[ { "path": ".gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/latest/usage/project/#working-with-version-control\n.pdm.toml\n.pdm-python\n.pdm-build/\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n*.jsonl\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n" }, { "path": "README.md", "content": "

UltraEdit

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\n\n\n\"Static\n\n\n \"Static\n\n\n\n \"Static\n\n\n\n \"Static\n\n\n\n \"Static\n\n\n

\n\nThis repository contains code, models, and datasets for UltraEdit.\n\n## Introduction\n**UltraEdit**, a large-scale (~4M editing samples), automatically generated dataset for instruction-based image editing. Our key idea is to address the drawbacks in existing image editing datasets like InstructPix2Pix and MagicBrush, and provide a systematic approach to producing massive and high-quality image editing samples. \n\n**UltraEdit** offers several distinct advantages: \n\n1. It features a broader range of editing instructions by leveraging the creativity of large language models (LLMs) alongside in-context editing examples from human raters. \n2. Its data sources are based on real images, including photographs and artworks, which provide greater diversity and reduced bias compared to datasets solely generated by text-to-image models. \n3. It also supports region-based editing, enhanced by high-quality, automatically produced region annotations. \n\nOur experiments show that canonical diffusion-based editing baselines trained on **UltraEdit** set new records on various benchmarks. \nOur analysis further confirms the crucial role of real image anchors and region-based editing data.\n\n## Training\n\n**Setup:** \n\n```\npip install -r requirements\n\ncd diffusers && pip install -e .\n```\n\n### Training with stable-diffusion3\n\n**Stage 1: Free-form image editing**\n\n```shell\nbash scripts/run_sft_512_sd3_stage1.sh\n```\n\n**Stage 2: Mix training**\n\n```shell\nbash scripts/run_sft_512_with_mask_sd3_stage2.sh\n```\n\n### Training with stable-diffusion-xl\n\n**Stage 1: Free-form image editing**\n\n```shell\nbash scripts/run_sft_512_sdxl_stage1.sh\n```\n\n[//]: # (**Stage 2: Mix training**)\n\n[//]: # ()\n[//]: # (```shell)\n\n[//]: # (bash scripts/run_sft_512_with_mask_sd3_stage2.sh)\n\n[//]: # (```)\n\n\n### Training with stable-diffusion1.5\n\n**Stage 1: Free-form image editing**\n\n```shell\nbash scripts/run_sft_512_sd15_stage1.sh\n```\n\n**Stage 2: Mix training**\n\n```shell\nbash scripts/run_sft_512_with_mask_sd15_stage2.sh\n```\n\n## Example\nBelow is an example of how to use our pipeline for image editing. Given an input image and a mask image, the model can generate the edited result according to the provided prompt.\n\n

\n \n \n \n

\n\n```python\n# For Editing with SD3\nimport torch\nfrom diffusers import StableDiffusion3InstructPix2PixPipeline\nfrom diffusers.utils import load_image\nimport requests\nimport PIL.Image\nimport PIL.ImageOps\npipe = StableDiffusion3InstructPix2PixPipeline.from_pretrained(\"BleachNick/SD3_UltraEdit_w_mask\", torch_dtype=torch.float16)\npipe = pipe.to(\"cuda\")\nprompt=\"What if the horse wears a hat?\"\nimg = load_image(\"input.png\").resize((512, 512))\nmask_img = load_image(\"mask_img.png\").resize(img.size)\n# For free form Editing, seed a blank mask\n# mask_img = PIL.Image.new(\"RGB\", img.size, (255, 255, 255))\nimage = pipe(\n prompt,\n image=img,\n mask_img=mask_img,\n negative_prompt=\"\",\n num_inference_steps=50,\n image_guidance_scale=1.5,\n guidance_scale=7.5,\n).images[0]\nimage.save(\"edited_image.png\")\n# display image\n```\n## Citation\n\nIf you find our work useful, please kindly cite\n```bib\n@misc{zhao2024ultraeditinstructionbasedfinegrainedimage,\n title={UltraEdit: Instruction-based Fine-Grained Image Editing at Scale}, \n author={Haozhe Zhao and Xiaojian Ma and Liang Chen and Shuzheng Si and Rujie Wu and Kaikai An and Peiyu Yu and Minjia Zhang and Qing Li and Baobao Chang},\n year={2024},\n eprint={2407.05282},\n archivePrefix={arXiv},\n primaryClass={cs.CV},\n url={https://arxiv.org/abs/2407.05282}, \n}\n```\n\n[//]: # ()\n[//]: # (## License)\n\n[//]: # ()\n[//]: # (This project is licensed under the terms of the MIT license. See the [LICENSE](LICENSE.md) file for details.)\n\n[//]: # ()\n[//]: # (## Contact)\n\n[//]: # ()\n[//]: # (For any questions or issues, please open an issue on GitHub or contact us at support@example.com.)\n" }, { "path": "app.py", "content": "# import spaces\r\nimport torch\r\nfrom diffusers import StableDiffusion3InstructPix2PixPipeline, SD3Transformer2DModel\r\nimport gradio as gr\r\nimport PIL.Image\r\nimport numpy as np\r\nfrom PIL import Image, ImageOps\r\n\r\npipe = StableDiffusion3InstructPix2PixPipeline.from_pretrained(\"BleachNick/SD3_UltraEdit_w_mask\",\r\n torch_dtype=torch.float16)\r\n\r\npipe = pipe.to(\"cuda\")\r\n\r\n\r\n# @spaces.GPU(duration=120)\r\ndef generate(image_mask, prompt, num_inference_steps=50, image_guidance_scale=1.6, guidance_scale=7.5, seed=255):\r\n def is_blank_mask(mask_img):\r\n mask_array = np.array(mask_img.convert('L')) # Convert to luminance to simplify the check\r\n return np.all(mask_array == 0)\r\n\r\n seed = int(seed)\r\n generator = torch.manual_seed(seed)\r\n\r\n img = image_mask[\"background\"].convert(\"RGB\")\r\n mask_img = image_mask[\"layers\"][0].getchannel('A').convert(\"RGB\")\r\n\r\n desired_size = (512, 512)\r\n\r\n img = ImageOps.fit(img, desired_size, method=Image.LANCZOS, centering=(0.5, 0.5))\r\n mask_img = ImageOps.fit(mask_img, desired_size, method=Image.LANCZOS, centering=(0.5, 0.5))\r\n\r\n if is_blank_mask(mask_img):\r\n mask_img = PIL.Image.new('RGB', img.size, color=(255, 255, 255))\r\n editing_mode = \"Free-form\"\r\n else:\r\n editing_mode = \"Region-based\"\r\n\r\n mask_img = mask_img.convert('RGB')\r\n\r\n image = pipe(\r\n prompt,\r\n image=img,\r\n mask_img=mask_img,\r\n num_inference_steps=num_inference_steps,\r\n image_guidance_scale=image_guidance_scale,\r\n guidance_scale=guidance_scale,\r\n generator=generator\r\n ).images[0]\r\n\r\n return image, f\"Editing Mode: {editing_mode}\"\r\n\r\n\r\nexample_lists = [\r\n\r\n [['UltraEdit/images/example_images/1-input.png', 'UltraEdit/images/example_images/1-mask.png',\r\n 'UltraEdit/images/example_images/1-merged.png'], \"Add a moon in the sky\", 20, 1.5, 12.5, 255],\r\n\r\n [['UltraEdit/images/example_images/1-input.png', 'UltraEdit/images/example_images/1-input.png',\r\n 'UltraEdit/images/example_images/1-input.png'], \"Add a moon in the sky\", 20, 1.5, 6.5, 255],\r\n\r\n [['UltraEdit/images/example_images/2-input.png', 'UltraEdit/images/example_images/2-mask.png',\r\n 'UltraEdit/images/example_images/2-merged.png'], \"add cherry blossoms\", 20, 1.5, 12.5, 255],\r\n\r\n [['UltraEdit/images/example_images/3-input.png', 'UltraEdit/images/example_images/3-mask.png',\r\n 'UltraEdit/images/example_images/3-merged.png'],\r\n \"Please dress her in a short purple wedding dress adorned with white floral embroidery.\", 20, 1.5, 6.5, 255],\r\n\r\n [['UltraEdit/images/example_images/4-input.png', 'UltraEdit/images/example_images/4-mask.png',\r\n 'UltraEdit/images/example_images/4-merged.png'], \"give her a chief's headdress.\", 20, 1.5, 7.5, 24555]\r\n\r\n]\r\nmask_ex_list = []\r\nfor exp in example_lists:\r\n ex_dict = {}\r\n ex_dict['background'] = exp[0][0]\r\n ex_dict['layers'] = [exp[0][1], exp[0][2]]\r\n ex_dict['composite'] = exp[0][2]\r\n re_list = [ex_dict, exp[1], exp[2], exp[3], exp[4], exp[5]]\r\n mask_ex_list.append(re_list)\r\n\r\nimage_mask_input = gr.ImageMask(sources='upload', type=\"pil\", label=\"Input Image: Mask with pen or leave unmasked\",\r\n transforms=(), layers=False)\r\nprompt_input = gr.Textbox(label=\"Prompt\")\r\nnum_inference_steps_input = gr.Slider(minimum=0, maximum=100, value=50, label=\"Number of Inference Steps\")\r\nimage_guidance_scale_input = gr.Slider(minimum=0.0, maximum=2.5, value=1.5, label=\"Image Guidance Scale\")\r\nguidance_scale_input = gr.Slider(minimum=0.0, maximum=17.5, value=12.5, label=\"Guidance Scale\")\r\nseed_input = gr.Textbox(value=\"255\", label=\"Random Seed\")\r\n\r\ninputs = [image_mask_input, prompt_input, num_inference_steps_input, image_guidance_scale_input, guidance_scale_input,\r\n seed_input]\r\noutputs = [gr.Image(label=\"Generated Image\"), gr.Text(label=\"Editing Mode\")]\r\n\r\narticle_html = \"\"\"\r\n
\r\n

\r\n 🖼️ UltraEdit for Fine-Grained Image Editing\r\n

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\r\n

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\r\n Haozhe Zhao1*, Xiaojian Ma2*, Liang Chen1, Shuzheng Si1, Rujie Wu1,\r\n Kaikai An1, Peiyu Yu3, Minjia Zhang4, Qing Li2, Baobao Chang2\r\n

\r\n

\r\n 1Peking University, 2BIGAI, 3UCLA, 4UIUC\r\n

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\r\n \r\n \"Dataset_4M\" Dataset\r\n \r\n \r\n \"Dataset_500k\" Dataset_500k\r\n \r\n \r\n 🔗 Page\r\n \r\n \r\n \"Checkpoint\" Checkpoint\r\n \r\n \r\n \"GitHub\" GitHub\r\n \r\n
\r\n
\r\n

\r\n UltraEdit is a dataset designed for fine-grained, instruction-based image editing. It contains over 4 million free-form image editing samples and more than 100,000 region-based image editing samples, automatically generated with real images as anchors.\r\n

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\r\n This demo allows you to perform image editing using the Stable Diffusion 3 model trained with this extensive dataset. It supports both free-form (without mask) and region-based (with mask) image editing. Use the sliders to adjust the inference steps and guidance scales, and provide a seed for reproducibility. The image guidance scale of 1.5 and text guidance scale of 7.5 / 12.5 is a good start for free-form/region-based image editing.\r\n

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\r\n Usage Instructions: You need to upload the images and prompts for editing. Use the pen tool to mark the areas you want to edit. If no region is marked, it will resort to free-form editing.\r\n

\r\n
\r\n
\r\n\"\"\"\r\nhtml = '''\r\n
\r\n Limitations:\r\n \r\n
\r\n'''\r\n\r\ndemo = gr.Interface(\r\n fn=generate,\r\n inputs=inputs,\r\n outputs=outputs,\r\n description=article_html,\r\n article=html,\r\n examples=mask_ex_list\r\n)\r\n\r\ndemo.queue().launch()" }, { "path": "data_generation/Grounded-Segment-Anything/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\npip-wheel-metadata/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n.python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# checkpoint\n*.pth\noutputs/\n\n.idea/\n" }, { "path": "data_generation/Grounded-Segment-Anything/.gitmodules", "content": "\n[submodule \"grounded-sam-osx\"]\n\tpath = grounded-sam-osx\n\turl = https://github.com/linjing7/grounded-sam-osx.git\n[submodule \"VISAM\"]\n\tpath = VISAM\n\turl = https://github.com/BingfengYan/VISAM\n" }, { "path": "data_generation/Grounded-Segment-Anything/CITATION.cff", "content": "cff-version: 1.2.0\nmessage: \"If you use this software, please cite it as below.\"\nauthors:\n - name: \"Grounded-SAM Contributors\"\ntitle: \"Grounded-Segment-Anything\"\ndate-released: 2023-04-06\nurl: \"https://github.com/IDEA-Research/Grounded-Segment-Anything\"\nlicense: Apache-2.0\n" }, { "path": "data_generation/Grounded-Segment-Anything/Dockerfile", "content": "FROM pytorch/pytorch:1.13.1-cuda11.6-cudnn8-devel\n\n# Arguments to build Docker Image using CUDA\nARG USE_CUDA=0\nARG TORCH_ARCH=\n\nENV AM_I_DOCKER True\nENV BUILD_WITH_CUDA \"${USE_CUDA}\"\nENV TORCH_CUDA_ARCH_LIST \"${TORCH_ARCH}\"\nENV CUDA_HOME /usr/local/cuda-11.6/\n\nRUN mkdir -p /home/appuser/Grounded-Segment-Anything\nCOPY . /home/appuser/Grounded-Segment-Anything/\n\nRUN apt-get update && apt-get install --no-install-recommends wget ffmpeg=7:* \\\n libsm6=2:* libxext6=2:* git=1:* nano=2.* \\\n vim=2:* -y \\\n && apt-get clean && apt-get autoremove && rm -rf /var/lib/apt/lists/*\n\nWORKDIR /home/appuser/Grounded-Segment-Anything\nRUN python -m pip install --no-cache-dir -e segment_anything\n\n# When using build isolation, PyTorch with newer CUDA is installed and can't compile GroundingDINO\nRUN python -m pip install --no-cache-dir wheel\nRUN python -m pip install --no-cache-dir --no-build-isolation -e GroundingDINO\n\nWORKDIR /home/appuser\nRUN pip install --no-cache-dir diffusers[torch]==0.15.1 opencv-python==4.7.0.72 \\\n pycocotools==2.0.6 matplotlib==3.5.3 \\\n onnxruntime==1.14.1 onnx==1.13.1 ipykernel==6.16.2 scipy gradio openai\n" }, { "path": "data_generation/Grounded-Segment-Anything/EfficientSAM/EdgeSAM/common.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom typing import Type\n\n\nclass MLPBlock(nn.Module):\n def __init__(\n self,\n embedding_dim: int,\n mlp_dim: int,\n act: Type[nn.Module] = nn.GELU,\n ) -> None:\n super().__init__()\n self.lin1 = nn.Linear(embedding_dim, mlp_dim)\n self.lin2 = nn.Linear(mlp_dim, embedding_dim)\n self.act = act()\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return self.lin2(self.act(self.lin1(x)))\n\n\n# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa\n# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa\nclass LayerNorm2d(nn.Module):\n def __init__(self, num_channels: int, eps: float = 1e-6) -> None:\n super().__init__()\n self.weight = nn.Parameter(torch.ones(num_channels))\n self.bias = nn.Parameter(torch.zeros(num_channels))\n self.eps = eps\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n u = x.mean(1, keepdim=True)\n s = (x - u).pow(2).mean(1, keepdim=True)\n x = (x - u) / torch.sqrt(s + self.eps)\n x = self.weight[:, None, None] * x + self.bias[:, None, None]\n return x\n\n\ndef val2list(x: list or tuple or any, repeat_time=1) -> list:\n if isinstance(x, (list, tuple)):\n return list(x)\n return [x for _ in range(repeat_time)]\n\n\ndef val2tuple(x: list or tuple or any, min_len: int = 1, idx_repeat: int = -1) -> tuple:\n x = val2list(x)\n\n # repeat elements if necessary\n if len(x) > 0:\n x[idx_repeat:idx_repeat] = [x[idx_repeat] for _ in range(min_len - len(x))]\n\n return tuple(x)\n\n\ndef list_sum(x: list) -> any:\n return x[0] if len(x) == 1 else x[0] + list_sum(x[1:])\n\n\ndef resize(\n x: torch.Tensor,\n size: any or None = None,\n scale_factor=None,\n mode: str = \"bicubic\",\n align_corners: bool or None = False,\n) -> torch.Tensor:\n if mode in [\"bilinear\", \"bicubic\"]:\n return F.interpolate(\n x,\n size=size,\n scale_factor=scale_factor,\n mode=mode,\n align_corners=align_corners,\n )\n elif mode in [\"nearest\", \"area\"]:\n return F.interpolate(x, size=size, scale_factor=scale_factor, mode=mode)\n else:\n raise NotImplementedError(f\"resize(mode={mode}) not implemented.\")\n\n\nclass UpSampleLayer(nn.Module):\n def __init__(\n self,\n mode=\"bicubic\",\n size=None,\n factor=2,\n align_corners=False,\n ):\n super(UpSampleLayer, self).__init__()\n self.mode = mode\n self.size = val2list(size, 2) if size is not None else None\n self.factor = None if self.size is not None else factor\n self.align_corners = align_corners\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return resize(x, self.size, self.factor, self.mode, self.align_corners)\n\n\nclass OpSequential(nn.Module):\n def __init__(self, op_list):\n super(OpSequential, self).__init__()\n valid_op_list = []\n for op in op_list:\n if op is not None:\n valid_op_list.append(op)\n self.op_list = nn.ModuleList(valid_op_list)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n for op in self.op_list:\n x = op(x)\n return x" }, { "path": "data_generation/Grounded-Segment-Anything/EfficientSAM/EdgeSAM/rep_vit.py", "content": "import torch.nn as nn\nfrom EdgeSAM.common import LayerNorm2d, UpSampleLayer, OpSequential\n\n__all__ = ['rep_vit_m1', 'rep_vit_m2', 'rep_vit_m3', 'RepViT']\n\nm1_cfgs = [\n # k, t, c, SE, HS, s\n [3, 2, 48, 1, 0, 1],\n [3, 2, 48, 0, 0, 1],\n [3, 2, 48, 0, 0, 1],\n [3, 2, 96, 0, 0, 2],\n [3, 2, 96, 1, 0, 1],\n [3, 2, 96, 0, 0, 1],\n [3, 2, 96, 0, 0, 1],\n [3, 2, 192, 0, 1, 2],\n [3, 2, 192, 1, 1, 1],\n [3, 2, 192, 0, 1, 1],\n [3, 2, 192, 1, 1, 1],\n [3, 2, 192, 0, 1, 1],\n [3, 2, 192, 1, 1, 1],\n [3, 2, 192, 0, 1, 1],\n [3, 2, 192, 1, 1, 1],\n [3, 2, 192, 0, 1, 1],\n [3, 2, 192, 1, 1, 1],\n [3, 2, 192, 0, 1, 1],\n [3, 2, 192, 1, 1, 1],\n [3, 2, 192, 0, 1, 1],\n [3, 2, 192, 1, 1, 1],\n [3, 2, 192, 0, 1, 1],\n [3, 2, 192, 0, 1, 1],\n [3, 2, 384, 0, 1, 2],\n [3, 2, 384, 1, 1, 1],\n [3, 2, 384, 0, 1, 1]\n]\n\nm2_cfgs = [\n # k, t, c, SE, HS, s\n [3, 2, 64, 1, 0, 1],\n [3, 2, 64, 0, 0, 1],\n [3, 2, 64, 0, 0, 1],\n [3, 2, 128, 0, 0, 2],\n [3, 2, 128, 1, 0, 1],\n [3, 2, 128, 0, 0, 1],\n [3, 2, 128, 0, 0, 1],\n [3, 2, 256, 0, 1, 2],\n [3, 2, 256, 1, 1, 1],\n [3, 2, 256, 0, 1, 1],\n [3, 2, 256, 1, 1, 1],\n [3, 2, 256, 0, 1, 1],\n [3, 2, 256, 1, 1, 1],\n [3, 2, 256, 0, 1, 1],\n [3, 2, 256, 1, 1, 1],\n [3, 2, 256, 0, 1, 1],\n [3, 2, 256, 1, 1, 1],\n [3, 2, 256, 0, 1, 1],\n [3, 2, 256, 1, 1, 1],\n [3, 2, 256, 0, 1, 1],\n [3, 2, 256, 0, 1, 1],\n [3, 2, 512, 0, 1, 2],\n [3, 2, 512, 1, 1, 1],\n [3, 2, 512, 0, 1, 1]\n]\n\nm3_cfgs = [\n # k, t, c, SE, HS, s\n [3, 2, 64, 1, 0, 1],\n [3, 2, 64, 0, 0, 1],\n [3, 2, 64, 1, 0, 1],\n [3, 2, 64, 0, 0, 1],\n [3, 2, 64, 0, 0, 1],\n [3, 2, 128, 0, 0, 2],\n [3, 2, 128, 1, 0, 1],\n [3, 2, 128, 0, 0, 1],\n [3, 2, 128, 1, 0, 1],\n [3, 2, 128, 0, 0, 1],\n [3, 2, 128, 0, 0, 1],\n [3, 2, 256, 0, 1, 2],\n [3, 2, 256, 1, 1, 1],\n [3, 2, 256, 0, 1, 1],\n [3, 2, 256, 1, 1, 1],\n [3, 2, 256, 0, 1, 1],\n [3, 2, 256, 1, 1, 1],\n [3, 2, 256, 0, 1, 1],\n [3, 2, 256, 1, 1, 1],\n [3, 2, 256, 0, 1, 1],\n [3, 2, 256, 1, 1, 1],\n [3, 2, 256, 0, 1, 1],\n [3, 2, 256, 1, 1, 1],\n [3, 2, 256, 0, 1, 1],\n [3, 2, 256, 1, 1, 1],\n [3, 2, 256, 0, 1, 1],\n [3, 2, 256, 1, 1, 1],\n [3, 2, 256, 0, 1, 1],\n [3, 2, 256, 1, 1, 1],\n [3, 2, 256, 0, 1, 1],\n [3, 2, 256, 0, 1, 1],\n [3, 2, 512, 0, 1, 2],\n [3, 2, 512, 1, 1, 1],\n [3, 2, 512, 0, 1, 1]\n]\n\n\ndef _make_divisible(v, divisor, min_value=None):\n \"\"\"\n This function is taken from the original tf repo.\n It ensures that all layers have a channel number that is divisible by 8\n It can be seen here:\n https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py\n :param v:\n :param divisor:\n :param min_value:\n :return:\n \"\"\"\n if min_value is None:\n min_value = divisor\n new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)\n # Make sure that round down does not go down by more than 10%.\n if new_v < 0.9 * v:\n new_v += divisor\n return new_v\n\n\nfrom timm.models.layers import SqueezeExcite\n\nimport torch\n\n\nclass Conv2d_BN(torch.nn.Sequential):\n def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1,\n groups=1, bn_weight_init=1, resolution=-10000):\n super().__init__()\n self.add_module('c', torch.nn.Conv2d(\n a, b, ks, stride, pad, dilation, groups, bias=False))\n self.add_module('bn', torch.nn.BatchNorm2d(b))\n torch.nn.init.constant_(self.bn.weight, bn_weight_init)\n torch.nn.init.constant_(self.bn.bias, 0)\n\n @torch.no_grad()\n def fuse(self):\n c, bn = self._modules.values()\n w = bn.weight / (bn.running_var + bn.eps) ** 0.5\n w = c.weight * w[:, None, None, None]\n b = bn.bias - bn.running_mean * bn.weight / \\\n (bn.running_var + bn.eps) ** 0.5\n m = torch.nn.Conv2d(w.size(1) * self.c.groups, w.size(\n 0), w.shape[2:], stride=self.c.stride, padding=self.c.padding, dilation=self.c.dilation,\n groups=self.c.groups,\n device=c.weight.device)\n m.weight.data.copy_(w)\n m.bias.data.copy_(b)\n return m\n\n\nclass Residual(torch.nn.Module):\n def __init__(self, m, drop=0.):\n super().__init__()\n self.m = m\n self.drop = drop\n\n def forward(self, x):\n if self.training and self.drop > 0:\n return x + self.m(x) * torch.rand(x.size(0), 1, 1, 1,\n device=x.device).ge_(self.drop).div(1 - self.drop).detach()\n else:\n return x + self.m(x)\n\n @torch.no_grad()\n def fuse(self):\n if isinstance(self.m, Conv2d_BN):\n m = self.m.fuse()\n assert (m.groups == m.in_channels)\n identity = torch.ones(m.weight.shape[0], m.weight.shape[1], 1, 1)\n identity = torch.nn.functional.pad(identity, [1, 1, 1, 1])\n m.weight += identity.to(m.weight.device)\n return m\n elif isinstance(self.m, torch.nn.Conv2d):\n m = self.m\n assert (m.groups != m.in_channels)\n identity = torch.ones(m.weight.shape[0], m.weight.shape[1], 1, 1)\n identity = torch.nn.functional.pad(identity, [1, 1, 1, 1])\n m.weight += identity.to(m.weight.device)\n return m\n else:\n return self\n\n\nclass RepVGGDW(torch.nn.Module):\n def __init__(self, ed) -> None:\n super().__init__()\n self.conv = Conv2d_BN(ed, ed, 3, 1, 1, groups=ed)\n self.conv1 = Conv2d_BN(ed, ed, 1, 1, 0, groups=ed)\n self.dim = ed\n\n def forward(self, x):\n return self.conv(x) + self.conv1(x) + x\n\n @torch.no_grad()\n def fuse(self):\n conv = self.conv.fuse()\n conv1 = self.conv1.fuse()\n\n conv_w = conv.weight\n conv_b = conv.bias\n conv1_w = conv1.weight\n conv1_b = conv1.bias\n\n conv1_w = torch.nn.functional.pad(conv1_w, [1, 1, 1, 1])\n\n identity = torch.nn.functional.pad(torch.ones(conv1_w.shape[0], conv1_w.shape[1], 1, 1, device=conv1_w.device),\n [1, 1, 1, 1])\n\n final_conv_w = conv_w + conv1_w + identity\n final_conv_b = conv_b + conv1_b\n\n conv.weight.data.copy_(final_conv_w)\n conv.bias.data.copy_(final_conv_b)\n return conv\n\n\nclass RepViTBlock(nn.Module):\n def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se, use_hs, skip_downsample=False):\n super(RepViTBlock, self).__init__()\n assert stride in [1, 2]\n\n self.identity = stride == 1 and inp == oup\n assert (hidden_dim == 2 * inp)\n\n if stride == 2:\n if skip_downsample:\n stride = 1\n self.token_mixer = nn.Sequential(\n Conv2d_BN(inp, inp, kernel_size, stride, (kernel_size - 1) // 2, groups=inp),\n SqueezeExcite(inp, 0.25) if use_se else nn.Identity(),\n Conv2d_BN(inp, oup, ks=1, stride=1, pad=0)\n )\n self.channel_mixer = Residual(nn.Sequential(\n # pw\n Conv2d_BN(oup, 2 * oup, 1, 1, 0),\n nn.GELU() if use_hs else nn.GELU(),\n # pw-linear\n Conv2d_BN(2 * oup, oup, 1, 1, 0, bn_weight_init=0),\n ))\n else:\n assert (self.identity)\n self.token_mixer = nn.Sequential(\n RepVGGDW(inp),\n SqueezeExcite(inp, 0.25) if use_se else nn.Identity(),\n )\n self.channel_mixer = Residual(nn.Sequential(\n # pw\n Conv2d_BN(inp, hidden_dim, 1, 1, 0),\n nn.GELU() if use_hs else nn.GELU(),\n # pw-linear\n Conv2d_BN(hidden_dim, oup, 1, 1, 0, bn_weight_init=0),\n ))\n\n def forward(self, x):\n return self.channel_mixer(self.token_mixer(x))\n\n\nfrom timm.models.vision_transformer import trunc_normal_\n\n\nclass BN_Linear(torch.nn.Sequential):\n def __init__(self, a, b, bias=True, std=0.02):\n super().__init__()\n self.add_module('bn', torch.nn.BatchNorm1d(a))\n self.add_module('l', torch.nn.Linear(a, b, bias=bias))\n trunc_normal_(self.l.weight, std=std)\n if bias:\n torch.nn.init.constant_(self.l.bias, 0)\n\n @torch.no_grad()\n def fuse(self):\n bn, l = self._modules.values()\n w = bn.weight / (bn.running_var + bn.eps) ** 0.5\n b = bn.bias - self.bn.running_mean * \\\n self.bn.weight / (bn.running_var + bn.eps) ** 0.5\n w = l.weight * w[None, :]\n if l.bias is None:\n b = b @ self.l.weight.T\n else:\n b = (l.weight @ b[:, None]).view(-1) + self.l.bias\n m = torch.nn.Linear(w.size(1), w.size(0), device=l.weight.device)\n m.weight.data.copy_(w)\n m.bias.data.copy_(b)\n return m\n\n\nclass RepViT(nn.Module):\n arch_settings = {\n 'm1': m1_cfgs,\n 'm2': m2_cfgs,\n 'm3': m3_cfgs\n }\n\n def __init__(self, arch, img_size=1024, upsample_mode='bicubic'):\n super(RepViT, self).__init__()\n # setting of inverted residual blocks\n self.cfgs = self.arch_settings[arch]\n self.img_size = img_size\n\n # building first layer\n input_channel = self.cfgs[0][2]\n patch_embed = torch.nn.Sequential(Conv2d_BN(3, input_channel // 2, 3, 2, 1), torch.nn.GELU(),\n Conv2d_BN(input_channel // 2, input_channel, 3, 2, 1))\n layers = [patch_embed]\n # building inverted residual blocks\n block = RepViTBlock\n self.stage_idx = []\n prev_c = input_channel\n for idx, (k, t, c, use_se, use_hs, s) in enumerate(self.cfgs):\n output_channel = _make_divisible(c, 8)\n exp_size = _make_divisible(input_channel * t, 8)\n skip_downsample = False\n if c != prev_c:\n self.stage_idx.append(idx - 1)\n prev_c = c\n layers.append(block(input_channel, exp_size, output_channel, k, s, use_se, use_hs, skip_downsample))\n input_channel = output_channel\n self.stage_idx.append(idx)\n self.features = nn.ModuleList(layers)\n\n stage2_channels = _make_divisible(self.cfgs[self.stage_idx[2]][2], 8)\n stage3_channels = _make_divisible(self.cfgs[self.stage_idx[3]][2], 8)\n self.fuse_stage2 = nn.Conv2d(stage2_channels, 256, kernel_size=1, bias=False)\n self.fuse_stage3 = OpSequential([\n nn.Conv2d(stage3_channels, 256, kernel_size=1, bias=False),\n UpSampleLayer(factor=2, mode=upsample_mode),\n ])\n\n self.neck = nn.Sequential(\n nn.Conv2d(256, 256, kernel_size=1, bias=False),\n LayerNorm2d(256),\n nn.Conv2d(256, 256, kernel_size=3, padding=1, bias=False),\n LayerNorm2d(256),\n )\n\n def forward(self, x):\n counter = 0\n output_dict = dict()\n # patch_embed\n x = self.features[0](x)\n output_dict['stem'] = x\n # stages\n for idx, f in enumerate(self.features[1:]):\n x = f(x)\n if idx in self.stage_idx:\n output_dict[f'stage{counter}'] = x\n counter += 1\n\n x = self.fuse_stage2(output_dict['stage2']) + self.fuse_stage3(output_dict['stage3'])\n\n x = self.neck(x)\n # hack this place because we modified the predictor of SAM for HQ-SAM in\n # segment_anything/segment_anything/predictor.py line 91 to return intern features of the backbone\n # self.features, self.interm_features = self.model.image_encoder(input_image)\n return x, None\n\n\ndef rep_vit_m1(img_size=1024, **kwargs):\n return RepViT('m1', img_size, **kwargs)\n\n\ndef rep_vit_m2(img_size=1024, **kwargs):\n return RepViT('m2', img_size, **kwargs)\n\n\ndef rep_vit_m3(img_size=1024, **kwargs):\n return RepViT('m3', img_size, **kwargs)" }, { "path": "data_generation/Grounded-Segment-Anything/EfficientSAM/EdgeSAM/setup_edge_sam.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\n\nfrom functools import partial\n\nfrom segment_anything.modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWayTransformer\nfrom EdgeSAM.rep_vit import RepViT\n\n\nprompt_embed_dim = 256\nimage_size = 1024\nvit_patch_size = 16\nimage_embedding_size = image_size // vit_patch_size\n\n\ndef build_edge_sam(checkpoint=None, upsample_mode=\"bicubic\"):\n image_encoder = RepViT(\n arch=\"m1\",\n img_size=image_size,\n upsample_mode=upsample_mode\n )\n return _build_sam(image_encoder, checkpoint)\n\n\nsam_model_registry = {\n \"default\": build_edge_sam,\n \"edge_sam\": build_edge_sam,\n}\n\ndef _build_sam_encoder(\n encoder_embed_dim,\n encoder_depth,\n encoder_num_heads,\n encoder_global_attn_indexes,\n):\n image_encoder = ImageEncoderViT(\n depth=encoder_depth,\n embed_dim=encoder_embed_dim,\n img_size=image_size,\n mlp_ratio=4,\n norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),\n num_heads=encoder_num_heads,\n patch_size=vit_patch_size,\n qkv_bias=True,\n use_rel_pos=True,\n global_attn_indexes=encoder_global_attn_indexes,\n window_size=14,\n out_chans=prompt_embed_dim,\n )\n return image_encoder\n\n\ndef _build_sam(\n image_encoder,\n checkpoint=None,\n):\n sam = Sam(\n image_encoder=image_encoder,\n prompt_encoder=PromptEncoder(\n embed_dim=prompt_embed_dim,\n image_embedding_size=(image_embedding_size, image_embedding_size),\n input_image_size=(image_size, image_size),\n mask_in_chans=16,\n ),\n mask_decoder=MaskDecoder(\n num_multimask_outputs=3,\n transformer=TwoWayTransformer(\n depth=2,\n embedding_dim=prompt_embed_dim,\n mlp_dim=2048,\n num_heads=8,\n ),\n transformer_dim=prompt_embed_dim,\n iou_head_depth=3,\n iou_head_hidden_dim=256,\n ),\n pixel_mean=[123.675, 116.28, 103.53],\n pixel_std=[58.395, 57.12, 57.375],\n )\n sam.eval()\n if checkpoint is not None:\n with open(checkpoint, \"rb\") as f:\n state_dict = torch.load(f, map_location=\"cpu\")\n sam.load_state_dict(state_dict)\n return sam" }, { "path": "data_generation/Grounded-Segment-Anything/EfficientSAM/FastSAM/tools.py", "content": "import numpy as np\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimport cv2\nimport torch\nimport os\nimport clip\n\n\ndef convert_box_xywh_to_xyxy(box):\n x1 = box[0]\n y1 = box[1]\n x2 = box[0] + box[2]\n y2 = box[1] + box[3]\n return [x1, y1, x2, y2]\n\n\ndef segment_image(image, bbox):\n image_array = np.array(image)\n segmented_image_array = np.zeros_like(image_array)\n x1, y1, x2, y2 = bbox\n segmented_image_array[y1:y2, x1:x2] = image_array[y1:y2, x1:x2]\n segmented_image = Image.fromarray(segmented_image_array)\n black_image = Image.new(\"RGB\", image.size, (255, 255, 255))\n # transparency_mask = np.zeros_like((), dtype=np.uint8)\n transparency_mask = np.zeros(\n (image_array.shape[0], image_array.shape[1]), dtype=np.uint8\n )\n transparency_mask[y1:y2, x1:x2] = 255\n transparency_mask_image = Image.fromarray(transparency_mask, mode=\"L\")\n black_image.paste(segmented_image, mask=transparency_mask_image)\n return black_image\n\n\ndef format_results(result, filter=0):\n annotations = []\n n = len(result.masks.data)\n for i in range(n):\n annotation = {}\n mask = result.masks.data[i] == 1.0\n\n if torch.sum(mask) < filter:\n continue\n annotation[\"id\"] = i\n annotation[\"segmentation\"] = mask.cpu().numpy()\n annotation[\"bbox\"] = result.boxes.data[i]\n annotation[\"score\"] = result.boxes.conf[i]\n annotation[\"area\"] = annotation[\"segmentation\"].sum()\n annotations.append(annotation)\n return annotations\n\n\ndef filter_masks(annotations): # filte the overlap mask\n annotations.sort(key=lambda x: x[\"area\"], reverse=True)\n to_remove = set()\n for i in range(0, len(annotations)):\n a = annotations[i]\n for j in range(i + 1, len(annotations)):\n b = annotations[j]\n if i != j and j not in to_remove:\n # check if\n if b[\"area\"] < a[\"area\"]:\n if (a[\"segmentation\"] & b[\"segmentation\"]).sum() / b[\n \"segmentation\"\n ].sum() > 0.8:\n to_remove.add(j)\n\n return [a for i, a in enumerate(annotations) if i not in to_remove], to_remove\n\n\ndef get_bbox_from_mask(mask):\n mask = mask.astype(np.uint8)\n contours, hierarchy = cv2.findContours(\n mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE\n )\n x1, y1, w, h = cv2.boundingRect(contours[0])\n x2, y2 = x1 + w, y1 + h\n if len(contours) > 1:\n for b in contours:\n x_t, y_t, w_t, h_t = cv2.boundingRect(b)\n # 将多个bbox合并成一个\n x1 = min(x1, x_t)\n y1 = min(y1, y_t)\n x2 = max(x2, x_t + w_t)\n y2 = max(y2, y_t + h_t)\n h = y2 - y1\n w = x2 - x1\n return [x1, y1, x2, y2]\n\n\ndef fast_process(\n annotations, args, mask_random_color, bbox=None, points=None, edges=False\n):\n if isinstance(annotations[0], dict):\n annotations = [annotation[\"segmentation\"] for annotation in annotations]\n result_name = os.path.basename(args.img_path)\n image = cv2.imread(args.img_path)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n original_h = image.shape[0]\n original_w = image.shape[1]\n plt.figure(figsize=(original_w/100, original_h/100))\n plt.imshow(image)\n if args.better_quality == True:\n if isinstance(annotations[0], torch.Tensor):\n annotations = np.array(annotations.cpu())\n for i, mask in enumerate(annotations):\n mask = cv2.morphologyEx(\n mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8)\n )\n annotations[i] = cv2.morphologyEx(\n mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8)\n )\n if args.device == \"cpu\":\n annotations = np.array(annotations)\n fast_show_mask(\n annotations,\n plt.gca(),\n random_color=mask_random_color,\n bbox=bbox,\n points=points,\n pointlabel=args.point_label,\n retinamask=args.retina,\n target_height=original_h,\n target_width=original_w,\n )\n else:\n if isinstance(annotations[0], np.ndarray):\n annotations = torch.from_numpy(annotations)\n fast_show_mask_gpu(\n annotations,\n plt.gca(),\n random_color=args.randomcolor,\n bbox=bbox,\n points=points,\n pointlabel=args.point_label,\n retinamask=args.retina,\n target_height=original_h,\n target_width=original_w,\n )\n if isinstance(annotations, torch.Tensor):\n annotations = annotations.cpu().numpy()\n if args.withContours == True:\n contour_all = []\n temp = np.zeros((original_h, original_w, 1))\n for i, mask in enumerate(annotations):\n if type(mask) == dict:\n mask = mask[\"segmentation\"]\n annotation = mask.astype(np.uint8)\n if args.retina == False:\n annotation = cv2.resize(\n annotation,\n (original_w, original_h),\n interpolation=cv2.INTER_NEAREST,\n )\n contours, hierarchy = cv2.findContours(\n annotation, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE\n )\n for contour in contours:\n contour_all.append(contour)\n cv2.drawContours(temp, contour_all, -1, (255, 255, 255), 2)\n color = np.array([0 / 255, 0 / 255, 255 / 255, 0.8])\n contour_mask = temp / 255 * color.reshape(1, 1, -1)\n plt.imshow(contour_mask)\n\n save_path = args.output\n if not os.path.exists(save_path):\n os.makedirs(save_path)\n plt.axis(\"off\")\n fig = plt.gcf()\n plt.draw()\n buf = fig.canvas.tostring_rgb()\n cols, rows = fig.canvas.get_width_height()\n img_array = np.fromstring(buf, dtype=np.uint8).reshape(rows, cols, 3)\n return img_array\n # cv2.imwrite(os.path.join(save_path, result_name), cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR))\n\n\n\n# CPU post process\ndef fast_show_mask(\n annotation,\n ax,\n random_color=False,\n bbox=None,\n points=None,\n pointlabel=None,\n retinamask=True,\n target_height=960,\n target_width=960,\n):\n msak_sum = annotation.shape[0]\n height = annotation.shape[1]\n weight = annotation.shape[2]\n # 将annotation 按照面积 排序\n areas = np.sum(annotation, axis=(1, 2))\n sorted_indices = np.argsort(areas)\n annotation = annotation[sorted_indices]\n\n index = (annotation != 0).argmax(axis=0)\n if random_color == True:\n color = np.random.random((msak_sum, 1, 1, 3))\n else:\n color = np.ones((msak_sum, 1, 1, 3)) * np.array(\n [30 / 255, 144 / 255, 255 / 255]\n )\n transparency = np.ones((msak_sum, 1, 1, 1)) * 0.6\n visual = np.concatenate([color, transparency], axis=-1)\n mask_image = np.expand_dims(annotation, -1) * visual\n\n show = np.zeros((height, weight, 4))\n h_indices, w_indices = np.meshgrid(\n np.arange(height), np.arange(weight), indexing=\"ij\"\n )\n indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))\n # 使用向量化索引更新show的值\n show[h_indices, w_indices, :] = mask_image[indices]\n if bbox is not None:\n x1, y1, x2, y2 = bbox\n ax.add_patch(\n plt.Rectangle(\n (x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor=\"b\", linewidth=1\n )\n )\n # draw point\n if points is not None:\n plt.scatter(\n [point[0] for i, point in enumerate(points) if pointlabel[i] == 1],\n [point[1] for i, point in enumerate(points) if pointlabel[i] == 1],\n s=20,\n c=\"y\",\n )\n plt.scatter(\n [point[0] for i, point in enumerate(points) if pointlabel[i] == 0],\n [point[1] for i, point in enumerate(points) if pointlabel[i] == 0],\n s=20,\n c=\"m\",\n )\n\n if retinamask == False:\n show = cv2.resize(\n show, (target_width, target_height), interpolation=cv2.INTER_NEAREST\n )\n ax.imshow(show)\n\n\ndef fast_show_mask_gpu(\n annotation,\n ax,\n random_color=False,\n bbox=None,\n points=None,\n pointlabel=None,\n retinamask=True,\n target_height=960,\n target_width=960,\n):\n msak_sum = annotation.shape[0]\n height = annotation.shape[1]\n weight = annotation.shape[2]\n areas = torch.sum(annotation, dim=(1, 2))\n sorted_indices = torch.argsort(areas, descending=False)\n annotation = annotation[sorted_indices]\n # 找每个位置第一个非零值下标\n index = (annotation != 0).to(torch.long).argmax(dim=0)\n if random_color == True:\n color = torch.rand((msak_sum, 1, 1, 3)).to(annotation.device)\n else:\n color = torch.ones((msak_sum, 1, 1, 3)).to(annotation.device) * torch.tensor(\n [30 / 255, 144 / 255, 255 / 255]\n ).to(annotation.device)\n transparency = torch.ones((msak_sum, 1, 1, 1)).to(annotation.device) * 0.6\n visual = torch.cat([color, transparency], dim=-1)\n mask_image = torch.unsqueeze(annotation, -1) * visual\n # 按index取数，index指每个位置选哪个batch的数，把mask_image转成一个batch的形式\n show = torch.zeros((height, weight, 4)).to(annotation.device)\n h_indices, w_indices = torch.meshgrid(\n torch.arange(height), torch.arange(weight), indexing=\"ij\"\n )\n indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))\n # 使用向量化索引更新show的值\n show[h_indices, w_indices, :] = mask_image[indices]\n show_cpu = show.cpu().numpy()\n if bbox is not None:\n x1, y1, x2, y2 = bbox\n ax.add_patch(\n plt.Rectangle(\n (x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor=\"b\", linewidth=1\n )\n )\n # draw point\n if points is not None:\n plt.scatter(\n [point[0] for i, point in enumerate(points) if pointlabel[i] == 1],\n [point[1] for i, point in enumerate(points) if pointlabel[i] == 1],\n s=20,\n c=\"y\",\n )\n plt.scatter(\n [point[0] for i, point in enumerate(points) if pointlabel[i] == 0],\n [point[1] for i, point in enumerate(points) if pointlabel[i] == 0],\n s=20,\n c=\"m\",\n )\n if retinamask == False:\n show_cpu = cv2.resize(\n show_cpu, (target_width, target_height), interpolation=cv2.INTER_NEAREST\n )\n ax.imshow(show_cpu)\n\n\n# clip\n@torch.no_grad()\ndef retriev(\n model, preprocess, elements, search_text: str, device\n) -> int:\n preprocessed_images = [preprocess(image).to(device) for image in elements]\n tokenized_text = clip.tokenize([search_text]).to(device)\n stacked_images = torch.stack(preprocessed_images)\n image_features = model.encode_image(stacked_images)\n text_features = model.encode_text(tokenized_text)\n image_features /= image_features.norm(dim=-1, keepdim=True)\n text_features /= text_features.norm(dim=-1, keepdim=True)\n probs = 100.0 * image_features @ text_features.T\n return probs[:, 0].softmax(dim=0)\n\n\ndef crop_image(annotations, image_path):\n image = Image.open(image_path)\n ori_w, ori_h = image.size\n mask_h, mask_w = annotations[0][\"segmentation\"].shape\n if ori_w != mask_w or ori_h != mask_h:\n image = image.resize((mask_w, mask_h))\n cropped_boxes = []\n cropped_images = []\n not_crop = []\n filter_id = []\n # annotations, _ = filter_masks(annotations)\n # filter_id = list(_)\n for _, mask in enumerate(annotations):\n if np.sum(mask[\"segmentation\"]) <= 100:\n filter_id.append(_)\n continue\n bbox = get_bbox_from_mask(mask[\"segmentation\"]) # mask 的 bbox\n cropped_boxes.append(segment_image(image, bbox)) # 保存裁剪的图片\n # cropped_boxes.append(segment_image(image,mask[\"segmentation\"]))\n cropped_images.append(bbox) # 保存裁剪的图片的bbox\n\n return cropped_boxes, cropped_images, not_crop, filter_id, annotations\n\n\ndef box_prompt(masks, bbox, target_height, target_width):\n h = masks.shape[1]\n w = masks.shape[2]\n if h != target_height or w != target_width:\n bbox = [\n int(bbox[0] * w / target_width),\n int(bbox[1] * h / target_height),\n int(bbox[2] * w / target_width),\n int(bbox[3] * h / target_height),\n ]\n bbox[0] = round(bbox[0]) if round(bbox[0]) > 0 else 0\n bbox[1] = round(bbox[1]) if round(bbox[1]) > 0 else 0\n bbox[2] = round(bbox[2]) if round(bbox[2]) < w else w\n bbox[3] = round(bbox[3]) if round(bbox[3]) < h else h\n\n # IoUs = torch.zeros(len(masks), dtype=torch.float32)\n bbox_area = (bbox[3] - bbox[1]) * (bbox[2] - bbox[0])\n\n masks_area = torch.sum(masks[:, bbox[1] : bbox[3], bbox[0] : bbox[2]], dim=(1, 2))\n orig_masks_area = torch.sum(masks, dim=(1, 2))\n\n union = bbox_area + orig_masks_area - masks_area\n IoUs = masks_area / union\n max_iou_index = torch.argmax(IoUs)\n\n return masks[max_iou_index].cpu().numpy(), max_iou_index\n\n\ndef point_prompt(masks, points, pointlabel, target_height, target_width): # numpy 处理\n h = masks[0][\"segmentation\"].shape[0]\n w = masks[0][\"segmentation\"].shape[1]\n if h != target_height or w != target_width:\n points = [\n [int(point[0] * w / target_width), int(point[1] * h / target_height)]\n for point in points\n ]\n onemask = np.zeros((h, w))\n for i, annotation in enumerate(masks):\n if type(annotation) == dict:\n mask = annotation[\"segmentation\"]\n else:\n mask = annotation\n for i, point in enumerate(points):\n if mask[point[1], point[0]] == 1 and pointlabel[i] == 1:\n onemask += mask\n if mask[point[1], point[0]] == 1 and pointlabel[i] == 0:\n onemask -= mask\n onemask = onemask >= 1\n return onemask, 0\n\n\ndef text_prompt(annotations, args):\n cropped_boxes, cropped_images, not_crop, filter_id, annotaions = crop_image(\n annotations, args.img_path\n )\n clip_model, preprocess = clip.load(\"ViT-B/32\", device=args.device)\n scores = retriev(\n clip_model, preprocess, cropped_boxes, args.text_prompt, device=args.device\n )\n max_idx = scores.argsort()\n max_idx = max_idx[-1]\n max_idx += sum(np.array(filter_id) <= int(max_idx))\n return annotaions[max_idx][\"segmentation\"], max_idx" }, { "path": "data_generation/Grounded-Segment-Anything/EfficientSAM/LightHQSAM/setup_light_hqsam.py", "content": "from LightHQSAM.tiny_vit_sam import TinyViT\nfrom segment_anything.modeling import MaskDecoderHQ, PromptEncoder, Sam, TwoWayTransformer\n\ndef setup_model():\n prompt_embed_dim = 256\n image_size = 1024\n vit_patch_size = 16\n image_embedding_size = image_size // vit_patch_size\n mobile_sam = Sam(\n image_encoder=TinyViT(img_size=1024, in_chans=3, num_classes=1000,\n embed_dims=[64, 128, 160, 320],\n depths=[2, 2, 6, 2],\n num_heads=[2, 4, 5, 10],\n window_sizes=[7, 7, 14, 7],\n mlp_ratio=4.,\n drop_rate=0.,\n drop_path_rate=0.0,\n use_checkpoint=False,\n mbconv_expand_ratio=4.0,\n local_conv_size=3,\n layer_lr_decay=0.8\n ),\n prompt_encoder=PromptEncoder(\n embed_dim=prompt_embed_dim,\n image_embedding_size=(image_embedding_size, image_embedding_size),\n input_image_size=(image_size, image_size),\n mask_in_chans=16,\n ),\n mask_decoder=MaskDecoderHQ(\n num_multimask_outputs=3,\n transformer=TwoWayTransformer(\n depth=2,\n embedding_dim=prompt_embed_dim,\n mlp_dim=2048,\n num_heads=8,\n ),\n transformer_dim=prompt_embed_dim,\n iou_head_depth=3,\n iou_head_hidden_dim=256,\n vit_dim=160,\n ),\n pixel_mean=[123.675, 116.28, 103.53],\n pixel_std=[58.395, 57.12, 57.375],\n )\n return mobile_sam" }, { "path": "data_generation/Grounded-Segment-Anything/EfficientSAM/LightHQSAM/tiny_vit_sam.py", "content": "# --------------------------------------------------------\n# TinyViT Model Architecture\n# Copyright (c) 2022 Microsoft\n# Adapted from LeViT and Swin Transformer\n# LeViT: (https://github.com/facebookresearch/levit)\n# Swin: (https://github.com/microsoft/swin-transformer)\n# Build the TinyViT Model\n# --------------------------------------------------------\n\nimport itertools\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.checkpoint as checkpoint\nfrom timm.models.layers import DropPath as TimmDropPath,\\\n to_2tuple, trunc_normal_\nfrom timm.models.registry import register_model\nfrom typing import Tuple\n\n\nclass Conv2d_BN(torch.nn.Sequential):\n def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1,\n groups=1, bn_weight_init=1):\n super().__init__()\n self.add_module('c', torch.nn.Conv2d(\n a, b, ks, stride, pad, dilation, groups, bias=False))\n bn = torch.nn.BatchNorm2d(b)\n torch.nn.init.constant_(bn.weight, bn_weight_init)\n torch.nn.init.constant_(bn.bias, 0)\n self.add_module('bn', bn)\n\n @torch.no_grad()\n def fuse(self):\n c, bn = self._modules.values()\n w = bn.weight / (bn.running_var + bn.eps)**0.5\n w = c.weight * w[:, None, None, None]\n b = bn.bias - bn.running_mean * bn.weight / \\\n (bn.running_var + bn.eps)**0.5\n m = torch.nn.Conv2d(w.size(1) * self.c.groups, w.size(\n 0), w.shape[2:], stride=self.c.stride, padding=self.c.padding, dilation=self.c.dilation, groups=self.c.groups)\n m.weight.data.copy_(w)\n m.bias.data.copy_(b)\n return m\n\n\nclass DropPath(TimmDropPath):\n def __init__(self, drop_prob=None):\n super().__init__(drop_prob=drop_prob)\n self.drop_prob = drop_prob\n\n def __repr__(self):\n msg = super().__repr__()\n msg += f'(drop_prob={self.drop_prob})'\n return msg\n\n\nclass PatchEmbed(nn.Module):\n def __init__(self, in_chans, embed_dim, resolution, activation):\n super().__init__()\n img_size: Tuple[int, int] = to_2tuple(resolution)\n self.patches_resolution = (img_size[0] // 4, img_size[1] // 4)\n self.num_patches = self.patches_resolution[0] * \\\n self.patches_resolution[1]\n self.in_chans = in_chans\n self.embed_dim = embed_dim\n n = embed_dim\n self.seq = nn.Sequential(\n Conv2d_BN(in_chans, n // 2, 3, 2, 1),\n activation(),\n Conv2d_BN(n // 2, n, 3, 2, 1),\n )\n\n def forward(self, x):\n return self.seq(x)\n\n\nclass MBConv(nn.Module):\n def __init__(self, in_chans, out_chans, expand_ratio,\n activation, drop_path):\n super().__init__()\n self.in_chans = in_chans\n self.hidden_chans = int(in_chans * expand_ratio)\n self.out_chans = out_chans\n\n self.conv1 = Conv2d_BN(in_chans, self.hidden_chans, ks=1)\n self.act1 = activation()\n\n self.conv2 = Conv2d_BN(self.hidden_chans, self.hidden_chans,\n ks=3, stride=1, pad=1, groups=self.hidden_chans)\n self.act2 = activation()\n\n self.conv3 = Conv2d_BN(\n self.hidden_chans, out_chans, ks=1, bn_weight_init=0.0)\n self.act3 = activation()\n\n self.drop_path = DropPath(\n drop_path) if drop_path > 0. else nn.Identity()\n\n def forward(self, x):\n shortcut = x\n\n x = self.conv1(x)\n x = self.act1(x)\n\n x = self.conv2(x)\n x = self.act2(x)\n\n x = self.conv3(x)\n\n x = self.drop_path(x)\n\n x += shortcut\n x = self.act3(x)\n\n return x\n\n\nclass PatchMerging(nn.Module):\n def __init__(self, input_resolution, dim, out_dim, activation):\n super().__init__()\n\n self.input_resolution = input_resolution\n self.dim = dim\n self.out_dim = out_dim\n self.act = activation()\n self.conv1 = Conv2d_BN(dim, out_dim, 1, 1, 0)\n stride_c=2\n if(out_dim==320 or out_dim==448 or out_dim==576):\n stride_c=1\n self.conv2 = Conv2d_BN(out_dim, out_dim, 3, stride_c, 1, groups=out_dim)\n self.conv3 = Conv2d_BN(out_dim, out_dim, 1, 1, 0)\n\n def forward(self, x):\n if x.ndim == 3:\n H, W = self.input_resolution\n B = len(x)\n # (B, C, H, W)\n x = x.view(B, H, W, -1).permute(0, 3, 1, 2)\n\n x = self.conv1(x)\n x = self.act(x)\n\n x = self.conv2(x)\n x = self.act(x)\n x = self.conv3(x)\n x = x.flatten(2).transpose(1, 2)\n return x\n\n\nclass ConvLayer(nn.Module):\n def __init__(self, dim, input_resolution, depth,\n activation,\n drop_path=0., downsample=None, use_checkpoint=False,\n out_dim=None,\n conv_expand_ratio=4.,\n ):\n\n super().__init__()\n self.dim = dim\n self.input_resolution = input_resolution\n self.depth = depth\n self.use_checkpoint = use_checkpoint\n\n # build blocks\n self.blocks = nn.ModuleList([\n MBConv(dim, dim, conv_expand_ratio, activation,\n drop_path[i] if isinstance(drop_path, list) else drop_path,\n )\n for i in range(depth)])\n\n # patch merging layer\n if downsample is not None:\n self.downsample = downsample(\n input_resolution, dim=dim, out_dim=out_dim, activation=activation)\n else:\n self.downsample = None\n\n def forward(self, x):\n for blk in self.blocks:\n if self.use_checkpoint:\n x = checkpoint.checkpoint(blk, x)\n else:\n x = blk(x)\n if self.downsample is not None:\n x = self.downsample(x)\n return x\n\n\nclass Mlp(nn.Module):\n def __init__(self, in_features, hidden_features=None,\n out_features=None, act_layer=nn.GELU, drop=0.):\n super().__init__()\n out_features = out_features or in_features\n hidden_features = hidden_features or in_features\n self.norm = nn.LayerNorm(in_features)\n self.fc1 = nn.Linear(in_features, hidden_features)\n self.fc2 = nn.Linear(hidden_features, out_features)\n self.act = act_layer()\n self.drop = nn.Dropout(drop)\n\n def forward(self, x):\n x = self.norm(x)\n\n x = self.fc1(x)\n x = self.act(x)\n x = self.drop(x)\n x = self.fc2(x)\n x = self.drop(x)\n return x\n\n\nclass Attention(torch.nn.Module):\n def __init__(self, dim, key_dim, num_heads=8,\n attn_ratio=4,\n resolution=(14, 14),\n ):\n super().__init__()\n # (h, w)\n assert isinstance(resolution, tuple) and len(resolution) == 2\n self.num_heads = num_heads\n self.scale = key_dim ** -0.5\n self.key_dim = key_dim\n self.nh_kd = nh_kd = key_dim * num_heads\n self.d = int(attn_ratio * key_dim)\n self.dh = int(attn_ratio * key_dim) * num_heads\n self.attn_ratio = attn_ratio\n h = self.dh + nh_kd * 2\n\n self.norm = nn.LayerNorm(dim)\n self.qkv = nn.Linear(dim, h)\n self.proj = nn.Linear(self.dh, dim)\n\n points = list(itertools.product(\n range(resolution[0]), range(resolution[1])))\n N = len(points)\n attention_offsets = {}\n idxs = []\n for p1 in points:\n for p2 in points:\n offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))\n if offset not in attention_offsets:\n attention_offsets[offset] = len(attention_offsets)\n idxs.append(attention_offsets[offset])\n self.attention_biases = torch.nn.Parameter(\n torch.zeros(num_heads, len(attention_offsets)))\n self.register_buffer('attention_bias_idxs',\n torch.LongTensor(idxs).view(N, N),\n persistent=False)\n\n @torch.no_grad()\n def train(self, mode=True):\n super().train(mode)\n if mode and hasattr(self, 'ab'):\n del self.ab\n else:\n self.register_buffer('ab',\n self.attention_biases[:, self.attention_bias_idxs],\n persistent=False)\n\n def forward(self, x): # x (B,N,C)\n B, N, _ = x.shape\n\n # Normalization\n x = self.norm(x)\n\n qkv = self.qkv(x)\n # (B, N, num_heads, d)\n q, k, v = qkv.view(B, N, self.num_heads, -\n 1).split([self.key_dim, self.key_dim, self.d], dim=3)\n # (B, num_heads, N, d)\n q = q.permute(0, 2, 1, 3)\n k = k.permute(0, 2, 1, 3)\n v = v.permute(0, 2, 1, 3)\n\n attn = (\n (q @ k.transpose(-2, -1)) * self.scale\n +\n (self.attention_biases[:, self.attention_bias_idxs]\n if self.training else self.ab)\n )\n attn = attn.softmax(dim=-1)\n x = (attn @ v).transpose(1, 2).reshape(B, N, self.dh)\n x = self.proj(x)\n return x\n\n\nclass TinyViTBlock(nn.Module):\n r\"\"\" TinyViT Block.\n\n Args:\n dim (int): Number of input channels.\n input_resolution (tuple[int, int]): Input resolution.\n num_heads (int): Number of attention heads.\n window_size (int): Window size.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n drop (float, optional): Dropout rate. Default: 0.0\n drop_path (float, optional): Stochastic depth rate. Default: 0.0\n local_conv_size (int): the kernel size of the convolution between\n Attention and MLP. Default: 3\n activation: the activation function. Default: nn.GELU\n \"\"\"\n\n def __init__(self, dim, input_resolution, num_heads, window_size=7,\n mlp_ratio=4., drop=0., drop_path=0.,\n local_conv_size=3,\n activation=nn.GELU,\n ):\n super().__init__()\n self.dim = dim\n self.input_resolution = input_resolution\n self.num_heads = num_heads\n assert window_size > 0, 'window_size must be greater than 0'\n self.window_size = window_size\n self.mlp_ratio = mlp_ratio\n\n self.drop_path = DropPath(\n drop_path) if drop_path > 0. else nn.Identity()\n\n assert dim % num_heads == 0, 'dim must be divisible by num_heads'\n head_dim = dim // num_heads\n\n window_resolution = (window_size, window_size)\n self.attn = Attention(dim, head_dim, num_heads,\n attn_ratio=1, resolution=window_resolution)\n\n mlp_hidden_dim = int(dim * mlp_ratio)\n mlp_activation = activation\n self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,\n act_layer=mlp_activation, drop=drop)\n\n pad = local_conv_size // 2\n self.local_conv = Conv2d_BN(\n dim, dim, ks=local_conv_size, stride=1, pad=pad, groups=dim)\n\n def forward(self, x):\n H, W = self.input_resolution\n B, L, C = x.shape\n assert L == H * W, \"input feature has wrong size\"\n res_x = x\n if H == self.window_size and W == self.window_size:\n x = self.attn(x)\n else:\n x = x.view(B, H, W, C)\n pad_b = (self.window_size - H %\n self.window_size) % self.window_size\n pad_r = (self.window_size - W %\n self.window_size) % self.window_size\n padding = pad_b > 0 or pad_r > 0\n\n if padding:\n x = F.pad(x, (0, 0, 0, pad_r, 0, pad_b))\n\n pH, pW = H + pad_b, W + pad_r\n nH = pH // self.window_size\n nW = pW // self.window_size\n # window partition\n x = x.view(B, nH, self.window_size, nW, self.window_size, C).transpose(2, 3).reshape(\n B * nH * nW, self.window_size * self.window_size, C)\n x = self.attn(x)\n # window reverse\n x = x.view(B, nH, nW, self.window_size, self.window_size,\n C).transpose(2, 3).reshape(B, pH, pW, C)\n\n if padding:\n x = x[:, :H, :W].contiguous()\n\n x = x.view(B, L, C)\n\n x = res_x + self.drop_path(x)\n\n x = x.transpose(1, 2).reshape(B, C, H, W)\n x = self.local_conv(x)\n x = x.view(B, C, L).transpose(1, 2)\n\n x = x + self.drop_path(self.mlp(x))\n return x\n\n def extra_repr(self) -> str:\n return f\"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, \" \\\n f\"window_size={self.window_size}, mlp_ratio={self.mlp_ratio}\"\n\n\nclass BasicLayer(nn.Module):\n \"\"\" A basic TinyViT layer for one stage.\n\n Args:\n dim (int): Number of input channels.\n input_resolution (tuple[int]): Input resolution.\n depth (int): Number of blocks.\n num_heads (int): Number of attention heads.\n window_size (int): Local window size.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n drop (float, optional): Dropout rate. Default: 0.0\n drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0\n downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None\n use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.\n local_conv_size: the kernel size of the depthwise convolution between attention and MLP. Default: 3\n activation: the activation function. Default: nn.GELU\n out_dim: the output dimension of the layer. Default: dim\n \"\"\"\n\n def __init__(self, dim, input_resolution, depth, num_heads, window_size,\n mlp_ratio=4., drop=0.,\n drop_path=0., downsample=None, use_checkpoint=False,\n local_conv_size=3,\n activation=nn.GELU,\n out_dim=None,\n ):\n\n super().__init__()\n self.dim = dim\n self.input_resolution = input_resolution\n self.depth = depth\n self.use_checkpoint = use_checkpoint\n\n # build blocks\n self.blocks = nn.ModuleList([\n TinyViTBlock(dim=dim, input_resolution=input_resolution,\n num_heads=num_heads, window_size=window_size,\n mlp_ratio=mlp_ratio,\n drop=drop,\n drop_path=drop_path[i] if isinstance(\n drop_path, list) else drop_path,\n local_conv_size=local_conv_size,\n activation=activation,\n )\n for i in range(depth)])\n\n # patch merging layer\n if downsample is not None:\n self.downsample = downsample(\n input_resolution, dim=dim, out_dim=out_dim, activation=activation)\n else:\n self.downsample = None\n\n def forward(self, x):\n for blk in self.blocks:\n if self.use_checkpoint:\n x = checkpoint.checkpoint(blk, x)\n else:\n x = blk(x)\n if self.downsample is not None:\n x = self.downsample(x)\n return x\n\n def extra_repr(self) -> str:\n return f\"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}\"\n\nclass LayerNorm2d(nn.Module):\n def __init__(self, num_channels: int, eps: float = 1e-6) -> None:\n super().__init__()\n self.weight = nn.Parameter(torch.ones(num_channels))\n self.bias = nn.Parameter(torch.zeros(num_channels))\n self.eps = eps\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n u = x.mean(1, keepdim=True)\n s = (x - u).pow(2).mean(1, keepdim=True)\n x = (x - u) / torch.sqrt(s + self.eps)\n x = self.weight[:, None, None] * x + self.bias[:, None, None]\n return x\nclass TinyViT(nn.Module):\n def __init__(self, img_size=224, in_chans=3, num_classes=1000,\n embed_dims=[96, 192, 384, 768], depths=[2, 2, 6, 2],\n num_heads=[3, 6, 12, 24],\n window_sizes=[7, 7, 14, 7],\n mlp_ratio=4.,\n drop_rate=0.,\n drop_path_rate=0.1,\n use_checkpoint=False,\n mbconv_expand_ratio=4.0,\n local_conv_size=3,\n layer_lr_decay=1.0,\n ):\n super().__init__()\n self.img_size=img_size\n self.num_classes = num_classes\n self.depths = depths\n self.num_layers = len(depths)\n self.mlp_ratio = mlp_ratio\n\n activation = nn.GELU\n\n self.patch_embed = PatchEmbed(in_chans=in_chans,\n embed_dim=embed_dims[0],\n resolution=img_size,\n activation=activation)\n\n patches_resolution = self.patch_embed.patches_resolution\n self.patches_resolution = patches_resolution\n\n # stochastic depth\n dpr = [x.item() for x in torch.linspace(0, drop_path_rate,\n sum(depths))] # stochastic depth decay rule\n\n # build layers\n self.layers = nn.ModuleList()\n for i_layer in range(self.num_layers):\n kwargs = dict(dim=embed_dims[i_layer],\n input_resolution=(patches_resolution[0] // (2 ** (i_layer-1 if i_layer == 3 else i_layer)),\n patches_resolution[1] // (2 ** (i_layer-1 if i_layer == 3 else i_layer))),\n # input_resolution=(patches_resolution[0] // (2 ** i_layer),\n # patches_resolution[1] // (2 ** i_layer)),\n depth=depths[i_layer],\n drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],\n downsample=PatchMerging if (\n i_layer < self.num_layers - 1) else None,\n use_checkpoint=use_checkpoint,\n out_dim=embed_dims[min(\n i_layer + 1, len(embed_dims) - 1)],\n activation=activation,\n )\n if i_layer == 0:\n layer = ConvLayer(\n conv_expand_ratio=mbconv_expand_ratio,\n **kwargs,\n )\n else:\n layer = BasicLayer(\n num_heads=num_heads[i_layer],\n window_size=window_sizes[i_layer],\n mlp_ratio=self.mlp_ratio,\n drop=drop_rate,\n local_conv_size=local_conv_size,\n **kwargs)\n self.layers.append(layer)\n\n # Classifier head\n self.norm_head = nn.LayerNorm(embed_dims[-1])\n self.head = nn.Linear(\n embed_dims[-1], num_classes) if num_classes > 0 else torch.nn.Identity()\n\n # init weights\n self.apply(self._init_weights)\n self.set_layer_lr_decay(layer_lr_decay)\n self.neck = nn.Sequential(\n nn.Conv2d(\n embed_dims[-1],\n 256,\n kernel_size=1,\n bias=False,\n ),\n LayerNorm2d(256),\n nn.Conv2d(\n 256,\n 256,\n kernel_size=3,\n padding=1,\n bias=False,\n ),\n LayerNorm2d(256),\n )\n def set_layer_lr_decay(self, layer_lr_decay):\n decay_rate = layer_lr_decay\n\n # layers -> blocks (depth)\n depth = sum(self.depths)\n lr_scales = [decay_rate ** (depth - i - 1) for i in range(depth)]\n #print(\"LR SCALES:\", lr_scales)\n\n def _set_lr_scale(m, scale):\n for p in m.parameters():\n p.lr_scale = scale\n\n self.patch_embed.apply(lambda x: _set_lr_scale(x, lr_scales[0]))\n i = 0\n for layer in self.layers:\n for block in layer.blocks:\n block.apply(lambda x: _set_lr_scale(x, lr_scales[i]))\n i += 1\n if layer.downsample is not None:\n layer.downsample.apply(\n lambda x: _set_lr_scale(x, lr_scales[i - 1]))\n assert i == depth\n for m in [self.norm_head, self.head]:\n m.apply(lambda x: _set_lr_scale(x, lr_scales[-1]))\n\n for k, p in self.named_parameters():\n p.param_name = k\n\n def _check_lr_scale(m):\n for p in m.parameters():\n assert hasattr(p, 'lr_scale'), p.param_name\n\n self.apply(_check_lr_scale)\n\n def _init_weights(self, m):\n if isinstance(m, nn.Linear):\n trunc_normal_(m.weight, std=.02)\n if isinstance(m, nn.Linear) and m.bias is not None:\n nn.init.constant_(m.bias, 0)\n elif isinstance(m, nn.LayerNorm):\n nn.init.constant_(m.bias, 0)\n nn.init.constant_(m.weight, 1.0)\n\n @torch.jit.ignore\n def no_weight_decay_keywords(self):\n return {'attention_biases'}\n\n def forward_features(self, x):\n # x: (N, C, H, W)\n x = self.patch_embed(x)\n\n x = self.layers[0](x)\n start_i = 1\n\n interm_embeddings=[]\n for i in range(start_i, len(self.layers)):\n layer = self.layers[i]\n x = layer(x)\n # print('x shape:', x.shape, '---i:', i)\n if i == 1:\n interm_embeddings.append(x.view(x.shape[0], 64, 64, -1))\n\n B,_,C=x.size()\n x = x.view(B, 64, 64, C)\n x=x.permute(0, 3, 1, 2)\n x=self.neck(x)\n return x, interm_embeddings\n\n def forward(self, x):\n x, interm_embeddings = self.forward_features(x)\n #x = self.norm_head(x)\n #x = self.head(x)\n # print('come to here is correct'* 3)\n return x, interm_embeddings\n\n\n_checkpoint_url_format = \\\n 'https://github.com/wkcn/TinyViT-model-zoo/releases/download/checkpoints/{}.pth'\n_provided_checkpoints = {\n 'tiny_vit_5m_224': 'tiny_vit_5m_22kto1k_distill',\n 'tiny_vit_11m_224': 'tiny_vit_11m_22kto1k_distill',\n 'tiny_vit_21m_224': 'tiny_vit_21m_22kto1k_distill',\n 'tiny_vit_21m_384': 'tiny_vit_21m_22kto1k_384_distill',\n 'tiny_vit_21m_512': 'tiny_vit_21m_22kto1k_512_distill',\n}\n\n\ndef register_tiny_vit_model(fn):\n '''Register a TinyViT model\n It is a wrapper of `register_model` with loading the pretrained checkpoint.\n '''\n def fn_wrapper(pretrained=False, **kwargs):\n model = fn()\n if pretrained:\n model_name = fn.__name__\n assert model_name in _provided_checkpoints, \\\n f'Sorry that the checkpoint `{model_name}` is not provided yet.'\n url = _checkpoint_url_format.format(\n _provided_checkpoints[model_name])\n checkpoint = torch.hub.load_state_dict_from_url(\n url=url,\n map_location='cpu', check_hash=False,\n )\n model.load_state_dict(checkpoint['model'])\n\n return model\n\n # rename the name of fn_wrapper\n fn_wrapper.__name__ = fn.__name__\n return register_model(fn_wrapper)\n\n\n@register_tiny_vit_model\ndef tiny_vit_5m_224(pretrained=False, num_classes=1000, drop_path_rate=0.0):\n return TinyViT(\n num_classes=num_classes,\n embed_dims=[64, 128, 160, 320],\n depths=[2, 2, 6, 2],\n num_heads=[2, 4, 5, 10],\n window_sizes=[7, 7, 14, 7],\n drop_path_rate=drop_path_rate,\n )\n\n\n@register_tiny_vit_model\ndef tiny_vit_11m_224(pretrained=False, num_classes=1000, drop_path_rate=0.1):\n return TinyViT(\n num_classes=num_classes,\n embed_dims=[64, 128, 256, 448],\n depths=[2, 2, 6, 2],\n num_heads=[2, 4, 8, 14],\n window_sizes=[7, 7, 14, 7],\n drop_path_rate=drop_path_rate,\n )\n\n\n@register_tiny_vit_model\ndef tiny_vit_21m_224(pretrained=False, num_classes=1000, drop_path_rate=0.2):\n return TinyViT(\n num_classes=num_classes,\n embed_dims=[96, 192, 384, 576],\n depths=[2, 2, 6, 2],\n num_heads=[3, 6, 12, 18],\n window_sizes=[7, 7, 14, 7],\n drop_path_rate=drop_path_rate,\n )\n\n\n@register_tiny_vit_model\ndef tiny_vit_21m_384(pretrained=False, num_classes=1000, drop_path_rate=0.1):\n return TinyViT(\n img_size=384,\n num_classes=num_classes,\n embed_dims=[96, 192, 384, 576],\n depths=[2, 2, 6, 2],\n num_heads=[3, 6, 12, 18],\n window_sizes=[12, 12, 24, 12],\n drop_path_rate=drop_path_rate,\n )\n\n\n@register_tiny_vit_model\ndef tiny_vit_21m_512(pretrained=False, num_classes=1000, drop_path_rate=0.1):\n return TinyViT(\n img_size=512,\n num_classes=num_classes,\n embed_dims=[96, 192, 384, 576],\n depths=[2, 2, 6, 2],\n num_heads=[3, 6, 12, 18],\n window_sizes=[16, 16, 32, 16],\n drop_path_rate=drop_path_rate,\n )\n" }, { "path": "data_generation/Grounded-Segment-Anything/EfficientSAM/MobileSAM/setup_mobile_sam.py", "content": "from MobileSAM.tiny_vit_sam import TinyViT\nfrom segment_anything.modeling import MaskDecoder, PromptEncoder, Sam, TwoWayTransformer\n\ndef setup_model():\n prompt_embed_dim = 256\n image_size = 1024\n vit_patch_size = 16\n image_embedding_size = image_size // vit_patch_size\n mobile_sam = Sam(\n image_encoder=TinyViT(img_size=1024, in_chans=3, num_classes=1000,\n embed_dims=[64, 128, 160, 320],\n depths=[2, 2, 6, 2],\n num_heads=[2, 4, 5, 10],\n window_sizes=[7, 7, 14, 7],\n mlp_ratio=4.,\n drop_rate=0.,\n drop_path_rate=0.0,\n use_checkpoint=False,\n mbconv_expand_ratio=4.0,\n local_conv_size=3,\n layer_lr_decay=0.8\n ),\n prompt_encoder=PromptEncoder(\n embed_dim=prompt_embed_dim,\n image_embedding_size=(image_embedding_size, image_embedding_size),\n input_image_size=(image_size, image_size),\n mask_in_chans=16,\n ),\n mask_decoder=MaskDecoder(\n num_multimask_outputs=3,\n transformer=TwoWayTransformer(\n depth=2,\n embedding_dim=prompt_embed_dim,\n mlp_dim=2048,\n num_heads=8,\n ),\n transformer_dim=prompt_embed_dim,\n iou_head_depth=3,\n iou_head_hidden_dim=256,\n ),\n pixel_mean=[123.675, 116.28, 103.53],\n pixel_std=[58.395, 57.12, 57.375],\n )\n return mobile_sam" }, { "path": "data_generation/Grounded-Segment-Anything/EfficientSAM/MobileSAM/tiny_vit_sam.py", "content": "# --------------------------------------------------------\n# TinyViT Model Architecture\n# Copyright (c) 2022 Microsoft\n# Adapted from LeViT and Swin Transformer\n# LeViT: (https://github.com/facebookresearch/levit)\n# Swin: (https://github.com/microsoft/swin-transformer)\n# Build the TinyViT Model\n# --------------------------------------------------------\n\nimport itertools\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.checkpoint as checkpoint\nfrom timm.models.layers import DropPath as TimmDropPath,\\\n to_2tuple, trunc_normal_\nfrom timm.models.registry import register_model\nfrom typing import Tuple\n\n\nclass Conv2d_BN(torch.nn.Sequential):\n def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1,\n groups=1, bn_weight_init=1):\n super().__init__()\n self.add_module('c', torch.nn.Conv2d(\n a, b, ks, stride, pad, dilation, groups, bias=False))\n bn = torch.nn.BatchNorm2d(b)\n torch.nn.init.constant_(bn.weight, bn_weight_init)\n torch.nn.init.constant_(bn.bias, 0)\n self.add_module('bn', bn)\n\n @torch.no_grad()\n def fuse(self):\n c, bn = self._modules.values()\n w = bn.weight / (bn.running_var + bn.eps)**0.5\n w = c.weight * w[:, None, None, None]\n b = bn.bias - bn.running_mean * bn.weight / \\\n (bn.running_var + bn.eps)**0.5\n m = torch.nn.Conv2d(w.size(1) * self.c.groups, w.size(\n 0), w.shape[2:], stride=self.c.stride, padding=self.c.padding, dilation=self.c.dilation, groups=self.c.groups)\n m.weight.data.copy_(w)\n m.bias.data.copy_(b)\n return m\n\n\nclass DropPath(TimmDropPath):\n def __init__(self, drop_prob=None):\n super().__init__(drop_prob=drop_prob)\n self.drop_prob = drop_prob\n\n def __repr__(self):\n msg = super().__repr__()\n msg += f'(drop_prob={self.drop_prob})'\n return msg\n\n\nclass PatchEmbed(nn.Module):\n def __init__(self, in_chans, embed_dim, resolution, activation):\n super().__init__()\n img_size: Tuple[int, int] = to_2tuple(resolution)\n self.patches_resolution = (img_size[0] // 4, img_size[1] // 4)\n self.num_patches = self.patches_resolution[0] * \\\n self.patches_resolution[1]\n self.in_chans = in_chans\n self.embed_dim = embed_dim\n n = embed_dim\n self.seq = nn.Sequential(\n Conv2d_BN(in_chans, n // 2, 3, 2, 1),\n activation(),\n Conv2d_BN(n // 2, n, 3, 2, 1),\n )\n\n def forward(self, x):\n return self.seq(x)\n\n\nclass MBConv(nn.Module):\n def __init__(self, in_chans, out_chans, expand_ratio,\n activation, drop_path):\n super().__init__()\n self.in_chans = in_chans\n self.hidden_chans = int(in_chans * expand_ratio)\n self.out_chans = out_chans\n\n self.conv1 = Conv2d_BN(in_chans, self.hidden_chans, ks=1)\n self.act1 = activation()\n\n self.conv2 = Conv2d_BN(self.hidden_chans, self.hidden_chans,\n ks=3, stride=1, pad=1, groups=self.hidden_chans)\n self.act2 = activation()\n\n self.conv3 = Conv2d_BN(\n self.hidden_chans, out_chans, ks=1, bn_weight_init=0.0)\n self.act3 = activation()\n\n self.drop_path = DropPath(\n drop_path) if drop_path > 0. else nn.Identity()\n\n def forward(self, x):\n shortcut = x\n\n x = self.conv1(x)\n x = self.act1(x)\n\n x = self.conv2(x)\n x = self.act2(x)\n\n x = self.conv3(x)\n\n x = self.drop_path(x)\n\n x += shortcut\n x = self.act3(x)\n\n return x\n\n\nclass PatchMerging(nn.Module):\n def __init__(self, input_resolution, dim, out_dim, activation):\n super().__init__()\n\n self.input_resolution = input_resolution\n self.dim = dim\n self.out_dim = out_dim\n self.act = activation()\n self.conv1 = Conv2d_BN(dim, out_dim, 1, 1, 0)\n stride_c=2\n if(out_dim==320 or out_dim==448 or out_dim==576):#handongshen 576\n stride_c=1\n self.conv2 = Conv2d_BN(out_dim, out_dim, 3, stride_c, 1, groups=out_dim)\n self.conv3 = Conv2d_BN(out_dim, out_dim, 1, 1, 0)\n\n def forward(self, x):\n if x.ndim == 3:\n H, W = self.input_resolution\n B = len(x)\n # (B, C, H, W)\n x = x.view(B, H, W, -1).permute(0, 3, 1, 2)\n\n x = self.conv1(x)\n x = self.act(x)\n\n x = self.conv2(x)\n x = self.act(x)\n x = self.conv3(x)\n x = x.flatten(2).transpose(1, 2)\n return x\n\n\nclass ConvLayer(nn.Module):\n def __init__(self, dim, input_resolution, depth,\n activation,\n drop_path=0., downsample=None, use_checkpoint=False,\n out_dim=None,\n conv_expand_ratio=4.,\n ):\n\n super().__init__()\n self.dim = dim\n self.input_resolution = input_resolution\n self.depth = depth\n self.use_checkpoint = use_checkpoint\n\n # build blocks\n self.blocks = nn.ModuleList([\n MBConv(dim, dim, conv_expand_ratio, activation,\n drop_path[i] if isinstance(drop_path, list) else drop_path,\n )\n for i in range(depth)])\n\n # patch merging layer\n if downsample is not None:\n self.downsample = downsample(\n input_resolution, dim=dim, out_dim=out_dim, activation=activation)\n else:\n self.downsample = None\n\n def forward(self, x):\n for blk in self.blocks:\n if self.use_checkpoint:\n x = checkpoint.checkpoint(blk, x)\n else:\n x = blk(x)\n if self.downsample is not None:\n x = self.downsample(x)\n return x\n\n\nclass Mlp(nn.Module):\n def __init__(self, in_features, hidden_features=None,\n out_features=None, act_layer=nn.GELU, drop=0.):\n super().__init__()\n out_features = out_features or in_features\n hidden_features = hidden_features or in_features\n self.norm = nn.LayerNorm(in_features)\n self.fc1 = nn.Linear(in_features, hidden_features)\n self.fc2 = nn.Linear(hidden_features, out_features)\n self.act = act_layer()\n self.drop = nn.Dropout(drop)\n\n def forward(self, x):\n x = self.norm(x)\n\n x = self.fc1(x)\n x = self.act(x)\n x = self.drop(x)\n x = self.fc2(x)\n x = self.drop(x)\n return x\n\n\nclass Attention(torch.nn.Module):\n def __init__(self, dim, key_dim, num_heads=8,\n attn_ratio=4,\n resolution=(14, 14),\n ):\n super().__init__()\n # (h, w)\n assert isinstance(resolution, tuple) and len(resolution) == 2\n self.num_heads = num_heads\n self.scale = key_dim ** -0.5\n self.key_dim = key_dim\n self.nh_kd = nh_kd = key_dim * num_heads\n self.d = int(attn_ratio * key_dim)\n self.dh = int(attn_ratio * key_dim) * num_heads\n self.attn_ratio = attn_ratio\n h = self.dh + nh_kd * 2\n\n self.norm = nn.LayerNorm(dim)\n self.qkv = nn.Linear(dim, h)\n self.proj = nn.Linear(self.dh, dim)\n\n points = list(itertools.product(\n range(resolution[0]), range(resolution[1])))\n N = len(points)\n attention_offsets = {}\n idxs = []\n for p1 in points:\n for p2 in points:\n offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))\n if offset not in attention_offsets:\n attention_offsets[offset] = len(attention_offsets)\n idxs.append(attention_offsets[offset])\n self.attention_biases = torch.nn.Parameter(\n torch.zeros(num_heads, len(attention_offsets)))\n self.register_buffer('attention_bias_idxs',\n torch.LongTensor(idxs).view(N, N),\n persistent=False)\n\n @torch.no_grad()\n def train(self, mode=True):\n super().train(mode)\n if mode and hasattr(self, 'ab'):\n del self.ab\n else:\n self.ab = self.attention_biases[:, self.attention_bias_idxs]\n\n def forward(self, x): # x (B,N,C)\n B, N, _ = x.shape\n\n # Normalization\n x = self.norm(x)\n\n qkv = self.qkv(x)\n # (B, N, num_heads, d)\n q, k, v = qkv.view(B, N, self.num_heads, -\n 1).split([self.key_dim, self.key_dim, self.d], dim=3)\n # (B, num_heads, N, d)\n q = q.permute(0, 2, 1, 3)\n k = k.permute(0, 2, 1, 3)\n v = v.permute(0, 2, 1, 3)\n\n attn = (\n (q @ k.transpose(-2, -1)) * self.scale\n +\n (self.attention_biases[:, self.attention_bias_idxs]\n if self.training else self.ab)\n )\n attn = attn.softmax(dim=-1)\n x = (attn @ v).transpose(1, 2).reshape(B, N, self.dh)\n x = self.proj(x)\n return x\n\n\nclass TinyViTBlock(nn.Module):\n r\"\"\" TinyViT Block.\n\n Args:\n dim (int): Number of input channels.\n input_resolution (tuple[int, int]): Input resulotion.\n num_heads (int): Number of attention heads.\n window_size (int): Window size.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n drop (float, optional): Dropout rate. Default: 0.0\n drop_path (float, optional): Stochastic depth rate. Default: 0.0\n local_conv_size (int): the kernel size of the convolution between\n Attention and MLP. Default: 3\n activation: the activation function. Default: nn.GELU\n \"\"\"\n\n def __init__(self, dim, input_resolution, num_heads, window_size=7,\n mlp_ratio=4., drop=0., drop_path=0.,\n local_conv_size=3,\n activation=nn.GELU,\n ):\n super().__init__()\n self.dim = dim\n self.input_resolution = input_resolution\n self.num_heads = num_heads\n assert window_size > 0, 'window_size must be greater than 0'\n self.window_size = window_size\n self.mlp_ratio = mlp_ratio\n\n self.drop_path = DropPath(\n drop_path) if drop_path > 0. else nn.Identity()\n\n assert dim % num_heads == 0, 'dim must be divisible by num_heads'\n head_dim = dim // num_heads\n\n window_resolution = (window_size, window_size)\n self.attn = Attention(dim, head_dim, num_heads,\n attn_ratio=1, resolution=window_resolution)\n\n mlp_hidden_dim = int(dim * mlp_ratio)\n mlp_activation = activation\n self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,\n act_layer=mlp_activation, drop=drop)\n\n pad = local_conv_size // 2\n self.local_conv = Conv2d_BN(\n dim, dim, ks=local_conv_size, stride=1, pad=pad, groups=dim)\n\n def forward(self, x):\n H, W = self.input_resolution\n B, L, C = x.shape\n assert L == H * W, \"input feature has wrong size\"\n res_x = x\n if H == self.window_size and W == self.window_size:\n x = self.attn(x)\n else:\n x = x.view(B, H, W, C)\n pad_b = (self.window_size - H %\n self.window_size) % self.window_size\n pad_r = (self.window_size - W %\n self.window_size) % self.window_size\n padding = pad_b > 0 or pad_r > 0\n\n if padding:\n x = F.pad(x, (0, 0, 0, pad_r, 0, pad_b))\n\n pH, pW = H + pad_b, W + pad_r\n nH = pH // self.window_size\n nW = pW // self.window_size\n # window partition\n x = x.view(B, nH, self.window_size, nW, self.window_size, C).transpose(2, 3).reshape(\n B * nH * nW, self.window_size * self.window_size, C)\n x = self.attn(x)\n # window reverse\n x = x.view(B, nH, nW, self.window_size, self.window_size,\n C).transpose(2, 3).reshape(B, pH, pW, C)\n\n if padding:\n x = x[:, :H, :W].contiguous()\n\n x = x.view(B, L, C)\n\n x = res_x + self.drop_path(x)\n\n x = x.transpose(1, 2).reshape(B, C, H, W)\n x = self.local_conv(x)\n x = x.view(B, C, L).transpose(1, 2)\n\n x = x + self.drop_path(self.mlp(x))\n return x\n\n def extra_repr(self) -> str:\n return f\"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, \" \\\n f\"window_size={self.window_size}, mlp_ratio={self.mlp_ratio}\"\n\n\nclass BasicLayer(nn.Module):\n \"\"\" A basic TinyViT layer for one stage.\n\n Args:\n dim (int): Number of input channels.\n input_resolution (tuple[int]): Input resolution.\n depth (int): Number of blocks.\n num_heads (int): Number of attention heads.\n window_size (int): Local window size.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n drop (float, optional): Dropout rate. Default: 0.0\n drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0\n downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None\n use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.\n local_conv_size: the kernel size of the depthwise convolution between attention and MLP. Default: 3\n activation: the activation function. Default: nn.GELU\n out_dim: the output dimension of the layer. Default: dim\n \"\"\"\n\n def __init__(self, dim, input_resolution, depth, num_heads, window_size,\n mlp_ratio=4., drop=0.,\n drop_path=0., downsample=None, use_checkpoint=False,\n local_conv_size=3,\n activation=nn.GELU,\n out_dim=None,\n ):\n\n super().__init__()\n self.dim = dim\n self.input_resolution = input_resolution\n self.depth = depth\n self.use_checkpoint = use_checkpoint\n\n # build blocks\n self.blocks = nn.ModuleList([\n TinyViTBlock(dim=dim, input_resolution=input_resolution,\n num_heads=num_heads, window_size=window_size,\n mlp_ratio=mlp_ratio,\n drop=drop,\n drop_path=drop_path[i] if isinstance(\n drop_path, list) else drop_path,\n local_conv_size=local_conv_size,\n activation=activation,\n )\n for i in range(depth)])\n\n # patch merging layer\n if downsample is not None:\n self.downsample = downsample(\n input_resolution, dim=dim, out_dim=out_dim, activation=activation)\n else:\n self.downsample = None\n\n def forward(self, x):\n for blk in self.blocks:\n if self.use_checkpoint:\n x = checkpoint.checkpoint(blk, x)\n else:\n x = blk(x)\n if self.downsample is not None:\n x = self.downsample(x)\n return x\n\n def extra_repr(self) -> str:\n return f\"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}\"\n\nclass LayerNorm2d(nn.Module):\n def __init__(self, num_channels: int, eps: float = 1e-6) -> None:\n super().__init__()\n self.weight = nn.Parameter(torch.ones(num_channels))\n self.bias = nn.Parameter(torch.zeros(num_channels))\n self.eps = eps\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n u = x.mean(1, keepdim=True)\n s = (x - u).pow(2).mean(1, keepdim=True)\n x = (x - u) / torch.sqrt(s + self.eps)\n x = self.weight[:, None, None] * x + self.bias[:, None, None]\n return x\nclass TinyViT(nn.Module):\n def __init__(self, img_size=224, in_chans=3, num_classes=1000,\n embed_dims=[96, 192, 384, 768], depths=[2, 2, 6, 2],\n num_heads=[3, 6, 12, 24],\n window_sizes=[7, 7, 14, 7],\n mlp_ratio=4.,\n drop_rate=0.,\n drop_path_rate=0.1,\n use_checkpoint=False,\n mbconv_expand_ratio=4.0,\n local_conv_size=3,\n layer_lr_decay=1.0,\n ):\n super().__init__()\n self.img_size=img_size\n self.num_classes = num_classes\n self.depths = depths\n self.num_layers = len(depths)\n self.mlp_ratio = mlp_ratio\n\n activation = nn.GELU\n\n self.patch_embed = PatchEmbed(in_chans=in_chans,\n embed_dim=embed_dims[0],\n resolution=img_size,\n activation=activation)\n\n patches_resolution = self.patch_embed.patches_resolution\n self.patches_resolution = patches_resolution\n\n # stochastic depth\n dpr = [x.item() for x in torch.linspace(0, drop_path_rate,\n sum(depths))] # stochastic depth decay rule\n\n # build layers\n self.layers = nn.ModuleList()\n for i_layer in range(self.num_layers):\n kwargs = dict(dim=embed_dims[i_layer],\n input_resolution=(patches_resolution[0] // (2 ** (i_layer-1 if i_layer == 3 else i_layer)),\n patches_resolution[1] // (2 ** (i_layer-1 if i_layer == 3 else i_layer))),\n # input_resolution=(patches_resolution[0] // (2 ** i_layer),\n # patches_resolution[1] // (2 ** i_layer)),\n depth=depths[i_layer],\n drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],\n downsample=PatchMerging if (\n i_layer < self.num_layers - 1) else None,\n use_checkpoint=use_checkpoint,\n out_dim=embed_dims[min(\n i_layer + 1, len(embed_dims) - 1)],\n activation=activation,\n )\n if i_layer == 0:\n layer = ConvLayer(\n conv_expand_ratio=mbconv_expand_ratio,\n **kwargs,\n )\n else:\n layer = BasicLayer(\n num_heads=num_heads[i_layer],\n window_size=window_sizes[i_layer],\n mlp_ratio=self.mlp_ratio,\n drop=drop_rate,\n local_conv_size=local_conv_size,\n **kwargs)\n self.layers.append(layer)\n\n # Classifier head\n self.norm_head = nn.LayerNorm(embed_dims[-1])\n self.head = nn.Linear(\n embed_dims[-1], num_classes) if num_classes > 0 else torch.nn.Identity()\n\n # init weights\n self.apply(self._init_weights)\n self.set_layer_lr_decay(layer_lr_decay)\n self.neck = nn.Sequential(\n nn.Conv2d(\n embed_dims[-1],#handongshen\n 256,\n kernel_size=1,\n bias=False,\n ),\n LayerNorm2d(256),\n nn.Conv2d(\n 256,\n 256,\n kernel_size=3,\n padding=1,\n bias=False,\n ),\n LayerNorm2d(256),\n )\n def set_layer_lr_decay(self, layer_lr_decay):\n decay_rate = layer_lr_decay\n\n # layers -> blocks (depth)\n depth = sum(self.depths)\n lr_scales = [decay_rate ** (depth - i - 1) for i in range(depth)]\n print(\"LR SCALES:\", lr_scales)\n\n def _set_lr_scale(m, scale):\n for p in m.parameters():\n p.lr_scale = scale\n\n self.patch_embed.apply(lambda x: _set_lr_scale(x, lr_scales[0]))\n i = 0\n for layer in self.layers:\n for block in layer.blocks:\n block.apply(lambda x: _set_lr_scale(x, lr_scales[i]))\n i += 1\n if layer.downsample is not None:\n layer.downsample.apply(\n lambda x: _set_lr_scale(x, lr_scales[i - 1]))\n assert i == depth\n for m in [self.norm_head, self.head]:\n m.apply(lambda x: _set_lr_scale(x, lr_scales[-1]))\n\n for k, p in self.named_parameters():\n p.param_name = k\n\n def _check_lr_scale(m):\n for p in m.parameters():\n assert hasattr(p, 'lr_scale'), p.param_name\n\n self.apply(_check_lr_scale)\n\n def _init_weights(self, m):\n if isinstance(m, nn.Linear):\n trunc_normal_(m.weight, std=.02)\n if isinstance(m, nn.Linear) and m.bias is not None:\n nn.init.constant_(m.bias, 0)\n elif isinstance(m, nn.LayerNorm):\n nn.init.constant_(m.bias, 0)\n nn.init.constant_(m.weight, 1.0)\n\n @torch.jit.ignore\n def no_weight_decay_keywords(self):\n return {'attention_biases'}\n\n def forward_features(self, x):\n # x: (N, C, H, W)\n x = self.patch_embed(x)\n\n x = self.layers[0](x)\n start_i = 1\n\n for i in range(start_i, len(self.layers)):\n layer = self.layers[i]\n x = layer(x)\n B,_,C=x.size()\n x = x.view(B, 64, 64, C)\n x=x.permute(0, 3, 1, 2)\n x=self.neck(x)\n return x\n\n def forward(self, x):\n x = self.forward_features(x)\n \n # We have made some hack changes here to make it compatible with SAM-HQ\n return x, None\n\n\n_checkpoint_url_format = \\\n 'https://github.com/wkcn/TinyViT-model-zoo/releases/download/checkpoints/{}.pth'\n_provided_checkpoints = {\n 'tiny_vit_5m_224': 'tiny_vit_5m_22kto1k_distill',\n 'tiny_vit_11m_224': 'tiny_vit_11m_22kto1k_distill',\n 'tiny_vit_21m_224': 'tiny_vit_21m_22kto1k_distill',\n 'tiny_vit_21m_384': 'tiny_vit_21m_22kto1k_384_distill',\n 'tiny_vit_21m_512': 'tiny_vit_21m_22kto1k_512_distill',\n}\n\n\ndef register_tiny_vit_model(fn):\n '''Register a TinyViT model\n It is a wrapper of `register_model` with loading the pretrained checkpoint.\n '''\n def fn_wrapper(pretrained=False, **kwargs):\n model = fn()\n if pretrained:\n model_name = fn.__name__\n assert model_name in _provided_checkpoints, \\\n f'Sorry that the checkpoint `{model_name}` is not provided yet.'\n url = _checkpoint_url_format.format(\n _provided_checkpoints[model_name])\n checkpoint = torch.hub.load_state_dict_from_url(\n url=url,\n map_location='cpu', check_hash=False,\n )\n model.load_state_dict(checkpoint['model'])\n\n return model\n\n # rename the name of fn_wrapper\n fn_wrapper.__name__ = fn.__name__\n return register_model(fn_wrapper)\n\n\n@register_tiny_vit_model\ndef tiny_vit_5m_224(pretrained=False, num_classes=1000, drop_path_rate=0.0):\n return TinyViT(\n num_classes=num_classes,\n embed_dims=[64, 128, 160, 320],\n depths=[2, 2, 6, 2],\n num_heads=[2, 4, 5, 10],\n window_sizes=[7, 7, 14, 7],\n drop_path_rate=drop_path_rate,\n )\n\n\n@register_tiny_vit_model\ndef tiny_vit_11m_224(pretrained=False, num_classes=1000, drop_path_rate=0.1):\n return TinyViT(\n num_classes=num_classes,\n embed_dims=[64, 128, 256, 448],\n depths=[2, 2, 6, 2],\n num_heads=[2, 4, 8, 14],\n window_sizes=[7, 7, 14, 7],\n drop_path_rate=drop_path_rate,\n )\n\n\n@register_tiny_vit_model\ndef tiny_vit_21m_224(pretrained=False, num_classes=1000, drop_path_rate=0.2):\n return TinyViT(\n num_classes=num_classes,\n embed_dims=[96, 192, 384, 576],\n depths=[2, 2, 6, 2],\n num_heads=[3, 6, 12, 18],\n window_sizes=[7, 7, 14, 7],\n drop_path_rate=drop_path_rate,\n )\n\n\n@register_tiny_vit_model\ndef tiny_vit_21m_384(pretrained=False, num_classes=1000, drop_path_rate=0.1):\n return TinyViT(\n img_size=384,\n num_classes=num_classes,\n embed_dims=[96, 192, 384, 576],\n depths=[2, 2, 6, 2],\n num_heads=[3, 6, 12, 18],\n window_sizes=[12, 12, 24, 12],\n drop_path_rate=drop_path_rate,\n )\n\n\n@register_tiny_vit_model\ndef tiny_vit_21m_512(pretrained=False, num_classes=1000, drop_path_rate=0.1):\n return TinyViT(\n img_size=512,\n num_classes=num_classes,\n embed_dims=[96, 192, 384, 576],\n depths=[2, 2, 6, 2],\n num_heads=[3, 6, 12, 18],\n window_sizes=[16, 16, 32, 16],\n drop_path_rate=drop_path_rate,\n )" }, { "path": "data_generation/Grounded-Segment-Anything/EfficientSAM/README.md", "content": "## Efficient Grounded-SAM\n\nWe're going to combine [Grounding-DINO](https://github.com/IDEA-Research/GroundingDINO) with efficient SAM variants for faster annotating.\n\n\n\n\n### Table of Contents\n- [Installation](#installation)\n- [Efficient SAM Series](#efficient-sams)\n- [Run Grounded-FastSAM Demo](#run-grounded-fastsam-demo)\n- [Run Grounded-MobileSAM Demo](#run-grounded-mobilesam-demo)\n- [Run Grounded-LightHQSAM Demo](#run-grounded-light-hqsam-demo)\n- [Run Grounded-Efficient-SAM Demo](#run-grounded-efficient-sam-demo)\n- [Run Grounded-Edge-SAM Demo](#run-grounded-edge-sam-demo)\n- [Run Grounded-RepViT-SAM Demo](#run-grounded-repvit-sam-demo)\n\n\n### Installation\n\n- Install [Grounded-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything#installation)\n\n- Install [Fast-SAM](https://github.com/CASIA-IVA-Lab/FastSAM#installation)\n\n- Note that we may use the sam image as the demo image in order to compare the inference results of different efficient-sam variants.\n\n### Efficient SAMs\nHere's the list of Efficient SAM variants:\n\n
\n\n| Title | Intro | Description | Links |\n|:----:|:----:|:----:|:----:|\n| [FastSAM](https://arxiv.org/pdf/2306.12156.pdf) | ![](https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/assets/Overview.png) | The Fast Segment Anything Model(FastSAM) is a CNN Segment Anything Model trained by only 2% of the SA-1B dataset published by SAM authors. The FastSAM achieve a comparable performance with the SAM method at 50× higher run-time speed. | [[Github](https://github.com/CASIA-IVA-Lab/FastSAM)] [[Demo](https://huggingface.co/spaces/An-619/FastSAM)] |\n| [MobileSAM](https://arxiv.org/pdf/2306.14289.pdf) | ![](https://github.com/ChaoningZhang/MobileSAM/blob/master/assets/model_diagram.jpg?raw=true) | MobileSAM performs on par with the original SAM (at least visually) and keeps exactly the same pipeline as the original SAM except for a change on the image encoder. Specifically, we replace the original heavyweight ViT-H encoder (632M) with a much smaller Tiny-ViT (5M). On a single GPU, MobileSAM runs around 12ms per image: 8ms on the image encoder and 4ms on the mask decoder. | [[Github](https://github.com/ChaoningZhang/MobileSAM)] |\n| [Light-HQSAM](https://arxiv.org/pdf/2306.01567.pdf) | ![](https://github.com/SysCV/sam-hq/blob/main/figs/sam-hf-framework.png?raw=true) | Light HQ-SAM is based on the tiny vit image encoder provided by MobileSAM. We design a learnable High-Quality Output Token, which is injected into SAM's mask decoder and is responsible for predicting the high-quality mask. Instead of only applying it on mask-decoder features, we first fuse them with ViT features for improved mask details. Refer to [Light HQ-SAM vs. MobileSAM](https://github.com/SysCV/sam-hq#light-hq-sam-vs-mobilesam-on-coco) for more details. | [[Github](https://github.com/SysCV/sam-hq)] |\n| [Efficient-SAM](https://github.com/yformer/EfficientSAM) | ![](https://yformer.github.io/efficient-sam/EfficientSAM_files/overview.png) |Segment Anything Model (SAM) has emerged as a powerful tool for numerous vision applications. However, the huge computation cost of SAM model has limited its applications to wider real-world applications. To address this limitation, we propose EfficientSAMs, light-weight SAM models that exhibit decent performance with largely reduced complexity. Our idea is based on leveraging masked image pretraining, SAMI, which learns to reconstruct features from SAM image encoder for effective visual representation learning. Further, we take SAMI-pretrained light-weight image encoders and mask decoder to build EfficientSAMs, and finetune the models on SA-1B for segment anything task. Refer to [EfficientSAM arXiv](https://arxiv.org/pdf/2312.00863.pdf) for more details.| [[Github](https://github.com/yformer/EfficientSAM)] |\n| [Edge-SAM](https://github.com/chongzhou96/EdgeSAM) | ![](https://www.mmlab-ntu.com/project/edgesam/img/arch.png) | EdgeSAM involves distilling the original ViT-based SAM image encoder into a purely CNN-based architecture, better suited for edge devices. We carefully benchmark various distillation strategies and demonstrate that task-agnostic encoder distillation fails to capture the full knowledge embodied in SAM. Refer to [Edge-SAM arXiv](https://arxiv.org/abs/2312.06660) for more details. | [[Github](https://github.com/chongzhou96/EdgeSAM)] |\n| [RepViT-SAM](https://github.com/THU-MIG/RepViT/tree/main/sam) | ![](https://jameslahm.github.io/repvit-sam/static/images/edge.png) | Recently, RepViT achieves the state-of-the-art performance and latency trade-off on mobile devices by incorporating efficient architectural designs of ViTs into CNNs. Here, to achieve real-time segmenting anything on mobile devices, following MobileSAM, we replace the heavyweight image encoder in SAM with RepViT model, ending up with the RepViT-SAM model. Extensive experiments show that RepViT-SAM can enjoy significantly better zero-shot transfer capability than MobileSAM, along with nearly 10× faster inference speed. Refer to [RepViT-SAM arXiv](https://arxiv.org/pdf/2312.05760.pdf) for more details. | [[Github](https://github.com/THU-MIG/RepViT)] |\n\n
\n\n\n### Run Grounded-FastSAM Demo\n\n- Firstly, download the pretrained Fast-SAM weight [here](https://github.com/CASIA-IVA-Lab/FastSAM#model-checkpoints)\n\n- Run the demo with the following script:\n\n```bash\ncd Grounded-Segment-Anything\n\npython EfficientSAM/grounded_fast_sam.py --model_path \"./FastSAM-x.pt\" --img_path \"assets/demo4.jpg\" --text \"the black dog.\" --output \"./output/\"\n```\n\n- And the results will be saved in `./output/` as:\n\n
\n\n| Input | Text | Output |\n|:---:|:---:|:---:|\n|![](/assets/demo4.jpg) | \"The black dog.\" | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/fast_sam/demo4_0_caption_the%20black%20dog.jpg?raw=true) |\n\n
\n\n\n**Note**: Due to the post process of FastSAM, only one box can be annotated at a time, if there're multiple box prompts, we simply save multiple annotate images to `./output` now, which will be modified in the future release.\n\n\n### Run Grounded-MobileSAM Demo\n\n- Firstly, download the pretrained MobileSAM weight [here](https://github.com/ChaoningZhang/MobileSAM/tree/master/weights)\n\n- Run the demo with the following script:\n\n```bash\ncd Grounded-Segment-Anything\n\npython EfficientSAM/grounded_mobile_sam.py --MOBILE_SAM_CHECKPOINT_PATH \"./EfficientSAM/mobile_sam.pt\" --SOURCE_IMAGE_PATH \"./assets/demo2.jpg\" --CAPTION \"the running dog\"\n```\n\n- And the result will be saved as `./gronded_mobile_sam_anontated_image.jpg` as:\n\n
\n\n| Input | Text | Output |\n|:---:|:---:|:---:|\n|![](/assets/demo2.jpg) | \"the running dog\" | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/mobile_sam/grounded_mobile_sam_annotated_image.jpg?raw=true) |\n\n
\n\n\n### Run Grounded-Light-HQSAM Demo\n\n- Firstly, download the pretrained Light-HQSAM weight [here](https://github.com/SysCV/sam-hq#model-checkpoints)\n\n- Run the demo with the following script:\n\n```bash\ncd Grounded-Segment-Anything\n\npython EfficientSAM/grounded_light_hqsam.py\n```\n\n- And the result will be saved as `./gronded_light_hqsam_anontated_image.jpg` as:\n\n
\n\n| Input | Text | Output |\n|:---:|:---:|:---:|\n|![](/EfficientSAM/LightHQSAM/example_light_hqsam.png) | \"bench\" | ![](/EfficientSAM/LightHQSAM/grounded_light_hqsam_annotated_image.jpg) |\n\n
\n\n\n### Run Grounded-Efficient-SAM Demo\n\n- Download the pretrained EfficientSAM checkpoint from [here](https://github.com/yformer/EfficientSAM#model) and put it under `Grounded-Segment-Anything/EfficientSAM`\n\n- Run the demo with the following script:\n\n```bash\ncd Grounded-Segment-Anything\n\npython EfficientSAM/grounded_efficient_sam.py\n```\n\n- And the result will be saved as `./gronded_efficient_sam_anontated_image.jpg` as:\n\n
\n\n| Input | Text | Output |\n|:---:|:---:|:---:|\n|![](/EfficientSAM/LightHQSAM/example_light_hqsam.png) | \"bench\" | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/efficient_sam/grounded_efficient_sam_annotated_image.jpg?raw=true) |\n\n
\n\n\n### Run Grounded-Edge-SAM Demo\n\n- Download the pretrained [Edge-SAM](https://github.com/chongzhou96/EdgeSAM) checkpoint follow the [official instruction](https://github.com/chongzhou96/EdgeSAM?tab=readme-ov-file#usage-) as:\n\n```bash\ncd Grounded-Segment-Anything\nwget -P EfficientSAM/ https://huggingface.co/spaces/chongzhou/EdgeSAM/resolve/main/weights/edge_sam.pth\nwget -P EfficientSAM/ https://huggingface.co/spaces/chongzhou/EdgeSAM/resolve/main/weights/edge_sam_3x.pth\n```\n\n- Run the demo with the following script:\n\n```bash\ncd Grounded-Segment-Anything\n\npython EfficientSAM/grounded_edge_sam.py\n```\n\n- And the result will be saved as `./gronded_edge_sam_anontated_image.jpg` as:\n\n
\n\n| Input | Text | Output |\n|:---:|:---:|:---:|\n|![](/EfficientSAM/LightHQSAM/example_light_hqsam.png) | \"bench\" | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/edge_sam/grounded_edge_sam_annotated_image.jpg?raw=true) |\n\n
\n\n### Run Grounded-RepViT-SAM Demo\n\n- Download the pretrained [RepViT-SAM](https://github.com/THU-MIG/RepViT) checkpoint follow the [official instruction](https://github.com/THU-MIG/RepViT/tree/main/sam#installation) as:\n\n```bash\ncd Grounded-Segment-Anything\nwget -P EfficientSAM/ https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_sam.pt\n```\n\n- Run the demo with the following script:\n\n```bash\ncd Grounded-Segment-Anything\n\npython EfficientSAM/grounded_repvit_sam.py\n```\n\n- And the result will be saved as `./gronded_repvit_sam_anontated_image.jpg` as:\n\n
\n\n| Input | Text | Output |\n|:---:|:---:|:---:|\n|![](/EfficientSAM/LightHQSAM/example_light_hqsam.png) | \"bench\" | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/repvit_sam/grounded_repvit_sam_annotated_image.jpg?raw=true) |\n\n
\n\n\n" }, { "path": "data_generation/Grounded-Segment-Anything/EfficientSAM/RepViTSAM/repvit.py", "content": "import torch.nn as nn\n\n\n__all__ = ['repvit_m1']\n\n\ndef _make_divisible(v, divisor, min_value=None):\n \"\"\"\n This function is taken from the original tf repo.\n It ensures that all layers have a channel number that is divisible by 8\n It can be seen here:\n https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py\n :param v:\n :param divisor:\n :param min_value:\n :return:\n \"\"\"\n if min_value is None:\n min_value = divisor\n new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)\n # Make sure that round down does not go down by more than 10%.\n if new_v < 0.9 * v:\n new_v += divisor\n return new_v\n\nfrom timm.models.layers import SqueezeExcite\n\nimport torch\n\n# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa\n# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa\nclass LayerNorm2d(nn.Module):\n def __init__(self, num_channels: int, eps: float = 1e-6) -> None:\n super().__init__()\n self.weight = nn.Parameter(torch.ones(num_channels))\n self.bias = nn.Parameter(torch.zeros(num_channels))\n self.eps = eps\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n u = x.mean(1, keepdim=True)\n s = (x - u).pow(2).mean(1, keepdim=True)\n x = (x - u) / torch.sqrt(s + self.eps)\n x = self.weight[:, None, None] * x + self.bias[:, None, None]\n return x\n \nclass Conv2d_BN(torch.nn.Sequential):\n def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1,\n groups=1, bn_weight_init=1, resolution=-10000):\n super().__init__()\n self.add_module('c', torch.nn.Conv2d(\n a, b, ks, stride, pad, dilation, groups, bias=False))\n self.add_module('bn', torch.nn.BatchNorm2d(b))\n torch.nn.init.constant_(self.bn.weight, bn_weight_init)\n torch.nn.init.constant_(self.bn.bias, 0)\n\n @torch.no_grad()\n def fuse(self):\n c, bn = self._modules.values()\n w = bn.weight / (bn.running_var + bn.eps)**0.5\n w = c.weight * w[:, None, None, None]\n b = bn.bias - bn.running_mean * bn.weight / \\\n (bn.running_var + bn.eps)**0.5\n m = torch.nn.Conv2d(w.size(1) * self.c.groups, w.size(\n 0), w.shape[2:], stride=self.c.stride, padding=self.c.padding, dilation=self.c.dilation, groups=self.c.groups,\n device=c.weight.device)\n m.weight.data.copy_(w)\n m.bias.data.copy_(b)\n return m\n\nclass Residual(torch.nn.Module):\n def __init__(self, m, drop=0.):\n super().__init__()\n self.m = m\n self.drop = drop\n\n def forward(self, x):\n if self.training and self.drop > 0:\n return x + self.m(x) * torch.rand(x.size(0), 1, 1, 1,\n device=x.device).ge_(self.drop).div(1 - self.drop).detach()\n else:\n return x + self.m(x)\n \n @torch.no_grad()\n def fuse(self):\n if isinstance(self.m, Conv2d_BN):\n m = self.m.fuse()\n assert(m.groups == m.in_channels)\n identity = torch.ones(m.weight.shape[0], m.weight.shape[1], 1, 1)\n identity = torch.nn.functional.pad(identity, [1,1,1,1])\n m.weight += identity.to(m.weight.device)\n return m\n elif isinstance(self.m, torch.nn.Conv2d):\n m = self.m\n assert(m.groups != m.in_channels)\n identity = torch.ones(m.weight.shape[0], m.weight.shape[1], 1, 1)\n identity = torch.nn.functional.pad(identity, [1,1,1,1])\n m.weight += identity.to(m.weight.device)\n return m\n else:\n return self\n\n\nclass RepVGGDW(torch.nn.Module):\n def __init__(self, ed) -> None:\n super().__init__()\n self.conv = Conv2d_BN(ed, ed, 3, 1, 1, groups=ed)\n self.conv1 = torch.nn.Conv2d(ed, ed, 1, 1, 0, groups=ed)\n self.dim = ed\n self.bn = torch.nn.BatchNorm2d(ed)\n \n def forward(self, x):\n return self.bn((self.conv(x) + self.conv1(x)) + x)\n \n @torch.no_grad()\n def fuse(self):\n conv = self.conv.fuse()\n conv1 = self.conv1\n \n conv_w = conv.weight\n conv_b = conv.bias\n conv1_w = conv1.weight\n conv1_b = conv1.bias\n \n conv1_w = torch.nn.functional.pad(conv1_w, [1,1,1,1])\n\n identity = torch.nn.functional.pad(torch.ones(conv1_w.shape[0], conv1_w.shape[1], 1, 1, device=conv1_w.device), [1,1,1,1])\n\n final_conv_w = conv_w + conv1_w + identity\n final_conv_b = conv_b + conv1_b\n\n conv.weight.data.copy_(final_conv_w)\n conv.bias.data.copy_(final_conv_b)\n\n bn = self.bn\n w = bn.weight / (bn.running_var + bn.eps)**0.5\n w = conv.weight * w[:, None, None, None]\n b = bn.bias + (conv.bias - bn.running_mean) * bn.weight / \\\n (bn.running_var + bn.eps)**0.5\n conv.weight.data.copy_(w)\n conv.bias.data.copy_(b)\n return conv\n\n\nclass RepViTBlock(nn.Module):\n def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se, use_hs):\n super(RepViTBlock, self).__init__()\n assert stride in [1, 2]\n\n self.identity = stride == 1 and inp == oup\n assert(hidden_dim == 2 * inp)\n\n if stride == 2:\n self.token_mixer = nn.Sequential(\n Conv2d_BN(inp, inp, kernel_size, stride if inp != 320 else 1, (kernel_size - 1) // 2, groups=inp),\n SqueezeExcite(inp, 0.25) if use_se else nn.Identity(),\n Conv2d_BN(inp, oup, ks=1, stride=1, pad=0)\n )\n self.channel_mixer = Residual(nn.Sequential(\n # pw\n Conv2d_BN(oup, 2 * oup, 1, 1, 0),\n nn.GELU() if use_hs else nn.GELU(),\n # pw-linear\n Conv2d_BN(2 * oup, oup, 1, 1, 0, bn_weight_init=0),\n ))\n else:\n # assert(self.identity)\n self.token_mixer = nn.Sequential(\n RepVGGDW(inp),\n SqueezeExcite(inp, 0.25) if use_se else nn.Identity(),\n )\n if self.identity:\n self.channel_mixer = Residual(nn.Sequential(\n # pw\n Conv2d_BN(inp, hidden_dim, 1, 1, 0),\n nn.GELU() if use_hs else nn.GELU(),\n # pw-linear\n Conv2d_BN(hidden_dim, oup, 1, 1, 0, bn_weight_init=0),\n ))\n else:\n self.channel_mixer = nn.Sequential(\n # pw\n Conv2d_BN(inp, hidden_dim, 1, 1, 0),\n nn.GELU() if use_hs else nn.GELU(),\n # pw-linear\n Conv2d_BN(hidden_dim, oup, 1, 1, 0, bn_weight_init=0),\n )\n\n def forward(self, x):\n return self.channel_mixer(self.token_mixer(x))\n\nfrom timm.models.vision_transformer import trunc_normal_\nclass BN_Linear(torch.nn.Sequential):\n def __init__(self, a, b, bias=True, std=0.02):\n super().__init__()\n self.add_module('bn', torch.nn.BatchNorm1d(a))\n self.add_module('l', torch.nn.Linear(a, b, bias=bias))\n trunc_normal_(self.l.weight, std=std)\n if bias:\n torch.nn.init.constant_(self.l.bias, 0)\n\n @torch.no_grad()\n def fuse(self):\n bn, l = self._modules.values()\n w = bn.weight / (bn.running_var + bn.eps)**0.5\n b = bn.bias - self.bn.running_mean * \\\n self.bn.weight / (bn.running_var + bn.eps)**0.5\n w = l.weight * w[None, :]\n if l.bias is None:\n b = b @ self.l.weight.T\n else:\n b = (l.weight @ b[:, None]).view(-1) + self.l.bias\n m = torch.nn.Linear(w.size(1), w.size(0), device=l.weight.device)\n m.weight.data.copy_(w)\n m.bias.data.copy_(b)\n return m\n\nclass Classfier(nn.Module):\n def __init__(self, dim, num_classes, distillation=True):\n super().__init__()\n self.classifier = BN_Linear(dim, num_classes) if num_classes > 0 else torch.nn.Identity()\n self.distillation = distillation\n if distillation:\n self.classifier_dist = BN_Linear(dim, num_classes) if num_classes > 0 else torch.nn.Identity()\n\n def forward(self, x):\n if self.distillation:\n x = self.classifier(x), self.classifier_dist(x)\n if not self.training:\n x = (x[0] + x[1]) / 2\n else:\n x = self.classifier(x)\n return x\n\n @torch.no_grad()\n def fuse(self):\n classifier = self.classifier.fuse()\n if self.distillation:\n classifier_dist = self.classifier_dist.fuse()\n classifier.weight += classifier_dist.weight\n classifier.bias += classifier_dist.bias\n classifier.weight /= 2\n classifier.bias /= 2\n return classifier\n else:\n return classifier\n\nclass RepViT(nn.Module):\n def __init__(self, cfgs, num_classes=1000, distillation=False, img_size=1024):\n super(RepViT, self).__init__()\n # setting of inverted residual blocks\n self.cfgs = cfgs\n\n self.img_size = img_size\n\n # building first layer\n input_channel = self.cfgs[0][2]\n patch_embed = torch.nn.Sequential(Conv2d_BN(3, input_channel // 2, 3, 2, 1), torch.nn.GELU(),\n Conv2d_BN(input_channel // 2, input_channel, 3, 2, 1))\n layers = [patch_embed]\n # building inverted residual blocks\n block = RepViTBlock\n for k, t, c, use_se, use_hs, s in self.cfgs:\n output_channel = _make_divisible(c, 8)\n exp_size = _make_divisible(input_channel * t, 8)\n layers.append(block(input_channel, exp_size, output_channel, k, s, use_se, use_hs))\n input_channel = output_channel\n self.features = nn.ModuleList(layers)\n # self.classifier = Classfier(output_channel, num_classes, distillation)\n \n self.neck = nn.Sequential(\n nn.Conv2d(\n output_channel,\n 256,\n kernel_size=1,\n bias=False,\n ),\n LayerNorm2d(256),\n nn.Conv2d(\n 256,\n 256,\n kernel_size=3,\n padding=1,\n bias=False,\n ),\n LayerNorm2d(256),\n )\n\n def forward(self, x):\n # x = self.features(x)\n for f in self.features:\n x = f(x)\n # x = torch.nn.functional.adaptive_avg_pool2d(x, 1).flatten(1)\n x = self.neck(x)\n return x, None\n\nfrom timm.models import register_model\n\n@register_model\ndef repvit(pretrained=False, num_classes = 1000, distillation=False, **kwargs):\n \"\"\"\n Constructs a MobileNetV3-Large model\n \"\"\"\n cfgs = [\n # k, t, c, SE, HS, s \n [3, 2, 80, 1, 0, 1],\n [3, 2, 80, 0, 0, 1],\n [3, 2, 80, 1, 0, 1],\n [3, 2, 80, 0, 0, 1],\n [3, 2, 80, 1, 0, 1],\n [3, 2, 80, 0, 0, 1],\n [3, 2, 80, 0, 0, 1],\n [3, 2, 160, 0, 0, 2],\n [3, 2, 160, 1, 0, 1],\n [3, 2, 160, 0, 0, 1],\n [3, 2, 160, 1, 0, 1],\n [3, 2, 160, 0, 0, 1],\n [3, 2, 160, 1, 0, 1],\n [3, 2, 160, 0, 0, 1],\n [3, 2, 160, 0, 0, 1],\n [3, 2, 320, 0, 1, 2],\n [3, 2, 320, 1, 1, 1],\n [3, 2, 320, 0, 1, 1],\n [3, 2, 320, 1, 1, 1],\n [3, 2, 320, 0, 1, 1],\n [3, 2, 320, 1, 1, 1],\n [3, 2, 320, 0, 1, 1],\n [3, 2, 320, 1, 1, 1],\n [3, 2, 320, 0, 1, 1],\n [3, 2, 320, 1, 1, 1],\n [3, 2, 320, 0, 1, 1],\n [3, 2, 320, 1, 1, 1],\n [3, 2, 320, 0, 1, 1],\n [3, 2, 320, 1, 1, 1],\n [3, 2, 320, 0, 1, 1],\n [3, 2, 320, 1, 1, 1],\n [3, 2, 320, 0, 1, 1],\n [3, 2, 320, 1, 1, 1],\n [3, 2, 320, 0, 1, 1],\n [3, 2, 320, 1, 1, 1],\n [3, 2, 320, 0, 1, 1],\n [3, 2, 320, 1, 1, 1],\n [3, 2, 320, 0, 1, 1],\n [3, 2, 320, 1, 1, 1],\n [3, 2, 320, 0, 1, 1],\n [3, 2, 320, 1, 1, 1],\n [3, 2, 320, 0, 1, 1],\n [3, 2, 320, 1, 1, 1],\n [3, 2, 320, 0, 1, 1],\n [3, 2, 320, 1, 1, 1],\n [3, 2, 320, 0, 1, 1],\n [3, 2, 320, 1, 1, 1],\n [3, 2, 320, 0, 1, 1],\n [3, 2, 320, 1, 1, 1],\n [3, 2, 320, 0, 1, 1],\n # [3, 2, 320, 1, 1, 1],\n # [3, 2, 320, 0, 1, 1],\n [3, 2, 320, 0, 1, 1],\n [3, 2, 640, 0, 1, 2],\n [3, 2, 640, 1, 1, 1],\n [3, 2, 640, 0, 1, 1],\n # [3, 2, 640, 1, 1, 1],\n # [3, 2, 640, 0, 1, 1]\n ] \n return RepViT(cfgs, num_classes=num_classes, distillation=distillation)" }, { "path": "data_generation/Grounded-Segment-Anything/EfficientSAM/RepViTSAM/setup_repvit_sam.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nfrom functools import partial\nfrom segment_anything.modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWayTransformer\nfrom RepViTSAM import repvit\nfrom timm.models import create_model\n\ndef build_sam_repvit(checkpoint=None):\n prompt_embed_dim = 256\n image_size = 1024\n vit_patch_size = 16\n image_embedding_size = image_size // vit_patch_size\n repvit_sam = Sam(\n image_encoder=create_model('repvit'),\n prompt_encoder=PromptEncoder(\n embed_dim=prompt_embed_dim,\n image_embedding_size=(image_embedding_size, image_embedding_size),\n input_image_size=(image_size, image_size),\n mask_in_chans=16,\n ),\n mask_decoder=MaskDecoder(\n num_multimask_outputs=3,\n transformer=TwoWayTransformer(\n depth=2,\n embedding_dim=prompt_embed_dim,\n mlp_dim=2048,\n num_heads=8,\n ),\n transformer_dim=prompt_embed_dim,\n iou_head_depth=3,\n iou_head_hidden_dim=256,\n ),\n pixel_mean=[123.675, 116.28, 103.53],\n pixel_std=[58.395, 57.12, 57.375],\n )\n\n repvit_sam.eval()\n if checkpoint is not None:\n with open(checkpoint, \"rb\") as f:\n state_dict = torch.load(f)\n repvit_sam.load_state_dict(state_dict)\n return repvit_sam\n\nfrom functools import partial\n\nsam_model_registry = {\n \"repvit\": partial(build_sam_repvit),\n}\n" }, { "path": "data_generation/Grounded-Segment-Anything/EfficientSAM/grounded_edge_sam.py", "content": "import cv2\nimport numpy as np\nimport supervision as sv\n\nimport torch\nimport torchvision\n\nfrom groundingdino.util.inference import Model\nfrom segment_anything import SamPredictor\nfrom EdgeSAM.setup_edge_sam import build_edge_sam\n\nDEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n# GroundingDINO config and checkpoint\nGROUNDING_DINO_CONFIG_PATH = \"GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py\"\nGROUNDING_DINO_CHECKPOINT_PATH = \"./groundingdino_swint_ogc.pth\"\n\n# Building GroundingDINO inference model\ngrounding_dino_model = Model(model_config_path=GROUNDING_DINO_CONFIG_PATH, model_checkpoint_path=GROUNDING_DINO_CHECKPOINT_PATH)\n\n# Building MobileSAM predictor\nEdgeSAM_CHECKPOINT_PATH = \"./EfficientSAM/edge_sam_3x.pth\"\nedge_sam = build_edge_sam(checkpoint=EdgeSAM_CHECKPOINT_PATH)\nedge_sam.to(device=DEVICE)\n\nsam_predictor = SamPredictor(edge_sam)\n\n\n# Predict classes and hyper-param for GroundingDINO\nSOURCE_IMAGE_PATH = \"./EfficientSAM/LightHQSAM/example_light_hqsam.png\"\nCLASSES = [\"bench\"]\nBOX_THRESHOLD = 0.25\nTEXT_THRESHOLD = 0.25\nNMS_THRESHOLD = 0.8\n\n\n# load image\nimage = cv2.imread(SOURCE_IMAGE_PATH)\n\n# detect objects\ndetections = grounding_dino_model.predict_with_classes(\n image=image,\n classes=CLASSES,\n box_threshold=BOX_THRESHOLD,\n text_threshold=TEXT_THRESHOLD\n)\n\n# annotate image with detections\nbox_annotator = sv.BoxAnnotator()\nlabels = [\n f\"{CLASSES[class_id]} {confidence:0.2f}\" \n for _, _, confidence, class_id, _, _ \n in detections]\nannotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections, labels=labels)\n\n# save the annotated grounding dino image\ncv2.imwrite(\"EfficientSAM/LightHQSAM/groundingdino_annotated_image.jpg\", annotated_frame)\n\n\n# NMS post process\nprint(f\"Before NMS: {len(detections.xyxy)} boxes\")\nnms_idx = torchvision.ops.nms(\n torch.from_numpy(detections.xyxy), \n torch.from_numpy(detections.confidence), \n NMS_THRESHOLD\n).numpy().tolist()\n\ndetections.xyxy = detections.xyxy[nms_idx]\ndetections.confidence = detections.confidence[nms_idx]\ndetections.class_id = detections.class_id[nms_idx]\n\nprint(f\"After NMS: {len(detections.xyxy)} boxes\")\n\n# Prompting SAM with detected boxes\ndef segment(sam_predictor: SamPredictor, image: np.ndarray, xyxy: np.ndarray) -> np.ndarray:\n sam_predictor.set_image(image)\n result_masks = []\n for box in xyxy:\n masks, scores, logits = sam_predictor.predict(\n box=box,\n multimask_output=False,\n hq_token_only=True,\n )\n index = np.argmax(scores)\n result_masks.append(masks[index])\n return np.array(result_masks)\n\n\n# convert detections to masks\ndetections.mask = segment(\n sam_predictor=sam_predictor,\n image=cv2.cvtColor(image, cv2.COLOR_BGR2RGB),\n xyxy=detections.xyxy\n)\n\n# annotate image with detections\nbox_annotator = sv.BoxAnnotator()\nmask_annotator = sv.MaskAnnotator()\nlabels = [\n f\"{CLASSES[class_id]} {confidence:0.2f}\" \n for _, _, confidence, class_id, _, _ \n in detections]\nannotated_image = mask_annotator.annotate(scene=image.copy(), detections=detections)\nannotated_image = box_annotator.annotate(scene=annotated_image, detections=detections, labels=labels)\n\n# save the annotated grounded-sam image\ncv2.imwrite(\"EfficientSAM/grounded_edge_sam_annotated_image.jpg\", annotated_image)\n" }, { "path": "data_generation/Grounded-Segment-Anything/EfficientSAM/grounded_efficient_sam.py", "content": "import cv2\nimport numpy as np\nimport supervision as sv\n\nimport torch\nimport torchvision\nfrom torchvision.transforms import ToTensor\n\nfrom groundingdino.util.inference import Model\n\nDEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n# GroundingDINO config and checkpoint\nGROUNDING_DINO_CONFIG_PATH = \"GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py\"\nGROUNDING_DINO_CHECKPOINT_PATH = \"./groundingdino_swint_ogc.pth\"\n\n# Building GroundingDINO inference model\ngrounding_dino_model = Model(model_config_path=GROUNDING_DINO_CONFIG_PATH, model_checkpoint_path=GROUNDING_DINO_CHECKPOINT_PATH)\n\n# Building MobileSAM predictor\nEFFICIENT_SAM_CHECHPOINT_PATH = \"./EfficientSAM/efficientsam_s_gpu.jit\"\nefficientsam = torch.jit.load(EFFICIENT_SAM_CHECHPOINT_PATH)\n\n\n# Predict classes and hyper-param for GroundingDINO\nSOURCE_IMAGE_PATH = \"./EfficientSAM/LightHQSAM/example_light_hqsam.png\"\nCLASSES = [\"bench\"]\nBOX_THRESHOLD = 0.25\nTEXT_THRESHOLD = 0.25\nNMS_THRESHOLD = 0.8\n\n\n# load image\nimage = cv2.imread(SOURCE_IMAGE_PATH)\n\n# detect objects\ndetections = grounding_dino_model.predict_with_classes(\n image=image,\n classes=CLASSES,\n box_threshold=BOX_THRESHOLD,\n text_threshold=TEXT_THRESHOLD\n)\n\n# annotate image with detections\nbox_annotator = sv.BoxAnnotator()\nlabels = [\n f\"{CLASSES[class_id]} {confidence:0.2f}\" \n for _, _, confidence, class_id, _, _ \n in detections]\nannotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections, labels=labels)\n\n# save the annotated grounding dino image\ncv2.imwrite(\"EfficientSAM/LightHQSAM/groundingdino_annotated_image.jpg\", annotated_frame)\n\n\n# NMS post process\nprint(f\"Before NMS: {len(detections.xyxy)} boxes\")\nnms_idx = torchvision.ops.nms(\n torch.from_numpy(detections.xyxy), \n torch.from_numpy(detections.confidence), \n NMS_THRESHOLD\n).numpy().tolist()\n\ndetections.xyxy = detections.xyxy[nms_idx]\ndetections.confidence = detections.confidence[nms_idx]\ndetections.class_id = detections.class_id[nms_idx]\n\nprint(f\"After NMS: {len(detections.xyxy)} boxes\")\n\n\ndef efficient_sam_box_prompt_segment(image, pts_sampled, model):\n bbox = torch.reshape(torch.tensor(pts_sampled), [1, 1, 2, 2])\n bbox_labels = torch.reshape(torch.tensor([2, 3]), [1, 1, 2])\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n img_tensor = ToTensor()(image)\n\n predicted_logits, predicted_iou = model(\n img_tensor[None, ...].cuda(),\n bbox.cuda(),\n bbox_labels.cuda(),\n )\n predicted_logits = predicted_logits.cpu()\n all_masks = torch.ge(torch.sigmoid(predicted_logits[0, 0, :, :, :]), 0.5).numpy()\n predicted_iou = predicted_iou[0, 0, ...].cpu().detach().numpy()\n\n max_predicted_iou = -1\n selected_mask_using_predicted_iou = None\n for m in range(all_masks.shape[0]):\n curr_predicted_iou = predicted_iou[m]\n if (\n curr_predicted_iou > max_predicted_iou\n or selected_mask_using_predicted_iou is None\n ):\n max_predicted_iou = curr_predicted_iou\n selected_mask_using_predicted_iou = all_masks[m]\n return selected_mask_using_predicted_iou\n\n\n# collect segment results from EfficientSAM\nresult_masks = []\nfor box in detections.xyxy:\n mask = efficient_sam_box_prompt_segment(image, box, efficientsam)\n result_masks.append(mask)\n\ndetections.mask = np.array(result_masks)\n\n# annotate image with detections\nbox_annotator = sv.BoxAnnotator()\nmask_annotator = sv.MaskAnnotator()\nlabels = [\n f\"{CLASSES[class_id]} {confidence:0.2f}\" \n for _, _, confidence, class_id, _, _ \n in detections]\nannotated_image = mask_annotator.annotate(scene=image.copy(), detections=detections)\nannotated_image = box_annotator.annotate(scene=annotated_image, detections=detections, labels=labels)\n\n# save the annotated grounded-sam image\ncv2.imwrite(\"EfficientSAM/gronded_efficient_sam_anontated_image.jpg\", annotated_image)\n" }, { "path": "data_generation/Grounded-Segment-Anything/EfficientSAM/grounded_fast_sam.py", "content": "import argparse\nimport cv2\nfrom ultralytics import YOLO\nfrom FastSAM.tools import *\nfrom groundingdino.util.inference import load_model, load_image, predict, annotate, Model\nfrom torchvision.ops import box_convert\nimport ast\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--model_path\", type=str, default=\"./FastSAM/FastSAM-x.pt\", help=\"model\"\n )\n parser.add_argument(\n \"--img_path\", type=str, default=\"./images/dogs.jpg\", help=\"path to image file\"\n )\n parser.add_argument(\n \"--text\", type=str, default=\"the black dog.\", help=\"text prompt for GroundingDINO\"\n )\n parser.add_argument(\"--imgsz\", type=int, default=1024, help=\"image size\")\n parser.add_argument(\n \"--iou\",\n type=float,\n default=0.9,\n help=\"iou threshold for filtering the annotations\",\n )\n parser.add_argument(\n \"--conf\", type=float, default=0.4, help=\"object confidence threshold\"\n )\n parser.add_argument(\n \"--output\", type=str, default=\"./output/\", help=\"image save path\"\n )\n parser.add_argument(\n \"--randomcolor\", type=bool, default=True, help=\"mask random color\"\n )\n parser.add_argument(\n \"--point_prompt\", type=str, default=\"[[0,0]]\", help=\"[[x1,y1],[x2,y2]]\"\n )\n parser.add_argument(\n \"--point_label\",\n type=str,\n default=\"[0]\",\n help=\"[1,0] 0:background, 1:foreground\",\n )\n parser.add_argument(\"--box_prompt\", type=str, default=\"[0,0,0,0]\", help=\"[x,y,w,h]\")\n parser.add_argument(\n \"--better_quality\",\n type=str,\n default=False,\n help=\"better quality using morphologyEx\",\n )\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n parser.add_argument(\n \"--device\", type=str, default=device, help=\"cuda:[0,1,2,3,4] or cpu\"\n )\n parser.add_argument(\n \"--retina\",\n type=bool,\n default=True,\n help=\"draw high-resolution segmentation masks\",\n )\n parser.add_argument(\n \"--withContours\", type=bool, default=False, help=\"draw the edges of the masks\"\n )\n return parser.parse_args()\n\n\ndef main(args):\n\n # Image Path\n img_path = args.img_path\n text = args.text\n\n # path to save img\n save_path = args.output\n if not os.path.exists(save_path):\n os.makedirs(save_path)\n basename = os.path.basename(args.img_path).split(\".\")[0]\n\n # Build Fast-SAM Model\n # ckpt_path = \"/comp_robot/rentianhe/code/Grounded-Segment-Anything/FastSAM/FastSAM-x.pt\"\n model = YOLO(args.model_path)\n\n results = model(\n args.img_path,\n imgsz=args.imgsz,\n device=args.device,\n retina_masks=args.retina,\n iou=args.iou,\n conf=args.conf,\n max_det=100,\n )\n\n\n # Build GroundingDINO Model\n groundingdino_config = \"GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py\"\n groundingdino_ckpt_path = \"./groundingdino_swint_ogc.pth\"\n\n image_source, image = load_image(img_path)\n model = load_model(groundingdino_config, groundingdino_ckpt_path)\n\n boxes, logits, phrases = predict(\n model=model,\n image=image,\n caption=text,\n box_threshold=0.3,\n text_threshold=0.25,\n device=args.device,\n )\n\n\n # Grounded-Fast-SAM\n\n ori_img = cv2.imread(img_path)\n ori_h = ori_img.shape[0]\n ori_w = ori_img.shape[1]\n\n # Save each frame due to the post process from FastSAM\n boxes = boxes * torch.Tensor([ori_w, ori_h, ori_w, ori_h])\n print(f\"Detected Boxes: {len(boxes)}\")\n boxes = box_convert(boxes=boxes, in_fmt=\"cxcywh\", out_fmt=\"xyxy\").cpu().numpy().tolist()\n for box_idx in range(len(boxes)):\n mask, _ = box_prompt(\n results[0].masks.data,\n boxes[box_idx],\n ori_h,\n ori_w,\n )\n annotations = np.array([mask])\n img_array = fast_process(\n annotations=annotations,\n args=args,\n mask_random_color=True,\n bbox=boxes[box_idx],\n )\n cv2.imwrite(os.path.join(save_path, basename + f\"_{str(box_idx)}_caption_{phrases[box_idx]}.jpg\"), cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR))\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "data_generation/Grounded-Segment-Anything/EfficientSAM/grounded_light_hqsam.py", "content": "import cv2\nimport numpy as np\nimport supervision as sv\n\nimport torch\nimport torchvision\n\nfrom groundingdino.util.inference import Model\nfrom segment_anything import SamPredictor\nfrom LightHQSAM.setup_light_hqsam import setup_model\n\nDEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n# GroundingDINO config and checkpoint\nGROUNDING_DINO_CONFIG_PATH = \"GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py\"\nGROUNDING_DINO_CHECKPOINT_PATH = \"./groundingdino_swint_ogc.pth\"\n\n# Building GroundingDINO inference model\ngrounding_dino_model = Model(model_config_path=GROUNDING_DINO_CONFIG_PATH, model_checkpoint_path=GROUNDING_DINO_CHECKPOINT_PATH)\n\n# Building MobileSAM predictor\nHQSAM_CHECKPOINT_PATH = \"./EfficientSAM/sam_hq_vit_tiny.pth\"\ncheckpoint = torch.load(HQSAM_CHECKPOINT_PATH)\nlight_hqsam = setup_model()\nlight_hqsam.load_state_dict(checkpoint, strict=True)\nlight_hqsam.to(device=DEVICE)\n\nsam_predictor = SamPredictor(light_hqsam)\n\n\n# Predict classes and hyper-param for GroundingDINO\nSOURCE_IMAGE_PATH = \"./EfficientSAM/LightHQSAM/example_light_hqsam.png\"\nCLASSES = [\"bench\"]\nBOX_THRESHOLD = 0.25\nTEXT_THRESHOLD = 0.25\nNMS_THRESHOLD = 0.8\n\n\n# load image\nimage = cv2.imread(SOURCE_IMAGE_PATH)\n\n# detect objects\ndetections = grounding_dino_model.predict_with_classes(\n image=image,\n classes=CLASSES,\n box_threshold=BOX_THRESHOLD,\n text_threshold=TEXT_THRESHOLD\n)\n\n# annotate image with detections\nbox_annotator = sv.BoxAnnotator()\nlabels = [\n f\"{CLASSES[class_id]} {confidence:0.2f}\" \n for _, _, confidence, class_id, _, _\n in detections]\nannotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections, labels=labels)\n\n# save the annotated grounding dino image\ncv2.imwrite(\"EfficientSAM/LightHQSAM/groundingdino_annotated_image.jpg\", annotated_frame)\n\n\n# NMS post process\nprint(f\"Before NMS: {len(detections.xyxy)} boxes\")\nnms_idx = torchvision.ops.nms(\n torch.from_numpy(detections.xyxy), \n torch.from_numpy(detections.confidence), \n NMS_THRESHOLD\n).numpy().tolist()\n\ndetections.xyxy = detections.xyxy[nms_idx]\ndetections.confidence = detections.confidence[nms_idx]\ndetections.class_id = detections.class_id[nms_idx]\n\nprint(f\"After NMS: {len(detections.xyxy)} boxes\")\n\n# Prompting SAM with detected boxes\ndef segment(sam_predictor: SamPredictor, image: np.ndarray, xyxy: np.ndarray) -> np.ndarray:\n sam_predictor.set_image(image)\n result_masks = []\n for box in xyxy:\n masks, scores, logits = sam_predictor.predict(\n box=box,\n multimask_output=False,\n hq_token_only=True,\n )\n index = np.argmax(scores)\n result_masks.append(masks[index])\n return np.array(result_masks)\n\n\n# convert detections to masks\ndetections.mask = segment(\n sam_predictor=sam_predictor,\n image=cv2.cvtColor(image, cv2.COLOR_BGR2RGB),\n xyxy=detections.xyxy\n)\n\n# annotate image with detections\nbox_annotator = sv.BoxAnnotator()\nmask_annotator = sv.MaskAnnotator()\nlabels = [\n f\"{CLASSES[class_id]} {confidence:0.2f}\" \n for _, _, confidence, class_id, _, _\n in detections]\nannotated_image = mask_annotator.annotate(scene=image.copy(), detections=detections)\nannotated_image = box_annotator.annotate(scene=annotated_image, detections=detections, labels=labels)\n\n# save the annotated grounded-sam image\ncv2.imwrite(\"EfficientSAM/LightHQSAM/grounded_light_hqsam_annotated_image.jpg\", annotated_image)\n" }, { "path": "data_generation/Grounded-Segment-Anything/EfficientSAM/grounded_mobile_sam.py", "content": "import cv2\nimport numpy as np\nimport supervision as sv\nimport argparse\nimport torch\nimport torchvision\n\nfrom groundingdino.util.inference import Model\nfrom segment_anything import SamPredictor\nfrom MobileSAM.setup_mobile_sam import setup_model\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--MOBILE_SAM_CHECKPOINT_PATH\", type=str, default=\"./EfficientSAM/mobile_sam.pt\", help=\"model\"\n )\n parser.add_argument(\n \"--SOURCE_IMAGE_PATH\", type=str, default=\"./assets/demo2.jpg\", help=\"path to image file\"\n )\n parser.add_argument(\n \"--CAPTION\", type=str, default=\"The running dog\", help=\"text prompt for GroundingDINO\"\n )\n parser.add_argument(\n \"--OUT_FILE_BOX\", type=str, default=\"groundingdino_annotated_image.jpg\", help=\"the output filename\"\n )\n parser.add_argument(\n \"--OUT_FILE_SEG\", type=str, default=\"grounded_mobile_sam_annotated_image.jpg\", help=\"the output filename\"\n )\n parser.add_argument(\n \"--OUT_FILE_BIN_MASK\", type=str, default=\"grounded_mobile_sam_bin_mask.jpg\", help=\"the output filename\"\n )\n parser.add_argument(\"--BOX_THRESHOLD\", type=float, default=0.25, help=\"\")\n parser.add_argument(\"--TEXT_THRESHOLD\", type=float, default=0.25, help=\"\")\n parser.add_argument(\"--NMS_THRESHOLD\", type=float, default=0.8, help=\"\")\n\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n parser.add_argument(\n \"--DEVICE\", type=str, default=device, help=\"cuda:[0,1,2,3,4] or cpu\"\n )\n return parser.parse_args()\n\ndef main(args):\n DEVICE = args.DEVICE\n\n # GroundingDINO config and checkpoint\n GROUNDING_DINO_CONFIG_PATH = \"GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py\"\n GROUNDING_DINO_CHECKPOINT_PATH = \"./groundingdino_swint_ogc.pth\"\n\n # Building GroundingDINO inference model\n grounding_dino_model = Model(model_config_path=GROUNDING_DINO_CONFIG_PATH, model_checkpoint_path=GROUNDING_DINO_CHECKPOINT_PATH)\n\n # Building MobileSAM predictor\n MOBILE_SAM_CHECKPOINT_PATH = args.MOBILE_SAM_CHECKPOINT_PATH\n checkpoint = torch.load(MOBILE_SAM_CHECKPOINT_PATH)\n mobile_sam = setup_model()\n mobile_sam.load_state_dict(checkpoint, strict=True)\n mobile_sam.to(device=DEVICE)\n\n sam_predictor = SamPredictor(mobile_sam)\n\n\n # Predict classes and hyper-param for GroundingDINO\n SOURCE_IMAGE_PATH = args.SOURCE_IMAGE_PATH\n CLASSES = [args.CAPTION]\n BOX_THRESHOLD = args.BOX_THRESHOLD\n TEXT_THRESHOLD = args.TEXT_THRESHOLD\n NMS_THRESHOLD = args.NMS_THRESHOLD\n\n\n # load image\n image = cv2.imread(SOURCE_IMAGE_PATH)\n\n # detect objects\n detections = grounding_dino_model.predict_with_classes(\n image=image,\n classes=CLASSES,\n box_threshold=BOX_THRESHOLD,\n text_threshold=TEXT_THRESHOLD\n )\n\n # annotate image with detections\n box_annotator = sv.BoxAnnotator()\n labels = [\n f\"{CLASSES[class_id]} {confidence:0.2f}\" \n for _, _, confidence, class_id, _, _ \n in detections]\n annotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections, labels=labels)\n\n # save the annotated grounding dino image\n cv2.imwrite(args.OUT_FILE_BOX, annotated_frame)\n\n\n # NMS post process\n print(f\"Before NMS: {len(detections.xyxy)} boxes\")\n nms_idx = torchvision.ops.nms(\n torch.from_numpy(detections.xyxy), \n torch.from_numpy(detections.confidence), \n NMS_THRESHOLD\n ).numpy().tolist()\n\n detections.xyxy = detections.xyxy[nms_idx]\n detections.confidence = detections.confidence[nms_idx]\n detections.class_id = detections.class_id[nms_idx]\n\n print(f\"After NMS: {len(detections.xyxy)} boxes\")\n\n # Prompting SAM with detected boxes\n def segment(sam_predictor: SamPredictor, image: np.ndarray, xyxy: np.ndarray) -> np.ndarray:\n sam_predictor.set_image(image)\n result_masks = []\n for box in xyxy:\n masks, scores, logits = sam_predictor.predict(\n box=box,\n multimask_output=True\n )\n index = np.argmax(scores)\n result_masks.append(masks[index])\n return np.array(result_masks)\n\n\n # convert detections to masks\n detections.mask = segment(\n sam_predictor=sam_predictor,\n image=cv2.cvtColor(image, cv2.COLOR_BGR2RGB),\n xyxy=detections.xyxy\n )\n\n binary_mask = detections.mask[0].astype(np.uint8)*255\n cv2.imwrite(args.OUT_FILE_BIN_MASK, binary_mask)\n\n # annotate image with detections\n box_annotator = sv.BoxAnnotator()\n mask_annotator = sv.MaskAnnotator()\n labels = [\n f\"{CLASSES[class_id]} {confidence:0.2f}\" \n for _, _, confidence, class_id, _, _ \n in detections]\n annotated_image = mask_annotator.annotate(scene=image.copy(), detections=detections)\n annotated_image = box_annotator.annotate(scene=annotated_image, detections=detections, labels=labels)\n # save the annotated grounded-sam image\n cv2.imwrite(args.OUT_FILE_SEG, annotated_image)\n \nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "data_generation/Grounded-Segment-Anything/EfficientSAM/grounded_repvit_sam.py", "content": "import cv2\nimport numpy as np\nimport supervision as sv\n\nimport torch\nimport torchvision\n\nfrom groundingdino.util.inference import Model\nfrom segment_anything import SamPredictor\nfrom RepViTSAM.setup_repvit_sam import build_sam_repvit\n\nDEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n# GroundingDINO config and checkpoint\nGROUNDING_DINO_CONFIG_PATH = \"GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py\"\nGROUNDING_DINO_CHECKPOINT_PATH = \"./groundingdino_swint_ogc.pth\"\n\n# Building GroundingDINO inference model\ngrounding_dino_model = Model(model_config_path=GROUNDING_DINO_CONFIG_PATH, model_checkpoint_path=GROUNDING_DINO_CHECKPOINT_PATH)\n\n# Building MobileSAM predictor\nRepViTSAM_CHECKPOINT_PATH = \"./EfficientSAM/repvit_sam.pt\"\nrepvit_sam = build_sam_repvit(checkpoint=RepViTSAM_CHECKPOINT_PATH)\nrepvit_sam.to(device=DEVICE)\n\nsam_predictor = SamPredictor(repvit_sam)\n\n\n# Predict classes and hyper-param for GroundingDINO\nSOURCE_IMAGE_PATH = \"./EfficientSAM/LightHQSAM/example_light_hqsam.png\"\nCLASSES = [\"bench\"]\nBOX_THRESHOLD = 0.25\nTEXT_THRESHOLD = 0.25\nNMS_THRESHOLD = 0.8\n\n\n# load image\nimage = cv2.imread(SOURCE_IMAGE_PATH)\n\n# detect objects\ndetections = grounding_dino_model.predict_with_classes(\n image=image,\n classes=CLASSES,\n box_threshold=BOX_THRESHOLD,\n text_threshold=TEXT_THRESHOLD\n)\n\n# annotate image with detections\nbox_annotator = sv.BoxAnnotator()\nlabels = [\n f\"{CLASSES[class_id]} {confidence:0.2f}\" \n for _, _, confidence, class_id, _, _ \n in detections]\nannotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections, labels=labels)\n\n# save the annotated grounding dino image\ncv2.imwrite(\"EfficientSAM/LightHQSAM/groundingdino_annotated_image.jpg\", annotated_frame)\n\n\n# NMS post process\nprint(f\"Before NMS: {len(detections.xyxy)} boxes\")\nnms_idx = torchvision.ops.nms(\n torch.from_numpy(detections.xyxy), \n torch.from_numpy(detections.confidence), \n NMS_THRESHOLD\n).numpy().tolist()\n\ndetections.xyxy = detections.xyxy[nms_idx]\ndetections.confidence = detections.confidence[nms_idx]\ndetections.class_id = detections.class_id[nms_idx]\n\nprint(f\"After NMS: {len(detections.xyxy)} boxes\")\n\n# Prompting SAM with detected boxes\ndef segment(sam_predictor: SamPredictor, image: np.ndarray, xyxy: np.ndarray) -> np.ndarray:\n sam_predictor.set_image(image)\n result_masks = []\n for box in xyxy:\n masks, scores, logits = sam_predictor.predict(\n box=box,\n multimask_output=False,\n hq_token_only=True,\n )\n index = np.argmax(scores)\n result_masks.append(masks[index])\n return np.array(result_masks)\n\n\n# convert detections to masks\ndetections.mask = segment(\n sam_predictor=sam_predictor,\n image=cv2.cvtColor(image, cv2.COLOR_BGR2RGB),\n xyxy=detections.xyxy\n)\n\n# annotate image with detections\nbox_annotator = sv.BoxAnnotator()\nmask_annotator = sv.MaskAnnotator()\nlabels = [\n f\"{CLASSES[class_id]} {confidence:0.2f}\" \n for _, _, confidence, class_id, _, _ \n in detections]\nannotated_image = mask_annotator.annotate(scene=image.copy(), detections=detections)\nannotated_image = box_annotator.annotate(scene=annotated_image, detections=detections, labels=labels)\n\n# save the annotated grounded-sam image\ncv2.imwrite(\"EfficientSAM/grounded_repvit_sam_annotated_image.jpg\", annotated_image)\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/LICENSE", "content": " Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n\n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n 1. Definitions.\n\n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n\n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n\n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. 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We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n\n Copyright 2020 - present, Facebook, Inc\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/README.md", "content": "# Grounding DINO \n\n---\n\n[![arXiv](https://img.shields.io/badge/arXiv-2303.05499-b31b1b.svg)](https://arxiv.org/abs/2303.05499) \n[![YouTube](https://badges.aleen42.com/src/youtube.svg)](https://youtu.be/wxWDt5UiwY8)\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-with-grounding-dino.ipynb)\n[![YouTube](https://badges.aleen42.com/src/youtube.svg)](https://youtu.be/cMa77r3YrDk)\n[![HuggingFace space](https://img.shields.io/badge/🤗-HuggingFace%20Space-cyan.svg)](https://huggingface.co/spaces/ShilongLiu/Grounding_DINO_demo)\n\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/grounding-dino-marrying-dino-with-grounded/zero-shot-object-detection-on-mscoco)](https://paperswithcode.com/sota/zero-shot-object-detection-on-mscoco?p=grounding-dino-marrying-dino-with-grounded) \\\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/grounding-dino-marrying-dino-with-grounded/zero-shot-object-detection-on-odinw)](https://paperswithcode.com/sota/zero-shot-object-detection-on-odinw?p=grounding-dino-marrying-dino-with-grounded) \\\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/grounding-dino-marrying-dino-with-grounded/object-detection-on-coco-minival)](https://paperswithcode.com/sota/object-detection-on-coco-minival?p=grounding-dino-marrying-dino-with-grounded) \\\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/grounding-dino-marrying-dino-with-grounded/object-detection-on-coco)](https://paperswithcode.com/sota/object-detection-on-coco?p=grounding-dino-marrying-dino-with-grounded)\n\n\n\nOfficial PyTorch implementation of [Grounding DINO](https://arxiv.org/abs/2303.05499), a stronger open-set object detector. Code is available now!\n\n\n## Highlight\n\n- **Open-Set Detection.** Detect **everything** with language!\n- **High Performancce.** COCO zero-shot **52.5 AP** (training without COCO data!). COCO fine-tune **63.0 AP**.\n- **Flexible.** Collaboration with Stable Diffusion for Image Editting.\n\n## News\n[2023/03/28] A YouTube [video](https://youtu.be/cMa77r3YrDk) about Grounding DINO and basic object detection prompt engineering. [[SkalskiP](https://github.com/SkalskiP)] \\\n[2023/03/28] Add a [demo](https://huggingface.co/spaces/ShilongLiu/Grounding_DINO_demo) on Hugging Face Space! \\\n[2023/03/27] Support CPU-only mode. Now the model can run on machines without GPUs.\\\n[2023/03/25] A [demo](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-with-grounding-dino.ipynb) for Grounding DINO is available at Colab. [[SkalskiP](https://github.com/SkalskiP)] \\\n[2023/03/22] Code is available Now!\n\n
\n\nDescription\n\n\"ODinW\"\n
\n\n\n\n## TODO \n\n- [x] Release inference code and demo.\n- [x] Release checkpoints.\n- [ ] Grounding DINO with Stable Diffusion and GLIGEN demos.\n- [ ] Release training codes.\n\n## Install \n\nIf you have a CUDA environment, please make sure the environment variable `CUDA_HOME` is set. It will be compiled under CPU-only mode if no CUDA available.\n\n```bash\npip install -e .\n```\n\n## Demo\n\n```bash\nCUDA_VISIBLE_DEVICES=6 python demo/inference_on_a_image.py \\\n -c /path/to/config \\\n -p /path/to/checkpoint \\\n -i .asset/cats.png \\\n -o \"outputs/0\" \\\n -t \"cat ear.\" \\\n [--cpu-only] # open it for cpu mode\n```\nSee the `demo/inference_on_a_image.py` for more details.\n\n**Web UI**\n\nWe also provide a demo code to integrate Grounding DINO with Gradio Web UI. See the file `demo/gradio_app.py` for more details.\n\n## Checkpoints\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
namebackboneDatabox AP on COCOCheckpointConfig
1GroundingDINO-TSwin-TO365,GoldG,Cap4M48.4 (zero-shot) / 57.2 (fine-tune)Github link | HF linklink
\n\n## Results\n\n
\n\nCOCO Object Detection Results\n\n\"COCO\"\n
\n\n
\n\nODinW Object Detection Results\n\n\"ODinW\"\n
\n\n
\n\nMarrying Grounding DINO with Stable Diffusion for Image Editing\n\n\"GD_SD\"\n
\n\n
\n\nMarrying Grounding DINO with GLIGEN for more Detailed Image Editing\n\n\"GD_GLIGEN\"\n
\n\n## Model\n\nIncludes: a text backbone, an image backbone, a feature enhancer, a language-guided query selection, and a cross-modality decoder.\n\n![arch](.asset/arch.png)\n\n\n## Acknowledgement\n\nOur model is related to [DINO](https://github.com/IDEA-Research/DINO) and [GLIP](https://github.com/microsoft/GLIP). Thanks for their great work!\n\nWe also thank great previous work including DETR, Deformable DETR, SMCA, Conditional DETR, Anchor DETR, Dynamic DETR, DAB-DETR, DN-DETR, etc. More related work are available at [Awesome Detection Transformer](https://github.com/IDEACVR/awesome-detection-transformer). A new toolbox [detrex](https://github.com/IDEA-Research/detrex) is available as well.\n\nThanks [Stable Diffusion](https://github.com/Stability-AI/StableDiffusion) and [GLIGEN](https://github.com/gligen/GLIGEN) for their awesome models.\n\n\n## Citation\n\nIf you find our work helpful for your research, please consider citing the following BibTeX entry. \n\n```bibtex\n@inproceedings{ShilongLiu2023GroundingDM,\n title={Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection},\n author={Shilong Liu and Zhaoyang Zeng and Tianhe Ren and Feng Li and Hao Zhang and Jie Yang and Chunyuan Li and Jianwei Yang and Hang Su and Jun Zhu and Lei Zhang},\n year={2023}\n}\n```\n\n\n\n\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/demo/gradio_app.py", "content": "import argparse\nfrom functools import partial\nimport cv2\nimport requests\nimport os\nfrom io import BytesIO\nfrom PIL import Image\nimport numpy as np\nfrom pathlib import Path\n\n\nimport warnings\n\nimport torch\n\n# prepare the environment\nos.system(\"python setup.py build develop --user\")\nos.system(\"pip install packaging==21.3\")\nos.system(\"pip install gradio\")\n\n\nwarnings.filterwarnings(\"ignore\")\n\nimport gradio as gr\n\nfrom groundingdino.models import build_model\nfrom groundingdino.util.slconfig import SLConfig\nfrom groundingdino.util.utils import clean_state_dict\nfrom groundingdino.util.inference import annotate, load_image, predict\nimport groundingdino.datasets.transforms as T\n\nfrom huggingface_hub import hf_hub_download\n\n\n\n# Use this command for evaluate the GLIP-T model\nconfig_file = \"groundingdino/config/GroundingDINO_SwinT_OGC.py\"\nckpt_repo_id = \"ShilongLiu/GroundingDINO\"\nckpt_filenmae = \"groundingdino_swint_ogc.pth\"\n\n\ndef load_model_hf(model_config_path, repo_id, filename, device='cpu'):\n args = SLConfig.fromfile(model_config_path) \n model = build_model(args)\n args.device = device\n\n cache_file = hf_hub_download(repo_id=repo_id, filename=filename)\n checkpoint = torch.load(cache_file, map_location='cpu')\n log = model.load_state_dict(clean_state_dict(checkpoint['model']), strict=False)\n print(\"Model loaded from {} \\n => {}\".format(cache_file, log))\n _ = model.eval()\n return model \n\ndef image_transform_grounding(init_image):\n transform = T.Compose([\n T.RandomResize([800], max_size=1333),\n T.ToTensor(),\n T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n ])\n image, _ = transform(init_image, None) # 3, h, w\n return init_image, image\n\ndef image_transform_grounding_for_vis(init_image):\n transform = T.Compose([\n T.RandomResize([800], max_size=1333),\n ])\n image, _ = transform(init_image, None) # 3, h, w\n return image\n\nmodel = load_model_hf(config_file, ckpt_repo_id, ckpt_filenmae)\n\ndef run_grounding(input_image, grounding_caption, box_threshold, text_threshold):\n init_image = input_image.convert(\"RGB\")\n original_size = init_image.size\n\n _, image_tensor = image_transform_grounding(init_image)\n image_pil: Image = image_transform_grounding_for_vis(init_image)\n\n # run grounidng\n boxes, logits, phrases = predict(model, image_tensor, grounding_caption, box_threshold, text_threshold, device='cpu')\n annotated_frame = annotate(image_source=np.asarray(image_pil), boxes=boxes, logits=logits, phrases=phrases)\n image_with_box = Image.fromarray(cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB))\n\n\n return image_with_box\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser(\"Grounding DINO demo\", add_help=True)\n parser.add_argument(\"--debug\", action=\"store_true\", help=\"using debug mode\")\n parser.add_argument(\"--share\", action=\"store_true\", help=\"share the app\")\n args = parser.parse_args()\n\n block = gr.Blocks().queue()\n with block:\n gr.Markdown(\"# [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO)\")\n gr.Markdown(\"### Open-World Detection with Grounding DINO\")\n\n with gr.Row():\n with gr.Column():\n input_image = gr.Image(source='upload', type=\"pil\")\n grounding_caption = gr.Textbox(label=\"Detection Prompt\")\n run_button = gr.Button(label=\"Run\")\n with gr.Accordion(\"Advanced options\", open=False):\n box_threshold = gr.Slider(\n label=\"Box Threshold\", minimum=0.0, maximum=1.0, value=0.25, step=0.001\n )\n text_threshold = gr.Slider(\n label=\"Text Threshold\", minimum=0.0, maximum=1.0, value=0.25, step=0.001\n )\n\n with gr.Column():\n gallery = gr.outputs.Image(\n type=\"pil\",\n # label=\"grounding results\"\n ).style(full_width=True, full_height=True)\n # gallery = gr.Gallery(label=\"Generated images\", show_label=False).style(\n # grid=[1], height=\"auto\", container=True, full_width=True, full_height=True)\n\n run_button.click(fn=run_grounding, inputs=[\n input_image, grounding_caption, box_threshold, text_threshold], outputs=[gallery])\n\n\n block.launch(server_name='0.0.0.0', server_port=7579, debug=args.debug, share=args.share)\n\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/demo/inference_on_a_image.py", "content": "import argparse\nimport os\nimport sys\n\nimport numpy as np\nimport torch\nfrom PIL import Image, ImageDraw, ImageFont\n\nimport groundingdino.datasets.transforms as T\nfrom groundingdino.models import build_model\nfrom groundingdino.util import box_ops\nfrom groundingdino.util.slconfig import SLConfig\nfrom groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap\n\n\ndef plot_boxes_to_image(image_pil, tgt):\n H, W = tgt[\"size\"]\n boxes = tgt[\"boxes\"]\n labels = tgt[\"labels\"]\n assert len(boxes) == len(labels), \"boxes and labels must have same length\"\n\n draw = ImageDraw.Draw(image_pil)\n mask = Image.new(\"L\", image_pil.size, 0)\n mask_draw = ImageDraw.Draw(mask)\n\n # draw boxes and masks\n for box, label in zip(boxes, labels):\n # from 0..1 to 0..W, 0..H\n box = box * torch.Tensor([W, H, W, H])\n # from xywh to xyxy\n box[:2] -= box[2:] / 2\n box[2:] += box[:2]\n # random color\n color = tuple(np.random.randint(0, 255, size=3).tolist())\n # draw\n x0, y0, x1, y1 = box\n x0, y0, x1, y1 = int(x0), int(y0), int(x1), int(y1)\n\n draw.rectangle([x0, y0, x1, y1], outline=color, width=6)\n # draw.text((x0, y0), str(label), fill=color)\n\n font = ImageFont.load_default()\n if hasattr(font, \"getbbox\"):\n bbox = draw.textbbox((x0, y0), str(label), font)\n else:\n w, h = draw.textsize(str(label), font)\n bbox = (x0, y0, w + x0, y0 + h)\n # bbox = draw.textbbox((x0, y0), str(label))\n draw.rectangle(bbox, fill=color)\n draw.text((x0, y0), str(label), fill=\"white\")\n\n mask_draw.rectangle([x0, y0, x1, y1], fill=255, width=6)\n\n return image_pil, mask\n\n\ndef load_image(image_path):\n # load image\n image_pil = Image.open(image_path).convert(\"RGB\") # load image\n\n transform = T.Compose(\n [\n T.RandomResize([800], max_size=1333),\n T.ToTensor(),\n T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n ]\n )\n image, _ = transform(image_pil, None) # 3, h, w\n return image_pil, image\n\n\ndef load_model(model_config_path, model_checkpoint_path, cpu_only=False):\n args = SLConfig.fromfile(model_config_path)\n args.device = \"cuda\" if not cpu_only else \"cpu\"\n model = build_model(args)\n checkpoint = torch.load(model_checkpoint_path, map_location=\"cpu\")\n load_res = model.load_state_dict(clean_state_dict(checkpoint[\"model\"]), strict=False)\n print(load_res)\n _ = model.eval()\n return model\n\n\ndef get_grounding_output(model, image, caption, box_threshold, text_threshold, with_logits=True, cpu_only=False):\n caption = caption.lower()\n caption = caption.strip()\n if not caption.endswith(\".\"):\n caption = caption + \".\"\n device = \"cuda\" if not cpu_only else \"cpu\"\n model = model.to(device)\n image = image.to(device)\n with torch.no_grad():\n outputs = model(image[None], captions=[caption])\n logits = outputs[\"pred_logits\"].cpu().sigmoid()[0] # (nq, 256)\n boxes = outputs[\"pred_boxes\"].cpu()[0] # (nq, 4)\n logits.shape[0]\n\n # filter output\n logits_filt = logits.clone()\n boxes_filt = boxes.clone()\n filt_mask = logits_filt.max(dim=1)[0] > box_threshold\n logits_filt = logits_filt[filt_mask] # num_filt, 256\n boxes_filt = boxes_filt[filt_mask] # num_filt, 4\n logits_filt.shape[0]\n\n # get phrase\n tokenlizer = model.tokenizer\n tokenized = tokenlizer(caption)\n # build pred\n pred_phrases = []\n for logit, box in zip(logits_filt, boxes_filt):\n pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer)\n if with_logits:\n pred_phrases.append(pred_phrase + f\"({str(logit.max().item())[:4]})\")\n else:\n pred_phrases.append(pred_phrase)\n\n return boxes_filt, pred_phrases\n\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser(\"Grounding DINO example\", add_help=True)\n parser.add_argument(\"--config_file\", \"-c\", type=str, required=True, help=\"path to config file\")\n parser.add_argument(\n \"--checkpoint_path\", \"-p\", type=str, required=True, help=\"path to checkpoint file\"\n )\n parser.add_argument(\"--image_path\", \"-i\", type=str, required=True, help=\"path to image file\")\n parser.add_argument(\"--text_prompt\", \"-t\", type=str, required=True, help=\"text prompt\")\n parser.add_argument(\n \"--output_dir\", \"-o\", type=str, default=\"outputs\", required=True, help=\"output directory\"\n )\n\n parser.add_argument(\"--box_threshold\", type=float, default=0.3, help=\"box threshold\")\n parser.add_argument(\"--text_threshold\", type=float, default=0.25, help=\"text threshold\")\n\n parser.add_argument(\"--cpu-only\", action=\"store_true\", help=\"running on cpu only!, default=False\")\n args = parser.parse_args()\n\n # cfg\n config_file = args.config_file # change the path of the model config file\n checkpoint_path = args.checkpoint_path # change the path of the model\n image_path = args.image_path\n text_prompt = args.text_prompt\n output_dir = args.output_dir\n box_threshold = args.box_threshold\n text_threshold = args.text_threshold\n\n # make dir\n os.makedirs(output_dir, exist_ok=True)\n # load image\n image_pil, image = load_image(image_path)\n # load model\n model = load_model(config_file, checkpoint_path, cpu_only=args.cpu_only)\n\n # visualize raw image\n image_pil.save(os.path.join(output_dir, \"raw_image.jpg\"))\n\n # run model\n boxes_filt, pred_phrases = get_grounding_output(\n model, image, text_prompt, box_threshold, text_threshold, cpu_only=args.cpu_only\n )\n\n # visualize pred\n size = image_pil.size\n pred_dict = {\n \"boxes\": boxes_filt,\n \"size\": [size[1], size[0]], # H,W\n \"labels\": pred_phrases,\n }\n # import ipdb; ipdb.set_trace()\n image_with_box = plot_boxes_to_image(image_pil, pred_dict)[0]\n image_with_box.save(os.path.join(output_dir, \"pred.jpg\"))\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/__init__.py", "content": "" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/config/GroundingDINO_SwinB.py", "content": "batch_size = 1\nmodelname = \"groundingdino\"\nbackbone = \"swin_B_384_22k\"\nposition_embedding = \"sine\"\npe_temperatureH = 20\npe_temperatureW = 20\nreturn_interm_indices = [1, 2, 3]\nbackbone_freeze_keywords = None\nenc_layers = 6\ndec_layers = 6\npre_norm = False\ndim_feedforward = 2048\nhidden_dim = 256\ndropout = 0.0\nnheads = 8\nnum_queries = 900\nquery_dim = 4\nnum_patterns = 0\nnum_feature_levels = 4\nenc_n_points = 4\ndec_n_points = 4\ntwo_stage_type = \"standard\"\ntwo_stage_bbox_embed_share = False\ntwo_stage_class_embed_share = False\ntransformer_activation = \"relu\"\ndec_pred_bbox_embed_share = True\ndn_box_noise_scale = 1.0\ndn_label_noise_ratio = 0.5\ndn_label_coef = 1.0\ndn_bbox_coef = 1.0\nembed_init_tgt = True\ndn_labelbook_size = 2000\nmax_text_len = 256\ntext_encoder_type = \"bert-base-uncased\"\nuse_text_enhancer = True\nuse_fusion_layer = True\nuse_checkpoint = True\nuse_transformer_ckpt = True\nuse_text_cross_attention = True\ntext_dropout = 0.0\nfusion_dropout = 0.0\nfusion_droppath = 0.1\nsub_sentence_present = True\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py", "content": "batch_size = 1\nmodelname = \"groundingdino\"\nbackbone = \"swin_T_224_1k\"\nposition_embedding = \"sine\"\npe_temperatureH = 20\npe_temperatureW = 20\nreturn_interm_indices = [1, 2, 3]\nbackbone_freeze_keywords = None\nenc_layers = 6\ndec_layers = 6\npre_norm = False\ndim_feedforward = 2048\nhidden_dim = 256\ndropout = 0.0\nnheads = 8\nnum_queries = 900\nquery_dim = 4\nnum_patterns = 0\nnum_feature_levels = 4\nenc_n_points = 4\ndec_n_points = 4\ntwo_stage_type = \"standard\"\ntwo_stage_bbox_embed_share = False\ntwo_stage_class_embed_share = False\ntransformer_activation = \"relu\"\ndec_pred_bbox_embed_share = True\ndn_box_noise_scale = 1.0\ndn_label_noise_ratio = 0.5\ndn_label_coef = 1.0\ndn_bbox_coef = 1.0\nembed_init_tgt = True\ndn_labelbook_size = 2000\nmax_text_len = 256\ntext_encoder_type = \"bert-base-uncased\"\nuse_text_enhancer = True\nuse_fusion_layer = True\nuse_checkpoint = True\nuse_transformer_ckpt = True\nuse_text_cross_attention = True\ntext_dropout = 0.0\nfusion_dropout = 0.0\nfusion_droppath = 0.1\nsub_sentence_present = True\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/datasets/__init__.py", "content": "" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/datasets/transforms.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nTransforms and data augmentation for both image + bbox.\n\"\"\"\nimport os\nimport random\n\nimport PIL\nimport torch\nimport torchvision.transforms as T\nimport torchvision.transforms.functional as F\n\nfrom groundingdino.util.box_ops import box_xyxy_to_cxcywh\nfrom groundingdino.util.misc import interpolate\n\n\ndef crop(image, target, region):\n cropped_image = F.crop(image, *region)\n\n target = target.copy()\n i, j, h, w = region\n\n # should we do something wrt the original size?\n target[\"size\"] = torch.tensor([h, w])\n\n fields = [\"labels\", \"area\", \"iscrowd\", \"positive_map\"]\n\n if \"boxes\" in target:\n boxes = target[\"boxes\"]\n max_size = torch.as_tensor([w, h], dtype=torch.float32)\n cropped_boxes = boxes - torch.as_tensor([j, i, j, i])\n cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)\n cropped_boxes = cropped_boxes.clamp(min=0)\n area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)\n target[\"boxes\"] = cropped_boxes.reshape(-1, 4)\n target[\"area\"] = area\n fields.append(\"boxes\")\n\n if \"masks\" in target:\n # FIXME should we update the area here if there are no boxes?\n target[\"masks\"] = target[\"masks\"][:, i : i + h, j : j + w]\n fields.append(\"masks\")\n\n # remove elements for which the boxes or masks that have zero area\n if \"boxes\" in target or \"masks\" in target:\n # favor boxes selection when defining which elements to keep\n # this is compatible with previous implementation\n if \"boxes\" in target:\n cropped_boxes = target[\"boxes\"].reshape(-1, 2, 2)\n keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)\n else:\n keep = target[\"masks\"].flatten(1).any(1)\n\n for field in fields:\n if field in target:\n target[field] = target[field][keep]\n\n if os.environ.get(\"IPDB_SHILONG_DEBUG\", None) == \"INFO\":\n # for debug and visualization only.\n if \"strings_positive\" in target:\n target[\"strings_positive\"] = [\n _i for _i, _j in zip(target[\"strings_positive\"], keep) if _j\n ]\n\n return cropped_image, target\n\n\ndef hflip(image, target):\n flipped_image = F.hflip(image)\n\n w, h = image.size\n\n target = target.copy()\n if \"boxes\" in target:\n boxes = target[\"boxes\"]\n boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor(\n [w, 0, w, 0]\n )\n target[\"boxes\"] = boxes\n\n if \"masks\" in target:\n target[\"masks\"] = target[\"masks\"].flip(-1)\n\n return flipped_image, target\n\n\ndef resize(image, target, size, max_size=None):\n # size can be min_size (scalar) or (w, h) tuple\n\n def get_size_with_aspect_ratio(image_size, size, max_size=None):\n w, h = image_size\n if max_size is not None:\n min_original_size = float(min((w, h)))\n max_original_size = float(max((w, h)))\n if max_original_size / min_original_size * size > max_size:\n size = int(round(max_size * min_original_size / max_original_size))\n\n if (w <= h and w == size) or (h <= w and h == size):\n return (h, w)\n\n if w < h:\n ow = size\n oh = int(size * h / w)\n else:\n oh = size\n ow = int(size * w / h)\n\n return (oh, ow)\n\n def get_size(image_size, size, max_size=None):\n if isinstance(size, (list, tuple)):\n return size[::-1]\n else:\n return get_size_with_aspect_ratio(image_size, size, max_size)\n\n size = get_size(image.size, size, max_size)\n rescaled_image = F.resize(image, size)\n\n if target is None:\n return rescaled_image, None\n\n ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size))\n ratio_width, ratio_height = ratios\n\n target = target.copy()\n if \"boxes\" in target:\n boxes = target[\"boxes\"]\n scaled_boxes = boxes * torch.as_tensor(\n [ratio_width, ratio_height, ratio_width, ratio_height]\n )\n target[\"boxes\"] = scaled_boxes\n\n if \"area\" in target:\n area = target[\"area\"]\n scaled_area = area * (ratio_width * ratio_height)\n target[\"area\"] = scaled_area\n\n h, w = size\n target[\"size\"] = torch.tensor([h, w])\n\n if \"masks\" in target:\n target[\"masks\"] = (\n interpolate(target[\"masks\"][:, None].float(), size, mode=\"nearest\")[:, 0] > 0.5\n )\n\n return rescaled_image, target\n\n\ndef pad(image, target, padding):\n # assumes that we only pad on the bottom right corners\n padded_image = F.pad(image, (0, 0, padding[0], padding[1]))\n if target is None:\n return padded_image, None\n target = target.copy()\n # should we do something wrt the original size?\n target[\"size\"] = torch.tensor(padded_image.size[::-1])\n if \"masks\" in target:\n target[\"masks\"] = torch.nn.functional.pad(target[\"masks\"], (0, padding[0], 0, padding[1]))\n return padded_image, target\n\n\nclass ResizeDebug(object):\n def __init__(self, size):\n self.size = size\n\n def __call__(self, img, target):\n return resize(img, target, self.size)\n\n\nclass RandomCrop(object):\n def __init__(self, size):\n self.size = size\n\n def __call__(self, img, target):\n region = T.RandomCrop.get_params(img, self.size)\n return crop(img, target, region)\n\n\nclass RandomSizeCrop(object):\n def __init__(self, min_size: int, max_size: int, respect_boxes: bool = False):\n # respect_boxes: True to keep all boxes\n # False to tolerence box filter\n self.min_size = min_size\n self.max_size = max_size\n self.respect_boxes = respect_boxes\n\n def __call__(self, img: PIL.Image.Image, target: dict):\n init_boxes = len(target[\"boxes\"])\n max_patience = 10\n for i in range(max_patience):\n w = random.randint(self.min_size, min(img.width, self.max_size))\n h = random.randint(self.min_size, min(img.height, self.max_size))\n region = T.RandomCrop.get_params(img, [h, w])\n result_img, result_target = crop(img, target, region)\n if (\n not self.respect_boxes\n or len(result_target[\"boxes\"]) == init_boxes\n or i == max_patience - 1\n ):\n return result_img, result_target\n return result_img, result_target\n\n\nclass CenterCrop(object):\n def __init__(self, size):\n self.size = size\n\n def __call__(self, img, target):\n image_width, image_height = img.size\n crop_height, crop_width = self.size\n crop_top = int(round((image_height - crop_height) / 2.0))\n crop_left = int(round((image_width - crop_width) / 2.0))\n return crop(img, target, (crop_top, crop_left, crop_height, crop_width))\n\n\nclass RandomHorizontalFlip(object):\n def __init__(self, p=0.5):\n self.p = p\n\n def __call__(self, img, target):\n if random.random() < self.p:\n return hflip(img, target)\n return img, target\n\n\nclass RandomResize(object):\n def __init__(self, sizes, max_size=None):\n assert isinstance(sizes, (list, tuple))\n self.sizes = sizes\n self.max_size = max_size\n\n def __call__(self, img, target=None):\n size = random.choice(self.sizes)\n return resize(img, target, size, self.max_size)\n\n\nclass RandomPad(object):\n def __init__(self, max_pad):\n self.max_pad = max_pad\n\n def __call__(self, img, target):\n pad_x = random.randint(0, self.max_pad)\n pad_y = random.randint(0, self.max_pad)\n return pad(img, target, (pad_x, pad_y))\n\n\nclass RandomSelect(object):\n \"\"\"\n Randomly selects between transforms1 and transforms2,\n with probability p for transforms1 and (1 - p) for transforms2\n \"\"\"\n\n def __init__(self, transforms1, transforms2, p=0.5):\n self.transforms1 = transforms1\n self.transforms2 = transforms2\n self.p = p\n\n def __call__(self, img, target):\n if random.random() < self.p:\n return self.transforms1(img, target)\n return self.transforms2(img, target)\n\n\nclass ToTensor(object):\n def __call__(self, img, target):\n return F.to_tensor(img), target\n\n\nclass RandomErasing(object):\n def __init__(self, *args, **kwargs):\n self.eraser = T.RandomErasing(*args, **kwargs)\n\n def __call__(self, img, target):\n return self.eraser(img), target\n\n\nclass Normalize(object):\n def __init__(self, mean, std):\n self.mean = mean\n self.std = std\n\n def __call__(self, image, target=None):\n image = F.normalize(image, mean=self.mean, std=self.std)\n if target is None:\n return image, None\n target = target.copy()\n h, w = image.shape[-2:]\n if \"boxes\" in target:\n boxes = target[\"boxes\"]\n boxes = box_xyxy_to_cxcywh(boxes)\n boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32)\n target[\"boxes\"] = boxes\n return image, target\n\n\nclass Compose(object):\n def __init__(self, transforms):\n self.transforms = transforms\n\n def __call__(self, image, target):\n for t in self.transforms:\n image, target = t(image, target)\n return image, target\n\n def __repr__(self):\n format_string = self.__class__.__name__ + \"(\"\n for t in self.transforms:\n format_string += \"\\n\"\n format_string += \" {0}\".format(t)\n format_string += \"\\n)\"\n return format_string\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/GroundingDINO/__init__.py", "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Conditional DETR\n# Copyright (c) 2021 Microsoft. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Copied from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n# ------------------------------------------------------------------------\n\nfrom .groundingdino import build_groundingdino\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/__init__.py", "content": "from .backbone import build_backbone\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py", "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Conditional DETR\n# Copyright (c) 2021 Microsoft. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Copied from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n# ------------------------------------------------------------------------\n\n\"\"\"\nBackbone modules.\n\"\"\"\n\nfrom typing import Dict, List\n\nimport torch\nimport torch.nn.functional as F\nimport torchvision\nfrom torch import nn\nfrom torchvision.models._utils import IntermediateLayerGetter\n\nfrom groundingdino.util.misc import NestedTensor, clean_state_dict, is_main_process\n\nfrom .position_encoding import build_position_encoding\nfrom .swin_transformer import build_swin_transformer\n\n\nclass FrozenBatchNorm2d(torch.nn.Module):\n \"\"\"\n BatchNorm2d where the batch statistics and the affine parameters are fixed.\n\n Copy-paste from torchvision.misc.ops with added eps before rqsrt,\n without which any other models than torchvision.models.resnet[18,34,50,101]\n produce nans.\n \"\"\"\n\n def __init__(self, n):\n super(FrozenBatchNorm2d, self).__init__()\n self.register_buffer(\"weight\", torch.ones(n))\n self.register_buffer(\"bias\", torch.zeros(n))\n self.register_buffer(\"running_mean\", torch.zeros(n))\n self.register_buffer(\"running_var\", torch.ones(n))\n\n def _load_from_state_dict(\n self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs\n ):\n num_batches_tracked_key = prefix + \"num_batches_tracked\"\n if num_batches_tracked_key in state_dict:\n del state_dict[num_batches_tracked_key]\n\n super(FrozenBatchNorm2d, self)._load_from_state_dict(\n state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs\n )\n\n def forward(self, x):\n # move reshapes to the beginning\n # to make it fuser-friendly\n w = self.weight.reshape(1, -1, 1, 1)\n b = self.bias.reshape(1, -1, 1, 1)\n rv = self.running_var.reshape(1, -1, 1, 1)\n rm = self.running_mean.reshape(1, -1, 1, 1)\n eps = 1e-5\n scale = w * (rv + eps).rsqrt()\n bias = b - rm * scale\n return x * scale + bias\n\n\nclass BackboneBase(nn.Module):\n def __init__(\n self,\n backbone: nn.Module,\n train_backbone: bool,\n num_channels: int,\n return_interm_indices: list,\n ):\n super().__init__()\n for name, parameter in backbone.named_parameters():\n if (\n not train_backbone\n or \"layer2\" not in name\n and \"layer3\" not in name\n and \"layer4\" not in name\n ):\n parameter.requires_grad_(False)\n\n return_layers = {}\n for idx, layer_index in enumerate(return_interm_indices):\n return_layers.update(\n {\"layer{}\".format(5 - len(return_interm_indices) + idx): \"{}\".format(layer_index)}\n )\n\n # if len:\n # if use_stage1_feature:\n # return_layers = {\"layer1\": \"0\", \"layer2\": \"1\", \"layer3\": \"2\", \"layer4\": \"3\"}\n # else:\n # return_layers = {\"layer2\": \"0\", \"layer3\": \"1\", \"layer4\": \"2\"}\n # else:\n # return_layers = {'layer4': \"0\"}\n self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)\n self.num_channels = num_channels\n\n def forward(self, tensor_list: NestedTensor):\n xs = self.body(tensor_list.tensors)\n out: Dict[str, NestedTensor] = {}\n for name, x in xs.items():\n m = tensor_list.mask\n assert m is not None\n mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]\n out[name] = NestedTensor(x, mask)\n # import ipdb; ipdb.set_trace()\n return out\n\n\nclass Backbone(BackboneBase):\n \"\"\"ResNet backbone with frozen BatchNorm.\"\"\"\n\n def __init__(\n self,\n name: str,\n train_backbone: bool,\n dilation: bool,\n return_interm_indices: list,\n batch_norm=FrozenBatchNorm2d,\n ):\n if name in [\"resnet18\", \"resnet34\", \"resnet50\", \"resnet101\"]:\n backbone = getattr(torchvision.models, name)(\n replace_stride_with_dilation=[False, False, dilation],\n pretrained=is_main_process(),\n norm_layer=batch_norm,\n )\n else:\n raise NotImplementedError(\"Why you can get here with name {}\".format(name))\n # num_channels = 512 if name in ('resnet18', 'resnet34') else 2048\n assert name not in (\"resnet18\", \"resnet34\"), \"Only resnet50 and resnet101 are available.\"\n assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]\n num_channels_all = [256, 512, 1024, 2048]\n num_channels = num_channels_all[4 - len(return_interm_indices) :]\n super().__init__(backbone, train_backbone, num_channels, return_interm_indices)\n\n\nclass Joiner(nn.Sequential):\n def __init__(self, backbone, position_embedding):\n super().__init__(backbone, position_embedding)\n\n def forward(self, tensor_list: NestedTensor):\n xs = self[0](tensor_list)\n out: List[NestedTensor] = []\n pos = []\n for name, x in xs.items():\n out.append(x)\n # position encoding\n pos.append(self[1](x).to(x.tensors.dtype))\n\n return out, pos\n\n\ndef build_backbone(args):\n \"\"\"\n Useful args:\n - backbone: backbone name\n - lr_backbone:\n - dilation\n - return_interm_indices: available: [0,1,2,3], [1,2,3], [3]\n - backbone_freeze_keywords:\n - use_checkpoint: for swin only for now\n\n \"\"\"\n position_embedding = build_position_encoding(args)\n train_backbone = True\n if not train_backbone:\n raise ValueError(\"Please set lr_backbone > 0\")\n return_interm_indices = args.return_interm_indices\n assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]\n args.backbone_freeze_keywords\n use_checkpoint = getattr(args, \"use_checkpoint\", False)\n\n if args.backbone in [\"resnet50\", \"resnet101\"]:\n backbone = Backbone(\n args.backbone,\n train_backbone,\n args.dilation,\n return_interm_indices,\n batch_norm=FrozenBatchNorm2d,\n )\n bb_num_channels = backbone.num_channels\n elif args.backbone in [\n \"swin_T_224_1k\",\n \"swin_B_224_22k\",\n \"swin_B_384_22k\",\n \"swin_L_224_22k\",\n \"swin_L_384_22k\",\n ]:\n pretrain_img_size = int(args.backbone.split(\"_\")[-2])\n backbone = build_swin_transformer(\n args.backbone,\n pretrain_img_size=pretrain_img_size,\n out_indices=tuple(return_interm_indices),\n dilation=False,\n use_checkpoint=use_checkpoint,\n )\n\n bb_num_channels = backbone.num_features[4 - len(return_interm_indices) :]\n else:\n raise NotImplementedError(\"Unknown backbone {}\".format(args.backbone))\n\n assert len(bb_num_channels) == len(\n return_interm_indices\n ), f\"len(bb_num_channels) {len(bb_num_channels)} != len(return_interm_indices) {len(return_interm_indices)}\"\n\n model = Joiner(backbone, position_embedding)\n model.num_channels = bb_num_channels\n assert isinstance(\n bb_num_channels, List\n ), \"bb_num_channels is expected to be a List but {}\".format(type(bb_num_channels))\n # import ipdb; ipdb.set_trace()\n return model\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/position_encoding.py", "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# DINO\n# Copyright (c) 2022 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Conditional DETR\n# Copyright (c) 2021 Microsoft. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Copied from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n# ------------------------------------------------------------------------\n\n\"\"\"\nVarious positional encodings for the transformer.\n\"\"\"\nimport math\n\nimport torch\nfrom torch import nn\n\nfrom groundingdino.util.misc import NestedTensor\n\n\nclass PositionEmbeddingSine(nn.Module):\n \"\"\"\n This is a more standard version of the position embedding, very similar to the one\n used by the Attention is all you need paper, generalized to work on images.\n \"\"\"\n\n def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):\n super().__init__()\n self.num_pos_feats = num_pos_feats\n self.temperature = temperature\n self.normalize = normalize\n if scale is not None and normalize is False:\n raise ValueError(\"normalize should be True if scale is passed\")\n if scale is None:\n scale = 2 * math.pi\n self.scale = scale\n\n def forward(self, tensor_list: NestedTensor):\n x = tensor_list.tensors\n mask = tensor_list.mask\n assert mask is not None\n not_mask = ~mask\n y_embed = not_mask.cumsum(1, dtype=torch.float32)\n x_embed = not_mask.cumsum(2, dtype=torch.float32)\n if self.normalize:\n eps = 1e-6\n # if os.environ.get(\"SHILONG_AMP\", None) == '1':\n # eps = 1e-4\n # else:\n # eps = 1e-6\n y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale\n x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale\n\n dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)\n dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)\n\n pos_x = x_embed[:, :, :, None] / dim_t\n pos_y = y_embed[:, :, :, None] / dim_t\n pos_x = torch.stack(\n (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4\n ).flatten(3)\n pos_y = torch.stack(\n (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4\n ).flatten(3)\n pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)\n return pos\n\n\nclass PositionEmbeddingSineHW(nn.Module):\n \"\"\"\n This is a more standard version of the position embedding, very similar to the one\n used by the Attention is all you need paper, generalized to work on images.\n \"\"\"\n\n def __init__(\n self, num_pos_feats=64, temperatureH=10000, temperatureW=10000, normalize=False, scale=None\n ):\n super().__init__()\n self.num_pos_feats = num_pos_feats\n self.temperatureH = temperatureH\n self.temperatureW = temperatureW\n self.normalize = normalize\n if scale is not None and normalize is False:\n raise ValueError(\"normalize should be True if scale is passed\")\n if scale is None:\n scale = 2 * math.pi\n self.scale = scale\n\n def forward(self, tensor_list: NestedTensor):\n x = tensor_list.tensors\n mask = tensor_list.mask\n assert mask is not None\n not_mask = ~mask\n y_embed = not_mask.cumsum(1, dtype=torch.float32)\n x_embed = not_mask.cumsum(2, dtype=torch.float32)\n\n # import ipdb; ipdb.set_trace()\n\n if self.normalize:\n eps = 1e-6\n y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale\n x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale\n\n dim_tx = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)\n dim_tx = self.temperatureW ** (2 * (torch.div(dim_tx, 2, rounding_mode='floor')) / self.num_pos_feats)\n pos_x = x_embed[:, :, :, None] / dim_tx\n\n dim_ty = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)\n dim_ty = self.temperatureH ** (2 * (torch.div(dim_ty, 2, rounding_mode='floor')) / self.num_pos_feats)\n pos_y = y_embed[:, :, :, None] / dim_ty\n\n pos_x = torch.stack(\n (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4\n ).flatten(3)\n pos_y = torch.stack(\n (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4\n ).flatten(3)\n pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)\n\n # import ipdb; ipdb.set_trace()\n\n return pos\n\n\nclass PositionEmbeddingLearned(nn.Module):\n \"\"\"\n Absolute pos embedding, learned.\n \"\"\"\n\n def __init__(self, num_pos_feats=256):\n super().__init__()\n self.row_embed = nn.Embedding(50, num_pos_feats)\n self.col_embed = nn.Embedding(50, num_pos_feats)\n self.reset_parameters()\n\n def reset_parameters(self):\n nn.init.uniform_(self.row_embed.weight)\n nn.init.uniform_(self.col_embed.weight)\n\n def forward(self, tensor_list: NestedTensor):\n x = tensor_list.tensors\n h, w = x.shape[-2:]\n i = torch.arange(w, device=x.device)\n j = torch.arange(h, device=x.device)\n x_emb = self.col_embed(i)\n y_emb = self.row_embed(j)\n pos = (\n torch.cat(\n [\n x_emb.unsqueeze(0).repeat(h, 1, 1),\n y_emb.unsqueeze(1).repeat(1, w, 1),\n ],\n dim=-1,\n )\n .permute(2, 0, 1)\n .unsqueeze(0)\n .repeat(x.shape[0], 1, 1, 1)\n )\n return pos\n\n\ndef build_position_encoding(args):\n N_steps = args.hidden_dim // 2\n if args.position_embedding in (\"v2\", \"sine\"):\n # TODO find a better way of exposing other arguments\n position_embedding = PositionEmbeddingSineHW(\n N_steps,\n temperatureH=args.pe_temperatureH,\n temperatureW=args.pe_temperatureW,\n normalize=True,\n )\n elif args.position_embedding in (\"v3\", \"learned\"):\n position_embedding = PositionEmbeddingLearned(N_steps)\n else:\n raise ValueError(f\"not supported {args.position_embedding}\")\n\n return position_embedding\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/swin_transformer.py", "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# DINO\n# Copyright (c) 2022 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# --------------------------------------------------------\n# modified from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py\n# --------------------------------------------------------\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.checkpoint as checkpoint\nfrom timm.models.layers import DropPath, to_2tuple, trunc_normal_\n\nfrom groundingdino.util.misc import NestedTensor\n\n\nclass Mlp(nn.Module):\n \"\"\"Multilayer perceptron.\"\"\"\n\n def __init__(\n self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0\n ):\n super().__init__()\n out_features = out_features or in_features\n hidden_features = hidden_features or in_features\n self.fc1 = nn.Linear(in_features, hidden_features)\n self.act = act_layer()\n self.fc2 = nn.Linear(hidden_features, out_features)\n self.drop = nn.Dropout(drop)\n\n def forward(self, x):\n x = self.fc1(x)\n x = self.act(x)\n x = self.drop(x)\n x = self.fc2(x)\n x = self.drop(x)\n return x\n\n\ndef window_partition(x, window_size):\n \"\"\"\n Args:\n x: (B, H, W, C)\n window_size (int): window size\n Returns:\n windows: (num_windows*B, window_size, window_size, C)\n \"\"\"\n B, H, W, C = x.shape\n x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)\n windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)\n return windows\n\n\ndef window_reverse(windows, window_size, H, W):\n \"\"\"\n Args:\n windows: (num_windows*B, window_size, window_size, C)\n window_size (int): Window size\n H (int): Height of image\n W (int): Width of image\n Returns:\n x: (B, H, W, C)\n \"\"\"\n B = int(windows.shape[0] / (H * W / window_size / window_size))\n x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)\n x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)\n return x\n\n\nclass WindowAttention(nn.Module):\n \"\"\"Window based multi-head self attention (W-MSA) module with relative position bias.\n It supports both of shifted and non-shifted window.\n Args:\n dim (int): Number of input channels.\n window_size (tuple[int]): The height and width of the window.\n num_heads (int): Number of attention heads.\n qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set\n attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0\n proj_drop (float, optional): Dropout ratio of output. Default: 0.0\n \"\"\"\n\n def __init__(\n self,\n dim,\n window_size,\n num_heads,\n qkv_bias=True,\n qk_scale=None,\n attn_drop=0.0,\n proj_drop=0.0,\n ):\n\n super().__init__()\n self.dim = dim\n self.window_size = window_size # Wh, Ww\n self.num_heads = num_heads\n head_dim = dim // num_heads\n self.scale = qk_scale or head_dim**-0.5\n\n # define a parameter table of relative position bias\n self.relative_position_bias_table = nn.Parameter(\n torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)\n ) # 2*Wh-1 * 2*Ww-1, nH\n\n # get pair-wise relative position index for each token inside the window\n coords_h = torch.arange(self.window_size[0])\n coords_w = torch.arange(self.window_size[1])\n coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww\n coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww\n relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww\n relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2\n relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0\n relative_coords[:, :, 1] += self.window_size[1] - 1\n relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1\n relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww\n self.register_buffer(\"relative_position_index\", relative_position_index)\n\n self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n self.attn_drop = nn.Dropout(attn_drop)\n self.proj = nn.Linear(dim, dim)\n self.proj_drop = nn.Dropout(proj_drop)\n\n trunc_normal_(self.relative_position_bias_table, std=0.02)\n self.softmax = nn.Softmax(dim=-1)\n\n def forward(self, x, mask=None):\n \"\"\"Forward function.\n Args:\n x: input features with shape of (num_windows*B, N, C)\n mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None\n \"\"\"\n B_, N, C = x.shape\n qkv = (\n self.qkv(x)\n .reshape(B_, N, 3, self.num_heads, C // self.num_heads)\n .permute(2, 0, 3, 1, 4)\n )\n q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)\n\n q = q * self.scale\n attn = q @ k.transpose(-2, -1)\n\n relative_position_bias = self.relative_position_bias_table[\n self.relative_position_index.view(-1)\n ].view(\n self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1\n ) # Wh*Ww,Wh*Ww,nH\n relative_position_bias = relative_position_bias.permute(\n 2, 0, 1\n ).contiguous() # nH, Wh*Ww, Wh*Ww\n attn = attn + relative_position_bias.unsqueeze(0)\n\n if mask is not None:\n nW = mask.shape[0]\n attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)\n attn = attn.view(-1, self.num_heads, N, N)\n attn = self.softmax(attn)\n else:\n attn = self.softmax(attn)\n\n attn = self.attn_drop(attn)\n\n x = (attn @ v).transpose(1, 2).reshape(B_, N, C)\n x = self.proj(x)\n x = self.proj_drop(x)\n return x\n\n\nclass SwinTransformerBlock(nn.Module):\n \"\"\"Swin Transformer Block.\n Args:\n dim (int): Number of input channels.\n num_heads (int): Number of attention heads.\n window_size (int): Window size.\n shift_size (int): Shift size for SW-MSA.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.\n drop (float, optional): Dropout rate. Default: 0.0\n attn_drop (float, optional): Attention dropout rate. Default: 0.0\n drop_path (float, optional): Stochastic depth rate. Default: 0.0\n act_layer (nn.Module, optional): Activation layer. Default: nn.GELU\n norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n \"\"\"\n\n def __init__(\n self,\n dim,\n num_heads,\n window_size=7,\n shift_size=0,\n mlp_ratio=4.0,\n qkv_bias=True,\n qk_scale=None,\n drop=0.0,\n attn_drop=0.0,\n drop_path=0.0,\n act_layer=nn.GELU,\n norm_layer=nn.LayerNorm,\n ):\n super().__init__()\n self.dim = dim\n self.num_heads = num_heads\n self.window_size = window_size\n self.shift_size = shift_size\n self.mlp_ratio = mlp_ratio\n assert 0 <= self.shift_size < self.window_size, \"shift_size must in 0-window_size\"\n\n self.norm1 = norm_layer(dim)\n self.attn = WindowAttention(\n dim,\n window_size=to_2tuple(self.window_size),\n num_heads=num_heads,\n qkv_bias=qkv_bias,\n qk_scale=qk_scale,\n attn_drop=attn_drop,\n proj_drop=drop,\n )\n\n self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()\n self.norm2 = norm_layer(dim)\n mlp_hidden_dim = int(dim * mlp_ratio)\n self.mlp = Mlp(\n in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop\n )\n\n self.H = None\n self.W = None\n\n def forward(self, x, mask_matrix):\n \"\"\"Forward function.\n Args:\n x: Input feature, tensor size (B, H*W, C).\n H, W: Spatial resolution of the input feature.\n mask_matrix: Attention mask for cyclic shift.\n \"\"\"\n B, L, C = x.shape\n H, W = self.H, self.W\n assert L == H * W, \"input feature has wrong size\"\n\n shortcut = x\n x = self.norm1(x)\n x = x.view(B, H, W, C)\n\n # pad feature maps to multiples of window size\n pad_l = pad_t = 0\n pad_r = (self.window_size - W % self.window_size) % self.window_size\n pad_b = (self.window_size - H % self.window_size) % self.window_size\n x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))\n _, Hp, Wp, _ = x.shape\n\n # cyclic shift\n if self.shift_size > 0:\n shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))\n attn_mask = mask_matrix\n else:\n shifted_x = x\n attn_mask = None\n\n # partition windows\n x_windows = window_partition(\n shifted_x, self.window_size\n ) # nW*B, window_size, window_size, C\n x_windows = x_windows.view(\n -1, self.window_size * self.window_size, C\n ) # nW*B, window_size*window_size, C\n\n # W-MSA/SW-MSA\n attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C\n\n # merge windows\n attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)\n shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C\n\n # reverse cyclic shift\n if self.shift_size > 0:\n x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))\n else:\n x = shifted_x\n\n if pad_r > 0 or pad_b > 0:\n x = x[:, :H, :W, :].contiguous()\n\n x = x.view(B, H * W, C)\n\n # FFN\n x = shortcut + self.drop_path(x)\n x = x + self.drop_path(self.mlp(self.norm2(x)))\n\n return x\n\n\nclass PatchMerging(nn.Module):\n \"\"\"Patch Merging Layer\n Args:\n dim (int): Number of input channels.\n norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n \"\"\"\n\n def __init__(self, dim, norm_layer=nn.LayerNorm):\n super().__init__()\n self.dim = dim\n self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)\n self.norm = norm_layer(4 * dim)\n\n def forward(self, x, H, W):\n \"\"\"Forward function.\n Args:\n x: Input feature, tensor size (B, H*W, C).\n H, W: Spatial resolution of the input feature.\n \"\"\"\n B, L, C = x.shape\n assert L == H * W, \"input feature has wrong size\"\n\n x = x.view(B, H, W, C)\n\n # padding\n pad_input = (H % 2 == 1) or (W % 2 == 1)\n if pad_input:\n x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))\n\n x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C\n x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C\n x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C\n x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C\n x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C\n x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C\n\n x = self.norm(x)\n x = self.reduction(x)\n\n return x\n\n\nclass BasicLayer(nn.Module):\n \"\"\"A basic Swin Transformer layer for one stage.\n Args:\n dim (int): Number of feature channels\n depth (int): Depths of this stage.\n num_heads (int): Number of attention head.\n window_size (int): Local window size. Default: 7.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.\n qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True\n qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.\n drop (float, optional): Dropout rate. Default: 0.0\n attn_drop (float, optional): Attention dropout rate. Default: 0.0\n drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0\n norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm\n downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None\n use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.\n \"\"\"\n\n def __init__(\n self,\n dim,\n depth,\n num_heads,\n window_size=7,\n mlp_ratio=4.0,\n qkv_bias=True,\n qk_scale=None,\n drop=0.0,\n attn_drop=0.0,\n drop_path=0.0,\n norm_layer=nn.LayerNorm,\n downsample=None,\n use_checkpoint=False,\n ):\n super().__init__()\n self.window_size = window_size\n self.shift_size = window_size // 2\n self.depth = depth\n self.use_checkpoint = use_checkpoint\n\n # build blocks\n self.blocks = nn.ModuleList(\n [\n SwinTransformerBlock(\n dim=dim,\n num_heads=num_heads,\n window_size=window_size,\n shift_size=0 if (i % 2 == 0) else window_size // 2,\n mlp_ratio=mlp_ratio,\n qkv_bias=qkv_bias,\n qk_scale=qk_scale,\n drop=drop,\n attn_drop=attn_drop,\n drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,\n norm_layer=norm_layer,\n )\n for i in range(depth)\n ]\n )\n\n # patch merging layer\n if downsample is not None:\n self.downsample = downsample(dim=dim, norm_layer=norm_layer)\n else:\n self.downsample = None\n\n def forward(self, x, H, W):\n \"\"\"Forward function.\n Args:\n x: Input feature, tensor size (B, H*W, C).\n H, W: Spatial resolution of the input feature.\n \"\"\"\n\n # calculate attention mask for SW-MSA\n Hp = int(np.ceil(H / self.window_size)) * self.window_size\n Wp = int(np.ceil(W / self.window_size)) * self.window_size\n img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device, dtype=x.dtype) # 1 Hp Wp 1\n h_slices = (\n slice(0, -self.window_size),\n slice(-self.window_size, -self.shift_size),\n slice(-self.shift_size, None),\n )\n w_slices = (\n slice(0, -self.window_size),\n slice(-self.window_size, -self.shift_size),\n slice(-self.shift_size, None),\n )\n cnt = 0\n for h in h_slices:\n for w in w_slices:\n img_mask[:, h, w, :] = cnt\n cnt += 1\n\n mask_windows = window_partition(\n img_mask, self.window_size\n ) # nW, window_size, window_size, 1\n mask_windows = mask_windows.view(-1, self.window_size * self.window_size)\n attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)\n attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(\n attn_mask == 0, float(0.0)\n )\n\n for blk in self.blocks:\n blk.H, blk.W = H, W\n if self.use_checkpoint:\n x = checkpoint.checkpoint(blk, x, attn_mask)\n else:\n x = blk(x, attn_mask)\n if self.downsample is not None:\n x_down = self.downsample(x, H, W)\n Wh, Ww = (H + 1) // 2, (W + 1) // 2\n return x, H, W, x_down, Wh, Ww\n else:\n return x, H, W, x, H, W\n\n\nclass PatchEmbed(nn.Module):\n \"\"\"Image to Patch Embedding\n Args:\n patch_size (int): Patch token size. Default: 4.\n in_chans (int): Number of input image channels. Default: 3.\n embed_dim (int): Number of linear projection output channels. Default: 96.\n norm_layer (nn.Module, optional): Normalization layer. Default: None\n \"\"\"\n\n def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):\n super().__init__()\n patch_size = to_2tuple(patch_size)\n self.patch_size = patch_size\n\n self.in_chans = in_chans\n self.embed_dim = embed_dim\n\n self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)\n if norm_layer is not None:\n self.norm = norm_layer(embed_dim)\n else:\n self.norm = None\n\n def forward(self, x):\n \"\"\"Forward function.\"\"\"\n # padding\n _, _, H, W = x.size()\n if W % self.patch_size[1] != 0:\n x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))\n if H % self.patch_size[0] != 0:\n x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))\n\n x = self.proj(x) # B C Wh Ww\n if self.norm is not None:\n Wh, Ww = x.size(2), x.size(3)\n x = x.flatten(2).transpose(1, 2)\n x = self.norm(x)\n x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)\n\n return x\n\n\nclass SwinTransformer(nn.Module):\n \"\"\"Swin Transformer backbone.\n A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -\n https://arxiv.org/pdf/2103.14030\n Args:\n pretrain_img_size (int): Input image size for training the pretrained model,\n used in absolute postion embedding. Default 224.\n patch_size (int | tuple(int)): Patch size. Default: 4.\n in_chans (int): Number of input image channels. Default: 3.\n embed_dim (int): Number of linear projection output channels. Default: 96.\n depths (tuple[int]): Depths of each Swin Transformer stage.\n num_heads (tuple[int]): Number of attention head of each stage.\n window_size (int): Window size. Default: 7.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.\n qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True\n qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.\n drop_rate (float): Dropout rate.\n attn_drop_rate (float): Attention dropout rate. Default: 0.\n drop_path_rate (float): Stochastic depth rate. Default: 0.2.\n norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.\n ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.\n patch_norm (bool): If True, add normalization after patch embedding. Default: True.\n out_indices (Sequence[int]): Output from which stages.\n frozen_stages (int): Stages to be frozen (stop grad and set eval mode).\n -1 means not freezing any parameters.\n use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.\n dilation (bool): if True, the output size if 16x downsample, ow 32x downsample.\n \"\"\"\n\n def __init__(\n self,\n pretrain_img_size=224,\n patch_size=4,\n in_chans=3,\n embed_dim=96,\n depths=[2, 2, 6, 2],\n num_heads=[3, 6, 12, 24],\n window_size=7,\n mlp_ratio=4.0,\n qkv_bias=True,\n qk_scale=None,\n drop_rate=0.0,\n attn_drop_rate=0.0,\n drop_path_rate=0.2,\n norm_layer=nn.LayerNorm,\n ape=False,\n patch_norm=True,\n out_indices=(0, 1, 2, 3),\n frozen_stages=-1,\n dilation=False,\n use_checkpoint=False,\n ):\n super().__init__()\n\n self.pretrain_img_size = pretrain_img_size\n self.num_layers = len(depths)\n self.embed_dim = embed_dim\n self.ape = ape\n self.patch_norm = patch_norm\n self.out_indices = out_indices\n self.frozen_stages = frozen_stages\n self.dilation = dilation\n\n # if use_checkpoint:\n # print(\"use_checkpoint!!!!!!!!!!!!!!!!!!!!!!!!\")\n\n # split image into non-overlapping patches\n self.patch_embed = PatchEmbed(\n patch_size=patch_size,\n in_chans=in_chans,\n embed_dim=embed_dim,\n norm_layer=norm_layer if self.patch_norm else None,\n )\n\n # absolute position embedding\n if self.ape:\n pretrain_img_size = to_2tuple(pretrain_img_size)\n patch_size = to_2tuple(patch_size)\n patches_resolution = [\n pretrain_img_size[0] // patch_size[0],\n pretrain_img_size[1] // patch_size[1],\n ]\n\n self.absolute_pos_embed = nn.Parameter(\n torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1])\n )\n trunc_normal_(self.absolute_pos_embed, std=0.02)\n\n self.pos_drop = nn.Dropout(p=drop_rate)\n\n # stochastic depth\n dpr = [\n x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))\n ] # stochastic depth decay rule\n\n # build layers\n self.layers = nn.ModuleList()\n # prepare downsample list\n downsamplelist = [PatchMerging for i in range(self.num_layers)]\n downsamplelist[-1] = None\n num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)]\n if self.dilation:\n downsamplelist[-2] = None\n num_features[-1] = int(embed_dim * 2 ** (self.num_layers - 1)) // 2\n for i_layer in range(self.num_layers):\n layer = BasicLayer(\n # dim=int(embed_dim * 2 ** i_layer),\n dim=num_features[i_layer],\n depth=depths[i_layer],\n num_heads=num_heads[i_layer],\n window_size=window_size,\n mlp_ratio=mlp_ratio,\n qkv_bias=qkv_bias,\n qk_scale=qk_scale,\n drop=drop_rate,\n attn_drop=attn_drop_rate,\n drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],\n norm_layer=norm_layer,\n # downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,\n downsample=downsamplelist[i_layer],\n use_checkpoint=use_checkpoint,\n )\n self.layers.append(layer)\n\n # num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]\n self.num_features = num_features\n\n # add a norm layer for each output\n for i_layer in out_indices:\n layer = norm_layer(num_features[i_layer])\n layer_name = f\"norm{i_layer}\"\n self.add_module(layer_name, layer)\n\n self._freeze_stages()\n\n def _freeze_stages(self):\n if self.frozen_stages >= 0:\n self.patch_embed.eval()\n for param in self.patch_embed.parameters():\n param.requires_grad = False\n\n if self.frozen_stages >= 1 and self.ape:\n self.absolute_pos_embed.requires_grad = False\n\n if self.frozen_stages >= 2:\n self.pos_drop.eval()\n for i in range(0, self.frozen_stages - 1):\n m = self.layers[i]\n m.eval()\n for param in m.parameters():\n param.requires_grad = False\n\n # def init_weights(self, pretrained=None):\n # \"\"\"Initialize the weights in backbone.\n # Args:\n # pretrained (str, optional): Path to pre-trained weights.\n # Defaults to None.\n # \"\"\"\n\n # def _init_weights(m):\n # if isinstance(m, nn.Linear):\n # trunc_normal_(m.weight, std=.02)\n # if isinstance(m, nn.Linear) and m.bias is not None:\n # nn.init.constant_(m.bias, 0)\n # elif isinstance(m, nn.LayerNorm):\n # nn.init.constant_(m.bias, 0)\n # nn.init.constant_(m.weight, 1.0)\n\n # if isinstance(pretrained, str):\n # self.apply(_init_weights)\n # logger = get_root_logger()\n # load_checkpoint(self, pretrained, strict=False, logger=logger)\n # elif pretrained is None:\n # self.apply(_init_weights)\n # else:\n # raise TypeError('pretrained must be a str or None')\n\n def forward_raw(self, x):\n \"\"\"Forward function.\"\"\"\n x = self.patch_embed(x)\n\n Wh, Ww = x.size(2), x.size(3)\n if self.ape:\n # interpolate the position embedding to the corresponding size\n absolute_pos_embed = F.interpolate(\n self.absolute_pos_embed, size=(Wh, Ww), mode=\"bicubic\"\n )\n x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C\n else:\n x = x.flatten(2).transpose(1, 2)\n x = self.pos_drop(x)\n\n outs = []\n for i in range(self.num_layers):\n layer = self.layers[i]\n x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)\n # import ipdb; ipdb.set_trace()\n\n if i in self.out_indices:\n norm_layer = getattr(self, f\"norm{i}\")\n x_out = norm_layer(x_out)\n\n out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()\n outs.append(out)\n # in:\n # torch.Size([2, 3, 1024, 1024])\n # outs:\n # [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \\\n # torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])]\n return tuple(outs)\n\n def forward(self, tensor_list: NestedTensor):\n x = tensor_list.tensors\n\n \"\"\"Forward function.\"\"\"\n x = self.patch_embed(x)\n\n Wh, Ww = x.size(2), x.size(3)\n if self.ape:\n # interpolate the position embedding to the corresponding size\n absolute_pos_embed = F.interpolate(\n self.absolute_pos_embed, size=(Wh, Ww), mode=\"bicubic\"\n )\n x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C\n else:\n x = x.flatten(2).transpose(1, 2)\n x = self.pos_drop(x)\n\n outs = []\n for i in range(self.num_layers):\n layer = self.layers[i]\n x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)\n\n if i in self.out_indices:\n norm_layer = getattr(self, f\"norm{i}\")\n x_out = norm_layer(x_out)\n\n out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()\n outs.append(out)\n # in:\n # torch.Size([2, 3, 1024, 1024])\n # out:\n # [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \\\n # torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])]\n\n # collect for nesttensors\n outs_dict = {}\n for idx, out_i in enumerate(outs):\n m = tensor_list.mask\n assert m is not None\n mask = F.interpolate(m[None].float(), size=out_i.shape[-2:]).to(torch.bool)[0]\n outs_dict[idx] = NestedTensor(out_i, mask)\n\n return outs_dict\n\n def train(self, mode=True):\n \"\"\"Convert the model into training mode while keep layers freezed.\"\"\"\n super(SwinTransformer, self).train(mode)\n self._freeze_stages()\n\n\ndef build_swin_transformer(modelname, pretrain_img_size, **kw):\n assert modelname in [\n \"swin_T_224_1k\",\n \"swin_B_224_22k\",\n \"swin_B_384_22k\",\n \"swin_L_224_22k\",\n \"swin_L_384_22k\",\n ]\n\n model_para_dict = {\n \"swin_T_224_1k\": dict(\n embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7\n ),\n \"swin_B_224_22k\": dict(\n embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=7\n ),\n \"swin_B_384_22k\": dict(\n embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12\n ),\n \"swin_L_224_22k\": dict(\n embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=7\n ),\n \"swin_L_384_22k\": dict(\n embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12\n ),\n }\n kw_cgf = model_para_dict[modelname]\n kw_cgf.update(kw)\n model = SwinTransformer(pretrain_img_size=pretrain_img_size, **kw_cgf)\n return model\n\n\nif __name__ == \"__main__\":\n model = build_swin_transformer(\"swin_L_384_22k\", 384, dilation=True)\n x = torch.rand(2, 3, 1024, 1024)\n y = model.forward_raw(x)\n import ipdb\n\n ipdb.set_trace()\n x = torch.rand(2, 3, 384, 384)\n y = model.forward_raw(x)\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/GroundingDINO/bertwarper.py", "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint as checkpoint\nfrom torch import Tensor, nn\nfrom torchvision.ops.boxes import nms\nfrom transformers import BertConfig, BertModel, BertPreTrainedModel\nfrom transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions\n\n\nclass BertModelWarper(nn.Module):\n def __init__(self, bert_model):\n super().__init__()\n # self.bert = bert_modelc\n\n self.config = bert_model.config\n self.embeddings = bert_model.embeddings\n self.encoder = bert_model.encoder\n self.pooler = bert_model.pooler\n\n self.get_extended_attention_mask = bert_model.get_extended_attention_mask\n self.invert_attention_mask = bert_model.invert_attention_mask\n self.get_head_mask = bert_model.get_head_mask\n\n def forward(\n self,\n input_ids=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n head_mask=None,\n inputs_embeds=None,\n encoder_hidden_states=None,\n encoder_attention_mask=None,\n past_key_values=None,\n use_cache=None,\n output_attentions=None,\n output_hidden_states=None,\n return_dict=None,\n ):\n r\"\"\"\n encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):\n Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if\n the model is configured as a decoder.\n encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):\n Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in\n the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:\n\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):\n Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.\n\n If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`\n (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`\n instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.\n use_cache (:obj:`bool`, `optional`):\n If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up\n decoding (see :obj:`past_key_values`).\n \"\"\"\n output_attentions = (\n output_attentions if output_attentions is not None else self.config.output_attentions\n )\n output_hidden_states = (\n output_hidden_states\n if output_hidden_states is not None\n else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n if self.config.is_decoder:\n use_cache = use_cache if use_cache is not None else self.config.use_cache\n else:\n use_cache = False\n\n if input_ids is not None and inputs_embeds is not None:\n raise ValueError(\"You cannot specify both input_ids and inputs_embeds at the same time\")\n elif input_ids is not None:\n input_shape = input_ids.size()\n batch_size, seq_length = input_shape\n elif inputs_embeds is not None:\n input_shape = inputs_embeds.size()[:-1]\n batch_size, seq_length = input_shape\n else:\n raise ValueError(\"You have to specify either input_ids or inputs_embeds\")\n\n device = input_ids.device if input_ids is not None else inputs_embeds.device\n\n # past_key_values_length\n past_key_values_length = (\n past_key_values[0][0].shape[2] if past_key_values is not None else 0\n )\n\n if attention_mask is None:\n attention_mask = torch.ones(\n ((batch_size, seq_length + past_key_values_length)), device=device\n )\n if token_type_ids is None:\n token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)\n\n # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]\n # ourselves in which case we just need to make it broadcastable to all heads.\n extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(\n attention_mask, input_shape, device\n )\n\n # If a 2D or 3D attention mask is provided for the cross-attention\n # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]\n if self.config.is_decoder and encoder_hidden_states is not None:\n encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()\n encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)\n if encoder_attention_mask is None:\n encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)\n encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)\n else:\n encoder_extended_attention_mask = None\n # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':\n # import ipdb; ipdb.set_trace()\n\n # Prepare head mask if needed\n # 1.0 in head_mask indicate we keep the head\n # attention_probs has shape bsz x n_heads x N x N\n # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]\n # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]\n head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)\n\n embedding_output = self.embeddings(\n input_ids=input_ids,\n position_ids=position_ids,\n token_type_ids=token_type_ids,\n inputs_embeds=inputs_embeds,\n past_key_values_length=past_key_values_length,\n )\n\n encoder_outputs = self.encoder(\n embedding_output,\n attention_mask=extended_attention_mask,\n head_mask=head_mask,\n encoder_hidden_states=encoder_hidden_states,\n encoder_attention_mask=encoder_extended_attention_mask,\n past_key_values=past_key_values,\n use_cache=use_cache,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n sequence_output = encoder_outputs[0]\n pooled_output = self.pooler(sequence_output) if self.pooler is not None else None\n\n if not return_dict:\n return (sequence_output, pooled_output) + encoder_outputs[1:]\n\n return BaseModelOutputWithPoolingAndCrossAttentions(\n last_hidden_state=sequence_output,\n pooler_output=pooled_output,\n past_key_values=encoder_outputs.past_key_values,\n hidden_states=encoder_outputs.hidden_states,\n attentions=encoder_outputs.attentions,\n cross_attentions=encoder_outputs.cross_attentions,\n )\n\n\nclass TextEncoderShell(nn.Module):\n def __init__(self, text_encoder):\n super().__init__()\n self.text_encoder = text_encoder\n self.config = self.text_encoder.config\n\n def forward(self, **kw):\n # feed into text encoder\n return self.text_encoder(**kw)\n\n\ndef generate_masks_with_special_tokens(tokenized, special_tokens_list, tokenizer):\n \"\"\"Generate attention mask between each pair of special tokens\n Args:\n input_ids (torch.Tensor): input ids. Shape: [bs, num_token]\n special_tokens_mask (list): special tokens mask.\n Returns:\n torch.Tensor: attention mask between each special tokens.\n \"\"\"\n input_ids = tokenized[\"input_ids\"]\n bs, num_token = input_ids.shape\n # special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens\n special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()\n for special_token in special_tokens_list:\n special_tokens_mask |= input_ids == special_token\n\n # idxs: each row is a list of indices of special tokens\n idxs = torch.nonzero(special_tokens_mask)\n\n # generate attention mask and positional ids\n attention_mask = (\n torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)\n )\n position_ids = torch.zeros((bs, num_token), device=input_ids.device)\n previous_col = 0\n for i in range(idxs.shape[0]):\n row, col = idxs[i]\n if (col == 0) or (col == num_token - 1):\n attention_mask[row, col, col] = True\n position_ids[row, col] = 0\n else:\n attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True\n position_ids[row, previous_col + 1 : col + 1] = torch.arange(\n 0, col - previous_col, device=input_ids.device\n )\n\n previous_col = col\n\n # # padding mask\n # padding_mask = tokenized['attention_mask']\n # attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()\n\n return attention_mask, position_ids.to(torch.long)\n\n\ndef generate_masks_with_special_tokens_and_transfer_map(tokenized, special_tokens_list, tokenizer):\n \"\"\"Generate attention mask between each pair of special tokens\n Args:\n input_ids (torch.Tensor): input ids. Shape: [bs, num_token]\n special_tokens_mask (list): special tokens mask.\n Returns:\n torch.Tensor: attention mask between each special tokens.\n \"\"\"\n input_ids = tokenized[\"input_ids\"]\n bs, num_token = input_ids.shape\n # special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens\n special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()\n for special_token in special_tokens_list:\n special_tokens_mask |= input_ids == special_token\n\n # idxs: each row is a list of indices of special tokens\n idxs = torch.nonzero(special_tokens_mask)\n\n # generate attention mask and positional ids\n attention_mask = (\n torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)\n )\n position_ids = torch.zeros((bs, num_token), device=input_ids.device)\n cate_to_token_mask_list = [[] for _ in range(bs)]\n previous_col = 0\n for i in range(idxs.shape[0]):\n row, col = idxs[i]\n if (col == 0) or (col == num_token - 1):\n attention_mask[row, col, col] = True\n position_ids[row, col] = 0\n else:\n attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True\n position_ids[row, previous_col + 1 : col + 1] = torch.arange(\n 0, col - previous_col, device=input_ids.device\n )\n c2t_maski = torch.zeros((num_token), device=input_ids.device).bool()\n c2t_maski[previous_col + 1 : col] = True\n cate_to_token_mask_list[row].append(c2t_maski)\n previous_col = col\n\n cate_to_token_mask_list = [\n torch.stack(cate_to_token_mask_listi, dim=0)\n for cate_to_token_mask_listi in cate_to_token_mask_list\n ]\n\n # # padding mask\n # padding_mask = tokenized['attention_mask']\n # attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()\n\n return attention_mask, position_ids.to(torch.long), cate_to_token_mask_list\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn.h", "content": "/*!\n**************************************************************************************************\n* Deformable DETR\n* Copyright (c) 2020 SenseTime. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n**************************************************************************************************\n* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n**************************************************************************************************\n*/\n\n#pragma once\n\n#include \"ms_deform_attn_cpu.h\"\n\n#ifdef WITH_CUDA\n#include \"ms_deform_attn_cuda.h\"\n#endif\n\nnamespace groundingdino {\n\nat::Tensor\nms_deform_attn_forward(\n const at::Tensor &value, \n const at::Tensor &spatial_shapes,\n const at::Tensor &level_start_index,\n const at::Tensor &sampling_loc,\n const at::Tensor &attn_weight,\n const int im2col_step)\n{\n if (value.type().is_cuda())\n {\n#ifdef WITH_CUDA\n return ms_deform_attn_cuda_forward(\n value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step);\n#else\n AT_ERROR(\"Not compiled with GPU support\");\n#endif\n }\n AT_ERROR(\"Not implemented on the CPU\");\n}\n\nstd::vector\nms_deform_attn_backward(\n const at::Tensor &value, \n const at::Tensor &spatial_shapes,\n const at::Tensor &level_start_index,\n const at::Tensor &sampling_loc,\n const at::Tensor &attn_weight,\n const at::Tensor &grad_output,\n const int im2col_step)\n{\n if (value.type().is_cuda())\n {\n#ifdef WITH_CUDA\n return ms_deform_attn_cuda_backward(\n value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step);\n#else\n AT_ERROR(\"Not compiled with GPU support\");\n#endif\n }\n AT_ERROR(\"Not implemented on the CPU\");\n}\n\n} // namespace groundingdino" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.cpp", "content": "/*!\n**************************************************************************************************\n* Deformable DETR\n* Copyright (c) 2020 SenseTime. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n**************************************************************************************************\n* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n**************************************************************************************************\n*/\n\n#include \n\n#include \n#include \n\nnamespace groundingdino {\n\nat::Tensor\nms_deform_attn_cpu_forward(\n const at::Tensor &value, \n const at::Tensor &spatial_shapes,\n const at::Tensor &level_start_index,\n const at::Tensor &sampling_loc,\n const at::Tensor &attn_weight,\n const int im2col_step)\n{\n AT_ERROR(\"Not implement on cpu\");\n}\n\nstd::vector\nms_deform_attn_cpu_backward(\n const at::Tensor &value, \n const at::Tensor &spatial_shapes,\n const at::Tensor &level_start_index,\n const at::Tensor &sampling_loc,\n const at::Tensor &attn_weight,\n const at::Tensor &grad_output,\n const int im2col_step)\n{\n AT_ERROR(\"Not implement on cpu\");\n}\n\n} // namespace groundingdino\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.h", "content": "/*!\n**************************************************************************************************\n* Deformable DETR\n* Copyright (c) 2020 SenseTime. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n**************************************************************************************************\n* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n**************************************************************************************************\n*/\n\n#pragma once\n#include \n\nnamespace groundingdino {\n\nat::Tensor\nms_deform_attn_cpu_forward(\n const at::Tensor &value, \n const at::Tensor &spatial_shapes,\n const at::Tensor &level_start_index,\n const at::Tensor &sampling_loc,\n const at::Tensor &attn_weight,\n const int im2col_step);\n\nstd::vector\nms_deform_attn_cpu_backward(\n const at::Tensor &value, \n const at::Tensor &spatial_shapes,\n const at::Tensor &level_start_index,\n const at::Tensor &sampling_loc,\n const at::Tensor &attn_weight,\n const at::Tensor &grad_output,\n const int im2col_step);\n\n} // namespace groundingdino\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.cu", "content": "/*!\n**************************************************************************************************\n* Deformable DETR\n* Copyright (c) 2020 SenseTime. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n**************************************************************************************************\n* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n**************************************************************************************************\n*/\n\n#include \n#include \"ms_deform_im2col_cuda.cuh\"\n\n#include \n#include \n#include \n#include \n\nnamespace groundingdino {\n\nat::Tensor ms_deform_attn_cuda_forward(\n const at::Tensor &value, \n const at::Tensor &spatial_shapes,\n const at::Tensor &level_start_index,\n const at::Tensor &sampling_loc,\n const at::Tensor &attn_weight,\n const int im2col_step)\n{\n AT_ASSERTM(value.is_contiguous(), \"value tensor has to be contiguous\");\n AT_ASSERTM(spatial_shapes.is_contiguous(), \"spatial_shapes tensor has to be contiguous\");\n AT_ASSERTM(level_start_index.is_contiguous(), \"level_start_index tensor has to be contiguous\");\n AT_ASSERTM(sampling_loc.is_contiguous(), \"sampling_loc tensor has to be contiguous\");\n AT_ASSERTM(attn_weight.is_contiguous(), \"attn_weight tensor has to be contiguous\");\n\n AT_ASSERTM(value.type().is_cuda(), \"value must be a CUDA tensor\");\n AT_ASSERTM(spatial_shapes.type().is_cuda(), \"spatial_shapes must be a CUDA tensor\");\n AT_ASSERTM(level_start_index.type().is_cuda(), \"level_start_index must be a CUDA tensor\");\n AT_ASSERTM(sampling_loc.type().is_cuda(), \"sampling_loc must be a CUDA tensor\");\n AT_ASSERTM(attn_weight.type().is_cuda(), \"attn_weight must be a CUDA tensor\");\n\n const int batch = value.size(0);\n const int spatial_size = value.size(1);\n const int num_heads = value.size(2);\n const int channels = value.size(3);\n\n const int num_levels = spatial_shapes.size(0);\n\n const int num_query = sampling_loc.size(1);\n const int num_point = sampling_loc.size(4);\n\n const int im2col_step_ = std::min(batch, im2col_step);\n\n AT_ASSERTM(batch % im2col_step_ == 0, \"batch(%d) must divide im2col_step(%d)\", batch, im2col_step_);\n \n auto output = at::zeros({batch, num_query, num_heads, channels}, value.options());\n\n const int batch_n = im2col_step_;\n auto output_n = output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});\n auto per_value_size = spatial_size * num_heads * channels;\n auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;\n auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;\n for (int n = 0; n < batch/im2col_step_; ++n)\n {\n auto columns = output_n.select(0, n);\n AT_DISPATCH_FLOATING_TYPES(value.type(), \"ms_deform_attn_forward_cuda\", ([&] {\n ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(),\n value.data() + n * im2col_step_ * per_value_size,\n spatial_shapes.data(),\n level_start_index.data(),\n sampling_loc.data() + n * im2col_step_ * per_sample_loc_size,\n attn_weight.data() + n * im2col_step_ * per_attn_weight_size,\n batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,\n columns.data());\n\n }));\n }\n\n output = output.view({batch, num_query, num_heads*channels});\n\n return output;\n}\n\n\nstd::vector ms_deform_attn_cuda_backward(\n const at::Tensor &value, \n const at::Tensor &spatial_shapes,\n const at::Tensor &level_start_index,\n const at::Tensor &sampling_loc,\n const at::Tensor &attn_weight,\n const at::Tensor &grad_output,\n const int im2col_step)\n{\n\n AT_ASSERTM(value.is_contiguous(), \"value tensor has to be contiguous\");\n AT_ASSERTM(spatial_shapes.is_contiguous(), \"spatial_shapes tensor has to be contiguous\");\n AT_ASSERTM(level_start_index.is_contiguous(), \"level_start_index tensor has to be contiguous\");\n AT_ASSERTM(sampling_loc.is_contiguous(), \"sampling_loc tensor has to be contiguous\");\n AT_ASSERTM(attn_weight.is_contiguous(), \"attn_weight tensor has to be contiguous\");\n AT_ASSERTM(grad_output.is_contiguous(), \"grad_output tensor has to be contiguous\");\n\n AT_ASSERTM(value.type().is_cuda(), \"value must be a CUDA tensor\");\n AT_ASSERTM(spatial_shapes.type().is_cuda(), \"spatial_shapes must be a CUDA tensor\");\n AT_ASSERTM(level_start_index.type().is_cuda(), \"level_start_index must be a CUDA tensor\");\n AT_ASSERTM(sampling_loc.type().is_cuda(), \"sampling_loc must be a CUDA tensor\");\n AT_ASSERTM(attn_weight.type().is_cuda(), \"attn_weight must be a CUDA tensor\");\n AT_ASSERTM(grad_output.type().is_cuda(), \"grad_output must be a CUDA tensor\");\n\n const int batch = value.size(0);\n const int spatial_size = value.size(1);\n const int num_heads = value.size(2);\n const int channels = value.size(3);\n\n const int num_levels = spatial_shapes.size(0);\n\n const int num_query = sampling_loc.size(1);\n const int num_point = sampling_loc.size(4);\n\n const int im2col_step_ = std::min(batch, im2col_step);\n\n AT_ASSERTM(batch % im2col_step_ == 0, \"batch(%d) must divide im2col_step(%d)\", batch, im2col_step_);\n\n auto grad_value = at::zeros_like(value);\n auto grad_sampling_loc = at::zeros_like(sampling_loc);\n auto grad_attn_weight = at::zeros_like(attn_weight);\n\n const int batch_n = im2col_step_;\n auto per_value_size = spatial_size * num_heads * channels;\n auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;\n auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;\n auto grad_output_n = grad_output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});\n \n for (int n = 0; n < batch/im2col_step_; ++n)\n {\n auto grad_output_g = grad_output_n.select(0, n);\n AT_DISPATCH_FLOATING_TYPES(value.type(), \"ms_deform_attn_backward_cuda\", ([&] {\n ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(),\n grad_output_g.data(),\n value.data() + n * im2col_step_ * per_value_size,\n spatial_shapes.data(),\n level_start_index.data(),\n sampling_loc.data() + n * im2col_step_ * per_sample_loc_size,\n attn_weight.data() + n * im2col_step_ * per_attn_weight_size,\n batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,\n grad_value.data() + n * im2col_step_ * per_value_size,\n grad_sampling_loc.data() + n * im2col_step_ * per_sample_loc_size,\n grad_attn_weight.data() + n * im2col_step_ * per_attn_weight_size);\n\n }));\n }\n\n return {\n grad_value, grad_sampling_loc, grad_attn_weight\n };\n}\n\n} // namespace groundingdino" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.h", "content": "/*!\n**************************************************************************************************\n* Deformable DETR\n* Copyright (c) 2020 SenseTime. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n**************************************************************************************************\n* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0\n**************************************************************************************************\n*/\n\n#pragma once\n#include \n\nnamespace groundingdino {\n\nat::Tensor ms_deform_attn_cuda_forward(\n const at::Tensor &value, \n const at::Tensor &spatial_shapes,\n const at::Tensor &level_start_index,\n const at::Tensor &sampling_loc,\n const at::Tensor &attn_weight,\n const int im2col_step);\n\nstd::vector ms_deform_attn_cuda_backward(\n const at::Tensor &value, \n const at::Tensor &spatial_shapes,\n const at::Tensor &level_start_index,\n const at::Tensor &sampling_loc,\n const at::Tensor &attn_weight,\n const at::Tensor &grad_output,\n const int im2col_step);\n\n} // namespace groundingdino" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_im2col_cuda.cuh", "content": "/*!\n**************************************************************************\n* Deformable DETR\n* Copyright (c) 2020 SenseTime. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n**************************************************************************\n* Modified from DCN (https://github.com/msracver/Deformable-ConvNets)\n* Copyright (c) 2018 Microsoft\n**************************************************************************\n*/\n\n#include \n#include \n#include \n\n#include \n#include \n\n#include \n\n#define CUDA_KERNEL_LOOP(i, n) \\\n for (int i = blockIdx.x * blockDim.x + threadIdx.x; \\\n i < (n); \\\n i += blockDim.x * gridDim.x)\n\nconst int CUDA_NUM_THREADS = 1024;\ninline int GET_BLOCKS(const int N, const int num_threads)\n{\n return (N + num_threads - 1) / num_threads;\n}\n\n\ntemplate \n__device__ scalar_t ms_deform_attn_im2col_bilinear(const scalar_t* &bottom_data, \n const int &height, const int &width, const int &nheads, const int &channels,\n const scalar_t &h, const scalar_t &w, const int &m, const int &c)\n{\n const int h_low = floor(h);\n const int w_low = floor(w);\n const int h_high = h_low + 1;\n const int w_high = w_low + 1;\n\n const scalar_t lh = h - h_low;\n const scalar_t lw = w - w_low;\n const scalar_t hh = 1 - lh, hw = 1 - lw;\n\n const int w_stride = nheads * channels;\n const int h_stride = width * w_stride;\n const int h_low_ptr_offset = h_low * h_stride;\n const int h_high_ptr_offset = h_low_ptr_offset + h_stride;\n const int w_low_ptr_offset = w_low * w_stride;\n const int w_high_ptr_offset = w_low_ptr_offset + w_stride;\n const int base_ptr = m * channels + c;\n\n scalar_t v1 = 0;\n if (h_low >= 0 && w_low >= 0)\n {\n const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;\n v1 = bottom_data[ptr1];\n }\n scalar_t v2 = 0;\n if (h_low >= 0 && w_high <= width - 1)\n {\n const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;\n v2 = bottom_data[ptr2];\n }\n scalar_t v3 = 0;\n if (h_high <= height - 1 && w_low >= 0)\n {\n const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;\n v3 = bottom_data[ptr3];\n }\n scalar_t v4 = 0;\n if (h_high <= height - 1 && w_high <= width - 1)\n {\n const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;\n v4 = bottom_data[ptr4];\n }\n\n const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;\n\n const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);\n return val;\n}\n\n\ntemplate \n__device__ void ms_deform_attn_col2im_bilinear(const scalar_t* &bottom_data, \n const int &height, const int &width, const int &nheads, const int &channels,\n const scalar_t &h, const scalar_t &w, const int &m, const int &c,\n const scalar_t &top_grad,\n const scalar_t &attn_weight,\n scalar_t* &grad_value, \n scalar_t* grad_sampling_loc,\n scalar_t* grad_attn_weight)\n{\n const int h_low = floor(h);\n const int w_low = floor(w);\n const int h_high = h_low + 1;\n const int w_high = w_low + 1;\n\n const scalar_t lh = h - h_low;\n const scalar_t lw = w - w_low;\n const scalar_t hh = 1 - lh, hw = 1 - lw;\n\n const int w_stride = nheads * channels;\n const int h_stride = width * w_stride;\n const int h_low_ptr_offset = h_low * h_stride;\n const int h_high_ptr_offset = h_low_ptr_offset + h_stride;\n const int w_low_ptr_offset = w_low * w_stride;\n const int w_high_ptr_offset = w_low_ptr_offset + w_stride;\n const int base_ptr = m * channels + c;\n\n const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;\n const scalar_t top_grad_value = top_grad * attn_weight;\n scalar_t grad_h_weight = 0, grad_w_weight = 0;\n\n scalar_t v1 = 0;\n if (h_low >= 0 && w_low >= 0)\n {\n const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;\n v1 = bottom_data[ptr1];\n grad_h_weight -= hw * v1;\n grad_w_weight -= hh * v1;\n atomicAdd(grad_value+ptr1, w1*top_grad_value);\n }\n scalar_t v2 = 0;\n if (h_low >= 0 && w_high <= width - 1)\n {\n const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;\n v2 = bottom_data[ptr2];\n grad_h_weight -= lw * v2;\n grad_w_weight += hh * v2;\n atomicAdd(grad_value+ptr2, w2*top_grad_value);\n }\n scalar_t v3 = 0;\n if (h_high <= height - 1 && w_low >= 0)\n {\n const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;\n v3 = bottom_data[ptr3];\n grad_h_weight += hw * v3;\n grad_w_weight -= lh * v3;\n atomicAdd(grad_value+ptr3, w3*top_grad_value); \n }\n scalar_t v4 = 0;\n if (h_high <= height - 1 && w_high <= width - 1)\n {\n const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;\n v4 = bottom_data[ptr4];\n grad_h_weight += lw * v4;\n grad_w_weight += lh * v4;\n atomicAdd(grad_value+ptr4, w4*top_grad_value);\n }\n\n const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);\n *grad_attn_weight = top_grad * val;\n *grad_sampling_loc = width * grad_w_weight * top_grad_value;\n *(grad_sampling_loc + 1) = height * grad_h_weight * top_grad_value;\n}\n\n\ntemplate \n__device__ void ms_deform_attn_col2im_bilinear_gm(const scalar_t* &bottom_data, \n const int &height, const int &width, const int &nheads, const int &channels,\n const scalar_t &h, const scalar_t &w, const int &m, const int &c,\n const scalar_t &top_grad,\n const scalar_t &attn_weight,\n scalar_t* &grad_value, \n scalar_t* grad_sampling_loc,\n scalar_t* grad_attn_weight)\n{\n const int h_low = floor(h);\n const int w_low = floor(w);\n const int h_high = h_low + 1;\n const int w_high = w_low + 1;\n\n const scalar_t lh = h - h_low;\n const scalar_t lw = w - w_low;\n const scalar_t hh = 1 - lh, hw = 1 - lw;\n\n const int w_stride = nheads * channels;\n const int h_stride = width * w_stride;\n const int h_low_ptr_offset = h_low * h_stride;\n const int h_high_ptr_offset = h_low_ptr_offset + h_stride;\n const int w_low_ptr_offset = w_low * w_stride;\n const int w_high_ptr_offset = w_low_ptr_offset + w_stride;\n const int base_ptr = m * channels + c;\n\n const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;\n const scalar_t top_grad_value = top_grad * attn_weight;\n scalar_t grad_h_weight = 0, grad_w_weight = 0;\n\n scalar_t v1 = 0;\n if (h_low >= 0 && w_low >= 0)\n {\n const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;\n v1 = bottom_data[ptr1];\n grad_h_weight -= hw * v1;\n grad_w_weight -= hh * v1;\n atomicAdd(grad_value+ptr1, w1*top_grad_value);\n }\n scalar_t v2 = 0;\n if (h_low >= 0 && w_high <= width - 1)\n {\n const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;\n v2 = bottom_data[ptr2];\n grad_h_weight -= lw * v2;\n grad_w_weight += hh * v2;\n atomicAdd(grad_value+ptr2, w2*top_grad_value);\n }\n scalar_t v3 = 0;\n if (h_high <= height - 1 && w_low >= 0)\n {\n const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;\n v3 = bottom_data[ptr3];\n grad_h_weight += hw * v3;\n grad_w_weight -= lh * v3;\n atomicAdd(grad_value+ptr3, w3*top_grad_value); \n }\n scalar_t v4 = 0;\n if (h_high <= height - 1 && w_high <= width - 1)\n {\n const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;\n v4 = bottom_data[ptr4];\n grad_h_weight += lw * v4;\n grad_w_weight += lh * v4;\n atomicAdd(grad_value+ptr4, w4*top_grad_value);\n }\n\n const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);\n atomicAdd(grad_attn_weight, top_grad * val); \n atomicAdd(grad_sampling_loc, width * grad_w_weight * top_grad_value);\n atomicAdd(grad_sampling_loc + 1, height * grad_h_weight * top_grad_value);\n}\n\n\ntemplate \n__global__ void ms_deformable_im2col_gpu_kernel(const int n,\n const scalar_t *data_value, \n const int64_t *data_spatial_shapes,\n const int64_t *data_level_start_index, \n const scalar_t *data_sampling_loc,\n const scalar_t *data_attn_weight,\n const int batch_size, \n const int spatial_size, \n const int num_heads,\n const int channels, \n const int num_levels,\n const int num_query,\n const int num_point,\n scalar_t *data_col)\n{\n CUDA_KERNEL_LOOP(index, n)\n {\n int _temp = index;\n const int c_col = _temp % channels;\n _temp /= channels;\n const int sampling_index = _temp; \n const int m_col = _temp % num_heads;\n _temp /= num_heads;\n const int q_col = _temp % num_query;\n _temp /= num_query;\n const int b_col = _temp;\n\n scalar_t *data_col_ptr = data_col + index;\n int data_weight_ptr = sampling_index * num_levels * num_point;\n int data_loc_w_ptr = data_weight_ptr << 1;\n const int qid_stride = num_heads * channels;\n const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;\n scalar_t col = 0;\n \n for (int l_col=0; l_col < num_levels; ++l_col)\n {\n const int level_start_id = data_level_start_index[l_col];\n const int spatial_h_ptr = l_col << 1;\n const int spatial_h = data_spatial_shapes[spatial_h_ptr];\n const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];\n const scalar_t *data_value_ptr = data_value + (data_value_ptr_init_offset + level_start_id * qid_stride);\n for (int p_col=0; p_col < num_point; ++p_col)\n {\n const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];\n const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];\n const scalar_t weight = data_attn_weight[data_weight_ptr];\n\n const scalar_t h_im = loc_h * spatial_h - 0.5;\n const scalar_t w_im = loc_w * spatial_w - 0.5;\n\n if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)\n {\n col += ms_deform_attn_im2col_bilinear(data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col) * weight;\n }\n\n data_weight_ptr += 1;\n data_loc_w_ptr += 2;\n }\n }\n *data_col_ptr = col;\n }\n}\n\ntemplate \n__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1(const int n,\n const scalar_t *grad_col,\n const scalar_t *data_value,\n const int64_t *data_spatial_shapes,\n const int64_t *data_level_start_index, \n const scalar_t *data_sampling_loc,\n const scalar_t *data_attn_weight,\n const int batch_size, \n const int spatial_size, \n const int num_heads,\n const int channels, \n const int num_levels,\n const int num_query,\n const int num_point,\n scalar_t *grad_value,\n scalar_t *grad_sampling_loc,\n scalar_t *grad_attn_weight)\n{\n CUDA_KERNEL_LOOP(index, n)\n {\n __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2];\n __shared__ scalar_t cache_grad_attn_weight[blockSize];\n unsigned int tid = threadIdx.x;\n int _temp = index;\n const int c_col = _temp % channels;\n _temp /= channels;\n const int sampling_index = _temp; \n const int m_col = _temp % num_heads;\n _temp /= num_heads;\n const int q_col = _temp % num_query;\n _temp /= num_query;\n const int b_col = _temp;\n\n const scalar_t top_grad = grad_col[index];\n\n int data_weight_ptr = sampling_index * num_levels * num_point;\n int data_loc_w_ptr = data_weight_ptr << 1;\n const int grad_sampling_ptr = data_weight_ptr;\n grad_sampling_loc += grad_sampling_ptr << 1;\n grad_attn_weight += grad_sampling_ptr;\n const int grad_weight_stride = 1;\n const int grad_loc_stride = 2;\n const int qid_stride = num_heads * channels;\n const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;\n\n for (int l_col=0; l_col < num_levels; ++l_col)\n {\n const int level_start_id = data_level_start_index[l_col];\n const int spatial_h_ptr = l_col << 1;\n const int spatial_h = data_spatial_shapes[spatial_h_ptr];\n const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];\n const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;\n const scalar_t *data_value_ptr = data_value + value_ptr_offset;\n scalar_t *grad_value_ptr = grad_value + value_ptr_offset;\n\n for (int p_col=0; p_col < num_point; ++p_col)\n {\n const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];\n const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];\n const scalar_t weight = data_attn_weight[data_weight_ptr];\n\n const scalar_t h_im = loc_h * spatial_h - 0.5;\n const scalar_t w_im = loc_w * spatial_w - 0.5;\n *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;\n *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;\n *(cache_grad_attn_weight+threadIdx.x)=0;\n if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)\n {\n ms_deform_attn_col2im_bilinear(\n data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,\n top_grad, weight, grad_value_ptr, \n cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);\n }\n \n __syncthreads();\n if (tid == 0)\n {\n scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0];\n int sid=2;\n for (unsigned int tid = 1; tid < blockSize; ++tid)\n {\n _grad_w += cache_grad_sampling_loc[sid];\n _grad_h += cache_grad_sampling_loc[sid + 1];\n _grad_a += cache_grad_attn_weight[tid];\n sid += 2;\n }\n \n \n *grad_sampling_loc = _grad_w;\n *(grad_sampling_loc + 1) = _grad_h;\n *grad_attn_weight = _grad_a;\n }\n __syncthreads();\n\n data_weight_ptr += 1;\n data_loc_w_ptr += 2;\n grad_attn_weight += grad_weight_stride;\n grad_sampling_loc += grad_loc_stride;\n }\n }\n }\n}\n\n\ntemplate \n__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2(const int n,\n const scalar_t *grad_col,\n const scalar_t *data_value,\n const int64_t *data_spatial_shapes,\n const int64_t *data_level_start_index, \n const scalar_t *data_sampling_loc,\n const scalar_t *data_attn_weight,\n const int batch_size, \n const int spatial_size, \n const int num_heads,\n const int channels, \n const int num_levels,\n const int num_query,\n const int num_point,\n scalar_t *grad_value,\n scalar_t *grad_sampling_loc,\n scalar_t *grad_attn_weight)\n{\n CUDA_KERNEL_LOOP(index, n)\n {\n __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2];\n __shared__ scalar_t cache_grad_attn_weight[blockSize];\n unsigned int tid = threadIdx.x;\n int _temp = index;\n const int c_col = _temp % channels;\n _temp /= channels;\n const int sampling_index = _temp; \n const int m_col = _temp % num_heads;\n _temp /= num_heads;\n const int q_col = _temp % num_query;\n _temp /= num_query;\n const int b_col = _temp;\n\n const scalar_t top_grad = grad_col[index];\n\n int data_weight_ptr = sampling_index * num_levels * num_point;\n int data_loc_w_ptr = data_weight_ptr << 1;\n const int grad_sampling_ptr = data_weight_ptr;\n grad_sampling_loc += grad_sampling_ptr << 1;\n grad_attn_weight += grad_sampling_ptr;\n const int grad_weight_stride = 1;\n const int grad_loc_stride = 2;\n const int qid_stride = num_heads * channels;\n const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;\n\n for (int l_col=0; l_col < num_levels; ++l_col)\n {\n const int level_start_id = data_level_start_index[l_col];\n const int spatial_h_ptr = l_col << 1;\n const int spatial_h = data_spatial_shapes[spatial_h_ptr];\n const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];\n const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;\n const scalar_t *data_value_ptr = data_value + value_ptr_offset;\n scalar_t *grad_value_ptr = grad_value + value_ptr_offset;\n\n for (int p_col=0; p_col < num_point; ++p_col)\n {\n const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];\n const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];\n const scalar_t weight = data_attn_weight[data_weight_ptr];\n\n const scalar_t h_im = loc_h * spatial_h - 0.5;\n const scalar_t w_im = loc_w * spatial_w - 0.5;\n *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;\n *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;\n *(cache_grad_attn_weight+threadIdx.x)=0;\n if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)\n {\n ms_deform_attn_col2im_bilinear(\n data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,\n top_grad, weight, grad_value_ptr, \n cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);\n }\n \n __syncthreads();\n\n for (unsigned int s=blockSize/2; s>0; s>>=1)\n {\n if (tid < s) {\n const unsigned int xid1 = tid << 1;\n const unsigned int xid2 = (tid + s) << 1;\n cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];\n cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];\n cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];\n }\n __syncthreads();\n }\n\n if (tid == 0)\n { \n *grad_sampling_loc = cache_grad_sampling_loc[0];\n *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1];\n *grad_attn_weight = cache_grad_attn_weight[0];\n }\n __syncthreads();\n\n data_weight_ptr += 1;\n data_loc_w_ptr += 2;\n grad_attn_weight += grad_weight_stride;\n grad_sampling_loc += grad_loc_stride;\n }\n }\n }\n}\n\n\ntemplate \n__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v1(const int n,\n const scalar_t *grad_col,\n const scalar_t *data_value,\n const int64_t *data_spatial_shapes,\n const int64_t *data_level_start_index, \n const scalar_t *data_sampling_loc,\n const scalar_t *data_attn_weight,\n const int batch_size, \n const int spatial_size, \n const int num_heads,\n const int channels, \n const int num_levels,\n const int num_query,\n const int num_point,\n scalar_t *grad_value,\n scalar_t *grad_sampling_loc,\n scalar_t *grad_attn_weight)\n{\n CUDA_KERNEL_LOOP(index, n)\n {\n extern __shared__ int _s[];\n scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;\n scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;\n unsigned int tid = threadIdx.x;\n int _temp = index;\n const int c_col = _temp % channels;\n _temp /= channels;\n const int sampling_index = _temp; \n const int m_col = _temp % num_heads;\n _temp /= num_heads;\n const int q_col = _temp % num_query;\n _temp /= num_query;\n const int b_col = _temp;\n\n const scalar_t top_grad = grad_col[index];\n\n int data_weight_ptr = sampling_index * num_levels * num_point;\n int data_loc_w_ptr = data_weight_ptr << 1;\n const int grad_sampling_ptr = data_weight_ptr;\n grad_sampling_loc += grad_sampling_ptr << 1;\n grad_attn_weight += grad_sampling_ptr;\n const int grad_weight_stride = 1;\n const int grad_loc_stride = 2;\n const int qid_stride = num_heads * channels;\n const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;\n\n for (int l_col=0; l_col < num_levels; ++l_col)\n {\n const int level_start_id = data_level_start_index[l_col];\n const int spatial_h_ptr = l_col << 1;\n const int spatial_h = data_spatial_shapes[spatial_h_ptr];\n const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];\n const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;\n const scalar_t *data_value_ptr = data_value + value_ptr_offset;\n scalar_t *grad_value_ptr = grad_value + value_ptr_offset;\n\n for (int p_col=0; p_col < num_point; ++p_col)\n {\n const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];\n const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];\n const scalar_t weight = data_attn_weight[data_weight_ptr];\n\n const scalar_t h_im = loc_h * spatial_h - 0.5;\n const scalar_t w_im = loc_w * spatial_w - 0.5;\n *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;\n *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;\n *(cache_grad_attn_weight+threadIdx.x)=0;\n if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)\n {\n ms_deform_attn_col2im_bilinear(\n data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,\n top_grad, weight, grad_value_ptr, \n cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);\n }\n \n __syncthreads();\n if (tid == 0)\n {\n scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0];\n int sid=2;\n for (unsigned int tid = 1; tid < blockDim.x; ++tid)\n {\n _grad_w += cache_grad_sampling_loc[sid];\n _grad_h += cache_grad_sampling_loc[sid + 1];\n _grad_a += cache_grad_attn_weight[tid];\n sid += 2;\n }\n \n \n *grad_sampling_loc = _grad_w;\n *(grad_sampling_loc + 1) = _grad_h;\n *grad_attn_weight = _grad_a;\n }\n __syncthreads();\n\n data_weight_ptr += 1;\n data_loc_w_ptr += 2;\n grad_attn_weight += grad_weight_stride;\n grad_sampling_loc += grad_loc_stride;\n }\n }\n }\n}\n\ntemplate \n__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2(const int n,\n const scalar_t *grad_col,\n const scalar_t *data_value,\n const int64_t *data_spatial_shapes,\n const int64_t *data_level_start_index, \n const scalar_t *data_sampling_loc,\n const scalar_t *data_attn_weight,\n const int batch_size, \n const int spatial_size, \n const int num_heads,\n const int channels, \n const int num_levels,\n const int num_query,\n const int num_point,\n scalar_t *grad_value,\n scalar_t *grad_sampling_loc,\n scalar_t *grad_attn_weight)\n{\n CUDA_KERNEL_LOOP(index, n)\n {\n extern __shared__ int _s[];\n scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;\n scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;\n unsigned int tid = threadIdx.x;\n int _temp = index;\n const int c_col = _temp % channels;\n _temp /= channels;\n const int sampling_index = _temp; \n const int m_col = _temp % num_heads;\n _temp /= num_heads;\n const int q_col = _temp % num_query;\n _temp /= num_query;\n const int b_col = _temp;\n\n const scalar_t top_grad = grad_col[index];\n\n int data_weight_ptr = sampling_index * num_levels * num_point;\n int data_loc_w_ptr = data_weight_ptr << 1;\n const int grad_sampling_ptr = data_weight_ptr;\n grad_sampling_loc += grad_sampling_ptr << 1;\n grad_attn_weight += grad_sampling_ptr;\n const int grad_weight_stride = 1;\n const int grad_loc_stride = 2;\n const int qid_stride = num_heads * channels;\n const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;\n\n for (int l_col=0; l_col < num_levels; ++l_col)\n {\n const int level_start_id = data_level_start_index[l_col];\n const int spatial_h_ptr = l_col << 1;\n const int spatial_h = data_spatial_shapes[spatial_h_ptr];\n const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];\n const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;\n const scalar_t *data_value_ptr = data_value + value_ptr_offset;\n scalar_t *grad_value_ptr = grad_value + value_ptr_offset;\n\n for (int p_col=0; p_col < num_point; ++p_col)\n {\n const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];\n const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];\n const scalar_t weight = data_attn_weight[data_weight_ptr];\n\n const scalar_t h_im = loc_h * spatial_h - 0.5;\n const scalar_t w_im = loc_w * spatial_w - 0.5;\n *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;\n *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;\n *(cache_grad_attn_weight+threadIdx.x)=0;\n if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)\n {\n ms_deform_attn_col2im_bilinear(\n data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,\n top_grad, weight, grad_value_ptr, \n cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);\n }\n \n __syncthreads();\n\n for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1)\n {\n if (tid < s) {\n const unsigned int xid1 = tid << 1;\n const unsigned int xid2 = (tid + s) << 1;\n cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];\n cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];\n cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];\n if (tid + (s << 1) < spre)\n {\n cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)];\n cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)];\n cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)];\n } \n }\n __syncthreads();\n }\n\n if (tid == 0)\n {\n *grad_sampling_loc = cache_grad_sampling_loc[0];\n *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1];\n *grad_attn_weight = cache_grad_attn_weight[0];\n }\n __syncthreads();\n\n data_weight_ptr += 1;\n data_loc_w_ptr += 2;\n grad_attn_weight += grad_weight_stride;\n grad_sampling_loc += grad_loc_stride;\n }\n }\n }\n}\n\ntemplate \n__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks(const int n,\n const scalar_t *grad_col,\n const scalar_t *data_value,\n const int64_t *data_spatial_shapes,\n const int64_t *data_level_start_index, \n const scalar_t *data_sampling_loc,\n const scalar_t *data_attn_weight,\n const int batch_size, \n const int spatial_size, \n const int num_heads,\n const int channels, \n const int num_levels,\n const int num_query,\n const int num_point,\n scalar_t *grad_value,\n scalar_t *grad_sampling_loc,\n scalar_t *grad_attn_weight)\n{\n CUDA_KERNEL_LOOP(index, n)\n {\n extern __shared__ int _s[];\n scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;\n scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;\n unsigned int tid = threadIdx.x;\n int _temp = index;\n const int c_col = _temp % channels;\n _temp /= channels;\n const int sampling_index = _temp; \n const int m_col = _temp % num_heads;\n _temp /= num_heads;\n const int q_col = _temp % num_query;\n _temp /= num_query;\n const int b_col = _temp;\n\n const scalar_t top_grad = grad_col[index];\n\n int data_weight_ptr = sampling_index * num_levels * num_point;\n int data_loc_w_ptr = data_weight_ptr << 1;\n const int grad_sampling_ptr = data_weight_ptr;\n grad_sampling_loc += grad_sampling_ptr << 1;\n grad_attn_weight += grad_sampling_ptr;\n const int grad_weight_stride = 1;\n const int grad_loc_stride = 2;\n const int qid_stride = num_heads * channels;\n const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;\n\n for (int l_col=0; l_col < num_levels; ++l_col)\n {\n const int level_start_id = data_level_start_index[l_col];\n const int spatial_h_ptr = l_col << 1;\n const int spatial_h = data_spatial_shapes[spatial_h_ptr];\n const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];\n const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;\n const scalar_t *data_value_ptr = data_value + value_ptr_offset;\n scalar_t *grad_value_ptr = grad_value + value_ptr_offset;\n\n for (int p_col=0; p_col < num_point; ++p_col)\n {\n const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];\n const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];\n const scalar_t weight = data_attn_weight[data_weight_ptr];\n\n const scalar_t h_im = loc_h * spatial_h - 0.5;\n const scalar_t w_im = loc_w * spatial_w - 0.5;\n *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;\n *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;\n *(cache_grad_attn_weight+threadIdx.x)=0;\n if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)\n {\n ms_deform_attn_col2im_bilinear(\n data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,\n top_grad, weight, grad_value_ptr, \n cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);\n }\n \n __syncthreads();\n\n for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1)\n {\n if (tid < s) {\n const unsigned int xid1 = tid << 1;\n const unsigned int xid2 = (tid + s) << 1;\n cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];\n cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];\n cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];\n if (tid + (s << 1) < spre)\n {\n cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)];\n cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)];\n cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)];\n }\n }\n __syncthreads();\n }\n\n if (tid == 0)\n {\n atomicAdd(grad_sampling_loc, cache_grad_sampling_loc[0]);\n atomicAdd(grad_sampling_loc + 1, cache_grad_sampling_loc[1]);\n atomicAdd(grad_attn_weight, cache_grad_attn_weight[0]);\n }\n __syncthreads();\n\n data_weight_ptr += 1;\n data_loc_w_ptr += 2;\n grad_attn_weight += grad_weight_stride;\n grad_sampling_loc += grad_loc_stride;\n }\n }\n }\n}\n\n\ntemplate \n__global__ void ms_deformable_col2im_gpu_kernel_gm(const int n,\n const scalar_t *grad_col,\n const scalar_t *data_value,\n const int64_t *data_spatial_shapes,\n const int64_t *data_level_start_index, \n const scalar_t *data_sampling_loc,\n const scalar_t *data_attn_weight,\n const int batch_size, \n const int spatial_size, \n const int num_heads,\n const int channels, \n const int num_levels,\n const int num_query,\n const int num_point,\n scalar_t *grad_value,\n scalar_t *grad_sampling_loc,\n scalar_t *grad_attn_weight)\n{\n CUDA_KERNEL_LOOP(index, n)\n {\n int _temp = index;\n const int c_col = _temp % channels;\n _temp /= channels;\n const int sampling_index = _temp; \n const int m_col = _temp % num_heads;\n _temp /= num_heads;\n const int q_col = _temp % num_query;\n _temp /= num_query;\n const int b_col = _temp;\n\n const scalar_t top_grad = grad_col[index];\n\n int data_weight_ptr = sampling_index * num_levels * num_point;\n int data_loc_w_ptr = data_weight_ptr << 1;\n const int grad_sampling_ptr = data_weight_ptr;\n grad_sampling_loc += grad_sampling_ptr << 1;\n grad_attn_weight += grad_sampling_ptr;\n const int grad_weight_stride = 1;\n const int grad_loc_stride = 2;\n const int qid_stride = num_heads * channels;\n const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;\n\n for (int l_col=0; l_col < num_levels; ++l_col)\n {\n const int level_start_id = data_level_start_index[l_col];\n const int spatial_h_ptr = l_col << 1;\n const int spatial_h = data_spatial_shapes[spatial_h_ptr];\n const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];\n const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;\n const scalar_t *data_value_ptr = data_value + value_ptr_offset;\n scalar_t *grad_value_ptr = grad_value + value_ptr_offset;\n\n for (int p_col=0; p_col < num_point; ++p_col)\n {\n const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];\n const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];\n const scalar_t weight = data_attn_weight[data_weight_ptr];\n\n const scalar_t h_im = loc_h * spatial_h - 0.5;\n const scalar_t w_im = loc_w * spatial_w - 0.5;\n if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)\n {\n ms_deform_attn_col2im_bilinear_gm(\n data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,\n top_grad, weight, grad_value_ptr, \n grad_sampling_loc, grad_attn_weight);\n }\n data_weight_ptr += 1;\n data_loc_w_ptr += 2;\n grad_attn_weight += grad_weight_stride;\n grad_sampling_loc += grad_loc_stride;\n }\n }\n }\n}\n\n\ntemplate \nvoid ms_deformable_im2col_cuda(cudaStream_t stream,\n const scalar_t* data_value,\n const int64_t* data_spatial_shapes, \n const int64_t* data_level_start_index, \n const scalar_t* data_sampling_loc,\n const scalar_t* data_attn_weight,\n const int batch_size,\n const int spatial_size, \n const int num_heads, \n const int channels, \n const int num_levels, \n const int num_query,\n const int num_point,\n scalar_t* data_col)\n{\n const int num_kernels = batch_size * num_query * num_heads * channels;\n const int num_actual_kernels = batch_size * num_query * num_heads * channels;\n const int num_threads = CUDA_NUM_THREADS;\n ms_deformable_im2col_gpu_kernel\n <<>>(\n num_kernels, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, \n batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, data_col);\n \n cudaError_t err = cudaGetLastError();\n if (err != cudaSuccess)\n {\n printf(\"error in ms_deformable_im2col_cuda: %s\\n\", cudaGetErrorString(err));\n }\n\n}\n\ntemplate \nvoid ms_deformable_col2im_cuda(cudaStream_t stream,\n const scalar_t* grad_col,\n const scalar_t* data_value,\n const int64_t * data_spatial_shapes,\n const int64_t * data_level_start_index,\n const scalar_t * data_sampling_loc,\n const scalar_t * data_attn_weight,\n const int batch_size, \n const int spatial_size, \n const int num_heads,\n const int channels, \n const int num_levels,\n const int num_query,\n const int num_point, \n scalar_t* grad_value,\n scalar_t* grad_sampling_loc,\n scalar_t* grad_attn_weight)\n{\n const int num_threads = (channels > CUDA_NUM_THREADS)?CUDA_NUM_THREADS:channels;\n const int num_kernels = batch_size * num_query * num_heads * channels;\n const int num_actual_kernels = batch_size * num_query * num_heads * channels;\n if (channels > 1024)\n {\n if ((channels & 1023) == 0)\n {\n ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n }\n else\n {\n ms_deformable_col2im_gpu_kernel_gm\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n }\n }\n else{\n switch(channels)\n {\n case 1:\n ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n break;\n case 2:\n ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n break;\n case 4:\n ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n break;\n case 8:\n ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n break;\n case 16:\n ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n break;\n case 32:\n ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n break;\n case 64:\n ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n break;\n case 128:\n ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n break;\n case 256:\n ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n break;\n case 512:\n ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n break;\n case 1024:\n ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n break;\n default:\n if (channels < 64)\n {\n ms_deformable_col2im_gpu_kernel_shm_reduce_v1\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n }\n else\n {\n ms_deformable_col2im_gpu_kernel_shm_reduce_v2\n <<>>(\n num_kernels, \n grad_col,\n data_value,\n data_spatial_shapes,\n data_level_start_index, \n data_sampling_loc,\n data_attn_weight,\n batch_size, \n spatial_size, \n num_heads,\n channels, \n num_levels,\n num_query,\n num_point,\n grad_value,\n grad_sampling_loc,\n grad_attn_weight);\n }\n }\n }\n cudaError_t err = cudaGetLastError();\n if (err != cudaSuccess)\n {\n printf(\"error in ms_deformable_col2im_cuda: %s\\n\", cudaGetErrorString(err));\n }\n\n}" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/cuda_version.cu", "content": "#include \n\nnamespace groundingdino {\nint get_cudart_version() {\n return CUDART_VERSION;\n}\n} // namespace groundingdino\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/vision.cpp", "content": "// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\n#include \"MsDeformAttn/ms_deform_attn.h\"\n\nnamespace groundingdino {\n\n#ifdef WITH_CUDA\nextern int get_cudart_version();\n#endif\n\nstd::string get_cuda_version() {\n#ifdef WITH_CUDA\n std::ostringstream oss;\n\n // copied from\n // https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/detail/CUDAHooks.cpp#L231\n auto printCudaStyleVersion = [&](int v) {\n oss << (v / 1000) << \".\" << (v / 10 % 100);\n if (v % 10 != 0) {\n oss << \".\" << (v % 10);\n }\n };\n printCudaStyleVersion(get_cudart_version());\n return oss.str();\n#else\n return std::string(\"not available\");\n#endif\n}\n\n// similar to\n// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Version.cpp\nstd::string get_compiler_version() {\n std::ostringstream ss;\n#if defined(__GNUC__)\n#ifndef __clang__\n { ss << \"GCC \" << __GNUC__ << \".\" << __GNUC_MINOR__; }\n#endif\n#endif\n\n#if defined(__clang_major__)\n {\n ss << \"clang \" << __clang_major__ << \".\" << __clang_minor__ << \".\"\n << __clang_patchlevel__;\n }\n#endif\n\n#if defined(_MSC_VER)\n { ss << \"MSVC \" << _MSC_FULL_VER; }\n#endif\n return ss.str();\n}\n\nPYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {\n m.def(\"ms_deform_attn_forward\", &ms_deform_attn_forward, \"ms_deform_attn_forward\");\n m.def(\"ms_deform_attn_backward\", &ms_deform_attn_backward, \"ms_deform_attn_backward\");\n}\n\n} // namespace groundingdino" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/GroundingDINO/fuse_modules.py", "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom timm.models.layers import DropPath\n\n\nclass FeatureResizer(nn.Module):\n \"\"\"\n This class takes as input a set of embeddings of dimension C1 and outputs a set of\n embedding of dimension C2, after a linear transformation, dropout and normalization (LN).\n \"\"\"\n\n def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=True):\n super().__init__()\n self.do_ln = do_ln\n # Object feature encoding\n self.fc = nn.Linear(input_feat_size, output_feat_size, bias=True)\n self.layer_norm = nn.LayerNorm(output_feat_size, eps=1e-12)\n self.dropout = nn.Dropout(dropout)\n\n def forward(self, encoder_features):\n x = self.fc(encoder_features)\n if self.do_ln:\n x = self.layer_norm(x)\n output = self.dropout(x)\n return output\n\n\ndef l1norm(X, dim, eps=1e-8):\n \"\"\"L1-normalize columns of X\"\"\"\n norm = torch.abs(X).sum(dim=dim, keepdim=True) + eps\n X = torch.div(X, norm)\n return X\n\n\ndef l2norm(X, dim, eps=1e-8):\n \"\"\"L2-normalize columns of X\"\"\"\n norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps\n X = torch.div(X, norm)\n return X\n\n\ndef func_attention(query, context, smooth=1, raw_feature_norm=\"softmax\", eps=1e-8):\n \"\"\"\n query: (n_context, queryL, d)\n context: (n_context, sourceL, d)\n \"\"\"\n batch_size_q, queryL = query.size(0), query.size(1)\n batch_size, sourceL = context.size(0), context.size(1)\n\n # Get attention\n # --> (batch, d, queryL)\n queryT = torch.transpose(query, 1, 2)\n\n # (batch, sourceL, d)(batch, d, queryL)\n # --> (batch, sourceL, queryL)\n attn = torch.bmm(context, queryT)\n if raw_feature_norm == \"softmax\":\n # --> (batch*sourceL, queryL)\n attn = attn.view(batch_size * sourceL, queryL)\n attn = nn.Softmax()(attn)\n # --> (batch, sourceL, queryL)\n attn = attn.view(batch_size, sourceL, queryL)\n elif raw_feature_norm == \"l2norm\":\n attn = l2norm(attn, 2)\n elif raw_feature_norm == \"clipped_l2norm\":\n attn = nn.LeakyReLU(0.1)(attn)\n attn = l2norm(attn, 2)\n else:\n raise ValueError(\"unknown first norm type:\", raw_feature_norm)\n # --> (batch, queryL, sourceL)\n attn = torch.transpose(attn, 1, 2).contiguous()\n # --> (batch*queryL, sourceL)\n attn = attn.view(batch_size * queryL, sourceL)\n attn = nn.Softmax()(attn * smooth)\n # --> (batch, queryL, sourceL)\n attn = attn.view(batch_size, queryL, sourceL)\n # --> (batch, sourceL, queryL)\n attnT = torch.transpose(attn, 1, 2).contiguous()\n\n # --> (batch, d, sourceL)\n contextT = torch.transpose(context, 1, 2)\n # (batch x d x sourceL)(batch x sourceL x queryL)\n # --> (batch, d, queryL)\n weightedContext = torch.bmm(contextT, attnT)\n # --> (batch, queryL, d)\n weightedContext = torch.transpose(weightedContext, 1, 2)\n\n return weightedContext, attnT\n\n\nclass BiMultiHeadAttention(nn.Module):\n def __init__(self, v_dim, l_dim, embed_dim, num_heads, dropout=0.1, cfg=None):\n super(BiMultiHeadAttention, self).__init__()\n\n self.embed_dim = embed_dim\n self.num_heads = num_heads\n self.head_dim = embed_dim // num_heads\n self.v_dim = v_dim\n self.l_dim = l_dim\n\n assert (\n self.head_dim * self.num_heads == self.embed_dim\n ), f\"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads}).\"\n self.scale = self.head_dim ** (-0.5)\n self.dropout = dropout\n\n self.v_proj = nn.Linear(self.v_dim, self.embed_dim)\n self.l_proj = nn.Linear(self.l_dim, self.embed_dim)\n self.values_v_proj = nn.Linear(self.v_dim, self.embed_dim)\n self.values_l_proj = nn.Linear(self.l_dim, self.embed_dim)\n\n self.out_v_proj = nn.Linear(self.embed_dim, self.v_dim)\n self.out_l_proj = nn.Linear(self.embed_dim, self.l_dim)\n\n self.stable_softmax_2d = True\n self.clamp_min_for_underflow = True\n self.clamp_max_for_overflow = True\n\n self._reset_parameters()\n\n def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):\n return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()\n\n def _reset_parameters(self):\n nn.init.xavier_uniform_(self.v_proj.weight)\n self.v_proj.bias.data.fill_(0)\n nn.init.xavier_uniform_(self.l_proj.weight)\n self.l_proj.bias.data.fill_(0)\n nn.init.xavier_uniform_(self.values_v_proj.weight)\n self.values_v_proj.bias.data.fill_(0)\n nn.init.xavier_uniform_(self.values_l_proj.weight)\n self.values_l_proj.bias.data.fill_(0)\n nn.init.xavier_uniform_(self.out_v_proj.weight)\n self.out_v_proj.bias.data.fill_(0)\n nn.init.xavier_uniform_(self.out_l_proj.weight)\n self.out_l_proj.bias.data.fill_(0)\n\n def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):\n \"\"\"_summary_\n\n Args:\n v (_type_): bs, n_img, dim\n l (_type_): bs, n_text, dim\n attention_mask_v (_type_, optional): _description_. bs, n_img\n attention_mask_l (_type_, optional): _description_. bs, n_text\n\n Returns:\n _type_: _description_\n \"\"\"\n # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':\n # import ipdb; ipdb.set_trace()\n bsz, tgt_len, _ = v.size()\n\n query_states = self.v_proj(v) * self.scale\n key_states = self._shape(self.l_proj(l), -1, bsz)\n value_v_states = self._shape(self.values_v_proj(v), -1, bsz)\n value_l_states = self._shape(self.values_l_proj(l), -1, bsz)\n\n proj_shape = (bsz * self.num_heads, -1, self.head_dim)\n query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)\n key_states = key_states.view(*proj_shape)\n value_v_states = value_v_states.view(*proj_shape)\n value_l_states = value_l_states.view(*proj_shape)\n\n src_len = key_states.size(1)\n attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) # bs*nhead, nimg, ntxt\n\n if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):\n raise ValueError(\n f\"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}\"\n )\n\n if self.stable_softmax_2d:\n attn_weights = attn_weights - attn_weights.max()\n\n if self.clamp_min_for_underflow:\n attn_weights = torch.clamp(\n attn_weights, min=-50000\n ) # Do not increase -50000, data type half has quite limited range\n if self.clamp_max_for_overflow:\n attn_weights = torch.clamp(\n attn_weights, max=50000\n ) # Do not increase 50000, data type half has quite limited range\n\n attn_weights_T = attn_weights.transpose(1, 2)\n attn_weights_l = attn_weights_T - torch.max(attn_weights_T, dim=-1, keepdim=True)[0]\n if self.clamp_min_for_underflow:\n attn_weights_l = torch.clamp(\n attn_weights_l, min=-50000\n ) # Do not increase -50000, data type half has quite limited range\n if self.clamp_max_for_overflow:\n attn_weights_l = torch.clamp(\n attn_weights_l, max=50000\n ) # Do not increase 50000, data type half has quite limited range\n\n # mask vison for language\n if attention_mask_v is not None:\n attention_mask_v = (\n attention_mask_v[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)\n )\n attn_weights_l.masked_fill_(attention_mask_v, float(\"-inf\"))\n\n attn_weights_l = attn_weights_l.softmax(dim=-1)\n\n # mask language for vision\n if attention_mask_l is not None:\n attention_mask_l = (\n attention_mask_l[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)\n )\n attn_weights.masked_fill_(attention_mask_l, float(\"-inf\"))\n attn_weights_v = attn_weights.softmax(dim=-1)\n\n attn_probs_v = F.dropout(attn_weights_v, p=self.dropout, training=self.training)\n attn_probs_l = F.dropout(attn_weights_l, p=self.dropout, training=self.training)\n\n attn_output_v = torch.bmm(attn_probs_v, value_l_states)\n attn_output_l = torch.bmm(attn_probs_l, value_v_states)\n\n if attn_output_v.size() != (bsz * self.num_heads, tgt_len, self.head_dim):\n raise ValueError(\n f\"`attn_output_v` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output_v.size()}\"\n )\n\n if attn_output_l.size() != (bsz * self.num_heads, src_len, self.head_dim):\n raise ValueError(\n f\"`attn_output_l` should be of size {(bsz, self.num_heads, src_len, self.head_dim)}, but is {attn_output_l.size()}\"\n )\n\n attn_output_v = attn_output_v.view(bsz, self.num_heads, tgt_len, self.head_dim)\n attn_output_v = attn_output_v.transpose(1, 2)\n attn_output_v = attn_output_v.reshape(bsz, tgt_len, self.embed_dim)\n\n attn_output_l = attn_output_l.view(bsz, self.num_heads, src_len, self.head_dim)\n attn_output_l = attn_output_l.transpose(1, 2)\n attn_output_l = attn_output_l.reshape(bsz, src_len, self.embed_dim)\n\n attn_output_v = self.out_v_proj(attn_output_v)\n attn_output_l = self.out_l_proj(attn_output_l)\n\n return attn_output_v, attn_output_l\n\n\n# Bi-Direction MHA (text->image, image->text)\nclass BiAttentionBlock(nn.Module):\n def __init__(\n self,\n v_dim,\n l_dim,\n embed_dim,\n num_heads,\n dropout=0.1,\n drop_path=0.0,\n init_values=1e-4,\n cfg=None,\n ):\n \"\"\"\n Inputs:\n embed_dim - Dimensionality of input and attention feature vectors\n hidden_dim - Dimensionality of hidden layer in feed-forward network\n (usually 2-4x larger than embed_dim)\n num_heads - Number of heads to use in the Multi-Head Attention block\n dropout - Amount of dropout to apply in the feed-forward network\n \"\"\"\n super(BiAttentionBlock, self).__init__()\n\n # pre layer norm\n self.layer_norm_v = nn.LayerNorm(v_dim)\n self.layer_norm_l = nn.LayerNorm(l_dim)\n self.attn = BiMultiHeadAttention(\n v_dim=v_dim, l_dim=l_dim, embed_dim=embed_dim, num_heads=num_heads, dropout=dropout\n )\n\n # add layer scale for training stability\n self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()\n self.gamma_v = nn.Parameter(init_values * torch.ones((v_dim)), requires_grad=True)\n self.gamma_l = nn.Parameter(init_values * torch.ones((l_dim)), requires_grad=True)\n\n def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):\n v = self.layer_norm_v(v)\n l = self.layer_norm_l(l)\n delta_v, delta_l = self.attn(\n v, l, attention_mask_v=attention_mask_v, attention_mask_l=attention_mask_l\n )\n # v, l = v + delta_v, l + delta_l\n v = v + self.drop_path(self.gamma_v * delta_v)\n l = l + self.drop_path(self.gamma_l * delta_l)\n return v, l\n\n # def forward(self, v:List[torch.Tensor], l, attention_mask_v=None, attention_mask_l=None)\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/GroundingDINO/groundingdino.py", "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Conditional DETR model and criterion classes.\n# Copyright (c) 2021 Microsoft. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n# ------------------------------------------------------------------------\n# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# ------------------------------------------------------------------------\nimport copy\nfrom typing import List\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\nfrom torchvision.ops.boxes import nms\nfrom transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast\n\nfrom groundingdino.util import box_ops, get_tokenlizer\nfrom groundingdino.util.misc import (\n NestedTensor,\n accuracy,\n get_world_size,\n interpolate,\n inverse_sigmoid,\n is_dist_avail_and_initialized,\n nested_tensor_from_tensor_list,\n)\nfrom groundingdino.util.utils import get_phrases_from_posmap\nfrom groundingdino.util.visualizer import COCOVisualizer\nfrom groundingdino.util.vl_utils import create_positive_map_from_span\n\nfrom ..registry import MODULE_BUILD_FUNCS\nfrom .backbone import build_backbone\nfrom .bertwarper import (\n BertModelWarper,\n generate_masks_with_special_tokens,\n generate_masks_with_special_tokens_and_transfer_map,\n)\nfrom .transformer import build_transformer\nfrom .utils import MLP, ContrastiveEmbed, sigmoid_focal_loss\n\n\nclass GroundingDINO(nn.Module):\n \"\"\"This is the Cross-Attention Detector module that performs object detection\"\"\"\n\n def __init__(\n self,\n backbone,\n transformer,\n num_queries,\n aux_loss=False,\n iter_update=False,\n query_dim=2,\n num_feature_levels=1,\n nheads=8,\n # two stage\n two_stage_type=\"no\", # ['no', 'standard']\n dec_pred_bbox_embed_share=True,\n two_stage_class_embed_share=True,\n two_stage_bbox_embed_share=True,\n num_patterns=0,\n dn_number=100,\n dn_box_noise_scale=0.4,\n dn_label_noise_ratio=0.5,\n dn_labelbook_size=100,\n text_encoder_type=\"bert-base-uncased\",\n sub_sentence_present=True,\n max_text_len=256,\n ):\n \"\"\"Initializes the model.\n Parameters:\n backbone: torch module of the backbone to be used. See backbone.py\n transformer: torch module of the transformer architecture. See transformer.py\n num_queries: number of object queries, ie detection slot. This is the maximal number of objects\n Conditional DETR can detect in a single image. For COCO, we recommend 100 queries.\n aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.\n \"\"\"\n super().__init__()\n self.num_queries = num_queries\n self.transformer = transformer\n self.hidden_dim = hidden_dim = transformer.d_model\n self.num_feature_levels = num_feature_levels\n self.nheads = nheads\n self.max_text_len = 256\n self.sub_sentence_present = sub_sentence_present\n\n # setting query dim\n self.query_dim = query_dim\n assert query_dim == 4\n\n # for dn training\n self.num_patterns = num_patterns\n self.dn_number = dn_number\n self.dn_box_noise_scale = dn_box_noise_scale\n self.dn_label_noise_ratio = dn_label_noise_ratio\n self.dn_labelbook_size = dn_labelbook_size\n\n # bert\n self.tokenizer = get_tokenlizer.get_tokenlizer(text_encoder_type)\n self.bert = get_tokenlizer.get_pretrained_language_model(text_encoder_type)\n self.bert.pooler.dense.weight.requires_grad_(False)\n self.bert.pooler.dense.bias.requires_grad_(False)\n self.bert = BertModelWarper(bert_model=self.bert)\n\n self.feat_map = nn.Linear(self.bert.config.hidden_size, self.hidden_dim, bias=True)\n nn.init.constant_(self.feat_map.bias.data, 0)\n nn.init.xavier_uniform_(self.feat_map.weight.data)\n # freeze\n\n # special tokens\n self.specical_tokens = self.tokenizer.convert_tokens_to_ids([\"[CLS]\", \"[SEP]\", \".\", \"?\"])\n\n # prepare input projection layers\n if num_feature_levels > 1:\n num_backbone_outs = len(backbone.num_channels)\n input_proj_list = []\n for _ in range(num_backbone_outs):\n in_channels = backbone.num_channels[_]\n input_proj_list.append(\n nn.Sequential(\n nn.Conv2d(in_channels, hidden_dim, kernel_size=1),\n nn.GroupNorm(32, hidden_dim),\n )\n )\n for _ in range(num_feature_levels - num_backbone_outs):\n input_proj_list.append(\n nn.Sequential(\n nn.Conv2d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1),\n nn.GroupNorm(32, hidden_dim),\n )\n )\n in_channels = hidden_dim\n self.input_proj = nn.ModuleList(input_proj_list)\n else:\n assert two_stage_type == \"no\", \"two_stage_type should be no if num_feature_levels=1 !!!\"\n self.input_proj = nn.ModuleList(\n [\n nn.Sequential(\n nn.Conv2d(backbone.num_channels[-1], hidden_dim, kernel_size=1),\n nn.GroupNorm(32, hidden_dim),\n )\n ]\n )\n\n self.backbone = backbone\n self.aux_loss = aux_loss\n self.box_pred_damping = box_pred_damping = None\n\n self.iter_update = iter_update\n assert iter_update, \"Why not iter_update?\"\n\n # prepare pred layers\n self.dec_pred_bbox_embed_share = dec_pred_bbox_embed_share\n # prepare class & box embed\n _class_embed = ContrastiveEmbed()\n\n _bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3)\n nn.init.constant_(_bbox_embed.layers[-1].weight.data, 0)\n nn.init.constant_(_bbox_embed.layers[-1].bias.data, 0)\n\n if dec_pred_bbox_embed_share:\n box_embed_layerlist = [_bbox_embed for i in range(transformer.num_decoder_layers)]\n else:\n box_embed_layerlist = [\n copy.deepcopy(_bbox_embed) for i in range(transformer.num_decoder_layers)\n ]\n class_embed_layerlist = [_class_embed for i in range(transformer.num_decoder_layers)]\n self.bbox_embed = nn.ModuleList(box_embed_layerlist)\n self.class_embed = nn.ModuleList(class_embed_layerlist)\n self.transformer.decoder.bbox_embed = self.bbox_embed\n self.transformer.decoder.class_embed = self.class_embed\n\n # two stage\n self.two_stage_type = two_stage_type\n assert two_stage_type in [\"no\", \"standard\"], \"unknown param {} of two_stage_type\".format(\n two_stage_type\n )\n if two_stage_type != \"no\":\n if two_stage_bbox_embed_share:\n assert dec_pred_bbox_embed_share\n self.transformer.enc_out_bbox_embed = _bbox_embed\n else:\n self.transformer.enc_out_bbox_embed = copy.deepcopy(_bbox_embed)\n\n if two_stage_class_embed_share:\n assert dec_pred_bbox_embed_share\n self.transformer.enc_out_class_embed = _class_embed\n else:\n self.transformer.enc_out_class_embed = copy.deepcopy(_class_embed)\n\n self.refpoint_embed = None\n\n self._reset_parameters()\n\n def _reset_parameters(self):\n # init input_proj\n for proj in self.input_proj:\n nn.init.xavier_uniform_(proj[0].weight, gain=1)\n nn.init.constant_(proj[0].bias, 0)\n\n def init_ref_points(self, use_num_queries):\n self.refpoint_embed = nn.Embedding(use_num_queries, self.query_dim)\n\n def forward(self, samples: NestedTensor, targets: List = None, **kw):\n \"\"\"The forward expects a NestedTensor, which consists of:\n - samples.tensor: batched images, of shape [batch_size x 3 x H x W]\n - samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels\n\n It returns a dict with the following elements:\n - \"pred_logits\": the classification logits (including no-object) for all queries.\n Shape= [batch_size x num_queries x num_classes]\n - \"pred_boxes\": The normalized boxes coordinates for all queries, represented as\n (center_x, center_y, width, height). These values are normalized in [0, 1],\n relative to the size of each individual image (disregarding possible padding).\n See PostProcess for information on how to retrieve the unnormalized bounding box.\n - \"aux_outputs\": Optional, only returned when auxilary losses are activated. It is a list of\n dictionnaries containing the two above keys for each decoder layer.\n \"\"\"\n if targets is None:\n captions = kw[\"captions\"]\n else:\n captions = [t[\"caption\"] for t in targets]\n len(captions)\n\n # encoder texts\n tokenized = self.tokenizer(captions, padding=\"longest\", return_tensors=\"pt\").to(\n samples.device\n )\n (\n text_self_attention_masks,\n position_ids,\n cate_to_token_mask_list,\n ) = generate_masks_with_special_tokens_and_transfer_map(\n tokenized, self.specical_tokens, self.tokenizer\n )\n\n if text_self_attention_masks.shape[1] > self.max_text_len:\n text_self_attention_masks = text_self_attention_masks[\n :, : self.max_text_len, : self.max_text_len\n ]\n position_ids = position_ids[:, : self.max_text_len]\n tokenized[\"input_ids\"] = tokenized[\"input_ids\"][:, : self.max_text_len]\n tokenized[\"attention_mask\"] = tokenized[\"attention_mask\"][:, : self.max_text_len]\n tokenized[\"token_type_ids\"] = tokenized[\"token_type_ids\"][:, : self.max_text_len]\n\n # extract text embeddings\n if self.sub_sentence_present:\n tokenized_for_encoder = {k: v for k, v in tokenized.items() if k != \"attention_mask\"}\n tokenized_for_encoder[\"attention_mask\"] = text_self_attention_masks\n tokenized_for_encoder[\"position_ids\"] = position_ids\n else:\n # import ipdb; ipdb.set_trace()\n tokenized_for_encoder = tokenized\n\n bert_output = self.bert(**tokenized_for_encoder) # bs, 195, 768\n\n encoded_text = self.feat_map(bert_output[\"last_hidden_state\"]) # bs, 195, d_model\n text_token_mask = tokenized.attention_mask.bool() # bs, 195\n # text_token_mask: True for nomask, False for mask\n # text_self_attention_masks: True for nomask, False for mask\n\n if encoded_text.shape[1] > self.max_text_len:\n encoded_text = encoded_text[:, : self.max_text_len, :]\n text_token_mask = text_token_mask[:, : self.max_text_len]\n position_ids = position_ids[:, : self.max_text_len]\n text_self_attention_masks = text_self_attention_masks[\n :, : self.max_text_len, : self.max_text_len\n ]\n\n text_dict = {\n \"encoded_text\": encoded_text, # bs, 195, d_model\n \"text_token_mask\": text_token_mask, # bs, 195\n \"position_ids\": position_ids, # bs, 195\n \"text_self_attention_masks\": text_self_attention_masks, # bs, 195,195\n }\n\n # import ipdb; ipdb.set_trace()\n\n if isinstance(samples, (list, torch.Tensor)):\n samples = nested_tensor_from_tensor_list(samples)\n features, poss = self.backbone(samples)\n\n srcs = []\n masks = []\n for l, feat in enumerate(features):\n src, mask = feat.decompose()\n srcs.append(self.input_proj[l](src))\n masks.append(mask)\n assert mask is not None\n if self.num_feature_levels > len(srcs):\n _len_srcs = len(srcs)\n for l in range(_len_srcs, self.num_feature_levels):\n if l == _len_srcs:\n src = self.input_proj[l](features[-1].tensors)\n else:\n src = self.input_proj[l](srcs[-1])\n m = samples.mask\n mask = F.interpolate(m[None].float(), size=src.shape[-2:]).to(torch.bool)[0]\n pos_l = self.backbone[1](NestedTensor(src, mask)).to(src.dtype)\n srcs.append(src)\n masks.append(mask)\n poss.append(pos_l)\n\n input_query_bbox = input_query_label = attn_mask = dn_meta = None\n hs, reference, hs_enc, ref_enc, init_box_proposal = self.transformer(\n srcs, masks, input_query_bbox, poss, input_query_label, attn_mask, text_dict\n )\n\n # deformable-detr-like anchor update\n outputs_coord_list = []\n for dec_lid, (layer_ref_sig, layer_bbox_embed, layer_hs) in enumerate(\n zip(reference[:-1], self.bbox_embed, hs)\n ):\n layer_delta_unsig = layer_bbox_embed(layer_hs)\n layer_outputs_unsig = layer_delta_unsig + inverse_sigmoid(layer_ref_sig)\n layer_outputs_unsig = layer_outputs_unsig.sigmoid()\n outputs_coord_list.append(layer_outputs_unsig)\n outputs_coord_list = torch.stack(outputs_coord_list)\n\n # output\n outputs_class = torch.stack(\n [\n layer_cls_embed(layer_hs, text_dict)\n for layer_cls_embed, layer_hs in zip(self.class_embed, hs)\n ]\n )\n out = {\"pred_logits\": outputs_class[-1], \"pred_boxes\": outputs_coord_list[-1]}\n\n # # for intermediate outputs\n # if self.aux_loss:\n # out['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord_list)\n\n # # for encoder output\n # if hs_enc is not None:\n # # prepare intermediate outputs\n # interm_coord = ref_enc[-1]\n # interm_class = self.transformer.enc_out_class_embed(hs_enc[-1], text_dict)\n # out['interm_outputs'] = {'pred_logits': interm_class, 'pred_boxes': interm_coord}\n # out['interm_outputs_for_matching_pre'] = {'pred_logits': interm_class, 'pred_boxes': init_box_proposal}\n\n return out\n\n @torch.jit.unused\n def _set_aux_loss(self, outputs_class, outputs_coord):\n # this is a workaround to make torchscript happy, as torchscript\n # doesn't support dictionary with non-homogeneous values, such\n # as a dict having both a Tensor and a list.\n return [\n {\"pred_logits\": a, \"pred_boxes\": b}\n for a, b in zip(outputs_class[:-1], outputs_coord[:-1])\n ]\n\n\n@MODULE_BUILD_FUNCS.registe_with_name(module_name=\"groundingdino\")\ndef build_groundingdino(args):\n\n backbone = build_backbone(args)\n transformer = build_transformer(args)\n\n dn_labelbook_size = args.dn_labelbook_size\n dec_pred_bbox_embed_share = args.dec_pred_bbox_embed_share\n sub_sentence_present = args.sub_sentence_present\n\n model = GroundingDINO(\n backbone,\n transformer,\n num_queries=args.num_queries,\n aux_loss=True,\n iter_update=True,\n query_dim=4,\n num_feature_levels=args.num_feature_levels,\n nheads=args.nheads,\n dec_pred_bbox_embed_share=dec_pred_bbox_embed_share,\n two_stage_type=args.two_stage_type,\n two_stage_bbox_embed_share=args.two_stage_bbox_embed_share,\n two_stage_class_embed_share=args.two_stage_class_embed_share,\n num_patterns=args.num_patterns,\n dn_number=0,\n dn_box_noise_scale=args.dn_box_noise_scale,\n dn_label_noise_ratio=args.dn_label_noise_ratio,\n dn_labelbook_size=dn_labelbook_size,\n text_encoder_type=args.text_encoder_type,\n sub_sentence_present=sub_sentence_present,\n max_text_len=args.max_text_len,\n )\n\n return model\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/GroundingDINO/ms_deform_attn.py", "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Deformable DETR\n# Copyright (c) 2020 SenseTime. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------------------------------\n# Modified from:\n# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/functions/ms_deform_attn_func.py\n# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/modules/ms_deform_attn.py\n# https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/multi_scale_deform_attn.py\n# ------------------------------------------------------------------------------------------------\n\nimport math\nimport warnings\nfrom typing import Optional\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Function\nfrom torch.autograd.function import once_differentiable\nfrom torch.nn.init import constant_, xavier_uniform_\n\ntry:\n from groundingdino import _C\nexcept:\n warnings.warn(\"Failed to load custom C++ ops. Running on CPU mode Only!\")\n\n\n# helpers\ndef _is_power_of_2(n):\n if (not isinstance(n, int)) or (n < 0):\n raise ValueError(\"invalid input for _is_power_of_2: {} (type: {})\".format(n, type(n)))\n return (n & (n - 1) == 0) and n != 0\n\n\nclass MultiScaleDeformableAttnFunction(Function):\n @staticmethod\n def forward(\n ctx,\n value,\n value_spatial_shapes,\n value_level_start_index,\n sampling_locations,\n attention_weights,\n im2col_step,\n ):\n ctx.im2col_step = im2col_step\n output = _C.ms_deform_attn_forward(\n value,\n value_spatial_shapes,\n value_level_start_index,\n sampling_locations,\n attention_weights,\n ctx.im2col_step,\n )\n ctx.save_for_backward(\n value,\n value_spatial_shapes,\n value_level_start_index,\n sampling_locations,\n attention_weights,\n )\n return output\n\n @staticmethod\n @once_differentiable\n def backward(ctx, grad_output):\n (\n value,\n value_spatial_shapes,\n value_level_start_index,\n sampling_locations,\n attention_weights,\n ) = ctx.saved_tensors\n grad_value, grad_sampling_loc, grad_attn_weight = _C.ms_deform_attn_backward(\n value,\n value_spatial_shapes,\n value_level_start_index,\n sampling_locations,\n attention_weights,\n grad_output,\n ctx.im2col_step,\n )\n\n return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None\n\n\ndef multi_scale_deformable_attn_pytorch(\n value: torch.Tensor,\n value_spatial_shapes: torch.Tensor,\n sampling_locations: torch.Tensor,\n attention_weights: torch.Tensor,\n) -> torch.Tensor:\n\n bs, _, num_heads, embed_dims = value.shape\n _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape\n value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)\n sampling_grids = 2 * sampling_locations - 1\n sampling_value_list = []\n for level, (H_, W_) in enumerate(value_spatial_shapes):\n # bs, H_*W_, num_heads, embed_dims ->\n # bs, H_*W_, num_heads*embed_dims ->\n # bs, num_heads*embed_dims, H_*W_ ->\n # bs*num_heads, embed_dims, H_, W_\n value_l_ = (\n value_list[level].flatten(2).transpose(1, 2).reshape(bs * num_heads, embed_dims, H_, W_)\n )\n # bs, num_queries, num_heads, num_points, 2 ->\n # bs, num_heads, num_queries, num_points, 2 ->\n # bs*num_heads, num_queries, num_points, 2\n sampling_grid_l_ = sampling_grids[:, :, :, level].transpose(1, 2).flatten(0, 1)\n # bs*num_heads, embed_dims, num_queries, num_points\n sampling_value_l_ = F.grid_sample(\n value_l_, sampling_grid_l_, mode=\"bilinear\", padding_mode=\"zeros\", align_corners=False\n )\n sampling_value_list.append(sampling_value_l_)\n # (bs, num_queries, num_heads, num_levels, num_points) ->\n # (bs, num_heads, num_queries, num_levels, num_points) ->\n # (bs, num_heads, 1, num_queries, num_levels*num_points)\n attention_weights = attention_weights.transpose(1, 2).reshape(\n bs * num_heads, 1, num_queries, num_levels * num_points\n )\n output = (\n (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights)\n .sum(-1)\n .view(bs, num_heads * embed_dims, num_queries)\n )\n return output.transpose(1, 2).contiguous()\n\n\nclass MultiScaleDeformableAttention(nn.Module):\n \"\"\"Multi-Scale Deformable Attention Module used in Deformable-DETR\n\n `Deformable DETR: Deformable Transformers for End-to-End Object Detection.\n `_.\n\n Args:\n embed_dim (int): The embedding dimension of Attention. Default: 256.\n num_heads (int): The number of attention heads. Default: 8.\n num_levels (int): The number of feature map used in Attention. Default: 4.\n num_points (int): The number of sampling points for each query\n in each head. Default: 4.\n img2col_steps (int): The step used in image_to_column. Defualt: 64.\n dropout (float): Dropout layer used in output. Default: 0.1.\n batch_first (bool): if ``True``, then the input and output tensor will be\n provided as `(bs, n, embed_dim)`. Default: False. `(n, bs, embed_dim)`\n \"\"\"\n\n def __init__(\n self,\n embed_dim: int = 256,\n num_heads: int = 8,\n num_levels: int = 4,\n num_points: int = 4,\n img2col_step: int = 64,\n batch_first: bool = False,\n ):\n super().__init__()\n if embed_dim % num_heads != 0:\n raise ValueError(\n \"embed_dim must be divisible by num_heads, but got {} and {}\".format(\n embed_dim, num_heads\n )\n )\n head_dim = embed_dim // num_heads\n\n self.batch_first = batch_first\n\n if not _is_power_of_2(head_dim):\n warnings.warn(\n \"\"\"\n You'd better set d_model in MSDeformAttn to make sure that\n each dim of the attention head a power of 2, which is more efficient.\n \"\"\"\n )\n\n self.im2col_step = img2col_step\n self.embed_dim = embed_dim\n self.num_heads = num_heads\n self.num_levels = num_levels\n self.num_points = num_points\n self.sampling_offsets = nn.Linear(embed_dim, num_heads * num_levels * num_points * 2)\n self.attention_weights = nn.Linear(embed_dim, num_heads * num_levels * num_points)\n self.value_proj = nn.Linear(embed_dim, embed_dim)\n self.output_proj = nn.Linear(embed_dim, embed_dim)\n\n self.init_weights()\n\n def _reset_parameters(self):\n return self.init_weights()\n\n def init_weights(self):\n \"\"\"\n Default initialization for Parameters of Module.\n \"\"\"\n constant_(self.sampling_offsets.weight.data, 0.0)\n thetas = torch.arange(self.num_heads, dtype=torch.float32) * (\n 2.0 * math.pi / self.num_heads\n )\n grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)\n grid_init = (\n (grid_init / grid_init.abs().max(-1, keepdim=True)[0])\n .view(self.num_heads, 1, 1, 2)\n .repeat(1, self.num_levels, self.num_points, 1)\n )\n for i in range(self.num_points):\n grid_init[:, :, i, :] *= i + 1\n with torch.no_grad():\n self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))\n constant_(self.attention_weights.weight.data, 0.0)\n constant_(self.attention_weights.bias.data, 0.0)\n xavier_uniform_(self.value_proj.weight.data)\n constant_(self.value_proj.bias.data, 0.0)\n xavier_uniform_(self.output_proj.weight.data)\n constant_(self.output_proj.bias.data, 0.0)\n\n def freeze_sampling_offsets(self):\n print(\"Freeze sampling offsets\")\n self.sampling_offsets.weight.requires_grad = False\n self.sampling_offsets.bias.requires_grad = False\n\n def freeze_attention_weights(self):\n print(\"Freeze attention weights\")\n self.attention_weights.weight.requires_grad = False\n self.attention_weights.bias.requires_grad = False\n\n def forward(\n self,\n query: torch.Tensor,\n key: Optional[torch.Tensor] = None,\n value: Optional[torch.Tensor] = None,\n query_pos: Optional[torch.Tensor] = None,\n key_padding_mask: Optional[torch.Tensor] = None,\n reference_points: Optional[torch.Tensor] = None,\n spatial_shapes: Optional[torch.Tensor] = None,\n level_start_index: Optional[torch.Tensor] = None,\n **kwargs\n ) -> torch.Tensor:\n\n \"\"\"Forward Function of MultiScaleDeformableAttention\n\n Args:\n query (torch.Tensor): Query embeddings with shape\n `(num_query, bs, embed_dim)`\n key (torch.Tensor): Key embeddings with shape\n `(num_key, bs, embed_dim)`\n value (torch.Tensor): Value embeddings with shape\n `(num_key, bs, embed_dim)`\n query_pos (torch.Tensor): The position embedding for `query`. Default: None.\n key_padding_mask (torch.Tensor): ByteTensor for `query`, with shape `(bs, num_key)`,\n indicating which elements within `key` to be ignored in attention.\n reference_points (torch.Tensor): The normalized reference points\n with shape `(bs, num_query, num_levels, 2)`,\n all elements is range in [0, 1], top-left (0, 0),\n bottom-right (1, 1), including padding are.\n or `(N, Length_{query}, num_levels, 4)`, add additional\n two dimensions `(h, w)` to form reference boxes.\n spatial_shapes (torch.Tensor): Spatial shape of features in different levels.\n With shape `(num_levels, 2)`, last dimension represents `(h, w)`.\n level_start_index (torch.Tensor): The start index of each level. A tensor with\n shape `(num_levels, )` which can be represented as\n `[0, h_0 * w_0, h_0 * w_0 + h_1 * w_1, ...]`.\n\n Returns:\n torch.Tensor: forward results with shape `(num_query, bs, embed_dim)`\n \"\"\"\n\n if value is None:\n value = query\n\n if query_pos is not None:\n query = query + query_pos\n\n if not self.batch_first:\n # change to (bs, num_query ,embed_dims)\n query = query.permute(1, 0, 2)\n value = value.permute(1, 0, 2)\n\n bs, num_query, _ = query.shape\n bs, num_value, _ = value.shape\n\n assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value\n\n value = self.value_proj(value)\n if key_padding_mask is not None:\n value = value.masked_fill(key_padding_mask[..., None], float(0))\n value = value.view(bs, num_value, self.num_heads, -1)\n sampling_offsets = self.sampling_offsets(query).view(\n bs, num_query, self.num_heads, self.num_levels, self.num_points, 2\n )\n attention_weights = self.attention_weights(query).view(\n bs, num_query, self.num_heads, self.num_levels * self.num_points\n )\n attention_weights = attention_weights.softmax(-1)\n attention_weights = attention_weights.view(\n bs,\n num_query,\n self.num_heads,\n self.num_levels,\n self.num_points,\n )\n\n # bs, num_query, num_heads, num_levels, num_points, 2\n if reference_points.shape[-1] == 2:\n offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)\n sampling_locations = (\n reference_points[:, :, None, :, None, :]\n + sampling_offsets / offset_normalizer[None, None, None, :, None, :]\n )\n elif reference_points.shape[-1] == 4:\n sampling_locations = (\n reference_points[:, :, None, :, None, :2]\n + sampling_offsets\n / self.num_points\n * reference_points[:, :, None, :, None, 2:]\n * 0.5\n )\n else:\n raise ValueError(\n \"Last dim of reference_points must be 2 or 4, but get {} instead.\".format(\n reference_points.shape[-1]\n )\n )\n \n # if torch.cuda.is_available() and value.is_cuda:\n if not torch.cuda.is_available() :\n halffloat = False\n if value.dtype == torch.float16:\n halffloat = True\n value = value.float()\n sampling_locations = sampling_locations.float()\n attention_weights = attention_weights.float()\n\n output = MultiScaleDeformableAttnFunction.apply(\n value,\n spatial_shapes,\n level_start_index,\n sampling_locations,\n attention_weights,\n self.im2col_step,\n )\n\n if halffloat:\n output = output.half()\n else:\n output = multi_scale_deformable_attn_pytorch(\n value, spatial_shapes, sampling_locations, attention_weights\n )\n\n output = self.output_proj(output)\n\n if not self.batch_first:\n output = output.permute(1, 0, 2)\n\n return output\n\n\ndef create_dummy_class(klass, dependency, message=\"\"):\n \"\"\"\n When a dependency of a class is not available, create a dummy class which throws ImportError\n when used.\n\n Args:\n klass (str): name of the class.\n dependency (str): name of the dependency.\n message: extra message to print\n Returns:\n class: a class object\n \"\"\"\n err = \"Cannot import '{}', therefore '{}' is not available.\".format(dependency, klass)\n if message:\n err = err + \" \" + message\n\n class _DummyMetaClass(type):\n # throw error on class attribute access\n def __getattr__(_, __): # noqa: B902\n raise ImportError(err)\n\n class _Dummy(object, metaclass=_DummyMetaClass):\n # throw error on constructor\n def __init__(self, *args, **kwargs):\n raise ImportError(err)\n\n return _Dummy\n\n\ndef create_dummy_func(func, dependency, message=\"\"):\n \"\"\"\n When a dependency of a function is not available, create a dummy function which throws\n ImportError when used.\n\n Args:\n func (str): name of the function.\n dependency (str or list[str]): name(s) of the dependency.\n message: extra message to print\n Returns:\n function: a function object\n \"\"\"\n err = \"Cannot import '{}', therefore '{}' is not available.\".format(dependency, func)\n if message:\n err = err + \" \" + message\n\n if isinstance(dependency, (list, tuple)):\n dependency = \",\".join(dependency)\n\n def _dummy(*args, **kwargs):\n raise ImportError(err)\n\n return _dummy\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/GroundingDINO/transformer.py", "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# DINO\n# Copyright (c) 2022 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Conditional DETR Transformer class.\n# Copyright (c) 2021 Microsoft. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Modified from DETR (https://github.com/facebookresearch/detr)\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n# ------------------------------------------------------------------------\n\nfrom typing import Optional\n\nimport torch\nimport torch.utils.checkpoint as checkpoint\nfrom torch import Tensor, nn\n\nfrom groundingdino.util.misc import inverse_sigmoid\n\nfrom .fuse_modules import BiAttentionBlock\nfrom .ms_deform_attn import MultiScaleDeformableAttention as MSDeformAttn\nfrom .transformer_vanilla import TransformerEncoderLayer\nfrom .utils import (\n MLP,\n _get_activation_fn,\n _get_clones,\n gen_encoder_output_proposals,\n gen_sineembed_for_position,\n get_sine_pos_embed,\n)\n\n\nclass Transformer(nn.Module):\n def __init__(\n self,\n d_model=256,\n nhead=8,\n num_queries=300,\n num_encoder_layers=6,\n num_unicoder_layers=0,\n num_decoder_layers=6,\n dim_feedforward=2048,\n dropout=0.0,\n activation=\"relu\",\n normalize_before=False,\n return_intermediate_dec=False,\n query_dim=4,\n num_patterns=0,\n # for deformable encoder\n num_feature_levels=1,\n enc_n_points=4,\n dec_n_points=4,\n # init query\n learnable_tgt_init=False,\n # two stage\n two_stage_type=\"no\", # ['no', 'standard', 'early', 'combine', 'enceachlayer', 'enclayer1']\n embed_init_tgt=False,\n # for text\n use_text_enhancer=False,\n use_fusion_layer=False,\n use_checkpoint=False,\n use_transformer_ckpt=False,\n use_text_cross_attention=False,\n text_dropout=0.1,\n fusion_dropout=0.1,\n fusion_droppath=0.0,\n ):\n super().__init__()\n self.num_feature_levels = num_feature_levels\n self.num_encoder_layers = num_encoder_layers\n self.num_unicoder_layers = num_unicoder_layers\n self.num_decoder_layers = num_decoder_layers\n self.num_queries = num_queries\n assert query_dim == 4\n\n # choose encoder layer type\n encoder_layer = DeformableTransformerEncoderLayer(\n d_model, dim_feedforward, dropout, activation, num_feature_levels, nhead, enc_n_points\n )\n\n if use_text_enhancer:\n text_enhance_layer = TransformerEncoderLayer(\n d_model=d_model,\n nhead=nhead // 2,\n dim_feedforward=dim_feedforward // 2,\n dropout=text_dropout,\n )\n else:\n text_enhance_layer = None\n\n if use_fusion_layer:\n feature_fusion_layer = BiAttentionBlock(\n v_dim=d_model,\n l_dim=d_model,\n embed_dim=dim_feedforward // 2,\n num_heads=nhead // 2,\n dropout=fusion_dropout,\n drop_path=fusion_droppath,\n )\n else:\n feature_fusion_layer = None\n\n encoder_norm = nn.LayerNorm(d_model) if normalize_before else None\n assert encoder_norm is None\n self.encoder = TransformerEncoder(\n encoder_layer,\n num_encoder_layers,\n d_model=d_model,\n num_queries=num_queries,\n text_enhance_layer=text_enhance_layer,\n feature_fusion_layer=feature_fusion_layer,\n use_checkpoint=use_checkpoint,\n use_transformer_ckpt=use_transformer_ckpt,\n )\n\n # choose decoder layer type\n decoder_layer = DeformableTransformerDecoderLayer(\n d_model,\n dim_feedforward,\n dropout,\n activation,\n num_feature_levels,\n nhead,\n dec_n_points,\n use_text_cross_attention=use_text_cross_attention,\n )\n\n decoder_norm = nn.LayerNorm(d_model)\n self.decoder = TransformerDecoder(\n decoder_layer,\n num_decoder_layers,\n decoder_norm,\n return_intermediate=return_intermediate_dec,\n d_model=d_model,\n query_dim=query_dim,\n num_feature_levels=num_feature_levels,\n )\n\n self.d_model = d_model\n self.nhead = nhead\n self.dec_layers = num_decoder_layers\n self.num_queries = num_queries # useful for single stage model only\n self.num_patterns = num_patterns\n if not isinstance(num_patterns, int):\n Warning(\"num_patterns should be int but {}\".format(type(num_patterns)))\n self.num_patterns = 0\n\n if num_feature_levels > 1:\n if self.num_encoder_layers > 0:\n self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))\n else:\n self.level_embed = None\n\n self.learnable_tgt_init = learnable_tgt_init\n assert learnable_tgt_init, \"why not learnable_tgt_init\"\n self.embed_init_tgt = embed_init_tgt\n if (two_stage_type != \"no\" and embed_init_tgt) or (two_stage_type == \"no\"):\n self.tgt_embed = nn.Embedding(self.num_queries, d_model)\n nn.init.normal_(self.tgt_embed.weight.data)\n else:\n self.tgt_embed = None\n\n # for two stage\n self.two_stage_type = two_stage_type\n assert two_stage_type in [\"no\", \"standard\"], \"unknown param {} of two_stage_type\".format(\n two_stage_type\n )\n if two_stage_type == \"standard\":\n # anchor selection at the output of encoder\n self.enc_output = nn.Linear(d_model, d_model)\n self.enc_output_norm = nn.LayerNorm(d_model)\n self.two_stage_wh_embedding = None\n\n if two_stage_type == \"no\":\n self.init_ref_points(num_queries) # init self.refpoint_embed\n\n self.enc_out_class_embed = None\n self.enc_out_bbox_embed = None\n\n self._reset_parameters()\n\n def _reset_parameters(self):\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n for m in self.modules():\n if isinstance(m, MSDeformAttn):\n m._reset_parameters()\n if self.num_feature_levels > 1 and self.level_embed is not None:\n nn.init.normal_(self.level_embed)\n\n def get_valid_ratio(self, mask):\n _, H, W = mask.shape\n valid_H = torch.sum(~mask[:, :, 0], 1)\n valid_W = torch.sum(~mask[:, 0, :], 1)\n valid_ratio_h = valid_H.float() / H\n valid_ratio_w = valid_W.float() / W\n valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)\n return valid_ratio\n\n def init_ref_points(self, use_num_queries):\n self.refpoint_embed = nn.Embedding(use_num_queries, 4)\n\n def forward(self, srcs, masks, refpoint_embed, pos_embeds, tgt, attn_mask=None, text_dict=None):\n \"\"\"\n Input:\n - srcs: List of multi features [bs, ci, hi, wi]\n - masks: List of multi masks [bs, hi, wi]\n - refpoint_embed: [bs, num_dn, 4]. None in infer\n - pos_embeds: List of multi pos embeds [bs, ci, hi, wi]\n - tgt: [bs, num_dn, d_model]. None in infer\n\n \"\"\"\n # prepare input for encoder\n src_flatten = []\n mask_flatten = []\n lvl_pos_embed_flatten = []\n spatial_shapes = []\n for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):\n bs, c, h, w = src.shape\n spatial_shape = (h, w)\n spatial_shapes.append(spatial_shape)\n\n src = src.flatten(2).transpose(1, 2) # bs, hw, c\n mask = mask.flatten(1) # bs, hw\n pos_embed = pos_embed.flatten(2).transpose(1, 2) # bs, hw, c\n if self.num_feature_levels > 1 and self.level_embed is not None:\n lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)\n else:\n lvl_pos_embed = pos_embed\n lvl_pos_embed_flatten.append(lvl_pos_embed)\n src_flatten.append(src)\n mask_flatten.append(mask)\n src_flatten = torch.cat(src_flatten, 1) # bs, \\sum{hxw}, c\n mask_flatten = torch.cat(mask_flatten, 1) # bs, \\sum{hxw}\n lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) # bs, \\sum{hxw}, c\n spatial_shapes = torch.as_tensor(\n spatial_shapes, dtype=torch.long, device=src_flatten.device\n )\n level_start_index = torch.cat(\n (spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])\n )\n valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1).to(src.dtype)\n\n # two stage\n enc_topk_proposals = enc_refpoint_embed = None\n\n #########################################################\n # Begin Encoder\n #########################################################\n memory, memory_text = self.encoder(\n src_flatten,\n pos=lvl_pos_embed_flatten,\n level_start_index=level_start_index,\n spatial_shapes=spatial_shapes,\n valid_ratios=valid_ratios,\n key_padding_mask=mask_flatten,\n memory_text=text_dict[\"encoded_text\"],\n text_attention_mask=~text_dict[\"text_token_mask\"],\n # we ~ the mask . False means use the token; True means pad the token\n position_ids=text_dict[\"position_ids\"],\n text_self_attention_masks=text_dict[\"text_self_attention_masks\"],\n )\n #########################################################\n # End Encoder\n # - memory: bs, \\sum{hw}, c\n # - mask_flatten: bs, \\sum{hw}\n # - lvl_pos_embed_flatten: bs, \\sum{hw}, c\n # - enc_intermediate_output: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c)\n # - enc_intermediate_refpoints: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c)\n #########################################################\n text_dict[\"encoded_text\"] = memory_text\n # if os.environ.get(\"SHILONG_AMP_INFNAN_DEBUG\") == '1':\n # if memory.isnan().any() | memory.isinf().any():\n # import ipdb; ipdb.set_trace()\n\n if self.two_stage_type == \"standard\":\n output_memory, output_proposals = gen_encoder_output_proposals(\n memory, mask_flatten, spatial_shapes\n )\n output_memory = self.enc_output_norm(self.enc_output(output_memory))\n\n if text_dict is not None:\n enc_outputs_class_unselected = self.enc_out_class_embed(output_memory, text_dict)\n else:\n enc_outputs_class_unselected = self.enc_out_class_embed(output_memory)\n\n topk_logits = enc_outputs_class_unselected.max(-1)[0]\n enc_outputs_coord_unselected = (\n self.enc_out_bbox_embed(output_memory) + output_proposals\n ) # (bs, \\sum{hw}, 4) unsigmoid\n topk = self.num_queries\n\n topk_proposals = torch.topk(topk_logits, topk, dim=1)[1] # bs, nq\n\n # gather boxes\n refpoint_embed_undetach = torch.gather(\n enc_outputs_coord_unselected, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)\n ) # unsigmoid\n refpoint_embed_ = refpoint_embed_undetach.detach()\n init_box_proposal = torch.gather(\n output_proposals, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)\n ).sigmoid() # sigmoid\n\n # gather tgt\n tgt_undetach = torch.gather(\n output_memory, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, self.d_model)\n )\n if self.embed_init_tgt:\n tgt_ = (\n self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)\n ) # nq, bs, d_model\n else:\n tgt_ = tgt_undetach.detach()\n\n if refpoint_embed is not None:\n refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1)\n tgt = torch.cat([tgt, tgt_], dim=1)\n else:\n refpoint_embed, tgt = refpoint_embed_, tgt_\n\n elif self.two_stage_type == \"no\":\n tgt_ = (\n self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)\n ) # nq, bs, d_model\n refpoint_embed_ = (\n self.refpoint_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)\n ) # nq, bs, 4\n\n if refpoint_embed is not None:\n refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1)\n tgt = torch.cat([tgt, tgt_], dim=1)\n else:\n refpoint_embed, tgt = refpoint_embed_, tgt_\n\n if self.num_patterns > 0:\n tgt_embed = tgt.repeat(1, self.num_patterns, 1)\n refpoint_embed = refpoint_embed.repeat(1, self.num_patterns, 1)\n tgt_pat = self.patterns.weight[None, :, :].repeat_interleave(\n self.num_queries, 1\n ) # 1, n_q*n_pat, d_model\n tgt = tgt_embed + tgt_pat\n\n init_box_proposal = refpoint_embed_.sigmoid()\n\n else:\n raise NotImplementedError(\"unknown two_stage_type {}\".format(self.two_stage_type))\n #########################################################\n # End preparing tgt\n # - tgt: bs, NQ, d_model\n # - refpoint_embed(unsigmoid): bs, NQ, d_model\n #########################################################\n\n #########################################################\n # Begin Decoder\n #########################################################\n hs, references = self.decoder(\n tgt=tgt.transpose(0, 1),\n memory=memory.transpose(0, 1),\n memory_key_padding_mask=mask_flatten,\n pos=lvl_pos_embed_flatten.transpose(0, 1),\n refpoints_unsigmoid=refpoint_embed.transpose(0, 1),\n level_start_index=level_start_index,\n spatial_shapes=spatial_shapes,\n valid_ratios=valid_ratios,\n tgt_mask=attn_mask,\n memory_text=text_dict[\"encoded_text\"],\n text_attention_mask=~text_dict[\"text_token_mask\"],\n # we ~ the mask . False means use the token; True means pad the token\n )\n #########################################################\n # End Decoder\n # hs: n_dec, bs, nq, d_model\n # references: n_dec+1, bs, nq, query_dim\n #########################################################\n\n #########################################################\n # Begin postprocess\n #########################################################\n if self.two_stage_type == \"standard\":\n hs_enc = tgt_undetach.unsqueeze(0)\n ref_enc = refpoint_embed_undetach.sigmoid().unsqueeze(0)\n else:\n hs_enc = ref_enc = None\n #########################################################\n # End postprocess\n # hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or (n_enc, bs, nq, d_model) or None\n # ref_enc: (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or (n_enc, bs, nq, d_model) or None\n #########################################################\n\n return hs, references, hs_enc, ref_enc, init_box_proposal\n # hs: (n_dec, bs, nq, d_model)\n # references: sigmoid coordinates. (n_dec+1, bs, bq, 4)\n # hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or None\n # ref_enc: sigmoid coordinates. \\\n # (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or None\n\n\nclass TransformerEncoder(nn.Module):\n def __init__(\n self,\n encoder_layer,\n num_layers,\n d_model=256,\n num_queries=300,\n enc_layer_share=False,\n text_enhance_layer=None,\n feature_fusion_layer=None,\n use_checkpoint=False,\n use_transformer_ckpt=False,\n ):\n \"\"\"_summary_\n\n Args:\n encoder_layer (_type_): _description_\n num_layers (_type_): _description_\n norm (_type_, optional): _description_. Defaults to None.\n d_model (int, optional): _description_. Defaults to 256.\n num_queries (int, optional): _description_. Defaults to 300.\n enc_layer_share (bool, optional): _description_. Defaults to False.\n\n \"\"\"\n super().__init__()\n # prepare layers\n self.layers = []\n self.text_layers = []\n self.fusion_layers = []\n if num_layers > 0:\n self.layers = _get_clones(encoder_layer, num_layers, layer_share=enc_layer_share)\n\n if text_enhance_layer is not None:\n self.text_layers = _get_clones(\n text_enhance_layer, num_layers, layer_share=enc_layer_share\n )\n if feature_fusion_layer is not None:\n self.fusion_layers = _get_clones(\n feature_fusion_layer, num_layers, layer_share=enc_layer_share\n )\n else:\n self.layers = []\n del encoder_layer\n\n if text_enhance_layer is not None:\n self.text_layers = []\n del text_enhance_layer\n if feature_fusion_layer is not None:\n self.fusion_layers = []\n del feature_fusion_layer\n\n self.query_scale = None\n self.num_queries = num_queries\n self.num_layers = num_layers\n self.d_model = d_model\n\n self.use_checkpoint = use_checkpoint\n self.use_transformer_ckpt = use_transformer_ckpt\n\n @staticmethod\n def get_reference_points(spatial_shapes, valid_ratios, device):\n reference_points_list = []\n for lvl, (H_, W_) in enumerate(spatial_shapes):\n\n ref_y, ref_x = torch.meshgrid(\n torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),\n torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device),\n )\n ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)\n ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)\n ref = torch.stack((ref_x, ref_y), -1)\n reference_points_list.append(ref)\n reference_points = torch.cat(reference_points_list, 1)\n reference_points = reference_points[:, :, None] * valid_ratios[:, None]\n return reference_points\n\n def forward(\n self,\n # for images\n src: Tensor,\n pos: Tensor,\n spatial_shapes: Tensor,\n level_start_index: Tensor,\n valid_ratios: Tensor,\n key_padding_mask: Tensor,\n # for texts\n memory_text: Tensor = None,\n text_attention_mask: Tensor = None,\n pos_text: Tensor = None,\n text_self_attention_masks: Tensor = None,\n position_ids: Tensor = None,\n ):\n \"\"\"\n Input:\n - src: [bs, sum(hi*wi), 256]\n - pos: pos embed for src. [bs, sum(hi*wi), 256]\n - spatial_shapes: h,w of each level [num_level, 2]\n - level_start_index: [num_level] start point of level in sum(hi*wi).\n - valid_ratios: [bs, num_level, 2]\n - key_padding_mask: [bs, sum(hi*wi)]\n\n - memory_text: bs, n_text, 256\n - text_attention_mask: bs, n_text\n False for no padding; True for padding\n - pos_text: bs, n_text, 256\n\n - position_ids: bs, n_text\n Intermedia:\n - reference_points: [bs, sum(hi*wi), num_level, 2]\n Outpus:\n - output: [bs, sum(hi*wi), 256]\n \"\"\"\n\n output = src\n\n # preparation and reshape\n if self.num_layers > 0:\n reference_points = self.get_reference_points(\n spatial_shapes, valid_ratios, device=src.device\n )\n\n if self.text_layers:\n # generate pos_text\n bs, n_text, text_dim = memory_text.shape\n if pos_text is None and position_ids is None:\n pos_text = (\n torch.arange(n_text, device=memory_text.device)\n .float()\n .unsqueeze(0)\n .unsqueeze(-1)\n .repeat(bs, 1, 1)\n )\n pos_text = get_sine_pos_embed(pos_text, num_pos_feats=256, exchange_xy=False)\n if position_ids is not None:\n pos_text = get_sine_pos_embed(\n position_ids[..., None], num_pos_feats=256, exchange_xy=False\n )\n pos_text = pos_text.to(src.dtype)\n\n # main process\n for layer_id, layer in enumerate(self.layers):\n # if output.isnan().any() or memory_text.isnan().any():\n # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':\n # import ipdb; ipdb.set_trace()\n if self.fusion_layers:\n if self.use_checkpoint:\n output, memory_text = checkpoint.checkpoint(\n self.fusion_layers[layer_id],\n output,\n memory_text,\n key_padding_mask,\n text_attention_mask,\n )\n else:\n output, memory_text = self.fusion_layers[layer_id](\n v=output,\n l=memory_text,\n attention_mask_v=key_padding_mask,\n attention_mask_l=text_attention_mask,\n )\n\n if self.text_layers:\n memory_text = self.text_layers[layer_id](\n src=memory_text.transpose(0, 1),\n src_mask=~text_self_attention_masks, # note we use ~ for mask here\n src_key_padding_mask=text_attention_mask,\n pos=(pos_text.transpose(0, 1) if pos_text is not None else None),\n ).transpose(0, 1)\n\n # main process\n if self.use_transformer_ckpt:\n output = checkpoint.checkpoint(\n layer,\n output,\n pos,\n reference_points,\n spatial_shapes,\n level_start_index,\n key_padding_mask,\n )\n else:\n output = layer(\n src=output,\n pos=pos,\n reference_points=reference_points,\n spatial_shapes=spatial_shapes,\n level_start_index=level_start_index,\n key_padding_mask=key_padding_mask,\n )\n\n return output, memory_text\n\n\nclass TransformerDecoder(nn.Module):\n def __init__(\n self,\n decoder_layer,\n num_layers,\n norm=None,\n return_intermediate=False,\n d_model=256,\n query_dim=4,\n num_feature_levels=1,\n ):\n super().__init__()\n if num_layers > 0:\n self.layers = _get_clones(decoder_layer, num_layers)\n else:\n self.layers = []\n self.num_layers = num_layers\n self.norm = norm\n self.return_intermediate = return_intermediate\n assert return_intermediate, \"support return_intermediate only\"\n self.query_dim = query_dim\n assert query_dim in [2, 4], \"query_dim should be 2/4 but {}\".format(query_dim)\n self.num_feature_levels = num_feature_levels\n\n self.ref_point_head = MLP(query_dim // 2 * d_model, d_model, d_model, 2)\n self.query_pos_sine_scale = None\n\n self.query_scale = None\n self.bbox_embed = None\n self.class_embed = None\n\n self.d_model = d_model\n\n self.ref_anchor_head = None\n\n def forward(\n self,\n tgt,\n memory,\n tgt_mask: Optional[Tensor] = None,\n memory_mask: Optional[Tensor] = None,\n tgt_key_padding_mask: Optional[Tensor] = None,\n memory_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n refpoints_unsigmoid: Optional[Tensor] = None, # num_queries, bs, 2\n # for memory\n level_start_index: Optional[Tensor] = None, # num_levels\n spatial_shapes: Optional[Tensor] = None, # bs, num_levels, 2\n valid_ratios: Optional[Tensor] = None,\n # for text\n memory_text: Optional[Tensor] = None,\n text_attention_mask: Optional[Tensor] = None,\n ):\n \"\"\"\n Input:\n - tgt: nq, bs, d_model\n - memory: hw, bs, d_model\n - pos: hw, bs, d_model\n - refpoints_unsigmoid: nq, bs, 2/4\n - valid_ratios/spatial_shapes: bs, nlevel, 2\n \"\"\"\n output = tgt\n\n intermediate = []\n reference_points = refpoints_unsigmoid.sigmoid()\n ref_points = [reference_points]\n\n for layer_id, layer in enumerate(self.layers):\n\n if reference_points.shape[-1] == 4:\n reference_points_input = (\n reference_points[:, :, None]\n * torch.cat([valid_ratios, valid_ratios], -1)[None, :]\n ) # nq, bs, nlevel, 4\n else:\n assert reference_points.shape[-1] == 2\n reference_points_input = reference_points[:, :, None] * valid_ratios[None, :]\n query_sine_embed = gen_sineembed_for_position(\n reference_points_input[:, :, 0, :]\n ) # nq, bs, 256*2\n\n # conditional query\n raw_query_pos = self.ref_point_head(query_sine_embed) # nq, bs, 256\n pos_scale = self.query_scale(output) if self.query_scale is not None else 1\n query_pos = pos_scale * raw_query_pos\n # if os.environ.get(\"SHILONG_AMP_INFNAN_DEBUG\") == '1':\n # if query_pos.isnan().any() | query_pos.isinf().any():\n # import ipdb; ipdb.set_trace()\n\n # main process\n output = layer(\n tgt=output,\n tgt_query_pos=query_pos,\n tgt_query_sine_embed=query_sine_embed,\n tgt_key_padding_mask=tgt_key_padding_mask,\n tgt_reference_points=reference_points_input,\n memory_text=memory_text,\n text_attention_mask=text_attention_mask,\n memory=memory,\n memory_key_padding_mask=memory_key_padding_mask,\n memory_level_start_index=level_start_index,\n memory_spatial_shapes=spatial_shapes,\n memory_pos=pos,\n self_attn_mask=tgt_mask,\n cross_attn_mask=memory_mask,\n )\n if output.isnan().any() | output.isinf().any():\n print(f\"output layer_id {layer_id} is nan\")\n try:\n num_nan = output.isnan().sum().item()\n num_inf = output.isinf().sum().item()\n print(f\"num_nan {num_nan}, num_inf {num_inf}\")\n except Exception as e:\n print(e)\n # if os.environ.get(\"SHILONG_AMP_INFNAN_DEBUG\") == '1':\n # import ipdb; ipdb.set_trace()\n\n # iter update\n if self.bbox_embed is not None:\n # box_holder = self.bbox_embed(output)\n # box_holder[..., :self.query_dim] += inverse_sigmoid(reference_points)\n # new_reference_points = box_holder[..., :self.query_dim].sigmoid()\n\n reference_before_sigmoid = inverse_sigmoid(reference_points)\n delta_unsig = self.bbox_embed[layer_id](output)\n outputs_unsig = delta_unsig + reference_before_sigmoid\n new_reference_points = outputs_unsig.sigmoid()\n\n reference_points = new_reference_points.detach()\n # if layer_id != self.num_layers - 1:\n ref_points.append(new_reference_points)\n\n intermediate.append(self.norm(output))\n\n return [\n [itm_out.transpose(0, 1) for itm_out in intermediate],\n [itm_refpoint.transpose(0, 1) for itm_refpoint in ref_points],\n ]\n\n\nclass DeformableTransformerEncoderLayer(nn.Module):\n def __init__(\n self,\n d_model=256,\n d_ffn=1024,\n dropout=0.1,\n activation=\"relu\",\n n_levels=4,\n n_heads=8,\n n_points=4,\n ):\n super().__init__()\n\n # self attention\n self.self_attn = MSDeformAttn(\n embed_dim=d_model,\n num_levels=n_levels,\n num_heads=n_heads,\n num_points=n_points,\n batch_first=True,\n )\n self.dropout1 = nn.Dropout(dropout)\n self.norm1 = nn.LayerNorm(d_model)\n\n # ffn\n self.linear1 = nn.Linear(d_model, d_ffn)\n self.activation = _get_activation_fn(activation, d_model=d_ffn)\n self.dropout2 = nn.Dropout(dropout)\n self.linear2 = nn.Linear(d_ffn, d_model)\n self.dropout3 = nn.Dropout(dropout)\n self.norm2 = nn.LayerNorm(d_model)\n\n @staticmethod\n def with_pos_embed(tensor, pos):\n return tensor if pos is None else tensor + pos\n\n def forward_ffn(self, src):\n src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))\n src = src + self.dropout3(src2)\n src = self.norm2(src)\n return src\n\n def forward(\n self, src, pos, reference_points, spatial_shapes, level_start_index, key_padding_mask=None\n ):\n # self attention\n # import ipdb; ipdb.set_trace()\n src2 = self.self_attn(\n query=self.with_pos_embed(src, pos),\n reference_points=reference_points,\n value=src,\n spatial_shapes=spatial_shapes,\n level_start_index=level_start_index,\n key_padding_mask=key_padding_mask,\n )\n src = src + self.dropout1(src2)\n src = self.norm1(src)\n\n # ffn\n src = self.forward_ffn(src)\n\n return src\n\n\nclass DeformableTransformerDecoderLayer(nn.Module):\n def __init__(\n self,\n d_model=256,\n d_ffn=1024,\n dropout=0.1,\n activation=\"relu\",\n n_levels=4,\n n_heads=8,\n n_points=4,\n use_text_feat_guide=False,\n use_text_cross_attention=False,\n ):\n super().__init__()\n\n # cross attention\n self.cross_attn = MSDeformAttn(\n embed_dim=d_model,\n num_levels=n_levels,\n num_heads=n_heads,\n num_points=n_points,\n batch_first=True,\n )\n self.dropout1 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()\n self.norm1 = nn.LayerNorm(d_model)\n\n # cross attention text\n if use_text_cross_attention:\n self.ca_text = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)\n self.catext_dropout = nn.Dropout(dropout) if dropout > 0 else nn.Identity()\n self.catext_norm = nn.LayerNorm(d_model)\n\n # self attention\n self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)\n self.dropout2 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()\n self.norm2 = nn.LayerNorm(d_model)\n\n # ffn\n self.linear1 = nn.Linear(d_model, d_ffn)\n self.activation = _get_activation_fn(activation, d_model=d_ffn, batch_dim=1)\n self.dropout3 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()\n self.linear2 = nn.Linear(d_ffn, d_model)\n self.dropout4 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()\n self.norm3 = nn.LayerNorm(d_model)\n\n self.key_aware_proj = None\n self.use_text_feat_guide = use_text_feat_guide\n assert not use_text_feat_guide\n self.use_text_cross_attention = use_text_cross_attention\n\n def rm_self_attn_modules(self):\n self.self_attn = None\n self.dropout2 = None\n self.norm2 = None\n\n @staticmethod\n def with_pos_embed(tensor, pos):\n return tensor if pos is None else tensor + pos\n\n def forward_ffn(self, tgt):\n with torch.cuda.amp.autocast(enabled=False):\n tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt))))\n tgt = tgt + self.dropout4(tgt2)\n tgt = self.norm3(tgt)\n return tgt\n\n def forward(\n self,\n # for tgt\n tgt: Optional[Tensor], # nq, bs, d_model\n tgt_query_pos: Optional[Tensor] = None, # pos for query. MLP(Sine(pos))\n tgt_query_sine_embed: Optional[Tensor] = None, # pos for query. Sine(pos)\n tgt_key_padding_mask: Optional[Tensor] = None,\n tgt_reference_points: Optional[Tensor] = None, # nq, bs, 4\n memory_text: Optional[Tensor] = None, # bs, num_token, d_model\n text_attention_mask: Optional[Tensor] = None, # bs, num_token\n # for memory\n memory: Optional[Tensor] = None, # hw, bs, d_model\n memory_key_padding_mask: Optional[Tensor] = None,\n memory_level_start_index: Optional[Tensor] = None, # num_levels\n memory_spatial_shapes: Optional[Tensor] = None, # bs, num_levels, 2\n memory_pos: Optional[Tensor] = None, # pos for memory\n # sa\n self_attn_mask: Optional[Tensor] = None, # mask used for self-attention\n cross_attn_mask: Optional[Tensor] = None, # mask used for cross-attention\n ):\n \"\"\"\n Input:\n - tgt/tgt_query_pos: nq, bs, d_model\n -\n \"\"\"\n assert cross_attn_mask is None\n\n # self attention\n if self.self_attn is not None:\n # import ipdb; ipdb.set_trace()\n q = k = self.with_pos_embed(tgt, tgt_query_pos)\n tgt2 = self.self_attn(q, k, tgt, attn_mask=self_attn_mask)[0]\n tgt = tgt + self.dropout2(tgt2)\n tgt = self.norm2(tgt)\n\n if self.use_text_cross_attention:\n tgt2 = self.ca_text(\n self.with_pos_embed(tgt, tgt_query_pos),\n memory_text.transpose(0, 1),\n memory_text.transpose(0, 1),\n key_padding_mask=text_attention_mask,\n )[0]\n tgt = tgt + self.catext_dropout(tgt2)\n tgt = self.catext_norm(tgt)\n\n tgt2 = self.cross_attn(\n query=self.with_pos_embed(tgt, tgt_query_pos).transpose(0, 1),\n reference_points=tgt_reference_points.transpose(0, 1).contiguous(),\n value=memory.transpose(0, 1),\n spatial_shapes=memory_spatial_shapes,\n level_start_index=memory_level_start_index,\n key_padding_mask=memory_key_padding_mask,\n ).transpose(0, 1)\n tgt = tgt + self.dropout1(tgt2)\n tgt = self.norm1(tgt)\n\n # ffn\n tgt = self.forward_ffn(tgt)\n\n return tgt\n\n\ndef build_transformer(args):\n return Transformer(\n d_model=args.hidden_dim,\n dropout=args.dropout,\n nhead=args.nheads,\n num_queries=args.num_queries,\n dim_feedforward=args.dim_feedforward,\n num_encoder_layers=args.enc_layers,\n num_decoder_layers=args.dec_layers,\n normalize_before=args.pre_norm,\n return_intermediate_dec=True,\n query_dim=args.query_dim,\n activation=args.transformer_activation,\n num_patterns=args.num_patterns,\n num_feature_levels=args.num_feature_levels,\n enc_n_points=args.enc_n_points,\n dec_n_points=args.dec_n_points,\n learnable_tgt_init=True,\n # two stage\n two_stage_type=args.two_stage_type, # ['no', 'standard', 'early']\n embed_init_tgt=args.embed_init_tgt,\n use_text_enhancer=args.use_text_enhancer,\n use_fusion_layer=args.use_fusion_layer,\n use_checkpoint=args.use_checkpoint,\n use_transformer_ckpt=args.use_transformer_ckpt,\n use_text_cross_attention=args.use_text_cross_attention,\n text_dropout=args.text_dropout,\n fusion_dropout=args.fusion_dropout,\n fusion_droppath=args.fusion_droppath,\n )\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/GroundingDINO/transformer_vanilla.py", "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nDETR Transformer class.\n\nCopy-paste from torch.nn.Transformer with modifications:\n * positional encodings are passed in MHattention\n * extra LN at the end of encoder is removed\n * decoder returns a stack of activations from all decoding layers\n\"\"\"\nfrom typing import Optional\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import Tensor, nn\n\nfrom .utils import (\n MLP,\n _get_activation_fn,\n _get_clones,\n gen_encoder_output_proposals,\n gen_sineembed_for_position,\n sigmoid_focal_loss,\n)\n\n\nclass TextTransformer(nn.Module):\n def __init__(self, num_layers, d_model=256, nheads=8, dim_feedforward=2048, dropout=0.1):\n super().__init__()\n self.num_layers = num_layers\n self.d_model = d_model\n self.nheads = nheads\n self.dim_feedforward = dim_feedforward\n self.norm = None\n\n single_encoder_layer = TransformerEncoderLayer(\n d_model=d_model, nhead=nheads, dim_feedforward=dim_feedforward, dropout=dropout\n )\n self.layers = _get_clones(single_encoder_layer, num_layers)\n\n def forward(self, memory_text: torch.Tensor, text_attention_mask: torch.Tensor):\n \"\"\"\n\n Args:\n text_attention_mask: bs, num_token\n memory_text: bs, num_token, d_model\n\n Raises:\n RuntimeError: _description_\n\n Returns:\n output: bs, num_token, d_model\n \"\"\"\n\n output = memory_text.transpose(0, 1)\n\n for layer in self.layers:\n output = layer(output, src_key_padding_mask=text_attention_mask)\n\n if self.norm is not None:\n output = self.norm(output)\n\n return output.transpose(0, 1)\n\n\nclass TransformerEncoderLayer(nn.Module):\n def __init__(\n self,\n d_model,\n nhead,\n dim_feedforward=2048,\n dropout=0.1,\n activation=\"relu\",\n normalize_before=False,\n ):\n super().__init__()\n self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n # Implementation of Feedforward model\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n self.norm1 = nn.LayerNorm(d_model)\n self.norm2 = nn.LayerNorm(d_model)\n self.dropout1 = nn.Dropout(dropout)\n self.dropout2 = nn.Dropout(dropout)\n\n self.activation = _get_activation_fn(activation)\n self.normalize_before = normalize_before\n self.nhead = nhead\n\n def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n return tensor if pos is None else tensor + pos\n\n def forward(\n self,\n src,\n src_mask: Optional[Tensor] = None,\n src_key_padding_mask: Optional[Tensor] = None,\n pos: Optional[Tensor] = None,\n ):\n # repeat attn mask\n if src_mask.dim() == 3 and src_mask.shape[0] == src.shape[1]:\n # bs, num_q, num_k\n src_mask = src_mask.repeat(self.nhead, 1, 1)\n\n q = k = self.with_pos_embed(src, pos)\n\n src2 = self.self_attn(q, k, value=src, attn_mask=src_mask)[0]\n\n # src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]\n src = src + self.dropout1(src2)\n src = self.norm1(src)\n src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))\n src = src + self.dropout2(src2)\n src = self.norm2(src)\n return src\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/GroundingDINO/utils.py", "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n\nimport copy\nimport math\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import Tensor, nn\n\n\ndef _get_clones(module, N, layer_share=False):\n # import ipdb; ipdb.set_trace()\n if layer_share:\n return nn.ModuleList([module for i in range(N)])\n else:\n return nn.ModuleList([copy.deepcopy(module) for i in range(N)])\n\n\ndef get_sine_pos_embed(\n pos_tensor: torch.Tensor,\n num_pos_feats: int = 128,\n temperature: int = 10000,\n exchange_xy: bool = True,\n):\n \"\"\"generate sine position embedding from a position tensor\n Args:\n pos_tensor (torch.Tensor): shape: [..., n].\n num_pos_feats (int): projected shape for each float in the tensor.\n temperature (int): temperature in the sine/cosine function.\n exchange_xy (bool, optional): exchange pos x and pos y. \\\n For example, input tensor is [x,y], the results will be [pos(y), pos(x)]. Defaults to True.\n Returns:\n pos_embed (torch.Tensor): shape: [..., n*num_pos_feats].\n \"\"\"\n scale = 2 * math.pi\n dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=pos_tensor.device)\n dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode=\"floor\") / num_pos_feats)\n\n def sine_func(x: torch.Tensor):\n sin_x = x * scale / dim_t\n sin_x = torch.stack((sin_x[..., 0::2].sin(), sin_x[..., 1::2].cos()), dim=3).flatten(2)\n return sin_x\n\n pos_res = [sine_func(x) for x in pos_tensor.split([1] * pos_tensor.shape[-1], dim=-1)]\n if exchange_xy:\n pos_res[0], pos_res[1] = pos_res[1], pos_res[0]\n pos_res = torch.cat(pos_res, dim=-1)\n return pos_res\n\n\ndef gen_encoder_output_proposals(\n memory: Tensor, memory_padding_mask: Tensor, spatial_shapes: Tensor, learnedwh=None\n):\n \"\"\"\n Input:\n - memory: bs, \\sum{hw}, d_model\n - memory_padding_mask: bs, \\sum{hw}\n - spatial_shapes: nlevel, 2\n - learnedwh: 2\n Output:\n - output_memory: bs, \\sum{hw}, d_model\n - output_proposals: bs, \\sum{hw}, 4\n \"\"\"\n N_, S_, C_ = memory.shape\n proposals = []\n _cur = 0\n for lvl, (H_, W_) in enumerate(spatial_shapes):\n mask_flatten_ = memory_padding_mask[:, _cur : (_cur + H_ * W_)].view(N_, H_, W_, 1)\n valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1)\n valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1)\n\n # import ipdb; ipdb.set_trace()\n\n grid_y, grid_x = torch.meshgrid(\n torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device),\n torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device),\n )\n grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1) # H_, W_, 2\n\n scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2)\n grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale\n\n if learnedwh is not None:\n # import ipdb; ipdb.set_trace()\n wh = torch.ones_like(grid) * learnedwh.sigmoid() * (2.0**lvl)\n else:\n wh = torch.ones_like(grid) * 0.05 * (2.0**lvl)\n\n # scale = torch.cat([W_[None].unsqueeze(-1), H_[None].unsqueeze(-1)], 1).view(1, 1, 1, 2).repeat(N_, 1, 1, 1)\n # grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale\n # wh = torch.ones_like(grid) / scale\n proposal = torch.cat((grid, wh), -1).view(N_, -1, 4)\n proposals.append(proposal)\n _cur += H_ * W_\n # import ipdb; ipdb.set_trace()\n output_proposals = torch.cat(proposals, 1)\n output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(\n -1, keepdim=True\n )\n output_proposals = torch.log(output_proposals / (1 - output_proposals)) # unsigmoid\n output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float(\"inf\"))\n output_proposals = output_proposals.masked_fill(~output_proposals_valid, float(\"inf\"))\n\n output_memory = memory\n output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0))\n output_memory = output_memory.masked_fill(~output_proposals_valid, float(0))\n\n # output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf'))\n # output_memory = output_memory.masked_fill(~output_proposals_valid, float('inf'))\n\n output_proposals = output_proposals.to(output_memory.dtype)\n return output_memory, output_proposals\n\n\nclass RandomBoxPerturber:\n def __init__(\n self, x_noise_scale=0.2, y_noise_scale=0.2, w_noise_scale=0.2, h_noise_scale=0.2\n ) -> None:\n self.noise_scale = torch.Tensor(\n [x_noise_scale, y_noise_scale, w_noise_scale, h_noise_scale]\n )\n\n def __call__(self, refanchors: Tensor) -> Tensor:\n nq, bs, query_dim = refanchors.shape\n device = refanchors.device\n\n noise_raw = torch.rand_like(refanchors)\n noise_scale = self.noise_scale.to(device)[:query_dim]\n\n new_refanchors = refanchors * (1 + (noise_raw - 0.5) * noise_scale)\n return new_refanchors.clamp_(0, 1)\n\n\ndef sigmoid_focal_loss(\n inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2, no_reduction=False\n):\n \"\"\"\n Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.\n Args:\n inputs: A float tensor of arbitrary shape.\n The predictions for each example.\n targets: A float tensor with the same shape as inputs. Stores the binary\n classification label for each element in inputs\n (0 for the negative class and 1 for the positive class).\n alpha: (optional) Weighting factor in range (0,1) to balance\n positive vs negative examples. Default = -1 (no weighting).\n gamma: Exponent of the modulating factor (1 - p_t) to\n balance easy vs hard examples.\n Returns:\n Loss tensor\n \"\"\"\n prob = inputs.sigmoid()\n ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction=\"none\")\n p_t = prob * targets + (1 - prob) * (1 - targets)\n loss = ce_loss * ((1 - p_t) ** gamma)\n\n if alpha >= 0:\n alpha_t = alpha * targets + (1 - alpha) * (1 - targets)\n loss = alpha_t * loss\n\n if no_reduction:\n return loss\n\n return loss.mean(1).sum() / num_boxes\n\n\nclass MLP(nn.Module):\n \"\"\"Very simple multi-layer perceptron (also called FFN)\"\"\"\n\n def __init__(self, input_dim, hidden_dim, output_dim, num_layers):\n super().__init__()\n self.num_layers = num_layers\n h = [hidden_dim] * (num_layers - 1)\n self.layers = nn.ModuleList(\n nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])\n )\n\n def forward(self, x):\n for i, layer in enumerate(self.layers):\n x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)\n return x\n\n\ndef _get_activation_fn(activation, d_model=256, batch_dim=0):\n \"\"\"Return an activation function given a string\"\"\"\n if activation == \"relu\":\n return F.relu\n if activation == \"gelu\":\n return F.gelu\n if activation == \"glu\":\n return F.glu\n if activation == \"prelu\":\n return nn.PReLU()\n if activation == \"selu\":\n return F.selu\n\n raise RuntimeError(f\"activation should be relu/gelu, not {activation}.\")\n\n\ndef gen_sineembed_for_position(pos_tensor):\n # n_query, bs, _ = pos_tensor.size()\n # sineembed_tensor = torch.zeros(n_query, bs, 256)\n scale = 2 * math.pi\n dim_t = torch.arange(128, dtype=torch.float32, device=pos_tensor.device)\n dim_t = 10000 ** (2 * (torch.div(dim_t, 2, rounding_mode='floor')) / 128)\n x_embed = pos_tensor[:, :, 0] * scale\n y_embed = pos_tensor[:, :, 1] * scale\n pos_x = x_embed[:, :, None] / dim_t\n pos_y = y_embed[:, :, None] / dim_t\n pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2)\n pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2)\n if pos_tensor.size(-1) == 2:\n pos = torch.cat((pos_y, pos_x), dim=2)\n elif pos_tensor.size(-1) == 4:\n w_embed = pos_tensor[:, :, 2] * scale\n pos_w = w_embed[:, :, None] / dim_t\n pos_w = torch.stack((pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3).flatten(2)\n\n h_embed = pos_tensor[:, :, 3] * scale\n pos_h = h_embed[:, :, None] / dim_t\n pos_h = torch.stack((pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3).flatten(2)\n\n pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2)\n else:\n raise ValueError(\"Unknown pos_tensor shape(-1):{}\".format(pos_tensor.size(-1)))\n pos = pos.to(pos_tensor.dtype)\n return pos\n\n\nclass ContrastiveEmbed(nn.Module):\n def __init__(self, max_text_len=256):\n \"\"\"\n Args:\n max_text_len: max length of text.\n \"\"\"\n super().__init__()\n self.max_text_len = max_text_len\n\n def forward(self, x, text_dict):\n \"\"\"_summary_\n\n Args:\n x (_type_): _description_\n text_dict (_type_): _description_\n {\n 'encoded_text': encoded_text, # bs, 195, d_model\n 'text_token_mask': text_token_mask, # bs, 195\n # True for used tokens. False for padding tokens\n }\n Returns:\n _type_: _description_\n \"\"\"\n assert isinstance(text_dict, dict)\n\n y = text_dict[\"encoded_text\"]\n text_token_mask = text_dict[\"text_token_mask\"]\n\n res = x @ y.transpose(-1, -2)\n res.masked_fill_(~text_token_mask[:, None, :], float(\"-inf\"))\n\n # padding to max_text_len\n new_res = torch.full((*res.shape[:-1], self.max_text_len), float(\"-inf\"), device=res.device, dtype=res.dtype)\n new_res[..., : res.shape[-1]] = res\n\n return new_res\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/__init__.py", "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nfrom .GroundingDINO import build_groundingdino\n\n\ndef build_model(args):\n # we use register to maintain models from catdet6 on.\n from .registry import MODULE_BUILD_FUNCS\n\n assert args.modelname in MODULE_BUILD_FUNCS._module_dict\n build_func = MODULE_BUILD_FUNCS.get(args.modelname)\n model = build_func(args)\n return model\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/registry.py", "content": "# ------------------------------------------------------------------------\n# Grounding DINO\n# url: https://github.com/IDEA-Research/GroundingDINO\n# Copyright (c) 2023 IDEA. All Rights Reserved.\n# Licensed under the Apache License, Version 2.0 [see LICENSE for details]\n# ------------------------------------------------------------------------\n# -*- coding: utf-8 -*-\n# @Author: Yihao Chen\n# @Date: 2021-08-16 16:03:17\n# @Last Modified by: Shilong Liu\n# @Last Modified time: 2022-01-23 15:26\n# modified from mmcv\n\nimport inspect\nfrom functools import partial\n\n\nclass Registry(object):\n def __init__(self, name):\n self._name = name\n self._module_dict = dict()\n\n def __repr__(self):\n format_str = self.__class__.__name__ + \"(name={}, items={})\".format(\n self._name, list(self._module_dict.keys())\n )\n return format_str\n\n def __len__(self):\n return len(self._module_dict)\n\n @property\n def name(self):\n return self._name\n\n @property\n def module_dict(self):\n return self._module_dict\n\n def get(self, key):\n return self._module_dict.get(key, None)\n\n def registe_with_name(self, module_name=None, force=False):\n return partial(self.register, module_name=module_name, force=force)\n\n def register(self, module_build_function, module_name=None, force=False):\n \"\"\"Register a module build function.\n Args:\n module (:obj:`nn.Module`): Module to be registered.\n \"\"\"\n if not inspect.isfunction(module_build_function):\n raise TypeError(\n \"module_build_function must be a function, but got {}\".format(\n type(module_build_function)\n )\n )\n if module_name is None:\n module_name = module_build_function.__name__\n if not force and module_name in self._module_dict:\n raise KeyError(\"{} is already registered in {}\".format(module_name, self.name))\n self._module_dict[module_name] = module_build_function\n\n return module_build_function\n\n\nMODULE_BUILD_FUNCS = Registry(\"model build functions\")\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/util/__init__.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/util/box_ops.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nUtilities for bounding box manipulation and GIoU.\n\"\"\"\nimport torch\nfrom torchvision.ops.boxes import box_area\n\n\ndef box_cxcywh_to_xyxy(x):\n x_c, y_c, w, h = x.unbind(-1)\n b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)]\n return torch.stack(b, dim=-1)\n\n\ndef box_xyxy_to_cxcywh(x):\n x0, y0, x1, y1 = x.unbind(-1)\n b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)]\n return torch.stack(b, dim=-1)\n\n\n# modified from torchvision to also return the union\ndef box_iou(boxes1, boxes2):\n area1 = box_area(boxes1)\n area2 = box_area(boxes2)\n\n # import ipdb; ipdb.set_trace()\n lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]\n rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]\n\n wh = (rb - lt).clamp(min=0) # [N,M,2]\n inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]\n\n union = area1[:, None] + area2 - inter\n\n iou = inter / (union + 1e-6)\n return iou, union\n\n\ndef generalized_box_iou(boxes1, boxes2):\n \"\"\"\n Generalized IoU from https://giou.stanford.edu/\n\n The boxes should be in [x0, y0, x1, y1] format\n\n Returns a [N, M] pairwise matrix, where N = len(boxes1)\n and M = len(boxes2)\n \"\"\"\n # degenerate boxes gives inf / nan results\n # so do an early check\n assert (boxes1[:, 2:] >= boxes1[:, :2]).all()\n assert (boxes2[:, 2:] >= boxes2[:, :2]).all()\n # except:\n # import ipdb; ipdb.set_trace()\n iou, union = box_iou(boxes1, boxes2)\n\n lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])\n rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])\n\n wh = (rb - lt).clamp(min=0) # [N,M,2]\n area = wh[:, :, 0] * wh[:, :, 1]\n\n return iou - (area - union) / (area + 1e-6)\n\n\n# modified from torchvision to also return the union\ndef box_iou_pairwise(boxes1, boxes2):\n area1 = box_area(boxes1)\n area2 = box_area(boxes2)\n\n lt = torch.max(boxes1[:, :2], boxes2[:, :2]) # [N,2]\n rb = torch.min(boxes1[:, 2:], boxes2[:, 2:]) # [N,2]\n\n wh = (rb - lt).clamp(min=0) # [N,2]\n inter = wh[:, 0] * wh[:, 1] # [N]\n\n union = area1 + area2 - inter\n\n iou = inter / union\n return iou, union\n\n\ndef generalized_box_iou_pairwise(boxes1, boxes2):\n \"\"\"\n Generalized IoU from https://giou.stanford.edu/\n\n Input:\n - boxes1, boxes2: N,4\n Output:\n - giou: N, 4\n \"\"\"\n # degenerate boxes gives inf / nan results\n # so do an early check\n assert (boxes1[:, 2:] >= boxes1[:, :2]).all()\n assert (boxes2[:, 2:] >= boxes2[:, :2]).all()\n assert boxes1.shape == boxes2.shape\n iou, union = box_iou_pairwise(boxes1, boxes2) # N, 4\n\n lt = torch.min(boxes1[:, :2], boxes2[:, :2])\n rb = torch.max(boxes1[:, 2:], boxes2[:, 2:])\n\n wh = (rb - lt).clamp(min=0) # [N,2]\n area = wh[:, 0] * wh[:, 1]\n\n return iou - (area - union) / area\n\n\ndef masks_to_boxes(masks):\n \"\"\"Compute the bounding boxes around the provided masks\n\n The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.\n\n Returns a [N, 4] tensors, with the boxes in xyxy format\n \"\"\"\n if masks.numel() == 0:\n return torch.zeros((0, 4), device=masks.device)\n\n h, w = masks.shape[-2:]\n\n y = torch.arange(0, h, dtype=torch.float)\n x = torch.arange(0, w, dtype=torch.float)\n y, x = torch.meshgrid(y, x)\n\n x_mask = masks * x.unsqueeze(0)\n x_max = x_mask.flatten(1).max(-1)[0]\n x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]\n\n y_mask = masks * y.unsqueeze(0)\n y_max = y_mask.flatten(1).max(-1)[0]\n y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]\n\n return torch.stack([x_min, y_min, x_max, y_max], 1)\n\n\nif __name__ == \"__main__\":\n x = torch.rand(5, 4)\n y = torch.rand(3, 4)\n iou, union = box_iou(x, y)\n import ipdb\n\n ipdb.set_trace()\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/util/get_tokenlizer.py", "content": "from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast\n\n\ndef get_tokenlizer(text_encoder_type):\n if not isinstance(text_encoder_type, str):\n # print(\"text_encoder_type is not a str\")\n if hasattr(text_encoder_type, \"text_encoder_type\"):\n text_encoder_type = text_encoder_type.text_encoder_type\n elif text_encoder_type.get(\"text_encoder_type\", False):\n text_encoder_type = text_encoder_type.get(\"text_encoder_type\")\n else:\n raise ValueError(\n \"Unknown type of text_encoder_type: {}\".format(type(text_encoder_type))\n )\n print(\"final text_encoder_type: {}\".format(text_encoder_type))\n # tokenizer = AutoTokenizer.from_pretrained(text_encoder_type)\n tokenizer = AutoTokenizer.from_pretrained('/home/zhaohaozhe/model/bert-base-uncased')\n return tokenizer\n\n\ndef get_pretrained_language_model(text_encoder_type):\n if text_encoder_type == \"bert-base-uncased\":\n # return BertModel.from_pretrained(text_encoder_type)\n return BertModel.from_pretrained('/home/zhaohaozhe/model/bert-base-uncased')\n if text_encoder_type == \"roberta-base\":\n return RobertaModel.from_pretrained(text_encoder_type)\n raise ValueError(\"Unknown text_encoder_type {}\".format(text_encoder_type))\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/util/inference.py", "content": "from typing import Tuple, List\n\nimport re\nimport cv2\nimport numpy as np\nimport supervision as sv\nimport torch\nfrom PIL import Image\nfrom torchvision.ops import box_convert\n\nimport groundingdino.datasets.transforms as T\nfrom groundingdino.models import build_model\nfrom groundingdino.util.misc import clean_state_dict\nfrom groundingdino.util.slconfig import SLConfig\nfrom groundingdino.util.utils import get_phrases_from_posmap\n\n# ----------------------------------------------------------------------------------------------------------------------\n# OLD API\n# ----------------------------------------------------------------------------------------------------------------------\n\n\ndef preprocess_caption(caption: str) -> str:\n result = caption.lower().strip()\n if result.endswith(\".\"):\n return result\n return result + \".\"\n\n\ndef load_model(model_config_path: str, model_checkpoint_path: str, device: str = \"cuda\"):\n args = SLConfig.fromfile(model_config_path)\n args.device = device\n model = build_model(args)\n checkpoint = torch.load(model_checkpoint_path, map_location=\"cpu\")\n model.load_state_dict(clean_state_dict(checkpoint[\"model\"]), strict=False)\n model.eval()\n return model\n\n\ndef load_image(image_path: str) -> Tuple[np.array, torch.Tensor]:\n transform = T.Compose(\n [\n T.RandomResize([800], max_size=1333),\n T.ToTensor(),\n T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n ]\n )\n image_source = Image.open(image_path).convert(\"RGB\")\n image = np.asarray(image_source)\n image_transformed, _ = transform(image_source, None)\n return image, image_transformed\n\n\ndef predict(\n model,\n image: torch.Tensor,\n caption: str,\n box_threshold: float,\n text_threshold: float,\n device: str = \"cuda\"\n) -> Tuple[torch.Tensor, torch.Tensor, List[str]]:\n caption = preprocess_caption(caption=caption)\n\n model = model.to(device)\n image = image.to(device)\n\n with torch.no_grad():\n outputs = model(image[None], captions=[caption])\n\n prediction_logits = outputs[\"pred_logits\"].cpu().sigmoid()[0] # prediction_logits.shape = (nq, 256)\n prediction_boxes = outputs[\"pred_boxes\"].cpu()[0] # prediction_boxes.shape = (nq, 4)\n\n mask = prediction_logits.max(dim=1)[0] > box_threshold\n logits = prediction_logits[mask] # logits.shape = (n, 256)\n boxes = prediction_boxes[mask] # boxes.shape = (n, 4)\n\n tokenizer = model.tokenizer\n tokenized = tokenizer(caption)\n\n phrases = [\n get_phrases_from_posmap(logit > text_threshold, tokenized, tokenizer).replace('.', '')\n for logit\n in logits\n ]\n\n return boxes, logits.max(dim=1)[0], phrases\n\n\ndef annotate(image_source: np.ndarray, boxes: torch.Tensor, logits: torch.Tensor, phrases: List[str]) -> np.ndarray:\n h, w, _ = image_source.shape\n boxes = boxes * torch.Tensor([w, h, w, h])\n xyxy = box_convert(boxes=boxes, in_fmt=\"cxcywh\", out_fmt=\"xyxy\").numpy()\n detections = sv.Detections(xyxy=xyxy)\n\n labels = [\n f\"{phrase} {logit:.2f}\"\n for phrase, logit\n in zip(phrases, logits)\n ]\n\n box_annotator = sv.BoxAnnotator()\n annotated_frame = cv2.cvtColor(image_source, cv2.COLOR_RGB2BGR)\n annotated_frame = box_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels)\n return annotated_frame\n\n\n# ----------------------------------------------------------------------------------------------------------------------\n# NEW API\n# ----------------------------------------------------------------------------------------------------------------------\n\n\nclass Model:\n\n def __init__(\n self,\n model_config_path: str,\n model_checkpoint_path: str,\n device: str = \"cuda\"\n ):\n self.model = load_model(\n model_config_path=model_config_path,\n model_checkpoint_path=model_checkpoint_path,\n device=device\n ).to(device)\n self.device = device\n\n def predict_with_caption(\n self,\n image: np.ndarray,\n caption: str,\n box_threshold: float = 0.35,\n text_threshold: float = 0.25\n ) -> Tuple[sv.Detections, List[str]]:\n \"\"\"\n import cv2\n\n image = cv2.imread(IMAGE_PATH)\n\n model = Model(model_config_path=CONFIG_PATH, model_checkpoint_path=WEIGHTS_PATH)\n detections, labels = model.predict_with_caption(\n image=image,\n caption=caption,\n box_threshold=BOX_THRESHOLD,\n text_threshold=TEXT_THRESHOLD\n )\n\n import supervision as sv\n\n box_annotator = sv.BoxAnnotator()\n annotated_image = box_annotator.annotate(scene=image, detections=detections, labels=labels)\n \"\"\"\n processed_image = Model.preprocess_image(image_bgr=image).to(self.device)\n boxes, logits, phrases = predict(\n model=self.model,\n image=processed_image,\n caption=caption,\n box_threshold=box_threshold,\n text_threshold=text_threshold, \n device=self.device)\n source_h, source_w, _ = image.shape\n detections = Model.post_process_result(\n source_h=source_h,\n source_w=source_w,\n boxes=boxes,\n logits=logits)\n return detections, phrases\n\n def predict_with_classes(\n self,\n image: np.ndarray,\n classes: List[str],\n box_threshold: float,\n text_threshold: float\n ) -> sv.Detections:\n \"\"\"\n import cv2\n\n image = cv2.imread(IMAGE_PATH)\n\n model = Model(model_config_path=CONFIG_PATH, model_checkpoint_path=WEIGHTS_PATH)\n detections = model.predict_with_classes(\n image=image,\n classes=CLASSES,\n box_threshold=BOX_THRESHOLD,\n text_threshold=TEXT_THRESHOLD\n )\n\n\n import supervision as sv\n\n box_annotator = sv.BoxAnnotator()\n annotated_image = box_annotator.annotate(scene=image, detections=detections)\n \"\"\"\n caption = \". \".join(classes)\n processed_image = Model.preprocess_image(image_bgr=image).to(self.device)\n boxes, logits, phrases = predict(\n model=self.model,\n image=processed_image,\n caption=caption,\n box_threshold=box_threshold,\n text_threshold=text_threshold,\n device=self.device)\n source_h, source_w, _ = image.shape\n detections = Model.post_process_result(\n source_h=source_h,\n source_w=source_w,\n boxes=boxes,\n logits=logits)\n class_id = Model.phrases2classes(phrases=phrases, classes=classes)\n detections.class_id = class_id\n return detections\n\n @staticmethod\n def preprocess_image(image_bgr: np.ndarray) -> torch.Tensor:\n transform = T.Compose(\n [\n T.RandomResize([800], max_size=1333),\n T.ToTensor(),\n T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n ]\n )\n image_pillow = Image.fromarray(cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB))\n image_transformed, _ = transform(image_pillow, None)\n return image_transformed\n\n @staticmethod\n def post_process_result(\n source_h: int,\n source_w: int,\n boxes: torch.Tensor,\n logits: torch.Tensor\n ) -> sv.Detections:\n boxes = boxes * torch.Tensor([source_w, source_h, source_w, source_h])\n xyxy = box_convert(boxes=boxes, in_fmt=\"cxcywh\", out_fmt=\"xyxy\").numpy()\n confidence = logits.numpy()\n return sv.Detections(xyxy=xyxy, confidence=confidence)\n\n @staticmethod\n def phrases2classes(phrases: List[str], classes: List[str]) -> np.ndarray:\n class_ids = []\n for phrase in phrases:\n try:\n # class_ids.append(classes.index(phrase))\n class_ids.append(Model.find_index(phrase, classes))\n except ValueError:\n class_ids.append(None)\n return np.array(class_ids)\n\n @staticmethod\n def find_index(string, lst):\n # if meet string like \"lake river\" will only keep \"lake\"\n # this is an hack implementation for visualization which will be updated in the future\n string = string.lower().split()[0]\n for i, s in enumerate(lst):\n if string in s.lower():\n return i\n print(\"There's a wrong phrase happen, this is because of our post-process merged wrong tokens, which will be modified in the future. We will assign it with a random label at this time.\")\n return 0" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/util/logger.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nimport functools\nimport logging\nimport os\nimport sys\n\nfrom termcolor import colored\n\n\nclass _ColorfulFormatter(logging.Formatter):\n def __init__(self, *args, **kwargs):\n self._root_name = kwargs.pop(\"root_name\") + \".\"\n self._abbrev_name = kwargs.pop(\"abbrev_name\", \"\")\n if len(self._abbrev_name):\n self._abbrev_name = self._abbrev_name + \".\"\n super(_ColorfulFormatter, self).__init__(*args, **kwargs)\n\n def formatMessage(self, record):\n record.name = record.name.replace(self._root_name, self._abbrev_name)\n log = super(_ColorfulFormatter, self).formatMessage(record)\n if record.levelno == logging.WARNING:\n prefix = colored(\"WARNING\", \"red\", attrs=[\"blink\"])\n elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL:\n prefix = colored(\"ERROR\", \"red\", attrs=[\"blink\", \"underline\"])\n else:\n return log\n return prefix + \" \" + log\n\n\n# so that calling setup_logger multiple times won't add many handlers\n@functools.lru_cache()\ndef setup_logger(output=None, distributed_rank=0, *, color=True, name=\"imagenet\", abbrev_name=None):\n \"\"\"\n Initialize the detectron2 logger and set its verbosity level to \"INFO\".\n\n Args:\n output (str): a file name or a directory to save log. If None, will not save log file.\n If ends with \".txt\" or \".log\", assumed to be a file name.\n Otherwise, logs will be saved to `output/log.txt`.\n name (str): the root module name of this logger\n\n Returns:\n logging.Logger: a logger\n \"\"\"\n logger = logging.getLogger(name)\n logger.setLevel(logging.DEBUG)\n logger.propagate = False\n\n if abbrev_name is None:\n abbrev_name = name\n\n plain_formatter = logging.Formatter(\n \"[%(asctime)s.%(msecs)03d]: %(message)s\", datefmt=\"%m/%d %H:%M:%S\"\n )\n # stdout logging: master only\n if distributed_rank == 0:\n ch = logging.StreamHandler(stream=sys.stdout)\n ch.setLevel(logging.DEBUG)\n if color:\n formatter = _ColorfulFormatter(\n colored(\"[%(asctime)s.%(msecs)03d]: \", \"green\") + \"%(message)s\",\n datefmt=\"%m/%d %H:%M:%S\",\n root_name=name,\n abbrev_name=str(abbrev_name),\n )\n else:\n formatter = plain_formatter\n ch.setFormatter(formatter)\n logger.addHandler(ch)\n\n # file logging: all workers\n if output is not None:\n if output.endswith(\".txt\") or output.endswith(\".log\"):\n filename = output\n else:\n filename = os.path.join(output, \"log.txt\")\n if distributed_rank > 0:\n filename = filename + f\".rank{distributed_rank}\"\n os.makedirs(os.path.dirname(filename), exist_ok=True)\n\n fh = logging.StreamHandler(_cached_log_stream(filename))\n fh.setLevel(logging.DEBUG)\n fh.setFormatter(plain_formatter)\n logger.addHandler(fh)\n\n return logger\n\n\n# cache the opened file object, so that different calls to `setup_logger`\n# with the same file name can safely write to the same file.\n@functools.lru_cache(maxsize=None)\ndef _cached_log_stream(filename):\n return open(filename, \"a\")\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/util/misc.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nMisc functions, including distributed helpers.\n\nMostly copy-paste from torchvision references.\n\"\"\"\nimport colorsys\nimport datetime\nimport functools\nimport io\nimport json\nimport os\nimport pickle\nimport subprocess\nimport time\nfrom collections import OrderedDict, defaultdict, deque\nfrom typing import List, Optional\n\nimport numpy as np\nimport torch\nimport torch.distributed as dist\n\n# needed due to empty tensor bug in pytorch and torchvision 0.5\nimport torchvision\nfrom torch import Tensor\n\n__torchvision_need_compat_flag = float(torchvision.__version__.split(\".\")[1]) < 7\nif __torchvision_need_compat_flag:\n from torchvision.ops import _new_empty_tensor\n from torchvision.ops.misc import _output_size\n\n\nclass SmoothedValue(object):\n \"\"\"Track a series of values and provide access to smoothed values over a\n window or the global series average.\n \"\"\"\n\n def __init__(self, window_size=20, fmt=None):\n if fmt is None:\n fmt = \"{median:.4f} ({global_avg:.4f})\"\n self.deque = deque(maxlen=window_size)\n self.total = 0.0\n self.count = 0\n self.fmt = fmt\n\n def update(self, value, n=1):\n self.deque.append(value)\n self.count += n\n self.total += value * n\n\n def synchronize_between_processes(self):\n \"\"\"\n Warning: does not synchronize the deque!\n \"\"\"\n if not is_dist_avail_and_initialized():\n return\n t = torch.tensor([self.count, self.total], dtype=torch.float64, device=\"cuda\")\n dist.barrier()\n dist.all_reduce(t)\n t = t.tolist()\n self.count = int(t[0])\n self.total = t[1]\n\n @property\n def median(self):\n d = torch.tensor(list(self.deque))\n if d.shape[0] == 0:\n return 0\n return d.median().item()\n\n @property\n def avg(self):\n d = torch.tensor(list(self.deque), dtype=torch.float32)\n return d.mean().item()\n\n @property\n def global_avg(self):\n if os.environ.get(\"SHILONG_AMP\", None) == \"1\":\n eps = 1e-4\n else:\n eps = 1e-6\n return self.total / (self.count + eps)\n\n @property\n def max(self):\n return max(self.deque)\n\n @property\n def value(self):\n return self.deque[-1]\n\n def __str__(self):\n return self.fmt.format(\n median=self.median,\n avg=self.avg,\n global_avg=self.global_avg,\n max=self.max,\n value=self.value,\n )\n\n\n@functools.lru_cache()\ndef _get_global_gloo_group():\n \"\"\"\n Return a process group based on gloo backend, containing all the ranks\n The result is cached.\n \"\"\"\n\n if dist.get_backend() == \"nccl\":\n return dist.new_group(backend=\"gloo\")\n\n return dist.group.WORLD\n\n\ndef all_gather_cpu(data):\n \"\"\"\n Run all_gather on arbitrary picklable data (not necessarily tensors)\n Args:\n data: any picklable object\n Returns:\n list[data]: list of data gathered from each rank\n \"\"\"\n\n world_size = get_world_size()\n if world_size == 1:\n return [data]\n\n cpu_group = _get_global_gloo_group()\n\n buffer = io.BytesIO()\n torch.save(data, buffer)\n data_view = buffer.getbuffer()\n device = \"cuda\" if cpu_group is None else \"cpu\"\n tensor = torch.ByteTensor(data_view).to(device)\n\n # obtain Tensor size of each rank\n local_size = torch.tensor([tensor.numel()], device=device, dtype=torch.long)\n size_list = [torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)]\n if cpu_group is None:\n dist.all_gather(size_list, local_size)\n else:\n print(\"gathering on cpu\")\n dist.all_gather(size_list, local_size, group=cpu_group)\n size_list = [int(size.item()) for size in size_list]\n max_size = max(size_list)\n assert isinstance(local_size.item(), int)\n local_size = int(local_size.item())\n\n # receiving Tensor from all ranks\n # we pad the tensor because torch all_gather does not support\n # gathering tensors of different shapes\n tensor_list = []\n for _ in size_list:\n tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=device))\n if local_size != max_size:\n padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=device)\n tensor = torch.cat((tensor, padding), dim=0)\n if cpu_group is None:\n dist.all_gather(tensor_list, tensor)\n else:\n dist.all_gather(tensor_list, tensor, group=cpu_group)\n\n data_list = []\n for size, tensor in zip(size_list, tensor_list):\n tensor = torch.split(tensor, [size, max_size - size], dim=0)[0]\n buffer = io.BytesIO(tensor.cpu().numpy())\n obj = torch.load(buffer)\n data_list.append(obj)\n\n return data_list\n\n\ndef all_gather(data):\n \"\"\"\n Run all_gather on arbitrary picklable data (not necessarily tensors)\n Args:\n data: any picklable object\n Returns:\n list[data]: list of data gathered from each rank\n \"\"\"\n\n if os.getenv(\"CPU_REDUCE\") == \"1\":\n return all_gather_cpu(data)\n\n world_size = get_world_size()\n if world_size == 1:\n return [data]\n\n # serialized to a Tensor\n buffer = pickle.dumps(data)\n storage = torch.ByteStorage.from_buffer(buffer)\n tensor = torch.ByteTensor(storage).to(\"cuda\")\n\n # obtain Tensor size of each rank\n local_size = torch.tensor([tensor.numel()], device=\"cuda\")\n size_list = [torch.tensor([0], device=\"cuda\") for _ in range(world_size)]\n dist.all_gather(size_list, local_size)\n size_list = [int(size.item()) for size in size_list]\n max_size = max(size_list)\n\n # receiving Tensor from all ranks\n # we pad the tensor because torch all_gather does not support\n # gathering tensors of different shapes\n tensor_list = []\n for _ in size_list:\n tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=\"cuda\"))\n if local_size != max_size:\n padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=\"cuda\")\n tensor = torch.cat((tensor, padding), dim=0)\n dist.all_gather(tensor_list, tensor)\n\n data_list = []\n for size, tensor in zip(size_list, tensor_list):\n buffer = tensor.cpu().numpy().tobytes()[:size]\n data_list.append(pickle.loads(buffer))\n\n return data_list\n\n\ndef reduce_dict(input_dict, average=True):\n \"\"\"\n Args:\n input_dict (dict): all the values will be reduced\n average (bool): whether to do average or sum\n Reduce the values in the dictionary from all processes so that all processes\n have the averaged results. Returns a dict with the same fields as\n input_dict, after reduction.\n \"\"\"\n world_size = get_world_size()\n if world_size < 2:\n return input_dict\n with torch.no_grad():\n names = []\n values = []\n # sort the keys so that they are consistent across processes\n for k in sorted(input_dict.keys()):\n names.append(k)\n values.append(input_dict[k])\n values = torch.stack(values, dim=0)\n dist.all_reduce(values)\n if average:\n values /= world_size\n reduced_dict = {k: v for k, v in zip(names, values)}\n return reduced_dict\n\n\nclass MetricLogger(object):\n def __init__(self, delimiter=\"\\t\"):\n self.meters = defaultdict(SmoothedValue)\n self.delimiter = delimiter\n\n def update(self, **kwargs):\n for k, v in kwargs.items():\n if isinstance(v, torch.Tensor):\n v = v.item()\n assert isinstance(v, (float, int))\n self.meters[k].update(v)\n\n def __getattr__(self, attr):\n if attr in self.meters:\n return self.meters[attr]\n if attr in self.__dict__:\n return self.__dict__[attr]\n raise AttributeError(\"'{}' object has no attribute '{}'\".format(type(self).__name__, attr))\n\n def __str__(self):\n loss_str = []\n for name, meter in self.meters.items():\n # print(name, str(meter))\n # import ipdb;ipdb.set_trace()\n if meter.count > 0:\n loss_str.append(\"{}: {}\".format(name, str(meter)))\n return self.delimiter.join(loss_str)\n\n def synchronize_between_processes(self):\n for meter in self.meters.values():\n meter.synchronize_between_processes()\n\n def add_meter(self, name, meter):\n self.meters[name] = meter\n\n def log_every(self, iterable, print_freq, header=None, logger=None):\n if logger is None:\n print_func = print\n else:\n print_func = logger.info\n\n i = 0\n if not header:\n header = \"\"\n start_time = time.time()\n end = time.time()\n iter_time = SmoothedValue(fmt=\"{avg:.4f}\")\n data_time = SmoothedValue(fmt=\"{avg:.4f}\")\n space_fmt = \":\" + str(len(str(len(iterable)))) + \"d\"\n if torch.cuda.is_available():\n log_msg = self.delimiter.join(\n [\n header,\n \"[{0\" + space_fmt + \"}/{1}]\",\n \"eta: {eta}\",\n \"{meters}\",\n \"time: {time}\",\n \"data: {data}\",\n \"max mem: {memory:.0f}\",\n ]\n )\n else:\n log_msg = self.delimiter.join(\n [\n header,\n \"[{0\" + space_fmt + \"}/{1}]\",\n \"eta: {eta}\",\n \"{meters}\",\n \"time: {time}\",\n \"data: {data}\",\n ]\n )\n MB = 1024.0 * 1024.0\n for obj in iterable:\n data_time.update(time.time() - end)\n yield obj\n # import ipdb; ipdb.set_trace()\n iter_time.update(time.time() - end)\n if i % print_freq == 0 or i == len(iterable) - 1:\n eta_seconds = iter_time.global_avg * (len(iterable) - i)\n eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))\n if torch.cuda.is_available():\n print_func(\n log_msg.format(\n i,\n len(iterable),\n eta=eta_string,\n meters=str(self),\n time=str(iter_time),\n data=str(data_time),\n memory=torch.cuda.max_memory_allocated() / MB,\n )\n )\n else:\n print_func(\n log_msg.format(\n i,\n len(iterable),\n eta=eta_string,\n meters=str(self),\n time=str(iter_time),\n data=str(data_time),\n )\n )\n i += 1\n end = time.time()\n total_time = time.time() - start_time\n total_time_str = str(datetime.timedelta(seconds=int(total_time)))\n print_func(\n \"{} Total time: {} ({:.4f} s / it)\".format(\n header, total_time_str, total_time / len(iterable)\n )\n )\n\n\ndef get_sha():\n cwd = os.path.dirname(os.path.abspath(__file__))\n\n def _run(command):\n return subprocess.check_output(command, cwd=cwd).decode(\"ascii\").strip()\n\n sha = \"N/A\"\n diff = \"clean\"\n branch = \"N/A\"\n try:\n sha = _run([\"git\", \"rev-parse\", \"HEAD\"])\n subprocess.check_output([\"git\", \"diff\"], cwd=cwd)\n diff = _run([\"git\", \"diff-index\", \"HEAD\"])\n diff = \"has uncommited changes\" if diff else \"clean\"\n branch = _run([\"git\", \"rev-parse\", \"--abbrev-ref\", \"HEAD\"])\n except Exception:\n pass\n message = f\"sha: {sha}, status: {diff}, branch: {branch}\"\n return message\n\n\ndef collate_fn(batch):\n # import ipdb; ipdb.set_trace()\n batch = list(zip(*batch))\n batch[0] = nested_tensor_from_tensor_list(batch[0])\n return tuple(batch)\n\n\ndef _max_by_axis(the_list):\n # type: (List[List[int]]) -> List[int]\n maxes = the_list[0]\n for sublist in the_list[1:]:\n for index, item in enumerate(sublist):\n maxes[index] = max(maxes[index], item)\n return maxes\n\n\nclass NestedTensor(object):\n def __init__(self, tensors, mask: Optional[Tensor]):\n self.tensors = tensors\n self.mask = mask\n if mask == \"auto\":\n self.mask = torch.zeros_like(tensors).to(tensors.device)\n if self.mask.dim() == 3:\n self.mask = self.mask.sum(0).to(bool)\n elif self.mask.dim() == 4:\n self.mask = self.mask.sum(1).to(bool)\n else:\n raise ValueError(\n \"tensors dim must be 3 or 4 but {}({})\".format(\n self.tensors.dim(), self.tensors.shape\n )\n )\n\n def imgsize(self):\n res = []\n for i in range(self.tensors.shape[0]):\n mask = self.mask[i]\n maxH = (~mask).sum(0).max()\n maxW = (~mask).sum(1).max()\n res.append(torch.Tensor([maxH, maxW]))\n return res\n\n def to(self, device):\n # type: (Device) -> NestedTensor # noqa\n cast_tensor = self.tensors.to(device)\n mask = self.mask\n if mask is not None:\n assert mask is not None\n cast_mask = mask.to(device)\n else:\n cast_mask = None\n return NestedTensor(cast_tensor, cast_mask)\n\n def to_img_list_single(self, tensor, mask):\n assert tensor.dim() == 3, \"dim of tensor should be 3 but {}\".format(tensor.dim())\n maxH = (~mask).sum(0).max()\n maxW = (~mask).sum(1).max()\n img = tensor[:, :maxH, :maxW]\n return img\n\n def to_img_list(self):\n \"\"\"remove the padding and convert to img list\n\n Returns:\n [type]: [description]\n \"\"\"\n if self.tensors.dim() == 3:\n return self.to_img_list_single(self.tensors, self.mask)\n else:\n res = []\n for i in range(self.tensors.shape[0]):\n tensor_i = self.tensors[i]\n mask_i = self.mask[i]\n res.append(self.to_img_list_single(tensor_i, mask_i))\n return res\n\n @property\n def device(self):\n return self.tensors.device\n\n def decompose(self):\n return self.tensors, self.mask\n\n def __repr__(self):\n return str(self.tensors)\n\n @property\n def shape(self):\n return {\"tensors.shape\": self.tensors.shape, \"mask.shape\": self.mask.shape}\n\n\ndef nested_tensor_from_tensor_list(tensor_list: List[Tensor]):\n # TODO make this more general\n if tensor_list[0].ndim == 3:\n if torchvision._is_tracing():\n # nested_tensor_from_tensor_list() does not export well to ONNX\n # call _onnx_nested_tensor_from_tensor_list() instead\n return _onnx_nested_tensor_from_tensor_list(tensor_list)\n\n # TODO make it support different-sized images\n max_size = _max_by_axis([list(img.shape) for img in tensor_list])\n # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))\n batch_shape = [len(tensor_list)] + max_size\n b, c, h, w = batch_shape\n dtype = tensor_list[0].dtype\n device = tensor_list[0].device\n tensor = torch.zeros(batch_shape, dtype=dtype, device=device)\n mask = torch.ones((b, h, w), dtype=torch.bool, device=device)\n for img, pad_img, m in zip(tensor_list, tensor, mask):\n pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)\n m[: img.shape[1], : img.shape[2]] = False\n else:\n raise ValueError(\"not supported\")\n return NestedTensor(tensor, mask)\n\n\n# _onnx_nested_tensor_from_tensor_list() is an implementation of\n# nested_tensor_from_tensor_list() that is supported by ONNX tracing.\n@torch.jit.unused\ndef _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:\n max_size = []\n for i in range(tensor_list[0].dim()):\n max_size_i = torch.max(\n torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)\n ).to(torch.int64)\n max_size.append(max_size_i)\n max_size = tuple(max_size)\n\n # work around for\n # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)\n # m[: img.shape[1], :img.shape[2]] = False\n # which is not yet supported in onnx\n padded_imgs = []\n padded_masks = []\n for img in tensor_list:\n padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]\n padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))\n padded_imgs.append(padded_img)\n\n m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)\n padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), \"constant\", 1)\n padded_masks.append(padded_mask.to(torch.bool))\n\n tensor = torch.stack(padded_imgs)\n mask = torch.stack(padded_masks)\n\n return NestedTensor(tensor, mask=mask)\n\n\ndef setup_for_distributed(is_master):\n \"\"\"\n This function disables printing when not in master process\n \"\"\"\n import builtins as __builtin__\n\n builtin_print = __builtin__.print\n\n def print(*args, **kwargs):\n force = kwargs.pop(\"force\", False)\n if is_master or force:\n builtin_print(*args, **kwargs)\n\n __builtin__.print = print\n\n\ndef is_dist_avail_and_initialized():\n if not dist.is_available():\n return False\n if not dist.is_initialized():\n return False\n return True\n\n\ndef get_world_size():\n if not is_dist_avail_and_initialized():\n return 1\n return dist.get_world_size()\n\n\ndef get_rank():\n if not is_dist_avail_and_initialized():\n return 0\n return dist.get_rank()\n\n\ndef is_main_process():\n return get_rank() == 0\n\n\ndef save_on_master(*args, **kwargs):\n if is_main_process():\n torch.save(*args, **kwargs)\n\n\ndef init_distributed_mode(args):\n if \"WORLD_SIZE\" in os.environ and os.environ[\"WORLD_SIZE\"] != \"\": # 'RANK' in os.environ and\n args.rank = int(os.environ[\"RANK\"])\n args.world_size = int(os.environ[\"WORLD_SIZE\"])\n args.gpu = args.local_rank = int(os.environ[\"LOCAL_RANK\"])\n\n # launch by torch.distributed.launch\n # Single node\n # python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 1 --rank 0 ...\n # Multi nodes\n # python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 0 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ...\n # python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 1 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ...\n # args.rank = int(os.environ.get('OMPI_COMM_WORLD_RANK'))\n # local_world_size = int(os.environ['GPU_PER_NODE_COUNT'])\n # args.world_size = args.world_size * local_world_size\n # args.gpu = args.local_rank = int(os.environ['LOCAL_RANK'])\n # args.rank = args.rank * local_world_size + args.local_rank\n print(\n \"world size: {}, rank: {}, local rank: {}\".format(\n args.world_size, args.rank, args.local_rank\n )\n )\n print(json.dumps(dict(os.environ), indent=2))\n elif \"SLURM_PROCID\" in os.environ:\n args.rank = int(os.environ[\"SLURM_PROCID\"])\n args.gpu = args.local_rank = int(os.environ[\"SLURM_LOCALID\"])\n args.world_size = int(os.environ[\"SLURM_NPROCS\"])\n\n print(\n \"world size: {}, world rank: {}, local rank: {}, device_count: {}\".format(\n args.world_size, args.rank, args.local_rank, torch.cuda.device_count()\n )\n )\n else:\n print(\"Not using distributed mode\")\n args.distributed = False\n args.world_size = 1\n args.rank = 0\n args.local_rank = 0\n return\n\n print(\"world_size:{} rank:{} local_rank:{}\".format(args.world_size, args.rank, args.local_rank))\n args.distributed = True\n torch.cuda.set_device(args.local_rank)\n args.dist_backend = \"nccl\"\n print(\"| distributed init (rank {}): {}\".format(args.rank, args.dist_url), flush=True)\n\n torch.distributed.init_process_group(\n backend=args.dist_backend,\n world_size=args.world_size,\n rank=args.rank,\n init_method=args.dist_url,\n )\n\n print(\"Before torch.distributed.barrier()\")\n torch.distributed.barrier()\n print(\"End torch.distributed.barrier()\")\n setup_for_distributed(args.rank == 0)\n\n\n@torch.no_grad()\ndef accuracy(output, target, topk=(1,)):\n \"\"\"Computes the precision@k for the specified values of k\"\"\"\n if target.numel() == 0:\n return [torch.zeros([], device=output.device)]\n maxk = max(topk)\n batch_size = target.size(0)\n\n _, pred = output.topk(maxk, 1, True, True)\n pred = pred.t()\n correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n res = []\n for k in topk:\n correct_k = correct[:k].view(-1).float().sum(0)\n res.append(correct_k.mul_(100.0 / batch_size))\n return res\n\n\n@torch.no_grad()\ndef accuracy_onehot(pred, gt):\n \"\"\"_summary_\n\n Args:\n pred (_type_): n, c\n gt (_type_): n, c\n \"\"\"\n tp = ((pred - gt).abs().sum(-1) < 1e-4).float().sum()\n acc = tp / gt.shape[0] * 100\n return acc\n\n\ndef interpolate(input, size=None, scale_factor=None, mode=\"nearest\", align_corners=None):\n # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor\n \"\"\"\n Equivalent to nn.functional.interpolate, but with support for empty batch sizes.\n This will eventually be supported natively by PyTorch, and this\n class can go away.\n \"\"\"\n if __torchvision_need_compat_flag < 0.7:\n if input.numel() > 0:\n return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners)\n\n output_shape = _output_size(2, input, size, scale_factor)\n output_shape = list(input.shape[:-2]) + list(output_shape)\n return _new_empty_tensor(input, output_shape)\n else:\n return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)\n\n\nclass color_sys:\n def __init__(self, num_colors) -> None:\n self.num_colors = num_colors\n colors = []\n for i in np.arange(0.0, 360.0, 360.0 / num_colors):\n hue = i / 360.0\n lightness = (50 + np.random.rand() * 10) / 100.0\n saturation = (90 + np.random.rand() * 10) / 100.0\n colors.append(\n tuple([int(j * 255) for j in colorsys.hls_to_rgb(hue, lightness, saturation)])\n )\n self.colors = colors\n\n def __call__(self, idx):\n return self.colors[idx]\n\n\ndef inverse_sigmoid(x, eps=1e-3):\n x = x.clamp(min=0, max=1)\n x1 = x.clamp(min=eps)\n x2 = (1 - x).clamp(min=eps)\n return torch.log(x1 / x2)\n\n\ndef clean_state_dict(state_dict):\n new_state_dict = OrderedDict()\n for k, v in state_dict.items():\n if k[:7] == \"module.\":\n k = k[7:] # remove `module.`\n new_state_dict[k] = v\n return new_state_dict\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/util/slconfig.py", "content": "# ==========================================================\n# Modified from mmcv\n# ==========================================================\nimport ast\nimport os.path as osp\nimport shutil\nimport sys\nimport tempfile\nfrom argparse import Action\nfrom importlib import import_module\nimport platform\n\nfrom addict import Dict\nfrom yapf.yapflib.yapf_api import FormatCode\n\nBASE_KEY = \"_base_\"\nDELETE_KEY = \"_delete_\"\nRESERVED_KEYS = [\"filename\", \"text\", \"pretty_text\", \"get\", \"dump\", \"merge_from_dict\"]\n\n\ndef check_file_exist(filename, msg_tmpl='file \"{}\" does not exist'):\n if not osp.isfile(filename):\n raise FileNotFoundError(msg_tmpl.format(filename))\n\n\nclass ConfigDict(Dict):\n def __missing__(self, name):\n raise KeyError(name)\n\n def __getattr__(self, name):\n try:\n value = super(ConfigDict, self).__getattr__(name)\n except KeyError:\n ex = AttributeError(f\"'{self.__class__.__name__}' object has no \" f\"attribute '{name}'\")\n except Exception as e:\n ex = e\n else:\n return value\n raise ex\n\n\nclass SLConfig(object):\n \"\"\"\n config files.\n only support .py file as config now.\n\n ref: mmcv.utils.config\n\n Example:\n >>> cfg = Config(dict(a=1, b=dict(b1=[0, 1])))\n >>> cfg.a\n 1\n >>> cfg.b\n {'b1': [0, 1]}\n >>> cfg.b.b1\n [0, 1]\n >>> cfg = Config.fromfile('tests/data/config/a.py')\n >>> cfg.filename\n \"/home/kchen/projects/mmcv/tests/data/config/a.py\"\n >>> cfg.item4\n 'test'\n >>> cfg\n \"Config [path: /home/kchen/projects/mmcv/tests/data/config/a.py]: \"\n \"{'item1': [1, 2], 'item2': {'a': 0}, 'item3': True, 'item4': 'test'}\"\n \"\"\"\n\n @staticmethod\n def _validate_py_syntax(filename):\n with open(filename) as f:\n content = f.read()\n try:\n ast.parse(content)\n except SyntaxError:\n raise SyntaxError(\"There are syntax errors in config \" f\"file {filename}\")\n\n @staticmethod\n def _file2dict(filename):\n filename = osp.abspath(osp.expanduser(filename))\n check_file_exist(filename)\n if filename.lower().endswith(\".py\"):\n with tempfile.TemporaryDirectory() as temp_config_dir:\n temp_config_file = tempfile.NamedTemporaryFile(dir=temp_config_dir, suffix=\".py\")\n temp_config_name = osp.basename(temp_config_file.name)\n if platform.system() == 'Windows':\n temp_config_file.close()\n shutil.copyfile(filename, osp.join(temp_config_dir, temp_config_name))\n temp_module_name = osp.splitext(temp_config_name)[0]\n sys.path.insert(0, temp_config_dir)\n SLConfig._validate_py_syntax(filename)\n mod = import_module(temp_module_name)\n sys.path.pop(0)\n cfg_dict = {\n name: value for name, value in mod.__dict__.items() if not name.startswith(\"__\")\n }\n # delete imported module\n del sys.modules[temp_module_name]\n # close temp file\n temp_config_file.close()\n elif filename.lower().endswith((\".yml\", \".yaml\", \".json\")):\n from .slio import slload\n\n cfg_dict = slload(filename)\n else:\n raise IOError(\"Only py/yml/yaml/json type are supported now!\")\n\n cfg_text = filename + \"\\n\"\n with open(filename, \"r\") as f:\n cfg_text += f.read()\n\n # parse the base file\n if BASE_KEY in cfg_dict:\n cfg_dir = osp.dirname(filename)\n base_filename = cfg_dict.pop(BASE_KEY)\n base_filename = base_filename if isinstance(base_filename, list) else [base_filename]\n\n cfg_dict_list = list()\n cfg_text_list = list()\n for f in base_filename:\n _cfg_dict, _cfg_text = SLConfig._file2dict(osp.join(cfg_dir, f))\n cfg_dict_list.append(_cfg_dict)\n cfg_text_list.append(_cfg_text)\n\n base_cfg_dict = dict()\n for c in cfg_dict_list:\n if len(base_cfg_dict.keys() & c.keys()) > 0:\n raise KeyError(\"Duplicate key is not allowed among bases\")\n # TODO Allow the duplicate key while warnning user\n base_cfg_dict.update(c)\n\n base_cfg_dict = SLConfig._merge_a_into_b(cfg_dict, base_cfg_dict)\n cfg_dict = base_cfg_dict\n\n # merge cfg_text\n cfg_text_list.append(cfg_text)\n cfg_text = \"\\n\".join(cfg_text_list)\n\n return cfg_dict, cfg_text\n\n @staticmethod\n def _merge_a_into_b(a, b):\n \"\"\"merge dict `a` into dict `b` (non-inplace).\n values in `a` will overwrite `b`.\n copy first to avoid inplace modification\n\n Args:\n a ([type]): [description]\n b ([type]): [description]\n\n Returns:\n [dict]: [description]\n \"\"\"\n # import ipdb; ipdb.set_trace()\n if not isinstance(a, dict):\n return a\n\n b = b.copy()\n for k, v in a.items():\n if isinstance(v, dict) and k in b and not v.pop(DELETE_KEY, False):\n\n if not isinstance(b[k], dict) and not isinstance(b[k], list):\n # if :\n # import ipdb; ipdb.set_trace()\n raise TypeError(\n f\"{k}={v} in child config cannot inherit from base \"\n f\"because {k} is a dict in the child config but is of \"\n f\"type {type(b[k])} in base config. You may set \"\n f\"`{DELETE_KEY}=True` to ignore the base config\"\n )\n b[k] = SLConfig._merge_a_into_b(v, b[k])\n elif isinstance(b, list):\n try:\n _ = int(k)\n except:\n raise TypeError(\n f\"b is a list, \" f\"index {k} should be an int when input but {type(k)}\"\n )\n b[int(k)] = SLConfig._merge_a_into_b(v, b[int(k)])\n else:\n b[k] = v\n\n return b\n\n @staticmethod\n def fromfile(filename):\n cfg_dict, cfg_text = SLConfig._file2dict(filename)\n return SLConfig(cfg_dict, cfg_text=cfg_text, filename=filename)\n\n def __init__(self, cfg_dict=None, cfg_text=None, filename=None):\n if cfg_dict is None:\n cfg_dict = dict()\n elif not isinstance(cfg_dict, dict):\n raise TypeError(\"cfg_dict must be a dict, but \" f\"got {type(cfg_dict)}\")\n for key in cfg_dict:\n if key in RESERVED_KEYS:\n raise KeyError(f\"{key} is reserved for config file\")\n\n super(SLConfig, self).__setattr__(\"_cfg_dict\", ConfigDict(cfg_dict))\n super(SLConfig, self).__setattr__(\"_filename\", filename)\n if cfg_text:\n text = cfg_text\n elif filename:\n with open(filename, \"r\") as f:\n text = f.read()\n else:\n text = \"\"\n super(SLConfig, self).__setattr__(\"_text\", text)\n\n @property\n def filename(self):\n return self._filename\n\n @property\n def text(self):\n return self._text\n\n @property\n def pretty_text(self):\n\n indent = 4\n\n def _indent(s_, num_spaces):\n s = s_.split(\"\\n\")\n if len(s) == 1:\n return s_\n first = s.pop(0)\n s = [(num_spaces * \" \") + line for line in s]\n s = \"\\n\".join(s)\n s = first + \"\\n\" + s\n return s\n\n def _format_basic_types(k, v, use_mapping=False):\n if isinstance(v, str):\n v_str = f\"'{v}'\"\n else:\n v_str = str(v)\n\n if use_mapping:\n k_str = f\"'{k}'\" if isinstance(k, str) else str(k)\n attr_str = f\"{k_str}: {v_str}\"\n else:\n attr_str = f\"{str(k)}={v_str}\"\n attr_str = _indent(attr_str, indent)\n\n return attr_str\n\n def _format_list(k, v, use_mapping=False):\n # check if all items in the list are dict\n if all(isinstance(_, dict) for _ in v):\n v_str = \"[\\n\"\n v_str += \"\\n\".join(\n f\"dict({_indent(_format_dict(v_), indent)}),\" for v_ in v\n ).rstrip(\",\")\n if use_mapping:\n k_str = f\"'{k}'\" if isinstance(k, str) else str(k)\n attr_str = f\"{k_str}: {v_str}\"\n else:\n attr_str = f\"{str(k)}={v_str}\"\n attr_str = _indent(attr_str, indent) + \"]\"\n else:\n attr_str = _format_basic_types(k, v, use_mapping)\n return attr_str\n\n def _contain_invalid_identifier(dict_str):\n contain_invalid_identifier = False\n for key_name in dict_str:\n contain_invalid_identifier |= not str(key_name).isidentifier()\n return contain_invalid_identifier\n\n def _format_dict(input_dict, outest_level=False):\n r = \"\"\n s = []\n\n use_mapping = _contain_invalid_identifier(input_dict)\n if use_mapping:\n r += \"{\"\n for idx, (k, v) in enumerate(input_dict.items()):\n is_last = idx >= len(input_dict) - 1\n end = \"\" if outest_level or is_last else \",\"\n if isinstance(v, dict):\n v_str = \"\\n\" + _format_dict(v)\n if use_mapping:\n k_str = f\"'{k}'\" if isinstance(k, str) else str(k)\n attr_str = f\"{k_str}: dict({v_str}\"\n else:\n attr_str = f\"{str(k)}=dict({v_str}\"\n attr_str = _indent(attr_str, indent) + \")\" + end\n elif isinstance(v, list):\n attr_str = _format_list(k, v, use_mapping) + end\n else:\n attr_str = _format_basic_types(k, v, use_mapping) + end\n\n s.append(attr_str)\n r += \"\\n\".join(s)\n if use_mapping:\n r += \"}\"\n return r\n\n cfg_dict = self._cfg_dict.to_dict()\n text = _format_dict(cfg_dict, outest_level=True)\n # copied from setup.cfg\n yapf_style = dict(\n based_on_style=\"pep8\",\n blank_line_before_nested_class_or_def=True,\n split_before_expression_after_opening_paren=True,\n )\n text, _ = FormatCode(text, style_config=yapf_style, verify=True)\n\n return text\n\n def __repr__(self):\n return f\"Config (path: {self.filename}): {self._cfg_dict.__repr__()}\"\n\n def __len__(self):\n return len(self._cfg_dict)\n\n def __getattr__(self, name):\n # # debug\n # print('+'*15)\n # print('name=%s' % name)\n # print(\"addr:\", id(self))\n # # print('type(self):', type(self))\n # print(self.__dict__)\n # print('+'*15)\n # if self.__dict__ == {}:\n # raise ValueError\n\n return getattr(self._cfg_dict, name)\n\n def __getitem__(self, name):\n return self._cfg_dict.__getitem__(name)\n\n def __setattr__(self, name, value):\n if isinstance(value, dict):\n value = ConfigDict(value)\n self._cfg_dict.__setattr__(name, value)\n\n def __setitem__(self, name, value):\n if isinstance(value, dict):\n value = ConfigDict(value)\n self._cfg_dict.__setitem__(name, value)\n\n def __iter__(self):\n return iter(self._cfg_dict)\n\n def dump(self, file=None):\n # import ipdb; ipdb.set_trace()\n if file is None:\n return self.pretty_text\n else:\n with open(file, \"w\") as f:\n f.write(self.pretty_text)\n\n def merge_from_dict(self, options):\n \"\"\"Merge list into cfg_dict\n\n Merge the dict parsed by MultipleKVAction into this cfg.\n\n Examples:\n >>> options = {'model.backbone.depth': 50,\n ... 'model.backbone.with_cp':True}\n >>> cfg = Config(dict(model=dict(backbone=dict(type='ResNet'))))\n >>> cfg.merge_from_dict(options)\n >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict')\n >>> assert cfg_dict == dict(\n ... model=dict(backbone=dict(depth=50, with_cp=True)))\n\n Args:\n options (dict): dict of configs to merge from.\n \"\"\"\n option_cfg_dict = {}\n for full_key, v in options.items():\n d = option_cfg_dict\n key_list = full_key.split(\".\")\n for subkey in key_list[:-1]:\n d.setdefault(subkey, ConfigDict())\n d = d[subkey]\n subkey = key_list[-1]\n d[subkey] = v\n\n cfg_dict = super(SLConfig, self).__getattribute__(\"_cfg_dict\")\n super(SLConfig, self).__setattr__(\n \"_cfg_dict\", SLConfig._merge_a_into_b(option_cfg_dict, cfg_dict)\n )\n\n # for multiprocess\n def __setstate__(self, state):\n self.__init__(state)\n\n def copy(self):\n return SLConfig(self._cfg_dict.copy())\n\n def deepcopy(self):\n return SLConfig(self._cfg_dict.deepcopy())\n\n\nclass DictAction(Action):\n \"\"\"\n argparse action to split an argument into KEY=VALUE form\n on the first = and append to a dictionary. List options should\n be passed as comma separated values, i.e KEY=V1,V2,V3\n \"\"\"\n\n @staticmethod\n def _parse_int_float_bool(val):\n try:\n return int(val)\n except ValueError:\n pass\n try:\n return float(val)\n except ValueError:\n pass\n if val.lower() in [\"true\", \"false\"]:\n return True if val.lower() == \"true\" else False\n if val.lower() in [\"none\", \"null\"]:\n return None\n return val\n\n def __call__(self, parser, namespace, values, option_string=None):\n options = {}\n for kv in values:\n key, val = kv.split(\"=\", maxsplit=1)\n val = [self._parse_int_float_bool(v) for v in val.split(\",\")]\n if len(val) == 1:\n val = val[0]\n options[key] = val\n setattr(namespace, self.dest, options)\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/util/slio.py", "content": "# ==========================================================\n# Modified from mmcv\n# ==========================================================\n\nimport json\nimport pickle\nfrom abc import ABCMeta, abstractmethod\nfrom pathlib import Path\n\nimport yaml\n\ntry:\n from yaml import CLoader as Loader, CDumper as Dumper\nexcept ImportError:\n from yaml import Loader, Dumper\n\n\n# ===========================\n# Rigister handler\n# ===========================\n\n\nclass BaseFileHandler(metaclass=ABCMeta):\n @abstractmethod\n def load_from_fileobj(self, file, **kwargs):\n pass\n\n @abstractmethod\n def dump_to_fileobj(self, obj, file, **kwargs):\n pass\n\n @abstractmethod\n def dump_to_str(self, obj, **kwargs):\n pass\n\n def load_from_path(self, filepath, mode=\"r\", **kwargs):\n with open(filepath, mode) as f:\n return self.load_from_fileobj(f, **kwargs)\n\n def dump_to_path(self, obj, filepath, mode=\"w\", **kwargs):\n with open(filepath, mode) as f:\n self.dump_to_fileobj(obj, f, **kwargs)\n\n\nclass JsonHandler(BaseFileHandler):\n def load_from_fileobj(self, file):\n return json.load(file)\n\n def dump_to_fileobj(self, obj, file, **kwargs):\n json.dump(obj, file, **kwargs)\n\n def dump_to_str(self, obj, **kwargs):\n return json.dumps(obj, **kwargs)\n\n\nclass PickleHandler(BaseFileHandler):\n def load_from_fileobj(self, file, **kwargs):\n return pickle.load(file, **kwargs)\n\n def load_from_path(self, filepath, **kwargs):\n return super(PickleHandler, self).load_from_path(filepath, mode=\"rb\", **kwargs)\n\n def dump_to_str(self, obj, **kwargs):\n kwargs.setdefault(\"protocol\", 2)\n return pickle.dumps(obj, **kwargs)\n\n def dump_to_fileobj(self, obj, file, **kwargs):\n kwargs.setdefault(\"protocol\", 2)\n pickle.dump(obj, file, **kwargs)\n\n def dump_to_path(self, obj, filepath, **kwargs):\n super(PickleHandler, self).dump_to_path(obj, filepath, mode=\"wb\", **kwargs)\n\n\nclass YamlHandler(BaseFileHandler):\n def load_from_fileobj(self, file, **kwargs):\n kwargs.setdefault(\"Loader\", Loader)\n return yaml.load(file, **kwargs)\n\n def dump_to_fileobj(self, obj, file, **kwargs):\n kwargs.setdefault(\"Dumper\", Dumper)\n yaml.dump(obj, file, **kwargs)\n\n def dump_to_str(self, obj, **kwargs):\n kwargs.setdefault(\"Dumper\", Dumper)\n return yaml.dump(obj, **kwargs)\n\n\nfile_handlers = {\n \"json\": JsonHandler(),\n \"yaml\": YamlHandler(),\n \"yml\": YamlHandler(),\n \"pickle\": PickleHandler(),\n \"pkl\": PickleHandler(),\n}\n\n# ===========================\n# load and dump\n# ===========================\n\n\ndef is_str(x):\n \"\"\"Whether the input is an string instance.\n\n Note: This method is deprecated since python 2 is no longer supported.\n \"\"\"\n return isinstance(x, str)\n\n\ndef slload(file, file_format=None, **kwargs):\n \"\"\"Load data from json/yaml/pickle files.\n\n This method provides a unified api for loading data from serialized files.\n\n Args:\n file (str or :obj:`Path` or file-like object): Filename or a file-like\n object.\n file_format (str, optional): If not specified, the file format will be\n inferred from the file extension, otherwise use the specified one.\n Currently supported formats include \"json\", \"yaml/yml\" and\n \"pickle/pkl\".\n\n Returns:\n The content from the file.\n \"\"\"\n if isinstance(file, Path):\n file = str(file)\n if file_format is None and is_str(file):\n file_format = file.split(\".\")[-1]\n if file_format not in file_handlers:\n raise TypeError(f\"Unsupported format: {file_format}\")\n\n handler = file_handlers[file_format]\n if is_str(file):\n obj = handler.load_from_path(file, **kwargs)\n elif hasattr(file, \"read\"):\n obj = handler.load_from_fileobj(file, **kwargs)\n else:\n raise TypeError('\"file\" must be a filepath str or a file-object')\n return obj\n\n\ndef sldump(obj, file=None, file_format=None, **kwargs):\n \"\"\"Dump data to json/yaml/pickle strings or files.\n\n This method provides a unified api for dumping data as strings or to files,\n and also supports custom arguments for each file format.\n\n Args:\n obj (any): The python object to be dumped.\n file (str or :obj:`Path` or file-like object, optional): If not\n specified, then the object is dump to a str, otherwise to a file\n specified by the filename or file-like object.\n file_format (str, optional): Same as :func:`load`.\n\n Returns:\n bool: True for success, False otherwise.\n \"\"\"\n if isinstance(file, Path):\n file = str(file)\n if file_format is None:\n if is_str(file):\n file_format = file.split(\".\")[-1]\n elif file is None:\n raise ValueError(\"file_format must be specified since file is None\")\n if file_format not in file_handlers:\n raise TypeError(f\"Unsupported format: {file_format}\")\n\n handler = file_handlers[file_format]\n if file is None:\n return handler.dump_to_str(obj, **kwargs)\n elif is_str(file):\n handler.dump_to_path(obj, file, **kwargs)\n elif hasattr(file, \"write\"):\n handler.dump_to_fileobj(obj, file, **kwargs)\n else:\n raise TypeError('\"file\" must be a filename str or a file-object')\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/util/time_counter.py", "content": "import json\nimport time\n\n\nclass TimeCounter:\n def __init__(self) -> None:\n pass\n\n def clear(self):\n self.timedict = {}\n self.basetime = time.perf_counter()\n\n def timeit(self, name):\n nowtime = time.perf_counter() - self.basetime\n self.timedict[name] = nowtime\n self.basetime = time.perf_counter()\n\n\nclass TimeHolder:\n def __init__(self) -> None:\n self.timedict = {}\n\n def update(self, _timedict: dict):\n for k, v in _timedict.items():\n if k not in self.timedict:\n self.timedict[k] = AverageMeter(name=k, val_only=True)\n self.timedict[k].update(val=v)\n\n def final_res(self):\n return {k: v.avg for k, v in self.timedict.items()}\n\n def __str__(self):\n return json.dumps(self.final_res(), indent=2)\n\n\nclass AverageMeter(object):\n \"\"\"Computes and stores the average and current value\"\"\"\n\n def __init__(self, name, fmt=\":f\", val_only=False):\n self.name = name\n self.fmt = fmt\n self.val_only = val_only\n self.reset()\n\n def reset(self):\n self.val = 0\n self.avg = 0\n self.sum = 0\n self.count = 0\n\n def update(self, val, n=1):\n self.val = val\n self.sum += val * n\n self.count += n\n self.avg = self.sum / self.count\n\n def __str__(self):\n if self.val_only:\n fmtstr = \"{name} {val\" + self.fmt + \"}\"\n else:\n fmtstr = \"{name} {val\" + self.fmt + \"} ({avg\" + self.fmt + \"})\"\n return fmtstr.format(**self.__dict__)\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/util/utils.py", "content": "import argparse\nimport json\nimport warnings\nfrom collections import OrderedDict\nfrom copy import deepcopy\nfrom typing import Any, Dict, List\n\nimport numpy as np\nimport torch\nfrom transformers import AutoTokenizer\n\nfrom groundingdino.util.slconfig import SLConfig\n\n\ndef slprint(x, name=\"x\"):\n if isinstance(x, (torch.Tensor, np.ndarray)):\n print(f\"{name}.shape:\", x.shape)\n elif isinstance(x, (tuple, list)):\n print(\"type x:\", type(x))\n for i in range(min(10, len(x))):\n slprint(x[i], f\"{name}[{i}]\")\n elif isinstance(x, dict):\n for k, v in x.items():\n slprint(v, f\"{name}[{k}]\")\n else:\n print(f\"{name}.type:\", type(x))\n\n\ndef clean_state_dict(state_dict):\n new_state_dict = OrderedDict()\n for k, v in state_dict.items():\n if k[:7] == \"module.\":\n k = k[7:] # remove `module.`\n new_state_dict[k] = v\n return new_state_dict\n\n\ndef renorm(\n img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]\n) -> torch.FloatTensor:\n # img: tensor(3,H,W) or tensor(B,3,H,W)\n # return: same as img\n assert img.dim() == 3 or img.dim() == 4, \"img.dim() should be 3 or 4 but %d\" % img.dim()\n if img.dim() == 3:\n assert img.size(0) == 3, 'img.size(0) shoule be 3 but \"%d\". (%s)' % (\n img.size(0),\n str(img.size()),\n )\n img_perm = img.permute(1, 2, 0)\n mean = torch.Tensor(mean)\n std = torch.Tensor(std)\n img_res = img_perm * std + mean\n return img_res.permute(2, 0, 1)\n else: # img.dim() == 4\n assert img.size(1) == 3, 'img.size(1) shoule be 3 but \"%d\". (%s)' % (\n img.size(1),\n str(img.size()),\n )\n img_perm = img.permute(0, 2, 3, 1)\n mean = torch.Tensor(mean)\n std = torch.Tensor(std)\n img_res = img_perm * std + mean\n return img_res.permute(0, 3, 1, 2)\n\n\nclass CocoClassMapper:\n def __init__(self) -> None:\n self.category_map_str = {\n \"1\": 1,\n \"2\": 2,\n \"3\": 3,\n \"4\": 4,\n \"5\": 5,\n \"6\": 6,\n \"7\": 7,\n \"8\": 8,\n \"9\": 9,\n \"10\": 10,\n \"11\": 11,\n \"13\": 12,\n \"14\": 13,\n \"15\": 14,\n \"16\": 15,\n \"17\": 16,\n \"18\": 17,\n \"19\": 18,\n \"20\": 19,\n \"21\": 20,\n \"22\": 21,\n \"23\": 22,\n \"24\": 23,\n \"25\": 24,\n \"27\": 25,\n \"28\": 26,\n \"31\": 27,\n \"32\": 28,\n \"33\": 29,\n \"34\": 30,\n \"35\": 31,\n \"36\": 32,\n \"37\": 33,\n \"38\": 34,\n \"39\": 35,\n \"40\": 36,\n \"41\": 37,\n \"42\": 38,\n \"43\": 39,\n \"44\": 40,\n \"46\": 41,\n \"47\": 42,\n \"48\": 43,\n \"49\": 44,\n \"50\": 45,\n \"51\": 46,\n \"52\": 47,\n \"53\": 48,\n \"54\": 49,\n \"55\": 50,\n \"56\": 51,\n \"57\": 52,\n \"58\": 53,\n \"59\": 54,\n \"60\": 55,\n \"61\": 56,\n \"62\": 57,\n \"63\": 58,\n \"64\": 59,\n \"65\": 60,\n \"67\": 61,\n \"70\": 62,\n \"72\": 63,\n \"73\": 64,\n \"74\": 65,\n \"75\": 66,\n \"76\": 67,\n \"77\": 68,\n \"78\": 69,\n \"79\": 70,\n \"80\": 71,\n \"81\": 72,\n \"82\": 73,\n \"84\": 74,\n \"85\": 75,\n \"86\": 76,\n \"87\": 77,\n \"88\": 78,\n \"89\": 79,\n \"90\": 80,\n }\n self.origin2compact_mapper = {int(k): v - 1 for k, v in self.category_map_str.items()}\n self.compact2origin_mapper = {int(v - 1): int(k) for k, v in self.category_map_str.items()}\n\n def origin2compact(self, idx):\n return self.origin2compact_mapper[int(idx)]\n\n def compact2origin(self, idx):\n return self.compact2origin_mapper[int(idx)]\n\n\ndef to_device(item, device):\n if isinstance(item, torch.Tensor):\n return item.to(device)\n elif isinstance(item, list):\n return [to_device(i, device) for i in item]\n elif isinstance(item, dict):\n return {k: to_device(v, device) for k, v in item.items()}\n else:\n raise NotImplementedError(\n \"Call Shilong if you use other containers! type: {}\".format(type(item))\n )\n\n\n#\ndef get_gaussian_mean(x, axis, other_axis, softmax=True):\n \"\"\"\n\n Args:\n x (float): Input images(BxCxHxW)\n axis (int): The index for weighted mean\n other_axis (int): The other index\n\n Returns: weighted index for axis, BxC\n\n \"\"\"\n mat2line = torch.sum(x, axis=other_axis)\n # mat2line = mat2line / mat2line.mean() * 10\n if softmax:\n u = torch.softmax(mat2line, axis=2)\n else:\n u = mat2line / (mat2line.sum(2, keepdim=True) + 1e-6)\n size = x.shape[axis]\n ind = torch.linspace(0, 1, size).to(x.device)\n batch = x.shape[0]\n channel = x.shape[1]\n index = ind.repeat([batch, channel, 1])\n mean_position = torch.sum(index * u, dim=2)\n return mean_position\n\n\ndef get_expected_points_from_map(hm, softmax=True):\n \"\"\"get_gaussian_map_from_points\n B,C,H,W -> B,N,2 float(0, 1) float(0, 1)\n softargmax function\n\n Args:\n hm (float): Input images(BxCxHxW)\n\n Returns:\n weighted index for axis, BxCx2. float between 0 and 1.\n\n \"\"\"\n # hm = 10*hm\n B, C, H, W = hm.shape\n y_mean = get_gaussian_mean(hm, 2, 3, softmax=softmax) # B,C\n x_mean = get_gaussian_mean(hm, 3, 2, softmax=softmax) # B,C\n # return torch.cat((x_mean.unsqueeze(-1), y_mean.unsqueeze(-1)), 2)\n return torch.stack([x_mean, y_mean], dim=2)\n\n\n# Positional encoding (section 5.1)\n# borrow from nerf\nclass Embedder:\n def __init__(self, **kwargs):\n self.kwargs = kwargs\n self.create_embedding_fn()\n\n def create_embedding_fn(self):\n embed_fns = []\n d = self.kwargs[\"input_dims\"]\n out_dim = 0\n if self.kwargs[\"include_input\"]:\n embed_fns.append(lambda x: x)\n out_dim += d\n\n max_freq = self.kwargs[\"max_freq_log2\"]\n N_freqs = self.kwargs[\"num_freqs\"]\n\n if self.kwargs[\"log_sampling\"]:\n freq_bands = 2.0 ** torch.linspace(0.0, max_freq, steps=N_freqs)\n else:\n freq_bands = torch.linspace(2.0**0.0, 2.0**max_freq, steps=N_freqs)\n\n for freq in freq_bands:\n for p_fn in self.kwargs[\"periodic_fns\"]:\n embed_fns.append(lambda x, p_fn=p_fn, freq=freq: p_fn(x * freq))\n out_dim += d\n\n self.embed_fns = embed_fns\n self.out_dim = out_dim\n\n def embed(self, inputs):\n return torch.cat([fn(inputs) for fn in self.embed_fns], -1)\n\n\ndef get_embedder(multires, i=0):\n import torch.nn as nn\n\n if i == -1:\n return nn.Identity(), 3\n\n embed_kwargs = {\n \"include_input\": True,\n \"input_dims\": 3,\n \"max_freq_log2\": multires - 1,\n \"num_freqs\": multires,\n \"log_sampling\": True,\n \"periodic_fns\": [torch.sin, torch.cos],\n }\n\n embedder_obj = Embedder(**embed_kwargs)\n embed = lambda x, eo=embedder_obj: eo.embed(x)\n return embed, embedder_obj.out_dim\n\n\nclass APOPMeter:\n def __init__(self) -> None:\n self.tp = 0\n self.fp = 0\n self.tn = 0\n self.fn = 0\n\n def update(self, pred, gt):\n \"\"\"\n Input:\n pred, gt: Tensor()\n \"\"\"\n assert pred.shape == gt.shape\n self.tp += torch.logical_and(pred == 1, gt == 1).sum().item()\n self.fp += torch.logical_and(pred == 1, gt == 0).sum().item()\n self.tn += torch.logical_and(pred == 0, gt == 0).sum().item()\n self.tn += torch.logical_and(pred == 1, gt == 0).sum().item()\n\n def update_cm(self, tp, fp, tn, fn):\n self.tp += tp\n self.fp += fp\n self.tn += tn\n self.tn += fn\n\n\ndef inverse_sigmoid(x, eps=1e-5):\n x = x.clamp(min=0, max=1)\n x1 = x.clamp(min=eps)\n x2 = (1 - x).clamp(min=eps)\n return torch.log(x1 / x2)\n\n\ndef get_raw_dict(args):\n \"\"\"\n return the dicf contained in args.\n\n e.g:\n >>> with open(path, 'w') as f:\n json.dump(get_raw_dict(args), f, indent=2)\n \"\"\"\n if isinstance(args, argparse.Namespace):\n return vars(args)\n elif isinstance(args, dict):\n return args\n elif isinstance(args, SLConfig):\n return args._cfg_dict\n else:\n raise NotImplementedError(\"Unknown type {}\".format(type(args)))\n\n\ndef stat_tensors(tensor):\n assert tensor.dim() == 1\n tensor_sm = tensor.softmax(0)\n entropy = (tensor_sm * torch.log(tensor_sm + 1e-9)).sum()\n\n return {\n \"max\": tensor.max(),\n \"min\": tensor.min(),\n \"mean\": tensor.mean(),\n \"var\": tensor.var(),\n \"std\": tensor.var() ** 0.5,\n \"entropy\": entropy,\n }\n\n\nclass NiceRepr:\n \"\"\"Inherit from this class and define ``__nice__`` to \"nicely\" print your\n objects.\n\n Defines ``__str__`` and ``__repr__`` in terms of ``__nice__`` function\n Classes that inherit from :class:`NiceRepr` should redefine ``__nice__``.\n If the inheriting class has a ``__len__``, method then the default\n ``__nice__`` method will return its length.\n\n Example:\n >>> class Foo(NiceRepr):\n ... def __nice__(self):\n ... return 'info'\n >>> foo = Foo()\n >>> assert str(foo) == ''\n >>> assert repr(foo).startswith('>> class Bar(NiceRepr):\n ... pass\n >>> bar = Bar()\n >>> import pytest\n >>> with pytest.warns(None) as record:\n >>> assert 'object at' in str(bar)\n >>> assert 'object at' in repr(bar)\n\n Example:\n >>> class Baz(NiceRepr):\n ... def __len__(self):\n ... return 5\n >>> baz = Baz()\n >>> assert str(baz) == ''\n \"\"\"\n\n def __nice__(self):\n \"\"\"str: a \"nice\" summary string describing this module\"\"\"\n if hasattr(self, \"__len__\"):\n # It is a common pattern for objects to use __len__ in __nice__\n # As a convenience we define a default __nice__ for these objects\n return str(len(self))\n else:\n # In all other cases force the subclass to overload __nice__\n raise NotImplementedError(f\"Define the __nice__ method for {self.__class__!r}\")\n\n def __repr__(self):\n \"\"\"str: the string of the module\"\"\"\n try:\n nice = self.__nice__()\n classname = self.__class__.__name__\n return f\"<{classname}({nice}) at {hex(id(self))}>\"\n except NotImplementedError as ex:\n warnings.warn(str(ex), category=RuntimeWarning)\n return object.__repr__(self)\n\n def __str__(self):\n \"\"\"str: the string of the module\"\"\"\n try:\n classname = self.__class__.__name__\n nice = self.__nice__()\n return f\"<{classname}({nice})>\"\n except NotImplementedError as ex:\n warnings.warn(str(ex), category=RuntimeWarning)\n return object.__repr__(self)\n\n\ndef ensure_rng(rng=None):\n \"\"\"Coerces input into a random number generator.\n\n If the input is None, then a global random state is returned.\n\n If the input is a numeric value, then that is used as a seed to construct a\n random state. Otherwise the input is returned as-is.\n\n Adapted from [1]_.\n\n Args:\n rng (int | numpy.random.RandomState | None):\n if None, then defaults to the global rng. Otherwise this can be an\n integer or a RandomState class\n Returns:\n (numpy.random.RandomState) : rng -\n a numpy random number generator\n\n References:\n .. [1] https://gitlab.kitware.com/computer-vision/kwarray/blob/master/kwarray/util_random.py#L270 # noqa: E501\n \"\"\"\n\n if rng is None:\n rng = np.random.mtrand._rand\n elif isinstance(rng, int):\n rng = np.random.RandomState(rng)\n else:\n rng = rng\n return rng\n\n\ndef random_boxes(num=1, scale=1, rng=None):\n \"\"\"Simple version of ``kwimage.Boxes.random``\n\n Returns:\n Tensor: shape (n, 4) in x1, y1, x2, y2 format.\n\n References:\n https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390\n\n Example:\n >>> num = 3\n >>> scale = 512\n >>> rng = 0\n >>> boxes = random_boxes(num, scale, rng)\n >>> print(boxes)\n tensor([[280.9925, 278.9802, 308.6148, 366.1769],\n [216.9113, 330.6978, 224.0446, 456.5878],\n [405.3632, 196.3221, 493.3953, 270.7942]])\n \"\"\"\n rng = ensure_rng(rng)\n\n tlbr = rng.rand(num, 4).astype(np.float32)\n\n tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2])\n tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3])\n br_x = np.maximum(tlbr[:, 0], tlbr[:, 2])\n br_y = np.maximum(tlbr[:, 1], tlbr[:, 3])\n\n tlbr[:, 0] = tl_x * scale\n tlbr[:, 1] = tl_y * scale\n tlbr[:, 2] = br_x * scale\n tlbr[:, 3] = br_y * scale\n\n boxes = torch.from_numpy(tlbr)\n return boxes\n\n\nclass ModelEma(torch.nn.Module):\n def __init__(self, model, decay=0.9997, device=None):\n super(ModelEma, self).__init__()\n # make a copy of the model for accumulating moving average of weights\n self.module = deepcopy(model)\n self.module.eval()\n\n # import ipdb; ipdb.set_trace()\n\n self.decay = decay\n self.device = device # perform ema on different device from model if set\n if self.device is not None:\n self.module.to(device=device)\n\n def _update(self, model, update_fn):\n with torch.no_grad():\n for ema_v, model_v in zip(\n self.module.state_dict().values(), model.state_dict().values()\n ):\n if self.device is not None:\n model_v = model_v.to(device=self.device)\n ema_v.copy_(update_fn(ema_v, model_v))\n\n def update(self, model):\n self._update(model, update_fn=lambda e, m: self.decay * e + (1.0 - self.decay) * m)\n\n def set(self, model):\n self._update(model, update_fn=lambda e, m: m)\n\n\nclass BestMetricSingle:\n def __init__(self, init_res=0.0, better=\"large\") -> None:\n self.init_res = init_res\n self.best_res = init_res\n self.best_ep = -1\n\n self.better = better\n assert better in [\"large\", \"small\"]\n\n def isbetter(self, new_res, old_res):\n if self.better == \"large\":\n return new_res > old_res\n if self.better == \"small\":\n return new_res < old_res\n\n def update(self, new_res, ep):\n if self.isbetter(new_res, self.best_res):\n self.best_res = new_res\n self.best_ep = ep\n return True\n return False\n\n def __str__(self) -> str:\n return \"best_res: {}\\t best_ep: {}\".format(self.best_res, self.best_ep)\n\n def __repr__(self) -> str:\n return self.__str__()\n\n def summary(self) -> dict:\n return {\n \"best_res\": self.best_res,\n \"best_ep\": self.best_ep,\n }\n\n\nclass BestMetricHolder:\n def __init__(self, init_res=0.0, better=\"large\", use_ema=False) -> None:\n self.best_all = BestMetricSingle(init_res, better)\n self.use_ema = use_ema\n if use_ema:\n self.best_ema = BestMetricSingle(init_res, better)\n self.best_regular = BestMetricSingle(init_res, better)\n\n def update(self, new_res, epoch, is_ema=False):\n \"\"\"\n return if the results is the best.\n \"\"\"\n if not self.use_ema:\n return self.best_all.update(new_res, epoch)\n else:\n if is_ema:\n self.best_ema.update(new_res, epoch)\n return self.best_all.update(new_res, epoch)\n else:\n self.best_regular.update(new_res, epoch)\n return self.best_all.update(new_res, epoch)\n\n def summary(self):\n if not self.use_ema:\n return self.best_all.summary()\n\n res = {}\n res.update({f\"all_{k}\": v for k, v in self.best_all.summary().items()})\n res.update({f\"regular_{k}\": v for k, v in self.best_regular.summary().items()})\n res.update({f\"ema_{k}\": v for k, v in self.best_ema.summary().items()})\n return res\n\n def __repr__(self) -> str:\n return json.dumps(self.summary(), indent=2)\n\n def __str__(self) -> str:\n return self.__repr__()\n\n\ndef targets_to(targets: List[Dict[str, Any]], device):\n \"\"\"Moves the target dicts to the given device.\"\"\"\n excluded_keys = [\n \"questionId\",\n \"tokens_positive\",\n \"strings_positive\",\n \"tokens\",\n \"dataset_name\",\n \"sentence_id\",\n \"original_img_id\",\n \"nb_eval\",\n \"task_id\",\n \"original_id\",\n \"token_span\",\n \"caption\",\n \"dataset_type\",\n ]\n return [\n {k: v.to(device) if k not in excluded_keys else v for k, v in t.items()} for t in targets\n ]\n\n\ndef get_phrases_from_posmap(\n posmap: torch.BoolTensor, tokenized: Dict, tokenizer: AutoTokenizer\n):\n assert isinstance(posmap, torch.Tensor), \"posmap must be torch.Tensor\"\n if posmap.dim() == 1:\n non_zero_idx = posmap.nonzero(as_tuple=True)[0].tolist()\n token_ids = [tokenized[\"input_ids\"][i] for i in non_zero_idx]\n return tokenizer.decode(token_ids)\n else:\n raise NotImplementedError(\"posmap must be 1-dim\")\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/util/visualizer.py", "content": "# -*- coding: utf-8 -*-\n\"\"\"\n@File : visualizer.py\n@Time : 2022/04/05 11:39:33\n@Author : Shilong Liu \n@Contact : slongliu86@gmail.com\n\"\"\"\n\nimport datetime\nimport os\n\nimport cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nfrom matplotlib import transforms\nfrom matplotlib.collections import PatchCollection\nfrom matplotlib.patches import Polygon\nfrom pycocotools import mask as maskUtils\n\n\ndef renorm(\n img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]\n) -> torch.FloatTensor:\n # img: tensor(3,H,W) or tensor(B,3,H,W)\n # return: same as img\n assert img.dim() == 3 or img.dim() == 4, \"img.dim() should be 3 or 4 but %d\" % img.dim()\n if img.dim() == 3:\n assert img.size(0) == 3, 'img.size(0) shoule be 3 but \"%d\". (%s)' % (\n img.size(0),\n str(img.size()),\n )\n img_perm = img.permute(1, 2, 0)\n mean = torch.Tensor(mean)\n std = torch.Tensor(std)\n img_res = img_perm * std + mean\n return img_res.permute(2, 0, 1)\n else: # img.dim() == 4\n assert img.size(1) == 3, 'img.size(1) shoule be 3 but \"%d\". (%s)' % (\n img.size(1),\n str(img.size()),\n )\n img_perm = img.permute(0, 2, 3, 1)\n mean = torch.Tensor(mean)\n std = torch.Tensor(std)\n img_res = img_perm * std + mean\n return img_res.permute(0, 3, 1, 2)\n\n\nclass ColorMap:\n def __init__(self, basergb=[255, 255, 0]):\n self.basergb = np.array(basergb)\n\n def __call__(self, attnmap):\n # attnmap: h, w. np.uint8.\n # return: h, w, 4. np.uint8.\n assert attnmap.dtype == np.uint8\n h, w = attnmap.shape\n res = self.basergb.copy()\n res = res[None][None].repeat(h, 0).repeat(w, 1) # h, w, 3\n attn1 = attnmap.copy()[..., None] # h, w, 1\n res = np.concatenate((res, attn1), axis=-1).astype(np.uint8)\n return res\n\n\ndef rainbow_text(x, y, ls, lc, **kw):\n \"\"\"\n Take a list of strings ``ls`` and colors ``lc`` and place them next to each\n other, with text ls[i] being shown in color lc[i].\n\n This example shows how to do both vertical and horizontal text, and will\n pass all keyword arguments to plt.text, so you can set the font size,\n family, etc.\n \"\"\"\n t = plt.gca().transData\n fig = plt.gcf()\n plt.show()\n\n # horizontal version\n for s, c in zip(ls, lc):\n text = plt.text(x, y, \" \" + s + \" \", color=c, transform=t, **kw)\n text.draw(fig.canvas.get_renderer())\n ex = text.get_window_extent()\n t = transforms.offset_copy(text._transform, x=ex.width, units=\"dots\")\n\n # #vertical version\n # for s,c in zip(ls,lc):\n # text = plt.text(x,y,\" \"+s+\" \",color=c, transform=t,\n # rotation=90,va='bottom',ha='center',**kw)\n # text.draw(fig.canvas.get_renderer())\n # ex = text.get_window_extent()\n # t = transforms.offset_copy(text._transform, y=ex.height, units='dots')\n\n\nclass COCOVisualizer:\n def __init__(self, coco=None, tokenlizer=None) -> None:\n self.coco = coco\n\n def visualize(self, img, tgt, caption=None, dpi=180, savedir=\"vis\"):\n \"\"\"\n img: tensor(3, H, W)\n tgt: make sure they are all on cpu.\n must have items: 'image_id', 'boxes', 'size'\n \"\"\"\n plt.figure(dpi=dpi)\n plt.rcParams[\"font.size\"] = \"5\"\n ax = plt.gca()\n img = renorm(img).permute(1, 2, 0)\n # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':\n # import ipdb; ipdb.set_trace()\n ax.imshow(img)\n\n self.addtgt(tgt)\n\n if tgt is None:\n image_id = 0\n elif \"image_id\" not in tgt:\n image_id = 0\n else:\n image_id = tgt[\"image_id\"]\n\n if caption is None:\n savename = \"{}/{}-{}.png\".format(\n savedir, int(image_id), str(datetime.datetime.now()).replace(\" \", \"-\")\n )\n else:\n savename = \"{}/{}-{}-{}.png\".format(\n savedir, caption, int(image_id), str(datetime.datetime.now()).replace(\" \", \"-\")\n )\n print(\"savename: {}\".format(savename))\n os.makedirs(os.path.dirname(savename), exist_ok=True)\n plt.savefig(savename)\n plt.close()\n\n def addtgt(self, tgt):\n \"\"\" \"\"\"\n if tgt is None or not \"boxes\" in tgt:\n ax = plt.gca()\n\n if \"caption\" in tgt:\n ax.set_title(tgt[\"caption\"], wrap=True)\n\n ax.set_axis_off()\n return\n\n ax = plt.gca()\n H, W = tgt[\"size\"]\n numbox = tgt[\"boxes\"].shape[0]\n\n color = []\n polygons = []\n boxes = []\n for box in tgt[\"boxes\"].cpu():\n unnormbbox = box * torch.Tensor([W, H, W, H])\n unnormbbox[:2] -= unnormbbox[2:] / 2\n [bbox_x, bbox_y, bbox_w, bbox_h] = unnormbbox.tolist()\n boxes.append([bbox_x, bbox_y, bbox_w, bbox_h])\n poly = [\n [bbox_x, bbox_y],\n [bbox_x, bbox_y + bbox_h],\n [bbox_x + bbox_w, bbox_y + bbox_h],\n [bbox_x + bbox_w, bbox_y],\n ]\n np_poly = np.array(poly).reshape((4, 2))\n polygons.append(Polygon(np_poly))\n c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]\n color.append(c)\n\n p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.1)\n ax.add_collection(p)\n p = PatchCollection(polygons, facecolor=\"none\", edgecolors=color, linewidths=2)\n ax.add_collection(p)\n\n if \"strings_positive\" in tgt and len(tgt[\"strings_positive\"]) > 0:\n assert (\n len(tgt[\"strings_positive\"]) == numbox\n ), f\"{len(tgt['strings_positive'])} = {numbox}, \"\n for idx, strlist in enumerate(tgt[\"strings_positive\"]):\n cate_id = int(tgt[\"labels\"][idx])\n _string = str(cate_id) + \":\" + \" \".join(strlist)\n bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx]\n # ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1})\n ax.text(\n bbox_x,\n bbox_y,\n _string,\n color=\"black\",\n bbox={\"facecolor\": color[idx], \"alpha\": 0.6, \"pad\": 1},\n )\n\n if \"box_label\" in tgt:\n assert len(tgt[\"box_label\"]) == numbox, f\"{len(tgt['box_label'])} = {numbox}, \"\n for idx, bl in enumerate(tgt[\"box_label\"]):\n _string = str(bl)\n bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx]\n # ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1})\n ax.text(\n bbox_x,\n bbox_y,\n _string,\n color=\"black\",\n bbox={\"facecolor\": color[idx], \"alpha\": 0.6, \"pad\": 1},\n )\n\n if \"caption\" in tgt:\n ax.set_title(tgt[\"caption\"], wrap=True)\n # plt.figure()\n # rainbow_text(0.0,0.0,\"all unicorns poop rainbows ! ! !\".split(),\n # ['red', 'orange', 'brown', 'green', 'blue', 'purple', 'black'])\n\n if \"attn\" in tgt:\n # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':\n # import ipdb; ipdb.set_trace()\n if isinstance(tgt[\"attn\"], tuple):\n tgt[\"attn\"] = [tgt[\"attn\"]]\n for item in tgt[\"attn\"]:\n attn_map, basergb = item\n attn_map = (attn_map - attn_map.min()) / (attn_map.max() - attn_map.min() + 1e-3)\n attn_map = (attn_map * 255).astype(np.uint8)\n cm = ColorMap(basergb)\n heatmap = cm(attn_map)\n ax.imshow(heatmap)\n ax.set_axis_off()\n\n def showAnns(self, anns, draw_bbox=False):\n \"\"\"\n Display the specified annotations.\n :param anns (array of object): annotations to display\n :return: None\n \"\"\"\n if len(anns) == 0:\n return 0\n if \"segmentation\" in anns[0] or \"keypoints\" in anns[0]:\n datasetType = \"instances\"\n elif \"caption\" in anns[0]:\n datasetType = \"captions\"\n else:\n raise Exception(\"datasetType not supported\")\n if datasetType == \"instances\":\n ax = plt.gca()\n ax.set_autoscale_on(False)\n polygons = []\n color = []\n for ann in anns:\n c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]\n if \"segmentation\" in ann:\n if type(ann[\"segmentation\"]) == list:\n # polygon\n for seg in ann[\"segmentation\"]:\n poly = np.array(seg).reshape((int(len(seg) / 2), 2))\n polygons.append(Polygon(poly))\n color.append(c)\n else:\n # mask\n t = self.imgs[ann[\"image_id\"]]\n if type(ann[\"segmentation\"][\"counts\"]) == list:\n rle = maskUtils.frPyObjects(\n [ann[\"segmentation\"]], t[\"height\"], t[\"width\"]\n )\n else:\n rle = [ann[\"segmentation\"]]\n m = maskUtils.decode(rle)\n img = np.ones((m.shape[0], m.shape[1], 3))\n if ann[\"iscrowd\"] == 1:\n color_mask = np.array([2.0, 166.0, 101.0]) / 255\n if ann[\"iscrowd\"] == 0:\n color_mask = np.random.random((1, 3)).tolist()[0]\n for i in range(3):\n img[:, :, i] = color_mask[i]\n ax.imshow(np.dstack((img, m * 0.5)))\n if \"keypoints\" in ann and type(ann[\"keypoints\"]) == list:\n # turn skeleton into zero-based index\n sks = np.array(self.loadCats(ann[\"category_id\"])[0][\"skeleton\"]) - 1\n kp = np.array(ann[\"keypoints\"])\n x = kp[0::3]\n y = kp[1::3]\n v = kp[2::3]\n for sk in sks:\n if np.all(v[sk] > 0):\n plt.plot(x[sk], y[sk], linewidth=3, color=c)\n plt.plot(\n x[v > 0],\n y[v > 0],\n \"o\",\n markersize=8,\n markerfacecolor=c,\n markeredgecolor=\"k\",\n markeredgewidth=2,\n )\n plt.plot(\n x[v > 1],\n y[v > 1],\n \"o\",\n markersize=8,\n markerfacecolor=c,\n markeredgecolor=c,\n markeredgewidth=2,\n )\n\n if draw_bbox:\n [bbox_x, bbox_y, bbox_w, bbox_h] = ann[\"bbox\"]\n poly = [\n [bbox_x, bbox_y],\n [bbox_x, bbox_y + bbox_h],\n [bbox_x + bbox_w, bbox_y + bbox_h],\n [bbox_x + bbox_w, bbox_y],\n ]\n np_poly = np.array(poly).reshape((4, 2))\n polygons.append(Polygon(np_poly))\n color.append(c)\n\n # p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4)\n # ax.add_collection(p)\n p = PatchCollection(polygons, facecolor=\"none\", edgecolors=color, linewidths=2)\n ax.add_collection(p)\n elif datasetType == \"captions\":\n for ann in anns:\n print(ann[\"caption\"])\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/util/vl_utils.py", "content": "import os\nimport random\nfrom typing import List\n\nimport torch\n\n\ndef create_positive_map_from_span(tokenized, token_span, max_text_len=256):\n \"\"\"construct a map such that positive_map[i,j] = True iff box i is associated to token j\n Input:\n - tokenized:\n - input_ids: Tensor[1, ntokens]\n - attention_mask: Tensor[1, ntokens]\n - token_span: list with length num_boxes.\n - each item: [start_idx, end_idx]\n \"\"\"\n positive_map = torch.zeros((len(token_span), max_text_len), dtype=torch.float)\n for j, tok_list in enumerate(token_span):\n for (beg, end) in tok_list:\n beg_pos = tokenized.char_to_token(beg)\n end_pos = tokenized.char_to_token(end - 1)\n if beg_pos is None:\n try:\n beg_pos = tokenized.char_to_token(beg + 1)\n if beg_pos is None:\n beg_pos = tokenized.char_to_token(beg + 2)\n except:\n beg_pos = None\n if end_pos is None:\n try:\n end_pos = tokenized.char_to_token(end - 2)\n if end_pos is None:\n end_pos = tokenized.char_to_token(end - 3)\n except:\n end_pos = None\n if beg_pos is None or end_pos is None:\n continue\n\n assert beg_pos is not None and end_pos is not None\n if os.environ.get(\"SHILONG_DEBUG_ONLY_ONE_POS\", None) == \"TRUE\":\n positive_map[j, beg_pos] = 1\n break\n else:\n positive_map[j, beg_pos : end_pos + 1].fill_(1)\n\n return positive_map / (positive_map.sum(-1)[:, None] + 1e-6)\n\n\ndef build_captions_and_token_span(cat_list, force_lowercase):\n \"\"\"\n Return:\n captions: str\n cat2tokenspan: dict\n {\n 'dog': [[0, 2]],\n ...\n }\n \"\"\"\n\n cat2tokenspan = {}\n captions = \"\"\n for catname in cat_list:\n class_name = catname\n if force_lowercase:\n class_name = class_name.lower()\n if \"/\" in class_name:\n class_name_list: List = class_name.strip().split(\"/\")\n class_name_list.append(class_name)\n class_name: str = random.choice(class_name_list)\n\n tokens_positive_i = []\n subnamelist = [i.strip() for i in class_name.strip().split(\" \")]\n for subname in subnamelist:\n if len(subname) == 0:\n continue\n if len(captions) > 0:\n captions = captions + \" \"\n strat_idx = len(captions)\n end_idx = strat_idx + len(subname)\n tokens_positive_i.append([strat_idx, end_idx])\n captions = captions + subname\n\n if len(tokens_positive_i) > 0:\n captions = captions + \" .\"\n cat2tokenspan[class_name] = tokens_positive_i\n\n return captions, cat2tokenspan\n\n\ndef build_id2posspan_and_caption(category_dict: dict):\n \"\"\"Build id2pos_span and caption from category_dict\n\n Args:\n category_dict (dict): category_dict\n \"\"\"\n cat_list = [item[\"name\"].lower() for item in category_dict]\n id2catname = {item[\"id\"]: item[\"name\"].lower() for item in category_dict}\n caption, cat2posspan = build_captions_and_token_span(cat_list, force_lowercase=True)\n id2posspan = {catid: cat2posspan[catname] for catid, catname in id2catname.items()}\n return id2posspan, caption\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/groundingdino/version.py", "content": "__version__ = '0.1.0'\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/pyproject.toml", "content": "[build-system]\nrequires = [\n \"setuptools\",\n \"torch\",\n \"wheel\",\n \"torch\"\n]\nbuild-backend = \"setuptools.build_meta\"\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/requirements.txt", "content": "torch\ntorchvision\ntransformers\naddict\nyapf\ntimm\nnumpy\nopencv-python\nsupervision\npycocotools" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/setup.py", "content": "# coding=utf-8\n# Copyright 2022 The IDEA Authors. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ------------------------------------------------------------------------------------------------\n# Modified from\n# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/setup.py\n# https://github.com/facebookresearch/detectron2/blob/main/setup.py\n# https://github.com/open-mmlab/mmdetection/blob/master/setup.py\n# https://github.com/Oneflow-Inc/libai/blob/main/setup.py\n# ------------------------------------------------------------------------------------------------\n\nimport glob\nimport os\nimport subprocess\n\nimport torch\nfrom setuptools import find_packages, setup\nfrom torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension\n\n# groundingdino version info\nversion = \"0.1.0\"\npackage_name = \"groundingdino\"\ncwd = os.path.dirname(os.path.abspath(__file__))\n\n\nsha = \"Unknown\"\ntry:\n sha = subprocess.check_output([\"git\", \"rev-parse\", \"HEAD\"], cwd=cwd).decode(\"ascii\").strip()\nexcept Exception:\n pass\n\n\ndef write_version_file():\n version_path = os.path.join(cwd, \"groundingdino\", \"version.py\")\n with open(version_path, \"w\") as f:\n f.write(f\"__version__ = '{version}'\\n\")\n # f.write(f\"git_version = {repr(sha)}\\n\")\n\n\nrequirements = [\"torch\", \"torchvision\"]\n\ntorch_ver = [int(x) for x in torch.__version__.split(\".\")[:2]]\n\n\ndef get_extensions():\n this_dir = os.path.dirname(os.path.abspath(__file__))\n extensions_dir = os.path.join(this_dir, \"groundingdino\", \"models\", \"GroundingDINO\", \"csrc\")\n\n main_source = os.path.join(extensions_dir, \"vision.cpp\")\n sources = glob.glob(os.path.join(extensions_dir, \"**\", \"*.cpp\"))\n source_cuda = glob.glob(os.path.join(extensions_dir, \"**\", \"*.cu\")) + glob.glob(\n os.path.join(extensions_dir, \"*.cu\")\n )\n\n sources = [main_source] + sources\n\n # We need these variables to build with CUDA when we create the Docker image\n # It solves https://github.com/IDEA-Research/Grounded-Segment-Anything/issues/53\n # and https://github.com/IDEA-Research/Grounded-Segment-Anything/issues/84 when running\n # inside a Docker container.\n am_i_docker = os.environ.get('AM_I_DOCKER', '').casefold() in ['true', '1', 't']\n use_cuda = os.environ.get('BUILD_WITH_CUDA', '').casefold() in ['true', '1', 't']\n\n extension = CppExtension\n\n extra_compile_args = {\"cxx\": []}\n define_macros = []\n\n if (torch.cuda.is_available() and CUDA_HOME is not None) or \\\n (am_i_docker and use_cuda):\n print(\"Compiling with CUDA\")\n extension = CUDAExtension\n sources += source_cuda\n define_macros += [(\"WITH_CUDA\", None)]\n extra_compile_args[\"nvcc\"] = [\n \"-DCUDA_HAS_FP16=1\",\n \"-D__CUDA_NO_HALF_OPERATORS__\",\n \"-D__CUDA_NO_HALF_CONVERSIONS__\",\n \"-D__CUDA_NO_HALF2_OPERATORS__\",\n ]\n else:\n print(\"Compiling without CUDA\")\n define_macros += [(\"WITH_HIP\", None)]\n extra_compile_args[\"nvcc\"] = []\n return None\n\n sources = [os.path.join(extensions_dir, s) for s in sources]\n include_dirs = [extensions_dir]\n\n ext_modules = [\n extension(\n \"groundingdino._C\",\n sources,\n include_dirs=include_dirs,\n define_macros=define_macros,\n extra_compile_args=extra_compile_args,\n )\n ]\n\n return ext_modules\n\n\ndef parse_requirements(fname=\"requirements.txt\", with_version=True):\n \"\"\"Parse the package dependencies listed in a requirements file but strips\n specific versioning information.\n\n Args:\n fname (str): path to requirements file\n with_version (bool, default=False): if True include version specs\n\n Returns:\n List[str]: list of requirements items\n\n CommandLine:\n python -c \"import setup; print(setup.parse_requirements())\"\n \"\"\"\n import re\n import sys\n from os.path import exists\n\n require_fpath = fname\n\n def parse_line(line):\n \"\"\"Parse information from a line in a requirements text file.\"\"\"\n if line.startswith(\"-r \"):\n # Allow specifying requirements in other files\n target = line.split(\" \")[1]\n for info in parse_require_file(target):\n yield info\n else:\n info = {\"line\": line}\n if line.startswith(\"-e \"):\n info[\"package\"] = line.split(\"#egg=\")[1]\n elif \"@git+\" in line:\n info[\"package\"] = line\n else:\n # Remove versioning from the package\n pat = \"(\" + \"|\".join([\">=\", \"==\", \">\"]) + \")\"\n parts = re.split(pat, line, maxsplit=1)\n parts = [p.strip() for p in parts]\n\n info[\"package\"] = parts[0]\n if len(parts) > 1:\n op, rest = parts[1:]\n if \";\" in rest:\n # Handle platform specific dependencies\n # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies\n version, platform_deps = map(str.strip, rest.split(\";\"))\n info[\"platform_deps\"] = platform_deps\n else:\n version = rest # NOQA\n info[\"version\"] = (op, version)\n yield info\n\n def parse_require_file(fpath):\n with open(fpath, \"r\") as f:\n for line in f.readlines():\n line = line.strip()\n if line and not line.startswith(\"#\"):\n for info in parse_line(line):\n yield info\n\n def gen_packages_items():\n if exists(require_fpath):\n for info in parse_require_file(require_fpath):\n parts = [info[\"package\"]]\n if with_version and \"version\" in info:\n parts.extend(info[\"version\"])\n if not sys.version.startswith(\"3.4\"):\n # apparently package_deps are broken in 3.4\n platform_deps = info.get(\"platform_deps\")\n if platform_deps is not None:\n parts.append(\";\" + platform_deps)\n item = \"\".join(parts)\n yield item\n\n packages = list(gen_packages_items())\n return packages\n\n\nif __name__ == \"__main__\":\n print(f\"Building wheel {package_name}-{version}\")\n\n with open(\"LICENSE\", \"r\", encoding=\"utf-8\") as f:\n license = f.read()\n\n write_version_file()\n\n setup(\n name=\"groundingdino\",\n version=\"0.1.0\",\n author=\"International Digital Economy Academy, Shilong Liu\",\n url=\"https://github.com/IDEA-Research/GroundingDINO\",\n description=\"open-set object detector\",\n license=license,\n install_requires=parse_requirements(\"requirements.txt\"),\n packages=find_packages(\n exclude=(\n \"configs\",\n \"tests\",\n )\n ),\n ext_modules=get_extensions(),\n cmdclass={\"build_ext\": torch.utils.cpp_extension.BuildExtension},\n )\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/sub_8_new_83748.err", "content": "slurmstepd: error: *** JOB 83748 ON dgx-hyperplane18 CANCELLED AT 2024-04-16T17:44:38 ***\n" }, { "path": "data_generation/Grounded-Segment-Anything/GroundingDINO/submit_gpu_task_83747.err", "content": "slurmstepd: error: *** JOB 83747 ON dgx-hyperplane16 CANCELLED AT 2024-04-10T01:07:12 ***\n" }, { "path": "data_generation/Grounded-Segment-Anything/LICENSE", "content": " Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n\n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n 1. Definitions.\n\n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n\n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n\n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. For the purposes of this definition,\n \"control\" means (i) the power, direct or indirect, to cause the\n direction or management of such entity, whether by contract or\n otherwise, or (ii) ownership of fifty percent (50%) or more of the\n outstanding shares, or (iii) beneficial ownership of such entity.\n\n \"You\" (or \"Your\") shall mean an individual or Legal Entity\n exercising permissions granted by this License.\n\n \"Source\" form shall mean the preferred form for making modifications,\n including but not limited to software source code, documentation\n source, and configuration files.\n\n \"Object\" form shall mean any form resulting from mechanical\n transformation or translation of a Source form, including but\n not limited to compiled object code, generated documentation,\n and conversions to other media types.\n\n \"Work\" shall mean the work of authorship, whether in Source or\n Object form, made available under the License, as indicated by a\n copyright notice that is included in or attached to the work\n (an example is provided in the Appendix below).\n\n \"Derivative Works\" shall mean any work, whether in Source or Object\n form, that is based on (or derived from) the Work and for which the\n editorial revisions, annotations, elaborations, or other modifications\n represent, as a whole, an original work of authorship. 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We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n\n Copyright 2020 - present, IDEA, Inc\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n" }, { "path": "data_generation/Grounded-Segment-Anything/Makefile", "content": "# Get version of CUDA and enable it for compilation if CUDA > 11.0\n# This solves https://github.com/IDEA-Research/Grounded-Segment-Anything/issues/53\n# and https://github.com/IDEA-Research/Grounded-Segment-Anything/issues/84\n# when running in Docker\n# Check if nvcc is installed\nNVCC := $(shell which nvcc)\nifeq ($(NVCC),)\n\t# NVCC not found\n\tUSE_CUDA := 0\n\tNVCC_VERSION := \"not installed\"\nelse\n\tNVCC_VERSION := $(shell nvcc --version | grep -oP 'release \\K[0-9.]+')\n\tUSE_CUDA := $(shell echo \"$(NVCC_VERSION) > 11\" | bc -l)\nendif\n\n# Add the list of supported ARCHs\nifeq ($(USE_CUDA), 1)\n\tTORCH_CUDA_ARCH_LIST := \"3.5;5.0;6.0;6.1;7.0;7.5;8.0;8.6+PTX\"\n\tBUILD_MESSAGE := \"I will try to build the image with CUDA support\"\nelse\n\tTORCH_CUDA_ARCH_LIST :=\n\tBUILD_MESSAGE := \"CUDA $(NVCC_VERSION) is not supported\"\nendif\n\n\nbuild-image:\n\t@echo $(BUILD_MESSAGE)\n\tdocker build --build-arg USE_CUDA=$(USE_CUDA) \\\n\t--build-arg TORCH_ARCH=$(TORCH_CUDA_ARCH_LIST) \\\n\t-t gsa:v0 .\nrun:\nifeq (,$(wildcard ./sam_vit_h_4b8939.pth))\n\twget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\nendif\nifeq (,$(wildcard ./groundingdino_swint_ogc.pth))\n\twget https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth\nendif\n\tdocker run --gpus all -it --rm --net=host --privileged \\\n\t-v /tmp/.X11-unix:/tmp/.X11-unix \\\n\t-v \"${PWD}\":/home/appuser/Grounded-Segment-Anything \\\n\t-e DISPLAY=$DISPLAY \\\n\t--name=gsa \\\n\t--ipc=host -it gsa:v0\n" }, { "path": "data_generation/Grounded-Segment-Anything/README.md", "content": "![](./assets/Grounded-SAM_logo.png)\n\n# Grounded-Segment-Anything\n[![YouTube](https://badges.aleen42.com/src/youtube.svg)](https://youtu.be/oEQYStnF2l8) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/automated-dataset-annotation-and-evaluation-with-grounding-dino-and-sam.ipynb) [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/camenduru/grounded-segment-anything-colab) [![HuggingFace Space](https://img.shields.io/badge/🤗-HuggingFace%20Space-cyan.svg)](https://huggingface.co/spaces/IDEA-Research/Grounded-SAM) [![Replicate](https://replicate.com/cjwbw/grounded-recognize-anything/badge)](https://replicate.com/cjwbw/grounded-recognize-anything) [![ModelScope Official Demo](https://img.shields.io/badge/ModelScope-Official%20Demo-important)](https://modelscope.cn/studios/tuofeilunhifi/Grounded-Segment-Anything/summary) [![Huggingface Demo by Community](https://img.shields.io/badge/Huggingface-Demo%20by%20Community-red)](https://huggingface.co/spaces/yizhangliu/Grounded-Segment-Anything) [![Stable-Diffusion WebUI](https://img.shields.io/badge/Stable--Diffusion-WebUI%20by%20Community-critical)](https://github.com/continue-revolution/sd-webui-segment-anything) [![Jupyter Notebook Demo](https://img.shields.io/badge/Demo-Jupyter%20Notebook-informational)](./grounded_sam.ipynb) [![Static Badge](https://img.shields.io/badge/GroundingDINO-arXiv-blue)](https://arxiv.org/abs/2303.05499) [![Static Badge](https://img.shields.io/badge/Segment_Anything-arXiv-blue)](https://arxiv.org/abs/2304.02643) [![Static Badge](https://img.shields.io/badge/Grounded_SAM-arXiv-blue)](https://arxiv.org/abs/2401.14159)\n\n\nWe plan to create a very interesting demo by combining [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO) and [Segment Anything](https://github.com/facebookresearch/segment-anything) which aims to detect and segment anything with text inputs! And we will continue to improve it and create more interesting demos based on this foundation. And we have already released an overall technical report about our project on arXiv, please check [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://arxiv.org/abs/2401.14159) for more details.\n\nWe are very willing to **help everyone share and promote new projects** based on Segment-Anything, Please check out here for more amazing demos and works in the community: [Highlight Extension Projects](#highlighted-projects). You can submit a new issue (with `project` tag) or a new pull request to add new project's links. \n\n![](./assets/grounded_sam_new_demo_image.png)\n\n![](./assets/ram_grounded_sam_new.png)\n\n**🍄 Why Building this Project?**\n\nThe **core idea** behind this project is to **combine the strengths of different models in order to build a very powerful pipeline for solving complex problems**. And it's worth mentioning that this is a workflow for combining strong expert models, where **all parts can be used separately or in combination, and can be replaced with any similar but different models (like replacing Grounding DINO with GLIP or other detectors / replacing Stable-Diffusion with ControlNet or GLIGEN/ Combining with ChatGPT)**.\n\n**🍇 Updates**\n- **`2024/01/26`** We have released a comprehensive technical report about our project on arXiv, please check [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://arxiv.org/abs/2401.14159) for more details. And we are profoundly grateful for the contributions of all the contributors in this project.\n- **`2023/12/17`** Support [Grounded-RepViT-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/main/EfficientSAM#run-grounded-repvit-sam-demo) demo, thanks a lot for their great work!\n- **`2023/12/16`** Support [Grounded-Edge-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/main/EfficientSAM#run-grounded-edge-sam-demo) demo, thanks a lot for their great work!\n- **`2023/12/10`** Support [Grounded-Efficient-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/main/EfficientSAM#run-grounded-efficient-sam-demo) demo, thanks a lot for their great work!\n- **`2023/11/24`** Release [RAM++](https://arxiv.org/abs/2310.15200), which is the next generation of RAM. RAM++ can recognize any category with high accuracy, including both predefined common categories and diverse open-set categories.\n- **`2023/11/23`** Release our newly proposed visual prompt counting model [T-Rex](https://github.com/IDEA-Research/T-Rex). The introduction [Video](https://www.youtube.com/watch?v=engIEhZogAQ) and [Demo](https://deepdataspace.com/playground/ivp) is available in [DDS](https://github.com/IDEA-Research/deepdataspace) now.\n- **`2023/07/25`** Support [Light-HQ-SAM](https://github.com/SysCV/sam-hq) in [EfficientSAM](./EfficientSAM/), credits to [Mingqiao Ye](https://github.com/ymq2017) and [Lei Ke](https://github.com/lkeab), thanks a lot for their great work!\n- **`2023/07/14`** Combining **Grounding-DINO-B** with [SAM-HQ](https://github.com/SysCV/sam-hq) achieves **49.6 mean AP** in [Segmentation in the Wild](https://eval.ai/web/challenges/challenge-page/1931/overview) competition zero-shot track, surpassing Grounded-SAM by **3.6 mean AP**, thanks for their great work!\n- **`2023/06/28`** Combining Grounding-DINO with Efficient SAM variants including [FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM) and [MobileSAM](https://github.com/ChaoningZhang/MobileSAM) in [EfficientSAM](./EfficientSAM/) for faster annotating, thanks a lot for their great work!\n- **`2023/06/20`** By combining **Grounding-DINO-L** with **SAM-ViT-H**, Grounded-SAM achieves 46.0 mean AP in [Segmentation in the Wild](https://eval.ai/web/challenges/challenge-page/1931/overview) competition zero-shot track on [CVPR 2023 workshop](https://computer-vision-in-the-wild.github.io/cvpr-2023/), surpassing [UNINEXT (CVPR 2023)](https://github.com/MasterBin-IIAU/UNINEXT) by about **4 mean AP**.\n- **`2023/06/16`** Release [RAM-Grounded-SAM Replicate Online Demo](https://replicate.com/cjwbw/ram-grounded-sam). Thanks a lot to [Chenxi](https://chenxwh.github.io/) for providing this nice demo 🌹.\n- **`2023/06/14`** Support [RAM-Grounded-SAM & SAM-HQ](./automatic_label_ram_demo.py) and update [Simple Automatic Label Demo](./automatic_label_ram_demo.py) to support [RAM](https://github.com/OPPOMKLab/recognize-anything), setting up a strong automatic annotation pipeline.\n- **`2023/06/13`** Checkout the [Autodistill: Train YOLOv8 with ZERO Annotations](https://youtu.be/gKTYMfwPo4M) tutorial to learn how to use Grounded-SAM + [Autodistill](https://github.com/autodistill/autodistill) for automated data labeling and real-time model training.\n- **`2023/06/13`** Support [SAM-HQ](https://github.com/SysCV/sam-hq) in [Grounded-SAM Demo](#running_man-grounded-sam-detect-and-segment-everything-with-text-prompt) for higher quality prediction.\n- **`2023/06/12`** Support [RAM-Grounded-SAM](#label-grounded-sam-with-ram-or-tag2text-for-automatic-labeling) for strong automatic labeling pipeline! Thanks for [Recognize-Anything](https://github.com/OPPOMKLab/recognize-anything).\n- **`2023/06/01`** Our Grounded-SAM has been accepted to present a **demo** at [ICCV 2023](https://iccv2023.thecvf.com/)! See you in Paris!\n- **`2023/05/23`**: Support `Image-Referring-Segment`, `Audio-Referring-Segment` and `Text-Referring-Segment` in [ImageBind-SAM](./playground/ImageBind_SAM/).\n- **`2023/05/03`**: Checkout the [Automated Dataset Annotation and Evaluation with GroundingDINO and SAM](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/automated-dataset-annotation-and-evaluation-with-grounding-dino-and-sam.ipynb) which is an amazing tutorial on automatic labeling! Thanks a lot for [Piotr Skalski](https://github.com/SkalskiP) and [Roboflow](https://github.com/roboflow/notebooks)!\n\n\n## Table of Contents\n- [Grounded-Segment-Anything](#grounded-segment-anything)\n - [Preliminary Works](#preliminary-works)\n - [Highlighted Projects](#highlighted-projects)\n- [Installation](#installation)\n - [Install with Docker](#install-with-docker)\n - [Install locally](#install-without-docker)\n- [Grounded-SAM Playground](#grounded-sam-playground)\n - [Step-by-Step Notebook Demo](#open_book-step-by-step-notebook-demo)\n - [GroundingDINO: Detect Everything with Text Prompt](#running_man-groundingdino-detect-everything-with-text-prompt)\n - [Grounded-SAM: Detect and Segment Everything with Text Prompt](#running_man-grounded-sam-detect-and-segment-everything-with-text-prompt)\n - [Grounded-SAM with Inpainting: Detect, Segment and Generate Everything with Text Prompt](#skier-grounded-sam-with-inpainting-detect-segment-and-generate-everything-with-text-prompt)\n - [Grounded-SAM and Inpaint Gradio APP](#golfing-grounded-sam-and-inpaint-gradio-app)\n - [Grounded-SAM with RAM or Tag2Text for Automatic Labeling](#label-grounded-sam-with-ram-or-tag2text-for-automatic-labeling)\n - [Grounded-SAM with BLIP & ChatGPT for Automatic Labeling](#robot-grounded-sam-with-blip-for-automatic-labeling)\n - [Grounded-SAM with Whisper: Detect and Segment Anything with Audio](#open_mouth-grounded-sam-with-whisper-detect-and-segment-anything-with-audio)\n - [Grounded-SAM ChatBot with Visual ChatGPT](#speech_balloon-grounded-sam-chatbot-demo)\n - [Grounded-SAM with OSX for 3D Whole-Body Mesh Recovery](#man_dancing-run-grounded-segment-anything--osx-demo)\n - [Grounded-SAM with VISAM for Tracking and Segment Anything](#man_dancing-run-grounded-segment-anything--visam-demo)\n - [Interactive Fashion-Edit Playground: Click for Segmentation And Editing](#dancers-interactive-editing)\n - [Interactive Human-face Editing Playground: Click And Editing Human Face](#dancers-interactive-editing)\n - [3D Box Via Segment Anything](#camera-3d-box-via-segment-anything)\n - [Playground: More Interesting and Imaginative Demos with Grounded-SAM](./playground/)\n - [DeepFloyd: Image Generation with Text Prompt](./playground/DeepFloyd/)\n - [PaintByExample: Exemplar-based Image Editing with Diffusion Models](./playground/PaintByExample/)\n - [LaMa: Resolution-robust Large Mask Inpainting with Fourier Convolutions](./playground/LaMa/)\n - [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](./playground/RePaint/)\n - [ImageBind with SAM: Segment with Different Modalities](./playground/ImageBind_SAM/)\n - [Efficient SAM Series for Faster Annotation](./EfficientSAM/)\n - [Grounded-FastSAM Demo](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/main/EfficientSAM#run-grounded-fastsam-demo)\n - [Grounded-MobileSAM Demo](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/main/EfficientSAM#run-grounded-mobilesam-demo)\n - [Grounded-Light-HQSAM Demo](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/main/EfficientSAM#run-grounded-light-hqsam-demo)\n - [Grounded-Efficient-SAM Demo](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/main/EfficientSAM#run-grounded-efficient-sam-demo)\n - [Grounded-Edge-SAM Demo](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/main/EfficientSAM#run-grounded-edge-sam-demo)\n - [Grounded-RepViT-SAM Demo](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/main/EfficientSAM#run-grounded-repvit-sam-demo)\n- [Citation](#citation)\n\n## Preliminary Works\n\nHere we provide some background knowledge that you may need to know before trying the demos.\n\n
\n\n| Title | Intro | Description | Links |\n|:----:|:----:|:----:|:----:|\n| [Segment-Anything](https://arxiv.org/abs/2304.02643) | ![](https://github.com/facebookresearch/segment-anything/blob/main/assets/model_diagram.png?raw=true) | A strong foundation model aims to segment everything in an image, which needs prompts (as boxes/points/text) to generate masks | [[Github](https://github.com/facebookresearch/segment-anything)]
[[Page](https://segment-anything.com/)]
[[Demo](https://segment-anything.com/demo)] |\n| [Grounding DINO](https://arxiv.org/abs/2303.05499) | ![](https://github.com/IDEA-Research/GroundingDINO/blob/main/.asset/hero_figure.png?raw=True) | A strong zero-shot detector which is capable of to generate high quality boxes and labels with free-form text. | [[Github](https://github.com/IDEA-Research/GroundingDINO)]
[[Demo](https://huggingface.co/spaces/ShilongLiu/Grounding_DINO_demo)] |\n| [OSX](http://arxiv.org/abs/2303.16160) | ![](https://github.com/IDEA-Research/OSX/blob/main/assets/demo_video.gif?raw=True) | A strong and efficient one-stage motion capture method to generate high quality 3D human mesh from monucular image. OSX also releases a large-scale upper-body dataset UBody for a more accurate reconstrution in the upper-body scene. | [[Github](https://github.com/IDEA-Research/OSX)]
[[Page](https://osx-ubody.github.io/)]
[[Video](https://osx-ubody.github.io/)]
[[Data](https://docs.google.com/forms/d/e/1FAIpQLSehgBP7wdn_XznGAM2AiJPiPLTqXXHw5uX9l7qeQ1Dh9HoO_A/viewform)] |\n| [Stable-Diffusion](https://arxiv.org/abs/2112.10752) | ![](https://github.com/CompVis/stable-diffusion/blob/main/assets/stable-samples/txt2img/merged-0006.png?raw=True) | A super powerful open-source latent text-to-image diffusion model | [[Github](https://github.com/CompVis/stable-diffusion)]
[[Page](https://ommer-lab.com/research/latent-diffusion-models/)] |\n| [RAM++](https://arxiv.org/abs/2310.15200) | ![](https://github.com/xinyu1205/recognize-anything/blob/main/images/ram_plus_compare.jpg) | RAM++ is the next generation of RAM, which can recognize any category with high accuracy. | [[Github](https://github.com/OPPOMKLab/recognize-anything)] |\n| [RAM](https://recognize-anything.github.io/) | ![](https://github.com/xinyu1205/Tag2Text/raw/main/images/localization_and_recognition.jpg) | RAM is an image tagging model, which can recognize any common category with high accuracy. | [[Github](https://github.com/OPPOMKLab/recognize-anything)]
[[Demo](https://huggingface.co/spaces/xinyu1205/Recognize_Anything-Tag2Text)] |\n| [BLIP](https://arxiv.org/abs/2201.12086) | ![](https://github.com/salesforce/LAVIS/raw/main/docs/_static/logo_final.png) | A wonderful language-vision model for image understanding. | [[GitHub](https://github.com/salesforce/LAVIS)] |\n| [Visual ChatGPT](https://arxiv.org/abs/2303.04671) | ![](https://github.com/microsoft/TaskMatrix/raw/main/assets/figure.jpg) | A wonderful tool that connects ChatGPT and a series of Visual Foundation Models to enable sending and receiving images during chatting. | [[Github](https://github.com/microsoft/TaskMatrix)]
[[Demo](https://huggingface.co/spaces/microsoft/visual_chatgpt)] |\n| [Tag2Text](https://tag2text.github.io/) | ![](https://github.com/xinyu1205/Tag2Text/raw/main/images/tag2text_framework.png) | An efficient and controllable vision-language model which can simultaneously output superior image captioning and image tagging. | [[Github](https://github.com/OPPOMKLab/recognize-anything)]
[[Demo](https://huggingface.co/spaces/xinyu1205/Tag2Text)] |\n| [VoxelNeXt](https://arxiv.org/abs/2303.11301) | ![](https://github.com/dvlab-research/VoxelNeXt/raw/master/docs/sequence-v2.gif) | A clean, simple, and fully-sparse 3D object detector, which predicts objects directly upon sparse voxel features. | [[Github](https://github.com/dvlab-research/VoxelNeXt)] \n\n
\n\n## Highlighted Projects\n\nHere we provide some impressive works you may find interesting:\n\n
\n\n| Title | Description | Links |\n|:---:|:---:|:---:|\n| [Semantic-SAM](https://github.com/UX-Decoder/Semantic-SAM) | A universal image segmentation model to enable segment and recognize anything at any desired granularity | [[Github](https://github.com/UX-Decoder/Semantic-SAM)]
[[Demo](https://github.com/UX-Decoder/Semantic-SAM)] |\n| [SEEM: Segment Everything Everywhere All at Once](https://arxiv.org/pdf/2304.06718.pdf) | A powerful promptable segmentation model supports segmenting with various types of prompts (text, point, scribble, referring image, etc.) and any combination of prompts. | [[Github](https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once)]
[[Demo](https://huggingface.co/spaces/xdecoder/SEEM)] |\n| [OpenSeeD](https://arxiv.org/pdf/2303.08131.pdf) | A simple framework for open-vocabulary segmentation and detection which supports interactive segmentation with box input to generate mask | [[Github](https://github.com/IDEA-Research/OpenSeeD)] |\n| [LLaVA](https://arxiv.org/abs/2304.08485) | Visual instruction tuning with GPT-4 | [[Github](https://github.com/haotian-liu/LLaVA)]
[[Page](https://llava-vl.github.io/)]
[[Demo](https://llava.hliu.cc/)]
[[Data](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K)]
[[Model](https://huggingface.co/liuhaotian/LLaVA-13b-delta-v0)] |\n| [GenSAM](https://arxiv.org/abs/2312.07374) | Relaxing the instance-specific manual prompt requirement in SAM through training-free test-time adaptation | [[Github](https://github.com/jyLin8100/GenSAM)]
[[Page](https://lwpyh.github.io/GenSAM/)] |\n\n
\n\nWe also list some awesome segment-anything extension projects here you may find interesting:\n- [Computer Vision in the Wild (CVinW) Readings](https://github.com/Computer-Vision-in-the-Wild/CVinW_Readings) for those who are interested in open-set tasks in computer vision.\n- [Zero-Shot Anomaly Detection](https://github.com/caoyunkang/GroundedSAM-zero-shot-anomaly-detection) by Yunkang Cao\n- [EditAnything: ControlNet + StableDiffusion based on the SAM segmentation mask](https://github.com/sail-sg/EditAnything) by Shanghua Gao and Pan Zhou\n- [IEA: Image Editing Anything](https://github.com/feizc/IEA) by Zhengcong Fei\n- [SAM-MMRorate: Combining Rotated Object Detector and SAM](https://github.com/Li-Qingyun/sam-mmrotate) by Qingyun Li and Xue Yang\n- [Awesome-Anything](https://github.com/VainF/Awesome-Anything) by Gongfan Fang\n- [Prompt-Segment-Anything](https://github.com/RockeyCoss/Prompt-Segment-Anything) by Rockey\n- [WebUI for Segment-Anything and Grounded-SAM](https://github.com/continue-revolution/sd-webui-segment-anything) by Chengsong Zhang\n- [Inpainting Anything: Inpaint Anything with SAM + Inpainting models](https://github.com/geekyutao/Inpaint-Anything) by Tao Yu\n- [Grounded Segment Anything From Objects to Parts: Combining Segment-Anything with VLPart & GLIP & Visual ChatGPT](https://github.com/Cheems-Seminar/segment-anything-and-name-it) by Peize Sun and Shoufa Chen\n- [Narapi-SAM: Integration of Segment Anything into Narapi (A nice viewer for SAM)](https://github.com/MIC-DKFZ/napari-sam) by MIC-DKFZ\n- [Grounded Segment Anything Colab](https://github.com/camenduru/grounded-segment-anything-colab) by camenduru\n- [Optical Character Recognition with Segment Anything](https://github.com/yeungchenwa/OCR-SAM) by Zhenhua Yang\n- [Transform Image into Unique Paragraph with ChatGPT, BLIP2, OFA, GRIT, Segment Anything, ControlNet](https://github.com/showlab/Image2Paragraph) by showlab\n- [Lang-Segment-Anything: Another awesome demo for combining GroundingDINO with Segment-Anything](https://github.com/luca-medeiros/lang-segment-anything) by Luca Medeiros\n- [🥳 🚀 **Playground: Integrate SAM and OpenMMLab!**](https://github.com/open-mmlab/playground)\n- [3D-object via Segment Anything](https://github.com/dvlab-research/3D-Box-Segment-Anything) by Yukang Chen\n- [Image2Paragraph: Transform Image Into Unique Paragraph](https://github.com/showlab/Image2Paragraph) by Show Lab\n- [Zero-shot Scene Graph Generate with Grounded-SAM](https://github.com/showlab/Image2Paragraph) by JackWhite-rwx\n- [CLIP Surgery for Better Explainability with Enhancement in Open-Vocabulary Tasks](https://github.com/xmed-lab/CLIP_Surgery) by Eli-YiLi\n- [Panoptic-Segment-Anything: Zero-shot panoptic segmentation using SAM](https://github.com/segments-ai/panoptic-segment-anything) by segments-ai\n- [Caption-Anything: Generates Descriptive Captions for Any Object within an Image](https://github.com/ttengwang/Caption-Anything) by Teng Wang\n- [Segment-Anything-3D: Transferring Segmentation Information of 2D Images to 3D Space](https://github.com/Pointcept/SegmentAnything3D) by Yunhan Yang\n- [Expediting SAM without Fine-tuning](https://github.com/Expedit-LargeScale-Vision-Transformer/Expedit-SAM) by Weicong Liang and Yuhui Yuan\n- [Semantic Segment Anything: Providing Rich Semantic Category Annotations for SAM](https://github.com/fudan-zvg/Semantic-Segment-Anything) by Jiaqi Chen and Zeyu Yang and Li Zhang\n- [Enhance Everything: Combining SAM with Image Restoration and Enhancement Tasks](https://github.com/lixinustc/Enhance-Anything) by Xin Li\n- [DragGAN](https://github.com/Zeqiang-Lai/DragGAN) by Shanghai AI Lab.\n\n## Installation\nThe code requires `python>=3.8`, as well as `pytorch>=1.7` and `torchvision>=0.8`. Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.\n\n### Install with Docker\n\nOpen one terminal:\n\n```\nmake build-image\n```\n\n```\nmake run\n```\n\nThat's it.\n\nIf you would like to allow visualization across docker container, open another terminal and type:\n\n```\nxhost +\n```\n\n\n### Install without Docker\nYou should set the environment variable manually as follows if you want to build a local GPU environment for Grounded-SAM:\n```bash\nexport AM_I_DOCKER=False\nexport BUILD_WITH_CUDA=True\nexport CUDA_HOME=/path/to/cuda-11.3/\n```\n\nInstall Segment Anything:\n\n```bash\npython -m pip install -e segment_anything\n```\n\nInstall Grounding DINO:\n\n```bash\npip install --no-build-isolation -e GroundingDINO\n```\n\n\nInstall diffusers:\n\n```bash\npip install --upgrade diffusers[torch]\n```\n\nInstall osx:\n\n```bash\ngit submodule update --init --recursive\ncd grounded-sam-osx && bash install.sh\n```\n\nInstall RAM & Tag2Text:\n\n```bash\ngit clone https://github.com/xinyu1205/recognize-anything.git\npip install -r ./recognize-anything/requirements.txt\npip install -e ./recognize-anything/\n```\n\nThe following optional dependencies are necessary for mask post-processing, saving masks in COCO format, the example notebooks, and exporting the model in ONNX format. `jupyter` is also required to run the example notebooks.\n\n```\npip install opencv-python pycocotools matplotlib onnxruntime onnx ipykernel\n```\n\nMore details can be found in [install segment anything](https://github.com/facebookresearch/segment-anything#installation) and [install GroundingDINO](https://github.com/IDEA-Research/GroundingDINO#install) and [install OSX](https://github.com/IDEA-Research/OSX)\n\n\n## Grounded-SAM Playground\nLet's start exploring our Grounding-SAM Playground and we will release more interesting demos in the future, stay tuned!\n\n## :open_book: Step-by-Step Notebook Demo\nHere we list some notebook demo provided in this project:\n- [grounded_sam.ipynb](grounded_sam.ipynb)\n- [grounded_sam_colab_demo.ipynb](grounded_sam_colab_demo.ipynb)\n- [grounded_sam_3d_box.ipynb](grounded_sam_3d_box)\n\n\n### :running_man: GroundingDINO: Detect Everything with Text Prompt\n\n:grapes: [[arXiv Paper](https://arxiv.org/abs/2303.05499)]   :rose:[[Try the Colab Demo](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-with-grounding-dino.ipynb)]   :sunflower: [[Try Huggingface Demo](https://huggingface.co/spaces/ShilongLiu/Grounding_DINO_demo)]   :mushroom: [[Automated Dataset Annotation and Evaluation](https://youtu.be/C4NqaRBz_Kw)]\n\nHere's the step-by-step tutorial on running `GroundingDINO` demo:\n\n**Step 1: Download the pretrained weights**\n\n```bash\ncd Grounded-Segment-Anything\n\n# download the pretrained groundingdino-swin-tiny model\nwget https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth\n```\n\n**Step 2: Running the demo**\n\n```bash\npython grounding_dino_demo.py\n```\n\n
\n Running with Python (same as demo but you can run it anywhere after installing GroundingDINO) \n\n```python\nfrom groundingdino.util.inference import load_model, load_image, predict, annotate\nimport cv2\n\nmodel = load_model(\"GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py\", \"./groundingdino_swint_ogc.pth\")\nIMAGE_PATH = \"assets/demo1.jpg\"\nTEXT_PROMPT = \"bear.\"\nBOX_THRESHOLD = 0.35\nTEXT_THRESHOLD = 0.25\n\nimage_source, image = load_image(IMAGE_PATH)\n\nboxes, logits, phrases = predict(\n model=model,\n image=image,\n caption=TEXT_PROMPT,\n box_threshold=BOX_THRESHOLD,\n text_threshold=TEXT_THRESHOLD\n)\n\nannotated_frame = annotate(image_source=image_source, boxes=boxes, logits=logits, phrases=phrases)\ncv2.imwrite(\"annotated_image.jpg\", annotated_frame)\n```\n\n
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\n\n**Tips**\n- If you want to detect multiple objects in one sentence with [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO), we suggest separating each name with `.` . An example: `cat . dog . chair .`\n\n**Step 3: Check the annotated image**\n\nThe annotated image will be saved as `./annotated_image.jpg`.\n\n
\n\n| Text Prompt | Demo Image | Annotated Image |\n|:----:|:----:|:----:|\n| `Bear.` | ![](./assets/demo1.jpg) | ![](./assets/annotated_image.jpg) |\n| `Horse. Clouds. Grasses. Sky. Hill` | ![](./assets/demo7.jpg) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/grounding_dino/groundingdino_demo7.jpg?raw=true)\n\n
\n\n\n### :running_man: Grounded-SAM: Detect and Segment Everything with Text Prompt\n\nHere's the step-by-step tutorial on running `Grounded-SAM` demo:\n\n**Step 1: Download the pretrained weights**\n\n```bash\ncd Grounded-Segment-Anything\n\nwget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\nwget https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth\n```\n\nWe provide two versions of Grounded-SAM demo here:\n- [grounded_sam_demo.py](./grounded_sam_demo.py): our original implementation for Grounded-SAM.\n- [grounded_sam_simple_demo.py](./grounded_sam_simple_demo.py) our updated more elegant version for Grounded-SAM.\n\n**Step 2: Running original grounded-sam demo**\n\n```python\nexport CUDA_VISIBLE_DEVICES=0\npython grounded_sam_demo.py \\\n --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py \\\n --grounded_checkpoint groundingdino_swint_ogc.pth \\\n --sam_checkpoint sam_vit_h_4b8939.pth \\\n --input_image assets/demo1.jpg \\\n --output_dir \"outputs\" \\\n --box_threshold 0.3 \\\n --text_threshold 0.25 \\\n --text_prompt \"bear\" \\\n --device \"cuda\"\n```\n\nThe annotated results will be saved in `./outputs` as follows\n\n
\n\n| Input Image | Annotated Image | Generated Mask |\n|:----:|:----:|:----:|\n| ![](./assets/demo1.jpg) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/grounded_sam/original_grounded_sam_demo1.jpg?raw=true) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/grounded_sam/mask.jpg?raw=true) |\n\n
\n\n**Step 3: Running grounded-sam demo with sam-hq**\n- Download the demo image\n```bash\nwget https://github.com/IDEA-Research/detrex-storage/releases/download/grounded-sam-storage/sam_hq_demo_image.png\n```\n\n- Download SAM-HQ checkpoint [here](https://github.com/SysCV/sam-hq#model-checkpoints)\n\n- Running grounded-sam-hq demo as follows:\n```python\nexport CUDA_VISIBLE_DEVICES=0\npython grounded_sam_demo.py \\\n --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py \\\n --grounded_checkpoint groundingdino_swint_ogc.pth \\\n --sam_hq_checkpoint ./sam_hq_vit_h.pth \\ # path to sam-hq checkpoint\n --use_sam_hq \\ # set to use sam-hq model\n --input_image sam_hq_demo_image.png \\\n --output_dir \"outputs\" \\\n --box_threshold 0.3 \\\n --text_threshold 0.25 \\\n --text_prompt \"chair.\" \\\n --device \"cuda\"\n```\n\nThe annotated results will be saved in `./outputs` as follows\n\n
\n\n| Input Image | SAM Output | SAM-HQ Output |\n|:----:|:----:|:----:|\n| ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/sam_hq/sam_hq_demo.png?raw=true) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/sam_hq/sam_output.jpg?raw=true) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/sam_hq/sam_hq_output.jpg?raw=true) |\n\n
\n\n**Step 4: Running the updated grounded-sam demo (optional)**\n\nNote that this demo is almost same as the original demo, but **with more elegant code**.\n\n```python\npython grounded_sam_simple_demo.py\n```\n\nThe annotated results will be saved as `./groundingdino_annotated_image.jpg` and `./grounded_sam_annotated_image.jpg`\n\n
\n\n| Text Prompt | Input Image | GroundingDINO Annotated Image | Grounded-SAM Annotated Image |\n|:----:|:----:|:----:|:----:|\n| `The running dog` | ![](./assets/demo2.jpg) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/grounded_sam/groundingdino_annotated_image_demo2.jpg?raw=true) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/grounded_sam/grounded_sam_annotated_image_demo2.jpg?raw=true) |\n| `Horse. Clouds. Grasses. Sky. Hill` | ![](./assets/demo7.jpg) | ![](assets/groundingdino_annotated_image.jpg) | ![](assets/grounded_sam_annotated_image.jpg) |\n\n
\n\n### :skier: Grounded-SAM with Inpainting: Detect, Segment and Generate Everything with Text Prompt\n\n**Step 1: Download the pretrained weights**\n\n```bash\ncd Grounded-Segment-Anything\n\nwget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\nwget https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth\n```\n\n**Step 2: Running grounded-sam inpainting demo**\n\n```bash\nCUDA_VISIBLE_DEVICES=0\npython grounded_sam_inpainting_demo.py \\\n --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py \\\n --grounded_checkpoint groundingdino_swint_ogc.pth \\\n --sam_checkpoint sam_vit_h_4b8939.pth \\\n --input_image assets/inpaint_demo.jpg \\\n --output_dir \"outputs\" \\\n --box_threshold 0.3 \\\n --text_threshold 0.25 \\\n --det_prompt \"bench\" \\\n --inpaint_prompt \"A sofa, high quality, detailed\" \\\n --device \"cuda\"\n```\n\nThe annotated and inpaint image will be saved in `./outputs`\n\n**Step 3: Check the results**\n\n\n
\n\n| Input Image | Det Prompt | Annotated Image | Inpaint Prompt | Inpaint Image |\n|:---:|:---:|:---:|:---:|:---:|\n|![](./assets/inpaint_demo.jpg) | `Bench` | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/grounded_sam_inpaint/grounded_sam_output.jpg?raw=true) | `A sofa, high quality, detailed` | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/grounded_sam_inpaint/grounded_sam_inpainting_output.jpg?raw=true) |\n\n
\n\n### :golfing: Grounded-SAM and Inpaint Gradio APP\n\nWe support 6 tasks in the local Gradio APP：\n\n1. **scribble**: Segmentation is achieved through Segment Anything and mouse click interaction (you need to click on the object with the mouse, no need to specify the prompt).\n2. **automask**: Segment the entire image at once through Segment Anything (no need to specify a prompt).\n3. **det**: Realize detection through Grounding DINO and text interaction (text prompt needs to be specified).\n4. **seg**: Realize text interaction by combining Grounding DINO and Segment Anything to realize detection + segmentation (need to specify text prompt).\n5. **inpainting**: By combining Grounding DINO + Segment Anything + Stable Diffusion to achieve text exchange and replace the target object (need to specify text prompt and inpaint prompt) .\n6. **automatic**: By combining BLIP + Grounding DINO + Segment Anything to achieve non-interactive detection + segmentation (no need to specify prompt).\n\n```bash\npython gradio_app.py\n```\n\n- The gradio_app visualization as follows:\n\n![](./assets/gradio_demo.png)\n\n\n### :label: Grounded-SAM with RAM or Tag2Text for Automatic Labeling\n[**The Recognize Anything Models**](https://github.com/OPPOMKLab/recognize-anything) are a series of open-source and strong fundamental image recognition models, including [RAM++](https://arxiv.org/abs/2310.15200), [RAM](https://arxiv.org/abs/2306.03514) and [Tag2text](https://arxiv.org/abs/2303.05657).\n\n\nIt is seamlessly linked to generate pseudo labels automatically as follows:\n1. Use RAM/Tag2Text to generate tags.\n2. Use Grounded-Segment-Anything to generate the boxes and masks.\n\n\n**Step 1: Init submodule and download the pretrained checkpoint**\n\n- Init submodule:\n\n```bash\ncd Grounded-Segment-Anything\ngit submodule init\ngit submodule update\n```\n\n- Download pretrained weights for `GroundingDINO`, `SAM` and `RAM/Tag2Text`:\n\n```bash\nwget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\nwget https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth\n\n\nwget https://huggingface.co/spaces/xinyu1205/Tag2Text/resolve/main/ram_swin_large_14m.pth\nwget https://huggingface.co/spaces/xinyu1205/Tag2Text/resolve/main/tag2text_swin_14m.pth\n```\n\n**Step 2: Running the demo with RAM**\n```bash\nexport CUDA_VISIBLE_DEVICES=0\npython automatic_label_ram_demo.py \\\n --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py \\\n --ram_checkpoint ram_swin_large_14m.pth \\\n --grounded_checkpoint groundingdino_swint_ogc.pth \\\n --sam_checkpoint sam_vit_h_4b8939.pth \\\n --input_image assets/demo9.jpg \\\n --output_dir \"outputs\" \\\n --box_threshold 0.25 \\\n --text_threshold 0.2 \\\n --iou_threshold 0.5 \\\n --device \"cuda\"\n```\n\n\n**Step 2: Or Running the demo with Tag2Text**\n```bash\nexport CUDA_VISIBLE_DEVICES=0\npython automatic_label_tag2text_demo.py \\\n --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py \\\n --tag2text_checkpoint tag2text_swin_14m.pth \\\n --grounded_checkpoint groundingdino_swint_ogc.pth \\\n --sam_checkpoint sam_vit_h_4b8939.pth \\\n --input_image assets/demo9.jpg \\\n --output_dir \"outputs\" \\\n --box_threshold 0.25 \\\n --text_threshold 0.2 \\\n --iou_threshold 0.5 \\\n --device \"cuda\"\n```\n\n- RAM++ significantly improves the open-set capability of RAM, for [RAM++ inference on unseen categoreis](https://github.com/xinyu1205/recognize-anything#ram-inference-on-unseen-categories-open-set).\n- Tag2Text also provides powerful captioning capabilities, and the process with captions can refer to [BLIP](#robot-run-grounded-segment-anything--blip-demo).\n- The pseudo labels and model prediction visualization will be saved in `output_dir` as follows (right figure):\n\n![](./assets/automatic_label_output/demo9_tag2text_ram.jpg)\n\n\n### :robot: Grounded-SAM with BLIP for Automatic Labeling\nIt is easy to generate pseudo labels automatically as follows:\n1. Use BLIP (or other caption models) to generate a caption.\n2. Extract tags from the caption. We use ChatGPT to handle the potential complicated sentences. \n3. Use Grounded-Segment-Anything to generate the boxes and masks.\n\n- Run Demo\n```bash\nexport OPENAI_API_KEY=your_openai_key\nexport OPENAI_API_BASE=https://closeai.deno.dev/v1\nexport CUDA_VISIBLE_DEVICES=0\npython automatic_label_demo.py \\\n --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py \\\n --grounded_checkpoint groundingdino_swint_ogc.pth \\\n --sam_checkpoint sam_vit_h_4b8939.pth \\\n --input_image assets/demo3.jpg \\\n --output_dir \"outputs\" \\\n --openai_key $OPENAI_API_KEY \\\n --box_threshold 0.25 \\\n --text_threshold 0.2 \\\n --iou_threshold 0.5 \\\n --device \"cuda\"\n```\n\n- When you don't have a paid Account for ChatGPT is also possible to use NLTK instead. Just don't include the ```openai_key``` Parameter when starting the Demo.\n - The Script will automatically download the necessary NLTK Data.\n- The pseudo labels and model prediction visualization will be saved in `output_dir` as follows:\n\n![](./assets/automatic_label_output_demo3.jpg)\n\n\n### :open_mouth: Grounded-SAM with Whisper: Detect and Segment Anything with Audio\nDetect and segment anything with speech!\n\n![](assets/acoustics/gsam_whisper_inpainting_demo.png)\n\n**Install Whisper**\n```bash\npip install -U openai-whisper\n```\nSee the [whisper official page](https://github.com/openai/whisper#setup) if you have other questions for the installation.\n\n**Run Voice-to-Label Demo**\n\nOptional: Download the demo audio file\n\n```bash\nwget https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/demo_audio.mp3\n```\n\n\n```bash\nexport CUDA_VISIBLE_DEVICES=0\npython grounded_sam_whisper_demo.py \\\n --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py \\\n --grounded_checkpoint groundingdino_swint_ogc.pth \\\n --sam_checkpoint sam_vit_h_4b8939.pth \\\n --input_image assets/demo4.jpg \\\n --output_dir \"outputs\" \\\n --box_threshold 0.3 \\\n --text_threshold 0.25 \\\n --speech_file \"demo_audio.mp3\" \\\n --device \"cuda\"\n```\n\n![](./assets/grounded_sam_whisper_output.jpg)\n\n**Run Voice-to-inpaint Demo**\n\nYou can enable chatgpt to help you automatically detect the object and inpainting order with `--enable_chatgpt`. \n\nOr you can specify the object you want to inpaint [stored in `args.det_speech_file`] and the text you want to inpaint with [stored in `args.inpaint_speech_file`].\n\n```bash\nexport OPENAI_API_KEY=your_openai_key\nexport OPENAI_API_BASE=https://closeai.deno.dev/v1\n# Example: enable chatgpt\nexport CUDA_VISIBLE_DEVICES=0\npython grounded_sam_whisper_inpainting_demo.py \\\n --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py \\\n --grounded_checkpoint groundingdino_swint_ogc.pth \\\n --sam_checkpoint sam_vit_h_4b8939.pth \\\n --input_image assets/inpaint_demo.jpg \\\n --output_dir \"outputs\" \\\n --box_threshold 0.3 \\\n --text_threshold 0.25 \\\n --prompt_speech_file assets/acoustics/prompt_speech_file.mp3 \\\n --enable_chatgpt \\\n --openai_key $OPENAI_API_KEY\\\n --device \"cuda\"\n```\n\n```bash\n# Example: without chatgpt\nexport CUDA_VISIBLE_DEVICES=0\npython grounded_sam_whisper_inpainting_demo.py \\\n --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py \\\n --grounded_checkpoint groundingdino_swint_ogc.pth \\\n --sam_checkpoint sam_vit_h_4b8939.pth \\\n --input_image assets/inpaint_demo.jpg \\\n --output_dir \"outputs\" \\\n --box_threshold 0.3 \\\n --text_threshold 0.25 \\\n --det_speech_file \"assets/acoustics/det_voice.mp3\" \\\n --inpaint_speech_file \"assets/acoustics/inpaint_voice.mp3\" \\\n --device \"cuda\"\n```\n\n![](./assets/acoustics/gsam_whisper_inpainting_pipeline.png)\n\n### :speech_balloon: Grounded-SAM ChatBot Demo\n\nhttps://user-images.githubusercontent.com/24236723/231955561-2ae4ec1a-c75f-4cc5-9b7b-517aa1432123.mp4\n\nFollowing [Visual ChatGPT](https://github.com/microsoft/visual-chatgpt), we add a ChatBot for our project. Currently, it supports:\n1. \"Describe the image.\"\n2. \"Detect the dog (and the cat) in the image.\"\n3. \"Segment anything in the image.\"\n4. \"Segment the dog (and the cat) in the image.\"\n5. \"Help me label the image.\"\n6. \"Replace the dog with a cat in the image.\"\n\nTo use the ChatBot:\n- Install whisper if you want to use audio as input.\n- Set the default model setting in the tool `Grounded_dino_sam_inpainting`.\n- Run Demo\n```bash\nexport OPENAI_API_KEY=your_openai_key\nexport OPENAI_API_BASE=https://closeai.deno.dev/v1\nexport CUDA_VISIBLE_DEVICES=0\npython chatbot.py \n```\n\n### :man_dancing: Run Grounded-Segment-Anything + OSX Demo\n\n

\n\n
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\n\n\n- Download the checkpoint `osx_l_wo_decoder.pth.tar` from [here](https://drive.google.com/drive/folders/1x7MZbB6eAlrq5PKC9MaeIm4GqkBpokow?usp=share_link) for OSX:\n- Download the human model files and place it into `grounded-sam-osx/utils/human_model_files` following the instruction of [OSX](https://github.com/IDEA-Research/OSX).\n\n- Run Demo\n\n```shell\nexport CUDA_VISIBLE_DEVICES=0\npython grounded_sam_osx_demo.py \\\n --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py \\\n --grounded_checkpoint groundingdino_swint_ogc.pth \\\n --sam_checkpoint sam_vit_h_4b8939.pth \\\n --osx_checkpoint osx_l_wo_decoder.pth.tar \\\n --input_image assets/osx/grounded_sam_osx_demo.png \\\n --output_dir \"outputs\" \\\n --box_threshold 0.3 \\\n --text_threshold 0.25 \\\n --text_prompt \"humans, chairs\" \\\n --device \"cuda\"\n```\n\n- The model prediction visualization will be saved in `output_dir` as follows:\n\n\n\n- We also support promptable 3D whole-body mesh recovery. For example, you can track someone with a text prompt and estimate his 3D pose and shape :\n\n| ![space-1.jpg](assets/osx/grounded_sam_osx_output1.jpg) |\n| :---------------------------------------------------: |\n| *A person with pink clothes* |\n\n| ![space-1.jpg](assets/osx/grounded_sam_osx_output2.jpg) |\n| :---------------------------------------------------: |\n| *A man with a sunglasses* |\n\n\n## :man_dancing: Run Grounded-Segment-Anything + VISAM Demo\n\n- Download the checkpoint `motrv2_dancetrack.pth` from [here](https://drive.google.com/file/d/1EA4lndu2yQcVgBKR09KfMe5efbf631Th/view?usp=share_link) for MOTRv2:\n- See the more thing if you have other questions for the installation.\n\n- Run Demo\n\n```shell\nexport CUDA_VISIBLE_DEVICES=0\npython grounded_sam_visam.py \\\n --meta_arch motr \\\n --dataset_file e2e_dance \\\n --with_box_refine \\\n --query_interaction_layer QIMv2 \\\n --num_queries 10 \\\n --det_db det_db_motrv2.json \\\n --use_checkpoint \\\n --mot_path your_data_path \\\n --resume motrv2_dancetrack.pth \\\n --sam_checkpoint sam_vit_h_4b8939.pth \\\n --video_path DanceTrack/test/dancetrack0003 \n```\n|![](https://raw.githubusercontent.com/BingfengYan/MOTSAM/main/visam.gif)|\n\n\n### :dancers: Interactive Editing\n- Release the interactive fashion-edit playground in [here](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/humanFace). Run in the notebook, just click for annotating points for further segmentation. Enjoy it! \n\n\n- Release human-face-edit branch [here](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/humanFace). We'll keep updating this branch with more interesting features. Here are some examples:\n\n ![](https://github.com/IDEA-Research/Grounded-Segment-Anything/blob/humanFace/assets/231-hair-edit.png)\n\n## :camera: 3D-Box via Segment Anything\nWe extend the scope to 3D world by combining Segment Anything and [VoxelNeXt](https://github.com/dvlab-research/VoxelNeXt). When we provide a prompt (e.g., a point / box), the result is not only 2D segmentation mask, but also 3D boxes. Please check [voxelnext_3d_box](./voxelnext_3d_box/) for more details.\n ![](https://github.com/IDEA-Research/Grounded-Segment-Anything/blob/main/voxelnext_3d_box/images/sam-voxelnext.png)\n ![](https://github.com/IDEA-Research/Grounded-Segment-Anything/blob/main/voxelnext_3d_box/images/image_boxes2.png)\n\n\n\n\n## :cupid: Acknowledgements\n\n- [Segment Anything](https://github.com/facebookresearch/segment-anything)\n- [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO)\n\n\n## Contributors\n\nOur project wouldn't be possible without the contributions of these amazing people! Thank you all for making this project better.\n\n\n \n\n\n\n## Citation\nIf you find this project helpful for your research, please consider citing the following BibTeX entry.\n```BibTex\n@article{kirillov2023segany,\n title={Segment Anything}, \n author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\\'a}r, Piotr and Girshick, Ross},\n journal={arXiv:2304.02643},\n year={2023}\n}\n\n@article{liu2023grounding,\n title={Grounding dino: Marrying dino with grounded pre-training for open-set object detection},\n author={Liu, Shilong and Zeng, Zhaoyang and Ren, Tianhe and Li, Feng and Zhang, Hao and Yang, Jie and Li, Chunyuan and Yang, Jianwei and Su, Hang and Zhu, Jun and others},\n journal={arXiv preprint arXiv:2303.05499},\n year={2023}\n}\n\n@misc{ren2024grounded,\n title={Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks}, \n author={Tianhe Ren and Shilong Liu and Ailing Zeng and Jing Lin and Kunchang Li and He Cao and Jiayu Chen and Xinyu Huang and Yukang Chen and Feng Yan and Zhaoyang Zeng and Hao Zhang and Feng Li and Jie Yang and Hongyang Li and Qing Jiang and Lei Zhang},\n year={2024},\n eprint={2401.14159},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n}\n```\n" }, { "path": "data_generation/Grounded-Segment-Anything/automatic_label_demo.py", "content": "import argparse\nimport os\nimport copy\n\nimport numpy as np\nimport json\nimport torch\nimport torchvision\nfrom PIL import Image, ImageDraw, ImageFont\nimport nltk\nimport litellm\n\n# Grounding DINO\nimport GroundingDINO.groundingdino.datasets.transforms as T\nfrom GroundingDINO.groundingdino.models import build_model\nfrom GroundingDINO.groundingdino.util import box_ops\nfrom GroundingDINO.groundingdino.util.slconfig import SLConfig\nfrom GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap\n\n# segment anything\nfrom segment_anything import build_sam, SamPredictor \nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# BLIP\nfrom transformers import BlipProcessor, BlipForConditionalGeneration\n\n# ChatGPT\nimport openai\n\n\ndef load_image(image_path):\n # load image\n image_pil = Image.open(image_path).convert(\"RGB\") # load image\n\n transform = T.Compose(\n [\n T.RandomResize([800], max_size=1333),\n T.ToTensor(),\n T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n ]\n )\n image, _ = transform(image_pil, None) # 3, h, w\n return image_pil, image\n\n\ndef generate_caption(raw_image, device):\n # unconditional image captioning\n if device == \"cuda\":\n inputs = processor(raw_image, return_tensors=\"pt\").to(\"cuda\", torch.float16)\n else:\n inputs = processor(raw_image, return_tensors=\"pt\")\n out = blip_model.generate(**inputs)\n caption = processor.decode(out[0], skip_special_tokens=True)\n return caption\n\n\ndef generate_tags(caption, split=',', max_tokens=100, model=\"gpt-3.5-turbo\"):\n lemma = nltk.wordnet.WordNetLemmatizer()\n if openai_key:\n prompt = [\n {\n 'role': 'system',\n 'content': 'Extract the unique nouns in the caption. Remove all the adjectives. ' + \\\n f'List the nouns in singular form. Split them by \"{split} \". ' + \\\n f'Caption: {caption}.'\n }\n ]\n response = litellm.completion(model=model, messages=prompt, temperature=0.6, max_tokens=max_tokens)\n reply = response['choices'][0]['message']['content']\n # sometimes return with \"noun: xxx, xxx, xxx\"\n tags = reply.split(':')[-1].strip()\n else:\n nltk.download(['punkt', 'averaged_perceptron_tagger', 'wordnet'])\n tags_list = [word for (word, pos) in nltk.pos_tag(nltk.word_tokenize(caption)) if pos[0] == 'N']\n tags_lemma = [lemma.lemmatize(w) for w in tags_list]\n tags = ', '.join(map(str, tags_lemma))\n return tags\n\n\ndef check_caption(caption, pred_phrases, max_tokens=100, model=\"gpt-3.5-turbo\"):\n object_list = [obj.split('(')[0] for obj in pred_phrases]\n object_num = []\n for obj in set(object_list):\n object_num.append(f'{object_list.count(obj)} {obj}')\n object_num = ', '.join(object_num)\n print(f\"Correct object number: {object_num}\")\n\n if openai_key:\n prompt = [\n {\n 'role': 'system',\n 'content': 'Revise the number in the caption if it is wrong. ' + \\\n f'Caption: {caption}. ' + \\\n f'True object number: {object_num}. ' + \\\n 'Only give the revised caption: '\n }\n ]\n response = litellm.completion(model=model, messages=prompt, temperature=0.6, max_tokens=max_tokens)\n reply = response['choices'][0]['message']['content']\n # sometimes return with \"Caption: xxx, xxx, xxx\"\n caption = reply.split(':')[-1].strip()\n return caption\n\n\ndef load_model(model_config_path, model_checkpoint_path, device):\n args = SLConfig.fromfile(model_config_path)\n args.device = device\n model = build_model(args)\n checkpoint = torch.load(model_checkpoint_path, map_location=\"cpu\")\n load_res = model.load_state_dict(clean_state_dict(checkpoint[\"model\"]), strict=False)\n print(load_res)\n _ = model.eval()\n return model\n\n\ndef get_grounding_output(model, image, caption, box_threshold, text_threshold,device=\"cpu\"):\n caption = caption.lower()\n caption = caption.strip()\n if not caption.endswith(\".\"):\n caption = caption + \".\"\n model = model.to(device)\n image = image.to(device)\n with torch.no_grad():\n outputs = model(image[None], captions=[caption])\n logits = outputs[\"pred_logits\"].cpu().sigmoid()[0] # (nq, 256)\n boxes = outputs[\"pred_boxes\"].cpu()[0] # (nq, 4)\n logits.shape[0]\n\n # filter output\n logits_filt = logits.clone()\n boxes_filt = boxes.clone()\n filt_mask = logits_filt.max(dim=1)[0] > box_threshold\n logits_filt = logits_filt[filt_mask] # num_filt, 256\n boxes_filt = boxes_filt[filt_mask] # num_filt, 4\n logits_filt.shape[0]\n\n # get phrase\n tokenlizer = model.tokenizer\n tokenized = tokenlizer(caption)\n # build pred\n pred_phrases = []\n scores = []\n for logit, box in zip(logits_filt, boxes_filt):\n pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer)\n pred_phrases.append(pred_phrase + f\"({str(logit.max().item())[:4]})\")\n scores.append(logit.max().item())\n\n return boxes_filt, torch.Tensor(scores), pred_phrases\n\n\ndef show_mask(mask, ax, random_color=False):\n if random_color:\n color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)\n else:\n color = np.array([30/255, 144/255, 255/255, 0.6])\n h, w = mask.shape[-2:]\n mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\n ax.imshow(mask_image)\n\n\ndef show_box(box, ax, label):\n x0, y0 = box[0], box[1]\n w, h = box[2] - box[0], box[3] - box[1]\n ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2)) \n ax.text(x0, y0, label)\n\n\ndef save_mask_data(output_dir, caption, mask_list, box_list, label_list):\n value = 0 # 0 for background\n\n mask_img = torch.zeros(mask_list.shape[-2:])\n for idx, mask in enumerate(mask_list):\n mask_img[mask.cpu().numpy()[0] == True] = value + idx + 1\n plt.figure(figsize=(10, 10))\n plt.imshow(mask_img.numpy())\n plt.axis('off')\n plt.savefig(os.path.join(output_dir, 'mask.jpg'), bbox_inches=\"tight\", dpi=300, pad_inches=0.0)\n\n json_data = {\n 'caption': caption,\n 'mask':[{\n 'value': value,\n 'label': 'background'\n }]\n }\n for label, box in zip(label_list, box_list):\n value += 1\n name, logit = label.split('(')\n logit = logit[:-1] # the last is ')'\n json_data['mask'].append({\n 'value': value,\n 'label': name,\n 'logit': float(logit),\n 'box': box.numpy().tolist(),\n })\n with open(os.path.join(output_dir, 'label.json'), 'w') as f:\n json.dump(json_data, f)\n \n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser(\"Grounded-Segment-Anything Demo\", add_help=True)\n parser.add_argument(\"--config\", type=str, required=True, help=\"path to config file\")\n parser.add_argument(\n \"--grounded_checkpoint\", type=str, required=True, help=\"path to checkpoint file\"\n )\n parser.add_argument(\n \"--sam_checkpoint\", type=str, required=True, help=\"path to checkpoint file\"\n )\n parser.add_argument(\"--input_image\", type=str, required=True, help=\"path to image file\")\n parser.add_argument(\"--split\", default=\",\", type=str, help=\"split for text prompt\")\n parser.add_argument(\"--openai_key\", type=str, help=\"key for chatgpt\")\n parser.add_argument(\"--openai_proxy\", default=None, type=str, help=\"proxy for chatgpt\")\n parser.add_argument(\n \"--output_dir\", \"-o\", type=str, default=\"outputs\", required=True, help=\"output directory\"\n )\n\n parser.add_argument(\"--box_threshold\", type=float, default=0.25, help=\"box threshold\")\n parser.add_argument(\"--text_threshold\", type=float, default=0.2, help=\"text threshold\")\n parser.add_argument(\"--iou_threshold\", type=float, default=0.5, help=\"iou threshold\")\n\n parser.add_argument(\"--device\", type=str, default=\"cpu\", help=\"running on cpu only!, default=False\")\n args = parser.parse_args()\n\n # cfg\n config_file = args.config # change the path of the model config file\n grounded_checkpoint = args.grounded_checkpoint # change the path of the model\n sam_checkpoint = args.sam_checkpoint\n image_path = args.input_image\n split = args.split\n openai_key = args.openai_key\n openai_proxy = args.openai_proxy\n output_dir = args.output_dir\n box_threshold = args.box_threshold\n text_threshold = args.text_threshold\n iou_threshold = args.iou_threshold\n device = args.device\n\n openai.api_key = openai_key\n if openai_proxy:\n openai.proxy = {\"http\": openai_proxy, \"https\": openai_proxy}\n\n # make dir\n os.makedirs(output_dir, exist_ok=True)\n # load image\n image_pil, image = load_image(image_path)\n # load model\n model = load_model(config_file, grounded_checkpoint, device=device)\n\n # visualize raw image\n image_pil.save(os.path.join(output_dir, \"raw_image.jpg\"))\n\n # generate caption and tags\n # use Tag2Text can generate better captions\n # https://huggingface.co/spaces/xinyu1205/Tag2Text\n # but there are some bugs...\n processor = BlipProcessor.from_pretrained(\"Salesforce/blip-image-captioning-large\")\n if device == \"cuda\":\n blip_model = BlipForConditionalGeneration.from_pretrained(\"Salesforce/blip-image-captioning-large\", torch_dtype=torch.float16).to(\"cuda\")\n else:\n blip_model = BlipForConditionalGeneration.from_pretrained(\"Salesforce/blip-image-captioning-large\")\n caption = generate_caption(image_pil, device=device)\n # Currently \", \" is better for detecting single tags\n # while \". \" is a little worse in some case\n text_prompt = generate_tags(caption, split=split)\n print(f\"Caption: {caption}\")\n print(f\"Tags: {text_prompt}\")\n\n # run grounding dino model\n boxes_filt, scores, pred_phrases = get_grounding_output(\n model, image, text_prompt, box_threshold, text_threshold, device=device\n )\n\n # initialize SAM\n predictor = SamPredictor(build_sam(checkpoint=sam_checkpoint).to(device))\n image = cv2.imread(image_path)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n predictor.set_image(image)\n\n size = image_pil.size\n H, W = size[1], size[0]\n for i in range(boxes_filt.size(0)):\n boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])\n boxes_filt[i][:2] -= boxes_filt[i][2:] / 2\n boxes_filt[i][2:] += boxes_filt[i][:2]\n\n boxes_filt = boxes_filt.cpu()\n # use NMS to handle overlapped boxes\n print(f\"Before NMS: {boxes_filt.shape[0]} boxes\")\n nms_idx = torchvision.ops.nms(boxes_filt, scores, iou_threshold).numpy().tolist()\n boxes_filt = boxes_filt[nms_idx]\n pred_phrases = [pred_phrases[idx] for idx in nms_idx]\n print(f\"After NMS: {boxes_filt.shape[0]} boxes\")\n caption = check_caption(caption, pred_phrases)\n print(f\"Revise caption with number: {caption}\")\n\n transformed_boxes = predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2]).to(device)\n\n masks, _, _ = predictor.predict_torch(\n point_coords = None,\n point_labels = None,\n boxes = transformed_boxes.to(device),\n multimask_output = False,\n )\n \n # draw output image\n plt.figure(figsize=(10, 10))\n plt.imshow(image)\n for mask in masks:\n show_mask(mask.cpu().numpy(), plt.gca(), random_color=True)\n for box, label in zip(boxes_filt, pred_phrases):\n show_box(box.numpy(), plt.gca(), label)\n\n plt.title(caption)\n plt.axis('off')\n plt.savefig(\n os.path.join(output_dir, \"automatic_label_output.jpg\"), \n bbox_inches=\"tight\", dpi=300, pad_inches=0.0\n )\n\n save_mask_data(output_dir, caption, masks, boxes_filt, pred_phrases)\n" }, { "path": "data_generation/Grounded-Segment-Anything/automatic_label_ram_demo.py", "content": "import argparse\nimport os\n\nimport numpy as np\nimport json\nimport torch\nimport torchvision\nfrom PIL import Image\nimport litellm\n\n# Grounding DINO\nimport GroundingDINO.groundingdino.datasets.transforms as T\nfrom GroundingDINO.groundingdino.models import build_model\nfrom GroundingDINO.groundingdino.util.slconfig import SLConfig\nfrom GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap\n\n# segment anything\nfrom segment_anything import (\n build_sam,\n build_sam_hq,\n SamPredictor\n) \nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Recognize Anything Model & Tag2Text\nfrom ram.models import ram\nfrom ram import inference_ram\nimport torchvision.transforms as TS\n\n# ChatGPT or nltk is required when using tags_chineses\n# import openai\n# import nltk\n\ndef load_image(image_path):\n # load image\n image_pil = Image.open(image_path).convert(\"RGB\") # load image\n\n transform = T.Compose(\n [\n T.RandomResize([800], max_size=1333),\n T.ToTensor(),\n T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n ]\n )\n image, _ = transform(image_pil, None) # 3, h, w\n return image_pil, image\n\n\ndef check_tags_chinese(tags_chinese, pred_phrases, max_tokens=100, model=\"gpt-3.5-turbo\"):\n object_list = [obj.split('(')[0] for obj in pred_phrases]\n object_num = []\n for obj in set(object_list):\n object_num.append(f'{object_list.count(obj)} {obj}')\n object_num = ', '.join(object_num)\n print(f\"Correct object number: {object_num}\")\n\n if openai_key:\n prompt = [\n {\n 'role': 'system',\n 'content': 'Revise the number in the tags_chinese if it is wrong. ' + \\\n f'tags_chinese: {tags_chinese}. ' + \\\n f'True object number: {object_num}. ' + \\\n 'Only give the revised tags_chinese: '\n }\n ]\n response = litellm.completion(model=model, messages=prompt, temperature=0.6, max_tokens=max_tokens)\n reply = response['choices'][0]['message']['content']\n # sometimes return with \"tags_chinese: xxx, xxx, xxx\"\n tags_chinese = reply.split(':')[-1].strip()\n return tags_chinese\n\n\ndef load_model(model_config_path, model_checkpoint_path, device):\n args = SLConfig.fromfile(model_config_path)\n args.device = device\n model = build_model(args)\n checkpoint = torch.load(model_checkpoint_path, map_location=\"cpu\")\n load_res = model.load_state_dict(clean_state_dict(checkpoint[\"model\"]), strict=False)\n print(load_res)\n _ = model.eval()\n return model\n\n\ndef get_grounding_output(model, image, caption, box_threshold, text_threshold,device=\"cpu\"):\n caption = caption.lower()\n caption = caption.strip()\n if not caption.endswith(\".\"):\n caption = caption + \".\"\n model = model.to(device)\n image = image.to(device)\n with torch.no_grad():\n outputs = model(image[None], captions=[caption])\n logits = outputs[\"pred_logits\"].cpu().sigmoid()[0] # (nq, 256)\n boxes = outputs[\"pred_boxes\"].cpu()[0] # (nq, 4)\n logits.shape[0]\n\n # filter output\n logits_filt = logits.clone()\n boxes_filt = boxes.clone()\n filt_mask = logits_filt.max(dim=1)[0] > box_threshold\n logits_filt = logits_filt[filt_mask] # num_filt, 256\n boxes_filt = boxes_filt[filt_mask] # num_filt, 4\n logits_filt.shape[0]\n\n # get phrase\n tokenlizer = model.tokenizer\n tokenized = tokenlizer(caption)\n # build pred\n pred_phrases = []\n scores = []\n for logit, box in zip(logits_filt, boxes_filt):\n pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer)\n pred_phrases.append(pred_phrase + f\"({str(logit.max().item())[:4]})\")\n scores.append(logit.max().item())\n\n return boxes_filt, torch.Tensor(scores), pred_phrases\n\n\ndef show_mask(mask, ax, random_color=False):\n if random_color:\n color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)\n else:\n color = np.array([30/255, 144/255, 255/255, 0.6])\n h, w = mask.shape[-2:]\n mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\n ax.imshow(mask_image)\n\n\ndef show_box(box, ax, label):\n x0, y0 = box[0], box[1]\n w, h = box[2] - box[0], box[3] - box[1]\n ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2)) \n ax.text(x0, y0, label)\n\n\ndef save_mask_data(output_dir, tags_chinese, mask_list, box_list, label_list):\n value = 0 # 0 for background\n\n mask_img = torch.zeros(mask_list.shape[-2:])\n for idx, mask in enumerate(mask_list):\n mask_img[mask.cpu().numpy()[0] == True] = value + idx + 1\n plt.figure(figsize=(10, 10))\n plt.imshow(mask_img.numpy())\n plt.axis('off')\n plt.savefig(os.path.join(output_dir, 'mask.jpg'), bbox_inches=\"tight\", dpi=300, pad_inches=0.0)\n\n json_data = {\n 'tags_chinese': tags_chinese,\n 'mask':[{\n 'value': value,\n 'label': 'background'\n }]\n }\n for label, box in zip(label_list, box_list):\n value += 1\n name, logit = label.split('(')\n logit = logit[:-1] # the last is ')'\n json_data['mask'].append({\n 'value': value,\n 'label': name,\n 'logit': float(logit),\n 'box': box.numpy().tolist(),\n })\n with open(os.path.join(output_dir, 'label.json'), 'w') as f:\n json.dump(json_data, f)\n \n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser(\"Grounded-Segment-Anything Demo\", add_help=True)\n parser.add_argument(\"--config\", type=str, required=True, help=\"path to config file\")\n parser.add_argument(\n \"--ram_checkpoint\", type=str, required=True, help=\"path to checkpoint file\"\n )\n parser.add_argument(\n \"--grounded_checkpoint\", type=str, required=True, help=\"path to checkpoint file\"\n )\n parser.add_argument(\n \"--sam_checkpoint\", type=str, required=True, help=\"path to checkpoint file\"\n )\n parser.add_argument(\n \"--sam_hq_checkpoint\", type=str, default=None, help=\"path to sam-hq checkpoint file\"\n )\n parser.add_argument(\n \"--use_sam_hq\", action=\"store_true\", help=\"using sam-hq for prediction\"\n )\n parser.add_argument(\"--input_image\", type=str, required=True, help=\"path to image file\")\n parser.add_argument(\"--split\", default=\",\", type=str, help=\"split for text prompt\")\n parser.add_argument(\"--openai_key\", type=str, help=\"key for chatgpt\")\n parser.add_argument(\"--openai_proxy\", default=None, type=str, help=\"proxy for chatgpt\")\n parser.add_argument(\n \"--output_dir\", \"-o\", type=str, default=\"outputs\", required=True, help=\"output directory\"\n )\n\n parser.add_argument(\"--box_threshold\", type=float, default=0.25, help=\"box threshold\")\n parser.add_argument(\"--text_threshold\", type=float, default=0.2, help=\"text threshold\")\n parser.add_argument(\"--iou_threshold\", type=float, default=0.5, help=\"iou threshold\")\n\n parser.add_argument(\"--device\", type=str, default=\"cpu\", help=\"running on cpu only!, default=False\")\n args = parser.parse_args()\n\n # cfg\n config_file = args.config # change the path of the model config file\n ram_checkpoint = args.ram_checkpoint # change the path of the model\n grounded_checkpoint = args.grounded_checkpoint # change the path of the model\n sam_checkpoint = args.sam_checkpoint\n sam_hq_checkpoint = args.sam_hq_checkpoint\n use_sam_hq = args.use_sam_hq\n image_path = args.input_image\n split = args.split\n openai_key = args.openai_key\n openai_proxy = args.openai_proxy\n output_dir = args.output_dir\n box_threshold = args.box_threshold\n text_threshold = args.text_threshold\n iou_threshold = args.iou_threshold\n device = args.device\n \n # ChatGPT or nltk is required when using tags_chineses\n # openai.api_key = openai_key\n # if openai_proxy:\n # openai.proxy = {\"http\": openai_proxy, \"https\": openai_proxy}\n\n # make dir\n os.makedirs(output_dir, exist_ok=True)\n # load image\n image_pil, image = load_image(image_path)\n # load model\n model = load_model(config_file, grounded_checkpoint, device=device)\n\n # visualize raw image\n image_pil.save(os.path.join(output_dir, \"raw_image.jpg\"))\n\n # initialize Recognize Anything Model\n normalize = TS.Normalize(mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225])\n transform = TS.Compose([\n TS.Resize((384, 384)),\n TS.ToTensor(), normalize\n ])\n \n # load model\n ram_model = ram(pretrained=ram_checkpoint,\n image_size=384,\n vit='swin_l')\n # threshold for tagging\n # we reduce the threshold to obtain more tags\n ram_model.eval()\n\n ram_model = ram_model.to(device)\n raw_image = image_pil.resize(\n (384, 384))\n raw_image = transform(raw_image).unsqueeze(0).to(device)\n\n res = inference_ram(raw_image , ram_model)\n\n # Currently \", \" is better for detecting single tags\n # while \". \" is a little worse in some case\n tags=res[0].replace(' |', ',')\n tags_chinese=res[1].replace(' |', ',')\n\n print(\"Image Tags: \", res[0])\n print(\"图像标签: \", res[1])\n\n # run grounding dino model\n boxes_filt, scores, pred_phrases = get_grounding_output(\n model, image, tags, box_threshold, text_threshold, device=device\n )\n\n # initialize SAM\n if use_sam_hq:\n print(\"Initialize SAM-HQ Predictor\")\n predictor = SamPredictor(build_sam_hq(checkpoint=sam_hq_checkpoint).to(device))\n else:\n predictor = SamPredictor(build_sam(checkpoint=sam_checkpoint).to(device))\n image = cv2.imread(image_path)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n predictor.set_image(image)\n\n size = image_pil.size\n H, W = size[1], size[0]\n for i in range(boxes_filt.size(0)):\n boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])\n boxes_filt[i][:2] -= boxes_filt[i][2:] / 2\n boxes_filt[i][2:] += boxes_filt[i][:2]\n\n boxes_filt = boxes_filt.cpu()\n # use NMS to handle overlapped boxes\n print(f\"Before NMS: {boxes_filt.shape[0]} boxes\")\n nms_idx = torchvision.ops.nms(boxes_filt, scores, iou_threshold).numpy().tolist()\n boxes_filt = boxes_filt[nms_idx]\n pred_phrases = [pred_phrases[idx] for idx in nms_idx]\n print(f\"After NMS: {boxes_filt.shape[0]} boxes\")\n tags_chinese = check_tags_chinese(tags_chinese, pred_phrases)\n print(f\"Revise tags_chinese with number: {tags_chinese}\")\n\n transformed_boxes = predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2]).to(device)\n\n masks, _, _ = predictor.predict_torch(\n point_coords = None,\n point_labels = None,\n boxes = transformed_boxes.to(device),\n multimask_output = False,\n )\n \n # draw output image\n plt.figure(figsize=(10, 10))\n plt.imshow(image)\n for mask in masks:\n show_mask(mask.cpu().numpy(), plt.gca(), random_color=True)\n for box, label in zip(boxes_filt, pred_phrases):\n show_box(box.numpy(), plt.gca(), label)\n\n # plt.title('RAM-tags' + tags + '\\n' + 'RAM-tags_chineseing: ' + tags_chinese + '\\n')\n plt.axis('off')\n plt.savefig(\n os.path.join(output_dir, \"automatic_label_output.jpg\"), \n bbox_inches=\"tight\", dpi=300, pad_inches=0.0\n )\n\n save_mask_data(output_dir, tags_chinese, masks, boxes_filt, pred_phrases)\n" }, { "path": "data_generation/Grounded-Segment-Anything/automatic_label_simple_demo.py", "content": "import cv2\nimport numpy as np\nimport supervision as sv\nfrom typing import List\nfrom PIL import Image\n\nimport torch\n\nfrom groundingdino.util.inference import Model\nfrom segment_anything import sam_model_registry, SamPredictor\n\n# Tag2Text\n# from ram.models import tag2text_caption\nfrom ram.models import ram\n# from ram import inference_tag2text\nfrom ram import inference_ram\nimport torchvision\nimport torchvision.transforms as TS\n\n\n# Hyper-Params\nSOURCE_IMAGE_PATH = \"./assets/demo9.jpg\"\nDEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\nGROUNDING_DINO_CONFIG_PATH = \"GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py\"\nGROUNDING_DINO_CHECKPOINT_PATH = \"./groundingdino_swint_ogc.pth\"\n\nSAM_ENCODER_VERSION = \"vit_h\"\nSAM_CHECKPOINT_PATH = \"./sam_vit_h_4b8939.pth\"\n\nTAG2TEXT_CHECKPOINT_PATH = \"./tag2text_swin_14m.pth\"\nRAM_CHECKPOINT_PATH = \"./ram_swin_large_14m.pth\"\n\nTAG2TEXT_THRESHOLD = 0.64\nBOX_THRESHOLD = 0.2\nTEXT_THRESHOLD = 0.2\nIOU_THRESHOLD = 0.5\n\n# Building GroundingDINO inference model\ngrounding_dino_model = Model(model_config_path=GROUNDING_DINO_CONFIG_PATH, model_checkpoint_path=GROUNDING_DINO_CHECKPOINT_PATH)\n\n\n# Building SAM Model and SAM Predictor\nsam = sam_model_registry[SAM_ENCODER_VERSION](checkpoint=SAM_CHECKPOINT_PATH)\nsam_predictor = SamPredictor(sam)\n\n# Tag2Text\n# initialize Tag2Text\nnormalize = TS.Normalize(\n mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225]\n)\ntransform = TS.Compose(\n [\n TS.Resize((384, 384)),\n TS.ToTensor(), \n normalize\n ]\n)\n\nDELETE_TAG_INDEX = [] # filter out attributes and action which are difficult to be grounded\nfor idx in range(3012, 3429):\n DELETE_TAG_INDEX.append(idx)\n\n# tag2text_model = tag2text_caption(\n# pretrained=TAG2TEXT_CHECKPOINT_PATH,\n# image_size=384,\n# vit='swin_b',\n# delete_tag_index=DELETE_TAG_INDEX\n# )\n# # threshold for tagging\n# # we reduce the threshold to obtain more tags\n# tag2text_model.threshold = TAG2TEXT_THRESHOLD\n# tag2text_model.eval()\n# tag2text_model = tag2text_model.to(DEVICE)\n\nram_model = ram(pretrained=RAM_CHECKPOINT_PATH,\n image_size=384,\n vit='swin_l')\nram_model.eval()\nram_model = ram_model.to(DEVICE)\n\n# load image\nimage = cv2.imread(SOURCE_IMAGE_PATH) # bgr\nimage_pillow = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) # rgb\n\nimage_pillow = image_pillow.resize((384, 384))\nimage_pillow = transform(image_pillow).unsqueeze(0).to(DEVICE)\n\nspecified_tags='None'\n# res = inference_tag2text(image_pillow , tag2text_model, specified_tags)\nres = inference_ram(image_pillow , ram_model)\n\n# Currently \", \" is better for detecting single tags\n# while \". \" is a little worse in some case\nAUTOMATIC_CLASSES=res[0].split(\" | \")\n\nprint(f\"Tags: {res[0].replace(' |', ',')}\")\n\n\n# detect objects\ndetections = grounding_dino_model.predict_with_classes(\n image=image,\n classes=AUTOMATIC_CLASSES,\n box_threshold=BOX_THRESHOLD,\n text_threshold=BOX_THRESHOLD\n)\n\n# NMS post process\nprint(f\"Before NMS: {len(detections.xyxy)} boxes\")\nnms_idx = torchvision.ops.nms(\n torch.from_numpy(detections.xyxy), \n torch.from_numpy(detections.confidence), \n IOU_THRESHOLD\n).numpy().tolist()\n\ndetections.xyxy = detections.xyxy[nms_idx]\ndetections.confidence = detections.confidence[nms_idx]\ndetections.class_id = detections.class_id[nms_idx]\n\nprint(f\"After NMS: {len(detections.xyxy)} boxes\")\n\n# annotate image with detections\nbox_annotator = sv.BoxAnnotator()\nlabels = [\n f\"{AUTOMATIC_CLASSES[class_id]} {confidence:0.2f}\" \n for _, _, confidence, class_id, _, _\n in detections]\nannotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections, labels=labels)\n\n# save the annotated grounding dino image\ncv2.imwrite(\"groundingdino_auto_annotated_image.jpg\", annotated_frame)\n\n# Prompting SAM with detected boxes\ndef segment(sam_predictor: SamPredictor, image: np.ndarray, xyxy: np.ndarray) -> np.ndarray:\n sam_predictor.set_image(image)\n result_masks = []\n for box in xyxy:\n masks, scores, logits = sam_predictor.predict(\n box=box,\n multimask_output=True\n )\n index = np.argmax(scores)\n result_masks.append(masks[index])\n return np.array(result_masks)\n\n\n# convert detections to masks\ndetections.mask = segment(\n sam_predictor=sam_predictor,\n image=cv2.cvtColor(image, cv2.COLOR_BGR2RGB),\n xyxy=detections.xyxy\n)\n\n# annotate image with detections\nbox_annotator = sv.BoxAnnotator()\nmask_annotator = sv.MaskAnnotator()\nlabels = [\n f\"{AUTOMATIC_CLASSES[class_id]} {confidence:0.2f}\" \n for _, _, confidence, class_id, _, _\n in detections]\nannotated_image = mask_annotator.annotate(scene=image.copy(), detections=detections)\nannotated_image = box_annotator.annotate(scene=annotated_image, detections=detections, labels=labels)\n\n# save the annotated grounded-sam image\ncv2.imwrite(\"ram_grounded_sam_auto_annotated_image.jpg\", annotated_image)\n" }, { "path": "data_generation/Grounded-Segment-Anything/automatic_label_tag2text_demo.py", "content": "import argparse\nimport os\nimport copy\n\nimport numpy as np\nimport json\nimport torch\nimport torchvision\nfrom PIL import Image, ImageDraw, ImageFont\nimport litellm\n\n# Grounding DINO\nimport GroundingDINO.groundingdino.datasets.transforms as T\nfrom GroundingDINO.groundingdino.models import build_model\nfrom GroundingDINO.groundingdino.util import box_ops\nfrom GroundingDINO.groundingdino.util.slconfig import SLConfig\nfrom GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap\n\n# segment anything\nfrom segment_anything import build_sam, SamPredictor \nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Tag2Text\nfrom ram.models import tag2text_caption\nfrom ram import inference_tag2text\nimport torchvision.transforms as TS\n\n# ChatGPT or nltk is required when using captions\n# import openai\n# import nltk\n\ndef load_image(image_path):\n # load image\n image_pil = Image.open(image_path).convert(\"RGB\") # load image\n\n transform = T.Compose(\n [\n T.RandomResize([800], max_size=1333),\n T.ToTensor(),\n T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n ]\n )\n image, _ = transform(image_pil, None) # 3, h, w\n return image_pil, image\n\n\ndef generate_caption(raw_image, device):\n # unconditional image captioning\n if device == \"cuda\":\n inputs = processor(raw_image, return_tensors=\"pt\").to(\"cuda\", torch.float16)\n else:\n inputs = processor(raw_image, return_tensors=\"pt\")\n out = blip_model.generate(**inputs)\n caption = processor.decode(out[0], skip_special_tokens=True)\n return caption\n\n\ndef generate_tags(caption, split=',', max_tokens=100, model=\"gpt-3.5-turbo\"):\n lemma = nltk.wordnet.WordNetLemmatizer()\n if openai_key:\n prompt = [\n {\n 'role': 'system',\n 'content': 'Extract the unique nouns in the caption. Remove all the adjectives. ' + \\\n f'List the nouns in singular form. Split them by \"{split} \". ' + \\\n f'Caption: {caption}.'\n }\n ]\n response = litellm.completion(model=model, messages=prompt, temperature=0.6, max_tokens=max_tokens)\n reply = response['choices'][0]['message']['content']\n # sometimes return with \"noun: xxx, xxx, xxx\"\n tags = reply.split(':')[-1].strip()\n else:\n nltk.download(['punkt', 'averaged_perceptron_tagger', 'wordnet'])\n tags_list = [word for (word, pos) in nltk.pos_tag(nltk.word_tokenize(caption)) if pos[0] == 'N']\n tags_lemma = [lemma.lemmatize(w) for w in tags_list]\n tags = ', '.join(map(str, tags_lemma))\n return tags\n\n\ndef check_caption(caption, pred_phrases, max_tokens=100, model=\"gpt-3.5-turbo\"):\n object_list = [obj.split('(')[0] for obj in pred_phrases]\n object_num = []\n for obj in set(object_list):\n object_num.append(f'{object_list.count(obj)} {obj}')\n object_num = ', '.join(object_num)\n print(f\"Correct object number: {object_num}\")\n\n if openai_key:\n prompt = [\n {\n 'role': 'system',\n 'content': 'Revise the number in the caption if it is wrong. ' + \\\n f'Caption: {caption}. ' + \\\n f'True object number: {object_num}. ' + \\\n 'Only give the revised caption: '\n }\n ]\n response = litellm.completion(model=model, messages=prompt, temperature=0.6, max_tokens=max_tokens)\n reply = response['choices'][0]['message']['content']\n # sometimes return with \"Caption: xxx, xxx, xxx\"\n caption = reply.split(':')[-1].strip()\n return caption\n\n\ndef load_model(model_config_path, model_checkpoint_path, device):\n args = SLConfig.fromfile(model_config_path)\n args.device = device\n model = build_model(args)\n checkpoint = torch.load(model_checkpoint_path, map_location=\"cpu\")\n load_res = model.load_state_dict(clean_state_dict(checkpoint[\"model\"]), strict=False)\n print(load_res)\n _ = model.eval()\n return model\n\n\ndef get_grounding_output(model, image, caption, box_threshold, text_threshold,device=\"cpu\"):\n caption = caption.lower()\n caption = caption.strip()\n if not caption.endswith(\".\"):\n caption = caption + \".\"\n model = model.to(device)\n image = image.to(device)\n with torch.no_grad():\n outputs = model(image[None], captions=[caption])\n logits = outputs[\"pred_logits\"].cpu().sigmoid()[0] # (nq, 256)\n boxes = outputs[\"pred_boxes\"].cpu()[0] # (nq, 4)\n logits.shape[0]\n\n # filter output\n logits_filt = logits.clone()\n boxes_filt = boxes.clone()\n filt_mask = logits_filt.max(dim=1)[0] > box_threshold\n logits_filt = logits_filt[filt_mask] # num_filt, 256\n boxes_filt = boxes_filt[filt_mask] # num_filt, 4\n logits_filt.shape[0]\n\n # get phrase\n tokenlizer = model.tokenizer\n tokenized = tokenlizer(caption)\n # build pred\n pred_phrases = []\n scores = []\n for logit, box in zip(logits_filt, boxes_filt):\n pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer)\n pred_phrases.append(pred_phrase + f\"({str(logit.max().item())[:4]})\")\n scores.append(logit.max().item())\n\n return boxes_filt, torch.Tensor(scores), pred_phrases\n\n\ndef show_mask(mask, ax, random_color=False):\n if random_color:\n color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)\n else:\n color = np.array([30/255, 144/255, 255/255, 0.6])\n h, w = mask.shape[-2:]\n mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\n ax.imshow(mask_image)\n\n\ndef show_box(box, ax, label):\n x0, y0 = box[0], box[1]\n w, h = box[2] - box[0], box[3] - box[1]\n ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2)) \n ax.text(x0, y0, label)\n\n\ndef save_mask_data(output_dir, caption, mask_list, box_list, label_list):\n value = 0 # 0 for background\n\n mask_img = torch.zeros(mask_list.shape[-2:])\n for idx, mask in enumerate(mask_list):\n mask_img[mask.cpu().numpy()[0] == True] = value + idx + 1\n plt.figure(figsize=(10, 10))\n plt.imshow(mask_img.numpy())\n plt.axis('off')\n plt.savefig(os.path.join(output_dir, 'mask.jpg'), bbox_inches=\"tight\", dpi=300, pad_inches=0.0)\n\n json_data = {\n 'caption': caption,\n 'mask':[{\n 'value': value,\n 'label': 'background'\n }]\n }\n for label, box in zip(label_list, box_list):\n value += 1\n name, logit = label.split('(')\n logit = logit[:-1] # the last is ')'\n json_data['mask'].append({\n 'value': value,\n 'label': name,\n 'logit': float(logit),\n 'box': box.numpy().tolist(),\n })\n with open(os.path.join(output_dir, 'label.json'), 'w') as f:\n json.dump(json_data, f)\n \n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser(\"Grounded-Segment-Anything Demo\", add_help=True)\n parser.add_argument(\"--config\", type=str, required=True, help=\"path to config file\")\n parser.add_argument(\n \"--tag2text_checkpoint\", type=str, required=True, help=\"path to checkpoint file\"\n )\n parser.add_argument(\n \"--grounded_checkpoint\", type=str, required=True, help=\"path to checkpoint file\"\n )\n parser.add_argument(\n \"--sam_checkpoint\", type=str, required=True, help=\"path to checkpoint file\"\n )\n parser.add_argument(\"--input_image\", type=str, required=True, help=\"path to image file\")\n parser.add_argument(\"--split\", default=\",\", type=str, help=\"split for text prompt\")\n parser.add_argument(\"--openai_key\", type=str, help=\"key for chatgpt\")\n parser.add_argument(\"--openai_proxy\", default=None, type=str, help=\"proxy for chatgpt\")\n parser.add_argument(\n \"--output_dir\", \"-o\", type=str, default=\"outputs\", required=True, help=\"output directory\"\n )\n\n parser.add_argument(\"--box_threshold\", type=float, default=0.25, help=\"box threshold\")\n parser.add_argument(\"--text_threshold\", type=float, default=0.2, help=\"text threshold\")\n parser.add_argument(\"--iou_threshold\", type=float, default=0.5, help=\"iou threshold\")\n\n parser.add_argument(\"--device\", type=str, default=\"cpu\", help=\"running on cpu only!, default=False\")\n args = parser.parse_args()\n\n # cfg\n config_file = args.config # change the path of the model config file\n tag2text_checkpoint = args.tag2text_checkpoint # change the path of the model\n grounded_checkpoint = args.grounded_checkpoint # change the path of the model\n sam_checkpoint = args.sam_checkpoint\n image_path = args.input_image\n split = args.split\n openai_key = args.openai_key\n openai_proxy = args.openai_proxy\n output_dir = args.output_dir\n box_threshold = args.box_threshold\n text_threshold = args.text_threshold\n iou_threshold = args.iou_threshold\n device = args.device\n \n # ChatGPT or nltk is required when using captions\n # openai.api_key = openai_key\n # if openai_proxy:\n # openai.proxy = {\"http\": openai_proxy, \"https\": openai_proxy}\n\n # make dir\n os.makedirs(output_dir, exist_ok=True)\n # load image\n image_pil, image = load_image(image_path)\n # load model\n model = load_model(config_file, grounded_checkpoint, device=device)\n\n # visualize raw image\n image_pil.save(os.path.join(output_dir, \"raw_image.jpg\"))\n\n # initialize Tag2Text\n normalize = TS.Normalize(mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225])\n transform = TS.Compose([\n TS.Resize((384, 384)),\n TS.ToTensor(), normalize\n ])\n \n # filter out attributes and action categories which are difficult to grounding\n delete_tag_index = []\n for i in range(3012, 3429):\n delete_tag_index.append(i)\n\n specified_tags='None'\n # load model\n tag2text_model = tag2text_caption(pretrained=tag2text_checkpoint,\n image_size=384,\n vit='swin_b',\n delete_tag_index=delete_tag_index)\n # threshold for tagging\n # we reduce the threshold to obtain more tags\n tag2text_model.threshold = 0.64 \n tag2text_model.eval()\n\n tag2text_model = tag2text_model.to(device)\n raw_image = image_pil.resize(\n (384, 384))\n raw_image = transform(raw_image).unsqueeze(0).to(device)\n\n res = inference_tag2text(raw_image , tag2text_model, specified_tags)\n\n # Currently \", \" is better for detecting single tags\n # while \". \" is a little worse in some case\n text_prompt=res[0].replace(' |', ',')\n caption=res[2]\n\n print(f\"Caption: {caption}\")\n print(f\"Tags: {text_prompt}\")\n\n # run grounding dino model\n boxes_filt, scores, pred_phrases = get_grounding_output(\n model, image, text_prompt, box_threshold, text_threshold, device=device\n )\n\n # initialize SAM\n predictor = SamPredictor(build_sam(checkpoint=sam_checkpoint).to(device))\n image = cv2.imread(image_path)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n predictor.set_image(image)\n\n size = image_pil.size\n H, W = size[1], size[0]\n for i in range(boxes_filt.size(0)):\n boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])\n boxes_filt[i][:2] -= boxes_filt[i][2:] / 2\n boxes_filt[i][2:] += boxes_filt[i][:2]\n\n boxes_filt = boxes_filt.cpu()\n # use NMS to handle overlapped boxes\n print(f\"Before NMS: {boxes_filt.shape[0]} boxes\")\n nms_idx = torchvision.ops.nms(boxes_filt, scores, iou_threshold).numpy().tolist()\n boxes_filt = boxes_filt[nms_idx]\n pred_phrases = [pred_phrases[idx] for idx in nms_idx]\n print(f\"After NMS: {boxes_filt.shape[0]} boxes\")\n caption = check_caption(caption, pred_phrases)\n print(f\"Revise caption with number: {caption}\")\n\n transformed_boxes = predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2]).to(device)\n\n masks, _, _ = predictor.predict_torch(\n point_coords = None,\n point_labels = None,\n boxes = transformed_boxes.to(device),\n multimask_output = False,\n )\n \n # draw output image\n plt.figure(figsize=(10, 10))\n plt.imshow(image)\n for mask in masks:\n show_mask(mask.cpu().numpy(), plt.gca(), random_color=True)\n for box, label in zip(boxes_filt, pred_phrases):\n show_box(box.numpy(), plt.gca(), label)\n\n plt.title('Tag2Text-Captioning: ' + caption + '\\n' + 'Tag2Text-Tagging' + text_prompt + '\\n')\n plt.axis('off')\n plt.savefig(\n os.path.join(output_dir, \"automatic_label_output.jpg\"), \n bbox_inches=\"tight\", dpi=300, pad_inches=0.0\n )\n\n save_mask_data(output_dir, caption, masks, boxes_filt, pred_phrases)\n" }, { "path": "data_generation/Grounded-Segment-Anything/chatbot.py", "content": "# coding: utf-8\nimport os\nimport gradio as gr\nimport random\nimport torch\nimport cv2\nimport re\nimport uuid\nfrom PIL import Image, ImageDraw, ImageOps\nimport math\nimport numpy as np\nimport argparse\nimport inspect\n\nimport shutil\nimport torchvision\nimport whisper\nimport matplotlib.pyplot as plt\nfrom automatic_label_demo import load_model, load_image, get_grounding_output, show_box, show_mask, generate_tags, check_caption\nfrom grounding_dino_demo import plot_boxes_to_image\nfrom segment_anything import build_sam, SamAutomaticMaskGenerator, SamPredictor\nfrom segment_anything.utils.amg import remove_small_regions \n\nfrom transformers import CLIPSegProcessor, CLIPSegForImageSegmentation\nfrom transformers import pipeline, BlipProcessor, BlipForConditionalGeneration, BlipForQuestionAnswering\nfrom transformers import AutoImageProcessor, UperNetForSemanticSegmentation\n\nfrom diffusers import StableDiffusionPipeline, StableDiffusionInpaintPipeline, StableDiffusionInstructPix2PixPipeline\nfrom diffusers import EulerAncestralDiscreteScheduler\nfrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\nfrom controlnet_aux import OpenposeDetector, MLSDdetector, HEDdetector\n\nfrom langchain.agents.initialize import initialize_agent\nfrom langchain.agents.tools import Tool\nfrom langchain.chains.conversation.memory import ConversationBufferMemory\nfrom langchain.llms.openai import OpenAI\n\nVISUAL_CHATGPT_PREFIX = \"\"\"Visual ChatGPT is designed to be able to assist with a wide range of text and visual related tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. Visual ChatGPT is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nVisual ChatGPT is able to process and understand large amounts of text and images. As a language model, Visual ChatGPT can not directly read images, but it has a list of tools to finish different visual tasks. Each image will have a file name formed as \"image/xxx.png\", and Visual ChatGPT can invoke different tools to indirectly understand pictures. When talking about images, Visual ChatGPT is very strict to the file name and will never fabricate nonexistent files. When using tools to generate new image files, Visual ChatGPT is also known that the image may not be the same as the user's demand, and will use other visual question answering tools or description tools to observe the real image. Visual ChatGPT is able to use tools in a sequence, and is loyal to the tool observation outputs rather than faking the image content and image file name. It will remember to provide the file name from the last tool observation, if a new image is generated.\n\nHuman may provide new figures to Visual ChatGPT with a description. The description helps Visual ChatGPT to understand this image, but Visual ChatGPT should use tools to finish following tasks, rather than directly imagine from the description.\n\nOverall, Visual ChatGPT is a powerful visual dialogue assistant tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. \n\n\nTOOLS:\n------\n\nVisual ChatGPT has access to the following tools:\"\"\"\n\nVISUAL_CHATGPT_FORMAT_INSTRUCTIONS = \"\"\"To use a tool, please use the following format:\n\n```\nThought: Do I need to use a tool? Yes\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n```\n\nWhen you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:\n\n```\nThought: Do I need to use a tool? No\n{ai_prefix}: [your response here]\n```\n\"\"\"\n\nVISUAL_CHATGPT_SUFFIX = \"\"\"You are very strict to the filename correctness and will never fake a file name if it does not exist.\nYou will remember to provide the image file name loyally if it's provided in the last tool observation.\n\nBegin!\n\nPrevious conversation history:\n{chat_history}\n\nNew input: {input}\nSince Visual ChatGPT is a text language model, Visual ChatGPT must use tools to observe images rather than imagination.\nThe thoughts and observations are only visible for Visual ChatGPT, Visual ChatGPT should remember to repeat important information in the final response for Human. \nThought: Do I need to use a tool? {agent_scratchpad} Let's think step by step.\n\"\"\"\n\nVISUAL_CHATGPT_PREFIX_CN = \"\"\"Visual ChatGPT 旨在能够协助完成范围广泛的文本和视觉相关任务，从回答简单的问题到提供对广泛主题的深入解释和讨论。 Visual ChatGPT 能够根据收到的输入生成类似人类的文本，使其能够进行听起来自然的对话，并提供连贯且与手头主题相关的响应。\n\nVisual ChatGPT 能够处理和理解大量文本和图像。作为一种语言模型，Visual ChatGPT 不能直接读取图像，但它有一系列工具来完成不同的视觉任务。每张图片都会有一个文件名，格式为“image/xxx.png”，Visual ChatGPT可以调用不同的工具来间接理解图片。在谈论图片时，Visual ChatGPT 对文件名的要求非常严格，绝不会伪造不存在的文件。在使用工具生成新的图像文件时，Visual ChatGPT也知道图像可能与用户需求不一样，会使用其他视觉问答工具或描述工具来观察真实图像。 Visual ChatGPT 能够按顺序使用工具，并且忠于工具观察输出，而不是伪造图像内容和图像文件名。如果生成新图像，它将记得提供上次工具观察的文件名。\n\nHuman 可能会向 Visual ChatGPT 提供带有描述的新图形。描述帮助 Visual ChatGPT 理解这个图像，但 Visual ChatGPT 应该使用工具来完成以下任务，而不是直接从描述中想象。有些工具将会返回英文描述，但你对用户的聊天应当采用中文。\n\n总的来说，Visual ChatGPT 是一个强大的可视化对话辅助工具，可以帮助处理范围广泛的任务，并提供关于范围广泛的主题的有价值的见解和信息。\n\n工具列表:\n------\n\nVisual ChatGPT 可以使用这些工具:\"\"\"\n\nVISUAL_CHATGPT_FORMAT_INSTRUCTIONS_CN = \"\"\"用户使用中文和你进行聊天，但是工具的参数应当使用英文。如果要调用工具，你必须遵循如下格式:\n\n```\nThought: Do I need to use a tool? Yes\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n```\n\n当你不再需要继续调用工具，而是对观察结果进行总结回复时，你必须使用如下格式：\n\n\n```\nThought: Do I need to use a tool? No\n{ai_prefix}: [your response here]\n```\n\"\"\"\n\nVISUAL_CHATGPT_SUFFIX_CN = \"\"\"你对文件名的正确性非常严格，而且永远不会伪造不存在的文件。\n\n开始!\n\n因为Visual ChatGPT是一个文本语言模型，必须使用工具去观察图片而不是依靠想象。\n推理想法和观察结果只对Visual ChatGPT可见，需要记得在最终回复时把重要的信息重复给用户，你只能给用户返回中文句子。我们一步一步思考。在你使用工具时，工具的参数只能是英文。\n\n聊天历史:\n{chat_history}\n\n新输入: {input}\nThought: Do I need to use a tool? {agent_scratchpad}\n\"\"\"\n\nos.makedirs('image', exist_ok=True)\n\n\ndef seed_everything(seed):\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n return seed\n\n\ndef prompts(name, description):\n def decorator(func):\n func.name = name\n func.description = description\n return func\n\n return decorator\n\n\ndef blend_gt2pt(old_image, new_image, sigma=0.15, steps=100):\n new_size = new_image.size\n old_size = old_image.size\n easy_img = np.array(new_image)\n gt_img_array = np.array(old_image)\n pos_w = (new_size[0] - old_size[0]) // 2\n pos_h = (new_size[1] - old_size[1]) // 2\n\n kernel_h = cv2.getGaussianKernel(old_size[1], old_size[1] * sigma)\n kernel_w = cv2.getGaussianKernel(old_size[0], old_size[0] * sigma)\n kernel = np.multiply(kernel_h, np.transpose(kernel_w))\n\n kernel[steps:-steps, steps:-steps] = 1\n kernel[:steps, :steps] = kernel[:steps, :steps] / kernel[steps - 1, steps - 1]\n kernel[:steps, -steps:] = kernel[:steps, -steps:] / kernel[steps - 1, -(steps)]\n kernel[-steps:, :steps] = kernel[-steps:, :steps] / kernel[-steps, steps - 1]\n kernel[-steps:, -steps:] = kernel[-steps:, -steps:] / kernel[-steps, -steps]\n kernel = np.expand_dims(kernel, 2)\n kernel = np.repeat(kernel, 3, 2)\n\n weight = np.linspace(0, 1, steps)\n top = np.expand_dims(weight, 1)\n top = np.repeat(top, old_size[0] - 2 * steps, 1)\n top = np.expand_dims(top, 2)\n top = np.repeat(top, 3, 2)\n\n weight = np.linspace(1, 0, steps)\n down = np.expand_dims(weight, 1)\n down = np.repeat(down, old_size[0] - 2 * steps, 1)\n down = np.expand_dims(down, 2)\n down = np.repeat(down, 3, 2)\n\n weight = np.linspace(0, 1, steps)\n left = np.expand_dims(weight, 0)\n left = np.repeat(left, old_size[1] - 2 * steps, 0)\n left = np.expand_dims(left, 2)\n left = np.repeat(left, 3, 2)\n\n weight = np.linspace(1, 0, steps)\n right = np.expand_dims(weight, 0)\n right = np.repeat(right, old_size[1] - 2 * steps, 0)\n right = np.expand_dims(right, 2)\n right = np.repeat(right, 3, 2)\n\n kernel[:steps, steps:-steps] = top\n kernel[-steps:, steps:-steps] = down\n kernel[steps:-steps, :steps] = left\n kernel[steps:-steps, -steps:] = right\n\n pt_gt_img = easy_img[pos_h:pos_h + old_size[1], pos_w:pos_w + old_size[0]]\n gaussian_gt_img = kernel * gt_img_array + (1 - kernel) * pt_gt_img # gt img with blur img\n gaussian_gt_img = gaussian_gt_img.astype(np.int64)\n easy_img[pos_h:pos_h + old_size[1], pos_w:pos_w + old_size[0]] = gaussian_gt_img\n gaussian_img = Image.fromarray(easy_img)\n return gaussian_img\n\n\ndef cut_dialogue_history(history_memory, keep_last_n_words=500):\n if history_memory is None or len(history_memory) == 0:\n return history_memory\n tokens = history_memory.split()\n n_tokens = len(tokens)\n print(f\"history_memory:{history_memory}, n_tokens: {n_tokens}\")\n if n_tokens < keep_last_n_words:\n return history_memory\n paragraphs = history_memory.split('\\n')\n last_n_tokens = n_tokens\n while last_n_tokens >= keep_last_n_words:\n last_n_tokens -= len(paragraphs[0].split(' '))\n paragraphs = paragraphs[1:]\n return '\\n' + '\\n'.join(paragraphs)\n\n\ndef get_new_image_name(org_img_name, func_name=\"update\"):\n head_tail = os.path.split(org_img_name)\n head = head_tail[0]\n tail = head_tail[1]\n name_split = tail.split('.')[0].split('_')\n this_new_uuid = str(uuid.uuid4())[:4]\n if len(name_split) == 1:\n most_org_file_name = name_split[0]\n else:\n assert len(name_split) == 4\n most_org_file_name = name_split[3]\n recent_prev_file_name = name_split[0]\n new_file_name = f'{this_new_uuid}_{func_name}_{recent_prev_file_name}_{most_org_file_name}.png'\n return os.path.join(head, new_file_name)\n\n\n\nclass MaskFormer:\n def __init__(self, device):\n print(f\"Initializing MaskFormer to {device}\")\n self.device = device\n self.processor = CLIPSegProcessor.from_pretrained(\"CIDAS/clipseg-rd64-refined\")\n self.model = CLIPSegForImageSegmentation.from_pretrained(\"CIDAS/clipseg-rd64-refined\").to(device)\n\n def inference(self, image_path, text):\n threshold = 0.5\n min_area = 0.02\n padding = 20\n original_image = Image.open(image_path)\n image = original_image.resize((512, 512))\n inputs = self.processor(text=text, images=image, padding=\"max_length\", return_tensors=\"pt\").to(self.device)\n with torch.no_grad():\n outputs = self.model(**inputs)\n mask = torch.sigmoid(outputs[0]).squeeze().cpu().numpy() > threshold\n area_ratio = len(np.argwhere(mask)) / (mask.shape[0] * mask.shape[1])\n if area_ratio < min_area:\n return None\n true_indices = np.argwhere(mask)\n mask_array = np.zeros_like(mask, dtype=bool)\n for idx in true_indices:\n padded_slice = tuple(slice(max(0, i - padding), i + padding + 1) for i in idx)\n mask_array[padded_slice] = True\n visual_mask = (mask_array * 255).astype(np.uint8)\n image_mask = Image.fromarray(visual_mask)\n return image_mask.resize(original_image.size)\n\n\nclass ImageEditing:\n def __init__(self, device):\n print(f\"Initializing ImageEditing to {device}\")\n self.device = device\n self.mask_former = MaskFormer(device=self.device)\n self.revision = 'fp16' if 'cuda' in device else None\n self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32\n self.inpaint = StableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", revision=self.revision, torch_dtype=self.torch_dtype).to(device)\n\n @prompts(name=\"Replace Something From The Photo\",\n description=\"useful when you want to replace an object from the object description or \"\n \"location with another object from its description. \"\n \"The input to this tool should be a comma separated string of three, \"\n \"representing the image_path, the object to be replaced, the object to be replaced with \")\n def inference_replace(self, inputs):\n image_path, to_be_replaced_txt, replace_with_txt = inputs.split(\",\")\n original_image = Image.open(image_path)\n original_size = original_image.size\n mask_image = self.mask_former.inference(image_path, to_be_replaced_txt)\n updated_image = self.inpaint(prompt=replace_with_txt, image=original_image.resize((512, 512)),\n mask_image=mask_image.resize((512, 512))).images[0]\n updated_image_path = get_new_image_name(image_path, func_name=\"replace-something\")\n updated_image = updated_image.resize(original_size)\n updated_image.save(updated_image_path)\n print(\n f\"\\nProcessed ImageEditing, Input Image: {image_path}, Replace {to_be_replaced_txt} to {replace_with_txt}, \"\n f\"Output Image: {updated_image_path}\")\n return updated_image_path\n\n\nclass InstructPix2Pix:\n def __init__(self, device):\n print(f\"Initializing InstructPix2Pix to {device}\")\n self.device = device\n self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32\n self.pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(\"timbrooks/instruct-pix2pix\",\n safety_checker=None,\n torch_dtype=self.torch_dtype).to(device)\n self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config)\n\n @prompts(name=\"Instruct Image Using Text\",\n description=\"useful when you want to the style of the image to be like the text. \"\n \"like: make it look like a painting. or make it like a robot. \"\n \"The input to this tool should be a comma separated string of two, \"\n \"representing the image_path and the text. \")\n def inference(self, inputs):\n \"\"\"Change style of image.\"\"\"\n print(\"===>Starting InstructPix2Pix Inference\")\n image_path, text = inputs.split(\",\")[0], ','.join(inputs.split(',')[1:])\n original_image = Image.open(image_path)\n image = self.pipe(text, image=original_image, num_inference_steps=40, image_guidance_scale=1.2).images[0]\n updated_image_path = get_new_image_name(image_path, func_name=\"pix2pix\")\n image.save(updated_image_path)\n print(f\"\\nProcessed InstructPix2Pix, Input Image: {image_path}, Instruct Text: {text}, \"\n f\"Output Image: {updated_image_path}\")\n return updated_image_path\n\n\nclass Text2Image:\n def __init__(self, device):\n print(f\"Initializing Text2Image to {device}\")\n self.device = device\n self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32\n self.pipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\",\n torch_dtype=self.torch_dtype)\n self.pipe.to(device)\n self.a_prompt = 'best quality, extremely detailed'\n self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \\\n 'fewer digits, cropped, worst quality, low quality'\n\n @prompts(name=\"Generate Image From User Input Text\",\n description=\"useful when you want to generate an image from a user input text and save it to a file. \"\n \"like: generate an image of an object or something, or generate an image that includes some objects. \"\n \"The input to this tool should be a string, representing the text used to generate image. \")\n def inference(self, text):\n image_filename = os.path.join('image', f\"{str(uuid.uuid4())[:8]}.png\")\n prompt = text + ', ' + self.a_prompt\n image = self.pipe(prompt, negative_prompt=self.n_prompt).images[0]\n image.save(image_filename)\n print(\n f\"\\nProcessed Text2Image, Input Text: {text}, Output Image: {image_filename}\")\n return image_filename\n\n\nclass ImageCaptioning:\n def __init__(self, device):\n print(f\"Initializing ImageCaptioning to {device}\")\n self.device = device\n self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32\n self.processor = BlipProcessor.from_pretrained(\"Salesforce/blip-image-captioning-base\")\n self.model = BlipForConditionalGeneration.from_pretrained(\n \"Salesforce/blip-image-captioning-base\", torch_dtype=self.torch_dtype).to(self.device)\n\n @prompts(name=\"Get Photo Description\",\n description=\"useful when you want to know what is inside the photo. receives image_path as input. \"\n \"The input to this tool should be a string, representing the image_path. \")\n def inference(self, image_path):\n inputs = self.processor(Image.open(image_path), return_tensors=\"pt\").to(self.device, self.torch_dtype)\n out = self.model.generate(**inputs)\n captions = self.processor.decode(out[0], skip_special_tokens=True)\n print(f\"\\nProcessed ImageCaptioning, Input Image: {image_path}, Output Text: {captions}\")\n return captions\n\n\nclass Image2Canny:\n def __init__(self, device):\n print(\"Initializing Image2Canny\")\n self.low_threshold = 100\n self.high_threshold = 200\n\n @prompts(name=\"Edge Detection On Image\",\n description=\"useful when you want to detect the edge of the image. \"\n \"like: detect the edges of this image, or canny detection on image, \"\n \"or perform edge detection on this image, or detect the canny image of this image. \"\n \"The input to this tool should be a string, representing the image_path\")\n def inference(self, inputs):\n image = Image.open(inputs)\n image = np.array(image)\n canny = cv2.Canny(image, self.low_threshold, self.high_threshold)\n canny = canny[:, :, None]\n canny = np.concatenate([canny, canny, canny], axis=2)\n canny = Image.fromarray(canny)\n updated_image_path = get_new_image_name(inputs, func_name=\"edge\")\n canny.save(updated_image_path)\n print(f\"\\nProcessed Image2Canny, Input Image: {inputs}, Output Text: {updated_image_path}\")\n return updated_image_path\n\n\nclass CannyText2Image:\n def __init__(self, device):\n print(f\"Initializing CannyText2Image to {device}\")\n self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32\n self.controlnet = ControlNetModel.from_pretrained(\"fusing/stable-diffusion-v1-5-controlnet-canny\",\n torch_dtype=self.torch_dtype)\n self.pipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", controlnet=self.controlnet, safety_checker=None,\n torch_dtype=self.torch_dtype)\n self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)\n self.pipe.to(device)\n self.seed = -1\n self.a_prompt = 'best quality, extremely detailed'\n self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \\\n 'fewer digits, cropped, worst quality, low quality'\n\n @prompts(name=\"Generate Image Condition On Canny Image\",\n description=\"useful when you want to generate a new real image from both the user description and a canny image.\"\n \" like: generate a real image of a object or something from this canny image,\"\n \" or generate a new real image of a object or something from this edge image. \"\n \"The input to this tool should be a comma separated string of two, \"\n \"representing the image_path and the user description. \")\n def inference(self, inputs):\n image_path, instruct_text = inputs.split(\",\")[0], ','.join(inputs.split(',')[1:])\n image = Image.open(image_path)\n self.seed = random.randint(0, 65535)\n seed_everything(self.seed)\n prompt = f'{instruct_text}, {self.a_prompt}'\n image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,\n guidance_scale=9.0).images[0]\n updated_image_path = get_new_image_name(image_path, func_name=\"canny2image\")\n image.save(updated_image_path)\n print(f\"\\nProcessed CannyText2Image, Input Canny: {image_path}, Input Text: {instruct_text}, \"\n f\"Output Text: {updated_image_path}\")\n return updated_image_path\n\n\nclass Image2Line:\n def __init__(self, device):\n print(\"Initializing Image2Line\")\n self.detector = MLSDdetector.from_pretrained('lllyasviel/ControlNet')\n\n @prompts(name=\"Line Detection On Image\",\n description=\"useful when you want to detect the straight line of the image. \"\n \"like: detect the straight lines of this image, or straight line detection on image, \"\n \"or perform straight line detection on this image, or detect the straight line image of this image. \"\n \"The input to this tool should be a string, representing the image_path\")\n def inference(self, inputs):\n image = Image.open(inputs)\n mlsd = self.detector(image)\n updated_image_path = get_new_image_name(inputs, func_name=\"line-of\")\n mlsd.save(updated_image_path)\n print(f\"\\nProcessed Image2Line, Input Image: {inputs}, Output Line: {updated_image_path}\")\n return updated_image_path\n\n\nclass LineText2Image:\n def __init__(self, device):\n print(f\"Initializing LineText2Image to {device}\")\n self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32\n self.controlnet = ControlNetModel.from_pretrained(\"fusing/stable-diffusion-v1-5-controlnet-mlsd\",\n torch_dtype=self.torch_dtype)\n self.pipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", controlnet=self.controlnet, safety_checker=None,\n torch_dtype=self.torch_dtype\n )\n self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)\n self.pipe.to(device)\n self.seed = -1\n self.a_prompt = 'best quality, extremely detailed'\n self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \\\n 'fewer digits, cropped, worst quality, low quality'\n\n @prompts(name=\"Generate Image Condition On Line Image\",\n description=\"useful when you want to generate a new real image from both the user description \"\n \"and a straight line image. \"\n \"like: generate a real image of a object or something from this straight line image, \"\n \"or generate a new real image of a object or something from this straight lines. \"\n \"The input to this tool should be a comma separated string of two, \"\n \"representing the image_path and the user description. \")\n def inference(self, inputs):\n image_path, instruct_text = inputs.split(\",\")[0], ','.join(inputs.split(',')[1:])\n image = Image.open(image_path)\n self.seed = random.randint(0, 65535)\n seed_everything(self.seed)\n prompt = f'{instruct_text}, {self.a_prompt}'\n image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,\n guidance_scale=9.0).images[0]\n updated_image_path = get_new_image_name(image_path, func_name=\"line2image\")\n image.save(updated_image_path)\n print(f\"\\nProcessed LineText2Image, Input Line: {image_path}, Input Text: {instruct_text}, \"\n f\"Output Text: {updated_image_path}\")\n return updated_image_path\n\n\nclass Image2Hed:\n def __init__(self, device):\n print(\"Initializing Image2Hed\")\n self.detector = HEDdetector.from_pretrained('lllyasviel/ControlNet')\n\n @prompts(name=\"Hed Detection On Image\",\n description=\"useful when you want to detect the soft hed boundary of the image. \"\n \"like: detect the soft hed boundary of this image, or hed boundary detection on image, \"\n \"or perform hed boundary detection on this image, or detect soft hed boundary image of this image. \"\n \"The input to this tool should be a string, representing the image_path\")\n def inference(self, inputs):\n image = Image.open(inputs)\n hed = self.detector(image)\n updated_image_path = get_new_image_name(inputs, func_name=\"hed-boundary\")\n hed.save(updated_image_path)\n print(f\"\\nProcessed Image2Hed, Input Image: {inputs}, Output Hed: {updated_image_path}\")\n return updated_image_path\n\n\nclass HedText2Image:\n def __init__(self, device):\n print(f\"Initializing HedText2Image to {device}\")\n self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32\n self.controlnet = ControlNetModel.from_pretrained(\"fusing/stable-diffusion-v1-5-controlnet-hed\",\n torch_dtype=self.torch_dtype)\n self.pipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", controlnet=self.controlnet, safety_checker=None,\n torch_dtype=self.torch_dtype\n )\n self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)\n self.pipe.to(device)\n self.seed = -1\n self.a_prompt = 'best quality, extremely detailed'\n self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \\\n 'fewer digits, cropped, worst quality, low quality'\n\n @prompts(name=\"Generate Image Condition On Soft Hed Boundary Image\",\n description=\"useful when you want to generate a new real image from both the user description \"\n \"and a soft hed boundary image. \"\n \"like: generate a real image of a object or something from this soft hed boundary image, \"\n \"or generate a new real image of a object or something from this hed boundary. \"\n \"The input to this tool should be a comma separated string of two, \"\n \"representing the image_path and the user description\")\n def inference(self, inputs):\n image_path, instruct_text = inputs.split(\",\")[0], ','.join(inputs.split(',')[1:])\n image = Image.open(image_path)\n self.seed = random.randint(0, 65535)\n seed_everything(self.seed)\n prompt = f'{instruct_text}, {self.a_prompt}'\n image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,\n guidance_scale=9.0).images[0]\n updated_image_path = get_new_image_name(image_path, func_name=\"hed2image\")\n image.save(updated_image_path)\n print(f\"\\nProcessed HedText2Image, Input Hed: {image_path}, Input Text: {instruct_text}, \"\n f\"Output Image: {updated_image_path}\")\n return updated_image_path\n\n\nclass Image2Scribble:\n def __init__(self, device):\n print(\"Initializing Image2Scribble\")\n self.detector = HEDdetector.from_pretrained('lllyasviel/ControlNet')\n\n @prompts(name=\"Sketch Detection On Image\",\n description=\"useful when you want to generate a scribble of the image. \"\n \"like: generate a scribble of this image, or generate a sketch from this image, \"\n \"detect the sketch from this image. \"\n \"The input to this tool should be a string, representing the image_path\")\n def inference(self, inputs):\n image = Image.open(inputs)\n scribble = self.detector(image, scribble=True)\n updated_image_path = get_new_image_name(inputs, func_name=\"scribble\")\n scribble.save(updated_image_path)\n print(f\"\\nProcessed Image2Scribble, Input Image: {inputs}, Output Scribble: {updated_image_path}\")\n return updated_image_path\n\n\nclass ScribbleText2Image:\n def __init__(self, device):\n print(f\"Initializing ScribbleText2Image to {device}\")\n self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32\n self.controlnet = ControlNetModel.from_pretrained(\"fusing/stable-diffusion-v1-5-controlnet-scribble\",\n torch_dtype=self.torch_dtype)\n self.pipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", controlnet=self.controlnet, safety_checker=None,\n torch_dtype=self.torch_dtype\n )\n self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)\n self.pipe.to(device)\n self.seed = -1\n self.a_prompt = 'best quality, extremely detailed'\n self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \\\n 'fewer digits, cropped, worst quality, low quality'\n\n @prompts(name=\"Generate Image Condition On Sketch Image\",\n description=\"useful when you want to generate a new real image from both the user description and \"\n \"a scribble image or a sketch image. \"\n \"The input to this tool should be a comma separated string of two, \"\n \"representing the image_path and the user description\")\n def inference(self, inputs):\n image_path, instruct_text = inputs.split(\",\")[0], ','.join(inputs.split(',')[1:])\n image = Image.open(image_path)\n self.seed = random.randint(0, 65535)\n seed_everything(self.seed)\n prompt = f'{instruct_text}, {self.a_prompt}'\n image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,\n guidance_scale=9.0).images[0]\n updated_image_path = get_new_image_name(image_path, func_name=\"scribble2image\")\n image.save(updated_image_path)\n print(f\"\\nProcessed ScribbleText2Image, Input Scribble: {image_path}, Input Text: {instruct_text}, \"\n f\"Output Image: {updated_image_path}\")\n return updated_image_path\n\n\nclass Image2Pose:\n def __init__(self, device):\n print(\"Initializing Image2Pose\")\n self.detector = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')\n\n @prompts(name=\"Pose Detection On Image\",\n description=\"useful when you want to detect the human pose of the image. \"\n \"like: generate human poses of this image, or generate a pose image from this image. \"\n \"The input to this tool should be a string, representing the image_path\")\n def inference(self, inputs):\n image = Image.open(inputs)\n pose = self.detector(image)\n updated_image_path = get_new_image_name(inputs, func_name=\"human-pose\")\n pose.save(updated_image_path)\n print(f\"\\nProcessed Image2Pose, Input Image: {inputs}, Output Pose: {updated_image_path}\")\n return updated_image_path\n\n\nclass PoseText2Image:\n def __init__(self, device):\n print(f\"Initializing PoseText2Image to {device}\")\n self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32\n self.controlnet = ControlNetModel.from_pretrained(\"fusing/stable-diffusion-v1-5-controlnet-openpose\",\n torch_dtype=self.torch_dtype)\n self.pipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", controlnet=self.controlnet, safety_checker=None,\n torch_dtype=self.torch_dtype)\n self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)\n self.pipe.to(device)\n self.num_inference_steps = 20\n self.seed = -1\n self.unconditional_guidance_scale = 9.0\n self.a_prompt = 'best quality, extremely detailed'\n self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit,' \\\n ' fewer digits, cropped, worst quality, low quality'\n\n @prompts(name=\"Generate Image Condition On Pose Image\",\n description=\"useful when you want to generate a new real image from both the user description \"\n \"and a human pose image. \"\n \"like: generate a real image of a human from this human pose image, \"\n \"or generate a new real image of a human from this pose. \"\n \"The input to this tool should be a comma separated string of two, \"\n \"representing the image_path and the user description\")\n def inference(self, inputs):\n image_path, instruct_text = inputs.split(\",\")[0], ','.join(inputs.split(',')[1:])\n image = Image.open(image_path)\n self.seed = random.randint(0, 65535)\n seed_everything(self.seed)\n prompt = f'{instruct_text}, {self.a_prompt}'\n image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,\n guidance_scale=9.0).images[0]\n updated_image_path = get_new_image_name(image_path, func_name=\"pose2image\")\n image.save(updated_image_path)\n print(f\"\\nProcessed PoseText2Image, Input Pose: {image_path}, Input Text: {instruct_text}, \"\n f\"Output Image: {updated_image_path}\")\n return updated_image_path\n\n\nclass Image2Seg:\n def __init__(self, device):\n print(\"Initializing Image2Seg\")\n self.image_processor = AutoImageProcessor.from_pretrained(\"openmmlab/upernet-convnext-small\")\n self.image_segmentor = UperNetForSemanticSegmentation.from_pretrained(\"openmmlab/upernet-convnext-small\")\n self.ade_palette = [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],\n [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],\n [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],\n [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],\n [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],\n [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],\n [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],\n [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],\n [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],\n [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],\n [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],\n [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],\n [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],\n [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],\n [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],\n [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],\n [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],\n [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],\n [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],\n [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],\n [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],\n [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],\n [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],\n [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],\n [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],\n [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],\n [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],\n [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],\n [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],\n [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],\n [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],\n [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],\n [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],\n [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],\n [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],\n [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],\n [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],\n [102, 255, 0], [92, 0, 255]]\n\n @prompts(name=\"Segmentation On Image\",\n description=\"useful when you want to detect segmentations of the image. \"\n \"like: segment this image, or generate segmentations on this image, \"\n \"or perform segmentation on this image. \"\n \"The input to this tool should be a string, representing the image_path\")\n def inference(self, inputs):\n image = Image.open(inputs)\n pixel_values = self.image_processor(image, return_tensors=\"pt\").pixel_values\n with torch.no_grad():\n outputs = self.image_segmentor(pixel_values)\n seg = self.image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]\n color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3\n palette = np.array(self.ade_palette)\n for label, color in enumerate(palette):\n color_seg[seg == label, :] = color\n color_seg = color_seg.astype(np.uint8)\n segmentation = Image.fromarray(color_seg)\n updated_image_path = get_new_image_name(inputs, func_name=\"segmentation\")\n segmentation.save(updated_image_path)\n print(f\"\\nProcessed Image2Seg, Input Image: {inputs}, Output Pose: {updated_image_path}\")\n return updated_image_path\n\n\nclass SegText2Image:\n def __init__(self, device):\n print(f\"Initializing SegText2Image to {device}\")\n self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32\n self.controlnet = ControlNetModel.from_pretrained(\"fusing/stable-diffusion-v1-5-controlnet-seg\",\n torch_dtype=self.torch_dtype)\n self.pipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", controlnet=self.controlnet, safety_checker=None,\n torch_dtype=self.torch_dtype)\n self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)\n self.pipe.to(device)\n self.seed = -1\n self.a_prompt = 'best quality, extremely detailed'\n self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit,' \\\n ' fewer digits, cropped, worst quality, low quality'\n\n @prompts(name=\"Generate Image Condition On Segmentations\",\n description=\"useful when you want to generate a new real image from both the user description and segmentations. \"\n \"like: generate a real image of a object or something from this segmentation image, \"\n \"or generate a new real image of a object or something from these segmentations. \"\n \"The input to this tool should be a comma separated string of two, \"\n \"representing the image_path and the user description\")\n def inference(self, inputs):\n image_path, instruct_text = inputs.split(\",\")[0], ','.join(inputs.split(',')[1:])\n image = Image.open(image_path)\n self.seed = random.randint(0, 65535)\n seed_everything(self.seed)\n prompt = f'{instruct_text}, {self.a_prompt}'\n image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,\n guidance_scale=9.0).images[0]\n updated_image_path = get_new_image_name(image_path, func_name=\"segment2image\")\n image.save(updated_image_path)\n print(f\"\\nProcessed SegText2Image, Input Seg: {image_path}, Input Text: {instruct_text}, \"\n f\"Output Image: {updated_image_path}\")\n return updated_image_path\n\n\nclass Image2Depth:\n def __init__(self, device):\n print(\"Initializing Image2Depth\")\n self.depth_estimator = pipeline('depth-estimation')\n\n @prompts(name=\"Predict Depth On Image\",\n description=\"useful when you want to detect depth of the image. like: generate the depth from this image, \"\n \"or detect the depth map on this image, or predict the depth for this image. \"\n \"The input to this tool should be a string, representing the image_path\")\n def inference(self, inputs):\n image = Image.open(inputs)\n depth = self.depth_estimator(image)['depth']\n depth = np.array(depth)\n depth = depth[:, :, None]\n depth = np.concatenate([depth, depth, depth], axis=2)\n depth = Image.fromarray(depth)\n updated_image_path = get_new_image_name(inputs, func_name=\"depth\")\n depth.save(updated_image_path)\n print(f\"\\nProcessed Image2Depth, Input Image: {inputs}, Output Depth: {updated_image_path}\")\n return updated_image_path\n\n\nclass DepthText2Image:\n def __init__(self, device):\n print(f\"Initializing DepthText2Image to {device}\")\n self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32\n self.controlnet = ControlNetModel.from_pretrained(\n \"fusing/stable-diffusion-v1-5-controlnet-depth\", torch_dtype=self.torch_dtype)\n self.pipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", controlnet=self.controlnet, safety_checker=None,\n torch_dtype=self.torch_dtype)\n self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)\n self.pipe.to(device)\n self.seed = -1\n self.a_prompt = 'best quality, extremely detailed'\n self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit,' \\\n ' fewer digits, cropped, worst quality, low quality'\n\n @prompts(name=\"Generate Image Condition On Depth\",\n description=\"useful when you want to generate a new real image from both the user description and depth image. \"\n \"like: generate a real image of a object or something from this depth image, \"\n \"or generate a new real image of a object or something from the depth map. \"\n \"The input to this tool should be a comma separated string of two, \"\n \"representing the image_path and the user description\")\n def inference(self, inputs):\n image_path, instruct_text = inputs.split(\",\")[0], ','.join(inputs.split(',')[1:])\n image = Image.open(image_path)\n self.seed = random.randint(0, 65535)\n seed_everything(self.seed)\n prompt = f'{instruct_text}, {self.a_prompt}'\n image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,\n guidance_scale=9.0).images[0]\n updated_image_path = get_new_image_name(image_path, func_name=\"depth2image\")\n image.save(updated_image_path)\n print(f\"\\nProcessed DepthText2Image, Input Depth: {image_path}, Input Text: {instruct_text}, \"\n f\"Output Image: {updated_image_path}\")\n return updated_image_path\n\n\nclass Image2Normal:\n def __init__(self, device):\n print(\"Initializing Image2Normal\")\n self.depth_estimator = pipeline(\"depth-estimation\", model=\"Intel/dpt-hybrid-midas\")\n self.bg_threhold = 0.4\n\n @prompts(name=\"Predict Normal Map On Image\",\n description=\"useful when you want to detect norm map of the image. \"\n \"like: generate normal map from this image, or predict normal map of this image. \"\n \"The input to this tool should be a string, representing the image_path\")\n def inference(self, inputs):\n image = Image.open(inputs)\n original_size = image.size\n image = self.depth_estimator(image)['predicted_depth'][0]\n image = image.numpy()\n image_depth = image.copy()\n image_depth -= np.min(image_depth)\n image_depth /= np.max(image_depth)\n x = cv2.Sobel(image, cv2.CV_32F, 1, 0, ksize=3)\n x[image_depth < self.bg_threhold] = 0\n y = cv2.Sobel(image, cv2.CV_32F, 0, 1, ksize=3)\n y[image_depth < self.bg_threhold] = 0\n z = np.ones_like(x) * np.pi * 2.0\n image = np.stack([x, y, z], axis=2)\n image /= np.sum(image ** 2.0, axis=2, keepdims=True) ** 0.5\n image = (image * 127.5 + 127.5).clip(0, 255).astype(np.uint8)\n image = Image.fromarray(image)\n image = image.resize(original_size)\n updated_image_path = get_new_image_name(inputs, func_name=\"normal-map\")\n image.save(updated_image_path)\n print(f\"\\nProcessed Image2Normal, Input Image: {inputs}, Output Depth: {updated_image_path}\")\n return updated_image_path\n\n\nclass NormalText2Image:\n def __init__(self, device):\n print(f\"Initializing NormalText2Image to {device}\")\n self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32\n self.controlnet = ControlNetModel.from_pretrained(\n \"fusing/stable-diffusion-v1-5-controlnet-normal\", torch_dtype=self.torch_dtype)\n self.pipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", controlnet=self.controlnet, safety_checker=None,\n torch_dtype=self.torch_dtype)\n self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)\n self.pipe.to(device)\n self.seed = -1\n self.a_prompt = 'best quality, extremely detailed'\n self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit,' \\\n ' fewer digits, cropped, worst quality, low quality'\n\n @prompts(name=\"Generate Image Condition On Normal Map\",\n description=\"useful when you want to generate a new real image from both the user description and normal map. \"\n \"like: generate a real image of a object or something from this normal map, \"\n \"or generate a new real image of a object or something from the normal map. \"\n \"The input to this tool should be a comma separated string of two, \"\n \"representing the image_path and the user description\")\n def inference(self, inputs):\n image_path, instruct_text = inputs.split(\",\")[0], ','.join(inputs.split(',')[1:])\n image = Image.open(image_path)\n self.seed = random.randint(0, 65535)\n seed_everything(self.seed)\n prompt = f'{instruct_text}, {self.a_prompt}'\n image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,\n guidance_scale=9.0).images[0]\n updated_image_path = get_new_image_name(image_path, func_name=\"normal2image\")\n image.save(updated_image_path)\n print(f\"\\nProcessed NormalText2Image, Input Normal: {image_path}, Input Text: {instruct_text}, \"\n f\"Output Image: {updated_image_path}\")\n return updated_image_path\n\n\nclass VisualQuestionAnswering:\n def __init__(self, device):\n print(f\"Initializing VisualQuestionAnswering to {device}\")\n self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32\n self.device = device\n self.processor = BlipProcessor.from_pretrained(\"Salesforce/blip-vqa-base\")\n self.model = BlipForQuestionAnswering.from_pretrained(\n \"Salesforce/blip-vqa-base\", torch_dtype=self.torch_dtype).to(self.device)\n\n @prompts(name=\"Answer Question About The Image\",\n description=\"useful when you need an answer for a question based on an image. \"\n \"like: what is the background color of the last image, how many cats in this figure, what is in this figure. \"\n \"The input to this tool should be a comma separated string of two, representing the image_path and the question\")\n def inference(self, inputs):\n image_path, question = inputs.split(\",\")[0], ','.join(inputs.split(',')[1:])\n raw_image = Image.open(image_path).convert('RGB')\n inputs = self.processor(raw_image, question, return_tensors=\"pt\").to(self.device, self.torch_dtype)\n out = self.model.generate(**inputs)\n answer = self.processor.decode(out[0], skip_special_tokens=True)\n print(f\"\\nProcessed VisualQuestionAnswering, Input Image: {image_path}, Input Question: {question}, \"\n f\"Output Answer: {answer}\")\n return answer\n\n\nclass InfinityOutPainting:\n template_model = True # Add this line to show this is a template model.\n def __init__(self, ImageCaptioning, ImageEditing, VisualQuestionAnswering):\n self.llm = OpenAI(temperature=0)\n self.ImageCaption = ImageCaptioning\n self.ImageEditing = ImageEditing\n self.ImageVQA = VisualQuestionAnswering\n self.a_prompt = 'best quality, extremely detailed'\n self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \\\n 'fewer digits, cropped, worst quality, low quality'\n\n def get_BLIP_vqa(self, image, question):\n inputs = self.ImageVQA.processor(image, question, return_tensors=\"pt\").to(self.ImageVQA.device,\n self.ImageVQA.torch_dtype)\n out = self.ImageVQA.model.generate(**inputs)\n answer = self.ImageVQA.processor.decode(out[0], skip_special_tokens=True)\n print(f\"\\nProcessed VisualQuestionAnswering, Input Question: {question}, Output Answer: {answer}\")\n return answer\n\n def get_BLIP_caption(self, image):\n inputs = self.ImageCaption.processor(image, return_tensors=\"pt\").to(self.ImageCaption.device,\n self.ImageCaption.torch_dtype)\n out = self.ImageCaption.model.generate(**inputs)\n BLIP_caption = self.ImageCaption.processor.decode(out[0], skip_special_tokens=True)\n return BLIP_caption\n\n def check_prompt(self, prompt):\n check = f\"Here is a paragraph with adjectives. \" \\\n f\"{prompt} \" \\\n f\"Please change all plural forms in the adjectives to singular forms. \"\n return self.llm(check)\n\n def get_imagine_caption(self, image, imagine):\n BLIP_caption = self.get_BLIP_caption(image)\n background_color = self.get_BLIP_vqa(image, 'what is the background color of this image')\n style = self.get_BLIP_vqa(image, 'what is the style of this image')\n imagine_prompt = f\"let's pretend you are an excellent painter and now \" \\\n f\"there is an incomplete painting with {BLIP_caption} in the center, \" \\\n f\"please imagine the complete painting and describe it\" \\\n f\"you should consider the background color is {background_color}, the style is {style}\" \\\n f\"You should make the painting as vivid and realistic as possible\" \\\n f\"You can not use words like painting or picture\" \\\n f\"and you should use no more than 50 words to describe it\"\n caption = self.llm(imagine_prompt) if imagine else BLIP_caption\n caption = self.check_prompt(caption)\n print(f'BLIP observation: {BLIP_caption}, ChatGPT imagine to {caption}') if imagine else print(\n f'Prompt: {caption}')\n return caption\n\n def resize_image(self, image, max_size=1000000, multiple=8):\n aspect_ratio = image.size[0] / image.size[1]\n new_width = int(math.sqrt(max_size * aspect_ratio))\n new_height = int(new_width / aspect_ratio)\n new_width, new_height = new_width - (new_width % multiple), new_height - (new_height % multiple)\n return image.resize((new_width, new_height))\n\n def dowhile(self, original_img, tosize, expand_ratio, imagine, usr_prompt):\n old_img = original_img\n while (old_img.size != tosize):\n prompt = self.check_prompt(usr_prompt) if usr_prompt else self.get_imagine_caption(old_img, imagine)\n crop_w = 15 if old_img.size[0] != tosize[0] else 0\n crop_h = 15 if old_img.size[1] != tosize[1] else 0\n old_img = ImageOps.crop(old_img, (crop_w, crop_h, crop_w, crop_h))\n temp_canvas_size = (expand_ratio * old_img.width if expand_ratio * old_img.width < tosize[0] else tosize[0],\n expand_ratio * old_img.height if expand_ratio * old_img.height < tosize[1] else tosize[\n 1])\n temp_canvas, temp_mask = Image.new(\"RGB\", temp_canvas_size, color=\"white\"), Image.new(\"L\", temp_canvas_size,\n color=\"white\")\n x, y = (temp_canvas.width - old_img.width) // 2, (temp_canvas.height - old_img.height) // 2\n temp_canvas.paste(old_img, (x, y))\n temp_mask.paste(0, (x, y, x + old_img.width, y + old_img.height))\n resized_temp_canvas, resized_temp_mask = self.resize_image(temp_canvas), self.resize_image(temp_mask)\n image = self.ImageEditing.inpaint(prompt=prompt, image=resized_temp_canvas, mask_image=resized_temp_mask,\n height=resized_temp_canvas.height, width=resized_temp_canvas.width,\n num_inference_steps=50).images[0].resize(\n (temp_canvas.width, temp_canvas.height), Image.ANTIALIAS)\n image = blend_gt2pt(old_img, image)\n old_img = image\n return old_img\n\n @prompts(name=\"Extend An Image\",\n description=\"useful when you need to extend an image into a larger image.\"\n \"like: extend the image into a resolution of 2048x1024, extend the image into 2048x1024. \"\n \"The input to this tool should be a comma separated string of two, representing the image_path and the resolution of widthxheight\")\n def inference(self, inputs):\n image_path, resolution = inputs.split(',')\n width, height = resolution.split('x')\n tosize = (int(width), int(height))\n image = Image.open(image_path)\n image = ImageOps.crop(image, (10, 10, 10, 10))\n out_painted_image = self.dowhile(image, tosize, 4, True, False)\n updated_image_path = get_new_image_name(image_path, func_name=\"outpainting\")\n out_painted_image.save(updated_image_path)\n print(f\"\\nProcessed InfinityOutPainting, Input Image: {image_path}, Input Resolution: {resolution}, \"\n f\"Output Image: {updated_image_path}\")\n return updated_image_path\n\n#############################################New Tool#############################################\nclass Grounded_dino_sam_inpainting:\n def __init__(self, device):\n print(f\"Initializing BLIP\")\n self.device = device\n self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32\n self.blip_processor = BlipProcessor.from_pretrained(\"Salesforce/blip-image-captioning-large\")\n self.blip_model = BlipForConditionalGeneration.from_pretrained(\n \"Salesforce/blip-image-captioning-large\", torch_dtype=self.torch_dtype\n ).to(self.device)\n print(f\"Initializing GroundingDINO\")\n self.dino_model = load_model(\n model_config_path=\"GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py\", \n model_checkpoint_path=\"groundingdino_swint_ogc.pth\", \n device=self.device\n )\n print(f\"Initializing Segment Anthing\")\n self.sam_model = build_sam(checkpoint=\"sam_vit_h_4b8939.pth\").to(self.device)\n print(f\"Initializing Stable Diffusion\")\n self.sd_pipe = StableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", torch_dtype=self.torch_dtype\n ).to(self.device)\n\n @prompts(name=\"Get Photo Description\",\n description=\"useful when you want to know what is inside the photo. receives image_path as input. \"\n \"The input to this tool should be a string, representing the image_path. \")\n def inference_caption(self, image_path):\n inputs = self.blip_processor(Image.open(image_path), return_tensors=\"pt\").to(self.device, self.torch_dtype)\n out = self.blip_model.generate(**inputs)\n captions = self.blip_processor.decode(out[0], skip_special_tokens=True)\n print(f\"\\nProcessed ImageCaptioning, Input Image: {image_path}, Output Text: {captions}\")\n return captions\n \n def _detect_object(self, image_path, text_prompt, func_name):\n image_pil, image = load_image(image_path)\n boxes_filt, scores, pred_phrases = get_grounding_output(\n self.dino_model, image, text_prompt, 0.3, 0.25, device=self.device\n )\n # use NMS to handle overlapped boxes\n print(f\"Before NMS: {boxes_filt.shape[0]} boxes\")\n nms_idx = torchvision.ops.nms(boxes_filt, scores, 0.5).numpy().tolist()\n boxes_filt = boxes_filt[nms_idx]\n pred_phrases = [pred_phrases[idx] for idx in nms_idx]\n print(f\"After NMS: {boxes_filt.shape[0]} boxes\")\n size = image_pil.size\n pred_dict = {\n \"boxes\": boxes_filt,\n \"size\": [size[1], size[0]], # H,W\n \"labels\": pred_phrases,\n }\n image_with_box = plot_boxes_to_image(image_pil, pred_dict)[0]\n updated_image_path = get_new_image_name(image_path, func_name)\n image_with_box.save(updated_image_path)\n return updated_image_path\n\n @prompts(name=\"Detect One Object In Image\",\n description=\"useful when you want to detect the specific object in the image. \"\n \"like: detect the black dog in the image. \"\n \"The input to this tool should be a comma separated string of two, \"\n \"representing the image_path and the description of specific object.\")\n def inference_detect_one_object(self, inputs):\n image_path, text_prompt = inputs.split(',')\n print(f\"\\nInput Text Prompt: {text_prompt}\")\n updated_image_path = self._detect_object(image_path, text_prompt, func_name=\"det-object\")\n print(f\"Processed DetectOneObject, Input Image: {image_path}, Output Image: {updated_image_path}\")\n return updated_image_path\n\n @prompts(name=\"Detect Multiple Objects In Image\",\n description=\"useful when you want to detect two or more specific objects in the image. \"\n \"like: detect the black dog and white cat in the image. \"\n \"The input to this tool should be a comma separated string of two, \"\n \"representing the image_path and the description of multiple specific objects. \"\n \"Different description should be separated by symbol '&', \"\n \"like 'black dog & white cat'. \")\n def inference_detect_multi_object(self, inputs):\n image_path, text_prompt = inputs.split(',')\n processed_text_prompt = text_prompt.replace(' &', ',')\n print(f\"\\nOriginal Text Prompt: {text_prompt}, Input Text Prompt: {processed_text_prompt}\")\n updated_image_path = self._detect_object(image_path, text_prompt, func_name=\"det-objects\")\n print(f\"Processed DetectMultiObject, Input Image: {image_path}, Output Image: {updated_image_path}\")\n return updated_image_path\n\n # modified from https://github.com/Cheems-Seminar/segment-anything-and-name-it/blob/58408f1e4e340f565c5ef6b0c71920cdcd30b213/chatbot.py#L1046\n @prompts(name=\"Segment Anything in Image\",\n description=\"useful when you want to segment anything in the image. \"\n \"like: segment anything in the image. \"\n \"The input to this tool should be a string, representing the image_path. \")\n def inference_segment_anything(self, image_path):\n image = cv2.imread(image_path)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n mask_generator = SamAutomaticMaskGenerator(self.sam_model)\n anns = mask_generator.generate(image)\n plt.figure(figsize=(10, 10))\n plt.imshow(image)\n sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)\n ax = plt.gca()\n ax.set_autoscale_on(False)\n for ann in sorted_anns:\n m = ann['segmentation']\n img = np.ones((m.shape[0], m.shape[1], 3))\n color_mask = np.random.random((1, 3)).tolist()[0]\n for i in range(3):\n img[:,:,i] = color_mask[i]\n ax.imshow(np.dstack((img, m*0.35)))\n plt.axis('off')\n updated_image_path = get_new_image_name(image_path, func_name=\"seg-any\")\n plt.savefig(updated_image_path, bbox_inches='tight', dpi=300, pad_inches=0.0)\n print(f\"\\nProcessed SegmentAnything, Input Image: {image_path}, Output Image: {updated_image_path}\")\n return updated_image_path\n\n def _segment_object(self, image_path, text_prompt, func_name):\n image_pil, image = load_image(image_path)\n # run grounding dino model\n boxes_filt, scores, pred_phrases = get_grounding_output(\n self.dino_model, image, text_prompt, 0.25, 0.2, device=self.device\n )\n # initialize SAM\n predictor = SamPredictor(self.sam_model)\n image = cv2.imread(image_path)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n predictor.set_image(image)\n size = image_pil.size\n H, W = size[1], size[0]\n for i in range(boxes_filt.size(0)):\n boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])\n boxes_filt[i][:2] -= boxes_filt[i][2:] / 2\n boxes_filt[i][2:] += boxes_filt[i][:2]\n boxes_filt = boxes_filt.cpu()\n # use NMS to handle overlapped boxes\n print(f\"Before NMS: {boxes_filt.shape[0]} boxes\")\n nms_idx = torchvision.ops.nms(boxes_filt, scores, 0.5).numpy().tolist()\n boxes_filt = boxes_filt[nms_idx]\n pred_phrases = [pred_phrases[idx] for idx in nms_idx]\n print(f\"After NMS: {boxes_filt.shape[0]} boxes\")\n # generate mask\n transformed_boxes = predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2])\n masks, _, _ = predictor.predict_torch(\n point_coords = None,\n point_labels = None,\n boxes = transformed_boxes.to(self.device),\n multimask_output = False,\n )\n # remove the mask when area < area_thresh (in pixels)\n new_masks = []\n for mask in masks:\n # reshape to be used in remove_small_regions()\n mask = mask.cpu().numpy().squeeze()\n mask, _ = remove_small_regions(mask, 100, mode=\"holes\")\n mask, _ = remove_small_regions(mask, 100, mode=\"islands\")\n new_masks.append(torch.as_tensor(mask).unsqueeze(0))\n masks = torch.stack(new_masks, dim=0)\n # add box and mask in the image\n plt.figure(figsize=(10, 10))\n plt.imshow(image)\n for mask in masks:\n show_mask(mask.cpu().numpy(), plt.gca(), random_color=True)\n for box, label in zip(boxes_filt, pred_phrases):\n show_box(box.numpy(), plt.gca(), label)\n plt.axis('off')\n updated_image_path = get_new_image_name(image_path, func_name)\n plt.savefig(updated_image_path, bbox_inches='tight', dpi=300, pad_inches=0.0)\n return updated_image_path, pred_phrases\n\n @prompts(name=\"Segment One Object In Image\",\n description=\"useful when you want to segment the specific object in the image. \"\n \"like: segment the black dog in the image, or mask the black dog in the image. \"\n \"The input to this tool should be a comma separated string of two, \"\n \"representing the image_path and the description of specific object.\")\n def inference_segment_one_object(self, inputs):\n image_path, text_prompt = inputs.split(',')\n print(f\"\\nInput Text Prompt: {text_prompt}\")\n updated_image_path, _ = self._segment_object(image_path, text_prompt, func_name=\"seg-object\")\n print(f\"Processed SegmentOneObject, Input Image: {image_path}, Output Image: {updated_image_path}\")\n return updated_image_path\n\n @prompts(name=\"Segment Multiple Object In Image\",\n description=\"useful when you want to segment two or more specific objects in the image. \"\n \"like: segment the black dog and white cat in the image. \"\n \"The input to this tool should be a comma separated string of two, \"\n \"representing the image_path and the description of multiple specific objects. \"\n \"Different description should be separated by symbol '&', \"\n \"like 'black dog & white cat'. \")\n def inference_segment_multi_object(self, inputs):\n image_path, text_prompt = inputs.split(',')\n processed_text_prompt = text_prompt.replace(' &', ',')\n print(\"\\nOriginal Text Prompt: {text_prompt}, Input Text Prompt: {processed_text_prompt}, \")\n updated_image_path, _ = self._segment_object(image_path, text_prompt, func_name=\"seg-objects\")\n print(f\"Processed SegmentMultiObject, Input Image: {image_path}, Output Image: {updated_image_path}\")\n return updated_image_path\n\n @prompts(name=\"Auto Label the Image\",\n description=\"useful when you want to label the image automatically. \"\n \"like: help me label the image. \"\n \"The input to this tool should be a string, representing the image_path. \")\n def inference_auto_segment_object(self, image_path):\n inputs = self.blip_processor(Image.open(image_path), return_tensors=\"pt\").to(self.device, self.torch_dtype)\n out = self.blip_model.generate(**inputs)\n caption = self.blip_processor.decode(out[0], skip_special_tokens=True)\n text_prompt = generate_tags(caption, split=\",\")\n print(f\"\\nCaption: {caption}\")\n print(f\"Tags: {text_prompt}\")\n updated_image_path, pred_phrases = self._segment_object(image_path, text_prompt, func_name=\"auto-label\")\n caption = check_caption(caption, pred_phrases)\n print(f\"Revise caption with number: {caption}\")\n print(f\"Processed SegmentMultiObject, Input Image: {image_path}, Caption: {caption}, \"\n f\"Text Prompt: {text_prompt}, Output Image: {updated_image_path}\")\n return updated_image_path\n\n def _inpainting(self, image_path, to_be_replaced_txt, replace_with_txt, func_name):\n image_pil, image = load_image(image_path)\n # run grounding dino model\n boxes_filt, scores, pred_phrases = get_grounding_output(\n self.dino_model, image, to_be_replaced_txt, 0.3, 0.25, device=self.device\n )\n # initialize SAM\n predictor = SamPredictor(self.sam_model)\n image = cv2.imread(image_path)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n predictor.set_image(image)\n size = image_pil.size\n H, W = size[1], size[0]\n for i in range(boxes_filt.size(0)):\n boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])\n boxes_filt[i][:2] -= boxes_filt[i][2:] / 2\n boxes_filt[i][2:] += boxes_filt[i][:2]\n boxes_filt = boxes_filt.cpu()\n # generate mask\n transformed_boxes = predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2])\n masks, _, _ = predictor.predict_torch(\n point_coords = None,\n point_labels = None,\n boxes = transformed_boxes.to(self.device),\n multimask_output = False,\n )\n # inpainting pipeline\n mask = masks[0][0].cpu().numpy() # simply choose the first mask, which will be refine in the future release\n mask_pil = Image.fromarray(mask).resize((512, 512))\n image_pil = Image.fromarray(image).resize((512, 512))\n image = self.sd_pipe(prompt=replace_with_txt, image=image_pil, mask_image=mask_pil).images[0]\n updated_image_path = get_new_image_name(image_path, func_name)\n image.save(updated_image_path)\n return updated_image_path\n\n @prompts(name=\"Replace Something From The Photo\",\n description=\"useful when you want to replace an object from the object description or \"\n \"location with another object from its description. \"\n \"The input to this tool should be a comma separated string of three, \"\n \"representing the image_path, the object to be replaced, the object to be replaced with \")\n def inference_replace(self, inputs):\n image_path, to_be_replaced_txt, replace_with_txt = inputs.split(\",\")\n print(f\"\\nReplace {to_be_replaced_txt} to {replace_with_txt}\")\n updated_image_path = self._inpainting(image_path, to_be_replaced_txt, replace_with_txt, 'replace-something')\n print(f\"Processed ImageEditing, Input Image: {image_path}, Output Image: {updated_image_path}\")\n return updated_image_path\n\n#############################################New Tool#############################################\n\n\nclass ConversationBot:\n def __init__(self, load_dict):\n # load_dict = {'VisualQuestionAnswering':'cuda:0', 'ImageCaptioning':'cuda:1',...}\n print(f\"Initializing VisualChatGPT, load_dict={load_dict}\")\n if 'ImageCaptioning' not in load_dict and 'Grounded_dino_sam_inpainting' not in load_dict:\n raise ValueError(\"You have to load ImageCaptioning or Grounded_dino_sam_inpainting as a basic function for VisualChatGPT\")\n\n self.models = {}\n # Load Basic Foundation Models\n for class_name, device in load_dict.items():\n self.models[class_name] = globals()[class_name](device=device)\n\n # Load Template Foundation Models\n for class_name, module in globals().items():\n if getattr(module, 'template_model', False):\n template_required_names = {k for k in inspect.signature(module.__init__).parameters.keys() if k!='self'}\n loaded_names = set([type(e).__name__ for e in self.models.values()])\n if template_required_names.issubset(loaded_names):\n self.models[class_name] = globals()[class_name](\n **{name: self.models[name] for name in template_required_names})\n self.tools = []\n for instance in self.models.values():\n for e in dir(instance):\n if e.startswith('inference'):\n func = getattr(instance, e)\n self.tools.append(Tool(name=func.name, description=func.description, func=func))\n self.llm = OpenAI(temperature=0)\n self.memory = ConversationBufferMemory(memory_key=\"chat_history\", output_key='output')\n\n def run_text(self, text, state):\n self.agent.memory.buffer = cut_dialogue_history(self.agent.memory.buffer, keep_last_n_words=500)\n res = self.agent({\"input\": text.strip()})\n res['output'] = res['output'].replace(\"\\\\\", \"/\")\n response = re.sub('(image/[-\\w]*.png)', lambda m: f'![](/file={m.group(0)})*{m.group(0)}*', res['output'])\n state = state + [(text, response)]\n print(f\"\\nProcessed run_text, Input text: {text}\\nCurrent state: {state}\\n\"\n f\"Current Memory: {self.agent.memory.buffer}\")\n return state, state\n\n def run_image(self, image, state, txt, lang):\n # image_filename = os.path.join('image', f\"{str(uuid.uuid4())[:8]}.png\")\n # print(\"======>Auto Resize Image...\")\n # img = Image.open(image.name)\n # width, height = img.size\n # ratio = min(512 / width, 512 / height)\n # width_new, height_new = (round(width * ratio), round(height * ratio))\n # width_new = int(np.round(width_new / 64.0)) * 64\n # height_new = int(np.round(height_new / 64.0)) * 64\n # img = img.resize((width_new, height_new))\n # img = img.convert('RGB')\n # img.save(image_filename)\n # img.save(image_filename, \"PNG\")\n # print(f\"Resize image form {width}x{height} to {width_new}x{height_new}\")\n ## Directly use original image for better results\n suffix = image.name.split('.')[-1] \n image_filename = os.path.join('image', f\"{str(uuid.uuid4())[:8]}.{suffix}\")\n shutil.copy(image.name, image_filename)\n if 'Grounded_dino_sam_inpainting' in self.models:\n description = self.models['Grounded_dino_sam_inpainting'].inference_caption(image_filename)\n else:\n description = self.models['ImageCaptioning'].inference(image_filename)\n if lang == 'Chinese':\n Human_prompt = f'\\nHuman: 提供一张名为 {image_filename}的图片。它的描述是: {description}。 这些信息帮助你理解这个图像，但是你应该使用工具来完成下面的任务，而不是直接从我的描述中想象。 如果你明白了, 说 \\\"收到\\\". \\n'\n AI_prompt = \"收到。 \"\n else:\n Human_prompt = f'\\nHuman: provide a figure named {image_filename}. The description is: {description}. This information helps you to understand this image, but you should use tools to finish following tasks, rather than directly imagine from my description. If you understand, say \\\"Received\\\". \\n'\n AI_prompt = \"Received. \"\n self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + 'AI: ' + AI_prompt\n state = state + [(f\"![](/file={image_filename})*{image_filename}*\", AI_prompt)]\n print(f\"\\nProcessed run_image, Input image: {image_filename}\\nCurrent state: {state}\\n\"\n f\"Current Memory: {self.agent.memory.buffer}\")\n return state, state, f'{txt} {image_filename} '\n\n def init_agent(self, openai_api_key, lang):\n self.memory.clear() #clear previous history\n if lang=='English':\n PREFIX, FORMAT_INSTRUCTIONS, SUFFIX = VISUAL_CHATGPT_PREFIX, VISUAL_CHATGPT_FORMAT_INSTRUCTIONS, VISUAL_CHATGPT_SUFFIX\n place = \"Enter text and press enter, or upload an image\"\n label_clear = \"Clear\"\n else:\n PREFIX, FORMAT_INSTRUCTIONS, SUFFIX = VISUAL_CHATGPT_PREFIX_CN, VISUAL_CHATGPT_FORMAT_INSTRUCTIONS_CN, VISUAL_CHATGPT_SUFFIX_CN\n place = \"输入文字并回车，或者上传图片\"\n label_clear = \"清除\"\n self.llm = OpenAI(temperature=0, openai_api_key=openai_api_key)\n self.agent = initialize_agent(\n self.tools,\n self.llm,\n agent=\"conversational-react-description\",\n verbose=True,\n memory=self.memory,\n return_intermediate_steps=True,\n agent_kwargs={'prefix': PREFIX, 'format_instructions': FORMAT_INSTRUCTIONS, 'suffix': SUFFIX}, )\n return gr.update(visible = True), gr.update(visible = True)\n\n\nwhisper_model = whisper.load_model(\"base\").to('cuda:0')\ndef speech_recognition(speech_file):\n # whisper\n # load audio and pad/trim it to fit 30 seconds\n audio = whisper.load_audio(speech_file)\n audio = whisper.pad_or_trim(audio)\n\n # make log-Mel spectrogram and move to the same device as the model\n mel = whisper.log_mel_spectrogram(audio).to(whisper_model.device)\n\n # detect the spoken language\n _, probs = whisper_model.detect_language(mel)\n speech_language = max(probs, key=probs.get)\n print(f'\\nDetect Language: {speech_language}')\n\n # decode the audio\n options = whisper.DecodingOptions(fp16 = False)\n result = whisper.decode(whisper_model, mel, options)\n print(result.text)\n\n return result.text\n\n\nif __name__ == '__main__':\n load_dict = {'Grounded_dino_sam_inpainting': 'cuda:0'}\n # load_dict = {'ImageCaptioning': 'cuda:0'}\n \n bot = ConversationBot(load_dict)\n\n with gr.Blocks(css=\"#chatbot {overflow:auto; height:500px;}\") as demo:\n gr.Markdown(\"

ChatBot

\")\n gr.Markdown(\n \"\"\"This is a demo to the work [Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything).
\n \"\"\"\n )\n\n with gr.Row():\n lang = gr.Radio(choices=['Chinese', 'English'], value='English', label='Language')\n openai_api_key_textbox = gr.Textbox(\n placeholder=\"Paste your OpenAI API key here to start ChatBot(sk-...) and press Enter ↵️\",\n show_label=False,\n lines=1,\n type=\"password\",\n )\n\n chatbot = gr.Chatbot(elem_id=\"chatbot\", label=\"ChatBot\")\n state = gr.State([])\n\n with gr.Row(visible=False) as input_raws:\n with gr.Column(scale=0.7):\n txt = gr.Textbox(show_label=False, placeholder=\"Enter text and press enter, or upload an image\").style(container=False)\n with gr.Column(scale=0.10, min_width=0):\n run = gr.Button(\"🏃‍♂️Run\")\n with gr.Column(scale=0.10, min_width=0):\n clear = gr.Button(\"🔄Clear️\")\n with gr.Column(scale=0.10, min_width=0):\n btn = gr.UploadButton(\"🖼️Upload\", file_types=[\"image\"])\n with gr.Row(visible=False, equal_height=True) as audio_raw:\n with gr.Column(scale=0.85):\n audio = gr.Audio(source=\"microphone\", type=\"filepath\", label=\"Just say it!\")\n with gr.Column(scale=0.15):\n transcribe = gr.Button(\"Transcribe\")\n\n gr.Examples(\n examples=[\n \"Describe this image\",\n \"Detect the dog\",\n \"Detect the dog and the cat\",\n \"Segment anything\",\n \"Segment the dog\",\n \"Help me label the image\",\n \"Replace the dog with a cat\",\n ],\n inputs=txt\n )\n\n openai_api_key_textbox.submit(bot.init_agent, [openai_api_key_textbox, lang], [input_raws, audio_raw])\n transcribe.click(speech_recognition, inputs=[audio], outputs=[txt])\n txt.submit(bot.run_text, [txt, state], [chatbot, state])\n txt.submit(lambda: \"\", None, txt)\n run.click(bot.run_text, [txt, state], [chatbot, state])\n run.click(lambda: \"\", None, txt)\n btn.upload(bot.run_image, [btn, state, txt, lang], [chatbot, state, txt])\n clear.click(bot.memory.clear)\n clear.click(lambda: [], None, chatbot)\n clear.click(lambda: [], None, state)\n\n demo.launch(server_name=\"0.0.0.0\", server_port=10010)\n\n" }, { "path": "data_generation/Grounded-Segment-Anything/cog.yaml", "content": "# Configuration for Cog ⚙️\n# Reference: https://github.com/replicate/cog/blob/main/docs/yaml.md\n\nbuild:\n gpu: true\n cuda: \"11.7\"\n system_packages:\n - \"libgl1-mesa-glx\"\n - \"libglib2.0-0\"\n python_version: \"3.10\"\n python_packages:\n - \"timm==0.9.2\"\n - \"transformers==4.30.2\"\n - \"fairscale==0.4.13\"\n - \"pycocoevalcap==1.2\"\n - \"torch==1.13.0\"\n - \"torchvision==0.14.0\"\n - \"Pillow==9.5.0\"\n - \"scipy==1.10.1\"\n - \"opencv-python==4.7.0.72\"\n - \"addict==2.4.0\"\n - \"yapf==0.40.0\"\n - \"supervision==0.10.0\"\n - git+https://github.com/openai/CLIP.git\n - ipython\n\npredict: \"predict.py:Predictor\"\n" }, { "path": "data_generation/Grounded-Segment-Anything/gradio_app.py", "content": "import os\nimport random\nimport cv2\nfrom scipy import ndimage\n\nimport gradio as gr\nimport argparse\nimport litellm\n\nimport numpy as np\nimport torch\nimport torchvision\nfrom PIL import Image, ImageDraw, ImageFont\n\n# Grounding DINO\nimport GroundingDINO.groundingdino.datasets.transforms as T\nfrom GroundingDINO.groundingdino.models import build_model\nfrom GroundingDINO.groundingdino.util.slconfig import SLConfig\nfrom GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap\n\n# segment anything\nfrom segment_anything import build_sam, SamPredictor, SamAutomaticMaskGenerator\nimport numpy as np\n\n# diffusers\nimport torch\nfrom diffusers import StableDiffusionInpaintPipeline\n\n# BLIP\nfrom transformers import BlipProcessor, BlipForConditionalGeneration\n\nimport openai\n\ndef show_anns(anns):\n if len(anns) == 0:\n return\n sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)\n full_img = None\n\n # for ann in sorted_anns:\n for i in range(len(sorted_anns)):\n ann = anns[i]\n m = ann['segmentation']\n if full_img is None:\n full_img = np.zeros((m.shape[0], m.shape[1], 3))\n map = np.zeros((m.shape[0], m.shape[1]), dtype=np.uint16)\n map[m != 0] = i + 1\n color_mask = np.random.random((1, 3)).tolist()[0]\n full_img[m != 0] = color_mask\n full_img = full_img*255\n # anno encoding from https://github.com/LUSSeg/ImageNet-S\n res = np.zeros((map.shape[0], map.shape[1], 3))\n res[:, :, 0] = map % 256\n res[:, :, 1] = map // 256\n res.astype(np.float32)\n full_img = Image.fromarray(np.uint8(full_img))\n return full_img, res\n\ndef generate_caption(processor, blip_model, raw_image):\n # unconditional image captioning\n inputs = processor(raw_image, return_tensors=\"pt\").to(\"cuda\", torch.float16)\n out = blip_model.generate(**inputs)\n caption = processor.decode(out[0], skip_special_tokens=True)\n return caption\n\ndef generate_tags(caption, split=',', max_tokens=100, model=\"gpt-3.5-turbo\", openai_api_key=''):\n openai.api_key = openai_api_key\n openai.api_base = 'https://closeai.deno.dev/v1'\n prompt = [\n {\n 'role': 'system',\n 'content': 'Extract the unique nouns in the caption. Remove all the adjectives. ' + \\\n f'List the nouns in singular form. Split them by \"{split} \". ' + \\\n f'Caption: {caption}.'\n }\n ]\n response = litellm.completion(model=model, messages=prompt, temperature=0.6, max_tokens=max_tokens)\n reply = response['choices'][0]['message']['content']\n # sometimes return with \"noun: xxx, xxx, xxx\"\n tags = reply.split(':')[-1].strip()\n return tags\n\ndef transform_image(image_pil):\n\n transform = T.Compose(\n [\n T.RandomResize([800], max_size=1333),\n T.ToTensor(),\n T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n ]\n )\n image, _ = transform(image_pil, None) # 3, h, w\n return image\n\n\ndef load_model(model_config_path, model_checkpoint_path, device):\n args = SLConfig.fromfile(model_config_path)\n args.device = device\n model = build_model(args)\n checkpoint = torch.load(model_checkpoint_path, map_location=\"cpu\")\n load_res = model.load_state_dict(clean_state_dict(checkpoint[\"model\"]), strict=False)\n print(load_res)\n _ = model.eval()\n return model\n\n\ndef get_grounding_output(model, image, caption, box_threshold, text_threshold, with_logits=True):\n caption = caption.lower()\n caption = caption.strip()\n if not caption.endswith(\".\"):\n caption = caption + \".\"\n\n with torch.no_grad():\n outputs = model(image[None], captions=[caption])\n logits = outputs[\"pred_logits\"].cpu().sigmoid()[0] # (nq, 256)\n boxes = outputs[\"pred_boxes\"].cpu()[0] # (nq, 4)\n logits.shape[0]\n\n # filter output\n logits_filt = logits.clone()\n boxes_filt = boxes.clone()\n filt_mask = logits_filt.max(dim=1)[0] > box_threshold\n logits_filt = logits_filt[filt_mask] # num_filt, 256\n boxes_filt = boxes_filt[filt_mask] # num_filt, 4\n logits_filt.shape[0]\n\n # get phrase\n tokenlizer = model.tokenizer\n tokenized = tokenlizer(caption)\n # build pred\n pred_phrases = []\n scores = []\n for logit, box in zip(logits_filt, boxes_filt):\n pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer)\n if with_logits:\n pred_phrases.append(pred_phrase + f\"({str(logit.max().item())[:4]})\")\n else:\n pred_phrases.append(pred_phrase)\n scores.append(logit.max().item())\n\n return boxes_filt, torch.Tensor(scores), pred_phrases\n\ndef draw_mask(mask, draw, random_color=False):\n if random_color:\n color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 153)\n else:\n color = (30, 144, 255, 153)\n\n nonzero_coords = np.transpose(np.nonzero(mask))\n\n for coord in nonzero_coords:\n draw.point(coord[::-1], fill=color)\n\ndef draw_box(box, draw, label):\n # random color\n color = tuple(np.random.randint(0, 255, size=3).tolist())\n\n draw.rectangle(((box[0], box[1]), (box[2], box[3])), outline=color, width=2)\n\n if label:\n font = ImageFont.load_default()\n if hasattr(font, \"getbbox\"):\n bbox = draw.textbbox((box[0], box[1]), str(label), font)\n else:\n w, h = draw.textsize(str(label), font)\n bbox = (box[0], box[1], w + box[0], box[1] + h)\n draw.rectangle(bbox, fill=color)\n draw.text((box[0], box[1]), str(label), fill=\"white\")\n\n draw.text((box[0], box[1]), label)\n\n\n\nconfig_file = 'GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py'\nckpt_repo_id = \"ShilongLiu/GroundingDINO\"\nckpt_filenmae = \"groundingdino_swint_ogc.pth\"\nsam_checkpoint='sam_vit_h_4b8939.pth' \noutput_dir=\"outputs\"\ndevice=\"cuda\"\n\n\nblip_processor = None\nblip_model = None\ngroundingdino_model = None\nsam_predictor = None\nsam_automask_generator = None\ninpaint_pipeline = None\n\ndef run_grounded_sam(input_image, text_prompt, task_type, inpaint_prompt, box_threshold, text_threshold, iou_threshold, inpaint_mode, scribble_mode, openai_api_key):\n\n global blip_processor, blip_model, groundingdino_model, sam_predictor, sam_automask_generator, inpaint_pipeline\n\n # make dir\n os.makedirs(output_dir, exist_ok=True)\n # load image\n image = input_image[\"image\"]\n scribble = input_image[\"mask\"]\n size = image.size # w, h\n\n if sam_predictor is None:\n # initialize SAM\n assert sam_checkpoint, 'sam_checkpoint is not found!'\n sam = build_sam(checkpoint=sam_checkpoint)\n sam.to(device=device)\n sam_predictor = SamPredictor(sam)\n sam_automask_generator = SamAutomaticMaskGenerator(sam)\n\n if groundingdino_model is None:\n groundingdino_model = load_model(config_file, ckpt_filenmae, device=device)\n\n image_pil = image.convert(\"RGB\")\n image = np.array(image_pil)\n\n if task_type == 'scribble':\n sam_predictor.set_image(image)\n scribble = scribble.convert(\"RGB\")\n scribble = np.array(scribble)\n scribble = scribble.transpose(2, 1, 0)[0]\n\n # 将连通域进行标记\n labeled_array, num_features = ndimage.label(scribble >= 255)\n\n # 计算每个连通域的质心\n centers = ndimage.center_of_mass(scribble, labeled_array, range(1, num_features+1))\n centers = np.array(centers)\n\n point_coords = torch.from_numpy(centers)\n point_coords = sam_predictor.transform.apply_coords_torch(point_coords, image.shape[:2])\n point_coords = point_coords.unsqueeze(0).to(device)\n point_labels = torch.from_numpy(np.array([1] * len(centers))).unsqueeze(0).to(device)\n if scribble_mode == 'split':\n point_coords = point_coords.permute(1, 0, 2)\n point_labels = point_labels.permute(1, 0)\n masks, _, _ = sam_predictor.predict_torch(\n point_coords=point_coords if len(point_coords) > 0 else None,\n point_labels=point_labels if len(point_coords) > 0 else None,\n mask_input = None,\n boxes = None,\n multimask_output = False,\n )\n elif task_type == 'automask':\n masks = sam_automask_generator.generate(image)\n else:\n transformed_image = transform_image(image_pil)\n\n if task_type == 'automatic':\n # generate caption and tags\n # use Tag2Text can generate better captions\n # https://huggingface.co/spaces/xinyu1205/Tag2Text\n # but there are some bugs...\n blip_processor = blip_processor or BlipProcessor.from_pretrained(\"Salesforce/blip-image-captioning-large\")\n blip_model = blip_model or BlipForConditionalGeneration.from_pretrained(\"Salesforce/blip-image-captioning-large\", torch_dtype=torch.float16).to(\"cuda\")\n text_prompt = generate_caption(blip_processor, blip_model, image_pil)\n if len(openai_api_key) > 0:\n text_prompt = generate_tags(text_prompt, split=\",\", openai_api_key=openai_api_key)\n print(f\"Caption: {text_prompt}\")\n\n # run grounding dino model\n boxes_filt, scores, pred_phrases = get_grounding_output(\n groundingdino_model, transformed_image, text_prompt, box_threshold, text_threshold\n )\n\n # process boxes\n H, W = size[1], size[0]\n for i in range(boxes_filt.size(0)):\n boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])\n boxes_filt[i][:2] -= boxes_filt[i][2:] / 2\n boxes_filt[i][2:] += boxes_filt[i][:2]\n\n boxes_filt = boxes_filt.cpu()\n\n\n if task_type == 'seg' or task_type == 'inpainting' or task_type == 'automatic':\n sam_predictor.set_image(image)\n\n if task_type == 'automatic':\n # use NMS to handle overlapped boxes\n print(f\"Before NMS: {boxes_filt.shape[0]} boxes\")\n nms_idx = torchvision.ops.nms(boxes_filt, scores, iou_threshold).numpy().tolist()\n boxes_filt = boxes_filt[nms_idx]\n pred_phrases = [pred_phrases[idx] for idx in nms_idx]\n print(f\"After NMS: {boxes_filt.shape[0]} boxes\")\n print(f\"Revise caption with number: {text_prompt}\")\n\n transformed_boxes = sam_predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2]).to(device)\n\n masks, _, _ = sam_predictor.predict_torch(\n point_coords = None,\n point_labels = None,\n boxes = transformed_boxes,\n multimask_output = False,\n )\n\n if task_type == 'det':\n image_draw = ImageDraw.Draw(image_pil)\n for box, label in zip(boxes_filt, pred_phrases):\n draw_box(box, image_draw, label)\n\n return [image_pil]\n elif task_type == 'automask':\n full_img, res = show_anns(masks)\n return [full_img]\n elif task_type == 'scribble':\n mask_image = Image.new('RGBA', size, color=(0, 0, 0, 0))\n\n mask_draw = ImageDraw.Draw(mask_image)\n\n for mask in masks:\n draw_mask(mask[0].cpu().numpy(), mask_draw, random_color=True)\n\n image_pil = image_pil.convert('RGBA')\n image_pil.alpha_composite(mask_image)\n return [image_pil, mask_image]\n elif task_type == 'seg' or task_type == 'automatic':\n \n mask_image = Image.new('RGBA', size, color=(0, 0, 0, 0))\n\n mask_draw = ImageDraw.Draw(mask_image)\n for mask in masks:\n draw_mask(mask[0].cpu().numpy(), mask_draw, random_color=True)\n\n image_draw = ImageDraw.Draw(image_pil)\n\n for box, label in zip(boxes_filt, pred_phrases):\n draw_box(box, image_draw, label)\n\n if task_type == 'automatic':\n image_draw.text((10, 10), text_prompt, fill='black')\n\n image_pil = image_pil.convert('RGBA')\n image_pil.alpha_composite(mask_image)\n return [image_pil, mask_image]\n elif task_type == 'inpainting':\n assert inpaint_prompt, 'inpaint_prompt is not found!'\n # inpainting pipeline\n if inpaint_mode == 'merge':\n masks = torch.sum(masks, dim=0).unsqueeze(0)\n masks = torch.where(masks > 0, True, False)\n mask = masks[0][0].cpu().numpy() # simply choose the first mask, which will be refine in the future release\n mask_pil = Image.fromarray(mask)\n \n if inpaint_pipeline is None:\n inpaint_pipeline = StableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", torch_dtype=torch.float16\n )\n inpaint_pipeline = inpaint_pipeline.to(\"cuda\")\n\n image = inpaint_pipeline(prompt=inpaint_prompt, image=image_pil.resize((512, 512)), mask_image=mask_pil.resize((512, 512))).images[0]\n image = image.resize(size)\n\n return [image, mask_pil]\n else:\n print(\"task_type:{} error!\".format(task_type))\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(\"Grounded SAM demo\", add_help=True)\n parser.add_argument(\"--debug\", action=\"store_true\", help=\"using debug mode\")\n parser.add_argument(\"--share\", action=\"store_true\", help=\"share the app\")\n parser.add_argument('--port', type=int, default=7589, help='port to run the server')\n parser.add_argument('--no-gradio-queue', action=\"store_true\", help='path to the SAM checkpoint')\n args = parser.parse_args()\n\n print(args)\n\n block = gr.Blocks()\n if not args.no_gradio_queue:\n block = block.queue()\n\n with block:\n with gr.Row():\n with gr.Column():\n input_image = gr.Image(source='upload', type=\"pil\", value=\"assets/demo1.jpg\", tool=\"sketch\")\n task_type = gr.Dropdown([\"scribble\", \"automask\", \"det\", \"seg\", \"inpainting\", \"automatic\"], value=\"automatic\", label=\"task_type\")\n text_prompt = gr.Textbox(label=\"Text Prompt\")\n inpaint_prompt = gr.Textbox(label=\"Inpaint Prompt\")\n run_button = gr.Button(label=\"Run\")\n with gr.Accordion(\"Advanced options\", open=False):\n box_threshold = gr.Slider(\n label=\"Box Threshold\", minimum=0.0, maximum=1.0, value=0.3, step=0.05\n )\n text_threshold = gr.Slider(\n label=\"Text Threshold\", minimum=0.0, maximum=1.0, value=0.25, step=0.05\n )\n iou_threshold = gr.Slider(\n label=\"IOU Threshold\", minimum=0.0, maximum=1.0, value=0.5, step=0.05\n )\n inpaint_mode = gr.Dropdown([\"merge\", \"first\"], value=\"merge\", label=\"inpaint_mode\")\n scribble_mode = gr.Dropdown([\"merge\", \"split\"], value=\"split\", label=\"scribble_mode\")\n openai_api_key= gr.Textbox(label=\"(Optional)OpenAI key, enable chatgpt\")\n\n with gr.Column():\n gallery = gr.Gallery(\n label=\"Generated images\", show_label=False, elem_id=\"gallery\"\n ).style(preview=True, grid=2, object_fit=\"scale-down\")\n\n run_button.click(fn=run_grounded_sam, inputs=[\n input_image, text_prompt, task_type, inpaint_prompt, box_threshold, text_threshold, iou_threshold, inpaint_mode, scribble_mode, openai_api_key], outputs=gallery)\n\n block.queue(concurrency_count=100)\n block.launch(server_name='0.0.0.0', server_port=args.port, debug=args.debug, share=args.share)" }, { "path": "data_generation/Grounded-Segment-Anything/grounded_sam.ipynb", "content": "{\n \"cells\": [\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Grounded Segement Anything\\n\",\n \"\\n\",\n \"![gdgligen](https://github.com/IDEA-Research/Grounded-Segment-Anything/raw/main/assets/grounded_sam_inpainting_demo.png)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Why this project?**\\n\",\n \"- [Segment Anything](https://github.com/facebookresearch/segment-anything) is a strong segmentation model. But it need prompts (like boxes/points) to generate masks. \\n\",\n \"- [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO) is a strong zero-shot detector which enable to generate high quality boxes and labels with free-form text. \\n\",\n \"- The combination of the two models enable to **detect and segment everything** with text inputs!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Prepare Environments\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Obtaining file:///home/liushilong/code/GroundingFolder/Grounded-Segment-Anything/segment_anything\\n\",\n \" Preparing metadata (setup.py) ... \\u001b[?25ldone\\n\",\n \"\\u001b[?25hInstalling collected packages: segment-anything\\n\",\n \" Running setup.py develop for segment-anything\\n\",\n \"Successfully installed segment-anything-1.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"! python -m pip install -e segment_anything\\n\",\n \"! python -m pip install -e GroundingDINO\\n\",\n \"! pip install diffusers transformers accelerate scipy safetensors\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os, sys\\n\",\n \"\\n\",\n \"sys.path.append(os.path.join(os.getcwd(), \\\"GroundingDINO\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 187,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# If you have multiple GPUs, you can set the GPU to use here.\\n\",\n \"# The default is to use the first GPU, which is usually GPU 0.\\n\",\n \"os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"] = \\\"0\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 188,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import argparse\\n\",\n \"import os\\n\",\n \"import copy\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import torch\\n\",\n \"from PIL import Image, ImageDraw, ImageFont\\n\",\n \"from torchvision.ops import box_convert\\n\",\n \"\\n\",\n \"# Grounding DINO\\n\",\n \"import GroundingDINO.groundingdino.datasets.transforms as T\\n\",\n \"from GroundingDINO.groundingdino.models import build_model\\n\",\n \"from GroundingDINO.groundingdino.util import box_ops\\n\",\n \"from GroundingDINO.groundingdino.util.slconfig import SLConfig\\n\",\n \"from GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap\\n\",\n \"from GroundingDINO.groundingdino.util.inference import annotate, load_image, predict\\n\",\n \"\\n\",\n \"import supervision as sv\\n\",\n \"\\n\",\n \"# segment anything\\n\",\n \"from segment_anything import build_sam, SamPredictor \\n\",\n \"import cv2\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"\\n\",\n \"# diffusers\\n\",\n \"import PIL\\n\",\n \"import requests\\n\",\n \"import torch\\n\",\n \"from io import BytesIO\\n\",\n \"from diffusers import StableDiffusionInpaintPipeline\\n\",\n \"\\n\",\n \"\\n\",\n \"from huggingface_hub import hf_hub_download\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Load Grounding DINO model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def load_model_hf(repo_id, filename, ckpt_config_filename, device='cpu'):\\n\",\n \" cache_config_file = hf_hub_download(repo_id=repo_id, filename=ckpt_config_filename)\\n\",\n \"\\n\",\n \" args = SLConfig.fromfile(cache_config_file) \\n\",\n \" model = build_model(args)\\n\",\n \" args.device = device\\n\",\n \"\\n\",\n \" cache_file = hf_hub_download(repo_id=repo_id, filename=filename)\\n\",\n \" checkpoint = torch.load(cache_file, map_location='cpu')\\n\",\n \" log = model.load_state_dict(clean_state_dict(checkpoint['model']), strict=False)\\n\",\n \" print(\\\"Model loaded from {} \\\\n => {}\\\".format(cache_file, log))\\n\",\n \" _ = model.eval()\\n\",\n \" return model \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Use this command for evaluate the Grounding DINO model\\n\",\n \"# Or you can download the model by yourself\\n\",\n \"ckpt_repo_id = \\\"ShilongLiu/GroundingDINO\\\"\\n\",\n \"ckpt_filenmae = \\\"groundingdino_swinb_cogcoor.pth\\\"\\n\",\n \"ckpt_config_filename = \\\"GroundingDINO_SwinB.cfg.py\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/liushilong/anaconda3/envs/ideadet2/lib/python3.7/site-packages/torch/functional.py:478: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2894.)\\n\",\n \" return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"final text_encoder_type: bert-base-uncased\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias']\\n\",\n \"- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\\n\",\n \"- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model loaded from /home/liushilong/.cache/huggingface/hub/models--ShilongLiu--GroundingDINO/snapshots/6fb3434d67548d71747b1ab3a32051d27a30c71f/groundingdino_swinb_cogcoor.pth \\n\",\n \" => _IncompatibleKeys(missing_keys=[], unexpected_keys=['label_enc.weight'])\\n\"\n ]\n }\n ],\n \"source\": [\n \"groundingdino_model = load_model_hf(ckpt_repo_id, ckpt_filenmae, ckpt_config_filename)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Load SAM model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"! wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 189,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sam_checkpoint = 'sam_vit_h_4b8939.pth'\\n\",\n \"sam = build_sam(checkpoint=sam_checkpoint)\\n\",\n \"sam.to(device=device)\\n\",\n \"sam_predictor = SamPredictor(sam)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Load stable diffusion inpainting models\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/liushilong/anaconda3/envs/ideadet2/lib/python3.7/site-packages/transformers/models/clip/feature_extraction_clip.py:31: FutureWarning: The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use CLIPImageProcessor instead.\\n\",\n \" FutureWarning,\\n\"\n ]\n }\n ],\n \"source\": [\n \"from diffusers import StableDiffusionInpaintPipeline\\n\",\n \"\\n\",\n \"pipe = StableDiffusionInpaintPipeline.from_pretrained(\\n\",\n \" \\\"stabilityai/stable-diffusion-2-inpainting\\\",\\n\",\n \" torch_dtype=torch.float16,\\n\",\n \")\\n\",\n \"\\n\",\n \"pipe = pipe.to(\\\"cuda\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Load demo image\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import io\\n\",\n \"\\n\",\n \"\\n\",\n \"def download_image(url, image_file_path):\\n\",\n \" r = requests.get(url, timeout=4.0)\\n\",\n \" if r.status_code != requests.codes.ok:\\n\",\n \" assert False, 'Status code error: {}.'.format(r.status_code)\\n\",\n \"\\n\",\n \" with Image.open(io.BytesIO(r.content)) as im:\\n\",\n \" im.save(image_file_path)\\n\",\n \"\\n\",\n \" print('Image downloaded from url: {} and saved to: {}.'.format(url, image_file_path))\\n\",\n \"\\n\",\n \"# download_image(image_url, local_image_path)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 164,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"local_image_path = 'assets/inpaint_demo.jpg'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Run Grounding DINO for detection\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 168,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"TEXT_PROMPT = \\\"bench\\\"\\n\",\n \"BOX_TRESHOLD = 0.3\\n\",\n \"TEXT_TRESHOLD = 0.25\\n\",\n \"\\n\",\n \"image_source, image = load_image(local_image_path)\\n\",\n \"\\n\",\n \"boxes, logits, phrases = predict(\\n\",\n \" model=groundingdino_model, \\n\",\n \" image=image, \\n\",\n \" caption=TEXT_PROMPT, \\n\",\n \" box_threshold=BOX_TRESHOLD, \\n\",\n \" text_threshold=TEXT_TRESHOLD\\n\",\n \")\\n\",\n \"\\n\",\n \"annotated_frame = annotate(image_source=image_source, boxes=boxes, logits=logits, phrases=phrases)\\n\",\n \"annotated_frame = annotated_frame[...,::-1] # BGR to RGB\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 169,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAgAAAAIACAIAAAB7GkOtAAEAAElEQVR4nOz925YbuZItiE4zA5wRUtau7tNjnP9/6I/o1/M1vcfo6l1VmSkp6IDZPA8GOJ0MkooIhZTKXEKtUgZJdzgcF7tMu8n/+X/9/x4Pj//7//i3j4cHp6/ej31VGIl1dUQs1UIiyAiCWJZSa1XRQ6latNZCjwChSnd3Euy9g7Bia1uXpR7qoqoAllKLqIiEQIiiChGSIKiEwkRBikheD0BERASvaSRfdf2v9qv9aj9ne8NZPt1C/X5P+UnaxchfQiovbikiUFURFRGBBIPk6qtQw0NVgwGBh7fWTVR0UHOCIuIRQqppAEEGGBEeQZIOAOHBOgZHkgKQAQrhTPIPkggahEKAAtlG+Vrq/6v9ar/arwYAEi/nAf+yrRQ1ExWC2eYP3XuQBhWVEEa4e1er9EBFCumqSoaqEmBERLiTDJIRIQIRsVIEICkQACAhoqJB7+FqxgiSCmEIJCm+gJh/f739fRn4r/ar/Wp3WlKAVx3wHdHgIDz/3HYxP/uJ2hNPks9paX6jqoUR3ntEUOacCayYmmnRUgsjeu/u3rx17+6OIdFjsgEGiWQbKf6T7rGNLyICJwZD0oPOCIbn/0AHO8M9fPTGrWH/4Re5/7b2l8/gX/38X+1fpAkEkPirh/HXtAuucPXQkSwppJsqSY8gU3I3FS2KWqsqCHp47x2TEJuqqYqKmZqoMwACEhGqOiV4aMI+pKqlkpEqQJBkAMJgMgwAUIAIwCgCSaaVqgPxi2S8uuW64wJGmxwYAuCfLBz9ar/aaL+woGtYelLdYia11lIKgEh8RgsJhdZaFGjeRaSU0ntYKckbFiu1lqJKgoCptg4yVIQRgKhYMTEVkAJV0QhXGRh/RAQJhgAgIoIABAwaBGaggsI0EoPgpUbz42buO7T7pptvf7vRISVJ/LP+ctYde7ZK+8aHvqbJGJLw6yDfSZLRl97yq/2z2nZA3nA0RGQqvTf3zbf0/zO0W+PPj/eB9FJrLWamSiGDIIuZQI1WzBiOSJzHVFVFzayWUmqppSgkSbmImmpvqSIEkBfqeLwAgKoKEBjmAo8A0gmIHqGAAtkVg5Q0FJ8Gfwve+sb2kjn63j38RU13DOCvGXweztfe9PpbfrVf7Ve73oooBOjuMBEREy1WDaaiSymke4u2DtBfVUWkmJla0nVVdffh3QOQCFIFIEVUQFVNIMhUoQgGpsEZGKqApwaQh1vgCA1Jl1BMAfZdKOwtDv/tnP9beng/sUOSNvIlBH3qBy9pF0zuvXjetUm71uelWLN7Oom/H+v91X5ce0eh/sLi+hNiEldt5vfHViRoIkGCEJVqRU2FUNVSDDDpK0mCqmrFipmZaTrriIQgPLr3NdlAOvIAoprzIyokyFAVAOEhIt29R6gowiPontqAqAjBSLdQEYGmT9F+rt9Md36SRbrSLh0VvpmifacXvadGv63Dl3R3fs35GFJA+BuqX7/a37WNrfZP2XJFIKLqEQwXEVENhkJNBcLu3cPdPTwA6BT/8/1FJW24w/VnzklqCYAQ0/sTIDDDyaS7B0NNkvqTsd3MoUSMvxncaf0yuAo2zYAZK/bD5us7tO81+BuxFHy9RjVBeMnVvGAvzxjr33w9frV/YHtH4PDa7haRn1e+vNsKYA42D6JXKz0CEYuaCslo7p6WAdBUzFQEgQjSARIepDN6kOEezTvJtP6qKaBOURUx6cwgM5DD6EsOyAgQU9VJCwmG0FM+pIgICIEYhBCBEL57BU5T57cvgOxl0s2L5vqlF/vg3Nsszdtff95tLfKWZfgNntG3PMDuDOlSi5zCuu7cqq93mxGCz0Co92MKY422+Uifsp+Q6bxhpf527eo7fr8Xf0Ok67yFZ56EN/DPb9lFd+79YXvgDQ8qqtK7997VGBEzCMvCw4PdfcR3RWiptVQxDaCHiwiBNOd299QVIoJBGiHI+AAwVAwi7o1BAO49gmYWwaSwqirn65EUJwFtzSdN3Z+kyDWnrve2Doq8CJ0fkDQE8urH31mw72euuNo2X9vr/b/yXPAtk/Gr/WrfrfGX78D1ViDDJBsRDKGIhHqgO0LCR3TvkMQFCNDDVbUzxINp1ZWM9kpqPswDIsIIIVQk+chkAEFSVUmXremQGrbkP2lRyKZIW3W6CQl20rbIFiXwfI1P2NGOsN0mZndF/strLy0tL0S0f1wbnrUY0rLchS3vRFpw+AfnRelCeselbnDOTPd0S134CQX279Te3cf3J2xXV/PnQkXeEwK6iCy7ePfzx3B6Quan7zwh93Wvq78WUCThHnejMgLMhD7eEWvv3hgOs1Ksilh4UAkgugNwRg8H8uRDRS0dRjObG4f3Z1oRTjFfQESojowSskv5pmcMYFpcsi8IBRDKBak9fXx+2Lbe3jCf36U95xTKl/6Kub82es2BSQoAIWQX4bX1s91yMtKkypLf356anRPuZCeplMnN0zR/n/95kUPSr/arfd/2PSnvT0NZ3tRKJoCbHyfIAnR6CNy7OxWmVopWEY1woGJmeujh3r213iNImKqpjcxyGBKouwMMcjiMiqgpIKSKqGCH54hAhEOOHJRsUpN0EBq8XDlgl1svdhEJ/Y5t6+4lpG3TTbYmONszFETS14kiPdctuHFvmZfOS6YhHKmcZecyGeZ+vHu+cktj2o8Ke7VgYwRyRfrnadecc54dD7inNHxr+6Xd/2pfaa9XR953w/Iv36W3dO6SUrlHU9VwAVmKiVnzTgkIxUSlaoZwITEc72oR6e8vpBASznC3xVKIB5CQUPOuqh6eaX/SRJwdqaZ9N1TUgwBLKVBNNiAiA7UgehLGsSbC6fpnL6Dv+5W8s6r3F5yTFQUQGFEPiqu2CAAgORIrAQLoLpKZ53gLubdI7d4lhgIUO1o8fODBvTa3ba6J0Ax+cMMLaHujkxn15W2z8d7sOQ02uxY67AH7798TEcrp+4lsDv8S+NZ+B/4tYK5XmKYHyPMeb3HCi1I80peO4VueeXf/XcxD6b3DkrLQvZOstUZ4a01LZvIBw11YtTBIhPdQDQHIIXXKjjxHuIhGhELcnQDp7tG7u1NVBBqBmSWIACIC4IYFjZGSUEHGBUxCy/kOBCX/7y4BIxBg0t/YuEf+wntQxvOmz8A+Aj6/j4uekmpPSTxOYz//a3R0jRpef60hR3wl1uP88/eLVebzvTy/Gvtsfv0vQBK39j21nZ+mna38P+2Nv5O0/jPOUfl8/FIPhyCihRpqrQBaa9170qGIcI9lMSBaD5JSzSJUjEzUGRFBRilVRNwjjQrOTAuECEaw9yBpVkQ0PChhpTBDAER0hhkPqXaT/uXkjXnBu2JyVAAba7h4vZRzQzb4Gi7z8jNIhPtvZOI0l6bRmS11wBrJ8CYvGV1dHIwdYHX+9c2262F79Xdo7yl33PF4w2axGX+mwea+gH51bK/iWBcd/IUy+P0nP3+pn1NeflXjPR+Cd3rEOwkxO7nkpVlCX7kP70zEoAI/g8K0jaEEo/VOQkXo8fBYkBkaPAQwM2cPQiSvdIWysPdQQTEF6D4ShapIlgQTGMnoHpCdhM4k7CS7NzMD0HonmOlIZV47PIhEeCOZ90ZOOR1cNsB/94YnfDz3J5NQX5OveUmgryxJnH8dwMab5DkB4okUApd23TstUSO9eJl/RPu+IOjFFP91iNBrpeGfy1vmp2zfYX7+kgn/KTWAL8djDZZSABVRhcQo5yIpzjMGCfWI7n2xBYS7U2iqTmYQgIgSiEjZfWgAieKkQrBVeRzxYCJrW0VgqhPyl3RJGoIjuTGAzdx7icLM43aNZMvpgsEDkmC/ce2fqxg7PnSlybVbXv6sn3GzvKmdIUHX5uqmVebN4V3/mLn71b5P+4uY7o0n3iEi37+V43EFdCm1WlG1NAmvrZkZAr11OFSltx7imhbazBxUEBHNfW0ryVIsMZ9SVDJaUwt7BCEIiFgxJ7snIiCtdytmYqAoVMXSJXTTTlK2jzlFsrk3bgd8S312be62kII9Tv0tlEHufrx8+lufJH8H8vU6fZynHc7NLjLtMdnbM/vKmx4EzIv/Sp36W+79pQpctKu2rltT/arZ23fyji4JXzE1X1NMTwjJNaqx82L/LnujmFk+w2qJiKfjkw1SbO49IkRmpviAmKUzDwCEmFpaCEwFgt4aQlTLDAIgGVltWFVbb+ERTgFVRVWK2ibkpwEAwzVoD8fsUJ1Xvtu3H6c3EKCLW76fAfbH9P/tLS3tMiPJJtqWBp6BlUXWbvpF/X61Xw3AmREr7Z8XYu67nfdSSjU1KxYRrTf3ONQMDw6SpVQVIygCQ/EICmEiKh7eXSLCw4uVTANqVk0NAKjATPIsGhG9OzE8+SfQM8LGaik6iwxDTjalUyDY7n3PPIVe365y+/us4g2M5CteOvfv3f39wvf8diPqG9pL5KY0pKfOx6kE5M0ABOnFNVCidxkuyXvhIe/a8r3es8PXZNF51cWvHcNF+/GqyZ0n/phXfpcOXzHUEQF168eL4/EaenVeDU30zHevHJbHWtTUWmvH3hg0hcoo7lhKURFSVcVJ96aWzvvo7nQGHISomilUFTWLRULUoJn8OZkEg06CMsvLpB+NmurIQZ3A3IQF9jjdlvzn3ZfqZ1O6eb62m0H4b902pG5QfQjBAAzcfITedV1/1Jr+xIrX+zaZibF+zMz+bKfyH9zKUosmeqNU0x7ePUxVlLUUKwJq1UJAwikmAifJAAmFih4Oy2E5qKoGFGpFVZUAkcmBggyP7ozeXSBqdUZ6oRSrpZ7A+mGmHbDGpbA/fUPf3HjXXe151y/fhs8iCt6NNLxmDD9dO7OTbz5Ru5g+7Kj/m23mN5/6ndvbzdR328tMlJty/KPsmTvT3K/2wvaXWHcut8S16P2tlUMt7r1FF9Pu3rqblrTJFisiCtFSKxitsSo8PLZYUEEtRYuYqcBMxUStGoHo7r2vvZuIiPfo7iQpKiNxXFDLyVrg7sN/fLKBpP6bbUBETrSCL/XhvWg8mSRvzN25v8rLbbky7nh/cvA14fj7QIOvaXeI4K0fTrwg47oBHQEDNy0B9wObr178Qur8ZiK+36tv6+FWe73d+we170HRXj6Hb/Cgf8Pk3NppV817L2z3h7GXJN5lYp897hKt3Z5SPj48rtEb/cvTcW0NIeFOM12KiAKa4L5Ah+e+dyQmQwpQSrGiAFTNZGR1DncP93DvToUqk/qbmVkx03zhiIiIzhFMyy3u6Xw6XruEd+fv7jJcfHzNY3nuv/OaEON/3ca56CIvDsv51X6OdseL5g1d/S0wnwtC9I42mH3P920w7274KctSW/MgjscW3lWkNSnLo1khRVVrsaoWCArW4xoRpRQhRBUipkVASXVhGH4ZJEmPcHcRg6iHk1l1eNB0NVOR1nsxa4KTUpEuSTo0zgsUaGBEr5F0zyfr+sRtjzjL6HDLleuGtfP849k1ev77/Yu/HWj9wU5B3yoFC2bY3jv1L9/kuPXy9jM7X/3I9gbB/H4PLxSHvypWf/WWb/ee+PZ3f8NT3rHD4mDrvfVM25NAfJSSvqFSSj0s1SDNOcs3EhARXZbhQiqCUqqKBlnUQoIRvffW1tZXYCGkB9O5FABJFSUjIAoF4DFCw2RcIwT3maLxnf0df7AA8itH8kV7ZwfQX/VofrXbTOXyyx+QyOInbtqiN8/Kv33UfhloO0y1mhW1IIPee/fw3rt7F4XoKOBeSqnVpmc3s0ZY837sfe29h/fudOp0/RTTILqHZyXgSwWQIpRdm0Dr/HlnKP5q+06U/bXd8vx/Au7+95Um9/7H09/Jsb+3WDoTaewBr1d7Xe4NVLtkIfKME+zMP18dGOf733OnuzeovwMKMZ0gtqG+j+F8m+GvYhEv+fU77cBv6fbee93WF69Oy6uedf+aIdp+DZZ43uHzc/ESbne1FQ9mVXYRKANQUSEgqqXYUkpE9GjB6L4+HVfSHx4eKOjuphbiIoVZDowSgdZibX5s7g5CAkRn8pJqJbKKfHp/i4qIu2/GXhElQvVk+x3vM//YU/9b5oHvfZLfEhZw/vHcs5PPC+ju/3qJOUEmXvbagb2qXTxFcBrpq3jA2PX7g0fgLvX/6obOo/SN779f2Z8Q3pnzn59e5HX2or16PsMpct2Z9ju/vjtU/dpVOBGK8emSRJz2cHpsjH9vGhrPpwWyuwXP1uBVA95PEW+M86tc9oIH7GGSF1pWSkR49yzVosXSUdLMaq1mBaoSXVWbtwAjulnJumAyazF2J4DwSJeO1tvaVnePcEYIZDkstZRaiymQ0WBBkhoRM/RXRMxUdUYhTwYwdtu1efj5jujN6b4z0Gv3/JWi6Et56tvM3LLLqr17zjdjNn8H4f0naC+ky98o+/9gXeo6trP991oWMcGpckZ2MX85jfzKln92i7z1ZS/inK5e8gbF7g0ksXgPb95bExEtBoqZLaU8LodqBQKqMpwiAYqZSHEPVVUVUyXYmpPCcFU9tvZ0fGq9tdZ77yJSrRyWRUVUxExESvOewf8mmsJ+LcOckN3ml1em5GuCyWtf/m1Nrm0XecNynW58haXruS/aV4WXF5lPZt2u66DCvHlmdJjJfICtKsM2/5toT15WazhVhpGhBGBijnde+frb6QUS+G4wyLu3q2Txx4gvN4HvH/7Q5+076weJtm666f1nXTUFz98ux3keWHtOx2+91HPlcvMhzhYRk6cMbWOM/64q8HJefuvK0lpnhIlaESBgcjgsh1oXK8UsAA+0LPrYIwKCYMCKZrRwAOgMhxrDY13XdW1fno5PxyNEPj58XGrdo/keDtBMa1mWuqQV2MzUTCFqKu8dFfrd2vvzm/0y31qw+2CfnPDvl6IZMmpwZr6PScR3F+xIrOjUm11AwAgBApd7dA5CFFdCKybZz58EeHsk8Augoa8cgLe1F+rXwLczpl/tK+25DPS+u+JZb/tDinOlgbdE+4sQ14vvt972j/p2Pn3VSLCH6UiWhMnqUq2qR1e1pRxMioqaGSOyllf3IEUgRW2pleCxN5MCIHrr5nUp7n58Wlt46731vtRlKQWkAsVUNEVCmJqqLbVUMxFK5qEeFoGXwl7Pp/gN83WaCzwXQ/GcxMuN7/Oni4V/Fzlr34XwDOTerejOHnuqCf8cn93U1tN3mBZ/hUJOGsDF9G7yfl6zxW0IISNNB09j5Enzfj6Q/OZCYbol35xJby+zM3xVH3pbe77rXg1Pv5vcfZMefWu/ZzDc14/D6Ycbxpf5ynyu/+DGhNwN9+P5GGS7Us7SxlyherfaS8bwjNTs98BEijLbPIkhy19q7ac20YIZDc+RCJGUdKjPw3MJC3zlVV5uj7mYn5KPz++qLcuyLHUB1ERNtUeQAaGoFQWUS60m+tSPa29FAZXovbXWvbrHsR+7t+7Nin348LAsS7FiqqWYKEgFLUhTrapFIKIQUVEMx//hDnrvXd9Pmjvtm+tPlJeDPBeGl/fl3mcDei6QzGxqIjiTIc7F6iyiuRm+ppUMu7+wQfF78r0BPtyASUJ36vXpn+2rGwDmK0nmTo94/d3fo/00Zqd3HIbcoOzvqDDlidiOrZzo5vPR3Jvhi6FSdjW5RbZf34Ke3xnD+Hhy1nkG5RExqf+Ozu8Gms71ZEQEmccsLxquL8Pwjnwj2TWOtPa4QxhvAX173eiWzloAqAqA3nuttdRSzFRklHV0Tyd9RghQa6m1ktF7681DCEU0B+Dk2tYe3voK4MPj48PhYKalWCmlFguQBMkCMTVRoYqMQmCa1P80ZYMxzk2z2XK+ucnZU156Jf4K8iMiwYGSyLSEjy1/QupP/78BKWPV5Uww2wwog0RfWl03ASE/7cSE+atsH2X/0+7mi2/P+3ltewO1/WkI9N+jpcj/HuLUK3oQkckDbiHvX4HXrhrhLjfwu9sEN2PCc1xgqzP+jPonRp76QQRbJj8AGQQIUsVUk/wJQRWqqigEoXLmDHlB0F805BdcXGJQeQ9EJntIe+/Tuppp6y0YTmQq/1rMTLp7RKxtTYkyupda1tVba6nEPDw8fHx4PNSlmJqKZoaIAICs+aImolATVSEZcKWcoVQX0Rkxyoq9BHt9OeHe0bIXbbiXSPfvRYOyH92WH1Oilwm/bIDOLTfScxIv52+6o+bX9Nzze/W2Fnn2xF3bOPf5t3enTl90ZF8yw/dV+79L2ynvZ5rohWX97h7/aooNfvuGvejhhearl3z5Ncv5VSFmKLlDCno/meAWgs9z6k9y0H0k5nH60sObe2ekH6QAIt3MFEM1T09IAFlwV0idAbF7ZvBcw3j+ps9J5R4L2i4uHuEe3h0GEWmtqRYViSev1Qh4eDBERFXUlAyyk7GuzXvPcusUOBiMovbhw+HD42PVsiyLmVWxwe0FAmTSODPLqLBtpnDB375Bjft+QuD3k0l3O5c41/U2NEYmuR9fb5fJdu/bx/Be11y7eCdmigr0hgv1V3v8Vi3wlvXo6mU/qn39aRfK8Q/URS8m6qXPvUWnbl32mmvud/gXpE7PVIZ7/CcSMmEwgBT188tkAL31CHfv7iJIU6tOAKuUYmYQKZpJ+FVErJSiighVTYh8U0M2av5y/eDimqIiaqK1qEmQrTdVPZTaIgIUYThVRMyYXn0qdAlK9/60HhcrZkZyKbU+1odDfTgcVNREFytiAiAYCA64R6CmSemyw02uvxS0n204kWGmuiFHDv/Dl5CJu7amy2te1URk4FdJ8a71cVnAHAmpJ9dLbXDD4k/XnD8Fg+afWMA5IPM+lojLlm/3Eup9ocvv+YHIFUX6zhNf8BPnScBX6dQ1mvJi8nq5u57fu+1h7ECJ594BvE/Lno3wzb++vt2c8VcrVTf0MLx5zK/d0C+//sxXTYA7esxezSDAQJzJ/hFJ8OAT8Xf3xHLdu/feI9a1de+qorMOLklVKe6mamZUo5kaTRXoYiZqMc/OpnnIaTSjnoqekF/ufr154EoES6lUXVtbj2txq1rcOwl3NwUZZakQ7THygCanDcAZInJ4fHw4HGqtjw8PD4dStAikqIoKZbDDHBAy1Q8ZcMzTlOxrv1ppV7mwYGB7ld322akzEyt/mZzyLQLvZhQ6/3b/515mv+zk+UJIIvtECADonJlrV3K3VXe/X2h/uHpab7WXS3Z59SsEcRGZLiLnw35BD9dtcTevvU+4x5n++lNv3Huj95ub4OKmZ8PbFLYXDun+fL0nD5gxsl996Jvby0b7A/WwtwQhbqtHEkPM3yy9DDjRI3MsRG/dk5pF9IjMphPu4SBpZiqaoJV7LEsVoAdBaijN0nRKo/EUHrv9q5mLeXhRyiYHXYx16pF5Hk94QTkej1Dx8KcvT+4NS/XloTUH2Hs/LLVWFVEIzKyoibC7d/ellohq1WrRZamPD4eHZXlcFrNSzBCxer7FaFnxMXNBj1ElQTlv04wy3vDGwZ49TOxEcL/swZvbaaZOHqoEMLyWNggwzsniCbG51uO+bcoOkW48F1fyGZR/ksFfKOBcQB/PPr6kj7e3l9Pe59DBVZPPcxD26z3nxS+59PJZF1/cl9zfOaf11YW7HND7GzZ+JAJ2YwTfn/q/OfJ8U99JcgZsMehD9g+SHgwiyO7eW8tECyKQGCpCMAC4OwARSSciESmlgEyLbARLMYIECSMRCpM0miIBJhHREFGqsIiJyGXs5flLA5i1WXPnSAHQWuvubV2FrI/mHr01NXUPMzscDlBEUE1rqWs/AqzVVhces25weVjqUspS7FBLLTUYTqZxXOZyqupWgB6TuO8bptGcU8cZoxbJV91ulb3/PhIIGQ5UbzAeyEZ3d0u7LfccAITQS9+YkxHV5PIgbnv4vjy+qTZ6Bg3dxPPxYhn8vgT9KuYxxvRCansNar943H1z+pkJ5I2Gh6//euN1LpQJ3iHr9x/37Qj4S6689evZ09/Pie6FT78yhpfd8i0Pvb+pznr42kbeRv78FU4I0Jl1l1nxSqYNIAIEGeHePbPhT8+71BQADEx/dpXeQN3dQgQSQgBJS2swVF3DRBHsDEeIQAWiaqJmZSOz04VSrprBL+SJQlJUihiAWu2wHIJc11aXmiXakzyOQr4iIlJKNXoecxN9fHh4PDzUUh8Oh1KyIvxOSRERQFWLZb6H8fhRAv58WLzr50OkxfmZSDjUCMoITH1Fm+FPp6V9PmVjZhOpv+Xpf+5zGVPDub8ZT7TyFRL9bezpTafr5XLlG2jxTSD1nUS8vRlg9y9eIgV8yxjePM/vIrDLCywxP0CI/tnau7/yHmnZf4OJ/XAj9xP9nxRNyJjiPt29R4iITTE2Lb3eE1SX7EdEIlTgUBGoCnrvGmIkNCYCIQo4sDnmiKCY4SRxMgGb5FEbPH4xS9uLlBY9BfOHh0OtRoG7w1hhAXla27LUUooAtUgRExw8HI1q9vj44ePDb4+Hh1rKw1IeaqXk27qHR4zHDQZQNPM863B8PRvHUKXkjBNsKyqA7BZjixIYU7LBW9c43s3VBTQDmmYfRAZ0zA/b0zfUet//XSlCz4GPFwlxb9m9c4SvF+e/4coNBpGvsrnr/dymgufXb2x0f8HlH1e7vD385N931o5yXv/8zWTl6o03lZ7bI9oki1NF7BFL9KwTDKeUW93ej8p4bgh97Qgvb3l3JsSbI0ko9ezpb2K18/zPj1nXfP6UH/cwRgTDE8KhM5wUIKumwMkIJPUDekTP5PmlRIRAOoBkHiMF8hTcOaLrg0FquK+tgYMkiYqHC6hqYqWYClWFpkGRoEJcVU0MM0ThtG8A7CgnMg5ASZLptZlpfwq1mDqFIIMqWkwfahVIcwepKkstZuXjw+OhjgAyMnq4B3tEd2dQVAGqaik1w80m3DOx/mfc9Rr+M/GW+cM59c/+Xmz+3Tp+RrqEUJlcBIPPnP18msSXPeXlmenl6p+XAi0vf39F+w4i4VuFWbkeCXpjhM8J8Tlgt/WJl5gaMuHRbf5w6uwa+X4NPX1Fuwf4za19J2n42Zci76FnXOQd+UnanRFdCrrf0PaEKM7JVLiPzTcg6yHnp6zv7ukRJCcijlHjilQRZE5MFaqCVCkE1t6FoUinz8HKVBUiAQajH3vvHZI4caL/bipFqxYKi0PNzF0wTa0QSKjqSMwyedqVGSoqYlmR21QAVCOZ3vpgjpOmCe6X1jsQojDTRRaBHmo1zUxubN4aMw80IjEvUEVLKUs11UlB54RuiP9p6iPRkE0sP0evdoTjRPV3isL46cqSnrq6DDbS/TVjkmSLlgVfSDlvSnZfv/XeA06H+QZN+g42wK+2Od4JQ7wjuPHVB9/ElMaIXkQHfjrKdj7qy6V+bti6KiPsf78AKv81il69YxU47lqCPBjyfqQOMC4CCeHMlcAYTFMwKBOQsa5KZEFcMREVU5OISN/6OmuizMVipk2LCEf08GzzlKG1VVRqLTBViIm4aO8dQJhohBUzQCVICUwUX65r6uWgVUTMtB6WiOjeIVKXUqspRuiBkEVtrxmZmGhR0UOp1UwEnZGFxVL2h1g+UFWLiU7daqP+PG/5/slJh0y/J4s7+W7M66ZL4HQGBlh/dR+cZOmYa3Runp2C//if7L49b98Bv377ZS8yeX0nOHiIpndhqBuk523Dvm59ObsFV5bsMifST8EBvi61bO082d8zeIsn/rAT28+Pz9SP53fvzK0vGdTfvG1GwcEAyOm0ExGtR05uDMKlKhAS09+5mAmQ1bGAHckSUUDVTKVoCQkfVN7zJ5uAdqbdDdDdu/fULaZtmb03LZap4xZVd1Ii1DsQoWJa82nuxhF7lToIrp2dUm2pVZeHhUD3BtHqRQURoSVjkEVVixoxQpgFoqIqVq0eai3FCOneIjyytgwIhGKqNCMwAlTBLuxrjwIl3d8+3sFPRaTMvBGXsA9vQkHcqQ73V/+ONL4dn/sX/iTtZzADvo3MnFSMH9gu9JjrszfGNaHICw31pqPt6ePb/HHGbt8R+rMxnQsx+2u+Wm4Q784M/v6Nl9JpZL0sd2+9Nz+ZARhUEVNNM+yQUMevsq17BvTSIyEgFVURsyKku0tv1XRbhFy3EUe2pZTIjAzO1hvACBRERHgPapBgICSC1MQtcuAqmWg/ItJczGeO9aUsuiy1FGveAZHQAhW6qqgZICCFCCIiekSgMyghYmKGZdFStDNAemTkAyhUQSJFAFu4MnROyqTyZ1swpsk4J03TYr4pU/kvsWXE2L/GSQ0gKdeB91vE8Gw65rC+ZgJ9RXuD3fUqC7xPmF7O3m63vfOUnEfVf8VqeufXZ6Za3GWcN6juVWng5XC8nI3jdKOcRFc5vcQMyrjTTkt1aYg4Q24uZfxzU9ztji9X8zIU5Px+OftGoWf0/AWWW5GJMV2+DG5+Pmca50N4jzb55Ngvd3nUaTXktCIv4Wr766/8OuV/j+hBkuuonT4wag5vAikFEFOqgJsBWASqKFDIOFqiY6pTFC6qoMOliNHoEeEOIEjPBDtZZTdzAUUIlGwiIMWgBgXEwzu9wwFJR6Gk/R6swTCrUMKL6cg89MyJsSSL8HCSZiYh9NBitRTIqN8XEb33QLj33rvDCYhTaxHRtISAwoB7gGFZ5KsWHXRk53gx1yWexU/LbKZasiakjn8BKHfC00nkke18TO3/G3biOApfuejrffxFUtX7ifzfa/CTp99yM/1BUMKeEPMWOb02B89zeFy/9wUX3xve/XXcCOPpsr0Qc7erRF83nnf20yusvrzgke+9X7ZwptO7jh++emfS5nvmoq+3KUCPhxMgMmda755uQakQAEgXSqFbCvtFAUTE9KEf8PvA0iHBGDxAxCAhYqYUgwgjfJTSdROYZsocBAPzdaoViKjZhJ0G8fQcTdZWMVPV8Fgq0iV0Yien0KvtXQsCTu+9iahqFZFDqWXJtF0CCALuvTVdY/18/Ny89aDAHkt9qFWtpJWiRwikFiNFRZZSxWyUGp4V3ze1KFcqS8ZnuqQclooUVTMbCZJEplIwchbqkOf4DuT++kn7JgJ0KfS9iSLfxbiv//QepP/iTL9zUOuuvVGZeHOT58Tu/Nf9318R/V8zuq3yZbYJAGH3+fTgW8DQhH4u4CPZ/r4kcxe+ateI+q3XvOfoibOZOmVAPo3y4urXtDPd4lqH36fJc062G4+InGCJHBgZEb1HYinePWlrRGRuGwYJmCkJdxdkop/MlenJk8IjQDUvNstciIpyc3NnhHtoKcEARFW9d3qQNCuZSWFgShMvD5LuEQFCVaT3UgoquouZJmcQ2BZ2tYHwyKLwa1u7d9NaYSRNVQURLprSfaytd/en9uXT+nl1J3HQ5WM5APDekwupiC0VYHiUBJ7ADPVK0j5NHCKZDigCAg0JU1ACQqCaVVVTSy8oAAPGnCkuJthz5bx8lQg+x7++pd3v6oUPegNA9J3aDSejb27PitLM03V/MK+flteDdpfceg8NPQPi7ouT9wectDLTPeWlvEAevgYKXbCp/e5/DhVuS0k+yyMynd5kxxpeIik/p5P3xJQb319/kJzjZl8dyv7WZ/rWPlGWnnPbvcfadouM3JrPBsYcmmxSqI7ChQNJzwAvgaZ/TkZehXsnjZZknxyhSZucHhEImopHePdiMoR4H+ECAHxmTwvCTBEZZBBmtiyVJMENTk9ROTZTdYRSi9kAVsjusb2PUYsJwJiJRUWk/PHnH/VhJCJQqSqanhtBsvfWgx70IOLL+vmP9VMPFlg9FFC6OxEAJGv6AoRYMTXdgH4ZLq0wNRnKiARdNrQsNwGRNciK2vSG3aK9Rqad6Vf7fJNsUo/c2UVvpqF/IarzD2tfFbFf0dXrV/MezZrD2kb4Lspc9iibHI9JWb5+20007P69N0xEeD7xm6f/Bnz+7K6idwtH70n/Fb3+NfKNTKw8J1NVzRA0I2shRPsokKt7EDv7T1+dJK8kFNKDgcwLwR5RRAC03i0rIXLmEt35HqnZxniCNLMgDsuiKrtKAxTRWutz26Fo+t8oicT2xxPMRMT0bJOU//79Pz/Eh3owFAqKiVmRHt7cnWyrw5sXDfTfv/z5uR1F5IM9lkcD3aMxASdBekqpiJmpSkQWqEq/V62aFQAgIgGKGgDGKdTTVIpaVgqTncEsQT0dKNDlEs5v5M6BeUO7eoq+RQb/3vL7G9rLY9S+3tWFQXD/ss8Vtd3fFzz1pkieF2Ov+Mklwn5x8fkQdFMdrzRCz4aMWVeZTBr50rW7jq3fvvzKd4NOMdGCCd3qHAPv3XvR+1k05fRvuEBuTtOUwtVWnOOZNXWzyvJWAeC7Ly5bHOvzX3A+pNudfK3du/dCE72xUhP3H/I/h6QcY37o0eECM/GAmiIGsc6oVyIiKIIenvzDVMjw7qNiyiZ7R7hZg8R084dmqlAvpSRkAhETqXXpvdtittRkLZvtwaxs6otMa0QGgk2AyN0ZSlHRqX/K+Y4uT+sXO1pZHiJIDYj1CGfv3nvH8dh8bWYIiS9Pn5+OxyJF/+1RwB7dqJkQT52a8nsWNADUGGBmu7YMCCZEp5mcczAzXC4v21B/TLxo2qbG1ty7M5/TizdL97nwb7v752/XAM73TQz2LSf2NXxxg1Be8thd9o7nQsPVu0/E8SR7jJKJLx8jviaIbAj38ySvwGajvdzerxZunmure6zs9OsJ2j8nz3uKPL7dIWTX8ZL7A/oxqP6r2+VEjc8kiRmmKqKqBBBhpVSiiwgZIBmcAb84JfpMukuoYlkq0omGAQAeFMnUEd46jd57T8uys7WVZK1LQk06xGFhhJaSgbqtd6gyWK2qCrNWosgWZjw3bUbkSskcoQJVKao6yev2xiXgx3V96IsiqAz0ID3cPVrrfe3eVg92b8enJ1/boVaD9Gg9tIRFZ0YJWFFTNVUVJZiFjtMCniEPEhvnlbT8iowUmpZaxHY6RIgYMJduIOgODH1B4fiXtJNF+kaF0r9bO4exr9lU31kVSUD7jG6+80m/qfbdWq+d9DqLKkw/sRsGg9O359+TzPwl+feWtPukq+8NrrI9b7cK102a3HOmIeUAwPY4br2/zTYjN1IW7vrMxyQ2fqFezB/niyZvHJDRSGh5NqoLQezC7CDn/gQ/Q4aJi5oalDnjghMWk0Rh8+kUNSulQEQk0ziPfD+IyKQ9BMDIUChPG6eZqijJcGeK9QDIJNAChAcBD2/e3btCInhYzNQoMLVEhKyYFaMHzUJESxYOZspFpZT9sUgwBiKmVtSKaTUrqtOr6GwBiop6b21tiz0goqNHoPfV6X2N3nuw0fnp8+d1Xavax8fl8FDMpMzczkKYiEEWLUIwQkw5yE/MnNnptSSip3xvORiVWeUgdw9PpsITqiXXj+z11X1v35u/vLfb7dmhHU8/DeRdHnODjkx2flm+7DU935IfX6OoyJ54j7HE7sPZ+G+szJVR7HWF3Q7dM4yZe+1kqh0scZzGZ4bHXednKNVOunl+wVvgzZfswDHI7S32eNd83+3NTm6i42zOKd8lTzvxxj1gKz+vcWHjlDPt/oTLgfM/kDZgVc3kaWYGUCmFEsLWmyMg6qpId/4IAK21YiWfRRIiQYpwyLXOrOZyJNfWSh0MwkQ1rwQIlJI59kmiWnH4kIxFyTAVUwGRfQap0Ew3UUsxK7VoLRv1p0AnRRURlMPhsa1NoMuy9N4RPYjj+gSyrX58atBYfX1aVwGXpT58WMQAQS3VtIigqC51OdQ6kpDO7KbDsCEjA8Q4MJGVEUQhmcxaRBCgTI8skZtE4Ve70v6CqboE69/MZq5iNDevPWdrF/ed2SEuCp1fCBDvNmMyI1Dk3Ot8p1nu/zjJ9acO3tV29YJ28ayJ/AxOMLH6ZF0D/zqdyxP+PwzHgpEAYQiC20Mu/V2fyW8/1QFPG+zW0g8HwZhmmPGmaRYWEU2rkiiRmThJEyoFwegcpmARrOvKwvRfSXAmSGGomJrBT+VPCEbQTIuZigohCTGBpRRVEYql+SATQZeSJgpVRTAtu5rGVdMsEiCqpZhNCU2EEyXakHaU5XAoUh4PH0zN0c3KUzu21sPx5fPnta0q2qKxt6XUjx8/2rIIdLGa3pwGqVYsLeU5VTOlD0e+hzF5uS3yEAgparo5ewo4c/+KpEFdXnIwfoygfcOzgl+95juMganf7R73XSCXnX/WNen17vG9quPrtXFyJzzyWbcyCyVPFOZMxpcTspC+GvdGdG+4r2u3IDWegNW7GsY1VvSS4b3PKzzT5M5/3dHqa6x5z1Y5VARutF9wMkhucAS3fqe2QKVuehImWXyDFe6C6SZmtftSLjwBxnN2nv3pQL/zvkmyFe4x87uNrSaiqoO+mYqHJAcUQARiUtXgHhFV5QnhAAXeu1hZoAZBiKuoDilAEJlrk+HuXRCLlawCmc5FSP9QuKgA4XTTUmDN3dTEdNTaDcDDRMVGrNWA02dTIASiKCImaloUgtRACBCllkopGTkmImLKFd55fFrXL8fuzRbzvvq6Lg8PDw8PVutS6uPhsUo1lVItXZFGeTOIFdsef0FHBJBdVui5q/KnkbRuu/6O4vyPwOv37UIqvPXrmLkfMKBveYruBNtBD24slwLYk81n4ukOg8FG+OfFz6TKk5bwdkjqX6pdrjL5arzmTNAfR1721rwdVgTsrDK7BbxQCJ5R9pcPZW+CkT2tuCJDbDkj5ZQIhGR4eHgmZph5QLVYMZ2pCihCAQUjdfHQlZZS1tYSrm/D/Z4knVG0qErGRZFQk6ImArMiIa2vy7KYM4vGDD5KdgZCTG3taylW0pEmWK0EMp44OYaZZVWwTN9jKpJJHAYf0GmdVRuy/27WSi2lsbu7SxdF7348ttbWvq50NxDhxy+fTWRZqpkKtJaqokWl1mqLAcNpKQF8jxCRYiURqTjpAVQTVcsFSYvLphqfsMZnGvT+3/0W+a6kcONPt+jIfgzfxpD2e3b8d0fCuDsdX3/K/Zoe8ym7U3sNuLhyy25we8z3ou0WZUfWdwft4nLg0hmVG+8g9iJ1GkqZgrZcRU4upOlf1P9qu9hC5zttGi62X6/AbM+2+k6N4yS7Yy0SaNaLpyrO6pnNv/ZOBDcOOKeiOMnuMxVlk9hPB2d6csn5hUJyq447TTwbBJSumd09IgTaI+pwc1EGFelff/J/EEBVaykgl1LdY+3sKkBvIZrYPwBIrSXz3MBDVSlUVTFVhWa4r2aaB5Ls4R2eDvVVh1cySCAEVBWV9BTKpKRqppkxLY+JJTJjmrZokZFsZc+Si0cP0nsnNAydfT0+tdbWfnw6fqq1eGNr68PjRys1IAopamZSqmm1AMAAhxNPcrC0cJio2qCPyTohjIkj7pOcX5D408eNnDwjOT+E+uM+HbkY8Ps99ILiv7TzN/hQf3XkV8yT8oyM3OxNMqsLQb3Cjp59I9T59YX//ba3bkiNf+N26Zz/Q9sV2eKOdCUbDHQeZHCtw+2jXBFFZEiLsrl5YwOJ7pypIavfllOuvc5+MENMPckX3F0vgIp4chgyMv1nJkkLI0vGPAEaDAk46Ah6AFJURdXMuveq5lY9wor1fmxOg1QUBcJDqpiZDKd4grBiagaC3iksZoiR7nhdVyqXh9ozDyfphGa+TVUzAxERJqOYzPD/ARKz0hPIDsEpmyzj9PoFChHN31S1PfX1+OS9ff785+fPnw4PS/Mooo8fPooVd1ehmVopoUBsZQoYEUNOFKFuyqBkIWEyHxvpBHRaQw4T8ZY04uLff3DbwIx3Q7Tejype4bhbHc5b1H/bUzuxaMZU3VrKHTh8/sy8jfOqf+pe2PDPnwrV/IouCEy3jgnJ4g4Lu/rDCQIagM+L1ve+tjo3y7QBnCvxu2FcJP1IfQCQkfleTdVDBS4i4dHDQ0NIWiylSHr4SCZ+CKbTp5UkfbVUVHiwwzObQuuNtogKIyI8Q8GUSClfTR/sIX1s0/psZoEuqgXCcAq8N6uLZ3JoUaYBVyWz7Cf1Hy+SvkEzC7TIpKvYqK4wAruKfKWWEh6mWmoNCe/9uK6fPv3xv/7f/xCDFmlre3h4HOk5iWJqxaAnN6lZbSBlf4jAhJbsZyRvzhGIQElkWrwNCsQA/GX76yV74X3bjYfyNQT1nhXymmSEbV++5JVlqueXWu+ecIjsR3EpjZ+LR9cjabeAT2AP3maJuXG3CLCd/z1Ww9Nrzcec8nvuvty98hnAlYjB/uXOZMPvJiZfxcTuI9H3ob837OFbY3j+8YXtzFX3O1jNziDZcxZ/a/y3Rrp/rcFLdpNx/tDr318Z3tkFX9GkRSkEAqJQiqrWYiTDcTCARxIOKumkIlKYVRrW3tk8q6RowuOq1Q5SsVJdvK8d4QyXoJBEz3opATBEuZQaGZFN0gBR0+HG3xpVEBB3arG1uRClQggxIZhdmpmZqG6pk3UT/tOVJoEfCBwEfZuPJLWFQWSdy+CxPbW2fn769F///d/Hdnwsh6enJ6Mc0swbLoCoZtzE5hybepUg692ojaCDLdfdCQXCKJD2PNPklBN30Mv7xqt+//YcXb3zK3Cak9e0ZzL+GeF404SJzIJxe1ay/TPW5vxIZ5je+AYXNOa58nDj769+/BnbhS3qez/izU85v2vKDz9Ez/iWaRHhft+95Bx949MnpyckBDQVqClEChTSVUXZvJ/LTwJk5RN6JngD1r6qwGwRQFWXWgNcbHGlQM0MguPaPMJ7DxKIh2UppXRGgEqJUtLGUIoxlNFqPYQyPXZ6WzXDv3T4OctICKE2xW2ZGfQhkvIbZ5r9aeUZMpbM4jAlnOHs3hr4tD49rU+fPv3+6fPvpgqqt/748LAsCyT99kGRWQtNRMRGfZt8MERGTrttq21X7mb8nrD8gzWA93vcLWFw/BevF+VevZXPh3CfH8gWTX2hEOPs4zyPmzg5wPnnMtVOqL9oPL/qclgbgPA2Uffb27mwfPblDxjMfVXjhT1sf9/v6iXv8u5M4upDX/CUH8SrmCUOs9Y5KBJmEChQJAt4qVjvHp6+LuTI+0aEKMRJBhQevVNL1KRuZraw8vCo0O4uUDUFmjCzNLgtBaqiWiE9QoDMAuRwMwuwFKMdiFCVvvYiNlL/B6KxqI28+RvYsnl/AjLNK4Jh2552F061WxIsKiImCO+tSfS+/vnn73/8/ntf228PHyvKh4eHf/v4YKa2FDFNQD9hHVM7lFrLKW9zEMOJ9RvOzPd273mvdovantPBC1FuRlxeoziX/X+TGDVBkwuc5xU9nAM4k/rf6OqO9vMV9X+TE/Y3zm8vLI1/xa74zs/ccPD8+Abi++5nZbfxEgh85/4vnvKXGz9O4DMZQ96hZLStFBU1MZOyeouI9GkYAVoEQKEigkAIPSIYk0CilgLvstTumYZTSykKtPVIkaLLlOKLynCOyRvVFISZCQRSwBhhBh4MiSBAg0gpA3Ep5YS4qBogUEx30nGW3CfRcQBzkFoE6L31p88h5Y/Pf/yv//qP49PTQz0cloeDHT7+9uHDx6U+LloLBHU51FKLWS3lUOqhVhHO0ynBeB6C+Xch6O/drqquZ8L2G2TM+34+l9ABZsrfF9jYrmIx5x2+7aA+A632PX798MvQELi9/A/dTd8fh3wHNv/d2i2V7p/TJoGaHzmdlkRFQwFApYiIEgzxwR8YAFUlQsxKJvcHFEZ3h1BFsgxvqbX1VqsJNJghutLWNUunpNnZVE2tt8ZMmK9iotBQTTqtrXUiWmseYQajIWu2iyT1NzUAwRi6wCQv2WIrJ48B+KqKzqTQJcLX9cvTlz8+r/pfn//Xf//+v9Tst9/+/WAPplgWqw8Lizr5oT58ePiw1OWhHj4sD6UUAKInPCfHMQGn097eeMDeKPQ2FfvlF3Mama+q9js5/Y2lr64CLBNik2fdzmQYUwvbhiEiWQ7zop9gln04S1Uvz9Ignz/jpV8CgJw99Ma8vhFPuKDsmwVocxy4uHU6bpzSD5AER6bYcU1G4pzU2YvBZGTJ5lI5rRWvpLDfj6q+pOdvf/r9Ht6kYbxiSN/Y/ytvfw4nngjLWUr53a9f6RFQiA8+AAgphNGEi2jviEAgABUJQWRyBxMEGU4JgUcIA3C6YGRjAKmq0YJED+8aKrQkwiqmBihUlGxokmEAPQKZ4EF6RDBCootDxCACtVJKqdWKYWQQTEswiGBI2iiCQJZqjyxFmfmMAFWoQk20fPr85+fPfzw9ffp8/PzUvrDz4+PHWg+1LB8eFltUTClqWg51OdTy+HA4HJYZcHzut5On/MV0Yf/lK7f+S2xB1y/eVM+XPGbPsW4O5dJvRC4dGwbuM6DuC/7HaVB5DnPwBdMi18b2NSpwuuZC339hew0bHphXTMp+9zIMiBIZNhNn1oPpLCgJpalc9MCtl39p1fNee8l+/qna3QFf3043Fv2F5x2c0QLTPJDO+lSTAnN3hmTWZxGNaO4O0EzVTGagokeQYEQgrAj6BN+FQTcTNYEO4imaErn26L1DVEl2D5JqFhEkmvcefQiQYDGttZZMGrQLIwkSwkwp6uFpM+jhPbKCjVQzEzs9VrT8z//5f7fPn47Hz58//1lq/e3x48eH35ZSzVAOxao6tOhyKIcPh8dDXWopxYqoiPLk5ZlS/4zceu4X/NU999z97lw+vbj9R9ipLn59/gp7Io6TnHtGdwZ9nyTtRKeev++WrXV+c8L1pmvmyD8yd76M3XpJxPeW5/33F98+/+nWu7+q7TncUHaIUTmOmsXnppS+m9NxmZz8i/Pu2ZXKzGYuVFH6mYTH3X8ztiSr8l0d/49hDP+C7OfqK7+c39yasdsz+RUL0wufu38UwLOkNAM8IQA1gSgbnQ4wGBQEOz2gYqUWm4GvER7BCIqLVZB0rseV7FYVI030eDURUbHQSA99EFmPJWvEr6213p6OT6JQK6pWrCxWqxYVYwhVN/MuQAR6jEwW4/8YGZKgZkkbFCNyTCHl//2//2c1PfpxbetDfXz48G+H+nCoBSaO7o5azLQ+LB8+HB4OtVZRIYURFHBU+iV55rh5Y+ZfuBXunpxvJf2XrERfdEovaP34Mwaj2+q0pTo0/z2RwuBZi21LyfCYSiRIZtuDZhk5n12mCQobNj320OnlLkb94ln5Li1fPDDeH1BliJyI8mYSn3yRBGJM69lm4aivNHwdqJg+bYPcUzIzS4psoskcZWBy9xXTX+1XmxtwiBAiIoHhQDOqNoZsTpZZ4yW8uwexthUiVisWNVGhABJsx+4hvaoJsa7uLWwxMwtEUTU1KyaDD4B0d8/UOc2j9+7uWSumt+buxaxYPSyHQ62HeqiljNrpkHnACEcmsTjRmS0xs4oha8sN+qRQVS2f//jvxw8fjv0YjsPy+LA8qqiVIgWfnz6hmtWDiBxqrTVTi4qHJ3gVCN00gD0WdJrVK5+/ehQ3UfpbTGQvaue08xa9vIXhMAiciDtyHjTxvBMDyFU4WxKPjKsNPZkKRuDGlOhly/F9Xi9hN/Z3n5zLOfjGR3AzQA3qHrO2nogKTgbewSRCMhPjIP4xJm2QdRUtpRQrpsrMKLPZeciAnzQHQGGKUWoiRaxb2sC/YJNvdjz9R7bcPRmllBZTFQkVpQ7UdPc95nH26Am1tNaWWg9LUVGNPP6xRla+Mu+j2pfVkiqsidlMlTNhWTDYWuu9H9vq3UWEROtNBCb2UJfH5bCUkjBMKUUoQQYjAafu3tJjNSVdEhCng2liiJiClQCZSKj8+fmPxtabf/z4Pw6HD7VU905h8/b7l0+P8lt4mKBUg4mngAYF00AyCsxvsv9eUj77ewcK7dHnnTB4SqyWQjBuWiYv2+sO9vm1W/qo5z/t32Jfi/kkr4ZnKSBHzIALNQU1BKKjLCd3VeLGp/x7ShMiogBVHdAhfWQZOqVBIdQR4a0DBbrrWvMN7dTnt5P+8e4chfK6k8wUIZknZFrhT1pCxKhhmjHvXDkzMgIKOZQ6jmiomXF47mUVJxLu6ehGQkTVDaZKU1UM5erb3+ui/X05yjdiNW971s/DdS5Y4Gm7ghylSUYzURcWtUBmaaYqSykkPUIhEd7ZBHDvvXlTfzhYsQKRcKcEpERE6yuqikk6dyqojBSCRCgIEYhoBN376sen9Wl96svhwcODvS7L4+OHh8OymGaFr1qLmUWEkkFERPP+tK4J9yukiChmPJhspjEkJVEbddlL7916F6kPDx8eHj4IhBY0fv7y+cvxs6g+Hj5AJIOPg/SIrVwBTpNI7PKZXPxx+peApHg8Kf6llD9G+v2F/9l0CNfP20b9p9tvcPsPGYGInmBfp0cwY7CLIg1CVAkfKtiI4HAf+AYjIjJiOvsXQQREfAIXYmbj6QpOHpCzoy+Drf66JgAjGIHMldI9gx+pIlmuKPUcAhERYLo5ZBbGiOi9R8QTvHsgwiDFCurIsItSMBAgiXl4e7TeO6ftxNRYQtVgBZYOdSfx4i+dnO/btqP3z37N79Q2sWV+HlRriLCzbf6XuraM3bVSiHBvZBWRYkbAvSRStLb12I+1LJkms1j6/2QNdxPV4VM4Vea2tqfjl7UHFb03LSi1lFKqWS1lqXWp1czSVh1UAENWSofUEbGr3BGxzA9hZqWalZGiTQTl05c/tdi//fZvv338d1B7tLpYi/afv//XcV1/+yBLrbVWRebA4DYFabve0Amcs/dNZD6b2ZxGHTgUOJICTcrGyaOm2fPcf/Rd2p2uZELSSZzy06i7MMs9by1ThkdE671HD8DUTAMGpLElJCZFywkJ9+4xmSWA2LO5jDDMi0UkIpLDl1JgNpTO95qF10zLG1qWyuAoDDT4X+8uiXQNaF5mFe1wD2ewe5L+xDGP9IgwiNYqoskYUt3UIdxM/Cdi7W1tLTxEYVpMI8JKISAQQkaqwS1Mff++G2/4HlPxg9tmF+FbnOu+S/thgv+rXna/6LIlpOMgwpuWPcnAOKmcLnQQqNmy1NUXKytUskgkB7dINFglRIKtr65xqAqhqSxWQQ4gx1QEQ7aMUSs9HK17D/cn79EXLB85DGDFkhGYqgWpqowYmjYpwGAuoiCcxIwxzmazju/UvVE+Hz99/PjbstRaKxkCIvjp86en4yqCw+Hw28ePD8tSsprAOfCadP0WpPhc2jozFZCY/GOuxy4i40ft26tu8pNZpS/tAHASt8kLIqK5Y4AbvUdvwaJuYlLSt0VFJM04nIpDkjYAqiV5QP5vbD8BOS7YRpELNhrlb2LMzEoPzIpLDIa7e7j3eX5gajk/6S7RGa33aD1tAAK4B70rYLWa6FbKLs+nu0fSdDDA7u49okdESAjUpUgPhYdIV6miAUB1hKlfncG/w8ReaVf5lgiuUt2fhyv8PE2mz4VwuvAMNSCS+EsM7zMAWRMYZAZwpVFK1UIi05957x5BhiA3uSokSAcDkXnzixqyqooqiIhACDJDHJQpAkK8u/tKkcfHD6XUapa4f/rvDIh5aM7BoBHpJwRoipIEIVKKljIytImIQEFJP1URKR4pk6L31s2WWtZ2/PT5iwCFZdHDoSxZ8wuYpDHIiAwotpONdDIGjAtxzgM2C6dstbRnzohJ/U/25GS4X92pg5c9W9H57WTX+49b4ylWVoa9ZFzBjR9P6t97D3fON5po0DxmhDJTYTDcKeKj/tzQHiavHLeoDmI0Z2mbtJw3gSCCIhHUlKBVNCQUinfDx/Z9vLOMtpuYDQLqvXvaqzj1gLw4dYDe1vVp1eGyk3uMWc1CNKtkjzYmJEV5iDOSNydulruMJIKOMNHQyPRZp436j6OAN70vrr3sPx4He3nbqD8zWbEIRUNiSNUp4GbI7qyvJQShyIrwxR7qcqzLGi1IraYmzKArETVdWL+srXvn9BYRUd2y9e9CI4dWIbLUelQVkd5X9354eHx4ONRSzYYIPwKwQCAzTG+Vy5LlDFtCpmhWUSunFKEybcPhVAEFpYgOO2Swt27CBj/60Xt8OByq1izWy1EczXTYFkb6IWy0PkN9dorSc/Efg84y0Q+R/FenyVc2Jf1yna4RqGQReE4NeXbJ849JQPfp/SgnuA9AIDZl0GfrveflKb8jYthVIEVMNCJGUk2P0BAk7DM8scbTR2TJtMycs0khkchZovzplIANmErhlfJCErbN/4V+Nr/f9/BuVuUN6CMZwm0OW1unGVwIcF2XsqhZhsNs1qpgJHsFUIqZWSm21LJpQgmRqRESjJjODiQpKio6yw0JSQ0hJYIOyuS425nHsy36d2wXr3Cx1s+x2f1lt3bIe7UfA/7cf4sX4nsiAqiAKlKy9npMTB4uEJsUT0XSz0+0uAqc5MdPx6dOd8VDVWQYr6RtT919XY9JUiRG5EspZhmoAkbWKYYKoIoCU1GPHuyl1lIXUy2qRatpASCZDzTQmzfvW4ARkhhldTEETtmhN9x1kJXkQwnSlsVKLYuphSMsGmP1NTwE8vHDh8fHx3qoIgjEIImUAJURhFBNx4zGoGxzQi8AH5y+n1j/2e+3SP/9JX9bk2cf06FzBKLOBmAD8fMPkmZlf3iYVRayHhp9AkcxpwPnDIDAKOeQ+tdOUkM6cqUk8jxhKk/mnPdq72Zovzhjp+4n64oEgBIN48wci/QGlZAIdlCsWAQ6e2qKtdZaq82khzhf92Flzkp4GSwhp8QkoirTnI7dmnL6rf2SgrfGHQV53v7us3TJEZ+95vYNJ0iY4L2qkoAEnZl7Z9KFdNcWVTO1osVKgcqX4zEJoJi2tgInIte7uzLFF/dAPcV4DnIBAd2jJ8QkkGL28eNH07osh2opACEiQBMgyN579z6RjZONOkuAZYbQPGkAsiDkyQdv+m0SLEs5VKsmQngIn7qv3sxsseXffvvtw+NjXeqUhsOjQyBdtGaVS/cIwagBGVvmZzKZzs7msM12GocHg8DYYUPFv7WKr9qFV/N07nyhNuRoSOMxwSuefM/PWgzHxHH5LH02NBYRJT1v9wiNoGTu7M1fKGauB5HpG7oxmPFNhgVMCzDO5bYEl3BhGnkxlHF79l7NT164EPt5G7ZyMiJ9nEb2q1SCzdQdDZAERAF0BrlorbUuy5Iez5njXFWGJ7NHFvVWiEJhFALTG1nSXBLDQgzqFl9M8qL81t+dwH1L2yfifi4jb9rSxZd3Oryh9H9fPeAOTb81sOeXcRapUlEKRVXyHM+DPpjkhGvSVUVU1ELN8ri2cAcbuxCZfMGKZQ62DX5JYTwNoCQxyy4Fs66AhDuIw3I42MGdtRwO5SDMJKADi4n0JxQJskf06D0aKFUKAJFRl2VS4/TinGFsMnL4BCOCBare2dYV+B2uDV5qqfXw2+HDv//27w8Ph4y7ARDk2r0EUaAqgdhsTSpiKrGT7DXrQSYyZYax2wb6ggnezIn8judQn8UnJwolg+Lz5OuVDCB44fDDGbsrA5rPl9pZLLbLg0nTHZOyn6j9iQFshJ4TPSMpOhK9bntyI1qbPgVsTIDvJsC/oL1hdeaeCTKACPYJ9Q1FOr1BAYogE41nni0BBFJKqaXU4fOgmFHTkOA8rrnRRaSgUkYwJHdPn9OXKzjXS0X2VbH/lRtP9oA9M7i4aq+qXqXmPw8TvarNyLXEUBdXDoFs7qB51k5KJMjMn4DY8UJmQK0clkOAn9fjyk5vhRrk2lgieutA4vLp7D/pflo6Ocbm4d0bRNOF4lAfZIF3LnaoViSDBYAAm/dM8+bha2/dvXkjQ5CpmeVk703DdZLazWtTphWWJFkeHh4fHh5LKbboyt66WymPy/Lx8XFZqtUSJCK6+/F4tOhSF4U8MYYjaeoyxCiiICO/3Ub+ZDOwYHj5zJV4Nx38TpJkOQP7J/40Q74JhmzhcXuBfdDojaqPaDuZMM7JeQki4oCHu0/LAQgHpsVkw7i2gxSkMJiV1UycHplq/BQCvbMwT11px8fkex+6d+g9V5+qqmbFtJBdzTKgASIjHiDLElXtIe5EhKkWsWrLUpZalmKWAFvSbYL0YX3RWe80s1plrb0EVWXHP4MI0tJINZ2Mr3CAsSu/9b3/dm1yyUtqeBI4zqjniwT6v4QlbKLWuT/KGM/+pa5S/00U5PSnFMA5Y55AEh5QhGI6KwBJlYvqoS69e2vdySyyqCq9dfcQtayjlOG4jhlxpip0AAJlSESScCVZS9EsC2MlAR4ByOg9wpF2stZ78+7uDmeEUqyKSJUd35o84ORDgSGWjekaNrZSy+Hh4P2phD6UDx8PHz4+fBSzzEgk7giPiAVVQEgYioeoqoQBANHIdE8SFRpUEKqZcTgQpkpk7eUhdMh0zR4rdA23OV3w7NdTkuRb23EGm15sxRE5Kti8eKbgfqL42zaa8qQAGfUcGZArcrLEknRy9Y4AKO6RlZHHio85p6kBVNWINI6ODB4i7PTuXYAiWoqRkdWHTroAty29x7CevfF72PTeIuzP4zHIyHBJgFJUzLQW47IQq4qOiDYgRQchGZksA+lkYMVURZd6WOqh1npKiAcSPupPZyp1yYj0Qc3nyCPNA8mAQEu7VS7ZYOBTNMHcjQCGlo/d5vu7tZes+1VZnrs2PoIb1cBgAQJgOyV545g4Ds1uP6t4Kxu49Rb3ezuNfBzs4dwo5+2i87NXnm26fybQL0pNes1M4pLiSIgOm2oedFapD7W33p56uNK9K6U3//Ll6XhsKhoR7s0jUvxIK6CYGYMceR1NLYJUmFixCjAfIlAJttae2BWmKr23Y2s+M8m7h6mpmOxo/ViX7Zv5ytvrACimhZC1tXgCCmqth/L477/9bx8fH6oZAlC01sPFrLiHx+rEYUk2E6pptAQJOEWoZiRVxNSzdGxmINpe5vrSvoZk7Yn+NSQkqcIGNO1/uQgBH3t9ovx70sAdbjMHubMBYGY146YfEAg63LRM7ejk7Dic0FMl2qHkIJ3hHiC16Ey0EUo950f55tff++Js/Ej568oRipMHrQgyfyGm0Vt1ODuRQVGC3r1FZ1AgZalmptCUgFRPskwgZmRZhs4rhzOVCMYSbpEHMypYVDTzoDgdgIXC/q70/aK9ishua4QpIG8baL/JMghjuBXqnnoy3R022fEki4jIjvqTL9Xs77Cr5z+9vOdEcbcR7p1LLmjFxdY9++MkgJoVyIzgYQZcwR1imWhENKFIkrUuS29r72tzMpp7eByPT18+famy+LJ4nPT6fFSmK/Ee4CCP3bvaiJXldI8kufbu7MEwLcFo7RiRCT7hHgItZTGrM3XYwDBs6tw6aWycn9QCChh9XV286EFhD/Xw8fHjv/+P37RaOZQWYaq1lMOyEHCyd1f0rEMPIjNtYZLCQopIlOgR4hktIMFQGVtkx4326319bccufytkO4n4+GMEeXOIrBP9OR0AnjUAQm4OOUPnI+kRhKhJBN3DM9NNUKgANGu/TcBtuvxDVOiOkVIqBQp3kCKDCUgJHUEamyRFEgiMtN/EKXThPdubhbXkoONgDKbGWYKIJExUymJmZs195M8DHKAoMjVGdDJgWsy0VDNYKVZsEh6BmkQgGD06nSlPmVmGkhBwhnujIF0V1t7SmyLAWgoCptV0gLmbheDNL/6d2h2a+O23XCF22y+Rbn4TBh0Gu1H/OU0EOjvJC6ZXuwzisstlm50+F7evDubi++2ua/cKcDOO7/RqcxdON20qzDTB9eGAeOUuyCnYH4RIpDs/yZk/Z8uymWNzqsGKjMJN+bBiSyn92Fv36COy3TNp3MaSUgCykUgZAhGKmS1YmrdjXxubQgsKgNznzliPaxKbNCd4eDEz5AHRWqtZQarJHllNymxk/+R8cY+RNi5tyBApVYsCAkbv9Lo8lIcHKzaidGU6E9ViJSPyQSEk4yCgjOg9RlgCQ4TNMdSaIHWQBgFo14DXix3xsgN5BgFd+V02158dZnJm4xq23gyjjth+OD8kQ4eYTDXmr8NhUzkqsW1uWAl5WLq32MkOc2K5QKa9yYGmQX9z+8/xBp1DAskdmyk4kg0odtT/LyRe+4mKqULNuAfkP2PyEq2nqEiT7oGAKzyCo24GIKOYFwRqs/D0liiJhLuk31v3TlJnOhGDBAPOzu6M3nvrrfWWaYUUWpfq7tWqFiCmJPF2hOylk3OBL72hh5PYMtvNrnjPH+xMrtmCEp9BQMEzZ0fkoswNLBNR3ehfMm4OtxnhcDnXuwMdA9oMDs+p/PZ07H7d0AtM35Grd81XTHqbFBdz/OQWfrgDb0/HEhIhEWCAmV08hssZVE5TMkEAD0Jd06dYNTO7xZAvocWiNxLHfvRwkuEjBXwOL+dAmP6aVeiZErG3WNt6XI8PyyEflONf19aiN1/Xtharnik4xSLcVEsZcfU87QQxU0BJ9PA0IaQTxoQ7hrpWHh4Odami+mV9YvPlQzFVqdrYY2WVUaOSHqgJr7JYlhMzEXhOdxYcyCRzohgF0yaekalvrm3T3Q6YC3WNpjNT9G162TkEhJ0Bb7uXw6aKiTvJlCAm7JN7ZfP7xEn02HYhT+FtmVMSQ6wNMsJIiDhDRBUqJkWLWVE1Talj2jnI6N0x0GsSnKFmdFLCZbj/E8DwI5VttmR7U2ImVD69/fvwgFeRqtOqjT0dW6xcRrWkr6+KyaAUAGBqJBgZUJ2h6j2IPE2ghjsLAYQjxLuvGShAwL23vq69de9CgVgPEpI59wAGvLV2XNfPT5+Pbe0R0Xpdlg98PFSaGiTm+cXGATYGPVjtaab3n0bQxvMZuDpj2y56w8SeTe814njnHkl/v2c/bNbwTcA9SbJyOjgYEionfjIwzo0BRNrOx+nJ8wIAkUqyQojgyEw2CfW1+dlOWL7RFKr2126CVhaBU3KkHZuTuu/5jJMh+VJ4kJm1XjWVAVXhoJuCDEeU0+PItAQiD/dGJZjLn7MlqKW4B9kJjx4NvgoUUsoIiMqy6IVq0D50UARdWVLc8e4DLhaIAQJT9R7M8pOA0z3cybU1di+l9N6P7oF4Wp/CPTj8JyJ8KWWpRVUREQIhwrsPOpPK8WDWuYgQwKO7UyGijCitrbSgcu1NVs2zu/aeCrl7N1p6IHn3NGSncYRAS4VCmBiIiAhKVZvIeE4bgYu4JvK5JrerwXNrG1/X/3Yi3WQMnLLzvodTFqOYsH1WTZu9yNg/c4jzyCBtlQx6eAumu47OTEEe4YJM0iBTOtmR73T4FVW4B8CsAROkCKFgZ0Q30WpmRUWEnlWbxd1TiZscKnvfz8B7agAbUbtPs/akjUSmcuvdvfe19dXXYNqj1AQlnZGB8Ey3YmCE0z167509CPGdrAf05nnAu4mIggpHsK+trW0NDxGLXOlAgrCqEuxPx+Ofnz59WY/Hdnx6Ogrw24ffFi0m2q2VahTjbokxNsrYQJMvDNoD7DbkOR2/Rdw30n+VW7x8CTjAAnl+5+RX+4VP5V4m0cT5kE49R0KXZPeZpXaGJWLmI5OpBADY7FjjnaCiSgSGtrA700GqBJihQJqRRkgjzc6MthPwuUu0hckABmneJUhwjMQ4mvIldoi+4Lk7L1MfpYyEnnmMnSGhFFPjcEWkEKfAWCAYvjkPgKcyRjt2PnIwZBRVMII+0iSwt6Yiajbsu8EidoSIGlRCGPR03+/hHOgUA2lRlrGEEBK5Oq1H68dFrbf+1FaH9FjDG6DRXQsEeij28HCotSCEQZs8yOnu4QiNIZNjHm0RoUfrPXPVGKR8+vy7RYFK815qFSWA1pqIlMcSZN0ioAXFigy7jzhAUkWKFXDENJnp5G2acQhpSTo/E98ZuNigwjMPodx229E9QRbznpFcJi/bnEFPDIAMZ9qYEo+PzGdJUqEqCMioWiVbKK+OSLFU5dIFOD1aUoRiIEgWFS3Dgq8js6DMoDkIpt+8bAfq0o7yXu1CgL1zGbmFTQxVxt1b641doWalFAS4qBHaI+ghqgS7t+Px2Pr61FeBFrGsiZqhK713ELVadwdD1ATS3Xv33hygqqY61SlQpViY9t6/fPlyPB5bb+tx7eu61AOdvQ1jVa21adfMqUhNWXUUcb5GMS9e9haiffHlndl74XpN8Zinsex6O1HS0zfbFxP2vJB8JhXLQzrVtUDGF8rYq7mxYgKjwEhCzlmgbVLLEAzjCgf/gEDgERGqgkxGFkOB4CgBemKZu3cMkomQyPZyVBnsKpBJWZLUDwFOSJgqNPWyy5eV6Vk/CXtKHlPDY0gm4RdCKEMyJqjJOXxL/L6TYOdIOQ+g5LzNiXWBpCApDJmZetJFLYl7673Dn44HFVtK9VlIBJwZbQEVM7gQdArgzb2HFV176+Gu8Ogi6s6IfjgcHpbHx4eHWkopZdDYBJiDPYIzyFRVq9Wc+iQaEX7sq09X19L68fhl7e4Afnv4ra+t9VZiEQbpaunBKqXUYiVDCYbWvwMjhtnTbClFRZZMXW3FbMYkJ2E7F5duHYn7EijOISDgzCSAneiNs3QLQ9CbKYq5b7n5JjA48xrluscWsycq5uiRSkDqVvNhESwygp8T+phqyEC592+X6SVEtIW3cLib2gLxdNDK5dxcHUVMrVjdghLvzMz7tm3qdg/d0IKxcQkRaMYCZoZEtGgePRVGy/Q+BrJ776sH/bge/3z6/OXpc/OuYiZWrRa1pS5wuvdSFxK9ByAFRgFDwAwzkQgnQwKh4UAo4NF6X9fu7vRQymI1w2c82HuY9XVdVdWaKlSgIsrpk3baby9mfpjzcvH9rXu/ump7YTO2qpggpyUWm6/c7hm5USb4ww1HfdYzNm+HLTobGB7uIsP/LQ9qgtQAnBSJzCEmkEAgkspwiyQSkUxiE6Iq48iYgqIQ1+kqKWfjGf/zxI7zcTNvh2gGcU+fAuxc8gIwGiIgaQLCudcpB2Ik4ykgh80swGRJTPNS/jbVKSL6tnIxrRu5yZMXBhHBk58wKUBRje5rH0mCCYiGZeF1NSpNNA907721pgYGfnv82NyD4QgjSU3NLMjM0ykQFV2PK6iNvbcVqlCUYjmBh0P5+OHD4/KwLEstpWRJ+ZGIlGSEIyKyVjCNQp/FqQBwbe24HqFqZl1YvDey9N5rqfTwta/9ePAl3CK8pB3YtJQiuQd1yNaWwBwAILO11FozwP+w1FqqmRXLlKjfl3Lt+cG5anz2YTsADGZ+nzQCECHQncJ90h7zH3cXkXT/zE0qM43EeKiIcIRf68jxNCq2kwyP1HdzeClQRECV3SOCRRVgD9cgoIZ8hpiZmurkswrd84A9rbmDOTzHKPYzdylBXW+cLzR42Ux0tEGLkgXQSimECuoRTx7hPY6xqkKgAgnG03p8Oh6f1i+fv3z589MfX9YnFf3w8OFQDyb2sMQDa9ZBO/YoQCl12K8gZrXQe1979wCUmpRMVKDSeusepAhgKqa1WJG0DwjCfW0tQwxm4HnyEm52wf0UXaXjz5WA5x+3KQKStGzJUZ5P9HXVYezLIHcdyg5T5wRL56eTu2aCEzzpK7IRrAFnDqeHhHuYRsL94GMmv8qPCaBvuSSHXJkOJJv4F5HJBQi4h2aIjQYgIaGhIaN8yPbaQ6hOFMXzgGwny6GyReMnso30sxxyPwzYpZLnPFUYO3JKLcPONCnf0PxEdACIY5EI0GMCoIgIQnKeMtjLOSq8qyoEydwAMUG12j1abxFBgZWiSo+oVsO9llJL7dZrqevT8fj09Hj4rbu7e3NXVzOTzBo5oEkSFDMEgy5g6ysjailU0VJye3/4cHhYDsuyHJalmBW1rGolIuIZVNydTE06sxqVrKFEuvuX43Ftq5WSp6UkM6xmHz98/PDwQa14i756N7fWS3ca88zYePFEDCMilCx1yR1uqhnCUyzlflGF7FyJ9wdp23Ny2thjd2zLepVdcFDes35OJ2r7YrMGTCvOZg0h2Jmma/aZtk0AG5tGh9wf8DQUxJDJneFBgZiIFvE0xwCQ1IkhQDFLS64OE9mUFwSJSzrFgRhCjdBDQSvFg+KxZOiwZbJxMyumljHce8WGE9E7Ix9jP1/S9DHJvEaCnvFInCwhUzjKY7G7eByxsQ5ZAVtFmKl8CgNQNe093Lv31uAAMh3c2vqff37+44/fn9Yvra2fvzyFR/+tPz5+LLKoWFlM1TzCRMRMVFM01GLF5dj02KN7F1VTKENFAQkP9xChKBAqVlTNbNnmxAENb93NXNVNacpQjjxMKTkPaO7qRI2ZvJD9n6sCg2APvY+yWb9kZ+Sae/Ri8rN/H6HLCJ+y8SC3SeJ1Jhsezx3S90QwlXtGtWXWSCopAXVGEM4c0akk0W53nfhQxhmJSlGjGVL9iki4eXN0PglDwwGMHrkvcmOoyWmLCif06SOqwLM4oqhkYrSQzZk/69mm4OUaxZQwkmakZu7vk60TBAMCJX26poTPqFt3Ql1VbCj7OjkAJ/cYZpf0WsRMPctp6pNUkoDc8Tn9Sz0wcOxHghQGxLvrUqBmVg8LmreHDw9P/mS1WJFgyv8c8zCz0QN0htMlncLpIDxQSrGlimaqxBDTQ1myMEAxy5oykkq5GlmCruoK+Cixh5VMhEZEWmutd4+sHMYiUlrrIpKZputSD4eHpS5C6c1L7fQI97CSNQgyyD7IHiFkEN77w8MDkAoHstSxqmSKjCEabrt1R3K2M7PxgGEIunr2diDPidY9Q4pkb9rf3zz2xvBOH3UHnc2dCB012fIcnVRUEoRSwtPbnyBhoqlSEkJI4p4iMoBGkRhJCxIr5MxaL4B4sDN68NhDIjIBoJpSFCBEFQZw+JFudF9GEo88w8lSNkPH6fSe6PN1YBpnUyNTWT5pPbu7dnHWJ7Bn6w5TusRGEYd8R7LABBKqGu7aXaKvx3U9Ho/H9Xg8rl++fPnjzz8/f/4MyapqXNemuupyEBVO42dRm7KFQKBi6T4UYAtHRDENsGo1JCiEmUdVFabQYku4S5CEB6N3sVbMTINlUNhhD5wCypl/1V0gKHZp+y6uJ0/RJqrYdSNXr8+FHBFCHJvKk/q6R7paDno7gwnnKIbgL4GUtjiwmG1fTCUWoxDdwBQT2SdHpJ5OeOCk6kVEpho2M6WCdPfhUZMCn2X/s5ftpUTEh/Nixsyrkmqqw18enAD78BoOcsbCApk/bJPcg87Y9INw01pJFlCkRGS+hlnIhdzk+Em2Gd1HjCEpIUznxWRJ2C5GyrUpr+arDfIy+WA6jBAYprohLkS0HiJSlpqY+zD4Ok0MbGSoyuHwYJ+r2ixulKsAnWFhw2+1R09l4unp6enpi4iVUmqtLlG1ZsT7UstSlqWO+jBmZiYqyqBmriJpEcNo4QkuuUdrOB4z1ejTcYVAQbhTrZDULHIAkFBTydjNUtSMYKo/EQzxRHPonjrdJExInFBkWiqndJAkbH9U9uT5GQ+4mdfhTraffbtG/oc0lg7n3CSLrCEVnto/dfhXkkQw64dkBnmhCugZXKwjoVl6mc1tx1HcMwstyziaYxNREHB4ED7tb/SI7imBiaaTm45UShiRBJmvOz3kKVvOWIjnjHIgCxslf9n8bH+SOvf/xjoEG2WXDQe72dnUGE6wg6haQLJ4qGgWoggRdzZx+uo9IphAmburaKl6OByKWS1WrDAId62LTKfyPFeUSN0ZhIoE4S0iCAPqJIoiWop5CEwhS6mdoiZLrY2Nwea+ercozbuoiBaJZLwDngjILfF/m7WrH88pu2x5CDYM+9rtOwgiSX9wgvXIKh/O6O4ENVQljBrJArYulEKhjHyoFNAGe97WlRMf4fBuwRRj5yHdzzMFGBVM0x0lY4poliUvAJRSMpv8fCMhR17I9GDO6ZDpThYRNBptzyemrhNJ+4Y9GjMKmSNUMhkASUFyHxsuKAJxgY54pvGao2i5b8ylp9OleyZscBAuUFSlQTYz6QmH2EIZdoKpqpmSYIrUHSKcFIkg2XrPsBUSmtV2STXJWtRLPXx8wB/1s5qUcjArKQlHhIYkGNB693Dv3npf1/Xp6SnIUjamH2QEUKw8LIdSykikeEIGNZ13SKh4MQuz7pEBqCEIsLeeK9JbS5dYFZSqJfeFqaUSQDIYzftUMiyrD5MRoUnfRvpTUmRk+kxiPy0BZjvdcJD2u+20p+8F+n2tycAsZJbNShoVG4HbxJ6x7SLFeAo9GKQkyp/+imM4Q94RASVymhP/GCmO5+hVpZZSrM6SO2NTOkFnDxcRjynNBBCEiokNwGUyVCumxRx0dwJFRYhgCDWznE2fjZNGwI0f8AxmOJVGOSFRsiP4wDbQs0m88+t2zcbeMCcAGykByICkgAoopKgeapXHD8NrE1LMPn/+vK6tlvJweEjMS4tlZLCppHS5jab13taGzK9H0RCI1Fqq1QyJVDNHOLtpQchiS8YJl8QlYwQLtN5NW7FqpuoCSUmNY6KmRrShKLmFp5i7k3NzIs6/IaeLF4gY4gx3KuteCZgM9qzLxCmTnyVi34NEqHDQlySGhExHWIFM63giWsKBXWOatU6rOIPSc4dzy1S4X3dVddccR/OuTCk7/VTyXNv0l4CIppl0in1nGyVZzkbYVaaGLwiye58Gac4Cg5Kx9EH2UU3bk1eopBUiTkLeMOWEnEAUeiYZEQlknmRP57ThnBn0CJi5RUl5VSS5GbO+7gwLlQnxDSKmqmAp4h7OLpHEfYg/7AyPJIIjS60AQCnFoixSNOTD4SONptWkzMkJsIhI9+gxwsRIHttKwcPjYzhUNILFiojWUg510QGwnxgwySzWMq33IwbXN4+vQATUCgDvXVQDCKcWU9UydUwdNQfcW+tp0OjetRexGM7WeU2m4yLTIzYTuwMoZqUUK2LFTrbK+R/eQqG/Q9tLSOOLoU6e8NmxY/N8jJ9c09okACW28rxDs5GOPNSMFHjM4L6dsGplKbVuieKE3YMBD4/w1joEHt69eyRPDtVChEeX7lqWhMtKLQG0tUMkAmFSICJGUEFRUcSmZQ0nPAooVG4ZTMZrD6lqL+CfXKcvhdmrU7k3j+4neNB9nFxV8sPmxaSpJBGEQUzMNJY6ZDQRURuS7DCcw2qpdVkk4qGWpVSxcRoz28mw90ruN83Q8+VwqFrWtZVSAq7U5lCmbzVFxKqVUiKacRiuemNTy+LyBKpYUsnhAcMtW9GJMJ/vW+7njZOmT/I5lTNmCH7s+WkWiUrRPMc3buFYMuZ3yaZFkdnhx+YMD3fRUopCwxP8GXcGgcgKFJLVms4Y+Y4HSGbV1qQsjsHekpTkbkLMMBl39wgKmO6dOOkKqltFI9khQJebZBOQNkF77EcBye7efILMWSsCEDECTjbvHg7QPRBQVWmSorjs2igolxtyVm3q4Q46wmfQQ0RXSJECSDgC0c0UVJWedhZSlaoU1ZL5xzchQEUpoWoCVbMQd2dEwhsUmFnrfV2bisiyIH0WJGsJh2mpVg51CYNaScsFMAyriUZ0Twruvbfe+8PDw8Ph8fjURWBaTE0hxcosCjySSETOmtGZBcjQ6R6RvDMdp0eeMQADKSWICHfSRBgokqGYYO9tXVtdmgPg4rUeW4OpWWF4ahfQAccnDqYqWfFl+q9jfErcfwSZbADOPdF+aFUTBbpIyHgBAQ2T69anxNx15wLs0K9P1D+l9iGYxTjYEVmKAekbm7I4SWeMU5YPSQuBKj3AELKYZn6LYraUWmstZqmUkpEJ7p0I0pmLzNa6u6/t2NZWhT2cTitFESIdEBSls3sXkRR3VBL1EQx5n8rc+FDl9DQBKJIw0T5+6JlGleTsiq3k4mPSlg1d2vV5knbHFI/5DSYSv2lcAaEKTFAyRyBgclBCM+5BpdYlnZ1AMYUBomqlhsCQtCeFR08DgHs0psdemC6lVBM9HEQE0RvoKhxlNdzNZpE60Y4e7lmlIuNkBqLtGsKhMQ6df8Iaw9V3TMFQAlQmNc1pTAPFoPwbJqaC0G3mh7B8gtWGyWYnp5yb7iXjPlTgyQG8DZklSCnKZABZEkdEDDNOs0AsyxoOT/d8HOeqJ1dOxT0Ym9FjUFNKxj9FZMINGSQRGeWb+NJIjo7kNypIAHkcMUCQmLvM7Ku5XXLCE9xJM3OP6L1Hj9YbY8ja6ZmRYGmmK/DeBVlaSmlFGRquHaYWeRJyt6XYy2C4d6eTId4ZRIrAWbSkpJ8rNaclnBzh/aGkkkXUkj2TOoTdQrAwkFeGmkbvPSJEwEwfEYGI1d3pjw8PkJECWaghUpb6UMvRu0JVDRhuI4EQak+ncGVErKuDVsuDaS3CUqxoDVKKaTErVUU8nIEQIT0UHio2wqRT5k/8ax7MEdkGILp37xE8tlVVo1j3XiTF/1ndt63NPeBel1L6IqtW6zGz2EVQEJnmxgRq6XQA3TzWB/HXof0nKZErFOZC5twOx4vg7J20O9A/ALzQP8cP3GZipHHYRDqCdMa+rqakdLrXHebVKmnUVcgwvhGhRRVazerw2RzBeL1jUEWSpHusrZHsrbV1fVrX7o6C1jqpVdTURaCm2jvSnU4VJgkF9CAklVYRUShtC1vjnDWeQ78Yc74x1G2+rhpUrltZTnxkU7Lzqy2AUzZ3vYhYw8FRfyunFEJRyQhndy1kGiuP3bQm6igiSpfDsghgZgS6d4jBw3Qhxb0Hg5HA9Ch+tCylqCGzXm1BJgGP7muIaY0QkeY9JHr0xAFKqUN72cyMnpCeUMfscVpDiMldx8vOTbXxwpHpNv8+CQuyvytdbQbTlZMJd8cNhs/BFnsUkVbbZB3h7AwhRVTp1OmSCEFWO/MIDRaKSohDYVROmzYBZjoDwSxDIWqqtKGXBzNqN0czfI1NCyoRQ7CjithQmEEnbcvtTgIjBgkA0rn2ZN7L+IABR5DsMXSa3ntrPWPCI7NPj7wgBNi7T8vvtHUrvPdV8q0PxdxK3UDImAbPwQeywpbnsigRJxeBobZzlzmZnKGsEd6dEdNcPrPbRO5rHduiF+/e3V0EQTWWAjjYez+242M9hLuIAeqQZbHHD4/9y2cdOLli21NBDw+6QjNC3rQojM5aigDeXKqWMvzLs7wMSYgUt8TwRjS8SKZjab231pr33lqKKcl1k2ZNSh6tr4dDLRBJ5/2t7GpSMZLeeqO0usQO9FRRpZimt49GRoGl3mST/k8EKAd6lcrcT7r+NasvN/D7Vnd7+j99oEdsSe7L1Phjl7sYgKgYjLF5BI0bZDI0IQ1CVQG7h0KG05MkIaKIOiPt791b88xLFVlTPiLW3o7tCKI7mnehmaqrZvFDDJPaTK0lSJew1KgSbssce6cX3b/1DqqWvfTPE6F6W9vfx6E5TRLqGdIZ3rtHRu+P+qA6MwyLiUIZrLUSHgq4lmLuLWOyMq/usiwyYhrRU48Hes/kuq15T+TaZJinQDLPLU3FyNZaX4/NJUAUM5hBemfrrZuWUitUIjO0BD0oo9rehozNLCqbEsRBZmVQuxMKJGf77nJmuRMj5uVnKwWcjM6TAQTT6WTkHx7CSxoEoOjoFsIgS6oeIyWPAJqeGkrMgJU4+X9CRjowywCsiPSAjtQREoAAoGZDtaeKxOCsJ5gr5dakuJG8K7JeqQ2T7zQQbVaBRPC2t85ENQRIpzf3AQAF4MIZnZthN2Ykh2Er84o5we4Qtwgi3GUOjREZVpM2BXDGKyZ9HA5fgCgRZiJ28k+RE1Ogu4MxSH+mdmCGmmxirFBgtBqle+/eoAJ0J8W7iEaPMHbvLtG7i5CqmRAihRunObuHalggvJMBB1vrw1IiYmIwsjtEqpWipiLRe1KrTkKku+aYVTLfqUZEb7333t27R3eKiImoGYfyJwKUYkmOVK1ooqGkDjFKTKTUQonPn//88PCxre349HSoVdWGjK9SbCRtFEGq89MBVLclCSa/3MIsz47HWfRWZr6ezFzu+APtO5L935tMnxV1EvHmdmT3sb/A9BxIvT7FgRQ8UqebmatPkmyq9kmdIQpRGwjqkD0lZFrqwNCR1i28r2M1ps9b+uCSkDbY1PBGSD0OBDBi6NIXdFrzgkPzOSESp+iAS9VpSqCb1W0ktTodTZyQbtk8x3fg2m66U7M/GZPHlIZn1sShBLh3j9ZWjx7BcJ8F0cQkQ7AIQbFC1B5O0MrSmq+rK0vEkCTMLCIPoQFDw2jN1+49mqOLiGqppaqKQqkqKsEQPSauqqrh4b0zQkmoP7VjOB8Wiwjv0SRMT2VoBglOtpMzGaRMCwpl7i9hpnEZytaU3c88c2SK9EM1Q4Zlze2a0xvbXt8meaBoQxzpvbt77yPLXvqA6ZhzFYDdxSiqYPpCagTcQ1JK3TL54LQ/uOF1p10/GBymaDSzQFAAgzgy2bEg1Ye8MFyGOcyK6uDWQXAopnKyKZ5aRIsIQJK3kBZK0yqiQAeRyLUmtM5MAqI2rNYZUaFTV0Iwunsxk6EMDbZMwhk9mCiaSVZSYUYkB5FstSSwPhdOKSYZIcEJKE8O51J05CDaSbUI5L5LwUUVSqTBL0wtN4C3RmeHK+RpPa7reliWdV0ZoR+1GKU5BywcDn5ZW2bVDKfTBcws+hD0cK7BoAQAoYrD13VkSJG0jZkBTP3FI1prEKlqMUiKSkBM0YMc+dYJllpG6W0kKBbeG61IHKmqPMS2QTGgHl1qtZJBYRm8nCrjKePbtvu52QgxlePtoFzwA9mHfnH3/R3BdWrc2yY/yW2ywf3n+z4VAnJmxlDA52CyOAuji5qR6f6U/FFFFGKpyocLACtqkkjfFqyL9KKb+ieGf++AmDKiWLZA9kApJRy5fjZqO/jwp5oVBZJ0y9RX8zkRMcQ6mVJMEvrLQNCT3L8zmpxJ83MOr2tkMgHrbRYHapAh1Octt+PqbT2uPqWzWmu1sthSS0mFCVITkeneU3Vcn3pZUgEaOmikMXamlXx6Oh7XlSWcLhQskjGQsnlrTNBBVAVqWTRvCKiIzkR9evOm3dS6m6paSlyEUxKGzilhAvmEKGxnAEl5P0XgbXfm9pap655Uie2+TNW5rcvMNJkjx8wNNfTUk746jFURsXpHhJmpmjCEoqYqzIzNCb/IGYECRcus5s2dLgtgSww8OBkFKnCcjsfYUmY6MoIqRaBOF0H67IyIRwY9OLamCFA27XUnC46zlgQ3hgi2lCW6kKICCYGgd4DYopbSFZ2zGEsGoiY73rxcgpGm+xQUPOiEM7GuACEQU4mI3Aveu5gWDAF/nG4CZGdkhUUKQpDel5oJgkQFM5558AwGqRQzKaqa+axU19YALGYz0pk9Vu+9ra33/vR0jOBhWcysWBWV1lprzRnH3r8cj2trIoLeGsU07VDR4OailKKmUNUSES3aejyu7RgM05r+92bmmUsUyCA+HwGlpmpQEWrrvfW2LIfce0VVq9hiVUyd0b2v7lRfUPXw4O6CKEUOi1kRUVhRtQw6n0pfIv5xSpyadEZ5EihT+hkEKbnsnuIIbtAf3CD9874rd3GiP7nZkNHmw7t4QD0j5VWeaYWm14oi8zOJ+NBst6K+qqLIeC1EWr5GYkEzMR08PyPbs0znkItBCqmgqYRIENUqFzeRTlgtLj1TXenInidFS6ZdSuDYRkTFACOAma1zSOYje+KcqzHHwzA/vpJTvcOc7qQbY2kIZKYdnhjBCbxgGhJk5P/CzpOKp8yqDADGESXYez+uawq/7o7lYFLqKJIsSjMzjQYJgLUUFtjwnlMREyECmSdDrPToRz8+rU/Jq1UsuUSARijSKRQynEkz31zYmD9mpwI1Sspp6SuYezXTopGRhayTa4iKZHBGCGUUg57vnfOeQRsz0penrU3kr9N/B0inzem0MzjWkLsx9anZcvY5glI8c2+G9xbdYCVMYQJRsGhRFQp67yJisIhwR8CAgIrrfOiEmEgBdPjKO9LKmuVAUg1Ml3+PKAFKIKUQUKBpMvXwzJbjBJWLmpOSUnAGtYzoxZN/fW5XMxs5+iX95UURtap7yRAcI8RUDMXqUspiJTdfgMUqNneG4VoiGLn1KcgIhrkXg4M4pwQ9lkbSYJuPhgAKNambvwpEGeLaes9w1+0kJeVIw1ahiWYZItFNmdIi1TCCb92DLshcVIh+XNWBzh7sx+Pn3hpEy7E92ZMsD721tjYHjq19+vzpy+fPEBVZVWWpRaEkxFUbTMzUihawHf345fj09OXP3nvvvdZabTEt5bAcSvXwlDta703lcHgQ1eh9eHKqNpEATVRHAmKIpAkCLrG6d9VYFpPEJzxDaEEPqlGYXFfG0g639El7weFWJtvXk0yNrZ3yyUuDu97SZMinHCL4LrsJN1FrQH6ZWUxETRQ2JNwt/wyGP1iCgHlW0xZeIRDN99D0YAOIdLzN4DmSNFWUohlTXtGPzazYw4MKjz0oUouJJBKHUoqQtSylloSjRjag0f+ZL+2EIDaQ7Yw3yDl/PZ/tM+UgvxENPGsbLjd8YvLL8zaJ3tSrJu+P1gMspSSuMeQmVXCryxaptIVzqfVgFZBxUCGk9O4i5uGrrx6dYFtXQB4fl1qriQIyshNulCEzMIlkZB/p0VBNADEdJq7kl+mVgjQ7U1Q1NIanrEBiiqs6nfZkmHg5Zi+ZNWXzuZ+45TYrm8FY5Hyvb4ZLbIapk3WK6ScTQ1YmdhbOCAqLAZSiGsH0FCNH/V5GtBS8LF1xOWNDtqXOQC1i+CqNXC3IKMiR+E0ya7SYSkIHACC9RQ4lJyJjr1OoUzWDApG+gTJDzDbxP7eK06fek+ya6W9BTIqrVFNTO2h19d7dhVCoAlkoj7GpE+OYj9iz3NUD+RxZJMiEZabn5ajEh9T1mQEDKJm3AKpJB0TS5pQz3/uoBCkuNiDKmtnXNlmJMzdcUROKZGCXoIoEovvaWl9be1qPjw8Pyvjyx6doLhj5Ybz3J2+fnr58/vLp6filNSchKkutpmV4mqoeliWxcqE2X5/Wp8+f/zwenwSopT4eHotVEnYQEbh3RhD0kFIdUXKDDTkm2FrLaOKS7yEAg62vFCM6F229aave3L0fj8cvX76kD40C7l5qLVZ0JiEa6RAwzt84juNAY4AzJyFUQCX9GtTzLi3J/BB79m4qY29EpKw+pdfJybNWC4OESREgc4vIdsBFZvWwAFNBHqLkONGxoSLjuTPF8WpUFkggPIpWLRJrW1svZqXUWmrR8rBUAUxLKZY8M5EN7gg6t3Q/86E7oX1jszgroINnxP2S+54IxPk3U4XARue3+b3Cv2ViWaVYesRmYYAhZpGDVE1HAfchA7fWHkudsAG8050MitG9u3vr4dEBmJValkw6q5IZ2EXoGCJh9NZb74hMZUyPUUSbnAQCcPfujkmYIlTVJc380w3GTBPPIAaSzoltTmV27DWZOS/PduBpiU6b8tlFMw3CbkZPjsozLxxTtexZZMeLVpLee+oe7im+WGQ2hbwtFS8MmGZDSvMRKpmGQHTgoINOJ2nldNt3H344ufdVoVBTiFpq0en7ndRbMeLKtoSFwNZx+h5RRXRYecdxK2b5MPcWETO7gKUGZnkKMok4psCZi37uo8ENY05Qyp3B6DyuR1FdgDSQpiEwAaOTAINtEiyZFQwRMuA4956hxYSqVFMzH7bTkdWBJDIsYKm19f75yxenH72vqgg6+59//P7nl09/PP15XNf/49//P1jXT2szM0QgeGzHox+/PH3544//+uP3//ryee0eIrIshyJFVLSUWsqh1rostVSzsq7rp89/fv705/Hpi6oeloN0qpXuEb2pmEdXtZxAdnY43SVDbqCqpfU1PMKjiAgFwQyWo7uXIiLy+XgMlLp4X1vvva+t1tK7Cpi55VREtaioZVKmSTy4pxkCzoxUu92vk4KNfSCznAW+jRNMOzJmss4T0jrtlnHiBFvGwemuHN65jV1i5P2GyFZRIVNBUBMHUs1QdEzf6M3DIoZvb75tGouEi1ks1lqrxcSiMZxxKMvDw8NSl8NyWDKserhTWYrKuwnhIPEDc55tTv3pzAGzAgMTgRA55SeYisLJBjmxufO5PKkJCRhF2ubmMp1Ozrx8MIClHFTURJ/WJ6jWWmupO0sdMdFwzuDM9FDuvZdq3Vtr7Xh0b36w0qMd27p6z0jspS5LqYYssVB2U5D55qK7jwzqJfdesWVxdm/MSFIpI8pvkN0IiNhwgVCAOflbXKhmTuSJ7OcOmfLtkATTTho7cr/XcFMoUowEMvlrTNFhA9RG98OoNphh/lDMmsMjAE2NarjVAx5dTDhi0ath5EwYu3iTwE7LOhPZBwn0CNsg++1IDuYUKuagECNcIxMcKyLCmHqDQKyYFRGCtgsFPttPQ+K3ERojcMYuiakaNRUyzaKqakBauiKXY6yUjiIzQ2gdb7gDNydLdw8Ptt5zDksp4Z7pEMQ0cfKwtGzLlgUqbZyWJItkhBDRo/XuCIGsKukwmYJdGaZuQ4yYiwIrasfj+rQeGRTy89OnP//47//nP/7j96c/6sHa8cv/97f/o6/xn8Gnj4/B/uX4ZW2fP33683/9x//zx+//3Y5dVc3KUg9FFzJnmbXUj7/9VpfavR+f1uOXp+P6pfcmwIfH3/qhLcsh6wAstgTh2tWKlkKAvbu7kGKW2THUrHc/rq0wwsPhXUA4C0x1ae4xFEVTLdMfA946hoBgYwva2MMxvFPSLS1JEM+xiPE9Tnss80ukyw3eoTGFrowMHI/b5Jt8XF63yZ5Mk/kwu2WsSuZcjN57mt2m8KcKFWHMMIf9zpsC38gykWwjEAyOMGoyTUdpT4aqFinditlhqWZlqZk7GyJiMs2FU5Y8cddB0MentFVeUG/ZrthMk6cp4jlTeVFLL61U9RDgjPFJcWxOwMYz8tAtatLJYrXWWkuxAdFk7Gf6d7bjsQlFqb23py9Qkwy4X4+N7iVKj/V4PJpZb0exstSHpS6qqmLZAzJRa3pAt574gx9bwIOxLAcT/fK0GrStq2AAn95773DzPGxhqkrTUZC5Vt0UrCxrOrXbbbUplM1xTnBCWubcjoySY0HmXkyFMvXMvYKwrUgqIumZl3p7wu6lFGTa4bFrpyiDEEhHNzGdJWRDIFkxYVD1/SPyXhDwgTRhgwHygDArZGVlR0c6ciGrG6mGOGJkxiqQAEytiMRZ3qPn3mSTqHLY6CLtbdNPZCrZyYuUEg6Q039ToJao18Zwd6joFPHGXA/sN3Uhab0PF4XWRdUx+HpRC83cSRPxiakUTNw0/3D3Y1tHAUFBKbWUksV4i5lpyUShpjbcoHv0p/XYGuFfPv3x6Y/fv3z69PTl05+f2h+ffn/6t0///vC/8dPT5z8/OdqnL3/88ed//td//efv//2f7elJqKXUw+Hg9eGwPIpoa8cAllq/fP5EyTDG6N2PT5/b+iSQT4+fP374Hx8/foRZMatWRMXhPVb07l1phRESzrAINl/NjIjj8al4eGtD/yq1dgQ7TPzw8LgUOyx1qUtmf+1t7Sb/f9r+rMmWJEkPxHQxcz8n4m6ZVZW1o7AU0GgM0BgSLxSBUIYUPoD815R54gNFOBTOYOshBGh0V1VW3iVunMXdzVT144OanxM3s1EDcoRHcokbEfcs7mZqqp9++n2FKzuLSriiDHWNHAgYJXQGrR02jpQQGkODuEWKFwX1FzHqe9jCnw5V+P4W4j0PBhITJh5SlciTZodraOD0HukEZGY9PIrWxBZzGWacQuz1b85vk4cwKDJQ5+pkImHxMGRPbGzRBBAZxAQdQidEJJwWccjTZtjmZMttoAlxW9R3QHlvo+ywD8s4bUeSl3lmypXSTYt75IC3ioBG4SVfBp0/9cjXztqHOKXuBqgiTDwm58MGeslBoqWKqHpGWC2DNSwEWIR5tzAAvfUwVC1eFERbbw1wi27GhGbturYwnudKFhw6SZlLrZVZYniTICL61rZ16733gDMTq1i4stSqQXADsYMA52jBnUupyqpqpcycQkEROQsGocxcboccpZDkOJOZmAWsIqLZBM5btKN1yF3AkcL1t6XJwkjr3FsbJ7F+7IDJvhFAQayqFSDArZqFkAgXKkP8jZ2QcuQ6oHgBubtm93VPrsctGrjgzpVmSaXonK570WPKD8OUrl8EhzlISRHpfFscY9oru4CqKkSqI5eWXQxK7qJZfNuqA4BhAlgo0aPBHInwCJPs2yOgoLgdOdlxJSICkxOGAQoiSFP7aIQVcASBRbSEpXigExG7d2Y4BCBH8/B68/ZgItL94ASRR1gMJXgnhJlHAGFmW29t27pbAlUsXKfpkLCMSBFVUfJo1phJ3JfPz5e+ufXrejVftuvnZbmKFJyafe2H+YgrzNvz6dPT54+fP3+6nM5uJszTfDhMR5u2rS55IwI4R84vMyWy1LtZd3Mwegtr3vrWvF2Wh9cPj9M8ieq6ralQqSIsPM9TROne13UFcZmqPzyU3nuEi3Cq72bXjiMn9TVtaALobohgKIQ1pEYdS/vLQJwZhL7QKmImjFnI3Ck38ZMbXPG3oMn/vz145BPja05xpmRuiSiEiNyRGRITbCRUFGBPmWgPVR1D4FRLKRhiUkVUeA++aeVL7gPZuA3d877Ud0UVoiFPhUHNzvd2q35IRQisqrXUm2/DuKj5756V5P9GczLTzWASUkkNBdmjPfaFzQO/ffH44bW+HSd/4oEXHTZmViGGBOcEQ1YkGjE44PlPURkTdkT1PtOQEowUFNu2uVvA162ZqDDVOvlKkPBuffUiGhzb2kCcFTGDVXSeZpXRYmUmhzdrW2utt55TkC2UVYTd0cfkMBFTRLTezDoRTXWuWue5IqL3LsKigoAWVdK9s0v7MiYGUiSWMlKOAY3vX85buckjHxljWlkU5UwH/y1Q562mu7Pm8oqVNLP0bIEwiRRhBptbdqIEzCIenv0+4YFf3Ug4L2R4dluo8MxJRHQnzUSAUyk5fLTtI8LCiFjghTGVmmuaBaONS8T7/D8zdGRetxzDOVWB7zLbPKAg3sumtELq7m50S+n3THy/EuPq3AqX4EhJXClZtey1WsA9fEjhRIqQB1BUIwCPbHGHsITn2owId1fVHOQEU1LrU07BzZLBueV4lZtH9K1ZngruIJJa5sN8nA611qkUJkpmTmvNrF+fn7Zte35+evr03eenT96tlhpr87W/evVWVa/X8/Pp4+n0+Xq5bMsSEbUWMoiR9PDiyXYmELw37wAclB91X20Uvl3N1uV8vZ4Oh8Pp4bFMdZ4OygXu83zQqU6HqfeDqqzL9XS6REDn6dWbV6WbAVxrTbWN1PMstdRSiTglLyK895RBIBVzVVd/UWPdZCC/H254oAFEHLjnoLfE8160/a9/jLh5Q/EzEcu7ykIcyNyYjdO9BCwJ3AJKHKRB5t4jrGjJ9ZYnfQRBCKDg2OvVXDo2TTWLgD2b4NvJ9zJq71teVEnYAaTZ6RD0Fk7zzCRb5SF2Y9rvLcHxX4/gFxGZSZnH5P1+8e+X94fX9j56+qW85Z8+A24bmwbcIaokkOAISvkHIuGUiRVilYgxAAX3GFKOxDxElhAe67atbfUIEe6tLYRuLtak0rYuvtE8zWB4+lJZVM3xc82xr94tg1zztmzruq556C7XxTvVUqAaQeu6Zg9cpZh1awl5M4G5kqoQNREpRZkQyuQoRV7gh2PwjvfLmZ35Fwv+i04v7Ws+VwNymoH37HpfoLyvC/reluEdB9mVCVhEVedaAWfumQEk4CCSdSZEAsGc5IHhxTRC/j32J/qUwL2Mw8A8p0ojgoJlFysiFiKHR3Q3YspB7cgBvWwdZRr0Ze+b95VPY56MRfiHK0vGIAURE9zzLakWGTw0ZuZ9HhqQwXLO+QiEZNfV8yjovUdOdg+5icyrrPfWe3JvzLwUpUh3aOEUgRUqm651euzHduiDz8KMCHPfrA/kzYIAM9u2rbu1rW1jmQHu8Gitba2xyjTPZSrz4TiV4u7reu29w8y26+cPH07Pz5fTqV+XMEcxdEfzdllrnbZtuV6e+7bF1qMbkngmAQ+X9ODJixwYgvLh4TSEPynvMWhjYelKbrYu6+WqpRwOD29fvSFHWxYuykW1FiC8pebbrK313goBqqWUms2YUuZS6uF4OByOpcwIeOteNFR0Eic2MS8F91P3BuLvZ/UNWLjjgCP7f4GK0q2ZRvynAhDww3Tpf/nBIhmVmASM23YlpnQU2t85m90ZO7nV3ZwGzQPdmIWVUwvcsDeZPCxlPlWl1rp/atyGibKFgNF1yGxGA5E7yy2gOVg3JCTyHcaupLZzFIa2ST5hmhK92G0sIoWZcmLySyneL27Ei0B/61u8RM8Sgb2dzRgzxt8/y/dLRLrjGMxCCKQ8EhEJXBWM1Kkb7yFNwu5jnHdGlptbM4ISi3uAupCnRoy3MG/go3JVreGJ5xRR6a3lxPHhMG3e197W1i7L2n3r3rxb2wIeepjDoptzyQjia1tJUDR7WiqpERAoU0EAXMp9znrHVWjXttrbVjREK0Yv5odkKAwi3PfW7S0PIIBSq+YGXt/rtZf3a8dxlKWIFBEq1cP3+5/mhXmwsIgULfdwL7yLBt9XQp4BRONlI0u2zDMEHBE3UUXikYUPhJ08vLsTCExaeK+FKIO9jAIYHOmixcx54bJ3OAixtFOWsxJS4QiA8jDTkdhkCpUSivtzpYXGwPBGdhUgqLGLqVYtJbk9HpFiOG1bva99W9ZtTeApq6+8OyA4fJ6Prb2at8NQNRCJiGa9t5Yf2czgAffeu7n3rV3O58v1ysK1lFHmeazr2nqHihSdJhVl76311rc11u1yOl/P176ssHSz7hRMIXDqpbmbm990GmjPHd3di3kUduOgCLPePdzdsPtX5k3kVI8IDo5wlyZWm9YKhAof6tys9Wv3CFUBIo00jg+PUgtfNUX/yzTNpUxaailTrVPRuZS5aC1SFYTuhAyo3jrVJHeP6PaFQ8XL8HOrdl8E3/zhvXQG3Qu9/5WPfMXBkkz6DnEwKWvs/AwQSeBWHcfQsPKcxgbczVtrakozVy7ZW2QVZ2cjKO+VZg8CkWT9eAupI675mATLzqSqDrw1CO6tNVEaGHYqUHOG73FdkgdONCTXglJlwW8TxaPuLmXf2PeLgNFMu0f/H1yi26998cX+5c0f/DblN2IH9ox+VOW4MeDE94FdYYESBPkDGkli8o8y5fTYl7sw11oJej0tOeS5ressRxK427Y1C9cqh0nHricWFetGBISF8bq0rV0368vWW2+tb8t6bmv3zlMtRaRbT1jQEdbQbJPCYZFemybKjTh84phkIjIpRUeoppehO5P2BDB3zJP2TtaXCNvoFu8J/p7qg8aZyffT/Tb0+4Va8u2aZyM0bmIDwpz0dKEi6uy0A6uZBt5ea+Tmt6x/f9PMe4TN/2BvlxERDSHvvZ91r6fzHMr33KwTEZE6Uj8habLkETk0tHO94osnIWDMNIxURriky0r2XW+5AovQl6dgwg8eQQENQqDDOzzImVlFnItI11qFmTy6+9a2dVuX67Iup/PlcjqdmHcKcjK73M0tKGqZj69ezYejalXRxABTQy21e90N5t67uYVH25q1zbqbW2BQYgUQJjfvl9Zb69iYKKxvfXNrcA+Dde+t92bJ7CZyps4sqe5BL+rNWxghAgUis1FCAlt5FTPtyFQRfC8G8mlEpPYepXnv6/VaSim1gGDWaXRbUEqNCKmaWQNJEdFS62Guh7nOtdRaJiYdLGVmSwNNEHWfWDiY7rZtTLsAiPKwAdaBgiLTk9HKxJeZfBbG++3+0yjEn/jpl98fcy57FkdCoIjU7c13osQgSZkCBEVwpHGcdYS5G4HDGZE/hbupFoeThUCI4eGbbQiupSLgffhfq1aAfSCAm++UUGbJ9qiqWu/WNhRVLk5MSpw2ynszIf/j4L2L7BZh1vNQuQVliCThj0cQyC40qd6JKTS+/f3Lddvbd45s5kaE2zcpaHSj8SL672JS2e4fVxhKIfdARgJGgrWcU4LYrTOYOSKh59RQVlGqWtQmTmbOml3BgFu4GxNJsxU7WJxlf7Rgooju3be2Rdo3R4hTXC2ai5RapPu2ta3WSiAngpkwkUWAZBJ4d1NV1bAI8QgIK6MwIRyjFIvkwjGCeXBCgoypYFeovF0wShGpwfaMMTU9dkhu7EyYyeEelgTz7k4EZVW8lGIcBSgLJd4uhdmV2Cn7oizCQgVC2mFgKiPhuVUP4xASvg0JDrRqz+wD8KEVl9NsQSGJmo7TK30mSDiCJMaGd4+c5xJRcIAqE4lWkGLPHog4IIIcL6cUjPKgff0q5e2HSiKvLCW0xxgPuw3TMHN0BJl5s2YEgcNgRu5ogDO4cK06aynJyI2wvi3rer2cztfT+XQ5XS+XrGJSdBqebCAjZlWp86zTNM9HlZJG3A4k9J+i/whPxC6850BXVvkp0BSR44M7emDmbW3bZmbJMMk6L9L1bJcRAjhGRe+jAsximlKYhpjTVzJnU27cxcxrB8VRiAf5PHCjq2fcMw8PaJCIucrGTCAPc3cLZ9U6VW2LqIpqISLlMrSEkJCjgvKscCOyUpg4zD1MRIoOhkvGI+xo4D4WIcpyaxC96JLlJxm//OL7/397jFTtVo3QSMA4ACMKGiQ5IkpxT+TGEFHmMt6qaN4tInIP6mm8Gma9W4CgRcnzE5VUIjfz3q21lnYaES5iycoJRO9btx7ei8ysQiDZlYHuJxkPyCkdnFNJdPgjJeNiZEuDMPfia+bkmHJm8QMB+Fsvz8gfxqzBEIO8FQROoMhIJKOU+/4ZnPE85b2EZLAL9xFKikirJiEKt0Gd3TUyhkoGM4tw0VJ1mmcVKVLk2hqcihStKqLuJuzC5EFbW0kclt14B2C95xiHd7fW0/pWmYVwvlyWzV5VYVK3IMJcC8KGag6RyBAHSLEU2udg7+H6BT4jO4KSZT8zp33ey1oKwaDIfZ6SMvTCPTgREoDMu3nv3j3C4RGoUqpWGhk0D0fRoSSBxH/HNUt0hDgYKkzM1InHdG5Iub2TbCDwUMEY7/CF124iky+cdkR5jPkOPuUNNeIkLsPDIv2MAEo4dI4CFUldYhAGep/sKRktv5H/p71xAJpCXUwMj9iPJCACxGTjInqEwQ3efettbWvzHk7s6Fu/XM5P27qqHI/zw2F+mOcDqRCzW9uW83Zd1mU5nZ6X67Vt27atboZU7SfOdD4cKqK1soqUyqKqhUWHliQorEf4aExHRGQdAPJhYQ9CeDaTw+CDmOQjtCOGZ8APt14EggdA8HJP3fpJSALJuDX3JlMuzUDcd+CeXbws+nNH5Hmb+zo72ukwSkS+brxPf2TXSFWUc4XADSLBCo0wJ3RrwZ4vczgc3Iu7uSdmsicLewy6fZpx73cU6IutMoQgQISbhPoPiun/qsefKBryJW4J715254Sg996td0//HHMPC5hZYwLJ0DBO6pCIIMIB7ykUBSIEhXmIyOQTC2tRIph1c+u9bduyrkum7O6eylZMIiHdeusbmHWuqoXp1jUeHycIsbPaPeL2dQzLVhBRHcT67CCX8fx3hJfkT16Z22OQlBC31sXtHjFxSKT6qWQb5H5j73X9ODRpwPtE5DlhsQc+J8/Yl52MwdIZUpepCKc8yhcpotM8OfW29VpIqDC0qiilEQla75DeFpO9rMxAFoG+bK1tIKq1BKJZv1631mI+WClCEVqqqqTQdqmzlAQcoCrTNJVahXnSUlhrlVJrUmzzEmUGcYPFiJjSzjXuFy0Pi0RRRgh4AXqMyxsgcLferDW3pBsiggor110HaYgcBPbRtsxGMWKlI8Dw8OR3ItJWBBRBPrjwA5mk+y19uQtil58YHdu9PZG3DJGCSKDUbso0gMeUXMBb27pvzGAOYo1QFa5SiYggQIwpIsA9mCCDXToGuAY3DBREPpYLUsKesqoJEIkhmvfN27qel+WyXde+WnNvtjx//nD+9LG1dnj86vXrd/PheDgciBnwvq3rcrHW3Wxdl/W6bOvSt61tWx5cTCSpagUICy3MIqxFVIvWQYTltAD2QDBIGBFODDfL+Yhb0HtxPYdpT/apR4LsfmuRZjTP66ADSQ5mH1208YQ5/7FzBDl+eH7cMYD93LztWfqy85e/7MBtZjqFigQIjjDKcbadhqIMJqkc4g52c4eVpof5sGqgRQBFRZhKqe5u1oA5i4CICAhlfSl8U0y8nQGgW/6SgppjoAb7/FemNn/b6Mj9g/1XfvO/8NeHZxlTABQOM29bb8kWaJt7X9fFrYkw8yQCETWDmU+T0shuo/fevRHlFJC6eW8dQhHJIIh1W6/r9bou27qaWe/dHfM8l6LMQkatb+YtAG4vNPpuYMlI3xDpk7qP++AFTJx3LKN/US0jayFi7NUAv6wnvpe5vwgESSy4hVHhO/w9xI/zq9w3/KLfsD8J5Xv2uKvfmbu73YIORkc0b6303rdtW5d1Wa7btiGCIO6UpqZAsNDhcOzdCVFoqpouMRzq69Yl3K09n545DVhlvzWOrfXe+vE4b60Fx2W9LMuK4OhbBIqWwzSxpCB+mY5zrVJKnaZ6mMrrh4cyzwTMdVbReVZV5dRWo5E1Jx8sN16q8TBz+jQMGBf7PzQm3XZx5z2a0kBiem9r782tWU+FwRDcxoj3/ZzHD+fIT7r7UYr1hoMVHuYuw2F8/DXzrj5awTnMCIwu9v7kNGRqQeaRKRAR3w6eiH35eQSBB+N+iH56xNq2pV1avxK7R4/gohMzi0pQmDERbdYNYWal6F6CM/OQ8Ey53ByjA2jY9oY137x7YZUQEHWztW3X7XK+fr48P52fz7aaB1pbPj99WE5nOC+Xvlyu03ystYhwmIX3tq7WjYLcbduW3pt3s25BY7KXiRMapsjEjFWKaIF0Yk4tpXEm5lSCDL/GcAeS/HmHMCJhAwAEJ2R7+WXEjwHl+r0W3HPRgDH4ZUZPxLdi/raDvqwS7n2CcP/eHh/YQCKTL3b8i4MqsrocGD5RoZRGMaMIDytR2JV2D49uHdcIoBR9OB5UtU5zxBHp3RO+n1EZw8eUKDEFs9xYzbcrFhFCqbNI/9Xh+7/0wJ9sG9yuNfMQKOIxFisEjqAImHnrvfdmvW3bygwzaGGFuntBWHdmE6G9FxWtNaIUs3AJ6WwQ6q0T3N0v18uyXdfWsrzIh5nePn2Cv80sRI6HYxEdiSjCPSds71Oet2Qt72uG/eR76HiMhSK7XulNz+Bl5Xi7XF+mgZQxZxBNca8wRriPxCIkGSKpiUq3nYHRzAzkSeWt9QQ0ejf39C2ioCCW3AAi4t2vl+v5clnb2lpjAqME2N2sCxL1Jq3TBO8HmYUrMaYqTu1qqzO13q7LFRbEMs8zM5v1cGqrsRAY1/UilR0bKB6Oh9/8+pcPx4cgF65ra9vmKtPx8VBVpsN8PB7nKm9evxYtrbUqRVSnQ6FkuhMjB/YG2eFOkmGScEeyQ4GBuY+ubuzdn5G8Z7c8Bo+ae98265tZTp4rcXhEiVuhIPfNP54n3aQjYGYDKdk6JyFHxcMp1cudi5ntssAYzXzVUcCNCYTcrWkQ6+aUg1XgBJxi6Ch4lsEeSIskzxvt0Xpf2rqup+t16a8xTw9KohNj93/f+tbdvFi+cLyoKz2CGRKgYGUhohbu8IBtfWttEy5VKhyttda283p6enr//Onjel18c7O+btflcrEe5Nx6vy5LmaaUaRvsiUBy57LZm3X5DjpCMgF1ZKxKVD2UEMMEjESYFTmiDHA6IISPC3KjYe2ZTe4EAmJ3Q93j+/517DIDHsBwL8gEIrPe4YfMiTUMIP12BrwMZS+T/VuicDuLdmSYsYNF468AnOdQdq4SlUlwj3Y1FetNS0EqYrcBD3Vm81xnJbyoqOq2taW1aZrKZmWimQZfi0OTNneTZsRNYYNZOJXpEvYRSg/zF82ALHzu2+u/8vG98udP/GIgnKyHdTOPEAYLBuKSZhTuwlFUgRBh1cJgmIUwF6UUOQ4HuPeelZZJkkMNPgym1m1pW/PezXtCZAwhS2dgT5QJEk6hHhypd89BaGa37t2eqLmTBwPCIkWZS6qssRRVZSmsSiKkw7CbdxzoNnL3/QvFL77gOyI8Bs32oaVUmFQCcUT2PKAx7KX2BGS0jnO77edVxjuYWetrsx6JQDIx07q03g0g731brtf12qNz2plEEYZ0EhXzcA6LrgySEFZi0kLRWaKCsPVubVsvq8ikyhEIZwmBk4iY9VevjsdD8aj8emotpBx//ovffP31a+FY1+3z5+vpdJ6m8urx4euvvm5mReXdmzfm3msR0TppMFn3vXoR4eID0SGWNPkJ4WAGQShp2rvAaLb1zDK5tpfY3VBWYJj3/AWCSa2gYh2uMDUChDWY9on/lBY3s9b7FqCw5KaEG0SI2XqEqDpIVbUWISEI6sAetRRQUC6iXFs56AV3uMOJIgzZbmam8IAPsfQels0gdqLO+wFA1r21fr0sTV0wxQMdShWQkJYi7n1Zl80tcCpFldXSyi1ISiY7TB4cwqwRaPCgDrTr5bpcNyGdp1lIzdq2Xj+fPn5++ng5ndqyeXfr0Vsz69leFghHa2sTkUY5a3mnNoyTMyKzPQzjGk9mGt9gnEDAWZBkPSJnAlFBRCZS6fcrKTe9A+y077EMQc68YxyZCtCt7sNeRCaQnGYMzBwQCcl6MemIGPsvQz+/RGW/hwXtVTvdT5L74/txMDDot7cQGUjbehWRcns6dmciMwuQFi2linBESC15fvXWfKq2P8gz1bm9uZ0iCLq10nhvSFIipyDgphj6Beq/45CjDrqXWP/Vj9uRyPcbNAqtzFzSKsG8m7X8HtHgU7sNp+XkYOz9SQq4GRG7R7Z2LQ0cmFkVrbdaVLWYmcPcfV2WZV3Dh0JGNkgB4iAmTvla6x2FQTAfQ5iJSyWbJ4TASFPJBE2VOaUUBt6vmgJ8qjKsPIXSSuD2qb93Fr7MR2Kferg1Y7PjJ7lG7s8xQjxFCI1eUzZsbpBDPluOzqexn0WYeTPbrJ1O526mWs02AqyjN+/eFOhbO12enQ1MoDKXh+Aw69NceWInuLdEAlhYWVRpWT01JRO2W9e1FOqtmgdDJYYmQ+/+53/+j37z619UDnf/w3cfPn1e/p//w//rlz//+X/zT377468fX79+/fz5Ya7l3du3qnJZ1qlMUylVdS5FREDRrFcVFhU25mE+ZWbmQUy9W/pfRU22xBCjB/IqpMdT39Ye8AjvvecidISoIgVKRcnC3KtUFvJuTRsL4JHzUDtgG7213lozW1tLUJrB1j0sVNmcg4IYJReJzzIJs6Qpb1YkRVVKJUaQO26+yrZum3k399asuh8iqkgYwr2bNWtbrCwjnYPBuptTs621bV2Wy+VapHEUOE+lElhJmMKjL83O23W1xWECar016wTSoqCoJBw866RlAtipma3L5fz86XlbrJTDdDiokrmt6+X09PH5+fN2Wdwc6fFLBECYmMXdE0UP33kyN6FWZkqKzMtQOHCZ2LNrMEbCTHAQUYpRkOeARwb8F35PlFSOmwdHptEvQb6XOM/e6bylTaOsJ6KUUBkT4nvHNw+AyJZ+Su5/2ZnLp7qBS/dv5qzK/nhZ6H9RN+xhNX87v1XMeo6de3iJkpglBVM4kWgpEYCCeYyWDIHxHBUZRKDRyxzdvPHFPSTdclN6kZv+IMAzpQ3p/5ePl1fneyfH+PS3HNVHIpBR2Hpvbdu2zVrHIPBlWpu33j0IFL31dUNE9G4vzFoJCBFqndnEehctREgr5hyCVRVmLVKVlQgsGiQdhrC90mR6AQjmJQJgQCQDD1KEq5Y6TZwM31qLKOUN06G0/EJ6/U+VQTtGTb6XtDdckoiLVNXs8mEf/7kB16m2zAMzyki8nynZRh/JFqKHr71t23a5XLdmtdZlObtZOE9azfuytW1bL9sp2IMAKjgIFwoWiGMBmDxcSE27Uhet3bBtfXgtOMx825pIbb0TVEXAIcSPD4/E8enjx3evX//i5794++bhl7/69Xffffz22w/H+VCLkPuk+rNvfqIsBGfQ6+MDi3DK0afkPA/BACKRWkTZI2qol9LNttZ5OP+l0DeY9/wO8B7mlnbnrW0eHcC6ruOnCFVFkHnM00wW3r1RnyZyMpZgjr4T6kbjl9B6X9etWbssF3MTESUlYwR5BBn1CJIoykXLtN+0ilTKs2maeJ7ZLRnZZuER3b0nXbEvyXi0MBCcNXq4e7O+tPXcPgebSpnlWKJEdw/ufV2Xy7Zcl/PFmq2H5u+iaoFTVSU0s35ZttN6eTp/ejp92tal95Z0WlVB2MN8fJgfHo6Ph/kQQcv2fPr8ab0s6ICLlrlMEzOar9fr+XK+rNer9S4kjLSc4Rt5L+Nsso4y6oBiZLp74pypDN2SauZEqjOIZVsnB9M8wKkYRs4c2YonQmQitrfMCfeBzbHhXiRY90j0ErTZefCjXHBnVuj+V36Y6yYWl3IBg9w7dvjLA+bl3+AvH/jeW/oy5u7jGiBCMXPmHsGlltTKVq2yW3G6exDYWedhfj7+8oDUXvAiRkrP94j0AozY/5BGwSlc/kXI3tVXlMaww/8yxH/7eHvA2j/i4C6OgJcza6kYtbMU7y07ZrBILVMRIYogQ573nJk5egI/gWEXByS2K4nbexfVZLOIyPBmYvYIZipFq9apzhQR7BbEKgIVUUF6eRARBTkwhIWQGi3gyoVFShEV0aJ5UVV1KLzJF5pffzr6367S6PClZ7177z0PM5G0maJh0HGbes0KiYjvczxERIj93CJC1k9JC/ZUUO/bulrvsbkFW+/WWhgFdRG1Hst1Wdt2tTMxPRzfem/d2VhAxQmpyi4Ekg1cIqAevRn4VoCzu/XeD3Fkllqnw1SOVX78zU+++vrtw+EYgfcfPh6m6WGu33z91Tc/+hpIpiv2DOYmKZwiIXEjbKgWYUrHLMluWkYKFcQw6dh67x1IZyy+N/ncyL073BMEa7211nsjIhYGhZESyHpIEIGsW0QQR8A2l82qqghrlRKO1nvWncu6XLbr2patr6WIokiUqhPIzPtmHmFl4sN8fHCgMgLNJLsCHWbsmlZpLDDq7ua22Ha+Xq7Lc9IdVcvjw+PEEzkhopldtuvT+eN5eT4cDq8ObysmleIW13U5n54+P79v53Nr1i+NzJmjeX99nMPWdV1O5+XpdHp6/vjp88fr+RwWlg1j77Xom9evXr96fZlnldJ6P58/t20tXIS1cJHaexMAy/VyXc6tWVIxR4wf1WcmTveuQ4b5Efhuk//jO5HlwL0W4Nt8KgcF308I0Pg1vtvYM5KEthe/uWLuqf0P8/HcGbx3ZSPdW0e6n+KYQinnY566Jt87ORIrYdpV+rNzuZ9aL1/ivvl/QAGiFwdSqtbfnz8vjgiAcrsmvK9lFmWw8kAZREstRUWnUuc67bShbNnxrkDGzKKcZpGD9XF7X3tOuafkQ5Pqh6wfEKV0z35u/S9Mh92uxf2ijKud+fR+LuCFdzndRV6JiVT0MBfraTfiNgD4cHfIAGLSX4JAGTSRE8Pe3Y03YWESgdE0TWk2WWrRUoYZp0gtGpHyKBBRKaVwIZKwiNSpRSS3OyWjM7IXLVOtpZYxdCtM6bU9+NX79PB+of/0YZkXaUd9IlLvMMLc8xnMOxhKY7TgvnryniAGp2UcA3fxDyCHlYEg9wgzBhUpVYpTTLV4VN96+lGERdt6BITZmrW+CU2P86vRDwMnWMRCwhS9B69wTCVnYdnCmVlEs7h2J1Fi5l/+4uf/zT/+uw8Pj1q0qjJRW9tclSOdLoEYuCICxE7CQDCxkLjbreVOeZ55eGrgiECECG4mInOdVQUU3f1y3dbVxgjCOErDLQIOeGvbtq3rtm3btq3buHHKKoVZ29oZrEUi0L2TWLBv1w6glnI8HKdpYuJt3bZl3Vq7LJfLdlm2y6enD721d2++Pk6vitbet3W7FpncOxd89e7rQ5kbpG1NlUhYRNXrbJuyiHARFRRzX9q22XpdLufr0/l87s2PDw/Lcp7kULUySXc7X0/vv/vDHz9+y8w//ckv5/LQm3Hwul4/PX3btqttLQBmR0Sz7bI8L68fvC2Xy2VZ+uW6PJ2ez6fnvrbWzD2BRGKNi59jszOLpp+BmYc7fEQRVQBm7q2bt9j1iXLjZrxJAkJuctyg/L2SphfZ8f5FVq5EexOLb24N2PPH3bYkYhgV7FkTsH9Jo99LLPxSoeslUPODIuD+Nd8cIHYGs7NLyI0SmZjSiJH7qZSRMh1qX36ol/k+7dKB453se/UWFfNryT75/obySUpqK0naZIQHwcgQoMolqQVF6nzQWutc58M8H+Zaq1YphVlpFyABDzuFezr64hKMNx8Z0gN3UC2BshdOYuMMGF/G/Sr8LTEOkYTe4FGQ7B+Vebg4BSGF/SxskAGSjcuczrBCEqBaijMHeiHJzmi3Vrnm+IRZp2CH9963be29ZX9uXZZapzQ8ARTC8+EA9oTm8y2KCjFiH95mKrOm5W8pXERk8Isz0iZwRNAitZZah4+KjJ4JM9PubLj7OWX/9r/MoL1vhnGNKGcyw30UTQESBKXuAHNSLNMDByS5xbNtsM+j7RiXMg9LjWFWzkRBGlK0ljqJoJZCfOhby5l2jzDz8JhqLVyciILCjQqpTqWUIhTdmeHh3klKBHuAVYQkrPfNNgAqlVm1aK3l8XiYRN6///SLnx8fjw/pO398c8xBin0t7TrOTMQIQDmd2sZFkF1L1cPAwQoiCliYqCoHmDBPSkQRzFrKK53qum7NPUAI5s2iN+vetr4sy3Vbt62tvbW+mhuIYprLYX5gLu6BdS1FOtzJLr0v62Vr63Jdu/nr168fHh9UhEP61tfzclnPp+3p4/s/fv588h7fvf7u9Zu3FJQaMlV1qsUQ67qx6PHwWlOMWcSZcY1prrUUN1ettcwg2vq69MvHT99+990fPn14ouCH168P8/Htq68eDg/MtG3r56enTx+//fjxj+fr5W/+6nc//8WvH6ZjX7f1el4uJ7dmbhSkMm16uW7Pl8v7T0xtXT3QzJdtu17WdWlmwDCPUiURCAy+uSiFjEwr6TGZRIKMdjEV2jORGC3MASjwl6Hw9vUtlPwQU6GR8Q0UBTuInSp/t8CCoZV1B/QT4RFJtj6lGcJoBH8Zl3go3d5fnzmV0wfR8ybAzkMdJYY49k7FuAH0NFSNBzqCwC4kf6fL306dl8dAvvQt2X1ZtQ+wicfnyvlTEAohEBzu4CAIDzXBce2ZiViEudZpdCKlpHicahHigSO/hHRuXJEfhKVdmuSO/nx5f34Ytm6X8nt3E3vfZXxgj/tnBmfEzJH2/QDYJ498NwjOtxoIIlYtqR4MofwV9yAacbmUYs16bzm8l31gRDQggoRLzQujZZ5mVjL3Lw6tvIisTC6snM5NeaGJiWDm4+BkJkFi/VMt6bUyxqqzEkwV3xzPlFtv9nYFv7fyeVQ8ewZAueKxN2uImTj23Gd/qaHfKyKj0AUFUbfdtcss2WwpPMmk2RW7LfsIEtJUf2MwEZc6A+xbN+uBIGYRrfWoGvN8KFKEddI6T1UrBXViOIw5/WfGYCUizKM3iwCLKJfDPD0+PP79v/frv/PLnzPR48MxaS5gsjGFH8ycpolEJDr2v7s1j1pr+jtl0R0e5kYUoqIq7uh9y0la7CEJQO89NeenUmFOolHqp9Np25be4nQ9XZbzer2u1yVgEd7W3psxQZQeX3WVFPkPQhiFh13X8+n06XI6tbVbRKk6TROrPh4eWXRbtt63588f3n/3x23rLOXy+fKxvp/rNNVJuQBOQmUqy7Y286/e/ehQppy1aeFOLQWUzDCVQz3MLGLRPz199923f/Pxu/fb0pj48fH1w6vHD/P7h4cHYmrbtl2vbbteni/X8ynoevrw/Pj6oZQSrW/Lxa1zUC2TlgZmnMDkM6c7EW/dL2uLvFFDS5THhNOQfhsxa4cyiDmnFjxBxdtP967h97jwNEDlF5n+yJRv8NCXZLhb3n2LmDts8LcEFqKMvvwiAOVS5ptL9i24fe+4efk+R5WcBM90jLnNbdwRi4iUTrqF+4Fj5CcYzeQIv8HvP3y8jP75xS0YMrObU26lHSnJaJG1e3ELETgohbuZWMclTokQTKWm5Hcpk7CmQTqBEaNezgM1IbQchKNbwCJ6GZV4dGKGRMHLizioqeOqvbghGX7uwywvjwIAg3yLvZXuQ3iMBy5I96HqfQ+Puy4sWpR8MAhS0AAEiGLMReWbgvtg7ybEz5zT5F5LJSRPZ4hCi6hqQkzk4aMdwQyw+WgrK6vsN8nduw1qmqooZ2lQcxI1cbpEBEedNdZlwpVMu8TmaHrlXRsLN9c733bHrRmjIggJUVYQ0qSV78BdUkop1ZtTuAQAO1GPaGbdOiKUpWjosAzXrNUCxMKlKixEVKWG50Q6iaizDU28CIRO0wzEXKdSqoqqVpWqGiJeqgZX86hFYeIeLATOHi0haJoPrx5eP8zzV68ffvOrn/3smx8XKb5z5BPF6b2XUhAws1qnHI9IdjnCiZWZ5qkQUes9hk6NlCq72AaIphRrybvv5t1G1yStLg/TRMzn6/W6XK7Lebm20/m0rNfr+bJdV7D3vrqF9wA8yNd1Ox6OzGLWu3UQurXz+fP1dOpra92JeSNciLTIRT+TiIf3rS+XMzZoFMrZbYM1UI1SNIEt22S5rut5vXx1fnh8OB4OAHf3zS5t2yJEuBaZWTWA5Xo+ff64Xk/rulKAiNc4R7OLnD4SlSJEBPfW27ps4YyIdb1eT8/CqsQRVlSKKmPookV4LVJqJRGHb6tFD9q9lAfjOnVihtfCF2X9LVRlFnxLqF9msEC2cMC8kw1fhrMXz3N/hhewM9+g0pch+/b/F1JXe1i57abRcU7EnHYt9h8G+hv8QLTvRxpMGNwRm5zRGfkW7+MjtxMOO25z+2Nu3d2r5z4GnC+0jxl7Nmhpb0m/vCbJ38tB0kxlxgtxAFzcOrKdSEqcwxERPoyqipaqOs2HaT4UnURK0ZqCujkdPt7nzrQZZoZ7hjmu4p21Ou7CHYDj/abl53yB6dN+kcZhdTtB6AWUlnptgx4SmSx2d74DQveny0wyTyARLaUggobbeGT5VkpFNwgXFWZ1D0eIgALM1PoWHuk7a70LCwqYeShCk+zNDorB4guhhsDWu1unCCWuqpQqIqWC0N2ah1CIyVz5cNDRRaFb4QMWGfPCo3WRV2EvhTFkWCgYJPdrS7y3XsY6ZdzXpYhGQDgg98c+SizCetMUIkBYlNUFxBZBcAIjwkvQ4MIIO9w8nODk3XsAGX8pElUiYg5Y+GbWtVApNeBjBL1onaZpqpCtVCmlQJQ5VEpzh9PhUEQhIAGKKBX88pe/+Kd//g9UAXfrJoVGe4NARNnfzuVSSgl4W1ceg+DR1mU6HFXF3Ic7EO/ObMrp/MHCpRQtir35Zt16NyKISJHdwZbleJjfvn68Xq9P6/XTx/duvS/bcr5e2sVsu/F73WO7LP7qtbC0vvWw8FjXZVuu3pv1EEpDiKDsC4tFuFlPe5IiZdJi4dgFdN2cEMM+F4D51pcP7dvTw2Ga5iCER+9bWDwcH2ulFFno7tu2IohIimia1oV5w1qKErG3bJi7WYeBkdkrF1Q4CCQ0ssAxyMQQEeWS0AEc9x4b79hlpKnknrrT95P3PfFGZP6HkdIlrrLHRwDOpAO3z/o14qWGAO7LfS8efpCk3143UnrwRfS/Rd77s+10xoHgp5TnfjK9oHV+r2F5L8dH8r0P898kHYnoFotv4T4i9OaxegfxMxX9Wz7C+BCSud4PIPIM92O+J3NcjkjiaZ6+VMKMVIxANHHGCEWSTFS11ppeJbWUUqqI0sjieaA5dyP0EUOJSET3jkfcbm/soT75lrwnsnmvMtCQ/PATEtGeoH7/4+25/xhKHuCNuRGIAd/V+L68JZniEVEQIsxk9wdP3xLqABCOWpWVyaG1tm0DiIK2bUv2j/XORGkZlsoMqdMdwSkFnccRFJCw3s17VU2GScCtB5Fs1pnJHUW0TKVogY+5hUT98DKJ+OEVGP/hsVj3M3T87q0FxTsWNLIiTqbSnpJkoNzBny8WEqdANGUOQaxatARx9sEiRQ9FWYItrPce5M3bsq7rtlmjeaoMUVHsp/ZuwRzEUkqtteSrSSEt7CAVVSmkpSpX4RbGobVOzkZBDK5F3755/XisX717/aOv3vVmTNxaz6CRapmqmktDRAPh1hGx9Q6g6Aj3afdU6vR4fABKRDBLGi5p0Vx5EdHNsfcJai0RfgOFyCCqReQn776uXD++/zgX+XxerfvldD63k1vrW2MWFSbiUmrbNiIy62C23t2MAzT8piXDf2CHS4LS/1G1KCkNY6NkOgQxkfBNTotJKMiXbW1t5dGl56mKFOtexK1HmksUYd/zcCnqFmN4NoKztUPkzbx3DhZIurIwIQE5AmtOpdQ0BGcGVS0qysw+WrV3AcvMK5Q5mUi0Hwyxq1kOnsyd+5FrF5Gjc1/iP3iR+Ay9pX1nj0iJ+x64RaQRK/dfuOWH+xbKl9zPJx54Aw+GRxYcu8Q84+aFcEv1Xyaat7Nk33wZJ5HiHPRFQjxisehABV6Ew5efGrdT//ZaCQm4B+0A1djj+IKSlE+XVywTdmfZQRNmRjHvDFEKJohUBnmJWklZqmgRnqaqtSIpoCwR5tbDC6CeLNFgD3QPcIBEASCINFkd2XqMMYGUNzmIlV6wcbM1LZR6yyOO8X5LiNL7UMZBNiqA8f+cwE4mXx4A0S0CRDBzEDxQJHVPs9UMGXYZc7KxSLi1pqREyDlD80AoQaZJVWhrZoNuLEmfyajo7mZ9a0trjaeZmcytas3Qb70TUYR5EMjTf8nMYGweYQgCLVy8qkhNXZ9SVDU1+4Qr5WkJDtmPgv3QHMuahuD1rey66YTfAjiAvYscweEUERR+B7UBioCOPJISn3GO5P6PTTJEvzkPiR6dwUWViFvvEsFE5i0oOuy8rOvWemu+MQWDnYQ9+rUtm/XWwwyVSIsGyTBYKVxm0SrhqoEqhUtlhKQli2qpamaqlUgOh/mnP/nqn/7ZbwtJ35K81YdzrIiqulu3rbUNoFoK7Xu5atnaQlp1qiySUipFNROx7PiJStUp91Lv/fn0lL5vIoVZp6kS4OEjRiD5viwib18//t1f/ew3v/rpv/u3//6v/upvgsK2ZrZadw+EQ1WmOqYEMHioDGIETcPRh1mEwO6ZH0mK7pt7EFwt36REUOoHMcCie1jJcKRS9yoxA5YyUU5xphweEbe2wdMlrSLVxyNzFpcEYiwbe0xEEEgq10ewoDIzsYrUku4JRTyYfKoqqkzEMSaS8nwhYrkpTzILs6ZoCYUwEk3bdbB4kGBGpiKgYYAx0r9bE3OPj6A7WLPHzRGs89rm74ydQJKkTewkHPqSPS572M4RAGEWZSIIC7AnTwwSFqZUxhjB6b9AA71XHgN1HySZW23KL1mke5+AiHIU/+WzETmYVSmz/Xzy++ti//wDoqLbk2MABszMkyoTQsQs9YwigkrvjVQqF2YWsKjoUBEZ6sLZigQSWhWC5pyrmRMb81aUmclT7KIESsmzTplZOJn3mfTFbmmVJio3FaTI+0aM+6GQWAbfjsHvX9O9iBtl0N4Evs39gkYvPNUTAUmf2L1+EYZQhYcFUevd4aWoWx/1JqfTBcao84uX7b3nkUZBZra17Xw5RXhRde/EdMtsiNnMLHyzzVvr1lOLoncrMrXegzDjeJwPItl5yeONCaN1ycNqazyEB9NhD++8L8G8ILFD/yMc5LMhN1rAyD1Nxnw0xvdFHMzZsWBmtl2ldqyhzD9yZ9xmFyKcRwXnzcxtsxZC5v1yXsKDRTx6N9JKrW3n62ldFuuxro0i3DT1fExRAEMYQKphlMlFFZm0Wk/VSZhZ2zYGHefD1+8e/t5vfvXTb76ptV6Xa0qx6e6DaGa9N4QXLbnv3VM/mUZ6y0SkedRN06yqoz8UjkDvLYfswl1E27aab0SHaTowU+85KjF2mIoycao1iOjPvvlpkFNYbysTPn34Lumh4Qi4RLGshHOW8Kb9yMqk2XhQLZRoHjGTsHIyUJ2QM7kRAU8WOhDhdNdaQzaKRFju75Aph/7Zw9wgrBHovUegaHUB3HLzBdjgvKPmTMSiNMzchIhDSYI180lhUS11YlLREGitUlTcwRIsKKqBG+9l5HJgRkrFB2uhCBYKMMWu7UqA7zjwbWnekmsQZGTgLzNo5DA235ig+QPhfbToiz4BvXjgBuXkH3nApkPcT5hIRzhmlnTFSezk1r24kW1ePu2XCP49ajGl7O4tfOf+ck/x9djfHkQ07gNfL585i6fv6wLd6hgZWtajeKXb2cnjmggJc08CUt6a0tqG9HQkLiUP7rtpERGFhxZYbxuzMJmyuEgq8gJMrOm5zFxrBSaP0EzEdi4R8W1dAYCQIFWV7sE9iG7WFfvdftFpwX7Fv3cGjHv4Qg47T62ddzXqPTeX1FIrJZOEwgpAImqZA1RKtWZJVWitE0nRuk9FDTLpTZS/ta7CTOIevXdeFiKF91prKVWUkarI7kTs7ha+ta1t69aWtW2tW3hM0yE8DofjHNh13SQXBIfGsAkaihT3jyyjCEgi0OgFvLjlMfQ9913wQoqWiXp4t2aWZhYED8CFqVAJhAgCTEwSoSy5nobfxPD42iVk7owrCIh458ITlnW5nM8iomAP8+hkcV3Ol+3kzdvm7k7hQoBFSKjMLCw5Jm0RTDmnJgaUlKGOrW0otm7rdbmG2avXr3/9618nnKqiQbGrpI0kQFWnw7HWOtrsvY3TDhAphLSX4VqrlmLmvbXMeHyo+ZgE994ImCbxJUeQTQYcUXhXZgXALLVW652Ip+kY6D//+c/d8fT0fw+P1PznnDuIYCfmMY+XDgoQ0dG7Syo3R6RX9Yhf4xYSAE2upBQdemcjIcihlzzbhibA2DicnRxJgkV6HBKEiYoUkAgcI1MeSGC+FKtmdsyQjLksHKS7vUywsGgRUWEpIiKYiqrIBiMOURDdh/r5Zqu5T/HySC1DhHuQ765YcQe+7wj4CJ0pK/1lp5Rwm4zNn7yQVN11FHgctMOy5WUAyfgMukXzW2ExgCYQ5dtGCgHlZkhHRjCGasIXof8ekF+mqi8OA3oRCceezZUJEZEIqA49x3G7ZScsUfZm+W95ueTX75/p9qljl5LMt+9uLMl6y3kJJqKyrqvWSkQqUqTSjepYem4qOKw7HDBnt9auZavHfpzmearz3MzDp3kW1ZTaSZn6WsqL1qLQPiA2PnpESganjXiMk4RA++gljXLv9nm+uPE7XMj7UpLUPGT2SMnWiAhhjhwLBKroVEpCbclNBJC4rooXUQo2s8zQCMQl2wIw7xHuZtEN5uxAN2dGMgIjzCNAwu4xT+F0BheBExxZTFj4erlerufet9a7I4iCDTE9hBkCxBJAnjcRoHCBZA8gAhahIj7mVNP/N6M77bhhbtuIwE7Tv12cyB5DiqhY2Nrbuq2tG2HMxKrQXAOcChPOPBJJEIqWHA5JgwHHGAr27hZOu1KiMFl0uK/rel0uvfcIF0RbjJnN+tqWta3s6D0DNIkKFwVKYU1eSJVKIims3deVUAAStoBtW2tEvV8Os7x+9+o3v/754/HRd6Av059aa57Q0zRN08ysKaQnInXWcFfz3nvomPtP5yO3SA5urTWf5MBHg5GbMF3OJ/fm7qqFAC1FVG77VkSENRDsQ2StlJqpSO/+n//qd8pFWetUW2u5a3MrZleTAuDUpgyk8+NNxtk8gDsxkHdU+GUQIQLvIxsyJCRZNOHNzByH4TSyfAdRWiFnp0EARxr2acrjUmHJoJaNmlS5G/+KApByo+hkuNCihRGqlB65VogtRJgpRAbMktOFeZwBcKBkbs7MAhKmdMhC4hX3cHn7vGPqSl4kNwPD4Zco/55Dfj/+5u/wcKhnvuNC46y5oURyewFEEFgS6727iO+sivsT4Msz4P6sX+Tpd5rQywft/hmygzmyizRgsGOZx1Qy79SPcRi8fOm9Q7qHwv2N7RXDkOz1IBkWLDcQgYqNalTDwtXZLE1Pqmpr07Y2lcaBdLFsbUkb9LZu8+FwmI99st77/HAsU0nYrKpOpU51qkO9WEWLlmSs7a+8t9ESa4uc9QCRMo0IPW5kfrTYqcEjH9o5vti7BbQzD4SllJrju0Tg0IIgoJRSSxXJrk2yEYFAeC4jEEXvnl1oD99aL4pSxN27dbPWrWXXLrdHC0sAqrsBIPKDWdNtnk1rJYiKpAZyIHrrtjbrLcIDTkxdWkRHhBBHd9TgvWkbZs6c+y/vlY+8SaZSuGZnVoboc6JzGDaDvafzNBFR2aHYIVIZsOibbZf1ui4bgpVV0pt7MtGCnQxzO4NrrSMlpLurhMVwTd1tip2Z3byv27a13tu6Lh69r8tyXlRLINbl6uFCHBBzg0dvq4TOh4eqE2zjiW1zZimqAbZuLD3gonZZrs+na2n0s29e/5M/+7Of/uTHj4dHsoFZj7vIrJBap8PhIKrW3ftWSkmkhBMeUq5EHpmCR2YJBJ6mqahiyOQh5SyEZZqOccSHj9+Gm2o1Lcz9YX6koeUyBpKJ2KzfKiJiiwiVUrTM81GLMuf0eAI4GGSWpKvujX6AiJJ7OuDpHZnOij5u/93z07ExbjUxc6o9SBHdh8RpjM0GIqBgZc52LhEL5TiGEpkTQ4KIlAOAEMKZmMbxARBLSjfTaCtkSwoqUlUIXAqXUgASh4qqMqWNUp5DL4HcvcWdXBIRUUGkr06AiLEXsi+jZG79W8i7XZLMyPPw29tdN73lvQpmztM64gZy7uPFGTFSJgAQFt+Z+0QUgAaQaqH39gK9DP23EPwCsbknrOMLvrOebiBP7EZGOyEij4dgrjtsAZG9pNvnYfNv0j5RfD9veOQRsntBjZEFYmYKOAd7mDHqJMKswulqw0wlhZJp6JciJRGoUyslvTHNunI4YAQQVGTSEr2HhXfH0c3W63aiIsEB4bnOx8Pjw/w410MRLarzNGsJ1KIqjBRdGvJYMGcWgDwCgqkykv9OzKIQGp0oROy3llN5dSQBERHhuaDSX4VccDvgVMdxnenKUMT1FNwJULTdFabb5r4FYN3Mxbk5edTqhGVdzbbNt7Uvl8ulbRvg5pEcDBJesVhv/mAq2ru/evUmR7iEKdyJSVQifFu3IDM4sYpE7w0ImKMZTW6taf6dbOSmID/RPgJ2s9IECmVRmrU1Ah7WfWvurbXWeziEdSqlSCHiCPcw7968L748PT89fzoXLQ/zUakUIXTRosGFRJmp954LLhEzZhZWZopwQpi5de/WemsWGwFC2i2s29bWdVndeuvXz5+elvPlBnQysRZlLRQcgVplejgyynptFP1Qw9WKTkXFzNsagUUnIeqfP38+nZavp1e/+uUvf/WLX1HgerkSyMMCSF9G1gJO2w6CW3gQIjsZqsrEAgomsAiXzMEJxCyllFrrAJ+JnQLmbm0zF9UyTY+Pr8+Xk0eIO3O7XPjh+KBSwqO7pWLEnn4RcxGtIjofpl/88pttu1bnH/34nSr/8Y8fVXLmLh1RnJQDjEApArA7tCAhYaT6NBFzJuceAx3Zww0idekFNyHAgazQnidlOslCTq4sfGfMMAgiyc2XEAYFkRITmSNi5xGM02g8Y84I7ST+XIe5C0VlqqUWbQYmEnGViGBOZ9kAUfY0xvlGtDMzVAWh7J5WjUmCGvPqkZafe8QfaPiguGB05mLnkES6uhLSeiHPR7yAwiMioVQeba0RwZkTNR9nlO/D8LxvPKFs6Y1R/ozSRPzivd2/uD3nF9EZwC69nn9K0uItoyLAk4zNZReFzo+gt4m5ZNDsH/8WFYn3SaPkZdGeM+6dwP3qEQHkQeZRJISAcGKO4BIB0XGwJaOS0h+Uort123gTamxpFYCgwHGa+uFY6lWn6XSYmeAchra2dZqnN2/fPr569/D4o1mPxzodDsfH2R/mg4Kh4HGrOOA+RPN30R5WCkAYWRaUUgY8QIHIVEYgRDwAOMpZqsjECqNtMD5J5s+ikisuTUv3Kir54mAmD1u3ZVnO6+XivplZMwporQgpHkYcrS3Xy+V8Pp1Op+2yZutzCMMREZOodlUKTNME5yLTkG0e0+HU+rYuy7atLHBGkBWdzaK13lqfi5tZby2JB2kGQMIYkjt764cZ4VQnJKVMNJWXAFj0tW9r39Z1W9bN1j7V6Xg4FplEkpZqW29b31a7fv786fnz5VCOMaOqViHywkUMBJZwbG0zc1Wd5wkYwx4AEFFULRWfvUc4c6bhHI512y6Xy/W6ghDo1vr1chUSVUZgqJGxk1NORc3HwjZt54XBCi4MIXfzbV2vlz670xbet229KNNPfvTVT3/807nW6+l0OZ+0cC5foJQy1zpnfmTmvW8EUh5ZQF6rnYTCvafyK2vRqU6a9VkfRs5uPoo8QFUwzQ8PD8zkgeRAA7GuS51q7723DuoRIVqqVtUJMnL2V6+O/+Jf/MU/+2f/oHe7nK9mdjn3dbkSUYQxD/B+gC3uxMRGpdym2Cw86bigYQwYQaM3ycQpbZ54VOJgcgeAobrj2sLMnDkQhlF9jEDJQzBRwEm+5uHwLNj9QWlnQwwpqoFHe8b/2CWziCAyZZq7nz5CBHfwHca6p8z5/guEsEcuiQg3+C0ufy/FvoVs3tuneyRExop7o3M/nW5f7tk0R9yjc+JNkXaJcqdy3l5UhCTHYvdO8u2piG44UE6VxP1n/8WRgvHTnfOz9/P3kd38YwwlcMoICWCvGRK1JL71eO9V1c3cZRyxQfHygkcKtNzh9xQ+0GGfAxTs+LG7NQMFQ7p5J4RKHqgZhLD1vvUmQZhq9E1KISl1OkRY9366ntbtWid9fPv49t2P3319efvmR/Tm7TTXNODqZuyszCEUiO6t9ebm2FuZc5mIwEU8gkEe4UUTsc104LaNaZcTjfss8v3eINKaY/C0WCVJsm5Oe4ndrYN86+valutyPj0/Xc+fyRwhzTlQugYLiXKQPz9/ul4u1+V8ej5FR1EFobfORKnwbtRFWSmqKqmt17OUUrSIMiI8fGtr6y3CmeDRm5ke1XrblqXPDz4fKDzCeic2gCHpNMI6PmWilkLdNOAGn0oFA6Ii7OHN13VbzuvldLms1zUMj8cHBol0GTrA7bQty3a9rM/fffcH26AHLdAQdglBhbJFOGjZ+uVyZWA+HNZab7XmUCElNus7k8VAEdFz3Ldtbd22dd3czaO1baUAjckdWOu9tdS0AOP5M3GdXx1qmapyAZF5Y2dis9Z721hobVfv7cdfv3n18OrH794ep7m3vhs80Xw4aNUIKqoq6rlchUVLmN3HHYcqmhDRYNkiaLd0b9ZtqHy7WwBQoeDU8QdRTNM8zQ9MLFpksH4IQJ0qCKfnZ2aeWFwC1okYYBF99+7rn/zkmyJ0uV4/fvzw7bfvv/32w/l81rvEe+JPqaIFVsrhEqJxYSNy/l1pt+5LCj9wTz44lWZ2Dkwmz9nKHi+SIgx0C4/xZdsyEe+7bImwQLBTRF6MI94nMRMPTIyG0uQmHW53tJL2+iP3WeK7X2TDEQgWMxuq/gRldiZlNsQtjo8dvR8amQu/wIYIQHaPaVcMISa9ky3zPUta9AxyRHaSAffwoRM8rJ5vL0qUF2qAYDvUdkddhgEYkKjLfmhQrivatctG5KZb75mxS/TcjqYbbWHcwSRt7Redx3GqEcHKEhS7HdkOU9EtxRyqMnGngY5n3g2QcyFFMISG4DlAxCUBdQ/v1hwcTAESFoRNIjVPCiZz21prvTOLdzWbdCrMWtYajmXdTtdz7ytJPD99ujw/u60qfZriMGupEkKTBwNMKFXFaO3b2lYzzxpOSehwDFgoe4SS1mIjcyMmhruLcClFRGnv3SdiSJFbIk2ryIbPuxORRIjpuAmSALC5RbNmvp2vl/cf3z8/fVpPz325SPh8eOWYIgJhorxttrbt+fOnbb1el/P1dFEpxmUUqQBbNgCoKG0UAuBo8+Tqk8wTBS3rAopl3dq2uW1B0W0LNy/V2+Zb68t6VWaOBzp0T8YtGCxcRAqIsmcQFMEhWnr4bNbKfChTUSUi977a8rycPn3+8Onp07Jsx+mBA956KVWkEGFZrx9Pnz8/P338/O3T5w8HPcpbFHkndXaL5+dzj2YIjzift+W6TbXWedJSHNHNGDTVSUsNC+smJLVW907sRGFm67ot1y2nDFpfW1/atvbWKeAlB6TJ3MI8x03xsRthefCH6c1c5n5qtS1HfzAsf/z43XruIrRuy6HW/+5f/osff/XVtm21iJuplFev3gBQUTCLgJivy9ksVLWUWoqSCMxBlESIhPZHjJNkN3FEdNt672YWA8yJooVVjg+vRLj33ruZh6oQiTtCCeaqWrSqcKmz9dbaxsLhbtFbX6d6nOqBONblUrTM8+HHP/rxn/3jf/iX//4/PD9NzRYREGwAIQRCgAMS4eQGZtgwZESPUClMklliz0KBmfiOJ2SCmI0/kSEJubcocuoqSe2pqOTuLojMu/NHMajDie/f4RGRW85IO5KQUL0Lg0cFwCEE0r1zTJHdPKKXEfVlZg2AiYKpNRPhWqogZJ8xIdrF2/cXpp2YFC901hL/fRm186ubCv1AmwgITwc0GtBR0ujDPRuB2exQvPyoN9BptMQG2HJ7uZdEuwzrOZ+wM7AH/kb7gbmP3PE+iSW3z3V7SEq3crmF/T2hH88GH+r0t7dxe7BIEWUQh6fqM25aO/lZIiJ8T/MpmIOE9p8WSo85CiLX0CAKgmqhyCPWzCSAxCjMurCsnaxtWkqOwmf5v7XmEQGLS1xPl8vz+fPHTz//xa/81795F3aY3xzKrE5EMc0KwmLrsm1927KqnUpFdGrqRHAULik9nSOHIA93JipTVdHYC8D8IkmmgeGn2dx671lGEKjKpEUYnB7i3RJ3Wdbt+un06W9+/9dP7z9w7+peq7IqyJsThfKGra1rX59PT21dzpdTWAibSOHBAhx+bsKIQJCZ99btcLBpni1mQizLNcLXdVuXlcjzoKWwWpe2LVu7XBch6RZtaXOdpszsqpSpHESrD3zaVlucvMyHzeww9ePkmKKoINC8Xfv54/P7v/6bv/rD3/yBoG9evb0ezo/Hh+PjQ5mqmz19+vTdp++ePn14//GPy+X0eHjoy7WtX796fC0Uvi2IvrZtbf1yNUC1VFEh5nRBYOY6zSpFIEVKlZrsOC1M8L5tW+vb0jyA8N7Wbq1vq20WCHfNjZ3LtffmHluPZtbfYJuXh+OraZriGh+e4rqcPn34GB2i0jf/5kdfv3v31Vdff6XMZTowWLV4elEDvTdVaW29XK4gKqXKUYJZi5Iy3Hs33GI/CAgWAYKZtm29XK5t3RDdvBHzVKdSK4uq1vnwME+PjC07/InCZQ9/A0Rkmqdpml6/efv06dO6rDmMPh0mqlPrK8iZaGlb7zrN9TAf/uE/+u3f/O7b8KSPBXOKzjqIQtxDGCAO1nSJQABhnq2wnCOITNjG/Gc60uCWp+9xck8886igCORAEe8GQEakuxiXMCFHZjIdzMIif5/2Hhth535GsBJT7Pqq8CAzqLi7E8RsJ0riRXYbfh9X2g+ARKxyHKHuyieIoOyK7PM9+U728uKeamfb/AdEnME15zF9ACREnLd/D+6p5IQ9Wo9qZ7RGaFxmlhcpONNOyNmHYO6B+wXIMwApvqlT3JRwdlQqCTk3Svw9gu+4majcoj9uf3mH5+nFX4l9/DALDFWpqrsq515xUOYKQ6c5P2va1BKFEoIpgEIMDyehAJfsiY/6xMO9tUbsiVcyQUAAQsgc7p2ZU6LL3RDpDGIBwOj5w8k2s7aZtcu1Pb76+s3j24cyqdDWY+vbaT0/fX7q6zqJlqLzVA6vHlyLOReZHup0mA5TrVoriTp12xoH6jwTSy7UVP5KtkJKpuWUyhZYt809hKWIztWql7RUTPWV63q5XJ8/PX98//GPv/ubv2rXpYAmlsOhAhak1w6QcNC2LdftvK1Luyy9dRaBKCRkKA0M/wijII9AgRNicbN10+ygull4uJv1Nvh8FkLkzdb1fDqr2dLb43SYWQuJEvFxmh8OBz90aHULJt58e16fLtu51unVq3evHt++enizzcciyoRu22l7/sMf/+Y//M///vzpcpxe2aX1x+tyPE6nmZXN+uenT58+vr+czsvzc/RtWZpfl8uHD1r04VBr5opu14a1UxCxFGbuZlvbzK3UMh8eitYiddZpLjMJaSFVCmth5siZKUPAe9+2tW1bjriIF0ZqmCVBkAIBj9P7p/W6Hh4fjoeHaZrNo1u7nM7rda11quVQlOeJ33/3/psf/wTE3qlWib09atYdwcB6vSpT65vBo07drZsg4N2s94CxYCjBdSqlELG7LdfL5fQsCBGgsFC5fr6A4vD4qkwPHvDDw1QnC3N3KNhHrM0ZujUCgVKrh6/XMxOVUvvapjrPh+oerW0qfF2u5tPbd2/fvnv1+s1jRGO2bgNOYeIhTsrMQhxinNsp3JKNHCTptIMxihcmLAlyAqyiLHt44YjQu3QH76U1jwqgJ21NIt2eiJwCOdo33hBu2A72rDdzKwIoBxqUA0IsUsDwXWrXLFgc4jmF6XFPpfcIfQua5Wa8BzZzrpSfTCVf3vd5q1uQ5D3a3pN07O0C5n1kduiaE0WIChEUgBBsV1+4fSDiDJXZxA6Q3iAvIBn+gQCcQpGaiJTMV4UZv+B3JgVl/yMGaM+3cE/5ty2wQz23U2wcHnJ/ZDRlpjFKvcNRqWw2Zkuz1iBKBixlj00FpRC59PAAZFz5pI8kG4jA5MwOCkA1fZGCWIoI202ZJxWihxo0E8jdhzL8+JR5MuRIG+03mNzSvXro9kKYLNbr9vT+UwQu5+31ux99/aNvXj28KgL39nw+vf/u95fn5yo6TxMzOzUuAtFSDm9ef/V4fHh4fJgOBzC3HKryKKK1zMFiMbaPiJScfyEg3FonIA1Uibikg800Hea5llpEArG17Xz6/P79H//47e/e//Hbp/ffCfOkepgmRPFtdZLVzSxg6L15eG9b2xqBJIWoRqp1G4cihIOCEQ4WgsEtxPrQjWDi3lpEJNqozARys+169W7LVE9FtdZpPojWWqd+eIiHx7411pq998+n06fTh+fzZ1Z599WPfvT1N29eXx8Pr1SVKbZ2/fT8/i//8t98+9d/PWvlA0RYGK1vIhKwdV3Oz5+vz6e+ruSmRIW4RNDWYNz6xkVJAg7vcCPPbjCitd67BaIz+2bz8Rh1dunWN9LMPxy9CchJ1nXzCDc3t9623jqQA4I9KzMGwunGxgPi+nldr6frdGDR3i0Aaz08MB9fff3wy1/95F/9q//Tz3/6M2UGiijf6mWzHMHeoigQWzezPkk9X07MHITo3XsPOO8C57nfjFlUW1vX5WLt5OsaRHo4iIit63Y+nT/W45uvD6/eegQeX9dptrYhkJyi7AOHBwk3ploKEMt6naepcj0+HDIolFKZhSlUtNn28DD/nb/zy//2n//F//Q//uvff/ufaWw1jj1gRLgQO4Wy5gyYCPWe89spYhhEFAnRDKp4JN7L4IhBARKJF2gKEYFLMAuIfVAOg0iG2HAeESl5TxnOxjj0foAgwm/xF4BHpHu8DBSbwmO0GmXwaHED3LG34jBKgnHsBOWbYk4BItpnq1PWffzi+G3c+qJEOxf5y3rCJc8AHgcJIX9zYB55gO3jW0Q5GX5/BDNxKffThYgoUgIvEIXGAUuAMJdSvGd7iXbiKXbqTlKjfK8KhNLYCKnPg0E/G22wLCzuj5cnQTLnMx5jENxBqf60HyMyznhWHWwQSV2RoJH88wskbo9agRzSHyMiDJS8zQEWDH3DWqqqpsogAAp4gHm3VM6utwTTYCXn3SC35BmkH4EyR/d+2a781Jb29Onpw/vvjsejhLn18/X8+cOHQvx4fChFiXC5XltrBjscD6/evD0cHx8ejjrVZr5sTZgPdT4e5nk+grU5zDxl3VSliCLgZm6dmZnUEcRS8zHVaZqyiO2w03o9ffz49N0fz09P1+czzLQWYnLfrAWLWsTSmiXT3JwJ7j6YYQCHg5K6S+7uFkQgBktS+BrC1FO8S3HjfqWnGO3dqcxXzCwcvVkpWkpbmmqd5ol6Z+/W1gAv1s7rcjqdn0+f17ZKKb65N6yXdpifa1EiP18+f/f+9x9/93u/Ll69E11hvh6EJX3J27a2dY1ucBOCqtS0Ki7Mmnbwzu4CStIoB4EpzNGNPbMCOC0NgakXKaFKRKKsDA4XFnO0dW3dhixrWtpGjLabMCfOjVt6BREWgi3dl8aq7rAIIipaSyk/+/kv/tu/+Kd//+/+g7dv3qzrlgoOzDl2bltbP3/6SMxv3r5xIgTevHnLUvLKnq9n75v1tfcNRLVOU61ZsKdVp3tPtYaAPT99fji+mh6PWng+Tp+++3C9nF5/9aNtvZj95M3rt1qKpR6De8r4uLs3V6be++tXrz99+OOnTx9fv34jpbx5cyylEqioMiM0tJStrU9Pz5fL89Pnp7b147EykZPLaAw6M2cczrRKRRwQITMnSuOA0VtMnU4ao0wcCNkd7nhIf2fFn83hECo8sJGRer5A+XPQPQZgAoBiDL4zE4JBMnRN9vQ5MGTSiUUk3JhGqphEixw4TdgtAaBbMr8H3RCRgNP9Vzg4bUCZOZggtBc8e8i+daGB22k0Ch0iAkUSVJlvn9N5ZMDETDt6HjdqzQ1L2RsNxCmoxWBEztgLwLs038uao+i9XNpHEfAi0u40nwjWm74L0op7Pyd2fgrzrZcrQyop5zkohbDy9BDmokKEDvckOgYRUxFhQhU61KKqbp2TLryXHzoqn0EtHSdzSN4FoQigqKiOxnSosGqtmgKgOYkaSeQaSyfpCxQ7zMj30z0cCQ8imOBujHCO9Wy4dn+6SHn/MM+VicI9unWXMjVaN8Dc29YRcNj5elqeF9FSiopKN+8WUsrDPB+mOh8nUr1ssa5bjjrO01y10ID7a62lqlApATALMZeiqhKwbV2u23JdLtfTydaN3WDBrPCAgEScwJr33SjAAFMIoYp4ru/caMEIJ8IuCAVOEYkkhjITItDDk2PGLJrwX4KfPrKAXAlEFICFIcyM1941fAtbr1oMuGzL5bJcr1trjYipUOflhA/R2jwfVKRt6/X6dHr+5JcrWwSaI6JtIQuLgMmtu3Vyp4AALFSVq1LZlxdTil0HD9nnCAo43JwS96PkYZlvQb2FFt6VKKpyVQFzd1hvOZ2bRTFFIIxAICdVkqBUXb1h1gQhLsRBIDfyOEwpq1p+9s1Xrx4mFVXR6/UKYuLIZenRl3X5+PHjtlzm45EIgXj95k3RuvU+TdO6Xscse1t7X4pOMk1BI4nuZrDu1oNQS3l4/RXA23XRVuXhOD28fnxjwrC2Xv/4h3VtFPT69WtgSE24GxDMvC1XBrQeqvLPvvnZf7ycKeBm5/P59ZuplsnMPVpGhXl+/Ed/9o+fnp7+7b/99207Phzrslx5bxtGdGaVFFHMGUgWJlJhzyiWPLid/j1mkpLvnIILkXdDgiBUaH9iD1iH8P4UIw0c4S+zY84XBAiQrNiIaO+4MmK4Wwy1hmDSMapCIYN9GLUwM3eKiA4zir0QyH8G/SgIeyR27HKf6J0mVlauKq7i7iAQQwBnWEYaEAARCOfcXx5LRFwGH3K8y+CkBBEQPqqCLKOAiASRXqD2+2NYAhGxkOccRDixEhPCSeJlG5uJBEYMFeQ8fMIt+yExjosApcU8q2S3kF80mUfU38mdowMgQxJKhIoKg1SYCJrUhWS786ivlFjImWgSLcq16tZ6ainx0LXL2ZGbUF0uo/AIDRUBE5SplCrElZmqlqJFk68t9+CeRDEiYh6SAMBdoVWGGGHIPrnKLEkFAKJ7+MIG62AQ+mF+mGemyJ6VU+sc8DBzjyFkFqBozuRpkkIg6x0sWFuv0tfKTNeG87LlgOKWCjwkKjrpbFW9FhK1PPDguUzN+7ot1rr1zVqjUfkSC4iCghFMbMwM0sIMDmJOP2gi0hRMYQHBg5JGRpSFEY3RQ0Ka1xClRdZO1UrayehB3UG8UZlln0AUoNRHYY6wlYgCtLR2Xdq6dgKlMk9nIri1q4gKMXnvffV1YfeRBSHg5NFHDYhgH3tSiKqWKlKFq8rwkEBOAYE4hd4TE0beJpWMD9lZynhr2XwBpTqYErP1sL6N/Z7MdALz6BmNhCQ7nUwgZKJDFD2JGCTM9Jvf/Or/8H/87+Z5+urrnxwOD9/85KeliHl4BIuqFnPbtuV8Opl1Zu5tjQjVWkq9LOfD4VXRCvB6uaznp9au67KEY54Ph4fHWgoXneaps2idXx0P5t62Vh4e3W1bVid6fP3m4asfRd/W04UQ2+V0ff5YSxEpJDSc4ImmqTLT8+dPh8fXDD0+PP7057/49OEjgFLKul5pZlGB9exzzvPh3dt3/+TP//w//M//77/6T346PwuFqniQk490jAemXEbtTyxSigaCibNzORbsbhI9cAi+/YSYdptoIg9wEOvOQExqY9ZBYGYSkkz5h159am0TmFOdF1mSCMaUVOaSRCpEJe8oJ2EBRYgBhTEcsFFkDKFe5DNJKjKCKAIy6KiIMN+mcoCk50XKkiFxPKR5MwJImmPk8XUL6nkujq53umGMOUrcQBq9he27CUECMGCmWiSdDZR3nAksd1OlQO6DF/wrH0dh0HDZ9dE7YGKRCA+mFIQnOIko0jn19uazm0PYJRyUpYjKfg4QIU+NBJ0oBXiFx8CCDU3jTDNVSJNjkR6T2UPZZRAzMiOPfeSlz4IlgUZi5lIKMyuBaplLKaUosajeuUx5xUWy9QoVyvI9L8ou8J1QZqJUyb3zPAwCcO/hBEInmIpyEngivCMKg8IDkRN240YRkTvxGGhM4WUwSRFWCuoRbXUHiUaTrkVYVEpMjXDgECmzA25GYQ4D0lnDIjxS1SuJ4UKiLCxFuIjMypOKgbSoMgGkXJIuHDlxLhwBC3SW8aYJWbolCZ1FsnBmYVDsok7Iai4CchPDIjCCKd0+2c2CoCwIaeRkCQ5QN/fmgiAWSdUv77FZb5JXMZeUIIoyiJJPxjtrOuGKrFdyoRehqpnBR0lXykwpg0REJaqKBdJ60sxHs4WZki03Nl/i1DxwfWYKRzgDug9pgDEm7nMAtyiRMEYjhEEiWWVJgKuW+fjw/rvn3//u+S/+4i9+/rNfP74+svfr5RLgUmthbtvmbuu69r6qEAkfjgcienz1pm9b6/3Vo376+P53f/Uf+nZW8t5bu16X6/V3z+d1s7fv3n3146/evH17fPXm9Zu31rbL6ZSMD5YihQAi0Todjbn3p9P5+Q+///brH//0H/75X8wPb1hlqnVZVhaptT6+enW9npfrhQ4Ppc5aZ1JZ1oW1vHr1pvVtoilbxEUFEZfz6e3b17/97W/+03/4y8os88SppM1iAWUSihjdPzBQhxAxhxOpmMduty3YodcICiA53TkFzULKBLgFqnJRFiHNYxtEcAJk4AO8T5oP9EN53+vIAi1IQaAgSAYtJ0+cgKWOmBRphieZeMIYRnDarbqzoUuZ2TARUxKeUvYlk0sCWtuIiggXISra7lkyD5WGPWXilIDOwpRzISKPPRUVUCJRvJ8STFF0RD/dm9sZr4RYhWotDJRxoFGASGRMOHPIkI4I5VTqIqe8JtlIJyGwisc4hjKJ3QN9zhx5Cn8nDiPEirCBzCBAIsNMR3XoWQwBVUQWFUJUhooxgFCAA8nqZKYsatLqFPDMz8AUOWpIWYiPVmUKjQUY7swqQgCVQjpNJUBTLSMiiCaANBSciEQ4Q6LvdkVZCiSkrbUAiJvqwhhmkUCwUHhWEHslEuM0J0IEzCxXZJato9CIJMYOa0kVHXYV6XJExBxKkW50DIY7RCFemE14kiqR09NO4Tlj5W6SnHoKHakvC0FYisqkUotMhQ5T7eYOLlEiXIWZNSI8QDH0ez2wmbGRMIyGSWTN2i0Vu5OWdEMhCZQUrwSLWERIiERSK52xd6WIiYXg5NgbXu6AK2c4kCKc0n5jQkUouQHCNNVy04TKcjj1AzgVHVlSd2sIBhMpk44+UCAH9pkKy6SIUAdUWJnDg5hluEcmPVkiAhTCqCqFJQLCVIUdEghRYpKQ8aHS7bIUJpBSWmYmjoBAFBYQPT6+2tYtQP+3//7/+j/+D/+Pf/V/+T//oz/77atXj61bKVMR+fj+u8PDo6h470WVqBaVWudSplLK+fNTdH/6+N133/6OaTtMuq1t265tu4Rba8uHDx9Avds1bNuWy+XTh+v1Cua3797xNM2Pr2DeIqb5SMxcpnVdPn98//G7P3z37e8/P3365//if8d1ev32K1FelisQ796+e/36TWsrE8+HClBbtsPbw7ouKuVwfNi2dZpnkUmFCWTeAvbb3/797fov/83/9G/M1vPpFG68gxRMpAyCC2mt6qneiqDKwhLOmhouyNFd9nAf9nYjqY1sMmWRwlDJ1cI3LTVKfH9gTFl0JAQOYc6eBe5HPCGIC3vGQBVnYkCURCGKmjkrM1PknAUL0Silkw/syR8K5FRt7gNSSeCKhztRzmpEiKiIKEWFgFhC3IOEsn028njlncYD3CdmQ0A66tgR38ecM4mZqzI8WPMSJfgaLDTVOlUVzkQzmMizeNjJkvnNEfNZmamUwhwE8QhLDSMmHXDeSOvvDQEMEIB5VBWBAAcTUkeRZIBXUBblWiSjstCuGZVeMZJcT9ZINBmQHDtLwpGbdUF4Cn7lXAKFUA4Wx2hsp8Dsrc9NEEYQlVpLKRVDmIlEGBzCTAgZzm8Z6VGUlTUg5p4BelArmIR1D2HgNONy97CABwsRZ9lUlIWhKgBYkhSb7S3VIhRBw4o+r14gSFQlxqQ7ExChqsGkhamBOTUtSYgYBJg7x0hw3L1z4phhhZDCu/kashPoWGgqpQjVKodJamFWtaBKQiFCw8cnB9YSArIgERLmxkO8NKVOk2o68jQZ2e/OCCMW2VE8FCItKd5FwgyW7iY7cqIMZVFhj5iUiQQx5htFsKNzvqc5acFecl3nhk5fnJT9BSgsT2tioioyKesQFmVVcQvy9AKNUpRZEN5BEqzCpAUsOwQ6ytAUh1ZhZRRhEgbBXTQLTYqs/JhyNRMLq5KQZDtr4J5EEVFFap20qNLEJCz4l//7v/jf/vPf1vrQel/P57fv3n16/wcz00kKinm7LtdpqtNhFtbD8TGA0+mztUWL1lm1PFjbYrl6YDNflrUU/Xt/99eqejgcqvL56UM4pOrrd18x08PDI0DXfnn95i0zmdnl9Pzhu++2dREEC7793X/+y+Px7/z2H4nIPE/MtCzLw8Pj46s31+W6LpfjcSLg+fPn4/E4HQ/dmjRmSFbfza0U8fDD4eHnP//Vv/vXf7lcrtZXeK9KGgxNpRc4BYGVVZlUhBUMGeqeQKmqkkUYEzMCPeHyvS8ans4HkUpXTChKTCEsjoBbZGOTiSkNXdNMcUB/ueTohoXT0MxzD4BENJSbg5mmIlW5lkzJk1tk+aaUnMnTR2Pn5QcDKtnGJKLsTI3Yz7hNFbiyTKoheRByELuzB5kMCiRGQ5Xv51OmLyMyWMqNZNNNs0slLEwWwhK+O2wMrD5QCx0nZQrVMQDtTswwZg+oQLN+IiIGM0oWzMJFqFsX5xj7m3M0NZSt+0AKgrMbzEwqLCwByiNROHUw0niRiEIomIJgRCTCOthDRIzk4qgyBxUhFwYxsZIM45yUjDO3CBuBFs6kROmtECzMoHBwtm1G9gtlJqcipZQy9GOVU7l5pAeMqKUk01ayKFAGsfoA1mUgGtkJyK7CmEDsrZmbu3X3IKsAC9dSinIRApHHmEWmGMgjCQVTQsYkJEyqVJRElTpHRM7h16LkKCxTKYVFdcDKxJE0YHhnpSIQMmCMR+exX1QSCFXlPFmVZaqlSEyFp0lrUfUQCyaRYY9AyfMchDnAPA38WIWLcjcHU9FhSVJr5d0HAZSAODtBlbADqpU5hVJLHnjKYhwDGMn6AcKsLCKVzEZSB4hQymwDDIIQJG8ay6CqUTCxC4eM/DA8DCCglKyyparsbX5hIS2F2Ym1cGQpDZA4Aux5d4cQY7b+08OIkB4sQC1MzE5eY4iaMYuqpkZLstBz3F6EVaXsgz/CFBAPLXXatjZP0/F4/NGPv/6Lv/jnYDbgupwPB12uz+v1GeHwdxA+nU9CPNUJTPPhQbWcL5cIr7WCQ0UYpdnSWutb05wgj3h8+4aAh4eH8Lhcrq9ev3r39ddlfnh8+5WW6fx8KqWE+/PziQkf/vB7a6swv/3q3efPnxX47g+/Ozwcv/mlirw5Ho+9Y9va8eFhmvR6OZltl8v5cJg/Pz9/PU114lz163Ym0FQnQilaSp2Wy/Nhrt988/WHP/5eiD3EHMQcgnCMAVs4AyrKIkWrCMow5pvGLWPx8GyNUJpmAYk5925g8fBuQRRTYQaEYdm0zUnRbN/BGFqEmUcZKkzjj7zPvxIwhJqhWsNLcw34VGRO6FyEiHoPwDOOi3IRuIcgDbGz/IVK7PhoytfRToEZbaGp6KQ6ZXVQmEk8UuBWunEAlgz3TKYRAIcAJH5LcBMRZdXCZWgXMREri8DF2IejS2B025gZpVAVZWYiDXeoareNWINVpCipMKWYrLAIl1oqsbs307W325BboujmJqJmMGaBAHCEChXlMY1OrM7OXIpwBO2orFAkMV+ZRLgkEpAFhZBIUn/3+inH30EBn+ZpKkWFI80gmXhgfLlZc/R9cK9FyCKA1JQkTcn3OpUqhZg8goVVC+coAnPRUmtlFtrV9gEQMUp2KkrSv9JlKWVKaym1CBFtKlvbemci7jqsWqtKraoqwJAnJAJFDlskJgkQjVgqIqpVE5yEmYuglsIstejDPAuEmVSFhMMRo3RyIWU4A5Iy+DwG4ZhZREIFCGGppVQVIaqFq5apyFSlVnUPZR9NedHRrsguCwVAGpBuzMHK0klzjWgCLTzXwns8TlIFCIoU7qMIqEgVzbOQCaoCpybDF5N3WQ+iYJHClEqO4wBgKtmgGIAZRtLG2XMGIMl2iDSvy7XJFUApIipFeKpFVWVoxzNAZkyMkh204ApiQZAkykCUJqKD8otgd0aECgmNqtGdowgAKsrEqnvvMFkTImnJlUUus4x3SwSwRxzmCpK2rW/ffTUdXmmZwDJPdV3XLGWeT6c3bRMp0e3VmzfT4UCkqjXcbVtG41QqA+v1+fL06cN3f/z9X//15+dzLfX4eHx8/fpwOHTHm1dviOTN27fTNEuZ2rJu2yf03t0hUqSI8tc/+Ylt67d/+OvHh+PT08ePT6evSE5Pn7RMj68emeVwnN3cWgfR4fjg7ufzWUphovPnJ9Wvjo/HbVkD0VrT16+LsgW7eZH6D377D/7y3/1rZhym2d1W6o4QYq2iQR4gcklehyrSBEKkKD8c51pVRRN/aNYtAFB3swh3ygsFkgCpUAQmHRSZUXMZd02XKBGRoqwCHeOnEKHKY6JGmPLbREgpJ2ZFcAV7pJWD1Nz17gjpbhFgllrIC3uw74hvzVpQSMsAdauWwU0QKSI54V+rHqdSVWQHiz2QLOuq4gSP6GZ5ACabyMwhCpCZxy73xgJhqAxoNwOKEluBDc0nTh6X6ODeMrMwSIbyD5MG3FWmWncry0HKUpFayqQlIiY3VfGdJ5N1iZtId0kGV4S5UwTvqVwhYiMqErvCv5CwyFRrKTIVLaq1lBwe4MHohDKJShFxggvVqjFSOwDlOE3HqVYVbl2Ji6SHUvYdiBmFYAGW7LoMp+mMDJIsoEOttRaAzG3PZAfofzwc914BhmVVhsCMNNmpppDhxicE1CJzLUCIOKHsQ2isHgCqylRLKjXGjnxn+0VYzCMoUnIykx1RnUphQtXoQkxcipaSwmtcjgnmaKTkfYAZmqggnAlF2CNk4IEsI6/Jo5eq8FyVEEV4nkpVnlSKsEKmWWgorAqlbVsEk0TAPWqQMnNx6SyMKXTAM6LMVGsyuMA5NUqcMh5psRIAC8+lVtWqQ6orHKVIOAzIW85ERCrMhkQV9/M8sVpmwjDtorvrW8K9Ye4a5BmCAYCUFRF5ANSieQDkXlIVILqKUJ7AsFRKduTgbuY4MqjlWWVwMKeoK1N20TlKSWQvqQvJWxMRwughEVEgktorxEgvLCJKi1SwHh6FRKSw6FzrsplqPcxcSjl9forAhz9++6u/8/cPh0OOMFadIuDW27aa98eHBw8+ffz4u//4H3/313/1V//pP/3ub7778HQioZ/99EeX0/Wf/W/+2Xw4iDDBT8+fibhoiQgmLMtF6+HHP/1ZrVMOW30+X56enpdaejMiTJPCt/Pz+8+ff1QPx1mnOtVkpxwOx+zqsfDjw/Hzhw/XQz2+OoT34+Ex3NfruSqrlnVdjvN0PB5/9O7d6cMfhWEdDhcefBllZYYKlSK1FE0AUjXnNg6THqZJVZPMURoZEOBi0s1c2MVl6LQpM9yjFFYWZUbR6tyFe+rn09AdALkK3dw0VYZ0q0qi1lkMCDMXLYiYSS0ik7ZSRJW8J0FaQRwIVkZlDwlSQACk/0zCyEktmVKUfbSaJUk0+RnnqkI5H5XNtuiOLHcAcq/Ye6QB6h7ZY/YyDBQTuyrKRaWIpB4vOIike5hyEsqyNhUhgc5VVQadKQ/LqioiQVRrzXJch6OsVBVVmYoQ2JxVOWigZJnx9BYiW1HykGauwuYJBHHON0wlwSIBRAFhZdFpKtNUKnOtWopERKILEWDWrI1USEBSNa09srkDornq46EWUTc/1DpkBpKZki1PAQdFsHGQD3lXUIiUUvT/w9V/NWuSZVli2BbH/VNXh47UmZVZ1V3d6BGYAYcvJAGYURj5xgfCYHyk8ffRaEYzPIAkCDEQA8xM13R16axUoePKT7qfvTcf1j5+oxGdVZ0VEfcT7se3WHuttcm9zLtOSup6gnleShFWlU5L33eldNC88kT4ifQDRB1BjEljOlP0nRZkFyYm6ruyPQyl+CxyidKsdKrJaoX3uWLdhPBYDXzdotphTTYqVeVafcCCaeEiPO+LioyjReBjU8VergiSEHIUoU4Co21N7g2B0mhBLNQJFRUVVZGiokqzvnQiAE8QIt2dRVxBEpVwcm8QUBVhUbhtMN5QkKLabIRCwwiSlxDFxDaYuStdX6QvRQSEKKrVXEOTDBVETbrqnrag8Jlsk1s0dEnnmPzHmSIXyzlq9zRkEY6QGQJ/4T63tVEuoQxiwySsRLgYpoxhIR2Lh5tH7v9se/JcmvYUwT3Cg0oIWlpQPPGIJ4m2KD5MjqBZRDS7CThhepw/fPj0+UePnj8/Olqt17fMnarqbL7f77mU0nXb27W7d4sZIOlaD7VaX8p2t2UR6crt26s//N3fvX35/bt3b7//4ac///jm3e366dNH/c3tbrf77MuPHzw8Hw67169enJ9diMgYVEopXScspch+t6217na79fruu2//9Pb1qwi9ur4Zzbf7eHt5+/Nf/MX+sN/vN+u79Xy+PDo6EZFSilX3iK50EbE6Ojrs9rvNdr/bbzfbJ0+fvH/7+tB1q35hYfvD3upIgIQ6ZYriprnJwJXECMMkVeW+K2C1q8is076UvqgUjSDQMdVQyxVhMmErKiDNRnANVeq70rEW0SDSUTstRlQtuaMJkFDgMRYFpKn4Iy0K8zdVJQrsdqoBY7UIr+gQKkVhF6H9WJWwck+r7Vu7CIMFZuYwUyXV0vbOSkLnGC2JdJ32XRHMPaQQMUzxqlk1Nwq3ZD0Rk5vX6kYcFFY9l5x6iHDflaIsTEWlFGEuxDJWxyanWisimXIwUxHGWUVdAqJGP44eUbTgpHobHZROu6J911GQmc2iVHcLA0Xb3A9RmWelaK1WitdaR6tWnSlEOZfqoMvHRh5VFumKzDvtUTlKdl9CEu6AzftOmcgEbkmggWXVN+/LrIiIdL30VeHsCfVlqwY8zcOljgwXfFLBDJKDpEjhWacRJEEe0anOO+270ql0XRHVIDYHmMh4gC0CzROaJqyiR6/UlQ58xDwx48jCY/Ugxs3rtKgy7hZu230CKNWqBakKz0onyhjVKrNpSCMKlSL9rEgRlhFBhpkLs8HiX0mE50VFhDWIgoNFRBWur2EenN2cKFNfSldKURL2TqXLc4+sgWlYjqSZJYLHWkVcpTAbCxdljOIouRIwr9M2w6dqBmVOFlOBKr70fekUZo3hTlV1HKuZRaDRQh/H7FzanImCimo6ZDt22iSeGq2X8oA5FwQzYUYQkzDRrO+6Iij+cQSLCrWCMTyKcARbEHfMFhrELE7qARPdNHRj4pT4olONdGVCMpgk7egG0Jlg7VMESAzExF3puq7fHw5aeo+wakdHq1/+o3/vwcMnu806RpNeawgzufB8sTx/8GC/293d3cxXKyJRKYfx0Pf9drOu47BYLsZh+O4Pf/+7X/8bq+PLd9e///PLytotjofo/tE/+xeFD7vDfr/fnZ0++PSzr95fvivEXVekm4VwP1/03Wy73VJsah3qsP/6my/fvX/zh99/d3W7u93uu29/Ojs5en+9/9+cXhwdnWzXu/JAd7vNcrUqpYRXq5WYB/K+77qBNrd3qrrdbK1aKeVwOCzMF0fLqHZ+fvHRxx/v1jf7w7ZZIRAFB7myjCEUMSvSdTLvC+KFsnRFu9IoAxJBUkjgJcEiREL5HFGt5EFFxa32XelVu65jkv0weLgRj9WniIxqG2wOzN4K4jpHXwqnJTKjFxeVGZfkRLtGmDJ54apeSogoWIcqHDTrDboWEpE2plaUAllzp8/lfQLoi8z7Ai0LBEAU6kHDaB5RLSx9j4Uo8veJ3KOqV6+eGtkoOQNg7XTWFWH2oF7DOq5Wa4XvJiCuoIiuK2DO5Fcn7jqhoMQNmInVzQvWvCGwEHOoh4/ubjWCRMTNNUiEtVIVHquBIuHqZl5UAJh0InumThWDBcTMed91RTFMBdISwWFeihTVUiQ8PCB0B+sE4wGZdd1M2SNUaT4rpbRQ0Fh2EWEu1UwqCTM2xpYinXJhUpUy78qs74iohkbErHTzvuu70iWnRTxIPYtaVSXQAFPcg+4JbH1l5tJprxIUFiHSjypadBgde8ULcxEVZQ83C/MCsjNq2dG0agVHsC/pjdeV5iUpVM2DqFMFhoGBJEeIiqsGRq4czDTvVIuqhWoJzBiIIgx4CJGLSF9UNXd5FGVhE5GiSqIp4EX+TzAz2uSKiI2ZLNjImLkrMglngBpp0Um5aV0Bh0FFEm8lZpW+gBbhzDFWl0aGcHdKng+BQJmlFBGzYE26Th0NmJlEzXux9WPEQezEqk3JSdR32nUqzF0yUpt7cLiyk+cqkAgKp65oBEFc6k7JRINkMpLwzMIwACZjFqHggslCEWjWgMDiUoK6zExCpNqB13x6dn724GHfdbd368fPPjp98JClmIVXNx/Ksszni7Oz8/XdzW67efzRs7vNbr/Znp09MPOi2nVlGAZR7bru7vr229/9xobd+9vdf/U//Gqzq6cnq/1gN9fb/9d/9l/83/9v/+lffPOp2Sh9f/748fHZ+c3t7TCMi9WxKh92OxI5PTsbx/Gw281nc5L4p//8nxvN//P/4l8al+FgcbP91//671an5w/PH/zuN7/55V+XbtbDorxtJdPRxghni2G7o6KL1dLdj46PDvtDECnpbthz4b/5x//4wcXJH3//m1c//RgcnlKtEOYuzN1mHQ4nA99T0a5oV1SUSUOYOVhKp2xEPJoxIYeTq5gq0rG7dZ3OivZ9T0GLeT+audNQU55itXoES1p9NRAxJ4c91slnicGqUroCcBn2bw5FGUlIHGpM/JMiIqLVQUGBVArFgEdgGi5EpqqqCrcF5IB53806UANZQbtncaK+MwyKDPhpOLNE0GGsOSguNlRyC1SSzNGh0Sy66HshiuCaqmRxL7iqSGpEIcqeMgvHxNF9or0FERlJRAiHFmlLbllC3L2Yw28VbAtlZR115NFEmIrCxh3DYU7gqysiXN1RPnZd12mZdaUrJdreAG6uoT2WXagA4ci6ClYTQlK0Y+mULXxmhUmqOXOOVdBjeER1McOnrjVdbUJV+q4EcZnPyqzvmLAZjkspfVdmRUvRHFSwwOVJglSYlTXUcnwyrXsIEVKRTgknioi0FCFR1a4Lz5pREAcN5IwPXAPbySPI+VSLqgpT1xeKcDcRHS3lrEUU5XQ1QYz0AsSSKII5inDXl97joLUOnjShaGZSTMLcadGipQBuIuhmgOYHeTqICZxhGxmIIgpF6mAdXCZ3wBlJsVdlVRaSCMxw1MM9pHARZU0mm6hQp8JAmUoZxqrCrkiFEhEwY+Lmtyp4CJUKEQapxEzh2IxgwenbkiJbDg4ldtQSRMLcq3Sq1Khg1NpIDi5cXKBUZEmxjEQ0nkchkTSfgnEAnm0PSxILE7ZQdYLhMvQQeWtRQULy4wHSYRZhXOSjz794/OyTcRyOjk+K9PvdNuq43W+62XzZzxaLVURYjX6+PAz19Pj0+vp2GGvHfd93sFntuo6JX//04rDbudO//Xe/fXt1d3JyyqrzmXZK65u7//q//u++/vLLo9XRZnO3vl13pT85OdtstsQ8Xx5JNxOmxWz27tWb/fZwfHLSz/qPPpp9/uXm6W+/v9vst5vNann89s27/89//l89Ob+ww91Px0enDy6GYfBgkc7MZ7100q2vr9VjvV73q+WsL/18piLj4D4Mw3739sWPJ0dz1fLJ5z9z8v1mQ8zDaMIcZExiIeHSFZ51WkRzppKHSkW5zbNYJJCTwe/tUlsbFaZnEUReuq4v0nedT0KA4LEaHK0MrvhERFTrGDQtAXYVLqVDoyyYpGY5r8IUhq0hwuTk5oVmEnCyICru0Wkd3duywmR7uLubl6LaSUTtSmvvgzyC3IuWrhSVbEpyripcOiXKRQXYkYexXFekeozVxqpFxZIrLUSuwkVECve9qkgEK9xJIoKDnISjCBeFzt9V1CgovCugrQW3sWKEG4kF/r7IFBeIibSLLL0SSpUwCRFWM2YaxwHDu+KlKTaATnO1qkVUtZTSqcxKKdoRY5AaHJD4RGmVbtPO5J7ftEFW6lWKSK3Wd0bMXRQiDldKt9GI8LG6ReGhinCtHBFFuQcpkLgsFvO+oCJjZLeuSKdSIDkg8uCRiDxNREVCUutJ3LhEzGD4qeLmNcJjVzS8VPNqxm2jr6pGlGrmzTyUsiWkziUSI1PscFdlZR4qcdsUj/Rdioh2xVNmgtkAs4S7cpQi3OxxD4yRO0UAdM6whuqmF0YCKFjB2bZKYKGjyPSMyaRyIGKiion6aGSM1cDEgtQlzBkugZi4T6ZAgiQTTIIducB5iEWc3VRYqGCyJMQqbX0m5gAiXRvZ4YySSnrKI56H4UtBiZ2sCyKmKJLfTQRbxialOFgQMPIiZlIWIocLPjOrML5XyfuHhEIg1AmXyHgjIBoVRnKW5Bow1pQwh0TzQtRkhgiLrtfbT5ZHx/1MWPbbu/3d7Xq9roMtjxZd6Vl0u17v9vuTk+Pb66ur7ZWUTkoRkQjeH/Z1rP28G8fx7ZtX+932bnPXd+Xjp492h7rbjqKyXC2++Oz54/OTP/7u92cnq8V81i/mVIpo9+DxE1DDT1ZHTLRd32lXzs6OD1CMWe1ElovVn7972XV8eXO73u+J/b/5//2/f/7VJ2+Wi+effXb6UA+HQ1HxalbrcrU8iFy+e7M77OduT54+qebEpXTzWu2w3xXym3dvScvR2dkX3/zF7fv3+8NBtUo4jrW7EoUqqVCvhTmH8HBoAYpAlCStLJ6FQ7sQR4XSw6IDaaNoUSldQSQQkSAxd1j21eoQz7j7WMXhYp88bFYVFSAe1DKAiJAy7BrgM1YjNCLMg8aamD5R6YpHcwBhtrb/dhyrqnadRGgpUkqR5iuK7KRSNFmMMMTHtFOImvVuk6S4e1WulYqKFquGvVUsxETOQqVwUSlaiFlYemdvG8SYAP0jjgdTqAi6lKIa7lqUmMaxwssULtqpMRAuWSYyNwMNd2ZRs2D24JnIICPDRszbQhhUkNlCdWquqtp1RUWKaNdpkUKJblmOW2FJIOIe2uUgPag5a7j3HeIJMZF5J9MOe9cIx4uoyGhWzVS0DOy9hruolgTBuCxms15L0fxWIqwdK6dBUjBFMIvVinfHvBGXjCEM6rQwB8TMWaIwN5RPgqhE1NGcHGJnFSUmHqISSTAXbvNLanAHU9IfBHJ2FhqrMxncXbXQrNcINg/zEBLgQihz0sRN2JyzXIARB4s7K+dWDcRWZeSYVMlOkDpG1s06KhJ8lHAJJaVgp+pEhnlttPSeQ04QZmCwGomScZKdVBk03tSITR14hBAINsHtvwH2ODMzKZMIicKQEU8sEdy6oUBvRFJmVpa0IwqC4qxFfIxn2x5BapV6BBMn9AlLwdSOMhOlvCNHWMzEDoYtp5EM0mFWXrDnVRJUKcIt5SVZFUw+UV0sVjbadrNV1rHWOuwP+93u9u7Nm/ffffvD3/wv/oP56hhfbRzGcRyvb26Wq6PV8Wm1wYz3+321kQ5+2O9urt8xx4OLs3/6jxav3t2+v97UkQe35588/d/+7/7DX/7FNwgiRXW2WPWLBVxxVbtxOPR9GYeh9P3R6elusy7B8/lidbwaq53+q7/9619+c3e7ffPm1YPz1dMHq4vz+dXlm8HqR599Nlssh+NVdJ15HQ4xX3TjcDg9Ozny1fL4RET6fkbMy6PVfrvtOl2sVqps7vv94Xhx8c1f/aNhtMu3r4fdmh17WtzDgkNVey1MDLsxmOQBtjaLAAUD0FMEppiZ6iP7S+A22sgUhI2PTNWsK6V6uvabWTVjIasVfsuw5gRDrGjBUlgGBS2B8iymIsSjupl6PrYoOSoW0UaSBq2OkoBGThOCpKj2iH6ijZ2fDnQyuQAwEeaZKhFkluAVEZl5lToqqYlUgRgiM5DXotL1KswwemFWYQ3Mr5nDoxO6fwfyZAJ5ekh0RcNJRbD7YfSIdmmYqUAXBUwnwkmoYyJyDRE38+g6FTCdssFCEvLJfyxJNKRFi0jfdUkdDHRgsJZzIQbD3swMoVWk7XtRpug7hXWEqpPQYGilKKqloEhYmcVYKjPn/A1GHC3/UZl3pcvhhlJgxwRhYTNuG7R6jC0KLcqgEoncchbCjJaqfWOMOoGyMQigtRouuKqGRxToLzLmh1cSTaksMzESuDKHpllfFqpB0SkXYSLtlCF/L83Gws0cFobMKiFMUjm8QVWiqOJhgZR3J4iZvC1SAPiHti6AyKMdhQaWgcpwYqMCWV/jVCAVgKYDsTaJMlk4B3bIeJBwBLpaEbE0NowGMqXzI6cPR2BCl40hSQQ5OTfyJ0W7JW1Q3NhQhDFQ/oE7XnHyhm3LR1t10kS8h+qg1Dq55Eq2rDKE2POxdnJPSQpFShIh+8qBORMlBzhzOlGSkdEuBZu5lvLRRx/XWt9fvu9LrxRMUsdhOKznyxWlMVmIaLV62O3Izc322w1LDV/CqwABAABJREFUZ25u9bDfUVeu3r158/JHr+PpydHZg4ePnz3d78dhpPV2/OXf/NVffPOL9e3m6OTk+Oyon81ECxFHYXcjkW62UJVqUWZz1U77vu972NU++/jT//T/+n/+4++//c2/+8OTi+VyyceLrgjVcRzH4eUP359dXJw9OBflvu9sGC7fvt9sN5989Hw4DNfX19Xi6Pj0+PRsHA7DMMwWR1X4wdNnt3e3h2Hg9Wa+PDo+Obu7vPSuA/LLFB4j5zBQVEQDPbHk4IdYlbGlg5oKXbmBqDjLzMSkoCQInhBUPELMXSlmWDsZ1awVKypMkv4j+N+SVGygstyU7ezMIsrwsYDfgHF0RFAtMEspdG+PEx5SslhuUw1mKSql0047FiVIZLH3oBmNCWGulJgBORXNIy8iVusoouZUQ5SrOUZa4U6kIlxSqyQKsh5rBFmad7KEg2ZOFPnNktDgzFxEw52luIZVuHhyTAWqoKVlYhLikhViRJCKRERfeagsHEW51vSMa6+QpvAiEuTC3BfFpLe1663uypsGPEpKnoKc6AFm6EreteJOFDpW2EOYqUPUxSJMxbWoFQXAhVviuMhBUWZ9J0KqokWAAuCzSHP8lEDQSY8lwTod0M8DdrKkzAWCN6Jac3WGcJCblALvoJwrUkA4RZyoIQgtLphBkqbMKcMGOJWqxCQw2AdYVJQLHpDgRMwlKZ4WIVK4GaG6Gbz+IpwprQyFMEdnJw4ObMzMRygNCD0aRyIYTlsMDmx622afk5812S14CSaCcZUnTyPc8AdmlTOcMsjO1XysEADE5J1OWX+nlxyoNwjfSUprSejDGN7aUuc2sIbZP1Pup3MHwgkICM6LLXC4WLbyXqsHEYkKuSpRUWVPFxEmc4fJJ+Z+mRtRugS5uxZJvkF42msxt6UfqD8igrquj6DXb14fPaDz8/OLiwfjcNjtdvOj45//8kJKN5vPIny5XJLVzXbTqe6D7m5ubq/vzi4ehrgyH7abKrS+ei9epe+Wxyer1dHR8THrzIl3+2GxXL168Wo2W5xd9CKF8mQqMZmzW4AEZu5aShF2MIudtIhbHF+cHR2d+DjeXR2drpZ12N7eXR6G0UPfvnn52frLsFrrqEU6mv3xT388Wi1m8/mfv/3z+fmDzfpufXdLRMdHx/v9brFaDmNs9+PJ6cV+t2Ov8NuWUrqYUa78DSFyM8jrWFkhrqbWnSE24M5BbAi2AIx4KEozHxehIpyrUJUpfZK1VQQi7QjhzMnU2AVAwTxCaP0k+8NwFhzQ7JUNsYoJdAkUXsyqRMkYF0vbmnwjVBLacSmliBIrMQv2iJlDjAICoDK0vwI31vuKDVQzVR6H4Kgeqk5BTBIilFqKNGVRUch0sTIWjJ0GGOOxCBCVUECJCtZdiZRqRhQcEsRAjzPqy73hTwFulgp8CgorKiNGrgr0WzgZKzCIERF0ViwEvYJiHk1BzG55S5ESpic+prckFgUMrkWYSdSMoiiLgQkjAvBKhCdgQxW731yZHc0WzLgSuGGlkBREBCXcGDn8YZ9Gf0ALolV1+VQDKE5oGVg8il0i8sBbSvMtBQIDKyv4PAGezi4W3jGYw0RwsMJNU5L0wmgOCsiW6EQkORPurCKhQljfFRFAzEF9wxANHiUOYx+mdK72CJgd4kNGEEtmbxXVouw5FQ+A9IELqm3dBK527kPDaXWoY4IKTLpCPNyN4PnEEW4pUMSVhZ0TUFFceEC+1Kqzhk4REbXWHsV3sEUQexic1nWC7PFT0gbh+fOJJaNTYRbjMAvz2B3qMI5uQaJd3xct1GmokKcvqoUrpTCA269ssEAVdQxIIHpwPDHB9xP4MMK+pOrOKhcPLh49etJJORyG0nXzxXwYhq7MsSXtsN3f3txeX70lH+t+9/vf/Ha2PBrG/azvF4v5bnO7Xd8eNnez5QzRZzZfzFeni6PTruu7ftbP5qV0rBKBGq0wMbzp09lFuJTS933Xde52OAzh0fXdONaxWtevvvrmL1erk29/95vD7Q2xnQ6nN9dXN7cblVk3m1s1llqHsdMyU/Fx/Lf/+ld/9+u//0/+k/+LdFJm/WgjM5HHuN/9+Mc/Pri4+Orn35DQbLnouq4sZ/1qXgbdb7bKLgzXfE9iRLKBneAOmZhCStNRSQCGgBMagA200MqM1dUCZIRJiFii4pULRzUQLkREpeBgtA4csRb3kILFERmEFJ6SEHGSBzfugDCFBBMrwfmDCIonIMXSF8UbEIUw5SqnopiueZM/wg8sBS4CZ1vRlq48ECDI8YZUSATLcKcTyBwqrEXyi2g29hqlXVZ0TYyIRRwITKjhRChI4GNdSBEOI1vkaOB/+ugxs2ZPocwkbhTi7BzCpHBJ6EwjvfAMmLkKI+gqE6to494STaIi5sSnmEjIYPrLkY9s6rQ7dHgsIUosnFrnCGJ1x+hFmUOCiLSIN1tilH0RXt2KCMZ6WR62zE+iOcQEdBVZiuL+cRaWHxSgkR0pZ3oE1EEUWEBHzCkaSrgaCDIzJ3xCFO2QARKJjInAwigoSFMmXZJBC7YrTiBYOYTzH81bWoVLp1YjUphEkoaCQhRuhmyGk26UDDNssmNmFlbBruFMpBygnhKBPNzetqEwQmG4Ve138g8jU2eMAU5AsEhkUKXIrXJ5kfM4UCv98t9zPpFH/UPwJ/80KOBhS22eklCRRyg7pQcDkj/RZL7HIUzObOaHsW72++3h4E5Hi8Vy1in1Cf1lBqEarm2I0SqANhwmbo85pfoBbo0wMQ4KpghSltL1D58+++rrn58/eebOo1Vh6kvZDMP+MByfP16tTsj53ZvXL3/49uR0NVuevfrx5c3V+8fLfjHvZoslubnbsNsd9tvj4xMiVu1Kmc1Xxw+ePu+6nli6rnQljfegHLVqzFxrLaWI5p6vxiqOOo5CtFvv19ttP1tI6Wr1R0+fcfhv/vbf7veb0/NHq6PjxeXV3d1uqKMHjYeRKdabu+PT4/12+/e//juWbn600iLEYkEsenJyKurzTn79q799+PgRdTKMcXJ6/hd/9Y/7Ut6/fjUcBqoDVlEJqFhowpv5c0v/QYQlkajVBHoA7D3Wptak5CogCsNCMWlcgGEiQlS4gaUsEpZbntrPtiIhsozCLDZjCqfQhHIXQXiTiQCGZpKItjZWGlyJKg6jMoUhHatIOFpw2FrdNx/YGVFEC6PJEZSuFBQCvy5SCAKccOaymW2DCpb0Z8SjqvfNerrPYGoguSsMABeir5KDyipAS93TQwEhlLMgzr4EFSozC5OlAD5E2JwCbqbM6dIQzgGSIbOwNsMxhASWaFuOkfglEjVN6gcwMXRz0oQ3HqEFg38KZTOoj2UK1JxTw2CODkRhigjpHHhoazOoacxkOggTsvDhjFE5HCM+jph+hD1C8p7nuUPjyin6iNzLkOgMio2sN2B2RO3TOnMri9NRpwkz2joMUH+EiNibxJuTbuzAyCBXK8pM6t4Iq/iobWqEFJV1sRMHW3itWNMB0RScO4LIVdXD434vXeR0AF8aabr9HxDwNlUgD7LwCBcmbdXbZNbt9yvucLOZBdqcUG27OfONUCl4VtjCwiAFhXAayACuS1SqpVKCATVJEnCb3Azv2LGGmJaiqqPVu/3WRlPmcBeiMK8VS8oxHmfP+XhexoS9GqNj6mmIKERgOY0HJIKCNUSc5fTB4wdPPxqq7/d7odjttubmzI+eP3/6/OMgvb29urt9d3K8XM4Wv/31b9+8ffXxl59/8fXPHz//ZKy+ublEBKmjjdXNvVZ6f3P70Vc/Pz49NTMzG616eE89EfezWVAMh32EiVK1caa9WTUO1eLkZiOTR/h2s14uF0cnJ8SFRcdxOD6/WJycvbu8efjwyI0ePHoq5eaw3zPzbrsdhtp1fTefXb5/d3x8JP1se9h2tV8sl33Xscr8aLW5fX9yenJyfHRzffno2dPDYX+33qyWJ59//Zfb7fbu+qrWClIKsaC9zOjT/CspIBRv87fwoBQGoElAvZhcXSR+NybKHSklDxYaWGERZofdumeP3LD+Vm/eFy8xHTz2tLjPffItq7sZI5qTga6E3tpwBDIceZbO+UnT35ciLMBPkKmSZGbFX8swQQUzRY9wKkJOQsF94drKJxDiiBjQNDOEoVLJE2AAo5K4DUEzEAWHB7bMNuiD2IOYQ9rKE2qG9yihE83MqR1zrnMoRF4KrpsXCyPPBVhZOguFgdRF8EZsbAuaLn7LQoSXRmnJuaYYxScDYW/O8Mrswu5wN+BaKWoVoIVMwey5Xyyns2B/EVNpD2ryQ8IDW0yAGno1bDpEvMoMFJFNHj6ZTvyZxPga4DjV1m3invs2IR+h7Ebgko8DLK3YYTEzvFqI5EtkV+S45B4uwYw1PbVyg05wUjEFd3fG1t72R2AiMWGSwdVrRv9cZeQeWDpA5lVy3xkBwmv/0uzhskXG7clbhdESSwZBQHuTmqxtsoj8LpkdphDdOn+G6QWwzjynjW4sYW1HUb4vnJpotAq2EecVYGYxqEIaXDZVExaezAb8vlApokXNbH+Y7Yehcl3MZsLsbhZRLfbjSE6qvJh3gCep/eJWWbWkQpkfc3uGUKsxAngo6enDR88+/pSo+LjfbzZm4/bu2qo9ePL84ZNnxDoe9sJ+cny0J3/35t31+1fPnj/98i//+uzBE9a+DPt3L35c395e31y/v7weay2lm83Kcb9YHp8ysypUpWhMbBzGCOu7TtK4kCPMaiVIosxYuI5VmbfbnVmdz2ZWx2pDP1ssFovVcvH1X/zl9dWt9ouTxfzm5nqxOl4sl7vt7u5uTRRHq6Pf/+bXY61f/Oyr+WLZFT3s933pVkfHqqqljNV2u+3x8Yo8DrtDt5Bh3MlBlsdn548eX715bYfBPYVUGAM5ueb0xBBOhaGrilZOtU566sUIPDICUkQUueIXnNFc8dview5nnB31W2T9lqcL1ZE1KgcSkHPD93Ks62kwMCV+TsYLe0YZjggzgwN4pNwHUSdYm68ABVZO4q1xthXbftEVNIZF1j0YJ4AkkDHMEC1zPKkZlPAYZhnJhGEKt0TRngubPJHgdeFkIkAMFKSdVkLC6bqVzJSjBQRlJlIVsxARDXIzEiFg4KJwjwcoJyxBJKKCfV5tk03r2oJbxmLg20GRQmgKzvKrRZBsxbi5wkjz38YUpLqnwogIpHV0kB5Raq0ohTUXKmCpYY5MRYRqDTMmUmaboj4lrw/yzoR9GOUhBAOt8G2ji3bzWi3IwmRZ/idzMG303RJlDyLz6JJomHEfvgLOEE1I08BS/kgQpxpBJoAqr09Dt7Iwhjt3i634nNU8ExellwZayOkrME3T5QkXabmFWZhTM0uGdh7UXHY2Mo60lgg8KhnAhXPnNzELaJOq0GSwqiINEOS3+Eyc2Vcb3YwIOxi1WqB3iIjWTUvrHpK4Jpi6xcSDRiMWzKEsJ8uFBC37xVDrvJ+paK02emz2+/VuH05Hy/l8VqTItOMnCxawqCK8/UE7HtyGlhQeoSSii9Xq4tGT0s2sWh0HcvdxEObj8wdnFw9FZ06yXM32u41q727Vdk8/ffr5N788OnvSz1Zu8ebt+++//S7GenxyslwdDYdaa+1n82cffXJydjZfLsnJvQbWAQ2DEnfC5JY+/RHuYfXATKrYYedd6Yh5HMfjk2MVubq8mi2WIurks3721Tdfu9mP3/15tpgdRZz3XT+be3jXdSJlu92IdN/85c+efvwxBQ37bV9kfXfTzfp+PlsdHb38cYygvuvqWDfr7Uk3KwosTp599Omb7/98WK+zz5oKqKnlh7Os+3QUMUwhajZQwgnQYOULwWLCGxPLWMRNkA7S8wb3XZgsK46YBGMQivOHfWdE6+U5yAkkcDZHgcg4msRsYVAFEq6zR2hIcGC1UHsc2/+HZb4TtWWWMRU2+c05VY/BLCA04PQHR3NMg3UVCRaNREQ4a64VyY8eWPdFSc1D0ksq6D1Xu5VVyHL5vROTb4S6hnRF1pVZ/zsgO+JAHsHDBoMEyPKZWUhxg4RCFPJ/LG1WcjJcYqKE+jkaAoe1nWAZtlKK6X6kB2yGhO5358C/hVUYzQ9QjGnjZntCqUwjQfcc3lgEjFJJmnsZM1tonpcQbtdLyIPye2QyzRCXhIXWyjGYrQJhXg5+MdcVosJSpFnNMOMiYWs9E1uQJkcumJ2NCWN5a1siANtDHteAxRxMtV85RU+UIqOSU0TzSfVwIdL8q5ikJRwpDUnMoogpiJwjCEpbImYKU2JlKtAoT+V87gEOZk67lAhC+5glV7ASmwQFSq3CEMkyXJu70uVbRxBTCuiQL6HWwx0MyiMS0RISMXGowOg7Mb0gj3COkvUG42oxUyhFuHZydLTo+n6sZkFjuA00DvZ+vbvbbcLDwo8Xs1npcJqmdhXPUTUSEiN0b613aVAZqFQqVDqVoqL9cNjtN7c2DiR8dH4xmy1KPxuGwc1GkfdvXm9vLq8uL22szz79anl83s/monLY3r57+UP4ePbwQR3Gy3eXzHp2cXL28OHHn355cnwhfTcOBzEm0gilftZ3nVkd6+hO1a2OQ5jZOKJ/QiLvjrphv++KMsX79++YZdxv725vHjx64tKt77Zffv21e13f3Dz56Kyfr0rXzeYzEQ4PYf7FL/9GtKtuu83t7fXd5esXj5482e+3i8NqPl/OZ4sX293JYlbtsOrPa9Sby/fHp+d1GBbL1aOPP727fj9sBzOfiIkIk+02RbbBMB2B2ytGPOzZ3CGqCnMwrNBhyEHkymReI0c1QaJBoZzMkHxNQQSBWkpU08mSct6Ua2IQ28ndIZalYGZztxxZcavPJMLNnVlcyIkU5kJEKfMFap1n25lDOaNjy3otVEHbI8g+QdmEtylkkJBEbmfJHZZ0341G0D1/Pcl+yajypLFHw2uB8DTMlyI0OMTTeydwiaXxPlBjUWaRCGyBj3CGxV4UEmfi8LT21FyLLYRtME01ha+IGNUiJ9AlID4Z1wg3lFlb5U3ZCUqzZMlimIlKNnIJ52UubEUkgmIEFcSgCHIH6iPY6I5xxzT7DWyTSDVp9krRwBbUlxTMoARMnUFOzbnViRNaEoyReuCSkBZVUvPKxFyUnJi8klt4DSoh0hIXSsxgxj4Fgc9Mfm+ZPp4qTrAEmdUE/b3VUOzBxpFlNRdlZ8xV2BpJthncJQIIcyIPZxU3x9dE2qCmh8ipUhvjT+nHzIpwBaaI38ZfDS9F3cwoVXXKBDW8ihTVoqkBIrqvBVw9n3YEbgjegoTZE/lFWZR5T1wyUDB7kKSqEpEgmFlZUH+0R1+0+Oh8ONTRIjyG/Xi72W2HgZnmh2GsJjIjCkvqd+Ynz5W/2e1REKZ6ROTEWaYysejRyckXP/vFbL64uXxnVodxXB4fL1fHqt3hMKzvbsl9e3dz+fpl2GA23G62+v5G++Pl0ZnX8e7uhotcPH5kZpc376nrHz45/+SzT4/PzmeLYyrqdQwbyb3Ww2G/PRz2y8VydXTEMBWo42G78WEcDgczw4k5OjqyYQgzH4d92Djsz0/PLt+9G2t97/bo6XPtZ7eb+uU3P3/xww9Drf1ytVwuZ7MZE+/32/1ut9m+D6fddvu7v//V00cPnj9/dnJ62i2Xh8Ow2+8fPX363bd/HMznMz06PS397DAcdtv9cin73e7TL766fvPTiz/d5XiOm6FKDvFYpDDUrIggGUaE+X7UihonPgzbPnW0rlqDSVgBlSMayz3dIMdV+BeGi2IG0JaPsOEx7iE+hBInv9+2mOWxJb0OvL+MynmwsWI3GUqZxCCaDWwhJIIcDHhzlt4T4Iqg5G4RZE5m0VwCItOgIKHzhPyiDvEGTpIzuzh6Uwng+EJoZDIEN3QaLX9kJETbbNQCGk/0fcq+YooGKPhwN0lFWzGM5hlMDVyA1NEhMqQCCeE/I0X7o5ydZkxnxPsIwUAmLKJ60tcR/dt3uod22r0jIpJqXvj+EhNwQ2fyYMe62tatTQcykTPGwDYE7U57Owps/0hHBUjYmvyvmXmTuBkRNS4sI2qqMrl6uqtEisFRPmcsYffMwpHDxqAWTwmDmUZPVGz8Eg9XLtgQHBOmL0IUpCRBysxmzGwKI/9qRKGiLe5LridgJSbGMEmUgtxT2hYAYCjCPViAeiKdUuQUN1m0hPI/GjmXidlJU/bFhG1wRFRESkktZ8JQQUGhEU7hZsICdi4aNcfIhJEGcQQyxDfyA7OIR/OIblQwbg+Ku6cU1czCDuN4u95t96OImtluGMx9VkpRzd4iK85sAhqK1gDkPKTtnSkZKnlFy0zLbHfYEXPpumGzPhIVLqLlsN/N5v3t9dXV25eH3d3t1eVhPFSLf/lf/jf/4n/1v3789Gmto3nt5ouIuL19383mDx89+ejjj+eLhWiR0kU4WfVxDK/jbnPYrW083G43NhxKP7NhPGw2d1fXu+0GHYCFn56ejbOetmFmRcVr7YS/+9Mf3rx89ez5x8vV8W63OZ7Nrdah2oPHj3e7PWsJYqtRCmvprW6369s//P53m7ub5azstzfrXt6/exNcnn/2uXucnJ3+4q//vXcvfry6vLq+vnn4+OnJybk7Lnbt++Xp+aMf6VtIEdDtSWqOMtMzk+WmlamvTiY7T/0CoeHyYPFW1qFIJArQNIk4qRVgzoA9gRAET5t8TRzbHFBKw9On6N8q9AyQ0682J0VBJpZzzJxWQOeLuKFZseEEiuSAOR9oybFom4MSTzYkLQmhrAO2nL+V3xcQPyrfZiwRSbIF5lGZRYIlmkadPNNResdgvB0cIRFMCV2HRYQRBTJwPj2t7MNjJxwsIc6hYuYsXFgMZhX4+plrJatkYm+Z9f4y4hLe4/2RX4ASS57+mkWlTIrprEPE7u3ZJGn/noxDfNygcIrClKErIgjUeWKncGMo9/CylNBIyxTsTKKqRJjH4G5xKKtqkwjlDYeisH05BiDFEYoKhrioFOj+GJc/hQfKRCpB6XnJzFickrAkUUSA29euvrSmMDBagMGJYqVm7jTEhRVm5xxIVWYOUihdCmmaMjdIMPdncc5W0RAJJmDt7GH5KnM4kQSXD9BOJsD/ITLFQxbVYAL0ICXBIcHKOyZiUmIkHi05BOL0+YlwilLyVkS4WTCc/3K+0iKxEz5VtBzOrMTuqRNh5jBcLifLUg9F5DjU9XZ3u767We/co+u78OiLLLqymM1gDIlm2T44rFmEBmY5ExOVplODv1vHw+3lu7cvfjp9+IjCN5sNhHdjrZSEMVLVkwcPjk9Wv/3N37346YeTk7M/f/vT8vT4sy8/VxUiKkU3w7i+vev6vp/15q7alX6uROGV2Q+H3X5zO+52dTx0nc5n8/12HZu1R9xe37z46adhvxPyYRz72Zwp3K2bzVaLRfV6OOzG4XD9/g35+P7t6xp07FFmi9l8UYcKZakwRfhut4mIviuro+XhsB6Hw83793K6urt652E2WN/P2Uwo7u7uun6mpVutVpvb24h49PT5fLkiPlCEB588eHJ8erq7uzTEZUlzFZjXJgiefLaYvAlyhMktPGO5DLCCRmdA5ggSYq/hooGGMCNplm+Zt2WC39GkUiJAxErQnHG4wcNUMD5Dn5cThCRlB2E6mh+xvRxx4v5BjL2q3OBLkRA2WD4EETnTh1GOpsccvUc0vDu/SDYuHGTh3mVMT9R6klYC70JvREzuTdZLIRSkzOAiNfl/GA50iyxuKvDgSkACAgwYtAgzS5gBT2NidgtWMm85BQIIxHMODBjQLphziDR3AG9fEtU/u3nO/yLSK7slwsgPi/gWkZ3X9LgJIDML9GiOngW5JJgKygvgWRnuWYXIwsVaCxZtypBgDr50Bkdi2GgEJ1UgmnswEVoEkUmNFYk2Ik1GsEpaPmExRTgzmJYMPyIWdA5gRqooPJ2AJLXMlKVB1gmNgAREC60ipzwlLVUjctd2hBOLp4G/hwcVYQ/mlMJT6wOYOchFOdp8XN2IyZKLid75vhC770xSQpnEBm602UBPnPIHJpHC0iS+EIwUFSLJLgnhwCxY+cOG21GVUK3OlDYjnNOYSP8H3AkCYZRFREF8CkHziA+St69Wq9XqWIdhOBz2+2Eoh7KazUX75axbznoVBQkEKzjogz4gWqSXRAg/qBlyoMIctr29/s2v/qev//qfzOdz8lDWq6vri9KVUphlGIau9HJ8evl68/Fnnx2drN69f79YlD/89u9//7vffv7FF2bWd92w2/kwPHr2fD7r54v5bL4g5lKU2YfhwEJdUWMyM2JWNaa4u7m5Xa/v7jY3V5dMZtXcfayjh4+1np6dhVcOr+Nwe3s97vd1HFh4u7mZH63WV1f72b6fLfbDWMdhtZz389ls1m03m8u7m66UUronT57+9O2fXr/aPHh4/vzTz96/fies/XyuXXcYhsNh9+LFy9PV6tGji90wbDdrKV3XlWHYd8TSzfvZfHvXYgVTYypiDdykPo2U06LznHwgmKEYym44Ee3EkzGRpaRg5wxv+lnJUg9w7KTpzNFNwuOATzGIACXfJ9OeDB2Ub5GACeeDz+3UZ57KeI36iROGwjBOgowCfKcPGg5qjzlyXFPMG/YiB4UEfMuCMdtgCvG8EEKTcDqmX7Ch5NwZiIEDKI5tGUDC23nZhQhkjSCadO1EJFDBUnAjO0ISR4EtUoQtO9XDMtE2kpJEclXxOvAai6kOS9OuqdSlHAwEfxDd23kIZgr3IEcCaGmSmJs4wzyISjMSzitIUcDc9+y0OIgtjFmFeJo1otLMu5haFW46Y2Jm55Q+Jb+AqGhhogpTEb43GvEPqkJmwgIDciAxuBoVKI0KVwrNWQ4iZgSHqAbo0Rgih+OjgMkPZ5SAgqzgPuX4lKFNl2SjwWO5iHLJqYiHG2M0JQwPzJTTErlLKbCvIHISsgC9EXDg1IMHUz5GaMUaPwrSCnTNefGICVv0kLU0nWZB1cpfuMhKOahtz2o+5ihmjMXJLIRBPWL28AJGUbRwERNpjgjmLpXcnTQoyA0mqfDEQBEpVs2qDcO43x9YDuQ+74qK9CoSFb6VRA4iVkMAW9PDAEdzsMaJKRuzegT2dB9u3w7bq9XJZ+6+vr6cH52G1836RrULit1uu716P+yGbnb05Pni0y++fPTwo//vf/Ffvvjhz2cXZ4ANDvtdKTwMO3PTUvaH7ayfW8UX4LAIJynaLyAS9mEYh+qX725fv3y5Hw7zWREKlbK+O1xebVTfXzx4sDqaz/qiRVS6i8dPtOh+u7++vhyGw9e/PK22311v+342HHbDdu1ER8dHx6cnwX717v12vXn79m1/tPro+dPHjx55lM+/+cV+tw/WCFJRLkFOV1dXp+dn88XSxmHc7+az08Mw3N68qJs7q5WDSsKrEeSsMP7MgoGFOZQw1oRKoF1ej0gZUnDOXKiJXdtTlyRy7HkTSvE9PAiFOAK2J8CcG3YcraAJCgkiF5KgjoRFDYxiYtAskUKCqClUGt+IichV4MClH8St8Gb5qWEWXkTIuSJ5ZXOQSKY3jgwxMbddYlgETE6pactoRVNAz0V8AQom57MazDlFIHbC8+XuIaHttaQ5MaJzwWOq5MZZViW3gbE9M0W2jSsqwYXFSrJPQa0iUbQjRaIIQQWNK6zMRBYU1pB/T+THmDkTUHsH91D9UGVkoGWVEI82FWi0kYgUfjmF5RtBCUFMVEARznqNiUnczZt5GNT8SMUMyBiatuxPMyrpdKOYJQgmRJE+lJLBJb1B8t57WKbQe6Fgy2iAQJOZNlW6WTKoysSHA4MSyHJoqs5aTerop7I2aVUV+iq0I5TjNWCQLEHM6oEKILMzw8qNWSk0PWQIzCdmrsZskGXed+GU16s1nYmzt2/FSM+GfS8iLJDPS2OP0Qd6SEkTAGlFHl4HhZVQhJC4jBqWVifh5Jyym+zr0eUkcZojl/rhxVMBzk4eWHCaIwIs2xgPw+5wOKiKlTLry9Fi1hcOsmre7lpKw6b6NE9FBLpc8sQE8PmTesFUhOezPogOh52yC9vN9bvFfDnQzqzeXV56Hd++eb3frIN8uVqdnJ7OF/P9drNd34moEHOR+WLRz+YnJ2dKvNvtulKQP4ZhLKo6nzFZreNhv9tbPVT/7vuXL358Veu42+4OO6mH/bzvI4KkeIk/f/vT/Gj+zc+/OlkuO6Gh2t1uSxYnJ0fDaC9++P7ZJ58Wld1uO47Dcj6/vb759ne/OT49ffLRM+2kxvjxl5999Ytvwmx9c/Pjt99tH23OLi7Mxv1+K6U/OT1ZnRz9+Oc/X19fPXjcF+X9blvK7Pzo/Md3v/nzb/+ubi8LWcBNk1K2nXp/VOtt0bkEGap8YnSREoQuszH8p5Fd/pUWdJPrgrqQgQomKpBGsK2bbiTp7FUZuIgwzOpZRTncEIo5SikRhLWpEdEqqGSjT/UiGer/yJPTBnfIE5zGMPCgSBOre2pjtB01E9GA76eAWYBHw1HvK80stFs9mEW25/Vw2CviuzkpBqwUAtEyc8EnQwFWoGYmwlWIHDOgtWULZ+wdIEhlJaL5W6hMZgXUkjalqDireAWag4aFiaB2IhirTXP6SMFEBgP38OxYUESLqELc55kt030lJawtSHJ4FBZqBwd4C3kuMwFFLP/2JJ1tsThjC7DyVmHfKxTSmRuGA8xBRhMOzExGU2jmNOhvHjcQ5kXrtvIAA95LoxFHLsnUg4E73IdAY8kpVJiZVUaHE4lZTLTX7DnzfAQGBkKapMWUdjmzZN+VMHdir0aW3W6TulDEpC2cmvJMW1kWBQWmcC0RiDBomOnayKIZ5qU9sJM2jiLLujQLT6Jz9p5CbWEjtfEdKnSiDL451+KYDkZ+1Ezw2ZhTRHh1q+5DrdVMlFeL+dnJ8fnJ6uRoruLmhALfc4W94AJRI3IwlplQRCRFccpHaNyUI3zc77dlPKzvbjR8Tt51/Wy+qHUch4MKbba7YdgTm2j57KufDePw5OkTt7q9uyulaOlOTs88/Pjs4vHjx9v1tmgpXSciZlaU62Hcb+5ur68Oh+1+u1WdDSF/+vb71Xzx9OEZ2UUn7LXOuk5LMaPbze7Fu3cvXr5bHZ+sZs/3ddf1i77rhroXKRTjq++/U1Xp5lq63X47HuYXDx+Yj3/63e/chm4x22w2y/nSu/H9u8t//a/+p8VM/9l/8M+OVstBZdH19XBYh719/fqH77579vTx4bCdLc9ns+Vhd3d3+XpY3427Gxu32KKexHHBDhYRIm3ICgk5TBHbGB9D0kRX2pPSzqEwBSn2PAtPiEN7ELPvntA61A5EBIozUQt07UcoBLMewEQhEhM1PjMLoEThdDNORnaWW6AxhNVMMyLkNj3nFE3yllnCWzmFL5hTzcxQoBESt2oZHNNEz1p0ybjojKAEoQ+wgAkJyU3rVIOE4VrPrE7GePx5SiltYqHMUNhkF9ESVUZHymKdMGEWKqEGGKolLIrE6Tgn3cDoso1qtXRwk34TDFc4H+jWaSB/EGHCg5EytxSIeB7QAgsQ5/vgA70YE+UgpjE5AQq1+g4fj6DlFC4oiqkh7MjHTg1pnGDfwN2dBscc7sTBTGZVBOMsa0kFbjhZoTOHCJnnnUBpghDFRJ2wseTPkgTnzFlErOEPFG7VCU5w5LmcBELGxnmYgmdOwJL1GTACohxptljrEerEQRxamtG/EXwII4TNs5lIOB3pFEqfLGBawM23i1yzLOk6p1i2wSWFCK2VJTSxlXPDRmM9R0K7QPMlMxq2sLbGuV389si3cQOTEnvChSRMziEUKuTCqlyEZoWX8/74aKFSzo6W58cnx6tFV4Tc4GvBAjU/+NlMHFijTA0txTuL0H3pwADASJU57ObyXb86szrUWpdE5xcPwvmwP4THzdXNm5c/scpHX321XB33i9WLP7xws7GOu93dfLZg4dXx6c3t3aMnT4n1br0+Pj8jJmW6vb66ub7qRV788GchJ/bLq2vtli9evRfSz55+tJAYtzuv1vXzsQ6buzsiXvXzp+fnJPrH3/3x2eMzqod59cNhX8e63ez6vsxns/Gwt8MoXSGO95fr283ts6dPvvrmq/Xd+ur1m5ubm9evX3n1m+u7w273L/5P/4evfv51rTYM1cY6bLe101/+1V/cXb179+bV0emq1tPV8Wy+6P7++9/cvnqpMQq7UzoGcNsNkdNSdsIwA+RpSk87DyGHcgvwrzgyejA1ZaUwPDfT1YTaH0uGCGYKdgqKtE4Lz3U+4IMA3W/kNXJnCaJotm+cRqaoGg1nLktaERZRZqHGrnZnPIhOwSSV0v8RDxdyTZbq5iy5IQonJ+tRRLDwIrAmpVCQdBJ2nKJWNhCR+U1EEuzOXjjHJBYREZa5NAqjMc8HOXJMnjMLydmEEJMBh0uUDIA0eJSJnaXXJGEmEJqMRoo2X8HFxqdEuAgPx/JaEacQneZ5WUG29zEwZcNxmz31KEJEDLMALLqB/Rw5NUV1EDkch1S5wKQT1SgxEpxizIL8PBULkh7WAJGmkeaHxW1jFRM3FVlIjiOA6VHT/xJzwHIUNiTpsMMBupBIzuLBgg52UMoE1FahGjxZFyLCTrBJpuIA0O8QppDlYCQZkFkwSbS0TS3HgzEUAMtYMmdkPZ6OKUSkRYkR6Iq4pYsgHl1JcEybb4TACz/aXiPKT8AcTI6knVQfIo7sBBm6TTRjlkmc0rSb7xtpxHFiYUmmc+vwpxxORJT2Mi17R+hUalEoByuTRRRecC8UZAsVPl4uiPhoMZ9hbzWqSkqlBGc/EpEbSag1NmAN4/xx6+0IIQnrN5jp5t3r49OHnerr95dnTz/a7/abu81uu95tbtd3t1Lk6cefPH72yc3Vze///jdvXr0YDoc//OEPpxdnY60norZwVj06Od1sNmU267qu68qr775/89MPFxdnP3z77eb26tHDixqxXC23O7989/7Zo4/oMNyubzc3dxQxuFl4V7rCEr33fblYrW5urr77/seff/2l+HhzeXl7e7daHYnEfjhIPz978GQ3DME2n/WvX7y6e//+4ZNHb9+9ffXji8VqfrdZv3vz9svPP7++Dl30IQJzPBvr1ft3R2fHq+PV3/zTv3n35q1bDMOw2W5OTk5+9ou/ens0f/HHYXd74IZQI5Ql3xdYPhRSpDjZKJxaZp9AR2bgQ5RoHKWQgMCXbE9oPtT5T6AN11RzMMZ6TU1CU/0CojN/AJ9qBLdGkznY2xQAVSOjYkEQkyw8OdsFUJeDSxBrRtKmdUNJLEEM7wJuzzglUsECmwy2iEabYiYO9/ShwVvlBALlLUJloj/SoHwAoxEhGtjWGx7sgvcD/UPifmEUpTLfM2BHphhpjxNcJkHHpmj3qeEBSL/R+BKJIoWn/g9sGvIJNW1NIa751PaheEX7lX0AE6UzWCtqEeYYpVnWZ83Hi5mJCs4BVs4hdopIkHGSU5LcwkmeAUyo2RPyVPijNwomQ2ZPPVo0/Vlw9mMRIjiLsDVXQk/G0XRIUGRxEv7zxZUjVegoAYw8mpWzREikTrzVQBEeHBwEl2MjeDK3uhn9C+V4I/VUresJlFgcmI8RiYToBz0XDkIIh7owVSXxMA9XYYHTAidhShiDtdTZJ/BFQcEihC1wyoDj86sRB0jbbYQTWbIEVwrJho0/mJPll8IRdOZwcqPEfXN0R5ibUc5lo11cmPtzalbaAgaez0RluZjvDqNZhYDX3FBQ4BjC0ppRzbBEEIelgS8QNEonM2ZpfC3qQjRJzG7bu8sfv+2PziP87csXi+VR36uZHsbuk69/vjhaFpLb6+v3b17eXb9fLRZ3683f/ru/f/Dw4UcffxzONXg+X3qtu/2uWi1dufzpxz/9+lcnp8ff/u7vb969vTg/vr6+uttsZDZ37iX49Xc//nC3YTftymrZm/lhHGezed0Pu/EwesisD/bLd5fx9Reb9Xq73XWz2X7YL4+Xq9l8OOx323UNDuFS4vT8+PLd+7vb7u2rl7d3N/vD9t2b92a2WPQRR4X1+v1lx3p9c/flN4+OjpY2jKWbXzz5+Pz86cFG0W59dWmHnTJtbrb7/QEPlIIjQOiSqZorhYhzGBEM7lvNEuB4Y6iWZ4A4G2FLNBUMRYAE4LQgooWwKIKrJswPfF48JFJBBCACTaWHRbgRk/O0oJgZg1LJOmmCIBkLSbLvxUggz03yVZjJLYJrhJoi3MJQPMvnrLwFOKUKRWqMhMjZmLmoiEUNB3TRTPTy57jRHhIyzaoetE5tsEJoBHjxmAw6LqZ7cXcz0lQ35hfJ8p3g2JcX3z25h07AcCwqGbMJCXuzFE1LBiERJTKCvCALNgkJBuuUHR6vTKGgmEpUhxCXLJydhDUIodXa48/MXEhq/k4g4TmxypQNOPsjJ1UlpkLkokWKZikQocFcCoIjCoaUpkQEhF0sRGRmzFFKNlOtvsaZ4wYBOxYIN8pmM4nL4t6YSZqumj+g6MOFKILBWG/QdJBGNEPEpBRBawgtIpObM9b+hJMj/rjVbGiIcqKLnyMSUO2mpIrE3krZJIGhTeCM/sjEktaAREWpRoWahpMwSpiTM7E5BwWexclxKIsv+uCfgKlvlmw+kaXahU83U0HUT0jV86JNKaAxfAHHt0RPFmBjpE0MNUAmh4GJNakIkZOHEAtz6fr5vJ8Pw35/qBajk3ulCBVhFVUCjTbdVTgbbU7+LhFJWnJH5E0kViyayE40RGJ7+y7cHp4d02zW9725DaM//+jz5epoGIfXP73YbTbr9d3J2cm7N5c//vhiHO23v/ndbDYbhvrm7fuf/8Uv1+v1MIxd4brb/Om3f6c03Fy/MT+cna82m9t3l9c6m5/Oli9+ePXqp9f769vrm5vX17f9fPHVZ8/nhYfdvpvN31zdvHr7dqZycXL27NOPZC4//fTTuLl78/L1ycnxJ59+cnZ+MVseSZGXL990s+Xq7IRUgmgYD1dXl25WStkNw91m+/zp4wh/9uRJHfa3NzcqUvr+5uZqeXq2X2+G/bg8XvC8P6zvFovZ8enRiz/+7v0P3243tz5uijiqLeLCXEgKRdRxrGMNpa4knRozrhxV0hTg0vghzwPn+Wx4bxIH2m+i0mJOXpkomEU+uUpwnnkghBRAp9qRpAgKUEERW4LYA5k/wVko23OiENQUHln2NxwkKCqiGDZRTS+IiiTw0LUeNhvYYGFsxBQJpC9UvfnlJuSHXO4rV27PLxMLMK/pT9MmyZ2dgO/euz0KmDAhmSUpN5XkQzdNFlGlN9YRUBrWaFUScWJlApJlBteGYeQ9jBw7CJMjiWavjTYK65UJhgVt+iKtTOYWiqldACwGVGJRQcsRKc2QcBLBakWwUHPhMksoc3iQKicXGNCWGQmrqiQNNsxAUco9Bi26IYR5A7qJKMBCxbSSMcpI4GhqqqSBM8LMqqUBRyloRHYBzpWeeC35Wngw5HVhbtpmsQ0/ooioBnbP/TIjaTr4pm7JSJqZMpJohseHMZgDeVTuOyx8bBwpStENAxzTHLuQClNyJafZNV40W+mkF2OTU8N1otX9FM2rDleSCc1UzkWEKMXsuE34TxiIySk3b013k25mFw6AOR1hiND0s1CQM5sTcXSlWKlBkKZwuEvpglxhSAKgCv1Lo1dIXhliFqdGdcCbEzU2RSvvyPeb69Pl6uzho2EcDsP46Onz1er43Zv3y0Xfd+XqsD85O92u7/7Hf/0/hXvX9UR8d3vHLFJmTLTdbmezvi/l6t3b7ea2V1os5sLx6scf3CqLEvOPr16+fH39p+9/nDG/vbn+9fc/Dlbf3V7+k7/4msPeXb3/l3/7dzeb9RePnzw8v7i+vjmZ89X19UL5s88+3mzubq4uF8t5dXvw5NF80f/pj3/85IsvFifHdayzrn/z5s04DuZ+u16PZg8fPTw9Ob25vmF98dXPfnZ2ccGsQuLu5r5fr8fhUObzu7ub26u3x6dnF2dnr/5wR8Od5nnTHKzkuYvwGj5UZ6UiGumji4DuTs2UmZnx2N3H6JYdoulviKkhpRMUdF+RYFNGiDRbT8oO9L7EAHlAiNiNpJC7TeEW1buypikKms4Go+PRAAzQRoMAnCLrBHZF8TtBRfnxQChDVmi0luxNglmMwGd1ZoIjJxqN7CKCkhpCLWAj/4lEs8GH30skhh2pBQswmnIXrjA7R7s0YN0HtiDAHZimx7FxTiLCyDiEkiQJ/U/BDRDmfHq5eTwQpUiDJDwakSlY7ku8KeyZV2JlghUNqFmcY1c87Gl6lEM5zslkU5MkohdFi4rkVnQiZtEEEpAAmJmo1kpYFRmcxgZMjl3SmQO43eAWQDP2J/hQzVWAGTgRK7NF26SSKSsQj1MZR0kPJkq70nypDICBuZQZWIs1QktRs+rmpIqg5xwiBLNah2SEnBlBGl/2fobOrACmsh9rsGM7OjQF/fjwwcLpUXG3fKHE9DPS5nVJxD9zwz/4lUV43tu45+Q4JP3UzmkEhbkSeVuxkRXaxJ9qxLSg8ETImNmZoBSDARbeztO4CBckIb5EOd1ygVsSC1Up0yc2jnVMBN4hs7g7caJbrXYLLOwJIneLKcRI5PanJD5wkFG4qm7urvXqbbc8O3/0eD5fvH79ajwc6j726zsmmy0W3/7p2/eX749PT97fbU6Oj7pS9rvdx58+2azXblVkZl6v3r1livlydXtz9/qn74+Oj1YnJ+a62R3mXVnv+f1mfdgO76/eW1jf99v97nK9EYp319ebw561e3e7/c2PPz18dP7k609+8Ze/XPRa95v9+nbY78OHUrrLt28fP3ywPxxKkfXt3fZuraJd323u1ofDYbcbtttDsHz59Vf/6r//Vy9evBgPw2dfffn8s89E5Oj4eL5cXb17G3Uo0Z0eL17/+cWrP/26K0w0QrqfD2JE+Cgc7KQUomRE7MEexJjbS4YKqPnsPoFPyKy1p4+CQrzl3CYtY06KLnFz5yUlBvqZdrOT80+q6FEfopcF7l6JiFvNnk9KUlAkSxYP1oQdsryV1FEywBLy4IAdcERQJOY6xZPpuZuGWoiwPAFNhYPCXVAXTY9SSyF4xpwoWoGMQ0qUPUliI1kfJj7NnnxWsghNdTXMvILvtWg5QM54FDE2Ra0ZNDWUPnjCIcBtnFlRPwHBMCIRqnhxTr4nR4UpWcvRWQ1gvNMAicSdMp0EUa64JW6pMkAsohAFC6qBBJbwVwHoIMLpR5A/zoAEWEiCqRQzo7zuU6yK1pDdB33EtBYhw91asZsD2GZvIk2K1uJeK0sQRiXbSlJVd4e6ul1qmv61WfO7mbmjYYlaKykmN1FYW9R2MycK1eTActYg6BiZibAnIcF1cmEBrhTu1Mz68FKRQF8LbYjzKRq7f/Cy3YucsX1Y+d7nc/x8kmJxcFvJhQPo9x52EbB5S4wIWh4wr/GA5U/mjA2AZ3MsSdncB4ScqfxrT29+PiKWYBJlcQ6pmQs9rDp7GIgG9/Eeo3g0FgCFInFSzs52Kl4CWYHaKI9Y3KvtN7dvfnr02XHf9ev1OqLOCv/+N78Os9PzkzqOy8XRJ598fHN71/dlGA5Hq+V6t6eIN2/enJyddn1nw2Z9c7lczM1sOOyfPHl08egRlW4/+oNu9evf/GGz3Z+cn/759sej87PTB6ed6mJWXr1/Z+ZdXz55/nw/OjnvKc6ePn748NH11c3i+ZPNYXjx06uZCguVWVkuT3bbzenZUdevuv345uVLcr98936/29+tt5u7dbiP4ziaPXz88O9//bsvP/v0+urdw2ePZ8ezw2DnTx5Vou3tnZZeOI7Oz3a3r8bNnbulSJu0PQKOzpqJlUOKUG01WxhY/pj+0fRYtGODmE/toUvIGsH0XsE6xVLmxptGdVITB2AKN6t4AUegYwEgbxRCZJ7OlC1GxVTItLgbEWHNvngKEfBVCQp2ySMeQSwWLpQc1ulVp48arVJKpwBKpnaiKq2y/bDMp0bApohIo+zpm0ceeIFEHpUzM7yuW6nn4ZoO00h+8AWSTLvELTU4wUoryMzQRwAFT2gGj0YOMDBHxBNJRCyiTYsbkWw+pIEMGAT6O1aLNxC7gX+hKR51y1udRX+0AiBHIrmZAEhQ/tXSaZMpwg0ziAWe843jmQlZiYLdcowhYsBJQtvtma5aFqEfHAPg10qpQOPGMmb3dPwAwMCo8c2TlQDjWMXUKDxcgiyyRJAW1rERzIMK0mLeWOzhucegRzNNnCsj5dT8MRO31gknwtNXaXqCguHlTUKEfZvSrnIQQ82gyEY57EJmJg5iFUrT9FyUAGoBJNB4qLF301nSZI3Cw6fyguDEkoAYlK652KYhYfi0GC15CKf5KDMFcXP6zTY866M2PyBu+TdcSNF1Y3OEWOg9bMe1GjmFZhnlZMQEzw5qxQYHTaa8nObEaETI3ViZQyn3DmW1G2Ljbs1emUiLXpw9/OlPv7+5ebecH1++v5ovFm7++MmT3X5/fLS6vlsT02rZh9vrl2/Xdzf//D/49+thS+Kr09Pdet119PjJ06vr9dXNq+cff/rD99//9NMPJ8enf/XLr63WH1+8Pl6u6jgw626/dzKdlVK6OVOt49Hx/OHF6dNHD67ev/nVyx/3++3m7vaw335Bnw7VD8d2cX5qh+HN29vT8zMt8uLHVzYaEQ/jcH1zO593Evbqx5/EYz6bzZb9MO5f/Pm7Tz7VfjHqVf/w4dObbk7hpdfLF9+N+6GZ0GIeFSoM6lTe16wBOTgMgZddIke6BCdosBtwuZ1wFyRAAAWNnZAvYA6iXAjtI6qoaB4yTCTBkb05PkF1m3J4Sl5Egsk4hLlkK8doWeV+80+2wIGTSjiznthHnl1yTkM6VI7B4uTCJISuIaKRNROZpADBRInYcxMmO3KfYxgNKSyeR7TXDNmmT7xnBbG1fUJiFhYXKu4mJEzIdomtmuVSW/j+EFYQhtXpElqQs1l24MkyRIvjORngbDPgjhsUjpVVCIARpkJjUOUQJpEQC2mIKSlnv0fw36AIR941fKj8IiykkXsPOCe3wt7ol6pYBcrk5MmN5wLKEViliJyU25RaUx/Bjob+3p0DVwJyFISSKfRP+Tqa5yVRzj8jyM08nQkR1SPSmjYyY8LsudYQ5RKg4ajwaEFE6dwheZCmd/UINnORcG/7LZjZq2El3j0eOnUwrfzXVptIJOaXg2FrxBVUWbAwjKxhweacKmZSVTNrMAjA/8zvsDhCgQQ0toHwBM4dsaMpzv5GiIiw0TAoCGbika4SbqFOApMgnwYGYZjTRWRtRYyuolIoEUdwA2bzMotz6wejXUaeZuAf/GJmSYYvu7u5zUrBCfeMMh+Ud5LvG5TTNA4EDZRK4GtldTOxB8nJY7y9envx/NPF0dnt5dV33/3pZ1/8/L/+L/9ljfEXf/mN2f7k5FS1zGbz9y9fvn795vHjh7vt5u3bV999t/vZz75cLbrlcrGYzV7/9FPfda9fvf322x+ePH3y6qcX++3hn/zjf/RvfvXvjpb91z/7ZLPbvXz5utZBRVWKk99uNmZetD85Pf7Fz7/uVG5vr09Ojj56+vDf/pt/M47jbj/88Y8/PHq4PzvfM8dsOfvxx+/+8O2fhGgY9nWsTuQe+8P+/PwkwvaH3bg/1LGO+8P7d2/uurujxfLi2fNhf9jtdrO+32zWzPzw4tHdi9V+M6A6iwZ3NJCSW9T1IGcK82AOwQpzYmrarsRvIpzsvj1vNyZaORFEhIQtLo51VFPF2cwB2+1OXjEJZpycJHoyEPoaMcQhv5ep1MxaAhVQJE0Veqc8qUTEQqkhCZsU6lmqpCGiUOSaX2KyCIlcJ9NiDjPmzjihbeQtAlFYnugJgc1QQRGBJotbKMC4k4TBh0doZp/meRk9qGm6UqPszfuCgs2TduPRyHkRylNew7PILEqikln6vgPPgJH1Gz54m1rc/8Mp9GpT0/aUfdh0tZ8hssiFzFnUTsvOULMyesigiBLUCE5xr2CKCGzUw+UtRd2NSDy9lxjBxtybyiNyNQTdAyNTMGvFQFAwdBLJuc+g0wCTCbwgJ4MiDo7lRGERAELzNGC0K5SDSybymEZgAMVRYeahmT4VNWwR1yty7EYMYM+DFUoDBisOMx+ZJE7M9yMeyqPuAStQGF5M/df07znwaa7iiYwgTicBOTMLvHUoqG2ddAebPsy44bwsDB+AaLhNJHCVRKyIPK24oNCZKLVGud3B1t4gl0O0IRM81NRwzYQiQGIL89CJ5Jp7nNDPxD0mlB4wExYU2QoH830HGtmhRxBbUKxv3rqPxPL29SsiG8f9Znvdz2Z1HN3t6PgYT9xuu3vx8tWjhw+ur66G3Xa1Wl1fXRU+nq36Vy9fff/nP3/2yfMff3xxfX21WM5ev3n981/89asXLy/fvnvw4DzMPvn4+d1mc3Nz7e77wz4i5r2qynzRf/TRk+W8n3d6d3MtvqC6n8/7u7sgiuvrmz/96bsvv/pirOPHnz3fbXc/vXx3cX5ycrwch/Hl6zfXN7eLxWLW9/v9npmGcTiMe2c6OTnd73ZXV++WZ+fLY7+7u3WzRa+7y3evvv2t1720Orc1+wQF6RR8PDwit+wC/Ih00UWHwBMCwjwN9Kd7i1uH09rIGhyOqos4KMxtgummJB7p+SXq4ikgAAEV9aU29xjlXGiHZtubt1suomzhDPqyjCiYBibNPU3fOGHgcBJuY0FwGQhbsJqnZExta7C1ok5bEcqNT08tobZYRK3YwL6ECY+CgDTaKwU7UaUQaqYSKh6h+ArElAOBDLyeTzFFW4qer5RLbKmlIGiEKCvmiYmHVi57BhSJ2OmdPUNyqoVhwNqKUcVT1Grv6bXuiUqZGRFjW7fX8ktQUwyU9jCCtJTyp/bBiabryjm+tEjpmlnUWpmk67rJM48SSGif1ZFwM8YDbHR3BQDUch3dx4RobWOwRa6LIeYgYaruRAGwP6mZnLkkwsItcmyd+4QjSBiUpVatU/ZikyYubyrd66qYuIi4tRfPSD9RF6jl+Lyg0lzPmLEMqH3VTL74LzDi05+Qmnarvef9q1Fz7nJLEgKxe0R1pyhTmUThEhISEayiDgFmtAyaZRa1Xa3ZEU7+gkxQ7ITjNDA1yNglDe/u67XmfkFIVsM4xrxXLaoKmAc1F2R6uIwoTnCMPIfjWR1mPwwRKa4UbLqJ7LAfD/th3O136+Vssd6uP/vik88//9ndejuOl3UYlvMZhXddubu5ef3q9WKxWC7nEfGv/od/9c///X+vxMmLH3/c3N29ff3mu+9fqMjV5WURHYf9eNg+OD09bDZCtN9tHz04e3Rxut8fXrx8PY710aOL1XJOQT4M4/4wFinCHPT69at37y9vbu6qWZnNl0fH795fbtbb3WH48ccXRN1yuVguF2MnEX59dW1O+uzxbr+/ubk7Xi0///TjN69e/uyrn3nw6G5my+WiOpV5/+7FD5ff/872d1F3khP6xjfLIBmJzxF7uJtTsAo7aByRa7NwaFhCgsLv5dcQ5wWwklYDZ3HVOAXCMumCme8d14UoQDYVKaIujg5PWdKzhdiIEngmkTz290xrbjMt9AlIA+7E8OeMtmaVWVljDGp9uUjmiQ/k+T5Fe9ThiYtEvnqwg/6XV80p3KtXQEDSDIBRk1E+kR/G3xa0pl6J87FxgijMBUkONdXEb7WMpUEcou6Ue8fguc/sFMoSqYRtdyF733ycWLDWKsOLRPJbgBW3flqDBc+vJ7bRFLEUTubB5R7YSL0udjq2hgxgc/A91T5DjYgUzyCF7IX+xHEpsfMPAuGWDiIr7YiIMHNVIyoTkIAAFS2s4hq1Iz0R1GkaoDJltkirBopwowaVc9N6Ua5Y83bjJ/eihFVUZazV3VgUoqRWUHuu3WmUzxb+86NOuTKxOqLwMEsYEzb9kEYy358ZbiV+XsfsJLLWx3A6s1oGxHxl9ALeUnFDiXAxYZc0jZrzl3tU9+rBaqjZlEhEgakSkbtbRDXDcza9Qs7EI9K7K6d2re/BlfVGIZKpYrQs51uHhgOH6ovqaG7MyeiHrIAUUOaHSZY+uLK4RFNrIUQp54/pgeSIcPIqTEV4fXfdicwXy7OzcazezeZv370bDqv5fO51PD897lTubu8iYhiG9Xo7jvU3v/3dg3/+j1+8+NGH4U/ffverX//u4cXFer0hcgtaHR/vd3f7/WG/HWad9GdHZr7rys3NLKKfdWVWhIJ9rOPhsGMfDvvL3eb65vL1q3eX17ebzebo5KSbzVbz2fX1zW9/+/vZcnVze1fKx+M4LBaLvuvMIkiGYexPj6+vbhZ9//DBxYsfX/7xj99+/sVXRTuiqON4fHYRFA8fXFz9aR91n9ic/YP5GVj4rY8mijDDIrCirQacava0gILONaXqWcRHQxiYUZxEIqy58jCIiSNZm3mzmUtusaVgisKjeaYDZCfRIHarGbaglfHWPuSnbhVf+w65oi7nhUREmW+M4N2UYbpxQdDRAkxKAjiFRBRlvR9kA8CZ5I4trCKOgJRAMjVHDY9qvya4CmXIVIICLcCiC2F2KREc7EzuYRxKhFVZEmRgFuY9IzzaNrHcs/a6J/41WVwwbB48j36mh7z71BrsdNHwdsenAMRTr9ae7ft5Mk4IvNgi2iz2Pp60n86PVgweFK2sdXdVzTQUQfeM/gQryKfwRBiuSgNugN3cI8sBOllHQUmdyupWJHcoUy6wCecEdSJVqfjYGtGw7HAD6d49qgcxnIzDBcmAVJRzR1jDIlrqo2iz3QCHEqpnptzukkU+Iinq43RoQHAXpuBJb9Ea26D8suYx9WsonyWTWYI6zjnbjSQYMNKAp08TObVd1XmLsoukiIz+DoGzu7JUA+wS7AT3kqCwGlmDRFBEKQXHKoLYvLTJRiIK6CLaM48LgLMCaU9g6ZEwOeEh5yIlwuEpI1yUiT0BxZjMSiXb7Gm3hFJEUwwQon96B0E/EIpcLU4Udbh8+8qpF+KzR48u370tXV+68vbVq++//+HhxdlydcxCi8X84fl51yl2vpOb2/jq9bu3b6+Gw259u3nx+upuu1suD/thXPY8m8+G0b7/7k/L1fFidapdGcb64uWrYF4tZu7hNnplr9WNNuuZWD8M++1m8/LF++3+8Pr9Za0+mu2qySfP3OP1m/d/+VeP79bb/e7QnyytmrCY2TActut1/+zxbNYfxkpa/ubf/6f7zf745MiDF4vlOI4WZOM4DkO/XA67ayYn1vY4t3pC4G6f+ZudIqhGaJCQINY5ZrHMFCGsIdg+y1jsiTSPJKsJtHmQgRniFMzhtaLgTxonM6qs6TO4NwM1FlIKFhGVTPbOEczKReAF5WHCJcyyDGCi1OkHcEMWiYp+BaTUABqDmH2vGG7OAbnBDGwnI2JxD2MjjD0T3dGIEJUgEhYDcxrQlqUszqfVJO2XuwtjAUKCKDn+tMYQbUbrYU46TVOLS7Zd+BmqHITxZUxlr5PFvTKLaeIWRRMgk2NZDHnzSRSxhl6nrQraF+J0fU35VHAu+5FW+zJQVdwd8mgPIwWpU+6Qiay4kec+EAMKEXvhNovAE0vgnlHCUPhSE3YUKB09zN2CnLgiaEYWJXyfTmlykEKQBaWyiHhAFpAlYTROb6OgJfjALQ5SBLGoqDVWDBE7pW5WWDwyWbaFGFBc5y6aDEkwEsEqZhFVIWbgJtkRA170MOjByUVydgMxAdiirTzPx2S6MszkHqrE99Y3QZSLB5pJD1GENLuTJG8kqpfK+1ZJoZpoNwb3oK1cYjNmKnHf3JlHNTdzzoUwRK3xB+CTaEBG+ojWxU0j30TaoDLlrA+jNacq3KkyUXCnwqrCGaTukV4i+L6xTz01Y/VCcyltn6hNGjLPZ+NLHO43b98sjx+uTo/H/f7d29dm9XS/XV9fnR0ficjbN2/6vn/w4GK32e73Oy16cnpM5LqTN6/e/upXf1c49sN4u97sxvjx1ftw+/jZo8PrK4qrevD5LHab3WZ/OFQz83fvr3b7gwov5h2Nw2q1Gsfx+uZ2v9XtsF8tltfXN5vhUInvtvu+73b74Xa9Zffd4fDu8ubB48fv3r/vCnUnRx7uZsIRZtvtZrGYHYbx1at3y+XJxYOH/WzWzXoW7CW17ebu9v27ze2NMnv6Pt/f6LxWOcmf7qN4I3FRexbbEcsHOOL+NaZSMUE+ZkrhRaAUy7lsULh/YKGGF2t5HFwwYZhEEdHkMQ574PbfkaUoc1AzLKR7Hmp7ZPJBczd2Bj+AmavbVO9NIQhfJtEw6F0Rjyyw0wqYTPa1qGqZKNjtA1Ftut7wtE6c7jNtUHJuP7j0+fUdCFAriEdzVtGJiu1MnWp4Dc9pQz7DreCOaLSqcLB7PUxFzV2YGIMUPC88rXMA+h9C4EsQsiB0EkjYlMNCSErl/htN7NssIPHocVYBnCwC8sm2OKaeg4gguLX2P3UC5aeDlO5PyTsmc6/mh9HG6sNofSlYptLSPreAmNqiKVa6G1FYw3Yk79oHX4SZyS3aGQpyGAel13Y0FCaPLkAE1eJeEeIZ9k4qrRu+j4PKTJRrLFVFpODSZMJvSiv3cBrhUq4qmqedHOzJKCzTkeEP2nZqrXlLfkQUDHd9M5tk35RFObWfiaAQJgtPgSdxOq/gQXT7h9Uh7qNGUIUPJGHZm7jbWCtO9FRZEJGqAlDjaRjYEsAUL7IxDGreX8gHKG4IUaBo7mcDDolblP4zPKH+oTkDzIRq5LDJa8092CFIc+2S4bIJM/H+9vru6u3yeLW/2zDTrO+++/O3q9Xqn/yjv/7Dn/78xz9+O5vPv/zi8z/+4Y8317tHZ+fv3r2vdZzPFtdXV7//gz179qB63NxtdvtxqGMhf389v9u8mc/648VitLu3l9/rbH57t/aIy8srJ1bV/f7QqbDOrq7v6nj50dPH379403c6n82kL3fr7W6/DzouqmY0HPaj+Q8vXv786OTs7Gy73Z6eHhPRrO8Xi9nDhxfMfHt7u1qd3Fy/efvm3X/4H/9Hj549HcZRWbvSnZwcD5vr3e2lDXtp2ol2F1pER6C9D1ve8APhJsZNxkn7qXbYKYjSL61Zjzi4EvfpIJodS8po8rFq+/4io+5EcWBpK14jiIWV2WDjn5GCM2NFfFBxcUMjPmw08zXMTbw9SslsEZ4Enuh8nErhwAwp2pA0LSBTmauU+n0EULCGvP3C85lbVieExYOwhoVSoZtxv6XX4PuHBeJtcSfRCBhWkhPVas2G5h8ALC1kcgR75FYHvINQsyL2Vn2jVyMOeA95C2m5AYWdwtzcLViYlNOTP2NDhtn2cCX3E7XutGUevT5jJ8FUjdP/PAEQsXuAJYq/IDn7jAYNEWVnwB5h7kOt+8HMfDTvPVTFJ88xnmIKfmoCZKSCbyBpMvpBz4vlMzhsUd0VzRIOQpSm92i9pYenxBfGUfcAer6ENBf9/Kk0p0TwolzDCQfLBIvMDOxg+J6IEkdENRT1YAqhgLq/2e3bcm5STY/AVghk1eNuHKKAOHFtMNL64PAQhbWv6GETY4uSmTXFAvYg8xAJybojAhfCrI41glRFmEb3UgrqhUbaoVaXZP0/0SpaJiOi5jURmUOYYMWKJddRQAcGx/aDsjWzLNQkEZmLhbBtBgUqtzHAdAyn746LqF3n9fDq+28//8VfdRcXT8dPCvMw2Hazub65OT5erVZLd3/08OFht99v9+8vL9++ffPpp5/UsS7m3X7Y74bx+upWSzcO1068WM1Wy9ndbtiu9+NuNL+5vLkjUXdzInM2D9ye6Mrrt5fb3ShMb99eaacivZYuwpeLeWGZd8Wt1urzxXJ/t3779noYfv1//N//xxy11no4HFjk9PT4o2dPnj5/+qc//nl/GM7PTolofXtbmEPLfn8os5GIwuru7prCsdo0Eo2dYkVW4ZkO/kF1TOmGgpteXbRQPgfc2tMAAsmc6HljYqd1VoNaMh9Mbi4qGtPDi5KU6/1qupZA2rOTPGtMHHH/2wMW3JB8HL8UhqTRNYUIERsnNOvg+MNgmhqAIeEeZum9zPkulAlAKMiVhYQ9cu7Fbd0pkxCZebBQPiUfnFJvVzsbhSy5sg5nkZDgytw26ljFhMtF2A0yKcSIRIXdfHqLbLAR94iYJJrzALAJEWPiVCUTObuyuKegO4khyAOZo8MgQyUqpI0tJu4xEfBoqsOIhQCeIQHxtOcb6RCemNNHJWaJTAARQWYmUoi5ejq9UEY3nYKap/48aq3D6LXWXmUxn00ociKHNB2XjMl4v6S0M2PElFOSDOhBHzSMmfQR4cw4MBcIVXULiyxOhdWi4gpmiaFYUZM3Hu0VkpxMB5kzMYiw2z1xE8TLhCSIyMFskVzJClPC9DWDmR990JVPcFm0fJkYi7k3SRsq65Z1Ws3njaYqnBufLAErJ6bcv8Gtz6TwIHN3wkwNj0B+djMPD1FSYU9F4tSAt4zeKLOtluBojLwkg2KlMycMxWk0bQ2UcA+L0KnhI0Ilgm5GRIRJKHfHokya6vwGThGaCwJFIJibatxnQuvrq6Mnz07OHw6bdSn92zd/vr29/ezzTx9cXFxdX4nIxfn5xx8///7H77748rOLi7NxGIPiz9//WC20m3Wz2fFiVmYzCat1PDs+Gnhn1faH3WK+GmoFVCVMg1mnXJiLSjgdL5bKIRHLfjbvOzfbHXa96snF6XzRb3bbzXrdXZztdweVbjwMf/u3f/u//Bf/rI7DOI6Lxez89GSxXH7x+VfL5fF/99/997Oiz54+efHj9yp08fDRxaMnzHx3e9t3ZRgOEW6gKbu3uoQaBawJR8JzlygQAQmYSk1PTs7wW7OPkQzatFbOtoFimyuhZmz/ZC+IgJhavnabWlPRSv2pXUM50ozZsofO8jqIQgX1dp42zr/glKpZcvD08O9CLM2ikSM8WZEJ+0TDS6MR8MPZpRShYAOG6pAppcS1pTs8jSTBkfDtfSaI/LQTVxUXRtKoDtPBAIxM1NjV4Hm6eVFmTqbH/+w187iTY0MHGPjhXs3NKaICfM4ui9lhhpmT5ICPHkKWw5MULmrTWCJIssYKGPdWq9nMADZjZJOcOjCxuWEtNmYd7lPhRSFaiBjls7hoijc9cgaaih4A0BTk5tQ2RLj5MFbrOzPXpCWjOEXYJm/HVNNmHEE1JJqkhRn3zYIpQrGLjTJCUTiLgghZqUEP4FMyEYHMzInYUZ5ZUSi22P1+w3KLoJB8e5tZM2qdKevksTDHZokQcaLRvNf7tfVmSUyLhqeCKE/Bkx14VrXTBMPDwjV9mlIOhTM3dRKjefaFGqSwTsXoKIRFhcMNxDMkRXjbCUUBmmowhwiMxXCxI/IKNxgOtabgeqMbCnckbcm5bBCxc1QKctZke5OwetR20sncA/rK7Eaz/8REu0UeZhZ1JrIJYcRFzp6AsVfHHbuR4bOnTKqzxXKxWFnl9frNzeXlvJ9fjtfff/d9EH380cdayvLkqP7oR6uTiwfnm93dyfHxfnc4OTr+4fsXs+Vie9hbRPGqpTejRScff/Lp+zdXZCCxQCwXNWK928+6gtOrorMyU4r5sj9azu62d+9vN4dhPDpanpyuLq/vKsnRvN8fxr7rjpfLhw/OTo6W+/3u9OSUSY6Xi9Xy6Pj4PLg8/+Tjf2L11Q8/7YdDJ3r55o1VJyoPn83rOMxnXZnNt9srxRpeknB4gjOwWml0KhAsWk7gCLJ0F/dW76cgsWH3AntbdAvMpI0GEx5M4m0aSMSRHBLC3TdzEieiYNUcOKJnFSIjbohlY+JJ8toDiYoaEs4EwAFzh/gHpQcCg4YAvrJmMISyK5iYsyoJdtbW4k5ublgrSNWdKz4hMZMF1J8Cm/rAE+cR7KLS1pUi7SWGgQvSlvMFeapmsvhue2NUuT2jTcxMQeQZ5aa6Ktv9VO6gZsQfKheGlZJ7kFtomayXKJjZGo4X4R4GARr03AboPq+I10j+SKSPABnsimgq3Tg4lMjBoqeMIcRMytWILYTYckwSRMYUxWzK9Jp1JlJog5liKhMo4SZgymO11v2wt9MR0QJri20e4KRmmZB1TvLCxNKngSltxdidzEJbfMS1Mq+4Zlo4mEhyIxoAliki45gkxNmSaFbVRJM4JZwjESt8R6cMktmCmFskuYwknI1YOjDt0ATUWvNcMFOwKBZgTi9yj7XhknKCrckiMPJpYUCif4BmWMhc0sQcQBxhtecHw/TwgP6OYAVJ/A8ELxFOHF1RVg0K96gVRb2QYzAFM5XU2FFbq8CNts0uqffDDMkQBcQ4iwGcualUbNhCwyWz8FTKO8KcBWAeDYuA8YhJpANstoTE3eLi+afPv/wmuHv102urdnxydvn+qpv1j54++PTTz1bHp6Xr+1n//vK6dN16c/f+/dVqcTSfz548eXQ41Jev3l5eX2uR4jRXUaGjxXLVzepiNS8zc9euC2Gr1SPuZtvZrFfhcGfWZb8oIqvV7PR09bsfv13M5stF7xxXl9fX15vZYh7hTNZ1enp69M0vfnZxcTafz0rXdaXnZWGR2XxxdHJyGIZvfvHLTz7/4n/4b/9bGu3x46fLo2M3u7660r7f1+2s72/bldSGImZs5tRLmTvuiMNrICix3iBmcbOgCHZloMCTJgbwIZwOtDHnI7twMyjMGk4dQkSevCBSY1h4oJhiWAqysmBP/FSvTLEPHxu3OFq/D6q4cPYQWcmiz2CEbGMsjOJUwUnOERKaIEqU0p3v3xOpUVg8LKVGLKJ4IsCuERYnNiweNjMWVsBpJYvfRm9EyJVkdjjq3CQsNKiScxjgEdZCF4U7EKom4sf31unpyz0tHqV0zII2HiE0rFIUzt15rRjKLsSnUAu8ArAATG6IQik3d5EqubOQsJgnxJGi0ezugijjagJihrkF9ptMRYCHezEzaqKSppCTsAAuH0R2D+q3M0YZfKFdqObB7ZEOZ4eOthWHyCntY/o0awV8B7AyuTd52w0QT3IjnIEkEu6WUmtUIwIT6erknubO0S5rNKuJWq1k94MCnWs1IkrDuLRyTQgIXxLp1AypVKlaUS2iEGFEVDMD9xN2CCWzTmtAiYTBWUoWEEWwIVJzEDPq0JQwExK5uZMIBlSwOsW6HqKpicaxC0O7gHYNvYRgkYvRhMASk7PhcQ5sfkdn2nIl5+JYnG60VZFS5OwSBVtems0QrFfJwyvQoNY5EdG9xuSDEJHHiSfvxKkPcAomdsaIzyPEgrsym61OFmcPTk4fHPZ1uVwpRz+fn1yc1XE8Oj39+pufv3jx6vZubc7n5+fDbn+3XhPpdrOfz/vz81M3evv2cjFbjFF3h9qXsQgrycli1Z+V0WI0l66QkFWrdVzOZsvlkonNnYmLlFL0aN4/urj4/u2L0a8fnB3fbm6VCoYlp6cnh6FaHU9Pjz/95LmHr1arCJnN58dd6Ts9DIdgXZ2cd1355pd/dXL64D/7f/w/399sHn3y+dmjByGdW/3xj39Yv3/HzNVrkDjAVrfEYZjNI5g9srKzYBj05ugrcm7ISQ3BA+GBzVlATrEpEFG8QfsoWHCeslinMA9hFmNSdgMjyFmbqACINjOLBLU9HERBYe6JaLK6mTDDi6ehNo4g1CrCqa0Hqghd6/1ZQRwz8wwcKOk8pin5FCsFqYuDggS0nwhK6UQEOUDzIHLnsXpit4HFR27usHWzBGqnymoqBJv3YqIE6Y8y8Wfcg4Fk4dn8gJCDS4UnRRQOLJQVDmFVVniECIP9nJlbGviV9dNkrJuOEV4twkNCRQmj4PSqJ/Mg5QjBBNOdinIQIg8+UHhQNa/mY3WV1KYVYbOo5oWIxtFERMSJrE3xMc6Bpw36l+nmR5IJnNxjGMa9ynzekbKwgBIK6gcOGsB4TwE0u0PVwg0HI2JtB40aMB0eFOYALiVKoMtjD1gNt8gVOWXFaEEjoyS13892pvV8Hk7TGaMg1WlGFIZSPDEyzvKGUzJfB2PSUrAATxx9cbskljt5sgqjYCeLRrUkMNU8wgJWJxr3Ugn0+CDzmzk5wEegWEHknOr+1uMQU7AZxnHkFsLgb4equmMSE9DaCWlQODY3iDS6clCWGGjF0Ogk6yMiah51rtVw96FFGMeKOdJYDeziaLBecrSwhJnv+z98S54YgTRdbxccBg9yISndfF5mR8cPnj568kk3WxWxz7/62TCM83m/vr2+vb2uZruDHQ7jqxc/qbCWKCrz2WzW9dv15vHjT66urpj9aNlbHa/uBh/9UMZ+tjg5efDRg8dXfHvw2NeKpZQ+DkS0mM3ms7lXM/dSCpGo6OnR0dni+POnn/7qD98uj+ZHy6MaNtvqWH21OLq7e/3sydNvvv7Zarkwp/ls9dNPL09Oj4+PF8J0fLxar++WpEfHpxbls8++/uyLr1enp7/4m3/a951q2axvl8vFOjCO48jgouypN1RmS9F74ht1NJR1ATkJpy30ZPzOJDmanDq31h/nUDnysc3an9jCnQwjYiK2IPEgAeQcFThG7kvB1DXjnbsxk5sn5kjGQW5WtLSnOEI4whVovicl3yZXfhNWIQ84T2JQwG0+x3j4Hc/LBHtCkSrJWpRg4/Q+z7dMw8eINNFyC3cmJVMKc/cA+xIHOIKqg7oNT0f2CDdr2uoM5Sh7UY7hPx5BzZgTn5YlVAW+9OYeXkW0lCJUwpkU1tlujk9hbKwiDlv3LLMkwViiCBci7SAt5oDZpQdFDs1l+mAeHu5EPlpEYgZOIJejvmLA+5FyMxlrrRwiVETMnDkGsxKUGH+tNu2TFCUYLQc1gk17mM0q/sTDa7X9gWZdKVWFwzWxBSaqNUuHYGYzblYKoHliVCHQmyAQQtxFuUUKbQ8mMqONROTmKqSi0zQLfeV9msUqU2vVbtzfvKY7RX8RROQmRk0sHRmpmCduGyX8hqpYyFiomjsMNChSyiCE2ZXm6AdXj4I9jNB6Ad5oFrbaADtOkhwgJI6ElMgoaHTs22vtYD5qhBSTI0EyGP2Ts4I4TBh+wGcwKHkOeICqG7mICATDaSxDbpHoUhUpQBscRAK0JibMEW61juNYzcyNg6yamzEH2BHkEcJRsUrWMDx2sSTDTXicJ35BQcRiTNUpLIhCS3fy4Nnpk4+efPzF2fljJpXCy9WpdodxHLSb9/NVT7S+vTnst8N2/f7tm+efftTN+rvbWw4yGzeb7fn5xd3t+snjByJ8fbs+DOPFo4ubu22t9WS52t3u2UNKYVUictWiUlS6UkgxdVdhoWCVokZfPf3k9Oj46mb98dOHdT8Ssbmt12sVfvbs6bPnz4l1Np+508uXL2sdHz8+P1qtHjy8EJGu64bRao2o/uDp85998835xeN6GJ3s9PRi+/jZzeuffG0SlaKpaPIBcxdKOCIwc0qvWxFRvactBrEhfUcoQzoP9z2EerCC4K2fLhHUUn1OD5zMXES4UANlk3nnIRpM2exz/gN/AHANPmBcwpvC2fEkWUS0vRSwPzFu+9YJzWxIRlbBi+CBQ1wT0TA0EvhQyBxCLOxehNGHOzE55eOXfzHMycPNWtAkIXQVpCyoiCx5HoGcCFhSPDxhVrek2zI8flokyTA4sfektQ3MabeRl93MmRXSn2BGbQguuBscpB2jZkpMOWAiSRRw/1XhakYZryiIR8vZsCr1Ku5YOMxAZKuZOTsTU2hDAFnEPSIMTzQOTV6UYKUIpuo+jlbGajVMXBRZTYiEFF0MVaRnQtNnbmZkBPhmqOMw1hBaOM2CjIKhCSMEAEAKLpziB9QjllvkkifuTu4e1cfqQaxtwA1Th1rNCHA/R0RHotWZmhUS6ouGNIy1hqgo12oihHxs7uGeWTpvBcKiBTmW3CQIQRiU4ebjQ1CQswcrVXHjymylWeU4Ubh32rUYm9slsg+9x1qyenOCdS/aVxMmJi1diTCxBkFiaBMG5ylVPCDIZMDK0IC6E4eRUM6W0WaGEXM4uVsBAhNUMQJDP6kiDkF1pmma5H7uZO1fUEUaEqfgacInJHf4jVk1U6FqVgIU55SGGTuFEQeFm3PAtcKjVkMLDBG2Uzgz5M0REdKZzGfHF0enFyolwsdah+EwDLv9bmc2dl03jONYx8PhoKK7zfbNqzcfffbZ42fPln0/jIfLy6uj45NPPv10GA611hev391ttpfXa2XrF0XnfREhVQ4PZWU5hJeumxEVYTGvDg1t9Jj/eywX3dnx0Z9e3e6Hw3I2u+Z9hFWrv/zLXzx5/EBEDoMxx936drtZL+ZzG6yczo5PLo7OLki75XKpwrf73cdffPHk2cdevev77XZ9fXdd+qOzx59I3x/W1/vNjeQKkMQZWBjUPyKqtdZaPczCe5GIqX+9Z6BgVMrhQTAVApkaEY44S6AIzxnm5EaJB8c9qsW0qVqbMxsJh3ikyWhEKymp9W0+eZVbJWJrywqZldICgC378DCPjAfhysHs6YuZ+CsqBAsPLeFuGLkiSFcDM41F2MlYFGElKOieFBseNFq9j7aYCnjz/CLCJIWSdJeiTib39tii+5d2fdvvSTSW3TT3ynkvEfZw+b2ciCjYjDyql4B1Df5+m+Qg10Wtxol/puiaGlOWo4woLiM8opoP5m4uTIVIKqsowWy9ujmF+TC6cRQVViZXEYXFdK1u2VAFWhY0MS7c+iEq1by6qZBJDF7ZhZWIS7XWlgWFkxmN5mYOgbJFjNXGWrUrwClGM04PKFSXWb4Kc5iFAlQLiyBzJhOBIpGIyNzNHW3shMV7rYasDkots0tYREltCA11rFZtrMEaQW52qM4dsYbCs9u9mnOb1FOSGPDy1Ik2rhbKlsiMGTkLigiPUGXU8kxOIhiuuYeKVDMLxwqlYIrJjjxfJHIeA2IbHhYLSO89gCkB1WRRZQ+v2adHSoSVhQxJqwFokNB5m56C5qVpv5EjB/ORmZUE7NgGAVtoTMko6QE5sk7NJwi1FBTs8BqKCGSA1qEzC1ukG0c2EBbErswOVWdQkBOJYcipkRqYTIrmjkMGoFtCun5xdHrx8Pz8QVe6TL6GI8Gq4s6q7Ptax8Gs9rO+zPqbm9un5p9+/nmYHYY9Md3e3Zydnp2enXjE48ur7W7fFT4/PeNerw/ro6Pl3XZftNTGtetKgVs4elpi6kRnpaxEuyIvr99wIau22x2ePTzl0v3xzy9+9uUXjx6cz2c9s6/v7m6ubw6HvZayWCy7fkZSWMpsvqweIrrf75fLxaNHj/u+YxFS6eezcpjdXr0/fvB4dXayfv/q8jXt91uKwHyeI0qoJ1weiWZAuBt5fBuHJlnEDnMPhH5iEkUMbuzPHHL5fUXS4Lj8X3ge2hkJFM4J5QZI+nkeYQIQ5lbhOJGg0ORQh9LYcJYaqM0TKo+2HoCoKjehQuKxZk5MPtYAJ5yCWbIwg7saaTIWIprBLzSTER7VHLM9b7+msE6ExtNbmR54onBJm19AY3hL064zNyAhsVz8bCnw4442JcNfl+lwc9TCxc2cIrVBeSs98pmdoOZkc6WwJrtlY9Zq1krkhNYskk/XFWIKgXlSQEPmtRpFEdZqLqLhziyqZHVET3//fSj3ieCzlIr771HdgxxjRVToVA2mc9XM3Ed38xCiwbxW9wgRnfd9gRoNTn/MzOmIlD0tBYfAQYyJPMgdzlZUNAWECZ8x1m4lscQjqkVtL6Qiink6sTmNdRzNxrHWsari1LJ7VA+pRqpuMXqYR2ECMhgkVpN/nCGmjW8idXrZhyNV4kO2Z4YI9QzDIygP+mGsFMoqTTKTlLXp4bl/zgKzr8T3icTCzALEvQzuzfZLhBzCMRGfUlaCQAlAUc4JvFJEYJGIm5nVaJ4QKiKiUgrKk8DWB0AIyT6IVh5SgNSFzytteOutH8KhcDdVNqLRLFhqVlOSY0GKMJeIQJvSDI6yKPMsPjDEwzpvC9HZcnn64NHzj09OzyZnaWJUOkXni74r+8OOmWaz2XK1Wi4XHvH+zdtwPz05Dbfdrgj5u3fv9vv92dk5s3z0/MnV1U0lMRuvb69fL1afrB4saleH2pfusN+TRx2qmxHLOI7MKkW7on2R5ay72a//x9/8ahwP1WwYnYgvzo/fX65E4uz8tOs6LYWZN+v13d3t2enZw0cPT09PHzx+pn0/jHW+WLiHCPV97+61GouRctfPzi8edqpcD69/+v5wMJRGoNVCkAGoATXnBCrew9KY/Hta7gQo25CCh0GJlcldJurtNAXLWCZt/3bSeHIklizPNtFURNZJhAhiT6S8tB3pe/AjpjeCvCYyzLkHe4pVgihUmZiBe6BkpZznkbtBUOLm4D63aO/UNO3EHA51i0eE5BAh2nArg3VGZU0Lh2ZRMyWAHEa1C6LMNF2ZHP9xG4G02qUpPRvTPX88QHNvF6RNxTLbgekqaJjwysgxIpJUQ9yXCG/gWDVjxIrpU+YziMsVyindJQLUMY2vozIXj7Z+sAUWSgiBmMwNzHsPcoriTSbh5uFGRbpQMxexEGES9qjVq9eKs+k8DDZUI5bZrMy7XjiNQYDJ5MyJBJW6OyapxCLmqYGrEWr+wfEhoITwyfF2qM0IyEEpAkESsYTTaD7+/7n6r2ZLkuRMEFRmTg65/AZNVplViSqgGw002+kZWZndFtn9y/syDyMzMjM7u2g0OFAkK2nwiMsOc3czVd0HNT+RvQFBAsFu3HOOm5rqpx9RHyedRnU3B2cUACzmngs4ekIEVPViRkxWZ+fgy0fpjH21qQXpDmudDU+6sFeed/shPgmumSIQicTgCZi1MCLNLlKhao+XUHd2PguM3dVqLGVwmVTNkhESBF/fazOtamRoZG5OVJNAtBgRY810q9WaRI674jgnpj+v4yWlBB9/YKU3IAYD5OPhD8OQ499zj7WSWVwkrgaluBYLkNPUsxoyqtanFgAhHMFMiQNwpfkCQHcrJRblrqZEdcCF1An364vHq9PzfrFibprUmgFilkREacq2Hw6lTGaGgKntP//yKwAHYiS6evLk8uLy7cuXpi7SXF5cvX37ehyGx48eTYfh9eubH16+bhNPh8P9uFmm9KhZT8VylctZmaZiJYKkiIERG+G+abKV377+7m7aaclEkFVVYdWmy/P1fr/t+u7y8nKcStt2XXdAWLddJyIizWK5dMNpmhBRRJh5GAaz4fSEWcSm7EhAfH75+OHDq4fb28Nhp6olZ0kpT8XdkeLYVwpHAC8z53b2bACYadVQV0M46yrNHYwoPCIBvWZa/bwdjiYD5x9zVYoqHyvASr5wtCDXzQYgcFSgWU0qq93R3N3HN40IZB/ng4psBGvAXIsiOwAgMRxVig6GBKiICo5YAFCtcmG8io6jTAKDVgzdiVBLuFJgUA1myKUCOFFT5vugpo6bRZJ5tb2JcSHugFolwd0hyqv78VAcz5Ef2/afFf3YtqMfX1Do+GrbXXv/+Ofio/tZvEF8eXRwBWB3BDqSBOrkXSFvd1dXNGLEmpysqlAbOQq732MhBUAiJvZJS7ADwyatqBGiqqu6IHIwj6ONAAVDAnXAIswA5qqqVswqrlbKWEouGssuYipqaibCwkFSYPfZHi78RRwdjkYW4deGxc1LrZZeh0ysYUNmlf7uFbs0c+H6LqhbVh3HcjiMWkk/SGQRqlKKhndBNOPuGKnklYmIYbuJc73E2J9R7cGrIMCiha1mXDFiV+mQAhIWYCD0UjICjjkrlHDUClPe+NQJMASxBK4GCHUWjn8dITqRkHeGXqPqeN1AQd3QGalEKjKohqUozQ4j7uZZFTGwVNe4UczcvZgREjHBkRobIxJibXNqpNy8h5oZFDPuNW8QADwCj0qZsuapBKUgI5asQpXe7yHENgV353h1gFBJfEbzPm9eYyCCOWlBEFycna0vnzz95IvVyQki5VJMq+3rlIfdbrvf7RBCIOEpNev18u7uzgDOr6+fPfvk/bt3D9sNE07TtN1uwfnNm3fDYbh+dP3sye233/+0PwzjNL588QKux4vrftWlu2ECN0efSsl5QoOWm6hsiMiEr+7f/fDhVbGyPxwIoW2EiAHw02dP1hfngChNgyyqttlg17dN08WSY7vbt4sVuJeixFJyZua+7wGQEIUFANTscNhvdvvUNW3b2dgQWs7Tsbj8N9OeR5cWH1zUP0NADqfI2aUxHtvafqvNliJQlzkVuq/c6CP+E3TDmDiPhQyr9yUylChbzEwUpdMBqyotCn3wBmq7A44Q5LSKq/+8KT5+D3EjxbqAzJyD9mPmxypWD4eaI0pU7doeWTSSM14L7o5BXpwLswehqIqI0dX0aGRQIbNKPazQ2M9mBWPmYGGbV5bEseq7u1kU3Bju67VXCTwf32Sod2elPISrUx205xt3hhhMmYkIS1F3YBFz9EDCEUOLJEwIzkyoXuqSGxyRIxzCPu61zRxAY+FXawUFX6mqwQ0BEBjBiRxUDYuZOwghoddLlWEO90L0YqF0APepaCmVipDVipUIK6RaxRTAkTAJ28wO9IBakOpFCjC/TWSmYFaQvFo6G9QuMopVfVXzTRCfGGJ4XqMXs3HKh+FQimns0IFTA4jOhKqe1Ytl5sgdJbVK9sWfPQJUZZDoAbjUN6wyseqN7bHzqQtncy8Bx9ZoGGNCM1CzMUANj+baCEUYiUE8vJOUBaCm9VbTCkAAJ3ebgyc9hJUx8gbZBkDcwb3UR1BDMe1cpyxw92IuBOFW+BHmJbJ4Go8d3DxoMkarY2ElFyTfGWKof+zI74wKnrOVotNU8kx3HRz6pm1mS12bv2+IAkRWyU7gIlLvCPC6eyCsjHJJrt4uVo8/++Lq8VMHGvME4KahbzSz7K5NI0RUcgEiRjzshw/v31vW9Wr9cHf3+uVLYTLNueTb27vtZkeUNrt9v+hWi+70ZH27edgcxu3d/WLR3V8Mq9JAKQTuhMN+MNUkydHblIopqO7G3XcfXmz2Wy2GnLqmffb0ulu0THyyXH726afLxfL1y9dn5+d3tx92u10SaVL79PlzSS2JjNMkTds0jaQmtQ1LSqkR4UAPiAgZxmEsGhY5KE07DDs3CyPxijXMrX3QIQGAInU5uG/uVG0Wa9setgSVaQYUyCIKAyljDUC0audZoYGKhM4/osLGxRFwbDB5ojtOKUUnV8U01RGOZ6zO654YuIJIswPP8VKZf0rhxYtICqZz+pD/rHeeKdzHlhtn33IoVsyNK/YdxKb6thwtraLFjCLo6ISUizLPvCOoSgh08LrMs3pzuLmz1bed1BF/do3Nw1IdyOL2DL7Q7AAfo8Lcq9crsn5CiMjMaoYEaEcz5xgL4o2t3K8IFYupxDXyz1WI1FzBHVAh7m7DyAFyrx00orm6U4BPwemt8ZrgFPTCAJ/VzH0qakEZxyrV+wjqHS9GLVpKCcpvKVqKTUVVtRSLC2PGuA2r+8fP/notRUeGb+0oEbkascV9HUoLR0EUQg6FYowjHqsIQIJ43BBBi+ai4zjmrBaAhNlUih6fGgv7FDIDq6gDmM5Uhp+Nc16rkquq17Yprpz52q+cVJybgPlAxA+NPbdFac45T9M0jWPJJedpnPI0lWmaspbo02Z2iR0FDDoftWhdiHDGWOMNtPhmVMv8D5qrxZ07R8VF5HcN/IzkFkdgoghdsnp3zlYhDhBYKs4rQjNzqy0jHm/qI5AcGK4ZoHm0+GBuY8nDOCh4mfnh8a06zsv/eCoQZ3/52k/V2QgREYVT0y9OL6+vHz3h1BQN9Q2ie7EyTVPJOQqMmqmbMIvwZrNx1eVqKSmllNq2ffX69ctXrxf98uLiEgFU9fnzT4h5fbb6kz/5Cs1ubzdGfHO7+dvf/cvLD++8FARokBdNR0DIgkyNpI7Ep/zNyx++ff/q/rCf1Kbi69XyF599+vjR9Xq9Wq3XRHx6co7ADkgkp6enfd+vTtapay8fP8qlEPI4jFR13uQA6jDmHO9SuL/1/eL6+vrp80/6xSpGPqgiz7lo1kc08lOjqBwbUjxWGMTjhr9mXcQbrZU24FXdWYV89Y8d7wCYn+35InGfgafY2cD8yWoIJ2vzWxkE+PGZqd9Gnfjqpx1fNtg7FBXw+KBi5XXWVwwfX0gtxseKW33nIv3KrJRcStQgnakWtfOJN2R2sTR3B8OSdRpyHnLOWsyLOhz5bx9fOCBSSHlhFkgfn+H6Qua3ou7jah+JAKiqR0IU4MfMkCjy5JXsTkRMxBzUXPeweGEmJI571ewj4DOzssyqFTIxiQjOTtE+84vq++aGQbq1WbQZl+lx9orObN7C1wfD1N3F55/PnYUf17de5yOKzi8IPKoeBngV5ACMmsK1BlXuGfzsX2LmugCk6IMYQgjqhgASTEwzAnQkm6+0oxcQM4gQ1524j9OUc6DRqgYGUMzNnYEQIwHSAEJZH/GeZk4hvq2Y6Yx0I2KQfGIRgogzGlMvs9m4Puj3x+GteAVdK9mOkIrmUhyxissqXcOdEQVRFXgO56vNBgp+HEkIUYUJCcxLJWxBAE81Zvh4bqvg2d0dI5Exnvc4YGoZ5pmlft9RisJ3xz0SMuuhinMbk3V9U+rjPu90K8PaK+JgiQkIiupUipkzBZViBhYAINrM2NjEiBvAXXWdrE9g/J/F+mx5coYsJSs6haU+ECRgcs+qFiZW0Wgxb+7vN/f3VsrDw/3Z1QURnZ6djePBTK+uH52dXfb94sWPPzx58iS16btvfv+FNLvt/p/+5Xfuevved/1eD+VfP//lolu6+3jQ/XZzllJLDbu2STb7h9++/PHV9k6YuPH9dvf0y89Wi3a9WuZS+n5hgAZwcnF+9ejRan0y7LfD/uDMTd91/QL9Q5mG1HS77fbysm9FALDkUVKT88TUAkAphRlZ0pDtMBU16PvFuN/X41J5B/HhRWmL/RHN+LXO9TbKFh4BzmPXUi29vSbdIlK4nh1vjnkJXFuHuZn7b0APC/y0crrc1FgiuAZjP+duXLX08cdC6DPPoVCH1OhTEF1VmRi8OoxVQVr9c/7xNopGdcaaaH6BhKh1AVtRdJwvCai2GbUszlUroGtz95IVC4rUzgdnoAHmWoBYTSpnPr5V+c78TNe2qH4DfhwL3D2XjID1F83nASFEy/Cx30FEQrdAaWZNddClKo5Uf8RnULndDnOsevxzR7gLZqQmyhbh7PCB7lYUhYnIVSFEUBWGmj1QZ+DL1CRoVhUCIoKZe2RmFRKpTH0zU5gdMyrUZQ7gKQlH7+luBsw4S73UgY6PVExMMwAXdxPGRU2JYnrTAC603j+MLiIAIMzEhFhDyes6pPJb3Qlyjq16tCdR6INIi26gZsUUw7hj7vwRwMw4ZEGxqjbXeb927LbiMTveVbVbB2CqY4oDGAITO9vHGorgoBDhcRicqNo3zeh69esH9zDzIUKhGlZplVCj9X6aDW8BgZhmrrYZhYWY0dzXO2Q4vq6QS1Rj1Hr+qTqzOmPwsSsh4TjmxEcVHX29h91UnRAIndGlaXSKJrE03Nis1zd3i+u8XgDw82ZThFXJvcR0AQClKCK2XQcOxCSEw1gOwz4+HlUtZdBSiFlEVHW/227u78fhELCmSBNn7fz8YhyHpusAp5Oz89SKJLk4v7q9fRiHH7/84nnR6e7+LgHdbLf3u6Gn5k+efU4oeX/YbbaL1KyYSWgzbH/77vv348bM25bJ9dmzq8TUtq00DSVpFn1Rvb974KYBoqbvkckAl6v16ekFAK367ubd68XqBN3vENuub7t+sVwzglnJhWC+yVjk4upad5++Krv9ZpQkWsy9QFCjZ5wa0c3KDL8IU1VTRqmKrFb38EM+RoHGug3NYo3p7hrbAiL52DMC0rHu1H+rqsBgRlRo/oK1eNUGkYgcwFWtlDIHiNWvycxeM2Hq1wGcoyEiONLrUFirZG2Aqq30XILBvead1SNNSMhukKMtLVYB3rrENpzV5maqWrcX5qDmuWT3OBG06FqOt80BGHzmF81DV4nPp1rJu/vHE1FBqmOZPt494FBUI+Usbiz8CDTHAY9TGEEuFRHhWK/ObTcimhoHwgMQlDmYsYHo/7w6NgYZxoMTFRbF7FB1alDRn1JyxcdCzIOIiBH+OF8gGCCElFys1BbAZlXbzEiN0RKNtDrqY0gXo/H3mSiDAEeCI9TNTHV7Iwj/Oa9qNCKK3j2WFJUwqtYwuCMBJIKMwaABQkiMGNE9oTMM41KiutqMOdtxzIWEiQDBY2BEohJEsejUDaqhFjoAFDcCBNWIp5p9zL0mq/qMgFQICxDIowJCcDMcZzFhTN9NYoCisYKDmP9rWGZYRlWeBgAxeQ29iZ4lunpyV2YSJnXPM2qERKgVFDo2/xF37WF+NUe0IABRcNvLjAZ4ncbD3zCUo0yzm0V98HF+Jo6TLXidgRGj7YyNlItI2yQmzqQOfpwFj42bu3kFWWvjw0TE8TBgVVNUSQOqw5RzycVtNiUCC505EbOwGqemjZPMIovlKud8d38jCDqNN2/fPf3kU2fq0mqxWD1s7nOehsNut9kgoAAlplJszNPZ+dnZ1dWrn16Nd/fm/o8vvr29v3t6cc0OGceb3V1B3d1Ov3vx3UAZGE9X/SfPHgsTACWi09NTSQJIjoTcjDn7NO2/+Xa1Wvar1fLkrG0aU7vf3H14f3NxeXX1+NF2u9vc3+U8MdMw0JSnfrEKBZwiEkKTmtXqNF8/3j68n8Z9HkfTUpuBWCNVctfxrAKzMEscqFprqJ4mrOFsUKWjsWBypXATCpgOgRirM2iskefmt5r+Vvdjqtc+1F6qYt8/GyCgip+89lvgdepziHMyA6dO8eBZgVleG48GIJgazrkFSMdFH4hwdAaV/IRQpfIGqRHIYKaVOwQ+N+9zigc7InBYYlW8W3OprYyh5qKEyMjMeGymicIf2NzDFjfsWuAo0AMHAnI0nKNO8eN9EFriKguI7g2DnVPHpSPU5sQgQM7RI6qjYy23UXHigyWr5BAAADVzgjYlNyciAYt8BwrYAJCJHOwYW+kARqEJJK0VGIgALZrXGQ0MqMbBAaWUgvNCCWvhUsQqvwobmoC9mBkBQQuGdrKaXDMWVS3EnIS6GD0c5o1ijAL+sbDUfwct1G5EWh2uADyGJg9vOnQnBCEgIkbU6P2RmNhRiTDE5/HoqLs6pgqMB47GSFZKmffgDHOGOtStV+0d4kMQAmF0x1LtUaNex0MIiPVyjpun+rAhguBMlYPEbOrm4VGICaFudgzALNLFsIasVEfYI+w59wUohEZECYvOuQrz1OZmIKECq4WbQu0evX/AslS/bzebu0TyMJyaIxM00GGYsb64GecTXpWNbhGA5Fo3KAZOBIQkQIJQ3HMxk9lmwx0RBTlmwSj9iZjqlqxe2w6pbdrYfmJatP1imnKeMgC4FQBiZkUXYeYkKeWc3bRpG7OSVYlwu72/f/fWtey3GzNdrlZCpGO+v3/48bs/NiLMtNvt3r15c3F1/uUvv/zd77758cXrdzc3Dw+79XLVCu2H4ZvN+x93N13TGoLkzXTz081206w6VEuIT548PT9dT+Mg0n7xxaeL1fL+YWuOKTW/+MVnq/Xai75+9fqHH38katquTUk+ef58vTpdrk5OLi5BZH16kg8HIsjjXq203cpUhcjM8zgNRaEDILDUPv3il0Lw/e8fAAzmePP5wVBEZBYAJBQmIRKmGop+7ByJnLB4BeMhbtNAM9GOcjCY8c/gzoffOZgVrCZdHj17jM8RAVY3ZTOGzDN4CHX0rw93iFri3sKwWKxl3hGBQhPqBkDMqGpqpaKtUIV+AeIQRMdUm8jaUiC6maADAQMRyTTFqfXj9fixhpozAgW6UxEkCjQYZv4Iq0XsRn34LUzlK2Uzjg5BnGivDu/zuvioniGCep3PaujjQTVEB59XHXgcFIgCHWIXmHKOtsznzWscf5+3blgtHD+i6YiQiAzjwEcPGubfzuiMELE1PysXNi9QENAZQSlyt5GIoNq4AiIIM1vJc/X3eBDh4/jBEQ3DToQQNslhtqQFpikDeCPEhC1h+HRw7fjia+ARiJx1a4ZzWx1hnwxmFqYl8YQRi4sSuDWJRYSJALCUEptSZnKX5AZAWMmpdXgUScKBcgMgklaWY4yidNyaYWX8AkIpJdIOoe7IHKu/0BHoI5r9QOaFSjQOcaUhVptSCMTbtEY4h5lqrJ8RwNWYiIUh4KOfXYbxRRGAiJMk8FIcwDV8q9UDbDNyskhwnS9somgBvJ79YBgRmZq7oRoRmHkKP8T/FuStzWYAguCJBH+G48cDWaEBAoagEaK7OiAxe7ZcikF87MSz1wtVdIIE4/qJr0B1B+Bu5o0kB3bjYhhPqYdmnMjMmZiJjwDuOA7jeDjst8M4tU0S4nGatvs9szzc3S5PT5umefPhQ9d1q+Xqpx9/UM2r1QoAzk7Pb29vNw8Pi0X31elnRf3u7p4Rmn1qpLndbbfTIXEjbJMO6/N13zeec9e2TdOY+Xq9fnT9+Pzi8vufXpxfXF6dX56dXy5Xq2maGOk3f/anzaL7m7/6m3Ecm649PVkD4HJ1am5WtO/6hng87EqeWklWsuZcVNumdfeUkqrmcTpstm+++8P2wwtQdfjIN0NkB7OZ2wKOwgmREYhIwqerKgUgIlGtSqhif2hmbgxUi36sigAADFHoGCs5twqIXntwqNMuzFBhqHmgosbH598rOoLupvVmn4tdQIBHRprPz1iFp6owCGKlBwH5Btxarx+cT58jVI6Bz6R7cxSRoxn7sfgGY0IQa141aCwjRbhNklWxRmjVXRTXxrQyPuopMBURDwzOjztBhxkmjRFHOHYhgIjERE4QrfrPIaOo2hSDGlXabiAoMxQQOMPxAqulAIGIjygxzAr/OWMn7Hrixq1vLSFEyZ1x3PoZxmcVOzomEnIjBSQKYB4YzRFcmsSjlRlvqE4OH7E/hMQkzCOqZa2Xv6Op5Vxij0PA3AgSCmPEELor1jLlULnnMbiAm9cl0PECrMbMCIiMBIxkJsIInFKTmIlJzRxBhNyjqLi7mDkRZ9UCVnN+RRohJqgiGCZWKmp1+VMvxNr7IoDN5Gmv3fnHijx/a/UeBwCrwuFax+Y322uoESKBN0nUc5BAKiFBQa0miCZArhjJsT/4CMkFONk04BbLJrcQx88PejEjJzJHOi7B5liB2BiqmnlxqxnxPk1qy7bpUkeEDuEtHvlTeNx2wMe3BgMBMMTQfEBtZzimsUolQmBGzF5Mi2riFDcnQOhW5u3cTJML4C8gAyJAK+7CbevSNcvVcn3a972ZOaLN1DJJwo6HcZimYb/bbh/uyzTknFOTEJ0QmiZNU95tN/vD4fT8vFv0J6er4bBtW2Gi2/v7cZp+evHy5uZ9KfnJ4+uzi7OfXrx58+bNctl3q/5mu2WkJvGY86R+ulqfnp48bB8uLi+HcdweDu4mSSYrD7vt6elp03SL5er07KxpW0L6w+9+dxiGTz755Jvf/gHdz8/P+r7f73fcto9Wj/q23e93eRzzMHBKi75DbovmkkuSmFFBkgzD1vKIOuXDxmyMQsjESFEgCAzd1WJwq49uxLI7eE05iV8103ggw8ZqBtwqrFmblblhPp67Y/WvLecM59OMTsZSzee/8vOWJY7Ncbg4npr5v1Qt1X8GpB/Z1R95nfNvRIsVG1SqTMfae8ylo8Kn4E4ExBWnmm3U5ppX+USR0hH2eYyIuZSipqaEVBfX8+0RBTpEPPUkBFGFmei4jDvWgrp9QaQ6YAMRoLL7NB03CXNzRoQkKel8XcW3GJvawF98fqX1nQ84hFDrbfLzZjWuVUPkCvWbgzszAxC5MuMxinHu9MJEr1o6h1EQgB/veiMiVElEhdnrKnIGaua9PDMJIZKA427SUrSU0FG6meWcmQEStUn6VhrhKhJEZCJldDcmjl2Omoc8cv6HgqZWjaKj4XTwplLHWYhTZBMhCWMLUNSYuISBmBmmCAhFtbr5dHeYKY8VhsJ50nL42TNXL4Bo02dX3fiuDGeDhJlyAMQMUOk9c8men/iZtBqtlSByCTbTvO4Gz2qmyoiB75M7ojBzBWTNwQ2oRg04GTM7oYOX0DeihtbY3VWVESKiEurMUWU4kRUyTSVPehh1nMZDHonI1uu+kbZpgzwRo2s8pkwEhAgcfCA4Lh7cnVDVXDVsEpnIYq6sfZMFMyTSLrES2hCKIQEyVFiX+djj1CNqBJIwddeffHb57MvTy8fnl0+Em1LyWIpX/0stuTiQ5ilPg+bRpsOweTgc9qVo0zeJCVyT0Ob+vjg0Xbdar0Xk6tGTD4iumoaxmAP6YtGP4zgexm//+O27D/fF3ICYuG8XfdImyWEYluvFk+urh7vN2empAZ5dnOVxODs7vbi8ODlZd2378qefVqtTzeXFjz8tlqvHTx6fnZzuttsnT560TbvFzeXV5dn52fn5pYInpofbm+Gwb5uWWUQaD5SOJF5agPgOkPru5Ox0/75/axkowAoXRgwQAdDQFWdoh3w2YVMmnF10DIDMa0JszMkIwMzB4nZDZjEgICCImx/myl55IIhhajI3nLEinYVB9SIBcPdSShRHZj7OC+4/A1x+1snWpgKCUVMX1VQLrx07aJyhyzr+OBzb/zg7R2ff440F8+M7R8rM8Cl9lBRgSOYJAUCEU5KiXlTBVI6TByIzx7uBhIiMNYMMkBlr3gbMnmzzBg+AOPycCZFMS8SmJBE1taIwF3REbNqGUIJjT2FsMb9wP85EUUDm2yWqEBOWOZNgJpZEuCTNzWjcKACx3CF3MpyvjEqzqDXNENAxpj8G14KBJLkiMJMkxhFBPdq9SJCLLtkAkJGFmUmCSZy1qGopxSyWfC6EKXHbsjDGErVhQOTCaGalxGYJqhCVIAkDgJODlVn4VC9grLgJtylwFU4icZXVG6UogDEAIyqRQ9xE860bRquKhIyzNAXRieI0ABlgZENA0EbnpzToUVoXUsJYICwiKscowPDAJTzuN4rOq14I8WkQoLkLc8mKXOXNc5M0nxaPCbeCq6aKBI5RAWIbTchGFsRhtho+h2oQ1q6ACKRN5V+G6KE+ECEYnsayOwwPu+32sBemPjHCmsCJUYBhJmXMRxsZjZASs81vn1qpVgDV9K2+XI81GMaYYEqo5g0gAjEJAhiDYwVAuXZu/rEmADiCuY9T2Y3lN88+X65PA8eMewzchHkYSy4FEYdpOOx2036bp0G69vrsfLvfHvbbKWeWNA3jfjgsz86vrx9NOf/d3/+j5enDuzfDsHf3tm+bpl0n3m62BxgetnskQUQRPuwGYUqLbprG60fXF+enJ8vF+dnpWMrmYdd13Y+3d6tFdb9erBaX15cnJ+c3t/dv373/7NPPF4tFv+hPFisWRvCuaVar1Xa7bdv+5Pw8VE55msC0XSyBYBgPgHmxTIE3AkDXdaAOCpv7h91+n1KrnqOFT1SRGwQ0tWwawx5UdK7UKq7qZoGVoSNKU7Q4AXFlJ6i6GRSHhohCSOw1ozlWrFHKgrUZOud5/xxrg6gjdftVz5iaB0kbkAhFKLSJZh4XvX+kJwVwxLNSPE6X1YWyz/y3j+0qANY8vWrI4sZUr7G53f54MRw7GABX1SDyxdPl4CzEzhzlJuo5MjO0mGrfakoQBtrgDhRMB4prdgab6mabzYx59st2AHQERmRh1MrTdAqaImIxcFN1N3XpG+KkRVkSxl9Hm62BKuRbr0CI2lUvU4eo9Rg/i/7wyLdhQkIsoPGdA4RDfuwIA243Bo52a0a+EdEITdEobKRdkbBBBldhJogdHUpc2mEg7oAcLA5CQmeCJDwQFi15ymaGhG3DbZK+SV2TgqSWmFuRub9wqcRGmFVzGCJCRLdcSYHBNw637ngWhcgZjkCYqorU3fl8DVcRiJYSpv6qamq5FCJkQrCiboYUxkfonk0ZkIk1aDXuxBwqaJz/NzD04NrVe9Cr91OwbVCwqDoABz+GI9IY4tmLekcIIoxggqwAEEkU89Xts2fscYbwyjsy1PnTIkBz9GNb5yV8iwJ/UReJCcaK5ojIiMsu6N6AoFqGcRinkZoWg93BxA4iKedyHGNjKg9FwvyL6K5QUwyizYttPImImWsJ0pIQYjYfi7ZhHwqBv4KZQsSTQZDDo32Ll+/MjM6AkpoeUAAYgUzNzJhYwVikdTewUqYyjWUa8uFgDk8//Wy3H6XkBcE0TUTMklar5vmnn/bL5fbNu8Nut7m7ffHihWp+9Oh60S8e7u8uztfrk/Xdw6ZfLvGQzcrTp09++vHVMI7LxaLvukeXF4+uH71+9fLk/ARMD9N4d39/dnbGSZBpt993u/3nv/jlix9fDmPJxd+9f19Mf/HVl8+vLt++e5u1fPLp80ePrrvlsqh/eP/h8ZOnj58+Wyz7929eF9W+EcsTSl25i8iUc9HSpJZZuq5LTXd6en7YlDxOVDeiNgMjQPTRwjKqqIE7MAIwUgiMo36ZRu8fXmZ1A6qOFhQBmHv1+qWCxu3V0TPYCkF1czdQACKuZJCQkSKAU2X6zfzgoJlKNB8fcY8qbBKeeZZqhYL3Uqs/H9v/2tfP/X4diAEQ8CgeMge3WaP7M7qKA4CTanEAYa7PNBOCEyM5V2wKEABjN4iAprmUEhbz4dYZBen4fQSwzzw3anFVVsIVIoVzWoUWmMnRdDZ/rxXYXRoOhTASw4ymhDQAZ7c9qt0nHHGXGMjIXWuQMXL982jRJpsRxQRWZVzMHL7abqykleFCx7cXAeZZCN3IVC34TSIVCxfVHJ+YgyEoIRFzWFkTImPMGCjASbhtmsN+cDA3bVrpkjTCIe1NLBigGyECsrGIHqVnH9EXMxQGiF1H1RZAbTRisP3oQx2+oW4wTTm6b/UCgKqllClrKUU1xOtqRUrjVEoRRoaqoosPNubMWLJHAaYZ8gt8DTCiMgMA9WADx1OJwSBAjp08BemBiNBnbm+QFiBcO4qqCKMrgsWtCVpT35g4lLrqLnEr1qULHh+RefT1ebqAsP/W6vdZkVvwkAuGWLSqGeJtE6aubc7Wq75p+rZZdB1GsiUxIDcNBZWWmdyxHPkjc5eu6kQE6oSFwBKTuwsLERq5WVFwDPpWtqmUSKYmMKGESCUEk/GRE4pIBHfGY0AoqTtZXT59+umXi/WJSAumURqYkKUx0yQ8jMP2MORhZ3kAxtXyhKWZpvu+a8eDrlarB7Wm6YacU0q3d7dm+uj6cre5Pxz265M1iyQRYlbz5Wq1Pjnltnt4eEksSHR1fXl2etIvFm52sl5xgtW6B/Tr6+vHjx/94Xd/vL25W69PDoe8edg3bf/N4Q9t27dtk/NUSjk/P7+6uj4cDh/evv/qV79cpObm7buLx3j95KkbgvtYcmq76yfPbm9vD7tD17qgMNa+VZgRUE0BXbVM41jGiVGcDdCCbRhoL1WnsNgwRRMNXqMiA8uurHBEB/TQ2ODPKJ7urm6M4gDAEe0ZG12d+SUez9wM1FnAILOSptqFRrUN3N/cIOckQUj18FADAI6tf+zC0EPhCgBFFYo72QyFB3MJ58JK85RYQ1Wrg0zdMoTALSzX667ruGcNwAKRzNzJCYkYhclNiVjYZ2SMEPgI3iNYhOBCVHFACgqIQ329FRCuZxGPtH6PJU31xZuPDKs7MUdJjHcttqlxKuNKDmBn3o4BzmB7RaErbbJ6UAMiH02FERCBmeurRjTDeeD5ua6bEJWoWsAdV3pxqVk8CLFrQXefCywCIgqiC6MaOELDnJLwvDCMnUkocZC876GoBQBA6F2bhCkJhUsrVdJ3faFJWBQzACFZDSQKSKT22dULDaxoYSBCDDWxu7OIg6m6ubpFZkWZ33QgQlQABCYyrgv6I8TPHEhK3c7XYWsOyoknq8pGIHZKFM1EEGzIqtCFkJwB3JFjn4Pg5l4HKmZGNEZiYo4wPYQI4E1co28AnJmAwIBcnYmTyEwsdlVLwjSjY5EAjogwZ1bHTM0cS1QvRooa77ODRZY40+wwM18VSRw6JMJlK1lLm9J60ROjmgmFnqjuAGZahYXApJ4UcFVgCvLhcQ/FzI2DgRoKeTaIZDTQoiHpiOc4snRIi+WSwUFYyCElBgI1R0qpWcni9OL551dPnhOLqUarb1ocnAjNNOc8jmNK0pyc3pU8qS7WJ1MuTdOMu+1+ty+qqenyMBz2+2kYUGS/fdhvN4iwXC5Xq/XDZvvq9evVcvmweTg7Pd3shtu72+VyuVqfXFxc4iWOh2GactMk1yIij58+6xbLl69eHnZbIuoWi/vNFtyvr66IJGedxu2PL16uT08/ef7sxU8/vXn9+uL8vO/6y/Pzlz/+VHLJpuMwXl5eNd3i4X4ax/H6+mq11sPmYb/ddo4lT0HwMCuIqKUQBbxjGi6VoPXER50NX0BwBBQhxsqciXKJEa5nxsQOs2y1xmTEwrBGVkWYQOz9FJRmKlyVFjq5F0Ik5tl9Z5Yc/uwhqZdDNXclMytFm4bnGwgcmCXhbO6AAMS1BrlnYFMtgDPZu26a5uMJH+upzyQCmFFNmIXuM43GP57jGTgHh6opBojaIEjO1QlHRBA4WlEiAmCaRVVm4QBz/GpAFLvo8Djw4wtHBCJRK27HX4affScBHSERlWqQ8/OmHt0NycNPXjWMjx3mmyQqdRDT5z9f/3VCAq7ly63CVjOfguryb7425oskNsPxlY/rE3SIaCzAqifnONiCCEmIHQE4JWlSEgIg0nApIyKhWUfsVRvhJkJCJELEOI9f8WcpbD4YSJizTuZuFhe7Exm4tdIQQ+jQEMPDAIi4aZskrZYizPG0lJJL1gq9IUVJZSZWYmZXQLSI8OLaEVG9DmEen6NdnT+wwFd8fkznNVdY2cBc9EDCRxOqxN7BHTQCCUIzhojChIikNX+xpgAEhPrRir1WRnESZI4/Vu27olcItZ59PGez8Uj8BGfVW9Ok+alxEU7CgEqMTDK7LACRJkFCSYQAEv45XdMwkblGnzcfSw9+BSM5AUtIMioQLEKADsE3QEQgZnE3x4JaCQwY8ugaf2PHjbcWncYxMiMZse86XPbSprC1MKQnX3zxm3/zl227nMZhGMaSs1pRK+DGIgRYVJsmIabx4O3yBFPTLRY5GwLuttvxMKxOTrvV4sdvvhmHw4f3byWl77/97ocfvp+miZlT27x4/frN6/fMdHV9/rAb/uqv//bR1eWvf/31OGrX9WZ58zAuFsuubRE0NY05/vTTqxcvXp6frQOBXa2WIkIiDphzGYfx9PRksVp+/tln6+Xi5etXeTiQ2T/94z+cnp5cP3u0Pjnd3m/Gw/7y+tHq5Hx9si5m3Agx5ZxbcDXNOSdpauEWAaTsfnpxfWhge/saM7i5qlFIt9yFWZjMnJlFIu4N0QAZwL0a81QRRzWInT+vip8AeFGAXGPn1YDr2BkaTONolaKBdLBZW1yr8/wQV8B9xt2rStwgfPCRGJFDXFad0dwdMRjPgsldcZaDVKjEDAEr530+tcFrjS51Fuh+vIHmoxr/CUp53HvB8YhFHQbQEyi8z2rncD0BQmE2g5Kz6pFEy1ABFoxMD7NibjDrqGPCjt04Inu4FBxhqBiXEKxOSxSqdXdAJHcwKz5HPAXBhDkKfZkrxP8fBDTHEdYdx1y1uMIEobWM6cpNI497rjPhmarxUcUIQ+BIVF2e6mAFsdxkxpREImPdANyJiZgoNdWbPihQrj6Bhag6F3UAC0sAwdQICSFTNmVjdjMIp2R3N4z8uZAYz0ETdcXhDkRmUXFIhFJqmtSnlKYYe1EQM4AT1059nqAAABKLkhkas9i8PgkBXuhgQsD60R9rBtzrDXAUASLGOiF4muToHMwukLDnJqpr5ioEhQrmzA8VC5O5qoNZtXFAQHRiJq++2zMmWh8Omx+fIBowRXjorOOoHRD+vD9CJHZrmMOVpxURFkSBn/lJRDtBAMRYHN2B8eh+EhW/csRjjlY3AAYGqO76yIAORiGcA8YwsaiDqRMxuxMoEKgqOYJHvEK9IsM8SHM+HA4Ph904jgR8eXqWmpSatuuWQ0Fs+8+++KqVxTTmw7Q/7Pcl5yCILJd916/NLOfRXXPJxWwcRzcdx2mxOh2GQ7tcp65fLrqcs7kx87u3bwnx/bu3D/eb6KO6w6EUfffuw3LZfvWrLzYPu/1+2o95exj/7m//4acX12fnayEeBxXBi/MzMpuKIuPzT58fdvvzq+v3f/jmMOTLi7PV6mR1cvL1r37ZCH//3Q8PD9vlavnJZ59c/nB5f3e3WixWp2en56f96oSkefzUTUtRR2Yh8lzyNHJKF0+eAJFbpf7HNJlEiOnk7BrdU0Ok0+1hazaBO4iwY82pQGRGJmJEpyNHeV5PQZhd1S422uAIOI3GQtWL5jFXgxVh7lnqFyNwdQ62PCExAhhW4mDtTI/IiAO6OoI5UmiwMGbWDNF3HTESAyAWjwh04Lmzj1eQ54a01FQzAwAnEnCed07kVqrVGM5wf50l5s6DfsZy99r6xs4WiA1cmMkcIZAOcgMiZsJABMiRmefGDsyN6rvnoY7HOaoMAMkjYTXKoQNo1d7XKym0L9H7cwiv1LAiPDGA1eMGhNQ2TVEqRefFO9QEhbo+xLChw7qArLduhSqQPUyqHS0M5qAiE3MGfH0viikUi8WOOxgYzfgRIWTLbiUuNhEJF06aVXAQVBwHQIoIMkDk8N0rqoexHIbxcBjNrO+aJMSEjQiTAGEVMokIABEURyFKxGrOQRvxgD5UTRllhh4JEQg5pY4kATGnZhoGJghev6qqFpjnxLgmhYWppNQ4KqDGHV67FsJ42whJQd08djxUYXNEjN36x22JV4/+avMXjRUj+XwXI2LEEB876JgDAMMgAFyNZii/Kq6ilwZ3x8SiYEQY7t5ZlaJHj70pE0EEloF7mAQTUbVBg3kNgQBMKESAkERiMVCPbLB6HBAJQGFWMyBWXMY8YsMmxAbms3P8Vqm+mIA+jTn0xYoKVTXkHH1jEilJs4IVpZlNVC9XAyfXkqdxetju397fbg9Dg4m5WZ+c9MCcui+++Hx59bhdnimAA+SxmENqGlVtUur7pbtP45DLCODCAimt1ysrBTkBgAGenl+Aa5nGab87f/x4cbK+efP25vbm088/a7vF7d1dSun9zYfDMKxWq08+fbxY9NuH/dOnz7/97gcHPj29/HDz8Ps/fHN9efHpZ89OT9aAjJTevXunaof9/v37D+/e3+z346JfPHn0GAz++v/7N9/+4Zv/4b//754/f9r3d9Q095vt2dVVv1zevH+/2+12+/2jZ/Lk2fnNu9cpMaduGicvmiQBoDRdu1gSUR4mEe77ruTCJLGoTalR8/evX033N36Uds6dis76bGYSZOf6KFYWSXiaVEczhyD5IAFCUZ0BTjOz/X7aDhOQnS6XTSC8tSmaCTAIMYiCUTaNSbYKHeeyYgBZFQhpjq5D95xzmaDpo0izokWDiuAGrqbCXNmnVbVkWL0w4Vg9w9wAgMI7M0wDaNYkYt2BzXxBjzHHgmeMBhXARYhyC8gk7EUTIxiYOQUoXLm1dU/tAKqqobIjj14qLgMgRw8dUXUoQaxpInGg5nHkOBgBAjIJIrhjCZUvgIexmYcfAwqJMMcKR8TdKc6kzV/ZK9ET4uYOLcesNo2ZCRAYAorGeXFKBGiRNzJzZBCg2m7ElG/+s/rmHxnu7qCqQjWMZ94amdOs4zVzcx3VVDUXHyedphKy4b5t+65p2yYlEUmcWFiYhZGwclycEISJ0DySxXNkxAZQEu8a1RGKiIRTk1Q14Dz1Egq3wB6D8gx1Z8uIwCxkhYiYQR3AlI5twsc1UYwF9ZOLN5QQghnrXoPTDIwMZ+ivtt5ahVDHqcPnrQvOQ3R9RJlYq0Yc65LNnASEGQFyLsRMJHSM60P20MUTEwsFn9VmuezsQBofB4VX0UeZNCSRlBIzqRWa+xFXdYg9GIMfde0QmZARGhc/RIQ54NR6rmKCQMCwyuDK3qHiRoaIpICIxMLkoCBZuUyZiJqmKXXa5aCUqOowTrvD+OFmc/ewWXXLs9VZVjSSYZzevXl7+uhTIDpMhzJNBLRensS0Q4RTzuPhsD9sp2lkJpEGEUvW/X6/WCyL7tumaZs0Doc3L16MeTi7upz2++3Dval++tlnROnm5vbi8vKTzz/727/7+zzqxcVFkubVq9elTKnhFz/+CA4Xl5ePHz9mhru7u8NhNwzDkydP3rx+Pw7DJ58+PznJu+1+2S83m03Rcb1+hH7etcm1fPftt+uzi7Ztx3FMqVkuV1p0t9uOw3TY78NXYRrKul0QpxAedYuV5gLm3aIvxcAxSYeQj5CFm03TsN9tfBqqkSRAiHc80oEqL6Wq2KuVvVmYXVU+AAd7m4jIVRFJkAA8/P4JseS82W5Sz31JmkcUFOE4dep1bI3DYRE5OBsmH4GFOATm4qVg9ZM/LpndhoFFEgJhYhaakUBDczNQA9PIP5+xjtqbQMWv64xBGDHwcQYJ0a2U4JXEmaqNUPW4x1i8MbKbVo0JEbEwkScENwPzj+nTxMw/9723yl5Cd4qCFb6Lse0ljEA+PkpF3Q2JHGJ/HiYQsTdBxso/ckMmtbqrOQI7yCyMHBRzcHQNcx3yMPmfuzGuNgcBgYTZ2FwWzLB6rwJ4BN0AItsRfY3m1dzdwy0kPsIo9QBg85WLhO5objkXB5SY6MxdHcCMzXJ2BAfiUspsy88ewcDugNgk6drUt6nrmmAdEAtzqgQwdwSXsI5UG02hptUjzIwriMnNSc3DSjqAQkAN1AyiPwYHCISavNogSTRILEwaiQ3AZNWwYo49cvA5iMYR/bjhjDLNWBmfPDckiChEhBRE3uNDejwGdf0L84IZK3eUYlJJyYNag6zVudODJRY/O7q2mWIxMysUb5Eft3bu7lzDJYJ2hjrHUiJiEGERgAM4EgKT2Er7nGFNhGSx8OOKIc4QoruWrIWUKPQvGlBXGFXEdc8Yc2QMscaM5mSIoVNgQgfuEBwWZqAFW0mYlSglaRAtwr7dYSqWi+fJi7gquIKZWZmm8cPf/J//x5Mv3n3xJ396cnKaFn2QW8NqthSdyjgMh7ZtpikT6cX5+ThNahb1NKVGS3735pV7WS0X03Z32DyUPO63u/dv3j1+8vSHH356+/rtv/33//7i7OLVy9dd1+es4zidnC7+3X/4v4PT999/f3/3INI8bB/6fgGYHrb7w3c/PTxsAdxMf/mrr9rEy8U6q56eLadxfPzo+snj6/v7O1W/vn4yDgOAv3v7tpRyfXF5fX11//DQ9f3hcHD3cRilOaxO2rZtp5zPlsvt/WY8jIvlEhCJabffuEHf9+7GTIvl8vHjp9Pd21d/vEOfSQsVdjVTxYrDxDrm44weDW0A2WFA4oQUZCCzwDgRjQkZJEiNQpgIhCjF9AmmlXIe7kJO8WwZuMJxAXo8BVhHkQgh09oTISCgqhYt5iZRVTnaeyMi14IACErgVFGqkIcBIYarpKM7WBjp49z44LHDrcaZoKCEZB+PALqBItbTDQDgxIzIgEyoqgWrIYQFzFCF8qrzia7b2ijkCA5EQrFdcMCg+WuUAIRIe3dw4nlh6x4uaegSiWkESGwMrtEPmQETEyNzEmZCNkDwbMm1upYCUeTpzrditTRm1dBQwHyLuEdGQ+VxGbizhHNpXQXHArzuDRyAkBGD8hciBqqtq3pltJgDCTOYgcUyh5FFgjkeJCo3F2Q1t1IggtrMRDiJdG0jxMJMSVBSTI5Yo5vjfjVwY0Qk5CSIrnVeBZjpj8IcNFJTzeOBmCqBEiVivJiIkE1zsJ/A2Q2AHMMnq0wAkJgB0d1qt4zHhyfo+lGOMaJ7EfBYGgM0CYjHZ3ctNdVSwtNCZ8cVc2OP5jruJkKrWCwzqenHBQWAmoEpiSCRO8eUGFPnpDrmbKaMbcNkZTJgZEIAZip+vPyAZ707zGQHIsDA7MAZCRhMZ+oI1t0YkoLXsZiQYWZnQ1hhm09TYQ5WXOxiwoyHCM3NYyyDMItxIiarcgQSIgdiTs6Ng4xD0WINWUqNiBB7Kbky59xNAVGii1e3cRrdjCBxM56dri7OLx15GodxHEopwUQCcCa6uDh389WKRdJmu1PTxXJ52O3yeNiPw363a1Na9efff/ut5vzm9cvzq/N+dfrHP/7wy9Q9ur7+48PmH/727y4vz7/++lf7wwGB3Hy5WDIlRP7qqy9ev/zpu+9fvXn7fvOw/ZOvv1b1b//4zbOnTy4uzr/65Zer1fLy7OTFDz/99OrD9eXZH/74h7+/2/zn//x/W63602W76JK7qZZE9F/++q+/+OIXX3z5BRK3XQf4MQpYVblvye3u/m5zc2dqq9O1mZaSx3GMOto0bSmlaRtK3eWTTx8+vNnfvERinE3qTS0kTuEyIsLgoK7zIgzd3YurKjIwMTMaU82fxlB4GQAQUtc1q9K3DZ103aJftF3nqmblGN0T5J3KiaQAW+AIFVglicUWEv0jX+UY/4KmOk5D1LACQEg8F/MAXKF6RB/5DXVOD/4eVNiCad7Nzk2P8JHhFsffEMAJIPYYRWd7UfDgoACQ1+R0IAgVshqA5poOBREyMR//ir0YEGMEBAqThzMJYfSMDl5lEaoIAAREpKbx7pkbGrE0iB9z0I5zLcx7dGImYDckdnErJTNhTB1UT+i8yfg5tKMhsosdnsIcqaKqDu5KfsTAEMOhTdXIZ3FcvMBo/82YORARd4vNjZlLCm8GY4N6m4Zaeso53nwFDJcOn3E0ACAKSxYhaUhaJHIvMf0BhluJIQCQEzgTA5KDYTEHF6I0O6lFV1vjO0sJvi+xqFr9XBHBnIhYQtELpm4apQqZoyLHDY9MxJWV6u5e4Z+wzEVkkrimvVqgOBH43GJ4dUEBBDLHEgt2PPophhwgBCRe5+W6zA2v5fpkIzqzC0kYYzqIuQOQG7qDFj0MQym5TylFFEHsq+u0g4EpwUcRCoUBZJhiEYAToHDdShkQARDGP89ECuIVHQiYEqsk2hERi+mYsyRpErsbBR82JkioT3swdGtrhQyoEfuOiMRkzi2hJsklNzgVyo5m4ILExETVMjcJokB8DmAFdFK1An6y6C8eP1KAaRit5HEapmFnmsPht+8WkrrDYTMe9tPhoAAX5xfvXv6keULw4TB8+PButVxJ4u3Dw5sXPwLDF7/891nhYf9f//qv/j+PHj/+9Z/++sXLF6/evF60/ft37/eHPTGVYn/zX//u/YfbT54/ffL44vHj69dvP5jZ/f3DX/7ln/93/+k/NEJtm9ar07uH20dPnr169faT509/86dfJ6H/+X/+34vq13/y9Yf377bbhyRN3/enJ2enJ2f7/T4P42j66Omzrm0Wq9Pl6Xq9Wo/TeH97c3jYnlxcXT96/PDwcDgcUtOUaVD1tu3MNefJTJs2Nf1CSS4eP5/295b3RoZQhbzuLsTgzgH9AkD4HhCAH9F4MwPWaEm4GhsDhLGuORTwtpHTRQeC3fJEmhZRILEXD5DaAvEI3J6IgK1W+kAEKqxcF5txtAN/AoDZ0gAR3XwaJnRqWgYmnysBmIEZAxmKclH/SHVNEXEVAFTgL9l9xpwqjsFzTt5MZ8SASgjdXQxKdKXuKBKpCI5kTm5khkFaRPfiCm4EpOalFKvJ2ICIVgyCThHLaHCiWHrXtpFd0I0BIeZcK8TzPBFwXuAeVgi4SndN3cGMEWPXWlkaNKvTiJilwY8XMNbz7RTlmggIIeQPcYlixCLUkAM1cHFydxEkYDeramt3c2UEQbZKEqh3OVBdDxqSmgsG50AIDdG8AKOjMBOCmRISkFWhKoGI0JgJUZIEGZSJRAQkMQsgqppZMUAnJ0ZCBreiBuSMDI4SUbbmjUgsRwjnJtfcwebpJnZbDlUO5w7ARFxVWAgOpgYAQihMZiX2lgiCEOzMOVEeFIEYIpeKKtPA6ryGFBKw2m7Mnb5Hsrq5k5twRV7jqudocVXVNSZzptn6c2ZeizBiVelgtShRdHR0AhcCsBJNMoEBkIPRHJsTOE8pRZiO0z9AWJO7Qc0Hq1ug6FFmFU80NMKUiwEBOVFMwpHTDV7dSc3GaRLuklTufwR5WxUox6A+p1ZG5A7EQkmTxM2EMfhPOTtaVsg5J26hTltxhIDYo2GzYkFmz2pF9XA49HkCtZJHcDOdNI+KnFKbp8Pm7v1h95CHg5tdPX3+9uVPD3c3i67f77cAuD45MfDDfgB1c3j29JOLq0e3m/0vv/768ZPHkprPf/H5r//8zz68f//y+5/uHx66RbfbHVR9KqqGf/+Pv3/79rxfdMyp6xam9vvf/2G1Xj769Bkj/+M//rMj3N0+jNN0cbaepsPXX//y1Ys34PZwf7foF7e3N8+ff8qSJDVf/fKXqUlPn31yt3kwVXToFst+eYIkq1VThqnsRnXo1+vTi4u37942qXEtU57MlZhNTEuZcpua1C+WD0AOokUlAQCahgUCBJOFiRjIwJhI/5v9Fpp66HVCRQWR960WQeGqGMYvSdgYuWmBBLlBcGJH1zmpwys4QBhBLDVXsDa0gTUpVusIc6/KLJo3ohCSVfOcC1LGGY+dG20IWhw5BjkzRk4O40hzprCxq314pBjH5WfgYWVbO+sQ4ZMjWkDqbgAoddaIEMCPFo9B9kGL8QJQVX8ehozHzgfATYs7J1ZzClVdPWBx5B0Q1d3cCgA7e8z0ZghQioo4cdJc3BXB3U2LgiABzds3JQqK58fIqQC4ZlMmqFp/IoAMH9N14ncs6PJBvNaw/HMjJC0eJgDh9xzLg1gZkWLgQlU0YFH/yczMUQ0QKvTNqhZGMSGT83k1AcEsRGREIWqb5pBHYUFAYgZkiepvOk053CAcNEYYRZSUyKP0Ic/rFSQyMNPYC1Vveq9pm0HlrP9DREfbWJ9rHLEzspYCSEIISViYKAknAtBYIRi5KTg4mKoxY31QAWJZivOPj0qK2sHHk4FmXrsSd66aTJ83SOBVSFNXWjOmBMdFMYW7MXFYf5Wi6E5gTeLT1eKQJ0mkVkgSARzpd8TowdVGJyKNlEfV6lwEyNUP3oN1jLOihzDcXoyFHFDjIfPqK40/ky+6WSl65BdjQMeRCFDTJFzBdV6XIWIojeKMxBWuOua83+8PjtAJTTn1XRtqgMTcJhFhYU6MSYSZWdjBi9pw2O+327OLnKecp6GUqeQCSE3TosN294B5tMPBtXQnJ81ied4067P1dNgf8nB+fb0+OXn58mXXdqdnZ9y1U8nDkC8vr8s4LfrFVMq333y3OxwQYLPZLBaLw+0HBLi5vRmLmdOU9e5hO+XStW3fNV/96qtf/OLzt29eQhnPzy4S06vXb+jRxSefPPv0+bOuS27+9de/2u+3eRrfvHx9dn7StImF9of9o2dPrq4etX2/vrh4/eLFuB+XZxciCRF3+0G6/vmXX+2nSVJyACYpUwZ0kUSEYaVHzGUqQszSKBAwm6MXj/ofTzugx7sX3A8kgqq6qI+be2TYqQRSgQScLBRHFg9BPPJhzgGIHPlRRMwkGSYDU1UEguqIeQQtYO7GfObae/BGcskUNTu0n5Hz4hE0j+ZhuQwpnjrAesgR2UjtCCEELU6ALKBUqhXX4wUSVVFkxWqOzbKZRwIHkRGEK4q7F3VTUzIwZ1Q3V1VVY0mIIfclt8zsADYVrbaRMdzMN0HJxQkTE6KHlg0qh31uBNUUMIfXEgATM6K7TVNOqULKauZuakDGRl5yoUjBcig2pzvNdaNWIZ/3tGY4c0OJUICPH6O5EdBswFyvkJyLkjcs1UUWAOadAEKVEUHNbIA6HAI6oJq5KSEKcuVBEmJ8ijx7SYRlkhqCxrVdW8O5+mGShCRFSynFzJ3JgIQQwJhZxHM8r5XRGIwoVtVAYNw0nLKF2NxUCxGYu1XdL1aTuNiGIyNBOAmGTQohYhIoICxtu+j7JYLt95tcJmS2ArNHRzDNEIIFP5vlxiLl2FwcP5JYCqlazA5wrPtqPvtjgYGBKsbmNvQjplXBFPYiVNWEiAnZGRw0oaApdEkSC+EcAo0Ue9gAvxxRYvIyRA1gKqZNdCCs+JZCnbvrpwGIQojmjizoxREghJtmNeDCHVg49jBFNTnPcpt63NUqgBALHKxcVGI+Mq89aNqMRct+HA5A1Epb1BCBhXQCRE9Ci74dVZum6Rdt0wohxF4sDAOiOqnmaRglNQ6QUrvfPBBSUQCibnF68ejZcnWyubs77Ibt/f1qsVyu1v/4d/+wvX/46le/Wp6e/Ga9vru5efPqzUW2k/V6YF4CvHn58ttv/tD3fckll7zb70Qk57I/HH79Z795++btzfv34zj9yW9+9X/9H/7T48fX65OTz54/fvvq1W6zOTtZXpz/ql8urq4unz15+v79m+12s1z2XSvf/OGPwzB1XdptNov1CXOzWK0BaVJnhw9v3262u3/97/5D0TLuD4fh8OTpUxFxwt1m8/DwgIgmLJK6RSvScMjrmcFxmkpqurOr6/3dm2Fz625x1CtPE2qg61EYBccWBBywJs4pEkUMK6MAucfHXuaWxwGAODweqj2YmSMYE4F/bG6odmLgP2traklxP/ZIFP4AFRLCY5cWDcORyBBAuh+1u4DC6GZDKWrWYAPojiaMRBg+CnOaXHS9EBRGCJvr2r+EuVx0TEDoRkeDtdisKsVOLpp9BzfnRIAMDkTsx/hXdaNYsRAT1m0wAJqDxIsKjyxyh2KVaw9QLcjQnRCTpLiL1b2UDI7gBlUsBmpOkag1jQDepGRhsBuzPAT9r/IGnWL5oli1n/FWB5BllQtWt4keLPA64xCaV7I/AhoYAqoZozkKVlZZ+OzakVCmWhzR0UQrv8WD3YUOYASVgRR1Hksg9cxqZgHAz3YhpuERXSL1HhAhhHwV2NNKv48lgKFqyblAtY12AEOOW662G8GocgsyVSw/CQzDTQEJnbBojopD7sQNSVou16vVaZ72qpNaQQBki719PDZB3Te38OUixJkoNc+D1YzoyETwo6VNdfGN0ReryV6AofGgm1kppRLagBDDt98JQkAAjOQkaAYKQTLzqjE0QKrKc6zHjDEisCvag8yIrGq5lKLaRBYOzJMLcaU2cV0hotZvHsGYKcziIay7agEB1boknM8UzCJ1qNshmDOZAAFCT2/xZBAYQWGqzlESgC+6MLlg00jXtctFb0S99Mtl37SspubYNF3XLZqmbdve3acR1ycnLFwsKNA7NlfmZtk3/artFsPhsN3cv3/75ubdm9P1erM79N3iz//1n49appzB3Uq+v/2AAJK4XfYpNdePr1fr1d3d7e9++7u729vPPvt0+7AnTqnrl6v1h3fv7+8e/uTrX/5f/uN//NM//c2bNy/c7Mmz53/3N39/d3v///h//o/qenl5mSedpvJwv+va9vT8NOfp/mH7b/7NXyCV3W4LSB9u77/86mvA1EjSaVz27bs3r7eb+27RPdzcNinlcXj18r2OIyFKSov1KjVtzbtyJJKojQIQQt/16VmzWFJKoNlMI6ZEzc0dmXBm5Xi1lQE3UNdomIsBOQIw1ZQri0NHwObVU+BIpoyOFtzQCroyIiJ7WD/O3W44vVpEQs4aKTjuijEMP+YmFGKF5oYgIlCJzojgGuIYBDCNWcCjILoepilraVKTUjKjRqR6luLxsqjNJs3dldeLQRAUKlG/QsVhmIYOZpbzFOQ711KpL3NcO1Ho3pRCsVxK0WLoQgm4auMdHYmLGgAxAbPU+QO81CR3hJo34EIsxAGsOKBV6qnhvEJ3zwjIDIg0TQ7VGy4+STczNQ0OjEP8rdrNmXv4T0baT8gq3C0y2dwIGILWYWhY6Y4wz/OV+17MkGyG3LAhLlgB3VAvuKOaiRoZQU0gBgREjVXsXB4RgLDGr2d3R+cA+QDV1FyL6pRzk7ooS2agkRdoBoBBgHdwNyjupRR10/AjdEDiaMLdIpiL3AwhRkLyOpNiEFXNgRGYnBUVDNA5xGyyWCzWnFiVqlOfgwMRiWoGd2REQgcDCurb0ZikavE8WA1VG4wz5SbmbgyInBAN5tE8uJiRCBTDqRuHCxRJlYsQIDEQmjkrOZkCIggDuhVFAiczLxgp4E4/69YQPbLfFRSrSU7UaURnDDYeCYC7OiKRUFWGgSeMOyn83OFopBpQcvwmIkZEMLIDFK9EHAUndyMHIAgxOIVLmRkSqrsQihNIWrXt1KtwaljMXLNzmA2QLPv+dCpdavpusewb4fArxdR2/fqkbXsgatplkxIQaCmkVkpObZvBG+wQuqbrY4HBkq4eP95vHw7TePHs04vLKxdBQy/Dzbt3d3e3zz99xtLe3Xxgln/6238QQgD/4YcfPry/OT079WLDsLt+dH1zt//9P/1+cbq6vn60Plmfnqx3u93F+dVus708O3v65PF+tx3H6ZNPP91tNprLdsynqzUnvuyvfvjhh199/ctPPn+2eXjIOTep1VHb1BILE2LTrs7ODL47bLd+cX7z7u352endG/32X3677LvFapkWfdN1J2ePppJdCwlFK0lALELMgKDK4Z6FVtSgmKmDGyjVLiuoXnVes4rQRQUOThfME3MEQjNDnVcd1JVFEiYCACjgTq7FFcAJHRnNKiUfDBFMwfxnm7jglyIBMbBRQYIqLlUiqpoBq6OA+5HqHeYIoGDujhZ2FAbgbtl1zFbctejUNQ24tk2DVX8iVhmqQXQoZhSvFVkgNMQYen8Do0BWKySDFmh4RFUEZ8LBiRDJEbTO3EjI6ISgBm6AaurB6olaj4buLnKU5Bx37BG2V02zA+iO8m9gEMl/oAjoVonmkf6UElYRkzkgE2KBKXpq1fAsinhkCnQ5ym6ALsKERgISWjBHDOEUV9iKMBKbwIO9J8xOVV0Qk5yBEXFKLaBNY469BVVQvJJ3A9ZQAg4xYuXeqsZC1R3DqdvdCXDZdX3bIIKZlSAeIVHlYAbgAlqq8UXc01YF0wGVFA9mm9V0quApYqjYub76QCZiZxOGbmbuaOgg1XIkxMJN060xyZCnaRq3+x3jUbAHzF5KwdkZ3KHm19ViO7viRJwJIgIQHaOrwU1DVQ/uVd5GRIRMkhyUEBNzcDYoHMiRiCjCuwP1Co8gSlDlAQAEwO5aShxacyiRHeCO8ahXj/JgKTl6BYXrmBAZyQ6OH817hQTiMJrFfl4RvWaxRWBAlfxwnGWmkFiH4/mxY4mtW9yLVB/DKpUIHY57eELosuvG7Foc3b3YuBu80DiOWowRT9crAGzbdrFoEVHNgVCatlmskAUR2yZNo01l1KJu5oBd36eUSp7UjFiIBRzGYQTH06vrk9Xq8smzknOe8mazGXYPkuSTTz6ZzF+8evnJ82f5cGgEdttt13cn69U3hwGJdrtdcXj/4eU33/707OpR13UI/nB/t3nY/OpXv1qtlgTv7m5vr68vd9vNw93dcH397t07IUySFovFcrns+/7k5HS57FSLSNO0HRK/+/Du4f7+q2fPdsMhtd367PzP/+Ivm24lTbM+XU+qC+Inz542KZlZzvpwe9/1JyfnZ5v7O4RI0ISUUhh1EPHp2cXL1DiSGmT1rOZ1AnMHV1VE0rojdCumxWYABqk6oDiDzmNb4DHxywgISAyA4G5e3MxcAzfBWeAC7j8biGNqtsqrxrqRqv8lQgUHD8uA8KESktDHaBTCyOV1cMBSzMHIwmsGwKFhGhHNvEwTJlEmYZopqR4KVjSHSBk1wOrMjO5BNvN6tqtLQvz/jhA4ElolsyECmcfIBASGgOSOZhgPfFiCVZuHyufBwD2IkdAcdAZ4aRYTwezBhDNKU4mDCKbKyMBUSjG1iBeJsIsY24jIkOxnDlpV4qqaqKqhq9bPI4EPzFyEGhEDN6o+vkeVcnwLNK/ywDUCtCxkc0QWe2ZiInGvOAERMhMVjF2FxIUjkqz69sydeGCI8U7W29gIsOXUt6ltEgJosWLzthAMqrDZAK0SKKM3CZafQ62/GQwMGTgsOmZ97dF/VYTNw3o+BiMI6XzQJWsHzolYkJq+XzfdchqnMQ/3NzdkwQQiB4wmZf76c5uDFZ6vMut5FIhPFzy2uFT3HPO+HmaxJRFTUDznDTS6uVsusXcPrQASSV2+ISKBhdcQoBkCGbHT/L7NCyIFcAoSAHKlscbCLDojhNCtmBMCOaIBajT6pO4cm/849AAgIqgGEdvN4frlRAxEZiCS3DVMAQlh1kRCxYARU+yfXVUdKgwVl6WH0puOH5P5lEfQIhOVnCNwfNG1qW2EE7EUMyRpu8Xp5aPlyTmRJJZK7IucAgQvypzirdZpIhZHKiU7OEtzdvW47/uc8/3tTb9cMkMeDy++/3bYHxz50dNnbPbDy5fX19eEeHZxYQCLZXd2fiqSfnrxumn6//Dv/t3+7m4ah2dPH3eN3N3e56mYwcXl5e9/+8+Lvvvkk0/evHn94d27r7768pvf//77777/zW9+3fXtN998k5KUIqo+jtNi2TdNc3F58Xd/+1/PLs7aZX+/uSc3ESKGfrV6+umnNze3BenxJ58jQx4nLQVQ8jiCe56mrW0urjpwKKWAOzGXoqnh86vrV39kJzbPUaBia2tHPB28wjM2G3NiWBbicdEFNVCibsisRlAhzrxNU1U0dvA5Frg+YKpuoF50NveHet8DAEQ7EwUyjlIMnm6etQAANxwaQmHMGuQdi55Qgz4fR8DCNIUXTVtCe8Qc/4qWEgiUmrqVKIfuGvtOq3YLwbcks7AkpKDO185JXU2t8hNnk6DwSZqjW0y9xnG4Y6S6CuHPwu7jRqyXK6AFIyhu2Xr2ObRBUQtqi20OAGH2ji6ZzL3Mhg3Vt9WpKo48AqnM1D2WlERhG6cEjszupuqqExE3bTd3ewURGLhCEXVnGYt9J4ieONy8HQwNNL4DU+cqnAZC5CSshFqVP/GRBbnVapDtrLyI4j1DHjXMCNGFpUkiQogY6ZCIyITgZlpo5gorhhwQyGt+BCEwARMmIQBoG5ktY7EuKBgAgRGFRR20jGqB42OdRUyCdZDDx5WYU5/aJTpqyZvNg5aSUkPEgETo5K5aJNUbIxYXFORaVPdQl1Bw8YNKM3vd0/HnVD0YKO5FJqGaP5waSY2QWVEz9RyLIqs+InC0Sg/G1BxN7xr0JDIwR2AkaZvWNWsejTBxMzfgWC0GY+0GSAY5+5SdgpcXywkwYQAP9Cbe55o/44bCYSJGAObkxCH7YCFWmKBMXkwkFdDInqe6fCMhIuZ40II/ZXVMif6vdl5F/TBOuUw7cGQbhrFN3cly1XTYSHKo7lYI1C1Pnn3x1dWzz5puGZUIiBlBKLmbw+BBWbUS09h+u3GHq0ePTbVoIaJSRndvRNq0HB7SdnO32Tz8m3/7H3Px3//2t9ePHv30w48vX756+vyTi8urP//zf/XNH7+9v79Ljdx9uAfAXtLi7PRktVy2TRL+/vvvf7P4ddFy/egRgV1fXGrJf/+3f391ef6v/9WfXp6frU/W0zT+4fe/e/LkyaPrX+Vxevf23bPnzy8u17/59a//9//lf/1//6//y6efPUdAQlezfn1+/fQZS2r75Wq9ZiQgUt8ZEZOktgXHJjX7w2E87NUMIHVdx2ZEpIrctJSaPBwA2YEdjCrtG2ZOx5HzU0kx8eR6WEKFc7oIzGrNEBLXBdy8i6qb1OrzGBdAUAk+pnHY8deh+juHeWNFAqp6q/4A8wLFLdWGGB28xFURTUydcXMRrFxoD7CUKbtVLx6r5l0EQKAOM7wBBICm6kABCWgFcesPdpw9f2fPQjUgDkpbXR/G7xpo1SS4zfnJJIjMxGzqM427zv3uqGRk7BSXQjTlhMizg1u9M3z28SckNfP5d6OXihJSM73cKexSzeIIVLTH0VEj5ffoAGbmSLHJZnInCid5tfg2KnJtOHtBhqtEJX+CqaohYZBrsrmjpOQYG0FIInkqho5AApWGGeAzVewD4q3FaPmiK/dhmntoNFNAUos/SQGah0LVK9LnijHIIs56tUSoQXBHEsLUNMfP0gEDLUzSxLLGIZeidQ0ce5KYOt2csGk6afrULgylWJ60FNNmsSCPkseuGotoC27jDM0TKOJxNwLEFDTciKmEqhFTMwNCJjSL+8CDIR/75EoMo5gzHDxOnBEHqd8iez2EDegwcxzEafZ4sYhJJREhEmGZHMZxBC8ckCuiBt8AAYiKWs52v9nHbr6oFgcEWLZpuUimYMbh6jR3cNWvikI1A1gciASpIU5M7Gp5HLIaV6oGVIpVuH2ZR+xZLMHdsKgBAnmAh+pgueTNbtyNk5YsiG3P28Nht50IUt+3RRWQzRUAUdJuPwwlp25BLIREgiUyH4JwhTSWcb952B+2i7731BrSycWlm+c8imnOEwAi4YcP79H1h+++ZaBHT59fP3v+9vX73/3zb28/fPhw8+H07DyJqOrbtx++++7HlNoff/hxUnj39sPjk5NnX39u03B9fSUpbR7u3394m1JzdXG+u7+7ublNKXVN8y//9M+PH19Ph+H1y5co/Ke/+c1yuUDE/W5/sj7pF4tipl5+82e/cVN0m8bp5ubdMEyffplyyU3q2tb6fhkZmegwDHsHYOFcCiGenZ2Ge7NIUtVSctf1gBgflGrNWqhFpQIOSBiej+FPXhtUD2mPATKDAwCZunvRUnzOjMU4mhW4ZBFhBNcSXpFqwe4/suDiL83tcKDTBk4VJUEI93IHAA4lTXDQVbFmSVJMI9Ham0X+FAGQmZtDeOJE3Y+cu4qjBOMTkJAD2om9cWzdyMLgKi4gJ6/c/GjqmMgBmMy1wuxhXhiNaYy07m6OwXIzQw/FOzGyNG2LwKWUnLNV7klkJnuBDODMMQohEjMDWnXCMDdGcncWRpSiigA4k+ijr635WFAxjPgQo1JHdiQiqRWcUeYZTUFC9qCPYxbECAUAcEY2NMKaaxuEsIAM4lNk5pSSmk+qU86AyMRq5rF2dAtAiokUENzlSCpGCt4lBXkKEQEhqzELuSdm4bARh7HolJUMCjIiJ6RElCictQ3BHc2roSkoYo0niKDJSO4hAsSgDYRupZYeRwsPaweczQEJgUiAoVksVuszabqc9eLJ58vTK8TIQvY8TSVPM0lVAaDkbObECA5FlQi4cm+U0dGVIFQw5DOrRs2YSM3GcYxdNYSkxtTBhVlIrDJqpElt2zbMqJp9v/OlInLXddFKMBGTxO0Qe/xgX5VSGlU160oxs5TSYrkI2iggHA57tyJciRbgscBAIO6c+qy02u+GsFWABrFvZNW1bZImEUPI4TmoaoTs7jlPkTbubo7E0oi0IVs3Gw67Dbm1jSCCajCDjBGJOTElTpGg2k4ZZgNFLwqmyKjFeu9WPOB+VFPQkpjWKwbD1HbSLKVbBHzrRpz6ZrESTo1ITKmqGhplNTMr47Db3N/n/TZv7l69ebG6vH722S/zlMFBpJnGfc55yiMh/PjHP9y+e9u3qWnk6vpx267bdveLX3yRy3R2fvbp5583fXdxcXG6Xv7lX/z5t9/90DTNslvYYXL3frFYnq0++/Lz66ur/X7/z//4T3/xF3/BLP1qDWDDsP3ii8+ffvKEid6/eve//W//x6eff/L02WNKNJZycn62Wi7U/OH2tu2a06urtuun4SAEzvDTt9/t7m9f//jTs08+IwpOCDUsfeclZzUDd0KbyjRpTk1qUms6DeMgKSVrxKVpWuSmGJRiozqAInFxRGcCZnBzUrCj7awFN6aokzCwAhAaOWIIXiH4ioaUAAiQgSIqm4q5OROSoU2ao8aWiAVEUPA6txkAocXiYMYSEULgGowEBndzJ4Riqm4SFJJSVA3NgB0RtBTVQuglqL+uhCQpBUOpWqqxQrRLZhDmCkhWIfm6pQsSZ9hGQSzSiGKI96r+NffgkLtgckQFI2Q3VyjumK1eIRozknvDqesWkpIWBXAg1VJInZvoYSNXYJTYSwIlSQqQPXnAR3U/Xzez6BXuQcTUJDV0ACZnqXTKgMAcUK24OZK4TwAeXnIOBqazRw5Y2FGie3ZIggiC4OhICAUYAAUte1Ejwmwk4YMaEluAxJzDVtscHRSgWAYkdkQzdBfGwmTuMmuCoO6NKKyA0MzUsBjGwMdESVLsbMcxTw03HQF4OBpz5b5HqkMk6wIiKsAse5idFQhJKLjE8zKdiGr4HEQ/CFQsoBQKvPnk4uL6yZeL7gwAJ9XbzcO37w6rcmCicRgqNs+cJCGiNI0gFpyGaRIRQhqHIQK5WCToE8JkbizSpnTE+oNZqabr6qKHca9ozuM4AToTNW2Lszdnv1jENrVRi6V6XAA558pBnsfAmBFI+BjQycRqamopSeyyicndjhLiKU8ll/iuWLhbLCq9KudY7TILE7ppxUFNg3EWbIHoI3KeSskQS0Jm98pLAMTUcmS2WAz+6qWoahEREUFCtzCzMzU1t7brmejm/bvd5iHnPB32F2ftpyenTdNNY3794sXD/d0wHFhwsVik5ZIXfWq6bNB2i0Tp7Pzk8tEXiClPo88QKhqMw2E47Pe7zbC5n3YPm9v3b96/+3yxZMYyTAD+cHv/cHvjVpou7be7kvP5xcXVo6up5NXZFTGblour87Ory6LWL5ZN2958uGmbZpwmYTw9WZK0i/XJTy/eMNCia+9u3p6drS4fnf/3l//pZLHM00TC0PcsaT9uP/3iy9ub9zf3//LFF58+3D18ePfh8vr68uqsa7s3b169e/f+s8++aPvlSb8kTsNu55Yvrh+Xooj09sUPd+8/dOvTr3/zZ8RtMINpNm8horCNy9Nk6oSTgws3rtk09cuz51//+fWnv3Q3B63BfIHUhC5Fra6xEFnEQ+Jo1eLRVLXkkidC02k67PdJvekX3WLlDl3Xp7ZpkhARI5VSpjy4WdGcUuLIKQMwjXURH4asppIq+A8AqsXdmLgaIMQuAly1zBuI0AYHqc+qHx3UvZLUHZIFhwE55IqgqpGeR7PeYRYbA8z5izlPM8kRzbQSEgAQQQiJxQHNrBStTo6zQD8Ic+5KVCmPsQswdy2GBH23lJRCDhTRhLkY63D//oVNObyriSCDWpCzua5ItSjUFXI05hStt1aSPzLHaoETR3YW1L1s5RkBISVJBoZmke4CcZl8XD1WmZipjzYpQdsIAoSMyStj3t0sG0BK6CjVkC4WNMhI6NnC1hgs8hwCzzEPwiuQo8RbG/XCwRkRmUyhuBW1UlRoNlRzZ0ZzzKZTKUmTJA7fH6iGqFQ9dhBpXoFqvR8JvMxE09iyhLl3WM5hLG7MLZeQIWDAMUDY9IvnX/zp6fkn2832xYvvH7bbn958ePOwa5YnBJjHnJI4ADGulqs4ZuvlahgOd3f3WUssYhDczBbLRWQADeOkal3XdW27Xq8Q6XA45DLud3s1JcS27cz95sMHLYWAch4BcbFa9V2XUiKinKe+X6SUVB0Ak1C/6JhEtZRSmIg47XdbdxCRYnm1WkWPzUzhNQXgTdPmnMGNWSLsTERUXUsZx/Hh4YFZiNmsdH232x/ylJOkpmniE2NmYXYHtUII6jqO43K5ZGFw6Jq+FM15bNvW1IY8HobRiuZx0JK5SU1q3GHMJRdF1zxNItz1fdv25jAOA7gP02RaALzv+iT85tXLt+/eainr1eL87OzZ86fEzX/9u3+4u7kB9+1223bp/PQspa5t27ZbgaTlcvn08fW/ff7k9PJci5WiSGBWwuPzSG0PiacCLNanT549Z+JpPGzub8bddthtx2F0gKZNl9ePUiP9YtkCLU9Ocp4e7m5Ny/39Xdt0E9KHN+8Ou91XX33VdO1Xv/zqv/yXv/7jt9+dn67/8Ps//vX/+Vf/+X/89y+/f99KSiKr9erD+3eCdNjvc86paa+fPDs5v94chj/7y78gtf/p//U/odPF2fn2YfPbF/9yf3f75Zdftn13mHK3SqenF6BoJT/5ZLU4Of3w5g2a7w67xcmJCOc8pJRMVVUl8Tjl/X7HzMvl0t2nKSNAKWWaRi1GKCxNWpxvimx3W/eEWdw17DdL0aImQm2TuraLY3Y4DKomIkDQtElz/vDh3dvXd6rj+3fv9/uRU/f42WI1WN/163Sy5MWI3DYtABiV3bTdHfaIvbg0TbNY9Agw5YmIxqnsYSia192iaVt0SE3DiDmPh5y5qxFSeThY0JOKsnASHIfMxIvFgoVLnsw8ooOJWZIsFj0hTtOUS4lei4hzKVosiVDlnmDOIwAwsblFY1O0xHbbVMNsMlyvo5KGvCA1KTJKqy8QAgJmKwFDzcfEtttdLtr1HZinlJq2OzK8OUFRNaV8uNnsD6D3juA2uioSErLGcBHPqnvRgsDE4m7EEWsDSMQijBYOyMLSpJC6hslxJeNFHLoAGjRVBBrCNJtTIYE9bFSCnapFCMGcI/I+KEM+xxcjlpJJUi5gaCyhCFFBFKKpRAIlmntRV69ryWoCAiTxFmAoimYj6SCcqoE5qprUzB1jwkJQ1IrazD8oVhA7jpIfC3P06kpsNVk+8CSqRlBYN8uVghCY9YxdmIWVeeVdqkPT9oTp5ubDjz/9+O7thxev379+++b8+qnth6ze9j0D5ZK1wEN+AICU9g9yP43TlCd1J6I2NaWUUnIeCzFP45SLSpLdw7ZN7cPNwzRNpeSc82G3m0o8x0sDff/+7bAfGKFtU7tYqcJ+s2/aJu6J1XJFxEkaIhqn4fTs9PzsbL8/mOlysXTL4ziJpNQmQk7c9a3c39/lotOUVUvTNOgcy4MMo7k3KaVkecxTnsZxOhxGosIi7nZ/tylZx2ls2nYch3EcASCMFgJYTMIGoOYnJ+sga4pwzlm1MDMLafZxnMx9Gkct2b0kkWGYJi2A4dyLTZPCz4Bis6mWSwnUKw+DlXwYtmOeri6vrs7OV6vl5u7+zbt3P3zz+zxNxKxqZg2an6xPS54AeH3SDbtDzrpYLobD3tW1lJwnQKuC61IcsV2surbtVyfry8dN318+fnbYT9NwmPY7K9P10yfDOG1v70moXSzMgCQlJi3Th3fvX7748fNPn+Ws99vbtutT0z579nyYhru7mzINX//qy1Xf3d/eX12e2LR3zcu2++Pvf391ddkweylGYmabzfbq+vqzL77cbner1cl6uXzx3fdTzn/9V//lu+++e3jYNI3827/8i+VieXtzf/3s05PTM7Xy4eYtGJxcnD/7/MuuW27v706Qzq8ej9MgqQ0EL09TahbL5WK323n0wmHCR2jm282u7RZ9jyK0Wi1//8fvv//pdeCRu+2u63pT3R8GImrb5uryYtn3TdeY6X532O0Pqtq3TWIihIeH25cvfvzDH//w8vUr4u7588/HokmatuslpZSk77u263fbLRFpyZFwGmB50DGalMx9GCcE2O33bZdSahBQJDHzOB7uHzZt06aUSin3D3emzsTEslz2TZvGcWKW5WJZSo4I+KKac+6ajhN3XVdKkZTMNE8ZAJBI1bRMTdOs1ycpiYi4eyk5ompVbbffq340UJAmmeqi65HgcBhjDE7Ei9XCNZJbnBAJ6TActvttksYBNrsdIlxeXDxsHiqdNMmi75rUqJmqllIA/GH7YMVPl+3Z4nGiU/BsZa9lr3lvbhHEaSW7KbojetHCjk7oRQEEQvQFzijF1N0JWSgxgzmCg86rWiJhZECStrOSzZTQCxQA1WIsghhEVopDUswRPIdOFV0YgoLvYWeARMgOYBWCAqGADpGZ6++4m0FRJWRVBYxXQIAkpoZ05LrUTr8Kgz5W6hllsLAZCYkdEoGBIgpSTBmxQGEwnX1tY8FAOPtXHGk/dX9dV0pBXowNZGBGQXEGJAKkFy9e3j7sP9zc39xufv/NHybV5fosFVXknZYYsZs2OZKqMg1VnIXogJJksOCSFBam1BYdzNQV1U2EpwK7/ZaIpjzsh+0wDG3Ttq2oaZ5yKdkJe+6apmGilJIVNTORVErpOpFGTJVZwGAcpv1uz8wHPKiqiACAmo/TOH4Yo0+p+TRICFhKGccxRFwAIL2o6lSymqmWXMrp6aJtu2E49N0ZOGy326D1JpG2aYeci2rTtFYKkyCYWQ7Bt6pOU84lI2LZD/2iOz85bVOz2e2Wy4UBaJ7cfcGpUc0lg6VxHAOvZqK4CRSKpGRuuZRWZBoPu2EjSfq+32wefnzx08NmczgctpsHRkopEfGyW/b9QiQBgGnRUi6ursbpcH9398vPv/Bie9VcJvcSIqauW7CII5ScOa8IvGmaw2HYbjZ37z883H5Yn67Prx8P6mBQVBer02GaypSnYbvdPAz7IUnq+kVD0ha7fvyoXa7GYdj8+P1PP/707PnTp08edan5Dr77T//p3719+3a/3T598vRuu3m4vbu6uLSUmq4nSciy6Pu3r1+9fP3661//OomUUn7zp3+Sx2m7293d3q0Wpw/39y9+ev2rP/vX52eXDw8PQnB5cf7dtz8szs66fgX4IbWLru/vbm/zNLWLfpz8/OzU3MZh7BZ0cnIyTZO7D8MQouj1SbM/7EvxacpN23QNX5yuhsPlmEspIzsBogciF8bpAAlx2u9UjRFaxuyQEHcPD22b1osFqB62u6vzq88++/JXv/x6tewPY44NoqOLSB5LSwkA+q5JIurgYH3Xg1vTtO8/3A3jcHZ2ZmbgVPK4O+wWi6WaKpornK5OmIVJRhja1Bpb07SIbOqbh904DIi03+5NLSKsD/vDOI1takMnFvcBIqTUEMAwjnmauq5r2mbzsGubRt3a1E5TFgl2bHLT3X6vRUWYONZaed92ajpOU6xO2yZ1XasayDO1TZNz3u12u/2WSXKuc8OHd7cAsFgskahp0sPdZprGALKIuIzl3Ye37v7p42d5AZAPoMWm/cXZmsU974jQgyAF7lWTF+JhcgC3WOwLgls2Yo7YTgdHZKHkDkVzBEQiIyWpXffR6BXV3ZE5fJfDiELQiikgqnkpZggUKHoFjCqhSJqmQmShXUVnmNUNBBXkVS/FwvSi+uUBgYGEexHG/jCWOkgYQRFgWlSDTxurH3dGCYVxeHEAIKCZqgIJcdj6RZGv7M+fGR2HqB2ro0J4GIZaL7bmDI5uqtXMzpJgMRpHu9/dvn2/vd9s3n24ub27bRe9uoE5QAFnJVctPgIxqSowmilWZwUyotiuEydAAlMRRoSmbUvO0zi0XZfaFtzLPpecuyadrheJ4Ha7AfKpTJLI3MYpS1JEZJaUGgdgSWqW80TExDjlqWyKu6PjYThMU04pEdNYpjxNwzCY+dXVVdM2OU+IeBgOzDwMIzGJSFi5jtM0TmPTtFPOeRrzNI3jpFbCEWiaclzVzIJESSS8u9VMEoFqalgSB39XVRd9j4h7AEIep4yMzGTgbdNA0wACmKtZFKY2ZwRw07Zri6ogcYpQBKVMbSO8hzM/Y6Tbu9vbmw/3Dw+xelEzSozCIg0xdX0rSdqmb5qu77vPPnn69a8+//Wf/QrApjyN4yHng1ppm7btFszJtMzaT9/ttreH3ZTHYbffbm60ZAB62OxOzy+n84v9ft+vVjgc3m7u8sO9uy+Xi8fPnvUnF23fS9M1bXM47A+73fZhu9/tTtcn6+XJ2cnuyy+/HIbp8vKChdXs13/y62effGJEIOny0ZOuW6hNeTi8/O6H5WJ5fnGV2kbatmk700Lu43AorquT09Xl1aNnT+/vb1YnJ1232BZ9/Mnzx08fD/v9w929CKemsXz77sUtEF49ez5NDQvutpvYeex3u75f9F1PJIDIKS34ZLfdlyBtKzx78riovX53Yy6AUEomZiTK0wRgH24/kEAep2nK4zTmKavpA8D792/f392crFfvXr6+vnryb/7yL68ur4hwu91tN5umaZumERFwQyiLRTOOY1GddCrFHKDre0faHva7YTeNI25AzYbDwQwiEspqgKWo6Wq1hhR6QCFyVct5yFMuYZcCWhNIwKZxynnSkm3Kfb9ITSpaipVpnEQmJpqmaRxHIJCG9/t9zkWERSSoT0WzDkoIwzCAO3MXmy1mnkp2cEAoRQGgKBd1d8hTJqK27ZAFkBA5PHy0FKQA9MlM2yRMNBkkaTab7WEYVsuFm3Vtx5J2h4ff/ctvV00jbuxld3FxftH0jSdAYiIrQlQich7RwVyD4qTkDC7g4DpC3V7nGvN41GgyIBAyI3BKjc1CisrJdQNkRRdO5sjo80o40JJYp1rOzmE+4erkHO4FhIjoqkCMwIYlW1zcFtqdygOvEZdYzByVWcRm+UPsG4uGCU1ICrEUBTBE8bp+dCFKLG2SEP3OiVQeYmAEZOawpj4GRlT+JAARC4OXEjbfddcRBCmE2qoQqFronoJ7PAzT3fZw/3C432w3m/v9YSdtg0hqmlKK77/tOgQopsSh4YKccymFWSQ1KaXKxLJqwRoHsut7BGdmKjYc9oyIbpuHzfs3r0vJ3aJ/8uzTcRiJoe8Wy/WJpJRSSiJd1005u3vTtF5DV8Rn65IgAol4koSEpoZIImkYhu12K4NE1oeIEEHbtrvdbhonEQHzaZqmMg3D6OZ5GrebjapP09C0jRq4R0coh8OhlCIiFb4jKiULkbq6+ZTzMI5mdpqaYO+P03Q4HJh5HMdYjANA7KubpgGAnDMimpmInJyc3D88AGC4YU3T9HB3u14v27Y9HOTDu3eb3UMsyZsmxaBzcrJu2365XC2Wi/VqnYuB48nJWdM3bd88efr81cv3i647Wa1YOKXUcZdSF1MCAKrmYdhP05iHwc2JZXVy2i16dpiK3tzcuHvb9yjJDDTryWK5UwWEpmtXFxfLxQqYcs6bu52N04tvv/1w8+Fkvf7dP//Ll19+JW17tVynpjMtJeegvbWLJaV0dXHRNOnu9ma7vWfC1cn68fNPuUnFvOn6R8+e39/evP7xx2effZGt/OLrPzG3+9ubxXI1HLYl62q9Eknf/cs/73a71frk7PKCmDhJ1tK3/fbhPpd8fnqeaSTG7W7btI25IlApkzuoGREzQdskJIzDeHt7++rVa2JpmkYQAQKuBXcgltvbDSFm1f1+WK9W45TRNDXdfj+g869/86/6vm+6fjeOTWqMSJroVCoTBQgPw6gGfd/nXCRRPBspJU7p8vKilGKm4zhpSim1ZhbQzeFw4JT6tEgppaaJ5qaP9mK/z00pJYuIO7BwYF9urlqKGhHv93tnRCJh8WQw6ytFpGmanDMzpuRt2zIxN8QszDxNk6qmlNq2XSwW2+0W1IJOY8FRQXCHnHPIqgMCGcZhHEc1bdoWEJloGIZSSkop5xy5PYjY9+005VKymuacmYiY27a9+/DqX377u8cX58smLbsW4fYwyefPT5xdhBwwZweIQ4cAkNU1Z0nELLEgp7iwEdwdJ2ibxmYDSnAi5qZpzQCRSICUVafwsgjNR2UTQQh3hVXVnTwyw6sdFMQNAMDMsVdL1Y8P3TwXcy/18qvpN0ETBfj/UfUfX5IlWZondomQ956qGjcn4UEzszKLN5me7gMyK2CLJfC3YocZAHMOcKZ7zkxXdxUqs7IyI8I9nBlT9ojIJViIWmRNLOKEe7ibqak+Ebly7/f9vpMi1n4ewQbi6GDtuqImBqdK/CQwQFyWiuCNCAkGwJAi910MzNUUTmi609Wobdzm0Bx35tDSzZvLrJ0DJ33ss/EKTioWAHA40YbVABozS8UnGR+fdk/baZrnpcxVa4whhrAU/Zlh38IJ2BQQCZgIaw1tr+/6Vd/3zDzPc9PbiIipxpRSTE2ztSzLPB4f7+/e/vj9siwAHiOv1qsXt7eto3J5ebPenAFijLF9DMQ8jiMzM7OItHkXM5tpOwb6vm+StZMCpOv6fogxtB0zpZRzTim5e845d13zqCxlOY6jiEzjsdTFTVxhrqXW2szlucsidZxGWhABU84pJVFRVUZU067rzGyZZ3O/f7hvbaj2so/HY9viRaSUombtJKsibVaWUqyl3H2+y12XUwIAqbJ9enx4+Pzu7Q8xtreUUoqlFAAfhr6Uen5+8ebNm3meh2G9Xq+vrm9z7n766QPH2PVr8/if/tPfnV+u//av/gKxIRstpY45mkmVUmp1UyLocooA8zKmNAQKpRYrpT+Lu/3+/dsf+2F9cXW9VEHC1XpNBGVZVBQZ97stBQIgNy8iZ1dXb779+vvf//6nt29//4d//vVf/uUX33ynCtNhZ27zPKP7vJQOSWv93e9+//H9u67vzy/OLy4u7h8eYtcD0jzPat71qz/7879Y5um4TNz18+NjnZcR4Ljfvf3+x5Dj5fk5mJ9dXPDFuZsfpwMAnN46leth2D89IpFIJfdlHFU1567rV4huUili27lqrc1k2/frhrZOKbVPrZTKLW0VITC7WVVjjsRxve4YEYm/mEvXdV+8+WqRqg7oxDF3HAG4lFqqcoCccy1lWSoh5tQj1sN4jLElemJK0cLp4TwcDs07VmsVVSbOOTd7/DiOvbtIdYcTmQDA3Uopp0eIooq2X8YYpRQmllpLKU0nU6XGGIjYXZsdvYmUdVQRaUPyWisHbsuzEfNb3FVrDpu7ioq027ATspkdj0dVTSlN4yQiIjL0PRIdp6kpc1qhJiLurqYppgZaR4ClzF3upmlU1bKUZSmfH3Z4cdbSLlO+rPonI7S3zgrhibDb/DqqTIbN/xzYC5iag5XFSs6B4sk5bMaREQmbgAq8AXbMmgbPzD0lbvKiNqONIYo0Hpw3D3ajTFBjxD7v6areQkhOuxPxKaIVWoeleUlOAVzMZA6uAObBgAmaE9S8WS4MzLxJgNqouYo0YLGaRaS+yzkxM1VTNSVrp1SNjhgbWqApP0ENHFARDQ0BxNGcAiO2kFNEQHRxMG/AGkSEhkw93UvcAee5HA7HaapF6lKqqro5ICy1VLfN+Rm1n1ssxlBFkZGJrWXIPffZ3Ww+BfLxyUlP1LSY43SQUt/+8Mff/fYfcgoxBnfdbNZv3nyxWq1uX9x2XT8Mm3adCSHM81xrNbdhGLqua7VMO59VW/CcxRhTSs1dUmoBgL7rc87PC8ZVxGJsr6cf+hTT4XCwqo3/t0xzLaXJ+Vdnw3I3TtOSuh6BAhAhZuJhNSylxBBWfTcjdN0mcDiMx/Pz86enXYyRmM2s6YJiDKUx4MyWZWljYStSy6Iq87yoionEGHOK8zyleL1M9Xg8ztP08f27/f6plHJ9fZNSqlLG6VhL7foud6nvh+vrm1prFTk7O7u4uATEaS7DejOOU8j909P2my9f/ebPfrnqu3macspudZ7Gdh4jeiAmpnmelmWaDocq8vKLS+YIRIt5N6zV4bg9fHr/vi5L7vuco5odt1vVykzLoYja+vyy79fc59XZxYv4JYDnmF+/fP3HH76/uLpZrc+PxzHnTrV2fZc4HI/HWpf5sH/3wx8+/fT+l7/+s8icukHmaZnHeSqr1Sr1veWcAN/+8P3qbDPuD/f3Dyn3F5eXYFKmg2maA56dnQ3rgYjH8VhKAfecUlFZrfplPD7e3a82awUPIddlQcSc26QNQs4hJGpDPPda6/F42B+OgJRzFpFWr3AgKK05S62YMDMRGcex9RjnKhcXVxeXl0UNKSJAVdsdRxWd5+nUplOVZT4cx6UUJqbtloCOxxGxpcK4iIgszOHNmzdNgdZ227b1tw29XW1rrU2LPI7HEEIppZb6TLn34/EIzagoguDTMsc+xBDa8lmW5XA8IEGdCzPnlA+HQ9/3DuLOZrbbPYkI8Sk8vMu9u4/H4+PjIyLGEE8vA2FellbTppz1aGVZmGhxEKmEpCIi1dxFaisoEVBV2iFhpsf9Ts0OxyMAqOmyLKpSChKCqCxLETMyWESnUquoY3BobSAGqA0f0ywAKQSF1itGAhIRcxdTJIxMqkoQGmKfOBAFB1RTVVeX56Ot7WonqjwTE7bgWw/MgelZ2g1I5A2/xoTPHqZmRSXGE5LOqRmPApEzqSoSPUuGAKzx0AzQiEKANid2NTMOZOYqjRgFp7kKs7uIudppg0sxdDm5SrvauHkVTdHcrRSFFN1dGhYV6AQQcAcwx9D26uY5UFc1kfYAgrKjO2itJrVhZ80cIJRSpqWKaq2qojFEJpKqQz9I85UgIQAR9v0wLcs4Lbv5KXeJibt+YObD4dgUxE0m37qN4/HQdT2Af/78YbPaPNzfrYbh1e2tupnrzc21I759964bBjYd5/lnfT0AVBEzDwHmeW4xQ3DC44SUUkrJwadpFqnzPLcGi6kdj0dEbP2o1rifpqnrupTSEY7v37//9PHjajXklAEwpVRl6ruYAjw+3on41e0LRzYRAxj6/uLs3MDnZXGDy4urEMIw9GcXZ13XD8Nqt1uXUlopJFXcvOs6VS2l4Inh5e25q7V2OYGH4+Ewj0eTILV++vCTqh72h+PxuN9tx3Gfuy5GjjGoVjPt+nx+ftZ1/Revv2CK87Lc3Nw2XyIBVpEQwtXF5fpi/dd//avf/Orb1aq3oo2DPU3TUua2/hFJrMzzPM8zoTPzanM2rFbLUsfDbhynfj0Mq/XL11+sz4Z5mg7bJ95sDrun7dOdmq6GLjAT8NnlVUhduzVeXF2Ox32putsfzq+uL69viMhU53lm9GVZCvN6PYjEMs9EuNoMAL462/SbFQaajvv7+6dh9W1Kvaq+/f77Kjrk/unpiUM8u7rs16vD7lGkmilfnilYqWUgTH2Xhm7aH8SsLlNZFkqQu9Rm/rnvr29uSymlVgNkDkRcajV1Zs4xmfvT05aISq21KTKXpXkyqhRV50bJcKhSWzejSX4RUd37vhPVpSylVADvuv5E8K2ldflQ0B1EbJxHBOxz32p2cGgVumgl0qenp91uv91umUMrI3JKalZLac92U7u3fGMiEpEGpcCTmQjghP/A8Tg+bbdoeH15LaBiyqHJgaogppSAoLUxGdnMpmlalqnt0e2wUTEAbP1MQhKu0O4ZXTd0XSllKcs8SruOnEL/ADabTRe6UktbmCJSRbRIo+mJVARQlRCjSFUzIlqW2dQIBWqlk5aEGpF3nKaigpiICBSZInGLVKIYgjsoAJzS5n+GpLiDRw4tldqwPfluDum5/K9VHa3WYiJg9qf4cTMRYcY+JSQU0ZZoe3IVnd7g1lE/QbCJmmfM/ISHQUJApxDIT+nihnoCNDS7bkMmpcjBANxArF3OS3MYiIghqBqYB0IxdHMAJyI+YXDJkQHI1Q3AQJACshKAizRZq5shwWmk8JyyjK6G4FAR0ImWWp9BQSd8hroD+om6CqgKc5FxWuZSWz3CI3NIHOLQD+bGxA4WY0SiWpa+S1eXV//4T3//9sefvvv2F2gGbJvVytwRoUn4EXFZlsfHx1qLmrx68dpUY863L243Q09In+4/Pz7tbXcYVutzRAeu0Rtdq/EbVBXAzbh10pdlqbV2XdcOs34YTHW7fWr9zaaHA8Ra627/tFqtutSHEKpUdCjT3K+61Wo99BnAd7un1TBs1ufjPM7jXuftdvtk7sihSxmxMXDJwedakRCImg+TzI7jWGo5HI7DsOq6TlXGcTF1kTrkVc7dSWNH2KVIRK7GgRfmYRhcHRYpCFWUgMuymNtms9nttrvDvtbl+va2y526MfNmc355cQWAq2GlCl2OVxdXQ7+SWqeldH3ucjcvy8X5+Te/+Oq7b77IOZZlkWKqMs8zIMYQ3L0Z9JoOuk/JwGstKXfgeNg9fv704eLyap5mBw8Ru2E47Pe1FAQXk5DSmxdf7Pe7Tx/f59U6Dz1SnOclhlSm5f7je5GiKquzsxjzeBy3jw/olofOzO7fv7eby27ol3li9KFPq2Eopc1f6nI4Tvvt08NdzvnTT2//6bf/8Od/9dcYo7lf3lxf3VxJrSJ6++rlw93nUsvt2euuX4lIVHGkELMsC6pJKWm94ZiYYsw5xAjEZxdXIrosc+uKop+AyhQDAVxfX85FVvt+u6vjOCJizrnrOlV3MwXnlBAohli4IFLf92WZpS4pBW8Xy1oaZKbSacCGRGYeYwjMbTdk7PuuO+HSADOTzDofjwIgcsr+SDEBYmA+DXZVW3d7nue+7829lLIe1jHGaZkIwc2Px0NKiYhlWeZletxtu5RNZVoWYrJ5msvSVnqO6XxzZuB1KZvVejwe1YWYzR2Ack6tTqrPo6wcgxmqqgGa67Isiyz9ag2IKlWWCYHcsbq14d7I2PV9y9ettSzLDObipdkhMQZum9fJj2mgzsRtozQgJJyPo1RDVxUz0LkaAKFzi6JkDA4OTsyhVbJAwMQYIiCZIwdDhWbxbbFpbk5EZmAm6ApWTUtVNRE4tbUcWuwVkoNxSIjh1Fg4DQB+5jE3baYTIjvWZzhqO39U1RBCYI7B1QIERFeVBnEws+aNaE14AgxLKYinqp+xgQZJQRvWyOGUKdDuCxxis8MhYbN/OYCCW1WxycC7EEVBXdQ9cASgpnNnBGpyUoeTldDdAcROkScnBI2fbFtuVpcKiHO1eZplHtEBidu5E3PKOYtqlepeiQGZE3FVg1pvrvI3X3173B92T/fMFGI+O79C4pASMTaryDRNXdeP01Glnt2sP/z0/vryKqbYhF4hdqqamLrU5ZhSbOp4WEptS8jM3A3RmLmW0j5deHYI/9znQYBW77NqYx0T4NANqnY4HIZhYKIu5S9ffbFar87Xm/PVZpwmBOhy9/2Ph3c/fH+2zhjg9cuXKQ+XF+e73TROC4XYyKBSZZyOHIK7ByQFE1cw3O9HJKxlmecpxhhCmqZpmud2iUQmRwgpd4DuXqoupeqyTMv4/v1Pw2qVUwaEi/OLNp+oteQcX7283WzOnrZPqcvXq+txnKZpOhzGmNJqvb65lrQ/OkApMhbph6GKwOF4e5wI2NTH44gG5uiOMUQIsVEITwyiQq6GVp+ejl23irkcj8fL6+urq9vdfv/w+XNA79crIk5dhkD9avXq629vrm/ffv+HcRpnqfO05ER1GiHqH969A5AXL16Caew6da9Sz8/PpEWxd/n86mLc75fD4fvv/yiqDjROUxqnMh4RfFlGJpc6jfunn97+cHN9jlbrLDc3l4HDp5/ei1RXu7i6XK1WP717Bz/++OIL+Oqbq77rH3e7QGHoh8fPn/aHw/nmYn1xCRwACRzKUtyx6/rAsUqJEVLK1vThqjnli4uLH9++V5XWrOv7vj0/OaW2dAOH1vAsHPq+zymDaYpnqg4OgUOfMnfsP8fbdb0DLKU02XEDxXQ5D8Mwz7O7qZo0EidSDJxSYuY2nRIpKgpupsaE1ezp6el4PL548VJNWidQvS7TuN9txbQspcudG8zTOE5HdT9/82V3eYmc3Cq4kIs2kXGIQ9er2zJOtVQ1q8vS9YM/Z1cRkT2/J+BWpklURdXBCQHBFYmQ+tU6pjQej3jKFzFgJsBlnNCh71eEUOa5oZOIWZqtqgqHSDFWqeDec3JTRBTXllLghOOy3G+fNpsuJ5ZlOU5z1SEwxBOH39vwD04h5A4OTBEpVHWiEEIuoo2r2CrOVrm3lAuAdpo7ngJ7/BQ608hrBu1TaKu1iT5qec53gyZbbd5aZPIYMFAIxE1OSo6OVkppEQs/a1IQnRkRg7uBewrkyCFwUNVT3ot7kzMatNRSaDkB2OLh/Tm4qf08cIK0aYNCq6ATFmJEpgjAMQSiIOamxU4etj9xplogTqOOtKsJkgGckh/aBcobvhWoFFl3XVGdqoAJIpj7OI3M0cDbeAwaHQFRat3tt1K071ZPT59fvbz55qsvRX1a6nQ4zjzP09JeAwDUUsoyf3j/IYRwe3szzXOXe3AvTWWgggDgmEIIKZRSwNVMpdbWxWJmU1nmubnz2+qiZ5R033Vt+trmTsxUyrLf7V+8eLEa1gc6MHPkAIiftw93uyczW8oMjGg4LsvjdncYj7c3m4uL86vLy1q1jMdEEHqKMXR9RvKixLTiwCpiVea6OKJUQSI3NxepVaSmZO6mZillgAZwFoelWVVDCI9PT1bnHGLXD/OycKC2UsZxOhyPpho5R+ZAuBkGjunu/mm327fBBpO5OgHVKksR5lCXEmMCh/E4/uGPP3R99+aL1znF1TCAed8NDkmloqOCllIIMYQ4ldGq9CmnnNws5ZxSSDkPojum3cN913eX11dVKoKxxM355f3TDpivb27vHu7BzbUcnh4Q8P1P73/zF3/edR0zt0vPaui11kNd1BSQhvNNYDw+Pd1cXb/86ktz//j+43a7ZabN+QpQl+WYwu3j/WeQcn5x/cM//3OI8eLi4ulp97Tb/u1f/8393V1Vu3n96jiOdVl+/OMfc7968ep1nzsRRUJ3e/j0qYvpVqVbb4gjAnN04jhPM58kcH6CoACiOQGcna1vb67ff7w7Ho/TNLlDSqlBtttYiJlTSqUu2+22lKXW0u7lZvD4uCVCldpq5zbqDxyLyHa77Yd+6AZzfXp6ZA4Nd1hKAbMDtXGu5xiRUFWkotTawuJDjIDgpbahdIxRpIbAMYZpGsfpWJbpeNgBISEf93s8kYMxpbjqB3R///nzfvcwHnbvP3/68ssvX7/6Msd03O8NIFBoubClOIL3fQfYx5QC0XEcEVFKIfBpHJkZ3FR1rktZChJ209xga8OwWpbZVdEMHUWWeVnG4/7q+qY9jQAgqkyOTOZuopWoiLupihQxUHH3Yqpg5KKijU/HxH3fi+pSahVNhBj8GcTCz94pIobgqE1gYw0DLESnPRmfM0isip/kPadLFXijrYBWaT0lAG/0VjgdGCQqRIyoxMFM2zjXHMi89U3aPaPRUpvUiJAMTFSfE21brDsSkbkFor7PcCoIQnADefY1VFVq1AtqfG9TEQ2hAT3raVIBqmZiekpHdAVDQNNT3KUDIgeiNuw2IveAzUbXhl12Sh904BMbtcmI2h3nORkAsRGDLC5FHMAcahV3JgyPjw9MXc4dMlET3jB1ObfztqqKekidY8TQifM4z1WaWlXbQOykjiLiGIAwd928jDF3HKKJrDcbBxgPBwAax1FbmgpSrUtjf9daETDEQISiUmtNuVO1aZqa30dqbfO6nPPZ5gzRh75HgMeHh8jc5XzSriGWsvz0YTTVnLOaDV0fiLfb3dPukHK/CN3tlo+PPwSO5+uL87NN33frYWjXcCS25qEzJ4DH/WER0UBuxiGWWmd3Iro4P4sxPT49ppRp6A/HiYjneRrHYwyhlsoILSLu4vJ6f9iaaan1/v5hWZZ5mojD5vyqGzZTkfcfPrtZqWJqjYQRY+xzJsDVejPXOs+LiMzj1B6AcZzef/i4LPMXr1+dnZ91MRPatIzSDAFSXUVVHbwuZTwelnnaXFzGlAFhWSoR5S6fXZwHMhEp+32/Xh12e0Z4uPu42+76ED/+9BMQBsaP7959+uldv8qrIYYYttvHsiyJyU0daZnG8XAAcE8xUhouzss073b76nB184JDevf9958/fhiGr5ZptiqH/e7pYS/Vl0UMqFttVptzTvnm1UtEuru7f/HmVbdaX11fdxwfd/t5nkspqQvrs83T/Z245RTG4/7p4W5jChyI0ub8At3cfC7V0ZlZRJE4crN5eQrh4uIshtCEJaqy2+2YuJZyOBzaVH+zHpqkTVVDYCJStZSymR0OY6lL7rqGNay15pjNfJwmRJQqfZ/LspjPuUtENC8zmkVyNa1FxRQRf6YypBBNbBynEAKHFipiKaUGgJunWVRW6/V02Adk4NClbNFCCEz4sH0CosM0lmXZ7vcxUFmW9bAioPFw8E5FZC5Ll5JUq1pjCG1kBcT90DdESgihzlOpoiru6giqYqZlmarqbrcbx8N6dcbMakWlMrKaL8tyHMd5Wcbj8fz8ot23Sl2qVObQ5CLTXA0wpahu4zRqrTFEJAghnK1WIURXH/qeiVRURMtSzVy0bUTm7qaKp6mHEQUgFGl01VMkcgzcYt7hZ/wNokpt0E5pyDwk5gANHYyOCMSBiVuF7e7uhoghhHZCwMlU7AoekBBb78QcgUJAQhUx0IZSaGWoNwpSC9cxY6LV0CGiaNOMYjjlPT3/SA7OASMFdVJWEZmXQkQIINLyv1QM1KFdcKAhKogNIHAAJDFIgbHtzehs7mB0kjE1lLSfYkcBvf0Mz9NVB1czYgwcHYE5aeUishuPFKIhV5WqPoTILXkECQFT1zFzTImQGuWVCXLvr7/4EokftodSSggRufUfg5gxk6qJCtEKAZ7221LmnLOJllKcaL1eXd/cmqqDhxi7rnP3UjIHdrO2oq6uLkOIu+2uSh2Gdav9Qwht1tp4olJrnztmJKKry8tvv/k6BJ7mCdDNdBhWl5eXcy0qoqrLsriamMcQbq5vVzk1+X+pJcVoFnaz7qXcH0uK+xRjC6bBllADWGttnD/ggACBMcWYUooxrFertmWUpRKiqZhqKUsK68A09P2CCOpVhDmMx4kAl2UJzF3OFxcXL25fbXfHT58+NmFoKUtzCKeUhmEAxHEaKYSqJrV0XR/i6absZjfX13/5F7/ucuhSiIGncZmnMQSyxZ4e7sGEiFbrFZOHwPFsk7uMzCkGN1RVMQ0hiCiBXd7eVtNA7FKfPv9kAnfTJMuEMe3324e7j/vdQ63p6+9+sX24e3x4ZGzxCN6IeF3f1bJ4FWCeRd6+fw/qgFwNUu66YVAptcr9/cNxt//97/9IGF69+Wq4uI6bi/Ory/Oz808f369Ww8On+9z119e3yJBzf//xI4WYmN2cY8x9n/v+9Zsvp/V6vz+klHPOQNGgdbsUzEUrMddamENoSAazWisxDkOPCEzcd52DH49HAEgprc7WUuV4PBzHMTDFGEMI4zgiYYjJljnnjEyk7KcwanL3aZwcPMZAdCIDElFkNtVaSplnQjiYTOOEROZARP1qBYhNmwHmgalWmZfFTEMI7kbEz5ofAMcu98UBiLRoi2hFoD7nqcyfP38EhJzSqh/WN7d6wlWr1sohtAkqEUWMRORAgFhFp3ECc1cbpyk8G2uqVHc311oLnrZXZAQ3AT4Nf1vgIDPnnNyNEAIzITbUXTNMAUBIqePgxIAgKoGCaoMIWYophUANQicSBEIgEVEDUVVCfo5FMpMG0WgRKqKnuaxDC7aooNq0+uqmriec2gmuqiJyAiQ83wkaXr61+KBRgMBatlRrNzGRgVcpbtpixNX0FLcLgMRN3AmEpg4tUqzJLE+fljJR13UhRHfsAs1LcfdQqlLLFgQAUydHikTkjoEDkapIFQHAYi1HFJBIDU/5Yc/hBAEYERUhNI8wAgc2MTcz0cBI3BRUDetaxZzRmTHG4IDVsM19xTQgpBQQOeZhrMtUiqgH8ibgrap9N6yG3gBTSrnLq/VZS0wlZACfp6mRjSgEpGAq4Ca1hMCIKLXGnELk43jQKkZxqfPD0/1h9/Tq5sXrL94cl7gUQeR+GJDAzXNKucvgTUQMZVkCsqFHDkTs5kyhXY8BoJTSZjVlWabxOI7H9x9+AoeU0mo1TNN0f3+PTMjU5269Xg19//LFSxF9eHzk0z2OiXC9GnKKiLQsszu4QTUltaHvEel0zIiklKxU1aUuyzyPIUYmjl2uVaqWZak8zVPRT3eP8zKlGNu9JOVESH0e3ACccop97kpZQiUAM9GUopRm7XF1Xa1WD4+PqupugND3Q4gxxdQkHNM41ShLKe6eUlYNh92k7sOwYeaHx8dFlqEL6LjMyzSNImIKTJxirIu0ZDED5Ji6oeeYmia11GW7FTc9PD4+fvy4uTrfbrdSq8syz+N0PAyrDTNxDEut82439LFuBmbePT3d3z3e3z9+8eY1oJd5DLlflmmaji1xevvxoe/7y+vrYXW2PjuPxLMpIH7x9VcPnz/9+MMP6NR3q69/8YvrV19c3L7Ifdel/OnDx6fH/fXVzWo1xTdf5L6f9vtpWiCmi4uLx48fc9evLy9FNOWBmExkLjUEDiHmfoPM5l7KzMSAAA4IbqritQupdatq0Yi8GQYXr1Vak9pEwazWIqqmukxHc0N1TpGZY0ybEEuZwa1Ms6mSw7JMgFSrSCkiwsy6TKVWdwCHlGKZ5nmeRXRYdSq2lMIUW6NSS13cp3k2x83Q5xgccZmmLgQkLNWmeRaVWgUI6zIjtycuN21F63jkmCOHGiSmeNKHths8GnMEDI1ro+qMekrA01pUHCjnLLWAG7m7SAhBQowpm5lbDcQSUkvTNFMiMC21lFordk1f66thRUSAWLTWsbYt9RTuAYCIkUMVUfdSSmQ2dPVK0JLiIXfZTs1JaXiF/VwmsY4JGu4fvVmYRAWrh9jkjtSADarqqrUuDh67YGhAiECBWWLQWlvmrIgbSfNDNVxCy+MCQFURt+ieUnAgd+iHFQJILWBaTdvJYdC0/hhTCiEgcmCKXABqg8QWEX6u7wEpp0hEgMzM9bklEtSkpTxQiyA6pcd5IMJIDml+Ng2ZW4yxb18GT+hWxNauadFFiI5VjRun0FVlNhM3dSKHYEAEgAQcYqkz6Mkz1aISGv8BT3kPlGKXu46P2txVpbja6QNuet4qWmpRrbXqarM5vzjvci8i69UaHMCt1hJC2h0PbRjTxrBVileogiqKTF3X9UOuy4QqKefcD6Eb9vtDjK1AYzVDxHlZAGBZZgJsyQ8hxMPh2OUcwskgnHNGxBADODBzjjEF2pxt5mUejyMRjtM0jVOIYVqmcqxzGPe7x9evX2+34fzi6uHxYbs7rFdrdM9dzrlbSpVaVSqfemUGCPM8hxCXstBJjIQ5JWZEwFKrGYQY3JE5IlFdxExjDLVUcnA1raIArgbMOeYm6BbFLmcA7FIvnTTUSUppnud+NaSUc8xd7mo/tBXe9CFd1z3DjhQVzTRwcLdlmZ6enhywFLm6un3//uN//I//8//xP/z7FLpaqzU8r6m798OQQhCTpZS+GxAEAfbbbRv9yVKOT0+Hp4fPn94/PT2+8u9uODOAlmX7+JD61ersfJ7GcZpevn5RS9nvdss0HQ7HUsqylMNxSl04uzib9rvdu7e7x0cmXMpMRLXq7x8e/9V/82+vX90ixzLPnz5+vLm+Po6H3/3un9fri+vbF7evX3/xzTddN7RH9PHu/u7DRwVTczVPOXNIsevS0H3x1Zfo8OPbd/nhfn15qaIucjzs5mlGxKfHJw6pVuMUc9dzCI0+hkjgCQEhoNQCDjnnGGMt5eb66vJ8ff9QtFZGFlBZZqk1RM59VzRM02Qu1vzkSPM4IdPxeKzzUkvJOYnUZjVAACZCcBUBs3bD1rmaanukzQu3zrgZOWr1NoRrtMRCWJdZRKdlocAI0MLN0RwA0olfgoS0LEvTL7p7rSxSl2XJOSPgMPRMZGql1uM4ihy6OGPgqjWFaByA0MEJSVQppSI1hdj0ytAABKre4sel1NMEDgHcTNq1+3g8tksendI4WE+2KkwxQwBzC0iRmRxFdZRJilDgiITmrp5CjBy1VjDPKZtZ++uqikjLUqWa54AYKRiptKx6anlnalWlKjhQE8u7CbqriikRhYaAfw7lBRN3gyqVCTFEhJaeC0QMgKpWS3UwZnRnJIwhEqCbxRBVxMwNWn/6pYEAAQAASURBVGqmq2pb6f6cmhlC4w6Jqqk3B5wBeozxBJtAVJVlmVWViIM/hxczEzVgfGN/mgViymkqspRapDLRkFOXEzEBAhC6QjPIWYt9dwAxcUVwBM2RtRZrkSZI3owF1CIpAIBULQTz1sQ2oRZbQcSIIaTcdYjo1iIllCKdHl/VaRqb4TvlblRJYoC4Wq8WmGOM5phTmueRmZZlllq5XaOQKGUznee5Sh2Ph67vY8oENHSrq/MLZNofj+C4LPOyNFi5mVmTUQO6aDUxkdoa3Kt+NcYgtQKgqHVdl3M+EaIQUgjDsHIwB10NN13uWtny+Pi432+Z+fz8/MXtzZ//5s/uPn7imK7+23//X/7+H+8f7mJqbURPKZmqAqhq8xPEFEV1qbU5n0OIMabVejNO4zjPjQ8fc1dKbaMOB1C14+EQmGOI7i1iDA0JACG1p0FrNVUxt8ABiYlDCjB0+epqlVK+u3sk4L5fjdPYIaYQHHG1WqWcYoxI2DISRKQ1O5tKiphF6vFwdISzv/jVarW25pYgnJeitTaabxE5jsdhs05d7zAtx0Op9eziUkT7fpBpevf999vtw8uvXn/x5ZfMscyjgV1cXV3cvDCDstuO8/SLq6v37959/8M7LXNgaiP811+8yl0SKff3nz9//HB7fYWA47Fc3NwC4Lsf3/6X//x3f5O6YVi9++H7y7Pzjx8+/M//83968er11998++r1F6nvgHie5/E43n/+PHT9ZrPJq2G1HnYPd9vtfnN5BYS3r14xsqt+8823gLAcj0wM4A93n7XU2xcvdvv9Yb/vRQ1pfabr84uu79xARFzNAawWc4jRm/Yxxvj61cuPn+/meWzhjhDCvAgFav2E1qavqip1nqclzESc+g7MpVZ3HeexliXlLFXNPITgpgCwLItoBQdiAveyLG4ObghgZswhUgQEgIQITABu8zKZqqqJKlpAwIDYBnhVxFoB526ixHz63E95GNJMA7VWUem6DoH6fqgqZlNM0RzMlTAREadAxEwEhIrUio+Geg6AAMDMgMi1KkNKqXWFRKSU+eT277pauY30QggpJkMHwBBSP6zVtEphohxTQJ6ltD4EELZlkphjClYUA66GLqds1qST3LZLUXMgpISU0AUBzKqoB0I3EJNSpahxiA7QUmuQMMeuyTHc3VvH2ogIPVgpxVRNT8zOk9gGyQ1M3U+DfQUAphBih26mTUB/spH6KV6FiMjUnJ6D0JixChi4g4gCt3EuAgJzMPNlnrWRNMwAMERmM6eTLsFa5nlT96OpA3UpzbkUqQGo77rURQrYXqM7iVRHcofATMSqVk0IlCBETKdgYAoAbA4q5qFpnxiIG1eUAawpbM3UgAKnmHIeAH2ex8Nx18oZFdEWUenOHAIHi9D3w7IsMQQ3Oez2KrLZbBw8nV/udk/NL9YSF1OKtbZ3DUMIgVnmmZG1CBDn3HEOAF6KoFkgVlAyjzHUWlMIzeOSKA5nvYpcX10vdVmWmTnMczHTQuIn6re3NVDmeQ5zqctxPAzDWlSbdwbAQ4jE7ICi+vbtu2Ho1fTu7g6s1mWs1b/+6pvVaoNAomcPDw/jOK7XazNrVJOGN5JaU9LW69xtt9M4AiLHsCwzIJm7igBg5LB9ejJTBzD31bBCBIpMFBoXJeVs2gaABIgh5b7vI5lrDYFKmfuhn+dabUk5NeNY45iGEChTCNFMG54aEVLK+BwMElK3WW/69arr+pQimJHRcZRaatNcA2Luh24Yct/XKvv9bto9iohKVTMEq8uoKK/evL69faV1GY+HSMwpX1/fHg9HDqHL3X63rbXcvvnqVyKyjK7y+PjQ5e788mpappS7UupX33wTQ9zudv3ZxdXLV/ef7371y18d54U5uNkff//734t+/PDhzVdf/uVf/c3Z1XVV2e2OdZpLrTHGMs1ffvnlarN20/32aZpG1frHP/yTOX751Zci9fHu7vzs7DjuP314/8vzs6Jay0JEIeUXrzdPj/cqJaRsqof93neHnLuu7zBQrQIAgYiZHXyaphTTejNcXW3+8AeotUpdTtZfNzMNJGZKLb6Pw8npCsXJAUBqjcztUq0qqoKOdRFzZyZmTGkFJy0wpphLXVLqWrLQvEwUWNVKrXbSjCUCctdai4gF85yzQ8vXo8SBmFUVn/koTbxoZiI1xnB2dhZCGI/jOB9Lrat+3XVdqZJTyilXFaju1cVMRQAkcgAmDhGR5mleliXHBDGqCIVATCEGrdyYGU2rGmJERHNLqTN3UWHm3GVCanR7DrFf9eM0mUDgADECUiZy9wUrBo4pJseyLEgE7JEDx5hyOjXlm8O0JTUxIwengC3f18wQqwEReLsEVFHzwAHAkZyRYoqIDE2+TwjIgQzITJEIObRk2eYQPuHx3cEbL/qU7WTuQMzQqHfa3i1phSkApBSehzccOIhqi4NTV3MvWpFiInb3Fi9qakVqrRWeXWtNCSoO2JSYgZgpiGmzJiCfAh0RgBAiExMTBgdTrc33FBiJyBREBYkCYQuvrlKbo0Sazxiw6VUCEVEz5bqaPyc4I5ITYWCOMTGz1qmUaZrGdkVVN+IEajHGi7Pzfhg6wFbzimufBwqY80pVYoofP77/4/e/f3l7M/Tr8XAkppwSIBaR5rfrUs59J6Kxee0DOUGV2uZErRNl7mWepIpIFdUUEgCJqYoihyoLEgb34zy2D6qZJGOMhLTIDO5EmHOmwKt+vVqt9vvDdvt0dXUVLuJSFjP7+On+w6fPuUvo8PnuDglT35mUWus0TbXWvl+ZKbjd3tzGEI/jOI4jPg9zpnna73bH/T6GMI8jMXfcOwEzxRTPz84Ch3k6llK0FkIy8xwiQAMJajM3pJxEhJwRoeuyitZSPt09qizzPC5z3ZydE2EtNQRWqaUUQnp6fFpv1s0oICrSphHupSzr9ZmqOeAwrMX06vrixe1Vw5WLKDj0/YoJl2VyIw5IgUuth/1uGg/zPKbAiEaMdS7LPPZDv9vvkMjhblitbq9vKYTtw+M8HYZ+9fH9u/Gw/+Mffv+LX//Vt999Z1LvPn0cx/nF61cPD4+r9YpDiAbMwQHF4M3X31IIFNOLN2+Oh7ELcb/bXV1d7ra78/OzVy9fgvnj/QMx5a7rhoFKyTlx4JjT3f3dH3732/V6Vefl/PwMmfrVxsyWpaw35yFFmg8mdb976larlJK6UwxdP8jdpzqO3eDrzVlI0YFiDA3RS4Qx58ChdTa441KKmr58+eL65urhcZe6nlpCCBoiBmYzMTNGAjoNyYGwy52oNgxiYI7DupSFYiLiUhZGiuHUzGxbTGo42AlyzrnrAWypBYm0SiTUegISuJmKtqmnqopU4hYTBtgSs8Xa+FHNCDGlqGYA1hQKfNphs7tzIFUNHEJMZ5tNNaHDbj7OCNAEyxDNBJJBzjnmnDicWjrMhoDExORmpNbGsCHGkJAQc0pNdkEihMgQmEMMqWoF5DYIjSEGDhRCTpnMaxVgpsANyFXKUkqpSGBWqqSYWnXCgduoVk8hkq2pRo1X1gBvizqgz3MpUpmDR2ViJgpMMTBibFhmMnP0plFx1tYiVpVmhGla3qaPDzG6m+pCCMzcMNSEqCa1LOZioCLq7sTMHBCZKIiqQz2hqhHMfS7VHUUNHbouQ2NUNP29/wyJowAIBmji4sboTB4BEJqYCIMjgaE6ugeGEDBSRAzq0rB0SEgAzSfizAjgjiJawAJhjIkA0UxbRwwaqILAlRAAyQBEPTI0VSoSRU4xRiAHNxAxcAdgpsgsYlVqiHx2tg4pzqVSoKJlgKGxL9Rku31iot1u7653D5/PziTFoYo4QGhw5nm+u7s7Pz9floWIxmUMIVIld8Umo0UQbSnKpm7mXlVUa+aIgZwRnfbTIcfYxY5iyJ0ejyOHtFptYowxRDcjwK7rxHWa5i6m3HchRiQMMeauUzEESjFSpq7r3GG/37188UZNVWuXc7sVxtSZ2mq1+vjxp75Lt9cvUkjxvAPw3Perfnjc3u92j7XU9TDEQIigS8GUgd1U3IwjMYcu5WJ+cXkpqg2l64QtWUxV52kGd46hiU+ejtt5msoyLfM8zdM8TofDoR96V+tyvjw7i8yHcULChpkAxBCo6UxSiiGyWi2lTPNyeXX55ddfv3nz6svXrxDRFBAxpaRWW/akmdey6DJVqTIfQQq6iyoSxZhrrYDhsD1ExjJPS5WuS3OZDneHw+GQEr/959+1Fbl9enj4+BaAdrvDH37/z69fvUCi3MV+6JZpBncnDl16/eZNHoZpnC+ubpgp5O7x/v4//cf/+K//7d9+98tf/D//+//h4/uf+tUqdsN6vQ6BOMRu3U3jFFIYp/3x6eHdD9/n1P3mN78WrWhkWrfb8eL8uuu6cR6rOQUed7syzyqKgR0x5Nx1A4oy83Q89kip6x2szBUJY87Y7HilLEtpjFgmVtHN2Wq92ZRaXaVFquecAwczEZMmDGmNQSRKOZc2KxLNMaaUJw6tvxdjdIA2pD1BZJlDjKd2eeCQ2Ixy3+eUiGjoe16tx3FsQbhd12fsp6UAEDWrowOCKXpgghjMPaTADipiYDEGapRKswAYQogWPz/cbXc7Qnpx85JDUDdCDhBijA2yTzEigYmCeVlKJmrxG+qGgdu1Q1WBms4dzbTIQhyIEcEbmSZwJsQG9QSA5J2ChRBySuebM6JQl4WJzQUC5dzTzyh1DhiBDJpVM4cQGIHI3F3U0FfUA3LDnRbRIlW9ihhAa+BAFV2WiihusX2CTqSqKSYgUlUFZSLnhsHnwBEAtNlGEUQlcmhdIGSWZWEK7i1M2t2tqLRmWtUC7moKBsyx9dU5sLpLmQjBmpfNwR3B0AGNseqJn/oc4dmkR1SrBDA3hyYVRVeGGOnncHpvPGoOhA4EEImY0U4AZDJrbDxgIgik3losjfaKxC1uDM2xCfnNFRFbbBiTt/6hak0hMYMjumPgyBhNF7N60k2pMkA4KbkcwDmEoe9z7rZPT5v1Zr0+O7+4PL+4LGWZppmJb2+HcewdvO9Wm815rTXlBIi73S5SfHX7qsk6RU+bfNEqUlNMMaacs5vXKtjM0AzMRB6gXQbF3Iw4xpiqCKgwh9Vq3ei47S4cYzeshpbso/YwTdMJyIfw4uWLvutK1dZza+OWRuVk5nmZQXCeFmbKOeducPeckxlsd/vLq9tZ6lKXwKFKrdM0LqOax5gdEImH1bpWUVVbPISk7ofxOE9TUfHATuiOKefVahVicMAYYmOyI0DKuRF9TTRyoK6fpqkN/6dx3O62arpM4+31zS9+8YvWdb24uChLEVVkoufcUCLOMWtnDm5Wv3j98rtvv61VTmA/JACoVUIgU621VCm1LnWeyzSW8Tgej4aUV5uLzQWZynG3GvrAdBiPT49bFTnbbOb5yAhS5f7+4cXti198913qO0QQVQC/vDq/uL5Ags1mA65EoOK73WFwvFqfAVBKScsCjuvzjTO+/uqNAQ6rzauXr1U098PFzSUBIViZD9unbY6p6/unu4fdwyOD/fGffnt1vrm6uTiO436/Pb+4Atenpwdzu375+uLy+vHhfhqnvu+G9Zmqm/vlze2HeQoh7HfbWmvqOk45pr7r+xbpERhzzq291s6Ai/OzQARmstQYOeXcMk05cKQQVIWkwbKsdURjophy7qUsbsYxdNipmpk/C2aAmVpf/mcfE3MIMbi7gCJS4DgM3OVMz0nRTc6gbkE1hkSIjX3bcsZjCDFmUQ0ckECpnV4kyCfkIhBRCBzO15sQ01JkO+7iHGrpU879MCDS8XioIu4K4GJKFNAZyzJNY46xMXdaGwQRIrMiuDvH0DYoV63m7p677nlfw5RiTAmRj8uErU1O3PdDCaQiyCFHFBEwK8tiITATqKkWkRpCREJufRHPgdndwalWkaoYyVRFZJ4XBIghtoZMu8DBM37Z2Z9tr0uMkcDVrKqjUo4JEZtcgjkDNBceAyAhOdLP7GhEqrU6MhCL1FKWJp9TcfA2t3cVD4xVxEzRzdABXET0NGZFAzf1WUvzrbmht6wsIARUtQAAria1qimYZnRIwa35gEG8VnGVdrsMrcGE5OSnPb4prQMzMy4iDoQIRgnAzCEgubsbGLTZKJiZN+06tTm+t7wAxJYnGWPMDlaWqZb5BPAzRbcUslZtsQgimmIKIa+6VdUaUry4uNicbWrtpmk6Pzsj5MenBwAfhhUAiKS2vYYQckzPREDzxZiQOcSub0jeyJyYPUZUVyTDE20vMDtAKSXGOPRDeJ41iQrAifLXxUTxdG+ttbpIjDGl6Gaist3vVuvVxcVFrVLadV21GTuJkDCYWYoJAWotalpFHraPrsYIL1/e7vb7w/FYanWCWoph5Q7ALIWIgK3NOJfF3JuKuT1YZlK0tCzO7X7HzBwCTEde2AGefctSSjVXVRnHY62l77pAQ4yHYbXyzi8uLlMK4zh+/vxxLktD2plKU9e1DUJU3AFqYWJN5o5dN+Tc7/fb8bjP8Sy2+L4QzBOglWV+fHrUUrTOZZ6klN3DZ3Md1ptutelylmV+erz/9OnjOI7Hw75fDb/4xS+WWn77299eXlxu1usiVd3/89/952++++72xe3Nbe773OW82azMJCBvd48iNeVuGDarVaaWdglUqy7TLCaisloN//a/+bfLsnz6+OmHH37MXb++u1ttVsfdzt0+f/pUS+lyv96sDXTVp4uz9U8Ev/3tP3xdvooxpdz/4Z/+ab/bn11c3Ly46Yb17uExpnz78mWKabvdb7dbCmGzXr356uvt40PKmQiXeR5CxGcoYgghxvyzObG5hHLOpZZaSwgcc6ylEqC6z8sE5hyCmmg1IjxJJ8DRjJHSMIjU1pNsgjQRjTGUUkVEVUQ1MA/Dqg0lGanac4o1YcumlipM5GCRg5m5tVGTM1GIDGgIRI4hsIoSoZqYarPJAzhRaJ36552Gc+pCCrnL7z982M7L0PU555yzmdVapmksZVF3NytdafYed4/cVC0xcCAmd/CUWigKOjGxqgQOMQYRbfzUNoVW1ejAgVvSOBKWUkIMuctSqEkhCFxNI6ObjuMREaWBAeY5xoRIrf+OiEwoaiqmpmx0UoaoIkB1aDeA5vP6WQdlp1hDRwLCU4+krcZIgZ5Hso0jd4p7aTPYfwGdbkYzkdpw/ngySkGb1hJRG/MSCYgitnQEY8LTraFB/+Hk2XI3brPg52/XNt/gTWSuJlLcxe2Uq+Bup+mTtZBCbURyMwHm9skyIwClQDFGFckxAJC3A8JUVZQZANVF1FSVwikGkpA4YM65wT0MnEIC55B7jkHqVJZFliWkBEDELKhF2yFlKuXx4TMgrYZ1RC51WW/W036Hpjnn7756k3I29RzpOB5TTA6uTIBYCgxdSjEAgJs/bZ+6GHPOZalMAADjPFLu0tAzZROZyUEhOBJSCKRmJjYMPbnUIi4cABAgprQa1jlGDuwIRFRqVYFEBCLnwwr6oajs9rvzzfmQ+4Mc1qtu6LOW2riJbj4vs9eqqibCCC0HmL2ZBCHHGAObCbiyeghBVEudUggvrq9aEXeSvZ2QYFqnaR4PtRRR4fa4IKoLaJ2XiYgoJi1LM3aZqpYZAEwVVEzqsizD0Hddujg7H1art+/ejuN4c3O72WyaCmsg2h0P7i5VQiPM9DnFtNqsz84v+m51d/ewzEpAt9eXq37dRnntGW7bExGrSDkczKUss6rE1OV+3fXDPM316Wk87p4e79+/fVtK+erbb69vX+72u1YFjfNIHF68fLHU8v7jeyeIgc8vr8yh1BJCmOfp6ekJAC44LMvCITLz6Rl2Y3JT/fDj9/v98exsE1L8+OFT33fO2K9X4/Hw9HRfl+W43YaQdtNiJlULmX2+/zxsVhcXF8N6CBxc7ft//v39w8O/+/f/fprGz58+7Z+2l1fXHFJMw+Vt5u32uH8ErSmmfnPWrddWZZonYoopVlkCZDdnDq0v3zRUiBApvH71+uPHbS2Su7afIrg3SZw7IHatjDIANcsxMVGtsixz5KyqHiMRTT4hODOtVj0AzDME4xijSFlUI5MZAHjfZ2aMKYQQwL0WrbWY65CzO6np0Hd97sC9qKgbgsWYEKGU2dyJT7redqXOMZs36hcRUcNvkKO7nQ3DCKBa99vp4zQS0asXL1dXV/cPD7v93kxd1RFSTOa2uIcQu653DuBAxF4l9blfbS7OzqfjcZ6ORGimQ5/dtMV+NNALIjBjzknM5qm0uSSwY6NjCjBzw9SXsszTSIRSxbzdqawxMdvqwGZuJjCXUq3WqmqtwG/OJAQ0O9X+DdPThFIiYgABOVCzrZGhqSqeOELeyuUm+MHnaMkGRPqZiGNmTgBqrtLycJijWWvDgLu1lM32WpgQmNCBW2DAMzr0+d9OgMSnw6N5gYOIiGmRUkVC82+qIkAjthGSaTUzb1G2CG27RkTidpJhCifmRUB2p5CSuc7TBK61iiOKipg26pG5OVAz8aWIiAZmzIFCdEwpZzVTKSIFkQCQQ2QKzmpNiIV03B3/+Mfvv//xXZc6bubblPq+zzkh0Wq1TjEyhXmeSq0NnNlipgGh1sp0Yti6ewyh5fBIlSZ+iDENfY/Ex+PxMB7Mm7zC2wweAXPKMQRDeBZihq7vU0wxBGISEVEttUbmFmeMiKZKkc3s84e37r6UkroMDilEFVURYizLoua1FA7cprWqQk3OrLXUQsyPjw/gzgBiqirgyMwhxK7LTSfn5gje5c7UapUitTUNCPHZXN5M2Mbtas6hkWbLsjh4DM2zY7LkaRql1nE8Pt59evXy1WG3f3p8EpXtds+MKQZwGA+HYRgisxLOk4FvCH37tASG2+vrd2/Hea7MEGMoZSZi1aqiSynN/5liqugGlroeCCMBUiDC8XiYxxHNmrS/6/qU8mZz9vjw9P7jh29+8fVhv394eDw72+Sc//LP/3y/Pzzc3//07n2pagAPD/dff/UlmPddl3IKIS7LPJZyG2LfwzJP4ILotSzjYe+qy3xUS7WW3/z5r8+vL0MMx+O+7zMTrPoXb394F1Pu+/7x/eNht0PmX/36z26ur7o+l1n+6//6n4+H/f54POx+DS7TOA1dBwD3d/cU9kOf+5zI9PD05EDD2abrun61mqZxnsZutRr6QRSaFLvF+LTQPQdTs+ubSyJdlqMKqAohVhFo1SVAjLGKnCyebkycYljmZbvb9X3X6sllKdM8c+BlKYFpWK0bU1jED4fDNI5dzk3YwyHUKtunhxQTMx8PB28GAjNwKFJjjKNoWcpUZmJuSwLdVdTBU0qEYE2RUauEwuGUwXeyOSGW2ULgLiVTPY5jTMFHm+flsN9vzs/OztYh0OF4rKWoWnFv9HU1B8AZPBCHkNilWillmY9HJpJaYkqth75arUSXKnCq+YoejnsHICYVcYMKQGBMWOYC7qI+zfOyLEwU+CSWB/AYQlnQHEpd3Fct2Zc4noDYMTVcT6AA2OYR0FgmrcWCeNoaAU6OXwdv1LOTJwPhpMH0NuOlWusp1d1EGzni5GFquxc2dFrT4ZAiMrmRuam6mKA7M4G7qUHAJtFH96YGU1NEMlUzBUZHZAiRg7mBk7uHKtKM0K2hE5gQoIgUMzCJHMwADRmpDawbZgLb7J+4CyEiiNdGbUupJyIwD4GkKMAJF6r2nF2JaA7qiMCAkgITAYfW2+wUSUuRpTIToM+1jktBhBSDSEs8tvk4Pz0dcw4NHmcmbRTTcNMNPtqamMRMiKonhwgRmQO4IToiB46E7mAt6ObE62g3MkJRLVUQ0c3MlE53NHym9AEhESIRNPs7NSjrz4dtCy0FbOQmB2sJcv48PmHAxpFV0xO0D09BvkTtY2/kvOdOATxLEE6mQaDG9QRvOu4mIWAOKSVvtTwYPt+4murU1BolBqAB+E6vR6V6Q8PjySfp4LXUlknyh9//DgAP47GqAkBOkenZfI/orUVLSMxd7szk4uL8L/7yb//59z9Krde3w1/9zZ+vuzU4ai16uuGiiiMihTCcXeSu4+P+8Xhcxi0cIeU+d10t0nj3kuTHtz+9mOe63CP6NE4vXrxEw5/e/ahgm9X600+fpmn64ss3MSYONHXd7mnbpYSOy7jslv3Nixcvv3yjYuNhB27g6iKEcHV1WZaSh84ARe72+717MTAw3263MaXDdnc47F68/qIbhr/867/9/PlzmaeyLP/4D/+43qzevX3//qefbm5uhvVKa7n/9Onq5qZbnXHMzNHN5+Px+Lg0iicxz8cDAnZd72bLPNXVhijEPKQYCbFBxEJokRtkYoh6f/f2f/1Pf6daGyarzVUUTmt7XApDU7KflkADCIWWVoVgYtWlpU0RU+DQokPaqMasQeug1socmINb4z/4c0eIfq4fEVEN2nOFiA6Op4Xg7i3tsa2en4XafEopIXIkc2PmUz3k3iK9aq2q+vRwb45dn/o+z3MZx6VKbaA0B0C3VtRjA5+FwITs5IBAwIHb2mhtT24FMHEbLhJyCNyqaFFFwsABAEUlIIF7KeUkNEJXV6ni7jGGw+5wHMcupfbTEbEjtzQUpQpgzw0ZeI4za+8rMGMMoRnKljo7OVMPSBAiorM3TYmLalVzgEwBvJmzyFxFpUrDMYO7gWGT+DyzM1ttYIQeyau5EUROIWetUsriqoGDKzgaMdIza8fUrG2eAByQ4NQ0RA7uEIpoS2RB98AhhWDuS6nFjAAJmZmJmZQQ8fkYii3VOsbQcBxqWkoxbUP1dqNBNWNyAIzEHAMREEKLvwnBT5Ifgi4HCpRiNghjkWWZa1mInBCLyFLKUoVaixkBiavIeDggrv/sV78mjrUubj7Nc9d14D7Pi5qYWYxxmqZlWUJIzRbYknpMxUwbcdpdHU+Z9U2F28bfSOgIbm6i0IBDgC3zot21EIGgMYzaI0Kn0Pv2tQChoTXwOeDzdLCcmEfuRuAIaEDPd0mgU5ybIp3O2ZMqCahNPrwNS9wJ0J8Pm9Z8BAdsRvfn79Wez5//C5HsmVndfoPwlKf5rAz0n/+uu7chjzswtck7qpuqns6JFjAELfLhBCppI2AEv7u7//jxfhorc9ru9iEEZkZAqSaq4fQaXKUSp9XmDBp3DymnjEzAIa8GynppV7Us24fHv/6bv4kpIMZXF2dLmT6+/xBDODs7CyktS8lDf31zQwyBMcR4fXVlamUuDc46Hse7u88K2uWOCUudp+Pkag8PD2dnZxcXF7XUx+3+4/t3D/cff/nLb4loHucQuMzLcbefpvHh4T7m7urFzcs3L20u/6///n/8+OnDr371zdmmf/mv//Z4OIYQYuTjeFiWZVhj33cc8/FwXMocmSJzSimEyN3AMQPganMG5vvHh6XU61c9J1bROi05pxijg0nVFhL49PD4j//427JMIoudUFrUus/uYKdcQG0xHyewzCkX8NQ8dDNCQmiFAD5zAZ7LlIYB1uegbAQ7PQunP/Xcx0dE1MaR+FOdZD8/anh6jE5ftj2QrfMM4Ah8KtFaf6HdYuyUDKyO2PofDAzB/kU0SiuGn78FtgaLgwJRACIwR3SEJm9tDzXA6cbh0OjC7Vs1KJs3DcLp9bvb6aWetvNWRRFRLWUppY1RT+sVTg7Z1gkJMRC5SBGtqm4GS1VEII6IlAIZBaTUKJNMHAK7mZi4I3iDqp1iRdpHpqYiWkRLbaNzBHM1YYxArUdkjWNPBAgIISagjgOFaE6zWBsLgKO5EwAjBXJkrkJFirrA6ehHD+AE4ECMmWLLJzN3IOLTfu6nNGAAsufPsL13olrFuo5jjIAYmFyruY3zvCxL4rDMY+jX6K5VG46DOSRkNwc8MfCIQETAfRhyjiElDoH73I1FVOo0jwzQEJXtuWjoU0AC8GZxal3LX/7ql+DkbuAwTtNms3GHWsu8TIj4+vXrd+/eHQ4HZq61TvOkoq1BL7W6w273+Hj/GduODej23CxDcAVEXPdxNay3u8M0l+b6hhOuurXvGMD7oWOmw2GEBgdpxXkbq8AJgH36ndPf/Pmf9iepfcPnxQl/2r9P6+15IbT/xlPKJyISUrNqPP/J5+9yuqch0L88AuB/85JOX7QdPw0DdZIttOPNsIW4nQzs/pz1xMzLXNwc2nbv7ZA7HUV06trR09MBML356tsq9vjwmK4TEalKA7y66TyPx+N+vTnP/bqWxZFCiOVYxsM4nJ2ZOYfY92up9sWbrw/Hcbvd//W/+ldg+vYffny8v7s4P09dXq83HGdiur64QrDd4SB+mMcpxXR5fq5mpZQXL1+O07h72sEGpC673SMSErKDidbtbmsOIvXLL187+N3d3dAPBLhera5v1inE/JC3293j3R0zWq33n+/+69//1xcvbi8vLs7P10+Pu+/v/nh2djbuD9Nx3OJjTv04LqvNGXNYSqmmjDgdj7XKzavX1y/PxCCkHGJsKHz7UziGNU2nmohVdQGwh4fHT58euj5N0zJN0/ND4Ahg6gZtgGSnm/XpqYL2vJ0euOfnEZ4/sD/lb7flBc+53O4n8d4pivWZY9yGmYiO/3JfRmy75p+qjdPTcpIPgrf+hbujE/58avyMpnl+yQJI7ugqKiHktrp+/sOA//IWQqLV3ShEAgzgLRL353PJAQiZKBAhNTIaQDsAWrv8dDgBmLd7sz+/Yc9tdwAza6V033WtIAuEbtrOlRhTTBEBynx0EwAw86VKlZY3FVq+Qk7c5ZUCckgp5vYRN+ZBK51EFdROaeoA5l5VS7UqBkhtlCduMXDm+Ex1dgQHA+YQOD6fdsGRIHuti7qr24kphNDl2H4+iywqLXXAzMU0GMGJMxqD2899YSakQHTyemjra7RnAZudQUUK1hg1BGACJlSHpdalttBSB7d5mZtRgpgBiJBCDCdCHoCblVJiBHQwVc4xp5RzZkJYrNbi5uoCZtjEp4B+KgUciayla4ExM3MDRjox9kPHoU0mcqNfmXqOHayQA9Vac861VmJqidXgIDpHuvzu62/mZQYA91NQjrmBAzFfna9fvrj94e3bh8dHDgEAGMncTovWjInWZ6sYwsPDU8umb5vwqUAyUNOTCg0JvB3q1tonjkiBXdyk4s90eGQgpCbeMG0+VbSThJQ5OmgVIcIcEyER07LUUhYHN3U75fIhEXVdx0ymujSlpoOZhhBrrbWWtg2YGqA3ZXdjPZ0Wnp/ypAnR3E5kQYDA/Ob1m/vHx4f9NnDA54ZAW6ntbSHCFMOf/fKXHz49fPntq9vbS0SsUkPLF6RA6Nv98enpMQbYnJ/V2joGJFKnpaQ8nF9cxa5POT8V+eLrb0MI97s/fPXdLyjnx8+f+pxsvQ7MAJS7DpkQ6eL6evfw+PHj/T/+9nfztLx+/frf/Lu/2azXuFDo4yZvRITMD9NUi9zcXhNR3/fvf/qJiN58+eV0PAQGEb//eH/sDn3f7Xa7qxc3MYT1er3b7/f7HSE8fPp4d//w9ddfvHpxu8zjD09Pv/vtP202Z6vV2e9//wczK5f19Zuv+9WKAp+dn5trmY6Pd5/32627V7PV2Xm3Wk/H5TAec+5S39UqZa6uQEyidZwOqmqmyzwFsm++fPXLX7754e33IUGHse2MrQd72v5/3nqftzY4ZTo9H9hIYAbPbeS28Z0q81by+M/lxen/6GkACQ6G+ryJAwL+iyLmT5XM6ZT535QpDQrW7r7tF891R9uG4OdtBQHA1W3V9S+vzn+6v1f1P5VZiPB8d0EHd7m6OhtS/nj/WBzluQ/TruwAqKabs4ubm9twSrYJLZRbTYd+iCHu9/uu62KM4zS2/k/rxxKhuRLSvMzv3r4zNYoUU2pbTy1FaglEMcQhdRiIkchU6oJ26ueoOhKouZpxzDEGAIghY8gcGIlLXdAZrIgqhxCAl7lUQUGFZ6La6TBoWo7TsQRmxuAI0BSXRJRSVCdt3l0kBwJ1MLBGPUVENYoUIgEw4CmJE56LSzGvaolPoaShFqnW5tfETCFFIk4xiP0JugQnP5QVUSSpUpAxYwQHU2lNCkE3N6tqBojUYqd+vjM+zyHteQMFDzpYCMwpxRiDiJiKLBOAiWggIkLXVuK2ey60SDNTO3XgHYjZ/ESUa/tsjKldrOZlpkDBOOVkZsy8Xq8dYbfbRotu+td//uscMaU8zwu6MVPrsddaqwmHwMiPT9uzs2G9SUhIFFKISBiwdUWMkJqz4cvXLxx8NQwGPo8TMlsDupoxcXvl5K5uYs4hIQRDRCbX5ipAJjbR5+4amWqODCrLUpCit5OfqJFN8bmSSpGfk3WAmMGhSst25kCsaoEZkdzc1Io+K0lUkRwAUK3LOQRWrSb2vDYRThLSxqsEAzNwBIoxEvGbNy+QToYgawESdHpFOaYYmCL1w/Dn8zeXVy/+9V/9pu/T6dNhNhNRMTdCaqQwkdrcZN1q3a3OVmebYbUKIY3jsZrdfPE6cry4vpnmaTzswPX29sVmvX58ehrH8f7+/ub29uLi+nA4fv/9j3/84w93dw9ff/X1v/tv/91604uUrutqLaqKZu8/fBrHY87du3c/XV9eAcJhv++6brd9+v6H769vbpgDBdofD8hYVY9v55e3L8x8XBYVCYymOvT51euXQ9eNx/Hh4eni4uLs/Oz65vLm5vowzkQ0zvPF7QtDetrviTnE2A/D1eUlMRfVp6fHC+LAfHZ+ZYjD+swdSlkQCAwRvSzLvEwpBFnmpY7rPv3rv/71X/7ma3QB1TYCUG2TrBbiAUigZqqnulrdTQ2fxzyEaI7EDGYq1YDEzKwSABpUFXCzWkRUALu+I+Iqcqq0pLRqmRjBoVYxdRFxbFtP0wQ6IJxghXJiPTmfZN3NkVTF1BopDQBAT3qQPzUfq9YvX7387usv/6f//He7Y/l5eiYibe9wMwCY5+kXv/jy199++//+H/+/+9YrgedI9CZWLLZZD69efxFOAzquz7EcIYTIoekCYozzvJ7mCdzVrO975uaStXE8vqf3LfeUidQkMIF5w1gF4hBCk8qCSVOYtuwPd2QkVUPmEGLTgVLElBISIrFXrCpmCgaIRoAhcFlmQMQ21DmNc7yJpvyk2CdVYorMZGIUQuSABKYgxcwNrDKSgSICNfIzoJA3/lxzMXPgDK4Kaq1hgLUaQgD0EDgAcpXFzSMTnfQAzY1ibggGduoLgipUdRKJtTg6mJBnJEwhBgpKauCROUYGRDU38MAphOhq5grQrGgKAGLGHJt8mBBFyrKU42FPYGZSpTpDioGIpAqCR6a5qohoqW0U0EwnzTTRiFeEbGYphRgCEecu11LiMCDhNE0AsFqtHHGcl4DVVW+vr7fbx/2iIjwd9loXjoE5ArAgsnNkUmAQBEerol6W+el+++BFCQEJAzEyMxGgM6EjGljiEIkMARRDYGamkBTQazFX7oaYBsTOAzoCAjGgyxwDRSbgKEWlVEQIgco81bpUadnWLRJlrvNs7ooGRoEIAdSsBcsiGDhmDu3SH1MiJjdn5HalAFB4Lu0MgIwCBUOoKugYmJGarTQygRV1AKCTIDpxDCGKqbk1CvrPV2pGcPATHhxD7vPLVy/n/fjwaffh/af/y//t//of/v3/YR4XNVeZp3lExGG1aor4usy1LGrWrdb9sAEwqXLY7e8+feIQ0Onzp88Pd58uzjYIskzTfikO3rIvfv+73717+/a7736Ru251sf7Fr37xV3/71998+81mvf7hj3+YpvHlyxcxRHQ47MYP7z90Od/evJjnqUo9vzi7uDxfSgkxEcaffvp0fXO5OT9bmfd9V6u0Bub79++7frhYrwDh4fHei5+fn5VSAPCbb74x0/14WMpyc/tic/Vis7ngFLbb3cXlhYJtnx4ZvJYK7mfnFznEYb3JMW3H3erskpgQqC11IBARJgRXN5GqT48ff/sPf/8P//D343EGd9OF3NS9SAUzpmDNHGtmjQysZm4NG8Ati4laB9AR2b3tQsAUAFVQAyAH6lI2U2MEB04pDz0SAaEDhJjUKqox0jwd0c3VQ+45RmY67nbk6HiaNAAAh0AtdoqBY0COCBgYEWgspWplgI45xlhVpBYiyjnNpVh1N3Ny8fKv/83fiHhr+ugzVhkRc+7cpN2iqpe//Ve/GedSxRAM1FuEVNF6fr4ZR4eYI1OIPC0LEbVce6l1nKfc577rq9QQQ/YsIg1IDIBEbOqI8KybwKbkaH3QWotqdXAFi8RiAqeBhDu0jFtv8nlHNLOU4iz1uEzdZpM4iSi0EBiTxElVTZ0QTWs9gZOdmWKI7erlgAqkWhp3WNQCMTiFmNrHqi5LFUc2FwQDKSILOBCSqlWxGCMjq9RAxH0sU5msNlIEGABREePARWoQU1FtQcONKySiRBw5FFVEMhPVP3Wn2/3QVLVJMIkAMaXkYFprSiGlBEhVpYo2CQ0AmjW+uTy34VpMgHJgMxOT43hs4PtalzaeaWOJxqAnJijSmlXtmtmcp6aqpuBopbRhYwgBkHLXrVar4/GYUgJ35jDPc7OtRkKPUQh/98Mf0e36+otSCoeOmlxVl5vrF7HfOEQm5EDsoHUuy769GDHf3n0IiCFEkVrL3BCA4BCYFK1gvb24YvSQQkwhdyl2A/EApg5mgIf9weTYd6sQkwO5OVAE9j4nNaSQNMaqJubDKoPbMh4YMXA0N/N69/H9u5/eIVoMeTY00xih73PkoKKHaUnd6vxsDYBEGAK5u0pTbUHgqGJL0VpdzJ+mw2GZA3EfkgGoSFU1t5TzV1+9XHVJRICQIVaRLlEI5E7WBtxILQsJARkxJlbwcdGPD4/Ht+++/+P3IcXbi5fjPC7zCOAU2MGrFGuLh1lNl7JIrdM4lnkh9EBhWZayzKWUoevPbm5jSqk7DH0vtdS6DP0w9P08jnWaCOzm6pKIDvvtPI9dn//i9a9z7sdpevvuR0Lsum4cjwAQmOYyrVY9IHZdevnyhaptd08p56vrmxS71WozLRWRr66uVQ3Ax+PDUpbvv/8hpfjtt99IKaUsOeXtdvf0tM05n52d11KmcT6OE2HouuPZ5XD96vUw9H/8wz893d8Nq2HV5/3jk5TlsN8fDsehX5szQpP9Yd/3h/2RQ2yA6SqFUqpaSykEftjv3/34dtrvl2l21ZzYCA9z+f7HH0yVQ3IAbeKWE2cSHP3q9uZ8vQJVRDSXFtE+jUdysBCPk6nMKsWkuGqpZX+c5lrJKTBzpPP1cHF+sVmvmMlqmaVO40SOtS4RkcAfP34ezs8vztZzqUypxbMwITkErmrQTFiiYk4hMrqYm0hrO5SyLI6OcFL1xBjBwU0bxI1CJApd36+6HBjV1B2kqgPEEE2ViFOMh+OEgOv1YLWCmqk1i8Am9K9e3374dDgK5L5jDupea40xNe4IIuaUY4xmhgGJqJSip2CvZv1VZqZ/MXWIKTWhTym16UimaQy8YebqzXTznLr7zO1hwuadIiJUW+Yp9KHBERDc1BSUkFWrASpgERGwVe6Imq0qIAVArKq16mn8a4KBgBgoALG7qlYHXUpVM0YgEzMFIIUTtaksRVMCYiaeRRpElhHEDADNTUWp+rofgqhK1ZbgRO1ORwQAMaYqS61a1UqVZxHCaTKLAMSnPMI2d2dssUgUAoeUM8Iyl1qBADHwUq2Kup0UBYjITDmlEKKILGVZlpmY0S2lrCIE4ADNy46EIsrMpq0rBY09AO5ICAo/z8AQkUOIOXIMFDjmzCFgK0+IisiyLOAWOIrZH/74w9cvr9cRTEPIZ4Ri5mXad7SQmIrFFEEIgephNx23aqCIL866//O/+t/fXqyZ0RFL0Zh7pFDBNsMQOP3Xf/7hn959SLGPGA085kQhAaeAiGgtG1mP2+SFzWNKBlRCFINaUbREdDVQETctUmUe0WsKkTSaVTM/W29u//I3v/r69flqcKAUcBhoPQxDWoXY/0//8I+//+Mfb87OXIUIc+4A4Hgca62GwIxl0SJWVXeH8bur9Tdfvnh1czXkZIjH43w4zAA0lfru84c+RILMRLXM1oWbq3NvA0qxpUpzrDR/ZhPREngK/h/+5td/8fWbWsvf/fZ322199fJ23fdmFmKcp2MtC7ZywX0cjwgw9IOpjMdDjhGYOUVSOeuHzXp9mKb9fhcRU+D9dnf/8LDfby8vL2Qph8N+GsfHp22M8dXLl1X10+fPXTcQcUzp8ury1asvUuR5mT+8/+m43+cunV9e1lrff/zw/Q8/Dv1gphy4Fnn74x9+/PHdF2++HFar9WrtiCHyOI6fPn86X29ev3ndNmUAKEs5Ho+rYXV5ednk6ua2Wq3Gcdxt99cvvsx9369X/bBaDnurtdbKRBcXV4fjcVivU84hxsPhAODLMg/rVc6pqjk4EqVWjKvllME15cyIN+ebOXib81Zw8frf/Yd/85tvvvaqtdE+1XKOiJ671fcfPnx43F2cXaMTIrlbykldD/unHAKnbntYiKELoYxjGY/zNO5l/ubNq3VO+8P4+x9/rIvkELRIHvqzYVVkHt2lKKU4DN1+3OVlPj49gtnZ5qJPSU0Db4YukzkxGlDKnbuVeRJRAFCZpnl297mUTw9zDinn2Io4EUHVyFxb1K9CxwMAMwZwZAqELCqEzsxgXqsEsmUSxMgcWJ0BIVAtRdUdRGf/3X/8XxbJv/rlX1DAEEL23Ax0zKmUQkTNagcAKaVlWRCp66I9i45OzAhqo3ByNzWlJtRmOk3+RJYyN0Gtu7fJMBMTYYwhhACAjiCmTRxnZlUqAkqppubgZSk5d4ikVczdHWsVidpxiswxsDotVQwAMZ7C2s2NLKaESGrPmgGzWqq5OUJAJ2Jz0GfnhAKIWdOMVZWTexgMHBrYLsbm0bPg5maOkWNqYBAAx0Z8JWJHa49aGz0RQEsOwJaJTNBsKSoSm/OaMYYYQnQEDQ4AgYKYwemk5Od3Gfr+xEJxh2kapWgIyVy7LpiZ1QpuCE0k0+ZARM9jLnQMISJRK3YQsM12zAwRKUQxA8R+tWqUj77vf+4nolOK8dSW1KplBM9xWOccGdBX3WH7ga0mqTJbqWpuUq3OIq7GYdV3X9yev745X2SJMYbYcexi7nNOhq6LXFxcSAzvPh8XIQVQ7AgiqAUE0ZICxRSnp1rmKUeGoXMkxIgYzCyC1bHUJm0ygbpk8hga6odA3EsJkf/sl9/+q199U5eJQlj1MSaLHIZ89jSahnB1+zohIGhMbKqOyJqUkZFEarEySV3q/N1X1//ub/7yxdWZmTkQgLe2vwOKWP9P8b/87h8vVqvgdHa2evX6tktxHo/TcRH1cS7mWEWrWFEtattpXMr8zZtX/+bX3315exX71KX4f/9//H9SCkwAiGp6OOwOu20/DEyUuw4AS63Ovlqv7u4+dX2/3myenh4pcMppv9/ff3qPzAwoZdk+Pj083L24vbm+uJzGcZ6n3f7Qd12I8eP9/d3dAwHGuBLRFy+urq9vHfFxu18Nw2q1qVUvLi/PL86l1mmex8NxNfTLMvd9n3NOj09D38cUL87P53m+ur4Ghi/evAH3j+8/7Pe79dl6aXwV0/u7p2WWzWYTQuj7YZ6WGOP19ZWpq6loXZY5pbid5mWeVxdnN9c3jw8Pm3R5dfNinMbcD8t0lCJa6jQdQ4guOk+FYx76jXphirlPpc6x7zeXNz99vCsUCUwdDuNxs17/d//uX7+6WI/TKGpVRNRW6yEEBuTjfHjYH0qtOXVIhIDjNIlMiG5e9/fbFLqm9Rw2Xd/hmaSXRL/85ss/+/rLiPb5afsf/+s/vX33+XhcpmkudTlfd33Osy1SZSnl7Oz8//S/+w/77fZ/+E//y3b7ub++7mOIbANrTCHkvJ+XGCFwHlJzlkAKPQDNtW4P+4uzs3/zF3+xzuxg+2ka56nLOYdIAErh//f7Hw+zUogpEqKBmruXSl1oEyZPkGJghFSUjCN57ZmXMitRJKzqYP60G2eRE67GARFbVyDFFALP89K8S+7eYDDr1Xqap3mZW0+Zif15ut0EbvM8h5OShf05AFGtqlZ6tnHFlklOHLllKLQdFOy5aWJaTaFxOMwUDAEghDCXusxyknK5U/NXIk+lmTByEQ3koU3xEcBNtbnZoA30A5GaIzghtyPqNIxGUIDStE9AZialaWtbj9EBPcWYQkCAoKYAGJjSM4lQUFWVQyIkMDQDVUN4lsI2XStHQlZxBVepzyICaxikBppSNqnFTKsIOLbAGkSMKSDCqu/73DnCXKooYIhAmLqcgKqIEluZxU7+PAAEasMeb3eCFEIgnrVU1ZxSOxqIw1JrWWqXO1UnCl3OWitxwMCEFDgQeEiRSyGEgEyU3AOEJM8+lzovaQh+mqGRSHOtESgY0nHWd58eb242gOBO5hgoEJBUK67o0Of45ur8xw+fiLrgCLpQw+eC94G1HpfDVqW2WAmXBTmIViaMhFX8OB4O87Tqz88urxnXXW7GuhIRzSEiFJ2K1KkKA4hUM3L1ivhP7z//L7/9Yb9Y362rmeiCFU0REbg75+zkhrX0sav7/ZfffPV//m/+IrBNpRBGAnYAQEFXR2D2X3/75f3T/f6wv7683KwHJF/muRY9Tos5mVMVmecyV3VkATxO43dfvfyLP//lNM+7wxjKfHV58atf/HKsZbXZxJjqcTLVaZqWacx9tzm7aG3rcR43qw2HlPuh3eqatv3+7uMyj6I15vRw9/Dx/tMXr7+4uLg4HvZ///f/sD8cbl7c/s1f/dUPP7796e1bQn/96vX55ZWIf/HV191qLVqH1SoxA3iI6fz8gpm7vOp6ubi4+t0//iORX93chBjefP0NADtaZH77w4/jePz666/rPH/88PFwOLx8dfPx/Xs3GPpeVfu+2263Hz5+evXqZe67+VNdX1x8/fXX796++/Du7dnl5WqziSlfXF6+/fEHcbv/9Gkaj9/96s8NYV4WRHbDvuvnac+JU+rmUkLKMaQuZ40cI5tKimm1vvjqF7/66fPTfB9DTl2E6e6nX/36l5fnZ8syFTVRFRFDWKoghsf97v5pGzDYcqy6OLKZgRtYDTEeD4fDdne7uQjGJAWIGjagivyXf/jd3d3jX/3mOyDQUk3dHc1tHEeVQkC1qrj5svzb7758fXP15cuLF6+v/u7vf/f7tx9S1511w7EekDDOCQiLSwV2rcs8l2InLiSxGmw2ZxiIAiJQjKHDPsaYUupjXKr1OSB6Smwqp/hfc1IR9V1Znh6fUk5XlxerGMkkpxOixsy6LhKF41xMNBIuDkQUMHAI7HqiqzIDYk6eQmiuHVW7ublNKb3/6SdwjDGGyCKVsZl72hXK3UHUmqhUDaEVsBgJrUyju7bKOsfQcrlTiCmymy0CKVLzHph4EakN0GQQYzqZ9pGACM0RqSk8m5eNCMkAmRw4EUZibdY5M7dWEwMCRg6qBQAR+KQDAW4KESaMxISkDqra1FLozTcCyJRiTDExQgwx1CpEmELMKcUQkNiRkYIDmsH/n6n/+tE0yfI0sSPM7BWfdBnhITIidVaW6Kquru7eafbM7sxyQfBiAYJ/J0HwmkvMYpcY2dNb3aUrZehw7Z94lZmdc3hhng3mRSKRQCI/dw9/X7Nzfr/nETVVAzU1TUlSym3w93McoywiksWEAAEFTJVNy9d2X0qyKU8x5gIFNCaD0lb1s9kMEBRgiAmdI0PnfBVqVYNxQAAwv4tTSbTdfwRDNTIAgiJFyqUlAQB934cgJX/tnPNFl1oFRDRRJGbvCYGFGZGdM0CRjETAHBAw7nPsk2pFuW2qKSUGRdX7gaBaWXahaop2dduNQ5y1FZHz3rNjJDRTVGNyCPj4+Pj44GKzic75YlpTUVPhAIwWTebzOWiqAgfnAYk1g+qU0rbrqjb827/7+6u74eXlbUUVM65mwSnEoevGuB0K2NXGmBeVA41q0Gc6P7/+7tW7fjfMK98QRTUH2o9xmCYAC3VN7ITdME4o08lqdryY9WOctx6BAQiIuLg6BYidGtQ1/PjTz/7w/bdHD44t5xSjTNkExGBKEYGmGEUtK4w59lP/7Ozkr778rM+pExxibpDZuV/8/Ke//tNX3TgBIig4du1sNux2w9A3s4VzbFlcqKZpauomVPU4TfP5fLvZmAETDEN3cfnu8eOnJ6dHH3/yEbPfbTbn2x0YMLu/+Zt/dXN7PY7T0dHxer0+OjmepmQI7WLpqzrvt9MwZDBTc6Garw8kppQykPX7frFY3t3ejDEu66aZhYPjwzevXr1/+w6Rbq6u0GS/73bbzTgOOcf1ahWnvNvtcs7r9QqQ6rZl7533Rycnn37+xfvXr7//7ruzxx/cXl0yO1cFJTg9Pdnv9y+++aZtm77bsQ8EEKcRASfJm+2dISyXB5JluToMrgKAEPxkOcVMiCgy7m+G7cWwuYlVE03AqJ/yzW5/Mquq4iN0ThGD84R4d3s3dB1MQmYqUxKYxlg55MC9xH7fr5q5sbn6B/lwUjNkJs/u7buLrOId3t5u+2FQoHZWV55VshWymcHZgwcfPn5kYJPmk/Xqf/jVL54/ef+Pv/vD1d115RsQDVWqPDdNbaApRs2l+CRmkkVDVXV99/2rVx8+PTtcLRskGGMxIAK6fbdJSYL3BAb3PUdUk5zzMEWR/LPPP7+4vnz7/m3t65Oj45qQmGMy7wOCpVQkHcLMSTIyO++BzHs3jCCiwZNKrptmtVymmJLkFHOc4m67MyuBwvtdo3OuQFzK7req6iwZANh5Ys/smAiNTDPep2oNkMCECUPwnh2Xy3QWQSVkSSJkSTIgeleZKLEn9mpiVpSuyuDUIKcsXohdqLxZzpoZiwyS1FRSKlEokQgAYEVRIEQEhsQsZqiIpATiirAQuYBC0KAI4hFBmQpBBomcdwDksphjcj5UdcsuiKoZAfIUcz9MKUtJVdJ9P9WyaIyZ2atkMS1NODETy86Bv28xgAC6Kug0ppyQyECJCZCR0TG3zayAaMZpTFlcCM6w9g27UEpMXZeyaXkfW8EjKTByKZ0Dc5EEcQRDLIK0Eu42xCwym82IqBAdkioxV1WT06SqVVUxFamnphxvr89DNdOcNPYGsI3Tfur7MT46Pqqdl6wpSRQrMrIMhkR3XX+56dcHKwLk+6YMFPhHEgOE4hDfQFJwpmZ5INPAbCk5tMqRKY5THLoYQvAhTJNsxi6m/qOnZ//mX/31Zx998uZi8/Z/+f/kNAYLk+xHySoSY85JxphjShdXt82TU6CwTfD26vLm6torHM8qRCONCIBgWUYDmXKOu5jUhLBiOGjrpvZTvzm/DrPZE+/IjJDIUMgREhMQI2XT+bx99uRMc8ZQgTiAzIzkuOsHBXRViNlSN8p++9mHj//qJ1/GoRu6EVkvbjdHq3betpXDH3/+mSd89+7NcrauqgYJQXWc+rquALi77uo67Hab+XxhZre3dwfLmSmIiXfeER8fHYPZ+7fn33/74vDoAETKnXK+mLtQbe62pw8eqohzrpm1L1++vrq++TGAD75t2900XlxezWazetYKwHx9MIwjM86Wi/XBweJyQcyO/TgOy/UqBPf999+/fvWO0Aota71exdSM4+ScDv14c3MzX8yRKCYhdKvV4TBOJ6enWXI39s65qq7LoNU5Nyrc3t2dnJw+e/5ht99ut9ujk4c5k2ZBtWS2Xq/7vk9R5usDwn9hVWHOWbKAo27s3717CyKHy+WQpn63PTo46rb7P303VZ9+sAxeRdUDSAnITe/OL2JSLiTzHEWKMMBV3t3e3V3d3i0fPQ7eK4CkVLam97UPRES+vLhDRF/VR0eUJde+dsTDsBfG4OuK8MdffHq4WrEmRIqSRdNnz88+fvLgH3/3p//8m69ShsOwQueJvWeqgleFaYoKSkAxSRJBcne7/R+/ffH4wYP1cgUUiNCjY3YxpWkag4XgvRnEmGLMOcqYYjfFz54/+7/9n//dbtr90x9/97s//PnVm5dVMz84XAfn2KzyDrOWHWAJhlCZTgOoQXCB2RFRuQQQe/aAQEwcpxhjLAuAKU6laFym1FAyfs4574hpGPr9fq9qxPxDC1SRkKwIko0I78vexCL5vjejYGRiBiKSBZDIkeWckrnCXLsn0BiAmFIhiRIXTruzbKqlPo0yRhUBs5STGTCzoRlCqGrnXM4ZFUAK/pMQEQhcqBCBCdFxzlquB0X6jj4wsnfeeadiTlXZOyZ2zknJ8hFlkSmmMUX5gfHPjsvVKYl4VVExtJw0xVxMDg6NGEQzmBpRFeqUU1H4IppjhoJhc1SH0NSViKSchmFw3ucUiQM7cgzsnFphyaICqt73UkpaVjUDZBewaYKnatQJsYgWqK7rEMIUU/Gzm9mUopohcUE1mRgjE6GoxpjAgInbtk0Z932fp/0wjkkkaa6qEFVn3qlMREQq2SSJKCIr9DF22ZCcZxIDMCvdcyDOgJuu/+a7l29fvUnK64M2i+Q05jy5ugrBkZnzJFmRCIiz6Ha72/TD0WL288+//MVPPz06WnVjf/bw9PmTJ7///R84V4lQCsTCUM3QICc5v75r5zN0/uLqYtrfBMLK19kwKQA0wKS2T6qG4KtgwDZMlKeaA2adhjTmLHpzenq6qKvSLRewol0CI2S32d5dX18iKqsNKQKiA5MpgVgI1X6M+zFlMSX88Ref/82PPhuG7QRKCFMatyrz2tXBo/J6eXT57r1R9fDHZxf73TTFlFLKOaa0Ws5zzt1+Us3OsQGM4xBr3zT1OPaEqJI3t7e73f43v/mjqP7Fz758+uQJE68PDh+cPTbE+frAM68Wi2EYhn5Yr9bn5xevX7349IvPibGdtX4b0Lt2Ps8iwzQt5vOccwJT55arFRLknPu+ny8aZm7v9aJUOCLr9boUXkWKey/M2tnN3T7nfHF5aQCPnjxqZ7OU0uHhUY7JOTebtUPfcQhN29ZNm83mq5WCPn7yxFdhvB27zSZO0/GDs0dPnl5dXFxcXFezeYzJO/LeZ4nTOEnOTTtDTXfXN6IQkSFUJwcHJ8t2Snnb2Z9fvfvs6dm8bSUOCJkZ7q63m10H4LM5V/kqNOQikmPnJplOVkcnq/X59YXoLDivpqBWAEQZdJyiSMnsg/NusVgCyuZmm4HUIIqqyMnh8vHJ0X0npEwqkFVttmj+zV//5WKx/F//86/vbm+r6oEPAUXUlH0IYIggYmrQ1JWYZgHJ+u23LxaLxfHJycFigZ5jjH3fm9k4jpqziAz9kLOakRmZ6tMPHlEwJ/qzLz5+cnLw+69f/fMfv33x4uVyuVrUVXW4ZnZ1TUCRAEUk5jSz4INndinmpmlCCEikYCVzWCoIVV2VgHh5dzhPouocV5W/h0Yg5JTLEz+lIl5jkQggJslUShySHXtmKPQtYkCU4ntCRTUgKQtMMBQBIi+qiGQF0mz3NCEzB1D6HJnIoQkhEDtTE5UouYBgUlYASDmr6myxDFWTsyBGydlyVgMAZEZf0ueljw3mCKXcNhCCc+RcgScRoqK5e/Ul4TSlct4WywaqoEiIVtgAReHCAJDEUhaXMzLFlEtxsLA2JGfnSNFSzog5phRCiDHmlCrHLjgkRoSmCgwaJXb9XpI4BEbyoQiMilxzCqEK5Hbbnd4XFIXZZTUDOT1e/t//p7/3s8P3mykheipN97JmBh98wNDOWlOTTksrmMmJlLUJO+eSFCEyzOaLarbY3Gw2/W5ewY8+f/r88eP1fNHM5//x17+5ub5bLOactTLMYkmyA6xCcDVHQFEBRkRSg2maEOhiu3l7dfPy5bvz99dpnDLaFCj4Bi1j8M6HUHmJNpsvUpqIHHRjN/RE+Ksff/6zLz49WjWKMqY8D1ST/9mXP/721cvgHGapQwAzybmPyYwRaLcfv3nxLk6jTN3xvAKCbRowtApMxFlVkqkPojZMo6aRFGp0li2yMpiKdFO8vr2dP3wAAIDK/9LkR7zbba8vrzSlUJESajbNSS1O05SEgIKY78ehH8Z21lRtndFMMhgQmJqYFSQvKeD2dhNWdHh06OuqbWbj0DE7x0HUYkpgFsdBTfqhOzp6sFwsVDN7l+J0c3N9fX11e3u93fUpxmEc37x9k1NSwEePnx6fnCLxweFRjtGHuuuH/W5//v7dwWrZ7+6uzt9VoQbJjjHFiQnrUG3uNnEYZrM2xnh3dzv1+7Zth66/ubvpu4oB+10XXDg9PZnGcb/rRPTo6LCdNYSkAqYwn8+7fnr69MnDR2dnjx6enByPU9zvu6aqlqul954QxxR3u92D45PZYuFclaapadqh74cpzeeLuO/u9jfjchynlEV8CLP5HAoGI8aYxn2398yoanH0KGxxu90OWZ6fPVZXGTARXdz2Y3zz2YdPF847zwp4fnWFRF50HHbTxN6xaU6GlnlK+S8+evbBycG3b17/5s/f7LpuMZs5ZjVjYlKrnBMyNVQlSdLvx6PTYzwKF+/f930EZgT56OmTCjWqmGZPSEbsQ6gcGGaVTz/6YNeP//Tbr96fn++67vTwAMxsikQUnLvPjaMwABXgq4fNZr/Z7o4O188eP1JN+26fs6iK5Ryq4CsvORX0YzubPXxwiiYpx36/R4MvP/nw6dmjP3797R+//n4iyqo555wEAb1ziEnBEAHUVBQAmqYJoRpTAjQRde7+QnC/wvXeOVa9R0bWdXVwcPDu/TUhIkCMscBXEDDGLKIlRogW0VSsxNb4XkcoMspgCCZSFpfMnv//mTpmpb4aU8ySC4Wz8CsMsADZRKSwmxgRmQrzC8sK0QipjO+y96Ftl8TBeRuGfU6ZmCBbSbXWVeAy1BVNqqClq2EMUHlXeAHeUWmlOUNTMBHNKsM0SpYQ2LNTB5IBTYmACKvKV8EREiKkLDQl51lVchZL6j2LqWYNoiUqCGyBXSRyzknOiBTIIaP3LjinWeKU4hhdYRkyq+WcU4H9oTEjoMMQ6nt2PBaiIiDBzz5//u/+5hd/eLd/ef3OEZQ4FNZVRlWzyleFjkuOWmlSyuM4ARY7vaEZIZlmyRkQqroGRYB8vG7/h7/++acfPnVsqMBV00+f/vv/8I8jOnCG7Br2NI2IxKEGR9e9nW/ixyczMOnjtN12m/303atXl1c3cRAybOrQ9d1wd02zpZmS9wIa1YNpqLwxjYGG2+F4Nf/bv/zZB48eRInD1DNzRYHQGeTHDw8/ePrw1fevKiQgxwiOMThWzSKa4zT0lMYdW+56JKIoApyd80BdzFnFpqzZ1Cx7tVDkeUQ+cO1w0XiatVd324PlatU2RdUDRhnx9m5zfv7ecmrqGbCbRDKxcVIldbQfukliN0pbzw4OH4S6Ut9eTdMC75HgCFDSEDFLu2genD5xi9XB+khSJoeLg3UYQh1j5atuvyOiup31/W672S6Xh82szkPXbbZvXrwY++3Bet31+3yzWcxnhPjh8+feh7vdfn18sjg8FNFZ28J8PnXdq+++6/r+7u7GVBhP379+YUAPTh8Q4DRNUz8Suaaur66ucpqWizkTZslXVxfr9fpgvY4pBucff/BkuV6Jym632V/dvn717uT0dDFfjNNQVR5xhsTHp0dd15vKYja7fH9+t+/WhwfEWNcNGLx79+7o5NREpmmqqiqmDGAEcHH+/uzx01fffffw4WNft+1ibma7fadAdd1W9QyARITZV6ExyUYUs8WMXHmHcFiHWSCVSABxHNBwJ8Ofv3t1drR+dnby/vLq229fSwYmIs0i2cAzCIANo33w4MHjB8dm8dnZ6azxv/7tH+82d+uDY1BlTEQsYoQoZmgCACnm25vNbN6cPHiw2+yubu+Oj9aPzk4lR2VUUAas2DnvkNgBJYBh6rPkg4O168eb29vd3X6+aJfzdtG0moVDCOhzit57hXs+KKLFFN++e7fdbNfr5TDFcmBFAEJwxMAZDaLmD588OT1YxpRUTAyygaapDvzLv/jxg+Oj79+8zVMchimL+hC8c0TgyuCYHUOu6qqwxhBBJKecQaVYYkrEHhGdCynnLODZMXHbBmQwAFQLzuXC3gOMkqeYNKWsiUkRDJAdu5KdSz8Qm8tsDUQBsCGPCGJFBwZATEQOUS2nlKZpLFrdwm4DgJzVLAFAGd8HrpjRgEidIqABM2VVJFwslnU9A6pE4r7blfZZSimJlCqyc8EkwT39FVQLYw7MjJmBMOYY2BOBkx9gQAWIz0gVVSBCoME5Ac3eqWoVXOUclMEXISBKVs0iIkiUcgbLvkgOTEBLOhO88965QpstpvKmrWOKU0rjMBKWK4UogpOJBEQIkQHIQKV858FUFRWIqfiOD1czYIwKTAxJFCh4Xzc1B2eIoQop55KsYmYz9MGcc9MIUXIgFDW9j0LpNOx74sD08ZNnT88equRsSuRU5PT4+PhwfbXdrecLUSFIjDrl3MdpyLLv53+07Imq2r27uTt/d9nv+zil2lVcSwgVoKmqqMShk2xEkJhGdkTcE045avCnDx7+dz/54snpeohTTImpvLYZgdSsqvDDxw9ffvfCVxUoMFpdBYTEiFkhZmVUqqr9ZpI8FTSiWAIRAE0G0fD67lZBP//kk3nTIKAL3rlAJt4REfcpTQkuNuO8rhnUwCaz87vN9dVVBYihjkZjP+36idgxU1KXiLDxOgye+9WqbefzUf2Y3N1e6qZ2bgpMZo0vCTGVKGn14PCDz770YRFjyikBIJKras4x9t0eAJmdd06G4er9667fTbtdGsccp+BdTqnbd8vVogrVzfXtcr44OT1d7rvDowNV/f6br88ePTp+eLbbbK6vLrth2GxuD9cHbdPEYezG4eHD0ywJTMehC00jIjnnpqlBRVN2TFfbuzSNi+VyGIawCrc3t8MweO8MoZ3NDFERiMlUQhWGYQKAg8P1fD7/85+/zilXwa+OTo6PT6/O36+W6/2+++pPX/mqOXv6ATGnQSTnnFOcpoPVOk1TP/TtfM5N3bS19w6JSimsqcmIHHEIFRNNY68K20Eu9xoz1i6sax93twAlki5JwID3m+356/PLi5u+7++2Y8HzURlaDdl5Ro/eu2ePH1QOc0JAeHh8+Pd//cv/9rs/vXx/ebBce98wY+NqBWAVBsoiOUOWKQ55sVzOmsN6Xj159JAYx5xM0FA9O3YBFc1MQA0gjilFcc4fLOvlfHF7u7nZ3u6H7mR9OGtqp7msJGNMWcRUCR0COXJittltKx8QeLFoJSUwVZUcEyEqKAf+6OkjbzKlSXP5b9GIp5R1yqvl4qhfXVzcmqGILULV1AFQC13MVxVRnWIkQuf9bNYGkRDqcb9T05wFAVJKRb4GUJjPAojlmGtmBOC50EMpiYxxmuJIWQBKzwuYPRF7JiUbUxQ1ZFTJksuDS9RLiZCIQRZBBXSFJwxmlsuZ39QAmAgQs2QzNDCm4lhyjKigzldobDkJZ9NI7Jz3xI59SMP0L/FIkSyq7ELRBUdJBRSoBiompmimZowkRSMD6JCc/PBXjNE5Zueg5GRSMjByFMwlyaDiHSGXdJDlrGomokhYzDQOieA+rlSoy8zEzDElYiqRr6qtiCn1ad9tC4VcRAoQKg6mosTM7BXAxAgg5WRqTASGOScRIIQqBOfCNE1ZBTWrQfAe8R7LkPM9+iPGiIjOc8zEzMSEjgTNsHjpEUwtxTjsBFFzHofBgOaNJ4KYJnb87Onjm9/+lix7NBHJpjlLP2Vk58mGKL/77h0w7rveqYVQ1VUjcj8ylpxmbd0PAyIAquTyQsgFrBAVWLU6CAYyDHu5FykXRXBB7EMap+P1ej6fbfbD0tdmGssfLDUTLTCiqgqpqiWmrDaJMmJKOSMlSVMcP3ry4OHDB++ubxI2i3ZmwAkYmUcDzZiE0bnrwVZdPpr77TC8vry53HaQ87qu1HiaoqqkKTMDMuSUxpi6PhLyol0i4hhHJdSJr82a4OdMgSkpIznynpx/9frlTbc7fPDg6KRVEV/VMnamOcY4DJ3kPI5j5RwZxL67u3i/29wF79jxweHBZnOz2e+W6/Vquby8vFouV9vdXgBOz84c0vs3b/+3//Xf/+3f/u3h8VFdN4v1wZQSIoUQ7u7umqYJ3nW7nQ9ht+uZuZnNq7p6+vgMQP/0pz/s7zYxjs7zfrt78fLlfLls6ubq+vrli1fPnj2tau8cnz08OT9/v5xV7JymHGPsx+H777+fzRZDP05TOjo8Pjw4dM5/9/3Lv/jZwfNPPtl1wxSzqlVNnTRxpDTyXdcz+cWqfvrs+TDsh2FUWYBR27bEPueccqyalhCZidmp6jCmb79/uR/T7fV1yHubVarmvfe+LGmMGPKUY7Y/f/N6tZw/ePD4/fv3+/3WcyDnC+0vm/z8i2fLWaUmiODYGdiybf713/zlH7598aevX1x1sJ7Vs8oxokeqvMtlxQ5OFaZ+METHPMXUT7kNTvPoHBFSEvHsPVBMKate322mKc3a+TRFInj44PjR2YObm5vru5td5xazWRUqQiwS1GmKCKRqqlk0hxDQoUxxGAQR6sp78gAI3TTEqV00x8sFSjKTktDPRT0pJgrdMMYk+R6Ur2ZaeUJDQXLegSlTSIiVC865cZyapg3eJ6acc0xJctExgqoSMjNmQjCrfXCMhfWi9xRmMLOh7yULYQlGakCfxQwUfhCjlryUFrOMCCKKQRIpOFZEA1RVLfJBFVG5RyMTMSAys6nknEWESEIAU6PADhypmbExEAmiNm0V6joEr6DT1EuKVsZHqjkl0EqLF0KhmF5ErQhfRKE41rNkK1AmAieqYCqazbzq/eiKTAFBVAgJTZnQMSJaQRapWlHXlFYxgnrnHFrRxRdkW84pK00pAbNDh+yc91WohrEfhqHreuc8EVlhZKpmMzUj553HlEVVKudNhYkYCYhTzADkmOq6EoWYxMox20DUVERzdsxlfQ8AZcmMQIViL6LwAw9XVUwjmNXtop23XdchmkA2Y0KvmkXAAB89ePjVt9+Nw+iAppR2MWGYrQ6PFvN63pClaew6NSOTitABphRBwbLsu33OKmZN24iBWFLJhbcvIsWjYKb7/e5uv13O1ozeEbMjRVQV1STKMfazEH7yxef/4T//gzqRH0BgQECGIoqazRw5Pw5jUVMgYBLdTkNw9nd/+eXf/fKnvmr+l//0X6+3u6CVWmJ2jN4AsgFTRpCpTy/fD+9Zx5TGMVtWZtpPUdGZIiALUE7SD3FIaRSo28P5Yo3skmjKiYGD9wZ8OyL5OrCgatS0G2LXbW+3+/2o/+0//cMv/ro6On6YVPddZqbJJMU4jSMiNLN26PdFLr2YL8ax89W8ns1iyifsmXAY+qadzdpZSun3f/hj1TRtPf/266/OTo6mbhOHrpk1Z4+fvHn9iolmbdv1+8Vi3rTN+7dvj45PwGy/3zXz2aw93e+2b968SnFqm6pufAjh8vyyClXT1De3N33XOxcQqKqqnPuqqcDg6urmyZNHNzd3796+e/jo0Wq1On9/kUTn83kza+fLJQIEdm/evDl++PCnv/h5v++3t7dVcHUTMthF13VdF7McnD44Pj3+9k9/+t1vfvujv/iF85WKLVeLEplmoioEVS0daVP91S9/hjp1d+vNzfXlxTsCMYJiVfYeCEliTKqgdntz07bNw4fHtzd4d7szQSCybOtF+/mHzypmKf1NR4CGAA75l198fnJ48h/++TebvgfCmp03ixILWNAQc5JpMiAShGu5A4EHJ0fLmfPMxX5CRGoYs91su3cXN/t+JMwpilguavNAvFyvL69vhpTXq5XHUhN1zt0DBVQJLDRt40Ndt/OcchHUIKMLoVKo0U5PTpezRjRnSWJG7BAlFTOsWExpiBMze49mQGhkYpJVMjlHCACWcxqmKff9ZnMnWQlYDWNKWYSJS3TQOUfMGqMhImMVKia6x1oTUsmdF1mTqqPCVOeUzfmCVFQCYmJj1JzvoQQEBiQKWbTI0YhKxgIds6oy3QuifkC2OMdsCKqiWvBEaqBMBOggltec3Zu7HLOv2Ps0TapZcko53wulobDkUMSIGIDUBMD0Hq1akJEoogpWymvunhBX+MbMRExICCYiSXJgZ6bBETtyXCyNRg7VIGczNGIupTtCQCzaKUspi02ikkUBMVSV98H5kEX7ftjvu5gysS91AjAq6mQDKM84JALUokL8gYp/Pw1ihFlTixYDBhXWqJqqSJxiTCkge8SCyTI1NRnH0RF7dqJS+4qBjXS5bJ4+f0Z1i74hL0UsV5gWmgttjNbL2emDkz/87k+BAni3XB/N1uvQNIyUxykLqjEgeTSDhEyeaByGmKNzTiSX7O04TQbgmO1el6GiRgaIuOuGVxfXx+v5ovblMMLIpppl6gbRPHquTg+Ojw6Ouym6plEBzeq8QwXMoioYqsbrMKUcRwPpVcc0PTyc/ernX/zypz9CkDhNv/rJz/7h978fh771AQEgT5omUFAxkzyqjr5WsKnvi53N2BOgQSbyqrlC3HbD+c3merudr48+e/DUMRlkJPSVJ1CHAgjDMO7EnTSzGnbbPl7ebvoIg4QY/dcv3n/ys34pU7cf0jR5R/5+BQeL5cIIXN0+fPKsu1uev38tEx2cnrqqPfLtfnO722+nKT88e/T9d98/e/7ss88+Ozo8co41Z8+429x+991XX/zkF2ePHy2XS+eoqio1aZqm23d9P/DtXdvO2LHm+P7tm4vz90zYNHXwPnjfdV0Wcc6p6jQM4xinMU5TDJN3zhHQ23fvu3k9a9uhH5n9FNNqMT85PumGYbVeuuBdFRiRkfIUr6+ujs8eHp8cjd1ue3MlKnVde+cODg8PTk7nBwfG/O7925ub67qufQjTMDFxYXeX2ysAFAnabhv/9IffVSwfffqs363+UNl3L19VwRsROB/qZooZ2yrUBikTQsyTgD589IBcuL2+A/JM8tmHZ2enhykmyZkQCoo7Z8tJJcezw/W//dUv/uGPfz4/v1jP5otmNqYcCk5AMjID3VNYnPPbbrjbvTw+WD59eBQWDYID4GGKUfT6brPZDeOUVKJkyKJmltVijoz2408+9QTfvn6zS7JeLJuqrn3lHRYmflXVIfhpHA2g8h6ANWcDwhAc0LKpHp888I5FU1mWOiNQkqwpiwJGkTFGct6pqAozuVA9ODxwdC+rIEcKsN1v2YembWMqbxFGJsmJCj8TiYjUTMCAKZu0VSDnkgoxsedJppJCBwNVNQRiJnDFWoYMAOA8a0YGEi3Q95KnoJTFEO/Bv8RA/+IVLPKy4jGE+6W0YxMjQbk/hZdacSEdOVTLkkSFvffFoOI8xnivCrh3HiARp5SRiBwjgKrLkkUUkLDQipCIvDPIpoAMyA703nDGhN6xc8U/BWogWQQIwIiMABDRExlRQUKCKQHSfZMCAc3I1FAUJ8mo94kQQHTIRJQkxWHqhrEfJyQyFdNMRIzExKYZEADZueC9U8txHHKS4sBGg3tOOKHnkBUBqGLOVUhZmajMQLOKs7LwgGKhSylLSoRoKqxWczBRQDg5XP78s6ffvL7KSuwrZrQ8ITgEdg4zmAI4hx8/ff79i7fA/uj0yHPIObNERhbVnCFlIzYkcN5nMPBehALXoBk57qfp/eV1N3Tztl03C8gGWsgeoGSEKDFdnN++W6+Xz5pypEIERBlzsikBwsVu94ev33ZST8QZqxIoswSSxbsKGL2vQku833V9N8XBSP7qJ5//zc9+PF81krMYEIfDg/aDx4/++U9fV20gNSBUCTmLoRGDaQIKhm7ob9kbedfFMcXsPBqC5Jyz3Q7TGPOPnj+LgPu78wcHa8egaGTgGQ0hZjRXxYlvVD0BGEhMTL4iYRs/+fTZ2aMn+31PAFVV5RwBoApBJTofQtXsd9uYkjERudX6aHlwaIBdTvPV6nazaWfz9erg8Hjrg3/69EMkcJ5/+tOf/ObX/zDF6eW338UxPnr05Gc//fH19eXm7m6xXLDjaRydc+uD9dHJ6Xa7vTm/7PteVRbz+fZuU8huKSUzMbPgfJc6Ajg6PBCRw4PDb775RsTaWdP1/cX1zfPnH/78l7801e3d3fXV9cnpcV1VbdOQo5jyycMH+802DYPGGAHYMZjdXd+A0bOPPhymcXVwkOMEEk3VuaAqRdsLhJLS5u4aTHOc6npmZrv9Pub85vVrmHaNHTSe/vJHn3jGr16f+2ZWU0jiJgwW2KFSmAJxZTNAcW396PmsmTfvzq/b0HzywSMyLYz0EAI7AlJHpmhCFqf9sq0//eDpsNtfX9/t6un49BRcKJUlMgtkqpBFjVwocKTrjUiWx6eP6yZLzqrdMN7e7SRlh5zBxPR+s2qgSnXFj49Xy1lzcrj+w7ffvXl/Hqr6cL0G9SGUBSyIZpWU4lgquyK5pDT7fuxj/HrMIOOTsxPvWSWaqME9GcAIYs5pVLBJRFV1jNrM5l9+vKocI6LzoQ5hx3sRaEJDCLfbTUoTGbgQnCoyYUElEDLxEEdDQ2LHnghEtMwXsuSio3QF2UzIRGqARFOOjB4BPVLwLkKskEUhmiYBNctFCaKZGZwDMjAiQEUwBEYABGBy3jkfnGMSEzDQrIJS7H6aUwacplEUiIwYnPPM3ruGjCsXHHsiRspZJeWEaL7kaLC0rpxnbwJAaITMRTGJpqY5O2IVdUSICJ5LTMTAdJoiE/ngq1QxoYCKlmeoScGWgxU/YjGWFIWQgqGBmkJKDIBokgXZlYcvgI7DNE5T1w1giIQlBItU+Bxk6NUMgKvQVMGNU8fMQ4z/IocolCVEZGJRzNkICZGyZMdU0MdFJXrvHFJDQjUtVl4XfHDOMyuhUz+v+Gw1e/n2nEBCVXnnTDPkZCLoPDJZBkU4PDj4+Pnzu66HKQ3DNSNk4ilrTCVog4SYLE8EBDQBAXEdwjR1t9td1/enh0c//uKzm81Nt+2JGEEZAc0QDEGZ0LJcXN99/PRBU9EklrKhY0ehi/H9zeWL1+/GXhgQUgKm2lVlsUykRX6DOmq2+bK9uzFf4a9+/rNffP55zS6nMjpzHAISPnr48JtXb7eT1r6SHBFrqNjAJskjqhozYD1bknS1d5WjGEjAGN0mjzf9hgj/9V/95IuPnn3//up337zYxzBr5+hCGTU3DpzBmCfjsB/RQ65UYt9PWUGxWS2fPzm7vrwidMtFK5pSjPdwJ0NRcM5hwVgTNbN5uaKmGDeb3bxtjg4PwfQ3//zr5XoRqjq4um6r3/z2twz2+MmT77///vXLV+/fvH3z4OXhwUGhNRDh8dFxnNLd3R0S7rodIKQsWXLbNlXbzBZzIry5ubUYH549UhFEiG3b1E3fT2paVc3hwcl333/n2M1mc+8rZN51++3trZkenh7NmnnVtqEK/XZvCE+efXD5/nzf7avr6+X6sJ7NmbCZza8ub9C5gE3X9f1+5x22zezzzz/z3u22u7adO+9VNcap67aLxRqxeH015/zLv/zFzetvLO8B2CP+xeefNLPFr3//x4H7dnEI5jwDa6odMmlK0bOXFIltdbA0wCcPjk4OFpOkouFsyzfZRFQQwMQEqRPt+v3Dk5N5u3pz/v7Fi5fLg6P1+rD13kQgQBSEbEIuoQOvjiGqfPvi1X6/++DRw6aZDeNdyjkE7wgGS1mMrBD6BcHm86aufZK4Xjb/+q/+4vzy9vfffHtxc9VW1eFq6R2rqOUMauVKoEkAAFWmKVoWzHJxfnlzdffd6eGjs+Pjw1VwDomcI7Y8TanvelCYYiw0sGGaal/NKkeeS2Yaq4qQHEPwIabRTGKKi3bmnbMqmIKKoGGCHAJVziMSgTEbE4oUtAwzsZQJCiGAEd93U9VsjJHVvPdmEAIDBYnRLBcsf5lPpCwAGohQybC44+/zSIjkmMtLqHjicym0MpchPoCp5ChqpT+mxuyrqiIgch4AGcm5wgcFAyRmdliFoKYASOywgHtYAVEASuCiECiYCNHu8XX31l8iJMqSnZU8CtdVpTknVURCYiZHRGhgqmBWxBR4LzY0NbViPVZhU0QnqmjinUNTkDxO474bpun+B2b3K2ksN6DitAJg70LlvWmc4jhOUQpI7t6ZCGbF1KspSUxipqVAUfrD3vlisywnkTJoUFUkNDBi/kH14AIbonhImsVVaw6NKqom1ExUmdjQj+9v7t6/v7i5uhyGCbOQijLHnBUwpWT33LRCLVcDNSQiHNLUp2E1a/7ul7/8yx//eIjj777787fyymNwRm3gEgJISRRRwXa73eWmr51HA2IfM17eXr58/e72+oZVasJhGqGfpj20h4eaEmUxMKeuRIYVDMk9eXjy5SePn589tJyyQ0BSBeICL+Hj9cEvf/rTf/9f/g9Az0pGDMyoGnxAzilJEwKHg9gjeyRUizDs+m6crjfbk6PVv/rLnz19cCSSHh2tru6Wry7Oq4dcBXOsKBGzoqmMWdC7UJtqUjFQtDwM4rx//f3LD9vDw4NZimPX7ZlZcx6n0VdV07ZZxTk/m80QrJ3Ph74fh7EEwLrd7uhw/et//Meu2x8erh2HlKbKqhT1N7/79cOTwyml+WI+m83qpu66rus6Zn739v1ivjw8XOeUxnEygNvbu+12B6Drw4OqrmNMm7vt3Xb32aefEtE49peXl8fHJze3d+O4cd7lrA8fnl1dXm93+4P14vjo+PjoeLvZNu18tmirELp9v6qrbui63dDM57PTdn14mCVvtvt2vj46Od3sNmrp6NFpN40Hi+UwDCmlfte/+P57H+pnn3xiqnfbzdJg1jYqOaccYzSzqqoODg+vLqaTw4N02077sQkVEjuAn3745KBx//s//NPF+xcnx6ckHjXFMRcPSQbKqoVF5kI4WK6BnUjKIlYgwCbO3cPuTQ1dte2nzWRczVu2Z83q7bt3NzfXKY6PTo5LT8ejQzMQFkUTVaJh15vmP13fbO52D05PY4rFFI9E5BzEAq0xRSS2B0crBlOwlDMzfPj45Ozk8M8vXv/p++9ev393enLaNjOJAiZV0yDiOI4IkFSGOIESI9XOZ7HLi9vr6+1i1R4eHZweHNR1YNVh2I9jZucqhJzFzJioCm4+nw9Qns+aUirJemau69rvHSL6EOqqSinlnDy7+w+sSkiOzEToB+ykFUcmgkkJhQKTK71PkRxTjjGTABKLqag5ZnBclsOI2Tnn2KnYlCISoRE6VtMpSpaUVYpBsLSLy4pSRAyAmeV+tB85sOasOTGigDmmpgrZUCEDk1MM7ICpqitC8kGQoGma+WzmXVDNSEiOHLClYtkBVUHDEqfMkhqaOQCjfzmGqxWriJmFECrnJeVkOeeoZc9Syu5IRhAtiiAhIgEipBTVsiMwsGzKTkugxTNWlU85TtPU9SPcWzHLQPy+ZuycyyKOC1IBCcA5Hqcxi6mV8Aya2Q9CeVfUNllyzlHU2FBU70sKBpX3cZqKKAgRnfOEgIjeOWLOKjklaFwURXaogKiO2PvKiK+7dHd5cbcfL65u9/t9mkYUYxVGpCrElHNOiFS4e0X0GJMokKHrpmGM+9P18svPvvjiw2dPzx55YkR3dnR4dbfdR0DwybFzqDkmyEQsmpXp/aY7PjyofLjd9lebzeXFu9iPQS1rFIDATiiNXXd3dVlkFUSU7z31aEBC+eR49fjkBFWUivbPDCyrkEjwnhmOV7OAab8fVvMFeQCwiqHGnNMY2Swnx+jbGVpKcdp18WYz3g27j589/rtf/PRgMYtijn0d4PnD06vr683N1ePjQ7aECDlZiuIQ2KL145RhiLJYzJyxameI5NzjJ4/22912c4tEx8enNzc3s8VyvVxmQ1WBSve7zW67ETUwcVg0YXG9Wrx5+/qPf/z9J59+vFjO27aeJrq4OD89Pfnqj+y8A8Lgg5ru97tZ0xYFoKm9ePni0ZNH1azZbXa77Tbl/OTp04cPT0Xk+vrq7vZus9n+6Ec/qptmu91eXV+LGTD34xizlIPSYrms29YQV+uDppltd93B8bH3AUz6fv/+3fmrV6/rpjk8OF6v1+yd7HPO+ezR4ynGUFdyp1OcTh8+vHj3frfbLxbzs0eP764vP/38i4PjE3ahbefsgpheXV4iWjtbEtE0TeV3AUxvrq/QwDE7JnacVcehO17O//Vf/eL//Z/+4fb66uzoWCVpikqURQGdiBUAnAj84esXU85PHhxkTXSvYFRRIyJjRAZRvb29UwXGezfko0ePHj08vby+fHV+sWyqhqitXRuqaRpSyuwqxkCACuQ5XF5cbbbb2WzW90OesirEJOUsawBAUHl+cLz0DqMCAUjWDsfgw5cfP3369MGfvn11e7fPZs5XgBANJOuYVUXHGEWRANlx21ZJoqgB8jjmN2+vrq83x0er1Wqh5hyHSaac70udCBBCVdd1PygzF/JzOQVWVeUcBl/+jRBg5cLYD4RUAvhQuIpiCKamREXfVyKaYAbFCKsGBqZYJDAKAFkk5pzVWLUoVcoe0TGVwbihMVNMEZEMjQxVRKTozIDLGKo4+AqKt1yOi2aP8Ad0MgJC29bOV0zg2IMkrtg5Y4ImVFaa3zl775u68o4QVDUBSJH3ERuVAJBkUZmmhFQSheqQ7lnRRU5WPr6rgnPkkeIUd6kHzeUSQQCEyuWfmbLeO3tzlpQzgZSjugGBYlU1VV1XVUiSum6/7+MUc+UdmpXQmwFoFuHyC4imBqZg2QynOPXDEHPS8g4vrmIDxxxCFVNKhRoogoSA90i4nDM5KoBxQlRRFeGilDNwzjE7ctzt92OWbnSd1a5qFHCf+Gqv4113dbvbDUmURdRj5b2jYAZKgBxcAKiTxGl0KauZpJRFo8kU024YF7Pwl1/+6Jdffna8XmSRmPbiKkA4Wa8eHR39/s0VVGE3RZxAARE8K5XfmKvd7tXtHtG/eHc1DlMQ50ODaqZeTUKhjZr1Y2JmtUxWcqyA6Ewsk1zvunc3+2dnx8WiiWCKBiYlqS05ebZf/fzn//E3fwQO4ANmFZVOVTIZUMoTCnkf0jD0m/3tdt/F8W//4stf/uwzUx2n6NgjEjhaLxYfP/7gn7/+albVjcPAOkWZogTGUAVEUMlZZdcPqRvNtF00X/zkyyz59vY2DfvlwdGURAzXB8dE5FUd1+ebzW67q301xtERd7ttN/Srw7Wq3F7ffPDs6dB3293mg+cfquJ2u1HTnPO7d+8PDg8Qoa6b9WKhOccpiojzrmnazXZ7fHzMiLfXt4eHh6v16vbubt/tTI2YP/rwQ5H83XffMvPV1fXJyYmIjMMEACIZyIjJeV74edO23dA/OT1eHx7nnPebu6/+9M3rl6/Wq9V6uf7gg2dCVCj/b169/pu/fzSJ7LY7EHVIEqPmfH17R0TOMzr/wUcfH58+uLy+jTlXzs/a2cW7tw7N+8qvDwu/JJbouuO9puLgdEhqkhCnODat/9XPf/7//Yd/Pr++PV4vfTUjBElRjYiJwFTVOU5x+vPX300xnh2vgrs/5BEpGgsCBbx+/WZ/d1sD2dirCIhmpaSwrGdjP3z78s16Pnt4crjygZkDIBA5BPY8TqJqKta0japOw4Tm9H4Jyc6TgIHY2dnJat4SSEwZIVdVG6rWe58lH3j6i88//erl+W4Y0HlwYbffxzEbekDjOlSOSY3Iau9Xbg4gvqoBeJqkj+Nu309jLMUjvSfN4A8PcQUDx56QwDCE4L0TuedCO6QsklJGwKqqQggqggTelWuvpZwYMYuUFhEgFoWyD1V5XBaYjnOMRE6yyznfZ5OIkBDVOQbHaiZqjl3T1mlKFEFTVMjEnpEIgBFiFkS8p+v/IMwtGRxFYDDvKx+qENiAAUgkV5Wvm9bQGTJqlHvMDTTBTVkCe++8Yw7MqKl4OQW0+JeQEERyFhQxvL8papIpJ0cKmiWpVKZQ7gyABlaFGtVEJzSqXGiqJgSfRdSKM049Y0opAxIxoTEhqhKjoZaXi3fsmVvnU85JdN8NJiBk5JmYmB0SZ1XOpqIuMInmHFPqEH0aRzAoQzJ1XuDeherIPGPKWXImMPaekBRATVWUAB2zqTGTmRXmzDCNTFj+oBChKJmpZnm/GUblYB6TvrvpLy82mgbJWqBRoKCOkCAnQSL2DsyFENibkWevCHnou+3tNqq4in/89Nnf/exHT86OVbLIFPOkVmrexGAPV4v3Vze3cV+x9/d+NUi5Q9Pa15Vzlzd306QyxcYRMTlEUKkpTCn2w2A5AWrTVoA+Dp3lJObYEYGIgSQc9tPri9vTk6PWezFhQgIUwJxS4uSrdrPZvbjYJHVxyg3nNMU4TGjgfADEaUJiaLwfxb+93ja1+7/867/9/KNnKQ2DJDElYzCHRt7Tk0cP3l5dXWw2Dw5XbBA8270zFJx3nKxCRiJhSjFPY9xuNgKcc0qiU0zx5qZtZ4QwTYN3PE0x5bRaH1hOBtJtt9vN5vDwIHf919/8oa3bJ0+efP31V3e3d3GKfbdrm2q33X76yYdjt1cAZFqtVpLFe19XzVdffb1YLA4PDsY0dV3vmBcH65TSxfk770IdakSUmF+9eBFCOD45cc6/Se9KEZcYVfNytZ7P51kiMQBQVYeDw/VyMZu67fX17asX303D7vT0cH10/PT587vdtq7ryPTu9aup79+/fX14dHp9eeU9hSrsd/v5bDHux+1229ZV7Idc1W/fvQt145yrqlDX1fHJyds3rxYqqgX1pTmN+90dI3jHpOIcMTCYoAGaRYNo+OD04Xa3O7/ZHB0eH66PcrdPMQVfmRqBVoEIMoJe39ykFJ89elQTOTbiYBnQkSS5u9ulIQlCOUEbkUgep7gbxuPV/Gef/+q333z96uJiikdtCAQqGrMPzJhSGoexrmsmHvadZiHiEsbLKQuYqIVAZ8cHjOBcCERqGuqmDnNiDxTBkkzDMA5qlg0hiSQBIkAUVSYwTYTIQHmaPAVkkKkHQ0lqKQ1RNrk47a0OPjBqzlltirLbdqfrtUJOaprSrJ058mDFz21GqFkUVBlRwTuXDcreBX94h0wxNh6Z0QxDCK7AoUFUfqAbu+B9IMzeifOJFNtQMRE77xwwAiOqWlJzzrVVA77Z9ftUpmIAtXegmgVEculem6l3RKiIUKqgBmCEtfdt8N55hCyqjokpIBBzGa0bGKoampjkeyMomHf0Q8o2iUQrEAgzAgiOi0BZDQG1TF9yis4zu/sNtwEaERKSqSXJeUpTSgDgXairJtR+nAbLBqbOsSlWgVKOJkkQGPQeew8KhZON2FZ1Fao0pDglUyNEyRkRGIBLpwWKxEwlFxF5jnHC+0yqL3C7H8K4WBzNiNSPk5ahG0CxxJQvFQFVVcAcOQUR05gTOwIDNWtCcM6lYRS1foy7bYf1IQEAcsra73vOAyMwWhZh54d9yimTmRGxD6KgYCF4Vc1JhjSlmDTrfNHWdXj+6MGTB8dZUs4GWBzTYGLe14Q4b8Oj41X35pIBHYqaxZzqwAfrQ8+cpmHa75igIeMM45SMOGeNZvuuH6akhllyzGMUWa8WJmqCpmgOxEDENKf351fnD48+fHTCSMBIgBJBEYXcq4ubf/7tb2/vBo/UD13UsW3bxswZMMuUYnCaRHOXHOPZ2fHPP//0J59/sNtuDcCxS3nKpg4ADbzj1bz98ecf/uc/fNVTWB4dM4KNExoaWMriWvVMAJjzXZzSu7cX/8//x//r//o//8/L1UHTNKJQ1/Vs1opklRQ1D31f1TUBmGdmvDw/Xx8c5Jzfv3u7mC+qqq7qZrFYmdm333zX9/sq+Pl8Ng6dafOnP39ze7d98vTx47OHDx48ePH9SwBeLJfEfLQ83u/3ZkXwm66vryNHN7mc8/u377r9/rPPP1uuViFUddMQUvlUR4eHxyenTPj+3ftut3v48Ozx40dI+Orly7Ef+q5bLRfHh6vrm1uVPKWpCo3meP7+tttt5+3s7vLqcH2kmtk1TVu/fvlqvlg9fPQwW566DtTGYVifnJ6cPhynKU5T8OH49HSapn3Xt33HzjvmnNKL7769e/fdPOBqVqMCOjA0InI+jELbflvV7aPFcrvfnp9f7oZxtpgDUyxoF+eMWQwJNDh/tdntu28/fHz60dMTBlPPuz6+ePV6HCfJ0idJxVNiRszEnjl+9vyD549PHpyuf/fHP3/94m3brmd1AMk2RjUrUsDQtMnADHzwUiRVoGaqiJOk5Xoxa+pQNyEESyllcb4OVUscLLmk6aa/2/ajIQXPFVPlCcjFGImxqj1Mo1MRicwkmTRJ5bCpaiZgFxATYRbDLJYlW06e2Xk35UmSVMHJlAFATJMph5BjHHNiohBCip3k5J3TJGAgIiWffd/gAhMVK0dZs5QyFRYxGJFzzjnvfaiIK2TzWSpNhDxrW3a+ruvgGUEsSwk0MXPlXXAVEKLjlKJ3XIfgHU1xMoQoOabJuxA8B+8RzTM7HwApiwTPjh3e44acGjA7pNITUkJISXPM4zj2/TDFNMVoYOwWwd2Xq0SiqpQhIhEBc85F0mtokZCyiEl29wZpRu8dlDoioarsu44Ik6aUU920TdOEyhNC7LucFMwcB8flAwEzERQpsgECEYbgK+9D5ZGAmUzVczFyFbI/I+D9prs8Lq3smSjHhAZiCgY5a0pZJDpiQpQsxGD3WwG+zzJ6T1BsM1ymcoaYfygkmxmoZREiJGbRXFQFSCxpt1x71fLaY+LKeWYET4iaYxyZEJlNJabcjwkJDfBut1cgBfbN/ODhejGrCUxVesWbmJfB5dzHqZecTQ0dAGci8mwPDlav317uuq15h8DL1aJta1Iduj5PEyEoiGvqAs8SkWGapimNMU3RVKkbh+2wV5Oqrip2GjOaEDomLrX8NI7fvnrz4OR4Pa/FxAyZQYnfvX/31Z+/3m9386oaxz5OfYrdfNkGAlZLsXcWJ4lpyhqVqvD00YMPnz3ux74EOlQUAMWySCqXYkJ4+OD0wwm+e3t5O0rVtJkDxJxTzgkcMpo1bdseBlfN2+X6Rz/5i6qeV6HxVZVibNt2moau6+I0FS/rwcGBqqQJM8vxgwdx6Le3HSKePDhLUWZte3b2WCTd3Nx9//33JyfHp6cnVag0S93MaDteX908OD252+7fvHv/+Mnjdja/vL5+4DiEUOBfZtY0jaoGH7a3d91+37ZtaGpyPFvO1wercRwenp3O5+3d3Xa724bgLi8vlstl29YvX764vLq6u7n13v/kpz+RmM7fv1+sluvD47pq1qv19fXFNE7Oubqu4tB3+93xwzNXufdv3qDZfDbv+2mxmt9d3wzdfn18HIKfptg0jRmkLD5Uq/UBAAxD37SL4H1d1XXVDmNqyKkqEAgUKQYY+5vbbT8lAx6mtFqtZk376s27q4vLk+NjIkA0JtV7iL+YKANsNpvfbLcp548/eOSr8P78zdXlzf02DwABq7pOIt0wpjyenR48fHiSZZoF/OWPP1utlv/0h2/6cTg5PK4dxCmjc207C7NFBHUzR6oEqJKnlNWgTxNm//TJk6Pj41ndmsGMZKCJfQDCmIVdtd1tL8/fY07BhwrNUpQ4iVpWTSJXN9egeTVvK8e1gUpS1YgBwWUQ9CGgcJUFZJpkGu7zJIqGBHXtq+CZ1LMzMCJkxzJlNWVET4wGOSbnARG890PfDzkR833pVwpEwDOxK3tg0NJRillNwYfK+cr51gB8imbZO1fXoaoaZucICCkbMBMxGCqgOUdtU2WNBCWnD8F7x6Sm+x4MjBgLgjQwMxI7NqQpARqqgoGlnFPWnBM7cWbkvBVjLMA4jP0wDeO067txGolYJUvThMo7R6UinTR615T9LheBpefKOWaKKYuBm8/ns7YqC+4qVM45BMhJ1JTJFRQSglXBeWbwXhybKQIDeRJzFgwNSQkMFQAUUZnZe/bufppkqnUISKyiqpoklyBk8dBnyZTQAYkKIYoqEkkx0RsBYBnLGoCBeee99xkpS86SiTDlzMRcgHEGpRdesCneOQthFCVUIkKmLJrBLKfTk1XbuhFQmcc4UQh1u5ThDi2LCKOhmXfEngmrISacUp9kiikatPPlYnVUV633ZBbJBwRQ39x0scJqikPfd2UO3xKhKQA4RiLwweftbrmYr1br2azNKdo0NcFnVJOM5lQxAU3o2TmPPuPIQBLH3bSrG/77n/48+PDd6/eGzjlxSEAYQggKWcTAhiHf7vrj1ZJzNHY9wdvz89evX6vKvK1Tip5sUYdd399eXS4bhykjIBE0lTdHypoUNrebq83meNWU4hohutKUUZMCzwph18Xby6u8vb3bb+eHK8/UcgA2I4spxpgnyIvZYrl6+Nf/6v/06Mlz70PbzgvHqt/vYrpPvIhIO5vVzSxOQ5oiADWzucSYczaw2XwxDNOU4s3tzXIxe/DwwatXL8Zx7LrOOT44PPhRNfv4E1mt5tvt3Tdff/vw7MHzDz/IKc9gLiLX19cxxqdPn3rvV6sVAlxfXt3d3tZVxUxxHMph6uzRo1cvX5rZ4dHh69dvN7e3bVuHKhDgt19/k7LUbbtcLlV1MZu/3bzddv3h6cODo+P5cnV9e4voqrqZzxdNO+/HiRxXbWMiEtOsnS9W64Pj2nm3vd3sN5u+7zNi6GM7n+23uyT60cef1HWty2UxnWYVdu6DDz9qHOX91dTdZFQHxQqMU5zO35/H0ZgrUxkHYeLnTx6/efPm4t3b5Wq1ms8dmqmgAjvKeUKzOvhhGH/9mz+/O785e3jUjSOTyzmKGCOgZ3Z+ypLVFODjZ09nTZWiWs4G8PnzJwfL5a//8M3lzcV6OZ/PV6FqXaiAvSMNhDmmAmF23omq9FPtXRynzXZo6mXdNBIjqaWsVUWoljVdXF9fXd/kKUuKcRhizFOMMWdFVFNjyjm9Pj9v6+qgmR8eLF3w3aQ7UJG8mM2QGQm9r1zDBjvWYDmOMVaVD56ZoCR4QJUMQGXse8m6mM/LmpCZwaAELp33aRhyzkRYuEClGFzIPKWzBZbTlJA9EjtXhdCSDyoCRAX+fJ9lARAxKHKQUvpHE0liUkJHAKhmZcTORlUIwQfnExEUiJ13Dn+ouxKCqMac/iX5IgbBe3LOzHLOOU5oMMbYDUM/DsM49cNYuFVoFqILgbFwEgwAEjEhUNEdhLoq8/AQakVybTs/PFy1bSUpOu+cCyIZIEtKgaitKgABNDB1hBj8lLyImFpKmdihFoe8gAigEiLSPWe1qgIRqYmahiogqUrOqqwMAAJW2o+IrACiykgF1V1sLQZoaqaFIWiEVGQIzG6IWUSIqRx1Cl4j5xzHER1P01TAiKFyem+iJzXLORthFmGExazZS97uU8WIFDIwUy2iud+qZCL7ocVGqjZm2XbD9X7vvHv+7Pnx0cmsnevUV2wqSWOH5PIuXWJewqoUEMBADRRMQQhADdFXH376WTW/cKCEMOz3DtQTlpu7Z6cmU86j2JgMMJnhrps2+13O8cuPn/53v/zpbNZeX9/tx+nl1baZLVJCRADnVFVQnXda1eddOh71qFncjuNX785vrm9RPDsyzc5xBeCQ69n8enM3kgUkMPXEwfkW4+AsTtLv9998+/3yJ587cmbiHCLhOGYBSQrNrL7abP/41df7zbaRvN8Nu9QdLBuumpxyylnVQCzu6fLu7vT5s0dPnzw8e+zY1VW13e/HaW+ay8zRTJl5Pl8S8zCOWaRuW8lxqoLzvm5qH+pxyl23ub6+urm+/OSTz+q6JqLdbnt6cro8ODp90uz2+4vLq82+//TTzx49Ps0Sm6Yi5Ovr69vb28ViwczlJnh9df3uzRsRaUKFgLvtrtvtELGqwtOnT2Kc2rb94IOn38bomObt/Ory0oDqthn64fr66uzh2fXl1Z+/+vb5hx8dnzyYzZdJNIkuF/OU0mK1duxnoTo4ORWDqR/nzQwIb7d3h8eni3l7eHz07Vd/rPa7Zw9Ox0m2m00cBnL+7vaakHxwhhglaaLg6ODwYLg7f3u+1WlEqtCQkYDwzds3026L2Y25A8SseRgmIjY0kfTu7at0crxeHZBRsScBMBGBTIwgat98892+2zbtrNt3IALAYpSSjPvdmHVM+ex09eh4RaBIHFzICGz45PTk5PDw+7fv/o/f/2lz3p89/mDhFzGLQx3Lc85gPyXnnQga+co3wyjfvT3fTfnjTz5p2sabdN3O7K5u2inG8/fnoNBWDTnshkEBnAtRiiue/uanX56uV/u+//bNqxdv3+3yeLRck5ulpI5ApbeUVZNGh1SZlSlQDiGQFaAKIWQVQcSUYk5ZVbuh887VzpVgCNF9hoed894XT3ox4+I9fL48h5XKKJvI+VA0gqXNW2LphjBJ5pyRkxYFsUEpwOac2VMhUqOoY2cwGYAY5CyMCFZS91BqWAiGYIgkqmI6xUkUphQdOwCYUjIiUSCzwrATSSaaswCCATpyYChqYxTmqKaAHgkQgMipWRZhKkQIUFUiR0i+CoDkqqapm2XbNDTTadgzAhAXpDM3dQllIvGUMzM5Js8cQc0ImUqPzrGTrEaOAFEzgjFg5akKHgBVFRCRuApOhUnylBIWKw87ZnKAqqaWyxQODLOaqIpZzJEQER0SMlbMI6iNU+oGAaZsCknY+RL2V1UxBcWimotZ1KYhJslJzPqhrxcrMzIDdMSOBBDYaTE+i42i6CsBIDBGl8puJdt2GMZpXC7nf//Zh7uhTy7M29pB9MFAJYEZIoF5sqnv3t/Yw4OFZ2+ouYzEVNF5bpbgudu+zvu7FMVITRQNY44KYAKImPJkSCo4pjylPMapj9Pxavl3v/j5X/3sJ1Wgu25PDh6frK62WzCt6qos3g3VnE7Tfkjj+VutGW7Xq5evXg59JENHCpqYgADTNIoqGLQujEPqdMxxql3FxMGzqKpkULi6vH799uLTZx/k1AMpGhIZMPq6fXe7/earr/vdrScyhyF4zeLASRZNotm0iDLE0iTvX737L//pP/zb/3E5n6/E+Xm7QE3TNBZqFTMtlisRubu5ncbeOe8cI1VVM5+v1qap39559ip5sZh3XW8ATVObiKQ8jZOZXF6c//a3f3TsHp2dztr2u2+/u7m9Pj4+RsDbu03fD+vFEkRTTv3QX1ycL1aLaRzTFIk4hDCNY9d1q4ODpm4vry4uLy/quj59cOyIpylmETO9OL+83exyEnbXZ4/PfvTlZ4dHRwYmOathVVXNbNYNwypUd1eXYbZs2hbMJsl9383ms4p5HMY72FRt+8Hzj5r5rGnmWbtpFGanIreXV+zccr0iZgWNNL29uvjqD7+7OX91VFPNmFMmZGI+f3/17vWlCqeY+mlSQDHop5wlisTPPzp7fLz43VcvL68uTg+PTEUmcByQNKXknIOUFou2adu+60QyA6LllFLKmBQUYVbRj56dOYdqRoTmXEOEyCo2b+q/+6tffvHJR//7f/r1LguopjyMw+ir2rkqG0TwU3YK5prWVRU4L0jvr24Fv3306OGqCpWvpjgCT5eXd7vtUIXKUVnfmSMwcgEqGYfVcnawnK1XzbMnD370ybMXr9/809dfvXx/FfP1rJ2t10tEz4GGXsZhFNkTEKoCYpyiqDG1jsgsi4mK6oBjnIIPAcmHKqsagJUZNROkkkVkUQEAQkZS570BIaFYTjkH78tsOaUY41iy+YAIaqaWs4mpacwKzNl7L0hcTnuAJqYKZeaMjqtQ99NoBDmLMaY8gQoZFjpAloLqKf5FjTEroPMOkUVkSgkNIdRqmZhUNGdRyZKVkRwT102KKak4diIUwZDMERACIyJgFk1qCObAbDTnnAsVIBM7pzlXTR3qppCrU5pSiqZqZn3fq2nKCqjDlIILRAgG3oesklN2VeXY5RiJEIByTg6AmZ0v43SIOcWUCtkCkYmUQyW2VzPvg2N27BwRqE0JJEVEyFmSgoiNyWLMAOSrWiSqZFVxzuWcRMkADYrtGIhKe6PsawmRsMzyHFOccrKcE5oxUTaUJCYGQAYEZlknhgBGSIzVsmqnBcec4vZ22MZRVZbt7N/93V99/smzEPjtxc3/9o+/vdntA/vALMpZgZgRceaD5LhLcIyuYog4BXYOUDF0WN3e7V9fbjd318FXJeLDJmmcMCECksNxTAqsosOU7sapH4am8j/65NkvfvT56eGBSBwjhOCa2h+tlo8OD19db5q2ZRPHAiJpmuLUhaqpoNndXNy8fxXHiVUBYfrhdDNOU04iKathzBItHx6uK+JAxITM5IDYUz9G1fzizZvjk+PDWWUSs4ijCuv5u9v9y1evHOrJ8TGoTDFVtW33+/04WfCSJoeMAI6cqXrm3d3+3/8v/2vfp3/37/6nJ08+ILQ4JWYX6pBjFMnTNF1fXXbbzfpgneJY1RUahFCXvntKIlF8CGdnj7b7Ts2efPDBxft3lxeXXf+qbsP788t+393d3O3u7ppZRcRHB4dtO0MEJFqvl2dnZwg4TmPXdXVdhRCaptnc3O66/fHpife+aVtEvLy8GPo+5bTv957YiIlou9uLaN00dV1NlM7OHjnvEO3Fixer5eE0pdXh4Ww+91WzPDiQccpiHiCn7BynNPV9N/S7rLo8Poljz0gHR8cKNozjNE6L+Wycxsv35/P5DAnu7m6cC9550/inf/6HP/zmD5eX58+fnDx/dNogZMp9P7x7956QU9KUBADNUFWRWEUOlrOffPLhyap9dHz2n3/7uzeX7w7mi5YKuZJVZN/3U4xnZw8doUkq1fri+lMAJNQUHz89fXB8kHJkVxFR6fkDULakql3fna7X/93f/Oo//tMfcs6Ezpwjlf3tpZELzXzKoGlyPnkFipNTNNHtu2H3/k07X5yePlwsZ2p2dXUFhfxnqgpMrh+GKNGQgsMPn5w1TcXeq6n37qOnT54+fvj64vr3X3/75+9fvk8TwFFbBe9DjpJTzGZMZABTjKaWq0pFtBx04V7A4r0PvmJiIK7qWiQTcznslwiQGYCZ90FyZOckKyGBqZTJgWYDyzmnWLg0GckVO25OqmieKWdTFSKHBAXJXdoDWUVUHLAZELGZTdNE7HPWmJJZCbBYTCk4JynnnFUzlGjW/WtKRCRO2QyqKbU1pSnlKZXZDgIyUuWDMjZ141RMwbFHgpTUGBnNNHlFZUuSwTQwe6JQBUN0vjJAZyDj0M/aFoANOamO46CqU4xTAhExRDUSATGacsnysEp27BmpNHVVZOh7R1DX3jny3gNiyikmk6wFJUrIykQIdd2KSvDBO0YAIjbSQJUg5DylFA0Z0GXN2czXNYCMQzK0rIldKBWnnIWRDA2ZCk/iPr0LUAK2TMSIaAAAku+zvTlJcYTt++H8+srVR6poqEDkCYnr7JvX15f73SZ498WHTz599uHDh6dHB0ty2I394XL2+bMnv/v+dbM4yEmdr0RFUkKw3jKZbWXarurTtnE517PFmOH7m+7N9c2QVIWHgYNzZsCQK6a2dau2KWCOHZMy7/spJUgYP3n25C9/9MmqZe+w6/dMs4Yr0Oy9czR+8ODkcrsf+m7dNg5ZzBRwtTxYrJaICilVgJWvRhEzQ2IUAUBmRfJj7LddF3Okyg8pPzxYs6Sq4mI1BiR0IyCJwYs3r9effYwKFGpXL1+/fffu3UXNDJUnJDFT1WmaHHNJM4eq0RTRIGdBovVq/bOPvgjrg03XvX93/ujscT92fd+Fqlq1M1OTnPv9fnN7GxxKit1+X9UNAI3DoGpNO0ME8mH/vlstl4cnDzfbu6byTLjZbMzszZur//pf/xGIPv7ww8ePHxno4dFBHargPTkqfMDtZtsPHSB67w8ePRKR/X5fogHTOE311Pf9Zr/3zofKi6lMU7to727vFovlwdFh1/WLxTybxjQh2YvvXxyfHIPhOE1r53yoQ9049vNm/vbqxvvQ1PV2u53P21JqHcdpu90083lVz4goplzPmrqZlUnm6enJfrsZx+Fg1mbVnIQMb29v3ry92O66JPD1q/d9il88ezLz/vW7d0M/UuG+mwGgiGhR9YF98fyDg3mbFVbL2b/51S//2+//9NX338F8VhN6ct67aRzatokpjX2XUk6iBKSAAAQgarkJ+NlHH1S1N9PiDyciZJd/YCqOMbKvbvd7ci4mFCVAbzKmaQQX2jloGgnTvKoclQKWgpmopRjvtv3F1d2jx4/axex2P3T9yKqllWkKRj7nKGl68vDo0emhI3SOy5EOHZPYs0cPnj06/ennn/6Xf/r9FNOYcpxGIoeOLYuYEZH3rggMs+SyL0Gi4L2aJplExDkIwY/ei2Ym0nz/3SsFWBEhA1MCMzFFgNKzERWxwqh3hcKiqjElUJUsRGTFeFlYlmYlQQPMpR+laqoqogqAiMH7aRrHNJrBmKKooBkCSZY4ZYU8pahy76txISCSAESRVJT3Qyea2CjFKJKJSNWQjInKgtoTSFYzcM6pZrVMQIisQCnJlJKZJsyeMauIgRo4X7s0jeO4n6Z5CFXMEmOSnFRVTVPKY4zBB+8tq41TCsHFrCklcg7QlcoymHVdZyqOHXvHDp0rg6eUkiI6BC5U0lKYIOfMyHvviJnZVFM2Jp91AkNmb4iFtW1mzjk1AENTsQKWQM45mRghC+T7rh4gIalaGSfFnFQ1pyQ5gxqUHzZxuXQwOASonLUeJxVCI0mSclUFH8LbQZbt7N/89V98/MFjR0zeiaQpSkq5CuHjx4/+/M030+6qdV4TakwqJilFdoDMHt+xVk+fEIY3l93Ly003qGIw9JJUMCQ1Jmaqx9RPElcNW44p5S7lvhvQVYcPz56v15+fHR7VtN3eGmBV+yJldkaqVlcVoX1wevCbP30dZFHXAR0vD5ZotNtuJMUCBdRsGYC4kBfRskwxKeh+2C9Xs7/96//+YHnwH/7Lf+nHaV0FFEuafXClUi9qaHp9ef16tXz04GFUePHixc2bV40PU8xqOk1dzllFJQsxe+eiWGDOik1dO+Qhy+zwZH364Pmnn7fzZVVXBjCOfdHr5Syl/zjFeHh4CJpijMMwbO9uq7pJKQbvbreDr+r1bD5fLGNK68MZIHTbuxDCer1+//78n37z1R+/fvHs+eNf/s1fomLhnV1fXe72igAxjn3fxzghQokAXV5eTtO02+4k5+OTk6qqNnd3/Thm1TjF5WoZ49Q2bQkm5ZzPzs5iTCnFbuif/+wnXdcFXx8dnGx2W0Saz5fL1VpBwWzqxzTF2WK2WMwdY05Rc0aEdj431SqE5XpF5G9vr5Ewxunq8qqqgpo1zXw2o3GaELmu6+DdlPM+5lEJq3lS/eZd18d3Hz073e47zZqTZFNAMpGS64jTdHZ0+Onzp0RmzJMZI/7qyy/ryv/x22/GSR1GMK3qup3PFMyAfMHJmMUYJ1VHHCV/9vFHR6uFmgCiliUqkCeHKkgcx9HA3w3p8nangilnRpI03W3vjEM7X2cBdK5uZurDPqtIdlQilIYelGVUefF+uxilPXjQD7G7vd3teiAjAlUsbc2HD04r7xwhmDETEmg2Igdgpvns4fGHt48uzu9UZS+SDKYpqpjz3gCdqySl8uYBQCaKUyx8gzFlNMQapeRdERFQRXLOiFj+Xh6mzIxUZLkEUEy3mkXcvWodUkwxRAYqRGoiQnRlqq5ERMTsCICI2LmYJr1nnZHJfda0wGxSzinnLGqmopBzjpTGKP00MEJVVYgISUPlVXUcc87364F+GMEgS7Ista8MLaWUypZVEgA57xDRFBHIANTusZjl/5dzcgTgXTG/mhpxdCoJTaa+M7FpHOM0IgIiOOcEgEQATFRFNataTFkyIGN5e5uB2TD0ZuYcI6KaOfbO+yK4UVUqnJ9y4wMkRCJXBJflByD38E4wQCJHaNkE1ArHYxoTsTGzkjp2dd1UoSEyzQMhAXHZ5/zAfxNPpD8of5332lkVQsqp4aau66GPoMZms8q3DNRftQrTPsaUVGXPqC48ffLorz9//MHpYUx5sKmiGjKiGpMndMuWP372wZ++/jq0My3ookCZEBgBPSL1Xfrq9WWX8a6XJOidZzYsXz8hkSNCQDVwSXUYp2kc7zZDnKb5rDk7WruqAdYseUhk3jkffKiRghozoiPvXDBLnzx9dH55frfZL5cPfFVJyv12L2mC4p9TiymrKDkSsZRVFCaJMe7PTo/+x7//uw8fPUNyP/r8y//yz/8tBFdldQw5TmaAajlmZCemry5uaHF0dXG1uXg7Y7Q0cml8laIHs8ak2RRxs+uapl3MWt/Mnj59Xq+PP/nRTw4fPFwslt5XPoSck5TzUE59vysnxKZpCOH68txUm9ks5dhSU1Xu7eXN9fXVw0dP1EBUkVlUJaUU0/n799vtLqU8n1UnR4enD05OTo7fvH5X1fV2s/v9H/44n7UH6wMAdcyuriRLnGLbtp4YADa2cc4hYdu2s3bm9rsk8ur29fHJ8WIxv7q6Sjn5ECRL1/WI5L2fzeYhhO++ffHxR58wcV01KSsyKcCUEhPt+66etQA6DMN8tTCDEII/OFDNonJ9+X7XdbPF2gcPgN65g4N1jGm73XX7/bPnz8dh2Oz2oaoaz/PG29S3zhR0TNrFtO83b99lG2JwXrOpZYUfkiamzruffPnpfF7FqYdsCjSNwgw/fvZk4enX37wYk1a+cs3ct4spRVE2NYdYVUFo8CzB+0y0PDis6nYa9yUfgoWMgkQOSe9r9O+uru42e1OsGEnilLo6eK0WAjyOAyBUwCIEzhs5QM4ymikh+NCUHILG6D2cHB8eLJc3Vzf90JuJBzPT05OTw4MDA+CSQpTk0JEjA1BFI991w37Xa0nlA3oz9tUmD93QI9HRwWEdqmxiWmQkuRjbTTSnhEgq2bmqMIL+5XEhImaQcyYusBgkIipdJjNmB2ZEqKrkXAH5Ss5a5rYIjhHJI7qUZJpS8FVKGQFUlJBMNaecUwIfAH7I75NDQ4Asaimle55EVmaNKQ9TdIhIzrNXhpQyYUF4kpqNYybElLNoJlPvAwKIasqiomoZlBCxqiozmlRKFklVEVAkx2lSFStlcXPMREio6hyzpNT3O5GU4mhqiGWIAowQGJl98I4A1BSNirAQAIAAAOIwqQoCmoIxqELlqvIKjVNWRcAkyaTUow0DU1EiOHaQo2k2gBgVjQA55ZRErGidkQgIbJKYELBkcyvvqqpGigLGAKW3hogheAVDgJodIwKSqYJaVqmcB0LW0hZQAAPPpslEhaoKEUh9xWaKhL5u5us6I2/2ewWtQp3jhOycq5AcOzLKz5+cffX9y9Gs9j74qmI3qo5Z0LN3GMd+yjJgA5kCmlNTQzFARM1iiGJQMQZMIMPVdtvt957xaDU/XM4ryHm4G3d6abL44Gnt2DvnfePYqRkCWxE6VyG48OVnX7x49caxS+M0jQMhIFMWERVEYsQMCBnK2H+IUTX96NPnf/2Ln56dHA6xQ6yePX704s3Lt+dvHxweokOHBGCQ1WNWFd/UXFVvXr/NY5y384osjoNZlpTLhXecYowyZe26m8XBGnxlYdYcHD//4kef/Phns9XhbLEM7GOMwzjEOCFyUT4AuOIlUhPJOdRVmqa5qwQsiw59n3PyztfB356/vb28OHrwIKf8/t2baervNne3tzddN8yq6unjU03Tdt/5Kmzvbl58/+rd6/fzRbteL46OjjTL0PfDMJjIfDYrCeMyW9jv9+M4rtcHppBT3m53b16/PTo67PaDNHZ0eOScx/3OQNFs3jSb221TtWDKwTtTXzMCTF1nCH3K4zQ2TdM0zeb6ene7aWczripkkjT219d1Ncv9EImJFmba7zZo1LSzYfz/MfVfTZYsWXoluImqGjnMSbgHj8uTJwpVXQVgMEQwIiMjMj94nuelezBdLQCqUZlVyS8P4vwwM1PVTfrBPBITL1euyCUSJ46bqu39fWvlrl/UKm3Xl6qmqoxWK9ccrRQd83bfp/Ti/KVLFUAkZca+SfuiKM5IVeGTZ+efPj8Dq6rzpAFUVUQL2nLZ/psv37y7295PGjcnh6xTRoB2mMbUtJxJpUWyOo4h4PXtFXo5Xy+WfadiFEMICUNjTiqHmLpR7fpuqyI61Zg4ReKuzxAHj+6yfbg+DsdV351sTlPTI0aKMfrUt6RVVKEJhDbqeNgfPHCIzE+erEWWx+OwP+yc/Pxs06Qg5o96dLEKlVPDGAggO94d78ZpMq0OwJHrmMFt06VpoquHh+3D9tnFZdsvEAm1qikCaq3zthRrEZUAaSYhz7dhR1eVOYcynwizL2uO+DiQAoAoIjhiiJGIU2yY4gznDESRmRGr+rFKEXG3Rd+mkFSE1MhRRaXkGlJKvRBwSAAAZqAyt19r1RBRnaq4mJlBdeMiQtDNIQHHmSohZlUN5p8aE5i7cA5iXmqdwUUBCZFUnNiIwJwRHjPxiKiqouaMgOZVY7BISqaBiNSMTY7DodQSA813vPmOFpj7NrVNigRoigiIDEjE6Go5Z9MaQ6jz/dshhkgcRBQA3HEWTFap4liqYMuIj6tac0NwJs4ll1IjN4CuDvLRS4OkPpc8EACRAyNgjDGEME/u0Pzx0GbmwFMpAclUa8miSkBiMyi8lFJSjFU0lykyxNAoRAjrDM1k7v0qhCC1xACBnUMq2BbzAFMpY8LYxqZNKVAA8mwQQjjZnFw9bJt2VQsIqmDwEGt1ERMxBm9brYcHEAkpOgUnepQTzKqawKPWh+0dI5xs1mfrRRe4SwHRbcoBcDoej7k8OztrQ2ja5G4iRUCrVAMOTXO/O+4n4dTVMqE7I6qruCiAzUQrRzFRhWMp03BcLdr/8O//7m9//TP0Mk5H5AbZzfyzNy/eX73dj0MDHAM/FjuRuV8sFj2UCjbGednoaESloEFo23Q0VxcxGKby05989e//7m++e3f9m6/febO8Pw7iQBzqVBVlRnkDQIwx8Fokm5iKcuBFuzLTPB2P7iZWSnGAGGOKKYdw/7C9v7vdH3ab8yfMXGvd7faqCoDDcbi6uasKVcr/8r/8z//p//p/+bDdXl1dl1LGwe9u72aE1n6348CBw/39/Xa7HYYhxsjMp6cn05RrLX3fV6mL5WIcpxgjMX14/4GQV6vVer1x0OPhMIz77394f3JychiOCv7k4rJpW50pNESi0nVdjLFpmpOTzW67NbPUNkSUFquHm7txml6+eTbm6bg/mPm034cQXp2eb06fHA57DhGJm7YdhzEQ7Pf727u7w24/TaOqnG1OXUyrEJErEBGiMYAxZTDV8tWbZw37MFZRzTmrqrszAoDlPPVN+8ufPnu7Pd7tsosyRXVo2gUz15wBSB1qHteL4MP4w3b3NsaXr16+evnM5yVwjGiEIaTF8rtvv5mOuwDECZmcwKrUipy6bru9/vzlixcXF7/907++//B93y6Xy03XL9Fh2k1tiyQFZCi1TmVCCpGrJ04pMeLJZtl1SV1PTjbuqmalVkbP4AFiCAiBAblqvb65UzVTzbWqmgOqqhYBTE1qbz68j013xsEdZnNWIHb3GOPcD61VsBYkUrOp5Fqrqagpc+i6TlRqKTMkweHRakDMi67ZHXez7YSJmQMhEhNjYPBHaoD5zC8AKwBibQeiogJAWQRzIc4IzETz/o8CVqmIR0ASK1ohRSXmXGoVna3qAFCqAFUEUNUQo9YaZg2CG5HPa3xCdMf5Kss8lxLAzf0RcAeI2DTBzLEKEeP/MDlSrsLMkTjM+5q5R9tQVClmhkiqM56CFl0TZouPmzsGjoQzollTINePj3sABCSkXMXAELBrH9VdVazMMA4A9dmHZYaUQiyl5FLFnIPXWkrJ83+GOHwcxsE8G6ql1iohBEQM8wEAs+/eRaXUqiqANL+mqc22nkec3iwfF9WqyhTArVTNWSw2ikRI1d2QzBDASOBmlHW39DoFBOYQiAJR17SGPuRxGI4vLp4ciu8qmSNgBOJIDMHM1cIcr0qc9LB7i8IUAiFmVRGrImp6zOVYMgC8fnZ5uuoZITCYllkIB4aS8+39/fPLF33bApuaGECt1UPwEL/78OGHdzdZADAA94jC3JAqSmUmqcXMRM1GPw5FwT55+ew//Yf/6fWrZ+CeK5ojORpYreVk0b969vx3f/rzs5PTMg2I0DTN+vQMQ5Rh8JrdhRgs+7FoFhuruTogZNUx1/1x+PzzT//jv/+HhsPTpy+WTz9r+gWmdirGHCMHYnaAWov7fJFRRHZ34tD3Sw5hPB5UZRyHaRhibGJs5rZK1y1Ciqq22WyenJ8fjoeHh91qta5FmqYzh/vd4e7+2LXpD//6JxILMTLjl199nlLIeby9vW2bxlT7vjez/X6/PxymcWzb9unTy7Zt5ikKMXV9+/rFi4ftrmvbs5OTPOWH7fb+/v6LL74ghGGc9schV5lKOYthu91yiBwDAJdaQ+D5XlJyWSz6puuaUqXWlgIAEsfl+oSQUttiSovlevvwoFMmIhVdrdZEPP+gErPUWkoOMZycnd7f3jv4s6dP1qtuPO7HcXzsqvpsFTLgeDxsf/bFm5fPL2Z42Uext7kbMEYEQgoh9n1P9zurQ2QycQdom2CaY9QQ03a/7yN0AQJps+qk2o9vf5xqfv382ebkFBzAJEa+vT3cXt1gkVIN3IsKEQzjpAwxljbwz3/6+ZNl9/xi+f7u9vfffPvD1e3S0+n6TKa9DCOYAKA6IDDPuHZgUxXVWg9AuFiuOEZgBkJDo0AGZmBVaqQATNv73W63m/tWs9N8vvmJ8sM4DmWkyMhRZjjlfE6aq2pqIn/E64tKiMHAVWaFzCN6c06C/tUtWD/uElar9aoPu+OOkGIIRBgipRg4hFozus33a0ZAMNNazQ8qqtLHiIRarRblqKI11zFwSim5k6rOXhkzEHMELyJILGoiBgRVlUNywClnIopMzNiHVqqKijmr2Jx1dCQHnaclcwoGZr60/5XO5YAQQ6hBiWarpaupI5FYrUYphPnflCqLvm8S5zId9gcVnQnMZlZymcFtyOgA6pLCnPNDd2hSIhIAULXUpMBYpChAE5oQGtOiornWXK1purmNNb8HeQjAXmoxd3Astc62T2YGJEAWzXPjDH2287iIMoX5e+Duf418ufs4TeBuRCJKrLVWSqSmiJhiCjHMkj/mKCIIdDjuj+MxUYshmtrjKwXgpF73WWk8XS6fcBMTUWgcQ+AWKFQtuagCbs5Onwl/8/Yam84gAaAgtilMOQt6YJo8NMtN9Nyg+mxRFkW1abe72W6bJv7688+fPb344epDdk0cHUB0DjDP/WifDoe7h/vNyxcM5u7iDiEej8e//OnP2+0hpgaq5lJNDd3czB0MIMUIwPthnKSoY9P1z1+/+g+//sVpG+93R0AChyYldHJ5NDF89ur119/9sM3lydkpup2s14hYhr1VMakOjoEkS6k2iU3FTc1A99M0TuPf/Ztf/8Pf/Bt04H795RefvP70y83JSVV1hSamEAKgu3vTplzGcTrOXEaa54+Eu919Hof9YXt7e7NaLNbrtZhzCOvTs/by4urqvak8u3xuUnZ3t6VMpbBqHYbDYtk/OX+SJz3ZrE/WJ9uHw8np6uR0TUzPnj4VkTEPgZh7GsfxcDiYWd91832qlJIzL5eL/f5wfXXt7k/On2zWm+OwZ4RPXr/58d07qbbdbkOYm53IgU/Pz589e9ot+mmctttd1/WbzdnZ2XmMQc1SQym1qtqvVirSdr0jNl03lXdnJ2eHYeyWfdN1l10PUqVURxCpTZNEpFYpOYNDrfX9hw9iGmJYLNPJ6Uq1ai21SAWEGdWOCIxV6+l69asvP58pKPSxBzMPuAOFwBgb7Jbr3Tg9HPYqBYwYIxKDjg05MRyHB5yGk/UqRnIi5BhDY9Vu78uQrywsn1+cEyIgffhwUyctWXJVEQUVdKtSqeXh7v3nr55dnJ3kcuyWzd+//vXf/fpvfvPHb/7X3/zh2x+/XS1WbewbxhSUao5EgcyJq2PJQoxAkdyPh+Hr47erZdd07cWT0/WqDQQhhsABwIvK1e11zWWuYIUYzdHUtCo4llrePLt4dvHLP3//znHOB0p0jxxUVeXRDhsQmFi1BmKB+RYMMcbHPfCMVicyc5nxmYjjcETj+Q6qqm6KAACmUtEd0efJEZoyOs2rYNPArMRIEBipS4TzDMNEixaIsTFVNXMgMVAFRnCDWh93wjbvMFQTBwAAc4ofEWs+p/6DiszC4sARQIgZEdyNMMB8IzZXMQB39Ln49uiDZFNzVZuKIHFgQ9KAiK4eInepQbAmJOu6/eE4f59MJUttAJjZkJgoMc0wZ0Qkiso4vy44YtskJPxoLidHDCGVOszWlCrCpc7LK59nwIwzwJ2IDGZ9AAAhhYhI5jCOk0olRlWbWd7MwRyqyOMR507M+vHrz0Q6Q4AAiAnBidHUGJnnz8t9Bvlf77eT+aLtpcqMZSXEWVFpTvvjeHsoTy5WSAUwIEWnMEwDMEPouvPFzWG6G++LYBcY0Q0dIEgREyMOhKQOzqFbnaDUqdZJc656c3Mttb588eLXn78+2awV8VDy7XaHXTD3pu3YvIqAVgcYdvd/+eZPm3W7TmEurL19f/X2h++H/b6JLYomNyeQmZwBuB9GDuyI4zR9uN02y5Mn509SwJOzlcXw48PDmHNMXRvi0nzRJgrJiZhpvex+/fOf/rd//R0Fvjy/kFzKOETGEBtVnl/AlDVW95IdZBxtd5yGPP7i5z/5m1/9jAgxdecvXz3/9Iv1+pwIGwqceG7TmCq6A3jXtYeD1FoBgAwDh+PxqFpm5d7Z2UVAMoDUNNvtLgTCEKdxLDm/f/8OCVaLzbOnl7vDFhFD4M16k4s2KfaLzlSGoyJB2zbr1SrGdHJyejzujofj7mF7d3/X933XdQBQSokphRCGYUSkm5ubUupqtZzyuOiXRMgx3T/sD4ehbdrvv/sxprBYLksupnZ/exsDptQwx+VqvV6dMHEIgSggWmzjNGZKabE6IcRFv7jfbpnjxcXlYX9o+8VwHBzu+24RQhyPg4rOSXhVEa3z0FWqLPv+4eGhaD1fnQNxLTXERWpTtcdJbgVNKR6OU7daQ9OmfqFlQjVimsHpDtCmBEhtt1Dib75/NxYqFdENGKpaBFet1W0cx9C2EhvAEJGq0MMk7rzoesny3//4l9v94fmTJ/cP9zfboXqsBJW4ksSY6jgBumhdLhefv3oW3JVC0zZN04fQ/p//7n/64s2b//W3v/0v//tvHiRdnl8iASGIq7gxhMiJee6pAlhlIlXZH4ab++3V9d3D5e7504unzy+6RV9HOez3+91xRgXNCuU5UmKAh1qI8G9//pO+766P+3ESEREVM8UQ58QsIs6kQQIEoq5phiJTrR/X3YhEjFRrnQdo8wsZAOz2+xiWc7toXhfPzmyb650mDjOUVAkhRlaBUkotoiEye4zzHDuYypQnjtEF5suuqFWzx4mTu5nPZl9RQ6Z5mm6qCGhotThjoo/MCXCYM6lmUF0fI5CPDAl/lGY9ToBATUUciMGBiAkNycHAbL5rulQNc9RhRhRxiLlg0yEgH3E7jaMblmJTrhAShxA5BiYiYzBTDNwIIiK7GzM2kWuVau6gGg0RCIAJGSESoT8uvmcnsiq44Wx7cACdgRfOgWIkNgAAVNNcK9Dc9RVwjwHFZJhGqYXc1JyRTBQAEGHe/gM6gqcY1QzUuCUSokdYuqsDm8aUGoswr/jnjTEaEqWAjNEA9sO0t1WfCMyRQnHX1B+K/PHD9v3dfrsr+33x0B1rQYQ5cFbNkMhdQcGKUMseWy1Sitw/HETzy+dPv3j1/HSxQPCqLlovT07udruxyKZJ4uYcCJETo4B7no6Hd1cf/Pzydrf95ur68HBIGC2d3ZdcRBkiUKxSGQkRNJKRT5InlYtXb9rulNuWrZDi+/dXIYX7h4cYj11KuFoxbSIqIoEjx/D61TOOPBzHcjyOxwOaYUAlrKUMWZAITKcKh1pKtfvDwRH+5pe//NVXXwQkRV4uFovlaRN6rTLXK4GUHN3dVUSEQmKOMTbMgQhrLWM5qhoHXq02bdvv9w/H7cN+Gp89fToOOyYKm03OhThIVTe9na5jw2gQQ1os1h8+XDex+erLZ/2ik1IP+/1Mi9rtdje3NyY6v/Qx06effBpjmPJ0e3s/g+fW6yUi3N9v8zQh0PxDOE7DOOYx75qm++yLT/e743Z/uL+67w7TVKYiqg7rzVlqYt8vL58+DdzE1ABhlWoiIYSqcrY4dcRpGJdr3mxObm6vu8VSDAJRbFKtxWMiDu1qk2sZhyGmhIhIISQUyQF9yfTVF5//8ZtvldPDiFOO6g7eQggzADh2YZwmIKfQ/e7tnXp4vm7RhAOGiEipliouBDFwend3tx/ykEGUwQyAHZB9ooAP22F/2F88WRqQeS3FsmO1iAhVxkA+Zvnj4fv3H26QMBth7N2yszmYMkBLXjNGfHlxueoa8RwQV6t1anuCcBym1cny//Wf/uMnTy/+3/+f/6w2POxzZGqbhOpWxh6gbxcAnLU6agSi0KBDS03Ow9ffv3t7dff0+vbzTz49Pzm5fdge9wcGmKS6GjGhmwNUgCz6/OnF04vNMOWmaXM+VrPHEAgTMS8Xq3EapCIhM2AkLu5qoqbuNuPwVKS6iRkiuImCM5ODAaKqM0ZEQ0acZ0doM3sR1BBdTU1BqqiIAQKSu1apACGmkGJcLk5E5Zj3YtXc1MkccxGptaq4G2OkGMTmkKi7o4obGwAggZoFjkQ4pyKzVHcUA2aWGf7gOGvq1bTO9zVABzKfhTBCSHN0ydTNHR9tie7ADuTogYiI0ExyGRPEpgnJQ+SI4KauMjmiAzkgE7VNG5gI1aQyuQFxYBNooIvoYCpmpRYO4ZH2Bs5MIfJ8PQHAuYcFwA4QmIDwI0wJ3XGWMiMgz5Q3DszhfxzCAIjkZmrm7rWKI0hWapuZtTSP8EIIIlJKmU/qeQLoc/EOISCIVAAiZp/LxEyIYOYMFig0IY4lT/vd+1uOF6vnJxvoTu6m6Yf3b9/++Pbh7l6KOmCHwYmKiqmriJLFGAkIAN2UEdAtxSQpfXj7fQL5f/7H//CTzz/dj7u31x8sCzgSc+RwdnJyc3e9bCIYAkAVSzE0kQIAuV1f3727Oby7uq8QxQlBQ6Aq7BBsBk9D4yoEroY6jYH55PxN6paObG5MDOC3t7enpycEPBzHRYqBgEiJmQgoEIS4nVQtlAqqjqFDwqxKjhYSBxKpUx4ySja6un9oE//tL39yfno5o7ylSBIjjhyYaTaQmrupgrurioMTYhEhosWiq7XOrj4imO2duRRTI8LVaoXwCMUV87Pz85qn42Hfd92Yp3GqSOHrr//48LAn5rOzs2cvLjebtamUKedpymVCdAQfS52myQF+9rOfLlfL7fahHqq7LRYLIsq5uLuIxpgIiZkPh6ObIzMSxRiePXv22WeLzebkN7/9l5RirjnnfHt7++HD1Weffdov+jyVQfLL168Xq9XD/T2nGJqU+q5pm2nKDv7w8NC33XKxqFK6ris5xxhXi9Xd7Z2pnj95Umrd77aLZZ9Ss2jTNJUaoki9urnebJb9sr+63aXUPUIFwGMgk2Kupdhu+3DaNico+ert727fXZ+evn75tE1tyQUIiclqjYvF3Viu73aqIqKBOaVUzQksIOaaay7n6810eECfuq4BM+AUEkmVhJCI1Kz6VA6FAzcI3DbAvU+SAOf7tUQmyM8vTyM62kyMmUELc0kfwOwnX37+44e7d3e7w3HcPTyUIn0XU9MWC3UEYDSMZpiYAwURcYTQtyaqka8ehv3wp6fPL4/D1l3RIXAsWms1VS/VxlKI4Geff9q3TVaLITo4EceQiHieknMIzDyNUyklBEZGcJ+hb4+zaIBaJQQiIjOdV6dMPBuAmR63v7MxUUUrVAWdnxmmau7HcaoigIQOMca5TplLCW1ctd1isVIzRZumIwDlUocxj2MuVc2MGEMggPmRpmYGAeZZtJlyCARoZlJF1cTQjMRMRd0Z0Okj1W4egxcRUg3E6gimDoroKuYA6lhFESmE4I+/caxVIscw93gVPRdDMkBv20UMrZuXXA/HQYpEkWTz4zmkQCLqABSiOzoSRWyZQWT6uFwnBp6ZcIw8n9kuqp5rjSHO71kx8FTqPIMzd0RywDmjzYSGNk2ZY1qtgusMLnVEiIHdvJQCszMghnn9EEOYmR4isydIxuFoiPLxl6oGjuZmCKL2/t1bXVy0KwLwRy2zg5Tq7jmLqLDbdjucbdbb6l9//fV3330r+x2LLMCdfMxFHYgIquRqq9WmljEYMwefS2c4X4EnBHj+9OLvfvrZ5y9fulWpAg6qFjhQ4BTTq6dP7+5u7re7zXIJIJEIaq5zyAFwf3tXgc1D5FjEikkwJw7gAAyBCdxNZBqPh9326ZPT09OLyWJWCBFmflWxGmI7DcPm5Gx7/9DGpm2Sg5FbbPrJ/E/fvvv+h2skPgyuDiGkGEKKwdRLzo5OlDwlwyx5fPLs5aevzp+draepiHYGTKlZnz9r+wUihhiY5vCVu6nPSjYxkQGJY4w5l2kaRSSlpKrzl9LdkXCzWZvUv/z+667rYmCZctcvX3/y6e//9bdqtlqtj8NxtVwjB3OvpXz97Tf32/tPP31zcf7ETZGpSc3heBwO05Rr6prXr1+dnp9///33w3DUWmOIzOFks1mfrHPOteowTgTQdl3OuV+uTk9PVfXdu3d//OMfNpvT9Wrzd3/3t1dXV1OeloAPD9u37965++Uwtk337Nnzk9MTZl6v1qZ6PBz7RZ9zCSFsNptpGLe7XWpS0zTb+/smNYf9PnXdxdOLm+sbJF6vuqsP7w577XptkgUOy75/cClS7u7vxt39aReXHddarRohiI7MVN3f3e06gCf9IughgKHy7fX1brd98fLZyerEj1tDiIvFoPDnH99vt7tATECgClAjBnOs4DmXX375+ZevX/3lx2/+8PW3Inq6WQenUpUQwNW0RkYrEojIFJEJ6iJRcnvY7vI0FDGO4atPLjeLxlxUsFssrIrFKiaEMxY+TCqxX8VBV9z2/WI8Hg67h8OUY1o2TUvuzKSKk3lQM0QidiRnNwT1ehynq9tbAmHE4sox9rHJJcuYq1Yxu7w4fbpZzytfdwipDakBLKKiqohgpswsIin9VV0c5kf/vDjBOVcdo87zblUmmBsAMYUmRREBeDSW1Focqkhtu3Y25c1r1SLiwCFEMxO3qdaSx2bRdH3HHDhwKmMuxSuUWmqVnHMu+fHJ4+5uM6l07pMxMyAQopur6qTWxFjVhykjkQMQzrZhVVEzRfK5+jDnU52BkGYeaeAIpj53GnDuRaiZi7i5NiHZbNKVKq7AGKUWImQOTEnUYjtiCGaG4EgYIhOCyJzVcQAGQDcHxNloPNV6HIsh9BzbpmWaYdo4L91trrCbEXHO5TBIVQkc53cAJprLkKaiaI4eQ0ipY4dxkFmjTMRt0zIxuIvOY59HDUCIUT4uwcx0Rj5VKaJSCpkbM8F8LwFQ065rLIUAJvRomnd1wuCGoWm6tksk0evVD9+9/8vRSm1FDKV4RUepyurgnqcxxJC16niIXnSsFNLcQiNwYsIQBOmzzz9Zr5b7YZ9SQreGQ4ZCxBQ4MC+75VevP/3nP/xLjMxqbYwAPomIuRhMRSyk1J+6Q8PISKo6Z0EIFdRqybvtfR0Pn79+Fdo45VHAKDUuWh3VHT13CQNgFbu8vCTJIhYSx0V3uz387usf7reDKLQtP4oDkfJUhY1DNOJaBrdaREWp3zx7cvnk/GxZywNQNkNuVy8++/Llm8/7xTKGgIhVqs1N+Rk8JBWBQmxCCOM45jya2yzp3m63KTXMITCL+4e3777/+g+mvj45u3xOq5PzYZhKldOz8zyNbdeN48BIX33+xeXFxdt3725v7qVqqdb1i8161fWtqX799dfX13fXVzepCS9fvPjtb//l/v6uX/QpxqmUGN0Zkfk4TA+7XYpRVaYyhZRCZGJS083JpquLtunmt9K+7549ffruww3ifr3cbNYnq+X6/Pz84uLCzLd3d67GyKo1EwHwk8uLWoqaNU0zIw0Oh2N73nZdd/3+/cXTpxeXF7WqqoUQj8PR3FWNKORhNx13THDz4XoRUyAkmRJYNUFwl+yhybnW4/Hi/LRL6G5MAQ3Jfb/f/+4P+ZPXL5+fb6SOBegP37+73x0jcWAqVcxmPzwKUTZ7fvHkkzcvifTzT14ul+vf/PHPP15dnWzOOaYZri6mLUVgruoyFQ6hRTStXWzi2fJhD0MuTQpvnl60gcWViJiJEKSUIkYxIpgavL25v99PqujAbbtIHBeLfru9G4YRKayajhzNFdwRnd0RjKgRc3EFVAYn5DJOeRJAJEapVkSLiAGkJr1+dtEkcIRA0R1EbYYRl1olBKS5n0shxnmEEOOjqKqU+tEjOc/KbR7l+eODxR+5DoFmkXLR2dKeid3cwNwRRWzM2QCatlMDBOQQc6lTntxdTAzmdisSkanlLKWU2Uc/bw4Q2R3mwNJ8LM0NNUQ0AK11mCZ3H5nn6GtMiegxtgP+kXvBEJinXOa1qJE5ACPMSLTIAR3UAcnBTSTPkRGY4TwcgpmbSUjRzRDCLFMMsWkA6fDAgdFw1k2So6uI5jwNzIwc3BkhMuFY8zCN41SPQ+n6jjgSBXoUraGbmbqax5QQeA4s76eh1Hp6ctKlxtRC5BgjIoCpmxIHYgbEnCeptZb6qBIjmnIude6aP/4ys1KKqjRta6rTNCFQrbXUAoDTNDlCiknVZ1pTk3jz/GJbKAUo2U0FAVMMTQzrRdtEJis6HUFGOU5k0nDk4FLEzRURYgzkXoubglFAOB72J6veWR3gcQAHAdUZHNj3+132p6yepDaRlst+LDIfXSFwAP/i5Yt3d9c39w/LkIZhP9dVZhODqkmpSME5tY5NDBZ9yhnACHQ77O7v7roUfvLlm59++sUPN9fvH46xbQISzJURQqag4FX17uFhuXzVpUCMnPq/vPvw529+nAqkEBkLWGWyGFJgrm5TrTlnDihlLLXE7vzs7AlHOubpUBYvTp8eb973y/Xrz756+cVPlss1E82TuhDYAbNWMwHwpmliaky9lFxrbppmGMbYBHNn5sVilaeJALXKcDh23XK5WgIGc8q5ACEzxyY1TZqmPI1Zcr27ux+Gw2q5qFUP40CE4zjWkpGBCZerxS9//YtxGAlhsViO48jEdze36806hAAA11c3b9++a1Jzfn5OAOAWUtzu9+/evbu+uWEOfdevVsu2W6TY7A/H/X779u27Dzd3IaTlcv3J558/vbxMsSGk7f29uROikptqrbVb4jQMN7e3TLRarUup62756vWrw24fGK2WDx/ev3z9CSGK1NCknpCJq1o+bPe3P97cfPj6m+8Oh6GLMZfC6MQYYhiPo2MYq+52+02X2ohVK4gTo6KPtapaHsuf//SXkl89f3b544erq7sDQUyR0J1RzRTQXLUOJaTm5599sV4upYyg5ZPnT56cLf/pX/7wx2/fzWKDPCUkqlI5UC2qCl0MY5EGXLxQSCcnJycBN+v+9OQkoWudEF1ESslKj36lENMwTrfX11oOoMoYyB3A+tT0Z+fHrn64v/3m/vvTzXnbLhFQdfaJ0ewKRER1YE5jNVESagksMGnNhnPFJHdtf3l2ggwOnkJMTeKxzDVWMzU3l5mcrPOl0Exz/sjwcQOY60PBzcAdAcs8XaJHu4aZAbiqAIa+7+futwO0bTvTFQHQzBWM3Pt+CU4IGKKM4NNUt7vDcTgu4nIaSy1FRHMutRQpuZYsUpsUkMgdSqkf98wz61aYGcTVfchZRAMHRExNNIfIHMKjxatmAHStAoEB5zsBzwwL4pBioEcqWjCdQcsAgKUWNQ8Atda+iSGXzIhIiSm4g1RRMweMIUZOTdOpSIxt1y7aFM0E3VxrLjnECBSJueGmIFTR3fHIj8l9VPNqmhBn9nZWByAVA7citVQxgGW3OOuX69WylixuITaqBV1dAUISJE7RJTsiMwVmcYgBs6oag9ePHp/HZc68RVFRUwshKGItJXAUMw7sjlWqqoBjijFGBHUAZiCOgQK3KbQpNqiYt1YHlgJQuzaikJtVU3UTteoQY0QyIogxTDmLuJpNtUZAM1EFUQgITlDM3ODDh9uTs9Mvnz8HgHmP0vZVc20pICCm0HbtT7766faf/lndYpvQAcy5aWdGVXB298DWAjRoCo4g2/Fwt30otXzx6sWXb14h4Hb/0MaQ2IEEQNwJmYsUV68VB3OK8OFQ3zw93eXxN//6zfXdroojx1Itj2VeRmZRBHMHERQxG0b3st6crk5fmnuZ9lXKLoZ1z4vN+er87Mmz5+vVJnCY1Q7EAO4l51onYkqhn0mKIlpKASBE7ro+BDoe90RICMyMzEB8dv50c3pGzCklNQPw9XLxsM3IzCGu2v44Dnc3N+M0/vDDj32/qGrffPPd/d3dJ2/eXF5c9F1qUjo92WxWy4vzMxG5u787HvdX7z/0fb/s+5iSqD087JZ9//LlM3d/+/atlBqaeByHacrbhx0AL5eLzWZ1PE6iioyHw7Db7WupgNStutPzs9VmDea1CAE1bSSmaZyImQDKOIzbnYJ161VKcV/ydrc/PdnkaRqOx3EcG2QTM1Op0vZ9Sm3TtMB88+5tHqc/ff3DDx/ukEIp0kYG4qHWYOaETvyw3dUqp8uVu+exqji2JF7GUQgjo6vmH3/84TAVQUxMx8OBSmQGkzrlqYB3kavZV8+fn6+WLvPhxWqw7Jb/t3/4+89fvv/Hf/7tjz98c3r+bL088XmxFZrQBIS5W8XoKLWUYQIEzOP7GD958bwNZFJNS85KVDk0kaoSvb++frh9zzq1BICMBtFdxjqOw5hrOT4w1Jv33xGHi4vnq9VmbsiiVIiIHBCihzBOaJ4ghBDYEJnGLjVKQzK7OF23DSEgKSBWJkaDUpXQTOqjR/eR9TbPXQNhnM8DJjZzBAKAuThF6Gbzb5lkdhOYt20zjKMBdCkRgRhQ5LmWNHOiFBSZu6btuz7GpNWm4Tg6igIK7B72LTVIWCWruaGZqlaptTqAOTHCYzVUVVXBEYkUXM3csZrmUrJKi5SIwZGBEkcmdp+5KuKu850ZAZmZkMgR0ZvAKSYEcsKq7uQU2FVmQEVVIXLzBEih1hKbFpEoBAeb+X9VJDB3XbdarcC9a7oZ46WzRpJZRMdxSg2GYAgWeXbnCIWAAOZQVUQfi9dVbCoFjNq2KaWqA1LYNN2T1Xq9XFAkQAsO7iCqblAJzCxX6dqeIo/DYT7RibBJsTqIWy4FAGKK+Fj2Iq/1cT0AML8lERIzAVNq0lz3sKpsgDT7g8XRKIQ+NjGAyaBVuGXQYuAhRnREh1LFzIrMxwVJrkMeSzVXLKpFRUSPw/E4jq9evWR3UkuOjzJmcEISs/cfrl8+uehiSkRdCKuuOypw0/SLRexX91P57mZ3KJiYnpycqLqrIwWvj0kqI0yLpgzD8XiMzHe7/f3DfWri3/3kF1++fG5a74+H7Tj0XX+2WVztxyruFbntDbBWcaToCO4P9w9THnb74343qFjghFqzVxU3V88ZAea6Y9ZJaiWt5+vletG6PpAig6W2De631+/54omFKOo0A1QQzLVWk1pFChKFkNSk1jJjr1Q9cASHGKOZiAgT5iqAlJpmc3o2xZRr7rou5wlE3P3m5moch77vVA0cTk9Pp2Fo2261XgfmcbdnZje4vbllJt+sHmq9vr5q26Rq84d/f3d/dn568eQJBwbE3W7//v3709OT5WqxfdiK1N1uaw7E1Pedr1eHwzh3YAnj9v6hiKQmXT65iHE31dqlBh0Ch6lMwzCkmAyUmUIgqVJN1HU6TpvTk/mH+uTkVESGMYemW4XGHR0x51yl1lpj06jpOGrbdJuTk7/8Sb778ceap7YJ8yhgyrUYCLAaSS1AjbMda3GM6I5oh2poyEghEWoopU5Tlg/v28Vi1Xcu5fbDlYOhIxjEFG/H6XSz/uKzT0xFVarOtX9EJObwky++ePPy1T/+t3/6x9/+65SHZ6fnQOiuCITugWlGtjEBmprascjvd/u7m7vXb15s+s6rkpqgMAKmZtjt371/V6aJzWNoDGiOlJc6FitDPr56dvbv/s2v7m5vf/v11x9uP2zvb8/OL2NaAAVEVqmRTXNGbh6j3o8LZqpVmZiY3EGFqtgA01gVxAjBXAIzzbNMxDl2OM/6/zpjmWMwImpmc5wdZoWmmqoazalKlKryKPhlVxXwkkvoe4T5IymlFCRcdItFv+q7JRF5AJGKiAYoarv9MXKzWi1ntYipFpUqknMNKYD/taoFpjr35fFxI6wcoqnVKqKqrBhSCCmlxEzmqlLN9DHias6MkUhEER6jLjg3xR5joyxaPkJzANDn5ZypmNUwt8joUW/2+JufY6dEGAMTwLLvKaU6f4JmgB/hEiUzxfmDdtVF2yETAtZap0JMlhhLrTOxyE2DNsgR3SPzcrO+ePby9Ow0pAaJhuP+7vZmEsnTsWupVs5Vx6GSjDVnREICZuzbdqxYpFZTQgzugWleBiCiwyNvT1VzzrWWGGbCrSO6m1WpQBRRy3gks2Wou/yQLIZiw/EB0AlXPoe7KKBbUcm55lyKqJg9Bn8R1bGoj7kM06GJ8YtP3zjgpNb0Sy2g5hRDDOxuBBgpKNP1IZ+sVkwCYIkDbDapWwinb364+vrb7x8edohJVQ+TAgcTUxEAjik5aFWJQM3mZHs47t6/R9LPPn354snTVdeY26x9LkWaUF5cXGS52U0VYlIrMVAARrNA1CQkG2XMAagnp2DM9TEJh6pENvdIXYvIzc2HSPj588tl1N4GBDQ1cQRlP0JFjk+fPXv1pl+tgWjeG86bKNUKYIj8mHHXCgDgxhyIIwWag1nzvUy1mJlqzWWCgKebJ6o6TFPTdVJrniYVOe73qlZKNbcmNf1y0S+WUmvbdm9ev3lyftZ3Tde1i66ptarKNI0559Skvus+//yz+cpTcqmqwzCWUk5OTqtaatuzxVm/WPz4448impr29ORM1VbrVSl1Gq/7RU+5EEHfLdq2v9tuay1Sy3AcPnp0BU3bvg3E0zSllEKMMWlIkTmM4zBrZ6acEaBfLZBohlBO09T3vYiUUmMME2QCR9cEsG4CQwnM5nUSrUOdkIpp18R/+OVPgfxf/vzHd/f3m67viBwkYGJCkWxO7gaGWutxv+dSl30fnl3eXF+XsaTYpKZFD1998XnXxJLzrDMh5HlZysy55Cby//3/9O+Q6R//6b9/mMZ2sWqW6+CGzFkqmmFI6ADUuGmVgszXd9uHw/HsdP30ycnFZgNqUFSi7IfjzfW15ozuzK6AKSU1HfMIhE3iX3712ckirheXTy7Pvv7u/b/86c/v3n0fmsXF+bN+sSL0FEPJY4BJzNxokmpqATGPg5iXKb8bR8ny5vXl2cnSgQkfZV04+6eYH/luSIA4B80RHxtFROReVXUmC8xPs5luC4FpviEiAWAIUUqRYCEFDiFycvRSi6q4G0DomkXf9oQ0nx+ICEyiZo5FrNQ6jtkdwbiMdfoYAAJzIJoT/Y8hSCQiMp9hDh5jqFUCBweCWSXJgZhmRvJszNV5OwuACE1quWc1m0ohwBk2h0RTLrHtRETV/vqcREYEkFqmaQozkR+ZxKxJITDNzLwqOk2jaW1SO1+yNTIAk868IQohqJmIVpuO01ByZeLYJAcQ1TGXxEiGaiaqJuoYplxTikTh1etXf/sP/zHErmsbU324uRWBpy9XT55/9u2f/tXyNTI3bb/fHWquMSQAg5JjiIGQwBHRAXiuhKjSYw16NpjDnAWCuantXmoBhLEZx2kEBI5BZLRxbAJ3UEcfSQZkCoSittvtAdQMtFRTReLjNB2nYj5/k6iaiWopZar5fLP++1//4sXl2SrFodT/7Q9/ziKpWZZq5oDGAKSA6MRKb2+3qza9PlsSU7NeocP7q6tvv/5uvz8w2AlTrrVImR72q5MTIhCat3xi7uyVDhXapm2CrfrPXz4/XXSqVqTOvTxE7NsGAE+75fKz1f/vt/9aCZvUzr5W4uguVTSgBcQ2MLetjw+rhpDMHapBdRIHd/9wd3t1c9sv+kRhHIeInY5jG7yLjZlrFRNrVucvnr25fPJ8vdrQPEp/ZONgAHIDouBupUx5PDJHpJiagCEQAjF5lfmQDkz7w36/fVBVJOAQQ4iLRf9wf98EPj0/H4bjfr8LMS7Xm1qKtF3O+d279w5wenKyENls1udnJ5vNxk2maTLTWhfjON7f7zRZzjkQHY+DqZVaSymr5TqEtFguz05PRepuv5/f8Gop62fPDofj9fV1nuo0jV2f+lVDwG5wdX0LaBw457Lb7debTWgSe9N2TUzJVBfrTZMSgLt4yWXZtjGmcRhEJKbU90t3WKxTnY4lT4vFsmk7JgKgpm3doZSJgCxXHTNHqF4NPVetVdVt1PyLrz578+y06PTk9N/+yx+++dO33zXrdUByU+Rg1dVrjIEZ1V1N6zhozYt+8erF691+f3d/d3N3/eUXnz99cqbzzdbN0CULOqoDhTj70IeSYwyvX7zMpdw/POyOw+bsjPouIgLiVIohOwU1yNUXITZta5Kvr+62293x8uLi7GzJbZ7KDz/+OOwPPHN13KvolCmGFFN7fX/z6fMXz87OFIoBMPqb55cXT87fXt/8/o9ff/fNnzbnF5988okhATcOGhPVUgkU3KrYVKsDp3YJ5vdDzd+9X94vz07W/bLHw05VEVnVHcBEzIwDw5zwAWAOIQbK8xP+ceM618pqrf74YEEkAoQYOaVYaiVic8gi6kAc3WdNgIYYKLazKKaIGRoBcWwXyzNOS0JMgUMXs0oMQVSr1CmXacxiFgiZI1OsII+4GgSmQISqwm2IMaWkMQRDiYFjDMTERGqzI9jcwcGZCAC6rt2sVow8TGMuGYgdqZj6rIsbjkw4t3dD4L7vWk+BedG1zVz/mjd481/nnW2eRtE6jIdpGhKFmQmBiDN9rW27UqqIhUDqejgcD8NgYk3TmZgT4ON5aEbk7q4GBlnqaCXEkNr24vJZSt2Pbz+U6ah1knEsU64O7eo0K4JobELbNX1vRadS2JF8VpaYq9lHYQTOEs/5aTKf53Pz+3EbiRACQzaYbQ4qkYMruFpMDQZEh9VqvWx4KHVmkxtpE7iMY2ABdwNAowhJ1bKUqnIcpsD06sXT108vX10+eXK+etheSzn2bfvq+fnvvvvAS+qIwV2LcgjI7A5YcbvN3yVarhdtDPvd4Yfvv7+5eqvDGIlmaQQrgtTd/iGP+za1ZsYc0REc1GRCNKL+dPOrn/28R3cTQZ8/BnOPjOtFI4oM4cnZ6aunl//7X75eUyBHUMQASO4mgBYxZC3cxVKo1tK1cS7C5fEwTOPDdrfdb7/6/NO//flP31/d/usf/7A+PVHkytC1vVYQwc3J6sXnnz5/9UnbLhgJzBRcVZkZ0UpVQlSpw/Gw3d2BStP0oYG2WzCzqpRa/5rD293fP9zdMUIKkQJP0xSjdW1y0+MwzYv9tmlzKe7QL/pMWEphon69mqZpu3sIAZfLbr/fuWqt9Tgcbu9utw875nhx8dXd3c00Dpv1CSMN0xQCx5AA4JNPPkHEqw8fjsehaZoY4zCMNzc3RPxwv3dHM8hTjm04jsN+PxyOo3xMr/X9EhkX/TKmxsFyziklDpFDcFMRmQ3VWqXvulJrycX9mFJCInfPpZycnQVOqtp2AZEdEWouUmKKg5mJMnERzVXEfKrl+eXZly9fiIi6NSn+3c9+2sb2N3/5EwE8Wa1gbrQCEAETkmEMWIswmIxHit3m5LRb9A8Pd88un6gpmtZSci0zNJCZq4oDtKlpQ3p7e3u73/frkyVS06S3767vPnwoy8Vq2ScO7iEDYkBzpNA6J3NE5Bi4Fv3+x6v3H27Oz84W6/XN9S3jI0xGVd0sVxt0hMDq/unrl4xozCDOTLEBRPn06eWT5eq//u6Pg6Ka1SJAQUOLjsaVArCr58mLh9iv1ieEiORlGgd1PBTECRECs5iD28wb+FjuxVk2Pr8dzBN3emz/GACoKDgwsZO7+3w4cKAYGMFjSu5kCtVgJl0q+JxJLerquj3uqxqnsN8dTzZP1+sna6R+0T88bDmkiyen07S7eXgoKkVFTJGIOXCISMjMADa/rzAzEIJzoDlCGGIMQNimJoboDlUquNVaZlkKABAxgC/6frNcppia3AEHAVDVWuowjI5AgICUGk4eU4xmrq5MuGib2clISNDEEBMLGDO76pR3pdbtbi+1dtzGNrNbjIk4BgKyHKJFmWUIICY5Z1N1hBBSCgkRS9VcNDJVUQdUd1Ed8uQTrMB3h8Nvf/PPu+1uHAYi7PrOqm3v78PucH397sUpEoWmWRAdYoDVqj8OVcwj6SOXQ23OnLi7mM1Qjv9xpIM5WBUjojyNKkIxmZmbqlQx75k4kpOrViu5AosFNTMTw5CFjBuKiZlUtAtdqGU47B/KWGr56Refvnl2+fLpRd8l9enq4b2Kni03bbv41ersYT/e7rbr1JpUL6LElFJsWlPtl0sHe3vzwco0HYrkfNIvoG1mhheCuVlXUmC4O+6UgitIzoRMCAZQwdyl3N6fbTbL002eSmTmELgJMQSOvACrVcdy9K1fbJZPV/1u3LfdOqs4WArQMKxizLVk8QDYrE/zw61NBUN0tcN4uLq9Z4T/9O/+/hefv1HXiOf3h2fXN9dvnr1Ek5In8+AWU7969flXz1+/7treH92Ej03sPI1Sq6mWPNY8QsnILFoj9RSYkYCDmweGrsNac53GfrGYfwgxkNdqokxhdlI1sb0/3hEzIe33WyIy1anklAKCH4bj9fXNMIw3t3eB2VSa1IQUY0wvX73p2nR3d39zc59SLCKb1UbAp1yenF9++eWX6LDdPez2237RLZdLNUf0EAKAp7aZxtx0zfE4TNthmKbjMAGSO+x3x/Am9IsFM4vqtNvFxAgQmH2eFQgW85O2Y4AP79++ePVJ27XHYbScmyZOw545hJiOh2OMOYZIHB1s7ktw06XFQm+45GmxpEBBxlpNQxP+7a9/sVn2x+kAhuOQwaeff/XJk8uz//xP/+2769sn69M2UKC5zQ6EBAQcANyISeswlSn2iy++/PLs/GzMI6iCzt9NRvT5bldLDSHstVzdPaTUq2IRWa7Xb4CmcTyU8sPV1Wa1WS3WYIaWAcQdS9UqEiMFphAol5qr2O64GzOIAnhVRQBmwrkij/j+9vqLF8+enZ8iGyE7WkjJsiK71YIBf/aTn+4qbIfiCBiShQRG4goohOLMoY0hNYIREFIIadkRiFmpeYduFAKpAKiaupmZzsh4w5klY+bqPs+JwHz+WzN3/7gIqAqu5IjA1KTAjO4QAqWm8cBzHoc1eSFUd9NhmCAqMdXt8X/+//635y8++eTV60XXG8LucHD1++2T05POzESquRpa4hhnacoj5cwCh0A8Y3z4sQpAAJBiAi+EZKqIAA6qojpb4eZWLEXGvkvLZd/1J6nULLo/Tq4WOIUg4zRSiDNcMxBiiiIGRMzYxJhCDMxMs67XjJgQafYNj9M4HI7uPjUjDTGkjjn0XZswavFahTm4ObqmEFMIo6moAkiMiYld5TjmnItoETVHDezrvkcmIry/uXZMh8Ox5hxiPOz26iYi9Xi4u7l+dfoUESNBYs8IgQOzAjiHgETDVIZhENEmpvmVhQg/Qj9g/kLDx73KOE3zLaCUMgwjgbmBgyKamKKblrEI1GpWBRGzeRFRcwDoFr2I7MfpOI1M4emz588vL16dbxKAux/HY6kDYlivN127WC2W7vizTz7/x3/+bTFJzG0fq2pqm27Zp7ZZtK3nMny4Aq3qgGZI6KbTOBWxlCIzUwxd1z4JlNXAsAwZ/CPMaT7eivzw/dvT1fJkvRYpxCHFtOgatAYBx+lws9teHfeb8yc/++kv/vn3fzSA0Ha1ask6EO9HA0duulqRU+TVOh8PJH7zcPfh5ubV5cU//PoXr589MVMyXLbdLz7/4r/8679e3VydL7t6rESpX5xenG1WyzUzMyGAzcVJdywlzzFn+OtQM/CMZQohzjRdBERgYK3KpubR1IGZYwylVsQxxTTlLGYM2C0XYjZOIwcOTTcc99OUEbFWAYBxnNxcq0pQDkyBY4z9YlFqmcbxsNu9e/dusVicn58xh8Vq+eTZ5VxB//rrbwDN3Ijw5cvXwzDkUme04Gq5/Pb794fjcNGcxsDosVmnNjX745GZl6vF+eVFt+g5sFZJTRMiI2KIKYQwV9OfnJ+nFEUKEw/HY9M17rZcrYdh2O+2p6dns+9pypM7BEcKcW7InJ1dXrx8NRyH4YHmlRUhK+gvPvvk0xfPTUt8/GR8ebJumv6L5fn5yZP//F/+6x+//e5ktXqy7EMgcEdiQAw0Q6Mpu+ZSDttSS2bGs/Wi1r9SEJwogCMCc0ix6XbHw8P+CB4eU4Vi62XfMG141e27766u315dvTh7crJcxEAZEE3EzT14cAhAgQNHZBqOO8+51iqqANhSY6rZ6lGsT+nXv/hJ20YwVbM5Cx0jiogDueP67HTcDn7IDhRCwhClVCZwQAKyeYSIUK0SU4XIiKaGOjtAZ5Muq2nJxUTjNM1BoL8SAZBmOZP9df6TS0GYhYPzcahmCuC11MCBiRzYXafxiI6xSeOQwZkZRWoudaySAPrQ/u7P3/7l2x8xbs7Pnz3srkXqNI2i9v729vJs3YYsVdGRiVPTxtQEDrVM8w6GHiPzbm7g6G4wV68+jjQUFOlRdotI4ioqRKQAbZNijCcnpyF0x/IQYmwaezxLMIBi1hxC/3GuFQKDA3IgJqS5VgDgPrsyHOZi12MwSQQAai0lF6Dg7iHEiAhSzFwVZtuXG/R9j8zHcZx1MWYOgMOUTc2tYqCUYhexbxcUYxEZ9genOA55msZHSrI7Ejrg3d2D2dMicn/zoQx7cFf1x1TPPIybc1SOxMTM81YbPwJRHR7HQTb72IhVhJik1lprIFBViIghMi8qNQNWIayQMQSgQO5NUnc39cMwHMcJQ3t69qJfrpar/ux8lWV0BIocmBts2m7RNstV13VNrFKfXZwvuubusI+LFTK3ffP08jKmmHO1PLEpIGAgqNVd9bGLDiEENR+msc4mI4Ah565fAHPJmZiB8PEFR3HYD99+/+PFr3+xWvRFJcUYQmxiNDF1jwuAmLLjh+sbk2LgEMlEQKoQc0pd0wWGYZzqJIuuG4fx3Y8/iE5/9/Ov/v5Xv9h0TalVHd0dQbvAX37y5je//8NuolXTgdPy9OT1559/9unni26B7sRsbvOr9Fy35jkFYKKqtdYYU5MaEa21xtC5KRMA85iLWkXEvu8RPOfpuN9Nw5CaZrFcn52d7x7u3Wy5Xi9PNnO6FxFur2++//6H7f1933e11NPT08VysVwsn1ycE5FpndPQoHZ/d392dn56erpaL9arBbjk8eiAV1c3P/747tmzZ13ff/rZp276sN2enZ1MU2YKiHQ4HO8eHhDg/PRkDlOEEJFDrpJSenh4QObTk5N21SPOXHafu6Y5567vGTDnCQBDDA93N2rSL9chhKPoX6ueqjoDCcxgkZp5Btt3fey71dMLSNHKUPbbIef1evmzLz8HVQAQM0dObdumddusXOuq7/7j3/+tI/z2d7+L8flJu2LRRxcEIgG5gamiOTuWcfzLn76+OdmcrjfrRdO2jKbqFFNCQiAS0+vraxV1dWQPAQSAiGJgR9osFm8u4H6//eHDj2M+O1uviRmAmWboIlZxREht6wDVIzAiRqoFzHI1AHKO+93tv/3yy+eXTwgdnHR+a58RLuaq3vQLYzgeHlwKAKHM2cXJtAKYu5YiDoRGQGgOMwAGFNiDKKrZOGXGMFN1YH7Bcc85i8hsBoXHcfFj/mceqM+PEfsIg5unKzNLhpnNKDDUksHn5dbkDuVRK/OInB+n/Mevv8vVSpbb7d6kmslciC9TKePx2WXvDilEIo5hhlTDIxj04+UVAESUmcx0DhACADHNzLd5fpWa6OZQsc7/CzDmpmuaR4Y+B1E9HA4iBg4qCkimUkqJgRHQ1AnJ3BwQEEU1zCw5NUELDkSEs3Nu5laDz+mOCjmXnAEAkVVNzBVgyjWLAXIKATlwSmWSjx8iGEKRCm5tjG2kLjWr5WqqkqWWkovW7e6w327HYcy5cOTFoncOb6+vdtOnF+dPmjg1q67UeL/bE5qKarTD8XgcBqQ5HgnzY3H2lz3GS5Dmn7F50MxcS63TOCFHVQFDd0UMiCxOOeteIyqgeQocAwMYBcw5T1IkrlbLy7bpurZjRCn5bnd4erZJjG0MzNiERZs6DskdDL16MctPz0/346Ff9Yu+j4F1OpZdAffIwQnQseRay+TuZqAOk1SkAMDqXLTuj/tcS4xNEqMYQAR53nTMr9LsRLvd/ser61999UWs1cxU3SKFrl0tV4f7u4cPH8b9Tsdy1rfHqd4/fDg/O0kAhMKB0AtZDVTZmrI7VK3dsvuHn//NL754PcfUZuyGukxaRHzTLV8/e/Xt+7frk4u2aZ68fv3mq580bTvz01XFXBHxEfvjNmNYALGKxBhTakTEfer7NTioVGZU0VprrSWGoCLjOBwP2+Gwb1JDiDlPfbdYn5wetg+l1q7vkQgAu65/9uz5MAx/+cMftg/3ueTlcnV2enZ2dhpT5JQuz8+attne395cXfeLrl/0hJRSBMRpKuM43t9v7++3JydnROHNJ5+cnz+5/vDh8vLyOByOx+Mnbz67v394cv5EzR62W3NbLRda7HA4VLHUdNOY7+/v15sTEVEbQ0ht36aUAGD3cG9mp2dnkkspGd1Tinkcx+GwWK27pqt9BVdmjjEOwzCOYwgBQPrlam78caDt3e3xePQm9YvOEOM4/fLLz5ZNmsqkJlW0WyxD07VNT4BZ1K2k5C+fXfzw/Q8/vrvaT9Pl6WkDBm4hNmMRVUNkJIgIam5m27vtYXdYLdo3L56drFa5FDMjDjHF66vru6sbFUMjFUALYFhcq9kwjQ74ky/eLGL6+vsf/vz2x+8+vO+6xXJJIcYEQJSMAhIumsUwDNUThpYQFLNqQafQpgj6ZrH4yVdfcGBQFXVxBzcyR3ADjU2Ifff1+yvJlcDda817rUMdR5WsKtM43Y1ludps1mdas7nzAt2dEEKIITRoUks1BqkW+yAuMcYZ9yQiiGRmDgYO89pzLpDOpV9wB0RVjTGCw/zPhBCaJolQmyCwg0MuM9NtDlEKR3aitmn/5fd/end1v+gXaPWwe+iWyyoABlXq/pgnsPPTNnCoWhiJkMBBVEqppupuKUZCEhFTAaR5N6DqxMTOakZMHLmJIRCYqBsXJKlZEciVEWfAzOyVzFIfRfLqU6nmTqwiyoHHnCOzuY9latrkoh+pbYhqajrzGuiRL4qgoqZaSzGgUvKMVKvmuao5TCLHqQbkZdcxxJiaEh5BzY4QOHg0MCcMxJjaRsG2x11RA/dh0h9++P6wOzRNs+gX0zQdcTxkud3u7w6HL09OLyaSccy5TqUWOUxlPOnbccylijuAm4ggzJXneRNss6ZyXg8AAMwtO7NSCgeYHbmBQ9s0jrnWKVLyaV+KxIDHUXMtaiIm7WLZL86X/UkISaUaQEIBKzJqrRa7vm1iIG4eT3JVgO1uqlpi037xxef9Zn3Y7xlch0FzTkSAc0reapaSZZRcVat6KWrmTlTVh2nMNfddevPyxcvnL6+ubu4OY39ySu6Pe3rAx5oW4vXu8DAOZ4tFnjI3TViux5zf/vj2+uoqH4eZzYHIbdMuzE2l69MiYSkVAVQkkXsdymF0oC8/+eTTN6/nMJEReq0y5WxVVByRgM7Wqw836e37q1/96pdvPv0MKdZcYxcIUfRRd10l1zKJCodAzONwzOPgbsOQKTSXz192fWdgzBHcVCoi9t2CQzzsd7UWYj47u2iamEutZuM0lmmcA9o1ZwMUVREpIpeXl8N+d9jvU0rgNozHbmpjE1OM19fXu+3WrIL5XDm+295eXevp6WlqmlIk50rEbdt98sknT86fDMNxtVrfP9wej8fdfnd3f8shnJ+fnZysD4fj7nAYcz4cj8NxIAocUi21adv1elVFEL3vl23TmuntzfWjCk5qTMmBgHzZr01FHUspuUxENN/2zL3vF+AuaoA8vxOMw7EJYd10H3Y7A9i5A8GvfvXLz1+9mMaRGKXm1C365aZpegQr+UgkIYb77f7u4f7V8xfHXK7u7r57d3Vxerrsl1kxGxFxiATRiABUySy4q8Fxql9//+HJuTw5XwUmZiw1v//wPg9ZTOfxgwMSIwJU8/1YTjfLZd8g+JefvXrx7OJP333/9Q/vrsfp5OSE+85IY7/qVz2hgld0JTBQQ3LGZBSgbTwPbnr3cH+y6bsYbZ4yWDV1c5rN9cec97tJLbhrVUV0MABuiEjyUKssEtXjw9Vhu1huQkwaiZCJWWtBEClZGwYkMAeAmBIxmeoMY3C3+d49TzvmioCKINFHg6HP7QFRRSQzVzMAdDDm2IZFICpSiIgZOUaigCKLJm4Ph//+r38oYss+1DKWMuDE17cP7pbzmLOsu05EU0PBQ3WbyWP+UV01kyhnOdCcRGKeszkWmWutxBhjbEJsU4xMFlRlRJ9jHWimuVQRBZumcRiHodYq80+LWK1l1qKVWmlec5gictY6TCO6B4LH9x1EoBDHKQcM5pZrmdcNImKeyWGa+pIF1MYqYykETMAAMlVJjXYpgWoIYOY5T6KzghgUXE0pxJSii6mpqSP4/cPDw8OulDqVehwGQmxpud0dpyK3D/vULRZrqxwCj13XHKbJTQKCg6t6KRUcGQlDcgdRMTcAZw7zWwEiuoOqowkSErGpBEImMjCOFEMzoZniNBxkHEYVB1ttls+eXQL6h+2+axtwAbVIyK4M2kaNAagOTD0GBsM8t2zRRCTENnWbw3D8cPvu9vomH/dxnowYFCJTBUdmKqXkqllUzKuBKJYiWfJUptDwl599+sWrF5cna2Za9Wn8+u1QHRwpdE3sAKlqVdUUU1X89sN9fNMsFj00i+9udt/++ON03HHJ8dGUACBTQO4YD8djwooaAyihVzMVL7UCYjLY3d992G6Xz59GF9UZLugxtoHdTMxhjc1PPn3z9durIu7I4J7mup+ZuxGSqpQ8qZbAMaXmsNsO+y26AVBVXW+WsV3SR5Gdqbl7Si0zlVrAQUpZrlcxtNN46JerY87odthtzT3G5Oapbdida5lilDxRCE2bzHQuKhLz8Tjud3u3R+xlyeV4GO/vHw6HvZoxh6YZ3THG0LbN6dm665vb2+umSbvjcb/fDccRzB8e7jcnJ4tFczhICiEFzgXbpgVHVSPiqZTlctkvFuOYY4xMiO53N9eSp0Xfb4+Hh7v7i2fPVptN0zRSKxy2DbeuNo5DjG1qOkQijlqKO4SY+n4JAKXkKgURP/n007/88Q+Hu2t3EHc23Z6ervtuv33AYKu+77o1IufpQXSKAUej7672kwULsOrXcXny/dt3310/rDd+sj4FAjCpguaYmoSBTaubOmIMoZp9uLmtOj29OF+1cfvwcDwe5dEl4u4OBMiMzlOWUurZZo0g4GiOy2X3qy8/e3Fx/vtvv3979aHk9cnpZcfQtYFMuzaO7ioSmRqmsWiI6Jo9D+z2p6+//nD14dPXry4uzgwxF6nqhNSkBLF9/+5HEVDzY85qCo6zAXae0Pdd/NWXn4rKd2/fvb25mqqlbrlerk9Xa8QqebJaqiQgBgRzA7NxHABApJqpmxuazi3bWSg280eZ/8eGAMnc1cHBp3GYbWtSx8H1cnMWQxD3LlIHDROVkgOQuf/z7/7w7nabUkcEuVamcH11fXP3gEwhcKl1ZEIKqnX2NpsbIaC5mYgpMzo+gppn8CgToyMiEiAjOkEK3KUmxcCERhiY3ARcnXASGYsM4wDMUjO7T8exihCFWivhvOwNSG7uIfBHgQCpGAAEDklUA1MIwQwBUN2klHnbTMhV1BTIIKRxt9+1MZRcVMyRkEPg5KbTmF01xmhq8+an5FJFY6B5+Q4OiVPbpn3OU6nHY95tt2pQa1UxYjxZrxBwv9+bw+12PxVLTd+H5mYa3WR+xwkxqtpHevDc7/DHK/+jy/5xezEnOhBBRDnQfLwDwozvCEgEPlV16kYDGYc3z5/8/Gc/efn0YrZH/uNvfnM3bhf9mkEjQB8DB4Cq7FaG3f2NpqfPgzspNl0r5iq2H2+3D/eH7X46jli14eDzAkS15hltgOAgZlW8zkNP86GU3fHYpvDl558+f/H06enJpm8jk6ls+v6z55d//P6dcAAHckLwJgaPYd5cXd9sV8vF6Xl6/+7th6s7ycYemkSSdw6G3DhUdmsZoet2+0FibtjayKWIOquBugPgbnf43e/+tG67Z2drkb2pUGiAopMn8pSiQ4AwXB3s5uGQS12vFjHGvyaDAaBqVfMZNitSh3Gcc+7H49g0sWu7rm1jTKVWqRXMkJAMSp4UILVN03YOGAJffXjfLVdPnj2bhkHNd7uHzWYT+37uHCFR3y/ArGmaUsXBm64FRFdLbcQQEKxK3W130zRJFQAPISya1LZNCIzIM4q8a5th2DOHWvPhcMxTHodxsVyY2d3tbUzR3Y/DUUSWy2XX9SGE29v7/f54cXF5eno2jFMMKaWGQyhVwPHk5MxNgXgYxpxL17Zd193cXBPHxWo1xwqZqV/0xGFGGWIp80tqjHGapnnEe/H06dmT88NhG4jEbX/Y/cu//PaLzz7brNapiYgRgKSWUkewCtD/cHXz4f0tGFQ1UEipe/P6k/1he3394T6PFydrdkEnQEoODmQATgjm5IJgdao3b/f77f3TF88Oh6MDEQOHhA064kzRyVoOw/HZ5eWTs9NACo+PKXK289PNP6wXP7798N//+M3d/d355UXOSu5uBBAV3A0YAIkD0X7/ENz6rmFqqtQ//OXr99fXF+dny2UPblU0NP3t/fb65kEMAYObM6KZoWUrXiWjy+nJJqXQUfybn//056o/vv/wL3/+5ofvr+8Wq8uzs4a51hpzSdzkcZKuGs6NqhhmGS/TnAKak4SBuSIyzotDmP8sRIQQYyAEH8dJVZZda0VmkVwplTkERqYFutUyYsTf/u7Pv/n9X8yoTcldxzLlmg/Doda8aFYnJ+vdbhcCIQE4imgx7WIkgnnuYqqmoNFmegAzxxRnbVGROu8wiEOMIaUUiMwVDRANyAAQMZbipdTd4RBTGsdxdnNlUXdh5hRTk2KTAgKI1NknM28bzDyXKRBxngoTujsxAIUZduEG7uCIYmBopJqnabfb02oBiDE0qgZA4HM+MYPLNE2IPLP3iMlrnabSdu0jrF8tLZpFv0CeTFxVaF5pM8bIxHycxlKKO1zdPuwPx8uzy+P9Xa2FEIjQzBB5LDJO1dz5r3uTOTOI6I879McwaEoJgdTmTQbMAJD5JECkInWYJHao5peXm//Hf/r3fRNzmcYpU4gvnz15+NO3ifsIlggDCiqoey4CWKecOTTLvuuaph6Pt7e32+12PB5SJALvIivicSyIEFMyKCHGWkUFqqgBOKI5jipjLSWXZ08vfvLZZyfrFaG5a1Vxn4ttdtqndcMPuaTo5JkcyckBq0nbNOuu2T48/Pju6jiVFFsyKHVCBYSo5oQBkLuEnrOKMPJYp6ZpHSA1TRVQ9S5EIsap5qn85vd/6v7mlzSXvCkQETr1iwW37dc/vv3dtz9OEj77yU/OL14kbt1dRAGdEEWKmTBTFat5mjlaXb8chyMicuCma2OKKmZi4C6m7qoqxHOFGNq+B0Q3G4fj6mQzg74pcCl1HEdwX6w3oiaixBxSSqkBQgXY7na73e7i/Mk0jsMwIME0jm4yU76QcX2yWW9WMZCJlirjkNfrVZXaens8Hj58uNrt9ky8Xq122525dV0PACmlJ0+eHI7DVGtMvOj7pmnu7u/VikgNHJaLRWpaAJ+msV8sur7b3d/HFPu+nwEgc5I1NV3TtvOmBMGZKMbkSOywXLGKIpJUJaKU2uGwNfPzJ09urt6LlETUdZEQvv72L6cn51988WWMbc6l5qORBub94fjdN99gngKRW8WsOgECbWJcPX9xfXv1p6//cnZ+dnG6AROpRhwceKb1M6KbIHmt0oR+Pw6HcQBidwSix3BFbEot+4f7Rd9/+ekniS3FGV0JgOBIDtDF8Ouffb46Pf3u6r7WLFkChcCBOJCZqSI4M7pVEG2bGJkDY2RW8/12Px3H1Xp1utmsNmsBeDjsOQaZipu3kUyrgljJzMG1ouuia6sYBGoIzper89PNy2fPfnj/4U/ffv/Nd99cPrlYNUnrDCtTUZl1T/MjQnUuJmmepseUoPvMjJlPiHkrM00ThzADE9R8Gsum6xNHZU5tV+qM4keiYDYB07c//PBff/uH+/0YuWXCXAoijdMUmPq+XXQdIzYprhc9Aqq5qFatrVsIRA7uVkXAvMQS5pbCHKpDmEnRAKBm/HGsbeBmRvCYGUJmQmZuhjEDYorNMOYiyhRCCODAgWOaxUikKogYAgcODsAi4M7EwcwNYRjHRdchRbRZ3swzgAUAzWcDPSF4yVPpWyIITZKxzuBpVTGVYZjcsWm7kNKcczB3YKqmgXEqeT8eYwoORggvnl68u3542P1ltVq6m9RStL6/vxMTKbp9OLz99pvL1eaw34FZ03RyfygiTiTmk0gpBQECh/nPMvCcHIdHLo3hPANRFUIChMe1tumcoxKH7WF0DGbQNenzN0/7FsfjgzmI1przIjXLtq0lV8DBsQncp6BGahoI3O1wOBxyOewP07AnsMjUhOju1dTdwIECuYqJtDHWqsVd1UotVaGIiaq5nm2Wq75/enmxWnagYgSlAkBmwiZEZmhieHFxvv3mG/BHlCASnJ6dLVfLru+ZaBwHVj9vgshkoCmaVRWpuda5s1dMc85DKcNwzDri82dni44cYC5Po5dcJOcidjgOXdv94stXTIwcODWpW00if/79725ubgOHTYzPL06ePn3CPBPVnYkBXaQ4KCFO4yhlRCSKCQgP4+gqsW2YY60aAMBh3sqWYrO+rdaaYkptT4QP19cxhOVi4eaq1i9W5+fVzIZx4qbdnJzUqrUWKSU1ab3evPvwDgTWy2XTNHe3t7v94wjoycWZmTn6ctkFjgA2jKOp5qm6OyJPU15v1iJ6PI73d9u2bea022K57PuuaRozr+U4i2sOezG1ECIRidSUYogcYgCY536Bm0ZFzX21Wi/W69Q0xLGqtm0XYoqxM6vDcJzfWU1tuTop6uZIiZmimQ3DUbRWlfvtdpimqmZqDEDAiCBu764+HI7DV1/+5PmzlyE6AADyn7/982G3b0OsNdcqKgUQq4KoVpPUpC++/HK3Pby/252sFz2hqDrSLJx2QEc3t9i3q9XycNhPw2SKJjZn55qua7ruOE3Hafzpp1/0TQIoGBANDeadvyH4nBv86vPPus35n394p0aErqbu6BghsFl2rdM0JWYmMiIzczMknI/G2/v72/uHi8vLzekpEzEYW3UHBCwyeC5S61FcEc5PN33biCoTOaCoB8CLk83T09NfffnVb373x9vdgRBqrQSExFUVDWKc2zIBfBSp86x7fhQCoAOYWQiBmcdxnCnFpZRSChBVhevb+/PVKoYAqrMKVKpJIJVctT4c9v/bP/3L26t78LDomxixVieEPI0pxBQiuI77w1TGFMjMApAjMHEMIXKY808qaga1FouBgM2NmABwTkg9KlQJzUHNHcxU0aGKgz/2K4ixiOphJCpjqUV9RpHP68m+75rUmJlO+rgvJXL7q5jXg5gNw9gENvdScozNfFOeByaISIQc5mpyYkJwSLEpUM2L1FpKLWUaj3sCS6mJ0pQpT6WIqJqmGOY9xpiLmxGTui269mS9evni2Z+/+cZdQgipXT5sdyJKSK46DuV3v/v9Zy9eaS3VYbnelB/eiygATtNjGElm7KfNmrDHpfV8zseP+G8glFrbrrU5FQpIRCrChEAERoGwa1pTK1NmjiYSOdSca819137//nq5OWWORimrMmAgCughsOTp7n4/jqVPlFIA0ykXIkB3cGea2aUuVk11mMYskKvn6rkWkbro2vPF+nSzCTGslr27lioIUAAKU2oiAvah2ayaftnfH7dff/MWKizadHJxsjzZxBgRuVa1WruUIlNBnIqiQyRHL24lFwEPY84Px0MWOzs5WcX19e1DywFFQ4xZdL7ut13P6jHg7e399en6+cUpcUiLk+v94Xe//9242y77fhUThbhqGV1ynhCRmUVl3r3naXADM52vUX1KeRym4yE1jTmqiGmtDmZOgUWyqsxD15jC7IwopRyPxxBizaXt123bEhK63d3eptSoWM41xiiC7tA03Wq9HvMYmdeLxZx5Timm1KcYkYgQ+0W/2WwAYL/fu9t2P4hoSikmvn94uLm9VfVxnEJMTdP2fdd1fWySmT88bHMufdcvl4vy8FCr7naHtu0Iw+Xli7brCWmaJnebN3Wz/qzru5ks7eYYABBjalFqSBGdximOU44hAM12DRCVYTguF+sYYwjJ3ZaLRYgx54kIHbzmOlRh5hBDqfVmvNk97D/77O7LL1+3Tfr+hx8/XF0zYJWqBu4Ec87HvZrv94cvv3j9d3/zb2S0//7HP/7w/seSQp9ak8IpxJTctEwFwJfLPpei1Rh47paqKpjpOA2l7Ha7l0+fvnnxHMGAcH7XV/dAHOZcAwA33WR4dbtFaAMzUlAHVauqbhIiRuREISAWc6sSCZvA6ooAFLhfLEspt3d3Ap5Cs2wjSx2HsdYKUlCqqecpc0irtlcRJ2ibhIhmXkFjRCLuu+6zTz+FH98+3N8xs4OKic7b3o8XxLl8OwfhPzrJbdaKDMdj23Xw//fr4xDBjuPoTBwppOhg0zil86XULFYPZfzN7/74/bs7M+66uFo0iGYALZPUgkgiZf5fTHlaLToENzBEatrYpBgC1VzNrIqiuyubqaMDADoykYjkIqJKxHO3o6pqrkTz0NWIA7jPfeZSCnGoMqmhI8fIy0Xfd62bMYUsdVYwiWRCAIBpyuNYa1UkDMdpqFUDUxU1q+7Qtp2ZAbGDOxhSZIpMIYZIRKBGQAgE6GPOwzQdD8c6TTFwkxBMp0mz6DRlIkjMKQRwOI5luz8wh5PVsosNgjeRwSXnPI7ETSOqJ8v1fn+MkUXh6+/f7Xa7k/XyUCZD3O73oMbuKhVmdDizu4sKIsqjwIGBQFVCaD6OegAIQghzSn1uUjhCm+ji9OT+6sCzv20qBCGGYD4yMwJM1U6X/TvC/X57en4phtmgiw07GXk1FAMFBmQVg/iImZshSzN+BxFKkf1xKmJqlqtVBzBrU+w3fdcv2ralwOJ+OBxX/VKgPoZzFdycQ+z7Zd8lQP301YuH7f7/IOq/nm1JsvNOcAkXIfbeR1yZqrKyClUsCAJoNGk0Nmfa+nFs/uYem+m2bnI4DQIECFUolMxKdcURW0SEuy8xD35uMh9v2s08Z0ds9yW+7/ellG+urlpr9+/fgUGOCQBVtREhsQOpu6mQmdRmBgbhvJT780Mc07/50z/9k5/8tKr+b//x/3j3eLoaMoASkpqrowKoo4kMo3/53XeY4ovnL3/zzftf/+53uirT7rFC9jCnIeQdQ+yJGABPJENpIlLdeg5zAEBtUre1rqeYEsd8d/ceAIc8EZGplFLcHcA5EBK1Vk/39/O8m3e7bT2pqImklFuVPscTEXCvW1FVadLUUho+/fQzJny8ey+1brU6+scfv3727Pn9/f3XX3+9bev1zdW6rk8azeP5clnn3bxs6y3d7Ha7N2/ff/vdG2lyfX3dmtTaEOvd46OKhhBDDGspz549P53Pd3dvQ4jM4eb2+ThPKY0hpFpLjLGjyBPljhq4XE4xz/M+SSsxxBBjrVvZViKc5znnqdatK6prLeIi2pb1sueDm4eQ3PFwuM55yDEYxkYoolWFA2cOom4q//hPf//2/Xc/+OFn3379LZjlGETVFMBJrbkjIFax25ubj1+9iAHnw/zv/+zPf/v61d/94udv7++nPMxhlNbMfFO/fXbNw3w6XYBGix4SYlKW5m6cYlV79vzFT3/0oxjJTBMTIahK76dDiGqNA3sefvHLr98/rMwJyMQaUGjuBh4Mzf1xvUwOlBIAbFutgYpQD/MAsZxHZ1azt3cLwhIIKab5KprYuiy+LWDFAK/mmQEu6zIO2cyedIv9MOPQHO9PR2bKKTRRA+NAzKRiIoKIIYZeyLo7EceYVJTIu3NFVHvt30UN7uru6BAZz8sSpnFgktJuDte1lYfj+XairZa//cd/+qu/++e1WM55mlOO4bIs0gSYpNV1q+tW3J0CN/GPAiE4M2fIMfE0ZkIw1dqaqEbEQOzWZeFo6tJE0LdaDSBw91yyqJRWsY+DAIhCT3AyBXVlQBFD4hjCbrefhiFHtiatSqAg1MCUEACs1PWybdtW11KZIWhrAC5NKj8pNPr8p38izDGElPMYQjB3AG/atlpEtbVWS5PW3J4Ae0QI6Kp2uZxFZcw5UJjHKQRZ1rqs9Xw8z8NQVWzdVE1El7XUIlUfxnmappkYU4xF5d3Dw9/+49//L//Tvz3M0zfvHr/77jsEJ3QAbyKt1v4NfMLvgZt5R9I/gf3MzIw59DGRGyBiYHZ3AiJgZuhokBjjfjf0mz+FpA7jOA4jMW+vbk+//fbNtlxScqCwiKM4oTJzZ6HkMbz/9qvTo0QGM3BAN3+KmiNooqXaVuV8uQy78Wo3PttNUx44MsUYxj1xAKsu1U1Sytu24ZMEmDgECiENIzO8euk/Werj8aFsy8PdUZqhQ+XWmX/ak0kBenqFqjZQEaulIeFHn3z8wy8+/+LTz2+vbgzl3/zpn/6nv/y/GmJCJHQOsTYTaaZ+2drjZTlLS7vr9wt89VjEMnIAETVbG17P1/P+Sh1ElCNxeApqbqrMqayn5fhQtxU47EJal0urhcAjcx53MeQYEyKKtJySubbmok1rbaWqyuV85JBCjBxDD99DBDVLKXVbvLmTGyGmGOmwDxHffPdN2bZNbS1bHoaPP/7E3c+XS6tt27a7uy5P0m7QffHiReddqerhcPjhOO93h3fv3qcYgVAB8zge13Wrzddy++xZbXJ3f5/HCZDVnBgPV/OLF7fjOPSisqPTmDmkgGrlVGUty3JGxhjjti3jOA3DYAYiVaUi9m84bFtprXDkwMGfcC7UAcLz/ur585fffvk7EXW3FIgcAlMgpu5cRTezb776+nw6MxAiu/ekJXVHdaxNSOTHn/1oN46lrJDIBT99+Wo3jT//xS/+5Xe/WVvZ7a8VYL66jvPVaVkVWHp6NxISKnuINO7n5e4BwZZWxikjupkSOSEQAJGnFMFjHuff3T/eny/MbGA91DEwK4LW6ujr6fjmze/J7eb65np/QKJSRA2IIiEbOJUSMDlFYiLyIi2yM+G4n9Pxq40qAAEAAElEQVRuHks5SJOvvs5jFBOXFphNHRwJgIHAwM0u63I6PW6lmlkMMYRgVkx7BEUXgmut9fuTodf4ZkofUox6Slc/BJ4Cx92IcVvX3/32y//bv/t3x4cHYnpx9QJJFln+6r/943/5658/PpYYh8g0xNTbDSQUh1rqWkTMTQ3EwCGkaO7syIyMmDigufSwGgdkoCd+mojqVstWKhB2GxpTd52ymoJDbbVPZ/pywx1EhBgBsElLifKQ5nEac1YTDMGbNhFRy5EjDQ62lrXU7VKKuo7DFNCdiNREWrcaoYjUJrVWAyciptivTfdmZqVVNdtKK1VUFAFTjAoeAkWO4NBa7SZX7cRtosgcOTCFJnq+LDFFxPrw8Ghmj6eLGwAhhyAqw5CGPLy7f9yq/O0//fzli5vPf/Cj3/z2y/cPD8wYAwG4diFth9P1PcmHTJiu5H2a/wD0uS34E/KJmFprvb3ywB2kxyEQPwVyOrgZhpQ55tHD1dXN9dJUWk5lSIyIGPvWu4lBlcrMOYfLcRkjm0IV7RAJUd0Mai3btiJ4CHSbdp+9ejYFNPOizQXatqVhSkTTNKrBOIzuINK6eLsj3kLMp8vxV7/57XffvQURqYIOPXbYAdWhfbC3mLt0hqvpuZRay/Pr2z//458+f/nMgCJH4hRD+OSjj37605/+4l/+eTfu1KEKGTEOyCb7ia5uDnGajgWsthAyo4qVOCRwmac8Rv3yt7/Y7ecffPrFOOY+cHtiPkvdlkvZtmVdb5+/ZOKyFVCXUqS1ac/jMHDgD+BbAIBW27Kdh5xTjIXosizjBFP3fCHWWsq2ASExt9ZCCh3n7eBqUsrWWn3Klmit1vLi5cuU0j/8wz+8e/fu+vp6mqfe84LD+XImaojY3bbLZdm2bTfv9/v9MI51KzHny3K5ffZsPOzfv3v//t2793fvQwj1/fvxw2QgxTTP82VZ1nVJKQcOMcYQYggBCNFh29bW2o5wuZyneUak1ioAEnGK8e58BHMOMeXUZcrgXboGZhZTRCJzmeY55LEqFIW6lYgO4GXbNISYohqkFIchb9sKgKoGLmJPovLuJy2t/ODjV6+eXRN5DxYXB9nKGNPPfvTjeRp/8/Xv7+7f7Q43u2kvWyUTpoBmQIRoog1B0eH4+LAt57S/+urb707H0+vnz28OO/SqVpg6moVTTIvY119/Z7WZgAKIAlAwc3AJstVWA9T/8Kd//PbNd1+/e/fV+Xi4uR1SRmQHFCMxT8xKT3rECICAtQm4Yc8RB4g5/fjHXww5X07HsrS11sOTxQfdXABo4PvjsdRqpoEYiVLsn9zTzVprKaW01pj5Q2nY/aNAhMz84UbnJz4E9I+u7zDhl7/8zR/+9GeffPTR+fjw4tULJP///O//61/+7T+/fX/hmKcxX0/Z3HrCFYdgALXVKooUgUgN0DSHgO611RCYOZIDgEsHSqPTU6BGF+m7qm2tukMTZeKUYgiJmaH6h+NOW5PwFObYiMgBnxilABw4MCMROi6lnJaLqDBBCJHBpRUw65EDRBSZQkCmCDGkcYiBmTm60baVXlwTEtJ/V9yIiqsUL+tWmoB2GCAxxRi7csUcHN1RRBlMTUutBqDgHCK4b7Wd19UcluUCwGrg5j0+VU3zEKe8u6zb8bJ+/fbxf/tPf/3xr7755W+/9E6MCZyH3CGDHV+VUscBQed990f+ZARD+BAR/DTyq+imaqCi1Ts/HR0Iz5dLk+aBgRBDTvubVezrx7v3F4Owr3IUgLWIAxHYlGKMIXL0dcUA+8OhbQ1NHHrIgon7Uupp2xjhxbOrH3z8ajdPb969A6KtaSfjO8d+dQM5AdSytWrX17fH431vYjgkjum3X331y1/+y7t37wNRYjaFrnLqGvyOCBc1NQWiFMJxW97d3015/POf/eEPP/1otxtLq4Ch1lZED8OQp/bZZ6+/ffvtu/N6mPfiVBUJMGV++eJqnPbHcxElShxA3VpAIRcCs1Xuvr1IPf3ZX/wP+6udlFprB2OLmZloX1rtr2+vbp/XojGmh1YeHh7mm1eEEBIzhVJL77xqtdpqpwOt5UJM+8MeCEXqVop6b+nETYl5LducIhPVsi3L5Xw+n48P0koKMcW41Bpj2u92//Iv//LVV1+N43h7e/vmzZttWS+ns5vFFIeUXQ0dc07McFmXN8sbMYghBg7zYbc/vDqfz+Nud3N7U7bSoUZv374dhoGYpcnd3d30zZ7jeD6fhqENw+wOKmYuKYcmom4UQsoZmaW1EJP3dFmXwKCtjsM0zjtzzHlIKR7Pj9oscFqWJcaYiBF5GHeffP7jV19+/fWXv5vng6yn8+XRHAyM2JnzOE3bttXSCFCablopUBOvzdSx1O3q5vCzP/lpcnDXadq7EaIauIm66e1h//r5n3z53Zvi1KP9YgimnggA1E2YzMBKqZfzMs/7OU/u9f50XteyvLh9dnM15llV1DRxrgZ/96tfrUuLSGIrOjIQOER0IjC183b68Scf/ezzH/yrzz8+rcuvvvrmF7/98v1dm8f91eFZiAHMiYl78YsIgE8EBIStCLiaCoBdHw5xnF/e3Jbz5Xy8U/emzuyOznkSo8fTBYjIXKyBOZgxEjM7WOCgqqKChK01Ikop1VoB2N17BYmIPSeytSfZJQAggoOZ0nLZ/tvf/d0wxUj4Dz//+999+bu/+Yeff/32LAo3c/z05e3r57e/++abu4eFmag3Qt1BZhWRTfH2an9z2Depa605hnkcvE+azPoKDSn2vbopMAcOARBqbT2OGBy4C7MCB+UmffjhDkDEpdYeexUjAqKpmMpTOLOWy3ZZ6qJqOQTTDo1MKQYi4sA5pRxTyEMConmaDrs5hahmD49HA1D3p0QtMAdUN1dH8CZNDEqVD8HCSIwU0tPsjEPrRaFJQm7SWv+i1MbYuaRUtqZmxGzuiGzQkKlUCYlzimoSQ0yhXS6X33z5zXdvH1SNY3BUerJIsJqR6fdEJyLqRL+n/u7J6YCtNTdXUrOnCZubupWlttNyiXEOKWbc59SUMxJdDI6rrHdf390fj6cLYFDKAtPDIkwoADHki2HOySuID8EQ8zDccDsfMyiEelwul/MSiH788vqLzz+9vb0BcAArrX799t1hnIN7SiBbEwMOAdxWAmRW3cZpN85zLRsT3z3c//JX/3L/7p32RFDgrYgBmqM07b5nQXPw1qSoYEARa+Y/+MHnn7969enL50R4PC/qRmzEp3ma91Mac97l4cef/uC//O3f3cv765sXUOzqarq9mQJjWc62VALUctm2Fd2Y0bUxMWFoAPvDbsy5rnVZLgDOjKYKrlLLUksYxtQV/THEPBLFzk7MeXBHVSFEZGxNay0IMIxj2UqtdRwHadpaWZfLOM4A3SmWQozbsjKziTAHIIwx5TxIHir6u8fHkFISIcL7+7vj+TQMwyeffCIqy7I4ADoihxDTbrcbhpxSaq3VJoerm1LbZVlV1Ky+efvm+fNngcO7t29KKaVs5/OFiBDpcroM0zgOQ61tmvd/9Ef/2txqrdNMKWcCcrBu6BmmGRBNdch5uayX5e7V648iRxE5HY+Xy3nIwziNDw+Pbn64uhqqrLqEEHop10SIMIb8xRc/vbp+/nD/7s1Xv/1P/9v/CstpHJJIa6ppIAdrtbqbqIlbaQJC5liblapDon/9h39wezUv5wtACCFZQwdM0VeRrdVlXVOcfvbTH//qqzf3pwXA0Z0BW2sIzoSgBoCltEDhMO2ZojtyTgD+7dt3D/f3z29ub2+uc04Y4u+/+vb9+5M2T0/DQBnGiZlFllKr1O12N/3w9StCG+b88vWLP/njP7s/Lb/49a/++u//7vff/XZ/9fx6/wzMHDAMAxoggqmiEwCquJsjMoV8XFqx424/7cfxJr5M5GkYCIkp5Hn6+s2353WNHBEYAUqpv39zVyxOiN8zk1NMCOjurVUze9L7E8UYa6kdB/QhO+xJL4oATAHBHfy3v/+ytHWahvfv7r578/b94ybq05CuD7ucUx6HZzfXW9nUjGLQtREpAYoZIqj4Z5++Gqe4racmAmiAFpjEDQgdkAkJsKkBIAeiGFKMOeatyFYKAKWgtQkgMlLgMMTcWgshiHo/P/tFYi6ESMTgUFsLIaxb2Zatbs3MInUsozuomwfmIcZ5GnfzHPIYkOJ+d9jvDmPO58sJ8bS2VptczXOIwdyaVuSMgNLUAGpTMY9MfaoOCqLVHInBpKmq90xlU3frvFxwN9cYE4eo5mZ99hLcAQlFFVUJQoqpNqEQhiGbKxE5MjExRwWFQIEjB/4+grwPeWOMvTNiog70eMqH6VNJYtWOe3QzRef/71/9fUj843/1J2K4u7q5vt4dRZbj+f3p/Hi+gNoQh5EgD+4GK8bLWhFkymOTRhTNRNSIA0EAtPGw31o9nu7LeiGCzz99/fmL56+f7eZpvJS2qiGnzz76yAwfHo8BgUoBoP78GCk8KT3Efv/Vx598VEp7vPvq/vjQpCVmZhb1p9cLuYdSWE/uVAAydawOsmwpx08+/vizH3x2GJLUAsYKXltDUeK4rUvb70LAcZie3dz80U9+8lf/+Pfo9tnHLxDacroHgVp6/FlT8/49MDcR7ervq93V/sXNw93j1a6aekwE4JfLeVsvhHi4eaEq4LauizuknPa7Qw/RiTGZuFoz7wObrZbKTN0V3Nd0IkKAQ8wAYOb0BL8DNwtOJgrJiDjm4RACuZ/O3n1hkXm9XLZtJaSXr18Q07u7u5ByjBnMh2nEwGA2ThMRrdt2uSwPpzOFOKTEEQ3ADL777i2HEEM8Hh/f392v6xpDzDmDOyHmmA83tz/5Vz8zt9PpPO/20zTHFLd17dPFEFPndoXU5f8DmzEHAHOzYRinaXc8ncfdPoRwPJ5CSOMwEzISxWjuTh9wT8MwvHz+Mif+6//ynx8vZZoO7E1Bh3EaxnFbN1VnJGRsYg6kAiqtqRniH//Jzz7/6FUrJYTQ59yBQrHqriK11Hop6z4e7s7nt+/fc9w5OYITkoKKNEKMMYtbczjsdohQWwO0yIiEnHJT+/27u3fH0w8++SiqPS7rPO6OchYHh14pulsry3lZVmb6yec/vJ6HEHHe7ffXz6bh+tXzT3/2oz/4d3/xF3/13/7rf/ovf/Xu3e/2+6vEQwDHNDhgb7JFzAEBKBCbIjOawPH+eHl4QPAY+fowPbu5TTndPb5/9/6NSgM0tp6l7X/3i1/vnn1yc/38KanUrFtEibkfF13736cFHDhSLKXU2vqJ8YSW6UHx0NOf2q9//aWBl1prMRVPga728zjlS9keL+fD9dVlW969v4txEGw9bAYA3HWe52e3V60VQIwxjENOMaYQyJ60xU9IbkCgTlONY84xRAesTWu9uDkHbtIIwF17sGNPvmqt9XmBAUoVRKCogFRV1H1dSxMXUQcAJAWoIuiKhPM4xpRTinkYwzyNwBljiOMIRBiCIjw8HgNxJCZAR1TRhi0xEwcEdRdwj4Fziq52kbI0UcdlazmGLqPq+YIdwYFIAK7qwxTNDIGaahF5/fLld2/endcK5Ak6IBaIQkpJ1cZx6re0qgLoE0nY0RH6U+wdXN/3PqmAunsQsa+DDJ8aAndXM0Zyx63K12/ev/7opSNfLhcXQXOQAtZaFTYehzykUNbGDrW23ZDd+Xw+JxZfL1ZCiCkzS+U8pLJdlBCtnpfHP/7JH7x+djWgBTNkd/KUMwg6IkT88Wdf/M35H6tubIBggBS45ydbrUUNjo/HWjYCL3VDxCFGAlB1VXB/shEaaJPiSGpgTtJ0a+IAz58/f/7sWc7p4f4hPL+93e2301GkiSoHEGm11Vorc4wxjtP4xY9+6Kzbug5Bl/Pa1goA2hqBDzmU2gBIRKRJrc3MMITjtr15/7iWmnIIkc3bui6qLaZh3u0Y4fj4YK08PLw39/04IsHDw72n/OzFx/sdEUFHlNTWujR73RZAmOc5pewGy+W8buVwlftKZhqmdb2AWojBzPRDtoIZwW7nLsM41nFzUyAkwokTGhyPRzHHwBHJzCiG58+fa60hcGfEjsNQRN6+v5uG4fr6KhCrWc7Ttq73p4fT6eTuu/3eTAMz4jAMw263u729aa0+PDwcDlc3t9dEVEtV1RhTDDHntF4WAKylmcE0jjEl0xZSAEICPhyuS9XWbMjD1VVwhz5l6idRH1r2d7i7Ulupn37y2ds393dvvzo+nuZpTOO0lrpsm5SWYwwcEHrCqylAM49j8hg8j0Sk5yUPGRzUtI8jWmtVCgUGCt9+86YZqgoAmyG6q6AIUWCMuayX3X4/50wOxugGhmBm5IhAFMJS2u++/naaxhhSyh5raD07D0GVgBiAONDHr56/uLlRlTQO43SIYZjmHQGZ2+31zf/8P/6bj5/d/q//6T/f3V8WW3w55d0+5x0BKVID72Epzd1UEkbu1X3dciQCfn9cz+evA6GqgNoYk1Rx8HVrRNxaLaVwYHPrp3w/93OMhNiJ5d8bxL4fEffxdQdbISISigqSq4gZlNL/igfmwBwD7eepbtu2nedp2F8dHJFioMCKLq692JZWf/L5Dw7TCLoF5EA8hCFxYmQ1jxQI2Z9+AFB1IkwxhziIeil9DytmKqbTMDBhYOxZjU36cBvcwcwNvKkxoqrXJjE0JXNEDjyMAxKnnFPOYM3NQogK5OocgjuE3TgZ5zzuKCZ3A6S7h4cmknNiZkQqtXYZBqeUczKnGI055BSGGJVsqVzNjqflatwRExgA6TCkIeUQQuDQA9q6ZDUQE3GR2qTe3uz+4s/++P/3N3+LxCkFN2ulCqCYEVPEQMQ559bathb1Bo5NtJTaZzrwxPzxPv3vcI/vHyoz98i073fCHBjVRao6ESdV01ZX8zsPTEjuImzA26ZJHCxKdZOI4gBj1c0vFzJpIOAeOFVsfllzhFbrLoW/+NlP/u//9n+MoO/v3j6eT9IqAkzjNGDqicKc+PWrl19/93VInPOIyMSMAARYmjTRDoACE3bbjaO7MoIDikKnB7sjkruKA53W7f35JE7D/rDbHfaHK+HQmrspner19S2nqsuG3peUZi6mlSl6ivub6/Na5uvbc3nz9rgyjp4mQA0R3I2ZuDZtNaStU46XdXs4Xl7vb/79//QfPv/hD7ursDVprYeAZnd7OB6XyykFMtfAsba6rMtlXW8C9yO+SSGiGCIMMOQM7rWjKImaNGI+nY7jNBNxn+QRAyCt2xqIKQQivJxPiDQMQ1/rAUBttZSCRBzIxJd1cWZmBncXV9X7+/sx5Y9fv97atq1bCKHWmlJ6/uKF1FqqTLtEzp3R/+Xvf8/M8zzHFAGAiQlRRJZluTpc7ff7ad5d31yDQyklhDCOEyGqeW19htlU2rTfiZxn3ANCW1pno4YYxmkG5FIKESE6EfUIge+Hzh9GzxgC3zx/8ef/5t998ZM/+uef/91//N//X251LdVUkCjm7IDCkcchcHOpwS1vdZfT/Zs3v5nC65ev0jgyU6lNmqsb9uBfwmcvnn373dvzZYsxGwAa9MBkNxcVAjchkJaHIYCjKaCDQyvSRBkgxIDMIK2t8rAuRiHmMSd2FfU+YUdtrWzrNOWreSST0nQMOyAmCoDesyDWpkrOKc27K7fZVYrK6Xi8+HEYxnHeM3ZQs4t7IDbAtSqCMwYzl2YMfq4LuMaYA3HkPO6nZVm2evIuFTAnZPdqZjnnPhJARHziPWgfASGiuddS+hHRcdDWj1XoRPSOFGsxEgBzIHQU9RgZ0VMMw3C7rttlWcy98+OAAodkdja1Tz9+/ZMvPnUrbsrMIURGVjPtSwIH917PgogSYrcFmHaztUuTWkovb1trMXDfsyKxmEvrgQqEgGKqbhxDCOF78ME0jv3tYuYcUyBSQwAkJjQn9/7rh5wi5x3n0QCktffv71x9TDlycIdePphjCJYCAwIRj0N29yH1tE3nwO6+m/bTOCFr5hjjfkg550x9Yd+dp0Ax0DSMRLzUjRkzwWefvHzYfvzVV9+oNOJoBsu2iCEC9S8P0tMewtRa0602R4gfNKn916OnPAVk5u/nQt59bjH0zE9idLPOoXNzQoxEDTDGNKSkqkWNEQZyMV+3kmIIMVDwmHITRwptfSAwB4SYBCliQFOINCRF1zjukPL1HIF9c60XQgiEHFM2NQU7r8vN4bCup8u2UUrmGPLQS6eUUjToy3uRguRNhcAwEBGmQLU2IEOkSGFRf3+8P57P17vr5y+f53kPGAKyiDYQdW2rf/fmzfPDLoQgrUUOOaXEjIgh5yby1Zu7X//+m7fvHkzQMak5uIMTuDESopsiWMA4Y7I4TIEv13H8X/7n//DF55+C+2VZVPs77GqGrZZtW7d1nCYH2O0gh3D37m2pdZrGm+ubJxIL0pBHVe2JEmY6TiNXqnVtrQWK0zi9/viTu7v3tVymeR/TtK5Lb+pr2UCIAOq2ubq5N9XWGjGHGMwUwKyrhEARoJWG7kXlsq0Pp+OQMwfqs6ac093jqYoe9gczO58uIcaeJrTb7xG6zM63rYzDkFN6uH+Yhmmapx5d8IRuR4gxMvXaCLrijwJKs900l1ZVmqjlPEhrVcVcYsjz/jbnvG1rVclp6G6bTip+ki14J0GyAziF5zHF8Edq7S//8i9Pj/eEETEgeRWtmwcOTqExq5QQnQNJrd998/bu/vHV89vXz17UVtet5TioirpN87Ssy+X4EDxWWQ0scgYzMJUmwXW71PdvT4yINzc4TwguxUKIDmhq6tC8MXsMsYc9bLXSVtKQ9oedi58fH7dtU9VWyu72aoypthbGXcqDqYpWaRul7ICuIu5fvXvnQPN+NLM901Wd7u/vtu28qnAaAmBn8zfEriQENyBUByjFrHHAMIym5mbM5GDIsFVRaY6o7qpibgTUpwjuXlsd8jAMg8g7NcXuGxXp14O7d6Dk9w/CzYAZEfOQENxMYyB0qs2JyQHSOMYQpDapwsgqljIlxJ/8wR/UUlTlo5e3ma215kiuZCZEcNn6aApaq91nS8zdo9CkkVQ15Zi6X1dF3QAM3M1S0oCBA6FLv69UOybdDQP4OAxDSkNKOec+y0oxmmrgyEhPWxBp4P5ER3VHhOBoKWUFbk1VfK0SYgoUcx6am1S9rFsTGfKQQhhzDDFgYEAIzLWUpdVadUrjkAcOtNvv8xCHOOSYzXTdVhNbtzKM+bIVBIhM6haYOqoBTfbzHELcli1HbqK9pDKFGDO7SxM3QAyOrbRaSjV7Crf/72blDzz63gf0JuBDJgyqioO584ecAnHUxL6LOoWYIjGuBvXZGAIqm6hbKcUcwGkrjXQIjtLqZT0D8O5w0JDdyZ045LNIQB5zfCz6qzfvnv3hj64TXbZykiNxQoodh1KWtbWCgLv9/u54VDju9wdG4YAASAhm1LVtEDiHw3Y+uhs7qBkgBEZwvCzrm/P25v4ByA+73e3V1e1hX0XIJTI69EWKkbf6uL3fTofdgQCYCNwxEGT+8rs3//zr33715mGrbmYcM1JQqYRg4LWsTOwI5p5SBhfTklJ+9fnzl9fXwzRsy6WWAhBUnAOaCQCAE3G4Olwj4bat4zCW9bIsl1bb89cfP3v+qjNLUh5VZCtbq6W7ckqtpkqERKFsGwcy99rkeFpiGpEKEw/THGPsGRdMTCEuy2W/3+/3h/fv303jFABP7lXFTF3MxcTUxFIIjJRiDJFL3bx4iklFY077/f54Orvrbjd/byUhonEa3r19ryqnY2ut6byTISMxx7gs67ZtquIe+1eWCEWauwXiy7ZN0xjT0MQNgImQcDuviLTbzQ/398ty5lAopDyMRFSrH0/n29vEIXSIYS+fe4wadoOKO1FgjqfTZam2WSCwQKQiaiGlrA7NTc0CQN4dOIbm6sWKlLJ+++7dw/Nnz/e7g6ztdDy6e2ntu2/fuLnpRn3M7QLu5AJaayllXb94/bI0ef9wd3c8Xl/dJCJBM0Dr+RmmsVeRHgCChVbNtGqmfHV1yPPYatmW8/kRbw57RG/WiDGG5MZL2YYhBHJ1VJP74/3Dwz3ZRoBGyMQhGV/P6xLOSzk9nKq9G3bXKU/E5EhNdUg5MnIA8IKmedw7M3pxEzF3sW2t27aFmAMFLdXcw5OGkjvHPlJIKTVpIuKm3rffCETo/rT+9S7PBQcEVROEjqRlslaNKAQK1eS0rs1smIfz8eFqf123YqoApI5E4eZq/+Lmk21dtu3cpIl6MyU0BARgIosMRGFrta+7egnbF6KiZm5DoHEYQ4whSSdocQeFhpBSAkRwgp53RZxTmgOrCiASM7gTdG6mg0GtDTM7oKg11SpGbkjhg/IVQuDQpHJKYNZq6QJzJHZEUVm37XS+mDs4lVR1yiFyh0hUNUO8bE0M9/MhJ05j2u+n/TzfXr0Y0rC15bItZSnH42mV5qUCUAyhla3v2RGIkahHNvRL0I0QPnr57P394+PpNOYxBAwxsQTbtLVWapUmKtIv8L4/DCECeGc9fj/y+17m9eGqBDP8XgVMaCQrO7EW1zVCC4WLaDcHqoE5iPl5ax3dc9wuTRWRkD7LuxuwQAxFKgCIuTmEYL/5/defvLj59Ha/y0ObRJp3eFSVdjpfpAkTTSl+9PLFw/lSttWDqDhx4JR7N+dOzSHFMF/dtm0BF3RQqaJ6XrY37+7XUv7g88/+9Cc/HafxP/3Xv/7NV1/eXN8wUiNjAARMkdxEm59LcfOXL18SUkixqvzXv/mvb9+/N2AWzArmFhHMmlubp7m1GrExmSBB4JSwbS1lnncjoCPWy/nxu68h5en1J5+HMKkpEiXOKeaBBpG2ratKO6+Xuzffnh/uwWneHfr2FRFarWtZWikc2P2J2Lqsy/v3b6bdPI3T+fy4rstut+9Ew9Pxcb87cAiqSsS7/eF8OnUvcBPJKe92h1NraFplkroxNDdR81qFABFxyDnmOOdBVdeyMbOYXo6njk/phuR5nqd5MtVaGzN3asiG4AAdM7Lb78dxMvBxGmMMrdVlWQhxZ/tuJCyi0zgBACI9e/6CmS6XMyHP8w6IADCGyCGp6lYWIiIKOQ/u67ou07iTJqLSG9kQAgA0EQflQGVry1ZqqdoqurmpAwFFRmQQc3X14HqznyjkIl5F3S1CMOaH4+V82T777LPbw4FW0oZv37xZzgsAMwdiBCJV77g0EVkv6ycfv/7Tn/1BoPD+4f7vf/GL92/fDMN4c3vTQ1aZY2Z2qSLOIXnHgiLGkAn5eFlUy37O+3x1e7U/TFMra4x5N885ZDcsZZPGCr5WO9f117/93en4qK2IuYMrkRSVptu6iShY8dqOb89AYd4fpvnAcXA3MO5q42EYhzyutar2PTYSRfeGgKU0EwPsJlZmDt16Xbby7Pp2OV/WuomIiPZpwTgOzHS5XFRFtX1fUBIjErpDjDFGBjNzrEWkrYj08vnNxy9fAMN9K+fLuptzaXXczYC8Nc05MQGRIaE2NcVamwMiAFPKKWylONRam4i5o7sDIhM7EiBxwMAfLgBtzjYN4zSMMaVhHA/zrGaXy9LquUmbx5xjGnJsKlutrbVClFNINICDARYRgy2EQOitta0UBs8Z+3TLzYOZWyliqOq1LOCybqWKcKlOXksTtabivo45me96fqaoO9GyrCJuACGmaR5zCofhcNjdXB1uU4jZR16TDZbT9LgslyLjPMecE7j5QyCMhIAQOZCjuTm4tMrgP/z4NTF9++ZtYHKPHY+ACA5oCABPa/0PtX/P+XVEVLPvLwYOwZ7s3W69T3RTNTEDt4jgauIOCAODVV1auzudW1N1WErjOJj5ZdvOy8rsX3zxwy8++/R0PP3m6zcGkeMEGsSkmTKzuKfm61b/yz/9Kv3rnx6G3VjtZMuyrOq+bFtVG4YxhlBLmYbRzWuttWxaJcQEWtUdgMBx01Y5EjgjRCYVXUu9e3x4eHi4ubr+6U+/+NEnn7ycDxzCH/74x3/zj39flss8zeSuZoGDiWnTLgPY7to47169fPn23bu7h7u6rpGZAwG5UAOHQEXMhsgENQVNe9JalqZ1sXa2/Txe7/bmZVnKpR3xMrnJL39hteqrjz8LKQECes/g8adgr8uxbauZ7eadNLt/f/fw8DAOO1UttdZSHTyEeLmcVWWeZ03RTBk5xbxe1mFYd/vDzc1Nzvl0Olm3KKqKtHGc+r4fEE/HRxEdUrqEgBo4htDIkaqbmHWUoQdConkYAaC/BGLW/b1iyszTNFp1cUslRuJuARqGkQg48Pw0tMR5nl88f/Xq1UcxhMvlst8fUhrcTUT6uDnnnMehbFvOOcZwOj6ItP1+RuJxmi+XU611mqZlXcu2MYWu/ZimDBDWdSXiXp2EEDrFCNGbqIqkDhsKzK4JirlaK1WREMhcxMAwxdDUYtCYQhWoptqcVULA0uqvfv2r94fDzfXV3elyXoqKM2rKuXZSJjICKeJWyvXh8NlHH5mLm3/68tlHz29+9fuv/u5ffvntN19f7Q9TjpEtUnZ0ilFMVIGcDdFVxymq6fFS7pYlZT7kpGohDIfD9TzumcJ52YTbslWrsIr97uuvvvzq6yaGSA4KBq1WbVKr9JHMmPOPf/BpSrGpni7b+XJc15OM++H6NgQSRYqxI1s4Jn9y9mLIeZqmh+NFTaHnB8ZARMty3sqGAOu2iFmrRaR2lWBfw/Qp3Ietops5dZsYUkwBEQhhEz1vbVnKp69f/ts/+6OffvGDHPj/+C9/9cbwvKyHq10wFUN1S5GncQiRccOuDVPTLqUD8Nawp251yhszmmEP1+5xvyHGEGNKOQ96fbXnQIl4N8+qNuRpnqchZ1E9L2vnHAempzWnWVMprYpKCEQUmsL5clmWlZjULHGodXXQwIhNpmkyB1UN21aatWZrillaa9LPQAciNzGzVsWpp1aaW48z1Srq5mrOITBSn9MRYl+gmAOnSCZZBwMF5PlweHc8IeE4DiGGq21ppRKBmqbAKcaTdtkoEllADSkAgn8Qkoo0M7w7XmKeRLVf1E+Oyq5G//A4ibl7gInIpEs/1N2J2MzNzd07T0MMtlYsJsIMCMYlDBkDOGC15eF4XKo2k5vD+IdffP7Fp58e9gd7+cop/P79Y86BIZIRuaWcQM1E1PGbt8d//vLNv/rkVcgjV2m+bE2AI3M6r5sa1irSmpogIIeU8ySifVonompuCiqbmYFZDOGylnd37/fz9Bd/+ifPrncxxtrK6iWavbi6+vyjj3771Vd127qPcEhDH/UVkXUrSLHWX7/59g0gFmlIMaVxrS3H6EyqWsWaOoYAiujqrt0632pL7Ls4Y9seH47LVgKHY0oP5/LFF+Pd2zdxCM9ffCSqjCBSpIlZOx0fzqfHyNyZUdt6XtY150zM3epJhABU6ypSc87uWmrNwziM47bVaT4QYU9sPT4+DuOoIqU+2R/73rWUgoi73f58PiJhyqnWDenJ4t9EirQck5txDIFCrdWcwDwwl1KXZdnWdXfYp5z7qPdyPp/UhpxDDOZOhN3aRsSMNO9387B7fDy+e/du3u+vrq5TTGvZuvozxjjPc86574rLtsUYYgimkZg4dBsEEdPlsnRAYwzBTB+Pj/O8m+Y9AJv2Ww1ERFQCB0TqedwU4jDkHmBeNkOC0hogY4zV3MAphDjOtRasbRgjh4hI5Oo955ZYVR5Pp0spxGE83MRcQBUQCFO3InacU8zp5asXAcFFJXgxv57nv/jDn/zoh5//7c9/8Ytf/uZ4oevdfpoChxAhOioHsicyuS9rGYeMFFVcBRdv6/ltDIECv3z1kkJctqNnXKsJ27vHh1/95ktpwJTAhQlVFJylKRCFRInC8+ur63kacry6vt6Nu+P58s+//fJffv91WR9urq72u7GprGVlZmJSsS4cSENG6vMzFO24AFfVdV1rqWMeiEhb/WC/+u8oARVBRGZyp/4svl/OE1LPFClVLmsNgf/sj3/2sy8+241MgAQuautWVI0+bJhNZUjM1FPI+p3efxJjZg6RiAPHnnPuAK21D0pG5ZiYmZDykCeDm2sPIRJAYHbUkNIwDCmm5Xy6bMvWCgKupaSYkIKIbFstrSDiuhUirgLnZbmUkpjcvPZ7zuWwn5kjA6lJlRbWZSsG1SglYQQFE1UHDCGYQooppSgqgSlwd1wpAJTasXGDqyFyYAZTQKy6NWsCtupWpYg2cprHcZ6nUmvkuJvmy3rejdM6XBwdiGqtkcN+ms2wWjNXCggASEHVXY2JRWlZ289/+Zuvv313uHnJ9MRRAey8B3ky9cETIAM/oB3gaUr9FBUZOARicDCkpdlScGl2It9P0zzM89DMABHGg6bpGHMMhFe74bPXL5DD0hoHf/HimSAjpN5wxDQSoKkoJAZIkZfL+c1d3OXsyBiCVn08ns+XpUqLMe3G+Wp/JardRgvg2pSIKFJrTY3jMIE3U3MARKJYCfDHP/jo808/Ol+O7kjI6mBSxf3m9qYZHI9nZkwpBY5uqirQhOJERIywLksehhSCoqsqUzQjMzMkJYLAHBgBrIlxDoluhz27mrWYEoDv9jdxUERQ9yp6/3A3jUPdbhLHQEG1lXW7nE8I1srGADHGc9l02x5Pxx+8+ni326modX4fkrSttcoE0krZdN3WZ89fxJTr1p49f3FZjqqy2+2Ox+NyWcZxDMydMtVEWpMYY06pbCXFzMSttdLqw8ODNEEkYpayDZk67WO3G0Vw0+ru2mxZ123bdldX4ziiu4hQz2YBmKdZTcWUiB/uH9zs5uZWVQl4WVZCerh/+OQzPVxdPQ0/a40xDsOQczbV4+V8OZ+1tnHMDtCaEHEIsdYWmMM0D8MIhARdeYHMvJU1psSciADJaxVTizGKS5eEmno1GdI4z7uch0Rs1g7gyBRCRHDXxowcEkzDlAgAQsg9v1tNwCWnYI4IICKOPMwzT5O05uDutDUNGJgBxuTzMAxhWY7m4zTkIQCRpyF8vLt6eXv72ctX/+///Jd3a003rwyjQ+REff2DzmnI4OZIcdgh1pg5R9RtEW13jw+/+NUvn18fgULgJKqB4Otvvl2LxjRLq0MaiOx4PgUKw3SorTW1ye3F85vszggiAmbP97vrP/nZp6+f/fxXv/nNl7/+4z/84/08rZczYHAicHJHNS+t1lJ7UVhbXdeVCIaB+qhlGlFFLpclpsgcOj8LCToM7nsokJn1M6Tj+M0dwXvfiQjPrg/PdlnbInECZGRmpt77MiGF9Ac/+tF6fNjtZrRmu5kYzazU1cyIcByHeX8Yxxxi5JAcEVYgxm1ZQw8hcGDmPpvKKTsAEWlt0qqYFZO1VkB6vJzP21pVwT1IWMvmmkprtbQqWsUcvImcL9uybk0VzKTKU3IueVEfAAFQRMtWQinboqjOCEQpJR5Cyg5i4Mw05HQ47LZahxhzYgAwQHNHIgJkpDwMhpBjEKnBkIhb3bb1aBartlbbxNm7AU6dAQhpGKZpKNM0bFWcgosh+m43IcU3b9/eXg27/b59/b7rwfUJ3OHa9VO+7g5K+KT26WtuM+vD4r5V7y8B9uESMSKHQIQMCMx90eCuzd1jGlNkdAkhqFmMERG2dWOym8OYhpGIzfS33905cUqJEbfaXLSnJCC4bAUBTRXBHKAWfyi0PLwlCICuprWJqCFSYmZ0LetaNyREQJPaRcda1ZQIwaQ+UUjMiIiA5xzm57eltC9//y0FNncH+PrtWyJMw0iIQ0rp2Q0hMDEwmYOIZDUEekoZtU7FgD5bAHDsUZ4AIQUOEdy0NU6xCyACEigQsTTlECIzsAOiuinC4/HRzPbPbiigVZfag+ABHaQJMeVhKKWVy6oO4zQFTsQkWnsYBhEPw7RuF1NtrWzLOaWUcga0mNIhXJ+Oj+fH4+5wVVnv3r/Nw9C7tzyMAO7mOQ/btl4u5/uHOwcIIfalrJkDYYrRzYZpKq0dLxcA2GpRsw4TDSEgwOV0RsBpHnNKTBRiAsJaN0Ja1rUPD/f7/el0PJ/O21bn3e6H14dPPvk4BO7VDxGllFPOqlq2ZV3XcZx4z2by3TffxJTNrLaqIiEEAGyt5JxiiufzhRCHPG6lrGuJsYMX27KcxzzExBwiU1BFM6+1hIiffPz6m2dXWFcE7QeKA5pZCmPn5S2iHIiA9oFKEwnBjZqAuiMQEyOhgZsKxzikiUNQsRGecmhz2qOZq1itlDKGtFSVhzOHDfBkamnI//qPfrY0TcOBOKhZayIaiAgxUAiurqaBggdgopjDNGVy382TSPvyzds85rSlVto0rFq3KQcOicaYA+sHmsiQB6QRiYiA3K0JUhC3Tao611pf3Nzs/nh6+fK5A9Z1q6WZdQMcUQzNrZaaYwoc341pPdZe4wNaa63Vum3b6r6uC+AUQui3LDioKjN3qWjXJQMAI4eQOp8HPpgJmGk/DUNOTGxmFFGtrdtaRWqVV8+epZQO+3k3RHHcjVPgwIit1jMuHANjGPKYwjAMOyRw1zGGgFBbQXcOjEhNKm3Y2lxqjSGnkBuLsq1LW8raTANg79Gl2fc6bGY2d1UDYkBtoq1qClZrLa26Y2ki0oAgEu/GUatJgg20iRZpobaOpEYlNTJ0SiEqADONKcXGgJByHhOPmYl5K6WK5ZSJaMwDY6gmao0YiMjEXFVqkVrERFXTAGZ6fHhMMcRApgLozJTG6VQv//zzX37z9XfN4Xg6XV0dXj+/+R//+Ecckzv29rA1XbdNpCJ4p2t1A0y/FZpIf5zf2wJSSqVWeyLFMofAKuCYUlJpyByYUdvD3d1h3AERpgjg26m5agihK8O6NiPGs5g2VX8SZgBz6HIBIiKEpyh6MzMjejLAEQJSt1gS9axKwq7jcncC6oGlBITobhACuzsAAjg4GFjfF6krOgEBdGGvGyGriSP2jB8EUhP8oB9HQEfvSduEhF1TZOjgXWHyZIUj6CWGixIRMSGgqYUIfZbSjwwEUHVHVHuyZYqoqFHglFKa589/9BPXji3iENjEQmDRZmaBaN024LC/ehbToCofli89AaOhQ2DaNrUnsbk/Pt6nlHa7AxEfHx9zTg50OR2RPKUBwUUaMxcpnYWwrBczHYdh29aU82ZWtkVEzAwDmlmI8YNLPMm6rsvKzOMwEhEhpZRSSgCY85hyqq2GEFWVEK+uDimkN2++Yw79D4dpfPXRawAvZSulufs4juM4mrlIp3HTtJtrq8t564HJ7k6IgUlExmG8e//ucj69+uhjRAwxbetm5inlbluptZZticylboDEGNRM3dSsrvXu/u7N2ze6rarNrHV8qKoiYLfIldYTVLiJdN9jF/moKRIyhy6WIyYKgT5Yz55GHIE7jAfAEfBx3dxNVUwaIjCxebfZITgUKSEENW/i9uRJJdj6+4oOXqtIJVDubc7WGhG76dKaqf1evg0EKZCbm7ScQlFQkxgIn6Rq4uqlWGc7Wq3t4WEYB2Ry85TSum6tyvF8Xk4LACLzmAZCbHUFZnSMmRMggqEbIZlJn/iL1FJDHsZ5t+uLQ5GGTwvwBoCAKCLuT0zJ792jIRAimfdsZRtynHfzPM2qzc0iwuV4lKbPXrz4wWcf/fPPf/F//p//8ZOPXt9P05/90b+ap8jE27blca2mBJRzzCkGDsAYYzoMecjp4fGuMoI7ECh6reV8uXBIllnFzLy2VkTUNbhwoCadDGKtaa11QRpistRDUbqOjER1XbfaivcMB/cqzdE8RLOhNVH1RUptW601gKOZI4EDbq2JWWAa4jilNA1ZtMU1nZcNwXMamIOYxcjEMUVOkbfaHBxMMQYRzQnMrJRiZqWs6Aa1NPHz4yOqEpjIVkQBMKT09v3vvvnuzeHq+vHxlPOwnE8/+cHrj14+++bhlFIyA1dtra4LSBP4QFAyt95LA0AfCvdnhgAUgoiYWkpR1SLHwNyo226CmRr47c3+X//hT3/529+ezr9AE3hyHj2NlJ6avq5yQOrHew/S66tm//C/dgd/Mnz7kyoVHBwYQ0+k936QA/a2qU+o8Imjb4hE+NSpwFOJDk/2E8SnVGtHh6fLof9/3LxneXW7igMQormD9zHX0w/ZNyR9eo7Y90uA4Ni9PR8YWA7oYOCAPVMHn1rfHpaEAAaOBN7HUUjmwIF38/xwKWl3+A//7t8PKZZSzpeLi8QYDfT0+FCWDZA//+Hnrz/+VNS0CSCqGAC2VpfLGRGvr66mceciAFhqJcScEvMTBHtdNxWJgdF8u5xbrXkY8rwLzIJGhM+fPbt/9+7L3/0qxCBN+kSeiGqt67oG5mGaupQ7T1NOWWqfIOVhGELox80TLXJZLt1RUmsNIaj6N+++XdclxnR9dTPP+1rky99+dX11e7i6UtVxHKdpQsJSSgjEzOiO7jkm2h3KurUmrRYx/UB2ibv9/v7+rrUGjoQ8T9NWKgBM06QiRNQJryq6+QYJAHtYlR9Pl3f3p//rv/387du32OOO+lPvb2RfULqBGSGJeWemAjoiPUEDHQABARkRkQyhX/DgjkQ9VoqJvY8+4ek/j47ugAEJ0NSfXj9UAGQkBHIHI+cn/03PjnU3YGRCAEJDjxx7cUNIKaeyFdU2DNnVwC3GCGA9Q8bduukHwGOIACCtpZS2bVvXNaZ0fXXNgU/Hk6OLWlmLmdamRByZiCjF5ER9e4sxH64nAyXKtQkA1yrm6zTthmnqZFBV09rR0J5S/PAVxicZYWsqipE5BABvVaSJq87DmDq8AVhE/uCLH17O9e5cAuNf/83fnE/neXfdqn75/psXV9c/+sFHRMTMQ4wwZeY4jiEPHBNRCClSCAwWEZEJRN1ETd0YS5NLKeqUYhJTNyWCHHgexxRDKc1MW6vruribsDxpFj+kd5lZbV0hbOGJ3x2aUFN1s2aKIkWEXNZSRDR4p7kxqauKgVMg2k3TYZ5yjqWsTS0ptLqutZGaAwJSSpRCaK3VVovUIXA0VlMz653U5XJRqW6KgKJ+Pp2llVLWZYUi7sAMfLU7fPzxx3XbAsH11TWQXZbLVrdxyPM8I2LnZHepDzJlDoFZRHIec879XB/GsZYqIh0OauBxHFIeVJVDNCLgyDFxTNaklHXa5R//5Ke/e/Pw7u4+MTqqd93v09UPAIhMT1WSgjl22/3THhz79wGRoW/5n05h6zY7bOZERIH7qe5uIvp0O3eYZ/9jADAneILA4FOMTM8s0z6o7VgSMwOCp5UHkxsSITL1f+XmDgb49KUlIuQnD8T3YFtCRCR36Wf903XpbvB0mfXfCT+ANIjI/OnO6WZdIAJHdyelam353dfP/uvf/cWf/8Wzq+vz6fTw8EBuIYbWyrosZS03z1//6Cc/jSn2hSoCDsPorjmncRhLLSHmHkcz5bGW0qSJtHW7jMPw6qOPz8ej1DNxIA6l1Lu79/vDFccEMQD4tizL+Xw+n9+9u0s5EmFXAHdpM6I83D/krb569SqE0Fobh7DfH9Z1HccxxrRtSx/ixxjLVh5Pjxw4hNB9wm/fvNvWLaUAgI/HE8AlpPSzP/zDYRhVdTfvUkruXrYSUwpMpVyW5axiz1++DCHEmJoVN2OAbb2InPMwpjz0vIdpmvpR6+ClFGZWsyYt5/Q9tEBV05CJKRB/9c3XX33z7ubFDzDfLOfjVjbHzhB0cjTve1wIiKqanuDCH8RESMwcQ+qlAAKEEAG99621NextQT/4QiCiJprzwBz6S0KRmBnMVcVM13WTVs0scEhpjCkyMxK2WkMMMUZpEmOaxxkQFMTNCQnckLpmlDiwSusdg0hrtboDIYbAIq3P6AhhWS9rWbfGTknD4E6bTkMar1++mKZpLQUBU0yllcv5/P7dWw5h2h2ub2+nceaAQ0qq5kBDHtzt2e3zZVlTjOM0DsPQWj2fL9u2bdt2Pp+3bVMt8zxN01xru7raTdO0LYuYrZezmyM9oRqIaRoyA9RaxHxZV0b89//2z//ly9//5V//w3539emnN3WtoDZOczPbWpVacopXu2FSRgxPpGbGfiAhoIG31rbaTCHETIh5GJGp1oJOgSMhIEKKPA1pHqcAoEiByU1Nu/78yfdqT1UaqmppoOLgkEKI1J+yi4mKmVtp7Xw+M4ObT9MUYh7YHDk7wFarqATiyDyOQwg4jtdF4bRVDGmVFpHcFIGHAaQ1M1GrZmJAT/I7cFF99+795XJGhNZayqmU9ng8EeO2rSF6NRJhxADA37199/D46Oqt3s+7+aPnn6g0UIocdvv9+f4Ejj2BNnewRAhqtq6ru6eUpmm6vrpCCpfLGRB2826cpjxMNze3TMEBRGRZLu7AxOtyUa3zkA67q//n/+P1w+MxMMccmVhUWq3gQBgAn/IJEB3USy0AyMQ98yvGgEghhBRTYIZOZiZea2vSuqMQ3A+7w+FwpWbg3lo7LefAIcUgTUvZDKzW1mrtvIGUUreENJFSNjeX7hCOqe8DAMAQCCmPQ3+JQoxuzkyAaKLcb/7aEKGU8v7+3bZtatZlAw6ec8Ye2CpSa9XWStku69K3T7UUN0XEJ988UWnFzA+HK3SadzsO3FEK4zARkph89Or52+/ezXnsnYw0UdVlO6Pjy48+PVw/O53Pt7ct5EkV3CDGZCamGoaBGcu2lLKGQHkYai11W1sIp+N9HsZh3I3z6K4h545XG4apSlOT9bwcHx63ZamtqikAmLqZurkTjMOch7wt6/l0Op2XL7/8/SeffNLbAiIEMHdtbUOEGFOMKaW4bYWIe7HZ1QRmllIWqcOA0zgdT+cf/uhHP/3pT5nJzNS11IKIHVS1bpu2VmubxtBapRCub67LspZaYk7H03HIcx9D3dzciLR1XUNgIjYT4vQ0dHZAIg6cUnJAAgTodbo/f/7yo48+m+fnJlrLWurGIZZSzHQ/782tSUOAgCSdzaESUxLRGOL+cMUUatlCZA6hqYYYxpj6rHCrJafkDk3EzUutKWcOYZ53OcfQozIoAIKBqWvd6raUx9PD+XLO43h1uB6HkQkBfFkvCDSME4CHwEMack61NjcPgRGgqdRaUxqYuLat1IpETFy2WktprcYYCeF0OZWtMNNyWdSslK2UUretlI2Q3B2UtPk4zARwfXUVc47MX3/91cPDQ22tbFVqdUStbdrNwzzHEEPkaZpyHmKMSBA4dCDVtm3Lsqzruq5ra62UMg5jre3h4bE1mcfh9atXX/5udTemICqqEkJIOZnraVnFWVRb2/a70Vq5ub16/vL1b375q8vjcnN7re6X9XZbl0iWIzNm9yDOYhyYu7/XmBR8LbVIj1AG4hCQdtPcB6YeodVKgGOKQw5EOA9joIBY53HaH2b1VrYWY6BO5AQABGnNiAI6ElIIjNQntCkFVdvWTcyb2aVskXAc0piGkPJgZj3NkBDPuiKzioDrmCdzPxwOS9P7h/scg5u1Zt0H7944BjZLDjnEFGJOmSlc1vW4Luu6Rn4SISxbq2qZg4G32rYGW0MA/tVvf3t3d3+1PyzrpiIPj8e7u7m212aUQ9yN0+Xh3L/ggDCOIyKp6uV8/l5N8f7d+7dv38YUd/Pu+vpmnqaYhv1ul1O8LEsplQjEa9mqtJZyHqcdIZzXS87p5vkNIjIRM6/bOo4DAMQ0gJs9WQe4NanSiDmnJKsggaES+TDPKQ5MARHEFABy5GSptupdJWXtuJ66mhMAgcBAYxrTmOWsESCmUBqLSE6ZkMh9v9sty3rZLsOYR8oA0ENy1LSWyswhMKIhWJcyKZoTpBQbChAwMpGqqlTlyNECiwyR590QY9rvDhTj+XJGgrqVbV1Fpufh1f3jw36aTeTxfOxqB1A1s5v5lonHYdjt9zGmXmXHGFuTVuvAmRT+/u//6f7h7rNPPxqnyXLWVkM4TNN8dfO8tMZEfezmrrUVUkKEPl8+nx6k1dNl2e0PbhpC2F9d7Xf79m7dljO6h5SbSIw8jjkgmD5/eHxYTktKKVBYt7Juq5vnlIgo57SuPaSX9rsdIcYYc853D/ffvfnuxfMXWymimoeRQ3KzPGRCDCGae49SOewOpi7NCHkYBiY+naS1lm/iJ1efvP7o9TjmfvG7mJKHEFKK7kZWL5fzbrfbH+ZaSwKYd7v3798vl/MwRFSNTIwO6PO8K2V7+/Y7RLq9fTZOkxO10nrlW7ZtHEaOERzUpVnLMTtgHlKz9t13X0UKrRVESDk9Hh/GaRQtpZa1bOgQAwPR1qqpxEABIQQU3daiqkaCyNRERIWh8x5DXz8YABO3Wou0EGNO+f70npkQUER68Ma2bcwIAAFDZ9it5bKup74eQAQkNLEYIqdAhCoaw5NfWkyICNRarRxDV9xvZTP3nBIiShM1H/Oo2omFJSCJ6tJKqaWupa5rd2ltZTNRjjGmmFL8KoYQ4vX+6ub2RlTevHnz5s2jSCutoQOHkHOexikEjiHNu3m/36sqgK/ruq3bZbn0MTUAttZEJcaYUjqfz601011OARwAOIZQalHznIjAtrI2laZOSCkOSHx1vV9/9ft/+Lt/qFsNlADg+Hj8l1/++uX1NETIKaTAjKGoW/FairImIhVFpioNgEIYqgoQjXnKOXmpPbK9R/6aUo4xxgAAooroc0o34x6blyQcQ8qpjw2elqTIfT8UmYmZKQJgwoARCaiIoFtrJaSQc573c5imXSlFVfpmiYmbOgCYCoLHwInsxc1tjpG0tVIuLDllJhiHuYnuKDCRu4YQwb2UrdXWb6QUQooBkWtCxLVnMav6Vtp5aYCYM3/x+adTGpatqsP7x+NlWdSBiMZh2M/7O35v7grGzPO8Ywqn87FWcce+vnf3po0JL+dTrdV7MV4W+Va3WlyUiWJKbl6lms+qYqqEGDh2s4d0brV0WlNoptu29TC2DrLHwGq6lK1qQ4cu18NtwXXrqmN1K7WqylMGIXiMMTA9Hh/WZSFiN+tRznccHKFI69pgEVHTrvyNHE6XYxOp1rbjsi1LYAYiN+tzjL6V6qZw4oBMMUWHp8iIGGPXQWoX9zYpZa2ltFZ6XNz9w3vkYO7gVmpRkSmPMcXWxsAcUjT00/k8DkPKuZUaKHRNlKq01vqEvbX28HDvALc3t+u6PZyOFODV82cpEDFrgxBiq3I6HsU0xrSs6z5EUam1dJsrETYptawxptevPwKkWjbV2rWVSOxWmXAc8rJs33777bNnN4GjqY15AICUwrNnN/NufH93Z02Ox0dTiyGtsNVWZ5xrq+ZW6oYIOQ+iejydt3VxwJvbmx4waWadk9VUA8fD/mqa5vP5YuTDMADAVmpIeSvlsq3Xz17s9jtza60OeSRie/KUmGmrZX14eHj+6jURbdsG6LRRYJ6G4e7tm23d5vnQpL59++7Zs5chUAhBpF0uyzBNZs4cjAyRReTx+LBu6ziODohE4JhSHlL6+NXLX//Lrx5OJ1cVacTc55Hreim1XtYlcmwVFUzcAtK2bg5QTbGUphJD6qGtTIQOlQAAYnI3a01MhLvUBaF5a027DFFEWmspRFNdl5XQU4oxxFZrrdXcahqqypBy5BBD0I6svCwhRQAo1IjY3cEgMIO5SIOyObiqu0NXzuQQSy39WgUHNymlFDUO0QxCSJSRAQCetDplXbdaS9kQe8tEUpu5PX/+HBGXZRGRWmutddu2VsrlydkF67ohYowJAdzc/L/zP2IMKaVuh97tdmb2hBvQBoQhps4uCiHmEKkrOZAclIlyHkLIbng6XW6vbuINahNFOByuAFlEPEaDJ72/liZqbq7BiTkgqYiqBA4hMrQaQwoxuFtOAaqhAzObKxKu6yqamEM3MjHTYZrBoUgjfhJySGuuyggIFjkOKXU7iDoOKYMaEo3DCHUL4Hk/jznOwzDkGHIeLst5WVYzJ+raCYgxo0OrJaZhSDlEnofxcnw8qydzRux8MRXh0BeM7O7bVhCxbFsMIYzjGGPOEQC3BgFpGscYUgzRoFWRmOKr16+X3/72919/hRTTON3e3rTLPWOgGA/74aMXL959+2atBYAB7Pr6OsbkYJcLdJhrPxZbbYJP7oR1WdTs+u6GmERkGIYYQuDYg8O0VK/WRCiGnBDFHUHcOPRqNVKXHgMwsZtv69allD38r7/cfWhOSNJaZ1ICQrcw9vOXmTAiOhGwiAWm+CH2AJ3cfEijPukNKPYYS0YR3Y6bujHztpRWmpL03bKLEaGqqwghKrr11HfJ7tBaDTHiMAMABDK1nDPmrK2ty3oqF1F7/hyHcaRmblZqKaWkFNDdRYeYxdQRpzyOccxDBgBJbSulFx3u3fOPfVE+z7uUEwBupQEjU+KYQqBaKyADQIjx7du3TSUPEwBwiE0tpYSIrVUzX5fNeqVCuK2LSG2tmcjluLXWeu8v3k8cPp/PV4frNOTLupxO9+HE4zgCOLi21rqMr9Z6OBzOF1bx8+lCxIhBVLatllLAadvWGEMKcZ5nZi6lcAj9r9dWnz1/FlMahuHheBzHcRznrcg87QCobO3du3cvX71srU7TVFtNMU67OYQIrirt8eHx+vr6+vqm25XR47KcQqDjuyMz7vZ7ZHp4uLss5xiimtW6MQciAqBuow4h5eQ2tWU5r+ulljoMQxqHrVZCMrMcojW5nC+maqoUurYMh5ynYQoc+5je3dWNkJ5cMEidW0yIAbB1rQuAWyfIBEd3R1PoWwBH0L7CQnJHwjAOUWoDIERCBxEIKVBEduwZETHGlGI32QUOGFOtJYZkZsgdAo9IoNIQULVLAoUpxJB6yq2rERCo11Zyzt4TXomJY+grN05DyjHlWrYY45By3FZR2battVqK1dYul8vpdGLmlNJ+v+sV/fF4vFwuANCpD7WU0+l8uDrkjjKn2ke4alJr63Nz+B4h7N7te+q+1jUP05AzczzMY86ZOXTm2DBOKaVpnFzssJtffvR8W5aH9w/rVovoH/zoxxwix4wEHNnBg3IEKU0QyR1U9WlPG0OM2YFj4MAYA4sYE4rJsl3cLARuKutZDJD6CBp8qQURpzwAADOLSwhcRQJBTrHnSNcny3EhxBiiuw4pjXmPjCmFFDjHIK0FBFy37XQ6qTpycKBhyIEpJo4xlLLm6SpTEmnTbqxtU1dzi0y9GjWAWksIcb0siMgckChy4pDGFPryMsc45JxjnMcJkUTPDcya/P6bN99+975udd1OyKfD1eEnn3+cYraAt/urb799O+RgbqUV6rpL6NPbCMAhMAA0kVqKiLgr4WbmqiZN8jgws6nmnGO0VlsIHC0jEJhL2WqshMRE5hAs9ACbWkuXG/ZucRxnItu2rSNkayscQ/zefW7m7k8OfmaPsZdOfV3cpPUhNRKupRCBiOSYSq3AXVmH4MBEZqpVy7rVWilwDLGz2MgdwLqtoZf/qtLMECmniID0pPkBEy3bSsSgSD11NMamEtcwz5O795XJk/gfgAhV7Xy5pJS6+klE0KFzF7oMpK83EPsCBnoGT1+Su/lWVnRkCqW18OGajyGslzN9CBC9vb1NKfVH426matpKrQ7GiLWsTWTbipkOQ97tD5flXEpNcRDV5XjaH67GYSzrioBpzLPsxmkgsPuH+6+//rqV2j/qWmsYwziO5n46nVptXRxpYttWjucTh6Riw8App5xz76j6xv58OVcVjuFyuXRbiZrmlAISEh52OzfrEAszba2I+G4/94aRmNfzedvKMM79I0L0u/fvHGk374dpN8+jIy7LcnVz/frVyxSGx+Nj953GlMZhqtpM3ByIKaUs0gJHAFR7ylOqtW5lK7WM8yxv3/GTxsNFGthlXbc6CREDYw4xxxTwA6k0ZwDcSu0RScRRATGQmsWOB+8oAgdFJ0IwF+vqUUNkInLsIlE09abVzbRYSHEeR+jnlpmoIhAhAom0FlX7N673i12YLCoASOCIBK4xRGmytQWYKGUCYABx7zwGBECiEHLgYM0xcI5RW3PEjrc0s1brtq3btq7rVsqmpiLy9u1bROxb/XGadvN8e3ubc75cLk3kyU4kuixLX1LmnHvdtixLCG5q/UXq3NBWKzF3fQcg7OYZVQH52e3tNE5IRI6Habef9yGEyPjRy9tpyr/96vfrWi/HZUixiX375u0PXt/kFHKatGtzI7MCNDFTEQUDdwHE7iiMeTKVGCAwS1NCaq2UYn2i4AD9PhVtiBgDS5Na6jxOOSVmltrcjRA5xCGnRNRU+lJQHYpvzDSNY+JA6BQwDykA9UDvUGrpC/GmlvKY0+CmRDgMGcAcfN2WcQyqoipqzV0jJ1Mdp9S01VJLbetWS20xJ1RvTRx8P4whBNO2XJbLpTqAmxF2p5JelmVIuZQtxfD8+vXxdHk4r6fz2dVELKRYy3J8uAtMTO6uAOFyuZjBtq2qiggiPo5jSmlIeSul233V3KxLico4TUPOpRRRU9Gc88SMTCINzAkBQgBjRnYz/6CM7guiD6AJGoah515N0wQAZSvbuvUbov86y7J0D3qX/cOHfAl3K6VQ4K4taSpb2dS01FJVp2HqLbkjqWnTFkMIxOoWY4pDJHBtVVtV9yLiZiFG5EDkhNCD4FutQF3h2Q8WZWd33y6XaZ5ur69uDrO0+vh4dPd1uTj0n9BTSo6u7mhqTQFBTCMHRASovQ4S7Yko+L0tvt8HMcYUEgeSqoEQ3HPKw5hjCCpay3Z/f4+IV1dXpZTWhKhKK7Vsps201daYoG7rd999++zly2HciVCMaavFDPa7Q0zhq29+P00zEYbAq/u2beM873a7rZSH9+/XtYHTsmxlK8yMRCGGPAyAeDwdSy1N2raVy3l5f38XcxZRM7u6uhqGoZTCxH3ZW2tdtjWnfHd356qmOu3mcRxraWstpZbD/jDs5mmeb66vEOH+/v76+pYDPT48TOM8pKjanj17nvLQmgxDyik9vHu321/N834Ypg77fXF1CwC1boFsf9gDQim1bDVEMbeuY17W85OUjHAYh04yTDG6w2Vbv/ru22XbxmEybdiD0sC7gRwAzXHbasE2Dx5iSCkDoTmEEPhJlNcbXELo5hCotfZ+jpmBSM3QnJ+ujhACd1eAijBHYJ+meV2WWupyPgXCzrNCJAYE8+aq7toaIbjxum2qspv2McRiWqQhd7MzqDR3MzFmsl6xARIRAMcYKbCKUU9xQcwxOgAj0TAIQE8bIyKt0lprrW7btq5Lk9I+/FNLqbUuy3J8fNztdjnn3W4XU2qlqXa/hXShUQgREHshz8zI1BFj0hqHEFN6Sr9higFTDD6NYjDNUx7HkFLGsN/tc87gYNKYUMROx61sdR4nIjyeHy7npRTVCVz77wGIFjjkPJQqZl6luoujzvMeAIZhZAICAQBiWrdtXda1VTOrTfI0mFhOo6iWskqtDp6HnDQGI3UVNbcuXsFIFFIqtTjAmLN5t2hUQqCUppxDjKbmhO5QWgtVS2SMMVStao4cQojTEN2qApq6ait4VrPLcqq1MfKQIxO6OoE7uqi0JjEkct+2bd0qpzQNJOJqeq7l/nIyQwn8/vEMxJ1ad7Xb/+iHn53Oy3IutbRh0ONxi5EVkcSKrKfzWUTM3Q3Eu08PECnGpGpmtixLCCHGOA4DMwPCMAyn07G1Ss7o7gAcIgMDI4UASO4QmBE9cgBHpN40qwO6YYwxpORmtVVwQCYzZaZxmoZh4BBbk9qambTWatliCKUUdYkxpphFpLXaWoVuBGiiJnVbO22WkMwdEbuytTWJHJgICIwwhOiiBJ5j6hQXNXBg0QaIOQ2R2WIotUrr4L/ab6kn/TLx9wtt4uAmNzc30zx+9+0DWcc+drBxNIMcs5mLCXPs2xRC1j4mRlIzB5DWCFHsiasalHuZ1nWKPVMJkVTURFzCtm1VmhNzjOu6YOH3798/f/YqhABAZm6OxHEMCUEf3r8VlefPXzPHu/t3iXlTMfDzcn4+3rx4+UrUAXDaX1PMl/NJHh9EGzjtdvvAAcyl6boWBJrGYb/f9+ovhuhm67YhkgMShavdwU3neXr5+qMqTVobUiIOpbR13Vx8XdfSakphiHlbtmmcgdgcT+elVr25veEQ379///Dw4nK59A+7o0yrYBxmDlRrvb0+iMhyvjDiOO3mw5VKffftV2Urrs0QxnEW1e2ync+XEJKZmhYzYA4OerlcYgwAvQom1dpUACjHAZzMYF2XdT350ziG+rkuTWKMTOzuSlxqeUoUIwrg3YRobsfz6SlzDbzbRGJOiMiq3SESiL1PlkNADr3PRqKOSTbzedqZWit1uVya6qvnz81sa23Ou0ShaTV3dReDRDjksaoUkd65EpKbuxkSEwZEt+DqSiGknAnRwc1AzBExx4SO6uZEPalta5VTQKbubSQMRp4ypxSHcYgptlo7BnhdV1GRVkUVHM6n87Zt8zQfDod4kx4fH7dtIwzLsszzHGPo4jpmbq0GRoT45PZCzDm7GiEwhu6dAXJC3Go1RGbcjfOQh8Bx2y7SVjfZ1m1d6jTmw2GvzVK4HHYjMalqaRW1q6hB1PojqFKkVQJwsGmwlBOi5ZS10/DNl207b5fzsjCHJgZEDKQoZiACW21bLUNrCbnrAJdt29aCCIc8R445JAAQqUiYkNwdiQITcTDkWgTIjaWVWlsL+2mywz6FuBRRQ3efUs4cTKypNrGQxm1bsOvT3Q2eHLBiEkKIYq4amHNMtTUR7QE2a9n2w16bbbVV0dQjhdValW0rMcQc08sXL97e3f+37/55qy2n+OMffPLFDz4CREW6bMu2lVKLiKq7P7WWuQ8xYnyK0OtLGzcjZmZurV1f30zT7O4pddtAjBgAIeaUYqytGjiCr3UT6cGtHpgxsKrEmBEJHMY4OMDj5QgcKHDKUUQdCRCY0A2YqVPA5pk7cS4MoZSipn0GQ8g4+LqeL5fLmIZOoeuQ+hiftPjIpOBdu87EEKmv8Z2ROUZEN/dWsZZAFJgNsOKTsSuGKE+fSfedUt9qklskqOfT/emRkVqt45Dn3e7xeDKvh/0hhNARAVOazK1IyTnFEEurBBhCIA4cqHUiTQdmmYk5EYlqW9f+ofUi+geff9KbdxGV1mKM43hbv6sA/vr162EcEDGEOE1zrVtrVVVPDw+nx8uY59aUOMQYzazVNs3z5Xj69puvr25vu0su5YyIb9+8cRU3fXx8fPbs2bybzZuDhpSw1sTjME0hxnVZaq2i2moLMU3TNI5DDAEJv/jRj2JKj8fjPA7EfLmcz+dLa7IuS+duLOtKyCJyd393e/t8GAYVPW5HabJcFkT6/AefX1/fxJiIeBynnAdzH8ZU66aq93d3wzTupt3leOKYOYTL+biuGxOVUqrIMMyHq8M4zXmYVE3UmEPOcV3XngZTSo0xAOiyLiKKiK028nDY7/6HP/+z3bz7xT/9093d/eV8Nlc3a7W2Jtu2uHmICYmKhVLWVBMSpZRbYGKWWkrZUooxJnMXE0DIKSOAqAQkDAH8SaVGPYjGoVvrAdxcOQRwmPcHM3t4/7bVUmthhraWDSjFQwjMKT4ROziEEMjEzRGQQ+BuRjFPIXbNa4pctYo2U405q8q2bpSiqtZ1Yw596Rozu9myLDPNgRiQAjG60/+fqT/7lWzJ0vywNZnZ3tvdzxDTHXLOqqyq7qrqQd0A0WRTJNHSiwDpXxUIENKDKJASSJCE2CJVzR6qsjIrM+8Y4znHh71tWmvpwfzc7gggcIcIPx7HbZstW+v7fl9KMQghDk12b3UUKNOcALDVtm0bE9fe1FTVzpd1v8O7u7vT+dRa66332uiZ8zPk88xC5AAInoejkCOimTCZ91LHMIwv2+Xx6enHr27mOCFirvnpfGplBcDPX7989/60pImQMNDPfvLjVy9vAdRdS6vDRCch0BgDEpdawNzQ3W3btmlaogQioBj7trkbMdFIiwcMRAw4pRiC9K6tE4/xDgsglJIB0FwlCQDEKU7zlEIIgc1jiBKYS+utdVPV3i6tmVrp+eawQzVVFQY4LMsU5kPXdStrKcsURQKgkgMTohuOW971ZoruXlszo91uTt3mmNSd4Op/dYDeWy0b+BJEmAIipxinmMaIxn1YVmCIakblEiT+4z//s8/ubt6fLufank4rEJXWuipebY84nF/Dy/6D92coFmAAdRFDCPvdflhqSdiRwjyBea2l1UpEXRshDVTsNTMSoLfWei+5xDQJc61KRKenp1PXOE33L14yca5lxNyPweMcU2/96fiERPM8a+8/WGqJ8HYXlxjPF9wn6d2qBm81xgiOJKxmNCMTDYXDcOgCoKkCQmuNmeOUYghmnrdtPZ/clJDmadeo1FzANYY04tJ4PHlBzEEBRFh7H0zDNKVu/fHpQUK63e3naR7VcW9Ne/ORT6HWrAKBXdNRoJSirakqMrn7qBNHpraZmdcRixhCOJ1O7z9++MmXXwQh4YTEgLRb9jgiDwGIOYZQm0NDIg5BwsvXu3nvoOO+X2u9HI/7m0MMgRkfH467m5s0Lcenx1T7stvd7HYPDw/Lsvvw4ePf/u1v9vv9w8NDzlndDAEcS2tq9v3b9+fziiRpXpgIiUotxHR7e7i7u3k8nph5tyyXdc05q9r5dFYzQpqm6ZJXczP34/F0e3u3PywppQERW9ftcr501dvb2xcvXszLrvcuIQw0aWs6TQsxu8LrV28en56EeTiT7+5fCctasiQwg23LMcZlZB/ler5sNze82+1G9TrSvZnD6XRkJpEYJQkzC80pHg77mFLOeXiVh+t8bAdjmEFBBvqGqptZr3XMUM1sSpGYa6tXxzlSr+rgpm4IeAWUjAmTSog/YNH8qvKkEMM8L8LUyno6Pj09fdotSxRay2WaIjJ6B5LIzI7QtLfeRu1vzbQrIE4ShjMdEbt2U+u1l1w32USktqa1CND4i5EjiZBIQFzSFICFButG3QwIKSVXMzVAIh7FlBExIvjkKaXWu2eYedbeR6USY7y9uam1bdu25W2EBj3jXoKqgoMIEy+DIUoECMCBerVam0yx915LK62wsKkD6Hlbm/XhzP7f/eWfhzB9/e1bd7tcts9ev9ovs6mZQ8vDEy5uxiyARHAFtror4tiE25QOSKgKXTsRCYkQ3+wOhAwGIcg0T0TcVXsvCHFOskxpP03TNJn7VjJgaL0HYQkUhATIgFIIjOTgZmqKtffW2kgg7rUSIABK61XNpjQtc+j62FyZkJgdUFsHgNYqEQbiab8vTbfWWu+MfrUgIczztG65WwO0GENKERnJ1HpzgNa7mQdmR2+tNe2ltS2Xx+NRQvjw6fGyrqoGnnez7Pf7D1stl3K+XNZta10d0B1GXk8umRupJgAYwsSxWH9A6CFhzrnWFkIAgFmW0SCvrSGQBEa8TtJFhELsZqaKgEAszLXW3LJ0MvfWW611kqC9a1dguGyruRJTb63Wqrm6e4ppnucBToghns5PEgQAjqf23eNjqW13c4M0mpnz4XBjqqXW4WgzVSRi1dEaijGCuanmLffatfUuDZnClG5j7K2ul7M2GzQIBkwpTTi7X6V7AMASDRAJ4k6SBHNDV7W6buvd3UsAgmtuAorEMa7AUfUxa6+9tSklYgYkkRDThITruvXeDXTc/czd1FIM+8N+ignAv/r6qzmFQKi9x3liDiEmdX06Pr1+8zkz51Kst5RmmrC0zbxKROY07Xfn03kI8ga293C4OR2fpmmSGD99/MT89OLFy91uWbdL1/bllz86Ho9v376ttZXabDQ0JAxgwMPTo7kzUTcrtRJRyWWZ59ev3pxO5w8fPnz55ZettW1d3b3UgkwxcMk1xLDMMwDEKSGTmQWRZVlqrSHEGNPlcvnd7353f397c3OotagaArRazSwEjiHGFLdte//h3TSlaUq9lRhCdYhpdmYRYWYz72PDNp+mBIDreYsp7XeHy2XTbrUWN6erWEcHxvCyXr5/9/arr7768OHD6XS0a5Q5Pl/CHQC6mddKYERk4KM+vO5ukZHYzIBIWAiQJbCImVWvQkG1gvuQxl6nVkTjX5EQ0U07MyM4C796/VqQPj58EIJeagyJmM2tdxVQjhxCMPCR1z2mR1eccqvo1lp1G4YlMANXMPDSKzE21W07p8H1I0HXan0kA9e69U7XlLcYa1MmArPaa+9NKDxzLnhQ/pdlOa/ndVvfvHrjP1xh3RF5QN/Wdc05u7mwDCrUyIAys8A8tIW9K0Af3+HW9GK5NdWmCOxAjkRIgUmJO1JtbUr8p3/8k+PxVJojFu2dAAixdyutmxlG7l3R0aB3VXBX1dbK9bLlTuDuUGoxtZHSNaUJEAeCGQFSEmJutduc5sQhyM2y200JCbdctPfWHR1d3XqvgGqWUmCWwExBANEtl1q2vDW1w34WliBSzhd5fPg0pYXRVduwH5Raco0OkEuNYQyFQASHZmatgyjrvXcVCpEVbIJYmxrCPsZ5ns1tCbIEWbc8zYnOWxBmpuO2dtVSynm9mFmYptobEbEDgDIjh8lQwOHjw8PD8QiD7jEoPWZRIjOzyDD7jEdrKASCiAN8+vTpdDoty3J/f+/u5hZDiMRAGkMc4DO/Ri8gkICrBHEzRDAHVeva4zy3Wkupu93usCw5l8fHx7u7Owc382WZG7G7TyEykprVWkcINSKKiBBvaz61Ui+bqipuu/1BGAHwcllVu6kNpMSgv7q7myt47J0cvGvTTkSAoODeWiklxhhCOBwOw5hDMESZoNoYJaU0BEicIhAD+G6e9/NuW/NlPcYhpKs9TAsHJpLWOrgjYSDqJQsxIVG7GtXdXCQIYoix9zbPADC7dkQ01dparfV4PHbthPRw/Ph0fH9/e3h5e5svq7S62+1jTJftYgbbtulA5zOnEIh43S7Hx4eWzyGG3c1tiun+/q6X2k1LyYz0xZc/ckBzfPPZ599+84fHxw8xxlK2nPOy7Jbd9OLFrbua9VKdgGOMl8v56enYWgspEmCrbQCZVR2Al93uq6+/nqZJtX/9zTdTSkDoCMMbVbVD42WZ3S3nvNvta6sfP3wYM1KREGMC8G3N0zQD4OPDQwgR3MejiwTM5Gbm1no1M3QjwNIKOAJhmlJvteQ6Lzvtigi1ZCY67Hcpzut64SS3N7du7uaXy8ldRSgXBaLbw32u9ePHT8fzCZj2tzeX84WECbBWbbV6EOaARODupTtTj6N571faJSC6ARuAiwg7DKDsWKhIiIaAOMD6CDTIu1fyAcC41DKCaQf3GNPt7c1lO9dawe10Ot3evdwtewIKHEWomboq+qA0NlNjQEaqNWsM2nXI+RFpbGojLXxwJgBjbXUA87VrrybMqAamXXvtnQDUujoQmKs27bXVKSRAJMJxKUckEnHH3bIff69xwx4jq3HR2cqmpsu8+0Hi/OxQuaopxvmnCsRi5uuaCT3nMTkHkcgp9lYjUQMyg65gqgTAHGBIzwODq3bN7t0QEd1Q3cCag6upmZp2dxuRAbt5Hkkqo42RcyGiFKM7yiggCKcoIbBNsJtCtxaIpxBTkFozg8cQSqtucFmzagshOVDtDRFlWYQlSK9ce29XYiDiskwppGYmJZcQZjUsrV3qWmshwNAbOSBA7cosI/qntP543kqpHJiEh7zdHRh8CTJoN8LzYX+bAu6mKIiAT/OaD0u82QVA3DJtudauuZbAxIRTSiISEqIrEh1LAQMHWjcttRPR6JPM8+7+/mWMKUgYLSAWBofx4Q17STcFh5ub28PhsCz70ovBaI5biGFgT4DYidUNAWjQcoUJ8Xg6AUCcJuodJYjDQSKYOcBoixNiDAFiJCIJ8RDiej6zQC4Z3W8ON4YAiCKp1XY8nwORpCkQhhC1t2m5iTGqaS1AkUekFF7XKLqBqlozdQfwbsoA4yo9Ljo/LOIYQkxzYDGzbtrb0JSTE0qKEqJIYOYpptPlstVMTOdccs4pThQc6Bp5aqrmkDgx87CqAZNgBAD1hkhOaKba1Xq7KkFFWCQRpZSA4Onxsfa6fdzW9fLy/uVf/NmfBkaw1nudlwNLZE7utJsPqmZWt+2SUnLTUoor7OKSt6zd5mn37cdPzFDzFiSkaX789CnNu3mZ5zSVLectE0ma5q2U1urT6Wimu/2hPJ6YhSmAI5gHTkjAjGFOTtS7GbTj+fx4Oh1uD8zy/v371nW3j7lkRNSutfbeVNi1GyIGie7w7TffPz0dWcQBgDzEaGbEoqo5b8wSY3Q3sw5ACIzE5haYlamct/P5OO8PIabeqvUeQijaYMCkhJv22goiM4euLZettWLuap0Eu3VtrSsgMjq6mXUF9bpurrbf7VE952yIIQQ3M3WAPkzabsYiQULz1npFxIE3i8RiMQA0qh0ZenvWDhABt96BcACqiHC0Q92daBCrwMw+PT0MRE/Ltea8mxdC2O2mb79/+/2773/5y18J85pX6WLugODWXQ26kWkF627ae9HuqgQItbIEJu5mTXuS0LR37cSk4O5OauY2qg0yR2QDQ8fmbrUzQcmKCFtrbq7s5upgNKTYZmDGgDeHw3BoktPzSOPa4by9udm2vG3r2FiGg3LUmETYu6mqmaY0zzVfLqdzLmDOjoAWJEQJ0B0NcrM111yquyvEUrNqrbV2NTXr6mpGWplHDx+doJtpb+bdnkNpxkBvEMOYGQHdkZhROyGOXgKihxhTSikKk+QSWi8IQIC1d3Vq6muuBs4BgyR1q7kighrPczJ3RuKRrQLAROo6hTjHWSREyQJAgGTgW6uXddst883NTUixlcJMaoYOptrRH0+n7z89GcBhWW73u6ZaWguCU4xEHIFIEJxrq3c3d/tlb9qmpQLgHONhTgb4tDaEMlzfvOzU7HI+996nNIMBAllX6L3UNnSZZrZfduNbtSwz87XLP6TuP+TAANF4Su/u7yWEw/4mhLS2zcFZEgojEYswMyCa+WgcIQCoDtk+M8U4dkMasYUEWOp2vqzLstvPi7kt8+6HYcPlfFbTwIGEx9l9teQRlVqW3W4MAGNMY0w/WlJBAgIys/uAbsJ43tx8iGoQARHUeq01pugDU/ccGz2W6XA1AyCLPBMfvdWepgkBwb21XoYLNAavzRzStExxAoDa2giIGGR8M3O1rgYAMlwR6H3sHO6jYYF4dYG1NnygjZnAfZ5mQCAkCXw+r4+Px2kOe5jSDIg4L/OXX/7o/v6emXtrW/beLUZM0/TmzRdIGEJwQBJH4rv7V6VccilpmoXDlOaUkqoTB7Myz/P+cJNrvayXUquZg1NKk8NpZMD1bjFO5tVMiQSgjf3N3Uupf/PXf/Pll1/sdjsAPNzcbLnknFWVRaKPZ8+HBlxVzUopVc14ZLsjj567quace+/znBCJCFprRDTvgmk9Pjz01l+++izFOO8OHNLp9ATeg6QfxlcOICGSiISo3bupA+S8nU6n3nUQIMwUkcBckgBRd9tKWbdt2e1qbefzmZiXZcklD2DRuKyPbbq1FhFLKee8drfdvEzTROP7YFZLCRLSFHKpZg7gLOwg2vuASpWchQWZ3WFcqRFgrLpa6zhgWi251rplN43zNM+7r96++/Tp44v7u6enB3AIIRqY1SpIAERCOpbWKLkQo4SmfWjVGEGmiIhcjYRZBJFKLkkgoCg6AXatas2ZJSYEJ3etxQA48jRNDOzupg0BATGItFYBfMDMzaxrYyR3u8aWuqsqMQF4yZu5C8uI9hwiMkQC0DFsmNKUS5xS2ra8rWWSkQwM2mrtfc3r4/GYcwZCACy1baWW2roaXX3UHdGYHZBFBBDNcWTLtN6vmn1iYh5Si7FNpzijtxFPvYT4nIQCZuNQZGJJ00QVt7w2tVJy6yNxxFkEAeY019p6z6UUM1G11pupDzQTqKeY9klev3ixWxYJspZNkEJpvVk+bxdEvju8+OzV50ZwPh17Lahde0cER2iqx+1SqyUOlmwDzbUQ+Ivbw5Rm4hSAAF29r7neHyZH7IylV0cgxBCiiLAEgDH/pIF7QiRGtkE47kaEDjhN8zIvhHg4HE6nE1yJ8NJaHW0TIjLTqwxyMLYRkUlEXr56M03Lw/FTroWRgUdaAAGQdlXtIQR37+6mKoiImNJ0zemtlZljiFGCuVbV3eEQQoChWUYMIo+Pj+BwONy6+34f0SFveReTgRHRUCWez+fWNc5p2Ez4meFsNqIUZHQemQgBjUxtZAyQuRFfDwwH760Pf8oQIJoDGDATAIYY3J2JAaHVVmr12hystNpVSZiQiioYoFDvSgxNm5oRk7mT41DmIyATcQiGaGAAYG6gPpyoajpoU6NZH2OIMeYtNxyhuG7m58v2u6++3i3Tm9ev4rxvrYaYRghza733HqNM03QdP3Borbo7MZspuO92u1zW28MdMqvp/Yv7Usvp9HR792K/vx02w31IMYYo3Gu/eF5z7U1jkMv5knMuOat5ECEUhNHF7oj49PR0OtE0zZfLRkQInNdc6sgIafv9Pobo7oNbsOVyOh7Pl3NMaYzrg4SUoqoFCTEGM885m9k8T/M8IyB0u5zP6+US0pyWva/26dPDm8+/YOaWa+89pmme5st59QlrrSklIm51BZcgcnd3V2s9nz+aOpEjAAlP0zTPMzJr18Ph8Itf/IJELueLqrbeRmX+g5B/BF+PFTJIdoOXHiXMIVpXQOoO5B7imHb6cFCONWDu27oS06hEtFUJUuu1HG69D2XpeNlm2t1K77WW9ZvvXtze3R5uHx8+mtXWeopzCJGYKiB1JWIFAIfIQb2rmwSREHoxZB5PbQgBCNXUzVUdAMd9C8EH5l2IgahrdzNJkVBMtbRS1zJNU8QQWUKIQ1PU8RqnlXMetSMxtdYIyR1iTABotp0vl/HbLsfTcIb7c/T0mMOFELb1UmoZRpMhjDUzxOBu62UtrW5lO19WIgocTMHBa1VVJ5aYEviQwnjr3ZyCBDcnIW3ezVrvCI4S8Kpr4dIqd5EYCditDY8nErdarzWfe0YUCaM2qyPaUbW0VmorrZti5MAsjBwDdNcx/Mi5BGYRPZ/Pl/VCBNOUbm5v7m9v5hi6qbkKCrfaDbq5hRBv9nf3Ny9PZQ2pgdlWyihUtXceXGAHV1zX3KweL2chXKZEFFASoJhVR1dVBySW3HppnRGRubl199Yas8Q4jbAkBxjf9MtlXbdMEps2CSFKuNkfkHDUqswhxinEiIhD5Twusq01RGQXJGCWIDLvdynGURePZwMHUBOfI+LNgJyY3HXcM4iJgEiIlAgHiQW2bUWi+7v7ZV5UzQ1M1bQ3991uNzbBgfNf1xWJiFgoxJRMlVmWnc/zxCzbtj2TimnMLWKMvSkOG+WgMgMMJY+q9taJYTzPY28i5JGAZoYxxuE4EwnTlMaFQES69uPxuKmNQygyta41FzAb2FhARo8IyEhmToBjSMWAw2PdVQnACRxcu6JakEgsCm6g4x49OsvjO5ZSAoRudnM4/OVf/IPLej4+PX797fevX392uWzzcmASRCYkF+itDVqUyNRbMeuIBAiq3q3lbbPee+/Tfi+Mnx4/MeLdzY1zOj0dY4yqmttqveV1a7XV3nMuiLCt28dPn1qrbpbSjISD2oiIXbXUmrcyz/PpdHl6OsYY7u/vW9fe9XQ6D+zXfr9HxKvn0ux8PudaliGcN48xMguADin9tm0pTYfDzbgViUhdt3XNL159dnN3j+zb+fjp48Pt/Yv7+1cPnz4dj0dACjEsu4VJbITaqSKimRJSCOHNmzePj081X5ghSJyWeZqmaVpCCGZej5fj8fjx48etllHcjBmwmzk6XVcrXtdA74iwCDOzqzIxRzptufR2c3PYypa3Yt1ub26QGQBDGOzlXmsRZus+/CJbqVcikKmZPcOlcSikEZkkqNtW6u3hpru9/f79YX+4vZkRSUK03v3ZdyISxtMaKBGTj1sdoQ4bGkDJWUKIIbZa3IFYvGvT1lTBLS67EIOVrM/nEAaZAlnJhgOeEFqvag5IAIOqa8zB3ec5KHiQCO4jAFKfS7Facs553GjHEHGgSkZHqGtHwPV86aoxxt28REm9VWYqvT4cH3Mta84ENM9LTHNv/Xy+mA55PKY4edtUR8I2mjVz79oD4YhbN4fAPExIbgYO5tB6v6wrQmi9+zXKSQn9euQjXlrFwUGI4dq5AoCUWIJvWy5q4CzkAIPCTcRMaGrrZVXXrqqmMYSY4pKm0US55K3UKnZtfqmpTdOyn3duCmitl6fj4wD6t1aXKQaRKSY0ra052NPpsakuKdbaQ1Dt2TFKcGYMIbXW1MvluPam05SYpXVd85Z7c4RnniIQUu99HPiPx1NxNzBGEZb9fsciQnw5nuI0zfNoWkVAUL3m/Y7SWFVNHQGJeQoxxbBt2U0ZKTC7OQEKkqpGIkMUIpZgLrU3eB7GjrEYIMJoeanu5zlIqKUOvMGQcDBjCKH1PmK2DGAcS+OWEEPspEy08LJulx90e0P8as8cgtGlNVdG6toJruwTdx//UEvNJY+CUUTGpflZn2dMSHi1HLdaa7tu4hzEXcd1F117q6jaa2VmCgRuQ31sZsZmqvQs1DD37sqAaDAMoRwYAUDBDbe17FIaVdt1au3OQQ63N8t+90c/+/nf+5M//e3v/24r5f72Zne4Oez3P/3Jz2/v7q+0yF4QgJlExA2c2T3ogAQwOYo5IMtwve92Bybe1rWrsqAjntdLiLH3XvK6bpfT6bjmWnKrtZ6Ox7xttdWhBRosrSF029b18eERgJDoeDw+M798nudtywDkjk+PT6Xked4B+JYzuMcYcy2EPE/LmL2Pv+/9ixfu/vDw8Ktf/cmU0tAC1bpul9PNi5c39y9LPq9Pn95+983Ni9cxRuJ4uHtZazmfzze3tzGmGCMA1FbMbJqmWtuWc97Kze3Nmzev/+2/+cBsL1/OYxVtOTdVd/j4+PDNt99+/c3X5/PlGVODLEwYBvcQmdwMHc0MBIip9S4SzPTxfIox3N+/YBHV3nLVWslJa3caga+M4OQAZrnUFKOO/KmcASDGqGOlEA8s6FAH7PZsbrlV6+Zm8zx/9tmXKURALyUjQ+/NwRSA1M0p1xolCDI4dlMgNARDn2MEACUem+/YPgnRAFRtmpKEIEgssuOl995UibHVtp/nyHLeNp7FEJsaCE0xoWMMcb1czFwkIImgO3LvHW1c8vroIb//8B6R9vv96AT8UGWPZ7PkYqoxhGn/wnrpa4kSCjgyqunj+agGudb9fFh2h3maC+U1b906EjCQmQ3Qnps6gjgM+3HvXXVESwEAPbeyhZi7ds9+ahemCACtZ2IivAZW99679tpVzXe7PRKquxAKE4YwTzzF+Om0nrdNhBhwTHpVlUlqawiy5rWbokOMiVkG0cCEH4+nrVTR6sCDY4CR+bKdIOhl2959923JmUW0VwYLQoxy2O0ccqltLVtTdwdz7+bdLLet6jmluJt3KU5N21ou520VFiFxJEefUopzlU0SGBIhUzN1V3NThXOpnc4pJoDIQsieYhqhNg7eWhcRQDcz7X3Mx0WktY40anESIiZSU7U+kopM21CqsZCZm5lqi2Gn2pp5NwUzBlL3zTcSVlUEGNDgbkrOQ/g/pSlwGDvg8EBdKVqqKcbRKnH32sboAltrrbZROI3rWGuNEK3rcD4MoFAfnVb2Uopq6727Q8SAAEw8Mo3hGXsXRFpvDiphUe/r6RJDzLm5GzEMjy7h1Rs8wMsBOYboYFdqEPi4BGhrZipMZm7QzUEJ1XokAcIG5hQYUbUnkrjcIGHrfZzW6k4x3d7ffv7mxV/82Z/+4uc/AbCbw5+9uD0cbm9evbqXQDIFhCtcCICIZHRle2s2YusBUoyt1tLalNKcJrXeyrqenmKMMYavv/7q5v7VsszuBwBiCTkXjlNTK7n0brXU60W7FFcFxNDDoOH33mttIoJIzNh7M9Npmt2NOYgwIux2u3VdL5etNQPE0f0jQkIm4nmeETGlhAjMLEG2rbx5/eWyHID49v6Fu5eSGWFZkvUCbt1wvnn18vWPYpzMLcTw8tXr4/EYYxQR887EwgGQmMM8JTMwPR2Pp91uur8/fPjwvveGI+QLgAAcKEi4u3/x8tWrXnutdfA+R3YQAAjiMs9uaEN6aeYIZhsizvMyrht5W/e3t7e7m7JuS0xm4ADjRVwt9wJmNW+nLccQ9rsDgIcQrvl0fh0DjLupk09haq2cTqfAPE0zKqBTbxnA5ribdwtTsDgsBy4spRRsIFFijIM66g4szERBgrYaCKy1UhsQulpKifEq19CuDQyDIJEDCA8EgH58egwh9G5bzjTIjMAA1E3P66qlMl8xD8Te0T6dHwIyE/VeaytdbdkdppRub29bu6bQjAv6AAW21oB4ignFzm3lgGBkKRJ4r7oV761zDJLS3e3tft5/fPwIACOAE9Rdu7p3N0J4zumDa5qhqhkwxSDCTGFE4BEBQOl9y5WwB5GaKw+JmSNLcIJcW62NpBARuMUQnZwB0hzRkQwOywSgblY0b2UrtYsIhJFfa8QSR5IukRMa+po3RFxz2bYmubb9LqF7DEwI5/NTrqfHxwewDmi55pozg8/zbooxSDTPpVVwEBJAn1Iaiq7eW661my3zHpAM7LKu59PFzWtrXf32cNONABi6r9s2pVhHiCuiA5TWau8HnKeQniow4zV1Ea/+2CH2arUhEg6IB6KI2Gjw0Ij7ZXA8nc/j3mdm9WpN9q6dSEotAK5m3dwQdRBxJY4gYHY30xijP98DxtQ3hDAinABgXF3DeCTcr/rLEZvq/gO3efy9nhXW144kEWvvetUAIBGOCWRKabSGgkhXHWtxXEp6N0AjIkIspWzbKpGZhwup+TzU4FZrNzNGCiFga6Vks07ETAIA5jpOR3L0ayil47C7gIcgQFzGmAgBDLQ3h44h9lbRIcUETN68m8d53h12//Af/vlPf/wjIScECcgADPQnf/QziSHXmtIyx2lUW0gQOThw76332lWHBxOJtpy9q11jwV21XS7n49P57vZuJDQNAl2Mk7uPLK3W+uefffG77Q/renR35hBDKlxabUjQatszjzpORFJKpVQeBlezy+Wcpjh1TSnNc+td3f181lIKDQtIHtDgMH4dL0JEIny5XH7xi5/e39+pqgQejaYQpBXOuadJYlr2NzwtbdDqu5prJ3IROp+PMcYYE6dZApMzAJp5iHJ7e7Pl9eHh49PTY8nlfDoj4O5wCDENs/iU0jxPN4eb7XB5enzsQ4zbbaQ8DiwQEZRSgzAJIyPifE0z7t3BiEmtf3p8sK5jADMuGYAYJXawVkuQgLbxEMW7TdMEDrmVHwQXADAoAMzcGiIiOPTelzilKK4BhUc6NwstsgO31ioAxBDHvmN2HU8AgBkiQC45rytc5/YgHDu5mhKgBN4ul9qaSPQCY1IKV3nSDx15MFUSGdv3DxBGjjKSWZEoBGl1Wy/bYXcYoTsxTENRI8LyfJcdTtLn8x6HLz3G2HoLKcQUtbpYcOvblsucem9zivOym+ddnCYJAoDgYGa19sEjcXNkZJYRXOju2ltXdQMMDIjEgsQhxGuN2FVVm7Yx5iulBGEmgd6HlbL1rr0PJcI0TZFJBLtijIFDCGpCpG4KMLYbdwNXFnawZUqDB95Vz8cjgc/TrK2ta25dJZccIsYQphjBNee1PG4iFBjNeNtKziWm2NQOYajqGYBC5CkmYQxCwsJIgweg0GvrTbs7nI4n6KrNm/tW2y2HV7d3tdn5spn6NKXtdOq9l1omYkNAwP28S9Py1It2RUA3b9a69puYpmli5hCSqZrp2F6ZOQjUPgJOwXEMkJcppdzeufow3plprVXERmwZIrha69pVg4ghOGHkOMJvB7qpt2bovZThNljXtbU2sNLDBQrwQ9NmtDtlREyMq/rYPuAZ5InP9c9IbTQ3RGBhkQDDjms2TdNYjuNIYGZmGl3DkdDt7gN1+ewdhVILM6n2gaEG8F4H7zm7KdJQH4o5AdA4l4ZsfEx6URHMTQ0dIrHSlSoRWdy9a6u9TmmubtulsOA//Ud//vf+7FeHm91nb14HZNPerDsYGjmYcDiv2zTtvvzsxylOAGBXRZzjldc9riaWpmkItLtbWcsIrihlZYn3L18CYDN/+fqNGugwStbGxFOa9/vDtubD4TAIdyFIGU+Xas1lnhciHuP0eZ5P50trbXx8Q8pyOV/cfKSHAuA4dLec/wM6fBhb3hh3j8Y3M8/zvFsWFqz1+m7dsdbGIS67Q4gR3ImV3UKQvK1dVSR01aEtmdJMzMMAaA6InNI0jhZAB3ix3x+OT8dWa8mZmVRNg4WQ1m2ttex3u4cQ3Ny6Ag12IQQJ46gbeXlm5mBBYpqmKc3gWCCrNgZKEiNLq622dskXbjwu/FOMI7eagO4ONykk7QoE2vsYclwNOICqHQkFhYhSiiJ3p/P5eDoxUVvbdtnuX77c7RYicdOYQinXK7ID1NZGoUZEzOjX2GkHAIlBOyaJ7lBVkch8xD+BuWvvCMh+7dKMWoqRbm9ua62UCBBGIXg9GBDdvLu5mzAzEaL0qofdYQoTPA94Day1OjqotdYBkrlC4J8D/tS0q1Zr8zLWALCwNWtdS2tIQCz7/SGlSR3UkTgQy1jeQ8PKI8kj8tgx3K2rulluNVGaQmAWRG7aI7A7urqwOPnzboBd1Q0pIhggYopxy9cAknVdV/cUJQRRQEKKIby6e1FaXbfMRD4ZgAuPD4uDiAzDf2+ltW3Lw4rkDq11UdXWOyMyUZpiaa2bi/sUBA2OuoaYLmU71BpIYggDMz6laT8vKUlgDFdXh5sakSNQb7322lsNIl17TNHAztt22N0S42k7l1KRYZAGxuEBDszXx9J0660PbmWp2dxTSvM0D5tkKQURUowSAiLW2lFCTAkcALG5u0OaZpaAgGjO7q3XkfY79DNujg4EOIVITBKCu8PwF3W4prW7swjjdVmMw2ZIiYfFxp8BmWOjHyX/EIkO8dywvdDzD1VtZgOPpW4iUnsrrY5WTwhhqBdijKP1DAAiYZqWUtp4wsFBOCBDbW10ad2996Zq7gZg1yOzFnSTa6k0dG/AzDzuUogsor0R0vBQugMCCgUKYM8pWYaAiBIihfDFF1+EeSLU//x//8/evDwgujkN3kucFncvpZCAqr148fqLz380TbO7m/VWc9Nqpu4wTYmZWm2mrWYzNx72VGIz672o2n63kxBLabWWWruECOAhcO91jKNqzU/Hx7EdlGLjExlqpdbbi2mqtQJe5+3+HE89z3POtbZup4s7phR/6PnyuGnC1VAiIgMk+cNnN77Pu93CTKXkeTkcbm5bq2Y2TXOKEQlrzaYmQlPajR1ZhLVXBF/mGZx6720IWBGYAxG6q5kPmp6qvXr95vHxYZxSvbd51x3W3tSQHx4fv/n6W282TYsBArhMYcRNqzoRI0GYhIkAcVi1gQgUggRhIiImVCNhDiGIJkKyrm6m6qrqTUebq6vWpihsZITYhhzSjOg6vgKA1ptpN7NWK4EJ++PxoTfb1wMHCQKIpK3ReC6eQ4TGYX8dWY/EaTNEXJb5dDp2sxAiG/wAuWNCIIwpIfJIQRn3rd5VohCx4/Xjg/EDgYmv1wI3dQsozNJ7c7X9soy0kjHgE2Jzpue3RIM30/sPs7reOyGZ6rauMS5TSs21q3V3IOrmKYYY42j8OuJlze4473ZIxELE3FtXcCZkkauJ1R0BdOBRhceco1tDwhhkHBLsQCLoYNrYyVSZEYGQCLQDOALWWq+tA6S65hiCA00pcqTAcZqmm/1BW1fT5/SH7uBEzIDau7bmbiJRRKx1AJ+mJDEIulmvFGMKTAQKTjDCZfRmv3v/9KhqIoFZiNhNCTFGub89pBQBrJXcVUstquqgvbXL+dx7BgARhtpZGBmd0MC76Vrzum4AUFXHxGZEhc3ThIFLa66OALVU30OMyRHMbQR7jRzg2moMMU1Ta1XdjWhwLselVUJQVXPjIN7NW4shIkHvDQjdEQECS2BUN0AUFmEmwOPlNETEXRUZnwVzfDUNII6qgYgGnSNv27jqjvJ8nAf4HBozCv8fBqemxtfCBEur/Ky96a1bV3CgUfqpPg973d1EhgXMe+9528w1TvGH6dDVPGJGhGbWVK33IStCAABwU2QyMDS9jmHBiBBEvDdwN/MQAwzIpemwRAAFV2UWCfjq5euf/fRnf/7nf+ZWTZuaMYIQNuiOQOD6HCQQY/z8sy+maQZwJNDaWi+9l9babtmB6bfffZPz9urlPUfQ1op2psDESOFwmLft8uHDu2VZmIMInc8r966m2tXdzufz0+On3pq7matq790QzQG7mZkv04xEMcRuWmsdQ9fRlLu7u2OiXhslKqUK8/iQlmVR1dqamaWUhhsjxigS/Ll7Oz6Lb7755sWLw83+8PLlq207X4exjEGw1d57TynFwI7kZr23UUIAYGtlNChyriEmhKg2tn5062o28uOGWxsAtPfLekFmJPnd7786nbdc2/F4YucQQkoA5iGGEIN2HcWFIzVtCNhVicm0IyKSD+zoSEQBIhRGpEmCsLRae20IkAIboqM3tUFHkBhiiKZGpub2gwdwXA0BAIgdzHubcNZS5hj3L24RuecKYiKxqf6H9tppmkyt/8BeHkGnrTs4Iql5nGTeLTvhbV3dgUMAMycOIbTar3mH4y5F6sOlTKRqMHJOzACRiTp0B+cQvDc1dXB3I4RAzCGYGY7i3ry37m4l55xzKWVdV1X9ISxofHCqWmvpVQJxx06ExASAqsbMN/v9FGJv7Xg+11KPx5MTjSyHrqraDYxxILqG0l8HXGeEkW05a1NDmKZkZqrdwUVkhPiYEiJ0bEigrqRORIQeg9Q2whZ95A+W2nrXvp9uaZnTRE4APsWIQubWtQNEQK+9a2+IjoxsSFf/k4Ob9iZzlBiIGAwtl5pCnGNEU/Oe5njKZ3df4mRqx3y+bJcxNmHCEGQKsWm9qJXSaq3WGwHVVk4XZ4TenSnEiFOcE0+BWHtvXV0NyUW4dXODrgBEIsOvhUWVEU11nH7MYuC1b6Vtbug4ktI818IiXbX3xhJMdUjBmHnN2/FyqrWGGAEMaVjtgFlYJLfqwuLYmjqCX++8gCwIQOC9V+0K7obAEgTJEMbckogYSUgQUa7S/mu7H23AzWF0ten5GRAOY+UHERpJQ6oJU0qptUa9GysYGBiTWFNwALABbSei4eoaX6L2QkS9K5jD+GnuZkmimglRbe18Pi/LQkR98NyJ3CFy9CHlberoOqw4gcEAVEc6FZBBN3RUABGZUiqqn33+5j/75//JT3/0xZwCMzL66IA3aw7mpqV27dpac+T94SbFCdyALOetlNxaK3llptbqu7cfHj5++uzLn6RlX7Zt2y6onXcHIAf0rsoSlmVPhLVnAFyW/bZt23pJKRJyye3777457A7uVltVG2NzPV/Wp9Np5L4RIBJul22aptbaGB3lXMDh9rBvpRgAgOdSvMDt7a0DsMhuWUqtKcbxeA9Z96C1kDAHUbOPHx7fv/v48sWr3rtaJyZVd4NLbznn3f7A105Uy3WzDtO0IMLQ5HdtZhDiNIwR5kDoZuPKTczAzJ9/9nmv9de//huksUJ6Xrda+/Hp3LuiYWkVCRFIIiOhCI9gA2EBQqiIiKNXUsFjCILUzbSqxNTNCYCZrSshgGtk4YhjDVcNhhAJVXWgDJkFAgQHRNSuw+HPRJEEwRF9zevateQszLd3t4jEIkAsLMhoakw8SiIWZhciG9o2JAwSaEQOAAL44eZWWEgECeM0gTu3pqCQBBFwSNcc3V3VYkyqg1VekXyINcYLIRICqbcRdmZmtTdBknkGgpFMhgQDOTUu8SMHZYitkTCm9NynEhsUERICM4fa2jzN2hWIQwy3tzev7u8Z8JK379+9+3h8+v79eyLOpTQDMUZHIglJgggjFW3jMh270rq2XM7qJB3cSajXtro6YBSiK3YHReJ1yuPu5oGZI3sIyzSre9XerQXhEJKZucG2VYEtxkiITkg+dsJrC3qaWBsVg46qbrXURh2QkFiYRUIgBgDrvTdkCTIl6cVE4nmt25YByczV7LKu27aZ2jLPhJxLUbPSalXdcqm1m/loSefiQihEIlLdB5/A0NeaSy2BJe0Odzc3IZb3Hz+6KwIh0sdPn168PIxUBzdLMaZparVrM3cAgyBhYPwMfNhSR8+ERlIGArhHllLq0O3UVtEhkBCNqychQmtVmyKJDnuYs5lZ1y1X7YZXgwyg45ZLnNAAEUHNRcR6cxwMUamlmXqQSEQIOMjD40o64rRGVG8MUwgydCZI2GobVcD5fB4Gv2VZRkSwSDD1GAMRX1mniIgowa5mcQlXzhSLm6uqEwUSCaKql8sZB5BVbZomIiaWgQ0jYgdHoKstOYZSa60bACBSiPF8Pm/bGoIMxnVXc7Nf/NEv/8W/+M9//pMvBd2gCzPYuI4omIJrb7XUFkJUs9EVHTLEmvO2XhAM3CJzydv7h8en46fP3nzx4sXd2/dvl3kqdSvr5eCu1hGpdUPCFGYWQYRPD4/LNMcYS5FtK0GCCKQ0mVtKqdaHUspomGzb2lrLOes0x2XqOY+JfW+NYMDu/enxcb/bHW5vcymqqmaHw4GI1nV1AAkBEB1wEHCvxgmA1vs4ulvVy/nYuzOH8+kcEs9xCYHOx2PN683NLYK1snUkZGHAeU7C0FrtqjFOgtNoCg3vJ9iIsVxFmCWMJliI8dWbzx4eH0/Hp+tIX/10upgZuNNgTEnoXRFJe9tWCyECoIKxU2B2BwpsZuoeJDCytcooRIyugIiOgeUqxAEQCYNFIyhOGFMa22KvDRxiTEjYdXAGyR2CCHZrtbRWe61uMNBAqgqA07Q4XSO9IAxnch04WBiyH2YDp9EgJpJwDWeutdfaQ0hgEEPC8T7JzAwQp93svRPROAMIMKSp95bC3GpDGIkd1lojppQCVm+1zdNkpsN1bzWHIATQbHC4rfY6cK3Xbg9RSmm326U0tTa4wg6ISBjjNITjcVB3WpMody/v7g+HWYKZPjw9HdfL77/+6ulpjTHV1pnFfdQPMKWUQhzWzsCCgGMevpUs7tRVREy79tYHD3kiNU0pDV7juLWYjm41j3wyQkJCLFmYiBjRER0Am+lxWymvzDRUZ6NpcaWlEccoDOKA26mVWoRZJBJxnGbZShHGGHiYp2rXNKcpRDO1SwWkXFYA7Kq51NZ0SmlUwed1A9y6Wy6lbNm6pZQGUEFV3VxSFOEY4zxPUeiSt5xryVsgnOO0m6b3nz7VXokE3dX84emISCxsZsIMIExY1Kxpr9pqS3FurSHTMs8jJZKZd7s9Al62LaZAiC9f3G5bMXcA//jxk7vv5+U5WNHc/HZ/Q8wImHvLtbJ7iIEQXXsMFELc7feX83lMbeZllhD3h/3lsl7Wy7Qs0zRFCaWUKJxERiQvISFBbx0JTa1umZkkBEJkdDclZkKoo5Z3C4S5dmGa5x0zK/iQaaUUgwgxCRPQNfRROA0VEyKYSmIBgBSlmwUWMGfmU1lrWVWVCM201uLuPvRFzKXmLW9C4fbmFhFjCG76+HDuvU3zvNvNKYX1YmZKhPM8SUg/+elP/vE//gdffvbSewYhHtpJZnDXlrVmM6slq+put0hIMS3TNI9ETgecpkRgvWyfTo/rurrjYX+Ypunjd9+8/e4Pbz7/0f3d/RH50/t3l9On2/t7RL6s53l3Oy0HEbFeevM53oQY3717h+hq9ubNm3dv31228+W8rpcVEGq9zkJqrRxDqR3BW6tpfEZbdtUlTZfLpfUmIZpZiEEkDNpz732EQ8UY3a+96VE09W7EyExmlnNRtd1+19rYdEIpRST03mNMRKi9lVaFOaTZTIHx9HT89OnT/vY+SDDDvK0GluKS0kTMKc2Xy3nbyqhLpmlRdVV7/eazVtvT4+PT6SnEtCxT63o5X6Y5AZIwhyhE1BogIgGkGCQE750MKQgJo/DUoyCBQ5yklqa9qyMSBRECVNMpTaM1X2sVYUZyxCRsRGbqw2Qu0QlNaymVAYjJrZkpQCd0AAf3+/v7AcKb0hSYe+/EkoKMEdmYq3mtZBYlObiwODgNDYIZmCO4gPPA7Y/qyTwxAWFthgTo5mAIEIQQgdCjQEDOeQNtI2syCgNBy2s1a72aWh13gml2t94KOLfWWqljx79czqXU3vvQno8mlap++vQRHHLJfg0+apfLBgvPN3u13LSD+5Tiyxf3L24OBH46Hc/reatlXct5zbhVZiQ0V1XrgLDMyxynblbPTUTUhgZo4Fp8CASep/duQGruYLnWq3TQbNjlCEnNwCGGMAYVpsFQJAR0dHdAMLDS6pD6NLMoMqXkiOrmBtLxegIRp5QGDgeQDCmXIjnnFIMwBhYQaWYOEqdJe5UYa2vDDLzmPCVEoiQhBTHzpr313lrrZu7YzWaWOaUQ4mU9ozATMUAKcTdNKVD1/rg9ti1PIvOU3H09X8Bxyxsic0i5NnXb7ZYQz8zgQAamqtp1W9ePnz5uORNRjKGUzQFSTG788OnD8XTu2u/u7qJwZFy33HtHwm1dQwinc62tCfO4JKiqhCDMpbU158Acami1rpeLmk3TlLe9qY10Ie3LPM+u9XQ6bTmHGI6PHkXM1EYMi9mQAGnvtVUcnE64TqjG7GQUgMu8lFp766q9tTqaofl8ar0BIYsAAMKgORKAl1qtK+AAiZEIi4RWy3o6H24O06AFIOY1t9a3bV2389Dp73a7y7kRMxG6NQLcSn58epqneZkjIORyFhYmKL2tZ3PVly9f7Ze51HI8Pu72+/sXt3/x93/1p3/88ykxIyAND5q2suX1dHp8WLeL9j5N87I7aO+1tRcvP5MQWy2EOMXYan/7zdef3n1XW/v8yx+b+eH2pnb7/u370/ES4+O25iGv+O7bd2/ffvjpz3/+/t37V68AHA0Q3N6/+36ejxIma/3Dh3cOXmrPW/70cHx8fEIiduZJxv0/PQuoSs4xhv2yPDw8drdmOu3mdurH00kkEMu8LIh0PD6ZeYwxBHb/AZEttQ4UMNRa0zQB4FB/melwMAxBEYukmHa7xd1Kbb1V7XWZJyJoTZ8+vc/bagZdNa/r7e39PE0iETkAQClNAuz3h6E61a6tFSJJMeYgcYqOvq6Xb37966ag5jmXMd1xtWGBvKyXwBJDCCEiUi3ZXGOaYHgJ0U0V1FlYVWtprXcciiMHAJ/ShISDRIIOozXOLGO4MqQBxAyApq2Uep0lmQ8qu5t31d77+fzYWxuBATEE9z5GYF2VmBlFzUzVwa/KyLGwEd26WR+eVTMdZigEdHe1QR2HazCyD2YujPIIgIXJVFurbgqA3czNB1pRzVQdr1NhdHAgpGE/se4GZmBjdnT9cTVdMpE928GG85lI3PV82T57/eKwzKXWaZoYUNxvp3mKMZfydDmtJbfaSm2XLRPJbklDOtW7hhDmlKYQT6U2tVxbIO7ddssswq11NTDVkR3SzJBJ1dU8IA2ddG1dTVMYKixFcAdDQiYOMThASlOKEyK2Vtcyou3aNCUAcoPeDK+YSL+0HiUQjqayxEjMoua9KzjKVcsIwESE2N0vW90t+zgJ4mkg+hCgt44zT3NgEgYgsEvZ1m0TFEYCpqrKwlNKHEKL0aybqjoSCwGkELWW9XQmByLq2rCNyIDWakuTjHMpSLi5uXncX96ajnm3qmnrl8vl66+/RkRmGgJecBhD167azZk5RgnMcP1/6HCNJVK9huVd5yduTAzm/arNJwQ005wLIZLwADZo6+7KTCwCgMOihYij7SjMzKxmo3tDz/lZY/ENB4MbDDHomPeO1T+m03adGF/vJYhAJPjMyAU3cGi99d5HfssPipSurbUaQ+BBmDJAx2eHuQ6X4VjHPBChLEO4pKYs9PDwftzPkLCVXnIZMsdvv/762hCIYb/f/cf/7D/6kz/+ZQpMCEiChOB6evywXZ5aWVutkampM0IrZTtf4nKIMeZtM7MYQ63bw8cPD58+Hh8+pWnS3u9evFpLcaCf/PJXb8pPlnn+9Ondx/dvX969+vN/9E+3nA+H26rYtfTW1NFM87o+fPzw8uVn+92St/3D4wMTxZhyzk9Pj8t8iCldc1RERi5SFL69uZ1SWteVmNKUADCl6ePHj9u2zTMmlpLziJgbUisiVnNiBnBHJA6quZTi7jGlq9nCfJ6np+Pj+fximmYiONzc1NasKzH5IBAw5pzzdrm7fdUlfVo/vX712eHm7vu33z58/ODot3cvX332k/3uIBIcgIiX5SbnNSaqNas3lrDbLYjw27/7zb/8l//fT58e1q0AoJqVWobdY4jERywwEyOx6fCU2FAfuI8xmf/w08zNHIkGXwBwEIjJ3RDA7T+QLjg4Gl7b80NANxyLIzIC3MDMnzH/NJSUiKNZTYgwXgquVter3HO0bhDQR2b9eKln8y1cZwHjzwL42LnHfjw02Vc1w1DxDA/L9T+4KQw9HjgMkj3CIGpe1VBI4/WeX8PMwUeiFV5hnDDe0Xhqnp8fBwArtV8u25hdiEQXF5DEcdu2rWzqqmZde20tlzYl4avtEcx9mWcmJuTaOxLV1o3dwW+WXQppzdvpsgKgEAKCmSMABnR3lsBEtQxBjQ/mhGrv2pEO04QkDO5mEEMKITJziskBL5cVgdAxhTTq/VJztkqIwJ5bZR9lQAgBgsRuVnJt2GR8o4SQBAMH63pc12lK98u+dTNGMpgSCxMAxZCEJdcM7r01BwCnECT33HsrOQNCEJqC1NoJqWrPucaYAKxshZDNe20t977MnGuptVU1Vifqc4r7ZZlidO2GUExvOBqM0U2ptV9XFgzn77//5MYHTKMRM1YyXrWeyDTanmOh4qCVX0U9Y+Ff5fwjQ4OJtutaHL4CGEy+YS5xAASnEfyFigjuI9lsrCl4LkDA/PoY0fUdPrM/8WoNGG8H3ByGMxxHRsEAfqIPSK+DgyOMffy6QPHZBjTCLccvZoMIcH1G8BrQBoBX9y8wsl+OZwR0MEAfSTvjaCQEcESOu8PuT6fw0x+9SRO7qwOPm/l2OZ4+fb9ezsRhv7s5n59O5+PD8fH1Zz8J03JzdzfU4syhtXa+HInw1evX54/vt/UcI5HIPk7bujHzze3N6fi07Hc532StL16+OnTvXX/2i189fHz/3TdfTcu87HbTvNfWnh4/xLTMu/npfEwxfv/2qw8fP3bVWktroDZc4cMsE8d6eDodQwyHw+FyWYOEDx8+ltK7QutG1LsaIcWUYpyIpDUlYkbuNrI7FBlLaxKig+e8tdZinIXD99+/u7+//+lP9rvdbpn3l8slxIlFynomt8u6hpgAgiHHaf/6zZfv3n77/t33LNLVdvvDerp8U35ze/ditzuEOKVpFpmI8Hw5qjYzT2lGwHlKv//dH/7ud9+UvLU6cHtqDgg4WtOAOEb/Y6mMdWrg+O/3MgMgBwRUdAQzJyDAURApICMRkrrh9TeP4uRZ9X99psbqBwJwe37GhrIQnB0dQJ8fi/EkjvcGgOgIAIZOY7Ej6vPqR0QEs/F2x77v/uw3GMfTODQAnp+2fn1uaHzB4XMaZwteTY3k9nxoAbqbE5grAoLCUOX7dVNHdPxhK2huYysYEvBrzXj9Bx3vtLWMACKDNEAd/Jw3tKbavGtkWeYZwRFZWCKzAXbtLDLHiRGrWdUeQyillVZ3S4oiSabaterjlFIUQkLtNsYByIyAgowhrdtm4KqWWxVmR1QzVwD21po59NaEOHBwN+udwIGhaq+1hiSISMTqttbs5FMIIU6mhkQi5K6BJTt0N+nNVNRchtq39XZa10C4C8kdatNumsIUgxDhPKWhrd62bWBsOXA3PZ4vpVd3f9VaTDHGwASEmHvfWnn38eOWl8fTuV0p9r0DLgtLmKo9qiOMuVAQIGit9aJEQoyBg3Z1H8M5d4Sh5h0TlbErAwI6IngfSx3Q0OgqxAQy++HJGCBfBxuLx65nAAwg+Ei8GldLuIo4xxo1fD5XxsNBAOOBAhg7+r8vG344j57PgvFaMCibY+nBINFcUeDXkCRE8jHqup4MV0r7eFu9dXMbIaHgo2KF8QcAwF1HYURA1/fj4wsZ+MgpgIH4tOfHGq+H4rNRHQCRQ5TP37z5v/yf/0+/+PlPwZVQhBDBSt7Ojx8eHz4h0f3d6y1vx8fHj58+LofD/ub26ZwZRVURrPTSWkXrYO3p00dXfXr89Pbt1xyTo2jXrls7tlqzdpvn3ccP787Hpxcv7kOYDOPtq1fN9Hw6l9anaV5Pjx/ff5A4P53OT8djCNP/9r/9m1KVSGquw8bVWnNVJColt9oI8e7+fr/bnU5POZenx0di7mpmXmsbT/o0TbsYRGSIQF6+fOkArl1bLTUToXYrpRJTrTlFQbfT6fj6zf393ctl2ceUzH233yNirTmlUNZWq8YUbu5fLsvStXGMv7y5+/j2uxDDluvh5kYkbHl9/PTp4eMnErm9u3/1+nMENFXtFsNEGIDNCR/P6+m8aq2jNnB0f+YCX0tc8Oea5nnLez4A3J0cYTTSwNDRn1foWGoGIHQ1eRGgX6teGmal60aJ//7V8Lqun++m4DhSZJ6fvR/UzzCW1bDQuxs6jR0W0ZHNgRDcHJ9fHJ7V0lfB+ujlAQIqAAEYgAESXk8Guu7R4+AYZuJrVTYORwdHJ7qWUOM0ugo/6Pk+MXTLo6oy4fG/EGkUi+PFUN1GUAQiqToACUc3mKfJzE6XUwAH70R8sz84wEiBW+ZJRJp2d1qWg4SgNpA0GllMfN2akMDzQPfmsL/dTS/v7mrvJXQU6q7YXVXTvLjDWvL5clJ3NVXt0zTFkGJMtbfTeQWw2uo8zTFegoRcMwlHgBgCE9aWVU1dzY0Q3alsveYziyCjdCYkEQeCeZ5EEcZFuHYFqyWXbduO7udlZw65NZnSsswiHIIEDspaWj1dzghAIrmVWmtpPdcK5uu6xRiWxNOcam3d7PF4FJIt59Zqra201k1z8940xTgyakwjeMxbIZZ52ZtBa2iK2vx0vFjvNicJ4mojIAUIxhVpjNHGdosIrn4tQQY3eWzw5j9E2evzBjuutddLMuhzYT2qgdGu8ZGdZD/UGley+9AIuTOOuuW5evrhFn79bdeNHeD6bMDzaQU+3sAgRY8vOO7A4446TiV7PoccXJiTRO29mzugmRGCmhMigl3rNcCxXcB4mIdfZDx3YPjDYYLs9sPtB0cVRIAIHgV//KNXn3/2ArEhUG8ZFFurl/Pp7R/+bsvbFz/5GTHnXC6X0+l02h1uS85MyEjr5Vzrat6FpWyX8+NjXdfHp4f/4X/4H//27377z//Tdri9q7VxCGma13V9fHz68vMft638t//tf/3nf/H3/v5f/kPd4P3H999/++1h2QujAJZab+7uTqftcr7UXN+9fwISB89bQYTeu19rQgAzd3XXFy9f7/f7j58+ffvtd+fTGfCq0zcHNUA1d9i2stupBS+lXTVX2nPOI69tmpdmxu6XdWu9hiDH45FZlmX35s1n8zKXUri0/c0BABJiXs9rad35zZc/JSYAO5/OH969/+zNZ6fz8dOnh8+++EK1z8sCsCBQLjWIPDx8AoD97gbchWVKU85btVpamaZ4d7drVdzUzZEYAPVqImMhBgJgGB8iAIEZPnc4B9ELkQy6ag2cEAMQCbObhRglBtdm1kvdtDVC4RBjiKMWweFnhNGLd2bmIRA0UFO8dljG8gZmUbVxJLTeDT0MvC1iSomFpxjMbTA4Cchcwd2vN9lrP3bY8u0aKM/CYQz8VdWtDcUzIYHj8E8MbXjgsMxJmEzblktrikRBAg8GJmKMYZ6neZqmlAYnqauqWi0tl3LZ1l7rfr8PEszMAWqpjOAAZtC1/7u/+8Pbj09uuK31fMrguG5tnhiqvvt43CdGsP3hZkpzfzpVJQlTNzznLgKlWetQ1fW8EgkhDsCUGdTSksSuLYV4e7t7cbvbp5RbM6DSrfUx53FEksDzPNVank4nIGradywpRiSqrT2t51Zzimk352fTKEqQw7Jjgt41l62U7A40AsMpGXnpZW0XYpzSPMdp0C8AXAxIwkRkrTVzKzWr9lLq8XjqDu7kroEoTVGmGQkd/ZLX03ZZYgKH1tVdW63kmOKkrq03iByYTRwAzTz3Vmt3sJyzms673S+++Nnrzz6f/+73knZP58tatrvbV7/4+c9fff5Hkqaf/FHD3X0z3+1vm8vdq1d3h/nl/QtXTSEK8+hejsxPEXY3EZqmFDgCQK992y5dqyA1bbnpaPf12pt1wmuUvA1lp+mAgQzD4TIvIYah/di2rAocGEbKSq3ajYVHIqOCitBuWXbzLMyltsu65pK7KhIlGbkQjQDMPdcGDiHIOAnGTdfcmGVO05SSCAMYoscQphQAsLT+8Pg0JmPLNL+4v++tD6EOE4QgXXtvjRERvPVemrZuZl5a8zGYAF9iiiJV62jluNNWKhC6Q++qXUfaDDiYI5B//tlrYbLWa1lzKSXnWsvl+PT+m2/3NzcSZ+3AHG5fvnn15svvv3/76eMnA9gt+65WyjrPk3I/Pj3V9fLt13/4q7/6q08Pp8ulvv3+//r555+FlOZ5/tWvfkXC3vrx6dPHT59Smv/ut7//4osfhSm+//67f/NXf/Unf/onNzc3xvzh4yeS8PLFy6+//W7dyrdv36p67Z2Y0KGrQ21uvm4rMrnbYbdXgz989fXHTw+Pj8dRM5ZcB8ySSEMQIlLE4/nSzRCJQthy2fJ2GRtJ64DF3M0HbE4/fPyEwIf9DgDm3ZxSrDXHGGrJ8zwjUGutlHz/4p4EHz6+69pLrq9fvyIwBP/Nb34dY1zmhQmL6cPjw5df/hiIX+92uWzH01kEWQKAbvl82S4B+b/4T/7jv/zFj7fTp1o2gJEAPeViRvDixf1h3hE6PqcDmUEpZc1bb52Ybm9ua661FsMmDHPaOQiFMKVJe3vx8tUcYsv54+PH9x/fn8+nGOJAGow7ZStl2zYzHYszzVOKaehta60RkVhMjYi7miHAoJCCb7V064tEFkKhVy9fvtjvg3DrdQTuDuKsWwf3XHsuteTCwkMq6ogxpZhi5GAGrfbWay7ruuY2TFIUzL21NtRQu3mZQkDC0ttlyyVXJA7CbsrMLBRjXObpsFt4+HK1125N7XzJW6nTthLRMi8M5Oa5NzUVIgcEInVfp5vDqfZmSPjyy88SYwqy3x+YaUmSIt3e7mNI83KY7n8sdz8PcQLEKYUPb7/6+P23b15/9qM3L8r5cd3K0/lJtY/Fdt42Y9yF5bM3b2LAu/3E4MCCkubuj6fz03q+JRYRd2PieZ7Xks/bFmMMMUwxAcB5u6xlA3WAZuahlilNh8NhTmk/T4SYcynlMrgAQ4h/HZK7llIJCY2teQohpZhikCnFEAJib3WrvQGgMEeRreS19lpKbbn7/ibGaYroYF0vlwsTm4G6Sgx5bQi+TNPNfichgCkiiMiaMwPO01Rat6aqzsJznP7F/+H/+NOf/+rt2/dM+NMf/yjtDx+OT28+++nPfvrH7v10WX/1D/7Rr/4StGsu/S//8h+GQARg41Jj7man0xGJdssyTTMz1VYBLMWY4iQirs+Rm+Durt2neRqXXDMYKsDe+2XdgggzlgFMJuq9hxDsmQ8FSL1fDxoAcMTaOvP1wmFm27qq9sNuF0O4bOeu3QHUjFkiy+l4UtMoYg5GBO7DLuBm5o2Jh5WREJLQbrcLUba8CUFkdoOt1k+Px1qrmQtzCiHFab+frddSSxDu2keLtmsrpbSuToIULufzyJ0gt8QcYwAwB0cOLHHbMrG03td1DSK7/W5MF3tvzdqPf/I5YLqsveR2Pm85r9q1Fnz55S9evHxlNNfaKe72aT+n6cPD+vS0qRvQt3krpv327mbbLlGItW3bejqt5zX/4evv37x+VXJ7Oq2tl3/3b//6j/74j3704y8v68WA1Olf/a//6v7Fy3mJnx5Ptbavv/r6T/7en1zWy++//vbjx8cf/fjHHz8dReLpUnKuIcQUAwIQEJI4aJx2iCQSQorH01obhrS7uReRmHOe9w7gpjoKJWIKQWKKImFe5lE2xghNAYAhOiIGIncSCa15a7rspm4gIZj3UrTkbb/fmVrO3ksp59O7b75urYTAXXuKu9vXLx36h3ffzfPy85/+YmD6Hx4eSs2/+c2vu+qf/f2/UGtmOs/z6fi0bh9vb+9LySW3y7nud7fyxc/yzYucL1NKyMGMurkizsu8nxZwm5eESKVUN8+tXvIYv8d5mqx5KcWhLUsiEEcwdO0mzGmZCQU4vL57cf+TP6q1uds8zSO+eNwaHRyfMa5DIcEsWynrug3HxoijAKDrVZrQVE+nMyAkkcNuSfME6CkImNpVAM2qNs+zai/blqsiUS4F3JkFEGNKgINNjkESESPCZT27QXcbPu2UUi5F3RE8hRhDEJbaqwP2fk0XcPDBqhmoGHNf1/U5SXC0cKmrOUBMUZhA3cyaGRO6mjsM6aQBttpUMQSOV54GLssOibz31ts8RyYGFEcQZiJsXYPw8en9v/urf3mzm0nb+ni4XMqfv/6H/+7f/Zu/+fWv1SHnDCK3+7spppt9Im9rrao2T/tOesILMQF6mqZaywiJ3O/2gDQt0zLPRLTmLZdsPr4oT9MswiwcQ7i52S8h1ZyHuIoJA4cYAgDmNuC5FQyIyf2ajpm3PE1JUgxzktYdkYMAcowxBYBu/ng85vXSrOfehGEX2YC3jyshIxMi7VJCxOwWYphT3E/zEsIcKUWqrTpAJJokbqWp6TJNbu0Xv/z5j7780bdff/39u3fn87G0Pt3fAcX7+xefHj6QsLuLBIkSUgjTMH/7AKeI8Pi3+bAfXLMgIU1pj55zNnUMwQk5sGAa480hrRmFhroOi/Sn9USIEOnFZ6/fvn33h+++GRCYEOIsEmMkpquac5fGjDbGOGxow1sBALX1y2UtJQ8D4W6O5rpMCzGPruLN64aIzLzMMzO0WokZzM/nEwve3t2V3GptpZUwiLtmIaYQgrsJy04h3L1xt/1uJuKBKY0xrZe11AIDm54iM7fWRm+RSIiktSpEy7xsZUO6+jmHh4SJRQaN0h6PT3MK9y/uSm29eustBFbVb96d18tpsAx2+xsU/PLHL9f1cmpaLsYkLKkDvD9u84sf9dYS4nkt798/rOen4//2QGj/9B/9w6+/+eZ//P/8L1999f2aNwxxOdx88flr4of5cPPV199g+OZwd7/m/K//9V9/9fuvt8vlf/qf/ufXn73cHw5q/nQ8/S//8n/9/vt3371/LKV/eqp3L14k5B99+QsiuvL4gkicAvE173tc3ltT60S0LPM8JSIquXa1knMupdXatAPicCSNEeokMQYBxhBjKbmWOj5iVVPtYyhk5rnkj58e3r397sX9/fH4BAjLblk3Y4Cnh4e333139+oVIM7zXiioKYu8+fzH3331+5/87I9q33731d8RyN3d3fm0/vZvf/vjH//4dDmeTk8vX37h3c5PT+tlnaalK7tE3N1O6XZ56aW1mBIxoaNwUPAr/gwR3UMIMxIz2bNUoKviFVpFQsBMdu1DKxHHKZ3O53laCHnZLW42auqhfRhy8mlagoi7jiuyejeF0X6JMaiO2J7OTEjUWwcAJAQH7cbEABqEg7ADHI/Hp6cnZwcAVZcYN2SKs6TD1C1N04sQxr4pLDEEZjbXGGIIobU6AC3uPqAa18u629i8ADHIEI8OyLiO8FoHGFmegKDu25oxppjS8GnbWBuD6NUVEZS8tGaq1X0YAoZXXyS01rdtOCU9JgoixG7acy9BWEIY8LBaahmpCcwew6s3n/3Rr/70t//u/9fXc8utVfhid/cf/bN//vVXv3v/6dTVtapqJwYzfzydq9YgUXonJBaapyREWyvECIS73U4k7KYF0FMItbXLthFJZA4SUkhTmGKQeZ6EBQCLNRdwAY4hYgyETOzm3ZyUAcBAkYQIgnAUIcS8ZTksMzN3bRJoJwE5uqN37dumvc8x3cXldp4iAeHILfUhBp7nZU6p9SqMTY2Y1RQRlt1iquAE7shortrqFOI8JZbpZz//ecn16eFhW8+mKiKPT6cYZ3cMKdVa1st2c3c/pYlGn8S8ljKMGClFIu69pTQNFFqtNYh092XZXy6X9ZKnJQ0PbYoxxDiysx291no+n0dcZ0pRWJj5w6eH0tqLV68HVEeChBTVLKSkZmq22+1kOKrB0SEIu0MIgogkHGMwOwzUfu+tt5ZiHDZCRBhoCwBnhvFkIiIyLvNSe3l4eNrWbfRsWYKiqRkh1VrN3QKa+ZBUl66qBQAV8NPj04DAAAASrdsGAHVQmBxab0R0f3eX9geKKM5+pY45IpEgMji5CE1hLlpAtdSy5ZxzcwCFKBw4TLcv5hhJtccQd7uduT0cz5fz5gAizExXcPTQmzIzc5puPnx8+N1vv1XN779//91333/9zdvI+Od//0+/ffvxt3/3B9QaUjoc9n/yJ7/85S9/FlP8n//lX/03/+1/fzgcUpTff/31OV8Q2Z3fvf94vqy73e3u7v7+s5spJAnRHff7/eh4jlg1IzG1mGY1G2WCakeEaZ7QndyYGRCCOTGwYE8yzmZFQCI0AEJG9G4cQkppd7gtJfda/Cp8QXWNElrt67Z+/c13/+V/+X/7L/6z/+T1q5clb/vdcjqd9su0Xk5vXr9utaJ777n0LcZ02L9Us7Qcdnti8vfvvpuXkGJ4cX8rEi7rpebyzR++XqaD9cbk23p5/+F4e/9mWpZlf/vvtWLgrTftJhLNnYVGCGfXHqY0RqkBUZjcwNwJGRlab+r9fL64eYyJReKUYgycOYYQY+q1/uDyHVkgYzpkYKVltQ7mXdXBejN3iCEMKBAhutu2ltY7PGepmxoSIqH2HphYeJritJuAqNZWa5cIEgIxBZEQ4kD9WTUWRqKuNUaJSZgTIvTehuRiJCgMCQ8zrutZVXHECSOa9oF6fcZBa611jLiFGBGAkBnneVJVVUOk4QMgEgAstV1P05j4WecX0jRoQaiKCDFGM12WZUphLPXuFph670/HEyKOeaQQu8MIlFLVV6/fPLx98e3DJwRPM/3+q9/+0Z/86tXr19+9fY8opdect8u6lrL1VtbSiLsZTSEB0pSmZVkIoJQizAK4v79vteW8XfJWetOmKcReawwxxjhNKcW42y0O1nurVcelar87EEIgcvdt24bLQZhTiPtlN08TAbhDrbX2KkuM5iO7maYUkWKuPU5p6zVMYc/0o9cvXt/MUbi3rC7uICJIPKQ+3ZUAEGHNawyMsJSSd1MaAruudS3Z3GIIRDQv87Ts3r59dz6dLufTmgtIuNnd/tEv/3he9oebw3fff6927q1+/+7i5vycmkiEalpqG81ZcCTm0cgrtanbMs+7ZelRWy+PT2cz2+/3nDmlydVLbb13pkgx7PcjRqZ1VW99mWaNab/bzcsiwrmUAWlI0xRT/A9E0Fdhsog0rUyMDkQgwtqx98ZEIU3TNJmbmiE6pQQ4EtpwFKq9dzAPIcSQzH2ZFwCsrbl1ZpFnwYYpEJIw7Jf9UDGbhmFR2e8P4D545RKD9ta1hRTBryedOwy4/5YzEYYQVJWZAEHNmzZm7mqt2bauhylF5uXuvjU1wBCD9q6mAIAE40RERFD4/MsfXc7b09OTA6QY0HzLufcWJDhiijPu4P7m1eef/eQ3v/mbf/XVr1X15ub+l1/eM+Ff/+1vE4dpt//D11/9q7/5zZ/96a+ejk+5tt/85pvzVqfFLevTabusTizMASm9+uzlbn+Y9/vAIUpgGt9IL6WMsEwouQO7gyAR0wid3i0zC5vayGZyADPt2oc/fhggbDgAAuN12o5oEEO6rOvgDPo0lVL8GjVhjECYHUy1/uEP3/9X/9X//Z/8k3+UUrycjofD/us/fPfxw7vTef30N3+z3+/O2wWQ7m7u5zmCAbPNc/rw7u1vf/Pbv/zzvzdH/sf/4O//7W9++/vf/LqrPX56eHj4WHL+7rvvLlmddz/l5ZbFcmUSESZmJooSKQyTlA0zcNdWmoH1HxIBVGFMxVXLOKGJpZIcT+fTedvtd7nU29ubIPL23bv9ft+1a1cJYV4mZlKFVqual1yJcZ4TAJh673Y8XYgkpUjoY3W5jyQyXqY5xPjvKbm9gXuthZmnOZJQCFNrOhR0xOgGAC4iNqWQi5mxsJr1Omoyd9cRufzMUbfhgBsY0VLyum4DzR1CGCmYqlbKOgg0iEQirSvwVX4XY2Ams0EhHTj0q/R0v1uAEAlFAoxMYDPVDsDmg7WFvdVlmfb7HTNc1suW85AJjfqgtX4+H/f7fZQwsNLMxEKtZyWphvvA+zl+PG3vv3v7xZc/+je//puBKys1Px0fd0sC91zrINirubvPKQVmRjqdjlOKKGEE8SLg+XIyhMQxsmhMPqJkEWSEJbAPDQ4RiXBKszCiQ60VWAAbEwfxOc1znKaYQLW07gCtdwEGbVpKcS84pxhIwiwiHfyua17z3eHm9avbLZ/PpXa3ALybFtMGCNprYoqRL826KSC2VoUmZjYFROpuj5cz2lUTnKYpl/rhw4fT42Mp9bJmpfIX/+RnP/vZz373d7/75puvTqfzF19+uSzLVjZ0H8aKGFJMUdVijOBQWiFiVQtBANBdmWRZ0m63s645l66H0/H09PAoIoeDDraJ9mFA63nbCHHLecDfW1Nzr7WEIMenbd02U5UQQpCOOFIsRGSaprFex3e8925qCGh61f/QlXDlqr3Uqu5mVkuFgW4nmqbUWuutmZmEUFtFpFwqMUXh3nquhZiExbt3bVd4CCIOFQfisC+1WpGACFz1KhMaxSJCkDh0pIgDCnT9DeOO7D6SxEdUjp7X9fXLFyhihCiEpqrNwFurzGLd3Uy7EZGDM3OK4fbmZtzK3TWkQVhCACAkQXzz2WcxBgpy+/bl+Xza8qVz+vbbj2G62+9mBWnGufFf/evf995u7m5V5Sc//+MUZE4xxBTCNE0pxAkJYxwzN1Qz4dR7Q8IUwmg6I6K5NfMpRiGWEMxtTtPh5tBaHeb0kmveNuEw7naNOjPnUhx8mucpTeC+tcJEYaQg7JcQAiMxcwpCzDCmNtqFUQJPKe2n3fH48F//P/97Yf/i81f/6T//Z8fHD3/7619/+92HZvajn/w0RNktO6ttTtJrri0/qf13/93/+P/6f/8PD4+nf/Dnv+qtrTnvD3dbaR8+Pf32//Hf/PKXv/zbv/3KMP70F7eObADbtqY0kSRTE4rCLEJDghxDZABzWuY02G0svObc1UxBzWqrphRDyL2fL2szLaW1rgCet3x7u//Nb38bQvz8888/ffzkAE2bmbaBXg9pt8zW27Zebu/ubm/valM1f/ni1TTFy+VCjIgoIaWUCElYWEY4ApupBWytM08iAdFLrgAQIxMyIIQgz9By0NKFyAmZKQhjSn7ltBsLkjERmSkAsMhgShNREJ5GQwzRbAR7gLsSwWhzuQNqERYzdHDtfb20Mb0bSxSelbOq2noDQCIc074Yo8P1eRns7lrrtm0jy9O055IBsXXNOWvrIyazlDJP8zA4MLG7rduqWj++f9fz5fVu9pup5Hr5/u2bz169fPFyyzUGmedECEKoam7e0UptSZoQozuC994Bfd0uro5UiGjdVgcjIEYiRHe/5G2aJtkyAkTrFAARhTjGNOoGAKht9D2vV7cgIYWwm+ZpSq23rdaq3QZYsPe+1YLemJDQY0xRInEginlX5mXe7ZaUpD+tD+d8WHa1ac7WtS/7ZUkhzNOg6hH4GJlo0yiihgP2REC5VQqUpvTx08Nl3bZaujog55xPj09/95u//vjhExAFgo/v3r5TDUFiEDXbLbvT+XgVJoPvlh0yld7TNLF766rWjWhzXS/Hx8fHIHGel+PpSbUx81//9b8l9J/99GfTvLRawa3mbGbzPPdWHo4Py243zwsxPB0f8rYxsQghXA+VGAahAQcPdkSvxMjFGhCHwWsbNi4EAjI36XDYz0NOum1bqYWZ52mSkS5ANOqalAIA3ux3EuPlcqn/f6b+5FmSLEvvxM5079XBzN7gQ0yZlZUggGYL2ZBGN8kNKfyfe0muuIAIKQDZgKAxVaGmzMrMiPBwf4MNqnqHcw4XV58nQmLhmeLxnpnqHc7wnd9XyjgM67ZW1ZrzMAynuyOTLMvt/Yd34ziC+21Z3EySHOdBRGopVaEjt/dOlKqqB2F3dyMHN9V9uEbdAYQDCzOhGZ7meZ4nEVa1ahASIVKrBsERydBv280BxnEiBCJnCikwAqobElAfKeXduVTvrOTDdBzn0/HLL0/Lum5lXZbbePruw/ebaWG2x3e/Ho6FmMdhHqeUS3HAMUUiLKUcDof3797FOKi21iqxMDMimbspENMQJaUooYMHABl6K9hMQ0zM7GZVaLmqug8pijCi9ws7xLjPd3pHI4eay4kOKaZSsnpzh2FIqobu8f7Yr1Qi1NYcoZRCQNq0NPvy9Pn56dMvn7/8P/6f/695Tk9fnp/Pt89fXu7+3X/4F/+Hf15z0Tr8w3bZ1nXbluenl//vv/l3X55u/+pf/evf/+7vHh4fAPl8We/v36vj3/7t37+e8w8//Pb7X/368f3D8TDN4/BwOooEA3XzThV8M5PXYhUM+u0FZg5et0pmiam5stBhOPTMuENT9ioiUYqCAGOKd8fj5Xoz1WEaTI0qO/gQ/PHx4Xg8JLHldi358Mc//vFv/uo/T4c7ALCaf/XrH/J2OZ6OKUUHEHZCE9EtL6bWFR/unnMhImY3Q2Za16XU0g99ByQkEZ7n2a2CqoNtWXfpKlG+WW21E7lzKSUXInSAeZ7HcXx+ena3w+H4+vqa36RKaRhyyT1RQ4BSa801xSjCz8/PzJRL6ebvZoaArVaWrjySbV1braraamuqImwAKUYHuC23ZVlEwmGaSq2q2pHvhFxqJebI3OHnqlpKyaX0yWi1ZqqB6On8cjwd5buPXhYjqoR3pf7lr/7ifD67++kwDUIImh0AUVvjRADOgszSVM2UiVtr13WBTuYAZwpBhAHVTB3UYd02MGhahyFN0wDoGEh1nwtkJnWszfsYVW9bIpIBbKXWWh0JgIIECcQ3bc2B1a0pBjVr4EJ9xh0MGYnlOIwNx2zn27IJU6mFGUOM33z7PTg2pF+ePo8hMGGuTYKQ8FbzsmzoQIgKluvWmqp6rW2tbVnrL1+ez9fLz//L/8LEy1pEAvGuWzIHJm7auj87kHfR8TB0lFX5c9OGqDd5amtmNozjYTpcbwsR1lJzySL07/7X/3Wapq5rxu5r8TaBG0Iax0lNX85n1XaYD7v3EHOaYoxBmA/D1EdKurFnSsndCanV6maIGFN0QDcbhgERUwhdDtTtvXLOEkJP14OImi3rknPuRzMgAVLOpZZMBCkla/bu/fsf/wi/fP683K7ffvMxpWEYUmf399tmnqd13S7XK0sgItgtw/aZtdpqkNhhsynGPpAJ4DENPb7Ztg29sW/CaAYhxDXnWveD5uPH9+M4DFIZtS4LS5Aoz0/npy9fEHEYU5CgqiEIMXZz8J7RA4Bwff9+nKf3Zvrl6flyuS7Llktx9+l0/WrlhojbtgFACIGJmTBImJIAqFpRLbmsQWQYxjGGeZpU7bZtW1ZuOcppnkYUaKUut1sMIQVvVpflWmodUophQAZw0NbGcRSJ19uOMRcRQKyqQUKKqTuWmYJqKwWY2d3UcJpGIrrdbkBOAPM0ulspXrwdT/M4/vD4+O623J6fv5SGEqcQ8qdPL5+/OzMhwvGlrP/lv/zdy/l1W7bbUlNKpznerity+vnz8+t5+eF7Haf52+9+OBxPju18+bLml/Cp+xWmGIJpI+IQuJPfQwh3dydTfX55NTWW0H2AATEIdxMxQmDm2uq2ba1Z3qqqSwrjPJpqXpdhGP7wuz9Utbxevavv3UXYmm6Xz+fL69Pzp1arGTw/vy639ePH7yWEn3784//n/90c3miWRPM8EbEIldK0VXOPEtBBzcA9pojEQLiuax8afdM9A7inmNwU3mqq6MDE3dKntNp6+RGxbxwETEMionVd0XxIw5JLLqW1Cm8jC9Cp8UTm1poRkXWPje653Vo/HDpJfx89I0J3BiKm7s2A+2AkGABJZ7H5LVwMvOQuC3ZHcLN5mDSymd4u1lHSpaqqGfY6u6TAaUivL7f6Xmkamvr1dlvX8v7x4f4wmwGhC7V1W1uuYUgJ0hhjDEHNqb9sJBEppT1fXoHwbjqkEGlgJIws67ZVrblUYR6CO0CtmnMlQkLdtjP3dkTPkxy6Aqo/LnPfauk9/1wKAESJEkJoTVvtTTJGpKZ1Nbhu+XxbUkpbtsuah+lwOk1b9SVnc2XeA8D3778LHDTFopWKrqUZlcMwV9Uvl/Pr7YqIx3EcJbDQtuUlP21buV6Xl9frz59+WdaVQ0Ckddu638sOw+u0kzdHdX6rNmy3jd4aAwtcA0v/+021/00k+szsAL3DzCKXWrp/b5+h7ff22w8BEQb3Lt4nohC6ZYd1AZEwB+I+VqvgampqIYQuR8U3koRIMDc3D0GEBRH6gmNm7S2yLiWSgEil5KadogHa8fOO3gX+zL0wZe655NoaIv7Vf/lr303vSITArZ/1ptZBRk273asYuvWQCjuEwk1NiNwt14bg4zg1bbVUMx3G9O//7b8FAJHQKXLuqKpmejjMMRAi9tKquSvgelvXZQP3EKWPI6SUaqtfxzLTkEIIqg3cpmlmplKbubeqLy8vOecQEyKVUt8OBWTGXRrrsG1rvxsAYMtFJITQUclhWW6qu5klM8/TnFKqdWWix8cHCRyDHKbD6+vlH//wB+LQmqopAohwCDGEsJXcpW99RL5fk4hYWy1VOwf4zb5RgnD/GB0ARUT97NCmpRZCHFJsrZWSTbunCxwOxy+fv/zrf/Pv7u9P7979zw/vP/7DH/7VtmxJpNSSRjndzQbw+z/8qMbDMG6500y3y+38408/9lOpV9jdPYSIAG/GNYCAzDikRMQ55+7a1vt2MUbo5eA3zjNCt8vxzgZ3BHUbxxEJcq23y5JiCkGASFsDYmH69OmXy+W1Ve3u2QAYJDLTtlQkLKWUWjrmgd68xZk6AlH2sRYEswYAnYBLSKZtr8R/BQ69YX46IaVPA/eqLMDuNmNmhkBM3YL4v/XaI8AebBl4aRX3uUv4+ne6OhUAW6mw4+ScEEv/Ur7PQu9rgLkRsHFneXWzNqIOiujqQWit7IAAN/Ld1auUzXTvfnegXuvJWueMmQmnqm253QCtmeVq65qvt+X9wxiDiAihtbYZwewU1YMwAYBpa9ZKJrc0DFMaSmnOO2gpxCDuSBhYirYo8XTgMaVxGNwVAdyNua/JoqohxBhDj4xZujKwAlCpzQGDiDaNMRqAmwkglqa5lIMMbt6ark3d23XLz+fLlOpxSk8vr2kYD/MpRRFyAEsp9pJ0U//+2++KwPPTp89//BM6cDMiWbb1y/lyXm9jGB/v7mIkJQ9Jzuf1fL5u29bMx2HaaiXilEYzzzm3Vq23tPYbHvtJrWbwJqT5ivXBjsV1cAcmcnBzF8JuqgJMwFRbdTVGMjckak0dXELAN8KIq63Lkmtx82EcYIZeNjF3I4JmQNbvTAPvNLcOu4Yde4WAjrn4Do3AEAK4t7Y3BzqLrYda+/4BbFrVjAjd3NwVIArHIG6Yt+12W3s3jLvJ3tvUz77C1bp2CMy+oli6IKHzMnbCHSIC1Nq0NmLqPbXlsnXvMETUZq4gwqbe6k5hNIPW6h/qn7qBKiJ2km2phYjR0d16iRaRHKDV5q4ShJhMtVtamGnvXnQF/ldfKomRkN9Evbv4RFufSAQmZpFaizsIB+L89frv4ykpRlUF91/0s5kxUkrp08+f1RoixhDMsdZWqvbn7G4hSC9wd3xHq1VNY0h9PlG1KXgIodZaayUiERlTAnNkBreeNIQYaim7LsgauocgtVZtlTjU1kouHcf386fPP3369M//+W//5u/+5g8//pzCcPrm7h9//vJyuab4T5Dp9byO08mq16pErbXNkYIEZgYDs87HVJZqbxIDIgb31ioxEpGpE7Kb9ilo7ppjfxPCv7njttaERSTUWrYtL9dNwZu2um0ppmFIiMRE4zy1otfzxR1rbU11HMdhGPBNMZzzlteteV/MxKR97RmxmffaCFL3HnFCrFY64xB3/MI+POzwZ0xWv0l2yiRTHxUWeqO+mTX3N3rbLlHdcUPQfZmQHHS3vUNFhS5j1Q5VJEIEcGRSN3AX6XbB4Gaqyjt52veDpFf53BHJOvuuQb9hCZCI/C1YcTV3F6Kv902fEHIzQlRwM3XX2jiXklJAtOuyIo8xjbd1vTuG05RCZHQHr2MahAYABLdaNjMEt1zq6g1FxhgO07xpM7NxGFNM1NnOCGkYjo7ullIaU8ol11rcrU/24u5Q6621voUJUYRjTLlUrBWxQ0IghcBEpVa53NbLesu1ug/mXrbcQNa8XnIxR1VrZtdt/fTli1mHj5kwdZTpkKJiI+H3h9NPx+MnxlxrisOy5uu6brXVZh/v5vcPDyCWrY7j+Mvn8+VyQ6QdUmmmVrq4e3/ZHS34xlVG3JsziPC1gAA9FkAENiRUMyJSMEDos779zXX+vqMjYO/lxhR6vcIBegdrW7dmho7CvK0bEo/j0NdWM63W+jrYF3oIZNy7NPtxDNBjiq6mAIDaGgMyY1N9CygMEPQrsYfZkAwcgBwNiQSsO1nu0yp9aBe9aQMEAelHpGpjFhJ2s27546bURyy9mgIAUIdA93CMA5EDQ+dKIIBDUwNAZJZ+jtfSEMi8H9PWatvy1gWrtRQJodVmoM0qAlHnDyE2a+jYMwYAaKrdQr2bijgAEyH2mkCPktGBtIHS3gnsxtvdRrT3rIIECeIOwgyu7Ehs7mYADtZUqe1Ijg4BReZcWu3JH3jgCjuOgxy0url6U1VV2nkcaO61NG+AyE1bNVUzCa0b+XVlAZgTADGbaqk1xphz6aFxv1gJSRVqbarmrQJ6U1NzJEZEAPrf/sN//sff/+58rUxW8h+3qiFNf/zxswRJ48jCiOwmrXW4FZhBt/gFAq2tpwGmToSq/UxnNQvOPcYQtt4jJeJu4NyV6HuuzIxE5lpUgd1QDVrN2XaxIzSzLqEDgFLW1/Nl2W61NZFd0jNNE4Bra9u25FyafyU3WG26y37c8S0DdutUaUQR984yQN2rJjuFCokIcLfhBUBVA1A3a28Wjw7SM3437+ULgP4199MWgIncjZiot2d6nYD3hYcITIiIzgAAIrHTOnf9DzowIe/0oTd2r7/VhbCf5gjQzByg6e6bB290F3dvrXZ9br+HehADf6aAISA0awA+DZEZEXirtVYstTSrIsNhmgLzjTBv1SKjQ20FvLXmHkTdz7eLOkUZTocjMS95YySUvdCRtRLT8XQcYpqnUWtdN7ptbuYxhP7Q+pZMKaXuLk7ULX2aeSQ0RyEchkGYAzMSyfmybLl1IFq/6HPebltpTiGEEEMMMYhclxXh+TTPcxq2IVczRnZr7lVtiyKHwzGNs1eLMTqhxIQOcxqnYUgpKDaSsK7l6fnigLnUUot7PR3mz88vLy/PwtLzX3fowy493u89oq+OjADQayEsfSDGugS+c3t8J1d1Yo+JSJCh1lxr7UBtN+iBpH91sh3Swe38eq7WzI2F8I2cDYqtVgocRADQ0fvW6rNUAGCupn3I1wEhBqmltlpRmJBUuz3vruTpVdSmis0dADvFE6HP7CE4WAN0M2jqTqBVO2TREIj47XQzJukIIURnYQd6w9j2MAvdcceHEZh2ht4O82pqiMjCe3aiRkR95qkHzUg4pNRl3e7gaK0jdDuhixAdm9bWWgf8lqZMGFD2c7ZnSGaKuMsubc+f3KHkzEGAUFW3bbvdbqVUBz8ejmlIudbmNo4TADqjk5uDWkeYoTnkUvoJ3PECKVqI0dTqbuQYmPq93+rWug2Vtqat9faj90RE+xpXNTX3bshq5l0q7r0YiN0pE5op2Z6jIKKqcXdfdm+tJ2BqbuC4HwcoavYPf//HvDWm5O5rMeEUWAjBHSXEbhlk2PmASIxqzQCBEAyau7uj7iUdAkTE5tYH/7tXAeCOqGUmIgI34H6j9zkpZwImAYCai/drGLp8BJXc0XPNImKq5Zpfz+c+39taG9Mwj+OQ4vV6WZfFHdWt7yM3MG89an5zPO6aSEeEfTi+ozoB1LDniAY7OM60AbGDm5u/YeC8/+s9JwQ16Bcs7DC3vTTfzTOlyw1wr3CqKQt7L+G+RYcGwLt7mFdtBmAA/Zeiu2t743PhWxjiTPsQgL1pDoMIEUXm3QNhz0Wgqao5mXutPeYT4RjFHVprpbY+QY1A1/V2OswikdxqseaYc45p+O7DN0zQVDdiZE2S3NRAU4oilDMsueRaj6d0Ot3dzccQg770gJvNbCs5t8bE8zDM88SM2poBpJSYqFObHKCU4mb9WwCgA9ai5k7EBkgAIjJPUw8II4BspY7pkEIIQVRBIscAoRGCuPswDodpmobhmtfn89maDhxO01zBSmvoEBGtZSKZ58Pj/YNvdZoHR4SKQ0grbqXVauZghrBmXdcGYKUVZo5BttJqbWOKwmxutbbWams1hNB7rQDeA//dDg3RzIi7XTAjOht781wy0F76AHRkYqNhSEGEEFJMzUyiuHkIAQBqr2m0VmtNQzrhad3Wbv1xW65DGkII7grkHDiNg6ruJH2EUkp/8apg+x7A7sS7XK6E0D859pfxFge5We5dCnBBcoS9GwYG3X/DFHZPR4d+/7n3aKmvQyJyNVCD/Uh1JkIiM3N/Y753RpgD9F0H7m6qjZh7zyqE0KPy3gLcdXLo7sBM7uS09+s6AWOXwzP3nL5Pve2yBzcgByIHbzs5gLrW3t88ErS1/mmZmQitNYqhf+CS87ps3QLHHUIIzMEBO4mm4xv7ntyDq75vXR1hdxZk7iZohDylVFtrpYkIM/hOWt0PGVWttf75lkKgELy1Pt8UgnTpEYD3/sle9HNX057s71pbAndEot68QUThULQQgQMTszuYM3JkUADo3hI9iHEzIgYHdW3W0IOZ9jgaHcm7q7DugQ7CPniyG9PbHjWYlZrRoYvtWvMekn8V+xIROEgQAKvVtRkBAPXgibAqAFyu1563dSVMf/ghhHmeQwzn6/lyubTaenDd/wFycngzJiLf3VYQoEMHsX/sPS120P3K3J9SP0T7knCAt5wMGMnQhIR6Z4ap7X2avc9nOx8i9HcNhgBQWvW3S6LWikTaWr9v1NyaoQOwqe28OSICd2v4NfUHdAkMaog7Y5fdcR9q20sRSNRFqNDZMK0yc4zJtL6t516FI0RpqqqOiKVsWy7EATnqdmXAbFoytArzfBSC2kptjWJyh7xZ50ASEbMg4pCSMA8pMnFrmlux1lxNzS7rspZ6mObDYOBem5LI4XjA3dQbW2trzjGmHgDWprk0Zi619RFRFhmGFGOaphkBmzZgFInhGEOrOEbuXZpxGMKY1upbziLh7nQKEpXpui7XZQ0HuptGdb1s6zSNkUOrOo4xsnx491huawxyvl5aa6rWXM/b7cv5JYlgkKen25YLgqsBIBNxCNi5vojEewbg/b1qb8wiMSExo2Cr1R34TYkfQohREJGYHu4ftpybm7CAuwgL7Y3qXtMkQolxWZdaK7ydecMwnE6nng8uy/Ll6UnVpmmMMcYYzCzn8uc08K0rNY5jzx+7qqTbhuScwb3UaqrbtoGDuXXcYJcoOECXmQMAoXdRRQfk9qjk7aABQuAOK90vjA5f5H749ptvP/TfDur+8RAxvJEqbK+bve2l/+afXil2cyAD6AVbM9tbLH0dIxKA7hnuV/lEr+wjmjt9PZcB7O0qwg6j7LuCuiKZEGGv84p4D8rcoRuWipj77XZDxLu7O0RstSKiNqW3bUvEX5vGveQKO/CAiVn7CCxAc1N3Qzd0R+jPqtMTmioSOgIQqgMhmCr1odL+rLr33tv51RdhT1/66/uaWfd3Wk2bK5j3xQaIX73JkZBgf8u9pd+9gxxATZG4aevH8dd75eufVbW1Rjvw/M9vysy+ati/nrbm+8HRm1IxRgAwc5G9oEF7PKQd7UDERGTZWq19zew/GXEc0zhOw5D6viPAIOGty7AvIVN1QNrfKfcP039IN2gioq6O798d6l4a/YqqhreOztdF+Of1SUiEOx0rUIdxvV257o5o+obutZ7TEHXZgg/D8Cb2973+TxREHIDdqC9LBFNDh56eOgE6kvdiwr5B+h9a24mBve0guy+3E3mthIhMwNR7Y33ezHsIkmJgIgPnBq1k1dbfDlphIHdYt83AOhpPOCLF/v3VtLXKiK3pOMRhisfDKURetuv5er3ebmp69RWBztdz1YZop8M480wsYEYgjKjatm2rrQURRMzFtpwdsPueOhMSBWNiRugySw/C2tyayTAM6IrihL6VTZgQIYmkcVy2rZXV3Q+HqYDN07ycz1VVEMYUkOU4z+OQzLTWGghDEA/SM8Gt5MvtmksG9uty1TAEny63zcyEe79FzayUcrteW9fzurs5EUoIwzB8XX9mVmvZtq3HU331iEhKKQQ+zDMCMXNcllwLIuHuxh4APMZoXXBGVHJupZh75/mUUty8lFJyRsS8bSVnZk4xSggioeT8touQmXs4cLlchyHFGLqaRXfKGDCzsBBQa9X24iOKsBogIrkgaC9PERF4YyYHUO/uxz1ELSEEkUDkxBCC7HYBfYYFAIB67R4BmXbFNSCWWoMIC9fa+O0UoxAQsXdf3+Cjb3sK9lx7N4/eG7P75uxYXgQsBfoNt1Ooe2RK6NbJ7j0e3E9AFu7luv2G2xFMof9aehN0EVJH8zLz/f19yb+sOR8Oh/v7+5TS110NAGDwtrH7wUH9wCJmUN1PaQRi7v1SB5AYoPYOnvebsif6KSX1njlZP/JCCP0rxxh7oEp/vq66AZN+LT/2s6A7CALspzMhkRDsJzgKSU9MwdwZpb8qYd6nGbDrS5DQgEFhxxYLfy1K7Jdrv4rfqPH9YkD3PnhVve07Yu9hYkixlgpvzdL+kHvOUWsxaztWwRzA3JG7jR0RE/UBKDM7nU7H4zHn3K3Q1LqWQQAopYSIIYR1XVT3zfg10d57Vb3IY6aojPx2d+rXKxMAOk8FAfol4W9H/NfLwIlIeqqx/3g3I6Kusgdob8oHEOJGfYoXqYsUVOGtXMzM1UpfYEIcQujxUWeIdSENUHfFwb2u8NZy6Oe4BOlceERAJLNu3Ejc7bu6oY13+0kkphQFEFvbeiUJwMxUgtTaUNVNS27NdVu3bVvJVSSO47iVisjm1mqLIkwYo6QhEkuMw7LdzufL0/ll2Rbq9AswDhiGcDymx/d37x7fmXvebmBQc11yrq25OaiaeW3adG8LiwTtmlfEVtqlvPbCeWF2t1aLHKbhulyHFEvZDHwrJTEOYUxBmjIY1bohwjwkOJ6wtq0WBDbicZ6mYQaCZlU3bbW2knPdhNDcb9u6lizMU4gRZUhjVn15eVHzECILb1s11VJKqfUwTcM42hvjuQdfXc3KzP2I6dr5HlaEGAGg1gpgpdbE8svT51IK726RllLSFgEg56XWhkQOXmt1wJgmAC+lDMPgap9+/KnUGlMah+Hh7v6yXJd1PYXQSxl7Grv/LhzHcRhS37PMAtDMrNYqb6EuETJRCEGUEIFFeoxGQLXWLnczN9VOuQLsZkyA8c30kZCyVvA9rRMRIPyaGCEyeJer7wJWc2dVbbqfIr3A6m+Z71uQ6O67K3I3Bn2b4EfErjb5qqMzs75dRXYtLDH3qWMgRKfwlov4W2UqhNBnKbsAxXeXMeypSd9LAKgdIEMgIiGEIaWSa/3ypQ/3v21C+ppt0FubvV9UXzVge9c9BiISERZF3MtBIL25bYjdkdq1VBYBbQjq5Obe/e2/esP2A6vHFp2cgSj+phdE7NbwILJnSExMiAwoIgiQcwZzIe69x+alp6TVTELY3wKiqYY35WI16/q8XlH8+nK7TQ++jYD3I0nViRh9r8W11kiEfTdwBfM+Wtgxbb282c9Wsz150qaAVKsC7NL13mdmCTEGYhqnEQnNd2XL14UR4xCC9JiBmXuTKYQQghCi4B6q01tDgFmY6WuiGUJQNVeHNyknIDrsX4e539P7d+9a5C4S6rXWPunbQ9pOQhbiN7cM6FkX8q4M7pEB93KMiKn1C7fWhghIFESE0bx3WwgJ1dRas26LRAQAaXcx70dQYyYRAhBwz6XtDAzEYRCAPnzjAA7me94GuFe+AFRd1eq6vb5ebxtKCsu6NC0REUABKcaIxDGFwzS3klvNMcj9w71wVLOX84tC4wgnHiOFcRhZxKEFwfeP798/fpymOye4XPB2W9amTX0tBR3Fdp2hEGPA7tklvdjComZV9fX6um3XFFMKobQiCB6FreYoTBAYnRmFEU3RNAgNw0jIUxKv6qe7L+fnrDANQ0wpRGnaWqmtWS1tyzXXJnFglO52Mki4O5zuT3cxpu22rlt2x67DIcQwpCUXBEAGEWQeHIBZlmVRbf11IhIHRsRWlYE4CCBEZlDNrZQN1uul1z0I4fxyRsQgZE1bLD1ec/e9v9eaI97WzVSHIYlQyeV4PDIzMcUYJYRTvm+ttKaIOE0T4/4TgggiuDciYOIhRQdP6a7W1mpjQnOvtWHCYRwJu322EZMII2CtFfCtom0eQuxyzP2Mfov13LuLUre+AHdIITJLfZsqYAmI7GZVa0zCJKqWhgFgrxILd+mMuRkxRhJiqrUBQGu1mxFzN3k3NcRxHHuY073xeqEAmcGdRXamFoG9iUh8T8sIdrvVXooFB6Pu1EndOISIsLam2ogYu7krUm+kD8MgxExyd3+/5o1o56sgMjP3nyss3aL2a3geWJzM3BuzhDAMY5/GJOeeFRFxiuQ9KwdkYTdvAACYhqmpbuuGbjHGXhoOIazr+vLyAoAPD/dfi+l9trvW2lrrWcDbm9r9n4l2S8IgyR1aHxxxZRaIgG7kiK3R/jQJETEwvhXThFiICbDrgHotmYnVFBCZpCeFhNQTrZ5O9ZJJr7QA9RHCN9sh6GcoE6FI6F001TaOEbHf+n3dWak1hChEhiaB5+lAxATUSgsUUEhViWEYJ0SMIbxdgSzSY+GuD2V0YKKO5EKiXmwJIXC/7cy6nDcEAEiMfebgTezL5G5N6eudx8TdmKavQ0AIe2sX1dRVAwczRdrNnWIQEjIDROis0z16YAAHkt7HMkTflYbVVKuBvRXOEByI9jaG9zkg957A2VsiZTs22fo8hpll6zXYfaChh6rurtb/bGZWm+ZSwdVMOcbrui2LDTguy+qGIKwGDNaTQyTSWtCIMKSUQkwpjX2xIdjd4SAs3G3LkKZxGIZ0Oh7H8U5CalrPBuA+pKSm7lZbs65gfmsKokAzT8y7NJZwiAOClVp6XUvNBFC91kFkGuO6QowcU4xpMMPAVBvGkAAAHVKKVevpeNpyjjGlGB18W7dlKyW3qvp6vnizgcbu6iBEIYQUExH3PmFVBQVTc9cYQsm9DdVaa9ayt4LIzaGsq4Ej7CNghGkreVsWJGpWCDvXmljY3RX3Wi8RxxRrbaXW1rRW6SvMXLHzY5FKq1uupq2W7XY5z9MhxdSVhK1mQGCWFEIgWG6rti3GwNz7QxBYBBFUiQndUqB5jC9lcy25NNiNT/uEljv6myC3IiK6BcKeKhOioxPuRq2E6ObdNr4v+hAYAcENAad51tYIdo2aWzO0w3wolXJeh2FkkdoqMQ9D7AVs7CC3LkqrDRBqqYQQhJiotmaqNRckjDE1rW+5gsMu2UYkN1XYBYYdZYpEjAR9V1AIXVnbHMFNiBg6b6u7SmlIQZjMac/99/IGGJI5vvVaLQS+f7jrN0drjUCCIACCOyMaEiD0VD8QC1FT36N+IgRDN0HQ7m5Mnb0KjF27rarWb75xHKdhuN0Wj6LuYfcCdHdPEqxpUxuHKcaEgMMgiNBxwcxvLSghByBkB+h0kK0UcGJqLOTqzATOxApBtFUHs8CAJCy0fwtycDPfu0csJCgc1AyQ3azbvIwx9LYKi+wvBInAgzAAoZMDxBQcXJtyL+IZ2O4WirUpeIuREXhda86bqpk6uVet5q5aUwhDEhFOaUgpusO6bu6KQJHAiZVA0LSpa4V96gvIFbwyMUFnPIPW0tWUQGQOhu7KwtxxDjum2/1NQWAA5K5gQMK9KO9vAksAZOKOWgEnImi1mlsXKHeaxX5Kd2eOLo6wLhDbEw7fx2J2jxqAt/yzKyLA3JG6WbwjeB8N6b29/f817G+oix3cncz6R9x/qYEPKYUYt20tuVgXXBDgW45uaq05kDBRLbm2TCzjyKUUMEEIgM7MWiuCM6M5gTdENWsiKYYU0oBmp3kmUARMw6CtqRkxD8MQRMycAim4gqYhNa0EDXAMwtfltm2buTEjO1cz70JBpxCjtjK+3dArwjSNj8f7tWyybYu7hhAQPARJMczjHNJsGE3IFyN2IFPzqgXRGHCMKYZAxK35lutt2Vz9utxez5daW1Vb120rdUjpMA5g9bq8hjR9fnraliWFCEiqbW3N3ErO2239edt+eRsx7G8T3DuMZY84ub91JsCOsO1KtK+lT0J3dAJEpj6zRx1VDmg9akMiwlLLLpcmJpTr66uD9cILILgrYq/k7Noyd0fohVpCdOY390eAEJiCrOvWVMGMiP0tnxfcvd1JOioH+2f5GjEzo9nepuu1CHgr3xDRPhAKjg6ExCH0UoyZgxsiPX3+ZKY9JjLrztdAyD1Q9LfebE/M3b3bRfVHAADatLZGhMOQvtafwQwAe7OadgEG9Guph1fCggRmbl/LDn1ibp84JyRstamZO6QQulJ7757vRWqwbo78Vc/HpLoXaV298I1Yev33q7Lyawu7vxRVra0BeBDuc1L+pgDujxQRtamjvbk2d4oHaK8C9z6871rE7mZq5q0uRAQORMiIqtpU3R0BWN7yC+oj0ExIW94QEBkIqdUGvS8Auh8YvbJh/f2x7fc3vg159AIXmLmp9aOICcxBTbs5bY+yDTraDyQGBHK3zmqFLpwEF2JAMti7xKaO6N1dca9iwVeFNIhIy8vz7byNYwjCxH1OomvVesu5F766RhZ3F99deGrWfVXhLVyw7gjm+1axfcO9HbfgX9+97/FIV2L3YbD+30EfWe9/VxGAUPphi/vsF5jvjq/ue3i+FxVt/w19vyNg//uIby0z3GWmiH0uev88PU01eLPf7mnI2ycF7pJTaNpqbWa2CwoQACBvKqEAohqauojEQN1FmYjcvFarTXPezLr6FERoq3vdbVluaRgIvJba8kISGKlsNwdwEncDMAdloe4zCEgeYp9S7iXxbVtv19dpOmrNAC0EFuZow21bONLJjq0ZoOctmzmzIFJttbWKyGkQdwPiwPEwH+6OR1pAzMC1mSkAIYvEFEICpOkwKeoQWYRE+Ha93W4XUy21dXLD3TC1prl1ICa4Q2u6lrw8lZJLqeU0jUMMAKpGEen8et22jdBV8a3CTkjYL15gMQfPFRGbNlAFgz9LCHoWyohmrns62buPX/PxN2XBLlChrzO0tKswu3R91+AjuvZ1hO7Wx+a7n+oucu4ZNhjshr2wH6n9I+3Lu9vR97P77byBt3MXnNDhTQKxH/BAfTHvOwTA/U3WuR8P/vU6IKQ/N4AROlEVEd/4Wg62w3+wpxd9uGxfbID41mnALurfTxMEMu+ybHybYsE9VNoLDfhn/fM+nfe2q/o2ensa1C82c4A+DURvZda++bB7TeLecEO33k31vu3/HIJRV+/29i3R/piIiHlvtZoa9Mgd3HHvQu6ZhZl+rT33ist/8zGRsOcmvksS8e0x7gKq3jDGPdqGP2uroHuTu1Mf2fC9pQnYZy/QwQiRvBfB3gyWezBhBvt76svJux6l3+7793VwANuj4D+3RPti6WQC2xu4tHeRqQ+0gTXtDRokIH57Pd6XDX7tLff4pgMbCNEcAeByu709+rc987ZO3x4iAHaCL+7/0wDBielt5TuhYx8y7M7zvu8L2qume/Lg+/HtbmBuxB3i58hvBzp1wBoTI7j11qhp3X81WAiBmIERAU291AqmwigSmAJxHyGXzvxpWlQbAIF1VHI/5Y0IuuohiPS5hdZKF4r2GEICCwkCPF/Of/jTj61ZVbM+LYyE+ygcdGWgiPQtbaalat9lPfASjsTYNF8uF2YuZXPP67YxcxrCckNtzay5aqsVicZh3GN8SkioqrRzyomISRgAiRjQEbC10nsfrRYCJ8IYg0hS9XGeas09DTLT3Ao4BQohhFy263IrLQPv/q8nOX68e0hM51sT4QjBY4j7ewixObhZdHh3/6Atg5urulmSsJmaNUBqxatpHIeJpBlorTHFeZrM7PlyLrmMYZzHAwes+SYSUxpabXlbCbS7KhZtHENz60eBBFJVNQcHBnSitxwNcI8TkEDfv7t//3hvRMMwxJjc0d2stR6qEAUAdGullvP1crleDYx5R9eaGTqg9UBepzGN40QozBxTUG3EIZe6rkvXKgQJQYJaM2sIoKqqe6Oy56Rqu8s89pgZsGf3jITQIRCtbwAHNzVV12YOaKBv+xPQTZg6rI2QHKGp44706SIQeoucUIQ6Ikm1hSAUqA8ulpJd3a3/fBBhkcCM5taaxhgIGR1DEFMDRHCsNXfF4Y5h8RqCpJgISVvL1piIkaypuRUzJp6mMUqgXeCvtdZ1WwGAhHsJm4kdHAiChCgRgExrKXldlp7IM2EMkSTUZg/378b7+5hGCVJyBoAt30Ic5+lYSl6uL3m9Xl9faq0ixEzH43FdV1MT5i9fvvRGbq2VmFOMy21R1S4KvC2LIZraWxXCu6mWo9dShZmZOv0ihgjeqR/c47h+PQAgkcQYA6eU4jAMh9Px8f5xmI8hDUh8PN7V1tzaz3/8x9vra86LExzu3//2L39b8xaYtNXX89Pry4ubmRoT9WBcWABBWEIY0pDMbVlveVvBvffbx2E4zAcW2R24am2q03ycp4kltFpu+bblXHJlkmmc3U295bzlLSMSUWDG1urXhNJUkWjsLulpYOYoTIi3280QShdWEIH7GAcRqa3m0gk6Po5Tz1bNdF2XVmsplZlTikGEidW8mXaZb5dUCbO1FmOIMTbVUgq4q2quJZeCTCNHQlK30tqat8Nhfv/4fj6c5vmAQK2Vy+X1eru21ogoBOkP5HA8uPllWZ6evmy3mzAe5vk4znFId3d30zgi4rJst9vr9XpRA3eMQ0IicMhlBdMUwjRN8zSj43W93fJSSzVzIk4xioTAIiz/+t/+mx//9IetqZmzMEc22Ccr6b/B0vS0w9xb2WVX7gBu4GU+TkSw5as7blsGoG3dxnGeptmsgNl6WzWXUtae7Kqpmku01gwZTB2A1Q1NAzDvelM01VaKQZMYhJE4IsOai7Ac5pmZ3FpfbKVmNU1pYInuvqzX0/1s7s26+zEHSRNHbcXZZB5G8BCGGGIa0sBMW95qrnGsYwoxRK8VSSx5V0+7g7Y6T9FbjcdHYQKjz0+f1No8jHnLZNiajsd0moeGdas4DtOQplzrsm4AhohmgORJyMAk0l4aAURmNwck7jEtAvYw21lJif1f/O//8v/0L/+FSHp4fPfu7gGBiurt9uKuJdckKaZQzc6vr08vz19eXziEh/nu3cPdMAxrzrXVT798+unnn13rd+/vP75/fzreK9gwxMfjUQ2vJf/8+UvTOsY4yDCmAa0hgbvXVmtttTVEIJFez3XAnpsjkZq76zgMKUYGa6XVpuDetDWwWqsB5Fy2LVd3JhFiM9faCDu5TEJIjm7oBH1MFwEw5zbEILJLIIYYVUFVSSimGEMA95JLKdtt2W7bCkTCYRrG0/GUhrG3BAlBWHqLSc1ueTu/nudx7KWQ2gpauxuH4+EoMVyWxRFGkcCSc91qziXPw/ju8THFRIStta2Ul8vry/lVW0tDChJrqSFFA/fmd/cP83jQ5lvdzpeXP/7xj6WWpp5iOB2OaZhyqX/xl3/57Q+/Yg7mtqwrEG/lNk2Hx8f3VfV2vebl9vNPf/rpx38s62VIw7t3j1veSt4I8Pe//30K6Xy93NZ1jGkahk9fvixbebi7m6fhH//wp8uyqmqu2prmWonCMKTSWkFLQ2CSZd3MPAXqZmmBpfexo7AEac0QcUhpPhw/fvz4m1/98Jtf//rdhw/z3aMhrVs+Ho7TfDRtf/Uf//1//av/dLueHx7u/+X/+f92/3DSbSPHnK+ffvnDzz/+DOrgRkhdShtj7G4zMUyHaQLwdbuu2y1vq7kzyWE+jeMsLLVm1+ZganZ3/3A6PQBQzuuy3dZt02YxDkCoarXkdbut6zZMExoamIFFCR0C3syShPlwnA/zMIx7Zxr9tl6W24JOpbQQgrCYulpbtzXnjRAQ/XA8xZSauanerufPz095y0NK0zjGEBGgtJZra6rEdJimFKI7oFsIzCzbut7WtYfRWytL3gxhoEju1/WqFmoL4zj8+tfv747v7o73bv76+vlnu56mEyMjMImkIdyd7h7u3pnBmm8vp+n19cmsgts0Ukr0eJemaWy1ucO6FZZKhimMwyDcOdXN3DVyIAbAYgYheHAIMbAEEbHmiIDQhIGoSQwThlKKuU3jHIVLbblUJgLwyuTg0HwnCgMTCAIAeoxDjDSHcH09W0kSojfNbasF5unIElIYatnM7OX8UsrGwqUWZnEmLlsqmyMwkrv2EUUxQ3NA7D4iuTUHB9QxCSIKyzSwGxBAYEGmVjYFF0KRMA7jMM6qLQZCdFMw01oLoIsEcspoY0pyf3dqrSH64XgXODBhqWqeS8lbYLQWWUytmivAVouZxSCRwUtppYzjiQ/z+caXcyYjgF26fnd3+v7Du0U3R0hh6MHu9ba5AaCj2TDFGIJjOx3nvJa2h2tO/EaIdUWCKY3o5NY+vH/4p7/99bcPp+X2+uHdt4eUQqBci3oLEa63m7oRU2u5qgnVuzlO8wd1/+7hm28+vAdwc3h6fXGrOa+g9f3d3ZzCfBiz1mW7zok+vvtw0OE4j+fLZVtv6NWKCsGYRgoBYUTCUltrVd3MsaNqCLl/69oqUSQi9dpaXtdszUKQcRokDcyMTLXWZVlq08ChlLwt2SzetldryoiHQyAWBAQgM8tlU9V0iCmGLjwWxmmIudSt+VZWQz0dxnkYXT2X5dPTl7Vc4yDTkI7jfDyMIY2qUiqha4hJQiLE6uX25TwMfDxOiLDmVb3FQKfjfDxMSuiCAnAaoyBtpV4zr1nmcUpRCM3M1JpBA1YZsS4NORhkBCvF1YERBbHVXFWX7bZsN3d115xLKVtr9TC3Nee/+93fXG7P6ABgwzxTiFXb7fpyvT5//Oa7d+8fmb45HI/zHD/9+Ie70x0zSeCfr6/r9dZqjRKQoDtYlLKAK6KZtRTDw91xWdfmX9uuFiMxIxYjBN7xmb2s6zEKEQuAIwWmMcW9UN+DEEaJMh3maU4S2UERrJbb80s21/vTPUe+LBcR/Oab94FtuT4JkBtpK2rFQRHJzYtmVROW0gCIciu5Vm1FAlfNAC4hrHlb83IrFV5eHA1Mj+MgIkQIlyd1B6Smddu2UioTq9XWzbxqVq0cKAQJLI7GISRJzHi5XqrpYZqnOPQGIHEgYvUqKSVreavjOMJuyeKlKaJGQeuO7W7gPsRYSxGmkIKDp5hCDEzoDt37iIRTil06tUMFrbnlLee1rEFiCAGZ4zAgYiS+3i6Oql5F2M1K3vBotRTVtuU112yOYxJyZMTDYWJGQOIgI7lrI4LWcikbsxBzaZVLVbXaMoCJcG3Wu0fEBIjo1cFyLRFT9wpNNDighJC6Hz1SrVvdMiKWVh3weJzd/Xq9IkAUadYAIcbQtJGbA6j3KmsXve0wFmSYhiRMz6/n81WO86mWvG651uHd44NrM22djaimWym6tRpyjIliDDFveQkIkaW1vG5b6DNP5n02JZd8WW4iAVG2XJipaVPTHvuXYq6tlK2VrK7TfOwOMx3dBuZEvdvJ2iq4kVCU+P7unRwOB0BWa0gcYmq1dBKgObyeX71VAI9pYglba0vOtebAk5oSttvlyzRERh1TFJHtmnOtSyvDkOYYHw6nOz6iA6A83N/9T//Df3++Xl7Ol9vtimi51NeXc/eZU/feCkLso1sWhEMYzT2G6N7+d7/5i3/+F7+JIqXmp9vKfD6MR2e4lRsDBsDIYWul1upu59uCACnFQIDIaRqWuiLAdbkRUQz8eH+yVtMwkASWIET5+vp6Wx7u2vEwH6fpNA6fX+Lr85dqGoYZQzB3RI4cIwhzBPAK7kDuYKb7SAX04oyWWnLetmVDxOptvjvO87En2kGG0/F+ToMIL+v1y9PT8/lSPTYoaYjjEMDJDB3RwRV83daHw3EcxlbrbV3BHYCmaY5mIS85121bD+M0TkMaeMnLlodhGO/muyGNw5gMvdbWqxyOEFO6O53UKwQ6P92Oh1NrhQUJdEpxnqdxnJWApGJVJlLTEINonXiYplFV11wc3Ik4xMd3H076ztTN2np7vdbLutXLWgJzCilEueXt5Xretq15IyYRyrmspTkurbasLW9ZSxvH4ZvvQppDl2a6+fPTE0sYx+N4mB8/fFTT7775zrS8vjxfLpeSq7tv69pxSUxsWruQtNR8vV6RKKZYm72JgLGfbg59smFvayMA0a4xB/fuOTNKKrVgIBIBZkAchjGEgCQhDsxEzIFDKXW5Xg+Hw/F4CBLHFErO//gPf3X/eEoyHNOd1o7XNGumqrUVNIOEWt0BmhngdrteQwyANsSYwlgMSj4XvTl4CuE4jsOUGKXUfF6ua1UiAYReUGata16rKZqTmxNI4CjCFEAgDeNxPLibI5ZaRCSmBEgiUSRAJ3iSpBDXuJWtAnit6qburaezaNZa61TaoM1U+0zWENM0DMM4AEDechCxWoWDG6JwqXVdllJz16mqNmT6KuYbYxIkIQoMpYZWWycJeWvbetWmCJhLdUDVtvs3xOBu67aEcA0hurlIOB7vai3aCoCptpJLd9cC8yGOgQNxEBYkqt3g3lrVMqRxGmdCEmYETuPYddWA5GgaOAMhtN4E6oBY3CcBDdwJPYTgYK1Zl8D1qj8xIu+9NGt6vd6+++b9D6f5Tz///PTyxUBM8Te/+v63f/nDcnvdtnPZcsmZOc4TL/laTcvtJrWFOIhESYlIaqsvr8/jOBm4doGJ6cv5fFsXIqqlmiExqTsLobnqKkzudl0ut9sF+z0vodas1vVUO8yu1uxqzCItsgSRQYZhBOJStlrbst7A1M16S+B6vdSymhuvNQ7d5QoBiTk0Q9UNaqun2yCRzcC0tpprBffTNE3TUFqJHKaY1lq8lf/xn//l4yl9enr9+fPzf/m7f/j506d1XbW7WiB413+BY5d5dvOsZlVbLcs3H9796lffPb1eFq2vL5fzZSnavq3v1WpAfjg9MoWclwpm1kqpQ0pbKfM4zvOBGWrLt+W23tZxHIcUHu4OaHaYxpSG4+l+qYXOz7m2p/MFmU/zPI+Dmrm1dcvDfAoxlVJuy/WatyDCRDHGUSIQg8Oy3uoOTAcAiDGpetM1V+3zuj2zUdUkIaaIO4JcD/NU85q3LYdBHKc0CrKBr3lzxKaa84bo4zSkFEMMW6tLyeV6mYcpxTiGFEOaxmFIkRDBkRHGGBLRKDEgWzfKMwMzJgyBY4gpTmmIJHGgMwIsixrAPMTj3DEhcW3V3Uqty7blnB0ICMdx2LayLLct59oKEqc4HY938+EhhqHqNqaEgEVffK1Z28v1Nabwervctq1umYkRUMEMgQg7cLuV8lKrmyLTy8vrt/PhMJ/cPaYU0lBydTurVkQ4HA73D+/yeitbHsfJ7/T16eV6uVRtvTFtar2Pnbd8Nbe9f46Eb1CEt7Zwny3p7U+RPqjLbo4MEmWMKbEgoYJT4JgGZOn88OZGErpnwLt3H37++dPz63MY0jhNIci2bi/PryQG1N49fKha121blvV6W7Q6ILZW+l3kvlMrYhKoZm4SaVkXc4wxvXt4p65u7TQdHk7HMY21tNfl+rpcL7fVDIhQXVNMFIKD1VanEMmwegNAV2tazHAc5xQHINxabqZ9nBuZW6tEDIAsMgxzU2GJK93KVqvrbV2u1wuiE6Cq11aEeRiG1Fqp5botgChMw5hiDO7QahMRAyBiBGIkRlBr1/VCzOh94NahQWCOMfamSwwyDAIwugNhqKXWmktZc85Esm1bVwyp1cPhgRNfl1dhvtwwpREByUGCSBhdg9bclEvZcBdWcYyjm0kI/b7PJTtA1k1dH6fH4+GE7sR9yBKrKQJKIELWfudVfZPhY9ciYqdoMJl5yQUIBAkQiZp6F6oBEag5ETLhlus/+c1f/F//p//hb//0D+rw/uG79w8fHh/ezSnn5fW23pZl1WoANAxJXd215K3WMsQYowA4MeVtvV1vrbYugoscWutWlVmbadVSjWUnUoQUIARtgAjrumzb5o7EF0AihC3f1JyREdDcaslNLexjNCOTyLoVEc5106ZaKoKCYRojIAbm6qCqTTc1CFEkxBiicGqgSy1grSzLfApjCgjmoLW2xJwkNLdzWcv5KeecrbVS7qbj3TwI2cNh/vx6/tPPP4uTMKq5geJ/I+kUwSDSFSMiHGJ8d3/3F9/9EMJnQtJmt7b99PyFCE7TZAFLbVvZalMEK2VzMGQ8zcd3pzswS1OqxmtejofpdrlJkCnFcUhCNE6nw3zPrcyH13y5bNV+fn69rcvxMDPB6XB4ePwQ0myOdLutZbtdzkw8jiMSR0YBMTetbbne3C2EoOpElNIwmYORtgoApbXOtkOkPuSsFMDULQvx3Xxs5tBiZBHwWym3vOWcu6z+7ng8jON8mJElDcMvz89bLmrqoMyMhMSCSEHCVjYA1ZYNcck3KFsfpHRCBjjO03i8S8NhHg8ikFPw05TXFUfJKDGEIfKYEouIA0FWK5flerttEoZxnBzotq7ny620er1d120NIb17fLjfbtN4YEY3ndJoDxaGtCxZiHLODBSQKCURLrUqaLWGrVWw7s0XY2DEaRgQfFuWYZzGaXIHbS3D2orVvK3rrZbi2sb5kPM2TbOW8vj4uK7rWnIzRfAuclLF2orW5oDNzN6Yr/0qcLcu/mHGWp0IhamDGpyAhHvfkhHYkTmEGKZhdGZzDzG64/Pr68PDAwKP0/zNd99f/uavr9fLu8e7x3fvfvnxJ2sWhCNHgdCK1aru+7i0uhuwt7zlqu6lthBFhMc0nk7HNAZmbObH8TilkcFzXol5SINwaEREgUSYXVs1gpSmu9M9AyLYwY8CXrYlbxUcSq6lVWV8fP/heP9QSoHLs4Nfrlc0CDFJ3JhlHOYIUwhJONZqUZIJirgjKHhZcy+fbLkej8kA3KEPfCm60y5FAjcmGtLgsKl52KXZRuhDTEQYOSKAu43TOI2jE+Rac92izJ21xcSq2GJbN1BrpZo5EiEzEYfj4XA8TKWsAxMxBLSA1tv07tpnyU241iLcRYKhmbXdSSaYtbVsua6lttw2DuwAgQO9KZuIKbggYRe3InMlJiG15uZORkhE3plRA0/X22ZuYH16rXu7OhIi72I9Iuha2OPpEMh+86uPD6eHD3cfxzStNS+X51bbsuZl2QAYrMaU5vmY67bmnGI8HQ+HhweSmCRseW21kOHGNwFMk2DnfqvVWsBhuRZAPN4d0zC0pjJTjDHnTQ2Q2BzPt6WqudbL9Vm1jcNhnk7uqE0R3K0ggKmHkGTbFgUjN3S3VpblSiIhJfQeTmd1BwRkJWdmJkAitGY1V3R/uVwO84GBUghN1ZsGFhHOpd2WZVtvy3JzsMPh8Hy9pjQIUwg8HkYKAbRLx5orEgtA6cT3zhHr6O8+Fvh4Or0/3eVSSilDik2VAB6OxyCsAJfruWljonXb1FQEhfn7Dx9OY8olr6168/cPj2QowNe8SRrjMLiX3JZlvTqHMUzZt9YQGZ+vy8v19f5wfDjdx/EgcSrVHAwXkTR4bVrKbctXWlgCIHz6/On8egkS5mlKMSIyMiHJOM21ZEQf4tgnB9ft1poBUGCLQiVXq5upzVEgShAOSI74uizX22KAY2B2A621lMNpimGMcWg7yVhxryRQVSOsqtVat9louSyluYOnNE5pSikgOALM4xCFkGogqIw0xEAQyQPjkBJy3LVnMeCGtVRAH6Z0dzrW2i7rpoREUTgZbJflKoLq9bp86UfEEGIUeX8/1aktZXs5X8A8hSgxdhMidCRc3SnFIYYowtMwDVFy3rTpy9PrsuXTw8PxeH8YxiCBwNe2Xl5ea17+9q//44fvvhWSIY0LXx4fHp9fnq/rrTatOTOxGbmqmefWFdjQzDsT5s1JHBBBRAi5XwwiFIIQs7lK5BgCIZgbMgcJwh1uQdM4TuPh7nj/crv8aSsPD+9PDzHG+PG772vNZj6PwzKn7i6Ts67blsStloA8xmSyjwXUwmaurkDkgKo6vzu8f//x4e6BGG7r1UoD91LbspUlLzEmQlF11YpqCCCB5+l4mI/DODFzLaW2qnnJTd3RVbPmnDOdhrv374ZheHr5jAz3D4+vz09/+tOfWMI0jEJxGcrxXk0diay3xICReJ7vamvmas1yqWkaj4dThyjkvA0pFW3CUmoFA2ImJmJ0Cw5IgRGABDnQjAN3FAcCEBznwzhOuebr7aKtgtcDHEKMIY7BsdWCrrf1xuzbskYZAosEGeNQakkh3s2TtsIiIYoBEQuJdLcirxKiNEvU2W6q7hnAu0CvWUE0IhOmGNM4DiGwmxlUcEAP7gAOqhXBTT0Jo2LrtHYk8wbuzRQlHAKdJRBRUwVi0wpgxAIAQG7g/WF41SGEMYVslZDEKOf89PxUrAaSIGOUcaNtq2uIA8d4Op3Mcmtbq2Xdrke4fzjdm7X7u/vzy2tebtfzErANMSKJkBCitXbe8m0pAJhrubs/DWPKhakjrYBUoapN04REr5fb6/kK4K2BcBzTgEQIwEwMXLbN1ARZ2Jvlpq3msi3byszTeGzVnp+fl9sFmfvgrSOuNQuRJFqWLS8ZEJ/xMo0HMKtNb9tq7t1FIOd8WZfLcsnbdhqnWq20rVUbhzEMIXAgJOI+LwXMQiwNm7u7OTKyhFbdQQHQDarakrdm1lRvt4WFhhgATDiWZenQxZfr5Q+fPkXh7969Y6T1diVdOcqWi1b/8nRDxIe7+w8fv0EWcifU8+38cv4iYb6eX2/rYugnnIhYrX368uROP8x3hJ4iA0335TFQup5fr8v59Xy5bVuffsi53JY1xqFUncZhUO0jOUMMx8PMQuZ2Xs5EvC2LO4iECjUTtLzVbam1qLnEOM/TNI/vpwMS563++OWJIbnbWrbibkTH4+Pp7r5fx3m7aqtbqV2it6yrttznDFwdBfc0f5ynYRoClbbqcpvH1VIwLXnbaimmBu4hhMhILEjigGY79jFICGlIKYF7NwAAwBjEx1HJ13VtzdfrurAquxmMkg7TdBxxmqaQYi41l9KaWVUkFuZ5nMyBKZ3mU0rDVnIuOY5padas3N+fDvfvH95/c/fwOM+zsCBojOHy+kQIavrXf/Wf7o/3tbZt2w7D8N//d//dtuV1/aUUMzcWVmtqpuqmZuC1A9x513HvE2G0S+4RXYS5e5sQBGFCcnUSQkdVj4nN4TBNjw+PFGQ+nZxhPNx98/H7NI5N9eM3357PL7fXL+AmgR182VZ/8nXb5mGKLEHiYTog7fzFplZqLa2WWpEAEebjPM/zNB2aZjf/6emX6+2WJBGJoi6Xi5CYNnQDpBDjIR3v5tN8OHKMpZTzeX19fa556VNCIlS1lVYO4Xg3H8G8WEljfH/3ITL8+OPvvzxdbtN4TKfHYbjcXi+vFwSSiKYa48gUiGQcDqqtUpaQ5nmaxwkQS8vdCkpVS6mupmJRAiEg2JhSiomFibC1aQrxdrspYJAIBOOYDvMBSXLOrebaMkAl8mEctoy780cImKXmouZby8JiVddCgJ7izDGZm7oHJHYkJOEgJCzkIraBGBOz1tZc3dX3cTxFhCEliYGqjNM8T1MIoZZSWyFEb+CABFTLZt4QcZQxBlazDht/m+cBCXw8pBhFHRwwt/L9uwc0//0vLzu3zhVJiMjJmRjAHD1JVPfPT19++vxTTOH9w4cUR+YkLF59nKfH9+9Px3m7PQ9DuNbiKNN4ChJq0Wmc3j+8u4LW7ZJvLyUOcb5jkRAi0nq9XX55ee0t7pBYrWAfVXHrrfthmsZhAjJZLiEOQ0yn4yFJDBI4cYdnCEvV9bbe5Hh3561sdLvd2lK2JW8AGOO1qj1dXvOySEgcAnLhqkvZEKAVvS3LshVirGbx6SUFOV+W27LmWuZhiMgoxMyAVN2dJFBCRjV/vd1GcwEGc0BAQhZBYDDoTI/WbEwJdo6gmbmjfX5+/fJ6WbZMgCFICCxRrnl11xgDCxOTeVMtMsyPx7sphW291OLXraThOKTpui3Pl9dcyz+bZvQ2Dcmaas3Xy3achQmybk+fXpf59O50QvDm9Pp6Q/zp/vFhGiYBvJunQxqF4Mv585fr6/n1UnNzBFNF5mK9P0ISIiKgG1OY59Hdvrx8add6nE5DmGop+bZutdRWGLH3ZUptw+ghTU5RotzfnR6uly+vLxICcABkdVu2hVhO8jBNd+gmqMui4xDcSZi1Wd60k63csVVbcx0TR04pJQnuEh2g1u1286b5tq6lWGtNEAQtjLF/+FpqqTW3Ckjz4WTgbp63zcGHIXJBM787Hu/vTsu6rsttWW9rzas3JjGBXOtW6r3CFIcpjS90vZZb83WwlCQYKQ3xNJ0+nN7HlJ5vr77gfHf/4YffnO7fffj43cPD3TAOjuRuYFa2tWwS0uBmP/zq1/Cn3/3888+ttXVdjvP4T//iL1ott3+zPNdrK9Wgqu0zdIBgb3O82KUg0NkvfRwNe9UnhMDMZh6II0ufiSKWkltKMaYQU/r+u+/ePT5ellu2No4HIULq2iIdU+L7+9cvn0prQ0ophHVdm7bXyznnfHc4TuMYY+peY332panWVvvkBAnd3d/HEGrdtrJdl9vaCkQJMU5pBoG2bW3rPFqJaRiHWdLQmi3rDcu2LLfb7XVZX2urkQOCI3Jt9VZWLuvtdpsePnz7zbe1rsfpGCB//PD46fn32/n8/T/5+PHd6ZeXXwBYzW+vK3Mwc+GESMJxHA7ubupjSoSgYGte3a21BnvxuzGQNTVtTJRiiiEkHpCQDELXcBPEIIfjKUQZhrRtOQS+O06lIrjPU3Sv4GzakCKwSIqeV46M6OYNEIqiGG4tYKVt24SFO/G6ZGrKzCKMAKptb+yDl7ptZQE0ACeiFCMy1abzdDwc7u9P77BZrZWAwUy9NVUvfbxPWci0+8tp55t2IoCazWP4za+//f0f/mSO7uig//f/y788vzz//vO/QQj0JLOdwgABAABJREFUNl5JgAowTfNvf/Pbb98dkoR1y59++WVZltrS+wfoPae7u4fjw31Mo7V6Ob+Ql8NhDjKdjh9Smlurr68vy3JjxmkcsmV3LetVEY3jMIy1Nn65OJghlZrPl9eDTcyMjEysqkh8OB5TGMzr4Xg3pilJZMQoHY2Dhsgi0A3S8ybzlISnS2AUat6qNlUrJd/ydlluWlowi4RbzsRy25ZcSh1KDLG25tWI2wufU4i1tZxzLaW1ZloJZJJQOSrXQGKIjNy0LCWX1hAghNDJycBEyFas5yaqlWmfHSQmgwaqz6/nP/z0YwgyRhEa39093D3cbXU1VZFgZqb64f6eDIQlxYhgtaznUm7Fvh0fUkyuXmt9uZx/+vLpfPnyeJwPU3g9vwLMpWyB+W6er1t+ulxMbYjSar1KvuT8dHn95t2Hw+EO0AnhcBjfPd6/nl+2NQqHdV379CN3uA+AuzEzk5jqcrs1a9u2RRE0KHlDw1br6/m1lBJExmGIacjlerldx3nWXsNjOh2nH777eDjc3U0HZi+tsogwumrd1hjFrLpXrRrTnGJywXVhJHIHNWstl9LA6Xw5C7twFA4OTOglL6VVbftoSTVFAXc2NYDWtG3bWloBh5RS1ZZzJuQQZErJki3bRu6HYTwOwzZP52V8ur143r7auJ6XWy768f5RRB7uH6bjodZCSMd5RoRmFjEOQWLkIbMc58fH+29++M233/0qhKEL5EJgRFS3yCSMTPD6+jKNYwrp7nja8nq9vP788881X0trd/en25ZVTU3ddrI/szgAmTv411O+Xwx9yNbdWTiEwEzeyVwkbsZMvb8yDsM0z8fT8XCcD/O0lPrl6enX336ft+12u5yObGrmLcV0d3c/TtPmTRBjiFvZlnW9rUtgGYehY/q3vOVcAfo0Lw/DEEOPxoQAwZwQjtMhDMkBpjgGDMBYt6VuGbyBtW69U7acVdVBJHRyHBLUVrUpgROGWgsR5bo93z5/fP/+m4f3a1kCpzE+/tPf/jPDoZXym2+/W9dzYvr4zffP1/P681JKQUQLhi5ErKoilHUzLYbeWmNXdzsOU/PdgwIda61VKxvEITSr1/WCCLnkZb1tJXMIJBSjhBgQMQSJMUw+TWMkxhgG4cQUiJg4qoNCPPhklhCgaXuDWkMpC7EXK8TsAFnrsm651BhCEE4Sg3CMwYzcDRkdvWntBqvCRCzTGNIwj8NxitOmi5oRiVnd8mLaQXYqgcw0t9Ws1trcocsKENHUTofjw+lOmGv1WsrHh/v/4z/7zX/96y0xdcK276OgQEjH+XR/vB+kY69xHIZ5nFqzVmqrpSkN41Dd8rYqlSGFeUj3pw/tgMM45ZLX5ayqMQ611ApgjFraptugSc2bQQhhPszHWsBhnmZ3W3NuZrXVaZrV7P7uMYQQU1TFeT7wzENI1pqbNW3WJ0tZuvgCwEVbRYcYQgvh/ngcRWqrrSoz6N1dq62Uqqq1ZGY1VWQMKYiEkGS53kDb69mHFM39eDzm+nzJq5xxigMShUB309hLq3nNa86rV9w0Z93FGAjEZG+uWYSo/QyVwGo9XDKDUsttXe74kCQSGRCNKR2nQVWB6Xq7XZZliMNf/PDr27JcyjJEzHm5Lq25jMN8Nz88nF4N/O50alpzzWoD8zQN85p5zRsjvDs9EMrlttZa8y1rbcVBVgpk5/Plm/ff3N/fCzGof7h75L/Af+Q/vZ4vgUnVADDGYRqGKOza1prBQJjMm5OP0/Q436u1dV1M7bouW83aWlehHU+nlNLr9VJKLjWXSt7alMJvf/XDNN6TI5Dnsjn4lIaYuLVtXWzLl1azA1tTSkSSYhjVXohAQkBHbFqsbGVdNhoiAhAJ1LoiWi6OPDGZ6lLzymMwE3EteWvNW81mltKYmKAhAEQWCYEcqtfIcr3dHDCN4+l0f7q7P16PX16elpwJgajzHiMyJUmn012I0RxqLZEFGW/bej0vWy6X662UbSkLp3D3/sPr+QncU5of3n1IIbRWCf18/vL5px/Pz0/L9VzL4zTNbsDExLLmspbBnE73D7fb5s2XnN1bN10RkaYNESUEZoIdq7CDYvoMdkxRAns35OqVCAmE4N5SDEEoCqfIEuU0z6cD//Hzl9fDbR7i+eWZWWIaTVtefZoPwzi2skBt5ETIKSVt1prebgsAtNa2bS0l59r68E5to5CkYWitDQmEBZliSGzi4FHSNB8QYSVwgNul3NbFzISCOyBHR3InJhERIs61uPr7u7uPH947MCB6qKdEwhaGkZNoU7Lpm/e/iiG6qqD819efJA5DHEU2AGqttNaGsaYwEyAzbCW711KuULiU2rRMw4QcAzN1LKv5VjYbQmASkbhbV3hMhJgUmro3LWtZgbFbUscUEUckF5YQksgATsydGaVEKuxNa0fnOrgQC5K5umdAJ3ICb1ZLzbVVJjCtAI4Ug1NrYKrNVKIMkrDDodQBMYY0hmmMo5vmvG51Y7BS1pfza+AUpVsAhFK3UkrebsuyAdAb0QWY6Xxd/upv/x6YtZRm+vHhcUjCETswX4Sbupt5b42T//zLTxeq5mZA27auOXe8TS65uYZ4Z9Y7seoWCcd5Stfb9ceffkrjcJindw8fAYxFzGomVWFAAvZWS86GiPM8fkNSck0hOrp6K7XYRYlpGKYYY0oxCBPBAMkVuomItupaFZSCkAjaDqiXXz79kubRzMCcOIbkxJKCBibUlmtdcsilNtXW7DhPwHSc74ZhjOPwGuO65KZ1yeuUpvvjsdXyy8vTbbkehukwTSEIgJNrq9ndI3MtLasVrQQYQzQ3NGq5dbgMAiJQXvUwY4iybRsLI8I8jw/3pzGICF2v27L9xAKPD/eH6aCtBQYD39ZVggMLmBKFYXwgKSkkLQXEf/jVD3fL6Tgf//jTP6YgHx/ev7t/vJvznz49/fj5iThEGe6nk5Asy5Jb3QDZdFlXAKtV81qu58vx7mEcJjB5vH8gpj/+6aen19cQUpIoLGYqQrW11+trzoWQWPhwnO/CMM6H8/KctbghBQkxMPEwj+Px+Pj+G3Dlzz/fbhdrmzesmnOrh2lOxEBUWhGhnLM2JUcCf72+lJoDMbFU09rqlIZxPI7TddsWEZrCSDw4uDCZtnW9MbFXcKiIgjwEhty2XLObNuVawdxyLlvJWgsSM4GBqwFLpCBb3qxs7lqbK9itbWhhZBKKQ2rzlA2gqR7mw5gGAiKE0pqv6zAexjQ6IIOr1cuany/n5bat68qMtZZzWd99+/2H+4/TNA/zTK7n89Ws/P53f/uf//2/f/3ylGIw85eX5+PxmIZRVxvHuazlw4fvvvn48dMvvwwh/ZfyN1sriASA1pFIToEDEDY1AaIdz7dj6YhwjDFRqKUwYsDAJMhE7mrOIgCoqjlvy+X1y9Onw/G+5fXzLz8df/PbnMvTl1+meTZXIkH3mjd0r96aKhJFDNWbur5cX5+vr4G4aiulNGtMHIm3uplbSsPxcDcMOQRhYQLS0qrXw2QhhDgMrfn1stTWmllrdanZ1JoaSYohzuOYS922sm3l/jD/8N2HX33/wzAcm6nW2zAGBkMn4YjYQEGCEUGKI4AgqyGYY3fBBAACJMMUMAghBDUpUAns9fK6rpu7EfgwMoCHYRDiwBKjmFdCDJKYiQlCiMxUSxsPt6UU6qYFhMKBEF1bAQrdOIgjc2JiM62tIRFY972BLs8VkhQGYc5lrZoDq0MzrwSQhmEMozBTECRMMaBr1VpNAXwaDoGjmp5vr6/LZZDh4BDSyOtqoEu+ac0cuJSt5s3YzBVx8FvrHEdDyK0h+leclDBfrreqMTcrtaLBfDhMY5pSDETm0ombaB4Qm6MQ3M5fIHEIg2LNWubD9P7+w7t3D9t2nYJ8+81HUztfz6XUaZqdSVVfnp7+5h/+/lc//DrFyYACcwrjw+ljCanVKyFmtfP1dWtddoopCryhycig1Opi67KOw5Gojw+jGrizgxXrTNVi4CnNIUQkBG/ldt5akcs1VyRVHWIigNLcm7n7dd1uOWuvqnZtPhExHU+nh9PDOM73ejrNx9uyvry+XK+XNAz3aUgpriW/XK63snUWnVlTaBJjSkmChJSuebvcXnoa61pczbX7RtFhnp+fz7nWkksI3XQUhen9w+mbd/da6+Vy/uXpadm2rdY4jKfjg6m+Xs61lVyyuw7DeBznwzRttfz4+SfzOkQp63mej9P9OxGeh5ivcL1dwM1Na71er08NJIYkIoY+pChMCDQBDCHmXav/dMvltJS7u4cxhSB+mg/64WMpao4xpeM8g8Oy3MyzmRVTtTJSPMzzcZp4CAlGOJ+FZZ6ndw+PKQ0xhbv7x7vTO22boxK6udWazdq6roxBQLLmrS4pDu6ylQa4NG3NsVR3woG5Nf309OX93UOK8f50ty6vVnIM4f00I1OrW2ullKq6SBQgEh4jQSfdf/vttyHI6/PL83UVaohUSi0lD/PheH9KcVKnjs/66cff/XL+LIwhDCOmarXVbKZOVrVd1/VyuznA8Xi6u7uruWzb6gBF7Zenp+l4uL97GNJYSlZVAG9a3Fsp5qC/+vDrBNFaJSF3vV6vW97+83/49//pf/v/lbUwCgKOw/DLL5/N/TAfYhweHh7X2/Lp559iIES9uz+cTtP1djWxpjtHvtOZetWOd6b/1wYABpFhGMCBETqhSIQocCBWE2YSxhh5HAYifn5+eb0st2Ubx2m5LSzcSn3OX9QqMdZt05rdfRgGDZpzLrm21hBxq3lZ1yTxDbfm3H10zc291bpt2c2KMCIMgctWVRzJtLbj6Z6Ymum2FWsWJWrZqtnPL19qbd9//Jjrmmu73JbHu7vv3j3OQxToEsxIAzfN23JjuaVxjnHUpnm7OUJII3OaT6fauBukERMihBCmYRxTCiESqvs4hABNa6zLlgGoNXNtJEwEMcYYJHoA7COQpmbMIcQhhmEcJY7bkm95ywjASGqWWy21LGWrRinEmGZ33HJpNQNhd8NUbebqYIQcQ4gxmhp1tq6gExRQNWuqx3gYhwEYiYncaqlr2ZqbMCOzIS1l+/HzL+frZZBwX7JwhAEcrGklAFNz9DgMgoGYa1MFZ0J3UnXfeegdErybA+eiWy6ltiCROKRpDsNgOwbSEZAQmCnXGgPOY+i2nsLh7v7x/eO79/fvzJrZ+vB493B/V0oz03ifxmG6LufPn375/R//8bYu18t5ns93p5MSltLicHAzQBiSxKbZHLfqALVkwMbBtlwZiREJMMaYa62tdl5NrVW1a6EbETatqjVKmEIUToa+rVtTdXIBZGuQ0jyOI4A78u315Xy9fn5+fj2/muphnIgZiIyotJq33MbC0zSO02GY120LTCmGNIzff/v9+2VBkp8/fcqlAGF2Rfdcqud8R3S6O3nZqGzCJExNWzfu7oPURDgfDrdl27aSc2mNOlIfALE1q+V8uV2u221rpcLttj09v9zNhyByW8vf/cPfhxDe39+LGwlr227L7XK9nm8vr3dnFihlQeLHx8f746lu69Pr63/93T/cH+YYBUnr1r48f0lpWPLK5ojUAIRDYNHaBOVWs24bhXX7XINQinxIcUjD48O7Ly8v5rhuWxRJKam1wzhNaXTyOMi7x9M4RHL/cPeI1ZblNoT07t37+8cHEQRgYiAKH96/P87j7Xyu2+puqmXLS2Su3iRwiANBMtPz7ZxrBhA1LK1upSLj8/V8W5YfPnwTQ5jHaXVzV+5xuDAib1s1g247W20DlOkQH073p8OxqT0/v5yvVyFKaVC38Tg9vnt3d3d/mE+GhBQEe//etnU5DhM4vt5utdZaMoAbqLmv29paG2KIjId5Ot0fx+kYZHw+v7wsL7yg8P3tttam6hoi1epTGn/zF7+a57u//pu/+vn8y//4L/7nAxx+/vTj7/7+7/76P/xHMp+HqTXTprVVDvL88uqw97Lmw+H5+XP9XYlBlmUNIiFwq0SEjsjM4zAoeG3VcPf7VVWk3ebxMM/CbK32XmKILIHjkE7zYRpijPL58+dfff/dMM+AlIY5xPSbv7y7u393vZzVWopJS625lHwt20oAYxznabqsz61Bs64/QnMY0hgk1ta81XFIhCwsCM4AMcYpjcTk3rRW94AOCPTyem7bs/zyy7uHxyDh1uz1cpMQ0DznElKUwOjeXem/+fDx8TgPDJfrNaV0n9JpniTw5Wq1lsv1M0lM4wxQnTi3hnm9P52+++6fXa5XMI07BJ6DoDAQku0OtwlhrLVtRdOoOWcFB4QUQhRJMXT+Zhdc3vRatW5NqTKwpBiHcQpRVllvy7Js2dFzyctyXfIyj8kUkDfpbi1WEPdZenDbYenCplpKbq2VvDmYM0Qe0H3NJTcduThGkcCItdStltxqU83m5tianZfL0+tTXjJNx7zVWpsnUNNaKwOoajMfhukYD2p2XTdJyZpW65aDzszdtbyjpa+XGwUBICEZwrBtzYzXrdam5i2E5K7dDNtMx+PIc3SzkMZpPHz8+PHd/UPNeVmvp9PxMM2dnJxSmucDGDx9+vn3v/v7l6cvQ5pM9Xp5vrxOYPp6uSgSaEvigCgip8PD8SRq9XY7b2VbckHiw3xi4LItS1mra/Hq2tb1UlrtfXsi8lYdoVibx2lu2lyv221bLzlvy7JKDDxN8zQfpmkCcInBDF7WVUlyVSFyJwBC4tp0Weu25T52PQ2HIJEImOl0OszT4TjPD6f7w3z808O7l9fX1+vl5XJWaIZWVde8ldYc0dSEqbUKACxszWnn18Ju98FNVd8MwZ0Iamuv58vT+XK5rtd1m9MYQ3x5fvkD4jzP43QcpuPzy9MYhyTxtp5LpevtNoSI0+lyvrwcXsq6IJiQ3z9+r9U/Pz29LuvD8W4e59Nc1vW8LMtai0T+5sOHl/P509OXMQ6CfNu2ZkaATGitKrbn10vJ2xjD+3cfDvPdNP//qfqPpsmybD0TW2tteaTLT4RMUQK4ggDa2khjDzjrf0zjmFMa2c3GbTSAe29VZVVmZEbEJ1wcufXeHHgAZvQ/4OZn4Pvstd73eRriLMdonEMAxrgUSjFe11JWDHOalqktpdUaCAczMcYQEmFIKSLcHO5FS1lJJjizq1qXwZ1eATzb8Fp3SjeM8ZzAOrtYb+y8afeyrlzwyzJjRil5KnGcrn3XcMGFvHknbpJ23h93XIgUM+dIjPmYhayqupGCWW/P18vlei6QuBRCia5q27a+Oz5orWNyPiWt6pxB6urHH/4wjoN3gTEWORvOV2IMETnRpm1zCs4ZSQjJBQ91rZumrupN03dbu12XaZqGFEFXWkpBUPbb7ePxsdL6P//5X/7pL3/+H5r/EQs6Z4fzq5vnN2/eLMs8DddSimQiQ64rjcBSTKRVSamqGmtd8GG36RjXw2g4F4wHxgj+u92X0WohhHDzTSLeUkAoheCMlVsLlDFGTAhOjJRSx7tjq5VSwhhTN837jz98/vK1aZqm6/eHOy6rEL1ZlxtC1a5mnIYcYkmlrXUqDL85vqPxjgt5q3pJoRdnWrkBzNb7kFKJqVK6rhqtq5TialaEYnz0Piyzs84F47kQzvn9ZsM58zGNdmWAldLf371nkt9ICcQ4J2QISivOsSCEYOd1kLoVooHghvkiVK/r5hv0GCiVpHR9X/VV/RrMmkqY1zl6LxgJzhijXEopGQqEnAuxqukSIZOC5f+mUCjgQmD5ZugkQOBCZEi5JB/MzQsmhSTk4abnLHld19Us4zL5HHJOhByMEwxyvjkK/e3/RXDBOUeEeJtXZHe7oGTI0Ue7Dk21DSEat9aEgmNKMqdvbwiQyzIvLiVpbQH0ySFhpRQnLhiXQqWcV2uMs5QLQI4pSqliKiFFhqiEcuCDM4tzzidEukWJboztxRqJugAQcSIWopdVZVxiTGHmjERMmb4lzNhusz/uj8Swa7Zd3W03G8Fw8kYKXgBCiOuy3HAU1rvn19Pz6XS+Dj6Eu0OrpcjRnV6+aqWXcRjWRSvd1Bq+UdL44e5esBpK0bqufIwFD/v7kmAYTtyMNvhaacTivVnNyoiHGEouOUafIvDCGJ7HU0rkk4/eEGVkt+bZzUYjRS5ZF82PwpU0zEtdtZqLuqqklDGXmOLFX4dlSjk6Z+uqk1zG6DKUrmu7tgIC0nIr91Lp0+Vcv75izjGHVKnZWSWrVEqIkThTWnPOow+c8ZDizWBSCn5zlt4k03BjmxNAsiGu1l+HaTF2WddaqJzzdRoWN98dj23d7w7HxdlxWaWUPpeH4+6odylzwPL55dfXywlLYQB13W02vq3qvm0XM226dtv3XEqlm8Vmk7KoVNc1hPh0eg3WJEbDMoZctNYyi5qrEH1M0YXAOA7zKLja9r2xFiuZRLTLCgUygMtRF9bqalhmklq39Wazdck7b2qmnbNfv84huu1mv91VnHMoCIV2203st9bdUdVTKrXShLBMU0xBcCW4FiQY8Fo3d8c7F+x1loxQMh5zhpgQUNdVyC6FJCTTQgspNvuNVBqItKyV0hkgIwBA9GFd11pV7x4eAItSSuuWGONcVFXHBBWfSzQxIycRo8UMfbeBLc9EvK5TAVZAMcagKE6NFt5bjFFIRhyUYG1T9Zsd5yKm/bwM1pgQMhS4321DMA/H+0rUX18+Zwp3+8Ob/RvGcVmutaQfPrwBLv/Tn/9lOT81Qh3u91KKEDznGgE55ySl1nVdN6eXJ+v8ZrsbrqOUwjnGOceb8ZLRTZGTCBlRgYLfxDAohUgphJi1VIyRkJIzScg3m83bt+8wp5JjXdersbv9fUjZOgMIxppGyLptQoqAmEoe52la15xyySUi1iWXlFOGWHKmkjEzYIoLIrw7Ht8ejq/X5/npq/OeCsp0gwrfiP7oU1yDTzHNbvEhYC7BhpJLSanWTaVVWC0T4u7Nw/322LWNi9Z567xLbj1s+rbtOEMsya8LIIKoBBMx3qiM2bs1ZQTAumpvwIyUXIxe63q/hVJyjmFdppJSgeKDSzkAoZIVA8Ya6LrWmDVYk1Iy1i/GExe6qm4jjgKJOKtl7V0oUErJwRsohSFJpWooCXKIIc7JheBTkMhWtCmRFKXklHKwbkkpKaH3/ZYzVSAnF4wzSMQFx4Ip0zibeTFaRy4YsZKLN8sIyIBYhpRT5kS1UAW8QKakjBmzqwoDyEiI3roSwOWQUnIxSMEBMZZ0Hc4xhVpVwnvv/GLMsE4muFy+Gb7KNxNFull/M6YYPZcsQfr6+pK/2Wyw/Lf9EgA+3r15++b9apau7gTjJTjrQkmhrqob16bkLLRazfr1y2ezmEY1237P+YpYvLMUESAjFMF4pysb0vkyGCkQU921knNG0DZNTIXQVW2ndSO54BLkzK0xVVUjgWQ8JR584ITW+5QhxLjvW0blOp+mceWS910HBYQUPDjLmXRSqEo566GUXb8PPr00TwIBEyBDZEwJxgKrleY8A8Bq3WxjTDnF0DWqalSE7JMTSUgh+77F26AZQTBKOY3zlBMIJt2aRFE7qhjRTQv1343eOYP3MaUEkFOmb2VCSCmn2bhpMYTEOfVtlSG+DmcXnawqqru6JsnUYbe/DKfZrQfZ1lV72O1ma2Zr3t4/OmundbHG2BAWMyOgkrJWGhkVxqqqeacqxrXJzAQ3z9eXyyvmLBgoJTI0qw+xQM559VZVYse7436XSrHGFg5AmREwxtu6lUIt65wpsFIKSy/j67yYzfa+kjUxpqWqpSrBfXl5mszS6ZqAJYC22QqhrvNrpavj7v1m393ff1zn4eXrL58+//z88pwTHLaH4+GQcyIqKVnGsa8PVdsLzgTTMfl1va7WYBIupqt5XaNpulZImUuJKTIQGTJxYpwXoMVamzwS1brSgjEqqqnreleISsmqqjkXWrciNAhFEAeAcRqyd22/75vdpt1pqdZljNYi5RKyQsU5yZsDWZSq1U3VccYAgDO52x1pV7wNxi59o6iUSlUxhl3f/p//7h9mj/fvf4DkNebN3V1b9SQrVdddpSoSu7b1ya/B5lKUqBhJQp5yxpIZ4uvLl5zibrfTgnsussgFgHEg+m8OWyQi4oiJZ4xRCs4ZC97dfDLEhKol56SU3G22Xb8pBbxZpFYh+pLLw5v3v/z6843dFJwlRMkFEBo3EeamalOKxq6xhJh5jMWEEEuJGUosUmIhzBz2+8394Sg12mR+++0l+MRqprRQjGYbBrMIIbumYyS4l+M0ZpMh55S8C7LS+Hj38FB2XIp6s6nrVkjRMl2i83YSvGvqXqi+lDhPr8RIMEmFhRQ8lG330NbbXErwJgTPmQYo63yNOTlvVC21qg7bvQvWO+NSwJIRQHJJREIqziRjLKUsuZ5xmsxkzBqcJxIxp5tptWkbrTTnnDB5b3MuMScIvnDBGK+kynVKKawrD1FYjwx5ijnEWLLLMYZghnU0wW43myrWGrqUIZUiOFNKlUI+hXlap8GswRUSLdciBUyYIMWMxKVQUgiChG0re97lWDKklBHCdrKLsdZ5ty5zU2VkWAr44BEx+eTAXtfVuXXfZsGk8d4FZ4KLJTPiMZWbUgpLIWQlf1PFuRSatjbLdBnmBICYiTFZuGAFCyCD3eHQ1i3kQJCppJwSQFG1ZlxK1DEGYsKHcDq/XF6fEVit61pr7711bjWWcfbm8U1G5FJIXYXxYl2YgnfOHICsXYUkIQVlzLl0lRaciMGmbRRnTtuSs/O+lCA5o4IpZyXF6m3fN5u+t36xbmYMtRScUyi5Uh2f5zGXwjmOJU5m8TEh50yQqmR0BnLOJfuUMCMB1ErWxBPElNG4HEISjDiSFtJb560vIe22O6WqqFRVqZzaTdsjQj2cU06SqXRB7z1lJqVYnc05p5xu6q4bge/WwbtBu277PCg3cW7uu/aoDynFcV3GaamrumnbislGN33bAiTGsKuULEAFFZdFZrsYpmrJ5abbBmNKTNfh2rU9F7Kumn6z3fTbxVjnDAlxbHfI2NcXmpfhnrWYc1s1b6VenFlt6Jqub/vNblspLXi1BOOCZ5w7a8fL1du4q+vD8fh6fpnncyW5rqR1i6y7w/6esTKv42oXHyxBRsbatj/2OymkCz4Mp/3u0Lad98nYFRgJEoxDzHE267DOKWVd19quVS02staaxWiJC6kkY0JXLRNCmEqsEwHbbI510xJBv93XVV1yzLkQIpYMJZUEBUgwCggZs6ooBCDkjAkgZAxDTBkCAEMgxrggpnhNxDjnq1mhAAJwwbu+q2v1jcZofYyJCy4ZA4yjPeUEIXrmV2JS1TVSKbkARuumEGxfbZEY47Gq9dv6uyyazDgDT6xWQrVNC8TfHPYS/kAxY04xhUxZcCqFxcwzChdTjFFI6YK5Xs+MRFVXxriUc4bCueBMBO8ZYzchKBYgRMG5EpIQ+c2tTqSUZJwzzpSUQgiptJQ6KFWfXxazTvP45v2Hvt+E4HNOMYZbvJuYyDlzzu62W+/NZSoZsK1aI4jc0gjqgDEgwVhd6+PdcbvZMMk2tH3Y34+XxaJjirlgnbGXZRy91bkQCsmRCNuuanZdrRqffFN3u81eS+mscdGKut4d79qq4iUGuzgBgiGXtY+xlNRt7nMJABS8N9FyKaWU3vmCxbnVey9lTVhS9DFluGVzeSVUlQl124VcUshKqVsJqpRizMqIp5RTjqUARx5zWaPjvIBZnLeMM2KsqRrFdaJYSvHOBh8yKzecS0EQUnRtS+VOaxVzqVVzulxjAcwpBLeYdVyWfHvhRnDexxJTioITR3Qhn4fJrKbgt01rJWVxfhxGHzNXlW5yqzohGAMSggNBCMXGNYXEtWIp8ALIOBCY4HMqa/Auxewh+lzV7WGrCXfbfkPE0FnVSutXSBlvEqGbww4AicrtLwggldL0G5/TZVpiyooxwCIER4gpReJMiGTdwHnmrChOnAkmBCADJCHkuszDcDHLuswTFMwAs1kWsxbE1dlxGqVUq7VCyaZqurqNOXhrvbUlw2psjLFtOgACKJuu5YTGzgUycUEEnGFG5AyMDyUD40IyxTkTFRNKmbBOy8g5dl3fNB0j9NlzqTlCmZfJO1cQFu9cjj7Fx/sHRsX6lQNHYgGS94GXIokzwqZuVhvmdUXERtd9VzNgOSTnvF0tFmzbOK/Lambj/HYrGSEJgRn6urmahSN6m+U31S0TAu3qbkuA2+CnlMwYfbMgIjLGtVKH/U5LCQQhBALUTAnONpt2v9vXWhLHrq3rSlZKjtfTaMzpL39x1h2PR6Wb4oNAeD1f/vLpZyHFm/s3XdMkACLa74+1S6udGWNKVqpqtFKbund2BUiVbhhiTMGFQExqXTHGEFCreq93XMl0wzC8fTdcBudCv+m3u/46bBott31/y/4DQKW1d6ntN0qrksK+QCVVI0RMcfYOqbCSatUlxYxZfVi1rHMu/fbwx3+jD3d3y7q8e/i4afsMPmVXILtkzGzbdlNXfUqeCa6qmjiWDG3bb7e7AomRQMxmGWP0QlbEIHiHRAVICiXafl3nEBLXFeeVFBUgpJxLKSmECCynONsrlFLLLudYCrR1K1WrVIMMkQGUQgTBrSEEROKcU4F5vVASVdUwht4vUkFOHAqmEHPyTbMJXvoQCqCSnKsKRGsTUYmKMUaaC+mDM+t5ncdol1xypXXXtAAZMwKykNFlACTkrNts6qb+5//0v43XS9911rgYEyAgUUkJETi/aeKREBIhECpGAqFwhkSCkxRMcMaZUFrVdV03tZCVrtR2f5x//WWahrv42DTNNCXvPSIFwIIkuKyq3swLp9J0bVOrebVaNltRNxVfzEJAmle3sObH9x+0rlaz+pyPxzeI4tcvv03rtHrbNr2sVSvYptv2Ta8lT6UtyQumiGTbPfb9TnAkYoR3iISS2s2mr5vkzTpcGFHOIeVsw4wodN05FwUXUqqwRoHchWD9lXPunLXW5oxKyAKZC4EJYkyCAWOy0ZJJkQou15GIxZSiDyGalLMxBhEY4ze7PREjxgXjWMA6Rx6byt2IXjffp/ehFC+kJC4gpdtgXHC53RylrLkSu/64GS6ffv2kEGXXfvrqOZNABTIRkPHGRwclxpRjytO0rqshQkbMLbZNVQw+ODuvSyLK0R0qLpuGcoKcCxUpK10L5rCsOfq4adq+YbWuCSnm7HIgYpvNvqsbLSuptEImOZYCMca+6ZSk6fJKgIUIv2mxCyEKwYCzGFNBBGLv79+8f3zbtS2HmW5uz2/2SuTEwLvivNaVEpXikqQg4rlAySVnuFzOf/3rX1II282uqusY0+lyyYhCSMH9ZrPp+77S+vYkg0pQQDCGkgOhVpUSkiGsy2R92u13pcRUwuKW4JPk6gY8YYIwYMpJa660JqwQ8+pcyLjbHDDDpttqqQrkkDyR4Ei0Tqsln6EMy6ybxq0L5tjV9dq2BByJJbtACFJJKsAYERYoKYRgY26rQsRWa3Iu47jkUoZp3m631prLMKSMWldtU692TdF3dQcAxtjgb4rTm6Py/0/TmuLNzP7tmZYCgstN1226NqY0m8UH32/bD/1htStnwCURKyl4zqhr2ujDYu1lnj6/vpSM/5f+UGmWrFmcGWYzG8u9V/ICOT/s98651ZqmOSzWpOBDARJ82+9r1cRgE2QplWA851gAC9GtKGudIUWVUkyIDIUTQ8S21nY1QlS6qjebukDWquKMT9N4OT/HmKq67ZRyzl5en5bhklWgulJCSCwZgADn6aJ0U0lxE89Kqbr+cISiOv316dem39zdP8bsjZlP1+dhGogxqSrOPXERvCHOK6lzyikmYCSYuhln66YLMiCiD5EgCSUBcowgdNO2W+91iB6A33yWjHHBJRIxUohYIJl1xYRISMSlqqXSQgokZFwCYE7BuTWmIIW6KXeFlG3ZStFgKsu63MTPt9Ui5lTJRhCf/IuzPgQVcxaVyhkgmSiFbGpEHOdxHi6UkTMCIAI0xnhrkIsb1hiQEwnOWNM0fdfP18uf5qVIaJomxBR9DCnmnAlJcF4KMESGxIglTDc1423syDknzjgXSspN27VtWwCQMcbYdrt/ev46ToPznjEOACEEIiQmiZNUqm03y3QxzgpWccS+raVQBdNp9M/nV63092/3tdKIaVyGYR6NN23Vb9qNZChYiQVFVQcbrDO11ofNgUvJGUFJwfucUwLY7Y5V1czz2dq5rmTbdiCIc1GAAUlVt0TgzJpy7JUGZDkjcV21GyzZmIlIcCYy5FKyc877KHgOmEJ2leZEPBRPgld1TZy0rtfFBuOx5Fs9NwMGZ0LyMYaqqpVSMQVCVExiLD56k0LJUWmV4W41U8GYSw7RpwxMSCglx4icQylASEBaaC65rur3VcMZJWtzjMM0xpwLlK5qJeMhRRNM8IYz5Cwi53ePR6n4Zbq879/2defWWde1auvzOGTCze7QtV32lnEBBIprJAKMCEkC5ZSVaCvZEuc+RSJikqlKa66IWEoBfCrJu+gYFC2k4AQpM2IRMd/iiQClFCVlSPEW7ZVK3m97Sq6plWCMGEeE4B2WpKQWpHb7Xcl5WY0P2QknokRkXEjOpffOWltKAiicsapqL5eTNZaEaOtacB5T2m63Uqplnp+GIUI5bDaVlin6X379bV3n8/l1XSl4D6Qq23BOWlWAtKQ5BV8IhRBE2DVNKdA0rRCilBScR8Vv6lolNGCRQjKCAlUuwNtuE2Iy3rkQBGeasUYqDKmSmhFLMdvFpugboRqtASAmv5hlNX5Z12GxnIBR1oqnVKbFAJALcXWuQAk+YC7j9SXHNjrPJCLLIdpxGmOmfNNVQ4kpMca+ya/zt+fOvjVsMxErkCTnpaR5nSazgsBjV3388buS0zyezTxfhlPJWapbmkMqqdfV1qp5PDy+v3u7muXzl6+fX59TjL9//Nj3Tds0kovNpi8Yx+tY1fs1+OfPPytOXDUPDx8552YddK2llExwSJAyaKURgAAFE5WuuRDEGeQspYScC2bV1KrqGOMxQsSCRZREWlZ9v8kpCimlahgX1szjfFnCahdXETVKc64i4Gm6wHjZd0cpNUAELokxH7y3hmEpJazLlCEClabtpK4ZcC50ZjxBIQCGxFkVkrV2TMmB7pSshJTEWh+8ta5Ey+Ut+BwAC0EWQtdcphR9cD6G6KySteAVZIgl+GCCT1JWld4g45wLLmXKcTUjI+JcEZM5R8ZISimEhAKYi2CyEjWhSsXnXLz3CBBSyCkxBO9djIEhU0oZZ4dprDNjCFic0ocMuUBJOZWSINOymhRjqxskDAkgeSg5pyJ0paouQiypMC7evv94vZy//PapUip37ThNycYCNzs5cV4YEhGCByGEkJIRFijEuZCSiRuWWTd1I6QgQqV0Tkkq3fe9c2Zdl7ppvgVoM0PKt5pY3fRCKuSEnHMqVVUz5K/jKeaoK3nc7u73uxTjaThba6TQGWPOaVpGKGG7afe7+6Y95pQXO3nvWEYAEEpwJllHKUefQtP0gkvH+BLXZUk5JabFZnOMMS5mjusU/ZpjQChEignho5WVYkLZdSqQAbIP9r8rrPu+16pNOVmXnQ+V0pJrVembiBwL7vudFsIZ46zlUsSYhstZC+a80bLWdZshVlGlXMZlvg6TC65tdYEMlG1YuGJcS+Y4Zaa15kQxphRThuyikSQwQ/J+tbNWbdu2U47GrJuuB6IMuVFaS8GBXJZNJTZdq2S12dz13XZaxu14PR4euqqbl1e3TMM0Nu22aprH+3etrj0BSQ3IKl3lnBA8K6nm8rpcpORN06qqYZwzQK4FYxwSAICPfvJXnzKSCBBCsDXJ4EPJBQrc7OQ5ZSFE1zbn4ZJi4FzXdRuK+fT6K0oKJTaivvlipRAxpqZVx8OdlJAylJyIeEohRidyJiGg5F3Xszdvx2HQXDZSj8i0rrgQSirJxev59PL8stvvb68dRFQpmQmW2W+7+jovf/35r4fD/u2b9/12L6X01lHhXa211M6aFIKUEqDEGIQQlVRE4H0IOSIKLVQhlFrn5EP0NnguCBnnd8ddCHY+rTkHLaRklEMahkvIMYU8jusyL5wDKOkROWfLYlZrrvN6GQZj3StGSEFrDQAhZSxMMnlDajNgMXtr15K8kqrXPS8IKfoYSqLb3SnGVPJtwoYIOQSXUkkps//mUUFEwhJi8CFAKUpLUct+s6m0nuaLjyuALzliYXDrVnPRdU1I6fcff9dUtXPmOl6vi7lcl+h9W1WP97uubbQQkMtoTYrIXp+0lIzk6+XChStI/WbLGBGTxHgGTAVjCsmsOUcEFFxiuYV9OGci+sW7JbglppJLVlXjnLXebLfvENDHKHkd0QlGknPOedNvHhiWhKudXl4++eg23cFnC4Xdkv4doiYevZ/LJUbHoey7XSVZyCbnJITaNHdSKEIIOeSClayRKKVYEDORCRaSF0xzrnII9O1AhZKwACJjHBkjhshiCpAAoCAUzuAbtUxIQMzRWjMas9RVz5goRICMMWXcMAyvHEkqrVQPWEouSracyQIpeZ9ywsIFV4RcCouIy2qts1BiiK5rN6wgIRSC1Zp1XUJwOQUhJBOSMaibfVX355fP4+vL4lZMQH3Y7e+qurfrjAhSiUIMGSrSGYELvrt788f/E6vazV//8ue6sQzxyZ1i9EILRELgBQowYIRMMCaQc1YoC8aEZEJwpWTdaM4xp5gLcMFTTjmH3e5wuV7MPNWVTikhcSSWc0KAFGPTddvDHUW3bRrrDCQIKWQALWVXVYduw1JZ1qkRXd/vmOCllBhvPm7GOHLOBWMoJLISlYwhOGvsMnLGpW4yIjC6+aagAKAIyfv5C1jBuew7iSVbv3q75hCMX6WqN/1RVg1nLKfIuJK6izFijoSYYybGhJJSKUSUihGym7si54yEgBScYRw2fWeUqFMvuFyngUGyhqVYC1lLXQvBtRq5nFN0pShjYb/fvb1/t6m71RcULIZcNx1HTjlfx4G44CRctEtYaqFa1WTExRufQ4qJCb69O+YYetOVAkppIVjB2PsKkRrddu1us33IkD6//DIuY9cd2gqB8yU5k/1ud9x1d63uQ7KzWzHYutoxEoS5pEhAJJBxjpwppaXQSLe+QbLO5lSkVES3hppXXHNQERKSCCkXIARCoAIl5rJv2+/fP56GEyL6GH93d/8PP/6h7/HD/b/+f/CvRIxK4oQE4EIQkmvOlVTWGx98jgWghOC17jSvQuHUoRAyeOf9+vV1/pdfPlkbN93W2dBWlfMp5di5QEwSZGfc6fV1Wce+bQ+bXUZ6Ol2qZv/m4QNi8cmFYACkkLLSDQLZMpccOGPImRASiMUSCVkOOaSl0w2XEjkPkGLOLrjVOSDiqqrqvqpCVUOrhUJAILyMQ0zpMgzzYkvO1rjFmOt1ypCc86s1k3XzYpUUJaZxWVxOinEqxDnzzvHbhwixhOhLzgA4XId5nKdxhgKMmBSCMV5yAoBSvlkZvmVAy62PB1AAEWOMp3HY9p2WUnG23Wzv2h0VXK0zLmouuk4zFEyw4P20msv1/HK61rwiYr98/XK6XJfFAqDSSmnV1q0SKsXovP3ly5dtf7is5nB39/Hj79PPf/be1XXXb/a3bANjImdkTCDiukzLOlRac5JmcYigdMOF9HZc12k1q/exrk1VNaXkwUyq3rRVH3JWqmKBheCBVmSsrdtKd1DY19dP12U1diWurfdYoG9bTpwAOacYvbMrF7xptoiFMUbEY4o5Y8ohROCcx+QIOSKmFKw3klDLqqk2Zhm+sTABCEkKlYiZUpCIc4XAClDKIaaABVIMzs0uWgAkwUpkJRc7D9N0DdnZwhYamFKcayEVY9x7N68j51XbpbrpiTgjAVC8t3Y21/mkVCX1FguElDgR58IO1xwDsuJ9koIbu6KzMXrBxbpML69f6rqJKT7Ah6Z5kLyydv365YsPgXMevE8FDse3mcB7n6zNSA2UttkSYzctc7PZff/HfygkT0+fUkqny1iKI8ZvNU7Agoyauk4pcE7EmERUXFS6YkJorbfbLTJy3kUfsBTnbAixrhprnbM2Bh9DvMXbrbdYCkEO3r55+M6ZsXibSyAiDpQRRrMEa71dHQlKpelarSrGGJYSMOSUvPeQS8S0+gAFrJkJct/2guWYXYSESQGTMQTLDEcuZb3ruXPWBUuSx2CsuTJibbODehuDDacvRLx8K51RDJGAUNchBiAGUFLKdMNOCSkEW03y3ocYv/H6oSCymyYRctlud4yp4DMBcp4Y5ZyLkI1SlVayb9s393m5f5jH6XQZhVaH7Z4xrlSNnHECxhQWcMu82rXptk3b8sB5Fo2sO90LXUVMOQUippSAUuy6praTXCqpOCOAONrzMC8Jyho8dw5ySJDGdaDTrzZNhJiYaHbHHGk2i1IVECvAYoiBpRRTKSnFUjIQMkGq5AKYU/aQgXOZE+QCxs0hWik0YL71aKUUzvjz5fT08hJSpEK3GnBKsWmbd28e/1//8f+bMqRc/vDDx7f3RyHS/X5fa8UIS85SCgJADIwRAKScffDeBygIKZvgpK7n5cY0ZTEFxvk8j79+efqXv/60+LJtNx/u72bjnoZRS77LJXpngn8+vzz0HTFQdZ0h1Vr/8OH7N4+P5+vT5y+fpJB9vxNcBe+Z4CkVxgQBCikhZyJ+25sDAgGN41UxJpREpirdNLqFlIbJDNPAj3fvmn53/97eLKhmNa+nL4VBJ+SwrK/XYTWrYCzFYhZj7ZpKzrnYFAFpu9l+//hQoEzWLHaFUhqitm2lko2UjLHrFFJMPsZhmGOMXAjOhQs+JR9jgoK3mD8R5lyIaLvdXC8TQGnbzlqzzCvGmHOx1p+HWWtJBLKqCJgJlgnxePdGEcbsU8zWmZRdTPH1Ogzr/DpelxSneVmNk5y9uz9oIbZ9dx3X03XMKUkpXUgupLQueKL3x4+Pj28i5A/vftfUdYwpBF8yAABjHG+ZpJyDXTO4aRpLzm27adrWmmW4XGfjQvDLuvRNh0gBwCxLLevpevF8VVXjUhgu4zhcmqq+v3tTVf1xf/z44QdM6bi5O1/Pbhkx5RsgHhnF4AGKEFIpVXJKMRXEnNGYKc+eMdE1WyY5Eqbih/E8m7HtDtgxXbUAkFII0VHGHBPTdOPuIRIB57JCYqUkwJxzWpbBjKuLplINJEglxBCsty75VJIkMG7FGJROxLiQYrs5OqFLBsk5lYyZUgwuGu8MFiwpTvNY6S2UNC/XEktVNyGEkpLmEpCFlF8vl3VdleL7/YOU6svX375+fZKy2u1DjDGlkHNGYk3bXMfrefq6hiBUratmGKdxGlOBzdbWP+7rqnYxF0Slm6bdbXeHp093//v/+r/89effpNaM8xAjMURCxhgyFgNwxoUQjKip68c3b6ZlVlo/vnlrncm5xOCGy9lZi0CCKykVIqZUCJkUmjMhRFZC5mivyxm295yLZZ7qblNJNVzOjElGArhf5iEbF0sc7SLGi5CCACSTSmoksLMZRw/IORfer5fLKyRodF1XVd/ttGZ13bvkcomrG7xzJWEMDkrmxCD5sA6q6mrdEpPGTdscGHHFK8g5+piSN2ZdjJNCMWLIRaVrYsxHH1MQkpdS1nUVgrdtD5Ct81pWUnJnbUkplaIr3dSNEEwqFFKbdfHOpURV3bXtvuTQVd0LvV6d56IipUAwSpohU1oQCSglBw8FsEBb11WRkxlrVTVNJ6uGeMkFOOcAeZlHKMA4U5Xu6gZzXpYxB6xEnVKy60RMbNvdm/07SgyRGq6bdseFXM06XU/BrSHavt9z8fD88gTfRKtFiCqEIIXqmY7FA0MiyDmmXKRsFEnFKOWQYhREAYpASJCcXxlkH0OMiVO+AaEJ0Vrz9PyspHI+Sq3vj1uARFS0FlKwgjlDYoxxopv/LTgHFYOUa13XurbWJkwxucvFIKJWtY+l6XdMyKredN3xX37+lAGJ8HIZcsrAcDF2WU0IPkL87dVuNxu2GEWkpNp0jVvHn3/72+V6aVRb6z6nYOIiU8VIcMFTzCVjjInlqIQqkDMiIvPWPb88CcWF0A93HxgxzIUKmHXldVt33XYPuUChjNM0ClFijAWQcZUBXs4nTmIezfk8LsaGGIRUBZngfFmXZV3f3D/0m43NIfooCi9YgnehlABorHXW++CtcT4GocRxuyfGxnm67dmRvp0B/x3TSIyxzG4UdWLf5E2QARCv84wM0tNXVVdVV2kpd+0mB+9CrFRlvHUpcs7aphVV8x/+8d+nmH/65ReMWTFqK70s829fvg7LEnLcbzZv7h/2u30tq2E6IeA4DIzztu2320PKITl/CyAREWMsZSBihMwZQ4AppZIzceZTWH3wGTkTpeRlnksqdbPxpbycXoSQs5mndDnyN7pqU07R29HNhPG4f9c2zd/97u/9amrdtm1/fvlSCVU1m4I0m9lH33fbSlUxhRtkG9kNNtkQ1VrXXbsRQgXvY7Q5BckUIYXgEbCq65yS9zaFICWLKaQSco7sdjEWPOUYvSuYY4rGrT75FJOnmDM0uuEt3+wO1j1YbwRXklTMgYgY44Ss0r2WnbOLc7PzRsmaMf56+rws4/3hXb/ZrdaO4zkGO41D8qkU7NqOAAqUnBJi+eW33z79+uv93TEXcdjvNptNKcCZTD5cz1+tNz7ky7xi8VyIktE6+3J63W9xXtbn15NSqmq6jFiQISOt677vAXAeQt/3QMiFqIjd4IKMc86JEQnGUpJIpGu17TcPjw/3d/c/f/oFkPXdhjibpokRW+dpXuambuG2KJYypnT7lphiSiWn4N26rsNleGmrtpIKPQghVFU3vtOi/vTy9dUZRYxL/ebh/VY3qWSltVZV8nGepmkeiOCwOxKKgkFz/Tqdv74+KSa2m939w/oGsOt2yClFRyiw0JTMuowJC4gKWOKcs7plnCig1rXkUjARvVuX0Zl5mGYTQqUbQbxqO95tGBOphAIJoGitb51eIUTwBiATJ2PWdR4FE+s81yH23Y4RKdVzkhz5WM7GTkLVglfWWRtdQXh8eNxu7o/7HZa42tU5y4kBkTXmfL48PT/lQu/u35SSQ/CerAsrCGpkq5UCKMYsAHATMkshAHFc5mkaXLIxFyzUNG3Xb5Sq2wIlE0BGxLbqkbF1Xrq2zYEHt3irAUFyqYTIMQBQ02wY44J4iOtkzggZco7eZiiEyIjDjSgUQ/AmOMMySBL7pq0rqZlUjN+eD2eMcWaseTlfVd00yemq+9unX758/fF4aKZpCiEAIQCGmIhjzoUYCzl+fXlZzLTd7pRuGGfJhdfXJ8bk4XBf1e12W5WS5nnYb8u7h/fff/z+er2eL+cTXIXFWolpHJ/O5/cPj8jktK7Xee6bJjGa1rXpGpapqVoAyYlprZBy9sGsUcmacxaCT7GsZiakh7q53Ti1lF2307KKyeaSn1+/LqvVgiMlysBfXj43dRdKTinkEKyzUKBvOi7Vfnf38Pj4cjl5n3/9+cuy2NWuuUQAxoXIOU3L8l//9pev59fj8Xi33x+3W63qYR5/+fr5b/N8aDeQS0gRkVV1E9dlse6tUH2H12liQsI3k8Jt2AOIuMxzjDHnXLA0XcuF9CF4P38bKcVYciylnIYzLuVxf1SFYg4x5bpWdw/vUMjhfLE5CaG6tudcWOc6WZ1OL59enl7Ol+s0R5+k4ilkLPTweF/zikgp3WXO6q5SVZtycc4UiIzznGNJVCCH4IL33rp5Gq01zpq228SYXfIkq7cf7iH60+lZVZWQmpEQLDNG3llEvKG/allL5KmfvFsYUUoph8gBkNi6jgWh6TeCaSZrY83iPBAAE8A45sSEFEoTsRQji8AYr6tG8ioEu6xnayZA2G8fhVLBe0IkoFq3UdferjnlmFOhgowxzgpmYyfvzTIPMUckmtfpy/Nv58ulrXfv3woXQFdSC8kQOXHJeK20TzLFUPLt4MuE7BZhxlyci967v/z0X9Z1pswOx4dGteM45JS7bp9jYowYEidazFpyJs6V0jHjeZjwl7/adZRS3N/d1VWNOTszeucq3dfdJru8327osDNuuY6XlBkTKiJs+/7Nu/dC6lywbbu6aYlwnUezTM/PT88vz0jEiUPJOUcpuRQcC1RaAwAQvnv//v7uXgnunSei7fagq2axa85FcgkILy9L2/QFSoYilYopEWNcqWgzAMTgCMG7+OX5V83obn9s+j0U6tr2sL9PmM/L6TpEXrX3h8f3b77bt9uYYoTMkV/N9evpNM2Dc+4y2Ldv3pYMmrd//O7ogrVmAQRr7ZenXxYzbncPWikAhihUtbHBSKnbZhvDupo5pqzqrmCWUgqSBACQrV28D8QEK6VgASyIEEMgZAho1imFpJTCUlKMmQmiG6gz335UDEFwQYTX8ZUTE6SxFGKCc1nKzUkLjAlZVw91q1QjRJ0hAbBOKmmn6+UacykhXYbLy/kkVH0erqtdxmXcdhEQSUnOlSARkseCgrFECJlhhhgyIWNcUc5KkCDSTKD3mQyDrKSwwfngS3LBR8WBUxWgnC4v3qW+23AEzDEn1LpWqrmR7k1Mr+MphVMleilEzOE6D4JJBiyEEKKP0aXsEQgQMpbrcD1fx4IEBaWUh+0WAFKKX18ul3nkQN6559fXp+eXks31OngfpJZElFMADgisbZpcsncOgFbr1Lw0leCMO+ebqsqxmHWRQqechVRIGGPcMpICrR0nzUICY5bFeink/eFwmS+V0gzAuUA1jev8yD7sD4+M+O5GzoHEmVpcnsYrY9embTljQothGqZx5kJXtQ7WJSgFYLu9E4JOp6c/f/rb5y9P7x7f3B92td7w8+lr8q4Qt2YuyQPjkusUo26art/tDvf7/dmHJKUcxhPj5el84cSEFBlTDEEgFqJxno1b26X++z/+8e/f/u5wOP75l5+9MZRRCYEAxDhX6nS9xBLf3x8nY79cPwNjiLlAAWQAmTM67jfXYbU+KCWOuw6Iv7xcXszgQ4jeciStm05VEGIoCVJarPHRd7VWXFa6q7T9tH5+HS+/e/s9IxzsGKJ1yV6N/dvT6XK9SCX6vpPAxtlEeI1YzuO47bd11bZtt9sfgGhcTnYd27ovUAAwpmCnMecbI6Us4/R6eRFcdLs9CLbtdoxrQprtwKuu6Q+aY/BriEFyEexi1zV5c86RCtR1x4UCKm21kUxEb0IwnGsC5qNru52S2pjFpoU4l0JQKQxQ6gaxfKP2iuBMdMGsc0y3zmn2QgoiDYjOLcP1Aoh13SaVta6rujPWpBwF04UX4ryUtMxT8AYKMt5qraVsvpy/BsS62Va8HpdxnCwnIgZQspatrboCGXJBwoJIjGtZA2JVbwXAsi6/fv3l02+fIKe395e+2yCxCKVqNp1Qq50JMPnwfHr65etPfdV+fPzhH37/9/fbwzCdCRkB2dUBlqbqAIATgRCCsX/8wx9vXMNxurLIei37TbXpju1203ebdw/fRy6EqttuAwDOWR/Cusxfvv52Pl1yKsRACJ4SZ4xLIZXkWmvv4/3D+3/4h3+ntDTrfD5fD8c3x7sDE0wp7VNcvN9tdwkwQcICOQPnAgBDjjF5JUWJjAAY512tH7b75Cym2Ajd6yZn4MQP3ebvfvdvSLC7zUMtmuCMsyvnouYqRY+YqkqGqBlxF+3L6xNnpJTaqP3h8OC8M251zr6eny7zSdWqqt7GsI7jVx+cdx6QeWkhlWCdmcc0vehmc9jcE/JSonGrS0VWO45YYS45l+R9dNxZxliJAXNxbikprXZOJUqhVNUAAKZAJVkzJZJvj+9SCZ++/BfNm7vdg+A6AmbA6JMtM+fyBhOu6xYB5vlUgOqmz9EnH4N3xqyYgRAf7x+bqvIpNO2mqmoiYFw1TccFj1AAWaNUApeyex2vo13aekMxPJ9eZutqziQhIFRabbd3qRR/o3TmPI4XBuhiKCWxnCDDOA8FiuZ8WGfv/abbaF3NzuqmkcjDmha/VPs+Jfr6+tWY5bh92+rWBZehuBCCc4J1UnHvl5fr85fTa4yFM3zz8PD7H77zzn95+uKvg5K6FJCcNBMFmZYNgigpQ0nEGBa6JVkaqZWQkHVdNUiQoYTMiBouXdv0yYVlHu06kuBtu21078BCyoVhQrLW55yFqpNJ++1WV7pNVXIOuVC64ojTuiRAIXW33c3TlQA4YyWDC3aYhmmZ+35zfzgSYQr+9XSpmtPvvvu+ktXX85fT+XI4PHDVILFts11qUwr4hE1V89WYu90bQLQpU4G+7VS1scY5axi7+SGiwKw5vH97PG7713HyISghNpuuVjJam2O+aTAzZk5Uafl3v//dh7dvn19eTuezseZ8HSa7tk2rq2ow63fdh+8+fvzzLy+cGGOEIYUcCEtMaV4W50MuZZ7mfdeSQCIkwNktVyehIGdNw7dtXQ9pui7jBin4QATHnDmynIoLsauafdfnFL0xUkhkNC7zaq3Q+sPjg+bsPExURN9uIOPr60kKSQSAkRATlGWZU4ilAvz21hNjCIScEy5uPS+Tqpo3b94dHt423aaumpiScyb60NYNAlizQEo5p0zJhRi9j84PziNpLlS32SIAITN2DdGE6LUsBeAyvlwGdnd4BIQYAmTEAsF5A5BizNlrrRnKEMM8X6fxKoRo244zBYwJUQmmYvDWm5QLERq7OL/6uN3ujm3XOxeIMYCYYwwpemtT8kr3IFQsoHX/w/u/65ptIzf9ZiecYaXhDL23zpuckjOLtQsBNE3NuYCcAhERCU6YC2es0vXd8SF6u6xmNS4CGbNw4rv9USnlnXW4gKBxcZfLqEXz7vHd/X5XV8xZP07zal1MUVZW197nlHIKzgXntNabXV/V0tqm3JKCvNT9UVdbICGkquoGkVJKjAhymsbx9eV1XU2MmQvBmcy55JwZEROChHi8f/f3//gf7h7fpOzrrm23e+/WVELMsWrapuuv47jZbBnjIUWGBAW4EJzLTD6lxL4p1zIUaqqaIZUQtNaVrkPwi19z8iGZfX/c9YdScnB2NUsIjocwBj/MZ5bhzW5/6Lbjslyns7Vz17aMQQg2xVrrSmlt7ZpLBlZKSlCi4KyUZM3ooz1PL3/66z83sn3cP0jNjZ2+rXOwBG9zLLv+TtUdY4ILmVKYx9NqZ6lETO7p9avgXKkmR+O8u9XlblG8lEqKKfhUbWql9NPzy08//eub+w8Px/dCKp8sMk5c5VJiSoViQQjezONpnq5CNTklsy4vp+foXAhRCvX4+O7tu+8QsW667W6fUvQx1E2vdR2iQ8aVECWby3z5z//6z77gu8eP03j6+vnT0+W86w9nt4Tkttt93bQppdUY4lxLPSzrdRkIELmQQnKk4NbT66u1C0f2t19/+evnX9/cP7y/e1Ratbv9sd1jQcllydnY1axeilrXbUHGCpeMhegFV0ppQCilOBONCVCKYHR32B12W2895zjMc4oxF8q8PL0+v47n7Vb6aBlHKVghZJwzxjJYpVVbV1qyumoZx5RTAcyYQ0nDPNVS5hCGdTbBbLfLcRcRaXVmMZaQbbbbxRhBPJakG6WqKufw/Pwcra+rZnvYVaqa19U6h4Vr3ThvQs4SoKvaCzsXQO+j8yHEIaWipA4xRihMcCkqwZdhvBQEl2Kl1Lu3bwqUYG1A5NGlp9MTEHpjIBfkqt3cI4nnp8/D9ZQhuxA4l6udOIeHh/13330XcoJSaikJIQZ/k4ZniIAolQwldAK2baX5/aarTXTd6fJ6vrZ1i4Kdrq/DurRtH0MggJvF9Rb8QaKccwgh5LwT7U2jKjjPJVvvZ7vmnATRsM6s5rrSz+fT8/lMnG2r5rg5KK1C9CmnWldK8nm+Xs4XzsV+t7s/7hY7Vbo+bPrVrF3X/G53vN8djVtXt755fKMrbd1q7BpSdNbWqorBL5PJt3cNJnKO43C6XE9cyx8//O54fFB1yxiP0VtnfbApJ8FlSRkyaFUXzDEVgiSZyBgzlBDiulrGZFPXsaRlXMbpwhibnY3RTvOVMV4wa10DotZSSs0YA86XZbheX+u6PuzvOReq7q3zKTpnbJFMVgKAQkzOryFGIp5ztHZ1fpVqWu2k65qQE3GADAVSTi65G8OEBDPBxqJq1RE8n69XJupWVQKUFJwxToDW2eRcTs6HQDkorZEEhMAZv/F1Qyp9s+l/9w+p5FwKMuF8QMqAqeRYKe3meTi9dkr9T//wP56nCxKuds7FX5Z5tUvfNG8ejiSEDTkj5Jy8d0io2qZt2q5pUwpmmWP0McfLemXFC93YsLZtr1QFAKWkGJ0z6zgOjIm27b58fdIAkrMYYwiYC3CpHt9/92//8T8c794s60JEXOqcSsnc29W5LGW72x+fnp6NWbWuvA+10kiMiLX9pqwmOMuIuqamFLwPUohKKmcNEQ8hrPa6epMhIi9Nac28rG6c5yn4AABYwJo1JSO4NN5K3ezErqnVsq6IN5chK4xSycuy2OC0bgTlGO00XypVE5WYvVIKkCBgrSuhKyDgQkjBWQYG2WWf0M3GtMCatp2WE0OGiLVuK906t0qpARgXGlJmTAglb/bHklMKPqQshJZMOrsE7xiRNavzrqq6m2m5qpp5mY0xFRXO2PU6/PLrT8s0cq6+f/tjhHydL3Gxu80+IRrn9puaGGMIOfiQIzFRVS0UKDlzjt6uZrp8+vzpdbju94+VlD9//vnPn375/u337x/f/fz5r8bbvt1JVo3z7JwjormMBNiqlhjTVVVSXKarXU2O+fnL07KYl8tlPE/nyziMy9/98COx6eviuYCCZV4mxes3x7dSq0pXzlgf7DJa533XN/N8HYdhXZZPv326jHMsSUhGDF5OzzGkqtKVkjmnmLLz/uFh1zeN5gJKSTEBUkoRb2VtAiS8acGUlEQIBDElwsIRCVHqinPhruE6jOfLeD4Nqm6aptlstp1ursN0mcdDv4mlLNY3fT/bxZf08+cvV2PePdy/f3gjORuur5xKpbRkajXG8wzItpsd4zLlzLlc1hWBPd4/Nm2HpeRUumrD78VsTSo5lhxy2m43McaSMiHyZTH//Oc/kaS//8PfY6Gff/l1dvbHH/4IiGZeqroiwBJz17SKsxSL0LzVnZIKII/Xq8+pb9qYYiwBc8kpnS+vybkCFGMgYiWHHz68+/sff88Yu5j15fycfXwd1lRyKoWwINxEGTcDeWaMAWO3jT5TkgvOObs77B/utyWj4nyJxp59VfMCOaXsY6CuA4LVLjnFtqq7WnMGLtiubUIqqzXEsWp0peR1GUMMu2ZTKJti3n54ezjetU0DgMgwxWjMDJDbuk3BGzMFb7VuuGDD8PrTrz8Ro812w5UQQmDx3hoiDim6Zc1IJVOKEQFDKlwIn6xxTpDgneJMcCFC8N47JaTLjgtxPLzhjIfkg7fHzZExzhhPJUFBpWopNDIBhPpBH/YPpWRCBjlXHJJuY5CcOACm7HkhIapSdAGfUvDeEYmuuUNOORezrEIoKYsQSkoNgJyJ63A+DZe2a4EopeCjDbkowZ1dWlWZ4C/ja0mJEwkEF3zOKXj/ZbgWxJyLklKriphARowrKZTWTUG67YpXs2BmWMCsU85lWi9/+/Wvyfnv3v5wv90XLPM8rW5ulPYleIiZipRUK8lFK4W01kglu6ZhxG90WKWqSulYMtCQACFD3XZ10zLGYgzeG7tOl/MrAd4d7z9/+nxzJRUonPOu67a7/R/+/h8//vj7ze7IhYQyL9PUVk0BWO1yOj9VuttQLYXWWpvVtE3z9fm50TUi+pikquoCS4qMEQMqBVK49ULLcH1hrCamxuVsgpWcbzddXJa5WBfd169fpnFmTAghtJKNroWSvpTk3Lbb9m13PEABSDkhYspxNS6nxIjFGICXZT4v1t5tHjjSbrOrdGutK22p6waQpxIlk7Wsc3Im5GmZ//b552E1f/zwjz6YYX7VqqLCSso+ZKCiq7rSG86F9wY4CVUxLlLyIbiSo1S6AMTkVzPnXL778Meu2RTMPlhGhISl3LSawawOChjrgPgvn7/O61Q3/bbdahJQ0aZrAbkLMcRAOeYciQFw0XcbIViKgREFOy/zeL2eltU93D22dY8AueD33/343f1Hxqjrd3eHx03bP5+fUgiMM+cclnJ/PDKkEGMO0dtlmmbrYVncOE0hl2W2xZaX8TwZv+93v6t7W5xGEUocplfJqr7eCqEgRsrOztfL9bo5PADi8/NvIcQSgROFGIlE2zTI6Hw5l/xNIcQF51x0unpzd3w4HDZNU2lNjFJOiIUhQi5EJAWPORYojFOKCQljDJjzbX9wyZcUckrZ+mSME7KVVb2pur5tpzIwvn54++7h7j4jPT8/IfHj3XG1y/N1SFAKsu22AyxmHsd1YMgZSa1UKXmeR0EohYo5K1X5EK0NlVRaCe9sjPEWsu+7ba1bIvr5fJVSd10vGCsp8z/98vM0r21blYSSq/N4eRq+MiG6up+eViTqqhYAM4HudoBgYyDknHPkJPaHsFpEjDEAypTS8+n5cn1dxsnFNCyTqlTMedttv3v80GB1aOtd95019jz9uQCkkkvKnHEtKUQvlXq82//627ONoW0bqbWPEYkYZ9tN/ccfvq9kx7C8vj4vwWolXoZLxdRds900G5ficH71Lt4f77ddg9Ebu94f7rrt3dfn59fLuW2arumen54etocPb98O65wgHvab94+PxEUukDIglpwCJ1ZygpIRS1XV281DKejj15jzu/v7bdsTJLuO9E0yiBkIc5GctNar91+fzsHbQ7/NWKCgFJo4l0orJUpJgqEPppS8bXda1s651+HJRVc3u0b3CWII681T+82/nSNB0boON+MFROPNYteckhQlWH95Oiul9vsHIimkzDkAARJv231VV+s6ZyhC8JySdZMtIzLBGGt140oMPiEkD8n4ZRqXVmgEm9tU1c20nJL3omqgpBzDYmzKEYgB49MwfH3+uu23la5dSpvN7nisiRExaZb1ej0prSrdELB1ncbxau2acv78/DWX8vH9x77vAyTGeCNqwUSAtIZgo991Wy25EJIK5JvfHVPO0drFrouUkhFnJBivhd42/Z2UKqfsrPHODNfLPE9CiJsBQyqJCIRU1dW/+eMff/zxj8d3H0TVEOPe2VshKKaEgg3L9PXp6d2DgBY4ia5t7WKqumaMDcNYNbV1LuXEGJNCMMLo7HwdVzMrJXKKKXvGVM7BOTuboW/6dVwGf0GuciyX67AsloC1TZNSnlezaZq+brXSVVU1usoIJWfrrPcuR684021jnc9FJxZLAiUUF0yKhjwFH7nSvCYtGyVqZJhKyN6v8+BjMMZWvG0OOyg5hNw2GyiQQkjZz9Or9T6k+PatkPpQ8ZbnyLm8HQDzOuUcu6areJdTCN4yJh7vv5NcrvYavGEMsWRnV4ZYSnFm9d7Uuvl4eCzfuetyFUSCMcHkeTqV4bTp+812z3kNUIAAEJqq51zO8xVKkoxP4+tqlnFeiNSu7TmXIaS73bHSEksqJW/6vlbtMJ5/+vzTx8NbiMUtU6X1+fXZxVS1HZR+Gseffv7589dXEuxuu71O099++61kNs7255eXH95+/PHjD0KSD3Gx6zgPOQ92m/6w3wHkdR2dM0KITdueLidnVqnq/XYbg4NcBAktFMQYnJdcSyaE4MF5KbiS8vHuvtl0g7tGiEQoOOFNWZAzAQhGztmc07zMjAlIJQMoLtZ1uVxGqdTj3UPV1RniNK+Ci2EYNk1vnfn08gUQf3fYi6ojIRqzRufe3T+0WpeMMea6qRNmOy/e2r/+9vm35+fDbv9vf/xdJvjrp0+t1oxY1TQpw7iszy+vx8NBKh5TKKVYZ7jQnW4ISHFZVQ3jvK5rhhi85z89Pf2b7374eP+gpSbOm6YdlzRcTl3VK1FN1zH6mHOMOW13h67rIKdvlX2fGSEK5pxz3uaSXHA/ff7185cvf/fD73/3/vthnp7OzwIpGPfr599u8pDd/oAIMQdGDAoAspRihgIInZbfv323LOY6zdu2A8AQAmRBAES5b9Sm35WcU7BVkPvjHRPq05dfOIM2bp9fX5z3u377cHdsdTMN45eXJ1HLd+9/r1R3Hl8kJ6UrQfDucP/+7ZuY4rTMDEpJsSDY4Ky1q50glU27PV+evbMhub7fI1DIYTRzravj5lCosAxmXaZlCdFNZg4Zd91e6crHqFTTNu3LOv/8+dfgTaXVcXusRRuji8n54BVnyIkYFyRGPxgzP738dhlOc3/35uG9UiqkSCgKIBIqzl0I1pkpjPM6IaS+2VZVI4SKIZScUnQhhYLogpvWU9ds+nYrEIfhYqN7YG901bkcVz9H467Dy99++YkDcqnv797c3b/RVW3tklPuZb828zxdpunKib17/Hjc3M3zlQCMsT4GJMYYr3UtdaNk+4v1q02leClVCnm4XnUblNSSmA9udsN39Y9Y0CxTDk4x/rvvvt92G0YMCL1z1hnnvGR1Qco5jvN0vZ4/yy/7w/7dw3d11Szz2a2FIw8xGLuuy+qcZ3V9/+Z3x8fv627LhSgFnTfer3adxuEKJIDlZR1XM0KJgotcSl93795+/PDdD6gr6wMhJSgxJ8FljElXCpBKQobyBpsUvLIsZITNdnu5XLK1vG6stazkFFxhklBEyKOd2ZqplNXOdZcP24dO62m9llQsBMYFEV2Xy5fn19Xaw2a/EyKlNC7j9Xrab/Yf3n+HSCHHG0695JiTNWYuBV+HVyXrTb/notroo5bCmmVe1tmbpt3cbx/FNx4pL6lASCEm592yzDHBpt1Zb0/nr1JpKSXkpJgsCZz3Zl2ts2Pzut0cdN2bYHKM3tqYog8uxdA3vRRVAmadKQgMaFnOw3wWW6m4cuv69PKLll3f7kvBXIqLgTh99/7d9/iBi0pwxgmI4+Lmwcx1u1GMVU3rg/HBAYBbV8AkBC85TfPFGRNtrFRDXCqpXAqt7qJb53lYnQUuq6ZHxt+/+dgI9fT1s/e2QGYkdF3VQk/X+f/9f/xvP//tyxrj2/fv//3D289P/zGmUgkutNoJlKy4dUJLTaXv+s2u6Z+vAyIIBG9mY9aC/LjfzePp6elXQmIiphxO18mFnCHGHMJtsh3cZbhKoSqppJC6Fl8+P//lp9/6XfTZl4IMqRBhyYikBF6ur19+exIcV+cO+2NXdUxwLEVypYTYd/tN2/dtpbX48vrq15AARzPN8zov6/3dMeeSMjS669p+hSsS7g773/34QwqlbutxmoyP3jtEFiPMk0kJ13X1Kf36+lpyPu72hCy5WEldCizrqqWstNKqyqUYMwnJOSMpRFXXpZRpGi/Dmf+7v/8fNIP3j48Z4DJctk3f1U2O+Xq5cCHPw+vsbIg+Rk+cLeuYcuJcaV1HH0MIxFgIcbUrIkglY8bFh2GcxmVmjDVVe52vu3bjYnodrtmHYTW60tNqcskIQISYCQgQSkzp89PXxRom+a0QHHwg4gBIBbxdh/S83e4fjvc+xYTwsD2Mp9dxXnbtqqSiUhqtlVJMClVVUqthHqblisA5wrvHe06ik2K76bm4Gejj+fJcN5Xg7Dpcvrx+uVyvD/t3HNgwXUtJx909I/U6nb6+PH1+PfWVSDkHnwQQFFpCWJdlmRddNTkDKxR9QFjuD7uuqf/25dPL8yJKJs6UUosxl8t5Mutuv9VacUSIZVwXVnIyngA50U1aNKzXRtd1DGMMhFwIzohJLpSQyVsoBRA44wQYQkzJt7rjWimttNScSa0apWvJxLouZpklq6RQ43SFQsiE9ckuq49n4+JqTVW3bVMhgGJS8KI4scIhWDudddfqqnd2Ac5Z0chiulmJEOu60XU9D7OSuu03grFlGo01+/2+1tWbw/Eyni/nkxQ8O5NibNtNvznc3b3POUfvxuFSChGxAqXWdS5qWs1lWqZ55l9/Gwbzh+++X80QQsqlhJycddfrEGP67vf3h7v3m92RcYGE3ltrlhjCPM8pRa3V029fv3z5vCwLZ0wIEWNSWszreDq/3L19iyWnGBkTkssQS0y5ZNxuDr+xX8Zl2d1TQWSCcyEZE0pT25dpmXMCyMQ4h7LkFJp623/oqrb59W8/DcP519NT27SC6+2mzXRXMruV7YjolqruuubuuGOCvAuEjDjpSuccf/nlJ8a51pXgrK618y7kSMCuy3RUuqpqUdVKyXWZhnHixLb9cbu5V1KXZHNO63QZr5dlHnNKBTgTom5aLrjxtqRo15lBLYXIOa/OxRhzKeM8la9f++7uwBW73V4RhVC77dGtJvhE6AgwpVwKCC6GyRuz5m1mStXdhi7V1SyL91rK4+GhJEjephwYw+ijdciZfHP/dvWmACIwwaVS2vn15hOXUhQgRjw4M0+rXdebnABKRsqQ0jhOZp5Sjrqqdrv7pmoYZEB3enqydlWVlqrab49I+Px6+eXXr//7P//F+cyk+vD4Xil1nce7u/2h6X6Uom3qh8NmHF+HZXq8e3y4e9x2m35zLFiiN/MyzsZUdV/p9v/40z//9de/Varq29Ys5nS5hJRuffsQY8rZeo+ME0OppJbKWvvzp1/E/0r/1//pxxs1ugCUnEvOJJhUahjH6zQpQU/Xc8qZDtQ2LWFumq7rtpxwWoe6UjdU+O7xcGPh5ARNvdFKXq9XUdVtXxOjmBKxQlzstseckpSCS22dT94LLj68eXvcH3yK62A+vPlQSj5dT9dhKAC1VLc2zLquMbh1nZuqLZhLyVRicEkJXkpezXKdrp8+/8b/5//b//z/+H/+3//T3/70/u5+nEYXQrfpUo55HiWXsYCSqt/s5mlYpgUJCqO+04SsqlWcptmswUdijAmuuPxw97isq43+z19+kciauj2vy6bb7tp+Wk1EmpZ58W6ZZ0gJoUBBIsoxAaJP+eVyylCElM47gAJQbrWAxZjPT7+tqzscHt7cv3+8f3Mdh+v5Gn3+cj6nUO4Px22/DT5MwzDEV630w/1DiM65FQEgRsHosOn2fU9CSc50hcjV56ff/vWn/1oyfP782ziNqqpatRnV6GLw3u43xVo7rKNW+vs33zOWAYX1fnITK3C5XhQTD4eHfrvjXFLBkOOyTkjwcHy77XfXhw8hOskUYzyvxnjvrOWF5nH4cvpy6LbzNFtjSPHD/rjZHpGJcZ1eTicn51Mun16fKq7uD4eu6ZTW0zI4s0JO0jm48WOJoVIJwbqQCubbiWkNI6Y5T4xP09lZXzWb6+lVa93p/sd3v88pJcgIJUJczVpJUUq6hvNpeIVc6rqazLSs8+7+4d2bP+h6M16fT8tTDK6q6gJUCgohNtutIM4ZX42BnIbhOszT/ry/uztu+r5Rzdfz2a5LrbAU8LFU7V7XOllbCENJErgkej29VLopiNGnx+ObSgxfzk//8Z//828vX477/r4/yqbftn3JkPmzrnW72ycsSAwZT/mWeXfBrs4aKWVw9uuXL9M4hHAz/fJSImJBLMP15KLfHR6w1Iw4EiNGnCiX0rXbx7cfsQATApAAkRjjQkCBru9jycGHlDJKDogphgLJeXc+n1djkav9/pC8+/r0OcQNEAlA72MIodLVYbf9mN4O6zzYcUvttu31du+8xVKWabLOEMOUu0prH80wXgEyEffRX8frvrur220McZlmKig4E4wL5NN0jn5ihV2Gl9enL9M4q6p6ePxuv78HgFzKbrOVVNZlmcZrSJFAtV2vVGXmtcSSQ7qcn4lQaBlj1LpuqoZxZMC8tTF6hjznyIUihimG6OMwj1xVum4fHz7M8xhTEEKpaseQrLkO19fr88tqlr7d3B/ftnVbNW3J2ftww01G7wCKt2uIJGUtK+HJVrqlwnJOpSQpOUJe5mE1VnDSsqnbtq4ayXmSXHGx7bdt0+ScpNRS8J9//eUyLXXbfvfhu19+++3u7v7f//HfTtPr/cOx7qwWctP0gnEf3LTOwzLV7XYTs0QgzEpwu0yny+l1nHZc1znXu/3y05/duqQIkgsXYvCxaZtSinMuhlCQGCObA+OMc2atLSk9P52n4R0jGXP2JSnGMaOQkgkptKyabtPqOtjr5aKFavvOONdt9/1md70+/em3P5+uQ1dVhZOu66ZqAEBdh8Wuwbvg7fVyYoyQSGhd6waIE7EUAwAyIfb7jSSIJQnG7+8enk7P3qbD9rDtu23TfOFPBSFiMcG1dSO48M4aYwSTQrJ5mhhxKIkLDTlzJvqmv7975Mf97h//8If/8i//9LcvZlN19/sdclFYut/dQYJpHqgULXRQ4ctvv1a13m63wbhIAnlkmA6bngvFGHPen07Pw3SuFMsxlRiavuVadl1nU7jaZTbTru1WY60zDCHHgPQtBFEAEagAzWZFLojQOc+I5VJC8KUU49zi/LiY5/Ofv75c/l0EH+1k5qZuxCj/9vq1CGqaxjt7PadcQqUqoSWxYuzovSUqCaJL7v7wTukOoIQQYoRhMf/1p392PqUQWynrtospfX15KQTruoYMHz9+p0SthDDcMOJdd1RV8OtwOT0v02gAnXM+57Zt2rrTdevmcLq8QsGu7WO0zhmqMZecU2SEZll+/eVnVStj5iQVyzEEOy9eqzamwgtWQhGyv359WtfFOX/XbUY5ZYCyLF+efr2cTveHu4e7RymlkLJvWmJMKUkQnDGTXVLJD0euuTDrchkuv3791Fb7jx/lOl+XMe63d3VdMeQZS4G0hKVrtp3UwzQAQVv31qzr6qx3SohhvNT16377sDs8rGYZrjEDlZJ8MLkkLEkIwlJSsKVAKuU8XJ1z4zy0Xf/hw8c3Dx/MMpxOP0MpXdOlYFPg63IJdv7y8uXl68u6mNMybJteS8UY2262d9tD228Xb4HFqm7227uq6ZiQTbvb7x8uwzkDcKmJ8ZxSCM44E5y1y5JTJsYup9Pl8mLWJedEjCGRUjqEkFJSUo6XE+NS6ZYLiYQhR0RCYEJW7z989M4xLgAoxiA458QKS5yoa1rnvHWrrhRXVUppmq/D+fV0OsVUYirehkYJYvDb02ci9rh7U1UVIlrnpBS7zXbxK5e03+38Gp+H1+Ddvt/eHe9SCsN8JkTvfcyJSyEEI2BKy8V6mwPkErxXUirBx+mEyJxYPn3+kyA8bA7LMrh1JeQx58v4avx6OyNSit6sp8v5l8+/uBAeH74DJoK1grHdbi+lyMmPwykPJaS4O9xDKeNwJoRa1cQ45Oj8Ms7XGKJzRvPaGTeOQ9/1jERJiTOUUpVSkLF2cyyAKWUpK60kF4yQgnPerUiE2K7z4O2ECM5ORJx6SkKWnNq24QDX8VI1bV01wa9CsKPeOm/P83Xyc11tKs6Kz0pryXmI3nkHBV3wi3eqVt89fLdpq0azH374sW/Fdfb7fZsgA3CfU0ESSh+b7v7+jVaCcTHb8XJ96XVPGebVv16HzOSHx4//5sc/Zm+H18GF2LSdECeAgoAhxpw8ASklY0ou+gzZe2+MIcRpWv7pn/7igkPJcwEpVQafMwARY7Jq2sNhdzo9TfMolbBmFVy1XVtKadt93x/++c//+vb45v3bNym6ELUQnAShL6nEpm0R0LnABAnGta6H+eq85yQY8RhdW+u7zWbTt7cr7zAB1DIEb6Kf7aqV0lrHlKq6qqpaKjHPUwZ0MY7rvBrjXKjrartVnLHqVpmrGx7jKgU/th1CCiV1Xd/Vzb9++tccXKWal+vr5TSsxjEupnVkCB8f3zBGXdMKTlDSh7ffbdqWC6mluFyxJM9zsiUyEHWl277btq1x7ul8WlfTaR1K0JWO/oYqu/maCClDLoSkpAqlEDJEDDEa43KmlPM4T+OskXjK8OXl5eHutGlrH8K2bv7xhz+mkndt3zbtOs+W0WG3U3Udc/LBu3FiJLbbx0yFIboYizeYy7jMl+sAmQUHX0+nWsmH7a6pW6H0OC6vl1NdVc+vz0Lpt48fJ2teLp87VStkIScIXki1a1oEeL5cTtfzYbO5v3+8f9Nt98fLpRjvyzwBAiG4ZWGMS876tnXbzXW4+sk/dIe+bhdkSjd8GmNywc4Qfcby5njX6M6sK0EWUoqqqVSzLtO0Bq4bYGqYVsYdQBmmkTirdX3odjmh8auSmiGVkudp+suf//Tl9el331UlZu+8NaNArJsNV3VKMaYARJVqOeeIY8lFMcUUN95wrjhnKbqn579N06XvdkJVwIdpnVYzxRBu63HMBQsxzkLIxNjD8Y4RMQLvrFnX4+Zh31YpXT//+mnX7CAGs1yG4Wm4XJ5fX2Zjal19335wwa3OMmJiWaHBw+Hu+7ZN2Vi3eO8LH8JUXi9nXdecs3r72LbbknMuKXhrrEnWmHUqOS92fX76ss5rjPkm/i0JpBYxpMvluj/clVKcMd4ZcYv4IWYojAEQ9pttCNH7lFJCRCkV5yKlJIRkXCDO1hnrayEV5JxjqKqGkH/+8gkI21Yg0E2Kp6Ta77ab7T6lOE+LMdam+PbxsW87zXTioa8VMap1o6QKiaRUMUVE3rSbqpLLepmXWTPVtT1xitGyFFPOr+fnLy9fHh/RruHzl9/uD0d5VLGkL6czZom8+JcvIcdKN6VQKWnX7RhyrWtkSVWtdYER6aoOIdgQYF05F4Csrfu26qJ3zi0EkLyTupGcdfVOy0SkShEp+5IzMcICjEjUwjubcgp+YQiCV7WuS793onLOzMuSEyKWGF0hmsarnWfOiw9uHCcuRUkZckjROztbv1a6OhzuKtkyzoSUxizjPBIiUEkQiXOpqxyTjd7lELEIrrt+82PdWrN0qm1qudlpJaWPa6YktXh8vMu+SK60qqXgVaVPlxcsuUA6Xy/jOK6jEShTgVSKD97Ztaqqx7sHJaoQ0vuHu19/fUJEyNk5F7zTUgmhQgreB06UYwwhSCFnY/78168FS8lQEuRUEElKlZFlYErX3XaXSqz7ZlrG3778+v33f+CV/PL6pcSiZf1w/3i3e9BC23UuABRYSLHfbHO6LGatSn2nmwjex2istW4lQkZKCs05mRScc3eHA1EBLCXfffrty3m8jMv4/PzVhbDbbNumUUIxxlIuMeZxmoOPKUXGmZQVWT8sU0tQoCjOjTXcmmla7c9fnz/u93cPR8HleZy+vp7W5brtNterfb0sl2EsUO6Ou2W6/ulvP1dKP+wPjVbGrpqrTLmteyH0fne3jAvRs72+5hIZgGDMRgs5P2z373bHTDj++lNyVqvqRu7DknPBCIUxxIKNbidjBGdAOM7LOC51rVPO1ocC6d3hkd9Vl9UWKE3dvXtkIYZOqFZXnAmhFZbMS7zb7arNLgNaY1IMSuqq6lLw1+H1dH1uZF0K+9OvP1/Ol123fXP/ZrAzFchIKbISS993i183umacm3n6L//ln3Rd8wLBuz+9/vPT6xdIhTERvDnudkprty7rYsZxrJrXZrNtmh0Dxhk5u1SqNevyfHqSXAPCpu0+vHl3Hi8FctNtmfSAaFO5jpe//vVPq1tFVe1298ft/V3XRUi62TTtIdiVl3jYbdd1bhstmY4xAeTgQ19VjW5DyNOyJGcVr6Mxa3Q5xr7tY/rGNngd5+vrVwYlZJAxFSg+2nqzaavOuHEYLmZZeFX37TamCICrCzGa3baGnIyZum5L/OF8Pr2evzrvSkFvV0KAXLiQQlaVrhtdlZgAS9s0jVTT8JLIG+9DodNwlbquum724dPltO33D3u5LGun+zVamsZON5Wuh3X6+fOn7abnvIRoX9PzGlwturrpcJbH47vHfg8IGVLO2XufvI9uMeuEyIbXl/F6sdYSYxhzihmpMM5SLMtsr/8/nv6jWZokS9PElBPjbs4u+WiQJJXdheohmB5ggA1kBPjZ2ACQBqQx3VWVnRmREfGxS/w6M26mXLG4WRDxta/cj6qec97nafpIUMmY91bpCWGaJIXWOgSPEQSAEYKdUyEECDGlhDFufcSEMRQhADZ4AFwMxHoX1NT3zcvlPGiDQ0g4OS/j8vKyytPi1cwOordaUJiJOk9l72YMyDhNFIZVnr3uRo9TQzD3ISxu2azvN/XemeWqD5e5+bD//ra+b/vurJ5IAD/99tOX58f7u3c0EjUPCZeUUgzwsW3/8vjlTf32Lt+0x+7heISIQQCSLCE0ebvfFsUGQJKkWYieMDb07TB1/bzsN1tEKIS0KGqCiHETI1zpZZ5afXnCiFbZulytJ6uv/cgJWjGp1bSMLUVA8FeKQ8AQRedGc1VqWuYpOE8oDQh0yygxJYxFhJRS49x6Y5qum7UtyoIQAoG3RkMEy7zClEOAXLAYUUKRd3FT3xEcT+PzuHRZUkqSQIm4SIXXixpwxHler9jdNDR6mVDkGShCNMZbH5xghOFErhJKCAAYIGy9TZN8W61smN2yeA3HZUxkIoWoyooQFgBIpFS6CBEmNCkFtc4iTCAAizLTtMzESiEBDDEAwcSgB4QxwkRrbY2BBIcQMII+hOB9RnnwoFzV79+8X5XZpqr68fL562eMKQSQAnK6PH/7+nVX7W5W2/v9PmJgNQLBtd2ln/RufZfyZJ76xUYEUSKyfriOUxd9oFhgCCAKgsi2PT29PN3t78tUIhDytPzhh6oZxr5r0zxfLpdxnCFGiFDqGMbIGAMhlEJghF/zaH7xmHFFTN8PjFClFLkOfZlXPsLD9Qo5hQA2/ZiwxHunjf/u/u1NZSyE1kdg3aODPz18yZOEYOZBnOb509PzdRzu7+5WxQYxHiHU2kKIiiLLs5QibBCKIChjXHBZlq+rCkeoHMWEYkwwhtbBv9sA/N9tMBAhH+IwTtMyI4oAQpSwMqu21boq1/WiAIoYxXVVcS6WWZ8uh+P1paqqm2orqEAYS5FFgNS8DOOgqVqUggE4a5ZpwQmnjI7j1A3D3c3b331XIUya9rpo93Q5IYQ29VoQ5p07t621Ts0Lw/h+s+m8PzRX53yWZNp0zdS5CPabXVlwCABiEkQcjGOMKr0YC+ZlEpQZ7y5NsyrroszVopTWTAgAIqV8NtY5x5gkcP76cr4Ml7vtPpN5P18zuc95hREfhtM8diyiu/X6BRil5sAQIThY76zurIMed/N0vRwzzjvXtMNFaVUU9XrzxhE2Gu0Rvrt7QyBYb26qeh0hHufBAxOtnab+y/Onz99+ZZSiqQsR+WAJwdoop9wqR4LyEL21S5lvBM8wgm3XjONIMAjGMEJSmYokEyKLzk3jdGmaxRqZlVUlpnG2zm/WO+9stwyr1e2b/bvJTDeru4QV5/accuGcBQhOs+qbC0QwQHDoLhmnCSGH4+U8DPvK3RIWAeJpJaTECIEAXjle0bllGuexRxD3bTvNc4yRYqq8eeXWUUpAjMaYaRqzqszSFESglGIcZllOCJ7nyTtPUIivqdjXeDOlhGDKMAQxRk8Ycdobo6hkFCMLQ99fDy+PmEjJUqPssEz93FNKp1G9vByarqUEJzIxPtrggw8hGOftuIzXprE2ME6F4JkspnkKOAghgPdNez1e27LYvrv5iCPu+y8Pz1/O1/7lfF7X9d3+DSNsmtq2vQAUNtmGQPzDm3dvtu+6rjkcTpemQ5hSTADAL6cLo7yuNlkqCcHauOPxqJZZW+Ni5FwiSAkl3ut+mAlmUhYQoQjC+foyztOZNlV3fbmcT5f2brO2y9iN/aE9J1L8+x//mCSJcz5yGEA0drZWQQgQwowKD2KMVsqUCoYw7cbu0LZTPyRCrqo1gmBetHHQOV2XWyGLce6X/mKtstqWWZmkBUJxnNqn54eI0DKPRbrKs02RrwX0CJNp6JWZU0QIgh5DpR2EmPPMGs2YwESkLAMhKLWkacq4nOexlFIydmoardSyGIhJgIBzvl5VPgTJOQRASu7sMk99384Ph6cQYgRQaR1DdD6EEJxHGGMIgbWWEgZAjK979d7HGAklGBPgIybUesso8WH+/PWRM+KcsdY4Z6dx0Gl2t9nPXTNMlzSVEYYsr1GeX66Px9O17WbB0tvVtsyKY3ecl2mbbjAI3qimaZwPnPMsLbarDXDOGAsRCQAO08gIX1frMluZ7Y0y5uHxoe0aG1SnWupFzhPGeFmuJGGUEO2NDsFbl+UFIdhoCwGSSUqQRwRCRhmGcZqWeXkgmOZJZoA/X04Sd+/2d5jSrCjnWYXgP70cjHWLMa65LlYjhDAE58uZMcliSKQkhDBCKcbOW7d4Tlm123X98JfPP1+by/3udrvafvp2FJzpBWKMacQoRghijMDHABECABhjtTYxAmMtQhETWuXrslqJhAeCCKW5yLx3CIYqTxeVP10fPIhJsRKEGOu74UoIm5flt0+fh7EvyupmvUUwLPPMsQwh7lbrUqZ5kjEub8c9juDh+DIqdbcFWuvnwwsO0XnvrbfGACkmtTBO16u1ddGDMC/joPTiAmHCGeMxdCE455ZlHtt5UhP02AefJEmZZdvtLvrQdC0EcFazdmZTVZRgH+N1GLS1GLN2UpMJaV6UeQFhdFYPDhyvvx6aZ8nTd7v7cRmHua/ybZpkEQAVQde2x/PXNDtzTgkEVKYQoEVNNsZuHD98fLN//+7admmS/2m7vd/cWDNhRBFGIbhh6ugy57IinNabm0Sk49D347iua8kFjNfL0J6vTzLBjLFp6J31CNP1alcVtTZ6nK6PX744Y4SUCAI9j4mQnDMppPF2nCcupHMxhLiq6mt3+fr8DXnCE0IpYIKvqrWFZpmGc9c8HJ+N8QyTqihklk5mRhDiCFHEznhO+WuDnkkWvA/ev663O+usVePQO6VcCH3fWmMQxC4GAACllBDCKIshWmOsdYwxAGAIASAUQwAxUsowVlZbayzE5NVJByHECEPwmlCPMZgQozFLP15QHXHwMbr727umvb4cj5t1jhBMZ36z3tjoxnmhmBWEOR/HqXURFmVZFxttVJmX45g1bScThilSyzItSyK4R0GpZRiGp6fPXIh9tZuGSen56fz8+fGpTFd/+PHHRLCuP7ftyQfjfZyMD4i+uX0DXDTz/HI8zMtsjIpAYZkmggnB52nmeEhlEoP76y8/nU6X/XpTlOnd/m5Tbxij1tvT+cU5t9u+LfOSMkYIGvOim8aIYNt112vTtP0wqzJNVmXpPHo5t+/3fQjOeJ+lRSoyhGDwMfrFBGOMciB4CALy3huCaZlXN3dv4R4UMrdOD0MHECaUsURgTq7tWdsJI9gO/Tj0hJKIkbamac7RkzStMU5ChNqqYRqp4EKUIJLggXe2H66X5qXpxlW9XYut8QZhSTBNZWH0NPnFu2ChDcGr4K212hiC8KZaKWsCggBBRlk3XH7+27/IJE3SPJUZFXFRfT9NzoUAgA8RAIQgyvICoDjME4Q4hIgwgPDvliqMMXAAQsQInrTW2kAI1DL+8ttfrVkIwQiBfhy8B23f7HZ3H9/9gRDy//4v/49+7ozVHGEIcT+Nalm01m17pRFgBPuxH8Y+T2XbXOdp/vz5yzQtaZput1trrXUKYyKYpBgvajlNl25cqrrO01Lmpcjytj1/e/506c9Ouzop0iSllDBCMITQwIQSCHCapcsyQxhD9GrSBHo/9l1wPkAQfezVeLvbpUVhKMaMqW58urwAGHn7sirWm7r4w/v3k1qKIl30HJxO0+Tt3VsT7DA2cO4SnlR5uapXGWNGaRc8EmmkLBGirqrD8XK6NASxcZmscwAA732Ir7D9GAGAAL4KYZxxwQMYQXQ+kkgxLbOVELIdWm1dVZSck3FWT4eXhOepED98932db9/s3gTgvTMQMxAhF3K7u8WEQQhAcMfzMdil5GRZgJ3VPM1Ga0a5WaYyydAtVWrJuJyn2RhNI4QQrqvKxdhNw3Ga9mK932+9c13fz0akNsnLoqgrbuzj6Tidj7NWEEbrdJXmeZp7GDHCCKDb3U3fN9+eL5lI06x0o1FqJgBKwe/v7i5NA4T7I//xeD054x8PjwgBq+yk1eI0hjRPamX8dWhtBILJTbnyMQBYFWnqQ5ymmTJWpAkBGGA8LAvjLMlLF31VrNO8XsbRWgMwiAj6YJXWL+eH5/NDXe5WaizydFOuUporp9u2zdI0z4qGoWFojNchRIyED0s/XCiR2mjv3WazSeT91M2Pzw+PxzOhZFmmKinKvOKCq0kP80Aonad+WDqnAYTRWfvt5XOaSCgwxCTiqP107o6zNpyzukyqvAogUkoFQ0N/1TDstzeQ0+D8NOvg/Lcvn9blBhMSQ7DOOmettWqelVLWOrMo71+dQgAhFGN8PQOMMa/YREao1gpAkmQ5QtBaDSF4/VhnCEIIQOfcq5LIWOO8xwg5Z0IIEHqrh7FDDKFz91Jlq//wp3887l+UMQAhL0UA4XA95kX5/s33/dyfTqcYUVYWdbbOhPzUNwSh/f5+Xe+MNjY4UITovbIjYSTPKjXPWZJECJvL5cU8IQhA9HVdv9+9lQK/XJ8u15bzZLfe323eaRwFSxzIZjOrUUUYi6LCXFpnNtXmd9//Pi9zPS/R+8v1cj1ffnv4KrgYxi4rk6IoEAbaLErpSU2MMghDBAECiCLglO13+5Tny6h6Puo0GmedczACEqCkTC2zUr3MU+Mm4zghlFKGYOSMx+DH4dpPo9UjijFLCswoCG5VbYu00kYxmTD6amhYlrlp+zOlDFBeFbuq2OVZSjCTCa7KLaEE0wxG5INazBigwyRjTCBEnfUpS7rx8suXL798fviHP/6HdfXGB/j18QkF9OHmHiLofdTGQoQRpoxyToj2qihLRmU7dB5Aa8y1bbupfz49GRNvbt/84fs/cCoApsZFhHAM3vuAXhkPKIqE9/MQI0QIQQgxgfDvHQsIIgzee2sgBAgjpWdjJkH47WZrnRmnnlNd1jUl3AVPIBOM7zb7hMll6g5HmGUZilgIfrxe//rr3651XWQZpamQ/HB+/Pb80LXj6XhhmOYSQIB8BNOyPB6esqzalsUyq6eXI2bj75NUJoECUBRVIiWXWL7QoIPkAkBQpBKiuMyz0TOOHCA6X9SyTBQTAKE2mvzt68+bevO79x+enp8pFjyGqljd3769jWBZhvZ4HJrm6Xo6XE+JSAuRVrnYb6sIwGgYpJBSXFXlpW8eXp5icEVSqUVjynZv1igBw9Abq7rBt9N8vFytD/00t/3fxiUQShHEAEZMMHbwFTYJIAAx+BCcCwhCH0EMAEbAGc+KlYLw15cHq9z74BklxgbrwOBVO3VJIfIspwTNZg4xckSrfM35RDC7396cLwe19MG7uqrLql48bJfjc9uD2BWpQAgCCKdx8s4PXd/P02z0Ks2jD7M1WV5IGBHGJoBL30tOVusqybPb7b4oC+1chGhd16frZVBLniYowrkfl0lRyaqystZYs0zzhAEcxsF6t8zz4zSuivL+zdu6XL+cTr98+3Vfbxnjn7495JLutyuwxQLKnK5kUnrr52VACN9u7hAk/TJSQiECiODtdpeXBr2CSpwTnK63N5yLN3dvmRBaL4texqEDMAIYhUxiiMO0GOUkT/b1nlM66VmDBQNECBMcvxwf2muqzaidlZwvZnHOvW4lO79cm7M1C2OE84xl+Zu3H0/nUz/1MQYPYSQ4hHAd+hXGQopLe2y7rut6TnmEwODwtr6tVitGmBqaYJZVkb/Z5d4Fgql1fnE6+jB00zSNUqSr1Toy4C24dmPTNR+/pwjhGEII3nsfXwFASimlnI8hAu/C69UshPD/F+FighFEmGAIgVoWiIhMUowJQnBZZu+MdxoADAH0PoQQEIYYw1cpDSEMAAhApBAwjGB0MNJmas7n88f9d3W96acRA48SeW7OWSbe3d1VVXUdrhARBHDbti/s2Al+ul5yIVfpAgge5qntmyxLsqwgtCjSvMpKjXDfyEvf6qASIRKeBGfFinJO+6W/DA0IsSYMREAw5ZJSgmlgWVaQSAGCZbnhSQpApAjX1dqBMFpDMLlcLofTsSyyD7fvkiTd7NdFlszz1I+ttm5VbrI0HcbzNLWS8nG4WrPkSeKtD8EnicCMEoIWPS+qR9BJybTTIWqg4On0YnKbSumMdtaE+BoVnqwzAGaccoDiOHURRKUGEGGaFRlnBFMYorcKhrioqRu7VK7227dCcIIgpSxC4KyBECEIIALRg4TnIs0pTQihDmtjjPWGMLZe7yeNkrTkIg/dpWmG6CBDEsMoJWOUQQgFE6nMCARXiAc927FHEBLElmmGaVZkK+DhvGhgoh5VWidSpt5HDLFzPkaPCHYuGmv3We2CuzYdiCDGACICEUQIEMaYIAgBITgCgBCI0ZdZdr+75wy7YOapBx5wJBglVs/j3Cg1rtMSQHS6ntXpsN/foQhTmULglV4CCItZ3m3vo/dfn748nw/DdYw+siQRkiecSUIBl7mQ59NREnI4Ng/PL/d37ziVnIt5noyaGWPrso5OL+OiZ9f2PQIOgajm2ceQr9dKG4pllldVWVKMnTHkz3/56U+/D5tyPS4KQyBpKmQqhIwh9M31cjlOSkWEAaLP7aWj477ebrO195FwhkG8dNfD6YEyHiHMsrLvx9PporV7fjllaYoQyCQp8myap2vXIoCqtOyUVrMjiGLMbNARIsYY0LONUTsbgo8+Bh9iDAhBjDDGkRACQgQARoitdafLVWkVI0pFvt+sL1PHCAEwajd6r2OIygQX0hj8NPbT0F/aFynZu3fvORWIZ+u0dJDNyixqMmZWdpmW5XLtGRWZlLOZ+2ngiJR50Q+jjX5d17c3N6u8csYoM3EpcomstRZ45yyEqErTlBNvImNcYeSsvfQNmOKiRkYIpxxjulntLv2lX+aMppNqevWSlquy3m5221+fvr5czmma7m9uP+xvBAODGj/e/y6n+Xm8Hq7Pp8upm4Z3t/dvbmpOBaFkWAZldF4We54gAKy1mLEiL7jMYgwUIB992x6v15O1RooMExZQMM6EEHab20Unpcij0+PUTuP8HA8IIAj9w+mAPeUMnbsrAuCleUmFvNvdl0mp9KLMzAgjhCMIndUFT/j+phl4ma2qomJCGueqzS5hMhH85fxEMTEhNENflWWalWWxSXh2ePncDefZac4ZI1TQBCOECMQBBG3ZakUZzWUCADhNZ+eRcfbDu+/f3H0ghHjvnHPOWe9c9MEH75yzIYYIwd8J6SCE8MrBhwhBDBAmCNN5mXkEMs19CD54HDGMQKt5Goc0rxCCWjtrDaXEGKWNdt5jgp13CFJnTbBexUXk6R8//rvj4fHx/ESwKNJkMbNVM8FxU5fGm2Pz7LGRklEq05gsy/Ryem7G1hQ5RYiLDERU5DmAUSbpXbUmIbaXl8Px8Xy5BBQRRpRK4+AwzpjieZmmZUIIEYQEIk4vDTilqPRozUVelZsBNAFiisV2veOCab1QIox3lCACYSpTKuhqVd+s9iZYDP35+ggimvVgPRCM62l6uT7GCMui+vbw+XR6XtfrVbVa1Tdplp67C8GMS+a03lZVMw+T0xh5ai3g0Trd9gsG0VurjUYYSS4Q4VlWZ1I6bxgVjJJ27Kz1UibKaWsNCgAAr+bp0p36ZfrTD5siT70z3kNj53lur6cDwLRYbSiTglLGpNUqehAJgT4kSWKdma3dbHfv3/4hEzlnSMokkVnTjL8+PBo9f/fujjG6Wq+TNB/H7uX41E+dsZYQCCNcltlHjzEqs4IgNM4PY9+9NC8iFSEE6y3Br4A8EKMPEOd5+cO7d7dG/af/73+l5O8WZYxf3VURYcgZRRBh9LrQGAhhDsSoVTdc5nmWPPfAn5tG6FmZGUGDCYYArdb16XJpm0uR1ttq22yOiOB1WSzWEBhOp0PbDYsyVbVapZWxziP0cjlOy8AJ50ToZf76+PTb01M3D+lwVWqBEFwuT835aGAsyooh4hZ9OV8mZca567quLtf39zsIIgJwU69FkrjgGKYwAJIJ0TbNPM7aqDc39yZEiJCxxqrlb7/8/K9//fOmXv/uw/c3692pa9qhE0JgxuZpOjfXLJXTRX/69rgq68XqVVpuq20/zKfu9HhqGcWbOt9vVto4KuT99rZrr+1wBRQhggGAMYYQAiIQU2o0DCFGGyllAEDnPcb41REGAUAYznMPgayzvPMoT9IqzwEknCaEsDov8jxlmHTtMNuFUzLOy6IspXxSw+eXz017/eHuDUP03Lanz7+WeVGt6h+//7AYdTg/n5vLYK2nWOYp4nRy1kYPIajLKs9yG/yqqiTnw9RYs1itHl8GG2JRrIwLykxJIjC+k5JGAtuuO51eVmUuJXPWGLNEyyjk3tsIwWpV2/MVY3S32+sYIaIvL88EgT98/OC1W9ebVb2WhHfD6adPf7lcXzbvKqHINAx93zngT9ezYHK33umgr81VUJLlZZomFOMQgnUueE8QUWq4NpcQ/LVt27ZJs4wg5qzRAAzTxCnLklSr6fn8aK3u9eg9AIiDGAMEhLI0SZWZMQSjGs5PL0VSxgCXbBIsWRe1lIlzPpdsm5XX63mKBlNMJSWCU8opZVmWUUSmoS9kiSrgUVTLDGIkEHVjHyA8D93Pn34BCNxsNgQxLxEjlDICEURcbPOkLuq2ux77BiFMCLq9uf3++x/WZQUAMMbE1x9NeG3TYu99iCDGCBFEAL0eAK9Tu9enAKUUQKiUgZglCL76eV6/wVirjErBay8oxAiM0ZehNdZQSiPItF4kyxKR6AUuy7hgVhZVmubPxxcflpQz691laMo0Szm/zpdpHJRWu+qWiSyTtTeu6S8uaBuFsr5IGSEEoJCmWZ6X0eq2b59fHpq2r+sbE9XhcoJoZAhF6Idp0sbkWV4XlTEKeugsWMw02KVYrdflZlNvgHcRIE5SSumyzNZqSlnCRcJZCCHPi7RKUpYQhoexMUZDglfFzgMAITF66btz0N4F8DwfX47nrusQJkVejGMruWAEDWpZtPJGIyEZxYWUEMWECc4EjHExyujFaA1iSGVeFivmAxfCx9hPLYPYGjrPC+ewac7GGeMUJ8RZ8/Xp0+fDF0KZtUt7fZ6WZVY2RnQ9H501nJKnx0OE5O27t7vNbpzaEAHBxMdY1hvJi6pYz7qH2DKGfFCU0pvdneRD2zUTi+f2mqXy7v4+WDNPI+NSBiUwTdL8+Xo4NGeG2X53u6o3iLF0nIqtQJR4AJUxSrsIYQDgVVMBAyiL8s3bNxjCf/1vP4tEz+MSQSAEAwgQhhQzCJE2GgAYnEuEmNXy7fBQZYn1nhKxXd8GGEbdH5uDD25fr0Y/SZ6WIktYMsyTDXbQqpsGG02/DIvxf/32OXpg9Jynxe8//rhKK+d98L5pztro3i7H80UwMS2WM3Kf3TBClR6Px6+//O2v3obLcCUcv92/z2TaLePx1Kzy6uau2u02VS7O5yPEnDMavTN6jlwsaiYQgySVhPI/f/k1TfI//vhHTNnQDdfr8el4PjRtlRYvL0cHYZ6mOIcBhFnPBobr2EYCXER/+/qU8MajcGmaj/dvbjebjKfP1857WxaJkJIR6qypi1Um0sP1iBkF1hl3wQgBD/5OPYsx+EAJxghba1+f869LQQCAaN2s51nPIYZSZFWarPOyrLZCVn3XtZdLdFZu5LLoh5fn/XYbIvzy7QsmLOG8LArGuJBFM8y/fv26qAFEv1vvtqs1onCzLhelFmPPbSswq5OcpaJ4yW7yui5XNgTMSF1XwLmnw9HqOc+ySS2Uy+3urizW5/MTZ+S7D380Xo39qK01TjW9pYwYo6KPeVpTLrtlOJxOv3/3w229O59fiiKv6h1h9OX5G4NeK4UgTZN0XW+CB93UeOdOl8M6KwllaZpkkxCc5zLV0/BtGa137dRjjK/dtSwqmcjgA4gxLzYiqZ2Px+vJWRdccM7O4xR8GIfOQ6Ct4YzLQc7LYJ2+dudBz2+293frnchWmPN56dwyN0PcFu8Ws7Rzt1nd5rLsp4byhGIumfARGqu1dw/taTbqZr3V3jsIUsGCDyEG512EcV1vUiltsM7Yx+eHZZ5sYZ+Pj4+nx4jAtlzf1vd5mhOEX+v10HfTMk+EOwimeeFcyjzrJ1XVu83uRkipjAkhRBBDCABCEOPrsDb+W+s/xPD3KC+hr56A4AN6ZYw7L2KECFHGIEQQwgBi8AECiBD23sXoCcFd116bM0Iwk5leFsopIZhiiCH2Rs9TIzBmACSCuuilJD6g2apoUM6rCucIAWFEWeWEsLwonDVllU92hJjtbu5LLqa5p5SkMnFKTXMXAYZYFitWrzfaKJakDHG7zMip4My8TIQQypgyzlq/rooqyXW0TdciABAmWbUOgJlFIQwYwyHAEAIVCFLiXVjUoswkObuOs3NuU64RpIJlGEsI4TIMw98jMvU8L+uiTijb7W8zkT5dnqzXnCcEp01zuXbXXb2uy4IgGGOw2gY3CyGjdV+/fVNapVmivcecwQiZGqF3y9IrSAgQEPN+mRGmCEWlZ7XErruemotx0Tjz+HRQnWrHAUIsSPJyvjTzpMf5cu3SND/10z/9gTCJjZ6jMy/NCQj+T3/8n+rVZlvstJqdXzBmRV58eP+huXTbuobEmWUk0J0On7UxDqD95m2epA+Hh66ftvlesGpWo8fBO13J5N/9+EcmcohZKsnffh2sDTGGEAOEKITIuNjutoRioxQT1EeXSuG8HaY5kRwA6EOI0QMIvXXLvMhUGGefTs/GZhiiLC3TPKWEYhG/Hb4yBmOMizZpUkYABBcBhG4YmqHVIUYAm+YKIX+eroLxTIgYQz+2DGHK+aRHmUgf/Mvx2M9jkRVpltEZOmelSFPOP3359Mu3hyqtIEBD20/JWBc1ISyR8ofvftzf7bWe5+kSX6f2y4Qxjs6bMC96Jp8ev3rs/tf/5f+qIT0fDrMaN2L/2LfPh0cpRZmXjPNJ6799+/Lh9j4ikKR0XEA7jlkm73c7DAlDzCoz2+XYXIG37/Y3725v3715c+17bRQAUSkTYZznabVaV1WtrZ7ab8H//a9LMIEYxxidd3bRaZK+NmT/rfiDGMG4qOfTUbIkEVJb5c2spnHSJi/1tWkenx9hCPOgynoDMBkmtVmvn16ehkXdrHdSiFVWL+M8jD0h8O3b++/ffff+7nsX/aU9QADqrBJJ+vEuaqWDtX9iv/vDh+9KnipjLl2rrF7UjGP0xi2zSmTy3fvvbvdv1vUN5/J+s1VqziSPkJEA1DJu1zUE0QUDIhKJ2Kw263Ktvfp26s+XUyHLXo2R4xtKpUxVUTEQZeq0DyHGbug5T1JZCJ4/Pj9C/1OWZpKxH96+QwH083QZG05pKtPdattN8/HSPx6vlLG6qvbrDWGMcz6Ozbm5euMF54sx3TCRYfReAwx9cN4Z4C0E4c32blVWf/7809Pp6CP+x+2bHz7+4Xx9/Pr11yovcykpJc3QUCJu9/cR3DkLF7UopSP0kxoYTd/cf3w4fKuLzdv7D8oq63QmC0Zp350XM+U8JTh2Y2+879TCg5vH0UdHEX6zuyllsSwaBJjKZNFDP3UBhlVW4ICuY4coKcuiKMptgFjkSZZHiEL8ezX/e6snRvDqkHAeE0wYdTog8grVxhCCGGMEr9cIACGEGEGIGRMBRACRlOnQNT5ETIj3fpqGJMkADAhHQTmBCMVICdNqdMFTxGBEwVsYjcR8X66fm8P58qytddaMs7uM4u3+jeAVRsA7G1BkglFMfvjwu3fvvtNWC0q0VdfxjBFJRB6MCd5FAKTgjBCG4HVqrXdCoNeEPGd8VGpUI+G8LreUi8VZ7d2iNYRwV9acc0JYLop5aqZ5ABGsViVGQpnRaFuttgCDUV+HeQiQvrl5Z602xlLvnXPLNOh5HKfFhQljXhaF1vOxOd0jiiNWk2rHRgorqIHe52kqGculRDG0fZclJcHcWN20wzguXPKEpwii8/mFE+rUFINtl64qtlW9dhGm0SUimZYRYhqBtzHkSXHtun6ax8Lq6TrNkxDpxYyYsB/e3X96/PLpeFHj1Hz6dLO/vyP10+E4zW03diRJHzcP7dhkiazyNYxBWxegn6cmBnuz3a6KrO1P/9s//3+u14uQsl7vdlsgk2q99ss8E0g/7O6sUwF4nifjNLVdj+aBMxFj1lwu0QcYAYweIQyiJ4QEGH/9/BtC4P729vOXw6TUNI9v3ty8/3D3y6evs4IIIYygVcZGiwjECFd5jaNjlNbVOpEykQJi26eZtaYd26rMBacwBILIa1gLQ5TKCkWkpg5EhEEkMEbvrUfTMlZpMfcqeCsZDzFkaSKSdL/ZRQy+nD4/HA6b+u7H737HuEzKVSIzEPVs1LXvqmJal5uyqETKjV6Wudd6OV5OTa9HpW922+D8OI3WB4IwppjwCP/jP/6H07uLV/bnr5//+uWvxOo329th7IWQ21V9ai/aGUTht8v1drMxTrtg6yJf5WX4+HHopr99+eXl/HTpehiA8S7NKgKYteHaN/MyQwg5p4lI6mpTZcW3rI0gBGcheEUfAx/8qko3dU4o//p4DJN6XdIAEYQQxkX13WSpVzJc2itHYLdaRSKPTT/MU/SARPTt+UCEWJeldf58PZ+ac5EWqyxthmswvm27prnu95v7+7siTZdl7JaubS8EcyeCNTbLs816PQzjZBZorfcGYXRzc8s41Vot88ST69PpAAja7m7rVQWRadrLPM0ehF69GOuX2bZ91419DD547ZyRKykYH8ZuGnoKISaQcrLZbwnh89xbpzBBEOCUZq8ap1ePeV3vfvj+j6dL83A8ZDKVXNZFzjFqp76fh1Va5DLlItmU28joqOZpmuuqur25W9R0PH3r+x4EIITknBvnnQ3aLpzRPC+NVtYrxknwzjizyss/ffz9OM8eEUDAokfnQ1aseEhwjISQ7Vpaq4I3giR5mRdF6bW6Nsemb1cSV5l0232WFsHacbpOaqjLOmVp01z6vrNs0WYZlul4bXo1ljiPwaVc3G33TXc9X8+Eccq4svbL48Pj6Rky8uHmzbvtfltvESGCcxBjKhJRVBhTHwJCr+vYBgAAAQgxAAQxIcFayijV1BoTIRKcE0xiBCGE4EMMMYAYIfQuAAAJwT4C70MMXqSpB9H76Nz8WoakzNThm9G2d+Ptzc3c94vqS5kUSWGN7vrj0/lYJCtMOIigm8du7q1VH27fb9d7DFmWFATDdrhQgijENEmNxgL4EISeZ2VVDP7QnGeltuWWY9w0h2EcKRFZnjZ9s8xzA07DMknGCWaQkFPXjMtYfp8xypV3EEHBOeeCEkYQNlobrZZl7NqLMWZT73bbyrrZO805IQSKgSHENqs9cODzw5emv66yFWPy5fB8PhwXrev1JpXZpOZ//su/mGA2xQssqr7tD6fLpqaWeQj8qkwpAlPXXPurA7jI6klNf/vyteun3bbeVKtCJC6YCAFFSGu1qMFFxwm3RoUIymrlg/NWc0R9gJtqY5L8dG2n0Z7PV4qIi3C2Yz/0ZVn/6Q83t7vNu/v7tutfmmsiGUSIMIajkAi8e/t9kRazGhFGVbkD3k/z0aj5cn1u+tnYBcG99ZHI4qX9xieLcfZ4eFyt1nVRxaKaFhW8BxBwkaZJiQFlkLTX6/Hw+LDoP//0yzJr8HrPgNCDGBDcVPV+Ldv++rGsIsI///orE+/+4//wT5tV3nXNtZkBJDEGiBChCCEYQcxkiqPd1JvNaptKNk/t8+OTj2Gc5yzNdjK3yhDCMYSECUIF1ipGmPB86gfoLSeoKjJMCCJIcg5AnJcBIyQ4S1LJBTfO22BCABhAQXiVZBQhAuP9bvvh7qPRXb3aIEC4TAjBIbp26PSCtZ2/PX9brIsQ9tOQzBxYZ22w0ZN///3vBWWfn79sd9uMS5pWPz8+/vPPf7srs7f7N3W9mucpScS7N7ePpwNQIHivlU4S0St3Ga4YIM5ZVmaUUUooxlQHP5oZ+4RDnEg5aWW92213lKAsy1dF4Z1HEGOMHPAUY4SwhxBjtKr4//l//qfgyOn6/+y6CcIIIYggIggoott673x4vjTDOEHgMaY+Qo+Qsu79za0g5Mvp6dSc/uHD9xGCw+XsfZinYZq6T9++LJOhmKdClFnFELte2kv3+dJdOKJluVbCEtT3y7gL0DpngxvV/HA8UCK2mw0ACQIwS/Lfffx98CECG5x/ODymgs/T2HetRyFNBCV86pZhaIssY5Q23RkghBGzehnUAkm8Wa8zKVd5XqE8EuKDH6fBW79Z35TFGiNgrPEhwBict5lIq7IY5x5iMhs1H0cMwqR0nqSMsXZqZTCrciOTZL2qMcYhehjBte1O15/zJNvvbjhn8zwjAErJr8MVAVymZUyzX77+ovRCIRzn+R6/vd3cEylsBJxSY+Y8KdK0mOZBq8Fota9vQrRD38xxshACCGHwIQZt1NUer5NfYpBEQuDVMvdj+3R4yHA6zmocJ0jA7W67Lot+bBNBGCecUeDtOI3KmnGcKDZKB8HEok2IQPXzr+7TvHTvbt7d376TnLfjFRCWYRoBCK+SBAhfdz0hgiEGAFGEIIJIKGGULgAiCDnjCKIQo7fWOw8AgBhBRAhmnPEYA0YkeG+snpdFJhkXcroMr8hMxlPtolHT/e7ee3t6eQYghswyzAAEvVpccAiLnEvOOIpI63mOXhCxSdeEcoiIta5tewTgJr0fzDCMnTW6zAuEsVd+U9xQxtuxH5als/ann/9qTIwQGacAiHmalUUKMeXJarPZNv3l2+UEQDi2Lw6jVbGFEXnvAADd0FOMrdbWaW0NwkwKxkWCKEIMEQDGsYshZCKDEPfdRc1WTePD09ev8YEiMfTT1I82uP3NfVHknx6/eBBu6r2L8dfnL4e2QTzZbncJp2psl2mag3Nmfmqu6+1bLtKf//Kvn749RIAhCNgDnWoIfSIYJYQLMZsl+hgjvDbPDniA4rD0Sqkiq/KisFZfu+7H7/+0Kk5qWQSX2loCYJ3niIvFTqu8+sN3H6/tdZVn8zJ0c18mUsiVSPO3dx9WWTGPk/ZWLYN3ASBknYcQX/vm+fAy3nfv7t+9v/vO2ng8PGpttLExBsa54CJGqNTUdhfhi4xnMMKxHV6enq/nU9f1X5+Py2K44BAgCKMPIUnEx5vdesVXRdLP4//xv/+H/8v/8t/PRsOIqjTd1cUvn14wIV4ZhDAEgGISvW+GcyJ5wEQ7255O//rTf9UK3r+5pzjJ5QoG7D3CnGEqnFKpKGajn88/KWGO52vCSZLLVVVwSgOEWbJyKB4uB2vcrq4ZYbNatLUBw5Ql3715/3Z7t8pLZfpj+4wwQzRmNF/Vd9G7fhy4ECgAAhen58PxHAB5f//memlWVQmDl0IaGK6qJ5wQ591/+pf/crterdKM5AWMdlOsVZyP4ylNRMQuQJdIHj3o2o4lNARPEEqF7IdunGZC6XbzxkHAmPh49+Y8nXkq/vF3fwCRPR9esizlnK7L1WVoD5cXyRjDZBqHGAImxIVgnX3NAgQItbM5zxAjEYAYQQgRAo8wgBASTChFKWfb8t4FH527tn3ACBJybVsYQzv08zzCAO7WtV2W6OPfHh9+/fYYIyyznDJYrUqI8WhsgPDaT79+fSpkghBjnBHCpmH4l+OLC956v6t32/rGOrdo1XbtOPXzMm3ztTLm2JwIZPF6IRynaUIIIRjEAAQR1d0GHUkzNFImafohhiAwlUxAjEdHIAxcSh9j9B44gzCSjCHB8rwEMGqjQIwhOKsDtMZrlSdpmhb7/Y1Sc99ex6n3ERTlqs7LYextNK3qEGdpUWCKm/bSdd21G9phCi4WaaGXqWlPDLOc8VymmPBXZo5zFiGU5DkiDBLqQBQYp1zMRrvFzWFu53ZdbOpyPYxtjF6pcZ57RjlF/Do249BG52Dwo+m6eWCcN4CpmREEgwnTYta7e0T9dZgEgKs0vymKvhHWSR+DUnOVZAnlMUaacAjoebyezKkuynWRSS6JZFLwVVVxRpWeEi6FSBFAEEAEISTkFQ37+mQMISKEXyM5nApDDITQOUcphRA45yBEr6tBCEJGKWMUAQhCfPWFQYCdDUvQVbFmPAGwDyHGYLfb3TgOykyHl1NzbW73dwCSl8u5GS6fn76mWXq3fUs4W65GIrrJVofjy7/8+c9tO93fvsGEpKLY1vfPx8ffHn5bVdvggjHu07fPIATtxk2+TlPpEw9B6OcBYuKjRhAhRId5srbLs/Tu9l1V1OMyXprLzWrlrbu2Xb3ZC0rGcWrngWKKEMYEOaMBDEW22q4YgBAi2I7XEAOOAkbMqQAoLsv0fP5KUVJlqzzLtHbReE5jud8AAKsihSDc3+7TTEoqIQKn4cIT+acf/nRbb501T2YxQ39/c9ON7b98ffj9H26kSFOZvdndAQCUmh6fD2WV54VEBNlpocoSJLOyplQACCKCWunn5+eiXGV5Lbho2mMqi/f3u/16Pw5NCCG4MKmJy6Sq1otetDfRhQjR3f0bH4OPLhcCISCTjHjQtFdj5ktzPV8vd/u725s3ji2MsHVeTURbb5uh50mx29wB7wEG1jvjXABwWbQNVqlJL2OapmPbtc31rz//9do08zhaY8Zl9iDGCCCAGCFog2R8Ws6ME8azPGFVUSgHUszXdSkxrMoSQwi8iyFghINzzjoAogW6m7XourysFmOEWL2/va2Lytgly8tJjRbYAEkA2PuQ5nnvdZrXRVo9vjzPLkiMIgic4iwty3zdDKcQw/HcMioTGQgkm6ogjG5XdQxxUVpI9vzyCANiAr1cvuxWd1Umn8/f+qm7lW8wgZ7jcfZ5Xr4rq77rAUCU0XboHYvOhX4eyOl83q5363z19fHpRTKAYJaV//6Hj4+nzw8v3yiSWSZsWCDwRSqic8fuSgjJZFJlWUL5NCmCxTTPjLKyrNM0NcRUZSmomBZ97a4Y4zf5LWcExPhyPQtMdquNMjrGiCBCCASIMMYOQCnkbr0BjgKEIogAAIwxhDDGwBhVZnHBV4W822wgQMZYpRRixMNIID5dzn0/Jox/fXxWw4wwhgHvqi0EsK7Kssqb9uqAizB03VDVm3//D/+UZuXL4zcMIoUAeoO9OZ8ebXBSZNcI10XNOb/2nbHGen9pO6fc4Xr+/PQwjfNmXW03q41IiiTxwccYi2qDCC+dtSg6ECWhgnAQvA42LXPqkkUNZVlJkik19MN1WpSPsaq2zlnnRqsUpTiEoJ2fQoA+xOiu3VlwerfeC0KKVSG4XJfrKi+mKb80J+vsNA2ECkhgP7Yuuu1qRQA01lhrz9fLb59/hQjnSbZoJUmSiKQZ28VMb3Y3ZVkzmaRp4byd5hFTrJ1jEAxz9/D0m13U/f4uRr+o6dO3T9FbjgWZp+vYNM21zEoMadePh+u5ykq4BBAMwwRxdnf35vc//GmYRgs0NItfJkNJtE5Nsw2gcYhBxAnlVPA6SdL8t+Pn5+fHXMp8vdqs1gACJmUqMr0s7dKlIsPMI+cYRBjjGKN/xdW/5vFjjCAi9Mp+phhjhJD3nnP+OiqglHgXXh1hhBIA4OtYOASntOKc396+WZYZQJhm+TD23gcIfd9ffvr5rwwREDyCJE2LLKuWpZ8XHQKmmCmlIQAAkTSpL8fj80ujlH05TT/9+nW32/zpx393t78TLPn5l7+U1cvN+jYErxY1LkOvmra9YopnZ4q0WGebf/j9PzgbYowhxNP15JyWCVtcjxVUyqyKwhl8Pl0hJHrScAVB9CAGzgREwDhnvI3BT9PIKQEg+BjLel9nVfQmxNC1l3noPAiD7mBUldxsNxtJJdDu3J1iwGoxD8fPJox1vf7u/ds6Wb1cnxB6iyDdbW8kTwhCRKLVdgVd/PnTb7c3775//wMA8e7+7e3Oa7UMy4wRwRQcr8ff/ttPGLOP7z68vXmTJDJGTAjPihJGeupOXDAAkLWxKDYIQu+Cs9ZoTSnllPFUruq1ZPIyNEY77+N6s6/rHYDRmnnqO2WWRc9W2VkphOJvv/326evX6buFUYJBQCTe3t4E5Q+nx18eP1Wr/dvt/sPd/6C9+3Z4XKx+OD3erDYQhH7sJj1fvvwKNLZKAwBu7+6/Pj0lMKKHYwwxQOgRpADBCIVgs5nOD2eMuXPBIwxR8r/+n/5v0fZtczFKxxC9c6+pgFfAuPcuRB89KrMyFenQokymjNFJTS76uW0RgMp4Z1uZyACjYFjI5J9+96c6rxLOKIHKDRC5AD2APvi5aQ5d3y7KSplt6wqBmImEECKYcB5IWSyqDxC+v/uO8Ph8egzBWje1w9Vaezg8iCTNUlkVBcWriOCXxweWysvQPr0cbrf3jLDoPdnU1cc3bxGhPwnx7flrlufbqkwo0Tp/1OZ4bsaJXC8NADCh8uPbd5PRSptl1pwyzAgjYp71rFSWpiRLRr3Uab3NN8b5y/U0TANAyH5TWt3AAL0PT9cTQIhwQQidxgFiFFGIAGCKyyxZryplACYQQoAhghDEECGE3gcPwbRM+7dv9/Xq2rfeA8FeswRQa80FWuVZzjPBhJBZ8B5CVOX5m91tlqU8YVJyTGiE+NQcPAD7j7sf333PCWqb08v5yCmy3gzTKJjYlWtEWTtd0IKEzCCiWZETIdv2yiRdVRXCqBDiw+3bdb3xxnZTTxnL0lKKnFFZFuUyD6MaOOXOaWddluYB+GXu+2tDCzT2/aW5uhgJwdFbqxdrx+i9jwRBEkOYl5lBPM2jDub5dCiFyLPULLNdJpCV4zQarfSsfIQEm2tzdsETgjjjHDMIwzwPpwj6eZ7tqwKF+QgcANO8XLoOoBgDCADNSjEiAIyX4XrqLxhzWKGEiQ/79wQL6511LiASADieLyEAG3zEIE1SG/A4z/OsAGTz4qzu84Qqbf0883LtnMvKbLOt1fVinLsM02JjMwwI4lqWzdiNRtdFTblQerJ6LrN0U5fJq3o7xnlZjAoAgJfmBED7/l1ys2YYYwig8857/zrbDTEAACBAEKJXdiyCECFECIEQvp4QCEEIoXUWIoQxASD6EGzwQjDrEKMiSRNMSHCeIJxnRfCuuZ6ev35LSXJ3c4+iX5YpOkcRlHkBb+5v97siz2NA1+biA0jyFcK8rnZGWYz58dT0an53/0EtC4yQMPrt8FueZjlPMPAciypbg7g8n591BFmSpVlSZjtBOQRgVgoQtCrXiIRfvv48jE9VWn24effw/Nk4T0FoLk1VnFxYhEjztHDOzMscokfAWWsRQpKLVVFlWeWd01r99POfz6dDlvCIoopTVgos4IqWqcxSipvl5fl0NTpou1innl4e3t6/Hcp6MiqlFGI6O5UXpcTUg7xIkt++fCrr1f/+d/+4LdeX/uyiSyl1FkUIq/UaAPfLt0/HtkGIrnd7wuk4Dd7oNJV5uU7T6vburXdhGsdEphSRSc1a6cXqAEE/joIxScXQtQscxnnolpkxVlZVBM4Y1XRXgrADAFiLCUYx9m3/dDiemn756Zfj+ZynnGX8/f27AN3ilY2REhChHeZm1qqbrtooRPC+qhBEeZ6d2sO16b+//z7nkmImk+Tjx++cMf/tp88IYxABiiAi6KNPpExYqU1/Ol+7bp4m+z/+T/8HicGXb4/zNKlFeRcgRJTjYH29WjNGnXN9O9zu96lkeppwBBiBcR60XrwPVb5ZrbdS8GFohmWACK2qddNdYAAYwj/94R9wCItpj90zBsjY8Dydzk1jbNjebNbrcr/ZLnru+naYhlQW+82bMq9itEWWSykRjlVWYYS+Pn7lJKvWbBhnE71MUpJgiMDL9SWtklJW09DtNzuCCUH4br8nQiSUscnqRSkaUVyMAIRghBHihAsmQ4guEmfs2LXzbDhmNvgQwOl0Pp+vHqB+nhAO5Y8/7Df7r58/y5yhED9/+02pBSC4WW9ShNtLMyzL2I89iudLO0yIIEApDhDbCFAEAISEUaOm07nT8xQD8CBECAEEEQBtXT9MzlvnrIeBcIq5jC6eLy96Uu3YBB8xZmkmOGWLVyHC2SiAo8gSyQQAcbPepiJbtNHw8bm5vL0bvfFJkiVJGpxHKDwfHmRS7uodY2kkIOMVALEuK0zIYqyQIpFstyo/7G99CPv1elPuECat6rV1ZbUJARijEQwoBooxCLEfrouaAIRvbt+3U3s4v/TNkPCEEmyUyvNcllVwYZ6bbriM3Wy02W1vN+ubGM1zezq2F++jjq4ZW+Pmx8tTxjIA8aw1xXiapmBDTbD1vXfhZnfHIO37hhJU5OXpcpnmRVKRr7I3+32EEEcSQSSSgBiSNLPeB+C9VxiRc9O2c3e736dJAglPk5TzdDHLME11vr6/fdt2rZ7MttpgQe/2d4zKp6ev2pqq3tzfvI3WPTdfog8IcGWXpm8KtOJcGsYCIgHT9eZ2OytlZ57IAGAh+WazhtafTk+Hw1NVVsZbYCJCJAToAsiLnGLy+fTl5XK+uf1dmhQQAWPM31mBMcbgo/cx+Aji62oQRAgSDBECEL6+Gq2zGGFM6WsnEb26Jbz13r0ukkYQQASJTIahf3UGDN38cnhJWHpzd1vkBULeWauMej49JozCECRn0UVtlHPGWYco+eH7P7xcT7/+7VcpMkTg4samPW/KMjgDvFn8fLo8G57G6N/s7lXQXw+/zGaEEKeM1XklGIcIM0owxYC+ESzHCH64jZfmqcyyMsvN9vbh+eX5+TgMy2THNOd32/fRqUtzHJ0mmHlrKMECSqP90HbeBkSQMwoBDDyGgRg7muDWH/abam/0LAlr+uNi9Wq13lQ3Wk/z3Cu9DFOLIBiU7jGpqnUeQd9fLSPWW29jnlerzSbjEuOQJmnXXoxVEAORiLLIh6l//+Ydpthbd3+zdmEZxlarJaKCTQ1l8n73UWvV9Rfr5mm2QgjBJYxAMK65fj49qMuhrraM0ePp8NJcAMYvzXW73gpOT/3R+4gge7d7I6i0WsPgV2W5W+05E789fx70TEcAvOWCi4xSB+qMR6cGuzyeHptpQADCCM/nA8YkBAcDyrjcr1YEosXYSassSx+/XSalMCTglRaNA0JQL/Pz8wsi4HQdD88XQdP+2v7083/7z//1PyOIzk3/SrEhlNmgkjzPpRimBgHcnNt/nf/5fn+7LrM6zR9fju04EEKMdaMeIAhaL5exLcoyT3IEQLkqMEWU0Bgt9DgRRXDgculCMM7Ddb3+8cMPaSKZTLbbG2vt+XqGiNXrbSIYghXjUkixWFMVO4Thdexv650k/GZPzl0TAaKJsM4kaf7Dqlq6FllunTlfGkbZ9nZFpCi7SX09Pv3nP//ZzGq7rj1AeZlqa4Fzb252COJL0xirmr5v+l5QiinRxmqlhmUGAC9OUY4fn49KxafHgxqmm/2OILBKck7Scz/gNNPG/fb8eG6umBBlXXTsNU/nQwQYvZI35mV+eDpcuxGEiBACrzNgGH2I86L6YeQCna5njGMikkQWPOWFzQ/XJQCEMXYeXIbufrtdr2ohi/1mr5aJIjJM07eXBxv9uzfvZZL9D3/676ZpGoYeAuCdqfIiy8oYg9KqjLGsa8kZQCi4aK1CkXgPvLN1lmeMT32rEUzTrMgrKdmlb87dua42UuTeB+sHrZa2u1prtVP9cLJKZcVqmse+7xmXWQG6thdc1GW1qTdltYkgnJrHl5fD9dwba0yERX3L0ry/PDTLCCEWVCzOLsN07Dol0eJOyipJmDcuumCARRhIJrWavTUh6ITzssh9UIIiADPl1KgGTpPVqlZOFVpSTCjnhFGtl0v3koiCUSG4kVTmPE04G5fF2ckCE0GAIIIAZqO1tjsmMIveGoDYoJbT2JQoy1dZLWsgtWAyF/tD94QQYohkSUEg0rOhhHLGDaan9nmxFgaUUQkBMNbNarbeKmsNCAQTZQGA2HkzaSV5MhoHMNvu32IiZj3ACAEAIYbgg3fWe/t6EgAIMf77rR+i13cARAg75yCECJPXNTIIAADAeeO9c9YhBJWarTWUMu99BABCOC/TNC3W+a+P36JziWSrYh2hn/WkOeEQUMupTJ0PRhsC4TR2V5KIJDsNrTA6yamE2TC1f/vtr2VR1HXV+CagwDiJEU5Te2jPL+fLtR2iC5fiojdzus68M+3U++AhId5TEGkmhGY8EXzSs7Xhw9vvVsV6michxK7clkI0l8OpPbIsW+U1BLDrmq4bJePDuDTjtUizVV6+f/tWadW2l2Hq01W5Xb9ZZ+tlvgZvA0JZsb1dv9tkddcdta0DgD4skop1SRCmRZJjgq/teYJB5rlI0hXn09hPUwNRZFhusmJSo57GuqhymQrBUsk4hwQjgnHfN1yIzXYHgB+mFiB0S98jBJUa5nkBADJM8zRjiHpvU24jjBDjMsmdMyHGl77rp6kow6LmEAiMURuTSC6EjBAqqz0wgYRqVb7Z7LOShxiGqY3Rh+idd/04fgMh49I6OwwdQXBZFgTw0+mQJOk8qcOlm9UMw0/rqhRCEiHjFMdhUNr+HUQG4qvjV6nl8XwY5+HYNMukY8rHef7nv/7ln3/6VXB+7Ubj3WJ0BBhD6EOUiRyn6+U4oAj8BtV5/rL0D+ejNmFdbwXnyzL9fPzWtr1MswABwOSv809vtrcZIZwCKSiAaJgcR2x2mnMRAQktqus6F4n1rl/a7XpdpEWeli4CiKPWC8SsKBKKMYA6UjHMF2vtomaWMeBctPY6TuPEQIxpmlGMDmN/ujZqccq6c9sMeiKTWa5j++3xQSkLAJ50/PRwlBe6328oFsGFGINZNGdJkcFxnpS2KISn8xFBMKnF+BBikIAdTofrpbGL4ZSIftwU5TSrRqvfDs8vnK9l4X3EiMYYF61wgCiS16hXiNEFjxA6t/1ff/tmrXsNfGKEEUIu2Bii83bs+0VBDAAKsCrcPCnOCKP0bnv3Hft47trD+cIIUcpobcpSlEXpjDLGvnTNLw8PCRfbev/rw8P9fv+7D98P4xBjDAFeh+urkWxSOk0T45w2i8AMIjTOg1pmynnbN0B7QljbDyCYRLi5b5vm2PYd43K/2Zd5YZyGiDjrrPFt22o/xuAFE4zicegQAG/3txQRrZS1LiuKRCYYCYjBuKTrcstxYY3erraccMzI7Xr/28Nv1i55XgZnX/qLtn6OmiIRXGymnmCcJmmIQDLGhVBGQ+QoI4tZ2uk6Lr0PMWXJqEySrm/qW2XUz1/+Zo3++Pad4IRTtig3qgkguq03pU8Fl7NamuaqtN6v7zgXc+yfTk/KqlPTqslgAniGLqcjIXI0erNaaTNf2heKIpe4rla78j4tMhyjVpNSU5amVb7xzgshIaOzHRPO96vd+XLq+i6T6c3tbRSgHadT39Np2Zf7Ks/spJ23nIs3t99pSLJqtZjFGCOoCDEE75131lnv/OthAADABP9bzwchjCFEhBClVARACuGci/82M/DOG2MxNgBG58yyLEII5yyCMAafyuTN3d3nL79dmsYa17Sh7cYkTYaxRTCuijyEMBt/bXqtlu+/e1+kozWH7eb23//pT3/56V/nCTFBlmUC3jFOZMI2xSoXmRCCELxMcyLkf/j9f7c/HI7N0dr4cj3rEKVIlNZKTQG4ACjnqTEzhsEY3euZUv7u7t2RPb207nb/bleun16+/frl86peb/Pt/WYHQUwpTmWxqjbPh29fHn/lJA4w9PM8mMng+Pbtd+/ffZSEazV772J0Oc+/u5WCZdooSrhMKghR2x0OxxdOGMIUuJAmgmIEAIoOUEhn3Z1Oh3mZGTuuV/tFL/3c71a3q7qOOCaQztYiEFGMal4SmSSZlILqJUIAT9dnDDAnbJoW78M0jX0/7ndWytRaY4wqs9XrGIQi9ubmA5Xppbls6vUyN16PqyTflVJQDp11NCa5bIbjeTgjglPJ8kxAELJk/fX48PxwgAib4MZpWWVZdC5PpOQpWctr14VArEEE8iLNX6vWZRwyEDOEQzDjOGnrIfx7azFE5GOoqkpK0U9NmdX7OqEUv7m5A8F9ePsegDAMihAMAaAExwAYT0SahhdwbMb3N/u6LBetD9311+dvSVZkeVEXOYb2y8t86foayQ/3d5uyfDo/D+O4DFch6G57J3jitQ7eYwy54M7B/eYmkcmymMlNCfJt1zDURx8iBBADzlKZFs67prmY4CWjWikMMIJYeauHeZnmr0+Ps563df3dmw/BYm0j4ck64dmsxjQ5N1cym/l6OY3d9X5dr6s1pfzS9f3Yd4MKMR7Pz1ZrQtiqqou00NrNzmujrTIQgF4r63yeJnmSpJx7G0UuCSedGpXThJBhmNU4R+duq/XdeiMxBRRSxrQiajJVtboOkwcYQYwxZZhRxCOEDBIY49/Jji5AADBG69WqXwalTExQ188A9EXK69U6SwvJknaYOWG7uqYIQAQ555xzwSkGMBUiSxKj1DJN0zSeLyTjwjgnpNjVO7Uozhmh7Mcf/oAgHpbx4dunZeggBg+nI4L0w90b5dTnLw/WegdskYlhGc1imr7LmLy9vX0+PI59Z70TImWMcy4Z42oYpcyTJPMghACzJPXWIwA4YwjjRMgiy5fZOWsTkeSyJphZq2WaZmmqzUIJMca1Q/9+vU9lepn6d3db7PCu3C56+fnhEwHhd7e3t+stxRhATFgCMO668+XyFKIvswojnDKhgB7GsS7tpAZrlkTICLwPLpGFsIlFsa5qQYSbtVb6+Xg+vDxLLu/3HyCMi9JtP/RLn8jk/fbdrJrHw4GTNEtzQuiPbz9qq4Zpatg1Ij2ObUb6jIp2OF3b4zyN67Le3bxJhAQAZjK7295QhNflBgH05J2yOoJQr2vIKcS8adpv81cUImOUUMw5e3P7nuUripBaFowxehVzgBhB8N5ZZ8K/jXQRBACCGCPGiDH696EwoTEGKaVW6jVn/npIWGs5DxC/CgMoBGBZJmN1ykUILpVJmaWcYJEW0zx2fTcts3GOUQKxEAKbOIeIIiLLYsdevXm3WWf5x5v76/GbUloKua1XRSI5FZhRQKplVJfZlmWFESrLepXsElpuNtvD8ThOilJFiEAQUyIBCAEjAKJzBiAfY0yzdJrmc3PMkqwoKwLR1LUvz8dxXN7cFcCFw8u3UXfe6e36NmIQMSqr1awHpbUNsMhX63pXpEUiMzXNCIZhaOZ54lzmRfHy8qy1QZSYrkWIhqg+HR6v10Zr9/377//H/90/IYwP5+e2HTblhlISEbLOWTth2F3GRlm9qXbn64uLoeCyG/thGFEAlEmL3W9ffpOMr8sd4QIDEnzwIFTlihJurFnmWTCRp0UIfp7HGINalnmeU5mtsuIPH37XlKdh6jV0nliGScEzjJH3wUaDYIjex+itna1dhmVGMSCM1DK2TesCqeqyEIUkIkAHLZJpfnPzNku6bw+Pox7WZYWRk5LmaXYd226Z17nhED0cDovSEUAYI0Aohogg2K9X61UekM7lmkDKOf/xw0dK8HpVj0t3vVwRiAwzSolV3oVIhSAEp2X68eO7lBEuGCb4tAzHa4MRBEgfLs/XsVsV6ce7/cf7m2EeBOHRhlmrw/VgIni7vTdaq2U2Eb5c2zzN8qyQXHjry2xVZaWe9bm7LmbWTss0rat9VSJCeTfPzdjt69U0LTAgDjCFGFI2hRBCGJUqQpiUKrNqU+/7eY7WSkb3ydY4TR6en501nAmKMCHYRntzt8nn/OnlNC2m642zDmGv3VVwGhGkjHtrOSYgBAIA4yJjnCKY5ylGFAWQJYkNQFlbZeXv7n/8dHj4/PKVEHCzWRFGJruU5epwUo/jizHGeU+YABC7CBghwFnJWCoSCMdXRnQIrzJavy6rvCzmaSKYDWpa1OKCtzF+eX7pxlk7s1+tU8HSTJR5va1rznl7PfddA2NYSWkYfbmceIwFF9emicF3bSxkuSpW1s0UgyIvAcSU4gPEX58PhOB50e1wji5OTnkfJGUeuHVZUMwf2vPDy/F39+8ZT87Xc9tfUpkZ4zabmyKvpmnKsyqRGRfpYmchM++csx5x+tJcIUaUUGddIosIIkRgVVZSpOfLQS+jc/bQHod5EJyXaRFCDCEmVDJE39zfcMzRjBin1mitJmNzpQIjbFWsKJdquOSCrVa7PF8btUAUEMVNNzy9PMTg7usNJoRgXBS5TGTqM4BBkSZ2MefmeLO+v6+3KYiICQhB9IEhttvsK58Hp2tRf7ugX0+PGIQqlYxSQWmaZ2hsN2kFsHUu+ODaafnPf/4vzfWUJ0lwnotMpo5gChAWXHJInQ3a+2bsAojj0GeFWFd1lq8klw+fv56bZ0q5NCVnaaBJXnOj5xAcE2mI0Png/SsM1Dtn/y0p/vfKjhEmhDIWIIAYYSGFcw4hzIV4NQG8QtEBAABAjAlnbJpGAEAE0RiVCrFbb7Scz9dHyNHHdx+Cd1ab4OzT02cA4Lv7HwCM1/aCIDg3F8LQfn/rvPvp538dxz74GEN0xkMABSVSyCQtwuBUVN7Fr1+ftnWxrzdCSA+0nRZCoUhkkiacMgAhAhBiCCnxAYxjDxAlSFLECHHN3DwenwXjMcTD87HtZ8YpBEAQcbh8uYwvZZ4eL4/duKxXt5vVpu+AtyHjifHRGHfq+2FRZZppMy/DqLUWmX26vFwuL4nIEASA4CpfuWABxtrHftG90jrEvm9/+fxlGNrz6bkoV5v1epXmhPAsqwCFs55B9H/7+pvgEq3vIuGcpXrSQ6+bS//l/HW7qoOGXHCWJNEHAI1zyqg5SytZVxAiiCKCKMQQnYMQMsExBcty8ZZh4Darkgl3bo6v2yDGO4o5htCoxVuLMWaMEwhJRN08nNtzNy/9vCAsU5bUSemdM8YFSLSnDlDOUmftqW8vXXMdnngi+2kYxnHUC4Xk7XanrTXWE8Q8sABgACNmKM8FE7CiecFzHGiRl1KQ9vrCoE0ZkoIgCEGEFFEHrQ/AA8AF++G7N9ttVeelsyoVqJvLiMCu2nDO0rxI0xJHpPXw109/XbRelMOb+1xyM9iu71OazvM89P3kvbJe+kAQYZgjBgmnGFOrtEgKwHh7fh6bqzIuALJe3ZTVarJKWfNweO5HPc12v95gihFlP3z/4x8pT7NEzTMkuMhLgIhzNpVcL8sff/w9uXZdcNEbBwHotW36vqqK3WbHhejHBQCQytQ5uywzxMl2u8kFP52e26FZr+oy99Z7xggn2AcjU5HgrE6rpMj7eaIIp0X6htxfhvM0j0ktWcq76yAYstZqrb01mDAiEuetj4FgpIzRw6Stec37OOdDAAiCEKNxOhKMGXEgBAgChJd+uA4dgohSvlmt3799c3t3w7gkhEx6WPRo9OK80c5a6613q7KUUkSEIcbzYBLGgbcAuBhscEGN1joPIrqt19+y8nA91XVFBEUEViJLqKhXpXVKcGGdNzZ4A9p+fHh+BAxQQrN0tVqtAwzt1Ck9I0SPbUfw3Kvh/S2OMTDMBM8m89gNLYbQhrBdu1ykRtmv8yeMiVITgjAArIxJRf6PP/zD2HcgvHoWE62V9wYxxjlJJO+c+enLl8fzkSJc5ytnrRS87U82mHbsIBVqngUjZVqUxU03tQ/fPjvrRAKjNUPXOG1C9DjEx4cvBBEKAQ6+zJNoBcTM6U5Isatr60Ov7OSWQ/M0m4kC6L25XI4U474b61V1e3NDCY4RLcvwOD9iTLthmlXgJEDC+3F8Ob1kUmZZgQi10Rs9QoA5y8d5kDKHCGMo1ahIBOuypAgvxjAXpmlMagmjDRESSiMAMfoQXIx/lwFEF7wxMQYI0Cu0GWGEMaKMeu8AAoSQAAKEgQsa/04KhQhhhNArzZ1SJoS01jIqFjTHCAilxWb7MX4/K73f7Pv+fBhPRuvNdgMBkAmSTPqwALh99/b27uYeQ/rffvnL6fCSJ0mW5VSwNMlBAIfjsa5hvbqp0k3Gi9OlWbRL05RjXFf5pK96me63W+XcX3/5c5plggnJkyRJo0URwjTJMIbXaUocsC4cL83xdFxlBUYYEZanyAWnlfaJBiG219GYUOawzEi06nC9NE0jWVJv0u1mq40nY6/naRgniFEkgiFeV1sbNWMMOOvt4gKA0Rm1SErXRVUV+brKrteTd4bzxBkbgrNWNW2jKEdgnq3tlqnIihDhte3Wa8wFT1gOAYx+RNgvAexWd3maESrnZe7mAcRQFNm1OXsPvnuXpiLXVjdDa6yFMWQiYyAFMPb9y/PzZ4CAZFmeVBEGgTPn7DC2BAsiBMAEICpYmvOUYgoZIYiPl+VwHZQNDmAEjDHGGocRXrS/DmeLSFpUWmtEmBB80iNmWAoupLjP8//60788H182MpsmFWOAMHofMY4BAkppRLEqqzWjbWeq1Wa/rbVbns7f1KR89AHEiCGkOGIYQkAgDP1ACb3d7rIsy/NcjX5ZvLbu7e7m/f17EKJk2bTM354ex36MGNrgrTESMwYqEvHpcpGQZ5LDIjd9t1tVUiTGWE5DnmSABO0VwoRTDhz57v33So2SJU7rZRwgRpLT4/Vw6q5aQRBPytlVubq5vYEQ4YgEI31vEpkjAlMvsrRmlEVtj5dH8uHde2/dy/G4LBpCAiJqmnGezbVprA/nawshyoSoqrxMkg/7m3WRRbM4a+7W+9no2VlGUYyGQkAhyGUGADpdr1ywoswBApyQ/Xp3ac5kGIuyFDdsHJT3nhBitOJZYmOMwPtgx3G6vd0JnjyeOgABhCCEiABCAFBMEsHysnLBIwiZIptq7b07NsdNWX7/9vtVvUkFFpwkMp/Ncrqc7KLo684jgHmeA4QAgMusuCBFuXp6OahpoJRggkhk3rhp6bpxQAhTzH734TuIwKSGIpOZTAhEVVFlMhnGeG4up663IVKM52We7UIwwgQOQye4sDB8fvwStf3uze+Uh0rNTPBNvY8gDmM7T621y+l8TLhc726asZvHHiLYTF3bNnVZltmKUcoFL9PaOX1B4enw1E/9br2+udlOytjoGYNVLjnBDNJL3x+Hdhh017Z3t1vBibFOuw5zQSHGmEJMAuJlVh2ZODaHGiNt1HQ8vL1562MkDAsuvQvzrD+NX/fbldZLyhPg7DAhziUipG3PwzxgRCHwRSL7fjp77VxImLKLUsuyu7t/d/ORs/Lnr5+dHdMklYTebLdFWSUkBcEfjs/64UuSrsqiXq3KPC/+VPzjvCwAREqoc/rw/CWJeLt/Hwj9fHx8fHlmBBe7/TJPACAhc0pAiP4VGOi8c846Z4N3EcRXspv3/lUAQCh2wQIIKOMBhhACIRTiv9/94ytZKnoAmfceI2QBQJBIkUAMF7VEZ6xdEITzND69PPTT9c3u/b66vQ7HeRmd1e14ZYzfbPZlmhlj6rqqi1WRZu10fbmcUp46PR+vI2Jp37eY03W9pVzsdjcouoDwqJVyjsukHfrn01E7Paq2rmrvzWxMRNhFC32cx5lIJtOKEvHdxz9u1/dD1/V9l6UpSqC2JkSwGEOYgJBe20Ww6unhAWAICD53FxxIN02bZQ4xIMIwQpyK9Wrjg0EQlnkJIThR/PzwqR06hKh1lnHGEaUYFpnc1UUm5PU61eVqXVTA6VW9cjEapbthfPj2KRKc5vmwjIRi4N21vSQyjcHv9jvJk+ji+XohnNb1elnG4+XbqTlQcZekWZIUgvPoQyaSZVkIJxHEXo0wgmDtMFxm1fsALAMoMsb5NCgqWFnkuawCgD5GmRbVyt0pgyj2CF+6XlnHkjQlPJXi88u3U98GDzarDUSMUCcYXcaOCfHh7du/fbOYgt/t3kcIq/z2D9/9Yb3d/PLz3yCIizYxRggBQn9fPccAXtrmpVlVRXV7e//xzTu9jA9PT9d+4IQmaQIihABChABGEQCrVfSRYA4CXKb56p0a+69Pz79+/na/u9mudiHEVVFzJkBA9J48HZ4uY7/N13VZUcoymSac1vU6EezaXzFB0RtKMudDAIEwSghQy0gRA9FxivOscFkFrD9OL5fmTAg7d4d+viYJS7m429zKVOZFCWNclpkRPLz042zud+9HM2CCpqkdAeKAPp+fSZHJ4IMPq6GfrQ6MMOc9wgRiDCGJhF6bpsiy3WYrKDLL/LRM3bzc3dwnaTYbfbuut5t1P1x9NFW5ymRx6Ybj5azUzH784/339843ylgf8DjOBOKb/VbNlgrhQwzhdfzjQwwBwSQReSIgZEwKiIbXXA+AMAKAECKYxBi8s0WWb8sKI44JoRTc7ve/++GPLphluj6/vCRJDwG9nq7TNGqlKWd5mu1lIgjJkjIUsJ8770Ii0iJPkiyFEGBMsKAuAO4D45wQRkUiJW/6Uz/3q6KCCDPGy6wSnF+7PgTgYoQMN9NILmSzLylVAJrr5aU3+svDAwawrjb1amdc1s9n5w2n8vH46Jc+OA8wGdTyMS1DcA+HrxCCVKTramf0vIxDnhdUSgi9EIxSXlYrIhnFJJWJkGnfts67VVbUGZZMJEW1fP5abzYMxYAQZUmIOBBICK3K0mv39fkZM1lnGU8EobTpxsnMHoY/pAVL0tPhuSoKT9C0vNho1mBV5qVZ9Ow1g1zrtu1bLmWd1RHEp3OnjW66LkuS1XpNobxO+tw/NdMSLK7KimGgJlVXJSckhjhPc1ZkMaJrP0zL8iFfp2kCISaMUyryckMwwS7880//2//9//WfBBb/8X/+jx/evD0v4/PL46wmPU4QUCYSAOC/pXeBD94566z13voQgn8t8a8TYIwQRgBj5EMIlCISafCREEYJjQB4ECkAIUTvPXYuUhqiU2okmCdJqvUIMByn4fH5W4Dw8/zl+fD4w3c/3O/fdmPz9fLgZjeNw6Lnd2/ejXp2wRNC1qtNwlOKEE84ojxaT6pVu8zt0B+bQ15lGAMueIaE1xYL4a2rZaooe7w2t7ubREiEYAyRUAowQYhcuiFGgAly1tkQbvb3FNOwM0/PDzLLqzxrjhfVWeNiJCx6nYt8lYub9b7tHh9engGiIETkaftiH47nPJOcs029Wd2+IwQTxOap//W3Qzf0Sk3Xy/m3lwNn+P52u9vs7+8+bLe66Q9pktzfvE1E3o1XiAJHlfchE2yKCI4zQMR6b71xcUkSIQjrh27QU5bkN/Uq4dIoy2SCKCYQb1ZrIcJffjn3U7PKtyBEo8YQ47WzwaP19hZRbKKlWLAIEbRZVnKWeBeMdT4ELgsQgtWBFsxaRylllBzPJ4JZ8PFyGcpqu1rvumUkiMAQlNNGu3Jd7zd7b3ylFQweRH+7rl1wBOHd7u27+7fnphO8LPPqHz7+kQe09P2iNfi7jPzf7p0YAhgPhyNC5Pvvs6enr5fzyXktk2wc+2EZummIAATvjZowisZ4mZQeht8+fdrXVb0thqVHlGAElV6+fPuKEUmkBDFKxqTgH97dbZZVzvNEiJ8//Q0T8N3tD7dvvuunq7T6lvNxmRnHdlIxGhidXuw8TWVOYgBaGU4TRFgAflLL4uY3t2+EElnMMEAwYIA89E5PnTF4vaqRd+Oki6JCENhgkA96Uf08JyK5zjMxeqGEUUK8s8MwBe8Eofv1Zrde9+OcJRl6++Z+u11V5ePzw+PpRXD27u7tbrU6tWdEEIE44ckyT+fjBQdCozydTv3QIQgen5/e372tV9X97R1nzfV6XrS6NM3hfFbGvw7urHUBEgBhQtmP339Y18XLufXQQwRfz4fXq5s25npt1hjLlFOGGWcxQsqxzESSygjcOLcQehf9569fOEm4ELv1NsSovSOUMCGi9017MiAqq9bFJpUJBGHsB+c1pVwmhUxyzjlCEFEW0sAZgdRHEkUiQUScCY5pVWfeOc6TCBEVcln0tTlwzqy1TdsjDy0mCFKlVdMdb3a3d7fvf/s6vxy/YEh//fRb8H5XrwGA3tvoDcI4QlJVa8mShPGffvnzy+FzmRY3d+8ISSRh1Wqf5RsIArDamRmisF6t5klj6pM0984XkP/hR1EXWSGFd0aQ1Gfu6fL0cjo67wSThPFu7KK31tl6VWjtbRcAAjbESiaztX/+/KmuNm/v342qvXSNYmQZlQMwzzPvzbSMKYDCQ23UNC8BgizPBWH7aoMQ+/8x9R/NsiTpmSao1Dhxzg69LEhGJAFQQFU1ZLqX3etezV+dkZGRbqmWaqAAZCYyg116qHN340S5zuIEROYH+M5cTe373vd5fjx84kLmvM1ZNx/PFqPJYjwTViBMgbUY2afj9qf3v1CMbm9ury9u0zgljh9GCcLIGA0teDyt/8fPPzjx+M3lqyBMAte7mM4V7zTnmnPtSaMtJRQAYCyAFlltlJTGaGuMNRoAo40yhhirf90EYIwQVEoFPkKOq7UxBhBKX5TTLxToX5cISgjRN3Xpe3EYh1LhF7sM1MghJEoioFHip53sj1V2zquBN5hPLn3XmYwmnudKLRpWIei8PKbK6CRNfOqVVZmmw7o6t6Kn0qVcQqkQ4LPhFGHSd+Vm/fS0eVpdXLy9fdd1fVYWTPKOdVo148EUG3vM8iiMZuPJajweRqFRSkF8fbUc1gGUgldAGcehmEDoYCcKE4yglYx1nZHGWBXFwdX8qipZWZa+Qy0yFhoATctaAFVRVfvT8WH9JJUiGPt+GIWO53gY4IvFdRD4m2Mc+kkSj+bzi322/vDpJ2nteDgpq/zTw0OajiCgAXEiLwLEJskAKNt0HXa8y9XtZDTp2sYAFYSe47iiZ0150pyFQSA4h6HVktWKG22OdePF44njuBQDYEXfB0E8Gc6E0sDinjEDONB6NVlpxeqqyrNT27V+4AVhBJCJ0rCqamO1Q93L6cIAU9aNNdbHVFsY+oGPHOBb2oKizA0IXMftmz7yfQqJ5jL2Qkjs4bSuy8xa3XDe9uLlU/IleKK09j3v7as3r1aLm8tLIXlRnihCg2BorPlQ1l3fh0EAgYXWaCU914EIOZ6vbMtlIyRvmxpCC6EZjQajdMB6XvEqr6Dve4PIt8DxXMcqkYnscNTbU/79998tlq+H42nZZoTSaTLnip/LgwUKQZWfj1xKgCBApda273hRVulgPExTSqlGJgqjMLo+5icMUF0UWlrfIUJxAFFTnaw0PnEcjLuukKwr8yzw4jieWKBrzggCJPYiDIiHnVHUlV1Xtm3LmjCKwsCTSr66vBiEYdt1CEJC3d989dXtxe35dKy68qubV0KZuumkAkXZ1XVbtfxcNVIp6uCyqT59+XR9eTlKk/l4/tOHX5qmPJzPWVkAHVqpX/6uBkAAIUYwSSLogCgNxsN0+5xrY/7jox1AgCGAksvlfDZIE+IGdV3VXRmF0Xg4M1oJ3kdhkMYzznDgepSQwPOGw1HFGmnlYDAGGpyLY13myNokihln6+22qWuC4HS2nEyvB4NJ2xV1nRljXeoaz0ttHEchIV7PRd/zqms3uw0XLPDc1ep6MpwAoD5+/kmYHiNS10ILRakX+JGSOjs32/BOG+khx1LkEH8+nO+zE4R4FA2Hw7HkvGgLbME0GTmu50C6WlxbaRyItVBWaAkEJK5DvKLMqyxTmuV17rrOcrG6mi9dx1dS1Q8fleh4D900tZgi4FLHbbq2auqub2ez1eXyipy3bc9cSijxbeJMphMtRceapvWiOPDDwKehQ0kvyqwuOoo85GLqWmSFMJPZMnADn/qM81b11PUmt1MrARd9XefayOFo/M1XX82moyD0RuGkKcvN44/T4Wo0GB0Om//xpz//+ceff/f1N7//7Xg0nDu+x1h/Om0hshAij3pSqX/43T+M49QlQHOWnfaSMR+7GW+3x12i4GBEo1gRCPV/HNvWGGuNNhIAYK15WesarY2xFtgXF5gxGiIDITTGvrCPXoSuLxUwY4xSUiskOCvK3BoYRiEGUDDRVI1Lg+FgvJxen4rzz59/+PD0YT6+fHP9G8FY4DqR57K+dygJfL9nTV1nqus8Nzie98cim03m0JrpMB0PwqzKhsP5anr74ctPZX123SD0Q4BwyRjxIoK9f//5B2iB6wdM8c1+iyBxaUAx8Ty/6zl1PC1lmR+UERhhgBG2UnZl12cGWeo4EGgh+PN+lxe5Q0jXVYDgQZL2rO/a8nI6mw1jjdGuzI/FERE8HIwRQr4XrJaroql2p8N0NP1+Ngx8t21aY6BDHYd6vpekycRYYICJ4iROB9h6w+Gs7fooTI2FAKJhPLAKz6cLqPjz7llyOUomgyA0QgEDEMLGKMWMQ/F2vznnWc+s0dbEhrpUSMGFsAT5YUgR1lIK0XZNpVnBJT8UGYKu73kAmSiIEcK9MVXXgrbZH3fUgePxyPWcui8qVud5NYxGgqtTcWj7bjlbXkyvetHvD/tM8ovVxevV5c6lBWOMtV1bS8FqpQCQCFimRF+lxtqqOZdVzvoeWAiAhRBCZC2w6XAwm01W89VsMj/mzw6GCDuEuKEfvLrWp3xbVRpDiBAgmCAEMUY9554Lecs+Pt3HnnOxnHHBLmbjJIzzsnI9DBFJgjBwcF4WVWOVghjTQZrevn67XK2oQ7VoXWL3bdZJsZovbGkBNEryyXDg+pEGNs92CJIoSh83d43k1KGdaKURXFSe7zjYSeOR7qUwPXFox2qg9NNhk1f95eJCmhMmSEqRlcX1xauL8bTu6iAKSVOJwNFWqXGS3qwuGO+Lpt2cTseyABhBB0ggz3WVeuEgSsuubPt6n+0+3n/WVt9c3RqM2qZBkLy5fHMsjr1QYeBzxaWSCIKur592T9T1Qi8aJpFgbVlXhHiSWQR+ndtigKxWEFgADcZumoZhFFgCLLdGKwgRwkhbCwnqRbs9H5CD51ESB0mZlxADa81m/9S0FdTj2Xg5Gc8dx63Lui4LiJHnOx5ykjCSyuKqYlw2dWGUKJqSCT4dTIAmbc/rtkziKI5iZSTrur6rrJFNWWqrkzidDWdoROuyyI5HC+ByfjEajl0X3T88CcmFVFz0TSMFV6ppmr4HFnBrPj08N70I3LAX3GpY1y0AJvSd15OV4/pS9p8/fY6jqB1NkR0iam8vbsbB4On5YX84OI6/WK4wpkpZBJHQpqi6omKTic+YLMpSijMEQAtDgdvUzR7vxvHQQvX0vOEtW44WcTKcTy6AAZEfQou6RiLqWAhfCHQKWmShESqKAoxh3zUQaN8hfde7oeu6ZD5ehMGQi9YY5bthEKc1a4zR726/cmlUFoePd3+9WI4mw+tpOpmnw040XVcqw12IFetPp9PHu8e+Vz23f/149/r6zXK2ZJLlxZEAaIE10A7DdBYPha/7tjoUWdu0AKNhOhwPFlzCw+kMcTSZrqSQGGEArDZaGw0AANZAa9GvuHAAILDA9qyTUmLsYYwtMEorz/OMVkqJX/PEwBprrDEvzAgAAMaEIIoQUkqxnvG2hZYaBb/c3zGmksEoSZN6fa7rYrFc+tOx6vqi3Nd147puFITY0r5mjWyVPUotrbLr/fpqceF7zpf1U8vElHXHfMu7PnE8oLjgUPTd9fJ2MJp0TQOR5/l+GPpFmfNeMCGyspiOp6tZwoSS0m63G2s1N/zFP+UAsIyGcRTf75/KovScwqG+7/p4QhFACJGyq+q2dQwumx7YnBv5+vrV0vFOxTlvirJvrlZXy+FESbGaTwdRlKQxsCL0wsgbFDXDNMiboqxqgh3laQ0CoVoDVNXkCOIkGo0n4ng6K6V/eP8xDsN/jKK2Kaummw5myMDj6WAR4IIP4zkk5Fhsm67+8Pl9UTZpMpwNRhi7EHvYAK5ba6EWfVXtHYQwMMfj4anpRukAGOPHnu97XV9ywXrWGgNXF6+NVVXXctk8H5+b8qwtvr58MwlGYRhKYdLhoBR5yTNkqWvpOJ2sT9sf3v+SuCFAADn0cffUdS2CII5Cl1ArtI9x5IZVnWMtJG8YlwAirV/umxACHMdRz1ineM2q/X7/5eFRWzIZTK4ursbjhes5n78crAUWQYixUtoB1nMwFKJpO2NMFLhxkozdMcFUC+E7xALNFM9r1SPcM+b5AXbozerVdDrikpXlUSlxdDBFkPc97ztsFDLAo8F6v1cWXq0cgonjuMYAgrDrBn4QY+LsT4djeRqk4ykaGmCLJj/m+7pu/bpRqvdd1HccQGIAfllq1k0llanafqpE09UO8si///wTBnAyGb57ddMzZqwMA+/mch7HPvV83w1d6n/8ctc3Z2kU1/z5sHve7Y/Z+Zuvv6VRKK0aeTPpyygc9EYrqyww2EALvflwOgzDhjECoTKaEuI4bs8ERgGA1oJf72iIIKstsJZgvJwuCQ3/6d9/UcAQC4y1GFhjrJaKIJhEPhe861sALAKQ9bKsM4JgVhUQWCW0MsYPQkpcY4Cw+pRndVs6HiWASm1P+bHnbckrdqqKIl/OVlerK6kgE+J83ipejkdThByt5dNm2zZNVZz7vhmP0tsbnSQDY7U20nGdMPAoBlVTS4CiwQwB8unzl7ouwyDAGDIheC+8eLCcT6PAf3x6OuXlYJASgn1F2qI8a4yp17Ku6xXriiTcpEnvuM5kNK1Ze795cgjGjkudAGNqdOcROpvMfS+8uXrjePCc7apDl5fVcrZ4c/Naa/35/ucyr7qi7/qmrEvqeLEz8ElkFETU8o4Nw3QYplqJnjfQWGMAJrgTfeBHFLrGaEzgxFsl4bhjLYBWiE5KSan3+PTFWn19FQVuEARp27dF248HkRDSKBF5ziAKKEF103RdgwA02jgorIomq3ZF1XIubpZXRVP98P5nJrtv3r2dDQesY1lVnpvcddzxYFrXNZdiOV8EYVLWdVP3lDi+G4WeoJjAl7PbvvQFNQDmV9kvRAgijJBGCEGMEDbGQASttYQQ13GFkI7jGqO1ltaaF470r4VhCKlDtVEI4fFojCDSQp1PGYCaWIyRG3lBU+dB4L9eXfIqL5tmu3lazuax6xEDi7xAar3Z7BrWUkSqsmGSLeZLxwGYwovJ6nhePz0/52VbZGUcRuNotJrM+rbrWSeFtgAZe/Y8fzZd7Hbb4+GAMGobgTB9++5bZEDHeDIaUYyA1VVTsoYJLaXikeNhgVmvHehqajEhYRgPx2PJmRLyyaiiKgElF5PVKBmdqqq37NSUX1+8XY2mJauZYLHvV3VtlAyDYDacCa3P9Rlij7Vt2xRVeQh9d5IOXUIdhB3iY2yABc/bB6DF29vfXoa3xPXv1//6sNkNBvFffvrpfDo7juOSVLiqYLVQfVPXcXieTxd5mW1O6/2pSL34arSI49B1AqVJNFjmrNlvNodj8QWoNA6EFJ8+3U+Hk8mATNJkNLugxFnvGLAAAO17vu+HANjffve79frul7sfGJeEIGTMZDDD1JuOpgYxJkuX0lOWEZJyoeuqhhBbjCBARVVvTt3N9c1iMBOsIxA5LulU36u27muAbMd6KTWC6GWKCAEEEBglszzf+M7T4xfWd0xqpQwEiFBqMWESIEwJptBCrbWHsVYCKMGbRhkdxaEfRZA44/HcoS7ragAJbBpqZFaVnuuMw8lsMpOIvr39hhCbFdlwOFas//z8ZRBFr6+u7+7vH+4e31y/atquKhkhZeASA0zX9VVTW4tGo+XFaEqhHSdx0zaBE6VuKoRlWiCXIOGmYagh3R22w2g4j1LPCyejQV4dDwRu9hljGkDqekHkpyTP6zwrvzw9O45/s1pssyO0Uku5mM/e3lxTEiIDHEye1+tttpNGtw3DiN5e3vzXv/vPyEGH8jAbzQraEc9Z6hVCcHtYA6X80FNGN0JybaFUgzSyAKCucX2/7YwCUBkLEUYYQ4yVtJBA1/ccQsuidIlLMbVQIAgBBMZarVQUBnEYuBr51BNMsE4eTyepOUXOzfxqd9xpbZu6K+u265mSBmNsje5ZNxgPjucCQmC4YG3lIhT4vuv4i/GSIscNfM+zjNXHw7Euy0E6Bogs5is9tcd9rETv+S6hXtexpm2gtUVdHN//OQhiDFFV154X+E7gBeHS90ZRWjdtVbSMi+3pOB2mMAy2WXY6F/sse3115SCPC7U+nAEg5zyrutbF5MOnh+W8S+LId7xhnPzm7TuAyXSygBBXVZMX59Pp4DgOoSQIfYRNHMSreDgbs0GUOhhJK5IwPnL5sN7vT4fRcOhD8OHu3kLiuI7n4vls5CBCkGOt6ZsuCmJr4eFw2men64vb8Wh6zveYONPhnCtVVqeiOPNOF6AdD8F8dsk5hxYbbgbRyKE+BNQY1BsIaewgw6WwRh/2x/lkCiGtm+5pd95udhBhZXXg+W8uL/q+61UrpTwddl1d7U+ZkhIBcBZZdiqNBW3b9j1fzOenc4YhBsaut7sojsM4UkpKKQnBv5Z5jQXWwpcONwQIoRcOBEYIQEgxhRAyxjzXtS92eAuABdYCrTXBhCACIXq55QEDtDZCsLZrx5MlwKSoyq5tY8dJfe943iveE4Tu7p/qvpdaC9bxtq2qumUcWBcCGAYBIQ6X2hjMpXZdbzaa5Xnx5e4eGUez/rjNRapH4bRuGQTQGPuwWedV/WIcq2Vb5OXpnGOMIUar+XwUJK7nSK0Ya+q21UL4nq+1gRYvhhdN3fx890gQDDxvu98Qp3/z5jfEdT7ef1SaU88ZpgkFKEQessiFaD5ZQmQ/fPqJs74VLBkOHAijcOAH3n7/gAzCNHp98/0oSX7+5S+b3ZZxDqwljnt1dR1aInQlJZ+mV/Rrv6rytqnHkwmcTL776ivOedWU77/cK64ABFyoP3z/LYZqvX/Kizr26yiKkmTQqj4vm9Fg8rTf4DOazlaT2UUaD74861Oeyc4SZNdHqaQ2ACloj0WuEZji154bYOS2XXfOT9TzIqUgMF3TKK48HKbTkVD97rAJSbRaDaXsDtk2P2dJEImepyPai7bqssgbLIZThMjQjC0GfhiHyUBpleXnuizKpnQCJyvz8SipWSe1gsgBv8JngIVGWZmXmbUKSu25tO1Fz0VYZdd69bh+6PvO9TwIAYKQIKy01qzv6oq3zPE9AilFXhCkcTRARinqxPFgMb0wWtwdnqAFCJAwjEgQ+76PgJmPJoSgXMnRME1TLy/yzeFUFczxoqwqJrMbiMTPHz+GUZhGQdPWnTTD8cICwbkcpMm5KB+eHgiElDrTYBo4sdQydeGXxy9NK5MYV02urR6jIUI0jSZtZzGGbVOd81PfK9JxRV0/SgLOZFE2VdFAoOMg1Arc3z9AiBfj8cVotBqPf36K3t+9Fz3rWee5E4oBU6yqzkM3NcoIJYLQS6KYy/aU85cqr+dHycDhQoV+jFys9+u3b98xBv/1j+8JJubF5gqB1UppmRXVbCLC0E3CEGMkXxAtEAILtLWIkLbrypohiCF0BDfPuwOC9iP8/ObmRjAleDsbXozn83/6t3/7+OGT53lcdJSStGi9wLlYzNN4IKRqWX86NYfD8TRq217NZ/OeqaI4I6NBQp82+6Zt59PparWkq6nR2vdCYEHTNNgJvHjEqgxi6NEQWDsbBS6lRV5KxgCAFWCHY7bdnajrOhQSTALXW04mVmsLwevbNwSiT3cfiqqhmJZNjzFdzFbP23VAq8gJ7u+fojCazyeH8+l4OsTxoKzqsqqzrMLIYgLOp30Quq7jOI7PGTv1HCPT8+54ynaHU88lIc4oGSZJ0nHRC2UUeNxtN+vNzfWF5/pC8ONxP0gGSTzoWhY4gRLilG+Pxz2hUAvVM4mQdYjjkpDgAFg6n14x3mfZuWpY2VRK69vL2yiILlevHOxw3oXBABh7Pp1Y141GU6FMEPkAGQfBy+m07zkAMo1832BgNVfMKIrdsO2KrmuzonJc1/ddbXV5f/e8fuqVQhb5jncuipoxNwxGk8YLY8+6L+mdXxnQ2r68DjDG1gJgAUQQI0QpNdZ0XUcpDYJACGGBhQi9cKRfQKHohUCllTUWAKCUbJs6SYaDJNa8c7Q5HJ6eu0YJ6VIvDKLACfuWHzf7OqsEl4EXSGkfnrcQgDAIKSaMsyDwEaSr5TUi6J//9C+7zVMUxNPBjBCSJBE0UEmLiXMqy92hvH96GiTpaDj8/PDYNK1UmguplDod8+KQJUnoBG6SJpwLI/VsPAnCqBNCFzXBpDNG1B3MLCLuYnFpLGzbfrm8nY5GP/zlz4djwVj/tN8ng9533ID6XPadVuv96eH5+XJ1QV47mlvB2t1+d7n0ryYXSRhCYyhx/TDhElR1qaw95fW3b7/2fd9xnCh0lRu0sNsfd1WTAwjn89FX7Oan9+9d6q9eL7q+k0oy3o0nSTxMNMTf3f721fVrpVnf5N+9eu1R70vXtYJ9fvhc9y2GzMX4enF9PhVR6FukszxXSvphmKRDJpiQnBJMMB2NphTjtusogU3TVGXBOua78fXldc3yh8c7hOnz5rnumg93n+I4jl8NomDoYVproQw/V+ftKbhaXV/Pr42xXAmpWNM1AKEoHaTjmZDKAC8MnJ4rbYxLsDESAGi0wRA5Dun7hhLkEbdquqppey1XFAWBrxlL/HCvjsAqLaUmVAvtuaTv2qqqYQ3Gr0dvX70m0NT1qWf93eOD7yfXq2sjROjHjPV92wsuiG+F6qgFxohzdu57drG41rpvkPqb3/zd5nCA0MRg9Iff/01erv90emjaFkJAHSd2Ud/XdeewnltglZKSib7nFhLDektsGoXNeb/bHy4urvu+PZcnp6qQ4/huQEm0mFJjYFM1o8FURpZcrpZt18ZRACDIqyqJ0jAIjDEAUM0lItZYiyiN4nQp51K1m926d2AY0NPh6WG/+7RbZ7N6Nb183m+z8nQxmRioIYZV2wQeAEAnaVRVbd9XoQ2m8WA1nee1+uc//vzC0DYIAmuBNRiiuu1+ufu8mk5W8zHFRL7sZCCEFiqp27qdjNM4dJAlopeMaQhwGicIOv/8578eqzwMAtdPW6l3pzNTljUsL0prtevkcehFbgQNLVtVd8JzvDSa7rbnsmxe3dRdz0+nw2Q40oA0Xae0kubQCy61wBinyQgC2HWttYBJiLA/jILRaPr58Yvk7GK+cqlflUcD4F32vDufN7v9MElHg/inj5/3hz2BcBiFcez5DgzDNAyjthNJHEsAXOr8/vvfc6XbvveC5HG7+evPHxbTkee5SgLONZcKUyqVOGTnMPQJgnkJESafv6y5YMMkCXy37/rtehfG6dVq9eH+fV5kgzh+c3sjlGZMjobTh6fHH365cwP3arF0vXi3PxoJRuORlKqu67v1o1IcAfPv5fu+k6M0fnN1FUWDMIoxQk3dWmAZY03fciGsBVKKvDxTiHjPmJbLJGE9BxZ9vL+nmy1GNPDIajIqszw77xsupNQ3VzeYoP1hV5WnMEquF7e1FLvTwWKsBeCS51WppNRKccEvl6toGd3evlJSaWUQIgQTCJHWUmltjQXWaKNfqKAvcmBt9IsOBUCIAEIYv2yMX74AXsLESGlLDXi53gHw4iEBwBhrpGSCdaxjVivF+91+fzzlvhOGvmJcGQu1BPvjru5ZFA8vJrju615wayzB5EXa59bObDpHEDZ1PRiOBBNt01IPDtMJRGB3OMZhogz8/PzMmfCC6FgUvdRKIWuJtYCxzgKQN6JTx7Tu4iTiTzuPkFeXN1yBANE49gbJIPBC3yFayvvHNTcqjFKMSBjGSZpgCy5mF/vtYdOsHcebj6dV3f7xx/eIgt+8/eq7b+Zta5Qm+11W5mXVNu/vPzvuMI6zojpKocqqenV5PR3Met6vj0chJWM9Z12SxIrXeV7UDYsHCSautWaQjObjnn7rvL5+E/nO8bSv6lYDqQF6c/kbcoVc4oaeR5B/DuNznrOev766tRjldcmVEpyvhstxsnigT5SgNBl2k7ZjdZqMJsOpBhoA1TT5YDBM0xnr2yQddYx9+PyhqhrfDafjkeMSFziry5s4SZqqKuvmkNVlL6/mV5fzedXmj+v1ZLxAyBlNhskwJhRbAwjFwCgehC/+JqGMS73IDyDSEDjGAvgfObEXvr/neGkaGW3qpva86Luvv0/SMAzd03H//stnpWxddxASzmSYOAgSrZRSqun5Zr1pu75qKoz1xcUMWH0usuXCqbouCcJVMqjaCgxMRL2qPOXnTexGvh9UfU0d0nWt7wZvX/1GVJWUzf5wenXzLnJpadQgTfKy2e/zOPIpBU155l0VBsM0GczHk8RPjJRcqsD3e5lneX4+ZwZQn7gFO2dlCyFr+59uVovFZDUeTuIk5YwRxxVKk3/43XfSKAAt75gDiUFgvhhppbI8dxx3Mh5G0UADCyCYDcZNne9Oz45LOOOf7j53yrg4UNpss13WVkKbqmkX0zEABkIbUAIUY3XhYffLl88I4LevXwnWfrp70MZAADG0CBFAiNJ6MYz/8e/+8z4/F8VZcIQwNi9LPggBAFIZrkAYDYVsCKXAwnOeX84X89lEayMh6bgeD0aY0OP5/NXrt4tJ8/D0zIVumqZjMgmCxXB2LKpP949AqTQeBF4AAdzujsbgpu++3H2JwnA4HMVRdH150Z3y5+2+7zpCaZKmgotBnKSx73h+mo6L7PS8P9Zlybn8dLfGxGmq5pzlRVUt5xfDZPq8W9ddvz2c/QAnkRcHvuRx4EZ1UwvJEDId6zEljPPdcf+H3/+uruokScKykkI9fnmKk5BC6juOltJgkBXnY5bBEiZhEHmR0sIYgzFoG5GdSmRhVXSOE10sLtanrbX6eM4d3x8NB07gjtMRtGazRwDj2ewyjaOmLrPsXLU9Yxw7ruclge85BD1v/1yWLUHO58ftIO2HXFZt1zW1VtJaDSEABkBKT1nWNFXge4zx9x8//PLT+zQdnE/nH375xLXGhE6GydVs4TnR/fNT1dXpMP64ubcAjIK079rn7f1ffvgkpUCUTKdza01W1VVZaG0IJJ5LJe/6Nr9ZzZJoUPMeGu07LsboP2SQFlkAjIZAW6t+zfNIbazRQlCMAQAORtbormsRgA7GBBNgLYTGAgsAxJhCiK21xpqu77hQTJiq6fLs3FYF67qy6E9Z4zvKDuAxy8u2danrEp/17bnaFmUjBEMYG6UxQH4QYAx93/nl06eyrpIo1FwpAwFxAj8ej6Yda4oi6xjD2Ald79ViOZ2MP9zd7U4ZJTgZJE3XB+kgiePlZDIYpr7nfvXmq6LM94dnB1Pfc9u+o5q6tCvLsqgLimE6Ssq6oZgowQvOmRKG9ZK1gUvGg0ESpoGfSk2cuheKF2WzXKz+p//yX9e73d3d3TgJTmUBoR/4g83zdr1/ZkI4bvDqkoyHi3NdUUqGSVJWxWa/8dwAIxT4YRjECCCPeAgTZMjt4vpmuoriiLFOGwusTcK47+TVdBKFwd36c/vYBK6vLfa8WAs5ns3iKEnyvKxyl4RSgqrOu6aGCClpCHaMAEBZiugoGnNZH84Hz8a0doXhg9FcKMm0ff3mb4aD4HB6zJoijMPwJW0E9Gp1gYhPMY79ABrTtW0SjVarSy5ZyztptOd4EECuhOTKGFvXlTUWYkwxnYwmng8RJBgQAJCFACGgjbaIXs7mr65m291+vpotF7OO9UkcV1X5f//zP//xrx+ktK7jKmUQ0VEQcdYLrRzPj+JYK/v5brvZndLYT4Lhu9uF70fY8UfpME3CLDuwvvEcFxNsG3XIj+7caYsaEW8RT7q+MW7EOO9Ezfq27rqFFpKVbVMzrqQFAJC6EGkczy+Gz/vNm+vv3MDred/0zaHYjWcLzWtiRNP3XdtrpbenQ9l243gSR8EvX36kyPiu53gRxtj3A0yIlg35t7/+8Ntv3/zNt7/58Pjw4e6hbduuLQGFbhBKAzlXRZ4rqNu2ngzHo3QYBsnplH953niu81//099Ph7NzWVrivLn2D7utVE0QhNQlievzvqXQVKeT0sih7uN2x7n2PHo4ZMQYCF7C3MZxIAQWQjNOUiYEq0sBLILo5fgHECCIhFDr3XE2ngKIi7pRUp2K/Ga5nIzHPWMY4zfXV67rIoIgRqPhqIpqB5OLBe/aXmohNeu1hBQ3TUsRLsu6bVlRN9rCc16UVVM3XBsMYFPXTeA4w3RUFg2ESEjxsH7f9/1v3r0jcGIsZD3Pymaz3xFEyrJ+WK/rpsMQKSmlVsPBdDQY1H2ttZYSUE38cJimaVbmj+s/hZE3X8yn0xnjEnBJESmKEhFECc5Ox0+fPhzO565t/NrFviOs1EYzwTEEcRjkdXPOa5viruu10aHvAgANxOvtdrffV0Z9I74Pk+Gf/vQnxST5y49JEqZB4HsBRmgwSBarZRpHfuClgySM4yIrosimg8F8sTifz6zr/pf/Eu7P2SnPTudz0TbYcRxKN5vdC2Wh73sIQJIOfD86F9Xj06ap63OWYYzA07ptOyG1MlYo8eH+8e7+aTVdDYZDKMTz9mAxcAlVicYYCwGFhEpDZGxTddrasqq0VlEcUoyAgb2Q691OcJnEA4OAHw1uMQLWYowJofLFDvofSB9grdFaa/VyfXtRfltjALAIAgQgIhgi8KstDEKEMcEEoZfVgBFCCKEQJJIJ1nbbzU4wLjUMgwhBeDhn2hhEsLEAYpIkaXs+b/cHYMHL/4dgW2dFGAa9VDX7nNXVajpru44x/s1X39xcXHRdWzXS8TzWMQhU6LvGyuNp1/PWC5xvL175fhCEcRwnQRBx3jsE96xZTgeXi4kQ9c+/fFBKLeYz3dq2Y4MkLutWGx36oTbglJ2rNkcYJ2xklNiuH+/W2yhKDCJV07mexxgrmmo6ngdeOEzS7Hi6nM08x8mrOo0Gh/0RKNWUTccF9m3S1M//8k95WSwm09D12q4pW+b5adV1VdPNJmMk4Xb3XHX9fLlMAr8uSq5lFARZmbddP9eT4WAopM7LNnJTQkGWZZxJgvEgTcPYRxhR4ggueyKkgW3fn/MCIqfrzWp20XT5x8e/jibb29XVOA07yR/Px1nUxFEIMdJKf/3u3XJ003d5OgghhFIJxpnsut1hnybT1Xyymk2h1l3XHE5ni7BVuqubmndd2hujqEvbpu47ZgAGyEvSBFPCWB84TttXRVVhhF80QwABawx16WQwRNpCbQZR0FbFv/7lL4Lruu2f1gfOgZRKKWCtNcYorT3PtUr6oS8YdShpirbvW8H5Kav/8O3XCiHiukHgfXn4cjztxvEAG5jJoqoqHzvI4r7tWlZOk2kUpo4TBmGoVSeNKdrmy8Pn0279vNuGcYAsOGdlUTV//4e/mSajrKz8KKYUDZNhdi58H/C+eyqPrO+URYN0hCjN6zocphM/9hA2V7ee73lOIKV8fHpyqDMZj1nfks3+MBknr+bFMBq6eP90WgvGfd91vX6QDDB282L//u7TbDT85s2b8XgUkjiT1fFYTMfDru73Yt9zfnF1e3v1duyHRbkXWgdeuC03m33mUjqI0q6XGtvBcGCUzbJaSaOlhBAaiFyHWqW0Ngjh0/lQ5JlHqCKKYAiAAcAaA16wX2XVPm92EFHfd8/nc8v7tzfXWolhmsZR4Hle23Z1UwOM6qbmXHAlKMXL+ZhxdqjPHx4+x144DMOeCUIIsDZNEgyBi0niBePBIIlihBETLXVo2/dV3VJMlFaci0E6mI7H2pjPXz4Hvq8NkNL0qg/i2PPDsu57JlyM0yR9XD+5jjseD60ylDqz+XiYBgF1P90/3T08hoErLDKAIoAk5+PhaJCkd48PWsub5WUYxkX9wJXGAWy46jb7tuvaurbG+I7vel5eVz3bDZJBFMej8WA6njEm87KdTC2A9nG9TtJBXbOqbDzP258La7VSejYe/v3f/M5YzfouDFxg9WgwYD2HFiZxgiD2PZ91PbSQMYYpHY/HgUM9DJExk/G4qhrX81omy7okjisFIy5luX7a7vqejUajJIpdL8JuezxnwKjJcBK4fuwnoR8jiJjU5yo3Hn0+5VLJV9evv//2u/Xm6XQ6VGWljKEIaWmKLIcYjgYTYBET5niqml5PFhM/jDChXDIIoOM6UjhK9IQQQojRRiNCMJE9x5RQQpRSAAKIkOM4CEIIMaYEAGu0RMAh+OUd8JLyePkigNZaAO3msL2/e/j0+TNjvef5oyjFECFqr68u39y++vj5w+Pzs1DaGlA1vbbWWB1JNUhSz/Nmk4kfeC5Fvu9qbYA1oe+GPvV8vN4dT8VRKpkVeRTEF8vLjvXHMq84Gw/H0+mcUOJ5XhC4wAqEjLXWWLU/bpq63Ww2Zd2GURD4ieM4eZE9Pm4oJYEXKKUxoXlRCtVBiEZJP0jjlovBYJyEg8M5bzs+TgdGGMGVMgYQ/Ndffv50f6eVxgBkWVV3XX4uo9D3CEIAh34gpZRCDJL0nOVHoWbz2XQydx3XGIkhFbIXnQSAcKE+fPrkUQIROOXF1XR2uVxJcdznZ+g4EwuBgQj7GMMkBieeVW3DtTyXZ4xdJWBZ14A4AXSkMKPhCGDHSNwwOV1etcocz4U1YJR852EXaFF1OZfd5+0DF3Y6GYnuvec5Wmvf8yByNId5eX46HPZ5fbW65LKDSuXn4pe7LxphLs3lYjHxIt4xqcQx3395eFwurseTpeM4nud0PcPI4YL3HedcGWsw0BBCYJHVAAOwP+z3e5YXedt0UplfPj21HRdcKou6XjDOXMchxFHGMCGQS7WxfddBCLXVCMHxZEqps94df/z0JY69xdS9+/ChaKq+M7UVJiBSNFapc1lUlUziqCrLos53p9r141evvnGc6M3r73pBvtzdFQRbBC1A2OCLxcW5/Plh8zQf+j71j8fjxWwsOYfEf3W1bKrTtqkaZsIwHCSjYTT0nBOHmvo0wM5Ej5uq2T1t4xHjSkKA+76WnJPQo13T//DxEyWuaMW5aH65v/u77759ff0qr+vN8SylNsAByMvK/nD6sjsc6rIh0Cuq7t9++NGh9O3N7fPDfZkXTddutk9JGP7+u9/ayaxtW85kVnVcipb3Uei/unz1vD3qjrt+UJYFcqjjOooxpRVEhDqO1CZwPSSY6zmEYqXlf8hfLbAAYdw0LcGIEscDVikhpBg6DjDw891d07Ta2LptPd/P8txa++b2FkGz2W+y4gyN4Z4feZ4UsusaCHAQeBaYXkjqOKkfuBS3bd33bedQCOnj+kkwnkYxdghOovX6mQm+3q19xx3GqdJGGp0m0cVi3nf9c7UDjjMJxsoYqVV+PkdBZK09nfdJuEyHaRpEg3gQRSE0uMzK6Xw2TxPW96fs3HRd13QUOIN0/Prm9SHPhGTHYxYHUc8Ul0AqmxXny+VikMR100RRZK1RQrnUAQC9fX1b5nlWnXfnXSy7V1eXfK5fXd00bfX5ad0xFsaBgYgrvQxjh3hcMqk6LpkU+lwWdV0TjJWSdw/3eVW9ev16Mpm6CBkjn56ehpPJfLl4enqWUhKKmeCH05FZ03Wd47gUOy7xlLIAoDhJwySFwAau982br5bzxeF0/OHHH95cvXqNXsVR5PtB3VTjQTqdpFI2DgFxGNRNUzeN4yQWQt8Pb66umroGFtxe3/asV1BR19PWKKkIIQQTSqnEFGOCCaEvDwaAgnOEset5TdNYawmlruMACxChhBKEgNESWItfumDWWgsQggQhBC0AVnB2Ou2BsavFqu86oWUrhUOJpdiNw2+++y4IvWN25HWLMZpNpxAihKDrkPls/ofvvg08t8hO08loNpkQZKsi7xhrqtNP1dECKxVv+14ZNZoMb65upJRvESzKMo6i2WSOHUpdRxu932+LvIh8v6qrMPSklEqo0PehBVopGoY/vP95fzjcXF/Nx+PA8z0/yPMqSZKL5bVDyeG8CaP4u8tXXz4/NEx6YeoG4Wp1iQ9uW1Z//eEvQpmWi9MxGw8HTOmyaU+inM3Hked41EUAIGM9Qou8wIQMx8OubVnOA4cy3joObbsuiQfv3nxVFCVUhjrQd914tRgGkWP0IPCJN2i7/r//8X+sVpdXq8ueS20RdgPD+DmrATBxgAaDEVes60XowyiM6rY5Z7uLxc3qchWFvkudHz9+OFbFLjsNfIy1PZY75NBzVmInurm4RMYYJesy58RB2AGAAACR4/tBFKaJ4v1ms2a9xK6/2x0cclguLlzsEgd3ff+8XveMvYiIpezrRlqD4yhByFGiV9IiiF7AwwghC6C1enfcOwQZ4xyyrsirqhFCas4FE6rtRZrESRR2XQ+tpQRpLY01FmLqBIQ6V6vhV69vNbBc8P050zqYpwMCkObGGPS8P1VtlQbBOBl8uH8CiPz2u9+F8eB8LhlvkoF+ergbpMMkSv7u+799e/mqYdV//9P/eNxsLqar19evvTC4e77/v/78JyPMcl6Y/tZiWLT6YnUhuzLwwslkFYUR0JZ6RAH945cPmnNvulTKtk3vOo5k0g2CKEmTKC7zjLxerpjgZVkKAB+ed1ld+X7Q9X3bt4RQA8DFbPr9N19N0lHbNuvNJvE44MhBaFsd65alIX4+HJuu7XoGjK3rKk1iaNEgjkMn6Op8d8q5FNAaZPDnT/eH/NwDRwgplfIDCgAUopdClGWz3my1BQ/P27JlQor/4HdBCICxRms1GY1e3aasZ03ZbE7bosiMEodz9uXxsev6OE4sxIzx6nHLpVgu5j2TUlTnc85a5rnOuj4pbRCiddsZawlEPWfGWt/3qYMD302iCGNclGXP9Ckv27ZtWhYFPuPsHPoY4eOhNFrXw9aLvMlkAoC1WgVBEMdh2/dVVfquz5Us6kpq47jePmu6pjKvtYOA5xDXdXvOkyiajkYYA4+gU1Z4rkeQW/ddz/nr66tBmhyPhyjwXhpP0+EIYHK/fr7fbKPQj8OAQhxGyWQyopjUdQ2gjochcrWDMGf9eBiEYfr1zU0Sx39b1Vyqpi3jyHM91w/cuquLoiSOK4TJy+p0zvqeAQi6phaSB0FQFyUFiGF8zjOMia2b8nlz2B/Gg8E4HhZN9bw7tl3nUDqbTAI/GKUj6rr3z4/aAkoIRuBqdbGcT5quqLp8upxOx+Mo9Muq9MPw94uvqzxrmzyOvOHgpiqbw+EM4N4PXA2s67mDNJ6MR8aA4XCkzsfsXHRtb40hhEAAzK/KFwQgghBjbBHRDnQd1wfAeBgzxjjnvu+7jqu0oY5PXQqMfNkeQAgRAsYaLThGiBBCCVaCK8l8h7opfXv1Kg4DDe3D+ok6znQ8cUKfEvKbr78p8vP7T1/6VAVBqJVq2sZ1iEtpUeVtj7Xirk8htYJziyzCiHfiXORKSSY1cbwocp43O8nN9eV16AdKiOPpAKCdzxeUeFhZ0TNMiQFAWbPdbYuibriQUhqjHjb32Y/1OS89z/eIF0ap1arIi6KqLURRUGmtnnbr6Xj4b3/+05fN+ne//Yc//Pb3P/zljz/+8APFDsamuWsQJgiAyWiYFdX+dIIATYbj0WBkkfL8oO7blnVVU3mujymt+q7vWBKnQJun9YYLoTWYTCQmLiTE9XxKCUWk7poaICVV3jW0obPxjDPWNnlV+kIK4riL5XI2nR122zzP4zjVhhGi42hogT2csv0hY7o1UEGrKARvbl6NhuPNYa2lYNz0nQ5czyXk0DFtoOjYOEmenu/avplPFqEftj13qfM//+d/tFopJUjgGSGssnEZesQZDScWGOjApquBVVVdty3bbZ+BFn3XEOIOJ9OyOZzP+8+f787ZyQL74h5/OXJc6vquD4AdpGknuJACloAJLoTkQhNKfw2nAIAhAEp0nEFIseM7yB0ORybWO2YAAQAASURBVN+8en05HUVJlJWFARwCAbHx3ABkgCJErMXaWqmhAbPBOMuzu7tPk+kEJTEAKj+XabBwXfrzp79gSBeDaVlz6tIBTgdpiij+/W++W8xH/6//9n/Wee8E7c9397PBxB9MesaEVIg4+9NBa5UmaS8ZRmgcJg+bdVl1GGPXIaPZhFKX+P5oPEIIjuCIJIPB08f3uABvXr25XMyD0B0MEt8lrWhd6lHo5NnZ8ykaD9JBjODVIEqfHp92280kGVxcXjiO9+nxvmXddDQuskxKcHXxmkn0892TFrIo21OeAwAi1y0NO8pGaEZ8lxKHOAQ7DnW8Fz5v3/e703E2u/CDWBnsEgyshRAigAw0xhqmuNJS9uJ5s/EpDYjTdfJ+c7c+nIzSjuMWDU/iFCF0PGejNB2nw+ftTml997Tt6ipwXeI5TdtyroTWUmtjgNFGS+V5JAjcKAoQhKxjx6w85ZUQCkFU1V3PRcAZKgqECGcCQuBJiSRpqtqnnoHW8+i3795ywQXnSirfD1jP6raLIQ7cAAJnezjHcbBcjlZXr5iQBIN0EO32h6fndcd6LuTV4mI8XG2Ou7LLEQIQwabvMcXaGAQhITQN49DxLQQAWeq4s8nE87yq7fqeYQcrbcIwVkyGoUMwdhy3YbUf0OV8QJD7uBYAC991T6e1UqZr+YA4t5dXy+nkPBrvT9lmvyuq2ho7SBxogRbcD/3YD8eTxSnPfvr5Q9cxa6zjusCivGirqrpczobpII7C8XCACNFgyQR3qZeEEYL6w5cfMcFpEK8mU0hw1uSlKL2Rf2oOrO9c4iqtyqZhnC3nU0JgVhdSSSAsodhxnM1+5/juxWrlepRAY5TCCCmjwQvWGSGAESAYIGi1RshgSqAFECHq9F3XE0QwcZSVECGXetbSF2kwghgCrLXVRkoLE+ohRFjXBY6XrK5Yz4uyLJvTuzdff/f262N28ilxMWqLU23NIImvF3PX9RzX35+PYzBwHfd8PkrFXRJEYQwAhAhrRLdlRTF1/ViXVRDFQ5eei8x3gihI8qax6+fLxdxa+9Pnz/T+Po7DMA6vFtdP6ydkwWp5kSajJE4dJ6vvnwHEbuACpd5cXS+ni7brLi9Xy/mirHKEkAXgcD4/rteBH0Zh+PlhXTVdPByulrO+r9a7TdP1TXPyXAdj+PB8N5lM01fj/flcVc18PHU892m9nS4nw8BX0ECMVWtPZT6ZTB1Mp+MJpc7T+nGzz85FiQnZZ0UjxP/jP/9Pceg+rh8eNse8qtI4Gg8Gvh88HfedMh6h2Jjt5qFh/WR2MZlMCUHJIAbIRL7X9SZNx/PR6HmzrqpyNBgx6azX28Pm9PrV61evXk0Gg0kyqLoyz/bXy2sEdX7O55PL1oiybiRjpyLrhQz8bhCm4ygFQ7q4uKrKXPDeGkOXbhzFWZ7dXnb36yfG+btXb/fn3anYIYIgwsf8xAWvitpzw7KohO257Db7fV0zYOELdAwCYIHxPXc6mnge9oOgaErXAUpJjHBVdxYpIVjdVsooDDB1EICQM4Yp7jsOA9f1PW76rC+YkRgBo2VV1//815+ulxdVXgBNEIAjL0DGNlkpOkmJ6xCCCSma/Hw8IxL97e9nvO//6Z/+tWOKEHexmK0mKyHkIB0RSgCC14urf/xP/5AXZZqOqyrfZoep5z/thWRdxwTCXhilvkOrlknBR2nciQnXkGkxmUyYFRhTC9TD42ejjWKSfLi7P1c1MNLZeRDTq+vL0Pe+3H8GSMd+MIgn57xa748//Px+OhlNR9OyqgyFyXi4Wi1Gw0ESJ1EU/PL+vQfxYjLzo+j7736jlX3+7zvRdWkca2MY567rAgR9ShajC67c4y7DGFprEEaEYmANRA7n5rA/hGlKqdBKCykQRC+UPmgRF6pte9nrsm4fq0ZKEYXh7fXtbLwyWjuuJ6Rsu45x5vtumsbA2PV263rBuWyyPPcw8VwnCiJjlNGW9UJKAwAEwAAOpeqqsqvrnvWs7oQGCAFIkAIEEWB913UIMkaPJkNtrVKSMe4gcjqfeyEthEapxPd6DOuqQRCMhsOmqSlGSRz7HgUu9cIopvDrV7fjxeLDpw+O48bxwHXzKBkcDwdotBUCaBOHUexHk9HslOd110spmTJ9eyrKPI3T8WRaVMW5LLTRxmqISN/1XHCA4bfffLuYDbSWgvOqriBXqbaybgTLD+d91R7Gw2FdcYxcBzuyZ+V5rwy7vr516agpsovp1EI4HgwIJfn5xPq2U2ZzzBkXdd22LbtTm7ys4yjqmeRMrXf7ummmk6GBglLHAsNZSyEKPW+3XZ+OGcKwRMV0Ok+Ho8V4HgXh8Xh+Pm6TMJ6l0+ycV03jEZo4jksgwci3zmI8mY2neVlqLYXkjHV1XRVVLhVX2kAICaHAWoIwJVRhYv9jaIsxBcbCF6wIRgTjF3oUwehlD4AwhggBBAGE1liMMKUOJsRxPD+KurKyCriuY6xWWp+yY+D7jLVNX/m+ezwf1ptN1/eL6RwTdL1avLq6pL5vlPn0+UNeFZKzXZnndfmt8+3rm9fE9V1CXepGcfrm+kbp/v/9f/5/tGJRMu4Za9v6/ql7ffXu5vJaG8UYe3hY10W/O2wGYYwRcgM/jqLZfBHF4w8fP5zO5+vVMvQD6vDhaOR4BCE4HI6jMGyarqibU5YjAC8XcwRAEg5+9/3vluPxl4cvFKOry2V2LrliGMHBcOD7ntLGDwIhhDLmcM4gRqN06FCHSSG0lNr0TBRFiSAej4ZlU5dNZwCijue6DkawZ1xbnSRR/4UfTlkvZZykvVAWcKFs3QnjI0fatq+7rr26fIUhMla3rE2TeDIaN11b1OXj8/PhsNPaaK3jKM3yNSHmcbdZH7fz2XQ+m1MEMQSnotyfTsqAq9X1MnRZz5QGbSd3+0NZtKesvLm8XqyuAPKart/vnz3HnY5nPVfG4vF4+v7zZymL/f74sHuuWXGxvF7OUmJAVTf3d0/7w0fXoVEc3F5fKqYFl5R4AAKE4K9lYIwQJdoopZRPqEAk9Lz5aJAEAVOacVG1Tdv2CIEXOIkfhIwrDI1k/dXqYjj0DvtdBqr5aLicTIAGp7r58rg97XcY0ulgDJRoimo0n7fGDtPJV1+/jsLRend/t30/HsK8PP71L386Z23TiZafiBsEfkxcF1KnZ8IAMEmHX795dz7uXBqZ8aTjMggTTGBZ5C7pp7PlbDJGhh+zU991fui/vr0B0BFCeJ7b9TX1vNAPd6cT0Jp1HbEQKKPSKHIc6vuBT7HjeczYtqqrqq47Nhsuu14d9lnbdAAgDJHWyvVdx3f9NCp51yqxvLnJ88oj6KvlUKrq/vGZ8WY6HgeuN59NPJcaJXoj664Moniz7bViGAHOeFlVrGuVFATHcZy2bb/ZHYhDMMUIIaBfctxSa8h62bYdDJEGQAM8mswvl7Nv376ryuJh88xYs1guHWf51x/+EgZunIQGaGXNwA2+uXrzp7qlDiGU9IILoSilUUDKutbaQkSVgUxIo03D8yQMZ9N5XTdCcIfQOA7jyMcIAozTgCahDxEiFLu+0/b94XjMyyaKk+vViitTt32UDrqWCW0mk7HvOsaoyWjwzTffDNPRZrc+ng6u717OFk1duY4bh9Epy8qy6vv+VJRV00hjXIRvr67fvb5p2q5t+81uDYB2PYexripLpfTT6bQh+zAKI88niNRtVzUNsOhvfvfbIAhoSIM4yvJzVtWUUMl5ko4U1FLBJJo9P20pVevtue9ax0cGItayw349my8nk7HWer3ZMCGEkBVjQZBYAAEiL3F6bWzbsigMrTbnMjudz1lx7lgTBkHbda7jBhdBURaH85kxBRHKeWOpu7i87nv24eMXwZXnBnXFsuMnaBFGKEoGwyQ6nbd1U0FM9+cz1yoIoyAMi7osynK/27phUJSFMdb13ABhawFECGOMEbLaQgOBtQQTA40BGhOMMTZGC84RJRAiCAHEL68FAn7lt0MIgEMpxshC8P7zl76sRulgOp3E8QCC6nQ6FWXeMa6tcTwPWdj3LAxDZSwTsmw7xvo4SXzfBwQoKTvGqR9YhJqGr3fbpq2BNp7jzScTzrvDaev7vujFfvuMCYUOkcaEYfDb6XiX7QW3COCqKK2xhzxTACxWS0ho3bA4Sr7/7vt/+9MftdJBFAxn02E6eHi+3x12gyTxHG+xXChr0zR99+rmeDz2HSGUrjePbZUJJRbTkWRiGEcvJM7lZCq19hz3arlYTseU0NP5TCgVgh8OLXEohsjBlBAvLxqphLCq7/u+kwjj5XxujOnbjnXscNghzSkkUZQmFM/ny7IqNICLyWw2noyHA0Jw3YRFkcWuRyE6F1lTVlrwU3ZABGME+76zCFHPUdpWDbt99a5nsmf9fHmJKJYaamPyqgXEa7nWBhRNjXk3Go5chxRl19RCKXR7nU6mS8cNzsXxebs9HQ4EkSyruVCr1epwPJ4OZyb04+OT0nJ1Odut92qgXQzv7h/X280xK5TSURS5xNkfcm0t/bUEgF6a4wgA1vdt3wCTE2CU0kBrD6PrV1dMiKrtHzcGKCWVscBIISkmFBstmLIqSpLL5UUShogQ3jXnoup6yVoJjRoP5nXfVG3rOZ7xg3A2enu5HKfJw9N9VXZQ25vJwlr7L//3Pz0fjgrQdBQNMeaC3z+vIcJSg9e3N1qrTgotWFY2g8RPB2k4wHGc5HnOpZrMlkmSxmmqZBck6XAyxwgeswN1AMaAMTaIBll+hAZeLa+gtXVVEcb6YRBHvu85zmw8IRR3TfP2+vXnL1+K7JiGME1iSnRdVloBKfR8NW3qxljDpNjsdtPx5Jt3XwVeZLR9erorq8O///WHuu5fX1+ng+FiurBa9U1dV1lZ9VXVQOR2bdu1FWt74HtaG2AhtIhgNJlMvvvu8mm3ftw8QQip42ihtLYGAICR1CaMoul41LD++6++vrm8dH36488///T+PVOmb/uHzfb1za010Bgd+f4gSd/c3GRZwTQLQjcOAwwhdZysLAdx4jhu2gRdz4VQmGAppbGWYngxnw/TgVCCUgQhTgYpdbBUoq7b6WDU17UBOkqjwTCmVV3VfTpwPM9HCDY988M4HQyvbgb7U1bkGbcaQ0Adj3MVROF4dfkvf/yXfZ7PhuOedRDg3eF4f39PCBZKF1VjDMAIb/Ldsa5e1ZVHqYPpIImCwPW9/njI2q47nE5d1/mRS303TlOPUgMBQEhwvlk/BX4QeB7CNs9P7uoycAMpOMH+IF5VeYF9TyhzKs6H8wERPB1PtvtCcX7K8rzpPt0/aCmzMseEDtPBbDQFmPQdV8pggoExRmuAsTaSumQ0GDd1gSE97rO9OUZh4Awcxlheli1rh+kg8EOhZRRH2836YfN8rkqPeKHyEAaxH7mu67rUo7ThXRCFl8RJBqO+7SwAi+lse9j1QmpttbWM87ZtIcQIYeABY40x5oXnY5Sy1qBfh7FWK2WtAQD0fe95wKX0ZeULIbQQWAgtQC8/BNZaC14CfEZDxw0AobvjSSslOOecA0SlZuei2h0+L8aTi9UqitMojLXRp6Ko6mpmoRdEdc/ysjbGvnr91qVUK/30/IigKYpyOpl5gcMhNhA5nmsVAIA3Tev7zvJyEccB0HqUxKPBfD4dludyczzuzycEUN/KVzdz33P3uy3EKB2mSBsh2Ggy0pJNxgO6mDVVlefHCZ2PR+lXb19Do60x1AnKpq76ervbGK0ms7FLHeIQpaRD6Wg4POYZooQ6qOdCGOFF7jk7f9k8WIhfX99Ox2MpLUYEIcL65nA4WwBno9kwipSS5yJ3PSdNI2Ns13SH7Z4zOY7GrOsAhDdX10EYWKMBtBbYdDSouurnTx+KpnVc0vXtKTtooMPA96kziFPqeVzr4WDmusFwMmnbuizrq4tb3w+ANYK1HvGt4oarc55RgoQUrCoFsIHjRXF6uVouJwstTFGcP3z+ualYHMYEYasBwbiqqv1hgykJiEMQxoRCjZ4fN7+8v3vePBsJXC90aESIcRxnfzht9iepgftroAAAABDCgzgGxrY9K7JiNkg8z+27bjGbzWfjzW63nI0QMLEfPG33FhiCsWCcEFJVJSWoIQgg4AYuZ72FBjiYG1X3DBMn8P0AWaV1OpnPPAcQWZVZ5KDn9WNTq5vlDGBRlbzrGFfg9vpV5DuO67gUHY+nx82+Ho0iP5Kaaa08LxwNp5T4EKKqzoCVgnOEUTpIXerUdYWQCaM4CiJsbd1UPed326fJaDYdjDfHM6Fe6DgYkSAdkaKs55MpY+ygzq4fDEZJEDnTyeX+eDpnJ88LIMBac2mEVvacZ++/fKKIfP/db2ajcVlmz9uHzeaZIOIHgbWAdboqmbVolAzDKBxPRrvt7vPTuumaMA5e37zVFv7y/rmtayWVFznWAoKptQAjoJX0XM8YUJe1UhphDDCQUiIEMUAUw9lo+OpipQUfBm6WH+5+fOo6Pp5Mp6NZUZSb3fZ4OhttFrMVxdQldDJMoLV5VS4X08lgyDmDCE1Gozc3N0kcvf/8sapbDJHrOUIJzpkFaDqYSs4YZ/FoRAj+5tur8WSQpmlZNkRT1vKW9Vzxp/VjWVdRkiziEWcsTaLpeBLGcRiGs/niH4eT/WH3+PgFAjUZjq0FbVPPJqvfvPvten3/5fG5qmshlNWg51o2TBnVMzZI0jhOJtNlmiZxmBBgAt8DwDLGm7oDBihtqqZFEExG0+V8gTGmDh0NhrPJcjRMeVP9/OnDajZ9u1p+9+ptK5TijCDQ1CUAGAL4vH8WmgeR77Wu0jrwPALRdDbve/GXn9+v9weCiNKKUMKYFsoopRwnWMxmXVe7lKRJ6rpOlp+k0dZCiAGXIstrBICDcEvqz23NjHADTwMzwcj1A8Y4Z51U7PXV5Wpx+e9/+Yto2ddvvvICzwLrOlgyfnl5PZstz3n5+eGOEIoB8ojbQ6Gs8jxPa6uUoYRAgOz/n9tdKSUEM0oZYLWWFlittTUWE2yk0kZrrSGACCNMyMt4VxujtaWEAAgsgEorSshvvv52/fxct5WWirHec93xdLJer/OyLqvW8YJkNEHUFVIDTPq+lxqtLm4uFkvB2OmUdYIPo9iBVrNufzwEQQAJQgD2fXc8HufjBe9EdiowoIhQqdoBpgGlwCoItGJ9U2XIivEgdlw3Cj3BRDochYTk51NRF/1hzzi7vbzo2zrPTrzriYMvb677FpTFibHO9X3FuiiK57PZ61eDsqo47zjnD08PSRp7Ds3z4ljXVmvfDS4vLqMg/Hz3CWHUdB0hOIoiz43rnkV+8ubmbd+x3X6XRBGGSCqVJMnlcvnq6vLzl49FlRuK67Z62m7c1SWTou2ar9LXAFhkUZoknu93rLdaekHQ9CV16PF45g9iPBw6FGOLPN8FABgF3CCGxobA+kEwmUy55MfTrmtZmg69wFOSCd5hpB/Wj5++fKDUsVZzIbTfQQhub1597Xj78+HD3YfJaEQp7Rs+SgeDJH2BRDVtleUniNF4PoEGAQmCIFwf1nXbFlUPoReGzmQyWs5mknPB+7apei4gAAgA/R/PFoJQSPm4fq767np5+ft3bz/ffYIQWgg/ff7ix/Hvf/v9H//4J0rdwznvtXEJBVhqbYwGThQYDHfHHWMNtMCnZDFbruaLi1U7mi7jOCqrbLPfVmX926++6drsw4ePSBprEEEoSfxtxiVEb16/k4r7YUggDHy3KrM8Kyim+fG0fXoKY79n7ZurWzpIm6pHBmghyuLIpa7bZnt4BsZaCCzQGhi3LgLiQOjcXFwSz5cQnfr+3ZvfX85Hp2Lfs873YuJ6QdN3RmkD9TE7brLd1fWVBdu6ydM0eXh6Zh0fxkNKcMfZ/pBlRfHm5gJZw3tOqZ9lxSk7U0zm48liOg/9aBwPPd/dbXc0o1pbgF0vTCGlw5i+u7rIKuGFcZz2mHMJITBaa6WNzYvyrz/+eDifD1lRZBWBxGhjjYXWvPCCpNLb/b6ta2Ps58e/QISrpn59e/tf/9PfD9O04d1PP/+smJxO5pQGHz59OBV5y7sgCt6+uimKAiIYegEF+HKxSiLP9Zww9B+3WwDREMVh4GkjXc8bTxJCJuxB7Y/n+XySRANK/PefPoeeZ7nuOyG1ZUJYgK9Wr8IgMEYzIYxRBBOpRNPqkUxZV0ArR0kotWiqwo+j3fF4zisuZZYVddNgRBzseKHv+35VVZAg0QupVC3Y//N//d8R0J8ePvaMd6xrWdn3vK650gACG0VRHEfLxeLV1ZXjACHZfDiLgwGA+OHpqajatuvGcez6ruhZVVVxEiVR9OXh+e7pmQnmO95//sPfSSFPRSaV+vL4WBRFXjRVwyAgXBhrLBdMqmPR1q7jrKaLySDZyl4D6wfOxWKeBC7CmAvWC9F0HcYojiKl1ON6O0ljL/aMVtTFk/H0m3ffKM3+7Yc/O8odpun15WVd11VdYgK1FEJwYNxjke3O59nscrZcAOLkxflc5oRSo012zqIwUEoK0adxgjC20EILAUIWwf8gg1rGmZTccz1CMCHED4K+75XVAQIYQ4wJAthCBAAyRiulXNcDAGqt26bu28Z3yNXF0oJ5VZYfPn2E0LZtTykdj9LVcjEYjIQFv/vN9xfzuRLsnGfGmvycPT2xOI6m41Hf1ISgXz5+6LoOWWSUGk6S+XwOAUWQPG4eD8cjZ+Z6MbbAasXrpurK5P7Ll/3pIKXUGliEfD/wAx8a27Ss7lmeZ3XfLS9u0hDWzdkaFfrBbn/45dMnx3N+/vzJxQRBrHR3Oh0xwW9u31RFMZny2XzGGSmKfDZMLQTn47FtO4KIG4aYYIjgKTvmRZFXpVTSdd1hPCSEOk07GQ0gsl1Xuw5ezMd57gip5tPJ7fWFNgpAOIgGSutTcdRCrA9bLwxiCwAybdPNptOPnz9zKf/2t98Hkde0VXY8OK4LLCirOgkTlzhRFCOCuJCI+lwZgFASR1LJ/WkTuH7qh6Jnn+9/zsr9ZDD2qbM97//15x9YJyZDtz1nQnIWhb3g348WgvOn58dBOuz7ruHm9e2NEHx3WDcdwxhPRkkYRkVdnLNcaRu7obLGQnh5cfHtNwmCZHfYEg/P5kl9LpF1NooppRFCv47/wa80CABM13MKKcXw4/2n434PjDnsdtyo7+aLu6f7z+vntuk7IRCiEFk3cIuiUkpSLwwCd5yMzkqlgzQJYt/1CEHz+eVosgiDqGqKy/lCy14plnX9OB1YY3zqDuJ0MhqW3Xw4vI5cb33Y7Y+HcZLOR+N//+nHvCrHycSDqM2OTw+VxrYtq9D3fS84V9mxPBGsKSBpPNls1oi4aRzts/10uvAQUZiEaThfLtNhWnTV/dM9RNYC4RNqkOibjJzPmes4fugzoL5sd1JZYDCZmUWctFz0nJ+bqu0YQDAIQqDtZJAOk2Q2mY4m0/V6m3oJGRNCaOR5bdses+P+sA/9sK07IdhwMnr95kow0TWwLo73SgjkAYyVQVJbAwGwxlqAMe56+dcfPny5f1ysLghEvO2gtRa+lHWsNbZn+vPD2nWpUgYCM5tOry6vXc/79OXTIPEc17G667umbrDUGFHguq6GVhsdRv5sNhaS52U+ShIvoGVbskM3isN//E9/2/XC9RyEoLQaIjBLBthxesWKPBKS//uPP3ess1bP0lHguI7rPW+2z7tdz9m3776ZTadCCq51UzWMcS4EpbgomySJEcJJ6FHq//mHD1qIJIohwm3X+UF0PueEkvFgaK0hCE1HI+JgoI3QquXMap5OJ90H+fnzF4dCiJXk2qHhdDYEGC1Ws6vZ9NXFarGYbo+biinG667tup5PJrP/5R//8Xw4YOo974+97BbjGUL44XmjLBiOptn5CAFom/Zitfp4f9c1XV5Wh1NGCHWIYwFQRllrXcdJ4hgBEwSBgbbqauyQ0Pc5Zz9/+AVDwlgPrBomw3EYf/3qtR+Fn+/uhkH06mJxrvLN8UBiNE6HhOC7512e1xSTKq/yc/b12zeH4y7LMqG01sZRrku9+93zH3/4y1dff/365nbjOD+8/2l3OAouZ9O51loKbY3VVlMEMMIKAK2NMQZB+BIJxRhLASCEjuNKKTEhECEpBET4BSKirX3JjUIArTUAWIQQY/3j00NbV1oagkng+aztgAWCC+o488l0sZwFXjAZjzFFVxfzsq4/fv50OJ60saPB0HXJ+umJQvT17TtjdN01Stu+ZRZYiDC00PXdXXYySg2TgUtc3jMDQd/L7f70tDsLqTElUnLP9WfT2ZS4vGlHSXp5MVHGYkpczt9c3/q+/+e//ltWlRTAvMiYElXVP+23gygeJ0NjFbAaWPvzp184F9f1dddXUBvOmOA9hHiYpqzrKCUOJUk6sNZWReX5bgrjvCgIpqPRWHJmoUXU/vTxB665Avr+6d5oSAkVvN3vtkprTGgYhdCCJPKjJGGiY0y8ev3aArA/nTgTxuooib/cfY5D73w+Vj2bTueYeI6PpVIWANf397v9bLGaTObQaC57a4UFMs8K6Q9Dx0vDUNelhwlUel9uD8fjbDKLgiFnrGizu4fNHMDZZC4NHEbh5XK5O5zjyHNdYoF63j1nZUGpAzXmNoGKDXy/gAhiQB1nvd0gj/pReKyyMAxJ4OZlfjgdNdOM9V3bMyHRr7MfACE0ACCEuNZVx6IweFivq6rGCBqtPOoQTBiTnx+eyrpHABkLoDFScoQgAsYYURUZBolPLm6Wb4bj8WQ04pKtnx/LZmcR9VzaN3nsuxLr//ZP/9I0feCFAMGL1VUUBVXHSgamHv3rh58BdN+9ese7Rhh9vVw4BkCjh+kwy8/3m20YJYfNaT5fxFHClVhdLLXm949PcTQKCXIi93p5kURh2/dS6dXFIvBo25T73cYSezyuJWNFflxOl5TSzfaJ7PMy8rxAy2gU/P3f/O1isjrtdj6lYRwjh751cFlUTdmOJsMgCIzSTVMQRHanQ1Y1h/1Ja2WBrerS8zw3dM75qW2asmsoocrIj18+caEH8YgQ53Ru2ponk4nR0vVdCYGEMKBUGQ9B7PteMhgQDK3SDsUUQwisAS/UL6OhNkZLpRbL1fp57RAshC7rpmVdQcmffykd4jR1W5SlGzyMh6MgCAAEXddHQTBMo+++/ub5+bnvm6vVcjG7+Pj57pzdGcMvLsZpAqRRhNLQ96M4ZV27O24Cl8JBzIR/PB3LpgzcwPSg6zoLUVmWjucu5ksIrJQCQMi4wMjpeR1GEUboab1zj1ng0dvby6vL2+0xC7ADAev63lo7GIxDP+hZ33dd17ZCqdB308iP/CCK4lU4U6KDRr++uMm3RyEbrq0QikCBoLIIBb4Pofnlyy9fnj/3TCRp2FZNFKfDySQKvPBmdbmapeFgu999efxcdVwZ87DbxUG8mi4dhE5Z9t/+9V/iNNXK3j8/SwMo9R2CMbSe58znE2gBNIbxHiAoGO8RuVgtp5OJ4DwvitMpAxBYq7AxSgjPdRGCu+PBdZ1vbm8Xk2F0PhEvEkyu1491W3Wcp9Fwf9hprTfHwxRIpSXxaN9K1/MwoZhjjMmHuy+t5KrnQsg0TLyVVzet74d5XV0ulpKJ9+/ff//b743VRhutXuCqCEJojMEYY4KFlJ4XQIisNZ7rU0IggtpogKCFwAILLMAIQWAFZ4RSrdVhvz/tD3EUOa7rOURqwXlnLSCcjtIojAICEWurrq8+f/hxfTxXZcd6kaQDCCEwpipqn7o4wI7rEsGIS6Hgq8lKA3WqKtx0RV1GXrApqqKqfOp4ruc7QRqPNsdjGESLyTyMAt9zZ+PRxWLWNDVCyPHd8/nskXg1WUKAeN9/9eYdAODh7suXu3sjgeTKoz4w4GmzTuJ4OhlXZbn58qiNbTpVVt1sNEbIdn1vLYQYVU07Gzuhi43oB8kgur39vL5Tms/GkzQexmF8FuJ0PhmrkiSG49H5nKXpgGLquR6BYL1+drxgOpkFfuB7jlLKcRwpvMVyiSDxPHc+W/71x792TS2V6PvGc52u79PhqKo7hPDFYtrU1cP6MQlTrtSL1FdJE7hOmZ3O+dkaHKKYhKTPmEtcZOHpsD/mu+PpdMpqCIs//O5vFpdT1jPeCojcd+9+0/eH5ksHPForrrRs6sbzg9AY1nWDINBtzQVHyMOuM0ujAAeC9YeysB3rWZfXpRTaWqOYAhpS6nNeS6ExwvDXBYC11iIEx4NRGPqeS12K15sD63mveFU3X7+5OdVd3rDV8ppA8Lw7IUKUVMBaQqjrugCYPD/9/Bkai9+9fkUwKpv6nBU9464bOBicTjtr0Xq7++GXzw5xfb/HLp1Pl3GSpngURCPfp/Hf/YPjpY4VX+4bjUEUxrvDXzen0013/V/+8IdRr855GQXh6ZgrDf04xNhVUkNE86LomkJXdVWyq+U8O59aoS9Wl9KKoiiUkvcPX7RQr1avMECq75mSx3NB5tN55LuOg17dXHz3+hZaMIpvPt0/PN1tbq+Wt6vbV9e3z49PjHUOgW4UXK+GhHqbzem4O202Gy74aDgYp8NkEDHBQTwa+BHGmEvJuBTM7HfHJBo/rbfnqgemlbtMEwKMJIS8urycRe5ffvwJEbhajV+9umG9EB3Lq0JqYwC06NcSDwDAGFNXzXG3dx1qDFhv9ukwbdvqarUcRcOu4wDQ6XQxHg/TMDmeTkkSuYhyKR6fN8fjGUJYtOXz82acTKaT6f3T47HMw0EUx2nXM933vus7xMt5+fS8m04mhEKfuqEMmq7GgBjk7rJd19aU0MgCzUS0iBzq1G3NuvZieemFnuO4eZb1fd82TeNiBZQfDd69ev3TTz9JJY0xvu9rJdM4HqUJREhp/fHuy/4orldz3nPskhmeGat73i2Xi4vVbHtUyOBRPAjdACG8PWdd1SClHvbrIq8W03lahavZLHBDzdXpdO5Fix2CMUni5Puvf3e3fiibPE3SyI+qqvqyXldtI6UsHqrpcNw2vbWWEEwwdCi5XF5wrbbb3TCOhmlU9gxYyyXnrJ+PhhqAT1/uTlnuEjoZp4L30mgn8Kuu67puOhxjivfZURkYhDGOQBCEVVVZa1eT8XiYSC1Gw4EWoqmbIAznNwvP8bqmPR2PVhnWddvtehLFQumyLEM3SJMoq6q2bwTvrZaM9S81Tm00IUSIFykM0lq/XNwY4y+gf63VS13MGiOlNNZgBF9QIhhCa01R5hChvmMPd48Ywbev3yBggdVAqzgIrQGLxTIMPCY6N4h3u13N2q5ju2PFO5lGacvFv//0E0UEW+C7wWoxA1ZxLQyGg0EymUyej9u2aVbT2cqdCSYO1bHu2unVxCrrE/f3y9V4sEUQD6J0PJ9qxRnrNpt1UZZZVRZdmxXF33//h4WyVVcOIs/1/CorrNZxlLa1eHf7xvcdIcTzdkswbqpeaxj5keM4dVXfMV4UhZKyqmupFBfcoW7dta7rlEUVx/FivuRSEkiiOIII7Q/7Y5YjQvzAt1prqRfT+WQ45FxOJ5PjbtNxxpSp2vbycnlxudiuN5Lby4vlarna704OofPrWRB528NWC/lw//D0vLm4uPDcgEuOEeJdp7UJ/Gg0GsdpfDpn6/VTGkfAsO1hnxfVKJ3UfcsOnZTSDyKjVM963TIP0K/evHM95/ZyJljxu6/eSA3zttO6wwhNZ/MJcAKfHg/PP3x8H8WD66urOsuy0/7T8fib3/xhOp7j01ppXnel51BiYUw86sJDdmy7ngvhUzcJEoeinTLWWESx/Y8aMAAWQuR5eJT6w2SIEbQGuF5QVCUFyAuDXz5/bFk/xdh3CMZYGyukjoJACOF5PsBESVC1PYR4Ohs/rR+YVC/QciHE3ecvVVUqY4uqWs0vF8vluTgJqQ7nY17n3371Runu8bF89frbm4tVU+xnk5FDXGrgcLoQbrC6eTeZXyZx8tcPH3zixK5LfV8AXdXVOTs5jp+mkyAIWy7PRVYX54enNQ3C+/GnIHCE0Nqo4zHnQrooMIIz1tacb04Z+d//t/8tCKnVZn86fPxy1/M+CkIh7GJ25VGn75k2DefCo45PaFOWyLh9f64qRh2apKmU4vb6epjGBpgvd1/6rhkOhkEQb0/n+WyBAez7dr3bnYtydzp4ro+wi7Tu6hYHIeDlYjH/BTILZJq6i+XwdCpaq9tDL7WGGAMLAUTQWASw1LqqO2MBdnCaDIhLq6r2fRdakEQhV9q0xnMdjxLXo45L6rYtypJzvlrOm66r69rznNXqykJUN1USRYz1u+0h9sJBOsDU6bl6//FDGAVRPGgbLo3cFaee9dPhcBCM/GjUNN2mZw5xoiiKwpBgctzvLQIEAWtF6HtFUQrORoPU94Km7/Ky/b/++z/95s1Xm+HweDwRhFzX3R8OURgJbTljcRy/e/1ms98ZS6J0uD5s2q4H1pyKAlroePjtu7dCCJ9in7jDdOR+vPvyeC84d6DTNfxZ7qokuLy4bNqu3tcGWqnkfDbd6914NG7a1igVe/656XKePzyts7IEyhJKMXY6rYeT8Xw01ILldcGkapk45pnFuOi6NE48xwXQMqOeD1v1R4EIKds2StLJYDCfjuq28n0fU+oQEo9m89ksir3dcWshpJRoKSQXXd9TSrQWkvG2b43SFCMp9LnPz4ez63ovHY/xcOy5Ltf8cDgMkxQolbPzAI1Cl9adlbyXgt/e3rgOlVL4nm+BFYJq8DKzBVJKrbWUwhijtVZKAgAcShFEEBmrrbLScX2EEIDQWlsU+WazZR3Xyhhgnx8f3cDL8pwiIo3temZ2O6sUE8xaKKW+ef36N9/csH/6vzvaIYQO+20Y+ggg3/VCP9DKUEpmw+EL8I4pIYVczear6byqyvvnZy5lGg+UtllVeq4XiQAT1LFO5EwC4Qde2zey0j3jddvttifqeEKBzf7guqgNnWOeH/dHjzpGg+EgWcwm28PGWjgbz8qmycq8KCvfJbDrLbBBELRtu90dCKFBGEVxpJVm3CKMnTAWBj5stg7BoyT1HI9xHgb+1WLleITxJivyKAyjIAYWfvX110qKvou/ev2aUh9A6Hk0CYPlb78/nTMh2L//9d+Z1PPFcjQcDJKY8+54OCTJIIqHo9EAWBN4bhD4LsKe0kmUONRp6loKaV1jtGmbxlg8nV46mAjJ67olhFBMoDbAGuo57xar0XDUtHmXbTeng9CWQNdw8bx+wMicztkwnrux++HLw9P2+Dff/8EliT/x87wZzON//J//1/KweX76dL958sMQWQwxch1HKY0BibyI9SdLrFCsKJuiKI15AUP9OgayFhBCILFlX1VN13X9w2YdR9FyPL1YXdSs+7vf/+7Tw2PHGgd5lBClsdZGGc2FUEYbayLffXtzMUoGqmvLqnA9VwjVNs36+Un0XdcLLwyTwYBSNJmMnQDnWS6BgQieznleF8+HA7O25nXoOL4bKa0Xl5f/lVBt4GA0jAOvqeHN69vd7ripihklHe+O5fl5v//7v/kvV9fvXA+1dfX8+OX9+/fI4JD6TVefC8E5+Oabr96+Rfm5LKrGJdgaz8XOdOCQ28tlVp38OJ7NZ0+PT7FvR4PhcnUJIP4//o//7/awH8/GTdkJwVaXk2OWP+604BJINBlPrm+u+r4lvj1Vx7yoq5Y9Pm7IZn99dUNoQIl7/3hXd00neNtWwzjJspx4xvdDQimhTl2c4VU0Hg5+etwfsnya7wHCZVUiAwnGVilggTXa/prNxRqaxXLmOK5SPEmGBOO+7ZTWP3z8JLRaDEcEw2N2Yqw7ZgXChGCcDAdJ4LqEhJ53Op3Kqvzq7Zsoij3qzUfTsip6wRXvB6Ox7zlairpuPdd/2D+UZTUcp1+9e9U0XS/VxTD59qvXk3GSFwU0Oo3iU3Eqyv8fS/+1LFuWZmdiU6y5tHTtvvXeR4bMjMisQgFNiG5DN42X7JckeUszNtFAVwOoKiArM0RGxImjz1au3ZfWc60peBF8iHHx2z/G9+Wz8aSp27/8+MPp7DyJUyYFxnjsWgLxum2yrLxbrWzLStJ8MBg4lpklSVYUpm15fhAErmOa1xeXVVkNAg9g0PU97eRq89h1zenp7MRzB66X52XZsQHCWEGb/SFJC8655KKlPSybT4+PKsRlXXVMpHl2t1wOHPtksUizoq6a32Y+WVEUVRVYLme8bJrr6+vrswso+XK3SWm7izOiaIS0tmlNpiNd0+fjWZ7H+yi68IPJaNh2bVVWlhlUdTnw7Mvz067vqqo+xlHW9b7t7g67uRzH+3QTHnsIzqbzfRKHeaZiJc5z07IwQo/bDZbIcZ2macqyoqxru14IYRBNI6RjnbAAIboAqcRyfdgKAfMiz6J0F8WXl5WhabplKpAoCkFYkRAKCX+rBv12IHZtK6UEEHDGJVakAoXgfddBrKiahrECEOZ9n2VpGoe845xRQpSqqZqe2qY99AMF40MU3j0siUIsyzpEoWk515dXoyD45qsvkJR1UQ49x3YsKYVKFF3R266jXYMVBBDiAJi6fnV2JoW8fbgvqqrnXDUN3/E0oqu46mn9sK2EEApSiKknRZoWsBMdVpT5dMqlABAZmhEnyWQ6vbl6ZhgaUB4BR5vdltLu6eVlkae0bnshatojhM5OFjeXV3ESZ1mq67oUQNO16yfXZdVkWcUlCzzPte2WNqznKtGbvlUgyou8bpqu68/O5rqFiYIlV3SF1C3t+3Z6ejGdTGhd0aoEACymcynlw2p5OB6rugzDuO04AMg0DF2Pf6GlRojoWF03AIGR71u6bhpGSxsFQd1wHIIlkF3X103btE3d1HXjJEno+oGqaj1tR8MJ9AdVnXPOOGO056rpc8l/+PmHKMkm4xEVTAqiqzIYDhWi5VmSpnmWNcfY9KzR/Ksbz/f24d6zjOlk8fzzL7o+e/32r0VRA0kUCQFAF5fXx+N+vT3Ylq6rynw4EIDXtFYJ+sQ3TEKIARDgt8UIkzIY+i+eXmV5vFnHRdMGg6lrGkVVb46HFy+eEZUIiDraZGkkpAQQY0WHSOFCcgkY7Roh8jTfLLeaqZumlacHwXmWl+vd9uLicuAFEoNe8jzPNVuTQHSMKURTsZpkJZNsODSzMs5+zc/mJ47lXZxfACRsXyvy/LC/TxQEgIIhsh0XERKXuW3b5/O5hBgTJWlqG+AoDte7CCD15MTbhYfbFSPYeHLzhe8ZUZhPh5Or06s0TznjAsiyrJRd+Nh0zAkCzdBni8Vut3tYPRAdu46v66oGSBTmCClJVtiOdjpbVC0ry3a/CR9Wa7vMbFPfhTHruaGacZxkeY2wQMrac/zdfrvZbl3XyTUtyTLIQZYVSschUpuOqYgJ3j4+rhFQMMDRsfj55w+mabFGhkmaVwVUdQwlA4BLCQACEjHOVA1MJp6CcZ7lpmH4pmZoumXqCsJD3z9mMWccCLmYzXfHQ5rlZDy8W63SNL+4uHjy9Ga73/3zX/58sjijPS2q3Bt4ZdPdfvgY//X1xdnp8ydPGONREhFdhRUKgiDwfaJqSZYixJ89ubq+vvrhl582m1XSlgghfzDMqqajfd+I+8dHommWZWEJ2qoGUg4Hg/ns1PdczrvLi1PXcYCUGiEQgsl4PPAHZZ1hpBCiso62TRMMrLwopQBNU1MCVVWHCDVNG8YJ42K3PWzDeHWI+o4ZujodjUbBCGB5e/8w8vyqqpmEUsrl4wYs5kxsi6Ksqto0TFXVyqrNshI6EEgBABsN3cnY2+33ZVkCRfnyxRdSIkYb29SJqti2CQHnUigYawR/8fJZ0zZV1ei69erN6zQ5Yowwg45hXf7u8nG5/PThowTi9v6hoUxicHoy+7f/w//w5v27Vx8/ua6vqKSnnT3wJfRExwlGznAMJQZ1QZtOAiCk2O8Pg9Gw6boPD7cASs9z8rJOkhwiJa+L5Wb/sFwvN5uLi/OT+dl8sRBCQEXhEHIIESaCC4RwJyjrmaqqQoieMQSAoqCuo4alQoiEFBohjPPwmGRZJTlfrje6oSEANF2zTLOj7dXV1XQxNRwLA2Touj/wdMMcD+wo3R+zfXg4aohYpna2mHWMxmkaJgcogWVblmEihI5RtN5sdUPHAOiauhiNwWSCFGxqOu+Y7Nq2bTvOhoPBzfX1p/uH1x/fCwnbrhGCSwGLqnFdD0GQ5ZmQ3CBQ1zTDMl/ePDmbL/KqzPO0573tWKvdPi3KZ1c3Z4t5J4Vt6v3IhxBBSGaLOWfdw3LJ+11Du7KualooBKmq1jHadV1U111PWdcZuq7pOExiTVUtg2gq5kwqirKYTU2i5U1UVpVpWpZj//zql93hcP/4WDaUcxl4A0s3TNV+eFgek8Pf/uFvmqIKo9iyHNb1xHW3x0NdVvPZhLFsMBy0tDN0S6O6hJAohLO+74RkvMhSx7Z1XTFNo27yssqHg2FDKcLKPgw/3j32EgKsm5rievZoPJ3PZ48Pd03dPr95RptOQnB2cuK7waf7BwnEIco8z9vHd8sfH0TDbcsP06oom9Fo8vL5i8B3qpJ2XasQPB4OmGQ6VdfNuuMdwhBjwgUDUEIAfvPHdTUduf7NydM0qynlj+ulrpKr6+ue8zzKby4uJ6PBL6/++ufvfgVSYAVDhKUAUCLXscPD/oHDs/MTz7U9391uWZzFxziLsiYo+quLkQCMAT6dDLu+lUyeThdt1z88PGCMe9ZUbdW2YDwc/9dPf5ouTkazKQJ9UWWHKNpvj7Px5MvPv2Q91eOj4y3iOBsPpxohlm4ApAjed5R/+nR3e7ecLgbYAkav9j0dDiYD36B1sdsfS0fenF8QgiCSHz599LxA2R+Ppm66ptnUTde2g8BfrVevXv86Ho40omCMetaquq7isanoOtSpqAauPwmm//Snf9p+2BCF6IYqBDiG90VWuLajqcZ6tVuJne0448EwcGzdMNq67mk/Ho96iKumq5pOwk5AUNRUIQhDDoTKKFoetoE3GA4HJW0EQoCL37TAQEgAIG37NC36nhmGnqXZTuzGfnBxdu7aVlzkb1d3AIDrkzMAsZBoPJrQnq92uyRNiaJs9jvd1DFRf3rz7i8//Wxbtue6NaVYUU3DIYrOu14Cblg6CuUffv+1bdt3jw/r45YQYjlmyxqIZV3ktq5enZ/4gf/VZ597trc6hP/43//7yWTm+w5AUEipEoUz3tLOcb3xeMIY46xrmqIo8sFgMBmNl8uHJNo5liYYlQoTPYvCoxRCSGBbNoZ4FHhhwos89x03L4soSwhWdVXXVWMYjA7HECsqQCjJEqwoWVmOgpHtescwUonquoO8pHG2gQixvo+TrOt6hRAp5D5KBOeea79++66nrWfbJ+PJOgr/5nff7A7x3//XvzcMnXHW0NY0DKKT08UiLfIffvqJs24+makKno6HqoIqWju2MXQDjMhoOCyKMsuKY5y4rvPy+fUfv/2aYCVN48nAn0xPLJ2oGGVJkmSp67iXlxd396u7x0cmmKHpBOHxaGxpRlmWR0pbShWVhGHaUYoQwRjqiiZUoSJy9/Hu4fHxf/2//69FkUsoMEKKquGuB7KTAkKJAMSMd5x2hCgI49+eRgBACQAXHELZNjVjYjKeh4cIqFi3HUrbyXCoaUQIVtMmzCJiaIqhBLbbNe3NzYVtWGES745HIFHZsA/rJULwzaf3QgpV0wyizUZDLvqyKmjX27Z7Mj/Jivx43HdtMx34NaUaUUbDYL/b6TqRUIoeYo28evdGAnBzc1VX9Wq7QVhTiHJzc3M+O+3b+vbu0/Zw+MtfD4J3T64vXzx76jrWcBD86fvt3cO957ijIBgGg7qu4jyxPY+YWlElOtGf3VybhrFaPfi2qV+eVU3TtdS2zKotIVaqqsUI6rbLWNf3vUYUBSsvn7yAGL19/7asi6qpDas0dANLeIwjhZB/8exlnCYcQAWTrm9NlXiex7g8Htd9HwAEwkP8/Xc/PXty/cUXX0KoFHn28f6h6eiTqxvPd7a7jaIRhai6plPDqJu649yyrN//7ndFkURRTIgeRdFyVbZdiyHc7rZFWbc951xOJvOeccu0hoG3PmzSuhlNpq7n1nWpEWU+mgAEBRQqRn/77e/W2+Vu/1iUaUWzuq7mwRxwhA+wE0Bg1HP+/Nnnp+dP379/fdyvkQJ1rHuBHYaHrus0TJAEXAL4mzOIS103rq+fqgTomuU4HRcwGAV5WagEdx01TVPXCcbAMHVFwV0PAAISCgCA4FxBuhTy8urSC/S2plDKgWNPh8PJZHKW1afzk9OTOSEYIHA4HjjlgRdoWD2GkeO5tKkZg4buO46lQFlWjZoVy9Vq5Dvz4bkO3Kk/5awNw02SRLqh0wo5hmFqqmWpfWsRzSC6hrBycnrie4HAVJL+7OIM9spsMhOSdbSxbW80HKZ51PeMdtSwdEVVlMM+Pp8r2+VqPjsxB0aaZV8++/z+4W7zuDMNXTVI27bb7c6xXFu3VqtjmodIwZ4bDALvm29+pwiYFmlRU43Y6ETaltE2VFMN3w+Iik+nE9vQDln87OYSSnhI0yirEEZZlgoABFYBUgDvFSil5AgqnIMkjU3ThAgLIcFvTVEgAeQYYwBkR9l04gku0qwGQrIeesHYsoyS9VDRec86DrAi1+u1PxhrqtHUB8e2PS8AQm42BykllMR3TU1V0ySXTCKMIRS+a1kaCcPw+smNbVu86yd+EIUh0VTWM9Gx5JhAAbI4nQxGs9mkqIr379/6XjBfnPzxy8+glJ7nMc7zslB1raOdphod6wmWaRxDAFraqZrmOG4YR03Xh3GS19R1bF3XLcvhAB6OEVbwMBj6gev7/iE6trTd7o9hGjeU+gZiuDN05fri1NSIY5m6rnecR1mWFfV+f3RdtyoraAPNMOqm7Trm2iaQsAXMsk3bsuIk6/pmMho6tmFZRkt7IQopkOd5u+P+/PJmcX8eHcO256qmv3j5WVrk42BiWtbjw/3pYqpgJHlXFXlRNRyJ7WEn2W3fs8ALbNupGvrFF58tphNa5X/55z9BBS8Ws9nsRApANHy2mL9682uUJlXV/OW7H45JXtZN31F9bNGuf1xvgBC6pkMERddHcQ4AIETFiLd5CSRAWDmEkaoSW7FZ3x+PO9MyNUUFEkKAuq5nQkBMEKJYUXrWE0AgRBhjhAnGihSyqmrBQVpkxzDKknI6GbuuKQBP8/x0cZLGkYDAMaw8zfq4m8wXRFMPu11RVWg82xx2um58+fzFyezkP//DP378dKupimGoA384H89oX2dVBeuaqOrMd04W8/JDuT+GEonVcatA8i/++De9kMTQR0QFCL358CEpMsZ4S+l4MLBt6/RsbluGrhpMSlUB85OZYJQoZBOGpqFDCX998861nfFkSog6HI45A33PTUOnjBFN7Zr2GMcIQgkhVKBp65bnRkWeFJnnuqahIwAMeyQ4m3hDCQFR9V7w9WaXZyntRFaXtOuivCBICexxVZeb1dowLQgVy/QeV48Kws9vnvw5+S6vSgBl1VSKqlueTQx1v9nlZU1IWldNmmUX51eOYw/Gw7wsBsGQ921VN6OxHgRueDzkeYkVpGqqruuGaTDRX3qBrpv746HpeZqXTVmqRJ1M54Qy2vGh52sE1nWtE/VsdoJVlSjQdX2I0e2nT21LT09OpJDLzdJzas92VkvZ9Xw8DhQbs74VEuqGLju16/mv717bpgUBUgA8nS36poUIzkbD75pOCkgU5beiiZACSigA6Ht+jBKMOk0lZ5c3jAGOZde3ou81omiG7vmu61iaTpACBZOs61SsQgQhVnoB/IErcfPT27eni+uJjrOowKwhtjY1DcvErM+Joh+2h6wsgkHw/ObpfrubTsa2bZVlmhdF0XZIUeqyurm5nM9nfVNuskQ7vzJVXKTJw+qh7QREmmNZ11c2xDBJwsO+OYahadtQUQBQri4uyirfRbuiyne7+Ob8mjPBoGz63rDNyWQSHw+MS90wAcI9E0pgOQRhDGFRFj0TURw2baWbmqKq++NeY3jgeoHj6bpuWObt3XJzCMdj33ZMw76UXEzmE8e36qY9O11QSuu6jKN4Mh6enp40daWpCiHYgbZJVBUpiOAoySUACkZSSom1hnETSUIIE+I3imxTFU3bAYKhEFIAzoXg8jc5mGEanueOx0ODWLbhbfa7qqq2h0gAHqXJfDh++uTZard5XD1OBuMXL17SX16B5ZLRXjKhqmqWF23b6JoioUm7xrYdTAijrOO0zNPLs7PZdNbRVkqpmlraFL3oy6Igijobz3ds/+rVO8lFEudRlh2iUCXQsZPfOunb474HzLGcqqnatkESHneHvCqwggXjRCES4a6jRVG0tOOc1S1t+75qivlsCjGEEP3GJViuHlePjxLA8WR6PIZRlFRt63u+YeiHcF/VTV6Utm2eTSa/5RYCSFS15wwCYOi6pqmmqeuaTuvaswypYHK2cG1bCDlvGhUrKsSGoXU9VYjSS9EJHljObDp5+fRmvX1c6pqmqdPpWNPIOZpczk6zsoB8bqhqVVW3tx/uH/dVxzRLPx5CKYWQcjoc66pKOU3zOE1j23TSNOy6WrcO15dPyro2DIIRuHt4PIaxZdgIKWVZl3XjOvbJ4sQytCRPhZBX5xev3r62NH1ge6PJBECwWq+JgpI0VSBAGBJNdR373du3RFUAgE+ubgaBVzXNerOdTMaGaTBGKaUQQCEkZxyh3yRgkHb0EEZZXiRZQimdjGdAMsl7VUFni9nxsE2z3DBNFkWaqfWiLx8eN+uda5pFcRz7o9loUjXVu/fvMFZe3FyeTCe6rgvJy7qO86RtatdzbMdmnO/D/cPqYeAPnz57sQ1368366nSuEj2MEqIqjuMcDgdNVW3H7hnjnBdVAyEwbEMhSt3Wk8mZbVllmUOkmLatN7luGp47CLOMSiyx6vsjTbW4hE1XFUU+G09kx8MkDfyAYKQZalEWr9+90hT9bH42nS76vi/K0jPNsip6iGaz+c+//mIQneg67SjCas/k9hDXLbWdgWOYukKqpnnY7gzTGPpjywH3q+W/+pu/LctSAEEMLQ4TKeV8Zg/dgHZdQ1tdM4fD8XA4Mk3DtgzHNHrGkkwnqn48ZrbjDQejj7eve8pevvgcSJAVmWmaddM2LXdda3NYL5cr2osszzGCAAJCLEjAxXTx5PKiSg7H6JgUBe6BZRp/+e7PRLPms2la1EQ147yAQCx3+44jIcVkdDIcTtJ027ZFXlfLzQ7rlm8Hfd893j2UZX11fTPwvSQ+5lGs6VpNq2OUtl1vGmrPpARAUZTxaOy53uXNtefYQtRFVe6PWwGQa3lnp4ssTZq2pl0ThXvB3L7rhOBAoirPu1ZhTPS8J1wZ+a6pg89ffn1+ehnHu20cE41cXVxNhhNEWZPnvOv7jgaBm2Tp+08ffzMd2bZhGQQChHBbNvXpfGZoNoC8zgrW0TjaZHl59/hAaec43mDgmYap6ZYfePf3j9v1RkhR0a7nXNPty4vLlvYKUmfDMyBEHB4OHI4nM9ewJYcI4cl0FsVhWVWeHyhEUyzbEJArmuy62rR8x3ejdTzwfVezOKeWaaiq+v7urqUNUUnLhOk4hKiGpZum1XfycXVo2jIIPExUi7icsZP5bDodL+YzCOA+PORFYWC9qSrd923D0ohathVEEhBEOYtbgTXEhZxMJoyxHmHVHaRF0fedpqm0F4qqYwUoCEkIXVc/Pz29OD25vb3TVMUPhlGW3z0u+471rO9a7nhBXbV11eGRksTHyXg4HA7rMlcw5EBoujYeut1vo1suQVXt9kfWsfFo3DHuOK6m29vjri4zqMCsKhGAGjEUhJabXRTFju2oBBMFnJ8tHN9dbzZ1y45ZhhGqaBfHcdN2EKtZmldllaZpVmQSAcf2OGNNTw3DQEU58IOT2UzwjjYNk91utzYNfeAPx8Hgcb2Ks7zr+4HvL7wBHKIjPLqOaerW/Wa13R8c0zJ0M6+q9493QkANa55p6kSxDGMyHJiGqmrks+cvJef39/e0qRVL1XTiuYaqagAEWRyrmHDOi7xQNXU0GymqKoQwdPWH7/+cp4lpaEHgaCYoq1RV7be3H8LwCKRgjEkB9ruwrhui6llYdB0f+F5NaV7XHaOuZ1uGudnuTKM0TKMDpM4q+fBYlOXAd8uijtOSMVAWpWZplLWWZf7uqy+eP31CaV21xWaza5tKJ0qYZaaqSsmyoqiqUvBesPr04tp07MPxwNri4e5OIjQeDLqTk7ZR/vrqJ6IoX372Oe06qum06yHECCDJAVY0RTPqjhZVGWfx43pdZqVpGKmyLwH8+LFsevr05rKmVV7nWCOHOPJdqxNcUXQC8fv4gxe4j5v1dDiI4/QYJy+fPbWGA8Z5Rdu8LADSJWADf1aW1XK1uby+UDGuBTUsZzpzsabShjumt93uHM/WDWNzDF+//6gpynQ2i9N4Op0gAMqyNBTVNtxSlI5lmJb2Yfvp9nG7ORx8P5iMR7Pp/NvZ32mqJnhfFXmeJ4J1YRTVtjmfzRtKOwEgBKapAwgUjE3TxgBAwMYDv6ZUUfHJZPrbhgNh9PT6iaFrSZLC2SQvKkKI57gXZx6lXRSFuzjUbXfh+6v1Vte7xWKmxWizW6VpPh6NVWIQaPZ9CxGaTmeubSMVOvvw8uL89Gze0+a4fchUXFU1F2g+OyuLoqGNqULWda7jQckEAADwrqOU9QohmkYUpFycXXeC/+WH7xSivXjy8ubs7JjEl2cnlBYP20cg8DGMNE2dzqcAIABxWdajIFAUHGdp25a2ZSRFnBe5qevHPDydTDVkcQwtO+g6hgG4urlpmiar6tl0zrr2KBi2VT8YmrZl2PbF9ZmpWbTpHNv1POvidOZbVsNpI8o0TR9X+ysAvvzsZXTcN3XXNt0hPJZ1ExXJwLeP+6PgEkoJIYIAScH6pgYaaXrkuMNgNKzLbOwOv3758piEz66fCC7LLlUVfRQMmo5qmAyswX57fHJ9KSWzDGt/2BqW8/lnX374+BFjGAwmXdsc2xYoynq7V5AyHS0URT05O4mSA8aqANj2AssvzjRHCmbqWk3rT+u7u9X9wHUtWy2qjNZ917Se7TVVVmTUNGwhOk3THMvQDU0gVNWNopuKAKyukpdPX3Ycly178ew6jpLVehNnSZInGKuWZnMmFF1/tjgdOsNff/n5ux9+dANnFIyypDrsD1kymEyDL7/+sp2MTQUZllaUuQS4ars0TTeb/XwykUjhgvuD0T4phIRSyp5xilHEaC/4Yj41dXOz3zcN9QOvbBshBHLB86fPaNvuD0eM8WLmPblaQNTpBimyVtOIbVkZ63XbcD337PxUMHY6n08n07quP91+evbsxfXlxXa7nAyGVduqhBAM7x7StmMXV+dZnNV1q2l6Xbe6ZX7365u6+dFzbQyAlLzr+uEwsGzLdtyedrPxEAFG26qnnWNqqgomoxcYqgpRpOAGgfswW+7ivuuTKKFNIyC0HF1ViGVZREHDYRAEw7KqEYQKAOPZSRanYRwKzg67GEt0dXojgWzq5mwx931XVU3DtOI0E4waujbwhwAS2ra6giht+46bljPw3PFkst6sB56rKEgCJqXCuPB9D2nYdfy2L4nKTUsFUkmzDCBYt23X9zWte9nbjZkfjx3r86xQIFIJ6TmloFs/bHxviKB4//ZD19PpZOr5g8fHRwGhG/hFUTqu7Q/9qixMQz87O1MUGKfRar/r214AnFel5VgIq13Pu77jXBZF9cWLz+uaJlmMFWmapuu489kwL7L/8Pd/H0fR0+snEEDaUoBgVlciwW3HOJMAKQARLkXPW6IhDMlyvS+rshqPxuNBuMP3t3fj0Wi73ylE5RK4rs/6vqMdxhgpRNP0u8f7oiyruhVMfPvlV4+bVRbFT84vP9w+PKy3XS8gkJzxrqOaRlpKTdvsah5lRdNx3RBV29RtCQBzbDPLkrYilLGKUk3XFEQwQEVRmboZxeluvZ9Pp5Lz8HgEkkMggsCLkoQxasQqZbzvxcnJ6WIygYBPhpOOdUVRCAjyqnLcQDe01ep++fjYNPXhGEsJEAKGrgnRC9FZptP3zNB91tdAEIUoeZ7rqjKbTObjcUdpGB09x/WCYDAaqAg2VdXTlkB0fXHBGWOcP3vx/MOHD47nGIbR0DbJ07xMTdtWGlDWcd+JhvYAyC+fP5lNF1xAhEDgaI6LN8u1pqo3p+fvP3xs67ZrYce7XrQC4Iv5cDrwx+Pp/rAVfW/pmuSYto3j+j1t6iLP6/Lt2zfb3WEDj7f3txCh09NTSzd73nHGIOcIINfzsIoW44mhO9Oxn5bHqsqWDx8gBD3tMFQd0xwEfpOXgvWBYzm23TR5XhWC86pMPc+ibZPE+XA4AgCkRRmlRVXXT548O+73GlFdy7J08uT6om7q7S4MPLcT1unpCRT915+9+N1XBpCwLiqAlF50OsEda5u2We/qQxiqyKzy9tXP7x7W9/vdQYEqhEAzrF70QtBDnDEBGO+JgoMgOBz2nPVt0wyHI8cbfv/dd5pCnpxfKFiejsdpnGDVgIoSVXuoI03XCUJZkZR1SQj+dPvJMi1VI4apU0GHk4GQwht6ELgccl3VdU27v/tEKc3r8gTjYDRuqQjGM973pqZNRvM8j4fDYHfc8WVfNfnFbPzx9v5hs5pPTj5//mK92SdZblumbdt1XRwPxyiKJrPZ2fmFQgoFE8Uy7IYWbz69N1Rb19TD4VBWdLXeNX3nOGZ82D69fjL1Z/tjOPYCAqSmYGMwzevsY3QLMdQdPD0ZYYwglk9vTm/fvaOs+enNayYkhsh3reEooJw/rtcnJxeu70OkAAkRxADKhoFeQKLrTFCAtPl0FHgBRshyLVVVj8cjhAgrgUCtAvB0NKzK+n59P/CHF6cn633omvrZ/AWX0g/86+vrt69+VTDO8pxoWpLmuq5blnlyOh95Xl7WlqaHYdi2bH8MiapNx1MuIORSRVjFBEBQV/V+cwASzOfTxXRyebaYz8bz+TyNY0qb+8dPYRh+8/U3GOFfXv9VAm4Qe3F6mmZx31efP3lSt3x/jAxFBQBu4wOCwndMyyCDgf/04qqsqoeHbTAcIULSolBNk6e4aftdlBVNy6VGGa0ZRRg4psYlGAyHw/Ew3G80gr/9w7dF0/7lL9/FSfT85g8YqqvNxveds9PFJAgE7ytanp7OIEQ9o7++/rWmnWwl591oMNZ0K4py2ou728coTBjrVE2bTqebXRglCVQUyqCmqF1Lm761a302ng28MQTg/OKiKIq27cKHdZqVnEuiG95giBHGUALTsC0ncJysLKbj02EwKYtc0/UyLwLfr+p24PnT8XB3OOS01VRdSsihjI/RbDJL4vQ/fvz7hrLleue59vEQGYbxm8S3KSuMsePZ88mVqmrHODxEh06yQxQjoTYdkxBbrvfzL68W4xnBREq53G6kkLqmeq7b0dbUDVXTmr6buY5lWnGcXV/e2JpaFLmhEqCb2/DoWDYhxjHMBq6tEmO52QshbNvM8tKxfAmAbbmL0cw1rfvVhgnw7ObZ6/fvy7x48eRpWTcA4rPFyfEQbncH3wtcN+gZq8rWcV1Ku/1uZ1uWrqrIAlnWd5QTldimPhr611cXeRofo6NtuReXz5ECf339c5hGmoIXkwvDdpeb5c2Tz+P4+Lh5CAJ/Mpp0XbdaPRqmbplWnOS240ymcwmhqhDWd6PB4BiGnucFnscYM3XVtkwM4e3tvZAyL8vrq2vDMAAA4/FYVZSGUcroZDTEAFZNgxVS19VwOG4pHYwCoklK66urG6TIcL9+9epXKNHp2XlWJtvDOm+7LMk833v9+lckuaHpX3/1ta4qo8B/WD7uwoOCUM8YW+8Wk7mhqWHcvf3wMa3K6XiWF7nj2LZpIyklxHme04YKyb22JLoyGXimZi1v31ctnc1PHh8fszQ9WSxsjxQN+OnVL2mSjmezx/W973jL3SZOC10lgWf2DZuP5lnSRFGmqSQnmWlbmCjR4aggVFTl3f2dYRJIIIBSUZEJTdD2n27vuqYKo4QLOPQDKfjNzXMAxPu3v5q2cX42PSSxCnUCyfv3j5vtVtWNnna+hU/PTtq+NVWd96AsGs4EQUhVSVEUXU+l4KzrhGx//OmHdx8+aMRYrrfBwPUcfzI9Ob+8uFveLXfHmnGMxNB10yIvi2K73fqex3rme17e0O16bVquaZuC8ziOVtvNxfnVbDicUgoR6nopseJ7vm5Yuq7UeZiEe94z2zYe7+8o71WEsyR+03b3y4esrjuKu4qfnl0Qw2R9lxdl01ZCoKJp1aLyy0pBirI9JEJBvu/GReWp/OZ8nqaxNxyfX53d3a8M1TAM+uv7N3/82z+cLubr1Xr78Mg5e/LiOUJYhdVgaJsOubo5V4j9f/z9fx64FpIQ68an+9XZyRlRgBT866+//nT3+LhellWRZbkUjGAohcAIMwigZiidzvu+7+l8trg8Pe87egz3GLA8jxrauIGv60gwkCRJXdVZ1dbVtgn6qm0dz5rNxgJA2nVJEqsqKatye9hNpzNDN/7pv/8TwoT1zUo+aKY9Ggx12/GH05bDtGhtmwGsB+PBxWK+XN16xNQx+e7HnxRMptOxbWpYgVVdp1kWJQnn7BAm2+3h979H98tl3fIwTcp8s89LRVGWy7uuFQCRzfY4HE38YNCBxjV1z7IRwkQl//zXH6qqhgg5Xd+UdV5u5uPJb2TKzz//YjocCy7ef3pj6nrb1GVd3S23EitnJ+d910VJ2TLmef5kMuz77mxxouua7Rrb7bprq7PpdH/cf7jfVnVFKeUSqKoxHU8hRN7AL7I0jkpK+XZ/eFjtWdcrGFVN0XHp2LZl223bxVFmm1ZdV8PhYOC4s9FoMpm9efOuaRrLdhoaF2Vlmfbp6VnXd2VROLYjRd+LTtfVui5Vgv/4zVfr9eN63bVta+oKhNJzbdZRxoACQTCeEI00tMcQSQB//vVtWdZNSxFSWsp9gAe+jxBQNB0rZHxznaQh0ZBmK3maC8kGQRAlSeAOfcu7v19VjI2G47vH+ze3n55eX2VFulothRB107C+NzT9/PRMQlDU1fpxNZ1OIETLx9VsPLp9+IQgqDvKpFAt4+VnT8uyMnWDEFVKrGqahNzWTA5gmG1QVxvWzd1ytVxvf//Nt/PZ6Z9++llAxXXdilY965IsqWkLFajqJElTwzBN01Iw5pj3Pe269vnzp1VW3D/Kuq49x7U9Z3/YNmWBMCiKQrfcJ88/1wz1ux+aMDz4tn1z/vT8bMb67LDfNXWtKerD7R2tmvOzS52oVVMrSL24vGyaxjQtz3U/ffqk6zpUUE1rx7W4YHVTlSULQ4gg6hjfHg9RnBwOe9/zTMcWjNmWLYDQFWJqRlU3BW0nwcRaGELwsiktXaGcGrDfHJa0q5uqalpp6RrG+N37D4RoA1cLLJtL6Xtu2zSaoWZlsTvuq7rqpYiSLM+KLC1a1v/uM/7kdOE5NlawAOxkPDavrpMsPux3VVnohrU4PVMU+Li6XR0KhRBVUT/cfszSfDyZ7qNXdVvpRMurWoJO08226wWAq80mSePAcR3HP52cWLa5Wt0eq5oo5uXZ+eF4nAwHfuCu1tu2aaXkm/22bZljWk+fXd39+APj/fnp+cAbtl3W1rRnHGCMEfY893A4fP/X7yfjge5oZVfmy3gazHVF6Rh/+fLz6+tnaRpbpnpxNn/95nXT5oewGQ0Gs/HkV3kvIW4pZVxgpLQN1TQ9iWLH0M5On1DKBOuyolVNiSD65acfJOLf/O53UMpPt+/vsjSNM4jUuqFCYgUrYZQYll22zf6Q1LRRVdWyzF6IKEuRgmbzad/3RV7quipkG4cxRPj1q1/W6+1XX/1+4Dvb7W44nviut1otFaB43oAySHt5t99ToLx4+ezh7uPIH2ZZK4Qo6lqvaymkqhPl4+1yMAzagjqOrwzdH27faSoOw71pODc3l3cfHw9REgSOrenzy5NfXr0TUBmM/P1xW5bV73/3jWHqDPT7MAagPFlc2KaRZOm7D5+yqLRIYtuaY5pXl1eqaaVFut1u2k4I1gPBCUKMcQ4B5AAhNAwGo9HY8dx3nz4ACHaHOE7iqsrGI5+JbuAPqrJdrdcIobJuLEPf030wCEzHqsuMciEBqOtCI2qaJefnJ57lY0U5hPumabGqZ1lWtOl2d5xNJuOBr+tqWRZffP7ij3/8F8PB5D/9H//73cMSQda0neO55ycnz6+vxoFTd+1yvaKvXhFFYX0fJonlDfbH+C8//AyxFmdNT/uf/vrr6enZfHq5PuScixfPnk8nI42Q7W4lAfyN8t8y7vsjVWvX283Dw2rgB2VVb8Thq6++mANACD6ZzOM4gVLYti642IXZMc0UVefrlUr0pm4et/tvvv769PRyPJy9/fVnRcUcyN1ue/v+3SgIAERhkqy3O9vxVMMc6e7DcpVl6Ww+NTT13/7b//H+cf3m/fvxZCKkVDAEEECIxsNAJSqlFHLpO35m6NPxRCXop19fL7LE8zys4vl84XoeRMD3/cl4+vHDe9PUzi9OIAQPy3uClKauT84Xv77+abVcCQGIonW0pV0fDAZc8OFgIHh/3O9a3ldVOx4ERCMMSKxqM3/Q0rYDDCogGLqn81mSpoc4u7u/JybJ2vIQxV3X26YZeD7reoyxjsFsPOBgYFuWY9tHGid50bRt09RVWVV1S9s2CIKipFz0GMPUdKIwJrr24dPd3cO9ZWq96F3bMyHkknu6MR8PoURJmgW+Vzc1UvDp6dyxvcXpXHDGOWupuLi4HHk+luJ3X7wUHJi6FnhemMRZkQ5G/mDg9U13MhvP5wvf9Ta79WQyqspis13RpmWMsl5AKDWDUFojiKq2oU2NILIcmMTroqrapjI0E0hZlYflXQUYtXRCiKtg5FhmGh7mk9FkMt9tl5D1s8Wsp+V6+VCVhWUYbddkaVxV5X63ok0znc7rurp7WHZMcC4OYdRzXpfNcDQ0TQtCgDE+JEfPsj578kxTVUPTmqaSknHBG0qTMkVE6Rop5SFOo6E/OF3MaVtvdyva0uiYTKdjzdZ/evXqfkOuzi9Qx/7y/V9pzwzbMU3N0DRjYjnO4OTkvMyzMCuDwbClDeNAM7TByKlodjgeJGcmtk3T2GxXWVFphkFUo20b3bA8L/Bccx/nJ2dneVI2dXeyuDBUFB2j6WASZ3GepbtDdHp2EwSj0XR0CDeO45a0bouEqIqq4k+fPu334Xw2T4o8zHPBlPHkZH9MOtpAgsq67lq62WyuLm9c11ptVgpUDVW5uTzvucQKloCvdhskCcFwPLWI4dxcPZsMJu/f/BqG++Nho2nK56fPX7/7qKqkrisAJJcAYuLYepFlEGFNNwzNujo/tX174Hm79XJ7OFZV7TmOYZD7zd3+uPQ8r4Pd435rEIcAVDVNXvaWaUnA8raumzIrmrKupEBXV084BFmV3j/effXyC9+xIRCEwF9//bWntO342/efuk789Ordi6cX6+2eScU0lMlktI3CyXj27776fXRIqroqSiqF1HQtSrOOgvl0cL99HI9G+/0OYax4jhNY5nA4GI1H59eXv7yt54PRbhdR1vS8qWknANI0Lc8LBFf/8o9f3Q6Hu8MGS+x51pMnV5t9iCW0NMdU9d89G/eMbXbh8mFj2RaHsKy6qum3211d5k8vL968+RhFBySFEIxACTVCpbQIzuuubJtmszbSVDAxHk/HI9L1EklsG7aQXUd7zkBDWZrnWIEAaawvGJBWS6uaOp6v6pqh6VLIKEv9kd8xun689z3ndDI6RLFre7Tv0yYtipL1fdf3nucwTv/xH/+j6/n/+R//IQxD2zVvri+no3nbto+r5f1SPKxWjuNKIHvGmrrSdG2yOPv5zYeWw9loWrY9kOD68uXVxRlRVCZA3/fPb56u95tPu1sFKmXRIkxGw0FdFz1vbdNZjAbDYBQEQ932iqaaTkYmUd9/+vCn5T8LJg7HiHadqhKiaY7jAAD3m4PtuOdn54wPBBev3/ysaXqU55hoCCIgFQhJDyClPeNIJ6aumpbpFlnR9Ww6O7m9/Rh4TlkVtq2fnp7oht72bdOWhmkgSLBEnDGIJGu7rIhNx3EdAyLU9YJ2PGKRqRtEQQgJw9Sapo7io66ppm1CJKSUCMK2awbjIE6zNMkkR6Zp6kSzxyPd0iFESZqulsuqrAhRBZctbRjwEFIcy6xANZsPFUXxQsN1zYZXYXLI0iJNyo4JV1Pmi5O2afM0cSyHYMW1bEVRiqJIs/Dq6sp2TE1TLxeLMIkfN2vXtoUAtmnZpqUQ0rSNqqoQoLJpk7xQVYIxqKqqaRrHcaqy4X2nYGyoJu94T2tTV6um9RwHInh1fWFaBt6CsqgoZWenJ2kW77aPSHSGpoZZuCoyIaSuqtPx+BjtAZcno8XF2fnucNjsVlVTjyZDTdcn4wmlNedCM4hCtCSJZpPZbDj+cPtJQdg2LUfXeUuPmz0QEAheFe27dx8gBEEwUBT1Yb3VdQMI3rH27btfl6ulkIL1dd81KtE3221aFpJxBKSl2azjm80OCMgYlkImedN0fRTFs9nUsawsy4SEuqZBCPdh3DJoI5K37T6KAYJN21RNmZVF0zIJ4Hg0eLu+tS3T0JS6bIyBvg+Pd48r3nPP8aIi8zH6/dd/fPP2fZwmp/O5qhlIlfP53DWNMD6atmHZ6nRs+5amEExU9X656nu5ifdvHt4FbsCZ6GhnmfZsNMmy1LY8yzSKuuCCQQSO4VFR5xeLxXgwbINOcM4ZXe+2xzgdjcaj4RhABSJ8fXXdsa5p8sVivrx/bFnPgXQN8/FhdXf/AAEoyhIS7Fu+awcj36ub3PYDIHiXl8ckglDhff/27bt9FCdJNhr4nj/6m2/+sHq8j5LEc4e2rmXZruwqGmeikx/Bu912F8ZZz8A3v/8siw+uYXHWty2FEEgpsEKIaaGqwFjRDZMQIiG4X94Wmatrqj+wA0A61kOIA2fMgZCSmRYxTO1sejkIXJOA+7tHzoVEcL3eCslGw4D2dZY26/XadBQgQZbG//AP/3069DBEw9lsvU8568dD72QxgUCfTufj6Rgp2nx26tlaUewbmh/CralbKtbOT882x0McRSrR3n14+/uv/ub8JKjbTArI+pZ3Unnx5BoiqGvQJGJ1/7FIi5E9AgI1Zc0lu7iceoVblsluv6tq2zQNw8KqpiKpXF9e0I5qOhkOF23P0ij89f2vZVlqRJ9NXM0ApydnQ39KVIRVI6+SH77/kdGu6zrGuRCCdlQqUEA4Ho54m42GQwihpmrT4cSx3R/++uMocEPeSYAk1H56c2satmEFmuZw1iEVmY6qaArWFFtRptNZFMddx0ajka4Z9/cPlmExwZM0PF2MSY6O4ZH2gvX9brdzXNs0TSlFGkdFlr1//x5K8Ydvf+86pm/bGBiv3t8ew8OTq4unzz+zTSuKoziOJueTz19+JiGMk+QPf/j2YnFxvxxt1ytNxaal50XpOP7idF40zU+/vtntd77vzoazxXR8cnFSNe1Pv3y32ewIIjdPn59dXhGMq9LAEHy4fW86ztywN5vVZDofBIEUPEzjlnYKVjjjaRpfnZ8omvLq9aukyJuGmbp5fTUfDIb7nbderzohLNsMxtOuqX3Xm0yn4eHoj56Mx5PVarnc7r//4fvzs7Ob64u8qObutGly13Us3ek6HifJ7e2t4Hw8HhFNCQZOnKTDwHVNQ9PUpm4e7h8s2zo9WZRl5bqeGARpmh6P4cnJqWVaeSEYl0mcWLqh2hbtOyqpgVUmWFU19Ldk+r7reFGScCGlBK5tu64XHg8qQUL0V2cnSEFvbj9sAW6bLk6L66vrgeeXeZ7m2cDz66ru69axrfFgqKuaqmkDPyjSbOQHrO8fV4+OZWIE/eFACOB6XlEUbdsihPa7Q98zIYRpGo5rm4bx//fJQAggmkynddMAKRWMy7Ku6vpkvhgOgygKoxgwxhAAaRzrmiG4iOIUKyQv87ZpBv6QcT4ZjzFSmrrdh0fWM8mF7TiapuZFFccpAMIwNYjEarcrykbTdU0z2vVGxQfT0Ou2blhPw+hh+dgxDhCqmzaO4rPTsySOSyp6JlabzXQ0FpyleapqupTrs7NZR6s8q4JgoqkahpByzgGIsuzt+w+j8aTI8ofN3nNc1w5sAAzD1g3NtLTzq1PBeE+7OEmxgr+8fjmdDCltoKofo7RtDioEpqZ5ti0QPkaRbfmXV5evfv0ZI9RLvg8jLiUTQiBgmlbHujCOuOQAYMPST08Xu8NhPh45tt3xPs0i29JVzKUKO84l6yzbDAYe74RrjnWiE1dL4qhq6o+fPt7ePbq+CwHIsrzt2s1qx5nAir563ASe5fh2Q2kaxz1DCGvnl9f77SYIvG+/+Xr5uHRt2/Wc43aNMT4bjeu2qasyy0oGkKqSuqkDyx94PpYiCddt2zymORIAI8iEcIPBj3/9FWKUVJVhOsSwirZN8uwYR2XdZVV7MvNt1+sLvN2v8uQWCBin+Wg4GY3dN+8/NW09H88sS9vuIimRlABChBUFQMAFF0LkRdn3o75j6/1BNTTOKscOOJRplJzMTodjPzyuDssjFsR2jG20Ez3dH0KM1dl0Mh0vThaz9+/f6Ip1oPndw51lW7pJmrLzbIN2crVc/3F0cXHx9O7TO98fKIqqEms2m40Hg999+VXdVo8P9xBqnuEn8fru/u7q4tp1LdU8efv+A0ToydMnjqVt94cozjkXL1+8tFxbwRop8/L1+0cFoziOFyez08nUNNTlsoySUNUMXdMsXTUM82F92O6j08VcN+w8S7f7fUPrJM+Gw8F+t/706d3ZYjHwh9/99GPPuaYqbVvfPtwaJinrajKanZyebtY7XWKA9eP+0EPAORcQhmHIhegZxwgqhECEHlcP6/3Wsl3LMXRVcVzPcYPrq+v7u/ssS/Mis2xLNbXNbqMg4ppWlsb/8u/+9u72E22qyWTU0cZzvLpruaBZnkTxASvINUzRC93QsQI9z+tp11bVbDxeb3daoPmWaenacrX6u3/1P/5L07QNrSpjhWCd6E+uzsPjwffdy4szVSEXCz+Ow9nEOhyQaZA8z47RoWdCIHhyfrrZ7hRdh0SFAO3DaBeukQq+/urbv/8v/5CktYLh8Xg4XczDsoBC9n13iA4+728ur0HfLCbDNM+LvNRVA2Nl4AXnZ5d/+fHHf/zzd8MgUBAaDU8dx+27xlCV+WS0OLmwXT/Po2HgFFWRib5u67LMT05nUCHb7RohPJvOh35gaprt2NPJiPUs5v3Y8abj2f4YH3Zb37YHg4Fu6GWV52XWdo3n2hqGrKkd3ciLHEq9a9skOixXDwgprusEno8l6LqOqAoTzDB0jWhREuZloaiKQnCSZUVZMcaISjBW4jgRUvSMKUi5ODunXWeoRALBeW9pZtuzqup6pClAHQbDrqHR9uAFzslkwSWoSgohStLycAxn08lgMCiKUsFKnpeqpi1mJ5vtlrZthzrP9ymlURSbhs6Z0DRD1yFjrG1bVyKiEEUljLH9fu+5bl7WXd+lSaqqqmVaXc+jJGrbqqpLhDAhKoJQCIkx6pmknXhcb49RNB2OWtodwrBq2rqq8qzSFGO13X94eByPJ57rEZUcDyFEkKhK13cIaKyvKW3GI7vvBW1L1zQVrNRNE3h+03V5WhZF6fv+cDjWVX02myEE0zTnnG8P+6xIsyIfTSbPnz49RHEcxppmQVISWJ5MFwpWaN/tj4e0yK+e3miGetjvDVNte6oZ5unFYrl8VDGRQvS8N12L8r7Iq8nIr/K0KMvZaHRz+aTr6nfv35dNedgsr66vxqPB+dm5pqpPri9N2/n06aNpms+fv1hvNlEYWcB0HFvF+mw2YaLXdc1QVYUgoqDFbKwo8sMtZaK/fVxNhtMyLxnrdVUP/KDr6OF46GmlEjXNK00vpqOBZiAu+7KiEIGWNj3vIVQO+/35yaKsy114AFgVTAjBP/vspm2q9erRMM0//+Wf97v98xefPT4+SgkEAI+rpZSSS1FUhWNpnudy4RqaCqV8XC0lZ3XbHQ+JqRlcMi7kgILhaGBbuqoZF5eXtCtv7+///JfvPdezbKulLecAcBIfC8mgG1iaRpCinMwWl5cn2+1eSDGaOBJ2h+goJUeIKKqKMVawAqSsq5ILcXV5vWDTY7TfRcflPrLKLm36J5c3lHX77e67H38YjOZIUaGCr69v+qYFksRhaGrqdDw6O53XRUTb3jLNOCtNy/3s5cv/9t/+0Xf9k/liNl+cXZ6cnCzyJGnbXlWNi/Or8XjUd82PP/9c1dVisehaSlT/s2fTqq4261VRJk/Pbj6/ebYJw6apgeizvNQM07YchNWmpspnL5+2Leskevfu/fn5pWXgJN261ujq7BpCBat4NPJW6w3CCla0qqoflzvbsean87qtB0oQhuHt7YcsK8uiAEICAU5OzzFRTFVXFYwQ4aJXMH79+mcA+JdffPbDL6+zNEdISoh6IRWIadsCAAgmEELGxS9vfq2qajKaBsGgo2Xft13bDjw3cE06H6V5BAFyLN/zPcSxaeimoREFmbpyfXmWp+l44rOeE0z++uqVaWptTXXdQFgdj6ZtQweBZxhanufEcQLff1xvyqYZjAcSSQ7kyfmpH9hJFhHCueyiQ+rZrm3ZuqYgKMomRwjuDqs4jqI0KasOYhQEQd8zohkt7T59/IRVdTIYtHUtBRgMXM8zAQBxHEmgGMT46uUzy9Bfv/1VJaqlm21HA9vTEXn39rWhGwAi3/Xm0/lytTxExzRLhEws23765JllaITg05Mr1/V++uX7vimqOhtNrf/lf/63Hz+8aevi5GT240+/Flnac5Hm+ftPt5pmjIdDKbjrOBjjPCsgBmmW9x0Fkj0+PBDFwBB4gavp6jE8RGlkGvpwOHZMM49iwVnf9WmaxmmCEKZNW5ZVS/uyrMNDRBSl472ma7qpx1ke7W77nkICPd9tWxrHsZRQCNm2lBli4PpNWUkgLy7ODUv3h77j2Q8P93XT2qYjaT+w/d8szePxmDZtXReWaSgQ0pYKJhrWaxrRbZtBsNnvXcc5PTv75dWrw/E4Ho3PTk5VVeVSeK77sFpalqmrquBCUbW+78uyMkyjaRtdI7Rt0yzVNN22nbKo4izRND3NiyTNLMOI06Rp9LaiLe16zrqeep6na4RoBm1p3TRt3zmmzaUIkwRCTJCiKGqS5JR3iqYek4Rx4RoGUpQ4SSEECOPRcGpZXkM7jFCeZVmRCC7aqk7yzLCt+WzhuwGWSlO3ioKTrEiLZDoZT2ez5e6wDyMB4HAysxw7zwvO5Gx6fjhGxzBBSHRMYAVJICzbOrmYPa7vDU3vGT1GoWnrx3ArRG/bpuD97cc71dAGAz8IAs9xw/jY9axpGlHWmkbmo+H56b/bRYd3nz7EeeI7bt3kKrJUDMss6+pm6AeWQqa+b6tq1VYDxzRV09RtjoAEcjwYPa5XVVkd9rsoPvJedIyXRampdlkXQkjcsfVh5zpW21bjySjLM8vRDUNLkijL42Aw0TSdRUxyoRJl4A0Xk4llqLttBpmQgg993w9sKLr9bk0Izopsud1AAB9Xm6ooOO+zPF+uVoZrQwkGjss6ut/v8rpGAOmqnhWV6xhIVaGqpnVDO4oIdgEgKmrrEhIdQRSFia7ZT2+eCd5tN+vBcPDs5rqpq3ef+vCYmKo+mwamZV1dX5VV3nSdoojl5pMQfd/T33AjjAuiaggi3ndNXRFFXW9WhqMs5tMsSy5OriRGYRyF8dGcqkmWlg0/NdwXnz1TdSXab/M8//brL+oil1zWtL399FFTMQTcoOrN1YXjOJNx8IdvvupprxuaaqFXb340HfLtH77a7ZamYSoKTNO454xDjIm5XK4D37m5edrWXZkmom/X4eYft8eBN3r+/Lllqoz1i9m8l1xyAaV0XU/5r//4309O5qYG/9U3Xw0G3j9/99/Wm/Ly4rljD66vbwCSAsrzC821/cFwLLi4f7gHiOd51NbNcrkCQEbWIRiMJuNZUbdF06pYtYjmO46UgPYd47wXICvrPEubuusoq2vKuIRCIoAQhBpR66owdV3XjY5zCPF4Ovvd518ej4eHh6NmGBAppqkfwiNnbDwYnZ+YHe1821iMn+iqnuWp6zuH7SoKY8u2e9Z7XkCb7vzshLatrhLL9j7d3oORnIwHSZKEESWapmN4zJKqaU/PT4mCNFW5PD+1LDvab6CgomO+4ZZ5XVQN53IxnwvOwl1YNU3elGXRItGphhHGSds0lqE/ezqdzhcaUb/7649IyieXF/tjBKQY+oOmbv/5L39+crE4HI9xkQrEKW2TLDdU/frmZjoe04Z2ZZmE0Xa9nIxHz549s20zjAGCEAGkK+hsNsrSKMuKk9OZQlxCFELcJEtt10mTHRQ9Z3Q0Hn/24sWb168xwnXTXVxc1nVLaQehjNNsH8ZV0+iapmnG23cfiYId01RVBauKF/jbaJfmeV01GCsI4yhKto8rTFTP97eH6BCFge+7puVYvmHIuqop53GSQwj8AYmzqMhLJsBwNNFNzTR1wYVt2gpR86LAGOmqatnmdDrZH/Y6wbqGqibDWDEMTQIephEC+Obq0tQ0QjBR1b5rbNfqWUtpixB5cnWuqZqEom6bqqmrtlYU/P2Pf11vtk3bQggtw3A9p2mbosikFBgrLe0Y545tU0o5713XCoKgbTpKW4Qwl4IxpulagAKEUNd1lDGsYFU1CVHjuERYURWCFVI3NM0LzjghKkBgMBi0tBMQmJbFhOiFOB6Sqm1d19YUgrECJCgqquk67WhRZLquq1hRiKZrqm1ZUsowSVf78Hw2l0hJ8my92nz28uVsMl0uV0VZKBgRhNbL5X67m01m11fPdRW3tAJADP1AMJZmeZpGqqrZnv364wfT0EaT4H77AJECAPJMLw4z13Z8rKuOFkVJXha873kPTMuklNZVDYXs2ubl519ydxjH4U8//1TW9cAPnj1/ZpsWBzwrS+WI0jgJj5Fh2IiQtMh+06jRvkvTdK2AwAs0rambRjc0DGDfdaZpPG7WRV6kedWz/urivK6b/e7Yc4YVYtk2VohumC2tfd+GEm02yyxJBUREsfxT/+x09uub2HNt1zUQEq/fvk+yrGk6omsC8pYWDe0QwlDCuu3itFQQZt3y8fEBYzAZDQeBb3vuwA/asq6aehSM2+0+iTNVs5/c3ADAPtx9dHxDCs4lwVip6+px2Xi2MRwaPaUq0bq+NQySRElTNSE/7lxV1bFhEi7k7eO2B/zZkyfv370/hAfP8U5PRmUr3r57e9hlUiIIIWeccy6E6HvetlRo8nG9n58Ex8MGA7gYjhrWEgxEz+5vbxFgi8mcd+z+w6dg5N/f323Wh+V6Nx2NFrMZF3Uapqynlmn6XjCZzmjXFGkkeJ/nRylcAXBX13cf3ga+hxH++OH2+YsvTd0cjsd5UQDJ/+N/+OFkvgBCqqo2GA/bvuq7Ji7yIotYV2mGsQ+PKiGWZWlES9MUQaCs94f1bnNzfq7o+vd//bnuUJr1aLU7OyUOkAhD0zIni1PORVFWu+2e1g1WcXTcjQdDSzO6jtK2PYbHYDhSEDkcw0N2vDk7NzVSNm2SJrTrbdu/vrwqi6IoMgghhgSATgIopBRSCozapjseD7PFbDyZPbm5aWmbVYVqks8/e7nb7gPfU1X99nFZlqVrmr6jBxfnrKVxtG2wqlnaMYwk68uqvl9tnz276Tq22+8Z6ybTCesZ5/Ds/Dqvi7ZvTV1/8ez5ITxCAgbeCEpS1mnTtpyR5WplWCYX0DCM7SHcH466qo7HQ8e227bOixxysd3vl7sdxmjk+4dj9LCOaE+vzmbL1QPnvWU6qqoWVaU17fObq4fHx93hIAHse8H6TEoOJMnz6hiGtO9ePpmPgsn2cOw5//bbv/t//L/+n8cowUR5XK172oleZGWV5UVL6dniJM+Lqqk/ffqIFc11/OXjve/ZBJGPHz4tl49Jlsz2h9lkqqqYMub7g9V6NRkPCCESANO0KO0s27l/eDw/cxSIDWJoqrE57GhPbxCifR/GqQpJ03SPDytVwbrrcCYoZQAqjuvPZ/OLy4vH5To8Rr/76mtVJWmeQwggghApRVE8Pj4AIcq8QggBKAEHRZkWTWmqqmNoeRr3baspODzuP96+9YeBZTpt20gJuo75rqNrOpRCU7UwiSSDpqHrrpsWWZaXWlVy0Usgwyi0DOtitugoVW1tJQ6EaAhiTJSizDRCjklYVI3oIeeiY1xw2dIWQzgIfNM2i7wSXE4n855RVVPbluqayntu6gYAoG26QTDeHw9tT08nJ2maplHeM8a4AAAMhxbGUlMJlEBIqWp6Xdc84oRohgZpQ5MoUQixbQciBLJcsF4luuP4ddvysrRNEzvWYj7ebndNR+OiIAA+ubjmQKx3GymlYzmmZQEg5tNJR/skr23TnAztnjMAsaYZpq4qyMzKYjobzydT1jMoUds1cZjVPSOqgrgo+hoCrBPCu67q6CGMOtZbhh2XUVoV88n09OQ0S1KElO1mKzD+4++/EVz85cdfEOk+PNxrGsYQ0pYeacyZ2G32NxdXmq5jFfectS2N4ph2fRQXhyifTWYKVh62G8alZRhVXUKIdMOJ7pe6Ybz/dCclNA2H16Vp2ZqmZWlBFCiBQtui73iaFEJA3TDiMOqaBiiyZxIpKMwSohodB2XbHw6JHwzWm3dFmTuONRz4hqYaqpEkMYRyXfdxVgrIozSfT0e2ZZqqui92TdNAKMeeM/Q8hDGAfBiMlpt1FMdEVSljvufattXU5f6YSUkkUgAEQTBsu7ZnXdf1nIOH9aHtKl0xpqPBISmiqPm1u8UIvXz+tKMV7XrIkWsGe15KCQAEtKNd1wohAUScyQZ0X3z51cli+L/9b//v/SE0jd1wEKgaQToM9yHgIk0KqGumpU+n49FoMvD7lrZZETNZA9BDCOu2tWzr7GLe1TSPwqKqVut1nhfz6el0PPvs5sXt410axc+uX1iWLwCDWGDYv/r5e8Nwzk/Oj8docXq62e98zxtNx3WT//Tmref5hzA6mZ8iiFraCs6PbVfWzXK7U/79//R/SZNks97d746HrMKqGgSTgeeaChY9Lcpuf4y69qMQvK7qpmk0zYQSYkRa2mMIupZmeQGVdhCMHNvICByPB1F6ELAFkECIyqKwTJuoajAYcCjxIQFAIogQgBwAKURTN5xzIEGaJvvD/u/+xd9qKoySKknjoeOxnpVFlZW7oqqHg+F4NJS8n04HOrEb2uyTpG/ozeniuN8Fwejt+1vLNP7Nv/7Xge9lWea5Xts2qqbocSL27BiG+lR99vRyMPLW+6Xran3rgL79uN5cXN90UtncL3VVHY3GZVUDiJmUbdcdHx4xwlghWRJrmk6bXkGIGdxUjc9fPj1EyXYf7Y5J1wvWM8NyLq8vCcYQSU0jo2AwmYzTODuE0Xy+6Pu+aZpSLz1/cHZ5STQtTrKOtsfdJvAcz7rWFLxZ7QzLoh1P0ixKMts0Hu7vDNOajmemblRFFQR+245VgjbbraFbRdntj9l2n9xcU00jk9GEqJqCFdOymrrSNe3m6qwqyzRN+eksjSPPtQ3dKMsizwqkKklWKFAZu0NdVTVdZ6wDQAgpOJC0bzGBYzuYTkeWbSCEORe7wwYh0LStaRnDwYCz3jJV09AlkwAAzntV02pKNd3sOe/7Li/rnjHGIsmZpqkCyV0U27bjOX5Pe0MzwsMxSbP5fDYeDTa7Y0/Z3/3t37RtoauaPtY455TS3fHYNC2z5Xg0UHW9ygqiI1d1VBV1fUu7TnAOIIIALebT8Wi43W64BFBRCMKM881mPxmNuAS06yGEdV1LAaCmEYKw0l+cn0MBGOeOYweBr6mq5AIBTIimqCSMosB3IQJSSIRxGB6bpvVc1xyYRFEeNxvOme97mqrTli7mc99zEQRlXe32hyzNuWA315ebzarru2Bgn1ozXTMZY1mWSckVBUsBAJAQQsdyyjJFCA4CV/A2To6DwdB3bMlFU1ZJnmOijCejosh4z59eXSZZHsd7iwvGpeWYtKVZ03a0F4bWNg0C4IuXn+12+9P5iRSCEBRFYc+ZYP3+9oNm2ifTKaXt+cniybNnP/z1z21fRXGMIDGJ3lFmW57nDaoqbdpO13QAUS9E0ZQccAxxnMTjwdBxnChJNputQhRD16u6tlzbMh1KKW07NwgwUQAUWMGqqjHWR0nG+h4KACBsW3p19QRh0DRVXVHP8xBBXdf1jGIFG5rxu9990TN2e0sB6tK8bZpQJYRgjDHiUqZlg1UDCe77QwnVu+VOQqISPYpjCTiCOcCYqPpqvfUc3/eC+4etYSmmaUGMbMc5Oz+vq5qxPk0yTdMmY92znSpNRqOR4wbDobPebWjVvnzx2d96vue6+/0qLzJCul4w13fmw2tTM477bA2PQgIIIVEUhGAveFl248VcKtX+WM1mJ5pqxnFcRnVPqWESzsXtwxYDUPe1F7gt6wzL8Afeel0omtF0tRQNAAAruGqSn3/5AUu1yIrbx3VeNhhpXKSMo4K2tjPwbfXj/f23f/yb+/XbD4/pyHLWy60/EG1bFFUVhomh611HN+ucMXh19nw4GpuWnpUVIppmOQpETOKh6TiOo7x7/5Hx/hBGnjfSDatpuq7vw+jhFX83Ho40onOAkjTVFRTYJpZMil7XTNPSsyzbH4+2aTb7EGKFIBKZpGhTAHCZlUjBTds7jst5DyHP86jvGEaENg2ETEIuASCKAuRvHFBYVpRJCbGMozhN05PTs6bMu65b7veTyYRL4dq2oihVU2+Xj7qmTBYXP75+9e03v//qs2+++/6f7x9Xfd9Np0MpxP/3P/x/vvnmGwDFp7sP3377h7/+/HOaRI4VBJdXELK37969vbtLy+jl1ZO+7rmkL18+nS/OirIUfftbZ1EwQQgJPIf1Hev60Ww0m84fH+7SND2dL0xdF6y3PHu923z9xWdpVL798CEtO6IoA8Pq6+by6RVWyfZ43ISH1X5TFLXv+EyIzWZzenKqaQZGUEFgvVpCwafj4fG4KbLYdezxaCwkQApREREDpusG5yzJ08fN5tP9/fOnT+umCePoZHHKmHzz7u3Z4vzi7BxCZDv2aOATVemYWC5XWMG0bQlWuq79/vvv67JEGFEuP326VTAuy2o8GgVBQAzNsd2+bjFCkkve9WF01DSimzqlvaVboGkhQmmShlHY9fD07KyqUgyB7LgkPA7jrCzzNHVMAyp4PJzEyVEFwh0N8rJWiX7MKilbz7WHI68qy76nk/EgTZPxcGJoZlgf4zw7RnFdNUTXHNcejgbb3eH1+zdQstPFYrPdCyFHk0nXC0y0MMvWu93LF89syxwFHlHJwPfbjpZ1LSX0PNvzfNd2GGvHk6DtuUE5UdQ8ywECEoPwEIZR5A0Gs/FEgZCxbj6d7g7HIs8NzWiaFigQY8R66rjmZDLOi6JuW9c1EYJd1/YAqKqmqfpkPFEUslqt2rZlTCgKJkQdj0ZtS3VVFZxned72NE0TQnDgDJIsRxIul8sO9PPp5Pr8wnNsIbTVco0Qmowmu82uapqLk5OyqvzBAADRts2bd4+E6LZttlWlKIphW70Q4/FoOhhWRX4M957rBVeXj8s1UrCmqH1b6brScX4IM6ISgjTPCW4/3euq9uT6Kk4i2tGuo1meCg7yuvl0e5cmWV4X8L2UvTCQiVhR19QMrOl4hDDM8rhoCilRmjddxzzPNg2y3R5Ny2JQHNNIcIEx7hnDLdrvDy1tHddlosMI+r7btnVVF4PAL+tyNhzrjsVpO5jMoISb3dYfjDBRR+MhRDJPE0IgIijPS0tTBeWDIBhNBj/8+P18NtINfbs7uJ5/eXZ2POyjODVNczYxdU1TMR5PhkVV065tm05yKSFuKTNMnffCsfTz05Mff30365ip24aqXV+eIoR1Q7cts21oWee+69qGQZv2w/s7TSOapu62S9O+HASDVb2p2+JkMfY9VVFG7V1c0rRjXVlqq76nnBFNkVJCIDGQjFKMIIQSAFFm6T/9lz8NA0tXNVNTNk2z3ue+Y189u9yHkek7nm0dw31VN0ZHG9rEUYwwenhYWbZbFMkgCDCC291BgXgxWdRNn+bVZh9qxFosLn73h9/dPXzyPAdDfve4/Ff/6t+gTtRxJRRXwapham1X6IZ9e3tn6KQXbDgYzGfzy6tnWMFJmpVVpWqGY7mS9XVRGaY5cB3l+uL6n7/78WJxen5yApG22oVxmoZh1Ped5wZpli23m/lsKvoGQWkbapiXu0OkGXpe5HXVMsbOTk8lhEmWOP6ZhgafPn1yDEvTzbuH91GSWaa53e2jMDpZzJM4/C2QGGOMsURICk4wgYZx+/D42RfPVV358PFTFMaS88++eBnHWf/2w4ePnyxDmw8nt9vb3XGrSjkdeZrpqarz6eMH3lR/+tOP6/W2bsv5ZLSYnb599xAn9dnpXFFQz0BdSVP3rq/PPn36xITY7I46sTRcV0VVVk1FW9TUFe2bph4OB6Zu5UXRc1ZUddc1BOO8yNMsjZMQQ1yWBW27KDyYtj27vBpLtSjKb//wuT+0i7IBCLqGdXF2dozjn399DRHCCkqTREHaY77uuk7XdYFgmudni9l2v99sNpqqxnEIodIzECcZY4ILCWlHiKppuj8crFe7lva0k7quXZxeMsHv8tVjt+Kir8ru5ze/Ms58b0A63LRkMJi1LTs7WQAgd9tV2zSMi7qpq7puu240mmqq2fedlGh/jAJ/wBsKHeB5flmWaZKqBLuW7fmO57n7faipel2WCEII0eX51eEQ044SjMs8E1ww1iIFQwgNovZVi7CEjmppquAgT5I4L23bGw4CBAGltN7vsyzDCjQt3dSNuqqrqk2LgvIeYui4dts1m/3WMvSmLrIsUQluGlpULYSg7Vng+Q1tNE0Djrt+WAe+P/AHnexHk8l2uyNYsy1bt9S+6bIiz8vctexDGAGoQAE93+3rThNE0wzDsDraHw9HKCWEEkFgWrYAEAgIMRRC1HWNMciK1NDrqqqrpnFdF2NomialNDwcsaJIKauq6voeACSAVHVtNB7XbYMRFlIWZZlXRdU0dduIitGuJ1j1veDFy8/e3b17WK3iJB66HgBQQGTrRlIUt8ulqmnnQGyPocCEdz2EAiva4ZB9vF0CKUbDoVo0ZdMoxCBI7dpWQhnf3Q0Cdxh4+0OY1Xnbtbqj2baNkW7qBmf9fnfoun48He7j/Wq1btt2OpkoSKmbpmp6IRFASpxkBGkDb5CX2Ww6NU0DCdB1nWFYWZ4d4qSnvWM7L54/OV1MPn16Px6MFFXrWWd7TpZmiKiDgZ8nWV1SCfHxkIzHA991R34gofz9ly8GhnV3f1tQSltKMJEcmoaJFfLh46cXL9TpbMpYNxqO2zZfrtd10y1eviRI71hHaXd1/sR1BlVTPzwsm6a5upyPx+b7jw9tTS9OpwjJKE7ef3hnmLahGavNDmE4HAWC9W3TaZrVNt1w4D+7vFFU9WKxIBAqBBumjhT0+PBY1d3T588sU8+SJM4SxrjruqxvqqZ+9/6joZtpntd1TYhyt+wppVXTEE0ZDLw0SWqF2o47Hg2hFABi1veMdYJzCKGi4KYoVo9AN87LuiKItF3bI6G6apIf67byXUPVlPFkOBp5/shzTCuLUwnkw8PKdcbjyTjLmK4RTQsYY1nVFk2DiWq7dlU0r9+9MT0lCLy+7/O6rNvuf//P/+WbL54nSRNnJRd8s3qEgDx58iSOD8cwNmxbN9yybqP4VtNUVdUYpfPp1NCU++U9RMgwlDg5KCez+fVlpipgvdtcXD5Z71aM0ec351laFGXx/Nn1eDp7+/496xoFmIam1B2jPWtZ3Xd8Pp3oClYUtW5pWmSD2Qhp8OLqss2bt68/rndHibBlmYaqX15eLXfH9WbLBYISciZkxwQQhBAhhZSCA5nnhS3Moii26/1yufnTd99//vJF4HuaSoYDv2so6zvey0OS/vn7vz4p6iorN/fx9//8o6YZgknOZF42cHeYTeZV23z/yxvb1F88f/btV8/+9Oc//+nPf2JdPxxP/aF3fnqeZKFhmH/5/ifBgOvan3/+xX/9h3+gq93V5flkOKR1QxDSNH253qRpDiE8hqkCUdtSJmRdl7//5mvPMc8WizSN/vrT9wAomqZjorS0PKbRIYrqusGYEIxOxvO27VZ5YdmmkGJ3ODa0f/fpIc9+rstqMZ81TWM6PlZwmWdhmFxcXqqaBgGMDmEfRk+ePOF9z6W0bctxzc12r2pqzxntqBt4xzjhAjIO0qy0bSc8xpR2RNUkEFzwOI5VTdc0M87q7SGeTRaX5+c9403T7vd7x3Fn0ynBWAgBEZ6fnLC+62gdhVGR567j+r4XhkcIpBAyzYooDieTUVHkn+6Wp6cLFYE0ybiQQIAoChHkm/0WSMQ5AAohmqarXeA5tuvd3z9Mp+MXL5+lWSoFq8qKCcGZrNraMvSz+Xy730sI67bN0sQwjMvreZ4XZVYZpp0mCQAV50zVFMt2MFb3xzBbbZ4+fQIRuL17KIu6pzxj5f5YPb252R4PjIH7hw3nQjeM4WDQ9X2RlwjA6Wg0Hg6Xyw1jDCtY07W24xK1URyfzk+JooRZYtm2puC2bcu67DjXdF0latd1URi2lCoYIwU2Td22FCFFUTXLUWezMcJIhWQ8GiOI7h/CMDr2nDWUeo47Go7TNPn48Onv/vg3lycnbz+2YVLwHmmqoqp44Pu/Xeuz+Xy53kRxqhDr/Owkinaaqn37h2/fvf+wWi2zomwiOh6NdM0QHNKOVW11eX5aF3nPett0HFP1Ri5QOFZU2vKiKJqmrqoCYdk1pec6E99tW901rAaAMisn41FZFF3fsU4AKMs2b3nVc6YJSLAaxSGL4tFwdHlyGvieFMzS0Yf3rze7w8XpFRcyLejFyalcnOyjGCI8nUw72kkoFWSfX5wGvl/EKSHKmzev27rt+m4wnnAGDMfvGG/zAmL1ydXN6XxW19Vuv43DCEGeFIVg0HdHlmFuN2uiasPReH/c2rb5xec3WZ5WZUwwPj+d9ZR7jnN3f9cLxKSi687J4myzWVdNblvmaBDkWbY/JBfn54vJcOgHHCGIYBqFQkKIcFGVTIgvv/oaQliVNUDK4RhnWe661sB3AcBQKh1llPZCgPcfbztGVU0zDKMrqrwoT6YTS9O7pknjGACAMMIQ9i3lnEsJpIRYwYvFWHKeppUUMi+arKyNXE/zpG07UzOwij3f9QNnvXnEgHStRAiPB5PRePH85bPddl0WtWWbdd3QHozGM80yRzU9HCJNNz98fDg7O7VuvIuzKwL1ME02DxvBGNa1KE0flpvTkws33L16/Yuuu1+cXWVFwVhn205XVsOBYmiK6NrtJt/uNz1nHx4/dn2v/If/9J9O5nON6NKye0oHvr9ercoy9UeBYpCmLqee7fz+i/v7JW3bu3UEERISIIwCz5sF3nAYPOyPmqn/uz/8G2/o/5//5b9E+ziKUwUpRDeggH0vXr64Wa5XP//yehAMMBJSACYk5pKoBCOEFFh1/WTsff70ORPsu8MBYmUwmITh/p/+6Z8d3yaaKkFH267jPUYIacbbjw+bQ8IF1LFiWZap63XVYETajt29efXy+fPz0ykVsKnat2/enJ4sPtyvoiR+enE2HAzjLP3p11dPri/+7u/+Hf71rk5ST4r9fmcbbpbled6UJX316q3lGM+vrwa+1zYUI+QHJ6znddPWtFE1lWBlt928+uUXYqjr3dY2Pc8FuMdlWURZ3DRNXXdnZ1OioPB4CKM48FxDIxVtD8dDmhW+47VNV1XNdrtzPA8r8PJ83tMJZ0xVUNs2luVM5/NjGHVdd7KY057d3t4uHx6JpgoJur6DCEopLs8uOtpNx0PPsauq6PsuSTKkYIRR07ad4FhIzMQXL1+enZ2yviWa4nreer2dTCYnJzOC8cPDUtPJbD5Xic65eP/h3W4X5XkmBBuPp65jt3m53h6FlEIwRVUQItPpzLFdxza5lJv9QTIOAIizWtM0S9Us2y7bBnOhEoIVghD+/OVLAFlR5AjA1ebYdX1d17qh25atamqSxLPpTDfMzW7vDF3TVMsqr+qCccZLNhwMOecKxnmeJ2nhDoKbp9eOaSMuGaPjk9O6ajVdT7Ps+x9/Wu8OjmPttrd5VmGsYAULzmhLW8r7KFMw9D1vMhrWtAFI9qwHWAIkup4ahja/OAvy+HGzVqQ6mU7SMlOFpHWfFdlkNOacNZRihOo6x5jcXD3BmEgphyNvMZ8irLRtnSSJqhkAgpZ2fuApCoZIHg4bVTOIrnLGfMfRFNWZ+FVZaprBWLfd7iEmvudKxkzHPVV1KEGcJXFeqEhdbx5t23zy5PoQHSzXvr487/u66GnPWUPr5eZRU0nZlKPBzDAtwySj4Wz9uNpttm3fKRi7rg2lfHx81BTFMjWEcF0VtO+DgWdZmkpkXXUQgN1+JyFzXHs8GO12+7PZ/ObJk55D33FMFaRptNzsmqaXApRNPQnawTB48/EdEL+8ePrU0E1F1XhHnz29zvJ0MR0x3m7Wy7zoNU0ra9p2YjpdeI5n6KpEIMvyKu8Hvj/w7eN+8/b2VtfNnlLe91BRri8uoywfTebXN0+BlEzI2WxeV03d0mDoRxFHkAwNa73dFW3DhAw858WLp1JKXbdvrv/F8bgHUPi21dNWSpFmqWsrPadxXOmG2bEeK3rVtFleOn4AsbrdbhGGrmOfLM50LWSc9kxwLtu2Bghw3rVtBZFUFZW21PM8ouAwPNZ5Y2g6xjJOEgAghAAAKVjHOGdMSgkQhk1ZVjHFmIyGg4Hp0rEIRsOWVbd3900nzsYTxuXdp0PZ9K5rRFFMMBkNRr/76gvH06s8ckwj8AdhHLVUXF6cHeI9bfn11Q2lbXiM/6///t+vt8tdtA+zw3a7/vCx1nXryy8+//arr9eb0HI83bAZ0zUjGA5nlGamrgveFWWyO6z7tl9t1gAARSFxkldVnZeFUrVFmOIXV9e+bT6udkIKy7S7vmO8aarCMMzH/aqtm/lsBqAaDKfhYQuBGE1nZ4t5U6bL7bYX/OnpCUFye79GPamLXkik6nbN+tHA+5//9b/eHw9RmD25ftK0dduUkgMppISCCW4ZlqmhKlE62qx3j5v1tq5pWVUEI42QYDblgO8Pu+36KBjTVFXBwDZ0azarmraHEqtKmucf7u5VVRcAqB0yDev9pwdbUy8vL79fbt++//X/9j/9m99/9fnr9+9Vw/0P/+c/GrqGMW4py6LI0jWiEMEBRqoEaDIdXZyfbXex68+iNLpf7lzX03RdVxXPswTHwwEUkj+ulh9u7z58ZK5lG47pu4GhWgpEnDHXcaQUjm3Pp4vBYLTerN6/f88FyIqy67S6bZfrfZYW+jUZT6dF02ZVi4iqEszZlHFumGZZFU3bqpo+CAaubRdlsd/vVU3XNA0QgpC8nM2ruiIEMc6BQMDQXEMTjFZVJiWwHSsIgjAOsWJJCcNj3KLeGwampSOgDwbD8WhkO3aeZ0RFHWVMiDxMmJCGpl6cX3z2/BkGwLHs9Xa33h5Ny5YSFGWlQKypSrgPry4vAt9p6ipL0+PhCAFwPY/SliNoWdZkMLAMcxeGYRTWrQkUqCjw+ur01evXu91BU3QElcHIJ0VOiEIU3LNecJkXBcaKpRt+4La0Xi1XRFUX0zmQ0nV9Svv9/tB1/BCFlHaYA8cwoiw2NC3LsiLPFUVpaLc4OY3j2La8uqYQAV3TGeNFWRFCdF1XNZVBudpus7yCEGIF27bdg75ru8V0kieRa6if3VwFjv64XMdJ1vftbDoTPjzsd8PAcSw9LyoAYEYUAKCq4slklKSJoStCsLzMur5TNIQQuLw8Z4x2PSWqkpd5lZejIR54vm1ZEAjXcRBW5pPx8Xgsa0owloAaumqZph8Ebd/qqlo3ra5atmVXdTUcDNM8rZv68mIMEVjvNq7tAAiPSbg9MCkkEEA3t/8/kv5rV5cuO7DEVkSsWOH95+32+/hzfpOemWSSzGKxVNVsNKSbhqA76X0EXUjQnQShUYU2UHeL7DIsssi0vz3ebL8/b8J7H0sX+RQTmHPMMT5//pTC/IflvCwqkRfsuVtV5aA/oEkaYFRUGJVEWWaOa3I8x3FsVdWyRMqKXGMiTmKM6ywpzcpTFX08Hrt+4PkuBPUqcF3XcbwozWpFksuKuLi5ecw84FixauDXL99Bmv75z3+exWG3o0cf3d12qahKFMRVQ4+6/QOOb3ADiSbOEkE3bm9u4jDiEM8wxOXN1d70wjCXBc32XZIizg4OZFmOd7uyjMPASuJ8cnCYh5WutaqqtB0TMTJJIoamXPdS4PnRpE+QuMwjTmBn808bmhcEyTC0KEn2lhtGSV6sAVGziAYEQWCAGwAIQFPo5OScZSReUPKiZFnKsfdB5D16cBJH/s6y8qJ2HBchCuCGpEiWYWgaAdAAgIMwSJMUMITEU7IsBn5eVRUgIUWRgiBmaQFARZIErvFytp2MO4LEbbYbo2VwAjced+IsQjQpyy1REvICq5KyN/dxFLKIpylKUWQAqjSN6rpeLjeABEke8byEQcOxsqExHUOHFCzyjEPE2dHherWOAq/bM5gAYQKZrjnu9rqtlq6ooqT/m3/zNwRRlmWkq1Iah2TdEGWdxdnediRZ01TDsiyCoIMg5XkJNlQtCKws8aAmDkbDuMa3xDxJbI4mzj9/4kfl3//69sHRkWEYYVr8yS9+9rtf/3PH0PR2+252f3x6oup6mhdFUbx5d7VYrBDDKCqv0dLh6MBotwf9ju85fugOev29aW23W1kSAMYEQfyx2ZcXWZGVJEXOFtu97XIMm2VVVVV1WR4cTqaHo1a7dXl19/HdxySLIUt3DI1laUiRVdFYXsDxzCzaVgQBSZIgCBKSw3YXN/Dyfn2z3EGS+fKzH3pBxNBRSxP+9Oc/Vd8q11d3gih8uLiK/aA/GlEE5ljGtK0wifZWzNJQ1bRf/cXPV+vNYn6fFWW322u3VXO3HU0OOJabz2aiILh+yDEsRULfDXUSYrI2XQcTANI0QpCiSLIpne0yDb3pqM+xvOM6VVHlRUU2oNMyAIFphuYF9njyANFEQ2LTcRvcSJrqrH2OZWgEkyQkMcEh6AYeiMijo8O6qub3NxA2LUNJkripS0WRRY7P8yyKkyLL6gbXDcC4sS1La2maJldNxbGiado0hE+enem6dnd3V1WV6zqO49EUU5e4Kqv7uzlJgvlyBSkyLzJNVc/ls5vbO45hRJHneb5Ic8u2trstzyFFVZI0c5w/fl1gxwsxAALLjYddyMDdfs9L4kSZMgglSVJj4eXL19u9qSgKhxBOqiC0McaIZkmCcCzH0Nvm1qyLGiF0fzc3DE1VWkkaMQzdbrfiKCFJ7HtenleyqJMA+n6oqvr7Dxc0TTM06nY6VVUBAsiyoh0dDgaj3Xbv+V6r1crLjOe4OElESTh7eBpG7od3H/0g1lSdBCRNMWVRUhRVZE2nbViuF76NkjRMs4pFzLDXaZomDGOWRaa9gxSKkoimkCiKPMeOx6Pdbh+GbpyH0fUnnuVURYWQ0lSmacr+oJem6Xq9isKYgpAiKNd219Lu8GB8cDC5vLgQWeZwevDp+jqJY1GS4qwI5kvLcfMia+nyeHJAUVQYBr1eu9PpeB/cYXfQa3WbumYRa9suxjgKU4bhirwkSUoR5dAPEsdeLOa8KE0nJ9PxNE7iKIqzNGVZgYZQlXjXdfLctV1TlqT+oIcYzLAUQ1OsLImymOVpmWcUpHabjR34cZEneRQFcV01np/4YVoVtaZIWRFHvjsdDmeLVVYUBi/6frBe3t/cXUCK9F1vvQvqmuAluN8uirL2gnA8GjAc+nT5KYljnuXqpnJdb7HcIobv91q4KXielxSJIqnru1uB433X8QLH8eI0rzVNo0iKoVGVV8+ePK8rvDU3/V4f0pAkge1YQex1e21Z0Xw3tc0lzzA0jVRN53i5qeuyros4oSiCplG312tAY5m2va8nU7HfURWBIkAT2FvbsbIspiHO87LbHeiakmQJpKmqLIsy9zyvKivX8fr9brvdggTVYFBWTZrlGICmwQRJURTEAABAEiRRlLXA8QSkS4xrkkyqCjT1crPOi+zg4ABBkoKU0VIMVbfMle85o8kIQsrznJt5DnBDI77dH2AIBUWKw+ibb799/PjzXre3nN2PBr2ySNM4kGVFFPlBvwcIw7Tc7d7xXI+o8Hg6lkUWg+r05HC1uPr9b3/fbnVbmm46dpKlABN1SQ/7h0cHE9uxtvs9IGG304YMy2V5sbPtltHW2y3z5kZXBVmlJRGxAty53tnJ4clkygr8kcARdfTwwbSjqySkt1soiQKiqbws4zAuy8b1QkgTWRkXZdzpqpqm//6rrzzfPD09v/e2DVEXRVGWNAkoiqJpSBEUQZMUAE1W1Kos/eSHn80Xq/u1KQt8FAWu511cXt7d3bEsP530dc2wLSfNoiCNBv0+i2QrTIqq4gSRL2sa0hRFIYYoqszQWzXQdFV+dv6IguR//If/JIucLEmvvv3tk2dPjibjf/zn3zphyvOxf33FsBAimgJwPByQBDns9dabVRjF98v7g4OpafvLzcrzbdzU0cWHTq+32m2qsj4c95I0oUjKi/zdbhtziaRKGIAGNwRFNgBc3c/G3WGUZFmRgwbv9rsqr+oGyIoAMI6jkKXpp+eniiKleWrZbp4Vo1FP4tHRZAIIUFd1gxuEaFHgdV3bmeZmtVJlieM407QEQVqtlrKkBNFa4HmORghCjuEuri5JCKfT6YOH5zf3N3GYGK0uBkDX1X67tVzMv//u28VipestgAGJSU4Eisr3ewYG0HY90zKzOi+rMkv3g35vPB6EodfttDBB3t3dYUzUDfnp8laUhNFo0O50kqK8vLpFiDmYTMzd1ja9vCnyIqsbLMpSVZdlU/mhT5MsgkyWZHmWQQTjKKcoatTX4iDqtAa2bdcNjpK0jqIkjuMozooCgzpNMhoyaWqTJDkej4ui4Vhmb5kkhFfX17gmIIdkSWFoRhUlP/BNc2sYLZ6nptNhdpGY1h7SFCRBv99tADGbzWiGaAhAUhTLIgAakiJxWUdxXNLM1tx5gT8ZDdM09YPwy88+77a7tmPhJqEoVFUFwA3DcgjSdV2JEl8WmcCz4sHRZr9mEJvECYIMhCiN11mWQkTLiixLiuCEFcZhkEZx8urN+9Vm/fDBSa/TTuKUoshBr79eryENSQQlQYy8JInDlMsdexdGUZoUHCvEcSgILIKUbVkURUoCzyHke2GFBAJQnMTnWVqXlbkziyIjIFfW0LSdVtsoqiIrckARFKB8PxA4RCGEGG6gtfr9XhyHQehRsFEUqSqq169fK4qsynJV5SLHipJURlQSJQBQtuP6QVo2DU1TuCnzIrdNO0syUOUspKs8C10XABBGEcNCo6U7ToJJstVuZ3GyuL/xwsT1o7YmSRKvaZrruDSF9uY+ywqOY7948fj65l5RNY7nfd+L8pQkaU3U0iQOPVPmNWe3d0lSVhSeZ7///utut0cCotc2yrpumqqUBF6mkiStCEIUxMPJpKkSloXPXzzebbcMg4IoLrPStM0kz3SKbuqcF/iqJDbrZVOniCKTKM6TSBIETuDzIk7z3HFcnuNURcmyjIAgCgND06oKY4A9L6BIUhHFsin3s3vXizEgMCAwJhDDkBSFQYMBQdPsH3sYdM1ggva8mEU8Q9GsgFazteNasiYLssKcn2d51B8O4zitmzoMY8Qyvb4uyQpC3GqzMQyVwOXyPl2v5r67O5weYhIUZbXbW5brarpKQ8ixrCbrDM35rldUNSOKZZklMd7vVwAQk/FRmScEwL3+wGi3GRqRADR1TeGya6gdQ6ubZm+Z0JCV5XrDsZzRat3O769urmVZirMkEhEj8rPN3Y9efE4R9O+++kYUOENV8jT6+N4naabbHSKI9qad5VnTNCQGJEkUddk0BAkJggJhnFzdrUFTFPmF7fndbn8yHjuOTUGMaJqmaUUS4qygIESQpBryYNDr97rFNy/zLINQ5XkFQca1LY4v2h0jShNZ16JNTCNab7UtN9T6XZqgplNxsVkmRUZRxB85pX67M+y2szTZWtvlcokIsiyKMIo+hvHdYnN+cvzs0QkrKuvVamftKlxlRVkWedtoNXUZJTHN8A3AjCAud+u6aADGtucbmsohfrvcQwpF/l6ADa4rgkZFVUdhmHJ5WmTtbpehoSKKJA05hmnp+tra+oGPEd9pdXemnUaxIitZmjAQjQYjGsHFai2rKqQ5npM9L2JoW5GVOIqaphkMekVZIJZlWY4CVEOA9Wbve16a5y9ePNf1tu8FDcahH754+tTzvLKqRVlJ0nRvWZChaZKGBGpKXNaFqsibze7i+irLC0Pv1Q1mWZas66osswxUdUVQtKaJPEf7YeDYLkJ0nMZBGEsCT0JomlaFscAJ69W6wQ0ninWDk6yYLZaCKHEM8l0nSlNzYQuC0G7rWVqWpdPrdlmGTbKs31JIjIMoZnj25PCkyj96nvfu7Yduu40BCKKoLGvIcFXTuF4ImkDV9MPDKSTq2+vr27uZKMvnp+dVWZdlwbAQg0aWxbZhFHUh8SLN0Jv9jqYRTbPb7TaIfFlUVU0uy6qsCkAAxCAMyOVq0eCqKRuaojzXFSUxjmPLMhuMm6rmeX7QG2RpURY1w7BXt7dllTd1FefJqDfe7bYkSfA0AwCGIlM31cXlJWgww3EkSVRpRmIyCuM0syRR6Pd6iiLf3t8auvFXf/kX88Vys94gBhVF2e/1HMvGAHcHQ9t2lsuV3tYZkYmzGCFS5EUC40FHczw7itPV0mzp3Zaullm68NYkpCCEq+UyihJVVsaDXhgnVd0QgJUksa4KmkaIYZsSYAyCMCrKipMEAmCJZ9F0KPJCnpdVBdIsFXmaJjnLsyzPmk6m19f3phtaXjTo1oaukBStikLb6DOIubm/FQWJQVs/DESJB1XOImS7vhfErMDledgy9P1+F0SB6zksB9EYHh+PiwJHQVBX1aOzc4Zl4zQJPff2dg7RlgBYkqS8rDRNPZpO9hvT90JGELbWTpEVnudlSWQ5zjDaX8g6Ccj57P7m7l4QZUHkLdvsdnbHB5MwTrIsFSXe0FRN4Vfr3cvvP4mybhiyF/iL1bof+aom0iRWeI5WlSSPMIAQMqLAI0iTBGQY5uLiE8cxIs8zLKtA4vGTp4vZnGrWAABFFUfD4W6zK6uSpkhF1fampSiSaZlpmu8tfzTqQ4py7bhpME0SmABVVVMkBUCBG0yRZAPqXr+v67quq1fX10EYlrgVOkHox5KsFWVDJsVu79JIIEnAsGQcJ5io4yi5uYwMQ5scHAi8lCRFVdfDaU+VZdf2aISKorpfbARR5AXOvrkrsqQBsN9pm7vdfu9+9oMXThCEXkJgOssKioRGqw0hxnXZbmlVniECKKp4fz8z7RwAoqmBwPGH4yFEBKZIYrXe8Ax9N1ubjgtosmW0yzJ59eZdkZQ3V/M4TyganB1O8zxzXW+2cuIkLsqarJt9EDueSwBclzWBikmv32l3Z5sbTmQiP/zlz3/88vXrxXIlS0oShSxCCDF1VVAkUTeNyKEkywmyoSFpm/GH69vhaKgrckJTj754BDARpwnL0nVTV1VTFEWWpBLPG4bYbykvnj0iafbtuw8cy3ESfTu/YxgaV2Wv1eEQH0TRbu8mWUHTMCmrIsxlCauSnCX1ty8/MAz5+PGjfkeXFd52XU3T/CBeLpeQoopmByElieyw11ptt2RDMTTj+H4UheeHh/1Ou6iryHNIEvIc7YaxrrcIQCiKqIgyx7GSKEz7vb1r7rariw8fakCqqq5yQhBHnMANBsM4DEkC5GXpRT5JEaqidls9RdPWq1VZFRQF/cAvyoqhqTCK/DAK41ASBIEVQEOQJF1XQBbkqigUWeR5lKU5xiBKYtf3LduRFE1SdcRAAhCT4ZQYwTdvX7MC4kVOktWyIfIsDaIg8Ny6KZM4Drw4jEKagbIsddqtwA8t16VpGtc1SVEMwzII2Y6NcY1oxrRMQAKO4SgIKRLmZUkRFANhlqRZllMMUnVDYBlZEgfDXpKGVVVTgKIIShJEmRP3rh2Z0el0Oh12KRJAhACuAUn0UNfa2hLPR3FAkYTRUo5PDhCD6jzLwiSKE0DRpr0/Pz7OyjrOojzPZJUvyhLVlKaIe8tuMKYhkjRlPk/LomkwaDAIw9gwtLahuo6TFbnAcSQgi7gqiAoxTNM0YRiKoogxSNMkzTKpkTRNo2ma5Zj1erVcrWRFyrLcc904idI8FRDP8Fxo+5Cii6TEoKqbqgYYsYzACbIiJUkiiPzRdLrb7RENCQA8z4JkQ9PEYX/sOC5Nkb3+9Orm+m4277Q6nCCkZbVbunEQCIw46g90TRB47sOl44UxzSKage/evYnClILIsmwGIZFXm4rMy9p0HEPX9qYJIZOmGSSByPOu51EQFjUsq0rV5NvZHYQglbjt3nz68MmgN2p32vf3S2vnHR8d2F6AeLEqQb83JGnWcaxxvx+EXn88/GMflCLJm1nFi6jfUQyF35sWSRAUBXeBV1SVlPAI0W4QOn6cFwXPsXGU396tIRTOT059jvl4eRnFMWLQYrnEmKoBCrzk0fmxbdsEQfV7vQZjiuWePX1+c38rCiIBCKIGe9OGJCzyJIojCFEYZQ0B56s1x3O6JpOQnK3XCLKBH8wWS01Vm2EPUtyTJ08wIO7u78y947uRZTndfisM4ulkpGoSwLVjOWVWQopCiEqSGFeYppEqy2VRpHneG/S+/eYrkoLtVsv33SiOVquFKouS1JnNF1EUNw1mEGIQ8vygqUnP9mWZazAgSBIA0NSYwCRDo5wsAEk0de2FEcuxnusheHJ22P/utbnfmJqmGQf6i8cPGJGxbHuxWBENjpIIMVhVhV63XxTAD0M/yNO0bOlqkmUkbNLSmS3mHMNzouCYe0VVaZbBNQjdOA291c5lnlP7nRUnTU9Tzf1G040vv/xSErnZ/a2i8u1Wq24q1/Nu7+ej4fQ333zLcuJwOFqvlhTALuHNlnMYxWGSBDwr39zN71frdkc/OBoCkry7XRJVPewMIWQO+923H9/ezO5v7udelEOSfvLgUQ2r2/3c0NsDqe87XhC4DAUCa1+VOcsyum7wfHN5cYcY9Od/8UuaYt++eSUrkuX6dUMATBCAkgQxyfIoiQBNNwSBGI5GSJLlJE3v53fT6YEX+HfzuaIqLV3SNbnX6eC6hgh4oV8vcVHUPUPHBGG75uGoxyMGE0QQhBRNff75DyXpGiGU5InjuTdXd0We0QwDGWK13bOs8u7DZbfb64/aqsLhOh/2OmeHx4Hvu6EPAAagNvcmIuHkaGpZzv1yjhB9t147UWTorZqggqT84WfPWI6frVZ7jum09QcnJ04cXd/fX9+lYRjc3M85jvnsxXPH8ZamWZVlS2shlp3NZ+1WO7Ks+9l8OOqrKlGU6d29I0lST+sKgrDbbi3L5CS5aXBVVHGUNnWTp5kkicfHE07gHMf9eH0NKWI4GvR7ne1q7fu1okj9XpdC9Ha7S9O0LvIyr/O8gpDmGf7+7q4sG5JAURw5rqPpehgEedFgQEmSVuFqu3WCMBEEMUpygixZCOsqz7KyaRrXdTXNgBQFGoxomkHMfrenKZgkSRRHgCTKoiIoGAfhYNDptA0CVERTpElxfXv/6Owhh9gsyTzH4xBHC3B+v6jqOs8zggSCwEZpTCN6NOnxiMWgOD+bSBKdV1ZaUiwUvCCqGxAEgWkxcRg9f/JEYNi6KuMotmxL01t7x6mbRtc0x3IZjp2OR61W2/U8kRP73X6DS12VMWiibagqusALh0dHSZou5os0yyiKGo36rmdLEgcA1TQNw6DJZOx6tmEomqZzLHt7M3d9jxM4xEACU0VZBFECMCFwoiiKBNFUTZ1maV6kSRbJklzn+ffffUuSNEXTYZyYltU0mOEE03J2u50oiovVqqhrURTKspRF8fD4eLPZWJZT5gXLs5oqJ3FUV7goapJABAE/XdxleSXLEoQwzXKCJQiSzIsySoIsyx6dn3MIJnllO06DGwqRksTJsoEr4IcBDWlVlUia0jQchPFkQKqy9OKzZ71Op9du9ceDGjSgLkHTkFR9fNiPgjCMwPffv+z3+oamA4AFUVitNkkUEZgsiirN86yo0hKToMmLBAMkYpEgCJZjCEjEQRZGIIo/mHt7ejApi8bybFWVZcWIwvhwciBJQlVmuKqKPOcYRhSFJIkQ0h5ORkmRUjSbxunF3X1ZNYiTItM1nR1N04ooMgg1ABdluVrv0qRQFTmOUpKkej3BD5P11jk/nu6369BLsqxqMGm7SYM93w/CMJNlQRA5UeQ1TdjvXMcpsizxA59luTTPIUkmWXF+dmZoehinZdVQNHN3N+d5ylAlgkCdbp8ki6Iq7mf3VVMXZcnQXIVxEKVFWdcYAEBQJCHJYlmmeVGWZVXj3NBbNE1HWf7x4+XRpPPg6KBIMSfQWouvcn9vRXGciCxtukFdNRVJ7nyv3eKGg4Oq2Z4/OmkbirNdu9vtzrcAQN32QFHEzWat6y3NaBVl5jvO3raX87kuaXeX147t8oLw+ttvrCjijerj5fc0AbIky/PKtfatVksReE0S6qogSSgpmmy0jZb+9tV3v/vmm6JpYAOxqqlto2/bHkUjPw4/XV5lWdXUhcZLLd2ocVWU5eMnzy6vLoxe9/PuZD67e/LgaBsHtmednQ5oWgzdaLNa5WXaYLCxLFiBcbvHiXLohTxLTSaTOMicfi9LY0gQADEMTVEkMGShAVWcRmVVlnXtOI6iyiRBGroeJtFstYyj+PHD816vu3dWkKglEeGyNjp6lGRN2cgCbyia43u4rASaHva6BEkvmqWq8VURPjg72pu7Xq+vyDxHszTN2I7V0jUCEBAxqq4TkFxv9wKPIGSvb68hjQadHkUQlu2oqiSLMs+yHKKaKvvyxXPLcXFD7DZbsgK9VuuLLz5L03C1XiKW7g27vmU5tkmxHKRgECeQQqqs51VuW26eVWlayJIgi7ysKY7dokiypWmgqRoC22HgR5G9d3rdru/5mqY3uEaIqnHlWF4YJlXVzJc7gig6ba3Auaoarg/Mvd1ptx0r8JwAITS/vNVUuds2jJb+Rz8hJ/BBED54cE6QB29evyUx9F2HplEYRxzHcwzHd3nX8ZgOi3Fj2U5R1GVZFEVJQZSmGU3RFA2bBoRhpGkqIKggiIuy4nkeQkRR9Hy1zfN80O8lSZ5mCc+xkiTohjgadz99ugyirD+cqK3uuNfxLLsoKhoxbVnhWYbj2OVibdsujeDR0aRrtLO8TJN0tV6SNE3TnGm6luvQiB8OUF7WnU7ftW2eEyGEUZpygmC5blrkiqILPA8wEAXRNm2j3RJ4tigK33OLLNc0PS2KNC3ysjH0PkRCVWUCx/c6bT8Msizz/YBlmaLIRZFXVT2OsjhKEGLCMFotVyRBRcEaYJxlhSRLiAQNhSFkCIrgBKGucL9t8BwbhUFZF6vtpqqB50ZF2jA8KtJUliSepjVNazCxXK/Logr8UFU01/EJApCQms0WPMdzLJrPbrvdHouY1Wq5Ws0dh5ckfjodc6bLIA5g3G13XS8ui5wEQJKlXqe7Xq9URa3rhoakqqn73ZqEyDD0piqamvC8JI7KKq+2uz0rcnlWDHq9vt5HiCYA7nQNSZbSONrvF+12ezafb2377PS0z3QBQQR+sNnaaZq02kMnyA6mo0G/jyuwI8zd1ozStKWpUZbCsjJUVRIZ3w+rpu52OzUu4yRFDFvkVeBHNosIksjSXBJFRVYoSDc1Bk0ZuOZuv4vCWJHVsqju7m4piqjKkoNovlmVVcVRdJmn+/1aEKVev4vJhoLk8WR0fX0/X+4BAY+PjmucRYFPEGTd4CCK91ncNNTrtx+LLFfUDs9hhDhZkfI0JQnS9eMsr6qt2Wqpo/FIkvL93qnqsm4IL0g5PiuyRBTFvbWfTCbbrZ0kYXfQyqq4ykvXjXf7he2EnW6rKPKqqv0gDJOsKkOWRpPxEABAESRJkpgAEJGAaAgSUBDihsqzLEtzkoSu63AMoxuiwLDvP3zgJNHQjQaTtmlyHKu1e8fHo8XyThQERZVwk//ki2d5XYSBz3ECi3ieFnlBUUW1yBM7TEejA0TDyIwPD4/v72YbCGtcr01/bZrdXssOWcePnCyLPEvkRVFUXM8vy11DUHkUJpHT1E0aBcssmYw7BIGrImt3eoykQZFmk6ZKslQQRZJAz5885Hhhv/Hev33Te9Dfe+7tbDbot87ODn/2oy8C3zF4ERf6bHFLcszjo+O3by88z394ckYRoNPurXbbpMhUXvju6z9wkmzudiTV7LdLUNOSKIKqYmm6xABCzKGmKaI8TQiCIjAAGGy3e0kUs7LmOIbEBIHBdDzutPUoCpI8rZO4p+mSIKVRkmWJ6wetdvfD9Udc1cfjQ9+3LHPb0lpH47Eg8kmW8yw1GPSyLAMY5ElcEOkPP/vi8voKE4Suqlkadrq6wPB1Vbq7fY2p/dayNuZwPC7KyjQdluPX6zVo6jhOjg5PE65gIP3gcJKlkawpsiJe3F71uv0Pny7zMjcE4X69qTEhympdEWVR9PotkqDysgYUeX54BCG5223Ipjo9mO5MazwanJ5ML29vtsutobUApm5u77udDsfycRI3BN7ulwzLuEG8XK55kR2O2xBSd3f3kuzimuh1O22jDSlCUSTHD3lBMS1HEfjdZr3buwRBdbvdGgDL2vt+mKYpDRHHIlkWR6OeaVlVmTMMMxx2BEFM4sQw1Nl8lSQJrnEUpyzDsAwtK0KelVmW4gYsFuswChGisz8e8RGq81ISREhSAEGMUZblNKTqpvp4ecnwQktVz06m99vl/eL6dHJsWS4mm6YpfT9ByEAMwXEsQRFhGAkcTwEMSWI6HEZJunc9iqIQKwNMViVWVZmheZaGHINkVbY8uyjLwWgQ+KEsy92WMVsu8ixjWbbCTZpnLMdSkCIqcjwdh77fNBVDMweHR0VVbzar68urzXrVarcZROuGznHcfHaHMTZ06o8YlecHgGgkQdrvrLosWy1NYAWGogdtPfDczX5PUKQoCAfj4cl0UhV5qYk1rjkabU0/wKEkcXGWGrpRVuVyvc6LkkZMURRpmkuixAl8mqQcy1zd3GRZEYUZQZOCG6y3piRLvCg2IV5v961K5VgWQmI06sZJkCQxxpWqCKqipnmiSHzvxfPlekmSxG5rfbq40nV1u7MYxNIUuTfdoigFngMYiJJs+16epb7jczz/5PEDlmFtx07iuKmr2XzW7fbG4ymv6izLnR4fff3Nd5Cif/KTHziOpShCUVbDUdfZb1iOPjo6MAwDEwSoql5HQTwrijLLshRt52mOWBinpSJrqqjGScqyiGhqnmVlUQr9MAo8zTAgRUJIhH5se2EU56reycsSYxJCerPbmnZwevbAcez7nVlVjSRj23XrCgMSn54deZ7n+9752TGE9LA/dEPPdz1IkZho5rMlQcIffPHk17/9mueV54fDzRoUTYUYSAJwMD5drc0srwDBAUBefLrIskLVRECVcUq7TipKopOlBEX/5rdf9zqtXr+X5kkwd5um7na7+51DgHCz2m93O0WV8ywviibLq7Io25oRBAGBAQEaigAUAaLAr6sS1zWCHK4qx/H6/T4NGUnkOYn9+uW789OzTn9gOv7Wjc7PzvuDfhJFDCcNBt3l6i7Py/1u57qXnu80TXP+4EFeV1VDCJwYJQEkiT/O77rIL+9uiypnEP2jH35Rg+rT+2sSoi9/9MMo9L0oS4tSZFlJMIy2kWWZYbTTNFmuli5iOm39m2/fqq0WQZMf3r03dA3SzJefvTg+PYVEAzptTZKUh+cPf/bljxiOJCBJnjMyQkFi9zs6RdKALLeb7auXbzheaEDlWtna9HRNuL3aNyT585//giQaTacURf10O/f9hKG4jzcLhpV2plWkwZNHD2nI0DSVlwXH0E2WcYgWqLrMs6yq0ywnAEGRVFU1UZzUGJd5Iclyb9Bfr5aea42nw8+ePl3M5wixnVbr5fu3dphUNa4xIwsCRMSw1wN1sbeSKEmYChOAyso8ijNMUL3BqMD0emNSuIoDu9NpKS1tcXvnu3ZLEWx3n+bps8fPeE7kBXlnmePJQZblQRhc3sxNJ+BYWtEMxDA//+mPLce5ub5iGcTw4tZyHj96Yps2DaGmqFRT+2EehHFW1pPxJI0jnqEQ4qOssCwnS9IaF3XTvH792jAMThLz1E+THGHqaDjO8qJE6Hx49uD81LR2ksIFUcCLTF3jdlt9dHZUN2WeRrIsNFXjOS7ZEKen54Aird2mKJMoinWJfXj83Nmb89WKohleYK+vr23Hy7KCQWxVV4hFUZzYgTvotvM8phGX5nlZlTzPTid9mmZt22maQhalg4NRVdZxHBkdY7ez1mkGEZIUwCtsU4GqauqmLLJMlUWEIEWBpgSIpo+PjwgCAFxzAlc3eLlaN3UTJvHeNPt6P0mzBhBeEH7+7DHLIMeyx6M+pOksr3/79XdPHz+ECIVJjBg0mQwJoimrIk4KggKKrERBTBBNXRYsJHqdQRhEFEmdPX/O8ez93Wy/NQkSVmX16PEjc79frzaSLLCIu764jGNPkeRurz2fXUZJdnBwKHAPd7s9gevjwynNsLvN5mA6SZKUYVkawqoohsNRnCQvX74kAPjjDsT1bMerNYVL08xxPEmSYyIqyvR+flflmaGoGGOJFxaVBRGltWQqoBRV5jnRDy9sP6aohGMYURTrGidJNhwM371/z7Icx3CW5Z4cnE9HPdtzMcCPTs98z6UoUlYkx3WGw6EsCzRTE0TH8bLRoFvkKSaahqg0QyxrzXUDSZJEUXj88NHr+h3LcGkUF1mOASiruq6qqsEsw7KIFQV+NBo+fHCe53GcxIIgmqbruMnJWSsvgWOZAQ2qIqmbTFE4TDUtik+ioNvtrpf3N7d3aVYs5jf9fv/ocLJcLWWNEyS+rkDg+0UetzoaABmkMA0pSRKfPnv88eNH341YjiEoiuVRmqWb7aauAewaaZZxDCsKKgXpNCt4UUA03O52RVmZe5NlEAb40cMHpmPzgsjQEFIQEawX+IrarooKUTjwN2/fXTKIZ1gWMTQA9IMH53nidwwVMULguZLIrcztcuP94PNnk35ndj9vtwwviBaztWaoRZF0ekRLl6o677blLA3rusnTdDIeyjJPwUagufnMqurKtUOeFcbTkW25SZoQJOR4mqYbjgeAIDXN2O9WNSAwAAhCjqEFlkn9BgIMSQIwdI35IEogTCVBWC43FORsP6SQ0gAMCZqBqKVzucgACnm+NT0YW46VpAUgac+PNUOommSx3EECHRwc3M7ualyGcWLuHc/z+8MhAZqtueZYBvGsYqgcK/S7ffXk2PXt71996LQ7rhvmRS1IDMvQBJnEkXcx2z14cHJwNBJEkeGYnWlTiBZknoLZzfX38G62H467fuHcfnp7OD4O7QwTzaDTe3A69iMlj7OmLMMsifJos9mDpjk+nf71v/oXy51j7VcKq3iuI3PiYrP88ssfhmFAkGjYHyuiGAVpkmQdQ2OgXlVViXGSpF7gMhwNCCzyHEcTFNG0VLEtirPbuxAh1w9Z3q2biufEyWTkurbnBQejQaelApyfHh/JvHJ9dUsz0tPREQmItt5SFZmC0Pe9+8UqzvIachyuSJgZrTZJ05brxnEImtq2HJFHRVVc3c8ommkAMV+sWYYlEAxTIiuB424BaaqaaplmnmXjUXfa+4HlPQAUrKsc4FqWULd33gDMMPThZLLebhCiGA5iXBVZFkdxU5e8IBI0UlV10G7lWYwJoqirJ49OsjjamdZsYbOiuHMsNo39wHNsv6rwcDxQdFEoq35H85z97P6G4Viaht127/5+HngWyKKqxrosOTvLNu08K3RZSuLQDgJIUY4f6ara0uVXr15vVjuCII/PTliWaxosS0qDQ4KEHIP6gz4J6Zubq6oqB4P+fLGBFF1i6AchoqnT47PhaEhusS4rZENAiT49PaxALQoiw7CapsgKt7dNXOA8bzwvSAiUZkldlyRJlHXD8WJZ1aqqAIAt12ZZhmfFuiIenz/N0zdhmB4fn7STBDFIZOiXL78TeCHJcgxwp6PxyheGql99uvLD0PU8gecG/W67pXd1lGfZemeTJMVx/HQyElg69D1N1dO0mM1nBIF1VVckOSvKg8mUgwg3gGVZgReOD4/v7+4sy61qjCkijpPxaJgnbpHljutwDHt8dIhJUhSQ55m8wJZl2ukaIscBQEVR2O/1kiQFJBnFGYvYrMz/8O3rjtHiJcXyfa5govdXvbZBNPV+56iy7CZpjTFiGJKkkcCqmqxKytHhpGqAwDFFlmdlIYvKH1nG1XbtOG4cxIhmem390YOT//CP/0DRaG9u6rLs9FpJnp49PGdoJorcpAiH07Zu1PudLSvKQJPjxL+5v86zXFNbgqi0Wi3Hd9I0jcOYRcxgOLq4vB1PDrI0ydMU0ayqaZohyyKfJtFqs7qZLVRJLvNa11qBH9Vl88Vnn9/df/zw6V2URKqk2LaXZqkstifT6R++/vbo8PTZswOWe58lUbfTgYjM8sC2bVyRcVRyPIebKkpTRdYVRXR9j6QGn33+4jf/9NvFcpumGcOi4WDU67cJkpgeTzv9blORFMVpqrzbrW7urrfbbVXWo9H4aDpFEFIQXt3cvHjxRJbYzWrFMbyiaTVRd3khcHyORUkSCwJLEpTIiyRFHx2eUnT55mY9HQ8t0/aDQNPEwE8Ign/8+Et7N8cElmQhiBOOF6sSUxTr2JG5t1uGgRjK3HtlgQWRT9PYj+w0zzrtAUlx/bZKgIZjWNyUqiIkaSbrEsdB303qkgijqNvtjke9l2/ehFFYV7gsAM/LJGlXddqUOccIgqRJBt9UWVPUHMdiwmMYxHKSJGvddutw2p/dX9cYC4q0vJtruqFosqyToqgpohDF1u9+98++G5OAiqMTjpc7XWOxnCMGLdbrrKgePTguqyyO02ePn6ty6+Ly4qtvv/2bf/3XVOT++Mc/zvJou9szPIsp0k88TaVQTGcl+Prlu+Pjg7queI4TZWmx2EiiBJHQFDkU1Q7LaJ5nbgrHNMMGExjgT+zVo4fnPV5brs1ux6BjNl5nnMQbqhS4zpvXXxutzrDbgpg+OR5xCGmKWhRFXWNN1jfblSJKv/jZz2c390kU3M/vwiyqSTJ0fZFn8qwEJFE1GCJWEFm6BlQFTZ5neI5tUJaknMAZmpKk/ng8ef74MaTIKLWzPJyOR3lG2GHIUOj+8vKzz562Na5pyiQMNuvlwWRIM5ymGev1muW5vMyKNCxxMb++lVm51+0giNwgfPz0qeuGy+VqfHAYZgVDUCdH544b51kmqUrT4Hartd7ul2uTgaQkSsNht8HN3truzVWfIlWJK8uirLKqyhEl6qL4+OyMoOjVap3nSYWbwXAoCWzk+4AkoyjyXVvkaM+1bdsJgpggMMdzDUGpRtuPMsRDjHGeJKIo7k2zLOrNxmUYWtWEwI39wBcl1JD1erXfrDcAYy9KukaLYZjtdkdDpqQABakXz57OZovN3lXbXY5hiqJUVTVJElmWB5OhyIskrgfDPg0ZkYFR6FZlpSoyJAmSpPMqJwlYYRwnkS6p4+GwzNKyqdpdbefsum1p1O9udvskjjgWVWTV7XZ5TqgaTIAyL3KMm8APW4YuSCJNw/Fo1G53AW4OxuP1ctXUzfnpKS+wmi5xAk2SxHq1oVmm22t7ftjgBnENzTGuuT05nMZxvliuMAkIgiQBOBwP67KUGJZhOd3Q4yiIk4SiaIQYTFDOysvzrK5qXdf2tk2SYG/t+sMepAgG0aLIDUd9DBqe5wmS8sOwk+VVXlp7x/NCfsDvrJ1l+0mU+X4gKU2SxxzLy5KcxunJ0bjdVsM4MfdWq6VzLLffmRRJIpbRZINkobm3CZrzkoBnmDirljubQqjV7cmKCElAU7BpcBzHisTTCCmisFzt3n26/Itf/pIA+Orm+tGjR67tcqzIMFwU7NI0+Ktf/TzKYnuzb2qyAk1mha5j8ayoadresZdru6caRwfTuEg8z0nTytCMPI+zLEuSrNXSHcepiqpuMMuRPMf/q3/11y1DvvjwHmM1DKPDo2kDqs1qQUOi3elc3dy7ftjSuwRJ+o476PX29sr3LUiRNIXWi3XbMFwvrnL3/dtPlpMeHZIXlx8EAW1WjioJkR/WuCoLMklKWVQ0VSnKpK7z1dYcTY6m0+l//I//qdcd/MnPfjabzR3Xoyjqiy8+w7jabMyWpqmHXRrSaVrPZ3OKKGmKKqumwSQABI3QfrfuteQnJ+O8jj9evqFp2XJ2WVP7XrDbbA8OJookzmYhpGjTtOMwRAwriGxZJwQG5t5J80yiqTTNBJ5r9dt56ZZVcnw83e/dMqsOJ9MGVGEYVXUdJ42iKFVd8TybEQXL0HleRGGGWAY3eNDryKKQhj6LyLzCZV0QZNky5LwIFBlBihuM1MnkyPecZ83Jk0en5i4GJMXy3NnDR+vNfjqZ5Fm+2q5cy6yqClT1eNwfMgMMiJahG7pmWrtP1++S0OcEEcSFrIq9XgfSFCbwlXtHEiwkQRgkR4dDy3Ivb2btVkeQJIpmHz8Zy/LKc+P7uztVU1iOW85nFAF/8dM/WW/2uiwysFdWdVECjj78dD2PykiWuJubpWUGQZLmZfXq7eXZ2UkdF3npSqLeOZxgIuNYDn75g5+61gLR1Ha1J3ARp4miKbP12nY8XddrAo/aExqTIsOybEvRpIwXqrLab3cEASmSZnla4rknj47THBeQnh4crXe7nem8fvNuPluEgS8KrKq33DCyHdeQ5aYGRVnrrMqLIicI+/nScxNCUJEYFqGXlcXQ6PUGhsgiAYFRX8UYIKdyQGbud7udJ3L0uN1pQEfgONu2eU7cmebW2miKtpndnpycDUf92/myKPI4ifOm9D2/5iuGpVt6e7Nf0TR5PB2msd9uqXlWCKL0/NnT2Wx+f3drm1aVxYFtPXzwuKwBgUiBo0Pft2z73ce3B0ej0PeSMDg+Pl6tVoiGWZTM7+5lRR6O+tvd7O13ryRJ6bY75n6rSLIbRUa7hXFze7+AECVpGYSJKPJZUZdZrhdxq2c4jlM1dc/oJUni+iFBwqYBddmUWanKim25y/Wu39FxUwMAWI59NBp3223XcxAPW0bLC6PD4ykN6d3O6vb6LMsjSOmaAgCeTkaTyeTq9hpSBIHJ0DXbnR6DKKeuwzgtyopj6L5u+FG02W0ZlhU5LvYDnmGklp4UWVWXdZppPQMQkBc5VkBZEi+d7cFIevHi+as3r0SexxgTBFFU5dHBEYHB3f3MsXadVmexWm73VLtr8DxX1MVms5VTPo7j77//frPe0gzTVBUBAYv4N68veVE4HE55lk6zWNcFRVEhpOIwuLq53W93uqbhyDfdnW4YvCRwgHQ81w+Cfn/oB0Ho+Qxij8bTJEvrumrq8umzF37gLperpgYUCXq9TlWXmaLVFfDjcr7aW25AIkR7ROiHmqJpmirJkljxcRx/+vSpKPJ+r0dCiuFIiq6TNEjzKMsigiAwAVaLFUGSuq4BqkEcn2QlTXP9cZvlWIwx0WCGYytMZFne0HWSZTzGdlGFcQpp+ubmRhKFMk/W89sXz58JAn97f2/owmq94AS0t2xc1dNxL0jj0bCdp0ATtaPDAwJQ9/M5RTJBEAVRmGe1prR0TQsDz3UDCGGe5xiDwaCTpfnl9f14PNJ1/vuX39zfzXhewhi8+/Dx5PhwPJ7a7r4zMP70z358fXN3dHDY6fSs/Zakmp3pSLJGUsxmc0OSDAWRbfuDfo+iiE5HJyC2tpblhJ4TCrzSbrVfv3snCMLR4aTdavW7rc1ue319v16v3r+9/t/9q3/xox/8LInCNIkliTdaOgGIfq/74cP7JE1N23n//r5umiTJiyyVROmPbu04SQRE7TYrioGyxrMwDwKLxKXnWqLcogiKwE2rpXMcd3M3K/MySco4ziGEFC4bUNuWB2nOD6MwCnemzfIMw7Fh4v/7//wfzo+OWB4pmpSk+cHhiOOY+XyTFVGFxbJqfC9K0xLRTBSndVVNJ2NBYhkGApzd3s7rotJbHVZiaJ4my2K7WwgCRFDgeTQY9JPcm63uDoYnmiLhp5TneuZ+L8raX/3qT/Ms/fDpwrJM2wkOpqNOVwGgAYAWRTnwvaosiiIvqSZOiyT1GJ7Ky9wPHIZGoiSGnqmwBC7jsi4/XS/63XFfECCEWV61Wsani09pUhKANu2gqBtJzMqyqCsw6PeePDr9+uvf85LQ4CKMAtwAjhO1VisMndHglEXewRQGYSjLsijwgsh5nj/sT5M0JKk6SSKoq6K5LiVZ6HefzWa3GtWikORE2d1yvti6LE/HYXEwmDw4PPTLEFAEJKgySZ89frJc74Ikury5WN7fDfq9dnfQ7vYR3U+ihwgSSZKcnx7dz+4VSeiPRvbL16IgQcSWaUGTgGcZCjHbvekHQQUw4gmeg111IAn8aNQ7PBwWRc6QpO3Y795/JJuG4bkrZ0bTHK7wFu8G40GYZq4dQDrebNeqpo7Hk6rGPMfqhhpnie/4iKIKACBBcxTNsEzbUJPIr9K0JponDw4d1xuODperdRwHB4eHeVHu9zsaCra1vbu/kWXlYDrNq7wGwHZDjlMpSjBdr2rqOE2Hw1FRVqBpJElOomS1XN3e3JdVIwii7/ksomVBJEGznN9BCHlRWG92DMt2+l3fc1VFL6qKJFBepnldkkV6cXWZRCmNUKelT4ft0HF5SBwO24iiP93e1VltKBrDsEkWTyZD2/Mbiu4arQ8XH3v9btvQ0jxRFMnyHALU3U5PEDiAS1wX2/U88NyyrOuyKMvMdgMKUookU4j/dHEtsqyulCSksrAEGE8GffXBA1VRduauIcBitRYY/vrufjyZHh0eEhjcXF8PR/3RsCuJHMsiTZGCMCrKsqqrOPbLsogzn8BEmgWyxCdRJLAIIcL3XZFji7yx3HixtmgMBV7cbs28ShRZrCq8Xu+avNYUNU5T13M3m62uazSCnCCUDbb9sKwLSMO0aKq85gRGVeVOux1GkR/GgiTXRRVEcZRGiKGn48l+vycpIs9ymqYlRZwv5pIiSYr86fKmaXB/2O9yvG2Ziiwhhjs6OTD3+/3e5hCNSEgjxAmCH4SYbIq68AOvaTDAhCrrYZja/nLYHwgs124bfuD3Bz0AgChINIQ3N9e+7+d5phtaWVRlWR6dntzeL6uqlkR5Mhg8fXgqsvx2b4Zh5nv+Ul9udmteEkFZRknUamllUVqWTdGA4RlEYgrSq+3648WndqdzejgpizwtsGG0EEXbtvXq5WtRkltGt67L1Wq5Xu1YxAZRWOGGFfn/4X/8HxVJbhndsmwIEppOOBzksiwMx4Obu0tVVU6Px6vlTBD4ycF0vd7ubbera74X8bxU1c3vv/tY5k2v29M1SUjCPPZ915/PtpKsa0Zrubzrdvue50kilyb+bB4BQDw4P5+MDrM8a+pqNBi8ffN6E0UUhLKCDUO7vvrou06/05Y4uM4qhmEFTTBty/EClpUG/W5/0F8uVuv9UtEl09tHrvPgYIxoluHqNPNoWDZ1alvxcrHd7fZHx8fjycAw9CxLJFls6W2Z58MgJPut3d607VCUREVlHd8lScraO74fCbxIkMS79+9PTqbHRwO1pbx5+9b3o6PD4/V6WxbY9x1N4/tD1XHdMiI5nqY5yItskAQ5iXo9A0EK0hRiUFVgjkOfLi7TNM9q6vzJD0NnvV2tbdOlSMZxg5/8pPfVH35NI/L580fbranpKiBzlhWHwzHP8zdXt0mcr9drmqYhBCSgPD/OmvSAE5oqLwsQR9lsthQ4lqaRKMoCIz149JCCJMtxiAVff/2bLMNZVks82pqrnQU3q/3Zydn3r9+oqqC32w2gV5ub+XxVV/jpo2fdbjdVBFBViqBcXlyQVWPtrZvAe/DwrN8dh1Fk2ybH0ZeXN8T/9G//nx+vrilE3N3fKYJwdv7g0eMv//Z/+9uXL7/2wsjoaeN+T+EEogE1TTiBdTQYxkHy4Oxsud7N5ouOoQ26fZZly7LiOLbbGWz2lmnZSRrGSUw0hCwKmCLfXl6Ahmoq4PvhqD+SRKEmSlClQRDkoGEZSdGU8+m00+oGsUcBYrlZ8xwTBRHLcVmS7ff7R08fJHHx8cMVSdYczxlGWxKUoq7D0M/yyFAVmqIEQSyqyg8iQ1YkWbYil+WEJCiX61W/1cJ1XeNys1kxPMtz7KPzR2mRB2HS6w9CP8yzamvu9vvVydEBxzJJmlVNpcpqGhUESamGliSRbuiKojAQ7ve7JAkpgl6t97/7w1fr3epgMpBkOUlLXdVYlg3CKPJ9XFeSrsVpOuz1IWIvr6/LuphMD1nI/MN/+ce8KgWeJ2qc+IEsiwxNQwoahmFZq+NRlxH1i5u5KAphHKdZ/kfgPUwiApC9TjsIvafPngVhlKSZ5wRRFPuuOxz2O52W5zsEwATGXpC4QUgRGOC61++zvEDRzOXVbafdETka41qVledPn7RbxmyxoBmaIinbticHh9++fBVFcUdVGoIABFmkqdEysjIejwdl0Wy21vXVDctxaZbKsnBwcHhze+f57qA/kHm+KuumxhzH0YiMk1BT9QZTd4v1xaeLIkmSNMUAsBxqmipL8zBJFUka9dt1U3telBVVlmWGrqmaUuRFXWOKInmBTaOU53hB4hRFzPMiTXOO4yFBddothuOqpvJ8P00K3dBW6wWoQJrlXugACvT7/aKoRF7RZAnghmVZURCLNBZlkWXpsiwJgr28vIrioG4wJknT3PMC6wee7ThZVmBMdNuDw8MTy7HXq1mv3WYZpqkbRKOizA8PJpquJXG63W62+z2g6CgMH50/aHVar969gxTLc8hQ1cBz1qs1IClOENM0URVBVRTH8V3XpSggiKyqKQxiojiiEdkArMpKkTav3ly0DCPNkqquFUViGcizQp4XeUHohoZBFQTh3f1st3cbTFRl/uz50zCOsySCJHVyfCyIYp7njx897LR0z7d9f88ylO+HoqTf3KzWm/2f/dnPB+MhAM2//9u/y7O8wURVlRzDNQ0BSDweDULPTbOkaZoP1+uD48PTw2FepGEQa4qa5XFTNwii7WYzGPSPj04Xq81suZhOD25vbz9++KRp+tHRhACN74eIRiSBSZIkSGI46KZJ4nqh7XqqosmSdHw81TuDv//P/8CxgOMZGvH23s6y1PYCAhBFkdVVk6UZotmDw7HRkrfbzWQ0UkS1aXCvN3j77vW3375UNc1oadPxEBJ1jSvLDt69vx4Muoqqv377ATe43TJabVEUOUGUREGcLxeAoKoSlGWFENY0gaaqogEkJRqSeDe7dl2vrMnBpM2LTJnWDEsJvAIJJHBM08CipCRFOZwcBJ65WMybGvd6fdt2eI537O39Zj2ZjhgEBZ5nEPfNN9+fPzrleJpl+MBNHcuDkOq21fdvP9h+cnR2bKjcam9Cku/32sv53XTQv55fHhwcdtTWzvYFWe+2lSAO33+8ssx9UZTDfo9h4d3txnfDYb9bV/nR4UjTlChtOAG9e/MhyYrhsI9YCiIQ+1ldNO/efuI4SdIkURHCMH38+GFZJrc3syRNT48PoB9li9WuqitJVmvQ3MyuV4u5a/uqqrd6fZonEUKtbme3Nu2dl9W560f7tfnh48XT5589OH+scvx6uyirWlW1qqzC4LLCRFaVlu9lWSTQLM8Zn26umgY/OD1iKNoPwmePHiuynGQxrvOsKPaBdzg6CkKvp2okZKKCrArAMRIJMITUT3/8g/v7eZwFva42u1smcdjt9drddl1VqiLsHQcAXDb44vpekaXpkHG8YL3ddj/vbjY707Na7bbtxA1BQo7hGUZSRNEwIISea3/1/cvJZKyrxh9+++uz81Mnjt9dvxMZ5vb6hkWM5bjdXrfJa1XXB6MBS6OqkmiGcT1vMb8PfbtpMMuJFBIcP9RbHZph9rZDELTrLjDR0DQa9QcCw3ihT5AgSoLI2p6eHtAI8QJfZvmPv/z8frHkOcFQlMX9LcsiCBFk2F67b7v+zcpEbDLo9RtcxXV8NJ0qcmc5W572+zSkyrI8PTvhOHG7dcMw/XR5pWk6L8kv332QBZHjGEngOoYu8LLjx37gP3n4gOPZIExVTTw9Olot50eTU00RYj+KvX2ZR2mWf/vqe1mSCYANozXqjVfrzeMHT8I4yOuyLPMkShDF3N/OyqLM0koUJY7nEaKjKLi5uu90Rzyr3N5cF1maZ+nnn39O16W98xbLxV//1b9cLtZXlxdRFMdBjEEjSnyeV2mSYoAhhcIo3VuOLPEIIQxISNFZXvpBgBtAktBxPDaEiiRTNKzKarPeY0yIolyVWNR4iOhvX71yvaBlGIosR1HKsTKu6ywvGJbv9ltnx8ehH1ZVDWkIMBGF4f38Hldl3VS8wLcNrcBA0MQwidbrNQbk5KBX15WuGkWOqzJI05KiSNfdVkV1ejIlMM6LiiRBU9eiKG42a5LEkiQTRD0a9TZ7W9e089OjvbnrtQxRUqIwsG2LhrSkan4QmXurbsowtHnu9GA6bnDte34cFVUZIpRBCEADqrpiNIaky+loaDm+7YZ5UVIUgoBoIA6CsKqaosziNLEtO4yzuiHSouq2O9vN3va8Ua+t6Xqrrf/sJz96+ep7x16dHw5Aw0NSZWi0Wjmet2U4odvrcTz7+9/+5tmjx//iL/7yP//nfzL3tq5LuioHccqIvOXsA8+naeaHn714/tmXaZk2dS5papLGuq7tdzliWQKA4WhCEHi9W11f39zP16enZ19+8YXvZU2Ni7zu9joM4osyRxR9dXsLEW1ZFk1RrMCzLGJYOq+Kdx8/HOexoYlJnKmy0e61W209jiLN9AmCxrixbRtC2DbamiYrCs9zPIIEborRYCjL3MFkbJoeICFFw+vb2+3WIkmaZuggzqEVpHlJI0RDFMYpRJAAdBqbrXP9s+df3s1mcRxCkpBlpioTAXG2Z4ehwx0cg4pCFMvxTORF5tbtdvt5hss8YiHH03yaljRCRFV8fPO1ZTurzR5R1KePHxHD8Jw8nQ4fn4lFWTIIAQBGg3HxtCZpLIlCFOZRmLRbGglhFEdffvEFy0uqJvveDtCUKLU6uhwG+1634wZO4PgyLXIQNXX2h999DKL8L3/1p2/ffPXmzfUOIJahJV4+nBzmWUgRgqEZe8vOAX1wdPwBfzo9Pjad7Zv39w+fncqavLzbs5IoS+Jg1F7t1wBSW2slMHQQ+Yqsb9Y74v/9//q/52nc1vSizOfr+c61YIVH/QMr9J3EQzSWBFFXDYGRrm7v3rx9TRMkQsyTJw8m0zEiGQqTHy/eJ0lydHyM67xt6CUG7y9uwjgsq6JIC1zhOE8lTet1Wr7nQpLsqOrZ6TFBkn7gZllOM3RX79zdzxmaHk4nb96/aTApsIokCgBUnutUVVnjWhToPC+//+4TIND0aJwkUa/diZLifr48f/hgMJxut0tV4oukzIqi1W41VbWY3fMcLSlyWYM8TzEGmMTtbi8JE1URory8vb5+cHh8cXFxcHRsRdm//Xf/vcxyJGh4jmMF7vTkCFKkqmuI4z3LSVI/L4soShCkkzCK4nhvWr/61V8DAC+uLqLUi9PIUA1EMYLIFxhzEHEMk6ZJlMSKKndaraauBZ7Ni+zDpxuMCaPdAgSQOK6uS4oApu3atvfkwSOBF99ffzRklajKMPZZme8NeklaxkFM07CtymfnD95fXkZRKnDc7c2d6fgkiSzbjWNflfjD0bCuS1HkKZrNiopBVFvXEIPCMCnrOq+KvbWtmkRiObIhKRIKknR69vjN2/cUgfuDdrvdoUiuKAuMS0WSsiy9urt99+6y3e2OJ8O6LLM031t7XpQVWdputrgGLz770vXDf/vv/l1TN6LI9br65589tUzn1ZsP//qv/zIMg//y669My2NZgWEgw9GWadV13R8M0jThOaEqCkgTVVWRBEFRNGJolqHTNKcICAjMc6ym6QCQi/kiL/IXz59ODyZhFEKKNjRtudlBxB4dTC1nG8ep4ziyyNV1ASlEEjgOAxKiKEtr0DQloCm6bhqKhFfXt92W1utpK3tNkPBofOx5yauX70gKyCJ3fHREUlSaZpvdjgAkSZHb7W407oGmMi0vz8qyLEfjUbdt+L7j+U6/3wnCyHWjp4+faAr3j//l1weHpxyH1ps9Q7PL5bLdbvth7LgehEgQmLpKBV6QREVV5SxP0zRpGuw5PgZYVtThoMuxKIxT0/RNy97tbZZjBBYpkqgaxt3d/c5ya0wcHkzyvHS9sCiLlqballkTpCoK/WHnv/7XfzXsaCRNr3cmTRIfP30cDHp5lodRvt3tvTDodjuyJO33dkdTHz06Wy73WVr2+prrOYKkQoSaunnz9r1mdE4nI0CUWssoivLNx+85jqUJdnG3bnd6kCHKotru9r4XhUE2X8w7He3seCpLGuJY1/MITAwH7bdvP6zXO0GSSAomYdBqGQ0o8zKrCowByTJMVZUczx8cHgS+zzD802dnosje3s0cyzs8PGqaCiF4dzfjGE7TVE3TwsC3LJPETRwHkqTVGMZJen17G8URw3CIZgdDo6lwjcH9bMZC7nY+p2hakYQyz6aTDgGJfn/6s5/99O/+7n++u109eHDY6gg8C2mKvb/bma7fbrVZGrM8lWSF5xWIRsenJ0Ve/sPf//OzZ8+Lonrw4Hy7XZj73X5vNTW5tyzP9xGDDg8OHj86zxP/fr4RZfXgaHp6ciTxdFamRrvz4f2174btls5wHEPDpiwUWUqi2HHMwcGUZSVI1G/fvnn35iPNMDTN6KpO0ZiiqPefbl+8eHZ21P304bVl5w8fP3NdFxPkaDrUVDFNU0MzNltTlDWOI15/95KgwMvXb6qaNLpKEPmGNkQUDZoiSQpelMo6pSClq0pRxFEU+24Krf1dnlWawN5ef2QFVlUllVeH/RHYkQohm7uVrmpxEkuC+qMf/VBV5Sqv1uvNH03OR6fHgRtIsiJJCssx5t46OBwijn//6aLOS07gB/3Jer7gJfZP/vQn+73lh25eVjTLzxc7hqW35joKE1UWrcWuIak717KjQOZ5DKi8rpI85Rh2u7PqqtRbRhyXFIFPzw4JAqmK8W6zu7QueqPeX/3qT3v9wzSvPMe6u739+c9/rshG0dREUy9md28v3v/sJz+7vbz2o0DVdIZns3pzPJ4Gnl2RQJCly9t7y/VEy6oA/NUv/3y32WLQuJ7rBvHvv3354vGTx08OCJraW67lhuZ+3zT48OBwb21Iojk8mGap/+XnP+y11X/6/a/LMgFV3m23SYbGJNVtdxH8Y4Evrat6PJ76ruO5Fi9IgmhkWRYnuSgyi/VC06ThoM+riiAFl3c3mi4hmmIgZTpWUqbH58eSrCThnqLQbD6HuL+Y3Qm8wCJmMhgErjuZTGzH1VWJhJSuKI5lJkVOV5hHhOvZkijwLN3tHYmKdnN7Y9n7NM/8ONgWDgXoTquLyfLy0zVNwzgMzJ0Z+kHdEEEYVE0lifJitiqqUteNKIy3m50o8J12G4Nmb9pffvn5qD+wbYsk86+//kMQxhRkUtNL05gkiTBIsrz8X//u33/+4umjhw+W622/N0jSZL/fqYqCMSYB+OmPf3xyfPzP//TPrmdRJIkbLPBcnmcEDVVZZhEzHA3KstjtTAyI/qBvGHpVlS9fvtY0tdft5mXWbqtN09j25ttvX1IU7A96nu9KPNvSpNv7+zhKBEXJynJt73DRqKKC64ZjOIEX6wosl1u5pSZ5fr+4l2XlybOHV5/u8xy7rq+qEkVhnoVRnAdR0esPDUOjCJBnDSlDmmEYBvU63eVyfXu/29shYlCe5GmaZVkkKCoBadfzdnsz9OO6KuMkRSwHaZQm2fHxse/aZV4IgtRut1brRRR7PMcJIgMwuTPNuqnGw36e5Uma7HZ7GrFZVv7RMlDUWBRFXhAfPnwoy+JisfT9CCJENLUssH6SEQ0mAEjSqChFCgBVVt68e2t5QVXXk34/J7Mw8HRD43k2TbPpwQQSxPev34qievbgbL2ef/x0p2raT376o16vW1bVcDRhIeG72zR0MIZnB0d3i1WS5o8fP8WglhVxv98wTCvvGKYV1qB0neB3v3/J82x/0GNZ9o9vFmEYTqfjomr8IKIR8jwPEMC0rDTNeV5UFQUDsNlZVd1Uef4nv/jZbDZrtdTRcLhd7y4vL2WFS7IQN7isCgISgsC+efe2KGtDUc2dPV+ZD84fciw6PjxcrDZRFJ2fT3mRTJKEQVCWDm0rTquOIomAqMtaIiFyAzev787t08n48P5u67g+K5Isq1EUm+UlDekwjLuHwyA0GSR12gxuKmvvNE0DIb3e7FRNL4o0TfOioh4+fkES+OOnC0DRHIdc319tdqvVnIacIOIqSwWWdl3zfn5fAzAYjh89OQWYrPI0TxNF1cqqQAhKshaFCQmgG4aIESVVBQT5/sPNoydK7oQUic8enR6dTJsi51lV4EOWIY8PDxqCyMrIcXLH9RmW22/XVZHGCARREMShrqnjydQLHJbm9ZaOKKTJ/GazOzg6SVMvKzKB5wIvGw8GSQbgfufwspRXVBBhy911J8MKUFvXNFpKEESdVk+TNJbmdUXDdT3sddI47w/6fmhRJAlw01RFXRVxnJRVzLDkx6uPNEJNXZ0dHftxMOj3Tg8Of/+HX3988+bw8OzZ48/evftgGINvvvlGl3hB5vept1x8+stf/CnDiffr9ez7lw8ODhAnIEHOsuT2dlYWuapK49GEpmBdZTd3t6PJkEXsixfPGYjcyC6LmCQKROYKy77bO7//zX958uRJfzT5z//p79+//1jB5t2HDzRGkCIJgMeDAUkBhgG27Sdl/ujRA4jRP//2HyWVJxryyeOzIIySKGqqimiIJE0mkwli0Xq/y5PQtf3d1pJkMQwjy7QZBmV5fHA4yvIAIXx0eFjk3TKJPd9p9boizxqahAFh2iYnCDzHbbZriiRVQweAmE56URTvTQtSsNvupXF09el6OJ3+6Z/8+Ovvvnf9IIqCJs8Ri04nwzRO06wYj6ccJwGKkmUeQhrSSFHlLEseP3tydXWNEPno4aEoqYqs3d3fFkXOQtp0fEltnRweCByz3pm8IDII9Tv9ME4mk6MsLYMgqspMUvhHD0//1//tH26uZyQg6roQRIFh0OMHD5abfUXAn//JT0RZ+Od/+l0UhJPRoCgK23K3m93333335eefvXu7/vDhE0nSht5xfJeCJITcmzfXsswLklDV9R++eSMJgqLIo0GHgvD8/DiJ4yiKRFECTVnn4V/++Y9cz1mvzd3epUga42Y6Hdd1zSGIGJhmoN0x6hprmooYJgpjkqR0w7B9xw8J1w8OD45819/v/cGgS1HkaDhu6vrdp9uiKifjSRS4LUlmaJYi4KDXr0FNk+R8sS7LJo5C20wgQxEk3u32ZycPfvqzL1arFUmRluv1+32pJgARclwtSTKuq7wsFInT1NbOtvMs8Wwzz/M0qQAo+ZrgGTZNojAKfT8oy7Ktt9K0iJK0ZbTiJKpxTtNQ00WCzM/ODzaLTRA4JNm4rm/tfZ7PFIWraipJMpsICEwgxOR5aRitNM3CJO60O4eTYV1XrZYu8ZwkCa7rELgIQ1cUxY6haopQNKSuaYghkyR7+eaDJCkEqF3H73aGhqo4nrPd7iRJYRBDkkzg2xi7nbaRpE1Z+g2ue/0Ryyvr9XJvbtMs0VUNArxcr/I4xHXl+T4t04innz17Ued5GHivXr9erzadTtuybI5jO22BQVRTaX4QrTf2wcGwaprZfNkf9k+ODhbLbRSnTQl6nXZW1g2gAj9I0xwiGhDlQaenqYrMS3HkXl1dl+UkCEIAiCRJ95YZp15/0N3tNqLoiRx/dXMHCLql9375q3/5P/1//5eybob9ru24g+EBQZBxZjqe7bqhJHGeE1Ek8+Lp0/lyBlA16bQ7au/txSXD8Z8+vFdk8Zd/9ieOZ5dFut34iEglWTMIwHJ8mhW+l4VJ/PTZ0/n8fjHffv75i0ePHn+6uGUFWWS5w8lQUQIIYZFnB4ejME7Oz0+Xy6Vtu0laW9ZMlETP8xbz1XazNC2zxODl959+/oufj8fD777+Jk6LX/7ZL1WZj8KIF2XLsX3fr8ucADTL8oCifvDjH263++dPz5umsG3r9uqy125TjFDWcRglmt7iWXa59jHBVDkp8EqD0Wy+7fZUyw77va7WYXwvEAVWU1lAkCcnB0UeOR7x8eNbCOnxqM0iquHk0EsaiCBETMuQB9O+qGuz+U1R5AejcZhEiKYRSRydnEZ+sjG3eZx2u+1+t5clJc+TacqGSYkBSVCkpmuKJu2tfR5XAIPZ/fUPv/js0cNHl9d3pwcHlukcHp7SEHBIUDjhh198ae62SeLTJM7L/IdffvH+w3s/inAUM6zEcpkb5VSBO6zomE671725vTzUdY7li6LMSiyp3aoAG9/q9vqgxjqCkCKuL98t5+vru02YlreL5eXNbbfXZSFHAkhWDSBoyPFZHGSm1Wvpmi5eXXw8mR6RCKZZqBvdusnIpmIZoczCyaj31XeLMIienD9OFiHLwQ+fPuwtd7dz4iAlSURRKM1zCkFWlHe7FcNK1zezKE1GwyFNdS3Lvrj5CrI8DdH79x95nsdNdX9zzfMcgxgIOZqlJRlB2CzXyywrN9u9xAu9jh6Xybs37/MkG42G09Hk+voq8F1FkVhB0IxW11B5lqmbZtA1puMhAcr1Zj+fmw1FSIbR6XSqPGc4WFex7+ftjiyKksgJeVGUdaXKynKx4LkeDSkCV3lZtDutIAhbIt95dHp3P3N8+2//7j98vFy6TiwKQrvT8QMfplVelyTRaIpg7tfzeZplMaTZ3/3+q+PDcb/bqsoy8vzXr75vmjKKc00zqqpUVG6/2zcNIUkKLzC4xiIvlUUVBQkAoKzi0WiKMbndVCxNnZwcVnX51VdfCaKAEFtjMB2PWI6P0qjb1+uy2u52QVwxHF+WBS/wmi47jts0pdGSVZUncJlkReynddHkZf35Z882u12eJj5Rz2ZrDvEY4Ivra892zk+O+m2jrpsoMBHHGC3jcNJ23ej4cJImyeXNNcBQ4LjA9wmKoGhc5FUUJstqpWr69ODANk2SAPvtPs0yBrGWfd0f9Eka3izXy42VlxVPEAeTyXo1f/X2g8ALgGh812Mh5wfx2dnpaGQs5suqwhDRGNc0TVr2/m52axgthqGbujGMbt3Uq60ji+pPvvzy4voOE4woyoIodru9PM8cxzk8PMKAuLi6oBHy4rQmoa536gaPJuM4iFeLlajIk+mQIqi6LBiazgjM8NB3Y57np9NJt9P53e9+LWoyIECSFgLP1ZqYJnkcxrou+0FAENTlxRUj0CwDyyxDqibwwpt3r3f7HaRIkgK62iZqwnbNchzFfvbx4/Xt7TJKCkkGYZx/urjhOJ5GjKGpBIEhw8iSwkNy1O9Yrv2Hb74XRO0Xv/jT+/vbq6tLWVYUUZYFsdszMMY3t9eIppLEO5iO37/7lGXlbusEwT1CNM/xDEIQad9+/0lT2zTErh93Wn2WZfM83qyXmqqlWREEHgnqg3EXIrjclN1uXxCULMviOIVkE/hRnJWdnkqDnCTJ6WjgJQlJ0+vNTpOVtqxRsEWQ9N7c/ejzJ/5u54ZxS++bjpuklmOtV8ulrrU+vPt0cvZoOp10ex0/9C6vrgfDnuuZnhtSEEKSXC9XFEGOJj3E0iQFy6L+dHlrOgGJqyRLNV2nKfbT+5uqqEkIAWzCLFF1zg3SQVefjsVPF58wIAWBvr2fI0b+P/9f/tvrm7f38wUJ6H6nbdvO928+nD84+8Wf/cn1zbVp7RlI0SQhiozAtnxrc3A0LfL09uaqPxh2esrlxevlZn9wPFVlokyS+/vLvIyTrIa02G4ZSeGEqeeYQZrHvcEQPnhwvl7fza/fKAI/6YgUbFMEFhlms9na5r6I8/n92g8DSeEpoqrKguP43/z6WwDw2cPHkARZXquqars7giB0rXc/W3phUjcA16TAi65rB4FPUUScxslyJgiK5Zi73brV0lhWkCWRF8XOcHxxeZ2EycmDZ2ndiJLi+O5qPRt2ewfj8Wa7pEh6bzplXYRx4PuJLkutXrs3GLx7+zH0Y5IgXr19wyFmOJm0u20E4cXH93ma0zzLMux4OtLa+t5yep3OwXDMsyhMQ1XrXl1f98cDhmM327UfeaLIx2n58g+/HXQHQZJmefX24+Ww31usd0mS0JCiaPD5l080Vbu8vg3DSOBESZID37NNp6pKy3dYBnY7A0U1ut1REKRluWtphrndyrJwOB0yCCqyGATxZHq422/n23W71c7SnGgcEoAkzmhGEClmb4c1Xhqq/OWXn+925v3d7dMnzxRV3izvzcDa2jZJ0jRDrNfWartXFOPLH/yQJChzvb6/uzUtRxIFRRFXy3sC1w/PHpquKSly0JR3t1eIZlrtdhAn682mKqp+f7Tb2xeXszDOL2+umxq0u512a0CSzedffJam+e9+97v1xhwP+rblbDa74XBEs0JRNQyv3t6tFYnJipKEtHd/R1Kwwvj9h3eeH0BIEwRBQXx8dIhBFcdxFMZ1WTEMVZTlfutdXt2fnZ+dn5zSNFnmCYSw1x9gDFqtdpykLVVdbzcNKKLUVUTZdtyqBgNWoCgKQjJMEs+PyryO4xxSLIW4nt4+mEwI0NjuVhT4dks2NOXbb14yDH801T5dXgmyZlvexfX9crsdjYa8ILx7dxX4L0VRGE8GZbNXBKGlq6vtnqIhpEgGwi9/+Ozb794GXgghXZXlZrs6PjzSVfUf/mEnihLLcXVZJnFkOa7tBqPxeHpAXV5cbHb7rGjCMC0LTNGUwPGAYkRR3G7XDFPqmpKXtWm5aZJ6btQ0WFNbAJPr9b6uK0GQFU1t9zs//OzFycFg8n5geXEQBm1DUyR+OjnDuFquNpYT/Pkvf4FYZO4sTVLzNPZDO80jWZF3e369cQVJMTQhz6I8JdotpciTwPfTrPz662/+5m/+Zr+30jxVDFnitSCIrm9vSUArsrIzd3GSVRV+8vTJxdWn9XpjqPp2s0uS1PPCIAo0XVE0MY6jKi/COI38JE2y+Xwx6PYaiuY4JEbSo8fPX79+d3g4fvrs8dvX38t6i6SI2+VaUVSGkTmGGA8HP/rhk88/P/2Hf+D3W5OmoCjygKiur+5cN0AsN56M5/NdktQPzh/tdqZtRQyCARGnZdYbGhzNIZI+Pz5umrLT7vIsqyjC9dXN/H7R6XQtyzV3m7xoBuNe3TQQV7a94nie58k0iiUJPTk/EGUpy7KO0SuqfOfs/CBs653ZzU3ix+PxmEGwrqvvvn8d+2GSVZLsa1JL5JW7m9s4qsJg4zhBfzyZHgyKPImyPCtS1wvu7xea2mYhQwBMEth1HY6lT477J8fDKq/nszoMAoQgy4i26YkiVxVxXsRaSyBCAlDNu4+fBFZiBW61XLQ63c1uu3f8X/7Fv0zzeDG/XMzmdUOfnz9QJeHly3dRUnDcLE/C2WJzcnx2Mb+URU6UhOvL69ls+d/+n/6PnZYYaSIrd1x/b+99SPFp0Sgk/fjpI9s0izLf7VZnJ49H49b9zPYcN00LyJAfP72BTVVc383yKKLqGkDEKYogSO1uP8urxWJHATbOioaA8+U2r+q6vnvx9HEQVb/9/Vffv/7wqz//s9HocLkxSYpqGYPrqzteos/ORu1OK4xCjkc1ruI0fPXmpapJpyfnV1d32+0uiYN+r/PgwWPP9TabTZImveHgYDQejKb/9IfocDCKP0YConGTUySeTiZqp5PFlUgrmm68cT9c39yEgR26TtPU50dDTVFODnTLtIMo3i7vdM0QWU7u9KaTg21/K8kcx/OTyahpqiLPJEEiKCpMYr3bAxCWRcXT7KjfJymy3Wm3vB7FsF1OIjDRAKBr2nq1oWm2KKJeuyXxHKKIUb+dG/Kj82MMiMmw1eu24jjNy9rz07zYeK6XZel2szU0iSUokWc6LZWk4c39nKwbXVfwcCByfLttGHp3t90djvoMjeq6VAxd0dtpVrjuniYbXJUH4/7J8Vg31OVitVqvcVPSDAIE+erVq8Vy8/T5Z0+fPaFhY+7MTq+dFVm1qo6OTl3H40V9NJrYvv9ffvPt2ckRx7DzxS4vKmGxtb1A0/TRaBoneZrX3f5Yq3BW1yxCvU67LEuSInab2fPnLyLvPM8yjpNWm4/Tg/GPf/Sjd+/fsxz9xedf/N/+r/+P1SrBBEiyRBKkqq4kSep0ejQUHM8tiqKqmzdv3/E8relaEIYMzbACX1TN2/fXosDsNlsO0RBSs/t7SeBIinj+4rOqqhfzmaHKw0EfWuTt9XW30zk6GkMSuW4gChLHckUBDL1DAMyxtGGokqaFQUAR8PvvXjWA7PdGvv/u++9eJ3H27PnzxWq+s/zt+ztR4FRJ2pvBaDoeTcYMp+CGIglAM8DQ5LqoHT85OT+vyapMfE0V6zLVNGkyGd7d3XMcDwg+S5KUZiRRSfKcpKhut+1YNsfmhkZzCBxMR4jC253FMJwoNXEcF3HZbrcECeVFEYdJWVSSJDYYp2nGIo4iYVmkCCGe57K8BIDIi1LATZ4mprnLUlfT2klRlJhptbvWfhsGEUIUpKCuqmkcIijLAsJNgslC05WaIIq8HI0HmlYcTcdhaG13+8vrW1EQDKNlWa7juJBhXn7/SlNbpbPPs6arc+N+33ddP0z9KDKtUBLkzXrz5Q9esAwdhGnThJa5L7K82++3eB6AioJEXqcMJ8RWcHs/T+NYlTVZVkbTMUHUWRp0+10IsRuk3UGHoZ9fXt+yfOt+uW4PDn/02ee2uby7v/rNr/+Roog8Dff7fRBGQegJvNA0pKyqkiT0ul2CoI6OTixz0+22z07OLj5dfvp0zXAUx6FuR29Kcr9dqbr66PEjFtFvX7/utHuOH6/Wu25bwyTz+u2nnWWJEtfptGaL+1ZH43nh09uL05ODp08ex1k2aLUenD64vLhcLJPF7F5RVVYQCYIM8+LJk7Mktq7uNrygPj2dfPXVN0W5f/75iz//1cP/z3/33yVRPp5O48SfLxJIQMe1Ma5xUwu8hHFdFElRZGVVpXlxe78gSZIRyCSMkzicTMfL1QI35YvPnj9+dPLx4/v5/AaQuNXq1nmxuF/1B8Pff/UtRVGtlnF68nC9WvEcHI8H716++vzRjyiO8gOvyFMakgQA27U9v5sLskQh8sXnX7x+9SrOKkXvyUH1P/z3f/vgZAxANTygh72he3h2eTsXGVnmle1qd/HpqtPt9jqjosj+l//5/ydJCkGWAif2+t3ReAhX9zND1pKsyXJAISJ2FgLLB3Hl+UFR4QpgqSXPZ8tuv29Z5tmDh28/3MRZ+eKzn1r7lesGq83XbuBBmjo/ffDk6eOXb77Liuibl283G5PlmGG3v1yu8qpWVMN0vL3j3s42ZZ6enT0I02w2X/z4B59BEntBsNvcE6DIMn+7x0WWBlbC8zSGF77vYlB//vxHs9s7QOCDgylCFIKMZUUf377+hsTtlr62rDhKEcuHSXZwMO12W5gEvMhNuFGahLIomqYdJaGiSCRFxHGoq2qNq7LIKIJgEctBZrfdnp1pT87PIc1Grl/XdavdohEtcnTgh3XTRFFgGIZhtF69fRPvgjBPSZJ4/ORckqTf/eabMIg9N/K8a0OXjw4PPn/+pKnr9Wo5Gg/rprAtNy9wS9ZlSd3tzKzILT8gANQ1Jc/zMAwPJiNO4h3fNG1XkSWJlyRRmM/vMVnR/KPv379d3N3JPJpOxjsn+Pqb7/705z+XWfbD918lYXxweErKallkuqQGvtc05bDXQzT99u37QW8o81LT1EcHR5ud7UcRQhwkqXZLS1drlkGT8Wg6PfjhD168ffOKJHEUZUkSjfq964vLi48fTo/P+r2Oqkgsy83ms/niVhBZjItWW42WeV01VYmzvKRpkgSYAE2/3+p0NJKCYRinaazpcpHnnXaHodmyrE/ODxgGtmTh5vLT9999R1IUzbC6rqdRuFqs9pYVBEGehTzH7ze70E82m0/j8aAq6iIvjo4Ph6OOJIpRmFh7M4m8O99sdwaDfhsD+uTsfDDsI0jF8fTudvvll08ODw8bEpU1M55SNCSKOD46PDw5HUJEcCy7WqyjwG/xHUWRaMiTJCmJDOS5V99/d315j2uq12uJojibze7ubp+/eDTo9fO8HE8H94uFHwTLxbLbbmmqCqP0jzw+y3ENrrOsDKO0qhpD1RGkF/ezpqklWSZAFUUJTdNV1diR/ejRQ4To3Watqsr5+dn7j594gfPccG9ZsqTyPLq62Smqdnczy+NSYOnReDSf3yqq0mp172ezqszyIkvzAoNGEEVJkGzPa+qAJEhRRB8+bWrAsDyTFpXlZJYT//THP/l48fHDxw8PH55vrc3F5VKTlftlGsSBqLSfv3hWN6QoioAob68/6arykx9/+frt2yxPaQoWeZ4V+XjaL6sgL4sKUBQi/NjPooRoqKLKHGdT14Wk8Jionjx58N2rDwyi06QIoqg76P31X/yZ7bhXt28ZhuqPxvPZ+u7udjganZ2fb7abZtHs9g6DuG6/I/DC1eXV48ePHj08/UQUv/n1b/7m3/xXkvR0MOySEM9Xd4vltqV3lps9ScOvvvn9D3/wg7NH574b/Kz3k//4H/+xqvLRYLAzXcTwdQ3+8R9/q+laFKd5lcdeVF6UtufrqnE4Bf/p7u/6/Z7Isb/65S9v7+8aiWA4ftTvapqCASAIstXtH50efry6fP/h5m//t3//X/83/9X//v/w3/z9f/h7jkMcxxq6UaRlGEWfLj9CyBEkvL2bP3gwzcvcdT1NV7rd9mg4ysrMd1JEc0mYckikWYphScvaB16wXlicIE36si5onz39fLXbfvxw9ezZU9u0SIxffPbw1at3r169OT0Yxon78e3HimhYRpittywjCAIj0EIUp9998/Lp06eff/kjnkNZnh+dns1mG4iB55pfff16ejBtGR2J80FJFFFWFVlTNlWBNb21WM5ODk8hJMsqYxk6DuKD4yEsCV5WpP9/CXe1JUmCIIjVmNHN3M3ZPTyYMhIqq7KyqGFment2JR3t0ZOk/9If6EGve7S76p6epqrqoqSIDGZnN2YGPex/3HO7nU5RwBUMWu5CEZWr6xuaJL58/WmWRoZtHe5tszS5s9EHUTzPUiILnx680A0tjx2GotdGG7+8+VlbzoQtRq7LQCWXWTUa0ARJtlsdUZQ1S8MwPC+ATrc7GHRRCCRR0nO97e0tbaUWVWEbjp+EMAip2irLY4JEt3p7k9V0Mp8d7h6kUXZ9cUriaJKme/sHvbVeWZaeG8yXq+nDIy0QGSC4UYIVRb3R4gVprlkHew2SZWbjCU0REASNRiPT0oqqZHheTFLD0CEQAMscBMo8yUmSrUvN0AvysqBpUBSYJE10bUFRVFmCKAKCWV4TiE67juIMjuMogmV5xjHc/f3EMDSWETvdDk0Rurr87NMXQFlkaXH98BClye3D/XDQR3E6jFZ0V8FI/OTjZRRHnCBSRAKCkGaaDM0SFJMXOcPQ3U5/uVyOJzOe4R4fp1EW1hqtwPVZmrX1VVOSO83Gk6N9EChcywUK2FSdOL66nSwmk3lXaeEkNp/PGrU6RpBxFIdVkUTB+tpQM6ylrh09PUJg1NI0x3ZBEOY4BgAy01rlRfLy5RPHdW9vHgWBBUHo5uY9RpC7B9tx6g6HrTSHTi7PWIFPovy//te/9fvdTrv1/vgMBKAkdRt1yfMj1wvLqkRQmOOYhiLlGcFxjO+FMAiP+r3N7c28Kl3XOzk+uxtrQRAmWSwrNY6igLJ49+4DjCAkRf7ww5sgSFw/JGg6yaDJeFnkOUWRJEFcXl7QBIYg2HyxSrIUJ8mvvt5bTG8tx2k2ewRSHp+e0wT9P/8vv2c4hkARWZF3tgdJUmRxqi9XrW7T9x0/dP/HlXh3NwFux5+/Lv/pN7+OIueXn39pdbtxGO8fHJ2fn69jxGi0vrW1C0F4XhRe4BuGNZ7M5ks1TSuapCAIkqRalqlhmJq6Jjfqv//nXydJMplpru96jj0eT1AUK8syrxKeoyEQ9rwoz8F2py2IbBjGPF9znADGCJqhoyj1g7jd6jA0nZUpThKu5RAIBQLVYNi/uLwsqxIIc8PyA99TGrK6MhaqyglsUeRREHU7bY4m75aLH382rm8eUYxoNVtPDp/r+ur5swMSAx8IdKmaD/dTCKJ6rV6j0TZMo9Nb77Q7s+l8Y2OzKjNdfbR1FUFwRqST1CdwmEBpGIGRAg39tDfooSgcer5IEwUILMIAQguSAQog8IN4pdk4RXC0v7s9wpAqzaJ2pwVDwMnJW7muxGn54cOHr15/s7m+0e20DcMEqnQ07BQ5UJY4CJWuG2AI2en2KgC5ubvh2No3X/7ade2r68vR2gZJQGXZSROYEWgcJWzLzdLk7v5e17RnT5+VZao0pZogFlW2sTna2dk8Pj7WDTuKCgzDCBIf9tdN07y+Xjx/VjdN53E8m840FENtz6cwNEiSQb+ztd7Xl8u10YbtJL2u0myy7R7XHryO/BAEs7og1WuCZrjKk/35bObYjlDj2u2+qussxwRRsJibJElneSXKJCuizTb17T/O+8NNCsc/fjgOgpgsqCJFfDd9+uQFRSs3d7Om0tO0BQghQFH+p//0r/W6dPz+l/n0HgKz25s7WVZ4kXNsVZZEzTUhGN7fP0RQtCjCOEhwkicwLAxjCIb8wCNJQqgxENYGi0qJ5LioOJbRVguSpHw3kEWOIDCSxGWJxwm02ZDLMsuSLE/yOC+yHBjfacgnn34SBv5quZRliWCIdu/g5+/fUiR+dLi/XI1zMC3yzPKDUf9oNp9GviXwhKY6gW+enp4c7A0VpacaXlXASVZ6viOyXFlWcFUUWS5IshO4SqsOYBWF4TRFEwQ1ntxsb49cO8pSiKXpu7vLx6nmmH5/s+2FkWuHFEM2Rbom8QUE4gTIECDNt65urjmO4gU2juwwCBarZZFXvEBjG6N+r/eEF2w34PkaL3CWrbfz6HB/3zKNOAwYiozjIM0Cw3Xbra5umRiGFkUWhTEEVFVZwmQpyLWiLO/vJ2KjZjpOFscQDCVR3O7MksfBAAA110lEQVR2YRjpt/unpycIUQoCq2keyZFcIt2c30wWquf7NE0M+mtFWYZBWAGlqi5Yirq7n4wXyxrHshRlWO54PKdp8vzqWqrV+ZokAFCn0zZNneXYJM9XqxVHYzCC+0EUiGFZFAxJAVU+GvQkmRcZ5rOnz+arhUpCpmWgNFlmWR4nQRSCZdkbjla6pdTastT3Pd2yjYfHKUPxne4ABC6ubx5Ega03OiWAbGxuKg3F1K1eb8Cx9BbPZFlEkyjNMHePj5f3twIrKA2ZZ9hvv/8xyYv1rfWszOazaZHHslTnuF6RVZezB5oRdNPZ3th48fQoycu7uwc/iCgSd12vJon9Qf/tm7d5UQIVAABIEudSjTRt4y9/nWMYJNeEuswl62tpWiVZINc5EkOSOCkKIEoi2w/yHICqiiBoudbgef77H35EoeLwYCcMk7s7lSTAXkfRNaOCka8/fdnoNP/t3/+NZ1kSx5bT2VqvO18syzJBIcLQlp4XWqblhfHaYNBuN/6/P/6JYjCcRA3ddb1kc2NXrvE4Si3mc9O2byZzw09ZhlBNbbA2hGA4ipN2p7G5NXJdczFfVhVMUuzeltxuN0WB+3hyYlkWhCAwjFAE2ZBlxzVVVS8LAEXgIIwQBAMRlKAp3wlN04dhWJIEUaCbjXoQR1WZv/r0OYwhIAC26vWr69u9vZ3BWpulieMPx7pmBW7U6TQljjo/PTUcB8XJ//y//uckDGAQ0NVV6EVghUR+omp6nlcNublaTFAMCxxXEHiBE4MoOD3/8Pqzlx1Fvry6wlCMwPEKBI6e7OdxiMHAzuaG47r399dra6N+r50l0Wr+mGcgQbEURcsNvipBjqptb667vl8CAEOzEFjOJ4v5SqtJQq/XA0HANAxD0zvDVmfQGt/PaJL56ae3rusrDaWBQALPq6phux7LcGCF6oa5XKobm6MSyIoircvNLz5vOY47W07CMH39+gucgEEIRFCERJiU48ezMYZTq9WqUedBEOB5xjBNmuFgECEYJApjVXNWmuH7dl7kjue5jvPFF58SBCKJQqfVOf54vb658emnLy8vjr98/Wngud1edz5fKnIzL1OcQG/v7trd9qDfVZez0F0BFZjnMEVSjr76+9/uUQwZDXvz2YTmUBgp5IbE8FyRhQJHl2lq6Ha7uea4ZlGGDE2Pp6oocqIgeq5rGDoKpHmajB9u65LM8dzDeA47iSjNTHOF4dhwff2zV4c0BZ+cPDCMiGBYnPgS363XRNu0Ld1GACCO7NOLJY5CGIY0OEkSG1CXTJKcYMiVuirLwjRMdTWvKwICVY/j2wqElktj2G/TJCVQDM+TBNoEqirPM8d2m81Go64EgR/FwPXl7fsPl41WS6rxLMtgKIaRCKLOxiRFtJQ6jAAIVMBA5YfO1sbg5PTj8fGbL7/8bHdn5/7uNs0iQRRnl6eP49nTJweMwGRlEURxr98nGEvk6R9++s7zvZrYPj07rcpC4Pi0AkAUW8zmUR6zddp1HY7h8rT86cefqgr48vU3UZDiDPni8xee7s7UKQyjuxvbgkR15cZioUtK5+H+JkShl59sHtEv3r59k+Y5TlI4gUMAINUbiR9Po5llmySDEVhZFO54PO90m0p9NH68q4lCWeVB6FEUrhrW43zVqLdvri5HrY7E8mS9adu25ThRnB6fnct8jSaZ6WwJAZWhagSOr60Nl0vj7Oz4X//1dwAMfji5gjE6CtKr+4vd7cMPURwXcaMpDft9yzQZiux1WleRv9I0Zn3jdjxt1OtQVQZhmuQhx7KO5+EYbdteva4M+gPXcRiKTMLo7OQ0yzMcAVhGqIm1NIqnD48AXLECV+aladvPX8h+lL85Pq+JTJqAmJ1CFRX6lSCStrG0dGPv6AAnoKIoMwQo06Ah8aHvzRYzAIba3WanVU/zeLS+BsEQQ8DDpzue49IkmZVFjZd9zzJ1PQxinhdoAr8eT3RN63RbGEk4rl3jOUsnbmfTIq1kqba+PmIZ4uZ2nFdgmETPn+7bri3yZBTGrueSBB7HMUNSO1vbWZ7t7W/wHMdz4snxuWGFOIpYfhRnkGOZkR+2u61Rs5PnmWUGvuuiOApAMEOyhwebWexpujVfqEAejUYDFAYcx3Fdj2VpnsV6/bbAiysj2Fg7yGOg3V6TJP6X98cMzd5O52AFgAh0f38XRaFt2EpdAUrIS7KH2QrHad2wWu0WRaH7B32axE1N86EiDJPACw/3Dv7yt++++uqLu/vrg92tRo1//+anVrsVEDgIoxfnN4Io/tM/fZOGIQSW4/G02azP1RXPYBzV8ZwARJHFo45jtGZrt3djnuNEkYNQuARKEgstw+I4lqbJKEom04ko1aIoevf+ndKu1+WaUCPqCjsYNIs8+uWXj6pqZmnF8/JCdaZzFYLA9eEaiIAcx1lp9NOPP2AokRWVXJPFGteQ6q7jGZppOjZFEyzPr1bWqL8OQmB/rUtz1Nn1dV5kXuCJjVoJFghW5jmQ5dn9/c3+wZO8KHAcni8e2s3mP/3zb9WlFiZxvcFrxgO/IaMV3us2aZ7/4aefG40mAJYIhlO82u93dHUeRhHHCqEbh2HIscXh/kAzvVZbHo2GSqNhmrplGo1Gw7Hdfr8HQrDr+ZujEYahrWbj5vZ6Mp8WaUVSWLMlmVZ0dXPx6rNnNzeX+/sHUFUyDE2zm0kczibTJIsxnKRIot97lsbpyccTACx9n8QxAkJAUZDv7hdsU2x32DRNgyAAAeBgf0dWWvsH+zdXH/0gmUwWNZH7xw9vhJrcavKO7Rdp9dlnr8I0VLUVWFZXp4tur9sd9kabGyWQ5yUDw4U6m4IlSFCk5mqbO8MfvvvFdRyl0Rht9POiyAtgMNyZTMaj4Yau2yxLsSz95t2Pll18++1HimRAELg4vyEIDifEw6PtMPIbirzSFg+zm36//vfvv3ec6PNXwyh0Lce4frzSPa0zaPiWyTAYQVMn17cgAqEAhkM4WE5++7vf8jXm5vqmJjcsyw1DT66LZ2cfbVMnCHo8XdIki8CA61ocIx4c7Id+4Pv2YDDMUh7DEdt2OIFL0oBlmc8//0KsSxQF6SsDQSBdXyBQCUk8ByFIUeVFkXmW0aoL7Yb087dvEz/HUUw3Db7GG5buulFVQZzA+1E4m80oBmd5crV8DOI8DDyKRGAwHw7bQeA6rn9weLRaarPFPLCsOI9tw0vT2HMDQzcxAvQ8683P/+A4Oa2qqkzkulhBgKqtTNvsdOsghEMoASHQcLiOVMB4OvGCCGdxpdtwAy9Stc3RRpYVWVMJw5DneRJnlrNbSRJBoHi8u8NhAEFKACw3djaLPAvCeLzScYLEMHxnay8NwiiKTNe4f3zIs6wCSttyvvrim9v7h+ubx9Fw2OqNxo+P706uDg+f7O0/O/140e50QICYTg1RkilGfHy8OTgYxWmOInCWxDACOZ5Xr8tNRTZMHUGgZ0+fUBRdltU/fvhptDYEqjItSgSBXE0ty0LTl4Hv7+3sXF/fQiCk6VZelhsDJM8yHKIInCUYzPF93w9EqZbkVVFkiiwP+l3HcnEMOdzbTQuIF+QVw03HD+/enkoNWVHkNAsNYxVHBQKjpqGyDJpkoOtbSqNhW4Ysy2mcnBy/wxCoITcgBJMkPs9wnufDOOUlPgtjVdN7/WEUhRzDiByNo4ggCmKtadp+Bcfh5f3d/R1JkS8/+/z92zcXZx/6/Z7A0UVZUCVB0YSqatPZ+PXnX/zhD39cLdTxwwPH0hQptBo9hqFKABEl8ePHY9Owsjy/u5mUOYiReGfQnk6nuwe7T4+ePz7eTx81ud7MC7AqSyJPXNe17VisCQSO1OqtvCTm6oIV5F5/ELpWv9e5vDxHYMy2velq1Wk2mg3l9OxMkGSQoMYL9fDJk5fPnz88jtWV/fz5y8lkvLszGo46f/hvf8RgWBAF07QAEIRgaGNzfXN7w/HMf/z8ZrlcoShuWVc8z6MY9vTZHggVD/fnoZuapv04Ga+trYk8i2DgarUIwxwP4heffBYGEUnS/wNZ1hsSAMGWYWMQKnBMt9M2dL3d2bAsazKdghVQ68nTiUrgTJKUTw6fNRR5Mr4bjbbz/IFmuWZTEQUGBmHP8WCg9ONgPpvZphpEiRvkQAVGliXW+OGwHwX+QtWVTtswtG63a5khAIMbW6N2V4Fg6MPJR45hG0orTLOyyrud7n/7tz81m816s3n88XgwGOIoWOTI9PFRFIR+r7/StNlk9ni3RGEKRzHDtv73/+P/fP78hSjwlqn1W1v7O1uWoUY29Hi37PWHh0f7jmvJNRYGwb/8+cfdvSc4ilVFEfmBKIo3t/eCUPN9ZzAYLleaVJdsy/jb37/rdruO4wduItfFEoQlSamJwvXlA01ypyeXnucqisKxvFQTLMMUazWaoWq12vhxLIrc568/OTm+WMxNUWYdT213lE+/eMYS9Ls375it9SDw+oMBThIHRxRYAWC+MRpsCjxLEEi92fZ9P80TmmV/+uHDRl4VZb6zuedYqutf+UEUhtHV1fXa+sCydcvVbdurC43kcY6TeFroz48O7u4X9/eTIIxpGqlJnOdqcRBQJLa73dV11XW19fXhyfFVrYYlSVpVZbc/zDLwm69eV4D/j+9v+70mVEI8yf343VsUpj578WmRZVman7w/vXnAOz0l8UMYq1I31sZ6kRUQAJQQwHJ8keY///DjfLn81W++OT195zhpkZeu91CTeBhGHccHKkjTjTiLcAyNosr/6ef728fPX7+cL6cYRkJhnqRhmpGddhuqQNdLaAbrdhWOpZIoLHIeqcmsbWlhkhi2sVzO9/eepFkym02VllJC5eXV7XDQj8IQR+GWUm+2Gme3NzTNX1w+UCxuuw5FMBVIgyAWJwkIA7o2J1BQ7DXD0BEErswzcb13cnEWBwkCoSiC1hv1JAldx7u8euAFpz9cR0EsCAICgESSQrt9IMs9137+7FkQuNpqCUKo5VjnVzcMR0dZtBgv9rb2wQpM46Sl1JMkwglcEvl6nVnMl4lb3tzeh0EoK9L940IUagyD347vS6D8p9/81vP80/OzUb9XQRVGYgzLRFFUAUW7pei66rrO7s6WyHEsw2AoVJPqIFjVhKamUjiOfvX1a9txtra378YP6lLf3xSqLMZJEkexIAyDwE/TRJIlzVga6qKrdOe6WYHQp599KgrCx9NTCEJ9L0ji4vzsOkljHMeLApzPF0FUIBiF4rjSUnzfe/rs6Ozs+vjiwvPdek0+7A39IABBCENxnuXypIwiryxSimZBsOClWgWWpmnppuGHoTpfzRf6fKlRJKk0xJpYMwxTadZxFMcxQpLkPC/u3rwLQ0fkua3t7Wa3niRRnmVFFt9cLhqSvLe3zYu1u7v7miwammrZFkHgNEutj9Z4UXAsT5YaFVgZuhr4gcyzpuXdT5cVALAMEccpx7EwDDue01BaZ6cP27sjjKAoivSjaGnZrz5/zZBoqyOLNX4x18sKpARClIQCKII4raDs/fH3y5nxZO8AJ/DtrfU8S8PAPz2/Go9nnusPBl0Cg4u8YGnu1YtngaPFWVmCKMNKOe5LMlKClWnoj+NJU1EwnDjcOfCD4PL2Jvy7vz5c++1vvl5p2vbmpshxv/z4JklSRhQQFFqspp1OB3S80XqfpvF+v6tpapDnoeOBFTBXjSKLFKWhaXqchi+fv3xy9FS3zOVqwZBkjRMpnKUoPAzCxXzW7/cUeQ8ESxzDaJq8vrurgGow6DWbiu+7CAK0u3KSxDCI0QzZbNYVUBI5YbFcGLoKwcVyueJ4+ZOXz7a2Nu7ub3R1pYiyJLJXd3eaoXlB0Ot2ajU5K6qnR0/qspgmqaatSJra3t4gGda2TQzDcJzGcGw2e/BsB0XQXrvDMszD4xhE0M3NEYVjVQWCEDKeTO9ubkRR4igyTzOa4t++PVktDZbnWq0OTQu+G6NwlRfl7d1ds6nYlvnjP35IkvjXv/4VWBaGZlAEg2M4SRECP4j8KM3y509fMlzNdT2Rp5vN1s3dA45SvhuO78ftrvfNb77WjQUt4DVFuri+k2s1QZZWukYn9HKpvX71Gc/ycRwiCBZEeRRlGBqPx9PTi6sXR8/39/azMk2L7G/f/219fbCxOdjd2cYJsAJjN46H6wMozdYHHYrEBKGT55m6Urc2Ny4uzsIgHa2vQ2AJgpllrCzLhiCEZrmnnzxzPZcmqc2twdmJJdakKC3+7c/f3tzc72i2WGP73c0keiRIGkeg+7sZimAMk3W6zaOnTx4ex77vqKouinKr3VmtFrEfaLphWP7Rs6NuZ302Wx4ebgg1zvb0Tqc56nKPD9bu7sbV1bXv5Sgai3y92ZQtR0cReL5abG6uD9c7796/SZNEpLnQSxiSI1kmiEOaYMCiKovKMOL+cNO0HNdNDN3mBC5O4sUyzpICACAEg1iUyrMiz6o8BW+u7wiCms5WKAbu7x8QJIwTGILimmbSNPPwMEVxMk4y2/HSOPIdH0lSF0LQ1XRu2GYQpG/fHiutJgRjX379+vr29s2bn4I4BkCoKiptoYr1GkqhAFjs7e+cnZ96OJLJ0MnpewJG5iuDoDHX9dMoHw7WkKoKXKMuKe/evptri83h2mh9OynSPE+SBI/CzA2qs7vbvKpqHJvkeRw6ZZXXa5KtzyoUWc1u06S0NasE8qRMW00pjoskzJuyvN5XZLmWpKmmawSKghUwflwIHElg8O7OerPTeny8lupyfyCGQTybPRRlHgfe5PpMdwIQAkma9JPIcdw8y4Io9Dz3i1efz+fLuiTHvovXOAwGYKCcPtwZjiXXJLkmYBjiWO7Hi3OBx9Z6bRTDdMvJyxJMYgbHGQIb7u3WpNp0NQdh+HG+UA2/QtAsT7969UrTzAICR5tDCATPzi8ZikmSBMfRLM8EUSgBGEZhnmN4TiBw8t//+pfDg6f/2/7+1dU5CiNija+qNMaQqkgRqNzd28yLzDRVGkhbNTqlSbBKUAIpwcJ17V6va7oxTMQ5XIE4YQex60dDgh6trxdZ7nm27Tg0S4NApShtEIbfvnkbh5GhWTTLIgQMI+j6SPn5zRscp2zXP7u8WRsMIRRqt5ocQxnGvCZKLMs+3E9Wy8V8qdm2x3Jsu9XSVXU51/I8Zzn26ZMn7z+8WRttQACwNuiQJEGRZKfTIjCsKBPb0uGilHiBIbjJdJJmSRhnK80EICyIYwplWs1hmpehbVMsTZCo66Q0RdakWhQlSVwYpg9DUbfdmE+uCHjAieyzg4HEwQAAfjj++ORwv9nu/v3bv0tyYzqd/KS/3RytgyXQanVFSYiiIInt4bAP5NX62ojnZc+xT04vQQi4vLr+j//xd0ABLMePJAwr9cZssSqLAoFQCAA9L+l12K2t5v3kGidR29azNFUNC4ZQR4gHw67AM5IoeIE9mdz3290iSwsQbDaGs9kKwyEUQzmepWl8sZhdX17HQfLkyS7HMa5jTRZqXQ5t28IZdjy78Fy328VEgdfV2ePdQ5pXun7V7fVJmpUAgGZIEEAHvWFRZQyFOIbK8WK/27FsYzqd1cAKgSpNX/KcLNdEBgdvbu48P4jSFISgMIxJGs6y/Ob6lkbJBi9PneBg9wBH8KqsQAghSUZRWvPFoppNW7/+6nB/e/y42tkYOq5lrpa+66dZnmTQZKqNx/P1tW4BoHmRB56XpGmv064rCo5jD48Tz/MEkf/w4azVVD795KXrBbd3N6LAepFnu0vf04sC+PWXX52wFyBYeI49GrSSLH8cz09OLz559uzs48lnr14meUazNMuyhumkOWh56XSht9r1bmfgOUFelGKNb9RqMFg6tvPsYLvEc8+wJLGGYTiKwnmet5RWXZIXDE/SQAVVVVk93E/fffiIEkSr2ZQVVhB4GAQ4jnj75rskSo+O9soSRgmcoXgEQR0rKBvV7sb2Sp8QNM3XGc/JwiRLTbPfH4JVGSdlrztiWOrt23eyLPm2k2Tgp6++2tlb39nZuDg5vb0d15X6FtU0reV8MVYNI83BerfXwygSInCSQHCUwXEIqeiIRlHAnEwBP9F1a55oFQBzPPfk2V6ahkWcmbqNoOjO2o7tmjW+hqwjGHxXQUCcIPO5SlP0V19/eXl1AQKlutRBAE3imKKYKM4cN/rmV5+bprY8XwwHXYFkxg/TGsf1un1RlgxrZTraF69eTe4fEVW34zS3XF8QxU6nzVE8AgBpnk7Hd4okyrJcb4rt+uCvf/lHozMoyxLMCkMzahsSR3DLqXN4KG+u557jtfLs4XHWH3RoRvSCgC6LNMrHjxOKojvtvqx0prMJQeOtVtd1o0Ydarabxl+0AshJBtUWdgGma4OuZbm+7+UE/fg4GQ5G9Wbz+v6KoDDVUPfWd3iG141VFAaubQq1WgXDqe8rDbkoIdu2EAwWRNp1DIGjp9Ppy2et+8f7X96/T/JkYzR6WNir5Wq0uWZapmUYMIblWfYwmQg8ZRsmipJZljYbSuhHlukAAMSxDEETq9VKqcvLhb538KRtu7/8fCKIHAYBbpbs7mwXRcoylKsbDIVmcbhcaPfjSZKk33z5jeHaVQpMlxNDMw53R2EUuq7faNVREJnO/TIpLcupifxar5nEGYJDgsCcnY7LLDfURZG5nQa7sbGlalYQ5N1uz/MCkmCyNCuqMs8q3bB9N9R0G6UIGEcQHLEdd5UYNVnY3d+cTKfzxYqiiMP9g9efvaBp/OTDyWitB5RglZWjQQ8nsPl00us04iBsd3srVe3ITYZhxpMxAkNyTSAJpsZxMAI1W60oiquqkmqNOE2m8/nxx8ssBxhGUFererPBcuzl5TUEI0WSEBiqrmZgVQBF3mrJVzdX+3sH49mjH1imYY7HE5YRK7BSmkqr2cYI1Pbd//D7/3B9fXX3eF+kwObW8GDv4MPJmetFlpNxHJVkYLvZ7XfWvDCCcbzdW7+/vVypKlBlkiz/9MsvN3e3GI512v1Op42jwP3N6a+//iRNCo7hwzjst5VOg5/M5me2ZpiO6zowUBZpIspSpyWUDX4bGiVRXMGVri71ha7qLgQjDVnGuriqmZ7riSLX6jS6bVFUKAhTwCz7eHHjhUkUlzCURll5/PGy0agfHu6JAu86wMn1B1oQEADzPPfocH9lWiyFsjh0O5tVVRaEVRj6lG/N5xMIQlpNBUUggBVqLI9BeFNRDg7XXduaPDx++uJFCeZ382scAmlSPNaWjuPvbm5VZRanQAVAcVpa40dJlm6u78/Or0AY3t/fzdJSDVVDXc1nc0lS/MBUNb33clC0K9N2YAiznIhAULAssjRZWxsoilIBpWPbqqaGYeT70frmpuP5vMBuErhju6qqbW0JAAjgFLQ+7HuOZ7nuw2S2ubmjrky5xrMCjyIwTmA0w7SbXcvxAQClORmCsbubO9M2UQJsdmTUR7774cfdtS1HN2Mvaym166uLrEwvricC1/jnf/5nBIFQAuNrku36QFHGQXgxn718+RTHmcFwvd1tV2U57I8GvdbP797INYnA8TTLZKUTRoE2XRZ52e11RUm2XZtj2CiM3r1/J/I8jqMrVfWDsNvvCZLsuB6KwPf3tyxFSTWuKsqzs+ssB7rtrNPqvPzkeRyGjm39+a/fXl9fHz09CIMcAEIcpkC2AEE49OPVUhV4rqiKJI6fPj2MQv/y8r7VbguN9t7+qN3idX3CsKzAC8NBb7GcLqf24eFeS9m6vZ+SDKc0JHOpUgwrNySgTC4vzp7uH6iafnY/tgzTsFwIQmAI3t1VfMtkGJJXlKqsFouV76k4jASuk8W+LLFxXEh8Y9jr69YqDK3nTw8njzMS49SVyrF4UVZFCXXa9SQMLs/vaJo2NNcL09393eV4GkZhpQP9jRYvkBeXp8PeGvL++Ixl2UG/m1UJhuN+EM/GiwKqNjc3kqwkcGI8mRhLg6JRhMR03TN1I0nj45MbBIJfvfqsqsCG3GZoB0QAmuE4jonjxPLiOMmbjbZjOPPZRG4qJVDCOCrV5UarDaNWu918mNwEUXj/+PDs6BAEQdOyUQDWVgaGo6Zj0RT37u0bhEBZgeJ58uzKEyVutViCIARAeJyUgT1nBS5wKsMwCRK3HddxLU1f1Gs1FAJxGPnx55/vHibb69vnt1dhlKAkvre/e3ZxTlLY+nDjv//pDyzB8BwXpS4jEAxdc/xIWy5YmkQQZLZYCjydlxUvCHmZ9wZdhsEEjlK1EiOJEsogEOAZBkQA17MxHFusFu1WUxSYTqtJUixD0UkSMQ1J1TSaoubLKU7QYAXgMCoI4nS1kgS5tdMsq5gkYN8NLdf57vt/8Cw3HHRwHIcg2LZt0zCACnRdV6xJ+4eHOAI/jsdlVXpBXFQVVRdRLHBs++rukuO4ly+OOu21n378OQhcuVZbLo3+YPQ//f5fcLSYT6cMQRZpQZHwq5eHKATMlovPXj63bfvtu1MviFAUJUjC86PJdBlGgWW562sDpS7O57MyY+Qa77veZLYQJWnQ64Vu5AUJAEJ6Q97aXl+t1GarNZ5MGI4rKuCHH9+xLMMLTqvTgSDk5uaGZYgbXQcBjOebVVUCEBgllWpYMIb/7ve/v398IAlEYMjlQr2+ujFWSwRFeY47fLJvagZDYFmSAFXFy4wTeJubClS52gIssixJ892DZ9/+47gCQ8cpqgpstsoiL/74h38Xa/yTwyd//eu3y8lDWQC246MYDkKgbQUun4Fl9vj+I4LhZZU3G1KZJa1Wq4ozisC/+PKLdx/Pg8g/OFhPst6Ht1cNhWn369OJ9uZPJySCymzie5HrRa7jK3U5iVMYxucLNS/yXq/d67btxJ1O5xzFF3lKExTPcmWenJ59NG1XlGSKpO/uHla6pdQV3ws9J3r16vl8NjYttdNub673bu8vEJRerNQkTxqt5s3d/MXB0XyhljlwuL9nGKppmRTNVBWIISiK4vf3E6nRWs8BhqEroBqO+mVVaMsVzfJxllI0edQ+qNVEmmGq8dgPA4IkNcOU6/V6o/Hw8BDHMQRDHMfxNaEES5Imt/d2MBS8uj6HYeTs8lppNU7OT8RavaGIJE198vLpYqmOH5evX79qNpvL+aIoEk4QkzSZz2dbu5skQZMM02g0/vHt32W5DqHQ3cNdr88f7ew7mvnT23cIhCEgtLM9qjeVMC6zglQazX6/54dOGqcbW1tB4BuqMxwOfW8ZeNHvf/fbx+k0iX0Ehli2Zlnu0cF+EkVlnlMENZ0voyhJkuyrrz73A+uXNz/WlT6KZPPZvChyGAqtRz1OYgiBL68vKYrKkthL8uVqpQJQVgyoID08ePYwntw/zmo1OfAsECjFGvHF66eGaXv2qsOTiqJ8vHlYqTqKIIO1YVFFEFwhEMRQjKWbSkOuiSKEoizPV1m8nM4BGF/f2MmL4uHxkaGZvd2dVr1eE0We5UsAxHFM5NgsT4PQmk0Xy6VH4q4fJKOtfVNXo9MLAMQ8x30Yz0kSaivKzfV4c3Oj0YCiKNT0xWi4BkNAXapfnN9KQ4UT60HkJnEq8KzD0CAAMRt9scZNlwtTd+Mo0nW93+9xHA2AWQ7ki9W4AvJGQ14uF9PH0jKdvADADoJ8+smnvmuZhhalsec/7O8cHn1yRHMMw7Cz2bLZ38in1fh+TJM8VqWL6fjJ4dMg8gkSv7y8UFpNgRfyLDs7PYNRcHt758PxL4cH+zeXD5YdwqCWBhHHsjzHsBT65Oiz4/Or5du3HM/s7qy7lz4via5tvj89QxHUc9zFWNU088Une2GS+IHZbCrXd9fAonIdGyXIdx+PoyDYXFsXJMly/fl8vMPvtju9j6fHmqrjKKmZhrqarQ/7JIkRBKka9v7uDoFgtuNmeaquZikrGpbnz11NtyugAuEy8j0IA47PjjcGm7wsHX3yZDVRP7w/Pr+6lSXZtoy9g13bsCHo7vDJPssxcRZf3F/RFN1tNyGwQhAExbCqgss0s2yDY6hWQ263+wInFHmUlQWO4ziOswRBkUSGFziOExT9u2++lGp8Ehd/++5bBAGVercm4jAEMwzNcZQsixfnV5ahQSBUVTDLMHmalGWVAVUcB3meNWSeFfjLj2e2qjqhDxRFYEeT+0cEABezsWmYDMv/9uvXrVbru7//GUXgp0fPui+HHz++8wIHBYsiy8qy+vc//91y3POL6wqAO522btoYii8Wq+FogCDId//4edBpJ3Fi2z6GIAzHfDj9KMlyp9XZ2FizbFcQJNMwJ7PpsNdTGo3hWtdyzDRNZaXOUIy2UiEA2t3aOXn/rqmIAFDGUcUwtKbN1kajRrN5e3fT6XQoDLq5uanX+K3ROlwhl1f3T58cja9uXd/SNJUkkDjJm0prPl+gGN7t92f3N+ObG6ACcZw8Of7ICPw3v/nm/YcPnuPfPUzzsqorkuMkf/3bHweDjfXRxof3p8uF1mo3RJGL43SwPtzc2b69+Fir15vtrq7qRV76rh959zWR7nb7HI1LDHV8/IBgQHegfP76xc8//VymiW4HOEyKPAsi2GKhwihJkVRWFCQKkyQBRpWmajRD5kW6GBtRWBWxVxXQMjIZhmy1GiCEMAzHsyyJIpujNU2zRIHfGPWOnj5ZrdQsTyugomkqSbLLy2mr3YQR9OPZXfrh+vf/+h/y0Etd/4tPnsVFUAFVTWpESbpYLhv1usDLgiiTFD4a9B4fHpbaCoaB2XSqqSpNs2map1l29/CYZtnWxujrr1+dXpyjJPLFV69oigp8v9Wq+74ny3UABKIoqNclyzTS0LV8P4vy7lo/3y6SIinyBISRKAKb/fbZxXkQ+BxDzef3OEmsbQ7Pz89ny0WWFpPpjCDIzz99yRcJAmY8R1qWqig1GIaVRsMwVkqjjWHUdDypK604zmqywkS5yIkkTUZRIApC4AetZtu2zSzPa/WaH4e2G1IM/vTpk+MPHwWBWmlzEEJYRigLQF2ukmwl1xvdrpDnhWXrEIx88vKb+WJmGEZvMIjjuFGXO73e+cVFFEXXV9cIgjSk2mq1SqoCJYi3J6eDbgeGyprA5kXm+TbP0avlcjabMiwfJ9HCNKswzuJsNZuFUaa02m9+eYuiCM9zmxujXrcLQWgVV7IkAFDVqEtx7J+efuz3R2lkF1kMFAUEliSBLlezD+/e0izfardpgasqrywLDIHqkoQjlFgTF6omicDB/vbOzrprO5Px4urqTuB6o9E2jo2jMIYRpFPvxFmB4ARH48NeBwawCgJWq9XaYAsAwdl0bqkqhuFpmd/dmDlc9YatrtK+vblvKrWaxB6fvONrfJ6AcIXMZwuKIM8/Xm6sbWZl/te//g2p8VwaB+tro4fJJI7SPA0f769gGGh3ult7L354+7Gs8LXRYHNj68fv3vWbTSB3fvurV6Zld9v1w93dvASjwKVIVtVm1SCzLGu5mKeJh0FglkS8wGMYXlWVH3hFEbYUocx5jqPuLy4JnJQbtThO7h7GNY6hcJqkGIbJHC+4vR//7je/qkAIBGHbsrKygjLk7PxB5BgMJUzTWOnWeD7rDzplgcAoKtW47c0NN4z+9CcPglDPjVwnoDCMxmEEhZ8c7EVxqK4W17cPIATBENJqNzMgAqrc9g1REvv9QZxlxsO151qe7nMc0261SILa2dmgKBwCqrxITMuwJmMYQ2kMIwk8jMLr66ud/e2iKGiGAasyD6MkSfvdvizXCRyHkeFS1yiSDp2wKou48AejNU3VJuO7jfUNx7Z0zRZ5bu9gV2Ckx8l0Y2PdMIwoDsaT+ePjWGlIAi8EfpTEsbpaQDCaxJHne2EcrfS8U/VcP3yYLpVWu1PrZElSVuXZ+U2QVEK9UxaFbTuT8dh0A4YhfvvPQpnncVpcXj90221ttdJNJ4hTEEEAmIjC+GE8pxkaRWIIBVfqKs8K03DnCxNDwGZLzpJUlus4SkEAmMcRzvKthliC1WjULsu4XhdBIJek2psPJ6qm1QRxYzRSNYmiGQAASAq3TDtJim6nD4BAvX6QlYVmWCwv3dyOTc1FYCzPytvrexynvvrqS1FklVbj3fvT//e//217a9hptX/+5e1g0BsMO4a+MjUDAgBBEBGCtAPyu29//vVvfotiuO+vlIYyGqzBeLW21k7i8Ltv/06S2HA0nC9Ux/VohpmvtBKCvvn69dsf7FanJYo8VEGL2bTX74kcuVJXv7z50G136iz3+3/5Ks5jS7fxGvblZ5/cPzwuVI+juTiKP32+7bn+zx/O86Lg+JpuqJ1OuybVHu7c+7txoyEBRdVWmvWGNJtNozihOXa6UA1VlSVJX6piTR4MhgRBUgxtOfo/fvju9mpab8jDURcl8T/89acgqJgwzXNA4FpPX+zzLP7+4hRFsPuHS8PVWV4QBAUG0HazhSEQgQIcS7iOdTeey43mgMSW8xlUVRsbI92wp5MFSbAoVN1c3TuGWVdqIAZnQLE+7NdrtdkkYRmaQCEErHiBzxnq5uZ6sVqCQAkDKI6TYRziFDa/nY+GAwBChsMhAJQYWb3Y2sMg+E9//FOcFi8+Tbe2t9Mk99wwjgocxz3Xu7+/QWGIoijb9dKbmKZZhsJxAm0r0vb62lsIuri63d7Z3dnaxFFoMZ9XAASUuchz6mIxTTKOZ9Y3hpZhd9qtJE6mk8ne3vazw20ILiaLSZ6XYFUxBEl0O5P5zDJ1y9AhCANhkGLITm9AsWtNrx4GgSAw8/k8z0uCoBAY//rrX19d3dquJUp1kiV1x6k32iSKfTg553h2e3sLhcHJdOq7QVUhIIjLEm+olhsVc8sPkmRjfU1qSHWZxTBM4MQSgB4fH2S53lAkksJX6jJPwyRObTtgGKMokywtRusD1zNnsxUEA6zAa7oZZ+lquVK1le8HkixRJK7rGgCV3bbCMvjD/YUfhPpKZxn+6GhfFGsQBMZpAkOg47hh7CIoUG+IkRd8//0PSqOxf7BfXuUL1Rz0Bzla/feTf4/Tst9rdzpKEAe6roVeUCR5BZS+F1GkWGSgZbu+ExRJJTcbw/UNx3Evbh7294/A/+f//r/u76bjx2uKxkmChoFKM631rXWaJiVZ4WVl8jg9O3+zu7VVpRgGVWHk3d49FGW5s7vL8TQvytrKCHw/ySPLsgkSX6lLDEPCIMZQWhJqy+VyZaiCyDZb9bIEYQhBEbAhyXfzxdXkPk8rGKgGbWU2mSEQynGCYa/iJHv16eHjdD6erEgSRWiIwdhGvaXUGzLLwiCSldW3v3wv8cyos56AcFWkgWXfjGdyXTra3YrjzNQNgWMXi+XC0rrdjiTXr6/uwygBscrz7F/96pv/8v/9FyAvLM+nKeRgfacEsKIC0RLIomx9tJHmRRxFhm36vr+ztQkA5eXlRVwVoiwTNDaZPGZRuTUaETQOIqjIir5tUyhq2S6IgEmSkAQJQVCz1QzD2POjIPDzPCNIsi4KFYaFQdCuNzGUcAMHR6EkyhiWT7PINA0Mw5dLzTTN9dEwCoPZZEHTTFqUEIKGYSBKouv7mm59+frr2WRMMlir2T7+cDqfzzc32rxQV1VjY7T59t37LI+HgzU/CFiGeXi4qwksiROLlQYCgG2acZ4DEDaZzeMkD/wAx+FPXjx1bNu0vDCMDg8PSJz8tz//ZX29227VlvNVHGYQinAcdbS/I3CsIIqaYQiccHf3iKIYxxEUzYxn+nS65Fmu1ZIYnjZtlyBoVVWLLB0OujRNA2CVpslsPmcFwY+Sve09a6XPl0sULAkMW+lqoy4JPKlqhqo7y6UehhFJYQRFoChsWRZNEIOWUuQxJwoVSFzejqsSiMJUbsggCJA43GrweZGPx/MwyKoKyPIszguSJOuS5Dru3ePjaK37xatP7m5u4ixPspKh+OVy8fRoLw4DmhX/9OdvN0eD7a21IA6iNIYhhGPoLM0UpXl1fW8bTlwWGJTFUfHL8UWS5ZzABJH39PBJlhZ//ct3oiCsrw93tkee4wZReHN7m2dVoyG3O+3zi2sURSGgev7iKYWCIFgKivzTmzdQhg4HmyBcIjhIU5wgilHsqEuVpviGLANFfHFxuVrpGMswFLVYLPcO9ve2dxazeeC5AFjWauJiuRRFyXLcCqiyNI3jRJYlBMcUpX16cq5rJklQUl2mKOzm9kxqNZwoePX0CChKUzc9J4AAuN6Q64362cXFfLGAYbjZkHhGwAg6iL2yTGEEX800gmTanVYY2ieXp0eHTz68/QBWEC/UaJYROLbX74m8MJvNbcsReG6lqRzDW45TAUgQ+ILI4BgaJn4QeBRJaStDFOqswMiSYK6W/V5vPJ0apnv09KhRr6+WK9M05XodqEAAKO9uL1iGIQlE4FhN06cLdanb+/v7NEW/f3s8X+mCID1/uh/FwXg6RXFUEIXPX30RhaGh6xAIBUEQxvGgPyzLsqrKq9v7KApbivTx/CwH4G++/pVjavPFvChyhsCSMKhKqKm0PM8tynJrc2OxXAVB5DrOw8MkK4vtvS0YAkLfU+QmhuHtbrMo8sViRVLU4+NjEsTrm5uSIs5m976dbG5tBoFrWxaGo27g+m7c6bSjOJo8zAzDiZKUoqmaIAqilGRxGofDQV+UyD/88c80ziVJDCHE0bOjIosuL276g46uGSt9KcliVVRlBm1vbKzUWRLHa2treQnhBFXkwT9++oUVGmkS8wJj2Hac5p1ma9hvCwI3HU8dN2w2myiKTCez2IsRAtneWVPnS5igZbmBnJzddgbry7fHu5Kc50AYuRvDHgjgb95fdhQ1y98VZUUSrG65L56+uLuZWEFm+1GR5cenF3VZ/Px1S5TrS325UqciLyVZ6gTOprLOi9jd3fL69uRguxumrBck+dzmKHJt0NFNQzWssoRgAKorwmI6mc7mvhtHkVWWBcuzgO+eX1/ppkMzdAFmumPmeIojmCAQk5kPgpDtaABQGH4gh35VVHrkCgQd50W707q4emBovK3IOAL6MePmUZyFlqnjKOK5nuc5QRwahpHmVZrEUZoSOJglYUNiYASHS9RFo/lyyQrSeLmqyzUUw4oiN0xNadRt3/vk6cHZ3QVJYFvtTlaWeV5yOEZCAMZTcRwP+p2Lu8ckzVqdrrlcJUGIIYgiMhZcClLddt2GUsdwTHeIyWpOY3SZF2YSSkpdWy0X85Xv+52OIolskoaqoa5UwzI8yglHo3UIgzXDkSAYQ1CCpBp1qdeWzi5O09jf3d0aDtssy1mu9/LFM22pdlp13TZNx2xI8mefvUYIASgzSaTjsrq9fvRjkGXoLMvKshB4CkOBssjv7m+bzebB7iYvUBSD/OZXvwUg4Pb6YjKdMRTT63RmS70qQFU3YQyFvHC5NCMvIwgKI6gsjX65/Li9tdVtNdb6A9sPNcfe3toiCfToYP/s/IRl4Q8f3uI4nmZ5oyHgWBWneY2D7VWwOeoQMOjZZr22dnx6Pp8WgR8brsNzfBoH48cpzQhpkSdx/NmnT68nMwyGZADwvCQIYpZmuy2u32vfPk5nc3U8npI4QRG450UYitZEIYhDkiGBMl+sVnmeqcvV9999y5IERZN5meWx32s2B73uDz/9wovCr7/63LEtjEQftQBBkI7AmLYXhjGOov/yzevvv/+RrklnV3e1BvX6M/qHn98naYFiRFWVvV67P2iRBDuezICqoEgShuGaIBV56dgOgSNNpTGdr6Qan6ZhVxZPr25Pr66zHDA03XXjXre5e7AjK/JoKL97p22u9T3b8Y3Fm3fHPCsdPdkCYej0fMqzPALk52fvUZSo1cWLy1vXTyRZSrKCq1FQkRcVFQRhVRQYgVMM+y//8k+L2UJuKEHo/vDTj0vDTOGyJvE319e+6zeU+u39VOBEjucXq4Uf+gQFZ0kVR1Gv0w3DxLVsEAExrEJRAifofq/lmXi/N8jzbMzW3SAcra/XOObq8vI6DLM8I3B8NBrRFI0gMI4SG6OBH6VZWUBgFQZ+tAghEKuJchYUBI4EoRcEbhSkSTmvS4rrFXc391laKM3Gn/7yd44Xv379/Pr6/v5hCoEgAJYQUKVpjhBEBUFzbSULUqPRHqyPoiQbbvQuL8+ms0kFQs+E5w8P45ZSpwg88CIcQ+M0Mox5U5Fn89VkOul0W0FkBbHfaCp57EJwiRJoV2ygQPbXfz/FKVYUxTiN8jIzzWW/UwdA+P3xmVCvoyjW7XSnk3EQpDfRvNtSEE31fcfQna2t7U6ztVJNCIbSxPdD3/Ej1/Vsywr8EKwAy/ejyMvz3HNdqIKCIKpJUpTmXlx+srtjqFNdXTq+WVd6h0/2BU4WGPqnt+8JHHbCLE6iu7t7DCVvrsaWZiMQ7LqBvlySFOGFYZZXMIDgJMXVidFoc3N7Mwrc+4fZztZ2kqZhkC6WBk1Ta/2+ppmKooRxunewZep+WuRhEN3cr2py3bbD/x/I1B79p9MjHwAAAABJRU5ErkJggg==\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 169,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"Image.fromarray(image_source)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 170,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 170,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"Image.fromarray(annotated_frame)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Run the segmentation model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 171,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# set image\\n\",\n \"sam_predictor.set_image(image_source)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 172,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# box: normalized box xywh -> unnormalized xyxy\\n\",\n \"H, W, _ = image_source.shape\\n\",\n \"boxes_xyxy = box_ops.box_cxcywh_to_xyxy(boxes) * torch.Tensor([W, H, W, H])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 173,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"transformed_boxes = sam_predictor.transform.apply_boxes_torch(boxes_xyxy, image_source.shape[:2]).to(device)\\n\",\n \"masks, _, _ = sam_predictor.predict_torch(\\n\",\n \" point_coords = None,\\n\",\n \" point_labels = None,\\n\",\n \" boxes = transformed_boxes,\\n\",\n \" multimask_output = False,\\n\",\n \" )\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 174,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def show_mask(mask, image, random_color=True):\\n\",\n \" if random_color:\\n\",\n \" color = np.concatenate([np.random.random(3), np.array([0.8])], axis=0)\\n\",\n \" else:\\n\",\n \" color = np.array([30/255, 144/255, 255/255, 0.6])\\n\",\n \" h, w = mask.shape[-2:]\\n\",\n \" mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\\n\",\n \" \\n\",\n \" annotated_frame_pil = Image.fromarray(image).convert(\\\"RGBA\\\")\\n\",\n \" mask_image_pil = Image.fromarray((mask_image.cpu().numpy() * 255).astype(np.uint8)).convert(\\\"RGBA\\\")\\n\",\n \"\\n\",\n \" return np.array(Image.alpha_composite(annotated_frame_pil, mask_image_pil))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 175,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"annotated_frame_with_mask = show_mask(masks[0][0], annotated_frame)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 176,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 176,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"Image.fromarray(annotated_frame_with_mask)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Image Inpainting\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 177,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"image_mask = masks[0][0].cpu().numpy()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 178,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"image_source_pil = Image.fromarray(image_source)\\n\",\n \"annotated_frame_pil = Image.fromarray(annotated_frame)\\n\",\n \"image_mask_pil = Image.fromarray(image_mask)\\n\",\n \"annotated_frame_with_mask_pil = Image.fromarray(annotated_frame_with_mask)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 179,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 179,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"image_mask_pil\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 181,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# resize for inpaint\\n\",\n \"image_source_for_inpaint = image_source_pil.resize((512, 512))\\n\",\n \"image_mask_for_inpaint = image_mask_pil.resize((512, 512))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 182,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100%|██████████| 50/50 [00:02<00:00, 17.54it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"prompt = \\\"A sofa, high quality, detailed, cyberpunk, futuristic, with a lot of details, and a lot of colors.\\\"\\n\",\n \"#image and mask_image should be PIL images.\\n\",\n \"#The mask structure is white for inpainting and black for keeping as is\\n\",\n \"image_inpainting = pipe(prompt=prompt, image=image_source_for_inpaint, mask_image=image_mask_for_inpaint).images[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 183,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"image_inpainting = image_inpainting.resize((image_source_pil.size[0], image_source_pil.size[1]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 184,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 184,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"image_inpainting\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.12\"\n },\n \"orig_nbformat\": 4\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "data_generation/Grounded-Segment-Anything/grounded_sam_3d_box.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"199d1e0a\",\n \"metadata\": {},\n \"source\": [\n \"# Segement Anthing + 3D Box\\n\",\n \"\\n\",\n \"![gdgligen](https://github.com/IDEA-Research/Grounded-Segment-Anything/raw/main/assets/mask_3dbox.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b3c4befe\",\n \"metadata\": {},\n \"source\": [\n \"**Why this project?**\\n\",\n \"[Segment Anything](https://github.com/facebookresearch/segment-anything) and its following projects\\n\",\n \"focus on 2D images. In this project, we extend the scope to 3D world by combining [Segment Anything](https://github.com/facebookresearch/segment-anything) and [VoxelNeXt](https://github.com/dvlab-research/VoxelNeXt). When we provide a prompt (e.g., a point / box), the result is not only 2D segmentation mask, but also 3D boxes.\\n\",\n \"\\n\",\n \"The core idea is that [VoxelNeXt](https://github.com/dvlab-research/VoxelNeXt) is a fully sparse 3D detector. It predicts 3D object upon each sparse voxel. We project 3D sparse voxels onto 2D images. And then 3D boxes can be generated for voxels in the SAM mask. \\n\",\n \"\\n\",\n \"- This project makes 3D object detection to be promptable.\\n\",\n \"- VoxelNeXt is based on sparse voxels that are easy to be related to the mask generated from segment anything.\\n\",\n \"- This project could facilitate 3D box labeling. 3D box can be obtained via a simple click on image. It might largely save human efforts, especially on autonuous driving scenes.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"153cbf51\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"OpenBLAS WARNING - could not determine the L2 cache size on this system, assuming 256k\\n\",\n \"OpenBLAS WARNING - could not determine the L2 cache size on this system, assuming 256k\\n\"\n ]\n }\n ],\n \"source\": [\n \"import cv2\\n\",\n \"import pickle\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from voxelnext_3d_box.model import Model\\n\",\n \"from voxelnext_3d_box.utils.image_projection import get_data_info, _draw_3dbox\\n\",\n \"from voxelnext_3d_box.utils.config import cfg, cfg_from_yaml_file\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"a9cd7d82\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"cfg_model = 'voxelnext_3d_box/config.yaml'\\n\",\n \"device = 'cpu'\\n\",\n \"model_cfg = cfg_from_yaml_file(cfg_model, cfg)\\n\",\n \"model_cfg.SAM_TYPE = \\\"vit_b\\\"\\n\",\n \"model_cfg.SAM_CHECKPOINT = \\\"/data/sam_vit_b_01ec64.pth\\\"\\n\",\n \"model_cfg.VOXELNEXT_CHECKPOINT = \\\"/data/voxelnext_nuscenes_kernel1.pth\\\"\\n\",\n \"model = Model(model_cfg, device)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"fe6e3c0d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"lidar2img_rt = np.array([[-1149.5, 940.9, 8.1, -646.8],\\n\",\n \" [-442.2, -114.6, -1270.2, -519.6],\\n\",\n \" [-0.95, -0.32, -0.03, -0.43],\\n\",\n \" [ 0, 0, 0, 1]])\\n\",\n \"image_path = \\\"assets/n015-2018-08-02-17-16-37+0800__CAM_BACK_LEFT__1533201470447423.jpg\\\"\\n\",\n \"image_id = image_path.split('/')[-1].rstrip(\\\"jpg\\\")\\n\",\n \"point_dict = {\\\"points\\\": np.load(\\\"points_demo.npy\\\")}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"665fb508\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/chenyukang/miniconda3/envs/centerpoint2/lib/python3.8/site-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.\\n\",\n \"To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ../aten/src/ATen/native/BinaryOps.cpp:467.)\\n\",\n \" return torch.floor_divide(self, other)\\n\"\n ]\n }\n ],\n \"source\": [\n \"image = cv2.imread(image_path)\\n\",\n \"prompt_point = np.array([[560, 500]])\\n\",\n \"mask, box3d = model(image, point_dict, prompt_point, lidar2img_rt, image_id)\\n\",\n \"if not box3d is None:\\n\",\n \" image = _draw_3dbox(box3d, lidar2img_rt, image, mask)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"9cc22ec8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(20,20))\\n\",\n \"plt.imshow(image)\\n\",\n \"plt.axis('off')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"eefb9475\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"prompt_point = np.array([[820, 480]])\\n\",\n \"mask, box3d = model(image, point_dict, prompt_point, lidar2img_rt, image_id)\\n\",\n \"if not box3d is None:\\n\",\n \" image = _draw_3dbox(box3d, lidar2img_rt, image, mask)\\n\",\n \"plt.imshow(image)\\n\",\n \"plt.axis('off')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"0b57f2c4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
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\"markdown\",\n \"source\": [\n \"![Grounded SAM Inpainting Demo](https://github.com/IDEA-Research/Grounded-Segment-Anything/raw/main/assets/grounded_sam_inpainting_demo.png)\\n\",\n \"\\n\",\n \"## Why this project?\\n\",\n \"\\n\",\n \"- [Segment Anything](https://github.com/facebookresearch/segment-anything) is a strong segmentation model. But it need prompts (like boxes/points) to generate masks.\\n\",\n \"- [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO) is a strong zero-shot detector which enable to generate high quality boxes and labels with free-form text.\\n\",\n \"- The combination of the two models enable **to detect and segment everything** with text inputs!\\n\",\n \"\\n\"\n ],\n \"metadata\": {\n \"id\": \"O49XQD8rwNUF\"\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Install\"\n ],\n \"metadata\": {\n \"id\": \"kAi4IkEfKi5K\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"uSJqZHr3JTSB\"\n },\n \"outputs\": [],\n \"source\": [\n \"%cd /content\\n\",\n \"\\n\",\n \"!git clone https://github.com/IDEA-Research/Grounded-Segment-Anything\\n\",\n \"\\n\",\n \"%cd /content/Grounded-Segment-Anything \\n\",\n \"!pip install -q -r requirements.txt\\n\",\n \"%cd /content/Grounded-Segment-Anything/GroundingDINO\\n\",\n \"!pip install -q .\\n\",\n \"%cd /content/Grounded-Segment-Anything/segment_anything\\n\",\n \"!pip install -q .\\n\",\n \"%cd /content/Grounded-Segment-Anything\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Imports\"\n ],\n \"metadata\": {\n \"id\": \"MRkQUrtjKpbh\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"import os, sys\\n\",\n \"\\n\",\n \"sys.path.append(os.path.join(os.getcwd(), \\\"GroundingDINO\\\"))\\n\",\n \"\\n\",\n \"import argparse\\n\",\n \"import copy\\n\",\n \"\\n\",\n \"from IPython.display import display\\n\",\n \"from PIL import Image, ImageDraw, ImageFont\\n\",\n \"from torchvision.ops import box_convert\\n\",\n \"\\n\",\n \"# Grounding DINO\\n\",\n \"import GroundingDINO.groundingdino.datasets.transforms as T\\n\",\n \"from GroundingDINO.groundingdino.models import build_model\\n\",\n \"from GroundingDINO.groundingdino.util import box_ops\\n\",\n \"from GroundingDINO.groundingdino.util.slconfig import SLConfig\\n\",\n \"from GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap\\n\",\n \"from GroundingDINO.groundingdino.util.inference import annotate, load_image, predict\\n\",\n \"\\n\",\n \"import supervision as sv\\n\",\n \"\\n\",\n \"# segment anything\\n\",\n \"from segment_anything import build_sam, SamPredictor \\n\",\n \"import cv2\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"\\n\",\n \"# diffusers\\n\",\n \"import PIL\\n\",\n \"import requests\\n\",\n \"import torch\\n\",\n \"from io import BytesIO\\n\",\n \"from diffusers import StableDiffusionInpaintPipeline\\n\",\n \"\\n\",\n \"\\n\",\n \"from huggingface_hub import hf_hub_download\"\n ],\n \"metadata\": {\n \"id\": \"6Osu6KERJeTN\"\n },\n \"execution_count\": 2,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Load models\"\n ],\n \"metadata\": {\n \"id\": \"IFnZ_nwcKuUk\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\"\n ],\n \"metadata\": {\n \"id\": \"004CGse3NRS2\"\n },\n \"execution_count\": 3,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"### Grounding DINO model\"\n ],\n \"metadata\": {\n \"id\": \"qzhOnA6ALQlo\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"def load_model_hf(repo_id, filename, ckpt_config_filename, device='cpu'):\\n\",\n \" cache_config_file = hf_hub_download(repo_id=repo_id, filename=ckpt_config_filename)\\n\",\n \"\\n\",\n \" args = SLConfig.fromfile(cache_config_file) \\n\",\n \" args.device = device\\n\",\n \" model = build_model(args)\\n\",\n \" \\n\",\n \" cache_file = hf_hub_download(repo_id=repo_id, filename=filename)\\n\",\n \" checkpoint = torch.load(cache_file, map_location=device)\\n\",\n \" log = model.load_state_dict(clean_state_dict(checkpoint['model']), strict=False)\\n\",\n \" print(\\\"Model loaded from {} \\\\n => {}\\\".format(cache_file, log))\\n\",\n \" _ = model.eval()\\n\",\n \" return model \"\n ],\n \"metadata\": {\n \"id\": \"dPTgK04-J1U4\"\n },\n \"execution_count\": 4,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"ckpt_repo_id = \\\"ShilongLiu/GroundingDINO\\\"\\n\",\n \"ckpt_filenmae = \\\"groundingdino_swinb_cogcoor.pth\\\"\\n\",\n \"ckpt_config_filename = \\\"GroundingDINO_SwinB.cfg.py\\\"\\n\",\n \"\\n\",\n \"\\n\",\n \"groundingdino_model = load_model_hf(ckpt_repo_id, ckpt_filenmae, ckpt_config_filename, device)\"\n ],\n \"metadata\": {\n \"id\": \"73fWWbE0KBZZ\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"### SAM \"\n ],\n \"metadata\": {\n \"id\": \"uRsDao4wLV4t\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"! wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\\n\",\n \"\\n\",\n \"sam_checkpoint = 'sam_vit_h_4b8939.pth'\\n\",\n \"\\n\",\n \"sam_predictor = SamPredictor(build_sam(checkpoint=sam_checkpoint).to(device))\"\n ],\n \"metadata\": {\n \"id\": \"QNyrl-WMLVQs\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"### Stable Diffusion (Inpainting)\"\n ],\n \"metadata\": {\n \"id\": \"ZiDBwfq0LeVh\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"sd_pipe = StableDiffusionInpaintPipeline.from_pretrained(\\n\",\n \" \\\"stabilityai/stable-diffusion-2-inpainting\\\",\\n\",\n \" torch_dtype=torch.float16,\\n\",\n \").to(device)\"\n ],\n \"metadata\": {\n \"id\": \"KmSGdNxYLkGk\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Inference\"\n ],\n \"metadata\": {\n \"id\": \"y86GkczAOAVx\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"# Load image \\n\",\n \"def download_image(url, image_file_path):\\n\",\n \" r = requests.get(url, timeout=4.0)\\n\",\n \" if r.status_code != requests.codes.ok:\\n\",\n \" assert False, 'Status code error: {}.'.format(r.status_code)\\n\",\n \"\\n\",\n \" with Image.open(BytesIO(r.content)) as im:\\n\",\n \" im.save(image_file_path)\\n\",\n \" print('Image downloaded from url: {} and saved to: {}.'.format(url, image_file_path))\\n\",\n \"\\n\",\n \"\\n\",\n \"local_image_path = \\\"assets/inpaint_demo.jpg\\\"\\n\",\n \"image_url = \\\"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\\\"\\n\",\n \"\\n\",\n \"download_image(image_url, local_image_path)\\n\",\n \"image_source, image = load_image(local_image_path)\\n\",\n \"Image.fromarray(image_source)\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 566\n },\n \"id\": \"dad-C9F_OWn7\",\n \"outputId\": \"37e94770-4754-4243-9544-6c588dd420df\"\n },\n \"execution_count\": 8,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"Image downloaded from url: https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png and saved to: assets/inpaint_demo.jpg.\\n\"\n ]\n },\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {},\n \"execution_count\": 8\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Grounding DINO for detection\"\n ],\n \"metadata\": {\n \"id\": \"NaEIyVwjOCam\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"# detect object using grounding DINO\\n\",\n \"def detect(image, text_prompt, model, box_threshold = 0.3, text_threshold = 0.25):\\n\",\n \" boxes, logits, phrases = predict(\\n\",\n \" model=model, \\n\",\n \" image=image, \\n\",\n \" caption=text_prompt,\\n\",\n \" box_threshold=box_threshold,\\n\",\n \" text_threshold=text_threshold\\n\",\n \" )\\n\",\n \"\\n\",\n \" annotated_frame = annotate(image_source=image_source, boxes=boxes, logits=logits, phrases=phrases)\\n\",\n \" annotated_frame = annotated_frame[...,::-1] # BGR to RGB \\n\",\n \" return annotated_frame, boxes \"\n ],\n \"metadata\": {\n \"id\": \"lm8fGM5XOkqN\"\n },\n \"execution_count\": 9,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"annotated_frame, detected_boxes = detect(image, text_prompt=\\\"bench\\\", model=groundingdino_model)\\n\",\n \"Image.fromarray(annotated_frame)\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 618\n },\n \"id\": \"YVjdmTWOPxTy\",\n \"outputId\": \"1943639d-6f61-47e8-81e1-38b41141fb21\"\n },\n \"execution_count\": 10,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stderr\",\n \"text\": [\n \"/usr/local/lib/python3.9/dist-packages/transformers/modeling_utils.py:830: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.\\n\",\n \" warnings.warn(\\n\",\n \"/usr/local/lib/python3.9/dist-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\\n\",\n \" warnings.warn(\\\"None of the inputs have requires_grad=True. Gradients will be None\\\")\\n\"\n ]\n },\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {},\n \"execution_count\": 10\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## SAM for segmentation\"\n ],\n \"metadata\": {\n \"id\": \"6p7cHFDKQw5W\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"def segment(image, sam_model, boxes):\\n\",\n \" sam_model.set_image(image)\\n\",\n \" H, W, _ = image.shape\\n\",\n \" boxes_xyxy = box_ops.box_cxcywh_to_xyxy(boxes) * torch.Tensor([W, H, W, H])\\n\",\n \"\\n\",\n \" transformed_boxes = sam_model.transform.apply_boxes_torch(boxes_xyxy.to(device), image.shape[:2])\\n\",\n \" masks, _, _ = sam_model.predict_torch(\\n\",\n \" point_coords = None,\\n\",\n \" point_labels = None,\\n\",\n \" boxes = transformed_boxes,\\n\",\n \" multimask_output = False,\\n\",\n \" )\\n\",\n \" return masks.cpu()\\n\",\n \" \\n\",\n \"\\n\",\n \"def draw_mask(mask, image, random_color=True):\\n\",\n \" if random_color:\\n\",\n \" color = np.concatenate([np.random.random(3), np.array([0.8])], axis=0)\\n\",\n \" else:\\n\",\n \" color = np.array([30/255, 144/255, 255/255, 0.6])\\n\",\n \" h, w = mask.shape[-2:]\\n\",\n \" mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\\n\",\n \" \\n\",\n \" annotated_frame_pil = Image.fromarray(image).convert(\\\"RGBA\\\")\\n\",\n \" mask_image_pil = Image.fromarray((mask_image.cpu().numpy() * 255).astype(np.uint8)).convert(\\\"RGBA\\\")\\n\",\n \"\\n\",\n \" return np.array(Image.alpha_composite(annotated_frame_pil, mask_image_pil))\"\n ],\n \"metadata\": {\n \"id\": \"k8-1lhHjQ2dm\"\n },\n \"execution_count\": 11,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"segmented_frame_masks = segment(image_source, sam_predictor, boxes=detected_boxes)\\n\",\n \"annotated_frame_with_mask = draw_mask(segmented_frame_masks[0][0], annotated_frame)\\n\",\n \"Image.fromarray(annotated_frame_with_mask)\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 529\n },\n \"id\": \"puUUEAAjSlug\",\n \"outputId\": \"61237cd2-8a72-4801-9b0a-5c382ce421e0\"\n },\n \"execution_count\": 12,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {},\n \"execution_count\": 12\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Stable Diffusion for inpainting\"\n ],\n \"metadata\": {\n \"id\": \"Ct786WclTVdN\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"# create mask images \\n\",\n \"mask = segmented_frame_masks[0][0].cpu().numpy()\\n\",\n \"inverted_mask = ((1 - mask) * 255).astype(np.uint8)\\n\",\n \"\\n\",\n \"\\n\",\n \"image_source_pil = Image.fromarray(image_source)\\n\",\n \"image_mask_pil = Image.fromarray(mask)\\n\",\n \"inverted_image_mask_pil = Image.fromarray(inverted_mask)\\n\",\n \"\\n\",\n \"\\n\",\n \"display(*[image_source_pil, image_mask_pil, inverted_image_mask_pil])\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 1000\n },\n \"id\": \"wmBncdm8TVF9\",\n \"outputId\": \"91abd19e-429d-487f-f617-934f070cbddc\"\n },\n \"execution_count\": 13,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"image/png\": 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},\n \"metadata\": {}\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"def generate_image(image, mask, prompt, negative_prompt, pipe, seed):\\n\",\n \" # resize for inpainting \\n\",\n \" w, h = image.size\\n\",\n \" in_image = image.resize((512, 512))\\n\",\n \" in_mask = mask.resize((512, 512))\\n\",\n \"\\n\",\n \" generator = torch.Generator(device).manual_seed(seed) \\n\",\n \"\\n\",\n \" result = pipe(image=in_image, mask_image=in_mask, prompt=prompt, negative_prompt=negative_prompt, generator=generator)\\n\",\n \" result = result.images[0]\\n\",\n \"\\n\",\n \" return result.resize((w, h))\"\n ],\n \"metadata\": {\n \"id\": \"VWKOb_ZWWx2W\"\n },\n \"execution_count\": 14,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"prompt=\\\"A sofa, high quality, detailed, cyberpunk, futuristic, with a lot of details, and a lot of colors.\\\"\\n\",\n \"negative_prompt=\\\"low resolution, ugly\\\"\\n\",\n \"seed = 32 # for reproducibility \\n\",\n \"\\n\",\n \"generated_image = generate_image(image=image_source_pil, mask=image_mask_pil, prompt=prompt, negative_prompt=negative_prompt, pipe=sd_pipe, seed=seed)\\n\",\n \"generated_image\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 561,\n \"referenced_widgets\": [\n \"5ea785dee24b4e18b03d16599374a1ba\",\n \"44c5fe1100f5436683e321c4a75e1d71\",\n \"d4faaaafbb9d46ab8d2966bf9b882013\",\n \"b26fa9fef20948d1893a6a1a7d1643f1\",\n \"6793deaabb8942d6b94aa68066439988\",\n \"eb0eda69168741c0a79e5159c1188200\",\n \"43f625de28554e49a30860a6760f0540\",\n \"f5cca83250ac4adb84844e52ccb0978f\",\n \"7258898388d144a791f03609a3626a8b\",\n \"a971b2e1116444f2bfe5518f14e5f155\",\n \"2e2c15b721874d34aee868f11070c592\"\n ]\n },\n \"id\": \"IkRMVCOkYU68\",\n \"outputId\": \"60455cd2-3ca8-47e5-93c8-dd0ef57e2da2\"\n },\n \"execution_count\": 15,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \" 0%| | 0/50 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tURAhTUZR6XTqxFa1zoHGfA1wNDe/k+04SWX4ixANguL1eAWHLbiyH7TvGLkH3GS+5NG6zjd8VjdonIWH33zC3RHgMKpN604jpXgpZi79Hhb697qqnvKGa/+AirfZJZXuWCB3Erh0H80DkrKA48otNPTUbfXfcPsffPb04CmdXmL/cuvJVSb+IOLfPv4K8N6SW2kMh+9/1BXkpYn/4JjL+Z7i/Xm+78trEV0n39693Yu8rUfj2uqtPr79v935W9i3q+f5STK5bQFrVWxNTilt3ei1Bb//3huqlmRuPv8jZBRIISKdRBGOMYUZQW3NTDMHrvK7zus7zui7x2CD3/7qB3gyA7w4YZmMM4xjDWo/9YZ71YZhH9wthNoyAmbUW0B8ihCIqMHoVG3JJoDDEgaAAbo7Whv4LIfbPf0V0L3LxTwZAB6wyJM9WaLo+ljurP/d+TbTGEtclM5PP9hgcAhfH7NJWAW73SSAEItYlRD14k45Zwo1LufXoTrRlbwtfurgIG4TEW0MnIuB4MYvKFJkvYFfSBJgb9LaKbs2ryCMBbFtTvKPgm6//9iUvX2/48Cp1tqFeB+Yn5dadV2qsqsX/HXBZm1y9eW3i61t/oSP/G434xSrfXxOWf1pMKSVSGLMPo+yPhqr6hTiU11vxbaa22dv/C038pavWhC97mddR5AzTQbfsjdGuC4c2Jqxz2Lgus0FSRR3TXQoYiTE83nMMG8OGjfQJx5x2+WDDImadFrq/CgSimiDqeiJd3Ydv/BWBCgETNBGDCRjJ5SLscRVmP6fJTS6u10J1vnncGSHNTIKSEO9K3PjwrYJ51xVmCMHu3V4/fIUCG7682fFcS8633Z2IWMar/YGtHUsBJR/C3yPVxjsDl71uTsCU9uX/8L/1441oxP79LR22d0qU3DD1zeR6LfUG0z+5brQuuV7BSqu3/HXj54oPG8f8CgbsRayyc5K0uOHflwSvuPaLc+0Xn3x3fdH9XxuXdRJ9IUvfrAPvVRPrgP2kO3/R022Utk1CP+nA/4WLWd3CIMvcmB9yq6N019PGMABU2hgmAhpIVfGAfZAC6b2JCgWEp3EDQIhe47qup2cB8p/9FxvjOU6BqGq8EnopIBqO0wruY+YUEoECAotIGRogoi6WspvmgY6EP/sz+m0wsqWuKlkpr4+jItjT3OTP2f2xBWEKTmvLnv+zpsuZ6JQtmPtUAjtY/vC1WW5GkXoJOc+jxnc8vVyy3N500rTMlN7w66Dhw7Y4We7ybt2Es3ihctvy3rDltRyaf+eSQEF5ncnZxkLHHXP/1gxcVaoAgy3idU0D8L4ryRSvJb2FqL3iuySfZVVhU3v494n5P79+UaZV9pWkw8tLq6D/qxJLRVpUjKqq8Mk5JCM6mDrZxrRbnfz5yKxVb1pU6QK2BRr/z6Tl22vVe5gCZ25z2doXGlwnGH5ZAUxIjGHXZb2btnae53mew0bn4RsEaBwcRqNAoaSddj6vp4dju6HIyGHDSBtDtYnCfQxmwzeRWQT6GHIzMLQcrcLMNxec7rpkIGpE1IjvCfA+TRmXzLPic0aMrdNi3gqSbfbpbWQCHXN6T8Rct1wLMgo+1VvMDcs3g+AE3DRby609bip5Pwd2XXdp51tOulnJbmxyL/1v4oQsTfDF4EsjVHXlPSapZ2V8+TsTMfzi9drs6fB6QYzXaRejUivXN328oQ9vr9bfl+wvVdmbNjrKaDY3OCoVop9cX1Bmw55dMPysqFsf/i9fPx9FvvuQy8PScZYnXon/08qDkfZxSSEQOQlSKcm6RdJicu/Cv4XYOZzZ/hm0nL87Ri87Gbg++HcvkQ2aZOGLvTxx+nQzunKeVnshZJgp1cYY14CIqkJ8t3Ab1zAxEaA19wSMyzhIgzShZgnwPV+kmZg6vw+jgOhxegBAG+yt5XbhEDBUiNbmPTFQQxjYov1wmHXtJcCzz7zRd4Ua/43rkExqAJuXNARC3vC3zDyP9oovzN+9tSva34awsrVlE+8KD6Oi0O0rhgvFq9nyWTLBraHr5U9zp4fUrRVrhOsDXu7Wrr2AkF5TecLtdQCZRHnRQEiIUlg1Fziu08xLL4H90+sVGFDJQpiihF8+bjJDp8CfaKA3bnmFLsFfN/ZLnF1JmiT8S0B+U91b+ZNrU96eeVEgtrx8/yNhsArNtYL1168qqOnJ6WeaRuw5q+4yjnsh85JUz+bgh4jZs4yJ46W9F9iz1Fd5eZtewG2HjG8ZEJDt/rrjtZvHt7lHGj3y5devDE7fGrRabbEEMPqH7vYdOuo2VQ0gzH1bUFVP2nZdF0SGDBVp2gQ6MEZq8RSotCYtXAVUzyvkLuEMBSIFMiyRydJyp5Iqf9dGqMEpZjl7GyLlxJzPCk0WCUt09XTC9s51K18mEC4ju1/lanD24aqZztCXhdIv315KrSpXfs5xquABLB1ZmL3wf5kBrMbI9mLI/xu2zoqlqLY2UtaHVxBcmF32vykmQ3RFgZLyYytCiv3myylDttJnKe8uubeZObi7s/i2r3e9JplkgQW8kdqvA/k663/lkuK5r95Mi+erbF5LkJ9R5n5VWNTKDZOH1sDAReRU2GA9/Gt9W5tVceeLGLgX9ZUMWFYA21jI9pE7Hb5sZwkTmWIkog9udlPSxE3TsrRio4PX+zVPzPZOZ3KCU2m099elXpxJUwiIyt9aBLwn521KZ+OmEtojoCTCdpQAzBhh+zQOAH7co+v2mukYaGPAxhjjGilJDWmZIc3MBKqtQYWD5TWIbKACD0ASs9a6t061iWjS0Uo8WspsCVkaRLXYKODRiLP/zJ4n0EfvicqcWULaf5s8t9KKcOO7rRDM9YkbuV9VkfXJMLshHR/btKlRSZ35DkS3cVy0iMk12/PvpdoNCbYff0Hjvl1SYvUFr17BbtVL7r9tXxd58I6pbzmgbhj2q5drOolMNTsnP/zF9etAjLfduBWxKAQlBL60KP3a9caKKG846W3hX8Hzz174i+smJfYhvG2m/bqIr4KGf96ynDM7r+4qjjgAsgTxu969Y43bBKhgoil3F0egI9hih1hdNKpz1pBroOBf9noRUT+7VhkGQLoKzA9kJI1DDAIRitHGGA6tkjICRGwIExsAB8wc2gli0GCmrQEYw8xGpIuGmNl5niBU051rxEQ9P/5XFXHUJKtHHoAUo4KAR3pCsRgoLhrHfO9GCtZWrBzbubm1ZO8bjWJmHqqhSfjc0H5+4dTkb2XdPuee5+2ncoft227niBVO7xmudk32XtVXzYnvm9T7lWsn9t0FTSyLrbWFso7QrfI1jF9+1v4d/f/9i4E7fCXwL71/e/Dvis7tHaa2gnT+x5f55As1f1Lj1AxKnOVsmsT9pQJfO7kS6u1V2tf22us7yQtbFX9BxZv04P71paFTVVohv/b+3/4h0nrIKXzfqnSv19KeUCy2ilfzDvP71liXGdsgzI07v8jwoeNO+VZT607YSZ7u8koVdH1aY0OpmY1hKqICFc1ZEt5dUekAAQ8HElEIBNKattbCgpXEcxuBDUvFnbTgEY8RUm1N1fMRiSJOB5B6GZXYOLdlVdKgwvGaQW9IQilJM5XH1TTGKuLdPNjCeH4ijyc2fw0nm6aQruUd5sVR4N6KMnGs6smtHe+nz1+oEJNLfgoq7yt6+/yXShonknG5ydVZNR/1wf1lQF5a/6vvTFb4UgR+TZO7wP23r7UKLl1YLnm591f1ygJ0+V1+Nm4r72/zoCYYpgC5+5x2STbfTjRdfn75vELWQtQpxHal5qaH4d3XpU0v02jeTu9UVSPry0yavbneMdfr4mrL6ViShzu1/B8RkDch7yrvL23eXFomL8ScMqzCTZYOdFWl0SBmpkpVAeSqU7pEWm++MjLjMCKODJOmDaGwiOTBME170+7PwBCH/hrNLLzryATPnt1TpbXWWlNtsc0gzDxM8WmERhhOCYEcn8IsbmO30CO/x/jm93eRcXLjqDfF4Su2Ci9B2XAL5O7V7HiSVsKXn9ZJJzPWMrPyOWvucuO19L3an19/i8t+8bq1RpCH+f26oMHPY6f/p9c+S/26fZblb71W8JDrtF/akfLabS5jn/9IRQAzNB1y8vEv4f7a1lVMfUF3vuNqSbTfowN+of76Z37+qQqyov02N7YSX1v7Wsy7CrZJXQD0hVN5QejNn3Rv/13evTbVOxWMUSCVgWZ7Q/+CeX6uzdxG1qXzrn+und0bLSJdCPMIzcuA0dqQCqtRiIr2JuKZQYeEi9ixWl1AiIhqcxHQem9NQQy1MYSeBBQeyq80evymGUQgIp4xtKm2poBwmOMdFOF3JYz0QKEK00zvYp0PxxsxkqqLsTjZjEjpslBxJ/CqkpSkmc+9nUwpWNM685PxWkfn9tPLddsDG4awN3rBl1zyAnA/uX4ZlX/xLSaEzW+4k36qJHs58l5K45c789LIFc1vxbyQKB65ya9kjQkdUp/+frNeUTGWtUkxjfCdvzMst07mRKlGLrxdqspX5edsu+m2M47htcoXfScfvfMr74/VWniRHW9JKm+Y5cuHq7gVm5dFwLsCqhs13l8X/14oy/KvoLZpTvrUAyXYf8I+X9W8wnzuZqgbCweUcbs04JXK3QP/fbeXjXF+PlVVNcJyeuu9Hb71N8UADSbwTD7m4ZuuxIu4PUcjI5AK6LYf5gnwZEa++8P5hi7AfHmj/VhcBWJjWL63BIfdKH6ziUt2eiFkSLZlXnEdbKknC3K5sr/sA/JvQBGX9952YnlyjUnkbOmycWv+y3dFrL15K3ZuIPjvXl+JmCAXX53LAglvfEyNl9q/pu1XE2Z3O8v+E3i/P0c4iqtoDWRkbShruhVU4zHb8Yuku1H+FTWZP2QOJH9tTyO4d+rLmyV6VzyLgLFyglWt61WtQKVkyTmQTd5eWau8F+Twh6zvVW1xEtcslUkmbqW8lPqmBa86WaL/W4nxk2sO7dsW3ypbeSERooYsnQpfiizBu3G81ckXciy+qxAj5E7iLL5uMuO2JXe4kujuhxzXOMelqgAOkXGZQAVorStUmzZgXH7oyxg2IKA1r0gVTbW1JpCmqqIDI1VtoymF1zVsRIBtIrAAETJqZOQfMkIBi90xEfOjMFrXrmmLT34rGntxL3M+Yryn2PVgsM1nGUrcGzYDytjyOiQx8W9QXrapn1+bKlAj+8rD1c9bw6QAiykQ3mFoTjzMv68N2T79GxLtJ6+tq6+XyZkDMyk4V6ohub+wfs5H3/3ydxp4a9wSm7ENwm3810QTCdfvyf/zWt83TaaQeffgC3e87wqQM2KGl/OL98owspRSOuvqslkYkcSLYnRvy18Yr7dJ8NXv61h/BaLC10Gb/XJl8/5WpiQIa/Qqp6rYZENU/dwnYxEsHceyZnZ5sykwkx1EedvseyVWmqpKVY1HMz8xy3EgVQpe+zrHakkkMBGke4vM7DpPgmYPAQTStLXeAI4xHo+HiNgVpwYDUD8RkkwLjvp+rtaany9A0pNDuEZ/XZfRWmv0hKEe9NMUwBhGQG2YxaioUMXRVwExmqgabBLQyTHJmPy94PEruxAZYpHskoP+TgfKUUmze1rcky8nhy8MJi9o8Xqt8/DXHDz71J8B1pwxrVOb48oD73HyXrisn9bnJ2NNbWMj2DYcP63sp1RZxyphdRa1lZtS/K1l6+9hcFY4C9rx7e3g7FWErf5X0H/1DeGnw1I/3eUOdlq8UVuWB5bYiJtm8761Upz1OpS8F1OMcYPMeDgjNpAq0Ys0qyF87WKW9sVwvsxVUpYwp0VvCABdEHLjVZ/V8Ur8vJF+4QsBvjwJOOSIrJL7TiwW3Cx3c0AxhQdWCZOrLy9REzHoajoX+ZT9XbqSwyFRDic7JBW90q5N1RTAuAZpTYSta2vapPWmoo+Pw9O3QWFmQhyt99abNghUVFtY/30vAQHfXAbQaOMk8+wwUY0WiWjzDcYgbAwzCMnmq21VqBIQY2YhVQrMc8Z5GmqJowR+Ehacu/sljf65Gtv+2wblxnmb7iQlPEJHcsJWAyjB7zHH3jbsFc1eWr1WvWD/bIsv2iIZkt2QQN6U9bMr5eetur3u9YkJ+pmw6G/F6vx1a/xDraFrhYNIzoT30Py3K3oppChYu8nv2t40vK3Yu9LqFZKrQ3fZ+vNOLPJ2neTYPt8ZdevJbV/eX1ySYd7zbxXqt7hRY+WL+Sn3JL6oQW+I8jOheWM/5KgsY4OYizELk74zCZKAvilpLpE3MrEKzpvbzKx/nO3SnLZcc0bW+5Xm8H5Niq1hoILpkK5RWxZdc6qVwKiJ8U4qb6s4VpdeVQoJeSHSRZ10JgI3uRMUReu9teOjP3oXUfi5YK5gttZiBQAcx+GhmZrJfFQjSocEyWsMgCq+UvBtZGDkCo6jsVKKruofBZoiUFEsJRIxpGsCslx63dUWgXuRJ0NNV8PrGN39eMvKS0r87nUi9Q2pGid+T1LfKqpHfiYi9gmWTZEc2fuYMk3VKzv+8hXN39eTkvvVVoGwCYe54SG9M3dU+sXq10VU/DfzXqSYvRX316W/Aq588WXtZCypXmVAlfjGzPdWBrzB7rvw/xkI1o+1FeevH703Z232z6TT/iGn2hIAtMsRAV5MHCv+3K4SJa+4/sXF5cObZzen0jYVZD0wXhLeXUvZWlpLyLfT8DZY9eLmBnED82JnZm0B2sd92qLiy8wWOeVKvpKqPvKlaiXTKqV1LtZKEWQn+cVIFIj4o15yp0dpEhDRJvSTuCAqOLq2j9ZUB8Pyk3WRAEltrfUOwDcM0w/3HeJ+giv8BcNovfXeuzuKfSewbzVQFYNGV0WN1MwF7cY7FwXTTFn5RJ0iGTfBt+M45zWQ671IJrcdMDKJG6SeMFRZRyFFAJmDVwuAxKzlmSks9ia9u73P2ZeZFWL5NYr3VpOUTvLr1zJxZa/6jVdlVj/9OKhK38LQX8316NvtyemneW9WWxxeb0Rs1f/+60IvqVi9mqpfDtGrPIZsD95A9z6MRY8FRt/VlNpEDoAstxdxtb+/iJZ5ejYnJKAsM6/ditey2rUX7+TIS+OzjhgSzhfvY/MXxrKvxOeUS/KmiLtwXEyg0bIdKyddNpvcHJUYpYUZVlFYJv40wJYrIewRewx31rj3KWVqvrTAV9QacsJjeEQg0IxeDGHzSj5WxovXzfIBmCmGECajfo0xbKzwaWFVE1U9mqpq/R5BOKSZafcoHgEoVFGJAx9p53k+n+f1PAeHj4OvGNKEn/vCIICqOsJH0E5cEGkpF6pjqx4o++jdO3tnC3LmgMA7K/ImQBbOm6rgIkDJ6YG5PxrVbb9UG7DfXqp8Dx41TphMOBWJvYIvPKPVuaXI/bf7s/enXspdRE1QZ1lvbtcvyQACcy/YUmsRcV32rwXPYZoeuF8UfllTqm5TBsyq0omK9Sazuz/DMr4hGlCDNqN6ZPm7vCTZwpte4lpM8dP2z9rMcFpxR/wJyrfW3sJHbq19kQp7GVMoBmhywalVNK0CeHebrLR/zzE1XNPCs7y4ssdmFUetnW6Te60xuXnpyCxwmXT3/ifHCjCmQiLLqzITGubAv/AtXIlMt3uG9FQx2zzPPvpSI5h+5aUba8id0m5BlUWr6sPsGp6cR65rHA8BqKLaPKZfBZGMf9hwiy9rm5h7aJfWDRvXuM7zeZ7Pa1yAuOFHm7bWVdWFR1Mlllx8ksjf4Ckh3E0gWxRCEf0vrhu3lsWkLBq+uWY5632bGAVlq0bAFAARQeUBSVKtQq6QwuwosjP0TcC8wYeV8XPgZwAHlwfezQ9fJPGNqgznuGQoTgFyn/MBahUouJa1l3v7JYF6Is1XD39V3hpr4ne4xM/dhBEmMV9ktuSU5Uav/d0SmBEW55Y7cVeGVOFT9L+GcC+g8q5/d57KawLE6w+z5PtitlSAKmLjHlnLnVJhe6sas9/ZlETJupLXi53+wv609i1NEYvrV6olq/L2+urWTkxKrMOWeuKLuFxaOemxRxIsdcxBYNlcsFihpyEe5VqY4MqQrwKUBb9WFMG1mRGAEyU8Aj64KlcJbv3Mk8QlO1S+xCBByVVU1EtZs+NgVrGpkAaRUpYIl9Fw3ijTU49WkRZJ+lW19aP3o6koCVUheY2LxjILejgQI18QtAlp17iu6zIb5/Mc1xjD3O7fWz/60Xtr2oaZ6khaiQhUcy+AI79GLKmIBg5oMg8X6fg3rng1VsDJSSuj5FObtrJO+qJeGZuYlBViNQ7FkGdBG3isNWzYtKDK3upQq5DCZ32M94e/BKScgYGa5RvPlIWbPbHm1IYOr4UCCBGi99dlst1rS6K4EhqLYzUJvDoTXNLO5t+Abq141W4yzeeK/nfNIH+RmpBB5lt0C0o129r6k4vLh3w2hSsWfqoWvhFyyzVHPIwCbx4lkMcVFVdXY3cm31omSxe3OfBCYDhQvP+p3nB+XZUfCaty3Mt2vBNFC9Yu3MH53oq1e//fFrWKU5nty+cWvzdWYJFUzZeuexNYCgnBORKyeCEjIsgFNQUz88FsVtjQ/bur8+YHX1UnGaA/uz777JLHPaO0mIDO87JyxA6VC9hJdVc6oNqbPBVAa9p7Ox7H4/HxeHy0ppG3zYa5dWdxnoCwwdMugM2a2fN5fl7nZWP8+Py8rovEcfTWWj+OdvTem28bpnlSbPgu4N67irTWSIvj4AGIiNbuEymi7VCYUHYbqvnjJIBktNa8tTLKMs7cf8fkon3pv3h+iux3xflNDcv9RUeaTZ8TUxK1q/R4aDX/TeyWisCYRu2MEd3blMY2BI+mcwlw59K6XpgdejfXVqzcFQ0AVKQ2NNufBBYpCbEsl5Ko2YRgN1mHant4SpL4l+4Mr8ihrbX3r3MEtjHa6pG3v7y/XqbbOgO3UL+X63ZrlwclzF7slJyPr4yTaB5EfNdU2T5nLM2tM7eeTmtD5lVEyc5pAwl/HTYNgCtTbQXf69gsTzXbF9Hw5YS6F/li50fKpylRv2KS7D2B6ipzEHxqCsTyeSeMuZ15j1ITog4zHDY8eMZXMmksEzMzxskrPn4GCqGebQEEQh12GKrDsAxQ+D6qInt2YlWBMosYdjOBCLpEKKeqqnbtvX/79u3xeHx8fIDGYRSa2XVd47pE2/TiksMG8zOv88ePH+fn87yuz88fItJbVw3nbw+lHibwLWOioqotc0EIfC0AF2USgjHktDu10moxnSyrGn8b0dQjJIGMCi2FIiX2+uyNVJzcykLXSEGaLB8ayTor5YVfv5iC0/CByeuspzO93jQbp4vp3l+Qojrna2lIdYjyDhzYRYIvxRjd1HwZrIH4OiPWvkdlm+L1yBQSWymLEuIfpxeOhbTJ50vRNXpV3rrIWsTKV2TfGnDHhjtp9/5yPZ5jjm4pkXx5YRnikrKvgmIaH6L/07sdiylUqEmNyB24NjktCyMXZxUzyos3OJ1aybzz0SIMFrYJSZ886U1Kgxqrcp2hmxZteB2SrwepzD0L3dIg8OZKjboW+i8aA1IAv0jnLYxweyGYM/ubP4dDNfYgDIhnN/ZnSUTSe4EC9HOxsmJKziuE05yqDXQBG97OgEISoI0ZXBSz1U9okWSeFemWBcMrA/qEDKCjANJVZFxmw0RVKNra49tHPw5tbZxmwHVd53XRoFBVNc/0b0Ofqm2Ma7TWAJrZ+fl5ndewS0R67x+Pj8dx9DD9B3eZH3MD9NZba55DyO0/AqEy4MZJZIQfTwD6QQOcK3FJhlrnVC0N1l82nCln7kaa5KHlwyonF41h4q9HwWd4TsjgjC7aBmJxN0yMlxQhiwliWWIsxvj7Nedh2XKynzvSx2eFQOVFfVuem3Nn9ny1e8216R4bOx95/zfdLlVsDsScnmuNS2n+0UDMmfoCnig6odgf06L1RmwVWi2yJFvCN8/tNb37KiK7HLk/9AXAcf+4NGHGCMKbumSjlXjgywoXqvq2lRxSKa5zEq2Pe9CfRPKh6tyiJrz0vcqeTHV7YEX72xj+9cVincmSMrXXpeClTlk7tvzL0rWKwW7jK+usxLrujVFY3MKxtpEZXZ5xQIC4qs9hofKTl49uBM6IDjNZh1IEwBiXQESBYdSw7UBAT7oDpuoJV6hEXWanW2D2RiZ1CERiwVKYor3LPGP3eE1R6V0Bba2rxAZgp9t5Xec5mmqpXTTSeJ0XLlzj6qrXU4fZeT7HNZxYRz+OHlecCiAAEJsBPMmE5qE3zDGaMpvL+FNTwSo1aDPHzLFMSNwCJqPQHKfbjFxVgddYiGSIuQm5AkBLDhSbMCWwbG5ULkOySKWqzutI/g1el6l8rJNyC9ZP03iabWo/jPdSQ8TEszm9o4kvqCVlE9n8Fj4C1YYvPQ17KNCKC6swBGvgZH0XkF3+5EO5KFme3IZwMZtmjT8BmSnbkj53LHj9IJONcuk9We+uK9y+A7wJ5b0l2Eu5+S1iJqxFLlL3tZC1EW/0G0m2uAXTrUTby1+KfNMNAWqHavQiA+FvREgEeidXX6racBxYIuOzc3IDi61Fr+XJbUTqwfurRYNdzqYmnQ4zJP+mYObEDRqJOOHRUyD7PjIjbJjBhg2YBQcluxy9N1U/XBHmSdUo0NDbBCDL08bQphGBqmtMa6jHFIgvV0v4A5va5yMhIv08Tz8RnozU/CSu00RG780Mz/M0mDYVawGDOkgbHB7tf5VUgKlnBdL+7ftHa+3j46N5JFGcD+4TWnrvIp5Qmq5/JJRvDq7AbGIeGV9MsHYkoD2gEyhc5byFYJxbUPCt2BdlrtBqMlxwR/FRzgGJMZ0r14KOWegdLkKqiUwZDQhotdb30mbAUfJdXcEZCbwTg6ZdISpBdWAqATK1F0DKbPSSAm39Mo0ee4++iMG8vf4ieIo2XMicD9/wPVmkuGXl6U3SrFdwyV3mLD//rctFJbOBYRkQ5KelPBZzMmVkfr7XT7y9nUXfzwhcB/ddA7PbL8IBtUYqveQnInN246tftnoDZdZmCWaUxC/Q+nXNW6yfoDUL/tXrRVZ8ySp7MyOyIKVBLaQyJid1Hj8Sy20jrvyPxXoRd0nzvVFjrLJQRIaZ6tzCJiLqCZY1YiL9lC13JwBC2opmRGaLT0c0yiI0NUju7B8v9jEGbZiZDao0Mxt2gSoGNXHfrze/tYbWhHLphYFxjed1judQFVWott764/Ht8eit98fjENHeuht3vEoRCTejx3ii+osK/cCdBXKOpdgvpW0dq5WfgVpObGb2DfmmVndH/K1qisi64l4+7LBWa0QpXF8XFtmtWpqUKiPzgaqfESA1FZXZ+K05yX9xkvPi1XA9bEHKtSkJQTJVNr90qtu7Kr4jwGwB6+9ew9vrDVix2iY1IxeKcP9atdde7oWezFQdNVdzhN+UManw1xfnB4aZZCJ+8PXs4a23CmFhYr6z0usGlW9btMxg7qT/ogdcyrs5tbBAqBYR6y/3R9eWcamuWDm1AGfL3OP52phZOZav1eH1g/+z59554a4vRPl+JbO/vbt27QsOAQS6MfckrUSsQbaSHhVJ5hpgWoFIcoxBcpiNMc7rJIwGT3gswLP1CJeXuFTV4qAsUjMfjusA7m9Is1Xgplb7FqKtegcSRRL6HSa7+3LP50lCVa7r/PHn5+PR+9Gen8/hp8AYtbd+HBD17bs0u87ndZ52DYP2Q1vTfhyPj+Pj27fe2nEcIw6BEaNV0wFRVTPT1r1TSMlHsxU7g7IMRNQwb2Qkmt9W3YBpWsSX8eTybbF9Fi2AgkpCtCSm5FaLKm+dQouJJ3g5wHQa3SQVBy9Ppw+GhekxTNVSkRqbVyZPH2RWXdG8MusJEoRhSu5bA5wnc5ELFcmcWYI1vIipItRLhXfLE2XfWJ9+L1Xfg9tqeVg7Ww1JMjB0pQkTAkJyzcBt5AWpof/C9a4FW7sLAebqqyxtS6/fvFsgKasJpYh/a0GKqtfyZPnv7/QJOWppiUhq1YDuRbLQYkahe3FcH72paQHwSY99PcGUN6nfcY7pMsolJIiiKYvn1l7JOiCbOvU1Hfj6yOzbi7jigj7JePWd8zuShA7xYfbxZcAwoxljPRCbpoDzusY4n+fJyJLveQbYjy4eEePZNEVaa4AMXI3a0QCYaGtQqnMSQ8AwMIqhVEtk25GEB0VJqXXlmYTuItHS1hpBG+P5+UPw6K2F58LVf+1dOyEUjw0a17jGNUCKULU9jo/v379/+/bteBy9d9UmY8BzVhCkgQ2CiHOSoh2aKjPKZwUPAkw1oFhVUg8K+m8egZQdmxkJk6XWuL9ki2SmTUNSnyXGVFoECHRP9fPN1E/Ynbyzg6oLZBaolLBOE2PKj1QTF+YOgC97H+jzOh8NrQApPha58kbZqkpdz7rhUXH2wumTSlXKjLZZYUvi5bKJ5s+7DLjP123AanRTA5DqSt5Y4IevxfyCTSOrKLT5Slys+FMoMO1/fy1lZpcn5dfXuDPfy/atd23Z9YD13n5lwQu1ZLMjYatNFhtV7SLmbfB2EbVRn6+ugkT624x87ditWSVr96eqyXJrV8Ur+YOZIX6rc7XArXMh3l8me/0453u8ILr6vW0wT1P3cB6acYzYIBV2FYbVnsYxrufzHAyritnwVg2ab4Xq7WhNRXDw4fhmZoA0imgyvwp9BZFTP4XqaNLmYKTrGLvPBxO/AKB7fM3xOAQRaGPjfH4aSPXzGrU3YetKUlUVEudBEgBF5ej9+/dv//jH7//4x3+01o+jU0iyqdowmsG7D1MJt2+mhLM4PQZTrJKGiPtkDUe6YDaTr1T6s+VmcUh2FlpfkMFh0wIa0p51CN+CHZoSw3NvJ9Kljf9FXEm0Kiius+lIK/CagUjqYUkbbwizmof3GV8YJAA3H2/E7y8tkerpFGxYrqkuSzRYPAJNJqWqOC+wRKksRE4cXOZffi/BugqD/eNLQMvS49C4khsAP5uaqkJaad81X2vwbjLpi+tVaP3Co1X80tgJGOsApHbmd0pkbIJm2pXXQv9aptyg/9eeJmpw166UUz94xJE1e1JL1TkxdoTfSCOhdywCMjs0oXbVR37SiZtXb1axC6wsb2lbvlbzLgngyggnSbA1EzNukjUBYnJkIcVjBGgUhtfXAz1d6/fjcAlXrEHaeV3Dhvc+HL1EJNckAY7r6q2DGDJIalMxg5kAClE1iKg145Awk4jZABFmI1AEqjporQwIBGGhWW4krDUURaQ79qnEIVzn9fQwzfM6j6Mfeqi0dEBH4H/v7RrNozd76x8f3377+P7b99++fTzceXGZOfdnDyG53VdyiRKDmaGUqpIxfxrTu2KYipFqs0m+P8GOVPHz7NfErdN17u3QFAHLenx6R1deKhFRX5bJwrSY5DNTkyxpvIGBf9WlZfHU3CCj2SGU53cxg256Wo1iRgMsaD4rjV7XPLjzwILwMeNzNEok5CDVS0hReAepZc0ck9JbV5KjfigRzLXslysBIqJHSk4nfWJ3uHtG4mHfKzPbpns7JyO9lYjvrknJQj/BRswFyHZGmkJ4yxC5gf07qF9MhG/avdy5ScC3YoMvH7aqppOoSuL+QDz2dRmz2ZuDe2u/pAgMhgIXfJ/gugvSnWQ3cXEbhKW5KquCu7nY5Pbo7HfcYBoXuPa5Qn12fxkBwq0jw4/UNZcCtRoQN/1fY4xxGc2MAqljcSEy3PziejBBiJlJUzcpXdelTQkRXCRUIve669beTSMbVQStqRlFzDctCSt0isgp7Qpm5l8WAirSXdM+n4OgW+oHRm/9+Xk+jodACdN2aO8Ccpg2saFdtPemIh8fH7///vv3798/Pj6aCAQuJK4xQD9tXiII1DlbRZsKPSrRZzdBwkSBy4aK+nB42CzcfuoO1FC9QyEUUtJGL/GWCUQiLcY+xBO3NsSc/HA3GofGFBZT3ykTSOTL1bA8TZ5YJncx809hTlASgCk4ZoORJrx5Z0OGAMWlRxJ71pJ10yJV6DU/rdBQs4QQVNC5VOTRS5sBTBvxS+HxlE25qUu9kqL0pzp3ifYUT4JwrgQV/GCJ1aCGNRzH27YazN504a+uVHo3LaSG+UWMOHOWDJ+6wAagP0V/7M/ebu5fvwb3ySPLp011Wl5c99VgH5XlcZma8/uWpWjOti0+NoGYhMfIG6TRtgWh78Rh/ZMvyQLAuZ6uueXmgYyke00JKHeiveuHp7X3b/GCO3aHA46qMqNsolJyjHGmg5cItd/gjt/LbNi4bIxwEvgp61BTiGsoJA3aWparvnS4rktFwEZRFeF5OWqaGVSuTD4W53GJkK21JubRIGZiTQXSJM5bjD5KOe4y7rDDzTNuAWhhkTdSRa7ras0PhYQoFE0EZqLaHG1669++ff/+8f3j8XG03lQ9Ina4NWwY4M7bOGDGfd0AVHNzdKpIzjuyRE2rpGmG0Ilqc0SB2IkXETMC389CsIlyzlu5cdcL/yYQ322d/k9wqiOPxIEE7lbbgp5XfC7c+WrbYlWfYm2dqquqs0Q9YpMxawUhnmqrcL6RSjtu6qKg9vDWL1hXiylrv2p4StMqrrpTDZ369ypfUkp9gcIrcpZmTY9plkUWrp+2nuX9LOK1OKyGMYbcyCXzSyex6ZqiWiI9ewUsvplpUJlw9dLXBLSlWdx/vBP+HbHk1uuNirWlqyzEYTtmMO5inPLxCFWKS/ELRX/FLhXvLcBfciW3BBc5SuDUWxvxXcWaZqeKkr5DeY7ZpHEqNJu774UH1pvbhI+9t5572X27cZo5kFOLsADQMcyGgX7gVSwIvBQPiKrqDKSwaQsnqCUTER7743W5ET+KiilMCFRtmJsUzcjhe2NFe28He56rDjNLXdg9rzlJV1W1bPAiJLuIttYFSqI/ugKe9YGtjWFm9MQ9TZo2hQkwyiB7HP3j8Xg8Hkc/RBWA2RikjeHWfzf69BaHwXhIKEnWFtdiRCHAQQOhEIiIxbHynjwj2u4rpQI1AWKBc2fRFZQqH2FEEK0gzxjWmnhc8TcAogw1Xpt+tbas40mTbxeD3GzPZGVxwbKx5gYHZVZ4PwFTT1GpLBdrxEa6yOnlTP8EUj+rSKeQX6orlO9/N62yysImrpaGY7XfzZDBpPXuJUgEqM6vVNRFKIaNoUh6jwPZfC14O0qOPKtZJtOC7l1YCDFpmQhX2svy4KRXtmBVJe7ujoDmYuy8l14rB5JtHbai8xzltUzv2aIHMId4pqrPhkm1aTV4vPxdenCv7qX2OU2cG0PawCoVRPY6eFLu47dWVaa96EbA89r5N6rHvEpyC/l+dIPKy8yTMCDlRDZyjIhtHXY5NAuUoMRZ5zbKyZv/xJdgcwHEMVdTALh5lHQjdRi9/XJGEsEYnuNnXHYhbek+zW0YAG1KHqQdrV8eKqqACSPmy3UmVsnZGmGqLCLSe+vSIA8Zw9rRhBm75GzsGWno5v426Mc6CiDHo3/7+Pbt28fj26FQ8ZRB47rMzusiCAOar0I8OEmco9NmQ/OH3LyWyUgBsVLGGeuAQPikzRKEkdptmGVeeVSwYlSxwX6HyWEhR7IYApnuo2Zz+BJior/UJjWvNqjf2rNz4JQuxZL125uF+8bsDmQLiEd1Cz4jg7T9NxeIW2nLB4byKFKFRGW6tzxE4x38mbaCHf+XfhcjJphjWeUsAmFCro96uIWwCpMC2pQodchrisLp09vhc74+gST9JVs3F81CgPKAJAMWZyXfrBRZB6pWo+VcWQVdWmnhg8PZcXDtb1689ye7k7+KzEVeysk65VhSuqAMAkvJyXM3usny5k4hQUoVulFu2Wqc9ZSmzqKR1Cx3wkbKC+71LWJB0qy40GK3ZeL2eZnGP5FestivYhh9sxUMZkYIr2EQmJEUswEAULj538wDpmKlkF0GAagKoWxBmHBJikLZIrnDMIBjEIKmzfssCjMzEGYO9+6ghYCxpQyq0o8eYArRMVSbUgwDELqBXEwErbcMM8wwrbK+kL21xmG99+MQI7SJH+viWZoDXP0sF4qH7Xtit9b74+Px8XEcvcEU5LBxXZeZ0S4QIp7hyEghByKR3CiwhRlhSA966i8p7+dCm4W2MYskxJhq5oxebI54Y18uPhIuqthcftQLi+kjfM7zeSbUArA5JYrJSpmPYPHlpYU1q8Awca26yW2mS3YWawEv/ZpgByQGE2nND+tVATyQ2QC26QUIxMQWc9H9ejuHdgRcil1O9d1olI7HFFEuDRYYSthJHdokbMaSKoBjIpEwWcLKYucVRHRjA678sbW9wnMQE//2xAJwVdiGQAsK3UYqB1aAPJ8FVdnmSy+EkupWNM+fnOEPxSf32mbnptDh9lseprfZczj5ZaWVd9lpM+dG0cyF4BKUkafsESoEbOfpECteeG6HWYcRsnRfZmV3q9xiKPopM679W62cs4dcrKBLQ4P+ZswgFrgxh2PYuOwawz284ed1V6pq+H8R6qn46eiSO2zCYyXF0603QFpr47IIBgUyYQ4VTZvSOGzYsDEuxiayy0YQqrUGoGnz2KMxjHg2VbXcPUyhQFVcd3IOMIogTSyAqHRQjkd/fHyIyHUNAMfxwfNTVVpvIp62U0gOGxpymyISeX5EheJBTcPMhlNI6PvXsITKgBZ+c8uD7cvCZil7EVNGmghyY9zURgC4QYAiqgoboasmfyXOlSGn7qeeseDQZJsFeSvga2PELEfn8mNnrG2ZkAyVcqnWrkuB5eiSCvyYE39Fzln5dlLFrGtfwc8X1x8nGibGQHZJyQwP+ImytE6VUunWK/BQaiRkAkpxw+3L1hdMMuUgVp2VHdaJ5ytHLFojxLmjhjeHKiyPQcXSzX0pOvFpGiQ2dbKKSt2Ji1TY1ABZXsiCnPEcV8IEbyq6WQcxR5UC2BREkhWFHXKBxFVmvvgZbnyYcoVcD9+sxZrc3nT2nGN1876UYJ/Cd6Jo2r6TPxgGqGQHTA/QlH/TKru0tpq0CtxlnmCpfxkDLm9PtN6uHPfiw5wLzJnqQZ0eou6xPcOu63xeFyi+1Qtk006gtSYSXlltzSU2U/HKdlPSlekHpKdctzEgIr5rLBrQzI26/qLRztO3XV1Oe20qgLVmw9gJwXmdamoqrXdVNYoGCAsItqYReuRoC20KiFC6qiO8iKooaXZ89MGh7htQbdoAIc0G0dp1XTYGiB412eCAh796zlMQRGthT1ZxUz6TmJEwW9JYG7qJyhij4k2RwtMDRwE2aQIhTTIjl29AQ1kSdxxf+blYo8b5ZtHeWMOXrPcJVcoQfTYuwaw5HaaSl1+BeF6wr78TyfxgsmLnUI24mQ8kOV2WlCovDP3+Pl8+yorOYpvdg4DWuuGtIMhmTRR6W28UIZiBG9mjorzPkfizTlFZP3gQ215uhYis1pmo0lK4rzGgsXKQQAtZ8INp3HJxPVd7CfiuXqz25NmSkv8TyQBMz78gIHvPheN4JRP88+lYy7I8XXWfmTXEZ4tDqHsI8nTANwPgH31boxBwBVD45il8NeLe0JQK23DnKLu/FALQLFQnIkRzvrjPlhWz04VXa5zp+QiGKVUCrlMVcRbWrXVLOplExIPuyZfZXD2bdvXqj8J3cLlJf3gmfDMzu65BczgetEFDa2zqW3fFbRjqDL1S13M7R8/SDyxNtUatgeCgq/ytNXPvq3F4LOUwp23QojwURBzoblRNT8s1TCIFQ0ejH9ZLE0FrvdX562GMZ2ea3kPsg03bx+PRe9cmkWOINi6Spirjuq7rQgR0NjMb10CcEUZxhvGMnx6K44fKaEb9EMgEj4yQmtA3IpgnMtKE8AC4Wg3K5uWDHt+wzssJBsudBQAXg3gOELa4sWnX3RCUaQ5icrcvqJEWJKmINWzqGabVG6l1ZGsXNJ0FZuMXNSnLfztFZxPnJH15Loysi/oLz8uELBqQTD/7/lqF203Y3sBDElz3yCpfA0/vJIrYNxU0478cAVjiY1Y7JW0ZmoLM6h1ZcB1rHWubN9M75pOlDk6PzmSEZfFYSvydYou02NVjFMSXRrC8EcvW6dEJLEuPslRLC7fip92etLsINtnqb8RKZEqwOWIJxjFgFVqJCdAogAaRIxVMlbNsI3J1CisUE4y9nETOiSxpWZvKWhgkrK+rBsI0V5WaldK8DlFZ+ohYCHn/kkJRJSHAACikkJHjzTP5wGJ7L8a4wAgGFHg8fwSnm1CFImKIiE+CFBpNRNwcLrFKhVvURRB+ZpLkeZ1Nm4gne6AAnqATOtPmaFMXPASuMfB8ttZEFSPYQ/OQRYLN1JQC3yXsHG+AumrSSSPFzFTcPgM/wdETFY1xifA6z8fjIPD88/rx54/PHz8ej8fjOFprvvEZNG+/2760KQhREfWlR23bSWewlC8iEigJRESaikfKapwUIBUjpTUHMsw3dxLkHM3Jsc/H29ws7anQsoB5Bp2FMCwQXvUpTCqG1KzTmVcmK8x4QWLJAqrp8xfKJnJuTX/TnVvftviml8fdwpNTlCVE58SPYygS5XYROltRd2SbXZgDAeiMRCy0r6k+C8mPSyVOWl3uvUjnRUjmIAVZFVNl9TurI6AMgFVNDcXscOA9A94XrMsIDawNkwnzG5EcuQFmEmmJATYyMuPu5A11aEXBpDeTZbM/Um6qeWl8F+bCLt/MQYh1gHDRl4JM6yAHXQm36qefeArHELopqMLiHxEnJcXmPF86VXMG6WDLtJAs0u20vF/lEbkxuSzvLk3NuiTLdsOIJjUnEjD8USIUhRo83tLoadsYtqAxzGxEnwwGE0Hk6y85LCJ+XrqIaiOttTbGoK8gqEqhZ5Wg2RgCeurP2kAQJnqVciw3nYkeIqrSRTI5xhAVNVfVh++16kcnpEnE7Kj4qVtuugiWJNlJnucp4sl7zPk9MqWbcdg5BgAVjGE/Pp/ndV7nefQO0sZAagbmjC7BuqpNhJqTK9wbSWtXrCLyh3Szvoj0tFX5+TC5EQziviVM5ii9B8mtu714gbG3HJWzPNmxDPGT2VeFZbGVAz6Ht1NeZJY3H02cmf5snyCsYJVFo+eUbD+9XuA4usyi7t7ZSZWXm1MB9vEQtf2Zl9bsY7C15eYZlVL9NrUvBklnWUIgDkTdHnE2msSdgQv7YDAWtR5b7MEZ2SKRsBOFJT1hM9E1fB4vwjLlRMqIrVkbDVPqv6XywkETEyMBn+XJVlNkmllQZ1qPVzkbcyQ1pwnTL+3hTu/4WVOmzBblCHLSfK5zJdM4yyr2OaFaIFJUFgJoaGVWSUUDgub28YWfyjOYlrAKxAhKfT0RmKY8vhD8xrJVXsl6b/ZcLrkNOu1p9biKcNl54HqrB+YL6u3Y9jtgHBHf6dq1UKnXGObbWiVU+7xgY1xAq7MTJmtXPulQEVQbYhEgqt2fCa14mloEIjYyuwRN6BlT1MagCs3EvRQ2ICKe0AfBm/0alwie51NNSbbW/Oia8EFzjDEAGm2M649//TmiV94Z09ZKDRJNZR907b1pLHck1TJ/zY1UmqYRXyp4Yon04onEEQISY+azIBlnwa4auIU3X4GwuFFQG88k9ySWGWFOEOeVFfEWfkvnNEr995ZU6zZtZLGCsP6XT8k6K392ycLE6834INH8ta3cmy1Tl1yWSoWPqxZ7Q/pbdQu0v7anwkde5iI4iYCwHfvXJpq+0mXdtEw1zq+JU9lp1uambJ0il9mrsMghl+o1JITLC2G5x7DO3uxSbunbxiJMv9TdarcsWJflyB7/5pTZlo+e/qiqE0rapZYd7/exWJc+wfmZMbvOKi+DJmIqVixGoR+mjadMd9PPKtn0luNLMdSpuBHqU+y1kg01gt6rKnOqb2+usqbVuG0/7nIt+vzm4ZqryXVwhjFY6pUO+xSFiPEi6cfnXoyEZi6vQcLEz82FeEpj0aZjjGHDIztJjuvyJHGQBiMxYGw9zn0cY0QSUTeoADRCYDYWoI/5knt1RJv21gNUfDevC49GIRRq1zVEmgpFCTOBqJpSjOohOpA+zGjjPC/tzeOZ9Pl59IOw6zrP50kbNCOf5/U8n0+Ifv/4EMA4aA2q47o800+o7yqZ9CFBvAbD9dzUyeCuEKc1pGtTkRZZr3O4FSLqKfGQgYXllJIJClzG9sYUwArqE+a3CfjKRYjl7KI1lc/tJa7AOWFpSyHshrZ3ppY7D9+b8NKRn143SSV4R4q9yTL1rfSdrqW8zrI3WHhr+Qx5KoHBgIsCXr+f5F38Q2XNc+pr+OS/RoQUxAAidMArzt1/LKyfBogExGpjPLfVUhjGbOpCjo0uLGNICeEwbyZ5N5NF9L2QNUUAgxSycx0wXZx30k8ZGQvxhd8KSau5JT6RNaK+hmhA/lMv+7RbJ9mURZa0jaYvJpClqW94aHrFOI8Ort/q8Rs/54vr+NwuLh+mYjdFb36dU5JI5aDYkAz5qNLYKkiBFoexsJNGFaioMcwboX4b3JHrlTiOu2Lvym+owEYR2mUgbAy3DnnOBpcZfmTWGCYCM3OLP5ZpJQJtzVPrw1PrEFQYLVBYEQamSxXmhhbtQjGG6qBQ6WYDxmtcMq7j6CA/yes6AT4/n9d5Cnid53XZ5+cPCB/HA98O4zifJ4wPsqmii3jCh1gvhWwsQ4A3WTLwJy1DEEBak0hrpzuLUtJUlwwlMlNgumNAXxmBt8FemEM2wOXy9z0X5USpL1NWMIMUN1VRqDOQBsVksle2c3SqYPH9tvy9dYTLfbzCYhk5br2S5U+mgHaCht04q3qPgiuKLKaztStLM2SpLgTuVMFkezQhKObj+gvo51742dV+MIcrC5xdrDVcNcf7L8jjYZB30jahKQ4SeZPnFgG9LuamR4FVdNTFbKrMulLhSMMyEqUzUgCpaUtQX6rsLFYsJgrm6tKLmOuGsG8vA7Eo6tPinU0PgkSc0WbdSnHNrVM1dJwTRwo5k1I7J7sIXKYLhLCYIyHxUhSJpDHnxvGrjMVtxk4eeov+t2uqFrWee6NDzFUavZOkWBrew0SlzQ0dVKVHmnlUZHCUq+RGAGNc17iGDY1TXAjfImux4cmtP64vO/qDiPTRbmxXD6/x3Almrrjn8WAp0sJG5BQSaa2pu6mVvlqQEDdxRI3nIfL+KpBbu4B+Pk+A4xqtqbXWGmyM5/MHgPPH01/+/Pw8z/Pz83kcrbdug3aNE0+aqUr7+NA8w0y1BegLYT7Zcm3pnt4JUkxLTyQ11Vg9xBNRDBpAlXYDX7j8KvV9O9jqi8He1K3io9KE7s/W9N5ZCmXGKbWseEhkgt9b2H697vpMQdbrqxOYq+V3MTCVswC41WW5/hTvlRHGv0pGMPMLIspLGXtTZfl3bWH1Q/bHX6by1PnSxzKFTxp6eCtoDmP61is19Gxn4v7GJS9OlwLNNAynsuEvEB7nl43ahCCXsIF83K2MuRYp1qjTHcPFXEZ+hFPRRSZnmxylOdX8wqto7pTcrxC9WFdkbW8anaJ4yjrJ/OWaYXPOlEcm6qIVG3KjYX3eBxfvrG6rVXT6edZYhBgJC8VgpXy+93rd58dcE6Q8ThtYuDEUmHnF60kVgTaB5y0wGI3qwe/upbRwEsfg2LAxBiBDpWkjPWWo+3yj9R6q6UZ/TxrqhweIaDtiJbaoxdJUi66Mvcomgqbdcy0DIDWwUURENMSJiNRfVfdxEBCoaP/x+elSSvRwNwQE4xzDxvn89AOyPp/P83mOcT0im5vLx4wlc4et4/fCXqlPcjF8O0rOfSWZKBSSh6ExGBvFKVIKEwIypvk4OeWXFIItFqXemKJge/gtM6UqWxFExAKgyWpfKSiysCLxOgtm495XX2paAkq+lbN6V54w+Zhr5bIiPyYwsaqZmLtZbiWfkvXnCSdYp2u9xpeOR3828F2Ex+QUCNKqU5AQcLi866U7Z4W52ZJXCDDiUySZ9h1pF5ZY/xYtUm+2hWKSHFCR+HdxIpAKtM2lrHsiciintEiRuS03tpYllE+GZ43axs2vPYOkTGO9mAJivprMMQMig9UpGeoTzcngDnDzqUKwY2cyXLpURGvLgiSImGGzOeWEvrkwAAT+Lo2fo3Yb1YWdQojlMntdnhYj5dse/G+IXb1uvMtscB7jHpAKQFqjIhw9Nppvx7VKbOCagi7zNjRbCTKnU5hp5vQxElFPnrn4Fz0wsrQfj74hpGlrrbkthL5NlYzc+wJ/qXXfACC9qTYRVXfJuyO4xyJhoDVX942C8zyv6/z88cMdtc/PzzEu8cRAKsNsDHscoipN0/ADjbkGB32jTShHWMgponlCZF4JbSyz6RLpscTskrlkdofzfB4Lt3x9vXtm8tE0Zd5eWgMtqlHO9BnhF6yC1KBmPrkV/fYJX39rQtQfFmOXuhJfcxk97RZzAiwIv6pML511/FJEYvD5nFT4eS3KC97fhCdtM6duJA0EYEaclxK5Kpiz6zK/zt7KRrCUM+mBrIOZRAqvIAUjkAUbkfLgBfolxclbeb0CHlL8C26hMXCtZTm6YaUII1SmuuK8zMTNImLhc1muZCXUbJTIK4/+5OIk8kKjSc7XgU2/qES2E5moFbTI9YqFHXlh7nLRFnkmn6bKUOBcy7VZ7QuOJ9gnfi5Rpvn48so6z2K8ishubq5B8ntbdW4wMdg1hrMZjRBCmvc69y8N603GsOB0ERE1P9gK16UisYtYRJjxNtVcMxMVoIUGawzDWKjsbK0dxyGqNINBRDzGv2k8UIt2gqqJRSFGROclImytNW0KaaJNM3eQZ4oABOg0NlFp0praMDS1YXaN6zyv59NAAc8ff0Ll27fvv//22+Px6P3ovT8ej2/fvj0+Pno/VERjoy4Ru5tBe7HRS6ywjGzNI3wkHGdQhyVmxqKF18tYJLkERqHMLyD/ft2hePucmJA8Iau+cStmcWauKgknb2JOvy9mrcxKVygvMM+PLFBWldzktAqRetWf/slV7XH9YtO+uMiu14au+l0+tu4aWy0ZRETuzrBMMD9PzE2LxjL7Z+MTiaZ6gFUdjEak7Y9ZLFNOAIBCKQZRpsyeDV4LvCNP/ruIcpdLUv1z404peHSRV6cYOeL4AXq5gp2iWbB1JcRCtWcVJ5uQvQcsvErg+1VgnyajkGOxtJK1VC6tEkkDVrWi1oYeK56OPCIFyZq7YwH94mmX2TsM31vtZNaljInr1R3R/b2NK5YPwYKLmAtPpIfxSwrIUPMHzWCC0HH9BWKotqhZIZTmS0qjwaBCKhTtaNJEPTfcGKAMo/hxj6LB+KLqynwG8YsbRNx0Q21Nuyf4Fxmk2ejdg2M89Y2ERixd4LqgqsggPGg0/K8qTVukB63dVJqGIELAliPVVaX31ltXDwAdNsZ1Xc/z+byucV2n8TrPZz/64+iPj4/H49GPx8fHx8fHx/E4/DT70hQEMFqsenUR3ZLYKiChqqHzpM2/prGsLCiC9OmG/E5WjUX3a1xEsLosfLNP7p+JjJtGAdSWiW1K7uVlVJ8/HKH+N+36q2sD/u1pqXgY/zFmnEvJCR/xt3If8U2Na7tzmRLbabzSKsdjihdibDiUqBYdRmmG87EYsXTz5FiViJGEgaWfy5AIpkgJfVKTMrK9MjvqpI61ZGxcJeCbWFkWl8368Y4yRdDgn1iHTTIzrf9SwgSehXAVoF5TYHkBaLgUFhfyAn7LwgXlYNhQ8sbHU1PYrhvP8/5jTNIS4bcTG7ApE9GUWtlkyyXlIOvxF85dlShmWvzSpGI+LxyxKh2FEdvlxFoz7Es2icvN136nehGiZxXAUYLvUaCH8SDEwHld5qExEMIPW2mRbsGPORExTE+gAL31C0Nbs2GBciXqJM6MbL5c0Fg3APQwIhVQVHtslI14IrNhJmYdcA8rQ6LRjwWOg3VBRewphvru295yL1X4ktM4L35kis9+gsreW1e4q8Ez3o0xzvPzsmGG6/P8Mc6LZOtHPw7P/ta1HW1+1eoVZ5BBGPQtM6CKJJ+QfqQZmDLD9ZHN3ZMSIVguM1jAzZiJ/jnZ94lQ1k3/W+LkKwRfWWzTf8N2kSHq96frqeih38+65f3T92udPBHptEYlSP2z93OiUwmRrwXb9HxEcfHSJFDsDxQNhX2BvqT5bGLlacm8YlkzS6yk0ucDtD6S9cpC03VRP4V6wGuQOMF1FYllsSoaCCPtFCQFA6rLkuqgcY4Pg+xS5Ij4oWp+EWnqKFK3ouyC+dX0EFE32V7Ov6W3LMqzU2rqAptdivOvFB2mMWUZ6uWNNzLACbY4j1MKbYA7JXDMTVkbFH8pIC0H18U+/ECtsviDJWZuzuWJ/msUQlTO+idjK3K1ml1AsXFO7Syu/oYoqbm1vrXGkYWiUANCj8i5rusa17hcz7lUuhy51Ssydhs8rgeUIRrJfZq461ZE1FNJw/3LPjE0bBuRe40Em6N9axoavWprHcB1nu4gtmF+TpeqDhvurp5ECiBV34zhm6/cB9Ca9tYl4bS07aQH4SsAmp2fz/Pyo1wuANd4Drv++Oeff/zxLxG6kceDsltvx+OQJjFDFUZromu8iUBUpLWGVpO22FHKMIrKMu8aBZcBy1GbGlOCF1M5C1FZUW2pYSXr3K43Dso7ZC5A5KpKCqgFz9PinC6ismrku68Vr9iy/LxoRXtblg4H/BazrvcBlIXq3XyflS9K3VLv3nMv39lie4JLvTFbZb4eEz1Xlu6G8rWKYfaOUw2b1CqGKZ2ZGxO4foa09NRbzhXiNorpKCu0l331FIIg5d/e9bU5RaSSZLn8LI7KnPW38Z62jRqaEDL5jbgN9IutP7n6rtNOaJR0KnytUPCLz0sFC44X86a5lS9yhXPc62uSpqgySYttbAsNlvKW5oc140a/4OqF4VkyRqa2sJJtG49qxxokNWlQselZr3dPykBREf3DxvDsxtZ6M1hr3VO5QdSIYTZsGCjaVBoFTVs/DlG5eIxrmJ00GkUaJDVcpafHVA2lHQQ5ZNggkUn4g3g0M5FxXZlYU0WEFmk1U6sOU4pqmJg8qYUr/ioqYW0JKlgOnwAq2uF7tUg/dPg8h11jXOP5+fn554/PHz9U5eiP3nvrfQyC4jQI9wUJihly05wA7hxu4nkwYLmdR9NXllyQFJ+KxlyjoSyUJVf8wMwp+JIB4oEyMLioKHac/Lbxwg39E6hSgZhSyw0CstvxF327qv9iXmbv1nkQ90vDvd2PAQ6PyFwmy6SAP6mFG1+gwl3G+ZVW0VzLT5nDECpJhYTt4Mk30w9livP2Er4dMjZyc+mizFfuzUwlHekejVe6Tl0vhiRossrJGHaaGGaLs5F5+jRmn/OFKGAtKkhSK9cdU4XlYF6ipcLGy0kgDxddSVuwPs0fWOXtpMKLLJ+iOOXQXErd6Phy5z4NkrtdkJU+HbjOnCZJ8lVs178liRdgzW6llrNJjrl8lfxW4pTBb8vUlo0qWGRGkLTGzYV9jGv2t6ZuRpGuvZQUPFj+F9af1FXT0yMgyPN5jms0bUcnGtj9RFvzRKEGqlIkvNOqArSjPxR+Gvw1cCnC6O+xOZ7rRlv4zwUwMVwUVK5Qx8vwOjMEAS4AwBgjEm2VWT9zpvmBu55J2o/EoMcVCwn4kSvBoTTVNmj9cTyaYNi4rgFP/nM9zezz8/nj88/z+Xw8Htpa064qrWlrqs1FS5irzKz3XF5A4siz7FsieUK8j2cdmCmV53Y+gAmayWEV6rGxICdErbL/pgYlJ0V4b/qXVo6+f17vEtiRe869+sOXCbtpUcnw8/f1tKaao6sy6wKnYkXKNcL8Fou4tCksrdwUsR0BipylDUbSzQQjWmpFEdxdOlqpXj4S2fUZEhNLotlPkqai4OrTS3E9dbxgCQATqos4AmHaqqcglzhAglU1Urf07S6hbRfhZT4Y0D0RBUv9s9ptMF/Ma1wHfEJ/UGzyebz6Gn/kRF9d6qt6/g7967OkkvNXq4F6ZZMBU4pk35bfF7GWDPFGr+HG0JurSoAlqU5Sl1svFmFAWxhs9sgmZstSTtmiFgmUmbVqF9wUwdWNknkFBLL8JhQRhQyPwVL3pTZRU7UGDDeBMmSECRr9CHhPDAeO4WoHtbXe2gV8HI9TxJOGjjFQCQGN7hl14KZbbDQIZhahMSIqoGrrrZEws+s6zVQw/GGLCtkq5Kd1SXYKsz9iCSuZv8CGqSpMmAGjIPvRD4+TU8EFo9l1Xdf5/PPPf33++EGz3vRoqio2rDX1A8Zaa92Pi4ljW/I/AO6UoE/c0cIDschtRCY8H5XJ/IC4A1kQOzwL8hXTBYc5WSSWizZHdB/99ZvMMLXXH+fLy09UKAGPAQ73xksJyVY74pYgW34xzqBpL1brQD6p2ecqDoFFDU/jnUR4shU5ZzOKBCqpkO+9ujVcyn81n6CnwsVciuU+Jk92ePfNlYDNfjoyZeppD8BXwDPnTjWcJeoKyCCMbFZYAAEi6aZbhyojgBOH4s8CPS5cILPRt6dDtul+xs7Sre0j7z/K9qCbo8pOUdKYdDauJHuCpbFlP3SreYVm3RvzrmnBNDeb6e3J1L93PNz6lSzt/OY6aTYwROibWbVo5FWN/MSHsV1Tn6BIbQIIlTysiLD0pyxhacEcScMKEBdw3xq2qguh41DqVAYmELg5BRAhlBJM3oQYTY6mrUW4Twxo663yGErVRbvGIMjOQ+BbdvXwU+UffjS868sEyWHDtB02DNpE0Lq21nwPlffC40cdY83aGEZyDNpwlFaBtK4aZ96pB3pKgH46GNzfIAKRDGYKYPGcRchFThfBuAaFEIxx/Xh+nufz88ePz88fx6N37d9//+3x8eGhqaAIpWk7jn4cXbWp2NEOH05Nvpf0OkASESQt1U59lblCLtfZO21FAAN1kQelecQoyDoleH9qKjbT4PmeJ9fK47klmf3NP1FTooC7mHErftMTV9NGfCpuxlSUFmsJcjEanz2iJRawi17FBJSavzkRpFS0V+yW+9dlAGeTqp679JOJC8zAhBRmsPSXTThBTfL59hqSjmpqmksWRY+L7E5d70ZenTblhOAUSfPRpbJcJuTPq3aZGku+m3J746vlH2AO0yqad9vHSjygPCplrcqxW6ww2z/7+0sv7sXzLqmLZAvjTj1qsb9VmQKhmFhOn7WYVdZGRIYUCYiVad41vHpbxk5vXDHPwmmL4XUaUZfJMX/DnKw7QSQ1FCmTwWJqDjOXqhCeURkq0vshYh40+TiOawx6okzCzwlQVaWJyy6z83x6PJgHrIhI14bjcZ0X6Yc0CmkwDBtqXTAgh6e/PB6Hiram13WBOMcFMtK/qaq2cV1jjEEj6aZ1z7vauudfaD0sM4qIwAxTFhNpg8ZGOp0Zm4wh7H/+8YcK+nHYGP/9X//9488///jjXz8+f5hZ78fvv//++3/849vH98fHh0KP3ntr2rT1ru77aAcgYbiCSQSBWOwN0w5h5rHLUUIYIEMexEwUgVhJftSATTvsqoIUc6wc/cJjk6HzsfdcyfW3bdJVWPcqkpaQm9TbHZHnzJ1a0IpTESVVQeGLpp8mSM5Mcy5FLXViAUQl8uIhzEguYDWP+Vx3JC2NXhXAhbxFp5wf4axJA1xwi9skw7i7WOImdkmiQPQ6R9yYz5R6K8ggIaJOAp9gVHkF5oJhiXiIuZz/FHLn9qmUkVrQLqil5jJ8KRkYYi95aVkMLBhabPv1lcvDGJpNWkxd9AZL5eXai1nXWS9iZitqEaib4pQicx1uzrZlmTHoZUdK6HZYq0cTeGVRNIJ0tV6JCECrKmc9q1b1loopCAC3FhpMdzvtQtKtnK24WGKmAiCySITC+wn8NSlyASMaSVjFtIn0pgMqFOg1mjYXDEYzYojvcoUI/CBEETGzcV0q7TgOVfHEPq21Bx7HcUDw+fl58YTI8/PHx8c35p4vz/nce2+tj3EN8w1gUM9ARA5v8CBJg7m5v/cWyZJFoZUXWp3hjQb6gWgaE4uUucVKI6BAWj/PU2DP8zzP53mew8bn5+fz82mD//j9f/32+2+9PTzTT+9H70fr3mjfcCCtdT+9RkVUmhvLnOCRdCJGYME1oBAtxnE75GcuheMhAvcDFadrcbFWLJyWBU9x6GzwhZet+GmboNMc7Q0qvl7r8DZGPXwpOlA27laQkhuVuD6QJEo+j3HLDuZzmRqhHuNs4UZhxPtLc5lYNidugmQ2LrZ7F23DcCYRK70izZxb8XZ4BzLTiGtLu4AuLI7m+8GkEBGDZQO9PGPuF8kSgJlvsnRNhw9NdnHm2PY4zerT81f4sa3q1mELnya/0hj2EZ62/Jef1g87a8YOhxcQ24zqXP5b302UXRDtVqeTVCCc6+9SzteKywjk0YRLxl0Rpe7tK7ER0niRwVV5KR7xxqJ/3KWd+tbPWEYgDQl7tMZa3FzN32dhsmzhfsF+JWMIIRMgCK1hLrqbOwTYSLr5srdDRS65OEhfZVoeBOCnsZsncRNVNVOzYebnQsrjcTTVz+enRHKGy+xTRCIvNKmOq63ZuAIjtanGpmJP9VMGBk8vESH7gEjo/r233nzzGEQUtGExgSAYNuDGZ0AYagcj1wVF2J/PTxVA5cfzx/N8/vnjj/M8ReT798f33799+/69tf74+Pj4eHz//vHx7ePx8fHt20fvXUVaj10GDUpYAlyorrEVNLKpCu7cXmu/4scZzzd1xOSnqdMV9803Fr2Ay7e1xoVBf2pmzfXsYjm+FRTezjRxMdtnGZNaaJxoE1hv7kqo5jCUzqnmMgx1dX7zqrEAmTjFdfIF0tKXu6PtvLPSIP/KcjO/qEwHM3MmSXTmhl8uAWKIogOkRFZEOrJnn0qw1akkSDP+HAtJ+cTkjMuPsMgJLADiHD7MUShGqs6JryRyPcQZB3THIm+qzD7PX1MGFMK9rOcmKaY4nbTP915vSbKPIMV2MqZN+VTjIsz91MH5c4f8TWm5XU63Opotl3Dbr1iirpwWsSJaV3zv1j83wb68vz398uZyw7WINIMuFsVaTr3t1PrPa+eT+ZY7Jqv3jrXsmwpEUt1ZTdlMh4xInGZ2CGBqZh6NQxUZMOM5xhlJQYnrGu5IFZHeGpqIhP4zbHRP5OO+MIs9ihCI+AD5ES4dY3huH8+XA8XF4TGpRssB9W1hIhBVPXKvF20EiMBllP8zgmcEsDhMxo8Wc8L353V6ROf/+d//9ecff1zX1fvx7fvHx8fH4/HND2vpRxfRx+Px8fHt8fjovvwIr6/Vmt7pWYkOc3xSZ0sXHPMPUGy9PQCJxfhcecZgT+WiCpDbgO8fyiaT3PWzqAmpAt9NqizB9cIy5kymm6YKWSVWNANMPwwQWzkYM1w3MPaJFNYIq/OP0gGQKMmMhOeCFclTqZNxCrQvsGKZdV5sfEjsBz1Jx7oAwtSMs9Jl1c8cTwcDIs66EsIPadWQobk6EYjZzObq3B9UUvX1tTfSD2JLgmfH57ikOsfoeACfwMjAwQriTDLP7k8psyoRNcQ3AfKOjDXSG7lngSnlVwsJlo6vasrSqORk/55cs0rztbQJwHN9M6N+9udnOQuEI284TTMOmXV/FkJdzWZB2WWxf+8MZwF0YhOl5Ezo3gjkg/bVtL0J9BR1CzmSZklbAZBhBTUzsrNw3qfr4D6vVDyZg+QUdzOtP+O+Y3WjDQSZhd+EHBjJqmX2hYGika4zLZQE4GGknmguZ0C2xc+ktzD++BYrz/gfXF2LNmP1xSRPrwT8lFCQTRukCXkEhEv/8a8/zufnZeOPP/58Pj+16bdv3/7z//UfwgaotqZdaQLR4/FxfDxa7xrZR8W3AXg6uZXYgQhMsJ9/c9i4pPpJ5gw9K99f1NBVCtR4z+e+1oF2dg7BUnYR3spdJmAx412wuPa2wOS6Ck+Vyp+bNmxX/ZNnkDEsoWDBkBFUdbqJBl8EeE0tJahRJ557jbl3ZGvslLTlFyizzaog5Rt026/LqjJPR59CSGVXZalnKykpU+TxFbOfqsreDsgoE34ShxCFc2j95+32rZZAxhRgQDO/coja1S4XkELzJZTMtP7lRQ4uLWlFMKQtCz6LC0QmJ2ABsOKeu2jdF5evn4OnJY7kfgdrs/4Xpt9o/h4S7+r3ZNsv6lp/TJMpoyTOdBFTpViFwVSnsqSg8W1G5jJ3oaKXBcl0W1VOoXB2ViYGvDb/RWLmtT69hDsYSmRxhsUTxgFEqHxkCpIA7oj/XCaV5DSFqKiJSkPzxJyqKhlpY2atN6kcqBJxCtq7SNPmYRMkV8cnAY/7HB5sel1+lthwQ5mfFeBrgN464LJIhpFGGxdhBk8jwWGjfIpM+48y5waktdb//PHjfH7+649/eeqJ78f379+/Nzlab3p07V1VpWk/uramEQ4Y87FWq1MBlKBrRGxN2wYmQPgTBu7Lzxr3uGlc+WIf8nxl0RVuetPODpSUAcvy+c3jLH1hmTs5BWYjHH0MaXtJfMnwtZqkzAPePD+g60gOeAQxjEHMSggoQrLRzYi+lyoOmtJAqVg2l31s07ZSd0+YvwuwVVWapHcc5assDbnOHOt9YcO5RLtTPEDXDZdjDH/NLZIZA50SwAgxACEqInF6SkxS6hRUjRWEb6LxmbmijS81QGts0LIsY6L/zC47R1YgKxovMzFulHMn+7sRdIXZu3l6U1DnUzKbtDxW48EUZ2+uVXF504T7sxMk1xdfOrgp7Yv2syz16s3XZqUK9/JbsA4E4UNKI9SK6Peupl6UuHFr776kXfUa4IWkE3OqSayxlrQnA+7gdYU2DZqck0lFqZ7IU4UUP1fXVFWGzcyaGmdiqQ3HBoxrDA/kBMY13KsbR/xKrgIY4+P+UyM9C4UfEXONcV2nh35Mu3/rvfWuLU8cNg657Brn09wrbTbMjJ7xl5V+SEWllwCDAP3H5x/X8/mvf/5TW1NpNLajHx8PVYHKeV2C/uEpSUWGWQ8fszlh3GJowiVBpQ+3MF3QTIWuRkeh4fnxA9Uglv69afHFIgwmR+X4v+HnIOQKUcknzgRS86CgrV4UkeVevmpWKVjXCcI0VMSF+mu9d9XYEm1mIEBedqXJO06zGASM8EOfp8gINWGclzZVqO8cEdfHYhEH33VYWyUEGXgfp54wB4IvRMpKykC0OlqyGekpJ1BROoDdxE06eENJmz/FAofIY0qNeR5Ga0ahSkBz6lYgEBnUCZsnY5sv1VVVhiDmTeuts8Y5+jGHweiJU6CejjommfipABb7q5G8h2psUg5xiJUkRYqcb0F2qr1J59SGmL7NBac5pTRQp3sF0O4Kz/bmOnhTJVkt7tnK+cqrK/UmkBKglxllOXUCT2U+u4qdV5y/02Krs9wc5O2Xr2i6S+NdYpSWsr76Ygp+6e8MmXKUFwojYAxCGwDosTMkc+us5dMKXwWr0FK9EkAUGO7XhZKkQps2iyQPY9jl+dWa6Rij98NPHdbYwRWJP8NhO4YZr+sabru5xhkCZAhEVXrX1lrrrR+99+6Y7PQdGGNcnr0iBMCgH0xGouWRi24/ElX4NmNB/z//+//3+cePP//88dt//P7oehxH7x0ihDx/PEkcvYN6fDyCVipG1lGtgLeeRGx03tHAkcuxQJKxa11fJktOnaOQKQS0H3zj0zVibKV8p3hdA7ww4G5hL2VwziPUSibLy9WfzPbl/+Bav29bsnENFsiZOVpGalWWCksz0lzbvejSjn50A0lkUm9vnq8Q4MvJ3ptSQ3KpKM0ovlkwlqjTuDExfdN/Zi+TCkuabdZyv7wasry5CPUF75NYKzDsVM+uO4BLIjqIi/DgB3WucK1fVYdn4E2Tog0bZqkOivdeVXsjCFVprUPC3u/H3g0bfp6HaTvB3n37uoqICUX98CYJjqi0C9HxpTvJw8Vid1yZjJzvlGcivnP98AKXK1hlK1bSznLeioFNuXnzYzTq5rffC0/OCJ0no6sqLjcl2AT9VQZ+gdrL5F+0QVm0iIWs8k6UvL9W2fO2r1VXUb7spSKIfPvVSFGj7/GMPVK+trRhRKp1WUGs4cXNuO74tFhGODgw8h83FcKMbIjJCyOhIGyYi311F6zb8T12pzXxg2gGxmXXuK7zdJnxPM/rOp/nRbOjd+36+Ph49ONwjO7Nj0Q0Dht2mV3nMI4rdg0M35/cVOex7EQcbubLaffv/vjzz88//3x+nt+ub8f349v3b2Z2fn5KUzM7z+sf//mP49uj9S7aoEFWC7013RoQiGLuJPQUq+GUYHomYpqQDM8nk0tlY4hYaScTxaRNVc57QqxTcGFB4N28yMvmSz6wvvNQqNpcOXPjp5u6LhsCT3cxsitkxrmPcbmdLsw7hHE0FYOYDUA9saB7cYDIXzAc1Bx4bYiI7xddET2Jo/D0ICLatGRTLkr87MCUltR33a858o4geY8lecumV7E6yxRdJ+uy+ycxJB5iyM+cG8Nc/HEMs+spc1dk2fpBMo/E4zCOcbm8UJExRlPtrbfWPcuIGIlm5NH7YOTNHZcB8MP1pDXVZma9sbdGldZ67HwPk1sdipKpsSbAsHTnJMKuTqTSPuFuot074qYAls0H+m48Sgth7vF4/9z6kyxFvta/jBHli7IC7mPASvTF8IdNFsilhtx5a+pOIa6TB0JbiibKdL2/p9LXVwnR1Y+x6Snz7uqdYpwkvR8bDRhbqjqEUbUC1yJDmpgrE1mBc4sMnIQQ7NoHCXpeaKUv01WPfvjkkwjVPwyEqKj8+PGj90OaAGiQ3lRFjt4z3c8w8BrX87yMvK7r8/n8/Hwa7RpXa9ofx8f33x7fvj/60Y+HR/03VbKUn5GhE0ITizPxQKC58aep9DifhYNxbjvZ/+t//x/QRFpv/bfff/P0diSu83o+n8dxNGlH612Oo/XQnJhKD0HzRNCkuEXbFThHMuQKn0xRnMO5qCeMpKAkfQNPiZaC+xpdyRjzFA/LuC5Kwi3cYGWUnIfJ015RmGGEMBdUaV+G6/cjDGuRa4xi1xgOc9cYrt8KxI1+PmOMAwyr9/AseoA/F3RJcKUyrbCBUq2pcWjmgoKKtFQyI87dG66+TaQm0job50TY59LLNXW2GIcd929FZk1Tq0p6+r8z0pWEGT0j1hgjeAI0Gb4s8CeNdobWcrmcGDZ8D72owNi04QNQpUmyvKnyvAiJ81ddcbuuy8aQYa2N0cLYSYpqc7Yu4J5qePnzpDRGhENm6czKYStGbkrsRuhdLk5/6eL9yKJYAUyzEK7v7Di5VpOOzKkkV/EF/S9vrWVJKstOhpc1i8+49eXXguT2QXYGnMDPZfbtrLqtDL5cFrygviw0CmveoiLuvoLsoA94SWUQJnFuOkRIDPpkJzHjAEoUeuLP3jqNftxQWq0pIk1bb0drbdjQpmoqAhtmg1Ca0fO1qWrX2skFqLiR1BXNa4wfn5+fPz793MMw+vfeWz+O4+hHE2+XEZXXAYCfR7kOnEsm8VSeTbWJZ64TM6oQJv08nyD+1398/+237x+PRz8e7WgAbIzzPFtrLltEIC3SvNGXTJHixEQaYsNqbD0J5cMMlbg/WZ2cXBVm3BAf4ompPb7U0Z+pp83lbIELkrlr0tSmdMEa8TyV4qkHeSkRj+ilh9zzwiMKEUYaUAc3u0dXgGHm4J6CIYwdTfUaBlwiamaO+AZzeIoUPiIVZJZEgKf5ztUnRQ4IoBzm+ZCb0ozawk8RS8s05nAF8bcBHz+bVYtKFzlpfGrMH7dXg0ZhQs8W5FgIYcjocdKTnwy7htnlBAzFyk+fjsl6XVck1zVzz5eZubz3SRLGIP9MCM1DSkkabNCGvziGjeHriwY5RUTQtMGuLl0jeooW3oFC9AXZVxVyUm5lvjtISWo4O023crgXtDxQeswmtlerSdYsew37mMhru/zmDYtfuGAem1Pa09afRJQFqVft7Qb6Oc22RgslgyBrVt7X6Guo5p1Ua0VFqOzIwqgLETeFL1vnH2Y4tbGgOw72ccwPiy5jyjLVPgFEJaBKDApR8EKtZs0M3ffH1qISBIeNa1y9NwGaZxRSAeEnzFSLWY0BxjXO69I84rEfvuWrHUfvvQlZx8svFm5RbQLftNvNxqC1ptqbJ25QePypuq5rNAG7dlHq92/f//H7//r+/fvjt2/nOZ4/zj9+/BEWDFfENGzT4pZb19KZnkhfGAR+JEsVdixILPmYM9dcS6uQzC3gISsyWGQO3+Qj511ZZsasZak0qVuwWMWUJuLHFE+LgL9gNNogDOYu+djxISIibvMZ5rhtI5RZmohHvMS5JG6fgOXmPN6mkxtDXHgU14G8rksUgu7nTbhr09ezi316iv61v2+UKM4dtLflwK6JLbNu2p5vOJRDMYM5Qu4sUJPLw9w1OYad5zXGOMcVcyVaH6YO1+KvcdnlhzEN+ClIvcHNXxkfZUxTF10MewZbsxFWOCfwIJU0G2M0EYoZNeZxrqq4dGrpObnRGBPalg87sCL1mxqIFyKvusjX18Khb8Q1F/IzIBPpmNq6svPJ1lF5ZQ5A6tyucgHH/F55JgVCOa3XZrqFTdFQEXf1fLV+U93vGsldIEz6u8rJckOVXWm2bS3U53TcEMS61iFiwoWw8hRw0k1Js/IouxpoHMJIZS+gR+Hz6Ja+LT3VJ3YE3sCIpipuhR/DzufpQKra+tElciH7Dm1XgwIPzSiAa+oCN5f6f+4v8AQ86mbqNEwwccVNPdIICse4mpgnC2raBG6kd77wXlJVOoyPj4/v33/77ffv375/19b++OePP/78148ff/7Hf/7nf/7Hf/7j9388Ph7qB9Z4qMYgYdA4wdc8VCYGxdxx4ppo6y2GqVbfi6Rnnf+XwC6hhJVTNPmk0K2WeMn9C+O4RSdvLv4gx/tQT6XYUQSA+rFlkTEj/xc0dduCh2RdY4Au/OfccEiS3F0dtnkHdDBMQJxXNTWP52QlMvQXbZiI0C5F882HqqXme8tE76gvORVrsgEVdIdFKNZM9tuy/LjC93vox4r+N9c65jsMIyHibOw0U9o1ruu6ONLBgsgSHStfG3aN87qucbmp3p1UzeMepPmefpnM4dvpQ10LoBFI80MzhQKLHTXtsiHWmkCad8T1Li27T5Jlw8cV/oqEN8K8Aeq6ZIrCX7uK7LILm1laddOZfbb0pWIis3Cn4rVU4YA9WybpkAMC4iWtZOtL/ihzf/Wt3ti9ngSNwuZSplYkXEqVaRb66srAzChMKqR7pVka8rJ3UfeyXyTdPBXzFkJqMMgaoUGqooZR6s30somqQrU1iKiYeqCkwNefFKGNcV3jOlQ9etDVw8utxAAy2/8CyhDE5i8Wkqtq770fvWkTgbbWejuOrnlCQAyZCdy84EZo5OJExWiizQ+YEZHeUpBkqlCnp9no5/P8f/6fb99/++YEOD/PPz///OPPf/V+/Pb9t3/8/vvxOJpok+YywIZZExgi3ePw/ESm2jyeyWOpfPVeGkftVAql3xcT5d0Ng2sLb4JMaHI+5DwAMydG8EZZlYB0MUiMvcvEGFsE04Q7QvOgR0YsYOre/mScuWNxRnLYJoyAgioqTegBnS7ELcLVxxgQcfM2EdmYsqmMORP99XSvJhIJnP0hFy9hLmdNlWmGDU4N3b/2023Or/p0txrI/bOkXjjrWem7P367S0+xsE5rCWgtY1Ysa4xmvJyUZkr17jsje46tpIpzhbSm/ejHcRyPx9GPlkdlI4cw5W9oPv5uY1OoaIsbucASiNnwE1fXAyiy7yUxX6hUg/eWBG8J+/LuL1yO5pz1uyCt42jWhhW7SzHslMwMo3VQZ7o79roy7EcwH0bR03OgQ2fk3koJ5rQrQQyE36x8K0xbbLRwirGqq5o91aKtnaGjSLUyBbS4crUvAnxq5mup1SeQzEdLTQFdgyjJ6oCmBEYig8OImbkkPRDnrogA2gBh7+CD3+w8hxuE01qsIu7Ncgu6FRkAaapHby0dnuKxOgAz/BmQ1trRj9ZURI5+xEExEBoGhq8BDHbRIlB0GIDWIy2cRkYl84nQVJt21PRKxhHV3lR6a4CMy8Z1/vnn57/+65/Pz+fHf37rR2cdTWlGw6A1baZh8eKIvcvjGqqhY4MmkKbUPCTAs/lrbMvkMAs5hMhdDUYIqW+Z45wPKa1LtEuJEAASyTV830FyPEGpJbolCoaDZ+UaV2Xik9vyizszsNPHI6UzaFTCFIFHNsYY4cKNRg+zeVBX8mRMRBoBD+QMHQ1+x6dMdpWT/WWiW4yaOvAu8yXQIQrLW4uu9dXF5aFSLBb1apmQjjA57RKPuJRDpzbElRKLCDDXUYZ5RvM0W4o29XhkAqCiA6JGBbvrLL0fx+M4+uM4tDVtTUo/9W0oIrEtERBRNmkhJ+lakD8siQtu0TUKBoE8eCNY87aUvFPpzYroq2sl+i9B/0rekEup2+/oH8M0Td0lFCKkgvXsZAwuCnZODeGivjuERxBzLgXMFQvz4OB8NOgQGk8tM3ymaerKMb4SMszLLAEaZYWBUUhZ47iBalkycEV7rLrJouzkSojIVUA58CT+yd0eMZVQlDWE+SVz8buRxyN6FBawUqsjkqQpBPSkvNYEvTU7HqrX5yl2ng4APE9kflDWEiYceHV2imjGNOQpYATox0X23vEBAM2zLot4qwmMQQcM3yd8XqdH/6tqY6OF3t+bDoYp1bccTJ7wTyqkdRDXdZ3PU/6XXGNc4xx2aWvH8VBpgT3hE/786Adhw0BACVdgXSqO62SEEbJJk4doCFatgXdx3lszmxlfp1U/9kNA4/DITdmHBLOXxlaiIWZCzIIZ70bUYiJ4jinwiRnkL5CRljQAhKx2NTdhMzgAQqqG6ioxnh7wCxvWWzMYp2dePR5WY/zM9Rl66PoYIIYNEfEY5BQVztiMc9OmozeRP5S41PYQ3zZF7ef4Iy9/373F2+/17qQ/ZC6qAQUtoqtLn3dfSqyxRUDRFinF/ZShawwV8SVAkyZNW2uPRz/60Xrv/fCofxsWVS1i0SdPcoYMGxSKtmIP5j6DWAiISUvxGzrBXyG1vHxYLy5/47FVYtw16I2s7+mLsmVOiPMHp6NhbqEP7y25nIyYHp8FvF1tjiWUl1pTg8nwi1xhgBVqmRE6SDFacGtqWukYXpaiDnqrZ91/CH1sdr56VUKmelx+CMzlEBfW9y05kiKiFoZmi1jU4MWcYlPDSXKEFcDZ2Y9rlBbcYVQP4WAsCyACVbEhqvpoqq1dY2g/VdRXAI7O4qExySFhjAoqeR25A5mhLj8eD48ndBtld05uCvc7InL7YMDMzuc1rmvE4cXWGlQNAkXr2kTQtTvBNRK0hWHPeYZGUe3Pz1Og/ej96M/z/Pzx+Xg8jv74X//4/fu3D5+DIOyyy85GmA3xo89CjV9ktEC9NmBcAKntUcKz1FkCniIjhEI6xGPw57p2sswmBpYfJbszgT6e8F1jW259TvaMKeOrwARbN9qHHTM3LrldKDan+ryBbxHMCQbfvDrMzC4hAFUolOrnJG8iF5bKj7+YeZ5oJpFfL/cEhLQfIhATqh9O1FJNQwTkxp6UfU3wN69EKT/GCy+bK7YnsSiSZTOyWHwFm/k+h8zu4zKZoqJs8NOPYnRyZjJbIKLieVV678dxPFrvMdCuwkRKuNDx/Syk5OewPIb9LGd2TJ7B3rR5Eh6j9ChtF2d/83qVHKnmxHy4Lx0S1BftfbuPYhakXrTGY0XYoqOy+FlpW6pLID2YgYaCOCUzdT+3ieXWD8l61IeJkaYwFtbicOO8ATdWA6ELO9lCqdZoneVOGwPMBxeE0eUZAWHljgwp7PDBMtSnDiS50Geq4KX4LPvWAnzEdUpfdxpHbERCcBcZJ+U6ENfyPBW75N9YxAjEsRmiCl4cLlGMAsHwE31VxWVLa13HuMZorcV8J20MADaG2biuSzLIpbXWIKpNtbmyO+zymLfe2xhq43Snmad5tjGa+m6y8fnnpx1dJLarjesyP48sB1ZJhUprJtJE3bYgTfwsL/qaTnJLDEFa//7bb746+ONf//pxPn/88XkcH9//1/fffvstppnD4Rg0ow1tejw+AHgAbAxILqBaa713ivk2JRsmgU7F+rEJRyRVei475t/qYnz5sPK7e/lDTXamZD0zjYCchppYCqRkMl94BIGSD1L9D0L5bj0jQRlhSGYk6gs3AYDrHA7jFBNEPh+JgEVLHSMaIIK0GRppLa3/pG/T0FT7vaneqEhNEYr3thbiT4D759eqqbqK/cVTmXzCZTmkUpYHV4UqCRHxbRDUAHXPXuUakPuikCq8qDgFbJioNPEjJx6Px+M4Hr6N0e03PlNDyTN63hVVV/Op6sBDuB2OEyl8nVoGVvpKMVy+GTLyoqn/9bU+/6rO3xk6NWG+vBOCEwn4sfJjbg9IrViwsHmMd7hZV2lTj87WlCU+GSZRACJBq4R1Pxfa56ewlDZAOPJURQbmqOZJDHEIbalwrCdFNEPuxCenIjbihXWT8EQdXCey5Gpm3kyL1BSiUWeyXOYPYarqfoIu2WL5ghIY3iFCw5VX6hxze3AcFzpUlGIMT6FvDfXjGKMcAyE4jgPA8/kcFttcSDvP67wuGwMS4aQsrScWab7IMFFVbef1vK7rfD495MTLBGBmY+Di5dAzPGh6XK49pqskom3h7moAgIoLpdS2A3mUNAFEpX98e6jKdV3/53//15+fP7T33//xv/7xH/94PD768dF6b02167AhxFB0NFyufGmunFw7JoDuK8vWaNY8+ZeIptcSsQhaN95LaiehK8zh2SfX13PTzxGrJ0sdWmd2LPYU6byNVVDiMuFmTSITaRfwTqYBfU+AWNMGwP0Gw6OxEphA0lxdFRHVNEGWu9Z5UwDzKKO8am1IMgNEg0tT+SlodmMbQw5hXbv/O5es779TS5F9exXSpW773CPEXO8JAyya6NG6UJrYyOGRuEBoeMrciyWtteZuX093BS3BDMZ2Z4moY4/pJMGILHK3PcHYnGHWWhOxJuoBpISbKUMMBG/cwfNvXnz3mQtBa/YhaJWKvJSungp6PJFiKxV6qQ01WTpj0qPK24YrZ0FxMRPeSbSGMhy5TDUAxcZWlXutKm1FXk/r4mp/7owRWvgBXFOZZ+cKSMuvRYHJUkGyDJNOXQYcBpHZ4zLnSqlPQdyA7EJoGzQOM81tQSJ6BasAYAthK6T4Ot/9H042I8dCLRFtHtRPDAyaXdcAZIjHY6o0hfrp6OLRO2aulfO6rufz6WjueUB9q0pvkiuOnAi+ilIFMIad10WgtUhk4os2O1On8R3v1zUuz/Wv2ltwTLGOc4y498DFpCCiM6UiD8dl/egPzy7x+eMP0h7H8dvvvz3643gcH48jInnouGkHOg02zHQAVNUxhmdsdy5zQIzjJv0WYk+PZ5pK00HOlGnEzrH++SScb87vNbPm2jLLlvI35TwDhAhrzqAhMlZ6HGeYSZOl8kMoKgpxJdTExNebS/A+bFiE22ok7fb6VNsw81OQzYwcmSHDea60r2Q7p6frB1gMunPZ6j3WiLh8r7D/jH43KnIbDU11vnQl1pyTZXRce046ud2Mgw7B4j7FGcuvasbrIsHhRltNM0AaJUUUELf/oDLoAgZw0E2rkkqmITS9UlEd5a4xxnWZ2aCBGDYOHDR6KnW6eNaINo7ey0Kdu+q8X29/mmpGzK+bjLy9JdNGlDppZaspAGRJftRCYCsm1IWsGWl58hdTdXYLudTufDdgljWT9AO3ne0uT9rKMC16VwSoFFEisQUoFL7IY+4rBV8QNICezByhn2tyW9E8FJowYXFj4JLKdEmTi2bHsFSyknMgiGMxkBOKjCAhDsuFlXqkGQRTXYLbLbNAD6N0og+r6Z+UF2pXnmbiZ/ZCgM42KGpNDmQjc+KQ53WOSuRJQuS6rmtc53V1yGG9llkeuDNo7gYw2rguCcunCGT4RpfrMjM5RSFGG+PiMIG0o4PwQYwtqTSjdW2h4VqeM1DpBnyDqhmA3pvS+M///u/zeR0fx/fffvvP/+c/juPx/dv343jAAyYI942gufdCaRwc13UhYcnGSO2WKhDtpdiGFGLYaoN/Q/MP19VyVO67CVY8fp9bNcEcK2tarVMTpada6IvuyPbG+maiwYjfjzSVYCmrnu9DQPUTQc1Iu0Q1V4vBK4SpdG2qrauf2En4kq2Jup/YE8dGAqH0w+WqMD4XM/nNTP0WPxfhSq7pHkfN278vpFrpOttfhtf6KeY8Ek4QKyliLq5CcHLQxjWMNoYlFju4u6nUmjYz3wRtGJel1TXrVBWYhmRwIpjRxoAohZ4k0ae4xokAgIqitSaqSoOZXXZ9ns/nj0/fIyaCfnQVQWsizSOmqcWFZUnWJTxxBjMma20y4j1ZJ02J9W3fLa05ymsRTBJvY8OkPBlzYq7/cuBzoZ+TogIp44Esf59J4WgxcqC2rgcNCI4x4Lu4fSEH+Kgw6hZIbv0vu6gxEguERdC9gOrKui8R4N0pc35oL2FB8vbEXEuyS8ZVh9s+yCjkEIW0eIpTmFQHbWTUsXcjDnoYUKOoqDjrlCjKprA0mExHnnIPIP00LjddjhG2JsPJ4ZjqW7aMNS6hCztk0+97konrGtd1EsfRh4W+6AZSG0HJWL4MG4j98DQOu57P08alEG3Nx4vG8ICFduaWItMxmihbSyGKzNgDEp741EaoU32km+J6jsf3bx+Pj96O1iPAzmh1zr2IwKBHhGOb2bgGadrUMRSAxz45EKgOdovoBJ0umGmrnvFhEchRq+HtWiIGptNunUyyfk+oSn05/jobMweIeaVz3ZxUpJRSSbrrBkwwFteZaIB6/ImsKp8ImkrznH/ueHf5R5A2GNaH8AYwLSdMJS3SVkVEQpl21mUiQA+/z5WNtNiqfjtG+6cXl7+SlCWQCR4WSjLpy1xEpTaX2j+G2Xld13kZIyGnT2cPboOItgbS8zgMyoBh2Lgir66RogpBBhIL0zxE40Ckx7quwcwO7WQUbSoE1A2hY1zP8/n5fH5+/vCoZVV52AOGx+MhhDSwxzxvrUEonmk1qSKxfc9JUR1PrRM71e6igKmA5zcJtT5WvamoLq+8WWj4YrIq9VWL12sZbVuVTyG6tc3bXspoKtQJdOe43M4JRy5YIrOMYcI8MAtsEfLgi7PKWDX503O3aKXYoUOiiM9pTh/CJKIXEaLGLO0SBf++LTniLJCdryHKNB4bCixzyWWmpx3xdSsxPEKBGRfrMllzeElanO7uWagyYVd4BAk/tYJRUVZxOTcKTbSJRpCha93VJMDJLEjD5DWu67xoBk9XBaoqbQzObarhUxyjtQaBr2vHdQmk0VxJgu8Ra75mDq+02WVDLslIe49QFBU/1BiRMCi3JrAb7Y8//nWdQ1sjTYTXdYrIj/bDFYrWtImfQCa9dxEZnuXGo1FTI8CMA9P6HBtZcx7UDu1ElXTDYaTWcJsku+KVfxaWn3OhPud35jxEMaHB9aqIVCQx6GndMDmbYQYKp0Q6lmyJBUrVbmpbAFpk90bEUUDVd7OKclx+BALACCUQ2kinEAtZfQpo7o9ICkQttRLwDkmtvn0hvwHBYmd4hzOTuEXZVcXcV1LMQZvDnDIU4Qi34Rt9zzH8Bd+D7sH+6po/acQYtMHr9PTPo+x+YU0iGIG2asaB4XkizutyJ4EIzvMymqr2JlA/ds+uawwb53U+zx/n+RzD4OMYxzCZyIcHNIQzwKe0x/ZFv7V6PtHfP3D5tnLkxnFJuVLZWaPHsE/WY9PCFkXFs5OdattDWCLg2lypUalDMTWcHPBYPSAsdmHESPAKyHXdx4xAnLqs2myMAHUbBEg2KOhptJOlvCVxxmeE/xeDqIh5Cn2GyFMBxdRBOya3pzaBwWwMAz1xVgqAMOUXmV33CpopJHZv5WRwzIXbuqacc/+cG69a7xRzAWBw30BYr1zaeiAHgMHYsxuLL2OqbxTIOE9mZjAmiLrUUG0qDeAYtqgGgRhGU5Phe4LP0ZprnBGAGNpleLB8bMViEWwS6D8MvGgCcNDtCr3p4+PoPfLE+fPX8LEDQpGlaoNCTAyRaZ3EOC/n+D7GJaYCtN6uc/zxx5+//fGH/t4iCD1je/IQspZKcyrFiTYQUZXWmvYWgd4Sie5Tf89VRDmRktkLVJLTb2ZtLv/4zJpqmEzEiknKmkYsr2loOKkUpTbk8b1mArGqhTAbHvBmkWF4BH7VfgA/cvy6RMQJ5dYa1w8M1iPMJRbe0fHpXXIuCaUjVCKy0c/ugap4gGPgvUjIhHBHtARrodDW3TK4L4lWmN/01oydmO7DMjqU9LypqxGD5GzhWJp7fQFP93aekaFZW/t4kEB3+1nuqLBxff74/Pz8cQ33dDWBjMtPvEPvTeTw6W++iOd0y/t5CqSd19VaY+dxHCoYHM/n5zA+z8/PH59//PmnuA9NZPRIRHqerbXmEwwhh5WpnAYSLDyWqvNOxZtoLN21eCymHpEQr+IpuiShqXifswAm9Zn6aJXpB8wiZGiCpDOUZuyA5zIJCbrOGubWxgw2SyU3xYCDQuxtF3cIe2LCQGEVGZdpRCFLHU80KeRmZwHBBjRtTIOQt8qDw2MN4MpgoK6NsLy7tQExX0JKiG+RcQDRFhqlikIqvU+cYCP0WsyL8fyL1xhu47FrNIWqOPqbCDEAUW2M/Jshta7hYsM1P9/7ipghrqMZBdK0ETQR+hkuomOc5BkRCq4OtnaeT6e3c2Dr47quYYOtOQ6PYdIUhNtNpSwioi6Gz/Mclwza5/N5XRfNjqNLBlxp670dH4/H8eigkDjtOYYp9OKVdGXvsRPCzWLn80lPOC8YZv2//+u/afjt9+/t0a/zeT4/r+sih0iGxofzGZlRSGhp4yNi6H37XNPDHcpHj/MxmwC+M9MH1VJYgAVBOcXC5MFStnbV3tliqknrAzlXwg7pv1fBsQM4dPzYpjrcpuxu33FZ85ymHkbiaqK2ESdVBbsvCngtF6IKn5Ctd1Ht2hVxyi8A2pUJs01oIsN3RvlxAhAxmsKP2BGSrbeMklwUz9J6VNMQQAiVroVlK1e76Kq9SuqZOz543GRhWpzC6Q66PAOsLpXg7lBppbm/d5DXZR74jVC3ZVzjk/Rksq03hUD4vJ7Drufzx59//vH5+Skix3GETUbbcRyCTjOz6zxHExmus0RvOMyucfqUol0CilCtuTvqPM9//vGvP//804Y1VSN7a9d1Xa0fwLgG67ixEmOZ/pQZIcxY/VtRKFiNBdRF4InRSNXbl9xpi6BHSfppECmyY+WL5P9key+3TOIZsQqSVI/4F59AHrEmgXdMU4VEArHiflcuL7uQVvVrXKzYs2FufHPDziifKF0HMoblW1R9tUQRmUlyZAg8ha/nPnQtynxLqosKmlEjEo4gzaJxlIqgAzDsijmUjYmFSDQFoqLDDTbWHYybtgaIKhGGWZgHfRhx2XAzlwsqBTwoyO0wonQda1gqy+4GN3om/WxAuEndLDnsihOxUvEVd0fYedkV+4lUAfUVimpr7ej9Ur2A5/PzU4hv347r7NZCgRs24nx4EkQTUWJcp+d4N7MrnRk2huds8P+5PebxeHz89v3Ru2eK8640DQF/XU9n0THQmomqT5xrnMbhWYyO1vvz809B+/MPaOvH8QFwjOv5/DyfH631ps3MzvN5tAdbHzBIpOmiL49cGLmlqLfj6EfvR+uVvEWnPSPtyZSaOI4omoAk9c+8apoVpK96bM7RfdXApYwcTV8Ac6TNIk2GALVL4kDaN5CKei736vHSBXMdwJhwzvSaZ6+5Pd+XOwjfvK9zxTjCorSs4Qkarfcea4bQ+2N/mGYePzh+xGoA8DzWGBrmyrmOmTaFXVCuV+pfMlMtpm4nCGUNafxNDVYZG08NgjyzXVUi3uc84yjU8zx7b3Jd2rRBjPw8nz8+P//5X//9559/fj6fAPrRMmGhAuytmch5UptqPwhlGDGjMR574NHbwy4+TbWf4zqfz/O6zvOkQX32ta6qvR0SITS8rmsYxxiqOixteLJwTsVRp9Yx15cSLOYgWSKzzkmrpdOMQo8lvPipgUzDjecYDkLnwIQyPQyQTCpZFePyPf2SeQFJCOjh7fEURZWmgjxD1A0dDHuCcyySja12tdBg0loLeUOSDOucEUCbx5J6XZazzS0wns5kuI6vQ1R1WAS0aBOz3PlXi5fkufgbwXiO1K5ixM4cX7L4LUeaJhhpUKKb+NNmQND9moAYxHwvknn+LomoagvbLcS3YinSJA6XpTbXYTCASpqb5o3ANVhnK3r4A8whYtgIMrVeq3Ofo4SNOBbSN3w5ETnM1Ay9x5J0GN3aE2nNDQC7unciTTDt49u33hsgx9H7cXz79nG07seKDRtHP4ZcvOAuA5IuogfN7WxjjHGdF4cCInri6s/nZ29dhpzn6ZYHkjbsui4aW1d4trtmlBhXxyIVIRUdLk61taN3z93lfgl3AbawpzOt1mkaSeVKpoI6letC+gKuCe8JwoIME3bO8rkhGVSX6B/5XjJ1lmtZHrhCN8gHT6Srwvf+ebbhCr9mWPsQtsGY5ZXgiPB8NmWmoeeEBf0oWgljD8T3qF/j8miL1rpGGIeoaGudRodSeDCphwP71jB3oaepwUFGALBRwuqdMb6bFI0bdxmQUiLtIohNcMHAJT5C9oV08SNzxTddkfSNkR29sQ0bTdqpz4AzFefnJ2nD/nx+/vHjz//+5z//+c9/Pp9PEem9i8rjcXw8PgBprUkTMVXBsFT+c9I6hnh4n4rYoMngZZdFGlEzQFp/tN5aa/14HK01M9g1oNrGOJ+nb29srVF1kCoyxKYhzEBP3cEE9bmmkgT2SchYDCFhiMEbzgE6s2CFEKu42WDoWOBk+kHCaNe0waQokYxeRawMfalmdVAttIGAUXJL1XQaR6S8S7Z03WXBiKSN0yzqeuLl0fRUta7qWOldTl7UaEgFdLr5BR7EbFA0T++ohsvT3YTfymjDY4WBQYxR3upIPxd20VqUgSIGsvdmvgNJRQ0KFZiKRl/9odDOOBgWGh0WZpVovIBozZOIIAViLPEYWSbSeusKiLlc9BVpiwAQl6k5z4yUQWDkNnUeRz+H5zL5QQ9QHgYRDxsaNhqbswKFF8c5rnOcz+t6Ps+QexG6QzN++/j4+Pjw0wR61360oz0e/Tj8EHKIUkHBCR+7M45gMoiohcrvGeuG2UUqrPfWtUkmm25+0LCnpivO99BfFYFRurTmaelF4EoHXSL6oX29u+HHg+AZ4acaSsCCQx5jWNiThtgZGJdTfy4Byss8FdpF0y/jJ1OHY8qDMi85CufBIWHqCdD2UM+cnStsluoSe81j2vrlwf4iotJEnYxSm7die4tVWRZH10IgI9b42VoRP8kBROtdRFvPA3w89eBsVihrEoE5JOguOHJZCr1T+ed9Qe3WSfqlgMlZlH/SBhpj5ksaq9/dKhrHKgxpDdqk9+Pz80nwx+fzuq5xXc/n8/P5/PH5+XyevkMy55s0bTzccCyKOAkNGY4SmqGI7zzyIDz1wyjc8ehGNRMR7V2PR1dpXdvRH4C7fCM6+rLRrqup+kmTLi81QipK3yBzkTpXXXO1VCMQiqdMvy8KWDE8L7qnDhMUl+eTyKAvX0XFrEhiW9iq/JhaC+i3nEYxCPBITDGIImJvmL6/WpGgWh0aTpDSszeUa8m3VoSGBA56rIdLfvWljPfBIxtaJuid3pIQbq7jOCFN4kRan76U9GN7vCARKBWLYe+pXJkmy5cabvuNSE2AaGLqRwspYXa50sWwv0FE4dmf6LrwFT469QO8IkesK/LR57TtGv3QLtdSK4tVrEHNqMIxRqr5KsJwJ+d6xBftim5i3z++Xc/r+TjP5zmny7KMM1I0FgTuJPPVsze4Gf0sgMfj8fHx+Ph4aOsAH+74PXpvTf20QBExpaob4HK1HAG9mmGgRvMNCurbeMW3aLb27bfvHx+H+I5sI4eRNsbVWqOpNN9S5oetZu4tenaLCA3QDF6pbDYq0rT74jSXgJnEKXSYSAIR8EhdzTgrvG87YfyL/00fniB9NXHFonIGQKR+49rF4IhPqWWqej7JdDfGSWeCOXEpmVFAIl4yFFPxFWXz/B6RawzkxatW8YAS6g6h6wrbjzZNDHQYbL0fTZuBbuxzwpivVBxMOBEg2rXE+xCSwm+lpCzEZD0ain5SONIcBWiUtPaHbcZyoQiSywVC0ufhynBvR28UaCh66Vc0mudYVNXWmku9pnocTVW1d+1dm9u7PFlQ8HPUBXHDxLhsCHuDarOIrxERPfrhWVpUpB1dtIGgDVCRWYAcEVpz1TnslMVWk5pIy4CsdKg1QchGQWaUCMUz+M1yegMUjzrKQQjbURaYi69Q9ulhT5E5OxWNQYGIikp41JwNjEPprk43e6ioNOTp1kD6rjxZk1nNFwmnkxsu52JDwvpIgdHc9WBmGqPi0x2DUD+aTea20tjOa4DDwnD38ujoIiIuTkSYARfmx6mOMa6BEHCeizk0d4v8Am5qIoAxroMHKILRevMBU991KBa6NElSRGmXeTCpe3YhbA3pj7TYnuqDH1qcCKDN8kaIYsRpV9BGwm0JvgwlBNIgI6d8jmvu7WrHcRwfHx/n559/jswx7xPYQD+K0i3CnuJzpHc+XEouqzVWtP04+nEI8ThC1RbkIpLujoUItIleydU0wsbwxDU0s8/PT5LuTDBjF0hrcWBkrIXMCi19UvgqwVdMYSQRFxIg2DzqR9XtPl1Vm3RtEHZVCkPR9AYy9HPXRKZCBCFiy/iirUzsvAuFUJpD43XdJ22s8Z7RowVia5LFDJU0hGKYeeIoETFaQ4tyVEB1ZsrXYtlTumJwT15uo1cRHxafvvTUzymXaIgFmF159HAYplw9aa37otzv+uEww9kvozSEEEWetOAwXATIb5iVlgxITIhVQxCzjGgyhaqw8ms6E87lWFI4liCsMpNhlZoLd1FtEHTguC7f2U+RQbNvJHCMwzeJRPyAhKsjlWV3SEuoWn6+fKYaMmM6PJ1yUHWXtXmKaUJAVTQoh102OC67mrUxBGgq56kIO4BopMu6rX2COrWVddIhT7VMe6Zr8TnK8VhpJrVgi5RpWKXMMkBZqUDgOrSRHkDi3kulAoQNnzjimEsRxRCKkGJhnDXR1F1ClOUV4eziSzhhHJEmbv92xsqdFhjnBYioNGVrSoUbg8SNLTStAYpebdW4BWMgHLBEhAIT9A1XI7Pox+tSZOe4RqwA3GaTWqaokmlPDq+uREClpQd+2a1mGb0HD/w9+jR3gRL79puvacLxppojp9paSAGaK+YRI5QLg4DKuTC1C75fVp2DH4/jeR3HxwPPM/e6R5ZlG2ZiiISgGDQzk0gC4U5W73ETbdpDje5de6Sdkwoj91FwSqbTxa0vFfhPj9Q+n6fBDjlExKx1lxoV5CDqx9U0G3aeZ+8dCsjDQANbECyUi6YiDa159lHJDAhusnY+s8gGHV5GX6mZG0k83iIijF0tmtiU8wiQ2MORoMUCvtCkNLWaAjP3xVNwgY4lRjMOSPqbY+wmx4YlMXQN8Smbw0wPPiMpMIVcuXzP+twHqu6gbyKMbICKuXtA3BIyrnGVedvTKRkF3XEnH6YL5lCTu+Ciqqmv2eHL8FhLE2BE7GjR1oV/2c1R1HRqRxcnqMWPhiRnGkEcAqGYwxdCd8+84Q5zDLdAtGaeP5AApWnXPrrIg7iuod+093Zdru4YgOPox6M3jbX55UqtoEsD0qTrSOEKRBwOLM2zr9DcbsDLOPzINBmDoxkE4+J1nWxNz9gO2YZqG40iBkEbw1qThfOYNg2/sUbTu4CIYG2mAS433cSQ0t2+YVbQpJgsQgTT2RVrS58lbsVbbArEGIYR6YIH0ywjnqCHArdq6PB0FwRgmjsrfWDKQhX+KVExV3q8A2nJqJWNu1KNY5gb5SyPFokySkBuDMZaBpHwjZVikTFQLBjOj7m+OIbZdY40k7KWswIxsytyqImM4NZwvl7jEPThM5EQosEj1HxN72sHlwcc5OB5hRgTGGI3SSjE3vemVFX1U0mINIuF7xi+x4smUGpuUnM9IDvr4+DKuJG+5nXlpvfmEZuEaOv9eGjuj7HBISRiIXSevpsSEJHmuvNUMd2G2cxQmf1FSNigS3G33YV7PxkrFHmPfxnu3PEz3kWhl4yuqoxtsKKQ1lQgjv7DBmn/0P+wi2NYa7EkKXeqqjbR3rqE5uuy2jeqmWv94NzTxFASpJbMiddztqVcSHTNJTPnvdI5l6fCPj2Nt5L6VDjlIKDmxo3Yy2Gxy2vAjwTQtELH2iJAQZLLL9eaoEePGAOvV0V7b36AFRj6s4Q+ZMMhn3w+zxiBa1xjiAp6U1EjaeMa47xO78+4hogcR6dRmzOZxxdRBeoBpSp+qr3mKbcCiqjLAhogbaFjof/yNQzGy10pZXeuzegB4Kmi+qO5+Y+x85c0YFg4zl3nEKgnDVd5tKZjDGnNNzc27Ve/zMzOi0DvrWkP+4iRyrAtNF+CwyWnTWsD4P7x4/D8uoNPDEP6nKw0NOf7OpDvUgF6GzbMlBpRHGjaig5MQgmLZ6cRqPjU0vovMlUw3wlFMNZgxe6hsjgs0w1ccDPLAv6RnwaxPHa4jONkBQZpGgHCCrEIQCKJiFcAhWjobi7zcAeJ2KEoP6eKn9fMOCeWRCQgDDeZG0tDJKe0EDLSdKT10Y/YCUEA8WklNT99AsI4yoEHTxQ4aGPwuq7S0NdF9nbWLd09JirauopKUx1qnmLLHV8CT3dMjwU4T984eDkahgfJ7VdmyO01CmWufYQhEQCSYVyKCFRT0wyKQ0MmCzJ6bkOqL0o0E7kTgIYX26DSeu9ApKLoHw9trbcuqjS7bGDE0S4//vw8r8vhW92pL+4K7BoxPNYbbNiTV1NTDf2Sp/leE4+GCOOhwBgOT9dpzutpI9JdhC9UR3fxAorGKQd2Pj/HdX18/3boh4ED4/P8oYc8HodoxP1ppLemHzHfVCPyU2RuDA/jzDTspEU+vFWQDfzFDar0fsdUqZDp28VSUZGRYqW15hyORQgid0fuPg/bau2LibAIoDZYM+ZDS6uwAjAzFXGBRopwhGsjTKnq0TowEYtD5TLsygdlkLzsvOy8xkkS1OsCOCSSCNKMz+fTbbIqcl0D0CYGAYZ2hjopxIApGjLVaFhthS7JpshK+Cq72Gb+CfpNytXiapqA8mFJae1KDyDGwQqTNQ4HokFmlj1oG8OX/3o8DrnkQ2A2rnNcev7/+fqzJcmWLEsM24PqMXOP4d6bU2XW1NUQNAUCtOCFIqSQzS/nC94J8IkNoUAIoEB2s7uy7hTh7mZ2juoe+LC2Ho9sgowc40a4u9kx1T2svdbaFhoRk5mS2qbCggV4jKV2tTMS5SFR2bWe6Yu1a996b11U3b21lpnuVAWuTzMHVcOWCWlmhocLm7sYUFJs/VAPj+SmLU90ZRUBzFXerYrjJINRoX4rfeY7LLAeVHUSK/BnCQwwOBMpk6iszbQcREE0IZkm9mRPGo4BbAiqAC2H2YopoIpEJpNktgaHywhxYhLWAnjQhYC/A/pDINXm9InghOQveLPCrTUzI6IinlOJ8swmFjXXEHgxIhYegSi+LjhlXSvKoqA49DcSmah8s+qx+qhRTsR5Nyml5g/iIZysJKlbehIjBqeyJwFgomlm7hbuHknRekuDnaajaDj7F3yaWc0+vAaxK10bEasSpXnwSs+9N3ydJrmZkBCTmcFdSnEfqk4KSmltCw+zmUlbvwyaom0e8/lyxaGICFRLsAYDSyJAq1HNyN5L6EuYGUiGRBJxxHQIBBIjDHcwcdncS1eGb7t+4a3CUIIoUzUykrOpaFvCLWayOQ+W3nvzJq5hBdJhlEeRjGGncIZQiYNojSSKIfteRyGar4BTKAIRnZfsjO5JSSlnwEKbS/RtmX+iRX/5axVLC9k5//F7V/ltR4Rxcp4NEpYyOrOExzuQVDmGSRYveLUUSaghsJsFJV69cSFi5WRix3ysMm2UPeycc9h0L4Iz1q6SpIYHKUU4w1GRq82JDKwfAzbCKZlp5CLKpV98f/8s2McBCEhycaDO55fvDKFvUmp+i0x8W+uiPQAskckQ8hQNpRrfZZCC7jso3NaFjmBpQanESxiEQW8DhZiNYXKYSZxg7mP1qahiEZ6E+2qkKi1FERVDSHrbtHV8jKrNPbTlmFPgDuTBoCwkR5BTtMiQcNwhIhSMGcoc3FiYHbrPmqhQwWtRWZYWL/Q9m+K7cJYnFP1FyP/mAVfPmjVJx1hVlgxkPUw0jLUkBCzlGrFkUKQHAEHK9YEw6UJlMARlIncYslGksCZRVMqWdUH4mzYwieDrIDhs+Cxx3vFhvfd/68REplBivP/eFzKhjK6CssBHhssTyfkeKZM8wL6uAAEqBuBvYsk1CC3idCLVQXEcxtxE3JaESlWyjBMwNqh8FOQ1LGERKTkOFbUSHBhATFTQFtVxdhdp2OklKl1KtFVvrYhDIcSQ0olIvGNiXNoxWWbuiETwLXHPjAkT/0xJYhYbE05Ma+hNrXUizM9SVXGdcQzCw2eyhMdasBqBTyqxV5IkKERwaLGPpKYlceKAFfow/ubau7Rdtt7bes1BHB6Wg7U1s/n0/KSqgMhb66J13lGttVJ9vW+cpwJkJLOiYmX3FdcqMfCJVoPjchaaxO+jx2/vUX0lxqR0fjNeMb5a7vXw6ySg0qdyOKlvj1qzbjZ6QKdQUab4VpEoQHTyfFUQ98uixXBSfMtxpgyHvVadeNhuuYcP2+ecZlmcmqRwUtWEiWZGeIpUw4S3JlV4JYFiGcoMjEdQ+Ocq8GoZEzP68Vxzdqqkee4/eQ/7C47glde+bbQqZ3IlAsqkb/fawEBluZZREkW6mc85IciiJBJJ5uu2NdLi9UcKMyllKiUTTXTHhCVf0JOIisIcjsOCg6ttfR/YkAhjUQVULiIK52kio6Qxh5sxpRmRtiy5r1S6ZGaSIIJqQIScsvh9Segnc9lyrVZqed58Q3n59njmmRLXqTv/eUZyjVQxR6rCwqMOMlRPcFOgFSKrNoadJBWnQCKRKIkoMpoqORFT+yZ/M+kkkyDVRiA2B5pGWOGed4OJCZ8ifgtotLKrY1sIDjmvpa64dpmRZiTy7oEiwmcDgN9XBFh+gGvZwDovYMozcZJqZfgVUmjx8ghIV3IZeRLwKcrgnDHTgphUCPNv1MjvsRJlLzNFqChJRtZ2aODYotIWmF7IdRUuDBk0MROplJm0QM/JRCqipMkSwe4urJydiLJlREyjlbkKAppzgM0hwuYOEMzclSg9zIL17K4ZCpXwsDl777ja5gHo5ZjzOGaW3XXB45C4YzKMWy1NUP6bTQBWHrHK83ONdp3UxsK9q4JJwdQaM1N42nBVijQiAsrPcOJFmhYhIoHtQasdfwvsf8+H36z9PbNC/aOkd0AZH8D6Y15p4i+L1Xz/XvibKGf5m3t4/vVVT0HtCEx6HaxMVKPJKawQqVe8roub1cxXOJBToYgqEOF+0ZUYK/eKc4W6pUC4ovGhqkL04PMp4LoEpcSJ0YiIKr53Df0Y6ANeHXEGkpNmnYITc1wNU739Nf6t6Jxnrj0f5nsnxQUY08n4WTUiru56xhU6MGTy890RB4xLKmT5GPM4jgh3IlGNsK1tvamwiHDvLVyZnWkCpo6MemFSny+ACySbsKqYPCyJEZu0tUV3pixubj3Sb5xs0szO5g/jTxFFK4IFTHhnmFwkOapD1Ax48ygWSQvOwYeZK2gyJaesEfGaZuIBx2mhskZJ72eT10nEnDJW1OJcJtgRy2Nvze0QmIEF4ztl5JLspGLLCXMsjIkpoMKU8+LUYBY6gfqVq+UjolwDCRH2gMlVnfasbroO+DvQVSdQFpnyDK9IHLJgIgpKMIgQFkCSEa7UTlmgWZG4cjVZRJmYIpb6XAD3RwZHeLBmnB6/a5VZgvKXBAKoqq4WpQypiFmIemtZs95Ct9wtSL6Z+dR5Wq+7SjzmWgmpIpm9iak0YfVw4mlmIDhhK5KbW3ig3WRiInOfw6JFBpkbjhCU82MOmlVDZ2Lyqri/qi3Jpk2bk4hgOJ+RZjMjhOWybb33zMxBRATN7/u9BcWRqyWgdSYbGm0bk7bO3DJy+tSekcF6FcKUiCmCuRORexCJCr1XwHD9XGmFzhxAicZUUIRyVZRZr4nf+euIszhVJyvoPwF8/r+hn29a2TMYV5uJwFaLnAsRoyCnknYsA2WEHl7osuA+1MaS6vqDWaV2iaDYF5Qq7z8MdYtnakSC6GMRvsp/IAmMZloaSNbQy8AlJkWoNW2bwpWLllwVbUqiy6k3zCgZnYJq+3k1NlCcivBSSdW9xvWpx3R2Smv6e4636g+qFeBlXwmWEf66g0yVtOYmSVnR38IdNH8Pc7c55nRTVQ7nS3Ju5WPFyhSZYfj2wuGVxkDDU0UsYUp4M+aYA9qcjECNtG299aatwXAUqzYyoC+tUpYy2dnJWQjanyzitsACFIC8Z8KYFXZMWHSaEfiUCfFwMawzspDmXHUUuiPG+gE+6Q8VjQrZ5Pemc9VGVSysmrWY17XrY9UQHhlpZuEJZ7Q8VZioIRYNgYlDRIgLX2cKIcL/oaWkoNVfUB2o+sBXX5swrdPMCGGPrGULTNCBVILL+tB5CZwzmDUhNaVCglE08SoJFSV8JlEp2dD7UMC2g5jYnZIgzYhqzkrCV689kjikSXA6ExHZechL3l919xLqZ3JWxseH5BHhRMn1GgXnSiHHiWDnhI9sVoTEljSR0NZ0MV3WsWCMiYKbCndhxkQD/hGrpyDm8DDzYx/D5tPxLCxmdtk2Jpk20TPB8HzOCVip9zZsuD/Qb/e+bb1r0yAKC3PPY0SGmcGTv7fmT8/NPAmLfRh8J8w8BBsMceLOjJCUng01RoSaTVZtZSGR0uIqTyIS4dNG6121FXUhwg3hqfIinjBV2c7vo9ukXNlB6hJSnujfKvz5FLnTWSH9Rdn/vxL+zyb2PUesYu+kc2TVEjgY5lZFZTUIUQARJSyPiIEEEFPtneClayNiFVGRNaX6C3yqiMUUERRO6atcXPhL5bkycVVeQhtni0ht2lvftg1roNF9IgGUcp3Wf5KSorYvlVSn+g/UpSfwcz4QPFiA1PWqc31SKyWvB7wasvpa5Ld1iZOSq6tdiSSqT1oPHc0vrfhn5klktS/bJaSJ5OK2A+cBPVkgH8dYFD8wCzh29/BsIsIRkA2rXqCB1+JQcVVw7zYHDHkNSC4klByeoQVDzGksnAKSSxKzRIp8w1cjwoeVCz9HCC/CSpRQZxkBfVsL/8URXaOj98YVmQBTuTqCK6oGEkNEaV7PW1plNyIRZuqLA4zvj1GTeUpmMHepMw9niFpJ/f7a8jyeK+EzUSY11YxkUS+rAyZd8j5ZZg/V9p2HLFczm0Ikpecg+Fflui3Mp2SW/GxVGQtniKAdETrnDbxEz4lSJ4mYgoNKHlhffx45CBci0wOa8FyP8BxBMy2fPqL3SlhOqy3wYIRShAK6LFCVMFQjZp9ZO8+ZKSJ4zTEzi9qfRq03qLqYJcLnNMoYc+z7cbvfklNbi49+2S4irKxmZtNIaJrdbre31zebxsLXyyUzbU53I5HL9Xq5XFQliAwyesOX1s6Z3trj9tieLqCfNW3swSx966XSj2q2ZK0yNHMRbVjkQkzuluNA69S361WvRGTTxpzzmFuPKvSCmTk4N6YIro0puDHrA6+4QYBQKHNtVqmgVPpJABFcowNiEotZtuzfzn7/k1tFuQrEXDHxrG6XXrVONyeXCxu2KEeFSTTaaNM8MV5NIiqXc677xdVzClSjZZlSPXhAcQWvrHqN4RmlcUGRBX8o0hCi1vuWkasgp8jkRpnZuratYQjaewuvMQOXRqkiCPxKiddMEYUKtiinUv1Z3UjKJJJkAj2oLuk3yA6eJK8Z4nvkC4wLaiZTnVq1bqs5O/8mSMZMxKRNvdwYVBQecHzmEnzCEV6fNHjXIbCFiYwuGxGzSqytGLjG4ziIMkKBFkFI2Vpr0oQ5KcLXqtEsKAGLFoRUWFWwe4/CfU5iZhVWknGki7SuLjndRUQE+hriGlqXTL0eQhUC4P5DaVAINy+g44TKvZ5zkUQLqgR3F0cIii6EpEhAFrBcoRraMeYhZMgdK3QRLR+CcmIqGoB7EmtmipB7EyUOSdAVwJ4FK3QxbqpBOYEOjLIb7iVm9mh86mquoJlrzIt3nsuh+puAXoJ+fItVlsSqZE6ApxDRutGLwwqWp+pZaCfi/zpHyGgLxTuRbI/qxVF2J/Z7EyX7TErDl4edZG+AAs5B1bkQE6UkBgopa5gIMDDYxwjiKaKtt8pSmQr6R9OkYh5LV3FJJ3d3n/t+d/P743G73b68fiHO5FRmny7MyjLN3t7ePGKM459//PH17W3sR2a2pkKsym3rrW/74963TbV5+BwTq4b34zCflCTatt4v2+Xpw3PvXVtvW4chh4Vf+ibMkKMzBAbVXpqrNLgwImKGu80JyzudrabquKLh+2P3OfvWm2rvXCA31gKjChNZM6Hz3lOWZ+EZclY0Kao4MSLsO+bwHuSrfHj/R3+ZBai+1bfdQsVFFPglOly++1H0AhAuC2B1y28Kej4jYtXvKG4IQrAKfXUNRGS9iiixcp6EhshVpWMUhnDQnHwJTTipVMKQeLemW2/Moh0xgLjK4QoNqFhWvktiKp+7qnTOR3FmxlqrDdwB9wlnvyQwy2VlPcKznq1ilpk0mIgMZTV4wryg8HUnaWULFdl6J6bY+hmw4CmLeO+RQlyelxZmcD80kVatQwSJeuAHhlm58jFbUpo79NLCJT6PzCRnptWbU0T6tGlTVK9XbJ2lcKsqFb1qayISTBawr4IUKDIzVZQzi9PFurCaMtbM85xUEY5nzWC7g+DPzIl+l7JmiQii679otRacyezhBLNJqnp/4bQstApUIS5HDZSWUL9mIJwVrESRrKEsHCwFVZG+l0fv3Ub1ALwmrivpEEzeMJXJE2t6v24payK8ZgNo96uaPs8g8wLMWSJTSJwKVuP14/DSgyJKj4HwsFJO6S7rWxGRNA6smBXFLOcsKfMsSriOJNAzPBZJZZYMj/RwJya1iEgsCaiuBSIBWe78icDkSXTMWXsLkphZRyMmFd1aI1WvSQmsMhiLHSnT3fdjfzzubn6/397eXt9eXzJTWa59O/hhY/TeIvP15Yt73B63n3788+12H2Mcj0OUOWnb+vOH59Y3Zm6tPz1dtemccxxjfzwe+z7GEBZp+uHDBxuDKPPpqhHEKSQRnhTKok2UFNcL+d/dU/hCW4vCLMLMWDSJYC4aEVZcaXGzOY6mAp9XYcaUo7XGy/N2gfy0yh6qDI4PvJg6S1F5Fh5EZBniYP7B0/gv6ED/q9G/pppn0KtOgN8TB632MIvRUQOBcqMB2wGJDecZ14DAtSQu11mmDE5IYXNxlgAhM5VwvlSC63UWaaeskvN00aIUZRLEdC1AmYSZL5fL9XK9Xi4qQiwqy8hh5UlhokUsOnPpib8wU4CJgZ9f2/vq6XB9LS4SZe07WnGfucrLqqIXKISrFQSQuxEHhRJb5PpQznuXJ21Dm0ZmT45WT09Yem+tKTMhCWSRFX36McZxjH2McblwuPicltnkGmHTLYn2Y7g7L48UN9PLlZlFG5FEkMMdmgXC68hwi+lj+mCX3jqRo3Nl5kbkLiHq7unuQoozSppJHCvxMddYs2F6tXL7evJS9akQ0dr8SUp4c5SUC3FLQJtIDFUqLMCKwP2vfq0wIhGuuI0HLIxQm17cRpGGuTiqLNwC4PORpMnE5OboMppKprNCzPUN9lv4SHUVFaj5DLwiKd+inGYOcEtFRIQEnUbNwZISOQoUXqJUFlzieomrSMGgQ1ZGlNN46iSDyKrtWYjJp2WVkas04dK4JAYCkeakSlnzB+IUFiUC2WaaTUqK2IjIwkGWp6RpTvthvXvy5VLlJxUjIaiRu5tXeYIhvk3b933OQZGtdxHdtg1KlO165ZwePsYBr4Uxdvfpc+73xxxjP+7H/e329et+fxyvb76Pbds+fvioImbzdnu73W5fv778+OM/7/vOTHB/21r39D1doLRt7f5SW3/HHGMec8yMZJLtujVK2i5hM+yjaJvXrfeNKbs+h8/9CJgJRcRxHOazNxXp061Fhf4REW3rSSWkdGx5tZhzNu1uPsdsrc85haW1COaISFIcpcKZEZCzRkur2UP0Pc/C6jiqgKrzR3XZ4tuy///Hr/e6v77N++/y3Ea6KqlVzCANVOF6bgDjqC2GC62idCXiJFHR0ycrpYqLs7TlEohVIipIsiQwFYtJVEDkq6SxXj1zBgUnN2mtdS0RFGkNmVdBuHCYIkZTZJ6OKCABFqyBeCQMSmUlnWVjQ0qaaw3ZeggwVKg08Bdsqqpt8aHUCK78AITZCwOEO0QKK75FcFC21pg8vDHRdWNiqkFt0Zs4KDzJwnGI4QwqYvDxZ0r35uHHGJSEHq2pgKGRUE+RggRCLLihEbUQ0szmPI7jmHNubZtzmDMzubmKBodPnzmyYbpeG80tidkrqHNLZk0JiaKGZuY5ccFShiy6KrLjmqVwaSaIKCUYfIOzKsUZOVMsR6ZyS45CRVArQdPHxezOgDrCI5C9UQJrZqLCB1YVhthf7R8Dm4V9AdEqrulE8WidCkJNyMyiSEsZ1fCjJsCfZgYlaytq1jnOOPOGLO0eE/EpjSt0kHn1SpUIcJtOH7MyZUwmXmsEq09zPk1NV7+fidFC5NI1JKCDjCg700wwIH1OM5sRaR7CHEnTDRNmRCTGGGiwngaXKw6Y+3SfE/NWN3czfxyP/fEIC2nCKSzy9Py0bdv22Latq4ibHSj774/pc98fj8ftcX+8vn19/fr19euX/f6YxyMznq5Pj8dNmY99f315eX19/emnn95eXiPi+vx02bY0p81jThuN1wgXTLDC/t0iAp3bOLod4+n56Xp9CnfRdh3Xy/UCw9AMJ9MkUm2YFUV6RoNkuhXXbs5I4iZLPkCqoipFJwccyRQZ5q42zZq2tY7iva9cEDG/I/S8Iq6sOJ0orxLXWShTRYGNZkJ/uZqJ/z/h/x0X5PUXq6atUC4EAjEVByDh68Rc3iCFKfsiGa4ZlzC5GxgmzNhovOZ1K50lgWhzYv0YrTh5vB91XAZalyCDsuTHvUnlJxEWaaJSPTgz02Ks8HIyrAYXBsNJaN0dWxiSU9AuUgkvwVCVb+bq1R4JrWKfVmtQUFKtSD41cLnCFVFCLo/BPjMTK0tSajE0SFPKAFXJQOgRgUs2DE9URIilKWYWnDzS0tMt3ADG5ZwjM1WFJppU35EALLQ3Yo6M4a6iLI1EvGAst/DhXmVRLSWeRInPDrAVampsoLXMMB+ggW99EyZMBURI2EojqdzxIQSk/2ezo+esZJXLsj61ClALjcAzLNI0fTtdxk0pPDzfjy0Rqg5mIm4Kd5tC7d1DRQuqX96F4c7UgL4lZ3LA2Y4KemrA7HDz6lXWJa1tOEsQR5lBwcEZEcs4giCoxSuD+KCSRHWWdbeYy1KV35FeqaYfTyMAJ571WBExGN54KJsqN51S9Mwso8RqyoQzIDvF8ktBXpOT6kzpFBY2bY4xxjQY65sZyjJeazdUZfYpLPb8ZOZx2cC8WPBpahMEx2OMOUDP8Tnn2+vbsR+ERZAekbn1riyXp+vz8/PT8xNTHscxxtjv++3t9tgfb2+vb19fX16+vL68HsdjfzyOY7fpl8tlu1xEZBz74357u91fX1/GOIioWwuRFA0Rs7RhuSj/Fu5m3zrF4qHCN8lsRjpxtrZl+BhH7y3ct8sm2iJCVMscIqKJUtLWt0YEw57z5su5TeNyvai20iYAPTcnJhdZbGuqLy3tN/AWWQXPOi15ghfV/FbVXMCIFtBA75D0f4L4/Ce/IGr65nucueKszYkZXapShGYy2NznkiZEPy42YO1giqqBUM6EhkDFk+dKPBTQFRHQSKNdhUWLr7opC27lVZ8sp2+4RHCuGEKtNW3Se6s9a7S0OufELkrOgEtzKncYaBFULsCTM3DBFnOIIRorEK5uO5+BCn19IUe04OC6gFKVVz1g5IkUkaAoZBc6Oq4BSWSwqKY6O1FoiBc0ilsusOGg02wyTyUpuQeTeetECXrDfgw4zAqpR2btFhRRIWGPhJ/JnHMcEzZBc0wbFnDa4yTGSh9CgsAbN0oMbkUFNXOnTJFUbaSUjoPRsq3CgisxsoByxlwE91UHf1Pag0SwNCKJW0HflCjVAONA4azRUhSiz2IiokgpLQSHZtPm6rF1mpSRorXGxAkEbfSWssqJYoUJajgpw4aVAHBwndyIKFFkr8Tyzc2rCby5RSYkd7AESkpOMACzlPLAhbjuX32zWEBhba2uwRcJY75Qsxmwm6gmcxifJ7rcevZCa1UOnl1EdX7BzkKUSlmlCRx1bLpbObDs+5hzpHtGos3OIFEZ81DRTLcx5961ZBQExbU2MbM5DeF/zjmmzTH2Yx/HkZljzHDPpIiwMVrvT89PT9drlZoU+/143O/T5u12e3t7eXt73e837MRwmxR8HMfT9cLM8xhzjjRvIiESETFtEjNhw4ogMC2ba6dI8iAPzlrN5mFBBsuxMDtue9u6tta33reLzfn84bltG9idY47wmGNu23a5dOsX+EYRrJxVW+utX7bL5fL8/Hy9PjXt2hqLJsmcWCSW0ZQI0ckzJPmEHIBbIq5wZgIszVOJuHSI/O74Q1SmWrmG8YDXK5TTN/+DAcEZ7esI/kWfUPcNbSkm9StFpCZmhuVEUIschM99LblIGRnJJiJGrI3YI0Tg2SNJ5BmRJOVyx7r4OpwJHhAGzIXNMkekxZw2MZhSLcyd1wtVkd5gXr863kQcgDgFFa6f2BOVJAW1EYWnaKLnWdcGjyEXllb3dsWgXHecEjaoCxSqm8a0BvJA0uB1tQaf1SigR8lz9CKkDOPfRswbuqryV0xiYg+KjLD37VR0zmKSiCD1Eoo8xj6O2XvfLoq2iaD0VRUW8rQ0ZvhWTTcbc8zpHj7GAEOOMiWZKD3FzdF8uDh8dIB9LfqpcJIi/WJrFBQooixw1UQApSSu3/L538iNURmuJERL44cKA88/18EkVOAUkVZ8AeeSlmhlbca8h88frU1aFtZa3x9CErihITcqqbIoC0uDqYbKO4+4MFBOIrfI1EyqQxW1dZ5FQM6rD+Wk1eO3NRLD80NnfNYMRe7JAqCsPjBmriEwl0VllRUZC/oiJs/Adg7U+ro4JGetErAz82rKIinSMwmsOalfHJGQo0SkTZ/H2B+PcewZFh5NVZri9VOk9nY8Hr233jZtRfD3NTJ0mPa6m9s4xhhzuu2Px9gPIgLsSEw2bc5hFtDSchlY0TiOMQ4PH/txv9/vb2/HfU/scDYffDATxUaSmU6Z4TaPUWdCPJinsOxC/YIPsZ5tEOd7yPsm0pKbUeQ4dmZpXVXbdr20rR/Hffty0a2b2aJ+cmRsbdu27elybRkBmUa/9O2yad9627Zt27a+OIhhbtBNeHhSb73VTi1wS5fiiFdMX3dkNXZ1Qr4d7K6Q/l7BF+4AlKXC/TdAT6FL30T7zLVMmN+zCRcZtC6SLCh7KTqTiKCZzsxgXvvtcREgwcROC4Q4xRvJSGSAs2YRFtEyRmKpBrboB/B3gvhdNDLhT5XVGzCLNVG8MmFWUSKoClHrI0hnvabw2tC53jaDOIV9gwufOPGps9akpc/A/X8fkfM7cEe0nM2/SQAFhCJuYFfK+xiu5MQiwkEpiaguxKziQUEpKdokMqevkhlf7pSRRp5l7cBuDk5zJomwuUmKT9/3wy0KFxROJxGOCFIs/5qZJE3DfMzp4WZzmI1jPPaH2bQ5s8rs4KZuDlsoFU5VYWmNZGvJ6eHip04qMcaU9T84gFxZj2E5WWUpnQ1AwRnE1UWvYUo9PspMkixiYvI6/ACx4LspLNn4hAFrnMQkCedLvKTghdp5pocRl0ua8hkDK02qNl0iEkBVBelFEpFqmRuycM6MCP1Gh3NSTgHYUeX+6ug9PT2ZSVvD+48QmHgwM+pir10rwbUfKYl57cOucyVMWEBCwaqZ6bks9lDZVTioJiEgp8VFG9PHmOm+Xbp5773j+wIJiYjj2B/3++3t9e3lZd/3DHOb0Kblymp4RHDCgadslUxMtTHQnTjdYxzDzECDnHNGBJgdUbszLSIOBdLoqpoRY84xjsz0Oeec47GHG0GZTZzuFGFziLBPDzcuviSVpQ7QtVUHF1NxAQLwycO7IOZSrSchjTKLU5Wxx+NxPB4iis2s+FzrzIhctq3p1mro3nTrF9W+9a1pE5WIMBvaNDPNTEQklSJYPcqvNb/9xczYq4FyeDW7QqtefAepqYx0aDGGzv+PeiIXVHqiiviAT1zoG4CIV2Yo1nbpVheYjUCKvyMCOQpxWYMGJWjo5j49YowJMEiYW08iVjdiAOpZypC1u46SWmvcKVUylgczsoi7TXf3yEj2TMJviTjhPV3yb1r0fxi+UQ1RMjKxOTLcoyoOqBVYMlK06jbIdUTeMyqqNC4qN60/qavKLEJr82W+9xp/gVyv3+IInp0Y07JryjNDJxGdk0+8+IyivlBQ05ZScAIl8ZrvgWCWHnNaFuuszEWyLCXCccQ8gRhFprm3hqwa7s4mMHbF055zHsc+5zA3d2Nii5kUsspSriUn9eg8kiPLIS4yhRPNgZ4rxWpfJjYb08p8NRNaDW0hGpSSvOoGzgVzUyEbUXcFWbjModymRQRFkBZptArpiLNSWlAeMTMLhzmCbsIXoQkzizJl0ZSYUphqvQ6fk6X1US4MPkrVV3cEw7CzgQTBR1lIOgkvhJ/dPSwi/IhQMxHtvYuEs2MnQtS3IyEQdjD5yzI+EpZyVCt2AjKNquY6JgJIymsqVachfI7DzCLDpj2O4WFuDgXlh4/P8KoZY8w5w32/PfbH7eXl5fXlZd8f7pYeXApWR3WjTSlJVJto37bWGktplaGyCg9WURbzGZGUeRxH7fOYE26dlDnGIJXVyDoin4fbtMTI1I0phcjcT08eMxvMIgy3ciJqrQ0/EGxVGz4Yn5ZBTnMFwKpsk1ErrqXc5XLKUkttCJQmIprHgfYSZ8rdRDSJmsr+dlPBnHhrqk1Im7bt0rdrl+XgSrWIyTKaeSSFxDl+XxGb178LwlzgBq34ndUpZoRoeZMg1KCzRpNI64Z9U0AtOcdZbtG3v1Y7kCsTrHtzEpIAjzOFBFh3aKIrShYFwd3mjEw3rBxJOIhRNcPFVMbRwfdwMxR0IuIm3IgohNjdxpwGVDrdzMG8sWm5cBYRzgjcGBDkefl+4J4XPIzDGuGZwLgzQ5Yklcs4ba00XJBQMdrObp/fH9NZiAvx2nIGAKecx5c4AK1VhTn8SwTrgos6JcKLhlGoroctNkcsmV8l5kAMBZiw2hQi+OlT056SukxAKVOUMK7PdRQI2i6iFIowSh5zRnjV+EzjOMYcE7/GSBSllC7eOzGR9MacNoMpW4MKK9JNpFfYhdMtwiJ4jThBaPUXZaxzpypECv6SM0bz0mQA3imq5WkQlCCqhafVyscwqBOyVpYzC9V+x5IRqOSkwkNoFXx1RjMjgoIbKyYUaNaJmsjyCF2jPV5MyoBua3WWoMZX+5thnlhJXddIWLI4r2bOSW6Gtn9Oa6p96713UIAIu4zW+w2cNyzdw8B+6aiJuDZiEVPKzJhm75VFEa2TMijJbM4xzebj8bA55xjTxuvtPsYxp7Xenp+eb7dra733Huk+zMz22/1xv7+9vLy9vu7HI8LDnSg9ws0Cw3JVlLB927beW4PXPRGRR8KcER+ru+GdHfsO93CzyQS+Ioo1cRh1UmYsdYtZ3Xhk2NIopzuJNibK8KwBCrsv9SNGebTYUcwZRlSuapgoCguTkDh54WkV+7hwykinshbEtNXBIACHZ8bITGsqxK7eVJtwE1GUDMpKTpnESpRhY5jZHMc8Dm2qvSc1pGXIrLiEdEuTkpwpQc5ELBLr8CJTiUh4smRkaokeqaIPJUGA/h7j+TyJ7431e2+4/v+aMS9oCamSEYWYRAjDq1j3sJ6teYErADunTVRkEC5surFQhHNwuCR6vcIu3m+IR/NgPG7PHDaP44jCx9zMp81hE5k7MnvvzDrGRMtyuV6zbGAzKRVNMTFUthGeRYWMLAuwZFURVmmtacMilbWuVRQqgzxj1ILM0BrBfgjL0WrUkZREvuJ+4kVybW0NWWuIiYhqrpdrqFtD6VopSJyUvjYlLfyLmDnKfynMHJ28ma8ullpTYm4gmQgTUSfBJKyp9t4I9DXPpLTD0oKYH48dcz4iTqZ5HOY+x+FuzMTK6MSlCYWJ6HjsvbeiSmU2Ka/3FNkakHMSSkVAz6R0xkpLKgo92i0nJwnhWg7B1UuSpjrgNMf8Jx3gxTKmIwZPrNYDwa3ezDDUtQjl3pN6U02VtePQ09E0RlhmhBtR1lAF8+Mwp2Bujg4tBYr3hR4IMzeBAgjXNTLZwjzdfbp7MUUpIqJLtwiiDM5kikjc7kzKsH1gwYCMcUTmvts1nmClUMrbCll1IZkpuRv+QS1cKReTJPCYEUU8w8Om2WBisFRQFnn4se/743Ec4Fbe7rf7MR5vt7vZdI/e+9PT8/V6fXp+VtXjGKi9AX3cb7fb2+s4johaAuKw3wS0wOUmyCzb1rGNlZlF1L0QUmL0cwhO0BGUcduaX0dEOIWHwxqgap4o/Aw/2cwTe7iIRSTMgzng4hqFVRKlqmA12cLW0OtHEpXFX5EKEemgBMmIACk8sXktzxFodfpRlhJw9a+GL4ikKaU3MCJaa1U0mTMJKUXocqNN2EL1vm3Xa9M25Lj0HheYKESuH0VU+B2XHGmV6fWhwxq32DuJm1aFAmp4prMpWF9K1bues0bm9xRx8i9WmYXfBiV5nrzShFEsgTZLmR4+bYLmNcYBRGKOOc2YuUmrQArf9kClRhE+bfr0BQqLiLiZSdWhGTHHnGN62JzLEM3mHBM4GzOLEFjr6d60n6MU1V7VhzsVE2Zt7YhyNlyVJuLbO+YLtFpPgQ3Hgvmr/s+kU+yFGu1ssk7qUz1QYHQnmAYvbBjhMQr+9xaNsIc74Z2eWDXnsfa85qo6z1No5tOnzX1/jDHHmO7RpKOi8XBOal2FOLcspS9LeKBNcifw5Oa0+2MnxvAMkh/z8GM/MsLctBXaY5bSetOuLM/Pl+Ow1vRyaartsm3a2/Z0fXp+oojWmrD0ptpUuB7mWbthpxK4ItARrqxRDjl1S8l5uXM4RGqBeh11BRNTULj7mObrI25bYxJSVU63ZK3og6ZwFuGkCMSQ6Xq9KAPtKyK3DQZBgv7DM6iRJEWSxSm1qbYOr6PkkLUisfzCcNcK34XhfWRSTrPMZOLp9tj3aTMijzFsWpBDyMLEEQ71u0cw03RrraOOjnCHzRiOmaFjSXM/xjjGfhwHmlkiMmY3m3Ps+/G438c8Hvf7l19+ffn69X6/PR4Pd2+ti8j16en5w/Nlu6qquWWkTdtv97nvc4zjeNicecbqxG4yRBWqSbWoZTqbvHMc/kIHtz6NjEBpTBEroUE5tgwAiJdwPN+/JiOYM4Ukyli+2OcMQiAA3oooDBEPLfhjiekKNVmhAJGNhbNgYFp3v2w8qpvBX63GPM70jCjolinS8DW0zBIASE2zYRNtOfAFLDvU3uY4ZlOz6dhwqyrM4QHUCFhblnHCwuKzdEwobWFQl7TmtqvRPyM506KYLRyijiavNEPrGFXZ+v5XYgWceh4w98bHVPhDWcICLzALcxB+p7uraLZyggtKTrJwp+Bkdx9z+jRgaioqzNHUjS0mFg1NG8MGiGPTbY5hNj2yMopws+YcY0yK0GtbLhpUrxM8hwgMkRMDnxrIpiy5peqieYgoSyH+5coHAEhWgP/m8S65JZ1HCFXM0gbgAWKiTQuBSa2RQWBXKfF6vsnEnOyJHZcB2oQVaQmFg0eElWMUeckrbd/3YxzHfiDjZhMiyQhujZmlKQCT1rQ1zSRbsv7MOI5xv9+CZM6ZASIgm82IECVWGbs17tfnLSPnMZX1j3/6w/P1Q2+674eb7Y+dmJ6enn7zux8+fP4sLPfbW7W/HQ0xY+tWneBwT6N4r3DxSqgTJac7M4skNr7hhkeEeczldFHTExjB2/SMMvOaRkzdtu2yabQo/+rlm5Rpbh7ulDUryMggc7ewOQcRYddZ1DJNEhWBWULvEQktG0cVhqtiLBI5or6ImrskYYshhtJoOYfPFcYSpF8zG2O+3W/3+52ZeuuRSUpd+3a59K1nJjnUqg5AtfWGDQ/ubj5p8cKXiijN5n6M/diP/WAmOE6K8rEfLy8v99vt9nabc9zebrfb69vr637sNiYTWW+UdDweb68vW7+oNtQx6e4TMt45bYADFkVWiFVoov0lKW+AVTwKM0vC5y0B2MoZgoAF0bIw+gZOcKzjXUb9+Q3qkXn6061blhnJUWubcfFXQMsVy0lWBXyGvlxzO3q/rYXYrr/IZ/QvihUZOp93fHZZeTM7eRI1ynSzyQgkWBDah1mAl8nM2npv22Wbc0rT3lqEe6K6wWVHcYIGuxoUvM4C+jLQUdKpg+fF+c9ceeA9A5yxitYfV+GwvvOaLSFJrCFv5kKwM9f3Wuki6XR2xCdivjbq1FK6AlWJW9GAqIhLGRQQ09c+X0wFVZU5mcnMiRLn5DiO/XG4TzPD9juPYDB80ExCFmwOT1ARiB6Z1zw8lk9RZSwmbNZVUlkEr9Zbb53LL4zXr5p6rFkKnwT/9/r//djmEpOEr78F1C5PvE6luqZMaOqKwkir46HMBGCaRGTuY8w5J/xtAIEhESbyCpFNO8Zxe7uNcRxjRBmK9MtlQ4cUxYynZfVTiJWD4T3n7Xa/3W6sehyDE4xc2i7dzbdLp8gPH55a165KRB8+X7///vt/+Q//8PHDJ3JAU/bTjz89Ho/vvv/u97/97dOHD+MYbNZac3cW7n0TFarWFNwJbymQ3oabsILU4wbUJwQb6iPBc82ME+kyN9DdUYR4xpwzM+Y0xFmcfVUZYwprVznpFR5ZKTXtGMPNg4KF3KzwXEo3E2ZKOQbw9YTXIwsrtagVhQV2gKFBRMk1BIoMKwkkczoyBmy93W3MYW69d2aKTCWd6Y+5Dx+P45GU27bFLT2tt+3jxw+XuBKxMB/7bj7HmJHRWkeVbO4wZkB3Jdhe4m4+9v045nEcg4hbMTVpjnl7PN5eX2+vb2Mcj8djfzwe97vNKfDMyKCkkVN7GzJwd6pv80jHwXUMzGkV4yuwcxYmpsqSUmCokK7TvfCD5U3I+JLlGkBwAC/0OSqyQSmyEkQuwjPV0y68HlsglnPSmhxxDUBqZJ8EAYWsWIgQter3b3JAZYgavPI3JNr3n1ttyQoDYGazZGYLc2MWkTGnR8zpTftaLsWiLBlGwUxykUqWtFqUMznmQnHwj5cZOKB7YYmw9xeb31T/QCu+MYR4f29c+eS9bUSSo/dfuYCfpWEt5LlC/zkcIUVBbz5B701KjzD0mWOMMWIxQMM8mlMGOIOJeRQB0HX3Ge7pjFWCRKnqXN7mOcYYc8SiyKJiaCqqzaBCnbYm2pmZcPtCJ4lPPVYGQAgSLkwPktrWmsC0UvWkeEAZxFJydl4HJNfJKF46mNtVtoZBOZJBInQ+/jrQGCWVeSxpUJS1S5L7+UES1lm7Y/YVPhzaeYzVbYwxbBGrVDNyHmOf+/12G8dxHANdY++BCTCToypBc4ONGgvGYnODtPKxP1AtdtWMbFtLlzBv1ydt8rs//ObDhydOur3cr8+Xy7bdX14+Pz9v1y1DP3z84dOHD69vt+v18vz8LMQu9OHDtbeO/R3IrtU7Zpi5jIymwLXGsMD+wYxYbvSOkWVyzf8StF2f5oE6IH2aYYrjERQ5pwUFRWprGWTmxHMIm2lbXBSvb2fmtu+7mcEoJYlEOeFOc9+1SY9kYlOLjKZgZ3JrrmtmqKoqkumyysPpNs3GtGMOOo0lmL0wgAT5fR97t9m0EbN5oMth5mnTbB7HcRzHGMf1ciUCr1GZ6HHb9+PxuD8inepzBHxqxKTCvXeoE8LcYjzux+N4jGMQjI/gZsy07/vr19fH/T59HPuYc/g0iKWFOGcEKGfKWFxLSwKZkRQhgChB2yuwZi1yOANRMLjFZ9m6gvsKnLJwaKIqdNFWvyPSQF2gXZZc0TlrPkpQHC2cHD8jqqvyKLJZ4oNNIq7OYOUZvBtamBItdswKfCd/nhdx4Zvu5L0GJhYMWkFSQ6ZgZm7QhMAZ1c1U3dUiQ1tXEQJDLQngQwEUK51UyOdTRkwMXXgpo5hqUlwI9glMZ0JaWb+h6gS+CS0rOaw5b2YW3zG/KWxrX1U1IO/pMd/bBjw/jGQI+xPAzzw/7eKkInUvz1x3h6x+gcDYy2RZRynDHQMXrIywOUHid3cKYmIVpUYSDmV1EswPjLWFh0V6L17SnAYoHyl6GfUW3Z+W14quuC8izAT3IGh9iKKAQ9jtvPdSlRMq4STH6nVOSjFB+PWOPXPVD+iDYVNMReGIzCi1UMEUvohUvnqXMebKqmO62TSPYFY3H/ux74/77YY/1a4Wvm0XyuzbNufEJ95737aNwFplIaxuz5xzzDHHcez3OzPlZbv07cPTE5N8+tA/f//54/OHv/rjXzWR7z5/vL3dWmtfv3z98suX7z5/ul4aTsXnzx8/fLhG1nt7vlxae8bGUM9o2ovmzxSUjaUxUaYHynbsajQixkeBWhw7nrGtySndfAybY5QhjVnpqNCmwcMaRZllUgSXYXMGU1JvgnRes9E59rHPMdw9KFUElpBzjmM/RGTqZOGtbyLSrhoZ4zgyw9YxuGxbtk4ZlsYiKPDnNHPomBxuTX1rIoqNI4/jsR/7vj906rZdVNWmoeOH1nbf93Ec27b5hw/hpso2Bh7dOI6Xl5eXL1/ebm/HMcB9BObFxFtvzx+eanbsPm3s+3GMw6aJsOrGa/Pg4/44Hvu+HxGGbkaSmESSybEpkijYpzMJcBbI95QE7fbSU7wPt2hdrvqDzIwkWab/mZTlkIFAmyvEZWamn6jGN9V31bAIb7w2D+YJuVbAXj873+NyQbHxjcaTmbNeSVDUBB6wdkW+WLV3vana0l1V4jLZea+Pk4hYKGvfea2GPevpFm4GsGC9bvIp2sicGzo1ODehO7SodVc1NCA6XdH5bED07ETAoCYXWUjNYpt8+/De63r+T0zJ1uh3xaaV8FaCBOuEgmsnNtOS9UJMGWv7NIJdRFGSV3okDHOzNYog54xUFZRsEbltW0QYIB24cJh7WFgkkbnNNlvrYK1wsrBGODM1VVHVJsSblFSH9/CMYMz3m8CqHpnmnPTiLyytBosKs+I3YGohATRVEcX8phwvzsF6tVZLRinlR4H06HnmLzQqDs6joirIRrnIDCsHx1KdIJWao0jCMQZ5FFvPcGwTNirHMcxtjPF47OGprUf4/rjf9/v9cXvsu43ZeneP62Uyy1OdYcbwkUhUVXsTbUQED+rwQuJAmw933vjjxw9PT9e+bU3097//3afnjzamkv7NX//p+nS9//a3v/78y+dPH54ul0wOMxJR0UY858y0rtem3dmcIAnG5wM8UChJpTFLo2zqRIIeDnMg1EOAQ91jzMPMM8gjbI7H/X6MOd1szvBgJlGtPTYqTKgsYo5JtLe+X64XIp42G4tKE2VzG3McYzzuj31/7PuDmbetieiY02xSBKjcLDx7b6qXrZuh9Y6TA5YR2xb1kWaa2xjHOOY+j+M4QCNhkst161vPjDHmy8vL6+3tcburyvX6tPUeEWMOItof+/12u91vx2Nn4cf97ePHT3Me1+1iVj3hzz//8vPPP//y688w5gNvmIl6a9ft8vHjh9YaUbpZpNvEVJuIWcUAt9q0Yz9gSZmZItJEw50iCc4N+A8Hk5QtdVJEnDoX+I5QFqXutGWp+T4DtId9Di/ZRxIJESTgdY2YGV44zLYyCQI0qstkxlCYkoKBa5/hrLYO4gKGp+OSI0wX2HpmC151a4JlTZkRxEFR44H8JjjWR8unDIjXfzCO9sj3DBCrIOR3pAZxsWWGOxFzRIgKBbEwKzGFSBNhVW1Nm7TVX9Sbgw54JaKCfCgyJT3wFGuFBrIDVRGaTMzKAEBqhrGSz4npINNmfQQrVJ95r2r74hdLKctgrY7JuOLjjMVvQcHltYipSmD8FFEFNRgyKyKQt5wyx+CMmD7NLGxOnzYNCnEiyPb0er0CvFZRTDvxg1hUWfvWCT27TbVGC+JovWtTDEhq/l4fP4mycG08V1GCY4UK+mtMe5WVa3AQVNhljTlkNVwoKil5QfySRVegTFoSuIArhpCwSL2KXCWGgEfMfnYL2Lt0ElB4Ec5qTCC15KSc9sgjzZwyG/fVLqRN/2Ztcq2+tWnnKGPmJOHLdsne6qhGKVmCYoyRHqrcmlyfts+fPn769PHx9ugi17Z9uG7X33zG+vWY8eH56cPzn5iYksGP5JI1kAarwn1ooAbR99tULSLKllLYiogIdvCBMGFrf0ZkHMcAUKOqlnnsj31/PB57ULq7TwtKIb48Xfl6hQUVmEBzTPfZWr/fm0VkRG9NRaKUR+5hj/1xu932x4OYrpeNmOecqJSZuLeNRapjl9w6KEEFDDLRcJ22LZpJms/7/b7v++PxuD/2zMB9eXq64jKOOd5eb6+3l7eXV1G5XC6X7dJ7Bzb49vL6669f74/bfn9Q0uWyPT7fj/1+2S7YFmoeP/3445///Odfv3xxs4zo2gBUXrdNM4dq9CZMZg5cdOGl5ZAtzBFhaHo8MZmnZGVBt3TiyyDkEJXvOuw7AB5LIWZ41diQSZiH0aogC3ZPBnyEsFLEz0Vik6ZozVhqNce3AEvWvjiqvEDFveCz9V7YB8IbQnXSEhbW361y+R3DWD5iGSUN4eLKVxwHClQRcf0XrSob04v1M3O9thNLr+lFWLQEQuaGBbMizDBMzxQP1eQkDham3rRvm7bWln6YaoFzYCFOgdW0AgNxoXBF+s4CbdCJ4MOoPJYLu1vKgfqA6gklhrgLDjpzSRWmdRRq929SuEUdkShVBRTbvPT3mMszk4iqZEoQk2pLjzGmiqCwcLfqGrDnzdLMQdVw7MUUoKJyfbperldEXFAskqhdttabWXgmc2NxFXZLVW5ba9tGIk6kRGMOSgYHkangflXl5e282qxkFmHYfwb6P2Ei8jX3wFrt2pdAWYtKqhdILAplJ4ogAL6wGMBCqVAVxh4xVmmoF9AyLHAqxhwYFIK4pk0x9R02mai1lsxB3FoXpdv90ZoSkZf6yX36GNB1Okub7JeN8VO4Gkr8ffNoWdSbMJuUqV1UtXfVS+vSvv/h+99+9/3Hp2fyiGmPfZfI67Z9eLpkhE0T9owabICWJKrQ5JgZijvsrkfUIWJngDKRxZ5kYgqfXqujuPd+ubBHPh67uREzhduYlLE/HjCdT8pjH2+vb3MOD7MxS2Kjykf6GAyl/Zzudr89LOY8ZmSMMVX1+ny9bJu25u5z2nHsb29vX798eXt7ZeHr9SrCc0xm/vj50+V66drxdpB1LtenzGytZdbOw+2yde24XMo8zR73+8vL68vry9vtzc20t61v16ergttK/vry+vL6+vWXL9Omivbevvv+e4zK7/fb2+srCM4RPo4+x+Px8vL09Lxdt2nz5cvby9evX79+mfvORCrKkUzOLBluYzyCnp6fKAnwToajoVyPXUJaRmJ/jzZOSnOnxC7fXvD1QoPB8cahneHBjtRHzJQcnAhDSXySS9aMjYIiqDaCcDLhU4YELCXDVdjduDUW1pPrQwCsCXQhX4F+QRvY3IwYFUnBkkLMzkLqHiySxJHkhU9Q/fUV/lH2JZFRFJYCIyAm4Ie8KB+Jfh3jfcTFWPPmCIo1FGH2tasV6G1kYZKtUlAB8EkSDONZvCFiEYZtRu9dFUPIVnOPyPR0D+Zo64tOJk6s4vOkIAAyonc/q1i8JzqjOyK98PvjrGSwur6EM88JihG5e1Sjk9XAU7II8Bxe0wo4mHqxdoG6Mrj0mCo1VWdWuGyLEHPA2y1r1WzpPsLnMSNChLfLBrdE+A8LM/fNHE5VhWB0jmm2RDUiCkaNEJOZb/DVyBDCmqNOwqIQeakwEQTA1WGVMp+Ik1xI3tMkE1DO6vkiqpBfVknruCzGw9m2CRZx18tnUEvr2K3uksAcdoASyKEoAjxhgyVNBfR/cF5FJSMvl8sQOvahIn1rfW7m1rbNI0S0923bOozLmQm7nlD8qqoo4ycCIgsKbQI8rTf54fPnv/qr3/3+d79v3J6fr58+Pj9drn/zt38lLOSkItobJoosUqqsyFpgTosRiJ9KBfkTQkmeOx6ISufIyQT0kFkiSFWeLpet92H22B/7/ri93fb9MeacNh+P3Y5ZOqZjn8eRRH3D3ue+bZuoZgbsK16/vu3H/fZ23499P/bLBTbu/fr8JMJzzsf9cXu7vbx+vd1ulAkigIhsWz+O/Xq9Nm3TTFTGcZjNp48f1nAnk1lErtfr1jeiFJHMPPb965cvv/zy69evX15f3+acl8tlu2zPT8/X67X3Zu4wqf/661eI5Ftrv/z0s6oCIHrc74mtCMKU9Cr84fn56emJRMacx/3YH/t4HBmpqmGe5KoqEmGcMKWf2PdggND4jEFw9TcjEsC5KDR0VcmJVH0ybQhKqLNGLIiBzvIyF0Fklfwn4gxyFq2JF0Pfzmh6maKs3dcsMOumLRuNulgr1tNZgVOB/mUIWIeorJtVT4yKIpMZkuAzp5zIBxLWyYJBbngvf2mV8+8ziVLwAAkveQdKPwoSxrvDMG9dauIW7lm75yqBZKRoWblrg7Hodr1cr9envm3b5aqi2lRFWbSWYgmnAAADcpqYiFeqWRD/cpggAuvx/Ci4EKHz+b1Pat7/93zAmRRMaEAiEqrTXObmGQmpHTPzdEdLhT3K1WHWe1+gUnFEwezkbBQOmnS5QBOLMEzvMEn2cQyPaE1aa8zcWrtcL7hdIpIT2+cVzQvEWh4B+bgqpHD4CMEiNsokJWZpvbUV/UsynwafgAIXKxwx0SL783nk6DyG+BFZpKg8cwRwI/Szolh4kLXwvlQGjE15VGvOgAglWJSFfaAFMGdh0UD+o+VZJGVPoZR5uVwyw8R676o6D5utX7cLZdp0VW1b37ZL713Adt2UnCJJWJGzYY0CZQQHK/OHj0+S/MNvfvj973//x9//Psw/fnwm+s0P333XemcKtOoR6TaT6LJ1ZmnKmTMix3GYOxPhjK+WugIEuHbVseOZcjU/hU3U8JFUWXtjChPmjLk/bB7H43GM+fbyOsbhEVCxuk1S5oc2bZdL760xs7mP49j3/e319ZjHsY/9sZvZ3jQp+9a2baPkaX4c+3HsYMpXFcyiqsdDxziu12tTne5MdH99ub++Xp6e8OqL1kZ8vV5624hJu6bFY9+/fvny5cuX2/029iMiWu+996frZbtcmrbI2B/77X47jgOSBRY+jodQ6WAxsG3aVJWSVPjgR5h5krkfj+FhJHTpm5C4W2Gt8C5hEeEswqyHR7WtJ67DmcERThGJjhDL+E743YPOaBVJa+5KsVQqJ4k+E3emIOaF0GShCgQHJ3zcEU7JzijrERokhJQLD8rqNgo0yVK8MqppyjNKrR/IzEmQa3EKczARi3jEKoGDlsH7cpTn/CZlcdA3rx6v/z0FoL5ZqRPIEoJ/3f4sPAupiynWgtLafJgq2qZ5U/QXCgIsZ6DCV5HecTZ6a6313rT13rD/UnjddIYQOSOD4YguZ3KEIeV6Kt+AO38xSgf9ZlUoa9LL9azXJAPfBSkc3ypXIi3sPtNAQF8JANA1M0MliGOkok17KhmV3CFKTiXJJJI2nSlS5MTciHmJ/oAhmUcINwghVo1Mbp6R+FnMPObMyNZaGcXkO/Ne17zH3QPdMRH6ZakGEOScKlaIwSlDl4MaZVmL1WDrrPkADiLqr6E7/HhP6JJRo+MI0zfVhHAyN1YRdMbo3uLkjdHyFy5Pz1UAeVKmmSVTmCOU+jQhySQz2/qFAOy4sUjrjbB6ojVRbdqZaduaiHj6uqNUxCOOhL1BhiqbURN9vl6///xp65vFUJK+bUQ57sfT8+V0UoGN8P3h8DeEyTvkPNrakz4RUUZgqJiZoO9i7oQuOzOg1wW6SMyqol0xtMRegA8fr/f7VVXub/evX79Ms/vjbuPY93G/3wEW4sH33h9NRCQ9zecYNsfxuD+QqDny0hqjW/fwY0akhYdNCdqk65WXQIQ4kjJszD1rjUREzjnnmKzqkb3Xt8rIvrXeeyZpKzLu/X5/3O4xTcD0dffMPWIeA2WQm5FFl9a6TlY3jxksScyoEFRURRvscEWVea3hdSyyZGZhFSJIpoFsqkLIKMysTCbMSWDBFAjBdcB5OVZ5oLJ37KRiyGorS1eMRygBgbW8BhaJnIXLWi6/CUG4CXnGkBVp0AgWzswwQw/Ijgo0qpkUikYknuqVM8/wvAJVMjKVIJURv5M0KqQBSS+YfFVzYKjJ6gogMFPsH1zGC3WXC9CvkhDPoUCqVdlg7MnEWAgD4uSKI9kqCCelRyZFrQ9MUpamoopakWFfDJdBklxPcMWTkhJz4uulWjeuOMcrVAOBklO2WlFrNXsnI33RbNf7fH+zq91ZFUQsYmZSLL4mACgMHqKAs1gjGlHRro07i2gtC6kGC8hPYIFlMpSCRAgNDne2+sEUEWHuHrBss2AhmJRFJDidZrOw54xjHGBFhhtUYDYNJMLG2rdtlZ2J2XYIMQQqjDlV1SpM7xY/q8HLZVFJGbEMQ1hqCRtO9vrLgAdx/Gqrl8NSlJVWiRFY2gYkIZOShEiInYRKLoTxDmfCAICIM7C6145ZzNA5pGlAKWxu5vu+7/tB1X9Ia4KGF/ymfr2sDAf/PRREZbPfmh579NbS+vfff/f99z/87ne/edqu+2N/frpmkpu3rqwkrBRU9ihgrZmpSo2y8QSAPDPG9QKnKA+LtTs3k5wNLwcctoD0UbRnFK/XaetNqH369CHytz//+sV8fvny5eXrC+hKj/uR5OM4aq9CNVlClB5uwyLcYzJLY92unYgiom8NI7SAW3lSUrjanOzkQRGUAgNIUXKyNG1NiJkkAjCnl7scJRG7R+ZA3YqlWZTZWKR3yp4gMVtGhiQGMeTmMR33panyGihKsjSRxq3ITKyifWsqQklzGgWTSlEL6t5KE10JAHPrIj2rKPquws6DqpYBNCySXgcSS1GUW540HlpkkHcAJla7W0gIosWJi9ZdqW+4RmPVMGOi9m7pzWVnhH9nEgUYPd+MVgvRrpLsm/qWVmHOxMFCmuxSY7kFXaN8K2VVlrusMJNyEstSckHdxqtVXXTK5U2+XnpU9fyeEqVIObTul6pCp7LibVJmwzwkgjPJJSSzbZ1FtAna9labmaS31rUh6xORm0XvFbGQ4ZjB/gGJtSAH4Vw2kEEJsBsVMyr9Ja/AoqhKaPTeRy2e/jtKVsV/JR+cIIgnPcx9uhEYn2aRtREGKLq2xplLPWsiMt3NrUqXCMVDRF8jTKRJmY5KERB3EKhEETZj8uFbn3PsjweruAdx6X4htVUVrII5jgOu4hkexsR0HL23NHO6bC26dEGUYSJqAujBw4gdIx+p0RdlvZ8zzZ/ITyaxW6COqYU4ucys4K/INWrLqMsVfho0aW02SI6kxkVVrDQKyChDipSU3Bt+JNybKbGI1Y+xowS3sBY0bWTGGIeouDkTico0EpLWGpOEOTOpqrKSkIugsXe31rbWVEW4aXgKZVP9/Jsf/rP//D/76z/99Q8//KYxX7eLqqCKbE0pac0Ia0q91Vpm1tZNbI4ETc3cIpyYoyDdcwkKiARhNpi5aWu9c9EBmZjGnBrRmpZrReTz8/Xpwx8z9Tffffc//+P/8ri9ffn1xd0TovNpeEe8gsIS+2LBY8KBAj5RLJJcW16CmcKTJIWEGxOHxoIlE3yLoPeqCOlOe2/aca2ZWFWYOIMinIxAauBkZYGBJzM11cp8y2uUgBcXO77chomoqTID7GUmbtouV+wOYVyOJAs+wRYWlSQ4nC24OhH8+KwFi1sPvPSsgAj7bojeteq1MQH6wNPqi9YlOEv0FWtWXvkGQagrUYrHrNCEaMQMK6eUZBZKDISFSYlYhN0WH5qSScp1hojy3J+0Iv8aIb0PItA9czEnMxN94UpmRJiphECQk/WZIo0JYQUVLdBj3X1eHtF0djX0XiausQHRYh4CFFkDUCaiZmYpIuUkJ8q5dF6UHkKM9SlIaEDcPUymGLF1G3OSSOcGr4POHb00so6hbs3z5RGRgEjFyXNixUp1CzAkT3gnLRZpLn9Yxuqu9080M5eNDhHcAjwC6kuAg3NO4C7QQrbSQ6YwSwiTMHnSbNJcysGTkhx8g4WWlXlysSZh7jncppsT82SeYzId7ol+t29duHlxTMmsQsu0Od3cPG0SUdtneH749GHrW2Y/BxLVo0GGlrBdcFjJEb/vGqS1qncdsCruwaIoSCcNxAbgoMXlgS95prs5GPwU7hShkVNbExYldk4P4+JVVXPvp3igfIEkBaKmmDZjOpzv3O2YMOs0Vbnf7mOMQda2NueRlNPS3XrvcLFGt3y5bK23pJSpWJ+QRKJdW8sMbQJTNSL6/N13f/yrP33+9AkT/r51d2chIRYhECvhDkTMgsa7xti5WnYmIg9PFmnqKKEcJggRmHd4eDonY+yvrYmIamNmn+6YWwpHamZKMDH/8Q9/+N1vf/PDb38jmv/d/+W/3R+7Mvt07gS3bGICNpjwwKgZNRcAQJi/SwQxxL6UTdgFIATs479VYiLAlvqUKQn1dYRqB1FMymQbf0jKRCyO/jvZXc3Bh5OQWOEUNSN36ZTikcTvxsXLMw4VWCh8JBk/xbRxEHsyZxmfacWjXOO2oAwKJUFrm7xKvcz0xA6FRao/MRaWqK+EwMEJrRGQuVjw0SoL36mEmcTFo1+3BDURnwVkviekhb6/r/apxIoMitlkeE0wiTkKQz1r1vN0VRkJcSrwCcS/1YlUFIPaq4Du4OAF06NqJUBNiL147iechWC4ZD+03HnpPRcsfVckcSNWEWpN53Qnd9cmRNTGMVsTcelbg6mzTZsymMUvnhluNnlslx7h04aYahNRlxBzozlXny6dGlMQubJkeqg01UyOdGBEWfvkChDC57pS3TedWiYBC1yy3qSUBDGHV6qH8ojy/KiT0msYcKb8rOCX9VCZCPwcxfIaIedhpl4b4N4NcsMxwV62iziNaXAon9PNVcVZ5jHR7xC8rDOzR+FiSQlLL/fb7TaO6WEwU9DWM5OZ5ZNmppajP2UsU+VVMKa4kIiwJkENnURCySzrtDIzLU0vB8HZHhMqAJFlTYzUP4E7THeP5YjLmenpLbOJhoj7cp6NzAjLCI8x55jjmBaZQpJUeApGtZhSRnhm3O67l3l23G93Dyen+Wr7frj7sjPlzCz8uOp1VhFrSkZJCeclba3kfO69axp9//m7TZtkphmJIpgu11LsBZxzTtxAJyg2agtj1sRRkA8wzoUUgUr8lWGUFMSZzpkBuUZVxBReoETOYe7RW8PSHiLq22Xr7fe//e3f//Xf/d+f/vvH/ZHhTk4z3V2EbDgRB0FkzhkpSw+PSluQr6rXUybnJhu1rKWJJIEVFympmcCVpalCqU0ilMEqhIDN0A5qASOUwpQSnKJE3EhVxNxsBiWfVezixgj2PsIRRERUmgqLEmUmqGLee+99EyYRodaaBzXC/Bm9EecZx3glFyLKxVXmFQTrOcQZuCuU8hkQkaO5fKqzbOUiKdPTsdKzKnpMZUlUStmzfhUiFHEa+ayVVd9s267QgqETlZyFOGdMYa5Im6cvD2U4rw+Ql7dxrvK+8sd7D0KLsELuRkQEHzGi5ChToVoxWAvJ63t882TwTDC+QsarFFjPuUJpwDGUWKVY4rKs2ESEkiKjuRtG7Ump2lp6RphZ6wYvM3djZlT+ImFj3i3mZT5dn1gV79LMWlObOrVtvbGwEijbHYImwPREScGRgTGDqKDdyzJZJCpLP6YMDjCTmFakr4UzNVCITIksg1l0XMzYYiFemSaIIjgKe1UVVm5cJ+CE+LnCzRi7u6s2qko5q9KCgU6QDTv2fRwD0hXMyu77XWfbwuDQfxzpvm29o8OGP+a+7/vjvj8e0yzNtbfWrfV2/fDsHtp6EIFk6JnCNMMlIQlWjgx24XQVFdby4wOYhrqBM4poH5lzjauqcuCaHUX4DB/HRLgbcxKySCzeztpv3BoxgQQmCSNxSTc/jn3anFbKlPK/TCIztzkeD8f39wzPL1+/2JzhqcSe5uFz2uvrq7Z2vV7HmL1v26Vpa0/PH7QpiAYsot3zUfI44WQPQBsZs7NsT9c//P6310tzGzacWxOWTTmTzcuniJt2kfQsxg7utkcyk7JWaxfYEYj5kXswpTKxama4hZA8PT0JC3Fa+XpG33rvDQVUNXYYy1bRQjatdfn+u0/MsEeyCMsgFjIrdaGqhDuXlINlFcOcko7T7ZQc5AsUIWJlDmJ2jgT/jYk4pQTJwUykuPxKzMQNgAmwQg8nSs+w4FxT7my6GGpM4bS0IxlpYemZDJPxAC1+XSJJTCCYmRvG4CCFJCUrc7JiaoPoibuJ3R/w4WdaNDGJCGcD+opZExNZuKPWD4ozcxAEYIECu3DrUhelwB4irOoZYgsXkFBU4GS2gIjA4abImnIFMbO5AWoXYpDvMYbglPAg5Qhv5a8Xy0K0GghNQcfi6bw0CGdKo4QodQprMhV8HNUTUNS+RSIYIIIvSrT4xwjeS4kDDBGZNBaxvlqPWgIKzjNQApWgUMXqBTuOvGzbtm2+OwnWWyUciTk4w7318NDaAg+n1sdDWZh5jkFENJq2QUxy1/lhDJutb5frBQcE8qVLv6iItrb1tsWladt6q3IAaSqI4RALIDQ8DHuJFTXxSpYl6EO0h3YGWGJipl5jFGJiFU3JCOyWUmLLIF0SVmFpHToGVRUhTkozltDAYmGmiJhz+nS+MGWGGzOLNPcJQy73YW5zzHEMmzOxRUPVI0TMPS5Pl5aJfGlMzGTuWFm+32GssptPny5TL9enD88otsIcy4PSzEmxXYYJy73C4U2d4ESIMCvUGZhUI/ElZ7I4hUVMs1wGVbIqEs+Y7sd+jDFtvR9cw/CkZRTJnE1VBGuThTjJwQsWSMDGGLEm6tMmIEmf02wcR33zMecxbd+Pt9fX8LhuG5Gb+5zTzPb94e6X64WYIrw37a0xyTiO1rWBjqWCpTI2h3LnDOH89OHDD3/33eePn373u98+X7Zj7PMYKqzK5kEL4mFhpc5C3IkE4zGgFTRrJQYJSyt3pqRg5FApPlsqs7aNRYgbqL+SQnAlyrRp2oRCMRff9yMprk9PvXdtosru9vL6tTVpIslaYFQw3CCqT6N096ZrqlMQSFBIIF1VW0ZJfOKdVMt3JEpNSbQGh1FdMVXVj5XwTKVxwcoK4KGY4gipSITg28siDkRGcAoFLfaYCJMoEwPqEZUIP0UjiFl921B7uhkzw7m6OnniOo0rUC1TmrIepgVkL4LCqd4NkDHAyqg6hpxzKWUX9QPQNLPXW2NKLvfZhTScZJMsBCaCWTgWmyjWvPq9HZGCVxe4zylJAWlunqSLUppSrbikRcSuDp4W+6ISeT2Qeu0Eo+YaRtQUQqtDAJNVBBQvIYlcQ+isVa94N3jXy5AuOKVOS4Fra3CYQaREjBhYpzGijWOAMKCqHgH3m8UWGIN56z0j7xR0Z5LGqpnULz18jLFvl+txXBCvtauK9ta3y3a9PjldB3nT1ieYDzUCExEboeLYhevmkclOW9cxjsUEZREhFcYyOSIPZ6rKHVBOfOvqw6naSASJUlTMnZIVuJPwstAv3wJ8sLBtdzfOdHMsD9JWUJI2caqwNed4gMJyHBMFobm2UAnRICKdcGDJFJms6Z7ENicSwLEf4zhgkeZm6qqiUWuaE9CTNp1mAHJZ2WOh/b6qGuZgich019aYYQhRV8UzxjQz248RGUyCpX1Ulm0+p81pt32/3e5jTLPQJk3a1pqoQDeH7NUv0ltLTzNLg/xK3B07c0go3LI2SnBm+DQLu99vYz/c49iPYW5zPm43d495ESGbPsOOfY9M1eP5w3NvurX+4cPztqlqs2k+pwqnO2fGNFEWypiWGZdNr5fn3/3mD58+fmCm49jdZnpM1VBM71W4cbEPcQtRIRYpVkQ23YDCIicyJff0iJjFAMRxAyaprJ4510Jwbb1dmjK7JQltl8bJkRnNp3tYUCM7ppsnp4r8q//8X/3D3//LGfN//p/+x//lH/9fuOraGxgHwGfOoAOqaziLUlIZ9hGBW4uwKRW8s+jicKBB+K+4go6GF2cDnowimB25EUV6pJKUsxQzKSXXNmsMvzlFyJk7pn3M0USIWGCbvNglkKMvohwmF8ycLuqMNdoEGgrQwoXcFi7hEQzaEImIsljU9iOkgcU9Qzp4n34WuM4VQ2tkD34ycNSaKDPhgQIglkowVKchC0sRpqIGUnoiZmdZQmbpbjnRoKEeBZEiOdZAqeyaY71AfFwZwCNQvNIJ4+QadDNoytj2XvAPc3CorvVtCxJUUXcrCKJ6ekREZNME168aufXtqq6gqiTDMb51OMKKKFHZ0y6JDfY0UgoxtlBkeExOaRlukdP2CGJtrBrpfNPH/X65XPt169sWnrWsUqS11rft48ePn77/brtcuvaPHz42VWFRFSISUTFGgZKU8NNHGgcaJuW+FJHLM5tKR4dIWFXR0vTC9QlcWF5DIRVhlHdMtOxcAJyBPTrnvD8exzge9/sxx+32+njcKYgj0Pd6aBLNMecYx7Hf3m7Hvu/HYx4jalmvWLI2DcoWJkqZsW0b0e6mSZwB6ewx5zCf4ZbhhD3QNiO9Fp7OOfvU3tJmRqgoUCvIIWKNM3C5W4ZHa06taQgLk3kk03B73Pdj3x/HnplNW2vtElcWCYfo9Jh2vLy8vby+jDnn9Mtla9K/+/Sx987CZnPf94hoTVvrlOmoXpNUNDLczHFTwpmhAJfMSPdp4/byuu8Pmz7mIJbwHMcxx5AIFrZZJl/ElMuKW7YUKULTGAelzzEoiTJUCEJ2VbYRwv3z5w/ff/cpzF++vDxfN5FsW3N3Ed56F9XwmDaJSBgir3MkRpkUlKpgHgMqK/IvU/amtdYvveiImWEzmKYPj+UgSNl7E+E5HUE2PVpr2NJlc1KaZUryH//4xz/96W+atmPc/+p3v7eZ/+//8O98FmE5KWiBn3wiw0zEtDrbiEz0x5BCgXJ+qoLwygssxZYAIqbGnFVoJTGRLiRDiIIT4ABx+RPXj+VsGLB7JAdgJU4JDkMlnUyYVHPV0MLcVN4jOrzCCRN3FpYm6iQUFBlg39WnUC17OXFFWcKIsCRjbzQYTkHYSlRmApXOmaXWktI5wCUmjnCAIVITLcL0rkr0VfzTyrfria8WouaEAN8ABwdVi0KYmxf87kHx3pQRJQTIC7HIE7dYuQDJlbLIrWc7sdqR9XJysYZQnmK6xsGNhJm1oVjkejx1XBb0DYqgKqLr+RPOHHBmPDNza9yKORNJgIACZsIqEubOYnNOkdmUIoR5f9yxMNIsgs3JzVybzrE/WpfWSHge08LCnVh7b33bvv/hh9++/f67H364XK5N9Olyla12zWPhkDTJSNgFU1K/9LHPy2VbraU3bRLVwHiWiE5FMRnG4A62GJUXsoI7ZZo7HijepChzK2lBEhh+cxzH/ng89sf9fnt9fX19fR3HrqzCCSdxYU6m4ziOfX887re3G/6/z1pnFy5EFN48w8r52J7iSUV7a9paZk6b85jzKO5QgvsFbcGcx+OwD3bsQ7iRKE5v065N14JfTpKoYpa0cbfcNrpcNJxbMhPBnfWw8Xa7v93vj8ddWLZt23o/pgHRjrRxjP24f/n1C9B5IomPH562p73R3CWZ5hj7fiRadOasTYSJf9KaYtmIp2e5kRR90OcYxz4et8ftvj9GUDCLo+kYYycgapGUhuFJODPHdCV5uj1oBou8vnxlls8/fNdUj8c+jkGR9OFJiJvIpW3X7emyba/3r9et9972/c7MbnHZ+mXbgKmqSuQimZEwSTKlCi55RG33XrcvaorOQhFEgQVbFasyWOiytQguE+QxfU7YvB4HXfoGeR+HcLr5bI05ck5/fn6+XC5zGFH8wz/8y1+//Pry+svLlxuIiUCvKZi1EAjmVfKGUXIG9lVkiRhzBbKqQ1esyRUnOVaRi03li0DBq2wObKuBUyZzMup+wIQBlRAAV/wMJiFSYUzKcomaVg+/HlBGBptzi9psg8Aj3Ig8OSqsFcaRuRi68AAECF317EkWjDURrMAYXJbvgHQqtAf8UyqWgsxGkdFE8bUFgiQFeZ5xt7qKirsIIOtZAksFdcMJ4lZaaa7Crq9w+u1nUQPvk816fiKcktXFL6IxZBpYwnNmIuIzlK+XQJTEylI5XNemUYpMThhc4+lxoTcqohqJhW/gj1QaWj0mPGtdVQs5JiKiFhkYIqHCgN7Jpox9z81Rh2zbpsoeOazIkKxS1lGq5g78d84JMKpt/eXr17HvYx7ff/fDh+vTtXUKRdOR4WCDuLtnMS/lIVvvkVci4uTWVLRMj5PQjbKghCPUQxmR7hN/jYrpU9ckIt2dMyBdy1T3oAxpShnTx5j7fuyPcXu5vf3yy6+//vzznFOZVCXJoE4EUf4Yx5hjv9/vt8dxHHYM92jKnGSEfsU8jZjHPMynu7XWtt6riI6Y8xhzH2PaHIE2kDUpj+O47I/j8cCV8HDtrbeWSZ7OdjY/UhZjmb1ra/2a1yBuKk0bZc5pxxj78fj6+vLr16/jOFTbxw8frpeNWczMpxH78Xh8ffny4z/989evXylj2y4xP8unT0oHJwfFMeb+2N3dLdzfh04ZwUxt23rrSRCBOeRRvbWI8DnGGMfjjjlHZETtcXTmtHl4CebMzUdMm8OHMeXnzx8jjbjNOfbHPSm3q7bWhIjcKWI+9iD5wx9///2nz58+PHNkuPXev3z59ad//vmPf/z95+8/i8j98WAiacosaVBrsLMTTAUoqYQTfEZ/4jWgx38sBGSkKC9uFkE+SCzD2JpG+rR9Py7bpixmmBJxSs7HOI6jd708XaFtmsPGGGbz4+cPf/8v/sV/99/+XyPeiJKFTnMpX0uSivspUnLGUg5j1MhJDloISC9n2cggCBIRFw4hgf7eVHXFf2Fid8/wd8JiFqhBdWGqbBImSl59SoCZGLXXCj8jM6MeIxFeXbiEu7Dgr3M1IKu9qi6BMkF1bRCdhEVKvPcGRFQC2ArFZ5JDZJca/ArGOB6OmSIGpgtDx7NEqc8lYAnCg86VOyPB3ixm6grxtSyJmCKzESRXCesfOKasmptKMVspryC8suVK9B0UXJbhq6RIAlcgFr81C87CpP58s8wJTB+MDBHJtRepniiDDo/lfbVQEHuzhXmZVHOe08HFGq7aOwLfnDI8sqEVAxVZqBAoXI6mjTXncVDJWfMY0xw6QxljV3h7MB/HMeZwC1aOIN45YlDYY7+P3+0XaTHm84fn3nsSufvj8diP/f52JykuIRE/XZ8+fHzqrQG8FlEYFOMJqra+ddW2XmECUkgPEcGHKlKS6NpLa84irfW+baqSkTQ13B73+/647/vjy5cvv/z6608///jy5VdhebpcW9M2OkeazWnTIrA+fn88jn0fxwwLIvImaw4RxBQeydysRVJYqurYetOG4zVtzjGwIJ6IJFmD/Bi2zTH215eX/Th6723bnp6fn58+9G6tN8IKWbdpZukonfvWr5enj/PDuMytd6IUEbOxP46X16///OOPf/7nH8Ptcnn6/Pnz89PT9XIh8jHGeNy//vrlly8///rLj/fbTYierhcfL+P1+fPnz0TsbseYj8djTnOPgGkaRJAUTNy3rtIKbN2KUVsJwMzc5r4fx9gfh4dFkDuGjQI3/BNX5PBpDwpXpX9OOvbb89Mz1oJ37V+//NJU//Q3f7peG0V+/Ph03S5///d/8/njp94005vK69evL1+/NhUWufQLi9r+gDwBax0FUxRhUAaRzJi5qzBzePrSj9scY8z9/kgiEQJlvrX2dLmKilPMYUTcet96bxd1FvcwM9gS2HRRESng+BgHq7amHz4+7bdhY14uW9v6n/7qT3/zp7/+5defbVatTZxn2UgR7Oms5AS+B+XKU+VMh5q0CIaxFtsyasGykCGMNlUULVBlPy+gNf2cylIGZ7gIJxUpqNTwKF4Tww0vc7ylz6+CPzmYsXAlM1gBmDlCcJxeiwt+qWHyu2ezRRC4+ZqL5LlsvIrUffbI9XqDkpKDCIsFwY9CDJZK2kTCUE5gRgov21pDH1ByVsOQqyhOTPVrSotATkVTJCVKVuJItHmOBPWekyoDLPAmlrZtNZBEGelVe0DEgOZgtU71nN5prmdG4aV4A8dKFKYVkwABAABJREFUlgv0+jBg1FncqFwTGqWCdjgJdhFrrB2nHJbNrUVrqq1rZEhm45JEYJ6QIgVvYAMiLJmQCz3CbM5hLFDORIj0rSfRPA5sLPJw4K02hk8b42CPa9fwSfzDdtkyaMzx5cuX2+12f7uRcAZNm0x0vT59/vxp2zZV6du2XS69dyJCq4AY1HtnEdRuZ/+EuXF4iFZhY9PBYGGW7XK5+AV0BxI1m69fv769fr293X799devr19++fnn+9u9t8af/dJ72Mz0cYx9P2ZYZMwxj30/jmlzYg5D0TD3igjidA8itghhzrAmzWeDTxzI8DanOdbLR7JmhhrPsY+9URLdGMYqT0/Pnz5/vlwvKh0kuWklOjiOY057er5eL082Pn/88LG1lpmqMm3cXt9+/fXXf/qn//jTjz/NOZ6fPozH/dOnj1vfMm3O4/H2+uXLl5eXX99evo593zZl3yV3359jPIg51kbfYYY5t2iHCCWThRkudahMiLm1Rplbb5QZmW4YZ5iNA+twI7xrZ5Yxhk1H9YHLNLHZax6//PTT5T9seIAi/OnTp679yy+/jv3xww+/+fD89P2/+Ps//v6vvv/03fPTJcxFaOtttu2777//9Olj693DhdJ8jmPu+/H84UmYbQwBFJBljh3pxHQ8hs8ANVCajFGyNVAPDjOM/batK/H1emmbEtM0c7NJdJVL743p6e3t7X57XC6dicYRvbfW5Hq93Pc93FPleByU3jZt0jjpw8env/27v/kf/h//w5y2WH4F9FN5IDJ2cihRpmMOHNXLUlQ8FS5zMAoHxkUsWhTUqvYlJCQlIiTgOCKqktB7V6Rl7AB1T4zEappKa8dPyYNXHCASYvOJBORZODV47+lSVCl+59JkwIM8ToADXQYsH9w9kkQ0Qe5BYxJJBOpOQNNQclWkjzUdQL3PeVp1RkS5UwEGdjfmhsXDmRK8klPVwJHfRHrk07X7hWr2ix8awY0oQltbYT9ZmLxst2BrIyylLOOi5JV4OaNJ83LTQmp9H2agqTkZWSAdgIZXKBooUmtHO2rdxFVkFiBy5LDTYArFIgvG1CQWo68S2/r3YmJlMkHXR5TUROolsWRBWJ4khbA4DczkWmsUwR6MwQ6sXGcuZZozR6ZVLFR2O/aHYOypnPf769eX73pvc5hn/PTjT2+vb0nRL1vMeDweGfn5u09z/laliep1u14uGzFj9bb5VG3b5dJaQ0sbWPVJFJnaGjwAPByy+1WzRFPtfdsuXUgzKZgft9uXn3/68uWXX37++evLy9jvc8yta98u8xbUGrO6+/1x24+91t5bYAdAWQoAs2otCaMrHFYm9ZmRZqHiTQPcOCmTLHKn0mI5qcwjHpkU9rg3Zm59kybhe/ixbRuSB0Wa+zHHY+yP232aXS6X7z5/l/Oxv7y0rbcuNn3s+5cvv/zzn//8448/3h+3y7ZpHHe2PN7atiXF7fV2e3359cvPY38o53OX1kTJc44Q3t+cBXcuyN2PMY+xP0amRoRHtNZFGitjGSEmxljrSr7h9rrbcYw5xxiHmdl0Dx/ClGxWoS7cWQQrFJnFjJjpdj/GPrRr6y3M5jwufft3/89/PB63P/zhr374/vPf/N0fj/tO6dpYlTPju+8+HsdQaU07sYxjZiQrffr8fL/dI+L6dIGkiyjHPgueDvJpEaGqqXq/mzt8yHy79tvbbb8fZmPfj6enJ//B9+P6+TcfpbGQCLG5HYO3vvXemupj7veHffzwQTVv99v3331ipsvWk1KYhHlGUrr2C+r5/81/8V/8u3//7//tv/2/TXPiFJIIZ2h0QVqnyBmivXGb4ajKI8IjWbDIYZwWUAj9KCWJkqRYoPjhmIJH+IxQUYo1Not0CqIUF4AGmQS4CZGnVv5Wa+vA54Uyw6EaIkpWJjhVETFRw9pY2K9SEpNDcZhxhjtwnYXIiYnJIiAU5WQis8jrpStLkhNxFw0PS0ODDwgsibiMrZgoKVhIyrZPMilYGCiJSoNPnhXy4ZyhIgYabBAl1+yOkgniOqRSWf4NzB5NWUG4ypCUGl4GGNoRHGBoYFqP15RJZ/OmrJmeZZ0IsC48g9Ii7Bz8whJNFeuGtEmTZBVSASRjmUYplC6cHCVzRbARUeYUYfchfAX/MWK4j6YX+NeXfbkoIPO1IzMonWrzLDUCFoZWyt2Zp43t0lRZFdtScu3IinCj9DBmFSRDwEqzqIfETLwYXWPf5ziOYz8et+c/Pz8/f9CmmCS9vrw+7g9trfXGRHPO8Hjcvr5+/SLM2tt22S7bhZjmmO5uEVvvHz9/en5+FhZsnU2iSAqP3nvbmrC4OwAHLV6ju/tluz49PwFYHjNeX1++/PLz119/ff365dgPphCiJhebcbBHk3Sx8Mf+2OcO7ALAaHGZUSJIngBx6S6II5ItPSNDInSJfaohfh/9R3oSUxzuEUNFI0lba5ct3XwOJDkRJco5577vb/fb/XbziO2y7bfXt6+/fvz06fL0xExmdnt7/fLrL7/+/MvXL18jPHpLO2zfb9smLJ5u5sf+8OMh7ipE7DmNm3IkOdGy7c5pYZ5zxjHSLMIiq2kXmcTsKjL4YJamvbfem81DW6OkOcec5mZhM9yxaSPL04eJSzFfSiYi5kifSSTCWxez2S7N59FZj31XlR///Offff/brpo2hcjdlGkaXS8bAJDLtm2X7diP8GhNu3RhAjnF3e+3u9nMzGOfLPzp45M0CbN9v7ulNG2Xyzh8HjsLb7131SFBFI/7/Xa7UcZ333/nPx0fvvvA2u6PY7teRbe317e+XViJJd3SpjXlcH/sj23bFjGSmFKVp9Occ7ts16ft+x8+/83f/vW//e//LQiRHta3FsnpkYyAKaKoohNAZhA5Lq4HcPWEe1MRhlBpGhHBHJ0LroNzdTCLZGL3ATGlE2UGQTUIpz9YXJ20ylO8W8Gt2KnLRgQAUVAVmywwgHTMETOg2wd3v5TVZUNS3zeBplByUIRVkRthmcpJTdo0w0GBsgFSdipdYzDGDOgDcu1pqBoba2vXfAepC7UuL/gEGMoSj605cXkTMOA36DvgVoZxayal48ZCdszvbQmIW4DpglbuzAjH61JolWq1OL5/VfjFjQJOyVLuT6QqosXShYgHzK4INDVCRKkLxKLkRP7IKuAyuJzZstxiqufjMKJMi+mmTZWQ3okaPAiaKGPfNNzfuKw/aGFITp6UIIBHJoeISjK7uYXnaT8n/D4GMeMuj/2eYS8vL1vvMBTbeg9zM29NbAiTgBa5395eXr4yMSu31rGCbg7zMDPvvWPphIgcYxz7jrWLTAR0QrVhUxZjOIm+VqhpwzzW3Kb7/fF4vL0dj8fY93RvTbWfpDb3GW7hYT6PnEYRnE5JCpYbCZVvWmZ6goGPnLYO2Yry4TnBkoz3NY1cf07kbsmRh+Nz9lAPV2bCxI+YmT3M5nzc99vjcTzuFr5tmx3H8fG2H7feemY+7o/H43a/3x6Ph9vBxGE5d0qzcQgTR7p5pBvNQenSmoqoUFNqSiop7LUST4g5RUkU7xIFBO61k5AbBVjh6USe4aGubhlpNseciWxzuiWAcAnlBk5zpoK3AA+GSLJI8q1rjJlBHz89/Zv/w//+en1+fnr6L//1f/mbH34Iz77psT+a6rZtvXeP/PS5X69XbGRsjVUlk1VYSMY4bm+vv/7yCydtTxciH7v9ur81beHxOI7pTskecX2+fvjw4X5/PO6jNe1NI+TTp+evX1/+6T/+x2nH7377m9b5w6fP16dreNoY5vb4er9ctuv1CmDw8t1Hptz3w9xbx9pUmpxh7mYqjYK2tn24fvjX/9V/9T/9j//jP/7jP94fD0qS1sKXFTJsgsiLyyDYRSzmxISHecoeK/wlBxcCWTxAIqqzWENJIej3Qt5nDpQJjj5wiAji5JS65IQopsVCIZBwyjCqIJ0UXqRQgNsR5qbCQsTwKg13x8jB132AcgFoVnBYEYmi6BqGnUJcEDioQUxC6e+uXFlml7VeuEYMYLswZ1nFFM6+sHpi5fAgSfLEU8CPxsNcekk4rTAWc/CinEN2SgWLEysL0XTk0pWMy0UNpCEm0G3qz4RYuZEUiR89QqwczslLDifMQoDx8SSK0opqk7lY7CtLU6FatUNqqf1XJZoOwjAYRcy1mAbZEeg+rXDUsIYQkG7XxizSyq8TqJyTI7QVhMRopSKSyIlU6LQnTIJR5CpTyjMp3T0s3cYcTfvz0/Xp+SrMkR4GengtcbTpGNELS28tKWGpOM2GsO2P+7aJyDGOcQxzuFpR79q0Mcl22VrT3vohIsxl716IYhxzeuaYI2dIplCSkFCSO7kLlNpEkpExGwUpRdmVYV4vzEix4BhUUY8nyUQCMZIK9JdRCCOt41HzIsaW6BrZsCx6d1ja5MkuoqriGWZ+jGM8dj/2nAOj+En5yDEfd2ZOwnziMacrc7u0TOJICSOLDI5kD88goYTLTKNslI2lMUkGZ3CaQjJEGUZKtDXljcZwDtzk0mFKMfaYOCUycqI0zUifM8wokRdrwVMS6ZpWCQM9AMmXRdg9uLEIPx522doctvX2+z/88L/7N//b3333u8d9/N0//K2wmB+tb/t+v14uz09XlHTMsu/Hvj9Eadu2mD7ncfi0eTwej59+/uX15eW77z7OGWM/bq8PJXl6vmxb741F9Zhmc7x8PcbjYFEbh4q03mPaZdM//vVv/+k//PMvP/9E6SK0bRftmOeLMu/mgw5huWzb7XgLd20KEoQKN+2tqQzJSG0qymNMbRzhv/3Nb/7rf/1fj2P8+3/3747jgLkgZXByulPLiGBWYCseIUJNhUlQAWM4eG4BWsD00le+6ywrWHFCRJOlpEc+zpIXAkqueQSqc0R/rooZJTETBdfSkpU8UjibMhN7YF9sCCjYkeGW7lmdQUnwMmhtrsssh7LawWlh4TNchJTebZGYFjUR3PbqoD3rW9KKgUQLFUcIxOBBkB3Ri52cUOSwgpB4HWQqXRwV7VuSIj1TSoPM1Q+hGKda586L3oPHxVhRRiLlGpRArjJCiC2zSEQV+qsdWd5LNdvAO1cCi2ExNTNhxCssi9hVm3Ko+KKnQVqZkmCxDRXEv04K4jEW8YSHAUsT5nbpHcYMsFqDxzdLZppHtVXYq8gsoRKc6hLr7BGX42tN47O2k+MszumU6XPALVQ5VVMbKpAIt/RMbA1VIiZ3cMs4Bc5cJARPVqnBPSA1IqnOKsJzuodGa82EmFpjjihxIRG5+ZgD85lMUkz3wdINYc5GJJFKqdVtuTIxSyPC/JOYMdciYkqBSMeDopgVSbXhILVqkFg2Q4RNXoURVlatXXrCVbPghMd05zzMRKQ1DTB6zdIOCetKW1NR4ph+eM7JOKwRErEpKmtJh49+wrI3gSUoCYlw48wm0pSbphBJUiNSSknHge7MRtmFZFNhmaE2sR0NngLMXGs6mINFmrAyOQVTIMegi1jvVMSZBOokgQWbqISHYPglREQfnq9mtnXZLvrzj7/8N//n/+b/9H/8Nz/89reP262JtK398uPL69eX+Pzx+fkq0ec0z3G73UX4u8+f3G2M/Tj2eRz32y3CKSfReNzfbj/dj/uj9U5ut1trW+/btl2ftm2bNh63nTyuz5en501ZiejVxuOxb1vb7/f7Y3cb20W260VH8+Dr00WlPz3329vdzb7/4fu+9TlmDTYyHo/Re2+OLro1bpR8v99FSUSul8vf/e3f/tM//YfXly8vXynTjSaQm+RgHG1IZDlJOCNJWaRVGAgUqosxGItykrnoH4SvwpWXAhOYQPwMF1rggBDwFcHOUeaIUC77GhAHkW48SzxTM16mptybbk0TomLixdQOikhzsiB3XiM6gC0LI4fPJ8FtR0giggML/OB+nUypCmFSyd+omJjExBGlzsH1ytV6cwYCOjx0pSxjiFmDDW6SDmP9GtJAcEfCpMtHQZb1SWiwBByZiJxWiScqKRlZe/1giIIwVIARkwhJ1D9ZSY+WkAxJgjHYpUKTVqpiZmVs44OmaV24FDTpykoqkRZBigRNQnSaHKP3AjIEFXkhg1EpB8rn1ZIRUaoI1nuxKKtKRoB6qst7PtmTg6XBfaR1hcYEEhXgRIiLnCRNEgtJmCkpMtBMGnmaCWvrDSEPO77djQP07Urp6eHmgoeUoqrmFg4VZFKGll4hwj3MUWzL8oGM4ZHsbL33poLlMG4z3SJIW1OVCA6yzOAIFemqKnzZ+HKRrSlzJqnjnJHijqCzA9QfyZlskWP6xAHlQjiwnwOZjBmGkihOWFmCypiQlyyZmbUxYSMCpQpGaJnJVsaASeGc3kAQh3RfYK3lxEBYA7tAElpBLhmXqCSs1COJsmn5rnSV3liVhFiVmyRDAa4sRCrZFGlPmEkiumKNNdJuEpEtEpsIq1JrYnNkOKojFu7KeZ5pWRoO3HiRpkoNG4eElQlmub2xsFs8P29vX19+/fWXf/mv/mG7djvG4zH+/E//hDWU16fr9fI0DXYYdr0+zzmnHzYGRRzHEW660fHL289//rG3Zj7GGCnyhz/+LoKnTd9tu27XrWVew5wijvsefaroGPPYd+G8Pm1/+ONvfvrl53B7fXn94x9p25oZ9abCrNrvxMdxzDm3rZm5CBPsrJuoqE8nSRWBegyLBqUJJX14evr88dOlb001hXqIkKQHZSiaealItzVNiukkGVIGnMkolWrNC9RdUQXIsgYiRW0ISwwG4AYaYmbAFBIm/MzEXOY5iolispAkJxYREWUnObF/2I71xl2liZDUpRRKBtMmgsFzc8+1fZAWUE+F22BegUYmRGBH6JTKyYo/EZloPd3p7LSZiFKVuSyScu0kKOwlly1uKYuZRDG7bbDFJOUIMIrwgxhkmN5haCQi4upm7MselNIZhhGQNylKLqYEs5OYRZIoyTKQfSKZVXztUqJcStUkaLC1PEBAowKtKVkSQ441kQXQliypTYVocjKWNLFwujuVxpu5N+mqNr0sf5Pq+xBTzSbwzWvagZwjTDAPb5dLKwgItLkkFWrKTVDGaEoyc5iTkEi5CJHCxgM6hVSm9FDR1jbBu8mMjENk+nRS+BHW3CLfgyCaUwuDgX6mQwehxBvUsKIhbJbgV3AERXZhatKY6kMvFtj/h6r/bLYlOa4FQRcRmVscfe8tDYAEQdHW821spufLjM3fbxthY9b9+ISRBAiUuvKILTMz3H0+LI99qot8D0XUrXP2zoxwX758+fLA+XYzGQYtShGe/FWeFYF1ikoQ4ZYWFWUeaqkitcBdV8zEkuO79EKYgz28NTcj9SC8jyawFVVVNCLSMB0zO724UxLgH+ROWFFj2I2IjB22pozxfQXJEYHlpkWgFwtCj0gkzaCyi4ReUKScSyBPIxYiDo1mEh4i0HtFKTIMqiD5hApEHejdMCvxMBQRsmCx4MYh7kUIWgwsy3GJcCLWHLsMJmcyCudQJlhO5UoNrPlhkXIxkyQuVcFSSBE3DyItKqLz1G7vb9bDSOHzaV6Pa1WdzqdhqMRDeLw8vtCNOAdRGYdVHer5dJiXaV3HEFvmM1E776fleFoXJl4Oh5fzND0+v/z13//9u7/73e3t7XJutch6qNuhrh9uTofzsth0ng7n8/XN9c3Vdn/YH/aHcVOF6P2vn3Yvh2G9fvfmq3FYb7dbW0KLrmpd5mU6T7JeM3aeuDNxKaWU0ubFGsVArTXVOpRCQW1a2H01DHdX1+th/NwWLSLEVSWUeClFVKnvZk5QKqVUj5CShtmotzng+W8RtODxkRCsI5LmBWwnOOUBhFof4mZhZoc9p0gIeOecMcjlpkhpHuxERSQiwlKCU1SKUCm4q1l+gKfBIRVygU4/+QfOeWlJ0A1IK4z2M0rG4DBhlSKI6xLkRcy4NTKGZVYWOYoZXY+WiQBCKs94yUESrKFKgkkpQltQlsXDAb1Jc/E1i8pQipa0iBXDuU0SDB0yVaBRrsruwkWEXYUjhJjdJYKkm8waDNws3AgWhcLB7CJsGKaiEHYOwyJaeNKD41AhnANRgMpgBdtGgwp8gDw81VmIASS1aBEJaUwu2XdHExjVXILOoED7UpiEPO12LArU3XphnSwokhcrRUrRxH5pCkIuXBxmUCKq6J42ZaIoUmupROxujpVYAe1k0WBiLgVrlISxqEjYxCJyaoWxflO4qEDzhzROKioMK9Ci7EyiRYgXW6gvcwI4J3ahqEUp3FtQRFGJqkWloyLO4XNWChXmIlwKj0OpKuNQlUNZrC0NbDWwEhw5WM3DC01Lm81EZVkEC22cSUmHWkSLCEcf0f6N2UDGVyKKEBHSUkuux8PYWk7NqKTuIp8Iq3CECxTZ+OjCXDQ9d72Zh3EQGB7iSJtX6sPjKr9NdUVkKLmKEY6R5jORMzNwPxGrsAc3D1WsyXEWcdCfjp8cXXujLGSFyVTQBcmKlkQ0IhZbRAqOkmrv7Umqy4KYiogWazGuxu1apvNyt717eHM/DCPUKtfXV5BFvTw9n6fzapms+TCuVtsNwb3BvZS6fz6cz+fC8fL0Mk3n4/nw68/vP374MM8zFVKpf/m35atvvn7z8Dbcd88vRetqvRamYaitLTf3N5vN5uXlRQvvd8dlOa9WtY4yT6d//d/+yz//yzIOG1gnqYybq/VidjqeRHWsAwcV1ePxKMqb9WZpizC3uQbRMGBQYxnXVUWaabA3W0R4vRnh8MrM4S4UylwES9MZCn5wBHXIJ+3ejSfDzMmJKpVgiRDHjGd4WwwgS4SLFimKjXtuyhERLThg3KuikPYyR4QrADG7SilSwRZbOsgThbkLE9W+WAmzYubOLKWQNabgomzOxYTC0LdIbK4ClaBTFEXbUYjRLSJ2V+VaVYtGUAtTUmZ1t9a0Lc2TWScWUi4e5B6a9qZw45SgwNYyFcLmQlEx8/QY42AoYjHzDExHpCq4tT3DBpMoFTA3wjIAh4qISi1KDH5LzRy7Q1CANGG0WRhmJBTOxOZElHZ8nIoaChJBmx/9VqlFVaWo1pLDNpD6X/iJcFPBGAKxsxSFCjU8VGWoWpW9kQqpoA2RMbF3MLP/6EkEMRoeGH8oYy3MrCrZVhcXkVK1iNRBS0HRhTk2VAgeodDzqChxuHlRrG3SWkqEh6tFiLJRi0UoNEKCuBSpBX04QeNKg4LIw0pRyGWIGNC/SM7DBYUU+BIxE6swwYJ8RlcbGvacVuZwDiezNMokk8D6O/aAvxNjtR+FqEoRUpFatBStRZRJWQpHJWKWwtqbVsysHmTmoqxNZ/M0lws2vK0iWH9NAT0y5xAj/OAxAuqBSQ2tWhS8TDizO3s2HHqLSASNq+LhweTs7sy4vAzYIMSkApU2EgOG+rHcjIiCWD0LmaoqQlW4imgRlhDMswpF38jhRqxsESQiLVTIjCzYPYgxSGHQYxuTcKi4KFcVHYu3JFIhfxASN4OdUVEqFQsnVUScvBSJoFKqgUSrOo7DuN2uV9fffvfN3//D363WoxJpYSBgDOvdPdyXwmY2zcf1poqMuQLB5vN8UqXdy/E0TU8vxw/vn3768b1U8aAPP34iit///vd2mtiMiJ6eXtab9by0uqrhrEMh4f3+0KwRc6163B+bz0MpMy1//fefotmbt29bm7/5+pv1dqulMNk4yHG/X717WOYlhPaH/XI605s3y3lWlUXP7l6Ut1dblVqHYV7mENehvPv67dImYguqYcHklR17fISMyJUrVsdL0aJShwqRG846WRArxpmJpQVFqIW0ZuZmSmbmTpcSk4u01tzhDo0RGRKhosRKkEGzsopyELOJcFFWluzyCTk49gBBL73wC2cyaPEpWMkthsIeEp5LnwBYMCIFdyIiVmVN+1AKCCWdFTGnKrM4KZEQsbm1JlbUcyAgWNAV505/ebPmpFDVaFbJqsq1CAtT0WCpKq01a2jaCTupYmQ0VFQVG3SCEGdEQofEUMIVOE2VKEphEeaqCKbLPFtrRBIk2CflMLd3b42bmyjhRXj6jVNhyPW5hnIjES6Fh6oiWquuBi0lFyUxZb2syk7BEPsQwhsTFbPWmo1VihCHhTehGAYO0sVMIg27hTEo4dEwwgZ/PedgD1OFfwooIUpPPEjiqmpV7BgQxtA1EWGBvYNi05xq78bjWqoitBIv1pbWoAcXdXMXVQUOFCqCqjlbl67i1IKzv16yiaZCiCjRjDFALRwqysI6aBWBBjUIMd+IQiU4TBlFAxkFdhKD/kLDAKiHIopqEapFa9Fx0FpUmZSYpKCBW0hYRRh2HOJBhlEbdtHQFA8zsl0pWrRCkyYsF30COm+pk6NA70FLLaXvBxfzSMFSeIQ4KJPs7Vu4s4MC7L16ZmYpHMTiJpDKgaRntIGF0USnUIU7ZimqzMpcqpS+6hmji80bB4ok6EbCwVuJFuXuWY5uAlxn3Z0vZRxVdWFK8B8CAirY2JsHC1cllLclLUuKuQ/jKKwWVIeRSIdhIK53N3d//NOfVIft1e1h97xMc5F6ns5Bsb3ebrdbmDMz8dJmUtEi56m9vLyc5+l4ml92h+Px8PS8+/Dxy+Nuv9mubzbX3/0wVtH7h7vNaq1ZbayYeF4WrBas47hM7enx2b1BDSGVjl9O58ORKpfCp+P5px9/OuyOTPT9Dz9Ys9PpFMQe1KzNbRbh6XRq82zWRHiepzoUyKaP+8PmakvsZm2Z5+ub7TAO5rYsZ6IYxxreVnUtTNPxJJJK8KIEPXhRWQ1VWGotSSLDxoiKmTvF3CxImhFxsAWHi0gzU2WVYHGVQjgVzKUMYESpS9Bz9a2wSgDsqxKrSTajgyGzjBKBNaq5lw+CG8JCSSaOUHW1qJobUCxcNFFYmgsRMYeqq7AKBIIAwqTMRXko6EAWInYKM3ZVU0hFmHILAsNuojUmErUwAKSIIuwRKiESzKaqIhJBwarsjdU59fHamc9apGrBckSUE+7sS6pmWViFMpcQjVWwM5Uwf8pkRcKJSEylNTaP1siUhJktqDmTo3NJHE6OLR6phlOmrPlCNURClUphzPeSObOBEdAikRJfCmEMdTORsAyDjkNhpqJwJ1CiUKWcTeXk9SlC8fdogGPlDZEKl9WqIlBhTJXQ6FcZh1KzWpOw0CTCpTP5lyWh6LAwC5dhQFAIotpkXoijKA0LCwmJCgWj2gL7b46GgEQLL4KCDt32oljCKipwBmOoYsDfqUqtRZmWhWjgnDU0jXBmLqrDUEQZS10FfR/AZfTxJOveolo4apFhqEPVWlQ4CnOeDmLBPmtRIAQnMgtpIuLNvXCIRJBYhHBagGbDp6vUGJwPi+dDZwovIrVUJFcPNxOM48NBlzBintnbLFncQsBFqJ0SDXFajnvu0nM3DYb1WiAXUYhIQeHOpMKlSJGCnX5BEWHCSnlRw5lhge4SzZ0IpQzD4SebVIH9Wow58sLKVMyMsrWjYM7cilGJiMKi+ARcQXqaeynqzqtxjQLo5vr293/4u6+/+e7h669LGZl5fziF2WpF59PErOvNehjqYX+McGxnt6V5s/PxFOH73e7zp8/zPP/5P/7yr//6r3/929+eHx/HcfzHP/7x//J/+z+/eXhY5mW1qrcPt4SPM5ShjtO0DKtqFoudF5un00QURbUty3F/eNm9/PTLz+fZ9Mvj27cPw3rc7Xc//u2n+4c7c6/DqtbKTGZLKTKdz0Mpbk2YvbW2NBFtrRHRdD6jC6Vavv3m2/2f9qfD7qcf/8YstWgYDUWLcGFWIneXkitLi2otUnHaAYmAvckNy8bMVcRIqktt3IwbUXOr2bQswLBNaVCJ5qRs3sKwZp0TbEYQsyon1SkCmz8G2AAfHdHpiw5kItrSBN52ROKOFq6qCYuaAG0HBTYHRIqfXZlxCEU4DXWci2gdah0S+xGLYUezhbWKsX+koyBa3Ci4WTO3ZmwgubKvGug2lQo0rehXmVFbyBu0/1SkRAQJqSSjBXgLBw4vkY1npuTKhJkZS+tASHrEooptr9TZqsVsWdhcmzSxEPaW/rKU6qRwZxAS4kIiXGsZxqGUIsrjUGop6O6GGDMVDSFTURoUXWQPRw/bi1DQOIzDWMPJqzKHN8FSoZyBImJS9EuK0jJ5ECmLqNYiIhzBZQ0HZhiohTGRatlsVmMdsVqCiTjxCAsV8wV2o5LVCsFrCaJAEi4q7iFFhIp4HVVbTTF5ROQuXk7WMoUxyn1IwdDqhOluH0pjaw0rpSJCFUpDHwoXGYjYvXl44AgLq0gdKlO4khlb6+5IaB4wdqdh5IaUYyiK/y0qCJEgivpsMxdRRL4IcoHAzhf3tDFhMTewVrhy3NEEdaWviMBTkokJBhW4AcIp5HTFID7OKxKGslCIKSEDELQTGGPP9M4Q8qV5V5BB8sEcqoDtRCEimg0HUuVaVGFxwCxExi7BYUEY4hbKUUoixQsSJk5VEq4iBRUN4CKQSykT4CBmxU5AonDxkBxzyYRekPsZrWGtwVqHldbxu+++/4d/+sfr61tzqaX+8tOvu93h7Zs7PNex6Ga9cbfj8eAeq82qteY+L/PclsV9Oex2+93+OB3+63/7b//bf/nX3XHnzes0/9tf//Pt919d39+xlvNi02w5305chlpXoy2+LFPRentzdy7neT5jLnZYj7/++4f/z//r/zct7e7Nw4cPN78/nTZX2/Np+vD+/Q+/+2GowzxP1paiqizDUFdjFSZv7cunT+PhdH13fTqdbu7ubu5WLGy2MLmKvH1489233335/LkORYWW8zSOKsGVRTTasmAQiJyKgjCholKL1FJEGW7RYiFOwiRCizMxKbMySXiNlPrnEiSVKAX77oOhcu8sMTlMv4WFhJTyBglzrUVUFSsVU3gK9hzzAWxuSlxE+uC/BnHRaO5VeYEdFIe7Zd0KyUs4coyWmgQCMQOK1VpKGiIIF2vQkQTaUBAyERExN2yVsdKsLU3Nm7mnVJSxuJhKqQWbjiiYsfO7WmsRriJFFWtDUgepLJeBgSBrAYYc2VGy6KZaa9GipUDfWYUt1JoTQ/HVltaWWuZlWSTUWZnMYCsdFzMJDxcSzF6IaB3qOA6qKsrjMECO5RRsHJTvohRlguwqwnOZMnQ+4zjUWs2MXIuQFYNlP3PB8JcbM3EzwXqT1hozlcKliIq4RVkPA+GlDljPG0XLehxrqSqSnHuwCmsRCTbn1lp0SRYRtVQekubpIS2iHsoqVN2CRvaI1hoBX8C92a1hR1+Q9DWQ7i1yNbaUmrmZmEOKORdGK4pFZaiqos0jItzgMG7pIF1ERAO2sY0NKnQWNKPyL8yLBAlTKVqx6J5FJCDbIAGZAWqdLwkgAjuUjJ2ZSqAHbqEqLKHqotobLQrchAvGzGk7pVGAgDR1asZu3riRu7EUVGcWlK6w+WgoyFGVp6gOfRRR3ONgJg9VJmdYOESWIUGcORWPrhS4jwhEesSVHGpQzK4EQasLbbZTvhxhd3Mor4iyTIHeEdM9qh5OnJUJp+xJlFIAmq1HESc1d9ZCrCzl3buv7h/ePjy8G4dxaY21TvN0PJyGYbW5umptWW3WRUWLtnme55kiKMb5dNZa0D49n6f9y26eTj//9PP7D78ezydrcXt7N6yGaZ7+x//4j7v7N3/8498piergFuvNSkSWpTGLNRPRVa3b9WbZbE6n8zRPFjs3+vzpy/F8cC4fv3x5//7D4+PLer36u7//w7SfNtvV5mrNpBQ+DtVau7raLNMU5hF+Pp+5KMtNW8zNhBULol9e9kMtq9X6H/7hHz5/+nQ6H8nbCiIOCq5K7MrBziCdpbCIVNEqMvQCjlJPnIJOHCwmMXMVKgwFoqNJh8KLiRw6HlKPcMI0ECZ21R0SRlLW6PoQzRsRCtm8h4v06cUgIrbU1LE7gcpnqSUW96JRm5i5kbtjyjDXAMMUSERUYSPEqGkUIRCcozCgNxNazeKWHqnMQsItwsNb42ahJcwoIiy8izmZhS8LyYlDWN3YjM2E3PFpImnhPJfgh6HObxbwRUZjPZUXwsCjpcCaG0jXvcKeOqxxMdGlMbtwVTfhaEbkWYXnDFZI1ZwQUBEMtxdVUS6lqgrlmknxcMVuz1KTjvAgw4wZo004DkWVG7FXFmFzNmOVwgzNCczbvCiZsZCzGHmkC4Ayq5SrzQr9WyZCH1hYSq16WS2qTI7pb2aPIsIV014CRYJEIUo0inuO9o6xlRTtsrlTraikuK/5XWheWuO+MTQgZUMCEMF8L4SSbtaMi5A1J4pSSq1DVSBUABCH8oWDh7Hii0REk2VpEJ9BKondCSqYwmBWIlWtRYtyLUWEixQM5StmcNj7YDQ74jl5QVSLgGt8CWWCK6sKxn76GF8QQSMY3fWbqRQttZSLY6Uym3OjJYLNHRyL5OQtCQmDVA/M40JdR5Bg4a9wdnIcHfBRkOaZGcY9K9ozHJc3hPqDIoiGxouJhQc7GjGgh7OUyXIDDiCeYiV301KgF7Rwd1NShtoTClQogckkZ9n7OCPWbhORqJHePTz87vc/fP3t96ojEbdmQhIkt/d3sDsN96JVmNxdlLfbzTSd27JsVquiGkWWZTodDsfDfjofd0+Ph8NRmES0mcd5sWZ/+/HHH37/+7/74x/HYSzD6BZaSwSfjjMRawUClGDRwuOKmtlYV9vr2+vbm2bhHNP5qMKfv3z685//Yyjl4e72+fHp7v7u9u6ezMo4nA5TLTodzK0dj8dpOr/76t1mtZp4IYppOo/rVVFd5mWs5eb66quH++l0+tf/+l+eHx836xWHkVthCo4WFOGC7iSLilSVEQgl57qIWYxDlZzJJNhDSFzFTZo2tCGFBag6MZRgHRObe3BEOCxWWrNcyJ6CEUgMcw6I840JF6jO018n3EzY2MRJLCeBiFmVxIM5VNMeDgOk2S8MCkypMmWwI7Qa+sgSEwtBlJlTUMRh6VOUyjqKxaO5LcuiLaSFpQdw6iaYiJVrKSg5ofkxERGtRbDLCEwAACykmAp4FhzOxdMPw7AxhhLgiij8EFH4wl3PApZObkW0YVGSi1BxYQ5e8oZm2xwhKnJDUVEZ6jDUoVSVgiKZmcIiCItIlZnRUuhzgkWYKF3pJLXyRB6uKuykDs86yLGDiMXNp3lpRFGIRZkYDUtE67KqQ0SUgluW/aFSalGlHK4Do4Qc4EzEeOV934EIRZgoqQjDC4GIWVzYQ9E8zB3ERBi5NGssLEFFS9qaM6HZSxyAi6UoYAIzmYs2s0YuuRmxQijhGV6Z03tZUmJExBRBrcmylK7LxGAKjjd6liIRqlrwRBQaew6SEBIp6JsDp1MHMBzC4uYhRGLe2AR9cGbQhdlfg69WkAsaJ9FVTIp9jQjfOSHMTCG5gClH1UUEl0Y4nw8YTCaMvykMWMLMQmDrjtOvYIvCqah4uGATtAi60+gggTIV5kD3KMLCkdNBLUBIjlSdzHMIFQlnIvHog6lE4g3W7qUTfKkoSEdFpDHY2zEXNczMSB3L8NXXX7199/V2czUty+F40Dquh8HMgYqYeRgHg+f00ohitR5Ox8O0WH249ViEfTodj4c9ke12L/M8XW3Wu/1qGLm1OLc5ohWtLKUtsQg1p6GOTIWFy8CllDqk9R4zu7tz6FQ2V1dlXP3xn/75f//X//74uN9uS62lSHn8+OXD1a9vH+6W5fzl46f1uI5rj6W1afKluc0s6+enp+en57//49+1pUV4LcWbRfMIGsfh5Xlfat2uN99///vHL0/hRm0RInYSiQKxdVjRKlrQblOsiMPjoCCGzbk67NHcGQSLCFVyLxgO4+yWAcapCGN9G+wCIsLc52VZlmbYyBGYgzUF6BApWoQDCkVmySAdcLfSZqYi5pKzR2nCI8WjFjUsfcPpTjcd7jwS3LqGAqDH0mfsCdsSOCEEC0uKpyPDTxA7Rw1v7q3o0mSexXIbAXMqsIlFKiTnuOpBoFnhfo3AR0wqCrlkzrmrQAhqThwSQWYWGKnFpeNcNYHBz0RnqH6hUpdUHqqwmwtTLZr9ZMJEApqpGrlpVcZhqKWUigm/gv6eB5E2I4OzRZZHyVoHBbl7sq+1pjZorAiFqZjKjQpOISFaRJeyVGVP9pWUqdTCQWU1VIoopUhRBB+SEJEKJoSCiRyjv0EoXfC78Tcs6mSihdAw6RwLM5tRMMNSWFJlLKmHVY2IqrpgMXr3zEsNkYgwIYPj2LtLYTFms2AmLdlT9HCmgqZ6hwic44fMRNxkKdLHoymZCUlSCPGaBH4gYMmZkvon+Ftw+vRxbgjiSBMqYg9XjwiCHLTXk1D797kD0PXEZGYX0jzlQSLRLdQBbWBbzyFMOesFe8nEYxn9/QLQkBVI2NNtk5ikz4kSWnyKz0Vd+wErJ8acdmqk2YTJ0bzFNk28bPcA9xIRzAO8lDFrZkYJ0MgimBWbRiExZGISUhZ4/AaBBMMDFWGJ0LLMrlVW4zrcD8fjeVp+ff/p+vaWmJs5OY+rcbjejqvBTawtXPWwOxxP52aNOM7zmdzdbV5m81mUiKyofv3Vu4c3b192x89fnkikqP79H//4P//P/6evv/umLTau15vVZhxHIiKm1Xrl5uMwssg8T21ZzH21dmvG0v70j//8f/9//D//1//1//3p8wcyClpenh+fvtx8/Pl9e3O/Wa2n46nN0246L8v5eDycz+e3b9/O83R9sx1Wg4eDiCDmaZ4iqC3tdDzuX+r1drMa119/9a215fOHn1WEbFGOkj5SXFhYJMd3RUvKp5goezBMLlrcvZRiHoiWFBxUmi3dcS//LfSAoM4wg/bCzb2ozEWteTjB7lcDfx5IrjB1awAWLHenIGL38KoSru6OCiJ9SLmWgOqlMkukdc9lEpg9jIlVSymKjUkigh2PnoahmS4oGPcUHegsR1iCqYWbexPBEKwZ9sOk9kKYRBOqg3pyc6wFhXgph1GyKk1hS2dE4YFDHEIUrpjzSgUOtttDESQsmJdWVkoph0lD1e8q4uoi3FqLHq8pSYICWzwRLipDGQp8QwTTZGmTF6HoyHoQHhRa9kDdwNMiCv19RKizU2TbBGwuOu9RWGlpVotaqbnTm0KUIH6CGpG0KJZtRc5HFOnpkSKMKAeNMR6by6wxTugcLCEY9edgIQHyKKKeHnWYdMVLBBBAMieWiz8rE0XGGpIkATkH7IRVlDSnUF1YBoDNojkJ2PWH+HpFNXlrYqFmFkRubgmfk3rEb0Blp3IZpscAtcM5HG88skjkkCCKkCAPEgkN8ZSyk4B1owuiZ3dTYXMHg0dmWkrCCAw0BrtHs6AIFFnWWvJVmcMIHC5xNw2hjPAYps/84USWrVthtsgdrcz9CxGRh0jarACiQycKB2P0qdHkaM3NjZmKKImaO9gYxIb0K3y1CO6qKg8Mf1EQ1MAs3TrOAyu6SISEnSiMV5vV4TD98uvH+4evgs2D15vVOAxPX56lljASpdOJy7CtqmbzYb9/eX552b1ooaLlfDp5tHA3W7Tq4bA/7A9DKT98/+3XP3x/OplZoMf2uz/8sL2+iqCrm6s6VK1jXY9wahmHsQ4jMbVlqcNQ61DrsCxbEVla+/73P3z19Ter1ea//Nf//fHTp8fPn65vtg/3tx/evz8e9j98+234snt5Xq1WyzyfzwdR0SrXt1fBtMzLajOir3Q6nW5urw+Hw6dPn1ergYmn0zysxj/9059u77f/3/3jfN4P4xhtFvZhGDlcWXOfFG5ABAWQfhISQn2sM+NkjvkTUVF0gxL8I9JlC4jYVZs7sZpFsWBeXBxsoZtj8xJ3jV8kbkhmMJiwsAUoJ3K5cLoeUN4vog4HGfNinrvRmQAtgoXR4VOEDSczN2O0KDmbnKl563ANwZqDqVJ4uKk0l1lyOwiObnQbk6RrVETEmQP727GIOYWMkU8mHazATJM7laqYoXVHAsAPd8pNWxQUSQPJJT2Iq3jRwlKYp3l2j0LSmDEshziSyTVbiiLCpVBRqpWJsvWGFkZEcU836gS1nMiT0OER9FPFPapTs3xb1swlmEKkIJO4eS01KKy/Y8qWPMSWRVLem9gZD6/bj8B/DgJcjOMFGp8Q3aCLz8xJf4c4cYiysDjDb4eCWKNmWgoSEmNs6fSAU2nkL0iCosNtEaI+HoWJFBEmZbTjYbWDPJ7rKBjTUqIqGBWBAwYzlPioCog4ssUFDpw4gtyDWdyNIj9VqltTwIDNFqhiM8VamMXlawV10xOmLPiExWEMeLGFQHoR6HxhnR7oyC1t4ZQYG3T90sVLCW6ok1i90MmD3FvNmUiZO2ODzwEDYQYEh4LrIiu+JJT8l0UifJ6n4+k0jMNqGIvXUguremuSbpRMTKxMrX8kFuJg6cUTWguM+UNOEAe9nztLCSItpdRaKlnzUobrm1uphYs+fnn+/OnL7f3td999L8Jtad7cis3L+fn5+fHxy263Z4qbuxsUiO7OyqfT8XQ8N2vXt9vVuL3aXN3dbr769uur7c3L8/7q6urm/u5wPK7WqzIUZQH2kSLjuAIk1FHmaU7dRZEw8ggVvb+7/1/+l//rP/3zn/72n3/+9OnD2zd3593Lv/2PP2+2I6yH2jytbm/+8p9//s+//PVP//jH82kS0cPh9PHD57sH315dHw/HcTXWWrDjjoWH9aBDUS3jari+vn379t3TE3lrZg1doezORxSWLpRPkqXTd+Ep4wE8YxEht4wxJFop6RWo+HpkAdQtACkeIV5U+mq9QAeY0rMyLQ4RekGCozIm7u626PPgUyTVHcyRAyKs3AXLnKcNH5m1sLAWxYwth4WwCFkCHWLYvKMRkq1pzHAKB5GFu7MQsVFUVyY3IeIGYpm5TzmjmuL8Qgavk+xsBQJupzEhuk6VKJpY8BE290izZQg0gauyOhGOIEVby3Nin6gwUzi10pYmCSeJKF0ghSlYWFCoKKb9QKondGRmdjaBGzdzqpDQe4aSPNAcVdVwdgfV4gGDRc7HGMAIRWE9aiJOQlGCgnO2mAvnBB0MLIklFSPMZC0NfPDus9faHc3wPqWPegNOmwOyYIeRBQU21zAxW85WEBOZWhgLkbPkJ0nrA6JXA0L8HuBOZfY8hFRKUdWggDdO/3RGROZ5yrtvhULqE85GDh4Rhqf9SEfgFeeWOAo3WKqZE5gRkuiagR5djdJs1FGfBp4ewhyyaKQbKOfEeBATGZkU5r6GJQJtW6zSDKK+mjVbxnG5QZ6dZCfJ/hjiPsJrynm4g6BkjPIfZlOiPybPlICmvjNDAATTSJtbO51OLy/PIvX+7m4cxlprWJQ6soeWEmHmLbssbjiUEuwevSvkRswerIHMAFNFFzZvIkxczZhm2qyvNpvrYVyr6GKhXFTqm3dvv/nm6/v7u/1uT7R4zM9Px59//vnXn34xb2GuKutlmc5zHevpdLLwT58fn15elmVZrdc3N9cPb+6317erzcYXf3j7sBpHlXJ1fT2Ow/b6inCGYUrDQoXmaQFuqEMJM57lNJ+naV6Oy1Dr9c31ej3eXK2fn75lWn76TxtqUdanx8fvv/+ulnI8Hebp/Pz89PnLl9v7Bym6vdqw8vPTy3Z705rdbVfN3Nyub6/Op/M0TfM4MOt2WF9fX3/97beHw8vUfBwGITdrJFxFOLCqKt8dNIl5JSJyUSA4N5K8iqkKSho1C9weVVARBxORBkdoKIa7SMIysRiZkQOQ0WUKrMdvSoRB0hcBSsdqiMhQeUrW2Flv56WgnjSIOLBsKkXeEUbhIVRIczaAmRmNShVRuDLgCwSRuLi6SqaHJoadxmrc1QYYmEzHHC5BFFQSFVEvRn6rSWHlzlQRY3zMsXcS0wWhUjj/CCK/ADqQsAg8TcBID0IsrOExN9Zud/YbrIxuBTMBNDOyVbdxzcqfFV0X5Ka8yEEhQp0zFibSouFCQWi6e3iTC7LL7h13229XMVaKgBs3YTaQ8C4jPExYwD9cPkjaX0KI3g8RjMI5uaoQVU6rJ+I++AcuKyLcGon0k8HMnVv37CshbhG/Qud8QfTqnooQJzCvlRQMCAkSA2UAluTakm+jbAmkQQax98FD5gTiWKVDHulFR2mbCNAgLCrc3CVKqAh5T8QQxUfA/puT5uvtT3p95pQedxQR5EzhLsZSEi151xVd7lcW3ZJgIONspkNH+cmZlTkRAeX0HIariXtSQD5KUj5LV1QSv6VmmzfKo0YRbObHw3H3siPRYayb7UabsVPV4uFsERx9QgIS/0v9gB+PwXeF/CmY0KKkvEnkQaUMUgfR4f7h7e3bt+vtlkiXaXan67ubcVWvtldtacs8lxLn02m32x32e/NFRFbrLf4iitPhfNgfrMX5dJ7OZwsT0WG1GsbVdnulOpiSVhEtzHx3e4PWERMtyxzh0Xyy5uRtcS1aK7ruPGyHMihJ7HbOLFfb7f7Q7u8fDi+7ZZ7X6/XVzdU8z1qS+T2/7He7g5m35uYuol9/8407mcXmatvMpvNcxyCK6Xxu1lbb0Zof23Gz2Uxzu7t9c3t7//48RSgJ+2KqiakBO+Jy9TDGTxQMR/gsnkNyOg9bYaTD9PwDgF9Z66UGwOnidi/wkLSw5i1JWvABaL32ptKlI4VYgFN0IQSIPPo/dXLlQsyeHlPZPORL+MSxxGdixtqYhAqiKTEUSaAGCznJciQ/RrCiD06sKubQCoajiGGC02xGHGgZsAAHD5NTwoDGcyRti2SGpMXpeMDYlEkEHzpERe8FDxMxqDlM57KolAp7bIgDUw4nmI2EHoRgwoohHu5FD3PPzz38yuUqd6IbbwdpOD+ziESpwo7JC4GnNhgBwexEZwmFlKHB8/AkfwtFhBFhBleDwpkKdS1RgnHyIMOrzA9LHRXgMwrsGDz6vJQHYWQ/uatMvK88k+Osec9XneZBNwfFT3cUR4MTb+FVUECvFWMwwecniQc4oeX7ohQ2Ov5sr2U9yM0i4BBCFpg4R/wNUN5qcGvgJUKVlIXgfI4BdE9MZvl3EUGkmdXydjBwF5H0NlU+tHCCgNJTvvNbiEQk8G2Qnq/CKaksivBGDRxgYitxcJqwHUrRHGgYvLMutCPiXKzk0D8kaYYKzN3cW7O5tcWsnc/n0zSVoTKpTwsqQopFMZTEzFhLHYRBT4oINmjpiIKx+DWYiZWEgrkUNIwiZBy333z/w7uvvlkNV+YuS9OiRVlZ3UKEr66vzuf9br/bvRx2L/vTYdpur66vbm5uboKktWWeTu68zK5aWvi8BJc6bLab65txvWEt6tRma+p3N2vImm1pKuzNRHhxO0+zu7EwR0wTn6e5qIzjwEQqUrSuxoFWw7Aq+93Lw5uHaV7/8tMvttBXX3/z7Xffi5QyDmRWhmFzc3V1d93M97vj3f3d9c12u725kHOqEiSH4/F8On39zVfjegxjj5iXtt5cffX19z///Kubb0ZVFRzJ/GCEkX5k2UzdvWKLDgdyRjcJmGBRTVuVDnhJLvXzJX4FMVY4CbkRkaiQM6z8hSkYumPvYIo4+08BJVAAqXgEallMiCBsOBulGCWChF/pSaJIy8DIU59LcIIx7wudAAsrw5vEIYO7TOQQyA1nZgsid2bH/KOn538G/azZUQxEQOmflyFl0B14JY7ivkYrnxqnkWNEUAiBZk8ok0CVKSAVFRYgdy4iiv5hsxYuQQFoiBjvqerIrkaC1RR65JvG/clPh0/CqaIhbDYBSw3XP9iNMhksSImsN2aCUrDETFnrEAWpiwMuF+qcfm/5JhfvSUCLRBgl3YjqT3qNlvGVo9MQScHh54F1RtyHs1ky9kTBLgLqCuUM9xyIdpd73yYqJE5G2WxhVBfEEa9azySp3LEzNYkkrKkgFF+Oe4HHzcwcnvE2N9eGNzdvDho/PJjCnV1CVQuzYMqJIJXzXHvqF/iTP7croKRnRtxQ7jVwp/LRPsfkc7YHKI1HiVBZ4xIU1ZwpIGAHSwnwZTMUE3xe8WxUNYgVVxeK7exRgRbLsQwJivQX4T6OycwkHqq0Wo2b9eY8m4hyCIWwyry0ebZlPnHYejMWxfpN6pOLgjY2XUrIDFhiHAQ3OQ6RQiJmsV6vbx8erm/ut1e3onWxqdYKTllFVuPIEsLU2nQ6TlL47bdvHt7ei+hY124iKqtxdZTjPDdrvr2+ujs97Hi3Xq/v7u+/+vY7Fil1mKfFV15Kmc9TeAxDraruzaydTvO8tKU1YipFqKGSi7bE+XwehkFLWW9WEaYiVcswrMZx/fjp6Yff/+EPf/8HFS3DEKxlGKvoD7/73Wla9i9npsfzabp/eDOO22FczdNcKq83axHeP+2ttcN+/+Xjl9/94Wq93YroZrPViJvr+83Vzfnw6ITFaa9hLpy0aBJ0qBdxCrkTjtnt4V4mRE/HTJyaDRGcNw4SKEVQUaa1AtCmKLsYGxA3on+PHTkehPKd8kX3VdcMBUNWA4AqcNIP6w7tuKywpM+tGcK5mwXejCwCn4OEvp266Cb40oEbg/1i780KYF6nLEt+c6Sjy+e8ExrZUkePl4PwcwP/LlO/IBlsUvqKuA/URBSeoLxXVWy5T4lFcnRaOMwkarDk2qisMLK2yviJmJey/R6lL7HvwoEji8ulFZMGfe4BjzZmhIe+5wf5BnJtvF9gtUzZqFciK7veZ8rXA/7GjLJFDjIHoBhPMr80chmIqcQHxCkkSUQp0CExK2IxgyVCU4s9SEQsTPL8MaSlkSpFKBGjd1AuGpgMMhHEVMIdE6oUBPMzDNHA8QjNSCEUOUAmaKlnGUjSj2tuUva8QI7VYEniJ4WXmv2s/nqqzn1ZxMbcpe69y8pAFIqkFg6/BbKgGskU5REmCoFSLS7ZJGdwgIMoQkTd2N3MjeDpkXkEKSNS/BNdFEhEcHoBPLmongOoDvVveB+MCKJSy4rW11ckXI+nabVak/C8NHW2ZufpPJ1ORb1Usla4KHVmMPiCvXDlejBAe58oJETU3DFfWFfj/Zv79WbTPKydT9OkRaWUwrLZrvKVuauKllKKuLsPVmVYDevVel1V5+lctLZm83wWltvb26vt1dfffvPu7dvNemPOZnY6TVzUliZBNcpyns5m1lqLZZoWizA3lRIuptCn5/n0UzBzrcVBUKpc3Vzd3d1sN9vHx89X16tff/ownefNdns4HIf1ePfw5ptvT5hPfLjXd998vV1fzfMyrte4dcuyTKczsZvb88vT1eOtaL3arsZxFdau7O77H374219ObTrXIkzurbGy92wfGX8iyDqw64CRuKPVRBpM5NFAxJMHMU4yBxELURaAF14Obw3kiaBmAM+g8jppCHsMzjjH0RfKU/9cWZkSEQfslDOQkUW4EgQziH/RMw+l8J7hS0rsmdIAsdMrghkdbEa/kWAr6iwhFsbOGZAiOJQyanIQ+oBIZ5kdegABYcYZ0lMF0fEhciXSWjB58G9CkAf3zcKoaZyI3JOsp+RbESYksGWWO/ILbEDCXEUmMyJNG4QkQZi4rwtLRoDBjWOKDC+4Jz92YiYHX0JB5BQpvEa49OhtIU4hfCSJmEx1we/BUcCdtXBBkzwk2esOtOlCUAgJS455JBWVQvx8stKPFnT/kpsjQecIs4oE2qwYDYdLDDlFWGPYDhNcItgpHLvxesWEc+IEh3r0Ut0uZLQwS1EY2/ecpLBTzkpGgpjMkjGMHAS/iGcYK/g6pgcRk/N7qbnwpGM7gsBfr4AC0R+43siDTEI8N2unvaoIY4Q90HWKbN2KSIFFeFVlLD1PIZM76JRcVpkgrMDaBP5FTBRojhNxVxJwzpJj61M6hVLAY5QVC45EqJYiJFrLuG5G3MKW86kObsvystudz8erTS0Tbzdr6pKtDCOcxQ0nwMLhBQjIrW8Cz2HhUss4rERLMzuez/NspZRxXYeh1lKm89mt4b2Ogx7255//9st0Pv/+97+7v39YjatlmubWpMqwHj583D89P3qzt28f7m/va1mR87K00+ksomNdzedzGYeIaMuyTHNbFidzc1YOswarDuPW2jxPbbFSK3jr27ubUnUoRYexDJWD7t+9mW1Rka+/+7aomvl0nuuwWsy++/6H77//dn84zqeJnHbPL839arsttVjzYK+1DnUYVytzf3z6vFqt727viaIUvbq5evvu3cdff57Co82qxdmxTEoSXfV82uNCf87UwS5nAM11JuFknDadoCcFouHo9Dsqb0rCg1U0YPHKStGANzN2scOiCioTAOdGS2JxoNuIBIuRFLNISXc16GfcSZPkDnKnJpDPaJqJEXrewZClKgBmLq5hfuUvAqjfI5wTXBMHByn8RxAyWXMv7QWx9YYFpzq5t9wc+/g0JY5MKpoieiQNpxDyzjREUFd44KeimckMUI06hgKTWIJ1MNhwycSSNA73WOYXF7uOnC5ZPB8tuBeHYZpD2k4s7i3Ig5o54Te6R4TlReYEs5yZUzKxRaqDAHOdqSDNBcpv8qCQ8FoqKS/WOIcOREVh3sbC2apgEqBjzKf/JjqDgMtsEUFswhVdefwKBEyRRPqCLQxFmgU5i4Z6zz2dQvFLTQRLkBRmKcrdzrczDglLWiE62UXCzOlBlKoddipcFrKIRrDZ8V5Vp0pXLjH9Un4mpUQkwlh7F4RiWfvAVv93Up2BtBJEikOOfErk5KJcpIiZUZBCGsbZ6FGY9qjUXJ+iHh782ueGO1amZlgfOXlX+af+u/dfOIkfIMFIjXnSxIoanhXDAFSGMrqzDufJlubnafFDO59P58OJ2caq9Xoc6lBE3E1EwkDoGcwGcntySoKo9DVGIBtFC4VLRBEWjnlZyInJiQ1bSeZ5ijA382iLT6fT8cP7D4+Pn0qpxDyuVnUcpqWRilEM65U5nc5LKTqsr8pq7SotfGlNVCjC2rxa1e16s0xnJiJqohZLC2urcS0R07KIVArytnDENM2LWx0Gitgd9mMdZ+Wb22u4yq7Wm2+//6EtPg5F2I+H03Ke1pu11FrrcD4tZl7H+vT0uH/ZTVN79/bt1fW2lrLerFZlePfmYbVamfl6vQm383wqpchQdvv9m6/fPfz69q//9mWQLP+1lDBTIY5QfvWmMsjqnZwciT2rLoLJDy6gdMBmXbYf7k2U0sXcI4Iz+hApi4ejt08UormyLcJQ+EJxHZTlRO8xoRsA5gZshhGRc2MR8oY2EEW4GSussayHulRScpCyOAWzGPdyAdUwaB/OOAvLMRFu1iIhKwuhSRvukXPpDNDjOYnvHaSgZneMZfU4m0UPgiFr4cR5ib6DiZU1GWHOaTKMbyJciyjB7h+3LEKIWMiCSxHzYOdQxoJBEcGQmmRzOGCcS5cWYtZFHcsG0QX3A1lHgCeDSFAhyMoJYVdKDieQqlHQ4v53RJslQGQHpnj0LB4WfWCtuVGyy8kAAroW1aCAdhYFD5zCGEsscQy5V1j4H86SzSnQRvXUrDskRvJK8VFVdQ4MuxIIb8w6oYhh4k5o4r+PpEVxDh04ATowyp6JJoTp3zMJ00hGq8BDmoSduF4IOMyeiTBL7rFC8xlLHfPViAgKs/4BkZNEeusWsRfHiDGoRhLuCvpFQljChaRTkPBTAlxX1iJVFaUAjpuZm7d8d1m5GZK/cHRir1eSeEddhIG3he8uOb3gTMxOTIJplcSF6E4TL7Hs9tPheJ6X5Xw6tXlej/VmuxaV3v7AGgOiIJXUd0Xwq2gJWnLOmjSwzdeaeYuw1prooMzBbu7T6Xy13SzzHGTmi9lyPO0//vLhsDt4eHObp0lFI5w1RDWCah2ubq7HzVjq8MP33799+46lXkYOx3E9jkMpNE/L7vCynOfVUFW4hQnHPJ3Mo83LskzpBFh1fTU6UZCryGo9FlEyalOrm6qq7Cwr9RrkHm2pWsfrwd1ssdWoT08vn58+h7cff/zxwy8fxmFViw5DGeuwGsaD7/7657/tD6c37+6FeCh1mc6ng682o4eNdVyvN1oGtyksOAguVZp9Qk7aM4KCzRsG+hDFcHg6XxzEbOZBkZEboRyMrkUS8p3w6B0s7o2HHE1nZidDv67X9kltMMIlWsuRHpW9rwxIknt8Kf8T6JKxfSDYNS8V5fw5S2e5snxHJXmBMNLd1Pv3Yw/rNIwn05F7yjOCgXwOYkh0wOlkDMhCPiiwHA1e6B5EnKu3OvmCzyNMQcoane7KxsmFdevkPmbfAkpyxZZXDyBFyREZ4dz4SaD7hTuV7ITbCu1RFnuZ6CgiO/AOyX5WPl1r3vtDQRHRR6NxEdXD2dP+AEkmOUAOCir5TeLCAYV7qIezUVAnkAi5VEQCe41FM4FTvkkVDs5YkCA29YvMFz2LMAUk8gZTIgRiKHoVQ+HiFEJaPAy5hzra6LJd7ikmyw7Gqoj+Dy+GE0wpSSYJZ9cgMyNNVk7w0IVI1YWCwl27LhPjC6k4xaAwyhpUSZAGM7GKQg4B2xxM8XQRUF5OJbJkMJnAuP/moYE0gQUHRvvwm0RYcjEOLCuEglTcPElc3CsidnNU4FCPiEpEcnToquMtOAV6I9gVAZCQHCvqACJKo12bl+U0tZfd6eXldDqfp3mZ51lZrut6GAtLuDVXDYy5BeI88BELK56OUpIXfX6GRHHT2d2meTG3YWRr0bwtc6tazKy1pdkcbXFvx92+tbP7eT6dnp53EvSHv/9DHUYlGqqWUqZ5urq+DvdxHN++e1d13B9PpQzMolXGFQzcZZrm/cuBmazNPs8eXooej6fTeWpmzf369vrq5rpoVebD6bTMy3C17QOQdDwem7dxHCM4IlR0PQ68qmWZrTXmYZ6mp6fHn3760dltmZ++PC7LJMrzeRqHda3VrIXTZrP56adfnx6f/uEf/6GMY4Sriptz8Gl3KlzcnS1EvFbFbDpz2vIHiEM3IBjvPaT0IOEUfwJXhifKoggRadRgAMgcWKukpNTnbjjJbpLoisxkWfkVxyXTkgBPBO1ADzLKgfDoxzLjiXe2B0UGM6Cs9D5civaSNid2lKLILhxMAkoHxCZ1n58LNIZ3hblR/AbeRyd+8LN7UyP4YqKRU/EJk91DI200XnG2J6DDUu4knZK7wI/muMCqQLjABi8GXg+wZSHU97dERv/seQCgqppZEj8hGcK5G4RlBMeMPabUgsmdHH4QmOXrH8IjrGtbPNktxjCJE4k1UxR2mZRBhVNBWiIOxravyNLmkpcUciyW4MZymTDM2AFIkMt3wEswMbyCOZ8MCpacgWaSEGXBvDKOjCAoSqoWhZk0lU2ZG4MY9ohozncMfOGd4DEHFg0sPA6B91EpZDbQAoRlrESsxEGwuY6gYHLcEHKRwh0ayUWYxuxOokS+QPD+G5k/fNYAO7J2u6RWULDpL0T5M3EgiEKKRkhhxcMVhnaSq75aK+HPh4hCyIGyNSTCnMTIgiOnyzEymi1o6qRS8j+YvHa8cFbUlMhFTm7uzbyZNbPTfHrZP335sj+fFxcS0dVmNaxWtVYKb20WqclyRhCZwwoi879SLzwCNiBCklagTMpLWw7H/dUyax3m8/L45ZGF3717S0y1lqWd6lBOx9M0n3/95df97nBaTp++fAqP56fn6+ubZV48fD6dfWlVahl1vVmFxf5wcnK4gtehevPm8/m4HPd7UZ6m4/5pZ8tSB2WiuS1Pzy/B6Ga1CLu9uS0FJb2rhM3TaZpVh1IK2MFaax2Kslq4L3Y8HI+H4zRNWuWwPy7LXAadp/mH3/3u+elpM65/9/vfX99ef/n4ZRjLPLfrm9s//csfx/Vqu9ma0dLm7fb6fJzOh+PVZvXNd18f9r//8NPfplMLy2h7wVhCWNoWIUbBwiUbakza9ZHMzCQXwo07BQtuqEUruO8q3mkFTo8ZciewPF3jR0yQJ0DzEwCaCOJh6CZED+ssiFqSICBLk3g97XBbUc6uYC6l6nw3pXDTjUIDi2ItCCOsnvL5nobQ8YsIC4PrOzP2wWdJBIZHOuvuPamBhfbckhywh6NIXlteB5aNMQHdyx4kSHz4HNyBWKqX4gk5szrIWy9InF3Ziucql9fKTEx9MwFRLl51yC+Z2CKHSzktbjgoN/Nyn0kO6L5RCaH6yomEThAk4LegsHwdyHYIn1HkEiAzlOE/+35esAgUHOwRSh1p49jh8kuuYUGgA5UAsBkpvyTuNV+QE4WoXFQ7lNwRNKMkTJ6gIFuj7BLizJS/grLGFFbq1QkRMav3A4f/vz9/FhIOMpxuB5iCbpJVBE774WHizKEhFAqjCNRniP6pwsrF7krmbMQczWFhapf+HAxKcfxhKhDhIDOBe6DyhOM5hs5KqYmRGabNml4fmWx/w/Uyh+IC4PmwiyNnQ2QU4dRZJ74QccQp+2GyYPXLOc5Kk5lg2dSsGTWPdjycdrv9br+blyhFV+vNOI7rzVhqYZWgcLfM/4HyIhs2UJYTczhjdlwSORI2TIXEsszPz0/b2/tg2u0Oz18+r7abcOxmkVqLzdMyL0+PT+9/ft+slaFsV+vVephOx7vb2w9fnszbfnfQKrXW7dV2vVorS70eVuuVqHCIall8mqflfDwtbSmqj7vj4XBgscfnw6f3nw/H/W63K7VeX18/PNwdnp9Pd8/Xt7fzsiyLRdjpMHnE7f3Dpmwl/Hw+n07nUnQch9Vqtd/tp+k8z9PxdJSJz/N5tRne//q+ze3v/vSHwjIOYxnqy+Pz9vrKvI1bXai159gOw35/vLq+tmbT6SxahlWNoAhfrzckolVhuMbumj6RwpzTFaTC4YQyK7UrTJSyTOqCkNQzRIIRgGOKYIFqBVgFsvTc8xXB+OcITxdOn1PsET1295gNlyqP0NTL5JxTsr7ZwmShvKmctvLkmFYA8+kdNSV7aeEww4B3NDoYnaJiaBlyz2s4gD9zAeriwMBNNu6QsRLiXJIhkG2YRUPME4FRqKiTi4k4q1CKRjm5kbx7ALjS1XronmKPaOoMIwh6EviqsjA7Z98B+rzU80TnS3K8By2OZBj8N4VXan49f12OFHGHv72resELAvuLJGKZxB3toLlwoTShR06n4h79LGVvgJxc3Ny1z5UzS7AxKorIqk0ALYlywghjHCya0tRLowXqOrALRJ2GwtfLd5Tyu9+01xNVW3KHSB+Z1ZyT2stqibnTVHlQcljMw4TT0yPyrHB+x3xRxNSTQiElLVHcCAQi8j2l3oaY4TMlEVGwA4u5mWkmqsghdrp8tSzOKAxiUcpznTmAsqXBLFrgRkeOfjIq+sumOtw6jN3nMUYXkIhQ5IW6hye13+cUO5ILimBjxpSLpQA7uljzUnj27E9BZm5tacvc2uJOIlqLDtgqIJzutNF55gQc7sFQcyH7hhBHoApPSgIWJRRLW/b7l8P+hTj2+8O0TBvZnKeT2VJUie3p8+d5Ou+eX4b18PbqzfZq+7I/uPuH9x9v7253u2ctBS4im8365vp6vd5oUSZdrdfn6Rzk03KO1s7nk9FC4fP5NJ0O++fHl+fnjx8/Pj8/Pj09T/NUpHz11Ttqs9/fS9j5cFhvNx8+fjLHN5OXl91333+/vd4yy+k8hVGtdbvdhnsdhnEcbx5ufvnpp8P+UGrZ7/b73W6aTuvV5uHh7WF3ePPmzTgMyyJzmzbX2/jw6cunx6HWt+/eCPHpeGjN5+kczU77/a8//22Z59zFE91xO8lI6dyzeJfyRg+afAG/rOHBnEZiwPigNBD2OEgg+owOK4g4cilU3oZEqtGhZ5JBnOQHQDf1KyhgxopWGPdT51IucmhwPAzrnMAsT0RoauQvnyI7Ej215bGMDJhCRBJkkbI7XAoIkLOVcSEFcEWCCH3s9CmQDCxJroNMYg9gXCBf81JEGZ42WQRIdx/QdFGg14jW3QzotULBxcJyJGGSkDCX1CuSX0oHhIjo632i33RQKeGUl5SjKz7oku65X3Dg/F60ZWMa0wqRxH563ER4wE8HiCwpoCTrOZ9d6uW7hocvR+tSGAL5d34wD0vADMfJmUKkSE5fZfsP08vUPzXisBCbedc4MqVjHzgyQSJiChT1RDC2Es59CPza9chpJ85Th3zjoQopkTdbDA7amnaVDHao83iSkpVuDpvVUxDlRBVf5q6Sv1d2bmD8s/HRsxYFcU5dAVp7dEul3mO/2HZKH0lUgI3f8mtoZXL/KEhXgfuRHlxZa3EwPF1ICLb+2X3p2YYTmGd1Cn5KiB36nEwkGI5mJfZAlB+GMgyqRuOqrMeyqrVWrbloL3PH61wLMVPOAxMJJgCYRSTCHA31CLLWmJTErc3zdNKq8zKpxDgMtszCFG7utj8eOXw1br5+9833333LKva3H//6t78dXk5m7bA/X19fG0Up9ebmbr3ebLbrpTVvMU/TPM21aDu30/EY1Jjj5fHptD8+ffj06Zdfdy/Pz89f5qWNWtZXw1DqZhhsmndfHjXiaVneffvu4y+/mMe7b7/e7w/vf/3Ewl/Z18M4tIYqqU3TfH19JcJRJBZn4nmelraI0ul4eHl+2myuVuuNWdMqL/uXYRyOx9Pqatxcrad5HodhntvVRmpd/fUvP/7tL/+53a4KxziO52OqPqRIxGUpXQZVIOXkQJNySfb7N8g84ze6XXnR8y5H8hIArjl8yJRDQJeSIplLRh+3n2piAGHR6ERLR8GRcbrLAoOI2T1gOKOsQlKAZzxSweFGEhHiBvs2DmEzEtbkaNNOWijgcsEBOwd8RDTMPK9F9AAs+ccvYSwU3ACTUxY4HCEUBm6IsSzNhTg9bCLMiIQK1i/2u08pr7oASZJu7djvPvfegOFZChdm8QiY0WSQ4EsG4Zzso8TEQNdMRGQo24Mp2NEhwNfJZw4VwCXd4IVABBYBj9V8UMEeRs5KnSNJhtiFucA6KSNwP0dgo/F7CC7eXUMiLBdo35MEMQml3IWYEKHTgcaJKfjCgfRAzZgdzAXCIhzQsHcJS04ABEWkj3Ay5sCXDbJ3cnKI5LKIyGeLmQCcbQsza5kF8SHzyV3aVnjJma16Q5wvZZZwJ8o9jxEqUQlSZQ1x9zBQHMTgjyS3Argl30696wLmFKSh4HSzO8/EMDBMbyk8zMz/GEwjsErYUO/E1PVDwUQWntiKe3TOJiaAIXcwkHgokhwk6pVVZiBjIRqHst2slnYjVJrHOI6rOmxXZT2UoWpREfesSd2xZqgfg8jPJpf5FrFM/ywsuY7EmwqRN2tLEaqlRNg0LeNqrartbOG8Wm++++H71bDarm7mZQr3w2G/98Mv76/f3L95fnl5fn65vbv/9rvvhnFE5VqKnk4nc2P2aZ6n+STiT1+eDk8vCNtVWJk3w/ruenV1sx3GgYPrWM7nc1uW4/Fw2B+m6WxzW19vVsPwYnsmP+73h/WmlDtrdp7mtizb6+vT6fT54+F4OkT4/d2tCDsZkU/TBEx8OOzP0+HTp48RPK6H9XYznc/h4WbjaiTyZZnW4+rNu9v3v5S2TF+ePs+n0/3dlc1LJy4ACBF5jRxTr5mrq6gRBZObMQQUGVYoUWtnXKOjVtSnXR6YpfyFwO9WV86Ys++QwjOoxaudSBYDfebbjYJJgzzlYYmVNEIigQ262RHBlmQzygUYBrk4qu38RQl8Ek8EwGV0wjLl7QLRBQu0G3m7gy8YqoNkZs5/TjlrzJ06A1kZnhjbFcEQ18dTRoibUjRN0aEmEg4PUsiMsvWa8bPPawf+V6HsDBfurhc5nOGJy7O7gP/LJUhJGzUAZiVNqjarL4SndOvAd3QiFYFDgfRA1slwDmYTBw5DpMMmlIgol3YuxE5gP0Q4e5seLIUowj1zTBhIn4vsL0sqwAgAzEhhl2dbmtN3SYSChASUIfS/RZNCMW+Sa3r6NxQyxrm+uBoFExVV5zBzRnM1konq1wXbEZtJC2zLg99bBNySFGMESOXoYUQjQsAK/LRkO6PX0ZkMnUnRCyImleLqzBgrMgoqIiJSmKsqzl8jCsdXzkFlFVFBT9gJoIIzVRch5iwru+tfQLPtLaXOaYoKa6ps/gRRKLORdYleNtSzg0ciTOBzo5for52gANEqzFqTywszv9psig7X25t5blpVmDerWpQKY2wTRYanZRdRX6+A5rIwiZPn9I5C7iXG5NSqFDOzNk/nY12tiKkOOk3n7XaotXjE88tLGYbVarMehrdv3g2if/nrn/eH46cPX+ZlIRa3YJbWGj6/tWbGQtzadHh52e2Pon51exW2PH562u/3bTqeT+fT8UDht9c3wlLX47L48bSzxb765q1qeX56/vjp0/Z6u7wsN7d3D/f303HSkPu7e2tGQeR22D2fT/Nqu3Vfjod2PB0/f/qEN77ajKfTafeyO51Ozab1aj1PJ+JovqzHNQcNQznsFxberLcq8vLlaTmdHu5v7u9v/vSPf/jpzz9+ORm5uS3emgQ19lrEDYZqoERAEmJNS4YbJlYpGA+JnCVMEJViiEQilEklEWhwdyfGq0tIRILpFYB1y1Ztpy6YEAFVYXMGqQ8HKYS8sHZIvvw3kodOqHK4k4iZgWWkqELOBsqjRwk0HRzEhzMxRwhiFwIxPpwwUcEAKDM7FPF8sYyLzp+KR5MgI8fMl6ROPDzIzMkj2LmvnDeM9QpG7YtIIcAtoS7QJPKQDrkoglHvdkbUgzDeFOEknhSTuTBFWDID7hgP5WRH6IKqBTkKTqtAZcJOEUFY3hkeLnDOz3TJlCZljI4nkkRmvGDmophcZWZiCWaXdGllbLg3KHEoYEKG0AfEyginyOg4CJKDnpk7s8nQ4QoKR7+w7YARnWDm7E5hRXXG4pxe6wVrBF2EWYHQiSKTnSDt92SKEMGxSx2/TUTCnSTMDSaJRubkFu7mRM6GjaVETOEpDUP2yqXtWRR3EJJZIgUsLIRNMkGBaV4NpiYY/OPs+kTv0lNRodDwMO/aMIRLJoIpYGBPbzJQGMrlTgSCnzE3ppBQaxAUpzdRUOAr4EZzdoDxP5E8LM6CZ0WZDaCOBykhC138wZSoFqVhUKnbLc1nO50nnK9aeKhcNFSIBUszDPHkwvtTNvCJOPQiCvLkr4VoVVawLAy46Z+OdVwvS7MWX321VtHpdKbg7eaKhc/nqbA24fe/fHh+ejJr0zz9/NPPtQ7DMH7zzXebqw0xPT49rjfb25vr0/H8/PR4mqYIPx0O83S2NjPZ/mX36ddPZLbZrE+nqVF8+fBpWVowjePw+fFZiE7Tcjouz8/vv/3uayJx4+ubm+311bz4aVpUZWnLej2WWluz58enWkpEqEqz5WX/zBLPj88fP77fbIaffvy0GjdfvXunwmTO4RHGHqvVsFq/21xthkr75/V0OuweXz7++pGpffXuTmne7x7dZmdnkQIZJIeHSWBtVk6jgFxNcUuvYlP23xs6xNlzSypEgnpDKUE8hMvEzOJiRUS6M5i7YepLAmgX9z4ME15BGtIxQNKKHFT7RqksXDlJIVGM3qZQOnV6EeFhzZxZxCJcQ2HEJr1Swf7eTDsU4ZGTp6kmTdwH60ZGt49+k/NS2ZnlME485/+RWUlFgiOdMpJt9e5RJhHYPJBrqcKDchQBDFcGNex3JCLIizqyR/2Vfun4QF1U70IEg2TKhShZYOUnhz33ZapBkszoeT1DVERkjyeveX7FTFJy+c6JMyMsqx78N8TElC076c55lGckPw/35RQQtCYllmE6fzdWmnAGLfx65h6zGSw+RkvChFlVIzGpJ+eY4Ji5t7o6Pg12ZsrlouCHRTCagdIgImCz0zoLQUZGHuQpi8Wfcgu3SMtrTyPobGlkHYzvRZeGxyWB4XcIM5RgWH2cGSSoUKEwjjRjgJifiZjFw1U0JJwtUuCcq7ecmnKBXJKJMauJFkN0v87E92yRw32ZRBV5V/jiVoRkjUExcgkYmPT+AMYvJet3XIC8hCwg6VKzKcRJJmgR5mBZFx+qtNbMm0ioRPYtiDjILEiCu5UQdzLtUmAnSZAFVEhQau2c2X06noTL0to8++3DQxFdpqXN8zDW9WY1n6fF2rgpbW7Pzy/hNKzqbv9ynJf3v77/9ofvN1drDw+h4/E8rten6fjXv/x19/JiYS/Pz6taN9sVk5+Ox93zrrXlfDr98uuvP/74fmrTl6cXZ3KK1Wr19v7266++WtqyP+ybtW+11mG92myGcTXN87Ic52l+fPwSX3wcx+31Va3leDwUFY+Ypul4PM7nMzN9/vTpcNgX1nFV7263m9Xw+Pnzdnv19OXx/uFus7m+vb/ZXF3/9NNPh+fd1WZkakXi9Pzl+fnL6Xgk96LMZEMtuV0PccltMYvc56yRPdlkSJyCQrLpEuTmLOzhHMq/oXqS50nhTZIkkpq0TrEzhWC2I9c0we4FjGNiPAsLk/T0yXBHv3FVy9tPFBGilK6GF4owBBv94OBv5iJCZETK4fDRCZZIPzSAG8lUk/5hiTNxWZhIBB7uFMzu0ZXorzgLH4xe+5zMxCHQm6ajo3uHesipTMFY8yhKsOf2zEuZ5RxBKcLBhqJf26dcCXpQszSVRj+/R2zitOMgurQNAoVD1tAUDvzaDfko5x0isV6gUrlMPWQyF778EafX788skUMB+ctRQERclk2zqvRfkF+TseYFh5CcyK2ZcoESJvclchYLlw+XaTKTWWfP0eTtuL5nGgQ5zE8l3klz7XQSQBVIyW9HnxnMPIiXkVSicBEVs+ZhCL5MFAR34rDmzZqKWFiAZ8FgVF/0KcT+2tzoZ4y8d5khy0NJRYTBOA7B3JU4sYiy5JRcqgdQV1/SDHGYW4Kui3T4EpEFxJRj/w9IxKRccdmjK4YxgZw9bKAjRgOgw3/qdzYZJrBJabpyuRK9Qoe9YB9lw9eLwurMFuSDFvGpUYQxCbELh7KARQhOwy1OOViCNiLKOilyQhBu3OYtglkqEc3TOZjjpBH61VdfCRErj+MwrMY61PPp1Gw5nmL/+EJKN3dX8WM8P7/c3t+e5rPWcjydbuo4DMPpeGw2f/r4/PHjh/v728NxvxrraqzLNLVpms/ncNvvd3/5898+fv7408/vXXl/nKQUrI7a748fP32xZhZNRP/pX/6n9dWVaJln2+8P++Mp3J+eHk/Hc611XA1fffv1vExu5u5E1toU0ZblvDvsfv75l1UZb26vNutBmZ8+P7XWlnOztnz//TiWMSyWqa3GOo5lPk3zdD7un5fpZPNE1Gqp3hoHSaiz5d2+QBEBx5x4tPd0wqlRKDEvvghK2YDdIrjK5KOJud8+ksvNhXqyS7qcLMiYu9FbCoPYGD8RnSfKxmzHpRcrwN5swPyaZmmdmLIjw/SpcMYesKTEyTnCjbJVy5JbDpLU6cCFfiNj59fF5AYPyyS/wEFfgswF3FMH1Tij+a8nTEQgp8svCsK+PpfgYM0+yKXRBUCHH2wdCwdaDdF7CLmkBewJ/FnAJ3eZap8XDWZicyPi/jFw/ZOOjui2n5kYsQUdOrtXAUBgAEswmS3B6WuZM4BJAGcGwGssmZuIRIWjMzQUxCSqGbKzVAymkuVktuNFIXbNRjxFOHiF7EHj0IHKEAmCU0JmjJxKQrJkx4IK4S56BRoGOnmF8tmrSfot4OpveI9Y9uVBErkgTJg8zMzMfV6mQVfBuXQeQOkyjubRVaq9gLmcheh4mfJXRvc3RzAlESENIhLq0tm8aRIRoswGTQaJaH//wkyXXRk9ygexYysdBcHRpGefvMDUWdzonGyCsIAEQNwN950yKWQnKE1xL7c9os/4oOmeQi4mLsTEEsCGEkWjLS2iuS2NwlVDC25BLxDzyiHRsUDwGr1ddem1YKxfA6W3B4u3eXKSOmxUuKisNqvTNOO0TfP5y5dPKuJLK1WKsnsbV0Nb2tEPteo0Tzooi6jq6Xh+/PK5jNp8CWursdgyLefZluXwsmMij3jZ73798OE4TY2YVDlXwJRpWQ6H49xmLfLw8IZ1aM5zxGm32x+POHnerIzFYnl6OUrhoQ6nZaqrej6d5/O82ayO+8Pu+Xn39Dy8ebPZrrfbrbsxSVEOlfk8h0cpyiKb9fj46fnl6fPx5TnaXIQ3q6rUVIuIGIe1xZuzkFS+sCXRgV2n1/ly/twXDkeHKPcCvlIluEqcd7O3hogJ6609XESh/SN2VXKDyhJzUkqvkwbEweRhMM/jHna6lwxDm4+IhdN/URZFJ3IyACMzWXYOKbnMjgpNQljEI1QUq9K7mhMNxiQbO5MjHiYuLg4+F4Us9x4aEbH3o49slJmInQgcA/cMle2+pPqJw6mPXoYEuzB7900LD8c0NH44QoJ5Dx355pwZp52JWYTy8vfUCM4eCl1U+oy8jLCU6ZO6Cgty9lecfkkPlnatiWfxkVM4mENMPWahPmOOoNKjFUrCtEbKwyY5COGePBR2VxI7JguITVhyKA4PLIKYg/wyDpZC0GQAcNQ6/6C9V8DochpHzQqISUVSw+yYUWfOMB+9ee6d/4mIsLYMZcAzsPTZdxblELclzMmpRSMKKsqKT9EvWHhXgiZnyrnyDaEy+oguZc3hefj77KRlLmdn0vgtkcTELuD8ODT6L5As2+LyQjBTERaBjZEe4WZhv7n9r00JSCeASNAL6LUAMcEpJZCbcJmxzZ4TTKatCneddgc9F0ly5ubMxOQeZq3ZYlSoVavkvXfwmsCywmZQEr1wzVtPgoV54HiZhMkphMnNg0LI2zSpkDILh7Efp/OnTx+fPn/ZbjdX2/WyTLvdXpiurjbH42Q+N2uqetgfPn54X7SUqkubWPnp+VHDp8OJw6+vtka0Jz4dTu6Eld3IbUHcprZej7f3t6t1/fjLh/OylJD1ej2uV8+7l8WX4/7gZOvVKjzMfVyNxIPwdNgfYhu1VoqwZuNYRWmZpuk8i6p5K6VEi+fHp9vbByXVTa3D0Ky9vOzuH97c3N6cXp6PLy9tnpWs+dzmE0VbpjbUQZnKUKwtuTUlG3oJEcKDYMIN+oec48KA9Mo9PIgYlmqIFx6ZpPGfnO8+92cBGaSVobJAMUGM8W6QqRFMIFiMgswsXzAiPEWySF0L0kt0pssB9372kRe449z+bTh164m+HJTLa/0ArHGphi6JpPe0OAeG+mPAj3qFxr0c4Ndf2v8MqiEgXHYsh8XD6tcKQcJchCjlOp79R++30jub/4odu+Q/UpcKw5jkZLIQys0or9e74/TL5016FaZvia/zI1JnXIlzHrYP9nLafuTHEGKGxR5RN7OhYKbSZ7IBTJEkU8R9GUQS9nAK5maNo8+Gg9anVHRyV6FGx/XhoaJ48wj4TsYh5q7ZQcmJQbiYIpq3UFFWKAuJQ0KS88jOb8AwKMwpgz+coQQb2D0iTWgvbKCwqLUliNALlcj+VPegw/nNQ8CdBmJiESy1YOqHmrMj5ek6SEERKprTeO5o2ZFwyokCmh4GC9/7UeB48u+od94SMXkQeUSYm0efsUWcTbkuQRHBXYZH3QIwEvD3Ixywu0jCB0MNXYuWgTtb35TmA1BiQGAe4RCqCgWFoeXrSbem72KeWgaUugCzQgljCMq7IEvynwyGbqIlix631ubj8RBu5+ns1pov73/8Zff8vNoMx8PhfNwL8zJPq83q8em5DnW13h73x+V6/uX5l88fP//Lv/xPwziWoufj4XjYc7RNXa3HVRVtNEXYajOul/Xbd+9eDufz9Os4Fg82Dy16Pk7T+Wzu2HFWymDuy3Jq3s7HU5DXUsO9WfOTD6uqtXz49f3pfLq9vjWzcVXPZ9u97D99+jxN5+3VejWOQ60ktN5uVqtRijCX1WpVallaI471Zv3w5l41vvC8+/JZaYkwJi9V5uU4lAIzbzMrpH2RXAC6y4W54a5+J88mGVGXMDORs1Cwp1/Ba8F6eSWO9+5hXaTsWcARsWFKhDhdcy5lAyObuwcBnkEXTXnmeqyK/EX5MQNBNcsCklDgAkkgghoaXyzcsREVIT/CyNg5bXY6x5Bfq6/owi7CC4ADGolLndCPej/2WQFnfulDVJy4JIv/MApJ/wRnz6WHqdCOS2roL4GCnYLTFq8XYXSBWhwicbn1SF2c9nYhJAT/zUvz75JFgpUu3FRurYDFQsZ97qXQRRIaEQAHiCw5NpTtk4tvHwqpwv+Hv3LsBJ9RelXUn2uoKARnYCXERVRTu9OTF54/nCnxORK9MtqRF04tKSmAeL58cQ6C67FSBFuYx2VRl1NIkGMkwZNa6GE4sOKRg8KsIRkGSmAiZu21yIUAueAR0DHB8GbjDoLBkgnD2aG/c8qk5Z1wZEyMSorXOKtdNCsyF0a/Pv04eoQSiwg6FrBCjsu7jzS55WAzd3e6SGspWDDM32ca+r8S+TCMgpjEsBUkXwiMW73j/V6X5V2ly41FcM+J9JzrYmbRUpalNTNz80Bu5GyhsTh5P9P93PazQL850bklwRlOG0SuqbGIeTpP51PINLW2Oxym+WzRYuHj8aAcS2sscnN987zaGVGpxaw97573++M4jKI0n89mvtjy+PQ4jvr27YO0WOZ2OB3P80KsouXNV+/O5vvD9HI4rFcrZ0+Wdp6LqipXLW8e7vbPz2UoZm1p8+7lRYhW4/j0+EQq929ug2i3PwRRKcP5fN5ut7vnl2k6Pz49Ho/H7dX23bu3V9vr+/v71WrVZp+miWjZXG8j/fRZWbRqrVKqNltEHKI4b1ZLJfJwI4yIA/T16HXhYgC8Mi2kNpGz1Z87hIX73GsHGPkioDfnCJwToeLkIAeKdMNL6nnFPZMESU/40YmdC47JWHsRVuSJzz5n6m7MTFUjoF8W+PsQuJp+3y733yMKuB1qfatBFi3eGWPoRbAmJAtf90uAYw5ljUgy53IK8XtQ5OdHT5962GqFp5AqUHyl10Tmu8vPShkkBwvx4i0JAacIMm/EoqT52xhQ+jfsFxO4OWUOFlBI+OnMHMHBIPcz/lBy8n0I6UJl9e9Flxed9zlDG8EfMMu+4Hy/iCEo36hg9hR+l70aYjg5vooDI5mQ/JeZidnMXFxD1ZlyYA75sT9uJ+e48ENA/EDBrbVessol54Gjgr+akhpBtMTw/LEUDTUm1yjJMxJTBCrWCAr1sKCI3tAOa5iRQbGUERY1Z1Y3EK4wofpFQuolVyeJmInZzZhI2C13OyMtXgJ8EBF1t6JkKz0VNgwpPiggCyNPCWowxlg4gsJYSqK16PQWlD3hQnCP7mDO3ZhF2DxnvsF5WThM4ZWFuQDrdcCOZg/kx0gYeJ/WX26OvEF/5cEOV/aqtGiEIBMQcdEupM7ZFmYWcFh9xi3Vbz21uKLHLs6kQcbM5AuzOHO4BC1PXz4d9y/bm7vj7uUvf/6ziLx8flyt6lh1Op13T8+tzXe3t/QH+vT58+fPz0tbflB2D5XB20nX9Xw87w/HL5933ubteLVejW1qnz/vIzTyktVhtXn3zbv5RzNUjmallM1mrUVOh8PN9dXDm7vwdng53d5eF+HPHz8d9vv7N3fT6bzdXk2nNs+LG33+9HzYzVr4fJxE6Hg8N/Pjea7DZrXeljqs1qvCZWrnw+6oZQh+HlZXV6LejAUXxoZNWa3rtN9Jp0TCLCiYSNN8MNE7BQcxeFVmY4ZFfbR0/mEOFqqBCJwQlFoEXCTCIsiEofHHzUxz3SDo95kleXZmUmVz5hyLxXhmMBOruKc4UhSLSDlymgzUIlAPOA9xc2IyM0JrEKbN7KAj3EFFRLfdQp2N7aGJlZiVnC2cwoVzvArkgPeFxPCDQ7hE4W/eKMiTz+icTCI4rFEQRICAITHAG4E8gg7UnMLcpBRzL6UQfLouN16oNSIOyK5wYz08OjmakCiH7iLzDcjlnGrlrBAuHO6Fj2JGyOLu9Jq4GDeqT2710j8SwuJLEkQ4eRs9CY1MCMqYEwDNEKpSuGP9jgHxt4KpDXg94t0kcXtBwpHetNF76z1PUETkljbqOdOZkrmmFL/08jCx6yXHg+nAoSQJsr69TjzmiBCXlGzm84aiBwUATkZcqIy0XorE/tRhf5Y7nWn7DRd00RJniZUlHmRn2SfIhVqdKyLwqJmK8wF2WrYnZ/x5SgiSuzw8HOw8/oFhdDetzsGEUgROUwQbOwtGsYmYKWXHr80dBiNMYhYmIfpad4S7M6pvDhRFIHn6M8/z1O0Ms8rMQ6gq2rfdM2NtQXgYhnWip9D+YC9DAYEV4xwUIsTBdpnZw1cQJtYIMSm6zGdblnmZnp+eSimrdbm9uX5+fH55fnL3q+0Vs9ze3JDI7njcbDcvz8+ssqr1L//x5z/+8e/Dafd0PJ7Pj58+2mxvHh7C7HQ8Pbx5ezycd7sTk9zc3szzMk/t5Xjc7faBtyysLNdX12/fPFxtr8L9eNircDDN8/l4POwP+3ma1+vt/f1Zh3I6nC2sLU1E3r57EOHj8XQ6nZd5UmGtxSN2L/uhDNbc2nI+Lc3o5vb+u2+/E2YV9cV2z8+73XOzxa1d6GbPJe14RhIRAtMnRDAO777mIMSVFYNS2P51OQaEPwnoHJ0vJ7AE7Gaq2k0GjJn6ImKg49SJRgRxzusA6zBREc15ROaMKyKX050sY7cHy1Gd/sFeUWskNu0imKRcLzgqgjBWdlHc5PGVbKahjeUezH65Vm5huXnV2ZjIBb5XF56n1zcUGZqJiYJBkqLRkKrAEMQQCSYo3ZPGyVZERIhS30V7oWvwC/C1nTHT2i8G9Urn9TNASkO9PuDsfLySI8SU1nivUQvhDEILSoo+zf1R7mX/EpcyekDqRQPSPogP9yhheAUMCRPgsirusSYFxOEhyJ6RATrJLjMjkaKaBcKFbLzI0JgohOAKbBYcYelt0qVjZJmVkmiEa4RTxKu/JQpK8TAyDnaSnDK5DFfgXw7PVJKTtxJCQDGOnULCrBehZgcImXVxFnoiw1sTuKowX4Rlnh1uoqCunbwc3k6kRFcdx2tewH+TDtVBbi6SY1/gVbP71XOiO1bPe267pJyZUBcR4ewsI8Hg9bF5WDg2iqAUlCy9Iit07yM6v7UOiMwAr8tFI7t2EswkVcpCqloYWiGsPxNyX5B6HdO/qSXgfO0EvIECHYgG3lMiJKR4y3n2hfnl5VnHYTqfalGPuLt/2G63//bf/+Nl96Ss6Pre3t+t1uvT6ezOQ63TPLdpbsuy3mzO0/LXv/3ty+PnH//61/Ph6Ga++DLN43r99GX3+OVJSMbtSrlc390sEcfjpKWMq1GVxnFUJZJyPs9htjQ6Hs8W0SyeXl7s0Y7707haz8tyfXtzPp/HcWhtEeEIWxbfvbw8Pz8Pw3B1vd3vTuTyc7x/9/CwXW/HYWw2tTbvXna7l92g9fZmfX29lV959/S8nA5CnirKC/oJ0b5cIYgopPdpM4ZzhiwcqUiQDpTYkzonGd4hMGhSQQUPY7IOUUHpcDhZsgicEgm57JtlIsZGTyFqAc+SjG1Y8uAdwmKK8/UcoVXQsRKlwI9IiGFtzxdehNDky4AXFGG9HsYEL3ALYDVMHeQ34CWpsGSXwxtpki/EsHSUi6KC88+nEDRe3deIYLyTljf8yk9QtmFQkgkL2qzAiNH7wgBRidOYnKKzu+hqXHzULgZv4JC79LtzCxR4qRxwdSLQh2gFo17Hei5EUk63zVd2iBnm1fQ6EphfPb8TsXDBnLI4ZkkMbNcraCUCAM0mJxFj2iKouXOQYRIYHJx5SlCZsDcMPDL3ngExmQf+uGYK7siBKPJAJDCPC14gTGGDBZNMPWmzjDMsXRVPIprXgVpQaAzMIcxODM+CohWIVpL5x4dDTM2eUr7FjhtIQJ/I5ej8prJMFJEAq4urcM3AgfV6wSPvRv8XcQ8zdUveDcqBMuq9F8eih0ytgdWgGgUjxOHk5ByCm2LhzYOClJnYMjsCcPUmASXWo8w1GR5wnfDqe9mAixmiolpKKTXCc8VS7hMoaKcTRUBScgF9lN6/wmR5l0lYlUvnJ1PojPvjzZ4ev1gEa/nq67fHw+nu/raITudT1XI+nl52u2ma97vDsF5zkM02uRPx4XC05sOwYtH3Hz48PT6+PB22q83j80sV/fWn98E6TfO0nM/HSY6FiLXWq5urRty8BUzhqy5L+/TlSWsZhlqHIYqcz+fnl/35PM/LPM/T7niUqjqWZZrvH26urq53u5dSyjydyiDXN9fDWFX1y8fHIhpzWw3D/cPd6npTj3OQbDbjcbd/c3sbYfN0pmYSIcyiQmYdJsRFoJGgj3KQBIxpZweBt2C2BmgTkZY44e6K9doAWWlkwoERXxHyaGHBAcArrEIl25hE1GFQzoF6gv+kL/jCRCWu5F5ZI6gJoXDvKkQWZkuSBMA4uhd1nxJAye69tZB4rl/DpCtQBjtp0UtQJiKCyJKJKaM2Ry9tO6XyekkvJ7rzzpyQuevc82JSRDCpXQoRil7Xk3VhbHjA0TFtZeK37ZqMyNBXUicgEIs5ujiXEt4mVMaMLVSlyJ2Rv9QJUS0ZtwQEDJllijyCLi8j7zzaQuCIyOkScPsvJvcoRJS6VkiJg4ic3SiNTyMiGqSt0N7nFC6buVuUSspkAB0IxPgS4WGkIiSCaUB3BsOvId4r3Mieeu+OY1QxcoAaXWjsIM1ECTV5emjk7UBd1qshtmT8cLhZIFUiFnYRKaWIkIioKmUJlpkuLgRNRD8E/dj0Qg5BmhOn9EvbETTB8TOCqC/miL53IlsyqbyMnFPLnpUT9ozKBT1xRlImJhYJczjBpB6aQiXba0Hs7kuYNfcwQw0oqqLw98KTyisX+ZQSCTk6XTkjmJ3tyz3phQtzYDcNXkvnu0KIQyQhWdZIHETdJSp/raL/LEHiuLF549BHRh0edj4f7ZHeffPt9XCF3TSTt5u72zBbj5v1avPjLz+dp0lK2V5fDXV0891u//z0vN1uV6u1lnrYH359/96W5dPnp8N+evf2/vOXRxY9Hs/zMh0PZxLhosEyN9OhclFrXgo/v+ysLfM0n+aTEt/e393e3yxze9rtKHxZmhRl8+Ph0OZ5Oh0P+932aiMUu+fnWouKvHlzPwzD1c31cm7Dqg6i0zR5+Fh0XA/r1fabb75qkzHTx18//Od//I/97hMsZYSIFIrASFZFGMs4EKe8I4Zu9gABAmeZ30m8C9rv+eIy5cedm02LgwiLcGdPD3uKYA/pxWRiU+fOR6GxJZgLSXP0JEJYCNIEZeBG45zjybBOAX8gDzBYwFw4VsllYlale6XnVerfAbee4jeqzNQD4kcEpY8jchALB9o7cMLxwLQigjiewqUyx/XnPOG49JykEAom84UXVZXUJREkiMq/QWaMJOLoHXP8Ngq/UmWZHPHzuzIja2/AUGYWtWgOJRJDMs3RxyUSvWXkSYENeHJUPhfMlkGJL0k5XYvgIIL5Yequ8cU9NOUlHPklKPVjHeIi10VEkGQPwNlamJl7mHmoSO6NSBKPvcu2WIKFIiQEoROsHD4gp67TA/4Fwczs7iJK2EkTxJxCU5Hi0Sg6iwIWux9GHCkmIrYI76O2eeA47S4UQRxLKPNEUCpJNTsSWC9MHeVcysVexgKlE0XfK5lHKkJYuwYLJ9XQqMCRkMLiztkCuKTX3iu4mGdRlgRgoUQ0PMnQVEMRSNIc93YiMzf31haLMAvR4CpYIJIkbQrOyMMxOBIUzZfoxFLyYNkfSAwGw33kXQoWEXKWECEmI2cnlPCdn0SbMSSyFICFUNaUylmKRi+ssLqDiMkjVEid2zy72zis33311XSeXw4vt3e35/3x/v7ufJ7ObW7m5j7WsdZKA7/sdsfj4fn5pZThu++/n86TamnUPOjp6dnND4fjarV//PxiYczUKM4v83lpzaOOq1IL6sBlblrIzXYvz+yxPxyfHh9Vy7K0onA9olqKqux3z978/S/vw6wOZT2uhqG01tqyrIZxrHW7Xp/2x9XdjRQ+T+cgnpe23Wxvb26KFGV6fnz68PH9ctoXdU1kDMvYjrMpQQhucpeB4Y0EEebmM0MkYZLBPkmMCxClZALp8t9BSJPHljncWdXdmDQE3pqECxIRpBIUBoezbh+grH1eJBlYT1ST0x5EeZGzT5duCRT9E3tYkYKPixoY0bgX/V2s0e9eVuPgNDw82849DyGcJZbiSOlqMBErWWBjB/6spyieMmYzXZ6UdH+ADLZo0BFsCtxNjDxt3i1yYhajl5fFMuGoYAJS89RqhyavCyoqq4/XEib6fkqDPwb1hmheRRfWIHbL6RvucDRe65LfVDXJ83Rbs76rySO8mYgIKUO+5wEKyNmaseKZEF8ePUqkTkujjx/hRpGLvS0izOGD0LnlVPtSBDsxOzmjCjGDBp/CSWAI2qlydJpYWJzh/snuISm4yeIXpwmW1Cl271XDhdliImEuWswao7dCXYrJ8JFjVYGdYRJH+QPc3QPGzpkwImeXKPn/fD7Rg3Y/KZynNF5vIbi4PqYAAwdmJg+XgI1DFrdBFtbZrsAmISYW0bCsGZEVnIleHRUowlPgRUFE5tHczL21gOK6NYOpoRRFpIUJL+O04NtitRi5uyFZBshcoAASYEruxZiwAJkyYB3eCxiGpPjTLE9BWmYlCl0LEYU4YGOjwERkygSwmEi1usU8zevN1dXN7V4PHz5/8qDm9vHzl+1m+/Dw9ng+HY6Hu/s77Exb5mX3stOynM+nq+vN3f29hz/7s7s/P+/a0qqoqKzX4+l4Op1OzXz25hStWVDMM1IsEXEYh1uYN2uH/W45T6urbXhw4WG1CvNlmVar8eP7j9vtuo7l+fHx4eH+7t3NuBpU6XiY27gMtdzd3/38118Oh8NqVff7/by0WketqkVurq6iLUPVMMN0BaxeUVhyj9dM4u4XPsEu/jICMbqgWsQxyCPQCZnUAGb1RU4h0eNPciqUFzpzM9zqKaGQZPcLIZxCyELI/CIyBxkuFF2QmHAaSAmfIODKz+mkAmAeTpdhHgJ6wE9ACuxts4CFQfKflH/AL3cETygZEqIuVMGjhIda6jjy5mk+F6IsPBPCRlBqaLNAYlA0+bCSF6UoBHLEYP7CoGiQpkDpCHGjjvIRFzCwk3PRkQWCGYWEBDv3nu1rGAP0zIrgEoU9ImfuDX7G0jt7KGQ8q5WUUBJTWAA9Z+zwRPVkEHGlX6zjLXgUDLPFgnPh2EdyQf8g8jK/BQcR4j68CTyiuUvuP6NeD/VZtfAQshw763xeCt5ASDHON1pHnU0msmDlZqYiZMLMzRtBr0loAmUrvOfRTrAh4mDKrgOefPvdAd/DhUveFxE2huWHBSanwqJPHTs4G1butCMFXCUoO+/0W01LfgBQc/n4sqoG0RwYawb3RRF9+I1ImPKV4J4HBbO6NyLum54h9ETNTEQwukgbNicK0IQU4dmYMXIiEuvQvD8odJTNrbuSoEBAFM8T5Yyjd6m7wSEKOszuRqRMSuy5/vEVq4UKiWhcShlikQjnZosIHIQiyBxGjnhyaBwVGtfr5+f9+urOnA+HaZljXuzx6UVE1lv6uz/93c8//3KeZ58bM799+3YY6sePn+bn5fHL83/71/++TLOoatHTcYJKlbyIy/3NDZnZvITPYqFMIythDsOcgopyIWYtczNx0WBlkaDzPC/zsr7a1FrM2nq1evzy6cP73e39zWZcr1bjarVar1dFSmu2LMtqtbq+uv7hd7J/3lvjeTZRX2/HOoy7w75qvVqvSOj27ubx84E1vMElOC5vh1gQ1PFGAEjcUq4eWQawA6pRn7PEsRPxV0UDd5wZHaYgP1wuS0IrXEpg/O4r0/vAeOfcFQPRgU5wsEmWpVnLgvzDmG9yUr3NhNLPw4nYwzUKorShSMWe1fxACeqjJxC69BPAGuaUUnQaJdyi1//pC0rMYUQc5qacUpzI+/l/6PNFZz4i5Yp9VoYFPnHwpgR/1SwJ20xV3q3JhBn9k34N3KPzW2mnSsEuaINyt7jPDgm+LzCy+wUHCDo7QcACWc45BZMEYdlDSI5nv5Z5qAk8chbWicwpB3udiKWZE4elxp0KHm6C5bhwWYSFnMI910e4uzmZGRE1t7k1M6ujsQgkHQwTHuqAmjiMuIaqdiDuEdHMNSyopDMJMzt1CW0yU7+xNcN+K6JeowhdTi+gAXxyQtLVDuFKsooKnJlE5UTh7m4e4o5307GLhDZHFsCP4UYkLUoVOF6SCDlnPxzOcMLQApAHYYwsz6VAHNZ7cpZGRSC+RCL3M1O4RRALniob/mWiwhJkLOwW6PX3ZpcF9SERVLuElBr4e3PHq5XApIw4S7pGEIaloRLDr7OI0FAiJk39ErpBKMn6GWIPEtVladZcijnGqJnc+HKXlJRZCjayS+33hzioefNwkeJuKoUimBWi8CBhFVaNEA8SkmlqonWe2jS1IJnmtrSY5n0Zx+HpxZyvr29Xq5GFVMr1dXz9zdfXN9e//Pzr+XQ+TtPTlydyb83W681Q6v317Xa9qqS0vdnoOM3T/nBawqbWiNk9tAwRpERjrUzkXEVle30lqo+7F5tbGbAIU4JiWubVZjOd58P+ZHetLYuo3Nze3D3cN7fVsA6n24e7+4c3u88v03L++OHz9uZqc3Oz2BIk+8Phars6Ho8eLiJtWSQcJSE0bSwpclEpr4wNYLNH35bN5qawO+ymVCi4IkL6MFdnvBHjM2b1/xcYWc0xFAtWWJthd1FuwQPwjVR0A+qDxGcRdQvkJxUB0sdMD5N2sAfqKCzaq8MIkYZE6OyoVDFfJpa1OEdkKxpsD2zrPXK0s4WpSiER546wLhp4ppBIh2EhCUzGhBOseJN96aWBZx3PCskjEfwuQtiFosCtWTCJI6LuVkppkbWQmQt8IFmsQUkJmxzuQTwgOhIJN2MWbyHC4iK59T4oE6aAlPMsiLJnSZQXsNmCSUyEr6LqnfLwxAQpRcXqSg8yuIcKO4m7ezCkhMJBIuHuFiLCHMXdibEHMA+SU8AGVXs/B/2bcCJiC6KItti8tNba0JoUFTMJ4XC9tDei12XuDgKakvcP5+Sygpi116Oven6wGirFPSINrwXednjpRbVZS100PlZqhzomyhPPXe2ZPdX+9q011jSl414EEuyT+zGhTqhFVAxjXMrSvBvsWRIDcjm2RlAeLZBKZkZ9eoIdy+QNU3YoNZAu8bEDNI9zC4dDHycxFNkIB97Pgjg1qBSX8W7Q7mi1CPc9DCg1IFtIkgBFWb5QQ4seoqQk7UiE2FMOljqHXvBB00UpmvAE+TDtYhaoZiNH1kHcabC7h0gJisuysCAO4WDxIClDMwnS+7d3X339req4O07x+Pjx4+P5fL6+vrq/f3j39uub6+lw3IvI+Ty1pdUy3N3eW/Obm9v7+/un55fPn760aQJTqSJjreo8lrq9u1umFuHHaT4v8/E8NWvzNMPM/Op6s16NQlJUV9v1uF4dpvP+sBchsIWlljqMy2JALKWWMtTW/Ob2/rA/TdMyrjar9ZpLcSNh+cd/+mfS+I9//8vhfPzxb7+Wst5u79Zv3h7P048//vL8+HmoBEE9eVcieCftmQ0UqLs7NafwrtjpdHZzExG3yEHaBESpsOxtgdxS8erSRT1ZZDvr0m9LsUqCLn7t9HLndRLY9moX04t50ygI8nHW35AFHOFwuORgCyfHvi3pDBYCAhmlHRo4mric9HSZv4wTEmNuDTRovw0J2XAFI8y7f5ZTaASHuQsDg0pyLRmPQFbjATHSVRD1S0SAQhwUZsy8LAu0pHjYS5tVJH+mAx87MZtj64t4X2Bw4eHcQjTzJD6FA68TwgB1pi7QGHMK87D0Lza8D5fgEEt5FnI9jhAHLMKYOreNoqGziOyNnIzxRgCdsRQ+DUcur1YEs1jCnKZ3hrlUlmBu1mZrS0Pry4aRmrlGULL8EOwnqWjkTMadVoJmQSkdMqiTfNbVOJFHVRDm8nuxkQGnQkpLzOLRMMofcB7vRzZNxyhFBZ33JHe3PhwAco/hz8Opg7KcQw5PIp3hXJ6GP6UwXyputCRSYhBYFNeBdvSCyc083L0VKbBUEyuBvJKJLWfBKIvl/omlZQeY1SMsLAeHIcQCL8DBpMTch9cEU7iR3TKly2V9JSvT3yP5Bk8RtRMp0cWOMbpC2SPFSfmIINZAPdR5Pe6tS3R9oYMA+GLInxm3K68xEaydAg7ZQUqkRIWlVi3m8vbdN9fXD3UYv55tGK+uNnfzchpq+eqrd+v1+PKy+/Tp4zLNNMp4U15e9tfX1+6uUu7u7kTK493jh/cfHCP9rdm8bO4fboaNTfN2vRLl63Wb5sVuSKu2efHwokoUg9ZaSxlUa23o/y1NLMZaxrGyYm+pbLZXm3F1/+Z20PL2q6+GcVhaaxZlGNabq2EYl8Wv7jZX9zfbzfV6fbM/H37+6VcdyuF0fic6zR6iHmyNSq3u5EtDEMKDhlrByT0Ep8f7zLhgFTfuKot5KGYJSSg43Vmo++lepCgRfmm/o1DgTu0ScbAtxiUg5wpy4hDM4mSLC5QPfLQEpj2IdRgEDRUih2UWwrWnAhVUZNctoEPrEWJkwQkXcDEXBF5xfT2w8WpN2MzcwPWLMFkmJfhCC+bgeiAJd3MzrJuPkPDc7IJyCuyERf78/Lch2aOkZqVrUvMyJAvFxPC7QieAQBf3JfFJtkOOn6gpgSQ7OTm4ZVJXVaTbjPYoZF6zL1EkY03wXUP/QMWZRYRNXDi7P53yCDcjIhchSn97fBlzMndPswSkzhxwCGEnKh7YxEOp5k5lS1fxgT5nBsNo2RVmh9QGdb4zBr2ZKFgwXR2g26FlsQA5B1gspBnHU9sQl++KgTz3JELxlNEkJwpREWXnDi2ZnAKcjFkLaG5hB92fCxOZd380phwDYYpGlJO9nIw/pQAmTXhAt6FbyeIczHh1HHCwdA4CwnEVCReR3LjtQJWA7fiIud6enXLbhmhJQpYlKJO/R189F8QsKuIEn4b/P1d/1nRJkmSJYUdVzdz93vutseRey0z39JCgDAkKgRcSwgfwd1MgAgogBB9GAIIPlEFPd09XV1UuERnxLXdxN1NVPqjajQSzuru6KiO/7153M13OOXoUV17I3AKFi4NSOCC0aDGY2Ec7jOieAkTE4JPFEaRCOEmSZy3QXYf9Kpm5uqv10Gli8C1DFVjCzybgzytlxddrHhxP7IzxzFaj1yJPM8FITwFSESAOmqbdN++/+/6HP+wOt6p+9/D29v7t7/7wfbtcLqczkV8uJ209zrL2/svnp5fXl3Vd52n++uvdNM2vr8fdsuz2i1rX3rSrqc61zFV6lxqj/AIugMiynxlSqhSw9h4ObiAo/NzWKqH28/BISRkvl/1hx/B3X73r53Z7d9+bk8jjm7elljdv3xSpZZ6m3e7z03G/v//Dv/6b5vrV199P09xUD3d3el6//90f5orTy6e2Hd0s539GA+cOM4phLkuEkofYI4oV50jakfuH9QOQ9aqPbUVAiicth3CCbBrq5VQrmDB3NbNGkkVyYNs8mEiiHDIy10H7wrzDwyo5iCtXd2L1dDEiS0KVzV1DOhJHX9PeMdXb8QOdzA08rupA6KO47qquZkZSACcyZ77K1SxLwtjQYlEt+dB3eBgTZ0aJmQnVoUYDLJaCWRRJUcdkU8/XAfssVk1t0OvZr0fHIpxLid0tEZBQ7YWawinLu0Sho9Eb/mXXXiBFd6N2D9VLZPVEAQ3wQkxqufQyeZcI8QoPuoOIOTolh6taD27BjIld3b1Hj8dGTijupAZxCggqEilFEzCgRWaR6Cp6dG3x1vO8tq7M3N2FOLIKDQVMrPTUKzQTbLWpkiM2EGSUzoelFqGXCQiOMlU53t1dqDB7LFYbRaUHO55FEcMhaTESr4GIaEhXLEUI1g1CXbVQ8E+Umwki2VyPQl5M8oA81JglT6eOMx1/sZVaQqNtpqEijX7SPLuIIbb0WabERz2OJuLBawCuMOICJ2ao+/DDiEBqmvhUlm+cuDkRySg/YB5aqVi8PNrLSO0EXDfQZRuR6Gf8SYqcGlN7DpCEtsmG2Vt0EE48htUoMzfM1QXGTkwaI/sx8xbVR/w8tatoKtgfdrAZAzKX3d3d27/5t393c/fGId0aMZO7lJ32LhO7ukgppdZpXtft+fn548dfT8djqeX29vb+7v756eX+/vb2/vB83F3W1bfurmZ9PV9Qd7vdMpHAUZqsJAaIM5FVKjCfS9kvi4io+6ptsw7zaapFpdRaqqxqINrtljdvH9mxWw5vv3t4/9X7ItPx6bjsdvub/f7mQFZubu5KnYss4ALnWuq3P3w/zfXTpxdTzLv9m3dv+3bS3rQ3FKAj6aEID4aYmhgSM4+ZVHbK2gwJE0UWh8VBVR81gZpRDtLDza524ZkqQINVzbycdDyZuLjHhK7HDlkNJikRGLsmdAwkKvi7LGQ97+xvhDtsoU9TdSCM2waUY4gbO9i8L1TvVU0IMjNV7a27WS5llQx+yGJLLW1vktLztAaFqxO5kQ/RENy9xx3KXbAh0MheKKHg7O1HfU2jLs8uIOGzhB6YKEeUR4UVnc4A2RIliLZDzd07aeyMGqV/MPZxnVPgPmJWlLfJJlIW0ETXL599kqmqmnLO548M6lB3UzMN+YhrJoWIuc7CIBQzgKybhrtLvMrsBtWJrxUcObxrb6pbb6338ArvqltrMQ9CQj2kw8Ojx+FBOmd3ESIwhnZDIY+JgIiVw864BxvMYBIQLNxAx1iDmhFLj+4DnN8uZqkoEX4fqmkHhJkJLGGyFl1t6lA8Fg2YgeExnZSwkiE+ZY5QwoGmXSDRNQCkbqqZrYihDo7lGVm0IFE8yzCPaH/Nyby5SgxGMDGJWfoPxlVxgJ2ExTXmdMaHdYsVqp7y02hWroIqiiIx2F0zV1PmbOsNNu4bZb1lNkrIuK5QUxFQdouasGkwUsillF1zh1/rfeu9VrqGk6C1zZPhYnbE7gxzVdP0Xoumw8Q5KIO47gopXO8f333/+z8cDvcskzogWqapFNa2uQHOZg0EKcLMRcrDw6OwfCAG+eP9w7ws29ou6/n27qb8LKWIF+FubtrWtbdtWXb7ZVe4qOlla2oG4r4124wMXKSAJ6lcmBqdt7V3JaaplGmqtzc32+cncjw+Prz/6j0Zbg/7b7//7u72br1sx+O5tTZN81R3N7e3u5vb/f4w1505NfdC5Ebaaa6LSBHm28MDvlYy+2m9XM7dh2XjlWPScWSuBQYSxXcKDMRVkJ3Z+EPqYCEyjq4bA+MBHNaHJkhy1mf4gGQYCj27B9kDV09FfZqRXmtUZ7io9QFThwNydKx5u8jZLW6qwcliC54jVMXxTdLg3MfP9SBsDXplB4LFiu7B0lQx0CgePkcRB4eQmohEOIQkAeLawKJGDZ/d8OAIA3ANWU0IiPgL8pT/DGXcCuWHxlJmGnOnmU/pC1ah7j4URARPh1S7bqSNfwnFhpBMOxFExjX33IgeVVRQCw5H97Q3gmmEJ3BQrKaqpmoUUG4xjkJQg4jFGB7sYz9lXFuQO6HEgHVXgGAaVhyQLMbVDRGbjGCGzXTd2tq2tbXWFaCt99AizrWodyn1Wk7mkc0IkTofShzNEb6Hwhj+goZgq11NY/91dIBBUxOBNFbJejY4BjWK9EaxfjOBJcsiwuGeFppw16sgK4O0wqOjRHJBHgRmflp4+O6Ta6ZdH7tPzVwjQRIIIsLqQxJ5ZcERFXLIzqIBIDO96FlIapmIihOxiMO1t+CvTI0YnVW4MJzpivqbWs/uJHN41nHsAElWHB4FvHkMzSNVC25GZC4SviJulkN6DHVTM6Loms28mykcagbJp6VGvan2rl03qKwuAkJl04F+qjLYOXyko+lhJicyNUsSCm7erddSpnmGGgmrAixS5q+++fbrb79hFtWuDtUW++jU1GHrerlcLtq3y7q6+TSVabm5u93f3OzX9XJ7e+sOvzt8/PDz/d397e3t6/NLz0LSS5W2tS6T3EotFU1o06nItm7MRO7zbplK2R8OIgKhSg7G1tfL+cKF5rksc9XWapXH+/s3tw/ny+lweyhS1su6bmvrG0iWZX97c7fsdtaNpbTWpLTX59Ph/jAvC7HMy66IsFvR+vbNW0F7ffpwfH1Rcwm/lIAuc5DHM7Ib3MksKsIuVOEQlhDpIWqtgCNDhm0xvQQ4UpqoQbEOAyvKITOL4OjBJ5NZD81WKHPFg4pwIg8/3SgUzLvneFfKIq7CeoN6qZya5FA0sjo5SkzhZdoZmBdRDgqaaWA3SGIsihwwczphhfA6V+RcQ3pkCnZVdxs7aCPcWqLT5Al0RUfhv23yR+cBikH8XMUdfwaRXa5WBX49wbjKUQZdHbNvmVjNiJHjPEQx7jA0R5l43WHpUR+ydfYkSzxutLp5LJYakpJs5kLnm0wvY1BEUX/B3cMnzQjXbjswnCsGlZmPhpEqSkhQAWMabtHxhjn7JDPXyICO1ntX1W69W04+hQQpIya13ooUAluuUoCqhsOBw2AWehp1RwykdU2OCojiQN01JSkDVwv2iUgE4fdP6j0o3fzmGOcz1dNuaWXuDruK8pHnIJHoMRgfcd1MQwzshm7KAIuMuozUNFI5MQtzoGrxYmI3eyjGrt0SBp6ILxmeAGQZQACp1Bp/AObEHCSNmsOUhN07x4Jhh8PUNDpiYpgGoAbmGFUApdtDriFV7ZpPfuLQmLkmrZU3IQbcQmftBo+uP4ODD/hVs6+KQr537U3N3E1FuIpw5gx3MwrQQaLl7uYuLMKs2cQmDylcmNmctClTNWcCLYfD23dfuRcwBzRIRKVIgGnatfe4FByCI2NuvQnhcLMnpmXZ7w7Lzz/9UufJO719++Z8PP/1+dhVnfi8rQ8397JUc7Tu22W7vJ6JaFs3roWBu9tpLtMyL5s2d79sq7pe1ou6Mpdaalgn7He73TTf391Osxz2u3maj6fj+XzW7ncPt3f39zd3t6VWN3p5Ph0OB6nSVVvr5/N6ONyIlN7bJDxNk3Y7X7Yy72SqermYgSk3ZkdHlxfRvHdN2nY4ORtiHo+u0SSucKBIBh9ma6PsHZC6mY31ihH3InIxgbv2IHIodJcJGLLDWIRHMZcMWuCN/uWoX0GP8CJMRi3N1a+/yxNkBMCRZrKO9vGj3a9XMiQPoepRM2PmEcDCynpMcI0dRa4532BurXcEMUAca6N4bEG2geTEn/QEYdg5zqyPWYosz0fcHpCZ+dialaBIUtm/aRoiTNBVJuih70lo1ZFS3YjLIeKiYSCWXRFln2SDLssiPEgVJ2bq2plFdXx/S8hB1Xjc7Su0FfqCxOCZQmwa1E4JDhTmZt3h5Dl4VomLSKIqZuaImV9nar27x8ZgYinx/7hTT35DhSgcLUJu07QFLcTZ36IKG8aOdYRaFA5WC4oyDtz13cbgIbpq9FRmvrWmrbfWxrkhN2d3duV4XqZmKiwbkxGKULYYY29cKexOEc+gREzdGiymoxJRUTUfS+q7qgiRqmpHbJ/woGxNo1MtYjlLHPYc7g4jRw4wO+DM1DYYWTrLOSGNS4mlaOthBS1OqgYSGy1tgH00GGYgKLY8zDFg5ebuatqtq5rWqbS+SswnEDw3oLFabhuX0X9j2Fubk1kouCjrMQDwrtra2rU7vHdzs1gOw96l8HCm5SA0ycAkZk1Vg+uz32icyMmpdgN4UitGPC/728f3Zdm5VCVpvcFRRHrb+radzye4L8tcREBddbU+Yarny2VeqpqFueDr60XN33319YdPn3b7u5u717J8FCmfX0+Xc5/mw/37tz5VU2IhMw0Pm6WU3W5HTi+vRyXUpTbXBltVP3z+7IWl1GmaSqHvvnn3/PRcBXMVoYUBZu9tY+GHd3dLneZSD4f95dK06243T3Nl4SLF3FmqlMpEAKtp7/1yPj2fzs+n87k1GCZhzsEjXMtAM6hr7FsXlECKCdF0mgPEpOaIldlEMX8TlT8bwtwQIIuxDjMQOpRj2Bc5bWQ+RMWRfCxdcMAaRkXZgqZ+IkWZqV/I1xoh1RXGPLZ0AKZRjgeNiZy0De9IUi2p7A4BGRMraRxyG5XfVWBGRG6mCtAWC12jujczwFKmR1m8xllv2hxgVCIqLOoeoIobAO7Dc8FCpEGBAjEx65e+YNh2DhdSijBBnO23J1oflU2EAsSgJZSY+sijSNWOhUlMykNpxPlMEk6Uu3YBYi5ZjgOc4DyD0FXFYeZSZG1bNElqmlgW+XVrGTFFJwQgXP08QGkFESOkOuolOiK4WzNcEbYoBAVg79rVzIKQMWuhyUr0mYVFRGisc8gmDOpJiqTjKdHVhsMRBnWmIAYb0qyQ3BQu8CCQY0FadFmhViRPyQq5m6m3bsGSZ0+Ygt0o18xDYkvu6kZmVCKMqprCWdjUiYwlHaNI2R0cucZihuKalFNYm2wDZd0Ch/cA0qHu6BrnP06SE9Q0Fj2mRNJUYcjJdYyTngxdpGtLKpiERVXj0oQUl77wUUMtaq5EMBXtBI7JQU2RQ/JVwcmPXt1GbjIWGfzbKF4SU8XoAiiNQUbrlLktZLnmal5KMTOCg6/S0hz+yEEQD/omBgqDJ/CJZF1bWSoRS5nv7968//prUFEzcmcW7Y3cvWtvjQkkrJs5zNUYwsRc6H6+XbcWReHuZv8vf/oXd98fDr+7vavTIlLa1n76y19N2vm8/vzp191u/+2796W7X5pU0c3mebff7/eH/WXdem/H46v0ScWP6/m8bU8vL7wryzIT0bpttdZ3b97Oc7XeXQ3E1m2ap3m31GO9u73bH26mWgGx7rXWUqqwiHCpRbu6O7FEV26OrbXT+dLNpc5undjD7yoi6hWE9gFYkFiqJhP3GKR9POCItBnCB9QSJHsYRhqQpzCSfs7Fxiuja9bJejMCI5l3VzJxImUW4RKvORbC4Yu0iAA26/E7c3nBldJ1H7YE+cUYsbeLlFXASEX1MA/4TRPNA+xJT52oRVUdHktjk2l2cmhoYd08tyKFIoOcCB3mbolopaAK3ZIYS+11ZkCjgdZGQKMxgX/tiv03wxAjiST9SNdB2CBIUr87BhlCnTke/CgzkwG+fgp3EH7D9o7/itKwC603FrZmoT7CqNERw5k85K1IlYUmphVtj1/VsuxEoKKaU0hSOHy84LELJ4apvfceUmRzsoH/mDuYpUippZQSdFFaD4+XHs58QOLpV0IVKc30tKC8+thJaP8pJxciL49ulolZxClCD7XurXWzHl2bSD7KqP1hnhfF4BJuN4HNxfE160YozHBiyq7QGEKjMYx2mwZ/m0UZmSV5y7854+NskwZFLMwmgjGW6OSxHSnqd8Q+jVRUqOfgeMhROcgNdXZ2FqGEbGPiL9AkXNvP+Lfse93URudjxixEzFRKCQgs5xeu3FV2HyknyzphNONX5JGDC7jWEdcL2tW3ZhMLCJqyUkSGJwqNaNRvNKxayNRN29Y2qbVrZwOb1zo9vnn78PiGmOKUCJN1tNZa29b1or2bWQpaiaa5ms9R9JxPl8t62db24i+n84lJACrzdHvY/WT29u3b48vrp18+ns/nj/7rbl4O8+5mWeC29u2w29/cHnprp8v5dDxuraFj4f350j6fXv7yy0/OboQyT04gZpHp+999+/jwWEvdHXYdtrWVyEuZHu93b94+7pabWudaicAs0rbudp4WY5H9TrR3Vzd1gk/zfLCb73/3g1Ri0vNTg64+GgA159hsZVFABHeuzEQsI1/nGE06GGSayDLePZ1i3QJazgqLx8hsVP+/PQpIlUYeAQ/TRyKHZl0PDrvQL4YHaR6VknflVNvkyUbmfvcRosHJagRwEuefjJ2Raie/fpxMZYRx9/KjWndnA0zZJELcCJrp7X49wplHTGPwEMbEkpZvMc3vQzXkzOQaqBNneRchnZN8HJk4pDhX1T4CnEUKWZJ0sczDCa07EAsWiVNyff2SGNf5Sw2WzyiKqd9gaFmLumknkGt3YqBwlK8eVHwsbaEEESJeUIin8sMPx+kA7UFExSNTmXu4b0Z94ECP4XzrgXfD1dDV1DQQsSj+MyolooXBr1hg12PhpxOFcQiGzjfsMK80BbLDZXaNpiGiRshsWBjxu6LCba231i6X1dGZuJQ05RCET5kZ3DWDpjiHAZJrSh/hHpA93HmscCOiEFplbQ11QEiyiaMvr8cMw8Mm438WB5ZoXCcSZi6jLcpX4tlMAECC9WTqoLRZTvNqaBjuODsQ+9ORvXO8VASDw37NARSnjbKCgedkIIgH3YPg3hIrHMX+8Ngd52zQ4/ngk1KzGKmMnV48sBxkgk4G3fPcRwxwGWwDRuHKHc3hBm+9Uy1GuFxWntu0TFJKPEnrHcxmzkzzVAm7tq1NG7gwedcWAqJt08t5PZ6OAHa75Xy+MMvlfH49Hs1svawfPnyYd7tpWQ43N5fzWeE/f/y4n2Z7824v1atsUGVvbk7aSC+6LfMOVbquP3345a+//ORM87zUeSGU5bC/2R/evX+zLLuHhzdO9nx8fj2+blvbNn377l1bm9urw5fdvlYmggizsHCWhF2bSMTgmC9gh6xrfz2et9OlilHKOWmEeMrcniA53I0hiU6EvZqNtVYaV4myvosGFg5KNWmUX1fy9JrvR6hN7CA6xYx3iIWLRspwIyj06okMdyMGdNQkie3l5MeI2vENONifXG1oPaofjqVMcURjIJFhCBwIV24gfjhhdDrkpp0IgFCXbIaG6DJrbw9UHZ7CCY1CRGTYrYI8jfUi7YZBF1/v5vj3nPb6TUqK2v6qi8q/aepBzGYgpLxV4UiWfj/BvMYU01B/ji83ouJvsrIH2TGgW7hTDJOF+j78w2kA1gTESRnqVI9F8wRmwFhIlENIG4hD+BYQHCVVpn71aMsXrBrCdLt+8QE+E5ggjMDCMJS/5mP4wfgaMpN5JFCsKAEsxgx6hjLEqh6PTQDxg5GD5BH8mXKJJifNpRZ6lK49qCa7+oeIM9ydTN3dKNQIqgbmQYFm/HVlE0/bhFFrQC1GzS3ciynai9RcB1QWhg8UoxSwBLWRNIq6uSnUmNlFROKwMeXu1vDUjWNjZrBAjMAkzAa33MJBPEqP38BNfC2qKaYaECnbnTxyPxOBmXJVJEKDxWOjBTCCNX0pPkAxkptAPgTp3OjkauHpyIlQEadnSBR1Tkgw074UKXlm4fgSL+KwC1MngLir7m8OzFPdzTc394fDbSkFXKL8iBG6wsJcYoy999AGa99627bee+8dsLv7W5FSaj396S+1SmsCpq44r+fW2v39/dvHx6XUz58+EdPxcvzTX//Sent3+7DM07a19ddWmGupzVQLMMmHz58/HT//+OHnrfdpnr/+5qtSJwPqND08vKl1mebd4fYwz9XJz+fzy/HlvK67/e74epqm3eMbV7P94Wa3q7XWOpWwb25tI+UiEBESMqNpnt+8e6u2tsvx1/VEtrpG+WrEZKY08JmMzyMsDRwIDpCAQszlZBkGR9QgIuF8DUlUJruaIoiMcJRQRLSDI9tkNZNh1xyS9X2O/fFQykfGiDAu5j2KcgzMkCjOI/UoP5kL1+QQRgYgipEro/C3TQX89WcA4Wceok4bKQahsQjP9PSehSv8SgM43Lqaupkps7gIaqbf7DU4tmuQucnAmUe4CuIUsXqehjnENRPgNyEXpnm3hfDFJjI5jlTmJ4RNZte9SfGeaGTu6MbH2xigbeQTJjJGMquEuNU05scTGQik2CiwVpSI/+QEYWZQiMcGMmsh7irZcfjgu4PGC9EPdQ9+lbKGiGYNw/DPkVsLY8AY5OYWNX9+z1xX5hxhyxNzYGMmFpIYu8uEG4+CQS7kAeITEZMQl6ttXpqZRx2e4GHaDIDATjrSfDa6ZuH0P5453FMbmm/6etzc3VLAFUw+vvykLI98XMChBebwuhkdY2yyAZnFCEXodYw8NiYATmOnLxwWzllMQkxFBISwdxKJ4S9HTO2aD1cFIiIJUwKoO//2wJmDC/sGNQsJE8whow9Lc9q8tRjXII4DOTknTJfzh9ESMMHZu1HEC2KwRRLW3nX0TFfomsYQBUChqSUyIQZQpKh79yhPaWt6d7f/7ocf3r1/X8sUQ8Wq2lsz1Ys27b1vLUbl2J1BMYfXW2tt673v9wcRKVOtU5n6bLNJqcuyXE6nZbc8vn24vb05nU7n8/r5118v9vpyPumHn7at3d3e3uz2l3Zepnnrvul2Xteny/mnD788X16eT6ebh7v7x4e7u3swX9ql9S6lTssyL9Nl3eo8RWjbzbvDzc3hcNCOm8PNzc0eEDiKCCcmSTIMR5kt/PIAIuJa65u3b08vX/v2enn9vK0XD3GBB9JulNMzeWojlDCzZN4ezodk5kSWcQQDOo5SNTqwXGYXpk4JBvIgr0b/Hq2cXavQDBRAFFUj6MbfvLZ8V+shptxOlUX4kPzQuCwZqQhuWWziS21//et6RaMdIooHxlG1+agnfUT6iCcx1I9+7Z6inXTTrmZddbgTwIrxVcc/gnlmxLxunIkWkZmciZlithPklDx31sWhBBnAmSWuEo8i8seI8nAeUABGp4WomkdCyfqaRg2VIgwWJoIQKBZEw6Ozj2F7M2MCx1q5wArMiDuZFOZMIqHMjtGcLGYDNOTCMqz4RrkQFaJdswJdw53DPZ2WzNxZSV1JQ1wSm03D48E9jNrSSeCa7qJC8TyVXwzcxvsQ4QSj4lnm6B9L7PGNkj6jlIdBEYQ5tFkAQg8Tj9vdLdytGaBAQD0+P5hIPHbe8ZirG7VPUFbRnkbZOxpdzgkBXAuy3+bqPE8eGl7myE2jO/ZocuPdc/gzk6ihFGYGMUlc71L8tzJjDygoMDsnd871uwzJm8bEDonRHXYaFrMEjiVPv+n3495GmZ0fF3B1eJj8YECIhljfHGjw0EBnXURwqFrrvZtIjrCQw51y2RG5EQsT0himxMVVZi6lGvh0aa+n43G1b7//VzJNqk6CuGi9NXPd1q33DWqlSC2FzGHaFWa2btvpfFov67aut3d3xHRzd+Ow3jaHwXWq9f7mlpwONze7/b7878v//D/9f8D++vnp8+vLuq2758/3tze99cPuME319Xg8nk+t9Uu7QHje73Y3+9v7Owftd7vldt+1k7A53Ol0utTCrTeQ7/f7h4f725tborIsexGJZ3NZVzPs9kud5gBLI4QxZQi3jS/n8/l00W4ixQyxVIkZ6Ab2AHXj0ka0iDctcl2FEu2kmzO78HCGR3pFqCrMUeS3OAnHNMCQsiBeN9xjZVOccx8ZAT5C9vV+0miVA1YC3DWNXTG08zwUMr85dtfa0WPBseeA4TX+EsIcfJgdwX+DD5NQ6MBhYY8RYTjopiHay1h+Bf8pn4derR8S7XLmeNJfFlh56BUtos3ov4k5TVbAzKQ0xnyIc8ujIe2iEcuLTc0D0pUvOtxo2j0qYwrd5xB9DiP5kUsGSPwlTeTzYYIlVEA65pwC0MdoqILgz4fpA4nKmjs+BV2RnojkIBTAWcTNaCCKAQi6DwRqxNuQBqqa9t5an6ZKTGrGqk4MiaAeX5nhmmZN8UZ5dH4spkrg0HSP75tImZTCRmY9uBpyxHceAJBZdsGBClFPR4OsZEPwK8wcoI3GfI2nb34cTiY2jgwRO9tGUYwxIuNX7pdTA5p2b/1/vbs13qyZk+QrIo4hbjNY4UqEsSQvTe38NxLj38KyMeFZBI1QpLiNwWbkTRx9tUfhHntXKGofkIcRruVhNYeAmCX1QlEdwqPbhfs4QNcklg8obpSH8DfRMf6NVCm6DXega9fe4HNWQpnjsusTJmKnQS8b4OZq1hVEQiim2O9vwBMx901FakiOc3jHHW5TLUrdTEm9te319fVyPm3bRm7BO29tMzXtSsBuv7tcLqfX08tlm8r0zbdf//rpUwzb7G9v6zLbM5Xd3FRX0/X8cmxnM9+vx9b78XgOSuz27pZF7h/u9vsbEp7maXfYH24Ox+O5lKJdiZiF6jzVrRYRVH56en5482aayrqeS7lVtdYvhAKm1trWtNY6zUspLCye7i5WhIvINM3TtJh5qnhDZDYwXZZYH0cZGcYUVQnRXcQGc7iJsLuYp084GZkbUWMIuDiMCEzibjF9nY6ARGZKw14AAF2vP5hiiHYMDI9Y5PF5omAydWI2MyIZSJAPBMV5UBggyqCT2C6lg6YrPPgeGVEi5aVR+mSbGgBTxnpP5iFdja7/i98WcYS46MTDdc3dzEhYonqWXJBnMbIVYEZ0dZnuIh4xI0jXVFpHk/slE7IwEFY0YeVi5MRgIxfB9dsHEhZQAdLmMpjEaNFYtVMIbuPZwQGo5vJl4ehynOPmI1skzz1OmahDUgX4sI4Kr6j4dE4c+mwK58iRNL1EwewSshYj70zCYIh4OBPESlDi1j2KmDAbUbNi3gFvvQgzFSbncPljIpcAYNzAIgxyg8KFmaWa9uDLiWJUVswpjkFhdhHXzqWAIQQhZ7aR6xxkRM6MUos3MsRuAREutRZJlWP4T5ibRRMWw2WBn6h3D3QqJLmZRKOTRYhMEUiF5NmKwoLgsXUouqq8BuhEEqAfKQlz8ANDCEtmzgaJ+Q3ADRAeeX0w+0zmvRRZptkc29Y81QVwoiEOVJESuSK0t7+lNUTYmXrwHcIgXC7rskyMAJcYmYYcAyPwMbMDwHJyOMsDZH8ONXOTa8vquYjE3GLiwKYi6epl7k4cXGLgs6Bwik/kS3gq0ox651Knr7757uHtVw+P78wwLQyibW2AE9xMl91MhNPLej6fTft6vrw8v6quDgORFKGN7u5ud7sl3oo7dvudqX769fM8ze/evfv10+feO5dyfH6RWnaH/Xq5fPXd19Z1PV9CvbpqP15ONEmsGeS57vf7m/s7Fpl3y7au7jZL/eP/7o/3bx5t01nKr58+vL4+//LzT1vbHt8+uuFyPhee5nlZ9lNvdj5vl/P57vGeiMIyV0qdpnm/jwl/J2IIgalM0+H28PD40LfXywrtRmrEbK6UJTkiowpzkRJQKP8GndCA62CJ4CI6WPvSzrqJBIyC2AWI0eOGMsDJwYMIG1regCqj0Ai9RvjMRHp2yz7Zybr1WE8pIiXq4uwg3QYOFb8+wRmCw4VorPnonMYJFgvOAVLrTm4c9oqjVkzhhQ7VpnlsxSHOFeFR7DKHVgQwUgi5EZUinmxgE46FSMZRxqeZdrRFzFdozA2JPRRVBUHhIQscrQUyaeXnZzdXhZtW4loLEbLdC8qO4SD1NkwOBnkHgSNs0hF9EtGQ2Cb07FA4CiNYVh0kXsn9trEfwrspUwIPpYhZBxdCRDywgNVZjB1FSY0cLkwlGAmK6H/t5sNzIs2lwcIAF/EYyDTz3rvAu3JB+pWZGhcpIixjM4qjaxMRZsnOD3BAuCSQkPMh7A5I8l0OLyI0Lb13gxGDBSk/49SOMlNokFQdAMcZZ2YWCT4/sjEFC47RQlBAdMISaHr2oNkBZA8QPJYljumFM39GSTTOcpS7MSNpRAH6UoBSKY5JQ6XEB6OmHwXG6NrckYNg3WFFihcyqGmkr9CUUaxkcZiqxS+KJGthSmjpPeghkdIeQilzd++8X1hmYnKNFs9GvzlaVBBAIv+rnQ0ReWLqIfiRUWRlLozf1a3XaDY4WqFsaAqHUzqrGcyaKgylTq27ORu41smJ7+4f37x5W+cZgHUrRQB3LQwXITelXDAFg5EYEXmPt09xiNydmedltosLyzzP3/3wbVvNzPb7XTPjwp8+Pb0ej2WapmU/73fLPP/5T39BkXlX0VEOy253+PTrr3Webx/utNva2s0yO+j2/o7Al/Xy5vFuXvZaVE/rJPXU1jpVqdJbn+aZiEphdT2+HlnKy+tLwDN1mohJSt22lYh6b3DMy0zsvWvbtOv2888//eVf/nJ5fVZtk4QuxkutAKkqDWQ3GNMB/gyEM4AdplgzmBBdQPmBgkQkT82AZceaNb7T6DYCTGQeKEGM1RONP8xXCCGmjAAnugItFABS/roUyKQbQfa4mYy+CO7DIzJ6aGTzHdCxYXy1wTLEUi7ynKCyL+LsFGQEszLK8vgkDnYuwqDCLM6dQiNPCKbtS2EOv+IssdEiIXxmSfVHhB0E70YjV8QzFGKDkzGlrW6KGyMXSxEmRqo5ow9jTfCdaPQR8VgGSkZXruR6PRkgIQk8yh0SvUI0TgigxOHkGoGMBOEPEw/FI3wpnCIEkUv8OzmoBJ7iYyZJcivQNYPnURESFOdm5uGE1tWUidhrnWqcw4zKIpTNkhepV7QuviOTSBE3V9U8AO4cDwqB0nCQfUTF0hWOyCn4FEtPYhbmUgI8NR5IZfRToaaUWIVjCflfQUxcoU3Kd5UAeABeTExs3YcnSlaz5JTOx+NqURIYQS4kByYD+0sVLMGz5xoIpWdSufaYgbsxMcescrHEz8PFOorvhONhpjYYbScgT4QzRZI07WGe2F5eXlX7+TQLv93vdqE+dANLddN050pU0rLDj1dgyD7IrwuifVSInh3M6JMDXY1oj3SQBAdBSTFoIk7GQgrfNCZwubAUKVKm27u7ZVlKEdNQFbNrdxFEInALv+++td66dtvWrWtvfVvXzbptW5vnLlRBrqbH10utVKdJ+3a5rIeb29P5IrUcj68ElCJv3z/udou57Q8HB3b75Wa32x1mgEWYpUxL3S6tW+oIprm6ubm9PL9O0wGE/c3+9n7/9PTp7uH2dDxtbavztCzLuq1b68u8e3z7loXWtbe2lVpswzTNRTgmmLjOJRzFewPj14+ffvnpw+n1KOTCsq6XuZRpKlzYf6N/T7D7ekNCfhVzPUOue8WSo/i4njfKEs4TER74BSM2FHr+5KihCfAvDgLZf4yfbRZu8xRrpWhg9K5x4OM+0YimTikDRtKjV1zb3d1ZCM4l9k5zIumIPIYxVDhEyg6PKMrOuY4FMZU5xBsDqR5xwJldisCJi3uX1PKFDieAmvwy8bmCVmUnYpCMqeAxgnu98Ty+aTA6SO6Lo5x3dtZuEdw4p6IpOm4GWcLEmSR/k4EiC8bzzIaeE8LJbBNBD24GJiZBetI4EEJDhwf9GX5coejzK8icyLMPGUvUxgxH9m055nANwETq6ognRUzwsPoiYRKEi4inC1KYQdRaapVSikSpHdh8LjlkCsfZ0RNFT+FJu0csZk9aFnUq3Ln1RuQsJYvbPCKIcrWUCjZVw1iA6aPrJbAwI+nYaFkdV78hd08tU+D8dq1E8vSk6ZASjVnEwbcwKEao4ynFnREpEisDNRmNoDyypaMr5xOzIoPiRXSIKZeN923ocaYjw2WXHuMpxm6m3UzM1I1d+MvPAUjDAr9vqnq5rMfX18t2mUs57JbHxwdmdh1WM8xXSNczvgwILKuO3/ZHuUOVYtXPIDPtKuRwYpG4OoBRNGQsUoSYxQM5F3dTxbwsmJZ1FQeXWpbdXko1y0MWdYXDwl6jbbq1vl62ddu6doNftu35+XNbmxQhpvVyqaXOOzodz8fX49Yux1N7/tPrPE1RzC3LLHP5t//Z351eL3/+T38Cuzadpnl/s69TORwObx/fqPbnp+eHN/c3t7evr6foIuPnn86Xx4f7t+/esLBZf3193Ur56t1bMxfhd1+9f309ioipf/r0ad227777fpmXm8PNNPXbuxsp1dXnZa61Ro4pVSLuGAXy6HWaak0DtcO+sqG1rXeL6XoaL+MKcMd7SHlGVPcIiI5pBHG6yn5GOckjuOc7pSztgC8V8AihIXzMP06M2KzjlmKI+AHMk5M6FOaQK9oR+INzIc8RrRT+RQ5wG8kgfy0jVZKZY3zIUinVrLEdIRdhBlTD4gBMw9ggt1BgECfjTBrCPC4SYvSoSBKt9wZAvoj00yFmPKdcEpkQev7lcJAQAdmJjchg7pxOjBGprrZBMPgYvAYRUZzzWPiB3/Q/mYXyX/jypsdfKTVJlE+GeDZGGQJ/UPcixZEEzuCHPYwWoowLMVN0iqWwqcNRkMcE4a/EDGZQcDguRCSlgMgI3YwIIhEVHdn9kTDXWupUpEopIlG/q5pZOLVGqxYgjRSREFQgmdERbQJEVhAxCwoZVDW+LVw99oDE+AJRGNywsJj1cNFUZWM2CjhXguJRiw+QCZAj/0fAi7JaiK5IOg3xHnJOBNkzRL6ID5tazARKmJgKDCRUWFyShAqwyBRMFJYKMXcWg11u7mMeGABgQZhm7R2byISd8gUyEKsItHVz62oiZiQUjnopVQ4KNf0nHB6Cmt5jh17I05gcwpn19SpJjqxO7HABqzGbGzkPeMlCJcjpe04cOwosXAaSq4qNTTl8RsnAEYTEHDKJNd/URWlr/bC/ffPmq8PhtpTau5EQHK137c1iiaZ57x2gOi8yifa5lHJZzwRaloUqbVukukupdVu3l9eXbV1fX14//fpUa729v6u1ni/r8dPT47uH0FjVWlB83s2n41Gk1FJqlV9++en4ei613N/f11o//fpZtU/zVLh01eWwUJHn1+PptDKx3N2Q8OHmcDy+Xo6Xm8Nhd9gfj+fedV7mu7vbWuvtzW0zXZbFzWUuU60wm3dznaQU6U1b3+B9mabb2/vf/6vff5rx/OmXbT2RdSKvtaSuVsPKPksH64aS3sIYng0pAwByIV3MMo5K0qP+IIIkNZNtfaopffzHLwE/QYeEfiS6WNjVlRPEJIWBkJgTdSWQsHAVJnYylmhSxl9RDovo1XdwAPaegy3Rao9yJiJ/hAd3eJzSNB4bsWKUszQkeYhfGlUvmNiDvWMhJq6cTc8Y/o0yKOh3jm0nSGh3FEMgynE2ZOJh96sINCJAIGkWSd3hNPw6889kXhoBk8UcpZTW2ljYFe3KQO0o4IArT3tN+9nDMQ9lb+aghOljQiDXQQ+hUUBqnsXrEARLEhcg4sIwL6UInCAWZA4JixCDhSi5SmGAiSTImXzaHr0CAvMv8T/RSQvHEEAUJGHOyuwR0hDiXnCIVWzMj1OOLFF8cLjDjMxImAg9asIxbzFkstke5uy3uZqJsZm5ZjebIFaQtuOPI1/IOP1JIwEwsDAxCSkZgXpvJOnCMoydEzWKuyEx6aON9CrJirSRymsaJZXDfIB9DgN5d5XfNBmjKssjOPq0BGE7EaIlDkLY1FKDwDbg0qhea5V5N+91X6ciwLybKB9b4au6XAOyStkIru2qwx3MzkMFHtwxAcIcccRdicLnPVjgkLs6EQkXJq6llHkiFnPXDrBLEZbSe3ejeXe4vb/9/b/619//4Y+7m33vBmYzmGvu8us9gWMQsUzzDFTzJoXOl2N/aKXwdmlt7dFXiXDrbT2tp+OpFLm7v3l5Oj4/vRwOh8vl9A//4R/s/+vTrjJh2c1VJhF6fLgnl/3NwuB5mviWHXw5rcRUaiHy3rXO5fb2sNvtSilm/S8//fX4fPr9D787H193+3maJ2LaeuN1q7V+/d03Zn4+X86Xk8jMbq11gLZ2Fq4ALeyq6qZ1mp2KK9SNmJ8+Ph1fjqba1o3cpsIEZnb11JRcsZuYt1DVK1CQtfO1n+cQmlH63+ZAqCduNEro7B4cV5nlFbXhq4Y7q2qEh4Gxk8FUe7z6rGqZeIyCk+S2VhJINoNRp1tY6bCbX+0Pv0CwY/74inxcqQsCaFx4AhPUROA50gSmXIqLDKFBpCWAcf1Ro8eIBMAx3+pePP2liTnZr0DAx0/z8SC+SFE9bTVCxFVEBEDww0PogAEpZysmzJT6rmy+4msL8zVxJXrlAFAiggPsw4gsfxpfk8nVUDU6AzCJszmIdLi0uEGiKjN1io09IyGNwBLPhdStCDMzSSlhHiBFRIJfY9ewxyBHYOBMa/cY0WISYWGphUUosSGGMAmBRWKkIZwykW7k7ICBhgkZpwdP+OcQ14nBzkTkSmZu3ZEQEnlU9hTVkF/XnQeCGG1hBvUrFpRDz0SDy8aXo5cVxMD4QV9Q+msLPYA/ilMWHXmswMgMz0REA2ECE22mMUGGPOsx4uBcKEedwewIay0K6JlieBmW8/oBrjhGEx0liBCDTAKfDHFsYvPRLTsAIZHqk8iBC0slM4bv9wcWEakMEEVPas4pfjN4LDHnMRpHSRWBhDx6k4g6REziRKZKoLA53dZtLtW58FQYIEHw8zDa1tXcujoVWpaiXad5fvP+3b/+t//Z4fDmzVfvu3Nv2p2mpVrv7hyWGBYQt3lX7drXywpSkJlqrXWeZrixaJ2qtS7C3t1aJ3cp7LDWVKpw5fWy9qa7w+SEtrWlLlOdDoe9UCGWrq2IiMi333yr6ptu62UztWmqL+vl8+cnPNyXqby+HoULE70ej+badP383J9f6e72jpjM/HS63N7dYetlmrgIQLXIbjeD+Xw592Zb26Zp6s2mysxiZoUFTKZ9rnXezU9ul/MldD7em5NmMRBgIGVsHhsq8y6lDCbiuzsbAZKab9Y81c4OqKcxHyfiHJwSfelkMQIKE8WegFQsJ5ACBEbnsSrIiYhNUvKdQ1XxG5gdTMLCFD5lGp9IVSmMkHwApGkYHJhtVtNZnMcMrfOXIZu4Sx49TTShfqUnMr84Ytku5QB6ZENyOIvAqTD7uM3btkWAveIPMeqM2PGVWjjiURo6gYVNDf+roJC1dgzhUUhNhAEIi0gRKcxsBlUNQAsa8tq4tQn5RFUfWvxECJHwSpohJ4cY2XGU4Ej4hJnJyNhjGyYoB1gjglxV5PFpNXvHL2hhKeG9kFkyeq1gwKEt8iKpO1NoSDys1oio1jpNtZZSJKX3USOGm7sRSCm5haxDsqHxkBuR57AKM3OZ5gKCm4JgsG5dtbubgkXylMAD1w8nCf+CjQZPjUTwHVBL+3smCiJ1PJi0yY16gZGcsQ2Br+fDRZHStTPFRN+VIcjRcBEOM24RUddQj7k7E+UkdJZROajF4EREHOwopRSRwrUwS3C/CFEbDMQMiqG+QCRZDEa1BHjHwlwkGvA8JWO4G6AiBQVFSi1iqmyoUuA2ZlCYyZlZFc7qSmTGQ7tNJESs3Cg2FhFS+RfsBzGX0lXZWIRbM3fX1nvvWWKyhN9i23Tb9OX1pfVepulwcyAqQlTm3Vdff/fNNz8sy50yoUPV5t1ehLsxMRnC8swB0t5VjUCliiN3ZIiUm5sbsx5a5FPvvekJ567dCa21p5cXM7usl7v7+93N4ePnX99/9dW0qz//9UPf9HK6OPz25qaW+vL0sq6Xr7969/j28fR6nvYz43hezyXUz27TVJdlOR9P4SBorf/d3/3d737//YefP3z+/Hw+rQ9v7gF8/vT89u1X8zKr2nZpr69HgA9TsW7abNnv3L2Uota76rZt+91BY8GRUi31cLhZ9jvGXbuc+nrOGtwsHIM5pQDEwiwCwFxdqYgwBRyU6DBw7XEpDy2lKCsunxkMYYJ9jfhD207urlnNjMoJoW0MiR9xVx2Rzw2ItXmFGcQi6YfMTEbOTCwyrqIqHAQWJs2DGtd2THz5iOgD4g6YNUU7dP2oWcKmT4xHYsv22vxak6VRIw1ghQBI+mTSFUgJAUwCX1H3+BW3ofTEZkp6LKlEDF3fYCziahtZNh8EFhJnR84iRVA0qF311q5OA5Pw1JMEsuS4zsMSMUvuVLOBNSQ2lzAVZefAJJxL7ImcQpUUAC0cOZcxfguuRyQBb2KhErRG0LlhqArAzIVFhDS8lc27+eXStr5t22puhbmIFOGp1qmWUpiitjQNr6sAHo3JmNKnZjx407AYc1OL+C0h7ROBiFoMW8UKIbh1C/vvMLZxAFRYuppbd9cgsDNCCY/0PAxrEqgLYHrgUoAEoZPQJ/KsjTkQFoaToMBz3wDnxDZhgHBMJLU6QCUUe2AXi4lzsjBSv4J1geBHFhGSUisXGkPglOU4EQiaeS7jf3YaInAS1SBXpVZhqpIloV2NndjZnNzVbDcX7VQ4tsP13lZhZq6pqnYKv7ZsJ7O0cGYqtVq49qqZO5FiIPsiJFzg5l5M67qeu5paWfskVWqtRWpf2/Pp9fPz56fPT9p9v9+/efu2cF2Ww2G+/eG7H+7vHs5Ne3epdS5z79t29iJgpg4t5E6TaiyRpDJVMpiycwd71douW+8217neVG1+uVyOl1UdN3d3z8+vp9N52c3rufWD1mV6Ob427T/9409mtiy7yefL527dv/r6q1LqeT29ff9ut1t+/vlDrXy6nHpvHz/8SkwiZWvtq6+/Xi8rvMPs9u79fj+fz+d5md+9e3tze7fslrb1aTnc3d+bYVuPb96+lVLcvW1Nu5mZqvdutbRpnqRw633dNiKaKvdYrsF0e/e4Mp61w7rAtZuZxu4rY4aAAmEvEiNNQcWHFRNRFAxhThVI9PDRZTJzV73ike7uMdtIcHYBOZxDyeXhXEdwUqhrz5gV7B1zKdTQPQgluLqLlKiLUyrAbCGij8/modIxYjCV2CBkxvAo+4gEQgVhQZKfPGB3RfgPjgCdJas7BRXFxsxmFruGuingAqm1JCwhJY50YqUwkSn1diREqqZFZCyqjCVLw2oCbuaSiiG4K7OISBh6R4Ue1j7hzhKrzMw0jPkoqYKEGcxNhu9LQl6xhY2ImAMSvuIRFDu8kogVgrg358SlzBzsAhk7nGJ+DQ73mAInEimqyiH6dCKwBlqTdDG6d0orrysDRIUEPiyUFZAEhGMYimI9g6opaNvW3lrvGxxFym5Z9vt53s21FCaHJ1IcvaGa/QZ0cc81pwaQ9jBF6hhVOwAWLoXNHcxwmHDvCY6FHvE3Z2E0XxjWIOH3QEOCmzQsMyU4HrwHMRWWXMaGAX6OTBzVfbymlN8lWHatmK59AIfCl4jMNIp8M4f1mGeJLB24XciFSsnxFAJJjksg8ER391DSXT8HwEmjATCiksgpQShri1Iq5xkLhy52osKwcKILOwmzrHs4PKJcjYmEKba2Rl0RSTkVWqUUVatlIu+ABlg8xamkNLhiXpiob83UzHS9lN3+NrTT3fS8XZ5fnp+enp+fT8TEUnpXAO6k3ddL33pXg7qTOirIyXrr5iBbLxftas5wZmInjZumrszkG6z7PE+1CDEJl5fj+Xg8tt62rZU6EfPlcvn89Ol4Oinpbr8nost6+fv/8B93u939/f3jmzf73a719uuvn9b1YvC//Pjj7//4w9bWD788LYeZGM8vz6fT6e7ublvXv9z/5f3X79+9+erm5mBq23rZ2uXt4/tuPs/L8+fnaVpqnS7n9fHhzePjm64ax2qaJp/IjuftvDmjN3VrNzeQSlEKqQa6wvf3d6enz63MLUaa2S3dGghpRhW4gkR4jmo0tIVgghlJ9JYweA8b52TojYjNumo7Ho9mvtst+/0hzl+wsWGREGR+/tIAM0YnTYTUYRNDSKvGvrEWDmidCkjhgS0QGQjiY354gKBRY0UfbKRf0FiM2zYW1UasjP/DyaJRDhUNeoBGc8DMzmzaMX5bAGVZZ4bhvF+TB75U0fabViOCYbS5NMYYRtUcSyjDsNJdhyRoNCxXcGOY9ox2JRjMhPGZ2FkiGBGTQLr134ad/MIUvGt6JqcIGwO4SJx67KGi9NAPSUB0gHFaLCU/kU5pnCUAEGIjp1yAkr+3wOEajq0Wxo7ETl8Ww4Xxj5mjd22t9d6JUGtZ5mme5rnWWphM3XJqL/7FDL2O40LVDcZMrKODMtOQbAakjXH4GOxkzFJLUdVsLS3JIzjA4FjcI8HgMJFIyk8p03ySMpSiaDcwOeUe9iugFoxnfFEKzcq1y7q+oVGMj5RBqRBOXTbF3+3as8FMGNEdFBoEZmEMQx9yRyx5TlkaMi9GyBukDQDP3T5R0kWfKCzBt0t07/EaySn8gca7jnc8FcmPRwTzEHERnCUeEZsa6EpuBL+QLq/sEVIAF2LxMYjBsSEQvl02Lqy9q2vbNhwOJNSbHS/n19Pr6Xy6nC91mnrvCtu6Qhpv5/P5dDmdlYsss3Y1K2q6bZt7+DaqFCmpSCqtWVs3V62FCeiO/X7X23Y5X9bL6r723pZlWWj/66dPwjLN8+3N7fl8fnx4/Hf/7v/QtdVS/uEf/3F/2MHxy8+/PD8916lOpUSMkCJSy+/+8IMTlv3uq/fv//7v/36/2/femfnNm4d//ud/ZqFvv/nqcrms2/btN9/ud/s61X68/PWvP3719Te3N7cvL691mh3oup2Om7kv80J3da4TwDqblNJav6zb6+vp/uHBFO7aTOdJzDYzXM7t+fm1a+eYHqcvxlyO4Mg8UUhXIg81jFOgKEhZ2rDqJnMRhikISgag9/756fN62b797psY1KxcOpqFXzqR5OrNtMJnZqeY6UX01PHBBFy8xBQxt0gxsawXORdPDIK6w8JKIrmMjD/Zz3KsvvHrR8e14hq0dPa+UecNz4aB32d2YhKwG7OxI1bGE0cGEIlba1fn8wS0wuolY9ogFzDwGVyxnVHvfSn4EjT30UiNmBur1jJxWOK9htiCzSMOQ0SYyUyFBAxLo5Vr/Pcv3ylyHuea9GhEzF1iwHlAR1dO02ABQg34Or6XpxVvRNX4EJ7xidJEOr9uGU91JK+IhvE4eJh8M3EonNTgLkVqrbXWIiIskkpHwnjpFGbiSKlYGCKGkxqHoXu6sNFAyoBImebqwTOHhdZ1QsuMjJK0SRsQYS5DLo0wVCNwIN0wSlaHiT3sSkVkYCoDpR+zuJRmOIHP4bqAN8vkcRwzr17ZNE8z0ji6woWY3MVcMZYwcY4MSDeNa2Jma++mRZhCguCxdzLDLMMspr2zUMm04cLMhbgwBE6m134nqvkUFDFBiUDOUiSaJmGJ1ts3s2JOqHUCYuKFolkmjjcNYpCAYvldmAsFS+VRXgmRi5VayzzPKEW4xh1WtXVbT+fzZW2t5SHdup7X7XjZFIXm7dIvzZspVQYEvbfLZVUzKcyuXKnUQtF3usJMKkmdiNDWlYXcoarbth3Pp9eXl18/P03TVEqdp3map1rrvFtub+9qLar9L//y17a2u5v74+vx9XgUKeu6mrns6eX1+XxeH9881lL+u//2v2Pir79+//L8Uoq8fffmeDoS4XJZb29vv3r3fj2tx9Pp7vbGuv341x9VjUh++umX8+nyw+9/v67tcLhVs776stsF9Hs+rbwwg+bdTqQ+bc9XRTkLXWfUidjBKNyaujFxMWsO4ytZluB+6giJ6DoWOfQBZg5hvmKYJIHvKROph1A6dxIsy1LrBLKuDUQihSjWgYZvT5b9QoziUAoxjLDE7wzfxSKiY4vhmNMidzI1pW4MV4IETA0AxGMDSoQhJrL010okJFNA2pFeG5EI/clSjBIto3TAqmAWYqdodCNRhQqRQMZkPVZZZCvtY3TJhhdmNhmRcn5rVxFTAzR+YYIJ8Z89HMeirvS8O1G1ZdcSlnCh9jYy5hKYuMEYLEVMx8sNkZc52CmWYGf8HEGHKDzbCJw3/ZpvPQHpxHMiUwDIabFBajhdiWMfKAqNatbcC+LDMYVnc4Dl8VopJs7ISzKGQRJLqXWqtTALkTigjnBoiB3AliE1akVUIujoubgMAwt2Vk3eI57CEB7YcBsPMC7/Vtb0yMIdRAQpJTxugDFiHFN8TBRGt8CV8wlT4kHi+Ji7y2ie2TNQQAotknukYsrr5dfOOMuI7BzHw2YUEbirj/m2cYgGdwOCq8WS87bMswjBydM+LX4BWAozWMhdMVjB+KhR4xAnG0YY47tEYCMHuYuT59B6FIXxnay37m59E4eLiAzbQ8+iMyqE8Aom4hT76ygIYtQgcjzBuGAqtc4HYq5ldoN2Xbftcrm03t1dqoB52/rry2l/cynzvG7rr58+P7y+Oi9USi2TMDnMXNfTpW0bwWtda1IbiKE2YlxOl963bV0vx5N2VWvH4+uvnz5pt8b8ejz11ve7w7Lsfv+73+13hx9/+vFyuvz68Vep9e3bt+fL6Xg8t60dDvu7+1s4EV8c+Otffnr77vEPf/zDd999q70fDnsWhuH/9l//m8+fPp/O57/927/d1vXp6fmrr79ql623fjgcXl6Opcq81M+fn/aHj/O8W3azdyehm9u9kHx+Pq6nyzJVB7T3rW1qenM4zHVq6+bmdaqBujLLvNvP875MS9vO4eMLEiO4adRBMUYfesvcqRSnN1pzt9HxxoAwhvHseI1cUDBPy1Tn3bIIiYYUPxnSLM0DRE7OyslyObynl0HUz3AhMmZXZSYbDhCxuTxWbMVNY5Oc9KTQHPnY83i9BZREdVZOgiyj/v/qQspfBMcA1qMSp4GvMgtcB2A1bACI2PLuEVHXHlp4gfjV6zxRltGkxMO4jt6O7nw0CvmrHR6OxJG3GMSlqHYniyISILP0bLj+FckpNp+UUnquHPE03Iy6L7MaMryMYMdMbmHY54leUDYf+R5pBMnxW4nIAkofcS1jZu50SeQgkmNJ8zJHUKkh4fJBMw9MwSKXEoNFKksNG/ZahTioImEuLNfeJAz3BGl1ENPmadsDMkcpKVa9VtNhyCcQJQiHWR7CXUOEzZwDAkUmSC6cq1aiTxUpbGzOTKkaApGAnEUofEWzR0lRwehK6bofJ10bxk4lpIMK+FpEEDO55vrLL8QAfEw1EhMroYx1HGm8Bji0dXfvvfXemWWaqrohXUeAMTxMTLVWdyIJJ04dTRWxQCQcweJmmsdIiV97VRtq2RzwHUyDuXvvvXsHe61TmulkZeRwiaTIoyJyZnOCaY7BJYtOYGIhEZmmaSoTkxQupORwb+4d7lA3NQM7ia997a6dTNfzx08f3j59npa7aZ5LKVKrE47H4+fPH+G2TGWap5a4Gfe2dbWu/fR6KoWPx9fnp2cROr2ef/rxp+P5/Mff/0Gm+vT0tFGblqm1drn09++/br2D6P7hYX84fPz48d/823/LXLdtvbm9KcJR6Ox3++++/+7+9o6Evv3m27/+5S//5m//zR9//8d//Id/aL3td4dgaj48v9ze3rx79/ZyWd3t9u72/v7R3VW9dz3c3Bz2h9v7m+20SSmFy2Vbe2vzNNVSzbxb760xqNQcTg+ZbI56GYixHPb3b++J++Wls3TyMPwCxRhAxP4YQIrwmT5uWfFFSUvDFoSQHp/IYUZj4t1uJ0VKrQYlolwtGRHoiwMppYjec9lcoE1ZLyGcMxOp5AGZEGUXPf6EWVABKUIcRecVOwm8KmMdATl4Ew13YESxiolz4DWk/BJWWhksEpDJbjx75ayrh4nGGDkjwNVi97hH7IcjZ4kNFBahA8tNEdEXWJ6u80YRTQ2WtGqGXmZxUwWEmUXCRHcEEYMGpwmLDp5FRKapmKupqsbnoxTyuXs4Orh7C1UOgcXNABs+oHCHQrMCZZdSaGxzGch3vDNKGGdUzYHfuGfKjs9f8g9EZ8ChfhrAMa54CZdSApMBnJhLneZ5nmuV4CGvtpKE0T2RgzxnlcEiGJ4LWQ4HkUvX8Bh+rSGYZO2eY4xCbkxqEhnNA6RgcY7qXUqRUmIyWOCwDmg44DJHAR0WaAEGjV2JeQ6RGU5KVLpmFt8ic+lgm/3ahLr/5vExxVIwIVKQEBVQIisxmuCal4CsZ+2xba1rL2xqalSube9oph1OXKhYeEDBjc1i9xmSDMzlCCCwc9YSARdG1U+mZJmcOJXkVIqYqWpT7dq2Kkwh4VBL+kdB+SIRCiFmN0h3G+OlRjkQxrUI3IW4yhSqRLjBIEKAt9a6dheZJedEQGimCnX4NE2lFHfdtnVbL5f1VISXZdlN1d16a2u7sEjvTWTSbstuWi/r+XQhonme+9anpUD201Sn3TLPS++qpk9Pz7vd/utvv/78/PnT58+11JeXl5hDnJaJhZ4+P2/b+vjmzeF237b57fu3D3d359O6bpff//73+/3h1Z7M9f7u/rtvv3t5eTH3w+HA4I8fP09TmaZpXbvpen//8P7d+8Pd4Xxc727v5mkpXJl5W7fe2jRNy7ywkJlRZnSs6zpNc6nSepNaY5y+d9+2NtXp7vahryesR710pMlTR0ImIizMhUjILShMxJKGWEHupNGiDSsSfIGB07NtnubAbFVVSjoWWJY7camYQGGAEKGdPF1KaETvKMXIwE5FWB1EEpqCFEEQYkYkmcnQUMKB3B0TsAIPMN/diQTgUmqsKCEQMzRovlGjxT/KY+RsANuUP8tJFXBTg1q/evCE/tyGDj0bI8sN9WF3TgDlfO814mWx7DF+Ffb17tAW/3jqxV1DcZr8eAzOAwxBQKmO/ExppIvrJgaAiJKliAL7ysBH5xYvOPDsUEcpe7qKYnyz4SsOEKkRsdr4vh62z54RhcY3woBPcjrLOEQawlCPZQz5liLym/mYbSU3ZebCEvQ2E01F5jpNtYQDjiUolfx1FLUx2QTPnDYwFh01hUUgQ/hsGxkRxzghpfV3/H86IKH8OZTXSmI3llQpNSIiw3VzwAMzia7Z3XiIij0YkHGyEyaJtVcpqoh0HD8BoOgF/IsmzWGkg7NVodjbzgRjQdy6VGQ6WaxMDKKJHCFbMg8n9QRxAkOKls5yGw2cSxHhGF6nrnDvRAisKPJaknZGTPFpB/XDgBejxK9i6z1xERaHYQvfFhrNr0cnHD2mhU4hyqwARhnFoNYdVLh6rliI4+7atEK5ihDUvDAXqbGGpvWNWGS/1FKI2VRZ6rJblt1yc3uz7Oa1bbrpZV1FSt3xbj+79svx3LbWtiYi5r67qSEBOB3PAJZld3d79/bx/Vfvv1Y3U1Xz1u319RMRHu4fzut2Oa/aTUp5fX39y1/+uq7rxw8fL5fLze3N8eXYeuvdpln2+8N6XJ/0+cef/npYlrvbu97b68vpsL/59utvHh4ev/rqa4c/Pjz++3//78/r5b/8L/6L5+enp1+f7u7vXl9fb2/ulml5/vRqbqfjOdgwACKy3+2XZdna+dPnz2/ePKrpy+txf3Mg4QRxtNM8M0jYpjrNbx5+uRy1dxaBMIzdNUojBmIDDA/sMaZJDT0msWkYZkYzKkRgamYhCzWoaWcuYRSfkKYpMwUDbFfjmgi9GS0SlPUvaGdWMWam5hFriKjExBBziNBsXDQ4fKg+KOtKd1A3MAdbOObkY25cSvce1XxgqUbqTIVFPQK2UxqeROGYbBXHPnMmVyO4qjXuLMLM0Y7AECpXzxZkzBCYWSzpyxg5kOhMAFmeRnfCWdNbGM7EJYGE/UIQfRLFfCRsygEy9N7FxURJhMaC+UTwrhAEE3k2BwSo6zU+RcLzkMRxxhWMbsSHJYCTO2mC0p6NintYAvmA2Tz6GzIGFICbdyg7laEFxxcJ6gC1zXr8RBZYGiwD7uLh+mIScmOEYjBRfKHYkKbx+eMhC9C2+BMhAnAOnNBB8K6bO7Ewhywod0IanA2xZDhgr4AVs3lhYpJCpYRbEYUBGFybZShEUCfZoLgZoDSMbdSVScjDr5WzDoocrGrWzaxIjblWUImkEQteQOTsOVBmgLkgRl3YQAiePRJ1TvwBcBFp6MQOzbBslrqvXFdPIDeBxMswAim7RD8iCT0GZeTGzuM4wcFpMuvEKARjAiMEEiESIDimMgmYmeGkvc/LjBizds3+lzmWNjNCzsxh6xdVXiiXtx6OIO7uqqvXwkQwY/Jlrne3u9ZutnYxa1X4Zr/s9jvtDbbsb5aHh4c3bx9jpGDm6fjyutvPzGatMbC2bmpEfLg9kNPaNgDzMsGovC/n8+58vKzr1lqbp/l2t2tb3/r2y88fzfT+/t7Mfv7w608/nw43+/PlvK5rqeXp+fO6XaQwCxls29b9YZmnwx9+//vf/fDDze1u286fPn168/bN7f7gbvv9flkWIj6fz/d3dzc3h9/98Dt3v7u/jV3UIsXU3n315nRcHx8fpqmejkdVu3+4u7u97T28p7rH0rTW6lRAUNP1cqb9IZy6zAxEMSBaS3l8uPvrnxyASOnWkJB3PP9RlY7uPqrYsdYpxk2SPo1jXkRa7/HPRuOujiJM7sxiSHCkmymnEJkSMpSsewINtOFCEOEjRTrO5ACnz44TeeoUGbGgKf65tLpiJm2dWOAo0771rqrdVEDzNE/LPE+lSilV1ot0XbuGp0glZ3NnFM/VqmbITRiqHp4zxHCIGrmbmqmSlKomlh2LdPI+lIjkIGYlVqdu1FQLuLAQJLJnWJwG/gIoO4rUMlXVvvUY1IwR+rTTCFVLcLmS3qKuCoYYyLQ3ayJmTvM810oEaFdH771r76aKWAdLNIo/IlBARxRFNLOqJq0ZsD3MvEcRaWYAawxSBDWCZOtCa0TMLKLaY14Pg+Ezs947C7fWCr78NbqEgTUm8OGIOYfgKUSkVCki15WOAcOFUxFzjkp46AaiM8hJ21giEWPb6N0KQBSAlMenjSeb+Et4g+caRbI4AcLIZWEeazK41mmaHRARuBLUlFjYepdULkaLkcvnOQbdQECY+AWr4OOa+eAkYkjN3VCmgCXJXJ2MLMklhAzXod4JxJgGwMk8GvD8aQATF2FzrtPkDJFCTOpaZMqlrh71hABgYXdycIe6w8Vbb2AyU3WuGKy0e1Lg6U0bU5dSUKMyQgjTOdXihYqJhMLKAdU4uOQO9bDgj+4c5m6miBcqnKJmhicfE04d/bJdSqmT11zfLLRM0/3tAd6XqThF4KWlTsy8W5b7+4ciZVttPW2xa9dgvXVtfZkr3IlpKjWt2InMQIXBONzcqOrT52e3nOrbH25Ol2dVP58v+/2h1PLzL798/PBh2e223sxNRGotl8ul9XbY38AhIvv9rjV9fHj4wx9+v9/t7u/ufvfH322nNk2TsEgphWXb2mX95fnp9fn+blvXt+/e7pbFuk1luru7NxgT11pL6cvunohPp+O2rq/PfNjtiUlcLuvl9fQ6zbVOFYw6l9771tqUd5KBsEgjmBC6mxMLiLoqEW3dDNmWQbuIBNPK2dWDxmwoACdXb0VmciI2N2dQYQGFEtMAT5+JiFmuzjHz43C77sNyVzOCDfNyuy55i4nAII+jhUWQRGSJEzlCvAYRdkqYhdJVn7gSgc29R0UbDCbB1FS7O/fWAqVkjhnYhLGZJEXZATBSzhXUUoUFAVAyihhco+i22KIwdFSBqbiZSIzSiZkXFuMS7doVj8oASO7uTCRSp1JHY8elUtMs40abn39F5gwtoHYLf7Pg59U0om2S4AGIR+M67IvJLOJemPlfYen8DcFsRwLIow8htpiYolyYKMwGUvRoCIOKpXQu0oCPwhEkmEbNj2DmVAKXcgKoECUjbyH2GMTCaBEgLLXUMIAgoozpSTeQEEc+GEhkwGZINnpgIJSYEqm2eEWljDyUHK+7uRrSmMZgpmYWn84Rt4PDVVqqUKx+qOLq5MWEW7fC5FDinJ2rJFvvTghOk5kLiJGwPsfyFXchgJRIY0uROF/jN1KRw0xcwEyceKIbgnZwhaNSCZYg50aMiKgQlxSFsouZl1qnsEaFW3RCnlqpYIXDhhfRzIbCLdsCdwdHkktOQm3Ms8cMARFYWLr1CAkUu+fIWSRsWBIEopjfYYI4ABL1yLnsSBIPgyMEguUDsYBg5r2ruTu5uhIwc2HQbqmMfRU+7PfmAepN5K5NC9WlTq7mTlvb2tZjB14pleAsMs+zMAkXc6j1vm3q7r1dLlst5XK+LPN+WebL5WyG4/F0Oa0kstvvW9v++pefPn78+PjmsZv++C9/eXx4CKCxSt3v9ss8qVqtdb45LPPuP/8//uff//DDm8eHy/H47t2708tx2c9BAE5LESmvTy93t7efP3369eOvf/jjH26/fn88nVjocLv75ZcPhVtrfX/Yl1LWbdvWdbebVNvlci514sLrun769Ol8PN0/3Jn6brfrqqraejPzviiD4dFBqhO4lq+/+vo//oePAlb3HjURyGC1EIYad9hQgTzU7x5NYGHxaIsDMBl6emayUHMyBzwQTrGhzom/lxA0KKVe4TIUi3yiBXTS60JYBDAVQENWasPfYXQsTkAqBjm07TTcfXoXgrkJG+Da9XLsAjBTkRoMuaQWwQ1qJAOC0oRNiCiAXw5I3OGxfdG0q3AluFu3JBuN3AuRxScBCIgFXSXcChBAlwWVF2oVGlNjOXgEYubCTO4JdzsCM85kNm5IQPA9Q3sIL2AwL37FoJCuc341EPMYGaMcLgsn/+QxhsIxn6STASwChDEgj0wZaRnEuakMHrC1hQo9Lm+8BBFPNGlE5QLw+EUhLhHAh5BpvOqkLJiYpYTxPyP3bilg7ha/MIJ4FNRm4e3sdkVBRiKJxHjtOYLvIEAk/YvMY6kcRy2kFEMpzMwGS8ucUkhEpJDEeCzBhaSHcStARaq55kgaQ4p0qLAkL03Zzzr5+MhO5MRBHhAsmGY4WaxOI4rWdogQHEVYDVIKYFRYamUSLkJJWwAMyVE30CSs4sTgRqDYyFaKxDRLM6MC13h5X5R5KMRSpXsE61oqQKWWuNIp2SIyN4spQuZBCIvDtLuTUw76OECFxVNVwBYbG1gCDyYnM1f0lhk3Lee6WuqspYCom4clhXNbey9NS+GuPYYaSqVD2R1u9+umrdlm6F3Rt2me98tBeC6VW+9SaiFwFZi2jeGY5tlc1fx0Pl9O5621ZbcDkaoRlEUOh32t9fn5pbfmhOPpvOzm/X7300/PZn2aqmr//PnT/d39zc1dMKX/9n/7d//0z/90ejlt61pqvbu5e/P4+Pj40Nfu7l999dXWtp///LOZ7aZ5nqevv/5qrsv7r965etMmhff7w3q+iFApZX+zbz/9+PT89Pz89PbtO7j1tq3r+suHD1Od7m7vmKVhe3p+EhICn15OXOVyWes8u6mbqet2WYVLEWmqva0MglGdl2V32M4vp2MjItNg8MQVKFljmfYUnXk26iCQFziMbPTrNFQfYVUbxbaD4eZGcHUKONRpBIzoWa+GzD4AJ1cDE8ecUSKKYREewc+sp/Vo2OszSKIipiFFw0AH3KmE7VXYnKvppsa9FCqlcH6xWJ3riJHmnJBH8hIAO7FnPTJAW6qFmYqGCDPgq6grLQeSADCMYsQ1POXNJMb4IwqbsZSAMsjNyUzdiEw5vfAMbCCLiOcAQnmIQdllKsjp33x+Mv5MTH5FUMco6l0Q1DfAV1g7gwbG1gFzYRIpIQYHXIwRkqbw1wy8gcmdyeA8MClzYgweOICVaA+ImYsUk2C1vVCYb9Eo9OI4xHLjkNLnnF9OWPGoO92tdYd1Iie3lHsCMFUoX8EVXAftfKhNwUKB8cX0AaeTT1K18VRtoBtBvEYCC8KUmEGCcNWUYFAYAAmT5iOB9WANujqTaC5jcYdHUozudxAbkeTcc64wjA2NJB/I4IkQM4ExOROLJoiZ1IQ4BC+BKpLDYMwgkIhk9meFFMhM3KzHrxRCvsBpnrR3CHczdiMqZkTMFtZOZN1WJ2cvTGSqDEpX22TaSmynprytURiMFb5wgQQfD+YYjY7hghQ+AflfEffe1czMhQFh7coUXKQwczcFschE3Mx5ay7SJheODW5uzFRrYa4hCyWSrmRWHu4fb+/up93iq5uhTsIi5qqNSplN1+Px3Hs3s21rYFr2u5v7m771tvVpnk2tNT2fXi+XDfDj8fXx7X2p9fOvn5FSa1zW87t3b5dl9+HjL7/8/JO5vb689LW9Hl/X8+VwuK1T/d0ffri9uZuX5eH+4Wa/3N4eWOTHv/y8nbd/9Td/PB1P99/c16n+9OPPp5fT7/7w/d3d/cvL8265eXl+uX04vHm4//jLh7//+/8w1Wl3OIBomqY///nP0zR9+913UkprCtDDw91X377fLm0qZdteCLy7Obh7raU15UXA2NZtO19EJqkVUqbl8Pr83Ls7epTXBMxTBUnoCMDipOHNDIKDzCxmoYYQcCAfHuqvvEIh+Y6GnEDQzPQWASNDjgc+QCN+JdN4rSg9m+GkBxCwKndtAMGF2D1cE2AjSQV64KmbBKlCQkftqtaFpPVGDPVAY4zIY2kvIYvZmG7Bb5JKOFLHuebw4gQVZjXNfXWBkBHcoaGgZy4CYiP2ylAYTNNgwCxjJAREaq0WCXGt9s4hpnUnRzTx3T3r6eydBmMLpOdXrjkhH3l6oDHI9JWqHxgh4I1RldPoBOLhRuMX/JunaGhIla7RmhMMIWeJain3jShlLe4EmJmaJs0AipF/WOslt8GlGX+ItGAUDnMYmCMhRu9IgG6mQWTAjMlC/B+GboGGcWL/oUYIHMNCLxz0FhN5+NGHmDcUmwS4UjrYETFH3FSnHp/DolCQAKyYK0nhUqXMRBkN3RlUpSx9PTMYoDBBqHNVM9MOgoXANoaXUyEx+HL3GLxgcYfxF38R4QFuJQWdIgEmGHntFkLdyVGQyuIc3WIuCJpEHaquZtSb9/CJWlsTgAnhpK5qbkS1Ehczd7CUamralHiSUtRQ5hlwNxgZEcU6o5CVu4W5FQFkBhB7Uh1J6hBLNDWBC6lFcegEdqMYvXMXWGhAkHYxROaYhONwERGLEFdVnF6P67oddgszu3VYn+YCksKOItZMyrS/2d/dv/v22x+m+UBUWZzFY7kxABQ379rQu7beYLTsllomZlFt4Z7Xu7obCypNj+8eTsfjp18/n08Xc7u7uX18vFe7Yfnm+eXFDL9+/vjh48fnl5fn1+cPP/9SpLx7+/bTp0+1FJi/fff+sN+9++o9zNvazO33f/zh5mY/1enh4YGJVdU3d9fT8dSb9m1rbXPTw81OWy9c3j2++cd//Meb/e2//tu/XZZlv9u5o23t5el5mRd32h32dar7/d7tzCyPj4+7m72UaVvbtm3EvOOZRUBkBDfiIvePb/7lz39CLcoCx7auu3kmOKi4hTYapj1FZHCANYpdtVpBFCZ9lhVk6ujcwU5qYJYS0HXwWxZajDGeDNCVbQuMMcN9FkapB3RLEBlRO7kj/CrdUUzAuaAr6CJ3F2QGiXAcxS9TKoHGPaIsDC0/W3JwSUVfxZ8jnYXHTI4vDobaCLGmMPMGMY/WIYBpR8w7UYzd+qadR82dwm4GEVt0SiPOBnDmbiIML3BFCT0N52AOM3RMgTnUkQw9kbuXnPeGmYn4yARjvU5EOiTpmU2QQzMFEiXlGSrL684PSWmLqY/mI5r/jOA2VEHRSJoG0OQIF4UE0+IfLO7GmWJHkRubs+JoUKJQ5jFrJcTae28bbczMYDIqUgQcnDyTBsiYPw+BZTGoCDdVj4YibcXCZCNIJ5BD4ORRkmamUjOnIpUBNuJShKUQOaSI1Gl3kGmp00xEpr1vW/O2KRylWSnTVGsFvLXuwiQ2zewwYTFXWGZXHvMIY5TEhAL8JlMrtdZwfyZBpFPKnWIi0YY6ESoTSLxHiySBtIsklw+iburq2jeDm7FXAVFnKzJzNWIzQ2xPJ+He2EG1TFLK8XRe29Z7V+sXqDBvm7l7YQl4t5TCTgQ2eNfujsJMoQENqXVgvIHocmCLFtpBjfvNHOZscKceN4rAiPWmCG6KWYcOmoXDusdNW2uXbb1cLsfTaV7qvNTzaYWtcXzb5jd30/3+9pvvfri5u4/Wv8cmj8j8FtC1EIiFTk8nc7q7u5mXiYQvpy7C+5v9+XhuvbMygUuZgPP5svZte/vu0WFcCErhgfb3/8vfK6zytF9uTqdzayaFp1Lfv/1qmZdlXv7ypz8vZTLHskzv3rx9++bd8XT65rtvrfXz5fL+zduffvzx3bt3+/3+22++Pr2+vn375nw+75c9CB9++WV/2B9ubv6L//L/9D/9j//z8XT6m7/5m4+/fHx8vP/Xf/OvdvO+zlPlaW3b7d3tsizbqqVKmapIgbtUeX56mebJ9OBiTFSKzMusvYlMr6/bj788VWZryjQZVQGaEguzE8zNvWsH1FwLF+TMF3flUgBiBbSnpzzF/hNCmNIomCGDrtTkuGLhHBH5dVutu3E4RKescAwpxTDwQFbcQW5MQhr0tJG5C7urYniVuqO7MXKsVBP6DhkzUyGHk4haHD7AoUa5/xfpehaTWfgCKUF7C+gDV8gKIHjPki4r80g8TGTWYxxEmMJAVEPbEqsITKNQilRSa1VtVUrmLYZ2g4GZnV2EYOZSiEvOWH3BtoEx9ZzoB+VMlbuBSrwXjNm9odm85gBTEGl0AQMLCgrVeuTCGAQQYXOOzSnI95RTF8DI0Mh+zcNf2AaN4+YeehmoKoFyACp6vjSHoTht0W/ErF38dguiLFlkV1g4TkdD5CHRD2rry6RfDM0xu4PVHaZqMKpVShVCyJmcKKaFDbFBMaQ76l395v5WykRlcuNlv2PiUiRlNdNUpoWluMNNfad9udPbt1Nlbd1hr68vy7Is83zlo6epuHvraTOXFtKQ0Ef0rkC4u5K6tdbneQrgbChEI73n8AiIzMNxl8FsihjXTjpXBIRAroq7A6Lulr9o27ZuW6nE4obeL5uxtd7DL3qu883hsD/s13U7n89OZGbMLIWFGQRTAzyUDJyzlHo+X9a21VJBdFnP2pqnWbnDneiadcNU9su9crhpFHVaSnFHa52ZwQiyRsCmJiwO7+Zbsw6edrcdu/PlctLWGzbxi/e+9XW9tNZ2+93N7var2zfv3n//1bc/zLvDZe3ezyyViXrrYqFOcyKUKn/+08enp083tzdE97VOIiI39XI5s5QYyf78+fN6uczT3LuC/P7hTkp5eXkJ1mi326/bejqfap1ubm8PN4f9zf7T58/H03Fbt8N+V6ocbg9PT0+vr6/4EX/8V793+OFw+Obrb0Tw+nr8/Ovnm5v94W5/PL+eTuf9ze58OX/+9ImJ61TPl/PxeJRS3r1/+/U33y5l/+nl83/60z+t5+3N2/d39w/3t/fb2rjIwos2tWq7eZ73izvF/EHXTkSm1psKKweEKJUUYL9/fPtP//TPHz5+ECbta2UJv8VlriISgLMqiGBOUxUi4lLd4CTgQg41U7E+gq+Doso0mHhxE2GJYNGdzTVCOoEo3G0sAe0ovC3GYsK8MNkCSfQpin+Cm5MUd3QFC09l2nQNPLHW4iHYZzIwg9TJiKmMmjAmmlkATsUEgaRGD0DiIX+IiEQxAkdmVyuGAXZHAmAmcO7MsACckIkGSmGV6gYDq1E3h0jv3d2nKqVWAOmCSUISM6jFwevWAKT8Pz+3JKQFcjVjyBebthivi4gYI9HJlfOY8zJzDBQhwbb86myxAQNUkN4Q7t57GM+ZcAKxwcZGVo2OXBwQF6kR/wdUFteaA62LvETgzPrMrkpExUd0wHXoY2S1SGRXgsMxfJSI1LRpm2sdM+roqmEiwUwsoojSciBIntN9ilxnRXnxiRlmzZzMQnTlblj7VqaJiJ3pb/7N373/5gcuU52WnM1IhxxoiENJRIo7mKzEScta237+8RcRrlPd75dpqrVI6y3r/ZH4OIZrg48x713XtpGUHqnJrQiLiLCAw9hvpEkOWxImgFkKV6mTqsV5ChdtojAoFOGYRmcQQR3O2vvry8vW1rZt521tbXt+eSKieZ7f3N1NpdRalmkhs+P5tHWHUDcVspoS8nR0WZa5FAFBu57PayeRWgPDPJ2eTRuaqXZYmBRaOKGrGcza1hIWM229fQlPxUH9fFkBTMs8zfO29vfv3z3cPxxuDm1rauqgVa3WhUAwvpxXddNupRCgbV2nue72u5u7u2XZTzePDbO6LlLrVEHYts100w4iP56Ox9fnruoORlnmpZbpfDyRkBA9ff50fD0+PT2/vDxPZXr/9u00T7vd9M//6Z9NFfBta48PD8wQ4ceHBynl/s39VKd35zd//pd/1rZp12WeD4fD23dvXG13My+7KkLCDthU6ul8Op9O33zzFQF//pc/vXnzfp7r5bJ++/2393cPvTWDnc4nKeXN46Pw5Iof/vCHfz393Y9/+Wudpl8+fBTUMIF5fnqe5mmaptPptDsshYM+LNt67r2Xwss8qbZ1NcB3864rTpdeCn/7/d/eP3zz/Px5Pb++vj67Kdxa61wIgGpfL+f1dKyF2raaFVO7ezicjxsztx5EkoMI4sRhLg+q4a0qu93NsuxrKVGBqXXEkCEQFJqIhGDR3Lppa83hXRXmpj1CgpmFjp9BXXW9XOZ5mne70/HYTXXr8+zndV2WmZnCbxpEXKdmBvNuGloeZ6CA3JnJWa5yEGYhsDEB3jVM46AazsGAu8biQxrDtWZwRAnXzttUqweYi/Dx5xgtYpqcXEGqbs4y7QvX8/Ek0wSga4OxCKs6Owqb5HSakDsT9bZ1gxC7qed8BtwsxnjgVnmCS0xlu3lXDTnnskwe/yA8LGZDVSNShpsASzr2AoHxmDmowWtQdFGY5+QUEySojMgvTGzovXdzc1SCEJciRb2bOxBswIA1UgovxGRuqu7mzFwGv5l7jaMq9GtvFf0MCCDrpl27amURkRK1aAK5hOQrknsR4rG3cfgexcpJEdeAxBFNVtTKkbNCbxAoc9QvUiZQOR7PHSvRhUQs3PHcEUXr1SxLhNwZsNwSY+r9H/7+n+4f73/3+9+dmz8fX017tIHujsLOQCwqNsAMBqgXqizFu27Wza0uNRah5uQFtayXU3JBZOiX1rbN1esyN+3dFYAh2RiE8ZGFGMBrqb4pU9ktkzetU61T4Voh8839ezXdz5MRyVQ/fPiFgUK0dWvml97Xy7kILVM1Ne0dQK2FmVStCNVaifnUiUohkPbN2srkhXiapiICeGwlJBZ3j2GFqRYGzLRbL1KI6HQ6dfWPH59O58t5u3TVUue7u8PX33xzONwws8jWWztvFyl8acqEWpZpf3u+bO79vG2uXsrcnVb108enqV6Upm++va1Sws44WtroxbdtO7+eT68X7f3+/uHN49v97rBd2ulybrq9vLyu57O7EXnvvbCwkJu9vLzUWm/vbnrX9vx8vlzcVFju7+/vH25b1/PpvFuWt2/fliI//fTT7rCb5rrsZm2dieZ5crXj6XWZZzgEIpBaCsyYZNsu7dzWS3t8++ikl+28rX1Zdvvd/nS8fPvwFpDdvLuc18P+IEXu7x/+49//w3fff0+FzscznvH+63fuIKFloa5+vpzb1s2tFGERdydCkdmJmnYnYpkhvUw7qZdFeNrvLKCJ8LZ0iDABz0+fzXSp9V/+/Ke2bd//8HelCMillL51ipWuNQXuMYQyyTzVWbiGgi+dpzjrUKQ7JsYArW29t7aq2bpeHKjpKeuqOeirqr333nWe51rK88vL06df7+8e3jw+7Mi7rufz+enl8+dfP8XcWS2xAEO2tmUiibcP2x325/NFuABh/xO1HcWIpbAQsbAECdpbN/Qv9njhDRbXSo0I5q5diVBKiYBDBCZR7VvbLpd127ZJ+HI6V5mbSimiXcHdGE4otRBDAUA2Y9rMTQlgQEqFxdRCmh1YNtaMoaLnqPCSL2VmrmUyd+9Kw9eaKBfgiMiXXmtMLVB4McJp+P6b+xUGyg4+BCwWcwbpbJSi3KEbZI5lQFGwwzxduy20p5ZIHxExoXgISDnNDmwARepucHXTmMB2AeVia6Ipl8Bn9g26JhAuDqohNVIOc03aKGEqqpJaV1zZj9T0BvIVlvnw7sTijo8fPv348el43gzCLDnsEHbNbmZQi8fIOcRg3cPvAvrp47MC5jQvU29NewuipXdzQQ/5ajSj5t5Uu7KzbrZufblfttaN3d0jU6urBdVSGCAzY9DCFd362shBhWUuTTuE6ly5kFtaaq3nVYTbpbHwzf5w/PxCpra1/TJP89RBZdlNux2BXsmgvbJPs8y1Xqwdj+et+eenFyo0zbWdt2U/T6Uw8el8LLUU5s609Yt1nLtDxLqDnFxLkcLctj5NEgYtrbckaMzclJ3CG0SEyyS1znAI083tAvHTp/Xl9bxb/N3X33TTXz7+fHo9man23k0vtnnWMVXKDHDb2ra13lvrF1M93B561zrVteluf6i11incCCSGO1s3Jrm5uTkc9rX+QMSmul7W0/F0aZfX1+dt20CYp/lwc5im6XK+vL6+ulrbOrN8/PCp9aaqU50e3jwS0c3tTamy2+3d8fPPP79///bDx5/b1s+vl/dv38MhXEwtilmcQM6H/Q05tWU/1x0xpmW5XC7PL6+3tzcffvlFmI8vRxBOl9P5cpnnnbu1tl3O3FVZmMClFjdrl+biMdbUe9duDK6larfz6ZTS5DoTqLCwCLGrdjDXIl37/rB7fX05b5f1fGT2qVYW3ppalJ0NzOVw9wYul9Pp5eRdoZhUlcCiHmPzvYHMnDrIQvlGfuGQv0MofMRSHeOa21Q8asAhIPFuUVSqdYt5AxFikqlOUoqw1FqZpVOnrf/jP//57u5m2t/KvFjbLq1v2lsPNJRN7bxtaKyqvbWkfSWkc97sfDpfmML2SkopV7LY1EAQKRTS+JAeRpaQ3KSqsLxfXIlJu3KtzEKFS+7gc3Prl15lWfa8aVvPF5kOwnz/xtZ1JWLtW+ut1FKmQoC7Fsjd7Z1bv5zP59Pr+fUV2gQpmKKEdQJjCa4FOU0DIY61plxrkSK51IbAJRwSUgKUGTpMJZlisJdZfSwKGNjQwPjd4UqAEjHDIrzBQ2CLlH8NvyRLatxHTR9nz8LQL60AxxxAPnBEqgtg23NlQ25ywZDYUvAZntkGuIqzfhO9g7QJ2M7gMQQYElCCB3oSe1xAUAdIONqkjP4x3+EUkhiin37++fl0abECRsQMJKFg9tzJFiOHUoSyk21tc6D3TkTNtz/9y5+XZZ6mQuaa203JGB2xRzkwMw9NmHdv6/b0+WV6mbkyTyU65LU1c1dTgEqtRFxrgZrLVEmgPk3TuV22vjZTF5RWSWiqMtd5fzjs5517/6ef/unXjx+fPjztlrrUcnez/2QK0LTfoda67IVrYbq72d3sd8uykJM47293/fn0lx//fDlf3rx/M5X6y4dfbvY7Irj62/ePwtx63y2zSGnWtkuHucY0nXmR8MsQN5cqpt61O9zVmTBxCbZJmOzsvfWoco6vpw+//sp1en09f/hlc9hUi5r21ufdFDoGF3fAVKkzS3Vnd/Tem2o329bVqAP89Lwuy57I3X1rzVRFyM17bxrEBnPM+php7+1yurTe29a2tYtInQoRTLHf7YXF1ZnlcLM/n89mdrg9qOp+vxOp5F643BwOIBxfT+fT2bq9e/P+u6+/e/P27W5ZRMp+v7w+vfbVnz8/m/bd7rDe9vv723mal2nXrb17++6yntet/fM//2nZL7tl5/Bff/30019/fHzzloh76/M8K/Tl5cXUDjcHBv/u97+bpmnr2ruCgnIUIpwvZzW7rJepVCK2brLEqga4+zRViGxbL+De9Oeff/xv/pv/++vLc7eNiaZ5qtNUyjQtyzRNRerd7e1uuRHi//F/+n+33t6+eTBr5n0qNbR9RMyCtECDM5ghbsQkcS3B3t0oWT8LoDn2gBKRah97FlMPIkKtNWHSbiwUlzBYt1Lrh59/+X/+9//D/+W/+j//7nd/+PXjk15eT8fn8+W0tXXd1qLV3bWrAz2WfBJxyNiZ3HDi3lvXpu4eDYqHxxlTbAWttcQMc0ubEISlnSNG9R2OcBskot4bUrMdgrdE2lmKFHp9Pj49Pb0+Pz99/jzN09/+7d/M0/y3f/e3P3z3/bybAe+ql/P5eDy283bY7V5fn5fduix7634+ad82IYSkIzv7FEaGiI6IhchExcQw3HuIiIqIEAm7m2oOgIU9wVeroikAAQAASURBVBcmIJoVFmOTpAgi5blqXGQNalsNYXrpQ1p1bQAiZlMOiVOKLRPASXW9Y3gDxy5RUIlFlAGsWyDzOQQGd4AdPZuUXEzkpqqqpmYiI9IHc40wbE0xkcUqg5Qc5Q8l8sIl9EPuRpy2lWRkmo6AqRUgUlUHva6vL8cz1anU2QDtvV868vuGrIgZFIO15qRdu/bezeGqFqzI4bAHUIhLrcy8bs0JEE5LueB4zazbdlnPx7NpOz1f1O3u8d6kGPyyrlvXsOPs6zbViiYwW89P+3m+v7mppBt16wp3c3VGLdNSp5v97v72pq/66enjP/wv/8uf/vk/XV6Ob988FqCdT25W66QCkmqQxzdvfv/777/6d/8bZuutzfPMKJV5erf7v/7X/9V/+9/8P/5f/93/cH9/Z9bvbu/3N/vHu4c///mnw27Z7Zf1vAKkbrGkp5bJibprv+i59eBXQMRFQF6nSULWFSu7nVhkW7e19dPl8vnp8y+/fPz06fPl9UzEN7e77394//f/4S+H2x0L8+tVbKClSGUqIt5YFV09JiW1aWtbf1qlTg7MS62zwNG7gWN8j5hZLTThqtqfnj7PU611IqZpqef1VIocbvbzslhXVWPGNM2muq5tW7twefvusUx127ZY2rwsUymy7PafPn16en5eL5eHx4fDzc393S1Ad3f3yzxPc/28+3R7e7vM87qud3d3P/74y36/U7VmjYnn3bLbLyz8T//pn95//f68nX/98OtPP/98Pp3vHx9YQETny2UHMu9ceF4mKVbrpGbozc1a+/9R9Z/PkqZneid2P/Z16TOPqeNP+ar21Q2g0d0YAMMZrjg73F1SpEiK3OAHhRS7EYpgxOrvkBQhMfSFona5pKQxS84AYzAGGNgG0N6WN6eOd+nztY+79eHJ00N1dHScqq46mVmV+Zj7uq7fpcu8TJIEKEzGk6qqqrIkzWYcx4Dgy5yNMUQKQgij1FICaCmzi0udKBTG8LLUBMBZo0pXQVWVBePCWFuVjWxWLC4s5MWkVk/OTo8RXa2esIT4ecsc83bxudfWCoLovPhHEAjhYAFhHhFzBIizTlvjP9LaGC8REXCA4KWCeU03Es6Z0VprU5alUmo0HB8dHaV5dnZ2cnh8NDrrp6OBEFQbHYTSGEuZYYxqYwkhxm+NlPnWXAAw1jlEQZnHGZlKMWUR0GjriWqUkqwoKJurpZQxAEhLRdCf7fzUigH4aQUQ9rddm4RSQpg/PVOi0nSaTrJKl+ksHY1GnLPhKN3Y2igqs7t7OPfFUzBGV1WFCp01WTqLpJCcUDRgweN/KKeEgcO5bZzNdcC5++jCegNeN/YSP2eUcwaUWAvzomKfEYOLAzRclBQ4Sy5SBd6vgXjx//wrIcQaAmy+PnsRfs5hdRaAA+KcteZH8fCVyurdvYgXyvl8lAXArUPqaZSePUzAP6B1znpIJ4APc81pbPi37hEyP/+7ix0LLozyF6U/Fz5df8ugc8cxIgLxRb9ftZOR+Y/8YdZfCdFqIQN/sTFaG+Pj6QgEGJ33HIGzABYoQbTW0jnSGRGd1UbPZpkMeKNRq1RBCVUIUObeTIUEiUci+7cLAiDoSqeziat0KEKrlTE2m82iJFbKpOkMCRNC+CGZYdyP5KhzoEtXZGEYUMGV1gqto2CqUpVUzXgeBOUsFYRPJ6PDZ3sHz3ec1cV0AtZS67hgjHIL6BgN42TUP3/y4EE2ndx64ZpSDc54FMbW4qA/er63e//Luwe7e1+MJohmdWX11Tuv8na3t7QQhbIocxkF1hiXV1Weg8PCZACAzvlOBicYoZQJFkQBXBwPfFicCY4GEQCCYDQaPH74eDAaaqUZo3EUBKHI0vT+/fsbG+tRFExmk/5g4hypN2tRKFRVzDlHzIc5UBtwDoIwFEFoLAmCsNFqXbm6JQQrMs05EwGnvsICHDg0xlhtADBJapQRgsgFs8YabRinYRBKEWhQBKzRhiApy1IpDRQa9YaqVDbNKGdA0TnCWZSlWVGqk9NTimR5aRkILCwuoIOyLAGwSAt0UK/XFxcWCCVlWU2n0+l00u42RSCzLGs1mo16M8/STrf7ne9+R2k9nUz8O6Td6tSSWhDKIBJ5UY4npTGWEqq1iePEOdTaGK0Zo1Vli6IUnMtQKKWULrXW9OJISpmftyKCs846i8YoSkgQiKWlhW++/Y3Pv/h82B8YY4AAZ5wLMe8dRBFJKRqUMzg42Iui6Mr2hgwkYcRe1DNS8hWrHryWanyQVbB5rtCzB3xvoTUAYIzx012jDPj+SYdordHaGG20cc7NO0qcdRasNUVRplk6HA73dnaB409/+DOsUEpmlGrUE8Ixqyp6EZIyzvp+OmsdwrzfijBinEXnHOPMT1iBFoVijCpriCVAiJDMAVjfKU0AraOMEIsEgDKGczyb8zZFYw27cLuDcVwKP3HR1jFPHgukTAIZhf3BCJ2LpeBSIqWV1tpo7z23zhljVGW1UirPS8FUlrVbSacRE0a0KryjmszP3h6v8xVG2Rnr+38cOGoABAJnyOeAYx8UQMSLtqb5iowXCqn7zw04/ngF4A+3PtExD4lZa//293xFWfB67YXg673pxMdmyVerPzpvsQL8quOEo7/7EbTOAgK1FL3/23hwiEe1UbyQKTwOyLeio++thYvZ2MXx35vLfCrdAcK86hYQgPteDDTOAeXM/3Ln5tkxdlGNIzgFsIyg4LQqDKXAPFLOWeLlFI8qIm6+2ZM5H4RRZrklQEmNp9ns4YN7J6eHC4vLvsaSUSYEF1xSSr3Fx6KbuyiRoHXaqGw6BQeJDMFhZTROiNa2MCUBSjkn3mbhnLPaGovWSM7JBSuRc66MLqrSgKXzqhakhIZBHEcRpWw0GjHGiLP5LAvDAAAsBaRYVTrkIThTryfpNPvogw+yfHLzhZuNpDFy01ocMwatWu3F27e21taOTk5m2TTPi2fPnp4cnnznt74tuCBUGOPQ0iSKus3W3FVg0RorhDDWaucIp3lVaOMn/gwsJomQUmqljk/Pz8aD49PBbDYrq0rKoNasEUA0Js+VNVjkZa1ea7Ya9WZzOP5yOpsp1IyQViMhFCjakEpKPN/B8jCgTALlPAjDKOn1lnqddpJEgYjMPD1gLFqjPWbdEApccGutM1ZrLQNBKHDOmJCBlAQIWnTaTsZj62yW5VzyOIrbrVapKpZSQomxpiqrNKWcizQv/RgYCRFCCBkGUhJGyryoNxt87iOjs+m0qqpBv99s1sMwCGRQFAWXot6sE0JKVbY73eFoLINyc2tLshAILF5aoIT2B4PxeFQWFQIEIiSMCimiMLLWEkLDKHTWokOP5GScBRAEQib1eD6Ynde2IAfCLvAAzlmlnJC80+3EcazqqqwKax04dNYx7zqkJAxke3np7Oy8024DoGCiFidCMphjZC5s1gD+wkSQ+IoNdIb6z6Uh1FkEZMQXJFmH2jkEawk6a7XVSIBYZ9EicRhw7l2wQoggkJwKzqmqSkro2eCMUfd05/mgf/azn/109dJqEtWKrmq060BRa6OqinBqjaVAGOeC+6y4N/1TLgUlrjS5YMJLiVpZax1QoIwah4wRxi7YcAwIEnSOeYbb//86SQhljFtjGOcAgEisQWQOgDEqVDkNQ4EYhKHUxlzZ3gIGSRSBsYOTsyAQQgrKKVAwzpSlcsArVVpjuKBpnoYho526zks/IiMEHVqLSJBaC4iOU3LRgUIYpYhonPWhawLE+diWA+v8wXxugpiPYi7OyH5s7i72AEIpowwRnfUUOkQHyPBiTu43ZP9a50Mf/AorNLfIEEYpJUT7LJ7P7Tm0zvowHkUKQDigT74SHwSy/rGc8568i3fsXPQgQDjjTDDGGSFkvm9Z52kHXw1/EOf3IkIIA2rRepvpPGvnJyQAaCkT7GI3m6eXnbEEiPGEMQKEgFYKER2C1rpSilPKhXCcUkoEF4TOH1cIIbjw0DTnXL1RPzs739l5XirtvrhPKAllEAaBdwgYZXDONhLWGcEFY9RUSmtttO602416jRAodEUEPT45LcuyVm+UVUkI5YwlUWKt1WWJFjmjQSijKHTGIgVEl2YpAjLCfJo2CqJ2uzsZjgnQKEo6jQZF1241W+3W4Hywf3ygtaGcl5VSSldlxTgxyj16+HQ0HL/y2iu3b74wHc2u3rh85fLV6WxWVVVSq1tr++dnX9y9+/D+gx/82Z9durT51m98s9VqtppdlU0Fo+h8fJMorVRlnNEBowYdJ1hpVSmjszydTqUUVZanaTqZzSZ5mldYb7e67Y6UoeA8kCKUvBbHrVbDKHV+eqYMXVlbefOd3/j+935wdjAIQj6cTEFVC53G+tpqLYnyolxcXZ6m2WScxbVaEgSMsnqtDg6rogAQAEApYVIQM79FWgPOOGOsNdqfjxhn1lrGab1W40KUZUEoyFDaeWYE4zgKg4hQmmf5eDSp1RNCwFpbq9Wcc4RKpfRsOmGUJXFCEIq8SCcpALKABmFMCauqXKmKMRrGQaPdYIRzyoXkCKiqSgbSocvLrCrKRq0R1xK0aI2LwyQvMqPMaDSsCkUINXYUhmEcx9ZYjykDIIyxyWQqQ15mOUEMAhnIQDBujLHEaW0o895xsMZxQbmnBhNwzhplZ2mW5vkF75cwALTIKOOcCiop4VWhGo0mZYyLIIrqSlWUgSPzc9vcB4F+jsqQ+uG9/1g7As53v1q0Dh3MM6xAKUGGjAgpQ0GFiITwq74QoQgooQDIKCcUtdIWjVJ6ks6yaXZ+PAzj8Oz8vCg1sZQJuri82Om1yqqaTCaeoQ8OueAUgFOOgEpbQqiQslaLQiHaraaztiwqbY2XQ4MwCKPQGjsXJ331OIIDEoqAMuSMkXn2CL1EJ6RgjPvREHrjCwKlQgZcSBFGwigXBqE12ZUbl6ejyWQ4FlKoqnSolWJ+N0FEjW6WKQcggOVl4UlnhHFgABYJJ/QCIuSspUgdcUT4YDyhlHIuHBA0lX/hlFqHFtGL8EgJIYx7kdM5Z51/to4Q6gehXvZlnJOLqjJkjlEfzZsf84kPOcynPPNx0VyygYsyAZ8VcACEMGDzGdaF6QYdMDYfz3AgxPm0wIVtc+72d+C8GwfA2rlFyToDBDmb06mNMYiUUmKRee+PxQtIn3OEgs9/GQuIQJjnjILxDnzwdTwUgFirrFEEgAHxtBnrHBcSqEUKximtTKEspQJ9e4mpmIxFIKqqYlwwxijnSLjx7TBCFnlZKqcRtSWMByHnYRRYrQmg1kobrbURXNZqSaPZlFwiAqe8Xos5p41mYzqZWK2rQm1d3r55+1az1VZGUcrP+ifv/vKX+zt7VzY3bt9+od1uSSEDKZHA8fHRp598enR8ePvWC5dWljjjYRCGcdhqthvNFmfiyZOnZaE2N9cX2m3Kod1qAnHPn+793u///tHhEQAwxgQXaK2utJCizPOqqv3Nj37c7S68+PLLlVamVBqtIaY/6Esh4jj+jXe+9e13vv2jn/z17vP9f/8f/v3Xv/Hmle2rnLkkCq5sb6Fz5+f9vCqtwQptOp2Vxhi0o8l42O8Pz/rpbFZPErBIGLQ7vevr6721tTzXxto4qQeBpJSCtapU07wiyFY3b+ZZNpzolc2VzsLmJH+mkYQiCmK89+T5518863VanXbnwd09KlgQR21Nygop5Qu9pSxNHWIYUkqZjxEYbRAtF8IYq5UyxgaBDIQAQGddURRCCmftJB/7cIazViuFBMNAhjKMogDBaa38zJdzHscxZaQslZAiSeLZZBonseQc0KqytNbEcVhL4jCUlBJK5IQABVKLEopE68o53R8M2q2WkF1KmbHaKlevJ0mShGHEKRuPRtbaQAbT8XQ6nDp09WajLIqyyFVRMkoDEVICjDPOkuFwaI1BZ2tJrLWN4ziu1VSltbXaGFNVQnLOubHWOGuMYZwSQq3V9WYDndPaaK39NBYQBZNSkHa7y3h8ejqYzYpOa+Ho6Pjhw6dJ7aTZaAaRCKQMQxFGoV8ULjxyDsEZRE4YFwyRGGuM0oAoAiEZA4CqUoEUURQGcSgoZ0SEMiCcWmOAEGecriwh6HepssiLotg/3H/69OmXd7+czWa1VjeIg6pQkyzTlTs779f2j3oLXcqINjoMQ6VUlqZZmiEiI4wwkEIyyrRSBCxnvNmsOzAEiQwDIDgdz8qyZISFYVDqKhRBrV5jlFKglhBECAPBGERRzAW3zgRCShkCIULwKIqFkJxzAtQZ4ywCt5SyIJJhFKWzqRT8krw0mQyttQIYoWCNFgHz7nOrbVVVqqgcIaUygiFBq7Sx1hBOGBeUgtUGHSHOEQRirCOAlPp/gQJhlFqCjmhtqKSAjlzYSyghwObAVufmiqdFCwDWqvng3VoAAsQKITyznQAnjFMAZ721E/0aOAdXUgZuzpuzxDHve8KLmBwh1guvlDpnPeCNAAJYdP6CQPmFfuGdR+gsOkBrnV/97dwkjISA1gbRCX/SFpygB25QZ6xlfm8CRoFxRsEBEGscoLUXZWh+Z3bemTUPh/m7AkGkPrhkEQhYZRXhwIAjQW3MLE3TTBkkxkClTaMWM8asy0MbBFISxgmhgIRxLoQUgZSBjOuNpYXe0tLyxvqmkAIMaKNrtSifzpTVBIixLgii8XiUZ8Xrr79WT5rZNAskcxa7C+1+//SjDz4ejoavvfLyyupqFEaOYijD7Y21wenp4Ohkc3395rUra6trhBKg1Dksq7I0Shub1JIXX3wxiRNKiQwCRllcr00nWVlVKysrG1sb9SjWprLGVKqstM6LHASo0nWbrVarKTmfTif9fl9p9XQ2DYL4//Mffu/Wi5+/9vqdJGk8efzw/PT8xtUbW1c2BefEgZT87bd/46238S9/+Dc/+fHPPv/408uXN69e3s6zVFXl6fn53v7RNM0mRTbLy1xVhLK8zFFrU+pmI5nNojiMklZNgaNSqsouLC602t3pLJ9MxkVeMsLiOKlHTWt04RzE8f7B6ckgRR6srl+xqPNp6ghsXLnRjJPLVzY2Lm84Yz/58LOnT55NptX25ctLy53FpSXniBCBkNxYe1GIahzaoiiNmf+MVqrZagaB0FojuqqsLDeVqnRZBUHIKA0C6c82URxIKZTS1ll/HgqjCKzzfCdEDALZ7XXb7ZbgHBEQXRwFlDK/niqlKSVBwKvKGGuJUUBcNs6NMZSSLE+ddXmW+zuwUWZCpgDu+Pik3W7XGnEcR0k9abVbSuvj4xMHzoJVuhJCEh8V5BCGgjHKGSWAgnOjdDqdWZ++ZcQYY4yhVHhSjFc8nUPGeKPd6vYW+4NhWSjv1K3yKo5ce7m70F2O4ijLs3qjndSbnd5Smk4n6WQyS5MkrCW1ZqMxnWZGGyElIBZlUWZVWeaEzhUXxigXPAqDMAhKVWldEQqtdiuO4qIoB4+GqtBgiFKqUhU4ShgSQhrNZqPeBKQAWKh85/nz09PT4Wh4enZOCe32FiptHCoZijt3Xtve3iYUu+1Oo9HURh8eH37/j7+/uLC8+cZ6p9vJs+LR/UeLi93XXnu10+poW52fn/X7p/fu3zs9PIld8tqrrwUytE5b52aT6enZ6WQ4nozHQIlgEjg1zpz3Cz9GK8vSWBsnSRAE6NA6ZJSiA2uNs45SwhgtVS54SBltNBOt1PLi8qWDkzgJL11a1kqnaQaIRtsoDLXReZbnpXKEOYQyLwq0gaBZnhVlFYcilBSMZgAGAByZXzPoxfT7wjnpffCMca/DzPFqF7MUv9D6zJ21FgjYecsece4igEu8yWJu9KGU4hyD5kfNF8kr4hvT7MV05UJrnT+El40ZzLN+zFrnO5gRgFPGOSeU8Ats8XxbcPYrqxBaTzFHzwDxZgAQQkgphORojPcWWGuIJtZSSkH4Klw2F3+c9RxCoIR5LdfNYXleh6BeZCZI0XhkIXpVnVFOCOGCARKl9WgydoTNJoW2ZtjnTPAoqSVJ1G40CSeMc0o4LRRjXFurynJhcaHTaDYb9etXr3LJwSLn8wouX7ZRqkoI+cknn3764JOVS0u9F7o2ZIQhpTDonwkhl1YWKSdhHJRljmgBMJ9OESGRcqHTXug1TFWk6VhKmabFLC93nj7Ls6LVbBNCyrIKg0DIgFAipFTKHJ+cDCeTF156KUpqlBAK3BHQJVp0s7RQlbIGW632N9/82vbWlqqq4XB4eHy4v79/dHxydHZ68MOTo5MTKsTu3l671Xzt629YZyTwlUuXrHGdVu9sdP7P/tk/f/mlL9599+e/fPe9e3fvXb2yNhqOx2me5tlLr93ZXriRa+MIoVT0Fnsh49RivRan4ylaN5yOz87OH957lDSaUa0mRBhFcbvT63a6ZaUG43Q2nRVZro2dTSaj/rjTbjcaLY4BWpWEMs2GpTGT/tnpyfDZk+MkjrO0EjyOo5gySUjARSJFEIQxoOOEOi6sdWVZ5kU+90EAOmvRQVWVQnCtNABoY7hgWquLhiEIpDTWAoDVBoUoy2o2m3VarSRJakmkSm2qyo800nTWaDSSJLbGjMdjzhnnvMiLej0mFKqskpxyxlNVZFm22lpmjE2GUySoTMk0McYyThkHY2xVlZPxJAzDVquRZVm9UavV6mvra87gdHy6vLiY1GKtTVUpSrlDxxgXAddaW2v9bZ0Jyiifpqk2JgijJEr8LEupinMuKBWBtNpah0IwgvD1b7zVbLV3n+/lWe6sDWXYbrRX11cZ0MdPn375xYN6o3Hl6pWlpWWLxoKdjian/eN0llk3NlrrSkdRrKvSWhdF8cqltThOoiiSoaCUIjpttNWGUIhqAaV0Nps45wQTqy+vx0HsDAABKSQXdGfn+V/+5V8/3dkljG9sboyHk/29Ax8TsxZ63ZWFpd7la1f6J2dffP4lKhtL/sarL7TbzbJQQIh1btg/Wei0XnnlpVfuvNJt97Is/2Spt7e7+8IL15vNtuDcWEMp3T/a+/4f/enznd2vf/2t7e2tJI6rSqF1pS4f33/8Nz/+8Wgy+s3vfvf6rWudVlsVejwdD4ej/YO9/mAwHA7TPC/K0gFYbaUQztpAiloS1eoRoWRldV0GoizV+eD0ycNHx6enly9frtdbWptZXjjnKmMrrZVSWZpp52rNxnQ8K9M8DIRzhExsnhX1WgvQUspMpQCREmo9FxGA+WwCmfcozTcBJHNYGqG+bXbu6EFw1hlrjLUXrFX3lXwKcIFlJhcHc5z7arxW4M0DXnOYZ2jnFE9GgBhrKaEA1GdOPeEUKFJgDBHEHBZICBFcUEJZwLl3EaFPCXy1G8wVA+usBa++WAPeO8p9PyC1hAAl/o1L7ZwbxeYd9H7kCOYrCjNzc5S+94qiZ1mAc+iAGIe+ss2/Zk/gBKSc8aLQk8lIqWqaF6PBNCtLZ6yxptnqBoFsN5rGWmMsIUQISRjJ8hwc/Df/8L9RVSUkR+fKNJdCUCYl55QSPyS1zmRZtvP82edffNHqtNbX1pzV6JwMuNJqNB4dHu6hQ28z06biXCijGCOVKpuNeqPRYJxoXVpnZ1m2f3D05b37Splmq15rNYI4JIIxzillSleHRyc/+vGP+6fDK5evdjpdK4jSFWOk1NU0n5W2VM4CgUurl65dv351c7uqSotOWYWUFGX1+Zdf/uLdX03S2WA0nsxSIOSLzz/71jffYhEfTcdLi8uzSQoEEM1v//ZvZrMsZJJSrKoyneX98wEI2h8M//E//d8ur2/u7e1ZQGOspLRZq3fqteHZoMpLICQIgrTMnj3fvfvg/uHxThzX0LhsmjsKh4enZ6f91dWVKAyoa7SSBrHEFJpzEbPAUVrrXuIMpWBRHEVB4NAEQURoUOXF/t7R+cno2tWbS8uXqkoLRgGtUqqqyqIoyrzwtSrOWXSOMIKA6Jy1hnMmBWeUgsNms0GBUEYDKaqq0saqSlNOqyov0gwbdSkEI9Q5TRnlQKuqNKrilBhVWWeVKtFxImUUheAcIcAY1dYIIQPJXS3KZmmr09KmNMboMgoDWZVlmuXOuizLuGTOYafbdcYxngdBWGSZlDKohWESnR4fO4PeLaNUBUBym1JWF5IT4vIidw5rtRqRRBlVFSqMYikCQqhWhnOGAM7ONWG/YYRhuLy8HITBjeu3jNZFXqhKmVKd9c+fPn766SefLywuXr5yudPtOSTKaACs1WuELZ+609OTY18l2FvobW5toXWzWUopMCIIEEBiKgRCOOONbmM8Gu3tHBLEhaVut9taWliSoUBHOJNlroSgjVZTW1dp/dkXX2xsbTHGD/YPW63ewsICFzQIIsFZrZ4sLS7UQ5HORg/uPXr0+P7VJ5vfeudbDkAbhcqOR6Net339+rX1lVVGaBKGV65tfvHF508eP7vzxqt5UYYyAuc2VtZ+8+9858++/+faaAQ7GPSdcx7q+fIrLzgw7/3q/W67c2lhwSidxAEX7XarefnqNpfy4PDgs8+/vH//QVWpa1e2bt+61Wt3QsFrSexH6bVmwztEldGff/rxH/zBHz58+FjIoNftFqXWShnrykoVZZllqXMwTvPRcILWXt7ebNWTYf80ywqja0is9BRbAErBaAsEKKGMcs45Xix74Okyvmppbvjx4/m5FUdrY6zxNcI+DuIDxJTRecr1IsoAX9lK54LxRWWMl0fnMEVGKfWKqzXGUmf9zB3nqV47V46BUsa8HYkR5muKkXBjLfHseAuIc9ivt8f4wIDnEhujnb1A519EwByCs3M9GxAoUvBSCOOIjlLkDMxc8aW+Iw596AGBEOI7v6xD7zXyL4sShr7yHMAakxfFcDB8+1vf3bh8pcx1XpXnp+dPnz397NMvJmWu8tI4q5TSXi8iNM3zy9vbG9ubZ0enYRI5b2l2TqnKaE3n1XreAuTyouQBPz4+Ojo6bLQSipDlCpwr8vLo4LDT68iAl0URRmGWpVmWTifTo+MjEfBGsw4OjDFFVown048++ejo5IQxtrDcC6O42+s6azgXQKAqqgcP7j59+gQdnJwdt09acSAIgziOLOBoMjbOAaedRufS2oqxLs0zwZlWlUV0FmqN1jfefGucp7/3e38ISOtx6Iz54Nfvg7EvvfDi5vra+fl5VZVHx8d5Vrz/i1+dD4br6yvXr94oq/L53tNHT558cf/e3S/v/V/+z//X3/nd/+rV1+8URWWJIVYT44o0r8dJQJlWpqrKWMp3vv7mt37jm3fvP7j38OH56VCGpTL69PBAGyPFypvfuBPKELTTleaMG2OKKivL3OgS0YFFyngSxZTRwWCoNapSUcdbrXa93hSc60rP8hSJd1BAHIcy4EIIn/71n4JAiKqsqqrinAZhQACkFEEonPGBWCCMEOfKsuQSnNWIVqlKqyqJIgKk0qVSqsiydDZtNmoIlhDq1U9AJ4NAW2NTl+eZkKwo8zSbcc53nx/O0lk6SzlnZVEyzhDAOpNXxXQ2bbRa9VqdcW50GYRyNBqPB6NGs95p1QJnZpNZGIdhGPazfptSxpg1llISyIAxjmi11sYG1FLBmOEEwWpVMcEh4MTnXZnHaxHBGKdCV1YZFcWR00aVJaMAYPb2n3/yyaeDQV9KsbaxGsUhUMir1KGpSlVVZVUUzWaz0azd/+L+g6dPd5/vLCwsvvLqq61GO46jjc31JEnSLJ+Ox2malUW58/QgnY22r22tr6202u3JeFyr1RmnBElRVIEUk8nk5OjsvQ9/vbu7Kxgd9vuz0fS1O69ev3Y9DCVnjHGitcqyfDaeDIfDjY218Wg4GQ8/+eijzY3tldWV2axKGvX33vtgY3t96/KWM45KIiRHh8PB6N13f/7Cy7fqUWS08qjdt9/62vnRUSAwlIxY7pyjjDpjndUry8u3XrjSaEUWlff/hZHQ2mpjnTaIFtE4a4UQS8tLV65sgzGUWm2LypiyrKb5LIjCKEqss2+/890v7t//9S/fu//g0eLCgnczpnlhtMnybDabShkAUBkGy5dWrt+8WQvD87MT7Ud2bF6nwoA5i4xZBGC+3Idy63wlM7mYAAHxaDkg6LNOF1Mga4x11lmL4Etr566euZqL4DVh7/31w3PCGSXobWb4VTsOOF/RwRgDoADeVuysNfMUl2dLIAACpRddY2z+WzhhFvDiXeizvO7ChHSRA/RqknXOaO2cY4zPvVdfBXZ9FAsuUg2EzJ314Hcb5oxBamEeGPbCNKJvYgEABxb99R8p+gQHYZSBA2fROagqNR5NFnuLb37jTaNsVhTtdmcynf6rf/U/HBwdaDAOLirYnLUGrdbddruW1E/xlBKmtQV0FmzlLKHEl/AwxkulKqXPTk+NMZPJRGmlVQDWKlUiWqWURd3v98/PzxZ6i1rposjPTs+fPH38bGfn5RdfdA7BYpqmw9Fs//B4d3evLEopA6stpQQdVKUySudFvr978ODBg+l4TCn/+U9/+vTJk+vXrtx44TqlTAjpHDLGeu2FF27d7nQ7nDOHrtLWosvLygFkRu3v7v/xH/5RkaVXr15buXRJVdX58emnH31y+Hz/1VdfvnnjlkXMs/xw/+Dk9PTGjduX19frcc2gjqNwY/3y8sraz959d//57v/7P/z7+3fvv/7GneXlSwzt2f7B4vIiQyiyfJ7BcGQyG7KcbW+t7x7v3L13kKal1abfHy/2ut16lA7OtBSMcEDUDhljcUjbrbazTimVp9l4Mm42ar2FBSGlc1ZwGYTBCy+90O520+k0iqWymlESR3EQcmVMlqaAQCmx4JSqOOMObJplWivO4igIlC4JRVVWhJIqLyujlK6sdaqsGAPGab1eIwBlkZahKIvUGFfleVlm4GxVFpzTIIyCkBNgMpCUkixNKRDKyGg0PD/rW2tbnSaXhFC4tLrMGOOcVWU1nk4HwwHjolavdzrdeq1WZEVVqWazCSafTWeI2Gl3fZ/adDxzFrQylFApA4WVDIQMAgRrHCqjyrJyFgmBKIycsVmWySAIwhDmS8WcLDgHCiAyTmfpzBpLAMbD0f17d3d2n5+enRBCllYW2r3m3tFBrVYnBDnjoQza7aZY7AzP+oP+eGNtdXN7fTQa/uRHPz85OX3nnbdXLq2cHB0fHByXRRWGkjAKFpvNxo0bl09OT0eDKUEaBMJohcDKssqzqszKnb2d999///1ff2CcDYIQrdW2DEL+5jdfj5MoS3NVFJUpZ7PZs6fPjg8P2+36nVdefvjo4bNHTz96//34N39DG/fxpx8+391ZWV16/nwnDsIwjmbj6aN7D9vt+ng8+NM/+tPf/d2/JyQt0ypP8zVYTaf5kye/WF5eljyoqjKKozAMGaWNWoLGWKsopdN8FoYBtYDWEaBKG61MOs2ctUBdo1EjFJCg1YYzAhSCUIZRaBwGQeg5PO9881uffvL5+Xk/S/NWp1WVlZDCOVeWJSJeWl3f3Nju9jqbGxvrq6unRwfT6QwRtNaC8flKigAUGWUIQBn1SGQf2II5ptgjl33QGhCQUgKGeF0eL1ZqRKSC+rXUk3/8tMffgh3xIyTPbmIIQClHNOjpyWRe8usVCE9AIJSBQQDinL1o8vU9JX8LI/LP0P+QUt864nzjFn7VFQCUAgJHisCcNtYabQwicm/B9VFidIi+bp756wghlBC/F3nWFPU5Aec8BcibZQE9pw4IogUkFn3THIE57pRQSiwiWEAkggtj3CydjQYDLqRWejqeTKbTyXjorDOgKGMEaFKLKGFlWUGFIpDWWi5EXlSqMsZWBJ0UHK1D4xCAEUO5mE6HPu82maV7B4fXgssEnVKGM5ZlZb3RfPZ055e//PVbb78tOD8/7z/beXb/3n1jbV6WJ8en6Nx0Njk97p+ej53BUESU0eFgdO+L++PBQAZSFcVkNh0NhsPzAafMoZ1MRkWWTgaDldWlepQQROt0u918/fXXXn3ttTAQUoYO0BptjNHGAKWPHz364V/8dave+Dt/57uvvv6a1fbs6Hiwen6wt3t0cPRX/dPxdPLSSy9zRpeWll594+U4qEUsFJJrXQopFhYWr71w/cbtW3/1l3/9dGf3R3/9F/t7T99+8+07r758ZWv97oMvL69t15J4OBoyytFo5ti4yj7+6Rf3nzxyzi4udFVRnJ4dnZ+Ww/7R6PxIcOYsogMmmBBcCinDoNNuN5r1yWzw6MHDx+RxVEtarXa92VxfWI1riQi5tqreqAWhpBQFZ1QQcMgIDUJZFGWVl3lR5HnW7XQAwRrNGRWCMQb5OBsNBtioJbVE22o2GWtjKKWUEmsMgGskEVLUuhoNBlVVxElstcrTmZQcwBilCCKgJQQBnCpLwRnjVBVVNptpXSZJkkQxWejV641ms1EU1WA4OD4+Ouv3q0rfuHmj11sMZMAYI4zW6vV2uztjrNXqOLSMMk5ZHMdpmhZ5EYURgmfFS2chCILxZCS58JULDq1SLggCn7W3zrC5OAhccALABHMGCRDOqc5LVMZqc3i0/+knnz5//iybzZRWSRIjuOf7z0fj6er6enexE8pAF+XBwVn/9DyUQafbXuh2V9ZWsjwvsurJs8ef3/s0SaLJeDyZTra2NgMRSREwTuIotmiSJDo7PRmen4WRbLabURQZY9JpdvfLBx9//vHB/mGz1bpx89ra6uqgf/7zn/7ii88+/dqdVy5f3lJKa11abZIwrMdRPYl6nc7Va1c77eb3//TPfv7uz2qtehCEH3/yUS2JJ9Pxh+9/gA4Zo1rp2SylhIKFL+9+Um/Gb7/1FqE0K6q81HGrfu8XT25+ef/y1SuhDKy1GrUM46osHt1/lGX5xuY2UjHLy3arU+oiL/Jmq7H/fP/w8FAGwXQy6Z/3y80NTqDM8zgSHh2dplPC5OnZWRhFgYyWVy8t9BaKonCIk9HEGJs0EmexKHU9qW1fufrO22/V4roQPI5CKcM4io21pdKSOUO4H7KBm5el+NmJBWu0sW6ux3qH6hwGaR2bIxMAAC56ABAv7Pn+BvDVEforOQABfd0fF9yho+AhLmDQuHndsKOEAvMpXO9qh3mzp095+oDWPJOLFwEB55xlwPzdgH+VKfBZXgLUvzUZJX7spJX20yA/c/JTIGcdogVwlLO56gzgAA1a5hgQ6x/LWOehhnOYBCFACbGePOrQov8j86qvJzJQypAAGmPBKotZWgBQIaTz7GLn8qL45OOPs6wKZdhtd7e2N3sL3SiOjTZFVT57/twZO5tNZ9PZ8+fP9w8OanEoZVCvx4tLvUa96axJy2JyfvLs6fOiKmUQIOLOznOldBQGZZlrVWXTDAlq6x48fDRL82arNRwMdp49M9bWkujo8DidZUpVWZ5rZRiT9UZd8EAEwXQ6fvZs5/TkJAiEsy6Jw6IoCKXtZjup1ZQuVVlNx5OTw5ONS+voUBdVt9G9fu1Kr91WlWaUGa2BgFK6zPL+ePL7//4P4lj843/8D1575bXJaKy1qa/L9aWla5vbDx89fO+DD370w7+pyur2iy8uLvaa9VYYhE5ZVWpHHAEqBBecv3HntXQyK9J85/mzpw8eV+NsfHz227/1mzevXxv3x0FcW1pePRuej2ejX/3k108fPWm2ml+/c2drc7vd7gG6T7/49INfffD0wX1GWLPRbHdaCJDn2oINAslAfP7Jx3lWUEYn42kY15YuLVe6AkasM0xwKSQ4IoLAOUeBWmuRMmt1luZVVXkHm6oqZ20cRVEQmUQRCoSAUtVwOOgPzseD83qjEYZBUeTsgkLDOXGOOmeHw2EcRZyL6XQymYyHgz4BmM3SlZWVVqdTKppnhXO23qhHSWy0MVpladbv95Wqer3uwkJvms4IkqLIZ7N0d3f34aNHG5ubt2+9tLi4GMcxUFIURSCDbrfHGXcO4zjmnDLOpRRhGLS6rTwvlVZFWTAhoiA01nDBq7KCALXWZjxptJqUsUqVYRRLIR2gNloGkiCgRePmiBFEcMZ58Pv7n376/vu/Hg77HtBtrD45Pj08OKacMcEPdvcBqRSi22426/Wtzc2kHgVBWEtq0/GEcXHtxvbZ+fFkNL3/5YNrN668/fZbAQ9kGBhjKOdaVVqRXqcXSjGbTHRlBqcDbdRgOPjko0/Oz/tSRl+/88YbX/va9ub24tLC3t5zQfnTJ4/+4s9/cOeNV3rtXpbnzqGUAtG9fue1ZrOexLVOt/3wyaOf/vzd733vjxe6C6fnZ6urK512mzMRRMIaG4iwXqsDwkJv4d79u++/9/7C4uJLL77c6nT39vYfP34yGox//NOfN1qtq1euAiBBcnJ28u4v332+u2scPnzwqL2wKINonM600kVV7N3d//zzu7N0FoaRdfbul3frcby+sRJyVpaKElcqHdYa0ywNw5AHMiuyg4ODSilEDKNgY32j0+kIKQb98c6znTiK8jRXhdJSE4CZnmVZVilljHHWEir8hMfZC6z/vAsewWBVVcpoyjhjnFxQ2Ky1jrn59OOrlgJCGWXI5svzPO50kQ4HuOjg9XMhRjkRfsGk1Dm0hBDrFSMCiMi9yYcyQAJkHp5ydu4gupjoIBDqBQXrjKSUALHOOQcc/zOoAwHfscgJeHA9IYoobQQ4YxkgcE6prynQ1jlNABjnlBCtDSVgjEEEdMgYE1I4BIe+Pm1e6+CfE+fMOTTGWusAiMU55gKBIOHOOSoIekKNQ3SgKp1nhVKaEqetdQ7+8i/+Mk7C1bW1f/D3/+vN7U0qOGeEUmGMPjk9+8Gf/eDhwwdffPLls91nz57tdDvtOImdNVyw5aVlRDg9Oy3LCh1wIRuNpqrU8cnpwdGxVpU2usjzbrfb6/W6iysnx0c7z/cB9pSq6km91qzHYXh+NuiPRmDd6ur61q3tlZXVeqsZyRrj4unOk/sPv3zwxf21jdUXXrwlhdzZ2SmKcmGxc+e1O866WTZTlUFtppNRUVWzabq+vtpsNmeTGWUEnCVOaKvR4mB49oM//8tmPf7ud39DUJZNx2tLK59/+kkSx42k1q43kjhudzsPHzz56OOPD44Ov/nWW8NsjBrqIq4niYyENubh4/sHB4fD6UzpSnL+6suvTifTIp99/PGvnz65d/PFW7/1nd/SxnzwyUcPHj047/eRwiuv3tna3q7XE864NWqh17t5/eZSZ/HhkwePHjw8OTsdjsfrmysbW5thEKTp1Gi3srK0tLSUZcXu890HDx6Nh4OklpydHC+tLCtdUIZBQIEYwSWLoiLPy0z5rscwChEdVWRpcTGKQ2tMUaSMEp/GKot8PBxlaQrGqkq12i1CiBAUEYu8yLKs1W4BYJ7mgrKyLPI800rv7uyFUdBsNI8OTwhjMggtWq11EMooDnd39qbTiRTSWAOEcsp3n+9GtdgaLLL8rH/SH5zfvH5jfXOjt7DAmK8RdQRolESU0dFwRAi0O608z5SqCIUgCOv1mkMoirKqVLPJjbUUkHPhhzwAYJ3Ns6zeaPjDDiO+oB2tRSDEGEsuml/tPAEjjo5P/vzP//zs7CyKAkIcOqt1ZdEQTkQQdLrdXm+x1WxZZaaT8drW6pUbV3Shp9Px/u6+NXo4GQ2G/f7ZYGl5uSqyJ08eLXR6W+tblhGjTMSFIfPOxF67s9DtTqfjzz/77MMPP9w/OOBcXL9+bW1t49q1q+12tyqLNJ1qpd56663bt6//9Cc/+dlPfvaNb3ydEDJL0ySOr1y50my0lC45E4Sxl1995Yu7D6uyaDbrzVYTndvc2Nze3KrVakIIaywiGmPKohxPxw8fPvyLv/hzyUWnt/jLX/3q448/bbZbjx49/r3f/4Ob16+vra2URXXvyy9Ojk9q9Vqapd/7/p/0Ll1qt9t+unH//sPpdOocIME0zSnjp2dnf/PjH9+588rm+lqn3aBgK6Wy/pAHURgks+ns9OT0pz/+yWDQbzYb/+Af/oM3v/ENGYR+nf35z37+2cefEYTRcBjGYRBINE4K6ZUhgCYgWKspUArUWrDWWQNAiFLKASqtlDHcgaceW+f8SBzn8av5IJ5Qytgc/ubRmXMu8lcV35RSyhDRGMs5AQuhDChn2loDxqEilBOLzhkghHPqgDok6JAyRpgAi8RvFXMOhEfzMG+7d+gIocaiq0og4JBwL/k6QLSW+Wp1IIxR4zyiDwTn1lpGqSeVep/GxWsCX2nqAT3onHPGWedDLv5KQeeFx8Q5JASZBwQRMoehwHyYNP85/2d0oT0QQp1P0zOmtdG6sNZNpunR0RHlop7UW43WbDoLQilloHReq9VarabW5te//NWDuw+VM+1mkzM+ncyAYlGUZ6cDIFhm1cLSwsbG5s2bNxFAKW2dSdPs5ORUhtJat7+7B0xQGWxubV++sl2LE+vc1avXer3uF1988W/+zf9zfXX17bfe/trX79ST+nzY5ZteGJ72T5Qz3cWF2y++eLR/mGYzysjq2uqtmzcZo0orQNI/Px+PxvuHB9PJ5PLlK6EMKaFaV9aaaVkoraqi+umPfk4s/st/8c+jMKyqqh7H42G/12nt7+9ubl0WgeyQFhW8ljRW1lf3Dw//+i9/FCbxa6++yrrL+7v7B4e74/E4zTPjnDEmCKPuQu/Ondc31ld/+tOf/uqX7w76/cFoJEW8vLL6ve99b3fv6Hf+y//yO9/5dhQGZVVap32BTFkUkoveQnd987du37z56PGjR48fff7Z3cOjs9u3b1y9vN1sN6MgmaWzKIxm06ngXFt7enRSazbICRmcDteWVlWnZFFirWOUEUorpYxWQEBwThgVQlACZVFOJxPGiNXWga3V4zzPVFU6a42uuGDGKM6F0cY6Y7TJi0Iw3w7tjLNFXlhjkzi+duOqNY4Scnp+PuyPtq9cRkAXORmEjIowjJxzgQy6vY4xpigLgnQ0HBtrR8NhVZbvvPOtTqvLhMiKzDljrZ3OpkbbLumhdVWZR0ncqjdqcZznuVaKEJyOZ0BACB5GkRA8naXaGGu0lDKMwqos/HBYqUoGoXNOGwVAgYNSGggRHDinFhGMZ/1SWxnUoEpjHVaVFpJRYL3ewuJCb3FxkYcBOOYcEkpoAs6asiybnfbBs927d7+sKjVLZycnJ+cn/ZWV5Tdef22h3Z2ks88++vyjX3145erVF155Qad6NkkZI6GUaT55tvP03pd3j08PnXWLi70wTDrdzksvvyA4J4iUkdlsHEhe5HZzY/03v/vtP/mT7//wr//6t3/7t9ZWV7vdztLiJWscIVApFQfh5sbWzRvXp9PJSy++1O52Hz94MJlMmu2mroxDx4WUQjh0tbh2/dr1Z7vPT05Of/HzX12/df307DSQ0dLyUqfTnUzGP/zRj6MwCAJ5sHtw/cbVXndpmqa7u0/vP33KOJVCcCaKUkkh6/WG4LzdbltrGCOD/vmHH3y4t7uzunppcaETx/W42Z3Msn5/eHxytPf8+fHJCSFw4/q1v/Pd37TWlmWpK51XZf/0/Pj4KGnWsjx3QKQMDCrOOCCJ4pgxwTgnvknFGWPAWmsdQUCnEAGs9Tkt5xeuC0sLpfNO4DmygjPmDJILZJPve4B56dh8A5gjHr7ixVHq4+VGGyDEWGucN28SZxGFt4X6dZt59IeP8fqxG7mAQ1x4QwHAzKPChHA/akL/ywEpI25OckBERykVUhhrGTPg+xVgXiyNOK92RUoo85vYHELMkDkHlOJ8pScXh3wAyr1U4Jd1mPOlfMCBEcaoYNxREzJpLfpGLYuOEFaWRVkqAnB6cmq0JYQgYhiH08nEE5HyLC/z/LR/NpmO++fniwvdjc2NazdvUKBHp8cfffxxXhwpoxlna5sb/+Sf/dMbN25ub29qpWfTGRLIi2KWpo16o9Lq//Vv/8fz8/5LW9tfe/mlF198MQjFcDCSQnIZRPGOs/Tq9Ztr65vdhQWnjd+r3UVicDgcxHG0tnIpiePj46PhcNButbc2thijgrMoqAVBaI15/nx3b3evUGW31wmCgIAnEZbEucGw/9d/8df9fv+/+9//77a3NutJEono7PzUaFWWWZ7lo9Gw1W3nZUEpLF1aaHVbt168fff+g48//Pjnv3z39uVrJ6cnQcibncb2jS1GRa3WsM602u3rV65ubW8tLvSM1j//+c8ODo7+0x//p1Z3IYji1954/dZLL/QWl1WeM0q9q9EqM5mMsjQnhDbbrRdfeKW3sNxu9yiR0/H0YP9oNBgKIZxznMmyKA6PD4GwIAiazUa72yMEjHXLlxbDKAJA65Q1xFkMggAcEkak5OicNsqinc4ms+mUMeopVGjdZDghgJwRBySQghICzhSVds5SSgHddDohlDprCLpGs1ZkuTNYqyWNWtNZCMKo1WnX67UszSinPoVTb9QZowRovVXTlcmmB0VVnBz3Z7Pp7ds36/WNVrslBPfHE8ZFs9U0xgyLEackjsJAinSalgBGa0IgjsM8y5mgDtFTSwnBssgnk6lSevnSEqUECPVcnQoMgmo0Isa4t+Kh/5TC3NBnzZzLFYTx4uKlr3/tzb3DPQoYJ2Gn02h2arU4VMpU2ujSGme00oVWSODs/PzTjz8/3t87PDl2xlmj263OlcvXvv2dd5YWlsFiWVbbq9tPnz4+Ojk6OD2kkt26dSsKw8eP7t/94suD3d04jt55553uYvdg72Bvb++jDz66fevmQm9xMk3bzYbRDp0LA+HvYbdfvP3Bex/evXfvH/6j/3UtToikcRRWJS+Vy1Ld6fVazdbi0sL6+loYxeErL336yeePHj6+feuWDCRapJQKJhSqtY31G9eufnnv7vHJ8aX11dW1jRs3brU7nUatVqlqOp5MhpPltaWj3YMwDnf3nldKbW9f3ri8vba2srCwWGTVex++/8nHn1rnLi0uXL1yxTk7Gg2Pkng47A9Gg8GgLyVxBurdZWPRKpXnaZHllBJCXC1O4lo0PB8gOm21KgulSyF5Pp0xxgGhLEu0xqHzJul5WgUNQUBrjQXr0FriEMB3DhtjnQNBCHWMXYxV6Hxq7s3wMF/3kFoGCL5h0AeD8auqSEp9E7ufL12QPcEvv3PD0zxLBQ59M7fjgrs545MSJAQpeOCnz5QhzPVnB4QQ56xXJpBQbh0gEIcWraW+dww0IQJ98xqjlDhK55xxQigjlDFiLXwlawAAowyAOOcIeonOg1j9HcFHgp0DQhkyNk+mcUKAUGIsICCxlFHGfdkvEIqScwtMoSKMWGezItPaeI6pVooyWpUVY2w2mQLgdDqpqsJblT741YfpdLp2aeXVV1+9c+e1IIqElHlZ3Hn99X/7b//t3t4hIHnttde++91vnx2dT4eTWi1p1OppntbjmmC82Wo/3997vrO7trH25lvfXGo0VVmqokR0ZVnFLFDKERQvvvxKq9VRpRaMKl35xtcsK/rDk8F5f2Gxt7qxmuXp0cnhbJZub2/3FjoeQMYoFZzV4jhL07Ozs6geNdtNoEAJNdakswyt/dUvfn3/waMXXrq5vHZJW1sWpS4No2I0Go5G/SiOnj1/7nZ3Sq0vra03Ou12b5EL8UoUDcfjD379YSOo3bx9bWNlZWN9XUZBGMTOkqAuJBNFllelGo9HL7/0YqPV/Juf/GQ0HGlkjRZubtTv3ns4Hc/W11ckZ0BQChEQWZRFvz9QRimrkygeTSbj4cg4VZk8HWRnp9ZaI6VoNBplWSll3njjzuXLl1fWVoWMsnQGnAgphQgYBS5EOsvQc6oDsM7vMmVe5Lqq8ixDRKOrsswpgUAIBMcZC2QgGQtC4dBkWe6ss9ZJ39wteL2W1JLEoauqknPOBKWUe8RWr7tQa9SqsvLsnaooGWVSCNlq+Y4RGQfra2sWrSpto1FvtTrNRiOdzcZ6RDkri0oGQRgGWitn7XQ2IQTKvPCdiWVeAiDjRJWVLV2YRGEYWGOtNVyIVrtptAUgvriNEaZRV3lRYBHIUIoAAa1GwiiAs8ZaioQAYeAMOmeVUmEQvv2tt9b2VqfTaVVlaToaDsfTMchQllWZTsvxaDyZTI1z1lgRyslH06P9g7IsOq3mjWs3b966efX6VU65FIExGgGSMH7xhZfyvHq08+iL+5+dHp0UeXZ0uBfK8LVXXnv51Ze2t7ayIk/Haevl5myS/ckf/9nb336n116cpjMCoIoyiGQtipzhN2/daraaP/rhT764e//1O3ewVJYDY5QJkc/UvXsPtHIvvHCz0+5KEUjOtza3Dw72AxmsrKxFURBHAWM8jMTK6uqbb32z1Ob+vXtPnj35xte/efuFW61WsxbVJtMxo56Z6tjX3jROf/9P/uTxoye/8/f//rd/87sLC11EpIRvb29TIA8fPbp948a3v/Mb3U7HWtM/P7t/7/6DB/d3nj0ZTwZVqY/7UyaCehRwwYxWURSqUu/tPZ+MR4CotZpOJ+fn/fFoQgGsc1VVGWs8lEwIiX6Yw6gxljrHKPjDOvqkFaInrDnnLDqwllgLhHDGyIVbfy7wgneIgmNMCG7Z3PAyXygv+J/0PwuBzU/Vvh0MwKK1xhptnbGUEWMtp8xaa6zxVbm+v3HewOhrgCkl4ACpT3n55+BvA5RSB457DpvR1lnDGKWUorOUEMKQEGeN350cAaSMME4pF5RSAARPraCUUea/sIjAHKO+W847fQAArbXWWMIYAe77Vwh4844FgsZ66yvS+V4IYcAYAwo0DAKYN8MYZy2j1N+anXVlpTzFShlDAMfjMSKen5/vHexSCutrq5sb641WnSDVxgDitSvXX7vz6mg6UWXV7fbKoopiAYBFnotA1pPacDSsyur87PSv/+oHURj87u/+TiyDospb9Zov3gJk9UZ9MBiGtfrq6prOVa1en42HVVVUqqqKKiuK06OjMOTLK5c45QcHB6enZ0Bpt9dpttrg5rgna12tnkjBR4Mh5d08zSihnPMwCitVfvbJp892nzNOa41Eo8sns6fHfepIIHip8yiO9w73p3meFUWzszCeZZoFs9xyLiaTyXA865+PNhZXFhY6hFMDGhVO01ktqg0nZZ4WcRINB0OHeHh6vLV5+V/+y80f/vCHd+8/Yp322dnRdDZ9cO9+nIQbqyvLK0srl5YE40mStNrthw8f3r//oH96PpnNsiw11nS7nWu3r9aT2t37d89O+g4hiWvXrq9/9ze/3e0sKGO0NqHsVkYZZaqirCVJVSjnUEpfvoHGOqNVkRdVUVprOWfWmjzLi7yIQikll5xVlFLBg1rgrG/hqpIkrlRFCEopGo1as16vlDo5PVFKccqJYFbbPCs458vLywwoIEghvAoVBBIdGGO8jjWdTKTk1mIQyhCDdDr1Nlyj7SxNy6q8dOnSbDoti4JSxxi1VnPBCHCrTVFm1loRCC5oVVSzqRGySpKaUjoKQ8LIbJamaWqd5ZzJUHIrrEuLvMjSVAghZQBABJcIlBDiLki+zjkZSKNt3ErCWRxEIU6GpS6LssizWTqZTWYTpbWzNEqipJYMzwaz6SSMY1MoztnW1la9Xo/rsSM2jiNCiFbaGMMZo5TV40Z3KVzcWGottv7jf/zDvZ2961evfPvbv3Ht8laz22aMu1x3eh0g9NrN659/ev9HP/rFt771zurKcjqdADiYQby9EUZxEMdhrdb9/P6HH392aW01SaIkqksRA2df3v/yvV//+tr1K81WM0nq3tG4trZ2dnZ2eHAoAxlHNduCKIoQMY5rt2+9FCRhWsyGw+Fo1FdlURacIkjB8zT3fvTZLF1eWlxfW02SWqfTZgTOT4+LoqSUW6PeeOO10aAvOAVnTFUYbdDaG9evdlqN4fnp/sHzOEpefeVOvdFc7HY7vS5axxj95OOP7t699/knn62urlaVms0mJ6fHiA4pKcoCKRqjK1XNg8qMe4wbAPHN6OgcAvXna79Ae70TgFjrqLlY4whF5v9WgVCkF5AGQiljnBDn10FrLbHOT4oY8/2BF7AHf8x3/pSPWmljtL9qMKAOnUFEApQy4uVfmDOiHTqjjSMOESmlSNlXUYM5W4IyT0TmiGA8i9Fa55ALxwi1zlEP7XHOWuPJRV6qYowxxiklvp2GUf8Po4wBZYiOUUIAjNWIwBklQBGNcYYCYZRY6zO/8yoSIKAr5Qj5qmxdCBaGDNFpjZz7hjInPHwUHSHgLAKC4KLIC2ONM8YXNxdl+ezp07PTkyiKWq1mFIXj/tAHNIqq0mbYabYpAjhcWVsmgFYZZTJATGcIQBy6NJ08fvTwYHfvH/3jf/DSCzeH/WG30zk/Pa3V4rLI0rSMkuTpk6dXtrdbjUZOMtQIzhmlijTN8vTk7Gw0OG81G5TA/v7e8fFJluVhGC4uLsdhNBmPnfVviBznIgfmabr3/Hm718vyYjgYnp2dnpycaEQhg93do+//0Z80mo2YN2IRUWYJRcEhm6anJ6e91dVS6WcPHuWV0cYarWaTWb9/YsEicYeHx8cHh9boS6trt27fzuJy9/l+rV7Dc7O4uFir1Te3trq93uLi4tr62t279//gf/lPeVFdv3Gj22kfHh3s7+5GMrzzxiurly45tA8fPdzbeT4YDcFBkiSNemNldXVrc/3ylWucM2Mthcd5npdlORqNj49Oo6ieJDWrCy5Zq9MJZRiGwRyBSQmhxGhTldW81ppRGUp0FkAMzwdlUWhVNWoRQaCEOGvKquAiJgQZp0JybXRVVRBAIAJOWZHn01lWlVU9qQVBKCW31s3SAgk4RGOtDEQsYmssoURwYY3VFi1YrTUjVFdqOptFYTQ47zsMYeTrpYjWFWM0imOlyjxPs3SWz7KFhQVr7OnJSZFlFt1CbyGOo3qj3mgmFkkcJZyLLMudQ9CEIAjO0WEgwzAMOGOSywlMlFJFXgAQGcy1PkTw2EdrrUNntBaCGa2rojw/Od3deZ7mUwAbhHRta/nF9q16t1mrtcfT8YfvfVqUBRIY9odLiwvf+fZ3Xn/1FUbI7t7znafPTg6OewsLa6urlLMojDjl1jrKqTJlv9+nSBcXurV6bX93N5tO4lochzEQBKRhGOVpGcrw0bNnP//5u3dee2VtdSXPU6vNcDgJIlGrNaxlstacngz2j86vXd9iUmjt9vb3Pnz/w3tfPLh540YcJWgBGAoh4iReWl7+0V/9TZqVb7xxx1g7GA6LspCBbHfam1tbt27fun/3AYJVqpBBxzkdxUkgpKoUoXQ2nc3SWbfTqddqqiqsUXmaOYdBHPAw3Fi+dPv6tTgQtiozQD8BQ2frtSSKglCGr7706j/5Z/9MCMkBgBIpBafsza9/7Qc/+MHPfvbTK1evGG2PTo6Hw7G1PuNE/WjFA+b9F444Y6ybz6z9yJoS8OW56LuIvcGfMNDWcHB+fk4psZQgMAqEsHlBAyIFAowzAsSnCiy1jHHvtJxfAMiFNxRhDstCMMb46wIl1MPenENK0RqLqPx3m5fH+P9aCw6QMbyghSIgEGCM+eyhs467+WXBWee8VuDtpNZYAOesReucdeCQEKBwUQIPQBihOO/hoswXVwhEdNYYY9DH4pBQQoFQB/NEhHXOgaOUM/DF8dzvKoILySVlhHMmJbcOjbNo0Ufj0Dm/eVBKOGdIiXVOaSWEFILlZVHk5WQ6PT099w3bYRiUVRlHQZbPjDbGWG2tIEQyrhAkZ/lsalWJBAgFwXh/NEagnU7j3hdfNGvRzWuXqbWRZKNBX3A2m4yV0UkUOFtKDt/+L35jfXUtnU7LYsoYRFLUFhfOh/Dl51/meZaleTqb9U8H00lKgPk6FGDCAQEgWrmiSPMiz7I8jkPn3MeffIpCjEcTq01ZVZxxQqUF3R9Mf/X+p5RCr7nYajUCwaVknJvzfn8wGRyNhllhjs4HQARlIpCyWatfWl7dXt+4ur3d7baH/fOPPvr81x9+dPfBo+s3b0yn09/+u7/twxDtbmdlfaXIy6wohIjXNjauXN360z/5wbOdJ//V7/zX77zzzv7B3rOnT37047/ptNvOGkKgWW9+88bNTq/VqDdanVY9aSRxJKNgPJlcv3Gj0Wx98fnn5+f9/b39v/yrv7p2Y/f11+5EUZydZc1Oe3l5mXOhKuVdAohonCaUACWmsP7Oa9FRRhgnlFFGKeeCEsIZc2hVpc7zTEiRxJEQ3Bhnjc11Tgm1xijEyWjs6wEJOs44IySQ1mNhCNGUEhlGhAulbZkVCE5VpdYaHfpjTKNRGwyH7W6LMToYjrIsp5QsLC0tLy2VZT48H2R5brXNVC4DcfT8YDwZaa2E4IHkXFIAhxSSWl1Ilue5sboqFWWMc9GIQip4IALOCGe8XhNCitksdZ5GxwX6Voa5LWJOekRAbRTj0F2oN9vxql22riM4pQSEZLVavXLq8cOHTx7v9gejpJGcnZdxo37j1o3L29uBlFEUvPDCC7dv3/7kk492njx5+bUXiaP9fh8JpOns4eNHR8dH+SxttxqEmNPjo8PnO0VVmkpxKZqtZrvZoVKeHJ+dnE0tdcPx9G9+/rNGK1rqLTSbrVyVcS0pq53+cHYyGKOMf/XRlw8eP2m3mhT1/vPDs/F51A5Ho/F4PAZC4jA2xhhlL11aDaPwyZMnXIqtrU2l1HQ6K6sySZJmp6GVqdXiLMsG/eHCQjcOYmc0p9z722v1xBrbaDRv3L4+Ho8oYhxFaZZZqzgTrVZja2sDAIVggrNcV84aAmCtlkJ2u92bt671up3JeEwoMdpqsJO8aDSab37zGx9//PHTJ0+73cVKVd72brThTnnmjnWOIXMIhDGtnbau1JY4zcBRAgSc7yGckzz87Z5SChQRrHUOkTKiDWXzQgbqnPXdd14k9oYIn7n1Rkm0BBHdVxZORDLXgxHRGe1UpRDmO4dzzhH/HIhD5wwCAGd8Pm2Cr6ZPxHnbjteCfScXAV9hQCnhXoJCQny7i3Pogw7aaOfsfDNxPhRMgIBDAF9Pj+ixFYxyypgQzL+FCQFjrVcIfKsZoZRR7qNd1KF2hlHgllPOvUaCCF5tJohScMoYEKAMHGqtteDcz7aYJ1dTYGyewCCCjIezvCirSmVZnmc5AgoZyCAkBFSpyrL06gylrKoKQlEI7tBUVValE0KdUoozTjk/Ox8CNQe7e2984zWK1pQZVooR0FXJKJ1NRpVSP/7R34wGJyuXFso0nYzOtSqcroTks2z27OnO+dmZEGyh19XWzbJMxizQIWFsNJo+e/Zs0O+XWTobT4fDQZ7nRV5SzjnhJ0dnmdbaWs4El0JrQ4A6wlRl8iyXoRBsdj7sO9TGVHFAgUJZZnSSUZkQSq5dv3l5+/LiwsJCuyMYYeDaScQEXVtevXL5yhf3Ht57eP9sNMrTYmlp+erVy4C4f3gwGPTHg9FkNgVgSpnhdHh5a9MBUaqMorDb7hywIBDByfFpb6ETR3Gz1Wo0m8sLy0vLS81mMwxCIFTZ0hoXx0lvodfpdNGRY328s/Ps+OSsTPMXXn4xEGGazrKszoRglBlngiDwohWlxDqnjSIUKBKFDhyJkthYjVFIGdFWO3AAEERyNMyNdUbrVrvNJUWCWZoHYVBVuqhKAKeVmkymUnKLQID6rGNZFfm4EIJXSiVxEshAKZWXuaoqa53SyjmbJHEYBlIISiHLijSbARBChTWmLAsEzPPMx3c4p86aWjNptJMiL5SqZrNZpau8SJU2nU4vy3NjfTc4DbkIgpAxWimtSy04i5MYCLXWJHFSKaWVAkKjKBJcUMIcgmf8UkqEYMbostJ5kQ0G/aoqg0CGQSgkV2X+4N7Dg/3Dfn9EuOz2uufDQRTEN29c39jYAALTbKasatebi0sLy8u/8+/+x393dnLabLXSanZ8cvzxRx+eHJ90m53Xv36HU9k/PqslSSBYVel0Ojsb9p88fXZ8cNdSCMJ6WZUiiIxjZTo5OTu6++X9SIaNZo1S5pBMyxJ5xIK4yM+fm6oWyoAS4hCVJs4dHx598N4HcRItLCy0Wm2jbKmqazeu37t/74vPvrx3755FDZYSSowxhJKyyMNIVFkVy2Rrc5MjF0I46wigdYb7thiCCwu9o4MjAoRREgjhy0STMOp228PBkBGqVCWF0EpZ56pSl1UVyKjV6RpVcopFkeVFlSSJ1noyHkVJsri48Ojx46vXrq1urlpr9/aPnjx94uvQCSHWWYfMGM04Z5w6ay1B4gwBIMwf0D36B61z84rcix5gfzEghPoSxbnBHxEt+u/MgIE/SvtuSTKf+swRDherPnFzH6nW1hjjTbSUMMYuumIIcc76Amqvws5DXUAJcb6mlyBBRPQoNjeXhxljhAFFyhHAh3UtAmeCME6Y71SpjEGfDUDqkDhkBBklnAIBa6xDywUXggtJCSHOWsoZ2DnXmnJCKUVCvSuKEEYIc0AcOqMRibacCcacs5wxyiCOA8kpBZBSOqAICMQ5dGWljHOcMd8qxglhhEjJaUGns9nu3iFxOJlMrDW7+/u5qgxBGUrv4C4rpSpFgTiLMpajwcRYI6VIp9OymQzOj53VFIhxLitLZfC99345LcZbWxtlluo8HZ6PCq1mk1RX5XA81lo9e7qDDL78/IPnTx4GgUySiALOJrNPP/tsOptGcVhvtprNzniWcRnuHxwiwmQy+eDDD3f3dsq0SMejMi+sMQBgtLPooihy6JQymSqtTpUxURz2Oj3CmEYstbIUWZE7ohEUYS6qt65cvpzO8kprHtUfP929ffPWW2+93W43Iy4Zc2g0s5ozlhfZpdWVr7359g9//OMf/OWP0ix/7733s6I4Oz1RleKCCcak5JRRyvjVxavJS69FUdxo1d/9xU+3ty//k3/yjw6OD+Iwmownw1F/cN5/+PDBZDoejMa1JGo32wu9BeBkPBp/9NEnQopGu1VvNKNa0j87G44mH33y6TRLa0nt6tWrnW6PUKK1jpMYKKmUJhQ8XIVzDmAdwQAkopUoLy1fMlr1z87Hasq4R//wJGlUqkRCEEg+y8fDqQyEteiApLM0z/I4DhFwOssZDRy6MI65ZGVRKl1ZZ6y1WZq2Wq0wCNJ0NhlPkyTkgitt8iL3d8q8yEejISUQyiCKI3BmNh2HUYzOzaYzoLh66RKgZQQCKaUUZ2dn/cFQG9MY1lvddhDklTKNZqOW1IUMEEmVF8Za61y9Vi+LslKqltTCIHQOkTigzEdyqNXAUGlDKaeUBGHorJVCEkBwsL9zGMWBrbvZbHp2en5+ejqb5ULwVrsT1eqV0vWoeeXy1e3NrVazAQSsxkkxmYzGlJJerycYPzo63Nl7/tkXnxZZvtDrvfjSrXajHQYyHaa3rl994fYLFKBShlG6s7fbbLwHFq5cvba6vv3o6dGkzHf2nz98fBfzGVICBMsicxbzoiqRBDXKCOOcZ3lqVdWMAiEYYQ4oDsb9994bO3TtVnNjc2NjY0Nrs7B8aXV9czD4/Oz4VAQ8DkNKmeDSGsch5C4spvp47ySbFrGMnHUUgHPqEPyWKSxfvrR8cHA4mcy6vU6dB7N8WlWlUdpHjIoyS5KaEBFlQanU2fkkK0zlnHcbolPWmkBwpUpAUlZGBkFvoXd6dr6xsdFotyhlTIaPnz6bZAUCBaRSSM6pjLhDZZyxjgFSguChVBSAEPStudYaoEiAMMGF4GjRgkFwjHBGKaOUEqSUovMl6AwQHYA21lDnKc3WgXXox0iEoveGEkIYpQ4IOmudc2gRLRAqOJ+nZTk3xjDwlwBEdAYtJdQXN1LggjE/pnJzKDMBRr3VngKx1jLKuHPOGKONRgAuuAyCuU2TMt96MA8uz71qjFLmD99eaaaEUcKcc9ppsMQYCw6dRQIA1JtPAZ33iXo/rDcrEe97FYzzOCIEOeeUUgI+04AOwGirKqOMmeecfd0CYc16EoWSZXlRVDs7zwlBCrQsi+PDEylDVhZhGAElValQWG00BcqESPOsPxwSRkHDg3sPUOW6KBgBay3hlMtwNh1/8dm9Wi2Jw7pSKp/MpuP0dHAuw0CXVZamg34/z2frm+uhlM1G4lHDR4eHZyf9Rw8fv/TKS9euX09q9aRe19oqqx88evzRZ58dHxeHx0cnp0cMGRhDwAVCEkoMGgBGGUtq8cLGRrfXazU7733w3uC8//Wvfz2O49l0WhSFYOyjD97LVfHdb313Y3N1e21lcWFxNJyORhPk7PGjfzPqj2azSTGdtesNIQCMpmicM3GtFvAwrNf/0f/mH++fnv74b36SldXO7u6lpaWtjY21jUury2tMUI8ASrNcV6q90LHOfO8//vFnn3xC0NXjBgixubmxtLxw3Dp5/OjRJx99avXHrU790tLS9uXLW1euHBwdHR4evfDSC7dv3RZcVFXVHwwePXr45NGTB/cf1Gq1Ii/fevudKA7KXGmtOeGMUWMNorPOCsEYoVoBk8QZMx0Px6oyWquqbNQbUjBsdfIsi2K0zuqqqkqltVZGtdptAHj85EEoI2ud4KExSmsFBKhgeVn0GjXOWRgFQEhZFsPh0DpXr9fGkzEAGmstOmO1DJg2ajSYGGfCKMgyE4SyXk8oY7NpxrmRkiNiIIRz1hqjlLLGEE6sNc1WnUtJCK0lNSFEmqVVVdFl1g4jKeVskhd5WW/UGWOMscl0EggRhHI8GZdlKcNQSKnCwDgrgxAIQbQAHJwDREpYVSgp5Wtfe+2zzz7ff/CsqrLpeGacSepRFCeUibLSIpIby8tc8Nlk4rTOZzO0tqjy2XhijEIgi6uXPv7ow5293V6v89qd115/49WF3sK0P3z++EkxHS80E2tLo91wPB70RzvPd589efLt73xne2sradRefekFGkavl6//T/9hfH5+zDgJItFpNRiFfn9k0lLpPAhkGMirl69uri+vL/fiMAgYd+CyaTaZTE9PTwjQMAwWFxeZEGGYJMmZDGW707ly5XKj3ggjGQdxFMYMmDbm8OTw6cMnz548bzbqCUKpSimE0oZySikDQmv1Rl6Wp6dnURLHSRxFkbF2OhkfHh1SAsZZ6kf8QSQjJOwQkSjtmOBWa+es5JIQatAD4a02RoZhs9VqtdrNdpsRttjLkjgZn54ZpbVWiCFjgjLi0PC/7XBnMAfdgHPOGKu1UVZfKLFACQXmj82Ec0bZRaOvP2cjEkDKqPOBAYsM4aJ8F9GBr3j3cgKlxCEyDxcjSAhhggMSP3YHINYho+BRPn7u5Ffk+VSG+rShs2DnSjQQJJYitY7MC6qBcGutZ5czxjkXkgvGKcxLXCgB/4R8VoswQpgvtbaWOjbnnxJw6KpKIaAzvvEFLjYf5+2ogJ4Qh87OX5g11hgjRCB4iOg4Y74kwGnnCFTGlKUqK2WdM9qC33/QWmfRGWeMLsvz8vTTTz+PkxgcaKsyVW5cWq+sskCms4IyZpydpiknZJLODo+PB6NxEMe6UvefPeiP+hvLa5wBoxIZal0NxzOgcaNzqahIOhnpPDN5ud5dScu0cWmxUulnnz1vNusv37h2e2vTOZdm2WiUzsbF050DJqON7WvXbrwoAi6lyLMCBJ/mxb1nT6I8BgJxIAMmG3G02Ov1Wt1Oq9NqtLoLi4EMS2JklHS7XQTYefaUAfnmN75x8+bNqiql4FpXpsqfPn30jde/trq+ura0aI0lIBzSzsLiQm+p3+8DkjAMGSOCU05plmWD8zN9jDKMVla3IRBlXgYy1Mocn5w9fPDgf/g//atutzMY9KM4ajbqMpJJbcmhqyX1yui/97t/70+/96d//hd/+n/87//VQrd1cHxktZNMvvTiy9tb2weHh/3z84dPnp6cn5+PB1lVdpfam9ubvYWeECLP87wqAAiXIggjwmgFOm4keV4QIILzoigZJQQIYxy01tooaxklYRhoIEVeVCp3zgnGgzAEcEmcxEmUF6UqS6NMpfLT89OiqPK8IATyvLh541acJFmWztLZwfGhNnbz6natEQVRYDQFRaqqMsZSTo3Rk/GEc66rKs9yh64si6qUUga7B/taqVar1Wq1kzhGIEVWAsJoMGKUhFEQBDKd5QTAoc2yDAGREOdwPJnUkhrlLE1TQohgAhyxxuVVYbRqt1uAOBwOEN1kPCVIPPRCKYWEBGEADikjBH1/LyHcF2BQZ2wQBlrbS8urp8eDs9PR+VlGBUuSGJCWhZWSd3vN3kKvqsp8lg2zTJVVWRSzdDqZjoyyk2ka1etPnj395LPP0iJ7+zvffPObbzeSgFjbTJK11aWQQSCYKkokQBgTYTDJiys3rq9vbVCJxuWOMCFojLi5tbx/0Fzqdt98/c7qpUXJWX802Ds+/+FPf5nqPGo3vvOb31pdWowYLPTa3UZLBoGz7ujoaDwaTSYzxtjGxlahVFFWUZhEQa1eq1+7dm3l0iUghBFaSxIpBBNs+WjRlvr05Hg63UgacZZnRgZK2cpWSrlOtytllI6Lzz+9v3RpJY5rxjjvw3n0+BFq2Fq7jJapUiPTxuLS0moQN8a7k9mEZ1NXVlYbDeAQKBXCOWIdqkqHcciFoJQKIRrNRpI0qvwAEBijQEBrpbVWWnPGpQwkJxQoGkcIogNnnHHGoJ9VEqDEujkLmjFGKWHMr3kEkBKggM4BErCUUV8ZZoxh1lrjCEHPzXcAaC2hvp2LUC98Er9gM0pwbsOhzAFQdMiIr1xxzlpr0DlgQDkjlDDKAdAS6/S8BcB7lZxDhgQp8SghbqwxVqNzVHiHD2OUOgQvgvvf6Vmb3l6N6BGGjFAglDhAo63xhyPrAHzbJDik4NBa73CjhFyIx16MdmCdQ4eMcikkIFLfaI1gtNYIHvzo3bUO5nZbcMiAxHHUbDUOT85UVe7v7TEuQhGwgLZbjVarkeV5VRZ/8Vd/OhwOYxlSAhRIqUpLSKPeTcKaYnoyGZyfjo73z4XkYNGio1xqhNK54Wjyi1++u9hpry4tNjvtLEtZJE4HZ1leASH1WoNJ9nTv6WySlkWVptXR2XlRVfV6O47rjHFdGUCgjAMnMgw4YZeWll+8/eLyYm91eaUehLU4CnjAKeOUAaNVqSpnRRglgXy+s3t8uB9HCaCdjUdWG83AOEXAJGG0vLyoi+J476jV6cW1ZDkIOA+/+dbXfvnBR9ZoKeqUoDOVoyAkLctykqZBnDw/PH6yu/voyWMeyOXlSzISHxzu/+v/+//jv//v/g+/9Xd/W1Xl6dFxqTUhhTVgrI3C8Btff+PGlSt/+Ed/+O/+53/79/7u/2plbb0o0karmySNJIle1XdOT09+9etfn/dPnz/fFTIMIjnoDyljztnB2XBvf+/k+JRSemllBcF1e21rrRDcGsuF4MY5tNY55xw4NMYYraNIWot5WRFGozBhlDFKgaBSmgEggDcsM86iOOr2enEQO3Bal83WJqHs6PBw7/keF8JYc3h8dNo/v3bjeqNscsaSOJZCKKayNJuOpysrl8oiN8YabbRWtWYSBKHWyhhfdSeds9PphFKKjjBOi6JgjClVESBSiCLPp9MJ4zSKawGn4+F0cD6o8rLX6xHCW81WrVaXMqCE8CiglFnjCIUwDLVSrVYjDAJCCGU0SeK4Vo+S2KN9KaHAkHPfSuisMZRgpUrKWK/bu3btxqW11S/v3t8/PpgMhtpa7iCqRdaY/Z0DChgEottq1ev1equeTmeD4WA6nlTG/E//8/98fHTkKMymxdOne6+8pNLxBG3JNJbZjDMWhKFDw4SkgmW6ODo/eeHWLSooI9ZZJMTk6XlSa22tXvpEBrev3bx1/WavXSfOLPbal1ZWHzx6/GjnQKUpA5ScOaW0UnmWGqWHo8F0Os3ztFaLmq1WGEogNJ3lcZwILjmnWVo0Wq1GrT4aDaIkRAfOucXl3p2vv/r+e++fnvWb7VZZVc44IKzMS6R0OB7t7e7mVbGzs/PZp3ff+PodSvG8P/jyyy+ePnvebnT294+MBgTHBDiAnecHhJLN7W3GotFkCq4SobAWjENVpEVZler09OycUpaXOZM8y/OyKAXnXFDjtBTcOaec9RQgEYgwDDhBpx2jQAhBgtaCN89YY4H5VmViuWEs8BvAHJ1P5rlgIACOIJmXMPuTrK2ctY4LNteCHXEWrLVu3iZEOBcECGeMcEYtRQAfyAJ0SNCil2Tn8Djr0Yf4twQGAtQ6R4kFMn94z18gc5QR4dY6Y5yzVvpvNNclwLe2eIOyQ2ecY/5Tiw6AUUYIzIGGzhpjtLXGF7NQKgQXvtfY+HLqeWRs3mvsHDqn0VC/W3DBnbU+VqEqpZWurDEOtbbOWqM1IQ4BtdZ03hZbcSbanfb65lUhRV4WBDEOonarybno03PGOA1dzrnkrDIVEoKUylDW6g0ecg6iHtVn6SRkQrvKOax0ZZWq1Zui0UJ0T3eePX7i4ih0tnJGO4eSsyhKoqRxOhz91Y9/AQat0ZUqtTIykM6RKKo9uH9/PB5JKZTWZalKUwwmw06neev27Rs3bnWadVMqcEYKDs4qpZBJjjwKpKSEiYACEuIiIRcWugQcQUfBUiSRlFc2NhMZJGFgNa+HtWwyy1RuKN3a6K2trblf/JJixYizpkI0RVWFoUBCoiT46NNPHjzZGU0yZLTdqr/yyks3b1+/vLXxvT/6T//6X//fjo4O4zjudbtn/cEsy3Rpykr1et2trc3xcLx+aS2bTX/6s598+zu/ubWxnpVlEIpOu+MIC8JAO/2rX/8KHS72emmeffzRRwgQhJJRns2KOI7iJA6DIIqjRtKYjicLCz3KmFEV56CUNcp4niiio1RQypQy2SznnAsuojBAZ6fTidEaiKvKihAymYwphSiMlhYvgcVZNq3FzSAI8jxngveWFw6fHx2dHc/SWa+3OBoOGAVOeZGmQRAqpSilQSIJJUEQRhE1WltravWa0zZXabNZD6OQUTo472utk7imtQUCUoiqKjnjgnPKiHEmz8tGOwmCQAaCMpbm06oweVZ0Op16s2bMxS2ckiiOKWWEAudsOplIy4WQ3knNOBeCOWspA0aY91QQSq01qnKM0TwrLBqkxDjV6bQbpHX3wcNGvTkdz4ACY8Qom/SiN998odvpBCwk4MIoajUbeZkjJccnJ7//B7//7ru/fOnll6/fuPqDP/nz+/fuf3n5s3o9tEUJ2poyB4fLa8s8iASXPJDjLD2Z9G9wLHQR0vlQwRp4vLMnkdSDgDorGFVFIRmYoiymaS2SzUQK6og1kRSMc8kFp5wiBpIzypr1Zq1eC8PYr1+M0VDKZrNRVuXe7mGS1DvdRhKHxhhrbZ5lRpvhcJTn+dOdZ2fnZ85ZxqgxVltzfHa2f7BPkDniyqL4s7/6s5/+8ifNZn02nQz6Ay6FNcO//PEPPU6YMae0TtMpIVTS3o9++GeMV4GktSh0SNBBqarpbDadpsZZSmlRVkEQTWdTh/a8f9aoh1WeEwBGqdLaWQeOSB4IGQjitNXzDcABY44Q8BMU4iillgiP2ERC/K9BAEJ9H+J8io0OHCPol0Fj0FkF6C8NFChQQgCJM+icI0jQOqBI+BziT5iw1nP4ifeAXiCh4cI9il99zTijhDjGHDprLqLG9CssBBICgMits84aZ/FCenbomN8e/ATfWl8IYNFL3egAOKXMH2coYw7dnFhBkFIiOJdSMs4QwWhrPQUCvSPIKxLeCeVnVYwS6vUPRGOM1VpnRQGUGAfWGGO8QxYcogXURhlrZCi3L19559vfWV5e0pVxToNDXem8nO08e7KytPjWW9+MolAIrtEwKvKyGE4mH/z6IyrEq6/cEYxatOlkPE0nRVHmWXY+HJ+cnsVJ3OsunJ0eZ3nKBCfEEQvNdjKZjotKddqdssj6/SFntFGrB2G8utYzqhoMJ9qUDx7ce/rsmf870FqXSiPgnTfuvHjzVrfXowCUh1aVBEEKTgApsLIqrbPaGSwzYGw8HAhOOSOcMee0UqUFCJxoJMmQEkCsioJpl5Xl+XiYF9qWyqkSqioUjBOtqgKddlodDE539549ePDo4KzvgCW1WlHqK1evv/7G6xsbqzeuXTZV+fNf/OzPvvf9jcuXv/sb327U6/V6vX82Nm702aef7Dx9uthrSRn+i3/6377/wbs//KsfvPbq66/euUMJ4ZxnRemsnYwmIQ9WVlfiWu1y/RpQfPzk4dNHzwTn7V671ew06g2DbjIeFWU1Ggx6vS5jVCmltfYUByGYT48apayxQBAYhCzmlAkhEA0hjFB/VNfo0BnLA1Gv1fm8LSdGBKUr66wqteDyys3L61fWrXX1RsMoRQhx4BhjhJG4FgHBNE2zPANAXSkZSMpJpapslqXZTMggDmNGyZRNpIyN0eks5YyH7RZaDJOAcTYajNN8OstmMpZ1a/PMlkUZyDCKmFLKOaTAVKE4NUIwq40lGIZsNp2leWqN1kY3G41Gs1VLkqKqslkmAhnXEiEF59w652OfRuui1FEYmkp//vlnh/snUsaZVvfu3+0tdDsLzUF/zAipJ0m7VT8/PFXjstNpNFoNZ3RZ5kar8/7g/fffPdzbe/utN//5v/hvq0rd+/z+dDo8PTkb9SkDEjBS5nk6np0NR1dvuLXN9VqznZdVnpfWePs4peA458gAbcUZd1of7h+oN17BCmUj5pQKwiIuiDFCQMDpYrczPO8brQvEVr0BCM4ZzoXgzFlDgAjBe90O5/Ly9vbz/d3D/cO9/f2d56pei6TkVanzItNKn5ycVlV5Ojgdj0boACiEQUgIOTo6qYzq9RbjOAlEmBWFUppRns4qQhg6ikiyvCyrsqyKgHNKgRCM4ohSd3p6OBqdSUnjMCSUAaBDygUjlCVJjVAynaWh1oPBAJBk05xLFkeh12w9ogcAKGNSSOa0BvBzebjoasGvClTmuum81W7O2pmvyP5Q7cFwF8rqRWrLz+IZo0CoIxYtUkI8EBd8za/PBQBhxDMW0GfR8AItPQeIXjQF40VfC6GM+jgCZ84gofNnO39QShCQW4u+lZjOn6+XFDzYCBDREwqtf1CPuAaklDHmy78IOKSMccYopULwIAgFF+QCaIdaO43zVwKIxAKgb/hjnFFK/C3KWoNgjNZKG2sUEgJA0Tp0SLzOjOCQVJW1xnImDApVaYIYBoFzDBzmk5QBiYOg12kyitapWEhimXWWElJPYudMKMOt7VUG1KIVdEMbzZmsN2pIye/9f//gy3t3/4u/+9uMknyW5sV0MhkGGPaWF9//8L1Hjx8vLi2tra7mxax/1t9aW19eXb5x9drg7PzjLz67f+/BxsblIE6sscZVQRAESSikWFxcWl9bU6UGdFYbQMIIrcqKU0YZSinzqkBCKCVcEr/6l+X/j6n/fLI0y/P7sOPPY683mVlpy1d739M9szM9O7szwAIgyAUFikBIECOk/0cREAWQIQVDlBQgwOUudjG7XDN+dqa9qeqqLl/pM29ef+9jj9eL59ZQ+aa7syuqsiqynuec3+/7/XwyiK1xxkIrCuGAbrYbhwdoMZ8iByVCpcmNLa2Rg7PD2/dub++sDS9OsnQiZTkZjy5Oz+fzpJACUPjCy7cg9D6/c6fUcntvh4cBhCiZL996/c233njr//av/68PH9zv93rvvP+tzUtbl6+6siy+8756cPderRa//NKLzVZXiuL+vYe//e1vAIDv/963J5PRdDZ/8vDhnTv3Ll1av7y3RxkDCHPOOKfAoGfP9keDcRzXEYKm0GUpk3kOMXTAar3KQCijpNKUPPeoOiecsdpRTIPQc9ZBAKx1ge87wMsit8YYqyMUUUI8z0MYzufzweBCa+2sJQT3e2tBGASe70ehMmo8Hmkti7LIstz3/CAwYRwijMtSVEV8IaTSyllrnKlEIkJkzrkg9CnjEICyEJ7PW62W1soYpbUWslwu52mWFGmWca/d6jSadRWGfhQ469I0r9VqQS0AGCphHYCUMaXMYrGYTKfAOamKqsSutfR93w98iJB1TpSKeYYjiAi11hFCpDRaa+7z21/d/os/+zHE2GP+yflZnhWhx9fXejXfc8bWQp8BUPM8jt18PJpOBoz6YRicDE7vPbh7dnb+7Xff/S//+R8z7p+fnf3h73/w4x//1fnpWafVMspQigjBpXNnB6fDefpyKa9cvZ6lhZG6LARB3DrEmE8h9jy+u82lE7u72xdno5Ojo0vdnrUeRqTTanSbLeYQ0KZMM0ZIrVYToihFmSFMKY2iqMhzKaV1jlLOeVCLo/7aWuiHPOAiL9IkOTo+LPMUYgcBcs5JpYq86PW6/XZvZ3vHDwKPc0qpde5iOBpeDEuhMMWM0i6C/X6vFtfLUjTipjLqYjDcP3zWaoWbG5eUFHmRYYTDKGw0O81GzSoNoQUGlVJACLnvZ1n+bH//g9//oBY3qrmLLKVS6j/++Z89ffYEYYQg1MY4Y51zxlatLoiqlK9ZHcFXT32IKsnoanqOKvC9M9YgAKpaQPXMtMY5Y611Dlnn3Gp84wCstMEYI4QAwBghYhASqCI/V0/0quVbpYWtNdq4qpxVtX5XwZmK+wztCvxpLUTIWgeet8sqNQ0wzgILIYAQAYiIc7aC+VDMKk6WqxCdlW4YIyfgagwP4O8q61UUtvqV0HNUEAaQUsooY5Q6iKyxBGO7ak6vZlUIYgA0xphTzhgB0EmlpBDAOetWNxEIEQQrOBKqlJdVWApCgCFh3IGKv+aEEI1GYBRWumQe0dYxTru9nlKS+zVjLUIIE1Qp1kYXw23f10oJoWrNuMgKKaW1mVIijGvJYqGlxBhSjK/s7VKK41oIDKo3auPJZHA+fOHWiy++9GKn08QATsdjL2CRH3qMj2aT44PTq9eutLt9rdT5xYW1mjLiBz6wYHIx9nzPWQMtAsA5CD0/LEWBrHNG+2HgAFRCiVwopaSU5UyVsixlKUSJoJ3OUuvsYrk8PDza2r7U69TzYcmDQCp1/943Dx/c397ZunPn9mw8uRidAwf77d7la5f7vX5nfWPv8mXMwsV/+3+///jZrVs3lRSM0DgKlz6/+cKtP/5n//Tf/ft///jRA0Cxc67eaiMI19Y3Qt+z2hgtx8OTIPB/9KM/+Luf/uzTzz/hAb929fpwMvzqzm0tzNrm2trGGkVMaoUgstrFcRRGfDyaPnzwaHd3Z3t3q9aMNzc21y71q+GiVKosSyklBJBiDwCAICaYWELzMiOEOAON1owRCLE1llDke15Z5B4PEEbAAoyQUirP84q+EddixthyuZzNZxRTgGBepsY4QgiEgHOOEAqjMAxCTrlW2lkXhJ6WWmk1Hk5KUWijgyAoizJNUiUlhMgqjRH0fZ8gLI3wAx84IIW01lpjESbGGCGlsS4IwyCKPO7NlzNCmMgloQTBVUyCIVSIHGPk+95iIStuvFaq+ovkeTzLC2O1FAq4kjIahqHW1lFjLBW5mE+Wo8GYeszqaX+9++ofvtbtdD2fE4zzJJlNp5yRbrPFKS0KMZpczOaTZ/uPP/380zzLfv/73//+Bz/o1BuzyRwU+uXrL3xc//h4/4gBorX2A58yWmu2C6W+/uabx8/2r1y5nBc51O7iZJDuXqn3OnmRYwRLKZnv1cLa1RtX81ycnQ9rURSLECIY15sb65u+93Wh9HKZSKEpY8C6LF9ejEdRGHDOKCZSSaV0WYqyLPOy2IzC9Uvd4WxYr9fiekwZOD44pB4N/ajRbMxnc1GqZrNunPnOd767s7vte74spdBKa5PnxUcff/z1na9fuPnizZs3wjCo1eq1WrMW1bVR9765+2/+u38Th41/9a/+lZASGDsajefz6WQ6Xe/3L22sY4TLopws5sA6xr2L4fD2na/9IGSM+b4nlcIII4EYY8YCqTRESCttrakmOcZYbTSuzuoIwhUscwV7q6TwGK12vwiuwMkQImMNsggYAABSWiuttbUIIeNMtQR1z+WNVcISYUQwNhoDB4zWzxsC0AHrADLWWOOq0NBKwo6QNWpVR141h6vHP3j+Xlh5AVb/c3U5cBABhDAAgFgAIUKEEsooIXQFIKquM6jaFrhK9IsgtMAqrTUjsCL9YITcc+kAAGCFgsMIY4SxI04bZpxTWgALnKnqZJYSTAgNwsDjnrPu+WQAoOqPEznf49ZZbRDRxmhrtEEIc+7JolDWZVmRFyVh0WqrYRylDAAzmy3zIsmLonLtaKU4ZaWShBChNMa40azHcbRczHzP8z0v4H4pSqOtVDrPUwDA7vYOhkAp4fue73tKyOV8eXx6cnxy2O11a/X6ZDz2OKGEMMahg2mWZkkmpex2291uGyIcNYO8yJVW08m0yPMw8mfTSb3ecM5xyjDCSZ4zRq2xnCHjnFQWU0oDyhEbjkfS6NlsfvvOnR/9gx+VUswXi+V89vTR4/FifOfu1yfnp1tXdqbD8dnR6cnp2XQ2xxhNRrMwCH0v2Lm02+v3tne2+71Oq93pbV9llM7nKQRg69Klbq+7nIy1lP3uGlDq2ZNHr77yWrPT/Xf/7t89vv/w8PDZBx/8IPbjbLHYf/LIw6zTapW6IMQLgtof/MEPP/7441/88ldPnh4Evocx2r2xfeXy5SiMtTLYGojoYrl8tr8vSl1v1I22+08PTo5Pbt66tbu7m6WZR32IgLVWVJd3QjhjFeYWAkgIxZggZyEAlBIIkTFGK4UQLfOCURqEPgTAGJOmyXy2FLlo1OsAgCiKhBCz+WQ2nXu+5weeVLosCsqoNnZtY8PjXtV4ZJTVajUphMd9g7UrHeNUyDJNcmeB73MpRJHmrXZTQVdRzAEAlFCPcyk19IM8zSmj3BpEoLF2OBzv7e2EQYQQJlkahJ7WWhWKe17ltqy4dRAirTVnLAj8KA7ns6XWlngsrtUZ457vK62VloSSNM0Qwq6a/TrUbDevXNsTWtVqtUZcQ87NxxNKKcZoc2Ot1+ooUTLG4jhUShNO79678/W9r5FDP/j+77/22isexzqVBCAMAANob3t7cDbY3Ny+cnUviEIHXFmKF8DLV65e++ijTw4OjighWqmvvvjy/PT4+tUra91us9UMuA8o2tvdu7S1fTYYp8vFIkljL5BGER5t7+1QL1wUi8FolBclZUQbUwEdG43WbDYtywIihBFUUs3SNIxqv/j5Lza31//Tf/rzrc2tb733dlm8/PDBfS/wN9Yv1eu1ZJE5aJ8+ffJ3f/vT/f39drtlrSGYIgisNZcubbz04gt3bt/xfa/eqBd5Pp1OEMJxFGV50u123nj9tcePn0zGI8/zppMJIohzXuZimaRXfL/MSmWNUgpBTBmjjDHmGWVAABEm2AFsrUxSynkYBEoqJRXCWGtHKa2m6sZY5QwEFVsNVo9/jADByFS5Bbgi5VTPYoJRRQCtApjW6WqWDgEwRgMIKvCDsRpCXCmCEYQQIEIgQrBK/T+P4DjsgDXAOqC0FlJaCzCpOrzQAWiMUVpVnWJjLAAKEKc0NNZU9xRbTfer/0DP32HQAWuJAw7A6sWDVx4DV+U5jTXOmIorDSqmHVqhqx1yxiIDzMpoqpSucCvW2MqECQAE1iEECUIYIm01BNACZ7WBEHHGOOcQIaWllBI4hyGhjDjAKNUQWSW1tkgoAR2srgIOQowxhMg6O5vMO/0wjmJGWZZnPg8IIYSibJwtFktMmZQqiAJjrShLFAZKKakkINALfaWlD3hZFlWVIcuSZZIKqYQort24DhGiEAtROmCUEFmecd/Ls/za9Ru1eoQRIYQIIY1SBENjjLVWK7O2se55nufFlJBaXCtFlqX05PRk+XChpbl2/YbvB8ki7/V6YVy/uDhvNVqFkFmWcc9nvp3PJ4Pzs69v3zXWKKU+/eQTpWS/35dl8ezxk9lofPn6lYcPHnx+77b3iQet01Jzwnu9zmuvvwmNmc3nnNPXX329v945PT0jCBpj8uXS67ZrcYwx3ty+FAe1Yj5TslgudbNWe/jgfrPXeuPVV6Uq/uc/+dOjw8Ff/PmPv/Pudy6tdynBr736ahh6QiurHWUUQNRqtv4///5/evz00c727pW9vc3Ndc54lmTG6tlskabF1/duDwcX62vrzGP1eq2/vjYej8/OT7/6lOxt79qGVoUWQgghtFEQI6MNgNCtNlEAY4QwcdYarSgmwhbMo3mWGasYw0mytM5B55RQQgjOWClEnucYYUJJ4If13Ybn8yRdTk/OhFBr9Vq33+u0uhDCspRlVkIEq8gZBAhBAgzsdntxLcaEQQcCnzllAMdaayGk7/tKqizPnAOS+0rr/lovzdOiLMty6YeBs8Y5l2WZlIpTL0nSIFzG9ZoWxuQ5hIgxDgEIg0Brs1wsu90GYxRBVIHcRSmtmTsAnQOEMkpo9Z7AmACHfMatsa126/r16//rX/1Eq8MPfv/bvU6HM2aMSRbJ/fsPrl25yn2KMNbGWAiTPP3ww49nk+n3P/jerVu3KMZ5krVrbWc1QbjUot/rxmHYa3fjqF5v1kpZWuPSNL1549arr7z+xeefr6/1MUJ37t6WZfHgwYOv733FiN/vdYMoOj292Ll6eX19cwRhusjHdFqWUmrc6re7vfXz0fzJ04PDo5O9y5cXy9QZwSgbjkc+96OIaKOttV6I06K00NYa8X/4n/5kdHHxBz/4oB7HURCUW9sXw4vJcLi+1q/XGr7nr6+vzaaz+/fvR3F07dq1KIwIIQia0cXI83in0wwCr9VqLDC8OL/AmMZRTUmZp1mr0RBlvlgkURyvX7o0vBi2Ou1lmswXs/kigQ4AgAhloiitdUpr3/eWacq45/ne787NhBBCWXUXp5QhhCxEjDIllXNOKVnN7yt6H4QAY4KwgVBVI3SM4aowhfEqZPO81VvlIQFYVb5XI3VnrbHaOa2UJhRBBImrDubVQdo5B58jGFbY0aoSZhxAjlICIamSB8Ap46qBuavcX9WSohoZWVPdSKpB/wpIV3EjSIUwqqTDdnWxcVpr66xS0lhjjLHVuKqKhiLggNPWAmOdc6giHGnjnIEWGaOtsc5YTHC12LLPSZ8AVKtgSAj2fI9SUqnAlZQYI4AgJRRibImB0DogMcCkdAhh6KCzFkHoMEIEIYz9yIcIKiEXi4WDgLU9YGC92To7OysKUYgCQYwgIZQ1m23tNMF2NBqfnZ+99dZbvhcwzkpRWm2qgVhRltPZtCxFr9dDGHFChRIIwywvnHNpmgIINy6tB35gjSmFSJOEEVoKzRmxDkipGq1GEASe5wMAm62mMWEYBgTDTy+Gn3/+eVEUr735VqvZwR5fLNLN3cvj0ViVgvmelPLJ/tPz89OHDx6NJiOnXbfZoZDe+eK2NkqUuce8y1cuY0xeeOUVSKAuldPA5wGByPf9N956I5nPnzx+cnRwMJqM3nz7jThoQOQmi+nZycnB0XGt1Z4v5rtXr1CMep3eeDzyGMEOOOSyRao76u233zs9Hf3Zn//FxeD8b/7uL//l//6/vnXzlh/yZJnkIg/8UGkNCTcAXrq0CRxIlkulTa+3LoTAhGhlR+Px8eHx4cFRt9d75923qMcno2kYRC9/8ArhGGESRqGSuiyLUkhEEAK/Y2A5rXVV96/Q5pWBBEIHnEmXSZokWZ4h6BABWVYQgLjHrDVCaVlKa1yVO7LGco8GYWidazVbZVGEfhRHdQBQKUoAQRAGSmrtFOfU9zxKqB94WuvQhNa5PM19RrQWSuk8y/IihxAQTJVUca1WFmWS5kEYEUwbrfoyTRn3lTRxFKTLrNP1HHSM0ZPjk1bWCuMIIyIKoeUUE8q5RzAhpIrMuSzJIIJhHGrjjDGMsiiOtLHVcgJjqpUyxhRlTjGlBG/tbDYa0cVgcH4+2Nu+7MWe73uBH2irP/v8i1sv3jRG+354cPDsN7/5dSHk62++5fn+aDpRslbfaZVKaGso59yaXrf34ssvdvqtWqNGPQ9Skhciy/NWq7W9tZkly26ns9brX79yGRFEEPntx7/5+c9/9vDpMz/wPvzss90rV+Iw8hijEM3m0zwrtHu2d+2KF4Ze4I/G87/6yU9+9Id/2K7XAEBGao8H0FW8NIQI0sZ2O93pcnFwuP/k6aHne9SjpSgQQP1+t9lqnJ2el3kBfOAxZrR77733Pv/i87Oz81oUe3s7GCNKSZqmeZobbReLBQBwrbfWrNVPTs8OD/YBdEqqTqdtjTVaWWOjMGg1W5PZuBTl4Pwiu5JzzgnGnPOyEFUkUWl9enrabrYhRKXICcHVlFsbgyAKgsBaV52GEYalEFIqpDUEClUPWVD1YjGCaCVhgc9NJ8+f2FU8x/3/qYArM6RDsDpDmNUox2pttFQYQecoBAAjSBDWAFVIhOon1NYardVqTL6aOTkHAMLWKWud1to5hxEEBMOK2eaAtdYYU3l5MUJVVgdiiCGsiHLE6JUkxjpYQamMMVUaR0rloLPGOGDdaviFIEDWAAdXIX9ogLMWAIcwrPAU1WvfGQtXliwH7Kp55hCglBHCOGMYQa2UEFJpDRCpuHAIEYIJhMZCKBVAleHmudoSAkQJI5iUZXl+/uj07KwWRp1Or9vucJ9vrPchQM1WE1jYX+t7vq+VKqXwg6DZbjx4dN8Z4Ewl4kYYIkQRggggCKwdj8cAunqtwRjFDhqlc5MWRQ4tXCwXYeg34kb1glwuE6M0dEAIQUmUZlmaZf31dc8LCKXOWt/3tcJRFMZROJ/Pb9+589FHH8/m6QsvvdTvbzgIT85OZ7PZZDi8GFws5lOpVVEWeZYRgn3mtVotL2BCCgccgdgAhwB2Dn7nO9/p9zeWs5mWdjFb5HkGLOh0upEfcc9fLBfffPOg3mzt7GxB58IwvpiOT89Gz44PTk5PXn39dQzd4ckJgU6Vdj6dLrNFnpXjxXzz8u6169e1tpT5F+PJL379KwTBdForRZbnZV4USlqI8MnZ2Wgy3bq0nWTJ08ePKUa3XrhpLQx8Pw7D+4/uI4S73W6n1e2v9Y7Z6WK5uBgMtra3oigIogBYiCCkhGAAtTB+5Hse18YIURilpJSexyklCEOEiCiL5WJRlAVAbjIeZ8vUAOVxz/f5bKG1tKKU9UY9juM4qjvnkjQ9PzxrNJq1OO51e3lexPVa6AeMcS21Mfp3gB2tjdbGOWi0KUshS0kJq9eIzxmEIE0yUYo0zSjlrWbkHNBaS6nLoihFobWRUjvgPI8bYRkjUqoiL+I6bTSb08lsNBp5vh/WIwzodDrzPN5oNCjFQeBPhlOEQZbnvueLUiBCEISUUYRQmRZKG4SxjymhdDlfWGCt0j/7258t0sXO3sbg7PTk+HR6Y1qrxwDCRqPhoH36+NnPf/6rl1998d439/7ur3+yd2XvX/yLf9GsNazTCKJmI7YQnF1cWKcRIvVOq5HmW9s7zUbb8wKAEUXIQpsXpVTSWre1udluNZWQELjZdNJpd/7gB3+QpjkEKIzjrCif7D8uynIxmwGjyixXSi3m2d17D/au37x588UHDx49evBU5OKNN16+eXmvWa9hiFdEJgSlFmWpx/PZ+dn53Xv3AQYY4/F40qi1GCUYwUa9tlwssjRJkkQKWZQFRnh9fe3e1/eyIs/zglHOOKvoZACAyWQipYRgRQXeu7KrpLy4uCiKXJbCOMsY09pSRmuNhts/GA4nymjuOCW0XmOilIRQznmRl4PzwQu3Xqj4rBgjYy1ECFMCISKYGlgJeJHRJk3TosYDWhF4NAAWQYwQWCV8VkiziqS5Qjmvel0QQAshhPY5AqFCZFaL4d8J4q212mpqiV2hiADGmFBSwZ5hpQtwzjgLLMCYYMYpYwhB5yC2BiLkrDPWPhdAIgRhxYewz10zcHWrwBhXYaWVbYYYo38He6tsAlpaa4yU8jkJzlUoBgixtcBYa41GqJphVUWB6t9XXLdKR48gAsBhgJB7vhkHkBBMCOOMc0a01kpJrZW1GjhU+WoIqfAXgGGkjK4yVRAAa4y1DiAHLIAYGG3KshhOpz6hzw72IcRaqe3trSj0kyz79LPPj0/PKi0BwiiMwna7PZ4sWs3m1tZ2XAu00UVeKqUQxpzRZqdx9qtzxmiz0UQIWKWUlVBDZ6znhyf3vnEIOOiEKJ1dST6ww9pYqfXFaHh+Mbx+66bH/Qp0qpUhlEpZ1Or1K9euPnjwaDQ6/+zLzw9OTiljEOI0W04nE5GVsiw4Y2EtrNcbOzuXt7Y21nrdZhzHcTSfzVu95vBiNBgP79y5I6UimAOAGPORM+vrwdn5IF0mnh8igOMwRt+BH3784W8//O0ye/GN198qS9mot4vcfHHnjizN0eFpXpRxFE4GAwoxQjDPyuF4dj4cPz04ni4XzVbr5GwQRtHB/uFP7S9ef+UVbY0D0BobhGGWFxDT7Z3tte5aGIX7Tx9/8sknZ2dnb7zx5lvvvHl6enZ2era7u3vtymWfMwihUuX52cnTXDx6+HDn8s4f/OAPKPWisCaEKIs8jkPGCcbIOltlqBFExhgFHHXYOpOl2WQyQQjMJ1OjVZou4kaEqC1lUWQCI8wZb3eaoR9RSiGCURgenwitJ1LKMIgoYwQTQojRhjICNRC5rGasURQCACqoOnQAIWC1RRAsl4lQygs8oQQmFBOMCQ5JOB5PHXCEMgAQoTQt0iAMIESUAd/zjDZSyqIoms1mrV7TSjFCfe45C4zWDMdSKGed7/HFbKJLaYxlnC3ni6hep5RigpTSxhiPM4JJlqWccc/jhGKtxenx8cHxYVQLHHRZnh4eHLZbLYJJkYv5csl9fu+jeweH+/P5Iq7XvvfB729t7URhYIwxUhZl7pxVSklZpmmhLEiLIm42jbWUcIddKcooqo1H43a7hRAMg1ArhQBgjHJCB4PzPtkYj8dhHPzxP//j/f3D97/3rWSRainS+bxMs+l4Jo0ZTKbEi4nvr13qGaROTk4uLk7ub/T3traarRqFWGhZFmVS5GleKmMdRHmhEKMWgePjQbPRaTfq1uooCNf7vaOjkzTLl8uk1+9KoZrNJoBwMZuTq1gpxZnXqDeXydLz+HQ2E1IsFwvoYBCEaZp4nn/12lUHrLEmS1POmZTSGEMwFkpMZ+M8y6Mg1MYQTBuNhpBCScU4WyaL21/d8UMvy7LJdIoQOD8/x4hgDDGBwGIhrNaaMlaWhZAiZAEwwFprrIXQEgyAhQhAgjDGBEHsrLEWArSC+DtnUIXghMA6q7Sudr0WOmuc0bbC7D//4SuOc/WZFQ238sMAaF1lJEAQIkSI53FKOcKoGsRIqVexJAeq0AysirXG2AowVD3DIUQQEUwQQavJlHXEWLvyxqOVyb56O0FYFYAdqFpllEAErLPKGGA1BrBCN1dzf+egta5y+T53oK1eRQiCagKLIGCcMcooIRA6s1qJVznXCn5tq/UzBA4hgLCpViUAAGusMbpaJDoHMEa1eoy5hx3I8zKMAsd0luWMevN5Mji/uHP7LkSIMMw8xhlnlFHOPZ9/+OFHGCPgHGc08P1ao9asNxF2zuj19Q2CoTHKOoMgQBggyphHHz54WG83qncpRAgAhCkklCBJIEZPnu6fnZ+HceCck0qWpZBG5rNUK12W2ZNnT6eziYEWWDsYXkil/CgKKOWM723vbK5v9Hvd3lpPG8up3+93PIqd1rU43N7c5D6rBbVLW5cIwn/9Nz/56os7b73zVpEXCGBCCEDw7GJwOjhjFDXCxtbO9tOD/U8/+TSuNRr1bhD6F8PR/rPD4/0jgtDDu3f/05/+x52ttW67AQ04Oz4fT+bT2fT8fDBZpmmWGmvrtQghzDAZjCe//M2HG+vrvbX1SxtrSZJDhNfX1rq9fqfV6HZ7v/f++z/+y7/4yU9+OhqNjTJxraakXOuu1ePG+sbG3bv3bt++LaXyAi/PssHR0CpLPeyAQ34gRGm1tqsuiFFaAecwRc5ZpQzGUCpRlJlSUivpAPA9L6qFWqtkuMAIc8+rN+Ned61eb9TjOoDQAdtsNbUxh4eHpRAYEep4rUJWUYoJKoVI5okDjlFCKYMWAAw0w1oqAFya5UmeZUkilQxDv9qklWWZZZnvh2EUlqUwNtNaQYikkHEQO+MwQs44zljgB0pqYAElBFhACLbGYUAJJnEjrujrAEClJMJwY2PdD4Ki1JQSxlkYhAgzEAFKPQCgxgZhhBG1wBRF/uZ7r87SybNn+8YAAN3p2Um73T44OLQW9HpdQkmjUT89PQnCwGjz61/90moVBD6nnFBMMLLOYghLKQtRHB4fPn6032i22u2eVlYJJY1aLBZFWRR5YYylBBGInVMeYyXn5xcDfxFzxmez2Xg0qtfjk8Gx1VYL4VPq1aJ+q60c3NOKeXXpYKMeOgR0Ryynk9Ozs9loxBh2RiulhdGUMc7DqNHcu3J1a8cfj6eD8/PHj/ZlqV588dpavzebTFutFufs7r377U57bb3POfdDb+PS+tnJ2Ww2a7c6pSw45+trl5rt5uFXR2dnZ41avdvteZ43GJwXRQmBS5Mky/Msz4QshRBSqNlinixTxvjx/rGWMk3TNMu0UsqYNM0450+fHp0cnxJCIILj8TgMQsoooZRTrrUFECGMtTKrHKcFUilUOdusRgA56CBAGCMHVnMK55xWuiLcW2uee31X+1djjbOOUOTg6mBdHZ2dQ86tAM6rkD+A1Rqg6vliQoAxBoJqygIchA5TximlSlskNcQIIgyq0dNzYhtG0CELEIDVA3aVNgIQI0wIQkBrXVGaHYSgGt5Yi+EqRVQ5SzCEBiEEIMSYYEgRwCtiaWWptK6agUGIVl574EAlJbOmWkJgDIEE1jlCCKWUMQoA1FYrUxGXbHUfgRAZB0wl3IEO2Cq5ZK0zhCCInNYSY6yUMVpzzrv9jbe+9X4zri0WaVGILz797MGjR6+8vE0onkwnUgiAndOuyEujYAm1x00yLx4/+zmwkGNKmWMUE0KiMIzjWp4XWqk7X922xtTDmHiYETJfLIaT0f1H33zvg+9bAwAgwAKMESUUUxzXuXXq2cGBg1BqfXp+NhwNh8PRaDSaL2ZCiOOjQ4AAxpRS2l3rB1E4mSx2r+7qrNTWXN29/MM/+EGn1S7yYrqYOAM9n2EHeBQxuoImUI8RwL773e99+NFnD+49vHz5SugHSgqjrdE2y9Lz00GrGTvpvMCfzxelkl/cvv342dOylKUorbLOAt8jWbL88V/8uTXy8rVtynmRF8bAZZmUmQCQdXsdxviljU3meY8fPZkv5kkuHj89LA04HQwQxO1Oa219vb++tr11CTrHGH/nnW+pUv713/7tX/71X7799jvtVgthhxEcDgaPH953zr5484W1S2tKS60d557WBiNU+Yu4xymnRus8zfIsA8YgCAklnseLPBdlUX2rUoqCIJ5qiTEeD8d5mnGP99Z9irGr6rbAhmGUplmeFXlZpHlWp3XKeWXCyrKscr5XxtYwCChloiw5YwgRXeqiKLI8XSwWRZF7HmU+scbkWS6lJBinaSaFjGv1KCLGOq0MRLbZbAaBr7WRpU6zPAwDSpkxJWO0Xq8vF2mF/IIYGGOU1MaaOIoRho1GvSohV4KaspQQYgQRIchYCiDM80JrVckMpDSiENtbOy++8NJ0slgmyywrpFT3v7nvBf77770HAQrC4F/+H/7lT3/yswf3vwmiEGL047/8K4hAv9vr9jq1MGKcUUIcsPP5Yr5IRpNFkivqRUWpCYGDi5OD42dFnlNGlFSAAKUNp4Rg0mm3zgbnCDlt9Hg8XSZJ5AfXdq8ki0VZZEhbSggE0DgHCEWEJ0K8+OJNA8zFYPzd775HlJJ5plXhtNZa56XMCymVtYhYi8JaPYwbQdx4+M2DR4/3R8PhSy9de/Xll+bzhXV2uZznRXbrhVsIozCIbt64ebR/9PXdu+++8+58vvA8vrO7+/rrb/zyJ3//m19/9MMf/mGeF7PpwvM85+zFxcW9b+5B4B4/fsI5zbLibHA2mYytdIyQO9989dkXcjabYUpQlcvyeK3e8IMwjMKqmfH6G2806o1Hjx49ffaMegEmbPXIJcRaoLWGEBptIFolN+FKh4gwxsYaAMBzA64DxoFKxbXaLf9OsQ6MtcAaiOBKwVJ5tsBqdQxA1SGz4DnSHFajHFTZw1zlVkEQUUYYowgSAzSwzhkLAajSPVUkHz4HU4PnTDcHIIR2NZBaHbKRNY48f/MAa5wzVczfQVhxqRzQwFqHACSYVIxSZx0CK4Q1QqhywjsErasaXnjlSHvecFt5jjFACBGEEUTarBgP1Q4EEbQKuwJonbbaOgAARsZZY7WDVlspdOmAU8Y4WBEjFPf8Wq0W+WGz3jYWJrNFWeh//I//yWgw1FrnKlmmszwti1IURZ4XWZIki0XqQc9YxxmD1kqtSpEvkoRP5ozge998/fmXH0MLIz/yQo4ddsAVMnVQE0p9z6/sbFKWea6xIdPpZHB2luVZnqf/8U//43KZThYTKTSGuNNqIwQQpp7nRXFtPB2/8crrL778CqGcUnJ08OzO7a8///gzD5Pvf/C9KIrjsA4BtEAjBy1wQmsNQCGkhthoc3lr5/sf/OBP/5c/eXz//nvfes8oWRb58OKiKMrRaDifToABDukHDx+cnw8gmZZloZUJ/LjXbd+6cXNncyfPEsIIJvCLO188evwMInBl91pvfYNSurO92+v1drZ3anGj2W588tln/93/4/85HU8hAD73t7d2ZFH6vvf43pOjJ0f36/WtrU1ADIH02rXreSm++Orzr25/xZk/m8zu37ubZVkQB++/8X6V6MAUAYzSLPH9sDQ68H3OPYSclloJIbW0RmupIAQOOISglKU1hnucMhpHda1kmqYYgU63rVoRwtgPvLKUKlDO2sViobTO8hxBxChtd1pKqTxPOafWmeFwmCSJUtr3g8DzvFaLUiKFss5Ca6QSZVFYZx2wy0XqXLBxqZ8s00KUAAJCKeesLCVIU0Y5JlhLnSRJr9fzeAB8p7mRpXIIEkptWRhrW622xwMplPFAEHCEkRRSGx2GIcWsXqsrrRaLZZ7lcb2RJFmz08qzMsJUa0MYwhhDiAmilGGAIcJtO5v01vqb25sH+/thHOZFiiB69dXXrly5kqQJQqjf7X3nO+9LIRbzOUEYQ3R8dDS+GPqB73NPGQmcm89nRVn6QRTWmub07KOPPwHG+j6jFGsttjZ3iqJYLhdayZDzuRS+z9Y3+uuX1mqtOkCwVHo2X3BMtBCx7zklADQAOIwRIwxgwjjHFEkEb71wPU0LytjNq9cCjpxR2AIh5XA8PTo7n8wW5+eTh9/cTz7+Igwba+sbvXa/yJaHR88GpydH+wcvv/CSH4QAuCzND/b3d/Z2ldWbW5svvvLiycnJ/fvfRFFMCHUAlWVpnH348OGl9Y3r169DDA4ORrPpZHB+9vTZUz/gZyfHZ6eneZFNphOtTbPWjOKQUJ6mWRiGYRhSypRStUbNGPetd9/d3b3MOJNKRnENY1SI/JsH96oyrpG2mqUYa1ZZeUaAsQ5oYIHDAP7ueYywdcBZZ4EFCBrlVowcCKGzKxnAaiu8qtj+7jVS1YUxwQhhAKBxFhvngEGrdfKKhAyxcxo8f5pCjAlC2DnoANBGP9/jwudDF4gQxBA5hDGypjJBguevCAgAsMYY4BwmmFSF4YrVCTFQpbLaQAQJIhXEsxK6Y4IJxajC/xgAADDAAeswrqDtDjsIAESIOYeFshYaRpmt6m7WEYgCP+TcAxAaoaVSylpttAPAGKukJtQQYoADABipFcBIaK2NKVWBOSacCiUI5kIaTJkfxUpZ4xzjnijNMk0W+bK/3lZGGFNyRhHncW2dYBqGkXUuL7LFcgENhBgpabQ0SpgkTQ8PD5RRYeiXRTKbT1VZAmQZQePRyEGACUEOcMZvf/HZk/uPmE8AhFLLIhdCiUa9Fnih0UoIeT4YSiG767293b1aUGeETAbjWqO5sbn21e2vzs7OhxcXu7uLTq/XaTZr0YutVvPZk6df3/16MZ/1e2t7e7vtdotxLpWGqHqjQoOxktCP6mkq+mtrjTh68s3Xscd5EA5Gw8+/+HI0uPj044+CkGHAaIC77d63v/fderNJObsYDEdno83NzX/6j/7R1Z0rCEOlJWHs1ksv/o//7//x7r1vti5t/bP/6r/SUu4/fdZs1z1KG7XYSX39yvV3337rb/76b/OsWOv3/8kf/4MiLYwx9XqspPI5w46GcSCKMkvzbn/NOffrX/2ackoIPD0+kUqCoVNKvfnW2866ZbLMiyJ4JQz8ADqopAEOGGURghgjqxRwDmPseYwQpJTSRnNKyqL0Ar5i7RqDCe63msYYhKAxkBHaaDaKoszzXJ6dh1EQx7VaHKdpUpaFtWp4cR7HV6zTAFgILKXQAjMejcMooJRQgJNkMZ1NFrNEabFYptZp6wCEDEDMvQBAVEqNqIIQSaUdwEabyXQSRAH3PKlUludxGGNEOPMdAGen54Ef0jbzPM9YV5alta7ZbMa1erpMzk7PmUd8TgjF3KPO+R7nACKjbJIktWbTD2iaZaJUjHEvCIwxABjPD+ByOZ3OpFLAQZEJxsjaev/t996CFiSLxWIxm89mcSMyWvmB3263tre3+/2ucbbIsuOjo0Wy6LY7W9vbQRCmWZkUxd7uZejwfDxQouQeS/Mky5LPP//i8cNHRZFudPvXr+xxTvMip54HINTIFrYsTSFdYS0FEAecl3nmgMOYVkfbPE2ieuN0OIRKRREDxqVpgoHvMWytlUplWXJ+etrsdN99502L8HS2PDkcjCfDIAya7XatGT55cPeLz786PDjqNLsW2Vq9ro0eDYeD4RBByDze7na63V6SJLPl/PT87PHjp5a6RCS/+M2vPv3iU2VKUWRKKWAcY6zRrDdqTaW159Pd7R0v9ERZTIbTS5t7v/dff5tzpowyxizmC6XV8clpt9tlnPo+JwRrpfJMGK0pWSFtgjDM0xRBhhATQlkLnQMQEACNg3a16AUWIAcxQivGjXXGYYyNthYCgpBDKyP8c0BQFQR1FeXSGFON5QEkFlS8IGiBAQYYCyqTM8LErS4GTuuqkguNtcaYCkRXwfytdQhBDB0CDlY5UOQActUM3gDnVpJLZJ1xxiAHMCEQVy8AtCoyr0B1lc4egMrWhCCCCGCMKuA1hLCaEIFKOANW1wwAUQWXWH25BhioK+Y1QogQSjGFACpVoe6qGwJalQYgtMaZqnQAnJRCO6CcLYWo6sCmGskRhDWyBmijhRKiKOdoFkYxpdgB2+t1lBbaKeScNpoQ4oU8y9M8L05OjsIw6La7mNC4U69o2/NF2mq05ov5rRs3Dg6eUIyjkHOfxUF9OBkbawtVXJwPvvjqq7OT8yCcC5UVudbAWQPC0E8Wi8D3MUSvvPTyzZs3e2trl7Z2uv2uEuKrT74YHA963e7N6zcYZWfnF1989gVw4PqN62pH1BuNfr8fB1Ho+be//Pzw2f7R/uH6pbVavc49DhHU0GHKNbDGOCmkKsv5bNTrt4s0efr0qZCykJJivL29led5HIe7W7uE4/ff/T2v7jdqLUzpMln++M9/PLg4lUoOJ8NmXCuFLIvZS7deevnFV+7du1+1JW/euOIRkhUZQUgLgQmZjUff/853j549ffR4/7d///dx4Pe7a6IQYRjUa3XmEc/37cAWeT4ejx89enh0clhrNSmFpSi1s+1e6+zk7PYXX58eDa7duAYJxIjcuFFWbURMMFQIIMc4dc4SQjnjhBJKSFnmADgl1HK+kKJoNGrJfJmmSb0ea6Uq6FWWFwTTre3NMIgmo0lZFqPRJMxC3/coI7VabTA4kyJnjGdZFkUhxQRjShlJ02w0vlCm4XEuhUgWy8l4ghDK80JJxX1vc2u70+6UZdnv90+OThfL5Ww27Xa7tbjpLHDOSiWKkbDKrl9aS9NkPl02Go02wRAhP/BLUZZScs4podVvM4pqzrpFskiWaeiCwKtVcBfOmZCSUk4wo4xJIQFC1jjGGUZYawshSrIcOL1/cPDZp5+PLoaXNtcvX7kSh/FwMPxf/sP//K13vgUBWu+vcZ/+/Ce/WMznN27dePWVV+u1enrrphRCaf3jv/iLxXL5rXe/de3GtWajdXhy9id/9qdvv/X2t9//ti2K6XBUFHmtVv/Zr3750Wcf+Z736PGTn/3tT8LQW+v1ur1Wu9vd3N4dDM4pokpaCJBzuMpgsCBYLlNEIYb42f6+0ebZs4OwVpsmc8x8QehiSWSZA6eAdg8e3D86Oi2l3Nrd9RhvdjqXd/beev2NwWj8xedfLpP08tWd9X7n6ePHs+VkMBskyyQMQqMt9WhWZMk8my3mmKJWq4MwWiyXs+kiyZbcD7jvMY+XopzNxmu9rsdZs9Zot9pra2u9bj9uxhBjj/rKyCJb/tmf/floMgpiP/IjAKFxCiN0dj7AGBVFFsqQVP4rZ6yzCCFCKYDOGeewwxgDhIxdNXUhQqscoUMAVhkgBxzAGBoHgIbOVSBP6xB01lkEGYLGWATQKkS/gj6trF0EkxWBmZIqnVOBpV2lRYEQ4YoaB61zSmmtTbXFJbhixwJrbIXcr2AVYJUzAghXcCFs4Ar8sLptAOCAQwARhAkmDiECYbUVqEioRKFKSQErkB20/9u0B6PfrTRcVZK2EFgLDbTVzAlAZKyTUiEMEdIQEGOMthYhjBGGEGllhBRlWSqtXPWmqpYfDlZhWAe0cVYIqZzRDkilrIMIEYgIRNg6a62WKlMywzaMPEIJRFYFHKky0x7HACzmC9/3izJf21gjmAY+KYtCCBWHCBPSareAgdpYI40W+Ww2wBh5DCuV9/vrYRQwylRpdve2fB4u82xn++ra5vZ/+rM/xQS9+9LbQtknz55eDId5nnU6rVs3b/7Df/CHtVrTD8K0yIMwDDz/Ikn3jw6lVduXd5R2rVZ7d2/HOSdECSCYTifj6TiOYopxs9N88cWXD/cPDo+e7R898XwfIEg454HveYF2RinNGJ9Ppz7FL914IeCMeJR5vL9+SQg5Gg7PBwNZius3rvuBXxQZDvBkPJFGQoha3VZW5nkhxnYS+kGv1/30s88VsM1Gux53rHNffvFFUWSmlMbZMiuXizn3gyxbNlrtH/7wR6cX/y9RFEenR29+6w0tlBISOptki+Pjk/lyPp/OszIrhdzeuvKd72zXo+D+w28e33/4+OF9xijAcDBUhcgtBLWo8cqrb1zaBEpJziiq+icESwgoo0YzXF0rEUYYGWuyLAsCr9FolXnhh0EYBWmSirIkFGprIERSqiKfUkogQlIKl7jDg6O1tT6lWGmtS4EROTo46a93CaGI4CIvnbFSamAhBKi6etbrTSnKYT4qSwkJtFrN5tPT09M0TxeTGSG0Xm80m22CsVBycjHWUiGCeci01gjjVrfme35ZlgBBPwqFlKPJuNfrSaUX80Wv12u1OtZaTLBzzmjj+5VvHYsiRwhxxj0/YB7XRjqLAIKUsurvQl5mFlit1W8//GQ4muxdvfL+t99tN1oY4bW1tXtff3P33t0f/MHvx1H08Ucf5Xn6+huv3rhxo9loQoSIJEmygAAZa2q1uNVtMcYXyaLdbhJAHt775sbVa8PjY+AMpYQL1ut1ktnyn/0X/+yP/ugfiSw/Oz0+Pzn68u5nTw/2P/30E0K8OKyNDse9oEeJBU4XZam0QAArrfcffvOzn/6CILi+ttZsNdda7ahWS9L8/PhUKIkAWMxmDx8+7Ha6r7z2aqNeZ5xg7JiHMCTdXqPR9o/ODjbW3201Wt96+9tSFYfH+3mSXowvppPp6cH5cHJhjCuE6LQ6aZaVUjrgpDFxs/XWm2+u9dcvra9TBLIk3ej0S1lGYWCMxgQhgAijpVQQO6tsHDX2dq58fe/R8GLINjxCkMeDbpfM54sgCJRUANjn8DQDIEAYWgecQ1WCXhsFn8M1KzIQrvy2zqHKgrKyrEO4yn1WIRpbAa6dhQhDSgiEpLoAYIyrp/n/ZgJGBGNCYPUqQBgiYK1xq9oYJNpZYoyTUmitqusXJZRQCiGyRmmrnLOoAvBXuhlrEQAIwEobCSCoNL+rY/rqYQsAhivctLOu8pZV5QBKiYGrF4tzVTrJIIBWCR8IAAAa2OdkImjtasjlIITOaesQ0pQQBJFdwUMdoxhT7ICrYnNSalDVEih0FgEHEMGgKlNUQaDKKbPSF0NMqIMIIgwBdA5qpZxxnNN2q6WkXiZLgomSqvrI8yLP806vG4YRpVxqcT64GA5GEKJLuzvV8gM4q5xKi+VwOrqyt6ehiGpBs9miBHPPS0G2WKa04yOCsYY7O3vvfvs9IYSxxiCRZrnS9oMPvv/H/+Q/v7q3rbVazBMEIMN+I24CgOfz+eP9o6s7u+1e2ymwTBadTvf4+Gwyn0trCiPzMs+F6Lab1preWq/b6Tx6XLt95w62Vkmti5yLPI6VcdZq26zXcLteC4Ire7sUw2myIIwvF3Pme2vrfT/0nj55PJmOuriLCKWYUuwBg6xxnheGUUQIhsieDU60U5gjbXQUh51OK0kSwtl0OhWlqG5dfuBJbeJ6PcuzWy+8uL259/Dhg/Fk9vTJwframpTGWA0c8GNfAel5XhSF29vbcRBGtTj0/BvXb369/cWvf/PrJ0+fOau7a74yxjiXFZk2GmGCkF4uEgBhEHrGWmU0JUQRLJVypQHO+r6HCPaDoNVqeZ5XsQY97otSXVxc+L4fRzWEcZ7mZSm73Y4SlX4dpunyYggajTrFtMwzbZQ2VhkDIJ6eD3rdLvMYoXixnC/mYLlMCCVXr14WgmNKBoMh8zwI8XwxF6UwQodxWKvXfc8nmJycnCAEanEUxZGQAjoIAK7X6+12lxKaLBMkZK/TzdIizbPFYrHWX2eEAQdKUTDG4zguS+lzFgRhIQqITJbm1gHm+R52AAAlNWE08P1SKEqZsQZCxAlLk0UQeK12K/RCbKnHfGdtp9159523fvGrX/7pn/wpctBa88KLtzY3LzHGAQbAgSgKMYYIYkKIdW5ra8v3AimlA3BtrX9xPghD3ut3RV4aKyEE27vbWllO2cbaOqXotddeVqL4x/M/enjv/jf3756dXkwm81/96meD8/OtvZ04jrQ1i+UszdLh8OL44JlT4O233njvnbfq9ToCqCyFbptlmp4PL473j+dJRjm/cfP6jRs3FrM5AlaKMgh85nGi0XQ40qJstBpxLY6j2OpgY72XpgnGeP/g2b//D/8eGvj+u+91+p1LG5tRLSpL9eTJ48lsdnp8+trLr7XbrX67Y4202uhSFCIVonAACKkAxDGpEUwRJBQ7jMn23s75aLz/9DDwY9/nDoA4jn3f55xJJSuUW7UHJQRhgrUxxjhtDQYAQOSAg2g1trEWEQAxwhUjUxld5d4RZlU6BwCHnnveK/gnBNBaVxkbVxN6AJ2uCA0OgOeQtdWQHjhgq3Wys8baVZbUGKu0UkoDAHmICMMEk6oZbLWBoIJIA2MMxBC4ShZZLYFR1R8GwEL8u/mTe37qBg64yiJfwXxWBQTgnDUrfNvqsoKfQ+kgWq2JwfNF8IohbW01arLWOeCY+13RAGEMEQMAaqmElKIUxljiUYgwhpVdzWGEKCaUEgQddthCBwx2gHjUQIAYZsBCZ6vCBTbGGeMWy/Tg8IQxhglbLjPq+VlWudYz5+zV+jVKGOFkMp1eu371808/v3xtDzrACDPQKuOgdUWZYwTXN/oYo3qtxijjnFrruM9LqefLuQXAWMtCtn9yunt55/jw5NHjR5Pp+M033vjf/Zf/zBRqNpu2Gq213vp4Ng+9MCtKrM0XX90Jouidb7/vh01ZFKXRYRzzwJst55PlLKzHuRBKq3XSjTttLSRGdNfuPT06yMu8lKUf+troxXKBEe71eteuXc3ThFNcyDJVpTIuK+bM84iUrUZzrbc2GY4ghEromh8ooYjPMCTc541689n+0yDkRZKWRk/mozTJL21sh1GNcg9ARCD1/Wi5XC4Xi/FoDCygnBPOr16/nqSnu5cvP9k/WKbp/uGxNCoKAk5onuX7z/ZlKV55/ZWN9fUizwlBNRQRD8aR/9Irr2xsbn599+uPPv1oOJpd6fZqcRTX641WA1hgnGE+AxY654y2xjgMIaFYaiWVwhhAjAglQRRigowzKxguAD738qxAiDTqNIoiY7SSZjqeDgYDIctGo8Y4q/rn6xtrfEqNMRhTIUrP8/KsWPI0jsNaHAshi7IoRRGSUEhpjAMOMsajKEQQZEnmeXyxmPu+zzkHABRlzjwKHbIOlEI0mo2dnd16rUE4ieM4TdMszxln2pi8zPu9fhB6yTKBCGOERCkQxFZb3/OC0IMAGqXLskySBEKstYTOEYyNc5xxiLGSmbNOKcU54zy8GA8vbW33Oh1GaBD4WiuMsHOut762e3nvs08+nU5m2zuXfN831iyThXNVjMrnXgAc2Ll8eTSZpkne6/bTNKGE3Xjh6t//4reHz55tr294ECMSzedJvRlvrG9wzqUsKfYH5+dpMVtMp5zSF27e/NH3/8GjR09+8qtffn33y8PTIz/wSqmTNF0mS2U0w+jb733rD3/0h7UggA5oKYClpTJREF3aooz7Z+encRSUoiQU37h+7dmzZ8JmpccRJPV6DDC8tHMpjnytpFYCIZtmOQRuOh51261et3Owf7i50X/73W8ZbYSSoe/iV1/54quvIi/gFMu8kJEgCALjjLWFyJXC7U7bGm4dIJgZ64x1xgLKcK/ff/OtV6fDeZrkzjpCCKOUUBKGke9xj3OEkdbGWgcJxgxrZ6yzECJrHUQV1g2Z5yd7gFfPRWOtMXpFzESsegYCCCFwmGAIESAUGFs9lwECDiCwWuliR6AxVgNojYPQOIQdsAAg6ywEEKwoQM+vE1VRzBiIKhckIdU8xUEIQbV2AMBRgg10FU20ksc8Z3eaFcEarPpkq38AYIwFDpBVWbhK5KyMlK4SB0PkjEErtcyqJeCcs9Vm+rmDALrnnQJQoe0c0FpX9DlCCYYQQai1VlKVUkilrHVQI0iwwxDAVUwVIYQQWpG8AWAIGYdYoao3b8ViBIR4nGNC5ovZ2fnFcDINvSCuNTjlSssg8J48eZomCaM8SVMAgZmZshBJIiilCEBnnXVOSKmkTtOsLMrAD+q1uiyFHwSUUQsAItgKhzFZzBPqeRCD0dnp4cHZ9Zs3AMRGO4bZ1St7vV43nc6hcePhCCOMiX/w7GncbH38+ecPvnn4D/7oH165eh06l9kUY9rp9sO4Np7PTs7Oa82W7wWj0UXj1UYchBDBIi0BAntXLz/Zf3I+vEiLtN/vW2OGowutxI1b1ykhRZY/vRjpUoS1ehB6y2XCKPcYjcIgCPw8y5LlwjoNEZHKUM+HiBpnsmU+Ho8jj5VllqTZZDovSumHEYJAFCX1KIJ4Op5NJhOtJSFkODgrcnkxHlPuC60p5XGzeXx6Ol3M+p1Wq9EIg8APvHancfXaFYopAOD4+AgjgBEyUjkA+mu9Xv8Hm7vbv/7Nbw8OTrZ2thvN+uD8YmN9wzgTebFWGgC7iqphSBml0igoIXDWWEaYxzxrtJCKEmaYBQhqaBnnAEJtLeW8xuvAwqPjo6IoGvW67welKPyAe55HKa1a70EQCqWUVIwxIUU5Kvq9fhzHlNEnxoxG40azCSHyPZ4XeRAEzjroYOB7lFCMMMHEWpcsF4yyRrOljW61271ur7PemU+SxWK5TLI0SWbTWa1ea9abhJI4Co1zhJFaraaEds4Zq4WUCCLggJBCKgnd74rtCIDK9mcBcIwSxilwUGntcQ4gGE/G56dnuhT1WswJTRLinAUQLpYL59zG5saz/f3km0W9Ub8kN5TSlLJGo4EwKZbLIhfn54M8y7MyL8uyOt31Op0kmQ8uzuphwAkJuc84HQ3Hvs+M0c65PE8dsNVoV6qCEdxqxO+89ZbU9md//8vlMkmLHGBiIMDcIwDW4mAwnN679/D65T0MkTOyokA6Z7V0Ua1Wz/PYD7Ik2X/8pP+td3e2Np88fTa6GDTa2iKrpKxAqmWeq7LQWmgpA+4pWUBrNnp95OynH33c63YajdZ0NgfA7ezsfvDd79z58m6+SFEUzUZT32cIAK1FukxHo6E2ptfvB8wnmBgprbVSCmM0xmxna9eqfSFyYxXGKM/zoiiVlEYrpbV1Vii5TJLZbHF2cUYpNdXJlRCrzHMcNLLaAQ4qnRaotrjGAQisMQDoyvELnEMQ4RUeFDlttdJGWYtcpT5x1RAerMjKVbTB2UqHZZ0BgFTP3SoY6lY+SQAxxoxBQijjjDNW7QAoXS0RKKGMYa2VNYZSjHAl9Fqd6xFEVcq0UvQaaAB09nl1mVhbKYON1poSXNHnAAKUEIyRc84CCwFEAEDgMEIr/xh4/jawFiL4O9EfggBjbJ1TWkMIMF0lh7TUWZErqY1zGGGjLcIGOfKcVb3S0GOCIQTaEcaYNhACqKTSSkMHtNI+Z9YB3+eM0tSWZyenwDnMqE+DVruNEXj29BlCwOPe6ekp4xxAVxYCIpjl+f7+EfP8vCiUVFqZ8WgspSaEAACKIscYF0WBCS4LAREmmC4WyxpG58dn9x8+fOHGlX67QdzupY3e3/6vf7NcLowUnBIklRSilLoQw2uXr999+OCrLz+7cn3v6rWtMl8KWeZFYYTauLS2tr5xcHjy9NFBusx+7/33Yh7U/NApkybLLM20A+sba6PpGKGTLC10S2+sb2ipDvcPfvq3P9nb27HKEgKiIBhPJzQlvh+UpKSUtNttIUSaJctFcuXq1el4BAjjfthpU2fhZDb+8osvd3c2fUbSNF0ulpPpXFunrfFpcPfruwCDk6Mjj/E4io1xWunxZDyazZyDxlrf8zBgwIFH3zy5WxaXtta//d47zXaz2WwQQrI0c8As5osLPqjF0eMHj2uNxt7uHqb0xRdedhiV6peji3F/Y516VBkDACSECCGAc0pp54BxphKdWuCcAcYCzw+C0J9NpkIo4ywiuCzKJM2COIYQWOeWSUogppSGQbS1tVVv1NIkHx4d1moRp15RlovlgjHWXVtDRamUbrabxujzswGlLAj8eqPOuFftWoUUnU7b9wJKGaYYIEsQqz60UmUpjLaQozAIAAC1OPR8JnOJMKQeNcaWpYAILZOkFtcb7SYwMJnPK8gkhqQsSi1RGPhVR18pVdGxKCXaOK20FAJjQjnXWhNjGKFZnlsLlFGlKCM/oJRNR7M0zQbnF1XDaJmkxpqtne3ZfDmeTH2ff/HlV4OLi2artbGxYR3UxsRxPBxcyDL3GBmenTXCEADg8wBojaE7Pzre7PYEJkWeGwAuLs4W81lZZFKUlBEHDILQWcc4X+/1W61mVkjP41EcDUYHwhhCPRqGmPEiK7PRdDyZLxfLJ3v7u5ubvU6LYgIx1BjnWoRBqJ3zPZ8RspjNbn/++cuvvrjRaz16+ixbLrVBi9E45r4TZUCQFAUEGgNbiCUhOCvSeiOux7XR+OL+Nw/eeOP1wGfLeXq8f/DmO29du3L56OCkFkVSFABY4CyGkDNfSrNYJIQzPxCcewgibTRmSBViMBhpLQ8OjtI0k1L6vidEmWZJlmdSKYQgQkhbk6XZMk8xIYR7EGCIiXmO6jFGG4sAhAAhB4x9LkmsDsRGOwiVQ6ucPkYYQIAgoogCZgEAWhpjjAWGOAIAxOS5A65ayjoLnIHAImedNaCafyALVuYsiRBFCHFGHMCMcc/3GKWEIOcAodj3uNaaUOyAIYpqLQkGhBAEIYIII0QQBriyw6y2t8A5Z7FzpBrkkOdh/crU6NDzkz7GhDFSvdeMNhAiQghB2FrjHFoxJqyBFaytgk5UkyNXraERRgQhiAg2xhpttTZKK0woqFbb1hroMEIQut8NoKouBYTouYzgd3AlqJSEIADOQgCD0EuSnHnMGaC0ARCWZRGEHkSwFLmUcjKdaK2V0tVugjGWZulgcBaGkdKaUYYgDnzeajSTJNPGXgxGURxmWWaMAQgPLyYQosHo4s6du5P5+FvfeWc6nQVhOJmPITQPv3nwxaefv/LiLQRtwNk0L0M/PNh/+lc//vG9e3eu3bqstVRWC5GnWSGBfvD40dP9Z5gSYNXR4fGTdi/w8N3bdzijy+XMOLtIM8ioHwaNVnM4kFKqfrd38+qNO19/def2vfl4GkURhCAKglar5XNfCuMHfsayohR5ISazebpcLpLFxsbWk/0jSrk28GIwPD0dnB0fDi5OG7Wag2A8mE7ns1LqtfXN+XLx4Gf3J7MphmC9f2ltvR8G4Wg0EoWAGCVpppQJo1iHsTYyisIM2PPT8z/5D392aWP9pRdvZWnGKYcIGGP2nx2+/NqrX9+7r5QC3webWzuL5TII4hsv3hycnp8cnW70NqSSlSSOUiqVNMYoraF1jBKEECUUIUgwqULRlDIpS4iwR+l8sdTa9vt9jMlkPD49eRp6XhgE0AHgYJGWQeBhjM5OB2Wp0izRxnTbvdlkThjJs8JojTBqt9utdts5m6W553nrG2sIo7IUy0UahlFci601GFOtNeOcEOJ5HqNMewH1CPdolhbDi3EQRMaCvCym49lsvrTWNhr1IAiTNG17LSXVbDYlmFJGO+1uWZTLZVKv1Wr1WlmWEACJJKU08ENtLePMOlu9G6pLNGO0KBHC0BpTZFmv1X/v7W+dnpxkWUowSfO0zItllp2cnp+cnwOjEaV5Xi6XmQPn88XyYjBqNo+10Ubr4cUFgmh7d3c4nMT1WjpPwzgcjUdK69F4PJ0vAt+DAMzmi4vRRVHk4/Gkt9YjDgGIIMJZWYacBVGonBZKzBfLZJkYrTj34kYjKUpEWG99fTqepvPZiRgUaXH47LDVrHeaLT8OWus9YYw2pigLh3HkeXGzMZkt7n59v9ls+J53dnZxfDoYDoe93ppWCq5YwsZa1+50y7JsNAHEuNZsnJ2fP336rFavb6yvM06X8+Xnn37+0muvHB2dGOesdVDLsigZodP57GI4zMuitKoWx0YZpVSWZmmaF3kxOBvMZgtjnRTCORsEgYPAWq20RhgyzKjHGAJhHG95fLlcno8utNFVqOb5EddaZxGphCqrD1StJIFzqJqqQ0qpAxDYiqNgIYIYEEAgdBooYLS10BEIq1M7rMDL1lSXBgIrnUCFxQXGrsg6GENX5QQwgZh43GOEVHlMayyEjhDMOHUSaAMQsgQhjCoIQ1X9qgztxDq3CtGYCqz8/ARfvQAQQivvmHNaWWsNwohxyqurt7MAQEIJ45wCZC2y1hlQxV4tsO55OwCCiklkjDGaEMIjTimtNg9KK6M1JtX6YiWwqcw4Va8TI2iJdRg4BI2xDjvrnNRKSmGMwQiFvp9nmbU6z5bXr1197dXa+uamVdA4q7Qdng8BtN1uR5SFkno0GS0WC2cAYZgzr5RiMhlrbSxww4tRFEWMe8vlPMuyIAoowV9+8ZXneQRj7vOL4SzLC4CAkGI6nUgjnj45eGaPJpNxslwg6OaT0a9+/nORLBtR5PMgrtXPDg8/v317Op2E3P/ysy/ms6S31vU9b75Mzs/Pz85HyzSBABmnjFLf3L/rMXT/q9vGGMopJMgLoqAWzeZzrdXO3nYcRePJdHtn94Pv/6Db73315W3tjNUmH2ZSyn6/J2TpIGCMGeOWy2WyTNM0Pzu98Lx4PB5/ffcb5sdKGUL5bDK/c+c+wURrDZw5HMwu9Tvbu5cJp0myyMtcK4UIVMosk2UYxQiRNMv6/T6llFCujY6j6MqVa5TgxXK2nC9ms8n9R48fP312+cpeLarNZrPpeDgeTyCGRrgPP/z4R/XGydlgMB7WazUE4HQyQxiVRelxrywFxtQnuLCQUuuAVVIrWXEKMcZIK+MswAj5vi+KstoYlXlZq9XSNB+ORrPZVHgBxoQROhqNKaNxHHLPgzgxVmuj46hWb9SSNK03akHgT6fTgIWEUOBclueiKAmjca22XC5LIc7OzxvNBqWsKLIojvM8q9XixTzR2jBKkmWSFS5gvraW+95wOFwmKfd5UQjnbBiFrVYbQQchpBhDBrTRSqgiL8qg0FotF3MMoe/7GCEHHaaEebzX71lXaTSQc5ZxWpalBQ5hijHkzJNKVn9/akHk+msOOM455Rxh8uJk+PDRw99++OFiOrt27ZaSRV5kQRRLaebJ+OD4fDA4m8+WvW53d28vqteOTs7Gs1nAvflikec5Y+HFaHH73sPtS5cgMlrr8/ML6rGizB1wDgJEkHF2OBp1Gw0Hwdn5RZIWd7/5Wkq9e3mv2elsbG3fvf/49Hy4vbt169aNe1/dW0zHaZEDY4fjMYZPAQbtS71Gq9Vuto02M1l2Gi1CAPf8k4vJZJH7Hs8KeXx0nqXFbDFP0rzerGVlrpRyDozG87hRgxauX9rtrV86Ph+OF/ODoyNKWRxHcb22f7CPGWMeIxgtF0kI/GSZQuQWi+VwOLx//yH+/It2u0UYqbBuopRSGM9jnX53vb8ehn6SplbbVqc9Ho8YZ77v9Xo93/cAglEcA+D2nx386Z//mVZCK4kIdtY835sChDClBDvrNAQIVYMVawGEBiPMOEcEK62UVNYagjEGiFHCGTPaCaVgXjoIqswnoswaqxEmGAFnCa5+JMUUEQIhdA6S57UCaKxFGCCMiOetSAoWOFuxVCqnjEUYEEhKLS10BBNMCIDQOQOhQxi7ygUJEXoeBMKUYAyN0RZAAgCs8EZGaUsIcA6AleAL4WomAxGEjHHGODIrRkQ1XnouCHMrxi9EdoXKBivwA2UIQVMxfyC0xlpoEMHV0R446ywCwFlgjMFaKWeQA0A7i6C2AFptoQMYI2CNUpISbLXFCNdrcb+/vr27PTgb+36orPns488xcnuXd6MobDdbAECtFKF0mST1er0sy9/+5rdB6K+trzfiFgBg78recj7bWF/v9jtZkkfx0ytXL2NCHz54PJsvKSdlIcpSMMqYT0+Pz41SSgtKKHQAI3x4eGiVvnH9erfdvfvNo8PjoyQXjPM4ii7Oz8/PByxg9XrTWJdkGaiY3cZC4HxO8zQNO81SZOPxCGHc3VgjnjcYXORFBiFut70oqiVp+tXtr1584aXtvT1K/ZOTY6WkKoVTRhSKezDN0martUjS8Xx2MZ6URZkV5YPHT84vhqPxvNHuWgB7a/0fvv1mFPgAGqP1fDafLRaNWiOMw5s3bxGE8zKDADgHh8Phz37ys3/yn/2TVqO1tr4WBTHzuFD6tx9/2Ihbv/e97zjrpBSiLPMi3X/27O9+8ne/+vVvrl65jAgaDoa379xttJvbO3WP8YvxaDwdf/bJpy+9+nJZiu3tzZ3drVarSQjRWhstPM9jnGotjEHWAYQQpQyvZEvOAYsxNkYLIYzWhJC4XiOEeB4DEDDGur1e6AetZoN7fDweLRZJFMUQIaVUp93r9/q9fg9TIrVCCFHGhBAOgAoThDEpyxI6KIXCCFX9/mWynE4ncRwbbSmjnBNrrdE2z3NK2Ww2L5Xor/WNs6UQxhmAEPe9Wi1GCJVF3qg3nAVa6cV87nm+Nto5RymtNxqEEmsNQriirUCIorhGMS6kUkZprSkjShtjDESYYOJ5TIhiNBoCAwkllXDcQeCAVUb7QVSrN/tr69/77vfeeettZMHhyeHx8fHHH30CpAAQaQvXNy5tb+5QSg/2D5bLRb6fYITSNF3fWI+jZjIv7tz+5unT/XotgAhYrTe3t/wwKPKScm6MWiTZyelZyP3BaJgtsuFwigh59923u/1+vdnq9tfCWu1/+B/+v4vZ9MXrNzc6vWdPHs+mY10URUmzNE2LPNk/JCdncRBz32cEZWm+WC4JwY1aTDDmlI6Go7IsfM6yZXJ0eHwrvmkctACUQiyzPCkFQpAxlmQFxFgpmxdlmuWUsTTLSyXvfH1Ha/ud731Hap1cDJfJgjFWq9dffOXlxXI5Gg3zNG/2OqfHZwBCxtjO9pbn87Xe2ub2ZqPeyNM8SZZhHJaiMMYkadrpdiBGxhhRlg5YYxRCq56TVkoppbVaLT6rozXCAGqAIIYYA2QcwMQRhBDFCBMIIbAOmBXFARNcqZUxwdparQ2qxtwIQwgq0j6lhHPOOWOUYIYYxQBaB4wxxhhojHPWQODwim0JrTXOOguc1lYqUQpZloU22kEoRAmsxQgprZ1zVTcNAIcJhAAhTBxc7WqrzwPgEISEUkwwqZxfEEFgHCKYUoyel7swQgAhDBElVRDTVr+N6rDvVjcJ8DsGHAAAIcAY5ZwzxiokNcaQYOwwRpggjCBGK3ElcFWG1Rhb9RycBQY4jFD1hUKEnNYOWKMVQUAbCYGtx3EcRUrITquVpjljhFGMCbLW1uOYe16eZwBBQjFnDBPs+14hCi9gvuetr/cODo+7ve7lnR2hSllKAF2r0zw6PB5NxllWaqsJIHEcU48YI9M8t1aHYVCr9RkjqhRFlispTs4vWBCUyj559CTLc0r9bqezvr5eq9Uuphe5KoFDyyx1EAqhpZRaKc6Z53n1IPjWu+9ArT/9+ONrt251+u3xbLbM8uVymeelce5scBH6wcVokn32ea0eMcpqjUaRppAzoC2E0GidF+V0tlgsk4dPnshS1mu13tp6t9d7h3v1ZqfZ6/zrf/1v33v/O//4Rz9c7/cIds5a4GAms+Oj008++eSdt9+GAPo+AwAqqZ88ezI8H7777rsb/TVrTCGFKIVH6db6pclkyjmDAEEIuMeZx19/o9PqdD/+7Uf7h0+Yo4WUd+/f39rc2NiobW9t+2EwWc5Pjk7myXxve/fmjRuhHzHKq+e7NNZaBxGgnLjSUEowBtABazQAUEoNIFJaKyWN0QC4MAoJJtYaIUpnbRzHnsfDOOS+1/V63OMH+/tKa1EKY22r1YqiiHnMQOA0MNq0mi3P48PxeDIaV/PfNEmjKA7CIKpF0EJMSPWgD+OQEmKsscY6AIC1CEHuUaHK87MBxoRyCqAjLCaINBrNIAiVlBhh3/cABFqrymJGEEYI+oHngLPKlXlZa8RCGQBWxyljLIQWOFcKIUqhtSqE9HwfIuQZarQC1lJGGIHOgjTNLXBMsVKb6XI+nIzb/bXdvSv1etPnnPk8qsUff/wZod5/83/6P25e2qzV6j4Pf/bzn/7ylz+9eetFRlCn2xqej6wzg+FoMl4YZ4/PB+cDg5D1vaCjFN0/HI+ncbNGCRkMB4s8S7Pi+PhCyMI5+NZbb25srse1elirMxZs9HoBJSrN6oG3cfnyzka/zPPFfJZlaZoks2y+WMwuxuPhaKbGk93NrXmST+ZLZe1kvuSUdpoNpUxcr2EAgQUfffwbjczLr7yWpSmw2lk7uBgqpR4/fvT48SMHIEBguUwPT04ms9lysVgkicf5Yr6UWnsez7MMALe+sbG5tbGzt+N7vlDi6ZP9rMytdftPDpTSl791GWM4mUwfPXzUqDeqqV0YhEEQlmWxWC6NsQQTa6wQAqBVCN1oDZ2FbqXhNdaAqmZV3ZMsRsBhiAjAGEBrLEKQUIoxsYRAAKCsLLkAQYQgNtBChCilGJGVOR1CRihBCDiHEWIMY4wxwZRVR2NrHcAYa2uc0EDpatoOnDVaGQgqRaUUWsjqBVBqY4y1UgoIqoczBs6hKrdU4TVJxeHBzkFjtNEOWEcQhgQTSqnncd/3fM9nhDlsIAIAOCkVobQ64j8XeCMIAcGYMVYJzExFRgUQEwIhctZCtIKjcu4xzjDB1gKMMWW06g2vWKkVb25VokAV7VprAyAw1gEEnMYWIutgRVzSSishKUbQOWcdwiDNltz3EHZ5WdjSaaPjetMYwz2vEnJqbRDClT14uVxMx7N2q6WkisIwjP39pwfb25ecNaPhxd27d7/88kslTLffjxu169evv3Drhct7ewi5vMiG49HB033OGIQwjuI0Sw72Dx8+eTxdpK1EXL3e2bvOzk/PZpP5Wm9jY33j2rUrjrjxfDKbL6aLxWS2GI8mh4fHC7moBfHLN1/qt5tvv/Z6vkyPnx6++/bbW7u7o9l0upjneZmmKaH0qy+/XC4SCNxUTgYXF6PRCCFALCAIcQwxgkIqRPlskUwm00ar/cMffLC9fenq5WvcYwbYbm/jaHDa6fRv3bolpJjNp4xgP6BGagjcs8ePHj98uHj/vUatgQDmHsNAthqNOAzrtTjwfUooyzMXxtrY/qX3/+1/+98fHx7V4roFttlsxFE8nox83/v+739v73B3OBy2Wu1lshiNJrW42ey2W732q6++ev6Hg1/84pdplkxmkyxLPe5hjLnHOWeUYegQIAxQAyGxxgDghCitNQhCa3VR5OkyXS6Wvu9TSinBxkLOeb1Wl1JJpUpZipEIw4Ax5gdBKUrngLWAUKq0efr0AEDg+55UspplOWOLPNdGJ8tlKcsgCALfD4OQM08Kaa2ZLxYXgyElhBBsjLbW+ZzXG3VrrNEGQljKYpEshFSb2zwMI9/ztVJSynq9EUc160yBkcc9xnkQ+lWygFFGfOyMI5W3yyohyrIoCcWIIOAAdCBNU6l0IYUDgFOmlUTIBREHzhqrAXLGqSzXIIwgprIUcVT77ptvdOvtMAwogrzw/DB46523RuPZzVsv+IwLKUbD8Xw2b8ad/8v/+b/xGacUQofmy+Tg4Ojk+Cyu1bSTx6dPTs+Ozw5PB8OLp/v7zlmAUBTFgDijlAfp3t6VKPLjetMPAgCg1qosc2vAfDaRIvMYbjZqHkWX1vtKSru5JqUQQuRlXub5ZLH4u1//9uHDh/31jddefw1Tcn52fv/ObVGWN67dbISRVYoSpq09GZ48efJEOQcAOD06mc/nWZFdnA+Gg7HUstPp1qKa0vrp02d5nllja/VaGEaYkuHFxWg8dtZyjwdBxH0/jmKMMPO89Y313/7mw/PT8yRNGOdJsnz5lVfqjdbB/rPhZFQKcWljQxnueX6aJWmaCCEwJhibUpQV28c5W8rSGG0dNNYYvTpxO/f8cAsRdBYjgiB2Dqx8WgBhTFa9XVeJAECFbwYYAQQ9Dqs1QBUVpZT4fiCERAhWiwWEcBXb10Y7oCuRCqgQbcAiY5DW1ukqfuos0NooraUQUohCCGetVLLa5wIIGCEUoefOdgddFbZcuQpWlWMArAPE45xxjzPf8zyCsLPEuorUZpRSQghjbEUlqgY3xgBinYWwAnwCbSqYxGpU5gBEiBDMKMUYV8MijDAlDAJUceactVob4ywElbG1KgRD41ZXAcSwc8jB5zALDK0DxpmyLJQqpRJaGYxVnqVZrvJChFGslGKMOWM5Z2mSlWVhHcjyHCGS5dlHH3+cFslwdFGL6xtbG9ub2/fvPzg+OVBCnp+dDi+GrUbr5q1bzVazKOVLL75y7drldrtV5Fno0yCky8nE45xg4vl+s91iYbCQcn7/8SQrE6Vba+vng2lUq3e7vSSZp8mi0W4GHne1GgCuFsQc0tOjY2DMxtra+sZaI/TTZSZEWW81mq32MklPLwZFWWJMqMcvbWxKrUaDsRAFBG44GOVhwQjURdmq1zvNGqdcO7fMs9k8i2r1zc3tV157dW9vB1rgnJnMp+fn53fv3KvX4vX1Hqcs8nwIATQu8MJaK37llZebnVa73VRCSykAsBiRIAx2d7ejKKi+uQjGFStKFMX56dnZ+YkxWitDEIIIlUUxGU0Iwdvbm5curRtjprPpvbtfP91/dnJ+2ut1r1zd8z0vjkLu8aNnB6+9/EotqikAKaHcYwRhpRQAEILnSQJrpFTAWc6YA87qldvIWqM1cNZijJRWfhggWBptk0VmtSYUi1JII5SSmGCE8Xy2zJIiy/NevxdFtdlsslgsEMRpmlDGQhaeHJ+UpVBCgRAQQqwxSioAwHwxl1IGPo/iOEsy7jEHOaEYEpLlWaPVpIxlWaGVwQw7AIq8UFJXRhfrgFYaYxLXapRR5nFQ5bWdZYRaZCtJqjVaCWe01Y5xxDEmDENrjRAFY9Wcn0pVlqJQVhihpJTGOK2FlI5R2lvraCHRfNmM6sjZIs0MxXFcu3f/rhL61ZdfqIUexhxCTOqs3e06Y3zf9z22mE3SNBdSMUbCOHz7nbdKkb/z7qvLxdwZUMpyeD7aP352cHRwcHQyHo+kFA+fPDs5v6CYWQPq9TiOA8/3gAXLLD0+Pi6SlACostxyBjCyUgtdWmsxBEHghZ6/ubOTWfPo4ZPFIum0W/VaPQ7CZhR8+ekXCIPt3S0GkdEOU9pe79x//OgXP//ZdDK12q2vr127du3G9RvL+VIIVRZFlmfz6dwYvd7feP2N1y5fuVKv16vH8K/+/lf3732zWMy//vp2VPNv3XzB94PJZHxycpZlmSw1cLDI8iePn1zavLS1tRWHwXA0tquTsuSMiVIWeTGdTNf6a8AB4CBBhFDsjNVKWWOsg1YbozUEbgXKr57qCCELq0mOtRY5U/WqMAIQIWMR1KAKxaMKqQmQthZhLJVSSkNgIcKUEO5RQrF1WmrrWWuBdRYobbQtrVXVBH5F1XTQauuIrb54qRVwAECkrdGVaF4rtyILAaU1lBoCCLGFEFgLKndL1aqpuNAAIEIgREhrQyoWHOWUUkoxscYaC601Siu1TJWSBlhGabV1oBhDCHXlAqsW1cg+F8KsluYYYowxwqj6jAUOIUgJRRit+gYIQWjg6o8TIoxR1aV2ruozO1ft16v9MTTGiFJIIQFFRVHKUhplSlMWuYriOkZYaU0wqTge1loAwWwxhxAprSCET589/ezTz4QQ7WYriLzA84ej8cNH96ejaS0OtzY3//D7v//qG69d2rj0t3/zd6PJ5MrujseIFaVVUhVZsVwUadqs1TzP576vHOj2L+1dK04G00VWpkJe7nal1leuXfYDryiWg4ths9NsN1uBX9ZqkSgVBugjqbEDtTDqttvYWCN1nmWilF/fuyedlNZeWt8K49D3w1q9dv/B/dl0FsZ+kRW7l3dfefNlJ829z2//4PvfXeu0OWf1Zms0m/3lX//0N7/9JMvlu+8Per016IzSSmuLiSnz/J133uy1uxRYjGCRp9ZKpZVUjcloSKBbzmZa6QQAjBGlLMuyIsum45EqFfc9gghl1FggpGy165PROA7jxWJR5pm15tNPPvUC//r1q87Ybqd9aWMTQFevxcPx5Ouvv/7k488++fiTOAx9n09GE6tAnubOOsYoRshqUypltLHPv3eN0UYb4BxCiGJijWaEhoE/m46Xs4RQHEYBREwWGiMcRIE1bjFfTMbj8XQ8nUykEp12p9lqYoKytMjygvu80awHgZ8mNEszz/OUNpQSz/MghFqprMgCERR5URSF0dYYE0YRyFJjjHOWEIwJzrOsFCIKQm21MTBLC4hhr99FCKdZTjDjngcRzPNClMJYTTCJ67EU0lkAEaQUK2nLsqCEaqOBsxhiz/O0NkIrZzmlOC8LTCjGuFaLucelVM4ZY6SxshSlVNJIC5x1WpVZhox99M03jWYHSpPJVJSl53n1Vs3n3nQ8+va33y7yTKoMWWgtWOt11rttaw2GyPN8AFAd4QeTR+PhEGFojTw9ObdGK2m5z2/duvHq668NRoN/82//ewiRtlqlubZWSpkl6dHRIfeodUZK4Yy1FrbajfV+b32jRwASstRGirJwFkgpLbBBFDFOup0uBPDs/GQ4HDSbdaBtPY7W+h2gbDZf1jc2CCJZWXhe8NJLLx2eHKaL7I//+X9x5crlXqdvjJ5OZ4yx8Xj897/9zXg4+uCDD370wx9STBFBAKweLvDbv8cJ/eSTT4qi/PLzL6fjabPVyrO8KGQUhjdv3phNZgeH+8fHRx99+KG1bmNtvd/rp2lCCLHGIoytdWUpIIR5kZeijIIQY0wxBRYYpbXSmDBnndYKPHc6VoVhTCgCDhNCENFKrRqLlfWk+hHgOe5/RU4DFe+TACekqNhomFCMCYBAKoO1klphRYCzShZClc5qSgkh1DporYUOa+ug0gZYZbRSelXMMuB5gB5VzVy00r1XVeXVeL6ywhhrrTHV4xZhBMAqjUoIoZRXmhZKMHbIAeUgRKKUxhitJUSQIqyEZlhbhH63rQPAIYyww9a66lcFK0Y2xASjapBkjdUKQAwRwpXrBRmnNUQQuudWTYyrOKw1ADhrjLECOGyq60/1RUKArIEaOASqZQXmhHf6HcjIdJpKoxHBSuswDNI0y4qMUbpMkoPD/cePnkzHE+5773/nOzev32g1m8t0+Td//VeT8WRjff2P/uEfvf32W0aZfr87nUwP9vdffuWlOAy0ltKUVqpkMVWljEI/S5aUkijqYD8oHLjO+dPj0/PTAQs9FnCHbKvVqsd1VeYXo/Pydn7l8mXMiBCCEAYIkFIA5ALfowiFno8xnE/nEMEPP/ztCy+//Oqbb8RhDAiCCBdlcXhwNJ3Oopq/tr7+0ssvTafj0cnA86gXEGUKJ5SdOkzx9etXj85Gx+eD4WjOWfDgm6+jwNdWez0PaLXW7yCtlMoXWSJEXhZZKdR8Nnr29BHxSBx6WmsEkVTSGlchNrJsqUotRW4doJRBjC1wzVZdykKK3GhhDUME9fvNw4Ojg323s7MLgVnrdpIsRRC89cYbzWbjZz/9eaPV2H/4tNltDy4GYRScnZ1t72xjgyCG1lhttLUOV3ZgBLTWADpGKSPMOSOsquSRVjttNKGYEEIZ9UIuCokxZD5bzOfnZwOlhRAiCsNOu7O1s0kZT9NiMZ+XQiilS1Eul0shZL/bD/1gsVjkLq/AusZoIWSaplJKYIDUstNpUUKkEBBhL/KtMsY6BJG2mlI2HA1KIVqdblyv+56vteWUR0EEETRaAeesNogQihj2CSYYAmiNc9oWeeH7PoLQAev5LAz9NEmtNhgBQjGSuFoXM0qqdRxCVJViPBhfDEZFWVbwFowIsGA6mnz24Uf/9J/+59hpmSTLdHmWZJjAVOYiz6wsh+enCDOMCEJsMh4arW7dvAqBs9Y4Z4pS+B5L0yRZzhgBWxsbi+XcOYAAlkpIbRazmVXy/Xfffff995zQJ6dnRtqf/+TnvX67Vg9ns9F0NsmzotK3FXlGMPaZp62ghERhqLUilECEHHbjyVRL3e40RZmfn5y+9OILnXbj8f0z4GyrVWu3m8A5SnAYhqXTWpr19Y3z04tev1erN05PTwB0ZSE93+t2uzeuXz89Om3VG/X6/4+p/3y2LM3uM7G1Xrftcdeb9Fned1c1CHQD3WjYGQ6GMQxOSDOKCYoiP+hPUuiDFCHGUBQJkBSBEQnCNrqBtlXd1WWzqtJn3pvXHrvd69bSh30SnI8ZkXmrMvKcvd93rd/vecbBe2LuO6kxhqwofvXXfu387Pzxk0fL1eqrr74aDAdlPhgNxzdv3RJCOes2dza++OLLh/cfdrZ9+6139vYOkkQ7GyIFKUXPvhdCeOuBIcQoSSqtiMiH2HXWJEhM0ce+qUQcA0URSSqFQiL2EbD1wbpvyzLAcxAER44hBKKe7kYo1yg17zxARIURiJgjxRDZuUixDUZ41/lge/xDkiCDYkJiBuLYuUAhhBCIpBCCBQAIKZTWaxHB81FS75iMjMBroAMS9wIaRFQkNejYJ1aBldRKaSlQEHCgyKFHda51fSFG2Z/cQwghkl7DO/t1N6zfO3//ouuH/CjXIoP++R+0FoC9lgy0UsjIobe8r1XKTNA7LYE4Ej8vFwMRE5MUAgX64IHRhVDXXVXVN2/sZumwC13vKWDAtuuEEcG7s5OzRw8fnpycnJ+fJya59cLt9959b2Njc7w5mc1n/9sf/8nicv4Pf/e/ee+9d7d3dlOTBBlm0/nnn995dnLyj//R/yCYkcHa1rrGOSuVHI2HX3z15eniso002dvDLIuAG1tbR8dHaZlNF5docDgusyy9sn/og3306PHR8fH1W9e1NtWyaZ0F0W+8qSjzRCuMHDk2bSNQbm9tvfLSS0fHJwJxOV+enpx8defLq9eubEw23377HaN06LoLgTpJxqPJsChi8BFoUTWHB1c2tre+ePDgwePHqNT1WzfOz46ca+tmPrs8PdjfOX92ND15EskRMlFgEAw2Rhu7rrO1t12IARCd9QRgEllVCyMS7wIxNfVKGcMCykFa1SvnGinZ2rpQ+auvvHTlcP/Jo0f3vvpCSfn+j38ktRwOJlXdXX/h+rWb158dP3vr3beMMVvbO6PB8Oq1q0rJVV0PhkMpJIfYy0T70l9nmQJFFFHGGHyM/SAQikHBnGklkyRNEpPn+eX5bDAsu64TCiOFum0QMStyBvQulmVqJgkir5a1d8FJF0IfaJaruprP51KKJEmsTYXQCKCMYuRm1VhvE5+mmQHktunWmqAsrVdV07QhUozkgmPmPC+UNMS+/9fMstRZETha51AKpU0grxMtpGjqtk/WzeeLPM+G4zLLMiV109rO2oIZhUgS07QdIzKA7SwCmDxrmna1WM7m87qpvbUCkFlghK7tDOKkzBMgdl23Wvi2PV8uPIfRIKlXS6MTpYJl0CZtm9X9u/ffeP2lqweHSaKiszb6pllZW61Wy81JmSTZqBwtVktg3NreOj89D9YPh+XXv/a1QZqbQig0SqqPf/HR197++tffe5vBN1XVdda6DhH/8A//3R/+4R/9+q9/czQcArBWOk1zH73U2nFAF269ML59+/rHP/+obdrQ2nFZbk7Gn3/02Vdf3NuebGZbBQoN7ExihDG7+/uMvwwhGm3SNCeI4/FGXdXMcO3qtYP9g1VdzeezPMv7cqsAVEkyKIuyLF57/XVpRL2qnjx6vFqt3nvv3bffeUugLIsCBW7vbO3t7n3y8SfPjp/9YPb9mzdv37p9c2trO4a45mIy50VeFIXzVggRAwMIgTIGklKGEPpJuOjn/j09p3/c96fbHrPDINaUBwFIPXJZIBJR9LGzFhiklqKH6BD54AiVVL0MBlFIYnY+WmudFxwDrQW9LCUB9G8QjJHXUxnvAVBowf10XMp+/0qRpPI9QY5CJCEYSIkeYY0ROMZAzFLKHjVnNEstEYTSSksQPY0zcPQu9JN8RuFCJGKdKOejVuulMjOFGH0IkUKE5yIEACYK0SsUSSKVZCWZY6AQo3drzTEKrTURCyYpAFEKVForlKqf85DnGGXPiX5+3cLOWwQVe/C2FEiaUJ1O5yo5C1GON4ZaxsVqbqOVTnz2+Z3FYnrnkzsXFxcK1WRj/O433rt9+6UsS1Doz7/4/E/+05/Ylf2f/0//0+1bL6DguquTLPMhrOb1//Ynf64UZkVJMbaujew610VHxahsUlc11eL0zEUWz05Y6oiybmrbOQaaTefDwUCjBIrDsnzvzfcaW3/y5Z02dNeu3YoUzy4uRhtDdx5my5n1bZIOyJO3ocyLvCgUiCLPyVNVrY6PT/7yL/9SCryyf/DqK69ubW7Wi+Xe9s6nH36yWlQodZLkoLnqquDi8dnx5XTKAB9++Mt/9b/+6/29cbQNYnxKvJpNQ7uauZp9oxW7EJ13zoWmXjXVMiszZ2uiIIWwvvPOt7bz3l1eXhRpkemEYiQGHxxozApju8Z2TVFkSirv2uBaBL5+7XA6n3vnFovF5eXF/GLWUjy4fqUoyzwtRqONycbGb/7WS1mWCylaR5ubu4GwqlbMQSrVk8WlFoDso2/aWqKUUiTGKKk64jRJmSkEjxJ7DLqNbiCxWjZ126hEyk4EH7e2t/YOd9IslVowx9GoTDNNFAXq4XiwWsKzk+OmroSSTd0Qs060SYw0umlsUzereiUEqtVKSDHM00yrrrOp0TpJqlW9qtpevuF9ZEYhdJKmknQIBIhplkmhnAtS60BkCqPJVCsrhlJIbGrbC05dIO+pKJSN/eVfB0/OEYJI0sQ63zatSYy1XX1RnV+cAlKWm6qeM4flqvY+xBBtHXY2NifjyfTs/PT4eD6fVnXV+K6uGikgUahFP6WIq2WLkpzrnj55NB6USkljRNuQVgLAXZwdbY1v29ZKxEwnJ6enAiFNEyXwrddf256M26ZOBiWTj8CIcHr27Oh4srk5JoppqrVRo+H4jTdf/+D993/2sw++8d67vcQYWCYmjYipNINR6gJtjCZ5mnlnGaNzzcZkNBkPnzx+9NVkQ6EejKNJUohCSlEWI2v9ycnZ1SvXQghJYhDFeGNSLWsG3NjZPD09v3v3/ksvvgAAiTFSKBRodBLYb25tXmkODw72T56d/NVf/tUPvv+3Wplf+ZVfKYpCSpkmaVmWG5PJw4ePP/30k7t3v7z/4P7LL7149dq1yWQjMamUEhCkUhhDjLHnbEqtvA+RSPSUWiU7ZzMvY4wCBQSK5JECCSlAxMDEKKDXr0cWzMj9NCTG6EInCYERCEBgiLFPS8YYrXOxP8ALycCeQvDeEWBkgSwk+shkSUhmBolARN4H7yMzC4W8RnMKhWiUjhijoISNtx6ZsS9aIXsCKWU/wKJIIURkklrIQERsQGttVC8jJo5I7JwP3qOUfU4UUQByDBGl6m82wcfIwbsYQ4wh8no90nd4e+6/WstlGCjGGAIzhxAQ1/ekXtSuWAKuIaQgBBFD/4plCDEqISmyNAKAiaKSWgrhg5PSEAFF+uSjz7789MHNW9c3NkZZMbhcLi4vL06t/+rzO9a1XWfzLP/a2+/8yq+9l+cD29rLy+nDx49//P5Pqrr+F//sn/3K2+9Ozy/rpm5aW9VNlhZ5nlfN6sbNW9lgdPb0SRSxaxvByMBJkvJq0TSOIyZ5HkA0rrvz+VfWOYnYVJUmyNOUOFhnMy3H4/Frr7z++YOvrt+8dePa7eHmxk9+9JMnz46Sunl6fPTRp5+9/eYbbtnGGLMs29zZqZo2hNh1bdu0n/zyowcPHvz6N7/16iuv3Lx2vambJDH1aqmUTLJktlxenJ9fXF6cX54/uP+obv2isYMiq1erv/27v0413r5+dTzKbdPmWRpd7YWIrguN9xRcjBwRlXTOFVgiMHNsbRticD6EGBDZus62dpDmWWKcDyCFAl2kyUpi29Ray4hRSQRiKSUQjcuB81ZLEbyfL1fL2ex7f/GX+bAsR4XR6tatW7deeMFZ39RtnktiSqSRUnQ2ruP/RimpUAgGCDESEKAmJqllkqaL2ZKBsyyTUjGzkLIoixgJAOqqIeLxZCSUZKbzs8sbt8tIYT5b1PWq/0guF9V0Otva3khSQ5CZyErLpu60VlmWWeeaai6VBAHKqPliISVK3BCCtVEosWma1Wrlg8/zPMvzQSSUAgVKIXsqrVASUehEgwOpZQheScNIfcqiaTuTpsjQtRYEgJBSJ7ZrldEoBDOHGNI07Vr3vPmOgaLtbFXVVdNGZmYUqLRUwYZgfQihHA2q1Wp6cbaYz1GSUmCIsVDzeT2/nI7GY8AQgUOMXdvW7ZIonD07IqZEG0Q5KorMJJ99+vHhzlZkkRojpR4OBsdHR5tbG9PL883JWApGjK5zWsnlqlJaV/Xq0aOHANfyNO1TUrP5bDLZ2N7Z+eLLLwbj4cu3X1KshEqQoXWOmHSI55eXF2cXm1ub1naCBQVmwr39vfv379979Hhnf4+NzlCs6iYfDACF7fzDh4/feutNay3FaJIEEUFAmmRZlq+Wy1/8/Oc7O9vDwcA7H0R0wQopUWCILk2Ka1evvfTiSwLxP/7HP/n5+7+4dvVaYtKiLNMsFVJeuXplc3Pz+vWr9+7d+/Tzz7///b/d3t56+dWX8nQwHg9ta+fzOSJ0na2bs5OTo7puYoxt02mtrW/XT20UEpQAIQUiRKJIEQRHot6DCzGEqACoh0hHJqJIUcQ+IBBjJGYiEEqC9zGSiMRCMSIzxsAIHCMzARAJZAmCmEX0SoFAicjEFAMRUd9LE2rNYft7Xmf/uJZS9CAK7OnNiLDGdwqiSERAAMCgMQYKggSQ8t61HTD3Ai8mJiSg58hqACACoXrSE4cYQ/Qh9mvgyP3Uq7+nMApAKVAIIZVk4BhDr6RnJkBmyYJiD3ZQSq8pQGINKuqXv4F8iAFRqkjIQJERkKLHfk0MIKVI0yREN5vPls0cBSqtfKDWWmRAgCQxr71669VXX9nc3NRSs/c+2vsP7n300SfVoh4Nx9HD6cVlnuXb5dhGT0wx8k8+eH/erk6nJ3cfPyh00qyaSbnlfWd2NEhx5/MvV1WVJtmTR0fLujs5P5tOZ1meGZ01yyrXRiF6a63AJYaBG04mo0E5UcJczqcPjh9dXJ5n6UCb6vJy/ud/8b279+7eOrwhQORFgkoRcrWqMp2s/PyrO19slMO33nh9f3v3yf2HFIJzVik9n80uZ5d/+Ef/frmYDYoyzc18sdzdu/L621enl4tHj58ghHp1+f5Pnuxsbw7L8rU3XtFa2tY5b9cjF6IQCYE58jri5fr4QPTOB++9DdE4JNlS7Zo2SROKQIKISEixWC1scIk2EjBTiTFaCiQiiCxRbk02hNBNpNVi8eTJ082tnddeemUyHiGgkjJP0xB8f9LpiYayDwgQew5a68QkQGvJtA/BSJkXaTksuq5jZGK21iohN7c2nx09a5sagE1ibNMNsjRQXCyXD+89Lgapdc42joiEEF3XaaOkVIhyPlsIFP1yWAgRYuzajijmJtVCIyBmNJvOAOZlniqtSQIAJGkihUzyDECglKPxGADapgNEoWTTNFIbijFLE0SlpKBIKNBamxjTN++1lkmaWe9QicChbTqKLFAKpYjZ+sCMSZJKpbwj7zgQ+MB13VnvnPe2tQislEy1AhcUxeXysrGV9Y2OYBBloqJhpGS5mOpE6jSLEYhASprNZsScJKnoK5oAySB/9ZUX/tX/+oeCzLe+8012HYcuyTTF+PTo6fn52ZVrV/qRbIjOaBOcBwhV7S4udVkWNBmVeSkYmrY1SXL7hRe/unv3b3/4dwB464UXkhyUkFW97Kr6cjr9+PNP67rZ3d71nb1z58u3X3/ddr2Gd3j/4YMkz15/85288G3nQCShixLkZx/f+drbX3/tlVc92WbVXJxfJEmqhBgNyiQxT58+/eTjT7/x7jdYA0IclAMp5aMnj58+fJblKQilVfr2219/9Pjp337/R+9/8IssL7fX8xVJkbTWm5ubk8nmiy+//Muff/Thx7/4ux/+KEszRDw5Oc3yLIborG1sYxsXHRmTM0ofbIyBOXKMsX8nP9/oxhiROdJ6y0sAMYLzgAJjH3EMIVAUEQmgH65IKYXUAkQkipFFPz9ijMxAfVynh6pFQiYf+xARJ2i06G8XkQJF6jevAIj9c7p/08RITMBrf7AQCNDjdFD0rbYYY4iRIhIASwARIQopSUYVKTrnkEGgVFKCkv0KgCjC+huLKAUL6KFxFOl5FxiYemSd6LUIQgjZpyiEWi8deqgdMzMjABGHSH0vrUfzEwMDB6JI/YIjOO8QhKaUmXshAzPF6JkUIgsQRplBXjRVE4Kz3qOQQCikSNPkysGVN9588/DqYSTPDLP54vjo6Oj46fGzs9l8IZSYzab/8T/+x2JQbE028yR3IdgQvLXL1TIflIu6+tGPf/jyCy8myjw7etrVVT7Wn3z8yadffmWS1Huan540ta+b2iglKGQal+cXyfamb92qqih4Z9VoWMtUDgfjtnU/+8X7T46efvc7v3Pl+q1Xuvru3a/u3bv/7NmFb/32ZGtME5PXKNXldJYk6fnZ6Woxe+Xll8H70+On1rXVarWYL1trj46fhhAKk7zyykvf+da3d3a3m6a9nE5vvPDialn/4oMP0szsbm8+e/L4yeMHl+eXzLBcVd7aEC3FiMAxko+E0njqoaGh6lpvnXPO++hdCI5CIIXKKCUYZdNIraTTgahruq5zMXILbaI0ZByCk0IKgTFGQAAQSWKAwSQ6TbP9/cOdnW2ppO1aBCEEJknSXxmVVOk4QSGiD975EHwMEQUkWaKN8p2NPhJwL8lyfs2OdcEWRaG0PuKjQIGYNzYmT6qnq3ldLevhcFitKmWUlCI1SZbnWZ5lRcZMFKhuKh+8lAqszbIcQdi2yfJMK5XnhdAyhvCsWhLHGIIPEYUQFJ3z/UcxVGStlVJwpK7phAx5UUiphJS269qmRR4yi8SoPkSepUprE0N0zhNRmimTpBQp+KiMllpTJJ2YEENV1caYcjBUWtV1LYQCkGlaWn+6XNYnJxcnx8/Y+zxJJIroglBP969dF0JEFL7tEKOQAMgq0a2zp+eX5XCgk6wnIVOkpm3apmMOWrBSSkuzu7MFEe58/mWaZy++dDPVWdVUMYTlbHl5cXn9+rUQwvOUICxXy/lyPhoPj4+Ps9RoI7UySSKqqkpMsr299eprr3/ve9/7z//lT38n/t5//4/+B9t0F2dffvn5548fPj29PBuNJtWiVgK+/OLuYr64dvXqsm43t3cePz2+9+ChGYyuX73Z1m42n11OL5XWMfof/+gnX3z+eVEU8/m07ZoYCYCEkienp1LKhw8fVsuKkWzXbU82hZYPHj6YTeej8XhnZ2djMlksFlqpxKjHDx/+1OibN25ONjYmG2NvndBKSQmARV5881u/dnj18P333//000+U0VVVxRiAAQD7ZGNelEVRxBhEP99em2sZhaSedYzI8b86UPqTSwwRgQA5MDvvKERmJtkfzdddWYGIIPr2KwPE59QcZkJSFEkKAQCRmJFi7CtdBKbnNzynEK35y9jTm2OIgWK/H2Ug8dwJ3L+oxPODc6+86beryIBExIKZYyRFMUYAkutyMADG9Qusv/ugWMsgBQP2MCEhBSBDgB5UpCQSSCKSfbxJKRCif78x9nhn6qNQzCJGYGIhSco+CQSR+XlQiSNRiF4Kwf1IgCHG0EdsOcbgnARptDw82FtWKxciSiGVHBWDw/2Dmzdv7exu53kZYmDGy8vps9Pjhw8fXl5eUuzfWyCkuLi4uLg4Pz4+BgYXAsWQZ8VwMEIJVd388R//cZoZ8kExFVnWusp2Yf/q9WI0RMDtLFuq5d7eVpkXZZkvliskB8Ev5ovHiHt7W4JIF0VelCGEz7/85PNP7v7BH/x3X3v7a8WonAxHp1/72k9/8qMvP/3y+NnRfLYoB4Pi6DEDV6t5nmcP7z/Y2dna3ds+Pz+5PL24uLxYzBarulJGBaLrt2688urLL966vbu9vbu3++knn/zy5x8sl8vEJJubIwFw68Z1hXByerysqulsliQpE3nXEBFGAkQXIqM4eXY2HExms1Xb1c45IgKWVWOjjwieiSSoHpROnXeL5aqqF8uldT5NsjLPNidjimQpQGQGLgdlZ10IJKVWAAd7V2/eur2/d5CXOYCInpRei456gKyQRhvNfbOxv0vHyIxKSWBg4iTVALBatEqrEAMj+ODbutVKG20mwxEyr+YrpeX2zvbZyelisayqVVmWzket9Xg8HMUxASUmFRJa23a229jYsJ0L0Xe2K/JcKVUUuQARgqfogvfnZxdpmkwm40hxvlgxkre+7ayQgl3oReRM0A7Hg3KUpYk0Jk3TnhBvvWVgKYsszyhEnSqptDLRuaZuWkTM8hxYMmOaJChECCyVJIsCY5KlSZYiCGNi4sN0Ok/SXKmka/xsujg/vxSIoRR5kT49u3h8shDJcHd3u8gzxQQcEMh537hutCMgxNmyVsZMJhtt26HAp0+eygDGqERKk+hEaULYu7q3XKxMos8vzrK0LIoyMlwuZg8ePHrplZdWVYUIzNFz9+jxg6ZrxmJw8uzZfDYN0apbZr5YVnV96+YtH8N733jv2enJL37xi7/+3vdu3L51enz2l3/xl+enp+Phxt7OITAvZov5Yjqbzx8/fnz3/oPDw4PNvd1rL9z8+JPPj7/3N+PRB2lSWNdmaT7ZnAgBj58++skHz3a2tojYuRYB27ZBRGOSJE0eHz25++geEy8XK6VViCyQI7nB+eDs4kRK0TTtarli5MVy+csPf/nZ559rrXf3tjfGm5PNrUFZSK2llEw83hh/57u/ee3GtWfPns1m865rY4x9WTVJ0/FkMhoPOttw5EA++BgixBj6TDsDE3HkKBgpMkshgBixL4T1jNDoI1ME7B1hSJFB9OYTJAYUEhGYsedjht7pCPG/RnYYYn8rEOuYPAolBEnBICMAIUEMARCI0DlPwBwZgIVcJ/P7yTz2Paoe0g/ILPrSGgNQD4QgpkiKmZVS/ewe1op275zrsadraE8vQ2AQUgiUDBADr502ApRSIFSIUQD0xPO1u16w6lHULkLPpcOe/NN3xug5DhXiWkXAiCwQKZDofTIEFGhtSQOF0LOz4e0333rl1Vdmi4qAJ5PN4XCQmoyYI3vXdVXbHJ2cfP75nYvLSyFQCWFSo43RShExM2qtxuOxdf7yYmq9M4nZ2treP9hv6sp1drmcd9YC0fnpaZHk737ra1euXh2U47qpjp4+feHqzcP93XI4KMr83t17VdtmeSFZPrj/4OHjR0WarjqXDwaffPypte63v/Pdb/3qN/f3d/qY1t7mxne/9Z23Xnnzwf0Hn3740dGzZ8dPj5bV8osvvlQK9rb3Nsfjh3e/Sk2ytbW1vTPeO9wSUk8vZ/fu3T883D842NvZ3hIAz46eVKslILddfXF+mmX5xsYGAKGA1ap+enwitAYUxqRGCaJolAiRfKSL2cVXdx9s7Ow23rVta50jjq5z1vlqWXsbTJJsTbaiB9X62Xx+9Oz4/OLcOmuyTCsNFLY2N64fXktSLYXIsjQC+BCMSSECEKRJZnSitA4+aiOVQCmlSYxAEWNLkZg4OO9D8M7FGIUQJtH9vdVZx8j9zVcm0nfeOQcISZqIFru27dpWa1UUeZIkiFyWRXrj2nK51EYXRb5cVD04pG0b11mTJGWRWW97pK8PXkhMTMIEdVsnaaK16nOiUokQPEKqjWHnvfed7bTRw/HIaOOsCyG4ppUok8RkWYoglNJKGQQYlENElFJqY7TWjgCEiDFqY9IcoO2UTpTWAtAFH2NUSiMKiSpJUqW1ENK2ThtjTOJDECCARVEMlNQxALPK8nxje+fKtSuD8fbx0enjk7MHR8fINEhNnmotJTGxAvvkic6zYTEcT8Y+0MXlRZbnl5fzRGgpQLIQgssiHwyG12/cOL+Ydq67en0PQF3OZrZuv7zzlXPu2bMT23bloAwuNs4+eng/WGuU2t3d/OzjT6cX5+Hb0HZtmqZlPji4cpAfFr/6a7/69Mnx8fHJv/+jf1cvmqcPn+xu7x7u7d+8/YKUummqL7/68vT8ZLVaDUbjze2dg/3tcjRSafl3f/fD6bm9fXN86/qNd9971zY2SZO8TM7Oz30In3/66Weffvq//NP/palqBrDOMvHFdPbVvTvf/vZv7ezuPnj46M7nnz/46gsXYtVVq6NFdMH7uDHZuHr1Wn+YWK6Ws8t5Va3u0t0kTfKiFFoXWcEMo8lwb2dXKrWzs5Om+Ww+RcCD/f00z7RSQskkMW3bCcBAoWvXn1teYz6ZOFJvDIjEDCQAGftT7t87evtkKIJ4rk7HvzdI9iOav+eMBh/7DrwQQMQICOsNL+CakSilUigIAULASIEJYoggMBKEGGGNb4P+2b+mPvdn/uf/x/3jPhIDMDJIZqlUry5W/aZbG6W0kiiYOZICYB9CCJER1iQKACmx99EAQ0AM3ktEIYXWGoXs9xGIghnWO2QhkBUzEscekQpACEDM67QsIyLGfpK19iT3ojCBsidG9FsJ6M/vMQRkcN6lqSl0ubd/GGKIBIwQyXkXmq5bLlf3Hz348qu7i+VKKj2ZjPd2d64eHt64cYMiNU2TJpkLfmtzezAcfPbFF//5P//p7v7+r//6b7z0wgscHIJQ2syWs//H/+tfquHGN77xzesvXs+N1lJlg2SxmI2Hxd7e9mBQKiUXm5NhHJh0sH94befw4JPPPjk7Pj394OfDYfnkybN3v/7ur/3Gr12/fV2jVFJwjJeX56FttaCXX7x989q18/Ozn/38g7/72Y9NnjC5cli88423RIjnJ6daCZUkQgrnubUNIw+HwxgiIvvgq1XlnetnY9Z2VV0xsLW+HA7m9coz3X3wsGq7/f394WCglJSA3vnlcvno6Mmzs/NffPhJoOC8DzFa56XUMQaKgIBA4mS8UChiDME7k5rbL7x8+/btjZ0tKfD9n/709OLkw88+DsFvbEzGo8l4vHF45TAKCSB3drcODq4IqeqmGY3G2hgU0hiTJKZnRvWMxkDRWtu1LcB6qWNSJYWKIUiB3npEkFLWzvc+phiDkEIrNZ3Nog8oZVGU08vz2Ww22ZyMxhMUIs0SpZM0TZLEMDHFiIhpmriFX60WztkQQr8XCyE0TT0YlCE4561UYlAW2zu7QIAoGZ3JTKSopM7SFAGDFAAY1ycUEkIQoUkSkxhmEEqGEJMkSdIUQKRZppTmSIzcd1zyouhXad75ruuUCiZJtWGptEINAELK/tuamGRne/vs9DyRZmdrR76qXn7hlZdeeen6jZtvvP1WVbV/+6Mf/uVf/c2jJ4/r1fL0MhgllECQ0DQ1IkaOSqnRZJKnudZSCHl5cXlxejYZDsosT7Okc2G2qla2kplatfX3f/DDIh8MxqPVdNl57zkKKQ6u7KPgIskuZheL2cVgPLx145D5IDr//s8//PkvPkhMurWzaa27XJy/+sprL774wq0Xbzw7fXZ2dq5RjjcGb7/9+q1bL+weXh2NJ23XXrt14+mjxy7Y1998fWNrUysldPLO22/eu3f3/PQEBB8c7JdlVpRpiHEwHAkp67pi5LzIy0FRlmWWJUIqKRQBHx0dDYvh/t7eeDS+du3w31xcZrE8ONiNPmipBOL1a9fzvMizzCTJk8ePf/LTn82mM+ucC7GuOxZiNB4iCFTCGKO1Mcb0oxWlZJKlxpi+VhU5WmcTlQQfvHeRIwMQAjNy3x8g6m2RTAQMuHbu9pj8Hl8jlVRSSxAQAyFgv1btR/UhkoAYIvtAzjsAkCQVYmQQgAxiLXIRCoXsVSwCJQtGiBSBmIVcyyb73uvfE52ZmYAoxl6sCwDMPbwZuCf3REIGlSgZSQRCQtWDPUXfU0DRD3yUZmJ23nNgkJIVSymUlFqp/l2nlc6zrBdmCikCMYZ+2gT9xkSJPvoKMQTrO+eDlFJJiYgMLIQQIFD2Usze7EaAjIBKSMTnkGpAYhb9rCAEF7A3bjJxU9eEgIyopAvBW9+0zfHxsy++/PL42UkMrI2+dv3my6+8NCjzxJjd7d0Y/Gw2T9MUANM0UVLtbG0Zk+R5Vg7LxWKOPhzuH7St29vae/2VN+4fHe1fv1EOShE8xWC0Hg+G4/FACeGttW1MjEpNSlLn5Wj32uHu9cOf/finv/zww+4yuEi3X36xLAYf/OhnZVlcu3Y4GY92d3ZCZ6N3RTnY3tmXUv3W7/zu/+3/+X//z3/5ZwL48Oa169dv5lLfvn1rOr30kaquqc/P58tFUZZSiwhUd42zbr6Yz6sVC/RELsaz8wtL9OGnH9+6fdtH4Rjn82UUZlbbUVECU/QxONc0zaqpEMTF5ayzDqQIMSqjJZFSprOtUUYp0zq/uJwNh8O333rzt7/73bfeenNre8t5B0D/+B/949lidnZ2+uToycOHD764c+d8MS0mo600H29OXnv9zdF4FAPlZTEcDvM89xRjjN6HGIMxRggEghB6gK2MMXgfMLLSCjmE4J3zazkEs/M+SRME8N7FEEWKSsmu7RKdEMXFcnV6dta07fb2Ttt2RDFL0y41w2GZF3k/IXU+PB/Bgta6bRtaVdZZKcRgUC4XK2RgIin07t5e2zR1U1vrgoveh2rVVFXdn+NCcG3bhPOYZPmgHCd53qNwhZTWWugv1JFdtJubm1pp73xnLaJi8AKlUII5KlYq6BiJgH0kqSHJUmedMSaG2HVt35IZDQch+OFg+Norr2V5nuZpnucIMBoOvvXNXy9H45/99Kf3799zXaekMEaHaKtqtZhNfdPUddt0NgQeDkoKgUJ8oh4P8jzRWiqRaCOk1nkCAvcPDhFgtpw9evI4+ogCi6I4Ojo+ONg1iWyqSmsxngzL0WA8GiXavPjSC5/f+eL8/CJN0+2dndOT00dPHk4v5r/6rV97++23fvnzj7quQ6Vv37j5f/6//NM8LxZ113Y2L/PRePC1r70pBK5WVedsX5HKUvPqyy9enJ48Oz6e3byQ4kUhsRzkTb0SKKL3MfjhYOisVVIbnTAy+djaLjF6tar6bOFoMHz3va/dv3//rTfeytOUQlzrhSP1k3KlZGKSh08fffbp56tVRQi9lXNnZ+/W7ZtpkgXv+gRWJEqkyrJcSslMILCznTFGS9W0TQw9Dn/dc1p3YBH7ozStH7rUX137D1sfz1daaaUFYgSIFMmDQKY+RxM8hUAsvLOdc1KgUkqgQFyLJ1FAD69FEJEYYuyfpc4Ha72QKJ4vWfsNa49RICamSMAcIz73t6/ruYDMyL2fkgEChhClkAhCIaJec3tAIKZJIoWwtuuP25Fij8EDBKEkCtRGQ4wCpDHSx9B7FpHJmKRnIjJFRIhKGZMgiN4QGWPoJ79rUQGi0oo9aaOeczaoZ25QiFmeAoNznpmVNsDgfTBKM0OiFDBaa6UxdVUrqZz1HulyNj8+Ojo+OppOZ8AQAfZ3d7/29XcESmMUMHXWmn4IhSik6tq2quvp5XQ4KDfGEyUkRdrd3JKA3ju3ilmS7W3vDAaZFkpoXHYL7yCEwD7Op1MhOM2yJFEqyVqhll0HWZENN1945a2PPrrTtlVW5rdeuF1V9WRzIqT87LPPhvlgd397/2C/HAwpRMGcZ0UxHhCCD/72zVtf/5Wvx0Cew/HR8d179wgxLbPTs0sScjQcSKGC93XTdW07nS3qplXKSCEZ4HK+mDetI7isOzRm0VjSaW3jyi7OLuYxeCkoUUmR5lk+2DvYbapOZVEqNRqPb9+6/frrb169etWopFrVFxeXP/jB97/kr7797V//7e9+9/q1qyG4R48e9I2qwbAYDIutnddeeOXFalWdX15UdeN92N7eBcThxgQAVaKVVtokIIRgVkYhonMYQkCI1nU+eGZOjIlRxhCAIXjfer9aLol8CL7ruuGwZCBETpJESJjN/Hy+WlW1c6613cnp6XK1qOt6MBy++NILQoqL80sAklLUdeM8AdFyucjSbDAqB8OSiPIsk0KsViuTSIVaIBqjUGRN3XVtu7G90bfW+3tDXhar5bLruuOjE6V0UeYmNW3rnh2fZGl2BTEvBgKEEjJNMkbOs1xpLaQEQKm1NpqAoo9FOVBaLatVmiRCKBQ+ete1do0JEAJRSCmFwK7DJEvarpECEqOBwaSq7WqTqjzPOIbFalXV9ZWDnfI7v/76Ky/ZzqaJ2drcXC3nbddW1XK1WtV1c3ZxVg7H9+7fOz85RaLofd20FrvOdlLKYjhYHC2J4KNPPpuMJ96G0Xg4Gg598Mj88NET5+3+/l5ZFMUwbbxLPNm229na2NvfGW+Mjp+dN007Ho3zMj05tudnp8+Onx0eHGzvbD97dsaCLk7PvO0gT8ejoTQdMGg17LoGkAdlWULZtm2Wdkbi9vbGYFACx9VyVternZ3dSLEoiuC9SUw5KL3zVdXs7uz0uRIPPlReKXl5eebsTSnQduHgyt7lxTT4UG4XSGCMIYre91Up6NpmOBq+s/nOoBh+dufO6dmpD3GxWMbIm5sbV69d0yYhikorgSJJE0SMMQrZC3ExBHLgQyDnPTMLxD4loJSQsK6vAgqkvmu9VgYgAguQQqQD7Sh2AAEAAElEQVRpaoxJ0gSFEOgDRx9igMAA1rnOWSGFtdS0dQgOpQ59ryAwMGsJUkil1gdo5/x6su9DiIEFsxBrpUs/IgGITMTYJ2mAAZHFWhW/3sICMSNST+AEjjFGHzwKAFSwtrqzUkpJpZTsYRGRYtt1QCD6W0Ev78GeTQFCSmMSEfpDHjFGeH5Y60dZGSIRCeRIBM/34ICAUvYipH6xDowoQLIM/UCNqM/ZuhhMohmYYhRSMUDnnOi3B0RKqxBjXTWd7eazxfHl2cXlzHZd9FEqzZJ1hM3trd3dPR+9s51A6LqWpIoUvfNKg3XWWj+fz7z3eZ4ZpZjiarkQxTB4R1EsVvPd3S0lQSvJkZn55OS0Ws5Toxa2GZRZVmRplgljSKjLszlJFSI8PXoKAorB4Oa1m3t7B11TzS9nSaI3N7cEka27u1/ddd4xgjSJEM/GGxs/+OsfAMs/+IP//mD3qqtaatuDwysnp+ePj4+q81MAyYCTjS2Uomnb1apazpfHx8c+eCFlmhSdO57OFo1zZ9N5OXgym889yKwstjZ2I4fz05Pa1sOyvHrj5puvvba7s/Mb3/7W3/3tjx4fPRVS/uo3f21jsjGZTDYmG8BoO3v7pdt1tdra2f6N3/yNwWT4+OhxvVwtFwuiMJmM9/f2AEUkisyEYJIE284kqdRmMBwMh+MQgkmM0VophSAEAnEMrnfBBYqklESEGAUwRIhSSilRaVXVVQiOOdrW1lWVGC2FtMFqRcwglWzqpgcHXVxcnp2eWdsVZbm9s1Ot6qLMDw72m6phAYlOrXMMUaqJ1sYkum4a712aJmmWplk6GA6QUSphvaur2rZWIC6XKyYQAFmWxUCD4XA8GrVtJ4SumqqpmlVTB08hUms7AkaBwNynFqRUjAACtTLeB8DOaK210RratmXAngImpdSJJmKUMkbqSfMI6LzrM+8h+suLS0QhJXgfq2rlnZ9sTkCw87aql/P5AhA4uixVwLEskps3r0lx8/jZUds2nWuDp0DxjXfe+fAXv/jDf/1vtRRFlgXnLk7PSKDr7Gy+BAEuBqX0slpKoaaz2Xy+0IkSzDGG6fT8wcMHErXUouu6+Ww1GWUMUYi0LAbMZ3VT5WVxbevq4eFVFJDm2mjz2muvPn7wBIw5Oj794Y9+9Hv/7e9PL87yctA0bTBSC2Fdp5RC5DRPvM+dwM3JRpomdbVy3gVyAJEoCFSI4L1XSqHA+Xy2vb3pvMu16uMxQorL6Xld1cPxsOsoT4vhsFws5leu7GulETmENRuNgdu2S9NUSHnr9q0kS588efro8ePz84vZbPbzn/98Pp9fu3otyzMlVZ7naZI475RUiJIpSGVijItmISRW85VSinvvilBMFCkyRwFSoGAByNjnfFD0a0sUUibapFmWJAkjECGH4ANRXJN8+uBmjP3hGZgB++IU8vO6lEQh+49KcI6pT2ZxpNDDJtZ/jpiRmfodb+zn5Ou2LiAKFqI/hgEzR6ZAkZgF9gbfKEKUKBUAUIxA6xqX1goABaLVWivFTEYro5VSiGs6DyKAVEYpyQgBBXPAPsYTIPgYyUspaR0CJeilkgyRIiOofrsLgkIwRvZ/jRBj9KG3A/acAAby5EEKR8FI5UMIMSpA4mg768hPp/Ozy4vVYtk0XWU7IpZKmUQX2dD7eDmbr5ecRL3SQAhBxEwUQjBpGiMJgW3TrpYrBFBSRSJGuFjMZvOZVIl3HSIZrQUACqm0Pju/OH16vLu1CYRaJwJklqZBSqZouw6V+vzzO59+9NnG5tb+9t5bb7/R1k3b2uFweH5+RsEZpYfDAQhetqtFXVkbkiT/L3/z59Pp/OZLt15+6VUEJZX0KAjUK2+8tXL+wScfCiXLbJhmuevidD4lgrtf3H369Ik2yauvvt60br5cTZerVdPifIXikhG9540sVyZJZLa9y5ubW19/681v/do/uHXjxng4Gk/Knd2dxjbW+xB929bDQdk2DRNfTqeICJJu3LxSDvKLy7PZ9LJZVsE5713XtcCxDR0TSa2FkiBkORoJoTa2tkaTMTlOsswYAxSllAzMSP1HjUIfowaBfagZiElIsf5UMQGRQAyBrbcxeo5kjLFdJ6WSWo1GY2eDVDHE0ENE++e1bbv5bKmk2tzcmIzHPgQKoaoqKZX1XdNYANY6iT7O5gupMEuzHp3VdTbGWNU1BJJSrOaV0nI4GgyGgzRJrbfz6SJQGIwLxti1XfBRJ2Y0Hg1Gw7wojDYIiAz9nBMJOBJKKaRo2zb6yMjGqDwrBGCM0TmLCAoVZEIa5V3w3redLYsiAvvOMuPJ6cn9B/eKLI/RV1WttEzSrG2r1UprnSyX89n0MlLsbwyT8UAKOV/MBoPhwcHB6ckxIjSxPT07f/9n71fVShs9Hg6vHV65dePGowcP2q5DwodPH11MLx48fpxkmeucZxdjTJQhStLUhEiN7aqm0ToFgaNROZ1O/+p73797997u3mHTrrRWAsVsPivLvLOtc3G5rJTW0/lUKREjB+A//+vvvfj6a1k+aC/PkySt667MM0DobKe1ZqYsS23XlmXprQNA2zojjXfOx8DeIYssS23bCoHz6dx7751P04SIlVLOuaaxF5czk6ZK6hhpOCzPTk5n09nGeAMRKJJzri9dxRCaup1sTkKg7e3twWB4eOXKw0cPHz96cjm9/PTTT09PTw+vXMnXTUOIkaQAbx0jE/kQqWlqa7umbnr8g/M+BCsVZlpzRLnOzDzfB4g+eQ/ILKXS2iRJIrWOxEIE+bz950PwwfvgGdGHGEIERqnWPExiEoC8BqoJIHDet7ajEAFBSRQoTJKiQkJgwtgT33rWEND6lQAgRG8whucbYViLLtavG+gXsTHGqGjdBGZmipElAVNvP1BCaGOEEolJTGK0VrBeX/Q7Zc2sejoqMQcfffSBQqSwfqf1pYS+uEbYZ14DSXQeEaRSiTGA2Cc0gvcUgjFaCQmEMUSpiSLHGPscq/WhbRrwsbNdU9VN1y5WVd02XddaGzwgClCMzhGgklJR5PlsdXk5zYpUAgijfQgKJABa67MS+jKa976pKvKUJVnDnpFVouquLofq+PR4/9oBEAFSiFEKrZLk4aPHt29evXXrOgqURjsfpTa+7QbD7LM7n338wS87a3/7t77z8gsv27r5+QcfXM4uXnv51USrs4vz0WAgppDlGTEgCk9+Pj3587/8i43Nzcxkp8fni/llCI5jlEJIJUEpYKlVcnF2Mb+6bOvu/oP7SuCzZyfHz44SnQJIEPLk/CRGCJGByIa6Hz6GECIF652S+tu/8Zu//3vfHY8GvrXA8OzpM6VEWeQZ8/bWxsZkYzyZIOGyWgDHRbVkCNu7+1WzOH72tG1a8kEwoIS2a46enXTBEaDSSmmTZXmWF+NJmWWZFDLJtZCiv+s5H3ok4npRhrxmrfOaNEVEfS5CEPvIQmBRFG3bqk4Nh6O8LIQA5xKltZRSjKT3QdX64tzmeXFwsL9YLGfTab1qNjY2iOPl2SwrsuhjlhqBolq1db2aTefEse2avExXqxURHR5kddVY64zRg7Ksl3UxKfIin17O265tmqZp20FZBCJAoEBtbWMkk5gsz41ODw4O9w8PBsNhb+ZjxBCCNgbWO15XDooeIWdb65Ta3t5CRh+8lMjAFEEo0IlBdCFEbbQwPQkA6qb6+OOPHz18dOXgQCvpvAuEKMTl5bRu2yzPptOZdW61rNMsMdoE75VUx0+PY3zS9z1jDJF5uVx+eferqmn6OOPO7vaV61evXrlinQ2evh7eu5xN/+Q//aenR8c6MV1rEbFzVmnVg5IEglKya22elVpqKIrTk6fzj++MnpxIJZSSG1ubX31194s7X1R1LZWIkWYXi6pZjAYjo3QxKB89efr/+y9/9j/+j/8H6zpEoZVqu04gBO9jiExkEiOkvHv/YWdtiLHp2ggxUGy7ziTady4rU4EixrhcLRaLRfAhywujVZZnAuXZ6dnTp0dXrlxb1jPnu+FgePTk6OzsTCtltGHmtuv6NUDbdZE5LfMkSTtrTWK2trYGw8G1a9cfPnj4xZdfPHr89OLycmd7O0mSjc3Nqq5jjNZ1zofgKFBg5jzPyjzPs0wrKYVQSvVh/95pspZgCYGCRS+EB2RgqRRKKZURQoKIUimC/kEXKTIFosiMzAxCIIAQUgupcC1t4j5E03PivPfBe+9IIIPpRS89YHk9C2GmfgUMIBCov0WJnruAggGZ6blCgHokBAOAFLDejrHiSDEEihRDcAzA3IdepRKJ0SGikmr9umOKzkeIwKCFlkIyA0Vy3ndd13nnne/p7cYYRAOoGJgQ+sBr8CEGp7TsExR10/pIiUnSLIUsz4xpm1qhRBSOPSCE3sUAHIld8E3TSQYfolAqL0uVZIW3x8+eNbZykaTQzEghUmOlsNKoZbU6Pnl25fBQSUyMjv1LEPrIUFRa93cx78PFxcViudBKdNET8Nb+zgcfftg5O1/M6qbyViOKrnFt67TWjx4+TfN8Y2PUHJ1qLVddV/nu2dnZV3fvVavVeLhx9/Mvnz06auvqyZPHXdMeP3ny7rtfN0pcBBeByhC0UlobIdrv/+AH3kVkOnpy/O//8N9tbI+bqradq1yNyM2qu3Kwn+TJJx9+CgBt2yil9nb3rt+8tbu7u6qb+48erhbVC6+88t6vfGtzY3u8uZnkxdnZ6f27DxfL6dHR0eMHT64eHLzw4nVkOH12OirzJsbFYpakGgGKPM3TRCAARWv9Yj5vO3t+djqfT9PUnJ0cL5dLIQUH4kA94dU6hwqTNFNBJYw7O2VeDMuilCjIR5knwBCJANg52yuj+yszMCOiVBIZhZCgmAJR6HPDwjubaJUmiRDCWysVKim0NlRwnzT2vhVKJUkyGA5jcNqovMy990pL8oFCjCo0bSsFrmq3Wq5iZAbKyyyEAJIjkXdhYzIeDgchxKZpmLLJ5kaWZ0VZAnMIrrOd6+x8PsuzDKUoB+VquVrVqxgIQRSDPEmKwWiUpGkM0VqfphmCQODgo5AyBO+sJYqTyeQ5cp1jJC2VQElEKBG5x/UKZXQGmTGaA5EIxqgW+PLismqqzjaRNCMJrQkIyC+XbrFcNG1HRJ1tlZZa6zRJemEIMS2rxXKxTNKEGabTi08/+xSFuHp4ZXd3Z3t3JzEJh0jEwN6T2NrY+s3vfvtP/vg/r1YrrSmGyMyjcTkoypu3rhujR6Ph+dn5j3744529za+98+vtatl27WQyWcznq2X18NHTy+lFXbVd1+V5lmVZkqfbuzu3bt86P714+uQJKf7xT398+8UX33rj9bZtMMuN1j64EELXWa3U/QcPPv7ss48+/DgEaNp2vph/9dX9/YPdGGKWZs6Hxpo0y6ujY5+G46NjRLGq6q2tLQAcjoYg6e69e7u7e+Uor6s2BD+cDJfVUp3p0XAoEDtrgVgZfTmdCSl2dnYihSRNrPXAUSu9tbk5Ho12dnd/8fOfn5yenp6cp1ny7OSkKIs8KyYb4/GwSNOMmKTQzlsphOtqQCCORCQkxxAQCBlFz4hmZhSx98ALEYkNCh9jYEIPBIxSIQMgrumW1MPTehGKgP65HKlHt/YNeWaOIYYQ29YGiut3BUqBkgAEMxAzrUOSCFIIISUCg2AWEjlyZIL1UpopUAyx3wdTjH1qlKRQWiGwYuLggtcupkowRgy9s76PGYiIPY9Xa43MJH1Pp+q6tSGz7/KEGHrmtRC6B9QJrXC9UlmTjPoTBwgGKyOHznaT8WR7d/fmrRvj4ZiCPzs+9V3X2e50eu5ijPxflZNrPFPvDkMJyFLLLFGodRe9MsXu3n6SphKRiKaXFxFDCP7evXvW2f3dbaMUgpBCuBCAeTqboxAXp5fzxaIo8pNnz378kx9tbW6BwDZ0F+fnZxfno9GoWjUff/ZJnuQ+xGq1Oj07MVkyWy2/uvtAJdJ5u6gXi+UKQKR5hgKLNCVv796503Wta51QOB4OHz58eHZ++uvf+iYKWNWr7e0dVLJp2g9/+dHl+bSfArZN9cHPf765PTEmCd7XtnXWGm2++5vf8T78+m/8xtfeeaep6mvXr9+4fk2bZDQYLZerv/nBX/3bf/1Hv/Wb333ltdcn482iHLgQ5oeHL9642dkmhvjxJ7/s6nZUFtVy0TVNM5+HGIGjUAKB08TE6OZT21RVoHh5OdNGX15cInLXNm3XOtspKZk4+ggIinWMhJWcaFMMyrIsB8PJ5sZmOSgpQmoSiWtcIzM464hjCKE/7wsErRVw7ydCRA0AUggABUTBO4rAgFIJZTTFUNfNZJwqpZy1kcg5j4AxcpqmRVEcPfXBxqIsEm2aulutjrIsCyE65xBBJ5qJpZICcTQeplly7979LE/HmyNrfaDQ1LVWOvgghPTWK60SkyDiarVi5iTRUqnpfLaYL4P3QioptVQSAEII3oWWrUCtlTZJ0qcGmbgPdCII74KUMk0So3X00Sid5Wm9CkQcQ0zyVEiphVBCWuvyPHedkwLzLB+NR/ce3O099WmesGNvo0l1D4NEAUdPjqWS2hipngc3hESJg3JwfnZRV9V0NlssFr6zjFjm+XAwDM6fPjtNtMae+YqhbjoiNonWrdJKGa33dna+8d67r7/+6mQy8jZIBYH855/dMTL57ne+EwMZLVfVSivz5PGjJ0f/xrfwP/3P/8et7e3Ls8umrZVR165e39zcunPn83//70+JRNs1f/Hnf7a9sbWzsxMpKpNGks63F7PLO5/duXPni9l8Zr3PyqJpu9Wq+bu//TtEGI2HRicmMRJV3daLxYLmvFquiKlputFwaJKEmInFbDr75S9+mZamKArbtFKK4Gh6MWubTgD64PM8k1bFGGxfAvEhUgSgEKkX3/aTEUDs78rWOiHFztbulatXTGq88wDYj5JiDBR5TeABEgIiR4iEFAUiIzOjD7HzwXlKTGKMFgJcCG5Zu0hMLKTIspwImPH56H/9/Oe1lqXHIvdANlh71oGZOfbn5hCllFJpIZVQcn28YIq8/mlSolQiTRJEATEAg6fAIQIyCgHAoo8BwfpOzkRAEPvFAoBiIACOITCR1AKBn7MboGc2GGPSLMtSjcSCYwy+t1EiotImEngfQohhPW1XSiopNaLAddmMOVB0PjgPAjvrlYx5mR8eXj24dri3vVMOi+Vq0TVNhCC1oo6VUp33xPg8VYuByFMIgCGEfpJFzAAolJBab21vv/cPfmWyMenajiPfv3f3zmd3kHF+uVwtPn/y8NF4NNBGK1QChXNusZwrpQWgUnp3dw8ZHj1+fOful3XTzJcLYiqH5cbG1vliWjdLjCiFEgiJVGmRBe8ePztK8tR2zsXOeV/maZYmRTGYTDaSRE/KCTBWq4qRDw8PF8vFn/35n37/b77/z/+v/2I4GDx+8NhH//Dxw/v37nkbsiS9dngdpCTnN3e2dvf3irLIysR29t5X9zc2No02v/e7v7O7szNfzIq8zLLM+aCVHInhyy+//MYbb96+9QKHCEwcYmZMp9TF6bMkT168efvgYPPs5FQCNKul9w4iO2+D9zrRIXohBBO44Jzzzru6bgqRz2fzYphBnyeLwXsvhRQAcQ1sghAjMY4mGwd7h/v7e0YnCOt4ABP4YPtRZgwhMlEk5shEtCZS9QVFZCYElEIwUPCRma13IYYQgzba2Wg717YdIIfgvScpZFpm0dN80QoUk8nkpDtr6lqUmGc5BpGkRjNJhSFGpUQMIVIQRjFE11pkFsCutSG21nbMkCSJBDkejapl7dlNNsZt28ymUxRiOByxYGddyPOmaZRRwYPrAmgfnIfIKpE60QREQBJFXqQxxBCEVkob3bcZhJAA4Lzrl2omMyGSs8GHoFAgiDRJAEAbRTE67xFhPBqtFnW9Wjlnh+OhMYZZDLhUKvHBoxBn5+edtUmWA6N3QSuZmkSbvr6afPTl5yenp0mSpUnS2LYsCgq+aWqHdtWnwYVs2lYpc3J0DMBlmd+8cePKlSuH+wfj8bDIcwoUo7Wdz/JsZ2cry/IQ2XZdcDIxiXPuxvVbv/d7v/9v/s2/SbX5xnvvLWeLZb2aTi83NjbTLHv33W+cX87+6nt/Y5Q+PTv9i7/8L3/wD//RlStXjNZ11T5++vhP/rf/dHpycv36jXff/Qc9TOnhvQetq+erZXB2Ol9460ySJGlSDkoUkryr266u67quF6vaGJNlOaKq2/bR0cPL6cXmxoQ8DYZDInK2CyHEEEfj4d7u7sbmRojx5ORkc2e7yHICFkJJKdu2nc5mJycnDx88nM3nxCyE7PeinbWdcz74uE6jREShpArRSyEBBLAgigIiUUSiSIAIgZhYhEiRmQWkRZakqQt2MV3Nq1oipHnaNwXXMAWCuI5NAgIqlCClNkYIjCH2iGpgFCARAWEt3RKI/ei+D/f35Pw1r4GilEIrmSWpUgo4xhCJ2xj72VSPrQYtyUaPwFqIfnQlEIQAYlLiOaSzrxUIATFCjDFE773vh7gCUAmtNUqG4L11tuedCklE/W8OPkaBQiq1fn8x+Bg5hkjBBh+IhJJdZyPBYGu8f3Dl1q3bSZZYF9rTy9l0upwvizxLtYpEMUKMxCgBQABSjMF76AVhvaY5RgJkAI7AESkiRfCWlErKcbHf1Hc++zwELxG9jz64rukYIxEDQds2McTd3d2Dg4Ob12/lScbMgeOqXjVNhwZb7y6nF3XdXjm8Ph4VG4PN1WpZzRZX9g4Pdg5QYiAaDAbHZyfvv/9+ORy+cPv6wd5BXVebk83d3U3rrBBmuVq46PM829jYePrs6G++9zcff/zpd77znW9++zd++uOf/OTHHyCg1PLalRv//J/+8zfefPPZk+OjZ0dJmuzs7ezu74Tgv7j71R/92z8yif7VX32PIuVptrOzQ0S8rJ11Td0kJvvOb357OCyBwHVN6HxeFo8e3Lt758tv/Oq7gmPs3PTs/Ksvv3jzjbe01pGDdbZpamixaVuKMTjvveWAnbcA3FbNqlplZdKD/qLziEA+JGnKxFqq3gIHAFeuXB0ON9IyD22kGIWSzjmtNTAwk3feuq6nYPX9eBBEzACMQiBRDLHrOkSO0XvngClGipG6rhsMCiDuROej76s1KDh6NiiSPG3PrGBMszTN0+Pj466z167lG5ub4/FQJwnFuFzNbWettSZRaZYpqaSUB1f3ow+M4JxtWzssB2U5KIdl13V1VTvvnLdK64ODA6ml875ru7zIpVJKKx9D17VVXRcFhhgZMUnTYlAgC6WVFFJrA+D7b1rXdVmWKaO9811j0zSJMfoQklQLSVb7pu0MsRAiNaYsS+edTk2oXQxRaVUM8suLs4vpZeu60XhokpSWIXhgYmHk/Xv3EcXh4aFCGYLrGFvVaKVAwJ0vPrt/9551Tko5GI7Gg+HjB49E5M2NjSxNmUGgDNGv6kpI+eUXd1Jt3njnjSuHV7a3t/MszfNUKwUERhsKxBHyrDg5O++sy4oslQlByLKMAn3tnXf++P/7H3/4wx++87Wvaa0m47ExGlEoIZu2+Z3f+R2U8Kf/+b+ELvzkRz8LLv6zf/Yv0mzjw19++B/+w3+ol+1v/OZv/O7v/P721oGQMBmN7969XzeLxXJeL1eNbeaz2SeffnJ+dvG7v/1725vbiNg27aMnjz/65ce7u7tZWR4cHEwX87/+q7947dWvFWU+m11Wy1VeFFrrruvOTk/PTs9Sl0olpdSDQfnVF6tffvghR5ZaKWWIYtO21aperpZt24YYjTEmSb3zTNB1lnskJ7BUijwxc/BhXUvpaZeMRIzAPcOMYs8Q6p+zrJJ0uLFdFKXtmlXVLS7niVQsZGKcMT2nTfakpkgsENbYNhQ9IBbXUcm1AF4ACAFSirimNVOIERGox+eEGCkSM0NfF1ZJkmitvPeIIk0IGWL/QwGVANbkHEoUKk1Eb/PVSqCQUqj/XdsYfQzU+9kjBQrO+T4eGoxHzIWQQkoUIklSgSikFkoxgVIaAGOIASIxJgkZBiElIhJFHwlAqCRZXsyattva2X719deKYnAxvWDi0Xic5/lwND45PUMhvA9N0/pI1hEjhsCChRQSiEWfeGIGBEImAoospOrZAzGQMYkUItjeOOhRgtEaUQNwYjRiIjQ6F4SU23s733j3G4eHh8NskGcpBY4xNF27XNUBwvbB9vd/+MOfvf/+r928frC3164a1iEz6pWXX2AGk2QhxCIfEgr2eOXKlb39PRfbzb0xeFfXC2+jSLx1tn8mVqt2a2Mz0ckPvveDt954u66bP/vTPzs9Od/d29Yi+cZ7v8IxHh09Ho/Gs1mqjWLrL09PBIqtyehwf+/k/Nnx0XGSJG+9+SYTVsuq7TrbdVVVnZ+emkRNRiNnPREuq9WqWT5+9HgwKL729Xfaqolsd3a3Pnj//Ttf3Hnh1u1EJ0QUXLTRNk3d2W42m9ZtZ61jIu9CiP7yYua839vdl8gQQSBTiKDiaDQCIbU2KNR4OB6UQ6VNiAKkJPY6SciHrmsoBiGkc51ERgkxADGDQgECuL8xqhiQI3PG3nbEDCiIGFFyJCW11olSxmSZ875tOk+ktC6UaZqmbbsiz23XWOeqatW2bZom3vsQXd1UCUcJaIxO0qSp2rqum3qWpon3AQWDlF1nY+Cd3e2dre08L7u2e/z4cV5kRZLPplOTqK3dja7tmKED29WdkFKgpOCdcz7EztnWOh+JECmy1jLLC611DLHfVHedtc6naYaIUuumbZMkIWTnvV85VEgQy7IAhs53q6ZOk8w6m5p0OBg1TZPm2f6V/cY1q7pZVavLiykzbG5tloPheGP0sx9/MLucXr9xtZovfNMBMCJaa5umWa2Wx8dHWZqX5WC5XFbL1a3bN/d39oqiGI4GWhnbddZ7Aapt28++uDMajq/fuLW3u7O5NUkTk+pEogo+AvWNNmFtHAwmj548m55fjDfGIkOtlWC5qhut5Dtvv/PlV189evjg4PBKmiUKVeAYQiQm23Xf/AffVEL82Z/++Sz4n/30ZyDE7vbO3/7t37ng/5v/9ve/+93fcd7PFpeChTF6MCwmG+U+7XHkrutCdNY5JfXW9tbNm7coRBS4f3Cwu72/d7g3Gm1qqStXP7h//8aNW++880ZVLZum7dpuOBoaZZbV6mc//SkTvfDCLSUVAT188Phydvns6ASfR3R6YWMMUSq1tb118+bttms//+xzAA4+BBuMVhQpQuwTgyyYQiTiEJkArY8CyUgRfeQI0iiTJiA0EKP1qDUIkRaFSvSg2lo11rYdNTbSPM8KBLA21I21LjKzQpQohJAChRKSmUIMxBGZGEIki1ITEwKIHuUYWCBEJmTZZ2dQCERGKU2aJVmhszQ1iY7UNa2zBDICBSBeC8pAGG2U1j28WiAqKQWKJDNq7e8VAlD8vYSLiGMk70IIHgUabTKbSsikUEprQ4yIKKUQMkZ+3pPjGCOCsIjrmgsyrZ/YyARAWBTlaDhZrerpfCFQZ1ne+UBdm5rUZOWjoyc7Wztd1yED9R27QKLnaYTgvV3nnHqeXv86IBRCcQSKEAOjBiWF0gqApcThcDAclyfPTmtnEVCRLMuBNnFne28ymSRJWjWVMqKPtiqjdSJX82Xbtmliloul0qZxtna1MmZ7bzIcjRaLhfOts7xcnJycn0mldvZ2AIVQOjFJIAcMVbVCp4mgbVti6JpOK1WW5WpZ/fX3/kZL/fjR0ebGxuX59Ld+693f+PY3FxezR/ce3XNfDcfjwaBEgKaq67aOHMeT4aMnD+fz+e7uzmIxPz+7bNrGWsfE3rnj4+PJxrifK9ZNs1wuVstVZ9vFfHH3y7tKYfQxNVleFJ9++ikzHBwcMnLrXFVXAti27cXZeeusdyH4ACCeHh89fvyImS/3p3t7O4UxWZkGsBxJSeEjS6V2dndffPEFo02IkRm10RF6/q2XTIzQuS54h8jsKRIAolRaSqGUXpeBJZNmSZGEEhhc9M45JVSSZSEEISWikEyIMkbSSgUf2q7pQ9addRfnl08fHdd1OxiVaZ7O54umbeuqybK0yMs0S4zRzvosy8q8WCwXVdUIwYPRYDgcAUFPj/DBnZ6dddZOJmOGuKpW08tLYkqyVApFxCFE7uG1LvZ0decDMxtjKMTVqh6NVN+zJCaKIJUSUmijO9uleaa0MmkSI7VthxK1UiEG//wuJUAyATO3TStBaJXFSGVRaqknk3G1qlaLFRPZJpwcnd64eT36cHl2tr+7e+XwIHh/dH6+WMy9DT764Hxnu43Nye2bt4uy9N49fvS4WlbJ1WQyGWljijx3rjs5OxMsHz6+H50/vPXCqBxML6feu729XQBOIS3zXKAAZqWUD2SS1HrvQmTipquHcogSmKPt/K1bN3/5iw8vLi92d/eCl0KgIMzzzHuntErS5A/+4X83yIt/+S//VdvZH//4R2UxAobDw4Pf/p3fGxSDk9OzGLrEpMFHJTUAaSVstKu6QgEbG1tf3LnbWdt1ViAqlNpopVVvTF9WK+ttWQ6brusV6kabtmkR0TmHiDEQAGV5IVEwxK2trUjx4uyybuo0y5hYKAnM5ai8cfPmK6++ur938Nnnn3326WcUOcTgnVdS9sSGSMQU++pvn7yJFEIUWoCPEYG00TpJlEmsJyImwEi0WNXSZIlJkqwAIQkxMFsbgTsEcNY5F0KIzCAkS4l95r8nNhATUVhr0AH477Ny+Bw5yryuGq8bvtiHpE2a9INGIRRzBCEIsCdsPufAYR/uZ2QUiFIKCRIFBWYGhSgYRU+y7m80feWZCEIInXUMoKUu8lwrrQCNNojYK1xQCA4xMjNAP+WMGlQkjoTMYg3AgBAjBVBaFaOhUKJ1nfeUGBGJ2razzquR2dre/uVHvxSoyjInYmNM13khBUAvuCHmfhXD/eIdWDDx2r/KAhiBEUEQRSkUIDLjxvbWe9949/jodL6cX5xenJ+eTS9n3rqiyNu2zXMbXLDWKyHbttVaD8pyuayW86pdtXmWElFXWwRVGHO4tz+fzVdNZYxZVc3pxeVsttjd2x0UZQg+TzOILITw7CpbSa+Hww1EbNv24vL86Ph4MCwJ6Kc/+hEw5kVBApQyt1+8rbSKkXWq09x89NmH7737jYP9/WpaN7ZbLBZ5kaMQJycn49H44cPHLvgYYlO1zrvRZLxcta11TWMvLi5b26wW1XK1WiyXVVU9fPTQGGm0GZSD6zeu/+THP/vr7/9gb29na2sbGbzrru3vY4ztahkoMKFAXNbLy8tL70NTNxLP67rKErmxNcqTfGdvmxSYRJfF8NbtG1euXeGIUqrUJACstWjrTgkOfd7Me+8C9xtTF4UQeV6kpTZaAgKDIMBIMfQ9GIB+L9Vv55Isk1IAAXFk8hKli8E555zL8lQpraQKPtRt7b1XWjvvgotKq9F4qKRK83QyGqVJGmKcTmfL5YowpplRSuVpnqapNrIs8qZpjp8dV1U1mUySLAWmgysHVbV6eP9hVTdFUWZZlpgEAKu6UlqM0xFLGYmTJAHmSCQoMvRuVOhjG0SktZ5MJovFom3bJEmM1iHExWKel2U6MJHFGp2bapSy66yzVgoZKVrnvHdK6xBinuaCEYHrupZSPn5yfPz0+KWXX/j2t3+dmWezWZroyXigBC+XVWvJd91gMDg8ONze3i4GpVJqa2vzk48+/fLLL713N2/dzDgDIR4/enB6djIeDnd2trQBk0gmfXp0sriY37h1fTgYtlUzKMskT6VUOpFKaW+9D67flIKEvmQTfdjZ2ZZKX5xf9ApBrbXWqqqWG5uTGMgkWmn927/zu3fv3fvzv/xrRBGcLQZl19aL6dR3TiCAACnQuqZX9yhYDyEuLy7HG5M8LZbLyjtvjGbCQTnoWgsAEkUIEUFwRGeDRFkUpUCwrUVGADBKKykXy4p7/6BUiUn29/aVkifPznzwTdMwwGRj8/U33rh565bRxnvXNLWUEpFj7FNrfayee4QDAvYyGBQixh7FDM57wVQkRZGWEbmOddeFCAJEbJ1dtZVMtdJaKgPYCcAQYvAtAvsQvA/eh3UNFnpiZg/QWdemhEAhUSpEFChBSETqGQ29/lFKKfrBOGNP8FFGm8Qkqnd2MfS0iRiIAiFS/0eFEFoBShRGyp4vRAySCEj1MIsYIwpkBCGkkEIwA2CI5H1g4ETbrmuN0iiVAKGljiL2utQYAjBLrYDXnAVAJCIfPAJSjL3DSxuZYU6RvPWQg1nzszCEiATWea1NUZTTxUxI2WuymXm9MOzVO7zmwvXvgp6b2rcd1teC55trIVUkEELkRbmzfZCno8ghxFitlpfn0yePniyW0/Pz6Wg09sG2rUzTLJJ3tR2OR0WZCS2Xy6US/REvSoTheLBy9emzE6WMtt5ZO59NrxxeTXQWKCSJiUyOohKi7bq6bTcnQx/iw0ePHj958uz4qG5aENizwxgwQOxan5W5Dfbk5HQ2m0otmOn0/Oyn7//snXfe1kZb57rOh8h5np+cnO3vH2xONtq2bduWAjvvFvNF59rptJnPF4vFLMbYdm1d16enJ8NitKxWw7K0nVdaj8bj8WTyxZdfnJ+c5YNsPBgWWX64vTsajp1vm7qdThfz5fLo5PTi/FIhHu7vbW5uHBzsLRezi8vzczclBKWSvFTFoNzc2VJKRYQ0y2DtHGfvXYQoEOUaAx4jkQvBOW+MUVokaaKV7rklvTIpEvUcTSmFD2ids9YnqUmThJi6ri/gMEXukRUArGRblkWeF+VgVOPKdjYvs+HmYGNrkiZ9zg/SLFdKkbcoUBtFLIo8z4ssSVOKsVrV3nlrbVPVw3K4u7MLDE3XjEejNDHNqgkxIoq+EN91djadAYpyPFJKJUpnWQYAWussS/v2UK/HFko469I07dUiPequLAexF4iv0TRKKd1zHmOItuuEwCzLlFLe2RgJBSopnfM9gz0vijzPhoPheGPj2o0rs9n84uIiy5LJxmR/f//27VtZVixWix/+3U+M0ZPJRp8PAYbBcPTCyy8cPz06PT1j5tsvvDidXl6cn29tbt6+8UI5KLOySLShwNrozz+/A4g3b97QSjGytl2eF7lOEdg513WddwGBlFCpMoOyDDbiLo4nw8vpVEjZrz2EENqYuqrSLHPOWeuk0EVeCAHBB1ByuVrOp/Pv/+j7/+Sf/JPlfCWUYBGt7xQqIkpUgkKMRqPlfDkoh0JivWoi9Q/xvmYXl4tVWQxDDJ3tGHm1XPkQiiz33golGDkSJWkCAjtru7YbjYbEMUmTTKY6MTdu3Fwslx/98uO2bV988cUXX3xBSuWcI6a6ahCfc9V6qs//bhYPTNBvDrGv+wIAplmeKFkWJQqxnK+qVWMjJ2WhVKJ1gqhQSGLwIRAzKOmD67mZvF5h9iUtFFIgrrVbzIjQQ4RQSqm1BpTEkQIxkmD1PF2jhBI9GocAmVgiCpRKyV5TE2II3ofoIwcAijEiskSU0CP1hBRyvRnum2MEiigSSe98r7mXgoWUiNT/MhCxZx+Cc95ZqxLAPl1EEIl89DEEENhHlJQQUq7R7/0Jj5+XzKRQwFg3jdYGECgSIgYKKurUKOdclmVXrlz9xQcfpEk2HgxCDL1yAQABsVdQ9sMvYog9apv79yD28lUQ6/pD74GKBE3Trao6RB8DIYrNje0rB1f3D/d+8sOfPHhwX6LIsqRqKiA22gBC5zqtDUWPkXc2N9vVCrI8TZOzs3NXN8H6NMkmk5QYd/cOikFZFKUQwseopQyWOm8vl2dHJ8+m09X9u/efnjztWkdEbdd6osQYBlRaRRcHg5Hz7Seffry3s8ORQRCy2N8/OH52/Itf/nJja6MocpT93BwfP3ny6muvnJyfLpdL7/poZSjy/NGTh7YLT54dacldG4h5sZhfTC8BcDadJyaxnW1dN7ucbWxtvCxebpbVslp0bW3b5unR0csvv5DlQ+to98rB/KsVIhwe7u/sbu/t7u3v7iapgUiffP7xvXuPLs4udvf2QUiTFcokznmTpqhFiM77aLsOmQFAabUm0RIBMEWSUqZZUuRlkmRCyr4Oz89FGoBCglRaCSdc9BSjlGgBYwxN23RdB8CJMSYxUoquc0rpGHmyMdlabkqJK1yVZTEYDWLkxbIC4qIonPPL+SLEoJVC4CxPtTFSiqau27Zx1i2AOPLG5sbu7m5RFD2EXCC33CVZqomc9c5770M/vYmR2q6x1itlgg9a6SQxUioAiDH0eChtdNd2PdRBa+2970WsdVUlSSKlYAallEkMEYUQvPc9k0trnaaJt1YKKUCUZfn06ZFS0jtXlmVi0spWRhuJanpxOZ8uKqO8C0SotdnYyEaj4dHVY6FklqZ9YBcEN1VdFuXNm7dOTk9Pz86s901d7x7sv/ba65sbm6gQIgmUjCJxzcV0+ujJoyQ1w8GgaZs8zy8up9dvXAfEznbT6ez6tZsUoWtd8IssSxOTDsx4d3d/Ol9Y57XREZk52C5IENZ7ZOisf/rkyU9++lNrgxDgPRmTFMP8gw8+SEzy8iuvjoeDJEmUQhc9R2iIhUjyvHzxxRdPzp9JLQLZrm2AqIUWEZaLpZQy0l6eFdr0lFaiQN45Z53tLDJoY5SUxhgims/nWZpZ15rEaK1Ho8n29lZdN8+OT87Oz9IkBcBetR58FEIIFH3o7e8jmAjcD4HWDgBkAgghopIEbHSaGMMo285WVVfXLStlQABIRkmEwYH1obOOuXeoMEVCKXqdCwr4e9YDR+6nG7R2v0NP3OkjQxKxx8OhEEpJo5XSWgrZN3pDjBF6uhoiQr+ydtZ6733wIfZyxYBCJAZZaSEQUazbXwCROQYmYIWit2tGEYOWipifG2SQGfvCAjGEEJ0PqidUCyagEINzof/9iFIZzT6uhZRMMUZg7vHORZ7KREfmuunftxBiCIGkkrwGLkYimow3InFd14lJEJkjxBCFFD0Skoh74unfF5w59tDUdeGhz8ciCK0SFAIit43VxoQQ66paLVfz+dx3Ps1MW9unl0df3PmCiVCgVnI4GAyHg/72RBTLwXA0Gn328WdVu9JSSykUogQphFRKZXnhnWs7myQmy3JlpHfOW7taVYtmCgyuDcakRTEYlIqYVqsqAi+WS22M80FIqNpaS/HJx59SoN/6zd+WUngXdJLuHxz0xY161UotpRSLZfXs2cmDRw+VVF3bpUk6GAzI+w9+9sH0fFoMi5/99CdSQpYPAPjx48fn5+ebG5v3Hj5QRgPHxcnyycMnCHx4sE87sW2b6cX5Yjb/4ssvJhvjb/zKP0jzwX/50z9ru2ZjY3z12rWd7e3haGC0is5X1WJzsnE2nD4+OlpW1WCyabIsEKskAcReDKqkwr63GINhFWOMFAnIu9BLNpTS2milep9oiIEZWEoRoyQOKIRSWmmtlNZKSqWIYmi9d54oCiGYuc+n9UdvgSiVUkpLlGVRGpOkJhNKdG3bS3qZWBuTyNQk2nbdfD5XWvV8mK5rrXXD4SBPs8lkkqSpsz7LUmO00dpam2d5DGRkWK5WkagsC6V1CMQoOjurmqptW2aWQikp+zh5JBJCxBAJyHVOSWWMkaInagnrHPYss+ClkkZrYJRSqT7Nzet3pDFGSrlaLZMkkUpY55qmrara+3BxcXl+eqGkfPnVV5umvri4WC2r87NzYNbGbIwmRVlKpcuyAASltRCibmf9cKn/Ut/96i4zj8ajz+/cKbIsK/IiL/IsF0K51sbop9Pp0fHRoiiDD4NhKVAuFovz0zOj1OnJ6fHTY60UIiklEpMMh8ODg8OtnZ2vHtxfrlabm1t1XUmpBbINIU1ypdXDx/f/9f/7/3N2dqG1BABrXfBBFOn52fnf/e0PT09O9/d2R6PhcFSeHJ8lypgkXS06oURZFDpRWqinj54uposiLwC4ruvFYlk3297FwWgSox+WpTHq2fEzojCbTueLeZbmeVHs7u6tlivv/MOHD8/Oz2IIAnF7eztJE4Gin0jazgohgCkEjkTO2jRNpBAkBcXQ16v6jxz8vUgFQBBwCNF7DkxMpGO/fSECEsITC0AGCBADRRd8a7u27pwNUgoGjNzLkwCZiQj72BAKZAwUe3Q+UXz+KMfngE8gohgCM0mhpRBKCa2klKqXE8S16ZGYIhEFH5yz1lnvnffOex+sI4pSaRkjA4FQzBxh7fXtXceIqNI0EYAUfZ/RBsRIEImDj317uL8y+Bg9ReuiMUIBx0DeBWddZCZA5wMzSqXkujrEgQiYtdBCq6wsdJqEAPP5SmkjhJL9fUeoECMRex+kc0maDYajs/MzJUWapiFQ23UIop8p9CCK/u29pqZCvw6OSFEAMfO6NCElgkS5vmwZk25smOFwvH/1QBBMRpOmbkIIWxuTpqsFCiVlmqXOWWedtS0Aa6Uh8mQ0GW2OmBEiB2eRoWmaxXTpTs/yPDPGVNOK+bzpWq21ADSJ2d7cH4xGEOSLL77w5OjJzuG2AFkU+dOjp3/zvR8456WUzjtmlmlKwF/d+2o4GL74yktaSu9Cmqfj4UhpRRTOL8/vf3l/MZtPJuMH9x6uFsu9/f3xePjs+Pji/LyqWyWlQnn27PjiYt7ZjjHu7R5ub++MJ5MPfv7B9HJaFmVeZC++9OJwWFBg8rHr6oO9nTuf3Xn06MndBw939q5++tnHH3308ZtvvfXKK69tbm7keZroRDC56D2Rp6gTo7RynoxJR6Mxs+jvnkoAE5FYm+mYYowETEIhEjjnmFkaqbVeQwupz64BCoSe9R2RAKSUxmgppMD1uUwIAcDWWhTIDL1rJUmEQMjSjIiKorStjeSGg1Iq0dvrhJYI0JflvXdNU81ms6qqhBCRgtYaEdqm3d7a2tzeQkDbdjFSUzfWdePhgIEHg5IiM1KaJc6FEEJnLQowxgzKgpastep911mWaZNorZlZax1j0F5LIRFBay2V8s4JoYaDQYjrNGFQXiklhFRaJ0nSdU6g6DfDWhulqSiKoiiNMX3QyzkPhEmSjSaToii1NpsbWZ4X8/l8tVx1nZ3P5sGHpm29WxRFJoXs79ynZ6fz6RwAfAhd17ZtK4TcNjuMMFsuzqdTiQIJkizr292I4uTZaZrOnj07CcH3MVamOByVX37x2cX5aZpk/dEzz/KyKA8Pr9R1kxjz+aefX712bWO8kSiNgoc729GHTz/95I//5I/Pzk6ZMTgP0DMoQ1u3WivbdXfv35/PLiXKW7euNqv69PRsNB49fXTWutY79+KrL3VN57x7+uRxU9d9YHE0Gh0fH63qvwXAoii3trZQ4C8/er+uqxhCmmQhREQhlKiqKgZ69Pihs3YyGb/w4gtZkadJyogMKKVSRlOfOaYopAKANEsIIvYWFuh7Rxgp9otZpgjECIDE/WMxSUwM1IfpWYCjaF0YZKVWRqJBkAIkBfIxEDOHKBB6uSLD+r8MyP25vre0MzFzhH6T2VNGcb0AeK52ZylYSdBSaiWFFBRZSiEEro0yLnjrWZPv2UnB9VTdEAkA+iUqo+jBPL0MvN9GKK0AQBmjBMoY1sS0/uf294gYiZgRBYAgAmcDMoIAAOVDaNuu7ToCJMC1BEdJgQIQvHfOOWBGI7RIiBkB+9lvT/SXQgBzoMiMMRIixsBSifF48vT46WrVhEhM3LZdiEEKBb0qk6MQssdfAAMAA7IQ0KtmmKmndDCse9J5UXgX2q5NTaaNSYxB4q2tbZOaza3N3/+93/M+etcNR4P5dH7nyztvvP56kmaXlxcA8PjJo1/9B99sXNN19vTkJNFmNBhlWcZM88Xi6ZOjnd3tLEuns5ntOoFyMCoQJUdubHdydPbF3bsXF+cu+q7uXn/jNWeDQKG1KYtCKtk2jRSyLAeu7T7+5JPzy4vRcLiaLz1FKRAQbOds1wKCknI4GlbLar5cVlWtlba2tZ0TUgzKQjAQYQhutVrGGPd29/d297x3idG27fb39ra3txKTuC5uTMbloKDofeeVTqrG+hDOLy+m88Vrb73+nd/9zt7ODhMXRVLmpWu7i/MTZBRClYOyLAsfvEl1VubWu9VyORiWkWKIwXvvnBUIQvb/pL0SCZh7jxuHEJx1QkqBmvsvVeT1qYL6uadMkhQYuq6NIfSZ596mQkRd21KMeZEXRclMg9FAKblc/P+p+rNeS7IsPRBbw97bzM507/UxPNwz5ojMrJyzmmQNZIlokk029Q9agBqQ9G8E9JPQf0Do5xYgSOgmu9RNlljFmoecKrIyMkYPn+90zjGzPay19LDsepUCiUxHpA/XzzXbe61v3JY8X1/XaZ5V5XA4glq/WtVQTY/zPMWOQ+DUdyHPIsrEq2E1TdPJyUmMkZDGaQqlmtnLly8N7HB9JdAYGQBVZFgN3dA/efLsOI0mEGK3Wq+Jwm63SynRDRvnuXKBAyH6FG8GgYOBSWuqst5sLi8vicl7BFXVDWJd1+92yEwuCiFEpjCshjt37/Zdvz3ZXV1d1tqMIHUdEl3vDyGk3W7Xdf3t23e6vh+P4/X1/vmz569enc+lzDnfuX2HmS+vLv/2Fx8T8q3bZ7dv3+66JKKPHz++fffOe++8P+fJ1EorV+fXiHB+8erW7TvrVc8cVNrp6ek4jdM86TxudychhvE4vnjxwgC71MXgITOkKhyTqf67f//vUtev15u7d26fnZ4++sY3njz5+q/++i+fPn4SKY7TMYXUWmVmE53rGLtuVmi13j3Zvf3Oo9vb3a5ftdxE7MMPv3lya/unf/KnJ7uTw/X4zW9+a9X3x/1hmo9X11cvXr5MXdzCpooO/dBaFVM08iwcFR2G1X6/twK3z25P87Q/7G+d3vrhj36w22232x0hlzlP0+jSeZ8gPW4NEWNMRNiaKC4t6LC4cT1lwVyT442OBAgmIURAFLVS2lyqMsY+htj5AB1i8vZc5qA3TngFVU9uM3PlgFtZ3e6LN2QSeI8vEhEFokAcYgDTyBz8Px4EjebIlZO3peRSkmgotUprKmLqzVoEgCGkEBI6MO7qGau11VqaNAGCgEBgizGBAqmBtFZKkaatNbgxXzVporGpkhiSldpKrQ44GYYQ2OljU5AqJZfWqjZDoNhFLdE6rbV6IxgzN1EkCiGYQlk2ZUWF23fuAODV9bWopNDd7GFQF0CJRNSZgKXlAAwJ1XydAfaMdWLHoBEc96LAgdjZbbq62l9fXhkAIhuU9WYdOCKjmYnqdrNarR6N0/jJr3+NZCnEs7tnr148F20xhdgFFTs53V5db3Ipj598KU0Q8Pbd2/M8j9N4dXV1ebl//NWTXFsX4vd/8L3737335Zdf/tVf/RUitDnff++dDz54/+9+9aunT57WXJzRfvr1088/+8wNfjEFBIyBU9fduX3nzQdv7E5OQBHA3njjjavzSyTcbbahiyD26aef/s1P/vpf/ot/sdueAMKjt95Sa/OU337rrU9//en3fvD9mueXL172q35/OOyv9sNqODs9fevdt588e/7JJ588f/n8zr3bq2FVc3vx4kWgcHGuecrzcW8gFAIAbre72HVN5c7du/OUh1ur2MdhtdYqhzx79QRTIEKVVkuptQFi16VxnkRyzmW1cimBy8XMBW2O3ZlpbeI/7lI3NvHHY7Vamek850VEvCTIWoyJNvTwGw+v95evXl2kFFarYXeyO1zvay0Xry6Gbuj6xBxiDDHF1TCUWvM8qUKXuu3JppY2T7kfOlWZprmUwkSW4jxOiGGzXa83KwNwrKBLXWs6zZMBrteb07NTIry+uuIQNhxSjODJFsDMrKqiWkpZrVcpdTnPMcZbt247BsXBLWMQOBChGeSc1+uNSCu1dQlUrUvdvfv3L64up8M0zcWzwXKprQkRhxA32y1zWK3Wz+3ZkydPnj19ejgezeDi/Pzn7RchUGu62+1+49vffuvtt7ebtSHmeVoNq+PhsN1u1usVAeZa1sP6cDiIyna9e/DgjVtnZ+v1ukvdnOdPfvWrJ0+efutb30JATwsYx7HU0qq8fPn8xfMXuVRFBaTj8Xg4js9evfz47/6WiR88uL+/3k/jMYY4l5mZiOzNN++fnZ0dDseXL1/W2sjUasmHfR+11REBz25trq+OX37563fe/70f//g3KZHKFzHxvft3+288ArTrq/2XX3zx5lsPf/DDH/3y40848B/8xz9EhH/xL/95nrJzntfXh5jS+fmrN9548Dd//TfPnj/Zbjff/OZHtZYQOlU3IsJ2t3v2/PnFxcVbb7/VaiXmfujCNRMyogSKXoqHBgpo5u21r0lME1UiJCY1RQVRmWqpKmnoiAMAMvPNyQ4qNzH91USttaZN/GxCWo5UNGi11VoRITAGppsCeXBhEHMIIYFp+PvoUVIxU5MmHpSphqJaWmVVNSm1qWlMAQlqMwDo+i6lFEJAJgQISJjLNM5q5lkAobVmqiotxoQIS18lQGut1OqB5kgoCrkUDtRE0CDncjyM4zyrAbACJTDQ0sC01VpqaSKgINJKKRRYj1BbBQDVBmCEBGBMnKX4GBhiUNXQpdOzWxevXobMhMyBTVHRoTlHfgyBxBQRVEG1AXrfgJiJZyYRISEJIBGLKiJxCDElpsX2dnW1353tKFDQaGgCMs/egQyltshRmt06u1WqmGKppdb66sXLeZrv3b/Xd32IEQl+8pO//ttf/GK927x5/8HTZ0+Px+N4nJo0ANRGkus733zv/r37X37xxV//9V/Wkt999+1vPHz04YcfvvHwwfsfvv/JL3/1F3/xl1eXF8yEAClGZv7Rj3/03rvv37p11vdDTCFQWG/Wgfn85XkI/NZbb9+5c+f8/Hx/vSfGzXrVr/uvnjz+zf/iN2+d3ebAV9dXanr7dlxv13/3y1/98uOP//E//kff//4PiZEYaqlPnz59+fLV06dPjtNxytNnv/60aVsPKzP74IP3CJjIWqm55mmcQwzr3SZwGIZVnueuS2enp7dv31aTVss8zXnO6N7zGGOIMTAgMpL6bL9QbeDDshp6luFiCUYPpPXyZ8g5q2opBQn7rqu1dn0/54y4RFG1WltbAiYQsO8GJCylqDQ1y3Px3LgQw+nZWUx8OBzmPJ+cbFPqjof98TgOq2Ea51KrAXRzYuZWRERjisPQl5pbbdKkNW0iJdcQwm67G6dpnErsOuZgooDIIZiay7fVDACmPB8PRw7sSidVI1Tm4O6wruu6dedPETM3qWSBOfQ9+LJf66yq85y7rlcBFXj48OFhPLx49kIE+qGPKQ3rIcQoal0Xh2Elqs+ePZ/mfPvOnQcPHiLCZ59/9virr7/xjUff//73v/HoG8OqdxkPc/j2d779J3/yp5/8+tc/+tEPS6kDrEMIrbbj4RhjQMRu6If10KWeI4UUiPndD97V2g7H8fTsdBonZt7utp999mWt9YP3Phin8XgcAanU/OVXX/7kb37y8ce/mOc6z3OrgkRS661bZ9/86Js//NEP337r7Xmef/qzn/1/fv/3pZmIlDI//urxW48e1NrWm5Prq8Ph8vrFkxfEFCl0XWcGasqJU4wx8uG4b7UR22o1iLXdyfbJkyeltZjSbtjmkkOKu90JIZ7dOj053e2PV7XW6+v96dnpkrmjiMDznF1BUWsLYYmARsQY4pwLqEmrpuoMI8ASleNEgIgu0hVLSKAAVXXOc201DiuOwfe8wGw3tb5u0GF08IUVxJ29sDixCAxMVUUAMTADgLuvdNHIEZgyoi8eYtCaqlWA5St3wRIRgqE2NbZaSqsViHx8FAVE7IeBiP1JVY8YNTNEU2tNiSjUWkRUWvU7AYP6wDVNc6stdsl73tWs1MZTAQULOHlL91wAkYMiIrGhmJq2mtUEEYgREUSltNxUSs68iNUMyTVebnBYbj1D6Lru/hv3L89flVJS6GJKagLiocLebobLPQDmTjOvt/HsVEQ0bTEGz/0PFKRqrZKnok1TTLEPKYYYIgKmGI/76341tKJXV1fX1/vVagVAQFRrYUJtrUq7uHie5/nDjz7o+2Gepnkao6Zay+Gw50Cpi+N0GKexztXQUuprlhDCgwcPf/u3fvvLzz77yc//Kkb6r//1f/Xd732HiFar1Xa7JbC3/8uHH370/hdffP7Tn/zk68dPEeB4PDx5+uT7P/j+97733dV6/eLFi8PhoKpZ5PTWWYjhMI7z468RUVSv98fjYVwN68gdUfCinvV6U2opc0HAe/fuAuD9Nx74DicVVOFkdzbO0+eff/HFF1/dun2rjx3FYGIvXjx/9uzJu++9/9bDR13fExEg748HuTpuTtYhRkzYdZ0T8mg4jVMu2Uw9SAoBRJpaM2m1VQUFwJiiiJZSDofDar3pup6Y1SPiTKyZLpwYqGltzQ0eCBhCIKae+jGMCBBi8MqUGFMuWZoQ0ma72e62lxfnh3Hej0c0GIbVZru5c+dOirFZU1UmQuSYYq2NiJk4xMgczLTk4jlFSwGGQdd1gRkBcvGrRjmmVeowBIUxdmlYrQwoz6XrBucJQwh+h5WccylBQ0qdB7cQMwcGwJQSInZdBwh5zk0EqyCZ13R4g5U/1il1u91utVpziCmEW7dum+Dl5ZWIXl1e3b//hm+zFxcXT548+frrx30/PHz48I03HuxOTzbrzcOHD3/+i188fPPh+++/79oR5rDdbIkJie/dvfvJrz+5e+fu3bt3zWyz3oaQnr94fjgc/U/nRUVOIrZer/dXh/VquHvn7jxnJE5dvzs5vX+/fPnVVxTpzvbuJs9NhIjX2/Wr81dfffXF8Xgcx4nQgtIPvv/dH/zwB++8/e52uyWC1XDyW//4H03j/g/+wx8SWCn5eJwvL49idrnPF9f74XT92ZPPWpZ7b7xxdufWeDwcj0evFeq7YbPZ7K+OFxfXyCxZTk9Pnzx+ejgeT9ZbFRv6VQoDRTo5u0XE291udbE57K+Px3GpaAYAs9QFQHC5uNfWModAS7waIYk1fd0gCgiGpl59oovIHHGJ/vQKkFymnBXMQUU1CIENsUgFBlWRpqZIiYhBlbD6VaTISAxAHvkMtTZkjEqKZga1tdrCIkFVD2UDMdBmYI2REFDUqjQBYw4cAhA0FROYpuya3RgTBVJDZo9edkOziKmC+kLplZAxxWBgtVWpjZA0iVkgRMd/EDDGGGMiJGlNpJEnMQiX4o42QCR3gRGI16mZQqDAFJiYA2MgNPTiMF9+1IQw+t2qaoSICCri/rbdycnQ98fjfuKoZjGE2pp5CY53cgKqKRr6XgQIzMgBA2MXQwwsrTktqdICU1wPkdhAa50j0+mDO/0Q3GnXDx0YmMmcZ1OTqmkXQwgh8DRmU61lfvbs6atXr9brtN9TLuX66vrq8vri4vywv95sVnWeL48TsIbIyEFbNTBKutmt1sPws5/9DQD9V//lv/romx+uN6tpnMBaHg9MsOq699569NbDBw/u3/vTP/2Tn/3k54H54198/MVnn//n733vRz/+8dtvv9N1XanVQcQmSkjbXcfE3YCiVksJMX33h987OT2Z5nnOWVSG1RC6FEu6/+DB0ydPXr161fe9V3nkkp8/e/6Lj3+RQvq3//a//sH3f/Do0SNA218fHj/++vd//3/55ccfr9fr7XrbagFiYq4mV/u9Imx3WwQy1ZJzSh0ioNp6GMzEFBC11gKC7nQRaVJbVUEkBJ7mKXVd1/UuljC33bcGi+SOnBEzM0fpXMyFhCEEHxdabU5PeV4icwgxSFMiRkRtmrrExF1KgcNc8uXVOYASh8PhUGsdx+nW2dl2u1utBjBAwibt+up6HCfmAIbWVKF6ZrIBGbof3kA9pxdrkRmKqTEH7xMPISCS1Lwo9czAbKEGHGVG9Fx0ABAVBPRiVEImAFFFQ2IKHBCJEJj6PJdhWAHg9f667/vv/OA7ZnB5cV1zffnqVW31uD++ePHi5csX2+32/ffef+fdt9frjZq12rbb3XvvvndysltvtiXP0iTG5N5JbfLBBx8+f/biP/6H//Bbv/Pbd27fIUYPFk1dW23WqUscAyFRoGMeDQwI51ahZUzUjtIUDOnu/XuPv346zznEzp3SeZ5rKZ9+8uvD4ZDzDGgxht/88Y/+zb/5l8OwynOe52Pf9dVMRX7w3e9/9smnn3762QR6eX25O9nmXL/4/Otqte/75y+eqsA4HW/dOWU6u7q+zPN0996dk+0JJ7raX11cXKaua1KGVV9qub66HlLHkQjIG8T6PgUOpycnL4bh1auXx8N4dnZWtIQYYoqpT2YaQgBTz7h0yDcE7vruOI3QSMVce+YzzSL8Nyc4yYAUwAha06Kttiq1UgwcogEY6OvuRVWtrYk1l3iZqLJAEvDsfn+1mN1t7N3tqqBgYo2RxC1PLuoXf2OQSZUcVNImUlszW+Q5BiDNe8qWPuRlGSXvDhMDUT/vzFprYoqBGAIRp6EP8zi1JmDWWiu1MQUDA7MQGLDrui7EaM2ktialGMQYBHFxIoMRMXFAZsDFq8yEhCEyEbPXwtgCpklrVaSpAjKKKbvsFgCJahMOLLUx0snp6eXVZWpKAxGxSGmtqZd1AqknQYAhB1FrIq3kZ8++HqdDCAkB85RFCoJ8+ukvm87qYW/WtOmt09Nvfuujw2GvqD//+U+GYai1iciXX305lbFqVmgXF9evzl8h65j3T75+8vnnnwLpl1/m2ioYNalfP36iqv0Q1tsVM7sH9fT0bLVadxSnnH/y1z87HA5/89O/QNJ/86/+9Rtv3JvzEUFCCMR8fv5iu92O++uQuAv8zffff/jg/v079/7qb/76eByJ409++pO//eUvb5/d/vF/8ZsfffTRbndyeXUJSJv15uKLr7abbZd6VQ4hffH5l19/9eT8/KLkejgezGxYDdvdbnuy+/j3f3m9v/rwo48QSMWT+dvnn33+xRdf/dPf/t1/+7//1+v1tuSSurj6YPjgow8fvfXov/u//nd/8zc/fesbb+22GxFppsfxOM7z/rh/aOF4HJegf9NWGiGkGMxQVaax5nkWEQAgBgS7ur6ure52J+uz1enpadcNbqF0lW2T5hYVVzxYM1F1aS8i6rJuCzOrqaMKpMYMgBBTyPOsBqJKgTrsAscYQohBzV5dnLfWrq6uSismErrUdwkAOXidkUhr3hhl5o1DJaYQGpbSWm1IoIi1VVWXnBlzMMAQYkzdZrvd7U76fjUMfWD2t52RmP0/3HUdAqoZk8e3s8MIxc2+pZiZiIYQPAOgtapiXpUFBKvValitx3E8v7gA1HRcPXj4xlvvvPOzn/z85z/9edUmtR2PxxjC6enp6dkpANbafKtGIgT85JNPvaiAGAciry5Xk/V6/cGHH/5v/+v/+md//uc/+MEPT092rYqHizFxqbXVVgFM7Xg41toOx+PQr0OwWufD/lib3tU743GsJV9eXRrC9fX1erW+urr6+slXX3zxeWsN0dbD6nvf/c5v/85vcQjjOPJSeAkxREQ8u3P2T3/vnz17+fI4jeeX11W/3G5Xq5Mu9Rs0CAOpyliv5bxUzS8vn+2vDrvd5vadOyGE+VCePX12dut2jB0giNVWS9/3RNxqC0M8Ho4eDnD37r0nT5/srw4XFxfr7bq22mqb8nR1dfny5StAPI7T02dPN5tt13d96mNM2+3uxcuXBiAqgORZzWoKhOpaRlAFatIoBDUwk9oklyoGQ4zkBlxEA1ADZFRTkdZqpUiBA5KipWOtYIZ80/8FoLbUwrj1XYBcYgeAqgimrao0I0YgNiQFUEUVbSJNPGCHdBm+TVtzjUXg0HV9jMEASsmlVBExQANdzM4AiF6TyEwcOJCqeZm9mUnzO0wRKTAxBWaqrTYRFVNy/xVR4JgSNHb43hDV2zJVDRQBFRBM0NBMAEHMCAjACyIVfcMyM1WMAQGbNGb2npA7d24/f/HcW4Qc3BBZyBhi3+CUPWFYTaSNh8Pf/uxnsNRRmTYVqYHDxatnF6+euwlTAQD0S6Rf/epvHZj69//z/9uIY0iOd61W/Z/88R+fnp5O03j+6tXp6W6arq73h93J+t13396t1qdnp94ANOfSpeinVUpdTC780L4f+r6bp/zV54+fPP/6Zz+5Wq2H+2/c7lYBDKoWEEUDQ2k1I2BTZISQUpfC7/zOP3r4zsO//Iu/fvnyFR94nKZPP//s8ZOv79+/f/vOHQRcDavbt+8g4Gq1XvVDimmzXX38i4+/fvL1W28/ChQoEBJdX189ffosJPr4449Xw3B9dY2ITJhzqa0+fvz46vzy0cM3TXA8HJ89fypipycnHMLtO7eHYfW3v/r48vp6vdqYqakdjof9fh9CeP/d9+/evzuPeRpzDNQP3Xa1AYWcM5iZNiSTLO5f0WatVlEh9rysjphep5y72sCYcHFvg7NQBoCEACgqRDSXAr6JSiMhI0UGYlbQqWQx6YZ+zrM0W69TSkmbGtg0Hvf7A6C2Wqdp6kW2m03YRRE9Ho/TNLkCjpj6rl+tVgbQd31k9gIpE51LmedMjADYakupX223m8329u37Z6e3PI5fREttA4cYI5h5zFGM0Zv/XEJOISB5eKP57O+AsssamIiZWxO1qqbMLE2I+fT0dL3ZPn3y+PpwfX09N2nf/u7m8vLy2avn8zQ7ds/E1/vD1eVVCKHrGiB0XTdN05zzZ599DmDvvPNOjCnnst2sV6sVEGprH7z/finlj/7oD//wD/7ghz/+8W6780CX43QUEUAgolrq+fmll4tN03x9vd8fD8f9YbfbnZ6cnJ+fvzp/Nc9znsuLl8/v3r3z+eef//F//s+gwmwxxm9/85s/+tEPAHU8XgMQ+Ppecyk5cIh99+bDB2dnp/vxqFqvDpcQ5Pf+6e999NGHXeqP00FUcqmllP3hMOfp4vzi6ZOnn3z6K1NQ0a+fP92dnN69+8Y8Zw50HMer/RUTtdoA4PLy0gB2212r8uLFi+v91a9//esnT772v92c8zzPuRRievz4q6+//mq1Wp2enj18+GBYbbquR0AAa1VuYna819wxG0fGtLWmqEBRxEqtec6mlkJkBFDzIGQkAAQFE1VRueEjA4AGDoJGBH7wLinLN1GgakAKRAsRrAZo4FJSAAIgJAJCf3j83hAxZAkURExBpFUkYgQO5CMRmFWYPczQlUz+CxGBiBiMmZEwrDZryRURmQiADMxF3Z7MRUjozWfoUcCm6OZJjjESBSACRBNz64CKmIkSAwArOYt+Y9OFQAGMVACCtSIxKICRB01VwQgI2GrdbndvPHjjs09+fXnZHOhXVSJUtVKqmjUTMGRp1RrHENmzNcAQYgzYgRoNMZWSa6uEYihkXGsTxHZZ+mFthk1Qm0ottRVmUtVPfvWJWjXV2MU7d07ffOONb310cnay46C79coQfd1eD1tvviJCCgDQRJQYcj6YVQBCFkKbxvGb3/rg1ukpkoJak4qg8zQFQgDJJXddMubjcd9aE4V7d+7+7m//zq8/++KLL754+ux5CLHU9vzlyyfPngUKBhBDDMwlV6dbN6vVnOfrq8v/8f/xP4YQN+tNP/Sxi1JlytPV9ZVo++lPfnJ264yZa8mXl9ef/vqz2vJc86vzV8fDyIFU7OmTZ5dX+68ef/n5F19eXlyNx6z6rEk1UWQjiCenp4G7FIeUuiptmjISrobqWEerhQgiMw8MBLWVaZxCYGhWa57mYy7rEIw5MjFGVCd+PZJc1YVy5rwYMwJKE7x5sZ0Wy7kQERODQStVmxLQaljN01gRY98FDoLKhNNxFJGujzEEb8c0gxgikX+pNTAzdwBAr6WciOM0lVIDBUU0Ucbln4YEACVXG2AYBmSWpkQUUwQzFU0xqaiQhBBTTBwCAHjHiAck1lIosMv7PAoihKBqZo2IUowqgkjM5D0k9964/8Mf/fD81Yv9eMwlf/X1k+vD8bPPvzgcjuDBJ4hN6lePH1/vrzebdZdSDN32ZDeN4/X1/qsvv7i4vHj2/MVHH3344MGDaZr3hwMh9UN3sj19/733Li8u/vRP/uzx46/a/ftfffl4HMcmbbvdfv7VF2Cgqv5g/If/7T+mPl2eX11fH5Dszu1bnz/+7MXz5+Nx3G42IYTnz58Nq36/33/9+CtTOT3dvPPWO9/7/rc3u44Qcs4xBUScSgkhahMYOpagqh9968NX5+c5VzPbDadv3HlwdnLWpBJvnAX0aS91SUXH4zjnfPHq8ic/+/knn3z6s5//crvZrHebeZpevHrxZ3+eVFRNUkg1V2Ds02Bm0zTFLlxeXj59Os15jDERBy8BNDW/Kff7/bPnz1++fHH37r0mQkw1V3dVIQAoEqGIn7aCjKIWYm9AuTRDq01qrQAYiVE9BAfYtRaItoh+jRAdE5QGyECeVeBp/IhO/hCRA4auAyVCUFAxU60iKtoBAS//pwvfYTmKRVRBlEBMVZogqSGZAAISYCml1VpyaVKdLUBCUTM2D1gjikgUUkzqPt5mtdbAwTt0nOnyog8wChwFEAlVQVQBkTkCehUaNKkirdaq6uJZIAYzkyYOtBGw2bLzm1prQozuD0BE9VvPzHtjTeGN+29cvrp4+eKVgoYUSy2IZGbHcaxNi1QwG4YBAbrU7bZps1l3/Tr1iZBOT3froRv6DszTyVquZTxO81j24zjn+egyO6qqRiH0XQrMm816vR5qbWenZw8fPPjg/ffWw0DMgaGLKNrAUESJTCWrOU1PoAiIqhJCqE1EhAzKNDNTiPDu22/3qzROY5UCoLVVI1XVZhUCKKqpAQMCy1xFqqLdf+NeSOHWnTsX55el5uNxvLq8dg3J4TATovuemHl/dYkErdanT44UqO8G16jV2vI8r9araRpfvnjpegZmDhSmPE7j9N//3/77zXbTdwMSTdN0eX15eX6pKuM4A5BBNkUDTbEDkJS6ru/mMX/52eN33n2LE3EgMyhzS5EQrZYMiCqSUnJhQ6ul66IB5FzmaQKDwDHE0Jos8R2EIODzjNchudHX569cWh86AwMEdv+KmblJW7SUgowhxNOz08Nxj0R5KtQhEQXioR9iDBS4tWoIZlZKjTGGEJaHM8QupdLq4iFHULEYuu02quhc5r4fmlZVm+a5lEIhlHFcb7Y558P1ns9il7rUxVqq+3sBgWPAvEz6IQSvsWutAWKpJWIKIXDnmO9ibHa4zD9bIkLAru9V5QxOf/D9H4zjMfxFf/Hq1eXF1eOvvmYKXepFxEFfBTjuj4frQ4hs5oVhy4DXVFprL5+/+PhvPz47Oz07Pe2H3nXtIQQAO4wHNfnssy8++eTT43gEhBcvnyPSfr8305iix6j83a/+ThUcqQsxHI/7J0+/3l/vu67zvp15HlvN9x/cf/PB3dt3zu7cubUeVmZqJHOZiZBUwbDUKlLNkIRzvkqpf+vdb/zZn/758TCGEHanu27dH8cRCVSVAqoaIBBDa6W2piCbzbDbbh88ePOzj778+O9+9eWXX+Y6Q7A5j4fDtYqFwH3Xt9IocOCw2WxOTndgYCCH40FaiyE2Fe/WNQCeGW+ggtrk/OJiKdBFFBNpwoFxCSJbunk9QNMzGkTNGASgNSVipuBBQgDm+c7qgL6KqgKBE5fL8quKTETIxGICyxugSIiAzLwUgwGqqtS6RJxaULhJI4KFxzYg1ValqhupmniSCoUo0lRElWrzoq5mZhSB3LjCi88M1DzEM3Rd32+6kGLJ+XgYzYAYgRCIkAmIDAyYIDACEIE2E1IEDIEZWJqoWXVFx2vJNoEztg2sSVODnhOgqIGaIS2RFAiLEk6kOfsXYjSgcRx3u+2HH37zsP+L43EysDkXIm7Srvd7z59mhlxmFVxvhn7VxcRIhiCBMU/HgBVkHrrUxTjEVCqeblaIaEBVBDHs9+P55cXxOKU+bbbb1PWMBAghhNPdydnZaepilcyEgASNGESrApljhWoCgCkFrWAKAKS1IkYEOyy5nMc79251Q//1k8f+URsoEyNiKWXhMxFUDRCdAqpN9/vDNM3HcVJtq1W3CeuTk5OzszNAKKUcD4fa6vF4GKdRpJGC+cNLjIi5ZRNopQKigk7zxMzu5TZdQj9aEwT76ovHChZDREZQVLSaa2AOFI3AQA0xpJAi33/w5nq1fvPNR2+/99YwxBAwT7nmHBmI1gA4jRmQQSWG0KVwOBykVUJjplJmomiigTEEREBCLk395I0YmEEVDvsppeiQpDsDjkfP2gRESCnGxCKCgKVJqeXqMKYUKaZuGMZj7lexC0Fb61NX5glJVl3fDKsqh05aQ8Q5lzxXNWmtqkFtdc65S2lDm+12ZWZNoUrRrC6CjqGbc61V1fDVy4uT05NSi5oMmy4EOxyvztLtoR8Q3eFsTLTdbGqt0zyCQa217/vN+qRZs1Fbq7WWruuYgpRaWu77gWICMPcQ1FbRkAITYgDabbc/+sGPVfDFy2dPnj158eL5q1cvc5nLsaZEfi0uZcuCzOQx7EQMYEw0zXOMUfN8fPz1k6dPHYI309qaicQuppBKFQWtVYjAdZB37t52lWqMqe87v1ZrK61VAOi6brfZ/Pg3f3j71llr9eWzF2dnu5PT7TcevbXeDAgaIgOASEXvzm1GBITkphYAElEkEmsnu1sP33rr1dXfVJkrSG6508SEhiJtOQ6Yg4EGjhhQmnC01ap77723zm6dfOubH704f/HpZ589f/6ylGxob7/93re/9a07d+6CITPHEImo1mLq8LAoSs7ly8+/vLy6VLRaxJcwj7AHJAJsVa/s0o9OYjKzpoZEnpfQaoOAtdVctc4ZQA/Hscztzp2TLvVzrkTcpSGGpN4do80dxYR+Azhq4+0xQEQGCEg30gFYZPGGqsbMHKOY1dZybd6tqIhi4L9AmpPAom6FFSFA353AAIxNRbSVYiWXmhciOsYYQkQOAJBrq8dJam7Sxv0+dKlPKcTUEVITmafS3NZPgISiYm3Jpnbbi5pnNBITq0ozcwcdM7UKKkrsY71TU2SwpByJeajzEoHnjghCdmkQc2ylEaMZXF3vKdBqNXz40YflZx8/efKkG/rS6jwVMShFkKGKimhKHQHMx9laI8olJWYMTNOBu8iBse/SZr0GxFpb6IIq5NooxGrCCZIwkGGA1IfAwRV7w3bFXSjaCE1Ua1UpzVpFBUTLtQC63QADsykSMyghExjF0k/j/N3vf4djXK/XVerTp884MBKYKRMTozZDXqwiBmCq1dsXamtNpSkhciTD2JooKAcmxhTjZr1yI3QtTaSqLJ3JHpUqqvM8lVqXebk11zGIyDiOIlLmDGRNVEwYqYkScisV2cU5wIEo8Gq9Oj05OTk9vXfv3u5kt92tu75TqVfXV6fHEzOYjlNgkqExBUAYj2OKHGPvTBIzhUjTNOWcU0fEDGCtVlVNKTFDbe7eYwRnWWmeKwCklGqrxa3FDMzsEVeOo4pIzcWjdEUklznPZXuyJQRGQ7NayjhOHgY1VZlzTin1Q1drbU1MxMy6LjnqmlJiDr6MGsGz589bayrKkaWJigJR6ruEGFJabVa3bp2dnO5SiggUmEXEn17V0mpD7++D10MW5VwOx0PXpRC8G1KUjSMw0eKsN1WzPOXURUdwyTRw6IY01/nuvfvf+c5vPHl6ev/B/Yvz819+/HGg1KReX157xfycc80FCExNWlNTREQjBQXCJoqqIo01xBSAEBSRsDUgM+5i6NgsxB77ruv7NAyrk91OVLQphxACE1GT1hbnhUQOm/VqGLq+66CPoLdzXqU+EVuIKNX1Mi7oUI+DVDU1SZENodbKaoSMyibyu7/7Ww/fenCcxtuntxA151FHado8pR7Uv/Xk6SCMJNJqbaVVkRwi3rl7lvq02508f/60zPXNRw8efuPRsFp58UmTBgbN+0JEjayJVMndENur6tlTsYt5yk1VRaZpzrlcH6+mnBHwuB5XuHI7t0pTJzZVWpHnL14RciAqZc5zjhw4JgBWy2gQiBkXIQyhmLOhZurhBcvnY0Toqv+61MmbqHhGJzMBQQjBbZWARMQGKKoiZqAEqKYem6Y3sngHaZgYQP0XAZGC1ZuwZgeoABnJM3JIFZkLOCRaSoghdF3v+YWxpmkqJVe/+sygtWqK/oQRkoKZ+iHvgVYgIguJZB7IgQ7wel82KZopIkkTqYK+J7F/0YTm1+FCE0gTABJpl1cXSEBndOvu7fc/eO9w3O8PhzmX6TipK40EOIbWhKjkQq3hnJEpxBRTDBwoMpUudSmMcz7f7wkxxJBSZwilNaSgAk0UAyJxKZW5dAOklCgGQyutKquBHq+OrRUTkVbpZidMMUZmDswkRMEcBzMAA6aiAt/88Ntq4DiAagMxbYpo6tAHeDyHa5kAkVIgRWUkjTJPrdXqeyECuHi3yUJMAWQksKbE7OTzTW4ImFlt1cxiiDFGP90chTS1eZ731/tXr86vrq4P82gqAqaMvq2tVv29u3cfvfnm7mQXUpAqIcYQwzSO03TcnWzo5Nazr58nTrdu33J1Zp4zmOZc8lyQum1KCAaMNZdpmg+Hqda6Wm1X/SqGhEjNzFuG/ckNLp4Qh/hzismNKqqaug6QQiSRZp7PTijN49SrtFpNpsNYS92sV/M8Iwgyj4dpnGYXcYuCx14xkYUgVcwWrKDrEjMDmMPBV1dXTWpVcYJKpLldiEPoumF3epJz4RD71Sp1KcVud3KSUsfEsUtgNk9iCO4GR0IO3Hd91DSN4zTPHDilzqOfzJb4ayRkYgNAU5GGEGMIFWzJpJQWU2xWzm6dGOh6vTrZ7c5fnTMHDjQdZ1VTkWkqrTU3FkiTaT6U4oLUmxoRAzVFwPVqtd6sYwxOxKW+iyk50EoMSBiZHB0ihDQkInSQigkxciACgBA4pmhqJeeYQr/qzRohzPPkCQKiARGYCYBa0xST//VK9ZuSamuqzUVHfRc/eu89Uy215mk8TqIiQEtEmgEyebZIIPQIBiEkNZtKOU5jqS0Eunf3dt/Fl69ePX7ydZ7zyfZkd3J6enriYR7Hw6jaPIZkLllazXk2tf1hP+diYPM05ZzVJOdiCiIKQAXq8TiGGMIqMNJcqogieg369PLVuTQJhLWUGOn2rdtdN7gckphDCETkAZ0AHo8LCKCiolBLcx3j4hBzCEFVVRanGSIRoWEIIUSvxDDRBgDclqnCRx+RpupqeN9gkJgIkJUMNYQYva9FRBREQRUQiZiRmDziXsHP4T4l5hSQkAMBoDf4LmUdIn5BNZU8FWnCkVOMjMxES9yOvo6fFiRC5m5Aj4P0FFEOzDEgB49k4BhQ3Tvv5x75FwKArs7W5Yul8/MLd4HevnX70cNvqNhPf/Gz8uocb25Y8FpIg5JbKwciQAJCLzdbEo1i5BiD52tz8IxoVFA1Y45ITDGm2K3WPcWkBGKQSwPKYsbjJNJaa+41jTGiKSKaSuQAZOiGPsAbSA5A/asTBCYOoGagMURDdowPyFxsgAuZ4xr3JdaO0JiCNIEuAiBRqbXW2hAI2fP+HEM2VkIDkaZi4HVC2DzAVaTBstiaw01qYKKEmPpwlk43J9sqbcqlSQVyPyKa6na7WQ39kDpE9C6JUppYNEQwHA/Z6vl2vQPU9Xro+y4wmVjOxVTMtGaRJqotz3ma5jLXPM1IOAz9sBmIyBviFls4oRkIIpAamqi0Jik5d5oRcRhWqlJKFlEXzElTMAshiKqh5Tm72IyITLSZmFopRUSTZ4USyryYjlOKMfASMUQIC83YzKy24qbKzWaLCIHDlOfxOJVSWM2AwnE8HEdT2Ky3d2/f6/puWA2tupobDYCYutC12ojJ2UsASCm2lpaoL9DxeKxNiNgddj4MAiJ6vIppiomZmkqrDcxiioA4jeHRoweHw+6LL7+8vLwAgDKXEEjVLBASq9lqMxCGlFIuY6tVzRyxqaXa8mMc+r7vOw4BEEARApk6F2mm4sorNW3SlghMQEQIgVUQiLUpAsQUY4gxcogMCA6s+f2KgF4HS0wuM6EQRZUQwQiJlt4oEGLwy6k2L21V83PIGJFUxRSbNgNotoRKecZgbgUJwUjAFBdHLoBtd7t+WM05n7989eT5s1tnZ6e7U7ele6hUa6XkVlo+Hg/TOF9dXk1zPhyPCKQmPr+CGSzwjBSBw/HgBwMxmbu5ROYp53mu9eV0HBlBRNbr/u7texSoqRhojNHLHb2LBf1+RXSO2lRUaq0FDGpFJAzKN3ko8Fp35AwwMzKRqLam81xtCb4EtoBIKr7vWYjL8GcLjoWIREAppS71gWIRMyADEtVSBLG5moiJVZUQmBg5dH0IKiJVEaxVj+F1fFHFFKCq6DTNqhJaQICAQIkIycQMl83aTBE4pUiBFxG3SqCQutgNSUXzVOe51NpqkeUv7hyLme9NzksoGCPGyLmWelUDcwjh9tntb/3GtxXsz//yL6+vj8jUqnCkWsUEYkI11UpIRqQegOd3wOE4IWKKYZFPqXGgmFKXupOzTd8P3ar3Bptamoi2ql2XEFnFAKC2qqIqjQnzXBGAA4cQKUQOCWIAQCWC1/GWTITBwyzReSZw+QeAX3ngOVBGQEsi/qLIBbHFwyGiXMRxRDcWKYtUREN/WdTpLVNVQV8gvDbBdPmhQpXSamNPWyVeNOKAiLQIvwIrDshkom6mW69WKm2cZ1fLSBMxqYcJDFabHk3H/cHU7t67q6oxJCYUlJqLisUYdqebJq3OpeSmasCmaK20JmJipQoHMsPWmosAAFCbYjBpUmsjxK7r3Q+cUuy6JCqllJTiMKyatKMIInV9F1LIeZoOYzf0CnaZMxC0LCriCY4xBb8CCR0daczRYzulSQjcmtRSS86AQAh91xN7chYqKDjixAxg43jMtbQqJycnu9OT7W7b90MtrVVJqbdl0LYUIyERk7SWc3YeeOh7EUHEmksuWRVbra4bdn8lMvnNV3LxocXtqYBmzVKM6/U6pRhCfPz48TTNiFZLU3/zzVpp5hSiNlNxmSIhphQRMQZWNZGAiDEGRGg1LyeFhcC8pHR4/xQiMwcmQKRAi/ZRZImjIeKIIZBrAU1VxGqtnogXQvSjZ7lZzUHtBd1FRENyChSMwIw5LC2HFJjReUv2+i0zE10cgarSlFkFTIAiQjWRqlWlVSm1EBEBISMBD93q3p14PI7TPH762ad5zmhWSlU1kVariNRcMyKWXGttNxOXp0k6RE8GQAja9Hg8lFpC4L7vRcF9GznnKpJLURW3KabEq3Vvqk1VTRf3n3kTIhqI+11LzYQqRXI+Hg4HDmwgasqMGNg3QiJCNFUxCGYGXgNMCqAGWlqBYkbUE4eIHktvYMxBTHzmIFIEAPMwNq9tZyR26lhBS23q+iEji6gKCMghEFKIMdTaJC29TrqotVG9cEC0ljbPs6pGiYFDCoxMIQU0UhNCjCEQE4e4Wq9i1zMHYgS0QMwcUorSZO7rxeVlzkWDc57qg4OX4MBNgpun4SFh13fjeLy4uEh9RMRThN/6nd+KQ////H/9T1rFgMyUAhlTLRUZnKptIq6maqLM6DJNLerGAERcb1fr7el2sx0260Dhxg3oxAZUEcu1NiVA74JrrTEhd2kJJU4xpTj0XYoREUyNCI181vZUSEJk1zqBGiICAZODHZ7w5DZzh7ZxmUm9gyjADSNCAotfo1P/xlFrqKq1NDAVd4moC45dr++UOqpqYLcFGagbbBWXJH3yD5iAySVpgUREVVNMakoBc26ux2i1iUrOVdVEWwzJmnZdr7Wt+yEgX16cp5REm6qLf3AaR0BtVqZ5LnMzsdZkPI6lVE/PFzFCiiF4gZu06rSHy4dSSq1VpsUZGwN1qROV4Jm9XZ8hI/EQueS8JOia5tWQ85x1UlUmVjYkzHN2qIUChRhiCohkYhgwpeghEMTUWqveT4DCIRiwNjWDFCMytib7wxgVhtXq4aNHj77xaLPdeulSTDGlhETogRAxqpVaijqOCeA+BkYGMFFBpL5PIYSb0Uo0eE8YxhhebyRgyIFrLSWXBF3Xdd4khYAphv3+0FoTkXYQhwLMFtRkGo+Ar6PkzeN4l8oRJO8eYFu0H5yigWkzsuCJFOQjiw9hgO61djaWfIrhm5o/s9K8pgmQmUMgZnXTA3ri8N8rkvz3MENEBjVPQEPCSEEUlJSQEAM4H+g0KZsZeGQmhOXpFdUgiUqp2EwqAAFjq1KqtFIWnT3TervebNbXabi6uri+vt6Ph5JLq4KGClaleBo5BWpivOzNnkJzQ9EiOvCSc37+4nngOKzXhNBKzTmbKJGVKRPb0Pf379/bnmyYQWrV1gjJQEGViACh5EqAgFZzAZE8z3kep3FMKQUmIGjibL4RY4zxphJAAcFAiYAoxJj6fkBY2iBDDF3fp5jMLM8FEAk0z5OIYANFQjVMUFur0ihEQ2uiTbRWMWxipgai1mlnZgiLwQsRw5wn/963JrjgM55qZ02t1JxrQcQAUc3Eo3odwPceuxAjQUju9l8N61U/dF5m1vVdl4I0PexHUSl5LsQxRM8CI28zY0f9kIAQUU0MLHVpzlOp5cWzF2AkCID4ve99v4v97//+7796da3STEHRAkd/LZ0vBHRQhEppTORRNaJCjCe7s/sP3tjtdogEBALWavWZPPDSxClVnZvFRanOgChiMaQudbHvOk/WI+c/lIjJi+uACAMsuL2hGYblc/Sp3xe95WJZdj4DQiI0NTJPtAYj90g3SdFwOTHAKoC2qsRgBsxoBmwIAGq4REo5EwymXrVLyERMFEJc4EVe0nBouacIeXEtDV0357K/ujy/uBRtoKairTYERqJ8nLqu71KcxuPTJ89++pOfttpOTndv3H+j62Pqet+3+r7LeSpTKXNBxtV68OYWYi8/IhZFIG/a0wUPFTPr+i5QMLNamzcjeTspMc95PtrR0UKOQa0y83a3S1087A/zNHepW+R1IiFGJmIKUrOTaTFGjtEURJvfyuM0IQCipRgRoJYqKt0qdX1fW82lqgpHRmW1TEQhhr4f1qs1AR2PY4hxNWzWm21t1efeEAIAqOg0z0TETP4XcWjPwfQUIyItrn2n76QV9XcnsFvoxTgQGilxuJHDeuRiP/Qicn5+buo5uLqslZ4mZmagMbKBqQoidp1X3AQvtozRkXRAQgebRdSSLcFLAOgDmMs23KW3TGSBwsLnhRBCCMThJlkxhMgcGTAikzkVyQiLsMbcTOs4r1d6A5OZApIH7RMxGCDdjOPmi6iLCMwZGtXGgQMYqyEztgKFEKq2JTA8tIBEoO5DBDba7darVdputxdXq1cvLq6vr/NcahNHGWKMgKjUiMkUDE1EmcgctIFFZ2+o8zybTrWVRaQujQCHYSDT23dOHtx/8MMffndYd9qalgIeYYLkYHBgEnae30cBrSWbSYjEAUPAQKRNQBXQCMBLsAFBTcAXfLAY03a36YY0Ho+l1a7r+qHvl6pq6ldtmvLhcCjF7dsWMBCYiMxTTnH2uMxapZSWczEADiBqKn70Lg8EAKto8PDnJZABb5BpAx9i8uzAIHAIsUvoXjcAXZY1A8SYYoxdoNivut3JJsZkYK3U9XodA6laya3rEzOnDmIMtozDyyElCK6U9WlITX2QyVKmSZ8+flK1lnlq0n78mz9K3P1P//7fvXjxPKWulCwqFNzXZhzQiWtE5MA+4Ktpl4bT05NH33i03e1yLrkUj5ZzGJ6JDYQUMDAFQvNEC3LgHikgUkpdSinF2CV/s3j5CGlxdoAZEYPPZIhuW/MPdPmozcSlGjd3gE9LYN4JpwpuuQNrxjEEVQVoAiLmuTQqgKjMHiwDSCTqI5LcGLBNzZopApKRfxRAQoiigQOEmDab7cmtk27oYowcmQN3qUsplFz++i//yjdoBy0RkCgQESqMx6kL6XB5OF7/kYqcnJ68+eABgI3jGGNiwtYaGoQQ+6GrUlprMXVIoeu71Wrl8YYG7BUCzn2ZCgAwh9T1oCYitdbWmjO0uOQrcK0VEJlIxT8FixyEY4ppIbqXj9ER0IgGQFBycZ+eQK6leSlK33eqwhzMTHKNMcQUVFREamlTztM0mlnwXAmN683GgHa73b3791LXiejhMMbQE1Ap1fPaidjUSi3SGnfd8u0ldBTep8xhGHKpzIQIIoaIpbZaS+DQDf3r8DGRhdWIMRyPo18YKaUupZzzxfmFcyfmMUkGQGACIkoEIXEpswGsN6vtdrvZrlfDyi8Az5r1B1HNEcLmvlZRT1RRMyBfGXAJsnNikxCBDNnVlYE4OjxhaMgBkJphACIAJAZA9IZWU/IIGMDF3U0Lyu07EtKSCO4jmyvuVYEAHRz1/RiJXS5GBNFFI55bUAOylFLFVJqYttaaLZ8jIOBqvY2p225OLy6uzl++ury6NPdmAbpmwqc8z+QBNCJP9XBYwgIHUAA2FSUCU2Cioevu3Dp9+61vfPDB2+++887Jyebrx1/lWsiMDFMIBP4M+rcd3DoQDJkwhrAautq6GCk5u2CqsKBmPiaCoYr6LKOqIVCMq147BMRp9Jo5FwYB0nHMtdRc5mmeAXDouxBwMco0oZEMUAGmnOelbpoYQESyZET0oQMRxaSUGoiCN5QyI/nNbYYAKQVDnIg5UAihd+gDwBCaKiFKsyaGCKIWUYmAkVSU0rLJ+MQqqtJayaW2SkQhsqoAoFgjxZssJG1S/TTz7O2as9Sahk6tvnr2QoqoWivtrXe+8X/5P/2f//3/8u9+/rOfmQY0mEvhEGwZq5fvgCk2qYi42azfePDg9u1bADCNk0gTETAyXwFdfiuADKqGDRtACAEBURGZEGNIMaUUU+pSnzgwBUTPajUzUDN/Ubw7E8xNe4g3CSELsUYYkG8eMlhuAf80b55dcOSUmCkyATEwK1EjUkIjtmjYtIEwmDfNmYEwOBctnhjnaLLr0xXVzJAYoPjdvtlt7t27t9ueDJshdoxIMYQuxRTT+YtX/+k//qf9fg+KKpK6DhQQ0VPpUWFYDfv94W9/8fG//Ff/ar0eVESaMGvXJzMjhMNhnubp+upwfbjuuq7rhhB6J35CiiYWOAAoIddmh+NcS3P2LOeccybEYVgBQGtS63Tn9h0mfnX+ygU0McSUulorMRFSjHGzWR0OVx7tE1NAQqktMIYQc2nzcQbg7W6j0ebrDAqtlWEYwIxjyDmDStd1MURRrSKAyMyl1DwXSaoCMaXtdtsPw9Onzw3o9u07m/WamZq02KXAwS//cZ5abZvtdp7nGAI46YnYRPuhG8cREDbrzQ0HY9M8d10HkHKe1azveh/F53k2sL5LXermObsr1VSYAxi05ooUxJsuSXeiuHYIZo0hnN06u3P3zr3794a+jzGKCqgSc2B24rm1ZiBLPRYRqNeosnnrORC4SdU1fORmoIjEgNQEF7iGjYGQGJCZIzEBooHLyRARicyhHwQgZLp54FX8C4Yl2n0ZwcBLcpDM1ABM3R8Ei0IfEBANkVBuYiDFKhoDNlVAVafPbNmypTUDIab1ZtMPq1u3bu8P1y+evzg/f1VK9a1bzZtCAAAc8vLaKzQXMqGYhMCgElO6fXbr3t27D964//6H7677YbMehi5dXpzXMtVaACD4jmXmX4KKWjM0YCIySCkFwr5PTGgmrUlrjZFCYBWrZurfEG9mZ85zYeTtdtP3HYUgakAYIrXSRhERSV2n0sbxcHFx2Urpul4lKgmICkBpVUxMgUMwMLdAIiEYqIpogynHGAIvp0RtEkzFuRpgNUDPywKzGGKIsa2bgCJR6lIIwVdHTwtq0vwoDxaJOMQEClJktpmZRKTkwhjUdM55nrOqdn2/2qy80IoIKSISBAy+L4u4R9LLOU1Fm7Rh1Zvo1eVFk9ZKM4N3333vv/1v/49/+Ed/+Af/8Q+ePn267aKLrBBBVYmYyAdDPj09efDGm+vtOudcSgU0BEQi80kMidgEIRADUpXmcZTu33NBbgypc/jfIf7l3EY/HP3xdIDNg++IQA2ISAnQUBVuhkL/uezmrQWu8hXZFIgB1JA98RSIKDKJS3cjkSAzqiIZQQAQ85o6U/r70BKxpWLFX6RFY6BmIK0xVpHcSq6ltcaRU4opxUCBGKVI1frmm29849HDn/zNT0VVmykANAOEwFG1MYXDeAghHMZjruOc577vd6dbF00CgKKNx2POJZe51QaGXRq2J9tu6P3M8qAHoija/LDgm9PGJ74YY5vnwNzFhIgueeq7nphrq9oEmUVbq+JA/MpWXvySZwKAwIEBzbRVMQCOHFMIIRhg362kFWL2y4aISilNWmD2SmFkCoFpGIh4qa1RHdarYVh5h/s8ZxFFpBhTbc3JSmfX+SbORVrLACHGwKFR63ruu67VhoTTNMUYvUIgxqiqtRYHJWKK6OGgCG7mEBF/xFputbVpnq6v9/Ocicn3FWeP/HtMDCIKBtt7m0ePHt1/437fd+ZKuQbI7GOmK8hqLa2KqFRRQ7s5NMlhHAUMzMRhyeRwmJeZnMtFFEVm8kJ5AzZgNQIl9poqcJcTe0TvzYyz4FVqasTgyAcAEhkqAqCBkvlRbF5zexN2sMBWBgqmThAjAqAvi/4GE5AheXMkvubXVAR84YbUdWfp9nq9OTk9efni5fX+WpowBVBQU2dx/NeEwIsiM0CI4e69u++88869e3e6mLqYVn1/9+xWl2IMJKW0PNc5E4EFVAFiUs+79GPVHQAiKabURQwhJhq61FrLJbtT1jU/RABN1QzN/IJvUlVTLZWJXUEYQjDQaZ7EVQ1NxvE4zeM4Hk2MQ3C5WyBPQbZW21wy66KpDEzm3cFiHiKgFoUYAWqrouqZ5kKIpOhNnipCSH3f98OASAIKhpEjByYjUJQb1MyWARaYY4iRmESbFa0IqmoIKaiaNqmG4ilgzpMs5EEMTEQxeOEZM0kFacpMXR9BJBCjAhFVkf3VNShRjE42/e7v/NYPvv+9P/6jP/7Tv/zzi1eXtUkpWZkR0TXsJ7uTs9u3wWy/36uKmQEhGIEXfhIyMQgSUzOhpkiokhkDQGIKyEyJOQanqUVBas21OnPvOOUNlU1IzKJETKyL4FOW5Y5eu+Jo0f2ILay9X0iA4DWbamIGrbrk0xvoCIiRAwX1NDXFBuYb3uLeMkAF0yVgQQ0NaHkX4YZgFtHSZM5lvz9cXl33q1XXJbcpEULokJBu37rz3d/47q8+/mScxhDY+4ZaayqGCAhCSOM8fvrZJ3/8x3+8Wg13bt0ZNikmnqcJCErJ43RstahKTDz0q+1ud3Z21g8DiCJiqbk1MFiCEkMMqetMbzJ5iJi5NwgxIMA4juM4ksPqRGhgYCmGVrhIKaWgQSnVxPrUlTi7t4Ajl6KuMURwKhyYcOj78dhqbapjK9X9XNJ0nqd5LinFYb0OgVPsQoq1tWmcm0jOc60SUkKkzTav1qvVeg2IJZeuIwhspq2Za46bq/Na8wwtFQEjNSPCGGM2cWRpcYMy+VmlDt+JHo5HvwAIUUNIKYqQiuik0zimGBwZWDghlw8s4i5QldWwevPNN+/fv79arfwnIMDCT6i6k8glMebd47AEtwMxIgOho26GZIDADEQGJIRgAISqhmZqwAIckBi0KhOIIakhKKO7gJRJndxCv12WORvxxvYPC8rtI5CaLvkwYKDqOlTXkLvGCRcSrIo0MQQ1EoXaROqi+1KHj9VT28QUTDx3HJp4KYuEQPfv3b91dufl+YtXL14dj8fSistvEMFUQghSpR+6s7PT+2/ce+e9dzabVQjR8+QTx3XfD108Od2q1vOXV9N48IgLa2TE6qQFWW1t4eOd1PUTj42QY0gIJCpmC4Hv8vdWlzvOvWCInnUpBmZoqkpIuZTWmjPeviPVXGrOPoMiYoyJAQCNVDwwjUXVKKRoi0BcaqvSmuvEjRUA8jwbYhDR1iQgVwAVq60SAZN36iXpdd3W0sRdJsT+vQZQC8zNgAgDc/BA3hAN1D2EqpBr6WJnALXU1qRJm+Z5PE611QggJgZBRDkiUfBUoNYkxtjFBOsVAeQ5t4pEFGNSwf3+KpdZmxwP0zcePbx7994//b1/9o9++5/88u8++eXffvzpZ5+9evWqX/UnJ2cnp6fjNDmq65zuYjk2VANavNMEToSKNQQyDAEXXsOTLJBAQdVKkYZNAbQ184N8qRNavOtu1ACiGwGc+dB/44f7+6GICB3mdmCWmG9wq4XGVVFEAAURqdVnIgQjBFpKc8AnJ0dUycUmaI4QG5o//Tfzl6fZCtRSxuNkeu5IagwUYmAgQuyHVOcakL/9rW//9MOf/uVf/BUHJiBRMweXxAuwpLb64sWLP/vzv3z7vbe7vnvxgpvIyclOquz316AqKq21EMN2tz09PU0hmSkzN5VamksgRORGNk6tNUABgy516pIngyZtmkcm5hBqqS6Z9Z0u9SmXUmvTJtN0zLn4h1BrQzKIvPirDXyHKKX6b9uatdakCQKklGKKFGie2Z3t5q2BpgbghHHNdZwPItavBgL+4L317uS067p5youFAz2RUfwUdhYtRK6letVBCMHHfCYObGD2Ou8EVLuuM9NxHKdxcpN2TFFE9nVfSlmtNxy8ApYCh+1u52eHazfdRuw0qokNq/7dd95+9513t9utr0rMXtuxCPxbVXFFiI8JwEvwFhggI7B4obeh25hujCoLfogKaurPKGFjCYRQKADCYmGlQEgG5moWuOFlAJeBz4kxc7xlkRstTz0iuADEi96QwFVMiHrzU1yxyYudRjkFZawCprWpmfhPEjBDMQOBhYs2AM+tBOdXaojpjfsPTk9uff31V69evnKzLoAR02azfvPNNx8+fPTgwX0P91arJo3MULUf4nY3INn+4jKXaTwejtcHJEVg8ZESUT2bTTUgh8jk7DS4OEONwTU/zKwaPD7EkIjJs0nQGMCYAyEQoomoSC3mpQCtVCaMqRtWPSKVUjyFXtRc6JFSIkDnBZuINPcMoSvgVbWWIq211hChmBWAEKi1hsyhNTE1QbFm7jNmDn3XbzbrYb3mxKItz1lESp6xSwFCII4pImIQX4TZ45B8mSilqoqaSpZCjRhzzq16UX2LKXmSmql6VJItVxSKSCstpS5w0BRD5stpKsTEOKwxhEiAtdSvn3x9cjKJttLq/Xv3iMJ3v/sb3/nudw7745NnTz/99LMnX3/tRaYiSr7OIomqCjAxETvXe2NmZodigNDAeViQJkJSsalYbRBbIrNm5sH6sBzWuuh8PFkP/VwGIjSPE4TX74Cz08rEjsUDLLSbCzBcACHu8FuAIVA3czdRNbnBSxEDISkJqHpMGiAFDmDVBddeZIq4IE3q/xbBFEou2oSJUoophBgjoYUQbNaaq4DErvtH/+SfPH7y9NnTFyoGSICeW9uqKJhRiKFPuea/+Mu/fvjwG8DY931MyRbTFnOISNT3/TC4/4ictcxTQVxE+h7RagKmBQFRsOQCgE7PuFwSDB2UP+RDk7ZercxgHmcD1WagUFo5jscpT7mWnMvSRtKSqjIFZp9bQXTZqIh5iCtPKRiGDfFN1qNrgaoYILOvz9VMRI2IREUFTO3s9pmKtioqEFNw3MZVAK6mF2nMPAxDrbXk3HXdEjsRkJhIaJxGaTqs+hBCKS0kMDUOgRBNrRt6XAhAqK3N84xI8zQNq9WdO3dOTk6Jqba6WIfUzZRkCmb66OHD73znN1KXTCojUCAVq9LAbioGVRxAMQADctBc1BQAxSVItgSQoRGDKhL6dx+MBEA8rnLhZosQEpEAWPDGJ2wuewdQf17g5h9yi4Nf7Is5GdCFqj4ogxH69urXBLzWosBrUanTaZ6j17Q2a01rlVLFQQhfjVzZ4Tiwa8PcluBfeSltkowAQz989OG36tv58ZOvpuN4erJ9+Ojh3Tt3Tk5OI3Np2VSkFUWZjzOonZ1uV32XIk/T/nB5PU9ja1VK7YZISEBEzL5tg7r+hMAW/gLMqlQ0ZUX19mwkQQIyDkwUVFWj96camAUKfkEuKUZiUtt0HHPNIYQUuy51RJzz3HVpGLp5bq7QRSJGktbMYHF43Ng6EG58ybC4LASttSYaACwgBlNjZgR0Wg/AIsdhGFar1Xq14oCtVDCbpsmNyJFDYI4hIFII4FCTNKmlmmHTOufZLU4qUk3MbJ6zo5ZSWykFADQtILyX1Jhaa61pa9oI6WR3QuH0Ol353Lq/Ph6O+ezsNIaEHPJcDnQoOU/TeHl+/sbDRymvoNnXT7/+xce/ePL0iYqfmG63BI+vwIWc9aA7H0HIk/7Mz5sFikRpSky1imoBRCYM3Pw9ERX/qy0wHgD4TLMMNv55u7BigXYIFjkRIgDUm5/nF8Dil/FnXMnfGlN1+nb5Q3xm1EXG4MuxT04+3wAhM6nKcq3izWZo/ncj83MKFA3teDxeXlzF4NVp0vfdajPU0lqrU53fePTmD370w//vf/qjw+FIQK7VAEMD4cCKUksTa7/+4tdPXzw/uX2y2e3mXEoptVUkzqVwCCl16/XGbohtaeq6LFWHtTB1SdVqESSc85yn2bur3F2kol3f9V2PhMxsYDGlWso8z7W1kvNU5quLy8uLi3maZbFEg6tXfaoNcYG/xFquGQ2Yeb1ZMVLNlUNwWawDz13fR6ZS6/GY53kW1doEDPr1EKrtTs9u370bYpzmiTlxCH0/IKGKIoELWF134JpOU51vknTNTKSpBET0iCEVDV1wrlJEYkq+oPRdl3NGwsgcYjgcDgBYSxlWFELYrNe85PK6YgcA0ARCotu3br//3gebzXqaJmcym7bWrNZmCCpgiw7DrGnzalWzZiqeEgjKSGqGFJGE3J9GbZE/kitFFzXD8ii60RIJCRoKFkRYWnQRDdkzyeAf3ALLP4tQxxz5MHT/rRk5Pw7k/hlcBif0O+FGKAFgSswgWls9HudSii0Ng4uh1wWyr5EyQNf+qKf6hMCR0zSPpeyJ9qth8+477+122/Vq6PsuxaCipcwiYqBmNee5i+n07OTW2cn50xef/vKXXRdSjForgA1DijEhBzEEDoGDGqiCW14ciEXEwMGdOt4PEJaCLCDkQIEoKEhFB+DQzNxQBgB6Yw1FRDMFMwRMMa6HFTKVmmtttTXiGb10yPDmD1FVC8SBA4eQYuTAKoxmCKqy5IM5TswhxBiCGwjBtFZFAmbGgF2fnLYKxKuhX+oVIxJ4bONyX5uCixUqlFpTazLnnEsGAHeEqVqrDljlmhsiWQMxaa06YoHkESAMCCkmMJxz6Va9gax26+F6uN4fkFhVry73MaaTs5OuSwuJd4jMQeCxmv36k8+ePXtaWlX1DwQNzMSIyYwBvHiSlg429edQDQnJ/4vQARZUYAdQTEyAQA18qRfzVE3X0fqRjga21EcvRXI3G/BCEbuGAc3kJgEUb8DQ11LT1+e1s7egKgYCoszhRkt0o7pddkqX+Zjd1Fb4iGVgHnULiOCrMy73kxOHZlBKud5fGajHD5ycnigoc5hzKbWq2rsffPDi4vzvfvmr6XI0UArUxaQmCAZmtZRmfDgcfv3pp9/6jW+a6ThP4+FYS0OEJpK6brPZrtYrwmAGtVapyEwxxZqrL/MUUVUJQ2uiTZYJxr2mpp4T4PUPHuufYpQmSCaitdRW6n6/v7q6FG3usI8cmTlGVtVcqveF5rnkViava48xcASwmGLXxUCsIkAQmPvVCsCu99c5VwOorR73Ey0vRxqG4fTsRMRaUd5xCjEErqUGZqYAYNM0uVMycPDHQkSmaQqBU5dqqaqWUr8aVp6vJaohRlxoJBuPY63VV71cMwH20LuLFYmmaZYmD954EFOapuwyQLuRKd++devb3/rWdrsdp8lMAbHUnHPxodg7mlRNmkhVU6nSAMjVY6rmcz4jEzOjoecTk5n5ZA1GtsTMgTtlAVysAeABZ+Qcl2tJ/dF1KspxKrs545f7wCdyuCGHX/9LXR7Rm3/tr45vOcv0D57kQQjamkzj7AO/29D8FfLV/MYYaTd6I/DPSkSb1BATITWpry5eXV3RPJ/evn2L+XR5s6S6mgPUtptNF7vNen11cfH114/Hq/3uZN1iZETiyKFjDEiREDAEWPaZhaemwCEG50QdsoebhTEYG0ViTikCYilunzNVYLfNIIn8/ecUmIdhCByRERFbFTbsYn+y4yaKFE0hcDQDL+porRFi8KM/xpgiExlbQAiMjKCqiEZDWjw6SIGYQnBtPiESQH3dY4AIIYTVagVmTNSklVJCcM2DAENr2kS8HTjn2QxLLaUUUQ0hpi7SkmtR81xqldSRaKvSSm2qQESiFjgy5pCiVK21ffXl49bm66uLJnL+6uU4zVINEEoRGHPq+35FZaoxJZ+Wv/j8s8fPnl1fHRAwxABgtTav5qAlURIAoJkAKC64p4IBIRsakSIxCyGRKjhcq4BNARk9tM2lsYqkJn7gL0jm6wXVTECYmIERAXVB+ZfBCQ18fANYNlNbKiAIbxhiwpvEWP85igjWxJWhPmaqo5Ye1KHNh8cbSZ06La8LE2BmoF5W4GCBywfVmso8zq2KqdXWSqkilmLMpRyPExjUuX347odXL68u7Xw8jqv1upbKTLVmRKW+R+I+xC8//+zi5cshdpFZWgspmloMabvb3r57N6ZeqjBzycWUiCIihcClzjnn1mqIIYYk0lxhPAxDLdUj+5vUkksJ0cC2202pDRGZSYSYXUFArsE1hZqbNg2BQ+S+XwEC8lREkHAe52mexnEmxN3JDhE9dSelaAohskBIKQXmJV2AUUS1GTGaaSlttdqkGPtuMDEiDExEXEtT0C4lIqo1t9YO+z0x73ZATOwDX63zPC++llpDTLFLMkkTsXlerVYqEkO4Uf0fc55T6kBtLtmBw1pq18UlRxxoNQzOFgRmR2Ju3z579Oit3e6ktZqzEJOI5FKaH/pijqt6vp40NZWqogpihsgIyEyAHqaKVpvzWK9pBgAQvFEygOFCxQMCMPMCCiGCGNhS2KCwNCPeHNo+8DvDZcvxv0wrCzdmYITkLpbXd4UPMP6O+YVgqrqEcTnX3hZzyXJUutXGuQoEF5ktMWuGLueGxXSPYKaQQt+knJ9fvnzxcrvd3rt3d7vZxBhiCP0QU1z3qyhTvbq4Gsf9/uqwXvXMpKKx7xgZABDZRa2EpCa81HlhlaqlzSV7EBwzsNNZIQQKYqaseOO6CEGJCYlQ0UviCMkZ4NYaEgNB7KIBEqNrhVOXOHAw61JnBmrAtAiBXE/sD5U7wMG1aohp6GPkyCQizjanrmOiWmtYwGpAQg4BDKDVVnIZeTQzjhxj6IdOTFl5tV7X0mopZc7E6DA4oDVpuWRaQp2cHgOwxBQDiS3RVO6W8ukCpIopROc9mIhC1w3No27rfNiPIgJKm9UupjhN+ThNgdP19cGIVqshxW6eypOvf3m1vypV/UPOeQYALwAiItMG4Aej/4+CmaEQAAApKBKpCbsaDoyJTEXRatNATEr+DIoJIHgptD/q4lywq5dFAQyZBE1NXOngy9uNGcA3WrrR+wO6jmeJ6XWHx1Jq5mowAJ/yCIjR5ylZfAeqaiog5prdxTzn+N6CFy2vpH9XDUDEFrel34VeFVFbzmU8TvNUttstIuZcaq4irYvdRx9++/Dg+hc//4WKdf2g0kwII4m1iFbm6flXj7/+/It33ny0WvWrvlutNl9++VhqiWEgSFeXxxj5duoQKQ2xNWml1FbneQbTGFmtHfcTEXUxeHpXq+Xq8vLW7dtMeLLdiCoTANo8Hw+HAyGt17u+60Ak59nM+r6bRlHH5QSCRSBEoMBxmkuzNud8+eoiJiaMKQSpwv0gKvOUEUCaBGYTrVDGcZzzFAKtN52Z1lqRiCnksbVsUu3y/Gq72c3TPKwCApaS+y5Ja+M0IkKIwVRrqR77QYjTPKUYoO+JueTiC80w9KKa57nVEmJExForADDhPFcCCJFF6vFQ+37ou7jarMfj3ObJv+nLaUgkrd2/f/+tb7y1Wq2up4PbbVRMpJppLtXVk7WKtOYlRcvUAeAWcmJDZAVGRANFMWZjIhXx59aPfaPl/SYAh5MQkYhU7O/PZkCPK1umFg86vZm68PVMfvOTbq6H5Wj22w7UHOoABE/S94VmWaIdNfIH30RFtcrNDqyGtrgWFqQbDM3aEpPlv0Fuii4NIHYSCdGCS1oZpzl//vkXzHznzq179+6vNqtu6Gup01Sn8TgejmnVxz6ZO+qRzACMvMJOpYIZEnGKKFpyYQYRyfO0v97rsBpWiTngTRoYwE0lPC82N2aOIUiRQMwYkNBT82oVYmzaiMihwlKKWZ7K1HVda6LawBQBmIwJdOm6Dx7RAYhmTSuYha7vuq7TwCmwf484ckophmBmQc1yLeTebTADz6YvSaKD42DGSOvVqklTAQvQalVRMwwciFn8HayCAZgQAqO6oYkiR0gLwuUCGEKPNPJ9CTmw/xF+wM3HaRpHaeXszlmMkZiYo4peXx3OX128fHU+rHpGMrXD8Xh5ebnf7w2w71eqlnMmN5KDojfVLJjjopwBMEAyBMaABOxfA6hrKs1VEAhkjAjGHkWJ6JoudOe2z9FqAAKIJgoKS5Cbj/Q3tPDri+7mR37R3jBdN5uui4WQZPn35s5gfzHIYx/8LXIyT5upqoipKFiTRjeRogbub4dFB2OLFuYGSTTxgchJEbO5TiI6jdPh+nj77p3NZiutTcepiZScY8c72v7zf/6/+2e/93tDNxwP++N4mKdjq8WsXV1evnz5TK1eXl4A2na9JaA+9YgYuZvGubSa0oaYU0xM7A2jeS5gllIkwvFYW6sxBhFrrfV9tyRXu70ToJR8k20shBw5tNqmaQSE1IUYw+Egc87MVAHVrDb3TsnhcCy1FCml5lIqB45dWq02sUv9MEhr0qqpItI8z602jjTXOZfi3idXv5UiIo17HsdxPI4nu9OuG5rY8TDGFLyHXZbkQV7AOF/CxIZ+SCma2fXVfney9V3Eayk9szPEEIgP47GWArCkVvR9DwgxRI9185JIIowpGCtHJqLd6e7W2e1h6BFQRA7jQdSY+CYkfinObk18LxdpLm03M88MVzBkYneqkACgx/6qqqC7fJfHF4GMgJgc/FweVyJTBQ8l9MMVaBmtFo7qNX7x9xvAMjXZ8ij70/iaGPDXHxcNxQIevQ5wRFgSb83A1Jq25QLw098MABSXl2u5W2y5weBmRb9ZlUG14E0eKxE0rTGSNDW1XPIXXz5+9vzl7dunD998gwhLyVZra3VYr90cawBNpQshpBBCUENRC4DInkjhfIOpKAWK3T/wDqmZgqjU1sxxNRLycgBYsE+/DJgYkFtrCuY9CAbYciOClDpV1aIl19ZarkXVAgdlRQEwYCZvPmuBzMhaAxANCQEChcDEXfIFCQM7LipVQlPVVpd+PmJcngdqrfl3zRUBpOhRokjEzDFFIuxSj8RNmj/Ky9yKgACi2rQlVGTqUuq6XkVDYERiBloWSPTUYkJ0mdA8TdM4AujZ7RMPC+tiP2x36802Dd1cChKo2HEcLy4u5zw7CV5aBcDg8LenB3sU680Y4k+vh7QhE6AuPYSmiGpAS9g+MgL5+asmTdADc0yFiBDZn1E1cSrhHxy0wMSGoFX/Ibp5Q3q9DoVYANWbrwpcP+fRIT6h+25o6uGLghQdL2qi0qpIE2mmvuibmdI/oOdu3rPFUvk6GszvQLq5YARqEwWwnMs08vFwGMfx5OQkhGACFLCWmvO8318iGMLv3r1z69HD+waAKGY6TdP++vzLL7/88vMvDsf9vfv3rq6vxzzvx+vAaZqncRID8DLCGCMCModSioGWmud5IsJSaisldR0DtNpuaABqtQGCj8aIOM8lhJC6SEAcuR5qnufSihuDfYwKKbp+obbaqhzHsbSSczkeR0eDV6t11/UpRERMKWbVqtK0znlCgD6tm8WcCyEN65VHhev1WGrb7/ciZgib7TZ2oZRqKqWU7W7VRMTU06K6rlMR//qBNMXUhFtrfU9d16uHgGpLlDzmCLxUTkVEfBjs+87Hr35I8wxmSMilljwVL2Z/++13vvMbm0ff+MZ6tfn5z3/29NmT6+srAyDiJgoOv5hK06ZNqpiB2IKI0KJEMyIEBlS3KIDXUvq8TiywYPqLFAEJTYDF4ZNldn+tZPCTm17DoMvBvqiPEUFE1eM0/+GD+f9/AZgHh94MPjdvqy/d1NSD8G82DPW1VsFc2OFbxeulHJdj/+YSWNKInD9bQFggQFBUUARrTQmgNTEjIEPDWrS243E8vHzxPIUQQ0DT1tpHH7xDHADFy99TjCkGdkVvNSIMHvZihj4QhNCl1MXOS1ZssWE0UfA6KVbwbC6HsYiJiWMIMUUkark0aWJYWytzbiK1FDOX6kBrVdVCiszMHIQAGwKbwJLkZGKtipJoq4gCZswoLTJFlxIAoqFqa9npYgCUqqpS5uqQKCJ4o5u7vMWWEho/kf0h7voUOHRdD0i55FaramuCSCQCCzGgihhMtbZmsKBdhKgKnkbimykhMbGKoMGq7/rYpw49UFdHLa3qOErTPM1AVlo7XrwqrZS5EnGKXJuIVmIGs0aNkHz0dZEpLquma2JxyU4jUVRagEZFRCP1byL+A4UaEEJDBCQAgXaT9baA9K64dAQSCdRBp2Xov+GnfBhf3hCjRRx2s/u+vgCWcPll6yVkQCQiJjYSRHLUqZTSWpHWVJuZOcTvjRz4en37e7DVAFCdoV7eUSUEZF6eSTMAqLXOE+QpH66vU9enlNxp6b/l5cX1//B//x/+m//m/9B1KTAx4/X+ejzuCe3Vy1fjNL08f3l6dkoh7Kdjae3k7AwMc2mm4P29XdcFDn3fiQpVlKrTPCL60AccGICaCCCFiBy4NUGE42E8OdmqSrm8zrV0XQSmQBi7uN9fX11fG8GwHg7HJLMwk0dxNVHXI7kT5bW8rRs6IgoxlVLJS+50yTgbhq4f+iqKxKKKZil1gHQ85jKPeSpe3ZdSUvd7E9VWWm3aBAw8rHQYekIOkUupiOBZoTGEYTUgMeh8IwVbIjoOh2NgRsKuS2aWs7RWwYAjlZwJkRgPh0OudRiG9Wb1/R98/5333+1T3/XdF198+eTp4/PzSyZWNY6hFlk08gCtSZNqAkBgYAQEy15qwGbmLnJFaIpoJP6NAANlBgQPA7JFjIkOIsvNQHMzv/js5KQn3fC8y+yOxPQah1F7Pem7NNNDuv0CWO4AMQSExdd4sxkDApBrIm6gJlsSHwzcDeevoJn7nGxxLeDfLx1Ofvnr6Y4G1GXPWJLfFlMVve6CZ/aQfTjsJ9NWS4mMMQZZsr9bJAKALsU+dTFGUEVFRkZ0xa2oqAEyhdVqvV6vzd84EzMrtam0UioQMCsghsBNm4gwhyGmlBLH4ELz1loVnediItOUW6vOslepTq8MsOq6joMi8A06BkQQiA1vciLt5s33WUMIlgYua9JEAJkicwhM4nW4UYnRQJlC5Nh1kQgQQao2Ed9f5lxElAmjV0SEaIAVq4pWaVospOBPlKf8ibba2jiPOeeU+gWta+YgtJeuifuUTQykS/Hu3dM0hGEYpmmeaRIRJNkfDk+fv7i63hNTq777MzI5acYhedKFgREt6dKLBMBunl0Pm0IwAObuBqzxHQ3NDMQbaexmOkckBEb0Ch1QF3WCC6SXSBM/bA0RF/mNL8y+Zd68AbbIh14vAK/pX/LGzGVfdeUC+TQDSKjIFdBT7UCt1tykahMXXThZhDdrNPoI8HriAvM/ERaKDgiMCMnlHz41ASJCqVVVSq3MxxgCIqzWq1JrLWW/v768uvrZz3/6L/7lvwhM/Srp1w2pvXjxoraqJp/86td937/19tuguloP683KAOBophBTQEQ1//4upUgxhq4/BQBpXkhLOdfjYeq6npn8E2Va4pz9l+ZSEKBfhdYkhth1feAjxDhP83q9SinMc17sVDmP4zSOUymltSYqbvuIsUOg2EVv0RKpIVIfe9GWUlTR1rz+UICoizHGFAIjQpFWWzu/OD/Oh3VZxdh1XW+TcmAKDN7wp6a4JDqMxz0HHoaVr3wxdmqaPCuJGIlCCP7BWnSVUaitGpioIgADAQAzm/nE2EopOec7d+5sd9ta2uXF1V/82V88+fqpGfR9V6toLgrIhB7kLn6vL1ikIQIvveV4QwsJqqK9xjaXp4dYDDxdyMyWDB5iuplUcHlz4bW4AQFAcXHxLAg7gGHD1yf+38tflumH9EbygsvgDi5XWOb3G8oXltcLUdFen+PLU21qTb0zY1kIbuas5a0Du7miAMEzOmEZs3zrdkIViUQaAXuuaqliYDEyEYXY1QpVas6FIR+ux9Xd29gsBhyGbne6HlaJOFhriOpxnqbqIEzXJWLoui4NvZZCZFLEVLPkVmptggjMYqYcQmvNALkPqeu6LgGgk/iqWnMpuRAZBexTBwoiTaaKSDHEPqUUI4cYmAAJbGnZDSEgARMjqDZD4BhiSiEERgBTa9IMfVAQaihEoZZKBF03UBikGqgxcz/0fdd5SJHL1GyBodXD1IKngvqButCU2loDMqJAxH3vAZoJDBBJwZCAvDlBTaW10tSg1cYhuJhbmjBziAERRJqqlFLUMKXu+vr61fmrecpd3znpompaqldukqGogJq5r9PA2oLEvB41UJecdGYCLyHyB9v7q5dxB5eJ/eZh9Flm2ZMdP0dGNH8z1PvgndRXNj9YPb/zBhNcboBlv8WbHdmFbn5bLaAQB78WiG6cAw7D+hnamrRSaysmirSwAq8HnuW3uFkFXo9scPMX8i9BXwOSrxsKEU3crtLUJkQkplqqqRpa1SZV/93//O+/893vDok54PXh+uWzZ9M0mco8TZcXF4+/OnnvvffjOoUQ4cbfEFMahqFLCQ2meVK1JrXWhmjkFugOmdDbRXw0BgGv2cx57tf9NM2lFgqUICksghYR7bpuvVmfvzzP84xAMabWVPU4zTMilJrNrNZaSjGzmFLqO2SmwCJS2/+Prj/rsSzL0gOxNey9zzn3XjPzKSI8hsysrBxqSFVlFdkkQUkQ1GoSjRakFimqAUkkRbVI8IHPDf0V/omWmkC39KSGSEEsgFOSRTSrWElmZUZkDB4e7jbe4Zy916CHtc81TwJyIDLNzc2uXbt37zV861vfJ6d5RvBxs42ZyMPhWJd6OC2n+WRiAJhT6fLZSMwsIm9fv/3m6zdPLp5utpuck2hJKSVmM0+JgrYbpmCtxd4yEJGKtNYQiTlx4tgGTCkhUjiK9tvoIYKriKiizEREdW7SdCwlQMzbmzsilKZNly9ffXE8HFMpshfmbA6i66kNHCI4idQPWOArwZH3sx9xD80dvURAaL3Q7oBJiOR0I6G4OOvQ17sQUT/LjggEbn3yG6PO7nj2TvsLwMzUIRHrp9fdvcNQnTjxOPkNhSCnlSIMHlQRZkZ36mQgPV+2kDnuWcDcgo3tvd3u65dICOimuN5LNZDIvEiOEG1rSzEIdwDE1vTV1998+5NP8nZg9800bKYpp4ycJCRFMcQZ43ViAFS1OrcIlUQUXB3yeKZRFrsDsCqgEyXs3HpGRGRYUQZgxpTytMllyAQQFknuXkrebDfMsYANqipi4ZJGRLnkFPzUUog8p5xTLkNBdzWRKMlNY5ivraVaK6e0Gcq0Ga2ptkZEw5CZUqvNQOtSDZxiQpE58G8PaxLV0EDlRMnZLPIHlqEM05DzgIDullM2jeGeu4GImKOYuJuocBNCTkPilJ4+e/rpZ58e5jqORdSaSkqDmN5cXy/LCRMdjsfMOYp7EckpdZoDGBicbQ5jGaDT5KNm6fx9cqdYCwuJgdhEiKE09PoB+9ZkQEe9OTXv3hFIeBYqdCIkSomTJ2aicNOStmq3xF1brwL2JS7s5RVgjBNjsUaN+9Y8xobv4002UXGT1lQUAFGD86aPcT6uj0J875oQ+t2LoUBo8cOKDCCiuWIfj5uGe4kjKtd6ZxoW9llhmef9v//pH/3ad7+zv7+/vX0rTYYx97CncntzjeBoLiLLMquYuU3jVPLgCKLamqTEAS4t82meZ0TMOY1jAegWK62qg6SUSskiDZGknVqrRFQ22R2WpYp0+tKy1MPxMNdZRKRFqVRrrUTURAGxDENtDSAUqjcpFSTcH06n07HWpQxZTN1MzW/u7vYPDwgsKtoEAEvKiElVzCzkGETr8bA3VyQUV1x3uSOnJuZS8v5hH9URWwfy1MzcxmFgJ07caltqNXdmHvKAiGZ6OimE/jDC8Xh0t5w4pdwWLWOapm1K+fb+jg+HXArg8urVV1998YW7mtbaWubiwMDct/8iBnbmb2A0US87EpoRABBEq9DzQQ/WsVtAvZ6PvtE7ewJ68U8QBBh37dALrkdPz8c7zputhAvoikDgSFRKYU6IYGpiEtse4Oqd/hbH8pwBemt8Vg8DxJzyMA4costu4VvR+4AY4cUTWSlPgIEBr517VF3YW3hcle0dHLQ/pqM54rxUAwEGQgTDu/u7pm0zbchlGBMxxezB3KILVxdzdHNAW2qTWveH/fG4JweijFHnGXRPCAB3q7WxKifmAZmJKSVmd09EBMjIIT7PmfNQck6M7OrbzUZaQ8RpGqMIdvB57jo9Dh7mHMOUSikFOUBpCslV9yptWZqIEiMmLpm1aYrwlDIzZeZwCEKmpCa1Leom0swhPYrsVzVTUu8NFZZcpAiQQyg+pjwM41CGUoJUV5lZxRo2bQpIouJApmruKmJ+MvfduPWKeZy4lLY/Jk+tNXfMuczHeX88JiqiLXE+lw8I2FQsxJ3jtBoYRMOLZxSmh9wOs0DU6I8joxXZR4ipGUTQ76cZ16PcR0vhAAlMK5yImDhW9QyYRLW12mpPAO9UQ72JXS8ogob5J5433xG539su8YCrc1F/2uEJ5eYUa27vJoC1Z/H1Jga76F1MyD1aZjPQ0OaKreJoG5gTOrqBaM0lgYOpi1RAbVX/8F/966vLq/3D7f39XWIc8kXJZTtOt3otte3v71Sd06juy7xstztmLkMBh5TTMDgz1dqGoWTmpS4i3SasNcmllJyPh9k7ZDHlXMwNE5FSTpQyA9LptByPx9M8S63zfJrnRdWlybwsdakxSBdRFXX3XPI4jE1kKMPF5W4cBzOb52Veqrn5XO9gnxNLs1a1VSF0XbHX2oSpv9TahDk5mJmN4xinYZqG7tni3j3jOI/jOC+nUPcAAE6UPWNf70wpMQAuS23S3Dw2ADhnC7pdgaEN+4d9q4vlpKLgVBdzg4vLSzWd59ndTqf5l599EeNxU8NzwDOntYpej2TvyR27QiVY4Iur4VdnjbuHhnFMV10DluxYDnTJ9M7S6MPhCJMr6u89UXjHW2P28Fj4x4f9SIPnbESkYqHqGsIEQL4uZgay1Od2fl6aBPRYOcgFARInD8/zvhYDZuZqiBZQUm9RMHBTQEB1WXtu1D4TBCZesVBy6LNmdzOAxMhOxBg7rcu83N7ebseSk6lqlQoAnOHheJIQbIiAEqRY9ePpdH93f3+/3wwlAkWy4M6umJc5oCEBhwl8iAcxmRhixwVS5pwLM6dcSsmJ2M2HkqQ1QBhKAYTampoQAFgIQiIShA9ESpkpKDcUTtFqNs/tNM9ImCgzp800SrjqZE4EmBjNUfUcS8C9a4AHbuGAtbalVVlEJ8jDmMNCDwmJcis1LU0qIo/jdPlkR5SOhwMiEEIpCczcRT2YwuSI7j4M4zzPyEw5ZUJ/ICqFKJw6jChxynev30gT4qTLTMzmvS4JGsza2+IZrVF0jx4wdqzB3Z2QEaL+8WBd9pO6jkrWwWlciPXTK8DZoU53A0HsLS8iMpMRqrobI6N3K+u1vUXA0KrFroQVb1MwKkRx9fJDpDBqpnMoJ1pjc+9sY/fLAKAFwfVMvFjhpUiKDoaI6hAlYfz6/dKva+URKDRqPkI3MBEDjVZ6mStSLIpCW2o1+5M/+ZMf/dZvLMt8fXOdiXJO41CuLi5Pz45tWf7kj//kvQ8+uLgkP6CITNO0zDWgmDhIvf8mxgys7OZA0FrjlLKTiJrbNG0cwledHu7v3W27vQBwA9s/HJu0YRyI8EGltrYss5mK6OFwYE7MaRh8vz+4+zAMAGCDpsTjWHYXl9I0DynKrjCkBHdJqCqIlPLgailT2ODM88Ip1SaAmDITkzZtdcmFERy7nyq5w2legHQYnnBO8/2duY/TmJhba2UspWRiToUJMVTgKVGCFAsrZharQ2Oa9g8P5gbu02ZCAEIax02t8rDfVxERmaYREE/z6fXrV+6IQG6QuMxLLXkMCViPkBLas0AKhvBIvMfe+66hvwul+GNMN0M83/31BCIjg8qZFRF6RSu7ATwKpGid43ts1WJbC64VODJ0UzfuHbEbgHVJZkNcJf1pTWLQXWLWWiyyGKEaI5lZt09BjHqmm5NHrO+Tv3UWEjJa6IBAcX0AgRDFjTTmf10pyR0g7jC4h+U9IiKKtOvrty/ff8ZOAlBbUzVWr0slSsTs4CKGxJxIXUTlcDiejnMmzJlT5xeEjktUlB4b2ikZYd/vtu4MCkMZiNkg5oBo5m2pQjCOwziMNmQmdHdtok1EpC0tLF2JeRxykCGX+eR52GxGByBKrbV5qYfjcX/Yl6E8uRqnzYaIh1S6dRx2o9H+hJgZCUopaqrmBp5yNnA1q7Uu82LuwzAwc2YK1DuaNYKUct5ebKdpa+bm+6aiTRIR5xgqITMRY8wMCvYJVa0VmfIwDuOUSzEVNd+Mu2GcXn31taoDmjmaCHjAOeu6a7x7Z0JYD/rrufQuiGDogQw5OBpQjHcBAHTF+87f1M9v5HTvfaSfhXk6OGSOSI5hZg0KBoZm6upRh3qgUogrLPMOWr/ei14sdRTXrevLUeCW3mFYAvAAJULdpecr76OXPpcD6NPseH7nH7J25h4DSwu0uKc56/M6aOoIj9nUxTlld1U1QHh4eHh7czMOJRHPp9NymodUhmF49vT57e3Nz3/x8812N20uKWnUl+M4pMQl55SSqpl7yBWBQ5iqxYlHBJHFXEspkQZjjJlSUmlu3qQ5GCfOpZyOJ055s9nO85xSPh5PalrKEC8CIllYyIbLivvpcDS15bioiD6E/69JFSR3cnastTZR4lTbgohE6TSfaj3mklR1KCXIRcfDMRYmmDiNZRgKAIhJvMaqcjwciBAch3HQJiItafIubZswZTNBxHEYvQATIaGp7vd7RAD3h/3+4f7eAZZlmaYx1gKIUhMh5tM8q9k8L3d3d7/84otaW2DeKFZSCUELNAjoP/YTe5iDtYzBADwNAd1lZSgAdswz4iUYGDpFj+u+LriGvv+6v+5+1vCB8C+HTi8OWgTQmlXWiSv0YSyAu5sqehhyuVuIKGMP+rGh8g6dc80hfi7NVKXV6ubI3A1lPOyz1UyJ0Lo2KAQtFgwAFWmtAaEDwkjgiI5GHruZq6ALIqAihoNCuO44gav6N9988/DJRy9ePHHA41xzKbHwmkumFOzwHtAQUEXnZdFYvzcIvgZqn771FTZzw6DaB6bgarK0JcYGhbOZy0r1NncXV9XQZ2BK4KbQHHxpc9MqapQ4lxL2QU0kVrUAPVE2U2m6zMuyLL0lzQNzZmJwSwAhyaCiig7EyJk4MSLklEGAqGdXt5hFSl0WFc05ydK0tZDB6nUscymFmRgTgtZaj6e5tebmmNC0u7EhkiMgIxJwTqJirplSyfnq8vKbV1+G5CmnfHt7c3+3F1UFMYPQF4rWF9agugLdUbVbB+5719ITBIYXReydgIET9T4xhMlXhKg/2AquIECo6kddgr3yifO8ZiC3oDcEa+68EwPnHS3Hdcml5xDs0R/PnzJ73BJDBIyYFi4mIRMBBr10W3e5evboZ7uXfPG0HdaDhSvsBB67bPEV65f2xzHz0P9cnQkMNFRiTWurm2n65tU3P/rRb40lmQkzNWnjOOndfeKhDEOtS61LHodpM0RbG1PEMCNBxiZiqokp5xQ6P6pGhG5ERDEvUQ1GmDJTa95aPRxPgFiGMo2jtBq7KcMwDOPg974si4qknM1dVBC7/BcnZuMqqofTze1bAD8e5qay2Y7gULiYujRpTZh4KKXkIm7LshznZVkqLTSUYbudxmmoi2w20+5yExaVSO5grWpdZgDcbCZijo2TXAozWUrrHB4okZmr6el0creUyjCUXDI8+iCatNZaSzmZ635/QgDeEACkxNM0qRoCHI/H/cP+Fz//xfWbt6otKnhCMnN3RQPrqAYioMGq+tRbS3D7FXbkyhwDw744iADoIXfivU4AN/C41P2s23nxinqH7O4Y3I64aQFL9qDdjY86EgsIKE3CqyC+WVXi662L/6+UBXCA3jRDjG37xBoUvGlzdPbUJfjNmtbon8AwRsoBenXfg94W0/lGQwDE8auC9olrr6hWkRfqZNO45aLt1TfffP3m7ZOnl3KapbaSU84jccl54JRD+SxI/YyEhMuyqGgMYzy4tALu4cVjUeWwGxP1bUc1N5eulUnEBAhNRNVra4k4WAMIlDMTkopB6FQmFiZE4pQypZyKIxyPR1MBRBIWdARfWptrVTPmNE2b7W672W7CICAhUxPxGdws5zKUIedEGFNrDe4JIBKxBqCIaOaiy/5hP/Nc25xSCAynXBIxuvt8rEwzgi1zXY7LutqKItLEOKdcEsfijEPJxRyIQ58kTcNmXmqt7WI31mX57Be/FIUqoZoHfburox9nchesb2/0vH3y1IdZPeivWA/00sBXbN27lNXasq7XxVfgv9fL8QXnWYODg7miu2MkAPDz1HdtmQN+WX/uGn2hPxL2uB2Q/Llii+dAho5g0AVfgRy6R8R5drdmK8C1McAVrnLwYN3FEyKkAGzJz1ko4oF3L1I3cxC3Xhi6gQc7AiI8ffb5Lz/++CMmv9htT6eTLDMA73YXH33y8e31rRocDqcm8N77L6an02YzlaGQMydijrXlxkylJEpsom7GiQlxHMkBT8dDF2BgbrURcWIOvRdKnBInTtLa/f39UudWm4WGfd83p9ZarS3SNRKKal3aPM/mBgwl5XlZaq3TOG42UyrpeDgcDofQa0upEJu202F/Op5OwcvOKQ/TME5TTvbsxdPtdpsKR2dwOp1E3c1yTnHAwscUwFsUQ4nNgOPKqKpZDMAjt0fUNrPtdtdaPZoFVnY6nuKC1Cop1aEMwzSeDqdxGvMhN5Nffv7LZakxqEgpuZl563g9Ytf/6+MuN+tQY7AIHrP9O8AMWXQOQQf+lbIgBqV98SrOyRlCRCdfdWvDxfQMmkbtfl6D93dJySDibnyGZEMzxiI691R1/tPziwcs1LMQups2RyDOQIiyem+t+a3vYvdcQthxYAc7z6kBghEEEDW/9wblPPMLZp+ttwjAwdVMZv3q1dcfffThUHKrsj8cn1zSZls4s7uLNkqZkc3MERxR1XztsBxczBPCynd3NQ38GMITp0NwoGLSWm2q2rem4u3G4ikncBBVIlQzRGfmadqUUqZRVM3MRa22CuDSoVdXldi9aE3MjDOXXKbNZihDYgbz1jwhuLmeTqKiPvo0jIRkZq2143E+LadlbiknRDJQiPrQdFkWaToOse1pUqUMhdK2pGJq8zxHxG9VwHG72+ScmHNdlri0zBkgCOnGOWNDTskMgqhpGsrAfH//8PXX3wRmruIAwMgr5+z8BkU9fcbvAbsn1hlAP7elnRXXD2wPgrSG+t4VP2aUjgXBWs8DAPQdYOTeGXfBpZXzECW2r3njTIN753T3u7Fi92tF7oixnRZDE0QjJwTHddklincFMEfrRCQA875+tj7rjiad00OcZTGNaUSgqn0/wjv+21HT9Ra5WUiEtlZzYkYCx09/8dnv/s7vvvzgvXmZHXCz3aRc8jZzKku1YUvAaK655GfPn5VSCIPbQCpqrmH+hhRiAe5gUjVy5LLMtQkYcEJOlDIBkGkywxBNFBFCGsehtXFZ5tba4XisS0VERA7XRpFmarzmg3mZHb21enN9k1JOJQWZJQ9ZzY7Heb8/DOOAyOCiZvvD8f7+Qc1qXaaRUk55yCI6jKVMBQjVzMnNHM1NYZpGIjJzTgTIbiYqdWk5W4ECAAKeEIjI3LfbnTQF8N7rRBtMWMoQW0hfv/r6dDputpvEm4jiQJSYA+duVb75+pvX37wObmvEhfXdRjcnQre+urWGst7nYuyvBBbaFU/eLUhWFc0VQlyhGwRAN+rlFmhsFAY7zt851r7WK/3ZPBYXawfu58vZJw3ei/deHWEs1/dx8no71/kWrAUUIpKxRTeTgIiY3Nb2AaPUIUcIKXx08LDV6oWaxa1wtKi2EKAjCb8yrojGANHMVtFrBGIke/3Nm69eff2tTz5KpRDn3eWTUgbEJM0yJzxHpD7lNQ9rT0CR4NC7NMHQB4VOJSQmN5AqleIBDIniiQUiZ25O2EQBK+eEagKK3UsqMaPjkKosrYqJ1hqdh6qmnJAoxGFE1dE5JyQah6EMAyAGY8JMo2NFkdlN3W2aJiJc5mWuy3F/mJe5Np1wm4sTU0qZcwZAUzc0A+ecU05u0FTcIeXs4O621CVyfM6p5MTIwzSeDnyclybaWs25hIVCYiImcIhFA0RMOc9Lvbm5e/P2TRMJMlNMuPw84O+HDN9pXc91hK+V+hnTgRWJj3DbMZpeLfiZaNOvTvwLIvqvhPFzfQIrTS1gPo0KxlfEZ/VshTXbwHqoHyO+rxXPO/fRu4tY5ADsg1hQj1WPbmwZmzvr8DiMhzpIhWeQCHvsXzFRXwuxwCtwxUPjGQOt85NY0mEHA3UpOYOrqWFO283u888+215sEqG6cRlefvThk6fPlnleZl2k1VO7fO/phy8//PDlh+bIKYF6tygKmRQ3UCCmYRjM8rw0cF/qMs8LGJRpiJQ6lIIEYC4qmy3270ZIKQ3DMG2m+/t7DX8jaWa2LIuImJo7BPRkam7AzCK6LHNKvIHtZjOqqTRZaj2dTq01EW2LDkNBwtN+VlEAMPEga4K5LDJOo1RttUanzonQKWdGoBzupCvyqSbchUOMUyolxywNPPzQY/PI3DyVlJFEGziM0zQOY8757l5oWYaxACL3PQMTlVbbmzdv/80f/Zuvvvoq5aQW25NR2wfKgo/nM+DOxyAe766da/814sIKE/pKI3gsRjoN2glATcHDl/fcqEYXsA7H0Ndx0zmx4GMPDWux7QBuSPiuDkR3szg/6HrT1qPr7+QSRAA06qu8yRJS0OTQCMO1NB7PCbqoVOxvRj1vFHZo62Vx8jO34h3oANeaKNhEZrEmquAEdP9w/JN//zNO6b333tuO05NnT6VJa33bjokNjHsMs/6kkQBRzVur7MCIzCTgjAkRc87Myd1FZVnCDAA4pW0qYqZmtalobdKiVky1Gptxihco58SJiAkYvXldWhhOSBN3pwEBoHU5H0iJnQkNxs0EgLUubipNWmsp5RQXTN1CH5gQ52XeH/bH4ynMHQGdEqWSm1ponpShDGWcNptpmnIqdZlDQsDccsoAwZJVdw/P17ybci5Sm5/mJrWppJSbyAQY9rVmogamggiI6fb27fFwf3Nza+ZhIx4ntUljTtgXdqPddQiiayyMBzcAVg5yWGI5OlkIVwUrwE17qO+dee+fz8klQut6BiPNvJsFDLpKosW/dX7d2v7+B1nj3Y/X1PQOJrlWOevaggOE8Qd6iG85Wvd70P5ssDOAiCDW3O08qIuSDrELpK+1PoTuHbi7gnXx/UgECCQBoSC49pqNU1xnVpOkcPLl1ZvrJ6+vf/Sbv/ns+bPvff/XP/7ooyZyPByunjw7HI+7zeXTp88/+vjjYdi22qQJOICEe1rPsebmaGEhheSEiIJEDEFBF8Mu6wA5JyQU9dZEtRGxgDeRSMxhvyeiTeppnrUJpxSJkzkhKRGuu2OK6K01xI2qzbXWeYmBwTzPrbalZQCfT7OjLafF0WMbsYmmwg5Waz0ts5jUKiVnAC+FAYAYmajOlZhSIhAqU1nlfVIpQ986BBCxlLlD6SHgQYiKrbUy5MvLy1///q+H46Ob19aaCFBogCMzi7b9/b4uzQ10kZAGdo91kHDrtD7GjfqA1llXB4Z6gWFosTUadctZfGGtoHEtgeKTwTSztXwBBOxhuieDvl5s54MMKw/vsbRazx8ArjaPPQc4QMjTQyfUwSOo2XOVr6opEalNo5tBacpdRWgtndSigMReRgXZn03BHRnNyVUeWeEOvg4N1/v9WANGYYUI2Dcn182467dv3765fvnBh5dPrzjl2sxcgnnu2MWJwm7e0QPHRyQHa1LFMFFXwcwlEVEpBZEAO87jiMHdTJyzw6lWEG1NWwtSUjiPi3D4dRsnKrmknJnCvSIjYawHaehGhGSeeRiuR/nCRAA2z7UhAsBpnlNKqbVGxAhYSiFGIETCsFT0+N3MIstRWJEi5TyO42YcxpxLMNtAUVWXuXqGlImQo6hHwpRzyRkdmCmndDgZeHjDxntiZmpNAliZyrjdbOfj/PDwcDgeU07NJOcsKgYBp1qv9wEAkIms2xA5EfUxJjgCWl8l7/BJlD4UCP6qlW+4vu1r+O5Q+nqHENfR1K/EcgdHoNgGPjfAKy0aHyuLFek534K+UrY24asMLvjKUYilLiTv9vXRovS22SAIQgAQPl+9ryBkJ3P3znSCOLJmK7kC0cPVw1ccCNzXiOxNgkGorsysKkzJVKUuOZXNbrM/ngqPf+b3/+xf+av/m9/87d8uOREhJ7q5vpVmz5/laTxtt7tPPvn2k8tLM1vmRV3CA8AtDPDQzerS5uMS8shlKITMxNM4xHsauXQ+zQ6eOKo8aLWpt8TATP01MA/GvarUpUkTac0BUkpEkHM2T8Akouc3zAIldQuHFuZUa3OAeZl9mfumLpKoJk6lDDnllLiMpU9ngMJeWNRyt5ogBDLwKq1gHoYhNrgjkL2zNxunAmOOlzipmZoutYI7JyamcRqXOu8udsuyhPTFIou6IdCQM5h9/3vf/+bH37y9vr65ufZpvL/fr8UKuqOBmWni1KdH/bxG8Amfwl5cIKCoMK/L5/gYyeGMpp4r9jWCx2v4CFj6GRg945zvlkk9rvoKgfpjxF8/tj6KxnNTAutD4zsXbb0/53YCEN0NQZfTnJgYOAgQgavjOkkgiqFCz9C+VpDAAAim9ig/txKZ8DEHMYBXEQQMNWJVcQVKVIZsIqK+vbycdhdqyDm5A3MKvVtwd9PAhiG0hiCIGKgiriCAA2BKQe5PlNK6oAytNXNiSuEv3kc4fb7er7OquZqxqiq4cOLwqIKcU0qbzaRmy8wAXpcWURvcIGxfECF8F8ybtbrMidjBWmvJ1EwsM3PinJKqmli8UqLiYLVKKsXM3SA0HYdxIOJhKJxCvUhDj3aptbWWU7m6uhwGEhUVRYLEqeTiDsTJofueIzh0ly01FWNCxJzo8mLz4vkVs5tZKcXcCENDKpxyfdXZdw8qhYOtFBs0CVZCLIAYKK4stehC4+3WiP4RO7F/PyJ4aCfHCT8/JqyIZI/6j5E8WGcYk7DeEDsCufUNW0RYk9M7J3r9n3c/2Qu1c1o4o5Cmqo9OwtF5S5Ocs4SJR2IAiHLY3Mz6ioADiDQ8jwGiBUAwFQ8OSVNwJ2Ly0IdSRycgIGDMnOjZxbYuMovSMP3O937wf/4v/9Zv/daP3nv+3rgdDvt9O9X7u1si2OwmbTJspmfPno3bQbTVZUkJGVKsxZmaO6XE4WoeqAMRa7M85rFkG7Ko1rbMR1mWpVZxs5OekElVRRZwz2UYpkGk1ToTwjiUA5OKtvCGy6zhowkhR4acS1NzxGkztaaGsNS2AwLkXFJR3e+PS5VaxUSGYcyZyNM07Iho3GzSODZ1qI5gV5eTA9VqakhKZTPmnMG9VlFpRMicIx7X2hCxlDKMhYDbIs4cO89mquoxsk6QzGye5yYNkUoZh2F68uTZ/f3dYf8wn057TiGjUsqQh3LFT/7KX/urf/4v/oX/x3/7//xH//j/nXMGR5UGYSBulihpC3tYQEI1YyQHNDMC6tcEAB2ZU0i6PKLeK8zej/daBHUopOeG859IEiGts2JCPdyvmD2s3e35qpyR2PNSpK+Y7Epr+5Uq69zvwxkz7a1BCqsPIzUJ5gKYhYpuLJaiUaCiYQiDqx+GSvBGARl7hdz75VDMXYswF0LOOSGSqokaElBOCFhnRSKkxFTA0rKImIJ4Hli6OSUAAjOZibtFeAdEQDIgkQrmHIMwopwLIIX5I5mDg4jWKkRJMOQijBOP04Z5EdWSO/0fwNzVusgEmqMjYaBBHtQDzNQAPSDkMHYEQEVVs7rMolKXyoRpyGqWRAURhnHIJQ9DSSkj4NwWR3SAeV6Op0XdLy+vAJGRNtOOMatZ5uTmompqrQkiNBFXGwdvO8GGbrbMSyrJY6xh1pqYmppG4xJnwN1EWig+Dak4+DRNH374QZX65s3btrQmMgxjOiMYio994no+VrcVXF2BAtJZFUAfg6+fq6eV0tmF2fpEKt4WBFj3Cc495TsJgFb+aEAw4Oev6c7XfSy9jgIe4/06fT2HfV/PNq4ofT/5BrFdgYYWDKjYMnU3Zq51Kbk4KGjnwzm4ipzvs3bX81WwNHBYgFhH8W6SHKPCuHQB0auqXu0uq8jt3Wmadkc5/fa3v/9f/V//qx/+6IcDl8N+f7y5kaU62LSZHKxJW+bl8vJyGAdTFdVxHETa4XASNY/xKFH4byCHTh9i6BkBRKVGDoihWhi+e2k+eVsakm82Y8qcOJkHSWZxcDcjJgcnRm+mCqaIqQsUAwIRpcQqgkSEhoCmJqKlRFPHTUITxTkX6OQcR6SSyzhOiYuoZARETCWXYRiGaRwnQo7oJdIcvKmQEi2VaThjEm6uzSCpiLgCM2dEQIpCiYjNXZoEUiQidVnGYdxud3e3d3VpgLg/7Esbrq5Sbc0MllNVsYvd7m/8zb/+Z//c7/93/91/+2//zb99uDuJOAAQIwISMzjEVmzHOSP1d7m2ftBdG6zUnRWccQxfoxWbXI/QejsfURHwc0Xam+zz2T4zqNfG95Fu0TvjtZpaA3vUvqtczzobcHA4S1KvT/N8YUBEEEEQpKZxnIhQmsQPiUkRE8TUkDmZKqKrWnC0evMB1As3d0SMsXzgxIjBuAZTJ1DGhLwOD4DMbSwj87BUqUtFwLu7u0ScxoESmRkzMRMBqIipccqcmJkdPVZh3KyZoaqaq4YXfCi3O2gDhJhNDMjmRsi5ZE5cM0uTMGuUsIqHGOZpE0FGFKQeTYGQhjKGWSkCht0nEZoBKoJaRIbaakmZ1BJzMrPEPJRUhpJzQSADRYRciqgsC7ZW8USneeaUDXAaNzkNyzybq6k2sdXCGF1MVTSLNjVO5takBjnK3ZosTRfRiuBEnFNmTsGFQiREnpelNSVIyyJPnz1//+WHVdTMW1u++uLV559/vpzm1poRhj1cRD51ByAHi0r8bLPgbqtwJ/qZVLDOilfQZT2voSBBfbRAHd/BwKbPE9UzVWBFjPDcN/fiJ7JJhzjpbAr2bsn/K9U//srf+g17HBi7xxZ83Oo+BIZo0fq0B8ndu8d33+dUODf1558SjCV07HAQcsfgIeJbKXkch8urq3le6nK4uzvePBwvLzf/h7/6v/pr/8X/7sMPXlqz5m0+nW5ur5fT6YOX703T5nTcz/PMxLvdtJ02KgqmyMCGJaeLi0lET8cjgDMhuJ9Oc601pVwwiyiYt7q4hzq4qLZxKgA0n2bTlgtz4tZavNVN5XSaj8d5Weq8VFFLKZtbmF6J1ImGXIqqiRmY5ZSEFRFSSoQE7m1ZWuiMug3joKIV2zgOBNSamHliHoZSOKM7moNCGnk+Lcu8HI+nmPBL05wMCVWi7tOlLkSemInDXptqrb4AEwPBsoip5DFzSh6mPhbPKru6J/PZ0Gmz2ewudvNyjBpPVI6nwzhsEickFa2J+bB/+PjDj//G//5v/tN//k9effU1Z3799esvv/zy/u6+1dYXaztQCODQ193P9TbG3CtifZTdHjXy2cZjPaK9OsK1FP/Vo3uWB1ivTyCfvZaAd/5lhZDO/38+jysCHznAYpYWQG0vg979jvWPOTKJVJFkVkL3x0wiwIKDWN/IbC7udi4MV4jrDO4GehtAKKq7xzIFuYkQx32hkBMvQyklXeyefPtb3/7gg/coZh9mb6+vx2l89sELQjYwM0ucAFykqmpKiYjNIBQ5miqYY1NEmlsDDhNAMzFGBJPeLiyOVIj6jJshJY4lNXftHhLBoEV0U20NEJGAqHjKhMilpFiGj9eZKCaVToKgaqrgToAOSEi5a/aqSTNiQQQEUnciHsfRAWpTuHswD6uZpgocxRyBNlNdAXB3aS0WiRDQwVSl1iqiKbG5qWttVSXIcJxSTjkjJ06xMZ+R6LTM2vTJ5bNnL579+1/87GL7ZL9/OBwOw1hevnz5m7/1m5vNJgKcqYpIFwRROS21LktdWm11medlWWJFaKk1vJFCfjQCg5sruKqCx8m1c8PqCuc6Gc7x8/E0/+qfdz4fmK+vvWsUt3iuZbBnl8cLdraHjJSxFv+PfmHYFYHiSjzittA/Jgo5mRQ8E2KitReJ+xrEoA4bITIR55JLKsyUugQuMQKQurba5tPh4f7w6aefHg4zIVOZLl+8+Jv/x7/5v/jLf/mDly/I4Xh4UNXTaRZt5iBN9w/39/e3JvLy5QeXl5fRXRGBNnG3lJESHm73S13GsUzTzlRTTqlcAeB+f3Bx8TZyEbG61NpqazVZMbV5WZq0kUd3E6kAYDrPzW6ub29vbg+HwzLPda6EyEgl5cMShqOUcqpLXVprtbl5+Mz1YSxCrRUQU0qAuJm2YxmOp9nNYoQH7omZmdS0tRajWlM/Phy/gW8uLy9ffvByHMbgbTORoWFaR7tmVXUYspsDmIiAYyoJEESrqhta4th06/ADNompwWY7hS3ENGwSZ3Fpc2XmtkibH3LJ0mQYBiCepqk1udhe/IU//xf+b//1f/2v/uX/8OK9D771rW9d/uhy7UQ7qbzVVms9zadaW+jc1baIiIqB967drfflUUlh51CsQRzAuzdX1DMOZyD0cQ4QezldZNTPC5Pw/+fKPJ7/NQHF5eilvyES9Kh9bpLxPHcIepK6AMBpmYNiW8O8TSUMB2NyHG4tRITgwTCMH8mJTSzuDidOxEhIlIZhyClxTinxOA3DkBMnpJRyKqWkVDiRNiGEnFlVwfz25varV18+f/YiBBQSJ7OQfoEwK2bmYFBHJA8pUFTB6rXWuN+Irs2YEMFSSgCIFM4W0cpzUGAAwK37piCQM2MPOwSOKrr4olITM3MiplwKByE6eGjgKiYgmZMRb4GZkwNQShAkzLbUY9PTCXLOKadcSqj6ppTKWJDZHERFRN3wcNgDeKtNVN2BGdxcqohKGTJTIibE4GIvQAhMgKBmS1M1MEdi4sScojUwEeSUD4fj16+/kSrjtNtMW2JGhg8/efnTP/l3//aP/3g+zYCQUgFwPD+3kksp4zjmPJScOadxKBe7bbidpXU3mokRz4JwSEQWDIug1Zibmge7BlOE49jUBvDwtQCwLknY+9l1QoMdnY/qI5Sk+hXBx+zhDmbq76Kh4RDZ1056UxKLNu5u6itYjtC3yQwAzEIqp2NVET78bJEBfdJm1s124mJYVyiV47w8PNzPh2OTpubSVK2FZLEDgBtRQjCkoTr93o++/1f/2l/783/xLz65fCbNltPh5s1bEVlOMyUsiW9vr0+no4q8ePFsnKaU0mF/2mwmQppPJyBnpK+++PL+7m6324ngvBxLKW5+OOyPDydxOR5O6DDtBneryzJMAwje3d4eDwdVJ+QhD47eqgDasrT7/fE0n5ZWT8vpdJrn06m1Fhsu8fq3pdWlqqjUKq2ZqdTmCKVkMzeT1kTNhmEow1By4mlMpajqfDgxU7JE4IjYluYGOSd3n4/zOMLN9fXdzXsqAgNwJjMjZmJSUWmSElFke0RpypyGobijiCTAnDOyI1Ko4Yo2Vy+llKG02kSUEyHRkMtutx2H8e31GxGddcYdIiAKqHlrUhfZbKehDKLt42998lf+t//FqS3/8ic/qUtlYhWNeEp9LZRzzsMw5JJTYs7pYnNRcilpQISSB06JKHzoyFf0BrGTD3rVssKnwcDukCYGxt4R096Ja3Aw0UMgsysY2PqFobXXc00UcNEJWe9cXUOApw+5wN2JkZCjBYhwFvhpeKxP03j19EkpuZQhEPN48oiYOeWSmDNT4syciIDiKYkpAUrkYbeQV1FzkaYSthBtWebT6XQ6HVuV2mpfP0q82Uw5pQ/ef/9bn3yScv7mm69fffnFe8+fzsc9uu4uLlLKRD2B5ZxLLiUP4bCdODOyqhqquNfWmDFxQkROwYDHTmslcrdWw+BUI8oHkZKZOFOnPq4pM2KGicxmiJhSSkyqllKKtz5iVheuZ479REMXNXcU0VRKsaa1LlbdxACAgkoXqnRAJef4eSFvfDgcRAVhlQxyDCFcc3VjzpmJEUGaElEZcs4ZgeL1jVgWaKCBI7EaIHsupd3ef/XlV/uHw+Xl06dPnl1uL07z6Tvf/vZ/8pc++eN/8/IP//APa6th2SOiEYuXWk/z6eb2RpqeMeUQ2I9hQShtPhYj50KCmBjDnZm4x1EkAqAVvYzKnALIIXrERyOLrEHHg2j4GLrNtcuIrIrQce5j3SbmUN7B+T6ahXUe1fPF2lq8M5f4laYaHvvileG5/oa+fiFBR3zWHARxjxnRkYmAUN0APJccaFlKJTxBhs3Fj37wvb/zX/5ffu/P/kfTdjTV4+HwsH94OOxNGhENaRBpy+l0f39/cbm9vLxMlO5u7lLOiVhlaa3WOjeRX/zi59fX17/zO7+LAMuyhEXtw35vKkBuoK3Wh6/vckkp0bQdAVy0AUHhlFJuUkF9WU7uNs8t8lmty3F/WpaltrrMlRCYeRjyfDJVmU9zTnkzbVptKdqiRMzsDq1FyeJExImREyKN40iICVitLcsSTo11WRw83KpFm0NZqqgIYd9W0TDCcKutmjRRACilFE4cImVE2KocjycEAnTVdvXkkjm11sowdE0UlZRYVbSZhodZU2Y87A9N5Xg8vXn95tmz5xe7i2EYRAVDnxUk2sJvfefjv/f3/t7f//t//w//8A/BodYK7qHcg0juuix6PB6wI80OPRaHBOx6FXBdpkd454h3wD9gIzoTZLDfJyJy7ELNEXWJ1oKTOKUcpC8mDlEQZspDCc2x4EJlTikFZN7FpiluqgcU1ecB+KgE5CvmqwDoZrWJuUpTUVmW+XA6RlJp0tpStdNSPLwvgj8SIGi/HUECcowNsV6Or7sNQ8kAzimlkihD4lRSaDLTdrNNnO7vbr5581rVOeeQdjA3xsycQDVzZuTEZSijmZieUvjCEzq4mtalumouKeeymYYYUIVEWko59OyXNotITjkk10rJJccLSyvfN5qKTucPqQ5VC5HdyLEp55QYCcEstvCguzsklxoLJKlVaVU6MYAAHLSqG6SUlMzFylBs7f60ScgBlaFwotgnjldT1VUNEGPlzBFKGdwhpwyIZq5i4qqmIlJlqbVyQkwejeSy1Lu7u9dff/3ei/efP3vxa7/2nX/yz/7pw/3D82fP/tyf/3NM/Md/9MdmlkpSc7VOMQRwIlTpZE0zx/Bn7k8qkHqACNDmjg7oaqCqYKYqbZGYrSOiamhL9UX1VfgwKMZn6mSIzOLjXBUeSTzx9f1TgO4h1A79TGM3KvV32t8+WzjPKmgt/dcGeOVHnDOQe6wI4+ocFsE/xtN9Lah3D3DOMGs7goDBd3TzlJO5q4k1K8PgTljKj//Mj/5Pf+tv/caPfgyI+/3eTQ8Ph4eH+/l4Gse8GYeUKPyJOOGTq6tpHA/HA6Bd7i7vH27dY04LD/v7V69evXz5wdOnV9KamjKX1hoRAJKpbaZpBm9tiSxsqm6hjgLiyomXZX542LdWwY1SSpnrUk/H0zwfA8LNiWPfys1L8ZyTeRMDBBQTVTFXBgqIWVXdrLYl1QQIwzA4AhMbIWVos0Wr5G5C4uiJU81LXaqKJc5MHIy1uggzh4aGtJYSYji1qiZCcycS8HGps5o8PBzubm/d9GP7eBqnh/0DU3r67Goso0gbSiklhz3T6XQEcGS8ubn+/IsvvvziKwD49ne+85u/+ZtX+KQMg7sf5yMylZTvlgdOuNns/vbf+dv/4P/+3/yjf/gPmVDFVMTMOXzmEIgJCd2AOxJJPQmcl+cdkPBMWz5XGp0H6m5gYo/D3jNJwdch6hndx3UfbR0u9BqGVgPICPMIPaVwYgQI67dgTEboh7ULCMx0fS6BiHr8h9jDDqx9MBFF1Y+MTDiMYzg1Rs4C6AK0jo7AHjMSgr7xGEVX4EzmCMBEJoJEbu4GpRSmhAgl5w9evu/mX3z+5Tev31xebMG0tTqOEzjEbq+aESWmFJaJpsi0lJITJ0HBkKQ2sSqizQymcRrG4g5ElIaMzCs0B2ZQm8RMyBEoJ0QkSqkwIqqIqpIaAgNQsMjDYhoAVM2tObhbivcgnhglRGYGL4iAbmrJzQlxab0tSjkzQauVvAs2IeCq9EZBysw555xTSgSoIVAZ1CQjAAgpIUIEcGJCJndQ0yq1NZVmrSmcFtmpqjElTAXQyzTsrq7eXr+9vn5z/3D35OnTzTR9+sufXz29vNztfvibPyCm4+mYcz7NM1JoZ2MEbleXJu4WurtVY8sBVCO9o4Uol8bWRqeKuPsq1O/9/6yHWu+f6DTlaGqjdw3wtFOxWjdn164I3mktGJtKEL5bGHacIT5u68WILgB64QFnZl40BwCPM7mVY/fuOCzSkMW9jD2+/mtQn3j3nV5//M4wHojTFXgoIibilDlvS61Nwf6T/9lf+Nt/5+++fPntknle5vn0oAr708OynAw0p4EZzaQuM6C+/OC9Fy+eues8H3e7batVZMEgHhEg0K9//7sXl5eH4zHEGptJbW2plQndQUQAcZwmM7l5eBDV2I28vNyVYXDz/X5/OOzN7XQ8vXr1tRgstR4eHpZlWea5tZZLNre6NHfLOTlAXWprp2NfS1xqrWpWoITYXLwgdVlURJpM0xTNXI3uxPzcMklrd/XudDw5IBE/ffbs6fOnCLjMi4KM45ASufs4DGVIhNjqUqXe3d8fD8fLJ0+kmIiMwzC9Pz579mS/v1/qjITLMoNDylSeF3AUaYRchuwVNrspD2mul8fj4ad/8tPrt9dI8OUXXx32hz/zZ//skydPCYmI3dVzjkL8cHi42F385//5/zrn9Af/+B/vHw5IGBpk4bMG7tYMiNBCItTWuh80tNIAAoP0cEv1leRDvRNdzSpgXaHqkgWBxEIfNXkwL2IHszuhe8yu8BzcOScEZKZ+8FJCROaQIiZASJQAnJCic4s80e9CJDBHc422va+zcAxvTN1TClY+gRsSEITzGlm49gEadLIcECZOjsCUADuMYmKE1IeUAK3VYLUgpO12s9QqrV5dXH74wYe3t9dff/0KCXcXF0y5NcXeX0WBTW7YWjP3MpQ6OxGXUnLOrVYiyoXNVzaAKTEP48ZEo+kKWCc221VFRE+zp5TGcVS1aZqGEZjRDEVUmyAhEjN7ymZmFLAGs6qoBkW/MbObigqxpRU0oLC1cki5ZDelhRap4MBUEQmgu7kiwtmEJNIp9x8R5jBaVao0Dxk8iDF0STktDWutogZCRCKqtWmtou5ACMC7q4vdxcU0bSmxqF1eXL3//vuf/uLTr19//dlnn/3+7/3+d7717Z/9/E9vr68vtttxGD/55KN5XsygDGVZFqIQAoPEyd3BQEQibNdWO+U48mQzN1OpqlqlLUtV7RhNrdWkOZiaq2hgkSJneOe84YBhPKEhEAgADuRIKQfxIHOOMx80KFczc2AGCs41OWIwF2NN3P2sPBey0hCtma+3ziOKdowVemt6nk8j9PsWxRd3sgcjn5tmIu7NRgeRCAjdLOqlyAgpZmJIiDjttv/T/8lf/Lt/9+++/8HHdV5Oh/l4OB4PDwZwPBwQ9OJiQ4j3d7fLfFLVzTRdXlzklEx1HIYYhdR5JiIgYmA1AYTDw8PrL18/7O9fvHh29eSJrYuy02ZyxyZ1/3B/PNS721sEnDaTO1xeXara8XQKTiQAPjzcf/bZZ818XhYEOB6Op/2pDCVeCyYOyaBWpalJW1QMCUOILtyzEICZAIhT9BuEhKLiACoK6EjIidMaXJZ5UTNHaLVtt8Nus73Y7VJK0mS73borgCFSLsndl7qcTkdmrsuyP+xF7OLqgolrXba73TwvYUZvqiWXpda7u4dW27OnTytSKmniCcET87LUp8+fff+HP/j0F58DuIjMp/b27dvPf/nLnHPJQxmKO1JtppaHzMRtma8uL//Tv/yflpT+2T//57c3t4sqEeXNFHv45qFHZK4Ea70TqyVn+kO0sdbPCfTqIVKhPiKT0KvvzgvyzklbxwBRPVFvL9wRzdXdwFNicyc1QIRO03cDJUIDc+ph30EREcFDg7R7dlMKfq2bp5R85ckxpzxkB4i5vSOq6DgNKaWQf845gTs6BA6l6khIiXNOahooecyKaxV3V9G4ga1JIm5So/ZPqTx7+vSzz3755s2bb33rW27+1Vdf3dzc5JQuLy7MbVlqaF0SM2FCphfvv7i8uiTipVZAN4dc8rSd3JSYhqE4YGtVpeWSdrvNZrNRkYAECanFphc6MUMAJqrgGDqDSCTNVLXWxVVS5pyKg7fWcN0+M3NzFzFETzmZNFNtIgCWmnBKzFwyhV5r4jAlTAwIatpUuDUA5KAYmrVaO9WkN43nybzV0OFSMVeg0JHu6QERY1FTtZnastTTcZ6XSoQpl8urJx9/8q2LqyfDNLQqKadps3l6+XQ7bb/44pd/+rN//4Pvf/+999/72c9+djwciTAxX11e7XbKnMtYVI2Jcs6qYmoqAo4BfQJC5zgxghoCxcJusFPC0q+JAWKrTa21uoRdbejf9WlGeG+5x3TDhCJpWx93qGkAcP2vGiigmAbGVZuYiZuIttbcTSUU+7oIFiKYeu+lo4a3zq3zd7S54m4+mtmv0X8FdBC7hHrATKvfRW9i9Myoc3AzgajOHMg8yh9kSkSRP/6X/9l/9tf/xl/fbS5anQ/zbCqH+agguaRiCQ3drYnsD3uVWnLZbid3u7u72W53OScVHYa0yaM0ubvfb7abr1+9vrm5EZNPP/3s7ub+9378O99hMrHtdsPE+/sDItZWb69vb+6uv3r11bTdFC3S5P7uIXJka/Xh4aEM5WG/X5b69uZ2XpZhKMf9EREHLpQ5ccopjdNUUolF31rrYV72x4NIM/NaF0Bs0rTZOI45p3GYhmlIicNdLtCMVisBTtOUU2oih3Rq0mqtS12IuLVGiMs8T+OmlGxK7q5S51kf7m/NlJkudhfRQNw/PIzTMOwuYpZoZiIViS6Hy3Ea5EZabcfTcRiLqZdhQEAzyzkRoIL/+Me/N+Xp+vb6/v7hsD9tLzdvXl/f3t49efokhLaWeSlDUTFMuJmm1uqTp0/+0l/6S1dXV3/wB3/w+S+/4BQr00aEYblIXe4S42wxpzNR4Vc2ENepkUNoncSIwHs5tfZHj1joOjtYZ8Ud8YlyhjMDGBGnxDGfIOKSU6emIw1D4QTDOCJy4pRyWv9wzGkyZ2LKKeWcGBMnAvRxMyFQSilxyiWHjzQxE6OoINA4DDEYY2Tq7biXPAKagYVqCCDU1pjDcIN7H9m1yIgQRZWYLy8u3eDVq6+XpV5sdy8/+vD2+u2XX361zHX34pmpi5o0GzebGPO21saxXOwuXrx4/3B4WOqCwbMCJKTNZhPdDzNyRm283e3GacqpcCIRonObjxC4hZnFDJxy5EhsTVSX+XRa6oLu4zhuJnTw1tS982ETs6ioGwLKvMReQpQ9ogAIhBjyVOaW1AyZOFHKjAAlJezzZVO1utSIQUyMK2vd1B3MG7QqtS5NGiGnTJwSEYd6lKmZaauLKoraYb/vRAhGpnxxcfns+QumpKpAWMpg6B+8fPH8xbMvvvri9u723/zR//CD7/9gmIb94eF4PG536G7393fX1zepFCIch3GzmUop4zDE/gYRgnfN6tjHou6A3c95zjljNoBhYADkCwIEXrf2o6U4M8Y6s61HCICAZmwVil1VqSEoQx5LTNaaqKqI1FZPtcV8prUWGSUQqi5LYHYecPWHxF/lVq+ZNohJ6zBu/bO259F9x1cT0nm1nt7ZpukYKmMYnSdOKWdmRsZgBH73O9/98e//3na7vX37Vt0R6WG/50y5MIdJknlbqqmoNgTa7bbTZjPPNTT8SylDKarLZjOaw1Dymzdf//KzT//lT/41kO8ud7/4xS9CSPPq8gkx5pLNrC2LSH315atf/OJTd7u9ub28uCrjaGoPD/u72ztVRaS3b67N7PLqYn887S63WnUs47QZKawFMA3TkJjdQUUxGygx05OnV21p4zgdD4fTfNJDGHF3SzjwcOPLTGxmDjZNAwFxTqaKhsM0wOJmzqjEDAgpp5RT01Y8p0Sq4gCn06G21loNNXBzdfftZtpst8NQ6rIcDodxHK6eXoZ/0+XzJ+M47vf7N1+/+frV6+fPnqnIMs+5pHmel2U2orv7h48++fDjb3388LAfx0lNv/z8q69evbq5voanME4TUkJCE60qKm0oBcgvL65+/OPfH8fp3/27n759e13n+XA4AsJSq7tL6zitgWtX3nw8fVEyqCgxgXcpIUZaaTUIiI7A8DgEjgPIYWId683E4YDGlErORGkcS855KCUQjN7CDEMppZSSUyImTiHqwcgUvVkipsTc7VG7X3dAykShfOUAoFExucUtUAmGXKDl0BFPcFNLOTElN42GgoMtgt7bcTfH5OBtrofDsdbatKnoxZPLRPlwOL598/Yf/cN/WIbpx7/7O0T41auvbq6vSyl5yKdl3l5d7nbbcRyJszqqOgA+eXr1wx/84Oe/+NNaGzOKyel0bCKJKRd2d06cHDBhoOUIECxFQjxDxEzMIyEmMUnM4bBLyGoWHadYtHEQRGdiVu1jg1h1jqyuak3EzQiIAZGjzAxMz9wtLbWGnOE0TilxGQapcjyezBcHb3XhROMU72CaOaFWaYqOjiTaRMURKHXOKTEFcrSOJk0bmMvpuMTceBg2733wwXd+/buA6XiaRfTi6kJFVawMw/sfvL/506225XQ8Oti0GZfTvD88bHfbYRhyzp9/9rmocOJnT559+PGHrdb93X1rzUTzkAOpTJmROOcc5QljSsxlyBHvcymAlLggOXb+XofvRRQdzGOHHhG7OlvoS1MnK9CZzLnG6A7SI3ThiT69DZMc6Ahq5JnAcHCd8vZQ36fVj0Sj82gtPr1eRHgn/p/T1jphfkwefV7n64whvr7TZ4mJOFgciJhLTpSGYRSV+9vb6tJapZSQTNXnu5PJQgg55+V0uL+92x8P6MAU8C4PQz4c6vE0g3urx93FllP+5vXr6+u3X3zxhWg9Hk6vX78ex/H2/vaf/ZN/sdlspml67733njy5GkqetuPtzV0pedpOr778WpqVcbi6vLq5uVmWmZmWeno4PNxc397fP0zbKQ+54jLPi6oC4NxaGdCPCyGWocTvHucamrUmx8OxLgs45lJUZJ4XxKoitS7DMOacA4N2tygG9XgE82VZzF2qcOIyJJX29u31l198tdtcDMOwHMlLinJAqpZcTEUD2gQeBy7DsCyLm9093J1O87SZttsxJX7Y31epV5dXmdIwFFmLsmVeTqfjNI3MVFsjpPl4Op1Oy1KDGvTy45dN9csvv/z69Tcv3ns+lMHd4jqIyOk0b7YbBHvx4vnFxX/049/7cezF6TqzcnczD9u+LnOp3ZMkCAudhdmXhiLOEsf4jmJkFjg+RY0PgA5OtNYU65ygs3mIENYPqCMM0Ol51AdfABR+vHEd4LxE0LEmiLVH62yODlEBiApE90ze3VVVwjWPGOussWSKCNpaCP202ppXQjADVYl2vWmrbVGRWtvhNB8Ox+20iZ3S3cVu/3D46b/76S8//ZKY3r69lqa//r3vv/f++ze3tz//+af74+HiameAQGmz3Vw+uUJK7rYskpjNPKX00ScfVWmvXn05n47meppP6K7qRECEFCpQKRORiDC2cGO1VeQ3cVplBE/qQCmWCti9oxShLpVSzjmXMiTmlHIw1MyMsQ9agjjetcecCCnHsgOTh7gQQJqPc0APzFzKmFMyUZG2tGpq0mqivJmm7XZbcm5VWjuJqRoQcz9JAFS6vDV2xpGllErKM+K8zKDOCfeHowM/e37xne/+2pMnT9pSRYU4NTHTejyelqpPnz3bbXb3D+3r16++dfut9957//Nf/vJ0mkVkHMaL3cXlk4u7u1tAL0H5J2JGkVhjsXk+HQ7H1mosicWBSpQyJ0OrS33YPwAiWE6Zy/onF84p5Vw45ZBqSpyIolygnBIxduEKBApqxWM1fl7pwpWuA+Chcd4JRCtLyPvZB7R3QZ4eudEel8MeHzCIQ4aKZ5LGY4aBd1PJmoDoPCzo6cchQFZ3N9EGhihhgk1ItVUL8lxty1xbW0opxGwqYpozL6fT4bDf7/fXb9/e3z9El/HmzZvdbjdtxt12k3MOdt1y2g9jEbV5Pu0f9mG6tdlsX758udlsROXh7v7zz3+ZSz7Np1eviqvmnFPJz58/O54O//7f/embtzcps5s3EWZqtd3c3MzzctqfDO3q2dM2izsMeWhNtNU8ZLXWpEmT+qa2WlWttTovzdRiFJNLHsZh8GFZYj+sLrXWWo/HYwBngTgEahn4moHnnBF5s91mzqp6d//wT//JP3369OmHL1/O86yaUmJAB/Kc+WHf6rw8f/GklAEdc8lNpLU2n06cSKy+fXskgpzT3d3tMi+73W7abvb3D6fDKT9N5nZ7fVe3lRI30fk0v337RkUDlhzGok0utrsPPvjgyy+/fPPNNy8//BBrI+ZlXkIO//D1NymnzTgBQniyerEo1ruEf+czgIft8/mgrifHO5jTT9Gjyj+u8M969vA8WfKVVe09xPh5yupgqiqxogHYRcj7x7aSoZHCjhVW6n8AzP3gnjlFvhKjmdDdKHWemwVU2ywEMGtdlqUGG9XdpPW92RggiEhoQvSFHTcgAHd0nFurSxtS2Ww2RDQM5etXX79581Zd9w/743H+0f/ot77z7V9LzK9effX6zTeiNoyju28uNtuLyzKOwfJuIgB+WmC724xDfvbe8+O83x/2rSlzArc6z3WhcSw5MTNH5S6txoI2MqRMnJhw4ERuOi9NVMKqGgCC2znPs5kiYy7DWMZxnMowEGGhwc1aa6qaU04lBbCJBCklBE6JSympJO5M+b52l2KQklIuQxnLlIjAPKdsaobWhMtQNtM05Jxy7BakiguY95HDeTSJQH3lPkATyCkh0jzPBs0B5+OSh+HJ1eXzZy/cYK5LbTKMVFuTJstSc+HtZjttxuu38vnbL548ffbhy5fgvszzPJ/GYWSm7W57e3sjrc2nU5OKKQNgAOtRViRmSrTJ2bvME6J75gwIp9MpKhNVRERVnefTw/6+74OoOgQETwiAHHVF4JqASLHZty4PMGfOwYTlxNG2dgIDJ+Yo3TvHOaDU2CU7R+d+kc4VO1iHAKNjeFyx9tWVKWYS5+p+fbbnduF8oRFXmjas3QPAulcQswuPnTJQtdBLAYDMqUnLKVdpKjKO4/Zyuru5/fyLzw8PD0R8dXV1cbG92F2O0zCUkYj6uY+sD/D2zbUjqMn9/V5UNpsdIJRSTP3m+m5///DNN9dPnz55eHgAABV5+vzpyxdPxfz1V28C63/z+puHh4erZ5fgcDqejofj5ZPLVJjzwDlQhURIKSWOoS2xuavKfDpJB9na8Tir6TiO4zBdXl2WnGut9/f3x+NxqTXmRmamJmYeFSwnZljf2sRDHocS5hmjqn796psvv/ryX/6rf3n6wW/82ne/c3G5c/C21CdPn1y/fRsO9a3Js2fPS8m1tfnuJKpA8HB4yDmXUo7HU6t1u92pPNLz3759KyrIfvtw8/pNG4Zhd3EpS91utqfTycFV5LhXqepuT55cgfm/+MlPfv6nn/3697778ccfSlM1p1DIqXA8HIkRkVLqAl++2p1at/IDc4W+k96HQ+dJ0pln0FGyHnydiBmxT6LiOK35I9ChcxGD6znvDAxA4t56cojzdBeYoAmZm1GId8WCjHUVnSidImSHulR4/rhZrXO3VIxL0untEGLGRDyOw7SZEMMXE2INlCFP0xgL9Q5elyoieUjTMDrA16+/cdFpHOJ+hUaAajudTqJ2cXHxnW9/dxjHeamff/7lcT6N40Bcyjg9ffZs3E5ANLcWgUFEpUm9lJLTbrt78uT59Te393cPGorPIuCeCkfpHj6QrdVmFZGGsUDJOWcskCVJk3mprbWYHTZpgChSw/2i5DyMnIfCKWFf8HJAjpWXlBgRWhOELjHNnJBDRGDF63JsxkgaSolik4lLzjkxuO+2m1JS4jSeCuW83WyYkAlyCEEQugMRlpylZIPA2Z0A0DF8VmttxoTuKfFcxQBTopcfvv/eBx+IWJV2qrWJOED45nHKp9PJzT/++OM337xuS/3Tn/3MVZjzw/6htbos8zgOT66uvnn1tXuYPfgyz8NY3EzNCak1QQJEb8tCzJzIzFQsmPil5Pd37yXOYNwjL50H2n15h5hUTcXEGriJiYrVGtKoqiZN2rL0bQa3LnYfTJ5gZCHFbDISAceGemQGcDhvvpwBVoQAogBiyoKPGzlrpe/nEgzPa2G9VehzZF+3y/yxgViHdasRa188i9F1F0YOspgB9AVLbcLMjpZT3t/fffFVffXlq+3l5te++91vffJJKaOa5pQDREg5AwEimLiaqLaymeb5tD8cOSVk3F3hzdu7zz//YpnrYX+IGyvaXn/9ptZqKjfXt199+VqbDlOmRK3JzcPNqy9ev729RndiSpTHedluLrab3bTZXVxeTNMUFleB3uScE3cbXjMJxfW2iIGP08ApMZGa12W5vb05HsMHJn5pMbUYTkIM6brBZBcJKEMxs2Gc3KGMA6D/4b/+V6/fvJ5l+e3ht188fybS3P2Lz7/45Re//OTjl9+8fs2EwzCI6d3NLTEfT/tPf/bpaT596zsfH/fHUsbddnea99+8Oe62OyLe7x+ub67fvnlb23J1dXV5eeWGblrGkksyt6AVeHVmakubNptvf+fbP/nJv/iDP/jyhz/44fsfvJdLkUVEBMldEdnPJOBeUwdZeHU3Ctp7p5DFLiJ4SEj1quIdAcNVRTicS8MunjpqdBbkXcEbXz0j4xxaVzfvLmChfRa70N019azUEaKK6zAaqeucryWLASATceLEiZIn5mkcOTOFdDIG8EBB0i1TGcuI4QxsamgUPbR1IzoIbXBVqd6ICTERpZSQsLWGiMXNzHIqrVpK6cMPP5ymjRtc39x+/fo1AjHnnPL7H7y/u7xCJDNfpE1jIiQxUdGHh/1mGhPn58+fH08nMXn15Zf7/R7cNPFkExHlnM2oVRFpUhsQAvk4jkzExGBo6MSYE0epyEhM5JRyUjczN6lNsxqrEnpTcAj4ogw5lxJ+Se6eUjLTlFLMdYJKFL0Uc1L1REh1mUUUAYZcSuKS8263E2uuQAmR0jQMJRdKlJgRXNUMnNwDOVnqIrX1eAQQYjWhvuLg02Za5AhmOZcP3v/w8uLq5ubG0BFZ1WZfkmpwi2pdVOTq4vLq8up0OBJSEx3H6fCwv7+9f/70uSvsttthHE7LqbZlWU7bYQNmQZ31REF9A0d3j7qbELgkAuKUVOtyrFbcbFXaIejcXUQDpoRIkDnnjEhDr3YAOQ0AXSqZVsoEn6es0bSuTTURxf1wfyear30Erh9HTdVhJEfAQABhXTDrbdVal3U3y/NouFdevtb1awJYw79Hy7+mlxhjY1C+o2ePVljdVHReTktdlmWutdW6rLmt3d5cby+2P/rtH3304ccpc50XM22tIgAndncwDLZAkzaMEy7Lz7/4rLVlmIaH+/397d18WuZlPp1Oy7y4AxJUkc1mSimnMjji9du7m+sbJCfmtsjcZgdcFiklTcN0eXH55MnVdrMdpmlzsdtuNjmF+3QGAGL27h6OhJSAHZGcaEqIUIYBiaQ2aS0oAZyTgSEjqaIgZAd36lx3CnooB0M8put9g9R3290nn3x8mk+//PSz0+HAieA3frgs8/Wb6z/6oz/68qsvXr/66nvf+x4Rbnc7IspjZk6ffvbNl199efP25svPv3z/w/defvDhZ599Vlt9uDs9eXp1eXVZZfnyi69u396WPDx//pwTqVQ1hUZuzsSYgIFSykDktXIu3//+94jw//Xf//c/+clPfv173/vBD7+/nbbuHl7cHf1bKT29ZiAiJMJYuersAO8g5GMf8G7V3+uIWJ5w75w37Xok0UGeC3BfbbD6ssXaQ/Rp+xrKkTD0MmI4EKu/FBIHgaVSSNf3doH6bQDmeNZMCGHjHmEcAGNRyzHoL/ZWanQ5cSl6akcgj6OCiM7IltXdYuBh5uhATmCuTXIZXP14PBJzSnnajldPnkTZdf3m7fF4dMPNZrO7uLi8fJJzQeQmyoktPByBAKy2VpsAQEr55csPGKkuy/FwAJdpHIZShqGM4+DqaPNJWrxsgd64uZhIayISm+oimocSK8E5JcLQ0zVOFJ7tYZFBjNo4l0KMbKnHA4iRjzeXlBMlpkRIaOpNRF0dPLmZiWmVBecTcmZi4qHk7KnVBjiM07Tb7TjllJI1HeeB9nsRMUubYRw302me3755a60uyzKUwfuSsps6AqqaSkNK733w3tWTq/v93e3dQxknj3vGxI2JOVz3XCGn8uzZs/u7B3Q4HU6baQKE+4eHWus0TUS02Wwe9vdBlFaqiQswMMSSGqz7vhbLVQCAYFwysbuTuxBjOCDiOquKRZN+ByJq00qy6X/pirXvWH7girWELZJHIQLdZaDH6LhacdZ73bRW9+slW7UZvc+K38Vy1sFuh4F+ZXLc7+p5IP0OpvtON4Dr44A/PmforCgnQgJCwl252PrOzZZa7+9uf/rTn13fXtsyL3X583/uf/zhxx+11pb5ZKoAmFNCPBd+LmJq4oC3N/dffvXl/X6v0o7LompOtJ+PDw97BNzuLq+eXH3w8v2ryydPrq62240DENHXX339Jz/96etvvj4dT6qu5pdPrzbj5tnTJ5eXlxeXu2mccirjVMo45pRjOE/d9AMBUU0NYs09bJmCcI4edVNOQ0I/xa/vMbkEAELq9gkAsagBhJS5M1uQgjBSUgF3UX3+4nmkiE8///Qf/3//P7fXb7/z699+c/3q819++ubt29evvkLEnNMzaYQ0bsrhcHzz5vqLLz4XbZ99dv/q9asvX3yVOO0fDtN28+Z6c3N9+8m3P7m6vPitH/32OE4XFzsTDTAw0HNbV8rNTatySqYyDMMPf/gbueR/8N/8g09/8bMf/OD7H370solkzkF+IyDimJDCCsh3LChOrq/0TYDOPqG+c7tWF2c5nmggOhq58oZW3H/F6PFMOIgCJDQ2o6QPHcs1kp9JyedFyzjYgRivOqari1k/xwH3u5s2DI9KiNkahL2Je1D+OrcpkD0Kn+y1DWkhnB+sGLRmrbU2UIHwEQXPmQE8MSFyrVKrnA6VkHa7i+1mG6pKr159Ze4XlxfPnr548uTZZrsl5I48I/edcAxA1ff7Uy65zQsyPX/x/PXrJ29fv0aXi91mt51K4pyIM+eMnOBg3prACnyJtLnO5o6E47hxc86cS865RFnAKS3z7AaqIg3VgRCkuTK7OzMlSto7e7TA2sQ4I3QdBDWRulQDRaTUf393RFCRurTEDm4GtiwzIATjPoYEqpvtsil3D2EeMo3jMBUifgPXrVVzC5v7ZVnAIfztl3kRkc12fPHivSZ1ORzrIuJOyI4IjDkVRIRVQYcTb7ebq8tLR7u8uBw3U21BZRPJkjlvhsnEtOlymi/HyR1yTgLqzYhRLa43YyymBSsAwA3MFNC1iTkQUxwcN0Ikj4CIjAEugSOy9Z1hY05I6G6CHqJMsFZb1HXmUK23H7RaScNK4gmprHVofO4BAAzPZZiDQ1cef6eOh7Wge4fjswb6bkTzmCvgP0gR/eKttWCQheDx5oE/inZZHBdX1cLFRb/8/IvleByGMeVkVV0UADmlXvlp9zcPeRNEOh4P+4cTAF1eXQ3DsNvtCMnMgu6dOAfGGCowidnciIgpXV5cPXv6/DQvD4eH0+kEhIlpGMZhSCWXcRihf2MIBgQmsMqTAbgDYwrdbTMPgT40Q0ZK3GmO3ulbBGRgfeSOQLGvzH1U0klb6xiSEIlYRVNmds4pP3/2nH+DOPPN3fWf/uJnS51VJJdSSrl++/aP//jfzvPy/Nmz7W672Y0Pd/tffvrZ9ZtrYjoeT3cPD199+XVsOHNOH7784MMPP37x7P0XL55vNhvVEAKipVUkdFU9r1k5qKsDmumy1NrqZtr+xg9/4z/+j//nP/kXP2mt1bbURWafzSxOS4Q/PAMr0MsUdHBYh1rvxNh3Y+1awfRRqZuHC0V/8HdO2Lm8WP/aK/7HvmJlNeM6R15Lkn4N3uk+Yvoe+BW8M//6lWf4SBXqvjUEZG4xqkYE5xD4ihVI6tmrC0p0Na5wynJESimDY9BkAEFUMLEhiMhpWfaHIzNtNpthHErJ5nJ9+8bdcuIyDZuLHfTNZ4RwuCIy0zCuUrV5rmIGbmw0DdPVxcWzZ09lWS4vN5tpjI3DnDg5ubtU5aQpMVMy09NxPtYjIedccs4IyCkwMAYGFS2Q+zoRoKkSoroTkoo1rFy7yqSquBsRxkuw9sqwzu899PITMyEwACRiTuyurUVl5L7qcpgKhBpGyhfby8uLw3EhV3727OnlkyeX82m/3z887BMnQgZCWZo7GDgxceEiZRjGoZRlqcsioorVun+HERgQMfb319x0mjaXlxcOMJQStdm65YvIWMYhlxwc50UaJmbvtriIEdY9cXokEIQVdLwciADOjN5rRocOoROETJajrZh7D54O5goKIecQxrzvHk3s+kpdG8sUAKT75PVhLAISgq92dBG7kM4DtNXD4J0rFUQMf5wUrxeul1e/cv0C7+lf1Ju/Xqat3As4s0FWBNY8AgMREDIQOEAGxAF/8MPvP3/vyd2bWwMbx3GZWy6ptcUdU2LrFrSAxGohWJjGcdxM2w/w5Trk7ub2IhKfIETuLsPoAMw5cY79uafPnz5PRU2XZUYiV/HuWwAqDuBEHE4snfIMZOct1jBh85BdAnAAQzBwNFAPgxhpoirnbY4VHrd418EBqUd9WoX/HnM1IwKVzI6eUnr67Nnv7jY3d9fgttttU0r3d3e/+MUvjseTqP30T356dXX1wcv3t7vtOA6U6eVHL9++uS5leH71QkTd4OLi8uWHL7/znW9vN9vNZkuEh+MB3NIwZEgErKIIsqbqqEVCw4dSSmoq2rDC7/749z768KOY5UiTGNEiU5eb6uoEj9G51/frNkAPBefGEtfJ7gqp9WNGPQo/9gw973bA0df94cfpk511qB6j9xmR6CaM3VX+PEwOo4p4d84ch8dfwVZeyfloxdVGD6kJ83DF7hdrNXUiYOtXZZ1ZALpjCCFwTyd9gOnIXMxxqXVeRNSmaeIUC154d3t3WubNdvPk2dPtbkeUjstcSklQ0J0JPfgUHg2HwLywCCFMU8klXV1dvnj2nNg224EMc07BxEEkd5IGIo2YSikAEOPGlFPJOefsnYoCAI4InMihL/ohI8YBZSKIzQmsTUSPIY4ZJEZEMtcQSE6UgMAQXBUAOHGKo5/XzToADA3heJ/UbD6diFBHpQRINA7DZjM2bQo4bqaLy4vt5e7u5o7Tm2HI3ecIpNYlVo2maZPK5uryChDqspgjMYs6QHNCAsa16Ig/xJQ5PX365Hg8aRMnclFtsizLxfbCkIdhSDmbaKjpqqpZcu/dR4yWuj4EQGfCGfRYH1MpIFi3mmEtQBDA1LuFBgBAcNT6uSVCA4yoRGdMp4Ot+E4n3G8LQucRuYOqAa6u6DHupU5W67jto5hPb7TPjcHjFT7H+ndZP3hOG+fk/h/gPggAYOiwvqP9Xj56egTfmokIsLpxpg8+eP+jj19mTIa4yNKa1NZaFWYWAQ91ATN0jAIMyChlxuTdJtziyrlDSsls3SGP8hxBm4pLlDDM3Onq4MSrUFestyBRCnXuDg0goLnC6rMTl6LDFWuECsgaEc0NLfK/9X9/x3bKV2IM9uTBEYMcuhOQefdTdPCQy1cX4rTZbnPmaTMSJyL81re/vd3t3rz5RtU2m81777947733UmJp7ZNPvs2ENzf3dallGADADabNNE7TGHY0sjjklHJKGQBFFLHjLevb70DoKiv2QkFm27eHabN9+vzpcjqpAzJ4REnVvvPbEUJYy/ZwK42Tecb5+zEzgzWn+3qiQqrEzHxdNFn3Fk3x3cdAjAIqeKTrkTtXIevx9fPpXSsuPz/JqLosLmIULfArRY0/UuZiSVXdwQFtbRpcTYPC48zxIOQhf9Rt6iGAWu8RAhG9pyjs5RwiELpjlW57Akw5Z84MbPvjXlWfPHu6vdwN4+RoACixzwmkZsFBcjcAcrXFFjJGV9U6Zt5uts+fPwdrZcwMOExJRNrSzD0lLiUjxFodOwAlDKHQcRrLMKiqhLVZFz1QNUVCRk6ZusRqLNEhqlqrTdQ4YY55b+bECRCGsUzj1DOJKjHXWgE8mcaWnBu4mJG5rZpnHR0QEWmtVkQcxw0TMSVTl9bqsoBDKWW72x0OR8CYriIg1NqGMcde4XYzffjJh4eHeV4WSgkoiai5U0muSKCYcAUZDRly4ufPng7jsL+/50RlTKLtdDooPCPGPJQylGM9SOz/repsviotAGAoVXUqdBw8xAAfIw6tEDysYbIPbOMwECB1gQVHQGIg7LXVebSGazfrZ6QFY+4aqwGhjQoBOkecRFrNVME06pX1/lhAV50Gcb4xSKHbBd05by3kEEJCfKVL++rjgYh9xo0AAQUARFCIym2V1eqFka9POxStwlJDwcG8WoOokREBMA8lUMWYvfUUgsRITYQJBRQRmCnEPgGdmR187RLIEE3Dd4nNFcIDHA0oyOqWE7tR8/htY5uQOhoB5h7mmGS9MOyhzlfbKw9dJTd1I2AkUjOM0gAQHCis49aur5eu1lECcCBfG6w+uuHopt2t5JGIVc01qlfXKgrw/vsfvP/+B8v8XU4pcRrHAdCBYDkt03bUqtPmAjz2b8ON1onCNoCJCRCbNHcIPeUmLaZS66a5ex8xARPVthAyEZp7a0trSMhheNmj7CoQu/7tfDx6XeLgwefpvxZ2sEvdYG0EIuSu7KG1i3RQ1djKBUBTfUwy7itd4p1qBWMBpucEh7DctIhYFQABAABJREFU66MGi85jbQHMQ5t81UbETm3o+9WPJzvedF8v0TqssH6Ru0Z7zAYAugJor4jcHAzCBGTtXwB60GMCJkcw89bU3CgxAE67DSd2h9NyDOq9AYpJXVrKiQncAbSLq2iHVgLLN3JNBOrUVFPhy6eX3hozEEFJZMwzQG0CiMNY4n0I1mLwEUIAg4hUQ07GzbTV5quTJTPnzKGgEWvUiMArlT2llEvJiYnZk3PmYRiGUqLTk9YGt8S01JpCn0BFc8njAClzZ7usp7C1ZiYOHopUSORqDCSAbWnLvADSNAybzbTMC0YD4ValyUnVTE2GaZtSvrv7uqkNzG2pFkROMUihz6q2OhonTJxSSmkYRDcbMx034+m4nJaqopQocS6pHPygqk20gItbMFtCnT9McNagt051iUCt6+3Fyezta5TpqxZPj49mjhhSowjoZGCrojMiABr24XEfRcI5k/QAej53Hqo858msE6wdeDw374OyCGTmBrp2snAe2ELfRj6jRv0TgRM5nCV7HYBWdd8Oj/Zr1u0N1jjRewS3bmbvZmqZGQk0DOY7o1wDgiCiDkmsjxKPbq45FzOljlRgRHwI27UAuBDAAhzouTIhrxDcSix0B4xwDECAkN1dzUIUj4DNDAgDwDDoWQLWYnIFDbBjj4GcuPpa/kfuN+jKOJHdo/cKECjeQIDztJ8MFRAJmBNDhFVmACNMBOzoagaOajYOU/zWIgqrdOZ8rOhhAQtMHIiGqpmhgydKasZEiGxuYGQmzCmaqnh/bBX9dgMxSSECYTGAcVWJQyKiiCui2EWxerXTbbTdQimcHgP1eRgEMRTuYC+cp7+rAQiEtwQgEaCbOtK77GPoWRTX3hTf6VL98QQinoe70HXo+kzgsVGI8wgY1Y135f71aa6y1fFd7/4esePQ7biDgMJA/VLHw/SBV3dJQqToBN3dQBEZENXcuqYkJs7jOKWUWxNkOs6nlEoqWV1FtakAQ4IMFsqp0az01yxSYA9F5irKCROltGEi11rRgRGHkphYDZhyTqLaVZdKKUSUSoqVZgeb53lZFjBXs2EowzCMY1pFqjGFUVgf1lsZJrPuDrTaliM4qGi1ypkSpZgkuxkRJ0er0kyUM4coEIRMHKFrcrdWm7qCg4iKChrllKbNlMqQUoojNk2by93Vvd+5e13aMrfTfDTr7wdSMoHrm5s8blIeW5OYuwKZG8YAnxiZmRDNlTMvyzIvS1ywnMaFbD5VhKSi4BCWZiK2VBkNIQxnFNzDrxNMjYhj2209L1GaIxGrekfuAaJe77OAcxVjAGhwnrKuMbNXMOhAHV3A3lng2qF2OGbdlwF3p/O3BT8JDDv1FN0t1BODIRfPwcN1HtZuo2NX/RtWwCeiYM8q56/slVFcivWWr4Cvn/9nlYnrjx43J6dsokjAyJAcHNRCpbb7RrlHIPcOnyFENy3WOlTmFoQa6HVfJChHpMBXIgjEs6KeUYMe0hEIXwvBuOqMDN6hVYLQsobo+iONr1hxj/5gwcRziFG+g2tnxDqQe0CC4Li+5eC0HowV9g89D0JkB4lJu7kBNlVwByYk5CYSemW1NiRcWgunSRFp0kKHBgCimeaUO2jlfb4MDk2Fkc0svB0AhJFdHSC4tZ1H72rr27hCOus0L4QHALq4G2Iwog0MO8LVW974UN0Bya1PRtaSZa0i+p8eUXuYDTV0gNDd8XOOgcep7jkMx6y384PWnxALAd5LFoc1O/a6C1cVk47z9HTSX/LYW+4Pv64YQP/9HdarBGaO0f/HMw8MDwNV8rjO57QWjCEMyABEAzfCaB1Uu79TrO8hpWVprXkTT6UwZ1NfanUkBceUswsaogn3iNB3Uh0NFZGhtfZw2j+93OUhJYZMsKiAq5slSjygOaQMpm7mdVnmZUZADhvFeDNFXbXOJzUnxM1m3G42m81UhgGA1JQAzL0trXuZuAOgxIIBLrnkeFVjJjeMwziOCGBqou6ACQFSSk4Us+k+0kRiJh6ICNsitVZAQAIRQebtbsvj0KrGEhm4j+P45ClUqcs8H4/71qQ1aa0G8QMAKdH+cLzgPC+zqKeUAUm1ISMRrotSGBMZRzqe5vlUVZQYI5Qc57mp5JI0WjbAJuKIYf3Ty8n4J4xVWiNESOciAYCg03HOrHpECM3+DnlqX0JZy/oOlGMnWXf4ZU0Ga4ju9fTKEY3WuA+XENFC+gE65hOwhiEg9BsVLEZcS2JENDiHcuh3A13PM+F+3XpcX39o/0Izoy66ci6u0Lyvg8H5vwC6Hx8I1DSaiejbYqswnqGuu9aBJUSiwgA7bHUGQQdAW+EoR6fVJERdw4fNVkgqfspaKfa86GsFbt23uncc5h6VQQz5Yd1xXWP42s6Bd1Q73guCDmH3Fy16gXUa7uvkJeiCtKYwMPeQy3YC7vkAydd2zcDNkAkDJWVOwbYUNTIHDDLXuhTl5g7SBKFLuiKhijExIYkqE3aGJrKYhH71WpGDu6OjgXYABM3b2hOoB59V1Ygy0ZodzY0UOkxia6Ue55PA+4SjR/9QH1m7yj5pjB41vikYVtjr8TjUaxryDup4P47vdATQ172C/BD/vi4kduQxkqETBhsuhsDoK+iHHsF9rXdWpk9kOlA3d9BIn6YeLiuuBkadXmFxdMAscVYTAFwzhIfZCzhIExWtWCfYgjmYq4qrIxBhcoMyjUjMKQGRuyGSNlFuSNiktlq4EK95ryNXxK6uYMy5qaSG7g7sRJQSC5OrASGnBEjqMAzZDUTNzZY6x/5HWDuYSZgeqyoxDWUYx+HiYjdO0zgMIlZbFWlmZt5VjCHgVtW4a6FW7A6BYwesT0hu4u6IkMBhyMkBSknM2KQBQE65ZB7KgIDMCoTSJDxkE0DJpUxja8Ypj1MJLahhyKUUE2uigDBtNvWuNRcwqk3uHu5Tyk0ktWYOFviqAQQNEYMGrEHMFIuVcHCEaMrMPCFEewEQzNE4ZOamMZ9w7cQedzBXcnckM4gDTVEDgCJmcCMnd++LYJ3IxYy8HrQ1agSV0lbRTUD0mKehm8WWnlkfqKo7AcRSduxWRrcQXS16b9L9nCKAzo2wn6v1cKg4l1WI2LX8HQIt6rXhGUeKx++YPkBQCx6pd2vlGFMvxD4tiBBAvjYNvV5ef3cmBkBG8u5zHLkKrTcctP4m66gR0YOAu+IA/ljxY98J7HcEwc9mBh0c6gBNNy+NRNK76cAhzZyQe/G4Pq71aeH6Weg7IISEgKqamNBQ0bRnJTQPImAXvI+/cqjhO4BFm4UIYKZMCXo/54+5030lBzgSUMjdEAC4RgJwAwM/I2A9SROEALhFnWMuTnE+0ZnQ1JGwiSBh8FkCvuyVyzrZBQAJ+XmkXmszmZlpbHm4O7jY2s6uhYh7AP2hkEvUyxFzJ6DoX3HN1t4pE10P2no9j+7YdRNhhY0Ae5rpbRh22hD2Q+fu5ATYkagVHMJ3MgQAgPkKeQZHC3phsPaI4HBWPdF+JDB6YlCtgdi6ODpxAFUhRyxRDqLUGu2oihAiITESWF9gA7fMZCboYaehdZmfv3g2lEBhEhgOZWi1nQ6nUgojQc5o7lUtixC5WyZGp+g9wjXEwJpUBDse2zwNl+89keVkZkQUnMOwgCzIwOxG2Vyl1bpIE3dKObu7WJPWTMXMEqWry4urq6uLi10pIzG6VWY8HdvSqiw1iiUidlNmTogiLQTvckoWA4/WuuCcOSBwTokIYDXw5JTcPaTccimJw6wHc86ZcxmHzFlNHSBRMtZpnC53F8fjbABuHnQOERmGsr3cHE/H0/FEZEh4uD/Mp/lqHGutQVdmAGRuTQgWT1mlcwsAQKquuAQYuLmraq24LMu0GQGciBIzuLsZumsTREzMDk4KGo0zworhA5F3s2t0U2HKzOyrJA4REiXoBGGkxNDVN2OgjZEYCGiNkISASBwRgYnA3dUo1rVDpRAQndxF3RApHr832YHD+tpCYweO4o6b+fnur5smKK7cbwj6+t+KbKzt9vpRzzfQkQXviQHPCNDKMTmXwu4rUBQTXgQnYpWASvrTCUzBsRPM1++K1HK2kIJOzO4JLKJHR4Ws32CnGMeuLJ6o9/uQD6APMSAKxpUPE/ngMV+BucW+RTAHA63CLsNn4Mkl4mcoX5h1L3LpUc2gFx4Aopq6jB7GW8AWNnjr++PAzDFiBo+RJcVnDIxjp8SMOID+zjuK3wsITbRzbdGxG6KAe5TwGHo4ASSGfbkBMLr3rYUIsqBg4RqeMBv2rRNEcEUzMG/kiEyGAOaMqCaxt07MfYvd+2zEYg7SMy6Q966rv520AqIOpgYYSCAyMjggsatikKxWbCfgIloPofZcgUCuruQUv+i5iXQwxMctDehVDro7xauHyL0UsfMUByz6QqdQ7qBI0kVEMpOSE3iMv80kJikqzc1LyvFaYUrzPBOAqiAMTBwW6qYOZk0kxi9Sl1YXHapqW2ZIe/Lmp/1pSoUBqgIjIQAjSm2MhJ7q6mXWC0FEAGgi4ALWljqGSmirc2LuNB5OzAmQgDgA4HEott3kxK02LsldaSZOaRjKxcVut9u998F707Qbx8HNJZRNm7iZSay7e8ocOSDnbjwwn+ZhyDkFQ4/CiA0Jc0oIiISJOCFxSpyHzEytWfQa0ziVUtwtCWdORJhLRqQwW+ZETWwYy7SZahM3aNFMhf5cSQCQcirjOAzjUAZRORz3026jakgJkLik4G+LNHBjJkICg26a7NCqIDoihaZ+SqnWOm5GIhyHrNpUmrSl1TRd7aQJIakamCI4QXgYAIITBTdRwRTVkBxIWxgAATIGwhwuickpxDvXyIadOBEQR0en1/AK5wViN+bo+w0R1CTCF3X9cjOvjw/rK/QdDKW1c4R143IFngi8A3yBp/T700FROPfb9m6xDedFnj6fPgdmgq4ejo8EQVinCLBSqwMvBHGl0NhBOqs9ngcq9o52b2SEwJtWnGttZ6BPM1YuXvQB1qlIyHbefYv7vaL/Z5bR+YF6IxJTiCjXe5PRowMRGxgAOXprS/I09KbPOSVblg7wR+eoFohjpEQmjg7UORbAwEDRgvCCANA7oVXzsgMfK4U3nhuuIJOveHpfTDHDzo0FcorT1Ger4dP9zgsWaS8hxzvEtB4zAHA+18nosEr1ACEqOmp0aY4c2Jyzr2IK+Ij04fpKauRzdSDQlfzTyePW3zQHZ8IwjwQ07WCdrrtUGKmO+sz5jLX1D6J9w97j9f7CzGidZHUEMB7BOpUD+3Y+uFnvHPtFXPmt7ozUtEUREEYgjKhu6s1MHV1F11IGHF2tRaYhpJyTgmZMOZGatVrdhMlDKAGBv5gP05Tq6Thzuru7HfIYXu6oTep8bC2X0b3raXdQGIGRFENakM1iFuluRuhtPj3cZ/roI0JvDsjdQiGlsOIicxQzQGCkaRgyJ5sUiFQ1cUslM5EDjuO42+1KGZgpHEdaSWZSEqsw5BRIMRPlkojJDaT5ULK7EqGaBy/O3RPncTOOY1G1lFPKQ2ZOpRQEsFJyTtN2HKYBAcG55GLFojEEx5RJFYkoJ84pm4K7E7KZ1WWRJpkTKFRZ3H273bpBSnx8OKrKfJqDjSJmTpRyIiJ0WBwSM2JX+Kut1dZMxdGlSl1qrcv/j6o/bbIkSbPzwHfTxczudfeIyKWWbjSaZIM1GLI5gxEZ4Q+Z3z0yQhEKZAg00GA3qquyMiPC3e81M1V9l/mgN6owWd8ypXy5bqbLec95jhC2dowz01ynIsbo97c3NLtspZ2n0kO2dDCPECbCmD8nMUFgNyOEpRZzz1IC3Nx1VnqCzePKRItMyj7EfL8o3OJhoUN0QvqL5olIjh7mw42JEgkEgts8efrjMDuVGfy26s5DPyLY3EoevMZvF/1vyPWZQwhiDg8zeOgc+Je1FR4z73kk9ylVx+TcPgzu8bg1THu1Y8xLPT3eWAefCrsGODg8ejYQnRAN6XEnePwKMJ8i+PMFH77V0E9X11wQ4XGdmLcFNPR5/LSHo2+WCQbOUC7afzNO+bNkFfDNHDQ/kYfIM+8g7lOSeny0BOHhYcAY6AAQ6r2NJfuwEeaErH24+RizC3Qi0gMciB5mKniMg+e++3ivHWB6lR53kMdljeY1KyCQIP6b4Qj++QD67d9OZg7PLXfKJ385aWN4PDYbeFh35s5G8G3G7TPW9ZhdREQYGuj8YKZgbnMhhRjWOZic5xYNf64KIYiIYRoRs3UFcVJ7HeHhZJvXM3fjP18nHdwd+LGvIQKiP3b4x7HDpmERIJC/7dWPD+rxp4qI+cgBfDskzF/ZAxEfCj6Auz0QcIhzbE7TNaBBADPH9Ljuz29NPoek6NHHcDdnUh0PYhoEoiOxg0FA4oQAFgYRROAd0SAtCcBdB7gRQW9nP4VTIghhWtZy3O86zMx++P578I0AiOj16ysC1HWbpQLzCMIkQgTC85Y/Ro8AIp7RBAj3off9hoQcMsu/eOZiZlf5zGcgImJKkjO7AVAgkrl7RMpZUmLGGfNDAmaY4LCSEkYwUc4yuj4m4Ejf1gefhx0zpwAk4pwmYRVyLmtel3UMm0swlZxzyeDAwsycksxEjiRJiadPVM0mlZRIzDCVhAj7vuuwMLvv++39HRDDomvb225tJMpgsK3r+/t7eHz9+lVSSrlKJL95ylkkEWF4gEXKiYlU9TiO4zzDvbU2Wn99exXm/f4uwvjXf/3h41OEhXfXcbmunz58KEV6iwm9G2Oo2d7uplpqFZGcS05ZzVo7EBHBmIu2UXJOicpSwgOIACed4nG0CcBvRssH1Rnm9XMeEWlKBPQ4rnx77Nsx60pUKDExuAX8+bA2Bfxvjh2khxPlMbQI5PnSw0OxfhzJAeDb8PkhinwLBMyXZ1obHlrst1fvkQedI9yYejE9ztdEQEGPs/t0z84VRX2O1kCYay0eFjbXd0QIEkIgjnhUYP7ZCT4Xgzm6JHiIYI97zGNlR/jGVptTDZofswEGxsSYgbvNfPK34cVc550Y3S0AzOwxvfSYvdgABgCurmEOgUZhoWbv7+8vL89urm0QksYAgHaeiKCq5hYWqICIM2wwX2ZmcRPIIMzTp85Jpq3omz4+7YKTNBkY5AYUMaYoD0BCHqAxg0rfhB73wIkWx0B8RA0DEImZwumRPANDRJtWC/jLOvtwHny7uk3/wjfRHQKCEFtrIkLI4eBghEww6aYYPl32/vgJAMLNwqZQ9zhpfNtPJkMi4M9paR8jAICIwv+8wMO3HwxiPt////98u8HOjyTw2+r/kOfw29EFHq7/adaYLuT53EsSjKlEwfwKhDh3TnpkKx/+VojITJhI1cCdAnV0IkIAmPrIGBOHwCSAaDrO1hkJyBDwbH30JoAYMc5zTF4h+v5+60PNrLUmiXJN+30frX/+5Zec09l7H33VCxAgUUoS4TnnJHmC7SKmkDEIsI9zHKf1c3SVIshMRMg4gTRmxjhzHJREGDnChBkJmBmFZ/GRJGHmB25PRxhBRCKkWrKIRxiYDjO1v2yuZq4W6NpVx5hCNkrqQ8201JJyBpZKIqVWQiw155STZAf3cEQ0Nc6ZhVgE8PGknGcfqojKSa6lDB2hRsz7ed5vu6mToM9egq5FSmZBYWEK8DH6++1Y1rUuJiOLiLkHnNrVzJIwE2vX3tt+7Ec7w3yM8f711cMIubWjH8eP3316f/W3L5/XUv7Vb3779/+3//m7T98x4+uXr++3d4h4e33PpS5LHTqLGH0OTMz8dnuHgPM8fv8vP+WUEREJjv1QNQAkmTbL8P9WmwQikZwmdjhNFvbkyjz+89weANWMBc+jv7+911prrSmlMToCzY5Tn50Dj7DaXMYfUiYAuDnwN98kTVMbhDsC5lS+KSLfnvvprSeMcLNpIZvHzsf44Zs8AN+U4jn2eajjD/6Wh87/SoiIrbd2njo0SZoZJVVn5omRnopSEpkfkf3lyz78VO5GPDuOKCIe85L4S2/lDDpNmYuYGSnCWea5BmeAw+OxX841FwCE2cyWZSszhhYAiIxkqqpqPnrvOlR9dsppWAzXr59fP333afblElJrDQDP85wFTO4+ucQyGUOME5Q5azSWsnBinuAKeogGjxoIZgpmIRvq5gqOiAlIe7fwxMImDj78z368MNUAZySiR9/WtwkBAIDwfJ6m4IPwOI8Dy4No8tB8Hlabh682vklfs3NFVZnI3fowEUYMfBQ0fjNAPUCeiIwT1h/+cBAjfsMMfFMM6XFljQgHQiKGGYN/GAy/rebxEKO+nTgAvpnc5h31277wbTD1kNscAv8iEnoAhXvMghSchaYeNvix4QF8s5TN/80Jqs+e7Yfa5qbm53GqjlQTwGARQso5J2Fw632wpHXNta7mdtz3ddv+DLVdavr4UTKXumWSRCQvXz7/yz/94fPr5+9/9eN6We9v95/8j+/3GwDlXNS1326ttUev0Bhufr0EE81MgLCYh5sFBEW46XEcd+23217Skxs4PebmATFMRxhnKSwlZ+3dDYiAmVNKwESBJJRyJiRFUxumgOHMCMJOiDVHoIObBwu5+WzBNFUdCuCzj/zbnJEdQU3NPYmIcBaWp+crAaUkpZSlVklJzc52jDEAHb5Z7oAeFGO1MXrLNa+6AeBSNxJ5e30f2pGgtTbzdjml55fnuixtN212/3q73+6m2pscR081i6R4e/WJNJ21xUjae+vnsR8RzsSqw8aYoNVlqT9+93GrOSE/Xbenbfnhh++frpel5pwY/CKCvXc3W5b69HxtZ29na70jIjMPiG1bWPiqK5PUmpnkct2O+/n69qamzOxmZ2vHee773s+OiMSEHY3ppG+QKaCY/Xrgj/w1IiG5BzPNCxdXKQJMD+i8O1MWAkDwR9GFaniwMIjQ1BwidAQgEE+PSMy3FJEIjIVhDi7A3IOQMICBCDGYgRiJ1G30MXX0wGAmBHQE1YGEIhjmNOVKoMBQMzdycGEx97XyzoFYX15ecso6dBaMqGrvEw7l4UOYeus2xuxOKjUBQHi0PiK85iVsOmFMh07c+1z+ISULmCz+uZqaayopSZ5g7bkoASExMssYw9z62YbqcZw2FAHb0XJNx20HjPux399vbTQ3SykBYWut9SE5vb++/fM//5f9dni4MB/HORn/kyKq6sw4hjJhLmU+3iTIyKnkWsocziAhc0pp1u6lMXTbtrpUV9cx5hr94cPL65eviXk2N+WU1TQCkZCFItBcj+NABGKUlMJ9DJ0rmpnx3BLicfOZd0mbsCkI/saLiW9kC/pmXsQAmJYDYgA87oeHtTZKSXOvn7V/sxeQEIkk55RLliwYaN26jvkFEGm2DOGf+SYI85Vh4vW6ubkOVR3znvTnpXwi/h87wJ/n2n8ZBsyr8+MuGu76qMIORGRhCBhjIIAwPz0/PT09Xa4XljTrCM196EBAlklydEQkejDIAXgCpSWJsJSciUVVZ6E8ED+4ih7HfuzHzkzrsojIUK0pr2tFotv9NkXv8FiW9fK8ISXmhMy/+s33P//yy/39nlKp6zpssPCHDy/7fR/nERhD+1B9v9+OYx+96+g2ceiAJAwQYwwdJkz9PMZ53N/f//SHP316viKAqbnpsA4Y2oep50hpnQVW07Ll7qBOhJhrnmWl4I5h4eqmCCgk4YA4nxkKdxEWFpRvs7qULKmrhRuWeaxklqwIsxEjJQSHLCxMCQGQyCEsgiJyygFhYQDQW8dCRAgO5j5zE+bWej97ey41SfJvG/XovZ1dEnlEYqm1llJZPBXhLO627/vt/WhqJGjmnMQeG8AkdLMPgwhArzlbuLtGRMny6eXj3/2bv/sf/83fXS7Ll89ffOiHl6frZfOh+/tNkzCiEDczRtTRzjvPBFwtaSYs9vuRcsq15kUQ4LjfwW0p5bJdPn78gAAObkPP1h/7Rh+l5JxLSimxSOJH9AqIReZ+P0Y3UwCYEbYxxv1224/j6enpetmEZT+OidXIKRHTPDi7+jR7MQvJozcYmfrQKTC31lWHGUJgWUopNecyWemqaqpuBog5pan111okFzN7f7+3ceZcWuuX62VZFndvvZtayhJqpaZS67IsQNRH//rl9Tjur1/fP376ICzHcXz8+PHjp088FwbAY997b62N2aDExO/vN9VRSqlLZWIgMPPzOAGgtZ5znsWnOZehqn3MgMYU8dvRpg10XdaU0/Xpsi611EVEUipTh/DA2e+RJJ29m6oIqeo//uM/fv3ymZD2+/2//Jf/8oeffn+007o6REry/HxNJQPCT3/8GRFiXZFpnJ0ll1IQUU1FuJTSe0MIYk4hjxH4dPUDAgRBsBA4ICILEXEEXi7btm4fPnz8u7/7H379V7/5l3/6/edfPl+vl9/85jdLLf/h//g/fv7pj//Df/93xKTD3D0AmEjNxhjm4+yNhUnoUcGm2ntHwCTiHkOH6aOT57F4YjwKFN3xW3sQAKSUEHHqAISziU5oRjot9n0/zqPWUkpd1oqIc/+IADefI0dm+fj9JyYaTf9bOUdVe29j9ACYhRAQUGpZlpKXamb3t/t+3m/vNyRMKeeckOjhvJpjFHyk0meYcd51Z9nQzH9MoIKqTuR9SgkAVMd+3wF827aXl5enp6dSa2+ttdMjzuOMiOv1sq7bfr+rWq3l+rS5+f127+N0dyJcynq5bh5xHKfqMOvgRIjtaLfb+yxxSiwnIhNHeGvty+efn58+pCJpq6ouIsuyuNqxn+vl8s//+Z/+/f/2v99v+9vbTYe/fPr48vHD9XLdliXn9Pvf/9e3+3tvzfpo5+mq53G8ffn69Pzy9HRNknIpMBPsbghx3o923I/9/TjuaaLFx6Gq+35MyjWznEdLnBNyYhbhAFBVdSNi4RWY3M2G9rMP7aYuwo7k4cT0uGf2Me9upZRvyevH/GoeJISFRQxJAGtOj4yiuhBIzjklQcZvJw5ABJGUUznOFuEsmjlb2OijH2MOK4aaDQ2MEQOBgNHB5zXcWhCi5MKJAeF+vyeT3/72t8d+/O///t+/3+4x9Lx3i4gDvhnvpoyghA/BxEzt1GUpv/mb3/7ud//jr3/968u2fvnl85/++C/tPJjx6xf/4ftPGDbaedk+sODoTYiSzASQ1lKEUu+993a/33obOfPL05WZEhGYhgO4XpYLEjFzmHcdOsYY634/zGxZKiDlnGwoEaaUZ0AOAB9G8iXNsZ0IA6KOXkQE6bIuFJ6TIJQxxtRaJcn8fE2t9zRJVeauakxUliJMM8Wu3ftoRHS5bpKSm1GMLBUikAAFAkmHCgkGppRylpTFgzCW+x5jjOfrmpMIRKo5EU46mwFctrrMFl/AnInAv4TKR/r1r39AACBIkqb1SJgzM+PiNeuYyNxw90SUikwZDBDVTIdetg/MfH+/u3k7W4R5bxCGMat+SCQD0oDoo42uvR3MdHv/8vx8Xdctpbxt17osKWViIRZAHF1LJhdBCGb+/rtnH8f1cr3fS2/vXz7/FC5BVJa6bksqBYl66x8/PN+PeztQVb//4aOZ397vEbYtSyp5dulNlQMFk+Rccu8NAIV5XddcUkp5qny55LAg4k+fPj1dr3/9r/7qt7/59eV6SX/zr56frrWWDx9eBOm7jx9++dMfAWypVTaZd1l3t+BOcTRbl8qJ3G0AmHURJExuLkKEmIWGqg4NiCRpZlHwv9HZ57wnS8olJ0k6RnzrlROicBg6EKhm6j333q9rmX+g2UY3bxUIwMRJUs2EyEspRI9ext76GLgu+X6/ne1MAm7h6gyeBEM7E5VCrQcTEIHMlCqGhwvh7BJPWUQY5vhq1rQY61AznMK9JCHAKNL7GH0gesqpVimJ3HVbt3XNidHGudRM5G+vb+3cmbGdiGFMXNeSSwo305ETmoKZ5VyzIDMUyQRwv5trcKYsomfzoTq6JKGAzFSXMrpqbyVnALtsz8u6hAcnVrXWzpR9P+5j7DN7RSyq5+//6Z/7eX749Onl04d/+z//23/13/3NH37/+3/6p39qZz/Ps53N1cdx/vzHn5ZlmQjPCcFkIjdDcFe9XrbLZQXwOYezof1sajpar2sV5nYc5FFfntzBLEZvfWgquZRkbimlfvahNobOksRHzhtpxjti7vMOpoAALAxoZuphMfUBBk7Igdo13L95UGXfd0kl11o8IiwivPehZABg5jYMKProx9laO8MjlceQwB3crJ3N1JFlzvaGDbMp6CcWEeLtuhztfH9/f/nw/L/+r//Pv/3bv/2P//Cf/uN//Ic//fzzORoRaXio84xmwGNmVUv9+PHlr//qr37zm99cLhsRnef9p59+f7+9j9FDrZS8bUt4mGpdSh+NFRFhtuTcvt4AYNvWZVkjnBARo9S0rnVZsqsttdyzvL++//TTbmZP12u5XCXnC6/hGOHm4/3ttu9HH6OZEWAuRYgiwNUASIgoMSFOGN8c0bTWwhzDJUsteamree592NReiCAgp5wSTwXAVI/z7K2xSMo5U3IIpEgEqOoYNaecyxiqvQ9whFmbE8xUM5ckwlKXkiRLykM1NGCxhiAAMRQQKy9lEVPrvV2etpzSknPJpXc1DEX+/rtP7paZdYx1WUtOgOiTJkiEwI5AEefe2ziJ+flpmzqjDQVEDK85lVoBgC/beezvX/f9tqcknJMQl1KE8xSQl5LdVCHavhNR26Gfuwcy8+X69PHjp8vTy7pspVaHMPPoNvmyHkHgx9ud3JatPF3XdcnCQUS5lkDU1gGgHScJwdQZACYb5MPLc75L730cZ6nZALTHnNoty4IIZdtcAxCFiZEYsOQ8ybhP12tZ6rZtz8/PRXJr/YMk3tjaiLDLtgkhmIH5cbt5H6UURnKPobof9/f3m4Etl8WcZmZNx2hnczcm7h0m2vrPEqjTyDmnkpe1TLfSTJjRg5Hi535v53mezc2YOae8LAszAdBSUj93Ag8fiXIqMl8oYprmuiR8uWwsgkQQKEzCAgQlpf0OYwyMsD5gLuuJmSLMKDFhCFNOIozm5q4IMucLOoapGSFhZF5E2NAfs7eh82LxqJsWKbWmlHISCENEhHC1xCipLCUnZtc+k6+qA92WkiACzF0HSgCSDdWhiOBqOsZofamZE7raMcboA8glcbj31ogjV4HwlKWUIoXM1d22bSOkdduuT5twUveYlbEABMEAtZSPLy/327+Y9bfXrxH0j/94+/nLn57+9PLjj7/68OHDf/+3/93/5Xe/+/zl6x9+/4cvX355f3/vrfU2xnn2E3ZwQko5IYKp1iovz0//97//+7/9m79BjD5Os3Ec52zSDoLjPJZlCYigCAxVn3nAMbT1zkySs+tcVq2Poa19a4ANlgTTp4gEED5sBCCEDrBQDwACIJypYDUXZO9mZiyMwWOoqUp8AwF6TAaoIVGAn8dp7gjQu44+3m/v4VHLwiKMODtL29kajLxWDbVZMAvRx5CUAUhH7O/H/f39fnsHDFdPSf7d/+N/+fu//59e397baK33t7e38Mgpe3dkzpLXdX3+sJVaas5fvnz55Zdf3t6+jK77fnezdavrdrleLx8/vXhYOxshEGJIAkfw0D50qFp3G/fbPZdcl+XpciWhdVkmI9TAEHH0vu9HSXlZyrRYIkESQmRT1sXnwBADRZgTTOp3yVmHTVsjiRA/0pEOPo1xSGTDLYWqTif4ttZcMsQ8rEESgQgmVAw3IQzkad42M51eRBZKzIlTySWJNGw61GMQYMqplDyHrykJC5FQKamUjA5jHGFW1iU82nkQYU5ZhHPKJVcADEVMNE8MACQMDmjDLpdLLRUizNVUbURweJiO0VrX0SFcmPt59ONEolST+2xVd+gQ5uAxemOEnAUAwswC/WyRPJfy9nobD0J6mRYjgBjaz3OY+dv7bT/O9evrum4iiWacM7wuBQPM+vvb63JlJj+Pe6A+PdXWpreIIsDYe1ecDdC9E3lizsJqqqNFGEEgY5iFGYUhBqMTOAsTYiAwUcmMBBAWPupar0+Xy3XbLldCctVckgi281Adw1pJ6bjfmSAXWbcCob2ZMC7XtZ/q3o79dhzvzcbQlkomxll4MAVPpkcP5bcpL5aS6lKWWnPK3/34qbUxeu+tt9HDPHMKDNOuo89uLAhwH6NDC5v50t6O9VLd+nk48Sqp1rWo2n6/j6HPz0/q5ww0eABRzNC26jDvEcqCRDH6UB3MbB5DkUE4iZkCuCQSR2JiBggPUwgjiul8AXIgMlUzdRseFmHuNm05wQjhOU3Bc9bzBgiuZSs5EQsR3W/vQ1WE44EqNQQkglJzkpRKCg9Vm+45ImTGutS1LszSeueKHmnWyo8xhkPNuUiatsZJHc8l11xySut1Symr+Rh9tqKVUgnYI/72X//r7/9fP/zDf/4//+M//MN//E//eLbRe/vll/bly5fX17df/fD9y4fn77/74dOnj7/58UdE1KFvX99Y6LjfzdSGEmPOeal1qcvzy9O/+uvf/vjjD+H2+vXLcdst9PZ+dx/rthCW+/0OKxCzSJ5+MSQCJAc/z0Zv+7pB5CBEc+t99KMBYgwHApEKwESMZK0dMVSyhBsguJsHYCJgBUB3IMI8o3wBZhTGqhroYubtaOauo6saETFTH6P3NpvxEGEWjHUd5pFTTkkIyS3a6Ig4Du9D9/OEhzaZAKC1dr/f8Rbj6D7G7fV1f7sPtRmArsvy/HR1wpfn669++NWHDx/70fuY9ZjOCabhqfX98+c/7fsdGczHui5PT9fn6/W77z+u2xoGrZ/EyMJzDNFaG10DvLfRzpZzKbmUlAyNiKd7ZDZVbetKP35/e7+lkpDQQ1vXYVRzYRKAqLmmDzyG9t7P/WjnUAkkDPRhoccgod4o59naM5HZiog5p/u+xx7t7BFRS1nXRUSIyMP62cKcGX1aWPow04SEBEkECYnQ1U+cri+dfgGM2NY118wsAODu+32XKQIjwkRJw2RG7UO7mSKSqh77kV/Ssi2EvC7rnyOdMxKGEWPXaW3MIo/XWkc7T4SA/JAgUiZAPs/x+Zf342glS13LbVdVp8RIlFLGGQ5X27Y1sdz3vZ2nBzLLAbt56BhILCnN5tdHoC4oLNyDCMzs3E+m1Emnd8xMicDGMB2jN8KImNPBLonP5mAuZSHkyWgEhn4Md2dBBHA3N2ttIAELuU1z7wMSNa2XjFO5RuZJlHBAMFVAT5lnbcmyFuFcljRbkVmkLvXTp2cI8DYAvLXzRrQudYx2Hqhdz+MM9xlNx4iUOCDOc4w+x55EM8UQPuecwrwuy/V6XbdlW5ZtWdZc5696f78d5wHuEJGZ07bGtk6NeV721aM3DXd39TAmDHAiWtZyfbq21sdox3nOff247xPMMVpzrRMdExHCUhcYvaiOublEoD4oxK6qEJAkIQAi0qNs0hHx2wyWwaJr66NPoR8AmZCJgEVSyiU9emIBpjIV7kz89HSpJR9HHzaOY3d3oppSTknUpLXTzFLK27YKp9ZO95hn/GWtknjbtpxryuJuATSGIouBhXuSRI+wAjLOnk9ellJyEZFaKgSeo7XjwGASEcGlVgdKKX98+e7HH3/9u3/7u3/+r7//D//pP/2H//wPn3/+fB7nfv/yn/7x9bu3Tyml71NOZS01m+unDx9+9ZsfEsu6rBjxUMPm2Ds0C7d2jNHu99vt/X301lonoXVbtuumbrUsdV2ZxS0mICjlfD/24XpqExMYkFKy8ONsvR0jRmMRIUdKqYqU8KkROBpOf7a6zqQKp+wBrTUbVlkSSxImQnd2d2QQJAyM3vpx3wO9Lour9TZgVgQgIgazpFTMXMcAD1UlxAgKxJRzCjTX8zgRKWURSdZt2vlrTilJ1tR7P0yJqNScSnIb9/t76+04jlBlAibBQAh/3MQLDaLWj96PnLiWha9cS74+XS/rmlO2bqP329u91n08P9VatOtPP/1x6MhFUsqqo+S0brWW2rFPC4IOZSFJKeecSylL7b0BROsnkyDxMD3baWY557osZVlaOwC9jQ4AxPjl9TXl3M6Dk+iDoT3FKKdJ9iICInWfTnVk4pRm2VTOJRx0dO2mvY+uFkEMM+ciRLXmKfq+vUY7j2mtM9XW+npZn64XAGrt7KqJuSxlqRUAem/nicR8nue+3wExwkvJLFtd6nfffT9NNXUtM7XvZoggwgDBPJc/ZpxxGRutmQ2IIKZcEjHaCaON8zyPc7fwPrx/Pc2MRWAgM4/eCTCxhIdIIsaUS+ujj+bnkVL28Pttz7nmkokeCSG1MYOa4U4kl8tlWbbL9ULMo6ublZJFQBA62LQIShLvuq3r9z9+IqZ2nIwowmCAGDaGjQHkNizMXDwcmMjATGeNCcyupemQoW+UN4hpQvWcEyfORSCinb2WZYzRWy+XSkjullJKhREjpdLPE/nROnKeB1G4m5pChPlACiRk4FTEXfvZXQfGnN/SrPpDBGFOqeac1mVd17WUAgDa3XwgYCaxUnprDs7MtRSahIpwHaP3BggcQIiqY1mrDePCSbiWfLlctmWtpaqN2/u9tb5eNgBo5zGGMrO5hcdSlzksQI9S8tlY7XHsc/cYnZwmG535AbMjxKBgZgGYgDNAcNfeRuvNTImYhQkZJGHOOWcP3/edTiTiJJKTTBsxM88MRG+Hqj4Mr26Scy4y+qnatTe6rIDWR2PmnAQ8qJbRBzMFWB92u99VxxiDmBFJWJZtMZPRhqo6OAblnObYHJGGzmRZ1LKG20xUTiTlOF1jEMbT9fq73/2b3/z2N//u3/39P//T7/8//9v/+09//NN93798+fL2/vrb3/6mrqnkjJBTyjoGB4zRhJGZZ/7qMZQ2cbf3t/eznWY6xohv+ZmS8tPT9eX5ed1W62YBOaUIJJYxdHRFHFodQgPp7ON+7O1+55NTYgCQ2ykp12WdRyohVAuzCAj1eDi8HSfLZKiCGiYQZlfbz7PWDISSc57uqtbPPg8vETqnCYAcNsdiKSd3H6NHxH7s4QFBXXW7Xq5JAIB4hg4hicxLnpkipcJ5+tWHdnQAdY3BwkSz1LiP0RA8wogo3NRGII9mEcEIT5fr08u1LoupokMtBSKO/Tj3M8DP/ei9EVOA97Pd7zsJXstlu7C7p5TWbdku64UuQ0dEaFN3L2tlTinnvOT77aZqiH3bkod//fL1OA93f356zmUpKYlISvnsp6ohw3n217e3fb/33mc7bl3Wmceta6ll0aFdu4hkyYg4O8vCw2z26gzVMVoPs+mf2PfTPNal1iWTJASWJMIcEb01+UYpsDFcfRpQl2V5fn5i4ZRSRLR2mg4iNB+9NXcf67quy7IupaxZUh/92HdirHVhERsqwkupETB9q7M+1NTO1h4RR3fVwYJmft/vY3QR3q7beRz97LNhLtAxaI4lej+Z2M0AOJdal3W7rLHD/r731gNjWRczV1NyREQ1V7cILzmXjNfnl6frc0o1c80l15csiUvNo3ei+NNPf/jpj78vaVYsaRLelq1fxpydqmnAvLn0R55tWACEPTyKk/OBjB7B8mC2EtKjM9oDMRAxiSRJNZf1ujEKC22X5dOn767Xp/Wy5pTdwUFFlpwLAPbRyjTfCU9UtXuM0R9fnzgloVklpgYRhMTEOaeSy7ouSSSnUktJOSM+6HI6+nEcr6rzZwZAd+tDhRkZw81gWoOQmJDQbHgYERJiFnFwRiTkZaml5lwqEQ4b+3601m14rhIIvff77Z5SWtZFEhMxYBAzi+SUzSwebDhX1TCYQhmKfAvHoDDCo0MZASBsFjU/UgFIKEmEOKXETCnl1s6ZWpTMRJRznhwnRDJXM9X5OxK08zwBrpdNaJm8r6G99xYONjSXFD6pSSTC5oYGY4ze+xjdIxISPcqwxEGE5WzNzYTTsq4iwo/pOCLjlraAMDXTEQ4kBEG55ohA4DADwOtlW2q6btt33z1//fLl//zn//pf/+UnIQzzeZRBQsIQIRYsReDRb2OEEWBnO2+tnccxxkgp1VoI0d2TSE45AmrO27qKlLA22eWTETnxUx4xxpgD33ae7TxbHyyuPtrZdThLWtZtXevT05Pkov6NyYIc6AFEIkKs4BHA6imlJAKuo3tEgIUsS3X3McUH92M/AWOM8YgUIdRlWerCxCQMiqP3draZAh+997OPdQRSTokZddjjGYkwHb1hzgnRl1q2dU25aNjovfcebuiuY9xv78d55FIZcToth1nYg6YsIktdkqRhIJmypHA/j+M8DwAoealLSZJM3T2enq+BQROzl9Oy5lQKMiZJwqlru73dADCISwVGzlLoivfbfejYj8PGOI929tPMmGnZNlWTJJgoUWE1ZHz59OH99q6mn7981THdokvOmYjrWWveRx+GsCwVF0wlq1o7GwDsx/08zqkz9LMJkySKgNGbmd99ED6llAmRiUrJ17hCgCQ29ZoTWIzREkuRtGxLSSkQkdDNS8pMLCxCJEKvr/fXL1+YSPhZqb29f1VXBDyPEwlzJHdnImFi5Ajuo4+h7+/vYwxzJ3msO+pmaq232/sNIlJKatZ7N/dcEjMhfiPZj0dhxXTJBoKkXGp5TpJE3Px2uy+1qNm8fZhZHzozv+uyPT+//PW//teXy5M7pJxzykBBiETYvQUGEtxu96gF3GbkGBBTTgDQzz5DLqO1mYWzMWeMEBHID/InzoSOKTBNWF18gxxHIBEmYUmcc0o55ZSn3DGGElMuWVhSEvfQoff3+/X56X6/jaFzjat1cbOURR4loNP5RIQcQYSUCSBnRHDwnEspZVuWKZ7MHvAx9PZ2G6OZGUAQgJpOkp6HA0Kf5O6ZSKfZiRVj9GmSp9kHAJRTMouU0raugjITuUtZf/juh89fv0SEqde8+BJffvnczhYROZWXl5fZA0GAIpxSmoVAk8sSs7WG/1KDijOdMlsiZmySiIlzSonT6IOYS8mzTV5Eaq4tFyGRlFJOqiqSSs5E06AWRCSJEbO67ftOCO5LuM0tedIBIiDnJChDtXclNUIiNQoMhyRCiIScsgBhSgmR0AGYcxKnVEq5XC74LZvOLA+IeCCSO4SDhbOpW1dzS8KZEwJSgd20lvzD9z/+9V/99nf/9nf//v/7D//0z/8sQqYO1kXIeSLsnBMK03kct/d3NxtDj/No+6FDay1PT5elLvf7XYeVnNdtAQhOklImAkAffSCzMEmS55dnM+td3eMxW21dzSZ5WIeerb2/HyS06nB43q4XEjHzmKDRaSNjXJY1sUjKKh1NM/OkWS1LnocS2bZ1MpZnWZp7qI+xKzH5vCUCCEkE6FBV02HtaJfLhkAz1qGqfQwzzVlG7zocA5Co966jL0thJhLOqZRS1M3dYOgYgxKLSO9DzdjUg9xd+NHOCh7unkpaliqctmXNSS6XCxP33t5eX8PjcnmeE9HjOPropdY++hgmnFLlnHJ4nGdTVmI+W3t/v0XAcbYZIam1MjMgnmd7/fpWcpLEHnF734/9tADhxJKWrZacaqnbZVvqasOIxdR/+tPPQw2w51pTycB0tBMQl7oi0lBNJZEQJ9ExWu9jDEQYvfezbWtlLu6BNFz1PMfl8qSqbsPMEOn5+UlYEPE4dgggIhtaShJJYdY8aCZEAErJLAIOJeXrdtnvuyRyG/t979pxp5Ty5boh4OgDYxZ++bDOJPPmPkZXsz76GAOZCJlpRlqAiQkZvhXzzSv2IzsLOGlO67pen55cYeg4jqMPPY7DIy4v12VZ3t9vL59ebve9ILV2jqGTm5NzIpLnl6cfvv/h06dPhDzMZuonpu4MbqOf2l8/v4b61P/P88w5oVBmKZxHDNWhZjaUAnrXCKDAWUJv7jArXEiYKQw9IEw5ybRIMiJgTD89s9DDOx8ibBpvX98IOQyenp7KdyUnUfP7fR99mNnz9RLuueZcazt2YiYhnEW6EUGBQIIIAcw4ZadAKLWWnItkJmRiRHD10bvaNH8HMZRaSUm/RaJa74AgKQnJDAqp2RjaW8vCiFCX6VzlkmvvVmvdtmutK0AMU0QqdWF82489qX/8+JEIlmUxtTkdLTW/fX0HDHOdAQKfxQ0OYTEbr3CW1sEsvaAkIpJSklk8MnTUUmv9phIDivD8GFJOtdQLX67Xy9yYj/OAwGWphLDfD0DMOQfgaB1GA4D5h4iAWuu8KEyUZk4ZiYAmXydmvG5eQYgFkGbZurqbO9ODz0TEKadlWUUyzKcrHkY2dzM1HQPcAWGYMhcRGkc3RPTh7sGYswiSCCbJFeLXP37/hz/8IdwQw91mv5u7kSQEMB1ffvnl/e291IJA4GbqKcmUehhl3qZyLinl3lvM+h4PM2utE3OSvKxVJDl4b/3t7b2Pjg/kN+WcI2KYD9VhAxyl9O6juxmAhtNkgTLP7LhwYpKlCJYKY4DqhOenXE0VGcUskHhZNmLBIEI82zmKmamqzzhM6wMQdZip9z5yyZLEHaQwUhz7PvrQ3uxU9AhzydJ7P88TMSwGOKRcPn4sqeSEJLkM/WXmYkut9/t9dms8OBgYhGQWjyricyDQui65pAe/0Z2Qnp6eRLjU1T3UlYQROZUEhHWtwsgCrZ+lVMAYHqFjnut77+rRj1ObllLWy0WEXX2/H+d5EtJ5tPNoIvL6+U2SlFqPY6/LWmvHlCg4r+tv/vqvrs/X7tGHIVJKOaXiEX3YRPFBQGuNGFNmNxt99LPp6BNQMpN3qSQEIqQkZT8OIup9jDEmmHh2PhASeLTeGSlJyqkkEQhovcek2eA81jgipCS1ViY2tdZPD7cTcsq1LkC4rmtoNHcmCY/eFWF4uHY1H4ho6u5gvSOjEFdel2VFRHdsrUe4sEAWAEcAIiZBzszECFgQehusWQ0oaaoFgLvaZa3Syzm6B6TMpJwRRYSSbNtTAL58/PDrX/36u+8+esT9vs+Eyu1tx4jjuN/297e39/vtTYSul83aaWNwySVlIbbnFwG+3d/ez+Z9JJFjb0lmjyDGI51EgSH8KNSM8JSFEaskgmAGQiRiJpGUiLk3Tdn4wkvN9/10swg/2xkRpSw0+g57781tEG0ezizbtsZsgHaIsMAgoeggDMyUWJZ1C4fRtbdzwIjhJiM8nq5PxGTuSCQpASAhMlOphXXuB/r586sDlZoRySI8wMx760OHmakFIREFEUgiuS4emsoGmGO22QClTEj2/Pzx2AcCglti+dWvvrduJFxqHX1EuJqahQc5kAdM2AezuCsjEgADqSoQlZpqLcJCRORQloS0zoOC0rQYBkQM15SoVimFw0OHT+jZUktvIyEd52nuuZRalrrA6P08zprXeTKXlJZafUaCJ4E3AgMySUeaoLd2dCyYchLi2amkwyZW0SKmo2viSJJMQOaM2EVvvY8++gCMaSOc4zoLVdNuDcgTZ0rIImOMyawSnloXlMyTQp84JWH+BmYkCDVt5wFgtSYKYvIkKSf57vtPW10R6PWPrylJzmWMbm5zWGAW+313M1WF8JQzE09kcK199K59IEBKbBpD9Tw7QBAzIqQsKcsEaAJAyrxuKzGHg7kRETFmKeGqbuFETK4QAJzE3WTfdyQUyQHBKaVSzj6Y2cPKUnJOLNlUexs2BhKxSG/tdjuYiZl7G8fZcklMlHMy01n+p6pt9MTozkxibrf7TiLLuqaUaq0BcbaWUpr1Fw82MUC4a0yfZYw+ENFcj/tJQZfLwiRmOkbXoYM4UJgZgyYaZD8OQHgqCSPmwRmJtrISc1i4mRQJCBE2s0N3c1smrzSl69Pl65dXVWPmellqrYA41HAMHGDufTRJkiQDxFK3RPnXP+4QeBxnrhWR+3F6hKm3oyGDjhFgkiTLft9vakrMgODgQnx9etouGyPzC6v7/XarpcxBFgs/ledlraZ+edpqKe0861KnAnDsx9AxhvZhRLhdl5wrYbTW1VSS1FIkSc656ziOhtdLrvn+fjO1utTtshGi2QOaPvpwN6RAJhIC15SFhZLk7bJdn6/u0Lsi0u12GzpEUk5V0uNchgDmMXFj00JWt9I6QQAKbtu6XS51Xd6+vo7+NgF8AFBqXteL5NJVIRwJgnxe++az7jEwfD9v+/12u7218wACFrws1/24L0suS6lA23Jh5jHOM4mwtKEPas2DF8+qChjCzPTABwE6Y2JiltkV8JeWSmHuY5RcIMLMaeFlXS7XS60LAKac4VH5CylxMCFFO09ElpyIRb3v54DwlHiy1TzcmymMmfwa3frocXRm3NZl3ZbWGxAQ81K2um5mKjkBoZlzH6w6tF+JWuvtbGYjlRyAOoZPHLG5EzNTKpWAnl+er5crvaTtciVOY1jOOWfJSfoY/PFjBLS+R0TKktLTaEPVCOnYz+M45m3/od8iIbiZhxkCMjIDgQNYzOY+QU6cmElYiDAAZrBoqA4d1vU8j2PfAcF0m0DZefAUSUkkX1IpabJqmRmJGYGgsHDueYwBAEuttZYx1D0m4dFTqgslSbXU1h8+BWKZIC9CmqtTa8MDItRU29knpl/BJ/jfw3obvfdhw8zMdP4/mQkDNfw8mulgwporMhFjOKk6BdjQGXQz1dG7u2N+tHs8chvhiAQPhqOTCBmvS3l5ec6SJcnPf/qlnRO6Zdu6IkZv437bZ3KFmUx13+86FHA6XbmkvDPNrvha6lt7B8S6VPOU66LuSNT7AIyYZAii6XdzN9Vh2gU5smKgq4YPCEcEj2nidvn69Ssi5VyQyR2EM5GouQ5LORNxO8/eW+9DiGtJSdZaSgSUnJdt7WN8/vJVhAPR7XCLSZtbliXllIUfZGpVd9uP3QFSSoBgwwFASKYaSMicaDLh5y8SFEQJYKgaxWi9pUbu534/zvNQ08Tp3nrKqeZCInVd7u97yiwkNnR0yymdxx7uZSnCAgil5FJyljRJhh6+bStLkpTMViTq2sFhDPPwGbBWN3AfX4fkdOx7rUvO6enpQynlux++Q8K39/fwWcwUh2kfHTFyTiIy9+GznWc7ELDUJSeJgJT4hx9+SJJTSiln0/j0nZq1++3+9eubals/LrXmMRQJl1oiTBK5xXnsX75+CfdSC6D3Yf7m6xY5SVdVNRL61V/9Kjz2+357v0tK67rWXMYYrZ3MNHrGBADRezvPNnOGS60A4V6EZV3L5XoRKankWpfpobhe7cPHD7f7LQD4m23Mddzvd8BpewiIaUGbcUIVdQhgwMv1uaRUcn57ewcANb297+Zel2W/771t67ImkVKXlESY3nsfo33+6ef7ftv32+vXN05AGOe51/K0bBUJp6crl/r09PT29vr17Q2JPHyoATMTs5A6AAAhCwsCjHBimNE9wkcTcIQDkORUa1mWhc6xrLXUPFE2S621VCZBItNwgXCYMQxmNovWmruN/uAzj9GJMJqnzMw0Y5Hu3ofjZNi5jTFaP/d7ftGXlLK7s/D1euUk7s4YEXG0NueiEe4ByMiZxgm9DyJ0NXiI8cEIpZRa6vX69OOvfiBiZL5cnohQdbBILjmLOAQnefn48fUVBGF2P2nSfT8swtGm3RMBJ0NuIjICGBgokIXmGodMJIxEFs7hRA9oiEcAPWpTS8nG1Ed3dB16nJxqgoChGhEOkXJiluETt+yAk1NNJMHAyMiZHxRYcCTIOU3mAcBsTIOgKItEgLkZqGqY2yMay4AE5oCOk9jGzNi4mgJQH/08mo4BGK21fnYzJaaScxIhZgsPBJGUUsm1AjggWEznbmJkNcuSwgzcww1nvRyC6nBjN0eEuhY4/P62P13lsm6pSMq87/tx7rfbjRmnPSelFGGlpKFdh6mqhzKLqR5xAGCpRVJ291LquiwR3ns7j7P3uKwXTgyMGnqe/dsfAohm9YRFmHZtrYdZYgI1EXbtEAbmiG7qvVugic4XJUYu+XrZPCoSHvf7HKnt972drffhZryuOZelLiyMATmX6+UaRM8v7+04HexYTxbe9z3lDAGtHW7mGoBhMyJiNtSZafShjxp6TCWrWYXZFGjmBoEoPEn2vY39vkc1s9HOY9I8eh+mBoC5llQyf/juZdtyydMCP7oyo6Skpnrq2XrtJeeCiOEx/Z8QcLlcPDylRCIAYG6rrdxEVdXbfM1mpXL3ad8Zbvbl82cifvlwf3l52S7XT58+ppzv7/t5nkSwLotHWWoqOZe6hBsz5ZySfHD3nGVZ6rosKadtvagqEomkp+uKGLe3V0a+XN96a0iMSCml1kf3OFubxLCuGgGchCUhOxKaRetNTQFRdQyzdV2Iqa5LWhIAPr+8SJLjfiDgsi6llJRyyIO72UabV3lVzbksC9eSr5enlMvsrAvvdS0p5Vzy19fX82w6eu8dMiChqpWcA2O0zsgB8YD6JU+Sl5I9VISul+u2bs9Pz+aqw75+eSVB6woe4NrPo7eRc0WgYNjb/o//+b+0Yzcbam2ME1FSFnNt7WSW/TjjPPvQsiylrB+//3S771/fXiN8+pdAmBAZYaLEaCJJA2ZbLCGx0ARUQwQyiaTr9fnp6WpmJFiWwpiv16cPLx+++/67kpc+xrkfk9Agsy27SD/62Y7zPO/77j7mMCbiQWw1M5uQNUDVERDCCRkCY6iamdzlcnkCiHGOCco/zmPZVmLazybMEx513o+hGuGzvAURJAmx1KXq0JLzsiy5pMt127ZNhxFTrUnVRZIIZxFiFGZErs8lCbmNSfejLH6PCEgpWzZSmhjgAM5MOU3F3Nx0KufTaDipDxY+fIROvT48Qu2RKM01p5TXbR3ab3o724l3JGSHCLOhxiKkY7LjzMdoAwKQiRiI+EH4NLPjSClJEkoE6KYaFm4eBh6WkmjX1hsRE9MYfZIHh6ow996Z6L7v7lGXhVhUzb3fb/fz7IA+bc2IMM8uYyghcZJEueQqxDlnYfZpeScMQEYSFg4vpdDc8yLczYwgwtTHUDUT5nW9rmWJsOeXZyI+j/N+O7Tpdt2++/47Mx9tiDARjuHHcR7nwcyTEyzstOBxnPv9XNbluj3NnrGcBYBzyvgjuVvKORDa6E0b4xEBSR4Gt1Drx+EQo482GnooEvjIkhCBwigAYK7Fw0CllpxydXUmQWRGXpflsl0kyXkcbrYsGSF693CDiJwSidiYIWpLiS+XpZQUCE/P8d0P3x/Hoaa39/eff9LX97uaspB7IFBKMxHFzj7rVOakSFUVDN2ZAAORH/XTKckB0XrXodO9nkSEZcKSw330MVVOzqRNYyqk2tdUJfE+zsBgYTXz82TmyQt0933fXyeL1PH6dGWWCIwgs1nCIDlnM885mbqNUUpm4lLz7aa99fM87ruUWgKi1kKM8cUkYallKRXCJ/yntw4RS6nb9YII+34HgJTy9XIBxPN2zNlaSjKbAUqp3333ab8fU5Se1h1Ad4RfXr8CwLIsH374NMsI52/T2jjO4/Z+S1mQGcNJRJKUmiWJua9bzSlv61aWpS6VKQGEqdV1MbU22gyjDbXtsj4/PSEQMk+CFwSaBSJJyqUsyzKQyLUwnwBR16pjqGk7Wi8twlWHNs1Jcik556fnlyRJUu69lZyerj/Muo9Pnz72Pvb3u4HpCEKeDOoxzN1ur29fP38m9JRJmNa1MONSc6mVs1DiftM5akaRy1W++/H73vsvn39urRNATKoVMYJzojADcETOmVt3ImICmfEHQnQQkWVd1sv68uGZWc7zDIjnp+v1ev3w8vLy/IwodB5mjkElMwsCQJK026FD99sdwtrRck4kEuER5G796AFWUslL3q7Xs/fzPM9+tnYCYcmVRdRGyikCzKz3drvdz9ZyyRZOy8LAMQukHAAwl7QsKxFGwLoty7KEA/NM5sayVmJMs8TNgyCIKQkhoQ57uEaFlmXtvfXupkopcUqzZ1llODj4/OgIYEZt2dxVx3E/WIJBZpEVEM+hkwPQLPaaWVEPSfO+xJWrxRMJt+Psfeg4SR7NvRY+gTlA0EdrQwnnNfLhXomI1nrvrda6rEtKaai6GxICh8UgIQtr/XS3UjIJjQHuOmmjp412NFVtvUFQrgURTU0x1DTCwqP33ntHhHVd61JV3RE9MJGklBgnkc3dbDqEfFICARGI6VsbqIcHmBonNjczH8OE+Lpd1nWxMbO+/Fk/j67rpb58fIYAG66LyuMr2NDx9vqm5sullpQCuLe+3+63+wEAl/UizL2Pe+szT76tS6nVzYFguI4xWl3H0FoLI5s6UDiAmmnrbgPcCbmju2kSSoRI6G5mOttSZdsuqZR2DoAQlt46AixLNVfeVmYys5zO45zyjk0e+H2/t3O8vr1drpeUEpPUbVmXlZh776P39+1Sc11Kvd/vFjp0HPu53/dl3SSJmWnT1jpnmQ0Pk/2NKMzo7uFg7g8MOxGAD+uZcym5LtXUTI2EAJiS6NB+dnej6bSAmMXFOdeUuCx19GHDJPH16WktSzvPs7XzOANgXcZ0vbh7raW1tt/3iJCUEfE4zvM8j/2eRNL6YO1Od0o7jtevX1Iqcz7FhJLytq3btp73Y9/vajpNbIGQkix1CfPb7eZuyEhIj3z16IsHEYgIQGyXCyKbWU5lXuTVYpje73cWvj4/f/rh+yzF3IQlcRo6Xl9fbRggQAIQuDxdj/04jnMWOyNgLevTh6eyrFOg7L3bMKgBCE+Ix368v72Z+9P16dN335vZROUgkls4ROt9Ft3N9NwsNW2tEdHo7fZ2m9uq9m5DTXWo1lkykdLT07OF99asq7HYMI/IkoX5+emiasdhnORyuUhK1g/hdNnW3/7216OfS82jdynEgq7GwrUWJFqu2y8///L6+kaMxIQBnz5+/PDyrGZ9vAMBMbGQ9+A5HIdgBBRxc5xufCaAhw6ERDmnxPnjh4+Xp8vPP/2yH/tsRCDmPobqacOWdSslsfC0uo3ez/M4jqP3Xpd67HtM0fxh/NcAZ6aU09PT08cfvt/b/vZ2869+7gcJpUUo0Tn6NHfGLEhmUFcOTjVL4lnbU5YcYL2P8Mg5EWK4r8vy8vwh5zSzjfv7+4yYSE5mdpz7PBzM6wgiRNhQA0U3C593CEIkvFzOs53tULOwmLfV6YNyj6n+AwPSiYH8aNaD3ptImryvKc64Ttde+ExECgvJtm3LWu/3+5dfvrS200AmnomwxpyyRMB9v6tZKlOEZ55gD7dJErnt99bbBNvNn9ktXK3k4uZDuyRhoQAgRk5CAQkQCGut99s9KMAx5ZxyDoDWztZa680t1Ia69tYDIJcqIiwcEA5uYZO5O5u7pvHEzMMJE46ho43p1aZH4QLE9GYijmGXLV8utaTcAHV4TiSJ61JSSmfr4LEsy/XpMsY476e7qdrb2/voXfViS6nbYt32Y2onlkuqSwWI1vrop3skkcv1EhTEeC1XIBza933f7ye4jfOMgFzyDHCTASFOYI3rcBSYm/pQDyAmRhYAOvYTwmuttWZ3a92RHvRUMx9DiajWZYyuarfb3T2+fPnSWy+1HPueSn5+fi5LirDRVXs3GznJp48flizn2Sy8tfPnXz7fbvtUG9rZVJWTMFNr3dyJxdBmDTwEstCgmJMWKcKAWfL1aa2lAsC5nwaaU8p1AUDtOnQwsxCr40yuA8JSy3bZmNPr+OoYLFLLAoBmRkTPL8+lLh8+fLhcr+Zxnq2EA0Vrfb/tLAEIt/e9txMipErOmYlqycyYktRaZHJREFtTQAfElCRxOmJ3cyF5ul4ffjgHDCw53yCG6tfPX0VETREZIhBdUppXzhqwLGtrjYkg/Djp/rp//fqlab/WC2AQYsqCGoQkOaVS5ns4DfL7vtdSRm+t67qttVa3WLZFWIRlfjtjT1Jm98AsSpRSnnK6Pj9JSuGRSimlPPrLiIG45OLhtS6SEhOyyLHvY3Ttoy5LAIZrueSU5NPHD8RUy6JqZakpp4QIAa11RMhFApGQeh9ZcmJIGWZHDRhs2/U8j48fP23rOs49MZ3jXLYK4Pu+m2uphQi3p01KQuZ2tHYcM/G4bOuy72vrOld5QgxHj4kFZ6FQZ6bAIERhCqB5fCV6sPguz9dt29SMb2LdiKjkBB7nfpj7si2SeVYeEuJxttvb+/vtPdyudatrIZyUf2BClpQSp5S25fLdD9+nkjWslFHqcn0GFiopI4JHDwxGnj5LZnYLRF6XbdqRc8pEtK7L6IMJn5+fRu9DRxJmDiRABzO770cfKpI3Ivcw7ykBIPYxPIJZZlnQDDMjQC2FiIZqKRkAh3ZingXFcy17NKrbBE1GEjG1KS27+X2/55TKUhEJQ/68YYAHAmpXNyglCwlAsuyIryxpEulzElU1dUBV1fM8h+lxOhKllJIkJHDzlFNE3G+3eSGotZZSUn58LwyYsUoRGmOoqkfknCWlJImE3SznmvZ37ZZTJkIAH7311s7jnLcoEUbMgNF7r6UwP8oN3N36ONtBgMxcJE+ZSFWJGBBjfizmU1F096HKxoBkNovWWIeVpRJBP7pb5On7qRkCWRgI7vf965cvt9v716+vAe4Row2veZzdhglRiFl0N3U1YsiZ2qHah3U9931ZFxZacsm1tLORE4yw0IGoajGcGASJEkpmIYKYUV1AADMIpODH3iXHuSMRE7c+yhiSHrWgyHHcz9vtbq4p5SQiObt77/2REwFo7TzOg5hVB2ComrufxzlUmTDnvG1bXRZKjECXp+evX74iUx/d1HLNnNOk4cCjSQ4RwGbBHiKzeMwzKJRJxc+SOQFSb2OiyZ9fnsZQU5/xwnAfo08oRS7pet2QJALu9+M8zszFHW732+dffkGAbdueX56uzy/X6yUAWhuudrlc13I5zpOIjvPMXAOtpFxKTkmECDD66MLy9PKs5ubOzKWUWou5f/z0cSk1wGstJZdt2yY0EZEcgoS3y8Xdf/7587w3PD0/tdHfb7d1W7NkQMillpzXdT2PYz/udVnMfzlbUzNJMreNPsZ5HIgMSNu2LbwGhEfMcfpxHgGxAb68vORcImZTprFoSozAtVTJSYcCgoZ6xNPTVSSv64IPzzc+wkxM6yqShVOaRduTrY8eKWUiDo8IL6WM3l1NiK9Pm0hilunkYxIAWJcFIFT1IcIlebpcTa21ftwPda95CLHBQISccs2yA5i2tRb0iIjnpytnnlUaZPr8dEXA969vpqFq59GYhZGZGHmWLAKEIfpEPQuxk7KQR0gSFp5+F2YWSeu6lJzn8e/jp+9Y+OuX15gEb55WIgwDQXF0pJnkTO1sow0WCvdtWSb0eKmLub69vmsbOad1XepSx+yBcOvnmXNatwWDdIwk00iTk0gtJZesZpJzqWWGMGtZlqXOK/B2WYX5ftvH6LWUx+dPIZswkkc8ELyAFjZfYR0aHpAACciRmXESmyHcnUlSToi4rutr/nLf72Y6Fa7eu+mMAYckEU5zkjH6GKPfb7cdsZ4LOOSS62TBMiPL7LsfNoSZS56dxtfrlYAn/4CZUzJ3MNPeFQDHGDoGMrl55EAiJjL13vvoY37lmYYlItUOCKUWc0ck4YREfQxTiww512neU9CcslBydEYiJB0KBsKcWCbhiiiziAjNlmnt6ua5QJC7jhg23J2ZA1mYhV09SQJCKRKzURO/9XHzbKsRVRtDz9Yzi4AQEScpS56sb5GkZsfR3vr7ly+f37683e+3++095yyUUhER0WEpp0Klt46E2vutjdYbE6o6QJyt//ILvJgt68J0zirKpXomadoFZQzl2SVpfQru01vMArPPpQ9V00AkYSaRP/7xT9tlfX56PtsZ715L5SRlLbfjfrTWtNtsccguksZQJnWHafk4zzZ01JqXUvf7Pquc931X9SQSEJNubiMgkJBKzlLKha8vL24RzGJhf/zjTyIJAc2REIj50SwUmDghHuY+zFaWy+U5iYwx+DySF8mJiZ0iKMAhiZTni1o/j70u2dSOvZ1nP46z60Di1vTLL19bP4+zMRFxx/cbSy45sWQkePrwDEAfPn4ytff3+xgj/WsJ8372nHjdlnVdzGy/38tStu06VM+zSRIdej8OVdvWddrwbdG1rBGRktSluLuaAvjz8/Nxnl++fDGz73/4TiQREzPb8Gat1hIwezYwEIlTyvlyvfTRPeL55RMim4aLB2A7D2baLltOWURmkoKFPPyv//pv+hhIEYEpFwTys80ZXSriEY8aSXBiulyuY3QilpT62Wn2d6Q0Df7hxpiBUAqHh5sDERCmknFopdVVc81CdNz3/ThyWVJKpsZCU40HAAcSFjcnopLzLIRyhMAw9976eZ65Zu+eExthzQXXcZ4+RpuSqw6lRyA3mSkzI/JS19vX29v7OyBs2za+s6+3W0w3BTETAiAxlyyTlYYAiSgnSYk9EIBSknW5lKUs6yLCTy9PZpFLzeU0N3NPmVnYzMfofTRJeR6cSCjVLHeeVV8pJU5cShZiU1vqoqrncQLifr+jyEyijjZa6/vtUB1M9PHjp5RYh021d3taz7230XOt5j563y5bLUVNS5J13RBQm065AQJKySI81Oqy6DBCZOHeezzOT4QIajrb2VV1Shrmjz6iXMqFLnVdzc1cPXy/38HnVFxcjYAcnYkkSe84KW/H2SYh0SxEEgCmVFgYAMIilyXCxhgeMGcPEbyt18v2FDCtMj50lJrv990DzD0gGvEcbrtHTfL0fHX30TsC1rWWXFRHTgkBcsql5qVUVQuHbb2w5JrXz59/CQ8EEs5EhEa1hA4tUi6XS8qpty6XlPPH8DiPc2gj4pKLCL+/v+sYCSmtZZYuhFmv3cwtIlG6XK7TrYdAXTsKN+0LITDNtCyCEAqicIL7fqSUlkshIgsYZn1ChZH0OMzN1N7eb58/f2VENX1/f//w8eX6vKYsYFRqyUsGg8yJmSig9fbLT78Q0XZZ61KUGQlb66kUSelobaiSkNSUpbCwqfYxjnsTSbWWXDISMiKQj9Z7b22ck1scQCwi9/v7++2NiFh43Pvt/cZZ9v2uZtvTNdXcjtZ77279NBsu8vjgUk69d1N1TdP6OiVRN09Z3PX9rbsZMuZaIejcDzMHVQY2nXYrEsI0TZmzGN5ZmH2a0GaOj5iJRQSJp7bAKK/xeh5HqUW7H+dh7pIEILQ7A9VaI+A8m5t7mAdulwszM0kEMMm2btOscewtpTsAMAoKjz6WZRNK4HjZNnMnwn62cinrVutSGFDVYIVZb1tSzrnMStXL5Tr0YQv77W9+HR5T8ZxlAL11IsbA/bi30S5P11rLnDO7uYUJSwS2PswcoiFi783MIvjjp+9++NWPKefRVEffLuuyrq31ORVQM2YBAus21Mjocn0STtgOHYOYa6mzZ33Gd10d8JvTb15yM30rnuL5GMwSeYcHjgYg5hIODADIicPDhjNJYEgiwJibxsNqBRERM5zqU0b0yCXnnIjmqQ3dzN0RIedJNA1wz0lKScd5P4/93O+uI9yAgpASk7aOwnP5I6DCmbOUTyXl4vZLG76obpfLvu86NAnbAAZOSeYvwUQByElqzSRynh0hkuSlLk/Xp1prRIik2RYzu9uIuNb69PTceyeh1tvMWCLgeTYMlJTWrSKGpFSX6mat9yQpFxFLZr7UpS512S6IdFm3dVlut/t+v4/ea62//tWvmfhs5/12L7W+PD3r5mbOkoZa7+3jpw/zbpwIa61hwc/kZo9ixcSPSGs83OhdRwCIyPyozUzVACIAzHQoSEoImCS7BRGqDTHBACa+bNfE02rVz+McY6gpBB77mWqqpejQu90h4vp8vd93QBg21PTR0xhQloIBRMk1ZqRgtlOVXCQlNReSNnrKRVIK4Ahoox/nER6SUs1LybmUsi7b2Q7htG388vE5cdr3vbd2Hu35+Xq9XHNe3l7fVdUDayq1iBtG2FLWmqtbUCICoivmnNd1JeQ+ukiqS8WA3rqOYaazzfuybhBeapGUmBgAtDUSdovhysiSczjkUnTMpkkHn3WYgQ9uHsxibY8QoAhovSOGB4yhAZiSAEI7eq4leBJ3dlP95fMvMduRzbRBziVlwaAADwgigIDE/N2njw6RUso151ImLrQuS14WNzuOw5pdtguDJJEwX8pacgUMDJrSj0eodmapK6PI0c92NLW4bkU+f/lCTPJzTsLLukBA7HG/7wGPhjqR1IcOHa4mIkAYACllJI4IcyNG83G046E2qs2KcFU99hMQFvOcq7mqjnP0KTIauPBOiY/zSDlHABggA86HOSIwHBwAJEkuGRFU7RxtEjOeP3z44ccfwuJswN/4rky0LDUVud/O1joQPj990B5m1r2D98FJWOqyiIiw5Jpn4aOqjr21s5tGLUti2S5bQIyhVAkiRISAJVEuSTKeR2vtBGTJiZGRWBjBaaL+CDnVLCwzIDL6CA9mSllSFU4pzNdrPfb97e32zWvhQjCG9TGSCLOMoSLzTo+TOGYWiTKnNLldSGTmrZ2AMIbOvw5CmDmiyfTYPjrH0SXC3DU4ERGq+gz9y6Nl1RGJEYXZ3SmCCOYNmBBm98uceiEBIgVFEIabCBHy9IsjizCNMY7zHGO4W568FwA3dY1S8rKUmb/po4/eASNnyTl7IIQjBmK46v39db+/FWEi6Kc6ERIaYYxHTS4gA0ymmDjI/T72pkO/ICILIYgwJWGAKDW5u6lP2aeU/GeCTcqcU9rWZdvWnDMiTyD7PPJ/C5Thdt34YPcIAHcjxlyyxXh+eVovi8bAoG2p62Vp7Xz9+mpuASUAJKW6LllyloxIDIwbMcm6rhhRS71eriLyDE/n9SSimpeuCgVSrsR8Hic4unnKhIDgQEg1V8RvRIMADUsZ0R3mkR8o3IlJmExDzdzNDInQw90cc2LhxCmnAugRIcTESLiMlGstY4zEB6O0dHRtk6YpxOaurstSZzv4kISME0eYcpobUs45DChxzTlw4rUDEafTuo8TWRZOkkVYpuRx34/9fq+Fnl6u27qlJNenp23dvnz9kqUg0rJVxmle6m4+u0iYczjM3gEGyZKvl8txNASCACIAAOzAJLlU5jQ5Zo/WFJ/zFhJKLEKAwjIRq+g0OdciGGAEUJOQCBK6zZ5HhQgEK0lkGo7/0lwfc7zkYZOsO03eyECBuZYsiZCB8H67i8jHTx+/fvkyek/CrbdJ25aUdRgRjNFmMtQ3EKFcBJGmHtCOE9xzSsIppXxv9/t9hwgIWJb6UAhrrbWmXLQrMjhoNEMEIuFEKSUiLlKWdc0piSosKeWc3r6+Hudp5kQYiK01VTNzntgjwFzKJD4e7ci5MHLKqW7VzYfa/ban8rB5BsADJRH+aJhiaq3fbu+AVNeKTNaGDiMlG+YTAjNDYN9qzec9NyCIOOVMzGfr59Eg4nLZnp9ePn33qe0HM0lK61ohEDDMxpKqdl+WRYRrXpTsDz/94ThOBKy5fvj4UVLa1q0uVVgI2Vy/vr+aWs61LCWnPADMde4oZUtunrMQs5pqG6MPQJAkrXVvTkgBOHrvOjCQiKwkwHC30XpA9NYBgeYDXbecCiC0dt5vx7TQICJuRDlLSjExKURz/V1qBaLROwQu24IQEDjUPEKYzQKB55+/LoVEzrO1t/d5nJkISfcJxjc1YwJiBkQImC8F4aOsCjECgggBEOkbzc/dLfb9mGEc9mnY+1YArjBnDxgY5HNAOg1dROgRs/w24s9MqVDTOSgyUzdDogh2sKFOBAALop1t//r1i3sXrswMFhaqXQMpZr1dSkKSUlq3iw5AKvjbMiI+f3lNZZGc+9HCR0qJCCTJGEMSO7oZzEICD88lC6V1WS/Xy7ZdHlA27SXXkisihbuZDvW5YlKW2d8egQ5BxNfr89D+9vbKmdZrrWu97bd934mojxHqzJJTiQREeQIWTS0cllKzpJSYCACmdyCxEAkyUgQIp1IKOnx9/Wp91Low4SSzl5xzktn7pD69l2AeKXHixMKjjYhwizF6bx0RhAWRhCQ40pSbRSjj0GGmc9iQUoYYRJhyXtd1xgaP47i/3/poZSm991zK8vKiXe/7+9v7raRScl2WBZFEHtQNZGCiXJK56VCIIGQnNLU+RkqybGsqJSVJY0ww3PW6AcTlugkJMz+Y2AHp++/dfL8ftZTrBsyUS76/3828rsvxvmNM7w+LcHiWVSSllPi478d5HPdDUlpqYARCoLuqtfPs7QQASWlOFcFj76cPb2cTEUkswh42831IWOqKROdxdrVcqrvu+46IgfF4cR6lKTgRgxYWGBPoqeqtnUObSKqFc8nHcZ5nSyI//PjD5bIxYTvP9/e3Q/chAgG11pzz6+trO07wKDkDsHtIEkC2odoVCcP87evr9PhGeBIJ9dmCN9pY13XbNuZ09/vtdpNEhJhLFmFhVnNKQszucbwd8m/+7b+xoctSVU3N3LVuyxjj/f3WxmhnY2ROkmuGmVy4H+qjlCEi4T7UwkLFrPl59PWybOua56AHAJEMHAjH0Pe3t/04cs4iDEDzBOOGGAEWjBDfaqwRAJEJbQJqxrBpsBtdTS0nWbc1zdb48Fzy5XLJOYeHjo5Iat56V1Uient9u+33L1++HkebD+v92EuthnC009QIqbX2+vWt1LJukjgRCyL1rkmYWAgp0Lt2Cem9jd7H6KVWQrzf78xclqxmb6+38zxTSdfL1R11IKZo/TQzt0hJEGKeiKcqZ2GcaRZFnUdLkhmYRYICGYk518LEKBzuc13NnCSxuUVEQEhKnCJlQSIkl5TM3FQniJuF533qAds1N3VIYC6I/OA5IgaEhZuaIcy2mTkiM9c5CTTz89gZN2dGFEmztiHIwQLCwyMIZgZRDWY7FQoLULh5b/049ogoJQPE/et9slGXtSZOCOFue2v73v16pUQ4RG0y/UdrIFJ6P1pr+3EnzlIWZsl1SdeaS01SM6enl/LiboEOCBhfPv8C5ogIbiIUCMyExCg8hpVcSsnDfIpRv/rx+3W7rnVFojbULZCo1JxrcTMgtPBQSDnXpfTRZ3PcnJ/XJePptVYkAGBTO4/TIRLzxCYuzPu5994BZboJETAJi0jKIkLncdxut+M8Cenlw8vz84sQs6S6VJE0AcLCXJYME2FmIcwmghQB6AGtm4eaqwghIeAMaQ/3iUvxnBN/+45IKCkj0CyumJj4Pjrio3/NHWaDC/FEVa+X67Wd53GezLnWFQlN1DE+fNRa6zqp48vKQr31mYADpD7Gcey3t/dUMrOUWkRyXZdleeQQ54cvzNv2V8dxR8DLdZvJ55RSztlsACAIIOCyVmFxD2FOks9jZ5Faa05lu27rusxGm+W61rIQorZxxjEPTyWnlASRwjxsQNjsiN3W5XJZmbn3FuFmKkyXyxJhpnp/v3sYADrMnBIi0XEcquN+39++vrn5g/cwJdLJSQ0PjAgffQg8muGHjjb6cZ6l5Bnfvj5dkkgu9Xq5buv65cvP//SP+vb67t6ZUlmKmrl7qSXnxEwB0Ho7zvNyueQs16eLSEbCn3/5+b7vLx8+lFLMDBmXpZaSb2+3XIoIR3gf5367L5dl2zYRQsbRx3meDi6cmJMIy+9+938d/RxtlLIgYF2Wupb399vz85fjPF5vb6+fX8cY0MFJzvvkdfvQMfqICABgJmTIKalqHIGEgMAiyGjqQzUcwmMMHX0gYC7JA1s73SKVBB5MRD7hsMYkiEQIFsDEADhGv73fwh0BSQDCX1/fkmQiZIQkstRlLtPtuI/e1Szcw2D0MXR8/vzldr8zMROr2ZfPXwix96ZqffSPHz58+Pjy4eOnyZ0upeZcMMDNmZmRGZkSdOtDR9fRzuNoJxC0jsd5pCwGdr/vx364Byea3catn+p6tIMA55rYWl9EAoCJJXGW/PLyoabFHkoLIbOpu9oYAwDnnhfgx37Mlj7HqFjMbLZHWhgCurnaA6iOAOu2ioi7CyeZLX+hMCF64eFhZvOwgj6pJeimQzsE5CxT/TdTGGFDex/mFuYISDF5l0yEDlOh1HmzGDFm+nx+RyKShIhoZPu+t9YCotSsOu73+77vRBzhy1oZRbvd77fz6HUtM0uasqxb+f3nn9wyoJ77fb/v9/2W8rI+B4Go47pdJWVKOaUllVoAf/jxV7mWCN9vxw47M0kWZlSbybjEEMRWllJKEYvL5ZJT/eFXv1JzyYmZhzsJI6IkeXl5PvYzpczCpoZCKeXeRpAjzdCTJ86SUl2rzVjKoUy8bWtEvO/3frSSs4dPBILkXEohArWhpn00Ndzb/n6/zdyJuY+hOefL9SmVSs5JZFkXCkw5zW9BFEjzoouAhMQ5JwskJ6Y0VTpmJho6fP5BSylzF8E55JGENIfvs30Q3YIFCAgTY4KpDSYWyFiW0ntj5n0/dYxSChKKpMvl8le//WtiHKrgsW4rE/fS9+OuqvN5vO23X778cnm61lw9YrtIrjnXkiTPFRkihAncwA0QBYlzmgb8e+/tOAEw5cTI1m3Z6jAdfejQfg5E5cDtupQlz1sZASDE7Ft9ul6ncJpzXur8zE2JJvG7lETEy1KYOcxtOHiUmkUYCc9bf7/dbAxz27YLIfbej/N0i/0467Lcbvfeu7Awy4Q2IhIx4uxyxNAw1RFJLBxgmlN5Ys0CQPv4BtWGlPLlcr3f3uff92w9SyWgLOnp6am3k4TMw3T0s3t4KaWU8vT8LCmNoa9vrzq0tQMBWAQJ1XRdVzcDmJVB/z+e/qNZjixp0wRV9XAj7n4JgCDJv+qqllnU//8PM7NsmWmZ7vxIZkYEgEvc3cjhqrOwG4VVLEIEwHWD2zmq7/s8fd+2xlXEoZLaC1chImvMuq12sOPkCEUHP4TgufYQgtLKh+CDjyn/9IfMItf79d///h+vL6/LupRWUkmMDCD7tqUtIoLSyhgNIDhPQFJLjppYGOHARLd+2BhCCOPQuClBZ2ztLQnnkgFZ4dFhZjoOn/CBPQcgQn0kQGJPgHy4O7TWvXWFKgxuniZnrR2MVI5r3ve4bxFRnLXBD7XXPUVCZZQFJGUdKQKRI9YU036/3bXSj0+PWhvvwuXyYJyjo0rWmlbE7aDHH5dIzilte2y9Aky98eG7wEOeB2ydPp0mYzQDb+vGwiCgSddSRSDl3Hqz3hrjVFMi4l2YhhnlAyjSmxBiF6O0FoDSSu8tuEBE2hhEZOG4FxRw3iEIIiEiKVRIxpDzFhgByWjKpSAiHaYnsswsAMccj4Vr4eOdZKwRxFJKKVkEcMdDwUFIRGS0AZFagCyNQzDGftjJidTxVkPorfZeb7fb7frug/XB+TB665QiBMwpHd9uAFBSqbXW1o6vxZjitm9KKWNNq6XWsq9r8J4UEhKIKEVEmHOKKZWWWeCY3TfuUHL5sBUCIBntGOTT50/n86n3+vbykuJay2atOeqjVhtSpreOWoUwDH7wwX/6/FyLzNMc84ET0FobRRoEiNTT0/N3fgHB/sF1aKQISAS41nrIWEqrH4cSIgCpNZMmqJhjrimXVoBQG0OojDHOO611q3Xf9j2uucTftxkcBp9z3vat9oKo5phI6Xk+kSZjNHNj6KXmVqq1BgDpkPkKC3RtNYEyKMaYD3U8KSJltNXGfFzFABhEASAdCD/ujREQgbQyhKCNPrCJwkeovYkRJCLCXrtCGoLvrRKS0TqEwRjtgz8SQSmn1rpxxjsvIPf79VB7bttyXEM/GB4lK20ERFA+kN2EVimlBL1rte1xEz52VKodCy2jFaEhVVstJSukLq2V1GtRWvlgvfM1Zc51nEfSyKVWTBqRQGuyh+mlltJqOWILwAIiNVdjIe3xsMPWVuSYZwKW2ppw56aNGf14ebjkXP7+H//+/v6eUq21//DjDznXWtrxoz4GoR/L4CNULyCdU8koDCjeeeccEuRUUk4IeEzGtroj3s/nszeWQDvvtNWllt47KXTeMff9PR35olrzvu2oUS3b5bGRUp2l9xZ8EIU55t7a5eEMIi/fXy/n2mpj7kRYy1GVQGdU2vZluWtjjNWl1G3bpLMI9yYaAJ31WbIgNu6tdf5Y3VjrrA++pNJru9/u1/drjlkpqrXv+y6dtdHaGCSqtdZSffDWWGddLTXG1NtHwN9O1hgT91062zAoTfc17fteSyVFqAFACIiBAalLBwaj1Ecbk5RS+mjq5pq11gaRNOYSj8MsKeq111JSTrXV3qpzbjqdzufzsqw+p5hKGIdYSqlFsgizM2qQkFPat+3X9mvnPk3zn/7050/my2G+YO76oNoCxD0iyrovLHy9XeMWjdPCcugSD5n88dYkUtZY6YLq45BtnDFkFJG2OsXcWoMMCFRyBmBrrXO+d66lHK87JBj9ePhyay29YRgHq3RrDQC5swj4YK11OadjLm+MRiBEIVK1NGbet9x6AwfaaCRUBEe981iRKaRSc2/NWMMVO3CvdV+3uMfHp6eDkj94r7VBQaND1x0AgnXHxYJ7JwQBUMeDD1Jz3rf1/e3VeaONnufT0/Oz1Rbpd6WUMDNnhFar0RoBmCXGbd+2znycaIxWOaX1fkM4KBr2/PDArUpvSmlrg3WDMVZANxYUrFVqlWEySmkgIkAFWgU1jachDMaY3g1hr0WI0DqnlClYyel5Pj0+Pn7+8iUM4fp+s941FgZWAATonOmNRWSaxuW+1FJIkT8qAiJE1KqgAWZIKWqllCJjjAiVnGJM+x5rKbU2UurkT6fp5MOABN5ZYY4x5RpTSaXUkmvJhQiVoePNKgKdOefMAuM0DmNwzjvyOUZhifseU/bOApAylpl7Z4AjaUWaDroRHAWaI6Uqv8veemvWWuUJUFptuWRgPKoGzKK0AsScszaGW7/dbr11pfU0zc6ZAzI2jqPzvrVKhNqoUit+SFqEe0+91VqNNrW13lmkl1JARBltjXGDb5UBQGllrCMi7p2IjCIkrq20Xpb7si4LInlvgh60Vp1bTbX1djmflVElZx18XCKJcs75YFlAK7MtC2MfT2NrfVtXa3zxHYFKrTnn2/2qCJwz54ezInWsnQDg/e29tuqcDyEoopgTJx7C2Hp7e399e3uruVwezoCUct5jJNLOa+1kOs0mFeOc0gaAjorSAZQGAGZW9CGGu9/uxurz6TQMftvjgfzTWnvlam3busYY4767swmjP5/OOZejTk9KCeB9Wd7f30IIxtomrfUmHSLFlHJKJe5xXRel6TxfEGBd6rZt/db3uAt3Y/Rx6kJCYTbGhNEv9y2mKGnPe861CEirrdbivNOKAER6a6WWkg/QciGtlVbbttdarfXzNAfnjdZLuRMSAh3cq1Y7KuXDYJ07n09EaJwd3MDCcF9aa0aZWmvv0mpTShOhsfp+u6cYW62ttQ/WNkqDDoRKEzYkVADUehMRrXQIDgly7EorY5XWyjmrUBGBCJecRWS9rzVnUoBGxZxUdNN8AkAf/MPDhRR9f3l7eXkBllpqNPTwcG615pwVqZJSUqqVHPfNWHNwpo5Cv7dOK8VQSWGKtbbaeg5ktdbeORqHVlpr/Og+xpqImGIsuQ7j2GtDIk0KpFtnQNg6e5yelUYRxSy5ZCJoveWSUQAEmTsippKX+11YiIiG8WMlTl2hEuwpxZyT0nTEFkW6IhLmWvPRRWTupWRmNtYprQ/sFoEgkfQm3LQC77RCSjmvyzWnjXurLcY1Pj091JoJxRgLAoTcat+W2+9JEkHCg1oBALWUIWhvqabt8fGHddtvb+/OGFJ6GD2gDIPfto25iygkUES9dZFujUlEAGI1GavaQZirZVs364z3wRhdci4pKTsw92O6nUur0lrH1hUp7/zs/GCdB5T79bZt9/v7W2/FGCXoemkHCNZ73zpbZ4Zhev7y41//7S+Pj8+36+3JeBEBrQ6sCyBrpXptKcYxDMH6bdu1NuNl3rat5A+8DCHt+1ZrZmMGG5QhZzwZfb0vWttxnIzR0tlYM4/T4TOJe+zc4x5zKr0356wPD4Cw3hckGIZgrDn6Wct9lV57S9t60woFAYBrK3tKr6/vwYWY6jMSIoFg722eZwTQClNKR67RahWC6bWzYJUPwHqvDbTWSlXuyJ1Z8t6d8wpRkxLm2mqX3ns/2AxaW1DUxJ/mSYBzryBwejhrpfdtu92vtdZpnEII9kAu1sK9KaVApHeprZfCrcKyRuum+fKglHHeHYc5FzRLxy5EcN/XlNa0xRz32iriDNi/f31NJY/jOAwjAOQav/32/XAEBT8cz54A4ACA8P3rW60ipZNRgLrHtZZWa932PcU4BEtKYtytcQDHnK105t6ZlFJG9cbGumVZAWKuedvzfd2vb9dfv39/v64uuPe3m3f+05cvqJXWbu0JAXrjDh846Q+/KJLIEZwjTcoOA/eaS5YNmPl+u7ngvXeJ5ZD/KK0QMefUWg3D8Le//U0r/e3r17hH7ktteUtRW9O4lVJjyYDox1C5AYGQvLy+zqfx05cfjLUi3EprvTD35X7/8sNnY8z9fr8vi1IUQmi9W6vPl9P9vmwxKk29969fv4a//DmXrFtvgpJz3ret1NZ6TTULyzAM54eLs6a3djmf//jHP55O8/7DZowrqYzTqLVet61zV5o+//DFBxf3hISn8TSdJmNtK229ry/fvm/bpjWJiLHOObusS0q51sYiJRV/8gdNG0HB74lmgGMBACz9+G9rzTgF56yxhpBCCAcTSoRLLHuKVqkQhvuyp1xqvxpna61yDCAAEOD2fss5GqNA+jSNIjxO4fn5+enx2TgrADHtAr6Vyr1/yMKImXrvUkpprc6nk3fm+OSMNQYsIztrlKb+UY4W43SpteTjVxHoiHiGMwB0aVorhOObFFqve4q1ZGMMKWWUEeADpsMsSilBKSlvAIq08wYBeu9SpdXeWmXBzpWiMtbIUdS2FgD2mD50ar1z60R4FPoB8SjsI4KwlJQPiblzblmWf/zzH8pQ8IMg55xyiiJijFGkuEmrzXqrNIqgAoUsRIiA1qja2jQOP/78JZV8u12d86UXb9SRKbLWdPZHSmLf93VZlNbOWe2N1rSnXYRBYJ5Hpc0wTiCQS0YkTTZxCePJA6zL2plJkbEsyFq788Pj85cv43RBQGFovZVa7vf72/vbfVliSr0djEmFpBG1c/p0Pn368vnzlx+enj5bY43dgWDboh3cuqyHxEoAACSnFOOutZ7GaRompdUBaVBKEVCphaXXVpUmEPZ20FrFlAc/xLgr1EMYNSoByKmkFG/3W0rp2DCN04iIipQAdO7jNI3TEIJPKZVSGzdQuC/br7/92qUDgjG21m6NPT9cwjRcX99Tyeu2W+e8D1pRb6wUsggc2HRpzIqQemutd4VaKdU7gwgzH78FH4kKaUQKAI3WiGS00U7vca+9D8N4oExrq711QNDa1FJraWLg4CPVWtdtSyVz68MYlFJKq8MOXWtz1tNZMWMYwunxMs/nYxigSGlNxNxrb60t7++vL98QhLtYr3Wnmkvc4+v372Ecx3EwWktv0CVuW0qptWY/GZGOBJ0l5bRvW6utlHy73r99e7PWPDycgh+N1c474zRIP7JbKadWqwgACBGdH87WOVRYcwVSZFWVzgKXx6dxPv3f/H8v670BfH54NNblPZfa0h7vsvUuzNBaF0b5cI599B2OC9jhRRiDRRABKKVYbay1BPj67TWndLAWT6dTrXnf123ZrDWPj09Gm+vttqwroHAXpXUYg9Yml4JEtVURGMZBRPZtK7UM02djTctlGkcIyDJtcd3W7fp+JaWYe+u1dUBFOZZhGIZxAMZpnq21//ivfw7DNEzD7XrT7+9vtZRl2ftBhD7PtdT7cs855ZKtNa0yKfXzzz/9/POPB6u55XrUhVKO+7p36GEYWIQQRKTUnHYFLIjkrP3xhx9AIKW43BdnzTxNzvn7spFG5zwLOOe6cBfUSvXehUGRyO/wWxFWirRWWrvjIkl48FvUvu/bshpjjp6RCdoaO4zjodATkXEcGUVlk1KcTuN8GvhaCYAUxZSW+6K0msbp8+fPPgRjbPAOCcuHGB3HIRwTqH3bU8r7ltxgCRUpxYzcOjg21pDSgFBbrrV6b612xdaWW+tcW2XuWuv7fRGRIQTu4r3POfcuzG1d7tu2W2suj5fgwjFc1kojHD+choJICMAfRG6toQMCWms/1LCIvfc1Z+HNOqu14catNuwABoEKNEEkgQ/jziGxUlYpwqMACiA+DPPposgoY1JuzjlDOqdUapNeSqn7timlrTVKGeeNNfbYW9Jhv0B8eHyOqWobeuPepDfOqYpAbV0ESq6t92Xd3t/evfPzPLngBdBaa62x1mqj6Vg5tBr3TXod/Oits84JYPDjwZHsDK0jKXO5PJ1OFwFEhFprqXHf1/f3t7e3a0qVUNWeEcAY44xxzp7m808//+GnP/00DpMPvvbGwrX0Yze+bzsKqBDivlttmWHbY/DBWWe0UaiO3RqSAoIYNwIYhqCNKiXvG1hnFOL5PI/T4L1DpF5bjnHbl+W2bHFTpIZhOM8na9zvrhoiVK23aRoAQGltS+3cCRV2MFa1Wpd1McZxBxyQABVqYTi8xD4EEBGW3GPvFQ+3AXNJpWWxzhxJLVTCUDvLMa2qtTbu/6sYLJyMMYJyUKFKra31cRzPp1NtrTMjAHNXhEarolWtFRWwsNEGENZ1BQBnXZjGWopU6K1b5wHJzt5oS1orpYx3WqleKpJSGg+wY+e+7ev1en1/fZ8md3m6IECOJae4ruuhEagxm6CW23KQY0R4HEZgccHWUhHVluP376+1tDVu//X3//zt2/dhcMa6h8fPn394+ttf/lJTSila0se2vJRGCpUigWOa16RyzkWQaqvruglwGKZS6zCfnn/48nh55N4FdlQ65nK73kHIBw9AShlEQqLeu1YKGA5I5AGH4M5aGWsUo7RaWToqyDnllATEe7Pe98uZtNLHtK21ZrXRRo/jcL+bWqvx7vHp8eHx0TsfS4SD7WqtNmbdtvfr+zD43qWUCsytNOsNiuIm67pe367GmnEenHNGmWEejjZ+LlUf5QFtPv/wZdv2X//1nVD0+/W1955TE2BEIoLL5dx7v97e40sMIRxKWGW0s95YU0otKvfee5dDKMok27oBQZdWa4sxfvv67UjaDuM0hdEp01u9XC4PD5dxmoTb9bYIwvlyRqV663//+98BkEUU0GHGOfqnIkxKOe+4d0KKKZWSFdE0TyAh7nHbVqWUM94WV0t23o3z6Ecf92i9m8ap9QYCt+uVOwzDoEgFH06neZrGWmtOxTp3Os8HVVcp6o1j3KWLM1YZ6o1zzqVWbU16e3t5/WaUevr8pDTtEQTFGEvMrdW0FQGxToOgc946Ow5jbw1ESiu365VICQIfJ1ljiDqRaa0prQiRgKwzPoQjsy/CSqQ16ocXtrfOcmSBgh+UoV76kVUw2lhnpUvO5YiRpJRbb1prVgeAAq0zh4oUEFujkkuOuzFGa22cTVHGef75D8dUF3vrziEjMErJNafEAiZ4ESjcey1bIWcsC7dWAcB7Z43T2pKWh8enuO9xT1rp49VFSM7YrZS47VzaeToBYoyx995bo0PNRcAseYtHXqiknJbIF7HWcOPOoo1BIETy3lo/+DCM49kYs++Fuaea1uV+fX/ftrXWSgTaUi6IQJr06XT+8uMPP/74009/+tlaX0qprbbOIESaoMr9uu77PoQgIjnny+WC3zHGRKSGoJDguLAzs1bWaptkn06jc6a1fr9ebylaa533IQTV2Gizbdvry2suufeqtLqcz9poH7w2StlDWyi9MWrUpFvtpRYEUKi1NuxQPakheBGQLpUrIOVa0pZaY+d9CMM4jlrpWuu+72mPwt15O02DsYY05ph6rT4EbYhZ9m0vuZwvJ6WwdVR4BMPqcbfuQrW3jwY4gHXWOeusr7Xd7/cOXGoRYWOsCLNILU0RWe/KWrZ1U1r7EA4THyFN86w01VaJ0Dh9LOdbrr12AXFGA0jvDaTXllOKIhIGb71rpXEDpdT5dP70/Bn+AiXXYRoV0rrtMUbnzDiGp6cnhRoVLMsChPfX9Xq9++Ck87ZvqezfX39LqZwfn/57+2+n09RqbaU8Pjxs28YSSq+adFwTEpZaW+2tlVIrKrOn/X5bWu962YWQNBnrUi2//OMfX3/77eF8MdZv+2qtb3svtVvrRY4NDPChqpEPHQspBISUojFBG0NE0lmTMd6M40BKKyKrrz7YXvv9fq+5xJgeHt6PRO3D5fF6vX35/IV7t9YuyzJNJwK9rHcWiTEeUcZ5HOK6/Sr/UkTQRG7ce9NGa622de3cx2k8TVPwwTgLCNu6xpJrrSLgnEdCaaBQjSent3WtrVrjrDVIsq6bMAt0o/U0hfN5TimVVLRWIVillTEkvR8eZO6ijRWSMEKueX9PtVVnHRnFIq233mpO6ba/915HPwyjBxAG1hb3PZdag1EMsK7Lic6ooKNorf7XLksEWmu9syZyzhqrS8nCopXWWiNCTpqItKPScorRh/rjz6dAPm65944fpBZba13XuNy2IYRPnz89Pz4+Pz1/+fTp67dvP/7weRh83JMAb9uCgPIxbdDAkFO+L/dSChKWXLZt897V1oS51KqrBkA4QvZQe+u3ayeFwzR45zyoXJL0vr6uMabT+XTENw9tkIhorc/nc20VQLTWgNBK6SKt1n2PB5XhiCAdf1trNaFSmnLKKcZaS2vNWTfNo1YWCZi51tp7R4HDeC4gvbdaEEBQ4UFCr62llEgpZ613XhvduRvrSmvGWQQkpVrnnKuAoFLIjIp6O2oGPef9/qHA6855UGR9IGvruiGitYaZp9N8DLO0Vohoq3OuGWOmeaytret2vORKqUhH149KO0oeThu1LXt7aT744KugtAaV2Xg/zadhtNN8CuMABAxcW8spLfd13/bay6EwSVupuY1hOF/Of/7rX/7y178+PD7Mp1PrIiI5ZUTw3ummWunbujnrfAgAiIjOOW3UcaKrvQscRkTqihi60kTqqFLXlFLrVVg691qKUlRK3bZ+X5aX7y8ArI121qZYtFHLckdArewwDPP5BIw5pWEcegNhOXgeh5kn+NEY03rNqTpLxjmFGhCR8MALhhCYe84NoA9TOPxSpImZe2mH7QUQTVe99m3beu3Omux8Z0alAPlArBxZzJILEQ3DoZIWRODOrRXmbowhpFKrVtxqA0KuveMhiadhHI01ztp93/cYhxCUIWFAoM69tYqolFaksffunNWKuHVA6K3GuKectFHPn56491aa0XT4h4m0AHTOnaUfBRUk5wMef2CQ2/s9ligAt9t747IuTWvVel/udxHu0lLel9vy/ev3YfQkyMypFCLduR8m+4NdWmsppQIC915KO5RKy7Joa8dpXNa9lfz16/e3t3cE0jq9vLwoVB2gpGysJbTcRTQeqXf4UGkicy+5O017jLo0PzhUx1eKMkaT0saaJ3qopeaU1mWxxlpjDgm5Mebx6cE5++nT56MV0Up/eHzsvf3rX78euw3ySgS2Pa3bahZ9uTxqwPf3Nz+4T9Pzo31MKTtnxmkYxlGh0lqVWksuWikmyaVw78EPpz8/xHVd7zetLf34hz9oMqQ1C6/b/p//+H6/3R8eH8+PFyRKe1qX+8PDpTSFlQ5MilKktSZN2mhtda319f0N8VZyAYAhjNYeZQSIOd6XW2/NGtt6a2l/v15Tyeuyvl5vnz4/G20OESR+YEs/fvGBHgMpuYTzdDqdSNG+bznHA3rFR+kOMafSWtPqsLFL70xIx47YWUdCp2muqV3Op3k+jcNkrX14uDw+ni8P53Ecaqn35WaMJtTKkAteGJhb7TmmI+DPnbu19vn5yQc/DoN1DgRIISIwH+5DYumlFoFOio4wa4rxEGFu2xZCIKTDAZJiSikO42iNBYSj91tLTT3lnLdt08ZYa1uvWhvujEoNg9faiEirjZm10cIt5wyMtbZeGRCd1Uopa/EQZSilj/tEBU4xbfvee4t7VEZb60nRgffSWnfuR7ASEUMISqmcM4v4EIzn1+/vdd21VdZaban0ekyES8kMYovdUySjh2Eo+bijeK30fd22bQ3eO+/D4H1wnVlYlLbamBjjDtxy32PKpThntdHO2SEMIMKVc0yloNa2tZpyZ4CjlDeMQ/ADgS6pCEvnFmPctiWVmGLe1r32ysDMHZEeH58+PX86zWeFulc5qI2AhAjGWG2M0vrl5WWapjEMpWR7mNm1ctYrrWrtpZaAXpFyjkpOB3Q257zv276szJ00sViW3nrrjQ+AuVJIZGLa39/eck691YeHh2meROB+7wIQghdApeiQlS/LklImVMxMmg6RpAgoZb07EGVOQIyx7qOSVhWRG6zTbrkvuaScMgHWWoE/pOc5ccm51qqNjinpdRFGQFRGkTJaH40V4NYaiNYKiXKKuWRhOQJal8uDgChFpPAozThnGaXWNk3TNE1IyK3f7vfee0wJr7d5nMZ5UEoJI3w0OxERtFatNlIICnNJHxBJa51zh/8DmHuTbatvb9f325W5Pz4/BR+OB42IuPL7vbdSv397EZT7ffn123fr3BCmeZyfvjwucblf75/Oo3au1FxaCeRDCEwAKLflZq09JvcheOucUqrU91IKkl7uy7YvDHC/LYL457/+zQXz+v3aeh3HobZSWt3jXnNjkXmeTpdz/qDzqqMJ8FEJgMOl2Rl6SgUg1VZ66y/fX44l3Ok8f/7yxTqXYiRST5+e5mlqtZuPNxMggPcuDKE3ts545+SjY62/v7xoo9O+r8vCLNu2hOA/PX2qtSzbytzjnMLgW6vLchunKYSh96Tyh/HCDaHWbktRiqx13I+OZ9XXt+s4zZoKKqWNMUZbZ48oZNy3+/VWcq61/Osf//Tens+Xzj2lqrQZ51EZss4c8pDTae69+cGv65ZyYmGHvpXWSq2tkCAgNGbmvq778b5Ne1zXzRijjZWDV80iDKLgOP937vBRpCKlFKqPYVZrXQbprS/3hQiVstZaN3qtdC3Nh/D49KCUUkp1btu+Bx9++jE8Pj5oZY0x3rvGRTojAgG+vr1+//7tKK04cM65bdleX159sN4EbQ5GjhqHQYRrLSGEXhvLoYGW1rs5CpZaDd7WUltrt9sVGHpv3ntjzDAOxwGh1l5LrbWWQ3WgtYhYZ+d5JkIAyiUfAflWmwB471ttpGgap3EaFGlEPKJKvVZjdE2ttoaISqE2CgG7CH7se3vO+cAXpBSv1/eSc8rJh+F8URpNa21rTQRIYastl1JfXh4fH51zgOCCJ61LSp278XYaJ1JUSzWtW+daa/u+i/C+x32Pt9ttnuba2jEDvd3XklOtFZCUtsZo5x0q3La9pZxrbcxKa9VUbeWgDDtvvbXNWg00jIOzzhirjJUMTmltzTCeT+eH6TQbYzpLqbVzb63EuO7bmnIsJS7rLcXEnQGwM+dc457inkRQe9eL7Fsipa0zrbbObIz9/PlT8MOxIHEucP/4UJRSKZV9330IShmlSEh6F0U6xT1u+5b2nBO3bq09nWatjPNWWUIGJDxyATmnWkop6baQVnqcRwTZ41ZbtcZsRs/zLAAxxW3dAUAp470fh9E5q5QJwVtjjTEsokgdvfF1XTq3XBKz3N7vNVWtURnTubXSmAUICKF2Ph4MZz0z19IAMebsnZ8n66xFVIhQahXurR1aY26t9taXdUHEEDwiG2O4Cynk1BHRGlO08t4fpyIhPprtx+cuwtY4Y0zvUlvb99w7G2uRoNdujBLGGCMIeG/neRqd71zjnnJuKadaauttzwkEUq7OB+WsqgUQgWsueVtXIfn+8vr69na7b58+f/78w2ci5fzwb//9v33/+n1bV+3dPM0AKIxyREJbr6WsW2RhhepyObkwdpb7ur6+vl2vt/u6eOcenp8fHh5v96XkbKwmUufLg3cmp3S/r8M03HixyuTaYs6oNdIxmCQBkQNnKAe4pL+9vNHRzApBK3OAWEgRc++tGW8RqffunDuklyJstCml3pY7COxxP3gk58tDadX7kZTOrWpt12Vl6K23A6K1x7WUKgLrvsG3b6fzqXd+v97mt/daa2dxzhtrz6cTclNWa9HcuNVaa9PGfPnxi357fd/35LwvtQDCjz/++NMffz7Np/v9+uuvvzlre2+392stZRrHWnsYAhnTuO3bfmyle+8lZwJyxhddrTnyUX3fdkWKuXsfiHDbt5RzmILSCgmn85RSPcBbAnK0lpq0j/Ab4pEfFwAk7J1rrQrUcQRW1oYQEPB+v9VarQ3jOJ3PZwDcY1LKPD5clNa11FLLvq+ttR9//JFAldpEWBu635Z93Vqr8Jn3tK/LKiBwEiCp1/b2+vb+9h7C8Mc//ME605m1sco5BCjNCPB9WWrNROqwE8+n2YcQvLfWj8P4fr2mmI7Yw+HZGMIAv6+Jai1aKUTsnbftlnKeppEIL+cHRYSI3rpUSs5ZALZ1dc53boQowMZYY3QuyWgtzL1VROW8kQ4Hpx5A2nFCr1WEt7jnlMZx7NxzyW+vb0dXwzuvlGqtl3JUu8EYU3JZ7guIzPNpGAYfQoy51T7OkzNWGV1LOVhptZQjYHiwBO73u3deGERkHCbhDiyn8znF1Lmv2+pDaMy998ptXZcDNag1KWWU6su6pLgbbS+n074n7+w8n8Z5RKTWxXo/WiOg5tN5Op0IdcpFAOVI1LVWcimlAErvPaeYU+q9A1BvfLstX7+9XB6fkBRukZSFg9ugzPHZ5RzP5wuB4t6N1tZaQRiGMaUsiNpoFu7cY4rBOelSWxXgbd9LLYiQYz6uWNaqYRiVDjW31nopJe19GMIwubfX9/h9P5zStTSArpQWAUQoJROdgaGWwv04HfHD6Xy5nJ3ziKSNPnbFfGBji+zbmktMOYl0EEyxeOedOOs1CNRWUopaK6utMaYzG6WddzmX2hopIkBjrTH2o8hKiAcD7+BFA1pj0FgRIARCqrVYa1prSlHKyVittQ5hOHSnB/HCO/9wefg4DzF37lq0UtQZDjZ1T62W6rxtTQCg1tJqsdYabWpvx45rT/F+v5dcwxCYYZoG0gqVskodscVayrqspXQ/+JRLzNlY8/z508PT43rbGI9i7YRIp/P85fPnYRid9wKQUjFKd6Qmfds2a11j2fY957hvkZBKLiWWY/0xDMPD02NjLqVM02yt+ViBAFrnxnFa1rVXyTmN9oQsgNila+F+cDoQW23bsj49zSnu+x4R4cunkw+uMaOg9zbnfEztPjavTbRWvTdnXWvtCKa/vr4+PDyycFHKeU+KrHfn05m5e+8fn57itu/75oNngMrdBqu06iC1tflyFgJl9H3ZjLXT2TDz++02w2k+6RDC/f2+XJfO7IOf5kH/4U9/vN/uLNxb98HFlNIe933/+tv3Zb0TILPcb7flfv/5jz/5wX99+TbNp2GeyKh132qtYfCALMLzPNyu7zXX0+WEgC/fXnOOQxi10kioSTfuuRRAWfc9llTbwd4zrdXa6qEEgt/hxQDILNI7M8c91lLC4J1zj4+P27bsezyfTn/4wx8BQZFB/BAmlFyt1YInOpjzCMMQRGAYRqX0IIAgMW7rGlNKMcfyS2ZgP/qUUufuvOtN1mWJMSqlWisp7XuKx2COiHJJMe632z3tSRultGq1H6aRbdv8uhmrty1aY8dh6L2t61pqab3t6zbNszCjAIPcb/dj59la04qWuz6y/jUXYw0pZazJKc3zPI0Tcz+0GErzcl9bb9MwjFMwNvTGKcV9Twdv5+iyCrB02df99e1FaUWExlhFVGox1hxMiCYtl5xTZhalkIjm0yw3uV5vndk5H4aBlEopAR+YIDmw5s5Z7n25rSlFY8wwBEKqpaIi4a4MEdpaq1bae59LYW5x35s1AhBjul6vMaZhDH44d+49xZRybc1ZT5par7/8+n7e8zSdjvaAn4dUK6CeHwgQe+/MQFof2aRlWV++v9zuV251eb+t65pi5NbHafTB11YFYNt370JO1Y/6IImipl64teqt86dLzjnFaI1xzpKiaRqRSBlValnXLQzhfDq32g6WyfX9bV9XpYEArNWltm1bcymnOZ3PZz0EXcunz0/chRTV1nrlluvD0/n56ZPSmpuYwxunLRCWkmup2qjZzJ17ybVyOeyK83mWDtu+Xa/XVltptZbSekkphckv93sIgbndblejTa0jKdy2vbY8UADE6TSFNhyIn3VdOguIPD48IeLRB0YFhJpZCOF6u3duRltSOE2zc5409tZijCJ8UPtrKcuyMMv5dEoxee+ddwdt5VjatdZKSs7bXpvzDgS0UoKICDlFImkARFhLIYVKI/d2KLGcNc77J/2MiAzSulhvOkuKWVvjnG6ti+AwjSknAHHOX84X68MPP/wwjqdaurGGGw/j9Pz5k1EfqzIiPEw8pGEwg3W99WqtF5b7/bavOwhM8/hQHq0PStO2bq32YZqN0/se074ba86n0+Vycd5v2wqAp3h++/5eewPm2prpTSn1kQH9YFfKMDhhJNLMfL3eNZlPXz55a0sq92XJewrBW2dRQCmc5pD2nFOCB0YCrbXRGkRaKyHYWrJI662WnE/nE3f2PoxjWJb111/+VXvXWq9v133bnp+fSNHtvj0+Pfz5r38FhOv1lnMprYfB51KXdSklTeOsjUYlhNK5xpT0588//Y///X+UWl/f3vZtR8R93+/X5b7cam05lcdPD6aY8la/v7yiwn2PL2/X0+X85fOnQ+O53tecEykah/Egj9yvt5JLTqm1VpX2wTLDkjdtlWINiMeezViltHbOHa4rpZR0ICQROVqvSqkY4/v7dZ4G752x2lnb2rHev/XWPz0/TtNMStfaUspwkL8Ea27cJaU4DMPT0+Ox0yslk9JjGFuvrXfjbSzpX//8TVk6nWZt6fi9rLO99dZ7ay3l1LnFfc85A0OpNca4rZu12oWglOLeSBMippjilojupNE655zVWit1MKRab23btlLqNI2tdUTIJTMzM8e4Mx+hmOa9N9Z65xGptOqd984BotZeK/32/nY4FQSktGKqQkERQCKllQgAQu0NAY7Uyp639+uVubfOP/34w6dPn1BhTrnWum53ENy2DYm0UgBKKWW0Pp9PKSVhWda7sQYErLGpx21biVRr1VrjnOut7dsW4/7lhy/CYoyutS3LopR6erSgWERIkUZdW02pECllKJcSU6q9de4MEnO6vl/fXr+lFAfvjbXW2bSnzrKsS0zF6CPUmzVZ4wm10sYiqc6t1xZTXNf9drvu+5pi4la3bW+1HSqCo0WBmli4N67MvRbIxMwiB5OKFGkiAkFrLIigQClVGcVyeChonIacc2u19Racp4ZK0bLca47bvqa0xxjHwYfBT9NstMkli4Bz7vHxCeFIcPE8jk/PD0QEjL0KInZm7DgMJqW87QcKbSBSKaZSCnPf1rX7djATt227vl+dd63VPe6t1XEapmlsvcV9f/n1pbXmjH170QIsKM4aEW5cO7D3btv2bd1KyaWU6/ttGCZQCgCIyLvgPRJBYzFGe+MBMKX4/eW7tdY6e7zy9z2GEKyxp9PJOiMCB2an986tK9JKKRYuuXTurbdBe6NN51ZrB2KiQyGqUoqtV0JMKYrw7f369PTQj7h6rMro88PDNE6t8zBP67rV2o+TUKtMSltLrdXPn78YrwD1v/75yyEMv5xP1/e3HLMmdTqfp9OALNe3mwBY04w1pZbO/fnpaTJunufbbVGaci57jK01UPT4/Pyjd9qq+20tuYTBhyEopUWaCKAibYzlvu2EgM5b40zZS5cOyIetAeHDgXHoS9ZtZe6naUR8LD274Ky1IJRiSns+4KxWW9SqM2ttwkitt2P99iDQuRFSCF4bve37FtsQhjAMWtmUExEN46RIt9Jab8qaVEpOmZQCFEHZ94hIInKIwXvn8+W8bmsrbQze/ymQYO+ttSYse4z68vyICHndXt9eucmxFI0ppZR742meFCrvh7/821+3fbvdVgamxnIDZ900D97bYwSEiBoVS0eAbV33bbfGOGsVUW0957htq4CcTudhHMZpEgBjrbUWEXIpZ6UAQSslzMcj9cHaI0w5An9kWZRW1hjvQ62NFBrrSGnubIzu3AFg9GEYBx8sALSqEKEz11b3PfbO43QyVsUYETF4/9svv8WUoHBKyWgrAIjy9vqecmLm3isCGKuP+34rBRBzye/XN2vs1Kqz4SjVOGtZBJUM0zAMo3Q+LJK9N2ONNvrooAMgES7LeggG3ORKKq03Zs45H3Oh3815VmvFnUlhrZ2ZU08ppfv93jt770TGFBMzA0LvLALOOu/nY2UiLOu2328LIs3zGLwvpYyneRxGFARAZxwgpBg7cweptVpjDka0iPTW9nXz1g7DaI3uXe/7frteY4zGmR9++FEEnHc+OO/csi5HrFMZM/iQctZWN+S9JIXUuffeBWTb2rZtLMidffDzPJXGAKy0HoZhDGMYBxAIYyBllvvCIm7wwzyhVsGP54fHYZq1s0eUtrYW933dlnVd4rbXUlpL2772w74kH2UrUogonVtrhURLLh96gs7GaEFERYBAqJxzJScWBqajV0VKa2PGaQRA7r33bqzClUtMnWtO+7Yuwr1Vcm723iKodV0U6VLb0WJGknGYwzARKUE+1vXOuZyrANzv67IuCGCsvTycvfXadhDhXmNau5TSkrC63e632zIMPuWslTLGhiFY54Hofl+X5a40Ecme17eXt8vD+fHxAcm11pf7fVkh5wKApdWUsx8HATpm1rVWBHWE0GNMB0g4xbTtm7GGlFADQOjcciosYq3RWhljU07L/eaDB0RC0loBcIxx37f7cpumAQGZ+b4s27oLiLXWey8grVatSSmFwV3f39K2+6C8H5D0vm21ve2x/PADBj+EYbTWl1p7b9zZGOOCU6RAOMa9cfvx5x/XdVNaH6bdnIv3/jTPp/NpGsf7/eYHf5Q2lFGncBaW0ps2FolO5zMR1nYDRQr1MI3DOABCSlmTtpMbp8H7MITxNI/rth7CF0VqHEbpbL0TFnkRBK1QAyMpPKiR9GEkFWPMEeFz6IMO3hprbSk15phTtvM4jkMIQaPat225bbWnWmpOdZ69D64UOlZQhMStH4Zk0tRaabVw77Vm68zD04UFSVPMsaTi/QDQ47YfoF9mMFoXjduytJzny+mQY2/bgkxv71cAscaQtlob6637v/6v/99//v0/lNI//vyT0frx4bHUsu27D34cxi8//qCI1n399dfftn15/fZqnMspjcP45dPzOAQi9N4f3iWtlTWKjR+nMI4DAi1xTymmPX6IfxGdc4gU93i73RCx5FJrdcYiEXc+XAqHRlGRJqSDDMTce+t+nk7zlHNlaeu6vr9dEXGax9a6NVYH76whxNaOsXOrtcacaqvGWEF+v97Stp0up69fv768vjrvurQYc7PdB98PP17piFhb2+PmxNVShVkrcz6flTb7tnPnlAo30E4rUq0ykRrGcZqmcZxqLkdL6GDkhiEoPNoMsu0rERmjxnGutQ4Pw/Onp5xTyinnUm7FWg+IxthxHHrnb1+/tt6ds3GP1+sVSU3joEjX0kpJ+7YBovU++NC5x5QQUBsjzNbYcZwul8s8n8LgCdF4x8wIYK2bp1PrFQFzza20zl0pKjnnko8TdKnyfn0vuTjnDengQ69137e0x1aL0Xoax/v9/p///p973oFRaYW0e2drLSmlzkwovbbeam9sFeV0QHGddkYRKTLe4dOnpzAGFLicTtbofd2s1eOojDO5tA58X1YymlATkfWudQbhY/27x72UnFJMOeeca8lxj525t0aHiIMUaWqtNW6NmyKATkjmCGvAIeshqtKVIkTsLDV3paH1UmvV1ngbggvruqQUiYAAQGCep1yJCMZhBOjW6XmerLWKNJKqtW3LQkqV0kREGI9b3fX9fd9jrd1Z2zsbS70zIhhrlSZuPe77vi0Ch7gGSin3+1JyS7EYYwCk5hzOp2EYgx8UUM+tlbpuK4IkawX47f2t9XrsG8+Xc+O23fdWWxiCtVZrez6fx3Fwzvdaa21K6dKKsHRpztqY4rJt77f3fdsJYZzGMIRl2azRAHi5nEUgl7ws6xBCLtlaZ60xRh+UDiR0xo1+dNrtKe3bmlMRkRxzrZV710o9PHxSiv71z7fldlOEmpQx9nyaUXjfYsXcamnaECnnrTKq5FpyHgZ/8DYVaWAABEP5f/7P/1laA5Dg/fOnRwSZT9PpdEZE7xx3ud1u8+PDMI7aKEQCgdparUU6HBKbYRiOjFPJOaVSSjVOa61KKkjgXXDOL8uy7Vt33Vg7zWNNjYWttcaY2uTgqgIQCoIcfmAQAG+D86aWVlszrBsSdyZEoxSOwzjOxrpSalc91/z2+rJsqzWGlAaCgwW9b3vndppPjXvndjzfJeba2/X9fRzG03xhET8EY9zTw7PWGgGt1qfTabkv99tqrVXKGLKffv48hFB7i3vk1tOW4HdZi7CcQ9DbsqiZ31/fXl7frDEp5edPTz//9HMYh//n//v/dbvefvjxx9PlFIYRjfr69duyrLW3nvgwWCz3RSGOQ3BOl9JLrSDy6fMnYEgxl3RAjsw0jdM8GquHYUIkhVRb22PMJXEXYUYAUoQCiNC7KBIE7K0jyNPTZZqCMITgx2EcwuC8c67nkl6/v+ecjFbMVVjM+WyM+si3cTdGMfdSs3AfQvDBd5G392tr3dfh7XUxxrkwbNsKgkoZa11vgoNmBqXI2RCGQRsFAt75aZ6MNcM0DoP/93//j5KLMy64oJWy1hqriajmvkME6MxVGPn3eOXh7K2lvr9dSynOn+bTdH2/xZSsNaXW2+1+v96HcRhGQaTjmF9rrbVO86S15s5IRAQ//PjDOIz35f76+m3bttZkHEejjYgc/9K8D0holFZahWE4Xc5Gq9v11o/+hNJEVGrJOdfWuLPz3jlHRCnGUgoAHO25faPNrt4Pp3kep+lIWx/hJWE+NHZb3IT54eFBW9PqoSoEa02KadvWkksI1nlnrOncKSoi5ZRVhMooAcSKhjRp1PZDqJ1zIq1LzlssiMmHiUsB1E+NOzOXCiwi3GstJXOvcdtLjiXFnFMpRYQFRGltrCVFAFJLFunyES9jRXAoUoWPWgOQEoCjo9QBGYi4SmsNkImQFPTeUoyIYEgJs/dWW/LBtVxab9Yp66xShjuP3i3LGnM6zafTeagl11pu91tN+fp+y6mUWqxxh8nZe//89AgIpaTW2v39fVtWbRW3dnm4cJd9W1PKvck4Ps3zmHPuvSOAQuqde+0AkFK539+VVufT+csPPwBIjBFv5IIXgZhSbVVZPYegtA7DgIiEJKSNVdY6EpVL5M73+22L8Xa7HdvF2vq67+M05D2F4HJpSCKM3tnaKtJYP0LDUpupteaYiHC+DPM0HzqK4wvRDU4RAWAt3ThdSqq1busqID44QE5xEz4K4fZ8uYTgBZgFoYo2ipz13vbaWy6ksQrUVEkrb71yZlmX+215fX29L4ugkCbSRIjGDOM0AMJ0mpxzLNJbTykJyO12L6U+PF4IlB98KXVPmVCO+cm+7UU3HzzvzI1TjNf3KxFZbVGAe6+t7HuKMQozV2bFqOUwKR3JdRQhJDd663ROAkqcMYpULUUEFCkTTBh9yilu0XuHRLWXmPbgLzFG603JFRBvt9set7e3V6U1IqaciZBZ9n377dev8zzHPbvgXfDD4Fm49mq0MUaFwVut9z1aZ51z72/vwzBO4wCIxpq070orAvry5XNKGYlC8Pr69tZKQkXzNOWcT6dZG/P95dU5N4YRBIZxYIbD7nR8liIShvDp8yerNBKs20oI67rtKeWSUVTrbfDBGuOMCcMAWv/69bdvv31tpY3zMI4zInSRQ7w+jAPS75n6LkcoHgF+h23rcRjmaXbODVOw2iilANAYrfWY5zpMXhMqowlgPAVNmlmEm1LkvM2pEKKxWhsS4FpaaZlY7Uv03j0+PX379u39+o4IXXpK+dOnJ2N1yint0RgDhCCotZnOs7C8X6/TPButT9NJgE/zHMIAgqSopNpb19aI9NbqwWsFBEBZt/WwQU3TVGq5L3cAuJwvPriU07pspaQUY0wZFVVmZ9xxxTbGDuMwjgOiCj4oewAW7fV+S3EDRFJqsMYHD4Ta6IPz7pw/X04lZSSaxjE4j4TOWxdCq3VbJG571bW1tseY4n46nYP3hGitG0IotdZSjn5ALQKCVutpmozRl4dLraWUsm1bLfV0OT0+PHSQ3lotlUZttK61ErV1Wa6391YL80mfLSL6EGrv1hmljFIqhBD3CAKIWEpdbovRmqWVWvKypxxv910p+vEPw7LtKdf/7X/7H0qpLtJbQ5FWqzC3VnPaW6nMPacszEorIvLOW2tE8MBHf8xoO4vqH9+Avxd3amNiIaXwd6OVNba3jngI1/qB6+pSAaQbrYmsdsiqpxaGQBpT3EuuSnFtbU/pt6+/McswThYRFHFtOaea+zCMp+lMmoSh9T4E77x13uaceuu5pm1bS8kp8yorEJCm2+2WUtGKWp+1pvNpent/U4TOu6M6p5W6PJ5RCwo9PD3+/POPvfdt22ptvffWW21lW3fvfZMGTK3zafTjMAjAtu0iUrncl3tvsqz3xo0Ip3E6X86kVOWaYypU7vdlWTZr9dPj0+XhUlsFAW3UIf7b962Usu0bAPlgWDohjeNACrc1IpHzplceQrDelJQB+49/+KG1zK0Dg7PGWG+s4i61trjG0/lEhkqurXKrrI02SnXAVpuwMPN23xp35zwpVXP+utx/+eVfTltjtFLknLfaAaCzruQMCK3W+22ttYRxzDlt604Knx6ftdIvL6/X96t39uHxojR1ocOT6q07kv1PTw85lSObd+SwQIS7CMsh6RT5eJA+2iWIvffeutEqhEBEwVsBSDELs9YKhfKe97h/mEdbXdd9ua0PDw+t1X3biUgZra1aliWl9Pj0NM1jikmAAfGXX35dbkspNad6Op8ONwMAG62D9+M4lpyssbXU5SipkFqW9f36fkhrDgDtTz//fD6f39/fAVEprV9f3uK2e+f+9re/vry8fv7yeRxG54I2+nQ53Zb77baUUlFhGL0AXx4vvXWrzcEQJ60IFZKquexx89azQFxjr90ofYz4OzSt9eVyZmFjjLK6pLIsy7buvw8ECOXwLfcPHhwiEipS3hrnHAF6Z63WCqG3KtwOzPqn5ydtlNHEwq3WaZ416d5aa2ydMUqBM11cLiXFyB1SqXHbrQvnwZ/g/Pr9pYsYZ5n5drvnVMIQTtNpnkZrDIvseyTU1upSek7p5eXl7fX6hz/+4ccff87lUJfncfCDD1s/AHaICPu2xRTveHfOa0O1tFyK1sr7Tw8PDyklJOy9Ka3HcVSk9n3zgycyKedeGxhnjTVGE6ne27rurVWltELtnf/1l1+v99s0DvN4fnh4HMeRSCltnLMH39QHf76cpfPhrssll5xLzagwbvEAoBNC8O7wHsR9A5EQgggzd65Va+W9RaKSMgjXVm/3+ziNAGCMLbXVUmtvXoXL+cIocdtX2U/nGQRzTLHsJUcELrm+X99F5HQ6GWetNczSarbWJETSOIxh6Z24K1JKkbVWaYXYBJm2PZX88v0FjTHaW2u10sjcWI4QAHOvJZdamDshcO+IoEkBgTGKiHpnIgKRwzMnzL134Y6kUQBEEIGFO+Mh9jueOm20blobRYIonGLstbP0zl3hNM8TIi7brbKeTgMhtFJiiikdPLcaY9TKikhrPcdKBK1UH4YxjOMwKq1SzLVUpVEbfbvdEdE633qVzqVm5y2R2mM0xvTee68iuC6356fnMQxfv/62yTKOExEapZ11f/zDn/9IfyylhsEPYUCiYZxyyceQi5RSxiqlpQOQOGusMzmnfc/rfSWltNHX97u26unpkZkBEAmASIC3uOeYBRgJjaVSD/R/M8a0WlNOwYfeS++ccm69AmBrdL+tp/NsjAMia32MmblZbw5kCAGK1N4aIimFQBCC886TwvW+p3Ux1uZdkdW1tAPYWVIahuCs00REVEvvne/vL6UVra0PAYiG4HMury8vpeT5ND49PD09PQhxjCmltK2rMjhMp97YGC3QS8mlppRzqQmQc8l7jOfzxXmPgs5bYxFFnaZJFOy0E2kffKmtN2Zh3IC5E2mlDgsMEREQdBGojbkjSevNOq+UijH13lJM0zQ9PFxq6TkXpZV17iDZpJSvt9v0MvrBLesaQvDeH/PeGJO1XpEmhaSol3aga0jhtq7O2nW9b8tyu90AxVnVmr7db8t9YenbGq0159Pp2/fvv339zRp7uZyOaFav/X5bAFEbqrlo54x1VoAVKe9Dq/10Pp+m89v1ffDDw+VijN7THveY/5lT3udp/PLnn9Zlffn63Xv79PRgvSNFpZZ9373z8zj98+2f91s/n0/B+X3ZtxIB5HI50TEOGmdm/v76/etv31zw8zj13gjxCP0fxBvShICKSIfgvQcBpai3vu77sSFRSltnz+eT9540tlpBsFdRRmorrQODgGCT3nt/e3krrSIgC63bWq+3YRyNMZ9//DJfTvu273H7x3/9Z1xTjhlG8SEgUtqTPoo50xB80Fot93utrdWeIB+dyVKr1urxKYzT1HsDoJzjNM1dOO0RMJeGCKg0hWGw1oYQtLY553Xdjp2BiMQURdA6VztbZ1xw67754FpNnXvnvm+b1joMYY+xlIwgl/Nl8IPzLgwhhHC4Po7ldmstxayVyiW3WnsXlrYuS/laW2uHknOtVUT2Pd6XO8LRZJHlvuz7xizWGO8dg1zf78z9+fnTtu+dH42xvfejNeq05c6lFRCY5tlZ70Ng6cLcep3PszIq59K4p5xxW11z1jqt9Z73mKKx9nSapXMtLadklGqxGqXGafJBZp608cu2a23Pz0+fnj89PD0e81aAQ2LJ3LnViiKHlY8IFOHRzDHaIJAihIPzDChdBKXXXnMz2gEAkRKBWhoiCHsGUUoxg1IKAZw1tRZhRUTDGNZl2bfNWYuE1lpe5XZbasnGUKkNkZibdNGkx2E6MAveh2EKy22JWwQhb2zJpe+9dw7OamuUUbWU02k0zraWUSSXcj6dwhB8CABgjU0lpW1n6SxVGzOOQ4yx1czCrZXa6nQ+a6O2+zaO8xi8tWbdVmOMVjqlGI3+9Gl6en7iLsce+zAC3m4rd9ZiACSXdL2lYINxmllaLV3gEPgYbT59eh5CAMQ9br/9+rWUfH44D2EAgJiiUkqAlaJxmozWApD2BHd2zjHAISOotWlthuAFhFmWdf3666/WmtNpJsJlkV4rKXV9f/v27YUUPT9/tt4zsyDN8zTOAyFpp1Fw3+JtuR3Mrv/P//H//fLjlz/9+U/3+z2XusdNEZEigO59GMPgnQeWUvI0j3tMiNB7a63mXEu5Pj8+KaRPT89pyuM4GmPCOAhzyUUEWunOKWEkwaeHp1abdlYgEuHBTTqQeoj4gS4+olGHHxiwt7ZtUStlNOeahQEQam05ZkFk6cKQU9q2rZZCpMYpdOn7vh+6aUDYtq22zswppdrqOE6PDw+DN8+fngFAaXr9+pZSKjm/vH5vtSFitPZ+W+7r/eX723K/z6f50/OnnIsP4enhaRiHx8eL846Z0xaXZa2tBu9STPr58+P5fPr223fXQvD+/f3WSt+27bevvznrpnne9vX71++nhxMUQcTDW8DSUomoAEn/XvGVnOv7+x2ABGSPu7P2W/qttLrFqLR6en7+8uOXYRiVUoi6nZv0Q1aIrXBvx/z2Q4xy9IxQIwkqpb13LFhyvd83QXl+fvTexT3FPZZcjdW9d2Ot0hoUdJZee4x5VUobta7b/XZvnefT5Jy2RvfG27p8+fLFWvv50ycA/uWXX+O6kZB3Jsd9T6mVSkjOWmsMkSq17XvOrQtzKbnmwp29896ZEHzK8ezPSBC3uO8baTWEwWjdatu2XWltjG6tp5xyKpfzQ85JhHrvAhhzqrWB0DCMwzCSQlS4LevL66smfb6crDH7tpIiUiTc5nkyVpUSnx8ftNGKZF/ugqCt7a0rg04ZhrrFfV3urTalVast7rFJq7nUWuZ5LrkpRcZaALzebtu+W+sAwTn/+DjHGP/zv37xwR+PNDPXUo5v29v9rjT1Xp22taZajFIkopSGbdu3dVVKPT4+Eao47srYbVsUkdKojXLBIrJnm6/l119+LSX96U9/fHx6XNf7tuxx20jZ3jozWus/fXbPgm/vN4U4+tE5v6cKpKyxcdtS2olYaRLpxiiAZo3SWvUGQEC/IxZq6602AEASJOrMtZUuQWoLxmttcypKKyTQinLrpLBzjWnj3lywqZSctmmcpmloPTurY4p72hGRO3//9ibcjDFffvj0cHk6hAHLur2+ve9rnIZTYympaG2cNSzMvVhrCLW1ShsDIE+Pp5y3t5e3WnY/6C/2MYQwjlMpRUSGwQq05ZoB+7q+Iwr3ZrVCkrzvQxiG8IN2FpAeT5dtW/d9a9XM42isyqmQYh++zPNsnMsx51SYS0xrLVVrVE5rZVhkGL1w/+3rLyGE1ur9ftPaPH96Ok+jQn55eS0ln89nQhqGYTzNIhRTXZb7vm1I+Pj4eLmcAWS9r6ShlmLsuMfaRUBIGJQ2OZW47X6wrZZt3XJKvdeHy0kh3m/vX3+NzCIs1+u7NvZ4c9yX9XQ+ny8X6/w8zj4nQsqpvL+/51LO59M4Dq3WUkrK8SBse299sEgq7fs4+NZrTBGRfNAxZiI0Rlljnp8eWFhrbZ0dBg+AnUUpY60VYGBptRuljDa9dWuMdYalt9aJVGfuhyRYRGsFB7oGkZQ6jLOkAEA6N9SyrPfb7T6O4Xw6D2FwPsSUUozDEJRR9/cboAxDWK121udcjxJAKWUaJkTyPpxOFxEpuRpdj7S3MSbG9P3bC7e2b7G2dr+vSOKcvb6/v7y95FxSznGLKefn589Ccr1de+8/Pz5oZ7ZtD8H7IZRWuPamugDqVvr/+X/8n0/PT8+fnsLoz9/fO/e///vf//HPf4YwnB/OhBicF+Zc8tEYer9ev7++vF3fHwnXuOWaiUERCsvt9n7kNAbvCaDW6pz54acfWusIGNctLjsDnM+XHNO+bXT0tbgx99aaIkuklSaRY9fIDAdsq7ZSK9dSc+VW6qytZeDGzQbLiCzgvfdDIBBhuZUlxqiQxtOQUs6laGNr7cv9um0bIPVW399eh3EEBmPs88Mn97/bnCMhppRut7uxyjo7DBMprYzJJZVrIYUA6qhx2uBO80krxczLfV3XjaXnlPYYUaExxmuLgFpr44yxpne+r4tR5i9/+cvtdhPoLFJTa6UPIYynGUW11n2w2qqXb99u92uV+u3bd+9c57bdVkRwzvVW7/c7MJCQDcZo64PjKtoV621KKe2JgZk57VlrUvqD4o8daq3bmhCoN/beLdu2bXsIwzAEEESFQxiscd47Zkmp+sGPQwAWYSZEAHx4uNRagg/jPBzQi9Lq9XpFJE2uH9AOVCKQU66lTvM0jiMzl1a2bVNGcWc/hD+cPBAs+3I22linVD5+StbZVhk+iDVmiBVJa6VLzK2ydvogahCRoAiwsab3Wms9Cg3CfAjqj2K5cNPqUND8vqhDFAYgPHo7zJ07cD/mQNJ7rxUEoNYKyHHfrDXTPOWcS8tCEks0xrZWa63jNN6uV+jNhyBdBEFr46xz1ohILbV31mR8cNMQrLbBO+MMMKSc9nhjEaPpvlzv99s0hXEe4xYVUdzzHlcBmWk0Vp/OEyIcCX3jCMUc2TZnrfNeUAliLdk6K6lbZwBkuS/X6w2VPD58ctYf/fPOfV2zfdTaqi3GFhswiNC+77XWYfDG6lwyKgIl93XJNXPvtZZtX/dtO8yd3HpHqWmLObXWEWC5ryWn4IP5AONIaVkre5jpjLGOqFexVuVabtf317e33KqQpJJ7rWnbbu83Rti3vXceR8g1t8635f71+4sz4ctPnz9//jRNEzBeLg8//vjDt5fv83n6H/+P/6G1ItJhCPM81Zpv97u2Jmgiq7a4t96IUBHGPQJIa731bowZ58kH33LXWpEiUppT995570qtwYei6zGPFpBcCypsvf8vkvbgQxvKtm61Chy2ePqQeyIhsAh3HxySxG3XWiulEVGEcy17jJrIDwFzTjVrRdoo573WKsYUxuCcBcBcigA8PT1Za1++v6SSGrdjHn6/31BQWFL+6BNs+1JyfHh8FIbffvttGMYvX76klLQ2qMhbM4RRgHNOy7poQqP1tuy11vk0DdOYU9JIcL3fSanS5Sf3A2n8/vX1119/sdaOg08xfmD4WMYQhinkVF5++/p2f23cPvg2rZKAdcM0T9++7bfrcj6fh2BJELUM42CU6p1bqaXmfUvG2RDCumz7tgkiCrbGyEeaCo51HREIQkyZub+9vQXnh2lg6TGnbd8HPwjjstyu77eHy/n88EiaBIA755r3uC/bsizLOIy8Ya3i3GCd0dpKI/8cAOU0n7hLLRUnVBqZxVod3Nk4U0u9nM/WWxDqrXdgIexda6OJlLPae++Ms8Y4b622KRWQUlLtULctxrTnksMQaD5po7FijFlprZVOW8669N6tc4JccuHOwkJKQQdldBiHEDxzf3x67NC+/vItxt06Nzg/hNB73WJb3u9v19dpmNZtVQkR1XQavfdxibhTabWkcmjUrbGny+ysqblu26qU0ca0IqSUta7VTkCfnj+dz+dhGIwxKWdjjYhojSGEnJm0BmkllXEMgIIKxmlg9r0z91aOC0VtrTbnvaDEPRatz+ezVmYYByS03rTe3t+vvefeOefqrDudzyiwLOu+7QTKGddaTTFK5yNJYSxM06y0JTJIap7OcpQsAUTwmPQIiwgYo1vVpIgUaqNba8f/dvzjNkaUUv0DdCMAqJRCxMNMebwQFCkAApFSijDf7/dcS943a0wI7rCOACEIpJTilpx1l8v5fDlrrRChtcKdS8m1VWdcGPwzPQcfpnli5lar1gpBSIlAz7mXnOMWS22oAAH3lJTR2lpm7gy19SPBvO8bEWirD+EUg+SSU85eB22sdV4ImFlbU3tblk24W2NO8wmYS8mn82S0885//fbrt68viHh5uGitv397sVrve0p7tt5YZ0FJjnX9voTBC/PR5pvP8PB43tYdEL0PSHC73Zf7UhuHg/7S2jSP1hillTNOaUKAuMXWCmm0VmrtMacmnbQxxrjBtVa8H06Xy/fv+bdfv6eYg/fUJYzTfVm+fnvVWoVpZIbffvu6x7SuyzyfXt9Uitkae368dOZpGL33AHSkEpjh8nARkLivv/32m3U+jMO2rsXaw86kSGuth0ndbvcjL/7Ffwku0ABx29O2D9M4jE5Aaq+AcowYW2mduNeutF6XnUVQaW5gjLXW0b4J/K8s8YeGE5E+3OEMYHAYvPQ+nUbvLKDELUFpNZeKMveZkE6XaV/j7Xpz1nz64VPckjL07durInVf9t4bKv3np2fr/fv7jVtNLcU1GaMRhaF7P2il5tNcayk5MYtWep7nL5+//PSnPxKggIQw1lLOD6eSc4wJCWvrzRXvLYAcVXluTb98f/n662/OuNtt+ed//df5fP7Xv/61Ldsf//Sn08Nca0t5f/vHm9J0ejjVXPd93/bVaDs8jc9fPg3TmGPMKZdapnmKKQILEqRYkCU4l3PZ4t6YDz1eq80Gd0g0haUzKyLpDPIhAAAiZjn6TTklAd73aIwxxtZamKWVum5bF7nd3rd1fX8/fYn5dD4B43t7zyUut6XWWlvNqZxOl/P5cr5ccikhDHJiRFSKxnHYt7gs95jWXFXakzB776GA1vrh8RKGMef8y79+zTmN8zw/PVljrLaXy/z58+eaGzM774zS84lymW63+/V6dcaHIdxut7Tvd0HrLAh4H4YxfLwqAHJJRhsBimVfl5WZD1BoCOHh8UKkc9m/fft1ud9Ejq2cGKcPCOW2rKUVAAIB522Mu0BzzVImpRV3tsYQYOdeawGEWuq2ChwFCpZt2dOerLfAOM3DMIXghx9++KG2qo1hAGtM7525SweljlgRfP7yyXv//ftX7hxTJKKS8veXl5STc1YZQ+rAALR1XbQy8RzHgYzWEAJL//b12/v17XyeffBIhIgxbyXX++3WWh3DqC3dlutyW4L1ImCdp069i/f2cnEuDEfZmxilC/d+qG36AWHVWml1eMGIjvgv8+/aEyKy1nTutTYyWhurlVZKaa0/4M/WaUPHRhiR4MChg6QUAdmLvV1vcY/Ou7hH70NtxRidckagUop1xgWDCq/3G/dmn57GcZznyQ/BGl9i2Vl6q11Yaey9H4NjZTUhxLgv96VDJ+RckvdBEFrv4xhC9/u+1VZSTiDcuetDnC2orT4Wj9u+LW3V1qWUBSB464NXoHKr3FpwLtf6et1+/e3Xbdnn08lahwDbulcFgrTn7ZffrnFLey6Xx0foXeDsgzPWpJS+f3sBQWFGkFrK7XZPKb1f31jk1OowjIRgrbbWAGLr5XbfuXVUYJ3uqYuAICBBiok7Pj48klIarXP8ZF0t5R///o+47+f5NA1D8L6LKG1qq7UxIJ0ul5e3f4+xtHavjeOYP3/6rNXhZeMweARYlmUMgzYGAccw/ulPf/ry0w+55LeXdxwHQDlaQdYpAdBKT8MYY1zXrdQsIDnV230R7k26DwORrlV1adIOaieCQAih1NJqq70y0BYTIR6V0lYbMx1GMDlOHPBxB3CDQ4BamvfOB0dEcd9BwDhTW7lf79u0O2dAAFG0Ia2VAmylvV+vby+v1runpyfrR+usscYH75zb9m37xy+t9cvlYo2u3/vjw+MwhlqrUvpyfiDE+TSdLvMwTMIMSNZZBNm29UCICENvvcQU9zTOIwIt63IgFfTry1uMCYnG4LswAuacx3msrZRUwhRKS623ytLfrtu6GqtdCOfHk1YmBI8Ew+gPeKe29vRwWt6X3rrR2jqLQK33220BotM8KqUS5m3d3tT1GE9DZ+6cS+pcSY2AJL9TNXpvpdYjCWqsFeSP8rD3tbXyfqu911w58HpfEckYnWPatr3VqrTqDLfbTYDm09kYnZYl5fLwcDHGQW/v7+/X6225LfsW59NcStnW1Wh9Os/jNG3Lvm9pT/vtdq21dUFUB0VAkBQzbPtGQKRIIdXeciklNeg4naZxCgrxTXgcx2MWMZ0mRWrfY2uttW7Ncn44HdZGbRWQUpoEAEk611oKaii1LPfNGDfbyXmniBq3XPK6rwqVUfrycHbe5ZKttbVUZJz8oMlorbrWpBAJ921n5rQn63QYh+W2A2KYwtFx0+YA6tEW49vrOyBO4xjcYIwTYW2qEBDRtiwAEuN28OitMaXkdVu2fUNEbQxqqqXer1et7HEgWretcVNacW+p7Mu6tNYFwXqnjV7XNS47kYppv17fp2nura3LVmp2xgocFugwTKMyxlpnjQeAVpvWutR2JJUObtqxyz1O9wcBFAl75dZ6b01IdeZjXn8MBHw4vv+V0hqBjDVaq5pzyUV1ZJYYdxYmwhRzrRUYemsVUGk9zyfj7Ldv32NM0zxNl/Ff//gnCM/nqXEtNRltSBGglFoggbXGBk16qqUozRqJmVur+15rrbmUZVnXZfWjr63FlJ0brHG9dkL0Y5jaaJ1ZlkWYc85dWISHMFnrSJE2upRSWo2lxJimaRQQUqpL4wP1WPO6baU2b9yXv36eTjMIvr2+5rpDhvfrcr/dSikxJgawzhiyiIKExuptb+9v7yB4ukwgICI+OOdN62Wex2H0AP2QsO+KUMQYk3I0xoQhWKtjSqRRKV1rN0Ypwtbqvm9Hysg6++nzl7/9278t97swAKlYijLur3/7Gwsv23q93ad5+sMf/6CUVmgeHh8I1ePjg3fBOxusFYSc8tvLSzufh2Gw1kzzaMyPv/z2i6+llT6fJiTJJQfnc6pb3OZ5Hsbh0i5EFLxH5D0u274ydwHpwoCotT6Iut47BAARazwkyqUst/Xbt1chnKaZkGLaa68gSuTjXiof3/6kNPaWlVbeW+F6JIWQSFDSFntvglxr8c4OYVAEJZImQ0paLlbTf/vvf7XOldSVUtMwpJyAofUW9+iD+8Offx6n6fr2Pj9M82Uy2iDA5XIG4W3bL+fz0/PT9Xbf9l2UdqSsc0Yb7sAlT6eh5lLSvqf0fntXis7ncx4GQNKo+PnTk3C/XC6Pj4/jMO5p/49//8+NNuOMB3e73sYQUkkpxZj3Lk57a41VSi/rOgaepsF5J4cGInej7OPD4zB4hVohtl6V0bkkAVDKGG2XdRUARMWNkcgHt+0bEgAyCB3eR5bWmctBPO513/fW2jSNjw9PitT3799qrtP5NM7nh4en6TQedWIWRqJxnghRBJPO2xZvt2WoPa55W7eaWxgHqzHHrOjDNQYgB1wzxaSV0lq/3l/frm8CdDmdx2l03nI7qCXlfruv92W53422YRiMMi445+w4D2HyOZdWZD5N42nQyrTWEWSaJwQEEUQBENKw3O7jOJ0vszWPpBQA9s7H3b+Vjl2OkP6sNYFSSiFgKkkZmufZOT/N4xTGmpt3/viL29FobUpuIr2UWls72g8l5VJrzGKMWfeVm3jv1n1NewKCn37+Ke7bEfx3zrfWe2djjAhWqMLSpLbWX15fvHcpxpzifJo/yJEKlSLSpLVRqOvQnA2ESgREeF2WYRxRhBnO55MLprW6Loswx7gPITw8PAJKjHHftnEcEMUYM0zT6XwehjGE8XQ+lcogCISk1HHaYpFWW+vtMBkIyOE37ty7HEU5YmE5zLSORERYjh0A947H+OgIhpKiRmRUb1UR5Zy2bfn+9SuDzKcJEWopt9u1lCJ9f3t7++nnn6d5tsbU1mKKhOScW9ZbjNuB3Q7eIwmQNC7pvoOI1gYRe2+AkEoRkft9ub6vAGKN18aM86Q0dYXX2zWm5JzvzKlkrcF7N4yh976uSy7lcJg45w4widFakzamrVt0ziitiXSrtaIId2PUsiQEsEYbb+bzVErd9/h+fas5K6X++c//Sik/PDx8+fHLNM2Pj0eZn7TRJZXg/DiMxhqtiUBfHi7WmT1uRDifpmmecqm367X1Ih3eX19vt7vS6nQ6PT8/n84jS3feK9LChITWhGEclvtCSh0wxHk6/+3f/m3fluW+1ZJLrkjwhz/8QRvz+vbqgp+maT6dlVK99W3fa6nWWKutDx4Basv/+u1fv/7yW87l6fl5mAZ/JDc7oFAY3L7vxqgYY04ppVxyZebd7Ij0+YfPwLwsy7qvMe1a69orx7Xkao0bptFoFjRa6Vpbl66t2uN+u1//6x//5cLQWjfG5pJrr4p+16z+7hw/0KpE6jCclgqkKATHnWstKe5durW290aEQ3DccgUcB6eNOnZIp9MMSJ3B+yAoANh6FeGUduetQvWv//rX92/fnj89KTIINI6jtUYYhgGUUqToOCdprZl7q7W2cr2+x30PwYdhPPwZf//7f5SahmkstQKK/vOf/vz+ft33uO1r7w0/fwmDt86KwO12O8pv93UlDefLuXXuvb29vSHhEIJSagjD/bYQkLWaRawz4/+fqf9asivLsizBzcnhlykBDDBzFpkZVSVdD/3/39CZkVGRGe5u4W5mAFQvPWxz1g8HHt0QAQQCARQqwNV79l5rzjH2/elwqmRDMfHGjvOICHKL09q2Tds2HYRgO64BACjBVSXmSYCM8kZxLwAXkgsooMScgg7TOJdUZCX6vpNSeh8oZYLLqm4QRIxzhEmOeXwsztkUM6UbpA61XYcgyiV772UtMcfroqZpPh6GruuaujFOe+MIwojAYehDjBjjUoqPflGrrKp2aOu6SSDnlKyzOeVlWc7nd1jKMOwxJZwzyikXnDFGCCoJOGudBwBlb2OKXghGKUEQCrkRZynGSCuDaWEcccq25i2mIJfobAk+Vg231i7zknMWQmBCS0nOumHX9d3eGlMAwIwSQiABOZW6rQEEhJKUSwhBKQ0hRBg755dVGWMAKNZ6KaQPHhKIIC4QhBRzSVQQKVjbVgghUABlKIHonFvWNZeScywlG6tzSd7Zt/d3KcXpeNzqbN4Hueh+6Kuqfnl5wYhuOS5rrFI6p9Q0FQyAcxJC9eXLl2VZACiVEFKKbd/+8eOHYdhXlYCwpFTqWla15JzlArTWAGGCESEEQogItiEiCFNO8R9Fp40YWL53MAuEcGtObhAbCCWjZAPkpVwYxQDBXEqIsZSCNiJm2RS9pOTsnF2WFTNCDeacQ8AQAj74Va3Xy22TfceUhBAhBMZxVcv389dpGjnDMQWEQM6RMVoK2ApulazWZV2XJZcQfcCYBB9DLHXTNF3ftk0B5e3bm/La2TjPqq5hjhEAQAjFBHHO+74fHw9jjPcOYxJC8MR7H1jbUM5YZnD776SEYJSdhwWWmCAoEGTOqdYWJh68nx+T8Y4QAgsoMHddW1Xy6emJcta1Q1WJdmhKBgTjtq2Hfb+us/dhGdembepKFJBiDEKwUjJnBGO4zKWUNE3z7X69XK7eucPx6JxN6QkiKLlsuoYyDArczLQ5JyElyJkQDHJBiDTdDiJm1rXriKiErCuKaQbfyaO7Xb8qva7KGOOdTzQVkSlHelnXdXl7ew8xxBSmcVyWeW2Wum4hAss4z2o2SmGK1nXNqRwOe8qwVqqA0jb9Vjs3Rjnjckq8qWIK3345c85kVbngNqLf0A/eBx+CNU5ba5x3wWcI7neAMaaEwYIRwAjhzQDz/Wz3XfgKconGhZKzFAURiCignNSw2oaWCONcklPeGkcIhgjGGCEEBGNQUIjpcDpQyqy219sl5QQykFxQSoN32szDvtsfdkPbQghCDMZYQTkoIKUYQwAAJB8QBKNaEUDj+HDWIgiDCxCUYdfnkj/FZK0lFANUlmklx8MzxeK//4//ngtIKc3LEmPa7XfGaGPdOM0FZIgQ4wwh1A6NNVbdtfcBI1zVMkZfSok+GbMllqgQMhUgm4pSqo1e1tV5zysxr+tjfhDGZF2FkFPOtOSYknU+plAKQBBjgkOK3+dqsMQYg/fLusQYTvRkvc8FYIL/2z//n5TSb1+/QIDqukEY+eIIwd7DXLI2EWNEKen6vmkaTIi3PsSQS6mbNgTPBKecQYiaunWIeucwJpRkAKCUgnOJj3R/OCirMYYAFqvMNE1aK4SR8y6EIIX49OkHLgTGhFKSUrbWckEJohAV53yBaR5niBEi0GjNGHXeOueZZ3VTt20DQAYFxBxiisGHeV5DCN3QD/3Auci5EEqMUlywGIP3tmoqCLF1ZlmX6cvcde3xcIAQQIisNQSTLYEzTTMAoJJVTMlZzxk32lJKMy05591uRyndhsht20GIpGCbx5wyGnzw3mntYowxBcaZTZkSrLVCGEopKylD9MooZ61S2ijtrJ+msW37T58/M5xS3Pwh2XuPMMwlaaNKLiH48/VsrX5+emaCKq30amJMw24nBKurGgCwLuvWxlJKAYCv91HI6vnpw+bw2qB7OecUv2NMSgab/2BjWyKENuQGxnjTyQFYMCEQIgRhzglAAAHIOZcctw+1/ZGUU4GlABBjIgQzxkKIydumqY6HA0akrZq2bpumvl/vj8f48nLSSgEgCSHGWAiL0lZwNs9jiK5tGu9D8klwmVMKMaQcU0zWW8Y4QIBQ7J03xpyeTjmXqmoJo1LUmyQop9z1hHPug0PfNahUSJFyYpzFFK01MSYfeM4lxVRVFRcypazWBZQQCzDezfM8T3PTNm3bUMYQRIwzTAihhBKKCTqejkY7pdX5/TJPy9PL0/1xzblIKaSURlkpOaX8p9//iCFyzodU2qbijBRQ+mFIOaYUtdJD34ewxQDi09NpU1oSTrz3y7xuTQtQcHDBGL2B8FKKwzAAAGBGUsin/f4+PpxznDFr7Dg/1lVzwYwxVd0QjGOK1hmlVinkus6M0nlZYgoAFkTQqlbjTKPbprdcsGVZUkp1U4cQqroGAFhrKSNN01jnQvSxJFKwMQ4A2HStUS7mYI1Vy1rVtu4aAIoPYQu8gQy37BAAoKrrlGJKEQCIQEIYYYJLhrlsQF5UCoIAYorncfTe9F2DELjcdGVkKWVjFmlj+r6r68ZZs07LpslklEEIEEIpp6ZtCywg52UaKWMpBWNdUzV1xQGAbdtSTrumwwRXosIYa61oSzZ5FMGIINo1zZgyAjg6jwkppewPe5CBrEQpoCCYXD49PWVQvvz2m1J6nVfyL//jX3/66XMB8G9/+9vxeOx3e0Th/ni4Xspq9PV2E9t+CeNl0dYZjFHb9Lthl1OexslZTzBa5rWk1DYdIdh5dz6flVJt0y7zMk4zhKmu5MuHD+fz+2oVk5wx5mOINlvjjDX38Xo47RvUAEAwJjEkiLf7PaCUfW/iOT89pqqSh8Ph9fUlxDQvY9f2tawf03g+X6w2Xde1XfufUsmY4jIv/W6QjUzzmiN4ej5wzrLPMSbAYFPXUghrLYSQc4kxhAjEmCiijHFZV3/7j79td9JcCmWcM2ateX5+qqu6bltZiVxAjCGFKCtOv7ejwZYHTSXBmL2zlRQYoVrKv/3HfzDKPnz4YJDKKbdd4118PB6EMYzx/XbXVueYEIJSspwiAIBiQggugKWQlzDnklPJAJV5mUvJXPC2bWNKLoS38zsEqJJSygpjBCB6en6CEB5Pp2VZzu/vzgVCWckZECRrQQXVWiu15qGFEFFPY8x1UyMMKsGUCgUWIflmpYcQME5/+PRpHO/zPIcQIYTt0MGMbHApx3VZ+7Z33pWSMUYII4wgI3zod+uyPMZ7KXnodpWoEcRp2+UWsC4rJbRkgDHu+yGE8Jd//ytlDBP6eKiuH+qqb7o+plQgQAhvN5JNM7hpyr/fIymFEOacKSWc822BmVLCCCNKUkrgu4wHllJSjqUUgmkhJcf/fBhAKUTT1KKSzpqQ8UZ+7bqmrltr7KLVPE6U4vP5ChGQC9vtdpWsrFPO+sftVjeVWtV4Hw+Hw+vrKxP0fDk/Ho/90JcC2rZyLr6/nzmvRFXr97e+755fXp5ejuNjrKvOR2+NCs4SjNSqjNUbBlJKuSyrs15wmXLOzqWUvXMJxZQyY6Kk4oy/XK4pegiK4Mz6YKwllDVNBzKAGBFECAaM8ZTKNC5c0Ofnfp4XtShRVeP4IHj7i5Z1WTAmxurD8ZBSlLJGCOr7kkFmnFjntFKi4k1TW2thAj/9+PmHHz7+9a8/g4yMctM0102dYm4QjMHVdUMwWdY5peisTzHKShJEcYURQjGElPNu6GNM9/t1Vco6G4KzTnnvdznt9wcpeYjOe5dylJQhjKqm1r/9Oj2mXApEGELYDVhbveoZkNw1FcY0lzKOIwDZxxRjwgzjiG+3R4hJMGGc18oIyTEh421EEPGqQggu88QZgwVMEJYCpKgoZcbolMJu6P/85780XccpqLhIqWzmcIIJJWybfOZcUAIIwfe38zyNUnJOmbU2ei+rahiGfug558YZylDTVj644ENVSVlJHqSQFaHUOPO43ZRRT0/PJReCUdtVq1Ip5h8+fGy7zhmrtAKpNG0rhdxcfpRTikgGGSDQD23JgFHio2+qylrfdy0RNIaUUnZeJ2sQwakUpbR1jrR993Y5ny/nVSmI8PHpOeesVvX+fnHB5FQARFoZykgpxViXc0YYM8oBLssy61V553POdS3HcXTOA4QgQtpowrDPIZVkjQopci4oZy6EaV2kqLflSUElbKW3nLerPIKowJxBKQnklFNOEFYIQiElwdQY++3tTSnFOJ/GJbhspPHRI4iEEP3QCynWVRllKKWE4JhyDLHrOyklghgitC6aYZpSvD8euaS6qjdZqzU2xoAw8tbOywIBAKjIquKCCSn7/YAhUeva9W3wwVk3jRMEgHKWYmyapm4rDJCzbtsIccExw9aYEIPxuu1qlJisq5zTNI9cCgyRWlfnQ0wRZTxN45evXxjjgou2a9q2qZtaK1VKetzv1trDYa+dedzu7ZYuBYAymkuOMVaNBACCUuZxzqDM8/x4jP1uV0lJNlNglB9++KiV7fpOqSWmiDCGECACnIuPxwNB3O+G4CP1GFIIMSygFJCDi9brAvP1etNW17JywYUcrTMhhaZt98MeYpRzQQAVCDAhoKCcASG46/q6qXFAwXtY4P/xz/9cyUprnVKiNR2GHmNWCsg5IoKenk4Fwq9fvmhreM4x6+t9qtuu7VsuhHMhxYgh+sfBP8UYAYRogznkvAF7SymgZEpI/O7gZhB9J+tu1YGcC4YQI7zlzSCEjDEfXC45JdK27eX9vKqrWheQEyXE7zpZy8djWuY1pCikkLLGCF7vFwDyHsLX15f7ePXOOue01X3bEYq3qEJOgBDCKE8pc8mdsxBCLoQ1NuUCIU4gaW0op6enpw187r1V6wRB8g6kyBBCEEDBBaUspqiU0tpgTFJKVVU3bbs90AiBXVtr017O78F5IdjxcNwPPYBoA9BihJu2JRgjiIx1UohFL4wxQsiPv/tJG/329V1FhTFknI/3x6pWKWQIPsVETjgG550DsMQcASwxhnVxUlYIQcbZ/rhfV13X1cvLC8QoeM8o20zuUm7Z1lzXNQ90q2oihB+Pe9d2COIUM0GwlJJibrsupDgtC4CwqupNE8I5izkFFwgmVV1xIQBAFYJSyF/X3wAEHz79UIm65AJKmaeFEGgBEAJro87ndyllJStj9TIrrdXlfP3y69ff/fQjExwUFH3OINVNw6lMJaYcrLGYEIhAiF7KBhRgjZNc/vb33whljFO1rqjGBRaAIKUMbZXVAr8L6yGCCK1arWpNgFmn5mn58Xefdn1fSo4pEEA24OE0aas1xshoo43u+ratO1HJUgoOUFvtrPHeVY0M3jvnAABPzyeCSUzRWfufjJMUU4jZKMWESCQprUIIQvCu7Zig4R5ySd47F0QqxYeQSyqgqNV0uxaAsiyaUUx++OmjUrr9uUeUrav+n//6r1VVhRDO5wvEiAt2uz4KLKf6CCHESN2uZ8bY/f4QjG+fBwCAIHy5XFNMbNUxRt+HqpLO2+Cj886HUBDMAIaUIMSI4piic14pZZ3NqUSfQywQYghxLgUghAlkknEpU4h1XXMuACwuOGetc06tmjOxuRUpRTEkQohPxXmPMY4+IYD6vmWCLfOCMXZmM63jnEqIqW8bjFHJZRpnteoNkwsR1Fpra2P02jiMQAGFUbYuaksmCM7naa6buu2aum3mcXLe1l1V15JzTinOORqntdEAZiFZTEgrbY323m62ccbw47EQhHf7IaV4uVwOh2P39GSdXbXaDMbrukCI6qrdDbvD7vAYR6W0d34c/1pg6dtBKb0sE4RQCjnsBjWr/WmoZUcIxYQFH62xIUTngxSCUso454IThhHCj/vDGFW3tXeBYIoRIhTnFDPIPnjBxbLOUlSIw65vQ8jW2JQzIeTb16/TQpq6EZxLLimmTds9P720XYsQ0dqsqxJMiG7TzwZEYIHAu+hCYIz3u10/9CWXeVUAUkoF5xRCbK2z1i/LSjE5nI5VVTNGv+uzu7brekoZJjgZtwk3StmwPDnlvF2cc9niQN/1jRAiAZF1bsOSg82o/b079j25DRHM27NiC/JSmlJMBEtRN139+PWu1FoJmlL21lKCvQ8hOOf9ljSSteBcDENX1U0lZfB+kSOhxBvvg9+RgWJslGact22z64aUI6JArWqZ1OF0oJhp5axz1qgVIuoYQnjYDYQwSiBBJcVAMeKSWuOMsQSTH3/8cVmW6FNV14yxEILR2jpfVxIBHGMsGU3jNI6TWhUV5Ph0QhD5GErKmCLKKKMspWSNBRAShv3o3y7vOZa+byklVS1yYnVdc8GWeQo+EIxzTusyO637vosxUIYRBgjhDKJ3fl2V0qqkohuNEfr8+bPg0nnrrS+gMM5Tyil5gimlcLsHhBiMMdM0cy6sN0lpyjBMWBvFGRNcYETaupG1LCBJWfV9p5Qex3Ebf2GC4wYAKbnpu9/94fdq1RQzgEAqSa1qXdW6Lvvjrg75djtfLu+CVV3XZZDUauqq+vDhVWvDpWSc1XWzLCuleBg6o/2qJpSB3HNr3TzPnz5+3vW7FNP5cg0x+OCMdQTTGDOAwChLGCvf/e/bHXR7hQEIAWMcIUgRZQy+fnjinK1qiSFZY6qqAgDcH7fb9VYKwAA2Tc0401qPfPnDH38vJJ/nqWnqx+OhlW66JqUEAKirqpK1kDwmMoBBW8MpZYwkAo2zMeVkjQPQOce5cNbf/IMySihLOcu6bpompmi8pZiwhsWQCab7YV9xabUm75cr5+zzTz/+r3/7N1lLrY2xNsXova+aeiNNO+sf49S37WF/MNqE4L99/dZWLZfYuyA4K7nAjAQjXde1XcMFQwg57xGEdVdjjVMOzhlrDWMix4IIiDkuy2KNFaJiQkCASgYZ5hgTQmALrsICu647vRydsno1jNFUovcBAWOMscZ663aHnjAagr/dxwxSxavn5ydKMcQouNhULee0qqVe9eM6LmotuZSnp2HXM8YJQQDAUoBS6na/OetWpUJwTdcgDNWqlVYxpgyhtXZZl8ft/vr6+vrhGSE8LaNSmAkmpcglrWv03hplKKMQQGdcQQATmEHyIVpnEcRN3cQQIQS367XtuuPzsRYt56JA8Pry3NSN1rrruuCdUkrrFWOSc6SEQFgAykIIRmkyueSirS6lPD2dlNK3c1HMrWrmXNRt37Xtsqxb5A4iCDEKKQbnCaWMk5x5yQARKKVIKZwvd+/9bhi8s9YYgrEuepOlOOc2yJo2xnnHKy4kr6vKOU8Jsd5jQtZVdU03DAMllFEeQ5rGKadcNxUnHADgrEsxYUgf18k544Mbhl1dNyH4eV62MA/BxPkQQhJcPD09pVQI5Yjwrt8BWHyIBQCEtrB/2dxpOSWw7W8LyHkDqEBQIMaIEBxjQhgjhLclQSnf3UK5lFIAIXhLB20A7bJxonM2JtRVfdgfCELWrMs6NW19uVwvl2tOWco68KCdrWwtuZBCRB8ZR4zTWjb74dDWbc4ZYZRBWdf1IAXnglEOYNFmZYxj7BBB/TAgvKQxOeswIqlECBFZcckQQUQJxBgZZWIJajFMcERxyWUeV4QwpUyKChB4n+5U8Kap1mV5f/8mZY0geP3wiiCQlYQIPR6PEKI1DiHIGNsuiCGGuqowwQhA54L3HiOcUyKIAAwwQhiR3bCDAA5d3w19BsmtblVrjC5kqJSWFQ8hFJDrusUYQkKcs5yJbujMai+XK6PMpEJpjDnHmCpZUUw5E5iQUjLBpO977z1CEBC0FYO0NS56GkjTNsfng4/BGIMxGce5lEI3xAciQlYpJut1ijHE+PTykkL0zk/TlDOoaumDN8Y+7vfogtG2FDgM/X6/v493hIisKwSxEFIIKYTACMYYBRecVefzdZoniFBdy5zLy/MLJtgHv1VVIMKnp2dr3TwtBQDGeYqJCZRSLrlsr6vtBwBgiolSuj8c6lpAmGPwejXBO8GFWrU2OufkjffWA1CQ4KtezcX0Qw93MOZgDIghVnUtK0kYkVKmmCEEw67HGFmrCSEAZAiL944JTikFEMKCQozBhwKLsSYEjyBKc2aUlZzrtskpT9OsjR36tq6bFEEBiRDcDi3GkPzP/8+/dkPPBemHQWlNfDi/XxBBTVPnUm63e05pU/NCBBllCGHrgzaaIlyKgAXY6DCBlRBd3x8Ox6qtSinOeucC50xwGmJwqw0+lAIIpQhBQjYbH+BCCCkRIrCAnMrmKsgQYwghRAVuGb+cStJGxciYoIJzLrnVtpQMUPYuhJT0qkpJbV0/vz61TUsI3lhUEILHY8ohcy4Ysz3qdrsdoZgQUhIAANRtxSjzzkOIKKeicPvQ7+/v3oWYA6MMY9T2XUpRa2WMOb+/YQIhgKUkxvg8z0opKbn3PsUkOMMUogi1cf8ZDsMEI4zDtiHnomrEeJ85E33fQYALyBDAum4Y40IIKWROeZ1XsxqAUNPXFa8JI5RS59zj8fA+PL+8eue7rtv4cVXdgLzRLbEUFShFiiwrSTAJKRKKnfXGWGAsxrBp67prqop746yzzrsUQ4EJwgJh4TXVyj6m0XkHMiSUjOP09vYNAiA4F1w651alKGZamwIKJQRRLDgPPsSQnLXO20pUbdNiAmNOIURMMGFIGQ8wauXQ1K1gVc7QuTEFH2MAsGCE5nmilHLBBROIMEw4F3KjEQBQAATl+wb4H05JULbx/Qbk3b4IMcEIwm0DAb//jrJBprafff/FUlJOCCGjTYgupgQADN4VUI6nY8rR6LVkSChb1f12fzR189S3EIKQixSiqavNsHg4DE3dyB9F27dGr8syp1yij3XV5FxC8MFHAPPjMRGCqqZmlFFMS8lCMMpwTB7FEmMO3ucCCUZtU/PvAby1lCKQWFb1/nYZ52noekwowgohAhHRq/miv+Yct2E04xzEHErGjPqQIMKUQef8rNb4iN45KaWUom1aCFHbd1WK0zgZq2OIlFDGKaWEEoIxpoRighmjJePM0uVyDdHyii7TClHvvJeVLCDnnDlnpRTKGGPcYo8IqtsmpZJzKDGnECOL67oQQvKYm6YuGYqKe+eNsVJKIfn7+806D23BlGKMCKXOBYQJJkQpvTvsBRchRAgRZ6xkgAlzzj4e/74sCpSCCQYQgpKu54tSyxadOh5OlDIAM+PUB7/tfrwPRi8ll7oJznpCEIAwhvLnv/5lmkdGqeQsxdC2bdu2Wtnz+ZfgAoS4klXX9YQQre04zaBAyUVKwPsE8ffjIwBgWzship3znIvdbpjHERYkK1YE44JHH+dlud8eOUYuWd+2hNJ5moKPVlvLzTzNUlYZ5GB8JSWAOaUgK4YQDsEH7yjpC0A+2HWdY0gh+a7tEcaUU8JJSiHn8v72Pk3jH//4R8JIyllKUTe10c4YCwEglBUACMXOxeii0lpKRh73W1ULa9T5/X2eFx/Dss5CSkbZJlRCEBGC9KowgjmORmsQy2G/B7mgAp5Pp8c4OmuqqhKMUUYIpinFnItzxnljDJ2nMceIEGaCU4opxQgAkAvCsAAEAUQQhRBzzohgCFGOGQIIAOCSe+vfvp6Dt947KUWMjFKKA0IICcERxBlkEHPbtV3XvL68Hg6HuqoBLMEHAMAW0MYISykoP2zFuXF5rPMyPSYhZSzpdDhxIU6no9ZaVhIi+Nc///UxPkTFGWeYEmfduq6MM0Lxsi6ncCCU5JIAyrGEGALEJYZQSimY+OiXdXHOtV0rJKcMIwKHXR9cRBBuH18ISQhblWqqNuUkK0koTTFxzjFGMUZC0OvLCyAYwAIKdM5WUiIMAdjFkBCGQvAU4/k8eRdeXl52/W637DDCGJMYIqN063AqY4QU2uhVrcknWQtRCYyQ4FUO6vF2i9FXst48MeQ7SdQ4N8aY9ruT4CKlACDgnGOCl3WZxgchlNTEe+eshVxM0wQycNZpZbzzEMOmb5lgPjhrDWWkqTsAAWcVQpgJRrYiVvScstX76/Vmndnvd4jgtm0p4ZjQmLL3BmIGIIIAxegRAt8P/zHE7QlQcgE5lwwBgHirg+VSSoEQYwwgTDmlFAEhueR/mM2/t/e/390RcME7ZwkhTVNZu+YMEIRt05GPUDDGOPXWE0z3u8PhtL/fHnXdn56OlCBl1pISxpggxCm1XCIAKKZGWwiRFIJiqrUBCd7HewEQFtB2bddKzjilzFsXffDBTz6GkOq2ygmkEMN+d3p6qprGOE8JAbCkWAglh8OpbzqIUEpAShF8eDwe67JQhjlnIaXT00FWlbXOB18AJJRCgJ6f21qtatXOGiGFFJUQogBAYtBKTY8ppBi8BwD2Q3c87mMM43ifp2Wephgj51zp1TpTSgK61E3V9a3SpqkrhHHd1IILUJDgvK4qp11dNdFlTBGXUkqYYgophhSM1d6HnDbVa4IIxBBLBhCjLcm7vW59cPOyxJyabY3cd5Sx7egJQEEoggK8jyFEAIBV1jiz2cpiDOuqfPCwlC0x1TaN9fp+fXAuZC2dcSBDCAATXEhBCUshMc4IRkYZDMjT0zPIOZXIGDPareuKIKrrhhC2HS6qqtnvT5i+p5if9qf383Vd7wKzrQu2fS8A5LwdNFHOmRDStg0hWBtdMmCcM++01cboEzt4n5wLmKDdvieEuOCts3XdeB/e379BBACqo4xt1xKMU87BB0IxJohSUkoKwSkFcimCS0r5JvRmjEAICiz9MEAAc8lN3QopYy5VUzHO67ZGBVVcEoSaqn6MD1ASSSn95d//cng6plSmcc6gnJ5Pm/Rj63PnnDGllBAAABekDqKWsu86b2zfNYf9sOu6rcnlY4QFUkKoIABBn92mVldqlUIgBBndavslZ5ejgyXBUnIIG+cXQlg22QIqBZaQQymFC4ERzoQ0jBG2mTJhSpELLmVzv07amGHoX19eP358YYxClJ3X3vl5nIUUCBMEkayEBDKmZK35+u23++0RYoAIHJo9pzxmzxjLMRFKCwSHw9FZ3+/2omIQgBjjJnNwzlttGaOU0WmcjNayqqxxnLFY1zFFhCFCOaeyrCrlQB0tiKeSjfacL2rR3rnXD6/eR1AyCNH5ANB3f9vzy0vd1sR6vSpvLUi5bZoEC4Dgcr3e73fr3Mvry/FwrER1f4wh+mE3HNBhvC/X65URWjUVxbiqqm1z7n2YxjmEEGMKIVRSeuQhBN6GdV4IRNaZdZ1TiPu+F4wiBNSiYsrOuhAjoVgI1jT16fREGYUIIgDWdfXeYYwf4+Pb1zeMad8NCEGjjOBinie1as7Y0HRzis5btWoqKOkRhLjtO4xILolgjCnNIDetIQxps0JVhOCc0hwCFRJjpLRaVxsz6Pt9SmGL/aScUsyggJJTyQlsFYCy0RghgmiDOSCEMEQAwJJLzoUUgCDaGI7bm3/OGQJUcs5l8wWnAkouIqdCKYEQ7A/7cQScsVLy08vLbnfsh15wUdcNAJAxSgnkjOy7nTHKeQMEJhhDLvuh10oti9o+VE45lYIgaYfudr0b42JMdUXqugrepRgxhhFmQjAlJIGyzms83ypZU4LruqaMhRB6wnb7fSkZE/q4j9szz3nnvbPGVFUfvL9d7yn5jz98UsuKCfqqlXc+pS1lKzinspaSCymFUqt31jpHCOaC76r9PE+rWgEABLFpmTZuFaMs+KiNUWolhOyGAaCMICKEYuIKBMpYgkkIiVBYQAkxrnp1wb08vVBKY8wQQUrw7fZIIaQY66pKKcaUEEIxZcZp1QiEUL8btkuZlLKCNQDFqcU4zygTjN/vD1BgKUBKuSWttTbGmA+vH6taGm3v99uyrvOy9ENHKY0+xBDfvr19fP0guYQDZJsYLkNCKcaYMl5JSSijhIYYKcEvr8/jY97uQM45KWvnXS4ZE8KZEEJ4H2PKWmshZVXXQzsIXiljp0XlUlJKpZQtAgQK3ES2OeZaVFBmztn2Kpoe82asOx0PQ/cHjMn5/W1RS11VOeVtMUXwd/Wpj6HrWikkJth76wrgnGJalJmxwzFEynAldxhhQighFBPsnN8yVE8vz0+vzwBAF/ywHzgVMaVckqwrgikCuGplDrksAGN4OAzztJD/+//9f/6vf/uLNbbtmk/0B7WqFIt3IeUIAMIATsvCLKurOviAABCSv758eH559tpZq0opx+N+l4cvX76tk6rqJoRAMKEE73YDACgEl1PglGC4Sb9KLjGGCApomwZCbHXwPgKAcio5p5wTQggDjCGJKWKET88nhABjFECAICCURO8hhLthaJthWZaqYnVTMSaMMdZ6KTilhHCCMSaMYEKstRhTxlkpJU2P4MPusNvthqHfbV0MBAFnXEgMC3QhfP4d3R7s8zyNt0dVSSGlXvXpdLqc3zgXCK0AwByzdz6FhBBKMcUUEfg+jtRW5ZyFlc6bcXw468fHw7uYEqCU3u7XP/7+T1Ujb7f7Np/hQjDGNh9yiEFNK0LIpVhAcdpRTDCEDNOUUiqJMYIwACVzJmBJX758Nat++fAiOffBSyEQxHVVO+OGvmdcgAKt1wQjF/w0z/fHI6VEKT6dTgiivu8JxqkkWUmlDOMMYwQxsc5SRzfSLQCAYBxCRJAgRMbxfr6cOReUMi65dZZzQQghFAnJuaQpZefd7XajjBPKpaiavoEAJV+I4IwywQRFxFpz2B+WdeacVbW8vF8gRuW7FRDkbYxAEkRka+fnnP5RvwcbDG7bvCH4ncm4bQQQwRBulwEItgtBASV/V7YCtGWcSvABIAABCD5orQAAnHHrrDHGGacWhRAQXECEHvex6/pKCkRwCH4e5wKy4HJZF6VWLujuuEMA3x7XUlBMSWkrK79dtz99/oww8T7erhdEMaV880BgDK2xxpicS04gwMgYo5QFH3LMiBAEadOKnHLT1hAhjGldt0qtRltvw253OD09gVKc07kAiEmI0QVvV2u0zSVba87XG6PscDh8/PQj4yzEOM3z3//jP0JMrx+fq6be73Z1Xc/LgjHMKSOEOOeVrIZh8N59+fJt0Wq/G2RVZRCNMrf7qPVa19U4rU1T5wwwwSWXZdUppbZuh93u6fTkfRzH+/16C8EH5xinnLMCQAixgLKsC0+CU/F06hFhXHBvHUIIY1RyzrkYp5QL72/vCCEuRF3VVhlKiTUWIRiCr6uaUV6fGghgJevPP/yQS8kpQli8DwQhzDHx3E+LUvpwOByeDt4F7wOCCBToXajrGiIUQ2i6JmVQChBCMM4wJnVd51hyBtFHrQwixDl/fzys93XTtl17vzymccYIp7xxcv9xsiiFUrIqJQXFBJdMxnHklH8fUSLIBd/Vw3F3eru9mWBSzITygpM1ZtjtGeU+eCFF3/dt20nOY4iPx0IIAqXISnpreSUwICHE/nkgkBLCQ4oxBIgKwghjUjdNyeWv//HXw+G4h0djzbQswXlZV4STzWjvnI8xau21VhhjohbTNY2xtuv7oevAK7zfRjJiCAEhFCForaWUVEKKmgvKIIZt03VNv4J5mWYMojJGCF5VldKKEDwv017sQgihhK7uBCcwHSnGEJRlWm6PByGYM0ko2W7yBaJUgPMhFwghzLmUWCAsMAOYIRWsqmsMQVVVXd9QQnIqKXnvvBC87Uld113f7Pf7DXxfSp6mGZRCCJFCMMGNseN9bLuOEIwJYYQdj6fNJBNjIBgEl73zlFGACsZI8MrZYI0mlDSyITtECG2bGj3B3bD75ZfeGssI47t9JSWAIMWEAE4lglJSyIKLStbzvDjkIERaae+CXhVCqB/6y/m9G/ptL3/9crPGNW0NC7zfHrKS1ljB+TAMDLFUcllXrXVTVxDWBGOQM6UElFxyhDlTive7jjM6L49KcoKKUUova902T8dTjimlLIWUTVVK0VdFCU0pO2OXdZFMSNHFGFNIKZd5fIQYT6cDgogIMk/Ba+1dcC7IquJMlpIJwlLUEBiMEEG0km3bdG3TFFBSTClGgmHft1JIALJSyzSOb2/vhFNl7evLDwhRAGEuBaNEKY4xcVbnBBIgqJhgk0rWrP4Sbm3bbrrBkCxCgDIEMvQhWu9jiYgARCD0BcBSckwxYowI3Yb+BSOIKYLwPy2QgRTMGEcApRgJhAQRjDdoKLDecMa0ydbZXOLteqnrenzcH48rwej56dT1rTMOQBBgwaQYu4aUMITjY1zWuanrDHKIiSOZI4QUBp+cc5SxkOLtMQkhCGX7rtntTuO8aPsbnlV6TvvdYZmX2+0RY8IIUcpjSaWEtm+Gtq+bllOxqDk4i5CAEHjvSsGEJq10yqVpm/e3t/tj+fjDa47FmggBQYDq1dzvD+8DoySmhAFJsUSYx2kR8ro927ReYowIwBSiNRYVWNfV0Pfeu2mZIQRPT0+VrKQUyzr/7qcf//v//NdlMcOQIIEFYrXqZdYFopyTD55RprUmhDRNN+wHwUTJZVWLEIJz1jRtyH605pe//xo/xK7vIfwe2Gvrum6aAnLTVAVlAGNw3vlUYMkwykre73eEASEoBkdIq9VKcN03rXZ6eoxqWTmnx+Np2HVd1+RSGKWVrGRTnd/f5nGUUhhlKaXjY2ZUtV3XVM0UZ0opFwwhgiBoWvm4+xhjjB4iGmJ8enke+u58vsq6ssoSKa/Xe9J6WdU4jpTxAtD1fn/c7pfrtWl6AApEaKNRIYgwxs4FKeT18sYIrCRfl/Xu7m3bQggYYYwxBOBvv/12vr61Vffh2PaHTjLhfGCCh5Sm67XrG84EgjiVqLSqqyqmwBmbpvF6ue4Puw+vH7gUZtUIEVmXZVor2VRVxZmACIYYvfOEEFDAt69ffQgxJgxxTJkQQj2BGQYfUokxRYiRC45E54/Hw7Is87IcdgcheS1rjNHj9hi6rtu1RqtpmgTnQ9/nmBBGWql1WtS6dG1b13WM0YV4PB0ghhFkyliMGROiFzX5R9M2Xd054xEEktfOnZUyHz8Mw25QSjNWe5f+/vffCoAQIQRBQghCjBGmjEleEUxjiNY5UEAlZcWlT35LNThrs0UFFcZoAcVbTzkdx+n8ftkNHRMspBSV2ZIGSuucy+9++olzUZJJMc/jknOqazkMHRYVQKWq65xLLoBznkOCCATrORcYoWnVlZR9t/vhM7hfb1sanVISQ+Q1BqCsKii15gyquhZCIkimcU0taNu+7Xa7vnfW//Lbr1Ly3e7Yti0CqKlTVdVVXa/zCiAc+s5LCQHGmDUtBhCmVISQbVsLKVKOlJJ1VRhCzniIAQJojU0pdF0HATDWAgByzFjb4GMuKcSYc0igCC6qqrLGhhAY4x9eXjHGpUCMqPN+vE1bGsr7JKsKIWSUz6lgQpdZex9lLSilCGDGDSakrRtR1YjiYRh2wy6k6F3U1mAIEUQh+8tNvb2dz++XX379ChF6fv1hf3q2PngfAAAxZO8iwohTjAoOKanZar0O+x4UYLWXdWKEcsGFlBgTWGDKeYMpYYxDKAAWADLcSCy5IAAhABgjgjGCsOQCC0AYIQQxIhhhDDGGmECCIEEQbswlCCHFxAdnrd363gWAx+OxLJMxpu8bSqkQ8nEfQwgAwLprEcUgJWNMgaXr2rZttTHG2RAzxjTG7YyJrbGykv1+gAAihJ2Pf//5b976WrYp5sdjWtV6fj/nnKSQnPOqApzxVReISts1lWyul5s2OsZonQMQhBgARNpoAGDX9UYZHxPGZCOg5IzqriWUOB+NCc7Z3ccPEGGK8KaG10qnlEJKCOGu6SsuKSMY4wlNBQBtTCVrxng/DAiiknPwQSlTVy2l9On52TmfcoEZcl4VWM6Xd3d1tZTTuDR13Q0bu0k2dSOr6nF7vL2/EYw4lwRhUCDjrN/1SmkfAqMcYkQp8T6s62qMAQiF5ENwzjijdcip73vO+fPpOUSPIGaMAQAYZ1vbg0ICSoawIIwABN7HjQ2FGkg50UqHEBnhy7hKKRE+7nZ7UQlGhdb/MARgyhiDEHrvrTVfv3x5PKan59PQ9ee393mcqro+HI6h9suiKCHOuZTTNE0//Y5QQqdxQpi+fvj4eIyEcIIJQggjDCBIOVOMEEA5Fef8xpj13qVYYYJhASmkBLPVBmZMEIUEL5Oqn9un5z0AaF2XGFOIcX84TvPIGN/vDylFvzrnnNKaCZ4LoJwLIZdp1Ua7GJwPMWVRcYQggKCksj/sv729Oe/ut/vnH388PT2Nj1FrM41j09QFZGWW8/uVcdYO9aoVef3w+uW3r3pRXddRRoIPQsjX56cUQk556PsfPn5s6ur56WVZFIJITYrv2cba3oA+VSWV0jpbwpg3JiQLATJ2XZZ5PwySM4pF1w27/nC5vM/a3C7XkCImhHNxOB5CyPOqo88gF4BQSQVsguACQCkYAYpxRtAZMz9GkGMMWyA4YYIxx8ZYtRrrYiMb5+23b+/W2uenp+3JUVU152Ke1PV6jSkZE06nw263iyFO4xRjcs4HHwsEwSXCGYYYQkAJLIIyyQRn9+stIdjWLedsmZfz20XpZdjvCSMYwhhTiG6dlxRKVTV9v1tXG6LnTMRUQCEA4N3ugABMyQefjBlfXz6lmBe9tn2rFn0536paEkKN9RACwSmlFGDCJUsQGGswp1Vb55Si9xiRvhuccxBBiKG1ztpQ101JMMZotW3bpuk6H9OyKKMtRIgyxjjjgkVZUUaO1l6u1/ExeRe5ZAAiCAljvHhvtNfFc87rpuW8BgXmsKaUK9FwKWIKBZRVqbpqall//PDD4XgSdWXHaVZzDIkTMQydYNXspnXRj2mBEBFCjdZ61af9kXFqtMklxoxAAtZkKZj1FmCICImpyLpVq/EuEQogQgiTXApECOZSQPpO/cwZgO/37vzdIQe2mi/GBOHtSzFBBBHCGAOI0OYI26yZ3/vAIMeYCEHBhS04hAmqq+qsFowRF/KwPxLGjDX3+yOl3NT15f1cQCGIAFAIxlLyqqpiyhuRQgiBSLUsy/gYldK//P23rj/vhz2A4NMnlEKCsDw/v1CCCSPzOqUEpKwoZaUAzgUjdL8blkldr1NTR6XWddGYYEqJsy6XjAhVq+FChBBu1xtjLKcCAGr7dtV6WVZCcN02h9NT8D5lqNY1hyCk/PDhw+5wwAhTShCGglClNCZYcEYot8ZO8/x4vB9OhxMTbdMVkEvJ8zgro60L+8Mhg8IZrxq5TrN2BQGSU/zd73/vQ/TOxZC8t9u62EfvnCu5rEqBDrG2gRBdbuO6rIfDHlHGJEcYlQzUokOMw9Cf398v52uKkUuOIPI+lAxKBlVV1U2TcwElM8KPh+OyrEqr6CMm+Hg8hpAwIowylVfBmHP+dh0pZQDCDDKvhBBcBF5Xzbwu3vppmnNOhNBSMgDFOe+cW9dlmiZK8bqsP//889bwkFXdNk3btE3b5pzlWvUxrcvqghelxBBeT8+pwOBT3JTw3wnkpaRScCmg7A87hACCUDDuMN3ip4wyrYwx2mg79MPL8zOABUAkKkkZ41RUlcwZYIowht65SnBnrdY6xLjMo7UGIyR2e0oYRoRx+hjHdqPzGqeUxggBCEoCP//Hz87ZlEq/6xGCKQYEofMOlLJMy+VyXhdltUMYxRRjiOTydj2/v202+rauEQCUoKbdQwRSSGa1kovhx099v/vwAWVQfv73n7u27duOYRaCl1JigpXSPsS6qTClLljKmI90v9/v+wFBrFZDSDzsOWfN4fAMEfvyy6+iqipWWWMe93ldppRKSJFCkkuGAJUCCyjGaK1127RCsJyS885aJrggHOWUfPBKmxCCNgZZzwlLMVlrY4zW2k3wxDkvpfRdN08zIRnAlHPKCccYlFIbBCLnLCuhrYMYMs70qmMMKaUe9pSwqqoAKIRRterffv36/v5mjD4dX/bH/TC0gktjCO4p5xXjbJnXb1/fnbMfPr1WsluW2ZlojDNaEUyH/e6Xv/1yPl998Lfbox6nlHNKmXISU0opg1SmtNS1ZIRm2G4euBCSWjUswFmjtSGEFgBKKBRhQnlcNYS06aoQY/BRaxvidRh2IUQqGcZ4VVo723UtBDCmtBENK1GlkEFEbdXvD3sIgXXOGnu93sY0d13XNA3nomt7RBCh1FozTmO0kUBKKavrGmNitTHaxJAIJAVC6+xjKgijAmBMRUi53x9Tzm9v57/85c+ccyHleHtgTBijEMGNw4ox6vvu+fk5pWyccSEuq9LO5gxiLLud46kqAEEIEYQpxhTj1gbashegFLR5Wbf5D0Zw80kAAGEhhED0nRMHAfwHaRygzRKfsvc+hkAZwYg0bXO7XiNAhGAmRMnw/pgzyLnkCFKKueTSPjXe+vffLjHGrut8CJfrFd0xJ2x/PCCI+3bAiFSybruWIDxNi1pU8DGmKESRsgMFlAjatq+ranvbuJwfQjDZiALL9XbzzqecCaNSigJgyoByxrmACGmt53nWynz+8ZMQFSUMAKC1Vas11vTWVZUsBV7PNx89JSQVYK0nxIaYhq6Tsmq6gTK5LmspCCOKYQIFci6kqCkVuQCl1OaU1srAUoQQlHFMUdvWIOXL9Qxgmablf/yP/6dtW0LIxvJ8ei7DUKoCEMZa2+vl/O39vWu7lBOl5Pn1uanrdVG5FI5ZKGFRs324t7f3DMo4js66pm0Oh0PfyRCDWhUE8HuMGwDaccKIENI5Z71Z5jWnUtU1IQS1aNNrY0xOT/T98rZMcyV5TnleZgABwcxZn1NGEDHBKllzzhGCk55CDCWVlOPxdLrf72/vbyGGYT8cj0/l9RlCxLnEBDPBgzGH43GdF1CQse5xvVPGKREbbyrmxLcpP9iCZ4liAktmlB6Oe0pxKaCr20pKybmsXoMPhFIpREwRIUQpxYjEmKwxIYSY4DJbpS1nglJcN402umu7/X7Xth0EYF3VsrzP8zw+pp0yJQOCqda6ruqQAoI4lfJ0evIhTNP0/n7GhIBcSi5Syrdv36y1JZXdbiCMYIQqKcm//M9/9ckej8dpHr13ry+val0BLE1dTdOIEYSwYERLBF/Pv/a7YbfrCCEEk75rQSlMcGsNJoRhQilfVmWtGe8jZaTtG2MsyCAllJJXxlRdT+axbYfXj+lP/+WfbpfLNN+/fn2/nK+MC7z16TCCEIECMMIxp3VdHmPfNg1CcHOlxioOQ4cAmm6zsm5/3DVtm1Je1nVd1+PxCAEooKRSlmWZp/lwOFV1fTgd5mlzzxrG+e12nR7T4bSr69oYG0t01hljNq3Kdx1zCKfTiTDqrQ/OBx9AKVLWm48wg3y93pdlxRjXTVMyCCExLgAECWQASS7l/ngAAKu6oVSe395SjpyLpmkxorfr9T0mxlmKOae82+9ycMbqEIP3jeRC1g2h1DoHEEixOGfGx+N+e2wj17qWwEOfgnPRWRfTlonGt9uDUsI4V6slGDd9U0BOERhjBWcpRWuc94FQUrecUKpWNa/LFhqTdbXLOeVcyaquqpTTJlnVWq96RQAJUVEWMMIYEcbENM0h+mHYNc1z8Gkap2ke397PCAEuRAch54Kxquv2Lrjb/S5Fta6LtZ5RyjkjjMzTzCgFGHIhU0kpJx/jonQuACEsZA0AzDFtmgoEUSk5hOCszznD/z8Rx38CODHGBf7jy3FrAENYAMylIPh9h5xTRhgBAFKM3rl5mg+nvQ8BQzgMQ4jucR+1UoSQb1/f5mnCGCeQMaLrtE7THEJwyjDBuQ8++K2cOs5zLmU7DzV1M43z5f16fj+P0/THP/xJCJ5iQhg/xul4OHX9gBAcH7NzVq1LCBFjlHJCEAz9PpaCIEYYAoTWRVlre95xJggh87Iu81JyiTFRQhFE3kcAIECl6apxGp13knNtNADlw8urkMJqFYNf1pUS2A0tgNA6N80LlyzHJOvqyEjf9aXkcRybuvYuKKUkF33XQYCklABBH8Pbt/PQ903b//rrL8b46f7r08tT27QF5BASJvecyuM2IYinaVLKQIiGnjAh1KqCSwbaEGMMCdUE5lxKsdrGmKq27vsd7EEpgGBKGSulgAwQQKjALSBTcsmpFJAxQbzix+e9WnTTVFJyvarb7THeR9zEecYAAQAASURBVMroft8TgjHF3sfHY2SMcsHHaRZCdF1/OD4hhJu6llLM0+St9zloY3IqWqmUIiEoZ+Ssc8aM9wdIZRgQwohzHmKqqurP//5XUd8et9Ebfzidnp4+yqpiFIECEEQQQAgKBshabfX6dNrFFEMIjDKEv99BZSUIwdvY2WgTY6SUEkKcdcZYpVbjnJSiamohefQu5ZKM+/XvXwSnrx8/hJBDcEIUQoixBkBgjdnt9lUtlnWlHLvVcckFoTllUYnb7RZDXJaVYEwpLaAQRl92u1q2nDNj7TxNnBOSUEoul1Q+//7z8XAABT6/nggmy6x2u904PTDCp9Nxk5k8LiOvmPdunmchuOCcM5FzopSmmL99/Xa+nb1398f9eNo7Z6P3r8+vjFUxpWmau35QyqQQfveH32998eATJogwXFL+LtgpEAAQUiglEUxiiOPj4a0VQlBKt38sQggC4Hw+F4QIx7vd3jo3jY/T8WCNe4yPw34fYzTWUEYXNc9qjSF671JO3rqcsg9+WifCt7QXDine7g9KGaWMEDhNs160tyGHvEm9gw8llsP+yLl03ocYg4shhAIgY2Jd1MbsneYll8wIDzYyQSEkKaecQAyhFGi09y6ui+KM991uWZaSyw8/fLTO//brF2vNNC1d2z0/AbIjLvhSkDY2Rd80LUYQQQwRzqnkUqwL0KdVL94HhPHjNgFQOKc5Z2fjt6/nmDIo5Xy7V1IwzqznJSWtbYqRUoYRKgCwDEqB66I2r6c1bjNfL0rJus4ZrEr3w85om3za7XYIwnGeAIA5l5QywYQQihFTWkcfMyilwPv1VlWV4HLV9vblre265+fXjg4pZaWUseZ4fJKCO+fXdTHGTPdHDMEs1ienlpVQijDamuHO+g3HiDEtBWyi480kl/P3HQCEm5wvF7DtBr73vMA/RkMEE1Dy9quggJJLgolQAkCJW1EM5BgjAGB1hnJStTKm+Le//V1whjFx3mljlmVt2yameL1elVbRe865f34ijHEmQ4yX822eFkopo4oxVkp5+/p2Pp8/fPxQ1w2jOOVsrbfGXq+X6+2xLmvKcZrm8TFSRkrJOWVnzMvL6+//8LttKJ9TLKWsao0lSVH3/W5dNIDAeX+73rkQKdy7vm/apkBgnQ4uEEQO+z2hGALYd72PYdUqRK+UiSFY63f74/l8ttrs9nuEwMAFhjil9C//8i8AgH/6p3/ijIUQK4k4ExghHyPZuHspWWOkFLvh8Lvf/wkBSCnTRq1qUateF0OI6Nt2XhZM2TDsCKFd2xVQjDI5pxiSsw4UIJmEEDLGh92+bRvGJcE0hqCMctqabDlnfd9xLiAAzm4WbuCcn+clBE8IPZ6OpyMWXAJQxnFKOYXolVm9DxiiSlTTNIYQuGAYE60MF5JgJgWFEGqtMULLuhhrVqW01jHmv/38d4CLFBIA6H14ez8DCBHCy7LmDI+np6qqurZfFv1v//t/vb1/W+Y1ldx1By5lzvn76y/nVDICOPig1OqaKgVPGaWEIIghBNMyeespIaUUZxzGeNuiE0xjdNZYLkTXDwhjQjFC4HJ5v14uEAKIUE6lqWpr7fn9UskaYwRK+fz5R4opF8wHvwEoIQJG66brGaU+RilFyup2u/Zt17YDQKBggiAOMWwypa7vOSfk9HzEGF/fz//7f/2v9cMHCOCvv/zt84+ffQhD30khckoAAJBB27cpBkppjOV+u+eSu67NpRBCKlnNy+y8raSUggMAcMEoQ4xICCElFVNhTIfg7+Ot5Fy3UjutrPYpGGutDQSBnAv6PtAFG/ULEpRy0c6klLmUshYYIQhABllwcTge3y7vv/79N4ww52KZ5mWejDWP++Pf/tVhDJ+en7mUy7TmlKu6oYzWsmnb2kabQFzWRRv75esXiKAQAmO62w8SAVBIDBFhgiC01iMYYqQI4ZBTsDaV4mNECDHBRVUbY1OMzvu//e3X+/hglKSU1mVtu2a33zV1/cOnz2pVIQRrvZRVTsUaF0KUspKyWlc1jes8T7frDQBYVXJ8jCnFkoF1CWB4u13Uog/7/afPH5Xx86xAAT7lYRiU0o9xTCkxzmJMKaRv394xgl2/KwAxhq1zJSUAkNFhHFfJaAjehcB5oJQDhGoAvAvW2q9fviGCXl9f1aqN0VVdb5+McwFh6kJIGTgfpBQ5F6VWZ733gXFGKdVa/frrbzFGhFAIyVojKnm7jV+/vvkQrEs5Iy4EKMAHP47Tsqiqlo/btK4rZaQS/Ovbt//n3/4txlDVddN1lZQI45xT0/Sff/p9XbUAw1Lyf2auS845JwgAhABjjCDI8fub+/Yt50wIAqAghCD+rprePAEFlBwTYABBBErBCCOI1nUtJd+u1820frlcvvz6rd91v//97+q609o8HuM0T96FeZx99NHHrmsJZVVdPz89SSkLKG1ba63v9xvj4oePn4d+T9lfmqax1j5G1bUdgvDrt3dtjPN+vE8AwWVZnXOccUxQTnGellIQZezp6altu5IyYRhAOE1z085DP7y8fsgAKKqmeUWrRhgTJtquppyE1Djnci7BR8Hroe9j8Lf73SgVU8IYIgjHx4QQ1doAAM7nCyjFWg8RLLfH+/ulbZtxnBmjSimtbSXFxrk/Hg5a68vlLChvhuaP//Sn4/6YS3HWOedXpad5UqtCiBImpCx93zPKQCkAwnVdcgbaOHOfjNEQ4hjLr7/9KoV8fnl6fnlJCVwu5xwL51wKGUPY7q9CiukxresqBF+W1RobUyKMdG23rmocp+v1tioFAeSMv7y8UkaVNsu8TOOojM4xUy6qmiCIOa+oYJTQkjKEeJmXb1/ebrertooS/NOPn6ZlrtoaQ4IpNsZ0be+dr6s6xrQsSq2rkJV3URulVqW1YZS/ny9/+APEECOICsjeBQg2sXRIJYWcLvdriZFx8vry8nx6zjnrmCCEG0zhert1Xft8ekk5eed8sHVb1XUrq4pgkksMyRNMYoyM0krIGNIyq77vmqbDBGllUio5p2bX3W+Px20O0VHIuq4Zp0Wt6ng8WO2V0hsjyzqnzJtzDgFYVdXx8NR1XTt0QlCrDSGIYwQxIo/HyCir62Yax3ld+64L1vV9LzjfuCsYIeuiWk3d9CGHVSnOOaF4N+yMtTHHvh/atvE+CHkBOVeNmB6TXg2lTNaVlNxHTwmFEFjrCsjeRW2s1tb5kDHYlAC5+BwyoYgQknIEsBCEEUaUUUopRjiGoI1JMUMEQ4iMUaPNumpnLSZw8/hcb3OI4XA6hRhntVjtdgVwwQmmRZf3b++rWkEu1i/vb2/OhsNxfzgdlnnpd7uha5dVIYSaoaWIrlqtRjvnCcVcyhCi2exuADLGtDPTfVqWZV5XiECIcV6WeZ7v010b/emHT0rrVanz25vWatgNlRA+hHGaSymC8d1+f7lcGJf740mtK+WclLwsGpQLF7MQbBwnKStj3V/+/B/LvFBKjTHGBeciprhAiCmNMXPBcY2XZXXer0o9Pz1vxhtR1xig2/W+qLWWPKW4KgURZJyWDLyP0zxDiA6n4+16H8f5fLmM46OWlTH2eDz1Q5dBLgAaY2/3G6V0XZZlXp5enqWUQenp27yqVVubQhofD+8dpfxyu29BiOCDrJpSwLIs0zQbrVNOtxvknNd1BVEOIQSKAQDjOBJKCSGX81lKSRilhDVNzwXfqCE+hJxy2Sb+ECKEQMkQws0qWr4zH3LKqQAA4fdm5uYJgP+5/IWwlAwgTjkBhEuBlBLGyK+//kYwziV7v4zT9PbtW0FFaf31y7dckvfhervFGJzzCCNn/booF7w29tOnT1VVbYnUeZoxQeuiSgYIYEr5MOznZVXKTPPd29APewBQKZAQ9vz6uixr2/bOecbYbj9UlawqCbdOGhcxbSw/VtfNvCxfv3zzLu52u8PhaLRrGimlDDE2XXM4HrXWX778Vle1NmYcp5gixiSnqFZVMuBM5JTrtqvrBkKcCkCgAAjHaQw5HvaHHz//OK3rusyPx9gPPYTYGGediyl0dWedvd1v1rrxMX6SnDL5229fvn79VtfV4Xjq2o4xvhtizJlR9uHlBwTB43HPOQ/DwCjzPjLG2zZdLpfD4UQp4YwjhEoBj/t8uV7O5zNG+Ph0auvaaIMwFEJgRChjOedNKgARxBA76+ayQIwoZ/b+cDYwSoWUXdsCCO/32zzPl+utkpWsa8ZYJaWUzfF4AgBghAHAXddczueqrurmc8qpqiQh5GAdZvh4PHHOIULWmfe39+2wMk0zoSykfLvc26b74Ycf9GqWaW27HkGYcqYAxBgFB7mkFGFB6X6/nd++vTwfMYSUU+fdr19+tdYyylJMlLOc8vvb29s3RAnjUkzjBCDoui7nghDa/HEgA4zIbrdf18X54KxNOa1ah5iWZUUYxZgej0fbtELWrx/q8/v79Xaf5iWmKKrqMRLZVJjgaZoRJm1Tv7+9pxj/9Kc/NW3ro788LoDCVUMKMcEYV0K+vryeL5fL5fb+7V0bXV3l6ekIQFZK17UcdnvGKed8XVbrrb0FpdaY8263u90nbX0lJSG07wZCMMYYFpBLiTE7FwqFXb/b7facs5D98XDIeUPABufDNCq12hxLyiCmwkrBCIcUcgYQZsGYVqoW9W5fU0oJY4yxpPKyrldziymM4/ry+nw4HEPwMbUppb5rOWPw/4bWu8NwmNc5RTDhGUJaVW3Vbpr7iAmva9nmwpiw1gEIjHE5gpgyKHBZVkLI+9uVUKJXnVKIqTjn2qFpmpZL/tuX+6INxnhd13mcrHNKreM4Ioi3rmzb9ymhZdX567d5HtW65FwwXqdpeTodl1UTgvuuq5o6Z1DXTdPWoIDHNDFKcwHO+1Wth8PxeHzCmHz7+vVyvcICMSGEIIIZgLiqZYql5CwkV4u53e/GmPvtXgkJCkQYp5DujzGGSCjZ74e6qkrJ+8Mx52y9zSlDAMiBbXZATNj723spgFIGELYuPO73aZoPp30IaVsLOesBhMu6hC9p22It63o9X8ImKcCIcxZjPl/OGEFRScLot7dvj/HBGMOExJzbpm3bdgNDIkxiiO/vV2u0lPJwPBCCL5fbOD2EaBAwP/3u9whB56yQNSUkoIAgJIRQSp01JaWcC8glxeic8z5spaRtnY4RJhhDBDHGhOBSci4ZAEgIRYiUUkLwW8d4mmbn7GWaEYLe+dv9tqxzjElwMY/zqlXwIYS4LtpYW1fb6g/O82KsgwivSpWSMcYIoU+ffsCQjeMKADrtD03TbbqIdVWI8MvlTij74x8+Usa5ECWXaV4QgP3Qdn1XyQpTaBZNGKaUPR63AoDRVmtdCqiaZrff/fLrb+f3d+fD09MJYdJV9evrq5QVZexyu379+s0al3Nc5qUS9cePH/fHDEuhjM7TMs+K82peHuf3C4Iw5jiPY2vd8fgUc8o5h5C3VnVbNUH4uqrnacopz4saxzmmUtUtQvjxeDwe9/P5SildF3M4HTGmpcCua4TgJScAMUY0BAMK6IfBGGuMARAwyp+enggiznmIUElg42hBgNq2pZQ4H1JKy6KXef3w+gogoJS1XRd8gAjn6I1120erqnrodowKQlDXDW3TWGubtn0q5Xg8IYhDCoxxIevDaV/VEkFstM4pxRjTppKGuN8NqeRSSl03ddsEH3IqLtrxMSu1+BDmebrd7uM4V01diabvh6Zu/vCnP473cZ21Mabt25hCsokxijDLsXBBpmm53cfDfsCcp5iul9vtekcYno7PlJFVa6ed9Q5DMj5GrIjRuq6ax31SqwnRY4wZJbmktm1TjvO8GGW01rf7tFHCNlEB50Jp85jWpqmaquqH3f3+SKloZdRqk8/G2nVRRpuqqYfdAACWtWybJjiXUlTKbPiZ2Try/vU8HAZnzf3+qKU8Hg9E0sP+sEzT9XZ/eXmt27ppGylkBjBn8NuXr+O0GGMgJvvdESN6f9wghP2wo5QwwrwNuYDoE0ZpNwyUUCkExcRZY7VhnAOErHPO6+t1VNpmCBAhzoRNMwI2ulcBCCIEUQwJY1JVFSE4poRj2jijLrr3b+9VXX/8/EHK2o9xf9hjjCDMEIAQoqykdkYr23c7TiSvKsZoCjDE3DQtFUwwTghp2kGptUCwzismSFsXzpemqUKMf/nzz6tauRBd27a7nkvhQ9TaykrEkMb7CBBcZpWihwgoZdSqNwctRIhwQnB6P5+rtUoxUko/f/rYtI1aVyFFjLGqZM6wJHA8Ho1xQvC6Cc6neZmM1jHe+mGwzmegEADTskzLygkjJFNGSYVyATkDhGlMYZrW4BwhpB86AOHtcr1er1+/fV3mJcSYcnp6fvnTH/94Oh4qWe0Pe8mkdnYcx6qqKKOlgBDibndQShHK9rs9ZYwxOk8zAKEP6R8QOl/XNYDldh8hGNu+ryqJMW36wVgLCxBStF1XUhmnyfvQDTsEobHj3/72t8Px9E9/+tPT6dT1fVXJeV7O5/d5mq0z87ysy4oxjDk/PR0JJSGGYTfsD0PJ+fJ+fXl5TTF+n/2UAgrYtO4FAFgyRFsNACAEMcIAwv8kvoEN0A7hliwg25s0hBjjlAsAOcVUAFjn9Xq9Ga2qplJGfXt7iyE2dcMFd9Z74wEAlBIhhHXBaA8xoJR6n0MIWuvHeBdC/PTTT33X9/3AKB2nCWPqU9rtDoTxkhPGXAoxT79RVlEu67qhhHRDf3qKBQCMYSkg5nh/n1JKbV8ZY758/bq57B/jSAjd5cNvv3653+8x5ZTSt29vHz/9kBEYx/k+jsH7nPPxcGCce2//8ue/Doe9tht7mStjlVZG23lZtdY+hJRi8NEafbncpnH+/OmzrKu6bmLKSpm6bhrRbdXp+zT5ixOCEk6HYYAQT49ZLwaA0tR127YYIUrxuprz2fZ9fxh6UeOylJyKd8H72XsvpXTOMUohhNroUgpIpeCybEYaWWFESoIAFK11ToUS/PdffhGMQwQxRvvDASBI04abDSnlStYYE1lXoACjLSgghPDrr79BBF8/vGhllmlmTDDGGXsRlQAIKrNqa+taOutAhkzIjS1tnM0l/fblK4DQeZtSDCHWdWWdTblgTKx3LMrr5TZOS4yRMbY/7LT2KcfyPQCavA8IQcZwTsU6b6yblwVD4L1LMaSSJW8xxpjiFGO/63rYV3XV1K2LHkAIIarq7/iy2c77fY8gSjGVBKSQu91BKbMumlEueEUIu5yvyuoQwvV6Px6P//VP/9S1eyGaaR5DSEwwWVcAwq7tEyjzvPz7//6L1uaHTx+dMdM0G2sIInVdtV1bUiDX62iMM2Zt267r2g8ff4AE6FXFtI1bk9YO4ZkL0TQ1ISSlpK27Px4bi8VvYVJCfAgQopTSbrdr2tpo/biPoOTD7sAZhwgsq9KriyFhTpZ5vV6u02PWzpjFQ4BLAmlTvJaSUwEQFFgwxDkniECMWa3aWte1bcrJGPt4PN7fz68//KCUpUhwWaWUY0rL9JjGSSsjhXz98GqNezxma02e5r7rUyohBaNNnFZCcNM13luzaCq4bGpKSY7AWWeUKRAYo8f7AxNSV1X36DdMIyYUE4Qgarv+fD5TTihF87Rs/ibOOSE4xyyZSCkZrataDvuBQlTXNYSIUtbvdjnllBKnrO0HBGHwD6WMEBWAY103f/3LXz59+h2E5Nu3b6XAnLJ1JqVkkzs2naiq5+cnBBFnLMawuoAKaprOBdP1/bDbE0KGoc8pU0qNsQCCrm0JoRAihDClNKUcQ86pvL2dGWeU0bZup2kWVV1Vbdt1EBatzf02AlhiTN+3rQhpYwCEdduGELmQACIu2PPzS902X3772nZdSjGn8n/88/8Vol/WVTD+9Pz8pz/8ESL0/PIshUQYU8YORy4r/uvff3s8MpchBL9VojAh+8P+fr1ba6fHXIkWbNCHf6x/t3EOwYQQnCFAoHwf8iCEMMYEb+bIbeIPIUAYIoQQhASTzQ5dYNlWmgAUQoh1BmMsOJ/Gx9uf3wglGKNadq8fPpyejs7F8T4WAHIuOefp+5JJccbmeS4FUk4RQafT0x//+CcpJYQIlHI4HlPMQkiIEad8WRfGZIxZVPUw7J0N8/JeyWpVWkr+4eOHSsplWYzRBRRC0fv7+/QYL9cLyEBI6Z1f5tUYCwBECL9+eMWE/vL3v/3lLz//t//6X1NOxlqrzP3+yDm/n2+3+0Wv9uef/zYM/TQtEClKKESQCaGtuz9mLihlJBeAPMUAhZCmZSWMG22NsVLy+31MMXHGY/TromLyGINu6F6fP2SQhZQh+Mf4mKbpw8ePlaxjyQBAxliK+f183u93taxzyl+/fCOMSMn7vtfabOx3a914n7gQjFFjbN8PXTf0Qy9FlXOsKmmMXZcl+CiF9M4vi46pGK2rRqpFh5hccJR5zoB3QQrBuZjG6Xq/Xi+3DcEGQFFKp1QYxyHG2/WOIPLepxxvtxsiqGoqUDZdoDLW5ZKMtTGlDNKvv/6KCf7w4RXiLVsM6rqSkt+vD2gIJpgQxkXz+UcRI4CAQIgQKDHkSDImsGHce0cIPp9v8zj3fTcM3fHY5ZzG6YFWVNdtAsBtKw1EueB9P1Tb6B9kUcllXdZVA5BTSJiS4/FpGBIXchh2AIKf//qzUmpdV+3MTz/99PmHz/fb7Xy5gZJlJU6nY9U0j/Fxv4/90NdtQ6VYF/3n//3X10+vw37vtRNCAoC895f3a465kpz81//Xf5tuj27ohl3POY0xLstMCCOMzdNqrbfWn1Lc1Fo5JQBhKZBzUdf19Xr7+uUb4wwBADGq29o499tvX+ZpYpQJIUpOlCyE2GmZvC8F5sdolTGEMWX05XoFAOQIAci44BTzPyIcIJdMAMSYIESsdeM4xRggAGpY67r21hFM6raZ5+nb12/wBVZVk2Nc9DKP4zzP0UdtrI+xFGBdfDwexrppXo2xBW5QMx+C9z7UtSw513XT9z0jhFBaDc1v//Hrsujr7VJLEXN6ezsjguq65pwJWXVd/9OPn41z0zxP8yw5r9tmf9q/ffUxZkY4r4kQwmoLQQwuwRYijN/e3q11KaVffvntd7//KefSvLQUUx9CgQAAYI2rqybm+Pmn3/Xd/jE+HvcbY7zve1bEPN/ati0ANG0jpGzblmACC+yGPoYQnP/2breB8svzyzD0jPKX5+dxnBljLx9eKilTSj6FZV4xJkrpmHPKeZpm67xa//zl61cIwevrq3WOUsa5QJjM8zJOCyjJez/Ny+12DyEcDvunp6fdfldKiSnLujk9PedUpGxu95sv9vR8wogYq9q2retGCPH29m1Z1xD859cfEUaP+wNB8vHjRy74Uy5PT8cUc8wpxZBLOR6OMaWcEqbYBx99DDj8A/RTcs6bHqCkBBGihKBSSuExBoRQLt+LXtt6ACMMIYR4QwLCTc8LMS0ZYIYphN7bfui30YJ3rqrkn/7pj4KJ4+kJQYwx9q8+pryuep1XweSHl+eYYlPXv/z9F6XUP/9f/4VRDgocuh2XWwTL55gIRkrp9X1NKVpj7+McnC8AnI4njAnwPkTvJ7tq3A9DiWAcH1bbVakU3e1xZYzVTeVduFwvbdulFKdpWmYFEYQINU3XNE0p0Pvwl7/8FSLQNV2MqeSMEOqa/nR4AhBM4ySqKkb/frnUVXU4HBAiCM1K2Ti5vt/t9gdMcE5JacOFKrkABDGj47gE74bdsC7z1y9vlOGq4i75D68fIUHaGKPdNC5SinmeORPG2OCCFJWx2qi1lNJ1XSkZYeSDPx4PXdsLIf5xuMuMcUzQ4zFaY72NfTs0dXM6nQjB72/v4/g4nY4//+XndVUQwmVZEIIhxvf3S4oJQLTpSSSXnPMYopT1mB6UsN1uhzBCEFWNRATBgilhalUZZEboOI7nt8s6rwDktm0RxMbZ4EMBACOCMBaUheC6rq+aSnCJCDKrZYwzwQmlBUJjjeDC+RhdKgABSKxztawgwhijUkDJyIeIMUk5x5QgYpgiiKELIcX4mOacEiYMYay1WReFEHniJ0Sw0QYShBBOaRrH6euXL88vz9ZYQgmG5Ou3r59//JFx8Xg8YswAoD/96b/cH9enw/NjfPz25YsQ4odPH2Mu1gW1GoLpDz98xhQ77x/ny/Hp1O36ddHLrIKzfd8TYpZlkVJ2bYsJJBghQoh3NsREGQMQDvt91zaikvfbNea8rmvXtUqftdLWOmPNoozW5sPrB+u8kHK3760xIcTL+y2lNI0PtZr9jsq6QgD4EKyx6zQbFxFBuUBj7Hy9Pe4PbXTOUNJKcOq0T8mX/x/knQAAEcQI4VWpEOImrSacQITGaXqM4+vrh08/fNLOhJwWva7zdL1dMYQu6MdlJJQuyyKEDCE552KM5/Plermlkr33W5MoxbTMK6X4erkTQglBTbNRGN08rQBCH5O1thSQfEgZVin5kKqqfnu/nM8XZ11KxQe1Wk0RyaUsarHet3WdCnDeMsETSN4nwaU3TsoGwDI+plXZ4/5QChynZZrGLWXYtB3xmlDadbvr/fb8/Lob9nVbgwK00pRzZ92q9C7E8THdbnfJxfFwFIIxQlnbrXpBCPVdS+m+lIIxqqr9px8/eR8RgoTQmrGYIoHEWT8vqzEmxFDVNcR4Gqf9YU8pizGf368Qg91uxzinzDoX1LosyzxN0+1+x5g0beSyRoio1UAE7reHNS6V7H3MOXsfOI8vL8+MvU7TGIJjlDR1QwgFEEzTiDFilMC6ijFCCHIGx9Oh67p5WrRWzruqroKLhGKM2XagA//I9mwYuH8UwQAo4B+Oe+idzyUjBDDGAOZtLlRAAQhgjAoAqQSUUIwJo4QxLN9nFMFqO/R917Ufnl8oowCgLWZ0OV9CTMO+Z5hjBI/H3euHJ855TBECwBmdl/X56SMl5DHe5nU8iVNd1xiRcRxvtzshZF1W40zbNo/bTVYyxpRS2u13XdfmkkHJPnitFczAGss5A6DETCgnfdcbZx6PByhIW+N9RBh/+PihqqqqrqSoIKxjjPfr1XnHORdMIggZF8u07nY7rax1doOanU4vn3/8vZBiGufH4/75p0/G2Ldvb8a4YbcHBQQQcojW+RhTSnFDAleV+O23L9frFSM4r/b9HOq2YUR8D9rm/PTywihTyhG85JLP1+u3b++MkRCcVvr8fn56OrVtJyRvmi6mDCFq28YaC5ETkr99e085Pz2dYswIQ23M7X5r2wZhFEO8qMVH/xhHzljbdSmXaZ7HcfI2HE6H09OJIBJ84JLvdwdrDUSwktXLyzPG2AdXYEYYWO2neRqnUXC2LGpelmVZ9KJSSiEnweRjGgmishKbyjSl1PUDYbzAQinx3kGEq3pDXeXtE4u0tE0bQ/rrn38+HJ+qpuGcb29xmH7fM93Hxzyvp1NPOUWIcCHqurHGyrry1vkQsHNVJa1xSquv32JVVQTjcZxD8MOwc95YY5yLBcD7beyGDhHiXIAQY0KOTyfKKMGkHdoCwNvlrRsGybmUcpnV+f3ifRiGHkKIEakkwZgYbZu23vU7iGAlOUZESsE43e0GSplaFfn1b782dRV8ttYJKXyIDefH41PdNIILpdYUcwYAAuh8iCFRIjAKGJO6btpdhwH23hPG9ocDJWxdlt3vfk8os0p5H3KO1lir9KIWhNixP8acrterXtQ4PfpuwIjCDEL0AJWYQ8rxe1snZwAIRLgA6ENK0XFGU4nLvHImh8NettXr88c//Lc//vzX/1jX/y9N/7V0SZKm52KuReglf52ZJbqruwdDgCSEkSfbeAU85wFvl9tsExgYMOjp6qrKSvXLJUKHa3cerOYtrBUW4f597/s8C4TJaEMwbqpqntlEFcLQOHc+9wAgF8M8LjaERWlEkHUhxEAwpIwgjACEESQXXIhQHTSEEGPonOdceJcw5QhARGDd1HkuD2/nt8MBYSpk1rb9/f1dUWTfvn0FCFWrhgk+jJOPsc6ET34YRwTRhw/fCc4SSKt6DQCoyxWmWCl9Pp+HYdhut5Tgl6cXKuh3330nRfHp8yeQ8Ha7GcbRWG2M8T4wzrSyXPKsktOogvPjOI7ztN9vpJCc1/v9XisthPA+DH1/oV3eP9w774d+ZJxjTLT6x8RDLwpAUFZVTGkc5qzIECR5lc/z0nfdsixvb6cYPKYEUTIpZaxnnNd13fW9c/5wODhb50VRNSVMKITQn07jOK3XDQJo6IefpxkTHLxv6kaKjAsGIXDBLfM8zRMlxPlgrUspUUIXpYqipIwAncZxas8dJrgsCiFA9AEhQClJCaQUIASE4BgpRhiggBBOCECQAEyUUWMNghBjAmBE8B9X+EtmKMWQAEoQxnRxCMMQk1LKGss4QwhZozFGQrChH7uu18YNfc8YwxRTbGY9l3nJOQ/ee+eqqtzv91W9ak/nrm8xQZvNmnOeiTzawAgtikwtmjEqpZBSME4wRuv1Whs99L0QgjCCIMKELHruujOnwlkfYjTKcMmzvCjKkmBGCeNCnM/nYRgZFxCiq+sryTNrzNvhoJYFEwwRWuYZI9ysmrKsMKYIomEatVHOWoig9+71dTifW2tNjMF5Ny3TsugIoswkIXSelsUqggjj7OXtjTE2LtP5eO67wQdHCIwhWu8+/v6xWa/rul5m1TTVdr/PZOZ90POFzQWCjwihZlVrbaxzlPoY2PFwiClSQgGEIfgYYnvutFaUsWkch3E5n89VXWNC1qsVInCepnGcQYKU0aIsq7KCCHMuijyIjdxd7+ui1lpjgqqyur66/vLp82a1GaeBM+6cBQBM46CNsdr76PtuoIScTme1qKIqrHPDMFLOQIVd8FleFHVFEKaM5nnBGFvU/Onzl9PpMI3zP8a5KTnvldbWOT+NlFBrfNu1RdlcFHWY4hRBigkAoLVijN3d39xc70HyGOHT6fT6+oYSVFqFmA6n0/c/fL9ZrxOEjLG31zfGGASQMrJar7quOxzeVs3qb3/7N8rY6XCmjOx2e1MZpc3b2+t2u2WU5XlelVWCCSMaQjgfjilAKaTRuqmzqmqWRYVhLMtqv98dD+fowOimumoymV8AKiFFo80yKqU1OZ+O1laEEGzJ6dgqrYdxqsqqXtU3d7ffvj2ej8e27fRi+qGHgNZNXddNlpf7m5uizC74aMrIqmlASoRRmAChlBLcD+PUq2maGKFSZpRzLrnE9P/8f9lzLr+9fLs0g9Q4n47nZdbW6xBCuPR8EAowgkvJMwIq6PXtTb2qxnHEDBNEnPdPz89MiEVphNFmt9bzYmyGIpBZdv9wd27P//Jf/4e1ARNqnVVaBYghwt6nmHBe5TAmYzRAIIVkbYgxEIwxIZRQggkEfrNeV3W9qOl0aIPzBEIM4XpdM842m3Xf93/44/c//elP0QVC8DgOEECrXSbl9mq73+/Hv//Chby/vb65vcGQSDHBBEIMUgohxfFwXJZFyMxohSkRgitnplE9v77N89ydxmlaQnR9PyCEqrKkjGACu7Z7euIYImdtTAETBABw3vz+6be6qgjBMXoQo6AcEmSxe3k+KKUhghXCKUJrnZ5HTPGFi5Ai1MbM8wIhtG76/dMnCFFRFhARpTUlxBnffXn8/PtvjFHGKcEkL3LOmXPueD4dTyfyDQuZIQRjCoTSptk4a9r2gBC2RkMEAIhfvn56eX0FAGxWqxSBUkpmwvlwOp0TAA8PD977n3/+u9aL4CLFGEMI0Y9TGqepruvNZkuJJ4wQTDhjKACQomfUpQhCAAgE4EECCEKQQAKAEEwARhgTQgghCCMIYEyJXCpjMAUfAIAhBedc8IFTNk/zotU4jqfTEUIEYUzJpuS7dnLecS5iDE67i+l3s1oLIZ01AKARAYhSnovrq6uhb799/RxBzHjeNM26QW9vB0ppSmm/22lnyypHCCIMyrogiAz9MEz9PA1jN+12O8F517dD1682DUzo6vqac3E+d8uib++ud7ureV76rieIYIwAApSQLJNMUMb46XTywafkirLSdgk+aGUQQsuyKL1UdX0+d9MyU0a8DcM4zkohhJdZEcqsCRBCBEkE4PX1wDlz1iqlp2V2wWGEY0qI4ATRMM4iL1cYY0YRJQCglID3DmN0td8hgBa9cE7Op/Nms+GMem8XHRGAyigQoZAixrgsan+1K8vCOjMvCyLABuuja6pmu9tgirjgzgcpRFbkx8Px67dvTbO+vrpha2mtPbydhnacl5licrW/ssY551KKdVWH4MdxHMaBS2q0FlkOEZimcRzGvMi5oEYbRCAm0AXrnD0ez0YbQnGR55KJCAJEEGO6Wq9+/vlnQknU+gIcV0ob6ylhhLN+GF5f3pZFCSGllBDAEDxAxAdvPWAcUcru725+/ON3UrDgvdZ6meau683LK0hxf33NmMSEyZI8fv0GIXx6fVazXq1WCNO///IL5wLhiRDigg8pQh/brttstw8P70KM8zITwhiPeVWklLYRLstMCeZMWKNvb++EECmlSz0PIVbWeVmWxtuu19fXNzJjbddOh9kaS1a4yHNCCHHetqeWcFo2FWM8gnR4Pap5effdAwgxBF9WZXvuYgJt16UIZZbJvIA4Ou+6dvDOZ7lMAHjvjbEff/vdakMJFZzHkDACWZZXRY4Q9jEJKRFGRVUymtWrap4nbfXp9eSs8z6E6P8hcY3/cGkACG30klAuRJYVBHPnOueUYLxq6kxmBNG6an7/9eNvv34kCIhMIAgRAFJm4zBa5y8r3DBFQihMsKjL8I8/dSaU1U2plKIEG6W1MdEFEINgxXq7KfPs+x8/GGOmYf7jj38s68J7dzycrm6uKKHGuIf7+6Hvu1NbFGVZlpggTuXd/f0vv/zCCP3pj38EMSmlvv/w4cO7D2VZKqV//+0jQvgCmr5kB9Uyd8ZeNCnGeWP8tIz3tw9VgxJIhEqIIMFYKzXPru/7eZrbtl2v14RgkJKzzhjDOdFqQfAdQdhZSynhnGGI66oax6UfeiElRBhijAgOIHlrnfVCiq7t+2kMIRjjlFGzUpyxc9tZb/O8qKv6kuWAGCOMp0l5a30MwYWqro01UuZZxruhJ4R0bbfd7VarDacEEwIhXK83EAJCke6Msx5BGEMSkk/zRDAtykpKybio6mace+esD55zXtc1paQ9d8bYeZ6ncZqnmTPBOEsQIoRRRCFGCBHGOITw/2fDhUsTOIWICaGUEkIva2GM8YUKgRDGiFx4Eta6EGLbnmMImKJ5mblgZVE8v4xM0Jfnl7fXI8SIM74sylorpVitG8G4ELIo8uPh+Pj0db+9vrm52azrsip3293Pv/z8/PLEGM9uyyIrE0z3D/dd2znnrvdX/TS8vby+/+5DkWfb7bpe1edTS1/p+XAMISqlPrx7p5WCFdSLnSfVtd3Xb1/neZFSWuOntAAY66Y6n04hBIwxxni9WeVZLjMZnF+WqT217bnf7NYIIiGZtZZy3HbdvMyYkhj9PGlCaFEUCONlmYuyMNpkeQYAWNRMCSUUpxQWZUECkgu9LEWRAwycdSEmpQyAAGFcleXQDX07FLlclMrz4u72DlO03qyWacqzAhPSD8MwDDe3N1VVqH5p6nUAcVFL250zWzDGirLMsxIgVK8aISQhuK5r5xwCC4BgmucQImPcuYAJKsoSY9y2bXt4u6Anow/GmFt/TSm5XHSC99Mw50UGUMKERB+0suvNNsXEuVjUsqgZU5IgVNpCONfrCmM8K7UsapwXkBImZL3eeBdWqzWlpO/HC6IDI8I4XDfbmMLL82sIsW4qKQXBlBCijMLIYwIoxRCg73/44fpql8tMCPb6+uqsvSgt7x8eCKUP7x5ABN8ev729HgRnWmuCOeNJ5tk8T87H9aoghC3L3DTN9fWtd+7+4T6G9PG3j+M86UVtt7vT6Wytvn944FzGmAhGBNOyLNSsvHNXV1dSZDFFiOAya+8ThOTPP/2lrKrz+WiN22zWMaayzDfb9TjMxHsfox+X6Yef/hBCiDHwjNngv339KoUkFLfHlhLSrJt8LrSxXHDGWQhhGsY8zxmjzjm1aBBAAolQ6qxTWiOE6qYRBM+LVouhhGx229P5rLT68vnL7e3D9c3VMi3zMu12ewDh3/76MwQgxIAwSgGEGH2MCSSlNUFccFE3zdXVjlGmjMqkNMYO/exCRBBFECkjdVkghPJcTtP09vp2PJ4pZUVZ3uxvZzW/vB4wI7msRcZX2/Xnj18ggrd3+2mYEohTPyQQCabt+WyM3e/WRVGoWWtldtv9el0TSpRaokvrZkUJeX55tdqklM7d8fH56x9+/MMffvhhWqbff/94c72/ubm12kjBGaUY4b/928+7qz0XTFtzPp9TAhCCRS9SCgbiueucN6/PrzIvbm6upcwFzzF2l/EuRBDApLV5/PaEcMIEXUQIWSYTiNbY7tzKXBS5wAQxwuZp4pIHkOyiMCUsI8uyAIB9bPO86PvJGR1CULPSTgOIhrHXxsQY50VZZ9vWWeNEnnMRjbZaK8IIwSKEFGMyLlZVLfMCYY5RAgmeT8MwDFxyb93L01tZVXkmlZrzLCurwnnf94vWertdZ7KQmQghYES0Nsba1WZNCfv4919fj6+73fr6+hqANE99WVaU0uADQijFZI0FMFlnMeEQY6c9AMiHFGIKKThro/PBRxATBJdQaIIoYYohJgChEKIPniJKML3wfwgh1pkQgrHKGksZCdFZGxFBq1XTdu3QD8rq3XZ/c3tdlrXgQgiZyYxcbhUMCclAAtM8+KjLqrw8b9M05XkeQ4reabPEmNpzCwBclkUtizbaW4dAVPPyy7/9rK0hlFZV/Yc//nHou5/+/EfOaITJGauVzvOsLMrddielmKYxeBswsNYui5rneeyHpmk+fP8ul3nwySi9Xq+rqoYQztNY1VWCAGP4+O0llxIjCCHmks/zfD51QvAsz+aFHWPAAORNWVWlUiZ4l5K/vt4+fXtMIFCCuZDzhBknEUSMeQKxtxohbE2AAFgXBBfDuITonWuF4EJImQmGYV5nhLKolPVhGCaMiJBFSCCGOAzT6TRgzLKigIjEmMqiqMo6RRB8MMpdmH7BpmlWlJKbu9sQ0qVKWRRFSkBwKXkWfRjH4eX1BSNktWaC1nVdNZnI8DT2RdlInhNKc5iGYWiaVUoJERRi6rueM8mFKIqScRZjstYzQqx1F4caBHCap6oqL9Fi56x1nhBKKF3V9aJVWRaccQARpSR4FwiGCMYYgw8gJQjSw/3ddtuk6CHEMMG317cUU1M1680GIey0jzFCgAGE1niMyY8/vheZMNp8+/rt9u7u4f5eaS2XrMyLaZoSgKfTeZ6Wr1+/JhDev//u+fmlqsplXsZpBAke3g56WSCCHx7eF1URfFj0IjPRrJvtdvt2OC1vh0s+re/7l+dX7xyjjAri+zDPS5ZJMs8LRAhC9Mvf/p4gWDWrS2TVGn06tsE77x0h+PdPn6SUddn8/PPfr66ud7u9lHmKoOv6eZ4JxULwcRxPx5PR2hj7+vq22e7e396+e/eAYbTOCZFlZfHy/MKc5oIOU6+djhGoRQnOKSXzopx1UooUQQIxRZACoJje3d3+8MP3eV44F2QmffTTsiQPrHMhxbu726ZZdX2bCc6Z4Jyfjqc//vgXq3XbdUVRpZiMsw8P77Q1KcG6qUWW6XnhgjOBCYaU0HVTcc6yTD5+ezod2+1mtdlsL7Dom9ur3W7LmFBavXsXfvvlo3O2KErBBaUEU+S9+9Mf/7KouX/+BiG8u7urqnqZ5hSSFAJiFFP49Ze/V1V9eRVgDJdlOR6O292WUrLZrShhf/mnf0KYTOPkXIgpCsGPx+OMphD9OE7WmfWm7vuuqdbrTdM0dVmUPoRxGIyx+/1uVVcEIwxh3dQpJKWUyARIECSw2a6HQRlvldbn04AxUIsKxo/fJhusCyEleDyeBGcAgmlWPng6jcsy397crlerLM8IYdM8EbzhjF/W6THEoixjTCQELoQ19ub2Wi3q48ffqypvmhorbZTTVi+LHoeZrGlVVUKKaZojSMM4pgRBQj76tm8hgOvVZrvZGq2dcRgQo4b+3AOKbLQRRYhgjMEbc3HCW2NiCBDCGIJ3HsQIQIIIMkwixghdwmMQIXhZAcSYEMAEE0xojDGlhAl0wfddP81TVZdFkSMMu7a1zlHKf/rznyKIVofVpr7aXUuZx5TmcbLeh+jaXs3DXJYFgPD15WC0gSQtszodj3e399v1dhi6cRpjiNO8IARjiq9vb4Sitjs9P2fX19fLPJ/OZ87kOT/3fffy+PK//vqvD/d3m83uUgENMSqtKWEIYcEzF+wwDYfXgxDSeZtwOvdn+oi3mw0lDABY5NX1fhOit5sVZ0KpxUefZ6IfhhDCfn9VluWqasq86IeBIMQZ227WzjnOaPABY1jXpTYKQ3Rzd23MxVMWyrpIIEgpKeMxBkJYAlHppcrLzWbDKffeTdOozHI8HxkT7969K8rq7e2Q0kIIburmdDo754uyUIthjEohizKPIPXDUJZ1VVRS5sukpmH2MVBKyrKKITHGVpSdzqenb8/jOLXn1ttIKXl495BJabTpuu7w9rberENyh9NhvVmVZT4OU/DOWzeNU1Zk2pj2fDoej4yzulkhjIWUIsutcwAipe08KR+CENIol2eSEGbdMk3TOI+SyQQhAEkIGcKS5RIAOExD1/XW2gjS8XCAiNpggEHGaCFECN45SyyIyU/9HJKtqhIRUNYXMoKs6xUA8HLCzkX+w4fvx2lMKQEInPPGGFlIyeWy6KLMMcKb7RZidDqe5mUZ5j6v8oeHh+1mZ40hmBqt+24oi3KcRm/cxWXbj/3L80tTN1qrkCJGJMVUlEWRl4yxRU0gpqqsVpvGGtOeu9mOapkJJjSlCDHSRm832w/vPyx6UosCKTDOAWdC8GkaeQhlVWaZyMp8t90xTqRgt/d3zy+v/skhjLU1p7YNKeVlUVZ4Hse8kHVTQYCMtQTj4+FwOB36vgcQrldbq93YD2rREKAQ4zBOECJjNWUUIeB8MNaEGCHAKYHz6fz4+EQI1lorpdbrTV3XmKJhGF/fDkIwSikmpKxKwTnBWC0aEXx1cxV8zPMiwmS1zYs8JjAvi/ehLCVCBKS4bhqE4DyNjNGmqlf/3Hjv1awxhlXdEIq1sl+/Pkopxn7WWrfnc1lUb88HiCCltG7q1WYzjsPpdKrrTZHVnDOtddu3p/YcfcJPLxCnp8eX+9uHvMh8cjBRpZU2Vjtb1jXj1GgzK9V3IyFkmWZr3X63L4vCB89lRjD4+W+PXPIff/wh+bRe1wjiLMulyCSXGOGqqkAMCXhnvbexrIqiLrngIMFxmiHAeZHNy/L4+DxMMwEAYcQY66dRWyUEhwAJwYL31huQEgQhJW+1NloRspVCTnjEEK5WTXH5JZXmjGuru3OLEdrttkKILMsmNuZlXubZer1KAaQUUwCCc3EtL6RDJgQX4ub27nxuAYLWOjWrsioo5mVeYYxjTHmZAwBlLhKAPoVl1tb6mBLCwBljtAnRhxBSTBiBFKPzFiYIYUIAQwIxAhAiTDAm/2h9AYQJRphg5x2mBCaQYpzHqW27BCIA6Xw6d23nvTsdT23XbbebDx/eZUWuFqu1HsZFaau1nsdJa00wxgSXeWGNO55PeZkbb5MBh9eTkEIp0419iCAFlECKKVrjn5+elFp25fbm5q5rh37oY4zeBUxnNGK16AjS2M9DMxPO765v50WdujNnjDJeN/W8zPMUUwAhBGVm5/3QD5zxrhtDiDF4ShiExyx7xgReX98gCE/tEQFUFGWel5gQzmhMyVlFMOGMB+/ncdJaEcpipFxw52zbno21Td1IJqP3DmKXQlnkAMIYo9EKISylzHhGEC7KrCgKxqh3Pi/k+dxGEPM8M9pY7R/u343jlFKc51lmGSYkpqS0NtbkWf7nv/zZaGu9r5sqEzmKcDEqL4oQnDbWGIMhwoj0fd+17bzM+/0VxjjGEAFKKXLGv379Mk79ar2qq9pZd3t3C0Cc5rmqygQhwJBQ0nWDccpYk2AEEBplCCPkQrVykRC8228JZvOsMylSiHmehZimaYIwDsMYYnTWQ4SstilFpbS19nA4tF0XfeRSIgiXZVZqIRlLKTrrKMEpgRCDVkuei2WZh76lhNzdPuRZBhOCCEIA2n6CEOVFHlKUUvbDMI7j4fWgja6actWsy6oEMd4/3HPGvffOuvbUYkibbfP+3fu+66gg3djlMnPOHo8HjFG1Wy+zijHmeb5ar+ZpASlZ6//291+ub24QQtM8aWtTCjf3N9a44EGWFZjhvu2WSZMUgnM2r6o//PhDAmB3taXkZujHmEJdlzGG0/F0PBwpJd9/911d1f04OOdTAi9Pb+dTizB6fnqGiECQmOBVUVZVWVZN8NbHNIzj8+trdzp56/Mi74Yuy7KyLNebNcZYGy2EMdpO09TU9YV0H3zAGCcACCZlXW+32y+fv70+PU/LlGIEEN6/u7+5u8lE9uXLZ8aplIxSoo22xsSQxmHQShuj53FZb9ZNU19gMsusrLGYUu+dUpoxVlWFWpSUfFnmelVVRV3XNUawH/tlXg6Ht3EcU0rzrGJMl8Rn9P7x6eXu/vbq6qrrekxwCmkcxsfHb4/fHouy+OmPP8UUx3HiVCCArNPzbOZlmWd1as/zPPOM++iHfsIYOWefn54Z529vB+/sZru5u7/lgmFAGGMphnnxlLAsL95/9yHFWBbl6Xh+fnp5//69kFxK4VyIKUSQ2tPJWL1erSBFECHOBWMcArAoUzb1MisXAoSQc6oXtYzKaDtOk5CcUwkRbFbNOM3TNGltBREIIYyJ0er333/HEE/zaLRRyyKlXK2aumnW6/W//Mu/TON4e3ez3mwpIZnMpqIAIFprfAgooW9PT6t142xsjydjzfX1zThNddVkOfI+tW3LKbv6/qqoslIWetEuhK4bOOcI4JjgdrcBCI3TYr1VSl+mjgCmmMIlQwkhjOkCj00ApJBCShFBDC9AwXihQyMAL8pnaK3FhAAIGaOYIELIdrPJZPby8gJi1Ertr/abzcY566w/H7vgQ7NuuKRGGWP0uT1fJEVqURCmsqjmaRmmgRBS19XV3bXTxoeACWacApyMshdRrJBCZoIxPvT9siwJRa3N1f46LzKtdWJpt9/cXN1ABKd5PhyPUsp5Xp4fX7x3IpNcMAyx1qZumhTTMPSUsODjMs+EIIyQUqOP4NwD7904TVywaZ6aauW9I5RgQgTjQsq8yPdloZQ+nY7a6pRSlmdZlss8O58UAABB6L0lWGQyiyFIwIs8R5hoa/pxlFLe3t4JyedxkUISTCillFAffNOsiiKHCBqlrQsvr2/jNGZZVtdVvVqBFBelxmlSerm9vfMh+OCXeeGcWeUIwAAhAoHWepoWkOLhcDy3Z5lJzkWMkWBc1fVuu/XRD8PIBa9XVYihrpusyDhjnLPT8YgRNtaeT+3hdKzKFSFknEeM8W57lcmMUmac1cq23XA6npumqavV5naXyWLsp0xmRV5O86yWOcRwOnR1UxmrpRQxxRgiYwxwluUSIJACXJYFYsA5hRAuemGUEYxDCOM4dq2BKDTrAoKklQ6EIDQbrQgmarF60f041nVdVDkhNLgAIAghUEYQke25q+omyzNGSLOqjXZqXtSyEIr9aP7lv/2afEIURR+5kK9Pr7ur9apZd+c+xfjuw3vK6TjNT08v8zyvmvq7H7779deP/dBxKnwMhBJCCEJAK+2onebQtZ2zBlNGQEqc8durmyzLU4pjP1R1ZY0BKDHK1uv1Zr3NZRZBLKq8G/rnx6f9/lpr64ODPjEsrm+uQYJd12mj86IkhH787ffz6VSU1ffv7wkleVlJyaMPWZXd3d1LKS+24lktnInZqxCDzDLvA0IQwBRC8N4TgpUyCKL1tlHzbK0hGGNMMER6WY7Hw/PrE6V0HIfr66t/GHMYG/reaBOSQxAqtThnl3nJi6Je1db5aRp98rc3dz64cRx89K+vb1Vdvnv33js/q8l7f2H+zUr344QAJhS3524aJ0xp13YpgWEcqroijGBKTt0ZwKSVHvrBWPPbr7/UqybGSDCilArBvQsY0RgSRCCB6KzVSmOKqizX2vT9WJTF9fVunuf23K7X66LMUwAEY15XMhebzaZtz1orjAhEiFDSt+Nf//q3x6eX6+trxjgXHGLYz6PVRgiZ5zkiTEhpjBuGvhsGRLCPYVZLTMk6O47TpSi7224Iwav1OoQEYCpKpBbFKJnUlMvi6uqKEuKdr+qqXpXduSOEOOv6rgcAFHlRluU0jMusM6nqqyvOedu2zrph6EOIu+0OQdS3I0QAYUwwPR7PWplmNRJCAADr7To4VxQFAmhW8ziOEIKszLNc6skus3I+8IwOff/6+nZZs8cQKEUgpRDCPzL+IAHwDy9AANE5Cz3ChHAGMcIQAYwQQAQhfKkXaq2EEEPfHw7Hru2qqoohCM4xxk2zOhzenl6eN5t1WZUxghCDWpa3t8M8LePQK62XeeGMXtLyV1cAQdJ1vcyzBMD79+80QI9PjwAkRrlSqiwr733bdjITZVkVpfTBY4pD8CGm1XrFON/s9hhhY/R6u+VcKKUoJlmZccn3N/tpmBCEzrs8z2/ubozR4zBZ6xKAnNEQ/DLrGEMIwcegtfHef/v21DQVJWSZFwBAJiRAgGJKGSvKwjhjrVuUiilBiIIP3vmx7+dpKYq8LCtCsNFmGseUkhSSUcaFcN7vtrvtfit5Zq0TnDfNqiwLAMHpcPbOc8GzLB/64fnllZLzJVdCCAk+EQEhohVlGOFFLU1dM8YpoRChqipjTIww73yKiXMGMIQJQATrVY0xQhiF6JVaqrK+v79T2nTdGcL4/t37zXav9QIS4JnUs5JZBlIMMbgQKeUyExChLMj1brMsC4CX8AC6PDCMU231sszW2D/89BOM4ONvv/f95L2exsk6Syi2zlhjIUCcMSlk3RQXCnmRV3kpp3F6+vYaY0IQUkKE4IILAGJKAcHQj+OXz19DsIQgrRWEr0WeYUiEzENwxuq2iz46QlhVloLz+3d3wV9//fplUbO1Wi0LzLKnp+fNerPZbi4xhxDcd9+9QwgxThxwlALvdHs+Cc6qOk8p/v7rb1mWlVV+fbufxjn48OtvH621i9IP7969PT3P45hnWZZlRV2EEI7Hw9vrW10Xq50g/+7f/aUoiocP70NMH3/9+Pr8GoK/ur7p2u7z56/jOAkpAIbzsPztrz8DBBllj4+PiOBxmJZlKVd1WeX4YtuOhGCilCYEWWPO9pgJQim9SJdSimVRLkp/+/bt8elFG8M5z/I8hPj0+MiZSAA45wgmF96WCy4v8rt3ty/PL8u8hBS98ySFLJfXN1fzPDunlVYQpfP5lInMOPP09GSt6buBcrrbbNebTd8NLviQwuO3p3lWPvhm3ahluZRHAIjOGO/90+MTF1wt6lJ0WW0bRPHb8yFAD2ICEFSrilLmnEsAYEKstymCFKN1NgbvfXj3/p2xKssyTNAyzvMw+xDqpqKMFWWeZdKnaJVWakYIztOcQsiKMpMOI1RVFaE0L/J5nphgCMCX16e7+3sAgDaqrmuEEGdimaevn79M83I+tS8vr+fTeb3Z7Ha7umkAgD6Ey117GMan55jJrO1aQtlwPE/LfDq3F6dKjHFRinPBhazqap6mqqr6fhCSr9frBNP31Q8ppKIsCUacMcFF1/UEEeddd27LqiCEqHnOpHh4uOeCe+c+f/kUfBSCb3c7xogLblHaxwghxAlJKR8e3iGMh6EL0TvtqqpBGC/TPPS9dXaaxqkfhJRCihjWeV4EEK0Ly7nv+4EQcq7XCGOKMYQYIgghjCnGAAGCCMMYLwoJ6IOPKZEUEUKE0AgSQhATQiiBCKcQIUjOu8Px+PnT53EYb+9uEUSUkizPpeCUkc1uI2XWnlsAE+fSGmu0wQgihDhnalnObQshyLOiKPIsz1fbehxHH4JaVJ5JRmhwfrbBOiuYFZmoqoJQYrX95eVJqWWz3nAphr43RgMIx3FgjBtjTofTPM/7q72UUim1XtcAQe9DVZU8iZeXZ+f8xdV1YbNLmcUY5/nkgnXWbdabqkKLWrqu3V/tuBAvz89qUZxz521TN4yx8/kUQkIYcyGiDyCl3X7rrMcYMcpCjNvNpsxL50ybF2qe66qGCHHOIcIyyxjnUoiUkrMWXc5hCP34w4//9ve/no5td+oubrUP77/DCE3zUBZliOHlpfXWueCyLFs1DWNcLyZG750f+oEQErAHEDrr5nlx1lnjpmmknG3Wq7KsnPfGaKPst2/fxmHUVhdFIYTURnVdl2eF825Z5n7oU4yXr5pgMs/K1bqOEXR9Ow6T0gohzIVYZn08nDljFx2sVn/Xi63ryhpLCQ4BWXPRe7C27QAAjPOEU4LJ2nAxsEbrKWVXV3sIcXeeU0oIIkIIZUybRS8LgCGlqJV2Tu3224vImgvenlof4ul0hgju9jvGOCTw+eVJLVoZZbR+eHioqsp5xxgLwQWP1byURZHLfBrHd/fvIALe+WEarDPaqnpdaWNCjJTTsZ9Exruh88lb4+Zl+fjrr1lR5lkGMa77frWqOaN93y161kbdXN+s16thGCBEh9cjubu9TQlQTFZNCX4AAEJGqTEqyySEQBulzaKVOZ9bKYXT9tPTJwgJhEBKiQlyxswTDDZgShhlb2+vKYKUAOPcWPf2dpJSGK2maeGSLVob7ajAmNCCUUr5erPOZJ7l2TROGGJjjDXOOgcQKIt8mqfrqz3n9O3tzTmLMcaEam3/9X/9dbNa/fjjH9Sifvv4i/H+7eXl7u5OcDGOw+3dNcY4uDSOw7LM2piL75BnHBPorbPGRhgwhIxyIeU0jatmfTgc1aKKvEwJKNMTQpp1Qymd58VnSS0qwNSsmqZp/D/aCmlWM0QIJdysioe7e0LpOPR9PzhjheQyy5d5Pp/PBJGyqs00xpRCDOvVJityStDV9XWWZav1Bsb09PpcZNnb21vwIYBUlGWMfhyH4/FNcFHVtbVGGwsgGcYJIAQweHl7sc5574/nk1YzBJBSRjAKMWqtN+utda7t+2VRfddrq9e7HcaYER5j4FJecOEAoKKsyqpCCJ9OJ220ZDIrJATIaoswvrraU0q/fP7KGd9st2VRCiEXpRgTVVkpozkXPvjz+TxNYbUOCYAQ47IsWZZZa733lLGLmBMjem7PIUQAUFEWIcW2bY3V3rqiqqw1T49Px8Mpy3OEMaWs7VoAwDhPp/MpK7Ltuok+IAQpwcaZGBIAACKcYrwwgkJKKaSIYrgggSJIAEAELoI5BBFAcRjG49vReRNBHPq+LMqiKpd5dtZeWoHPL89P3x4f3r/b7goIISYkpcQFhxAyQjHClFFGGSEEYwwgoJwOx3Ge5tW62d3sQATB+RwImACjhDa1NvrYHvu2Y4xprbNMZFK2XauWpVk3b29val5klkEAHx+/Usq01m1bvv/wzjk/zyqlFEKcp7nvuyzPrbXeh7btCMUIoSZfM0GbeqW1CSHmeRE8ULPyLnDOEUTW+L4fs0zGcGl6ZoyxS1Ci6EprzWaz2ew2282WcyaEePr6SDD6w48/cM5f394gTEWeI4IQRMMwfP70yVr3/PzsnVut199/+B4m+N2H97/++ivE8IcffvTWnacBwMQYz/Msxeij98FTQhhnGGNtNaFEj6NSCyVMZrJvB63NhdvqrJ0XtRIiz0ultbG6PbbG2ru7G6Ot8w4kNHRjNw7WWD23jDMmeOhiisk6W+R5WZSUU8b5hYW1Wq/8qy/rihDCKb+Ao7pueDu8WWPatv3xD3/YNPvVpv7ll+M4j4xSH4K1NssyKTMheAKJC84F54LNs2KUN1UBEkSANk1lTXTOCxYubRPOmRDrcWoRhuvNWmlllLm6ut5f7Q+vZ/8aEAGUUq318XRwzhGEIUhVVVhjpmm6EFCUUlVRgS3oTh2EabtdV2WJMF7Ustmux36al+Vuf+uCl5JTxsIuQoymafj4+ycMEWHk7uEeXkwAWi/LUpAiz2SWiXmeg48hxrwo/vDTj//63/9qXSQffnwffFDKdecepBSC7+b54mZblmUYx7IshZD7/f6C5tjud8GlBGJW5NM0ztNivc+zXGv9+Pg8jP3N1U1V1lmWSZmKIgMgFVVOKeWcteeu73vUQ8r5druFCE3zLLjcX+0zmYEEvA99NzjrfQqUQCEoo9RbJ4WoyspZJyhNMYQQHHCPT083+6uffvrTl8+f//1/+PdqNlqpGIMzIWskL8U8L2VZ5UUah3FapsWo29tbytg4DQGEh/v78+nMhfjw3ftvn7+15y7LMkoJhNB7ao1zwQUftdKZlFVVLYsq8uLu/naap6+fviIMY4gARCnFarUyTk/z+PTt0Tl/fXPdd51kHAHAOWeUF2UppOCMIQRvr2/rpmaUOO/Wm3WW51prxunb2+Hiok4g9d2wLAvGyBg7DuM0zev15vB2qMry/v7+118/Guv2+y3jbJgm3wWj5zwv1PJtWeb1enVzextBeH55eX19E5nIiwILMs9Lkee3d3cXKC5GeBonxqizNs9zyujVfj9OI2OsLutpGmOMKQAI0Lt37zer7evb63q9rptmGoe3t7f1ZrNerY6nEyXUOns6nhCCx8PRORei3213IQYEkZQixtC2bZZnWZ4djoeh7xEAACYM4aKU9y7LZIgBEWK9H6Ypns5Fked5NvTdPGtMcYgJIFQVufMuk5JSZo32zsYEfAgAQgAQhBgiDOIFGApDiJwj72ICHgCMCaWMDIMaxuHt+Ho+t/M0CSHObeuc44K353ZRCmMYU0IIjf0QQ5KZAAAIIRFEEKHNem2MeX17XWYVQeScQwSyQpRlgSHWSqOEnTd5nllrh34MKdRVU+RFmZe3t7cQwWmaKCdFmXPBfPCMs7ou80wKkVlvT4dzTAlTpBY1dGNVl5cW69AP2+12u91kWbYsauiH8+mcIlxvVqt6jRl5fnlu25Zzvt9fUUK1XjbrbYwx+oAQJgTHEDHGjDEphHPeeccljymsN+sQg1W2O7ch+qZp5nk+n1vO+XazKfKiHwZtTUJoGAZn3fF4pozN03I6HY/H09cvX2Um/+kv/1QUOedSCumIE0744Kq6cs5d9KIykxdOaj/0WqmmaShlSi0x2rIuAQRt28UQmlXDGMuLvKrLc9eez+cQ/PXVldYGAliUBZdsu7k6HA/e+7IpMpl7H9QyUUrqpiGUgIRASv3QQYgwRi/PL/O8XLztnPJMFpQyiNB2u/uf//N/DV1/eDvc39/PdOq6869//7UsyrY/A4AAAFVd7Xb7BKJzjnPJCMEYZ7Jwzl8KKCmmru3KcnWRVSAMCcEQJozwfrfPpBBczNN8PBw//vpRSsk4W/RitS7zylh7PB4zKcuqLMqCMz6NE8KIAng+nr0PHLO2O1nnEIRlWc54RhiNw6i17to+Jn9zezUvc9f3nIvddhdC4jz7/vsfEYRt1/70059iim+vh9fXF0ywt77erBhnjLLT8Wi00kpxycuiXJQiTgdjNOV8s95M0/z69sYYH4axH4ayzK11T0/P11dXeZ57761zTdNgQmKISimlNEJ41TSCi7fX1+16dXuzP55Op/Ob9yGASBERgm92m2meCSJFke2udhAgpTXG2Gj9+PjkjL+/vxWcn0/tOM7zMjvrZZ4VZamMiiEyylOKIEZjzGa7+c//5f+ulHp7e4QInbvudDz+8vPP7bmNPu33++Ddu/fvjXXTNAkhEogAofO5Q4gghLW207xQQrwPyaeiyKQUIcY8r/YQgZgQwnmRxZiyPHfOTeOcZXle5CmCPMsZE2ox1rh37x8YZ2rRp9NJqcUakyLVWi3LEqPHEH348EEImVIQQpZVpRadFbLMS+fc29sL48Qqdeo6AEGWZ8fD0RizquvFUIhxsO7f/vpvSi/v3j1gjEOM2uhv375ZYxFGMSUAAaU0zwuRZYfDwVoXnV2WpalXSqvDMYzz7K1dtJqnxUWvtIUIWueUUeM0McLqpi7LKsvyw+FwOp2EzNZFmUCijDJGL7Ss1QqVZXlze3PxY3DOp3n+H//jf2y2myzLjseT1rpZNYxR34Z/+ud/RwkdhyHEEGM4n8/Pzy8Yo/3+arNeM84IIcfDyWhNCOGcz9OsjQ4+qGUe58UajTGexmkYBkpFXuTHw+nl+bmsau/Dl09fpmlG0W22W3Z1PY1TcMEaF4IDEUIELjhQillI4dIZhhDFGCGMyfsQE0JB5tJak1JQSocQKKUIIgjhuW0ppeniFgCEYCKk8DE479KSYkxCiNu7m7e3Yzf0ddX89Mc/EUYfvz4ao621Sul6VRZlLmXWn9sUCefsckN0xk7jsNvvYMIhRswQdcRoDWGCCFBInx+fY4y7/VVZVErraZgXNaUEBOOP357e3sjN9Q3llDMOYiKMQQCLPJdClEWhlTHWxBjW5VZwWRZHxlhR5AjB0+HIGT+fz7LkVaqij8ZazpkxdlkWpTTBCGE8DRMGiDCSZEogffr9s/O/Xu/3281mGEZnHBfMWltUpXVOa3M8HFNKeSYRwv3QQ4jO5/NDfm+dxYQWZS4k7197jJEPcJomhJDgYrVqGGMAgMPx0LYtBLAoywvZm2I8DXNRFj/9VGNC+r6b58Ua23fDopdlWhhjCGNnnaGWCuIXPwy91poxmmdZSkEbfTicYvAwIcpoCGFZlmbdnE9ntSzeO+dcVVbLrGMAeZ5ZY0ECZV0UWebrmhD2/Ph0eDnmZfGf/2//6Xw+Hs4HkIKQ4ur6ClNsTZBS5nmGMY4xOu8xpkPfffzt969fno3xWVZKKaWUglMpOaXo/v5aihIilFLKs2J/feND7M7tH3/66YcffyIICilPpzPjVAqZlwUnLKUouMhy4ZwHAJxPZ73ot4+/bTebIqvmZYYYzOMYQpRRihy1rfry5QsXAhN8as/eRykEQIgxdnt7u95sh3EQUjSrpqyq6JLIWIrhdDwbbYQUD/fvIAKffv9SN+V3P9yTx8fn3W6DEJZZJnLpQuy7NqYQY0gJXl1d+eDmUTnrq1VFCdWLWW9yLBDBZJ6WEMP7D++iAwABgjDnYrVaf/r8CSHUrFfOmGa92m43wzD+9uvHLM/i168Mc8rYRXzRVJWz+vPnT5v15uX56fVwLMuKMcEF/fjxo7aaYayU+fD+gdAPWtnrm5th6AlC1zfX//Iv/4Ih8cFtN7umWl1f31BOlmmBAL68vI7jKDPx8vxKOXPOYYy32x1IIMUoZYYIMtpQSnwEQz/XVZ1lcpmXGNM8LQhja6zWum7qkCKCSAiZiQxAoK2BOPfWQghD8te3O4Lp9dUVAsBoc7O/Gqfhx+//UJZlTPGC8ZnnRRtlzHJ8PZz7k15UdswFY4TRt7dXY/TT0/PFFeeDZYx7F6Tk+6vtdrc5nzvOGITo1J+nYY4pxhRlJpumjgC8vL6O/VBWVVWsOWd5UVDOpnFu224cesqYzOQFkwAhzPMcQKC0yjZFs2ryPOdUTOPUGUUJoYRywfu+V8uSZVk/dLvd3jlnjaWUnk+ttnoax2VR+vFxvVozzlKKX79+RQhhgsqyBgCUVblu1u35XNcNwni33dZ19eXL12Hoh3G0xu73+2meHp++aaWZlMEFygkOaRynGJOUcrVahwjLqhJMhBAwpVVZ9N3Qns//n//9f7++uvpP/+m/pBStNpxQ53wCgEISYyKEMg50WFIEACHngg86xoVyLqWEEA6vb/M8LmpBEAoppJBCiL7v21OblznBZBiHosxvrm94xuZpEUJwxsqq9s4fDqe+771zvx8/xhjLqrTGpRgDiN67wmVW27Io8iILITDGMSFCiBSj0S7GpOZxnKYsF5RQB62aFUIoRG+MhogMw5giKKvq7u52HMeUwHa7PrweKWW5zNfr9dV2vyzL0I9m0ZRSCEGwnmHCMlYUhRRsnmdO2DzNkvOirhaZMUqur68wwRAijOCilLXWOc8olxJVVXU8HadxssZcXV+9Pr9qq+ZxkrnM8/Lt7aXv++/ef6iapmlWs9E+JoSRWhYmuMxyzhgiaJqnGIP1blmWlFLfDYSQFNNlX0IxhQhEHI02FyPDOI7Oei64Ntoaxyjb7rYpgufnl6IoBEF9P7y8vG63Gwhh9LEoi6IoKKVUkH7svQspgYf7bb2qu6HTynDO801OEA7BQQCnafI++OA5E0bpmBLjHEJU17XzPobkXfDOp5TaU1cUBaGEC/Hp98+r9VYb/d/+238t8uL+4b5tWwARxgRGGEJwFi6zLsvCGud9GIYBgmiMmaf50nFJAEIEQPSU4WZViQxv1muE4OO3R4RIiCHLsu12u93thMgwghij77///ur6ep6mGOMyTc26FkwcD0cffAhumZe2bUGMznqNNASgO7cXcmJNKx89wfT59fn27gYDnEnJGG3b7vntNS/yEALLuPNed+3V1dWyLN25GwYdY4IQQAQhRF3f11VVr0oqVmpayOvLa1nlCOO3t7fVdo0JOp97iMBuu0UYMsF1p9rz+er6KlhPEC43NZeSUXJ1fUsI+fzpy9vz236/l0KkkDBBq3UDwAfvXJZlMhPr7dpqZ7X97rt3ZVnFEM7HdrvfWGu7thun6Xg8IoR/jb8AmIZhUGqZF7W/vm6a8suXz1YZTLDgeZZJAOCXz1+aan337lYr7V3iGVu0WuaFMHZ7d980zTiMP//yC+MsxjCMoyiEs857LyQvmyL4eD632riqKZdxXpQimBKMlnkoy1JrA6AmhAYflmXpur5YFoSw9/7+/YNz7nQ+XWQpV9f78/nsrctXq6quMEYQpgQiIoBz/u3xKyU0Rn84HBEmUgoAQATh65cnyrBRGh3R3f1dGH0I8fXldbPdEEqEEGqBry+vX758ZYLleb5M/9gAv768KaUBSlfbK4hw13chRsZF6qe6WW02a0YJwtBaiyHe73YIg6FvCCE3t7cY43GalFaYkpjANIx5kS1KeR+2G7bZbFKKzrlh6NGMKSFFnl9EED//9W937+6tMotajLGYYGvddrOpV/Xb69vheAghZplcFiUEf3s7Yoy32+3xcNBaj+PIKK2r+uvXx2mcvPfLolKKnz9/ts565+d5XhOGMWFcgASberPaNItSX37/HBMY+qKpyvV6vSx6HCZrbELgfGhPh1MK6O7hpqrKosh+/vvPH75/xwU3xqYEGGeEYOec1k4KnmVZ3w+qGwEAALvn52djzNAP87JkeV43zbcv37TWGCGtNOcihDAMY1GU6/W6qmvO5UXb4LwbxkFrzQWv6/rl5eX48QRAur+9q6oGgCgzwRlVSo3DiBB0LmS5gBj6CGQupRRmcRghSikXjDOaQOKMTfNMCLPWLbPyMUCAirzgjPsYHu4f1s1mHIa6bFJKGOM8y+uqfnl+SSkRjHnF66ay2h3PLYIIEYgImqa5aWqQQF0XgssQvFJ6GEfBOSU0y7IiLxgT5/NZStE09c8///1idTbazvOyv9pLmZVl8fTom6a5ur5eN2tt1XnoMcKU0izPLxv4aZo3643SBmNUVfXt3R1jdJkWAIAQglBKKc7zAoCktLLGfvr8hVByf3tLGbPWLIsqq7IoiuDD6Xg+HA6ffv98e3vDhVytVlVVy1wijCGAeZYtix66EUJYFIUQfLVunLPTNBUFJoRyToo8G8bx5fllnieIEGNimuZ5WYIPq9WaMw4B8NN4OWV7H7RWAMBMZp8/f44gpgR+/tvfiqLMy2wcx/ffffjw3fvfP35abdaZEMM4XhyIsQ+UsgtxbxqGPCvqpgGwR5gKKTFC2iqGUVUVmZCUYkQw4wxBTIj0Pg7DSAnLpCuq7GZ7Y5RBGK7WK2eMlPwSedrf7F9enlfr3Wq1/vb16263wwQjgAjDarZZIYq8bFYVAFAI8ed/+qMPKcaklHl5ejHWEEyMsafTuU6r4N3lc1uUOefsdDxv1quyLjHGwzBRSgEECJFl0pgwst6tv3593G62IsvbtlutVrmUmKKyKIdxXMaxPZ6zLBv7IYaw3azHcdLaOOs57yBKm816vWo4Z1nMhm4c+nG9Xr1791A39TJNzy+vy6Q2m82iZmetd5Zzvt/vxmksq5JQul6tnHWY4DzLvz1++9Of/jLPc9t1dVU1VR3v37XnEyUUQvjDjz9gTLtzOy/q29dvlx4jF2xD10VWbNZrCOHxcCrzqqUdYxxAyBm7u7/T2ry9HaQQp8NpnuaYgLXneR4ll23bXt1cl0XJCCKEURofH78Ow1SVFRcsy2SR5yKTzvlMSAxRUeZq0U/PR4Qg56yuqxBj23YgpWm4wOB0SEEwLqXgQhJKrXOn83kaJ8apEMw5BwDgglGK1bI47zDCIbi+79brFefs9vbm3fcPEMC+Hw+HY993lNAL2hhChAmBCFVV5b1nnO/2W2uNyERdlEqpeVLNqtlstnf3t5QQQsg4TvM8lVW1qGW3303j/PXL581mxbDohn4Yh7qp6qrSRjvngPNc8O12W1al5ELrv2ut9LLEFIx2XdtBBGJKZV0WRcE4m+eFcoIwssaG6JVWSmsphBBcSkEIscZM47Ber9u2ZYxGH62xRZEHF9ebdVWvbm7urNXHwzHEoLVNIT28e3AuXGbohOI8y4zRjLGUYn8+D1P/y8e/u2B2u23XdREGiBGmNBnLBWOcd+fO2ZAi9D5igtu+fXp8TQAgTJx3l/g/oSSE0LbnaRqFkATjru9T6rXRICWj9OHtIDK5u9oxwvu+N8aURSmldM7WdX1ze7NSWgixWq1AipTheV7UrIy3CEDjzDwpxqmQwllHCUEATsvgrBu7Ea5KBEGMl/zlUJZVUaB5msdhqMu6abYApHN7+tvf/rbfX1dVZa1BGFMq8jynjGS5OB7ORVEAkCAAelYPt7eYYhed5LzM87zIcilDlFIIiOA4jhijeZ5jTIxyISQjjFN+eDsAAJx1V/urTOTBxVTHq901Jrg9twih5NPh7fBv//a3qqqY5CFZ59x2u9XG9H3fdu2P3/+w2mzmeQwhhhiELC7GHoggoZhxjjBUizodz4QSQnCR594HTKPggjEupIAQpZQIxRfAdVOv6lX9ww/fSykZZcM4KKU45zH6FENVV0KKvutPp5OUIkGwLPPFdZpJkVKsq/LCDL++uu76Psuy1d1qvV4jAK1xCOFpmrU2hODdZg8xeHx6qpt6GsdejZAg7VSNS0KJsRZOUEr5+vrKCYspTtOsjUYgySyvm5pgnIn8+vrqT3/+y8vjwVjPOc9zSRl2VlOGKcGvh7dxGhEAQmZ5Tuqmdj4ABPuh64Z2nhfOubG6yAvBGMZYK81wms14e3tztb+ZppkSihEKIcbol3nxwY/jhDEVWhyOp+fnp9u7u/3NtVJmmZamruX1zfHcTvOEEPLOUkZ9CF3XxRgTiCF5gJGxljPOKE8xdV3ftb23HiBAdvsdZXQYhrwqh34+vB2vrq6UUm3bO2+1UkLILBdam9PpzClxzjdNXezyECKh2IeYYnp5eokpMkas9UPX+TwnGHvv66Yui+LyrrzgwNSyMM62m+0yT4zQu7vbm+vbp+cnRsS/+0uz219N8zJNsw8egbTf7/Mse3l5ut5fG2PmsTXWUkb2+52xtigK52wCKS9yKWXwXhtVZMV3330Y5xHAFGO6bN6GfsSYXEK1nPGYUpZlKcSyrOZhnIbRaF1VZVEUmcyXReVFXhQ5JXS92T68v7fGL0plkm/Txjp3o/fPT8/es6fn5+16jRAqy0JrTShWVkOEMgITSBDDZl0xSsdp/u67988vLwAkZy2l9dX17nKngxAcjwfnXF7kX798iylmeQYhlpJHAPI8F0Iu8yKEuLq6CjEOw2C0ybJstVkDCGJKQz8m0L+/f5fnOaW0yIuyLIa+n6ZxtVrFkMqyqDE2xjw+PkaQyrqaRkWIb0/tKfqb29vb61s49PMyY4j7rgcwRZCst2WZG2uGtj8cD1mepxT1bC6T3yzLqqqSUo7TFII2zo7j1NSNkJxzhhCKMVprT+eTtYYQijFeNatpmm5ubjhnjIuyLM5tr9UyDMM4jda6pmlCCEIKY5boY8KR84xgYqzu2yF4RyndPtwTRs/nFiJIMIIAfv709f7+Xi2qrvLVeqe1TxCBlHwIp1PXdb11dhoXhAnhKC5pvV2HmKzVIdDr29tMZu25xZgMQz/P82azXm83SqnD4eC9F0KqZbmArbJMqkFbazmjZZlLKY1Wi5rzInPOM0qj8lRKhsGyqHEajsfjbr9DCD09PalFZSJbrKYcQYiN0QjCaZqlzHJZCSmhdmVZrjerz5++tOd2mqZhnN49PHDGqyonGCcYnXPrTVNVpVYGpMQYWTWlD9F5TwIexxGkqJVqmrqWYhxHs9i6rlfrVYpAax1TcsbOo8rzbFmy0+motV1mnedl06zW63WW5847GCKAiFIWYuRCOOcIpz7Esqxggkzp18Pb4XC4v39ACFdVDSAAKb08v0EEOWPncysz8eHDd4xRTMjxeO77XnBe5MXL6yuAYLvZZpm0zlljX19eKGVX+6vg46W5zQUXQqaUMCXEk7e3A6EYEsgESwBwwUFMxmi9LFY7hEHftV8+dVmeCcbLqpAygwgao5dFD/3Xw9vp4eG+LMsVInlZvDy+zIvKZLHMM4Swqsrdbre/uhqmuSoqQmmC8fX5BTNyld8AmMq6BADkRXE+nZdldsF1fYcu6J8Yn5+ehZBMUIzJdrfCBKllWZaRUT4v0353RRn5/fdPb29vXAitzOn4ppWp65pgMoKecapnRSkpy6Jtu7oqJZcgouenJ5llIcY8z40xWjvG6Pl0znMxTSOjBFMsi+y//tf/VtRVWZbr1Ta4aH2gnORQGmt9CG72Qggp+WVHVVXV2+srZRQCtN/vy6LSWm13G4jgNE5knpasyNbrdYxptV4VVVnkRVkV/+t//quxvlk3yo4pgtVqJbNss90kHwGEWZ6fTkfrYSGLRBLGEASYyaJpGu+CsXaeZ8qoNcYJbo3tu1FynteZLUtMMIaEC8b4EmPywXHOKSVVXfsQmqYJIb28PtdVyQgDKf7hxx+tMxAAYxXGpCrr0/kkBCcE9d1UVVVZlXXVvL2+XQC57969e3x6qup6mqfX19e6rpd5gQlUVSmFTABoZRDECcPHp68PD+9gAqKuqqq6CAQFFxgjzjmjTEpxPpwgRG3fnQG4cGPc4KXMrLUQQK31NE/DNCCAHp+eIACEUc7Jx4+fm9VKSHq1v1rUAiHa7bacMRedmhdnXQiBMlrWRdd3xpkS5ZTTi7Y2y7OmrhetCKR9P0xxzovy5uaWc7EsapxGQukFdNkNXQpJZPzm5ub19YUQAgFY5rnruvZ0PjdnbfRq1fz7f/8f3g4vT4/PWSayIkcIOmuneeKMdm0bfej6zhrbrGpMkbXu8HY4vL6FEH1wPnjvg1rUat0wxiGEAAIfvA/eBX/JyUkh15vVOEz3d/dKLS54bx1IgGASSZznWUiBMYYQ+uCjiZ8+f/72+M0aH1NKIEkpMSZSihjT2E/zPK02KwzSMAwpAAgTCAmg9O79A4jg9fiWF/msFkJwCulwOB/ejoTi3WY7L2ZeJr1o6xznPDjvXKzrVd2sCWMJxk+/fz63nV4WKSVEfJ7m9txdjM2M8ZgSYxxBlBeZdWZRi7XOOYsgDN4zQrNcdl0LUsa4WJbFGee8W6bjdrfBGK83G+8DQwyswNPjQhnLsgxB5Ny0Xq2qsprmMfhUljLGkEkJEAo+OGXLMq8qAhIchzGFuFqvCMFDPz09Pf/TP/2laRqIIPyH4xgBGAjB3nujjdba22Scblb11X6XYvTBowTUopx3nDPv7djNnHEAYIxpmmfGBCakqsqqqa6ulgSBdiYhQAk23s3L9HD/kJdFJgSAoB/6eZ4oY2PbYkIYYQihK7gXQozj8Peff16tGkKxNUYpTSkBCbRt17VpmZerq6sszyCC8zw/PT09PT8LwRljx3SUmbh/eEgp1quGUdadu5jS6XBarVfb7ZYxdhk5qmU5nc5csqLM9KJO51NV1xSTeZpjTBihoqrUskAIAUgyE4ILCODxeFyv1137aRxHrUwm5NvLwbmQZSLPMxf8Ms/a6ncP713w8zhZa+qqOhyOECVCKEQII/L49VHmMvnYDwNI4Hw++xA453meZ4V0evn8+fNqtS+rmmKmjX56earKklKKMEkAhBAAQEa7ploBCCCCp3Duui7Pc8JoUeVWa845xXSeZorpw/19SqGqaojwMPZ91zPOIkwylwihlEJZlYyRulmt100/TtOy/PSXP7dde3Nze7W7SQmc2tPYDyF6AKE2uj2dhRRCCK1VnuV9NxRVvr/aBZd8CB8//rbb7fK8cMEJIcjT0+vV9RZBpBaDEHp9eru9uwUprtZr78PhdFwWrZX+vv5uV+/0vDhnZSb/v//Hfy3KDCEE1qBp6iIv+nHAGFZVrZQKMZ6PZ4CAkAIBGH1YrWqG6LLMWZEFF9UyBR+C82/9azf0TdWklJ6fnghl3bkbpglCZI1pmopRNs1913ZjPzDBGRNaL865oe+KosQYv70dTufT/d2dMxqXudHmr3/9XyElrTQVlFIaQpBSOuu9j8aYGKLMsg/ff3i4fWCCjePQntvT6azUcjwcnbfX11dlWRFCEgxc0MevTwkASjFEgDJsrem6c1lUQnJC8MePH49vh+1uW9clZkhwvozLNM1lVVxCQcfDebffEeRmtTBKKSF1s4IQxBhkLp+env785z8VeaGUXm/WMaZv355AAv04eB/qWhBBLoNdH3zUiw/u8PZWlZXMMu0DwwQjiACYphFBFEOYp3maRuNViN5a07VnhNDHjx+/fv2m9NgPZ86lMuZ87CBCZSGHQfpdKKscQaiUCiGuV4xQAjHIpMQw77p2taqLssyynFIKAJiXGSF0OBzmec7ygpCL6sQQzF4Pr947BCAAYL/fU8pC8OMwzNNktAYJLPMcoh+HwWhjjMEUV/VqVa9EJlJK0zheQHvtqZWSIQi3m20I8fbm5nA6GGW99wSRpqn7drTGMMG1UsfTYb1aKaV/+e035x2IACGMMb7ab2OMSinC8GKUC34Yx9iFvMggRm3XDf2o1JJCquuaURZjbLt2XqaiLC/4kKLIIS5jCJQxY908TZRQzhkmCCZQFDnEYFE6xuScz4qsEatxGedpfvfh3TTOjLLgw3qzElxAhLjgCJN6XScIOGVVXTvvp3GaJlWWRZZLxuV6s4Eo5TJ/eM/OpxPA0AfPKYMAtG3XnvyqaUIMwXtvHUG4XMsYc0xJScjr68u0TM7qy8OfUtTGKGUuzZ4QQJZls12maUoQVHUpMgEBnNUis8woFWMKPk7TtNvt2raNMU7zrGbVDcPxdI4hJZBAAnlWFnmZUpjnxYfgQ9DqqJRijCKMndMhxP5L9/r68oc//Hg4HB4fvwYffZ6H4FNK1lqludaGUuqcU7OKIAIAQgx93zvnIATWWWusD35ZlAuMMfL2+vrrr7/VZb3f7WUmGGMQgwubRAjunb9IRBalvQvfvj1PaqaCVXUNEQ7WY4IY52pR87RggsZxfHp+IQwTQsqi7IfpksKsqur4chSZQBhgjJz3WqsyK/JCDMOg1QSAf7i9OTi7Xq9CdKfjIc8y5xwVBGO0zOPT81NTVpjiw9uRc9asmkuTN6aQ10XTXN6Bi1E6+IjRhTbhrYn7/Q2h5MvXLy+vL5xxIUQKaXu1kZlclvnu4f4i+nbepQh32z2l9O7+DgIEEOi79nQ+dO0oJKvrhglWFDkiqD221ruaEwzA8Xi4vbuZx8FaSyjRRpdVUeb5P/osx7dzVRcY47btEEaP37798z//s3p6Op0OZVk5a8/nrqhO6tPXPJPWWCZoBNGH0L4dMabbzbaum7ppnh6fxmE6Ho8JgrquUwIIYCHE4e2g9MIJ6/uBUTbO02azzvJC5mnRy263v9rtldXGmJQSIriqaoQgSHG33SKEyiqr6xIhpJRBCCOET4cjhJAxJiQDAAgpCSZZkRGCuBDLaXl5fRGZFFBABBCGt3c31rmvX78abQkmjFGMcLNa3dzcnM/HGOLxdBQZs9b0U39zey0y7pxLPv7y6y/TOBltUko++H/91/9FGYsx3N3dt107jpPSOsSotcnz/Pr6xlvHCS+rSmYiywqtDASJC3Y8np+fn2+urlfbBiMUYpjGiVJ6f/9QlgWlVGaZNZZLUZT5L3//NQHAKPvy+dv+alcVpXPeOXc4HOdpas8dRljkWYJJcLnb7Y1dnh6/HY9nKSUXdNHL8XSkhPRjnyC4vrlerVcxhhBs34+M84TgerOa54VxfnN7SxCVeSaEtNalFPNSTtO4KJUSyGT24bvvY/LzpGOMF0pHURbW2LIs61XNKFNaYUJuNptlnpdlCSFggp2xSumYIqWUce5DoIxCCOu67ruecbrZrc7nPvq4WW+vr64oI33fw7oCMBFC1bLITFpjFq2qqmSUbjZbBPG0jNY6zqWQriiLoR+MsdZ656MPvut6pXTTNBkXi5pfXt8iiFmWt+PgnWeCb3dba92FKGmtr+umruq265z3RVEgiqBGIAHK+Gq1ggiuVg1G5OvXr3037HZ7iPA4DIzxhrCYopSZj544n1I6tWfECAA4y4vgE0SJYPb105dFzVkmb2/vmlpqo4N3lDEqmA8hOkMQggQCgJwL8zxHAPNCzKMCEB/eDuvNClPcD6M5Kk4Z51TkIoDw9PScIthfbeZpMd7GGBNI4zicz90yz5iS2/sb6908zT54CGEMoDt3/TBAiH76w58YZ8fjaV7mum44523XJYiKKldWLUq9Ho7jNFlrMCJKa0ywWeYiLwCEKUZM6NXNlbP+y+9fnLcYI8YZwpBx+vZ6uAxR53G6CJk3261SerPZVmVZVpV1NpPZ88vLMAzzMgsu9ld7Y+3QDpRQjPE0j+fTMStyowyhBGKIMTJaPz49EYLv7u42m816vT6+HdS0BBCyPMMYXfK/86KWRVNGT10bfdTKpmQymU/zFGM0i3l6fFmWRUreD511znm/2W7youQia7s2Ji9Y4ay7f7ib1WJ78+Xzl4e7B87Zuw/vUoyLmi+cG8HYnbgr8+KCCCyr2jlXVUVeFIrzuq4ZJZddqzbm1LYI4/12++Mf/4ggASBpNat5wQQTQjCEzjkEMSH0998/uWDnZbbWXcCIi1mGfjTcqmX2znPOAEKcM0SRMfbctsfDMc8La12KICQvJLPGEUqqVfX45dvpeIIQHI+nYP31ze7l6YUgcv/uPoTgg3MOL2phnOZFTgCMu/12mZau64siz/Pi3cO7m5ubsqmyPIcYfvjuQ9t2375964fBeb3d7s7nc55nnLP/7f/xv2VZMXR9URba6PV2M45DVuR1XdVNY4zphz5BsKhlHCZMEErw7XgUknsXpRTXNzc//vhj23XTPCGCVusmxIQQens91OtqmZcQ/TQra/WyzIJL70NZcgDwT3/6qesGhLEzpiyqVVNDCNuum5YZUSwzDhHUSsUYOWen4+nVv63X681m8/r8ijHuuvM8z4fXl5RAvaoASEPXnc+H9WZ1c78LwY1j/+3rt6qqEUKMkXpdt6d2Xma1qLzI/vTnP6cEqqYuijzL8rY7d+c+y3Ips0H3hDBjrPdBLVYIDiMIJIKUVusGYdh3lxoUKMqCUWasPZ+67XZzeH07ta3g8ng8BR+klIQR6rD38XQ6vx0OgnEAEiH09uEGRLjMk3P/uM9e4iuc809ffr/a76qy2u02Pnil1el0ssZ8eP8hz4t5VgTJ9XbLGBunAUJEIKnX1TIrSmgEAQqYZVJrZa0DEVnriwKP4zANM5fi7v7WO98PwzRN4zg458umNNpyIWJMxhpG2RRGxmlwwWglhNCLcthijDBGWmku+DiMKcXNZu1dev/wo1KmrKuY3Dwv564ty5xiprVqtjXlbF607qY8r3hR6mnQ2oSULlkmcDnlE0wIJkWGCRqGURtNGcEEJRAhQovV4zByMQkpu2Ggil5d72MExjnjHKG8rBpO+Xq922y2u/0W4Hh6O18EakLwLMujD1rbm5uHLJOc88PhjXNeVbVPqWs7NSsu2DhMMUQqBGcSITpN87KoGJNSizbGGFtkZXscx3GRuWjP/TCroipgCtM4xxAhAkqPw9idz+fNdkMJIZSduvOX37+s1813f/iuqkpl9dvbMSskIbhuKpv8NEwJJ5BAikEr5b1X8+JjUGYROPMhzMt8PJ0TSNa6oiwShs1mtd9de+vGaaacOe8fnx4TAItWEaRpHgAAzboJPlxuw3mWMU4o5xAB75OUwipHKX/69tz3QwiREBIjxIRQTpZpFkJwLihm3//wwzAM2prX19dZLWVZZnlxWVbN08IoTxFACIqiCDFABEIMyYV1saIMa6W8s6tNTSnNi0II8fe//zxNS1luqqoe+2maRohS0RRSZn3ff/79MxciyzLvA0RweJlmNWGEpnkSQp67XnLNuYAQWm9kxvOyMN62j88fvn9/dX3FqOj7PgTLGC3KIs9yjMkwjTGGx8evggshxeO3x0zmyqh5Vn//22+Hw9t+t/2P//E/vnu3MVYrbU6n0zTOSmnOqZTSOQchIpTmlGhtMMUQAooIF7LvuxBiVde5FJggtaiX55euG7JcpAD+9ve/3d7d1WUNUbrE1ikn2qi+H6x1mOC264xxMUWRZd67WWmR5d//+J1W9vOnz1zw9XYDAFiGmXNWN80F5XA6Hqd55JLnZd6eWqW1c9bKTCmNIAYxkvPhTBDuh/7p+Xl/dfUf/68P5/Pp+elZZDKm+Pr1bdH67e1ltV5dXV3pZVHzAgEsy+pqt/v27dv+6up0Og3jKDM5DP3xeCKU9P306dOXeZ7HcQQAWOucc2px79+/E1IaY5t1U1TV1M+n9tj1rdYmJRi83+62l+UMwvBCuyUYO8encSIEb7cboy3n8mI1Agk8fnvCBClljFHOOyklSEBm2WrdqEUZ4xhnlNNhmIx+aTZNWRfLrPI8hwifz6d5Uf1YGKMYl2VdApCauqmb5tNvv1NM7+5vg49aKULJzc1N06xub28QRgjjtmsRQu8+fFdVVdu2426EAEGEbm4zZ/3b6xsh7HxunTX3D3evn99iTFWT//iHH+dlEkxShjljeZ6P46i1BgDGBJxx3oV5nqu6enj3QCht2w4k8Pz87JzLZc4EC+MouIwhOueyLNdqKcvyfGyHvn/34X2eF3d3D01TD0OvBwMB2qw2291uv9tBgPFf2G+/f2zW9TIvxBAAAEYoxogQghDKXBpth36sm4pzSQhRSl9GijY65MmnT5+tNZwLQnBZVjGFLMsh+gfiPMVovZeZfHp6GoeRYLzeIEqodQ4FqJSCEBpjFj9rYyCCmRCbchdSOJ3fDscjQtA5572nmACQnHOcZUWZZXmutP78+fO0zClEY82qqUECUshlWUCCQoq+Hy7IIIwxw9gYO07zPC9csgQgocxazyj3wZ9Pnfe+ruqqqgFEMYFpXtQyHw/HX3/BspBSSJlnwYVxmBFqpZBlVVDKtdJfPn9OKclcTNPMGEEYGWuzIi+rehontZjTqWVsTikBADhjxqCyKptVk8nM+aCs5kiyXAKIUoLG2OPplGVymWfG2ThOlFKlFmM9JSTBRCh5eX7FDBulMcGb7SYMnhLMGaecuuBOp5YQsizjPE5lUQCQqrpS2gzD5Pw3wigiuKwqY6zgwlpXFAUmqMia/dW1MnqeJ+c84xwR9PZ28N4xxqx1RptMCm3hohZlgbASIjjP0zzPRVECkDJZ7HfXfT+M47jerDmj1lkEsRCSELq72kkhlVIJgKenF875drtNIMUY6rqGEFFOx3G8fCZTAgiS1Wp1Pp5enl600ULIEELX9jKTWZYvy+JdRIhyngUXvfdfP31zzjFGrq+vhRCEMuf8MuuskMZa69w8KcH5/d29zIv22F6CvK/PL4zTIi/0okFK/6f/8M/TNCcAX19flVkSSFVVF3kuuPAhXu33MYS727vVeu2cqVdNxiXTbLvZ/OlPf/z69au2y7BMSlttVCZzjNHQ9T74oszPp9Y6c319gyC6bA6yvPi3f/vb9z/+QKmghJ4OJ8vtMk9VWVprfQgx2OPp8Oc//9N//i//OUbQ1M0wdJez+DiM87SMw3Bu22bVvL6+7PfXp9OpKKvVqinKcrVa98PUNE1ZlQhjKSQhmAtRr+p5mn799ePN3d33333vQ8AIZnlW5gXE+OX5RWtNKGnW5cvTM/x//7/+n5yxS9UCQkgpfffwLiXgvFutV6fT+eX1bZ5nznm9qqPzCOMYfJbnhBBnDWXs0tlRRgsmYgwxpqqu+mHo2tZa16yqEGJVllJmIYTj6bjMuqmrVbPhnP3y298ppavVCgAYos+yfBgGzvlqtVJKf/78+7wsdVlopbIsu7m5ARBkRXE+toTg9WbTdu3peAzOT8tMKbm+uarqeuiG55fXssx9CFqbw9shxnRzc1tW5W+//hJDquuSCd63fXtum/Uqz/OmabJMUEIvDdKyLD5//pIgKLOi7dqiLC/l1buH+7IuvQtD3zHBl2nR1hR5zpmAEJ1OR5AA5yymBELq+kEIbhbdD/2ltLndrKumuru9q5tKzYoLzijr+naZFQDAOeecH/sxIbi/2rXn7qJNX5aFEPpwfx9DgAgWeWGt6ftea5vncl6WL1++Tst0dXVdlQUXwlhbVRXBGAL45fOnoiqv9lfzsnAmxmkchskZxyXlnM1K/fEPf4ghTePY98O8zLe3N8uyWGOHedKzVlrHaKUUu90+hDAOE5cMQcQFjzE65/KimKd5HCdjTN2U1hhr/TAOuZCMcyHkhWYRY5BSGm2M0TGBPJPOeYiw4MJ423ZnSqjIRPQRIcg46899XuQhxLqqvPPjOCq17G/21noEYFmWjFAXQj90zrnz6TRNEyG0rqoQI0JwnCZrLERIZjL4YKylhJZVGWPUShOKpZCEUgyxd25ZFq1VCJFSlOWZEDKEeIkAGGO45LnIrHdd36UQpZRVVUAAQwxqWpp1w5lwwWPCUozO2Qt92lrjvE0+WWeFlBFErQwmtGpKBKHMZHReW8W5eHt5TQAWZZFAHPqhb4e7+9txGAhhlOCu62BMQnLOSZZnQgguBUL45fkFAMgpE4Jyxgki1tlF68v6lHE+DGOzqoWUUmYE4/OpTSk19eru9m69bjCmEMPff/sEMfAxTOOICZ6mGSTIOIMIvL28dm0vJK/KsloX1ga9mLwoBONl1VRFyRiz1lprtV6MsxGkqR8RRFf7LcHYOQ8QOJ/PalH7/V4b45y98Hl88PM8U0J3+50QYujHCPzr89PpeIoA7LbbZr3CiKQIyrJEBM3zbKzPs9x79/r8QjAmBHvrizI3xh4OB4igNhZjDBHMizwlILgoqyqA8N//j/+OESzLsqpyiKAzblFKZhwT8vG336+vry4ypWVRMpcPD/d5Vry+HqzVWhvG+c3N7aVa9fr6QgkFACiljoc2Jnd3fwcB9MEtk+r7HhO83a44k845gGBVlavVehymz1++9mNHCZZSVGVTNzVMaZrHsswZYfM8E4yMNtM8/vM//QcfrQ9unpe+G7JcrNerrm27bkAIpgjavo8pOuswQpvdrsyrvMx/+tNPwXlnjbVGCBljwhRtt5uu7Q/nt08fPyul6qbGGF/tdhDA+4fbcRhfX96yPLtsuV6ensn3f/x+nucIEwSQMu6sPXcnrUwIcVF6t930w5gAWDVNTEnZJQGAESQUp5QOh5Ox9vb2ehrnoiyrpkoAGG0ABEWR101ljT2f2guRURvtjN/v9uyeCSGmYbbBEEqqqr65vu3HblGLMXqzXRNKAQAheJAiiOn+/i6TUmlVllXfD2Pf1001jOPhcBin4Xg8vf/wfrVbpZiapjHOxBjqupymabvbUarVohlnZVksk5Iiu7m94YxppVHEeZZvtrsQwna75pwb84/DxbIs0zBBDG+ubrz3MaYYkpRymVTf9T4GvaiYIhfCGNOd26ZqjLGPT8+3N9cpJoigs44QopWpqkoW/NvXxxD9NE0AQhC/vb6QqqxBShDDaZyGYQox3D/cNfXq/0fTfzVLkqRpmphyNTVuTg8LlrRId3VND1lgZnGBFQB3K/jBkL2D7GJ3SHd1VVdVZlZEZsRhzo2rmXJc+Ow/OOLH3Fz1+973eRhjTdM+fXkOwZ8vtRAR59wa0/ddVVV5kTHKk3R72B2fnp6MNoxGv//9P3z+8hkT8qtf/2YYhl8+/4IgWm/WcRIP42i07rtBWzV0w+cvXxjnEMFJQSEiyvk8zdMsx1H2fYcI1sYQRgGETGvPfQAgTRd5liqj2ra11iGC5dimaZKmGQhg7IdLfdHaUEq10iEEHrHYxiEEYyxCOoAgh5FHHCOcpilCyBqDEErSBCPiQ5j6yXu3WGwY5+fTxTkvh2mzWadZNo5Sz2qaJ2tMCCA4zxiZ5TzPM0tZCCF4wBlfLteccwjxcrmYJjnNsxCCMuatv7m96bthkjKKImMs55xgYq2xxiKEAAzWWxc8YYQC5IOd5llpDQJQeuZRRDEdx7Ft2usvB0IwhHCNpRtjIIJt14EwEEriJAUQyGm+sqS6rkMQFUXOQqSUAhBSxrQxznmA4H63F5FAGB72J60NgMgbl2Qpx1Hfjc6G25t7jDElOMvScRwwwWksMEEQouB934+UEOMcQBBBEgLo5XitTyKEkzSFGCXBA4hEnFhtTqfTOEjv/TwrbbRx97FIIEKjHAEIwyC1VUmSMMqiKA7etm1ntKGMsogVZe590MokacIZCyHUl8thd1gtlwCC5WJxPB6atoEQDsOQ5amaVTPPdVNDBDAm/TCwiFtrYyG0NvM8QQiddQjheVYgAGuN0qrvx0lrEccIEwgxplgp9emXnxHCPOKEsOPxrNR03YQ55+Zp6sYh4hEimDCqtNXGEka4EJOcjHU//vRj3w0QQynHS10jDAghGKF5ViH4+4eHm9vbxXJhjaWMZgR7H5pL29Zt349ZnmFspJR1fWGMIYTUrB3xs5zHUU7z+M03X/d9fzwdjbJylmqe0zTZ7XeJSDbbNQJo7KXRVoh4u9ks14vT4YgxTrIEYRgcGMc5+LBaLZMkmac5SdO8LNu+SbLUW2icc8ErZYZRFtWibvrLpX3z9uH2/k6I+Obm/tOnv0GIp6GfxunwcgQwBOM8tEVeRpxpa61xTdOc9uciLyepXl92ZVHcbrcQoaEfh0Fqo6fT9O7d+2HoOCcE+BBxHkVCaX1N0Z6O5zfvHgimaZJHSfw//t/+06z0559/+fTzJ2MNp2wyJk5iqy0mSOBIawsgFCLSShtrtVHjIJMkGaWEEBCCl6sl58waBxLAGIMBGWMfHx8pxYvlAgf08y8frTNCCDkqAAClzBjtrFtWS87GIi9C8F3bjcMoxwkTorQ2xrZN66EjBNXn82q9qqqyPtfWuQ9fvQUA7faHiHOjDOccQZzGaRRFaRbFIrl6RcDgEUFaqzRNh2GsL81yuUzi5PX1NQTw1TffMEYwIlcGjpRjnhcPD3dPT88vX14ghAGE3eteqTnLMzlORhtKSdf3lDJvvXO2uXTv379dVovDabdYLBBGSSySJGnrerb2/v5umqbPv3wpyrxa5HKahn5QSlNKIYJJFv/86ee2645Hd3d3Qyk7HA/jMJJXvF6vb2621ljOeFZk8zQPfR9FgjPurPvphx8HOaKAGGFyknEsSJ4abTw0p9NJyhERyAlHCFPGGKFDPzRN7bxXWgUVnuSUVzmlBCPIOS2LjBAmhBBQIIxFFCGE2rbFhGy3m3EY2qbDCBMSMEZ13WCMV6tlnIjD/oQxMsakaQogtNZ5HwAMGKN5dhwiSiiPIgAAxNCDCgY4yTkv0ohHk5yLsjDGXudCnPFExP3YSzkFGLz1AAAMsQ++LIpZzcvl8nQis1I+eGOd9wFCxCjmKc/zwls/TzOESGsNISSUOOsAhH5W3js5yiuAjFEGYJjnGcAw9NI6r41L4tha66wjhMTJ1VeB5lkD2GOMpmm21gKIsjTlkQsgXKE3GKM0SQMIkRAIIwBBCABhhJSScsIIaW2SOL428wMAEWOUsRBAFEUP93eUchbxWERd1w+jBB5QTCGEhBA1676TSpngPRMCQjQMcppl13WcM8YiIUSeFwFCFkXjIDHBIQSEcZqlCEHOI2PMx48/393cQgzlOCKC5CyV1l07UIaLsjTKNnUzK+2c5ZwNk7TGaa0JwUYbSljb9AH6vm9DAOfzUSnVdz2PGGOMcxEC6Pu+ri/zrIqy8D6MwwggSOJ4nuUwDM56xhh23mjjrFPzPMhRxPHN3X2ep/M0n4+nYRwAglfDmvWWYD1N8+lwVLPKspQy0tZt3w9XJkeSptc73zzP4yi991mSGG2iiM+znOY5QN82fRwLBInWahgHa927d++stRDCYZAQekIIxBBDDAAghHgPvPNpmjJKZ6XmSQUwAx/yPGOcWuu0NmmaPXx/3w89xmSSU9M1wfqiKAkm/dCnWUoIdSFACxnn16DC+XiJBNdawRA+//K43azbtocQIIT6oV+tl8vVOuIxWdDD/jBp8xCJ5XIVAKQsMtZR6/7yl7+E4H//+99Po3x+fgneXy0pcpw+/vTx9u4GQGCtkeNIKQMABO85pd9++22WpqfTMaIcQhg8MMbtXvfn02m5KMiXn58v9fnNuzci4vvdnlKyXi/HYVytV7OWcECPX56mabrGBo6HQ5RFjFFnvZyk0YYy5qzVWr++7t6+e5ukCZlw23Zaq4jz9WoZELDaTHJK0tQH1/f9NE6n09E6k2YbgjAh2I4aAnSFqFRlcdgfOedRHCeJiJru8fOjtRpjfA3mU8YAgFJOCCEAwDfffrverJu6OewP2ioI0c+fviwWy/pyiVjEGWeUrFdbEcW/fPklBN/W3WJZhQAWy6WU0zAODw8P9bkBEA7DACGknPVdL6WsqtJ5L0QcJ+npdGra9nypfQjG6DhJjVEeOOvc6+tuUS0IJuWy4pR54N++e/fHf/lTXmT3b+5lP05yxghjBJMkhgAghCnGfT9orRaLhXU2TXMexcF7a13TNJTS3W7vnNvebBaLRdf0s5ru7m6zLHfW8phlRYYpnvXUNPVut0MQiTgGAXz+/AtlbJulCMKmq9nElFJFkTPGh2FYLBaLVWWtN8o0bXv9oZ2UrJsaAhgnwjrnnZdSXlVR4yD7fsjz4ng4jvMgRCRHSQgZh2Ec5SwnhPF16MEYQxAlcZxkiYhi70OeZgCCSESCRxgh65x1psoXXgjGuTVWzUpOE2Nsmmbt9Ha7HYbBe0wRZYwhhJv6VDf1crHknMexSLPUOgMQtNrO8zzJKckSKeUo5fXN7p2T4wRCKIvCOdf1g3N+nuayrIx1RmsRiWv6AkQAQYQwAiHA67hGGxBAkgiDsPcOhKC0DtM89oNznjEWRRHnVM3ae0cwJpb0/eCdV1r5EIwxxlptTJak1bIiiCRxPIyjcy6KRWRt1/V6NF3fGm198HIcq7KMInE+X4QQCJHj8YgQ3m63AKLFYtEP/QyhHCfGGEYozxMEoTHaWmetwwRr5aScvR210QEE6z0NgEeRtf51t0OUYIKcC/vDgRHqnE/SGAAAAwwweG/7oTPOGGuHbrDOBRgIpz4EH0KU8NQl1FLgAwjBGMs4Vca0XV+VFRfcXWpjjTXWWRdFLM1TgMEox1kqAIE39ny5AIiyLFsslwAErXTf99M0XeoL8ABjLISIIhFFUZEXEMJhGuWkvG8Op4M1eppmQklzbrM0V1p3bWedm+Us4ojz6FJflFLDMGiteRSN01QZ7UPw3md5OgwDQphSWlTF+XTs+oFyluXpdnPz+PhIONrebFnDvPO97FGNgAurzYqQawpriCLBRRR8iCI+DMPxeDwdz5zxNE2iSFyfH8aYsSaOE+cswrgoSwzxw929VNJZhxGeZ/X58+P5dDHWXn0nbVunado0XRxFzrs0TQ+7/WazSbNsmmeCcZwmlDGEaNv2EAHO+Ga7kXI67A5VtYAAf/zxo3OBEnyFZr9/+yFN0zRJMcJt2zzcPyACX59fAQyU0LzMBReMRs8vz877t/fv7u/e/PS3H8/HIyVMa80Yf3h40Mo4ayHC5OdfPjfNRRstIlGU+flyoZRaa+G5rqqirs8iEqOcfPAABkxI07bv3r7VRp1Op+ABhIHxaLPZBB9mOTFK86LAlEAAIhEBEHjE+7Z/fHzSSr95+0Zw4bQx1ggRCc4oxUmSlFUhRNS2XX1pjLHWWxxwmRSUsUhEUcSen54W1SIWcZwmmBCltDY6EtwYm8QJxrhpGqV1XuTDMNzc3uRZ8eMPP4AEYIQjFtV1M8nJaN00DQIoz/IszyBA1vg48iHAOI6Vnk/H0/F4XC1X0yTrurHGiFjEIpZSDsMAIbr60FfrDed8HId51p6HSU4BhCTNMKLWOSGS55dna/U4utPphCEqFgVF9HI+X2kthJLrTsU5m2Zp1/bDMDjrlFb9MFDGRCS+/uar/W6vtHr88uWwP3LGg/fLleKMD6Mch7EoCh9cJKKqqjiPGGNd253OpyROm7q+Yi3ff3hHCJHTtFhUIXgAUVvXEGIXPERgHMa2aXnE4iT2wSGCEhGF4OU4E0IBBFJO1tp+7C7ny/lyybJURELEEcaERwwAkGUpBCBLM2tt27V61FxzKCDGeHOzsca2bTv2o3WWMWaMQQiv1+vVeiXHcbfbzdPMOecRd5Pbvb4yxrwP0zwxxuZpopSOw5Cn+bVhZK0x1uZFNoe5rmcEEJlJgEBrba2hlHofiiLth14bUxQFJnSe53mehYhXq6VWpq4v1jnvPSEkjmPOmdYGQIgRKqsSBOi9AxBclSAeADXPwQcfAEJQa6XmmUfcGosgujL6IYAII0YIIWSelXOuto2cZJZlWinvg7UGU4IxFiLmEeM6wsiOcthsN6fjKU5i56w1OIoIgvhqSL3e34dxBAB4a7MitdrOymZ5ggmLRFJU1eVSY8wCgm3dGOuzPEmypMgLPelZy8PpRCghhF5ZrZZzSsjpeAYhUMoIwd46NRtEQJEX6qImpcqqEHEyjuN+tw8o2NnwiFFKEcYI46Efj8czhBBDbK1lnIxygACmmQAYtG0LYHDWSTkhCK0yGGMMUJImWhsQgnXWWgsgdM4HF9I845RdsZ1JklBMKKZv3ryt28uXv31ZLpeE0DhOCGGXcz0rNSs9DqNz1jozK+XNtQJhAEJJmnlvKadDN2qjAQSUUMoI4/x8OudF9atf//bLl0eEIEH03TsiYhELsVgs+75fbzaIwGkclTbeYYDCMCrvwyTVSOlytbTWtE272WziRNR1Qym7dpWvwtcrcsoaywVP8xQi6H24XJrDfjcrLRLR9wOLKKE0TuKiyPe7vQ8jgmC/O3ZDiyEcxrFpWoyh0XazXWNMKKPPL6884uvVSojYDxPC9OdfPsMAIxHnWVpfGkRQEotPP3/67ptvQgiXSz1P8vnlCRN8Pp++PH6+2d5AAIuyCMYYbTarzWazHUY5TzOPhHfeeS+iCEFcVuk1m0f+/t/97vC6S9NksSj2u33wbp4s46zr2rZtAABynBAhXHAMkZTycmkoYbOWap6LskqzZJ5mhBGNCEQwAGCsGeXgjLvUl6Efy7K4udl89dUHgskVtH33cLO93U6TNNpkZVrl5eF4DN7ziC9Xi6ZpRRQ9P7/uX3Zff/01JeT15bWqrpno9tI111gFY2y5WlFOjbbGWG31brf/8vnLV998Pc7zn/74Zx4JACAAUKTxzfoWeHg4Hr777vuyyHnEp3kiiDw+P93e3nLGCcbrm5UQ8TAOSZqOUlaLJUTg+fmlLIosz4UQ+/3hL3/5K0IoQB+LeJ7mPCvKoqCUXU5n75F1dlFVztmHt2/auvn88+eu6z68fVeUuTYqLbL6cjkeTiCE9XrZ9b1z1jo/TfN0vsxaVWUZQvDe/8u//pFgEifi/va+qqr7h4e+762x4zC4yDAeNU3z+vIy9GOcJsvFQsoxiRPG2O/+/ndK6eeXl3meFlV13B/rS00pa5tWjqP3zjqvtanrC2GEIFJfGmX0N19/FYBvVccYwxRpZTDGlJG6uTDK+mHw3qVpslwtT4ej9W5ZLQLkzjkpp+sFdprnvu9FLJRWHz99tNZe6yMIQcYjPerrXt1a+/Onn6dJWmPjJFFaO+cAgt01p0/pNUL37bffRpGYlYqEeHp6evv2bSzi6ymvbTsIIMUkBBCJ6Hy+GGMoJUmS8siN4zWnm0AIu75z1rX7tmlaIaJYxBih3W7nfSiKPE1TY9w0Tc65sqy++fabrmt/+fTLJOdIREma+C5ERaSVJowgiJy1atba6GsjJE0TEUfDMPRDz2mU51mWF+vN2lk3jsPV2UIpQZifTmdjzfv3HzDGeZ4Pw9B1Xs1qu9kSRp6fniFEd/e3IhaYEMY4Ie755YUQkqSJsfbx8QUCQCldmwWjlDCCAIy4YBlP8ozRSI6y61opx7GX1nkpRwCBUuaKc8AIT3KaQvDOizgiGOtZe++0HhfLCmMcgMcYX0Wwl8tlmuYoipyxk5wIZRAByggAkDHOGD0eTje3W++CURZj5D3Qs1FapWkCIYo4x4h476uymuZJaztc6utcS4iYEEIwYZRyHlFK+66/qpYwQt6H8/kkZ/nuw/skjvthJBhnWZ6k2e51z3lUVeXYD1ob6yxjNICQsARCJKJoe7PxIWBE+r7jUZQmSZom1ri2663zhJAkFl03jGosq7JalGmcOO/Op5MQQls9z9oYWxblJKVW9orb0sY0TbNarY0zAQTnfVEW1jptjBynS31hjGEMm7aZ5mmzXnV9d3t7O44SIRgJ8eXxCQJwOtd5kS5nw0WUrVaRENqqx6en4INSqq0vIk7HYVxv11madf0AAKARowSfz5dFtVSz6ob+fDwjhF6fXt6+fVOWhQu+PrcIIR7pP/7xT03bzXKCEDBO3394n6aZc+67774f5fj6ug8wvH3zLq/Kw37/utt996tvIi4oI1abK7+gadvT4ZinKaEw/OpX3/zTf/nnsR/yPLu55dYYFvGu69q6H8dRJLFW2hsikkgRU5b5PMu6vsRCiFhcD5LjMJRlQSkbhh4j0rQNAFdlGH56frTWJklMKYEAYkIIzpx3Yy/HaRzHcWiHfuic9RAhhNHpdCEEJ0lSXy7d0C+rsirL86WepORRdKlray0mrCiy193+zds3VVW+vrwejydtzbm+dP88/O4f/kFKaYzFCNaXmlD65uFt3w7e+7ZturZFGF/O5//Lf/wfGKVNU+d51jYtJsg6K6Lo5fmFMnq+nObpuioZht3ueDhM83xtEcdp4q1Ms7isijTJsrxIkiSO0/s398CFl93L6+uuLBf4GzIMPUDhUtfXOSn673wuLOfZaHM9WN3e30SxiOIYEzzNs58kBKAfhq7rrk7m66qKEqKNadrW+8s1IXf1rMIWyV723cgjNs/Keme9mZX23kdC8Hi6anaiKLq6z8pFUS4KhPAVSyeS5OZmK+fJOVfk+SglW1E5zp9/eWSMEIrP54v3jkdssajq88Uoba2RUoIQTscz4xRjAiE02s5z03ed0hphJEfpvE+ThBAqhDDWNm3nrM2LvBTV9TUdWTuOEgHEKEviuOt6iND93Z1S6nI+K6UwwkVRGGteXl7GUeZlNk1TEidlVSGI0yzJsvx4OvrgjdFymhBCd3e35/PlUl/kMFrnAAAQwqEf5kldsZpxzK/kCQShcw6EsD/snXMBeB/8lRHNo4gRHiCwwR13R++8945xYrQ11gLo265tu0YbxxiNoogytloti7KEADi3OByOu9ddtaiulYLlcjlNUwieR5GUssjzYZRKKedtHMfGmNPxRAgBAWCC3757Rwi11jR1ba0FAQTo21M9TwMI1/pIhjEVQkRpQjiLgp/mKSOIUAQgAigAAIyx2+1WxNHudTcO0mh9c7sRkdBaSz0JHllvMSbXKq9IYqWc0SoAUBQFZVQO41WpLCdJaZZkiQ9ADmOcppGIj7tDmifXbLeIBCXUWhd8qKoCBJTG6aRm6xxG6Pb2dpqm63LguvhdLCpKGUYoz/KrE3QcpTE6EqJYlJTQELwPwTufZ3maeOAhjzhntL40dV2fTyfO+XK5+PTzz866OBZZlnnvKSEQgqIskjQhiHR9Xy2KcZD7/Y5TFsexd9goo6bZ22Csdj6EAEUULxfrvMwh8KTHTlzdL9Ekp6tgDkEsR3k6XQAAZVktlgtMyDD0jFJMcJalcSwooWVR7na7/esrhHhRLX73u989Pz9/8923wYVhkPX5ggJwznVt39QtY0QrXVSlECJPcowJAMFahzH+wz/9c7ko373/kCbJ+XKWUiIEjdFFlctp+vL0ZI3xwfbDgDFmhKVp/G//8R/O53p/OLRtl+YZJvR1v2OUffz40XpfLsq//7vf9f3w/PSEEBCxeP/hHSU0hCCn8XQ6YYpnpwiFeGy71WoxjrLMch/8+VSrUedJXmTF8/MLIcQaI6dxvmKBMX5+fE6z1BpXXy7A+yv/CwBorCGWjGqQUkopl4slokhO8+6wT+P0/v42AH943f3y8VPbdyE4xihE8JcvP09yuru9pwzvDwcRJ5lIAEJ3D3fO2f3h2Fzq4N2s5jfv3hzPp8+fv/gAsiJ7++5DUzc//PBD13VPj8/90BdFPg6X+4e3wYcsy6uq/PTx03l//Pzz52GQm+3WOw+CRwRhjH/68W+v+/1X7z8wziBCx+PpsN+HAACECMG+H0Y5FG0DQOi7AUDovIcIxkmcxLGzvqnbcZSr1Wq1WLVNK6X8y5/+2nWNEGL3vIMILKpFkRXeB601pmQc5flynqfZWau0Ypxro7Ux/Y8fkyRhnEIAIYSLZUUQFUIjCNMsO53OTd1cs4MYIwzxze3WOQdAqMoFIaQf+rquF4tVSllZltbaQQ6EsLbrooirWV0JB6vVKiCYpqmIxSinaZqzLBORcN5PSgEEgQvTPEOI+m6w1hVl3tSNNe7d+7dKaefc89NLluUQQYTxNM1RxAECXd9fE5aYkqEdRikZY0LE1piIR2mahgCss2maggDGcRSRAABMYXLWex+8c1merddrCOG1FrdYLJz1Xds0TZPnBSH4WvmOkxgh5JzDGIs4Pp/Owzis1isRx0PfEUJDmLxzx+O5bZs0S9frtbFmVmqS09XdGkCIoijPcwDA68sLJlhrXZYlwthao7WpqhIAOElplImTWGtDEV2tV33bY4IQhj7yeMSMs+uXEyIbJ+Kbb77KywJjctjvu7YDIJwv56qsQvB1fYEIBRB+85vfpmmmZyWEeHp6igSflZL1GPFosVyez+f9/rDdboyx4yCnSSKAhnG4IgJnNR2O567v0ySW47w0jkXRIAfrfQgQI0QIybMqTmJtTBKn86y898EHb30Sp9M0O+8ulyYU4AosAwFcB54IQQBh8AGAMM0zACHP86pa/PLLz0M3bLabN2/faG2UUXpSTd1GPDqEkOUpQlhKiTCe1EQZdd4xzrKyDBZEjMVJfLqcMCaM8QAAZzyAAAkAkAEA5nm+enhGKadJOufTNGuHPgAwuiGOEyGiq+iNEJIXuXMGY8IYXSyqqy8BgPDmzUMAgDE2jlJrdT6fMSFSyiiKIIZpmjLG8qyg9MrzyC6XM2V0kCNCU5EXeV5CEDjn283N6/4VhsAiRihtm+50OpdVgQkZxnG/2y8WFeO8azqMyZ//9S8BBAgQmqayKLq2gwDGkbicz3mWV4sFZxFn/PH56fHz0zyp5WphvQ3BP7+8NG1DMFktV1HEhng8Hg6xEN9+/+0vv/wyTfOVulEuKhGJLEn2h/2Xx8+cRh/ef7U77GY5vTw/rzfrx6dHjNCvf/3b5Xq9Xi0JQbNRAMNB9o9PDYQgzZJv8+/6sS+XizhJEESHw+G7778rq6Jrm+fnlyhiAEA1qafnJ8Z5WebnU0PWN4vnx9eIsTxPJzkyyossxYRCDBbLilCirrBfOR+PJ61NFIm7u5s4iYdmSEWcpokLPop4COH5+aVtmr7vsyzfblbKqKk3eZqJWAxt/9c/d5yz4D1hxFqn9PT09GStXa/XcSw+f/7UjT1jHILLvJ6yPNPaWWu1mvM8U7NChPx//pf/ZVZKCKGVkcP40w9/zfPMWnuNUX714f04jtVicdi9Fln56eef79RdJMT791+lafL49LwmS2PscrkiBLdN54NnlD49PhVVaa05n0+UsCSJAQJt06p58tY1TVsUGULw5mbbdf2lvjhrGaMwgnmR9V0v5TREAxcR46DrujzPIxH1fdf3wzg+3d/dGatC8NM0N3VrvQ7As4gN4wiGUU4jAMhY670TTszTXJaFUopzFokoy5JhHNI0TZLYOVdUuewnZVSaZ8F6hLAQPMtzY3RRZXe3NyEAOY9JkujGWG9v7m6sccqYYRjTNLXO7Q5HRqgQUZLGb989UMyOx3Pb103Tcs7zPOMRPx/Ps1ZxJAglIEBrzXZ78/T0eLk0680meBfHMUK4KDLOo+Ph6L3vuq7tOs7ZtWMuRCyiKF4sIxFxHmmlQgCcc87YFRQsR2msGc6jEAJhTDEVcdy2LYAwivjj41OR55xFt7d3cSyU0ouqHEc5q7kfmmmSeZZfzucAgjHm9fU1SRKMife+73sp5Xq1Xi4XWlsfAmOs6/q+70MIN9tbTNC1oEQJnuc5TpIkSaWU93d3USSenp4wxpRQY7TVbpKzNsY6K2fJBaOUzpNq2265XKZZ8vryAiD47u2HKBabm20UcSknRKAHgVGSJHHf91HE4zi+1DVj7M//+ufbu7s3D2/wiM6nM4QIAt91/Wq9StIkuHBzc2OtvbnZtl1bN816teIRZxFHCCiloigKwXPOKeNt3xcYNfvmdGqWywWCMM0TkcQu/J+GPoKjiM9yRjFBEKdpKkd5Pl6MNm/fvCmL8nA4gqK8f/P+sNsZ45vm2PX9arHI8mK93ixXS2MNhijiYpqnWcvD7qi1J4w676dpHga5Xi2TOBnlWFbV8XBCGFBKzqczwTRebwglSZLtD/th6I02jLHVarlcrpTW4zjAAB2w0zxPcnLWxklSlDmLWdd3xhhj9NBr60wUib4bRilv7244Z6v1sq07KUchIoSQMcZaV+Q5Zex0Pt3d302TtNYrrRy2atZxGotEUErO5/pSN3lRJEmWxNnpdJ6VNs6Nw6BmbYxWRm3WS0KonltCSVEWlLH97kAo8cHv93s5zZzzl9cdwTgA+Jvf/vrh9k3fdz4EETHOGY+iy/k8T/MslZzkNMvVcgECYIwfDkervRC0LHLnQpLHVVmVrkIYXVnZUsr1alUtqnmeGeeX8/l0Pg1d/+b+IYkziMJ6ubSFe//+/f393el4/vL4SwBuv3v99OnjdrsZuqGsKkYj4yxBZLO5pYy/7ndGG8Z5mmfW2c+Pn5M4BgQiBC/nS5qnbd/yiD0/P3/55XMkYvK3v3w8nk6cMxFFZbVglDaNVEO7XK+HXhJENDCLqgoF+Oqr9y+vu/Ppcv9wRym9u7nTWgfvcPDXeLUQ/OVll17TPsMAfNDWHg7HLM+880M/5nlGMO7l2NYtYRhjQiglhDrvrycaqx1CwRijZwMgTBIh5fD58yMEIE4ExcwAl8RJHIOu7/tucNYDGGIROWuP+3NRFZSxn/7ytzIvMGWvTzvCSVGUAIL1evX6+rLerH/461/+5//3/yxE1PY951F9qa0xeZ43bbPZbIzR18D77d3t8XgEINRNs95u1stVnCTb7VYbfdVogACdc0rp0/FkrDPGMEaXq5U1GuUlZwLCgAgapcQYD4N0zolISD+pSSOCAQjeg6vOdLNdY0QRRHEazVIRQuJEBB+GXq6XcZonIYRZqSRLclyoWSVxdFNkcpSHwylO4vV6fTycrfEQwVHKYRist0mSaGvmedZG3d3eZlku52n3ssOUdt0wyU95ln/8+edxHDiLQhr2u30/DItFhRBmlFlrIxHFyfL19aWu62Ec/X6fpVkIgBDKeIQQktMEITDGKK3LosjSHGO8ubkRnE/TfDqeAQhJkhitn5+Ht2/fMsbmeTqdjs45OU0AgKLMnXdN0/z4w18Z5f997td1EecAgtVqWRRFFEWjlOM4XBckj4+PaZrf3t5dwa7WuWubGiEohBjlkOd5mqUE4wBAURacs2uiNEkSjLHR5svri4gEQvAqYMiyDGF0c7O1zhVlSTlz1lrrzucz5zxJBYQYhMA5jwQXsbjZ3ggeWW/evn1/udQ//PDXWMTjKEPwwXuMeRKnznXzNK82a+e9NfYajvzzv/4rxPCbb755fX3dbDbjOFBCp2mGCMzzXNf1brfnEUcI9/3gnAMAYowwohGD5WKxqEpt7GaNrLfzpAEAAIVpmq4zk7Zrrx14M1qMEefR1RzSDx1BtCjyLE+tscb6umnmWe32+9//w+/LsjwcDmmaqFl56/f7/TXvIETEKP/088/D2I3DCDGFCMzjHMcxxhgTxBkz1gIQEEbzPJ3PF8YEIRi4kKSJ915EESEE51jNs7HOWMMYffxyIYR452c1z9OslW7b7nA4jrNMswQCWFUlIaS+1EZrpWat1X6399bFsYgi7r0bh3GUo1LKB19VVds0Xds555RSWZ5e26bDMEzTdDld2q5VVhvlsrpbr1ZZnkEICCIIoCROrklCPZuu6XnE37x98/z0PMnJe++Dz3leVdXz0zPGWA5jUZVJkoooXq/XxihKiZm1nucsTSmjxlqldAhBKZ3E2W9/+/daG0LI8XxaLlMh+NXpfakvs9yvVouHh/s0zRijyigPwigloQQi9Obd+3ke/3uE1LskjrM010a9eXizXCwX1eLduzdfHh8BAEJEf/vpb99883UUidP5nOXpNM2UsZ9//vT49HR/e6+0Hl93x9PhN7/+7aHf13Wt5DT2QzFmry87hLEzLklijBBxIaRFSjFxLkAE3n/99nKud/t9fb6UZZWXGSG4ae1itVwtVxBiAMHD/T2AoO96SrBIBQDAGde2zc3ttqqW+8M+z3IEUdt186yu9Q3OOEY4ivg0KuccRIBQxhjlEYcAHvaneRpXy/VmvVF2Pu6OfTfc3d2dj+em7qdJxokghH77/ben83kc5diPjDMR+0lKRLBS5nw6Y0yiKGHM/fq3v359fAUAFGU59MN/+6//dHd/t96uk6Q4ny5M8Kfn5xDCarH8/OXzar26/gFZnmVZ9n/87//HerPO83y/3xNCt9sbpRWGeLfbe+/LMt9U67bp9ru91iZJEuddnpbny2mzWQUP5DTO07xcLgAMBBNjjLO+a4Z+7G9utlLOhHEIsUgio03bdtqYoiznWWPgyyqfxhl4gBm2Wu92Bx+C0jOdSdf1fTeUizzLUowxoXwcRqWViKO26U+nNuax0vN6s2KUdm03dD3nXGstpVxtVgjhvus551VZaWOSOAnOT9NUFDkhOHiPCXLecc4hgMbox8dHSmicJFoZQsgwjM7ZeZ4451ppH0IcixCuBg4YJ0mW5857TtD9/b11/unpJc0SQjDn/Hy+ABAwxufT6c2bt0VRaKXjJHl9fd3t9kqrDx8+YIKt8xi7pq63NzcQwGmWxtjX19ckTdbrdRwnV76x0eaXz5+FELEQPoRJyrzIhRBd10GEGKUPb95gBE/n83WpAxFs206ISGt9Op7SLIUIxiJmjHnvpZTjOJ7PF+cd8MAYe0XxYIyVGRDGlFEfvNHaWRsJnmd51zaqnNIs8T44564nQR/8zc121rPRhhJ66A8QouVy1fd98AFhPE2T1toaUxT5ZrNtu7Ztu3mWi7IKAXz19Tdd281qHvpRqfnhzRutTJoxztn5eB4HuVovYhHPSs9KXd8mlFGl1WF3TLP0dDyejygAjxE2WlPKMefr1Wqe59Pp1PcjwjDLCq30T89/M1bnaW6s45ztD/uIx3XdYEzzvNBKj8M4DiOCiGC6H/dNUx8PB6VnTDmE6BpSAABY6xC8JqZAnAgesUhEq/WGM9rVHSGEMOqCH8cxFgJCeDjsOedxIngUARDkOF7nVEJEmJBZzYtlWVaVszYEoGY1DKOU0gcnRMw4HccBIZTnqZwCYRhMYHuzSdLUey8nWRT5NE339/dplkxSnk4nHwJnVAhhrSl4PkmNEZnVTCdKCC3KQmt9ODRaKxHzNBPe+DTN+r6TcpTTxCgry5JQopWuqopSCiHM8hxBVF/av/308Xa7LYrs9u72cjk3dQsRNMbc3d0SQr31hDEIQBzHu92OQPLtd99mWbzfHdI8gwBADAmlZV4AFCilUcSHYTyfLwiBxXJBCI5EhGNcn5vRDyC4wYOu61BAYz/c3tzxiD/48PTyRAherZf7w/H+/v7u9tZo9/j4ZfeyQxhiiM/nU5wmSZyO8YAxwpicT+fVapkmCcJ4sVzFsfj48Zc4yRZlThzw1XIBPUAQzVq9vO5ELHjErXNM8HmeeUQrVOlZ/fUvf7m9vb252WCKzsdaqXm1Wiqlnffeun4cVst1p64+GQkgyIpMJHFTNxjTrmsggD5YrW0/9Mvlwlqntb4CIwMAs1Yv++fFuoqTeLFeYoycs5RT722apT74JE8nrYdxiEVSFgUT/Lg7thgzzp6+vBBGMYDH48G4crPZbrYbbY1SSkoJIDidT9rMNzd3b9/ci1QcDycpR854JKL9y2G9WWk317uRYCrlNE3TYrnY3t5YZzHEVVVZY+QkL6eL1nocJynHYRiMcQihcRzrS5Omadf11vp5lgjh8/nSNV1ZldubjRynEEJZlPWlPRwOlNEoYgACIaK0SMuiDB4AAB7e3QXvtbLLdWWNdcalmczzTESxHMeizJM4dt57B3hMz+fT/7k9i8pqMcvJGX/YH6yxy1VFCL65u/HO84iW1Y02Rqn5yqVilBptuqZzzk5yitPk/u4h4vxwOD48vNFGH/ZHysi1/OVdUFoRip3zfT8wxp0HEecIoXW6stZFQmilRjklcXItSWGCXQgiFpdzzTiFCDHGOOdxHI2DlJM8nk6n05EQcj2DO+sul0vTtCAENSkfXAhhlGPXtXlejOM4z2oap67v0jQ1xnDO4zie5/n59XW1Ws7z/Pj0mOf5lTv//t27eZoggl3bRSLquu5Kfg0hJFninEMIzUo556ZpAhAghC7nc5IkSum8yBiNdi976+0w9OVioZQihERxJN1og09E4oP3IQz9yBgJALi6mefJWwcA0EYBANIkNda8f/8+TRJjrPP+8+cvhJDX3e54PGZpBhHywb97824cx6eXp5fX1wBAVVUYoTRJMcQ8FtvNth+Gy/nSd0MU8WpZQYiknFxwWuumac7nc5YnznkQAAiAMSaljCIeR6mcxrJcIITqc304HqUcKWVaa4ompVXwAQJUFuViterHHgRgjUEIEoS6tmva5ubmRivNIgZwaC/NpCfCsA3EOUspU0rlWZ5kiVKqyHOEUfBBKwOCt9YdDnuMsZmMsTZJEozQ7e3N5Xy5ruJP59PCVc65eZqraoExVrPSWocQIiGqZaW0GfpWSskpY5TgLDkezw8Pq+ViMUlJKJ2GScopTRLOefBhlvO1YNx1pqwqrbW3UbVYsGG01ik1aaWqVclIFN3EVzQ6RoRxbrSZ52kapyjimOI0TrMsgzAorb/95ltt7OvLi/feGWe0oZSmaZokiZST9SaKOISQEjpJdd1CF1XZ9z0hxBgrx6lumtub22GQEEMAIILon/7LP8UJf/fVh/p8SYsUAHDtguhJDd2YJpma5zxLjb0+Lb98/92vjLYQgXdv3g19n6YZJjhOYyZYksQvu9fgPUP0bz/+7e7hvmte//CHP/zm17/erLZpms7TRCn/7n/8nkVERKJuGutt13aLVbVer+ZZ5XmulcmL/Ouvv7q9u7POyWEkry9HYx1nlDPqrO/6nnN+NRSfz6f1ev3leMySjHEqYj5N4+trG8fCO2eU6rpmUS3u7u61savN6vXl9fVlhzB6+/ZNkqQehKIo/vyv/3o+XeZ5FrFo2hZDUuS5sUZrnSapHCYeOR8cgMhYdzpdIhEhBNtOqlkpradpAuP4/t27h9uH+tLwh7cQImNNURbzoCapIARJIrz3eZZVq2WASM3m+++/apqml4NIxdiPw9i7YNuhH28e/vHf/Lvvvl0dDvuffvzRWqNmDRFsu+a7779799VbrdX5fNnvjhCDWarlcpFHuRykd2Fzs/Y+aKUnOQUICEFd23rv4zS+XM5Jklrrsiy1xmKC37x/s3vZvb68TvM0ykFECcH4m2+/1lpdMZGLxSKO437oRSSyNL1cLtd3IkaAx6JrOhFF4zBEUYQxDh5AhE6HU57lXPAsyy5NV2Tl9Rs1jXNdN1mWCRE1Tdt1PeOsKAoeCefCarE6no771119vsRRzDgjBMZJbo1DCCs9D0M/TVLOkzW2b/s0T4qsEIkQseia/rA7huBZxLumm2e1WFSYkOPxbK25nC9STgACpTTBZJqn9WrVNK2x+ubmRk4yuLDZrPM8FyLWWo1yDMFf35I8itbrjdKzUkrEoijzpy/PlFAf3NW0Y7Smcdz1nbGGM+a8G5rBaIMQIoRwyrIsI5gslourub6qFtbacRzr+qKNcc6mcZqVqbd+muau6wghSmnOWRLH5/NZjtI7TyjVRlNGjscT4yz4MOtZMHE+nspF2dQ1lSTiXMSRB9Z5W5Sp0TMEjDB8Oh2MNkkai1hcThetVQBAK53EyfZ2m6WZVeZaPVuvV0qboeshgOfTuW2axXIJA2ialkX8Or1RWjPOq7K8Ltg5Y4zyJI0AGJXSPKJTP43jCBFklIUAIIDL9VLPikdcJJFzDhOU0SwEP45zmqaE4u1mSyhhlDlrL3VNCfXA91KSoUMQK6Xev3/3QB9eXl5+/PFHCCFjFEKQZGmapCEAhPE0ysfHxwBgnMQIYnil5CvVdC2lhFEqp5ExNqu5bTsRieVi0ff9JCcuWNd33rqm62IhOOdKK+fcNE8AADmOGCHvAWMsLwvvfXCWEnpzdwMDEFE8qZkQVubFLGfnrZ1N3/ZRLOqm1UovllUkhHeu7dqhH7q2vbu/BwAOXd+1LUDo4f4hS/O2a9umRRWS46S0WiwWaSKmfjJKbTerUcqqqAAATXPZbjZKqyxPf/70+YotQRgZo4d+NNp450c5JXG8Wi2TNEEQMsZmPUGEh2G8SkOH4bN37ubm5ocfflhtlm7y+91+miXG5HI5QQiXq5XprfceQti3DQAgTRI5zUPXWee+ev+V9dZHsVbTfn903n399YfL6aQZj4X4+eOnhzcP79++Xyyrw+7QDk2SJl8+f0mz9Pv7+ziOX3cvTVOnWVxWiyto8q9/+Wt9qeM0fv/2fZ4mv/r1r+QwXmtoy8WqaRvG+dQ2ECOi5vnxl8eiTIWIOecY45fXXZqnp+MxAB8J/u79m2sf8jppLYocI0wYAjAEH6xzx9Nx6MZ+6J+enoSIRBJf2xxSyqEfGGPVYoEw7rt+s10Twtq2eXjzYK1N0nSU42F/mKdZcB7xCARote2HzhoHAEAQLJeVHKbtdoMoIhT7gNbrtXNuntW7d2/v7m8wxtZYY+ystHU2BL9abRAG3rlVtXC500sNIaGUSCn3h93/+v/7/97f3WOE//2//w+fP/9infXOFYuCUFqf6zRLq8XCgxCn8evL7rQ/fv7lMeL8ekqNBGeMzbOK4qhcFvOs6qZOk4zx6PnxCQRUVWVZFGVVNU2zWC4Wi0XXdJvtJjjQtm2axfNErXVRwmc1Qwibull8tZjnyShruMKEPH9+3N7cLBbldVvYNh83q23TNYyy9XrZXJrdi54mmRUFhH6eZD94pdRyWd3d3lNOtdY//e0n56yycxkX0zRHgq23G2stRri9tMbbSU59/2KsVkohfJXchbGXWZ5ihs+Xeprm5WrpnD+dzlHEjfVKKkxw8GEcZSzikzyN4whAEInw1sdxghG8onIAAFf3d57nlJBIRIyyOBbBewigiEQSJ13fYYyKohhGPIwjpdQ6n+U5o9SZQAjptNbGam2UVlLKu7u7OBbWWkrZdrOJeCyl7PshirgLLk2T2hpGWBwLpRSPOCHEWgdAcN5777QxSZoopbTW2gRrXZIKY4wPPonjqyuRc04Zay5NgMEFV2SFcxbAgPBVzarHYXDOcc4AAPvj0RiTJElzaVnDlmtVVWVT186FxaLq+6G+NE67ru+M0Wmc3tzcSilP7Ki1ctY57w+Hg/chy7I4joP3IYDgwzANAAJC6DTPVVVM08x55D389PGPGGNCyGJZJUlinVFaYYyjiDNKjTVymK4c3BBCCMAaI8eRYIIxZowtFlUs4rwozKzP9QUi9PBwP4zDMI6YYOd8FMXffvudMWa1WltrrLXOORB8nuURj7qudz5QRrzzh/1eG4MQmqepKPPFsnLetV0XQiCEOO+GcZjnKU2ytmtWq5UHoe87SsjPP/9MCaGUWWfVNCutizxfLCtGI8KpB6HuWjmMAIHlYpmmyaWt7+8eWESneXp5fkUIMUoRRpEQn395HKVMs7St67IqRRL3bae1Hvrh5fmp7XpM6TzN1qrVevv27bvjYTfP2mr9/Pgci4gLNk6DC6AsS210gEHEcd+P56Z+enq0wS+rBaMsywsp4qKs5nkKIAAIzufLPM8fvnnft1MAIEkjFmgAQSghxykvsq7r/vKXvy6Wyz//+S/OuSzL5CzTJC6WRUAgL4uu76u0aNqmLMv9fn97t0Uwf/fwflZzmoppVkVW1N1lGIYQwu51b4x5fXll11bmOP63//rfMMbBea3MPE9v377xIeRFfjlfpmlSaiaEtE2z+e5bhLE2erGqfv3r31SLhQ+mPtUQojjmfd9fjrXSpmu74Pztw5YA6IEPUSR+/Ztf715er1fyaRrfvX/75uYBApAX+S8/fxaxaJsuinheZvM4A8i01gAi75yU0/64b5tWzfNyubq/f+CcU84ow1maW2Mvde29y4vMWZflSST4OI0YoR9++IFzprUqiiLPs/rSEIziRFhnRckhRKvVMs+z19cDhPByOnnnnPNSDoRSxkksostlLsvq5mZzOl+ev7yk2arIU+cdgMjM6nQ6G6OTNOWR2Gw2Ly/Pl8ulaS6vLy9pFi+X5Xq7nNV8ObWEoLu72/Pp3NQNpfSKFx6GYejHxaIqF+Ug+6EbtNZZlmZ5aq0LDqRxwhnz3i/LMmZRHMdt01WLRXtpz8cTxPC0P90/3E9yksNIGUUIY4Q+fPX+6emxbdo8z/79v/93AXg5yngTc8Z88NZpAF2SifV21Vza+ze3CMFL57mIvvzyxQV7ubRFVVHORVTFKbzU3Xq9tkrVzWUc5cOb+7//7d93QydnOXRDFIunx2c5z4f9cZaTd8EDKCep1eSCddZDCDEmlBLG0eGwv7ZktdbNpbXaAhi6oe+6gVFirSOMqllnSRZI8D5EEdOTjpM4TWNOWQAgQD+paZYqWEcIQRB2TTex6en5aZ7mKIoQQmVZqJkP3ZAVSGuDIVZKJXFCSyql1Nas1xulVF034zhuN5uvvvnqdDxJqeIk9i4Ya50b+75XSi2Wi6ZuOeOR4AAANav9/iBElGfZNUEwjtJqiwAWiTDWAACds89PL94zSkkkIhGJaZpxhBfL8tPHzwRRFuF3H97Pk2ScuWC9CyEE5xxEsMgKyugge4gA59RZnWTce9DUtdEaY5TnBURgyRbee22M9Xae1PPwrLTmEccE13UTCxHH4rA/WOcIJu/evY/juGkahIarKt17dzWGjsMAAwQA3N1unfNam6IopknKWXbtkGUJQmi5XAz9MHqJEGSUdl2nlSYYdV2XF/nxeIzj+ErnTrMUQ7zdbsqydN4Pw7BZr7ebLaW0r7rj8cQYWywXl/Pl499+EiJO4kREUZan/dB3bX8+nxhlZVWoWU3zNI49gN45a4w2xhFMMEEE0964JBHTNEWCBwAggO/fv2+aBgBwuVxmpZI4Xi5Xsm2KoqgWixBCPwyznpXST4/PwziqaSaEAh/++pc/x0kMAEKI5Hnatf1+d1yuli7YxWJ5OBzHYZxnBTF0zj6/vDzc30WJKBbV0A8//PBDJLhyllGxWJQ3b+/6dvjzv/zrv/zhD5vNFmGsmilJVVHm0zQF7+vLJYpFmmZxEndddzqfeH1ZlIsrSWa/24/DYIyFAPz4l5++/ubrcZLnS319hAghZVWlWQocsNZlWeKcGaWkjBZFEUUiL4orPJVgDCC41BeTpqMcf/rxb7/6zffVupgkP58vh9OBUnI61YTiLM2899vbzWl/IYRgjPKywBB9++03Io4eHx8/ffr56fnxH//x3/7hn/8AINTKEIKbrq6KRQhBqUlr7Z1/fPyi1Hw4HG62W0rp4y/PXPAP796Ok6QUq3l2zpHtzZIz7j3805/+NHQDgkhKeXd/Y4xu6vrp+bGsqiRJ+65ljPV9F8cxJthaoyY1a3W7vSGYynGMY2GtSdMEwAAhUPOsldp+va3rS11fIIQ84tM0910HETocD1c25Pl8yfNUiAhDxBhlgjnrKCGr7RIBYp356uuv7h8egg+97IxS+/1xuVwYq7Msqy/1PMnz2W43KwRDXmRlWWAC6nOvtF6tq4D86XA+HHbv3n8lJ0kxL7J8GDohWFO3//zHP4AQltWyKBdD3/3tp78tFwtKGACAMnrcn5z15aJ69/79/nAYOikikebpdnsrx3GcRgjgKMeAQHdpIxGViyLL0qzI5SCjSJTl4nQ5ZEVGCcmyLHjf94N1NssyRlmRFdM0yWnw3iutrLXNpXVGt10nongaJ4SJdarrWxdMEqeLRYUg/O7X3zIeDcPw5fPjLz9/HtfjarUUjMteBmeDdYjgS32JReydH/qeMaq1Pp6OPgRE4CgHhEhRVkkqlFL90GmlkyReLJaXy0UpxRgjlF4uF8Z5P/R1TX3w3gcAEEQIQjTJKYq5nGQIIUli53ySRpzzcZga01wbLqtqyW55CB4EACG8ZlQIIQAA730IQWvjvEMIGa3zLOv6nlASp+nu9fW6Vh2HkTKy3qy7rpvU/PK6Ay4kaaq1IpgGDzz0hBAQ4OVUx4kAIFx3A23bLhcLTLFzPhI8eC8ikawTEIAPQSndtV2clP/dP9UPkYicdQjioshdcH/3939/Pp/iNFZKHQ6HqiyvkHfCeFO3aZas1iuj3TjIiIk0SzBCAAYRJ6fDiRAcCWGtAQBOclqslvW5bpqGUgEh1EpJOfZ933e9c3YYR4TxsirHYZSTNEYrNadpIuV0Pp2TNIk4Z5SHEPq+VUZ765fL5Xq7YZz54Ju2WSyLOIkJpnGcXH0MEMHFapEW2TwpCHzT9n3fI4wADLEQfT+sN6KqKiml1qooynmeEUKvL7uub0/nc5HnUcTzLEvjBCG0Xq+iKGqaRnaTiEWSJBgjjHG1KA+nkz977521puv6JI3zKPYhjOPgUCiLIgSgtQ4gnI7nvMjfv38fPDgcfpLjqJS+dscwRuMwno7nS3N+enzK85Jyutqs8zyfJvX+w83Q9x//9imeZgDBsqq6rm/bDgAIA1xUlVazMTr4sN8fyqoghI7DkCTZ+3fs8+cvs1IAhq7rAoDa2B9/0m/fPNzd3f6H/+HfvT6/BA+tM8qo8SC7tkUEZ2myWi/lNBtjnp6fy7IgmITg27YmlM7T3Nbt4XhgjNe+vbnb/vGPf3Tevf/wPkmTur5M03Q6X9Q8f/vt919/++00S0zIGqFxHIZBKaWkHEMI4zgaa9quPZ1OnPHFsmIRgxg1bfv89LwslxDA15fXLEvvH277rgcw3GxvCWRd30EApZQYwn4cAghZnr95eOe9++mnn27vbv75n/+5qbt/9+/+kXIGgP/xpx9/+3e/+d3vf2eN+emnHzfbbVYUf/7znyMeiVRYa9puAAgoo891kxtLqqwMECwXm1HKHdpjDJMsHoeBC44wXK/XIICubbIijThN0y1CcJrU5Xzu+x4i/PT8slwsizwXiQAANHU9jMOiWuRF3rbtv/7xT7vjq7YKwmC0QQA4FyLOOOVq1iH4zXa5e9175z989S5H6dCPEMKI864dOGUQwPp8Wa1WIQSIQ21NUaZRzOAcQnBpLiCqjHG73fMwjta4ecIQQa2nJMm0VmpSmKL7hwcIQZ5mfdc7b37/D//47v3b55eXT58+9v2gZuW8pYTFqcCUUE7lOOVZvqgWfT8Ya4y2eZrdbm8jFkUJn0ZpjaM4CsB/+PDNrKeYp20zTHLmTJzPl6+++oARvlzOlHBMcAhg7CejfSySeZKPn5/MrYUAAoCUUl++PBujVqslpcxZCwKijPAoaZtWaZXkyTROUSQ+fvqIAGKcyUlTxpzz33z/zSKvtFYwmIjH29vNcXc4Hs7GaE6oB17K0Xs+9KPWSinVtp2cpjdv3tzd3QMArozrSc5xKiglCBGl9TiMdV2vNuuxHxDBGCEza0IJJRQEF0VEgTD0wzhKgnGcJEZbH0IURwgiElgsEs44oUREQukZQhRCiGNRVZVzjhGmrSYIe+8hgIzTNM+MNVxw4OE8KUrpJCeE8GK5oIxGUbRarR6/PIpIrJZLyug0TcADzigmhFGmtEIIlUUVAkiTJISQxmnXtWY2eVnIYWYM9/2IELq5vXHG8aV4Ba8euOVq5Z2b5CTHCQSECJajtN55JwEAnFFrVVHk5arUk6qbZprmm5ubvEitsVVZWKuXi9XN7XaxrPphkKNMk0yk/HK8tE0XxTzN08vpYqwpysoad3Nz473b7fcIwTjhu9fXOInXm613HgBwudQIQGPNw/0DQhgh5LybJyWnqcgLreZpklaHy/ESPEiS2FsX8chaayZd3hQIBqv1NEnngpImKQRBZJTSWee9T9I4iRPKKZmJlKOIYxGJfugZp7EQxloHnFZG8EiOkjHetz3CaLGoIh5d7ccBQmvt0A+UU0rYPOlpnI2xEAeCGUYoS9O3b98Zp4+7E8Ekzwql5s1mM00TZxxh9PT4FHG+qMq7m9vletm17SSnh/s3SZpcIVp393fOeqmmMi+UnCIhOOM9HB7evjmfL0mctG07T3Pb99v1hmHSSWUmhQFAEL5//zbLsqt2dL/bXemQu90ehIApyvIUY+Rc8Nb/9S8/hGDzstTzHDzABC8WS8pZsEEbN0gZC9F1rTbaO1cURfBBGz1NU9O0oxyXq5Wc5r7t//SnPy+XVVFkP/3wI6GYUpqmWZpkWqlPnz61bRtxfj2beheMsafTqayqRQXyPLPaymm+hkrW65s0ja22BhOM8c9ffk6S9JcvX6pVtVyuy4pWy6JumusPbQgBAN8MYwCgKgutjffe+/Dl8xdjtDG2LMuX191qvYIgNE1r/knXl8Y4Gwvx8vrKGEWYIIJDCPOs98cDgGGz3nz97VdGKfLVt18HD6OIt22/WCySJJ7V2DR133XWmijmu5e9865aVjc3d1qr0/nUdV3wIU1TGjHOKEZ4tV5rrRbVYp6n3X5XFoXRxnu7P7wopeQwfvf9r5yz+/3huvkEEM5SQozwDBljSs2Xy0UIMfRdHCc0SaAP6/VKTbOcZF3X1pkkjYd+QBA8Pz39+je/MkrvD+1VLb1YVkWZD8OQpimGhDFaFJW1XhsDLiH4cDoeT8dTgIBRHvGo74c8y0QUQYCkHHa7fZqk1lqTWzlNNzc3hBCIkJSzs95qG6fp198UaZryiF9vu6+vr09PL9M0UUYpo8aY4EN9rq02+92eMbbdbp6en/WsolUUx3HXddropm4iIaSU/dATREicTtMYAJom1badNQZCSCk3xl7T8XmRjdMop2kchyRJ+3PtQ8hJnhWZmvSARufM6XC6udl2bfvx4ycEsbbqcqlHKa03FJM0y7Isy5JMiNhYl2e5MUYpfbPdaGO0McEHNWlKaBzHD3f3Ukoe8WuxuW2bLE/HQVpjV+ulc87ahjFOKPHee+/TLFaz1rN2zoUAszTNizxNkzzLAAJPj89ylOvNOs9y55y3/nQ+DYPcbtdylFpbOchiUSyWC2fDx7997Pr+7uaWMHI+ngIIlNIsS6MoulqIq6Ky1uwPezVr7MIwdNubmwEPTV2XRRWCe/zyFAlBMOxHWVblw8NtnEYueKsdAni5XohUdF3jXfDOG6u///Z7Y8zpfPHBj0MffDgcD0ab15fXNI8pwafDiXMWQFisFsbqy6UZ2rGqChecs6Fu6rv7OzXPl0u93a5Ph3MSx8v1su+HeZ4HOfbtEEXRu7fvvHdDN8SRwFncdu1qvZpndTgcu67brDdCRAFCo80oh8ViKWIxyUnNOgR3f3ff1Bc5TyooznmSJkkSF7xwOwshXK83hMDL+UIoLYqyPtfOu74ZurYHMFy1ZfOspkSVVZmmSRRFV5qxA/5lt9tstkaby/lSVSWhVAjRtk039GVVrNdrStnQj5e6BgF4EJxzBOFFWUWRCACsVysAPecMALjZbCEEz8+7iIvN5qZvBq1NP+wJpWqej8fj119/LdW82+/TNIMYBe8YY4SS4+m4fz2kab5aLQN0eSjiJN4f9l3X/+//x3/+6sNXCKEQwna7HYdhknNRLRAECCFGGaGIEAoRiESUpOk0TQhBrU3d1pTi3/zmVwTjuq2N1nfv3+xediH47e26b1o5DlHEx3EEFn3+/LmsShDAOIyY4Ic3DyIWzruX3cvt7R3BmFKqtS6KImJRnCaZNoSQ7d2NmifCKITYWh0Jsb3ZiDiRcrym3jHGYZ7iNJnmKc3Sr7/9ypnQtPWnT58gxACEEML9/W3f986Yy6W+nE8I4cWyopTc39+H4B8fP3/4+muKyeSn4+k4z2q9WhrnCCPOuVnPBFFE0DTJJE6nWWYFDyEwSk6HI2UUYXQ4HIwySZ75EJqmIRh7Hx4fn5xzQkQYkQBBXlQlxgASwhid5vnp+cg4V/OcJFGeZ4RhSnHbNA9v35RFWVWLWanL5Xy5XJIkWa3XRumm6ZIo4zTKs0KZ2RhDGYnE4nJpRin7vvc+KK1vttvFYlEW2bUiez6fx3GkFN3cbuZZBe9vtzcIQxiQmtRisVivVgAAY025yOujjzj33lGCMUaMYYTQNEsRMWc153RRVT4sRCQgBnIcgnceg+AsAOHu9ma5XCqt//bTx0+fPnno72/erFbr//Jf//PDw0MIgXG22WytN5dTUxbZrDREUIjo6empLKuu7YdxLPIszkQsIjlNl8t5GMZqWUURRxj++rffgQDqtj6fDlmewgCMVYtlhTCKBAve50XirEcEIxiSVJhWRzErirxreoJxHIssy5u20VoBGIZuDMAvVxVj9HIemq71zl/OrXN2s9lobZXSy82ybXtt7TiM8SahEfXSAgx3p1PdXGatEMQuuAAhgAAAUC4XUSQul8vt7a3zQOvucDilSQpgeN3tsjTL8ySKosPuGAleVaU19ma75lG0Wi7madrv9pST4IOIxfF46UdZVtUox6EfiyKPoijLMoSAMdZ7zyN+s91gRLwLcSwACoQiyhLn3VWZxDirqmq5RCKOpnmORGSc1UojhI21cSq01ZSRJE2s0QjhADyl9Obm5uHNvdGm7bv7h9ssz5q6k5PM8tQad6kvu/0+EtHN/UaceCTYarlYrZfOWjVNk5QIwTTPQPA8Yl3XzvNslMnzLBIpCFdPbPT6+ip4RDj5Kv+wf9nHqWCMWOP6vo8EghDVl9pZt96shIjkJAkjxphxHK3VeZETikY5Dt3Qtk2e5cqo5WK1XFRFVqRJen9/3/d9VRTH0yErijTJjbW73Q4AeAVkpVkafHj/4f39/X3wYHfYYYJvH+5Wq+r1+cV6XxRFvBUYUUjArGZkkPchjiMRRwhBIRIaUYppWS3GftztXrM8kXK6ud22bSeHHgKwXJVpmoQAlJoBCITgru2yNLPGBhAwwXEcW2vUPO/2u2mSi6oCcDTWTaPMiwJAaLihnF5Z9pMcMUKb7Wa93kg5vjy/yGlwOiRRtt1u1WQQIdt4O46DxOTh4SHPs8u5dsZkef5wd1dWpTbGWZdl2Zu3bzC6ftQDJlgbTQntu94Fdzqebu9ur9g47zxCSMTRaX/WxN4+3LZ1q60BEL0+7YZ+RAidTyelFQRBSnl3e48wsN60dXc6HKZpLIq8zGJOkTIaQti1HYuw0W6ep2+++TqAcLk0hNLg3Waz4ZzLaWSEj8Pw7sN7hpkcJUR4ntWH9+/Lqnx+eTLG1HWT5ytGqdbmUr8ghJfVcrEonfNG68Px5XQ6lUVxf3+HMVyx9TypxbIiGLVtXxT52EvGeZYnwbu8LFbL1TRPcp7Wq/X93UN9uThrMUFZlkGEpnlumi7L493razGXwIeyqgCAi2V1PvnNZm207YexbS95kSdJDCHGFLRNd3N7yxjNsmQcR0KplHK5XImIdcOwe93tX/cIQnLYH+qmhgExRhCGn7/8fH9/xzijnNw/3O93u0lNUSwEj36pz9f/XCySoiyP++NutycJXq0XlDIRx0rPSs2csY+fPk6TvLm9QbAM3n/99VeMsbbvtFFt3fCIU8ZEFHdthxEO3jEuIITTZGCA0yTHYZhndbPd5kVKGWGMUkYhBKvVaui7osheX3eLqtrebPMsT9N097Lr6j5L0lN9UlIPg/QAcsYhJN5779yvf/Nryuhmdf+3jz/Vdf3hw4fVerU/7g77I0Th9vZGRPxSt6fj0Tp7e3P33Xff1E0LILTGXs7nq4OcEjKMo57ny/HMIhbH8dD3EMCbm5uff/55nuf37z/EET/XdXOpGadpnhOIjdfjOI/jiAmy2jw/P0OIsjyHED0+Pvrgr9kVD7339vn1NU0ywumlPlHCpknFqYjTJEAIQHjd7QKAsYhgwM76x8cvIfhvvv7eWXvYvURClHlhjJOTfPvm/e3drZTjrFUUiSzLeSTcF5/lCGPcD33d1krr9WrlnWOcXBk4CCLrDPUUQ+CdzfOMUAKCW63WaVJ2fT8OQ16mz88vd7d3EMH6UkeCF0UpRKSUwhjrWTlvZj3dbO8eHkzTNBEX10XZMAxZnh0Ox65rq8XCOVc3jRAiTbPdfrcoF6vlWkQRpdQ7VxZlAIFgYp1ZrZZ6NqOUl9NFShlFwjmTZ+Uw9vd3d+/evUuTlGJy/3Dz+rJ/fnpZr9fL5ULNWs1qmiaEkJmUNXqe1TSMcRoboxGKKMFqnvI8j2Px+OWLUQZTkqSxD36eDYSBUDL0EiKY57nRxocwq/l0OolE5FmOKGy6ZpSjEJHztlwVu9edlDJO4itajjLy8O5u7EfrbbHMuiEyxq23GwTg3d3dPKvv3HdZlshxPJ8vxtph6HkUIQScddMwzqk4HU/Wma++es+j6Hyou74z2lBOsyzzzrV1Z63xzp3OMk0T57xznkccQRTApLX58NWHzXpVlhUl9PX1tWvbENx6vWmaZpKyvtQYY4wgjyI1z8PYQwS10pfTuchz6zTwYLvdTmp2zkEIAADjMGqjMUZCxEKIOI59sMtliVukZ+2hf315TZM0L/Lz+SSiuCyrPMsoo1ma3d3dDuNYVOU0zafTKfiglGaMAQi990qp0/M5y1LOeV7kEMHlYgUhvLm5e/z8pW06AIOcUABAmTlAwEXEAB/HAVFEKO26LhZiHMZqUeZ5MU9zksbbzRoiQBHK8sQ5Oww9QEgOAxeiqLJZqeWqMs7+6U//arzdrDehrZVW93d3q/WKcdZ1PWO067o3929EEmttRCIIJsMwcMY36/Xt7Z33zlnf9Z2aFWOcEBxFcdu2p+MZApin2STVx7/9DAAUcSSiyFmrlbum425uN7GIecQoYdMsT+cTIXhRVYvFsuvaH374sayKJI1vt7cBAAjDpObT6ZzEyeGwp5g8Pj3+9re/LcvFu/cfjqcDF6CXowfeOosJKavq408fRSysNV3X/vDXH4ehQxh9/+23WZ59+fKL874sqzSLn59eyMdPn7RWSRITDvqu77qubc+E0ixNByknKU/H8yTV3/3d393f33/55cvj49OHD19xHqVZ9nWWCSEeHx8vdfOrX/0q4vzL4+OlrpM4jmPx7dffvbw+X86X/e4wDH0kxP3t/bJcQQSHcTgejldANISwqIo4iur68sMPPwTgf/OrX/3jv/1d8DDLE+dcU9eU0yIv5CgBgFmWrpYrQlBZlUqpjx8/LquKkhIAYKyesaoWBcb0fDqXi0WWlYxxgmdG2SiHtm/+n/+P/9esJmPM+XBK4nS1WRuj1TTlWc4pgwQtq/UwjiISzruh67Mif/f+3VVOOyvZNm3f9cvVUohIa/Xp08eIR5SRPF8lSTQOwzj2lFCE0WG3W69XACDvTZzw4+HUdc00zRDhNE3brpFSJmmCEJRyQhhKOQMI98cjIXSzvu2HIaUME/y3j7/c3myzIuciPh4Ox9OR37Jvvvmqri/rzYZx/tMPP53rpiiL73/9m2EYLvW5yHLKqQBxuVg67+u6Pp/PSRq/e/ceY9LUl8+PX4qi2KyXggvBuXMOQmitjZNYDiNjNBLsv7tM84IQyrmJBFd5lhf5crHUWhNC3r9/dz6decRFxD6f60iIzXZ9jVdO81SWZZ7nbdteO7f7/R5CcNgfOL/yOHHEeX25KK1ACMfDsahKNc/ffPu19947CxGUcpzVfDxwznkAdpo1IcR7lyTJ+XSumzqJRV4SQtGkpt1uH8cizzMhIkqR0T4tYmXm8/EkYo4wyossAIcxPh3Pxp6TLIEIffrbR+vMNaYSiQgRpKRWWiVJQjE9Hc485lm8KfMiIDCOA4uZdw5BWJVVCM5YUy2W8zQfdvtJyljEhOK2rudpzvPs5flp6GUIwFnlgw067F93zlnnXLmo4lh8+fzL6XzKslTE0fFw0EbParI2ZGmC4Hq1Xl2vLJTSJEkQxgAELqJx7LU2ZVUYo8/HC6X05WnHI3YVIMtZWmOSJL67vbXWSjkuF8vvv/9OG9O17TiOV5ISgEBKub3ZLpfL0/H0/Pw0K0UoFrH44a9/Kasqz3PnbCTEfr+flQJjeLh/W0XlOAzBu65t+6691JdFtdis1uMosyzlLAoeeR8e7u/3h4NzNk5ia4wxZtZKSvnLp89397fL1epyvohYJElqjGvbNorj73+1knJSeiaEYoIRQTe3N3/4pz/MRtGINuc6L4o0E23bns6X+9tbjHE/DFxESZJUVXk4HG9ub4s87/veOXfYH6yzAbjF7a3S83K5qC/NrCQlGHhXFGkBiyxNAgRPX14ojZbLZZJl59OpvjTXD8EZdzqdtzeb0/l4NVX0bZ9lxXqzhhAEEJRSp+MZYbRarkQUBwAgQnKcAoCU8+PxtFot2awwxvd3t0WVNXVntNls1oyy56dnyvBmvS7KDPngnRvH4fn5dbFYHl6PlLPlapkXed93X54e7+7uKCEBgsvxvOt3Q9eXVRWLpK7bH3/8eM0NU0KXy3Wa5sH7oioZoxBirVXb1MaY4DwlTJl5lNIHqLW53mujhFrryGa7/NO//Ouiyn//b35XX5r/8l/+c1nmwQPj7Pl0SpPs/v5eJAJhNHSj1qYsKyll2zRZlgohpFRXC08IIS9K9PS4XCwDcGpWGCPvPQDw8fELZZRQarV5ePsQ8SgAZK3Z7w+MMqXmNItBCNaaf/j7vwcQ/N3f/fb2/sZq9/T8tFqtnfNN3Q7d4IMP3ufFnRDxKPvz+dK0jRwnEUX9OBZJhgn5+vu3BMK66RmLxmn+8eOPX3/9lZznl9cdo+zbr7+5vbtdrpZKTQD6y/kiOK+WCxQgi/jpeB5H+fT45Ve/+fUf/ukPSpn7h7uhG/63L895lok4TpOk7/qiyhfVIsuS4AFCYByHANzT4/PT0+N333633W6a82WepqLIsyzbH/a7/X4Yh3EYPQgQw7LMMUUAgcVyESdiniYRi77tMMI+hPpSr1ar5XJxBSRopWY9U8ayLFPzbIpisaiKIsvydLVcvuxerpvq1XpVX5r/+l//693DHUb4eDpd2stqtUaEOGt3r6+EUBB83/dVVb15+yYr88vp7INnnD7cPfR91/V9gKGr2yRNKCXb7aZtWusspWTWWitFMXYYtW2bZynwwXufxSm/YcqoScrNZl1UxWF/iEUCIfz46SNBpCwKyrlztus6TMjjl0fnLEToL3/563q7vuYalVKHw1EIcTwelovFH//4JxFx5zxjFAIYQKjrWml1OZ+vWHkhBIDQWUcYTPN0nhSj0zxP1XIpu/6wPwAIt9tVc+nmWRZ5ARHsuh5CfDmfT4cjRFAksXM+hBAJ7oM9n8/r9QpABCHoui5OBMTAOYcxpYx2bZ8l6cPb+2EcMcRudnESL1aL1XJJEJ71PM2yqevj4cQ5U7OSjPCIny+Xvhvfv3/LuajrRivngoMQXmVHaZJACD5/+VlpfTwdxnEglCCErLVa6zhOsjyb55kw4qRru7bthiRJrDcUs/rSJHlMGeuHQWs1a3XYH5z1AYbz+eK9X1RVxCOCyaePn4q8WG/W9aV+eXo9ng6r1fLNu3eL1XL3ujsdz5TRtm1jEb+8PIcQltXi46dPp/N5WVUAgsvl/PDwEMVx27V9P1hvL5eLiKOr75pzTgnBEE3TXFZgtVpqpcZxdDbESYwQzrL06fn56fERY4IJ9t4zRtWs2rb78vlxnueiKNq2w4gcT6ftdiMifjwcKCUQIa3M54+fj/vD4+OTNTZNMsrY7f0twshap5RqmnYYh1jE+/1+sVguF0ulNSWE8SiDQMpRiLjtWuf9/nCEMDDOqmVFyGq331lnhmGI4th5x7l49/5tnlXG+v1xnxbZ0HZJmkAAEEfffP/11UqbJsn5dNFaIwyuWJqnx+frlPXl5fn+/r5pu+VquVquPn/+vD8MGJPf//73zlpr9GqzXC0X2piIxddb8mq9gBBYZyc5zVrlWSriqFpWb969G8fxv/23fyqKYrPepFna1u0ox77tFotFmqa//bu/c8E9Pz4Pfb9aLavFou2GiHPCCMaIMHKpLxjjru8W5WKzXc/TTDA+HA95mWFM2rZt2363Ow599/bdW23185dXCBBJYv6f/tN/YJy+PD5rre/vbg+vp/XNhjIctrdN24QQlsvF0A4II8ZokWdRFL28vFSLar3eNE3Lr+K6IkcALhbVy/MrY6S+1GpW1tu7m9skESIWi9XqfDz98vmXrMhikSCA5DjkN9s4iYqiQAhu1us0Ty91/XB7J6c5EnSxXDrnp2mCAGhjKSc3tw9JLP75n/6QZcn7Dx/UZNSk//Lnn6zV93f3cpZGmzwr5TytYiGE2KzXzlvG6N3D3eVUf/31t8vlQs3qL3/+86wm59zheFitV13T/fLHP5VlhTBerlaMsl/9+jfTPAXvYpGuViuj1cPDw939HaHo099+fn58vrnddG0Tx7HRepJTkgilpv3rLskSwkhVLgjBUo5yktYorTWl5HQ+l1U5z1NsxGa7hgC2TUsJ3tysgrPGMjmOlHLOedf2IACrLWMsyzJKSNd0AASMUJ7naZL0fffSdsa4SAjv7Ol8eH564Zxd6vN2uwUAZiRr6rbrulmpfugIoc7Yuqm7rvn+u1/3bfcv//Ivi7L6/b/5PaMMESwika7ifhjKsnDWJUlKCf306WejbT8MWZolaXrVtymt68slTuJRDvM8d20XgseYynG0xhpiOGcxjxgXs9aMc4wwIUR3bV3XzlljzCjHvu+KqqKUtG1bN+0w9M75eZ7jWJxPpyxLryVtQkhR5mpW4yDHYVgulxghgGDE+TybpmmKIj+dD2M/AgTGYQgBBAfUrNI0TtIYYAhAiOOEUpIti8Wqqpt2HAZtVJZnEMCrYzZNskhEQz8YbaJY+ODrS+29MNaE4BBGQz/WTe2dS7IYQXQ5N3q2X331NkLcGMs4j0QEITyfLgAAmCMesTcPDwTTwJEc5eH1SCPCGZ/kVK4WjBHMQN1exnEUSVRkGecEE9zOE4CARVQb/fjlyzX2qrVqm55HMcYoigREUBs9z7Lvex/8OIxt2yKIAPTG2BCCtfb+4WHo+zRNF4uFs3ac5zTLvv7m6+Ph+ONff0iyNE3ih4c3AIKIcwjht99+d67Pl9O5WlTRxL96/04b03dDJESWpA/3D3lR1pcLI2yaZPBhsVwsllUIoSgLKcfXl1eEcJomnAlC2G63e311nDMIoNYWIjOcBqWUUjrLM5EID7xSs3MphFCkYnuzoZgCAO4e7j7+9DFAEItosV5cjpcoEtVtOQ0ySdOu7UIISSKSRFy1z1rrb779GkF8OO73+0NwIYCQpDHwoB/ah4cHTNB+t5+miUeRtnpRVQ8Pb4Tgj1+exnnknOVZJqf50jTffP0NZaTtGhFHjFEAQByLtu0SkXhv+74DIEAIKaX1pTZGbzbrYexlmNWs//VPfybXOp6cAICb9VopBYNfLhYAhjRLirwYR4kBWq8XZbkwRnPGmv1FzSrL08PuYJzxzmdZlsTx//Q//d9FJKZpGseRUrqoKkzI27fvhqE/nI5a6bqu+26AkKxWm7u7G230MMhhHCmdoyhq21bNymor4mjsx9eXF0wphIBzutluIUSMEe9c23cE4+BBtS5JEkWXSzP0IEvSxbKKOKuKJYQwAJBGeVUtLpe67wYMcFEUaZRaa7XSjNKIRnqa0zgGAcVJmoiYEZamWZ6NnLNpmuZp+ur912mcZF3aDt3xePj54y/Oue12u395bbtunlSe5z6E4H2WJ5xFRptpHMuijIQ+X46LaqmV8ZuwqBZ1Uzvnv/766x9+/GsAQU2ma4ckTq6ktjzLrPNJmjrrHAzO+3lSwyjruovjeLPe5EnRHNvL5fK//a//63/8v/5HShgh6O2bd2qeYACM0zxPAQjOu59++vHz518Wq0XEEwD87373D+ub1dOXl9VygTF5eX4ZpxFj/MMPPxhtetkfdjtr7Wq1mqTkgpVlPs/zOA6QwHmaoQPffP2tnCaEUdf3IIRquXDOr9ar5+fnz/X5w4cP0GPvXZamy2rpvEMYKwViLpI02Wy2h8MeY4Qhcs5BECjBaRxPoww+bLabn3/5pbk0u9c9i+g4yOBhWRY84pRipSbnXD9I4IOaZ6XnYRjGYbjZ3DHGCMbW2/pS921nrI1FzIWYld7vj1ma1XVLCA4AnM81ImixrPI8t9bVTWONvZpLIYBd177/8AET/OXLl4+ffu77HkJsjPrHf/zH1WaNETqfzpOcX19fX15ejDUQAKXVPM15nltrr0kMxth+v9tstlVVvr68XElt4yi11lHE67r2zr/78Ha1Wjx+fpymqSzL8+nEI56mSVkUlNG+H8Zu5BHTWq22S6X0OE8ftu+0Mvv+sFov0iQehv9/S//VbM2O52diQALpvVt+r21fc95jq0657mZryOEMgxEz/FT6BrrRF5FCERJnQmRPT7O6WV1Vx71+2+XXSu8TCUAXW3mPyLzKAPD//Z6nUhVVkWVJFH3fZ5z3Xc8BNw2zbpqiLBRZsR27LCrAAMaYA44w1jS9qevdbscBk2VF5EyW5b4juq7WTXM6RbphyJICgaCqCjLwwAZOueP6AOEB8J9+eNuRhpJhe8jni6llm7Ik7497Dhjn3DJNSZJGoxHCQlVUaZYQMjBOR8GoqkpJlvbHfXSKBIjqeisrctcR2zEd167qssgLjHFdNwgJEMDnhQAASZFtx/rVr78TsbjZbOq69gLfcz0AeFlWZVlijBnnz6Dpuq7Pz5e6pg+MClAwLWs2n/V9m2dZWdZ5XgIGi7LQVG325VeGbvz5L3+WZSUIA1VRKRuQIGBRKPLycDw1dX1xfqnrxsPTA2Bsuy01TT0/X+ZFURQFGYau7bIsEzEGnAEIIRIELNR1AwDwXRNAKAjgOT96cXEBAAy9cLfflUXxb/7+32R51neE0AFhqGtG0zYP949pll1cXnRd19StrhnPJ2kIARIRRA5lvCmrLM+KosiKNAyDMi9ev3mlyPL1zfXt/a0sy13X5kXOGUzixDC1rms+ffhwfnEe+H5RlBDwzXo1m80FJOiqIUliEkeu65mGvtqu26p99mRAASRRBAEYerrfb1+8fLlYzC3T9IMgT1PGuWU649G4bdvD/lAXtaaqoRcIQMiL1HXcpq17QmzbyfLEMC1N07Msw6IoKZKsKBgLqqoOwwAFWOaFKEm6oVPKJ5PxdrsVRdl2LNfzn55WZCCT8QghlKdZ33dZlmqqKimSoihd29dtq6vqaBxUdS3J8jGKdU2Ynk0MXcMMwGEAruNOzyaQM9t2BjZwJuR5rns6jxIhTZMoXy6WQRB8eP9+uz+cLxaGbji2E4R+07QPD0/T2SKO09l0CjgAkL9//y6chF+++bLIq4FTJKLJZHI4HA1dT7Osaxs/CAzTqJuq7WvAOQC6AIX1aqXpmm07A6Xb7RqL8nQ2gwL68P7D4RBZjplEyekUf3z/6ebqpSyLSZpijNIkN0yLcp5E+fLijIl0/bRZni1VXUvSzHXcru/u7x/G08nj42PV1N//+nvSEwoGUcDR6eiPPdu24rs4L4rF4izJ0tVq1Tbd2fnZ5dW1ZRofP3woimy/O+43G8Z4nhfbzbpu6zD04zgGEOi6fjodn73EAEIOuK7qA+0QR9PxpOsIG6goKX7gFnkxUGra5jMLxbGcN1+8VjSNEjqbTcejyUDpdrt9fFgNjAZBMPJcz7PDUZDEUVPWmqa8eHkZnSIAuYgRYzRLE1PXUgmZlnF1dXk6nCzH0mQ1y4v9fo8EhEVRFNFicb5eb5u2VmTZD/w0T3bbnaqouq6tN9tRGCKMBk7v7h8EQQCMAwi7vi9LYpimgHCWJYoiJ1ksCLhtG0KIYRiShCkj4/EYQnA8HN69ffv+w0dFUTFCqqr8/NNPkiTruoZFSVMVhBAAgFNqe56haUhAQRD4QbDfb0VRpgoLw2A8CgHnk+kECejh4UGSRE3TZFkehsGyrcl4LEooCH3Aka7rr754AQAg3dD1TRKfTFO/WC5PceQ4jqKofd/N57PT4WhaxpuvXkuiFEVRXTd1VVu2Q4euHwgZCBsYHRhSsCxJTV2rkibL4vLN8nA6/ss//2mgxLFtWVFVVYmjSMDQd4MiLylkpmEiAVVFU+RV1/b393eAA1XXDMNMkrQjjAxNluZ930uSuNltKCGGoT8H2CCCZVkQQnzfrarasi1CujguOeBYxPEpkUQ5TqKekOPxmKZpXbaKqggIkZ4ghPqOUEJlUaYqLapiGAYOBN8L+q6HUDBN482XbxaLRVPXZmheXV9uVltVU6qqggAwxgRBeHx48DyPMZ5l2Wg0KooySZLnMUPXd03dSJJ4eXXlOt7j45Mo4sV8AQDUTA1C4cXLl33fFXnZ9d3l1eV+u4MQGZYpawqGQpomum5xzjFCz4JlxllRFlVVPQ9CLcuKkzhJUgHCODphUTRNayD0ALht24wCwzSW58uBUEqpoirnywuIgCAIjuMCzquyhgLQTV2AQlM3gR84jgsA55TfPdy/+eoNALwsqtV6ZVnW/e0jgABCaTSaIgSwiIPx+OlpOwo9JAqqrO73R1XRDF2jPSzytKyyU3QKw7AqK01VTNNcPT05roMwT+MUUAAA1TVNwrhv2rauFUmOjxFgNE/TxWI5n8+ariN91/cdhKBp6x/+8tf5fDI/mymSmqVZnhemabquDQEgPfF8R5Kwbuizs2ld11EU92Qoy2r9tM2LzA/8s7MzzkCR55vNZrk4x5LkuM7pFOm6cTZfdn2/mEw0RcnL0rYsfLEsykrTNVmRBQHud3vLtCbTSV4Wsqw4tivK0mQylmWprtqmqRVFcR1HNbQ6q3GZE00zgpEf+v4pikRJTvcFGWhdNV1HHNuFTFjMFy9fvsIIQQDffPnG0A3fH+0Pe8Z44I/u759m09l6s3r+Pt8PbNvmnJ+OUd02Isar1VpAMByPvvv1r1zPTZM4OiVJ1Wi6fnlxiTGyHTfLk37oHdF2Pefx8bHvyNPjpmnaUTjOs7KtG8gFPoC//uWvf/jD34zCydv3P8mShBAaj6Z5nmmaen554breav3UNSTPCwDhYX8YjyemaXZtnySp4zkmNYLAf64OKFip6kqWlLZpNU27eXGDINJ0rapqxvl4FEZRjCDEIkrTdLvbVkVZlSXCKM/yoiqf/7/zs3ngh5IkpWk6nU6SJCWEbNe7i+sLVVJkSaGMDQxwIDzeP2ERIRERIpdVWe7Kpm1EhBnkoR/IoipKclE9K0dMCCFjvOvbOI51TT+bz/O8EBGqy0qWREoI6ckpjg3TAlxYXpy7vlvm1b/9H/8vA6GfPnz2PU8QhKZpzhbLpms2mw3g7Gw+s123LMvocOz7ru/746GZTeYcgPl8ISvy6mlt6HpRFp9vb5GIbctRFLnrh2A0EhBMkhRA4dP7jz0hy+USYxzF8Sgc+aMAJjGlzPN9wHlZVoqq3N8/moZhmqblWFVexHG8WJwNhMiK3Pf9zYsXGGMAQRB4j49Pqqos5nPbtj9//qxpmoix67kCgLZjS6LEOUMYr1YrQ9M1XW+btqpqsume+2V5VjAKRqPgbDkfjUcDHQQBlWV5OkVZljdNq6nq6mktiRIAAAiwaWvf86MoStNUlqXJdGqY5qePn0UJQxFyARBC2MBms1map4ZhyKpyOhxHk5FpmrZp9UPftT0AnHPAKMNIID0BADLO8qyoyxYAUFX7IPDpwOlAV6t1WZSiKNZts9/uMUaG2dqeYwjgWTdflmVTN2maAA5s2/ZHnq7rtKcChIT0mqZKWCZkIAMTRcmybUM3PN9Tdenu9q4npG+JLMqyJEEI4iQ2DSM6nQQI6UB1Xf/i9evtZpdmGekHUcSargVhMNBBQFhRJITwxcVl2zS6YaiqOhCy2WwLVH777dfDQChj87N533ZQEJAokoEpsvjmzRcfP3za706TaThQKitqUZV0GOJTkqepaVmWHbz56sv9dpOXBWfg4f4RYXRxccE4L/IiiVPGmSzLbdtVRa1pGjBgdDqR3tpudrP5vO87SZRVRVVVhZDh4eEeYUFRZMMwJQnbjpNnZXRKdF1//fpVWVbr1ToIQsqohKXolFxcnquKrmn6QIeho4IoQA6gALMsTuNcU051XQ99f3lzoSqQMyjLMsKipMuGqT89PVJCkICqpjZ0FTDu+35dVcd9ZFlWliUcwOlk6lou4/RKEW8/3X3z3bcAwIeHRySgw/6kaEpd1ZQOjmWmaSZJ0ukY7fdH27bCIKSUH3b7tm0GMoxHI84AALDIyjRJ8zynjDVVc/nlla7r6U+pACGAPAwCz3X4PadsMEzz5ctXYZgoimaZxnq9gQBeXV8dDkdN0yQR11Vz2Ow5AG3V+a5/cb6kjIWeX1ZNB1rbthnjddkURdU29cBpURSiKCGM8XQ+I32/254EQarKxrHD5fll3bak75Mk4QCMxuMgDIqiOOz29w8PX3755uXLeZwmtmv3ba9o6ne//k43tbPlMjpGTdfWdeP7HsYi8DjnVBCQYVpNX9dNVdbFdDG1gOW6bhD6CCFRFCkdPrx/h0WsKdpus7cdG4sIQK6qymF36JpeUeWGg1N0clzHD8KqbP/b538uquzi4tx1/b7vFFXvu+7hfvXnf/1BksTFYiGKQjAJBwb2u13ddmXZYLE3LFMWZVVXnt49blZbVZNUTaGEWrYxGo0Mw4yi5KeffzJN4/zi4vHxoa4rcTrSDPXd27dFXgx0GOgw1ENdt7ZjKapSltJsNnddT1RERdUQEjTdzLJM13XP9bGA8qJknJmaziBo2sqyzbpposOpIx0Z+rIq8iR79fqV67iu4/7880/3D0+M88lk5PlB1/WAM1VVmqZS1Pkw9GWW27ZVlPnQD+NJmOU5Z9TzPV3Xp7NJFmd12YxG47/9m98TMnx4/9E0dNPUB0purm/KspQkse06RZRfvnqha/rxGA0Dnc4n2+1+t93v9jvA+TAMu/2+bWpNM0QsvXz9IklS0zoviopRtj8c/FHYd0RW1Twvkjgti2p/OEAIZUWBAO52R8sySU9N0+jalhASJ8l4PP7m22/qqoqjFCE0m05Ho3AY6LM4SJaV7WZDGW/bZhSGnu8PZLi+uRkG8nyDMRqFjLI8z1VVvb6+oIyf9qc0y4q8xFgwDG0+m8ZxslnvnqtDTd1IsrjbHaqilCQ8nU41Xdtt94wxOjDKh9VqI4uSKEtt0+93B4QxgKws66poLy4vOAOark5nY0VVd7udPAxd13HK2qaNDidZlfOsUGStKArHsVVZW63WfddTxgAQplOX0gEAIQjDPEuTNKaMKYpE6TAazRBEeZ4rqiZJateWHEDOQZJk08l4Pp9RSjllVV0XeTWbzgVBaJoaAqGpmyzNCRl8z7+6vJRkyQ/8NI1M0/IDj1L29LSSJOnV69f/8T/+x7Isfc8TRQwARILwLDcVMS6LMs+6rMhevHiBELq7vZUlWVZkSi/TLNN1TVNVCAVZlgQE7+/v67rabne6ppmWqelafDolaTIwFsdxT3pNV+uqpgOLkzhN09Ph1HWdYemWbb9991bVVdtywBlECD09rRzL9jyvaRpRkjXdGOgwDEOWZpIkIYyDYBSGY0bZaBL2pL2/fXh8fJrOxoyx3XaHBEQIxCJu2rppwfNVe9d1nIOiKGVZns1npmFmWfbtd99UZQ2BgEUchn5VVfLF+Ww+lSS83e2qKkzTTMTCbDbr207ESlOScDQWRZwmKaV9nhFJkiRRXJwtzi+Wnz/darpuWsb97b1tW9PpdLPelGULISjqUpWVqmpm84UfBm3diaISBKO6rjjjvu97vouxyDgbKFmt15zx4/H4bN/MiwIKgu1YCAukJ13XjcajpmniNPM9//XrLwDk202DMYrjxNANSRFtx9Y1bbvdwf3OtTxRES1L79pekeUw8DHGAPDdbgchFxBgfAAAOp7VNu0pia4vriRZPEWxaeqM8bqqCCGGaYiiwAAfyFCVlaYp2PN8ztlAqKJokiincWaaJh0oFqXpdAYgjKJT0zQAwLprF4tFlmb3j49d1yEB65qxP+y6nmAsb7ZrWZJ83+27VteNtq0hAF1HVBnrhq5yWVGVp4enT+8/O56liFLbtm3XiyJWFKmsy67pXc+ZnU3JMBia/qzKTOK4advxeMQBlGXF0E3P9Ysqj+ITZzw6plXZck4fH56+ePP6+vpmftYICG1X659/+rknPRKRIODrqxfBVZgmCYLI0I3j/nA87DEWSDeU+fH65mr1tJJk1bTMrm0sy2zbvsjzr7/9Jo3T9cNT3/cCFkzbkGT588fPdV2PZ2Pf9QQBQQDTLFcUdTY/q/Li6WlFGdcMXYCCLCmCIBR5bBqGadkUUFESGWMIC1HXOo6ta7P379/LSJyEkyItjsdDnueGoUmi4tieoiqmYZR5TUjnui4bOALCxfk5goIoIN3QPM+TsFrWlaKpqqYXRQ4BGI1HiiIBBiQonJ+f102FEA6CUBTRaDzK83ykKgMhAILV0xOWpKLM/8t/+a9ZmjEGGKdpmquqMgykzKuzc0PRVE3TRUksymIY+v3x1JEeY4xFsaoqSoEXBIADy7F+/OsPnHERybZjIYQ44HleWJbV931VlcJJoIzOZjPTMkbjUVmUTdtwBj58/DCdTAAAhJDl+XmWZ4vFmaoo6+36cNjrmu77vq7rsqSIEjZNs+vaoqwc25ZksaprL/Asy2QDpZQRQgRKDdNQVVVR5e1mVxSFpspJkuZFEY7CsioBALquC1zou246ndiOvV5vyrKM4liAAh0GWdaqsizyPM3z2Wy+WMwkEYmyzCZjQkjbNm3XtXUrqzJj/WQaOLa/Xq26rmUDa7pWkmSE0HK57Hvy9PiUJknTtBLGHaWKpkIgyLJq2aDtunK3TeKT67pB4MkD5Zz3hEiiNF1M66rebreGqTuWo6mKIKAqLwkdhoF5XjAeTcq6kBXpuCeMUccKZFVBAjZMw3EcJGCEhMPheHY2Rwg/Pj6UZaWo6uLs7PBPf+xJb9kWBPCZyzSwYTk577q2KHIBCc/YatfzdE0TJdGwzK7tKKWSKN3d3nddbzlm23RFUbZtnab5crl8jkgBBkzLbA5N15G26c7Olrd3d5oqq6o2NPXl1aVpmrKq5HdFXhQA8MPhwDkwTEOSxKZu375/K0tKliW/036raRpEsOsbMhDAgW5qlmmmab7ZbhAS6DDstrueDBjjr77+SpJGaZrIkrzf72zb3u0PZ2cLWZY5AKTvv/3+u+gYbdY7AQpXF9dN03DAZFlMkujx/qlIS0b56nFzdXWpavp0PFo/rWg/vHn9uq6b+BCVWZ5GCYRQlKW2beq6/vZX3/7rn/4KBEFS5e1u77ru6RS//Yd/fPXy9Vfffd1U9eF4In0HIMMYpWmWF4WMpbptEELpbts2rWEZ+80uCMPl2QJwYFiGrKiQCWwAN1c3nAMkiIf93g/8gZKBDn7g04G2dWuahqppAAAI4GF3AAyOgrDv27qu26aLTqfV+mmxOBMgcl1X0WRZVk+niHPGBSbJsiiJaZo9Ax81XZ1Mx3VZV03VtW3f92nW4bZtdEOzHPPpae24ju/6aZ47rq1r+qfPHw3DJMMAoUBIAzgo63LoSfpjpiiKphpFcTtbzMeT6X6/BYAPwxAn8cCGx8fHPM8wEizHgVDYrDZQAG3fcQ5Gk9HASF41aV4gJBwPx/liPpvNDcPou94LnLZqT6coSiLA4WgysgyrZ1QUURCMy6Lc7DZ5nvm+xxg3XcMyLIzwYrl8eXPz7t27v/7lh6qsmr6OkxhycHl9NZlOMEJt2zIORCRYjplnhe26lmGUeSkgT9PUviO6YTqWTQ1ztVqbpilL8k9/+fnp6fHFixvLtiez6fF4xBglWZLeZlmWIyS6rmOYhqppQTDqmjbPi2EYNE3r+y7Py+324DrudDbr+55BJoki47St277rPM89P78gfV+VDcZoNptlSVbtKllUwmA0ny10yzicjmmcNHV7dXPVt11VloaupUlqmyanQ5UXrGUQ8OP+wDi0HMewdMM2bz999gMfAsGxbc5YVdS9RDRdZ0goyhRjvF49lVVJB3o8HERZfP/u8zAMYTBWNJWQHkIUhgEh7Wa90zTVsZ04SuZns6ou94eIDPTpaZ3GyX/4D/+RUvqnH/4kybKh6fAJcgb8cOT74NPHT3lRENL1A2maxrR0WZYhAE3bHA/7tuuSOPY8FyNBVmRd05q67vt+Mp28+eK1JMun0ynPC0VRSNcnSSIrsiSKHLDn7oWiSKqqxMckjtK2rknXd02rKKpl6UgQZEnpSLtdb9M0oYCpqrzdbuMo1UyNEEqHoRu6nhDTtA3LzqtS1Y2bFy+eqaJt3czP5lmSQQG0zTOmOxNExCFUFCVL474nfdcjBHXLEAShLKokzuIo7boWAKbpCgNMFIWmae7v77GIjofjwGjTtrIkSpJcFIWAsBQqlumdohNlzNTtqqxPx5NpWcfjaWDDcrHUNEOWlW+/+/XxcKqamgNQNdXF1YVt23XTOrbTdv3j6l7AaDQembbZtl0URZIkqYqCECqKfL1eccaLIuec13UNoWDZVl03w0AQQqqixkmcJIkf+KvVuu/7six7Qtqm2Wy2i8Xcsqy2bfMir6pytzsgLAAIh2FQFKUua8O2bm6u4zhGGFd1WVWQA26YpgFMSlhRZVmWW7ZDhwFwGWOcpmnbtOjszPO8i6vLX97+wij3g2D1tGaAa5re9X3d1pRSytm//PG/X9/ceJ7bdx0UwHg03WxXRVXVdcn4kMWFqimBGZIkF0Vxu9komipJ0t3trWEaXdfWVXX3+e784hwhYbvZC1AQIPBcW9f1pmkUWa7bIvCnnHHhSkyTWFUxEAQRKbpj7DY73dBJT7qmn4TTKDrZhr3dH8bj0LSt3W73849v94ejImunY1TWj9PJFAgQSZgx9v79+w/vP6iapihqEIZ3d3d/+ctPiiJrmrrbHzhgmq4qijKajpIo1QzNts3n7RGlVFHUltK26y3bxggLSGCc5VkThiHGSNdVRtlhd4BIoHSoq6rrCRZFzlmapZquSKJESIeQYNuOqqtYQodD3vQNEkrbtYaBPDw9GrpZ1TUWkR96h/0h22eUUlmS94e9gOBhc9J1A5dF9fH9R8uxRBHneSErctf3RVnsdruqrtMsK4pis17P5nPGmWXadV2Zhi4pyna9V1X1dIo0wzBMPYqjKIpNS99tN3VVzxfzPMsZY1Bg/tjr2271ftW0LfgADNsEHLRNAwRoWIZpWoxRASPMxKFnVVMHYfAM7xVFyXJNMtBnfJjQCgMlnPOyKvM8P0aH66vrLMt03UAQcEB1Q3laPe72Ow7gH37/uz/8zR+qsm67ljKmG4am6WVeDoR0TVNxsFwuOAR8YLZtF3lmWCaGSNFkXTdsy9INnTKyXC4HRvu+tS27rKvlcvny5YvH+6eyKDVVFaCQZ9nd3ee27Z6ZGwrUPM/HWOpJr5sGZbxpm7Iso9NxNBkZuvbw8DSbTT+8/4AwMk098IPoEFmWZRuWbVq6YZxfLL3Q0wyzCsuqrHzfr4qy7ztVlZMoKtIcYxgEwWw+2e+PnMFwPBpNRgMlx8ORkKHIiiiKHdsejUbhONxtd1AQyEA+f76VZZlzyhhLk/Tly5d5mf/7f798fHiaLc4uLy6Pp5OqKZyxzWb74ubVbr9VVXGz3QSBW2Z5VZSUEUqo63kDHbIsO56OlukMPUEiKrI8z4vnwC4SBNkwBQQBZ03bEEJEERdZXualH/h+GCBB8Hy374llW1VZSbLU9d1mswlHoySO3394f339Yvpqst1tH+8fDdMsT8VACBSE/Tb5+P6TJmtNVdu2NZtNJaxsN+vT8Xhxfo6xeP/z3UAIwjhLEgFCSZaX5+c96VzXNQzj7dt3AMJwFIzC0Y8//nh+dh6ORoxxTpkgCI/3j5fnFwDAIi8lWdR0RZRkxmieZ0Veapp6fXMVR5GiKEhEXdclUdy2fZpmlm0sL5fr1Y4yGkeRoqnDQLu+K6qSkoExFYsIQtR1XRTHumHYtrVeb2RFJj2VJVUS5bqsFFmtivK//pd/OF9e8Bf8FB0no4kqSYaumYaOkJCc4uP+EASBJAqiJE7Go7Kq8qJQVTnLy+l0JiAhOp36nuiGRimt63qzWsVRbDu2aZiyqk7GYyjA9Xojy9L58qJpGsuyAACmaSiyXBT5KVI447vt9hmrJStyHEVs4LZjGoYZJ3EYBNEpqtoaQqHIC4SRoii6YSCEJVG2nDd1VWm6bmYmJYPt2HEUU8bD0ajvSV1Xju0YhjlQIorSs7m6KHMRi47r6LrGOZ/MRqNRKEoYQtj3bZEVnIHpdFLX9X33JGJRVTXP9zar7TODC0CmGappGo8PTxDy+WKmqPKz/pOQvqoqsh80XTFMg/Z0oKTviSAI0/HY1I3xdHw6nZIo/uGvf/7i9StTMxRRLvJSVzViWbefbyVZjqJ4uz/YlnV5ddF2vSAIlm3zNdU6AAA9PklEQVQBAUIInx7XTd00da3oGqCCoqmaojZtg0VRZmygg6qoL16+kCSRDjTLMk3W9DPjdDx6jk8o+fTpUzgaWab16tWrsiw+fPioKEpZFYauIYSPh2MQ+iIWO97JitS0XZkXh9OpKioAIX4hGLpVVYVlW01dZ1kKAGiamgwkiiJKmSAIt58/dV1vWdbyXBIlXJYlQqhuGogEQnrLNGzLJH3/4tWlrCpYwDCKY0EUSd+blpWleZ7nNjG7tuv6rmu7YRjSJJNEiQy9YRiyLDMGhn6QFdm0jeMhytLMdZw0TT3XdT13IMNhv1+vVkVR5nnx8EAd1xUgFARUFoVlmFRVMMKGbtRNM5vMdrtd17UAAsDhZDRWDUXVNdf3dttdHKfP0hJRlPIsz/LctszxKPzll7dVXeZZudtuTdPMs3y9WaZxyjmbTMaiKE5n0zAMbNshHdnut5wKjmOvn1aQg9F4dLY8Y4yallEWtWpq4Tj88H+8BwKyDYtBWteV7VqT2ajrOgA4BNx1XNd1y7KK09gwzL4neZpLskR7Ulclo4Pnuaoqnw6nbiCqJhumaZgGp6wo8vVqPRmPNF07HY7MdyRRiuMYQkGAUFFUSqiu6+EokBUJcNAPw2632+y2EItnZwvHcW7vPkfx8bA/Dn1bF6WIxevry67pPn78+On2HsuKpqtRFCdJ/Gy3kBXZNM3j8ZgXBYKYQ2Y59jOFX5LEtm0NUx+NAtu0XNfBWBmHE9M2DMtsuzrNUlVRR2PP9zxZEW3LJqQ7HnYYo9X6YTyahIH/+PS42+2iU2Sa1kCHshoEJBDKeNMOhAaef3Y+7/ueMAIBL/PKMDVZlouynE+nkqJompplWV03pygOQi9Ls7qqoQAFAV1eXazWq6qoyiI3TE1XNcM0SN9ROpRV4fn+b37z/Wwxy9Os7WqExaqu7Kndts0v794JgjCbTSVFUjW13taMsa7vRSxdXJwfjgfAga4Z/+k//a/7/eHy6mog5Isvv7y4uJQU8c2XX/z4w484z/zQdzxHllQBoaKs57OF6Vo//vBj3xHD1CQR910HOGjqRlVUUzOapp7Np0hEb3/5cNhHHPC2bZGAu66ty5bxQWAAQoHTgQKOsAABjE5x1/cDIUEQyJKoaWroB0mSUHO4ub5mlF1fXt+8fFGWpZhLAxnC6bgsSs5A3w+e72IRnZ0tHdf+9PlzVmRffPnGc9y3bz/EadJ2Xd/3lFJFVSnjVV7kRT4whiXc1C3pB0mWlstlHMcQQgiEOE583/M8d78/cMabtuWcA84HOuiGvt8dVqsnSZbqqn7uQi6XF8vziw/v3x+OBwCEpmlNy0AI66rBGB+GVlFlz3ZtywYCqKpGgEKe5ZIs9h1Zr1YDGTrSO44jihiLaDGfVXX9+PBQloDSXpIEyzaHnlRFmUvS2WIhYrHIy/l8xhkcBmKZyvkSSaoYH5Oh123bdj1HgEJV1UgQMMa6rv31x0+qokWnKBj5zyaysqj6vlcVJTpFf/N3v7/7fPfDX36AAlgsF6qmpXGSpul+txuPx33b9apC+mEyHZdVKYrimy+/XG82Tdusbx+3aPvy5eu260zbmEymcZQQMgSBf9wfdF0/Wy5Xj6u+7Tljfderijyfjz9/vqWcSaLEGGWck36IkuT66lqWZMuxSU+++sa1TLPrydPqqWnq7XbjeT4ZSNvUX371provqqpSFSUrsucoxzDQuq7rthIgurt7cB1XlsQoiubz+UDp6XRKs9S2bcdxOOeSLJ6OJ4RRT8nheBQEJCmSqqgQgN1ud3t7Rwm1PZMQ0pOeMIrTtFicnc3ncwih7Tp3t/cA8A/vP9y8uLm8uthutu/fv3ccW1blw8Ox70gQeqZpAAhxg/tuME0TMNp27dPjU5WXeVbUTSVAaFmWbhhd1zV5Y5vWdDaVFElAUBAEOgwP9w/L5flsOtV03Wibtm1lUeGAtX2TbbLj7jiZjp/NTXVdBaOQUfa4euKch2HQE3J9c3XYH0Zj0rXt/eeH2WJumwaCUJblMAz/8De/r5vq7vZps1nXTXPYHV3Hmy/mo9GobRtRFMnQYYx2+13TtrPZXBTF/+F/+Lf74/7y+gJAgZBOlGXOeDgKTvujpEi6ppOuN3T9cDgUScYpRYJQZFld1aKER2GIRcSZUdcVwsL9p1vNNOezhaooVVU5jkspRYJwfXMzmY4ta/P09FRVlarIo9G4KSpkClma1HWj60ZdlU3TlnXNBbTdbn/329+sH1eMM9M0tuukKMvf//43k8nk/vY+zdLReFQ2XRwlx+gYhsFsNs2StGu6Is+zNOc8N00zzRLX9UzL3Kw3uqHNF3NBEBRFFqCgyLip2s12t4Bntu0osmTpBqH9w/3DLz/8LGvS08PDxeX50JPffP+7F69e/PLzz3XVaqr24f3b3//u77I8u7+/r+qKE6ZpGmNcQODx6VHAMAhDWZTLsjg/P1NUlZC+bupTFLVd5/t+liVFViyWZ3/5018FDLq2Rwg9PT7tt1sykL7vfvzhh6apRUmEAEiyBCGAEMiiZNi6JGMowNdvXp+OsYjFpqnjNH716oWiKg8PD34YkI68ePXqt44FOIziCGNxcXZ2Oh1e3rxUDZUzlibxfn9YrTbx4aTpqiAI+/2x7eqvvvxyvXp6cfPy9RevtutD23SWA23LKNKMUAYZF018cbFMktQwDIwxg1SASFHUs+UZEsXVatW2rSzLru+oknY8HTkASEQQADZQURIFiFRNlUVZEaUwDCgZur6jw9D13Zev3+RZRungOGYaJw/3j4wOpqZyxnVN+/jhg6ppsiIVVTX0RJTEoe/XUXTcHbAkAgA5h3lZPFOSiizrB+J73vJy+e6Xd9vNbrlczmZTgATTND9//qzpGgDcNI1yV2RpVpVlFJ/G4USUxeX58uH+4Xn4LKsygujlqxdN3UNByPPctIzL6+s4TQRBeP3m1adPnx+enr7+6uurm8ssTe8+3zVNBxgrymI+n/mev9ls27axTDsv8oGQsqwkUZxOppIkEkIgBIvZbLXZPD7ct00bn07L87PJZPrh3YfFfG5alqEbSRJbjp1leUfI8nIpy9JXX3+dpcnbX97KpdR2TZ4VVVP++3/376fTyYcP7zgbdNPK0+zTx09N3Ywmo67rD8dDEPj/+//nf7dd+/LyYr3euI5bllVVlowOX3/7dRInaXLKs5xSmiV5OPLni0WSZViSN5vN1YtrxuhASTgKIBSGbvB8d78/BH7w4sWLL74Q9/vD7//w+2eW7W67XZwtRCzrmr5er9brtWkamq7ZrtN2LQdUVpR/+ed/8QN/NBppml4U+b/+6V9d11dV7dvvvtlvd5IoyqIShH7dVOvNKo6Ss7Mz0zTKsrRtOwyD29v7w+FAuj4IAt930zRxXXc0Crf7nWmaNnaOu2NTddPJFCHUk75tWtf157NpVdbb9S6JY8u2ZEnerfdlUQIOGGd4GIjneYRSzlie5ooiO94yCAPDMpqmbdpWNwzHdcqyxAiblqmqSpqk69WmyAtV1yzHev3qi91m7Tq2qsnT6Wi73R3yYjKdChAWeek4znw+q5rmsD8YuuF4Tt+T12++kGXJsPSu6yzbGoZBUzXbtX758RdJUZbnZ6TvHdcRJZyneVHkbdOahiFJomHoAAhJGn39zdeM0uPpxAYOALBc582bN//5P/9vr754pem6oqppUsiSPAzsxYtr1/NOp6MoYiSgX375xXVsAHiRFY5nJ3GUZpmqaLbjKKqaxOkhOgSBf9jv+36oinIymhzYXpZlEYm2oQ/DgAVBkcWqIV3fLJdLhAXSd37oKapUN13o+6quC1Co26quqrbpRIzGk/Ew0PXTtq4b27LPzy+yJFUVdRwGfUfyvAQC74euaRqEsevbVd2ORv6nj5+wiD3fzdIkjpQgxJqmJ2nKIL++vlEMXVCkrhluXt2cDkfOgWEZj6dYEmVN1aAA66oGXNistrM5dB2vLOs8KyHgb1c7WZJeffFCkiTPcwLf/fzhA0RQkuQ0rVzXSY5xlheGrmMkT87m/8//1/+DC6DvO4BxOB6F4Wg8DQbee74rINh2bU/6i/Pztm1s06LD0LaVqsqWZZqWaRjGwCjj7POnW9Myr6+v0jTdrDc96cJxcDzsERYGMniuV9UV5BAhYTpd5lnWd/0wDBwC13UX8/npFD3er0UsQgEyQB3fDDxvs9nZti1AYbV6aptONwzLth8eHpuumYxHoiKdL5dVWa1XT6fo+O3yu+P+uLu/T5N4PPYpGxbLWZEX682TZejv3/4SjMZ3d/embbmOZzsuBALi+Prm2nOsYRi6vqurig4DJYMAgKWbnAPLtiVFyfLccdzLq0tOIeW0ruuOtBY0q6ru2k4z1MDzVVXVbf3T+4+KrDVtM5/NJEnKs3yxmFuOOZ6O99ttWdSM8aub85/++uPPP/3YVo0o4/V23XdEFJHtuKoia5pmOfbsbLbf7T9++KQY+t/93d8PlL1/+xMh9PzizDQN09S3+33Xtov5FAowSVLKWHdxGYThM/8DCvDi4lzTdMs0Xc+xTLNt2+1mretqmkYAcAShYWiyJLVN/+vf/opRHsfR6uHx1esXkiSLonh5eTEej/qu22+3kigLUOB00DQNY5ykaZEXo/FoOpsIUHh4eDAt2zSN4/H49Pg4GoVFnnekn06nAgQYCwBAQzMEgCzL/O7X39ze3l5dXdVNPZ3OBCgospKk6d3n2zAcxcd4tV2dXyyHnmRFphsa5cPHzx+/eP3F//Q//09ZmpZFGceJLMuKKtuumURpVZWe7yIBd23HOM+S7P2HD7/57ffJKbl/LOIoffn6xXalplnmek7fUcqGoqrqqllvtmdnS79t7j7fheOxokhPj2tpLIlY0jVdVeQ4jizHFhA8HvdpmgpI4JxDAOuqFDHWdb3vesu0HNcuiqIqq1/evhWxGI4CRVUe7u/3u51hGN98+40AwX53jE8RFrEoYcoGjBGjnDM4nU5lRRUQ9P2wbtokSb/55ltC+jROgyB4FlSkaUYG0nVtLVfn5xcjP8zz/LA7QQRkRb6/fxQxPuyOVVnZtr1YzJu6LspyMpmWZplnOYAAIyhxAggkrmsDAFGH+6ZTVBUj3PTNeDbuu64p274brl9eUjqYlq2pVDf0uqkhRJPZxLXcOBF0U++7PopijNFoPH5WkgZhYNl2luV1U718+bIoiqZtJuMxZbTveoSw6Vnv378/P7uo6iqJk8XZWRRFx8MRSxhjLInixeXF7efPnueKophnRds1tmWriloW5UAG27L+7m//Lo5jURQ1VXv54oUqK03ZOK5t6FqeZ4wBwzDqutpvDsfodH11paoKp+wUnSbTMWPDpw+3RVUtFgsIoSzJHz98lBTJdeyqqizLdOwZoyzPMkEQgpH/nJNTVUUUMRIFVVOC0Jck6XQ6aaoKGHv24lJGqrI7RifHtheLeZEXuq5TSuMo9gPfFE3GORDAZr3tfFcQoK6rSZIkcVrVlaRIECJNN/b7nSKp4SjI07wum/OLpW1bdVk1VTcMrCOENa2MgCRLf/zjP5uG4TpuHEe2ZTHOFFVtqrptOgghgABLoqLKGGPXddu6/u7X32w3GwyRqquyIiIBdn2bJgmA4PL8kgkcQ6Eoq/l8hpGS5XHTVI7jm4ap67qiqNvt4c9/+nNaZK+/eH3z8jrPijTNNFXRNY3ZXJYlRVNELDZ1k+f57e0dEhBlQ+B5lHPOwHQ6kyS5LHJN1xVJYYzOZwvGGcLo/vZWVgPbtg6HnjUMS5gz0Pe963uaoadxejicvMAZhoFzcH93/9vf/27o6Xq9SdNUkuWmaW4/fyrLmg2DIilNXf/58Oemadarzel44gwgJCiyJMkSBHwyGT0rdK9vruqq4oB3XQs4POwPlNCqLGzbTdJYkRXA6Hg6KqpiNB5pqpZEcdf3kiTv98fjMXZ9v6laVVE8zxt6djwe66rRDd3QjaqqHp9WdVOXjXx1fSXJ2Pz1t4/3q6auPMfd7jZxHCdJnERROBqNRyMIIcKIdN1oMinLXNEU3dAnczKQ4fHh0ZMEyoY0TRzbpWTo+nYynSi6ygUKAFAUlQylbupZVhRFqWrq//Kf/pc0SXb7Q9u2pmHFcTwaj6qyartGQIgzUKR5OBnJqjyQ4fb27lrTSE8lUfruu293+4OiyIqsyLLWlE1elKomf/vdt3mWHo9RXdWu702nk6KoAAdt3eq6dn5+JiCU52XbNYIAZvNZVVWyJI0nIzIQx7XLsmjbVtUUQRAYZ13X6Zp2c/NSVmTHcaq6Oh1OAyWnw6kqKjoMAMLZfK4pmue7gR8+rVYfP30SJVwkRZyc6rp+/cUXnu8eDofT6RCG477rsiwbTwJFXd7f3e83h+l8JqtyV3dn53NCCKNM0zURiR/ff2Q95QRsDpvNau0Hbl6Us7OFrsEoiuMoEzD8/je/beqmququJ3ef7s4vl0HgO44NAJiMx3EcpUmWJblu6FEUcc6qsppOJkWRx0lSV5VlWfvdwTAN0zR838uzYiBkNhtPxhNBxJIspXFStbVu6JdXV6NwUhQ5ZXS32eR5/uLVC9IPZVEgJESn2At9x/NtDhDCqioTMgAOP7z/JElYxEKaF23XIgFJEv7l5184B6PReDKdPd4/YFG8urrUDbOuq/F4HEXRfn/om/7Z8KMqyvpxs1jM8dl85oXe3e3dx/efCaGapjqurUiKZZl11ZBukBWFsSY/ltvVQZKwAPFoFBI2pGkOofDFF28Y44wx27aj7qTpWt/1RR5DFVKB9mSQRQUC1LWk64isKFjEWZKZllkV1f3tgxt4qqxmWaaoSnmsw1FQFMX9/b2qKr/7/W8RFDRNWZzNIECH/YEQIgpiV/eO5ciSzDjv+75rO0FAhmk+Pq18P1Bk7XQ6SooEAMQYi5JUV3WSZJIiXV4uD4f9eDISMeaCJ4rocEgAAhzQtms1Xe9J5/iOIMC8yNI0YYzWRS1rsgCQqippmmqa3nZ1WZd91xVFEYQ+4KzIc01V8ixLs1QQUNf2UEBNWTdNK4uyojZ1VRdF4XmOJEuyLKdJEseZpitVWQoQYlHQdFWSJcM0tVZhlJV1NwykaZvQG+m6MZCh77qBUDpwhMTx1MmSrKk7QijjoMhPj3dPiiwjJMiKIok4ibOmbueT2SicNH1L6fDpw63nO5quZmkGAR+FPoYQcMgpJW33EEUCBMfDEUnwd7/9TU+IAKFRVLZp9YQ8PO5H4di2PCTCKE7CYPyPd3/0XN+k/PbTHWNsFIw4ox0hrmU/3D+Go0AXjJ4MeVEoqmwYhuPYEKL7uztN0/7657+MpxNZlqqqzbL88vIyOp0MyzQNY7vdfPvdrzRNfU6+B+dhOArKstxud7/88MtoHDZtFY78uqwb0MymUwyEtu40TTd0nRCyXm+SJBZl6btfffMMEHZ95/H+SdXVYOQPhKxWj4uzM8M0JEVmjKuq4rr+dn3ggHleKAjiYb9Pk1wzjPcfP9R1/e1336qGDDmI48h2DMsyh4GUdZGkCQDQsi2MRQDg/Hxxe3e/2+4ZAZqmzmfTrm3rqvZcb7FYBF7Qs96xXcsyRSwmSdKShg3gw4ePiirmeQYAj6I4HI8939c1jZAeYex5wTAQz3V1Q3NtjwxksVjoup6nmWiIz6++1l6QjjakG3pq2c5kvhBPUddSJEhplh0O8W57mkxGiqIddidDt5q6tW1HwhLnoK27+XxKeqKqahh4UZROZlPbcsqy7noqKZrrBgzQtu6miylEgq5rq9UqDEJV1T0fjCdjACGnHEKh7RrGqWGZh/2p7TpJwl+8eV0WFSXD/ae7JE390C/yYuiprEhFUSRJKotSVVdJnNZtdXV1LYqipqn77b7vO1VXAeR933333a8fHh6eE65koJyDcBLIqmjZhuM6T49Px8NREsX5bP7i5StdVZI4nc0XjhOommya1vnZxf39vRf4VVXEUTKZzrCAAOf213Z8jLu2o4wqiiRNgyLLNVXzfH/7tLFsG0Jw3B9tx4mOJ1XV5vM5IaRtWk3WAOT77VGUsGkRzoBpm5DDPCvqqj6/OC/y/O72QUCCJMqe5/V9P5lNyryqyvvr60vbNlRD/e9//NPNzY2iKl3X2baz2+w55bPpPC/ysihIT4qytF1bU1XKhqIoCKGSJMbHWACo77vVes0Y91yXkE7RJIzQfrPDMjJ1NYrTuqYACG3btS3p+6Gq6ywvTqdoeb7syXA2X3iuJ2LMBkYpzYrc9wJFVdqqwqNRCATgem7fD31PTMvAGDdtY3FTFHGe5aQfFE0+vzwTBOQHbnSK9odjkiRZmvV9//H9x5uXN03TpEm62+/OFgvG2PJ8KUnyQAYBCghhidIkSxEWRmE4MCJgQZZkwsjj6rHfdd9//72uG2VdXV1fllUVBAEh5BlUYhj68XgcBiIArChyEPiAQ8PQdd2glHVdJ4sKpUOaxqfjsayKxdmZoqmH06Ef+qpqmrbBGEmSTGmvaliURQZo3VTn58umr9uuJUPXNE1ZlhdXl7Zt94SIEm6bRpS0cBTESYxEJMniMAySgh3Xfnp87Lq273vOmaJIgHM6DJxR3darokYCkGSxzMum6xRV9nwfQShAOJmHcZQwxpqm/emHn1RNqZpmMh0Fvn9794kS3rW9ZqiclhwyVVZFERFCBAEpijqZTPq+Y5S2XWtYpiSJjDKIcHSKFE0jA2367ouvv+j7PokS27Yxwk3bM4EjXdBs00b26XQSAQtGQVFkvueKkpClqabrA+n3u11ZlAhjVRXD0Bvo8O7d2/PzC86o49hpkoqS1JS163iShLGMsiT74+6fAWAfPrz/7W9+WzZlmZf90EVR5NhuhdBASRSdTseTbmkI4q7rdF0nw6Ap4nK53B92VzdXXd83VRMGQVWV796+832/LIssTbqOyFKNMZ5MJnVTm4alaSpj3LKsZz0v6QZdM9qmvry8tB07idOBkLapAYeyLNVVJUAoS1LXdpqu9j3J0lzV1DD0NVV7/+59ksQAAk55XuZni7Ohx4f9jnOaptl+uz+/XKqq2jRtOPIZG25vb/UP6nJ5JmC0P+xkWSyKIsvyrm9ty6WMWqblOp6q6xAgAQqAc8YGEWl5mkkitqcTAPh2vfE8rydiGsWKJB/LU57nWMCqrZ5fLNu2Wa3WjuNOJhNZlsuigkDAGPU96Xti2yaHIEkySZaqti7KkjOuqLJhWKQnAxk4AIzx2XSGkGBatohEGctpmliOHTTBf/vHf4K2kyU5FjFCEAJw2B9URVFUJRwFA2UDpZ9vb/GTbBl6MApGwdhxPMZA4IenY2zahqoqP9z9wDgzdXO/OyCE6qYSkNA2bZ73iqzYtq1q7HQ8uo4DBVSSwnXspqnropJFkTIqq3K9ra6dK1VVyqJEoqWqSpHnJQSSJHddOxC6225dz53NZtP59HjYc8ZNy3w2J//mt99vN9vdZgcRSrNU1dTl2dnABkrodDwJg2D9tNpt15PxND3FkiR5nss5r4q6qTvPd6bzCQBgsXj5/t3HLEktx9JV/ePHjwBARVW6un9x80LAggBhkiayImuqhiW03+0ZZwgJnPIP795dXV0v58uyKsuqzLNss117jhNH0WgyVhRF4LAsqsury2GgQICjSZglme2YjuesnzaSLKmKDBFkkE1n0+1244Ve13eqrozCcBgGP/QI6T98+IBFLED4HAN9fHjEGBu2ASFM0yychAMnAx3KuoQQMDo8PD40Vd3U1cXlheXYnHNBgFjESVpWVW1ZVtM0bdu6rmtZTpLGjuNpupZnWVPVGGPPdaEAvMCHUDi7nD/dPmAIaV03nFJFlbI8gwJ89WIWRfHqcQ0heP/uvesFlmzavv3hw0cB4/v7teua4/F4IENRFEVZ7nYH0tOyqgWA+p4oskK6oSwqAGBVN5KIx5Op63mfbz8xznVDNww9ibKuJZQxhFGSpD2hbdcWed53JByFHek457btrNarPEt9z0viVFVlRVUwxNcvLsui4hzkGa3qWtM0QRDKKlVUpWv7JI5lWcqLPAiC3XbvuNbl9fl2u16vUt3Q4uikqBJEIAj9dz+/PZ5OT6snXdcFiCCEk+moqqs0AZqhUjo0q+b8/MwwzLZtFUXRNDWKTpIkwWcTHgJFVsiKKD2PKyXU9x0UEELQsnXXdhVVe/f2HeNkYZ01bZNnuaRIEEMBCQih0+noOl7bdiKWEEKWZfd9L0rYsixRFP/6w09t08AbQAcqYtF2nP542O0OsiTquiFKkiTLGGMBozAMERKSJPnjP/6z63uz6RRA2DTdfncCEC8WCx5BXTeeVk/T6WSxXDw9PQyQKqrWd32R5aKELy7OKRssy/EDl3OIJVzV1e3tre/6VVV6ngchnM7Gb9+/d2zrcDpBzv7n//A/fvp4azmmKOKmrulA7+/urq4vR+MQC/h4PFqGbdnGfn/YrDez2fQ3v/m+LKrpfKrIyu3nz1jClmXmRaaoiiRJjw9PrmsXReU5jmkYdV2fLc40VRvoQCkFANR1XVWlJCnv373/+7//ewiENM7KojQ0Y6DUNHVRRN9++81hv/dHoeu5WZ4jQTgejgI2q7KKo5Omqa5nAQarqpyOJyIW8yzViDEOR9vNxnX9qqwvL69uXgi//PwLRuibr7/qSavrmmmZRVE8Pa0XZ/PnGmpR5vv9AQLh+upmPJ0IgiBAaNuWpqoYoaosurbVVJV2RJHlPEsZB6Zlpmn8rODI09x2bV3X+r57+fKlZdmOY5OetG2j6ZoI8Xa9URSVMSOJkzTJFmdz07QY5bZtFnm+3mxHgR+GIca4H4iqq303NG0DAJ/OxrZr7veHvu+//u7r4/4oqZIiK47rRlGs62pRFmVRcTaybPt0iHRdT5JkIKTt+/PFBcZSWdemaXZdLwDh84fbuq66xqCEAQBcxxYENJlMRmGYF0XXdXQYyrw0LWM0CsqyPB5L150tF8s0Tbq2U3UVMDadTLq60U1jMZtDJFRVHR1Pd3d3qqb2fUfZICCkqhrnvOtaQUDzxYKD4fHuUddUTVVI3we+u91vby7O67quy8IwTdM06rrGGEMOZEV+un/gAJ7Nz0SMPMd1LecQn6qqHejAOWUMeJ7HGFM1rW0aUZI0VWma9ub6ZuBEk/TH+8fJdCJiYZOlQoe6rvc8l3Nm23aWpWToFVUxLeM5PPLdt9/leda0jabrpOvTOFFUeT5f0IGpqloWpWGas9kMQsAo44BvNztJxnmeh6Mgz7IXNzeGbqiGnsTJfrfPs/z6+jpLs6oom6ZVNSVNU0mS7+7ur19ceaH39PAUnZLJdOIGblEWpB9szwlGQZHndGChH5ZVcdwfoYTCcHJ2dv60WgkCwiK2HOdZQ/v7v/3D/ecHu7ezPPVcz9DVpm7qqlY0OQjCIs0Qwni1XgtI8L0QCsIonIiyJGI89KQnnet6i7MzCIU8Lxlnfd83dRNHEQRMFCXKmCTLTdtKohScj3764QeEMOXcdtz4lDw8PkiS5PvBLk4urq7CMKSU5kVWldXD3f2XX32JK7zfHkQJ65qp5sV6s/Z9nzLWto2m6Ukc/+u//lnXVSQgzpiqKoqikp6kZf727bvxeIIEJKuqgDClg2VblLMw9M+W57/89PN6s+m7HiF0PB04oOv1OsniviWT6cjx7CRODvvjbrdL0jROEijA2WL26s1L07TWm812u9usN8PQX99c/+73v5UkaSBDXdXH4wEL6Gy5OB6PVVkhaQQ5gAIwLQNwUFUVoQQATkinmzIWpYENAgZX15dpkooIOa5V5pWAkGmY6/UaQPDV179Lk2zoqaZg27EEQSjzqqdExJIWaK9fvjzER9M2Lct0XTeOT3VT0YGqqgIBwggLgtDUjYBRUVZZnJR16Qe+JIsDJc+2sq7rmrr5+aefwlFomcZ4HFqW0XTN6RSFoa9bGuPM8mzD0JM0xqLYdg1IOOdAkuWBMUFAHIAgDFRVpQNVFPn1Fy/+t//3f3a80cXV5RdfvoYAlFVZ5mXXtq7nWLbZtk2WZefnyz/8m9+1Vbs/HLM09VyvyPK//uufEcYfP3z0/YADpusGEgTbtpMo3jYbhHAURZ7nr7fr0WS0WMxJP/z4008csF9/9304DrMsu7m+eXx6uLu7C0ahJKOf/vLTYX/glGZ5DgD4/je/Gc8nWBJFUXx6XAHIT8fT6XiaTMb73cFxrNevXp1O0Z/+9K+c0+9/831VVT//9Isfut/96lsk4//jH/7h4vIqiqK0SP7u3/xdXVf/9R/+4euvv/r5x1983//666+3m7Xr+1Vd13Xbk77pOlmS15tNlhfT+YRxUGR5lqV/+MPfOK5zPBxVWUmzbLPd6Ibu+2HdVIdDNR6PetLRgQ4D7ZrO9/23b9+WRXF5eYEQfni4dxy7aZrtbm9ZlqZrpm2RYbi7v6+rRjdURZXJQCfTycXFEnDQdi0jfCBD09aqphPS1WUZRclAaXQ4PT0+YizFUTyeTMIw8Fyq6frjw8PibKEo6mgcFnmORfzq1evVatX07fWrazrQwAuKshiGoarqw+FomKZu6OcXy67vnh5XRV50bSdJUtO2tuPomqxoysPDw/F0GoXh737/2zpvuq55fHjoST8aB+PJaD5fQATubh+6vsvz4uPHj5zz65sbDtjpcGq7rizKy7+9TOLkdIom49H55Rml9OzsjHR913Rt3TxvdZdnZz3pn+vci7Oz4+E4DGQ2m8qSRNoeY/z73/3u/bt3oiiPxiNZVYaBN2399u3bYOSPRuGnT5+kg6ioiqxImq67vrfZbMqyVGRFQPDTx0/jUbjfHjRNwwjnef79b78fBuq67ul4GgZaVdVhv4cCePHiBULCh//2gTP45ssvMEKKokzGE1VTVUU9Ho8ChALkjut0Xd/UtefbpmEIEJqG8e/+3b8FgPX9UNeNpmrTyfRX3/3qmZf3/u17WZablgMoNG2NEGqbLs8LQuhu9xgdT4yy4+5wOBy7pmvb9vLq0jLNIi85h7P5QjO1vutkVRZl6fH+qWv7qigFAa5X68Dzm7aJolPf9X3TX1wtg8C/y4siL7uW2LYOBQD/7/+3/+vxFM2m0ywrVF1FWKyyWpGl88uzqmqe1o9pmnVNDwVBECCAfP20kiTR8TxNV7u2f/PmC0kUJVm+u7v95Ze3um44ll03dd00l+eXX3zxxjD17W7HOOUcAAiPh6NlG37g332+++sPf97vjpIsu64zULY8OyPD0Da1YZrP0BjXtcui6klDGcuzXNNUVVEVWTEsEwB42B2CwE/SRJLFMAjbrn24e1hv113Xx0miyiqE8PxyaVn2evU4UGrb9jAMp2PkeV6WZwhh17P/+7/82TTtL796Y9tOXddt21BG0ySZTMYCEKqqAhxIWOSQL8+WAuK77W67XXu+hxCCEEwmY8podIwghGmWNnVj6PowcAYFzrlt2QghWVZWq42ERcfzyEDyrCBkcCznWYalaXpd1qIoSqLouB6EQk8aSZXnizmEQlWXhmE83D+UZWEa5kCIpMiyJL/75V1PBlmWs7wYyFCUJRmGtml1wxCxaFj6xeWFZZqqqkkSLvKiqkoA+OPjI+BckkRKKaNMVRRd1yHgZVnuD3sRiwACTVMdz0vjNI4S23U819vtdsuLpWWZVVVBhH/64Wfd0E3L0jRt9bRKkmQ0HjmO9eMPP8Vx6vnefDHP06wsSsM0JpPJfLF4ur8/HI7haIQQenx8SOKUcy4r8nK5nM/nRZ5//PhxPBmzZwbk+XlV1VmW9T2ZjCeWa3HKT6dD07amaXiulyTR48MjhPC4P8iyJMvK8uL8ecwVjkdt06ZpejqeNF21LOvTh09FWS5mC8/3XNfRDf392/d5WQyEUkZevny1OFscT8foeEIY932n68byYvnHf/onRrlpWHmeyrLSE+KHIeD8/YcPt5/vmrqWVTUMQ8aAbZoQC7ZhUUbfvHkjiujt2/eXlxeKoj49PUqSpGjqfrf//PnzfDH//ysqu2GxOAvH/vFwVFXND/zPnz5XVaVpGueckJ4zIEp4PB59/vxZFEVJlqqqdBzv+uoiCIMsTYu8fC4SilguyrKsqizNBAGF4aht2yzLoQAkSY6jGGOk6drhcLy5uS6L8uzsbH842LadZzkZyJdffV0W5WazdmxHUqSu79MkzbL01atX2+2mqevJdHp5cfHTzz9Fx1NRlq7nyJIiy/JisQAQjidhWdRVWWRpLstSmVeiiEnfK7IiKVgz9PVq4/rusxpEURXOQZomYTgKQv/u7n6z3iCMfvXrX3ddV5blerXy/EBEyPd9BNF0NiE9abtmGIbjKRIQFCCs6+bNmy/SLE3ixDQM23a7ruUUOLZdNw2ljAHedf1qvZ7NZ5KIm7Z2HGe/32Vp6jhu0zQfP3xanC2mk/HqaQ0hfP361dPqKU1SP/Q+f7wlAynL8puvvu4JyYtsPpsXRTFQOgwDGXqMcVmUXdd5rn+2XJC+a+rm4urKMI39dg8FwCjAGEEIbdvSdR1AmOWpaZlxFG2229Fo5HoeQmi/OwzDcH11XTft/f0dgqiqq6qqHu4fRuOwbdue9IDz+XxhGEZR5qQfxpORrqu//PROUTQBQdu0N5uNqmkQAAZ4UZRQAJPpZP20atpWVVRZkc/Plkma1G3TNT3CkA3McaybFzcfP35iA+16MhoFTd3hKI2d0DEcEyChruvTMVqenZVl+fnz58Af65ru2h4AoCiLJEooHy6vL2VZOh6iLMtdz5M1hQ/s4f6BkF6RZD9wu7aP0/hX3/16Np0nWXI47Zu6k2XR9VxBQIPrSLK4ul/TgbZNS2inItV2bMdxFUlGovT4eE8p1TStrEpJFtfbFR1oXddhGKi6hgVRlOSqakRRdD2XUHI6RbZlrp82GGNZkl69eC3Lat1UURw5rlPk+el4dBxnoCyJTkAQDMuo6opRVuQpgGA8GXdt33d9nmfRKWac5VkGISSEjkYeIQMShflszhjbH3aOZTmePTBCh0GRZVVTOONsYLqhC1AIQvdwiAQAkYBbQhhjoogBhJyx8Xj0rFQVILRtRxQR4GA0CtM0bZrGsg2ERcs0gyDkglCWCSFk6ImiarKEm6quy1pVFV3XqrLO0rwqq7KsRuMR5zxJM01XXM9RFLVtW8OyoACyNI+jaPX49N2vvhmFU8sw98c96fvz8+VAhzRO2yZ/fHhMk8R2XE2RBzpUdaVpqh8Et3f34P6x73vXceqmGY3Fr775qmvbqioxFjfb3cuX1wAKaZJlSRKGvogRhCA6xYvFwrRsWZY8x56E4ePjU1Hkb395u9/vVUVmnEqieDydiqKwbEtAwnNv47DfUzogJKyfnmzHjuNIkWU6MIQFQ9NOpwMUgG07gANNVuezmYhxkWVhGHRtF52Op9NJVVTG+fpprekaZSxNkqqq0iTpCbm+ukIIzebTy6vLLM8Ox8MIjueLsznkgiCsnp42m7WmaZQMpmmIsiwAYaBku97cXN2QgZyORyziJE1ESX7/9v1oPLJMy7asumkGQrabnaZro1GAMHY9Fwjg559/mkwn2+2m7zsAoWmanMPj6aRp2rfffoswytLMNE1FUYqiPByOGGEs4vV6AyEMRyPAOBYxxrhrW83QSN9jhCkdzhY3APKBkYGysqwwFpfnZ23bx9EpDDVZkY7HIx0G3dbI0H38+IH0g27oqqpAAfSkBzXHGIuS6HoOh0CWxbZtrl9cF3leFtnhcLRtE4uYDpT21DQMQYCAcywiUZKKPB8IZQN7DrOvVxtd13TDkBQ5z7KBDoxSz3UgB6IkQg4opUiAhqk3Tf3MkDgdotEkHMgAAGi7FgAYx3HXt1jA5xfnvu+HQTgMQ21WgiDEUTQ6W4zHoySO0zTpuk7XNVVVOKNZVmqa6vt+XZdZmvZdJ1gWpUNV15xyVVMVVcFI7AnRNN0PAsqoIouHI/2H/+9/RQiZtlnkxTAMi8Xs7tNt13SME8Z4FEdt22Z5hhDyfY9zLmFcVZXrObv9NjpFD4+PkixeXVx2BCmyOhlPHMeVZSnPciQIYeDruiZh0batgZKqqKuyUFUVIaEsCkmWsADzJD0djmyg68fVfrd3XDc6xWmWUcJESVQUxTIsjEVJknRN22w2dVWNxiOIYNc1ooglUSSEVGXJGA1GQVmUYThVJGlgI1O3VF3N8xwASEhX5qXv+03bdF2nKLKsyIauN029PFukWfL08AAB36w2Xde8evm6H8jdx1vXs/Hj7dr2yjTKEEKSKOuGTjmLoljTVAaGtu36flB1RZCw5dp3959HowkA0PEcRmkYBIZqcM41LUciCsYhJWy33yZJ1rbN+4/v6qry/UDTNYjA6XSSZJnSoSpLzgdRxk3dUQK7jgwDhwBlWV4UxTAMx/JYZvnl1eX6cXU6HU3LlES8WW8oYxiLQSBURWOYBhDg6XAyDbtq6qbtRJkTSimAhDERi03T9f2pzItTHAWBb1smEEQkIIxQVmXDMGy3u93u4Pt+15LjPsryzHVciGBZVrZtV0V1BJAOAwDi6mHFAS+rilFm6DpnXFIUQohEpbwoIIAAcNPSKASSLJOeYFVCAx0Yo8MgSnJHuqIoJUmhlCFJ8v1AQBAw7jqOoshJkmqaLGEZYVx3TV4WXdukaSKpiqjgPC+hIMiqFMdxHKWGYfRdhxGyTLNruqwo+77XNK1umihO6qbij5zSAUJhPB0HvtfW7V//8tfF2YwzsN3uetJnaUr6HmGEJLTZ7rqhN3WjrmsGKKFDWVd5VnDAu64/nqLpfK5oWpwlsiQVRTEKRgiJbUtEWZzOpmmaHY7HgVAJIlFSJCRxA7Rtq+tmdIzyLLcsczKe0WGwbMPzfDoMaRLblm0a5mg8EpCQJAkhVNMVy7b3bYtFEXT9fn9ACDVtI0sKwkg3TdM2u75FIr69vQ3CoB96ylhZVQijKE4cD4w1GVCelXleFFmWmbaJZXGz22FJGoWhbTv7wyHP8/lsESeJoqin0zEMwmA0ztKsa9uO9IKAWEc4JVEUTaeTw+HIOJ0vFnGUrdc7SSFB4HMAFFULJmFZV5xDWVa7riPDUNctG2he5CIWvYA4nns8RZwxAQmEDNEpGo8nmq6fjicBIYSwJMkQlrKiIUHouj6KIsu2ngVemq6XedF2DaOMUqYZum7oWMIIId5xJCIsSwMheVWqsoplsShLzoCma3leZlmpdkzXTKBwDniVN1jCVVVROkzn0yROMcZlVcuiFEcnVVXaupVUWdM0hNB6s7ZMU5Sk3Xbn+l5PCKVAkiTLNnf77dXNVd8TVVUxFiVRUnVlu9shhKPTyQ/93e4gK5KEpLppMEKEEEoGSZQ55V7gYIzyNC3LGkIoCIIsyrv9lnT2dD5tm5ZRfjwczy+WjLIwCIo8p4ynaQ6AUNVtU1WUckkSBSScnZ+1TcMBOx5PRVEMA/v57VvDMC3Lms2meVmomjqbulgUOQUIoaapT6dj37aB75Vl0Zb1cXswTIMMvWNanFJV0wZC8yQ7nU7jYJRlWTDyL84vyrLc7w51VY+C0adPn3RTY5TvD0cvdCUFAwFwgYuqZAmmpmuUkCzPVEW2bOvD+/dhODo/XxxPkaxIbdvE+7ht2zzLLMe0FPOnH39SNSNJ0q7vZtNF09R5MXRd/0hWsiyZthHHSdv1ZGBlWWOM/cCr6qbIcsA5RviZlG6YBqOEQ9zUjaEZZVkpsqppSlnSruuyrNV0HXAgS3KSxE3bICw5jjOdTi3DZIxhjEfB+Ie//hiOgsf7BwQvsGJKcXwssuz8cskY3W13b398++LVjWXrXd9ywDgQmrazLKuG5dBTRVa90BYEmMe5iMV+6ADnkiwByAQAkCRMpuMsLk6H42qzmkyn+91OlETXd7q2xxJ2HLvrmufn4uriFMXJKSFt1zV101SUUQB4XVbR6XRxc9Gw9u7u1rYd3dQpY3y30w1TUiQIYdvVXUsoI7KmH5IqjqKBUVlVNU0zLcO27CxNMUSmY9S1Eh2jpql001AVua6rOIkhhHXVQAiCIHjx8rppWk7ZeBx0XZ/GSXSM6qL68ps3tmUWRdE0LaMDpTRJ0jxLBQQ9zS2rGgoQISRJIhJQWRYMwIH0TVWXdS1irGs6FrGAUNPWTdN6ns84ZICVdUE6ommqkAnxKSZdH3jO6RQbpon4kCYxG1ielR/ffZBEyfZtVVXSJHnGypuW4fkup5w7jiQrgiTGUbxdbwDnpq5jLDw9PjZ1Z7uWALiqyqfjoWlaRREfHleSjOMkruo6z3PAAQDMsK2yqhXFUA1DQAAJaBioqMhFkXMIkyxXDG24JaIkChCcX5zrlnI47S1rdjgeJEnyA7coiobWQRg2Ta0o8vx8wik/bHdtU0/CEcLIDTxNVTgHdV1zxj3Xm86my4szzjllQFWV3XonIbxcnrFhcG378uI8juPD4Xg8nubzhes4bCAfP3zYrDYYY01TKBkM0xCxKIridDqVZWW93mRRijCmjOqaPp2MvMDbbfciwnVZCqOxqil3tw/ny2Xb17/89B4APlvMsjwTMVZVpa6rzXqzWC6BAN79/NZx3CRL2rbJsvTFixdIECzLqKpWVWXb9Z6e1nXZ6po2Xcw2620SVR/ff1RUBWFk6rrvOtvVigKe59npeCzqKgwC13XG46CqKt/3j8fTw/2DH/iyotRlmWVZUZRB4Lu2G4RhXRaP9w8iRpxzVVdVRfI973A47LdbAaLFcqGqahonXdfphq4bRtt1lDSB70+Miet6XdtLonx9fdn1fdd1jFEAeZZm/TBMF9PV3QohOJ3O0iQ5nU6aroZh2DR1VTd9S/KicB0LSyhNU1VVVFUKAme32cdRYjt209Z5WsRJ8vLVi3A0QgIMx6P9ZkuGAWE0UJIf8ttP94HvEsYMwwxGARRA1w1tV4uyKKvyQFmaJOPJyPW8q5eX681GUuSBMT9wsyzvSS9gaBj6+fkyjqIiyc7PzyQRCYBxzk6nY0+6vu1My/ADLz6esoyG44CDIUuSgRLT0rKksAf7vu9Gk5Hv+OvVOk0z33V1Vbt5efVw96hoygtTv//8cNjtzi+X4/FEN433b99/fP/BMAz97Kxpa0KGn378WVawAJHnOnGSeL4nQCjLKoTcMi1GmSKrgPG3P72TZCnwHdM0GaVt21DKkCDE0YmxQdP1vu/SJO76vickieO6q/1REEz87eZwfX2dpNnheOCcK6oiYVHXVcrp6mmVJqmmK2U5bNfb6WzSNe3pGFmGjkWUpRkhg2WbkohPp1NZ1fEpiU7RZDZRFE2WRK5qLWoGSkzL1FQFCyiOY0M3CO3+6R//cTydmaY2HoVlUUAouK4pq+Lrb151VYPHk0nr9kNHg1F4++GB9MRxLEnEh/1JVXVFluM4IYwhgCljl9cXjA/RPuIcDgPhQPj84fbsbC5hkfRtmmRYRLPlfBxOkiKVYnk8GSVxGkcZB7BvW0mSiqw8bA9YRAACyzSZ40pIVFXlFEVRFGm6SUhXd01VFp8+3LZdBwV0iqKqabzAq6qKEk4aomkqxuKz2rvrDlVR+6GXZbk/8oukiE+xwGHbdpc354Zp9Ky3uPH+54/T+azv+sf7h7ZrwvHo9Rcv+r7nFJ4Op6ZtFVXqB6IZxvnl2WG3D8IQYxSf4iKvZFkKxwEW8W67K4t8Mh0ZutHVHUZIkRUBobpu6rIMZyPX9+5v7/umG49HmqY1TQe4gBF2XVszVTqwtm4Y6V3H0gwNQuR4VlPXLen7nnDIgtGoIwNgVNXENM2aurZsM9pFgoBUTTntjoIgWJZNeypAcDgeRVHJ83T18Oj67nd/+33dNpOJhyXZ9a0sztuuI6THGP7y4zskiXVV7TenKD7ttrvxdPzq9UvGwHazpoCOwiCNE0L4MFCMxapoBIRN3WTdkLUp4NzUNVK368dtU7arflUWlYKV6TdfLs9ommYQQ9M02qY5nY6eHzi+CxBzfefxfkOGPs+75cVS0zUOGOU9FODDw6NteReXy+lsPJ5N4ihSFWVxNq/KggNQFOVoFDqOO5qMPc8tivLhYa2rRtPW49lEFqVngnQap6KIzs8vdN3QDSOOIt0wREk8HQ/Z5/zi8mIynfRdpylGU7fPFcXtdosxVhRZVRRF1kQJ+Z6TpbkkysfdKU6jui6vrs+RgJeLsPbc+893ZdX4Qbg81/M8L6talZXpePz2lDRFQweOMcqSDCGUpznioLGaqmoPh4OsyoADLAhnZ/O6bre7A6VMeM6LMv7MD5AV5b/9n/9nkeUXl+eqLh+P++iUWrZBh3673fuB9+Lm6vqFn+WLD+/fB+NwOpu2dXfYnOJTdNgeo13c973r2rIscwr4wNumiY6RH/oACENP3NDFCDPG8zzXNe3q5rJt27Zp+46cnS9kRTIMrScE8KZp27PlXEDCcX8Mg6BrusPuaFtWmZaT2dhzna7pOWV10awftnXRVnW9mM9UVU+3OwAEWZKqojINnZA+z0rbsauqIWQYKFU02TSkuq6DkT+ZjrMsk2V5oMyxXV3XfNc3TL3Iy/12zziXRRFj0fODrmkVTYEQZnlelWVVVzevrwzNFEUsSQrgoChLx3Ec14lPkaKofUcMQ5VlPDR9mZZ1WsRJohl6UWcCgrKuG44hy9J+vx/PJ/4oOB4OUZJMFzMsirKqnF+dUTB0hCiMcw5006QUJHkRRfFiMbcsU9XV57kdQth1rL7vry4uKKNNVRasUBQ5S7Oe5KqM97ttlmSz+ZlpaqQjddNqmnZ+cbY/nvpmWMyXvj/2fD+Osof7R0kRr6+vJUmCAqQ9pwMdjULHMy9vLoeepHFyPB5lWZ7MR0kcG4ZCSCdiW5akPC0op5NJeNgfi0wTsazoak/o2BtPAFR1DXD2+dPdfrvXrzTHc8oiEwQejgLGOQeA86Gpm75jlqPDAfz/ACeG0olvlUwEAAAAAElFTkSuQmCC\\n\"\n },\n \"metadata\": {},\n \"execution_count\": 15\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"prompt=\\\"a beach with turquoise water, sand, and coconuts\\\"\\n\",\n \"negative_prompt=\\\"people, low resolution, ugly\\\"\\n\",\n \"seed = 32 # for reproducibility \\n\",\n \"\\n\",\n \"generated_image = generate_image(image_source_pil, inverted_image_mask_pil, prompt, negative_prompt, sd_pipe, seed)\\n\",\n \"generated_image\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 561,\n \"referenced_widgets\": [\n \"a6cbfc25a6564c7ca1db07fab2528290\",\n \"e487791fb9844b43bb0a60bf38c8773e\",\n \"32ea2bede3734b8e9f08bc70a32a75ac\",\n \"9ec5b2691899461eaadc0e5f9d6707cd\",\n \"0946932f4eee406daa14559740763c6f\",\n \"48cbed72ca234ac08355604896d301ec\",\n \"b4e185870f8c4d70a9da3a8a8acac098\",\n \"d18c78e1537649f7b2e2304d41cf6e38\",\n \"045da1f1dadd4c2b827fd809dd1732b7\",\n \"cc5bf50da40e4aa48c4aec2b1ac27f78\",\n \"832e475b2851408794a49a0bf5f7597b\"\n ]\n },\n \"id\": \"s3LxZK1EY69d\",\n \"outputId\": \"03945524-debc-446d-8fd4-8e111d6bcdfe\"\n },\n \"execution_count\": 16,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \" 0%| | 0/50 [00:00\"\n ],\n \"image/png\": 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\\n\"\n },\n \"metadata\": {},\n \"execution_count\": 16\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [],\n \"metadata\": {\n \"id\": \"BFPPGHaOuTJy\"\n },\n \"execution_count\": null,\n \"outputs\": []\n }\n ]\n}" }, { "path": "data_generation/Grounded-Segment-Anything/grounded_sam_demo.py", "content": "import argparse\nimport os\nimport sys\n\nimport numpy as np\nimport json\nimport torch\nfrom PIL import Image\n\nsys.path.append(os.path.join(os.getcwd(), \"GroundingDINO\"))\nsys.path.append(os.path.join(os.getcwd(), \"segment_anything\"))\n\n\n# Grounding DINO\nimport GroundingDINO.groundingdino.datasets.transforms as T\nfrom GroundingDINO.groundingdino.models import build_model\nfrom GroundingDINO.groundingdino.util.slconfig import SLConfig\nfrom GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap\n\n\n# segment anything\nfrom segment_anything import (\n sam_model_registry,\n sam_hq_model_registry,\n SamPredictor\n)\nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef load_image(image_path):\n # load image\n image_pil = Image.open(image_path).convert(\"RGB\") # load image\n\n transform = T.Compose(\n [\n T.RandomResize([800], max_size=1333),\n T.ToTensor(),\n T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n ]\n )\n image, _ = transform(image_pil, None) # 3, h, w\n return image_pil, image\n\n\ndef load_model(model_config_path, model_checkpoint_path, device):\n args = SLConfig.fromfile(model_config_path)\n args.device = device\n model = build_model(args)\n checkpoint = torch.load(model_checkpoint_path, map_location=\"cpu\")\n load_res = model.load_state_dict(clean_state_dict(checkpoint[\"model\"]), strict=False)\n print(load_res)\n _ = model.eval()\n return model\n\n\ndef get_grounding_output(model, image, caption, box_threshold, text_threshold, with_logits=True, device=\"cpu\"):\n caption = caption.lower()\n caption = caption.strip()\n if not caption.endswith(\".\"):\n caption = caption + \".\"\n model = model.to(device)\n image = image.to(device)\n with torch.no_grad():\n outputs = model(image[None], captions=[caption])\n logits = outputs[\"pred_logits\"].cpu().sigmoid()[0] # (nq, 256)\n boxes = outputs[\"pred_boxes\"].cpu()[0] # (nq, 4)\n logits.shape[0]\n\n # filter output\n logits_filt = logits.clone()\n boxes_filt = boxes.clone()\n filt_mask = logits_filt.max(dim=1)[0] > box_threshold\n logits_filt = logits_filt[filt_mask] # num_filt, 256\n boxes_filt = boxes_filt[filt_mask] # num_filt, 4\n logits_filt.shape[0]\n\n # get phrase\n tokenlizer = model.tokenizer\n tokenized = tokenlizer(caption)\n # build pred\n pred_phrases = []\n for logit, box in zip(logits_filt, boxes_filt):\n pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer)\n if with_logits:\n pred_phrases.append(pred_phrase + f\"({str(logit.max().item())[:4]})\")\n else:\n pred_phrases.append(pred_phrase)\n\n return boxes_filt, pred_phrases\n\ndef show_mask(mask, ax, random_color=False):\n if random_color:\n color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)\n else:\n color = np.array([30/255, 144/255, 255/255, 0.6])\n h, w = mask.shape[-2:]\n mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\n ax.imshow(mask_image)\n\n\ndef show_box(box, ax, label):\n x0, y0 = box[0], box[1]\n w, h = box[2] - box[0], box[3] - box[1]\n ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2))\n ax.text(x0, y0, label)\n\n\ndef save_mask_data(output_dir, mask_list, box_list, label_list):\n value = 0 # 0 for background\n\n mask_img = torch.zeros(mask_list.shape[-2:])\n for idx, mask in enumerate(mask_list):\n mask_img[mask.cpu().numpy()[0] == True] = value + idx + 1\n plt.figure(figsize=(10, 10))\n plt.imshow(mask_img.numpy())\n plt.axis('off')\n plt.savefig(os.path.join(output_dir, 'mask.jpg'), bbox_inches=\"tight\", dpi=300, pad_inches=0.0)\n\n json_data = [{\n 'value': value,\n 'label': 'background'\n }]\n for label, box in zip(label_list, box_list):\n value += 1\n name, logit = label.split('(')\n logit = logit[:-1] # the last is ')'\n json_data.append({\n 'value': value,\n 'label': name,\n 'logit': float(logit),\n 'box': box.numpy().tolist(),\n })\n with open(os.path.join(output_dir, 'mask.json'), 'w') as f:\n json.dump(json_data, f)\n\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser(\"Grounded-Segment-Anything Demo\", add_help=True)\n parser.add_argument(\"--config\", type=str, required=True, help=\"path to config file\")\n parser.add_argument(\n \"--grounded_checkpoint\", type=str, required=True, help=\"path to checkpoint file\"\n )\n parser.add_argument(\n \"--sam_version\", type=str, default=\"vit_h\", required=False, help=\"SAM ViT version: vit_b / vit_l / vit_h\"\n )\n parser.add_argument(\n \"--sam_checkpoint\", type=str, required=False, help=\"path to sam checkpoint file\"\n )\n parser.add_argument(\n \"--sam_hq_checkpoint\", type=str, default=None, help=\"path to sam-hq checkpoint file\"\n )\n parser.add_argument(\n \"--use_sam_hq\", action=\"store_true\", help=\"using sam-hq for prediction\"\n )\n parser.add_argument(\"--input_image\", type=str, required=True, help=\"path to image file\")\n parser.add_argument(\"--text_prompt\", type=str, required=True, help=\"text prompt\")\n parser.add_argument(\n \"--output_dir\", \"-o\", type=str, default=\"outputs\", required=True, help=\"output directory\"\n )\n\n parser.add_argument(\"--box_threshold\", type=float, default=0.3, help=\"box threshold\")\n parser.add_argument(\"--text_threshold\", type=float, default=0.25, help=\"text threshold\")\n\n parser.add_argument(\"--device\", type=str, default=\"cpu\", help=\"running on cpu only!, default=False\")\n args = parser.parse_args()\n\n # cfg\n config_file = args.config # change the path of the model config file\n grounded_checkpoint = args.grounded_checkpoint # change the path of the model\n sam_version = args.sam_version\n sam_checkpoint = args.sam_checkpoint\n sam_hq_checkpoint = args.sam_hq_checkpoint\n use_sam_hq = args.use_sam_hq\n image_path = args.input_image\n text_prompt = args.text_prompt\n output_dir = args.output_dir\n box_threshold = args.box_threshold\n text_threshold = args.text_threshold\n device = args.device\n\n # make dir\n os.makedirs(output_dir, exist_ok=True)\n # load image\n image_pil, image = load_image(image_path)\n # load model\n model = load_model(config_file, grounded_checkpoint, device=device)\n\n # visualize raw image\n image_pil.save(os.path.join(output_dir, \"raw_image.jpg\"))\n\n # run grounding dino model\n boxes_filt, pred_phrases = get_grounding_output(\n model, image, text_prompt, box_threshold, text_threshold, device=device\n )\n\n # initialize SAM\n if use_sam_hq:\n predictor = SamPredictor(sam_hq_model_registry[sam_version](checkpoint=sam_hq_checkpoint).to(device))\n else:\n predictor = SamPredictor(sam_model_registry[sam_version](checkpoint=sam_checkpoint).to(device))\n image = cv2.imread(image_path)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n predictor.set_image(image)\n\n size = image_pil.size\n H, W = size[1], size[0]\n for i in range(boxes_filt.size(0)):\n boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])\n boxes_filt[i][:2] -= boxes_filt[i][2:] / 2\n boxes_filt[i][2:] += boxes_filt[i][:2]\n\n boxes_filt = boxes_filt.cpu()\n transformed_boxes = predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2]).to(device)\n\n masks, _, _ = predictor.predict_torch(\n point_coords = None,\n point_labels = None,\n boxes = transformed_boxes.to(device),\n multimask_output = False,\n )\n\n # draw output image\n plt.figure(figsize=(10, 10))\n plt.imshow(image)\n for mask in masks:\n show_mask(mask.cpu().numpy(), plt.gca(), random_color=True)\n for box, label in zip(boxes_filt, pred_phrases):\n show_box(box.numpy(), plt.gca(), label)\n\n plt.axis('off')\n plt.savefig(\n os.path.join(output_dir, \"grounded_sam_output.jpg\"),\n bbox_inches=\"tight\", dpi=300, pad_inches=0.0\n )\n\n save_mask_data(output_dir, masks, boxes_filt, pred_phrases)\n" }, { "path": "data_generation/Grounded-Segment-Anything/grounded_sam_inpainting_demo.py", "content": "import argparse\nimport os\nimport copy\n\nimport numpy as np\nimport torch\nfrom PIL import Image, ImageDraw, ImageFont\n\n# Grounding DINO\nimport GroundingDINO.groundingdino.datasets.transforms as T\nfrom GroundingDINO.groundingdino.models import build_model\nfrom GroundingDINO.groundingdino.util import box_ops\nfrom GroundingDINO.groundingdino.util.slconfig import SLConfig\nfrom GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap\n\n# segment anything\nfrom segment_anything import build_sam, SamPredictor \nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n# diffusers\nimport PIL\nimport requests\nimport torch\nfrom io import BytesIO\nfrom diffusers import StableDiffusionInpaintPipeline\n\n\ndef load_image(image_path):\n # load image\n image_pil = Image.open(image_path).convert(\"RGB\") # load image\n\n transform = T.Compose(\n [\n T.RandomResize([800], max_size=1333),\n T.ToTensor(),\n T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n ]\n )\n image, _ = transform(image_pil, None) # 3, h, w\n return image_pil, image\n\n\ndef load_model(model_config_path, model_checkpoint_path, device):\n args = SLConfig.fromfile(model_config_path)\n args.device = device\n model = build_model(args)\n checkpoint = torch.load(model_checkpoint_path, map_location=\"cpu\")\n load_res = model.load_state_dict(clean_state_dict(checkpoint[\"model\"]), strict=False)\n print(load_res)\n _ = model.eval()\n return model\n\n\ndef get_grounding_output(model, image, caption, box_threshold, text_threshold, with_logits=True, device=\"cpu\"):\n caption = caption.lower()\n caption = caption.strip()\n if not caption.endswith(\".\"):\n caption = caption + \".\"\n model = model.to(device)\n image = image.to(device)\n with torch.no_grad():\n outputs = model(image[None], captions=[caption])\n logits = outputs[\"pred_logits\"].cpu().sigmoid()[0] # (nq, 256)\n boxes = outputs[\"pred_boxes\"].cpu()[0] # (nq, 4)\n logits.shape[0]\n\n # filter output\n logits_filt = logits.clone()\n boxes_filt = boxes.clone()\n filt_mask = logits_filt.max(dim=1)[0] > box_threshold\n logits_filt = logits_filt[filt_mask] # num_filt, 256\n boxes_filt = boxes_filt[filt_mask] # num_filt, 4\n logits_filt.shape[0]\n\n # get phrase\n tokenlizer = model.tokenizer\n tokenized = tokenlizer(caption)\n # build pred\n pred_phrases = []\n for logit, box in zip(logits_filt, boxes_filt):\n pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer)\n if with_logits:\n pred_phrases.append(pred_phrase + f\"({str(logit.max().item())[:4]})\")\n else:\n pred_phrases.append(pred_phrase)\n\n return boxes_filt, pred_phrases\n\ndef show_mask(mask, ax, random_color=False):\n if random_color:\n color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)\n else:\n color = np.array([30/255, 144/255, 255/255, 0.6])\n h, w = mask.shape[-2:]\n mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\n ax.imshow(mask_image)\n\n\ndef show_box(box, ax, label):\n x0, y0 = box[0], box[1]\n w, h = box[2] - box[0], box[3] - box[1]\n ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2)) \n ax.text(x0, y0, label)\n\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser(\"Grounded-Segment-Anything Demo\", add_help=True)\n parser.add_argument(\"--config\", type=str, required=True, help=\"path to config file\")\n parser.add_argument(\n \"--grounded_checkpoint\", type=str, required=True, help=\"path to checkpoint file\"\n )\n parser.add_argument(\n \"--sam_checkpoint\", type=str, required=True, help=\"path to checkpoint file\"\n )\n parser.add_argument(\"--input_image\", type=str, required=True, help=\"path to image file\")\n parser.add_argument(\"--det_prompt\", type=str, required=True, help=\"text prompt\")\n parser.add_argument(\"--inpaint_prompt\", type=str, required=True, help=\"inpaint prompt\")\n parser.add_argument(\n \"--output_dir\", \"-o\", type=str, default=\"outputs\", required=True, help=\"output directory\"\n )\n parser.add_argument(\"--cache_dir\", type=str, default=None, help=\"save your huggingface large model cache\")\n parser.add_argument(\"--box_threshold\", type=float, default=0.3, help=\"box threshold\")\n parser.add_argument(\"--text_threshold\", type=float, default=0.25, help=\"text threshold\")\n parser.add_argument(\"--inpaint_mode\", type=str, default=\"first\", help=\"inpaint mode\")\n parser.add_argument(\"--device\", type=str, default=\"cpu\", help=\"running on cpu only!, default=False\")\n args = parser.parse_args()\n\n # cfg\n config_file = args.config # change the path of the model config file\n grounded_checkpoint = args.grounded_checkpoint # change the path of the model\n sam_checkpoint = args.sam_checkpoint\n image_path = args.input_image\n det_prompt = args.det_prompt\n inpaint_prompt = args.inpaint_prompt\n output_dir = args.output_dir\n cache_dir=args.cache_dir\n box_threshold = args.box_threshold\n text_threshold = args.text_threshold\n inpaint_mode = args.inpaint_mode\n device = args.device\n\n # make dir\n os.makedirs(output_dir, exist_ok=True)\n # load image\n image_pil, image = load_image(image_path)\n # load model\n model = load_model(config_file, grounded_checkpoint, device=device)\n\n # visualize raw image\n image_pil.save(os.path.join(output_dir, \"raw_image.jpg\"))\n\n # run grounding dino model\n boxes_filt, pred_phrases = get_grounding_output(\n model, image, det_prompt, box_threshold, text_threshold, device=device\n )\n\n # initialize SAM\n predictor = SamPredictor(build_sam(checkpoint=sam_checkpoint).to(device))\n image = cv2.imread(image_path)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n predictor.set_image(image)\n\n size = image_pil.size\n H, W = size[1], size[0]\n for i in range(boxes_filt.size(0)):\n boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])\n boxes_filt[i][:2] -= boxes_filt[i][2:] / 2\n boxes_filt[i][2:] += boxes_filt[i][:2]\n\n boxes_filt = boxes_filt.cpu()\n transformed_boxes = predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2]).to(device)\n\n masks, _, _ = predictor.predict_torch(\n point_coords = None,\n point_labels = None,\n boxes = transformed_boxes.to(device),\n multimask_output = False,\n )\n\n # masks: [1, 1, 512, 512]\n\n # draw output image\n plt.figure(figsize=(10, 10))\n plt.imshow(image)\n for mask in masks:\n show_mask(mask.cpu().numpy(), plt.gca(), random_color=True)\n for box, label in zip(boxes_filt, pred_phrases):\n show_box(box.numpy(), plt.gca(), label)\n plt.axis('off')\n plt.savefig(os.path.join(output_dir, \"grounded_sam_output.jpg\"), bbox_inches=\"tight\")\n\n # inpainting pipeline\n if inpaint_mode == 'merge':\n masks = torch.sum(masks, dim=0).unsqueeze(0)\n masks = torch.where(masks > 0, True, False)\n mask = masks[0][0].cpu().numpy() # simply choose the first mask, which will be refine in the future release\n mask_pil = Image.fromarray(mask)\n image_pil = Image.fromarray(image)\n \n pipe = StableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", torch_dtype=torch.float16,cache_dir=cache_dir\n )\n pipe = pipe.to(\"cuda\")\n\n image_pil = image_pil.resize((512, 512))\n mask_pil = mask_pil.resize((512, 512))\n # prompt = \"A sofa, high quality, detailed\"\n image = pipe(prompt=inpaint_prompt, image=image_pil, mask_image=mask_pil).images[0]\n image = image.resize(size)\n image.save(os.path.join(output_dir, \"grounded_sam_inpainting_output.jpg\"))\n\n\n" }, { "path": "data_generation/Grounded-Segment-Anything/grounded_sam_osx_demo.py", "content": "import torchvision.transforms as transforms\nfrom torch.nn.parallel.data_parallel import DataParallel\nimport torch.backends.cudnn as cudnn\nimport argparse\nimport json\nimport torch\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimport os\nimport cv2\nimport numpy as np\n\n# Grounding DINO\nimport GroundingDINO.groundingdino.datasets.transforms as T\nfrom GroundingDINO.groundingdino.models import build_model\nfrom GroundingDINO.groundingdino.util.slconfig import SLConfig\nfrom GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap\n\n# segment anything\nfrom segment_anything import build_sam, SamPredictor\n\n\n# OSX\nimport sys\nsys.path.insert(0, 'grounded-sam-osx')\nfrom osx import get_model\nfrom config import cfg\nfrom utils.preprocessing import load_img, process_bbox, generate_patch_image\nfrom utils.human_models import smpl_x\n\nos.environ[\"PYOPENGL_PLATFORM\"] = \"egl\"\nfrom utils.vis import render_mesh, save_obj\ncudnn.benchmark = True\n\ndef load_image(image_path):\n # load image\n image_pil = Image.open(image_path).convert(\"RGB\") # load image\n\n transform = T.Compose(\n [\n T.RandomResize([800], max_size=1333),\n T.ToTensor(),\n T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n ]\n )\n image, _ = transform(image_pil, None) # 3, h, w\n return image_pil, image\n\n\ndef load_model(model_config_path, model_checkpoint_path, device):\n args = SLConfig.fromfile(model_config_path)\n args.device = device\n model = build_model(args)\n checkpoint = torch.load(model_checkpoint_path, map_location=\"cpu\")\n load_res = model.load_state_dict(clean_state_dict(checkpoint[\"model\"]), strict=False)\n print(load_res)\n _ = model.eval()\n return model\n\n\ndef get_grounding_output(model, image, caption, box_threshold, text_threshold, with_logits=True, device=\"cpu\"):\n caption = caption.lower()\n caption = caption.strip()\n if not caption.endswith(\".\"):\n caption = caption + \".\"\n model = model.to(device)\n image = image.to(device)\n with torch.no_grad():\n outputs = model(image[None], captions=[caption])\n logits = outputs[\"pred_logits\"].cpu().sigmoid()[0] # (nq, 256)\n boxes = outputs[\"pred_boxes\"].cpu()[0] # (nq, 4)\n logits.shape[0]\n\n # filter output\n logits_filt = logits.clone()\n boxes_filt = boxes.clone()\n filt_mask = logits_filt.max(dim=1)[0] > box_threshold\n logits_filt = logits_filt[filt_mask] # num_filt, 256\n boxes_filt = boxes_filt[filt_mask] # num_filt, 4\n logits_filt.shape[0]\n\n # get phrase\n tokenlizer = model.tokenizer\n tokenized = tokenlizer(caption)\n # build pred\n pred_phrases = []\n for logit, box in zip(logits_filt, boxes_filt):\n pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer)\n if with_logits:\n pred_phrases.append(pred_phrase + f\"({str(logit.max().item())[:4]})\")\n else:\n pred_phrases.append(pred_phrase)\n\n return boxes_filt, pred_phrases\n\n\ndef show_mask(mask, ax, random_color=False):\n if random_color:\n color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)\n else:\n color = np.array([30 / 255, 144 / 255, 255 / 255, 0.6])\n h, w = mask.shape[-2:]\n mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\n ax.imshow(mask_image)\n\ndef show_box(box, ax, label):\n x0, y0 = box[0], box[1]\n w, h = box[2] - box[0], box[3] - box[1]\n if 'person' in label.lower() or 'human' in label.lower():\n color = 'green'\n else:\n color = 'blue'\n ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor=color, facecolor=(0, 0, 0, 0), lw=2))\n ax.text(x0, y0-5, label, fontsize=5, color='white',bbox={'facecolor': color, 'alpha': 0.7, 'pad': 1, 'edgecolor': 'none'})\n\ndef save_mask_data(output_dir, mask_list, box_list, label_list):\n value = 0 # 0 for background\n\n mask_img = torch.zeros(mask_list.shape[-2:])\n for idx, mask in enumerate(mask_list):\n mask_img[mask.cpu().numpy()[0] == True] = value + idx + 1\n plt.figure(figsize=(10, 10))\n plt.imshow(mask_img.numpy())\n plt.axis('off')\n plt.savefig(os.path.join(output_dir, 'mask.jpg'), bbox_inches=\"tight\", dpi=300, pad_inches=0.0)\n\n json_data = [{\n 'value': value,\n 'label': 'background'\n }]\n for label, box in zip(label_list, box_list):\n value += 1\n name, logit = label.split('(')\n logit = logit[:-1] # the last is ')'\n json_data.append({\n 'value': value,\n 'label': name,\n 'logit': float(logit),\n 'box': box.numpy().tolist(),\n })\n with open(os.path.join(output_dir, 'mask.json'), 'w') as f:\n json.dump(json_data, f)\n\ndef bbox_resize(bbox, scale=1.0):\n center = (bbox[2:] + bbox[:2]) / 2\n new_size = (bbox[2:] - bbox[:2]) * scale\n new_bbox = torch.cat((center - new_size / 2, center + new_size / 2))\n return new_bbox\n\ndef mesh_recovery(original_img, bboxes):\n transform = transforms.ToTensor()\n original_img_height, original_img_width = original_img.shape[:2]\n\n vis_img = original_img.copy()\n for bbox in bboxes: # [x1, y1, x2, y2]\n bbox = [bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]] # xyxy -> xyhw\n bbox = process_bbox(bbox, original_img_width, original_img_height)\n img, img2bb_trans, bb2img_trans = generate_patch_image(original_img, bbox, 1.0, 0.0, False, cfg.input_img_shape)\n img = transform(img.astype(np.float32)) / 255\n img = img.cuda()[None, :, :, :]\n\n # forward\n inputs = {'img': img}\n with torch.no_grad():\n out = model(inputs, 'test')\n mesh = out['smplx_mesh_cam'].detach().cpu().numpy()[0]\n\n # # save mesh\n # save_obj(mesh, smpl_x.face, 'output.obj')\n\n focal = [cfg.focal[0] / cfg.input_body_shape[1] * bbox[2], cfg.focal[1] / cfg.input_body_shape[0] * bbox[3]]\n princpt = [cfg.princpt[0] / cfg.input_body_shape[1] * bbox[2] + bbox[0],\n cfg.princpt[1] / cfg.input_body_shape[0] * bbox[3] + bbox[1]]\n rendered_img, _ = render_mesh(vis_img[:, :, ::-1], mesh, smpl_x.face, {'focal': focal, 'princpt': princpt})\n vis_img = rendered_img.copy()\n\n return rendered_img\n\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser(\"Grounded-Segment-Anything Demo\", add_help=True)\n parser.add_argument(\"--config\", type=str, required=True, help=\"path to config file\")\n parser.add_argument(\n \"--grounded_checkpoint\", type=str, required=True, help=\"path to checkpoint file\"\n )\n parser.add_argument(\n \"--sam_checkpoint\", type=str, required=True, help=\"path to checkpoint file\"\n )\n parser.add_argument(\n \"--osx_checkpoint\", type=str, required=True, help=\"path to checkpoint file\"\n )\n parser.add_argument(\"--input_image\", type=str, required=True, help=\"path to image file\")\n parser.add_argument(\"--text_prompt\", type=str, required=True, help=\"text prompt\")\n parser.add_argument(\n \"--output_dir\", \"-o\", type=str, default=\"outputs\", required=True, help=\"output directory\"\n )\n\n parser.add_argument(\"--box_threshold\", type=float, default=0.3, help=\"box threshold\")\n parser.add_argument(\"--text_threshold\", type=float, default=0.25, help=\"text threshold\")\n\n parser.add_argument(\"--device\", type=str, default=\"cpu\", help=\"running on cpu only!, default=False\")\n args = parser.parse_args()\n\n # cfg\n config_file = args.config # change the path of the model config file\n grounded_checkpoint = args.grounded_checkpoint # change the path of the model\n sam_checkpoint = args.sam_checkpoint\n osx_checkpoint = args.osx_checkpoint\n image_path = args.input_image\n text_prompt = args.text_prompt\n output_dir = args.output_dir\n box_threshold = args.box_threshold\n text_threshold = args.text_threshold\n device = args.device\n\n # make dir\n os.makedirs(output_dir, exist_ok=True)\n # load image\n image_pil, image = load_image(image_path)\n # load model\n model = load_model(config_file, grounded_checkpoint, device=device)\n\n # visualize raw image\n image_pil.save(os.path.join(output_dir, \"raw_image.jpg\"))\n\n # run grounding dino model\n boxes_filt, pred_phrases = get_grounding_output(\n model, image, text_prompt, box_threshold, text_threshold, device=device\n )\n\n # initialize SAM\n sam = build_sam(checkpoint=sam_checkpoint)\n sam.to(device=device)\n predictor = SamPredictor(sam)\n image = cv2.imread(image_path)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n predictor.set_image(image)\n\n # initialize OSX\n model = get_model()\n model = DataParallel(model).cuda()\n ckpt = torch.load(osx_checkpoint)\n model.load_state_dict(ckpt['network'], strict=False)\n model.eval()\n\n size = image_pil.size\n H, W = size[1], size[0]\n for i in range(boxes_filt.size(0)):\n boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])\n boxes_filt[i][:2] -= boxes_filt[i][2:] / 2\n boxes_filt[i][2:] += boxes_filt[i][:2]\n\n boxes_filt = boxes_filt.cpu()\n transformed_boxes = predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2]).to(device)\n\n masks, _, _ = predictor.predict_torch(\n point_coords=None,\n point_labels=None,\n boxes=transformed_boxes,\n multimask_output=False,\n )\n\n # scale up the human bboxes\n boxes_human = []\n for i, label in enumerate(pred_phrases):\n if 'person' in label.lower() or 'human' in label.lower():\n boxes_filt[i] = bbox_resize(boxes_filt[i], scale=1.1)\n boxes_human.append(boxes_filt[i])\n\n # predict and visualize 3d human mesh\n for i, label in enumerate(pred_phrases):\n if 'person' in label.lower() or 'man' in label.lower():\n boxes_human.append(boxes_filt[i])\n rendered_img = mesh_recovery(image, boxes_human)\n cv2.imwrite(os.path.join(output_dir, \"grounded_sam_osx_output.jpg\"), rendered_img)\n\n # draw output image\n fig, (plt1, plt2) = plt.subplots(ncols=2, figsize=(10, 20), gridspec_kw={'wspace':0, 'hspace':0})\n\n plt1.imshow(image)\n for mask in masks:\n show_mask(mask.cpu().numpy(), plt1, random_color=True)\n for box, label in zip(boxes_filt, pred_phrases):\n show_box(box.numpy(), plt1, label)\n rendered_img = cv2.imread(os.path.join(output_dir, \"grounded_sam_osx_output.jpg\"))\n plt2.imshow(rendered_img)\n for box, label in zip(boxes_filt, pred_phrases):\n show_box(box.numpy(), plt2, label)\n plt1.axis('off')\n plt2.axis('off')\n plt.savefig(\n os.path.join(output_dir, \"grounded_sam_osx_output.jpg\"),\n bbox_inches=\"tight\", dpi=300, pad_inches=0.0\n )\n\n save_mask_data(output_dir, masks, boxes_filt, pred_phrases)\n\n\n" }, { "path": "data_generation/Grounded-Segment-Anything/grounded_sam_simple_demo.py", "content": "import cv2\nimport numpy as np\nimport supervision as sv\n\nimport torch\nimport torchvision\n\nfrom groundingdino.util.inference import Model\nfrom segment_anything import sam_model_registry, SamPredictor\n\nDEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n# GroundingDINO config and checkpoint\nGROUNDING_DINO_CONFIG_PATH = \"GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py\"\nGROUNDING_DINO_CHECKPOINT_PATH = \"./groundingdino_swint_ogc.pth\"\n\n# Segment-Anything checkpoint\nSAM_ENCODER_VERSION = \"vit_h\"\nSAM_CHECKPOINT_PATH = \"./sam_vit_h_4b8939.pth\"\n\n# Building GroundingDINO inference model\ngrounding_dino_model = Model(model_config_path=GROUNDING_DINO_CONFIG_PATH, model_checkpoint_path=GROUNDING_DINO_CHECKPOINT_PATH)\n\n# Building SAM Model and SAM Predictor\nsam = sam_model_registry[SAM_ENCODER_VERSION](checkpoint=SAM_CHECKPOINT_PATH)\nsam.to(device=DEVICE)\nsam_predictor = SamPredictor(sam)\n\n\n# Predict classes and hyper-param for GroundingDINO\nSOURCE_IMAGE_PATH = \"./assets/demo2.jpg\"\nCLASSES = [\"The running dog\"]\nBOX_THRESHOLD = 0.25\nTEXT_THRESHOLD = 0.25\nNMS_THRESHOLD = 0.8\n\n\n# load image\nimage = cv2.imread(SOURCE_IMAGE_PATH)\n\n# detect objects\ndetections = grounding_dino_model.predict_with_classes(\n image=image,\n classes=CLASSES,\n box_threshold=BOX_THRESHOLD,\n text_threshold=TEXT_THRESHOLD\n)\n\n# annotate image with detections\nbox_annotator = sv.BoxAnnotator()\nlabels = [\n f\"{CLASSES[class_id]} {confidence:0.2f}\" \n for _, _, confidence, class_id, _, _ \n in detections]\nannotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections, labels=labels)\n\n# save the annotated grounding dino image\ncv2.imwrite(\"groundingdino_annotated_image.jpg\", annotated_frame)\n\n\n# NMS post process\nprint(f\"Before NMS: {len(detections.xyxy)} boxes\")\nnms_idx = torchvision.ops.nms(\n torch.from_numpy(detections.xyxy), \n torch.from_numpy(detections.confidence), \n NMS_THRESHOLD\n).numpy().tolist()\n\ndetections.xyxy = detections.xyxy[nms_idx]\ndetections.confidence = detections.confidence[nms_idx]\ndetections.class_id = detections.class_id[nms_idx]\n\nprint(f\"After NMS: {len(detections.xyxy)} boxes\")\n\n# Prompting SAM with detected boxes\ndef segment(sam_predictor: SamPredictor, image: np.ndarray, xyxy: np.ndarray) -> np.ndarray:\n sam_predictor.set_image(image)\n result_masks = []\n for box in xyxy:\n masks, scores, logits = sam_predictor.predict(\n box=box,\n multimask_output=True\n )\n index = np.argmax(scores)\n result_masks.append(masks[index])\n return np.array(result_masks)\n\n\n# convert detections to masks\ndetections.mask = segment(\n sam_predictor=sam_predictor,\n image=cv2.cvtColor(image, cv2.COLOR_BGR2RGB),\n xyxy=detections.xyxy\n)\n\n# annotate image with detections\nbox_annotator = sv.BoxAnnotator()\nmask_annotator = sv.MaskAnnotator()\nlabels = [\n f\"{CLASSES[class_id]} {confidence:0.2f}\" \n for _, _, confidence, class_id, _, _ \n in detections]\nannotated_image = mask_annotator.annotate(scene=image.copy(), detections=detections)\nannotated_image = box_annotator.annotate(scene=annotated_image, detections=detections, labels=labels)\n\n# save the annotated grounded-sam image\ncv2.imwrite(\"grounded_sam_annotated_image.jpg\", annotated_image)\n" }, { "path": "data_generation/Grounded-Segment-Anything/grounded_sam_visam.py", "content": "\nfrom copy import deepcopy\nimport json\n\nimport os\nimport argparse\nimport torchvision.transforms.functional as F\nimport torch\nimport cv2\nimport numpy as np\nfrom tqdm import tqdm\nfrom pathlib import Path\nimport sys\nsys.path.append('VISAM')\nfrom main import get_args_parser\nfrom models import build_model\nfrom util.tool import load_model\nfrom models.structures import Instances\n\nfrom torch.utils.data import Dataset, DataLoader\n\n\n# segment anything\nsys.path.append('segment_anything')\nfrom segment_anything import build_sam, SamPredictor \n\n\nclass Colors:\n # Ultralytics color palette https://ultralytics.com/\n def __init__(self):\n # hex = matplotlib.colors.TABLEAU_COLORS.values()\n hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',\n '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')\n self.palette = [self.hex2rgb(f'#{c}') for c in hexs]\n self.n = len(self.palette)\n\n def __call__(self, i, bgr=False):\n c = self.palette[int(i) % self.n]\n return (c[2], c[1], c[0]) if bgr else c\n\n @staticmethod\n def hex2rgb(h): # rgb order (PIL)\n return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))\n\n\ncolors = Colors() # create instance for 'from utils.plots import colors'\n\n\nclass ListImgDataset(Dataset):\n def __init__(self, mot_path, img_list, det_db) -> None:\n super().__init__()\n self.mot_path = mot_path\n self.img_list = img_list\n self.det_db = det_db\n\n '''\n common settings\n '''\n self.img_height = 800\n self.img_width = 1536\n self.mean = [0.485, 0.456, 0.406]\n self.std = [0.229, 0.224, 0.225]\n\n def load_img_from_file(self, f_path):\n cur_img = cv2.imread(os.path.join(self.mot_path, f_path))\n assert cur_img is not None, f_path\n cur_img = cv2.cvtColor(cur_img, cv2.COLOR_BGR2RGB)\n proposals = []\n im_h, im_w = cur_img.shape[:2]\n for line in self.det_db[f_path[:-4] + '.txt']:\n l, t, w, h, s = list(map(float, line.split(',')))\n proposals.append([(l + w / 2) / im_w,\n (t + h / 2) / im_h,\n w / im_w,\n h / im_h,\n s])\n return cur_img, torch.as_tensor(proposals).reshape(-1, 5)\n\n def init_img(self, img, proposals):\n ori_img = img.copy()\n self.seq_h, self.seq_w = img.shape[:2]\n scale = self.img_height / min(self.seq_h, self.seq_w)\n if max(self.seq_h, self.seq_w) * scale > self.img_width:\n scale = self.img_width / max(self.seq_h, self.seq_w)\n target_h = int(self.seq_h * scale)\n target_w = int(self.seq_w * scale)\n img = cv2.resize(img, (target_w, target_h))\n img = F.normalize(F.to_tensor(img), self.mean, self.std)\n img = img.unsqueeze(0)\n return img, ori_img, proposals\n\n def __len__(self):\n return len(self.img_list)\n \n def __getitem__(self, index):\n img, proposals = self.load_img_from_file(self.img_list[index])\n return self.init_img(img, proposals)\n\n\nclass Detector(object):\n def __init__(self, args, model, vid, sam_predictor=None):\n self.args = args\n self.detr = model\n\n self.vid = vid\n self.seq_num = os.path.basename(vid)\n img_list = os.listdir(os.path.join(self.args.mot_path, vid, 'img1'))\n img_list = [os.path.join(vid, 'img1', i) for i in img_list if 'jpg' in i]\n\n self.img_list = sorted(img_list)\n self.img_len = len(self.img_list)\n\n self.predict_path = os.path.join(self.args.output_dir, args.exp_name)\n os.makedirs(self.predict_path, exist_ok=True)\n \n fps = 25\n size = (1920, 1080) \n self.videowriter = cv2.VideoWriter('visam.avi', cv2.VideoWriter_fourcc('M','J','P','G'), fps, size)\n \n self.sam_predictor = sam_predictor\n\n @staticmethod\n def filter_dt_by_score(dt_instances: Instances, prob_threshold: float) -> Instances:\n keep = dt_instances.scores > prob_threshold\n keep &= dt_instances.obj_idxes >= 0\n return dt_instances[keep]\n\n @staticmethod\n def filter_dt_by_area(dt_instances: Instances, area_threshold: float) -> Instances:\n wh = dt_instances.boxes[:, 2:4] - dt_instances.boxes[:, 0:2]\n areas = wh[:, 0] * wh[:, 1]\n keep = areas > area_threshold\n return dt_instances[keep]\n\n def detect(self, prob_threshold=0.6, area_threshold=100, vis=False):\n total_dts = 0\n total_occlusion_dts = 0\n\n track_instances = None\n with open(os.path.join(self.args.mot_path, 'DanceTrack', self.args.det_db)) as f:\n det_db = json.load(f)\n loader = DataLoader(ListImgDataset(self.args.mot_path, self.img_list, det_db), 1, num_workers=2)\n lines = []\n for i, data in enumerate(tqdm(loader)):\n cur_img, ori_img, proposals = [d[0] for d in data]\n cur_img, proposals = cur_img.cuda(), proposals.cuda()\n\n # track_instances = None\n if track_instances is not None:\n track_instances.remove('boxes')\n track_instances.remove('labels')\n seq_h, seq_w, _ = ori_img.shape\n\n res = self.detr.inference_single_image(cur_img, (seq_h, seq_w), track_instances, proposals)\n track_instances = res['track_instances']\n\n dt_instances = deepcopy(track_instances)\n\n # filter det instances by score.\n dt_instances = self.filter_dt_by_score(dt_instances, prob_threshold)\n dt_instances = self.filter_dt_by_area(dt_instances, area_threshold)\n\n total_dts += len(dt_instances)\n\n bbox_xyxy = dt_instances.boxes.tolist()\n identities = dt_instances.obj_idxes.tolist()\n\n img = ori_img.to(torch.device('cpu')).numpy().copy()[..., ::-1]\n if self.sam_predictor is not None:\n masks_all = []\n self.sam_predictor.set_image(ori_img.to(torch.device('cpu')).numpy().copy())\n \n for bbox, id in zip(np.array(bbox_xyxy), identities):\n masks, iou_predictions, low_res_masks = self.sam_predictor.predict(box=bbox)\n index_max = iou_predictions.argsort()[0]\n masks = np.concatenate([masks[index_max:(index_max+1)], masks[index_max:(index_max+1)], masks[index_max:(index_max+1)]], axis=0)\n masks = masks.astype(np.int32)*np.array(colors(id))[:, None, None]\n masks_all.append(masks)\n \n self.sam_predictor.reset_image()\n if len(masks_all):\n masks_sum = masks_all[0].copy()\n for m in masks_all[1:]:\n masks_sum += m\n else:\n masks_sum = np.zeros_like(img).transpose(2, 0, 1)\n\n img = (img * 0.5 + (masks_sum.transpose(1,2,0) * 30) %128).astype(np.uint8)\n for bbox in bbox_xyxy:\n cv2.rectangle(img, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), (0,0,255), thickness=3)\n self.videowriter.write(img)\n\n save_format = '{frame},{id},{x1:.2f},{y1:.2f},{w:.2f},{h:.2f},1,-1,-1,-1\\n'\n for xyxy, track_id in zip(bbox_xyxy, identities):\n if track_id < 0 or track_id is None:\n continue\n x1, y1, x2, y2 = xyxy\n w, h = x2 - x1, y2 - y1\n lines.append(save_format.format(frame=i + 1, id=track_id, x1=x1, y1=y1, w=w, h=h))\n with open(os.path.join(self.predict_path, f'{self.seq_num}.txt'), 'w') as f:\n f.writelines(lines)\n print(\"totally {} dts {} occlusion dts\".format(total_dts, total_occlusion_dts))\n\n\nclass RuntimeTrackerBase(object):\n def __init__(self, score_thresh=0.6, filter_score_thresh=0.5, miss_tolerance=10):\n self.score_thresh = score_thresh\n self.filter_score_thresh = filter_score_thresh\n self.miss_tolerance = miss_tolerance\n self.max_obj_id = 0\n\n def clear(self):\n self.max_obj_id = 0\n\n def update(self, track_instances: Instances):\n device = track_instances.obj_idxes.device\n\n track_instances.disappear_time[track_instances.scores >= self.score_thresh] = 0\n new_obj = (track_instances.obj_idxes == -1) & (track_instances.scores >= self.score_thresh)\n disappeared_obj = (track_instances.obj_idxes >= 0) & (track_instances.scores < self.filter_score_thresh)\n num_new_objs = new_obj.sum().item()\n\n track_instances.obj_idxes[new_obj] = self.max_obj_id + torch.arange(num_new_objs, device=device)\n self.max_obj_id += num_new_objs\n\n track_instances.disappear_time[disappeared_obj] += 1\n to_del = disappeared_obj & (track_instances.disappear_time >= self.miss_tolerance)\n track_instances.obj_idxes[to_del] = -1\n\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser(\"Grounded-Segment-Anything VISAM Demo\", parents=[get_args_parser()])\n parser.add_argument('--score_threshold', default=0.5, type=float)\n parser.add_argument('--update_score_threshold', default=0.5, type=float)\n parser.add_argument('--miss_tolerance', default=20, type=int)\n \n parser.add_argument(\n \"--sam_checkpoint\", type=str, required=True, help=\"path to checkpoint file\"\n )\n parser.add_argument(\"--video_path\", type=str, required=True, help=\"path to image file\")\n\n args = parser.parse_args()\n\n # make dir\n if args.output_dir:\n Path(args.output_dir).mkdir(parents=True, exist_ok=True)\n \n sam_predictor = SamPredictor(build_sam(checkpoint=args.sam_checkpoint))\n _ = sam_predictor.model.to(device='cuda')\n \n # load model and weights\n detr, _, _ = build_model(args)\n detr.track_embed.score_thr = args.update_score_threshold\n detr.track_base = RuntimeTrackerBase(args.score_threshold, args.score_threshold, args.miss_tolerance)\n checkpoint = torch.load(args.resume, map_location='cpu')\n detr = load_model(detr, args.resume)\n detr.eval()\n detr = detr.cuda()\n \n rank = int(os.environ.get('RLAUNCH_REPLICA', '0'))\n ws = int(os.environ.get('RLAUNCH_REPLICA_TOTAL', '1'))\n \n det = Detector(args, model=detr, vid=args.video_path, sam_predictor=sam_predictor)\n det.detect(args.score_threshold)\n" }, { "path": "data_generation/Grounded-Segment-Anything/grounded_sam_whisper_demo.py", "content": "import argparse\nimport os\nimport copy\n\nimport numpy as np\nimport json\nimport torch\nimport torchvision\nfrom PIL import Image, ImageDraw, ImageFont\n\n# Grounding DINO\nimport GroundingDINO.groundingdino.datasets.transforms as T\nfrom GroundingDINO.groundingdino.models import build_model\nfrom GroundingDINO.groundingdino.util import box_ops\nfrom GroundingDINO.groundingdino.util.slconfig import SLConfig\nfrom GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap\n\n# segment anything\nfrom segment_anything import build_sam, SamPredictor \nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# whisper\nimport whisper\n\n\ndef load_image(image_path):\n # load image\n image_pil = Image.open(image_path).convert(\"RGB\") # load image\n\n transform = T.Compose(\n [\n T.RandomResize([800], max_size=1333),\n T.ToTensor(),\n T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n ]\n )\n image, _ = transform(image_pil, None) # 3, h, w\n return image_pil, image\n\n\ndef load_model(model_config_path, model_checkpoint_path, device):\n args = SLConfig.fromfile(model_config_path)\n args.device = device\n model = build_model(args)\n checkpoint = torch.load(model_checkpoint_path, map_location=\"cpu\")\n load_res = model.load_state_dict(clean_state_dict(checkpoint[\"model\"]), strict=False)\n print(load_res)\n _ = model.eval()\n return model\n\n\ndef get_grounding_output(model, image, caption, box_threshold, text_threshold,device=\"cpu\"):\n caption = caption.lower()\n caption = caption.strip()\n if not caption.endswith(\".\"):\n caption = caption + \".\"\n model = model.to(device)\n image = image.to(device)\n with torch.no_grad():\n outputs = model(image[None], captions=[caption])\n logits = outputs[\"pred_logits\"].cpu().sigmoid()[0] # (nq, 256)\n boxes = outputs[\"pred_boxes\"].cpu()[0] # (nq, 4)\n logits.shape[0]\n\n # filter output\n logits_filt = logits.clone()\n boxes_filt = boxes.clone()\n filt_mask = logits_filt.max(dim=1)[0] > box_threshold\n logits_filt = logits_filt[filt_mask] # num_filt, 256\n boxes_filt = boxes_filt[filt_mask] # num_filt, 4\n logits_filt.shape[0]\n\n # get phrase\n tokenlizer = model.tokenizer\n tokenized = tokenlizer(caption)\n # build pred\n pred_phrases = []\n scores = []\n for logit, box in zip(logits_filt, boxes_filt):\n pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer)\n pred_phrases.append(pred_phrase + f\"({str(logit.max().item())[:4]})\")\n scores.append(logit.max().item())\n\n return boxes_filt, torch.Tensor(scores), pred_phrases\n\ndef show_mask(mask, ax, random_color=False):\n if random_color:\n color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)\n else:\n color = np.array([30/255, 144/255, 255/255, 0.6])\n h, w = mask.shape[-2:]\n mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\n ax.imshow(mask_image)\n\n\ndef show_box(box, ax, label):\n x0, y0 = box[0], box[1]\n w, h = box[2] - box[0], box[3] - box[1]\n ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2)) \n ax.text(x0, y0, label)\n\n\ndef save_mask_data(output_dir, mask_list, box_list, label_list):\n value = 0 # 0 for background\n\n mask_img = torch.zeros(mask_list.shape[-2:])\n for idx, mask in enumerate(mask_list):\n mask_img[mask.cpu().numpy()[0] == True] = value + idx + 1\n plt.figure(figsize=(10, 10))\n plt.imshow(mask_img.numpy())\n plt.axis('off')\n plt.savefig(os.path.join(output_dir, 'mask.jpg'), bbox_inches=\"tight\", dpi=300, pad_inches=0.0)\n\n json_data = [{\n 'value': value,\n 'label': 'background'\n }]\n for label, box in zip(label_list, box_list):\n value += 1\n name, logit = label.split('(')\n logit = logit[:-1] # the last is ')'\n json_data.append({\n 'value': value,\n 'label': name,\n 'logit': float(logit),\n 'box': box.numpy().tolist(),\n })\n with open(os.path.join(output_dir, 'mask.json'), 'w') as f:\n json.dump(json_data, f)\n \n\ndef speech_recognition(speech_file, model):\n # whisper\n # load audio and pad/trim it to fit 30 seconds\n audio = whisper.load_audio(speech_file)\n audio = whisper.pad_or_trim(audio)\n\n # make log-Mel spectrogram and move to the same device as the model\n mel = whisper.log_mel_spectrogram(audio).to(model.device)\n\n # detect the spoken language\n _, probs = model.detect_language(mel)\n speech_language = max(probs, key=probs.get)\n\n # decode the audio\n options = whisper.DecodingOptions()\n result = whisper.decode(model, mel, options)\n\n # print the recognized text\n speech_text = result.text\n return speech_text, speech_language\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser(\"Grounded-Segment-Anything Demo\", add_help=True)\n parser.add_argument(\"--config\", type=str, required=True, help=\"path to config file\")\n parser.add_argument(\n \"--grounded_checkpoint\", type=str, required=True, help=\"path to checkpoint file\"\n )\n parser.add_argument(\n \"--sam_checkpoint\", type=str, required=True, help=\"path to checkpoint file\"\n )\n parser.add_argument(\"--input_image\", type=str, required=True, help=\"path to image file\")\n parser.add_argument(\"--speech_file\", type=str, required=True, help=\"speech file\")\n parser.add_argument(\n \"--output_dir\", \"-o\", type=str, default=\"outputs\", required=True, help=\"output directory\"\n )\n\n parser.add_argument(\"--box_threshold\", type=float, default=0.3, help=\"box threshold\")\n parser.add_argument(\"--text_threshold\", type=float, default=0.25, help=\"text threshold\")\n parser.add_argument(\"--iou_threshold\", type=float, default=0.5, help=\"iou threshold\")\n\n parser.add_argument(\"--device\", type=str, default=\"cpu\", help=\"running on cpu only!, default=False\")\n args = parser.parse_args()\n\n # cfg\n config_file = args.config # change the path of the model config file\n grounded_checkpoint = args.grounded_checkpoint # change the path of the model\n sam_checkpoint = args.sam_checkpoint\n image_path = args.input_image\n output_dir = args.output_dir\n box_threshold = args.box_threshold\n text_threshold = args.text_threshold\n iou_threshold = args.iou_threshold\n device = args.device\n\n # load speech\n whisper_model = whisper.load_model(\"base\")\n speech_text, speech_language = speech_recognition(args.speech_file, whisper_model)\n print(f\"speech_text: {speech_text}\")\n print(f\"speech_language: {speech_language}\")\n\n # make dir\n os.makedirs(output_dir, exist_ok=True)\n # load image\n image_pil, image = load_image(image_path)\n # load model\n model = load_model(config_file, grounded_checkpoint, device=device)\n\n # visualize raw image\n image_pil.save(os.path.join(output_dir, \"raw_image.jpg\"))\n\n # run grounding dino model\n text_prompt = speech_text\n boxes_filt, scores, pred_phrases = get_grounding_output(\n model, image, text_prompt, box_threshold, text_threshold, device=device\n )\n\n # initialize SAM\n sam = build_sam(checkpoint=sam_checkpoint)\n sam.to(device=device)\n predictor = SamPredictor(sam)\n image = cv2.imread(image_path)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n predictor.set_image(image)\n\n size = image_pil.size\n H, W = size[1], size[0]\n for i in range(boxes_filt.size(0)):\n boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])\n boxes_filt[i][:2] -= boxes_filt[i][2:] / 2\n boxes_filt[i][2:] += boxes_filt[i][:2]\n\n boxes_filt = boxes_filt.cpu()\n # use NMS to handle overlapped boxes\n print(f\"Before NMS: {boxes_filt.shape[0]} boxes\")\n nms_idx = torchvision.ops.nms(boxes_filt, scores, iou_threshold).numpy().tolist()\n boxes_filt = boxes_filt[nms_idx]\n pred_phrases = [pred_phrases[idx] for idx in nms_idx]\n print(f\"After NMS: {boxes_filt.shape[0]} boxes\")\n\n transformed_boxes = predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2]).to(device)\n\n masks, _, _ = predictor.predict_torch(\n point_coords = None,\n point_labels = None,\n boxes = transformed_boxes.to(args.device),\n multimask_output = False,\n )\n \n # draw output image\n plt.figure(figsize=(10, 10))\n plt.imshow(image)\n for mask in masks:\n show_mask(mask.cpu().numpy(), plt.gca(), random_color=True)\n for box, label in zip(boxes_filt, pred_phrases):\n show_box(box.numpy(), plt.gca(), label)\n \n plt.title(speech_text)\n plt.axis('off')\n plt.savefig(\n os.path.join(output_dir, \"grounded_sam_whisper_output.jpg\"), \n bbox_inches=\"tight\", dpi=300, pad_inches=0.0\n )\n\n\n save_mask_data(output_dir, masks, boxes_filt, pred_phrases)\n \n" }, { "path": "data_generation/Grounded-Segment-Anything/grounded_sam_whisper_inpainting_demo.py", "content": "import argparse\nimport os\nfrom warnings import warn\n\nimport numpy as np\nimport torch\nfrom PIL import Image, ImageDraw, ImageFont\nimport litellm\n\n# Grounding DINO\nimport GroundingDINO.groundingdino.datasets.transforms as T\nfrom GroundingDINO.groundingdino.models import build_model\nfrom GroundingDINO.groundingdino.util import box_ops\nfrom GroundingDINO.groundingdino.util.slconfig import SLConfig\nfrom GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap\n\n# segment anything\nfrom segment_anything import build_sam, SamPredictor \nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n# diffusers\nimport PIL\nimport requests\nimport torch\nfrom io import BytesIO\nfrom diffusers import StableDiffusionInpaintPipeline\n\n# whisper\nimport whisper\n\n# ChatGPT\nimport openai\n\n\ndef load_image(image_path):\n # load image\n image_pil = Image.open(image_path).convert(\"RGB\") # load image\n\n transform = T.Compose(\n [\n T.RandomResize([800], max_size=1333),\n T.ToTensor(),\n T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n ]\n )\n image, _ = transform(image_pil, None) # 3, h, w\n return image_pil, image\n\n\ndef load_model(model_config_path, model_checkpoint_path, device):\n args = SLConfig.fromfile(model_config_path)\n args.device = device\n model = build_model(args)\n checkpoint = torch.load(model_checkpoint_path, map_location=\"cpu\")\n load_res = model.load_state_dict(clean_state_dict(checkpoint[\"model\"]), strict=False)\n print(load_res)\n _ = model.eval()\n return model\n\n\ndef get_grounding_output(model, image, caption, box_threshold, text_threshold, with_logits=True, device=\"cpu\"):\n caption = caption.lower()\n caption = caption.strip()\n if not caption.endswith(\".\"):\n caption = caption + \".\"\n model = model.to(device)\n image = image.to(device)\n with torch.no_grad():\n outputs = model(image[None], captions=[caption])\n logits = outputs[\"pred_logits\"].cpu().sigmoid()[0] # (nq, 256)\n boxes = outputs[\"pred_boxes\"].cpu()[0] # (nq, 4)\n logits.shape[0]\n\n # filter output\n logits_filt = logits.clone()\n boxes_filt = boxes.clone()\n filt_mask = logits_filt.max(dim=1)[0] > box_threshold\n logits_filt = logits_filt[filt_mask] # num_filt, 256\n boxes_filt = boxes_filt[filt_mask] # num_filt, 4\n logits_filt.shape[0]\n\n # get phrase\n tokenlizer = model.tokenizer\n tokenized = tokenlizer(caption)\n # build pred\n pred_phrases = []\n for logit, box in zip(logits_filt, boxes_filt):\n pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer)\n if with_logits:\n pred_phrases.append(pred_phrase + f\"({str(logit.max().item())[:4]})\")\n else:\n pred_phrases.append(pred_phrase)\n\n return boxes_filt, pred_phrases\n\ndef show_mask(mask, ax, random_color=False):\n if random_color:\n color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)\n else:\n color = np.array([30/255, 144/255, 255/255, 0.6])\n h, w = mask.shape[-2:]\n mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\n ax.imshow(mask_image)\n\n\ndef show_box(box, ax, label):\n x0, y0 = box[0], box[1]\n w, h = box[2] - box[0], box[3] - box[1]\n ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2)) \n ax.text(x0, y0, label)\n \n\ndef speech_recognition(speech_file, model):\n # whisper\n # load audio and pad/trim it to fit 30 seconds\n audio = whisper.load_audio(speech_file)\n audio = whisper.pad_or_trim(audio)\n\n # make log-Mel spectrogram and move to the same device as the model\n mel = whisper.log_mel_spectrogram(audio).to(model.device)\n\n # detect the spoken language\n _, probs = model.detect_language(mel)\n speech_language = max(probs, key=probs.get)\n\n # decode the audio\n options = whisper.DecodingOptions()\n result = whisper.decode(model, mel, options)\n\n # print the recognized text\n speech_text = result.text\n return speech_text, speech_language\n\n\ndef filter_prompts_with_chatgpt(caption, max_tokens=100, model=\"gpt-3.5-turbo\"):\n prompt = [\n {\n 'role': 'system',\n 'content': f\"Extract the main object to be replaced and marked it as 'main_object', \" + \\\n f\"Extract the remaining part as 'other prompt' \" + \\\n f\"Return (main_object, other prompt)\" + \\\n f'Given caption: {caption}.'\n }\n ]\n response = litellm.completion(model=model, messages=prompt, temperature=0.6, max_tokens=max_tokens)\n reply = response['choices'][0]['message']['content']\n try:\n det_prompt, inpaint_prompt = reply.split('\\n')[0].split(':')[-1].strip(), reply.split('\\n')[1].split(':')[-1].strip()\n except:\n warn(f\"Failed to extract tags from caption\") # use caption as det_prompt, inpaint_prompt\n det_prompt, inpaint_prompt = caption, caption\n return det_prompt, inpaint_prompt\n\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser(\"Grounded-Segment-Anything Demo\", add_help=True)\n parser.add_argument(\"--config\", type=str, required=True, help=\"path to config file\")\n parser.add_argument(\n \"--grounded_checkpoint\", type=str, required=True, help=\"path to checkpoint file\"\n )\n parser.add_argument(\n \"--sam_checkpoint\", type=str, required=True, help=\"path to checkpoint file\"\n )\n parser.add_argument(\"--input_image\", type=str, required=True, help=\"path to image file\")\n parser.add_argument(\n \"--output_dir\", \"-o\", type=str, default=\"outputs\", required=True, help=\"output directory\"\n )\n parser.add_argument(\"--cache_dir\", type=str, default=None, help=\"save your huggingface large model cache\")\n parser.add_argument(\"--det_speech_file\", type=str, help=\"grounding speech file\")\n parser.add_argument(\"--inpaint_speech_file\", type=str, help=\"inpaint speech file\")\n parser.add_argument(\"--prompt_speech_file\", type=str, help=\"prompt speech file, no need to provide det_speech_file\")\n parser.add_argument(\"--enable_chatgpt\", action=\"store_true\", help=\"enable chatgpt\")\n parser.add_argument(\"--openai_key\", type=str, help=\"key for chatgpt\")\n parser.add_argument(\"--openai_proxy\", default=None, type=str, help=\"proxy for chatgpt\")\n parser.add_argument(\"--whisper_model\", type=str, default=\"small\", help=\"whisper model version: tiny, base, small, medium, large\")\n parser.add_argument(\"--box_threshold\", type=float, default=0.3, help=\"box threshold\")\n parser.add_argument(\"--text_threshold\", type=float, default=0.25, help=\"text threshold\")\n parser.add_argument(\"--inpaint_mode\", type=str, default=\"first\", help=\"inpaint mode\")\n parser.add_argument(\"--device\", type=str, default=\"cpu\", help=\"running on cpu only!, default=False\")\n parser.add_argument(\"--prompt_extra\", type=str, default=\" high resolution, real scene\", help=\"extra prompt for inpaint\")\n args = parser.parse_args()\n\n # cfg\n config_file = args.config # change the path of the model config file\n grounded_checkpoint = args.grounded_checkpoint # change the path of the model\n sam_checkpoint = args.sam_checkpoint\n image_path = args.input_image\n\n output_dir = args.output_dir\n cache_dir=args.cache_dir\n # if not os.path.exists(cache_dir):\n # print(f\"create your cache dir:{cache_dir}\")\n # os.mkdir(cache_dir) \n box_threshold = args.box_threshold\n text_threshold = args.text_threshold\n inpaint_mode = args.inpaint_mode\n device = args.device\n\n # make dir\n os.makedirs(output_dir, exist_ok=True)\n # load image\n image_pil, image = load_image(image_path)\n # load model\n model = load_model(config_file, grounded_checkpoint, device=device)\n\n # visualize raw image\n image_pil.save(os.path.join(output_dir, \"raw_image.jpg\"))\n \n # recognize speech\n whisper_model = whisper.load_model(args.whisper_model)\n \n if args.enable_chatgpt:\n openai.api_key = args.openai_key\n if args.openai_proxy:\n openai.proxy = {\"http\": args.openai_proxy, \"https\": args.openai_proxy}\n speech_text, _ = speech_recognition(args.prompt_speech_file, whisper_model)\n det_prompt, inpaint_prompt = filter_prompts_with_chatgpt(speech_text)\n inpaint_prompt += args.prompt_extra\n print(f\"det_prompt: {det_prompt}, inpaint_prompt: {inpaint_prompt}\")\n else:\n det_prompt, det_speech_language = speech_recognition(args.det_speech_file, whisper_model)\n inpaint_prompt, inpaint_speech_language = speech_recognition(args.inpaint_speech_file, whisper_model)\n print(f\"det_prompt: {det_prompt}, using language: {det_speech_language}\")\n print(f\"inpaint_prompt: {inpaint_prompt}, using language: {inpaint_speech_language}\")\n \n # run grounding dino model\n boxes_filt, pred_phrases = get_grounding_output(\n model, image, det_prompt, box_threshold, text_threshold, device=device\n )\n\n # initialize SAM\n predictor = SamPredictor(build_sam(checkpoint=sam_checkpoint).to(device))\n image = cv2.imread(image_path)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n predictor.set_image(image)\n\n size = image_pil.size\n H, W = size[1], size[0]\n for i in range(boxes_filt.size(0)):\n boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])\n boxes_filt[i][:2] -= boxes_filt[i][2:] / 2\n boxes_filt[i][2:] += boxes_filt[i][:2]\n\n boxes_filt = boxes_filt.cpu()\n transformed_boxes = predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2]).to(device)\n\n masks, _, _ = predictor.predict_torch(\n point_coords = None,\n point_labels = None,\n boxes = transformed_boxes.to(device),\n multimask_output = False,\n )\n\n # masks: [1, 1, 512, 512]\n\n # inpainting pipeline\n if inpaint_mode == 'merge':\n masks = torch.sum(masks, dim=0).unsqueeze(0)\n masks = torch.where(masks > 0, True, False)\n mask = masks[0][0].cpu().numpy() # simply choose the first mask, which will be refine in the future release\n mask_pil = Image.fromarray(mask)\n image_pil = Image.fromarray(image)\n \n pipe = StableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", torch_dtype=torch.float16,cache_dir=cache_dir\n )\n pipe = pipe.to(\"cuda\")\n\n # prompt = \"A sofa, high quality, detailed\"\n image = pipe(prompt=inpaint_prompt, image=image_pil, mask_image=mask_pil).images[0]\n image.save(os.path.join(output_dir, \"grounded_sam_inpainting_output.jpg\"))\n\n # draw output image\n # plt.figure(figsize=(10, 10))\n # plt.imshow(image)\n # for mask in masks:\n # show_mask(mask.cpu().numpy(), plt.gca(), random_color=True)\n # for box, label in zip(boxes_filt, pred_phrases):\n # show_box(box.numpy(), plt.gca(), label)\n # plt.axis('off')\n # plt.savefig(os.path.join(output_dir, \"grounded_sam_output.jpg\"), bbox_inches=\"tight\")\n\n" }, { "path": "data_generation/Grounded-Segment-Anything/grounding_dino_demo.py", "content": "from groundingdino.util.inference import load_model, load_image, predict, annotate, Model\nimport cv2\n\n\nCONFIG_PATH = \"GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py\"\nCHECKPOINT_PATH = \"./groundingdino_swint_ogc.pth\"\nDEVICE = \"cuda\"\nIMAGE_PATH = \"assets/demo7.jpg\"\nTEXT_PROMPT = \"Horse. Clouds. Grasses. Sky. Hill.\"\nBOX_TRESHOLD = 0.35\nTEXT_TRESHOLD = 0.25\nFP16_INFERENCE = True\n\nimage_source, image = load_image(IMAGE_PATH)\nmodel = load_model(CONFIG_PATH, CHECKPOINT_PATH)\n\nif FP16_INFERENCE:\n image = image.half()\n model = model.half()\n\nboxes, logits, phrases = predict(\n model=model,\n image=image,\n caption=TEXT_PROMPT,\n box_threshold=BOX_TRESHOLD,\n text_threshold=TEXT_TRESHOLD,\n device=DEVICE,\n)\n\nannotated_frame = annotate(image_source=image_source, boxes=boxes, logits=logits, phrases=phrases)\ncv2.imwrite(\"annotated_image.jpg\", annotated_frame)" }, { "path": "data_generation/Grounded-Segment-Anything/playground/DeepFloyd/README.md", "content": "## DeepFloyd\n\n:grapes: [[Official Project Page](https://github.com/deep-floyd/IF)]   :apple:[[Official Online Demo](https://huggingface.co/spaces/DeepFloyd/IF)]\n\n> DeepFloyd IF is a novel state-of-the-art open-source text-to-image model with a high degree of photorealism and language understanding. \n\nWe've thoughtfully put together some important details for you to keep in mind while using the DeepFloyd models. We sincerely hope this will assist you in creating even more interesting demos with IF. Enjoy your creative journey!\n\n## Table of Contents\n- [Installation Details](#installation)\n - [Detailed installation guide](#detailed-installation-guide)\n - [Additional note for bug fixing](#additional-notes-for-bug-fixing)\n- [Requirements before running demo](#requirements-before-running-demos)\n- [DeepFloyd Demos](#deepfloyd-demos)\n - [Dream: Text to Image](#dream)\n - [Style Transfer](#style-transfer)\n\n\n## TODO\n- [x] Add installation guide (Continual Updating)\n- [x] Test Text-to-Image model\n- [x] Test Style-Transfer model\n- [ ] Add Inpaint demo (seems not work well)\n- [ ] Add SAM inpaint and Grounded-SAM inpaint demo\n\n## Installation\n### Detailed installation guide\nThere're more things you should take care for installing DeepFloyd despite of their official guide. You can install DeepFloyd as follows:\n\n- Create a new environment using `Python=3.10`\n\n```bash\nconda create -n floyd python=3.10 -y\nconda activate floyd\n```\n\n- DeepFloyd need [xformers](https://github.com/facebookresearch/xformers) to accelerate some attention mechanism and reduce the GPU memory usage. And `xformers` requires at least [PyTorch 1.12.1, PyTorch 1.13.1 or 2.0.0 installed with conda](https://pytorch.org/get-started/locally/).\n - If you only have CUDA 11.4 or lower CUDA version installed, you can only PyTorch 1.12.1 locally as:\n ```bash\n conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 -c pytorch\n ```\n - After installing PyTorch, it's highly recommended to install xformers using conda:\n ```bash\n conda install xformers -c xformers\n ```\n\n- Then install deepfloyd following their official guidance:\n```bash\npip install deepfloyd_if==1.0.2rc0\npip install git+https://github.com/openai/CLIP.git --no-deps\n```\n\n### Additional notes for bug fixing\n\n- [Attention] To use DeepFloyd with diffusers for saving GPU memory usage, you should update your transformers to at least `4.27.0` and accelerate to `0.17.0`.\n\n```bash\npip install transformers==4.27.1\npip install accelerate==0.17.0\n```\n\n- And refer to [DeepFloyd/issue64](https://github.com/deep-floyd/IF/pull/64), there are some bugs with inpainting demos, you need `protobuf==3.19.0` to load T5Embedder and `scikit-image` for inpainting\n\n```bash\npip install protobuf==3.19.0\npip install scikit-image\n```\n\nHowever this bug has not been updated to the python package of `DeepFloyd`, so the users should update the code manually follow issue64 or install `DeepFloyd` locally as:\n\n```bash\ngit clone https://github.com/deep-floyd/IF.git\ncd IF\npip install -e .\n```\n\n## Requirements before running demos\nBefore running DeepFloyd demo, please refer to [Integration with DIffusers](https://github.com/deep-floyd/IF#integration-with--diffusers) for some requirements for the pretrained weights.\n\nIf you want to download the weights into **specific dir**, you can set `cache_dir` as follows:\n\n- *Under diffusers*\n```python\nfrom diffusers import DiffusionPipeline\nfrom diffusers.utils import pt_to_pil\nimport torch\n\ncache_dir = \"path/to/specific_dir\"\n# stage 1\nstage_1 = DiffusionPipeline.from_pretrained(\n \"DeepFloyd/IF-I-XL-v1.0\", \n variant=\"fp16\", \n torch_dtype=torch.float16,\n cache_dir=cache_dir # loading model from specific dir\n )\nstage_1.enable_xformers_memory_efficient_attention() # remove line if torch.__version__ >= 2.0.0\nstage_1.enable_model_cpu_offload()\n```\n\n- *Runing locally*\n```python\nfrom deepfloyd_if.modules import IFStageI, IFStageII, StableStageIII\nfrom deepfloyd_if.modules.t5 import T5Embedder\n\ncache_dir = \"path/to/cache_dir\"\ndevice = 'cuda:0'\nif_I = IFStageI('IF-I-XL-v1.0', device=device, cache_dir=cache_dir)\nif_II = IFStageII('IF-II-L-v1.0', device=device, cache_dir=cache_dir)\nif_III = StableStageIII('stable-diffusion-x4-upscaler', device=device, cache_dir=cache_dir)\nt5 = T5Embedder(device=\"cpu\", cache_dir=cache_dir)\n```\n\n## DeepFloyd Demos\n\n- 16GB vRAM for IF-I-XL (4.3B text to 64x64 base module) & IF-II-L (1.2B to 256x256 upscaler module)\n- 24GB vRAM for IF-I-XL (4.3B text to 64x64 base module) & IF-II-L (1.2B to 256x256 upscaler module) & Stable x4 (to 1024x1024 upscaler)\n- ***(Highlight)*** `xformers` and set env variable `FORCE_MEM_EFFICIENT_ATTN=1`, which may help you to save lots of GPU memory usage\n```bash\nexport FORCE_MEM_EFFICIENT_ATTN=1\n```\n\n### Dream\nThe `text-to-image` mode for DeepFloyd\n```python\ncd playground/DeepFloyd\n\nexport FORCE_MEM_EFFICIENT_ATTN=1 \npython dream.py\n```\nIt takes around `26GB` GPU memory usage for this demo. You can download the following awesome generated images from [inpaint playground storage](https://github.com/IDEA-Research/detrex-storage/tree/main/assets/grounded_sam/inpaint_playground).\n\n\n\n\n| Prompt (Generated by GPT-4) | Generated Image |\n|:---- | :----: |\n| Underneath the galaxy sky, luminescent stars scatter across the vast expanse like diamond dust. Swirls of cosmic purple and blue nebulae coalesce, creating an ethereal canvas. A solitary tree silhouetted against the astral backdrop, roots burrowed deep into the earth, reaching towards the heavens. Leaves shimmer, reflecting the stellar light show. A lone figure, small against the celestial spectacle, contemplates their insignificance in the grandeur of the universe. The galaxy's reflection on a still, tranquil lake creates a stunning mirror image, tying the earth and cosmos together in a mesmerizing dance of light, space, and time. | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/inpaint_playground/dream1.jpg?raw=True) |\n|Beneath the vast sky, a mesmerizing seascape unfolds. The cerulean sea stretches out to infinity, its surface gently disturbed by the breath of the wind, creating delicate ripples. Sunlight dances on the water, transforming the ocean into a shimmering tapestry of light and shadow. A solitary sailboat navigates the expanse, its white sail billowing against the sapphire backdrop. Nearby, a lighthouse stands resolute on a rocky outcrop, its beacon piercing through the soft maritime mist. Shoreline meets the sea in a frothy embrace, while seagulls wheel overhead, their cries echoing the eternal song of the sea. The scent of salt and freedom fills the air, painting a picture of unbound exploration and serene beauty. | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/inpaint_playground/dream2.jpg?raw=True) |\n| In the heart of the wilderness, an enchanting forest reveals itself. Towering trees, their trunks sturdy and thick, reach skyward, their leafy canopies forming a natural cathedral. Verdant moss clings to bark, and tendrils of ivy climb ambitiously towards the sun-dappled treetops. The forest floor is a tapestry of fallen leaves, sprinkled with delicate wildflowers. The soft chatter of wildlife resonates, while a nearby brook babbles, its clear waters winking in the dappled light. Sunrays filter through the foliage, casting an emerald glow that dances on the woodland floor. Amidst the tranquility, the forest teems with life, whispering ancient secrets on the breeze. |![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/inpaint_playground/dream3.jpg?raw=True) |\n\n\n### Style Transfer\nDownload the original image from [here](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/inpaint_playground/style_transfer/original.jpg), which is borrowed from DeepFloyd official image.\n\n
\n\n
\n\n```python\ncd playground/DeepFloyd\n\nexport FORCE_MEM_EFFICIENT_ATTN=1 \npython style_transfer.py\n```\n\nStyle | Transfer Image (W/O SuperResolution) |\n| :----: | :----: |\n| *colorful and cute kawaii art* | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/inpaint_playground/style_transfer/style_transfer_0.jpg?raw=True) |\n| *boho-chic textile patterns* | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/inpaint_playground/style_transfer/style_transfer_1.jpg?raw=True) |\n" }, { "path": "data_generation/Grounded-Segment-Anything/playground/DeepFloyd/dream.py", "content": "from deepfloyd_if.modules import IFStageI, IFStageII, StableStageIII\nfrom deepfloyd_if.modules.t5 import T5Embedder\nfrom deepfloyd_if.pipelines import dream\n\n# Run locally\ndevice = 'cuda'\ncache_dir = \"/path/to/storage/IF\"\nif_I = IFStageI('IF-I-L-v1.0', device=device, cache_dir=cache_dir)\nif_II = IFStageII('IF-II-L-v1.0', device=device, cache_dir=cache_dir)\nif_III = StableStageIII('stable-diffusion-x4-upscaler', device=device, cache_dir=cache_dir)\nt5 = T5Embedder(device=device, cache_dir=cache_dir)\n\nprompt = \"In the heart of the wilderness, an enchanting forest reveals itself. \\\n Towering trees, their trunks sturdy and thick, reach skyward, their leafy canopies \\\n forming a natural cathedral. Verdant moss clings to bark, and tendrils of ivy climb ambitiously towards the sun-dappled treetops. \\\n The forest floor is a tapestry of fallen leaves, sprinkled with delicate wildflowers. The soft chatter of wildlife resonates, while a nearby brook babbles, its clear waters winking in the dappled light. \\\n Sunrays filter through the foliage, casting an emerald glow that dances on the woodland floor. Amidst the tranquility, the forest teems with life, whispering ancient secrets on the breeze.\"\ncount = 1\n\nresult = dream(\n t5=t5, if_I=if_I, if_II=if_II, if_III=if_III,\n prompt=[prompt]*count,\n seed=42,\n if_I_kwargs={\n \"guidance_scale\": 7.0,\n \"sample_timestep_respacing\": \"smart100\",\n },\n if_II_kwargs={\n \"guidance_scale\": 4.0,\n \"sample_timestep_respacing\": \"smart50\",\n },\n if_III_kwargs={\n \"guidance_scale\": 9.0,\n \"noise_level\": 20,\n \"sample_timestep_respacing\": \"75\",\n },\n)\nresult['III'][0].save(\"./dream_figure.jpg\")\n\n" }, { "path": "data_generation/Grounded-Segment-Anything/playground/DeepFloyd/inpaint.py", "content": "import PIL\nimport requests\nfrom io import BytesIO\nfrom torchvision.transforms import ToTensor\n\nfrom deepfloyd_if.modules import IFStageI, IFStageII, StableStageIII\nfrom deepfloyd_if.modules.t5 import T5Embedder\nfrom deepfloyd_if.pipelines import inpainting\n\ndef download_image(url):\n response = requests.get(url)\n return PIL.Image.open(BytesIO(response.content)).convert(\"RGB\")\n\nimg_url = \"https://raw.githubusercontent.com/Fantasy-Studio/Paint-by-Example/main/examples/image/example_1.png\"\nmask_url = \"https://raw.githubusercontent.com/Fantasy-Studio/Paint-by-Example/main/examples/mask/example_1.png\"\n\ninit_image = download_image(img_url).resize((512, 512))\nmask_image = download_image(mask_url).resize((512, 512))\n\n# convert mask_image to torch.Tensor to avoid bug\nmask_image = ToTensor()(mask_image).unsqueeze(0) # (1, 3, 512, 512)\n\n# Run locally\ndevice = 'cuda:5'\ncache_dir = \"/comp_robot/rentianhe/weights/IF/\"\nif_I = IFStageI('IF-I-L-v1.0', device=device, cache_dir=cache_dir)\nif_II = IFStageII('IF-II-L-v1.0', device=device, cache_dir=cache_dir)\nif_III = StableStageIII('stable-diffusion-x4-upscaler', device=device, cache_dir=cache_dir)\nt5 = T5Embedder(device=device, cache_dir=cache_dir)\nresult = inpainting(\n t5=t5, if_I=if_I,\n if_II=if_II,\n if_III=if_III,\n support_pil_img=init_image,\n inpainting_mask=mask_image,\n prompt=[\n 'A Panda'\n ],\n seed=42,\n if_I_kwargs={\n \"guidance_scale\": 7.0,\n \"sample_timestep_respacing\": \"10,10,10,10,10,0,0,0,0,0\",\n 'support_noise_less_qsample_steps': 0,\n },\n if_II_kwargs={\n \"guidance_scale\": 4.0,\n 'aug_level': 0.0,\n \"sample_timestep_respacing\": '100',\n },\n if_III_kwargs={\n \"guidance_scale\": 9.0,\n \"noise_level\": 20,\n \"sample_timestep_respacing\": \"75\",\n },\n)\nif_I.show(result['I'], 2, 3)\nif_I.show(result['II'], 2, 6)\nif_I.show(result['III'], 2, 14)\n\n" }, { "path": "data_generation/Grounded-Segment-Anything/playground/DeepFloyd/style_transfer.py", "content": "from PIL import Image\n\nfrom deepfloyd_if.modules import IFStageI, IFStageII\nfrom deepfloyd_if.modules.t5 import T5Embedder\nfrom deepfloyd_if.pipelines import style_transfer\n\n# Run locally\ndevice = 'cuda'\ncache_dir = \"/path/to/storage/IF\"\nif_I = IFStageI('IF-I-XL-v1.0', device=device, cache_dir=cache_dir)\nif_II = IFStageII('IF-II-L-v1.0', device=device, cache_dir=cache_dir)\nt5 = T5Embedder(device=device, cache_dir=cache_dir)\n\n# Style generate from GPT-4\nstyle_prompt = [\n \"in style of colorful and cute kawaii art\",\n \"in style of boho-chic textile patterns\",\n]\n\nraw_pil_image = Image.open(\"/path/to/image\")\n\nresult = style_transfer(\n t5=t5, if_I=if_I, if_II=if_II,\n support_pil_img=raw_pil_image,\n style_prompt=style_prompt,\n seed=42,\n if_I_kwargs={\n \"guidance_scale\": 10.0,\n \"sample_timestep_respacing\": \"10,10,10,10,10,10,10,10,0,0\",\n 'support_noise_less_qsample_steps': 5,\n },\n if_II_kwargs={\n \"guidance_scale\": 4.0,\n \"sample_timestep_respacing\": 'smart50',\n \"support_noise_less_qsample_steps\": 5,\n },\n)\n\n# save all the images generated in StageII\nfor i, image in enumerate(result[\"II\"]):\n image.save(\"./style_transfer_{}.jpg\".format(i))\n\n\n" }, { "path": "data_generation/Grounded-Segment-Anything/playground/ImageBind_SAM/README.md", "content": "## ImageBind with SAM\n\nThis is an experimental demo aims to combine [ImageBind](https://github.com/facebookresearch/ImageBind) and [SAM](https://github.com/facebookresearch/segment-anything) to generate mask **with different modalities**.\n\nThis basic idea is followed with [IEA: Image Editing Anything](https://github.com/feizc/IEA) and [CLIP-SAM](https://github.com/maxi-w/CLIP-SAM) which generate the referring mask with the following steps:\n\n- Step 1: Generate auto masks with `SamAutomaticMaskGenerator`\n- Step 2: Crop all the box region from the masks\n- Step 3: Compute the similarity with cropped images and different modalities\n- Step 4: Merge the highest similarity mask region\n\n## Table of contents\n- [Installation](#installation)\n- [ImageBind-SAM Demo](#run-the-demo)\n- [Audio Referring Segment](#run-audio-referring-segment-demo)\n- [Text Referring Segment](#run-text-referring-segment-demo)\n- [Image Referring Segment](#run-image-referring-segmentation-demo)\n\n\n\n## Installation\n- Download the pretrained checkpoints\n\n```bash\ncd playground/ImageBind_SAM\n\nmkdir .checkpoints\ncd .checkpoints\n\n# download imagebind weights\nwget https://dl.fbaipublicfiles.com/imagebind/imagebind_huge.pth\nwget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\n```\n\n- Install ImageBind follow the [official installation guidance](https://github.com/facebookresearch/ImageBind#usage).\n- Install Grounded-SAM follow [install Grounded-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything#installation).\n\n\n## Run the demo\n```bash\npython demo.py\n```\n\nWe implement `Text Seg` and `Audio Seg` in this demo, the generate masks will be saved as `text_sam_merged_mask.jpg` and `audio_sam_merged_mask.jpg`:\n\n
\n\n| Input Model | Modality | Generate Mask |\n|:----:|:----:|:----:|\n| ![](./.assets/car_image.jpg) | [car audio](./.assets/car_audio.wav) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/imagebind_sam/audio_sam_merged_mask_new.jpg?raw=true) |\n| ![](./.assets/car_image.jpg) | \"A car\" | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/imagebind_sam/text_sam_merged_mask.jpg?raw=true) |\n| ![](./.assets/car_image.jpg) |
| ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/imagebind_sam/image_referring_sam_merged_mask.jpg?raw=true) |\n\n\n
\n\nBy setting different threshold may influence a lot on the final results.\n\n## Run image referring segmentation demo\n```bash\n# download the referring image\ncd .assets\nwget https://github.com/IDEA-Research/detrex-storage/releases/download/grounded-sam-storage/referring_car_image.jpg\ncd ..\n\npython image_referring_seg_demo.py\n```\n\n## Run audio referring segmentation demo\n```bash\npython audio_referring_seg_demo.py\n```\n\n## Run text referring segmentation demo\n```bash\npython text_referring_seg_demo.py\n```" }, { "path": "data_generation/Grounded-Segment-Anything/playground/ImageBind_SAM/audio_referring_seg_demo.py", "content": "import data\nimport cv2\nimport torch\nfrom PIL import Image, ImageDraw\nfrom tqdm import tqdm\nfrom models import imagebind_model\nfrom models.imagebind_model import ModalityType\n\nfrom segment_anything import build_sam, SamAutomaticMaskGenerator\n\nfrom utils import (\n segment_image, \n convert_box_xywh_to_xyxy,\n get_indices_of_values_above_threshold,\n)\n\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n\n\"\"\"\nStep 1: Instantiate model\n\"\"\"\n# Segment Anything\nmask_generator = SamAutomaticMaskGenerator(\n build_sam(checkpoint=\".checkpoints/sam_vit_h_4b8939.pth\").to(device),\n points_per_side=16,\n)\n\n# ImageBind\nbind_model = imagebind_model.imagebind_huge(pretrained=True)\nbind_model.eval()\nbind_model.to(device)\n\n\n\"\"\"\nStep 2: Generate auto masks with SAM\n\"\"\"\nimage_path = \".assets/car_image.jpg\"\nimage = cv2.imread(image_path)\nimage = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\nmasks = mask_generator.generate(image)\n\n\n\"\"\"\nStep 3: Get cropped images based on mask and box\n\"\"\"\ncropped_boxes = []\nimage = Image.open(image_path)\nfor mask in tqdm(masks):\n cropped_boxes.append(segment_image(image, mask[\"segmentation\"]).crop(convert_box_xywh_to_xyxy(mask[\"bbox\"])))\n\n\n\"\"\"\nStep 4: Run ImageBind model to get similarity between cropped image and different modalities\n\"\"\"\ndef retriev_vision_and_audio(elements, audio_list):\n inputs = {\n ModalityType.VISION: data.load_and_transform_vision_data_from_pil_image(elements, device),\n ModalityType.AUDIO: data.load_and_transform_audio_data(audio_list, device),\n }\n with torch.no_grad():\n embeddings = bind_model(inputs)\n vision_audio = torch.softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.AUDIO].T, dim=0),\n return vision_audio \n\nvision_audio_result = retriev_vision_and_audio(cropped_boxes, [\".assets/car_audio.wav\"])\n\n\n\"\"\"\nStep 5: Merge the top similarity masks to get the final mask and save the merged mask\n\nThis is the audio retrival result\n\"\"\"\n\n# get highest similar mask with threshold\n# result[0] shape: [113, 1]\nthreshold = 0.025\nindex = get_indices_of_values_above_threshold(vision_audio_result[0], threshold)\n\nsegmentation_masks = []\nfor seg_idx in index:\n segmentation_mask_image = Image.fromarray(masks[seg_idx][\"segmentation\"].astype('uint8') * 255)\n segmentation_masks.append(segmentation_mask_image)\n\noriginal_image = Image.open(image_path)\noverlay_image = Image.new('RGBA', image.size, (0, 0, 0, 255))\noverlay_color = (255, 255, 255, 0)\n\ndraw = ImageDraw.Draw(overlay_image)\nfor segmentation_mask_image in segmentation_masks:\n draw.bitmap((0, 0), segmentation_mask_image, fill=overlay_color)\n\n# return Image.alpha_composite(original_image.convert('RGBA'), overlay_image) \nmask_image = overlay_image.convert(\"RGB\")\nmask_image.save(\"./audio_sam_merged_mask.jpg\")\n\n" }, { "path": "data_generation/Grounded-Segment-Anything/playground/ImageBind_SAM/data.py", "content": "#!/usr/bin/env python3\n# Portions Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport math\n\nimport torch\nimport torch.nn as nn\nimport torchaudio\nimport logging\n\nfrom models.multimodal_preprocessors import SimpleTokenizer\nfrom PIL import Image\nfrom pytorchvideo import transforms as pv_transforms\nfrom pytorchvideo.data.clip_sampling import ConstantClipsPerVideoSampler\nfrom pytorchvideo.data.encoded_video import EncodedVideo\n\nfrom torchvision import transforms\nfrom torchvision.transforms._transforms_video import NormalizeVideo\n\nDEFAULT_AUDIO_FRAME_SHIFT_MS = 10 # in milliseconds\n\nBPE_PATH = \"bpe/bpe_simple_vocab_16e6.txt.gz\"\n\n\ndef waveform2melspec(waveform, sample_rate, num_mel_bins, target_length):\n # Based on https://github.com/YuanGongND/ast/blob/d7d8b4b8e06cdaeb6c843cdb38794c1c7692234c/src/dataloader.py#L102\n waveform -= waveform.mean()\n fbank = torchaudio.compliance.kaldi.fbank(\n waveform,\n htk_compat=True,\n sample_frequency=sample_rate,\n use_energy=False,\n window_type=\"hanning\",\n num_mel_bins=num_mel_bins,\n dither=0.0,\n frame_length=25,\n frame_shift=DEFAULT_AUDIO_FRAME_SHIFT_MS,\n )\n # Convert to [mel_bins, num_frames] shape\n fbank = fbank.transpose(0, 1)\n # Pad to target_length\n n_frames = fbank.size(1)\n p = target_length - n_frames\n # if p is too large (say >20%), flash a warning\n if abs(p) / n_frames > 0.2:\n logging.warning(\n \"Large gap between audio n_frames(%d) and \"\n \"target_length (%d). Is the audio_target_length \"\n \"setting correct?\",\n n_frames,\n target_length,\n )\n # cut and pad\n if p > 0:\n fbank = torch.nn.functional.pad(fbank, (0, p), mode=\"constant\", value=0)\n elif p < 0:\n fbank = fbank[:, 0:target_length]\n # Convert to [1, mel_bins, num_frames] shape, essentially like a 1\n # channel image\n fbank = fbank.unsqueeze(0)\n return fbank\n\n\ndef get_clip_timepoints(clip_sampler, duration):\n # Read out all clips in this video\n all_clips_timepoints = []\n is_last_clip = False\n end = 0.0\n while not is_last_clip:\n start, end, _, _, is_last_clip = clip_sampler(end, duration, annotation=None)\n all_clips_timepoints.append((start, end))\n return all_clips_timepoints\n\ndef load_and_transform_vision_data_from_pil_image(img_list, device):\n if img_list is None:\n return None\n\n image_ouputs = []\n for image in img_list:\n data_transform = transforms.Compose(\n [\n transforms.Resize(\n 224, interpolation=transforms.InterpolationMode.BICUBIC\n ),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize(\n mean=(0.48145466, 0.4578275, 0.40821073),\n std=(0.26862954, 0.26130258, 0.27577711),\n ),\n ]\n )\n\n image = data_transform(image).to(device)\n image_ouputs.append(image)\n return torch.stack(image_ouputs, dim=0)\n\ndef load_and_transform_vision_data(image_paths, device):\n if image_paths is None:\n return None\n\n image_ouputs = []\n for image_path in image_paths:\n data_transform = transforms.Compose(\n [\n transforms.Resize(\n 224, interpolation=transforms.InterpolationMode.BICUBIC\n ),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize(\n mean=(0.48145466, 0.4578275, 0.40821073),\n std=(0.26862954, 0.26130258, 0.27577711),\n ),\n ]\n )\n with open(image_path, \"rb\") as fopen:\n image = Image.open(fopen).convert(\"RGB\")\n\n image = data_transform(image).to(device)\n image_ouputs.append(image)\n return torch.stack(image_ouputs, dim=0)\n\n\ndef load_and_transform_text(text, device):\n if text is None:\n return None\n tokenizer = SimpleTokenizer(bpe_path=BPE_PATH)\n tokens = [tokenizer(t).unsqueeze(0).to(device) for t in text]\n tokens = torch.cat(tokens, dim=0)\n return tokens\n\n\ndef load_and_transform_audio_data(\n audio_paths,\n device,\n num_mel_bins=128,\n target_length=204,\n sample_rate=16000,\n clip_duration=2,\n clips_per_video=3,\n mean=-4.268,\n std=9.138,\n):\n if audio_paths is None:\n return None\n\n audio_outputs = []\n clip_sampler = ConstantClipsPerVideoSampler(\n clip_duration=clip_duration, clips_per_video=clips_per_video\n )\n\n for audio_path in audio_paths:\n waveform, sr = torchaudio.load(audio_path)\n if sample_rate != sr:\n waveform = torchaudio.functional.resample(\n waveform, orig_freq=sr, new_freq=sample_rate\n )\n all_clips_timepoints = get_clip_timepoints(\n clip_sampler, waveform.size(1) / sample_rate\n )\n all_clips = []\n for clip_timepoints in all_clips_timepoints:\n waveform_clip = waveform[\n :,\n int(clip_timepoints[0] * sample_rate) : int(\n clip_timepoints[1] * sample_rate\n ),\n ]\n waveform_melspec = waveform2melspec(\n waveform_clip, sample_rate, num_mel_bins, target_length\n )\n all_clips.append(waveform_melspec)\n\n normalize = transforms.Normalize(mean=mean, std=std)\n all_clips = [normalize(ac).to(device) for ac in all_clips]\n\n all_clips = torch.stack(all_clips, dim=0)\n audio_outputs.append(all_clips)\n\n return torch.stack(audio_outputs, dim=0)\n\n\ndef get_clip_timepoints(clip_sampler, duration):\n # Read out all clips in this video\n all_clips_timepoints = []\n is_last_clip = False\n end = 0.0\n while not is_last_clip:\n start, end, _, _, is_last_clip = clip_sampler(end, duration, annotation=None)\n all_clips_timepoints.append((start, end))\n return all_clips_timepoints\n\n\ndef crop_boxes(boxes, x_offset, y_offset):\n \"\"\"\n Peform crop on the bounding boxes given the offsets.\n Args:\n boxes (ndarray or None): bounding boxes to peform crop. The dimension\n is `num boxes` x 4.\n x_offset (int): cropping offset in the x axis.\n y_offset (int): cropping offset in the y axis.\n Returns:\n cropped_boxes (ndarray or None): the cropped boxes with dimension of\n `num boxes` x 4.\n \"\"\"\n cropped_boxes = boxes.copy()\n cropped_boxes[:, [0, 2]] = boxes[:, [0, 2]] - x_offset\n cropped_boxes[:, [1, 3]] = boxes[:, [1, 3]] - y_offset\n\n return cropped_boxes\n\n\ndef uniform_crop(images, size, spatial_idx, boxes=None, scale_size=None):\n \"\"\"\n Perform uniform spatial sampling on the images and corresponding boxes.\n Args:\n images (tensor): images to perform uniform crop. The dimension is\n `num frames` x `channel` x `height` x `width`.\n size (int): size of height and weight to crop the images.\n spatial_idx (int): 0, 1, or 2 for left, center, and right crop if width\n is larger than height. Or 0, 1, or 2 for top, center, and bottom\n crop if height is larger than width.\n boxes (ndarray or None): optional. Corresponding boxes to images.\n Dimension is `num boxes` x 4.\n scale_size (int): optinal. If not None, resize the images to scale_size before\n performing any crop.\n Returns:\n cropped (tensor): images with dimension of\n `num frames` x `channel` x `size` x `size`.\n cropped_boxes (ndarray or None): the cropped boxes with dimension of\n `num boxes` x 4.\n \"\"\"\n assert spatial_idx in [0, 1, 2]\n ndim = len(images.shape)\n if ndim == 3:\n images = images.unsqueeze(0)\n height = images.shape[2]\n width = images.shape[3]\n\n if scale_size is not None:\n if width <= height:\n width, height = scale_size, int(height / width * scale_size)\n else:\n width, height = int(width / height * scale_size), scale_size\n images = torch.nn.functional.interpolate(\n images,\n size=(height, width),\n mode=\"bilinear\",\n align_corners=False,\n )\n\n y_offset = int(math.ceil((height - size) / 2))\n x_offset = int(math.ceil((width - size) / 2))\n\n if height > width:\n if spatial_idx == 0:\n y_offset = 0\n elif spatial_idx == 2:\n y_offset = height - size\n else:\n if spatial_idx == 0:\n x_offset = 0\n elif spatial_idx == 2:\n x_offset = width - size\n cropped = images[:, :, y_offset : y_offset + size, x_offset : x_offset + size]\n cropped_boxes = crop_boxes(boxes, x_offset, y_offset) if boxes is not None else None\n if ndim == 3:\n cropped = cropped.squeeze(0)\n return cropped, cropped_boxes\n\n\nclass SpatialCrop(nn.Module):\n \"\"\"\n Convert the video into 3 smaller clips spatially. Must be used after the\n temporal crops to get spatial crops, and should be used with\n -2 in the spatial crop at the slowfast augmentation stage (so full\n frames are passed in here). Will return a larger list with the\n 3x spatial crops as well.\n \"\"\"\n\n def __init__(self, crop_size: int = 224, num_crops: int = 3):\n super().__init__()\n self.crop_size = crop_size\n if num_crops == 3:\n self.crops_to_ext = [0, 1, 2]\n self.flipped_crops_to_ext = []\n elif num_crops == 1:\n self.crops_to_ext = [1]\n self.flipped_crops_to_ext = []\n else:\n raise NotImplementedError(\"Nothing else supported yet\")\n\n def forward(self, videos):\n \"\"\"\n Args:\n videos: A list of C, T, H, W videos.\n Returns:\n videos: A list with 3x the number of elements. Each video converted\n to C, T, H', W' by spatial cropping.\n \"\"\"\n assert isinstance(videos, list), \"Must be a list of videos after temporal crops\"\n assert all([video.ndim == 4 for video in videos]), \"Must be (C,T,H,W)\"\n res = []\n for video in videos:\n for spatial_idx in self.crops_to_ext:\n res.append(uniform_crop(video, self.crop_size, spatial_idx)[0])\n if not self.flipped_crops_to_ext:\n continue\n flipped_video = transforms.functional.hflip(video)\n for spatial_idx in self.flipped_crops_to_ext:\n res.append(uniform_crop(flipped_video, self.crop_size, spatial_idx)[0])\n return res\n\n\ndef load_and_transform_video_data(\n video_paths,\n device,\n clip_duration=2,\n clips_per_video=5,\n sample_rate=16000,\n):\n if video_paths is None:\n return None\n\n video_outputs = []\n video_transform = transforms.Compose(\n [\n pv_transforms.ShortSideScale(224),\n NormalizeVideo(\n mean=(0.48145466, 0.4578275, 0.40821073),\n std=(0.26862954, 0.26130258, 0.27577711),\n ),\n ]\n )\n\n clip_sampler = ConstantClipsPerVideoSampler(\n clip_duration=clip_duration, clips_per_video=clips_per_video\n )\n frame_sampler = pv_transforms.UniformTemporalSubsample(num_samples=clip_duration)\n\n for video_path in video_paths:\n video = EncodedVideo.from_path(\n video_path,\n decoder=\"decord\",\n decode_audio=False,\n **{\"sample_rate\": sample_rate},\n )\n\n all_clips_timepoints = get_clip_timepoints(clip_sampler, video.duration)\n\n all_video = []\n for clip_timepoints in all_clips_timepoints:\n # Read the clip, get frames\n clip = video.get_clip(clip_timepoints[0], clip_timepoints[1])\n if clip is None:\n raise ValueError(\"No clip found\")\n video_clip = frame_sampler(clip[\"video\"])\n video_clip = video_clip / 255.0 # since this is float, need 0-1\n\n all_video.append(video_clip)\n\n all_video = [video_transform(clip) for clip in all_video]\n all_video = SpatialCrop(224, num_crops=3)(all_video)\n\n all_video = torch.stack(all_video, dim=0)\n video_outputs.append(all_video)\n\n return torch.stack(video_outputs, dim=0).to(device)\n" }, { "path": "data_generation/Grounded-Segment-Anything/playground/ImageBind_SAM/demo.py", "content": "import data\nimport cv2\nimport torch\nimport numpy as np\nfrom PIL import Image, ImageDraw\nfrom tqdm import tqdm\nfrom models import imagebind_model\nfrom models.imagebind_model import ModalityType\n\nfrom segment_anything import build_sam, SamAutomaticMaskGenerator\n\nfrom utils import (\n segment_image, \n convert_box_xywh_to_xyxy,\n get_indices_of_values_above_threshold,\n)\n\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n\n\"\"\"\nStep 1: Instantiate model\n\"\"\"\n# Segment Anything\nmask_generator = SamAutomaticMaskGenerator(\n build_sam(checkpoint=\".checkpoints/sam_vit_h_4b8939.pth\").to(device),\n points_per_side=16,\n)\n\n# ImageBind\nbind_model = imagebind_model.imagebind_huge(pretrained=True)\nbind_model.eval()\nbind_model.to(device)\n\n\n\"\"\"\nStep 2: Generate auto masks with SAM\n\"\"\"\nimage_path = \".assets/car_image.jpg\"\nimage = cv2.imread(image_path)\nimage = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\nmasks = mask_generator.generate(image)\n\n\n\"\"\"\nStep 3: Get cropped images based on mask and box\n\"\"\"\ncropped_boxes = []\nimage = Image.open(image_path)\nfor mask in tqdm(masks):\n cropped_boxes.append(segment_image(image, mask[\"segmentation\"]).crop(convert_box_xywh_to_xyxy(mask[\"bbox\"])))\n\n\n\"\"\"\nStep 4: Run ImageBind model to get similarity between cropped image and different modalities\n\"\"\"\ndef retriev_vision_and_text(elements, text_list):\n inputs = {\n ModalityType.VISION: data.load_and_transform_vision_data_from_pil_image(elements, device),\n ModalityType.TEXT: data.load_and_transform_text(text_list, device),\n }\n with torch.no_grad():\n embeddings = bind_model(inputs)\n vision_audio = torch.softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.TEXT].T, dim=0),\n return vision_audio # [113, 1]\n\ndef retriev_vision_and_audio(elements, audio_list):\n inputs = {\n ModalityType.VISION: data.load_and_transform_vision_data_from_pil_image(elements, device),\n ModalityType.AUDIO: data.load_and_transform_audio_data(audio_list, device),\n }\n with torch.no_grad():\n embeddings = bind_model(inputs)\n vision_audio = torch.softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.AUDIO].T, dim=0),\n return vision_audio \n\nvision_audio_result = retriev_vision_and_audio(cropped_boxes, [\".assets/car_audio.wav\"])\nvision_text_result = retriev_vision_and_text(cropped_boxes, [\"A car\"] )\n\n\n\"\"\"\nStep 5: Merge the top similarity masks to get the final mask and save the merged mask\n\nThis is the audio retrival result\n\"\"\"\n\n# get highest similar mask with threshold\n# result[0] shape: [113, 1]\nthreshold = 0.025\nindex = get_indices_of_values_above_threshold(vision_audio_result[0], threshold)\n\nsegmentation_masks = []\nfor seg_idx in index:\n segmentation_mask_image = Image.fromarray(masks[seg_idx][\"segmentation\"].astype('uint8') * 255)\n segmentation_masks.append(segmentation_mask_image)\n\noriginal_image = Image.open(image_path)\noverlay_image = Image.new('RGBA', image.size, (0, 0, 0, 255))\noverlay_color = (255, 255, 255, 0)\n\ndraw = ImageDraw.Draw(overlay_image)\nfor segmentation_mask_image in segmentation_masks:\n draw.bitmap((0, 0), segmentation_mask_image, fill=overlay_color)\n\n# return Image.alpha_composite(original_image.convert('RGBA'), overlay_image) \nmask_image = overlay_image.convert(\"RGB\")\nmask_image.save(\"./audio_sam_merged_mask.jpg\")\n\n\"\"\"\nImage / Text mask\n\"\"\"\n# get highest similar mask with threshold\n# result[0] shape: [113, 1]\nthreshold = 0.05\nindex = get_indices_of_values_above_threshold(vision_text_result[0], threshold)\n\nsegmentation_masks = []\nfor seg_idx in index:\n segmentation_mask_image = Image.fromarray(masks[seg_idx][\"segmentation\"].astype('uint8') * 255)\n segmentation_masks.append(segmentation_mask_image)\n\noriginal_image = Image.open(image_path)\noverlay_image = Image.new('RGBA', image.size, (0, 0, 0, 255))\noverlay_color = (255, 255, 255, 0)\n\ndraw = ImageDraw.Draw(overlay_image)\nfor segmentation_mask_image in segmentation_masks:\n draw.bitmap((0, 0), segmentation_mask_image, fill=overlay_color)\n\n# return Image.alpha_composite(original_image.convert('RGBA'), overlay_image) \nmask_image = overlay_image.convert(\"RGB\")\nmask_image.save(\"./text_sam_merged_mask.jpg\")\n" }, { "path": "data_generation/Grounded-Segment-Anything/playground/ImageBind_SAM/image_referring_seg_demo.py", "content": "import data\nimport cv2\nimport torch\nfrom PIL import Image, ImageDraw\nfrom tqdm import tqdm\nfrom models import imagebind_model\nfrom models.imagebind_model import ModalityType\n\nfrom segment_anything import build_sam, SamAutomaticMaskGenerator\n\nfrom utils import (\n segment_image, \n convert_box_xywh_to_xyxy,\n get_indices_of_values_above_threshold,\n)\n\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n\n\"\"\"\nStep 1: Instantiate model\n\"\"\"\n# Segment Anything\nmask_generator = SamAutomaticMaskGenerator(\n build_sam(checkpoint=\".checkpoints/sam_vit_h_4b8939.pth\").to(device),\n points_per_side=16,\n)\n\n# ImageBind\nbind_model = imagebind_model.imagebind_huge(pretrained=True)\nbind_model.eval()\nbind_model.to(device)\n\n\n\"\"\"\nStep 2: Generate auto masks with SAM\n\"\"\"\nimage_path = \".assets/car_image.jpg\"\nimage = cv2.imread(image_path)\nimage = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\nmasks = mask_generator.generate(image)\n\n\n\"\"\"\nStep 3: Get cropped images based on mask and box\n\"\"\"\ncropped_boxes = []\nimage = Image.open(image_path)\nfor mask in tqdm(masks):\n cropped_boxes.append(segment_image(image, mask[\"segmentation\"]).crop(convert_box_xywh_to_xyxy(mask[\"bbox\"])))\n\n\n\"\"\"\nStep 4: Run ImageBind model to get similarity between cropped image and different modalities\n\"\"\"\n# load referring image\nreferring_image_path = \".assets/referring_car_image.jpg\"\nreferring_image = Image.open(referring_image_path)\n\nimage_list = []\nimage_list += cropped_boxes\nimage_list.append(referring_image)\n\ndef retriev_vision_and_vision(elements):\n inputs = {\n ModalityType.VISION: data.load_and_transform_vision_data_from_pil_image(elements, device),\n }\n with torch.no_grad():\n embeddings = bind_model(inputs)\n\n # cropped box region embeddings\n cropped_box_embeddings = embeddings[ModalityType.VISION][:-1, :]\n referring_image_embeddings = embeddings[ModalityType.VISION][-1, :]\n\n vision_referring_result = torch.softmax(cropped_box_embeddings @ referring_image_embeddings.T, dim=0),\n return vision_referring_result # [113, 1]\n\n\nvision_referring_result = retriev_vision_and_vision(image_list)\n\n\n\"\"\"\nStep 5: Merge the top similarity masks to get the final mask and save the merged mask\n\nImage / Text mask\n\"\"\"\n\n# get highest similar mask with threshold\n# result[0] shape: [113, 1]\nthreshold = 0.017\nindex = get_indices_of_values_above_threshold(vision_referring_result[0], threshold)\n\n\nsegmentation_masks = []\nfor seg_idx in index:\n segmentation_mask_image = Image.fromarray(masks[seg_idx][\"segmentation\"].astype('uint8') * 255)\n segmentation_masks.append(segmentation_mask_image)\n\noriginal_image = Image.open(image_path)\noverlay_image = Image.new('RGBA', image.size, (0, 0, 0, 255))\noverlay_color = (255, 255, 255, 0)\n\ndraw = ImageDraw.Draw(overlay_image)\nfor segmentation_mask_image in segmentation_masks:\n draw.bitmap((0, 0), segmentation_mask_image, fill=overlay_color)\n\n# return Image.alpha_composite(original_image.convert('RGBA'), overlay_image) \nmask_image = overlay_image.convert(\"RGB\")\nmask_image.save(\"./image_referring_sam_merged_mask.jpg\")\n" }, { "path": "data_generation/Grounded-Segment-Anything/playground/ImageBind_SAM/models/__init__.py", "content": "" }, { "path": "data_generation/Grounded-Segment-Anything/playground/ImageBind_SAM/models/helpers.py", "content": "#!/usr/bin/env python3\n# Portions Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport math\n\nimport einops\nimport numpy as np\nimport torch\n\nimport torch.nn as nn\n\n\nclass Normalize(nn.Module):\n def __init__(self, dim: int) -> None:\n super().__init__()\n self.dim = dim\n\n def forward(self, x):\n return torch.nn.functional.normalize(x, dim=self.dim, p=2)\n\n\nclass LearnableLogitScaling(nn.Module):\n def __init__(\n self,\n logit_scale_init: float = 1 / 0.07,\n learnable: bool = True,\n max_logit_scale: float = 100,\n ) -> None:\n super().__init__()\n self.max_logit_scale = max_logit_scale\n self.logit_scale_init = logit_scale_init\n self.learnable = learnable\n log_logit_scale = torch.ones([]) * np.log(self.logit_scale_init)\n if learnable:\n self.log_logit_scale = nn.Parameter(log_logit_scale)\n else:\n self.register_buffer(\"log_logit_scale\", log_logit_scale)\n\n def forward(self, x):\n return torch.clip(self.log_logit_scale.exp(), max=self.max_logit_scale) * x\n\n def extra_repr(self):\n st = f\"logit_scale_init={self.logit_scale_init},learnable={self.learnable}, max_logit_scale={self.max_logit_scale}\"\n return st\n\n\nclass EinOpsRearrange(nn.Module):\n def __init__(self, rearrange_expr: str, **kwargs) -> None:\n super().__init__()\n self.rearrange_expr = rearrange_expr\n self.kwargs = kwargs\n\n def forward(self, x):\n assert isinstance(x, torch.Tensor)\n return einops.rearrange(x, self.rearrange_expr, **self.kwargs)\n\n\nclass VerboseNNModule(nn.Module):\n \"\"\"\n Wrapper around nn.Module that prints registered buffers and parameter names.\n \"\"\"\n\n @staticmethod\n def get_readable_tensor_repr(name: str, tensor: torch.Tensor) -> str:\n st = (\n \"(\"\n + name\n + \"): \"\n + \"tensor(\"\n + str(tuple(tensor[1].shape))\n + \", requires_grad=\"\n + str(tensor[1].requires_grad)\n + \")\\n\"\n )\n return st\n\n def extra_repr(self) -> str:\n named_modules = set()\n for p in self.named_modules():\n named_modules.update([p[0]])\n named_modules = list(named_modules)\n\n string_repr = \"\"\n for p in self.named_parameters():\n name = p[0].split(\".\")[0]\n if name not in named_modules:\n string_repr += self.get_readable_tensor_repr(name, p)\n\n for p in self.named_buffers():\n name = p[0].split(\".\")[0]\n string_repr += self.get_readable_tensor_repr(name, p)\n\n return string_repr\n\n\ndef cast_if_src_dtype(\n tensor: torch.Tensor, src_dtype: torch.dtype, tgt_dtype: torch.dtype\n):\n updated = False\n if tensor.dtype == src_dtype:\n tensor = tensor.to(dtype=tgt_dtype)\n updated = True\n return tensor, updated\n\n\nclass QuickGELU(nn.Module):\n # From https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/clip/model.py#L166\n def forward(self, x: torch.Tensor):\n return x * torch.sigmoid(1.702 * x)\n\n\nclass SelectElement(nn.Module):\n def __init__(self, index) -> None:\n super().__init__()\n self.index = index\n\n def forward(self, x):\n assert x.ndim >= 3\n return x[:, self.index, ...]\n\n\nclass SelectEOSAndProject(nn.Module):\n \"\"\"\n Text Pooling used in OpenCLIP\n \"\"\"\n\n def __init__(self, proj: nn.Module) -> None:\n super().__init__()\n self.proj = proj\n\n def forward(self, x, seq_len):\n assert x.ndim == 3\n # x is of shape B x L x D\n # take features from the eot embedding (eot_token is the highest number in each sequence)\n x = x[torch.arange(x.shape[0]), seq_len]\n x = self.proj(x)\n return x\n" }, { "path": "data_generation/Grounded-Segment-Anything/playground/ImageBind_SAM/models/imagebind_model.py", "content": "#!/usr/bin/env python3\n# Portions Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\n\nimport os\nimport urllib\nfrom functools import partial\nfrom types import SimpleNamespace\n\nimport torch\nimport torch.nn as nn\n\nfrom models.helpers import (\n EinOpsRearrange,\n LearnableLogitScaling,\n Normalize,\n SelectElement,\n SelectEOSAndProject,\n)\nfrom models.multimodal_preprocessors import (\n AudioPreprocessor,\n IMUPreprocessor,\n PadIm2Video,\n PatchEmbedGeneric,\n RGBDTPreprocessor,\n SpatioTemporalPosEmbeddingHelper,\n TextPreprocessor,\n ThermalPreprocessor,\n)\n\nfrom models.transformer import MultiheadAttention, SimpleTransformer\n\n\nModalityType = SimpleNamespace(\n VISION=\"vision\",\n TEXT=\"text\",\n AUDIO=\"audio\",\n THERMAL=\"thermal\",\n DEPTH=\"depth\",\n IMU=\"imu\",\n)\n\n\nclass ImageBindModel(nn.Module):\n def __init__(\n self,\n video_frames=2,\n kernel_size=(2, 14, 14),\n audio_kernel_size=16,\n audio_stride=10,\n out_embed_dim=768,\n vision_embed_dim=1024,\n vision_num_blocks=24,\n vision_num_heads=16,\n audio_embed_dim=768,\n audio_num_blocks=12,\n audio_num_heads=12,\n audio_num_mel_bins=128,\n audio_target_len=204,\n audio_drop_path=0.1,\n text_embed_dim=768,\n text_num_blocks=12,\n text_num_heads=12,\n depth_embed_dim=384,\n depth_kernel_size=16,\n depth_num_blocks=12,\n depth_num_heads=8,\n depth_drop_path=0.0,\n thermal_embed_dim=768,\n thermal_kernel_size=16,\n thermal_num_blocks=12,\n thermal_num_heads=12,\n thermal_drop_path=0.0,\n imu_embed_dim=512,\n imu_kernel_size=8,\n imu_num_blocks=6,\n imu_num_heads=8,\n imu_drop_path=0.7,\n ):\n super().__init__()\n\n self.modality_preprocessors = self._create_modality_preprocessors(\n video_frames,\n vision_embed_dim,\n kernel_size,\n text_embed_dim,\n audio_embed_dim,\n audio_kernel_size,\n audio_stride,\n audio_num_mel_bins,\n audio_target_len,\n depth_embed_dim,\n depth_kernel_size,\n thermal_embed_dim,\n thermal_kernel_size,\n imu_embed_dim,\n )\n\n self.modality_trunks = self._create_modality_trunks(\n vision_embed_dim,\n vision_num_blocks,\n vision_num_heads,\n text_embed_dim,\n text_num_blocks,\n text_num_heads,\n audio_embed_dim,\n audio_num_blocks,\n audio_num_heads,\n audio_drop_path,\n depth_embed_dim,\n depth_num_blocks,\n depth_num_heads,\n depth_drop_path,\n thermal_embed_dim,\n thermal_num_blocks,\n thermal_num_heads,\n thermal_drop_path,\n imu_embed_dim,\n imu_num_blocks,\n imu_num_heads,\n imu_drop_path,\n )\n\n self.modality_heads = self._create_modality_heads(\n out_embed_dim,\n vision_embed_dim,\n text_embed_dim,\n audio_embed_dim,\n depth_embed_dim,\n thermal_embed_dim,\n imu_embed_dim,\n )\n\n self.modality_postprocessors = self._create_modality_postprocessors(\n out_embed_dim\n )\n\n def _create_modality_preprocessors(\n self,\n video_frames=2,\n vision_embed_dim=1024,\n kernel_size=(2, 14, 14),\n text_embed_dim=768,\n audio_embed_dim=768,\n audio_kernel_size=16,\n audio_stride=10,\n audio_num_mel_bins=128,\n audio_target_len=204,\n depth_embed_dim=768,\n depth_kernel_size=16,\n thermal_embed_dim=768,\n thermal_kernel_size=16,\n imu_embed_dim=512,\n ):\n rgbt_stem = PatchEmbedGeneric(\n proj_stem=[\n PadIm2Video(pad_type=\"repeat\", ntimes=2),\n nn.Conv3d(\n in_channels=3,\n kernel_size=kernel_size,\n out_channels=vision_embed_dim,\n stride=kernel_size,\n bias=False,\n ),\n ]\n )\n rgbt_preprocessor = RGBDTPreprocessor(\n img_size=[3, video_frames, 224, 224],\n num_cls_tokens=1,\n pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),\n rgbt_stem=rgbt_stem,\n depth_stem=None,\n )\n\n text_preprocessor = TextPreprocessor(\n context_length=77,\n vocab_size=49408,\n embed_dim=text_embed_dim,\n causal_masking=True,\n )\n\n audio_stem = PatchEmbedGeneric(\n proj_stem=[\n nn.Conv2d(\n in_channels=1,\n kernel_size=audio_kernel_size,\n stride=audio_stride,\n out_channels=audio_embed_dim,\n bias=False,\n ),\n ],\n norm_layer=nn.LayerNorm(normalized_shape=audio_embed_dim),\n )\n audio_preprocessor = AudioPreprocessor(\n img_size=[1, audio_num_mel_bins, audio_target_len],\n num_cls_tokens=1,\n pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),\n audio_stem=audio_stem,\n )\n\n depth_stem = PatchEmbedGeneric(\n [\n nn.Conv2d(\n kernel_size=depth_kernel_size,\n in_channels=1,\n out_channels=depth_embed_dim,\n stride=depth_kernel_size,\n bias=False,\n ),\n ],\n norm_layer=nn.LayerNorm(normalized_shape=depth_embed_dim),\n )\n\n depth_preprocessor = RGBDTPreprocessor(\n img_size=[1, 224, 224],\n num_cls_tokens=1,\n pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),\n rgbt_stem=None,\n depth_stem=depth_stem,\n )\n\n thermal_stem = PatchEmbedGeneric(\n [\n nn.Conv2d(\n kernel_size=thermal_kernel_size,\n in_channels=1,\n out_channels=thermal_embed_dim,\n stride=thermal_kernel_size,\n bias=False,\n ),\n ],\n norm_layer=nn.LayerNorm(normalized_shape=thermal_embed_dim),\n )\n thermal_preprocessor = ThermalPreprocessor(\n img_size=[1, 224, 224],\n num_cls_tokens=1,\n pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),\n thermal_stem=thermal_stem,\n )\n\n imu_stem = PatchEmbedGeneric(\n [\n nn.Linear(\n in_features=48,\n out_features=imu_embed_dim,\n bias=False,\n ),\n ],\n norm_layer=nn.LayerNorm(normalized_shape=imu_embed_dim),\n )\n\n imu_preprocessor = IMUPreprocessor(\n img_size=[6, 2000],\n num_cls_tokens=1,\n kernel_size=8,\n embed_dim=imu_embed_dim,\n pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),\n imu_stem=imu_stem,\n )\n\n modality_preprocessors = {\n ModalityType.VISION: rgbt_preprocessor,\n ModalityType.TEXT: text_preprocessor,\n ModalityType.AUDIO: audio_preprocessor,\n ModalityType.DEPTH: depth_preprocessor,\n ModalityType.THERMAL: thermal_preprocessor,\n ModalityType.IMU: imu_preprocessor,\n }\n\n return nn.ModuleDict(modality_preprocessors)\n\n def _create_modality_trunks(\n self,\n vision_embed_dim=1024,\n vision_num_blocks=24,\n vision_num_heads=16,\n text_embed_dim=768,\n text_num_blocks=12,\n text_num_heads=12,\n audio_embed_dim=768,\n audio_num_blocks=12,\n audio_num_heads=12,\n audio_drop_path=0.0,\n depth_embed_dim=768,\n depth_num_blocks=12,\n depth_num_heads=12,\n depth_drop_path=0.0,\n thermal_embed_dim=768,\n thermal_num_blocks=12,\n thermal_num_heads=12,\n thermal_drop_path=0.0,\n imu_embed_dim=512,\n imu_num_blocks=6,\n imu_num_heads=8,\n imu_drop_path=0.7,\n ):\n def instantiate_trunk(\n embed_dim, num_blocks, num_heads, pre_transformer_ln, add_bias_kv, drop_path\n ):\n return SimpleTransformer(\n embed_dim=embed_dim,\n num_blocks=num_blocks,\n ffn_dropout_rate=0.0,\n drop_path_rate=drop_path,\n attn_target=partial(\n MultiheadAttention,\n embed_dim=embed_dim,\n num_heads=num_heads,\n bias=True,\n add_bias_kv=add_bias_kv,\n ),\n pre_transformer_layer=nn.Sequential(\n nn.LayerNorm(embed_dim, eps=1e-6)\n if pre_transformer_ln\n else nn.Identity(),\n EinOpsRearrange(\"b l d -> l b d\"),\n ),\n post_transformer_layer=EinOpsRearrange(\"l b d -> b l d\"),\n )\n\n modality_trunks = {}\n modality_trunks[ModalityType.VISION] = instantiate_trunk(\n vision_embed_dim,\n vision_num_blocks,\n vision_num_heads,\n pre_transformer_ln=True,\n add_bias_kv=False,\n drop_path=0.0,\n )\n modality_trunks[ModalityType.TEXT] = instantiate_trunk(\n text_embed_dim,\n text_num_blocks,\n text_num_heads,\n pre_transformer_ln=False,\n add_bias_kv=False,\n drop_path=0.0,\n )\n modality_trunks[ModalityType.AUDIO] = instantiate_trunk(\n audio_embed_dim,\n audio_num_blocks,\n audio_num_heads,\n pre_transformer_ln=False,\n add_bias_kv=True,\n drop_path=audio_drop_path,\n )\n modality_trunks[ModalityType.DEPTH] = instantiate_trunk(\n depth_embed_dim,\n depth_num_blocks,\n depth_num_heads,\n pre_transformer_ln=False,\n add_bias_kv=True,\n drop_path=depth_drop_path,\n )\n modality_trunks[ModalityType.THERMAL] = instantiate_trunk(\n thermal_embed_dim,\n thermal_num_blocks,\n thermal_num_heads,\n pre_transformer_ln=False,\n add_bias_kv=True,\n drop_path=thermal_drop_path,\n )\n modality_trunks[ModalityType.IMU] = instantiate_trunk(\n imu_embed_dim,\n imu_num_blocks,\n imu_num_heads,\n pre_transformer_ln=False,\n add_bias_kv=True,\n drop_path=imu_drop_path,\n )\n\n return nn.ModuleDict(modality_trunks)\n\n def _create_modality_heads(\n self,\n out_embed_dim,\n vision_embed_dim,\n text_embed_dim,\n audio_embed_dim,\n depth_embed_dim,\n thermal_embed_dim,\n imu_embed_dim,\n ):\n modality_heads = {}\n\n modality_heads[ModalityType.VISION] = nn.Sequential(\n nn.LayerNorm(normalized_shape=vision_embed_dim, eps=1e-6),\n SelectElement(index=0),\n nn.Linear(vision_embed_dim, out_embed_dim, bias=False),\n )\n\n modality_heads[ModalityType.TEXT] = SelectEOSAndProject(\n proj=nn.Sequential(\n nn.LayerNorm(normalized_shape=text_embed_dim, eps=1e-6),\n nn.Linear(text_embed_dim, out_embed_dim, bias=False),\n )\n )\n\n modality_heads[ModalityType.AUDIO] = nn.Sequential(\n nn.LayerNorm(normalized_shape=audio_embed_dim, eps=1e-6),\n SelectElement(index=0),\n nn.Linear(audio_embed_dim, out_embed_dim, bias=False),\n )\n\n modality_heads[ModalityType.DEPTH] = nn.Sequential(\n nn.LayerNorm(normalized_shape=depth_embed_dim, eps=1e-6),\n SelectElement(index=0),\n nn.Linear(depth_embed_dim, out_embed_dim, bias=False),\n )\n\n modality_heads[ModalityType.THERMAL] = nn.Sequential(\n nn.LayerNorm(normalized_shape=thermal_embed_dim, eps=1e-6),\n SelectElement(index=0),\n nn.Linear(thermal_embed_dim, out_embed_dim, bias=False),\n )\n\n modality_heads[ModalityType.IMU] = nn.Sequential(\n nn.LayerNorm(normalized_shape=imu_embed_dim, eps=1e-6),\n SelectElement(index=0),\n nn.Dropout(p=0.5),\n nn.Linear(imu_embed_dim, out_embed_dim, bias=False),\n )\n\n return nn.ModuleDict(modality_heads)\n\n def _create_modality_postprocessors(self, out_embed_dim):\n modality_postprocessors = {}\n\n modality_postprocessors[ModalityType.VISION] = Normalize(dim=-1)\n modality_postprocessors[ModalityType.TEXT] = nn.Sequential(\n Normalize(dim=-1), LearnableLogitScaling(learnable=True)\n )\n modality_postprocessors[ModalityType.AUDIO] = nn.Sequential(\n Normalize(dim=-1),\n LearnableLogitScaling(logit_scale_init=20.0, learnable=False),\n )\n modality_postprocessors[ModalityType.DEPTH] = nn.Sequential(\n Normalize(dim=-1),\n LearnableLogitScaling(logit_scale_init=5.0, learnable=False),\n )\n modality_postprocessors[ModalityType.THERMAL] = nn.Sequential(\n Normalize(dim=-1),\n LearnableLogitScaling(logit_scale_init=10.0, learnable=False),\n )\n modality_postprocessors[ModalityType.IMU] = nn.Sequential(\n Normalize(dim=-1),\n LearnableLogitScaling(logit_scale_init=5.0, learnable=False),\n )\n\n return nn.ModuleDict(modality_postprocessors)\n\n def forward(self, inputs):\n outputs = {}\n for modality_key, modality_value in inputs.items():\n reduce_list = (\n modality_value.ndim >= 5\n ) # Audio and Video inputs consist of multiple clips\n if reduce_list:\n B, S = modality_value.shape[:2]\n modality_value = modality_value.reshape(\n B * S, *modality_value.shape[2:]\n )\n\n if modality_value is not None:\n modality_value = self.modality_preprocessors[modality_key](\n **{modality_key: modality_value}\n )\n trunk_inputs = modality_value[\"trunk\"]\n head_inputs = modality_value[\"head\"]\n modality_value = self.modality_trunks[modality_key](**trunk_inputs)\n modality_value = self.modality_heads[modality_key](\n modality_value, **head_inputs\n )\n modality_value = self.modality_postprocessors[modality_key](\n modality_value\n )\n\n if reduce_list:\n modality_value = modality_value.reshape(B, S, -1)\n modality_value = modality_value.mean(dim=1)\n\n outputs[modality_key] = modality_value\n\n return outputs\n\n\ndef imagebind_huge(pretrained=False):\n model = ImageBindModel(\n vision_embed_dim=1280,\n vision_num_blocks=32,\n vision_num_heads=16,\n text_embed_dim=1024,\n text_num_blocks=24,\n text_num_heads=16,\n out_embed_dim=1024,\n audio_drop_path=0.1,\n imu_drop_path=0.7,\n )\n\n if pretrained:\n if not os.path.exists(\".checkpoints/imagebind_huge.pth\"):\n print(\n \"Downloading imagebind weights to .checkpoints/imagebind_huge.pth ...\"\n )\n os.makedirs(\".checkpoints\", exist_ok=True)\n torch.hub.download_url_to_file(\n \"https://dl.fbaipublicfiles.com/imagebind/imagebind_huge.pth\",\n \".checkpoints/imagebind_huge.pth\",\n progress=True,\n )\n\n model.load_state_dict(torch.load(\".checkpoints/imagebind_huge.pth\"))\n\n return model\n" }, { "path": "data_generation/Grounded-Segment-Anything/playground/ImageBind_SAM/models/multimodal_preprocessors.py", "content": "#!/usr/bin/env python3\n# Portions Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport gzip\nimport html\nimport io\nimport math\nfrom functools import lru_cache\nfrom typing import Callable, List, Optional\n\nimport ftfy\n\nimport numpy as np\nimport regex as re\nimport torch\nimport torch.nn as nn\nfrom iopath.common.file_io import g_pathmgr\nfrom timm.models.layers import trunc_normal_\n\nfrom models.helpers import cast_if_src_dtype, VerboseNNModule\n\n\ndef get_sinusoid_encoding_table(n_position, d_hid):\n \"\"\"Sinusoid position encoding table\"\"\"\n\n # TODO: make it with torch instead of numpy\n def get_position_angle_vec(position):\n return [\n position / np.power(10000, 2 * (hid_j // 2) / d_hid)\n for hid_j in range(d_hid)\n ]\n\n sinusoid_table = np.array(\n [get_position_angle_vec(pos_i) for pos_i in range(n_position)]\n )\n sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i\n sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1\n\n return torch.FloatTensor(sinusoid_table).unsqueeze(0)\n\n\ndef interpolate_pos_encoding_2d(target_spatial_size, pos_embed):\n N = pos_embed.shape[1]\n if N == target_spatial_size:\n return pos_embed\n dim = pos_embed.shape[-1]\n # nn.functional.interpolate doesn't work with bfloat16 so we cast to float32\n pos_embed, updated = cast_if_src_dtype(pos_embed, torch.bfloat16, torch.float32)\n pos_embed = nn.functional.interpolate(\n pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(\n 0, 3, 1, 2\n ),\n scale_factor=math.sqrt(target_spatial_size / N),\n mode=\"bicubic\",\n )\n if updated:\n pos_embed, _ = cast_if_src_dtype(pos_embed, torch.float32, torch.bfloat16)\n pos_embed = pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)\n return pos_embed\n\n\ndef interpolate_pos_encoding(\n npatch_per_img,\n pos_embed,\n patches_layout,\n input_shape=None,\n first_patch_idx=1,\n):\n assert first_patch_idx == 0 or first_patch_idx == 1, \"there is 1 CLS token or none\"\n N = pos_embed.shape[1] - first_patch_idx # since it's 1 if cls_token exists\n if npatch_per_img == N:\n return pos_embed\n\n assert (\n patches_layout[-1] == patches_layout[-2]\n ), \"Interpolation of pos embed not supported for non-square layouts\"\n\n class_emb = pos_embed[:, :first_patch_idx]\n pos_embed = pos_embed[:, first_patch_idx:]\n\n if input_shape is None or patches_layout[0] == 1:\n # simple 2D pos embedding, no temporal component\n pos_embed = interpolate_pos_encoding_2d(npatch_per_img, pos_embed)\n elif patches_layout[0] > 1:\n # pos embed has a temporal component\n assert len(input_shape) == 4, \"temporal interpolation not supported\"\n # we only support 2D interpolation in this case\n num_frames = patches_layout[0]\n num_spatial_tokens = patches_layout[1] * patches_layout[2]\n pos_embed = pos_embed.view(1, num_frames, num_spatial_tokens, -1)\n # interpolate embedding for zeroth frame\n pos_embed = interpolate_pos_encoding_2d(\n npatch_per_img, pos_embed[0, 0, ...].unsqueeze(0)\n )\n else:\n raise ValueError(\"This type of interpolation isn't implemented\")\n\n return torch.cat((class_emb, pos_embed), dim=1)\n\n\ndef _get_pos_embedding(\n npatch_per_img,\n pos_embed,\n patches_layout,\n input_shape,\n first_patch_idx=1,\n):\n pos_embed = interpolate_pos_encoding(\n npatch_per_img,\n pos_embed,\n patches_layout,\n input_shape=input_shape,\n first_patch_idx=first_patch_idx,\n )\n return pos_embed\n\n\nclass PatchEmbedGeneric(nn.Module):\n \"\"\"\n PatchEmbed from Hydra\n \"\"\"\n\n def __init__(self, proj_stem, norm_layer: Optional[nn.Module] = None):\n super().__init__()\n\n if len(proj_stem) > 1:\n self.proj = nn.Sequential(*proj_stem)\n else:\n # Special case to be able to load pre-trained models that were\n # trained with a standard stem\n self.proj = proj_stem[0]\n self.norm_layer = norm_layer\n\n def get_patch_layout(self, img_size):\n with torch.no_grad():\n dummy_img = torch.zeros(\n [\n 1,\n ]\n + img_size\n )\n dummy_out = self.proj(dummy_img)\n embed_dim = dummy_out.shape[1]\n patches_layout = tuple(dummy_out.shape[2:])\n num_patches = np.prod(patches_layout)\n return patches_layout, num_patches, embed_dim\n\n def forward(self, x):\n x = self.proj(x)\n # B C (T) H W -> B (T)HW C\n x = x.flatten(2).transpose(1, 2)\n if self.norm_layer is not None:\n x = self.norm_layer(x)\n return x\n\n\nclass SpatioTemporalPosEmbeddingHelper(VerboseNNModule):\n def __init__(\n self,\n patches_layout: List,\n num_patches: int,\n num_cls_tokens: int,\n embed_dim: int,\n learnable: bool,\n ) -> None:\n super().__init__()\n self.num_cls_tokens = num_cls_tokens\n self.patches_layout = patches_layout\n self.num_patches = num_patches\n self.num_tokens = num_cls_tokens + num_patches\n self.learnable = learnable\n if self.learnable:\n self.pos_embed = nn.Parameter(torch.zeros(1, self.num_tokens, embed_dim))\n trunc_normal_(self.pos_embed, std=0.02)\n else:\n self.register_buffer(\n \"pos_embed\", get_sinusoid_encoding_table(self.num_tokens, embed_dim)\n )\n\n def get_pos_embedding(self, vision_input, all_vision_tokens):\n input_shape = vision_input.shape\n pos_embed = _get_pos_embedding(\n all_vision_tokens.size(1) - self.num_cls_tokens,\n pos_embed=self.pos_embed,\n patches_layout=self.patches_layout,\n input_shape=input_shape,\n first_patch_idx=self.num_cls_tokens,\n )\n return pos_embed\n\n\nclass RGBDTPreprocessor(VerboseNNModule):\n def __init__(\n self,\n rgbt_stem: PatchEmbedGeneric,\n depth_stem: PatchEmbedGeneric,\n img_size: List = (3, 224, 224),\n num_cls_tokens: int = 1,\n pos_embed_fn: Callable = None,\n use_type_embed: bool = False,\n init_param_style: str = \"openclip\",\n ) -> None:\n super().__init__()\n stem = rgbt_stem if rgbt_stem is not None else depth_stem\n (\n self.patches_layout,\n self.num_patches,\n self.embed_dim,\n ) = stem.get_patch_layout(img_size)\n self.rgbt_stem = rgbt_stem\n self.depth_stem = depth_stem\n self.use_pos_embed = pos_embed_fn is not None\n self.use_type_embed = use_type_embed\n self.num_cls_tokens = num_cls_tokens\n\n if self.use_pos_embed:\n self.pos_embedding_helper = pos_embed_fn(\n patches_layout=self.patches_layout,\n num_cls_tokens=num_cls_tokens,\n num_patches=self.num_patches,\n embed_dim=self.embed_dim,\n )\n if self.num_cls_tokens > 0:\n self.cls_token = nn.Parameter(\n torch.zeros(1, self.num_cls_tokens, self.embed_dim)\n )\n if self.use_type_embed:\n self.type_embed = nn.Parameter(torch.zeros(1, 1, self.embed_dim))\n\n self.init_parameters(init_param_style)\n\n @torch.no_grad()\n def init_parameters(self, init_param_style):\n if init_param_style == \"openclip\":\n # OpenCLIP style initialization\n scale = self.embed_dim**-0.5\n if self.use_pos_embed:\n nn.init.normal_(self.pos_embedding_helper.pos_embed)\n self.pos_embedding_helper.pos_embed *= scale\n\n if self.num_cls_tokens > 0:\n nn.init.normal_(self.cls_token)\n self.cls_token *= scale\n elif init_param_style == \"vit\":\n self.cls_token.data.fill_(0)\n else:\n raise ValueError(f\"Unknown init {init_param_style}\")\n\n if self.use_type_embed:\n nn.init.normal_(self.type_embed)\n\n def tokenize_input_and_cls_pos(self, input, stem, mask):\n # tokens is of shape B x L x D\n tokens = stem(input)\n assert tokens.ndim == 3\n assert tokens.shape[2] == self.embed_dim\n B = tokens.shape[0]\n if self.num_cls_tokens > 0:\n class_tokens = self.cls_token.expand(\n B, -1, -1\n ) # stole class_tokens impl from Phil Wang, thanks\n tokens = torch.cat((class_tokens, tokens), dim=1)\n if self.use_pos_embed:\n pos_embed = self.pos_embedding_helper.get_pos_embedding(input, tokens)\n tokens = tokens + pos_embed\n if self.use_type_embed:\n tokens = tokens + self.type_embed.expand(B, -1, -1)\n return tokens\n\n def forward(self, vision=None, depth=None, patch_mask=None):\n if patch_mask is not None:\n raise NotImplementedError()\n\n if vision is not None:\n vision_tokens = self.tokenize_input_and_cls_pos(\n vision, self.rgbt_stem, patch_mask\n )\n\n if depth is not None:\n depth_tokens = self.tokenize_input_and_cls_pos(\n depth, self.depth_stem, patch_mask\n )\n\n # aggregate tokens\n if vision is not None and depth is not None:\n final_tokens = vision_tokens + depth_tokens\n else:\n final_tokens = vision_tokens if vision is not None else depth_tokens\n return_dict = {\n \"trunk\": {\n \"tokens\": final_tokens,\n },\n \"head\": {},\n }\n return return_dict\n\n\nclass AudioPreprocessor(RGBDTPreprocessor):\n def __init__(self, audio_stem: PatchEmbedGeneric, **kwargs) -> None:\n super().__init__(rgbt_stem=audio_stem, depth_stem=None, **kwargs)\n\n def forward(self, audio=None):\n return super().forward(vision=audio)\n\n\nclass ThermalPreprocessor(RGBDTPreprocessor):\n def __init__(self, thermal_stem: PatchEmbedGeneric, **kwargs) -> None:\n super().__init__(rgbt_stem=thermal_stem, depth_stem=None, **kwargs)\n\n def forward(self, thermal=None):\n return super().forward(vision=thermal)\n\n\ndef build_causal_attention_mask(context_length):\n # lazily create causal attention mask, with full attention between the vision tokens\n # pytorch uses additive attention mask; fill with -inf\n mask = torch.empty(context_length, context_length, requires_grad=False)\n mask.fill_(float(\"-inf\"))\n mask.triu_(1) # zero out the lower diagonal\n return mask\n\n\nclass TextPreprocessor(VerboseNNModule):\n def __init__(\n self,\n vocab_size: int,\n context_length: int,\n embed_dim: int,\n causal_masking: bool,\n supply_seq_len_to_head: bool = True,\n num_cls_tokens: int = 0,\n init_param_style: str = \"openclip\",\n ) -> None:\n super().__init__()\n self.vocab_size = vocab_size\n self.context_length = context_length\n self.token_embedding = nn.Embedding(vocab_size, embed_dim)\n self.pos_embed = nn.Parameter(\n torch.empty(1, self.context_length + num_cls_tokens, embed_dim)\n )\n self.causal_masking = causal_masking\n if self.causal_masking:\n mask = build_causal_attention_mask(self.context_length)\n # register the mask as a buffer so it can be moved to the right device\n self.register_buffer(\"mask\", mask)\n\n self.supply_seq_len_to_head = supply_seq_len_to_head\n self.num_cls_tokens = num_cls_tokens\n self.embed_dim = embed_dim\n if num_cls_tokens > 0:\n assert self.causal_masking is False, \"Masking + CLS token isn't implemented\"\n self.cls_token = nn.Parameter(\n torch.zeros(1, self.num_cls_tokens, embed_dim)\n )\n\n self.init_parameters(init_param_style)\n\n @torch.no_grad()\n def init_parameters(self, init_param_style=\"openclip\"):\n # OpenCLIP style initialization\n nn.init.normal_(self.token_embedding.weight, std=0.02)\n nn.init.normal_(self.pos_embed, std=0.01)\n\n if init_param_style == \"openclip\":\n # OpenCLIP style initialization\n scale = self.embed_dim**-0.5\n if self.num_cls_tokens > 0:\n nn.init.normal_(self.cls_token)\n self.cls_token *= scale\n elif init_param_style == \"vit\":\n self.cls_token.data.fill_(0)\n else:\n raise ValueError(f\"Unknown init {init_param_style}\")\n\n def forward(self, text):\n # text tokens are of shape B x L x D\n text_tokens = self.token_embedding(text)\n # concat CLS tokens if any\n if self.num_cls_tokens > 0:\n B = text_tokens.shape[0]\n class_tokens = self.cls_token.expand(\n B, -1, -1\n ) # stole class_tokens impl from Phil Wang, thanks\n text_tokens = torch.cat((class_tokens, text_tokens), dim=1)\n text_tokens = text_tokens + self.pos_embed\n return_dict = {\n \"trunk\": {\n \"tokens\": text_tokens,\n },\n \"head\": {},\n }\n # Compute sequence length after adding CLS tokens\n if self.supply_seq_len_to_head:\n text_lengths = text.argmax(dim=-1)\n return_dict[\"head\"] = {\n \"seq_len\": text_lengths,\n }\n if self.causal_masking:\n return_dict[\"trunk\"].update({\"attn_mask\": self.mask})\n return return_dict\n\n\nclass Im2Video(nn.Module):\n \"\"\"Convert an image into a trivial video.\"\"\"\n\n def __init__(self, time_dim=2):\n super().__init__()\n self.time_dim = time_dim\n\n def forward(self, x):\n if x.ndim == 4:\n # B, C, H, W -> B, C, T, H, W\n return x.unsqueeze(self.time_dim)\n elif x.ndim == 5:\n return x\n else:\n raise ValueError(f\"Dimension incorrect {x.shape}\")\n\n\nclass PadIm2Video(Im2Video):\n def __init__(self, ntimes, pad_type, time_dim=2):\n super().__init__(time_dim=time_dim)\n assert ntimes > 0\n assert pad_type in [\"zero\", \"repeat\"]\n self.ntimes = ntimes\n self.pad_type = pad_type\n\n def forward(self, x):\n x = super().forward(x)\n if x.shape[self.time_dim] == 1:\n if self.pad_type == \"repeat\":\n new_shape = [1] * len(x.shape)\n new_shape[self.time_dim] = self.ntimes\n x = x.repeat(new_shape)\n elif self.pad_type == \"zero\":\n padarg = [0, 0] * len(x.shape)\n padarg[2 * self.time_dim + 1] = self.ntimes - x.shape[self.time_dim]\n x = nn.functional.pad(x, padarg)\n return x\n\n\n# Modified from github.com/openai/CLIP\n@lru_cache()\ndef bytes_to_unicode():\n \"\"\"\n Returns list of utf-8 byte and a corresponding list of unicode strings.\n The reversible bpe codes work on unicode strings.\n This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.\n When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.\n This is a signficant percentage of your normal, say, 32K bpe vocab.\n To avoid that, we want lookup tables between utf-8 bytes and unicode strings.\n And avoids mapping to whitespace/control characters the bpe code barfs on.\n \"\"\"\n bs = (\n list(range(ord(\"!\"), ord(\"~\") + 1))\n + list(range(ord(\"¡\"), ord(\"¬\") + 1))\n + list(range(ord(\"®\"), ord(\"ÿ\") + 1))\n )\n cs = bs[:]\n n = 0\n for b in range(2**8):\n if b not in bs:\n bs.append(b)\n cs.append(2**8 + n)\n n += 1\n cs = [chr(n) for n in cs]\n return dict(zip(bs, cs))\n\n\ndef get_pairs(word):\n \"\"\"Return set of symbol pairs in a word.\n Word is represented as tuple of symbols (symbols being variable-length strings).\n \"\"\"\n pairs = set()\n prev_char = word[0]\n for char in word[1:]:\n pairs.add((prev_char, char))\n prev_char = char\n return pairs\n\n\ndef basic_clean(text):\n text = ftfy.fix_text(text)\n text = html.unescape(html.unescape(text))\n return text.strip()\n\n\ndef whitespace_clean(text):\n text = re.sub(r\"\\s+\", \" \", text)\n text = text.strip()\n return text\n\n\nclass SimpleTokenizer(object):\n def __init__(self, bpe_path: str, context_length=77):\n self.byte_encoder = bytes_to_unicode()\n self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}\n\n with g_pathmgr.open(bpe_path, \"rb\") as fh:\n bpe_bytes = io.BytesIO(fh.read())\n merges = gzip.open(bpe_bytes).read().decode(\"utf-8\").split(\"\\n\")\n merges = merges[1 : 49152 - 256 - 2 + 1]\n merges = [tuple(merge.split()) for merge in merges]\n vocab = list(bytes_to_unicode().values())\n vocab = vocab + [v + \"\" for v in vocab]\n for merge in merges:\n vocab.append(\"\".join(merge))\n vocab.extend([\"<|startoftext|>\", \"<|endoftext|>\"])\n self.encoder = dict(zip(vocab, range(len(vocab))))\n self.decoder = {v: k for k, v in self.encoder.items()}\n self.bpe_ranks = dict(zip(merges, range(len(merges))))\n self.cache = {\n \"<|startoftext|>\": \"<|startoftext|>\",\n \"<|endoftext|>\": \"<|endoftext|>\",\n }\n self.pat = re.compile(\n r\"\"\"<\\|startoftext\\|>|<\\|endoftext\\|>|'s|'t|'re|'ve|'m|'ll|'d|[\\p{L}]+|[\\p{N}]|[^\\s\\p{L}\\p{N}]+\"\"\",\n re.IGNORECASE,\n )\n self.context_length = context_length\n\n def bpe(self, token):\n if token in self.cache:\n return self.cache[token]\n word = tuple(token[:-1]) + (token[-1] + \"\",)\n pairs = get_pairs(word)\n\n if not pairs:\n return token + \"\"\n\n while True:\n bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float(\"inf\")))\n if bigram not in self.bpe_ranks:\n break\n first, second = bigram\n new_word = []\n i = 0\n while i < len(word):\n try:\n j = word.index(first, i)\n new_word.extend(word[i:j])\n i = j\n except:\n new_word.extend(word[i:])\n break\n\n if word[i] == first and i < len(word) - 1 and word[i + 1] == second:\n new_word.append(first + second)\n i += 2\n else:\n new_word.append(word[i])\n i += 1\n new_word = tuple(new_word)\n word = new_word\n if len(word) == 1:\n break\n else:\n pairs = get_pairs(word)\n word = \" \".join(word)\n self.cache[token] = word\n return word\n\n def encode(self, text):\n bpe_tokens = []\n text = whitespace_clean(basic_clean(text)).lower()\n for token in re.findall(self.pat, text):\n token = \"\".join(self.byte_encoder[b] for b in token.encode(\"utf-8\"))\n bpe_tokens.extend(\n self.encoder[bpe_token] for bpe_token in self.bpe(token).split(\" \")\n )\n return bpe_tokens\n\n def decode(self, tokens):\n text = \"\".join([self.decoder[token] for token in tokens])\n text = (\n bytearray([self.byte_decoder[c] for c in text])\n .decode(\"utf-8\", errors=\"replace\")\n .replace(\"\", \" \")\n )\n return text\n\n def __call__(self, texts, context_length=None):\n if not context_length:\n context_length = self.context_length\n\n if isinstance(texts, str):\n texts = [texts]\n\n sot_token = self.encoder[\"<|startoftext|>\"]\n eot_token = self.encoder[\"<|endoftext|>\"]\n all_tokens = [[sot_token] + self.encode(text) + [eot_token] for text in texts]\n result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)\n\n for i, tokens in enumerate(all_tokens):\n tokens = tokens[:context_length]\n result[i, : len(tokens)] = torch.tensor(tokens)\n\n if len(result) == 1:\n return result[0]\n return result\n\n\nclass IMUPreprocessor(VerboseNNModule):\n def __init__(\n self,\n kernel_size: int,\n imu_stem: PatchEmbedGeneric,\n embed_dim: int,\n img_size: List = (6, 2000),\n num_cls_tokens: int = 1,\n pos_embed_fn: Callable = None,\n init_param_style: str = \"openclip\",\n ) -> None:\n super().__init__()\n stem = imu_stem\n self.imu_stem = imu_stem\n self.embed_dim = embed_dim\n self.use_pos_embed = pos_embed_fn is not None\n self.num_cls_tokens = num_cls_tokens\n self.kernel_size = kernel_size\n self.pos_embed = nn.Parameter(\n torch.empty(1, (img_size[1] // kernel_size) + num_cls_tokens, embed_dim)\n )\n\n if self.num_cls_tokens > 0:\n self.cls_token = nn.Parameter(\n torch.zeros(1, self.num_cls_tokens, self.embed_dim)\n )\n\n self.init_parameters(init_param_style)\n\n @torch.no_grad()\n def init_parameters(self, init_param_style):\n nn.init.normal_(self.pos_embed, std=0.01)\n\n if init_param_style == \"openclip\":\n # OpenCLIP style initialization\n scale = self.embed_dim**-0.5\n\n if self.num_cls_tokens > 0:\n nn.init.normal_(self.cls_token)\n self.cls_token *= scale\n elif init_param_style == \"vit\":\n self.cls_token.data.fill_(0)\n else:\n raise ValueError(f\"Unknown init {init_param_style}\")\n\n def tokenize_input_and_cls_pos(self, input, stem):\n # tokens is of shape B x L x D\n tokens = stem.norm_layer(stem.proj(input))\n assert tokens.ndim == 3\n assert tokens.shape[2] == self.embed_dim\n B = tokens.shape[0]\n if self.num_cls_tokens > 0:\n class_tokens = self.cls_token.expand(\n B, -1, -1\n ) # stole class_tokens impl from Phil Wang, thanks\n tokens = torch.cat((class_tokens, tokens), dim=1)\n if self.use_pos_embed:\n tokens = tokens + self.pos_embed\n return tokens\n\n def forward(self, imu):\n # Patchify\n imu = imu.unfold(\n -1,\n self.kernel_size,\n self.kernel_size,\n ).permute(0, 2, 1, 3)\n imu = imu.reshape(imu.size(0), imu.size(1), -1)\n\n imu_tokens = self.tokenize_input_and_cls_pos(\n imu,\n self.imu_stem,\n )\n\n return_dict = {\n \"trunk\": {\n \"tokens\": imu_tokens,\n },\n \"head\": {},\n }\n return return_dict\n" }, { "path": "data_generation/Grounded-Segment-Anything/playground/ImageBind_SAM/models/transformer.py", "content": "#!/usr/bin/env python3\n# Portions Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\n# Code modified from\n# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py ;\n# https://github.com/facebookresearch/deit/blob/main/models.py\n# and https://github.com/facebookresearch/vissl/blob/main/vissl/models/trunks/vision_transformer.py\n\n\nimport copy\nimport fnmatch\nimport logging\nfrom functools import partial\nfrom typing import Callable, List\n\nimport torch\nimport torch.nn as nn\nimport torch.utils.checkpoint as checkpoint\n\nfrom timm.models.layers import DropPath, trunc_normal_\n\n\nclass Attention(nn.Module):\n def __init__(\n self,\n dim,\n num_heads=8,\n qkv_bias=False,\n qk_scale=None,\n attn_drop=0.0,\n proj_drop=0.0,\n ):\n super().__init__()\n self.num_heads = num_heads\n head_dim = dim // num_heads\n # NOTE scale factor was wrong in my original version,\n # can set manually to be compat with prev weights\n self.scale = qk_scale or head_dim**-0.5\n\n self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n self.attn_drop = nn.Dropout(attn_drop)\n self.proj = nn.Linear(dim, dim)\n self.proj_drop = nn.Dropout(proj_drop)\n\n def forward(self, x):\n B, N, C = x.shape\n qkv = (\n self.qkv(x)\n .reshape(B, N, 3, self.num_heads, C // self.num_heads)\n .permute(2, 0, 3, 1, 4)\n )\n q, k, v = (\n qkv[0],\n qkv[1],\n qkv[2],\n ) # make torchscript happy (cannot use tensor as tuple)\n\n attn = (q @ k.transpose(-2, -1)) * self.scale\n attn = attn.softmax(dim=-1)\n attn = self.attn_drop(attn)\n\n x = (attn @ v).transpose(1, 2).reshape(B, N, C)\n x = self.proj(x)\n x = self.proj_drop(x)\n return x\n\n\nclass Mlp(nn.Module):\n def __init__(\n self,\n in_features,\n hidden_features=None,\n out_features=None,\n act_layer=nn.GELU,\n drop=0.0,\n ):\n super().__init__()\n out_features = out_features or in_features\n hidden_features = hidden_features or in_features\n self.fc1 = nn.Linear(in_features, hidden_features)\n self.act = act_layer()\n self.fc2 = nn.Linear(hidden_features, out_features)\n self.drop = nn.Dropout(drop)\n\n def forward(self, x):\n x = self.fc1(x)\n x = self.act(x)\n x = self.drop(x)\n x = self.fc2(x)\n x = self.drop(x)\n return x\n\n\nclass MultiheadAttention(nn.MultiheadAttention):\n def forward(self, x: torch.Tensor, attn_mask: torch.Tensor):\n return super().forward(x, x, x, need_weights=False, attn_mask=attn_mask)[0]\n\n\nclass ViTAttention(Attention):\n def forward(self, x: torch.Tensor, attn_mask: torch.Tensor):\n assert attn_mask is None\n return super().forward(x)\n\n\nclass BlockWithMasking(nn.Module):\n def __init__(\n self,\n dim: int,\n attn_target: Callable,\n mlp_ratio: int = 4,\n act_layer: Callable = nn.GELU,\n norm_layer: Callable = nn.LayerNorm,\n ffn_dropout_rate: float = 0.0,\n drop_path: float = 0.0,\n layer_scale_type: str = None,\n layer_scale_init_value: float = 1e-4,\n ):\n super().__init__()\n\n assert not isinstance(\n attn_target, nn.Module\n ), \"attn_target should be a Callable. Otherwise attn_target is shared across blocks!\"\n self.attn = attn_target()\n if drop_path > 0.0:\n self.drop_path = DropPath(drop_path)\n else:\n self.drop_path = nn.Identity()\n self.norm_1 = norm_layer(dim)\n mlp_hidden_dim = int(mlp_ratio * dim)\n self.mlp = Mlp(\n in_features=dim,\n hidden_features=mlp_hidden_dim,\n act_layer=act_layer,\n drop=ffn_dropout_rate,\n )\n self.norm_2 = norm_layer(dim)\n self.layer_scale_type = layer_scale_type\n if self.layer_scale_type is not None:\n assert self.layer_scale_type in [\n \"per_channel\",\n \"scalar\",\n ], f\"Found Layer scale type {self.layer_scale_type}\"\n if self.layer_scale_type == \"per_channel\":\n # one gamma value per channel\n gamma_shape = [1, 1, dim]\n elif self.layer_scale_type == \"scalar\":\n # single gamma value for all channels\n gamma_shape = [1, 1, 1]\n # two gammas: for each part of the fwd in the encoder\n self.layer_scale_gamma1 = nn.Parameter(\n torch.ones(size=gamma_shape) * layer_scale_init_value,\n requires_grad=True,\n )\n self.layer_scale_gamma2 = nn.Parameter(\n torch.ones(size=gamma_shape) * layer_scale_init_value,\n requires_grad=True,\n )\n\n def forward(self, x: torch.Tensor, attn_mask: torch.Tensor):\n if self.layer_scale_type is None:\n x = x + self.drop_path(self.attn(self.norm_1(x), attn_mask))\n x = x + self.drop_path(self.mlp(self.norm_2(x)))\n else:\n x = (\n x\n + self.drop_path(self.attn(self.norm_1(x), attn_mask))\n * self.layer_scale_gamma1\n )\n x = x + self.drop_path(self.mlp(self.norm_2(x))) * self.layer_scale_gamma2\n return x\n\n\n_LAYER_NORM = partial(nn.LayerNorm, eps=1e-6)\n\n\nclass SimpleTransformer(nn.Module):\n def __init__(\n self,\n attn_target: Callable,\n embed_dim: int,\n num_blocks: int,\n block: Callable = BlockWithMasking,\n pre_transformer_layer: Callable = None,\n post_transformer_layer: Callable = None,\n drop_path_rate: float = 0.0,\n drop_path_type: str = \"progressive\",\n norm_layer: Callable = _LAYER_NORM,\n mlp_ratio: int = 4,\n ffn_dropout_rate: float = 0.0,\n layer_scale_type: str = None, # from cait; possible values are None, \"per_channel\", \"scalar\"\n layer_scale_init_value: float = 1e-4, # from cait; float\n weight_init_style: str = \"jax\", # possible values jax or pytorch\n ):\n \"\"\"\n Simple Transformer with the following features\n 1. Supports masked attention\n 2. Supports DropPath\n 3. Supports LayerScale\n 4. Supports Dropout in Attention and FFN\n 5. Makes few assumptions about the input except that it is a Tensor\n \"\"\"\n super().__init__()\n self.pre_transformer_layer = pre_transformer_layer\n if drop_path_type == \"progressive\":\n dpr = [x.item() for x in torch.linspace(0, drop_path_rate, num_blocks)]\n elif drop_path_type == \"uniform\":\n dpr = [drop_path_rate for i in range(num_blocks)]\n else:\n raise ValueError(f\"Unknown drop_path_type: {drop_path_type}\")\n\n self.blocks = nn.Sequential(\n *[\n block(\n dim=embed_dim,\n attn_target=attn_target,\n mlp_ratio=mlp_ratio,\n ffn_dropout_rate=ffn_dropout_rate,\n drop_path=dpr[i],\n norm_layer=norm_layer,\n layer_scale_type=layer_scale_type,\n layer_scale_init_value=layer_scale_init_value,\n )\n for i in range(num_blocks)\n ]\n )\n self.post_transformer_layer = post_transformer_layer\n self.weight_init_style = weight_init_style\n self.apply(self._init_weights)\n\n def _init_weights(self, m):\n if isinstance(m, nn.Linear):\n if self.weight_init_style == \"jax\":\n # Based on MAE and official Jax ViT implementation\n torch.nn.init.xavier_uniform_(m.weight)\n elif self.weight_init_style == \"pytorch\":\n # PyTorch ViT uses trunc_normal_\n trunc_normal_(m.weight, std=0.02)\n\n if m.bias is not None:\n nn.init.constant_(m.bias, 0)\n elif isinstance(m, (nn.LayerNorm)):\n nn.init.constant_(m.bias, 0)\n nn.init.constant_(m.weight, 1.0)\n\n def forward(\n self,\n tokens: torch.Tensor,\n attn_mask: torch.Tensor = None,\n use_checkpoint: bool = False,\n checkpoint_every_n: int = 1,\n checkpoint_blk_ids: List[int] = None,\n ):\n \"\"\"\n Inputs\n - tokens: data of shape N x L x D (or L x N x D depending on the attention implementation)\n - attn: mask of shape L x L\n\n Output\n - x: data of shape N x L x D (or L x N x D depending on the attention implementation)\n \"\"\"\n if self.pre_transformer_layer:\n tokens = self.pre_transformer_layer(tokens)\n if use_checkpoint and checkpoint_blk_ids is None:\n checkpoint_blk_ids = [\n blk_id\n for blk_id in range(len(self.blocks))\n if blk_id % checkpoint_every_n == 0\n ]\n if checkpoint_blk_ids:\n checkpoint_blk_ids = set(checkpoint_blk_ids)\n for blk_id, blk in enumerate(self.blocks):\n if use_checkpoint and blk_id in checkpoint_blk_ids:\n tokens = checkpoint.checkpoint(\n blk, tokens, attn_mask, use_reentrant=False\n )\n else:\n tokens = blk(tokens, attn_mask=attn_mask)\n if self.post_transformer_layer:\n tokens = self.post_transformer_layer(tokens)\n return tokens\n" }, { "path": "data_generation/Grounded-Segment-Anything/playground/ImageBind_SAM/text_referring_seg_demo.py", "content": "import data\nimport cv2\nimport torch\nfrom PIL import Image, ImageDraw\nfrom tqdm import tqdm\nfrom models import imagebind_model\nfrom models.imagebind_model import ModalityType\n\nfrom segment_anything import build_sam, SamAutomaticMaskGenerator\n\nfrom utils import (\n segment_image, \n convert_box_xywh_to_xyxy,\n get_indices_of_values_above_threshold,\n)\n\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n\n\"\"\"\nStep 1: Instantiate model\n\"\"\"\n# Segment Anything\nmask_generator = SamAutomaticMaskGenerator(\n build_sam(checkpoint=\".checkpoints/sam_vit_h_4b8939.pth\").to(device),\n points_per_side=16,\n)\n\n# ImageBind\nbind_model = imagebind_model.imagebind_huge(pretrained=True)\nbind_model.eval()\nbind_model.to(device)\n\n\n\"\"\"\nStep 2: Generate auto masks with SAM\n\"\"\"\nimage_path = \".assets/car_image.jpg\"\nimage = cv2.imread(image_path)\nimage = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\nmasks = mask_generator.generate(image)\n\n\n\"\"\"\nStep 3: Get cropped images based on mask and box\n\"\"\"\ncropped_boxes = []\nimage = Image.open(image_path)\nfor mask in tqdm(masks):\n cropped_boxes.append(segment_image(image, mask[\"segmentation\"]).crop(convert_box_xywh_to_xyxy(mask[\"bbox\"])))\n\n\n\"\"\"\nStep 4: Run ImageBind model to get similarity between cropped image and different modalities\n\"\"\"\ndef retriev_vision_and_text(elements, text_list):\n inputs = {\n ModalityType.VISION: data.load_and_transform_vision_data_from_pil_image(elements, device),\n ModalityType.TEXT: data.load_and_transform_text(text_list, device),\n }\n with torch.no_grad():\n embeddings = bind_model(inputs)\n vision_audio = torch.softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.TEXT].T, dim=0),\n return vision_audio # [113, 1]\n\n\nvision_text_result = retriev_vision_and_text(cropped_boxes, [\"A car\"] )\n\n\n\"\"\"\nStep 5: Merge the top similarity masks to get the final mask and save the merged mask\n\nImage / Text mask\n\"\"\"\n\n# get highest similar mask with threshold\n# result[0] shape: [113, 1]\nthreshold = 0.05\nindex = get_indices_of_values_above_threshold(vision_text_result[0], threshold)\n\nsegmentation_masks = []\nfor seg_idx in index:\n segmentation_mask_image = Image.fromarray(masks[seg_idx][\"segmentation\"].astype('uint8') * 255)\n segmentation_masks.append(segmentation_mask_image)\n\noriginal_image = Image.open(image_path)\noverlay_image = Image.new('RGBA', image.size, (0, 0, 0, 255))\noverlay_color = (255, 255, 255, 0)\n\ndraw = ImageDraw.Draw(overlay_image)\nfor segmentation_mask_image in segmentation_masks:\n draw.bitmap((0, 0), segmentation_mask_image, fill=overlay_color)\n\n# return Image.alpha_composite(original_image.convert('RGBA'), overlay_image) \nmask_image = overlay_image.convert(\"RGB\")\nmask_image.save(\"./text_sam_merged_mask.jpg\")\n" }, { "path": "data_generation/Grounded-Segment-Anything/playground/ImageBind_SAM/utils.py", "content": "from PIL import Image\nimport numpy as np\n\ndef segment_image(image, segmentation_mask):\n image_array = np.array(image)\n segmented_image_array = np.zeros_like(image_array)\n segmented_image_array[segmentation_mask] = image_array[segmentation_mask]\n segmented_image = Image.fromarray(segmented_image_array)\n black_image = Image.new(\"RGB\", image.size, (0, 0, 0))\n transparency_mask = np.zeros_like(segmentation_mask, dtype=np.uint8)\n transparency_mask[segmentation_mask] = 255\n transparency_mask_image = Image.fromarray(transparency_mask, mode='L')\n black_image.paste(segmented_image, mask=transparency_mask_image)\n return black_image\n\n\ndef convert_box_xywh_to_xyxy(box):\n x1 = box[0]\n y1 = box[1]\n x2 = box[0] + box[2]\n y2 = box[1] + box[3]\n return [x1, y1, x2, y2]\n\n\ndef get_indices_of_values_above_threshold(values, threshold):\n return [i for i, v in enumerate(values) if v > threshold]" }, { "path": "data_generation/Grounded-Segment-Anything/playground/LaMa/README.md", "content": "## LaMa: Resolution-robust Large Mask Inpainting with Fourier Convolutions\n\n:grapes: [[Official Project Page](https://advimman.github.io/lama-project/)]   :apple:[[LaMa Cleaner](https://github.com/Sanster/lama-cleaner)]\n\nWe use the highly organized code [lama-cleaner](https://github.com/Sanster/lama-cleaner) to simplify the demo code for users.\n\n
\n\n![](https://raw.githubusercontent.com/senya-ashukha/senya-ashukha.github.io/master/projects/lama_21/ezgif-4-0db51df695a8.gif)\n\n
\n\n## Abstract\n\n> Modern image inpainting systems, despite the significant progress, often struggle with large missing areas, complex geometric structures, and high-resolution images. We find that one of the main reasons for that is the lack of an ef-fective receptive field in both the inpainting network andthe loss function. To alleviate this issue, we propose anew method called large mask inpainting (LaMa). LaM ais based on: a new inpainting network architecture that uses fast Fourier convolutions, which have the image-widereceptive field\na high receptive field perceptual loss; large training masks, which unlocks the potential ofthe first two components. Our inpainting network improves the state-of-the-art across a range of datasets and achieves excellent performance even in challenging scenarios, e.g.completion of periodic structures. Our model generalizes surprisingly well to resolutions that are higher than thoseseen at train time, and achieves this at lower parameter & compute costs than the competitive baselines.\n\n## Table of Contents\n- [Installation](#installation)\n- [LaMa Demos](#paint-by-example-demos)\n - [Diffuser Demo](#paintbyexample-diffuser-demos)\n - [PaintByExample with SAM](#paintbyexample-with-sam)\n\n\n## TODO\n- [x] LaMa Demo with lama-cleaner\n- [x] LaMa with SAM\n- [ ] LaMa with GroundingDINO\n- [ ] LaMa with Grounded-SAM\n\n\n## Installation\nWe're using lama-cleaner for this demo, install it as follows:\n```bash\npip install lama-cleaner\n```\nPlease refer to [lama-cleaner](https://github.com/Sanster/lama-cleaner) for more details. \n\nThen install Grounded-SAM follows [Grounded-SAM Installation](https://github.com/IDEA-Research/Grounded-Segment-Anything#installation) for some extension demos.\n\n## LaMa Demos\nHere we provide the demos for `LaMa`\n\n### LaMa Demo with lama-cleaner\n\n```bash\ncd playground/LaMa\npython lama_inpaint_demo.py\n```\nwith the highly organized code lama-cleaner, this demo can be done in about 20 lines of code. The result will be saved as `lama_inpaint_demo.jpg`:\n\n
\n\n| Input Image | Mask | Inpaint Output |\n|:----:|:----:|:----:|\n| ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/lama/example.jpg?raw=true) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/lama/mask.png?raw=true) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/lama/lama_inpaint_demo.jpg?raw=true) |\n\n
\n\n### LaMa with SAM\n\n```bash\ncd playground/LaMa\npython sam_lama.py\n```\n\n**Tips** \nTo make it better for inpaint, we should **dilate the mask first** to make it a bit larger to cover the whole region (Thanks a lot for [Inpaint-Anything](https://github.com/geekyutao/Inpaint-Anything) and [Tao Yu](https://github.com/geekyutao) for this)\n\n\nThe `original mask` and `dilated mask` are shown as follows:\n\n
\n\n| Mask | Dilated Mask |\n|:---:|:---:|\n| ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/lama/sam_demo_mask.png?raw=true) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/lama/dilated_mask.png?raw=true) |\n\n
\n\n\nAnd the inpaint result will be saved as `sam_lama_demo.jpg`:\n\n| Input Image | SAM Output | Dilated Mask | LaMa Inpaint |\n|:---:|:---:|:---:|:---:|\n| ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/input_image.png?raw=true) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/demo_with_point_prompt.png?raw=true) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/lama/dilated_mask.png?raw=true) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/lama/sam_lama_demo.jpg?raw=true) |\n\n" }, { "path": "data_generation/Grounded-Segment-Anything/playground/LaMa/lama_inpaint_demo.py", "content": "import cv2\nimport PIL\nimport requests\nimport numpy as np\nfrom lama_cleaner.model.lama import LaMa\nfrom lama_cleaner.schema import Config\n\n\ndef download_image(url):\n image = PIL.Image.open(requests.get(url, stream=True).raw)\n image = PIL.ImageOps.exif_transpose(image)\n image = image.convert(\"RGB\")\n return image\n\n\nimg_url = \"https://raw.githubusercontent.com/Sanster/lama-cleaner/main/assets/dog.jpg\"\nmask_url = \"https://user-images.githubusercontent.com/3998421/202105351-9fcc4bf8-129d-461a-8524-92e4caad431f.png\"\n\nimage = np.asarray(download_image(img_url))\nmask = np.asarray(download_image(mask_url).convert(\"L\"))\n\n# set to GPU for faster inference\nmodel = LaMa(\"cpu\")\nresult = model(image, mask, Config(hd_strategy=\"Original\", ldm_steps=20, hd_strategy_crop_margin=128, hd_strategy_crop_trigger_size=800, hd_strategy_resize_limit=800))\ncv2.imwrite(\"lama_inpaint_demo.jpg\", result)" }, { "path": "data_generation/Grounded-Segment-Anything/playground/LaMa/sam_lama.py", "content": "# !pip install diffusers transformers\n\nimport requests\nimport cv2\nimport numpy as np\nimport PIL\nfrom PIL import Image\nfrom io import BytesIO\n\nfrom segment_anything import sam_model_registry, SamPredictor\n\nfrom lama_cleaner.model.lama import LaMa\nfrom lama_cleaner.schema import Config\n\n\"\"\"\nStep 1: Download and preprocess demo images\n\"\"\"\ndef download_image(url):\n image = PIL.Image.open(requests.get(url, stream=True).raw)\n image = PIL.ImageOps.exif_transpose(image)\n image = image.convert(\"RGB\")\n return image\n\n\nimg_url = \"https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/input_image.png?raw=true\"\n\n\ninit_image = download_image(img_url)\ninit_image = np.asarray(init_image)\n\n\n\"\"\"\nStep 2: Initialize SAM and LaMa models\n\"\"\"\n\nDEVICE = \"cuda:1\"\n\n# SAM\nSAM_ENCODER_VERSION = \"vit_h\"\nSAM_CHECKPOINT_PATH = \"/comp_robot/rentianhe/code/Grounded-Segment-Anything/sam_vit_h_4b8939.pth\"\nsam = sam_model_registry[SAM_ENCODER_VERSION](checkpoint=SAM_CHECKPOINT_PATH).to(device=DEVICE)\nsam_predictor = SamPredictor(sam)\nsam_predictor.set_image(init_image)\n\n# LaMa\nmodel = LaMa(DEVICE)\n\n\n\"\"\"\nStep 3: Get masks with SAM by prompt (box or point) and inpaint the mask region by example image.\n\"\"\"\n\ninput_point = np.array([[350, 256]])\ninput_label = np.array([1]) # positive label\n\nmasks, _, _ = sam_predictor.predict(\n point_coords=input_point,\n point_labels=input_label,\n multimask_output=False\n)\nmasks = masks.astype(np.uint8) * 255\n# mask_pil = Image.fromarray(masks[0]) # simply save the first mask\n\n\n\"\"\"\nStep 4: Dilate Mask to make it more suitable for LaMa inpainting\n\nThe idea behind dilate mask is to mask a larger region which will be better for inpainting.\n\nBorrowed from Inpaint-Anything: https://github.com/geekyutao/Inpaint-Anything/blob/main/utils/utils.py#L18\n\"\"\"\n\ndef dilate_mask(mask, dilate_factor=15):\n mask = mask.astype(np.uint8)\n mask = cv2.dilate(\n mask,\n np.ones((dilate_factor, dilate_factor), np.uint8),\n iterations=1\n )\n return mask\n\ndef save_array_to_img(img_arr, img_p):\n Image.fromarray(img_arr.astype(np.uint8)).save(img_p)\n\n# [1, 512, 512] to [512, 512] and save mask\nsave_array_to_img(masks[0], \"./mask.png\")\n\nmask = dilate_mask(masks[0], dilate_factor=15)\n\nsave_array_to_img(mask, \"./dilated_mask.png\")\n\n\"\"\"\nStep 5: Run LaMa inpaint model\n\"\"\"\nresult = model(init_image, mask, Config(hd_strategy=\"Original\", ldm_steps=20, hd_strategy_crop_margin=128, hd_strategy_crop_trigger_size=800, hd_strategy_resize_limit=800))\ncv2.imwrite(\"sam_lama_demo.jpg\", result)\n" }, { "path": "data_generation/Grounded-Segment-Anything/playground/PaintByExample/README.md", "content": "## Paint by Example: Exemplar-based Image Editing with Diffusion Models\n\n:grapes: [[Official Project Page](https://github.com/Fantasy-Studio/Paint-by-Example)]   :apple:[[Official Online Demo](https://huggingface.co/spaces/Fantasy-Studio/Paint-by-Example)]\n\n
\n\n![](https://github.com/Fantasy-Studio/Paint-by-Example/blob/main/figure/teaser.png?raw=True)\n\n
\n\n## Abstract\n\n> Language-guided image editing has achieved great success recently. In this paper, for the first time, we investigate exemplar-guided image editing for more precise control. We achieve this goal by leveraging self-supervised training to disentangle and re-organize the source image and the exemplar. However, the naive approach will cause obvious fusing artifacts. We carefully analyze it and propose an information bottleneck and strong augmentations to avoid the trivial solution of directly copying and pasting the exemplar image. Meanwhile, to ensure the controllability of the editing process, we design an arbitrary shape mask for the exemplar image and leverage the classifier-free guidance to increase the similarity to the exemplar image. The whole framework involves a single forward of the diffusion model without any iterative optimization. We demonstrate that our method achieves an impressive performance and enables controllable editing on in-the-wild images with high fidelity.\n\n## Table of Contents\n- [Installation](#installation)\n- [Paint-By-Example Demos](#paint-by-example-demos)\n - [Diffuser Demo](#paintbyexample-diffuser-demos)\n - [PaintByExample with SAM](#paintbyexample-with-sam)\n\n\n## TODO\n- [x] PaintByExample Diffuser Demo\n- [x] PaintByExample with SAM\n- [ ] PaintByExample with GroundingDINO\n- [ ] PaintByExample with Grounded-SAM\n\n## Installation\nWe're using PaintByExample with diffusers, install diffusers as follows:\n```bash\npip install diffusers==0.16.1\n```\nThen install Grounded-SAM follows [Grounded-SAM Installation](https://github.com/IDEA-Research/Grounded-Segment-Anything#installation) for some extension demos.\n\n## Paint-By-Example Demos\nHere we provide the demos for `PaintByExample`\n\n\n### PaintByExample Diffuser Demos\n```python\ncd playground/PaintByExample\npython paint_by_example.py\n```\n**Notes:** set `cache_dir` to save the pretrained weights to specific folder. The paint result will be save as `paint_by_example_demo.jpg`:\n\n
\n\n| Input Image | Mask | Example Image | Inpaint Result |\n|:----:|:----:|:----:|:----:|\n| ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/input_image.png?raw=true) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/mask.png?raw=true) |
| ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/paint_by_example_demo.jpg?raw=true) |\n| ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/input_image.png?raw=true) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/mask.png?raw=true) |
| ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/paint_by_pomeranian_demo.jpg?raw=true) |\n\n
\n\n### PaintByExample with SAM\n\nIn this demo, we did inpaint task by:\n1. Generate mask by SAM with prompt (box or point)\n2. Inpaint with mask and example image\n\n```python\ncd playground/PaintByExample\npython sam_paint_by_example.py\n```\n**Notes:** We set a more `num_inference_steps` (like 200 to 500) to get higher quality image. And we've found that the mask region can influence a lot on the final result (like a panda can not be well inpainted with a region like dog). It needed to have more test on it.\n\n| Input Image | SAM Output | Example Image | Inpaint Result |\n|:----:|:----:|:----:|:----:|\n| ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/input_image.png?raw=true) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/demo_with_point_prompt.png?raw=true) |
| ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/sam_paint_by_example_demo.jpg?raw=true) |\n\n" }, { "path": "data_generation/Grounded-Segment-Anything/playground/PaintByExample/paint_by_example.py", "content": "# !pip install diffusers transformers\n\nimport PIL\nimport requests\nimport torch\nfrom io import BytesIO\nfrom diffusers import DiffusionPipeline\n\n\n\"\"\"\nStep 1: Download demo images\n\"\"\"\ndef download_image(url):\n response = requests.get(url)\n return PIL.Image.open(BytesIO(response.content)).convert(\"RGB\")\n\n\nimg_url = \"https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/input_image.png?raw=true\"\nmask_url = \"https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/mask.png?raw=true\"\nexample_url = \"https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/pomeranian_example.jpg?raw=True\"\n# example_url = \"https://raw.githubusercontent.com/Fantasy-Studio/Paint-by-Example/main/examples/reference/example_1.jpg\"\n\ninit_image = download_image(img_url).resize((512, 512))\nmask_image = download_image(mask_url).resize((512, 512))\nexample_image = download_image(example_url).resize((512, 512))\n\n\n\"\"\"\nStep 2: Download pretrained weights and initialize model\n\"\"\"\n# set cache dir to store the weights\ncache_dir = \"/comp_robot/rentianhe/weights/diffusers/\"\n\npipe = DiffusionPipeline.from_pretrained(\n \"Fantasy-Studio/Paint-by-Example\",\n torch_dtype=torch.float16,\n cache_dir=cache_dir,\n)\n# set to device\npipe = pipe.to(\"cuda:1\")\n\n\n\"\"\"\nStep 3: Run PaintByExample pipeline and save image\n\"\"\"\nimage = pipe(\n image=init_image, \n mask_image=mask_image, \n example_image=example_image,\n num_inference_steps=200,\n).images[0]\n\nimage.save(\"./paint_by_example_demo.jpg\")\n" }, { "path": "data_generation/Grounded-Segment-Anything/playground/PaintByExample/sam_paint_by_example.py", "content": "# !pip install diffusers transformers\n\nimport requests\nimport torch\nimport numpy as np\nfrom PIL import Image\nfrom io import BytesIO\nfrom diffusers import DiffusionPipeline\n\nfrom segment_anything import sam_model_registry, SamPredictor\n\n\n\"\"\"\nStep 1: Download and preprocess example demo images\n\"\"\"\ndef download_image(url):\n response = requests.get(url)\n return Image.open(BytesIO(response.content)).convert(\"RGB\")\n\n\nimg_url = \"https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/input_image.png?raw=true\"\n# example_url = \"https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/pomeranian_example.jpg?raw=True\"\n# example_url = \"https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/example_image.jpg?raw=true\"\nexample_url = \"https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/paint_by_example/labrador_example.jpg?raw=true\"\n\ninit_image = download_image(img_url).resize((512, 512))\nexample_image = download_image(example_url).resize((512, 512))\n\n\n\"\"\"\nStep 2: Initialize SAM and PaintByExample models\n\"\"\"\n\nDEVICE = \"cuda:1\"\n\n# SAM\nSAM_ENCODER_VERSION = \"vit_h\"\nSAM_CHECKPOINT_PATH = \"/comp_robot/rentianhe/code/Grounded-Segment-Anything/sam_vit_h_4b8939.pth\"\nsam = sam_model_registry[SAM_ENCODER_VERSION](checkpoint=SAM_CHECKPOINT_PATH).to(device=DEVICE)\nsam_predictor = SamPredictor(sam)\nsam_predictor.set_image(np.array(init_image))\n\n# PaintByExample Pipeline\nCACHE_DIR = \"/comp_robot/rentianhe/weights/diffusers/\"\npipe = DiffusionPipeline.from_pretrained(\n \"Fantasy-Studio/Paint-by-Example\",\n torch_dtype=torch.float16,\n cache_dir=CACHE_DIR,\n)\npipe = pipe.to(DEVICE)\n\n\n\"\"\"\nStep 3: Get masks with SAM by prompt (box or point) and inpaint the mask region by example image.\n\"\"\"\n\ninput_point = np.array([[350, 256]])\ninput_label = np.array([1]) # positive label\n\nmasks, _, _ = sam_predictor.predict(\n point_coords=input_point,\n point_labels=input_label,\n multimask_output=False\n)\nmask = masks[0] # [1, 512, 512] to [512, 512] np.ndarray\nmask_pil = Image.fromarray(mask)\n\nmask_pil.save(\"./mask.jpg\")\n\nimage = pipe(\n image=init_image, \n mask_image=mask_pil, \n example_image=example_image, \n num_inference_steps=500, \n guidance_scale=9.0\n).images[0]\n\nimage.save(\"./paint_by_example_demo.jpg\")\n" }, { "path": "data_generation/Grounded-Segment-Anything/playground/README.md", "content": "## Playground\n\nWe will try more interesting **base models** and **build more fun demos** in the playground. In the playground, we will:\n\n- **Simplify the demo code** to make it easier for users to get started.\n- **Keep complete usage notes** and some pitfalls to reduce the burden on users.\n\n## Table of Contents\n- [DeepFloyd: Text-to-Image Generation](./DeepFloyd/)\n - [Dream: Text-to-Image Generation](./DeepFloyd/dream.py)\n - [Style Transfer](./DeepFloyd/style_transfer.py)\n- [Paint by Example: Exemplar-based Image Editing with Diffusion Models](./PaintByExample/)\n - [Diffuser Demo](./PaintByExample/paint_by_example.py)\n - [PaintByExample with SAM](./PaintByExample/sam_paint_by_example.py)\n- [LaMa: Resolution-robust Large Mask Inpainting with Fourier Convolutions](./LaMa/)\n - [LaMa Demo](./LaMa/lama_inpaint_demo.py)\n - [LaMa with SAM](./LaMa/sam_lama.py)\n- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](./RePaint/)\n - [RePaint Demo](./RePaint/repaint.py)\n" }, { "path": "data_generation/Grounded-Segment-Anything/playground/RePaint/README.md", "content": "## RePaint: Inpainting using Denoising Diffusion Probabilistic Models\n\n:grapes: [[Official Project Page](https://github.com/andreas128/RePaint)]\n\n
\n\n![](https://user-images.githubusercontent.com/11280511/150803812-a4729ef8-6ad4-46aa-ae99-8c27fbb2ea2e.png)\n\n
\n\n## Abstract\n\n> Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Most existing approaches train for a certain distribution of masks, which limits their generalization capabilities to unseen mask types. Furthermore, training with pixel-wise and perceptual losses often leads to simple textural extensions towards the missing areas instead of semantically meaningful generation. In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks. We employ a pretrained unconditional DDPM as the generative prior. To condition the generation process, we only alter the reverse diffusion iterations by sampling the unmasked regions using the given image information. Since this technique does not modify or condition the original DDPM network itself, the model produces highquality and diverse output images for any inpainting form. We validate our method for both faces and general-purpose image inpainting using standard and extreme masks. RePaint outperforms state-of-the-art Autoregressive, and GAN approaches for at least five out of six mask distributions.\n\n\n## Table of Contents\n- [Installation](#installation)\n- [Repaint Demos](#repaint-demos)\n - [Diffuser Demo](#repaint-diffuser-demos)\n\n\n## TODO\n- [x] RePaint Diffuser Demo\n- [ ] RePaint with SAM\n- [ ] RePaint with GroundingDINO\n- [ ] RePaint with Grounded-SAM\n\n## Installation\nWe're using PaintByExample with diffusers, install diffusers as follows:\n```bash\npip install diffusers==0.16.1\n```\nThen install Grounded-SAM follows [Grounded-SAM Installation](https://github.com/IDEA-Research/Grounded-Segment-Anything#installation) for some extension demos.\n\n## RePaint Demos\nHere we provide the demos for `RePaint`\n\n\n### RePaint Diffuser Demos\n```python\ncd playground/RePaint\npython repaint.py\n```\n**Notes:** set `cache_dir` to save the pretrained weights to specific folder. The paint result will be save as `repaint_demo.jpg`:\n\n
\n\n| Input Image | Mask | Inpaint Result |\n|:----:|:----:|:----:|\n| ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/repaint/celeba_hq_256.png?raw=true) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/repaint/mask_256.png?raw=true) | ![](https://github.com/IDEA-Research/detrex-storage/blob/main/assets/grounded_sam/repaint/repaint_demo.jpg?raw=true) |\n\n\n
\n\n\n" }, { "path": "data_generation/Grounded-Segment-Anything/playground/RePaint/repaint.py", "content": "from io import BytesIO\n\nimport torch\n\nimport PIL\nimport requests\nfrom diffusers import RePaintPipeline, RePaintScheduler\n\n\ndef download_image(url):\n response = requests.get(url)\n return PIL.Image.open(BytesIO(response.content)).convert(\"RGB\")\n\n\nimg_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/celeba_hq_256.png\"\nmask_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png\"\n\n# Load the original image and the mask as PIL images\noriginal_image = download_image(img_url).resize((256, 256))\nmask_image = download_image(mask_url).resize((256, 256))\n\n# Load the RePaint scheduler and pipeline based on a pretrained DDPM model\nDEVICE = \"cuda:1\"\nCACHE_DIR = \"/comp_robot/rentianhe/weights/diffusers/\"\nscheduler = RePaintScheduler.from_pretrained(\"google/ddpm-ema-celebahq-256\", cache_dir=CACHE_DIR)\npipe = RePaintPipeline.from_pretrained(\"google/ddpm-ema-celebahq-256\", scheduler=scheduler, cache_dir=CACHE_DIR)\npipe = pipe.to(DEVICE)\n\ngenerator = torch.Generator(device=DEVICE).manual_seed(0)\noutput = pipe(\n image=original_image,\n mask_image=mask_image,\n num_inference_steps=250,\n eta=0.0,\n jump_length=10,\n jump_n_sample=10,\n generator=generator,\n)\ninpainted_image = output.images[0]\ninpainted_image.save(\"./repaint_demo.jpg\")" }, { "path": "data_generation/Grounded-Segment-Anything/predict.py", "content": "# Prediction interface for Cog ⚙️\n# https://github.com/replicate/cog/blob/main/docs/python.md\n\nimport os\nimport json\nfrom typing import Any\nimport numpy as np\nimport random\nimport torch\nimport torchvision\nimport torchvision.transforms as transforms\nfrom PIL import Image\nimport cv2\nimport matplotlib.pyplot as plt\nfrom cog import BasePredictor, Input, Path, BaseModel\n\nfrom subprocess import call\n\nHOME = os.getcwd()\nos.chdir(\"GroundingDINO\")\ncall(\"pip install -q .\", shell=True)\nos.chdir(HOME)\nos.chdir(\"segment_anything\")\ncall(\"pip install -q .\", shell=True)\nos.chdir(HOME)\n\n# Grounding DINO\nimport GroundingDINO.groundingdino.datasets.transforms as T\nfrom GroundingDINO.groundingdino.models import build_model\nfrom GroundingDINO.groundingdino.util.slconfig import SLConfig\nfrom GroundingDINO.groundingdino.util.utils import (\n clean_state_dict,\n get_phrases_from_posmap,\n)\n\n# segment anything\nfrom segment_anything import build_sam, build_sam_hq, SamPredictor\n\nfrom ram.models import ram\n\n\nclass ModelOutput(BaseModel):\n tags: str\n rounding_box_img: Path\n masked_img: Path\n json_data: Any\n\n\nclass Predictor(BasePredictor):\n def setup(self):\n \"\"\"Load the model into memory to make running multiple predictions efficient\"\"\"\n self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n normalize = transforms.Normalize(\n mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]\n )\n self.image_size = 384\n self.transform = transforms.Compose(\n [\n transforms.Resize((self.image_size, self.image_size)),\n transforms.ToTensor(),\n normalize,\n ]\n )\n\n # load model\n self.ram_model = ram(\n pretrained=\"pretrained/ram_swin_large_14m.pth\",\n image_size=self.image_size,\n vit=\"swin_l\",\n )\n self.ram_model.eval()\n self.ram_model = self.ram_model.to(self.device)\n\n self.model = load_model(\n \"GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py\",\n \"pretrained/groundingdino_swint_ogc.pth\",\n device=self.device,\n )\n\n self.sam = SamPredictor(\n build_sam(checkpoint=\"pretrained/sam_vit_h_4b8939.pth\").to(self.device)\n )\n self.sam_hq = SamPredictor(\n build_sam_hq(checkpoint=\"pretrained/sam_hq_vit_h.pth\").to(self.device)\n )\n\n def predict(\n self,\n input_image: Path = Input(description=\"Input image\"),\n use_sam_hq: bool = Input(\n description=\"Use sam_hq instead of SAM for prediction\", default=False\n ),\n ) -> ModelOutput:\n \"\"\"Run a single prediction on the model\"\"\"\n\n # default settings\n box_threshold = 0.25\n text_threshold = 0.2\n iou_threshold = 0.5\n\n image_pil, image = load_image(str(input_image))\n\n raw_image = image_pil.resize((self.image_size, self.image_size))\n raw_image = self.transform(raw_image).unsqueeze(0).to(self.device)\n\n with torch.no_grad():\n tags, tags_chinese = self.ram_model.generate_tag(raw_image)\n\n tags = tags[0].replace(\" |\", \",\")\n\n # run grounding dino model\n boxes_filt, scores, pred_phrases = get_grounding_output(\n self.model, image, tags, box_threshold, text_threshold, device=self.device\n )\n\n predictor = self.sam_hq if use_sam_hq else self.sam\n\n image = cv2.imread(str(input_image))\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n predictor.set_image(image)\n\n size = image_pil.size\n H, W = size[1], size[0]\n for i in range(boxes_filt.size(0)):\n boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])\n boxes_filt[i][:2] -= boxes_filt[i][2:] / 2\n boxes_filt[i][2:] += boxes_filt[i][:2]\n\n boxes_filt = boxes_filt.cpu()\n # use NMS to handle overlapped boxes\n print(f\"Before NMS: {boxes_filt.shape[0]} boxes\")\n nms_idx = (\n torchvision.ops.nms(boxes_filt, scores, iou_threshold).numpy().tolist()\n )\n boxes_filt = boxes_filt[nms_idx]\n pred_phrases = [pred_phrases[idx] for idx in nms_idx]\n print(f\"After NMS: {boxes_filt.shape[0]} boxes\")\n\n transformed_boxes = predictor.transform.apply_boxes_torch(\n boxes_filt, image.shape[:2]\n ).to(self.device)\n\n masks, _, _ = predictor.predict_torch(\n point_coords=None,\n point_labels=None,\n boxes=transformed_boxes.to(self.device),\n multimask_output=False,\n )\n\n # draw output image\n plt.figure(figsize=(10, 10))\n for mask in masks:\n show_mask(mask.cpu().numpy(), plt.gca(), random_color=True)\n for box, label in zip(boxes_filt, pred_phrases):\n show_box(box.numpy(), plt.gca(), label)\n\n rounding_box_path = \"/tmp/automatic_label_output.png\"\n plt.axis(\"off\")\n plt.savefig(\n Path(rounding_box_path), bbox_inches=\"tight\", dpi=300, pad_inches=0.0\n )\n plt.close()\n\n # save masks and json data\n value = 0 # 0 for background\n mask_img = torch.zeros(masks.shape[-2:])\n for idx, mask in enumerate(masks):\n mask_img[mask.cpu().numpy()[0] == True] = value + idx + 1\n plt.figure(figsize=(10, 10))\n plt.imshow(mask_img.numpy())\n plt.axis(\"off\")\n masks_path = \"/tmp/mask.png\"\n plt.savefig(masks_path, bbox_inches=\"tight\", dpi=300, pad_inches=0.0)\n plt.close()\n\n json_data = {\n \"tags\": tags,\n \"mask\": [{\"value\": value, \"label\": \"background\"}],\n }\n for label, box in zip(pred_phrases, boxes_filt):\n value += 1\n name, logit = label.split(\"(\")\n logit = logit[:-1] # the last is ')'\n json_data[\"mask\"].append(\n {\n \"value\": value,\n \"label\": name,\n \"logit\": float(logit),\n \"box\": box.numpy().tolist(),\n }\n )\n\n json_path = \"/tmp/label.json\"\n with open(json_path, \"w\") as f:\n json.dump(json_data, f)\n\n return ModelOutput(\n tags=tags,\n masked_img=Path(masks_path),\n rounding_box_img=Path(rounding_box_path),\n json_data=Path(json_path),\n )\n\n\ndef get_grounding_output(\n model, image, caption, box_threshold, text_threshold, device=\"cpu\"\n):\n caption = caption.lower()\n caption = caption.strip()\n if not caption.endswith(\".\"):\n caption = caption + \".\"\n model = model.to(device)\n image = image.to(device)\n with torch.no_grad():\n outputs = model(image[None], captions=[caption])\n logits = outputs[\"pred_logits\"].cpu().sigmoid()[0] # (nq, 256)\n boxes = outputs[\"pred_boxes\"].cpu()[0] # (nq, 4)\n logits.shape[0]\n\n # filter output\n logits_filt = logits.clone()\n boxes_filt = boxes.clone()\n filt_mask = logits_filt.max(dim=1)[0] > box_threshold\n logits_filt = logits_filt[filt_mask] # num_filt, 256\n boxes_filt = boxes_filt[filt_mask] # num_filt, 4\n logits_filt.shape[0]\n\n # get phrase\n tokenlizer = model.tokenizer\n tokenized = tokenlizer(caption)\n # build pred\n pred_phrases = []\n scores = []\n for logit, box in zip(logits_filt, boxes_filt):\n pred_phrase = get_phrases_from_posmap(\n logit > text_threshold, tokenized, tokenlizer\n )\n pred_phrases.append(pred_phrase + f\"({str(logit.max().item())[:4]})\")\n scores.append(logit.max().item())\n\n return boxes_filt, torch.Tensor(scores), pred_phrases\n\n\ndef load_image(image_path):\n # load image\n image_pil = Image.open(image_path).convert(\"RGB\") # load image\n\n transform = T.Compose(\n [\n T.RandomResize([800], max_size=1333),\n T.ToTensor(),\n T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n ]\n )\n image, _ = transform(image_pil, None) # 3, h, w\n return image_pil, image\n\n\ndef load_model(model_config_path, model_checkpoint_path, device):\n args = SLConfig.fromfile(model_config_path)\n args.device = device\n model = build_model(args)\n checkpoint = torch.load(model_checkpoint_path, map_location=\"cpu\")\n load_res = model.load_state_dict(\n clean_state_dict(checkpoint[\"model\"]), strict=False\n )\n print(load_res)\n _ = model.eval()\n return model\n\n\ndef show_mask(mask, ax, random_color=False):\n if random_color:\n color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)\n else:\n color = np.array([30 / 255, 144 / 255, 255 / 255, 0.6])\n h, w = mask.shape[-2:]\n mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\n ax.imshow(mask_image)\n\n\ndef show_box(box, ax, label):\n x0, y0 = box[0], box[1]\n w, h = box[2] - box[0], box[3] - box[1]\n ax.add_patch(\n plt.Rectangle((x0, y0), w, h, edgecolor=\"green\", facecolor=(0, 0, 0, 0), lw=1.5)\n )\n ax.text(x0, y0, label)\n" }, { "path": "data_generation/Grounded-Segment-Anything/requirements.txt", "content": "addict\ndiffusers\ngradio\nhuggingface_hub\nmatplotlib\nnumpy\nonnxruntime\nopencv_python\nPillow\npycocotools\nPyYAML\nrequests\nsetuptools\nsupervision\ntermcolor\ntimm\ntorch\ntorchvision\ntransformers\nyapf\nnltk\nfairscale\nlitellm\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/.flake8", "content": "[flake8]\nignore = W503, E203, E221, C901, C408, E741, C407, B017, F811, C101, EXE001, EXE002\nmax-line-length = 100\nmax-complexity = 18\nselect = B,C,E,F,W,T4,B9\nper-file-ignores =\n **/__init__.py:F401,F403,E402\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/CODE_OF_CONDUCT.md", "content": "# Code of Conduct\n\n## Our Pledge\n\nIn the interest of fostering an open and welcoming environment, we as\ncontributors and maintainers pledge to make participation in our project and\nour community a harassment-free experience for everyone, regardless of age, body\nsize, disability, ethnicity, sex characteristics, gender identity and expression,\nlevel of experience, education, socio-economic status, nationality, personal\nappearance, race, religion, or sexual identity and orientation.\n\n## Our Standards\n\nExamples of behavior that contributes to creating a positive environment\ninclude:\n\n* Using welcoming and inclusive language\n* Being respectful of differing viewpoints and experiences\n* Gracefully accepting constructive criticism\n* Focusing on what is best for the community\n* Showing empathy towards other community members\n\nExamples of unacceptable behavior by participants include:\n\n* The use of sexualized language or imagery and unwelcome sexual attention or\n advances\n* Trolling, insulting/derogatory comments, and personal or political attacks\n* Public or private harassment\n* Publishing others' private information, such as a physical or electronic\n address, without explicit permission\n* Other conduct which could reasonably be considered inappropriate in a\n professional setting\n\n## Our Responsibilities\n\nProject maintainers are responsible for clarifying the standards of acceptable\nbehavior and are expected to take appropriate and fair corrective action in\nresponse to any instances of unacceptable behavior.\n\nProject maintainers have the right and responsibility to remove, edit, or\nreject comments, commits, code, wiki edits, issues, and other contributions\nthat are not aligned to this Code of Conduct, or to ban temporarily or\npermanently any contributor for other behaviors that they deem inappropriate,\nthreatening, offensive, or harmful.\n\n## Scope\n\nThis Code of Conduct applies within all project spaces, and it also applies when\nan individual is representing the project or its community in public spaces.\nExamples of representing a project or community include using an official\nproject e-mail address, posting via an official social media account, or acting\nas an appointed representative at an online or offline event. Representation of\na project may be further defined and clarified by project maintainers.\n\nThis Code of Conduct also applies outside the project spaces when there is a\nreasonable belief that an individual's behavior may have a negative impact on\nthe project or its community.\n\n## Enforcement\n\nInstances of abusive, harassing, or otherwise unacceptable behavior may be\nreported by contacting the project team at . All\ncomplaints will be reviewed and investigated and will result in a response that\nis deemed necessary and appropriate to the circumstances. The project team is\nobligated to maintain confidentiality with regard to the reporter of an incident.\nFurther details of specific enforcement policies may be posted separately.\n\nProject maintainers who do not follow or enforce the Code of Conduct in good\nfaith may face temporary or permanent repercussions as determined by other\nmembers of the project's leadership.\n\n## Attribution\n\nThis Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,\navailable at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html\n\n[homepage]: https://www.contributor-covenant.org\n\nFor answers to common questions about this code of conduct, see\nhttps://www.contributor-covenant.org/faq\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/CONTRIBUTING.md", "content": "# Contributing to segment-anything\nWe want to make contributing to this project as easy and transparent as\npossible.\n\n## Pull Requests\nWe actively welcome your pull requests.\n\n1. Fork the repo and create your branch from `main`.\n2. If you've added code that should be tested, add tests.\n3. If you've changed APIs, update the documentation.\n4. Ensure the test suite passes.\n5. Make sure your code lints, using the `linter.sh` script in the project's root directory. Linting requires `black==23.*`, `isort==5.12.0`, `flake8`, and `mypy`.\n6. If you haven't already, complete the Contributor License Agreement (\"CLA\").\n\n## Contributor License Agreement (\"CLA\")\nIn order to accept your pull request, we need you to submit a CLA. You only need\nto do this once to work on any of Facebook's open source projects.\n\nComplete your CLA here: \n\n## Issues\nWe use GitHub issues to track public bugs. Please ensure your description is\nclear and has sufficient instructions to be able to reproduce the issue.\n\nFacebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe\ndisclosure of security bugs. In those cases, please go through the process\noutlined on that page and do not file a public issue.\n\n## License\nBy contributing to segment-anything, you agree that your contributions will be licensed\nunder the LICENSE file in the root directory of this source tree.\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/LICENSE", "content": " Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n\n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n 1. Definitions.\n\n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n\n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n\n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. 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However, in accepting such obligations, You may act only\n on Your own behalf and on Your sole responsibility, not on behalf\n of any other Contributor, and only if You agree to indemnify,\n defend, and hold each Contributor harmless for any liability\n incurred by, or claims asserted against, such Contributor by reason\n of your accepting any such warranty or additional liability.\n\n END OF TERMS AND CONDITIONS\n\n APPENDIX: How to apply the Apache License to your work.\n\n To apply the Apache License to your work, attach the following\n boilerplate notice, with the fields enclosed by brackets \"[]\"\n replaced with your own identifying information. (Don't include\n the brackets!) The text should be enclosed in the appropriate\n comment syntax for the file format. We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n\n Copyright [yyyy] [name of copyright owner]\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/README.md", "content": "# Segment Anything\n\n**[Meta AI Research, FAIR](https://ai.facebook.com/research/)**\n\n[Alexander Kirillov](https://alexander-kirillov.github.io/), [Eric Mintun](https://ericmintun.github.io/), [Nikhila Ravi](https://nikhilaravi.com/), [Hanzi Mao](https://hanzimao.me/), Chloe Rolland, Laura Gustafson, [Tete Xiao](https://tetexiao.com), [Spencer Whitehead](https://www.spencerwhitehead.com/), Alex Berg, Wan-Yen Lo, [Piotr Dollar](https://pdollar.github.io/), [Ross Girshick](https://www.rossgirshick.info/)\n\n[[`Paper`](https://ai.facebook.com/research/publications/segment-anything/)] [[`Project`](https://segment-anything.com/)] [[`Demo`](https://segment-anything.com/demo)] [[`Dataset`](https://segment-anything.com/dataset/index.html)] [[`Blog`](https://ai.facebook.com/blog/segment-anything-foundation-model-image-segmentation/)]\n\n![SAM design](assets/model_diagram.png?raw=true)\n\nThe **Segment Anything Model (SAM)** produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a [dataset](https://segment-anything.com/dataset/index.html) of 11 million images and 1.1 billion masks, and has strong zero-shot performance on a variety of segmentation tasks.\n\n

\n \n \n

\n\n## Installation\n\nThe code requires `python>=3.8`, as well as `pytorch>=1.7` and `torchvision>=0.8`. Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.\n\nInstall Segment Anything:\n\n```\npip install git+https://github.com/facebookresearch/segment-anything.git\n```\n\nor clone the repository locally and install with\n\n```\ngit clone git@github.com:facebookresearch/segment-anything.git\ncd segment-anything; pip install -e .\n```\n\nThe following optional dependencies are necessary for mask post-processing, saving masks in COCO format, the example notebooks, and exporting the model in ONNX format. `jupyter` is also required to run the example notebooks.\n```\npip install opencv-python pycocotools matplotlib onnxruntime onnx\n```\n\n\n## Getting Started\n\nFirst download a [model checkpoint](#model-checkpoints). Then the model can be used in just a few lines to get masks from a given prompt:\n\n```\nfrom segment_anything import build_sam, SamPredictor \npredictor = SamPredictor(build_sam(checkpoint=\"\"))\npredictor.set_image()\nmasks, _, _ = predictor.predict()\n```\n\nor generate masks for an entire image:\n\n```\nfrom segment_anything import build_sam, SamAutomaticMaskGenerator\nmask_generator = SamAutomaticMaskGenerator(build_sam(checkpoint=\"\"))\nmasks = mask_generator_generate()\n```\n\nAdditionally, masks can be generated for images from the command line:\n\n```\npython scripts/amg.py --checkpoint --input --output \n```\n\nSee the examples notebooks on [using SAM with prompts](/notebooks/predictor_example.ipynb) and [automatically generating masks](/notebooks/automatic_mask_generator_example.ipynb) for more details.\n\n

\n \n \n

\n\n## ONNX Export\n\nSAM's lightweight mask decoder can be exported to ONNX format so that it can be run in any environment that supports ONNX runtime, such as in-browser as showcased in the [demo](https://segment-anything.com/demo). Export the model with\n\n```\npython scripts/export_onnx_model.py --checkpoint --output \n```\n\nSee the [example notebook](https://github.com/facebookresearch/segment-anything/blob/main/notebooks/onnx_model_example.ipynb) for details on how to combine image preprocessing via SAM's backbone with mask prediction using the ONNX model. It is recommended to use the latest stable version of PyTorch for ONNX export.\n\n## Model Checkpoints\n\nThree model versions of the model are available with different backbone sizes. These models can be instantiated by running \n```\nfrom segment_anything import sam_model_registry\nsam = sam_model_registry[\"\"](checkpoint=\"\")\n```\nClick the links below to download the checkpoint for the corresponding model name. The default model in bold can also be instantiated with `build_sam`, as in the examples in [Getting Started](#getting-started).\n\n* **`default` or `vit_h`: [ViT-H SAM model.](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth)**\n* `vit_l`: [ViT-L SAM model.](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth)\n* `vit_b`: [ViT-B SAM model.](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth)\n\n## License\nThe model is licensed under the [Apache 2.0 license](LICENSE).\n\n## Contributing\n\nSee [contributing](CONTRIBUTING.md) and the [code of conduct](CODE_OF_CONDUCT.md).\n\n## Contributors\n\nThe Segment Anything project was made possible with the help of many contributors (alphabetical):\n\nAaron Adcock, Vaibhav Aggarwal, Morteza Behrooz, Cheng-Yang Fu, Ashley Gabriel, Ahuva Goldstand, Allen Goodman, Sumanth Gurram, Jiabo Hu, Somya Jain, Devansh Kukreja, Robert Kuo, Joshua Lane, Yanghao Li, Lilian Luong, Jitendra Malik, Mallika Malhotra, William Ngan, Omkar Parkhi, Nikhil Raina, Dirk Rowe, Neil Sejoor, Vanessa Stark, Bala Varadarajan, Bram Wasti, Zachary Winstrom\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/linter.sh", "content": "#!/bin/bash -e\n# Copyright (c) Facebook, Inc. and its affiliates.\n\n{\n black --version | grep -E \"23\\.\" > /dev/null\n} || {\n echo \"Linter requires 'black==23.*' !\"\n exit 1\n}\n\nISORT_VERSION=$(isort --version-number)\nif [[ \"$ISORT_VERSION\" != 5.12* ]]; then\n echo \"Linter requires isort==5.12.0 !\"\n exit 1\nfi\n\necho \"Running isort ...\"\nisort . --atomic\n\necho \"Running black ...\"\nblack -l 100 .\n\necho \"Running flake8 ...\"\nif [ -x \"$(command -v flake8)\" ]; then\n flake8 .\nelse\n python3 -m flake8 .\nfi\n\necho \"Running mypy...\"\n\nmypy --exclude 'setup.py|notebooks' .\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/notebooks/automatic_mask_generator_example.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"5fa21d44\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Copyright (c) Meta Platforms, Inc. and affiliates.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b7c0041e\",\n \"metadata\": {},\n \"source\": [\n \"# Automatically generating object masks with SAM\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"289bb0b4\",\n \"metadata\": {},\n \"source\": [\n \"Since SAM can efficiently process prompts, masks for the entire image can be generated by sampling a large number of prompts over an image. This method was used to generate the dataset SA-1B. \\n\",\n \"\\n\",\n \"The class `SamAutomaticMaskGenerator` implements this capability. It works by sampling single-point input prompts in a grid over the image, from each of which SAM can predict multiple masks. Then, masks are filtered for quality and deduplicated using non-maximal suppression. Additional options allow for further improvement of mask quality and quantity, such as running prediction on multiple crops of the image or postprocessing masks to remove small disconnected regions and holes.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"072e25b8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\",\n \" \\\"Open\\n\",\n \"\\n\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from IPython.display import display, HTML\\n\",\n \"display(HTML(\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" \\\"Open\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c0b71431\",\n \"metadata\": {},\n \"source\": [\n \"## Environment Set-up\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"47e5a78f\",\n \"metadata\": {},\n \"source\": [\n \"If running locally using jupyter, first install `segment_anything` in your environment using the [installation instructions](https://github.com/facebookresearch/segment-anything#installation) in the repository. If running from Google Colab, set `using_collab=True` below and run the cell. In Colab, be sure to select 'GPU' under 'Edit'->'Notebook Settings'->'Hardware accelerator'.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"4fe300fb\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"using_colab = False\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"0685a2f5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"if using_colab:\\n\",\n \" import torch\\n\",\n \" import torchvision\\n\",\n \" print(\\\"PyTorch version:\\\", torch.__version__)\\n\",\n \" print(\\\"Torchvision version:\\\", torchvision.__version__)\\n\",\n \" print(\\\"CUDA is available:\\\", torch.cuda.is_available())\\n\",\n \" import sys\\n\",\n \" !{sys.executable} -m pip install opencv-python matplotlib\\n\",\n \" !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/segment-anything.git'\\n\",\n \" \\n\",\n \" !mkdir images\\n\",\n \" !wget -P images https://raw.githubusercontent.com/facebookresearch/segment-anything/main/notebooks/images/dog.jpg\\n\",\n \" \\n\",\n \" !wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fd2bc687\",\n \"metadata\": {},\n \"source\": [\n \"## Set-up\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"560725a2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import torch\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import cv2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"74b6e5f0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def show_anns(anns):\\n\",\n \" if len(anns) == 0:\\n\",\n \" return\\n\",\n \" sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)\\n\",\n \" ax = plt.gca()\\n\",\n \" ax.set_autoscale_on(False)\\n\",\n \" polygons = []\\n\",\n \" color = []\\n\",\n \" for ann in sorted_anns:\\n\",\n \" m = ann['segmentation']\\n\",\n \" img = np.ones((m.shape[0], m.shape[1], 3))\\n\",\n \" color_mask = np.random.random((1, 3)).tolist()[0]\\n\",\n \" for i in range(3):\\n\",\n \" img[:,:,i] = color_mask[i]\\n\",\n \" ax.imshow(np.dstack((img, m*0.35)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"27c41445\",\n \"metadata\": {},\n \"source\": [\n \"## Example image\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"ad354922\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"image = cv2.imread('images/dog.jpg')\\n\",\n \"image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"e0ac8c67\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(20,20))\\n\",\n \"plt.imshow(image)\\n\",\n \"plt.axis('off')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b8c2824a\",\n \"metadata\": {},\n \"source\": [\n \"## Automatic mask generation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d9ef74c5\",\n \"metadata\": {},\n \"source\": [\n \"To run automatic mask generation, provide a SAM model to the `SamAutomaticMaskGenerator` class. Set the path below to the SAM checkpoint. Running on CUDA and with the default model is recommended.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"17ade22d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sam_checkpoint = \\\"sam_vit_h_4b8939.pth\\\"\\n\",\n \"\\n\",\n \"device = \\\"cuda\\\"\\n\",\n \"model_type = \\\"default\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"1848a108\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import sys\\n\",\n \"sys.path.append(\\\"..\\\")\\n\",\n \"from segment_anything import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor\\n\",\n \"\\n\",\n \"sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)\\n\",\n \"sam.to(device=device)\\n\",\n \"\\n\",\n \"mask_generator = SamAutomaticMaskGenerator(sam)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d6b1ea21\",\n \"metadata\": {},\n \"source\": [\n \"To generate masks, just run `generate` on an image.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"391771c1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"masks = mask_generator.generate(image)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e36a1a39\",\n \"metadata\": {},\n \"source\": [\n \"Mask generation returns a list over masks, where each mask is a dictionary containing various data about the mask. These keys are:\\n\",\n \"* `segmentation` : the mask\\n\",\n \"* `area` : the area of the mask in pixels\\n\",\n \"* `bbox` : the boundary box of the mask in XYWH format\\n\",\n \"* `predicted_iou` : the model's own prediction for the quality of the mask\\n\",\n \"* `point_coords` : the sampled input point that generated this mask\\n\",\n \"* `stability_score` : an additional measure of mask quality\\n\",\n \"* `crop_box` : the crop of the image used to generate this mask in XYWH format\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"4fae8d66\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"dict_keys(['segmentation', 'area', 'bbox', 'predicted_iou', 'point_coords', 'stability_score', 'crop_box'])\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(len(masks))\\n\",\n \"print(masks[0].keys())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"53009a1f\",\n \"metadata\": {},\n \"source\": [\n \"Show all the masks overlayed on the image.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"77ac29c5\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(20,20))\\n\",\n \"plt.imshow(image)\\n\",\n \"show_anns(masks)\\n\",\n \"plt.axis('off')\\n\",\n \"plt.show() \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"00b3d6b2\",\n \"metadata\": {},\n \"source\": [\n \"## Automatic mask generation options\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"183de84e\",\n \"metadata\": {},\n \"source\": [\n \"There are several tunable parameters in automatic mask generation that control how densely points are sampled and what the thresholds are for removing low quality or duplicate masks. Additionally, generation can be automatically run on crops of the image to get improved performance on smaller objects, and post-processing can remove stray pixels and holes. Here is an example configuration that samples more masks:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"68364513\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"mask_generator_2 = SamAutomaticMaskGenerator(\\n\",\n \" model=sam,\\n\",\n \" points_per_side=32,\\n\",\n \" pred_iou_thresh=0.86,\\n\",\n \" stability_score_thresh=0.92,\\n\",\n \" crop_n_layers=1,\\n\",\n \" crop_n_points_downscale_factor=2,\\n\",\n \" min_mask_region_area=100, # Requires open-cv to run post-processing\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"bebcdaf1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"masks2 = mask_generator_2.generate(image)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"b8473f3c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"90\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"len(masks2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"fb702ae3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(20,20))\\n\",\n \"plt.imshow(image)\\n\",\n \"show_anns(masks2)\\n\",\n \"plt.axis('off')\\n\",\n \"plt.show() \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8c937160\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/notebooks/onnx_model_example.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"901c8ef3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Copyright (c) Meta Platforms, Inc. and affiliates.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1662bb7c\",\n \"metadata\": {},\n \"source\": [\n \"# Produces masks from prompts using an ONNX model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7fcc21a0\",\n \"metadata\": {},\n \"source\": [\n \"SAM's prompt encoder and mask decoder are very lightweight, which allows for efficient computation of a mask given user input. This notebook shows an example of how to export and use this lightweight component of the model in ONNX format, allowing it to run on a variety of platforms that support an ONNX runtime.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"86daff77\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\",\n \" \\\"Open\\n\",\n \"\\n\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from IPython.display import display, HTML\\n\",\n \"display(HTML(\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" \\\"Open\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"55ae4e00\",\n \"metadata\": {},\n \"source\": [\n \"## Environment Set-up\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"109a5cc2\",\n \"metadata\": {},\n \"source\": [\n \"If running locally using jupyter, first install `segment_anything` in your environment using the [installation instructions](https://github.com/facebookresearch/segment-anything#installation) in the repository. The latest stable versions of PyTorch and ONNX are recommended for this notebook. If running from Google Colab, set `using_collab=True` below and run the cell. In Colab, be sure to select 'GPU' under 'Edit'->'Notebook Settings'->'Hardware accelerator'.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"39b99fc4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"using_colab = False\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"296a69be\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"if using_colab:\\n\",\n \" import torch\\n\",\n \" import torchvision\\n\",\n \" print(\\\"PyTorch version:\\\", torch.__version__)\\n\",\n \" print(\\\"Torchvision version:\\\", torchvision.__version__)\\n\",\n \" print(\\\"CUDA is available:\\\", torch.cuda.is_available())\\n\",\n \" import sys\\n\",\n \" !{sys.executable} -m pip install opencv-python matplotlib onnx onnxruntime\\n\",\n \" !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/segment-anything.git'\\n\",\n \" \\n\",\n \" !mkdir images\\n\",\n \" !wget -P images https://raw.githubusercontent.com/facebookresearch/segment-anything/main/notebooks/images/truck.jpg\\n\",\n \" \\n\",\n \" !wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"dc4a58be\",\n \"metadata\": {},\n \"source\": [\n \"## Set-up\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"42396e8d\",\n \"metadata\": {},\n \"source\": [\n \"Note that this notebook requires both the `onnx` and `onnxruntime` optional dependencies, in addition to `opencv-python` and `matplotlib` for visualization.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2c712610\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import torch\\n\",\n \"import numpy as np\\n\",\n \"import cv2\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from segment_anything import sam_model_registry, SamPredictor\\n\",\n \"from segment_anything.utils.onnx import SamOnnxModel\\n\",\n \"\\n\",\n \"import onnxruntime\\n\",\n \"from onnxruntime.quantization import QuantType\\n\",\n \"from onnxruntime.quantization.quantize import quantize_dynamic\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f29441b9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def show_mask(mask, ax):\\n\",\n \" color = np.array([30/255, 144/255, 255/255, 0.6])\\n\",\n \" h, w = mask.shape[-2:]\\n\",\n \" mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\\n\",\n \" ax.imshow(mask_image)\\n\",\n \" \\n\",\n \"def show_points(coords, labels, ax, marker_size=375):\\n\",\n \" pos_points = coords[labels==1]\\n\",\n \" neg_points = coords[labels==0]\\n\",\n \" ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)\\n\",\n \" ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25) \\n\",\n \" \\n\",\n \"def show_box(box, ax):\\n\",\n \" x0, y0 = box[0], box[1]\\n\",\n \" w, h = box[2] - box[0], box[3] - box[1]\\n\",\n \" ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2)) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bd0f6b2b\",\n \"metadata\": {},\n \"source\": [\n \"## Export an ONNX model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1540f719\",\n \"metadata\": {},\n \"source\": [\n \"Set the path below to a SAM model checkpoint, then load the model. This will be needed to both export the model and to calculate embeddings for the model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"76fc53f4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"checkpoint = \\\"sam_vit_h_4b8939.pth\\\"\\n\",\n \"model_type = \\\"default\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"11bfc8aa\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sam = sam_model_registry[model_type](checkpoint=checkpoint)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"450c089c\",\n \"metadata\": {},\n \"source\": [\n \"The script `segment-anything/scripts/export_onnx_model.py` can be used to export the necessary portion of SAM. Alternatively, run the following code to export an ONNX model. If you have already exported a model, set the path below and skip to the next section. Assure that the exported ONNX model aligns with the checkpoint and model type set above. This notebook expects the model was exported with the parameter `return_single_mask=True`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"38a8add8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"onnx_model_path = None # Set to use an already exported model, then skip to the next section.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7da638ba\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import warnings\\n\",\n \"\\n\",\n \"onnx_model_path = \\\"sam_onnx_example.onnx\\\"\\n\",\n \"\\n\",\n \"onnx_model = SamOnnxModel(sam, return_single_mask=True)\\n\",\n \"\\n\",\n \"dynamic_axes = {\\n\",\n \" \\\"point_coords\\\": {1: \\\"num_points\\\"},\\n\",\n \" \\\"point_labels\\\": {1: \\\"num_points\\\"},\\n\",\n \"}\\n\",\n \"\\n\",\n \"embed_dim = sam.prompt_encoder.embed_dim\\n\",\n \"embed_size = sam.prompt_encoder.image_embedding_size\\n\",\n \"mask_input_size = [4 * x for x in embed_size]\\n\",\n \"dummy_inputs = {\\n\",\n \" \\\"image_embeddings\\\": torch.randn(1, embed_dim, *embed_size, dtype=torch.float),\\n\",\n \" \\\"point_coords\\\": torch.randint(low=0, high=1024, size=(1, 5, 2), dtype=torch.float),\\n\",\n \" \\\"point_labels\\\": torch.randint(low=0, high=4, size=(1, 5), dtype=torch.float),\\n\",\n \" \\\"mask_input\\\": torch.randn(1, 1, *mask_input_size, dtype=torch.float),\\n\",\n \" \\\"has_mask_input\\\": torch.tensor([1], dtype=torch.float),\\n\",\n \" \\\"orig_im_size\\\": torch.tensor([1500, 2250], dtype=torch.float),\\n\",\n \"}\\n\",\n \"output_names = [\\\"masks\\\", \\\"iou_predictions\\\", \\\"low_res_masks\\\"]\\n\",\n \"\\n\",\n \"with warnings.catch_warnings():\\n\",\n \" warnings.filterwarnings(\\\"ignore\\\", category=torch.jit.TracerWarning)\\n\",\n \" warnings.filterwarnings(\\\"ignore\\\", category=UserWarning)\\n\",\n \" with open(onnx_model_path, \\\"wb\\\") as f:\\n\",\n \" torch.onnx.export(\\n\",\n \" onnx_model,\\n\",\n \" tuple(dummy_inputs.values()),\\n\",\n \" f,\\n\",\n \" export_params=True,\\n\",\n \" verbose=False,\\n\",\n \" opset_version=17,\\n\",\n \" do_constant_folding=True,\\n\",\n \" input_names=list(dummy_inputs.keys()),\\n\",\n \" output_names=output_names,\\n\",\n \" dynamic_axes=dynamic_axes,\\n\",\n \" ) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c450cf1a\",\n \"metadata\": {},\n \"source\": [\n \"If desired, the model can additionally be quantized and optimized. We find this improves web runtime significantly for negligible change in qualitative performance. Run the next cell to quantize the model, or skip to the next section otherwise.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"235d39fe\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"onnx_model_quantized_path = \\\"sam_onnx_quantized_example.onnx\\\"\\n\",\n \"quantize_dynamic(\\n\",\n \" model_input=onnx_model_path,\\n\",\n \" model_output=onnx_model_quantized_path,\\n\",\n \" optimize_model=True,\\n\",\n \" per_channel=False,\\n\",\n \" reduce_range=False,\\n\",\n \" weight_type=QuantType.QUInt8,\\n\",\n \")\\n\",\n \"onnx_model_path = onnx_model_quantized_path\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"927a928b\",\n \"metadata\": {},\n \"source\": [\n \"## Example Image\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6be6eb55\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"image = cv2.imread('images/truck.jpg')\\n\",\n \"image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b7e9a27a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plt.figure(figsize=(10,10))\\n\",\n \"plt.imshow(image)\\n\",\n \"plt.axis('on')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"027b177b\",\n \"metadata\": {},\n \"source\": [\n \"## Using an ONNX model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"778d4593\",\n \"metadata\": {},\n \"source\": [\n \"Here as an example, we use `onnxruntime` in python on CPU to execute the ONNX model. However, any platform that supports an ONNX runtime could be used in principle. Launch the runtime session below:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9689b1bf\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ort_session = onnxruntime.InferenceSession(onnx_model_path)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7708ead6\",\n \"metadata\": {},\n \"source\": [\n \"To use the ONNX model, the image must first be pre-processed using the SAM image encoder. This is a heavier weight process best performed on GPU. SamPredictor can be used as normal, then `.get_image_embedding()` will retreive the intermediate features.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"26e067b4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sam.to(device='cuda')\\n\",\n \"predictor = SamPredictor(sam)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7ad3f0d6\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"predictor.set_image(image)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8a6f0f07\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"image_embedding = predictor.get_image_embedding().cpu().numpy()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5e112f33\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"image_embedding.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6337b654\",\n \"metadata\": {},\n \"source\": [\n \"The ONNX model has a different input signature than `SamPredictor.predict`. The following inputs must all be supplied. Note the special cases for both point and mask inputs. All inputs are `np.float32`.\\n\",\n \"* `image_embeddings`: The image embedding from `predictor.get_image_embedding()`. Has a batch index of length 1.\\n\",\n \"* `point_coords`: Coordinates of sparse input prompts, corresponding to both point inputs and box inputs. Boxes are encoded using two points, one for the top-left corner and one for the bottom-right corner. *Coordinates must already be transformed to long-side 1024.* Has a batch index of length 1.\\n\",\n \"* `point_labels`: Labels for the sparse input prompts. 0 is a negative input point, 1 is a positive input point, 2 is a top-left box corner, 3 is a bottom-right box corner, and -1 is a padding point. *If there is no box input, a single padding point with label -1 and coordinates (0.0, 0.0) should be concatenated.*\\n\",\n \"* `mask_input`: A mask input to the model with shape 1x1x256x256. This must be supplied even if there is no mask input. In this case, it can just be zeros.\\n\",\n \"* `has_mask_input`: An indicator for the mask input. 1 indicates a mask input, 0 indicates no mask input.\\n\",\n \"* `orig_im_size`: The size of the input image in (H,W) format, before any transformation. \\n\",\n \"\\n\",\n \"Additionally, the ONNX model does not threshold the output mask logits. To obtain a binary mask, threshold at `sam.mask_threshold` (equal to 0.0).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bf5a9f55\",\n \"metadata\": {},\n \"source\": [\n \"### Example point input\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1c0deef0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"input_point = np.array([[500, 375]])\\n\",\n \"input_label = np.array([1])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7256394c\",\n \"metadata\": {},\n \"source\": [\n \"Add a batch index, concatenate a padding point, and transform.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4f69903e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"onnx_coord = np.concatenate([input_point, np.array([[0.0, 0.0]])], axis=0)[None, :, :]\\n\",\n \"onnx_label = np.concatenate([input_label, np.array([-1])], axis=0)[None, :].astype(np.float32)\\n\",\n \"\\n\",\n \"onnx_coord = predictor.transform.apply_coords(onnx_coord, image.shape[:2]).astype(np.float32)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b188dc53\",\n \"metadata\": {},\n \"source\": [\n \"Create an empty mask input and an indicator for no mask.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5cb52bcf\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"onnx_mask_input = np.zeros((1, 1, 256, 256), dtype=np.float32)\\n\",\n \"onnx_has_mask_input = np.zeros(1, dtype=np.float32)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a99c2cc5\",\n \"metadata\": {},\n \"source\": [\n \"Package the inputs to run in the onnx model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b1d7ea11\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ort_inputs = {\\n\",\n \" \\\"image_embeddings\\\": image_embedding,\\n\",\n \" \\\"point_coords\\\": onnx_coord,\\n\",\n \" \\\"point_labels\\\": onnx_label,\\n\",\n \" \\\"mask_input\\\": onnx_mask_input,\\n\",\n \" \\\"has_mask_input\\\": onnx_has_mask_input,\\n\",\n \" \\\"orig_im_size\\\": np.array(image.shape[:2], dtype=np.float32)\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4b6409c9\",\n \"metadata\": {},\n \"source\": [\n \"Predict a mask and threshold it.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"dc4cc082\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"masks, _, low_res_logits = ort_session.run(None, ort_inputs)\\n\",\n \"masks = masks > predictor.model.mask_threshold\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d778a8fb\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"masks.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"badb1175\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plt.figure(figsize=(10,10))\\n\",\n \"plt.imshow(image)\\n\",\n \"show_mask(masks, plt.gca())\\n\",\n \"show_points(input_point, input_label, plt.gca())\\n\",\n \"plt.axis('off')\\n\",\n \"plt.show() \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1f1d4d15\",\n \"metadata\": {},\n \"source\": [\n \"### Example mask input\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b319da82\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"input_point = np.array([[500, 375], [1125, 625]])\\n\",\n \"input_label = np.array([1, 1])\\n\",\n \"\\n\",\n \"# Use the mask output from the previous run. It is already in the correct form for input to the ONNX model.\\n\",\n \"onnx_mask_input = low_res_logits\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b1823b37\",\n \"metadata\": {},\n \"source\": [\n \"Transform the points as in the previous example.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8885130f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"onnx_coord = np.concatenate([input_point, np.array([[0.0, 0.0]])], axis=0)[None, :, :]\\n\",\n \"onnx_label = np.concatenate([input_label, np.array([-1])], axis=0)[None, :].astype(np.float32)\\n\",\n \"\\n\",\n \"onnx_coord = predictor.transform.apply_coords(onnx_coord, image.shape[:2]).astype(np.float32)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"28e47b69\",\n \"metadata\": {},\n \"source\": [\n \"The `has_mask_input` indicator is now 1.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3ab4483a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"onnx_has_mask_input = np.ones(1, dtype=np.float32)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d3781955\",\n \"metadata\": {},\n \"source\": [\n \"Package inputs, then predict and threshold the mask.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0c1ec096\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ort_inputs = {\\n\",\n \" \\\"image_embeddings\\\": image_embedding,\\n\",\n \" \\\"point_coords\\\": onnx_coord,\\n\",\n \" \\\"point_labels\\\": onnx_label,\\n\",\n \" \\\"mask_input\\\": onnx_mask_input,\\n\",\n \" \\\"has_mask_input\\\": onnx_has_mask_input,\\n\",\n \" \\\"orig_im_size\\\": np.array(image.shape[:2], dtype=np.float32)\\n\",\n \"}\\n\",\n \"\\n\",\n \"masks, _, _ = ort_session.run(None, ort_inputs)\\n\",\n \"masks = masks > predictor.model.mask_threshold\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1e36554b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plt.figure(figsize=(10,10))\\n\",\n \"plt.imshow(image)\\n\",\n \"show_mask(masks, plt.gca())\\n\",\n \"show_points(input_point, input_label, plt.gca())\\n\",\n \"plt.axis('off')\\n\",\n \"plt.show() \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2ef211d0\",\n \"metadata\": {},\n \"source\": [\n \"### Example box and point input\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"51e58d2e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"input_box = np.array([425, 600, 700, 875])\\n\",\n \"input_point = np.array([[575, 750]])\\n\",\n \"input_label = np.array([0])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6e119dcb\",\n \"metadata\": {},\n \"source\": [\n \"Add a batch index, concatenate a box and point inputs, add the appropriate labels for the box corners, and transform. There is no padding point since the input includes a box input.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"bfbe4911\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"onnx_box_coords = input_box.reshape(2, 2)\\n\",\n \"onnx_box_labels = np.array([2,3])\\n\",\n \"\\n\",\n \"onnx_coord = np.concatenate([input_point, onnx_box_coords], axis=0)[None, :, :]\\n\",\n \"onnx_label = np.concatenate([input_label, onnx_box_labels], axis=0)[None, :].astype(np.float32)\\n\",\n \"\\n\",\n \"onnx_coord = predictor.transform.apply_coords(onnx_coord, image.shape[:2]).astype(np.float32)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"65edabd2\",\n \"metadata\": {},\n \"source\": [\n \"Package inputs, then predict and threshold the mask.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2abfba56\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"onnx_mask_input = np.zeros((1, 1, 256, 256), dtype=np.float32)\\n\",\n \"onnx_has_mask_input = np.zeros(1, dtype=np.float32)\\n\",\n \"\\n\",\n \"ort_inputs = {\\n\",\n \" \\\"image_embeddings\\\": image_embedding,\\n\",\n \" \\\"point_coords\\\": onnx_coord,\\n\",\n \" \\\"point_labels\\\": onnx_label,\\n\",\n \" \\\"mask_input\\\": onnx_mask_input,\\n\",\n \" \\\"has_mask_input\\\": onnx_has_mask_input,\\n\",\n \" \\\"orig_im_size\\\": np.array(image.shape[:2], dtype=np.float32)\\n\",\n \"}\\n\",\n \"\\n\",\n \"masks, _, _ = ort_session.run(None, ort_inputs)\\n\",\n \"masks = masks > predictor.model.mask_threshold\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8301bf33\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plt.figure(figsize=(10, 10))\\n\",\n \"plt.imshow(image)\\n\",\n \"show_mask(masks[0], plt.gca())\\n\",\n \"show_box(input_box, plt.gca())\\n\",\n \"show_points(input_point, input_label, plt.gca())\\n\",\n \"plt.axis('off')\\n\",\n \"plt.show()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/notebooks/predictor_example.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"f400486b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Copyright (c) Meta Platforms, Inc. and affiliates.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a1ae39ff\",\n \"metadata\": {},\n \"source\": [\n \"# Object masks from prompts with SAM\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b4a4b25c\",\n \"metadata\": {},\n \"source\": [\n \"The Segment Anything Model (SAM) predicts object masks given prompts that indicate the desired object. The model first converts the image into an image embedding that allows high quality masks to be efficiently produced from a prompt. \\n\",\n \"\\n\",\n \"The `SamPredictor` class provides an easy interface to the model for prompting the model. It allows the user to first set an image using the `set_image` method, which calculates the necessary image embeddings. Then, prompts can be provided via the `predict` method to efficiently predict masks from those prompts. The model can take as input both point and box prompts, as well as masks from the previous iteration of prediction.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"18ab8c70\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\",\n \" \\\"Open\\n\",\n \"\\n\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from IPython.display import display, HTML\\n\",\n \"display(HTML(\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" \\\"Open\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"644532a8\",\n \"metadata\": {},\n \"source\": [\n \"## Environment Set-up\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"07fabfee\",\n \"metadata\": {},\n \"source\": [\n \"If running locally using jupyter, first install `segment_anything` in your environment using the [installation instructions](https://github.com/facebookresearch/segment-anything#installation) in the repository. If running from Google Colab, set `using_collab=True` below and run the cell. In Colab, be sure to select 'GPU' under 'Edit'->'Notebook Settings'->'Hardware accelerator'.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"5ea65efc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"using_colab = False\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"91dd9a89\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"if using_colab:\\n\",\n \" import torch\\n\",\n \" import torchvision\\n\",\n \" print(\\\"PyTorch version:\\\", torch.__version__)\\n\",\n \" print(\\\"Torchvision version:\\\", torchvision.__version__)\\n\",\n \" print(\\\"CUDA is available:\\\", torch.cuda.is_available())\\n\",\n \" import sys\\n\",\n \" !{sys.executable} -m pip install opencv-python matplotlib\\n\",\n \" !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/segment-anything.git'\\n\",\n \" \\n\",\n \" !mkdir images\\n\",\n \" !wget -P images https://raw.githubusercontent.com/facebookresearch/segment-anything/main/notebooks/images/truck.jpg\\n\",\n \" !wget -P images https://raw.githubusercontent.com/facebookresearch/segment-anything/main/notebooks/images/groceries.jpg\\n\",\n \" \\n\",\n \" !wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0be845da\",\n \"metadata\": {},\n \"source\": [\n \"## Set-up\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"33681dd1\",\n \"metadata\": {},\n \"source\": [\n \"Necessary imports and helper functions for displaying points, boxes, and masks.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"69b28288\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import torch\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import cv2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"29bc90d5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def show_mask(mask, ax, random_color=False):\\n\",\n \" if random_color:\\n\",\n \" color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)\\n\",\n \" else:\\n\",\n \" color = np.array([30/255, 144/255, 255/255, 0.6])\\n\",\n \" h, w = mask.shape[-2:]\\n\",\n \" mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\\n\",\n \" ax.imshow(mask_image)\\n\",\n \" \\n\",\n \"def show_points(coords, labels, ax, marker_size=375):\\n\",\n \" pos_points = coords[labels==1]\\n\",\n \" neg_points = coords[labels==0]\\n\",\n \" ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)\\n\",\n \" ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25) \\n\",\n \" \\n\",\n \"def show_box(box, ax):\\n\",\n \" x0, y0 = box[0], box[1]\\n\",\n \" w, h = box[2] - box[0], box[3] - box[1]\\n\",\n \" ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2)) \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"23842fb2\",\n \"metadata\": {},\n \"source\": [\n \"## Example image\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"3c2e4f6b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"image = cv2.imread('images/truck.jpg')\\n\",\n \"image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"e30125fd\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10,10))\\n\",\n \"plt.imshow(image)\\n\",\n \"plt.axis('on')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"98b228b8\",\n \"metadata\": {},\n \"source\": [\n \"## Selecting objects with SAM\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0bb1927b\",\n \"metadata\": {},\n \"source\": [\n \"First, load the SAM model and predictor. Change the path below to point to the SAM checkpoint. Running on CUDA and using the default model are recommended for best results.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"17ccff22\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sam_checkpoint = \\\"sam_vit_h_4b8939.pth\\\"\\n\",\n \"device = \\\"cuda\\\"\\n\",\n \"model_type = \\\"default\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"7e28150b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import sys\\n\",\n \"sys.path.append(\\\"..\\\")\\n\",\n \"from segment_anything import sam_model_registry, SamPredictor\\n\",\n \"\\n\",\n \"sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)\\n\",\n \"sam.to(device=device)\\n\",\n \"\\n\",\n \"predictor = SamPredictor(sam)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c925e829\",\n \"metadata\": {},\n \"source\": [\n \"Process the image to produce an image embedding by calling `SamPredictor.set_image`. `SamPredictor` remembers this embedding and will use it for subsequent mask prediction.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"d95d48dd\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"predictor.set_image(image)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d8fc7a46\",\n \"metadata\": {},\n \"source\": [\n \"To select the truck, choose a point on it. Points are input to the model in (x,y) format and come with labels 1 (foreground point) or 0 (background point). Multiple points can be input; here we use only one. The chosen point will be shown as a star on the image.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"5c69570c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"input_point = np.array([[500, 375]])\\n\",\n \"input_label = np.array([1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"a91ba973\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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rXsVlv8KpcP7jP9f132DjP6KLxZ3G43kFUZISVd7z1yo/w6xM+8omfQ5y3CXeCRpIFdNJi1ftNcAXUtmshYsQyuI549w53P/ABzt95m81O6McRUeGKyAc//Un6e3dwveN07dHe00WH6zroepym7gRY33uC64tzwjASNRBU2ceRECIvffiDrNdJQ+47RDwqNn/RKc55hJHQOwZRPvnpT/HtH7zFuBvMAiFJ8Ow7ZN3x8U99kvXJCTGf76TZQxJodSOigaEfuPPhl5CHW974xnfoMHAoMRJCnptlbWFtlTCGewiM6rNXv1POG41gUe3nmgEdgi4tAmLUWLR+CLDueO6jL+GffZLVyYZ+1eN9RyfOQB8O7xw7DYwoKpGP/PzP8sq3v8MJIAqDi3zq879M9+wTdKsV6/Ua5/yMVoOds5GInnruffhFPvazn+B7f/RlXLQ9NKJchZGP/dKneOpDLyLrnn69RpwvSqY83r7vjTafKB/81E9z8cobXLz8mrWjylisW/N5KvO1IGAsCRb1+yqgfcczH3iBJ59/js1TT8JmDa7He7Mg9UHpfIeuPAGF3cjHf/aT3P/+q7z149dwfZfm3rGPgQ989EOcPf0Eq80G6T1u1RMFE7DV9qGIYsgGPvqxj/Lw4SU/eu11Ql7vRAvX6zWf/vSnTekmvnxXK7U0/Rz3I5/5+c9wuj7hC3/yBX77t3+b3/0bf4O7d+8imGVZFIZHV/zwu9/jW1/+Kt/75rcIu71ZycQUQc4nQVRBxCVaOwlI2uzFvH9bgDETnBZ40cF+b36/6Z2ltZ0/+17fH57TFvAtKQLrUitw23rKOa/aynJE++zSPLXzt8SX6+dbhXNtvViiXUtr1LYrkvZq9V1blj7Le7L9rqZzN4HkJdkAWLQQt2CvXbO8Bm1/l+a2BYe1vNKC66X3l+bj2D6v5wqY8eE8V6WO6rl5UUTTAyKMIdA5Qfcjw8UVYRfwQZEIHc4Iuyp4R5dAr08sI2KWYbWOLoPX6s9jfC5bLx+3/IUFSAD/8B/+Q/6D/+A/4POf/zy/+Zu/yX/1X/1XvPzyy/xH/9F/9Nh1ZME+qrIf9njfGR3RuSVhsg4Fuq6bgZ5cDzBzv1FVhmGg7/vZwW+F5fxedntpD2EGRebCYeBqtVqZ5ac5FF2yBmWAtNvtCsgZhiG1be/kz2twA5bQYrPZlGeGYWC1XqM60nXr8nz+SQKNIQRWqxVd17Hf7xnDSBwV1ie4xKjy+J2YhSqEsYyzNufm+aotcrX2cbfbzcZbH8AWsLbgNT9fz3MNZGvi1Vrw6rWtP2v3VE38cns1wWyJ4/SsWUXKevoOxsSs7PQTiezHQFh7nvvwB3nqQy8S+4g4oe/XjDg0MURBwDm63jMMI3effZrNvTvogwtr0zuCU3ZEfv6XPkN3dsLYKSIe58TACKZZEeeIOFxUNl549+qSMezZh8huv2fz1DM4cZydnhKur3n75ZfpxhEPIB6XNL/B96gki1Ic2XSOV17+IS9+5OOsVuYKOuwHXH9iYC2aGV7cfA0RMFtanM0zGC3tpeOh95x+5CV+6z/89/nj//6/592vfQN/fo2MwlMf/RA//duf58HDB6yAtQoxOjxm+TFLsU8ARokE/HrFEAPPPvcBHrzzDk8++wxXV1c88fTT7Iddmjc/A0jiU9+iZ+08UUfWJxs2d88Yz68TQTfQPMbA6RNPsbp7RlBwxVFEUDX3qmEY0TEwSmQXI2hgQE04DdFYhygxhmRFdkUwkYpJ1/vWieOYTrtlwO13+Yy1ZUmpUOiljYioSUtqlSEinDz1BM999EM8cgF1QhhHfDBhN3pFVcxNUhT1pig4f3SffQxsos1Wf7Lh+Y9+mCsf2A8D4zDi/FxoVFViCCjKNYFdGHl3d4V6IToDmlHA3znhE7/8GfabjiAG3qNGkmrlQPG0F0U7YXCgTpDkhhc0ENHZnOXfDWxOoHFJ0F1aE1U4e/YpPvzJn+bCDezHgT72OJQw7tHg2PgVLqgp/5zN/l5tvFloCzGiKkQPn/rFX8A/eWLa1MRjtHPpWUka7Yh4D1GJCC+8+MJEY026BIHf+q3f4qWXPkjne0Qmyx1Mbs55TnrfE1eRX/jML/DXfuev8dKHPmR9iskVcDfw9o9e5btf+hrf/to3eHTxCBcCfXQ4wDlhdBCdIEkydpDoZtmUUIGPWnF2TGj6X6q0e2/pu6qjMzyzKPDd0M503hOPc4cA6Vh/5nINs2fmZ/hQmF8CXEtC94FygDmPrOnJ/AxMPLsdb9tm3oua/zMii0zVzM7YzO3+CM2r52BJFmjHOQ+TsH68F3Buv2u/vam9Y8CrLcdAvVVkP+o9dAyQTpXM/5ytx3Gcb+Amz4tzCJHeezqSJ1WIuCSDRDBFDcYXUcElx8uIIyZ3u2Ng73HKT0oD/kIDpL//9/8+77zzDv/5f/6f89prr/GZz3yG/+6/++/46Ec/+th17HY7E4BipO9WyYUg4hvmlAGGCenzg9NaXbLAHUJgu90W68x6vabv+/JMTUhq95QMPrIrSAZI2ZUuAwdjqukgihA1a81gs16z3e3wyed/2Bs4CuOIakxudII4iGOk7w3U7HZ7vPdst9sCTrquA7E4BhFhu92yT1a0ruuISVNdg4wi1AqmTXRC1/WoCNfjng4lDhHn5360bTEQ5IvveJ6zrnILbDVhS+4TNeFtTfvW0ETo83zbuh4XAFuw0+6Huv26DidiglnTH7K1AvDeAOUQAx2gIRIdeGcis3cdewIn9+4QO8d2f0Xf9TjfE6KiviNGkAhCJKi5sQwi7DXSixDDSOjMKoVCf3ZGdGZVxGdtsRCJqFNElCi2z4NaH5+4c5e9eHzX40R56qkncaoM19fsry7RcTDf925l+xM1i1Byq1Fg2G9x697AYKKmq83aXNiGHb0Txjgmlb7F2KjIxPwS8zmgbRGieHarjv75Z/gr/97/hn95fc31N7+LC4HBA3dOuHjnDe6owyfqGyT1QjFwk6izqvLo0Tmu83TrFXeeehLVyDNnzzKG0cYlDhWz+ChKNkFpGJEYDDSpMhC42F6zEi0MW0NExTHEwC4MiK7o8KARjXmP2h6w9QDpPLGLDM6E3r5YYkx4DjHia9cakeTqMddktlx4SQHQMsulZ+vikOLV0wpOPtEeoqJRiWLPK/Du+SPeuP823D3lRARvUWF0YjFx3glOnLlMOtBOefKZZ9jcOSO8fUHnHFfXV7zxzhvo2Zq+7+iyQORcYsQkUOrwQOhs3br1ikGjVSxCFNjFwFYDl7s94j1rWeHU3EqcmO+8k+QZgLlLRg10pxuCKitxOIusM6tnVrgl8GExPTkCa7YIRUGR92P1lc2pwOXuiocX5+zXcHLHFFgSzWrkOqPhIQZQb4KFc+DBnaxs3qMSx0h3sjHXwc4xhBEdFR8jQRXpPOogxLzeSucdGkHU8eDBA4ZhZBwnjwCA5557FlCur6/wvp8L2Kp0fZdcoKDvVrzwwgs889QzdL5HQ8Q7s1lfvPOA177/Mt/96jf48Z99h3C1BQmmdCOdiQQySZ/ZscoKhmkf1sK/2v9KfI6UL5IVZ3FnH5b3EkaXBG3SOYVDkPKvi9VqvldkhMynFvrVtt2CxYlGGL2nWKMnoNmCmna8NZA4Vn8uS/HZEx+d89iWt7dzYH/kdS5/UhO7zD/aenSproW5Xir1+PNZiDFCpZStn12qawZy7IHZ80ulljlqT6UW6B2TV0SkKBDq72vl/tK6lTZYPi9Gu45bqYz/aFIDghfzjx2H0XhCkhMKds1tKkje64mvzUyGSx25gc/ZXL+PABLAP/gH/4B/8A/+wb/2+/urrfktip2kOAZc3+H9XLNUL64kZpg/yy5vrjkAqlqsNHDoZpV/z3/nDZ5jgLJLXf49g4KimVVSQoZIGAPD3sBN1/cgysl6Y1q1/YCGADHS+w68Yxh2dNLRdbYtx3FEnG227NKXwVGMEXEUV7yY3L9yn8GIWY4/GlMSB3NfS0KwCESP35ywv7shvHvJBkcUodVUiAi+cwYSnEMEOufY7/fG/CRJRgtzmP+u3QHymrSxRbPvpTELF8Kos/dz/eaOOSWSyNat1gWprq+udykOLX1Qgu2Jyk4iIklQsUh2+uhNYFS4PD/nbnzKLIrOI1Ho/Apcj/gN0vV0qgaSfEQU3Mma+OAKr8JeIk/sHbLuOH90wVObp9jgid6ZNcX5JJX30FXkYAyMuy3XD+7jNidcDQOXGvH9ms3JCScyonFgXEEnHZotlyL0AnRCcBFV6FyPRojDDokrduNAGEdOnWN/+S4nV5eMcc+ogTW9uSs5U2rY/DkKxswMVZVRAl1UNsFxstogd075lf/13+F/+PF/zXp3yaOLc8IwcOIcZ9Ix9jCsHA5zbZTOm+CkZu3oVAiXAzvZ88Snn+aHP/oyn/jpn+L+a2/y9LNPE/qObtUbCHIW5yfi8HFk38HYe/rR3ALixnP23BNcPbpAx4DPYqLC2ekJqpFu5YkupvGZoBc10nkh4llpsmSsek6eusdARNVEccVcLOM4JREIJDcvZzGxIURW2e1J5hrrurSCzVwAMeVMK2BBAkjR9rF2mDUlW49ROjHlj4mzk1vL6eaUlXOEqHSKuVSIxdFEDMBGb25rRrsdfr2i69cEf0VAOdmsePbJu7w9XpplzXclPkDEJ+uC4jCpf6VCiHB29x7adcR9RJLLY+eEEAZGHeh7o6mddEaDpKJEIohGNmLB4Peee463vv0y3SA4dXjt2Udz4Q7JXSwLPyHP7yKDztalvAYmqOZ4oJUoVxfnrE+fwrnOQJ+H3gt4T0y8zTkDg+oE7eDJF5/h7ZdfRUeh1xWdX9OfnnE9jsTtjtVqQ+ccvXOI7xBxxB7Ee+MnamscQ+C5558yK7NOPMt7xxtvvILqk9w5e9JAbYpHc9nFrvN00XN25y7PvPA8/WadUZgJedsdP/72d/nKH/wxb/3wx+yvtgxhJIpaYg+18QQj4XRiSq0s5KlN36TJD9GsS0nwNEuTFPflyZKXhP4kNEbVEmB/sDoVT3kvgbq1ruTPl9a8/v6mevMzS4AH5sJ2/rsux/pkz5lnQqqpKF9sWuxMtvXNZaUphrgWzNtY7Xr+sgJhCTTmP1uFSz3WNgFHUVwnRSCS9k3+s2p31v/FmZ7aWSp5rG28knOOtre1F9IxkKdp4EsKq2Njr4FSW5ase/Ox6GyO674cG3tpS8Ro/SIYmu/nYkHHI8kK5FUhBi7Oz1Es7jnEtGAak6tdqlcMjinMPI98PvNL89P8PZMFRRoQ/XjlLzxA+p9bzA0lmn/6GOm6PhHbvEEnxiVJSEEr/920KV0+2A0RAFuI2nK0RKzyxs7P1pqTkq2oEtDzeyEEhmGYgZZsveq6rsQCQTqkqiaQ5SDlEIgxGPhQS2ZxfX0NkPz2J2tICFOcUgYHqiao5v7s9/smg5yyT+53JuAIT734PPtH32fj3az+maYpmntLtqLl8YsIGufB1jVBLcHM1eGuiW9rQSr/yn6IxYVSxCwo9VrWdbYas5owLDGJXHLc0xLDimp7EY1cX10RYjAioUDUpFVWgpj2+MlnnmY/Dpz2PZ0rDmf4JLiZAJb2bwCJyrrr2YdIF5T1aIGrofc88+GX2F1dcCoCY7QsbpJBZRImnCDiUIn0ODqFte/pEIb9jv12y27Ys7t+yHB5wUoDnXNJM+UTczXte0AYgwKe/TDy5o9fZfXUU6zu3OHi3Qdszs7Q7cBwec24H8xKFGOyahlwKa4TzBmHAF1QuiFw1q0Q9bh+zdMf+RjP/swnOD//Bnfu3MXhkVGRlDhCotJTaex1IpgOcEPk6v59vvXP/4hHb7/Nn3z/x7z0kQ/jn32aDqFPYnd2yxMUr2IuQkT8qHi14OOV77iOmekbmxhCYLXaENW0YdkKU1yGUpBxVIUQkTHihogM0RJ8jBb3F0JM/0J1RuYaa6mZuOpRzrC0T2shomWG5Twk1zJN1ceo4KWAvQPBLT0XogUADePIPowWOEzEk3zOc+A2tp81gTCSompELVFJMHdEL544jojHAL9qWdsoMHphn1wSu9XK6sf2N0PkrDvj7mrDxdU10Q1E6YneAEDNdQUmN1hV+s2aQSP7kCw6oSfEyJDi6sR7fCXM1zThmBBU1qRam5NuhYwKuwBjTFHu9r3X5CarFkKU6+1Q1uLwQe1fcuZc9T2rvmc7Dkhn2TJFBZcy2HnM5S2M0cBDBqkh4tNe7dIZkDHy9Nk9ZD8SVyPST0o1SaDj5OSED7zwEqdnZ0Q1JYQGRXYj7771Dl/70pf55he/zPb+I7qgCcRgvPhQPqZok5t5rBV57X6urQrvBULe65mj67UgYNpWWRY8a54xxQi6xWfb/uV3Dp6Vx+t7y8Nm9Yj1XJPcWve1dunLIQkzYfgIGKjbumn/1688Th3t7+U5bgY/N5Wlfi1ZZxZBaWbGVf/zc63XS1G6i/GFGhAeow1L7b5Xf9uS6Xmr1G+tUUsgyayuU91TuImb0auDObMv0BgQMI8kMvzRxCuY2YIz/ZjJjmTg9HjjnQE5ezB98fgQ6X0PkCzmyJh2lzJFZRc7XwkQUY3ZI5JiDJJVgbSh8gZP77cakQxygIMARFUtmrUly0O78fO/OkFDPmRZINona1ILsK6vr/E5riP1dRKirI0WGMUYGcMAhGJB2u12E4BLTCfGyHa7LWMehoG+cwV8KaDiiF1PWK8YgwmLeZy1q5zvLFi/1g7k5zIoVdUSt1UTmvx3/c4Sc2nn2fbD3KwvYp9l0JqtY1r1YUn7lOevjVWrS91O/e44jvi+Z7Vem3AY94zDaOfXQUQYsRTKQQ3ChXHE9xajEcQyuMGIYFmuAsnNZm8C54gBATdEhr5nuHuCPHOP3dU562ixHZokK0uabMI+IqaxBTRZIvrNGokdvvf4EIj7nfkDxz2qEbzDibmP6cRVcd7hpMMxovvIer3B4bg+v+LRO+8i51cM2x3DxQXD9RaXzlXAZMDs/daejUwsLYY+0jlTL7+72/Ho4pwPfvaX+dKffZuu82hyFxxQs2biSsxCIeoYzRxVGcaRR6+/zRPB0Z1f8WB/yfjiCwzjwBiD0RNXEWpJoMQZsM+C+RjMkkBUkpHIQI9zXO92nIVAV+2pYp1O/6JYzFGMCqNl+RGxLGLJo5thtPibk65LzEQJaQ5tb4rRtOYMtOen3qvtc6gm18n5e/Y8tv6aQYC5l2pU2ztZ0QSlDoudUoYhsB32DLsBEFwvdM4XjX4oezLN5TjSeUtGEhNuudruuN7tWOMJG1M0dJ3iZbLEq9jaDxqJwbIA6jgm1z/rY68wbPeM+4EgDicDoxczqmZrSFKyxDQ+Eo2M2BnpcAkcjeyDpfl2nbeU/bUj2Gxql8W4ei0UNbfMcYT9nn4MEAJk5VxMiqXklpjpawgRhrRvcJYZzjtiStRwvd2Zxc17uk7wojixOVFHscaVuLgQiF4IEswNN/Vvt9sjCPvdQE+Pd53x2lXHiy+8yL1793DdCsQs4rIbuXr7AV//ky/w7a99k/tvvU2nghuCWfscE/iGA4vbTYopOHRDWprX9wKnP2k5dnYeF2gtjePYd7Wrfv7ehPPjY2r71M5BzZ9mMkhUkMOA/yUhveXH9Xe1nFSDrEUhfOH3ttTKy7q+xbEXH5f/5UoNCo8BwrbM6eYciFgChMdrN8t5P8neqvug1eetUrdWtC/JZe06F2VEBi+V/FsDPgNHqW7JV7/k1ODQwtlasfCTnVU9mMeWXtiHj1/j+x4gTRshmUZ9Mhsna5BL8QQ+x6OEADgkx86ACTlJcMwCW6679gPOG2a/3x8ApmEYyvMZeWerU70xlzZ+G6cUQiipvvP3uc6T01O2+13pS3ans/gmQejK5s0/c/+6Tkof636t+hUhzDNi5e9DsABbJ2Jax64nrlZsuw6/27KRaQ3ywRMxzaTzh3ey2DzIAeGrNYRLzK8NFm81JAKTH7rIjABkkFlbl2qNSu5LnUZ8yUVgtueaMRVGgeB6z5hjFNIeVNQyuShFAFbjT8T9wKDeDDQeRlFGHekkgAoDWQiNjMOerY7sneKcaY6vXcS/+DS7dcf1OLJWBd8hYtJ8AJBkTVWz4qxGZbsd6QbFb0c0Brwqvu8JpzCGHt95wn7AayRqQMUTZdKYK6BeGKMwinJxdcFTd+6wUnjp+efx48j9GHj09jsMV1v6cTShu1ozq+yQeY4aCU4ZHJxfPGTYX9Pduce7+x0f/tQn6F56jsFZBr99CGw7S8rQCQxqiTAQR0gCQBwjUSzF8KPzR1z2a4b9DpxyPey5CoPd3RHsDjUDSgmsi7DDYlMCBrS2IbAXtXvVorWn0e60ubi85Mn9wNX1ln6zoetTeuMMUDHQqs4sstvdnodXV2xFbaVVuLi44PzqCo2R9YkwRi0uHhPDNvEgW5ecn87akga6FmbKPtcpfqMVBFSx+CJVXGovhGCZCsXbOASGaMkYsoVbQuDRg4eMnUO3I6hH4wi9I/aKakTUUnSLwm7Yo9db1n3HlUazMAfl8t1LLi7PiXeh77eIc3R9z9oJ4n1K32vzHvcDcbtH9qNZpUIkuI7QOcYwcnF9zeX11rInOXNN7iCB6gnoBVXG7Z79w0uuzy/KHAVVu4No2HOxvUZjZLVeTUKJxmLRO0YzatfgSXFnd2gNMbK7viZeXtGjdBpZA/Qp5YiInUGMVo7jwPWwZ3RK0Ei/7pBeUO3YxsDlbkdwjug9Pgprl10co6Wcl+Rep8puu+Xd83PGOLKLexRlJLKNgYvdjn0UzvwOHwzQf/TDH+YDL77IerVOWl+HDpH95TXf+fLX+OaffomHb77DcHXNKpq7rBdfzpJojleYC+4tPV3S5k/KAbcIkOqf7WdLIudNmvpa0G15QZYdjoux7+3q1PLGuq8HvMV+Ofiu3W+tErae18Mx6mxPtv2q+5RLy7dbPlz3/1B5eBww1YrJtv91KWcn0SOV+Zq39K2u+73Wui7HeH/dxk2Ar17LrLh+L+BzbP+2c3mTEiAr4WoZpl2vFhzV3y3tGyUryw73ZQHvYq66zrmStdbqMt5ksUZT/Gh9N9TSnNRrVYfDtMahsr+cFKXOMVfapfK+B0gac3a6KUYlDAPifQoat2JJB9TiPLBnYrQg0yl1q5Q86nnia4JcE6MaENVarfx5bRnK9eTNmbUEefFrQT0/mwlO7VKw3xvzEpHiIpcBxTiOiWBrcaGr381t5f5lNzr7fbD7VxaInADOKeMwMIyB07MNq3tPcO8DHyD++BVkP8ysNplx5ZCVvLlzsonahW5JmKjnuv18idAVbQaHgYs1UKo1M3mcddZB5xyr1aoQlmwiPyAWR4SfMm/ZdUwTsHSOqOluKefBpXTMgA8Rvdzy4J1zfNfzzAsv4E8V1mt81xMv9/Q9+G7FGAL73Z54tSNc72xODKUxOOHnfv7TrILAo2vGJEytfDRNtHOE3cjqzDMOA9swcLHfcaWRcGfNhY9EC+vAASvtLBZhN8J+ZBwEv/JoFyy5gEtJKoAxWmxcF0bO33id3aNz9vuRO+sN9zZrrt98kwevvk7Y7fEZhGc7VJqn+g6GSeumDKKoRN59/TWuX3+Nk5OOe07oRPj0b/wq3/rSF9ndP2e83HG9H1ifrAhB0dWaiNBFn7JgRXQ/EneR09WGcT9wcXnJMOy5ksHiIi6vGWPABfBdD70izoBSQNAeJCgyKHEY2Y97vLOkJaOmeBpJ7kXbgfFiy3pzCjpAENxKLO26s2BlFyJhO7J9dMmD19/k4YOHjAo7VTrg+nrH+fklq1VK2oEjRnORzdo641qJRqkpM1qGuLRP67PX7uZl0A9U4mCmM7kP+S4p773FWO087/zodQaUNSv60xP8ek236rnzxF3OnrhLv7FU7Ofn52zvP2D38ILri8syHgbl7Vfe4MGjdxnvXhMvA64zK+fq5IQnnnmak9MTet9xfX7N/bffYv/uOfLOBT6kuEkig8JaPO++/YCHj+6zuXeHcDrg/Yqu7+n7njt37uBXKzrf8e7DR2zffcT12w948OobMAYUISY6ebm95tHlBQ7h7OxO0uwfRh7VdKYWVuAwa972es91CIRHgpOO4WpHd7pBzkbWJ8LJ2RkiDlWjS7urK67Oz3n08GFyffZ03puSKwSu7r/LxYMHxLM97BTfbYkndolyt+otztGJXVZ8ccnFo3P2j67xY6RL9YWo7Lc7Hp1fIIyE6Pjpn3mJn/3EJ7lzcop3Do9DQ4TdwBsv/5g/+mf/grd+9ApX754j0ehgn+egSjTiROjFz7TIx2jsDODozfu7LXM+cigA123MlAbVWrUAramkrPshCJpiAmvQ0irh2jG2pQbSGo5fn3Fs3K1wXQubuf4lcLBUFoXn9Fm7p9vP7Z0pA2/bzuOAmBb86QJCXQIRN/FumCevacdbW5FCM74lANKOXx+j/aX+P+5zh+CYIiO+1z6Zv3cIMKd3lucxy6yqWpIt5DPRdX3622HRs0zvLgDsMo6mj/Weq98t56LIraZ4FOeguorhvcr7HyCpJuEg4qMzYa3v6RE0VKbaYPE0Pt2e7hC65FONmquFuR7VyH9uhakTB9TuWbnk7/O/dvEzQKoPen2IaxN1BjgwbYIMfPZhKC5jtekUoQR0t0kErO2x1J9Tl1vGs86C4tt7lFQZ8+9potYnp3SnJ+w3a1ZnZ/hwPjsoBZjEsVhZasvSxDwOCVl+riaqs9SdHBKZvP4iUgKHW0Ka685zWX9ef5/nJrtLtgS+ZWp1PaWPmmKIvJu585mlL+0PiXaZnBt58OPXeXj5iO3DHd8d/4znP/ohPvKJnyE64Xq7487pGSenp1ydP+L6esv+4pLhzXdxV3ucpsDRCHdCx8W3X+Hdr36Xbed4O1zSBXjmuee49/TT9KcncGmWonfuv8WjsOc6wPmH3mRzejelT1WcOlDP/sFDtq88IFxfsupWnJzeY3X3LqvNyoBvmvdxv2cc9vh9oNeeZ1dnxCDouxfst2/y6M+/y6M33rRYCIx458QJGrH7npgzHjDXKFRgOyAPHvKV/+f/i49+/pcR1/HGxXdZiyO+c8HbX/0Ob735GneevIN3cOfJe2yef5rTO3cIFzt679lfXnP11jtcPbzk7R+8itsO7LstIvDMvbs87ddc/uhNdvsd450zC25fr+lXa/x6w8nZKS5Y/NDujXd5883X2XewffMBRNLluOBwhGFEr3bcf+UN+nceWRzhOHJy55SzJ+7x5DNPs16vefjGA17+7vd4+PAhD+8/YHh0ySYIqLBL1u+u7/F9z34c0WhAJIQwCWXVPiSdq6WyxEwnmnHoAnysZGavmMDmkutjiMEsptGUTcOjwJhij37w4OvgvN3VI4L3wsnphvWdMxDh+vKS/fUWHUd0u2UTPYTI/vySH33zu4SoXIxv0vc/IGKA3K1XdOue9ekJTz/1NJePznnn4bvofuBkhFWw58YQ8a5j+85DvvWHX2SUgOt6QkzW23TmTzYbnn76Gbz3vPrqK+gwWGa43YCMgX3ItDCAE+499SSd87OLy6WZ31YYaed1GIYiQA1vvGMAQoTr1+7j1j3amZeDl467d+/y5JNPoqqcn59zdXVF5x3Xjx6wCSaQXL3xDu5sDQTevvgeFxePOO867j7xJFEF33V03gDqnSfuMQx7Hrxzn3feepthby7AL8qGh6p437PbeKIOXL7+gBdeeo7f/PXf5IMf/gheHL04nMK4G7g6v+Bbf/ol/vzLX+P+62+hw4hEc8tVJ4yqxfqRS44Gje5mLf2xvXtMk/64wmX9bsuH4bi3QtvGErC4CVzUv9d8aMmtfGk8S2f0vc7ssb5O9R+31rTlWD/bvtXfzxI43VD3Es/On9cgpbUaxqb/pR6OUcP3BiqPs4/qtmp3vGPlJtpw0ztLe/A93uK9+MCxfh3vx/F3sgJNMNkzhkCIkfV6bf2WSj6S6n2Zn4Omo7O2itwlS6qo9AymJHckOv2Y5X0PkLbbPcmHKGmmBVKcg6j5t4tkjeeUiKFc8poWawwjYTSsmwlHFtj7vpuEArEtuM9Wl5DShjs3rWsRHNK9JarmM645O9ukk5UKALhkvep8xzYE0wrGyJisPpLSk1tohqVQ3e/3KYAc80dPAfWZMO32+xSroXjXAfmwWZ/GMQB2s33WyNi3Nl7vBOd6YlBEnR0W59DO4Vc9eCGEaIQK0yQoE+HPoLC2JMUYiSETFZIm3IKsi5ZaUiyGRiRlNmqTKkBKaBFDcZGxeZwnj8gAxXcd4uxCs1gDVUzIU7Iri6U4z5kHQ4zmshnT7ZMZXBVnuaS9SItqa+WJwW6Zt2xfI8N+TP0OiDhGBt74wSuIE9ZhRRc8599/g6/+6C3wjjDafIikpBqYS50PkS5AcJ7ohLDb8c/+z/83VJWzix1KYLuGTuH8R29aooquQ7wnCuhuYBXtTqQ/+sPvgu/NuoGyFWHfO/q4R6/O6dTc/PAdbmWCGyIlvft+HBj2AwJsVif03crScw8jcb9jd31l8VchmvVEksYOCGqxCK2LXS4umBuQ7Ea+9T/8T3zzD/4V+I5hH8B3+N2ed9XO7psdxDDiVz3xzprnnn+edx88sKxYu4H95RU6RJx4nj27h4ZglrDLLT/44jcYCOm8CiGaJkp8R98nS8PdM8b9nv1b73I97Nk75c4e+piyEzohxoBXYXt+yfmffx+iWAiTsyQF4j2nd+9wdnbG+VsPGIaBYRzoFVbB44PigCDCer0x61UIdr7SXW/edyVxAuZIeOB3fhNDzXs8vTABpOqZcvYIREJKqJFdhhL9SX/nc+hzYhQVvDg6b5Z6J44xRCS5oPl9RLeXXL5znlzSIn1MZ6nrGUfbS6IOlzIGdurpri0b16jAMKDXI/F8zztvPgKBtRjg9iFpfjuPjJb4YuU6xquBzinKYDExKZGDOIdeXXPx8FViVE7I/MD2vbgOlUiIdr/HnbXF24haFr9iSEtzYvMzF+qdmAUq8wOfsocNcUQBv9c0X6D7iG53Brg7S4pyfv+CRz94za4ISGu7jyPeKyvpiEEZL6+J+x0SI5dxRFBGhYf3r4gxgUFVXhfB9x1hGC3WKRrd8iJ8/M7TDGdK9J7Yee4+8wR/9Xf/Gj//K7+AnK4tE16IhGHk+vyK7//5t/mzr36d1/78u8je7jzxYNkXJd2PlMEMKVZLs1AlILqY1bcADaWirTWNXXZvWrJslOcauXFJGC97v/rumOuZZD5QrbFLvLettxZ061jX9wI7s77JlGgoe0XUILJ9bwlowNwjBShx2tnCk3m3MBdoW6vasTmpXXsPxnmD0qBVZr7XumS+XfOOY4D3QMLncM8cA3htP+tSuycugQ9JdKK2nvzrAtx2ntsx1ms4AyJH3m2LzTlQuYamWgq/yWCIvJb5jIhDk/uviLAfRlYrk8tMYW9yk5Z4MSULm1kWnQ/W2qhmjiJckc9PedDkPBFzhY+RB9ur95zPXN73AOnOyR3L7rXqLFOSWvpQl9I4g2U+iok55TjrGmXm70FT8qC8sSBfBBJQxLspBgO7hG/SboBPQETVYihMYMjuRCas5ztNxmGfiK8leHCdZxhMEB914ORkg6qy3e4IYTRBfTR3vM5ZutVxGCDanUjEmEBNgonOsd/vCtEOIdD7zlwfgGEYIQi9W9F3HVECMdj9H5aFSu3ujBgR8enZzgLKhz2dd7iVZxQYifS9Jw5jciHSFKM1EcvaqiYoaADV6v6naExQbAw+CQzprk6cUIKUWw2idz4J+JPGKX/vnCNIKKA1+wOLT5aybDFMc+eSUCpil366DJjLpatKSuBfNBfBkv6iSroADTR6Npu7CD0iAUPxOXV0miM6fOxwapc3ikAXFd0HYKRPcxdiTAfZYi6ykDAQC3DcXFwbI0vPrfYmFBEtK1QcRkQCXZkfZ0LrsIPRXDdR2GDZsUSUMIJzFkcVh0i8vi4+wDG5o64R1omX6vaaoFemSUqg0mmkU4v12FfWWQVGJyix0EchWyZSymgco1oShG6vyLhH2afkByZkWfwMuGQVIEb63ZZ333nZQDKYG246H65TiLb3NJjUpDEiIbJKls0xWJ49xNrbq7J//X5STggbVdbpHI2qBoSc6dGGbKENOvlLiwmOEiLxwQWP7p/jcaw0JoEeY2ydravXAGJpUWM689ltwkXPoKPtOOdBxySozRlw68uf704SsdvpSWfEeaMPPq0LtWJDA+otRiYCBKUjsHIuMbspq2TNeEPIGkNLfy2SgvJjZJSJHbrCOE0gi2MwxVZSUBHsUARRSyxQOLdaVrYQC/gstEQgWs5nfOdKDJXgIU4ychHbk9VcK8scqummd3swqpR4nS5MArFVmGiWtZDfmvZnjKiYe07IFpOoSMRc2XoH6ggiRffiXAISERDbICLm/prv/BMUJz10grpInxKaqKhZp6NdrWCyhYGT3FcJkTUuXeCoRFGiE3xMqcXP1vz8r32OX/vdv8LpE/fwnbOzGgLxes/Fm+/wtT/6An/+pa+yv7iyRL9JaMrr7jTHGWWhLO8Jypkl08s0W4UOSLVJNN8xZWNI9vsZGLqpTM9MtKf+/Jhl5aYY1PIcUzdLffHmCIhc37H4kPxZe2F93ks3WbCWxtHWO38nlvXJa1XS9Sc5J79bu8dnHtsCp7rd1lOj7fPS5zVAWgKSNQBrR16vGWQZe+LtmbbN/l4oS3M1yRzzeCI3A5eNUqSynB3bpe9l0WnrbPdubcEqfT5Sx3tbj+pYNZqx66QMqOeXBKoVLPuKB0dyOU/WPSLqQImMpH4qxV10AkN1T7RcGgtiSZPyE633XOmPZ1Tl2gs/OH/3YKzHyvseIG02J2hUhp0lTrCLAx3jMBTGHUOw+5G8CajZSjBL4JBcuywVuCubRJIQoDE0yQUkZZCakK5EI/cuJVXQfPFkNJQdogkzXQreX683AOkCW4dPvpSSwMAw7guDEIS+6wlhRMRZDFUCPeNg7mwuMZYcC7BK/dhvt4RxpHfWRo45sjEZEPGYtoMUUAeARrw4nDNL1m6/hyuld54xRPbbPRoCq76HaClfURj3++RCcninkFlXmLIGVhqEQogWNDkuaZvzQW6DdWdkv3KLa10N2wQVtWZvRtQq4lTXpaqM497mu3ZvtMoKu7esY1P8U16fOaFKgkAivqbAaRkHMwJba86yIqZodWaEJoGHel4z0YuKSiyCd86Elol51gahlg64nqesNTKh1hF1fsFy7mPRriWNlmXFm981otW6U3ZBXj8D67leVUVTvJ7qBHQVU3qITJY/wRg6sUqtWmlEc5kx+fxMEtpaTWie96wRzxcFl/kOAdK5PVivND7v/TTXjtn7Wp6NKWNhLBfxIqRMaxCDrVeIEdWIT1n22rmc7V8Oy3Qu3UwQqgUN7zxdqscurM0JC3L2z5S9TifFTxl3+n4SHE24qGc11PskWTg0bfj6fhZVuzNoVrlqUqroLL7FMLLtGydu/l49/jJfTDFdZW4muq6GodPZnrSrWcg38pXiJuPBFrNnYwpQTm6SaQAG2NOFtZkUWMarDG6tzVxp0OrM6nR5Zf697LUksNTzV/ZgtOyRWbgfRYm9YxSgX/HBj3+U3/m9v8XzH/oA0aczFRQ/COfv3Ofb3/gzvvYnX+Tq/rvIPtg9WTIP0p/muNkTFW1bcktqaW7+7HFjQdsyeyavY9VOS4/q99o4nvzzJsH6cfrTjuNYPTXfrN1hcx2tu9ux+pbmdPZ9aReyt4ZWc5VLuwat4F3XX7tNtcAH3jvd9ePOcQtO2zEv/d2uwU1xRMc+q/fB0t6p27mprpveb59vlV71e7PY8Rvg+Xvt0WNtt2d0Nq7S7rS+inlgDNE8aDJJkvSCqZamOmMlC9jDSRn9Hum6a0WKI2WBVdI9m49X3vcACTH3gJXriCHadRrEdEO3JDclMyV758tm6mQSlMMwGoASgRTXLukSwaxZ75LGtHOdbQExtyzPJFT7BJrCOBRgE8JAHO1OI5ekHUluV0VrGSwYfBj3FtyGafzDfrQLFlc9fdcxDCMinv1+R9f1OEdykbNb18UJzveowjhGeucIux2rFKgvYC4sqqw6zz6GFFgeEAe98+n28+nS1M1qVS7hurq+5t3zB5x5gTGyv75mBWkcwjgM9N7c0kRgv7dU4gdJHESKI3qtKXPOsQ/jLN6qEJyYXXqWXehYIIIwabtq7VIdJ9Ym4chJMywroD8gclmo1HT/1gz4ZeJZLChahE/ThMzTht6kDWxBA8w1WnnvZ8tBW1STabxhZiZgWV7qFgTEYoWcMzvnXBHuawKcx9HO96ytSnhrGVGel0xm6zazEgIml5J63orLZUuwEyjL61j3JaHaWb9Kn3XO2I9p3Oo5ywCtFiZCsoQa2DpkkPkM5Dkuonjuk0bGGBlTvGANoSwzp0BK+TwMo1kVJAs4cwEUDtNw18XOxmTzWBJAOmcXqipCUNMOjii95MDYLNjX9SYtJPN9LE6KpTu3XwurBRSki6SFLPBLcUtOb5p7ZLIkGkhJ49BEu6HQi6WS+2bKi8M9JNWcqhrImc5sel61gMCoFIVZbiDXE5ObiaIQYezMoioh0odI9C6BNC1jsHbddA2Faklfn4WOELJ3QoWwSGB5gb4IgqSrGaJzBG8JXnZeuffcM/zG7/1NPvnzP2fWf2fzL1HZXV7z6je/yxd//w9567U3kBBxSrJASXI9l9mZr+dyOrMTKFpSQLSAoKWT5R0ns3eW6sltTztGZ7RiCWTUvy+N5b1KS7fyj2N13KTVP6CV6AxwtnTqvYBSfqZNxFR9m8iV0eSl+muFY03327lqQVz7/U19bMtNoDQf8FphWssS+d3WyrXEg5eAaTtHqpQwhzY2uZ3T+fws3yF17N16vus+H1Pq1oo+hIP3DvtzCAgznbtpHW4qkhT7WdnU9yu2Y0yKvAkhlbMMswyENZ+Y9vqk+Ftuc/q/JwIDVyOEx8dH73+ApICGiAYLGtZo96EIlIXxyY1EILmMSUG8Of13l+4kajMP1ZeodqQsTd4zpmw/XoQoVk/nOqIGc+MTZRxGPJR034rDC/Rdb4xTDdhsVuuUNGEzYyarfMeTd8QQWfcrG99mw34YGAdhc7Ky2+2TD+l2u2M/7BnTBbNeoe/MAuS9J9taxmHE9ZbKebvd4lzHGM2Noe86i+sRSRczWpzH1fU1l9eP8OsVst3ZrcghprTNdgKCjsRg9y7ljT+OY7kEV9VSIXs3JUGoE154dWVtDGjVqdPdjDhARWQyZqgAUU0UljLoZcG/9u2uid8SA5a8j5gzjUIAY0wCEZxfnFusFaAaiSG7Ys0JaS3MlzYqgSMqB/3JAMiSAzCrs5yN+hxUddu8h1ldtcASm79LH6upuIk53DS+mkBbu3OANfUxa9N1xgCM2blJeKWWgm3tAoeXMquqpYWu9s0MuDEXwpbeL/NTNLoZHJhAmp9wmJAv1Z7M7xUtICnFdpjMDqrRzrXGWZ01YDX5SwijjTukZ/O+yucpxljOT067Wvcl/y7icE5K0oDZftYUj5XiiJxP2dyi4qLinGUP1UixrOV7jvI+KtaZhDjaRCp5DTLYyf/FkNyw0u6eWYIktWmmyWledHIvzGsX6z1b72kqoYjMwMtCIJoZNIXJq2JrKpneSOqzddAs+BNtyhb9ktJWBfGea6fsNLJWoQtCJJRMjga2tPTX0nF7A41ZwEqOwTGYK7fNZd7LuV96EKsSNVpMngiDU64l4u+d8Wu/9et8/rd/E3+2SfenGfgZr/c8ePsd/uhf/j6vf/O7xIstq1RfGEZLhe/NtaWllbUCqgZJrUKpfn7prM33ah5/XqZlIX65HF5C/jhl6dl6LEuCpqHjyZLcWtdqWlj3P9OkfAbrpE81vV1qu61zyQKWvz8AOwlwpyeAxGMr+pvfrbPf1mVpLdvf85m7yb2wnZfZule8wA5j5fbezHM7X0vz0JYD/to8I7L83tKeK/2GdDYX+HOzX28Eg3AwjpqvzEDXDfW1c7wkVyyNqVZuTGtq/NusNund1Obl1RW7MJgyxyqewI7IjC4f9EfsDs3y9w0gaRqXElxkF5VR3vv5XN73AOl//+//Hzi7c8eCxtOm7PquaHCzBQimDenTHRoF1SZG2BUXmLLFyqE0zYHdcH99dW3WowyqRFit11xdXaFq1odxHOn7vmje+74rmsQskDvnae8E2u93XF/vcE64vt4S0+WVPgEGRbm+viZGZbNe22WkMgmV4hzjMKaLcaesbOK8ZZsKgf1+b5akpGkPceTqesurr75aCJgBQJvDtbckFUMY7W6fqIyXW1YhacvV4rhynAGV0JkPacnapIq6MDvceW1yKnCTVSYCXgRjZMZIoTrIMmmO8nuhuGTZ3VX5gC8xjGIl0bnFK7fRMvbWApX76crZFE42J/SrnvHi0uJwNCLxkEDVP8vbM/Ciy8+JIIe0uXyX71/KIKUwOgA5okkUmY0pf1+IfUWs6nla6l89t3U6+3LLuLOMeSYd1u2lsTPtjXzBsdUxaeUy07UgTXs3Wx5uEpjq/pa9xaHGc0mYKeAqarnUdMbAJWEeyYB54Z6tBGxCnCyoMYm+ivlvW5DrPKZB1NzrxhhQjXTJmp0THrRjy8oJbQBhNRPG5Kp1rveCWWSkWOcjYvdBJaEcha5zxa1NRBJATHOYXbCSO/MYxhldmCsjshXXms5JXGKaL6O1+S6xKbOmuUTa7yKU7JHiLDFCXfLoVM0Vb05r0j15kq1YVmIDZqc5kglcgcUMUSBTcfmMGgkInSpBhR89eJsfvP06n37xI2y6OwwaZsKDNFZK76Y+KmqxqDG7P5vCYAJJGedVFvvKFXjoHFuJxHXPz/7KL/Nrv/s7PPXCs3Z+Ulwm+5GHbz/gq3/8Bb7xla+yu97itwNrUkKhaEDXeKtZ92K172q6fUxArfdbrQCpAUH93LRbdVHYbJ9dEsKXhMNj5ZigmOe0tYDVdKEWUh+ntHXV9dV9rMd0E0DJ/cz11nSn3hM1cLE6mj7JMi1s212ik3ltZ6D0CMCo36vHXNPw9jNVLXHFx3hPDZhmHiILbc8F/+Xxqcw/X9rfBwooJuvzsT3VrvNNZwaW90v9bqZp7ZzQfH6Mx7Vz3Y55sV0m2mrzEglxLMrWzGNctsC1/UqydlClTgue5chjY80luGh3J4rS8/gmpPc9QPrWt77F2elpudRxTAd7yX0oH4IlgmGMyG56d2LuUDnFtGACiHcO33WcnZ5ydXVVLnbt+p6TkxNiCDx89Mhc6xJQ8MlycpUufay3RRjHygSZmI5OAKsW1vu+L1aQYRgIIbBarVivVvSrVbGG5cN2dnbGbr8njCPOe1arlWV2QojjwMlqzZ07d0qK9Ifn52xW65RVrDJZYwHFEmEfRsYR3DDQdX0KcmdyJ8pMLm3orG0CDohVFmLadNr4aQz1Jb22bh2T+8m0btaJuc92AcKVIJYteVlIr+tYEqZbolW7m2UXtZphxhjpu45xtMyJV9fXDMNI1u66pAVu21hieDXzOsbYzQpxpJ4MHlgghPX/Fwh3zVxrRiMpFXF+vgag9fv1GBYZG5S9XJHf3O3SN6DcUTZnlqFMoyYjgojYZaUy1dXOserkTnWwf3Te33a+l/zmFct2pmluymV10fK/dcy1uDPmJAlFabZSVFZEMwAk0ER531zsKBajGG3sYwgGIJgLEzO3k8a1qYB8cQSd34FWVkRzEL0rlvGgWqzu2ZWtzqoqDmIcD/ZsXadW/Zz2WV77uv+UsyPOWRKZgyhd0h1o83dFDEwih+tZr18u2doSYwSdFA+qlfufNVa9ueCyJNMR16RdDRot42RUBpQ3Lh7x6uVDPhxH9iGgbkoWc3iGhOCWrAaWPVTVIVJbkUgKhKlPtfZ3S+ClT/40v/O3/wYv/NRHzWKUxuDFEbdbvvPlr/PVP/wT7r/6OhKULoHGUXIsZYqnzPNjjR64rOW2l+jbv34R5ivXfCuTAuuYMH4TnW8F/veiafXntVBbN32w7xbAwLEy0YT5uI/VCdPF8zfN+4weZxfNpuT3j10s2tazNC8zILbAZ9q2lgT4JaWm/Zx3u6bn7fxkWaSA8Wa+Wtp/ZLDlcEu135fWse5/beGpx9+WJTBf11e304LGuostkDtWWhB2bPjHABNUClpN162gxCj0fceq98mLQIqc65xLnlw6KZaSkcHmSolMCgYnx9OnzwASWHIaYNXf4c948+i46/K+B0ji0w3ooxHxEC29YPleLF4huxRZdrXs9mOTbO5fk3Df9/0Emryf1aUK5+fnE7JNms6HDx9Wz0yXjcJ8g83djSCEsTxfLCjNIYghcj1ep/5OsS1GvCLh4oLNZmNJErD+vHP/fnFHAgNqggV7e++4vrrm/NGjksXKOU/vPQETFsb9PgEKE/y6rkNHhxclXFzRhWDxSimlbxF0sANqSXCSBjpzUtWkUZmsaDWYqWOQ8nzVzN2AqhFzkUrQT/UrFWGKJiyMYUqM4Zy5KqJTetM8X/WlsUWAzm1i857TkGdeVVur8nshEVGNEWdpyGwM3lv/GssMIskF7whDjhGt4ivSTkzCcnIhSgTfBF7KOoQkqEqaLE2JDSJ1wHeuMTEpk8CTi5EWl6dpYecMjKTRhcpiUzMgTSnuEWqrVf7MGPSM9ZON6iMpmLNQS4cUomyZbtAUSC5mMSvEtcpZX+bWe3wS+W1+JsblJd/wHevOpHFmFy/bwxPDq+Yj5sBS64sAoxiQk0S4xxRzqLYBidHmIe8L2z8wxGRHEiljzm5lQUNywbP1d5iVKISI+GnvZLdU731JpZxvMRco8Vu2Vw8FxLwpolqmu6B2JYBX8FHZOcF0P7ZnJF1+LNGsKl5Il/bZeGNWGHmf5jDMBRy1pBy2X0kgJZY5DcNY+lwnOzGaOk7WpbyPJTFhV+2sOI0rv1s+0OSmqJK0nRMIgsntNIOgUmcYp98rgSNrPZM+t7hNDlHBOUJUrrY7rtYbOm/Ww8mIVAlKCvi5MJP3pGDWphyfZaKFpFjalHikM2ATUZ584Vn+9r/1e3zys79AEKAzmitj4Pz+u3zjT7/E6z/8EQ9ef4vx4ho/WsIeh6S03dmK79OoxDKMVouYabLRgrlg1wp4Na+sQU0bazGbk3rNKiVPWYMFofGgzInpwbs3AZdWmXa0bV1q4bCu9t261K7ilslvOUhfZF5Xba1v664ViNNnU8xe3tzFWlkpEutSz1UNgjIfbefF/slkWc/vp3a1WssWEBWVQN4LOW6yYg1LAKWMv+5HVdcx4HZsnCyMaenZ2ZhjxPvKJbx6b0nxubR/b3rupmeWSrvnlp5bAta1sm3aP1m4g+wBknlz5z1Pnt2Z6teKvtkjlA8SH8hzVKvA5r2YgPxEnXXaP4km7vsI7ywO/6C87wGScx68x3eJcVie2Mp1w57zvpsJtD7F9+QFz5mGohoDkySwhzgPtrf3fZWeei7w28FNRDTf+aOTJiG7fOSNE9M74j3iPYrFDGgCFFo9mxlvTCmmI2ay3F7v6dfrklI3xlhS+Za2R3Nx21/uidFSg5+cnNClLeJc7eoCq5XdhCyuw4vH48EJ+zAQ9gPjpSVokNS/4maWfvdOWHWdaW6SW4wTxxLz0arfwOwg5pJjmESYJW+QJECWDFJxEmJFkrsPwri3BBLZygaCis1vfbv45PplZmIDYyZ8Sbr7Z5b1rQJUuY9jGAhh5OriIeuVZ+clXVo53ZWVlteC1hOItXnIYDBbOyBBTZRk+aLaG5KFdBOuS/wCTEBVsvY7xe8kd9RInAuN0dLcuxT1rwmIuSQ8at3vQu+k/D+fj2z1yf2IasDUeSkCs0YTiE15mYXiymKEMJBAJU18g4Iwltu71V4s5yFMdNTmzk37LAwhAW43iwdTFNFoKY9TCu9sfYsqqFpSgpiAd1FSpD6WwWLuu845iy3RdAeQWt8KbUjKljHKpNBJMT+h0ugaOM7zJQQnxOhK7JHGSMj3IskUW4nYxcl2hi2OMe8kkck33jtfrHn5zOX9HJO9ptv0hMsLnEZCsGQNiieOmlwmSIKzQARHusA45J2RFkPE7r5K8zTNWQahEZU462eeF8WTyUFmwmWFxacEElp4tuTsfhW3jRmnF5BdovfQbIoky0GJPid6YGRlEsyykmIiZUYnNPGDrHDIdYWUxW8vysZv2ATP5eU1F6t79KM1p5osNN6BmqWuE4E4Wdo13QeQMwJKsuqoRuz2P0sA5KN5ROz3gc1Td/jV3/1tfuV3fp07Tz+BSrK0RmV/ccnL3/w2X/wXf8D5m+/AEMpkFYFSBC+mRADLEF8L7zOBSrN6Y1KgtHFIwMHP/HtWLBbvjG5BhNGK6mgjxLrDevOS+Qa0ln1RxPPq+QVwkT+veVhrQSz/kHm/qlJ7TeTv67/r+qZ3bR8ILnmi2J6d3FBDAaStoN6231r6irBbzVbUaVaW1qouLeBaejZb7mtlHSQlSi1oV33Oz9qHtk6kY5rvvDwm5JNHlOuVyu1RpJzP2fPaAs/KnVqnXXNUmbkAGo3WGr1pXfLqcS7VWYOdpbbqNvL3TszK/7hlknumM7EExGpDwXz/JIWxo3hIiSgnmzUrzPDg0nobJ7C5DzFU+GgCTyqAm5Qr8+KQJK+qZNnCvDXyZzbfw2OP/30PkBCPk0kQ8M6E8igxBQRrYt6uxB5pnPxwRYRxHNnutslf3+oq8UUVIwBbtDpQcablqUy47eZtD0d+PyPzshkds3pijNN9Tkng8Ux9FBFz74uR7XZb+p1jTnK8B8Aw7Evb19fXDMPAyckJ6/XahMcqo9x0cCMqnqgWkzSOA2Hc0xPT81OMCVRZ6aLivZsxuCVze23eFie46EpwaO0mkBlmfjfPZck657qigSjgs5rz7XZL3/eoKuv1egJjzIlUXtOgdjln7nMGZXn98ni7bro3qUuAUMRA7tXlJcN+bwTZmdUmg8VY9RHSRWeqOJeJiJCuWpwxvkIMshhS8c8sZKKTlk0q+dTmJdiFpOpwTNafonV3wuhTCmZVnJeUmc0V96hWEwYkkOcY0t1CJZ4kGgiK+f66RMQsrsOyf5H/2S6hKDaywKtagZxpPHlwJdtgtLtlWu3bxJDEAGVMoClObY9EosQKBGo11smCFyNFmZJhayUjl/5a0L59mEG8U9O+F9dElBApgn+uX3O/8l6vvhsVQoQQxe7n0XThbgpszee/PmOWiGGuAa7PTiuklpUQj3eOzWoD4hkjSLJmrMUh6ojBJtA57F4mEYbi1pbAU512tQDsad7yXEzJD9I+qRmkGi2SJo260ff6L5286nTuSpM+oix69ZU4i7PKFuo6OY+kejQpM0RThtM4n9NhDAZkYyhJccy6bi6CGoVdDGzWd/jwSx9npY7toJZiOw+aCbY5hd5B5x1xDOlcO4b9YJbBEHCS1y+muRZUIoNz+JXwM5/5DH/z3/47PP/BF5BVh0SLWxv2ex689Q5/9qWv8PU//iJuNyLxUEifJkhK/MDjlJqW5hiMJTfOpefbpDm1cFY/n3/OeAiP1795ZYdKmGNC8E39n9HEBYE2z0HtWVLLAMfaKjwpTn/ns1VAs5s/X9d/bEyTYF5ZeGReR/vu0nc1jW0F/gkkQAbLdZ0/iQumQEk8tPR0bd2Y2vyfV2bjbj5fAp6tfJOVJC3oys8UpWIjH75XeS/Q2j7XjmXpOXPZnVzPH6cveQzeOZwnMa+kkEsySVGCMlmb8/UfB/UxyTiFoVKPwaAxSrnTtN5ftVXxccr7HiCt+jXr9aYcSlWl71fARFhqwtxuTlWl63pOxARz7yfBoY7DqDMC5UMYQijueLnULmG1IJ+zpe12u/J7LcgsaaWyNi0L9jVgy23kvo7jWALZRaSAgP1+X8CSiDGd/d6A0m63Y7fbsdls6Pu+1HF2dmYxS8kaMA4j1xdXjGHE6cCm8+XmcI3jzG1wAn6jWSKquc5jrC024zgWoGeB4FqE6HZeWxA6MZocCDkR3+yHnX/v+56rqytWq1UBjZLcCGYWwKreNj6qJmIZMNWumHWK8FXf8+TZPV5xPySGwBiUmouFUAkiYnEGJtypWY+0BtZhAjlM1o0IhDD33Q4JVLgEr4qrI0lTGsGrWYWyYIDm4ElHUGWnoWjB0ZTOPo3L7nXVGdEHu0gZ5whjSEJmYv5qSMRciJLkn4TdGBWVCdhKmgst8zyBQbsLdNpHxaYm5JtJzUJX1Hx5bqGwSiHFCoHL8S1k4Amk9Z7cRAxU1YQ8qgEBWxuF+vZRpvkkzbsCLlslUbtBD02gwUBOiFIAQ4wQKv/sotXDHKhGlXIRn6ogwT7LiC8LmDV9C6HaP3n+Ktp4jHEKdk3Cut+gozGunLEyBtsThYmGQNelfZitjcklK6+jLW9NF60VzVNW90MaYVdBcAU018/VkClqnt9pz9d15PXOfSlfjUlADTbTdsFsUmiIlr9n+2HOuxmNcBFQhhAgjvi0PSSAiykjpcDp6gwZAvshMMiUBMhwYkxChzKqoxOXrnOAEPb2XYx4yRZyRUPAebsUmk546ac+wt/6t/8uH//Mz+JXtl87tfHdf/1Nvv2Nb/Ktr36di7fvswqCGyPqpot9DwQwjWWwjwMeMp8yRcm4+MzSvmuVkze1Uax72RLj5KDOxxLyGpGqVf7cVFcr+JZzd+SZ3OeaH9a8rW570u5nS/9c2C1yggZatUH9XAtYct31Z+VeOc0zokVR2Y770EVv/nsLkvLnzsksWdC0bq4A1EVapDoBzub4t2OtZaqbBOXHAmRtf94DQNfrVX8m1WdLz7dZbB+nry2AKWt5w7COgd48zvq81Qrj9+pbTc/tapG8V2sFoibPg7SWNWkvSqucnKi5pLmmt4m+x6wcmLaFvefcT2RBe98DpGkhHF03Cbm52O81im8JkGnfnOtwRMRpRdznueaXCEHtBlBn52o1KPnf6enpTHNQb8ia8BRBe7WauZTVfauJan4nC3e5LxafkNOFSqkjuzHkZ/reXOq22y0hBO7evYuIsB8Hht2QANJAJ5ET59JFo7HEA7SaMe/mF7Lm0mq3cykxW27SD9UgqQZU9RyZBWcCQjWYqYFqnqf9fo+IpPFCfVLremMMJXVlG/y5pAXKfc1As+s61ut1mX+vjjGMjDFmkabMU9SY4k4AQiGqaeuW8cjU0amfTJqYmbUzmOVnOgdTBpldhGySUTVG6DVpbdWsPlE1XVxs7e3DSBenGCNkcocREdR1lnoaEM1WFpJLkMVu5YB7hSphSUo2EDMoNbP8GALRxQJ2JjKbgZ1p0CvaWuLMakFjxhIEXOfNDJOHkcYnCD5IAdrZla68WBhB7VZjlpNpjhsNvCadmQBMmk3nBNVxBmJiMLAWYmQvWtbU6Jb1xYlZZsdxZAgjThWnybqRtHa5b/nnMAxlglolRnsWa+VFjJaqfwzmnutUS4bKUQNRA951yb/eBKwO289eBS+ugGCX4o4ORJYENgr4qOKF5skQBFGZXR47A61McV95HCJSBOa8boUJZ/pfQHZe3zQXaU6zoqfMD0CUMg5B5vvTGX/xDuiSIgu7niCOg7nKqbJylq/QqbJSCISEs62+OKT6O49qYIyT9XrVeZz3OBJA8KbEkq4DB3eeepLf/b2/yWd/69foztbEXhAv6H5k3I18+wtf4at/+kXuv/EmDIFVFMuwGSNBQfyhNaDWxj8OOKq/ry3vLS1dKvVevCkIvu7f9DKLWumlNma8PK9j9d0xxUENYFoBs1UE5u9qRV1ttV0CV0t9tPlPIQCNN0v+Pvc/19XKIfWctQBwnsUyzUiiifWY2nlo+WL9r+5ffjZGPYg9Bko6+xpglLWtlV0k2VqrM1yNt10f3mOP1nOS320tWvNxT+Op52TJKngMqC/JebmOti8tWFka72Ef526fS+C4/b1+dql/7bN137SaLxGmi9TjPGbJ3K9zWvAEbHRyW5z4q6I5+671qqy/YO7ozi4qNRpNUnKrWtt6MzBuy18CgAQmZE1muSJQuEykY0VY8onLlw9OiyviEWf11JaaY0Sz/qwlDK3ZvNVu1HXUB01E2O8tTqjv+5k1o36uPli1G1h9p5CqFuuTyJwoDcNQBNx8X0qMdnfKMAzcv3+/3H2jIVomOzFhDRE05hvh5wSjEDmNxUWmtsTUBDoDmpro12WJyOQMf/X9RXl8S8ynXrtMyDLIWq1XpX+5H3lOUTXhpMrEB7DZTHdV7Xa74vrXAjhxjnfffUAII8M4EvbBYlmSi5BzzoSblGo434PSAkoRIar1QSFZ2aZgjMDkfpLfD0nYbrVJmelFgVhbOWNK4iEUQJf3q/cp86Eqo0sJRfJ6kwGwJKJmgmbea2EMibFTLtjL7cXstucsLq8AQNlbkLkTdmGwk1oYQLW/UpC/yDQnY9JUF1FZsil/UkFJnPZVTGMQMfc3AlWMWpW9TDLgM9pie8SSnYSQQYwJx9Na5H4ky0AS4J2ze2g0XT6i6eESmxgNKBycBo2MQ0x3jwVcDFgyBKUX8I6ZUmKW1r0CdVlgncUa1S6+5PMcGeOIODhZdzx5dsqj7TVOnbn4OnMBdQl8SJpbSVYycZ4u7QXXWNmmOYpopitZCK+zzpVVz3Ry+nDOwKdn8tlI0BR1OSazUpikC499sQTGtB9ldpmz1deAbBWmg1ULSgI+lssPRboSp2pKAUcXBImRUWD0hqdW0WLmMtjJcZrZ4i8uA+hQ7s0zS6hnVMH1jiDgNh0/94s/z+/+rb/Bk08/BasO7yx2KF7vef0HL/OFP/xDXv/hK4TLrbnZRksQEx3E3hVQmOd3Jmg1a7dEp+qfUx3CMEzeF0vgYKke56bEOa0g3q7/bHmauuZA4CdzY2r7dOzzJVDQKt6y8uGYDNDWk/tbnjmK/EyWseyzkzzRepjMeHPthlZ9l/Qfic8fxsfUfWzX+ZgQPefDzHh9O69ZOVOHF8jByOeIqZW9Sp0/wRrftCeOAcT673Z+l2SZ9h2Ye8UsvZvnr5bb6j2xBLTacR+bn7b/7e9zXjAff2vpbOcB4Pr6qij8LGGVs2Q+TDSbqm/5XISYUsGIGPipAI+gxRNFNXsHpP/r5CtQx0C/V/lLAJBy8HTRcZHXTTUfUEnPNQFtM8CTgFXjI1sziRYg1RYKmBO62pe63jhLxCm/W2/Chw8f8swzzxw1ey5pPdq4p/xMthR5P23IDL6KEJyIaQ7YntJwO3AOL54YzU/dZpmyeXPbtaUrR0e3BL+O0cqfHSMoS+8X4bZZp+wq1xKcds7BwOEwDHR9Vy6uzOMuKU3FLtNdrVZTG4nJZReQvL713Jef4vBeODk5ZTy/xsLhKhCRAvZjCvZPPlOg5lpUM7p6b1FlkPK+M7eeuohpXHJmt1xCJRyIOGKVMCODa1Wl7zpiN1/jQtScue25iqjl3+3vvK8BbwkFQHGuIyefaBmkWbl8ESSdm9LVn8ZVAZtSjy+tT7Y+lHOR4kj0iNUXMaVJsUZV/bA4nSm9fv0vYoHzlr69Pu8geGDuHpvBdp6PSWmQMw0KPmd9M1SIlgs/M5BNezdlrTRgmmLVkptg54QeswA4NcAYo5TzWzTW6QyM48gwDIjIwTmv56OMz6JrWfcdH//QBzi/urZsbKKsxKVMhxzQqBgsHsY5N4k3wsziUu/pvB6ROS0oOsRkPSpz35KLlNZ6oo8uHwOypVDRWR0FEKXv6/64nK4d0lgqYbAFTGVrCc4vA0GAATVXtggDkeAtfsyHyV1EMihK+9GsrJbcJc+f7zpQJTrP4HpGp3zwpz/Kr//13+GFj7xEcBFWnYG4UXnww1f4zte+wZf+5I+5urpk5Ve4YEov57zhPe8YRGAMxSX3QICS+dBuEihrRVgIcfb3kmC1VEcrtC3xzNK3/Hvz98EaPabQXPfzcd9pBcj6+oX8fQ3QbgIg9fOl7kTrlsYhCbO3MV7HBNgDcBPrZ6szqYeCdAsGF+tbkIfss0kGm/VrJrPNla0xxpIcpC31nqrbsvn6ydb6gM8ujFcmgr0IIN5rX+f66zZqGbGWJ2vvjKU92HoSlXHrHCAcs8K2fW3XrZVLjwHC2nJnjyQ+6L3dybnbgaopLzVL68zuTQOg0NksEx3udZM1TZETmeJLi2pSlsd2U/lLAJAAlgM5y7cLm2zJJxQEEUuHHWJOmeoTE1VEDjdVXf8S+oY5kGrThh/2AdbrNS88/3wZlznQpAv5orlA1WCoHk9utyXYIjLTyDnny4bMj3o3xe2sVzmRQRqDOKI6S+O72+OwmA/S8/WdOFmodm467G3fJGm9tKQIzvoBmRMsrAsmpKdkDc4nbW+6oyFh4AzyckzRsQtfCxEKMd2tpCVoMGKXMI7DaAKEYpnCgqXrjEGJccA5V1Kyz+ZaIWLZ/p649wQf+sAHeGeIuKCmoWNa8xCDZXdzU/rqAjDQYmVSmiQRMgEPy4lgUl7Mc1mDCEwArP2cDZg0TMVtzFKULE/Wv1SfS+5SykzDnlONa9LeZJc8u/zUFeFTY8ruVaWotz0lIBHf+cnqkQARiqXkluwa2sSuJIEx+8mP41gsA9kdpbbs5fkoSg6xPe0qoDrGAKzIPv82XynWq2KCBTSWSZ5bdCfaks+VKwKH87bW3iWQJFIJkWleYiW414DCCdFRrtETlD6BVIcQx8p3W024d2KAbL/bTQqM9Izd+VZptPO+SxPsnOAwN9pN37PpPPthT0BZ4RBN1pskOGSXnNj7ch5hArGWRruioRVD1zzYnIQhjTs7YWSgVS5NFvtkijlKmDndR2XfpXuBfKbjybU1nUMbs0I5M2k8WrkDOqYFsB5M9AYp66oonU4pyKWskNW0dt4SKgTFqzCmNXOijHYYktervZvThzsMVIkzS2jnjB8MzvHkB57hV//Kb/HRT30CWXWoF0Q8u6sr3n39Lb71ha/w6JXXefDam+hux8bcAfBiGUkjmupNYN9JyWCZzxiaxY+8GTNNmQvJtpyHwqUp2fwB7c3Pt8J0XUrylYU2H0f7Xn9WywAHPKlZ4aWxtGMsQNC5RB8nPmOWuMPLbmth9Rjwavl2+Xyhj/mbVuGzNA9LY7GlNTBe9n5muGlPixx6ydSAoOWvNxZJZzWfi1hOGcqcdtZ7Z8rkU1HdRoivxzvxv4oB1nNQd2mhz4cAtKmvaqeseYxH57ptYum9Y/NZ11uPzdyqKWufhAc7w8zn5Rg4mgPcTP/yv/z+8t6a3pvqnZScHYpjHwO7cUxYR8tBE5F0zUpWFEwx2SZzZR41gSYBi21NYwlZjpDMtsRy/KgSHt+A9P4HSHXWuRbVL7nE1YCiRuFtQoCcHagskIuITPcr1RaDvAGXiPKSgF73J/+szcqaCOz0TtVGZQWrN36dlCDX0wKErFEmaQj7fjW5NmmKPUl3tThn2sostCogzuO9wm5fLG2zCxapD+OcodbuBSRCHEJKh1sIcx4jkPyVsxCc56Hz/cTcUsYv5yZLTv0vP5c1+jU4LVazFC80DpYaMobkduiTRWmsXTeTVUcmTW9hlEnYI1hCgkEjrvPosOfOepXiOCofcknAwU8Att4HZY8BQ1TLGJbWyjTcQErekBlb17Vie9rrKFEmsBsFsw4wuWjmhZnu2IIgJgC4DhQHQQvoRTW5BYKIT3Waa2LaYmWtszOmyLSeZR2cgBgQo6RyT4CoEPrpvKiakByjxdBpTriRQK5LKcwFcH5B9MltKwgRQiLYapYxEbH079m1KRowzEoKKXOfBXUHMqXcdjg00Q7LWKgGO9WEUZ8EEBE1QVUUdMRlYZ7MELKIzZSOnIgPiqeybGffd1OhTIKjuOLKGMPcxYxy5ibmLRX9KbRGs5VIEUa8RjoNaZ4mEGhnNm0aFJ/7n6c+/xoORTxVnd17rmluW/pG3ncxpBlK1zXM6CEJuExudjgLa5S0xzSqXZMwc7cy5p2TixQhIe/FWrggn4/p2QL4ERyuCAFQWHtKfW7WN0HwqUpNygBNYypTJi4BKJ9C2TzaCTsR1nfu8Lnf+Dyf+PVfYXV2gnR215VXuHzwkG9/5au8+fKPef3HrxCud4Rhh1foNK1/vkeGtH1R+iSsipvuKCuZprTMZik1r2sBxMQPHd4fegksCbbtnjgm5C8BnfJ3ZiwL77T1zgDQMejRgKT295hoRv6sjgHNMZU1Lc91LskmSzLDfH4Ohe/irscNWcGqulr5xEmXLF2t9SMBSGH2fB5H2+866VI7bwfnGJhiUesxHwHOaXwTeEvnvNkj9XwU0HE4HYulnpdj4KgAuZo3P8aetnHO571+573AZS3PlDqhZIaDij80+7sGsm177bgLLWOayzrp1WH/JgVi6VM681GEd88vCENEUgbYonxFEYUxauF1AOJTHD+YsgjJuvcCmKjOlX2e3KihuIeqwnALkKbSdZ6+7wrDmgTjWkiuN2FiWM6MfRPhmurMGpX8Tt5TSwJ4K9TmumsTaWv6zu/X3+cN6Nx003ALhEo7Oq+rLktEt/19qS9UMThthj3T9AuqIWmpmV2omUFSrWmylODz+J05eJp/Vvonh4e7AB0U53xpp2S/qxhwnvthGIrwn4FRbUHLWfpUld1uV2K1hmGg7/uDTGAzTSWHa5C/894zxgwqLbZntVqxCyk2SSti5w3GtAysXtNoZieEKT6mrIktUInnybu2CHf57ptqDfMcdynTjHOS0UuypqT3Rezi0Qz8xBIciMLQ3kpexc+I5CQEE+F0ngIuwSxDqfOmS0zg4TCouJ6HCYi31rC8xnnNgtbxJrM8Zgdzu3SGD5UaCbLkM0ol+6dLAFlwc6Xak+1eSQYBRITOe9S5g0ukDxQqCM4vK2PasbXnyvlll9+alrWxSPmZAzdYrTS/1ZzlUsdBlr4suNLWZ6u0l+pe6sd8fqdZaV38pjmYz3kWtpwIkqy/k5CddltFb0v7jfKsLnV7rUvnTcJP/iQdLUQpoAk1gdeyewl0jr0X4qbjk7/4GT7/V3+bu08/SXC2/xywPb/im1/4Et/4wpdgP3J1fp5TItqZQMGZxdSnM1/v1SXB75hg1Qrwx3iLCdrzNc6KyZr/tfu8fu69yvxs5dTCc0FvkgOm/h+UI9J0PfZ2Dup9MqcXUsadP8vvtO7eNa9ry2yeE71eavO9SitHtEJ/28fSv6pLdZvH1qyWU5ZA8U20ygTiOKMFIsLRndXswVpWWAJlszYr+rVUb1uOgZelZ9ox1ndg3gjAFtqt91j9nGHa431aGvvj7JUlGtrWV8/tsfHk+Pl+vaLrOwqoJbFEEfbk9PzG29szEaPFt+ZLu51z5moZLDmY0Y9MS+xS+UCS1cPtPUil2GTljXSIuKXWOsx+ZkawwLwk11Np1pXivlQTghuFksoyMPU3aZ8qi0b7vdov5e/MTPLzNbM51m4+lDXQOSBKWeCTrHOlZLObMX5J6H80XYiIXfwZYjhwbWwPksjk2lcY5Q3EQJhcEWtmGqPdx1TPQxbqasGunftWcJu1VX2f06HnjITDMB2ypT1VC19lDEkgcM4harEDV5eXxOtrE6qbNV1ioAdCISCoYSlJaxHzhawKKniZXzzoZE5027U3NyAKGC2ssjo/mQhb2swIUTEx65CROucYYzhg/mWuiqbIztGMPcl0d0+9hrWQXJ+ZPDc5hmYpLs2nO4py/Ei2gKI6c/EThS4H6jfCz2yMOvk7t2eonYuZ4GKDmNYlCQ71/p0FJDfC1U1nt7UOo/P5qvdDfrdVyNTP1HUttdcK0zcx9OlageOuRO38LQk7pb20drnUQvzSOWrrb/tX17EktLVCYEtDjo0/qlnD6vdqS/7iHGD70GWAZEysxJqNTgi944Wf+Rif/+u/w4sf+zCjg713KWFH5J3X3uTLf/jHvP79HzI8vIAxEPc7S7yCIM4TnQEkqvNb78ultblJqHo8AWy6O2m2ns25XtojXQVgDwRE1QOlXBlHI9TD5F58qIB5r/4f8rP6M9/QjTLqTF855M11Pa1bVuZzuY35mFNSG5krBMv32ZXghpL7MSlaD+Wfae9PLlPzz+Xgs/rd1ovl2Pmf0xtmigmY8++6hskdd/ns1/tm6fzDBAyPjWOpr8ikYKnHt+TiWc8VlbK+7nO9Frm0Z64AhcrTqTxXtXPTOTomc7Sl7VfdVu7HTfyu7oNzjieffopf+2t/hfXJhtVqZXOX5tF5T7fe4JJHU76fE4SYsh7VbYQMilSJ48huv0uJpbwperzD9yvE25UhV9sr/tt/9EdH17MufwkAkt1Wr5o30nSHCUxgJz1d/XSIZF/LI1q+WaCYI+qhi521daipqjd1XXerSVgCLarmDpLjAzKBz1aRbFJt45DaTV2nHc+AI1tdZkSmaj+PR1VLdrt8j4qqphiR6V6nmrXWd0Ut9a8QFSz+pp67PFf54tb60GUwkNc0z3EtiNWuQe18thaHlhDXddbPLMVOWV+meKtZfVrNg4Ptblf+FicQmbkjLBG4WrCaBMSYXGEES0mvkC6GdPlKaVIMUOJ5NcDIbZU2itFbEmBIxD8LaGUiQO0CotTvKcg2W5BmWZKadih1xlm1M91l2ld1DFsGNWOcskHmz2sw1QotRfhPVc8EAaW4auYU0jOXy0q4qvdMaWOB0VjbNdusE6IUR7MDBpznMp/pFsjkhDPLcZJLczw9X1vEyh5WZhbpXOrn67rbPdi2uxRH2VpW6ufrtViyDBwTaGb1N/M/szo1bbVjab9v6cSx/VQ/W/9d07nZcyESq7k/WIeqvfnfktwSQUWInWP0wijKveef5df/1t/go5/6GVj3sPKIKp049g8v+LOvfI1vf+XrPPjx66xx9KOd7X2IiJgm11I9mJXWLjB2B2NrM3nWe+oYgMo/H2cNHtfNruWPx/ZFK1gea3+pjZa+S0pBXH9/E28vY1oACHkul/rantvHmbvpeSkKrCWPFBNkFoc/63PbVr0H5nVPngBLVq+6tHuk5q01Pal5fd2f2uOnnscy/hpgcAhwDuaCOR2rx1nXUa9tO0cH86YKkq9smM/d0v6qaV04klyjpmEtTWjHsHQG8jNL9LSdn5bm1Z8tgbQlQFTXWa9py6e6ruP5D77I3/3f/T1iijvN4EjSuHMSZKtPzStFLEGTT3JGTFnvinwiaooe6wmqlZyt2B12qpxfXMI/Wpyug/KXACCZdkXBtNyqM5eI9ND8bxE0hnQRYBZylsz82ZXjUIA4RmxaZtj2tX5uqRRwkbIY1ULhOI4H1qX6MORnl7TqLSFoBfyWgeX2uq4raZedcynsRRDvilBcW4hKHWLxPPkA5oO0pIHN7wPmMlIxlDK+5CPfumrUWvEloakdZ3uwa6Kf26oFoJYBxmietCKHDC7GiARz8Qqz9iMhgMfP+tWuebsPpj7qnMiIkXiLeZ2SWRiY0hRzMp2Dw3EbABdvF/5WS2Y7Pe8dFI0ZTxghqznELF5FzFUsu8BN47K+y9HxHjIXW4P63qDDrIRL82X7cQIzwrRvVLW42xW3S60E8OLsNO+H+eIf7oNjzGMGgFN7dV15n06xCrEIVXYOzHKd5zArKVqml7PRle90EiRyP8p6hwjukEm2wnD9e312WgCzJOgdcyNu6z9WjglfS88dAzJL7x87Z63Q19LRm85k/d1M2G3oz008ou2jilg6ee/YdiD3NvzKX/lNfvGzn2V97y7RY/WPkdUY+dF3vsMX/uBf8dYrrxEut/QqIIrzHSGMrNfr6kJssYQnKc4jW3BrhVnb31p4rBVRS3N8U6nPr6oWt+saRNwEDpbqa/lV6TeTEi1/XtdTW5Jma48cPN+u9bH+1Hw2113H+d70Tjvf7ThzMSWHIjJZIluX75xevn23PbuzsegU03EgQDdyD0zKvVqwrgX7lqfWzxf5ZUHAhhusIErht0sIsF2f3GYbk57nTHW646w9/0vnoPRHKGenHlPL79p3210zo9nMAU5Le46eM2P2P9G5WSo1bV+qo5av2nmuz18d/w6UOzZHp4yiST2TPaMsCZEGnd2hKc5zvd3io2OdvEOkAqR57qMqiBa+6kSIokXBZLEIjz8H73uAZNHGOkP4zjX59huEnIVbp3MBElqtZ+NmocuEFOaar7zoOpcnZ0ShPhi55OdFxIZVfV9brMYYGuKk1GmU21iCpdiCmpj5RLxqAa4GHGTBU6DO0qRRLS6lOtA3CV3tgZ/maa4pqYnszDIk8yQL9bzUdbcuPnmseUz5rp9MSIHCuJfM2a3Wq45Ra8cjTiAYQ+v7vrjdwSEYrRlNOz+T4OxQzdYMyj+sxsp9bU58m4SL830tYgJ11mylNW5eQDUi3uMdiFq6d9U50y3zw9ziR8ZTUwOlrdk4F+Zw+iaf58O7iZbmPjeglTbYJ+ujQEnpDlTxUulcRdte+c0Z41+Yx3KWmc5p6//uvc1DbdXJ/1Sn+83yXM61t4dxS6kHqM7byQCp7mi9Ppbyffl8HmPC9XlstcM/CRN+3NLOK1RucGmnHyv1/N5Ujgm7S7TxJhDQ0qayj5IyINc5O48t7WNaLnEGjoIqoYOf/uwv8Lnf+13uPv8MuJSCG+iC8vC1N/nWv/oiP/izP+fq8grdDfQKq64D79jHEd97XJysbplKxComtBXMWmEp9/e95vS9yhKALa6wDUjK81iuWjhSWiGz+maBjFW0oFLOzXiim/P/dq2W9qa1RqGbNV3PvP/YfBwDLEt8DDL9M/fImm+UvggznnRMcD5weVOlTgZVl0illGN5znOdbf0ZCKtqcVk3ejhdJ1LXkce25HFjYocUJVUa4cH42jmprRrtdzU4anlyW+dszEn3Vvczv7tkQROR2bUx9fftPN5Ebw7fn1J5L5+Dqf9tn5aeWZqf+ozU9dTnZMkFPPPqYb+HYcR3yVMlg+DEM6OXopgYkjlptVmbbJJzcKldRQDJWKGKr/aO987CPmLEO4+6pBDVm+lHXd73AKkTh3fmdxqzhlscfZeIlCkoEsHK4GhiwDFOWdyceJzzCaWC5kQE4nApG1J6lBAmq0lMRNZlYVUc0CUBKaT2Ju2dqmWVIt8OT2aYk7Y797ElbMZEVikeylLF2oaoXIxkugslb/bazax+RlWJzhVXnxgjp6en1fwEywLlbJ4lRIh7u6xSKHE77Y3pkrQc9QGuNQ3RfJ7IaZY1jT+Mwe76yAJ1ITxaiGsNjkSmGKeauM6EFzG/1yKMQkpxfKiZz6XW7LfCQ56XJcYWBFyAblTLfqZCHyJITK6ROe+KprieSSuVrSZ5jrquS20ta7VN+MUu7RVJxNvuw/HOW7auDHQSwC31pP1dfHlNVWesJwVxayClh5nimjJksHms0lcXDRupL7FYO41AThawIuvqlGkvrXDhfV11Dm1fV3ediZBTL6hqyoSXgfuhgFrD2bHSmOaVyF0oGfw0xZOIVFbkac41facY4XakM6oWK5b3HFnoS9ZW73PqcYAphipDXJ8AQc4cGWMgBhD8TAtf96NkY6sEjJJApQJVJfUraU3zeeFwbLVgVJf8XX5Hqn+z0vSz7Xf9M/++KByVhrOgrtPc5/Ut9JYy55NAc6iNrWlUGVPTn8mrIDVeNggFAOV5kPR5DDGlPbe08MhkZQc7N6KWvc7lD5wjqOJ9x5bAUx//EL/6t/86H/65TxBcGp83sDM8vOTNH/6YP/4f/wX3X30DF5Ww3U4Xy3pnbWRQWblpaszXHnSzONp2bZdcKut5q/9u61jeLxmEpHmtLFKzrKZVmZQSk1CKkC6ASECgbDypn7Tfq24c9DEpavL5mIGdSsCrx7MECspPKYLFgbC8RKvr35f4TjvXc/fv9HfFK+1ZG5NL5z2pLpsz1yiM8/6WCnhUe8aqmOa15n1131pAUuhPBYDrecnvt+BlGuMyvQhiZ9K8yWcC3dH92ipNCl1t6OhNa9I+F+PEq+vnblIg2e9zENLKEjfV07ZV+pv+5/J+tlZmeyOPof7ZzlPdnxb41Tz0GMDKPJLcbrrDaHe9JUbBR8+439N5z34YWK1WJlOJMCaZc5WSNYibQJH3nckMITKGka7r0SRPFT7kOnQMXLx7YYrcVc+du3fx3W2ShlK8OHrviRrtNvqaaCkFgWZCrargLGZpEiCSMJMhTmaiLm/qdBMwFM1z53JcjNL1KRWr2uWd4EET06/FhyRoqvOEIaIExFsfs+Cu2e2vbFqzdMRktnfJNciEXor52S7OCqbp55Dwtgeldr1zMgGr2oIiImTFlIKl2B4G4hhwIRijTxq/ljH0fUdIlprcTjmMuR9JHs4CZIyCqgn9rl4b6/jMLalMqc5d+MqY8mdV4oX9bjfNRUPwMxiqS34vl2KVatwnaiISROlxdCrshgDOceI6Bt0xxBFhrsWEifhPdU1Msb4LYLlYhEFh+GKWT8jAxoY6dbfy603t5u8y80nwrQIDKRVxiDMmnIFXfkZEys3Z9Rzn9N8lo5ymcbo5Yz7QVGWA4bKWHiJzRuOkGpMmW4NL7qnOlbHk8fgUxGtxdToDVPUedoCZlELZp2XG01gMCOWsdgkoqeJEy51QWo+B1F4WGEXqhSEDHmECYN5ZG1nfJjK5bWSwkAGJSgJ45O0yCUiQaBdT2wdCXLXms3WoSqZTNSPNn087bHo3KzRaoa+uuwYtS65vqjkVeGLalSYdsWD5+gwWAaf873hpBQdNe8iVj1uBc+pDHqwWAaVKeCMkF9gyG3Nnh65jTyQ6z/rOGb/6V3+Dn/2tz+PvnjCKsBaPR9hf7Xjl29/nu1/6Gq99+/vsLq5QgX2+WBhFuo4x96GcjYkWhhDos5t2cs2sLeqtILS0B2qa2655zVta4TArd+pnWzfpA0tWmtcMgsp+tz/Lpdfa7G/m3LZd6KKMyr+3Z+9xygGwqfZevf+WgH49v3n8+fPZ+Ks6pn9urvPIGnnJ85wVXzmJj5RnSl/U3JbK3WdADmFVKEonYcq+lkvLc48Bu9qNsf7u2NxkelYnUJrNt2CWgewinJVYFejI77XrUPd1Bpaqtpcs6otjY2q3ba/td7u+7WdtW+2+aL9vzxlUiZgqmj6JCdNz+YwdgjZmf98EpGrLYL22MVZyceELJie8e/8B/+1//d+wHwfOzs544YUXSgz9vXv3GGMwBX7i1ZeXl1xfXzOOAcfk0dT3PcN+b2C789BZWEDnHKfrE0iyxxBGdsOeu/fu8fDRIx63vO8BEs6CUT1K33lLmZsIR9HOiiC9B7X4EBUBSTeSV5vDiyAaTLOfFVVJOIthLFpeEXtv03XTRYUpKDtUt1S5VVf6AnXWGrP6jOOeEEeQrhKKwfkOienyxwSexhBNuAsDfZLjhxBYrdYMYwJRCjGBlZaI5U2dYx1qYNECnPbAWKBhcsWLKc4ikdVaW1Qf5HEMxbxcM8LWX7cWirKgXR/fGrR16V6mrAmq45CAktksx2cA7IeB3XZb+piz9NWlJgQtEa+FiFxiJXwsEtNEqJxPaWczM9bpuZpghxCIYWq37/tiPWrXsS2tcDmlPp+7TxwIsKoHwok9a5qyNgVv0drkfUHSWCWhT5hkGg72UCUnMifI9bzN5rARJFqXSSeu+N239WkllM2YDROgbhl2vd9bJjuNbio148nPtXEa9Vmq+18LRvXnec5zffV+zZ/Ve3RR2FgQcOvyuHtpqRybp/x3LllRlfdk/v5Y3Emt7a0BUiuU5Tle6tdPWuq+18UE70h9O9OSgLI4VyKoSLktvnZz7dTAXBQIvXAtkbhe8clf/Ay//jd/l7PnnmbvzZq/Fo9eD7z6o1f43rf+nO985Rtcvf2Adb5HLVlM67M7vwRcgLk7cqj4Qj0H9dja9XycPVQLTEt70qxIciA0Z1pdC12PC1J+kvWer8/j13HTGSh78YiLYPv+Er851oelZzXJGG3/l3h1TTPrZ9p1BgrAfK8xt/26aW5aOeCmsS4J6ofgUiYBvKHLtZdHew9k3WZ+ZsllGg75x+OUln4svV+AhM4tmze905blOXn8MotV0zkAvykjZ93+sc9rnpr/zrKZU3j049cJ48ijEPnRV75pz8ZI1/e4kzVg1/R0fc9qteLs9IzNas39199IsrfjR2+8wbsPHhQAPyaDAZruEVRls14zjiN+1XN6csJwsMLHy/seIJmFI0CMdF3P2pv7U3Qe6TpA2Q87s6yIQoyMY6TruxJUDnnBA85hl0QmEdAufXRcnZ+z6TpOTk6wG+YxgBNGVv2KDof3jt04MuxHoionJydEHMMQ2O/37NJt9hMzCoxhIITAnTt3GIbBEjIMjlW3Qrzd8QSw2fSImFOQhh27/Z7Ow253xfV2j1utCxFos8nB/KDUzKjVsOR/EzFKt4Uzpf3MbhuShP6lQ7TEiFvhtNVMqCoqh5nKgAPhsY4RySBpu90yDDaffd8bgaqIQS2AFSBRCXEZgNXt1kJeLjOXBypmZS+U/nZ9zxhCskRCDIowD2LNoGZufeuLy0I9nzcx2lpgK4xa3GL/M5Sp52++ZnNBqfgiOzcT+orbHCkhB4dMzOqZAEbLaFsGUL9/k6UwyrQ2M+2j5uQqy8y5HnO7n1qhY2IChxrTnE621RK2a1TXW1tp2zVsBayltWkF03aMS8zuGIhaEiJueq9+f6nfM1qT6FCep7rNdi1qMLQUE/M4gkg7L9PDB48uvlu349o9/piCbqZdDnN7cSY5GGhKnwWEwcOuE57+2Ef4td/7XT780z9F6IR9uji4VyG+e8H3vvZnfOWP/pT7b78NQ6APkB1cQ3Kp7WQem1crPNq7RYpyzk0a3yW3xpmyqrH2Lc1Fy0cO5xfy2W/frzPnzdaYZDlo6PCSoHdsTQ76WGnWj4OQ9y4zT4jmnXm9RV10tNRz2v6snxGRZB447HNNv+rfMx/LfC7zvppm1ZeN5vdym49jMboJRNfPHaNBbVkGl1DiV46UFvjXezjz1pliFopgX49viX6Xfiwoydox5lKvTYzZOXR5Dy/NxU17+xi9nh46BL7H+nxTPW18Uc2fZu9Ue6NWhg37PZfvPuSdt96qxpQ6KGL8O68Tk7LQLuiwKzrW6xX7/VAU3r6zDJ6ojXCIdpXK9W4LIuy3wvXDh+zGWxe7UnbDnmeefYZ1v6J3Zn67vtqy24/0fc9ud83V9RXOweXlBbv9jt1u4GRzwt27d01ITbfNKwHv4eLyit2w52RzBq7j7Owud05POVn1bDYbus6Q9zCOtiFD4PzROaBst9vU7pZHj+6DdAQ1Ld6w33NxeVFZmQIhBtarFZcX56xWKy4vLzk5OWW9PuXs9A6bTRIy8Mk0rqh3+NWKd+8/4MHDc/p+zZ3Vurh3LG3umftQRTCKcM6U5Qcma0xUsxYpXfE1BYqwnB0gDrTAUDZ+dlWrD3cNRvL7zvniQ18LTLle7z27lDo7Ez+wjF77/R7nXLG85Itfo8zvjGmBz9gEjNalFr4nUFvFrqQyS/Cgigr4vuPk9ASK4OWRGBCV0tfcT5dcwZxfTsqRNTP1mpZ1qJjVQfrlBeYXYyxEdJkwT+vWMuECdaq/Kc8b0VryL6/7dJQBNeCkHlu9RyZCfaj9nuqa6mwFwXwGaqBcz2stsNd1tG3U85KFvPqC5XrOl97JdbcCV7tPs1vCEjNvP6v/1W23gvCxNch1LQG4us9L52Q2t1W/6nbbsd3Up3bt62fbeW0B2Gydjoy3nqcDS20lC900V3VxzllCSRWcCp0Txhjtktdgrraxc2yefZLf+N3f5hOf/SV05VDv6cQSL+0fXvL2j1/n21/8Kq9974dsLy7pNBLHlMUsKXzyBs9W9VaTDkrOeJZpR5mbal1qq367Nq2wurRPlwSmlm5IAiaZfuc6c/25/dblLwt5tUB/zGJf/75E00q/qs/e6932HLRFRIqg1p7D9MQM0zyOQNzO8wEdXHj+cXhB++wxQXvpHM3mb6FNOKT5La9qx9y2n92JRWSe0bahE7Uyu92HdRv13l1y2T0GEI7Na+lPkoTa+ViikbmYPDTfJ+263uQG1/a95QW5jVKXzpWhLe2t669lwGNjaufhIGSieq/mt1Ej22HLoOOBAoQIThUvUwy+xoiGkUGVkBS0u6t9ebfvekYNNvti8kt09s9IjBa5Zq8jj1ve9wBpux344cuv4L1n1a/wnUejJTIYx0uUQN97tvstD84f8e67D3jmiafYXl+icWSz2bDb7Tg/P2ezWSHe4sw637PdDoiDvhs51y0XV1cM+7cYx5FhHNHkisQYGbY7LDA9Mgx7vIfnn3+efqXcOT2zRAZrT9/Bbrfn9PSUECL73d4AWlQcjjBEdrLn/HLPW+88BEwANzcJs0xdD1cosOp6Tk9P6foNcRzQRqMIHMQA5FITHO8cWmmYsrXGrFED9J6u6xiGPdm33Q4BZGtDfr5YdmLA+yoWqBKiYFmbKJJcVJwroCOPPRPe+h6nYRiKu+BqtSrjzT/LmCtmnouqElLa9JaIHBMAJwFKZkBh9lMy04/sx4FhHFOSSwyMJRN0LUyragnobplj3X7dxzxvS8W+t381s6xdDPKaHAjfMPM/b7XIMmvDKhOUEJRYdWfOgA8tYUvuUy0DbAlvO8a8X7NV1vo6Z4QzAcvPM0HVbbZMa/ZTJ4m5FUhaK2L+u3Z5ms1X9e4x5lzX13537Gd+p03z37ZbP3usP0u+8+/Vdn5PRIpSoP7s2Pzmetq9f6zfS+ezFTDrMUgjcN4kGE99zv7083HcOJcCFt9hCpQYFfXCNYF40sFmw8//+q/yS7/5a5w9dc/iPZxpS/124NHrb/GFf/EHvPH9HxGvtoz7ARR8J6hLlzXn++9qHLcg/MJ038wwDHNhXwRNgL620i/RnXpe21Tfx4Toej7ts8MsaUvnOu9dVZ0JebVSJJ+vOgvagfWp6f+0H3SWabN9dnFN3+u5hh4eA2ktjVnav+14awUhTOBoqU9L7bWgvz7b0742urYkcLcgpK0/15Fp+VF+cYTOzGlU5knzayOm/ZGE32Z/5j7XNH6Jl9fA+6a5XDoDbbkJXC3zaAccXmpc11N7txyjMTeVJb5Rt1PPxdJ6PE6p6XYtzy3RcREDutKnRFEp+QLpWcekiM1nve5jsvXZ2nlTOod072MeacTclaPaHZPOOSTlJlL3eGOCvwQA6f/y3/wTnnj6KU5OTzg5O6NfrzjZnLLqTglhpOuEEPac3dnwxBN36VYrvHfcu3vXgr36HtXIk0/cY7Xu2e53IJ5xtIP16PyKb33ruwxxZD9szVVuvyeMFmR2fv6I4WrH9vKK66tLEMU5+JlP/BR//9/733KSLE7jOCbg5Lh394y+XxGDo/cb0xJ7z+XlJcMu8NWvfJmx6/HdqoAnUNbrNfthYPApKF4Dzz/zNL/06WfxzuGIqLgZ0TgmZC4R5cyAJrcHQ+SWXc2yo4k0cSsy1yLWpuy63vaQtn2riUsGNzXzy/VmYXi73eK9L/d95HHUrne5H8eItGICVAuAaiLT1mUE91BoKv3H0vVGVZz3iHcMwwidaXsVPbCo2e9zl6+W4LYgKb00G9ecCB4yvvIMZBxx6NqEacpYElJjBKQkBjBrVFrX6m6C8i/NR3Z5q9vLgvw0p4eCbA2uZ/MllPi2VggwoXHO7JcAw+KFyRXjngMfq7PdBzXQK8S9ugepbm+JsbfrXddzTFitz1Jb7xLoPNb+UmlBTf1+/n6JMbbfLZUlAbJd82Pjeq9x3CQMtPSlHevyPFAEsvY8HtDR/E7bJyeMDnTd89Inf4bP/62/xQsf/hCDBoJ3lvEwwoM33+LN7/2A7339z3j1ez+gs4zsBGc+907BUiN6NFmQfAJw2Q3twPqSLk7P1xmoTjSn3tfH5qae07q0gnE9L4dzmJQFahRlyash1wlzvqHRrPC163Pd57rUoK2ur+Zl9rvQDmlpjO1eyr8vlZtAX0svjs1XfRZq2rE01sdxGc2lpUvHQEz9TH3+68/bUs95BratS3g9tnY8bb0hBkTmVowCvjQW4tvOXV7jY8Cz7mOtoC08sHrmGO2Z1Snlf0fnph1v4alH+ljm4D1S299U5vydEpv7Xnu4Vib9pEBporOHc2F8WNh0nksBJScsm0qUZCXWScGU7cY+yRiaBjTRBodPnztRxpgyA6vCGEsstL9hXdryvgdIL//gx3xgVJ598QWi23PiOoLs6UMK6lePF7sk70PPv8C4u4f3U7rRu0/cZX2yBjXf+e2bb9P7jr4TNpszdruBV157FRCG3Z7tdsvV1RXXV9dcX1+z3W7BK5uzDU+8+Dz37t3j3t0zPvjCc6w2J4j3XG93XF9vWa9WOO94+tlnWa3WvPvogrjbc7W7whPo1h17lJ1z4Du2KSZJga7r2SsE5xhDwIndpj7sRvpuhSTBOw6H8Ud1StX8+YywNAJRFmCnuJ/sQuSII+BcyX4jzDVWhYmqaYRyGvCaWOX2c1sTQbb/xeRbKpp8iNOFphcp08l6vWaz2cy15dHmIx9OSP1znpxuliSo+3QRp6L4rivplTUJ/KgW7aCk32Oai67rUmp17OLFmNpLvEE0EoaRUQ1YrsSzcp7RJ8LvHJ0zNxhNwoO4lPkq1zVjroeELhMkIyJzLV95Ni3OlAUvEbKUqjRn5MnZjrIgU+rPaxLMXO6dR8VXnvVaLGMIRXNucyBlLD61YRA/AWrJmelck+hBDLDmNWj2cSHC6XnUMsiJ8xWIMdO9JqQkAt6BRvs7g9uc0YmS6CFM65HmLe8tTam+XTX3Bf6lCclMuBXmWjDQCkBLgkP9d6vlj2HKmIZI2rNpnjSd5bSmkhh6tgjmM38TY2/7UAvELSM8ZJaHwK5tT+qfaoK+a/b3MYG7FeByqRl8+51EZvczKBVwQkr2yDoboGkv27i99FyiJQGKS50ktzofHOKEQWBYec5eeo7f+b2/zoc++dPIqmdPxCn4ELi+/5CXv/M9vv31b/DgldcYtztcFmStWqJaCnDLgOgqQWKunOm67DptgxmGULLVdd4TYiSGlDSncr2qXfNs/mrak/Ypmdb4so+EnGk07wEtC9sC81aqb89zqwSr+VR2H6zLMVDT8pfDZ6XQ0rbO9r1jn7UgPyuA2jHnPVR/Vitgap7XAif7OaW0R11Zm7qttu789wEowRR2WfgUsb1V0ptWz7YCc5aR2nPdKg2W+tOu0zGaI2JuVtnSC4eulPm5tq0lWtPyyqU+w3zdanfPJeCT57GciYZm13W062PAQIpckeWlqc75HC1Zmdr2WuA37+cy35zvr/mcLc3P0vfLoN3GVn+vqgn0bjDDkdGv2Wuzcad6mM6OrU+Ov1NUBXVCyHJNtDPhxVKExxjMkPATKBHgLwFAikH58Y9eYT8GXvjQBy3IfzfQb3rWJyf4GHEhwjhYoOxg9+xE4PTOXTYnp5yc3sE5x3q9QdyKq4tzwjhwcf6Qcdjx5ltvcHF+zfZyy9XVNSLCycmGJ+49ycc+9jTre2tk5VAcXd/jVXF9zxACTz31NPvdnovL1xDfcXJ6yiuvvs6LH3gR1/XE/Y4XPvA8zglvvPk2J3fv4Ncb9lHBOwK22ZyY9QHn8JjgPu53JZe8TxvDLBH5XqS8f02azOnEa21j7d9da88LeFGzGoQYGUNmCkqIkRCVzs+JyuTOd0iwal/43F7LLJybwFQBXonBhxBYr9ezJA75vd5PcWEWUyXl0EWdE4SZZlJyCnVMeD+IR5AizFm9TPdFaAYZKQ12FvicM8FdsHujxkAUy+bkkxtOEj2SO2LqagIPpa+pDZIQM4slkHRPTjWuGdNIwvJUkfU7p9rOqdWTmqY8ZgJaih1I9VnmxrSPKkFIS92Z2SrZD7gQawHBl0dMiNMCyjPQqAn0gUtjUzQxG/tHer96QKpuFStXNN9ZyUlIOBCC58wia+K1gK8Y5lkO6/moBYxcR3uTfN1G216ttW3HegDKqjnIQnPNKGOoshmm6wBkgYEfm9ta41r3sxY4loSPMsZmbK2gma8VmASI6exQ0YyaDrQAqK3zJuEo128dYvop03e2jZaFo3qMwSUXMBU8Ho2RlUtxQM4x9h7/5B1+5bd/jU//+mdxJ2vcyr53MbJ/dMHr3/8R3/36N3n5299D9wNdiLhUf2SKFfLO4d3kWlzHbGb6ngUK0+Abjdzv9yXNfJ5vcfPLv3PcTz3XwiS0SWml2hckupUBef69CdWp1yYbkWq6m89DPh91v9IDs3O1tBePtdeuFyR3cEw51e6T+tmahtaW9VZQrYXsto78fbZY1cCobbPd5xNdN3rmpMuMIYGbZdBxDKSUvzMxTAoqJCusjoOIm7xPjpX6OVfNdV1nPUel3zKfp/x7tkAeAzxLoO1YaWMyyz6/YV/Ny6TQzGOrLZRLYHqpT/m92V5aWL/6+Xrs9f5dUh5klt4Cnvc6NzM+08hBN83NEkAUsSQLGnOaf9Jl7XU9c16SabBzQlSZZAsxRWq+7F1FLElNeS+36ZBu+cLfm8r7HiB1nV229+6D+4xx5OnnnuXOvbsEOSOoo+9XBliiENSxObtD13vefPNN7t27x9nZHdbrNVdXV4h4nn76WZ5+8kkuLx5xeXXNxfXAa6++TsDx5JNP8YGPfoQnn3xidllpYACnjEHxruNk1bO93hYGcOfOHV566SUATk9PGYaBN15/g+g6Tk5WKWhfWK1WadOnBASJSOfMbDkI19C5bYyctKDrOhP0G27VHqz6gBaBO42j7/uDAMuJ4GfAlZitd+gwXZZbCBpJEyiHQa5ZaGzvF6oJTLY21dn+Yozs93tEpCRfaN/LMR/7lDO/ZLFbKCJTFrIQ46GgUI07C5o5s1w9lpqZls+SgGOX6Fq/T53DdM7LBAns4BdBsWHiceEzSAJ7dfHjYezInHmIJHeIqr4WFGeG2s4HmJaGysWyFUxa5mW/A5rj1ZYY2ZwBzlzi9DhgaD87JiTUe76sjXMH84XMyXfNYLJbaRugWv9e931JQ93O0VyInIOXg73xHkyqLW09S3vncQSLPKb8+7FnH1dIgWm+amG/7MVEi469t9SPVoBr12PpmaW+10JZLci3e3yfGPgqQB+Fzq3Zx4Cs1wwbz4d/4ef47N/8a5w+9yTSebsYdlTkasebr77GN7/yNV79/g+5evCILig6hpQp9ZA+1zSxHlvuZ+2KrGoJgkTMajSn3fOg9TyW9t63mwT+FkTM5oXJkn1sfuvf6z7XGTyXgEfbn8ctB/RggZbcVH+7p+v5nNW90F77/tKeXWpj+qxWzglaxUAu9XfJLbg8I1gg/MIaHqMr9dlZomdLwOlxaFQNKI6Vw3UwZRYNOFg6z/V+v4nmJeHp4HzfXGwNMnA+4MUL47LxZmXbvA9ljWZjfe91aXls3Y8MGDL/ruu76fzWdbfztcRv66Jxrsx0LsWNn53S9X35rFUeHls7qeLo637kZ1zncd4V+bCdU+PrtwCplH7Vo8PA9fUVu/3WtnGIdNvA+vSU1WrEizI4uLje8sHnnyGMA889+zznjy75+MfOcN7Td4HVasOWPb1fc3pywt3dnuu98qlPfZr13buIm0DR5AohdH5FjIG+M0AGwm63QyOMgyVzqIWyp556Cu89F9sdYDcKX11f8eTTz7BarTF6bps4p6xerVaFKWZT4uQakTaPd2iYC5v597bUhC+3Ux+S3JZ3HjQWtw4qK0buYx5XJhLee2JKX94KQW3CiJq4hxAYkkUsxlhAyW63sxTo6cDVwm3LiJaIQv6+HWsGNFkAyWnWlw5vHlubQr0VQiGb0c3y1/c9cD1ZtHR6ZxajFXUmKNVt123MhPAFUFCIn1AsXYcxVtMeOJwfprVm/lytA1oSANq1NsEk9f/IPqzbz+9l15qg057Ka35MCKvbbGNoSt+qua1BbTuWdk6MKc5BaP7pxSFVEox6TQ804ywDjbqP7Z5tGVvN2Fsh+BgwyBfy1vUcY3o1A20ZUN2vOu6lFZTqeWzbqxUws7rF3AWXzvHiWlZ9WerrEsNvx3lsfEvP2AfgVfBB6KIl/Ng55XrT8ezHXuKv/s2/xos//RHCSc/gwUfo98r9H77C2z96la9+4UtcnV8wXF3TB4sl0gjaGW2o3Z4zba/noeu6kpSmPe95LbLWPSe5yXO+RFfq8ccYzQ0VWZy7+vy1+6uusy2qy+uW26jPR9l7HAqO7bos0fZj6+e9pQcOVYKhds+2fKPdn3nPz/owG2e9/xe7c2N/6/ftLFMuh7f4nOwkNz2/xPvqubL1isSq/iWl5VIfjlm067K0R24aY/397F3Vxjpav5O815vvalmirm+JB7X79Cb6slTK58ktseYB7R5q10AEjgHcTM+RZfpT11tbNGvZpwVmmdfm0noItaWdt3YfvtfctN/nfl5fXaN37874yU3zWzwKynrM+bS9v5BFb0H+uGnPtuV9D5B+9dc+x5/+6RcYLweG3Y7XX/kx796/zwsf+ijXl1f06zUQ2Xjb3GdnZ2haxNVqRdetGMeR/X7A+xV9v0biiHcdm7Xn7p27nJ6eEXD4vrN4mGqzjcMAki4iw6ERAhEfoetWdF03E8Cfe+45ALa7HRsVus7x3DPPc//+OyCOZ555xoTbdHCyVSeDB9sQEMbAyju8z37aHhFzyYD5hp2BqGpjlQ21wBDz91kDkn08c1xQiBb7YLFJU4DlMFgO+jAGvJPiGpKZd4nraDZxm30uxljGrar0q1UxHdfjgCkLTP4996Weh3pc5RlncTM18GmFvzyHcOgbfaDVFjMtR/NKLBeW1taLXF8rUGYXtLbfWWCohfsiXIgQmNfTMoLWBeCYcFzaYyJS9bsiKcboBmJe9620lbVnx4RRYWZ6z+te96nMw8IY2jmpGUn9nsjU97xn67WmEZBaAbpes5lwVz3TJp5o16FNN3/szLVrmffikqB7IMQ3awGTsuUYE2zfOSZ8HQOVB+3pXNNaj22JUebvWuXEewk/bbtt3/NeqgXxdk3rdbhJ8BaTdFhFQYMjiBBPV8jT9/i13/1tPvG5X2Ldd+V+rn6E/cMLvvedH/L1P/wT3nz5FUu4ECwOKWeiM4ELxB2Clnou83ft1QgwF3qdc4SkXW2F+ra07YmbXFaWhI3W/bi0oQDz59u9d3ROm74s7dO6LNG3lu6183fs8/wva6PbKx3afi29Xyf5Ofz+ZsHypvFlfmIWpP8Pe/8aY1tynIeCX2SuvXc9zjl9Hv0k2aRIig9RoiiSIilREtmkJAoee3Q9NmAMbAzGuB7A15aEESTDgKEZQAYMCfAP24B++JcheSwI/uULW8Bcje3rudKV5esH9eSrSTb73X26T59HnTpVtfdeKzPmR2bkioyVa1e1JAOD9s3G6arae618REZGfBEZGckAtZ+z/bXfRzkshnpdSmnRR9PVZsXU75y3G2SLXZuWt1p3EJLzOUxzfh51HRbbXLRfrd8tXTXtbLu79KyVOWVNKp0k9et+WCNusraM/J5gCtQYpbUOW7KzJW+1Y1bzCJGr5kzXL1hQdJ+ttzUHaQ5rvTc6HiNydF0V5qjnS5yZFy1veQPpk5/8BB555BH8+q//OjwRQhgQNhvcff01PHTtOobtBtvtGvfjFoSAvVUygmTStts+Cw7GMKQ7kTwYkRggh9VqH3v7BwjOJcPH5QQG0KmkMxORByMZCV3n0HULHB5eQtd5HBwcFIZOExngO48nn3wHPCKeffZbODldgxZ7KUxsuVcuMa0EUj7cvlg4eGKTzSsC5KuFohed97WgrBQMpkIq7VLE5DjByODeeWyGMXOclCq1dgbGGqRI27qIYhLPKNG4IPq+x2azgcs0h43dzSXGWBIEaA+DNmiknUoxMcA09lsvOk0jeV+8slaJt4AKgbBaLRsgb+xzURacjCodplILnOmCJyJYMVOH0qTBNUEpYzTCZ0obhEos/LxCnigIbnY/PRMZkXU8cRtk2e+B2sjR71jArzMspTNH9WWIhWayI2xAuFWe2jiS34PyrFtPnQZf8rlOWtLaFdPgXfMb0XQHr0lXWx9qXpl7V/Nyaz5bn8nfc0ZR67nW+pLn5pSnnQftvJgDEC0D6CI0q+W0AZRECCuPcGkf3/bx78ZHP/sD2Lt6BS5f6N2FCDpZ4/VnX8TNbz2Pr/7eH6I/PQO2Q76kNXmTA8eUnCKvQ+98xSNaNusdb+vZ106nAiZ2gETNV5YmGtS/GYA5VxK//snrkaIdBUDbUNf8N+1L7ZUW+aF36+x8S2kaYW4u2cIff4wVQC+fpfMc+py7nR/dZwnHl0pawHqu7RZN7dqwzqIWvVpz0KrPvmMBv+yiWbkyt6a1g0rLa110FlZrlEwMNP3uDrmpZc5c0e1R7sOc4WdLyzmh29d91N/VBsbUANrVX9v3yfwxUvZazbNqXg729/F65sWWI6PFj0k61vq38IU0q/qud8rl82G4eEbAt7yBdP/+fTz++GN473vejW9+45vYWx1g6HuE9SluvXqGbQhI4W8MRxHeIV9SGvPEjUT1foDvFvDOw1E6071a7WFvtYeT7QaLrgNAGLbj/TsOhM51YMrJDELIuxIOQz9gvV7j2rWrAEaBnM4ceVy7ciUxBwFnZ2cIIeLo/m0MfY8+ANuhx3K5RDoDkRdTBJzPnjmkXPBJwLv0j6cCERDBMe72iCKwW7d6oYuBRM7BO4cwBIDH8A3GkA+BU1E04jlwGBe/9qxLv6aGw2hYyXPDMGC1WpXvRLAB0yx8WnBb8DXn+W4BYnm2UjKqzPVbFrGnFM7BnOi63W4RYkxxs3E0kCqlk9Pa1nPVVjx6XksSBjl8Wz0DMI9KpwI96jNNn9Svup3aM5b+aeFlt7r1HMv7wYAkLWhLdi5VzotVJxrPkLWUoVUkQoPIXJIVaAA8DAN8vmC43tLX66jtWbTt2rFbBW0NqBZYkPG12tEZ33YBDVu08a3HZxWWve/G1tsCUFKsA6I1L3qN2TbmAJo8Oyc/9Ls2hHcWSACV4W7rB/T6Ss9GEPqFx2MfeC8+9qNP4eq73gbyHRbUwQ8Rfdji9N59PPe7X8LT//H3gPUWi8iIQ7qwOsmcTBtCuQeJMMovoY8GetZrbccMmDWsAJGVYVbW13KsXv+tNdjiezTW99x3trTmaa5o+a4NyBZN7LrTgUfWyWXXgOUTayDo3zWNbZ92jVe3p7+reL8Yq/lsyUw/LO30Od4YIphq/raOjFb/Kn2xo1hda8fWoqWtO41tuqMEyLzV82NlyZw80H2osFBmdCtHm3yj6eyma8GOx+qC1NS4NrXeHZMoTXXHlEb1XZJzOkBwTRnrjKzV38/NnW1bPi94CsKbU7703oMcTZw+5afi57r9EV/o9VTkkzpHrMeg5eT/nuZblV/+5V/GdrtBGAZsN1tcvnI5p3HeoO8jQoy4dPkSHnn0OrrFAtQ5pHCxgBQhk1JqHhzso+s8QAGEnNY0JAMBNMAvPOA7bDabJBB9ymDkfcrQJQCscw7DZgtggf39A1y6dBnL5bJi8L29PTx84zqYgH57Bloucf3hR3B09ACv3z5C2G4RwdhsN9hstgW8OUoHcB0DiEnwefYgEJz3SaBGBlS2ssxV6eCuqw+RF4UcQsk8JYtPwtucKHGiNM7Og0MAuVXOOpIXZeQUbsgpzTI4JUAARoUk4Xa2aI8eUbpkVV8KW4QcY+wn0uHT5DGtlZUsGOsdmYBNR4hhDHEDSUKE5GXSisReUqvpJ3WHEOBiBIMQCOg2ASvfYXBpHjryJeucc77Un7aEa0+49vCGGArOsMaOI5RUmsi7dvlSaZCX6U+JPyRCTu9CTBQnAYxRCNqt7KSuRMFILGHa+bPKQdrwZRpS45xGW51Lsj+LQQP1rlF6ZSTSnhGMLe8uGnPXdV2ZW5l3UV76lndtVMkcpTC7RJjIyXsvzoFkzJTew5GDZKvgfKJL6g+Zv5EVKpXdhmKWTpR9pfTVfLV2GTwonVHUiljkn9JPWtFYr2Nrfm2ZW3Na4UnRQJXzXJdU6gCse8ClitLc5OfkXgxKFaafun/CQ0K73JcIRqQIDwJydsmInG4fjAUcPCe69wDOOuDS2x7FD/7I5/GuD34AYeHAPl8h0Qf0J2d49vf/EDefex6vvfASwnqD0A8IMZQdHT1HXQYQkrZd6wdW/Kz7zEDJTkdqt18+J0U7TfMWqG8ZPkle6IuVEy0y96V0zMgAE0VkZlcdgQj5Qsj0RhYTSOqgBkO2b9ZIkQsk5/jNgtq57+2454C/BaUtkDox0jE9qzk+n+jRqlevY1a8keRTDtvlei2Sm9+L07vZum65IkScjumKhzybCRwAZkxaVrbOmllwqsdhwXY9x+MOHoirnUVyQOA60kTacUQIDQDe6tccL1jAnSJx2udrLG9oBwNjNFqp1JvwY6Sp4yf1qR0hUPgXlfhtOs/02Frrp6pfSNzQq5YHpQ3Li7Z+a5RVkTnp4arfUrcc/5g4PJlLiHEU7IXkNEqXvhZqJN7ICbiICF6opiJPHKUrR8b2ceHyljeQ+n47Zl8j4Pj4GIeHh/DOI3Lagdjf28eiW+Dk5BT3ju7DI51ZSWeQugxuB4S4BRDgyaNzS4RIePDgATbbLTaRwTQARNhstyO4Rc4eDAIoGTWIA0IgLHL96/UaAEoGN+89lqsV+mGDEALW6zWuXr2OS5ev40tf+TqWiyVOTrcoZ3yywnA553u/HQBOindBK4jwEdVYmD+mrfkRcIxZ4ESgxZjTIMdYMr/p+yg4hAz4RRknL3zXrdAtIoABoQ/iKskApT5LIn1iTpfd9n3agdOe0tHTwCUFbZXtjsv/ACRhP4SUVEHH3Gsgq9sVo68Ce6CJIJE02wXGGsGkvbzaiCntR0aPCHjC+sEpFj6fSxgCXL6vR7wksivIzIiYxrEXpak8V1ZhZ3Ik4BLr+R/MpaVjHSkjnRWUMcYyvzYEp/xUfRCAlM7fyV+mbxCDCuMuGVH1tH52DsRQ0kiFn6HeF/Aryitivg5Zg62dnAkgAipQYulVfvJIv7GzySAKCNk2TcCexeBU2SaLYVZAPBXNqemtFXArfHRXuAQllDrKLUrZ2MTA00bfnAGk6aMVa4tXLN+1vP2NBkbAZvuQv5PdYqf50hgUdtzNNZAdBogM4sSP+VojgNIZI3aEniO6a5fwic98Pz7wqY9heXAIRrrLjEPEcHaGmy++jD/4T/8Fx8+/BGy32Gb9oB1R2vvrXAqxRqwNb00b5nQWUK4gkOdaZ0LAyRklZzGd9xhmDIhdhoP+W9MPQLoPDdPPgeyUK/0uD8zyke6HBmcWXMvPXeBX86EF77aI3G61dRGAbduEek+PaVf7mr76PFmZh5hC4esLzOMESbf4pdWeFznHeRzyHWrZputqtWGNOvvMLoNV5iXmBFKOXNnRLrxMdTh00WHZERxjrI0V9W6Lb2yp6JzHPzcm/Y4Ob22GEgLZ1TV9f1zz7d3O80qLJ60hMqU/JjuG+ns7PtuOXiOt96ysED1meTtyxDD0RTdMzroyMs5IfMgQ2SttjhE+Yx+mu+nAiENG43COotPy34SBdOXKFVy7fh3ee9y8eRNEhD4EgAh9H3Dnzl14D9x54w6ufPSjJd4bqL0L5DIWjQChAxyle5WWS/ghIOZwoK7rSrY1ZnXRHhEWC6QLrSLj9r27uPrQJVy7egXAGGIHAJ7lkkqC8wu4LuD0dIObN1/DSy+9hH4A3HKJ/cuHcHknJ8Z0WzCTU4uW0rkk51KoBtVjKvH+itE0M3M2bHQ8p14kkiIUMQLE6nMqQK/rOhCnkLg4DAV8a8UvmcmswWE9FkQ5TaT3NRgwTK+Fd0tw2md0e3OKUozDItC5Ds2zfbdCQX533qGPAXt7q7TjGGMJuxOP06ie8k5Mca6kNspcGM+hCJu5cxxzRQuU9OsUIACoztNoGurx6n6Wd3djA1WyZ5XbgszSU3+u+bkFhMt7JpxGty2eJ12n/V1/Ngd6rCHRoouuS36fA3EXmcMWELBgRfdZPyOyx/JxshGmddi6WwC6ZXzYv/Wzc0Zpq8zN7y5F32q/1Td5PiVLoHSxcRKcIHKIDtg4Bh0s8e0f/TA+9tQP4vDGQ4iOgEjoGIibDU6PjvHV3/0DPPNHX8GD23exZMai8yWZjtDcnk0TYKyTy2h6auNK85nN9NUybnSWzRYvzNFkF33Lc1zPxVyx9bzZeY4ZFAPTUMBWv1p8eF6xIcYtMNmSP7bvmqfqc0BTIKvnSz87gr5at9cDbc/hnKE3R7eL0mlux0CPXerTY9JzoaMvEj+r+/RQO3q48XmRGaR2603bLcOjJZO0kVOFzu4oF+etFLmxKxS2hXla7dm/55xerb4JTrJh77b+8/hbYwvLO606dBtiyHJk7O8fTOonkmRPZqdM61wzVh3R8Kdd3vIGUoyMj3zP9+Dylcs4PTvDg/UpFt0CD+4/wHC6RhzSrsaD+ye4fes2whDgFlPhlBZkMpDIOwAerlvgm88+h/snpwhECEPEcrmsDomm9xxiCNj2W5B3WHgP5wkv33wNH//oR0Bch2d577Fer7HdbJO3bwjwrsODB3fwta89jbt3j3D/wQmuPfwwLmfjijmdSXJZiHae4LJCjDkkLQ2l7cXVwkovHhEYWjHL5977HMqWQk+SEZmsfudzWlmk3Y/IsYTYOecRwnh3k96V0oveKo50ZieBC32LellcqIWBjKe1oHX4TutchTxv49HF2GK0wWKJc1XnqsrCzQtdQh6dSgs/+uxGTw+QwvlKv5QQr/ofQyW85koloLnOCjYVdO0U8IKDWgaD/G4Ffsxhhfp7/a94eBQV5BereuaAihbMLcHdqsMCBKJxfjWfnwcS5zytGoToflrDRH6XedXzpPmqpQDsPGh+04rQgmXdV9nNtWuAqN511nPbDhfhyfwWpacATWtudH2tPu6ad01Pq5Btny4KlgkAmBLfEqUwXecQHSEsPB559zvwfX/mh3Ht296OwRPWADoQliD0D07x/Ne/id//D/8ZJ2/cAZ9ssBcZ7BibUB8a1v3UTgf5F0LIzq3xOXvWQNPD8r6Vd/ZzSye7fi3PtOZQPp8zdJIo46rvrf7qudllbMh45PNdxph+xu5ktowa3Q9LWysrzwOydh7OO+8h31k+HtsDkhPNJBNKb07e30WL1tqZzP0FvVotfrNGgB6f7of+XBIw7ZJTmv+ICJ13GGI7+Ys8O9lRbfSrko887k682aL7XugY59eaXQ/n0czKZzvf1hiZyGRKnGLlga6zpVdb/W45tKu6jJNVnhW8d3Z2Budc5Xgf60k7ozGHfabY/hxaa66l0LSbm2fBFamNSVdny1veQLpy5Qre8fa349LVh/CNZ74Jv1xgsbeHq4sVyN3H0Z0jOBDWZ2v8+9/69/jcU5/FtRvXSvptZh7PIAAAp/sgyDs8eHCKP/rKlzEgxVMuOle8g8IEadckgMiBnAMToY8McoSnn3kGt964hccfvoHtdlsSDsSYMrSFGDGEMbPW7//+H2AYQonpXq6WyaPf93A+nSOIMYU6hCFgJQA4e18cpUQAwjQSxlYMiAySNONLiJ2UGKcHOFNoGCFCUnunUDKCZtAc85zr6xaLIoC1EbPdbidgzgoAu5jK4jNKU4wvu+2sPbZE47064t3Y5ZEo/XAa1k/BttBPvGMJCBMgBzIZ8D5n38v8otPhElnPZfppUx1bUKPnSL7XNCqfZQUon9egbdyVku9a5000jTTAngMAWlhrWurvJcBBK2fdrvaAz9WlDeJSr3xP6X9W0QjldQpQG2NvAZ7un8yPpqUtlnaWVueBd+Fd+5nuh6Wx/nxOAVugXfFMI4y0xQcybvnbnheoadUe265x62IdKJM+m7nXdNN83PLoytqIHEHOIzKBvMcAwuAJB9ev4lM/9Gm8/3s/Apb7jJyDD0A8XePWzVv4xh99Cd/66tPo75+AtgEY0gXKUd1lpvtqx2JlSGvemLkkW2jxsh1rq/7z6K770OpPNQZue8SJqNIfrXmb29HXc2jXYqFBoy+WftqgOw/0WjljdYduv8Xfmm7699baan0PtHdk0nMRzLU+K3OcKq36rJ1oWjbotUBU3z2on0Pksv5b49Nrze5K7ALuuj5tEBGm60LwC2IN9MvYzDi13rNyS4+7hQ1Kf5CbjNOwdl2ER+13ds2W+aTxez32Fm3luV3rU9OoFbHQ6hc5Knxin7FySeqzclLe0yGPtr6iC8IUe4UQsOg8Npt8cbWKHgKQzzMmp5T3DiFGhBzCy2AQamyjz6631mcZyzmyrlXe8gbSdrvF//rb/yt6ZhyfnWDvcB9+0cF3HR7u9uBogeO794AY8Pprr+M//of/DT/+f/pxhDzxsohiTCm8iQmUz/p8+atfxRt372HISmDot9huNui6lKxBGGdvsUKMjJ4DlvtL9EOPuFiCY8Tv/f7v4wufe6oSygVsInkwYwTOzk7x9NNfx4PjE3DMdw0NAf12C8pncURROd+h65ZwWbi5fNt0CBHMU3ApCz1yLdwK06qFYbfmIzPYRTi/SOeRYgBlg4ycA3K62j4McHIolDmnI58uSGFwq5hijNlQdRiGlCpdklvIM/p5LYR2CTo9JrtbpMGePcMhcfUtZWkVUwG1NLbZDz2AFAOOCnTIzxSbPI5jZnHTmLHNCjD5vfVdmpt5L4xTYYka7IiAtcpH03Omo03gKmRJxjRGhUzJ06MS/EzGtato5Vf1k1AZ0rbYT4T3rAGwq7QUnAWWc++1HBAtQHue8rTv6Xdb37V2NfR7LVDbmvsWELwIzfT7dh1qwNcC/S2APVe3BRFzzzvnMcScXc47YH+J7/zU9+IjP/D92H/oEkI2jPYiQJuAzb1jvPD1Z/CNL30Jr73wAtAHuHwvXHTA4Bo7jwagzAEcy/MjL7cPxNvxXgRktUDQrnebBoD4INScMyfHmxx6t+Bc/5s63nafF5oD4FK/Ngi0x1vXMQ4CRSfp0Hg71lY//jTKeeu9GBIY3XJ6vYQYq/MewEg/G1Z5kX6ktmrHjy67eKNlnNgx6v6X7ziFomnjKz2nBm1pQgDMGpBn7Hht9AswOhwnO1cWXDfGrPlZ/808Xmxb5OQONpmTSUQ0JjxpzN15xpulY3m2MTZLMz1mG/asn7U00HKDqHXySp4Dttv6GMpobFE65kKUEo15n/ocA4LiD22Yt9rW4xFahhBKcrCLlLe8gbS/v4+zzQbUeVy7fj3HLhNiSFmbrl27BgwBx0d3EQLjN37j/4MffOqHcO3aNTjnS9KCGCOGfkDYDgA5bAPjt//976QEDcxYhJg8hYC6sFV7wTJT9Olg2hAiOAR8+Stfxcc+/GFcu3YVYiTJApPQPEcd/tN//i84OrqPGBmbzaYcRu6HAYgBy+UKC+chZ5HONmdYdh6He8vcF5cPw9eepCFnAPHeAznURhit5JBnLtuy8o6EBPocQgikLfIQImIkhO0Gm6HHohsXZAwh15Uy4+kFbAWqCH75PISAxWJR+i27czo7U0vptoSELXqBayGq29dAgrMgH3FKDQa1ENELeBQajK7zYDCGEOA4AamsI3J9IqQo2zFzYIHFzqkAoxYalp4xRp3XYRYsW8FLlMIDRdBYALMbjE0vISzfxJR1TDKzBY4jCFDj0/2wwt32g5kn59pSfW3DoXwm2phGWgi/ybPtWOfRsNW00Px8EYNG5qn13aSv59Rlx5hyYczT7CJhWFLXXJiYli2ye6rXQQs06TpsWKA8Y40Eu0u4iw5WxhR6lIdkLScagZBSzy86BEd45we/HZ/8kc/hxjvfhu3CYe0Ynhy6TUS89wC3n38Ff/Sffxc3X3oJYbsG9T18Tm8biMGeEBxAQ87geYHSMpQ1jezY7By0DAJrTGh+0/N2Hj1nf+epzGBOzhbLV5rfiOo76OaMntIOoSSnaI3NGhXjHXpT3VAKTY3oXWGfLRqc9+xF3t/1bh569Ww5x9aQM/W9g/P9mzNcque0wpspc4aRfLdL/qXv5Pwtyk9OArsC21bPF6eryLiZPtjxaYBd8Y2h1645bRlh+r3iqJt43lo2U72eItc6o2XsVMZIY3235laeae1UtuSulRe76pZ/JeEV5m1DiWASDOqcw6JbJDpE5IuplU7J0TaMMUJLt5lE+Pn69jw9rMtb3kBaXrqKvcPDJKTFe8XpoteBA6hzuProwwjEOD66h1ffuId/8T/+K/zFv/gXwcy4cuVK3qlw6IcBm+0WgTr8f3/7P+Dm3aOUvjEMONv06Pwig7IA5uTYIEoxsgyg61Jq8GHbI7qUKerWvRP8z7/1O/jxP/tnsN2ewTlCiEAkAnEAhx53Hhzj9/7wj7ANjNX+Hk7PTsFxQNiuEbdroOsQncOAAA4RDI/OeXTwCP2QlUmHMQlAw3sAAR0xp18MWfhmD4gRAiXN96JLl+ZySClr84pw3oOGHhTHxRKRvPchxmT4gdLWOZBkQ1SeARDiEMpOGTMjDEO6KZmBzvlkbC4WySgMEV6dS2oBIb3gBeCPB0TruFwNrPVh6rEe5F2Oemtf6mkBsvQvguMAt/BYLjs4BzgST+WYrrIG9gxQgBwUHw/pENJlrylDox2zBUC1wlcTrwoRVXXZnQIB2NJHO765osGcnRN0dRINDjGfZaOc/a7tGbKlBRYnihq1rtLKJaXwToYqGDldfgbkRNXOk6alq4zUlBiFWUAjV3zSmqOKFqrfcwpoFuQ13i3ryTlwUIpa+ouRneaAgKT8JQl9ZBQgImGaesdul2LW47SlBfjtd/J+a23NGQPZNZTTxLpKnlEO2PDIa9Y7RE/oVw77b3scn37qM/i2D74P3aJLu0bM6CJjOHmAOy/exAt/9DW88PQ3cf/eEeSkHYMxgFOadJfOD3FI/GXvmdP9nTVANQgCzP1L82O3tJ4DDTpSwia0sSFL1gAufUcaawHRAlxn+Fj3T8tQLVdmeeYcQ9+unbJWk7BN6zef7yzLQdU5Z6RJH23/dIiPpZOU1s6ZBpu6LQt6R5oTHI3RApNw2xyaJE7McpUHUHbHWoBet6eNagdFIOU8AFCybeqxye9NGTIz3mpeiZESEok8ZVC+u4PcmDyhaoO59FNuJCA4cEh1aX5tGTJS9HOE0fjUz+7iYz0feu5H+mIkXu43INcHsEpbr+iT/y9GRtLNNK43NWctfWHXrtTJqu+6r3PyU/PFnJyRZyfRFjHpct2e/L5cLLC32kNP27Sr5wjESW6Qr52WLstsAmGIiUfSdT0ZEMaU/Er3qFpXnJzQnuq08OeVt7yBtFitwM6DieAkdhGMGHtEZMI7wtVr1xBixNnpA3zxi7+Hv/AX/mI2FDjdNZQvjQWlOzJefPllgHwKlwspRbMU51KmIiCBvF4OoQXCdrtNjBQyw3nCS6+8gs12i7SnEBEiwOTRcYAjj6P7D7Beb8EMrJYrrJZLnJz2eaGkTHF9PwCuA5gw8LjLtYo+3b1CBJBD5KnAL+CGUIyi9L1s6ydm1J4BOSu06OqD/loODP0AuNqjCWSBY8CU1G09OrruzXoDIpcxp7qThsc2gOmFlEBtHOl/spDFGzG3XSx/S10ySPlMAx4NFHXbQMGWiR6IWO6tsL1/WtJa6kvj5N0CBijTQsBqAxzPKd2JcDdt1KUeW9WOUpLyndDbKp3asJruHMwB5aJoIpcdK/1eSwHPtTvphxHyrfNaEs6pdxFZQLbikToMdKy/usuLpnNlDVjNi02aN/6ee2YOfJ1Xdhm3BaiJcZz/hSwz7C7pHLDdZdS2+jsHSOx6tHNq3y+AQv7lRai/jw4IncPgCThc4Xt+8PvwoU9/H/YODwCXDZYQQcOA26/exMvf/Ba+9sU/wHB0grjepHHQ2NYYM5r61Vkwq9tuGEu61NcZ8ITWFkxbAF07dnYbk1ZmtcB+NECV2QiFbBzNzbe9rNf24yI8q8dun2+dLUnPRXC0NBhlqqVFq29zO5YXWj/GEDnvXUt7LZ9knPK5lXHl+cyPu9aHXYMtB195B9IHBlOtd+fWemvsGkiPz6VrPNL7mIDdlmyg0iNMMp8SOXRuTBmuy5zs0DRpzbXV85VuN/xr9abtm0jUKE4n1HNRzi2jwZdqjWv80lrfFQ3zkfa5Plue0MXOrzWu9XyWOUcbDzHn6KeMOSnTCPmdEKf3YcpMe+crvgPaIeK6b9XYJjXPl7e8geQ6D+czQ2cuFGvdOQfidDA7EuHatWvwHths1ogx4vLly3AuJV7YbDbovE+eGb9IO0niSULaMWFOSQa0JwxAYWCd3nUERBHDkHZjDg/3AQCn6w0CM9LOI4HgEYaI0AesT86wOV2nO4/y5YVhSHGbrkuLMHrGQLHc2xFiwBAiOjdVpDrMQZ+RkZ/e+9GYUwxXey9SSvKWWtMCSGd2Y6JyyFiKPS+kFd146JvRdYvyrMQT6zuMWgJb6tXpjO3Cld+18GgJwDmA2zKI9N9pZywboY6xzaGSzhEQxRPZApqUsVamJSuFDs5uoalHSOqZ84DtKnOKb+65io8ahUYd0KyjelbqEIDJU4/XnMKzczXpB1G5TBNAbcxgakjLZ5q7z2ur4gO0U8br9uyZtTm+K32ZAXG23/JZVHJKf17qzl6NFlBujVX63Cm6tM4k/XHK3Nj0d7u+t7whhksOAobjOhsWiBAdYeuB7YLwbd/9Hfi+H/08Dh6+AXQdiADPBGwDTm7fxavPvYiv/P7v4/6tN4B1D2x6ODgMnMNEicouVWuuWuMTQ1wXG/evZeIcTWxpAdYW3ca7/sLkGb2+5Xc7tpR8p4igC5Vd6/NPWmy687HvXDzTcwByDvQCiU72MHjLsCjvYgrGWnrB9qd1HizpOwZ4DFttyZQ5w8bKrxYQnjNw2qUenV17c7y3i1912FQlf9PLAFxzbPM9rK8vmSu2X5x3PVoh4S3AXd4zOEK/86dRrFFH5jP93VyfxVjWuECeOU+WtLCFrd/2t3Uvm+hT59KF2Jt+vKdSsI3FUhV/ESowIWfE07nwNs3+uOUtbyC1QsoKEM/UDJHLQfdLh5ew8CmL3Ha7LZ/3fY+D/X3EGNBjNJqc7KBwOuejQ7Z8NqhSP8aZK6CMGavFaA1vNlukO4JShpU+bhHjgDCkyyR5COmm4BCwf+kQne9SWJqn/DtAnpDOtKXU2tt+gxhDjlGmkpLbhgnEmNInirEhn0uacMIYgiaKIP1MsaTkHIYwVOFGzrt8v8+4IAsIjOMC16BL7zQRpft+ZJEkujkwj5kCZW5FydtFa+uXelpGjhWGerxTY6Fe8HoRW4VglW3aPWIMQz687FIyC/1epQTTh8lWYPGGU+kG5fTtVlja8U36bwWP+r4loHSdeuya3+cV2DxAtHUW2jsqBmNrbLoPFy0MrtKuameG5YNK6QCAa4PeOfoR0USY69LyIst82V2tOUCn22rRoaw5Fb46mVOenxe7s1GPbwTFOhNkq7/W+GoZchZ0zq2fVv3ybAvgMpB22rNyZQLYAdETonOIC49rTz6BT/zwZ/H2978HceHALodr9AGbB6d4+ZvP4eu//0e4+cJL6c67zRYduZTpi5KsY3FU8NhH6YeVHxbIzIFkPU+WL1tGWGsXeyJPDN/aDJS6j86lC9M3m82kP/JM0qVyFnWcj7ky5zCwNNBtaZ7Rz82tCy3rx/oweccag7ot3d9JqHGDL1t9t3Mq9GLFIxdZ2yPdUJIx6Dv55uaFmVO4sp+eK7VrTf/d2tGs+0qTsbVoJ/2wu8w2sZBupznHb0LGl/dzFj4dPmznyPJY+Wloossuh5ctcyGHRS6bzy1fWVlZ8JN6Xj/T2vUq7zDnKP1p1lKLEzR9WjSysqJVdL0TGihbpmAaEZ08deiUvqn69HhLHQ3+btHiouUtbyBFTtnZhFH14X7vfI7THQVWt+hwsLeodiaYGYt81mUYBvjVPpaLRboJOMcyR47gWHvT1+v15H4jET5lmzmODND3PYZhC9+tAMn4RmmHi+MA7yIcBixcAA8buLCH2G/R5Wc8AB4GOArjDkPnC8BOOz1tUBJCCucTA0/i0UMISbjGxFiSuEEYLcq5pkrgo4S/EbgYinXMOhd0ZYWzVebjgsipctV86mekbgvC7ELRn+udJ/lOPweMCnKxWFRjlPCi1vtW+ADIWRAZ3EcEjugWnbh0Mm3re5qk7aQgbOhfhKRXt8banMKuivHU1IJW7mVqKP0cDjAnPC0dRnpU3TRjaRuRnsbLmlvG6y7h1wI+rVIBCRrPiOj2St9U29orZuufA3ZSWvHvu/pn65xr04KAigYYlb59PymlqaPgPMNEHyK2oa6WfnY96vpaa29ubdvnWuOvAEX+zkksf+cREREWDkNHOLxxHR/+vk/iQ9/7USwPVogCpoYIXvd47YWX8NUv/j5uPf8yzo7uY9Utge2ABaf6mAjkHUKmYb6Ou9BE6NniV2vcyN/VDpd6dlexXlpdrEyxMlLe1eePpM3kuNtM6pL+lHHxdL21gJ0diwV49vcWSLxIsXIl/aQS9WHD8FqyRNPBOslaPGu/t/Nr69V6rNUH3Xb6PaQQqRky6LVoqNFsQ+vZOlxyuoNpxyo6XH/XesfOod4tnQtXtPqAiACqs5btkpljP0cnlawr+5zFG3r8FpO0aDCn7ywN7HfqoWY42Ti+Wr7aMVr6zrVT1jy4Wu8tvdKSuZbmLVlmacIZJNkz0ACK44U5Amfr9J4wNo11WhxIROVcsNVVMj7Nay0eezNy5C1vIDEIcGnXhAEETjsrFJEAAwjOecQg23xAjKEAZxEe6/UaIaSLYFndlZOAqsPQD+WchRgDIjREyeg7dhKwBjZbvVszwPsO3aLDEBlpSwjYP1hib+VBkXD1yj6W9BDYdTi8cgm0XAKdh/cdMKRUn77LWYFA2FvtIWajx/nx0tcW48UYq92j8nmI5b1690i8yNy8fTrGCEfTBRZjLDtLZZ54KsTtgvc+7VgxT+PMdQidXeD2911AUj+nhXl1DgDZzjSKsDUGLTBCiHAhpDM1BDx4cFKEt17c+p1SV6wvWyUS0FUrK+1NJJoagHNlTvFocEVEE2Fu+3peaQEF+XyivA1A1O3Y522Y2pzybfVHK3fNH5NwppmtoNb4x/5Mn51T7rsUnB37RfoAGGC6o315zqY2tnTX/EQz4KhVLC11u7bvLcXWWhf6OxsPr0uCxgTK2eTCwiMerPAd3/tRfOypz2D/8LAYOZ4BbCPO3riD55/+Jp758tfwxkuvYjhbY+E7hLBFl0FXRPLIxhxe1zVAdEuWzQG8OcMCmIYw2nnUcyBAskXDOT5pATugnotdAMn2v/X3rj7YiIbz5ErLyLKfT99B00nTGs8cANxVNGDbVecuGaCfsz+dcwBPs6JKsU618+Za66w5IDmRyeWz+f7bOmS3qBVKfF4pWMPQYU6na8yV2kdlXLXql58W5Gv6vhkd16r/vNJa81Z/aOAvY9LvA+MxhrniKO10a3q0HDK6zovOle7nLh2jNxDKVSbnrIciB9MnlY4eQ4On7+r5uygm0OW/AQMpwZowDMkwAlJ8aZCzM+mMTowBHBl9vwUxYbFYIISUPvvs7ExRn0ra0L7v4ZHSEo4GkitGU4yMGAeVStthGMZdFOccAuIYsw5KIXx9D9ct4L3DZrvBjetX8TM/83/H8b176E+PsfQO+wdXsA0RN9+4hYdu3MByscSyWyIOA8ABzPmSWedwfHycaADO6aTbilYWjTCdLJy0E4TJM+l75DjfWA5Bi5OLQAhhqAyvIshkfpL7uvRHh2lJOKAGq0JX/awVkHNgpKWAtREhn9vFPbdN3gIV5TlmQLXv8qFMcgRPHkMc0hkkziEQkTFEzrtCDjEGiCvFOYcgyRuyMBFPEIB8aa3qB49gXpSTHrNQfBewaPHFXNml8EvbkZFynDQArBJeWvDFmC5F1nPc8nrptuY86WXsyak16Z/+u02veYBu69GKmkHN56aAnzPLWIU8Pi8Z9oimIOxcBcZp4HO0E9qeB15asoORdoxZP2Ne1/3UGQGdBgE01ilY1q5T3a68SDK+3Bc915SC6wBHCN5hWDo8/r5343t/9Ck88u53YuMIwXl0RHB9xPboAW4++wLuvnIT3/jyV3H75uvgTY9ltwAQgc4jMMO7nA1JXbyoedieQ9XzbY1yIIddcwrVswbJHOiu8L7inZZH3M5Bax6Fvlre2TMhdq225ljLYtvnuZ3flizdtYNmbZ1z5U+Wx1Udjf7Njaf1bEse6PXDgFqzWicBAvJaxdJo7Mf0nj7rvNDjkhLVWmmNz/Jtayz6b2v8S2kZE015saOO+aKysXKdNtsaMyP9k0yVq0CatRrsIPNG6ns7xl19vihv6+LIpQgk1YdWqeZb1WvpOqenBBOQ2tXR2KrV7zldsGtMml/kDJhe0yJDztZrHB7sj+8RktON8xySoocWdHkMUE7hoh8VFnozoZBz5S1vIAFcUl0nRQqQAP4YMYSA9XqD7WaDs/UZwrDFIzceAsilS4AHRmSHrlvh6P4pwBF7hwe4/tAVIAwIYPQhXzpGcmYlhWJR2R0I8EXxJWE0DCFZz95j//AwPx+BGABOO1qMDt4xYmAc37+Ny4d72NAezk5P8Z53vh17+/s4/i/3ce+N1xPAyZn0hhiTp5/TjtliscDBwUHmrcRM3vsCNkUwxjjuOghTpz5zMUomi4mRszcBq4ExOGC9Iiw4YB8Ezs+HvGskQAbKmEwMLMItpyrNTO1So0ogpMbCMKRdHWaAY0qS4agy4Ky3Sm/12nh+KbKYdHx3E2gwILeYknMp1WimhygjRymkgzmllffOIUoy4EhYLPYQuw6Di1jGAGIPonTYMPUXxchy7EHwY9uUPfja4CyGUaIfYQRvkzHGODWsoDzf+vdKyFGl8HeF9tR1yzO5f04Euaij1s5APScyL3pnrwKMqr+zIZjgysCYKy2Fq5OYtIxv+76MWzITCr+PzzNCGEo4b/oc5acAqZbRwlwbD3NKoHynFI+U4kHMOyCttdFSMIXOkSGx5CkrE8AY66rWRB6UPEt5nci9dOOglFe0fDO24+QcpRqLA8FxkqzBAXC55RCx8B2YHdYLh+6JG/jkF57Ct3/3d8J3HQZEOE9wwwDqI45vvoHXvvUCXnnmObz83PPotyl8OS5dUtaeAJf4hwnQN7oLGJBsn5TPWKbwYwUQZN6cXAQudM5jjgw/jjrrKS4MQUJ/EwLLnN4jIlDXlbnhTGdyqbeU6TQX5tkCuS2QZGWDgK6K50xpOUK0MWnXm901qzusJAZDEnzmsMdpu0mmZUcVTx0pc0bdeeDKPm/XaFq/2kiYvmdpWQ1Tg17UdG+C4BYAV0a87bvIysLD5rzQHG1iZs45x1kLQGtH5FxfLQ2KHCUAJWveaHQSuTKn8vwo81DwjPCe5c3WDmlrxlvn/yZ9R87MRwTKV4/IKk4HIJqEAsDljKges+jbYhRkJa+NjZHHUpYUlmsqaOSrGCMij/cSgRMW4eR+zE4qBnmPEOKEJ+b4rLXOrQGvI4s0ZnCO4Lt09Q47pGsQlNxjFhknilDJ+oyh4TwGwapCI3mvyAOjg95kecsbSP16C+c8wEg38+Z7edZnZ+j7Huv1GoDERK6wPDzA9WvXcHa2xunpGtevP4zlcoUQGCFEnJ4cY+/gEMSEe3fu4fDKQ2BiLJYLlPz9OUGCADHv/CgYM4j3JOkNGZcOD8EAfLfENmxEFmDoA5xLmZQIhLt37wFI9w196/nn8OST78SNRx7BydkLAICIvFXaecSBy11Fkn1nuUhpI0Ug2LMGwJgRRH7XHkQp9cG4DNIBdJSABLskIAijV0vXCea82yY1TpWxPjcGJRDAo0EnC1kSOcCNgKPl5dTGk/1OF60gWl4IKyTt9xqcT0A6Ja9i4se81CmFrhHGCxUJKuU3YzJvBWwwV+PO1RVjSYwtHTol/bBKz9KjbTyOYK7+vK5D99W5dOGx9w61wcmzdaSfsjvZnKbqPf1uC+QVxYlpaJL+fXanjOo+zhlH1iCryxT8MHNljMc4nlG0/Ux+gpoeNkxibmy5waptKTEyGHUI7XllDsjNhWrqOXG2X6ZQ6w8CuqzAE/BQ80GErUvK0XEyQjw50MIjOkI8OMB3fN/H8JGnPo3ltcuATxc+dOzBmwGnd4/wyreew4tffwavfusFoB+wXaespb7zcBLWVIQ4irGnadGSJ7KzJuu10MbMr147ZR5zm9ppId/PAfP0Xk3z9AVGi4vr/pR5UbxfQmpDI93uzPztMiY02NLGjwb8b+Zs3lzbLcBW9UF9Z+W0bjfGWBKP6M9ahlzLwBnXKZl60++t8CgpdWIJ1Z6xc1oGnf5O180zz2jgP+cMtLotjarOENcG93XZpUOFJpqm9csj2CVSdTOX3Re745iTw1bjsP1sJdHCDj62fa/GmeeHxaEjuh6AOIKn81UbdtOxZwNL7ARVLD+29Le0IxhQ7zISTRiq2Zeap0csdq4cYC53IE3mNbcRYkBJeZ5lHnM+38q1M5alTkWbES+NY9b9qN79Y5S3vIEU+wByAIeAzbbH6ekJNpstXOexWq1w6dIhFoslYgyIkeEdcPnKZUSOuHzpSrn/iAgg53C2WeP05BQPXb6Cu2/cgUOHxXIfPueXlyLCOJ39ES9A8pyLoUYAHDH2FgvEEBFchwiHpV8icsR6s8Hh4SGcIzjfYbFcAQjAlnDv6AjHD76KGEMOV0n1S5sAqox6qT91wE8Fhozg1aFMITOuVWgj6E9hYxTHbW2rGMp8NA5KFrCfi3ceIQzVYhwFXJ1JT0oCmcn3ahWH/LMCdAJITLH9ap0DkL9t3dpAKEZfjAgcELLwlMv7GAIuczgEdIhR/qfIZvshym8i0FB7bexc2DnXNGrNtRZGWkloGsxta+tLVuXnOEc1zav3UJc5A2CnYaA+d3DlEL1+T94RB8G0H0mZWMOoBQgqsHcOaJSwNnlPz58Nd5HQV6ug9VzNglTmyX1S1olg359bF7sA0Bh6S5P52dm/C5W2ARuJ0DtGF5NxBFC602jh8OSHPoiP/cjncO3tj6WdIEfo4IDtgLM37uLWCy/im09/HW+88hpOju7DRwYPIe20qHX2x+23NWDkdwsY7K6dfce+a+db5nOO7nbOWsB6FvipcpFD8rbe1jqVNaKBljUS7fdzpQa39efVWIgqQbpr97sFDFs6ZRiGyT1w5V2u5Z6do5bBMmf0pHe4Anr2fbt2yzMNOSVr1bav+2XHW+uWum09V5UjQBV9oa7tb+vv0q/UiWadzGL5T9daqo8nvGSB/pvh5TkDUrCV1iOj/NjhFOKpY1HmZtc6nOsPkJeAeVdjpjk+Sbs9u3WAXcvnhd/btnUfnfNYLBYNeZeiZ8g4JcFcjE+pl5AdB819vz95ecsbSKFf5zuAPDwY+8sOD106gFt0OTsPY316nL1FjMgB+/sHCEPIoS8OIfTJE0DAtevX4bzDI488jEv7ewjrNeJ6C14tsB3SbhQI2XMfM8DlclmrCFPZ2TnYX+LS/h7ABCaPS1euwTuPkwcP4PwCnDPPkfcgdIhDBHkPz/kMlE/3PFUCrHGXTmLiWtlXAFgtDPGelcI1GKuUuSS7ECOQkb3gVBhZFlIFNngE7faAfGlWvVcWI7mqLgGOkvhBdp5kbDYu2yohXWw/rWKwYVs21t8CFl1noXc2jkJkbDfbyugAGCHme7RKgJGTaNu2wFTtTcAVjaE4kx0Js4Veg/QpUCv1l2bnt9/1+KVIuKEVqOM81buaAEpWSMZUMFswVcasnmkpPxGqQhPdB12X3kkFRq9gW0HPA9dc6QR07KKfXSva6Ezf+QlQ3QUidZ+1ApadK+/9eMbNjGlundi/54CG3SmYVc6VEm6NARAvrKZzkgmEZUiXQsIBQ+ew9/ZH8AP/xy/gHe97D7rlEn2OyeoiI56d4e5Lr+HVb3wLN7/1HF556SU4ELqYQk7Ipcu1xZC+KJArvNXgVf2e3bmYrr82zVuGhv17AtIbz5OrQ2gtONOypNVOEzQ1cFyLH2x7rWLXxpsDiaPDSdeVeLTeQW7JbT1Xk9ArI2PlO60HKtpQG2Banmg5KuzZtfH3qQ7XfdG/F2CKenemkmtmbHO0mPSDah1i6Wn7o/s0BcT1POp2iShnGp6W8hxqvio0odFe1jynadOa04xkilyT0tpxLXWRA2M0/uqz3AGuSuxlDF/Fk/p8d4tGrTKVnzXPWRksbVj+jxkztHSVLTvDX3XfUGNL3V/nKO9sDfDIOJeTnB775gw/5RB5mW/DS5aHdU90+xctb3kD6e7rr2K1XKHzPudZZ/RnyGmWF/DOwTEjbrYp9M13eOihK7hx4wZiTGF1Y6YMh7PNFmFgPPbow9hfEGJYg0PAEJNgDvnOIVlc280GISeEIHJYhyFvuzK87zCcLXDj6lWAHMgtAFrkaAiC9x2c60rce2SAyYmvAs77lEkpK3/xpch2pVaaOrZY/lkgJswlXu0qY1Wm5wQE0ihMxpIuc5U2gBEU6F0G26ex/rEt6X/pM4xRpBce6sWsvThzglloA2BCixYosX2SsemtegsydJtDGBA47RSdnJ5UwpAonzsSYAadHrOtGDXVbb/T/UrT77VAtJ61GOUMSbv/wl8tmljANSdA54CTfDeObV6QtZS3NXhs3zRwER6y3q9dYHUCCs3nrRCcFlhu0cnynn6nNiqswqjntdXHokhMX4rCBk/mZNfcWQVqaWO/m+tXu672nBOlsdu163PYnXMeAwirG1fw4U9/Ah/6zKcQL+9h8AQfCR059GdrHN2+h+3dY3ztd/8A9159DWdH9+EjwJKdkajwnZ4n6zjR32vaM2sA0M44ZgHwLtBoP5tN2GDmy/KQpfPcPLZkoK1XaDFn4Oix2z7qHeqL7prpz+04W+ultTMu/daGYSVrbB2Y8reWL1pvyPdV3zLwtfRuAcu5uWvxmx3z3K7hHD3suHXRc9OaO/lJROncMdpz15rLi+zUXARwT+YtphA7m6JesJDVeeettV2ltaaqOgydgXHcrVBV3a+WPpubJ/vupG+qD0S7k1CdR4PWWtb90vPakhcVUUx7m80Gi0WXj8CwOS9UgiNr5y5zkc/pnqtxwC1Z+yctb3kD6Sf+xv8Nly9dwmqxROc7HB7s4+DgEIuuw4MHD7BcLrFcLsv9PpevXsPRelNAg4TeJeITNpsBbuVw4/pD+H/+P/42Hr56BYQBIAfOh8a8TxN+fHyMe/eOsO0HLBcrDCFgs16j324RYkzpwIcBb3v8sXTHDqUQOwLBuQ6LlQdnIAvnkbqQgC8T8sWEo6AiaoPKmllG74kIVzEkmMfQOsnUV943QlruNposPJfiXWVni2LtAR8XURsIpGfGi/msEosxVEZWtWi5jnkF6sv5rEDR7wrYahWrhKVeICkonRLejkf/HmNMZ7RoNDi9c2VXjMshASR+ikJv8WmdXyZgW21Jt5SUBVzMuRc0nlmy/DP3fqsvpU5D+6khMpM0AyNgtbxgFYwViq1wjph3dWUNWE+a3llp9aPVjua1ufASO/YWv8hzdoetHm/63K6pXcCHOfGArrtyHMTz+6TrmlOqc0C7RStd13khGmOdgKwPkVUAQJ3HZrXAt3/0w/jeH/scDh+5huCABQguENxmwGa9wf3X38B/+q3fRn//FKsI9CdrDBwxcMrG6XKIspO2FKiy68R+pvtIZp21QIzmk/Z6MIC0QSdtuOlnLLiyjgMiGg9mG3Bm67dFz3/FFzNA6CJApbWud71/UX7U/QAyHUxIn5aJFuzZ9WDP+bXW6dgwKk+47f8cf1h+qN+lSsbIM3OpnW09u+aipbs0v9gzU9YJa9+d+3xOzmi6X6R/0o90pnvUr1VIZtZlc3hA/m7Wr/ppnaGWR8srasvKhpq3ZEKZy8ZO9UXXjfysnieq6mydLbRyY8QZdd2WPnbc5/HVXBHcmXQtgOyQletfiMQpxhjPbwERhJwHKP2vsY50f8fPEoZ6s8bTW95A+uD7341l1yEMAxZdBw4R2819DGeMYb3B5b1reHB0C6enp1gsFthuzrD/8BPwvkO/XSNGxrbvEQNjsVqBnIfvOgCMEDa4detFHCwdfLfANiSrVjKYdF2Hhy4tEWOH/b0D7O3tZVDm4ZzHcrmEW+xhG9JdTK7z5SI7BqFbdAXc+HxxLOWwqyR9qew2iSWd+MJ6vmRBIwOl9N2o/D2cj0CoPWV6UUgMrRaaQIojhScQXDp4HBkeDtt+i84w7y5lVoP3mol1mFyM9UWKNgQPAHye75QpSi1iTiF4BKrCi3yuJ4ZQpVyXsc/1ue/7agdIh/bpUikExwhDRIgR634L9NuUmh0p05/3KdQTeb687wA4xDhAGydVnxpGQowRHEJKQ2zeGY2W2pvdmhOrGLUN92aKFlhTEEAVT44vYRSO8m6jTj2mVtFjFKWqQ111ae1qphdRyG8Br+bFVr9g+IFjRDAgUMBay5DSdbXA9lhPpS80oYrsSGNLO5IxxkKL1D5lWVFehJ3sUWHa8KNEJAvG9BhqcGSNjNSuzHByANGYnYzT/6Q3ruvQxwj2Do+++1348Bc+h7d/8NsxrBzOwNiDx2oT0d+7j5dffgnPP/sczh6cYH37CCd3j+ADw3mHbehLPzgmOeecz46Fdhhn+V14KvcpyRpMJsHKvhbAl+8E7Gp+mANktmjwPIb3jPyswcIuQKp5b26HdXL2gGkiF3YBkV36QH8uY3kzxdK5gOK8FgnpOo0CFE1/ZPxarovDUHSG7ft0XXLycDcMFCtvJ2C6YUTUoBGF1qLXW8B1Spj0HqUHJtEHk/7z1HAZAf/o5CNKTpZZg7D8TAJKOzHnjPJdvC5htcgREqOIHftexLWAHlOsUdogU6VTW7J3Im9pVBIaJ2kHatOg0JXwaNRJbXPraEJn6Z+KCGgZ9bYfdZ/GsYneZePMkbZaPLfLYJLvXZ63q1ev4saN6zg5fqD0ifRhdJoK7ogxgiNSVJXQd4aPdZtiXInc+98NJFWe/9qXsd2s8cwz38AwbMEccXp2iqEfcHBwiIODA9y/fx9DP+DSpUvw+1fw3/2V/wGr/Q7UpTNAvluhH9ZwkbGfkyZQ1+Ghq1cxnN7BAlscrBz6AJydnuLywWVst1ucnh7j9MEDbDYPsF51CIFx//gEd+4eI0YCOY/lQ0/gqR/5s2lDcUhnisg5pGCyruw2ON+VwKsQIxx7AC7f7UQInM7nDGFIggoRkSn/SyJDzi6MIVsCiBiAywxJGZQHMKc7jMD12R59V1EA54PRjI1LUbhdH8AABocKZACJYb33iEP7zgZRBOPvU0GWBOLI7HJwfdV1aSH16eAsh6CezQqduSzGJKgDkFPrMjN4CIDPB0pjKEBWAxbdZxue11LoGjxHAMtI2AbG3rUrCZS+8joiEQAPsE/Ai0QAh6IUK7yulK0IkvMOdtpwOhv2KPUKgLWgvXyvRFIrhFGKpORWk9acc6Ja2Gvh22X1GpTxyqoH2jjRdcyFRNkwCH1w2IZEVX1ESrAidUu9WoFKHZMQJOFdZegRpXt0dvGT7NLanS5AeIIKL8t6rsCAooejERRy/i+Hcmcek/4yus4pwzE5WKyxnKrPOy/FaAoFPCcnh4xJG1Cpj+PalgQzyZZfMNBzwOAc2CdvYRcJjhngkECW77BxhP7KZXz8C5/Dd3zyE9jbX2HoCG7p4YYAOh3w6le/hZe++nXceu0V3L5zGzEkJwg4h+rGiC47mTpyYJejBjiWM2h67Zf1A4yGGxEiYgFq88CjbSzqZDjOOdy4cQNHR0fYbreFH3YBjl1AT3uLzYsgTnK74BHhHkIJvS0GBE1D4zQAS+2M1etnLKCS3+1n2ijT7WpjbBddS93jcCZGR1ZHIDDklOf4bL7414zL0lLqnZuPidFk5GIrJG6unpbRFGOfdjkzyEw8ChB5MA9V21U7IrOiRAik8UdmUM6ym85Mp/v3nEt1ytUfrHgi6dbROUAsjgsqjhglocf1QqPB2Bq/Bd1apiKO55C6fJ0J5wxxGbYkmei0UycDbaSxJedqMTtw3nUP1rgAgMih8ogUhxPJWqv5uYxVyQs79s55iAHP2Tk6mjq7HQ1Cn7kdRP3TfjfdcYtjmBtSArEs1HIdU/3d1LEz/bDP99ttmku5EiE9gLSblOSzGNWgtMMf5RA3o9xrKhd0DzECvk0rqYcjg6bkmC1veQPp5PQ+Ll86xHd993fi+PgYwzBgtdrDwf5B2q0BYQjpTh1HhNOBcPny5XKmRINI5ojlcgmOKdU2yOP4wSmuX1om5gZj02/g1x6bzRZn6zO8dus1xLDB/mqJ9WYLIF0EerY+wzAw3vW2y0DZ9g3ouhzmpUCn3FPiXLofCRDhBxCn3Sa5C0CAmPCkNipiBt4tMAq1mGyYEGV0rgFz7TnIwsmnLPUuSysGV9lbKi+KI3TUTcBkWkBTxVCAosr6JUUEhAauLQUnn+vQQi3IrEAZQkiGp6sPTlqgI/UDdchdqwzDgAWnsDo4SU4g4ZFFTJi5m3qfS7veFX7Rz47epPr9Mgbjna7aVLwnBm0xyFrPowY4+rvyffqgemcsI9CfnFGoBP/Iy1maNses6aP/FsDi/KgUND/oYuk96bF6X/62/WiFNGrwaekldUjomFXQLYBm+WGkCipwpgGOnVe5SNLuNug1ZA1BIB96byjIaTha/bsdu4AK2dECEbooRkhaE+wIwQOx8wiLDu/88IfxqS/8MK48+gjYOywA+BDBpz3uvPo6nv2jr+DFr34Tm/vHCNs1Yhyz/8W8PruuK0DRzoEev/6sgDaaT4PdKvV4p5dCSnuvv/76zndtsTJiTjbNgXph7PJudgTYOube13IBaiz6vjLhPb07NjW222u4tUZs+9ZQbNGnPI+al6s5Qf23Xg+tfuo2rYMmd65yUow4Ygombf9ba7weU0rWAm4bx7oO6et0DWs5ltZzMrhicbxWHqVcivyafKOG3tBjteE0LXM78JZf3OTKirotLW90eyMds0xRmEYXjjEZjlQ/Q0Q5wqd6Orc57rjZM71pzmfopOddz4tQTLU1twZbn8+tB4tX7HdWf9s6WjRpOSNbfbTf3bt3hIO9vXrs+Xz+2KBUYOprRNM45ya80BrjmylveQNpy1s8WLuU2tt1OLp/BnIbAEcAUkrprutwcHCAvf09XHnoOhaLBfphUwl35pQ1znXJc9P3EUweL778Gu7vd3AuAg7poi28AdkS7FYrUFyCCNg72MfBwWX4+yfYhruImx6PP/H4RFATUVqklBdq9hR57xFDnzKCEVVgQ8IAgNoDBWjvUwIgc6DWApvyLqYMLm1Wsa7eIQzJ2t/2PRYzc8LM2aMdi5dWpzt25Er4ivSpKJaG+S/GiyhlHZMt456EhKjv7EIqoIdQgVVddP0tQ0zTVtpPdx3lNjMdTk9PIaFkoJqeeh7tXOnwj7n4f1Dd7xpIRJDKS6+VqPYUnSdQrEe0xXMAwDvqkWyPwPxt8OlB1LtHqAGnLhbYjAZSvT6EB1uAcNfYW23q8Wpa6L+1oUJE5UyEBYMaZNq1rOubAOIG+NEKxPJVoiNGrzTqtb6/v594FNMzVhMPpKG1lRkaIDaNPe/QE6Njh0XIZ4Kcx9YDvSf0nvDIe96Jj3/hR/DE+98D6jpEcvCRgH7A5s4RXvrqN/DC15/B8dERTo+PQczYbrcF9Mt6lX+27xKaZg1DPf4Wj8waIGb+NY0sbaxBdpHSAr26vvNKi8d39XO27WLM1vOs+bZVr/68Bcik6DXzZkLu5kBSEziq9m36ff2cXYNzY9Tvtna5W6UFUCeytPRr9PinZ8ZdTvvsHM8yUHBEena+f601cd4Y9GcFS8y00TIE54w/4QNxTOt31B+ltTbdZ/Qb1Tv91fybR+f0QEV3C+7NvALzu0Da2LNttHTBeWWOpq1353avrXw/r+1mm0UPZ12dQ11z/Oe547D1A0jROOc8+2ZkK/DfgIH0la9+A6vVHrbbAS+88DLWZxsMfcBjjz2Ga9evI8ZQvDDkCD/wmc+j5cUnIgXck5dzuTzAiy+/jmce3ANowHbYZCGQgJjsDvAQUwgXCL5bgInwxu076BZL/Nh/93C1k1E8FSGik80Bl874LBYL9NsNyDm4CIScVlKSJZSsbd54RIQ5qS3sW0IUqHdF9PMV+MmkSmkulUdbaOZ8BsC2DVRhLHIpXzKe6hAqDQQphxVapaQX6FSRTD1F8rwOsbJzIL3Q8fzWGGj9Ls/oVNECIiKnOFom4OTkJIVtZd3s1M12rbqbChjjuSo95jQH00O9Mh5mrry8WtCz7GU1DJ8Y5RLktqDRNNf0YPNMDcan75ZwxrzWiEZvofOupNGPcVqn7Y/mEeccguIPy1+7jBFQW1nMAbwJGLHtACl0LJfWjo1ty46rRXuocTXbVW2OIGnK18xcLtKeG58OwWr1zxoVrbrKdwSwSyHBKXzYIXqHTUdYXX8I3/fZH8AHPvkxxMsrbIjROQKve2zvn+KVbzyDW996AfHeCejBKc5u3QE8YaBx10poKHdvyGfWmWLBh55n0QOE2vmwa64m84MGbwEVvc4rdg51vdZRdlGDQq9/+btpyKq+F5o1dKatY+48hC3aILA8aS+8nhgrDbBs22uCTaBEYcyNszU2G2Kt2xDHz5xcsfTVDlmra/VuXKINJ0ThHFAcXbW+s3xs5yE518Q5ksOaNGlo1CFS9A6g1KHbnNLAGJmOJnSWvk3kkhqDzcQmnztgcp5zpASKsTOVl2OIndWPumhZCCCH5avdLMXDWkfqsaTdOF/1z45bv1Pjq6nM1rJIy1nb79bvF9Hb9p1dpSVfyjpHCu0GTcN801pOobnVWiYqyamcm+4Oa0yj5YPodX01g52bi45Jl7e8gfTarWOAHiCECL/YRxc7nG6O8eqtO3j9zn04ohJudHCwj0ceexv6oc+HMgFHPguOFLMfY0r3zUxg3yFigT/40tcR4xaRdAz+GIbBQ4DLWTpCjOhDwGp/D+9+73uxWK7KZXO6kDOCjYHFYlEEmRZ+smNSFKwSTtXicGK8TD1guxYNMN2SHb9jOEq7Nj2Hklq67NqoG6R1fZ33YyivYvIYY+XJAzSASXVrBSrFhjq1BMGckNVtaUWFmWd13QJqW7tvk79F6TkgDD0evnoVJ/dPwc6B2aUwh4bSbIGmImh5BDQtECpzbgFphsVNRaVprPuS+sBVqIy806JN63drBKT+uDavqTnQSoMyEEphIVNQYIWiFuLSjA2f0c9OhHIDzOo+VX2bUUC6jUJn1Ubdx+l8Wj5ttZMUSN1P69yYAyEwbdlxaVroud51NmMXCGoZUyn+HmDnsek8gncYOo/v/P5P4mM/9GlcunENwQNwDisAtO5x8uobePnpZ/DKs8/itedfxB47DGcbDBwR2SGA4dUu9Jwhoz9vnXmRfuudBTFGLF01/fT7c59pQNziN/3ZXFstmurPhL9afKPbHS8dn85ha50XWiHJL8uzLZ6r5rzRd/2+ddIxj9dPiIPnPJpow8zusJR2CUXu7QJT9ru5+Urh7ARgum7t+O0/6Z8dm6UtM4pMTo/Eipd0mQsFlXqYx99bbWlQ2pIJLVrIuxV9GvS0v0/kJKZGl37WFq1v5TrGVqik9Mbqo6ZclblCez3Y9TXhIxpxl8UPLR1padXU07NlDM2z4f5zc6R/bz1jy645qORJEumz9SVMO2JWsDi0Zow5quepheHI1XJnyku7x6bLW95A2gzLrCBTCBw7YGDAuyVO1ls89NAVdF2Hvu9x6aEbeOSxJxCG8f4fZqWkGEgHkYHNdgD29vDEE+/E2fq34LzHdgjYbFNmsy4bPEMYsCIPTwTyhM2wRT9ERNfjkUcfR4xApDE+u4QYSSgWIYVCUfJ8pm30lIjBKqsinHOabP0vgaeaMay3msx3FVCdE/AwhgSUR0aBey1YbX810Ch9ijx5BiVGfPSqWWBpwYQUmc+WsaGFZ8sQkTr1oW3bln0HqMPzhDby92KxwGpvD3x4gPshAJCwu92e9qYCp7Fe/V6METHU/axC2Hw7Ht3GEVjDUs5w6H+6XUsT5tETpj8bAcHYaPUup//Je77zOaRCDIw2ILeKthKgPGNoKpq1jJHd0fMXKy3gU9wMM4CsVZqKIxfrfdZnuqrxaD5qzJcFsPo7q0Tnwlfn6tmlfJfUYXCEswXw+Afeg09+7rN4+3veDXiHsFgghgB3FrC5d4TXnnked198BS8/8xzuH91Ft+iwCQMGl51FPp1ha+1U6J0tKy9afa5CZ5GdNRgNppZMawIlU7RcsTS+SLHPze3WXZS/iAhe7WTscvzodyifI7S0awF8vQtxXl/kHbm43K7xyTmhHWDd1r2LJnbcmoYayLbAocxpPlrXpIVdi3Ptap1lx+OKM1KGXa/3XeNRPYZA8YpmBCRHcHvereyfkxe7SlNPmO8t79hiwbddR5HmjKj0tpVTziWZEVW7NT7g6j39/lxIGiARNufLAz1WAmZDxiw/VmM32PCiZba+C7zXmsu5+WPmdH8VjbqwfM/ybDsclVDjsQrz2GffJD+2ylveQPrWsy9juezyvwW8d+iW+4jssNrbx72jY1y+fBnOeRwcXMJiuUKMDI6A965Ym4nYKTzKdx6dT5P8nve8F3uHl7E+O4FfrOAjEEPE8clZin0nh5MYcbDaw8GlA4TACIHhI/Dkk+9C3/dgGndhZJaTVS23Io3eS+8dhqG4uwBk0Cv8RyiXp8mBcL1wUsKlqSczcn2Jnl7sMcZyb4YF7MhxvJJRROrw3gMkmbrSwcqYQwmZuZy9kN0zadN7n7yR0GGHOctLFGA+zu8wDONulRh1lIeqwDAxoet8Cc8bBVybb4rxx3nHJMSS2jQB7fqi0TlFmcInJdQC4JCM3iEErE9O0W8HBAAeydWlDT49PwlMq1A605buwwi+eQIORZD7roN8PBpfKHPKPBWyCRxStStllWRFPx49dJG5+Vz6u91Won9WSi6H23Es6XPzJpipa2q46t1ZQuJVHa5i+6X5o+wkcG5fGqWRHtZ0aglmq1DtWB21aWpLC2TZ8du13VJg2iPPMZaDz6265gCIrNm+77G/v1/PeVZkMZOL9WTJM5RixiknLInM6DuPw8cexqd+5Cm893u+E4v9PQwxwnce/WaL07tHOHn9Dbz41W+gv3eMWy+8jO3ZGbxL4xg4grsxnKXLqfo4faDFJOQcnlP99s4BkvqfU3a6CJRQbLhUX0r6IMBTPPiFSmW9SwKKmics4Ib6qelbfpvMjS52vlsGyhzfTSur/5wDGBoQpnbrC7f1+rJ8tAsktuRAq20g38UXQpmntE4T4XfdDSQJf6oxc02r+bOb9c6fHs+UlLRTTtt1KfpP5I7oNWtAS5GIbKIWj0373vqMCKNcS8pbscAo22T+tJNH17fLQLIyo5hkWY8CI9fPGkJzukYW2sz45/go6brRWHc5dI4ZxbCVIvyFHHrOaJ/ttbJWtz0mianxi+637mPhrXxcgvJQ9fq0fGnHqedrV7mIEVHaV38jf0aNZD1l/c1ggkTLhJ985xGGAY4kSislwmj3S+PA0biX70D1Om2N7wLDLeUtbyBdyTtEQI6zBdB1iwSUQoD3C2w2GxweHuDhRx7GcrFEP6SoXk8enhycYwQCNn2GskxYOgdHwI2HH8alyw/h5PQBMAwgMPp+izD02KzPAAAL73HjxlWEOGAIPQDGlctX8I4n3o6lXyKSBwdOQCIwFssFeNjAu5TKiZAu9nPksFyusNlsUrxljrlkSuedxCgawCkuF1mIRwaHZGxF1AdoK6CEKUCvvjdKIdHU5/NDAhwT0xJRkuCOAHLonMcmC1bvXDI4lCITpTQMQ+pnADin4JUdBo6ESHV/9vf304W7YUgpPuV7oFpkjCT4iAjOJ2M37VpJeMg0FJAobRFTZHiicqEmA2ByORylYZioM1LWW9oxI3pgiBHUJwEyeAffp3TjpNJUVgvbUbo0OM93KUraWkFbYsyVIE27kGIEjYI87bKquHeqw4kKX9DY5q7QP2uUkXPlPgX9rDgegHoHovAbc5HOctZB0spaOWcFYkuhitHcEp42y1bTgBHrGynFPWeQIlSoge/Mjl/d6fq8lzxnlH1lfFAyJiTRiVXKlh/LHBjNrPtHQEmMkkan+lfGjkIH6QMZ/pN3HCfgFQilryn8NsIhpdunmORH7DzWYHSXD/GBz3wa3/ODn8bq0gHIJRmAAVjfPUJ/9y5efuZbOHrjDt545SbWR8eImz6BcwbQMzxGfiainLo7wlG1VACO6dkMepLBm4BQ5wgcA0jomB1EkSMosggkpHujBkVbhvdpHWnjZwROQkUBtdpgsfxIEOSe5mjKz7sMaV2X5o0WWE5jaSes2GXI2PuAqkQ7andf6w4NtFvrVd6xbVs+k4vSnZd6sv5CQrhWlossYZfOnkhIT9KrVChetWGAv9QpMs+GW2qaJlky0qJVdkUraP0h38lZ5cQTAOd0/3DJIA+h4WSambvST0pOp9FAlEEkOafPfMmcW0OkkiMNftSfOdKAn8uOgIDblhyzdUzCI9Xn091ErmRpTZsUnigOi6otSuubaIygoHx5+7jLUc9Zi6dtiGjiUFbMRlV9ds5ETBCPsjeGnLzL13VXuI13R7noPlqZ0qK53sqS1TKuunb9Wq6JfNE8LxgWiACNjmQUP8fcDmJIuI0IRKKvpR+jo681vjdb3vIGkvbCkFqcSY4mhuj7dObo4Ycfzm9x9S8tkiELJYAc4F2+jJSBw8MDbDYb9P0ZNptNafvSpUvo+x5XH7oC5z1Oz87SHUbOYbVa4fKVy1juLRHiKBicjp+MEc4nAS7fL5dLAHXoH1ADrJgTCmjFNB5qG9PT6uQQ4o0DWhY3V89JSW2igDrKBIoxjheuuuShdd5h0XWlb9KHOYW9XC7BzNWB41YYz3a7zbHoHs5N+6oBpFa849mBKBxRFFs9vjrTXaEzoVrwtUEwPbxtvSfee5weH2PBSbnJIcMWaKjoM1aMVtH9deRKQgUtvFP/U0L2cedt9LoLAJHfq/h1JQ9bAmdOqVnjSD/PnDzyQB32OVe3GKl6u90aIhbcjHWM/dd1tgBia0wWuOixtp47DzDYdqSekL3cEyMHKEbvHPjRZzNabVoes4BjDnjrT8qaoBT6a0MdAqW0ydLnJHOT1zDEfN2Ad4idx7Ds8K7v/hA+9fmncPnxR0HegxzBMaE/OcX63jFuPvcCbr30Io5u38bZgxNsz9ZYb9bonM9nYIwhq2lmhkKVHTp/TkbPiaXhXCjTnBK2bchntQd7Gsa1qy+yrkfgPC3aMLGAvtWn80BTq35mzoJpCmLle8tzE3mqntW6S/pcnV1sdUT6yPU4dF9I6Q+7rlrj0s+2wunmSpm7Rp26TdFtk/PHRp7pOZjj09aY5uZY85A2Wq2zxRZ5tpWEQSeYaI1FnGkXpfkuWlc88ybe0+vM0qFuAMWo0nxHNDXU9c9d5z3l/dSPyYigjTQ91/rYhfRD5J0kJNBzXXQj757Li5RJ/9Eee7gAT7bnO2KzUYnNUiMzC7zW+9JWXe+bN4DOK295A0nSY1cAAcCi60COsNkM8L7Lu0iHKJ7EbBjFKIAe8NkNyRyLocMx4qGHruDk5AEWC5fqwAggrly5gtVyidPTBwgxIjCnHSCO6JbL/Fy6nLXrutzXdC6DnIf3taC052VaRpBY5oV5qFZcIrDkIkogMd0uZi60M8I63WwszVDZtfKgMUkFM2IYU1uHEODdGFZn6w0zysiCZy1A0i7WeL7GAgIAKU272R2TBAHSF/2M9pbpn845xFArg7q/o8CbeE0DJ695BpfyuZyVANWLfxdwnytFYPNoJE2A0QxNy3ia48IYGtH6rvG37XtLeThHCIGbvEeGd4Um2jM4p7Rbyiw26p+lwQw4mSjOHIqi264A3TlrSrenaaTXowYEjqahExqoaFCpxzmngC2Pyne7DDkBgE71M6izaVE8/JzC7RxTlhUegycEBwwLjxvvfAc+8aOfxxMfeC9o0YHIwRMhbHsc3bqDzb37uPXCy3jhmWfhYsDm5ASbszOcnZ4CROg5onOuXPRs6Zd7PKFna+5bOyZ27Z83n3q+LPhvPdeae0v/OQCo5UurDanPOooseJ6sE7M+dwHxsc9q9wFTB0Jr7C2a6vHsND4bvGnrsyFwMcYUqtvgBd2HVj+1jtg1RyKvQwgpIHqmn1q2z/GGlm1C03meqR0gut3WDpYOj2y90yq7eNk6Ee33+uyrXSPpIQAcmxB3p55hLs5KXf9c/+fmW+vZIQSQm2ao27Vmd/FRqx/mk4lcEh6Svk3WqamnNuSmToc/jWLHO9JmvKbDjm/XfDADp2enWeZAwIW8OdeJiXwd6//jG4Nz5S1vILWAAZDOroAk1jfdHi2XxQIAc0DMOzvD0GMYevhlMqqGfkiZ6Ryh8x0ef+JRXLlyGcxpV0bO1RweHmK5XOLk5AG2fY8hBqw362TAMGO1twIQ05kk74phxCz3cKQ4d6mvFtjtVNbMrFwUmZEVCJo8i1qZaLoVrw9QDu5KHaPQTV7bfgiIbqS3pHDUymlcuLXw7vt+BFYxwjGKAWoVnjWqKsXlauFgt9zrPgiFCCHW9NBx31ZoyfdczhkkvrFCXyvBcQs8h4YghZyFEFSYXky7OMpgkx2vJCwH4AJKrAYeaTt/F+DQ9C0HJl2dnUzTXPOHVbC63ppHxnf0762QmxZwkecrAEs5Xaw5xqqBru0PkBWL8WYCNWCwOzBzwLOsI6o/s+Nr0bw1zkroqzVrwaNOU25pO1enbtvy9dRpcL6iIUrbMHNhUx0DjimF0UUC2KEHEMlh3QGHj17Hxz/9ffiu7/sk3P4SQ+cwEEDbgPX9B9jcO8arz76A2y++gge372L94AQnpw/SHXCcd4q8nC+yMknTejwHpNekPN+aD00/m8xhFxBqAdl6PdalBbBbv5PiL/19qz/2fV1ErulinQOEqb5sgWjLSyn8uM7ipeV0y/Bp0dLKjjlZwkCzn3aMei60Q2xuTlpGo+YZPXYrw+z3UCHLeidAxjPHE5pu0xCtqREOoIR/t8bRmr9dc2H7o5+zRqeMxdap59PS1AL7EkniWjSc9kHTgM1Y7S6YyOaWs0rLgzmc2DIuW3JX3pcd3ZYztyVD9DM2hb2WPUKrMudEOcR3es2CvRtqjqZ2zC39UPo9Y3xYunvvdzoGi47glIHWkUvn1JGwkc1KMbdGNd1GuqIatcUBc9hiV3nLG0gaVI/hROlsBuJoIBGlSxE32w1CqBdKSq0tB87SjdPsKSVDyLsWIQyQg6rL5bIcWj45OcHZ+gwxBmz7LfowwDuHbtEhlEQKASEOYJazUg5dl87MyA6WnKMSgaJ3jPROkj4sWxIYzDBFtVgpxba2Frf83gLEMUYMOcTDkS8XgjJS7G6n3nN5bN54v8XAGAuX3SddWkrNboHLc1q4iNLRYQBlsRh6CK1jjCUFpeYFobfQzCq0VO8YE1z1Ia9gZkbgAYEc4MY5T+w1FZZ6/FbIiVCyC7/ME2pFapWorksr8OXeqqLz+FyEPnhu29N/V7w0I+gSzdu7R5qmk3HFCHbjZ9XuhVLik/4RiqEp89L3fbkPSxulQG0wzHnFxYkgbVpFcx4IqcYl76Y/JuPYVSwQuagyOG+dzSl0EFXnpzQI8DHCM0BMCCBsCQirJeLeEh/4/g/jez/3Q7h8/RqYCAGAC0DYbPHg1m0c33oDzz/9Tbz2wkuIZ1tszs4ABkgBAu99cTgSULzIGlSO4596/HcZPBYo2efsGgLaRpGmlV1rLXq2AGYLfM+B+1abrXFLfwVYVeA7MiKm9VuemrTfAKdEdbinBlKtOuXdlrOkogOV/+2koaal1AujD/S7dse1RVcLKHf1QX+ux2VLC+RqvTJX6vmdhnzOyY0WX83xU4t3WuOcWx/n1U+UHYyYhozN8VoF7Bt1F34iAjdIoA2Q1ru2f/bvOZmqZUpLrug6RloCQPv58/RFDLUhXWWbnZxprB1wwNQZZrFRkeXeI4TROLOGfgBP6hA9GsKQkgApOcOcXFpy9KLjDsN2Czn3ZsdezRFhEmI3Yh1qJpC6iA6cK295A0kTUf7FmBMXcMweaBYski+2Shd8+i4ZT8tuCVBacEMIKaSMHGII6HxKIS4Tslwu006Uc1iv11iv14iRMYSAbrFIYXYhlB2Sbb+B910OqWOAGOQ45e8PU8/NGG8+FW4CrGJmwCjnjM4RtLmWCtDX4XfzNC2XrXEERWDos1EWQj6vNR7OH0KAI4LvPCjUuxLVTgtR5cncNbfjuNvKSuZ7TikQycWUYz9EcLZ2QKptdxVGJ+F4aUES0OpP5CLQHRGWiyUWlLK4UAw5DJFKPyQ0pu97BEyVbEtxT3Z6HE3GPUfL9XoN7z0W+fyXbSPXCKIa8GngcxFhZEGG9jy3AIhWzDInIUo2u7FOnQ1xFuAzEDlU/deK2AJa3aYdQ6mXR0N0F4hq9cuub1nDu8DIRcsuw+zN1DcHBMFcvBJCJ+dyAgbnEDggEhCWC2xXS1z/9nfjkz/2I3j0vU8AjjF4DwQGtgFnt49w//U7uP3iC3jxm8/g+M499GdrcGQERPjFImebS333+SSvGElpUG+OHnPhUq15rMM7284a/VkrfGrOsHkzZboeL1akXTFY7PsVbahtXM3yAdA8OqDpqGkpsl4DptY7LbCZXsAkmc0u2oo+I45J2zf61ALDc+tmTo/YMicHCgaZeV/z4YX5hVAuYD2PN1pzq/tb/kZKgmHPD9m+Wp3TAuW7+qJl9nlzeV6ZtDdjAFljpuIJM8cXMVikThu1ottqgXUigJyHTmvd0nvyt8iV5Gwdnx+GoUqFT67t1LF6u9WGnhMg66YaclZ0AWo9putwRIiNdtLxlBS5VfSr9As6+cq83LRzSKBKBs3R8c2U/wYMpHTGJwGP0fsNABwjtv0mH4gm3L59G0SERT635Cl58perJeLQIzBjf5UudnUERJLscgwixnK5Qtd1CCHg9PQUp6enhdG870AAFosliAbcvXMX67M1mB0O9n06bAwAMQAxGxUxZU/yLmV/YgAxDJDFpA2+AlAzCD89PS2GCWPek1nClvJOmOxcSBIHIKXohqSqzuewBMgBaXGGIQDsEGJI33uPxXIB128Rojj9xHvHJeX3Ip/DyvZ/SmhCKcRI1iTnf7sAECkVrRdTy6vZEnbagJI0sNojI3/Ls1HytzQEvHS1KHkBApGLN8U7QnfpAJduPIx7R/cRX78Fx2PoXrZC4LxHkDNrXVf1fxSWcdxx0EilIdA0xcRbA6TzWQ8ePMDBwUG6n8ko9CKsUINiPWYrkFshT9WcmbmwytECjKpPOVwmhsRvLtNZhLLwfAsA6PUgTDYMQ0k2YL1oFsxYham9dXMKfpdwntAFM4CzQbvzynlK5rxCyWdTsvXlxpMRlOlMmRe98ymjJpIBS8sFNgRcevxRfPJzn8F7PvrdwMEKkQYQM2I/IJyscffFm7j5zedx9Not3Ll5E8Nmg+FsXTyFRK7IAO88skWaMmJxveNB1AqlrWmgQUyTtzitQfko1dOakVTs7pH+Kb/rHYsJjXcYUun3aX1zxtmuespPQrkQNhRaAVkgjEmBSl25XpH/QEUzohSmLGtefyc7s3aNA7VTrNVXO48Vvzd4vwWIKoOGx939OaNIz4mla0teJepQ+b82GWmsuPyUKzjmjCBLVzuO1vyWzxVLXHRs58kFjTHs+xUNaMoX9llbdPhgjBGsQuz03AuP5hbrZUjTsRW9CMbQMMKn68sYfCy7M/VuJlRYfUv22siFGhOQWsd5vRW2mBppLYyisYxgEL1bI79771PWW4kwYi74yxqhdt6kDqJpUpfW7zHGktBJ03Dsb53Ft24b6LdbeE/VewDKPZhzpdX/lKaY83UsyZnWwowX0ZtS2hL7T1B+8Rd/EZ/4xCdw+fJlPProo/jzf/7P4+mnn5508Od//ufxtre9Dfv7+3jqqafw5S9/uXpms9ngp37qp/Dwww/j8PAQP/7jP46XXnrpTfcnZTkbEGMyLEARfdhg059hvT3FdrvBMPQAM5755jfB/ZAMIwK6/I+HHnvLBfYWHRyAjggdEVZLj+WCcOvmy7h8sA8AxeMv2exCCOh8B+86EHksFysc7B9is+nx1a8+jWXXgYcAHhJo4BBAHOEpAQ/vHJaLDt4THAVsNg9AGKCtdi2wdSghkLZDo1JQLUEmyk8XDfxjDMVAiZwYMOXNRUqb6imDlVyvIwwuGZAhqIQRJGcV0vvk810juV5koRY4lu3xSCiprVkAmVpI+iyUpoUeq4zFnuPS4YpStIDYpTxIxZdPv0MxbCkjrZxEG6AFFuzQRcYxB9z4xPfgPZ//DE4cECilZyef74VBEoJ+0Y3eeTM2uTi1NEyk0qvvCOvBKOAEwKxWKywWiwl/SFzxHC0sb8nvc4K+ZQDZ74D6suLWeQYC5wP6aa1QjECMJQ14q05mTndRhbSLDGZ4cvCKf+cUoF07lZGF2vvWalu/b0N/9Xvlp/qnQZamhy4TQIh6R9SujUrJqbmojHAAHSjdx+YcqMu7q2Jg5ztYfCRQABwtwPAIqC4rlAABAABJREFU3QrDwSV85Ec/jx//m/89vv37vge079EhYLUNcPdOcP9bL+Jb//F38a3//Ht4+Stfw+vPPof+7Az9dotIAHySEZ1zWCDtGhEzHPLayjIk7ZImJ1WSi/J7UtA2dMkCXjvH4/PpXxJNDkQeyCtZ84Gmmy56l6TF83a+9feWZ6S+Fn9pud8Oux7/iV5IMsIVWREy4EqhBwyEWIxj5HXlGsaR/l232wL3ejytnzKOOaC4S/60DBdNZ83jc3SUfzokUNO+Dfzz2YeYHVsMyJ1pjqikaU6yJpHX05jy2Nar+6LHZ/vXMrbn6ttlsOwylnadzZnTK7pNrWfn+qbra/Uz7V5wEYR5VY8OU0OT0q9US0n3r2mn9Zn+vTxDPksbB3D6O+XtmmbAs3/Pyf4EczwcdblehzS06Xuahq26NZ3suEvbme8IiQfHn+05I6IS+WQ/nystfaf7Lriytc7yX9j22/z3GHY3/3x6RxfNW2BOuoozHmBOY96BRc4rf+o7SL/5m7+Jn/iJn8AnPvEJDMOAn/u5n8MXvvAFfOUrXykZ3v7+3//7+Af/4B/gV37lV/D+978ff+/v/T386I/+KJ5++mlcvnwZAPDTP/3T+PVf/3X883/+z3Hjxg387M/+LP7cn/tz+OIXvzg5aLqrxBjw4MEGfb8FI8L7tEDIe/i8QPxigaHv8eUvfQmvvPoKrl6/WsCicw63bt7C9evXEWKE9yn86M69e/CO8MpLL+JrX/saAGBvbw8nJydYr9cVc0dOOwd6Z8I5h1/7tV/D4cEB3ve+94PyxarL1QrrzRbMjLP1GsvlApFjNuS2WK83sEwiC5+Zc6iPy0o9Ce8YI4YhYBgCui4xS5W6GUihcKzTgdeC0oZD6JC/mBVAQBpn0gUppG6RGZezsUeUzm51XX1WRO/YsPpcFEnpS95GnShioCwG3U+9wKwSLWOKtWIUINFKv9ry6giNbPjbVLGkuQn9FuRRklucnZ0lmifrr6Kx1KsTQUzOWIhhpL2WFyi6f7I7lX5iMo5x3G2h2BpvpSBM2zVIfXNeHd13a0AVgJ/9ua16tTdf+ljOtJzTZmt9EFHaOVX9scJ4F6Cw39mxtMBDC7icpwBsmNjcGHX9A3O50JUZoJjWmWNgACN6AsFhG4EuOzyic3jsQ+/D9/3ZL+DGozcQVl0yrBgYzjbY3LmLO6++hme/8QxeeuY5UB8QN1vwECDhihPAwzzhbT1+nbSmZRBaWuux6s/Hn4Dm2hqk1u3bOZgDFa0dES1brTE1VyxwH2kwz28VbzEDbtzhFQeJc66A94usYW3QgMd3JOS41WdpR+S9BtH2OTtXFb8zrBqctKVpkt6PkyyW1iFmx6f7Z+cOQHGUzRU7Bl20DLJ91ZkHq/aImnOe7uh5c7vEmk67zh3bcdj+yHc64uI8A2oyN5gH7+khVBe/5y2RicwrczkzVtunliyXcy7y3BNPPIHXX38d6/Uai4Wv+4G2wW2N2tg4Z2R/13xQYRu1PmojcOoQkL/1v4pPGotGH6uo1vSOMpULbbxFVLeo++Nd4tn0ucjc80uLxwRHXKS/b2aN/KkbSL/xG79R/f3Lv/zLePTRR/HFL34Rn/nMZ8DM+Ef/6B/h537u5/AX/sJfAAD803/6T/HYY4/h137t1/DX//pfx9HREf7JP/kn+Gf/7J/hR37kRwAAv/qrv4onn3wS//bf/lv82I/92IX7c3a2hvcOq9USi2VXBM8QY7okLY7C786dO/jVX/1V/PW/8Tfgu4ghbLFcLnDt+o0EXCPjbHsGZsb+3j4ePDjGP/t//So2mx6+63D/wYMU2paVgFjkIpSlbRFGb7zxBv7f/9Nv4Gc/9J3YbJNRRM6n7HbM2ZtP8K7D0J/i9OxsdpwFMJJkw/PJKOoHRCU4RYjpOHABH9p71xLa+meQM0Y0hhZ4RxhCMkqTETbAK+CivQrAKOxtOkth9KmBMS5wWcga7DG4XESn69f16EQN43mW2sM6d+ZEl5iTFWgjrqSTnxgVejHn92PE3v4eOufB/YAFORBCZSDJcy0Bqv8W4auNjnNL1kha2BRQwwC5MT1wbYxK+FFbgNvx7uyCqlPT+M0IMFm7Ok6eY0DMOx425AcYlYxdE9wwkSYKRvGQGuzsAewW0LP9aIGglmE0NtcGjruUmlaA5xUNdCKlf7k1ePLZuGcsOBtOziN0Hv3C46FHH8UnPvuDePIjH4I7XCHmjOybkzPQNuD2qzfxypefxqvPv4j7R0fpUuh+SJk6ncsXIk7P+bSMQU3HCYA2Y5Z7zzQAmayjKgykTXMrG1uAsgUm5/qnw3bPM7JafGRljAUPlXNJnd+TbG62DoZcujh/wakdVxqDK1c3zPGt7qMGfNb5ZsfYogHbUCv1jq1vpAUX41vL+9Y86vFJHXq9nidnW7LfjmOOXwBMwhL12GIckzaNdxnOdmVn/6xsM09l4LpbFllaii68iDwvdTKXsDDbr9yTyhDRtJsN5TbN2jWh/677WK+jvu+xXq+TUR9jobV+Rr/fNGzhJ3S5CM/pvlbRQcYQmaOrfmZu/mQNalnU4s0LqI5S5zju6XdA3vmhdNYo4Y6LVS5o4c1gBNv2m3n3Tz3EzpajoyMAwPXr1wEAzz77LG7evIkvfOEL5ZnVaoXPfvaz+J3f+R0AwBe/+EX0fV8987a3vQ3f9V3fVZ6xZbPZ4P79+9U/ALh06RAHBwdYLlNWrmEYErjNuyXaW8kAvv6Nb+L3fv8PsN0OAAjL5QrL5R4YySu9XO4hhIjNeouvP/0NPHhwgq5b4MHxCc5Ozypr3AJW/bukA//KV76Cr371awAchhDRDwEhJENpf/8QRA7b7RZ7e3tw1CHFUddeagviUlgd52x8hJQ6mrBeb7HZbLDZbMqCkBAzRl0XoGNoUf6WdmNMiRjEGJDwizlLPYRQLqJN79RCRId6tUBRibOdMT4cuco40vTWXhd7Mzg1PKbyT9/dMFEQXAvGViY53T9mLuEBlIVC6Ad0zqfN/HQMqyk8bYibnp8WqJf354Bg+qOeoyoxBU09fJpmes6k2G1+274O89R0kc5IfVZpWGEtfNACbxXvNTyiuq+6flE+Ra/OAJo5sE6o32vNla5D3m+F7ehxWjCgdzV1HXMhLK25b/XNGg0Vrb1LIbTeocuXCTOAHoylX2CFBUAe7qFDfPePfAY//hP/PZ78/u8BX15hoJS58ez+MY5eeR1f+8+/h+f/6Gt45RvP4vSNu6DNAN70KSySkkGrk53MgbepIcAVjXTRoMLOuzPt6frnAH56v46rt2vCzs0c8G/JHTsvMr5pH6b3/LTmvjXvdregalvtEFu+kGftrq33vlyPIfW31vocWLP8DqDaXZrMremX7aP8tAaN7ct5O6n62RafMHM559Hqh52bypmn+jgvF9sOEH0WVepohSO36DsB7kbW6bmf+9eilZUru+bHtpX/SOGHWdfZCJHJ86pY+o/9GkF90UOGNnqnetx9GtdxjBGvv/76KIdN260+ad4q/VIh7fJ5S6cUbKUMFtvn9OzUIWrr0u231pjlcbvOK9rOjFnoZdssv5t3yhxQem7oh/w8lfPt+lhGi05VWB0wGb8Vl7aOBgvNlv+qSRqYGT/zMz+DH/zBH8R3fdd3AQBu3rwJAHjssceqZx977DE8//zz5Znlcolr165NnpH3bfnFX/xF/N2/+3dn+6HvS+HI6Lwf44KJCtUiR/zBH/4hPvih78BytcJ2GAAGhjBgGALCsEWMDO87/If/8L9hGCKcS+CqH3p0XYfFYlExAzOX7HXMjG3eLUqfA6+9/gbe974I3y0BpLCr9WYD77qcaIGwWHTZs5cOpjMlY++VV17Bo48+ahhIFmMykoYhIOQU3n0/lJ2tamHEOpQgvT8yoV7YlYeZU5xn5vECOpaLJZzzSUhlhifkg94sc9IWnHZhVAoHKHOllYssVH3wsbxjBNYuQNTyLkk7ti/MXIWSjEIuJnPaCqJSb0qscHL/PmLfJ89rzHe7uKlXSwvCicJhTFa8FrZzYCzdJzMdXwhh4uWqgIqjqj1LZ/v7LuA2/o3J9636d7VXtZkeLEBq9jmrsNOXsyBAfmpeijE2Qy6A6UWDuu25HSctN2zYpu2XVpytPuvnNIhp1WdBWfk8x64LAA4cQZ1H8A5nDnDdEu/6rg/iI5/7DK4++QS2HWHrHRaR4bcDjt64g+3RA3zrS1/D8994BvFsA8ohxIkOyYHj5MwcprzcAhFzxpCsSaJ691jes+C5OeYdgEOoIvOjZaS8Y0HtRIHvKO0+tQGzjF+Hyuk+zxklRFSSLWggysyIiCVUeTIu1PyinUIpa9aUlucVcdBZ2lgwZ+lD+QycjKEVgmT74tw0nHd+jqelJVehONYaIC1Dp7xn5saC3V19sEB/UvfMeHTd5+lDABXwlfVmnZjV86ZNKydb+rKsZaIqXXSla2ke9M8abeo3K4N30Wisf/y7NuTq2ufqmfAg13RqyQW7FnXd075jNjux7q9eWzIua2zsoqNu0M6z3v2dPi79zcDQ9g8JRkiWxLI1xHk3VK0HG3KOGXpz/cEsbd5M+a9qIP3kT/4k/vAP/xC//du/PfnOdvSiwmHumb/zd/4OfuZnfqb8ff/+fTz55JPVO2nBMkIOAWvVRCBcuXIVznXYbHow0tmcvu8Rh4gwpMOqQwz46Ec/hme+8Y3CZHrbG5CzPwP6YSh3EglQ8d5jsVjgxsOP4f0f+CDW2x7LvX30Q8CmH5LiGAL291bw3qHvh8IsaQFFrFYrPPLII3DOYRgGnJ6eolssQeTR9xuEkAy3YRsAJnTdAs4t0HV+Ah70vFgwbedML9oYYz4w7XIyjJRRLRmJhajwXZeSUDCPWcbijKBzAIfRYBGB4pwrQE0Edm3QTGd01+JP77bfk2IVjxThpZbhZGurgAmyxyVGPHLj4RTi2Q9wmApmrVhsPwpPkyv3R8z1u6msMcoZO9/SPw3Wi4EcUS7k1WNrjVf3w3qg9DM6VHFO0c+d75pRcYimn7q8WSFp+2yVm45Xbz1rizXCmyMwIEDXJXx/EZByblGg1s4LgHKw14sh3nUYPGHwwEPvegc+8fnP4p0feD/QeQQieHbw64Dh6BgP3riNmy+8hFeffQEPbt+FX/cIZ1u4bvT0ifq04EEbdHO0aoEIy0O79IwOs9X8J3/r9VG/P+2HljO6DnvWs9X/3WMDiMycNIww/fmu+sbfacJbRFSdY5n2rwY71ZyZ9ubrqOfA7qqUqyXy9zZ1v3MuEwXNvtixml5By+wWb7RKi87jHLSfb8lAC/Q0r8/JCxl7MSRmwr+TAzLpMssH5/GD9GfsnzyDiXHyZsouGajbTX8k7GV3RWXeI0932/+kZVd9rTNZSR7xrIPQlrk+VrqjwnTTtdI6o5gbRuTppeit8dX11deazI3DyhTtCLDPOCIw1Q6por+MfaR5U9dHpT4u0VzNnTP6k829lusXKf/VDKSf+qmfwr/6V/8Kv/Vbv4V3vOMd5fPHH38cQNoleuKJJ8rnr7/+etlVevzxx7HdbnH37t1qF+n111/Hpz/96WZ7q9UKq9Vq8jkjxVuDFTBBUgQy7SLgvfM4vHQZH/3ej6OPDBcCNqcnWC6X2PYD1qdrePJYbzdYdB2+/QMfxMOPPY6jO3ewXK1wQMDJyQk2m026u0YrR0oGStf5IvSXqxX+D3/2z6JbLrEdBtw7OsJqtcJytQKI4BdLRDiEPiCEHs55xBiS9y8Q+m2P5XKJvu8Tw2UPT4xpETOAPgzohz7TAaV9vSitICUaUzSO8ei1Ui7GHiWLvgBqULr7BABFBoVQspNIusl0MG/qiZffdZafMo+mXf2vAqSYCj1dv/5ZhED5n/Z6YOKF0O0BgCfCeGlq5qws6OVES3o+79DAFSNxy4zh+Bgu9HjjlZdSalGOYK49oBOwnQWLnGuLM0JDGyZpZ21cBwkkEMj5dHcGMKZVp9EI0vRqlZbxyOo7/RkzCugd3UWjkdYyiixorp5ToE0OeorEZSAhe3mOxhZFLLcMCr0DuQtk2P55ciVWOY0zlnuadNGgsAWm7XPz63N65qnwZiEqQfZjKlCViANWu16eAQ+Xsk66lEkxpW4FuggsODlABu/QdwR3/Qo++dlP472f+B74S3voQfBMWEZgc+8Yx7du4/VnX8Dz3/wmXnv5VVCIKQNdTBukoujgKGV5FF6IebpoHJNzaU1pw0SeGQ2dGvBbcDVqaFZ/c54r61yRuuq51r/LO9rA0vOlL1J2TjzmYwjn2FYjdFdNVFkb6lPKcj1lr+Q0w5wdPTQaAFz4YKZw4nXOshugvOappO0dx5t5hdxIpYYRIGvL8ub8GPUcjs5DLdMtjaWtjNwTjSmDvguAp11Gw1h3+/uWEXzBoxNV3SNfTyMSdu06y2fyr6aLRMlk3iWAkPVF4eW8i+OS0xdioJC42KIaEgE03XFgRo4MmeoFLa9axoc1xivaxCQTnDgQs1gQeZbUa5Heef27yoDTmKGch2Jk5yPKnXm7+JGzUGC05W7kOEkiNVeq8do2MO7uSNIunZjDGtC636Nc40KbDA1SdlFGSSWv15DoXqK6f3YXvD1nY5KM8h6PVwSIfC6SjXLW7ZK6XWER9VeIWVfmz5zmX6Mfk6zipDfaFE//z99HRes/bvlTP4PEzPjJn/xJ/It/8S/w7/7dv8O73/3u6vt3v/vdePzxx/Fv/s2/KZ9tt1v85m/+ZjF+Pv7xj2OxWFTPvPrqq/jSl740ayDt6BGSYhwZ2jmfPi7nPgDvO/iuw//5r/xlfPv7PoDVag9O0nPDJTDpO0QQ9g4OsTo4xP7ly/gr/5f/Kx66ej0LjgR8zs7OUnrvrsNyucRytcJyKVnxfGZmh8899Tl8//d/Ggf7h9hb7WO12sdisQKYcHa2wdHxCc42W/QhMZHL8bnM6exPDAEcxMPCcJ0Hu3RwOoIKeI4peTZC7CuQBqTF0fd9Dvsb89aP634EMNbzBeTbkCnf1UQEch2YOvRMYEqJKiSu2MsZCiVI5TupX3bdrALWh2n1ZYfVQqYpSGqBeD0W5yjvNDBAjMgBIQ6IHDLwqBVbESTM4BBTmmkieErpiJ0AU/Jw+V/OUZ74DCkkK/gFlosFVjHg6PWbadesAYClzcCxpFaXdLzklERS4yrjK2mQHZDPb5WrkQlpHigJOs71CfBK4Zh9FVIhXh9N18prZP4hg7n0u0t3geUMiym9qQjsdoz7RDja9lxKIBCIEYgz38vYINoVommJRsNPj0mLipahYs+STAw3ToopsRCX37u842nbsUp1bvxVGxjli43Pr/oVE1+mfuh+5d+F6jz+G/thYvIpp+/3Hn3n0R/s4z3f/yn8+b/5P+CDn/0huMMDuMhYBIY/3eDBq6/j5jefwdd+93fx9B/8AW69+DJ4uwU4YogDgovgjuA8wTuCAyuZk2Yv/RSu0d9x9Z3+XMBmy+gVj6HwHBfmcAlggcpP5HWrmGdSny6yRvWB+hiTbB8ziaa6YxR7hcpP3a79x4qJs/hId0xxkgXl9KGjlAHVUf5erj6veUjzHSEb9TLHoCLDigGGZCSP6cDrO2s0fUUutOjVWs/yu5aruo+y3rUjwYaHQgy7vP4II487qs/jhMb5x5bTRfO+LrvWprWQ5LldZ+laRpC0b/tn13nLwBvbYRCJhI8ARQABzAOYA4ginGOAIpwHiERiRsTYI+WhDQlfIACIIDcmJYglDF/+Tc9f2XLeLnBFg3yHY5JhocgvOQohulUukpc5t3qhciRl+Qfmshve6kvV/wa9ZZ2jpTcwNbrs3Fk9J0U7qicOB0M3y5uenJLtSr4r48KGqkoCLbvbo/vT1u+j3NWYmrPBWPQNZdkuP/M/rYs5YyAG52teRpzANK6oln4jkvlMRnUMsfyTez/F8UNAuprCYMEZNmyWP/UdpJ/4iZ/Ar/3ar+Ff/st/icuXL5czQw899BD29/dBRPjpn/5p/MIv/ALe97734X3vex9+4Rd+AQcHB/jLf/kvl2f/2l/7a/jZn/1Z3LhxA9evX8ff+lt/Cx/+8IdLVrs3U7QABDCJ3ZRY6ieeeAIf/q7vwmqxwOHBQbHq+75PgvYwwPtFUlIxot+u8a53vRMf//jH8b/8L/8zmBh7+ZJN7SUIZifEe4/lcomPfuxj6LoFlstl2jlaLsv5Je89jo9PAEoHpMktAB6w3aRdnWHIGUeQE09ABBZDYodDvkQTSCm3BxPvrUGi8w5DGGCFTAuIWYXm8uJ37JLQZcZi0eXvAYAKzcct9DHjjcxBaVctTO39CCGAYltpcAb2sryssNZGmBVG+jNRYGMd4+d6l0u3e54naQTSSUJ7cnAR2Byf4PiNO9jcf4DLPG5V63fSuHaXOeUvFLHCe66/8oze+ayFfP7MPG8NJGlDF1JCu+UV3jUmbSwWWmahCpruplg6aIAFHkGT5glAe7um4xZlY7/XY5oIcsqodmYGW2ur1X9tGNm2q/f1em543eeAayCgd4QFCItAWDGVO8j6zmGz6vD4+9+Dj/3w53Djne9AcElJLSKwJI/To2O88fKruPnsC7j5wku4ffN1xE0Pn/vRdV2RO957OIjXs71WgWkiDesw0A4SfTZCQlJGMJHob4GqpkHrDIylkf1cXzjaAtVSJvHzZj7maFCBqJl5k7937fLqfrQA2oQHZ4wB+7ztt9C6NZf2fb2W7JrR56nmZKsFdLtk78XOSbTXx9zcWNnQMoRa/SjPu+mat/qk1a6uy+qk+rNQ5mOsGxCQbnX8bFFfi/yW9+z6tDxlf2+FrLVobPlBGrc7VqWejF10O0Uex4i8IawuD8+g3g7VzKc2aCojpibLBBNpGlU7NzPLQvON5UFbLD5pYbS5MrdO7OeChVtr7jycc5FSv592Cr0X/ZbDBjFaSeethV2l6ANMefSi5U/dQPrH//gfAwCeeuqp6vNf/uVfxl/9q38VAPC3//bfxtnZGf7m3/ybuHv3Lj71qU/hX//rf13uQAKAf/gP/yG6rsNf+kt/CWdnZ/jhH/5h/Mqv/MqbugMJmCpYYdpV3tERwwQADg4OcOv1W7jxsMNqtYLzHotFh853WO7v4QwbhCgZ1wYMYQCGId1VpJSyKOzi6QU1gePpyUmVKQ1AARQhBOzt7aPr0qazc0AIhL5P7cYYcXa2BpAPTusc9hFlF0CMDTkDpUGB/tsKK71Y7YFOayBp40iY23cdooBHuXSQkqFEIqhht38zHWhkbp16UtOpeYaK5gWMHqu+HNZ6Da2SY64FiBYSWgja/s2B0Rhz5rohYuUWuPdqApMuJO9MNAZS/qOWyo1i56YYVlTTbPQouUnojhzytnxRtZNVREugi4E06QeLs6BNI+FbOx7m8T4Q/V7iERrbPEepaPoIuBUAVsuTdljD3Jzqfs+B39TV9t1IlhYaIFnvuVbALQNpHMF8mVMS4o/1kdBFAjmHrQfWHXD4tsfwPZ//LL7tIx9Cv+exphRy5/sIPj7BnTdu45UXXsSz33gGd1+7heFsk0LqHFWXDttQNOmFNeQ06JPftYy0Y9fGrmTlrNox/G/nSvo3R69dhov+bHzOQqhpGvJdxpHQ6qLAYFxb80ZSCxRrPtO8FcHlHJKVhbZPQv9kWI0gsGVAtEKHbJIJ5no3Ts91y6FieaXQREIPJ3RKAKxF2zcDAGXdixzd9Z6lXwgBDmlXX4cstQB2y/Gj+95ydrbkqAXhll7zA0Xx/MuFpqon0Kh/Dtzb9m3Rn9tMsOO7KJhBl7JOGgZG/TPVEWLKngYaIyHmip1TMbTn3JW6/ZYjTfdp7l1LD1vmwuCa4dZI9Jzed0nFqSh1zvHvHO/N4UZLhxYmkXcksiIlHxsNVo4RnGVJS/dqfGJlDLPRiSJ3yMiHN2Fr/akbSOcJdCB19ud//ufx8z//87PP7O3t4Zd+6ZfwS7/0S3+i/ly5cgWHh4fw3pczSl3XYeG7Kl0mEeV04MtiMG03GxCAhe8wDAOGvsemH7AdejBSikJQCqk7Pj7GwaXDkqih7HhQMo68UgJiBO3t7WF9dgbnF1XmsO12mxYD+ZTcALGE0i0WHfphDYDw3HPP4f7xfbzv/e/HYrXMI+ZkxIWY+8Doc9IHrWi0wZgAS3vrX8Yhu2lN8MijUkoKIxmQactTFk0dzxliKOB04mV1gJzq0HG5SYHarCxTga+Viw1H0otcwiBs3PtY/5i9S4pdnC0goH+fKCgi9CGAmLA9PcODe0fwuZ0hMNiEgO1S4i1hWrUn4XQKDKknAdS7EkB9T1YLVNmxzoLuCQit+2j7PFesYKy+U2B0TglZQSq7qzLG+pB+MmYEaLfCQ6zCaykAeV6Us+6H0Ne+p+fSAgQ77pbxVJSMqc/yp6UtEaFjgg8pPJSXHg8oID50gO/8oU/hw5/+NBb7+xiWDrwgYAiIZ2uc3XuAZ37vj/DSM9/C0e072JytEx/HdIbQ5bWtQ6NKn1V8vAbEWjbpneUWr1me1rStw2/TvNo51HPV2qGbA3yp3zWw1fSUOH/7uf7dznutvBu7K0C+yHvqqW8BK2ZGCTsztNe816Krrb81/hYtYxggxkKrWLDZAvYtI2GuDxocVmM3BmpNbxSAaw12zXOtObCltBunzjvdrl7nFd1Qz4mWC/KcDmfU89Yqmm6WJprWE73f4IlSpxjLIKTwvC6/K66wkX6t3W09brve9JmzlizTelpetWNKhgCDuJ5LXSQMvTjSGCC/e5fUfmrluh67fK/HYt+xz2p+07tTQkfpm93dkefk3WEYxrsXqzbTmOV97RhNj7SNyQnt1Lt2rC1+nMxNQxfzOJmIMWC7HY99EBGQdbLQxm6IWBlR7xArOuShxJiOJsyFtZ5X/qtmsfv/h/Lkk0+WsDW9MDnUCxpIhtPjjz+OGAjL5RK02kPf9+h8Sovde4/T9QbOewxDj+VqibDZ4B3veEe6SKxzWCwW2NvbKwJvu92mg/BGoa9WKzz22GM4vHQZq/2D6ixO13U4ODgAUQciBnNKzLDdhrLIYwx4+9vfjhubh7FarRDy2aEh36Mk3qmUtjk9D6TzJva+IKGD/GwxkBbUE9AuEcGUDMF0s0+Pfhiw5OSl51AndWjF3hYjca4vnPuPltduXJwiNPRdGnbx2xCBi3iXnXPlfJQUK5ws7axBFR1SvCwcVosFOt+BgHRH1JxTj0aQZ/vWmqu6f+0qRWHYeW3Rqvn+BYVMC6Drdlt12rltKf5oxt7qd2sumWseqQAIU0mzLs4TW5/dDWiBlqovVKtTC4AsXayi1O9ZAGZ3UcnwSWseW/0lAM51WHtge9DhsQ9+AJ/40c/i2tsfB5MH3AIdM+i0x/rkFK988xncffkmXn/2Rdy7dRsc084x5fN8IAJ1fhaMcWK+CkBautl1tQss23VQz89oKGmwrNuzYUq6Lg3k6jkav7dgoQVM5W9Nk5ZHuDbu9AzVczq3Rvu+B7O6xgK157kFWmq6AWx4qLUWNf3S59Nxl7XaCDO0MsCCLk2HFk/Myp9sH83xihiNtv+27Ardq/ohbeouzMjkij6KNm05VTtyNJ9KHS1dI+/ryIw5Op4X5izPSu9S/ZrXa92o267eN/zTjAgwdKtwgUtJJazDMde4Yx6TPBTTuYldWrRPtTbHIIat/q4lf+x4hIqtfrbwxGQkhvdb7akaBUIVXTfWMbcHNu23fm/Cv+o5HeIs8zu3Pu34hqFPTm+UpVvYak7PtowzGXeM41n9CCRHHdX1XQTfSHnLG0jL5RIHB8kAkZ2ZdDYnCZ2uSyF0oHSWJ4QA75bVrskwDNjf38eQD64v9/awWCzQbzdYLBZ429vejsPDQ2z7bTlHVN8lg5KSUYTdYrFI9xGpXYwQQroPIANx50SgDBi2axzdu4O+38Bli3h/fx/7Bwd44aUX8fTXn8bHv/d7ISFhHDnveg1wjNFQiwx2bUaXohlcA0kRuq2wMsrCyKVYwJLG3IWhuiS0MLYRClpZSPpvKUX4EsOhm/Gq1aBGPtfATLcn5yJk/Hbx66Lf1zHy9jJgmUftDdWLkojQI4KJ4X0eEzgduiakM7U71u6c4rRzWIGY+cog6bVbwHBOkBDR6I2boddcf+aASQtY2jlr9SMnbbqQ0NNtxFjTsFXsuRLdxk6ANu1pBR51ERnRAhRase1SNtX8i4ZpGB/jvKE4M+Tz4D1OXMSld70N3/9nPo+3f+A98MtFSjRBHUIfgE2Po5dfxXNf/Rpee+lFhO0W27NN6udikZLIEJWdcsL8DmNa4+2Ux7NUbPCENphteOj47G6jWX8mfdAh0i1vfDo/OT38n94fF3Fr3Pr3VghV6zP7PYBKHluPq/A4mTU4JysqQHNB8CB8aw3O1rjte7v42Y7frsPz6pc27JwBoyy0MrkFknf9XX3Ouz3pus9aVyCvQfm8lQ5ey2U9R+fJRT0fdq4vYiCML6D0VaJD0tfCR+02ddstvpszEnR/bJ3yd2t39aI865wr5/mszq9kUIPfSp93iH0tP/R7Sd7NA/uWEWC/k+91O3N0TaqgXkNFFrvda0m313L+2LG2dPtF+NR+T85BUtaI/tL0bPH0XNhteS7Xx5geKbloecsbSFev5h2WEAB0iXEY6IctHAj7qz1453D3zp2c6ADYbM9wenqKGCOuX7+OCEa3XOBsfZazfoW845OMAN8t8M53vRt92GJ9doaTkxMQp8P4jzz6GJZ7e3jxxRfT4uS0LbztN+hDj4eWHcLQgzji5OQE222Pa9eu4sGDBwA57O3toes6rM+2eHByhsUiMcUQQ/oXApgIq/0D9JHBERg2W2w3W2y2AX1ILHJy1iPGnKpReJ3yQqIxZaMUUbxyHkrup7CCnCNAXfKPeKKU3psI8B5+uQRvNiBSHkaidDapUhbGU5sengjdGCNi6NMOWOZ86b+9t6clUK3g0vW2BE6arvH+DS2opR29BWzDRKzgJSIgAJRiCJMBHAKIU9ZByqmARX8WXcS14pkYNS6DX3DKAiNtiXTgERyPJXuTmHcKwjLPZT5qcK37Yw+T132uwX4l+OUZXX2eWwKNwl4+IwmHo3LmqRrZTiGYxq1DLMs3jtLZgB27pfLuRYylSqlqaQ2ksboaDOifln80zVwOhxXjMGXwSbIlnQGUMaW8VEwEdol2HRxoYBAceqKUnOHGVXz8cz+ED3zv98AdrhAJ8Ozh+4h4doaz+/fx3NNfx8kbd/Hqc8/j+N49LLoO3jssuzSv3tVZu2IIkIs5U98lVIYgmTKLN9bRyONq/NbTHDKPlDUV005O4XtCyeZW6OESxUBqfUOA0Mh35eA8MIYrUs4OSuP6Sv1TU+oldxSS0yF/QZT6UUI1hY95ylcobcUc0pvu2SNKu3KWJhZQtcJ6gZz8K6/Z9IxyChCXLKchamO1wb8zf2sjLdGvvQ7OC2+xILBlMEzpNf6S1UX6SfXzus/nGgSqDxqg2c/rv0fdUhtA0zZqOsQyNyBGMeW5fs/qj1af7O/Mkoobmd9SxS0D2Zaq/yKzitBKcibJv3bY1a75bdFB972cRKEkhwEGB64ccrpOCb93qOVmeZb0+PPu4Y4dFOkHc8pm6zJwkagXsOwYzuw8NQyG8jvx2DJlOeCSLA8x32FFnHbgM0+RgyTSNaFkdf3TcHguU6blQ/oA43pBvd7FcWix0BzPzX1mQ/LmSmRG551K6EUlJfsuHa4dVwV7ZPnGCACSflPKp9S5C+O0ylveQAJ5gDqQc1jtpeEyGDR0wBARh4jNZo03Xr+N1cE+bt++i3sP7pdsdMuzU/T3e7xjfw/UeSy8LzLDkUcMAPkFrjx0HdvtGpcvPYTOv4HjoyMsuwWOj47wbTdu4D3f9m587RtPY9v3GMIA8oT1+gwxDLj12m3EGHFwcIDj42MsFx3u3buLyBHXrz8M3y0QQsT+4WUw99hsThFiwDYMCDHi+iMPg7oFNtsBznmEvHPUB0YfknJfb/qcb34MvxNaFKCA0Wutd7uAFHKkDYOykPzoMXYAtsMAFyP6MGDZLRCYQJGwIA8QY+CAAC4x9cA0BCRhhGkMswDUftjC+3SfkywCVkKh8vhgXLRakVnvpwZkMn75XEorxMH23Roy8rv89EhZVUIMcI6xdA6OUZIzZMiv2h0FXtPgAhIAVvf+RI6jXiuHmqSuEUxoYd5S7lPjQANd+3mbjmOdRdtOBVUU4yhLbrWD6LyDzsxY3supnVs7bnPj0IaVDqGrAAHQnDcZQ/poqjBa4VJpTaH8zCawdKqgOc1LWtG3xiKKOv1LwxcAD0oOCpezzHHWhpzzevuQ++Q7bAjo95b4wMc/io/+6Gdx8OiNlAY+RqzIIxyvcXznCDefew4vfuubeHB0jON790AhogPBB4AQ09UCeXyOxp0hn+dGsm2mro9nEvXcFDkj4wcm9E0GFQnjFmeTnn4GilwRoFxuduHUvqZtMXQpgRTncuISMdjiKHOQWTPI2koKoCwrMbgENEN4PIRiXHluAzkiQpC5yvXJ77IzpumgaSc/bax+ZOFVKoaWnOdI9M4JC0CjDBeeM+dDdLH9tudSW0V/p3fAWk4BnY3QJtyo5rm25DIAHcWBBasF6Jm+WSArgCs0oh5acoXBcOQmcyNjbBtVXIVcMY+6Swx5kQfSngXBNnR7bFOBX1BC4Yhg7hFjvWM2JyPL2EjMCi7OAUZUtJ6e/bHzaeuf7ABRWU2jEyL/lGspQALm2w4kvSvUOpOjZXHlcFOl1tdxlCucnB6lPTVVLdrNAvsk1IpTSC6+dV5yeqZRE2Vq8yibxBHRwhatuYQxfCzNhBaF8LmII6cV/l5qnsEMltb2Hf15+T7/XK5WACdMxFmHiGxpyR+bO6AYSERIl2qb3SLmfL/WFNOdV97yBpIOp0n3EDmEGECesH9pieFsg9u33sBmu8WDs1O8fusW4BzOTtd5Yj3WZ2s8+sjjYKSwtr7fIuSzKCfrU8TIWO7tIcaAEAZcuXIVZ6eniDFg4Ig3bt/Gd3/PR/DI44/i3/zbfwvnO4A8Hpyu8eIrr+LsZIODgwMwOQQG7p+cIETgZL3B3maLPfZppwiEW7dv4/Bwf0zzHYHNZgPfLbDZbBFDj2Gb7q/ptz2GIYDgsF4HhCFMBNRisUAIAev1upyDijE2L92V98SCT+GILjG075IBxqOnhfJu0AicuHhfGJzDCEclrHdFivBrGDKtxakViDZutHdVwuKkWEGa+KXO0CKKrLU9a8NatAAoCTqsQGcxMgPWZ2vg0kPjGHasW91Xe3BSl9aBylZdpa8ArJCzNKnqAyqj5CIHU/V3eu7GuUYxjhjq8lskG4JdDrtUnlAxuXaV9pZ6vpdB0aI8kwFu67CwpU09r5iMqUUP2xcbBiF17M7UOYbsWcUTCdg6wHG6/FUADIFBgbDPHmcOODtc4vp734VP/shTeOSd74BfLIAwoCOH2A9YH93D3Vdfw8vPPIdXX3oJDx4coV9vwPnCV1dAxtSJIT/tOtXFhnQWhU8JvrTOKGi5IkXmx9ZXh9kBcqWA7aPIjUm4sKbpOcBEnmnNY+tvqWcXCKnBWh1qMuEhMx4plDo56ccI/GvjRNdlx7erLZGxeuds7j1b5sYCTNOSz8kj/fsuvhNeiKaeFuBuAV1bf6FFjEk+Gfpr8DZHA91OayyaDtqxN9e/XKupk8sa0HKqRQPp01xobLV7ORnN1OiZ9m2sx37HqNe2Xa8tvioGqHGeFZqbi7pF1+mWdtHCzjeLXpoxAmyRz+ZCu3T9tj8Jh42/S/uts9KWRlQM2/NLvUama3uW741c2jVPmh563YgjLBYnkBuxALXxmaVb5YTY0fYft7zlDSRhKlEMe3t7ODg4QETE5uQUJycnuHv3LtZnZwhg3L59B4888TYE7rFcrXB6toFzHvcfnGC92WBgIAwB2/UGjoDbb9yBI8Kly1cAACEMuHfnDIvlHvp+gPfAvaMjHB0d4d3veQ+uXb+ON27fQWTCvXv34dweVvsHuHXnLpb7B7jxyKO4e/cuAghDiBgC4+7xA7z22us4Pr6Hq1cvYZHPB4Qhot+mZAjb7YBhSJlNQt+XtN4C1vvtFuv1GtvtXsk7L/QB0lmt9XpdzlBpT5+lpTYAIkdEdpnZk8EQYr60K6ZbuyMPBao5l+5bcs4nFIda+TDzePmtCuGQQnBwLi3KYRgqJWSVkV64Mv/CCxag2DCzccHXhlbpB1HZxZqAEyX0rCEnnznn4HwyuMHzAm1OWNV0G+lThb0QNZVOqc98bttqGk07lDOnP2aEJTX6PSfQlAAsikm8YlTAHxqK7rw2BCxoGlka2N1GC2h1vZpvLJjWP1vgFjyqak3zXUqmmFVqYqt55bwrwEBH6ULsgOSsOHWEw3c8jo9+/tP4to98CG7ZAeRAgUGbIWVVvH0Pf/Rfvoi7r97C6dF99EMPdClcODoAMV3MOmed2vAZTRv52/a7Wis7aJZMsimYnCuJpkkutfqizxNaY0E/bwEkURsMj8+jWUdGIBfg/3qXQPNVa2e7SQdKF6ZKRsYic4pDq214tACi/V73RXZ50kH63fNh+9kyYoAxhEavv2oOTF+0Q6EFascxTN0qdr215LzVDZXMUPJhLsxOvm/RsmUoa0CsnTV6PVg9NfY3jXOcC/1vd5kDxDXvy9hrGWD123l8IM+V383ejrTVMjA1JpAdGVsf8rdCi1zJxDg6r69tHmqPY66euTVm7xxqzedceKrm/WnIYr2DdNEikRot3bZLVlXz0ZBvVqZynocURknYbDYyqsJTrXVnadKi9065Sm0a7ypveQOp77c4OTnB9evXU2KFvsfewX7eXRlw+/ZtbDYbrDcbXH7oCjabLV69eQtdt4Dv1iWV4p17x+iHPm2JxmSIIEScPHgAjhGPPvY4QgzYrNe4/vAj6IeQzizFgBAjnn/hBbz7ve/FZ556Cv/Tb/wbxAi89PJNvP7GMVy3gHcet+/eL0rHOYdu2eGNO/dxcnKGvh+w3Z7hwdkDHF5+Lzwi+n7AZtNjO6Q2hiHg9PQUJ/fuwfklBnYYhoCeAxB6nK3PsF6vcHBwUOgjyrfve5ycnFTGUUswysKuAE9+nyl7Poa+1E0SuiJ3MuVL2rTwF4Yt55CM8B+VA0A8ZrSR3S6bwlz6JYtFFppOKy7vWG99LRxc8WS0Upwzj7tgFtwSUbVDZoG3qATJikckHv9aCM6FaWhwp0MMdN/S79MLQwGB2VMBZoWbnmciKvcojDRSY0bbIzu20VbqjBRKB87GsRNAmc0hUtCGkW/bTnzknYc1XqRoPi7jypXpBAjj/E0Bsy0WOCT+4kohWYBphXxpMxFsMne675M6gSp2vOJJBpacdpCIE88OlML63KUDfOAzn8KHfuCTWF6/jB4BLgKrQAhHJ7h3+za+8eWv4tXnXsDRG3ewJA/0PTpK5zKBmA7RZiNGQhwtiLHzK2v03AO1DaCs6SX2haWRBv6yliQcOFMIkoxkTmFrZ5A+0GzX0zifKWxJr2spIUyzQsp7cr7yPAVtwab1IFu6WbBMlHbs7T07FnDoy5K1Eab/ntup1n0t8f8GMEu5iNGl52FcV3XIaWvcc3Xa/ltDRNNqzjDQz+n3qh1DahusmsZzdGvRaYzAnba7a4205FX6LDkIiMRYmDqSbBsVPzXAbdL5NDG5WnrEft9ylAAy7LYBOweEAYk2GHGCJF8a+4kKkFMYjSQrB3QfWwC/zD1NDWtgmhm31d8WjSx+kGfL2UWeXldif99lGFh+bmGKFn1ba2OubhnHzjmuaJDjEHJSGx3WGmOE49owbI1V3mlFeljsqpbWmypveQPp7OwMq9UeiAgnJyfoug79dovFYoFbx8e4f/9+InIIeM973oO7d+/ipL8LZuDwMN1rdOnwEERrbIctumWH7WaLB8fHWJ+dYbtOdyW9653vwJPvfCe+9a1voes6XL12HTEGvP7aTSyWC9y5dwfHJyf4wAc/hKP7Z/jDP/oyju6f4NKVJcJpj67rcP/4PmIIOLx0CZcuXcKSk1U/JASJSB4npxvcuXcf1y7tI4RsJPU9QmQMIeLmq6/hpWefxXu+/X3olvtpJyn0WHYux0onxjs9PcX+/j6YuVwodnp6iqtXr1YZ+ICaKeU7rbTlv8hInsn8zhAGLPOCoRDSbhNidZO4XgRFYSvmnoYbcSUIW4rYLn4b5tIydqRUXhPUqVOtwNRKsKVwZnfhQkQEY3AooZqRGV0DVJ2neGNDqY7v7BJogPNTYW0Vru1DS+mV39MHkzbTewCUYiIa7/4AoYRYcLa/tEEAiDJmNaRkmOv50XXP9dEqN/3TZWOiRQf9bMurp5WEpVPrd/tTGxtWcU8UE49+0Ao0McPFCHIesXM4IwYO9vHOD30HPvr5z+LwbQ8jOAYzYRkAv+6xPT7D3VdexZf/4A9w88WXEdYbLOEQwxa+cxhCBMUUlugclXNKXdflGPFpP/RY7dhbCrQ801iPqraKh+r6R2Pf0lZ4HZiGurQBZfvSYkCnvQWAqdEx1rEDJDXk6tiB+lkx2Ow61PSb9fIqrGkNKBmDlpWtNTMHOlq00XRuvdeSj/b91g7sXCFQdZZ2rl9j3xKf2PFa8Dg3Vquraj3TDk8+D4C2+gmgZH1t9Uc/r/l5wrMxu1LIgciDMYaDzxkAlh67+1xHLrSek9+tTm7KtJkQu4uWti7O9xhSXnaN3cE/admlD/VOdYsfd+kC+V6uZ9m1Flv1REwNHP1ey2klutg6S1qGZOt7faH3XNHYphLnWZY5VX/1rPpdirSnd1nny3mRGe3yljeQLl26hCtXHkoXxe6tSrrvPb/C3t4e3va2J7A+u4ZLly7hnd/2LsAvcMmtAIjyAYYsiBbLJUAM33lcunQJ+/t76WxNZCyWK3jf4YnHn8Dx8X3srVa4fu0qHnroIaw3Jwixx917d/H4ZoOPffzjuHL1Bvb2DuEXK/iug/cdLl2+DALQLRaJWTqHPkQs/TIxR+cADLhz7y7i+hSd9xiGCIIDxxRu9thjj+FwtcLe/gHIL3BweAnggM4xusWY2np/f7+AVCBdzPvkk0/COVflv9fKWAu78bvklXfkECS8CymLTgjp/iYGwXcdFo6wHbaIxOAhVmBBwCFQZ9rTiyPdp5QNAxWbrXPw2yQMWiC0Fjpg0mBqAUB14Nt0KxsTA04vZAukrRfE5Qs1R2MsVPXoNq1AKu1g6hkZn2WA2557wlSRWYGk2y/CVgwZI+Qh/ZgBoOk+mnpHJx2KT3SOYmZTqqMtFI3HTrWljfpdBpIFldLPGGvDXZ61QrjQzygKTa+5HS0AFeiNje/tPJe2iMr5tZyebDr3RBgcwB0hLDyuv/vb8NEffgqPv+/bgS5lteqYgc2AcLbGG6++iq9/6St4cPs27t+8BRcAigTiCO8dgiM48iVzHIHG5AcxImbDSdPD7nZYBafnv0WfXUXXVc/dCPgtIEu7R/NtCWi0INmCh8IjRGCTbGE6f7U3ukrnztw0sO26seeadN/teUsLQHOFFY1qXsoZNGfAkwUic0XXydwGevpZ+5n8LWumBfib/aD0j2O7n/P9z0Z+bJ+x2ZVwYnZHLa8L6evc+63SepaZq3NSmudbAHAKVPM/AjhtUYM55jl3E15orcld/a2Grn9XY7e8WvcPbX5layK1jQBbCEg7Q03gn/lRQlVLf9q7W7odS5uLlqlsmuoaoH0O0+re9NmFm945Hu24axuobcOtLVvabc2Nw35fZGKMkPPAlYanWqe08ByQ5noYhtFZ59L52FafieR/b24+3/IG0g98+lM4ODzM25XKgkbOacGMmJMXpIxMDgFUPPMyKUMYig6TLGExh1N0XQfkrCSPPnwVzhHCMMC5lHFjGHqEOKSECc4DcPjEx787MX/28BAlo0IWVBCwTMn4iJEBYgxDDyKGRzYwOO/cOIf1ZgNcvQT/tkfTDouE2Qw9Qhhw6fAA3qdQlNVqVRhIvC5ywa1dJClELln3RKOAcRL7BMopi1O4kU+VAkMPigHMPSI5eNeBmBGHAT4dHQdzBPF4KDFGuaMKpX19xwkMkLdnQFqApWmcZKXcUha6tM6pAEn0kktIg8RDlY1FAbC2DzEmvvGZTp1zWHaLPM0OhHaWPDFI9O4JldTIU+9hXUbAVkILMl9p2ljgGcuzPAHhVmpXQrdp2AjtJHQtKW6iNIbIddhDC7y0DJJdh0O1gBXwBWAMc5KU6nl3ilymI41mhwCOVFcathXard2QlvJtgsbc/kgkFMPHceKvZJSiZIcDMzinBfZMcPkelgDGQIQT53HpsUfwic9/Fu/+7u+EWy0xUIQDg2KA6yPObt3Bq88+j29+7Wu4+eqrKePcdoADoevSOvQLPyZWAYo88p3PRhGaRe/ozYGvltE//j6G7mQuSbweE//od8a2Ioh8kSHpjKXIEAZz/V7LAdACBXp+dR+TgSRzKWGxY3is5l8dVhtDHJ0B4qmVdUD1ey1+t8DGyjV5r4AJA9zHeud3qFvPS/+n4E39w3QH1Hp228ZkGzDvksscuZyhskaYSBK5tkIcQaM8q+u1P63skp/6vEg1puw0YM7nbWXN8Cg7rOHYooVu3/nakWSjGFqGw/h+Cg0Xx5NeS1ZG1e8mvpaQLjkOMsJXKvKAqJ0sRPMeGu21dtdbf0uxO4otB2XBBcCUNrnnzmXnrcyfwha6n3Nrq1oP+XvBQuMr446mxiFzYLySf6zbFYObIW7Dlv5o6VdmlcVPyRMr3yZjKvW0+bOlz1uYSMvNXXWIDNKyV3Y2C9127PhoPhPjqLSh2puTbW5XNixT3vIG0kMPXcLe3t5EwAM1cAWmHg5A0tQygIVa6ItSl8RAMhOc84ohO/R9MoxAwGKxRNdxuSC2JaAllj4p5vECWWC0loGVEjRj+sfFYoEQD7DdbNIdTCpFYoypL4vOgfKOy3a7BTOXxAxakUvyA02bcpaAErgf19LoqZV7SIiyMI0JmIkXK+S7PTwcXM5MFmJeZMmdXmK6rdBrCUc7l3OLswU2ZEytcMLSLqZCtxL6aWQQo7GoExovI5V3Stv5kLsnB/D5F0PqduQxAesWZLbAhEay9X5Y2xjhhPzGv4FyJkgUZ6u9Fqio6T7SZ6wknd0h1Te9JqQOC5Tk53g2rQGijFKoFW1W+MzZts/GPY+GraaNlrPa2z2OeUrXOVCoxzMR05QVg4A4MbYJEmafxhA5v5vunoreoSfAXzrERz/9SXz409+H1aU9BJ8YZY8J25MzHB3fxb2XXsUbz7yIF5/+JtYnpylVtdCQgEAAPKU7h6CM16xw+74HEeVzPmkXqQW0W0rZ0sECUsqArjyuPgtcG17aG56eJ4QQ1Y5RW0HKjqAoZbt7MQckNOiXyZJ+xshwbmp0aBpoYyiKIpf+Nd5pgekWPa0RYoGvtK3nRZqdgpbx81b/W7tqYx+Ss0PT0Ibq6M91/+047Zgt0COiIjsnhZFh5VgPq/M3bPo0p/vngN9UvqZG1VRiNIxGoKfnSNOjBTxb3n47ly0ZO9JJnlE04N2gUdwRDMri2eVs91rvCKBVxMaUz+YMDTt223/RM009BjR5Sc9XU9aCCu7QZ5vPe78F7kv7nJzjRHp+pnMB1OH9Ld5Kz+t/I49IFjtLy4pmRmaVtt04J7sMjYquSte1jNNd82r7VNVrxuBcOm8sd6ctlgssvMPJyXF2NU133FrYQuNo5uwQagxV0McufTRX3vIGkgbXdlHo70MIKdEAp0x3gDDcCMJka1AmRpiv7/tJvGkLMDJz2RKUv4mm+d6FiVqHn6X+vt8WZV8OJuazAcjMIu93XZfSdhMwZINuu91O6pfx6YXMzIXnrOKWsQmEJ3Jw2TPgiNDHCObUryiANC88ju0F3wLIlRJFezG2PrN9tu3Z8MFdCsQqKsRYvJItgTfXF0+EYQhgArbbLe7fP8oAjbMx2SjMxTPaKnNjz19W7Vu6aL4t75Y5rd+bU876uZ19Mf0tf2OUa3PgZErjaahIC+RJHTZhQwvUk4y9oYg1DwrdxjU6HwLTosWcQVH9nS8IRcxrWfty2YG8w+AI/crjrCM8+R3vx6ee+iyuPvoI2AGRGB0DvB2wuf8Ax3fv4ZVXXsTv/c5/RLcNCKebMaQwg4c5A2FuLrRMEhrb+W+CSvWulcsaFOgwWp0YpjUnuh0dcqufZx49rHLxtQUuuxws+meLR214pR2rljP2b/3srh1hK5s17eq6p7ur9lk9/rm2dJutcdm1p8c2B84mdzaZUEzbB+bxfGp5hqYOn1a/W0X3sSX77XitDmnRoHVWzDqTbLY9PT7dho5saK0T3W5r7JY3bZs7S8Uz9ZiboaGY8oGVD/Lurp0FLrtudV9bddXdZYDaDlQ9JCIxAQGrSO3at6UlCzTPlrVvDEb5vcXXc/XPfd563+o1WSNy1KGlFy3eqp8ZjV/Ly5YfL1rOw1wAcHR0BE+A992IgeL4PoDKca/HXs0ZzUc2JONpt1xolbe8gSSlJXRE6ep00WIkaaWvY5OtEk0hbPWOizCrpIHWbeit+pYC10wvReqWPukU3lrhd12XDKQ4Ahd9cE4uJ9MeUzGuAJSxS/a1smuVzO+qr+V3TkkHBmaw9+XCUrmwMcaU9tuB4NVuGBmBZgWppk21Nd9Y4JZ2tt4pCKtD2WaL+a7qF+YVOkyMs57rkMPsOFe/v3+QQxiHpvejjEONx45zVgDvGJt9pwJXM+Nr0R1QToEZes2FRpTfU6VNJVvRYEbxt+KTLwIMJu0YoKydBS2QOdeW8GzLOLBKpwWUQGmXwQHoOOU2lMzaKXqTAeew9oTFo9fwA5/7QbznI98F9oTAmd79gM3pGus7R7j90qt4/pln8Pyz3wINEUM/oHMekVDi9+34pE86Zt+eE7FGp35f12OB8tzcJlmlzeWapvp3K8/1bqLQ3c4VUXJmybxqvtHvWGBh58kWZ2ho53puJ6Vac4rPdsmkOaCu+ylGewijEW8BoAU7u0CPXse2PfkeObRLf67HM9fOLvC4c1cPM/KAJBx2vj09v60+zvFrE5Q1xlH/3G2k6bECo96ck5ctntSfa5rN8XOr3tEYqvsooYpVHxuyQvjO8rMd8+xaYqDaJsd0fsUAsI7d0lclE6ROzg7bdB1JinIhR8W41utiTtfptlrzVsakhtOSBfp3Oze75nxufWhdo+slGkNLm7uspo6Cb1E74ltrt2XgtOrUv1usINi4yG0iHOzv495mU8IUJYud1qO2Pav3OUqIfE0/MIPpYrLOlre8gaQNnLmFLYaChFyIINTGhXwu4WmaKeV9ANXukDyjGUIznWVweU8DEWECMYBkPItFV87m6JTVFtAwp7CYRU78ABo9rMvlsrQrxpB8Z2OutaLVAi6N3SFsAxjjQWR5xjmfd7WyMDE00PXL30L7ETSplL5wiLT7vJHum1WGel71/EsbUrSQsQrRe5++o6nQSLQc6ad5qIzPu3I5mvCLpA62xlX6svaWWuGkFUbVV+Y6PM4UoYcGBs65yV6VBQtW+bXAn+Vz6af9LOFhlw9sTu/QsbTTY9b1WiUzB3Dsdxr4hUy78+6n0IAhjam9YzynJM8DjUIX4ryzwwAToUdE8ECAgztY4v0f/Qg+8plPY3ntCraeQN6BhoD+9BRn949x55XX8PxXv45bL7yC+3fvwjsHYk7ZL2MEdT6lA1drUPcrrXlCjNMLpu1YNK1bQKwVlz+VhTK307lrgXv5XXvmbUZJOy9WQY/jnHq4pR0boqY/a+3yy7stsKrHYH8/75ldAE7a0/1s1UskntQpyGvVKbpgl1GQvk9V/v/Y+7NmSZIsPRD7jqqZuftdY18zMyJyr8ysrburs3pDdaMHPcCMkBAZDGZkRoTkf+AfoBCU+QEUmcfhA4XkCIUPJEUgAkxDGqhGL2h015pZlZX7GpEZkbHe1d1tUT18OHrU1NTNb0Q2ZviQaE2JvPe6m+muR8931nV0QZ9P6XxeT65JGpuPnAkfYwJPYtDysaVnMN+PY/fLWJ1Dc8+07aGMKhf85XUTkdydJwrfVn8fo3ljZzCvS//J2irABBCCkJzERo7R93y9dE2VR8nXMqfrniXdQ7r26Rqn93bse3LH5TTKGIOYwVheGr6b8RDr1j6n9/pMbvmjVuT52oztpZy5X8ebjt1peZ1jvNDj9kJOt8OnyD9K+7Bun68r+VkefOZ98LVLhVuAms76TgJ15Xsg8lAJvY7zY2iwZ2PbUtEJNjjry9ceIHnf57zJN65qYnzCGCnD6hIGNr9MnXMDdR+A+KwuZFmWg3rGGNF1F0G+IVWjk4bojPwl9SZ6SpB8J+aCTdMM3jXGoHVdfC9n0HKJ/9jB1L8jY0kSltRYIwkpSf4XCZoJ9TmR4OSmPHm9uYROCVk032G/QgjXrXsKdseY0Xw8On/pfKYEYyip8YM1SOtSM0fdW6m0pDB98sa6dlguFyDSyH3DnBmReIJgDaELjF3a/3WEvr901l+yWke67rG+zFxq3fsrBH6kDzp3g34NGK7hOjxOw6ef5SZd+ZjyeRrrb/quIQrJ606W2unPNK8W82r/x/o+Ju0b678kRDZgxyE8PtBYoC0I5194Dr/5+z/A+aeuCsgxBhNP8HWH40cPcP/2Hdy99QU+e/8jHD/ah6sbWIgmyoHhxKUeVaAJJjDNOUBK6ZD2Of39SeYpXW89V2n+nXX7Mc7DCN3Ws5326aQQsznjkwu3cm1/Sg9Tpmis5LQybWesL2P0Z2ysj5vXfHzjv/fvDJgq7qXF+Tv5HZAyJzmdS+dJgxKsK2oikwsDx/o+1sbYPtF6sxlaeX7dPK8zh0z7c9J8ax05+B97Z2UNRsa7rh9P+kzafj7+sXOU9kujjOZVPpber7lPtYyt9xgAAtALWLF6J6fnNgX0RCesWfa5oVVN8eNKOt48/+PKPs3GlgpeTuI7UrPidXdgvtYn0bmTnjlpnKyRObL60rl6rDUIDYHv2LM6V6LhJrRth7ZtofTDOx+FLek5z+nsyCiQDiDOM0h86/4W5WsPkHKmV8vY5TgAGcklPHZppIxqVVURiOXgJj9E+SWovyvTPKYSTQ9nqi0iogjU9Kf3EnZc69M2VIsEYEWblc/XKhHniObXHXprjDhS60Ei0wOoYFZGcS6BdWA+9w3QomtoyJ7IgKZzlv89Boj0d2WyBodyTZ4H7z1cSHibEu7B2rsRSRSGxL3rWrRNG+m4/By5zMLPsixHL86cKGqfiAjw64jJsO9al3w+zNg+tifH2szrzs9bzmTppDj0xDevY6wt/Sy/bMfezfsytmcGkt0RsJnWkUu5e1DZ92NFsrWGWSMgmtSo6aUGioghvYngjUFrgdn5M3j9B7+Na995DXY6RRciIaJxqPeOcPTgEe7d/hwfvvcelkfHON47gG8lOh0T4KVBmLIXOhTWwhg7ML9NQZGW3AfkcRf2GGAZAz9j9Yy1oXOfn1vmXkubnrf8rOtnSrvGGL0xpm1d6Rm0cWZG93sO1NPf0/bG9v7Y/vkqTE/e156ZB5CEPh+7j8YY+VwDsirIWAVaKUOTRpvK5yG9jwcCsceMaRUMYEUDk2r71q3t2HyvMyc6CTCPMZXDOVoPOtL6tf0x4ZeMc72/3Bjd1Hpz+qVF+iwpQ3QZTzoBJ5nbjdWtQtr02fQMMDNgaGXf5PdrWuI8Z1rF/vt+R540ljHwkraf74N8zPF9GhnTyHyk8yZrvP6OAHqh/EklP2tYU99XoR/5edAzPH72V8eW7zP93BiNKC3/iqLnvQ1RPL/6Tn4fpOuQfs7AQEYT14qgsVS+cvmPAiClxDeVZKvJ2pChkVnsAZPYDsQN4WURrTFROu+7DkguzHwDAVhRMY9dNrl0WcGCvq+FSFD3kIm1UOZaIs5StLX1gfiACNYU0IhL1vYHz5oeQHZdK5FEYntidpVq4uI8GqBrGew1NzJgrIWzHEKpGzERYlFw6rgtmWjCpuNLx59KaxSUAQATwTPgg6+F2qwyEMz3xol2XldOtHWu0wvGh/1AJNoFT4mMgigGjFg5vABcnPOwTwhgY+LcWg9J7ksMR4ySAZAJiVJTYB4IPZt4eem6xLFTMJGgfr2IJGJhGvih63T9AmGGmFxSCCsL5hB1CRIWII4tzAlzFBRzmN8BaaTe2T/dq/odGwqXSAjOpoTOABYmnjVrBXBotDkt6RnKL5TViy1hYsKa6NopITYalSk4p+oYAWVu9dIFoPMywpTl/ciZ+PQ77ZMhgyIE5qi9gzeBsTMSutt2Bt4aNLMSzazAa6//Ol77zd/A5uldtPBg52E90O0dYPFgD0f3HuHj997Hgwf3cHh4IAIN52ALO2BO1bfJBNqHQONSRiDVqCOc+5wBieOwpvcHYS9CBSIUZRVoj4fnDiFoo8wjKNIUBoHIAsQCTtFfqGOmk9oHFZhEiTCyoA3pfvB9qHxOaT33OWcIAo51n0RalG5v9Oc77knvB22l+0JNNuPdkIwDGO6ldFxKm9J9o7/nWtP8/b4fAPeTLtJh6nmEyKwolyIv9r8nn8ceEwEwslYsCYlFcCYaZxWiaT9z7VvOxKVnJKehuoY9/ZD/eaXxSMdKA/rOGPZjjNnSZ9N+pe2vm+O0f2n/c5qXtp/v5zGGOQfuaV3rmNrHCa7Sz3Jhb1q/PAMAPtDgVLtL4TvVPuqLfd3raFw6F+n4U6A04IFoHKysWw/5YFwQFftIJElztQ/o6VB6Fsa0VGNrlc/dAMiv6e8YXxjrZclJFpgh9QQLDch9PGYCmwrx8z5KtL7hVMS72oQ5yc9a7Fc/p/E+IrmbI51PxrhOADT2t36m/zw8ipAvU+5kBF4x6XN2NnXs6ZketKXRlCG8YtCLRWG21LXSrbXlaw+Qcq1MehBSplgnXi+XnpHsP/chFxB7HyZ/iNZzO3bKPtd/Yxee9illUlSzxTzUCCmTnwI/PSxVNYH3ThwSrYUJZoMajMGadMkDo8ISSMEmhzaXuo0dRmEmSJKdQi5KJoZ3HUww32E3DAwBopXLVOcpgrWE8UmT1nJgXslYEORSNmov7VfN09LDrH0fU5Ona5MWhgDA0paRydG1GWNMdE3D1ILIAC6sRfycRLsk8c1D8mECO0lU6pnhkUrfVfUtviOUMC4xDwMPGZ8I81kiw6SEWfooHRy7YOPCQgl/eDcM0XtZ40iYBrtpfRkAq+xvlSZJW/0ekEt5NQxwfunkjE+c54T5HRJSnVdlwnL/GnmLqH9ef8+1Q319wz4+rjAYraUYfAGdR2FtDx4mFVxV4MI3nsOv/f3fw5nLF+DZwzGj9IR2XmP/0R7279zD7Y8+xb3Pb2N+eIhls4zCnVRrHi//Mc1OGEHKnA8uTKxh1HThWJhv773kTwpz6pxq1RH+drDW9Mx3chkqLcIIk5UywTqu9NylZpGpFjjS1XDmV6Seoe8DQJgzYEkf4rlCAm6S3/U56BlMmZDwec7oaRljgpDXke+hE256Zgwc7LX//Zn3q2v62L2rTKWcH6E1Mi7NCTh4OmNixvqen6f0eWUX87/z8zaYx7AXx/bPSv0jNDw/4/pdytyP9nWEsU9/z5m8/M5I50OEnavzlzKXOeOoz43SQmBw56VlHJCE8XuCCscUHOnrXtMP0KoGNR+XPpPzLimzmwKDdFyrDPwas8vBNh65j+WPvj7m4d2VzGc+77r2uVB71fqC4pyMrffJ/eZE85l8xliZw3zdV8Y5nI5sXvqx5XxBaDKrh6LgKD9XqbWPztPYeUjbi/Mb23fY2DyFra0tHB8daIvhihj3e03HPKBh2ZqCgpDKSO4jXiVRjy1fe4CUM7W5hGDMXCy9rLyX4AVlWYq01MnfYjMp9VdVNbiA0oOTMit6gWsbCjpSjUYeQCA15UtBkfc++jxpP1QjQjRsN61r7KJIS3ohaJt5vpDYHyXIQJB8G3ioNNrB++EGHqxFkOo8CTFKf6bj1eeMMTDWgn2vcUufyceUXkbppZJ/r9q3/JIbI065WUQE1clPQOiP0Qu3Y8znc3SdwyT0VxnVfH3yNoeRuLDynIyN42WrfYxjM2J2NcZiERGIabAmPRFCH6lw5CJeV04ay0rblLZN8ZKw1j7W1yT8NmBG82fX9TtnPNLf9fF0Lw2ZqoRpfpJ5IaArJHy3cYQJLNgxTFWgsYTJlTP4/u//AE+//DxoUoI9o7QWbtlg/vAAD+/dx2cffYzbNz/Ho3v3YdTcLumj/p4DpfRnziCN7XWi4R6I85kwY7q32raNfpxj5ohjF6fuY+fcqI9iej71c6VrkV5m9CNtT+vOpcRp/SvCkREmduzztP85bRrbo3kbKXOR//7Ee2mkEIlVwJjgZ6wfab/Tz/Tzx5VUTDBW30kgKd1rKf3SnHr6TM4sp/1LQVTKVJ5UclqT9iVl4vV3FVIO6cKQOUzvrrHx52cz78vj1irvf/533n4+Pyf5kOQmofLeuK9gWp5krte9N9gDWLUESJ/9D6k/L8KkD/9OAcjYeulz+R0Q5537YBf5uT1pXdef815o/rj5z8eW05DYp0zglZo05vsx5VvI9KAmfYaIBpZOJ5V0TuVciXbv8OAArmkABO3XGl+4k2jJoI2IlzjyW4RxXudx5WsPkNRvIydAOsmp+UjKEKSSokGQBx6q7SMzDDEtA7CyKbX0Ueb6A6MarvQi08tfw26nwSNygqyR53pzMfm+KIpBBLh1F15kOIJdaC6d1T7mkr4IBA3AHQtzZ23Qd4hqlkgc7tK57ZmTVeKl4yHqAWWquTLGRGCa9iVeCswrYYvXMT6phiyfl9gvEnOg3Pwvv1T183WStDEpqeYqUEAbwRStMrFjF50Sc+ccxkxndJytc7IOCOZQIaoPGQOO3j8YlRyNzXN6qeQAMx/3kDAPCexJ4AVAwoj0482Z9FyiFpkZ00vhxvZ6OkcpU5Cek/wMCNgf2vHn/X4ShqzvB8F6E4MngBhdaWBOb+G1330dL/z6d1HOpnBEsGBw5zDfO8D+3fvYu3kHtz79DDdv3gxa7dBHHgpU0jDl6/o6ZnqaMoVh5uJ7Xdf1DEKWzyY9WxSeH2Q5517Dk/ZjbE/k5037uG7NkVz2aX9SJiGX/qd91tx2+XcnMX8n7fu0nTi+0VpWmf+xPZu38fgynMthf8SUday+lAFc7cPq2e7v1qFAKq8zH0va1thnDI53h36WCxPTuY/WIMZELfRYm2OfrWdQh30cM/nMxzb4LqtnDETr72u1+Rjuj5xGpWWMHuf1rJuPxzHgOuc9OKBoWppHOUz5Lf17YCYf6hvci2HNteTjXEd35TZjiHZruHfH1jTWieGVl/YvFeyk85pb0KzQC/RrftIaPO6ztI0xi4WctjwOPDyOfg2A0Aj9LIpC7u5Ew6f3cfrOuvHmdCHu0aCSbLtuCLKEIVrpf85Drps3BUPDoGDDPj1p+doDpPwSBvrDp4y/Lrj8LtJGBSR6cOLF7IaaDe8l6Wo1mcCGiHdj7ad/A4jS8JT49wzzqk1sGlUvPTTaP010mzLc6Xj7/g436ZjJWb7xtS1tf6AmB8MWBZj7xKmGDJxrBgci1aBp22pjnjNp2l7a99S8JmUiYt+RhALPQGi6B/T9sWfT8TOLRMj5VQKQX1g5gdJ60yAZkcAFvxZLonmcbcyA8HvTNtFcaF1JCWUk5KY3TRo8C4B9Gva491sy6P0v0rVnZtEeJQB2cHkTYoLRdC5yoKn9I+GcAGaoT086jnTO0vryczMGZPO1iJ95D9B4fp+TGNn0+9VLZ7i+q0R2lQnMx5LuRxvMQwkGrjRoK4OnvvkyvvmD38LWpXNgFy5c57HcP8Ji/xAP79zFrQ8/xu0PP0W9XMIzo7AWoOBYHf7lZyGld6k2KY2wmc+BPp860OYMngo50rHpHrLZuUjXcaDJzNYuZ6LWgo1sndJxpEzsGHOb7nXtd6qNz/s1xuzkjHt+Vsb2Wb7HUw1XynDkazE2T2Nnc8h8D5krFTzpuhkz7juT/p0zinm7Q4aVg/BlVQg1fl4wuq8G485oRX7vpv3t19evjCEfT1rXGIOZ74N1e3FsXIMzDzGBToWfKT1IBaMDbcTIHOXzP1ZOYlQfV8boVNpeXqcf6dPj5iMHToN7kQNkGaGvuXnjYO29j6ZpeX/WlRQkjfUjByHpWqX8UZyLE85JWufY+V5Hc8KX4e4cH8c6mpHyWfpcWiXM+JlK+5HzgWq6ms7H2Hkcm7sx/giQe5rZw3gN7e3gObEISmijllyjNrbfgDBlOn/hk5QmPmn52gOktm0xmUwADC8MBRT5RhbncDNg5FVqasJmTSWzKcORM9mpxHKFwGTq3JSZSSPVjUl487DjWk/btuG9YbCD1ERgXX/SPuVETOvRvwdmbezBhgErNqlkA4fGApyo7YbMcmjH8JAo5xf+2AFO+5VeOP1aZRI8Gkqw0zI2Pu1jnIfHKGbztsb6nl8QTAx0PkpjXCdmRWNRak666Abzk4wtJZoSmYyCX0uQolAvXRHlTND0KWEJaydhxx9/2a4DLgMC5j1glYN//Nj6d4F8/tcRRH0vnikzNE9Yx+Ce1JdhW+v3gvYzXeeTpFxAiFppLVxhsP3UBfz2H/49nL/+NMy0FE1Q68F1jeP9Q8z3D3H7s5v4/LObeHTvAVwjdKssimhCwJC7wBoa7IExplz7qGcjvfxTZkBpkTLTOpZeKEEr/l35xZrOwVArNZzjfD1SaXU6dznoSfuTrnfe1oBxTT5fR2+GjNuwv3lfx4DAKNM1MuYxepv3eaxf6Z2RAr3BfCX9zkEc0Ulgvy/D9sfPkGgMAx0ZYRbz+dE9kgq91HxN+2eNjX5caf/SutL7PAJcGp7TfH70+bH9lZZcG7NujtIxDeZjDS0c21fav1TouK6soydPUsae1f7nzD8zAzz0AR28FyQxY+BtjCEeAwkrZ0s+XOnvGJ8U3w/33xg/NFZH+DBtcfB9/m9svnKaqv98kiw5bTufj3yuxj7v52N8rta98yRl7L0xurOuzXXtje3NsbUYK2VZovEdiAMddzxKS/J1GTtHZIwEyOK0vXQPPvmZ+doDpLpeRmKcRypLJzdlHHJGQZgAE89V+owCECHmHl3HK1ogfSdtSw95epH3Wqxeupn7IOUlZci0Xa2/aZooNXbOgf1QNapt6BjNCOMwZn7UdV2fLFWV1WEuOmIJ8W1t1JakhFHnFDwkoLk54BgR0hL7nD4T6181icilBjlwyU0nlDF0TjLG5Gs+xuDkzFkuGYxEAhSizgEgwLkO3kuY50Q/L0R/ZbVDyYiOx+oFLRMQtHTQqoOEDojaPgqAiUN/dJt6H0ZOCjyVVsdfBvuEogN3HFo/r+jf5wRMnDC89K++92MEO/uOQNG/yuucRqYp7NjB/GUtESV95SjRpJX2U8le38d8P/VzI8+QCZHjwKBTW3jtt34Tz/76a8CsQmsMirqDbTo0e3Ps3XuAvQcPcPPjT/Hw7j2hXyEJsSeCY+4DOyT9shqhLs5n7/RKYT2BIYNJROHM+jhuay26zkdmWosxmgONZH6MmOc6r6H4SVcCRIBznLQbpyrOV2S2wPGCZKDvS1iolG6n2i99PgWpevZy5nXou5fQWd3HCb1NmZf8ss9plMyL6SPXAdHUs99+JNG1Sc4Xg+NcEES7r+uUnLZIl8cYmrTt9G+i1bORjh3o74J+92Lwe+wZUewX81C4F+tPz8oIY5QL31JhVHpmtKTCxhwIjgk+Yl5CAph6WqRzp+eXMiEkCHFNVgBg0o91Jb1b0v6m3+X3hf6eM8fxbn5y/u2JyzowOMZwKg0MH4y+p/dG/7dcEn7k+XTdT2Lm0/v1JJClzwLJuoYO5cB2TGPwpNOb78dU8JPuxXgWodfNkPfL90M+P/nc9OOUGiPjn80BYqsnj2ywvoSYykJ5nZy/yt9JPhy2Ef48aW3XrUHfY6kv8oekJu+r7jD5fhjrZ5wjF9wVIg2UOo3pIx8/SfnaAyRJRNXCe5dkUF+9WGQtQmhtK6GpbWAYCmNhQ1jajnsQk5rJgSTx1WRSxTak6KKumo7oIo+ZaIjDskjSEAEGRaZBAEF4hyT0qoQuFkIR2BOwZ3RBiwOGhO8OnCqROInrJZ4yVSmhSUEGkZiD9cRCouAZI2ygIcCgRGEtrCnguV0BPXIoGRzAjJiIBV+fwopfhZFA48wSRpdYGCiDntikB5yZAY2Q5TloTwBmCQsOAJYMvBiXhTETiMP4TRE5eO8cYCwMTAwLPNxTJjA3gREHB8m9SPWJCBYFOg1UAWGWwIBxDDYGsAQDj9IUEv43ZIEOMvlgxraG4EWOKxA8FoZbbbERwApDEoOmoihLJvq0da5D51wAx2E9NXR4QZHgs1EiI8xEZMkJMJzsmSC0iReC9pdZwnpHYBTChrKChsAAKdOYFtK+cw/gWNTl5AgFhYAAsqqBQQI8iTZT9l2SW4MZ5JVZVwrfz2sXQn8bop5ZNgbEBMMFCARDQ5DI5CTyIOvMiwDLMwBTwDugIIKxBFca1JXB0994Hr/2e7+N7dOn0LlONK1NC7es8eDufTz4fA+3b93Gw/sP0NW1RD30gWYQw8LG84uoBfTw7GCpAAwSkxQO/U6YWTAMBRrGDKeHJIQbt1bOoS2Vzsg/2SbSB7mA+gtWwE3QrjMLLGSgsImGygAODh4cNZs6JuFwTFx3bwiMROARgvSKhFF8DuHFHwIUIiqF+WAYeNczbuqoqyhNdiHBEsHr2TQmhpkV+tALxWQf+7hlTNjnKigSMB7oQQhiAu53s2cfgrOEHWII7FWAIeZYAhIVxAOqwzYRR0nCaIQxMUuo3NTEiEhogYAtiRZqJI7+kHEES8qEcHf5QM8MGJZYaErY9zBW5pQgpyy0EYMoJACJmUEx9HuYhhHzspTRS2n5GIhI/2k9aX3MnPjrGqFrRODCAuhQsEfBDtYbEE3RsYMzshcNPKyXhBQcoiimUREVqKd39KB/yf5EIjRJ+bYc2K4DAbJ/e6Y4ZVpThjAV5KXPrAMfOfAYZ9Qp8CiBgOqdkQsz9H1W5pfDGenPFThn4ofvp2MZCF7oZHCYgpKBySyEfOi1qFWy5APp91Fcqx7Ma7/SeU37qu0qLc15oZX+j4wrX+McTOTmqmk/jA0GGKa/v+L9N5xc9HR6lXeI/SFlczKgOQJyVJAwFAIIj+IDM2DJSv7LZNw6HuWNUyFIOicUTMyZPTon7gzxAGWCiXw/rdtf8l3iu4geROvd5Nz4ORkrX3uANJlMAQB13S8W0AdMSNG69z4SRHhG7WrZagVgDOKFMIaImRnG9tKpyWTSm+aZYWjL3JwgJXhp3zpuA3OdOBBSABLAYCMCQ1Akl7iJTDN79SkoBra8uRRB/62LFpYSDe8l0pD3PrYZ+0FBqpsR56Io4rwAPZ9JRDHhm9avF3+cI99rvNLQ5r05I5I6lUgwNCdOTxgo+tE47yVXUyDyKr0gEk2Ptp2aTHJgBorCRk2dRu8aBPTAKgGy1sABYIOoRoaCOz8k3KDx2yKSQg6OzOk+1LbivPb16BzbosDG5ibIStCLerFE17ayVqxktq/VUlJ3uECFqcVgHZkFFFG6bwKRAg8J9yDHkEeiBRuO1SBlppSIhwvRGnAg1gLOwjgZmPIkCQEf+s8eHoCG+dCeK6NuQChUY6p9A2AZ6AhorR+cqXQ9TACpIIr1kUJEC3QG6IzH6acu4vd+9/u4cOUyHBjt8THgPNq6xfLoGHc+v40v73yJ23fvYrmsJW1FAZCVuYAhOHaBkQPYmMB0E4gNKu4TBBemlDUyYSwxb5Z0y7GDYUJBEn1SGT6AgA6wtkTjOnjTM4CpnrBgg4IsTMjRxeQDiCHZ3+EMM3sYshEIF+yCdtkP9xIJw680zIatq+DfsZcxexYtLBE4PK90klgEVWSsSCOdC32QdbLKjISNLQmfw96ygf/Q/UzJuAnwEUl7wABEPvSF4Um+Lb2XvRn3ghTPBG5N3P+6u+UseMSmIodN8YfklAtrJhtZmCQCPEwQIFHA+j0TWCJoKgmR6dWzSQHwgGTe9HcLRhFUKq5jdJ5BXuAsQh4zMnrHCLgiQ700JC061OxMrwMMaaRBMhSY3nGmMi/qm+sBeEu4cOMZXHjuOn7xzlvY2pjiwtYWDu88xJc3HwjdZgK5kGcOVsAB9VFLB+Ak017pGCjStn5cY9YKcY2Tu2wAVnMtVQZmcsY9nY98LteVx2nCYh3h/AruXQ1wtFIiXeewv3pz39VH14M+ufdWnx/7PdXMxc8J0eJGQ86vY7DjqQxdT+d2HUg/qf9pGyetx7o1SLWr+fu5tUz/XV5L1rcEuHO6pzPeZEwYASQancyyamxsREPfyxQk5XOWtmvi+TY9z8g+8jdj857vwVH+NPhQ+/C8WE8Bxqw3HVxXvvYASf1y+gAGNEC26cbUf477BFYIEgoUQYKZAQmgN+XxHjEkeAqO8kO1joHWn1FSwQx2DmVZDoCBjiFX7/dMuB1s/FxDlZuX6PuKsHMJSdrHdL4UTDjnJb+KMaCiN1HRKETx71RNnKnC88ticKiRONgGSbdGJ9TPc9V6OqdpyQlRakqYz1k65n6OhmY7LqyP1pGGOda1SNv2HNoKpi1t14n2yPEAWJx0maXt5yUdv/K62u+0/8vlUqTYOg+UXmwnmPcBQUqOyNj2bSPa967b32n/4+fZesTvINJzMMFzZIMj2AUFkyTmwJAGwsoGhDI8E8bDQf/ogUmGKJUhg2V4GxhmQjQX4UIYXmIGTMrMSrGeYHyfoFK1fyY4w3bkYbZn+O7rv4Ebr34DlhlufozWOXSNR7NssfdwD/sPD/DFzS8wP57DtQtMgkC1sIX4FYZ97oIWwpCBLfQCAwxZWEzBHDSvCJovY0CW0HKIWqh7jMW005CFpeQ8MKNpWxhboawMOiP29Woym+04kFUBUABrDHQW0ZxDGSBAzAKtk3XNGSAmBzKNLIpjWGZYGNGwgtGYVgCN3KxBe8qBaS9kb3gEgZIVEE6i1erPdJ8XyXkHTzImaw3YpmZqNDA5lrwvgYlgmTtDIiTpOheBsWzYXgMswqMA5kwPkFg5axahl8k31aBwf8wShpBItB4mHFxC8DsEwZJoR3TvK4KJADhgGmaAuDcjN+EMExl418EaPdOq+Vo1dZMtRX3fMDz3nGhcc6YtpfFKn/R3z27w/WBGsn0YLS+8R1EVOHYtnv/N7+LGD76Ho8M92HqJex9+hk/v/xk2GCgbhnGAJwNnRTNJmdWElvTe0/FGsJSAuDHmLb/X1Yoln6N8bGOgKJ+/x83JkwCi1fe0fu3/6jnNzSHzIs2Og5OxtqMp9BO+M8YUK0+U76d8baIbQ5K0PK1nHRDIQUpu0po/v+5+ftxc/G2+Gyv5WOK+PuH5sTs650fGANwYt7AeTIX65KnQTsJvhrvrcWMba6f/XlqIbBDJvdOfqxOrH5SvPUDSyVRwlCa3ikg28RlhZrR1g4ZFbb+5uRkYKBO3QUpEe9MRA0OJT092uSp61bbl895UL0fwzD3DnKL6ddqf0Y2bHRBNPKvmCKk2Tf4NzQC1pJdW+rcx4gxXFqJic9xnw1YmlowZ2LzG98KFml4a6XqkASUG4MesmvylczcqURjpex4II73sc18iba+ua1RVJXOUaB5SYqKSTGPMIDKW7gtLBmyCaY41KCph5D2H73i9lCPdGxEch3kcHT+J0Bc0JFCyPxy8TwAgUTBDUnK3KkmVTxU+hfVLqvcs0v0xkJfXMwBsGEqw4nwCIC8Xmc4NmEVbwcLgEEFMhZj7UOJgsGkROxfeAwBYwAVTGmUqexMlH0zMVLpOIGMBa2A8MOP++TADUn8wDfFgsJE9L8y3RVEQXnrxWTz/G98GzSosFsfgugU3HRadx90He9h7uIeH9/awnLeoly0KU2BW7qIqTKQtNgAQgNGSi2fLqKktA94YzE0BjpoDGjAEHDSexpg+FxczGlIgRdG0lYxBawwcEdj2kSsRsY6YYxmDoJU24ntoJMw6jImmmbpnY4Z3F/rlJEG1gqi2bdDVh4DrJJBJ6+A6B3QOlglFOwe3bQCeUq/zTmiMETrrSUCBaJsIhqwIcMBg6vO5OWYYU8D4DsweJUQTJiBbtM7qcwOgN2WDRCBk5wDHKK2BJUDzpNZlAW/zHFmpxLunhalQxsBGEEH6LABioOpIwDkjar4NgtaHe62LtTbsTRFcCc4T8+tIW4lgSNZIV8cUNpjGyj4zgc56b+DbTiwn+oO8QheMMcFGfZ10dqgdGLur8u+Ehsp5XkcP9flUsFZZg67zOLz3AD/70Y/x3G98B9PdXRzteezeuIIf/OP/BD/7N38JfrgEOTHx8WxgLGAS8/mchqVMeLquegdon/XZ9A5Nf1/xT8xopK77gLfI+IaceV03J+nzA1+ZNe8SURKJsP9s3bql86K/q6npk5ZYJz9OLJf0MRlX2oc06AyQ5EjDSNANArrkjlsHctN2x+61wfqA4/yl/RzbS2MAPG873yf5M3n969pL+y/7a/W5fA7WAcV8XOl+Xdf3vL+ACB8tdJ+PhOVnHuyjsfMyxispL8MsdwsRgSxlwmc8cfnaAyTnOlhbRLO1lCFW5jwSOyLMj4/RLBvsH+xjc3MTbdtie2cHreuwtbUlzl/JgkRTOVqVLuXELb0Q0wPddR2896iqqnc2RWCAqFdZphdreink9tHajzQkeMq8a84PBYtlWYY2Vx0P04OVEwlrbTC7EIaB2YOsgQUDTR/5r8uY3/RCScFJDpJ0PMNogAD8KrEeZeZHDnhqMpc+UxRF1Damc50CS+2z+u/4RBqYt7cKkmUdWu/ADLiuhSFIPcqwYXiRjl1oK0QxGV8+RmEuEAVzut+JRJLkwdHvInBTATQwxu4q3SNAD1Q5vKffjQHUMeKem3WuLTaYbOUEm4FCmdfIPBqQMejYo7addkAYQiWkHjBOJJZpNBtjCdRZFJgAROK7RQRrA/gnDpL+XgrnvZfABrDoQltMENMtz9g9fQovv/oyts/uYq+tcTh/JHnLmhaHDx7h7uERjusWB/tH8I4w3dhAuXsKRTVBW5XwRYGyKlGUJWBtAGNAWRKKskBVVajKMtI3W1hQYUMOst6s11oDUxYoZlUAMCbSAKIAZmxvH14WBawJQhNjYamIWgU14yICvGF4Exzhg/ZI91D0+QnbT0wlQ+4uFuNDVvAGwDuHpq7RLY/QLZeoD49QH81x9HAP+/cfYrF/AHt4BL9cwrcdXNsBnYMxZdgXDIaDsQbgEJBHHYpAsIWFMVbGYQxABhYA2IjfFgr4tk+zQMYGoByCzhRJAmswHMu57TxBzAsETFUwYKe0Q0AHs4f3DDIdPDsQNN1CAGSeAW+hvKUmdxaaIcI1KF3UqKlEIGJYbgKglb9F62VFUw3bCwhMAfhgwBj2gCFIfUFgRdagLKpwXoDOz2GohVXA51fvvnh2MwZq4CMS9gwwzpznoCHSjUxbn5acbkR67j2mxoI74IO/+AnufX4Pf/8f/yOcO30eB/v3UVxyeP0f/AA/+eM/R/NoAUuiEez9VleZZkULad/7u6oHtNqv1Hxef64bo9ar95x+PpaPMAeTaR1acvCQP5sChezbZM51HYfhyfVn2q/cZJB5GKhhXX/yu42Ze6HVmvt8HYjpui7ydulzY+4L+r5nDmdmHKzk85kz5qPMP60HT2m9+Rquu9/zudP3xjR4J4GltL8q/Bl7Ny3reI/cgob0/NAQWK3b54O1ESdRGGNRliXqegn2gadKeJa8rpPOAFEwufciIFM+RfdV/vzjyn8EAMlBZzv1W9EDrUlAu65DXdcgEP7tD3+Iz7/4HNdu3MD3vvc9HB0fYXt3B03borLDLPG66N47GLKDzZwezpQQpM+kvj6LxSISU9U2pJHp0qKETvMSab1pnVHqG0raBwUkQK9dU644P9j6THpAI2jhHukbMvCgqDEyxojpWHJpKgA02ZjS9pTgpX2JRD1x5Nfv0mdTojJGlPNndB7TnFgpcNXnrLXR1NEYA8OmP4gJqErXJ91vabHWovEdnHdouy549BN4lU6N9nswXxANSr5ezrkQZGD1Ao37LwAayV5NERRpM2PEVsBVcPIOpmikPjDBdGfsIsvrSP+lw15hTkwBTqSLrGCOCI0Pkm4OYwkSZ8PArtMErIFZV6miBZpSZs5YE02LiIAOhMaWoMLCEwBDaK1oSVtDcIX4b+m5q6oKZVnCFAWKqoItCwF0hcXumdM4e/4cWvZ4yEKDNlWD6x02XYfL5QTlZAZbViirCaqyEjPXwgKV9MNYI+uomcwJAgRUExQCDIBEq1B4jgtIgTnmsFGiT42uEfeaCh7s0eRCBIPifcTRXI1ZACMPt3ZSA/WXHIAuOXOGwtros2F/bwJgPicMIhMsA77p0NYNHty9i/3Pb+OLTz7Fwf2HoPkSbr6Eq1ug6WCaGpY6ON9JEmb2welb9ibIiF+SDaHLbQAJkwLonMy19yHRKIvGzVoYa9Eq3QuXs4ePkZB8wqx472G8gQn7MjK5CP5jYFCQlrITDQlDmHpiipexSmQt5Gy1kPwgIIKjoMXUs+pnPQ2yGtiGJf9ZWDelRaYgWBMEhQFkmaDJNmGPFeUE08kGbGHg7R7atpYz5jrR7LkOYzRWfqzx0VhD104q3Fca7+gx+rfyHgEdediWsNMaLN7/Agcf3cb2c5cx3dzCUX2M4swOXvydX8Nf/fGfoWoZFdvA5PWargHtCn0Zo2ep5iO9A9J7Pddo5EznOvCQfq/35xjQGns3p7f5mPJ3mEPABQzXM+3HSYzlkGle/W6sf+k++ipMaw5i0rQr+dykViBjdfBIfevGl9/lA1CoZoIj4xiAifB9rm3Mn0v7r8/nQO+Ezq5o4+K8GBrsoXX7MNfqpv3Mx5CxGCu8yBhoNETqbRp9U02wWEjnZazN/BwOeAnmoYCCBOin9T1urdPytQdIwOrkcjDFSMFHWZZo2xZt2+KjDz/EtevX8fDBAxhjsLW5BaA/JPpejuyZxbdjMpmsaCFS7lO1VunilmUZJaou2Xg5gdTxpAlb9bP0AOf+SVryfnnP0bQO6EFWSthTE44UwAEQxtOHKGVGwZFuTiTtJNKPwMD0EeLEx0H9i9Q0bcwfiaKzMa30MXB8cZ7zi5mxejii1s85ieimYC+AQBf2jPMMcg7WFiGWgkE0bzEEAxMlkIYMDFnJiwCK0Qgl4o9EWyNr4JpWOyYRtULEwiCmF2kP95J4H0NV5hSpp1E+mBwJ8+VBkAiB6XPxYgj+IhKoIjwQfvRaFzXz6DUSamjmgzmdAhMkocER9jzHLvZaDVDv3yOXSm++qqAGEGl5SwYIDB0D0WrOM6GzhYAHomhYBCJ01uLIFjAhIEkRQU2JYmMKt1GhmkwwnU5RFAUmkwnKskQ5ncBOKkymU9hJCSoKmNKiqCqUZYWyKGFs0LwAKMpSHL6hwzBgS8FHiAFr0YJhQSjZwHoZQWsILXGIjCdr5dFfkkSMyosTqwQuQIw6RiRmMHIXB/G17mvy8FYDknA0idO/mZLLRNeGpR92cOkMQ+gz+XDvEzypZi5EM+SE0QjzT4Bo3LI9qj8shwh0PJTseWa0Vsy9PBMcA2ZaopiUuLB9Heeev45nf+d1dMcLLB8dYO/OPdz97HPc++wmcP8BunqJ5XIBdhbsOyjmL6iEmgObQFussXDGwBclAAYVZTQvc96BrYE3YrJmApg2gQZ6Kz54KuQhMiEHCoHLMoD6nn4VRSF7kCeiJTJ9wm6AAz1cwgRrSfWP4sCwbjoHF4Q1McANM8AGaCu4zg3MXJgZ5FoU3SJGLlSQocBYBQPGCDhSTeWkmmE62YS1BFQz1PUc3rdYLhdAswC6wEIHq4ew/SSyHfMQGOv6sp7cpIQ+rGdVWIJt0NCntj8jq6Y+AMCWsITDhApUHYN9h5/++V/h9Qv/KSblFJvb2zjeP8SiAHirRLtfY8IE8gDDjLajbcQxJd8rjkgZtFxolwMjvQ/XgZf0zs/fSb/P2xz7PZ+vtL30GRMEAwIE9ftV5nas5Hc0Z8/n7UYz1zAvvbAVK8z2kwCXfA7HTB1XBJW6N8PvabNpi3nd+bzl63YSgB3THI39zN9JeZKUFxvLnThWf/o3OwZoPABXDsAGa7pmvGP1pCXnPeNaKE9MvaKCw+fsNdQTBnt/DEinffAcIhCn/AshGBL0z7nu78J894WBwtiYE0gmzYOsMG1lIZoAQ4AhxnRS4oXnX8DtL27jhRdfwNZ0A1NbCqMrt2KYbGH4lHksynApOgffiReyDc767CQIcQQzAIrUl8h7+K6L0gzdJKQDiMBFN3ZqopRuZN0IfaZ0IJciDRF6UQTTGXi4IB1MCaxGvfMefZ2hXWMI3on5iGMGeUZlCIY7mIkVp3BbwDsntt6OUZBFywzAwbMABDImbGyx7xdflrB8rNKQIA13BGITQEd/4NhJeFqNuKZMC4DgmxKS7FJiEgdl6MUB3QRmxbFHUVUhBG4JDydRqGBhq4ngONfAcAvljn3wC9EVM7Ag9KY5jGASYxh118BWYqYHMphQganzEpYaIr2uCfDGonTA1EkENd1DEViEg28DWPTR2ToxHyAbtSfe+T6BKiDS6AxkCpPDYtkGBqMIjkyy8Wz4DwZBgxC0Gkb8WMhDtCgmhBC2FMyPLGAMfFGACyMgwhDYErwRMCJ7rUBZFiiKEqYsMN3agJlUKKYT2KqELQsUE9HWkDWoJhUmkwmqqkIRTNJMUYj0P9SZ+juJvwrFcxYvyTjE4EsRgYfe2yYxE0I8xwL2EnoTmHKE8xMCJMPBgW1vA26hQcEkPLiFXszSoCPEcNHJlSSXuYaTBaLNe99wAE+DjnEILKDMK+sT8Weqx1Omoa+2D787vHMJ6eSx0qrwnRk8OxhF9BdL22QyAtS4f57j86G50qA6tYnJ7gZOXbuEa7/xKpqjY9x//zO88+ZbWNz+UsKiE4s/mSUUs21sbEwxnZaYbsxQbW6gmm2AygLVdAJrLSYTAcFFIfuPCtmvCGdLhEPBPI0AtSuJQ1DkbihGU+w/Dxe4i3xZmEdlOnTf9S/JNMi6mf6F+J6uA3vE4BnOSWZ67z3gPLhu0LUtmrYBs/jU1ssadduhaT3Yefi6hasbcOdgHKO0m5htnkFpCXzwEMv5PlwzBx3to5kfwS0X4OUSRdvB1EtQ8BFz3Ikflpf8I7K+HJidXjOR7jrvGayhCj0H6XawVGDE3FQpoxZnaORvDsy9tRZsCJ318AbYv3cbX7zxFs4+fQbzZg9FUWFnMsHv/e7v4O0fvYn9L+5jSiWQCAZTZja1tkjvRzU5zJnHlKamYCjudd0ngNzzRmiLpilID9lJAG0MvOQMbfpcDhLSZ0UoAhUzBXBkR31q8npXmOakz+k8jppnpWclBdZrSrzvR97P5yMFFGNzZzBcMy3BY3G0Xq1H/6UCYxVS6PMDoQSwsieA1X08mEcM934KYMZ8sJMOD1FeVvJ+xT1MiNFkJdhLmA9WDbqBTytO+AJNdq/1puNLxxR5S6BP8wKgrusQJt2vdD1f617eK0yQD7yvBgVLeRwOdJlIIq1qH5+0fO0BUpEkx1P/kVSLAgTTCYScIJ3DH/wnfygEIjAALthFOu9AjsGs4KTP59K1Q9OsdEE7NcvK/qXSjSh5IkJZVQMJizzT9UxeduB1LLmEQsvwIACARNhSkKff5RqjePgZYtsfrjkCItOttu8IYY49O5m7wqKYVWgODgOwE82dNRJRy3tJdsnM4nBsApgkg4pF8ueCr4iYjYibdGdlPp0LOYRUumDkaHXkonZJTYoIiP49UYoOGiSy9SHcrxIDF4CmRSfRtchgWlhU1qPzDnAeRKW0QeFih0QFY0vC4DID7AIQknFU3mKKCTxbYFKgOn0G9blTWNYtpkdLVEyYw2Ny9SJ4a4aWCX7psDw4wGJ+HNdF14sYKBMtkxAIAY+egKaQ0Mn9PgjgIGg14gXc6xREGxZMcchaAfpq4lUWaCeVaG4KiVoooGUCO53CVhXKSQU7qVDNpqimExTTCcy0gpmIKZqpCgE9kwplVaHiAiVJ5DGj5j6hryXE/MUHwOCAwIAhRv7Kz8FJUqZ1ZyM8qZA5XjCBL0fkdDHg6/uH8j85+5jCpRswBSUM8lg1EURkZeXyWHlitaRS/bHC6MFYXmsULsRvhs+t42VGZvqkL2Oto90cdD+sedgDMAblqW089evfxPmXnsPevYdgz9jY2pQ9awvYaoqqNLAFC9NuLFwwFawMRdTCzGibBvsHB/BgbG1vo6qqoa9N7MOqJBUY32NP8t3KfDxhnfr9CrPDgCUb4UhavBFTPYsQXMF5WBDQOriOYEwJ3zk08wX29x5gfvAIe48e4OjgEAeP9tDMF1jsH6A9PBZf07YDLY6ApgG3reTsYgYxw7IBTBcZbzFLFg06rIlWExSEY7kWIJ2LMcZ5jPnVu0jlARUIv/qbn6B806BpDsAwsKbA1mQH9cEclbdwTkydVTKfmyGlUmxtd2zrp4wz0Jv1qxY78hyJufkogBoZ4zrtz9jnY2BmAAISBnuVYUxFff07qeXMmCYh/X3deNaNDaGlAXUZaUf5uLyk/Uo1FWvvBhU0JFYqsR3CgMjneyDX/sXUHiNzkAupTwJ/J5V1QCp1GfkqdY/tkV740vuRqdWN8k6M9cA0F2SszGveJqkyQM+bR1N3A7/uvP7BT/T7XVPnWFKz+X430UmX32PK1x4gpSBES05QlEBUVQVrC8yxiJHVKvUtkBcBCFo1RvyOtI7UbE4PaF3XMeSzy8y3UpvqtB/pMykSzyO0pMRQN2wusUnHms6FfCb2HInAJz6fSpykTkCdsNM2BcCJxsUTSxJIJ+CpI4KtqpjA01ojZhsx/42F2suD9XIEAIIvPDo4eDUV4x6AAQxij6IAbCGmcUQMmAC0wFDNXpRwiGhEly+AX5VQkoDPkM/J2N4MxRIBRiKokTEoDUKOEBaNCQqJF2Uo+PsEBgydOIAHkzmHIuYNsWYKgNCxhzMe5sw5bH/nVSy4w+FP3sFGCyxKizOvvoLm7A7aokB9uMC5zS3sbG9jsViIM75yjZ7Rta3kewnSZjIGXduirpfYMeLrYEzwnwGJhqYUrQ2B0LkOy+US1hhMJlNU1RRVtYHJdILJdIJqOoUtC5RlCVsUweemRFGJlgdE4BDFjBD8ggID6wFxdPfBrCoiDorSKh2KmMkF7Y2I42BC+HNh4hFzGLLytSNnQn8fO+/y91CKulp6dr0/G397IntS+dtdl2nJ2YqTnhwfA4czmlYZL/v4v7S99MvH9e2JH/5bFyagKQnm1AbObE3x6OE+DpwPvkOEKTw2rcW0KmCM7DFHYhYLH3L8MGO+mOPhw4c4ffo0ptNZEAr1/pc9LRWJO3Ayw7fKgJwslV5XnhRYDSWtBKY+QuTgOcn2DIdOzmZBYtZdChh2YBBblLubOH9pA4V5RugMM9q2Q7tscLx3hKNH+3j45X3cu/0lFrdvYfngHtrDI1Dj4OsGvm4B51EaAtBJMmLygFWfQQR6PGSwciZ7zCn9cfOga+o7SRCLlmGWjA0OFgrwaHAI44CKKrSU5vta9flI785UE5PTlvzz9O8hiFs13/sPKTkQGPs+5xf+Q9o+CcRpEui8P+uAXexj1t/0vRTEjZmBpWNe53P0JOPQjjDWn918TeN3ZugLMzbPj+vXGNgYOxOPG9e6QkR94IKV0ptXpn3IgU/+bh7Ya2z+874O1hZJoKUcyCbPRi0dVulEXBMWvkB8wk4WDD5J+Y8CIAHDIAPOOdgkwptGLyOSfElFaWFs8POxRkw1mCWLuRdzPLBEM1KwBOYoEdKoZ9776OifSqaM6UNAp3a4udQqJc55oIe0jPkejR3O/mdKuFQVzdG3Adyrp8FK7BjMDt4FEGhN0Ax5ONdGh3/nWoAKsClgYGAdgTyhRIggyB28YxjysOQg2VokopLa8TsQOhBMYSV0uIbsBaFyoj2y1sJ6K6ZvwbcjMr4KphjBhMEChYTRVQDEEEArtqqyFmR6HyIK0fmczrU1cGUB2BJsOcyDCeFxg1lNYN7JCWOm822Z0TkH5zwelRZtRYDzaLzDnfkCs6tXYSsCzT2W9x7BTKbodk9hOZnAlxXKagtLX6Np5mDD8F2LoigxqSoURQmYGWyQfk6qCWazGapJJQB/OsXm1pYEmQjaU2stCmth0Utte8OoMA4WQBI+jGYHSqAA+b7TMRNQOo/SCQ/UX5SIlRjuL5Zh2hcnuskATDmgdiJCRyF0fOgH0gt25JyPSa+Glwhlb44x8avMA/Pw2ZMY4fSZtRfFCYS7H9/q+ytStOxn+tyAafISVlvpiNIi771IBmkoGe0lrUGYkjCAjJ5ppaS9PtjLcI5yiWP6/SqjtmqaMfx+tch5lh1qSwIs8MMf/gUOjhvsnDqD8+dO48XnruHi2VOYVAbWUMidFS5mmWwcHh1hZ3cXs40NaES+Mel+LhV93F54HMP6uJJHEnuS+kFhbNQLwOLuZo/C9VJYkAEbDmc+5GxiiI8cDBovQUa4ZKAiFNMJzpya4cL1S3jWvQzXenRHB3h09w6+vPk5bn/0Ke7f+gJHD/bATYtifgzTNQB3sPAxnLY0Im3nOWzyOTqJ4RoAz/C3D/S/MAauc5jaQtrjUjTSAJxnSYbsJfoq4Fa0A7kwNe9buj4qiF2nfUrr5VD3WBCfdHxjYx8Teqafn3Te0r6f9EzsZFJO0nQN12D8mRPbCjRqHb0bA3Rj87JC99acVebeJC4FYCIcffIzPPjbM9LoM/l6jAGPlMcb6/O6sWkZFR5wYoad0Rnli/L5CK3IE9nn+V06duek+zhN+JzPV16PBKsZtpPysrmpqyYfVx5bn4/AajBpMiKvmtDY9sr0ri1fe4BUVZOBxiYlEiloUmIl/g9iTmeNhBu0mvhRF9V38TuZeoYtLGxRrphjCGhwg4VXrZL+vS5xnPZR+z0wxQvj0RDhRVEMcgeldaQI30TtpUdRhMAC8OAQfhYsTsliwk/wXSeHjVRDJoEALMoQkcmjCCCx6foQto4ZtpzA2BIGAkCaWoIGFKaARxMidKUhTQGAUXUFJq4MDuAIFykAIiy5EYfqEJqcfB/sgaxkRJecQiEhpJMcMLWlGLyAyMR3fACHYnLX+ywxgA4GNUR7pA7eAtYMiBhELIEACgtblsFpv4ArCqAoMZlMYgAAGyJiYWJhCouKSXLr7GzD7myAZhXw7e+hRIHSWFQ722hKC4ZBwYCbOVCpa6qhlRHypIQ1C4wNVPVMBh2FhLo8lPIYD1CWbFjeFA2dCaAlVBcZeqbBw4O7hAhwxeDr+J5DiWDIE5nTE67OWMOKIQtzvINSTdBJF3HKEMh+WnOBE0DEgzkc1NPHFBnWkY2kv0DCCPRi0MsqwEzvRRPBnEjFVPIVbP+dc2LugCTIDDAwDVHJWryg1JE/MB1KO4pMcxYZN9Nr81IhEgXAKm1zCPcsYIkhQTTKSYXCFuFMdDG5qghRbBS2xHaTtjXIgf4T2/FxwDAOdvu/rXOSl4kMTu/soFk0uP/lIxwdOSyO5tiZTLFdFCi3Z5iUpZh6GYILqmVDJOe3lLxkXTfMdzMs4xLSsb6exOCnz+ZlTJKc1rWOwY31AmDqw+Wmz1sAlaeosUVIqAsgmtEosyT7MPTFdSBNPgZJ3gxDMBODYrqF82efw+VvvIBvLmrM9w7wxac38d5bv8LhB5/i+O59dOq/5D3KIOhxZnVd899zIDTGCA/egZg6g8Rx24DE0sAYdMaIabX38AQ47sCWxILY93WlZyttL+chwEMJ+jrGMq0LQOLLvCqBT9c3NbMHhtrydA5yRjvfQ3l5HNju6ZVZ+XxsnMnkj9aXt52vIUN8XJjGTL84czdYBWrr/l4rhAjbOgqJMlCQA+D077zu+HeypjnoXQe414HdsfpX9l72WT7WMYDmgyAytVDq+6EWRetBsL6Tf7eujIEk/VzcN4TeLJdLTIreZUIVC/kYKNxro/WqrItIoz1FYe7g/n3yGA1ff4BkjMFkMgERoa7rsEA80OakznyFLUHsoVPDQV2sC1JOQ8SrsoyH1jmHpnVoOo/JZNIzLQkR0/a0LzlBTdWSKSEeky6lhFX/1rFFZiPbRD2h94PDEokzEM3fkLTvnIchB7gWFJLCUkGYVAYUAh503mHe1GidBzgwPTCYnT6Hwwf7YsrmGV3bwbtO5sjMRBviHCTvR8L4dRaGLAz1+VoA4UkXcLCTCbgo4L3rIx0VBsb2+U6sNZhOpsKkWQNfFTABpFQKWIyBtQUKK87/ZIxoVqw498NaOAr+MAYwtgADKKYVysKiIAKsMHa2KMT8zBp4K5oPZVoHEsXA7GoEMIS/2QAtGRRkYTsHbwAyAlYqD7Ap4V1AJESBSRXpyLKgSDRkb4hpmGFG4TvFliB2kYg4YiyrNT5rYJjk4ooaJAIKTyhc72ptkz3WEaNLVEMpCLIOKEcuKiagM9xrq7JSeAxAUjwN3BO/2N4IYV+RdnHaq0xYyOKvlTMf+lxKWMcuXT336dlX2+icHqTnrz9rLgIRZI73uqbppZkKP5SG6d/OucDk94FgDPdR0JxzMXCN6zyWy0aSsoJR1w3atsV0OoUBo+tquE5Mjre2tnDx4kU45zBvazTBfvzKlSvY3d0daJHIjZsppXtUaZycRQNb9Alr9d8TFUYwv/QwRYGLl6/g7Q/uYOEW6KjD5/fu4dKls5jNKmkXIVIe6YVq8OGHH+LSpUu4dOlSpIP5vpIyLuletydOYlTT57Tk7a3zuXhcnUgC0qR1OgCdznsCCIhCKBLqRRMcg0ewBMdx4dzpMTIMwEkQEFsIPZ5V2Jqew8tXLuD5X/8WDu8+wts/eQO33nsfdz/8GN3hEUzTgsiCfA3mXuuSO5+v86s9qVixi4aDRFcVGx7AQfLNEUuEyIIAxw4emoS5L6t+Z/08DvyjfO97nJd1e4IUuCX3wjoBwJPWmX/2JMzrWBkw2X+7KlbKSXt78Bx6upYDFn035Wty2vA4YcMAOKU58NaYcOb0+iQwos+n2owxfi4H4GldY2MeG/+68fbfU7znx55hrGoD87nItTdqeQIMrbG0zryO3KJJn0vn2pD4OJN3gUeRetI0LysCBxnVoL00pH7/lRB5cW3o52ItYF5TvvYAaWd7E5NqguXSYlqVkpCwbcVZPzxTVRXqpsFsc1OY+a7FbDaLE84s0lDvPaYbM7iuQ9eJ8/10MgUz43ixwOHxAoU1mM1mcM7h8PAwZPMVd5iqLDCdTlDYIlmkcEjSpWeJqkbJZo0HKzwN9H5Kxhi0bRuZI/H3kfhZAiL6fDyTaSUgRy+gwAh0jWSUZx0viVR1e3MThWFMCu6lw+H/3ivzY9G1LY6aFiCJ9tTUDZauQXdqC6UtMJ1OMQs5pJgZpqqAELFIfb0ASECA7Sm8JcxmMzFHtAWqiYRmpnKCsppgMqkCM2ljkkyYkByMRLugmeUJBj4SpowpBgTo6dj88HKQsABB4GQNnIZAZgjAAaJfTBfmtGSHiYYtZ5b8IZE/N4FBYSBEuLMQM0FngmbLErxxcNYD7KQdXyLE2ZPuhShIYMak7QNP9IMKFzG5QCuUyOvXjInLCHwYNjGh8DZ+puMToGThKQkKTgSEIBHWs4RwTuc20CdnGGx6TxdOaFnlAp+lUus4VwQXfL5ie7ECRsfpnxnjwL09tZpT6IC8V+ZSPki/0yhgzD1hTc8cVn4Pc8SiWfXOo21bLJdL1PUSbdNC/VU49FPnTXI48eDy0dpd20tMXYhw6YOZnI61bVt0XYe269C1Lbrgh+adQ9008N6jCoIc1zmJlBnAkfcey+USzjm0TQfXeBzP5zg6PELTNlgsFiJ08R0IPQirqgovvPACnnrqKXhL2Dq1A2bGndtfYjab4erVK7h8+TKsFWGBXEp+AGY1OIpuHwohWSV3EGCtQVHYIMSwUZgBjEtTAREwICTzdQxceeYp1PxjtF0L2wJ39/fwaH6M7e0ZYAAyAHkDFzYVeULjOrz7/vs4f/EiiLnXTGeXqgrZhlJojp8P+AVWxi9oLvqN16+3Mkhx3w50OHGvRiYqgpiwe1l3l/rM6V0RPtP9RvJXS0ANCTZpjQFCzihDFP0tBQRp0JrQkjMgNmB4gBhUiKkyiFGRBdoOIIpWmQ4OXBEmT1/Ety79IS6+/Dw+e+OXuP2rd3HwxR1gvgDVBqbrAOckcSQYDsGMBgTHPHCal2iM2iNEk7oBEw6ADQXfWKEjURgBhvEMywA8o7QS/VOsCFf9gpHUPwZOiPrADinzlprH9vdlmtjdB7/VcQa632fDkvdvRQCU7amvWgZgQCdzzXNjUnxhYp+s3RwAq7l6/l0OTFKhMrAKlNKyTmOjd+UY06zm/Ovmbz0w6b/TM6e0zmOoJUzrWgeK8r6lAvN8z4wBbJYu9EEkAngiE3uYtjYcD8a1YCumolm764BePv96D5mw5pPJRNwsTjA5jcAqm8cx7SnSPvAQGH6V8rUHSGdmE2zMpmgqMffoXIe6adEFhNzUDSZViZkxqKoSm5ubmB8dwhKwsSFJ+BaLBSwxjusl2sJgsViCCCjLEtOpRJzr2gY8KVAWFpuzCt55TI0syKP9fVTTGSbTCUpDYC/qw+jrArkArZHcOQ7CjBOVAAgOQbJsLPo1Z+UuIuBomgYAwxCD4KO0mABMKgnXbUkiL5elgKS2bdE0DQyLdK2oCjS+g2ePykoYgo3CoJIbJbbr4dBBovpZqlBBHPHbMJbCVLj62ku4cO4MdnZ3sbGxEaSVOmZKzihFYkygGDo6AlQomF0xuEoKr/ymDKnw3hQv1d4BRuc+ySewcjZ7gsfhQBudf40OlzQYWCQ4mL4uvclDP+IPH8YbWihYwIziKtv1kQMRpLY9XUjsbQ0CS9Ez34h97PfXgK6HSAhKNMW0LrxKBKdEJ/u/IUKRSLxiLiuSsN5p5LWBBCp0IFV5KxDxHUGWQCZQc8AwM1yILghGyJ0VzGOY0RFiOFLvE3PB4DshRFL9zhCFB3pzxOnQMxgmKpWuKfDUbETGWJAmhA5hshwTOudxeHCIvUePUM8XwnACkPDNPm7KmN+Ggs29V98diOama+E6h8WyhbhpeMzni0j867pG23jUdYO6rlHXS3SdCEGOj4+xWNRo2w6u69A0DZZ1jbZtAACtF+DUNq1I18L6ORamVE3++v0ia0bJPiIi/OitD1CWJSpjcfnCeXzzW6/iG994CZUp8cG77+H9d97BM9du4MrVp7CxNRPmNCBluRQpJE4NFCVE6CQWsy3nGG3HMMaFsNtiemdNkmoAKbPoIcnXjSAlNji1tQnrG8AB3DKaxRJHRwu05wilBwplFAIwZwKuPXUN/+Jf/Av89vd/O5h/9Bes9wlLHtA9MzCfHwtQda0IhRaHgBftXdd1Mfqpcx7N0qFzQfhFCCa/Qy0Ce/GxVGm6cx4OVn4m5sR6vpmdaMZpqHUzoBj+HxRC9HoxDy6Clt0YEeZpwuOqLDGdivCpqsooZBPTR4rmMJ4drAW4bcVvE6I5JltGIGsMhYihEgm0LAnXX7yOp5+5ioff+zV8+N6H+PjdD3D80S34h/sojo5RuCUcL9EYhw4tykaEdEv2cCRgrvAE8ozaCCBxCWjWyFvK/KpJKQehQhH85zy7YFqt9JsEMSeWIibYomu+qp65k3WKplkQGgXT+9AonotR9UM90ozebcN8OeuY5FRjHNc9Awyr4B1r/861EGPtxTqZVup/kkLoQVKqkVinEYp7H0Nmfh1wPEmLk487fz+f83Ft1FCTKefQDQACkFrl6L6DXtMikEAAKNxb9ozNd65tWtGaZOAindf0ucH8BymC3HuBhzJy/8Anwa8Sum7CGRABuYmBDlKeIm8/B+ljfcr3cAQ6HLKkBMuqrmvhEXz6k/eM3tUMkB+u9+qcJt97EbJEnkr3wVfYz197gFQVFlsbG+AZB4lpga3NTTgWyc9iuQAAdG2HorCYTqfYnE5w9+5dHB4cgFkYmI3ZDGVVYb6scXx0KLlXStFI7e8foKlr7G5vo3MOVVHAlARrCG3T4NT2NqYbG6FHwQyM5KIiEtM/MetSoiyXpIcREzfX+x9F5op8/L2PuCMbpijKYHomB6EKzvxECNoMRhF8qDppMAC+Ao49DBht2whzWpSwZoIIBvSfJ7ATpsobRt22aJoOk61tPPf887h48TxsQbDWYOv0qYGp2RiKT7cspZKkFRb98SU9yuOXAZ3459iXUaLGJ79C2S9jPR7tEw1/76/5EzsHNXeJhIkop2Vr22fu6ycyUPjCPKSHyiQwM5yaF3HQ0CAAFs8hWaQ6VwpD2ZucyT9l8vQZ+YzQBfMyBSjikOnjZcuhY3EfsPDCzqXhgHsnIcPBHCDswbIshBH0FpUpgOBrRMb09QaGJpeG6UwTyVxozhr2jM55HBwd47Obt/DwwUMU1mJaTUAMTKoKbV2jXi6xrJdoWwEteuG2IUdNXddYLBZomgaHh4do6gatA46PjqMmp27qwHQ7uI7RdS4y1DQ4V2JGGzdeco4cODE56HeXRprMGREtBonU0nu0rsF8WaM0Fnv7h/jgk8/wp3/xl/je934Nv/4b38Huzjbe//BjfPDRx7h+4waeeuYqprNpjDwI6s810PdFpIoCpihIEr3naHYo1rLDVAlxP+s6MQAm7GxvoywNOhbNRNd12D84wLJuMCkEQMijJr63u7sD5zo8evQIOzs7g3noaRYD3uL+vYd4++13REMVJJpXrlzGpNzEol6ibRrYYoKymMI4B/gWZcEAd1GaLMIBEWS1bRv2uKi3jHA1aF0HB0LbOjCTgPPQn651cJ5hjOY/6zV9MQ9ZYOibpgWRWEtUtkDhgMPDAzkr1mAxXyQJuh02NjYCOBVfyu3tbUw2ptja2cT21iamsxLVRIRsxhIcE9A5EDkBUwEMGAsQtQJAyKCalrh87SlcevoqXvu1b+Pmex/io1/8Cvc/+ATt/Qeg42OYegnTGjTGxT3D3sEZFzWFKe1aiZpF6Om0CsYyplgSL+c78PElZ2jHyrAvBJVmp2awKWM5ZuI1xmimYxhr/yQQtU4zNQag4u+GoO4FY346eR09aER//yUgZ6y/aq4l94KLtCzvdwpSTirr+rdi5otxbaB0fXXM6+odC4oVvuw1hsLQxGfHEh9ryYVzY5qik0C1ji38FfmDIU80eAN98/09n44DkY70ADYdw7qxjAH5AWBVCxYG5vO5gLkQ+n8AwNLuZmNeOQcjIDzM1t8K7H/tAVJvngFMp1MsFgKICAywRxUCJmxMp/Gd4+NjlCEIgEhpa2xvb0dzBEtiera1tQUiwmxSYWs2xcbGBpgZ8/kcm5ubqMoCYI+iLFFVk8GBM0Zy1VhrYx/0UDoAVVGiY6BpxezAWs2bEKJQdS74BJUwhoK5jYC5pm7FcRo23C6S0JUgJgXGGLhOIve5thUzIO8xraaYVBWargFagNihsBVURaKaEvIAwaBEiQYerQfIFnj2hefx9PVnYUuLrm3gGo/TGTgCnixs69jh0/I4Ivm/RFFNC4ATCZQUlTaH85owpMMoB//z9YuSC2kg9VRuIXRJQE2M6xKl29pvBS49KEII/uHjReb1kmGOGbA98zC57+Af0HHihxMq9oHhBtlYpzaqq24CcAPnTLWAOgUt6qcDyNn2rkPTtlgu53CuRde1KAqLna1tnNk5he2dHdiQ3Fn8mRAlbqmkMGeG5Hcf5/mLz2/jzV++DQZhWlbw5LD/6AA3P/0MX9z+Ao8e7GE+X2B+PEfXdehcF03cPIf8W8nFI3OkebhUi8dBuhd8KnhoYkDcOzETibCD4gXTXxIdM2Cpn1N9P2gbhmPU/dNjD71k5DvAQaI4No3D/O5D3P1X/wZ/9eOf4nd/93fwG7/2HRhivP3Ou/jwo4/x7HPXceXqFUw3ZnHd+pIzK/3eUYayKCy8I/hgfqcMskabC68BAeiVZYnpZIL9+ULmGUKX66ZGNytDSPr+VUC0Bs8++yzefvttvP7664NM9el8t12Hf/PDP8fR4SGOj+doGjFv3tp6H5cuXAaYcHR0hIPDw2DuKFq9+fEcnXPogpaw7Vp0reRIa5pWTLedg/cuikc659D6FghMiYbxp/Bd03RRwqoWCYZEC09lMfBLs9aiKApUZDGz4kc7nc0wqSowM2YbG9ja3MZstoHOF/B+ia7r4p2JkMqhqkpsbW3g1OldnDt/BmfPnMap3W1sbsxQFgUcnAT0MBqa30W/MkcG1lgUtsCpc7vYPPtdXPv2y7jz6U189MZb+PwX76C7/QB+/xCel0DbomgZJRiePFrjJJG3L+McpSWXbI9FwhpjMntTxdUyxkjnzJ+2rc+PAZ70/tPPBhqbE9ob9HUNQMvvozGp/ZO8k7d10vtj76ZzqX3Ng2TldRojSUjzuUnfXRdQYV0ZAxUnPRtpDhDMfXtfm7G2n3RORLhKvbAqGRMwHhY7BxOppiT1c8r7n9Y/NkYFa9q+3jvy7ji40TtbhYfr78W+nVTLpvRnPLiGTIvkJyW09XJlD+Rj+KogR96Te+5J9kJavvYAySQLmjLqpTUw1qAsQoCAsFn29/dBRnximAVwHBwcRMmItQRmh6ZZwnUVNjY3Udey+AcHezBGggN414G9Q1FIMANpx/bRWIAQmlk2w3K5lA7HBWR0jhPpol78VXC6FtMHNefwzqEM2d6LSgJBFCGoQNs2wf5dwk13rgsHFmjqJpoudV0NQw5bkwlOb55BXS9F0upDoj8iEEzPDDPBeQImFV5+7UVMtnawbDt0TYuubbAxm8SIfWn5Khv0b3sgVstQMhUJ+KCt1bdOIsbKpK0SFiDz+e0JEPemHE8KEtPf9dJP308DA+gz8d3E3yNlGPQzNcNMAY8MR837WPxeEAIOeN+bpHEwB+J+Milhovv+i8S2816iPwZplJoXWk8okJqcBO0UC/BRjZO81AstFLD3/nfCLDdNCwvRkFLwYSpLi+XxMT747As0XYez587hxrPP4szZMyjKEjZErhyTQGmRy1KuCu+BO7e/xN/8zY9x7vwlHB8tsL93iLfffhu/+MUv8PDRozDX4rcR5z/Rivkk6lGUbHsxQ9VIimDxXxHfN4MgZgNYTBGck/pgFUy5BOSF+WTNCRFMjGQH9mOERDXUPqbCAA3v3j/b9wvGgEHiEM8ezbLD/M59/H/++b/Ej378E/yDP/wDvPj8c6ibGm/8/Jf4+KNP8fwLz+PqlUuYTCZSnyH0gTPEJCtnyiJTawnMAo6KQt8J+y0RPIjTP6MoLTrXovOinVzWNZZNIyDfSJTOdLWdc3jxxRfxr//1v8Hrr78+2AfpmTo6muP+/YeYTqf49NPP8d77H6BrJbF1WVl0XYvOdSs+Y01bS11emUbJPxTPY8pUgqGmnWwSxjPMvRwFAtgGLW7PfEcGPl1fI75F6keqTJf4eNpo7mdNgaKosLt7SnzBgv/ndDrDdDLDuXMXsLuzg/2Du7j1+R0wi5/b5kaJi+fP4fz5c7h69QpOn97F5tYmbPAFosKg5Q4MRlEU8IVoC2xRYWNnC89882VceO46Hv3mr+Pjn7yFj994C0effwZ/cAjDBNs2cF0LLgzImmBC298PY07j6b2f7u0caMS5p9Vn0vVP60+/T83CxrQ468BMGkwp73PeZt6XdAzrNEMngZ+UAU/NPNP5k3kZ9iH1o8rnIzLA6byOtJ3fXWmbaZ05o5/OVW6Fks712NzkjHk+D7G/of85M6/9TTUnORDIg3UoKACU3+vHkvdf5zQNkZ33L7XAGQOS6Zznwvi4FtneT8G93rn5GqTt5PO4bo/qs2n0wZV9mrzfOYedjS0s58dxzz0OHI2ZGebzpfvRY/15Oql8/QFSYobBzCjLMkjoRCOiauTOd+jqBpOyAoxBNZmgaRrs7+/HbOpVJQEOzp45IwEPJhOw9yhCvhnfOcznc5TGRIlfWZZwDAEXBDAT6lqcoztrUZYVrBWNjnNdQNI2sCFeGDzioH626FyDoiixUc3CQWaAJciAHjoJ/Q203AVzPRfCJjJs8CMywbTIe4fCFrBlgcmkwrSsUAU1ZwFh6LouBJqwRXhbchW1rsPu2XM4d+UqvC3RuODP0HUwBMxm01EHuieR/mg5CaDkz+Vl8F4aaQCZsDASgV7arsy4IU1gm7CJTNDo+qGlxKY3mKSl9C9eGhAfAy9Ew4ew7AI4BVgxEmaMVYqujDVWNS0pQQwDU5BmjAF3fb4n5/sw8ClTsSqRUqYzSKNINJCWxIdH/fdU86BMHpjQBR8hnUfvPVzXoXUODELXtmi7NgDzMMZGQLUxBmUpYcm7rov7Vv9u21Y0pW0L5xyWyxrHR0dYhgiO+/t7YMWhrr9IyqrA2bOn8K1vfwsXz12ANwZf3ruHP/uzP8cz157BjWefxdmzZ2GKXkqYXm7JQupqo6kb/OpX7+Cpp57Bndv38PDBPt5880288867qJtaPJaCUks0MRSc18zAACJVfggutWGb2kzDkZgaBObXB3O51KQibt20TnkIvdkm4txrE/031O93VuySMAqDPe2hQdeJDIogvKlrh08+vYX/2//9f8Srr3wDf//3f4AXX3gZDx/cx89/+gY+v3UL1649g8uXL0dNvTDuYgio+7Mfru53A9KcaQ7SvkbNJIp5oRmSW4PIw3sXnfTF/7QO805hrEO6cfr0GSwWCxwdHWEjmkVnAgr2mMwmOH/hImaf3MTnX9xG18pie2rguRk1B4pCEmVKiIJpkeSVQzLvAwqVBCoZrgIhdZpU8E0kdvZFFs4+MnMk/4goAqPQQVnHosT+8cMYQTFGGaQKlkpsbm1iNtvA7u4OTp85jd2dHbhuA/Pjz/HxJzdhf/oGtne2cO3a07hy+TIunD+Dnd0tkDHwUPNxMfOmpgGVBaiyqDZnuPTiDVy6/jRe/q3v4tN//xN88NM3cPjFHXSHh+DlEpZYol5JHooVRi8HAWNgKGWylbGNgDRjtMeYQOY+RUj+3ZM4gQ+Z0lT7uwqS8nbX3aNjzGM6H2PO/XoHjAEKQIH848eTM+Ya7joPopADs3QMcn8Mz9lJ4xxj3rWkpmw5yOnfXY2wNgRKq59rHanWJQW5eX/S7zwYuX/VmCXNWOoZbT/Xho7tl7Hf8zbGwKL+PgbwAYQErP34cjCdz2NeZ9oXY4JoKuwvYw1m0yl2dnaw9+D+Sl+eZE/kZezce/47DdJKcd4PNpcwniLh976DYSt2j61D29WYbWwCRmyrNzY20LZtDM1NBIlSt7MFgFBYAxCwtSkBCJxpMT8OiVO9lxxMJDbezgczGcuYq/NuK5qcwkzgXYdpFUKEB7OM2gNMJL5JRR/xpm0bcItYt7UW1lh0TszxqsJESUdd10BVACiE4DlxfFapY1EW8M6jcx2MITHBA4e58f3lHC53D6AF4AuLC09dx+7pM2gdsGw6MEliV8ceGxvTyOxqiPMxydz/kqU/HAB41dm1f2b4tzqCEyE4rw9DNKtzLrPrDxwLg+KCE3Z6CCMQCG2JSRuvEL+8XwpU0ksqld6tA4XxUiQD1kAdAeSob09sN6x913UxvKaEdrfwnqOvjADtDi44n3edBAIAUfjdAUbCdroQKZIg2h3nPZq6xfGRSIc8M9qmwXK5xOHhIepWIq4tl0ssl8ugIWU471DXNZqmiWOo60aYXhngQAql0RCrspIkl0QoygLGEm7ev42f/OoX8M7jxrXr+K3f+R1cuXIVH334MW59/gVee+2bePraU9iYSTTFNKeYzqsw1PLz8PAQV65cxeHhIW59dguffHQTn3z0Kdq2g4ecSwmVQrGfBGE6fACdqrVRMDlwwDdJ4BBZ7L4vQfubfy5/Um/OkfLVBKRaFkt9HUOANHR8FTEND/5mVsbQgSSkTGzbGs0z41DXHX72szfxwQcf4nd+6/v4we/9Hp69dgNf3v8SP//5m7h583M8G8Bpr1EKMZlD0XPhnQO8Jnb2ED8wyUfGkOh1SquCmzwuXrqIW1/eBUjugc57LJZLifZXFYE5XJVQX7p0CTdv3sRLL720oq1lZkxnU1TTElvbG3jm2tMoK/FpI7LwsGCqBnPZ7yNhDERrhqixUG1gCkQH62oSnDxYa16hDfoqAVFbFBcOCqYp+PZwuAfCHckcfMwYvhFTvz46G2B4AXiP/UPJN0ckUQ0nkwl2tnZw4dx5XLp8CWfPnkF3cIQHP/8F3vjFWzi7s4OrVy7h6tNP4dLli9jYnoEY6NDBE8N4B3QGbAjFpAQXhPPXLuPS5f8ML7z+6/jVT3+Gj998C3uffAHsz2GbGp6PZDRrAFE6HzpX6Rqv/OunaLTk4EOTwufal/TZeFesYcjGgNFJUvl8TP9zlxWGnYZ0YVRokfwdQVLnY9L3dQBlrOjHOagbY5RTkDTGOOeRzvLn1McvvXtV27Dar1X/GeWv1oV4z7VA+dwOwFg2nzl4TOtL+zP2+9h7ed1j4GZsXQbPmuG5yveu/p6bUo6dxzgvnYvC6KPjY5RlEe/Jdf1I6x7bg+k7w/4bGD45MfNY+doDJNWgjKFwzwx2nZiJGcLm5hZsUcBxL2EqqzJodYxoeIIdtzIpzGKXbY2EbyYS+3NAfJ4CX4W2bVCWkoNjOp2IyjWYxXh22JjNYkjwwlo0bQ1LhSRcNWIOSKBgDmcA3wUpqYAZIsbW5gxVVUapNQB03RR7e3sBRBVwnUTR2tgU6ah3HovlApUpMakKsV/vuj7MrZUNK1ophjcFqq0dXHrmGspqhvmyxXJRg4oCsAzXtbDWYLYxi/MvpTcnzIvu655Apt9mgCaAtRUyxr0kTv7s/WmYReLMvHrBREKVSN9Zx45eiqOEJgYcCFodNW1RUzTPXsXuYTy5dMnHpK2I4Cwx8wD3gQPCOFXy5Jz2o9/XXeeCRop74OK8RITpHFzbRe0UwliappGcVOpX5FzQ7HQoiwLeM7pOntP8Wt5LNLUmmF22XYvFYoHDw8MIoBZ1jTr8LuBKtD6qu+AulcwZTCYVyBg05GHLEtVkEgOX2KrAxmwHm8HfgkhyTZXBkbyaTLBsJWqjSy4BIpVMyWVHhlCVBXZP7WI2m+L44BAfvfMu/of/4f+Cv/eDH+A73/0ubt66hZ/89GdYNjVeeP4GJpMq2QMq9TdANOsB2rbD6dOn8e/+8t/j9u3bODjcF8YbBIZEQONwCat8gcN+CBtRfvUczV8HezMj/umuTR8bMlQEa4ugeaLBOaHsRWEKA0hiID1ROcNo9VxCx6J7vwOS3FpE0m8ikhDGLH5Wh4fH+JM/+SHe+Pkb+MM/+AP8xuu/jqZt8MUXn+P+/fu4fPkyrl27hvPnzwXgRoBAS1hD8MkcSoS3TsZa9DOjOIfCAhljsL21Gerx4dwxFssl2i4ENwD3Y0sYlpdffhlvvfUWXnzxpSjUIAUzYPHB2d7AYrnEpatXcfrceRwcHIVoVbMh6AIGGlZSU19K/Ot0cjOGdGzxB0wNFCANhSkcOpyGTfZeE+KG8xQCX8RobTG8vURdVEm2JiL33oO4QVPPsWwXge4RjueSmuHBg4e4eetzVG+X2NnexvkL53Dp0kU8e+MG2s7jnXc/wHsffIjTZ07j8lNX8My1Z3D2/Fn4iYWBg+0cCgK6tpZjVhVoqwqbNy7hN5/+T/Hy67+BD//mTXz0N29g79Zt+CMGtzUMu7i+jsI+9rqth6F+dY51vfUM+KDqTZnAdWUdU5v/PSZtz5n6Mc3Dk7Sv5SSwlDLow/dSv0IDgh8w38PGEk1zDpaEaKwwwnobqjYuLXqXakqPaH6WrEWkRKxWFTSgTfk8qFlaOre5xiKfj8gDUt+29k/P+LhIArHNXEORf67fRcAAYdJF+BdSYXBy9pMxKT0e20+5VkxBnZ55CoIxz0PfqfgsURSEDAIUxXVYmenwuYmgVwFG7qM5BorGAnPp3Sr0XeptGkktQUT9/CSCwPQ+WnduoqARiSl7wp9xPs9PUL72AClFupHQk5qrGLjgu0BEKKtCQIoRRpYhUvOF6yRpojGoitmgPgAgTzCFRVlOsL29A+ccptMp6rqG8w0657FsGmxumsDolXGBi8DQiKq/zxM0qaYgkuheZSFJTgEBKmQJbUDD1hhYAqbTChuzSjYk+kAOFhb1vEZRltjdmcGGvE1lWcZxTKcTuK5FEaSJHYvDpAMAF4iQtVg6YHN3F0/deB5kKiyOaxweHgsTagzgHCx8yJ1UJOOhyNykUru0yKZd1ZjIryq5Z3S+k/wWXnxSfL/r4TuXVhhDKgMkbhiBYY0gRx81Ms9IDrIXFBJ9eJTwaCFQvCAULHmnBF8NEXsi3bkuRD2TnDUuMB9NSMrpXAfnGnDIpQOIaV3d1KLRMRVAJkQy86LJCSYwne+iSZqapS2XS0kECh/z4NR1HW2cxfm6xvHxcdQE1bX8LcxCEdZMgHFZlSiLEgwn+VOKIhKtoigwnUxQbGxiO2iQprMZCs0PwoyymqAsxSS1bhrxfSgKeOfgQrhzIjH50Tw+rpUJr6oSy2WNup6LtBvAbDrF/Og4zmvbdVES3natBDng3gTixrPPYrFY4OKFc3j5tW/izp07+Of/8o9xuGjw27/9W/jk48/w5ptvw9oSN569LuH42YO5g5qSkSlAFHKaVSXufvkQn376BfYPD1FuFCinJdyyhocVZYZhFBRyx4QLHwBMiMLF1qS5PHvgFIQiA8Y4+UMvv56pSKoIjMsAGMnDsY247/UsYtBU0BIN2ZKhBB6wsOCQxyzVhGqoWKWwMhxC3Xb44sv7+H/9v/+/+Osf/wh/+Id/iFdffQWP9h7i008/w71793D9+jU8ff0atnc24H2H0gbpK1s5/4bhAiDrfAcK2jJDJmrlZJiiuzt/9hQKQyjgYCDmeIu6Q9MCriPYYpyJu3jxIv70T/8t6mWLybQKZ1FMlGWJHJ66chlvvPk+Lly+hqduvIhfvvtLAB2YLQiqoRdQwl6MUeGD1j9EF0XCxKmZoPqNpcy646HPgS4SA+DUFDRZT2Ey+nC9hnvgqPmObDAF986BnQNp3F0T6nIehfWivWOGpQlm1SzSGs8eXdvFMOdtU2O5BI4P93D//m18+ME7ePONn+LCxQu4ceNZ3LjxLPYOF/jix7/Emz9/D8888wyuP/sUnnrqMra2NtCRA/sOxgIWDGIHIoPSVjh35SLO/KM/xEuv/xp+/jc/xcd/8zMc3roNs38Au1yAuYGnFp6AgiwQzCvDQYPhMpyOLoIimRIFrKvMHdAnvUwFralWkRMakzJsKUOe8gr9GRqXguv3J2laxupIAUJahhJ35VsAIgn4weSglhD5O5LEE9HPkcHRlw0MMPX374Bpx9A3Z0g7+rnItSIWcu9qTryULrmsHdXc5MBoZQzJ87kWKf17UAePz23a9zFQm9al5zU+n0p5dC50jGY4V5GGKzBNAIFa4xgjkTgF4qaCNflf+s5wbYJRbgBH6edK9ymR4akAhmjo8ydV9MEz8rnNxx/bSYC0slRKt8uyRLOYJ8Ki/t7j0G/VfI/NPROpi7fw8RBLDpn/fh7pCcxgtfxHAZDSQ+SCiVl6wIDsIBsTt9zG5ia8c2i7DtOqQtu2MTmpbgJV1xJJwqvFYoHJZILJZILlcgmmDiUYx/O5MMtdF0KoFihtEaV11koi2bbtsL2zHSMdqSN8YS3K6VSYUvLwLMxaYQ2qwiYSjLC1jBzIsqpCMlVhasXELjl4gAAw79CxAKPWe5STqUg/ncOy9Thz4RKuXn8WDhYHh0fYf3ggPgQg3PzsMzz99NOwxgbN2VCVmztjjoEkLZKXiAWfeAZ7tWkWrYT67Mhn6s/je0lKYO56jQ/HCGvReVEPHgFkjThVOz8g3t65EKSC0bRi5uWdj2vSdg7OdRKBKoAT3W/KRFhroqmaSmPawFSoOZlzTsCMk6SiCpyW9TL43nSYL2ocHy8AEFznsKxr1Evx43G+RRM1N4mUJ4y/rEqUZRUDZpRFgel0hqqaYjbbBBFFfwvRBjGqqQgCJLcWsL29jbZtsVjMQYbjmqbnS89b5zyOj+fRPE+0X0K0Um2uzhM7DKTVKfOhf6dOp1Ho4VelsPFdM7RVf/TopwCAt99mPPfcNbzwwgt45ZVX8cMf/hC7u7t48cUXcevWLfzq7Xdw5uwZnDm9K3vBq3bRgyCavLI0OHPmDH78ozdw7+59mAK48tQlHB4usagdHGuiTS/gSJleJBcsIKqZ7CJOx5GW9MKm6PezasLi3appS1q/Msj6WezLyLN523m9RGJqZUy/VqLtRGDWQ0JMIhhrwd5jsWzwwYef4LPP/q945ZWX8Ud/9A/w/PMv4ObNz/CrX72D23fv4ZVXX8bFi2ckrw0oaOWGDuLpXvfeh4AROiYGyGC2sSHa97ifRPO3rGvw5gwSNns4x957TCYTbG5uYm9vDxcunh/MEIUlu/HsDfyrP/kLsJ3hueefx+37X6L1NXzrgUivhnlSlLFZnUeSRB0jax3HRP3e1jpk3MVaMy4TfGtyE2dDqjkJAX68B4UzVZQFrA1RUesGDpK3inTfkYExRcwlYiuH0nuQl+h8TVOj61q0yxrz5RL7h4e4c+9LfPDRh9je3MLTTz2D68/cwKWLl/Huu+/g/Q/ew+XLF/HcczfwzLWrOH1aEg93bQfTeLmXCg8uPIqywvaF0/jd/+zv49vf+y7e+usf472//jGOb30BczxHUdew3qEzLgA9E0CSMPPwwqjqfAzO2hqAlPMHKwznyPO61vkZOunOy0tKA/P29Luv4u806CMgcIejnD6A9f5bhHtPh6C+WloGQhrt60j/gdVcNTmNG9DvwNSmNCh9Xu+IMQY815qtG7+Ak1WNQjwfRgQuUXsxAjbGAO7YZ+kY0r6lfV0XcY5IApzovOf+YqA+9PngHhipK+3PyvzLH0m/KYCjBAhltY6t3TpQvw68p2I59oymrkWIY0nR2kpduXZt2F7P54mvqoKjnlfM77bHla89QOp8hzZEFJKkiB7EXtTwYXI1godnjsnd0gOvqtH5QkwLtrY2JXdIiO5VUAHXJQTDSh6Krusk+EFhUdF0gLTb4GheL2twkOwbY8SJmBnTqfjwkDEgFlvXopTkfWQIbE1Uf08rCSeuDDEgxE43wu7ubhyn9z6aJBlj4EJfyrJE6yXIQh1AIINQN7JpN7dP4dKVp+A8MK9rHB/NMZlMMZlU+NN/+6e4fv0ZeO+xtbU9IALrgjTkkqX+GWFGe0AkvgbeBRO2TjQ16ryvGhEFRmr2Fk3igl+QDxEBm6ZB00jYZ3X6b9nHxJoKcjQnj2vb2Jb3PkYUhDFYNg3q5RJN20a79OVyiS48UzcN5vM5vHPwzNjf30enQCbMu9qyy34TTZZEk7JRslSWJYoAckQSRphubWP7dAXXOZhCAhEQGQHwxsAWRey/IRPyC4g5WlkUmEyncF2/BgrYJDdPB947iNooIsLnn38R57RzHdoAnFLNGYCY54hZpODOSchiMn0o7z50N8MYdXZWKdZQwhYvEjO8GAiiUdAzkF8cjMBzhgsmXiQMfPD+R1guG7z00os4d+4cfvjDf4NLly5ie2cL+wd7ePudd/H6b/6GSP04kfqhd1gvTIW7d++iaWpsTTdw9uw53Ln9ANPDBebLVvqS9EfmimMEQP0e2blQOpCXMUYgP1d6xsfKgJHhVal33lb+/Lr20otS50YjIWpCTM8+2YMCbJZ1i5+/8Uu8/+FH+L3f+238wR/8Po6OjvHl/bv467/+Mb7z7dfwzDNXYQ0BxMEcoxdqKRgDEKOzMYuUm8JZPLV7SsyhTT+Otu2i2WhuBpTO/UsvvYSPPvoIFy9dCM+ojFWkqru723j66afxxZ37mGzsiElxzaLlavvQtgMm0RCgibIV4LCYyVnhSKCJtBXQMXuJUCiiVliywYQk5K6SyUeaQBehp94ADIKx5SCaKOl6sUVRiFm3AqXgVouyqlCVJdgJbYyCDc8wrKkAHMgJuCLfoTQWRTUBwFGjVDdLyZG3v4ejoyM8ePgQ777zDnZ3dnHtmet45tqzWHz8Gd597wNcuXoJr3zjJTxz7WmcP38GhsVMuOkYXeFAthHhZlli88pZfP8f/xFe/N638Kt/92N88O9/ivr2A2AxB/wRwC7SJTn3waQqASr5nl7HWI8xxmnJGeyxc6KfrQMNaVlr8ob+nOm9l7Y7ANbJuV0BZtxL8BOdAnpwFD4nESMOorExR1/VdfQj728+rycx0vm78bPAsKfzlgpJHgc8UyZeeIXhWq6AOAyfWSewGiv5HkjXYWy+9I7N+6uCkRxcqZBjnSDrb1uU5x3bSxIQqBdO5UArB0p5vWNhvtMSNZNIYNPYfBENfSsH7fV8r/L0qs2UOQxzlJgGPq587QGSKQwgQd4keo9z8GSiys2Q2DxqpCOZZB+0SITCSqCBDWsFgXq5gD2LlJpA8IDk5gBiKOQ6MMk2PGts6ESwjays5EVS0z2lTa5zYDCOjo6wZQ2KsoDzQZMUNth0NoWxYqLknEPrHAwYSJhV5yUqjPMOtijgQx2eOYCBFkUpGqW2bTHxE9SLJUDA22+/jel0ip3tHRRFgWXd4HuvfwPGTtB6oK4bEBm41uHW3Vs4OjrCZCIhvauqN93TkhLyscsoPTiaYNQ5DXgAON+hqYWBXx4tcXhwGAHOsq4jo922jZhmBUDTBsAiTLpF13Wo6yWWy1oCZ3QdjudzzJfLYOrl0bXiW9N1HawxqJdLHB4dwQUGou1aLBdLScQYpFAyPhb/DwomSEHLKGM1YX4qWCvmlZOJgB3RSBYoqxIMiRDnnI9Jg7tOJPK2EM7FOY4JIn0jwL9rW2AZzOdcD2K88+DQb/WpEg2PEAjXdcFnClG6EtdgJIt674DZazBUMyYlJVwkEluWfrMXqaQIHxQXGDgnupmUyCFeThCGkpImQDDGRhW6+BkgEG8bzJoC06yaDQJgVAvhYMnizu0vcebMGVy9egU//elP8O67b+Pb3/kOls0Gbt36HK+++go2NyZIk8+yZ1jbXx7L5RJ1XWPXbsf1FAxEaJ2DxiFnlcwaCgxxIAXJbOn415mCnHSZ52WdVHPsTObP6e/pOV3HBOVMmPZN3xEzLAdigoP4/Aitk3XrvMPB4TH+5R//Cd774CP803/6X+Lipat48OA+/uZHP8NstokL588IpqThfAwCSXiOEc1kr8mzs9lUABJJ8AhlqJbLpez9oohpIBRs6eV/9epT+MWbf4zv/9brATjp/IhG0VrCN77xEn717h/jlJ1ie2sXTdfCde3KvEYJKlF4m6Imv9fCxtHABc2oBvdhIB4tj0TTGOsLYCqls4Yk7xUI6qcaeoSCKCb/JWuC6XgRgq20gNfEs2I6bozB1FjAGHRBqOK8iwFbmD2oa9E1yxAt1cMAmG1WqKYzdN1S8uJ1HY6OjzE/nuPo+Aj3H9zHr95+D5cuXcaNG9dR1w0+++wWrly5jBdeeBYv3ngaly6dhyWLrmV416J1LQrXYFrM0FmLnacv4/v/xX+Ol379u/jln/8In7zxJuju5+iWS5imgIVHwR7et2J+k/hwpAJLZZ7WMZwKTE5iep+EWU2FhieV/Pynv+fWGGndad/WgTRAndUT5l83mY4L42Zr6fjyecyfSZ9LgWHOXI/9TMcBEgjnR+Y8BYBj//J+6ec+64O2FX31ct+dkfnOv8vnZ2zM6+rKtcAi3O37NB5pdk2f1D4tKTmtHtsbY2sxALQ0tORYNz/pWUnrUAHaoC+BnplCAizVi7m4vWTBc066D1dGLwxB4M1Sje644OKk8rUHSG+99RaeeeZpFEWBoijQNE0wf5sFW3CDuq7ROYfpRIInMBhFWWLv0SN88umn2N7awqlTp7C5uQnXdpjP53j48KFIKU+dAiCLWE0qzKYzkfAkKulqNhXag37TqxQmJS7eRe9SGGPw5d27OH/+PIqqBHvG8fExDg4OUDcNqo1pMIuTUOBiBiGMcNN6PLj/ANevXxem0DmUZTmwp/bsZdwBTNy9dw+FLdC0LW5/eRenT53Cg0d7OH/+PJ597kVMN3fRskfdic/IgwePcOuTz/BX//6v8E//63+Cq1evBmbErN28Y5KF/DNh5iW/kneE+XyJhw8e4sGDh9jfP8ByscRiPsd8vsDh0SEWiwX2Hj1C04i5Wecc5sfHWCwXckCchF5XC5amrgWUFEUMfe28OI6qXxYzS3Sm4EzKxmK2LZnlDUmwjmoygbF2YEpnjEVd12jbBtZKMAE1UZPw3hz9fZZ1I3MfCDIRoen6EMeeOZp2svfw0fNYJOUuaNRAYoMcnSG5D5fKzCHyVxL5TjU0QBJpSJ9H3MsmAUFixkgBvPaOj1LSiGeccP1anwATSnwuEC2KtQqCRi5LLwJmhDD8fdEko4YIpRETL47mHvI7Qxi/lBBHhl12GbwH7tz5Es89dw3T6QQffvQBvvHaqyiqCkfHxzg4OMTGxiyYdw3t4Y0xaOo2RNdDTAEgUnoOD6ZSXCvzoJf1QFI7PA9jl6l+l1/GOZMxxpyk76fv5j4S+XOp5verSCbjhUgEGAvfidmogQGzizmYiEIiRhYzr/fe/xD/j//x/4n/+r/5b7CxuYujw0P88pfv4vd//3dgLANwQSU4ImmFMOwGIdx+MB8SgU0Fa2z04XTeoV4u0XYtJt5ibGhEhJ3tbQDAYj5HFYJ2xM1NEgTl+edvgIgwny+ws3MKX979su8T82AdZb7FdLsXVoR9CwiADxJr1cKKgD81P0JkYBGYgBBPE2COjAUpSNRNywiwrBc6aNE7QQU6nSF0ak4cQJcBwOxgPGALG03CdVzOOXDTYkkGnhcAMdh1Ao5tidIYlMVU+hjM8OqmRtM6LJYOx8slPr/9BU6dPoXr169jvqzx6Wc38c6li3j11W/g2vWncOHSORSlaCNd59ECwKSELwtQYXHm+Wfw956+jFe+/x28++d/iQ9/+Q7m9/fgjuewbQNrRdDWCy1yMLO6r9btex23PpOaPK57L2U01525/L38nI/Vmfd57PsVpjhIqVb6yQrAGMwOMP29kdODFHjk//L+p7/noa5z5l/7tBJUAetp3bp5WAeSVKCZ1jXo50gdYyUHqzkwG6tjjL6uZ9o5MvgpgBLT2oHMKLZjMt/VfK7yPesybcrYHgy/QYnluj2c/p373A3nHj0tCsJSdc04eKQavv79Aag+4SrSCLPye752ovhYZ468rnztAdL7H76H2cYE165fw+bmJpbLJTY2NmGo13QUVYmmaeJGLAKzY8oCddvg8u4Obt3+ArPJFJcuXoRjj9nmBo6OjtA6AUyTyQSmsCiCBmWxXEZbyaprUU2n0cxBfZAYQOuEARbzukZMpozkGLJVic8+v4W2adAEc7Bz587h9Nkz2NjeQuccqrII/KXH/v4j7D16hNNnzmNzawvHywWaphGfoMKCnEhx26YFSPpxcHCAo6MjFEWBjY1NfHHnNrZ2d1C7DmVZYvf0KTz7wgtoHIsXhifcvnMXBsCZM+fwwvMv4MKFi2iaBltbWyumHlpO2pSrEjKDZulw7959fPzRxzg8PMayXuLLL+/inXfexqeffYrlcilMaVnCGiNBAcLcW2tx5sJFWCuA5cpsBmvL3pfIc4ykUzd1iD4mGpWevw9meq431Vu0bfQL44NDeNehDeGu1XTRq+kDifRCTepiQlbVOgVTvjGGNRJe7gENkxC+q1efhneMeRMiSVHQymjySUoILxiwQvCTeDOBwRJAHnh5IJE6DeEABr+rH8JYoeRR4eESKZCxYWwjQNkCIX+5AH3TP5dupRTAyfgoSPtsNBslI6aFHLRLIJa2AYAsDDnAtQA8Hj3aw3J5CbONGR49eoCjw0NMN3dQlhUOjo5wERcEULH8T4GkMQZNswy26j6A4DT8egLWEp4+XrzcmzIM5m9EKjdWTrpUTwJJ//8sapJRVBMQi68ea/jfYB8uEsRwWQP4+JOb+OEP/wx/9Ed/hLKcYm//GPN5ja3tEsao5nJ4Kce5iEIBhD1hAmgtYctCfJECbWqaJmo+mFfD73ovYe4vXbqE23fu4MaN60FIoAyPpIjY3NrBC88/h3c/uImNjU1MpjPU8/lK/6Izc5R/ydlGkAATCKzaTmDIfBPFfeZ9CP4SBBhEQIxC5z1816FjF99Rc7KAmKQ9Y0McBgGTKsQT5YqFNZPoA+C7Lvp2AiKssAYITcIY0XIZA3Teo5hMMStK+btr0TRLdF0LOAsmBlHIeTKrMJ3M0DYN2q7D4dE+5guLo/kRHjx4gN3dXVy9ehWHB0t8evMOrl+7ipe/8TyevfEULl0+DyZGQw2M72Bb0b63tkFZlLj48g1cuP40rr37Ed74s7/GnbffQ/fwPviYYb0Bdc0agYJqdseFDemaju2/nDmWfbJe45C+r5/nv2t9T8JMj4GTdX1YLekzBgDD2jJoZVdBn9KpMef8x7ZEQ188PW8rd0IGLkwCEk5qb4x5T0tsa+RruT9MFEQ8ScnXJgddOfB7khLXLvh7pFHj4ti5t+RI21MamPqIjY4zfK4WC4/zZxPXhb5/uXD/q4wrtp+8ymDxgw7CHUrmNR1fyqfkYxd6Oh52Xdv/qvfh1x4g/d4P/gBXLl8CM0J4V4JnQlVVUZ1qLTAtyj5nUVjAc+cv4LdOnUHbNrh//yE8gOnGDGQNtojABBwfHePUmdM4Hxhy1UJ0wdxpuVxgXteALeBBKMsCtqzipqdgl+6Z0XmGKQpU1QSzzS1473H6zNnADAoDb4wk0/QkpkllNQWB4V2HcxcuYbqxjc3ZJuqmwWxzK/hJtDBGTCW8c2LuZSVvkof4h+zt7WG5fIgv797Fo0d7mE2nuHbtOq5ceRpffnkPZ84/haOjQxzPF7h44QIsWfyrn/5PePbZGygKC2MkXxPlACkeglxN6gcHWpgcea9uHD765DN89NHHaOoGjx5JEs5f/OKX8OTxwksv4tLFi6iqCk1dS8hq7+DAwceoQd124KZFXdc4PJ6j6xyapg5hp+WwdJ0LOYCE+WiaRkJms5qiCYMgpmjDvEUaN7w/nBRBVSQEUGGd7KfU2VKZOEMkkjobXO/DHBUFBfDTz2XTdji1exrnzl3Ae++/j8V8AfG7CIE2VCSD0B39JfRGCKr0Spo1kYFSxh0IzBOZfu0CzZFnTQRlAOLeBDD4LC/GFIMLNRJkAjj4SkT2UaYnaDtzhh/xOQVVG1vbuHLlCtrgC/bwwUM82t+PZiLMogOSPGThovWigauXDQpbYFm3uHPnS1x/dhfGGAmuksxJ4MHD+kp0v42NmUTQazrUywVsIZL2wPbHec4Zn3RfpGWMWXqS8rhnxy7oMbOYlKnKPztJYjvWFkEvJABMKIsKjkw/9+zF8d+YADgkYtmv3voVXnnlFVw8fwFL1+HgcI7NrdOQvEdr2mbW2Ubg6uQSB+C7BtVU6F2QC6BuGiybFhudAxmKZnaDyxge129cw/vvv4fr168NmnPOCUpgj2998xv42c/fwk51DpuzTTSLRehJIjXW6IWCkCUlhJq0KpMYBBkgikIZCmbAeh/1ElyATYjSxCR9KS0m1SZc5yRcrvMBFALE6nNmIjmQmerHroIVay2KYgpiEsFc00h/wDDM6JomaMP7dAfioysdK0rx+ZpMJ9jwM7Rti/p4ga7pwCECIXsHayvYaYkJCV2u6xbLeom6qXG8mOPho0fY3jqFSxcvYVEv8Mknn+DGtWfwyisv4rkXnsP2hVMwzqNwDXxdo6wsUBi42RRNVeDKt17EUy9cx0c/exu/+Iu/xO0P3kOzt49iboCmhYEX7SWczD1z1Gkr1TVhy0ve7/485EIMBQupZm0d8NH9ld4lJ5ncjWlr1tGHnEEf01IFMU3YS3mGM8BYikGhDNLAJ8O8RDkQGaMdY2NJ+8Os7QY6gSGN0WdE2NRHI17XRj5XY5qc+JPQR8BN6wDkPvYEN/Lu2Hj0mRRk5LT1JBCd9jPto37unOvvauU1svYGQA288tnYPOX7KhXa9ZxEqB+UptEDwDF6aHKjIQp8wlgcc295ofPu9Lan2FfXuQi82PtBdGLGsP+Joij2m+PfPn7JEOsZRhAgo88B+aTlaw+QphunwGaGsihQAqhmYuY0b1xMsApmeHgwWXSd7yXOIJjCoDIlvvnt7+LgYA97R0cSWaxp0HqPZ196ATvbO4CxMLYCBwfWCrJBZzu7Mbx127YwRTC3Cf3TtfbeY3NnmpjAcSBWQhgsEYoQ+rtPNClgzxgLGIvWM4rJFhwblNUGrPe4ckWCJ9RNg2pSyaEPksiiqGA7j9Pnt1BNN7A4luSXN64/h7Ka4Py5C3jrrXfx8qvfxP7+PsgYbM5mmM/nWCwWWLZLnL90Ad47bG1ti+QRDI1cJYE7NZKIR6SH8lQESWIcrgEkgM9u3cYnN2+h7jrcuv0F3nzzTXz66afYObuLl197DcyMB3v7uHf3LpbHCzSLBdq2QeMadF07yN0RgzgEXW7/ux6u0M+kX7GsXAKyYkQEMuVAgtP7KCTPDC5QQgwpy/r9kAgNmg4NKrEg70EWqD3Dbm5gtnsKtQPABGOLBMD5WA8BKNA3skLk0z/TxVn5EglBCmtHvflD3v10Dik+xxJKeORyE03QkMCLQIiGPVJiFyVQHsYAx8cH2N+fYjKZCONVECiElicdf2QMxa+wYwDwcA6wdgLvFjg+nMMyoySCb9oQqavvg3eAYFwPyRVTAmzgOpG2nz61ASIHQxU8G4jnzfDSXAcuxi7Mdc98lXdzZm4M6KTMxJP6SKTv52PiAAIQzhkCc2+LEkXYMxItUfKtWUMojIXrOtTzGh9/+B4uXzwNawwO9+e4fPkcPHcSyCBrJ/7zEtGNEdbde8zKAtvTCp4JpTES64A8Gteh7jy8nntKJZShbjAuXjyPv/7rvwrgLYAv6GXsAFfjxRvP4NTmBPVigWk5BbzsPbHiGZqqcqDbSpdS+sBBWswISXwNIebphokAHUQgG8AOGIYYjjqYyQywBaZ2gmpjG0cHR1i2C0xMAQo0zwRNrQtS1hQkgcVyobRlEI4wiorg2w7LtpaE44wIfFOGzjBDT733DLQeZAuUZYnJtMKp2SaaehFTDywWiwAOpY7JZANl6fugOeHfcjHHwcFD3Lmzg0vnL+HwoMaHH97Gc89+ipdevYGXXn4BuztbQEHo6galtxIwojRoywrlbIIXfvvbuPba8/jlj97AG3/6l1h+8jnMwRGKxRJELQgdvHVi0hgDN/WUjBK6qOuVO5yrsEHN5oeCwEwghHGAsQ4EDffIekY3MpdPQDeUifTsRaAZEhd7ZS6N3NWOLdKQ16mPk/5cR1fy85T0WnZdQu8VHBGF5CIcwuBrzHnCUNiVtNlH7l0tKfjUNRiCNL8yz3EsRANAqJ/na5vSytRvKY2SvC66Xgqa9P2x2UK4cyMRYI45zsZoMEHoBxENosbm93UeRMb7ILQatC1/e5Ho9X0PZn7iU4+IqFKXAbBYLMUrACwClTAWZtPLYItCBDthzmKag3DnR1NiAkAGZHuf0h64KZhK1oh0bgjgYOJMfxekIRYbTNlYGQcilJMJ2rbF0fExNjc3w2YWybgpNGwqRM3ngc632N/fx5d374BIQk0///zzMcBC23WobDlgRnqmRaR4AAaRhIAe/aaMzYDwhsAO+plKqHyItlSWmk8J0emQmeHYh/DbkoirXS5FUmssyLtBDHwKfljbO7uYViVOnz4DIovFooYpCnTOY3NrC7YsY2LQruvwxhtvYGt7GxubG31fsrln7jfr6qURtnxgbgQ8AIv5Ardu3cLGxgbefvttvPvuu7h37x52d3fx0ssv4e69+3j33Xdx/949yd/RyQEV3wM/aJOoN6sxJKrkolBOX+c9GJwkEprY0wzEqGQcieQDyXdjJSfixthI7KWmtJLVd3V/GGPADjGZWucdQBLym4wGg5BLRfsWZUH9kEfWJ+3/+BgUTGrJI8fldY7OBcmFDC/Er5cQSR/XMfLpZZtL4QgS0MJaizt3xP9DzgiDvU2a7jVxRGluEwHPVVnCe8bh4SHAUkfTtLGuruuE8QWgdszWWkynExhj0IXIaJOqihcakhXIpZgnSUDX7aOvUp4UYOWX6xhjdpIEdaytMQl33o4xErTEOyMJjb0EmRFpqcftL77A0eEhNma7ODrSvFwG5E/ao/0eSf0mJBqkDZoqkfKzF/9L2XMm2vnnY5xOBXQfHBxgd3c3GZPQha7rsLW1g2efvYY33voAW5sbKIsSvmsDM8ORuRHw4GIEJo1C5UKESwDxoh/2RcClMiHeSyRWA7EI6JwDGwtrJKBC6yQoz/bZ0yibKeYHB/DMKLwHeRMBTa85kjQRIjgwmFYT7O3t4/DwMPpPqmWE61y8N1Kmv+s6GNtH3WQOvpfMoKqCtRTnsuskp2DTNFguF3BOZoeIUJaltBPSA7TNEk27RNc1mB8d4d7dL3Hu3HkcHe/jk5sf4oP3P8Qrr7yEZ5+7jlOnttDUDGMcTNcCjoGSQcUExWyC13//t/Hyqy/jJ3/+7/D2j36K4y++hD08Rlk32Gga+AJYUgMyIRKnicbNMGxggwAvjTiW0vbULHJMa5BH/0pDZo+dqa9qvjZ2TsfAS/93EP74TNuZCrekkq/U/hM8Gf6ppJ8Hn0ubqd/m6l2rbeWpWqS7Q6AzptURQVu/NivBBkbud2AIUteB1Xxv6Hua4PUkkDQQ+IyNaWRt87GFAazc2fpO/l4O1LWu9HNmBvFwRnTeVvqRvd8D4BFeIa43BR4w0PmRNdEiptse7HqtV5wznOw3mJ7FJy1fe4BkCivJpdAzm449qskE8+UCn9/+ApPJBDvbOyiTCGzMjIcPH2J+PEfbNijKEufPn8fB4T7OnrmCyWQWo9kVRQWi/qLNVZtK7FKiqIdXP88ZwFTKmEswInJmxmQyie1ofqfFYglblKIdIaCsJnDLJZZ1HaOjaZ1lWQabewGOrvMwpsCjvXv46U9/hn/yT/4JyrLE4fExjo+PQy6cBT7//HN8//vfh7W2jxa1JhN5LhVTJ0nVFGhxzuGLL77AhQsX8Od/8e/w2Wef4eDgAN57XLt2DQ8ePMRPf/IzHB4ewRqDqqiAgiLT2x8OIf4Elbz0zEZ+kRAMNIcbgGAiiPh8eCu8I1JxZgZiBvu+xf6t5LJhxOSZ6Xfp/ojv0ep3CtoAwFqDul4G37MqmLcEKVE231qeNCXaV72IdR1PIjajwAA8kJ6LgLa/yAZBS/KLK7tILBGKokR0gCdC23YhHLTsgZU+ITlnxqBpWlSVRKhcBud9CQdfR3M6IpHIIeQF8V4iFm5tbaMsS3ROIizuntrOzsBw7Pmc5PM+dimse1bL2IX3VRirwdyMAKOxNtZ9vu5iz4GX/rTB7K1txISEg7nd8fEcx/M5NjdOYR5yx4lk0g4PWmy4X9e0fWMMzp49i7t7RyiLMuauY9eJaW7XgatysOfy8T/11FO4desWdnZ2YmPEBN/J/vOuwXe/8xp+8sZb2NjYwnRSwTU1mHzvBxD2oeQs87DGommboSaCZH+pdDYVnKkhFAMxmINrHbhgeCPJwCV4jAfZEl0w6bMbU2yVBRYHB6gXSxhvUMCIlaxRqa6AtaooUBQGbdfi9u3b2NjYwGQyAYBougr0e1TvIC3OS7TCMHmgAJKKogAVfQJ0Yww2Nzcxm81CzsBj1E0tyWiDMMoYGwJsWNT1Eot6jrpZYrE8xsHRPh483MWFixcxny/wyac38cILz+K1b76Ca9eeweZGBTiHzrVwjUdrW0xnU3jvsHHxNH73v/hf4YXf/DX86If/Fh//5A24L/dQHixQcIuChKYbA3TcwRuGJ5Zz74Z3WB5MJY3ale6f9OxHAaf3o8xl+nxa97pyEjO9DhytPJM9P6RBNH7eRspJDPsqeFPaPO5j2gvMROA31lY6l4/rQw4CcpCj9UWezPTzkq9DCoBTXk3fTedxDHzkdeZ7KX0mFzSlYxz7Li+5xm/dezmg0c9W7vY17+XlpL03rAAq/xkF+ClP158JvxKBMPYle3/sHsxB8+PK1x4gRYLEHDa92nMDVVXh6tWrcM7h4OAAtKAY7a1pGkymU5w5cwYA8GjvEfb39nD6zFmcOn06TLxGWHEBjIwnL0ulTCmKHTto+q6qRuOlngAj3bht2w6kfEpUvBfDHmNCThIymEynwbSlBtCrc8uyDAw/o23EnryaTHHm7Fl857vfxblzF3B4dIyj42X0y9BgEVeuXEFZlphOZwH8KYEYHvT00EXmN0gWlP/3XsLvdl2H4+USH3/8MQ4PD8HMOH36NIqiwIcffIB6vsDmdBbnxkfJBolmLDJKQE+IldBSzzPHgwVEFR8Hq+xMGBPJOiP5CxjAj8RAt2dnRAKWlj5s9CqBWUtQiIIZJqOuxQegqkqogSCveZfjP06IG8UfYu7D4a7KJERrTOxCR3HtmSv4P/0f//f4P/yz/zM+/fTztRIfHRfrWkS8OgQwj7tgV4guazhyAUdiWyzgXgUVWu+g7vC6aH58MJ/qwy3ruVMNgzEBtImyARzaUW1wURao6yXqusFsNo2RMeFCvDZKA06cIPVbM3ePK2MXgI4vrzNngsbeGdtHqQY7ff5JLsK1zAEQLkeJNoeYxM+jaWocHh7i7GkXc4WJf1d6FpM+yM04uOT199lsCuwdhehrQbNkLeq6Qdc5MBexnlVppcfVq1fxxhtv4LXXXkucpA0omHcSeTz3/HVsb86wrJfY2tjE/OAQNvr7IUpIC6OpHhillT1HQAAHQv/Lsgzapj44AiPkTjLiFxgFTc7Bs0fhGb7tQEUBBw82BqYswSRAfrssUR8doT48Csl3DUpboDAGhhgUUk8YGHjXDel0AohUWKCaFJ03WU/pHzMDzoPCnhcTZzMQTCgwnEwmqKoSXddhsVhGH1EOhItsgdnGJso25FRql+hci7pe4PDoGF9+eReXLl3CwcEhPv30Fp57/jl865vfwLUrFzCZWsAyYFocOYdiYjFhj4pmOP/0VfzD//a/wmff/TZ+/q//Al++/T54bx9mziDvQN7BeAlq0XIHCr44ad8HY08Y3fTzVOiTSsVT5j6dk/ws5cKotN5cA7WOXq4VusXPhE6N5l/jnkaPmYmlda7zaVwVpOl3Q8Y4fgiKwQBkHLxyv6X7MweU6/xucsDBQMyPp+PrH+6DHOTv5QBDv0/BSA42xvqU9jmfw7SuHIiPzfHq3+P7Mm0772MK7sbAEkbur7G/T+Jn8ruoH7sIehWk5nOcz78xPa882HfS6GA90n7lgPpJytceIAlPJkn1TDiVZIyYKVixZSQwdk+dkog8JAznBm/0pg+ecfbsWZRVJYxCWaFtGzAZMIUNOHK5DkMWD0Nc5shYLxx9VxOSpkyN1pdKHnPGhQjonMfx8QLb29sgowRF4kIaY6G+P2nQALHlJNiywM7uKfzs52/ie9/7TRweH6GuOzSt9E8ZlmvXrmFrawtlSF47mPI1jF5qYoBEiS1JBxmLxQJnz57Bn//z/wnz+TyO7fTp05jP59jfP0BVTmBj1DL0QAcE9d7T45Idz/7X5As5/G74mGIIXnk8KycxtLq++eer0j7tR04U8nk0RjOJM4qiFKYk4NF1TKrjRFKZEiggJkaO7Y32cnVURIT/3f/2n+D3f/A6/jf/7T/GP/vv/vsBYVOiPBif1hk441g/8yBseHrB5GXAcCQgBGHtxZfiZLClTKc6g7Zti+2dTQBiKuSDv2DTNGjbFrOZmAWBEUxRpIqqKrG9vYXpdIr9/T3M5/MQ+t1E5pZ6S9YB0X4S6d/jyjogetJnA2nbE1x26bNrgW9yeY/Vs65PHPYbQQASO4K3Hl0nmoeDgwOJCuk95vM5prOpeMuP1a/0ZIQpOnXqNG7dfRR9gYwxgHdo2gbOdXC+PHG/nT59GkfB7zQyHMEsl5nBvsPWxhTPPfcMfvyzd1AFLX68iLM5omQ/GGPEET4kPNY7Qu34w5OiwZAeIWYyD8Ab3qNbLMEdw1QVaELwRQHYwEQbue92z59Dce4sHt75Es2yRtEABA9bFoAluE7ooO7/VCoeQZIGrPFDfxhmhus8bBH2eQBJYkbYmz2n9en9VRQljLGwtkTXzVDX4qdUN020bChKg7Ks0LUCjhZNjbpjLOol9o8OcGp3F5cPr2Dv8BCffXYTr730El75xou4cvUSyHo47uDg4BsPYz1QFrCTCs99+xU88/wNvP3TN/HGn/4FHr33MfzREYqmhmkBeIeCGd70grX0vs1DJKclvefHmOH0fOVnKGey0/l+XF3pmpxEI9Yzq9lzI++n9H2s/3lJAYLkkQPI9AI6HyKw2kKEmH4w1v5eynmmfOxjQud1wCYfe/LB4Lt1747Ry/yZFNRqHx9Xd5oA+CQhl9aXtjEEgONAO30up9npXlMz9LExj+2hsTK2L9N1SU0ZNX+jCD5Xfd3SM7H2HuXezC4fo9LWQSTAJyhfe4CkjFmcKCDaOJdVlYQHDugdYUGAeLk552CLCTY2NiQUt/fonEcRGD1htobqYiKKoZ1V2rxOrZ6a2uniq3mDLqZqelTSDYgGTC/afjMBOzu7mM/n2Nvfl/w+RRHENRSSy3bxIPb9kWAPs+kMy2WNnZ0dVFWFo6M5vJcDoxqrrutw/vx5MDNm02k0leETAUN2uNUAjiXfho5rvlji448+QtM00cdqMpngzp078N6jICtmcfCSYBQ9M8+BAyetf0D40mACPYO2YmucAAUGAL/uMFFPTEceWU87KL5w0jEdJ+YGbSuBKHSviJldCpAzwp1JNbUoQxb3zmOIXV7+0T/8AQDgH/7DH+Cf/Xf//aDPY5fFfxgc6MvwshA7df2nbaxov5DO58glFjU9Hp3rUBQUfCSW2NiYZm2HELeVDX5IU3DwU6rKMpz1oA3xDCbGyUA679/4eP82gGrsEh+jQSkjNyZhUxrzOGD3JOAo/U41m0RiPuqMARmPzjks5nO0TQPaJBweHuL02enoNEYmwQzNkvTnmTNn4Lr3RXsUxkAhQXPbtmA/BRXjDKZGJtvc3MTe3h52d3ejNpGYQHDiBwmLb3/rNfzlX/0MG7NtbMxmOGhb2NR0hgjk+/wrSgWIxITO+040TICY+OgcBfqIkLzZAwh2caCgVIJ38K6B7xjEBjwFjC0ANrCVhZ1Y2LLE9sYGLl26jIO7d3H744/RtjX8pIQhicyqqaaYuU9FERkTiT4Vg97kDDsYogiTsVlrQ265KoxjGLWNAsBzTnO9CAiqKvEPni8WmC8XgaFxADyK0sCaAq7q0DQN5osjLGuLpqlxcHiIe/fv4+GFS3hw/wAfffQZvvXqy3j5leexe24bnhgNtQLcnEHpSpSl+Cd96++9jmsvP49f/uWP8cu/+Peob38Jd7AP6mqATFy3nL6m2qR034gMiFaERCedmXWCCP08BRljQCDfv+v+zseggYNGQUDWz9SMcJ1AZGx8PZDqjUW9Z9E4gqMkUv2Q5VnlD5JxJCA1nfvH0c4cLGlJaeHo3XkCwH1cyd87aZ7WrefjaH9ef/puuitzoJa3MzbelIY+bo2/ShnQjQzELYN/dfpZDnKEFA7H0e+P4Xzlpou6b8zfAaS+OGX+iCR5IcvhrCbTCH4AcW0nIgl9zYyyKOC8R1M3wvz7YHajmwZq5uDhrYYINREVC6goQj6IFmUpGd1TsJQfnHRh5W9lAoVg6N96CanZmHzW19F5j9lsBs8eBwf7IBA2NjdRWAnzba1BUZh42ZmYEM5jNtvAn/3Zn+O5556ThKqW0LYN6mUdcjsBdb3AZFKgmhSYTCsJkMActQO673PAlBI0VuMvFtONrmtRlCXe+fkvMF/WwTdKJMBd5/Ho0X5w6EWY4d4srp/A/oMchgwOY2DMUu2JFgF6HOsaAKg1hIJGfk/HPiB+UKDes/McJMKIl+0qdPMBkElY8hZFaSQOLfdTsI6YpZKmdcQuldak/UyeiD++/e1XcPXKRQDAU1cv4dvf+gbe/MU7gzbSSzfu84AVVpYMTwYEVs4KIc5TDsfGQFLapprctm0XpU5126B1LciWcJ7R1B0ASQDM8GH5KQozGB7b2xshzLEMzhpdOzUloyBoQdROr/RnzSW6bq3WSfLGmKv8UhzbB2OSyLG+5HWVZRmFQOHTpF/jUtMhYx0e4xCpiixc18LQBIuF5H4z1mA+XwJcIr32B5cmhdMe9gPQa/W3t7fQdW2fd4kZxhp0TYu669B5D+M5YA6CKmWJxOyoKApcvXoVN2/exOnTZ2PIXYaHpL11sOTxwvPP4szuNo6PGmxMpzg+PAz3iQc0siRxjIpGcYo4RPKT/qbnZpj3BNAJE8GNdhShPw7OBbMT38EC4KoCkaSVqGZTbO5s4NTGFM9cOoVvPP80fvTXf42H9++jqRfY2tjGtJzCFgUmIf9f11Lvk0kAkfjnOOf6s8xAVZZgBiZVBVtYlLbolwMcEuOuMoAAhwTbav6i5wDY3NiALUo0bS3pF5wE8yAr70+LEuVkgsVigePFMZquwbKZ4+Gj+zh3+hIOD/Zw585tfPjxx/jmd76JZ559Bls7M9RFA2oYbdugKhsUVQVbltg8fwq/9b/+I7z47Vfxoz/5IT762ZtoH+2DF0vYtoZpm7henlkCrwbrE0DukrgkLJxAyqTmgswxn8u8jAH+/Bzm9aWfD+hwQif724Vi0BB9dqg1EKFq2kYKcNOy7u8h42+DWWoO0lQzH4BT8p4ICDiYN6/Su9RscWz+xmidvCuTsULXdI6SdwZ8yxPcTxGkjJh55b8r7U37pqZmjytKs/Vn5Bt1INkcpPfxVykK+hnDdRkDdY+tJ5mblMthAIu6hieCT0A7IMITBJ7Re47Cb1knAdbxzicTk2SH2Y71M5EIk//OB6kvltSUA0CYdM8eBgWc79A1cgkXtpD8DMH53xDBdw5tXaNrW3RLcaqdzqZwIQQwu8CmOwlryh5wIXSpIRsuLwsm2cBd1/U+P6Hkhz7VIon0BOg6B2v76FviL84hkRgNfC7YMwpJuIGtjcn/j70/j7Ysuet7wU/EHs5058ybc2ZlZVVW1qwaJIFBIAkkITHZBkPTBtyLZz/TD0+0oT0uvye7eYDpZ2Mbllcvs/yM2wbzbGPwwwyahYaSSlWlqeYxq7Jynu5wpj1FRP8REXvvs+8+NzNl6H5L7qi6ee/ZZ++YdsQvft/fSBxKhsNtRtsbrK6sEoVxufBkFJTmfGCI444DcxFLS0skyZTUjd8ojRSaN86cYWlpiYXFPp1uiBG1DecD1JdojZlxzjA1xoALg2wEZCoDGfDs8y9ijCCKbOCLIIiZjIdsb42BEO3rbkggMMaGwm4QqVLqMNMdv3XETobdzbmtQ5Y5gKob/K/qRrtJrfmMwL4bbVqYOQ/rjN2sdcbeuPYE7JgnIQTKCKeNMGTphP7iIkZaiW+VA2Y+wJgnLWp+55n62pVqzG4uvfbonz/xz/mJt/0EH3j/O/nq0y/MPYzAYjknn3DEanbe2yR8zf7ukAD6+S//KXtcvrbm/rLDqSLm5MomB5ZhgNKKSTZBRksYApKswGiBnWFvTiOciWNApxPRH3RdLiTLcPQHPa5vJG49WNBb+onNQPadB/jM4dEy3huB2+a9bUC1NZRsbb21HXZNAFX5L8Y2LYJo7m8v/Gnvs3ALwWpx3fwa4WhchtGCLLN5yaSEyTgF0wESDDuZDv/+hTAgrEG1BRuaXr9HkRdgDNII6xskJQpDrjW5kUhjo7kBSLfGy32nFIcPH+HJJ56yqc+0QAvvG6ExaLRKWVpY4eTtx3jqqWfpdgdEMiRXuZVhOCazFJ85IIAD+YGUhFFMroodTJWsASdjDD4BhTXjdOeFwNIGbTBZZhNY5xks9hGBIHJa/m4csLYc0wsl0fIK7/3Au3n2q0/z3FeeZ3PTsDwI6fcDVpYXuXr1KqrILHhx+yUQAd1BD607bG1tEciQMAw4dPAg25ubLA0WbP64orBCA6WcpsCeC34MdZCuGj5Pdtx2DXXjkCgURFFAmkqyPKfQyiWDlgRSsRDELiFtQpqMKfKEPM3YHm5wcP9BtkZjXn39PA/e/wD3PXAXh25bIY4jCAxZkVMYRVCkdE0XEcWs336A9/25H+C1hx7gSx/7LJdfPoPeuAZ6TCg1Os8AG0lQGUUgAL/ehHHA0K61yj+22kc76NGcPVzfu80AEE3NRz2ARivT6mmJJ4HVZcdIzmof5vWx2d8d50YLLauPWcqaX16jfr8utDYEshIiQ+1sqwld6oBgXl+bc1EHIzQAQ3mvM72uJyqtA7G2ttrmvE53d9O+N2lyc+7r/asD0/r8103xSnooq/b8uOcBmvr42sZm1047/9AmhGubm2ZblRDNnptGiDJVjcLunUB4wFNmIXOL18N8D44q3qKMillx/bYfeJ7LWKXJTZave4DUtDvc3Nxk0B+gA5ugTmlFFFs1fRgGTjpngzqMRiOMMayurLrkfpaZ9/XNvmx3COjKiTJwoU/DIEKpwuZPchGB2hasv145NXoNl0FrhXWorDRI2iiEriK6+H7ZM1WUzy4uLnLp0iWuXL3Mnj37KsZeVhtMKc3K8grj8Zhjx44xcRnhwzBEK8Xly1c4e/4cw9GIu+++m06nQxRGyNbIdRWjVC87JTV2wWujKQrFlasbXLhw0QFCzWDQR2vDcDRiPB4jhUBR26um3poDi+56/ZpxffGtVxIr3Pib0iJZ3jMzqpbN730KJM622t1XMuhQC2FZgSgjfN9MKfGx4GkWPDcdC42BNE0ZLC6V783MNQP82ks5xTVw5Mf/gfd/K1vJFn/ro3+LH3ngR/jA+7+Vn/+F/9eO+ZnRVhicyVmdxrWDqWaZx1jcyoFeH5goiatwYaadj4VxkbeWAhQwmdiknx4Le62QdhLxMAiIfWLoQjF0WoNASpS0jDFa0Da0ulSvzaytyYA0n2uzpW5jcubV02yrfr+vq614GiWltLlxGm3NSGh3aQ/hEiP7+wQIbBS7PC9Ik6T02ZxOpxR5QdTZLfTxLCPh114UhkyTBKgYKs9opFlmc8pJ77+207cGYDAYMB5PXJ1W014xa5Qmlm972yM89tgXCZ059ubmZhUox81JXfRQZ3BsYCCbwqFcG64r3lRP6yqCkweFFox6gYNwJncGneVkwzFFIRAiZNCN0KqLDCyoj4SgiCRvffsj3H7bcT7/mSe4dvkySdpjYWHAwf0H7ZkkBCKQRM63Sgi4fu06aZJwYP8BxpOxNeE2hsiZmHphYH2cbUxUM1BBc28bYzW1ncAGGTLGkKQp48kElStAuKBCHeI4JM+cQE+NyfKU8WjIlSuXOHLwCBsbV3n19Rd5+NEHuPvuk6zv24NWGkRBFAWofEQ3jiEI6HZ73Pe2t3DnnXfw9Gef5JnHHufqG2fJRyOMEQS6QKqcSNsgF8adF0ZSgl8PKhHz9sfsWNvoWpOx3m2e2oQfJZPOfBqw23432uXnYqfGqxk8oF5X/f563f58bLbt7wnD0Gr0a2DJv+OmKfzN+HPW++d/ysAMNUGIr8fzVfPAVht4mTd3TdreDNSw23nXbHPevU2AWd7nztdbORd302TeSrlRHTNrmdl5EUIQx3Hp76lNpbkCSh5qZm3J2Uignh6C9b+uSg1w3sJ4vu4B0qVLFzl69AjTqXIAJUUGC6RpgkewWZYymYy4dOkSb5w5wzTJePThh+l1eiwvL6GNwihVAiQABNYx1TmMK2Od573pnSoKAm1VpVlhUIVCY0jzzJlEuFwUUiKMsdmFtUIGgXuxTkMiDEHoQ1Aqq8mRPqmqC5dcWG7fLx6dK/IsQ2krwSuKnCSdcvnyZSaTMQsLi4RhSKfTK1XTcdxx/kmq9G3KsowLFy5w/tw5JqMxw/GI/mDAgQMHiOMYGfgEhjcmwDuZME/MtZ3HIOSF518gz4sGE5Zw7epV9/wuqlwBQgR19sMSZEeVZ42b7LablWzZ62WqKuMlVjXi3gaQwPkjUP54UDZznwfBDiyU1tjuITsjlhOv90s7M0/lVUXYQBlBGFgfOpW55Gs7D6lytHOASP1wuZG00Jd77r6D47cd4d9+9d8yzIb8zku/ww8/+MPcffcdvPjiazP3zhwKJdDyjc/2r+3wb/bhpgAQ1N5dVX/5Nx4cW6KqCmtm6n3zvIktCKZOSOArNcY+ZxM/W8ZssLBgI+IVNvz9YNAHsVkyzjPoqFpO5ZjqP3Xpa/NgrL8n7xvSnJO2z83x71b8gT7vwPd11p3U6zmEZvtUP9p2adP3U/j9WgmGlFIu3Hpo80xF1tx4xz5kJzPh+7GwsGBzLmmFlJ1qDwJpklDkOboTOcBWyinLMdsfydLSEpubG+zZs45SPgCDwCBdiG3FnSdPsLS0wGg0pdOx9HTGBNHMRsfyRWuDKQqMtudJPcADxmBELey3H597VpWMnhNIhDbQEIVBmgKtxozygtAUrPZjpuOExU6MEMb6z6mcA4f28oHveS/PfvV5vvKlpyk2MxYGi/R6PTACoSz4Utpq/BcWFhgMBghh/cPCMCyjN3q/LR/kxCftrq+jNvDumWIvKPT3RVFEYTTKfbd3aZFwY5MkSUmTxNI+Y8G17EqiqMvEhQ7P85xCZUwmI/as7SFJp1y8dIVXXznDw488yIkTx1hc6pOlilAakmyKiEOUMRQdTbzS5dEPfAt3PnQ/X/jYYzz/1FOMLlxETCcEqSA0AmmcN6wjcaamwXToqBz7XJ+lFoDhQUidNvpIc/W9VhfU1vfFrFaiEoY2z4c2OlIvzetKqXL/1wW6TdpdP8NmxsXs/vK/S2Gz1ghRBY8CSSgr7Vz9uXm0qo3u1X2upfRRCWfN1Ko+7jTRLp8zNx8mun6mNTU0zXnZDejVAV59fPMbrs6/mwE99Xmaq0m6YS03V2aB3KzJqGh8bgOkxpgZHySjVBU/wBjHV7WAoBoPcrPvD/4bAEhf+tIX+cpXvkwQBIRhQK/Xp9vtIgiQgWA0GrK1tYk2CqUK+v0BBisdDYOAzc1NC3ZcuG0RBqUkOcsc2DE2nGoUxVZiqFWptrZ+PBbgSJeksB5esh4Jx2aXty8vyzIMVeAGoy1QSqYJaWbtsn2iWi/BtFJlG4bYmvc51bvRqEIxHG5z+vSrFhzFXdbW9trcHd0uDz/0CEliTYPSNOXKlStcuHCBzc1NpuMJqijoLwy4//77EVLanEtyfrI7X5qSlzpBtyAmIEnGZLnh1VdfA0Q512DK5LQ2x1MEQSUFnmEwAYzE4COk4EI/Wwdw0aJl8SFO7Qa0TJqrCCO8Vmgn8bCDsT4LVuJEFUHNONOhGUbY1glgtJPKigpYauMPGGe605B8lZJiBxqTJEUVik4ck0yzHVLpr0USNEuMZjn5+hx84P3vAuA3n//N8vcPP/jDfOf738mLL762ox+lNN/VqF1Vt9LDHYfsf6WkyzPwQlAFaRG2n8l0aiVYjgZ4ICWlNX/ULhiLdv4pYWiTkHqGvtfrVe1IiVF6BhALOWtO0bY35oGcecxI2/g8QzXvALoZqWSzjSZjItw69hrzWcZNz2hmdwtjDBZMWj8hSxPTNHP0zNJCC5S6yFZfhFlmayfY9JofWyytjkotnFIa6fPD1cZb7UM4cOAA58+fZ+/efY7B8mZyoLTBiIyFhUXuvucUn3vsqdKCwJov26Jd1nlfb0nDtC7pid8rHhzhhTD+HXituJsnq401JS0RymBcQlqhjfus2b6Uc6bI6ApY7vQggn4nJuh0iKIAIwwPv/1Bjp84zhcee5KLFy6Sq4xBb5Fe3EUX1kJBhlaj5aW8vq/et9avC//ZB/fxc30z684/p5SLNuiYHyklcRyzuLRIt9dHG8XWxiZZlqCLAilCZBDQW5DooiBLE5I0Icsz0ixhezhibWUf4/GYM2fOcM+9p3j44Qc4evQwohMghEKlBTmC3GjCKCIOIwaH9vHuH/geTjx4iic//knOPvM8anMLMUnAKBvEQRcWYAuD8YmwTbtQrTneJqNcX8d1DdtuJmVt4aCrs9EgnOXJPP+WJlNatTHL0DcB2ryxNJn58h5jnDCzAg4zJsW1/Wv72vRb3Ekz28ZR/9zmQ+SFn806LO+wkwa2Me9toGUekGmrx/etLeFtvT9t2rrmPfPOhHpfmtGPd8xJC22efW+za7OtnXmlbb2KxntM03SGH27bO6XZqqB6TyXgouTH562ZWy1f9wBpfX0Pk8mEPXv2AHYupRRYLau1nx+NhnS6Mf1+j8GgTxDGvPbaK+4lwnA45OUXXyJXBXfffy/9waBc1IEMrDQ/shF77GGeVUydwYZkFrL0P/IRgsCZsNUlhqIe/c6CLb+wkyQhTVLCyD7TSTpWchdYZs4n5ouikF6va8PHKnv4GmlQRiGlwRiF0jmj0RCQrCyv0un0uH79GqdPn+bs2bNcvXqV6XRKkiQsLS5y4MB+ojjmyJEjNit6qUGa3Th2GPMJcPW3Za7yXJMkGVvbY65cuWollg6AZVnG1tYWMrAJHw2CQlf5aryWyTNS2nhnPlsEAYiqrdn+2DsqImk3XEk4Z+6jrLH+V2UbjdUqWCOhGdW9v7udgFjpdCCcxsmoHRoGP6/2gAzQxiY01UZbwF2TjJS13oBYtZUZRnoOOAL4zg+8k3E25kOvfAiAP3jlDxhnY77z/e/iF//pv9pR78w8OIbKCFO7NLt22p5tO2h2H4z/1c6EVH9aEKG0IgwDcqVI3d6VBEynU5TShJFAuLXl5ykvCpaXl1laXCJ0EvMkSRgMFsqAJzvGA85hvXplbXvmRvun6dTbZFB2AznzAFbzPezGANXfizFVxLI0TUumpw6y5x3udRhuvIDC3e/rslJzYc3kTIeZDXITJQwlS4uLpa+LHZDtc1EUjtZqjJFOMllJUOtzcejwQT732OMlw+rpniAoUymA4tFHH+LTn3ocpRSdTqekyT5UfAmsmBW4GKVt2PqGWaqPKlfOI06w48+xmmmhEJWGSgQSpSGIImRhLSCGl67x4iRBjVOOHT9Et9OlKwOECOj0umRZzuLaAt/2gXfz2iunefrLTzOabmPQxEEHbTS60KUGTrkxeSavcILEusO8Z+y9eXk970wbw+vfeSnZdwy+Z8wF0Ov3iLQmCkN63S6bG5tMxmPrK+t8s8IoIgxj0iQhzxKmSUJRGKaThM2tq2xt7WVjY4Ozb57jkUce5r7772bv3iW7KpUgnRYUuaYIC3QsiKOI2x44yeHbj/D8E1/iyY9/iktvvAnDIUGaEsgIqQubH0sA0qCrVFblGL1wtAl65km1mzSwjSY2628zW9RmJ02pv6PWz7VHrCBBlVrCtueafal/rs6w9vtK7Vit2Zuh9bsBxRsxxm1gZzew0FbXPNrYpLFNP7L68x4kNoMt1L+fB4DqtKTcIw23h93Oz93OihuNtznuGwnB6vfCLCX3427OdXWeVM8aT5epgt74fz2grnhALE9oTLkZtfn/a5DKEsexc6juzEhPhAhQqkAIQ7dnQ2ovLi4QhhEISSBCBv2B01xI4jhC6oBOt8Panj3WX8lJAMIgdKHAQ+I4QohFDFUUIoFEigAZSPIsJ4qjSvsirKOqFNbHyUvJDKB0TixjlCpAhMhA0unEBM7mW2ldSkL7YZ9ev1fmnvCmDkJWqvlur0uvs9cGcgitFq3X63HnnSfZ3t7is5/9LFevXmU0GpFlGUtLSxw/fpxet4sw0O33WFtbI44s4JMyQOsK8d+MNKfaIBZiJElCGEa8+uprFMqaUgQhpY9Dnuf0+wPL7JpZVXVd5W1xgsSGQajyioBAOTC0c2/L2m/fp2ojNclDXQo9c90A3ryvxny3l5o82BhwgTYoGadZwl5J6gUGXUpkhbD+FULMmh98reWHf+h7OHzogPvUHgWu2+1w18nb+Q/P/gemxRSAaTHl91/5ff7MvX+Gv/d3/zJJks4dNcC585f4t//uP//XdfYWyyxRr5hJr13wWt00z2xoabe2kiRxvn82QaeckXoqer0ukfNBEsJqi7u9AVIGgNphZCbsjdVKu0kpXNsB1wY6ZoHLfJDVBnya/ZjHWDTBkQ8T7E1vvHCo2lu2VH6VuxfhzNWyLC214mEYk0ynIFZu+PzO/gqCICwjvPm+B0FA4oRKWtvE05avqAtHqjno9wYkSeIsAqxpMcb7l2lkIMiLlJMnb2d1dZXhcEiv15uR2oLN92HH2TCLqr3PGaYFXAhzx8i5OTJY+u/zHAWODkpXt9HGBgcs8mrN5Ippbnjhqy9y9do17rv/Lg4d2UunFyGkIO52ICzI0oxjJ49y4NA+XnruZV596TUWetAJQ0xhQX7pG6k1prA+HXmeW7O9GrPk2677f7UFIWkyauWPmwOjNYETMCptKJT1cbImriFx1GEynjgfJIXRNnl7p9u3wYeShCybolVKlk+YTMdsbm4yHk+5enWTl06/xsOPPsDJO+9god8jlKALTSE1Sb5N0XEm6YsdHvy2b+a2e0/x+U9+hpc/9wWmFy5i0hRyg3TroTrjykGBf0c3sQ+q9Tu7D28U3rr1DK7Rrd20DDfbnzYhTP08btKfag00KWL1/c1GbruZvrXRwZ0Aozpzm2u17sdSv972uekLNSNo3AV8NPvk3RuavqW7JQSe16fm0rvZ0gSMX0tpvvv6nDSFTtVNs/3VSleCnprgztMDw+w6rATVVeCr+vcGbPRSX9ctTM5/EwApyzLSNLXJYEuUKsgLQWw0a3v2srl5nbjTpd8fgJBcvnSZuNNlcWmJMAw5cfJOtoZDFpdWSodRcIeGNsRdFw5YV5tUlvlpcIBClqYC/pD2wEpKCTUbY3ChQrUikJEDOQEytiZ+WhtEYKN/RHFs7S+deUYgqyztQRCCkGil6fb6CBRCBEhC4iBmZXkvi4NFfvM//RZn3nyDLMtYWFjgzpPH2L9vnbzI0EqTZQVHjhwppcXShtJD+sSFtTLv4Jvd3BblT5MMGUS88urrTKdTZBhbgi4D0jQnSVNkEFIUjuF0UjkbXUw4auAIs6gTx9lDSjjmp+rXTIcRXhphvCTb2bbXpTENHCJqf8gdB377nMweEdavrCQgVGMSrjErcaYcuxBQ5DloQyADZ8I1ezjNew9thNsYw9LiAv/gf/xJG93pJsqvPf1rOz7/mXv/DH/hv/vBGz6bZTm//TsfYTiaWKaw1r9muVUiPbu+qnU5e1Da2Ik+QIBdS3Yu0RqVZeRZDnHAJEnJioIojsr1YxlV60AcRzFxJyaKLQOeJgXdfoAQNvxzaRwlLIEuCXejv83D2xP22eAc7YxFG6PSnL9mOzcDuNrWS7tk2F4rHCNuQ/7vLg0v24ZKGiiEDZNcY/5HkzFpnhN1uwzHCVYZLvByimqs89eJAA4f3M/V6xuEwZLdWwakSzadF8pqabDaCisqqQQ4vpZABiwuL7O5vcWePauWzuvamtIaUxSsrK5y8q5jPPHEU0jZI+7ElulBlyYlzXdh3OHtD24Lnqk+GSx9KhkN26jQgsIvc2G10d7vTUhpzfC0AWfaZzVOOUYIzpw+y3Bri5OnjnPHyeMsrSzatRwIRAcEGZIOb3n0QY4dP8qTjz/J9njIoDdAGNCppijyErAAZY62UpItfJsggpAwBlHY3FHWN8kFr6CaRwskqncgvMwKy0QiBGibxqIorM9gp9uxCdujgHAaOyCboQq7FrWBqNcn7nUoMmuFUUzHZEXOJJmysb3BxvA6l69e5c03LvDwAw9w5NABgsBAaMhRFCal0BlBFNOJuiwe3Mt7f+BPc98D9/D5D3+Mcy++QrGxhUwSRDYlVD5GqzUrtnRcIxEWREkrGPPanjbpeXPv1IFj/Vp9TdW1DSXdM2YmqlmbqVW9vlKTIeoM82z6kTbasBtwm9mTtXF4UAB2D1bJ3h29FPWzfHfhja+v3i8rCcPS4LKvbs0Z4cLYNz2UZ+ldHVi2Bc6oF/8uZ/a2nA3K1ZzveUIuP46mkKU+3nqbtZpp0sS2Nnask5bxNDpcm6OqXhkEpQDA8i6z/A4lkKmeKem+85+WCNDWEkcbkCbAmCq/nUK5dezcKMqz2I8XDBotSu+rKl4Adm0JbddVbuYneG6Wr3uAJGXI0lKf6XRqGUkRgIRCF4SR1cqsiT0MRyPHrMf0en0GC4vE3R4yigllwLHbT1AoTRRXmighvPlctWG0sD5A9v23h+D04Ki+OfxGqBMOAIFry43HEi6BdgtGyBARBDb5lXCR7JRBihCjbXhoIUKk9Pda6WIoQgIZc/ttd3D2zHmeePxJZAgPPfwQ+/atIySk2RSlcoSwJoR79u4liiI7JkPpE9PGyLZJIWY+C2PDtirDZDTh/PnLpHlO6CymZaGYpqllioQAJNqAED4MegWErH11Fdp3lpg0fZ4aDCL+gBZ485pAuM3XRETsZMU8MfAuTiXDv+NO198634WXangE5PwgXDjvehZ6IezBqrG+amhDJ4oc4z0/TOe8UieMw9GYH/uLf4tf/F/+Dvv27uHV66/yV//gr3JlfGXHc+N8zHNXnpu59tsv/Db3/fP7GESDHfevD9b5Z+//Z9yxdgeXr1zjr//UzzIaTXbcV87IHCBwo/tm1uGu57MNW2wPFXu/DdJg/dSMKgikRIqQXKdkRU4fnzshcDRaYLSm07X+G91uxObmkGRacLA3sMywUAhCp4vzoNszs7P7o+3vNulnE8D43zPSshYQ3DZvbWCoDWTvVoflVVWjf7s+2ngVbsw4Hy2DYxqtOfB4OiZXBUKGjKc5WgmMkE5w4ato+vs1+m4My4sLXLh40ZlfBWhlkGGAEC50tNJE/r0aTwds8HxflDGs79vHpcuXWFldtr2WFeA1xrgojQUPPXIPn33s00ySiCiKSKU1LdKqnRERwpv4ui7XhGxgVyzMrh3jwJy0eKI6L6TXahlEaTboTNWMRiAxqgAt2N7Y5stPPcP1qxvcdfdJDhzaR6dj/bMIDHlgz5nFlQXe+53v4aUXX+W5Z15gEHcIosAKahCkWW6DKFDRLSsQtH3RQiKEA4EioCBHakNRKDCqPCsdqfP/lODGszxeoCiDKh1CoWzqjCiOrI9XIAg7AVkWMZ1OLa20xxVGCeLOABnEJMmUJEtQpmCajxiNtxiPxky2p5x74wJvffQh7rrrOMvLfYJSdFWA0uRKQWiIwi5H7r+bP3n7bTzzuaf40sc/w/YbZ2G0jWBCoO34jNAo09h7eFq/UyDRNLWqP1f30WjTRDUFHn426/xDnTbUNVI7aBAGY3KbvkRZP81KSOSwRwtgaYatrtMaa9XCTDvgfTdQNvMAAQAASURBVEEDB2RqKRx2oeltwp2ZOZlhkGf7449cPP10IEk7Wi1q9zdBYB3Y1NuuA8TdBH7+mfq81c1K66UOPNuEW/PouSeK9Web/l5NsD0z38yuzTo42iHoct/X+dQdY2cn76SxdEE4JKwNyDiGMAECBNYdxQg/7xqhQYrQtalnAzYAZZjg+hziu2/bUO3yu9bydQ+QgsCakXk7aW92JsyshDYMQxYXFzEGtra2mE6n7Nmzp7S51dpm8o7iTrmYobKd9aW5wOpMh6/Lf/a26fWFeCMJiWgs/KakAcAUFdPSZvsqXR6JhYVFFhYX+PhHPs50mrB+YI2jR4849aZGKRtFKE0z9u/fR7fbddqj3TOENzddG9NlsM7XcRTxla8+x2QyKc0O/e8kSZ2U0jIuNoR6e30gSv+OepSt5ty19tvjk/L3TknJjUqTcLWVee2XBGbOM1JaDaB2n1WhXBCR0Dr96/nmEzdbPvmpx3nfd/4Yv/j//Du8+53fyK9896/wI7/1I3zi9U/c1PNN0ATwbbd/G7/y3b/CoaVDfPKTn+en/u8/y9Vrm4BjesROGAk3N5f175vrDfx7awFWDmxS25NFXhBFcZnDxd9nw37nCNGj5N9dm0pr4thGgfQa4KIoiOMOYCyjqtw5dYPD3f9uO/Cbc9G81owQVvdBaM7PvHDiTelsU9q4G2Cq76sbAau2duvPaigjEimlmIzHZFkGQrggDR64tPej/tsXbQyrq2suImHFgIaOFqdZVkal84yTO1Kpm9RqrTl06BBPPvkkp06dosZy4leGMTYB6d333MVg0CeZJgwGsWMIrbRaInfM927jqTPJrcKDklGtInMJaSW6hfZmkC5ogDY2jLgxTltnE/O+9upprl67yt33nOK2246wtmcZQUC3Y/1AhbBr+9S9d3Ps6HGe+vzjXL1whWyaog1MpmOKPGNxcUDhtEIBzipCONMqh+6MEIQE5HmKzR9UFyrNL4EzGTfGuCiwFZ33Z3uv1yMMbcRD/3k6nTKZTMjz3DLgWiODkE63T5anpHliTWtzxTRJ2dzc5PKli1y8cJb777ubhx5+gKPHjtDrdzAuKbwqcopwTB7khHGXbn/AN7z7Xdx18l6++MnP8PTnP8/4+jWYbBPkGaHSxFqjEShpUH4utBeKe1NxnGl8xRi37cV5jO0M4Kj5OdnVOcuL1GlCU+u0g54K7ztUsbk3u+dneZvqJc+ax5uSAW8DD8376/W2zVM1YRW4ro+nEjxUz2gXKKUJFJrjbKOrzb3avG8e+Kh/nvFtbNR5I2GX5xFmwLZoGW+jz23t1ed/Lr80px+7fb+jHkdnA28xgCEVmre89a2kRc6VzQ22t7bI0oTpeIzOU0xRoJVB51aHpDEt/kRBSVDq60IIiRAh6IJQxjccky9f9wDJ+7E0Qw77Q3g6nTIej8v7wzAgSQz9fr9E3UIIul3rxOqZ7kqLtFPSI2uHvP9cJz5VYIGKCapHM6kDG19vySi7jRSFYWmu5+/x9cja4p8heECRKwJpVZmHDx1hc3ObL3zhCYoi59RddyEDgTReWxNiDMRxlyNHjpZ+XE01c720EfR5902nU6Koz9NPP02SJMjQhYoNAsbjsU12iA9rLEiTFJ+XoTk3lvbutAOul/q1ikhSSq59v9oI2tdaboZ4VL+99LcqniH3tFwICwSzLKPTtdq8QumZdfC19NsYw+Ur1/iRH/tpfvzP/xB/62/8OB/90Y/y85/9ef6nT/5PFLq4cSWuhDLk77/r7/O3vvlvUSjFP/iZX+Jf/av/yDzTq5vtdxsxb/3s/73hNDiApAogxrhoj3meg4hLhlcKCdJgXPJH7ST8cRwTRRELgwWuX9sgz3PyPHNjmU2KWLVYMdPzDs7m4Von9G3z1Lau24DXvPn05UZhc9v62rxWv7/tepM+1OvyjJKN5mYYTyYWIGFNM4uiIAqdLwsCEdRBynwByGDQY3t720Wrm5U228hJhfMNoTy4hdg5r4uLiyRJ4oIR1JmhOrNZsGfPKnfceZyvfPkFS9ek10ZZMNWkiW0MWNs7nafhEw58aRegoMz5JqQ1w/XXMRjl/CsCQZHbJOlowdbGNl/64pe5dvUaJ0+eYO/evfQHfUJrF4dwcxV2Ar75Hd/Cr//rXyNPCqQUxN2Ybr/LaDJBOoGBUQXSRU4zblqFtInTlSpAgggDdKEraX3tt6lfAAIXzMhbbNTPvvoceQsHIWywo36/74R8Kcl4jC4KBBAISS8KiYqINJ2SZjZiZZ7blB+bm9fZ2tri7LkLPPrIw9x77z3sXV+BUGOUwagMHRZkOqdQBSbusXh0jXf90Hdz21vu4rMf/yQXnn0WsbGNGCeERY4WAiWsX1OoK1NUF3OjjMBVX8L1PdNkYnej+fP8gZr3t9GhJiiQQqCF03I6cNfcx/OEKrN70oXYr0VqrdY7pStwc+3Xx9omDGs7B+YKIquH3G+om9/t4ON3ARH1euftX/9d08eqTUDWxis0zfvaShvYMWInCGsCn3lt1u9rm8t5dL1NENdGz1wtNom2+08J0GjuvP8eHvyGtxGtLpJMxuRJyng45Py5s1w5+yaXL1xme2PE9WvXHU2353WaJhgjAa9dAmEsAMPY5LBB1CGAUohzM+XrHiCdO3eOKIqYTCYcO3aMbrdLr99jkkwwxpAkCWA1SFeuXEEISRCEdDodhsMhg8GATqdT2cmaSgrZRPceFPnv/GchRBXJiFlJbx30qIYPUn0Rzyx+95taRKD6PTacrCjr95HyhJCEYYwuNMuLyywtLfP4Zx/n6tVrrO1d5eixw/iwxl5bo5VheXm5lM7Vw/neaOO0bSTfR+XyY2xubHDlylUrfQ9sJEApbKjx6XRaEiyfaNcS5zY74FnNgL/eJrXZDbTcCNDMK/OYxfpc7caoClGZxvifOmNUGJsfSpnCad9y+v1gBjw122+Ota1vfr3VAfmv/K//G48//iV+6Z9+kL/zLX+Hdx9/N3/2P/1ZXt98/YbzcHzlOP/u+/8d33jkG3nt9Bn+yl/7Bzzz7EtOjV6Fu54xf9ilj/Pmqs4AzIadFhXobYzVt2ucyrACVIIosn58PmpkGIVlxEAELmS37/gs0xKGAUVhBR5hGBLHMXmeWAbImBLk1gFcc5U118iNQElzbHUwdTNlt3Z2MOBz3kmdBtZDirftu6ZpR73f4A5/Y0BKENb3MkkSF0TBpjmwbVRtI0TNz1Oj9eyatmtBsLy84lIoqJm1E8cRKrM+lrbfzhzS1ddE2MYYp5VIWFgYUJ8mD4qVLugEMffddw/PPfMSOE2UFAFG7vQ/K+m71mUI+Po7rJv01N+vHR+l6ZyUVTRMG0TBYGqmTD6EuOVDbd4977MZBAHaaKbjlNdefoPt61ucuPMOjt1+jIXFASKQhJ0IJQTZNOXpZ19gNE6QWiBDyPKM9dW9TC5NSnNDLSg5f4O2oMk4nzVhTSi1sbkBpbS5mwxO8+H2l9WOVYEahBBWWCEqoFmfw/pa6vV6ZFlWntthGNKNYqbjCWma2La1IghjOsIG8sjznMJpKtM0YTqdMJlMGG6NOPvmBR56+EHuuOMY3b6NWGeMDac+1SOUTulGMf1ul+NvOcmBk8f46ice46uffIzN02cwoxGoAkXhyLsoBXreDNJathiMqfxommObt7/mnS/zwEIbQz/vXq1rIAa/v258hrYBlvpZ7feipSOVX0q9nnmAo8kTtdE9q7Gdd97X+DbfVs0UurxrznlezY2eAW9tYGWG9t+AxttkuXlD+7GzjuZzM2DHgdg2Ot4UsrTxQ21rqQ1QNXmetnqbAvyqXlMKBDQGhTUX/s3f/i3+8Itf4J3f836OHD3CtWuXGF6/DigO3XEbb/nGt7O8vg+tFJPJhO3tIbkqOP3aa1y+fJ1CBVy6cIHRcESepIyGQ4xy/vguaE6RTLnZ8nUPkAaDAXmeE0URnU7HJp7LC+cZUG20wcD6T3S7XZIk49q1a1y8eJGFhQX279/P2tqaS9haLYa6+drswWXv8TbTnnnw1+vMRHPh1xmlJigri3FBGkSF2GcWr7aMnpdaFEXhNDMSjSQIJEePHiPPCj7z6cfQynD//Q8QRlUAC+0S1hRFzr59+0vzxN3snqvu7S6Z8JLbTqfDV778LMPhsDZnNrS5BUeWASoKRZ7ltTpnGQZL3BxBlHKHaV2zDzPXW/p5Iyb9VsvNAJX5pWLiS8AkhNWuCbvG8kzNmBK0tX9zbVXl6Wde4ru+9y/wDz74k/zA93+AL//4l3nvv3kvT5x/Yu4zbzv0Nj7yox9hubvMf/iPv8//+Pf/CZNJOzHyIKFtnezWx6/13ZRE3M+T3ypak+WZdRg3lDnFYscU2PDSvpbKxlop5YBVRK/fBwyFUkRRXCZytYynqkkrTSuYbWNW5gHumxnjvOduBnzudgi3fa4D1Ob3Tdq4W7te8y2DACmsCWMytWtHK43Bhv5eGHRdI9Z8zDNczfrqpdvtlT6I9nvtNPYhmZqSFy7/nJQ1f42dAM8Yw4EDB7h06RL9/omSRnvm3hiDKXIKFfLgW+7nP/3m76AKBcYLleYIYIxlzNrmvnl//XywoNKZSBnwgqPCgBbKpRxwzLcAIV3kVaWtDytggoAsza0GCtv/Kxc3GA6f4dr1De646wQre1cI44BOJ+KJx7/AM089B0iXxT4nLRKrXYoi4k5sQ+UbQV4UdDodlpaX0MYwnU4ceLJRqqxiVhLFEWFoI9RlaUruAn4IIar96s9P4cDrDcCDDybkhZI2aJFgQYbEnQ7jyYg8SxHCIIOQWEZEkSHPErJ0jNI5ZqiYJlNGwxFbW5tcvHCRBx+8n4cevp/9B1ZtUJdUMJlOkWJKpxsy7XbodweEccw3fMd7OHnf/Xz6d36fZz7zWRiPINfWX7jcEp4SCnAhiucldfXlZsIp14MViBaa0yxN5r7JJNtz2/pg+tD7okVLUW/D04Uqsax9xtCWm8i3vVNYNK+/deFBm4bJGGaCLM3WV+305qXdzqTmOV63+mmaNu9W5gmhfFTitqAO8/rhr1fnhx9Me2m+1zpf6r9vA0Rtz8BO0+1bPbOkEIRSMOgPGCwuce6NM3zmk59k/4H9bG1tMBluY7QilJK9+/ezcuAgd911F088+ST9fp84jrnnkbfwzev7CTsDRttDRttDijTj0vkLXL18GRmGKGOttDa3Nvn3H/8vN9W/r3uAdODAAYQQXLlyhcIR7CiKMIV98Z1OxyWEtJnFgyCg37c5LLa3t0nTlNdff53t7W0OHjxE3LEHdMms1iTwdedKv2iapnL1qDX+Wj0Eqr9WJ1S+Pl+nPcznmIIZU9rxe9PCcsFre9wsDBZYW1vj9Cuv8/obr9PvDzh65Ki16zTWGV2KAI0ijrssLy8TupDSbZqzqulZaVTbd34+rJ14xEsvv4xSRclYam1Ik5ThcOhylDgfJHzywIohmmnHEcQdzp2N9v+owc/Nljo4vLXiDlBhmXeEceYuNhy1Zcar0No3OlhupUwmU376b/wcyTThR3/kT3Pv+r27AqR71+9lubvM//vf/BZ/74O/+EfSh3llHvNwq0UIa7pZFIXN64VNAB13LJOWTBMr2VV1ZoTy/jAMXWhjK0xIkilRaM18TFE2gjV7mj2UZw+12d9/3ONuljZJ4s2WpqAHdpqD7FZ3CbJ8SFs3RVmeW2mq0UghyUsNsr/H00l28Dr1Esch/X4fG7LdlHjVC5BU4UPrVrq9OvDy+1Zrzfr6Os8+8xx3nDhpx40sTS4tX1JQFDmHDx3k4MGDnH7tLGHYtXndiqysb5bJcOK6m5h/v7+bjNIMc4tLEi6kTZZt1TjOvElQ6CqUsLRBMK3ATQoQNvrdZJzw8kuvcHXjKifvOcn6gXX27FnhwMEDfKl4GoREiIBuL+bhR+/n5MmTfPgPPoIIJP1+35pGak1RFAzHIytctG+MMIzKlBRSBoSBFSj4AAwk3lzQarqQde2wKEOY+/G3Se39+7UJ4m0I5SzN0MJGxhJSkCQBaeaSU2obCiPuQBAappMRSTpFa83lKzmjrW2uX9lgtD0ijiIuXVrigQdO8frp83zqE4+RZkOELIiigCju0O302XNgnUMHD3DyW/8E4UKXJz70YeRWQSe32goZWMsOrSg1FwZmTORbVkC5LutjbTKtXkhj+Y0dipEdz7f9rs+nD9BQlZooYQ7t2BnxzWmjatHxZpnrdjDXBMFtAsc2AaRoPDsjlPZDmFPa5mHefXV+7WZKHQD6OfDzkCRJ67to60dzzJ4/swqkWwMpzXltgu8mX/Ffc17YSq01iSXl1twuGY3pLy6wGHcZXblGPhqxvLyEznIOHj7IyvIyaZ4RCcFrL71MNp5w6dw5+v0+Z157jbDXZ8/BwySTKSuLS+zfs06eTQkDWF5e4PDRIwxHI9aGSzfdza97gCSkZGFxkSiOKVRBGEVM04QgDKow24HP+h0ggxBRaPqDAYOFBbIsYzgcsrW1xenXX+f48dvpdrs7JKPNePVQ0zIBhTN78LmOcI73liBaZ1bjJH5CSgTC2k9WIykJjJSBDSHq7Oa9ZMfmdDEodxBbAmUJm9aW+CgKDh44gFGGL3z+CcajEfe+/R76Cx2yfOoWvkIKmwX+4MFDRFFUmun5BLdf07uoEQQI2dqacO7sRcBKYny48iSdkOVT9u1b5/jxEzz33AuMtiekucIFhLT1GOMYAD/h7QTR/11ub2NmHDT/OEud+NQ/t0rqPUPmPiGs2YE1cRAuopNEYPPNBIEgDOs5nOZLmJoHaPO7mWfLGi0BO3XqBEorfvfl3911rL/38u+htOLUqRM7CKx0M162ISgJedsB2DaG5nztOpbav75YAQYu3429TwFI68BptCRNcowpnDZVkiYao6W903nEGxdRR6mMhcUu3a5NYKpd/p7BoMe161u2PSnRRjkmvppf39c2BvePujTbmAei5zEF89ZOk1GfZ75xI6n1TD0Ya2ZnClCKzPkIhXGHJFMoIdGmIBDV27XPzpk3YZDCEMcd8gI6GKRQCKEJRIRSmiwv7B4zvi7PkMzOoRCCxcVFtofbGGPKADv1NWdMgCpg0O9x6tRdnH7tTXwwgua7mFd2Wwv1uVLaaieNdnTQVJE6A2E1Qj5xdWXaKpCBJNKhM8/DmnVp52MnDbkx1r9LG65f2eKr42c5fvttcOcxDu8/xB13nOC5r7wAcYdB3GFre5vMKI6dvJ3JcEQynrCw0GecJhw5epT9hw8gpSDPcra3t7ly+TKM7NksjaNvRtochEFOR0CaJBa0uTdbrlvsc0ro8v1qGw+L2habYb69IElK6XwMJSIQNsF71mGaTMlTRSgDrKJRs7gUMp2MmCZjVJhTFCnTdMj1zYsk6ZBv/KY/wXia8/qrb7C8usT73ve9ZPkEbXK0sebgG9sjNkfbXN8aksuQhdtuJz17lmxjg8BK84iEtEncBaSmQEnjklJXyaalo1nGWGf2otA20qbjE6wljM3FZYzbQziTMbnzpJsB096s0+9frw32EymEC4xU+UuBcPm22mlDW9Q174dn2xV4qYbRAowdR52nqluCtDHjddcEg+UFrF+gXff1dVDXLpX1uP2Bnyl7OM0AhLnAq7E/m7Ry3hnW9Jmr19dmijZPaHYjcCKlnIlmqZQ99+rnu6+n+Y7qYG9egJg24UyT12qbv3oxjrfw2VqENqhJyvbmFmbQQRSapU6f9b370EZx+dIlkmSClIJLly6jDeRKkaQ543HKeJrwlkceJsDu8Y9+5OMsLS5ilEEXijAIWd+7B6UUw1rMgRuVr3uAZIT1yUlza8u+srpKEIXOxKuL1nrHRgzCEGmsiYwHB3Ecc+nSZS5cuMDx48dn2vAbu0mUy8WGsHl8hJXQWckeGCeZEY4YCEFlU4+T+Bm/ECsTDSGkTXzlwFHFIEgQmjCKSgJkQ6kKbBhFQdyJ2LNnL8PNEV988kn6gy5333cSERoCHTopqyZ12qe9e/eW4K8NBN5w/msbzm+6LMsJZMxrr77EeDRFCFmaQChlM64fOLDOPffew6FDh8AYPve5J8vDHCmqQ6C+WecQLKhFg3HzUkWDFDME42spuz23A3wYU0YC9P5vZT31vwQgArQxCBEARe0OO4dgCCNZChWbEp56+/X+7Mawls/Ypcf63jXe+ugDfPKNT3J1crXWQ8HJPSd56dpL5bUrkyt86o1P8c63vpP1vWtcuXq92YDt6i726239udV77E6ors+M2RhsniJT5sDQ2pFrEQDSRrfKJnQ7KxS5PdBtiOLZg1ebgsFCh5WVJWykr5xpMibuxJZJDQIKrVDGL7b5EriblcjtBmxuptQ12vV2m8Dpa+lP/e/dDvXm4Vu1azDSIFySTlXk5GmCVgVC9kjSAqUh8Pyjo4m7SYIx1tR4z551pklOf9lgpAKjkcJqL4q8QClt8/m492z7VqvG9TkMQ8IgZDwel0lRZxgIEZDnmjxXPPjgg/z+738YrQuCICSM7HHbTAZZikRuQEd27GnjpK8lLzsbiMgDCi2cL4bwgg/p8i6ZMry6MM70zgiQGuFyq8kckmHKay+cZuv6dU7edbv1F0ETRAIhYTBYICtyMp3zlocfhFzxxJNPEncj1g/tY3Nrg9OnX6PT6TAYDDh6/ChKKd58802yNEEYidHShV8PCVB0RYci0GRpPmNyZIxBlLkSHC33xMq4KKei8uWtz6nXKAVhgFKRBbhBgAxDsjAnz3JUESJEB6Nyev0FojAiSabkeUbXRGTbE776zJfZnkx56zd8MxfOvcG+9WX2H9qLEYZuNyIMBQhDnmouX9vmuRdP8/wbF9lUAbK/gEpTlpb65OMJZpIRZBnSQKwdo67cWIQs6Y1yERwFPoWSwOb/o5wLt3wrc2yxExw1GfsSgPrvmqvSeNro+A9XP2I+A9ykA7OClMqUzpZqjPWNPC+0dX39l1EMMdZ9wJgSyHnQ4YWpdQGOfaYmNGjU3bSQ2S0HUbOP9fE2wVLzuea1mxGe7JbOoxyzELP5OH392gJJUdfItpxDNy2EnCMAq4Ncf/9OgGWf0U5YGRgbo0MGko3phG43ppOkJEpRGIMIA5IscW0KslyhEMTdPlevb3Flc5svP/1cmcR7OJ4SdXrs33/QtpMVbG6NMcZwtcmX7FK+7gFS3fQtiiK01qWZXQmInKmFBzjSBVDwL77b7brNBVeuXOXs2bPs3bvXHpZOMtVkMmbN4YKyL1BJt/x9/pndzK+a30sXMrYt07LOs5KgBUGATSRqEyMev+04EsHTX/0Kw+E2J+64nfX1vSA1oQwoiorILC0tsbAwIAhkebjcqMyTrNQ3YpomBEGfZ599lryweSy8aZz3zzp67ChhGHDt2jUCN8+qsDmeLNGuE9mdBGN35s4erH+cpY1B9Nfrpo/1e+taD39geZW5lJUNvgCyLHU+L5F1wDbz26z36VYZ4Pe/71uRUvKbz/9meW29v86v/qlf5TtPfie/+9Lv8mP/+ce4MrE5k37z+d/k3be/m+9437fwb37tt8u2ZnpSSjndm2hIn/5YSwmSKbWIRVE5xapCkWWZA0SCNMsoioIwmgUP/mcwWLA0wDmPSylZW1vj5ZfPWOHFDfxv/NhvNrhC2/N1k956aZvT3bQTt1ragNW8++r9aGP260w/wkq08zwnSZJSgOXNoWRNaSqEc5QO2tv3492/f53XzlxCqUVCU9HRIAjInH9jm6S9+betaz9XrlzhyJEjO8YigDwvUFpx8q472Lt3jWtXtwjD2K6RWyzNuasL49r2TTmftWtW/9nOyHoG2T/bBJy+rTQ1XLxwkdF4m+0tCw6jKGJ9fZ377rsXEwq6nQ6n33id244e49u+47089rnP8caZM9x7790kyZTRaATA2bNn6XQ6nDhxO9PJkDNvnnWMaeC0zCEEEimsEY7JMtslZxpncCDJCIRLNOmFX573mkdPPAPsTe+8z0fRL1C5tlqj6YgiMwTYwBdxHDOdTFB5RiAk0+mEa9ev8elP/yGmULz8YsGJ2z9Er9dj7949RFHogoxMuXjlGi+88gbnz11AT8Z00ylhrnnf9/9p4lDy+Mf/kNHZC4TbU3qTnDBTZGGOEhplXKJZBNoztbo9ep8UksrXGcCbxbmgTQ2hVAXs7QrxZNGUgjavwZoVFghhA2UI5tOr3YQmu9Ke2preTZA3c465hPVNuuY/+8BXTe22YRYMzmP2fXs3U5p0sHleNNuaN7a6f9bNmio3750HUkVt1M09UudFd2vLlzpv3UaTmtFX621JIcpk1uCyzkkbLMUUln+11kQZMohwwUutokEKwrhjhTihFTYHUsz0ZWlpkfF4TBAELAwWSCcJJldkeb7Lyt1Zvu4BUhiEMwCk+bcPr13mNhKz6k4hRKlJsr44EaPRiHPnznHgwIHy2V0XvCoQYmf+i+ai3unsVsnB/WYvo0E5Ztl/NxNJJQixJkDKEQZ7YyfusG/vPpJxwucfexwM3HPvPSRZQhAJIqeNslqswgInmAnOMI8ZmiflmR1PlTl7a+s658+fL8fV7fUoVMF4PHYMp52jJEkYDocuep43I8GNfdYEY7dSblZjvMyx1rGdYG63MdS/axKitjloO6R3ZPyevaP8LqiBdSEqp+vCRQGMfHb5mr+Ef/ZWmeC2+z/w/ncC8FvP/xYA7znxHv7Nn/o3HFg8wPkLl/muu76Lr/xfv8Kf++0/x0df+yi/9cJv8cvf+ct84P3v5N/82m/Xxte+ZuqM3M306Ub32nfMjvwI5SHhgLVnpCwNsPsqiiLyIndBTQIwhiLP0UpDLEvG0a9j+0xIt9sr39N4PEbKCJxpg+HGktYm0GgyCLcipZ0HtPz9nom60Rzf7Nqp056bfcYzLDCbh8XViGfbjbHJpD191TZrNJTmxc4v4gZrQsqAxcVFNjdfZP3QGj6CmPUhiyiKSkshSvTVLik1xrBv/z5efeU1jh07NmM5YO+3Y8mzhKXlJY4eO8ylS1eRRYyuaULqc1z+a3au7yYIqgPKtvdYrXPBjrdhDF6T6WmJ1UCJ8uvCmWm3paJQRrG5MURrOHDwMMdvOwqm4PTp10jyBCNhsLjIJJ0yujjmHe96B8888wzPP/ccd911F2fOnGE0GlEUhYtSOuHYsQM88MB9vPj8K2SJDSzhk4ELKYnjGG10GdlrJjGksaaBShfOX9W+h+6gVwpAb+RA7s92FSroQL8bkyRdtre2mE7HBLIDaAYDSTIcop0AJcsSZBKRTlNG2yP+9//991ldXWN9736iKCZJUkbDTbYnI65uDUmzjBDNYhQwCGOeevZ5/tJP/g/cdv+9fO7DH+eNL3wFde4aYjSBoLAWIk7ib4RACYkxmnhG41Lf+xLpApd4nzgrzzTWbC7YaeptVwLl/qn27/z9VJ7/pv2etvOw7VqbENWHpq5fq0cEnvcO/XsWQpS+V/W+1vdFCZS0aR3mzQiQ6u210Yjm5zrP1OzTvPrnzV2dbta/r4PP5tz6NusRItvaqwOlJn1p1lVvux6NGdiRiw9mLReEP3xrcmqjrUFwlqWEcQSBZDiZ8MprpzFGs7S8yGDQta4lCoxSDMcTRpMpaZITxx0E1X7OhUIIyZUrl1leXibudFg+fJiiKLi+sTH33TbLrYu0brH83M/9HEIIfvInf7K8Zozhgx/8IIcOHaLX6/Gud72LZ599dua5NE35K3/lr7B3714GgwHf+73fy9mzZ2+5fSFmF2VdVe8PgDpYEcwi3/oBLqVkYWGB9fV14jjm6tWrro2dtvT+epuasY0Zai7uugSkDlDKe2rgCGbNNry2oTS/c8/s37+fUIS88dobnD79OgcOHmTf/nWSPCHJElInrdVaEYURKyurbuHPEqe2TdIsbeOy4VNtQIFXX32VyWTiQtJWGyzLMptvyRG6ZJqwublZOmuLGaAmZxxB6+00N/TMe7nlVXRzpfkO69fLtndh5kTJGjaue4JSq08pjVaaKLaS6TrwnxdpcDcwV++/L6ury/yJb3iIz575LFcnV/n59/w8H/nRj7DaWeOD/+Cf8s3f+oP8g//5l9nT3ctHfvQj/Px7fp7L48s89uZj/IlvfJjV1eW5Y3WdAph5X217YXYedi8zIZ5b3n2dnzam2mc+0qMxNkjDNJnaNWmcrHHOodvtdunEMd2uNddNkpSFwcDtWVFKt+ftlyYY2u2e5pw072sDR8394etrzmvb3DYP6Xnvo0lXb+Y9laCnRlttVLbZ9ieTCXmWA5CkidMwzCZQFDVa2Izo6e/Zs2cP0+m0lBv7IABxbBMEW/OyKoJY21z4+V1eXmZzc7Nsoz7vxn3OixStcx544F5ClxohcIFu6nUaY9eWqdHY5juuM0X+nNpNm18/O0rBnz8zZt6lKPtcmp85awrfln/emqKDUgJMwNbmNufOnefq1au88vLLvPryy1w8d5719b0sLC8S9WJefeM0d91zN7efOMGXvvQlDhw4wMGDB+l2uy7dRp/Lly+jteb+++4nkCF5rhmPUrJME3U6yCgk6nYRgfW7LYwmUwXTbEKSTUiLCb2FDvfefzdvefgBRGA1wvW5aNsndWbTRyLEmWp1u33W1/exZ22dOOoiCAhkRN9FurXrcmzNwYucNE85e+4sr776Kk8+9UWeePKLfPXpZ3nx5Vd548xZrm9cZzQZMkomXBttM8wz3njlda5evMb6oSN894/8EO/7v/wQq4/cw/TgCpNuTBpF6DgGabXTATi/o/kMt5QhQRA62hO492v9kOftSXueVnm6fFL2JinxfBSm2jt18Lzrudag83UGvP5cPeCVv173NarX1UbP2vZ9vY2mkLc+d81n6n2/GX6nfu9OMLCTB/Ugq60/zXHWx1YP6NDkaWfoUO2d1AXrQuzUSO121jb7L4TYkeqlTUBWp+n+xz/nWp5ZA76+PLe5y5CSVCkypSmM4PK1DTa2J4ySnNE0Y2NrRJrZaNSDhQHdTocoCmsuMRFRFJG4AEydbpdOr89gcYk96+tz32Gz/LFqkJ544gn+xb/4Fzz44IMz13/hF36Bf/yP/zG/+qu/yl133cXP/MzP8N73vpcXX3yRxcVFAH7yJ3+S3/md3+E3fuM32LNnDz/1Uz/Fd3/3d/PUU0/dki+MUpowspsjzy3hbC40/7mNmahfDwIbRS0IArrdLtvb22UI8eZm9w68Qji2t8FMCCFq+YlmmS4PigJR5Rjx/fFgychZ6VidqDQ3kAdLR48cpcgVT3zhSYq84I477mBj8zrdpQ5ShGgtSKcpINi3b78jupbRuxET58dW/77+2Y8tTVOklDzzzNNMJhMQFRFUhdWq9Ps9et0uPgP7ZDyxY/DBLDzxodIetbV3M4zaH0Wpv7s2IueJYdthvaOfpro+A95rB5S/Py9yOp3uDqar2caN5qHt8BFC8B3veQdhGPKli1/iM//dZ3j74bfz6mtn+Mt/9YM89/wrAPyv/+o/8PjjX+af/ZP/kb/5zX+Td932Lp688CTfdPSb+I73fgv//j/+3sy4bEM4PwJT7otmX+p9n8eg7zo2wYyesKrHWA2CoDzkfbVxHJMkY6aTaeknpgqriRUyLPOO+X4URUG32yPuuHtNFd2u7Fcb4q2Nte1Qb5uDtrHvOv4bfDfv+3p/5pn8th3ebf2+Udl5v6OXzpxZa02aJGS5DTpT5EVN47i7hLfso22I5eVliiKv+VzY56MoZFgo0ixl0OuUQLg+zmZ/O3EHgDzPZ5gFe5OjwaYgLxIefMt9dHsdJqOcKAjnmEMKS+vYuQdvVObSZEFp0qdqdMkaTemZZzwwlEISyQCkKDU29VQRCEEUx/jIfVevXuPI4X1EUcTBg+t0+l0b/KATEIc9TCC4cPEcq4urvO1tb+MTn/gE+/bt48477yRJEraH2xS55OqVa9x96jBXrlzn+hUrDOv3uqzsXWZpaQmkQOkOeVG4JI8GESqWl5e5++67WVhYAAGj4Yi1fatsXR/OSLO9JYXWujyXvQa+BEuIKqGu1qANQRAxGCyRplPyfIJ2YYLzomAyGRH3+mgUhUkZp1tkKnHnZQgGlDYoo9HSzq8JA4IgpDAFk+mEl154idX968g44J4/8ShHTh7nC5/8NE9/8rNMLl9DjSZEJkcqZc0NG8t+9n3XhWieSdfu7wBjijnnkwVQdv0arCmeZ1rBmtlV/s92cVIF2XFlN8HNzdKHW7mv/Gl859es55ugxl/UPnsaMnMstey9Or/Wds77s73epx00oda3NsA2D6TUv/P9q/erGZa7fn+Tj91tLtsAaP37tjbq984Dlm3tz8xffW6MFU5kRUEooNsflNekDDBJwjQpnJDcgXkkMrC8dzeyuUuRlfbZ+owKNjc32bO27vaBYc/evbvOSb38sWmQRqMRP/zDP8yv/MqvsLq6Wl43xvBP/sk/4e/+3b/L933f93H//ffzr//1v2YymfDrv/7rAGxtbfEv/+W/5B/9o3/Ee97zHh5++GH+7b/9tzz99NN89KMfvaV+2Og+NoGjVY1XyeN8aZO6tznDeUmMlNLmP+n1yjE10XI9qarPz1OXMHgpQ9shWJcueFOHOsGPoog4jkv/pxmiB6WWpSI4sH//fgIZcP3KdZ55+lnW1tbo9XtcunKZN954gwsXLjAajbDO5or9+/cjHK4LgsrP6ka+EvMIpXDAJs9zrly5yvnzF5xZhI39b7MhpwRBSK/Xt3PrGM7ReITWakaSXw6WnRu23pcZ4uPn+48YN9UJY3MOmnNx80xkQwsiK0mgEKC1DVsbyGCH1LtJwG92DM0+fqczr/vLb//LvP3w2/nf/v3v8t3f8xd49rmXZ8D4s8+9zPf8qb/Iv/8Pv8s3HPkG/tLb/hJQmefNGV65F/64S9sa8Ae+nyNveubNENLUSp4ElIlEmweoMYYoilhcXGQwGJC6iGueQaE0F7u1vrbtsVt9n/6Z+oH0f5Qys66bYF5Kghr9StPU5a2DQhVYJk6ykzXa2YYxpozKtbBg82XU57ZubljknmH29bYLGfz7WV5e5urVq3OYENtuUWQcOLCPQ4cOzNDzHcyToBQUNE1TbqXMzKmrwjOMYRRZ7ZbxDG9FM6top7aEQUC30ykFeLNzVmmvjTEkyZS4E7O+dy9Ga/KisPlGooBuv0t/MGA0GpLnGd/5nR9gY2ODj33sY7z22mtIIVleWmXQX+I//of/xPmzl5hMMopcMBlnbG7YSJArKyvEcczS0iJ33HEHb3n4LTzytoe4+/5TrOxdpjOIWN2zjBI5K3uXKWrJ2r2PUZIkTKdTkmTKZDJhPB6TpmnJE2RZRp57U0srydbKEIYxg8EC/d4CnbhDHMeOThRkWQJCIWSBNimFnpCrIWm+QVpskmRD0mxEno0osiF5OkQVE8IQ6IVc3LhMlqdkU5uLa7BnhW/709/Nn/rv/wIH7rmPaGUNEfcIg5AQCLXeYRpuBYd+H/kfqxGSInAg2fMcAUJ4Sb697oxNyx8prM+yoH7eVJqDQAYNTYBb8Q3hZJ223gyjXq/jRvd7/skz2U3eqxQq1two6rSmuabn9cP3vVl/E4z4e0VjD9fHXu9DEyB9rbS9zrfW29nB9+xS2sZV70/b83XFgjdtnQeS2n6aQTDAB9ywQoo8L2wgiUAiXGTpuNMlCKxGVYQhncGAsNOh0+vS6XSIO1E5niAISnO7OI6tkEcbcqWZphnmFmDPH5sG6S/9pb/Ed33Xd/Ge97yHn/mZnymvnz59mosXL/K+972vvNbpdHjnO9/JY489xo//+I/z1FNPkef5zD2HDh3i/vvv57HHHuM7vuM7drSXpmlpvgWwvb0NVJocD5ImkwkLCwsIRyy01s5hLKg2lTGAtky5qatNfaJ3Qb/fZzgckiQJnU6nJCC+rRlmWdtcBP5Q94y+NY2qJDgGyvCMxpiSUfAgyZcosurDiXKZy/H2sK49Ibx1OQLrY3Fw32FQgi998Ytsbl3nkUceQZm8RNrj7Sn5MCcIY9ZW9hBFfcKoC0Igg0pSUh/XjTa0tYK3eZKFEBQqxyB46eXXGE6mGBe/NAgi8jRluD1CBtano9vtMZkkTEYJRntVcoEkqGkdBMbstJdtk6bs2s9dwMt8wLdTil6tk3aA1AZCys/YiC4+Aqr93kl3hfUX0MK4aEaWkcyylCgKCQJBZlTpk2Gr9n3dKcHZbTyeiC0uDHjHN78VgO3hiL/zd/8X/st/+Xi1hmvE1xjDeDzhp//mz/OpTz/Bz/7PP83S4gLveMdbWVwYsD0auwhaHqRQvcPGXDVByK0y9vMOhJ3jtqYkUeSZZkkQRoggIE8zulFMKOyeV0bbtWrA5qi0Ib2NUkggigRxLAlESDbNGfRiMBmhi0Io7cae8Yvyms82aZyX2jffUZOpbV7z93o7/DzPZ+q90V6Y932T+an/bvprzhNW1D83TUFmg9cYQCJMgFESlWuMUghjGfOiUJgY70pT7hOYXS91zT9oOmFAN4TQCEIdOsbQEEYhQkKWZ5bWl+ZGUIY2bqx1KQL279/PpUuXOHDgQMNU29J6ZIhShl5XcufJO3j5xQsoAyKo8mMJIRC1c2HefO323upPlutLG4zLMmKEsWHmlf2tbdIdJ7Eq3FknEMoxzaoAqYmjiNtP3MWZN99ka2sbVWikFGhTlNpSYwybW5qi6PKlp7c4fuI4Vy5dYU3tYWFhgTiKoRNRiIAsz9gabvD+73wvH/nwx3np+Ve4dnETg+KNN97EKDAFaCOQQUhhIE2sVujUqbvIi5SHH32I244fJe5ETJMhWisOHDzozk7DoSMHeO3l17nw+iWnNVAURU5RZBSFsnvWhdz3pmQVGLBTIoyN+pXrnHQ6ZdDvc+jQIYbDEcOtEKU040mCMIY8mdDtLGEMaKUq4YvzJ0MHNlqqthFnjVYQhXR7MQtLfYTARvGTFnB1uh3iOOLkW+5iZfX/zJc+/TjPfe4J0vOXiCZjgiIn0wWZMGQowihCFJpAYSOURaKip4bSt8zFB8Nrg7RT/4ggqEixsRpHu4Y9eRYIEZb72vIglomVtfW4Q9AlRKmltd3xfI//3i1TDN4Vxa9zX1+TNtaFkM1z3cy4APiG/LvYKfwrmX8/Ow2GvYyO1/B/aqPX9TnwpY1O1+/x39eBRn1MdXBXv8//rfVsupW6Ca4fW92MdPacr86eJrhqgiKvebUCgab2bbZtP74mnZ93hls+FbwRiRY2FY4xApNkhAhyDEEUEMYRQklUXljaHdiIl0KEIEwZiVLKgCiyAoypTpDSrpc8TwmjABlI8mnuBG03V/5YANJv/MZv8MUvfpEnntiZVPLixYuA1WjUy/79+3njjTfKe+I4ntE8+Xv8883ycz/3c/z9v//3d1yvI9der8d4PLYvHquCtt/5BaZK5rKK0GE/e7pTR/1xHHP9+nV6vV4p5fIbqr6wBBCGAVrbhesdTWVt8QhhmWEpZKkRqNflpZ11W04rKbRMRd22XjtnTWFsuMfV5TUW+ktsXL/Ok088Qbfb4dDhA4yn49LJPwhCVFEQIDly9DhBEBMEoV28WiPC+dL+NmJmv8BylGi0gSSZYpA89/zLNuy6MQRCEoQhajJhPJ4ymUy57/77uHTpCtNpwuXL19DaE2ebxNZTVYOekZjckKG+RWbbj+dm77tRH25Ul2Z2LrVSdLs2MXGaJWWYXl/SNEVpRRgGCFknOPPbnwfemp8PHdpHHEU89cVn+Kv/t/8HZ9+84OSTlQau7bnf+d2P8+WvPM8//cW/x6OP3M+Bg+tsvzx2B2at/3OkTjfq141KuQ53fa4mbTWzUeSUM70xSpOlKXHcsZ8bjwWBdAdVwKDfZWGhZ5e6sYx3FAXkGQRCgEvGKRrvr9m/tkPKfz8P2LYVD4yah/utgs22UqcBN1pLuzEU9ftnnhEgRUAgQ4SRqFyRJaml1UagigJjApiJyrVzbsp2pRWixHHI3tUlKBRGWX8OX4esmZT5NeGl880x+mv79u3jlVde2aHxEdJglMRoKHJLsx584H4+/Aefs3n4ArsQhHCm17IGvlrmbdcimh8dQ2VcBDTjJOXKZdRxUlkDGCnodCIGCz0OHTrE9nDCubOXkFKwujxABgFCWnNnEGwPh+RZTuD2ixI2GEAn7nLsthNcunqZV18/h9aatdVVbr/9dq5vbJAkUx588F56/R4Kzcb2Nu/6tm/j8+EXeOGZl+2ZUNgsRhZEaKRUDAYLGK0o8py9e9coVML5C29y7PhBZBACguvXN9m3/4AV4smAXj/gzrvu4pkvvcSFCxcJnA9hEMUgFUoXaO20ZcZQ5AqjtIPKkigIueOOE1y4cJbhaIMsn5JcH7I92kDKgEAKCCQisFGzijynyHKkBpSX4Cu7hLTBoj537AQCEQSEQcDy8iIH1tcR2CBEQRjYd4V9Z3mgWD6yzjf/qfdzzyNv4dP/+fe4+NzzqK0h4TRBFRmRE9gaAYSOD6AoYycYQBm7IgIhS15ABHZtGnApMgwCUwLE+h4C0UqbVAl6dtJnY5wQb6YuT+frAo3aemcnfZpH89o0PzP3zfR59r5m8IHdaFWTDs+jA3WgUb/eFDjOE742LYk8CJRSzmhn6m3NOzfr1+v1zoyTneOo/26eF/56GxCq97uuIW++o6aATbjFZpxAAuzfWmDXcV4gjBX0FVqjixyjNUoV1g1DWf43TVPyPC+1RAsLCywtrzh3mqLs1/b2NkcO3wbCmggXTnlyM+WPHCC9+eab/LW/9tf48Ic/XDJ3baX5cm/mAN/tnr/9t/82f/2v//Xy8/b2NkePHp15kVrrMmS3/1yvu8lo1192/XsfInRlZYVOp0OWZYzHY4QQZf1+gdfrr5tXNAlPsx9tBKL0PzLW9KSN+bB/a2sLLSSF1hw+fBhjDK+dPs2bZ89y+4nbCaMImQVo4+xYoTRJOHLkiJ0DKZFBTfLf6N+NizWHkeDMHuDypcucPXuWNEkJgohut4Mx1jE+DEOS6ZSNjU327d/HY499HozN5aSUY14a66Ftrvx3fj5uxGw03/3NlObc3xRD0/Jc7Rv7f22eZ6IrYnbUkTmfOgvMXbLgr8Gsq1mEELzy6hne8e7/E2fPXkIrddN1CiF48+wFfuCH/gqHD+/nzJnzt9R223u7ZZBqP7UCOUElHcVpV70pp/1sbdWVE5YYPOCookNhrNTK96/b69LtdMv+R3FEHEUkwuYUE9JpAhG7moa1MQQ3A3Cac1U/HG9mXd5ozr8WoFo/RNsY/jaaJ6Us6ZAXGGVZxmg8ctLMsMwX5/eKQWOM3EX44UyctWD//n1sjad0nbO9n6tO3LGhnpVCG42lWDNwfkfp9XqWGWxEBTTGM4I2JG1eFJy44zgrK4suL5iLGifljOR7t/m9USS2ZvGAX/mcMFLaVA1RSKfbJVcKJaA76HLbnbdx9PgxskITry5z/o0zJHnBob37uHjpCtevb2A1LZIgiDAGlMHNkGY0TnnjjXMoDEP3nra3Jly5smnD5QeSJEv5lne/g0AYurEgLwz33H8/X/3y8+gcJCGFypEBxJ2Qo4cPsba6xsWLFynylAP79tHpBHzkox/i0KED7D+wn1QXHLntOIU1DLOCDgTD4Zg0LRiPpmhtU3osLi6wsLBIZ9BFBVaTk6UpoRB2n04mbF7fZDwcEXYC/uT3fQ9LC4u8/NKL5HnGwlKf4XCLrc0tRltDPvQHHwIhSbOcTp6VvITwa9yAMRq0cDDCIAkwxgpUVlaXWd271yWvVxRaUWhNoCywjTsBWVAgOwFmdcB3/fc/zJc/8zm++PE/RJ+9RjBNIEsxWqEM5CEoowi0cqtWoLHnN8KCSalqa0iIGQ3sPBrRduaXvJED+O0CQa+F2rlO2+jKPBBS78du5+zc/WCs4Lgu1Pa/HQmZaaON1rbxhM2+3VRfdilCiBkLoaaWrO7e4PnApp/5vD7f6hlaf77+ua3umxkXzMkjhdhBXe0+UhRpSpIkxIsLZFnK9csb1gQ2SUmTlNhFjc2yrHRnARuIZ2FhAR9voK7BGo9HBIENABbHNw97/sgB0lNPPcXly5d59NFHy2tKKT71qU/xy7/8y7z44ouA1RIdPHiwvOfy5culVunAgQNkWcbGxsaMFuny5ct80zd9U2u7nU7HOmk1ij/ESgmxUmhjCIWYASxNxNwmBfDP++98cIYoikq7Zn/Q+5dTFDYEqQc3URQ11JSz5jLNqC3NhaqUDTPa1j8/Rms2aNvo93ssLy+TJFM+//nPI4Tg1N2nGI5Hlkd0IicZ2OSB63vX6XTiMtS2lNL6udQkCTcPIjyTpEuA88QTTzIeT6x0BBuoIkmmTKeJA33w6U99GiElvV6PKIoxxkqVtalJrVzfS/VyY56aa0B4rqWl77sxkU2m9VaBUP3ZG91bSv8akpkK9NQkRI5AKOU0SL5vHiQ11kaVI+PGY/D3vX7mPMJUCYub/W1j5PyzRVGU4EjU/mmu53nzcytz3Xy++dTs3m6+z2pNh2EAAmeGZPN1dTo+GSh2/bg+aaXLfTgYDBgsDADB0tISvW6PKI7RZuzGb/dAEzTcbLnRPDWLfw9tc73bOmyb8yZNrEfV3E2a22Summ03QVn5uRb5Dyc1nE6m1oQpCp2JqTdjsjvDVTRTd9mWe3fGQLfb5/WzF1jbu7cEJ8bYxODJdOpoO2XdiPYgGmCZmE6nw2g0KgMLWcZQYeGDFVikacry8hJHjuzj8pXLQGyDULiE3D6hY9vcfC2ljDjnmOUgDAmiCCMMMgiQQQBKIaREC83avj0UUmNiOH7qNrq9iLOvniYpCoI4Ji8UaLdutQ08AAYjreZtMp6SJKnNXirtGaKVZliMEVIShnDp4hWuXLrK3n026bgymg9/6ENMJ2MiHSOE7aeh4Oixw+zfs0oYhPR7MaNRykIvIgzWyNOM0y+/wt2n7qYIAi5evMixo0cJAguQz505w2/8+r/n8rmrKGX9daIoYmtzg16vx/LeNfYfP8LetX0gDCpLGW1v0ukvcvDofrIkY7g95BOf+RRLCwN+4Pu/n9uOHQeRsj26znBrxGQ85elnn+bC2QsgQanCsnrle3frSnvzMv9e7BLtdjusr6+zMFggiCJAoLSmyHOki6SIjohigYgkBZqNbMI3fc/7OXLXSb7wex/j9Feeobi6QZhlBEChrHY1QCO0NVMUSAwafC4pz1t4gQWAC/RUd2Ft0ocmc1xabCAQtfOqfFb4oBC70/lSmLULYKrTI9//Zn3z6vdjbKO5xt5AmxCtbaz16zs0xrVxzKOfzX7Wgaa/7kFP/cfX6+l5XfBVF562mfU3lQD1sXv+oD4nbXXMmwM/D80x1a83SykAq7c7hxfLi4IsyyBLGY1HXL12DZXnmMLS5zQviOO49MX3gHIymbC5uUm/3y/b8P176eWXKHLD6trqLdHXP3KA9O3f/u08/fTTM9d+7Md+jLvvvpu/+Tf/JidOnODAgQN85CMf4eGHHwYgyzL+8A//kH/4D/8hAI8++ihRFPGRj3yEH/zBHwTgwoULPPPMM/zCL/zCLfWn7rRXSirF7GJoWyTNKHd16YFfNB65epO3UvrcsnDr0XN8OFXrBGswpiiBU7P4BVUPv+qvhy5srE886u9TaDpxh4CQg/sPYgxcuHCRF196kZU9q0SdDkmRIYSpMJKUCKO5/cTtZXQuywjNEo82m9obFWMESZIxGU958cWXMdoyK5WUA/I8c9FHBNJYp2IPjmw+GQFGVAEoMM4mvj0hWdsamFc82zxP4rWDiWtc8/NSN/dr1nHDOdoxZzPyLZdELUApewgH0u4bsAC9TgBLDQqWiWnm6qoTZ1+ajqXlOnPRrbTWVeVzprJZ78wYagD7ZgBCUwLWbGf39zn/kPcAsqpHlCatlpE2ZJkzt9LKZmiv+X3JILCMRw2ExHFMp9NBSsH29jbj8ZiFhQWuX0tcaGQX/c5pVHfMTcv8NQ/htnc2b97qv28WEN/sup3HpLQxVrvV03x25vA11qTR+2gmaUJeFHRl15nYaWfq6Zm02fmZaU9U4PTgoYN85dnnSqGDP7DjKGI0HKKKohKiiPlzZ7D7Zd++fVy7do3FxUVKEO0ZZGMl+UWRI6Thgbfcw5e/8mxFq4Rw/d4ppW7O343MGpvflfdISRjH1mbfzanVWNjEukYrsiwhLCRRt4s0cOTwAZa7PbY2tpBGIzEIYwU0WhcEfvNrByKlnX+c6ZqQ9gdpTdeFsSGnz75ymr0rS4RxxOc+/WmunD1DhxB0BoEVTohQMp1sM4oFd91xB3kyYbh1jVAGhFIy6HTpBDFXL15m9dB+Ll28wPraGtFggXSa8B//3W9w9vTrBCZy+xyyPAUBRTImSYZsb19jevQIp+45RW95wNrqItc2rrK1vcWe1TXW1lfsu1Oaj3zyEwRScvzYQR584BT9Xo9Qhtx//32cOf2GPXuzFIHVLvvgukb7sMt2bnwU2CAQDBa6rKws0e33WVhYwgjrg+vfC3lBWIBUIJRmdWmR5597nuWFFY7dc4o9tx3mq595nK9+7NMMXz+H3p4Qao3RGUILpLDnYpErK9j0a8hHzHUaVeV9d2nQx5Z92U5LqpPGf0d5RZRWJ00AUb9/HrDZ7fu2Mq/vFaVvaI+EBfNazO4Z/50/C9ui0dXPyXnnXVt/6nvcl7pmq8lHNOlsncepC8DatGP1NnfQMGPK17bbXDf5m2bd9fZv9B7bAmcYcKaatb4IK1jIMms6F2rNyvIyuSosj2vs2R5GMZ1ulzAM6fV65XsqiqIMjlbnk6MoIk1zpskIuYWNnnyT5Y8cIC0uLnL//ffPXBsMBuzZs6e8/pM/+ZP87M/+LCdPnuTkyZP87M/+LP1+nz/7Z/8sYPNM/Pk//+f5qZ/6Kfbs2cPa2ho//dM/zQMPPMB73vOeW+pPHbDkee7U/gFx3C0BTj34QFP66jdMXTsDFbr2C6Ae0c4vhLrzbjO6iNaaJEkQwgKfPM/LTQnWhKPZl3qABG/C1+aDEwexRdaELK+soLXiqaeeYjwa8+BDDzKejCmKnCAKrR2olMhA0os7rLrcR1URdTq4KzHdIcUQ0kl8JUWhOXv2HKPhCBkEKK0ZDPrkec5oNLK22A44aqXo9XpOEmVtvUtbbmNKKYgnwpVEaGeffJ8b3+z4fDNYr15f29/N+3aro/U7e8MN+kB5sPm1HUVx1ZeZQ6u6py1qYv0w8J/ra1cKQX8wsMlSMycZMu1EsP58sxhwST7bCXlzfuqSr5l6zKzkqw003IgZ9zoHf5u/fzqdsnfvXqIwwhgbcj6QwjE//lCcaQjcdwuDBRYXlwiCgDSzkbHiKNphGjlvvPWx+b9vdH99Tm407psVZNzo2RkQUzuA2vozDwA2P7f1LQhskl5vFWAsckVQM0XxGINqzmbxuKiuGRAS1veuVT5mphKKhZGNgFQURblXmkB6pt9UAOn555/nxIkTVUCMUoBgAVCeZyidc889dxHHIVlqNXDazDIQfk5nmIhd5nJe8WedMRCEgYsEJQhEgDKGQttADUJDKAJWBn02rl9na7hNf7BAN+4RhwF5OmY63CQ0hdvA0BHa+rkYqykz2oInNBjjAsQ4gGSEQAtBISUyC7j46mucGXTJ85SnP/959DS3gTgESBnzPX/y+xhORnzqk59gdP0KGxcvgAgYj4ZsbVxnY3OT6WRCkWV85Utf4p37vx1TZIw2rrEYhXz64x/l1eeedcEoAvee3BrAUAhJNgrItq6Rb17j/Gsv8/Db38ad95xieXWFrEi5dPkChSxYXV5DGEGe5bz84st8/KMfhizlwP4D3HP3KfpxB4FGGq+BcYCgBNUu75TxQMGZEwnDysoyy8tL9AcDgjBiOBozWFzECOsjlBeaqdSovCBCEWnJ0cNHeP65F3jokUeIB12+8X3v5M677uKLn/gsLzzxFSbXtm0Ah3QboxVCa2SoUUWBMJowkDPmcArn6xxIaz4tdp5h9bXXBOtCiMqckIZgp1FHm3mVr6d+3w7eobYn2qIO159rE9b4tn2Qo3rgm5JnqPURKtM2vyd3A43Na/V2/d+eb/T9rZvOzQuy4+/3Z3Ddt6cN/DTN7/y9zTO9/OzMvZv31cdRb6NtnG117wai2kCkq2F2Xt3VPLeAqGsgimMOHTpkrVi0CzwkwzKCc6/XK6NP+giTQojy3PB9WFlZptvtYbHxzZ+Hf6x5kOaVv/E3/gbT6ZSf+ImfYGNjg2/4hm/gwx/+cM1UAX7xF3+RMAz5wR/8QabTKd/+7d/Or/7qr95SDiSoNEH1cJ5RFBEEUbkQ/X31Z3xpY8qayLm+CerX632tb866ND8MbWQcD5J8f72Dng/84F98vZ0m4QB7KEoMeZaztLpsTQyubfHEE0/Q7/fZv38/m1sb1mnXBVCQzvfiyPGjGGMIw6gMBhEEO/OhNJnTNiIFlOp8pS3j8eKLL+EZB2tuGJNlOcPhkKJQSBnYUKrSov4sLSiKGiPmpIIYD49m2/7aGMFSxjTznm5U6oRsnlp5bosNAjNP+jP/ebvJi6JAK0XkGLyqTn/jrDTIS258JfPmrVzPxqqtg1oIYGe4MBcM1ftR/o0lzPW2Z8bTmJM6o7hz7KZ5od5oVWHbUqjtQTsvTUnXrFZWRJWAo21p+e+63Q4LCwPiuIPRxubUGQyIwgil8sZD1ZxUwF40Pu9S/OE+I7eor+F6R3fOc1sRzQ91yV5lxOabr0B6nSFg53vc2WLVzzo9K9eKoEw80R8MWB4MkBJnjmtLFUkLF6dN22TTogq2M7OPHEg1xjAYLKCNcWZR1f6z50FQCpzmlSajuLS0xHQ6LT/vXJv2V5alHDp8gPX1vZw/e5kwCNEih9peqpvA1tuz+6B+jWqNiPqc+uXvn3FnQWD3nQ11qyxYkoIAiIRg4/IVpNAcWl3l0sVLjApNoWE6mZBNx0RCMxoN3YvXaJPbaHhGo3XhxmwAiRSyNO1GOPAZhGQEpNubfGm0wdWrl5kMR4TCJoU1Hcm7v+29PPLo/XQGfW4/fpjf+vV/z8ULF0BYp+orly9x7vx5Nq9f50KnQ6ff5frlSxxa38tw4zpdIfjSFx6nmE5t1DisBNl482QH1pTWFEYzuX6NztIinx4OuXzlCt/4Ld/Cnj17Wd27zKWLF9nY2uDwoSN0C8N9D9zP1bMXeO6LX+Hs6fN88QtPsdDvQVEQRJG1CAlDhDEEQtg8LcZgjMY4kztr4mYDu6ytrVnaEMUIIfnSl7/Cww8/QtztgAtfP9YFmdJEAroEdLpdCm24fPESB/etQBiyfvsR3n3o+zh4/718+g8+wblXTpOTcXD/Pg7v38uLT38Vk6cERiGNIFAB0mgvZbQ+l1J4D64aPXQasPLaTu2yEFhpvqDU5FKjAwpDIAMLHI3Vms8CA1O735/r881MbcS8ORp034eyb05w5QFEA7TMgCtazL5aTAdnmtsBEmTZet2/tDrXZ4V91fMtQ2kBPN76Y8f4GgL7NuFVG182T7BY/76tNJ9p9qP5/DzgNNNuQ6AlsPywyKxp8hLWzL0wxglpraVVoTQbGxt0Oh1OnDgxMweVuXyVZifPc8KgEtj7fKg3U/6/ApA++clPznwWQvDBD36QD37wg3Of6Xa7/NIv/RK/9Eu/9F/VdmmT7SSD3k9JCjAuyo89oOqgp9pUTfOtHQxng+ny46ub2BlD6a+U5wXeNycMbUQ2D4g8s+1VhF6i6REyWA1dGIYIA4EMSo7FaGtLH8kQpW2G4dXlNbQSPPPMc1y7epX77rsHRQ5BCQfsWLXCCMmxo7cDgiAQyACEdEEWpI3aIwQNadNs4IQdUnABQkqm44LxKOe10+fQSJTKGSwsIGWI1jlZpsAEGC0Jww4iCDEuK541j/FqWScx8kTRGOsj401VGoCjSWRFY+/XcEQrP10fl3+vzbU1r9wKWPN91u5g2lmHHavNa+EkYdpArtG5Iu51HG/pwDsC6cCkFi6casmZ14icmW2nXN/MMvA+fLCpXbuRdKllkC4nh0GbWSmONrNg1xO6JvjcIbkr96D9LP0B79oqV6nxY3V7VQjLjAi7d6QI0IVCF4pQCqQx1vQosKF4C2N9NqxZkVddCBRQGEMYhwwWewShwBiFLgxLC0tId/gLIfDGLsZoy6hgKKN+i0rarXWNxlQnrx2f/1O4fedByMychyXcMuUbczSCSkpZt3kXwk1Qyet45sX4mJGuGduWjT7ke1SjJH7tiFmNbHlQu3Ha/WyfktIghPfdAaEkvV6fY0cPM9zasiZSGJYWBnZNFwajnK+Lsc8rowlMgECW778EC5hyfIPBgH7cQeU5QimkVkgTEAUhoQzJ0hylFcrF4a4zXjPrWtuQ2V4AlqZpZebqtRfSJhs1OkAVgqXFAffcfYpzZy6DdmtbJtiQ5hE2LY2dX6Vdvht3RgkjamvZ3lPuN1GAqIRRkCOwZ103jjDCanp0bs1CtTFIDCorUKLH8PJFLl26wN13nWRy9TJ5lqPBRqzLM0QxJE+vu+hyVjtiHKMtS3BkaYwGcJHaMCCMBFOACCnylMvnNlCqII6s5qiQcPy+O3n0nX8CJRRZnnDfA/dy+9/+KX77P/4nnvjc4wSx4MzZ01w4f4E8n7C1dZWBWuBjH/ooRZbTjSKiQHL+zbOgc8eMK4IwsNHajKmtc4HGhuMeD4fkheLVp58jG0751vd+G3sO7OX2Q3eQ7kl5840z7N93gLgTsLq4hFAKXeTkKmc6HmKMjx4qKbQ1eQ6FcLmkbSjtUrzjpikIYpZX9tDpDhBC0un0eParz9ONFrjv/nuJ4tBq96Q3p5ekBRAbjt52O8+/8Bx79zyK0SkBhqgT8vA3PcAd9xzn6S9+hcc+/lnefONNQvZz97d/B8Io4ligkoR0OHG+aQXj8Yher8dgoc+FV06TXd20tNSA0AbhzCeNi47rV58V7DqNhobACTqNo2ueTkXS7Twza7ZtjCFwtQkjUM7XM3AMSSFUdRZbpEYoA4wMrBBZWX7CuPVmHDCzISnsd0opC/yksBpK4+61iN2lSLb0KRDed9kD2pKQ1fa+Pa+CwD5rwa9ACOMEAqKcKyEsALfbwAq+7T61rgqUtNFpGR0t9LSvfv7V+c62tA9Nwf684BE7tU7S5oaTlp8qwSezx0jz+Tqv29R+zQOSbQKfepHet92d/0JasB7KACYZpDYIyXMvv8TWxhaBgdXFFTpRyHA8Yml5mWx/YsPdCxvhNFcFKRB3u7jjF4QgQKILTRgEdOT/D4M0/B+tFHlOt9stgVHpC6SsPbU/5ITwTJvER6vyUr02LYXVtOycvjr6r99v69QluvXAiZrkxAdx8PV0Op1Smu5N8nYsSseAISwIsJIzQ6/XJ446JJOUxz/3OGEQcOLE7WwPt90CF64ty5wsLy6ztLiCUl4q6GLSm5qpCn5TN6UsVf/rm0A787o8V5w7f4nhcEJRWEIUxTHGCLa3hwh8PpoqCaFPZOcBZJskyxe/xT2TNjM/5fcNBrvs/47q5pZ6nTerabrZerVjINmFoAhHuD3DKoA8y+j0Bs5sUe3AQRUu8Mx0vT5/vaofSl55J8grUdOtj6/WGwTO5M4XKWYIZb0vzSKqhUc1G7W94O8z1b1BzQ/LaymMsAejEIJASooiRwoIpQVWRZ5RxEWFQ/DMvaMR7iDUxhDHgU1E2o2ZTidMJhPWVvcCmiCU1m/MOW0bU4X59+u2PIyFFwoYJ931TLCjEZ5OiLAUFlgUVV/XHmR78z6PDgVaWYYmDAPiOHKCF7vPLWPf9N7yj1dmrMKY2nSLxq2VkMlogRBBVUdjvJ5JkFjNBgRIAYEwPPTAvUgpGQ+30EqxtrLG0sISW1ub1rdShoAP++9GaUQJjqqFapmAwJnMRVHI0tICyXiCXl0ikMLGFhCCOIwt7amBqxL8NaWw2HcppWR1dZWNjQ3W19fdmLwk3oU6NpIsK1Bac+89p/jkxx7DaEMora+o5eIDhPHO/lTjMsb6ATlQpI3TimhvPqRQOIDkrmVZZv2eAkmhJ+VZU5q4upFJAUVmyKcTtq9f5+kvf4nDBw8hQ8fUStja3iSZjIlCrJ9MbvPYGe1Nxqq1JxxwElphsEGCXNx7CmlAG4rCRZZCYgKJ7Ibc8+ADFHlBOpliIsUTX3majevX2bO+h8FCH6MN5948w7Vr19BFziMPPci+/Qd45oWX2bh6DdXpsm99j2X6nCCpUKr0lTJ+3kqQbrUKQSDRhWLr+iahPMtHfvdD3PuW++n3eoxHYy5dvMgfnvs429vbXDt/niLPUEVuz0eU28caIUJ7hgjrD+v3qBWo1Pw23Rm3srJanjlFUTCdpHzh8Sc4cvgw6+trRFJaOYyyQNlIMDq3SS87HU6feZNDhw4SiRyjC4wO6C/EfPO7vomHH3qIc2cv8tILr7K1uUV30OXs2dcp8oxet8NgsMQ3vf2tHDp0iDRLGG5vcf3SZTYuXqZIc3Seo3OFyXKKPCfLcpdHSjlTbWv6NJlMCQpNx0VO9fd4gXNoNELb9WCUplAKf/bUA/kYFVjQrY17f2FFUbR2a9bmpZTGwRwPDJz4RxiDcHkShRGEMnTCHBsRUOGyQRpHT/3xITRGun66YBbCC81wwhZP44SxpqPGlEJYT1sKT8ulBXQV/+EETw6MaW1KvqvOM7WVOg9Z/7tptbEbP9IUepb0RDgzWCHK44EZUdqs6V6zX7OawPZzut5e00RwVgsvyjWBo9EIq0zI84zR9jbB0gBjrGZJFoY0SZCiQxSFFEVOkqUMOhHaKIbb21w5d8m6a0QhYRTR6XXd3jT0+3327tlzSxY/X/cAKXJRLup5hOpqzHrEOfs5qEkhd778pjN7U5UIlO1Uixrqh21z4XtgdKPFJqW04Q9dRnit1Mx3JbJHsLKyihSSs2de57XXXuXgwQN0ujGxCslVThRF9LsdMDAajTl+2wmKIicMwxL41TNp30xp26RFYUMmv/rqqxSFVW12OjFRFDMcjkiS1M27zbkkhDWF8H/799JW/465ouLfZY2hBKz0/yZKc6yzpgWz73jed7uVJsGqNCai1KTcbD0gyNIMISAMAorMh5Wv+iI9Dw2llsIySDfX32a5Fc1YfY/U52uHZMn9KxpSMIzZ0c8SQLXUYx9psu3M7A2NcblnnO09gBTEcVSTelEBKuODsFRjqmuQlVLEnYjBQp9up8vmxiZFkbO8skQYChInGPHawVLeaTHQDkjiI025lEq1yazRAiHLw2znOeuYEQ+QPBB0eV7AILRCaIX0GnQrtys1FRWoxjHt2lXTaK9+I02hhNV2QnXoCuEFGdasF5ePRjrAFMUh3/j2h7jn7lN84QtPWoa0KLj71N0uJ1XAyuoSNZnwjrYrIUrJCWGM8EHW2Lt3L2fPX2C/cn46MigtC8bjsTNtjm3gjjmb0QuklFKsr69z6dIl9u/fX7MamD2AvRXAnSdvZ2Ghy3A7odsNyAtjPZVNDjrB6KKMemqBq3uHOsf7/FSaOOzB72bbgwCUIjCaUEZ0Qstc27VamRwhbGJFIaz5XxRKJMKa1eUZcU0w53+8maMHRk0BlBdYYMAox9i7zyJgB3NcKMXC0gLnX3+DyAhWV1f5zKc+xbUrV3n3u9/NeDhiOp7Q7/W4fv06aZKwuLDAj/7Ij/LiSy/zex/6GINBn0OHDrK6tFTmG/Rnore4qAsqhZUiWmYrCLGJ4g3D7TFRd4uP/v6HSJOEIrPnlMpzG+2wDLrkNUTVew2DdhaqsjbBJrIMJGEUghQkLjR4GGrSNOHChYu8+uprLC0N6HRj8MIiU5njK6U4cuQIzz39Zfaur2OMIYwCtDZ0ujHGGAaLA07de5K77r6LPM/J85xpMiHPUjLX5urqMjIQkENnscPi4b0cCx+0dM5YaCBKbtmCS6UUWumaL3dGWBgCZYMrZVlGlufkWU6RZ4hcobKMNElsP4qCNE3J0pQ8t7+T8YQsSRDaUKQZ6TRFaoEubO4ro73G06diUE4jb8G29OeKsYaMWmmEtmaOQlvaoIGsZi5Yl4ZqYagosiktZDA2z5NBOPps/7ZCQ2mFEt4CQEhEYIGSBihMGRHQp3fAa8ZFsCMoQ5vAt87v1E3H5vGG9fNoHn/k+cP6udUEOs16b1TalACtAt0WkAdOSGoMAdLy3KLS5kkhiHsdlCkweUqvEzIdWdPoaZFRkNPt9+gvLzBOJ8g4YJokTIYjIhkS9has+bWUZNMUISTdhT5BN+bacIt+v3/D8fnydQ+Q/CIoQYW25gFtvkx2QVZhKts0R740F1Yd7PhFXi3Aqk4bljks62iCgDp6Lx2Ga+2XEbFqQK2+UGUg0UqyuLCM0YYnv/AERmtO3XMXw/HQRfoIWFoa0Ov2yDJFliqOHDpGltmkW7ZvAUEgZ8bZtlFLG2SYmSt/X5qmpEnCG2+8QZFb7dTa2h6yvGAymZCmCUL4nFFuw9fmtA4omqBk58uu+Lcyo71nJsz8jT8PDO9GdHx/mp9vCOJagLTt3s7ndjL/njmrTJmsRtQyeHlW2AzTNdbRA4Yy+p8DHaVttpOquk74p+pnSes4fV1tfd6NUM6Op+TCZzVB9b7McsHuPc5+saM/LXvVS9KRkOsU5RI7hoGkE0V0OjEyEC4wQEBRKIRQM++oORdeYi+kYWlpwNraGmfOnGE6TVhdXWFhoc/o+oaV8JsqEqNlaJkJr+v775lYVcpI7ViFsCZ37gxx91W2/eWhR+gkce5AwmmuEUgT2oiERqMLgdChNcf09TjQId2mMcbYaH1UMsZA2KSWGJfY0zTzAPlIWTlB4MyXtcvIjptLoQlDCyTiTsSetVXuuPMOHnn4EbpxyBce/wJbG1cYbm/w4IMP8dZHHuLsubM89PCDxJ0QUWr/qrXiNWrCiB3z6SMVGqO4/fajvPHmGVRhECIogWccx2xvb1dpHOZoj5plfX29TF2htY0O11aKImd9/x72H9zL9tbrRGGXUZaSJVOKVKPVBMxspEkryIBAKAvq5awU1wCBsJo/6Yif0BplbGZYoWxUJ6MNhXIhxYWVdqdJjglDqyEKQu4+dRcLC32+/JWvkrrw9XUH+aIoMEKUpjnlfDtaZHSV+NxYTI0158EJJGrnghBEYcS+9XWkMpx//Qyf/tgnGG1vs3dtD6+/+ApJMrVh3ouC0fY2SZLQ7Xb557/8y7zy6mkKDabQbG1ssnnligVGjrY1Ta19kVIiw5AwiEEGQICD52xe22A6HZc0MRCSQOO0dV5g4v3lhANXGhFWfHf97A6CoASxQkqCMKC/MEBpbdOBFIqiMBw9epQzZ87ywgvPc+ruO4lipx2mqtO/AykF3d4Cr79+hqNHjyCLwkrSlWXwYxlZs3sBBNCJYjoDC7js2vGJ7zUxXQyG0BiUj3anHP2xI7Tv1dFcv9G8JYwU0pmaVsLl8rwsnHmpqUbi15PWhTXpLzQ6y6FQqDQnTVK0hiLLydLM/iQ29UeWJEzH24y2hxRZ7gDVlHSaoIoCVeSYosAUyu7/QoFf7y7cO1jgjrGmhAER0ig0VhAROJNsgzWp1sLSOmuda0A7CxkhKYzyEjqU1ijtLUCq/GlCWjM8490QTDDDIzaFq83PPgiXEFWAifo55+e8CVLKN9VY/01tU12gVLYrvcmf2XFPHYzV62hqk5rnUfP8rBeBKPlYhLBmiNqgipxkPCQWin4oWY47ZGFAmhbIMEQGkgP71tm3fz+BkFbDnmWECOJ+j6gTl6aYPijFqXvvYXV9D5/+7Gd54+ybrf1pK/9NACTv3+MDD7RJ8JuanbpqsLlg/AuvJwmsTPVmD1ZvZmaMKm3/fanbctb7UG/bB2OwEqeQhYUFFyUrRjgHfb+JhLCM19LyEkKGXLt4lWe+8lUWFwfs2bPC1c3rBFKyvDBgdWnB5sTQGfvXD7DQXyKZjsp2/GYox1+bz9nNaFGIv1aXFGhtJWRvvnnWmkhom7gvCELyScJwOMRHXKrmGIyZfT/1d9ksM4Bj9htEXepOjfFo1NN01JyROt6ENKXej3mAel4piVBLHXPbogJLNsS7KCNx1QGPMdZUpi49C+rMvhUHl5qlqk+7j615vXmtKdHy980LEV0HPdUz1Xf1kbdNa1vUnvqNM3vRgRWtNYG0kvc77jzBxsY18jxndXUVbTImkzErq8sEYcDS0pL1DTKzbWqtKVSBVgULi33uPHmCZ555lmvXNhASHnnkLfzBxz6ONBofV8sm2ZQEwss4zc416Ri9UosjrSt1CWhR2Bxgwtnd+4NSIug4AOPqdpNYCgmMZap1bp+JZIRSGVJaCbkdl3L7GILQgqxKk+fNjq2voo8mFARBGe48iiIGg5jFJatV63S7rCwv0x8MCMPQ5YLZY5NmrqwSRoLNjU0uXLzIF7/6Ki+88AKbG9d5y1vu5/u/709yx53HOHX3ceJeh9F4BBiXx0i4NQNGeCnlLE21sy5LgLf/wDqWtzMuV469J47j8qxQWtsIcGI2dP+MoMYBhH6/T1FY0yMbfW/WHMbT8qIo6PUNd546zksvvIpRimw6IU8noAWBUNZvycPRmhbKao+c+R9+P1nGVdcAod3nGmPs+rBBf1Rp3hN4ywml7N9aYbRieWmB/fv2cuniRabjMZ3+oPR79ZpUm7+oEkB4AGCM9X01obagTHgaYM+tIAxc8l2D8ILBMODI8ds4duI4UgQ8/+xzdIIQ0RuQTqZcOn+ebrdLkeWEQUDq+ry9vc0XHn8cREBnsMR0MiEELl04j9A2uJAx1lzH05p6wsggjOyPDBEytHMnQgyCPM1Lfww7sVbLaRAYaU20oiiy5ovC7hG3pWoBjarwwn4NysACljCOiOKYLM8plCLLc4wRdDpdoiji7NmzbG9vs7DQQwTRzJqrhKOGAwcP8+yzz7Bn7z6EsIy4lM5sUAT2mnARDJ103ua8Mu7dWD5eOEAtjUBSCyaFsUlmhZgxtfb7RHo6FgBSOoBuQTceLPdKJ2coKVCl/ZTCBXnQBqkBpTAICiGsWMiHkNcGo5QzK7WaJV0ohDEWKGU52miyZEo2TSiyjGQ0YTIaMdoekk8SxttjhltbqKwgSxKM0uiisFFZc2vap5UFadL5mRpyBIUNXlUoRzOtyaSQhlBoa+VibN+lseutEMbRocKCI1Oj71AKouqgx9KI2QAtnm60CfHr58RuQYxmhOY1K6n6PXXeEtyRUTvk6udSk2eu88X17+f93QR3xgNPf9YJUfq2CgPTzS10mtLRgoUoZnHPPoolRZrnpKYgzhTJlet0opgoDAmnKel0Ap0OyyuL5KogSVOyJGd9/z7uvv12cg3v/OZv4fc+/Ac75m1e+boHSM3Q2FaCKWa0M/Xi322dyfef69f93/WocvWwjv7zdDql0+kiRDDzbDNUZR2U+dIGzsIwJM9z0jSlE8dleHCfMAshWFnbg9bw5S9+mdH2Ng8+dD+TZIqQsNhfsLbOsUQrg1GG244fZzyc0umGMz5Zvt0g8EEabo7przMFaZry3HNV/g8bljErVfNFUThmz2b8Vqpu9tMOaFpLTXsElATdMpSWkazPZZt0Zbd2bgTO5j17M333DI8X1rVJg/xvDyKFsGNK09QSXK+N9Ik2sYEatGNubRWm1MKAZy9rbdUkoa1I5BbKzayVch+Zqm3fj/JgnnmplVTTGGsPDrOHQim1qj3oNT3GWImiFoVzmjUcOXKEffv2cvXqJfasrXFg/zqXLp0jz602dd++fS7kfmHzjNS0vkIIVKFI0gn79+/l0KHDHDp0mNdOv8ibZ87wrnd/K6++dpqXXn7F7ncjUQK834LCRzJrzIcQDkDNzolf4UJodwDbdxlK+76EAK0yqwmSnlm1EywAE2rnf2FpUbfbdaFRDWEk6PX7xHFMHNnImYOFPouDPnEYuaTNEQsLCywsLLi9HDHo9wlkhAG63R7dbtdFC53Q6UTkuXIRKiWdTkyeF2SZ1RaPxyNeeO5FNjaucebMGc6cOcP1a0PiOOYd73gH73vfe7j7njs5cGCFQuVsbA9RKnf01U2Lw0lGGxTKgh9tV7aUFhxR+jNq1tZWWVpctKY/WQEDS2fjToQMAtI0dfSneif1c8Cup9l3NhgMmE6nDAYDl1NrJ5NhTdQK7r33Lj70Xz5GmkzodWOy6RaBNRJC13yPGngZbypocM70QlogIIKadljbZIrCSrqNgUI5TVNQRXWyghNZ5pSKwpC9e9Z44blnyfMMUqtt9YEnPEjEma1YkNul1+uVoXXH4ykeXKRpWlpOJMmUZDyywT6cIOzA+l5uu+02EJKLFy5w4fx5giAgcpoMrRTbW9vOj8n5KzrGSStN3IkxSlFkOYlM2N7aptfpgtMG6AZT6M/pIAgIZFiChkBEGALrbC+tmalDnaVPhMYGEYjjiKLIyLK0jJAG1sojCi04GgwGTCaTivY5IB11YlbX1rjr1CnW9uxBBJYxnU62Uaqg0+mwvT1k4/p11tf3EAfRDprviwxC4k6Py1eusbKyjDbWpFYgrG+eow1BKBEESBkhRIBBlfTAJta1pmXSCAJTkRjhNCMWIDmhkKj8Bss1KSkJjKCKGicAI2UpmvF+P6Vwxwd7cushkEG5rjI0eMCHtNrYULj7BJEjapaXE2XQH6G1i1ZpgYxx5oBoq1HWSlGkGXmSkSUp460tJqMJ03HC5vUNhpvbjLeH5ElCnmakaUKapejCmgoKZTCFra/QGQa7z1AaCoXUpjT7s/NagNQYU2CMDUhjmD036iUIdvJ5UPGUdV61KVSvaMQsaKmfhc3n6qXi8YLZSLO1Nprgxpc27VD9WlO4PSs89bS5OuuNO5v63S5haK1jimsb5Ea4dWL9yQI04+0pY2MIg5DIBSszAkwccPnKNYIwsL5vUnD52nU+P0pZ3neQt7/rHdxz113cbPm6B0jNKFildiaobELrqk+7KCsU34aA/eempNAv0vqitBqTgCAIZ0zG5qkd64uqHj683lYcx0zHkzLEbBAE1t54OiXudAmCkGyS8cWnniIMQo4fP84w2ybuRKysLLMwGGDIyYUijmL27zvAZJISm7DsgzGUkmlL0CrGakefqZhu/7wQdoFnWc4rr7xmJfCBREpBlmWMxxOKwiaz09pJV7CO21bieROgaJdSKiCMk17pds1O6+Y3tQoa9zVHXm9xFkx7INN4woMBf4buqKc2Ar/WHBURAsuAuQNHCkGappbJiSMHZH0EKUEQy+reQJbgy5vCBEKQ58rVUR/TjsHPqJWMk2TPDM6YCtyVB+78d7jTJtlzutW8iMa68m1A7X0I29KO91P7KN37sF2zL2AwGHDvPXfzwH338clPfJzJZMz9995LEEjyPKfQOQf27+fYsaMVyBbed7FCdMYYxuMJe1b3smfPKm9961u5du0Cn/r0p7n99jv4if/hL/K7/+X3eObpZ8lSxXiUkGYFBomWwgZgqI1JO01BUGMxwJrXhGFEGEiktEzZYDCg2+2WJoSLi4t0Oh3iOCLuRKytrdDtdmzW8ThkYblHv98vTXz7fcvgGi3Q2jIcYRShjRXsZFmKQIMRpGnmnLGLkuHf3Njk8oXLxHFMmqaMRkNrkmQMG9e3yfOColDWGX06ZTKZIKUkzw3D4QitreO3UgVBIBksDHj0kW/gkUcf5p57TnLg4BoHDu7DmJzReIssy62E2xjLDJpqnxmwmh9dRQ71q8ivRjBEccTBQwe5enWbLMtKmuwDV/hQ3/Yd73TmNWZ2f2itWV9f5+rVqxY4OqMa90JngH6RJxw/foylpQU2r1xh0B8w2rpiAxxItwvcQV9fyMbIciiiVreXuoIhjjs2Aagze5JBgCgTkRtkENrcRBgnTLHVTMcTm4A16jAejQmCsEy4aPdjFajHRtDzEmfBqVN3c/vtx/n4Jz6BDAOWV1aJoojxeFzu7XPnzhKEAYGpAFKv1+Py5csUaM6fu0DqkkJm2uYPk87MMYptKGw7VDGT701p6+Nx/fo1tFEUBqJAEoYRWjhq4vz5pLYMYBR3CIOuBZcECBmACBx4FBjh8xdZwGG0FyhYrcd4PCIOQzvH0pqOGhd1stvtceDAAS5duoQQ1qS/P+izuLTI+oH9HDh4kEOHDrG4tGjp9njK9evXbVRbbUiShCtXrnDijuN0RL9VOOqp5NFjx3jxxRd55NGH0T6gJQKjHE8itY1aiEAKXQqD7HfGmew5Giac/KC64s4aUYIY/75rKxJZVNFjcee2B1LSCPwi9u+u3C/G5zHzZ5rV7goh0NLxR8ZZz3htnhOgahcYwwjhHDhtH0NEWYc1rTJ23UTShv8nIuzHxAZ6CNY4jA5AS+OCPIDJC4osQxWKPNGk0wKVZeTTlMlozGhrm/FwxHB4nfF4SJFkTDa2yCeJfTbJMWmGLhR5ngIFoFDK+g9q5QWVDsi4CIF4QVdNEOPpjzYaEVj3DIEHTE7rYu2bZ2iTP3tLTV2Lq4Y31/SC8NAnEC7f8U7zu6aQuqksaAKhJpCqf1c9R7VG/HPKWjHEwr17Gbok0SFChFbrbwyFE2IIsH5nxvHIxiDSAiE1xmQY4ehEEHD+uReYjFLMN76dbhjv6Nu88nUPkHypExspJYGwttlKa2tSBjYsqZ6VAPpSD/IA1aLx1+qf64vEmm4wY+Y3g+59MlWHkL3FvyUcliBIaVX9CmPt+YUgii0oGvQXXAJc61i6srxCx8DTL77ImXNnOX7bMWQcECrJ3pVlDq6tEESCJCuQRrK4tEyutBOa2aznIKxNeeCD/O4Eh43ZdSDJKugN1mwuL3K2tiZsbEwJ45jeQBLEkBlBmuZgAqIoQhWWIChl0Gqn1OymiptL3LNVfKuS/Zzpe/mOyhHWmXNndoZ35Cwbqc2BpooU5oi9i60tSucS+92ML4mRlNoxP28YFykswIZYc72t5R+y0arsQa+KzDryG0OhcoIA+r2YEyeO8MjDD9HrdsGFnl1YWLAJTPOc3/7t32Y0mRKEIYuLA0SgmU4Krl7OoRAgNFrnzo8icFIeHFOqy2RtgWNA0D6vBq7P3gcAPDqswFTtsBcQeA0H1uRNOKatfO3+wHWmIXXwae3sDTIQM/uv2+0SyIAwCgmEpN/v0+/3Sul5v9+n3+uwtNRjz9oal69c4aN/8PtsbGwwnUxZW1nh1VdfZjpOOHnqFA/cdyd79gwcZyfx4fkD6UOyWyY6mSryRcUdJw8znmyxsfVWPvuZz/Frv/bv+L4/87386J/7fkT2p9i+vslLL73MC6dfZTuZoomIoi6DwYA4juh2uqUp1PJil16vSxiFxFFMHEcMBvZddiJJHFXBVJQz0TDakOWKJE0s4+ZM4tM0ZTpNyPKcybaNhDmdTsoAKUJLkoliezQky62GJsszRuMxyXhKYOwcJ94nIEsJw5AsK8jSrDyE7ZKwzHnkzHSN0QRBSBSFNmiDgqVBj/X1VeJOh8XFRQaDPuvr6/Zn7zKrayscPHyAxcUFJpOE8XhMkuao0kRMg/NZBAuAlbThpQstEQUEUYB0wZEss+J8QYzk0KFDXLxwzUZVUy56HIqoEzGdJhTGhWluk4n4VkWlGVpfX+eFF16wOTmMatxsK9HGoDLJvrV1jh09yPVLlxCyA7KDEIooFKR5WibJnalCm6phB4x8RDvlNERZ6s4noYlCSRhKotAqQ4pCkWcjK3EXAiNChImAAJULet0lJmPFeFiQp6ACm1AWApSRLvFqQICNnCaUQReak3eeYns4YjItiKOY8faw3KNgky8XaU4gYtdtSbcTo03I1saIkydPkk404+0UIZwm1OfmEy7lg/QmXIKOO0vT3Pr95SpnmiZEUUyIdBEoK4ouZYjC+ppFYUQcdumIPoFPnhvYpLZSCBdJ0dIhGYVoKSi0DUwxzSZgCkSqgZSCgFwqskCR58JGkuv1OXL4kPOzDZBhTNwJiDs9jIY8yTEKOkEHIQQbySZXr14nnRak05TpeMJoPEKh3RnfHiXMGEW/10NrxWQ0ptvrgvGCJmXxnfLgwWCESysi3bnWwPzCmQ8KD1i8xsiTddPGDQmUUQgvmCoJt33OvwVgRjAphKjOZYNlkIWuBMeyPIkRQtdqAeGDz/jzu/oCVTvbvbC2ko/407wCfdVeohQYWu0WiDhAxhGD5Z7l1xrPewBtlCZzgSemkynbW1uMrm4yvL7FZDhisjlktLlFNk1QaUYxHZOnKTovMLlCKo0ulIuVN7Vno1bWjNVYYXEhYBoom0zZyDK6r8QHtynwHIxAIo3Xegq0qOa9zZWjLXdQ8z23aZtgZ7RiUXshor5oTOWfpFvuRdSEQcamH/CQScoYjbZ71AXHAMc3eJNy4fukyvdojMspJ6X1G0Na3zpToNQYHWrCfoebLV/3AMk7u3mk7A/zQOrGOeQ3XsWc183z6gvL2xrXNVBNiU/9s9dI1Znz0gRABs7Z2Ti75tqzbsEYx2j4KEYGiMLQmvckU4LASu1WV9dYXFhEaMNnPv1pgkBy4o4TJGnCYNDn/8Penz1bkuT5fdjHl4g459w996ysqszaupZepreZngEIArMANMgACtBIQ4Dig8z4t0gPetSTzGh6kqgHGo2UKCMhA0kQM4OZ7umZ6W26q7qWrsrK2nLPu54lItxdDz93D49zT1Z3Q28FRFnWvfecWDx8+fnv+1u+v4O9XZomxkQbQ9vBhQuXOZvPMaaKFnIJITFGMZaSn6MtRLdIYPzeVV1x/8EDlsuOrbphZ2ebJ4ePsdU0JyDKPVS2dKpYbHCTy3nj40cbSIIbMF6xg4BOFpKQ20wWgL/eoYp/yCYV+8DHmlo55ri4u1h+UkK9J4U6DE0twFTxJhm2l2xUCLMLIdA0FSoE/v4f/D6TSU23WlFXFTtbO/jg6X3HB7d/wb3799BGc/nqJZbtgs8+fsSjh6cMRZEEmAhNc9oPYkK8CqQEeQ0DFbUq1ojvJTdGEeO0SyMClMU8m6YZ5QVOJhMmk0lW/KuqYjaTGP2qqjJdv4neX2P10P3xOX0Xi306T9uuJJciJnvPT4949OCM5eKUs7Mzjo6OhURkteL111/n0aNH3L17l93dHf75P/tn7O7vYo2i7fphlqhoQ0GLAq2EZeno8JjLly5y48Y1jPkOlW347ne/x//9//b/4Bc/fpt//k//kK+/9g1ef+EN/kHfssQT1JJVu8hypetSsnJgtdIslm1UMh2nR8fc+/QRi8WC5WoeaXddDh9cLBZybS8hGavVii56SHonlm7nxqG8ObfRGFG2vQOlMNaiY9iQQlMpk/MSJ5Mme6WMhdlsQl0L7WozmTCbzsQKGC3oVVVhbcV0OmE2m1E3Ndpqtna2YpjWBGsN1lbxPhrvFfPFkk8+e8B8vqD3TgwCKuYbJar0IKE5JoKjSVOh6hqjY0hpUvw0Wa4obXjmmev89V//hNVqWdR2CTSTJnu2x0ri+ZCVUpvY39/n7Owsrp8S5ZeGNPnbGMUbb3yJn/zwByKXt3fo2znaBSZBo6I33Rc5SEYNsgoff4uGiM6MFViPI/gOvIFgpK6XteiofCaFwjZiLW+ahhvPHdB2T1B6jq1XVJWGXFOqB4RVzwSL8pWAR2f47p/+Bccnp9Rqgu4NOlKtKyUGQb/omer9GC4l5CHb022Mq7FK4+YVdm64NrksIdyIcq119CBUTQ4fNMbQNA2TyYTgFTZMUU7RX2gxQWGVkK3U1oABrQzWVOgYUtc0E2xToyd1ZGrV1HWFra14ZZGcEVtXKGtZuY4nx0cs2xVzv8JUiu50zsmjxyxXS067BfcOH/PJvdv0fsn+pRmznYbt/R1WK/Be09FztDhj4TqWfUenAlSGvd1dZltbbG1v8eMffJfF/AznOk7PTqSwr/dgNhQeZqiJc/P557lz5w6vvfbahvNUYRuM+11RX210ZhiTAAyy/vN3xKQDq6SjxP1UHD7FjpfOi4axUNw3XxP30VB45tMz4tuMjBXr71BiPrlmDMpG/TKcOXqW6D6+AA46rxkg712J4EisfKC3JmxtTdi7fllAvbayhbpAaD2r+ZKz4xPOTp7w4LO7nDw55PjxE86eHHF6eMxyPie0K3zf061aIa3oeoJzqODZcnF/QIhWMEqMBsHhFBkQopAaTCbmq/qezzs2eXmSXE39tjG3d+36/Hm+Xzn3zvf8KCwQRjIW4jxhIFHzPkjtsnyfgWAqe+Hi4cKgH4e1FJoQHPsHe9im2gD4n3584QFS8uKcp4oWBXM9EU5CyXRWPtZD6krv0DpjXRlip9TAPpPosmEMpmAAZOnvkp5U5eKsKifUpVAH76GqJAdJKc3Fi5eoq4ammfDJx5/y7rvvsru7w4WLe4Dj4sV9dnZ2pF6Oc6AsKKlYv1w5bBFvnoSDIoa8BXK17aeFBpZHOqeu6xhG0GVqxbOzM7a2q5iXIAukqStcv4p9HrJlp+zbX3ZkwFP05QBIQawMktAOxWYRGCTxcDeGJR3WPi/nT9kXavS9yIxo3SmERYieFsloLu8Lqc7U+J7p14BOViHiJhag91G5qyru3v2Mf/E//A8oFVjO5/Rdj9UTFss5q3ZJ71qaymAqzcnRE7a3d7Fao/GYBIBCjF4wg6VIKZ1BiY21Y5q6pm5qmlosopOpgJuqqlFA0zSSkxHnjLFawmeSMu7cuD5JIOfWBYT1a7Vc0buOxWLJ2dzTP+xzmJco+i6DHx/nbqL59f0AIFIcdwYVztO1kqvTNA1vvPE6ly5d4ic/+RH7+3v80R/9Ec899yzOe5btKiq9wzwrNxcBq5a+czx5csxrr72KwvKbv2m5euUqP/r+T3j7px/wf/nwv+DWjedYrlYczk9Y9i19f0bXrfBO3iXTOwMdls7FhHozWNEVCux496mqKq65gEXy0abTCZNmK891ay2T6ZTk4bTGMJlMYsidwmhHMxVvlrJRYbKiZIoHKymWNl43paoVxsq9m6Zm0kxiOyXUScdcBGsM2hgZGxVwAXrn8zgu2hXt2YnUXenA9YHO9dFDKsUjnfekzKAQxyFp48ZoKmuYTRvc1OO9ENfoWlj7SnKNQOBgf4/ZbMKqXcXctAGwE5X7FPaSxnhYv+PdX0K/5HmLxYLJtBkpl1kGAS50KOv56tdf57/9bwzKe7a39zh67NArj9eDwmgL0aKLMMxSXHhi/kLxlXMOrVqUMdRGPJJehWi0SJ4CjalqdK3o247VWUW33dDYS0wmBowUTre2YjFf0DeSAzRrptSmwUdChFm9xdbBAXXdYI3D2jUSGJXCsiW8rWlqdrZ32NreFsuwUqhvvo61lq3pjGkzo7IVVSRSsFrCxZMRRYwnhqq2UHmsEUBpjcEoTW0sthbCCLlGqLyDl3IfPhDzYyLjnhbWV50s0SpEhSyw6lc8Pjymdz1aBSZGo/qAW3Z4D/N2xUf37vJ/+j//H3l4+JAP3/+QTz++i60ammab6Wwb3RjQMpd75zidz3ly+ITf+q3fYnu2zb1797h//66sD6u4ePEAY23c9zfnq6Tj8uXLvPfeeyNW3H/bYz3PulR+n3akNZOWYbmFZmMea3pT7NthQZ4P0Vp7SnzWeUX+/FnD84bfQ2GvGPYZIOdopvfVel138KTsjLJ9JXnOMI80frGQ0C8t8sb1HqMtzbRhun+VHXuV5779ZZQT412/WHF6dMzj+484vP+Eh3fvc/joCUcPH7E8nXN2fIJfLJjOG7zrpVh56CLrnhBpBB2LIUe9wysl1W8DI09hMsb/MoOzAMCxvvur6F+lEfjzx/PpxygnH7LHLkQSL6UHYJoIUp7WlvUQQZA+qqvq127bFx4gJQUpKRllBfnyKJWeMicpgZISFMEwEQZQsZm+MU2yJAxKUDVMgPHETaxBisFrlQkmKAWapqoagodV23Lp4hWU0nz3u99juZzz1a++jnMtBxf2BBzF+Nw+BAIaayf0HoibxpiafDhKD8h58HHeWjr0c2CxWOC9p64r7t+/S9d18WQRXtZKqKAPEkKmtcSZJqU8hEDbtufGadPzB8tREtolo1BKrk6CUEUL0QD+RPEau/blfPmp1qzKsK5ADdam0mrs/WCdSxVsUGTPIRCj6cYeSHmmRuhC5TytBcBka1tw+L7n4OJFjp8c8v2/+ItIDSzPq/RMwkkqxc7ujO3tHYJybG9v03fwwvPP8+LNl3BOlKiqtkynE4ypIIi3ta5rAda9o2uX+F4YrnonNVu88wJoViu6rsd5x/HxKU8e36Pr0zmOtm3pup4UepUIRpKiXBoJkqHA+X7k8SgFvYu08aX1M92j9PCmMZL1CdbU7Ozusbu7zc7ONoeHhzx+/Ihbt27xB3/w+3znO9+haSaczc/oul6o8xOzUnG/NO5aSb7uYrEEjnnlS69w4cIBs9mM55+9yf3P7vPRBx/y4N59FsslR2cnUqzP1Ri9ha3EQ5NY4KzVBL3MxAnTySR7TOrJhNnOFjaDIqkr1jQTKmuwSpTmpmmwRgBNVVVoo5nNpkynk6wsWythfZW1SAAvaKPBCDuaTxbFWCtIjJUhrqU0DsPYOC+gp1+19G5F70IGr33X55oqvQsQaXWl7o+TBGEfYp0Z8Y7k2lGKHFJaKicJICkFxkDb97RdRztpBFD5hqnSokgrHcNOnOQhXb/CRx9/yjLWakksZFoNMl8kw3jMS3mSDuccu7u7nJycMJ1NsnwYzT8k/6LrW1555UX++f/+j7BKc+XSZd79+bu89dOfcnx4NOxVKiWwQ/Iam2SZj6x01lZSYyeWZDDGYKyl73pRtCsruYnREmWtZdI0VPWE2dZOHvv9vX0mdc3XvvwVbFNLFXoUddVIbTYvsrJpDHUlz63qSgwesY1GKSptMFoMIFrrKDN6XBCrdwhB+toajDXgodce13uk1r2QTmhs9OLpfI1EgjhI3kPnCL3087IVQ4IKDquFBrrvuqy4r5ZLAeargFsmI6TMzaRJ9r2LczTQ+Z6u7zmbi6e2p5f8pD5IrkkwOKU5PjvjuelrcPwRZ92cs8NjFpxyFB4xmVTsXLhEPZF8ol4tWFnL/PEh88fHzHZ2ePutt1gu52xvzdjb2+frX/+6eMgpa7KN995cnw24fv06H330Ebdu3ToHZkoDTtq7Nn2f7ps+K5XLzwdJax7V4kjq8iCbY55gCEOpAoZ9cuxJKtsXoVYIOL9ZuQ8kfWGDYq6Gdqx/ppxEbmTQpJKek2TK0JYUCYKKXorYPoUS0p1UhFkFqZ0FzOcLXABb1TRtg60NNtYbq5ShqjWTKwfcvH6FF5W4gV3bcnZ4wpOHj3jw2V0efnaPR+9/xNHjx8yPj1DzM4zrccslFk8fQIcgIbiC1nAu6RYD4Cj1uvT3prmQar+tGwHL+ZCOUXQVnw/m15+T729KD08YfReUiiyKMXrKRQDIeL6mn+Xv6wZMeTchjfHeD97+X+H4wgOkMu8nWVJXq1bYZ6pqBG6ArF2XbsZ1FJ0GMddWKT5LE3ETu8e60pbQMgxAbjToiGKwLqzku4IwISrbW1vbLBYrfvDDH1HVlhvPXkObwGzWYK2kfbsgG4/3isl0h66TJDZlVJZP40WVQN15z8z6+5XgSBaQKBup+KHkEkgOxMH+PqenZygUi0WH1qLkSH0SlccuhV8lq25JS1n249i7Mwjf8Tnpvgm0DhTmKuYvya0DpfO2FORDn4fBypGMzUpJrYXo4TExt817hAVNx0rfJKAW85jUIJxAEsah9AwQaVoVtbWw53F9h1FQNzXPPfscL7/8MlcuXeLypYvMJhO6VjxymgYInM1PWLRnoDyrdslyucJ1PhakFIHetW0s7Ndl4OOiV6aLgr/vWgkBiePtXE/XdhlgJ89OCplMpBsyp0JmG0pJqBJXXYxRmveJTCUm46ZxSEnbwYfI/jWE7+W5qVTciFPIpoprRJRJaycxRNVzdjbn2rWrfOUrb/DVr32VL7/xFa5cuczhsYTfCUXxmJVnHYylmRWCkA+0bcfBwT7fuHiBw+NDLt7Y5+Zrz4i1vm3pnYvjLaxaSkldGG1ShfNAbcGaRByjYh5PLXPCGmxVUWflV2ONiUntQzJw2tQF6MaaRBHI+AgCFm7OvAXXO1wcbxeceHicFEX0IY1tGHKvAoCBYEmhKeW/3qcAYeJ4l1HoEjMfKNdoBLZBmNVCCIQ49lJAOQzrtdjE490xOtDmIqtumG/AbNLI3CFkj8GLL97i/Q9uZzprH0L2ViRqaF3Z/KxBrsT26/F8uHbtGo8ePeLq9asQzjOfQhAyAe/Z2dripZdf4smjQw4OrvIHf/8V/un/9n9H2y6Zz+ey9q14W5ViyCcIQo8sAkVq9Vhlcr7K4FmV0O2Um5HkeeqXVdfR+1QBiJiDE9iabrNsl7EQpmJ+No9rF9pVC0GjvBZyAucwStZn13Ysz6DvFKvlkuVymWVn27Ysuw6tDc47VqtYJNUY8XC3QjXtXRCru5Kk7L5zwtAXAavSw/5plaVyNnu1Q0zY1krl75IzPpUFqipLNbWYqWI6m9DUNZNpg7Exz0RJPpKKa9BZR2vEoHMWOo7bOV3fYY1DKYsLgdneFa586waHZ4e8+eHPefP9v+F09ZCuX9Evzzh6EDB2kg1CSQe4o97H1xUKqDXs7m3xn//n/wdeeulFOg/BjcHC+hpKyumtW7f43ve+x0svvTSSTyI3C/1DKVIg2rqiWyqt656k9XuWx+A9KkKrhm/zOl3/exwOF85dU3w77LnEfMBiLQ6KdBzqbIzcrPyX9wVigdmxfjD0hR9dVxJGuVznTQ2yQInOV4FEZGiDqh3zxRLd9yhjY9FTHWvcKToluaZar/AmeTw11aUJ1688x43XnwcUy5Xn+PFjHtz5mI/ffpcHH37E4Wf3OH78CDqF7qTgtwWC8/TK4TUZNI7H6zzwKd9fowqd5zzg3GSUDmHYb8ryNKWhcuM9EHlWprRsGrNEAV4+72m/rxsV8rtGI1rfdzHq5OkeqPXjCw+QNjHRVZXFu8H1qLXOYImwwYNSAKhN6DXdZ/3cMShS564DsbYUkiQPsFgRw5Cgu/7sGL4lXl3NbLZNXTf82b/5cx4+fMiXvvQidVNx6fI+W1sTjEk05B0hWFZtYLYzwaPEwqrHE2sgk0jU5Z9vHVhvv7UWHySBGeDw8IjZdMZHH99hd+8izz73LD/76c+k+nbf57HyIUC0WEv+gigtiSmrXHjnF3sgue9L8CkNk01HRWVZaVA6hpQh1p9031Jh9wnwmqE/iAI7Wc+NGWh2bSWbbdq8xdJrqGvJnamM0CjLNeJZm86mKCXsfsZYtmYz2q5FK81kKrTJJ2dn+L6XcJ7gWS0XBC+g8aMP7/DhB7eBQHA9BJ9zk3zn6foOF0Sxdd7R+/hd7zOLmChQ4/yUwdNaemYGsFluVsnjlfq47Ks0X6WaeKoIFKiMRdlxbbJk7Rzi4qNSu5ZoSghoF4uPFt8ngYhWwlSpJKcmgU1ra+pmwsHBHpcuXeDGs9d47rkbXL12mZvPP8/Vq9c4O1twenIqLGeE6PUUpb5ch+mnVmOL6dnZCYvFXMLcZhNeuPmskLUQ6PsuGSnxoRcFTmlsZQtrpwZvUQzJ+D6yw4mXxcUCloGudzjXZgDTeieANgKFvE4QL0paF2I5jSvECymLFD30QrMSZK74+LmPVlIhf4gKJUJdLvMi5LyZEMQK79VgXVxXhnKoy7nNOoIb4rsW7dVqsFrKzygHvCTX2yKsMr2bUiqyYVlMVGycd1y5ehljdfQgtdSuiSC0om1bIc7hvHzP7S8O7z1Xr17lzp07WXEczcd4jVaWrpfCkjdfeIEf/PX/m+/+mx+wOO3Y29ljZ3tb+q4Vj4mPALePfaIjKBLGrS7S20aWKyXtmC8W2bDRO5UNbN7JWvch0HXgqOOMVlFsyrxyocPj4rhp+j551DWBCSo0okAGh43e7ESsMFZ/hygJVVWSKxaphNOepYCJA4MolkZr0LKeJo3G6hpjxAhprYkGDo2pNEwctpLaW5WtqJuaummwytLYCZNJjbXSL5NJxWQ2odmv0bsw255S1xVNU0UDTFabx4DBReOO8/TLHu9dNuKESIxi9QSvDT95523+y//Xf8Pbv3iLh/c/ol0c0/WB1nfZgGatxdRi6NjZP+Da1cu88aUX+Uf/6H/Fa6+/xrLtofe5IHA5f0YGmfizrmum0yknJyfs7e2dA/KlTC0Jf8rjPPjf/N35CyUhnhgNMT4zjD4YjMeIJVeNP99wiXxUeo2ykXLQl0OIJCTR4Cj61mDaVMX/868hgYEwUn6T8WgdBMi6Gr226BKOCIxikd0AXkdSGKPY3d1lZ3eXru3o+liQNhbZjXQgOBXDxrIYlbloUr0wYwiVYvfGRS4/e4WvfuebtKdzHn56lz/71/+ad3/0Y7rDI6pVh297cLFtDLJ9EzAqwUT5nStedJ1lNt2rvD59liIm073W2ZdHY5rHe7xflN9rPQ6LTobo2M0buEUZvc+6jp364uREwrh/nYzzLzxAKsHPsFHHlcJgdUsKmdYD1z4MSZGbuOjLBVVOnvT52AU4XDOyBAWyQqiVGpQXzk8c8RyliZIc2bIh72zv0reOP/3TPwXgtddeZTptODjYo2kavEMoRUEWbDA5ltw7n4tnBsYxq4M37JwIHP9dtDX1tTWWG8/cYG9vlydPnnDjuSscHh5xenLEwd4BL730Am+++RbaKAmfQJi3Ushx7wQ4tV0bN6uCdlwNhdaIYT8qCRnGC7PcbJLCk8c6ypNybG2igI8x6pJ/UWVv2mQypWlspPAU2tqtra0YGuapK4sxmvl8Ttf3GC00zMvVCt8FrKmkDsZiHlnBljg3AMX7UQkuvTHOi7dG9BFH33cE18dijA3ee9rVkgSSspIYx9X5FN6gsldBrK4C7BJLXXJFj/Fw+ltF4a2L8J+hT0FyEJROnhBi7HBSzlX0hhiCH4CnVslTYjIQT9a5KlKVp5yBRJlvlKbSlVicI5BOQEsbqVyvtWY6nbK9vcXu7i5b29vs7u2zf3DA7t4uOzsTJtOKnd0ttre3CAGeHB7x+PETVu0qh3fJpqxRKbA7Wr7SItBZ+UhJooG+b1kdrdBPDiU0ykg7TVQMFSrSLg/hgBnQEElioyxJQDMk74BX+TkCdAYw79QQRlEq9QLuTA5NS3JOxblAUpC9z6F1PoFiF+lSo/KfWN+0ctmwEqJ3KeWVeT30BaHsowQOs0o6amcI5Ot8XLs+3yOF9eWzB6XJS40c13eRrreLQNKD6wlhylQ1KO3RSgwTz1y/xunJ2VB7zQmj6WoxHwG4jYpCHP5kQZ3OJnRdG/OsCkNY2oyjzPI+0Peeixcv8bu/+3t899/8JQ/uHvLozjHHn7UsV8uYeyeywHlPiEtNB/ln42h678HqyDZKNmplg5wSggJZk7KGK6uxlUNVMTdQSc4aASpjsLWmqsUbaaIHJ+WReQ1KG4wRJsemMjRVRWUNVWVEVlYVtqqorAABW1lx4+ihjEaKMLCVRW9JOYK6MpGoIWCNojIWrYQ4KO8n1ozIBmwtebRGxxqDcU5jiAyXkGojaROLkCJ1d0JUtWScYrFyBtZNpRQmaCxgaPAu5rMouc67NiqHgd57jt9/wnNfeZ6t5y5ycnTI/OSI3q2izJY8tbqWXL6drR0u7R3w6ksv8rt/93fQSvY68ZxGJr28j5W1bXQx9+V4/fXX+PnbP+e3v/Pbo+9G4EMN+o58twnsr914A2AZfx0KhrrxHpuuG+kv8YYpR3TjPddebiBuIrK7ndeJkgcpUAKA4Y3W85WG9jIUPo6fOecHeZbeJwGyQtcuWymlJxU6BJz2VEqLtyhEUhirJS3IqyF0N0i7Uj6T8CIpSXXQCr9qI7GKY2oCq8Wc3lSSN7jdsP/KTb49+wO+/rd/i7f/+ke881c/4uzefVQrkVG4EKMwZK6Gofl5jEpAkT7DJybB4byESAdD2yD31se7DOFL+upTw/mSIXNt7LNTIQGvkFgR5dzSW0go8pXW7g+ii6T9NBUXXi1Xnzuv148vPEBKOUchhEy13XUdylgpomnFqu+Jk0MPqLq0WqfNJ/09miAbFPCk7AzKuMphDt57qqoSRdE5NELVazJzjQjtvot5ASoqktGC7YOnDw6jpB6IwbI72+HDX9zho/fucOXaHpcvHbC/t0NlGoIDhccY6DH4XhibXPBUWuKvXW/oqkClldRniZYeHYuHCW+ZzqxC+VCyqZV9lvrN2opLly7y9a9/jX/9v/wZ7VnghWdf4tM7d2is5YUXXuT3f//v8dZbP+fTz+6xWK6AqLTjafsVnWtlIaiArSt00GIxTpbpON211ijLCOgOm7GQCxhjskcqLeS6mWCrSR5HpSTBXcIChU42zSHX96zaFmsUvu8ghozNT86YH5/SNBOOj49x3RLnWom/j+FMrhh7uZeLFnqfreDZ2ptD1AaXtYsKa1r+PoYBWK0HmlKS8EgKXQAVUEZRVRE8RCus0IIOFFiJpSxZeI0WIKKVeGIIkTEsUeMWFqJSKRuEr4S9SdiSAq+wWpQoa21W0m1knqqrKtbwqbMiboxisiXW4d29Hba2tqhsLTk3kwmT2ZSqrplNhR1tMpH8j6apqRqb21lXNns/RAFXkrDtPc73rBYdJ0f3WKxaFl0nOTGpL/I0D8IxGqTvS21k3XCRN0EiEOo9oXOoVZfnHYBDE0PGswVvCF8khhWFDHZzuC363Dgny6kLLq+JNIfydcWYlXH1aWzLOZiOACgjAC/EzSaDGKVE3UwGHRWBJJJvI8ApAjU9GDJCYXHOoC++j3d5qxv9VATwKiuOAqLiJqqCWGgJ9C551uas2kDXBXAeHxQuKCahYaIDygRu3nqe7/35XzKd7mBszQQhE+hPToT+W4VI6e4KgBcV6hjAVhpIp7Oa1WrJ1mwb74tIhHiOxqFRtMsls0nDK6+/wOVrB7z983dZPAzs71zk8MkRt29/SNu1JNVDDACRursy1LWVMFwVUMZHUGJj+KilqWtsXeEjgUYmWImeVFtZqtrGHDUzGBayzExe3RRaXe4DpUIS90W9lrUZSoMhTz8UuBS+FAlihvUzeB8pPs8KUNSU0jwk7wUhrof4m49MoT7Kvqjtym2igQwj7FlFW33hvehZigzPa5DILCvK++OjM9557yOUn7A1rbF2wtbeHiH0EI2v1lRMmwmVrdmeTWl8x62bz9E7T9e1MdcyYLPemBRJN7x3EB0lxEUYgCuXD/jp38xx/ZKqmUblUud2DmMQ52I09sjnhWEiu4FEnsg1Yzkw1izHHq1ybPpc/mJ8jdzjKR6Ftb9L7wZRrpXnDN8pgln3LA/XjYHh8LvPc0AOMVhF4y5jRTuRkytUssLG+Uj0rCu0V9jQo4NBWUUfuqzPueDxzhCcxvUipxUal+Z0lKdS+1mekWTtUofsTXa+AyU5fNoq7O4Wr/3dv8OXfuc/4N/8i3/JB3/9fczhQxrl8dGz30eAI+ubJKRHBvA0dglQyLuOekY817H/SqIKFfs1jVeSIese0PUxzsbp/IRijJL8UOevUwx1rwJDQXgB7LLo82XeS9hgCBgP29Uspjicb9PTji88QCpjr0sXXIluSya7dSS7PsClNbEUDGXSWvmMZAlPtNYJNHnvs8cgTeDkfUqU5FBYIOMmkX8P4lUwRrE122LSTPiTP/5jvOt57bVXgcDOzm5UKCsCntOzUxSGrl2ytz8jMLDmSS2azYl2Sg2WAuf8CCyu25nKJPq61mztNHzr27/Bz372Fvfv3+PFF17C+5YP338fnOPll1/hD//Jf8z9Bw+5/eGHHJ+esVx2keVOZVa07HnzPgr4IRchja9tpJhc6utknfbeQ/TqpZwoUUgd864FdUhWMoOPZAKSV+OCtCWFGrZtF+8XBqt1LBhYVRXL5VIsVH5sSSk05kGhVklIF1aV+LOK+UtpAJrCw1XOY2MMOoiHZoilT5tp8oYN1cptyg+LwjjdowQ6RhmqmBtjrInnWMl5sYYQmXSMsdTRMjqZTiPFs3hKqugtmc6m7O5sM5s2NJOGuqqYTKZMZ1OxqlYVVWWYTidUtY22omjx1gYfyBZQARfJ0+FY9UII0fc9Xdez7CXU7HQ5B6MGBYkhVDSB4NSviSRF1q54dcTaqbJBQma5B+/yPUfslwGSFTrPS6KSFwbDSLKiuhiqE5TkuPi0mSfwhQAj0ncFaAkM82fds+FDrP+V12tivBwIFdI1DpflWxlOcX79F5vX8FH8WxjpynOHKTsYL0plJ4QQadIZn1soPOsbWIJIWYdjTQ4TIHSghr7vveNsMc/U6fttz55z7AUPyjOh5urly8znUrDTec/BRY0xVfTyyJpGJ+//+X0ABuVAwuwu8+jRI7ZmO9nbXCplWhm0Eq/w6ekpOzs77O3v8jt/+ztYL7k3znmc+22S2iCW8ZgdEGn2lZG8B5kjA5BPYAJED3IJAJP2pdEorvX5+Dt5vRgGo2L0Tu5v+ZfkKk7kWPmM8ZiOj/KjoJ72fRjpt2r0HutK9bjt5Vite0s2bG8b71vuKxR5cS4QZVIsvG0tb79zm9UqgLI0taFqKhzC7mmIwNRYrJG8IxU8RjkODg5YLleknV3rQV/ILVZDyObQvpCWA6C4efN5bn/4AV969cvZ0FUwI8u+VqYAwtq4FzPgKaCn/O5pfTdqXzkexfQ6z0i3GcCk+eN9DO3d4AkqXrCop5PvlqDeuWbIH+P3T/Ncq7TqouxKcifEwq3xutRWH+tIOSRyIziPt4zySb1PIa5jnaDssxJcpEgFpVTMPQbdK/HcGk3fdVy4cJFHjx7y/e//kIMLV/jtf/APeObKFf7iX/z3LA8f0wQnRVRV7AMloFBHHWgTiFkf33J2ZMNFcf76sT5+m+6Z++1zPEtPu395vVKlzHlK28KQw+y9p10uaapfvUgs/DsAkMrJkBZciXZhc55S+jxRaK4nMJYuyk2gqPSohDAQA+TE0uL8EsmncEABLmMmksFSn2K4JeRgNpvx8NFDfvSjH7I1m/HSyy8zm82oqpq+FzrlCxf2OTk5FQYpJHbdVjUp7ye1azPi55cujuFcee++71E6cOXqAQ8fPOEf/sO/z3/9X/+3fHj7Dt/6xm9x+/2f84v33ubx/Xvcuf0B09mMupnSrpZ0rdAdu77nLFrGy9yYEMIopCcp+VUjfXd8fMxyucyKtHeB4MKQv1Eojc51EKRYZMo3SjkNnujC9wMjXQYSRGt2oay27RJrNd5JXpKyQy2FtKDjXYpwmOF7KappsgKfwkMFtOhY1X0YJ2MN1liCGxNapDmUNsuUw1VVVhioqgpjFaYhh6DNZlKstK4b6qrGxoT12WzG1tYWTSPhISp6fabTafSCpjwrsWD7lDBtdAyJEW8CqkfhC7KNoSZZ73uOVke4+RB+GgKR8cwIcAmyyYTgM7B3xfaXAEBW6FNNr+DzpjmQtbgIwAawKUAkwrPoRV5P5kybsIQsFp8X4C21IQElwVXDRq+IFmClCYmGloFhMf0ENbRdJTCtorf5fKx4nJxAYB1Ep9+Tdzz1V7leP+/4vA1xpICMlLsx+Byfs/l5w2OeooiVzRjdR8aOrCjLdx7Psu+YHzmenC3YPTzi8qU9rlzaY29ni9l0ytbWjCePH8d5XTOZCTV926XckVTA2I+el16r7JvLly/z/vt3eOHWS6MIgvLdE0Dve8d8PqeuZcN2YUUXrbvBhIwiEhBRSsUaIYHQlfOsHuqtrQHNkMaolN/5f6Vcz7Bz+D2k7hTlKoVTlQgjJPCe+mPj7+cVJF/m1arxd8lKjFIRiZzvZ9bvG1ibMaXSnc6Xv5+aS7t2j+T9DITY7z6q3AqljRQFVxrnNO/dvoMn5owoDcpgzQRrazRSbF0hIMV7qQ139ZmrVJWN69+vhaBuzhcq+6Fc2y+99BL/87/6V7z+xtdyH5aRM6Wx4tc9ngZgNrVrkCmQJtn58S/Dac+D0vX75XmrOXdeaaTNZfyyfjbkOq23c/33dZm4fmGIa0Bk2uDhBMCR15kn4DpHq8tyL0RwuplBbh0krRurQjRUaA/OK7RTOVx/ur3NzRducvvDz+hbz8u/8U3mXeCv/8f/L/boPqF3mNzmGM5d9HFqYzlPPlfWF+0szyvvUwLb9Vz+8hmb+mF9fDYdv+z7Te0GWYGL+SKC1s8B22vHFx4glSFVMGwYfe/OUX6XwAUYDfomK2sJbMrfy0kn+Uh6JPTWQVY5mVJbqqrC9X60mYxyCpSwxAVgMp3w53/yZxwdPeHb3/42la3Y3t7hu9/9CyaThjfeeA0xpBsW8zl1s0Xfe6ra0HtPiBWLyyNZJUuht2kBKYbFU/ZR13WgPDs7My5d2iO8eot/8k/+Mf/df/f/4Yc//BHf+o3X+Ae/93f4yU9+xk/f/BknZwtCEK+O81L/xMfwtLRRKxXDkEIYLUopKDpD15bVcsV8Mc+eMa0ljMyqKo9xChsTkFnHYqbEXBl5hlYqF6ccEwRIQTlFzIvZMB9MZJ9SSg8WvTgXU8HDxExW1xVVVed+3t7eisBWrJZ1LWFnOkBlLM2kYdJMYi0bi60Ms1nN9s4WW7MtTAqxqSScZtJIbZtm0qBjrH5d19STmqBDbKcazdmkGA4eDTkkaV7ymbxztL7Dd0XYoHdRMRrmeVK50r199OiUylTyKKT8lWQ7l/hhncNdsvUpSBhiYp1KXhm5ziPsiaFQ8gTEqBjKpTUxyXbsjVQIKYqKm+HYGKXynijt6bOC6aOlvYyZz2umjCeTu0ZwpqQopJIq4c6vKZQBghfrpQpJ+QiF0hpBQXzJ5FVCnd9AVAJYevNmWG5k65tf+izfp1jv5xSmkYJzXvnJ14y7JN87WcsjglyzPJf3Hq7JhiM1WNtxElohiqECbVm2K1bLOUdPHnH4eI/rVy/yzPXrbG/v8NGdT6ibCc10htKyJbarTooloxllDI/AxHAYY9jb2+Po6CgbbM5HIMh7pTDE5XJF23ZCQGDjeKh18Doo9QmAj0BQaIdxi0/IYMarPPcz7kmyOoQsv9I6TMrrEC4q/1eo0diV4aTl/fPcpHiHX6KYB5XGeay4KoXkZqydn2qBjeaeWgvxK56pYw5VehtfXlZelYwX8UWkHl8CRCJjhDhJAJDSGqUt7935iEePT6nqmUT9JaZSFQjeEpBQyFDINtc7bt26hTGSxyr5gHF9b+iuc3vu2jqs65q9vX3u3bvHlStXNp5X/hz1/9png4Hq1wNU69eVlyeDU/nvl3mmxrrOZmAzMjomWVYYk+SjRBTE2jPLy5JBqzB8MMxrX7jfgg8Snp3WUVEGBh9DG/tEWHKeMGCTfE3vWhrhcwi7jgXWQ8xB1BpPh3JiIL3xzDN4Zfng/Y/40Xvvc+sr3+TWkyM+/LP/ETU/o/YOTcBJ1TS8YhQut0mOl2C3bH9ZULg81sd0Hdw+bR9Y153K56+TRGy6f3n++poo257Gs2u7yAL6q8/tLzxAgjHjWVlHoOzITYP+NAGzjroTEApBPEDl58mDtOkZCRhtEhablJDSAin0p4HpZIrRhu9+98+Zbc1448tvoJTm7mf3eOedd/nmN7/OZDKla3uCV5yeLnj+4jMs5h1iZe+xlYRglUm+grTPW1XWGUrKqbY+SZfLDltZbr1wE+c+5Ktfe5XprOZ//p//FX/z05/y6Ud3+PJXv8rzL7zIYtny+MkTTk5PWa5WLBYL2lXLarXKYVSJejqEkP9O7eu7Ht92aGPYmmyNwExV1eApKtQPYyGFUcXKl0IcJA9GgE5dS60PYairaGphVkIFqspKhfYip2Y6m9JMKrzvQanoeWmwMUl/Mpmwvb2N1mIJ2trairk1FXUtxTyrSp7fNHUGapO6ZtLEPCFl0MbmmHqvlkDMHyk2qqH+UqLrThTMC1bzOc7F+hLRUprmNIjinpTyYQORjcVDvleqvK0HHtZhLieFP0DA4r3K3rhSSKYCoCibrcfBJ7KAHo8fKWIJSGlAZSYcCuXR46PltjxUINbbGa3CvDYVUleiZGQb1qWSIsNKZU9kIjbpgxeWwITGiueaTKY8LKP8t0ZohmONmHECcyRRiGByuJbC6h7y40SVG+LZz8ksxuGv6xvP+iaWm5ssp2sKTkp+XVdUh74e2A/XN8RN4RoDeC7OHSnC0g9aKaFBTkpE8DF3RYwaGiVlCxCPi/MBpXpCZAtUBB4+eMTR48ccPTnGxfpc8/mcrcUCY4VxbTFfRC9njN1P/Z1Bzrj/vPc0jRRmbdt2RNGfZU1SthnWjveSp6h6A3qtpERIa0iu9s7nqSA/Paie83Az9pewXOR5GbI8DwWoUONlkufw+TFlNGZPs+SW4Ipz5w0KafoRhmfENqW1uA6kZUuKIHLdyj86bwD8fviQc/002o8TOI3tUkPpAEyD6+Hu/Yd8dvceJ2eLmPqueXh4jK6moFPOY2k0k9IaKgpOrcF7x3TasLe3v1HZXLdvfB7ISbnOzjle/dKX+MlP3+LatWsjIqhN62z9PpsAyrpMWD/W1/X43JhDqdK6LtlPPZuMNJueP/yTKIQhBG0MkGyKwpBHMwCihHELL3qBjEq5P3rTEAbZVrxfIEUDxHf3nr7IE+2RiNMQXGHYPq/If57sLY318ReZU16iUnxw+JAiiDTTpuHGlUvMT+d8du+Ydz+5y0vf+m0WT+7z2Vs/Q58eM9ExxDuBxDUdc6yrjveO8vv1+br++yZgtencp127/sz0d3n+JkD3y448xs7Rdx31rxFm9+8EQCqT8iV8bVw49mmLNB3rkyjdLwGtElSkIq/ja4Z8hfWJtA661j1SpcIwtMfjOkk0398/4Pj4mEePHvLccze4dvUyyhr++I//hEsXL/Hiiy9z9eo1IHB8coZSwoFkTMz3UGoEjPIEDKFg8FpTaAtLh+w7Y7f5cBjOTlfMZoaXX3mB27c/4mV1k2du/HN+8P2/5m9+/GO++xd/xdligdaSIF3VDdpo2j4QtEFXDX27ijTVkj/jnaeyhrqaDE8yVsgElMIkIgKtaJoGhViYE6tZql81mUzY3tlGRZf41myL6Uy+77oOqzVb04ambsQbM50waSZSzM9q6qZmNpvlmjQSxlahtMmsRWncqtpidAIaAiyt0WhdRUVMvBsyR6Mw9sLC5ZwAmLZP4YHtkMcSAkE5ul4IAFK4TQIR6wbvrIB5UJhiHqbnBgHOMel9+D7NATHAJMpp772wxqWwk3XlKl7nfI/zQ3tS+3OwYvTepc0qAaSgxMNZvku2WMdwOxDgkz0tQaxk5ZyUWjoegs500ak/PMmTJoE0srb7DALkHprexfmfvUXy0wUf605IR6vC4xBGqtwQvid93UMrGllKjB/C4xQqJM9lYbDR62uMDBpBGH/WY8zjKcOc4LyCUx7jvwVgJ/m57o0vQzYofl8HSEnm+ZgvkHKj1i2s6ZpSFikloYXKxwR0J97D4PxQaNs72ljTSAXJD5T2OebLeQwzrfB9z2q55KOPPubDD2/TNJbf+Po36PqOtutoup6qqWm7jmTEHoPAYk2VhpYoCw8ODjg6OsrlDTZv5jGEVsn9snHCjcdn3K3ixQguzYeByGMzWClBaBxHUtPV4C1hqFCf3m+s6o7BfXn/pwOlso/OH6O9TDHy+ITcuvE6UgWAyV2z4dEiP9RTmrX+4RrICmSQpo3BO4fWho8+uc/Pfv4+Dx89kvZpQ0CjjEUbi8mhxirivbJfwvBPBZRy3Hj2WeraSkkGkjwrRme014/X66Z+DCGwv7/Pcrnk7OxM9rzPATib7ln+/jR9aOzVGSus6/t+2itCGAxi6fzSCDCEopXrSI0/VyLPU3TGmCAIjBpkbwI36btBJj8dCKZZk77WOXoG0pik34OleDePcybvBW0vDJV9rzKDpksRSrEsS/lum8LMUj845yCOoQoxzDfNTcSQZxV0yyW7UwFJTbPDh58dcf90wVf/3j/k+PCU9s77hNUZRvnMCvrLQMa6nlv21SaPz6brn7anPO268vySMXrT/Z4G7jc5OYY/xIN0dHj07wHS+rHewcYOeQ3rkwDG1pP1iZzyg4ZiqJs549fvlQRDAlEDADovELMwif8fFIVCUUVhbcVstsX9u5/xnd/+DtcuX2XvYI/3P/iAN954nZdffom+63j77Xe4d/cud+/d5ze+9dus2g5tpC5LqjORLNXBJ75+2WjShrQuTId2lzvW+X4neE5OTpnNpjz//A0uXNjjwf1H/O7v/z2++c1v8vEnn/LJp5/w+PEhh0fHsYimFBhMYSDC4uLRylAbw872NpPJNI6dKG1bW9s0kwkhCOtUCmWbTCZMJhNhRGsatra2sNZirWW2tcVkUoNx1I3k3yQXfRXZ1XC9UOEWuWilVasU9gmAB4z8Kza91WpJZqdjvFGkf2XdGtF+hrmXlEogVplPSoPCBU1OVxFYEy/XI6VWztd5Mx57G4YQMgI5/2YkUIMEsLkwAB8fPDqQa0BETBS/d8NGElymVc2AO3q4tDLpBdOqGcLt1NCu8ToN6YXlLxcthiEJ4k2sRx6FFo8UY+XPpXwDhucKoItjG0cyhIHhaliLnKsjNoC5YR0DeW3l0cpzR9qcig7iAxICuKaI4AkFEF1/piYldScleOivdUAjG3d8fhiUjHV5mLo5AMoNYS3nFKpkMEFhorAKIQW5hJyfFmditOpJfZ6268QL7B3eO1zfS9HirmO1WnF2esri+BQf14hzTgpPx1xDghN67K6LXuHIWlkZ2nZBINBMpkjeiGe5WtF1LdOpZf/DfZ57/iZt24ohouvQKBbLFdPZhEonJX4MHdYP5zzXr0vB2GvXrhX7xjCvx12ro8ztSWFYefHE8RlYBSXcL4OjSPVODgUjywxCiPTHcf6kj+M7BGSdpJBiH5+5KURzFJJWPEaA1VpYdvn959kcR9bj1C/let4AvFQMl4zybQTcSjyWcclm4Oa9H64tAVjcvyUHX+M9uKD56x/+mPc++IzOV6BrmeOx4LSxFpTGaKE9Dww19MbiKYjY9T2awK3nr0cgP85ry60Mm4BImhrDe4mXQud1/fzzz/PJJx/zwgsv5v6U7xgMCfkxYTQXkzEseXjTPAzFzzKfszR4SPvKiUK2meY1kOZG3ANlmxXwI6Ah9n9iUtSmAEnEenHJAzOAJ4WPNalSm8heyNSXA0ga61kDKBuWzvgYQFzqu5QzLO+rM6FWCAFbhRipIWU8ur7H9b2kC7jB0+SDF2KHSNyj1VA0O7UFJAJG2PZcTnnQWkkBbQ2t66hNYOkWXLl4gK5a5i18du+IQ1vz7d/9A7773/w/8f0Si6zlzvnRWJfAtPys7J9ybNdz8YsOHfqs3BfSUstAcyxD0ymZvj9OlU1g/fNA2dO+H3RpIbnwvqdpqs+9T3l84QFSUjyVGhLc265DrRUdTAPxNMCUfpbAprSClPlHCQGX4GrIT9icxFaSBwwTN0gid6RCdWUIToBpPWVSVzRVzT/6R/9rurbj3r27PPv8DX7v7/0uH3zwAW/+7E0+eP8DrLFcuXSd3YMLnJ4tmDY6uq0tGo3yFoOmMlVUWLWEWRHBCUPbNsWHhmKjFnkUv4vnzudLjDFMpzNeeHEH30tV9de+/BLL5ZK2bWlXHfPFgr5t6WM1+5RfZGwsMKq0UDw3Ung1IHz3ddNgTNpAEba2GPqmtBkJ+vTTeSGCEOEfMuVwCMKZ361WMZ5/ALip78UCPgZGSTCEKKhLJT8dngSYU0vJ90j9mLwOxV6GK5WAcgOTmVHM9/R+4w1wM/PgoIiMgZBHiHQGpT6Fk3kSC5lIcilmKX3kvR4ICYo2JA8ESQCqYZPxwCqW+oOxlQrABD0o+moYhxBAh4H4YMxKFHClF6d8NzxCTl3EqwNBJcA2hNaUtKdaDaFySYnIAC8kUBoKJSZtqAJ8Uu5Vli0qvVO0GGZFIqD6HuVDVGCH3BMApTWdjptcHCKlyLVTLDLXezfQtaa5nbwG64YOSHS1cr6L50phz0CIhUOT7OtiaGu7WNCvVvR9z2q1kppeXcdiPke7Hh/rdLVtS9/3LJfLOA/EY9j1XawD1tKuhDnStRIy1rYrqXzuSgYxl99hnNQuSlOqo1VZqcXTdwGjZ+zvXRClyxpsXUXK4MCFgz3ado7zLW2/ovc9nRcW0WXfM/cdu8pRaY32onl5DOPFyagdly5d5N133yPwmoxlofD4cD5UReSAjd6j6K2L88KFZHkeKHGTEoWVnARRCBPwlTaGNClQ9N5LfhYhaq0hemcGpU+pyGKZvFQj3WddoRnyOUoDkBxpTIZQwE19NPouKbRZg85PYgySChAeT9z0jE2fDcBNRdKEJIOKewT5zGh5du81f/nDn/Duh/dRukFXsa6NLr0bw9oAyW1NYBaFJMgDIXkMA+xvTbi0vUUIffZW5+Eq3jVLsjD+nGAKOVNeGLhx4xm+//3vc+vWC/jsaQxI6G2UyQXUTyXxojQuqPjHHoZNXofy/uuH90P4cal0ixE2zQHJe9ZGY0zyrJioo5lRqKJ8NzxHqTS/VC5LUSr2620tZscYFJaDn++d5sUAXgcdMY4BKa9KkYrFhiCFZ1Of+CC6Rd8L4VT2LqXIhCB7vHdeaNHjfjBgYwnN1CpqAy5INEWQeoLeO4KGThm0srgOtne2aexDZhWcnC2x+7vc+lu/yfv/0/9EvdTQuVzYOa3hcr9dTxspPfzr4KnsLxVcbnjqIoXKxoii2zCkciUKpWzuflWMWa4HWo7cBsBUtmvT72Ube9fTO8/de59i9vc2zI3NxxceICW3bKnglnlBcD45rtzERjHhjBXNUtEoF+WmSZQETwm21gFHGeq2nidVXiP1k2BvbxfvPW+++SZHh0d4BzvbW/ze7/8dUSK04Vvf/k36ztN1Pddu3EBrQ9d17OxY2rZDq4HmPCXtppo2iVFLkLfPm/1YAKn82WA9SGaB+M5ao1UQKublKnqFpAK1sKhtZ+UrBGJtqCF52BhNIIUlycIRwTNYkxerhTwvg4e04GOuBGWdGblJqlFELDqb274OMFR6tSHkKmoxESz5qKgMG89oXlCq7iW71Pp8SeFMyao1NMAXWsu6J4DinSnGKClDofhZKjmlBMqFO9O7utKKmZRscEQqaYa2i4UyiELcu6zsl4AshMFCXVKlRp9IQdO6DigLdSGDGZXDP6Uffd4k83hRUjP7rBYE1ABg8/epb6KgLtoREqgJPneOj88sgUvZ52ndBKDr++wFSFTjeaNJ75bmSwSiCgFdecxCGleZo4lCehyPH6Ki4xOcjHlSQ2hxAt+lbEufO+9oVy1t1+b8vuViSbeY0y0XzBdzurZjsVhwdnaG946z01O6dpXXYt+L9wegD33u0/TOUvw05ScIy1Q51lppdFB53XbtCu8FIB0cHHBw6SKT6ZTJZMLOzg6JPXEymTCbbkkYXdMwi2G0IHl+k2aGtYbE1i002yfcvv0eq1WLjbJAm1TjRFbO2XyOD/sRXMqcyiagvMblivQe0+ksFx9fn/+Rff/cviN7U9pLhnVBiNbyuDaXy0UsqB3XXcw/qasKa2LRVGPwvaPte0iEMErFgqVD6KxSQh40yLs4d9ZC9tYVzVKBOv+5H52z6Xia8XF8Ulypv4b1+POO8tL1PX14hsrbVyDw4Ucf8dHHn2JsTVU1wGZ2yHXFvGxznitI3iIh8Mz1qxithE02a46Bc/TXue2l/qHgHMhO33mapsZay+npKU09oWTBFRAdfedJf0kyJTOzpeYMhsR0fd5zwtCnTwNIqdh0qR9loKOS51FHgKSwNhEbmXNyrWhuMZ5D+5xDwmmzkW5ELxpvULzbcJPRXp6MkqS9kcFY5tzghdJ6IH0YfpJvrpS8q1EWawx1LUzBjVf0vaPruihfHc719L2m9302uHqX5qWEYzg/VvpT/2itwKhUnVJKCNDz/HPP8slHP2C2u8vh8pQbr3+dw9ufcvSzN6ltAOcooMxGEHRuHq+DKO9B6Tz/FJuBS6rpl/pYxxCT0u88mDvK4VHD3yHNX5G5wQcG2Xt+7j1NThilJAep7VCfU6x4/fjCAyQ4L7Bl4My5ULcSRKWOdmuduWkirMeSltenmkfJezVSkNYESK6LxCDIy3uln+J9EsVAa8WVK1dYzBd0reNLr77K2dkcow1VVVM3Dddu3MA5z/buHsu+zwxL6Z4p4Xwd6A1/DyE450GSCO30XklJTN/Jwu+yskQYQnsG2aKy9S2AgJZQeGbkFOmjmBeS4ntD8INimZTFeEEKhQSk8G1qc1S0U77PiFY1vmB6RvpMMabITAApKTclfWxSj0enJwAZlZIxQBoK9w0b4hjBhEKslF6Acl6UDRjNv5BIDnLDGQv3KLxCYUUqbpcL1JaAYdD94t8R1Wk9fFW8X8Yu8ZdM107AlZuVWl9jaztb2S3Bjc4rrW9jOt8hVFT4hcp5NWwOSYlZt3wKAPMkHgpfzLnkIc3zJisQEh7l4nwDhryqQJ7/SWkoX6/rhG2n73oWiwV910GUB67v6ZdLKULsHF3X03UtXQxPc90qhnX0tG0nm3AnIWwpTy21U9Z+2qydWDedz4nfzvXgPCb40TXGiByzsQAvIGyMRuOVom5qvJlgJxVGG2xlaZom1wqrmil102SG0SQbjTGoAIvFgqPDJ9z+xS84evKY4DzXb1zna7/5HbZ2dySP0FhQUtxQFKpUqFiYK/OmHqDSlXhRtBRMbuqa6WzKe+++w2KxYqKk/lCi8E0EKmdnc1zvUFVNuW4YbfHj+RlCYHt7i+PjY/b39ymPp8vXuH6yZUXFeaU5PT7h8YOHnJ2eslwuWSzmkf1uxXKxxHVdLug829pif39fishqQ+thf3+PZ5+9wWQ2iXNSIaGcLlvpByVJRRBdrJwNIGUTACqV9l8GZv5tgM+vBKx+6U3AM77PcN8AWiIm2t7z9nvvUzUTtK7wkeBnnSBg089RG9N9kRA7rRRXr12l61uG8PU0H8Z7/NPem+BGfT28mif0PTdu3OD27du88vKrcZwjAIsyLRmjxFBEZEcbAEfIZAphND9LEJ9eK6ztY+nwfijAsB4RoFBZwa8qizYS+pTW8DrF+Xr/pM8yKE1lESLIyT/zCIRijxpGZX200vfZeChvMnoHMVwnJtTzYJnindev07FQehWN9c67XG+x7RW98/Q4+oLsJxT9XQJ7GQvRu3RQmODQoaPWUufwjddf4W/eus3Olcscu55X/9bv85ef3Sc8uYvxfqRLlEe5dtfHjdh9Ku6jY91kMBSuA6unPeOXrd/y26xRhNgPpD44X6cU2Lg+8AFtFA/v3efSv/cgjY80KKXCnECLUuOQuHKxl2FwJQAoQ/ZKz8/6YK17i0ogtQn0JLCW7j9S3kYKW6BpGg6PjmjqCTdv3uTSxcsYLRaL4FaEAFtb2zgfuPbMDc7mCxbLJc55mqbJCqqEpQnldNnmEvjpqPQGVGaRK9sVEt0wUt8GEhNYoI/W6fIa70ThSss0BLKASRt18gzl/omW9REldEn/rCCHYiWAEwLGp0RPyduRTakIAfOyuRRmiTR4w+/xGNfUIrZzgxUj2qPSdEhU7+KWHzOyBcjhA+PNJgqqaDnxa2Bg9Lziz0SD66OA9TEPqLwuCTgdKURF+U4bS3pecf/Ub1422NTXMg5hOInoPi88oKCiAS9khTWBB4jhY+W6K84Vq+V4zSQFVi4exlRmZ1of0PtE4R3zqeJ9E2hJ8yfFlEtcuISipLU6rDdQDGEEua/XjBwhhOw9SCCjnS/p2zaHmo1kSu+Yn56yXK5wzjFfzFkuFhwfH7NarmhXK9pVm+dUeo54VQaPVCiALfTRQhvnqCJb/JLRulT0jDFUdUUV62RhDcYIM6OxBmstTVPRJHISY5hMGqqqpmoq+RcLA1dWYrurugIFJgKgNAZiSBB6ZLVGnBOIda6A5WLB2ckxQQV+8sMj8WAT2Lt8GVtPqOpKxkY0GpFLYZBdRCXERDY7XTcYJaQo1kr4zs7OLgGNd4q+8/IvkqAYrVFas1q1rFYtvqmxOiq0lBEEw7ws5+i1a9d59OgR+/v7YxAUxnK1nDc+hg/6WIBao/j4o0/44V/9gM8+vsPbP/85R0dHLBaLbIVWsgDzPM3KmNYEpagmW3zp1S/xrd/8Ft/57d9i72AfFb2Iw3iYGCIkiq02plAwn348Ddhs+nysRI7P2fTZr3LfTff4FRpdiPexoibePU/Qho8/u89i1eO0hSDh2uveonVFcL1MR2qj1ppIfsn21hbb21sQHM75TBddtiddN4jsQtnL8nNDXymZV3t7+7z99ju89OLLODcYf5NsRoGLe172Tq/NT/k8GkQYvoOBDEZy4MZgKslYHU3/I+9wkVuU8reNTax/JaAZ+m5sTPDnPs9GZFUAtby3xj4sANMQRSGfl8a40XfputgX5boygNFDyF+eF9F69rQ6W1qrmBdp470NTWVx3rPqWtquo1119M7Rd46+j6yzjPVQIOuHQRlMDGe1KPq2xVrL5cv7XLi3xcliwXR7j7BvePbrX+f2H/9LKTjvNgOVz12DIXnGVGTiJE0ISQMp7lHuLesOhrEeu24oHgO0/FlqWwjC6hd19nXZsX5t+UytNKHvOHz0GPvo8dPfc+34wgOk9QHIHRtkolVVhbV2NJDrAj0d61aDdHwu8pZPKQVGCYBKJrxS0PZ9n2vorE/iZG390z/5U/7Of/B3+MZvfJ26avjk48+4d+8ujdW89857eBS9h9b19N7jkJCfKhaITZb1tHjhfDxqAoSStL/JAprej3xuYkNzAamZk/OwfFZ6VSjF4RBznSwEPi6I9FMWyeCydSm8KQnpOKY5lygL0URyMDiW88JCwJ1iHGqS3iUL/niIQpE+FwvO4FUqNpHSkhT7VUfQMdivBuEw0HGX+Cxad1N/q5DL3qwfw9wD5VXkYVCZ5KLEfnm4QqDt+wgqUp/EflHnlb78L16flNPh3dMYycc+JKNCMT8SUFZDXk0KGSr7fiQcg5cwAi2JrFJYVgr5qoLJL60brXUE4MOY9r0jscf1fZ9zWQBa5+i6Fu8Dfbuia5esVktZm94L3bN3MR/JkTyWxhqMsdGbIjkzp6dnHB0dcXp6KpbBtqU7m9Mt2+yRyd7CGKogYVLiOUwhLLmvfckkKCFVaEWwUsdLCv9ajLH597qpsDG0ylpLM5lQVxVV1VBNZ5lu3lpLZa3U2LIWbc2Imjp7Ho1GGSN5iFHu5Aml0yoaDBwQvTooyWNTYn03YSC3CWseW0WUDUg2nW4meKW5+dIrvPPOe7TzOb1TbO8c4EJRpX4kQeT/SX4YW5EKP/cEUkiI5ItptLFYW9P3AdNDu+ojW2SS84Z22bJYrHBbM0nPUOPlt67Epc8ODg548803eemll8ZrZwRkh/OFgjcC8pjLcnJ0yr/87/9Hfv7mW3z84fucnZ3mMMa0hvTIiKNGa00UiyMOHz3ks08+om0XfP1b3+DCxQN5D+Xi/BUVwPUOpQx9340VoOL4dbw2mxSvTddvyvn9dZ+3fv3nXremTKW9PClhXgU+/uwewdSUXslNnqJ1T8G57yEq2DJW168Lm6wQkWgKDhjKvXWTIVXu/XSgKPlQXrwyWnN8csLWbJuuk/FMOdAeIoHHAChKsp4U4r0+R6XdKQQYyIQhJaCSOW6UyP0EhFL/GGPEoxwNJ6VSXRZjXzc6xe7JsjD1USrbEfK5A5BKcmXUWxvW6yZlOhk+83lxv1MKjILKGAnZVeO0CKWF0nyTwVzHtZmWqFISFmeMxlaGpqvoqo6uc6yWHa3uaLuerpcw6WQsHnREhVM67k1DCDmhJ6jAG2+8xL/58x8waWoez0+49tqr3Hvn57Qf3UGzOjfPNvXHqJ+K81MbBiA/jkYp13tKbylB7fCcweCw7g0aMZwyAKU0IE8zrGzWv2WrskEResf2bOvc9087/p0ASAmBG2No2xaQHBgYD8R6KFypeKV7wdMH4fM2gvR9ak8O/VoDW33fD+Ema0I3tSlNur/7d/9Djo9OaNuW7a0tHjx4QGUrrly+yGxrxtlyRR+gn8/xXYtXISs6ojSKpdlam5Xd9cmachQouP2HejMwVvYHr08IyLPDEFI37AVqDSAhVqCYH+SKXJ8SNHgfMgPZEAs7hLklZqcB3EESkSEyweiY+5AsEuJ3WFMIMugqoZscLnlyIs1uAjfjywsgoVIb4kYb9ECelEBQBhIDSBBAne4dS72FMFTyHvVdnJPiLsicDalfUk+XLESBSFMfkoqbhLb0zzrbWSng1oXdOrDpI/GFUkMYYwk2s+VvdG/yObK+ZAM1IeU8jTfOrm1xXZuZgBSS4+Kc5NKsFstMDtC2bSYRCM7RrZbZy5Nq4LRty3Ixx7uOPoWiMSjuBQzO45TnIEMobcqLK0lbVFQGhvhxWd/VpMkFfauqEur9OCfE0yGKzXK15OOPPubk5AQfPFcvX+Nb3/4WFw4O2N3dxUSwY6xle2eX7e1t5vM5JycnWQHXxtInauj4Hsko4gkEU2xOIeYNxp9xIKMSMCb7EKwjinpZxFMrM1CSa41G1qA2Bl9sdul8VKxxpKEyNXXr2A2GZ597gdu/eA9jG5lHMX8horJBFockk0WOuF7mUhc6OtdjtcZqRWMtverYns64cHCRjz/8FO9D9ha5XhjlrDEYbTk7PaPf30XH/vFZJmxOCNcadnZ2JDSy70fnBA+lxznLKgJ930lIZNtz8uSUv/zeX3H7F7e5+8ldzhYL+ig/lZXcTZIyqgcDSJnPpoDainx/8vgBv3jvbW6+8Cy7ezMuXb5E53qOjk5iXmjLbDajqacZmP8qh34KkErrJh3rysy6IvRvc3yekvu08+SB58GYikYkheLw9JjD41NQMXw0hZcyyJ71Pbvcc4onA4Mx1OM4uLAvxCO+xTlLDlcu7gub+2S492aAJIQgstavXr3KZ59+yrPPPj8Y3lDRABHlWbEH4gegk98jAY4MpKQHspJcAqRCZ1CAVxKZUobOpt9LI7NzsYZZIg9RZtQPw+8q2eDGAEnHiIAEnBjCAFNbBhTG2BORv/SDLJELh9IasR1932c5U2lFsA7nbAYKKVQ11REegaasKyYv4AAMocj7rqtYd9FTVzVd17NYtSzbPkceJHkiBhWFciYiL492At5C0Hjboq3mG6/e4vs/+jFf+fa3ufPue7z+2/8hP7j/32Jce35d/JJDMRhjtFKJl2njPC0N/Wn+rxOblcc6m3MIodAPh9IlYgD3BD1OUcltLPeD9fYHj0Gxs7XNzva/B0j5UPG/tMgSe08ZygZj0LMuANePcpEPg7SOfseKRAolSQLGOZc9ROn56UhepZQXkpT05PkyxnLjmRuoAHc++JC33noToyt+8uOf8PLLL3Pxwj7OB3b39tC2Yqfveefdd5lubbNarPK7eu8x1sZQFGnnumUNIttbWXyu8NAkgJTfr8ix8DGsZ7DGJPWSteL0g/UnnVUqzNInMSwurtOUyJ02AKFNjZtBBE7DGMVkfw065cqEQkkrkjjL8Ux9kn4PkDfYYeMhWvrzqUOce7lY47upGO5Vjvl6omTOSUlCIACGc8xA6fw+yHxL1yVMlmv/xDcIaxJtpLyVgk2RKblT+yCGzwUkRoPxOkjPUAw5Dal+U4i5M957ur6LRA5uuHfc+HyRB9M7x3IxJ6xWdKsV84WEiLZdS9e2En7WCmuaDx58wHUO30tNKOc7ur7wevrU/iCJqqOQ1xABnfRXoo5XkbVKKRXXiXhebFVRVxXaaKwVcFM3DdZYmklDXddMp1OqqgKrY50UK4V/Y36MrcRro+PakzDUgTkvEAha03U9jx4/ptrd4Qd/9ZeooJlsb3HrpZfYP9iPdLhKCgzbCm1qbFWhe4/XCzHEeBeTfUVhzmA6vrtWGoyJ8lFl+RhCMh8oMIVCnORHYTQRo43Mw4DPIW8BCbNVSkJCXCjKJoTxOul7R9AGcSpaMBVf+ca3uPrMc1y/do3FssVYk5mhIIWIaUIYlJEk17u2Q1sjNeSVWLYn1rC/s4WxihdfvMnt999nsWxZzFcsl2K1dd5RVQpjNadnp1IWYVLHeVLQRJcKZFx3LoaqKqVYLBYS8pyUOhcNAQEOnxxycnKC6zuM1WCk7yZVw/Z2w3PPX+fqpYt8+MFtfvDjv+bRo4d0BbOntZa6qqmj51C8hw3T6ZS6rqmt4fqVi1y5dpWr16/zt/7O36aeNjx49Ij9vQO819TVNqvliocPbvPB+3f4yle+ksT5hiMpH8Mnsk+p4vxBdpahRt7FfLscql3elbGsXsu7GI5Q/H/8XVaG05lhODcpp/lpa/dP14rBwPDpZ/fonSOYRNwT3aBhIFQihFiTRo37KgzGpvSBj5EX21s1k6ahbVcoL2GzidxGZORmXSLfLe23BSYdLPjxWU7yi7e3d3nzzltcuXodkBBUF2+ZDVOpn4LQZKf9Ircr7TvFc6QuX8h73WDYIs8NrXWk5E5KL6wz0iX5RvTGD5JoYHjLz82b7DikL+lYHmGB84PSEO8Vou709EMV7yZhtXEHUxoTIxaUUtiYsy0GuEBLQDtP8pBkMORC7oMBPMV/xMgfnQqAF8BAyzMTSZW1ntrV2LrCrnpWKyvMyysiZbgXeR4cGFFuXPB0BIJW6JWnAi5f2OHaxV3e+/lPuXTxGlW1w4WbL3DyzgnaSUkJF+ue6aDOGQ8Sm5z0k8vz3XkXqckj2NThXD+XMjGv62RcUmn+RPkRP8j6lgJ06bmNz4k/Q4h064Uhdx13lf4mkBBI7z3TScOlg4PPmRXj44sPkJTUPNJRsigGqs7kjSktBIMFe1D618FT6cVZexrDRlJ4NkZAalBCSkW3fHZaVGLZkfuKF0cA0rSZMrEVBPjyG69zeHjIxSsHfP0bXwHANIaz1YIHTx5LrLMH1Tvccokp8kPSAk8eJBc8OvgxYvc+AqQYt+4Sh1my2uicQ+JcykGSw4d+IH+Qi4ZFlRfKeOOTP8Z9HfLvAjJSGJ1YnhPICJiQNjsd68FEr5ICj/SnYxDY5DdJLYiLzqeNcVB2iOB0aKMn1aBIytroyPvLwEZWbjp5I1ADeNGR5ldrM9StiO3tfT8IK4aNKShoQ8gASQW5j47WMccQvhVSaFox18ZtDkPfFnljySvY9z2u7fCtgJxVu4qJpj1d2+L7Ht8K0chyGb00vaPtWlzf4pzQQYcQGZWcJxfDTcCmdwVDoUN1Hd73dMHhiFZHid/DG02IYM4qi/Zgg7x5VxchHlpR1TaHnRmjqeuGyaSJ4WmV5NfUFcEKHXQq+mu05OhYY2JohZCj5O+NJMQv266QCSGymXWsVl2cHwKKQrQEApja5PoWpQEiWRWDqegxnDnLjZuad3/2Dm45BzST2TYoSzOd0tRNDqOZLzq6k7nk86gKtCRC+75HKfHQDvItghttAT1SdBUpGTauGaXo0wYaxLPkg0IbqfnhnUJrG4sHa4TUQuqCKJWMKjGPMAiVd1KwvXexTxTeWQkL7lo65+nshN1nb7KyhsfHZ4QQsmEpeC+/a43SEvpnzGCN1MbgvcYEizZgtcPqwDPXL/Htb34NowKvvXKL/+q/+u84PG2ZzztWnad1Pdu1xqwCi27FabvETCqSN8GogvAFRntE8DIXd/f2ePzkCZcvXx6MYj6AV9z+xYf85Ic/4StffoNbzz/L3u4W9cxgKpMJdB48/JR/8J/8x7iu5/DREYePntD1knuUog+kIGgSRyIbm6aRNrmeEDqWq46Tkzk/+eFPOTlbcv/hQ+7ff4LSU3rXSY6i8nSuY3fvEs8++wwh9FnmDfshOYelEBeixKAIDPJC5lZ5oii3bPCMpCiGc/IzPWD8QVbKx96rMShJ5QBKoC/tSnTpqW0R7CpFJCDm4eMnaKNwoZdwVh0LWftkRZfrB5A8KHxlU9Jj5Jk9lw4O8JEdUgPed9nQl0BiyGHvOsuRsguCEg/m+l4SewaDZtk6bL3FfOU4na8w1uKBPoYYZ8NkDIVXwaGVExDlwQeFgxFQHXSYIfwuKb7SdJWV/qrSVIZR4evUQd57eor8Up8MnNFjkwu0irEsxDngvexpCZQNh8cRaHEDBI5UkT4EKZUQN+LSS5G8SDpPsUAwBrywxhmjozFVSjtoo9GVJQSTdZw+RmP0Ieavx+h/AdziWTeRgAICVjmM8YTCKK6JDMKpJIMaogeMCZKjVdfYxmKWS7RVLJdLus5DJ/ugQkCzV0LqE6zGKNC6Z6krXn7tNf7kT/6cfrtlFQLPfetr/Oijd5gcn2GCpzMepzUTp8ENYY7F4EskAzpGE3iCF49oWmvZAZHmAuQQSNG4GckNotzQ0eBEUCQoloy7IXZoSBqPSl7cEPMuDToMjK2lTpQwVp4TSuGMog/QLRaotuNXPb7wAKlcUBkYMA4b2nRNaWEuhfHgHh68T5sOsYj2IwFdPq+MzUyb4rrbPoWNlO3w3lNXFavVCq2E5vbKlSuCjqdTsYYYy9bWVCwhxnJ0eBxpmF0GXyL0RPg552Nbida8VP9FhJPQdooi43OhMQCpTZKEZkmqEIJc58LYO7I+JptdomOGnxK05mvXAKtYE6KiolW0eKWFNSSXArl+TdnXJWBIie9KpefEhbzW9lJBKg8XQo7xLjeTpF8Mm80AeWRRq6xMJhrkbHlRhQBhAF8gW3t8SQgB5wNd3IzbbpULbqbk7vR7ct2nf6kop+s6iB6fFDaWzu/bln4VXf7ZmuVwvctFPLWK+ToMiluPkxDPWCE8j318CwHrQ82jZG2rJhVaV9SxL1zfMz86wbUdvQ4cXLzI9WefY2trm2kzYXdrh+l0hmo0NoauidFBEmSHsAexaCeLVMK/Tq3V2NEK1wuV8mw2Y2u2NeQGxntWdU3bu1i7R5T9qplIDuCiIyC1eQTEgKlqvPOoyPBmjUUbLflI0dvWO2Eb6n2gbhr2Dy5w5dp1PvnwNk4Zlp3HqY5F54DT1Imgq0w+EbShS2x7AZSyWReV5PGSuGRg0ExrJK3zEWFHXisQfPQaMZgZXGTB08pLMq0fJ9OGAJ0HtIAXrSSJnSDess611HVD7zXKTKhibk5lLTNrJIfK2GjkGEgXlBLQpWIiOFERM9qK1w5PbWA2scyaBgNsT7f4nd/5DtPZDv/X/+K/ZLlYsFousxFoMpmyXC6YLxZsTacCviK4T8ajQUmEQYHvuXjxEnfv3uXy5StZlvV9h1aWP/7TP+UH3/9rvvfnf87WtKZpKnb3L3Dh0iX29vaorOX9Dz7gkzuPIYDFiqGhXdG2snYBFos5fS9MhbK2Xa5JZbSspLbtWXU9KAGRq7bl4YNH1JNaatBUlmba0Ewqnr1xjevXrojOr2CgRw55fZS7pSoMXONvCyAVQEh8VHHd8HvyJI/uOfzF+BgAktoQKje0w+dLnfNFWzwlnXZWaJXsNyenJywWC5KRk6z4D9bsTYalZBUf7WuxJQohLdjb3WWxXBB8J4qhlz14lG+U36nwlqT7BkCrHH4+AifxeRqVw3ubpuHJ4SG7u7t03mfiEojDmsKD/dCngcgoGjFrCOf1pE3G3mTUNUZqhAWUGCyUynm34uFJLIrj/U/0LQkbS+AvkUEl75oKegNAkmiNFsfaxiiAMZAjXbLROfWBjEzeUXtfzi+J4vAqFFEnjlTvKBm+08Pk91SsdugT5/ohykIHvBMWvOxVUiH+zcgwnsNwlaKqNFo3VNZQV5bKGpbLJUtW+D4S9hAIKLxSslbjntXSMpnMeO21V3n7nfe4cuU6W5cvcOlLL/HoRz9l1moar+hCXCO5GG2hsxZ9HYIv1vvnHyl6QBVzdH3syjD0cUguxViff56CHAY89iST7zUyjgSZQ1opjo6O6Nr2V3oH+HcAIJXMYymsLrn81hVjePpgpo5PE3ldUGTBXQiMEZreALLSs8tJMgq7CmKVLa/XWqzfqgjFSZSRkmtgePzkCUofidDzsFy2KCMu45TgnCYTIDkXiPASxh2VJ6eP8bg+DHWDkmUnhJj0GQZQmSzTIfhM31yGZpzv82FTWD/KPi77pejIdKII4yTVw3D9wHJTLMBsCZS/EpuKUgPQKxoxAmsjJSAVeEuLsPhGlPwhJ4MQIkua5NVkQRtCrmcSfMhhNF0rtMxd10lydrsSOue2yx4W8bj0qCAApos5FM55+q6TULR2KWAp0UA7l4V7H59Z5pSFZKHJYRBDaJ8izv04H7UxsaaVCHZbaZSus1eyaZqYNGzwBqppE/Nt6hwmZCNBgC0IB0TpjZXUjYnMcx7X9Zw9OeIXP3+bd958CwJcuHSZb33nt7CTBm0qTKylkR0iI4t33j3xhCFMIM0/BYYIoLWQDVRVxXK5op5OMfUEZWt8VPzx4FdL+pMFi9WKxWIhYXUoTs6Wcf7JvX3vCJ3U9Elhdq7ztH1HCO1AU1/OSyVEJ0ZbqBpuvvQlnhyecHD5OiuvaJd99IanMDNPoCMpeEnmye8KHMW8c4MdPfSEotgwqgglQoBIDltFRYAQ0Hh832KtyRu/rNNoBY85f0m2mSjDhMGqhhCyUkUQ1ju0R2lRgI3S8Z+itpbKhjwmxgipxHTaMJ00GEQZkTDHSQ4bDgSCAasNW7MJezvbTOsaAnStJ+D50muv8uxzN7jzyWcs5mf4XkJ/mmaCMRXz+YJup0epKo7PYDU9b2iBvnfs7u7xzjvvRiUzytEIkm7fvs29+/cxSjGpNHt7O7z1zvtobZhOp4Di9PSEv/jeX4nimZRDH3KeXRqHROWcvLJJ3khNKSNzyHsBPVrhgqNrW6xRGGuEhbCu2Nvf48H9uxLmGufA8GoqG4tGu2KSeSHloo2m0HCaPy/b/22OTZ6TTWclp9BoT41bw3pDswKnPA8fP5LQuVwUegjQJ51X/Bw9c+0c+V36p6lrlFK0bYd34knXqooOyQFkJ+KkFP6d9o0kyAJhVP9t9FyIXgwhl9nd2ePRw8fU9QSllcj6MOR2eBdLFARD8FqKEitPwOFUjDrwIRN3roOkdJQ5ldba6HExiO9GPAB9EVWitSm85cM+6FKYegJIGSyGIaIjjD1qQPSqlfMy/Ra92IgnyPXi9XGxUCteiSekMHIIcYRCGzGopT1qyAl3gI/7YTK2q9RqtPbkvOGoB6aQOh8NfqpPOUspN0tqQSViGwnFG8LsTTIUWjMCSUZplss+630hyH2Fx8fhlEZrj+97nr3xDB9//DHHx0+Y1/DMb3yF+7/4gHB0Ru16lILeSP6n8YNOun5IDqgnMb8+7Ri86eejr8b3G3vOykOCEs87MMpz1/XwdWP66LkhEGLO27pR5vOOLzxAKoVP2aEpSbFU1ksQUw5euk95r3XUK18NYKfrJNG7ZHFJzygT1srJWCJ3nVz7a2BLFFGhh6RQsANECwys+haUJrhA1zuck/ojOQZ8BDoiiOgBJWEG5bulopE++NHEStYu2Zjl3Usrm4+bcgIQaZ4P/VkAmLV+3nTIJhfO1T0QhSCGkG24j3hwVLZ8hAimyD9B8NDgFUteMglZG5JWISa2K4XEzw45Nt5JG/q+J3QSiub6ntWqpes7FvM5J6en9MsVfdtFV3mXSQS8c7S5iFw/CjULwRMicUDwQ5hDQMBcF2PaUdEzEFnRZG9VWQHVeS5Cqk1kjFjltRo2OV3JvwRybMxTq+qapqkjJXSVQ9OslTA1nfJqogVfR9ailI8g3oIExnwaxhQ5Kq+QGRUlJKkPEghTIVaj6XQb18Pt2x/RdyuquqaeTgnWgrUEbXEorBkzUEloko0bWwpqjPW3KPJyoqVJNm1HcKBtw7LzzNs5hycLUTST4cVLPDhKo3XFqhcCFu8i5Ws0GJDlg4QygHgZS2NJVgZQEAzOOzrfS62ituPitRv83b9/FTNrODxZFtN4YDZKgCQumghqs4qXr0nro6osYkgeEqrTWBEtyZUxKKsy/bWPbHTWBEiKglYYXRLLRLrswqAEsuE3xqCChMrVtQDqpmmo6wpTIbTiVUVT1WxPt6iriklj8FbWvLVJcYkT2wd0zu8pwpoR0NCHNn6nWJ7NOTs+haDEAOE6prNtrly9xFvvvMP8bB5z2QKqEg/8armi6yS0TZMCswY5NSh7Q78aY2O9oi7LOR9lTprvq3bFbLrNa6+/xo9+8mMePnzA8Ym803yxoK5qvHcYTGbsSmF8SXmQ7i4MUdIjqKDARy80AmCCVllBrIyFVqGWGmU0J2cn/N4f/B6emDMW508Ky01HcsCIaNe5FhhKZc9rAk2bj0D5jSr+n79NS2CUL5SxabZO52vWQFMo7lj+LvcZcjjzPYIsycOTY/HkojAYghKwkvtizVgnf4Q89iqHGxVt9oHZdBrrlTlUkFLboW9HOkX5HgGVIwcSCE0SK8GITUQOuvioaSZ89NHH7O8fYCqL05Ao4XEx1NU5uWuw9L4HPC50eOVzfqFiIKwqAWdpgKnrOu8R2miR/yrKb98NslUpQutyjlNpWEzMtyULbfouFKkOIeorg9cOgjJZJrvesVothXRlPmd5dsZ8MWe1XLFqV/mcvusJbmwkH3QsldMwEkiqqorJZMJ02jCbTdje3mY6nTKbzaJRQ4wfSsm+J158jYtrp9cCYHT0cGuj0d5jvEY7AZXWh9h/OstxW+xhldGYpsFE450xK7QOLJce8IQgkUhdL/NFY3Cmp7aWN15/lT//7veYHuxQHxxw8ZWXOf7hTzFtj9GavoqGzn6c51wa8FPez2j6bwBTpaE/6Zub1s45T2yhu0moZLEu2AyIynSRddKYUQRYlFOz6ZS2+/chdvkoFfaUk+DDsMHAuCM3IdJ0n3Q8TYkvP8+EBmG8CMvE9tIrlEKZYOCPt0aPAERpEUaJUOmjNUYmlSgwfaYjJt5fCh/Kxjls5GnTyZ6MzuF9UrLlvn0vScs+umHTRp1ARMqTSm0S5TL2h6LgyC+9NAOlcd4wy35Om/6Gfs6hFfkZ0Rvoh3hUlIQb5HuFccx0ysNKIWHKST+mopp9CilbLelWcwE5MSwtCd/edVlw933HfL5gsZizWCxRvSP0LufVpPA11/eE3ufnluA8jQFqqJtkjcF5sdgYPRYsqQ5K23V0vsMjpByT6ZRm0jCZTmPi9iQqn5Jzk4S9tZVYkCPJgLVGmMu0JmiNr2JOR5xTpLWgkpKd5rTMR2M0ylgJAYnJ+n3f46OQrZQmKIP3kVQBkzWXBKRL76sPgdoaDFpqQcSxW/Zn7F68xHR3j7OTQ1RVUU+meKNRRrw3Ckm4LteNaFZDgmyaCy4kK2kCKD4DJJQissPHTV3mtoSoiFdIxi6NTfLODYaEsq9KJcP7oaq99z57rcR72KNVHcMYW8nlc4HgFUpZWPSZklvi/A2pxpCE5UarrjWZlVABVfQeO+eoo1VbaxUHYLAEhyAsbtpInHdtUv0SHT18lrquaCYKhYSeVJWEGuaaMSBFWZsaa1MIsWE6mTC1sZ6IlXtmi70KEERR63sh69Bag+sIrqOPIYHdKmRFOss5xyjRPUSXgScQlORhik1JizE6hQ7j6IPi0uWLLFdnrBZCy963jjABa2u65YLFYileGUXMcBxv7ut7hTWGumo4PZlT17UYmJTMj4uRRY7gefzkCXc+vkPbndF2p4X1U4r9KqUJ2tMDzvd5rg7P8+fakbx/ae4GBcZWmKqS3yvJS1FKcigsludffIkvf/2btL3MyySTffBR8Q5538zitxDPiQAkPrHwAMRlx3lZvn5kw08hF4f7qzzm69EH69tEOI8d4j1A8pOKfSZeu2hbKUKuiIbJREgSc2iC2nhP2Y/Xowzipd6hFUyamrZbReOMGK60V4NMlbMLORHlkyJ7BLOuEs4bFYfnlYBSxULRAnpCzMvS0SPb91K6oOtX+NAidZQcvXf0MZ9MANJ5BbhURtcV03Rd74KAUS97fgp1807GKwGh4ZqB+KkM+w5xDjp8Hq4Q9/O+71m1HYv5itPT01xeIdWgC30PBT12qVATJMCuJFQY2uFRXgyvidE0Xd91UmvIe58NPJPJhN3dXQ4Odrlw4YCt7W3qWopTB1Jag7CtJvmYcidFziqMcThjhAwoheBpjYpGkWSYtVqhqyrWI1IYq9AGlos2pksECAqvPMHEcG214tLFCzzzzFXu3LtH0Jrnv/ZVfvDOe0x6j43kG+tOoRGJWdanSs/yWOY9TS8u580mQ/im9V7O7/Xvy8/T3HuaDp/Pd1Abm8NKf9Xj3wmAlLxHpQWk7KKyo+E8fWn2iKydt36UtI5pkAba6/Ucl4F+PD2jvH9SZJJVNn2fPFKpEKuPAiT4QF3XoFQkZnAMsd9a+GczKJHDOU/Sj1S0XHV9ogHv6bpW6tQoCCpSkxfudxWVhVxDiRSPW1qqKRaVGtFnpvfUSkXygGQ9YNQn5flGDzSZzjm6aKENIWAItKtVzr9xfc/p2SlPHj/h5OiYxXLB3u4ely5dYtWKUF0ulrTdiuVywWKxyIUYV6uWvlvhu1Wmi075FclimtpY5l6VABYGqlMAbTW2jgLQmFG4kDEGXQ85M4ktTSslXpKmihYz2RxWbcvZ6Sl/85O/oT+T4qCvf+WrPPPss8x2tpjOZpiqQiUK9wLclGFPPviszMQm4z1YNXg+y1C0pISqNLWidctoK4QGSrYxMUgM4kUSPHVkpxFLWbHA4pwZLHg6xFCsICDGqcjcYy319hYXr13n5PSYejIhRWD40IMXa2VPYemM89JHSnDvBsp0pROZhycolXPVlFKjfCnvwYXB2JLGXsDAUGckh6BBXKuFUllsJkpJfL7MFwtazjK2QusaY6bRutsJ4NQadI1SlkrpCJAkvC6FcRij47itWeq0BCwYPeQglu2oDFR62PCn0wnGWMm5aixbEyGmMEYAjryXzOds/S3WqMg3QSSDMUTGOIRAHzphSewCoS2UI+8xsY9d9IC62JfCQJhC/ATMqmyMEeVu3VuVLNRoFckjyFZnH+/tnCOsVly/foUQPGenZyzOFnStgJGqqljOz1jMl1S2krWr/FDdPU/h8wad2WyLJ0+ecPHiRQG+9CwWS15//Q2+92ff4+zkBEXg52+/DcFhI5W55MsNRDmrto/7FqPcMDFEjFBKQVHsMDFvMSAhVJUCrS3b+xc4uHDA/sEB1565wXPPv8DzN29hm5kYdFLIbdxjklz2YfBgDI+MgFyR5Ug5BiIzYkL3r3IEskd3/ZIQAdoYjG24dXjKHp1ZKkuPgSjCZ4u5vJvWBQBW4olTQkRUvvPwh4/rjXiv4SujhSpfQ8wbS/uZKODpXcv3I6SQzFDcM8RryXtvPr+43jBuozGG1Wol5DAJxCkPWmO1Zmtri8VqznK1wIccD5HDvRPh0rr+kgwpaW8r9RMfpL6ec/1gjIIIeEIMaxsY8YBc58cVZELpp0tGSN/mItyryGq6jPmCvhddJvWXkCd5tLYopakzi2jDdDqhqmpm0watKKIgTPbcaKNQWt7f9T0+CLA5OztjPl9yenrK2dkZZ2dn9H3P4eEhjx8/5sMPZd+eTqfs7Oxw+fJlLl26xGw2wzZy37ZvxWDtxcPe24D1Cu0NNoCKxX1tDL9TRuYRSX+NMq6qK7Y01HUMSVdzlkuJQJF8c2m76BKavlvx2quv8NmDhyznKy5fOmD/1nPMf/o2M68wqDy/zs1xiNFHg/FaIiiG6IRS5pZgKcP/DQCoPJ4GktavXd9DSw9XqS+eA/WI0bxrW7Zms89tS3l84QFSEqoZkJixtyZtquVC3wSG1oX+Ortdelb6mZWgDeelIwnotLitsVlQrAOtdF8dlaE+KnshFhD1GoIVEKSl6Ej09CALnqTADQJLBGY0kkXg4nqXhXbKh5EOUJLQmTavCGo0UtdHm4HCNYO+KNxc32dB17YtvXOslm0OJ+v7nuVSQIokj3a4PlmC+kL4enwvZAHtStzly+Uq34PesVqu8MGLZ6XrWKykHo7v+nPWjhz/PfLODOMiOUQu5zsYa7F2IjkkWlPZmrppqKJXJjGbURlMLV6aSdOgjaapJf9G2xh2luKPo4Xf2krcylGZG1lkVIqzDpH5TTxSp2enfPzoEfP3foEylguXL3Nw+VIERgZlrAAkim1UQVCi2AYlVjkDo5AGrRWWoU6XsBrGy1Oh3HLOx409uGGehgBGmcEqGGQeSsHOQPa6xEmY5yWBvvcorWh7N1CYevG6tV1Hu1zxyuuv89yzz7J/cMDjx8fYqpY2RiXYEXJIaQlmlSzekUFCQiMUaE3r+iwTyuKFPiiCEm9N6gvJJTJolcIIBYD2vWMymaC1UGGTZUy0xiWvmzaRUCDdL8ohwCiF0fJCSgVJetaWEBQTNdRJqyKI1kbyVKZT8dik+VtXQvttjcZon69JoZFaa5oKKhOyR1FF+RFCQIce5bts/e5WK7oo3yT8cfAuZiu3F8MLyaruk8dHFNBO6RGN/GC9VOCiIhXEmj+wXoFxorr64p5p03YEUAHlEuV+eqYYYBIpDRE0pNCwvu3pe8+k2UIrxXx+yun8NHruXOxnw2LVUa96rImhqXpQ0kumUkXypij29vZ5/Ogxu3v7dH2Pspr5YsXBxYv84X/yn/Cnf/LHnJ4csVic4ds2W8UBlPHM53MIAdcJ6JlOJ6AVfZ9CRFIOUupIUMpEj50CH8OZMBhdE3TDV7/xLf7J/+afMtkWOeacp+scs91dFm0XZeJgvS/HNAvIQZREldqDSgXCy70yFCDgKYBGlX8PSfoKMsNjvizJ7/FFUfwURjeGUD81fBjPzSEP8Rka1/csV22W+0qpCMblurL9pTxJLU5gfQzU5I+6qWXva6M3MO3lqQZdAX7SdeIALGXpoDwmq37SU7JeESQcOR1iWGs4OjphZ3c7K7aV1Tz//E2uXbnMtasXOTo+Zr48Y9l2LJc9v7h9hwePnhBCyjUcxiSH6CF5MqWSWrap93HdMni8shwIg5E0zank8fEREAXEu9vHnNm+71m2EiLnEzFQqq+jbCTLibMnOKaTBmsMk7oWT4v0sITWuQ6F5vRkTtvNCRH8NI3kdQs4k5FNz2hqCSufNA0XLx7w3HPPsrW1hbUVEFgsFjx5cshnn33Gw4cPOD054e7dU+7du4vWmu3tbS5eucCzzz3Lzs6OyM5eDIjWeZzVWBONznqIoLBB2PEknG6Q+VKnLpEOWcBAMCglESzB+ewhV97RdQJWZ5Mpzz9zg/c++pjD+RmvfO03+PG7H+L6BU4SVDF5o1fZGB1CDDdHCCtU3m/8MG/jEkl203Jh58/SWKyBnHPgKq3cQf0Z1rMar/5smFaR8a7QR8YGBsk92qprmmnDr3p84QESWuI7IRYgLQTOOrhJeQVQKtHjwUznrnt81gBrPnd9EpTfye+JWSUOcLRemciYIoJ3cA3bqopeo5h3FP9DIcpYAC0xJLG8KJjgMMFF3UBiq4Ny9N7Te09lalzboZxDQ078S27u1WqV85CWq8iK5lys3RIyfeliscjntm1Lt1jiuo7lcoVzfQw5i6Co68UlHIWg610GRMr3sU/Sqgtx03YSG52UH8gWUwA6iXvWlcFUFm1tHv+60TnfpsyrsVVFPdvK1rCmaZhMJhKK1sRaBJE4oKoqUTZtKhI3hHGJdy2OdQQ65RwYJloaZznW432z2hFiLky04oqLXaOUB+eYVjN8sNy89RL3PriNMbC1NcNWFcpW6KoCpXOoGXGulGnHPiqXCiWexDxHVVI9ZZOL/axj+KYUy01rQ5JaQ+gJbiC8KDf1tLkSChrxJBRjaJhnYD30IQidbQgopwhOalBICEEkqOg81FucLB2271BqADZohY/5P4kUIXvxEECoVQwd1EIEUVUWj8LaWCw6J/lH2m1touKbcgPlnzEWo4qCgCFg0vyIoCuBo0yxHoFXrQMmcqDKqaLcVtay3cDWtMnkFQnoqBBoDALK64rK2qGtIaCMeCD6rqPr+5wgnuZaecg4Cb128IG+dbi2HSkvOgm3wEgeglDLlh6FUJyT7PCDgYo8t53yuQ5Lspqno5TRae14PIlNc9T25MlVisGsA6HcuNOnYZivSfEOLgibWO852Nlie7rNfDlnvjxl1S/o+wXWTlDa0jlYtg5jhUTCGhPX0xAu5L0YE6yC4Ht2t/f58PbHtL2n94Gw8iyWPV2AW196mWdffpFV14qyvFpJWnt0ny8Wc/71v/7X/PAHP8CEgDGBP/zDP+TDDz/k3ffeYzGf0/Ut4mFc34sCWleSBysmHjCWa889xz/5oz/CNjWtH0J9bWWFQr9bFvNibJgLISb4bwA3IUg9lczF4M7vn+vzbvhj8Lpkg2SIY6n8+qVPPZKhJilR6e/kQS4NluuH5G306GgUIgJpuW5kP2M4IXZBGOYXDAa2EESueihyHobQ26eF4JdtLAkEyuuyXGW8dgYAGbDKMJk2HB8esbU1IyhRbmtjOdid8dnHv2B19oDJpEGjuHvnDqaZcO3qRR4+eoiPJAvEd0nLV2mdiSxGNYCKdrsCvLkYU5eIStaBUQghe4Z0ECXWEVj1HauukxIRzmdWO62ErKCpalwneaV4GaO6MgR6FA6lHI01zCZTZrMtmqZhe3tLaPitZbY14fDoMW3bsrU1Y3trG5REDfQ9rFaOx4/k+9nWjJPjEw4fH/Lo4RPmi4UAq+wt2mZ//4CvfvV1tra+hdaaBw8ecOfOHT755BOOjg55cnLI7Tsfsb29zY0bN7h69Sqz2Yy+d1gVw42thCmjPS6A80ZCYrXHWvEwCYGERnkt4BBFUxnUTCJ5tILlYiUgNEZAqKgHeB145aUX+eTeXbq25+DSVWZ7F+nbu3jfYrRESuQ5niwEyTgeDREy/ZLHjmwYzcAoMIoqSstHjEdqZIzd5GDIj453CMX1pbFzdE3Wm4brk54jkSJGjHxaojJ+1eOLD5AYwE86yrjFUoiXOUHr3oZ0n/VjEGKDwrzp3HIilF4l7/xoM1FrE6A8cl2XELDa0HcdCqiM5BfpIC7E05NT+q7j9PSUJ4+POD4+YTKZMp1MefzoEYvFkuVyRec8KM18sWQ5X2Sgs4xV4KWdWlgMupiTFC08fQzPEuWqICjwQuaQCsCFon8S+5UoTY5EI5uSnKtKYvXRIizato1uXCRHxiiMbpjUdRyrISfr9OSUkyeHQGBrNuPlV7/EbHubZjphMpsxnUgy5WQyyfkbKnnBzBB2lCzv6XeVqD0ZKww+BvGvz49k/QgIWJANUxeb3zh7wWgziikfQogia5gBW2w+QmIg11TW8Mz1Z6gnMwJgmym2mhCMBmViO86zJ6YQjUEYFsQPYVBmS6UjVfEOeKm74H2OIc8J6EEAxEgxCtHqGRn9UlifWHhS36WaCirXNVLRIlQF2QSUshgTkBwbi60aghUQbM0AVHXMyUFVYrElAZxhfFVB913+9D4wicVhE2nFIBtMBkJJGJuY02N9i41FDqvKDmQXMTxWwK2MdfIyTmdTZlOY1CYnOad5aI3FeImRT6QWonEpCB7lE2GH1FnKShmQQq6UEgvoslDExscAxIlzdVDyChm0piSXR1BqUIopLMUBdKIyWFe2YfBghgRmhu/8CCANRgaZb8Ozy00WKCqhnG/rurKe5b8LuNaBd/TOcfHCBY7vHLM4m9OulqzalqqSgr99NO54RB4lCt/y/aS/FF4bet+zNZ1ydHIcr5PvnJdCry7uAZPJRBSKZhLnirRt5+Ai//SP/hlBW37y/e8SnOPh48f8p//Zf0bXtdy58xH3H9xnuTzLBq2+65gv5mitWS5X/OIXv+Dxk0Osqbh09Tr/7D/9T6mbOucYpXlfVdVIGV8fs/yZH+bD6DwgqFSSem3+rM299UiK4NW5cRlmlDs35z7vONfeYj/9vCPlbMEvP/fcodaz0eLH8X8JEDwNKD6tr8vvy6PMC9lk8IBo1IhAYD6fx9w9heQZeX76459w9Og+D+/fZTKtuXT1EmeLlv1LVzi4dA2jFe1yhVc23W30jE2MvKldZf5QkkElMNoEkLwXAETcGzrnWLYrCa8NsoMZG2Vp9BKoEL1zrsfUkdq8NuztHrC3u8P21hZbk20m1TR75PqupXc98/mSs9MjVitH3wcOHz7BuYcZIEmJCk/KW18tV0ynU5659gxVXYvxVWm6vmexmHN8fMwv3nufN7slVV1xcLDPhQsXee2NL/PNb/8mZ/M5d+58xDvvvsvR0SFvn5zwi/fe4+rVqzz37HPs726jncHEAukm5jj5aGhzOvWri6x3QoSh1yMJcoqHpl11g1fODZ646aThpVu3eOv9dzmcTHnx61/hrf/lPnUrBEoush2DGBHrumI6nbI8PqNftQM1vPKMi7mO52CaG2yY99lpUBiQ140E6+dvmue/zpHmrQ9iwPxVjy88QFq30It7s8/frSd1pUW9LgjSUd6rHFQdCxWW91n3OpWfl0qAjkpyImsoz0+HUsKM9/jxY3Z2dvBdz+LkjI8++oiPP/mYe3fv8fjxY9rVilXXUlc1AH3vsVXN3u4+06ZhEpW6jz/5lMPjY4lvD0hCZ9uTWL5SHhIgrDd+eNegIuV3CKDF0pSssloNHjttdAY7NiqydV1T1RV1bWmamiZ6a1zfi6UtBJzSGCuFGW1lUYpYPE0KU1aVJIPrmMvjnOPh/fv89V/8Od57di/u88bXvsJse5t62qBthVLi+UkbeuxkAiG7jYvOjrWpIMWzqzTuaWwChKBG9azyxhb7KI9hrp+A5PqWVFAg3gldeBlDQIUi5DMt7kIBTcKvqhu+8o3fIgSwk23OVj3aWhHwQUIayvdKeThaFfeLirJ4q3xiVBWrXxhIC0TA+Og1SJs4OQdO1oDJiqu0WQwTOtbFUNHTlujB0z00A1AN0VIpyfsF5bdKwMRIGISV8AqKtaaV5ASpkKiv1xawOGnzPFAM+XE6tsUUgCuNq9UGiyFEFqoE3OvasN1MubgzYRYtkXVTS76KMdlzRn7WMPYhzKMS6AihRzkFDtqVR7kh91DqCflhyqihnltqZ+rHpNCECEBVUU/kfEcMMiiF1FHcU+ZeAjo+3zMdjjXFLlvwBkteqUQP56nx58U9XP68BIUFu1+6x+jeYS2Hfl1lHa/P5KXx0Yjje8dqtWJvd09Cd7ue05NTtnf22JrtxpIKXUzYVhibiozbId8qP1MUiJQP4oOXAq9hqLoSVGJPJEcBYCqIhCSpj2xt+ad/+Ec8d+0q3/vzP+PPvvcX/OUPfpiLwYpy2efyCz54CadBCnVWtVDDv/zKq/zWb/8Oe/sXhBxCD+GzaZzbth2N7UiehWRQ8aOxEjEo4+TFPHxu/vxSZSbo8f0p5nNJPf9LjvU9/Gl/rxMKDM/9twBHcpNzVu0cOcBgnH0aQNr0s9QB1kFQ2UdPA0g+QFCiT0hhbh/zHSV0cDbZYmFqtpttjo+f8NFyzqUr13FtKyFpPhav9QHPOEVg0HcGo2Ifc13KNv66ACmEEKNI+igTwCphWEQl4yEyz/uUX6PZ393h6tULHOwfsDXbEs/5quPo8IiHnx6ymq84OT2lbVuWyyWr1YrVUnKY+k5C1XvX07XdYLyjjzJewmm1NtR1RV3VNNNJJGYQMqSd7W0ODi7w4s2b6EnN2WLOo0eP+fk77/LWz99hd2+Xixcvcv36M7zx+uscHR3x05/9jI/ufMQnH3/Cp598yuULF7h56xZ7BweSp2wtpq+obE+IofjOazonZE3GSi24yprReCQ20BBAsWS5XGZDdxojaw0v3brJ7bsfc9Se8cJrL/LW97+LbVtU6OmjkVYMehIhUVc1i/4Y7ZNnl+hFlFC7QMj5ZKXzwXuPEQSV58a6njUKE12by08DTJuOXw6aZJ+oY9TFr3p84QFSuQnAePGmv8tQqPL3TSCl/E5FRTp+m2PRy2vV2iRIwKNUDteFTykw07Wp3T/+8Y958OABOsDDBw/4+M5HvP/eeyhgNp2xt7vLtWvX2d7Zpq5rus7hXZDimYDxPaenJ1y4sI+tpcaHthWHh8cslITHVbUUm6wjeJlMp2irMTGcp04saZWlnkypG/HM1LVQ9U4mU7GK1xWmihTQObFcx3wli7D+SycslktOTk5YLhbCHte1mUGm71uc7wsGGqiqGqPj9c6xf+Ua9Wybs/kZQVfY6QxV16iqQdkKpWthMo+LP48pgVDkiQSfwn9A6JdsBlIkZVoN7lsXlQRtkhs6iGWkkAthZHhVEYCkzcwnrJFDPF2ZwOpTaKEfJa0651gtl7Rdx9VnXyAEeHS0QJsVqXS1NpEYQd5SvFVr3pvk2QkiVSG3VaFibo1KLnaVQtaGWg5pjuuUxMkAWExpYDA6hpsVHpgEfEIDQY8IOFKYW6fF0rTOmhRCQI+U5tKKHzD4DNAkBDNZVz3arbKXJ9GVV1WF1ToXUcxrVsXwt6Dwrazb7Z0dtran7GxvMZnWWC0ge1i3PfQ9rkeYpUIaeaIhIc6dUT2GqDBHwJKUQ19MniyPTBwnBuA1jN0AXAcARAxV2LyJhCLMKfVxBjfSCrJRgOF9/DpASsCoeI/h88E7GQikWK11JdqnVhbKd3qDbIAqZGbyOJWvdu4987tIm3TkQ07eOd+L5+Xa1Ws8fvSYvutYLZa0q5UYruJaEblt6XuX3yXVFMv9o8DhMCiWbYuxFSenp0y2Zpkq3MfEb2XKulGGzPeZwFNQmHrCf/i7f8Bvfed3+OzuZzx+/Jijo+NssVdKgNr29haTyQSlNbPplP39A+pJQ91M0EYKzboQ0DHaIHV5qWivA4uNvxdCrQS+6yB4fU8bz5FysM7PyWQIST6pX6Ygre/TTwMOm/b0rIT9ktouT33251wVGIOdp7X5aUCpVBJh/F6ZbGojCB2o7s/OzuR67/BevB4Xn3mGmzdusJrPOZsfY6cGlKELii7WQoTE+Dm8XQJFNhqmSnADjNpZgqEyUmf9s/L33J9K0VSVGBRijmjveskv9o6LBxd48YUXuXTxAt55To6PefzgiA+PP+Xk8IjTkzOOj445OT7i5PSI09OTDI66NtYVdC0weLvKfXjoT5X3eiFOMXnPSntHpvqeTam3trh8/SpXr17llZdeAgKPHj/m4zsf8vGHH7K7u8P168/wm9/6Nr/9W9/h3Xfe4c033+Thw0c8evyE3f09XnzpRXb39jBdT19ZnJGyLtbpaCTWmOCwzuOdyh6klCJgjNRSSyVilsvlqJ+7tmU6m/DKrVt8cOc2em+LC7ee48mTI6yX/S+tPzwoA8tTKZ4ctFgXhYJ7CLMXeTXWWdN82LR2ByNhitwYGOg+7/jlIGiso6dr0j6u/RDC/KseX3iAVIbLJfrq9U5MCLvrukx/u36PciMpgUsJvBLYeZrnqLQmZeEXrXnrdIXJo5SsMEkx/MY3vpGv3b9wwI0bNwghcHJ4hGs7PvvkU8K9u1y9do2+d3zyyWdsb23zwgsv0rYt9+99yv37D3jl1Vf5xm9+m2YyY7a1JaFfukYpHcOEbC5kpqzFG5WVl2StlYRKCatKikxSjKPaMAAPyOEjAMFbAkPdlLra4uLOJdp2xeL0hC4WRpUq8atYILGn79tc5K6NgCloha6n7OxfYtE6bDNDV1OCqXHKAjFOOXoxRpsTYlEWKumYQ5O2vRh2lJSr6GiI7Q/Z2k0oNsJkRWe8AQ5zRmiGfQxVzAnpIVXVVqPNIymKzkt+lfdDMqsQNmj6XuZPZaVAn44C01pDyLkyg6c0zaU077QxI0uoRPeZrDgkFjujjbxV8u5E8EAQxT8DJpnwuZ6RPKjwciiy0qm0RocKXFyfndRlMLlYo8cjeVdZCCslHr1Y+C2vmxBiHHZAEdnfUMzqKm8c+7tbHOxUbG1vUVmLtVUOqdQw5BTEF0leH7GoFgUCVfSuuBUqqGEusCbIS2109J0iZkPlz30RQxaUG5T6MMyhiA4KWFgcQfpr7GkZrkkhkfIqIb+31GkaW9ZDHCwffF7X6+/2NGUakuMsa+HZmh6y36TsnuFvN36j0TNFhm5WssuaOZ+3kWqt6VNYs4LOOXzfc3xywv7BHk8ePeLh/QdcuXaF5UqUKqPFG9+2wkQma4m8jkfWfCW5WQHFsl2htPysphOR/RSENirJH8RUFPIt4lKUD/oQsJMZz7/wEjdffHlkJEh1qbIXOPeNEu9+gM55kV4j4+350PHx9Yw+y2NVNNKHp/f5aC6sAZbxkbIdz18bOA+uNt5hI0j4nCeugbbUjhI0/ipW6889V6lz4G8TKNr0eymj18PzSuPsejtK2SKej+jlCZG2H2HOfP+jO1TWMKkrCI6w7JkvW1Z9EGhvJ/TdSu60loA1EFqNdZWxEWTY79ZlxCawVL5vVVUQpEg0wPzsjOOjIw4u7PGlN17j+vXr9G3L4ZMj3n7zLQ6fHPLk8RGPHj7k8MkTjg4POT09ZXE2Z7U6o3fLHF42hIIHUD3ayN7b926Q8QBYCCZ15WivNEr212zwTgVfrYXaUr81YTKZsLOzw/7+Pi+88AIv3HqBqp7w8OFD3n7rLT756CMuXbrEszdu8Nqrr/LeL97nxz/5CY8fH3J0/BMuXbrE87dusrOzhTMa03WSg2QN1mpsZbDaEyw5lzqEMGIVrOs6929b5JUGFei6FS9cf4aH9+7x8PiQZ15/hc/e/DlTE4TgoRjroDX1dMZ8tSSYWN8wSIQLosFFpssxkC69qOtrYNNekuZw+fso366YR+vnlfMrgcVyXsl30pgQQo5w+lWOLzxAKgXKekemI4SQE+8/T9gmYZitTiOLydiWtK6IloKh9CbBUHQyXQeM3PLlvZxzUohMiWLTzKZ88tmnWDSh77l77x7VpOHatWscHx0xPz3lH/5H/5DLV65yfHzE7Y/vcOvVV/kPfvf3ePmVL0kyP9D1EHQTXc4uGaQxMawqhGFDF4XdY7UCH5PWo+fAF+9CvAfnJrZYI5LHQSkI1kr1emuZNDNWq47lakHbrqj7tgBIC7zr6WNYTIhhJX3vuHz1BvNlz96FK6w6cDpIcUQfCL7DGD9QVhebvgvkUKLSkgSgPIPyUShhHgEs5XiV863YbUcWaJVyg1Kh1AQSsiVZ5oQxkZlHCfBwusoxuwmUCYDRUpU9SBKuMQqjBwUrRKrTdevM2AOUxiahv/RdfF7sL/FuxXmep7rK4CQ6HkjKRrmKgoueK7lEwuiUkdpb4RS81HUySoOX3K8KzZZGNgUb8+8yA5ymrm3esM//k/WZyDWqKrG7BVRIzIgO1zlS6VhRKgrFpgBIEn/n8AjQ9qneRAxJFQAiL1gqhOtg3BfeLOg2ngcRrK9fX86r/Fm686aj9GQnb8dwrzHwOK+ICmg7rzCPnjA4FDIYCwAFU52O7F1pHTGq3bP+3uffLxkQBgbBUHRD/P2pAEm+V3HiifdI5VDOVLz19PSU6WSK6x2fffopz794k3YlTJv1dJplr3M9AmzVmhIrz0ogN6BYrVbs7O5wNp8z2drKSX9axWcXvjEdBsZECrAEkUxFiyGHuBe4yHiFl2eJI2sgxUDFfMdoPQ0x80jk+sDSur4nbQJI2aiohoTpTec97fi870ZGp7XzckjvWujdr3P/jc9cM5DGP9D8etZlGMTgxraV+0ihC8CwX3xe+9Lv6f3WwwPXz0uHVgizbByzvutomkbmMCHmDiqWXQd9KyymWLyGzil6FKa2ONeiGOduDwZnNcoXXAfZ62F16ftNgC+DrrgPur7n8ePHLM7mvPjCLX7nN38L21gePnzAT3/8Nzx++JBHDx/x4MEDDh8/4smTx5ydnbJanNG2S1y/gkjq1Hsh91n3kspzVTZAj/bH0A3eXGTd5WsYaAOS4VHF6Bi3VKgTiUx48Bk0TcN7P3+TnZ1dLl19hldfe53nnnsOgLt373L37l0uXbrEjRvP8Y//8X/MT9/8Ge+88w6ffvopj5884datm1y9einuYRZbWUyvqZ3BGkfofS5km7xaqS6e1kbyG+PRdRKN0/kei2WK4tkLl/n57Xd55cWb1FcvEE7mmKDoujYbk1utsROD2p2B66Vgfe9QHmwQavm+yGFOoCaBlKcZCdYB0Kbjc3Vxzu96m5wS4xsiIZZt+9Rnrh9feIDU9332Cm3aDNZBjzYpDCm540IMhRGOexct/ipaEgQ4iKVdFBrZ+BILXcodyPStRHd+CCiVWJB8VEpTDRW5VwImOiXOKYRFJi7fdrXikw/v0K5WtD6wOD0DwIRApS2PHz7km9/8Jl/92lepJxParuXmKy9w9ZkbmLrh5HROpS1WWfFmoHBKU2mLtpUwSimxqpswWA89Aa18pB5GakXEQ5l66NtiHJL1d1Ch5f+5Bo9SKCuavVWGqplQrSqWyyXL1QITAZJxNat2hdIdKphoFe/wHVx65hYXrj3H1vYWT47nVE2FqZZRM00FTW22FqWwCpcEaFQI0xipwZSbvSJJeRFhHlnN4rikc0z6rgDCWSBEaugkoPMWqxJtdLyfSiQUJnvgyEBnENACkqIXUokHQRFQWhTikOi6y5o/GQAMoRha6YR+o64ZC9dGCxEq0isrj1TVSQDPx+LDIdf8yFS0MhEIIVCpDq0CVWUjKUHFZNIwnU6ZzhSTxtBUDTvbW1S2oqlrmqqmUl58lKPNOQpYxjHtst5czJ0i08FKSKLHuVboYym8uWvKaMpbSnx/JEOI8iQyCyJ7Y8LSCegnADOyrFMwvZ3bLBwEn63N5ddBGVF4KTaKEH2b5XlJIQ5D+9NKHQOq8yF23g3EHyPIUio8JANBbP9oz5H1kDbH0WamVEyyPr9xJUV905E8VnL3dN0AQMb3Gd7x/P1K5ZEoXwN9F0NEvdT0cZHdc9X1HBzss7W9xdGjx8yPT1ktVvRdR5hIzQ9jo7ErDMQkg5Es9lSS6cCq67B1w6MH9zm4eBEb9wQfxyvlISUzxJpGfe6Nco0SZA8Qgh8dPYApVExAlPeR2osEmEiTNfbrELJ6nkSgNCamv+XaAYeKkSS1X62NSzlO62EtTzvv/PvG1fU5SlR6B7mgfL9NJ8JglFJrX2XrzgAe5Y/h2vFtRut5mJ/pxJDXdfk+aVUNfZLm0NhAURo2SgU9GSHKuTKELBXtj3Nra2vGfL6QenGxWb0TbsMQAiooTCCSLw1GDaWCFCcv9vbkoQhBcs4UQ+SM1CFLgDuGzSV6/7yOk/4le13wPjNwGmto25Z7Dx/h+56XX3qZl198iW654oP3b3P3s095cP8eD+7f5eHDexwePebs9ITlck7ftXRti/c9BMnpTJ50V6yNuKEXkRyDp8M7X8ydYswUkIGg6Hqh9E7ENG3bi84k1PkSLu76Fcul5vj4CZ999hnv/PwtdnZ2ePGFl3jjy29w4eACTx4+5P79h1y8fIkXbr3ACy++yE9/+lNu3/mQd999jydPnnDz1vNS5L3vqCqLcxZrHa3qaJyQ/lh39ANtAAEAAElEQVRjqULAeo/RnspKLbVJU0PwLFQsbRMUnQ/0PvDMM8/w/p0PUNry1b/1t3jw/m3a01MhB+raWLsLvFFM96/QNDOUD7jFivmjx/QnR1il0H2Lj0b1JO/z1hPCaL0MBZgLhtS0ixZ7u1IKZfS4+HfcfzTDvphlD8MhjLtr6zma8dvFgsXxCb/q8YUHSKNwhGKjTpa/czGL3meqWWOGMCCQxZEqq/q82OMQqUFZkQ1y+F2RWMVkzJyLRVej8im1AkSp7GOblC42uyh0tNYYa+j7nutXrnLpwgWe3HvAJx99zM7ODsvTOc45tmZbLOZzfAj85nd+k1uvvIitJxLa4Xvmbc/JcoWuLMZUGCwqOKmqrjTK1vighW0qJbQXFlqjFFqHvB+sE13Amh6VxqEAqdkqZuImUoyDDoGqAq1r6kozm9YS7tK2rDxQTdBdh6okT4lVi1c1od5BBU9QnmAVfQj0XS8gU01QysQCjzFHhlis1ZrR5pND0pSKHoMErsriqQUxQGF9ku8GT0fa4LLH0QxhVanv8rV6YNaDFL4jQruKDAJlKE06EpOYfF5saFpllqigC+U07nseD1GhIwk0J274BAxCEnhE6xAB4VOXGGTlBThYpZgYxVZTsbNzwNbWFrPZjNlsymRSs9MomkrlfLTkwRIg2jN4FVShY0Q6CB9QymeDQn6/4UUz0K6iSBusl1KLyScmHx/ovKHr+xx6kcIvXO/RQceaUynckijskwU+zpGoEEmfD9629dyAoBVelQrPWBEqyVlGimJwxXnpf0MXjY5AnhfDs+MXTzmGgqOiwnhTtMuPy4E+zVNQytPPy/tYV3D///E2bARjrHNtFWBCJU91GDZvQlQEFfSKPniCllDiS5ev8OG7b3N6eMxqvsJ1nVjkdSA4kQ2BEEsqpDDQof8TVk35WdWkYbGMRppkEElJ8AXe9ISnFyMtzgtxLg/vmbz9WRvIoZQqDGQe2WDBYHxJ99scl1/I6GiAkSKVwxmJ5EKeMS5aW85FlQBWGo/y1dbGcQymk2f9lx8jQ8GGOVc+a93QEr/MMj1/R+6l8/sZRZ8zzLf1owRiY+Cjsswt18hgtC3bPAAipZJRYp3QIu1dQlIjk9Bjq4pV34mMlBNF5hRv5mK3hZjnqhDFUGj8h3257FOfa/WNSS5SXl/IQzHUpAqxP7QWsoGqbmJkQeDup3c5OjniG9/4Bjefv8njh4/4yY9+yCcff8yDe/c5evSEB/fvcnJ6yGJ5RNst8L7D9T0aKyHXcf9TCHmKYN4YtBsGIE9i0YxMm8MAMRp/Gee1kc+LSK5Pir93YorU2qG9po96gXYxb0v1LLoVy5NDnjy4x9/88K+5du06X/uNr3P95i2ePHrE48ePuHL1Kl/96pd5+Usv88EHH/DhnY957/3bPPvsM+zv7xDoafte0iC0oguOqu+oq5oJAe8NlaliGJxFa8V0YlFUtF3AKQXK0mpF0zRcu3yFxdmC3StX+cvv/RXh7Ax6yaG1RpiDm60psyvXufSlN9jbvUgdDGf37/PZOz/j9O4nqCePMa4FnOgcSomZVkmhZBX7KKt5IRDJcOMcZ7TtJz3RoAYvv0zCqIuKrmyLfZZCH1eQ88HjUEkOvNJ0bUe7WPGrHl94gFQqoN777GosPyt/amNGn3mfwEyykoypvNcF7XqoQgnMkrUuW2HW4nHXw+zWwUS2lMf8k/39A27evMnWzg6z2RZ92xOU4vjslAt9x4VLl6gnU4ypMonC8fERSlm06jFWgba4IOEYXkGwCpRGUi2Sp0VTmq03eePKd3/a3+X5yf1fevDyPQECVMZQGfP/I+9PY21J8sM+8BcRmeecu9+3V9Wr6qrq6qpeubRItriMRciSTI1NDzyyRcA2QMxYMAQIA0OWBBuyv0gag4b0wRYg2AI4Q1i2DEkYWOBINEhKFJcmJUpqskn2Ut1V3bW+V/X2d/ez5BIR8+EfkRmZJ8+591UX5UEpul/dezMjY4//vuBDkjbrHLOqZlGKdKMsK+qqohyXArB9dMoPc1MSFtM7R27Goo1RrUo/BhYwqg22kYZ9RiVkexxr/DZBGCmzLf90E4hhyfGwl5W98QtR4FUMdx18qxr/JPAqCzivlRQ3ZyPwmC1LHpGfT36Tny75xkUMLR0ESCXhVn1IHKe8a/4ZHJk25KOMje0JG5Mxu9tb7O3tsr+7w872iPHYk5msmVOEh8a3fhbSv0ekffH+tGekwwx0pNk9JrzHbHT2qUd0+GCnbT2MnCZG6LPWUpXiBFyWFbaWxJllXVPbkJPJ0eZviglKGxNRuSUR8DsvGdJtrKtpcjoN3ZeuZDi9LMOEVTrXTvXes6G/V9VxeLC6I4Fe1dY6af8QLFjvfzLczuqXw/MQgdTq9vrjas6Gb7WIURNz+fJlvl1WHB+fUBQFi8UCZ1t/UGlDExNl9uF4n5BUSgVT4FZbPjTPQCMMvlPJf9eVvoBKJbCnLyDstL+Gge3Mhe7VjHA6/tFq/IJfkYpnXHU+XKcR6uPRi5YnqT903zzD5zwyJP3naZ+r1mvtGFKK8AlLKvTttxpxkxYbZra2tjibTgXABrifEv39JvrCznWO80M+Rv21SIXTEbZnIY8RznN8fMLtW7d56aWX+NEf/VGOjo748m/9Fu+89SYPH9zj4PFDjg4OODs5oVgkUT+J/qDtXpqEdos4bpWMqE+XpfTHkDCrv/5x7Zq5O4f3deKj1cKZLESVNSZorkqwteWdd97h1u3b7F25wnd//vO89ImXuH/vDg8f3mf/0iWefuoG127c4P1gcpflho3NSTPeTAc6I/O42oP1uHyEGqvWZFdpskwz2RijjaJy4JQEhFHa8PwLL/CNr3+djz17kwrP6WyGK8vAIGk2tjZwbsTm5j5s38BuX2Zz/zJPf+KzPP3yJ3n/tVe5+8+/Svn4Efhj8HMJgOE1GRLgSrGMv3wcH8NCtKH1Tvdq3R4O7rcTS5dqUXD3vfdW9tUvH3kGKeZvgXYjvBefo+jAFn8HYZCipiB+E+3PXZBk9xmaWA9a36EUMCiGkdMQIxTHF02D0gORhh8+OjzkvVu3GY3G/NE/9sd47/Z7XP/+6/zWl75EVRQ8/eyzXLt+A2cdr3/rWzivJGKd0kw2Nrl8/QZZnoMLSdxwIQKTSJIas604tjVr3Jd+rSodYNljJPtMZCTcmwAPWUYGZKMxWxsbOO+xdU3dZOH2klgtyG+VDv4kSBQqQ0ykFrUWrfN9TGiZhuxWgYFLwwd3pX+tGVNK6IYVofEH6O+3oitpTU2fGwq1wWX4ELLMR4mcbiVykdmJ+UmWgUNkfkLrSgIYtJG/wly9T+qKdFHjmIxzdrY22dmcsLs1Zntrk62tbTYmG4xGOeNRLhoV78k0OF8GJi8kLFYpElc4l5wR365Jn4YeItDXSYSHnvUZpFgUkTkWzU8eErN6L2Fmq8pSliVFWTEvSsoqMOG1FYYp3BdlRGmPFoMnHdZQxtrO25vE1HBptAFwDxI73Tn1id/u+8ACxz7Czw7i8b11CGa+3iM5bOxqRmYt0/IB6n2QIstrO0RJKz1f1iCtGleXQQp3KIQyrqqKp566QVVVnJ6eMJvNKBZF46MQJeZpu+edQWNMY6qyEnmvha7Lba5614E10AnKkX570X3qCyAUdCwJ0qKDCWP4EgkLEXR3SuP0MA4cms+TjjMl5hv8sWY+fefvRurMMj6K8+lT2heBR0MCw6F9fNJ7MygYQjUSdkksLgF1RqMRxcHBEo5atU5LMGKFBqk/ligoSJ+n/yJBPM7GaCW+ym9++w3yLOf//Mf/OGVZ8tv/8ku8/fZb3L9/h8eP7nF6fMjZ2THFYoazIehOkovOWhdMw7vnv2HMWBY+9QUFq4QDq/ZniLlu19LhfSsEj98WRYFROoSXVmhVk2Ujsgzq2vLw/n2++Cu/zO/89pf4/B/4A7z0yss8fnifk+Mj9HiMUTDOc06Ojsm0YWtrI5jMi6bQ1Q5vHLa21NTUxqCUR0m0GPFHyzJJYF17ahvMtx3sXbrM5vY2Tmle/PQneVsriumcjSxjlGeMJiNmxYKT2ZzTWYEflYytY2tjg/0XPsb+9Uu8tPcy7/7O7/Hw/tc5O7mFLku8ldQd3piG/h2K6NgRYp5DP/ZLn/keEk5FJYdG/Jt97Tg5PLpwHx95Bsm7Nh4/0DA/TXhHJ1LkLMuC7wStODu2EYjnvrNf/Jk+6zNNSqlG9R/764+jzyhFqUP/QDUMk3MUZcmjhwcoZdjZ2edk+i3q+w/Q+Yinr11jsrmJyUYcn045PJlKNmrnMFnO5mSTZ64/zQhxxLPKUTmYV6KudiGwQZyJRvXMuJaJ0P7vq4B+B/D0/o7f6YSBirp5aT9KbwNBmbWZzz2K2kXTtSjBlI8V4pelk3H1JXhDYVOXgGdi5mC9awBw/zyECARLa9CM2ydroWic+73PxPkfmhDZkanyvgrSs5YY1pHxwYtZHC3QF2Ih5nHo+7JEP5sgacaTZUoylI9ydra22N7cYHd7k61xTq49I6NkuZWYiyo82peBQXJQe9oWw5qqRCLXA4Rx/pHoTAnfzpr7YSKvWZuB39N1WAa4rf+BCtJUZWQVMqMZ5RmTSc6iKJlMRizKikVRUhQV84UwTs55qsqK/4o24K0kRlXR/DKZp0vPu19BYLpOHTnvFyeaOjU7RLLvvBQmQ/ZCuZboi2dhFXEwtM7nSf1WESL93/v9rWpLzDtNc7Y739M9eUMETV/gBcHUSKnGjKgoCjY3N9Fac3p6xnQ6pazK0EebTDi2sy54QBSqmSRiUpCrLJUUFqUE5dA6pHPq933euvYZqf7arCLw+4RvM8cEXironLUlc7UenFw35nUMw3lMVkowrftmcK4DZ1vq+YB7uv7Kq/DgEMMa37camtYsbdU+Pcm6kOIi367D5ubmUh992qUPK1McqVJB75q59TVaaXt1XTMej8WcXSuODg948403+dxnPsuLL7zA66+9xptvvsmd927z6NEDDg8ecnJyQFXOwdV4LM5XMiIveE18f01z71ed3VawsRx5OH22clVX4JWhtUzr95lPpaCqSiR1hkShtbbGGEl27mrP4eNH/Oov/xJf/9pX+P4v/AAff+kTzGYzdvf2KYsS5z3zkzOMlwTTDocta7RS1Lqi0iVGGxbzBfk4J8skUIMxkIeARqgMEyLvoTT5aMIzz32Me3fvcvOFF/n5n/9FyvkCX1VsbUx46eVPsLW7zd72BnZ2hJtk2HKDstTs722wv3+ZSX6DPNOc/IsH+HzB7Oghel5hq5gyZRnn9+ng9P0Q497HH+fdiz7Mjz6GWim2t7ZX7ne/fOQZJB20An1VcXyW53nDPEVnbp0tm9FJpBDVQZDpu1WMg7TRSvNSU40+1xy/i34RKSEQGTkdpAZGZVRVze995fd45tmbfOEHfxBtNC9/6lNoPKN8hNYZZSUJ1YQu0ri6ZmuUc3z3Lr/9L/4l8+mMzc1NXvnMZ3j6xY9T4KmMxypABY+dDpE1zKnHd+mzVcg3EsyrvuuYAHTkq+L7YwJF1ITkDtWNbuvJxwoar6MQuUklxFWArkOMWttKwxk29TUquIB2may0rEJmWvmWSvLtOGTWiR1/ouVRCoyuiX4wPozDZCEvg5JoNqM8ZzQek2cZJssk63YTyrnN3dIyStEe3DPKNXmuGWUZ41FOpoRZMFqjsaiGmA6MYdC6acA7CTZgvcInsvwm4EFg6rz34Wfkl3xcxQ4Dl65dvLbriI7z1ryztwTzk/iNCmakwUzOKY/JNFqPcZMxk2jSWdYs5gum84LZomRe1vjKNtEVGw8PFYmtpkPSQ7IKF/vI7Db3ohsJb928VyL4geWIRpj07mN6r1dF4Vse8/K7JyE2+t+sak8I8OV2m74uQFDH3/tRRdO9KcuS3d1dvPeiPSoKFvMFs9mMje2tJgBGKuhK2x7qSykVkjf64L86jCNcb7M6bYX7k+KcVQxqQ8jCEmyK36c4qB+mvA+3O8K/HjESGUzCHWrPvMzTh1DyXulONMRh87DlskoY0n93Xolz7Psdr+svfSb/lts8L2/LqvEPneWhb89bo5QxEbPf2D5oT2MaOp/PiULGOJY+TOm3q5SCJNCQ975Dk/RLeib7zNTGxoYQ5N7zzttvcXJ8zA/94A9RlyW//sUvcuvdW9y7+z5Hh/c4PHzMfDalriSCnlEKURLFfH5x7YJPqEI0+cnY05KaDPZdG9KxP0kZgsUy32VtVSxG+5BfKprrWpyz6LpGVxKlVRtJ0/Hg3h3+0S/8PDdv3uTy5es458mynLqyTKdzrHVcu3GNy9evoFASwl2J4HxjMkEZwf/Oiw+zdTV1VZKPcvb3LmF0xnw+Z2NzwtbWJltbW5hM8+InXubf+MP/JqcHR5SzKY8ePeS1b7+BN4rLV+7yI3/oj0C1ibYLNrIdblzZ4dL2GPYM192zfC7/IX7v12aokxlGnYEvJArlikiUKYM6xHyvu/9pKO9V+5f699qQx9Fpxe7l/Qvv9UeeQbIhg3CfA1VKTCqstYzHY8qybBYzlfqmSCl+H0sEFv2QnX3gFhFxKs2IJnSxfvp83SVuc+PA3bv3+J7v+Tx7l/YFXGiF947cGEkkOi8xOmuIWe89owxM5fkn/+B/561Xv8HJwSHeOX7rV36Nl773D/BH/91/j+zSFWoFXjkcquNv8ySIqb8mcR4Ng9R71xbfINt0bUQCbxMgTAswvZfwzfJV0lSPiA7EfcN80hIcfekQtMyhbxtYyRQ1VXwyiqa91u5W6AepY4Kvk9aiejcm9YPK0EaLT4+1EoklBDcwRhL3ZsaQm7avLpMO1lXgJVpX551WZDqTENpGM8phNBKfrzyLzueWGN/MNbMQfzW8l3cx4iPgvMX7upm78yE/lpffIxPgfVeSGsM4LwE6RTCFC5pb1SUt1p3FofuebmFar8PIB5M3HQNrjEQS58eOapSztTFhOi85nRecTudM5wW1b5MLN3scTF7EyXw1c9OfcLtGYmff7uXF7t2TMCH931MCtr8+69q86LieFHZ0iI+4ph+wdOBIAoPSw2CtZTQaobViPp8xPTujKArKqmTsNrp3lm6ujT4yT4mxPMvEzGYjW7kGTfqBobH32l03p2Y8LPPHfTyWjnuI+RqSuKejbwMoRBwRxxXnI4O30CSgjO11/bbW8rjfURmaZ/ozhdVDO6Oa+7teSHGR0jJn609yh/HpLUzqw7ySsA9CKYUQkhJ0ZVlLFP+ltEbnLMc9HJjz0PzTfEFx3fM8Rymx4Pnd3/kdrl3a5ws/8APceuddbr37Lvfu3uXO+3c4OT7k7Owui/kUHMEyAYmEKdLAxnIihqyHaCnSzWvT0Af9SIUr5j+0/qvK+czUsH+2RDP2KHRgpIILBZbMhIh6WqGMQWlNVZW89eYb3H77NngxJzQ6w3vRnL1/6z1GuxN2treZjMdMJhM2RmMu7V9i7/IV9ncusbu7y8bmhCwkm1UKxqMRG+MJQvk4yrpEhSS4Shu2tnd5/9b7jLXhhRc+ztM3b/KVr32Vd969Tfal3+Lf/OP/tpwXJ23hFdWmY+O5bSaPL2O2LoF6jGYBzLFKAn2sOy/nlaE1T9OqrKqbwi6lFZUTenY0Hl+oX/jXgEHSJsM5Se4HsmjGaGpnyfIRXklgg9FkIrbiLubb6Drh6hDNTXmC/T7gWn8KpTXeBqjkelxwKkHvXUxx7guDDURAak/cAHgvwM46i1aaylaMN8Zcu3ENVAhnqUJST+/ITMYoGzeEHgTaXnnuf/PbHN6+jZpN2c4ly3p9OuX1f/5bPLxzn3/7P/wPufriCyycx2uN81qiHJNEHgn/Wye9WUvA9g79Ut2hT30iyYvfpwxrh0jpXSyVmrj1ch012hHfSETDJON/UCqJpuLFKCcdskqqK5xEYOkRxipoazKjyLIcY7Imd0GWGZSx6MAgRWZAgLpG1XQ1ecT5eVQTXQ58kNjEudeBCSEwmEaL+abRmo2RYpRnIQGdmJgJcS/FxYAEKpXc+dC3a30c4jmFlhnH423drLPDN4ErpLQhtpufQwhLXLDC2qdS2JDvo/k7kWQrhVfde5YiR63aZy2QTpha5yVqmRd/oiwz6AzyLGe0YZhs5EzmGZuTjNMzw+F0zrSocNYKk2hdGJ/ChXxS5wkYus99Z7NXAf2h96uk832E3a+zRDD2GMxV411FNJ73+0UZpnbPaExpVGRuVjEUqXykifZFIPptQjSLUEkClGisr8lHI7QxlOWCYjFlsVhQFCWbIdqW9p7ReEJZVZS2bLWPzR77RpggoX4t43yEq2r0BoNwrTHd7RDhfa3FMpGeChriHBshHKl0NmarF41OPPPL0txW0h5NyuWbmABaxiTwKs1BJlYP1taJ2bprxl95F8ItIwSi82gdI41VOG8xJlhnqLAiDawP36kgmUqibgpMG9r/sLeJQEr+dg1MTeF6A7tW3A+fwJ54dlpcsiwx66OouH1d2wiZ06rAHfF5ykC2QlK7VD8KnogSvPCd1qqlV9Qwsbnq7vfP6nL0v2FTRmlLBANaw+zslC//9m/zmU9/mks7l/jaV77O++/d5u77tyVk9+EB8+kp1k0Dbu36+qnQXmMy3EQFlfNiXRLVMTBNSoe8Vmp5bH2hwKoInEPz6pfluu39Td/ZMC+lBB8rpQOFIL6teImq610dcE6GVoqqLMGD1hlVVeC9QquMqi5Z1DPmZ6dMJhMmozHGGB7ev8/G9i4bO3vs7e1x+fJl9vZ22d3dZWdnm9zkFEUpOQG1YpxvyFoZzdms4NPf9d2czmZ865vf5OYLz4Gr+XT5Kb70u7/Ht9/6Fle+8hR7l/bw/jrvvP0+z924xmh7RG0yshvX2HnhOe6++W1ybyXZuTLikjywpg0jna6nc03495amWF7zeA9dqCNHP9yZFftjFNiqZJR1Gep15SPPIOX5KIQp7UkPkEOhvQ9J9uSnR9TT1qaqeEEyPhCi8WgLoPYtUYVq4ul7L35LCoUN5l1NfpiEOJBDkJi2JABbK90kMBXtiWvMLbx37Oxvk49F9ZqZGGpY5uW9b/xSGnpfgclGPLh7D+YFmffC8RiD9uCt5fDWLf4//+//Fz/2E/8BL333d7NworpXPuT5wTdScQ+BkOszRsv70AF4qZSqrdG2opr/NGsU210JzJO25f2AKUVAkN0b6RvCS9O9kD6pGxFVfKMbpOvbPQxvtfKMM4XWBmMk748JxIRRFqPbnBLpGGvlAyMRkCJxlR2N0CxZE5FAKUyM3wp4b6l9LYQgCkvIeK0kaeoozxiPRmSZZpzBKE+Ckihhs6yXVWhD2beEZoukh3znRPMUCa0WYYPzdcfMpouUWk1ThxgBsK3/VGefEb+yltAJjGTkXFUfebfn0ujk7NhlLU18Ls8k9DcGdK4YkZGPcgldvrXB9saIyeaEw7M50+mU+bzGekkVEIxqaRPyqiWpWSpVb/qOjN8FBQ6rng8xVk/GqK0nDuDiUsCh8awrQwITle537935fQrMamg/1eYhEkm5wjvN5tY2G1ubHB8fMJ+dUpaF+J15j7eOcWa4fvUKt957X5JoRzOzOAylGzNa0VQbJvmIalGgdoWxG0oMrrVqzn+7T+1epX9L/TbaZnoXI+OSth33PlonpEGIYr34t7WtKXgKd+tacstIPrGaqiqbfiPeQvXgQtD6NsIp6xCyVaO8IlMiqNQ6OJwbg/NQOyvpwYL/fcNAJrCYyPwN7LlON7mdZQd+pyWF493nkfTqClT6ws/lEs1Ce0/T8SRjae//suahO+blMTZ/L+GpoKlXiqKUdBg62fcUdi+NLfwdc5J1iNkukm5Kak2jFGxOJhhjOHj0iH/+m7/JH/yDX8BVlt/57S9z784dHj16wOPHd5meHrFYnOJcNFNOzEMjU4cHb5szlsLRKIzrrElYJ2ddp24K+/oMU3//+3fnYiWlhXrfBH8pD+HsJlGLldwVF4QR3tVYZ8lzj9EjUFAHxgkFjlrgSOlwdYVyDh/MHwtjODk7g4cP0FqSpI9GI7a2ttje3mZ/f59L+/vs7u1y6dJldnd32dzclEBFRkGe871f+H6eeu4Znr5ymVeef57/5Wd+hheee5rX336Ho4MHzKennBydsFAGXxn2t7Z5XM7ZvnGdnY89g97ZoDrzWByucjjbCl7S9Y3xAWSvulZYMdcU/b3un9dQpzkuCUxI76AiJOO2juNHjy+4n/8aMEgpwmmk6iG0a1mWS4Se1pqqqjqXqUFC+JBUUa6kTlW4sjtEIZgnAVQsA6A4LjHNcaggMaudbRgirbRcmkCsOmcDorHgJEKNc04YIgcNBOkDjPTiO8fZyTFbkwkLL3b3Whmcl3wtuVPYx4f8/N/+u/ybf2LOp3/wC5TeiyQgthF8kzQB/6WR2FZEOQpcSFK/V8/3K1+sXEQK3a93HuHY1l1nVuED1xcuoRJftFGeM84Vk5FcrShljUUr0+yQSD/a0NtWWOnhCfguMm6AhYeiji9kPA7fSH+VEu1pnksAhnEuSViNUYxMCnwiExvnHBkdR8yb0hJwrnmXEnVR6xHfgZyHeF/Su9ZlkCKCGiBUVAxy0gLCWAwpAaECLaYi9dtZvpYZjYzwMmMiTFRgsmjNGbX2qFq0yLKfwvhOJvJzvLnJ1taC45MRxyc5Z/MFs0VBbW2TL2lI45KWi57j88qqCF7LhNcH62/Il/PDKus0XPFMPmlb6f1OYXlsU85ka7K0sbnBZHOD4xPFdDqjWCwoy4KyLBvT5el02rGd72vFVGC8fMgTlI9GgSkyIX7LMqOcEt6pliKNXNoR8PXqRgFbVVVLjulp++2cu3BNBcatL7wbgpXp+sV+G4uCDhMg7Rrv0daSGcPmxoSdrW12d3ck2JB1lFVNWVUUZcWiLJnN59TRtOqcLf+w7s1FGPbvpK8h/P+kY1g7pshIhOfG6EaAJlFpk8AkAwx0nxFQSolJF63PXZ/Ahfb8x3ultWacZ2Ta8OjBQ774q7/Oj/zIj3B8cMqbb7zJ44d3efxYAjGcnhxQ1wVQi3teD3StWo9+FMJVdeP5TYnzIYapb7LYF/xdtKwWVMEQJ933y4luF3meA1CWBZkhhAkHgmYw/oxpKuLdjfSgc4XkddOaxUI+efhQ1iDLRijE934cTPN2dna4dOkSl65eYe/SLjs7W2xsTCiLguOTE65eucrdO3fZMBm+KCjmC4rK8f79A6ZzzdmexY0yTu8esbV9mctPP8e9e3ck4JeVCJ6dJMMB/tS1Jc/zJca0T3v3/UZTHNdnnobOcr+cnp6dt5VN+cgzSNCVAjQhpFULLOLBjJK9FMHE0uRGor1Ahjb5q2iPbCNDMANR6Pol3ezoyCbArU00KhNAfEaUtKmMwTu5EIKgWmDVn3faF96jncWVJfV8TuY9GyYjMzlV7bC5wViFrTzV6ZRf+d9+ltJWfM//6YcpnIQBj9yQRolk3YWEo0trHvtNH6bjTC9FfBf/7koBnqR8EIItJaKGos/0W4zEdGtiEpLBZRl5nqGVQzeJWR22CZAgzG6XzRImx0NMaZc8D4CaQPg3YkISMzqFbXzmPN7ZwNRApjV5phmNcjYmI0Z5LmGtjWn4B5Esh5ATKjI3Ho9oL0Wb1Y7fE5gpJ4wdUYLnwXoJtx7bgBC50ccz3jJVrZkIQSCQSPFSgEk7TuiaUSq6Es2WYKUhzsSxnOSdD+He5cM2+W9sp+ogfaWiT5LwXMIkhXy/iFZuQysyBZNcCMCDkzPU8RnT2QJbVR1iuk/w9glf4l6fE8VuSNrZrMQKYu6iBFifSE7LurxGH4SA7AuvPkxC8jx4qACDwgZC3BiDBvYvX+b9O+8xnU6ZzWbUlQTNyY3A39l0tsRsDhFVKjAMG5MJ9+/f55lnnwVFQxT09x9YIt7Se7RqX/rEXR38blOiIcVFqxiftvhGUh8JZBmbaTLb94Md6CQkdCTilFZkJmPDaPa2t7h65RLXrl7h8uVdFJ75fM7hScGDRwfgPAtb4GuL8QptMirVnrUl4j0JjNTf16H7cF4575shZ3LaGS+VJ9E89Bns8+qtfK7av2O+PK00k8mkwySkDMEQo7wqYEf3WTfXTzyfk8mEDHj04AG/8eu/wRe+/wd4cOc+777zLg8fPuT4+A6HRw+ZnZ3gXIlWDrwNglXD6hXtzncdY9Sfj+A42/k2hfH9urGsi6YWy3l7ltQknVu/v1T4EJkeYwxlWWBtzXi8ASE0lOAzwUNKmWDaKjkU8zwX/snVaK+w3jc40Fqoy4IsG1MVC+bTU5RSPHpguJVlTDY2MXnG5uaYvf0druzv8f7162xsboC13Lx6jXu3b3P77XdwfsK8MszdhEWt2NnbwSmHIWfvqWe4bTJGGCQJ/Aq47ulYVfXvWJ9xTGFaf0/S8zxIx7kQrsv5vux0bfnIM0jpZU+5ba+7F8R70QDUtWxo5HhjYtn4vqrbDY/JI5XWGJOJtDtIa1ww8SrLks3JBnVdd6JqNJJMBUprbF2DVuKgZwXBZVkmuYlCWx4f8nk6yqJgPJ60Y3HpRW2lJrGvBlCUJcVshq9KJtow0prM5JS+xmmFcYraWyqrqKZTfv3/+3No7/meH/khKqB0LmTeDnb3OmjAloD8shYmMkZDeCMSsVEzcx5qWUdI9YFO+mwIaQ0RCz4w0c0cws+WGTJkRqONprF5VgpweFdT2brTfuyiuzYpARQrtOsh78Kl11nDSMX1ieOsvBBj3ntwYm6Zq2BONzaMRyNGWUZuDEaJqM4jfmt94ioyXU55amvDgEI9F7U8PebeRY0SOJ8kEAQxHYhMES6862qiItAXZ+LQY2C8OuxkHzDiAyMUmQrVLGFqgpT+VEpMIJvEwDYlVKPNvjSlvOTZ8LYm0xqjFFp7tHY4F+oqGccoU2g9JstysnxEno85ODri9HRKVdWdMbTnYPnsNVLaHoE7dGb7xPUQsZ1K7NJn64i3CDsucldW9b1qvN63a3+e7X/atlaKxsj3nPH3YVG67vH+WmvBeUzIi1ZUFXiHMYq9y5dA0QRoqGoJdKKVwLyiLJb6aoO+0BKqGjKl2dzclCANRmN9VwMztJZDa7EKfqXnICUS1mkSV61ZxDGyXvJcQpWL1tQmJksxipQJeQNFgKDQIaKWMWLekxlDpmBkNHVtuXvvHo8ePUDjOTk95uisYjovqepaYFIcv1d43cLgzvr41qyqv25DmrgnKavu5RATKxVZgkvpdymDu66/VfcyvbdpO0PBOqwPTIDURKPD/giZF/P8Dc0zPUOpX2afiG+J1+4crbXCHBnD4YOH/PqvfpHPfe6z3HnvNu/dusXB40OOjw44Pr3LdHaCOKSK2WbEiJ6uMHId03pRBnRoPdOohvHvPqN0oWAYSf2hc5PUos/49dvtM2aRdjOZ2JnWdUGWjYiRheWbGNVWsGZdVzSmeyK7lnyR8QwpEQnVZRnolRilUJD63E7JMkO1mDGfnvHo3l2OHjzgj/+RP8Jzzz7He++/B1nGa69+jfceHPL085+h0puQ5dQ6Y3tzgnWKnRs3GO/vU50dNbg8woqu4IbO877LAdAxRx66Q6mgJjXzXMZhnkyLAOz0+HTlfvbLR55BsnXdmMw1gMRaQHc2DgQhxgWv6xpjTMPYNIjHi5md9zFCHmRoirLAmDyKxNFoqrrm8OiQ8fVRZ0zpIbHW4a2EC1YosjwPKlUfQiTK4Y19uhCkwWRZo4ptEUIkhGLbXZ8npRTeWWxZgLWMlCJwYBiPULdaYccZVI5NB8V0zm/87M9hUHzXD/4A3mhKW2OVQSmHasywUqDbzBTVwWurpd5RKhVBpIrauRUIY6gMMUOrEMK6caS/e+8Qx2aFMRkxD5PRQmjroD3zNoQdEEo/bbGDPyPhH0s6LtczT0zPiWukRz2GBi/JZr2klM20Ymw0k1HGxmREPhJCJc+EOVINwSyOoSmj5cNZc0GqHut512qJRNOjRIOUqLydc1jvhXl2bTLa5r0P5zmclyhciBqnuCSe6CshcwoOgmE92rDlUtcFxigyREmQicjARA1f+FZpCRPf+IDpkEBY66AZUg3TpbVpogwSckkpJTb2JlPNuYiSWm00Yy2h10eZZmwgNxmPDo46SHnonPXPZ5r/qUu0dM/NqrML3Uz3Q4h+nUR6HfE+VPrEaIrMUkavhb/dd6vm0LSpUi+/5TH1iadVJYWJ3gYPPS9tV85i64q9/T2c9xSLBcWikGingdHXhEA9qkuIxzmrFMl7j3W2SQNhre3o2tdp44bKKti3Dt7F0jdFybIW9Xck6boNrR+/i4SG1hlpUJ4sy5q5ZVrWtCqrRvBXFgVz68S53FmMlogrOoFDlfeSgBlPjJoW8lt2xteZnw/pGppl7hLw8dk6YcS60hFUDHzXWefkVK4SKFyEmU+l50PvY+mYayd7Gn2VA0WK0aYRLIAkVSdYnPTbizRQKsQgzi7B630GywdBV1VVTCYT8jzn5PiEX/vlX+Pll17m3vt3uH/3DkdHjzk5PuTo6DGzxYlEoiWyDXH9guHzE+zVkzC//bp9i6GU0E73v2+OOtTW+WMWBuYi443rH/uNTIPAzEpM5HTrr97eAYdzNXUtQcayLAclScxtM7c4hoj7fUgyr3GqxigHLsNasLZiPMo4PDrk3Vu3+MQrr/DWO2/z7M2nOJ4V3L3zLmqyy3jnEseLMeYsZ5RlXL9yiUtPb/H6b+5z9H73/Nd13ZwxE6yg4hqvYzTXwcnYXp85St+BMIfWSXCu7a2tc/chlo88g5QSaTEZrMkyateG2daiq2zst02wNY/aowgYnPdUtiZTWWjTYkwWTM9UozWyzlF7QcT7ly5RlpWc5YBo4iZGJkgZjQp28LWtQ0jkHtEUaAoXTN1E09Ui5rKo8D6G1VxGkioCSmsl67l17G1tUxQF3np8rciBAg9GkSlDXtSMnGI2XfBr/+B/RwGf/ZEfpHROclsoifYVQ0F7lsOTer9aKtfbKVQn8phrvk/RZR8G9QSLKBX78Ul9T8wF1WdQ8P1xQQQkWqUd+NCOmGhKqMto4x36isyQp0PMdQjAxiOnN3svQDRCvD5R4HyrNUq/EdZJbLizTHyMJmPDJM+Y5BkqSHRVOK/4yByBc7rRSHWEAMhxi8wK0DBBEuJbN0xPXdeN5qd2jtq1DFjULEUzO0dct8gsuSWtVJdZA1y7Xl3jRJDQiq32R34Ghse3iK4xmw3MkAm+RFmeCyMUmV6jMFnqZ6RbqZf2OExjYud81CYByqBNhg4+6ZmBrXFGvreFycfoLOfRo8etjXmWkeUiTHHxPCbz70uHl89Jl7DqLEnvzvcRzxCD1i99qfGqclHhRdpGn5FaVWfduM77vv+8P5+4ZnmWM9IZ6IyxhqIyzM6O2NndRWtNUZYUpTBIrjHzoGHWIrzv3FVniakFlAflBH9Uldjipz5Q64jwdesxJElPGdD+frcO9qnpqOpIbqPvQ1WVZHmMvNhKcOu6JiaYttZirWWxWDS4Vfs22E30jdBKS0JMLTfYeI/GEJLsCfw0Nd54VCCQfQPniCBX1oBoMiYCDD1wRM4Thl20rBMmnMfQ98tFieK0rVXfpHvY918REOJjg4AK8MXhrGuI0yEfwu3tbZRSzGaz7nvVRmJN75EIlNucYOPxmNFoxGKx4Fd+5Ve4evUaD+8/5O7dW5ydHnB6csDx0SMWi6ngRqXxQdDbiWa7Qph60XX8IKXvzzJ0787rO2WiVsOh1d8PnaeW4K+JPq9KBQG5DoxsEJoGggNPyPfjJepde98zIpMmNFLEsZGxCPSLs+CDdt9anNXUVc29+/f5zKc+jckyirLgs5/9JMe/83WyzFGUU/xixDjLmU4zjieean7KpaeucvqaoZp3c8a159dielYKq2BYyryu0iL18UKk+xotUoAlJgo9L1g+8gxSVVvGNnCwlTAt6EDSK41Xmspa8GC9mDoZFLUHZTLxK+oQ+wYXLI+UMngHVSmSQfE7SbRS8TslRKX2WqRlXiTOSmvwlQSN8ELciWRdxm10htGmIUa9cxiTYZ1IBQQPe+q65PTkGK00GxsbFFWBQocEhbC5tUXUFhRVRVkuyLxnZ5RjqpLaeawBvGOEwjhH5sV0YmEtYxTudMav/oOfwxrFp37wC5TK4bEiQfdKiAGi6C9I4QkO8eEOdwIQeN84jsrhlv5jpKooqVQqAfyAostepOxL033QqHjv2woh/HkrZPNJAymQVglgCaNohNgSTS5qESMCilnRhXaKkrXWzKxTlMyh6TU5W/LMN3+kxA+qa5rWkY5oyV00Ho2YTEaM8ow81yhDiI4DtVchMkxgjrxv2pOzlWh7ABdC0MaoVtYGbWcAwJHYj99Ya0UCHyJdeSfZwl0QRHiXMj+Ourbhnae2NGuWBngAj/et/9PSUjb70pX4GiXmcEbLGSYyPFkuTI8SBGSKEm20JNs1GqNVCIMuCEkYJIfRhtp4jKpCNMKQnK/RIFmMj/kewBiN8pIn4orJGGUKX814fHSKR4QhulboHMl70QgvAjLxPhCLNJJy70VqrkwrXnfO0mrV2qhT7fr0pJbeB41cOH3x19Bknwg8jzBIGZY+I5LWabsf2sNlP5zBer3nq9pK21xVNzIHuQnJv4Ha1tSuxnnF3u4VMpVDVVPOFpSLQghMFQL0ONEq99dIawmq08zJCZwwIbKcUq1GM44rHXNaukRXcsZVC2dQNKHzUylsV+uz7FPbEdJ50QDUdU1RCDPocSEUt24YHvlOdiLCKOfbM6Q6MFrOo3UiMYjJmJ2XXGlKiUmeV+E4qlYAGVMpGGVQKqxPkB016xqIf4GlKggeXQOTXLLfHebD9+RdS8W3fQ299dHkV+B6q8VqL1L6ne713dnfDs6JHUjbJsx5SZDXzFc1daNIzUeahsCYh4ZjoBDtwfgYudG3/QfYMp/PO2Zm6eBMyNGlUE3aEOfBOo/RGRujMa6q+Re/8Rv4omChZ7x3+12mZ4+ZTg85PT1kvjiVdfPtPBP+N0xxeZ3669+M6gMyTH1hQopb+oEozusrvU/nj9HhezkZ5bUitRaIAj/Be9F3KGiTRmG8TgJomBAZL5EiCB0rEI08zxBLKRvajRrCOLa4AzIuiwVXYpzBIIG/aus5Oj5DZZrdKzucnZ2yvb3Fj/87/w5v3H6MxuNqy7yomI89jw/nPDh9gNY5i6pEexvwmW9xWGDK6pib0bfMqQowxjR0lG/cSrwnuHS0goBG4O3khDof/eMVJssFPiqFN0602B6K2WxwP4fKR55B0rSaA6UQ4OIEMKug0vdeorlleU5RlrhCbMzzPAetg9+REJi5yQLR2F6siLjSMMY2aKyUUtSWIIVrNQzWWZyrUNoDLUEYf3ovCTm1NsIQ2eAAL5OR0NFZhveOYrFgMZ9itCYzcHoqNpY7O3t4D1tb2zhrUVqJhqquyZRC1TUm5MgBj/UO4zXGevIwJ0YZtqrY1IbpYsGv/IN/yPalPZ791CdxRjRZxkvUnMggxSsX7dJBmtJJuDuXgMcODUdEtq0ZTgpWPG3oxg4wgiYnbAS/3XYdqeN7B3Yl7bT29xFoJpq8OHbnUF5C0rYA1idAZ3l8q4j8blkGuPHvFJj3gXymMkZZzigfkWU52igwouHzzuOtx9ou0SVpXVrn2kZ75JwwOV58kCJT5Jyc6ehTZGsrwSeClsg6K0EaUkIlME61taKljNokJ9G2CIyK8yoJ+NDIxJolaeiL/vL5dM2TMKG+wiC5FEwmERoJZgfGaHKtyPOsZYJ11CCJX1kW/CqkvuSoMkFzlBlDpg2ZcWRBGhWFcEq1vgRxb0ZasbcxQj1zA4vn8PBM1hmPMh6TCaOTZVnIG6IbaW88e33NSZQg15F4DX03Efd70jat5W7q5CY1hBa0WqzeeVunlRk6p33fmj7DtIoZWCW17XwfEfvAN0PfriJa0nMecyJVzlI7K/ngrOXKletMRhvYci4Rm+YL6qpukTXdu9yVQouZ9Gg0Qnuoy4qNyaQRvOR53hEspExNWhqtDcj5y0yTMy0yNWlKib7ENRVcxDs3VFK41P8+JmluhTQDnEP4O2p/4l/tOy8WPZAQgra5rQ0DhJxJE5hXCZixmsntELZJkKVoYZHWS8+DjiaCagB/xLF70MYst0E8l1nDfK5j2qP5mE/WSXpofSu79yN+1RlQM8CG/fLiaN7ch/BYB85OQaOZUUoYnDwIdJQP5vphJDFUfV8r2cw7DEwF+gdCPsbKBSHsCK0Ur776dd55402eunGd27fe4eT0kGJxwsnZEfPZKT5Y1LTzDAxRf569Mawq63DpeRrEobkOtTvEAA19MyQYGu6//3fXBUHaknotzRosiwJOaML4e5pcSX14JN+7IKhXybN+X6n1C6CEuRKBt6KqarTSnE7nnE1PuX7tKg8e3Ofe/Qf821/4Qzw6/B1clrEIwr3ZoiTTI269+S7PZYbSOXLvRaQSyD2vfCPIcwjzAjGiL41Q2lnfmZcKQpaUwW6sd5ILJngwBGeyDp2PsHjqYImigMf3Hy3t1arykWeQnJIMukopCNJZk2VBgu0wWchVVIcDpbOQ9RjKsqKqqmCKAFVVB98GQ1XVDbKygflQAbBHZqwKGiuFEEERgENrahHzGjUSHy+Sd1tbJAJIgbVCQEUkUBYLdrcm8n04JIvFglGei2lIUWBMyN5uxIyoycFXOzk4iA+VDhILjVg+iNWSwnoYBUGVNYqZK5i4DKZzfvnv/m/8X/+T/xv7H3uu5e5pJVzRtzb+GxLHNTlI+s8TE5TzSkoUdBHwcF04n1G5CPCL6566DKX1+gRf33F4XRkiWPrMUXymlCLPMvJRxng8Is8l+azC42qPV5LxusvIAT5Ka7s+RJEwss6JNshKEjvnhLmxtbwTgiRofyIx7x21ryUHk3XN+xgJT6PBx3VQkqTZBz8hPKYncIiR8IzPGyapj2BsKtkOBJwiSKkp8YgETOYu119pqHFUlemY0ZlgilglDJLJaoyODJIiM2KWZbMs5B4zwtQoj62F4cizLJjyabQRqZ1Rms3JhOeffZb5/G3mi4rK1YyswpWOS5f2Oz4AVSDGU2dTICTj7Jo8psxyQ8QOnCmluz48aenx9Z17tY5Q6Z/p/vk8j7Dot9OMNWoKVhAe676HNsv6qn5biTGQsIYeIUD29nYZj8eczs4oikWjYXHOoUwwr3NuiUCWtkXYZq1lZHI5U5VoHouiFO3sAHzrw4x0HZ23UHXHH7Wycm668xqCE32YsmpdO4xHz/xoidAfYDzXlVXwMV2DjmSfZT+6of5Sptd7jzIrtAB+mThuxpQEphgaY/8sDu390vl2vmGqunWFMB4SHrSdLk2hM57Bb3rfK9o1dc4F6b1oQRt/pYTB7s9p6N5EOFXX4ndktOLB/Xv8+q//Os889RR33n+P05NTFosp0+kp89kZDTHrfCQSumU9L7Ry/kNlHcOzTuiTnrF1d+Simqvz7sI6pqp/X9NgEtGHUgSRtbh49MYNcm/qejiC3CBslhfNGLxz+CCkKYuCR/cf89yzL/Dq117DOMXZ0RH14pSXP/sSbz6ak5uMcnbKwemCRw/ucePaJl5nWIQpj/dZ8oWGsaoOMBGGSGvR+ASc78OdjdZG+Bafi6lgOq/gyRYFdRrKqkDlgpOdk3ZPDo/W7k1aPvIMkvWOqq4ZjUZUAcnlSECGLMuoqzpJCivIzHrfmFBUVd0ctszkQarrG38myXosqs6yqkSlreMBFD8W8bewDaCC1rY7tq1UCKPq2whEMfoVtEixowaXLnBO7MG1Uk2ejvF4wubmFgTCU+yGnSQVC1I266MpoDjGEkyj8KCVQ1lH5hVj7ai1pSxLts2E+eMDfvHv/j3+L//3n2Ry5QpOq+DE7IW4VKADbvWrAIpSkeptLmyKIIdKHwj03rZAf+C7ISTXDmUZqEbJRJTELREfng6DNBTxJv4eAVX/+VD/faKzL9WNBHPUeIzGY8ajnDwwx3FcEtvASQjj0J2NbbjIINnGdDMSgM6JJrF2NbV1jeDAWfneNsyREGiRiHfe46ixvl4iALXRGC9mbpA1iLslHiyeLrEfx6JDHoW++YVIl8RTCqVCNMG4oAh0JGimItDFC7DGUtcepSzWGrSWHDUSiMNgjUMbiyprjJHw7ZmRkOl17TG6xihhpPI8D8xT8OXwMdiDhJCOmsssy9kYaV54/lm+/dY71NY3+3FychoiTbUmEFqrjkClc/ZWnFulVee+DZvMrC/rpKtDJb2T67Q6F/W3WUucrOh7ncZoqI+2nlgCpHBIKcV4PGZ3b5fjg4cURcF8Pqe2Nd47FFkDumK/HQfvcEzrusbXIojSWkyfq6pCJX5tqcZtmdFq4cDQvCMznAZ26a9HH16uYqDSZ/020rmtG+fy2q6G7UN7lT6PQhej2iS462B3HJ8wcUFQMoBHvPODZ03+gFVcyQfHSd02LsLss3oYK9uRs0CjZW91Bt20CB5JnkrM5egFZzole5xlWeMrt6rvPM+ZzWbkuWE0yikXC371V36F7c1Njg4ec3Z6wnx+xqKYMp+GgAxRSKYMfmCpLsJMPEm5aHvnnd0P2v4HrQtdYSjEKJJt9Lcm2bPOcE4sm4aYa0/LUJlEG5r2szROH7U0rY9w7TxlVXL//gO+93u+G+/g7PhMQmi5kqev7/Lmoym2WnDnrbd4+7Wv4heP+ez172e8vcP8bCow0ben0nswWkyQU0YepcTSBRe0x5EB8hKjnMRsNepnE7JA4cWsGR9oM4XPDZeuXWF/b583v/VtrNOYAQHKqvKRZ5DK0mLMCO8VdS0MQV1JFLuqSqMpCZFY1XXLOBAzi4ujm3M++BpFoG+aKD5yaPNGs6S1boI+RBvQaGeZmkBIiRHqfLgENN9FCUIE0jHhoDwXqUxd11y+fJmd7W1qa3nuuecaM7t4qSQWfnDqU8EJ32is82jvsVXdRuezNbXzktHcKJT2ZNph6wpXO8Z6g5M7t/nFv/d3+Pf/kz+FH4/FXCc3eK2DlKg9tv0SieN27ZcR90VKn3mI6qp1UqAnA47LBEK3Xlez0wduad0nAcYp0ZCaxfUJDYkiZcKFD2enjpypqNnLIMXEdzVFYlalxNTTts+FCRJ/DDlbQQvlWlvoqOWUNn3DICktfj9Z8OnRie16plQTuUqrNvGycx5nK2Lo+hh9xzuHHClPbuSe5aMQtTH4KU0mo8ZUrqpq6rrCWkdVVsznwQQQFSIAmsCgiTAgkLOA+FcpVAgwUWOdR9WqCVdcW4/Rnjo3gTmSeebGUNVBk5RnGAPWS0Zzow1ZrjDaYQxoK1rH/Z0dnr5xjffu3EUpg/eKsqwxJp4hAoPUPa/9szL43NOYzUVGOn3f/75tQwJXXKScO4Ze6RPGF2FoBgmiXvPr4MQ6YnZ53K0GLSJqYzL29vZ4T2mqsqIoCuogKEMRcraowTWO2oGmB9/6NsznMyabGw08WKUpT6XGvj/xzhwDgbCC4Rhifvp1hxiU9F33+fL56TPwq8pF9ys9y9bZzlqsY96buUIH5fSZoNWk9qqVHmbuL0S0K5olW5q/HxhfeO5ZPc5VzKJPPvJe/iO6+XYftdJ4JWaiUZiKagMvxD0diuAW35VliVKKUZ6jvOPVr32Ne3fusLezzcHjR5SLOXW1YDE7w9oKhQPvJDjHapJgLfz4MBio/n0Y6r8Pmy4C3y5a+jTJKljc/+l79zsVXGjtG+ui/t2I30X6pQ+nUtqpmbcTSyjnxbQv5n4ry5Kj02NGGyNMnvPW2+8wPTvD1iWbkxHagK1Kbr/9Ogf3b7GR1xwcHmCVofYqSN+78MRah4k5NeP8A/LzPuYqUkEBEMYcF6Rd1eb8CuxGAr8ohcpyyDMYj/n0F36A3e0dbt+9z1n1OIznYuUjzyAVRcl4MqEoK1Gja0NRLIJ/RcsgCRAWItB6cTSPyK1WtjFv2RhvhEhxgbiqHWVVglKYzHB6etZEdZHw4qCU6QD5LmJUrXM2qaOgbhIK2mSs8n2wna9rVAiTHLUU3jlMCP8tbZlG44BXeA3eGLYv73Flb4+TRwcsFmfNt7V3whgpD8EeNbOwoQ0qh2ldUVtRqd597Wv8i1/8eX703/sPmNoKnxusF62aigd8TTmPkLlI/aZuhypZ/V1KqJ3XXyqdWWZyfAd49YmQVYTk4NhX/D5E0ERgFjNhR6LfWvGhEPNGATTOe4rwM9VAgTyrrW1M5uI5q+u68S2K9wGvcK5l4GOSVwFqEiRAmAYnSXIT4qk5v3WJq8VUSWsl+YKyjCv7+2Rac+nSPqPRmK2tTSaTSWOTrJJcDyaLSFY0leM85DZDhXwRkVBQwnjUNacnpzw6OOTg8IjDo2MWZSnR9EIbWhmcU4FZccHHJNxtL8Swqy1Ot9K8UZ6Lds06MuswylBVNuR+iblhHJn1kFu0g7HxGGXQ2vPU1cucHB1yelYiEZ1Em+vxjalf65c4fEYHme7eOe+cu3VMSJ9Gu8DZHSqr7lSfOEiRcr9uLEOIPcZsWjXWVWNZWfqfBk6pKksuX7qM9475Yk5dV5RV2SDq+Fnf5yreLRUEXgQBgEL8WaNmpA9/lobVY2hT0r0PgxTrzaKGvxsuq75NGenukWrncVGz6H7pazmbvjzEgDKx3ioGqR/QovtLOg8aM9Pl9Vj2B4llSMAQx7ByXf36NtM5rDvD66JUDvY58Lq9a4kvWfzEOUajcXMvUx+5Iaa5LMsmv9XJ0RH/7J/+BpPJiIcP72PLgqoqWcxnFIsZxiAaKx8As1fQE8SsEx5+GOVJmJwhGDvUzkX7vci8hoQUqVApvRcp7JTAKW3Kgq4PY6sJGoK3q/r10ESk1cHJzWuhEw7PDih8wfd+3+d5/bU3+eI//adMtjcpbRXCjldUizOMK9gMEQ2dyqicb/Zf5pYwaYTQ/z76iwf4qXQQQkmJllbt950FTGhAUDoLDH/GMy+8yIuf+RTq8iXUxhbf+8M/zK//8i9zcnpy4X38yDNI82IBp7qTM6iRqATisjWPcoxHY2EwvDjCjsZjtra2RMLiAS85kqqqkvwygYlBKfLxqMlinkYR6jhLJwffe1F556ORMDxBvZ3nYrs+Go8aMxutxaSHwOlrV7O5sQneUywKlPdMJhvk+Qi0YmNjE60No9FEcl540RZ4pdjc3eaH/sAf4ODWLebzGcWsoCoXYjKlwedazKi8qCtxoCuxX/a5ZuEKjK3J6oovf/FXefEz38vTn3yJhXc4r7HEpIo0mqRh/4eIeFbt3hqRE8sATKkYna0nAYqIw6d9Dkj0et3I+FUH0MR2U8OXvrR1HaDtP19FLPWZpDQ0bxZyYLXSmNi3Q1kIjmE4oFLRbyjN5RUCJdi60R7F+TnXBl8QxsgTo+lE3GqyPIxFN2aNCoV2JQawtWW+mAc/OsXOzg5Xruyzt7PF9s42GxsbGG2o6opiUVCXFeWioJxOOTs8kEhaIaBDVXuclfD3cQ102OvMW4wSTdpoNCLLMra2ttje3mK0qdna3mbv6Ws8/+wzOK+Yzeccnp5x5/CAxweHHB+dsljUxNCmDTKKxvnhL+9Dwj1bB42XaMGsttQhUEPuhDGKwVni714Hsz7nmegMpQ2ZUjx/8yavv3EL61ppn0jxwOkoCeudxx7CXCKw4gc95BqfmRX3SKkuO7aOGBgKEzxU+gg4vRurhQ7Lvh3p77o3/qU+5DeiX0X4C3p3NWmh+U0lfzrvuHLlCgBVVVFWFVVZtdLJhImO9VMH+qYL74kaHjGxqztzvCiTFPscmsMQobOuvWa+T0jwDbWXEmZ95iXd73U5vFKCsDNOJZHsLsJQ9t+1bOyQ+dGK52s0SOl8Y3+D63wOPZzOTR6km9obfXOlV5skdto8Zwxp/T4u3tjYYDabNeZb0c8l3qnYZAysY4wYD/+zf/ZPmc2mlFpRFQXeVtRVyWI+BWxL0IZIi3Hth+azhLN7ZYihlfpPcrbXMayqc898c4/jmEnepX2to19Wl6ExD8FFpVh6Hplda7v3p5m3av7TYZBSU7shpkxoA4Fs8Zt4Hs5mJxydHXA0PeZr33yVT1Q1H3vxBeaLQvp1ljwDrSwb4wxtNPnGBkXtIHEx6JxDrVHBvSWNTmc0bYRMBT76UrF872R+MQCbwTvFUzdv8spnPsOVm89gdrY4q0uM85wWBbOqon6CDfvIM0jf++nPsbG5SbyY0bSntdWXoA2SuKoNuGC0aew4Y6JJG5K5NqprGkFRUzoHL7ySDewyRyng6xMN8adXEjJYQ8NwyDgzsDnjbAOAzU2Ld5p8vIm2EoJ2MhFmD6XJ87GM2udUOqN0ln/+9a/w7NUrVDubFHWN29tmXlXkoxGjwPRt5jlnJycsTk+Zn52RaU1eayYWSldBpmAx5Td//mf591/5f2B9Tu6MHCrjcYnAyAfJQPwrLX2YJQRIW61LHHTrSrvymwo2tE18/biWCZ/kPXTYG7+Em5oxebrAKpViyv1NBxgkqZ7+9JYJGnz3s/DM0xLL/RKZ7izLyfMxWud4p0FprJKxeCdZt5skrkDtq6AN8o05nbUSca7yMb62jMHWksDPx2gGARBLQEMVIjiC1jlagYQNFe3QbDalXiwYmYz9vT2ef+5m0AaNKcuC2WzOfD7n6Pg+VVVSVRIAxdYWu5hTFkUTatjWdfD5gLKoKKu60VqlzGKujQSnyHLGkwnj0Yh8lMu515CPcjY2NtnY2GBnZ5ednR22d3f41Me2yV76ONPpjAcPH3F0fMrDRw9ZlDWWjEznoMX0To6FaJc1Bo1p8pzV2qMNGO+o8WQ+I/MOHaLSGWcxhDDcpsZlYLQjyzPGmxP293Z4fHgczq8CFcJE+3gslpFfCjeWpO4QTOxSRk8OmiKaBodbk55JlgS7q0t6NvtETa/N/ncx44ZKnvXr6TDeZlzhdycHr6UtPUmesuAArlTnPWJzmLC6qucDITDaNDBEomuWzrF7+bIEy60K6sWMaj7FVSXYcbBEUM1kMi3RRJ1LIgrG+QT/TGUU09MzJLmxxtNqn9IE2cnQmvbX8zIX84FZzk+34rukPwlG4btEWmeAPcHXEs+hWD4JsR2Bz2niZ+jPtY2i1i8+heGq/T2Qs8PfJwzKEsPZX/4BJmiJEQvEZPr0QqSX701pmGcf7HcVblARucX6QZCUEr+N4JYWn2ilhZqxdYObjFI8/fTT3L97H+sAo6i9xWkxd1bK8ejBI776e7+LtzWlrVHKUtULFsUp1hYo5UO+9CS4RyqFGJrjwHH0zfu+YEbeCqrqMufdhqLJq/ipxjQSbbhpaScyjqlG1CNEvHcxdHxDFaQj64wnPj/vTp73vt3n6LMeBWatfzvehwjLFq2j5YLBOdAqa2Fd1MTKxgeBajzj7ZpprSXAgRI4aJSRLIvOU8xqpidzbjx1jVlxSlHPqOqS92/fYXt0mVovsErjtaEoasqqwhrF1Fkxr09WS6KtijVXbR0SpEkz2djg5s2bzGcLTg6OJIpecFXJg9JiXiywQXMmmqu4FhIl+ROvfJqt7W2mi4qdeQXVGVVZ8P7RLV790pewswJfXVzb/ZFnkJQSf4Co5VGIs7kJ2hgV6qhImHohtAG8dXIlrMdriThH8NNQxogfOB242/hHRCAFSHthH513TfQwRcxtEQbbI46djmFcaVOCRZWlhUjWKGVQOmM2L0RFXjm0yuSiBNwbEVKtFGxtkO9vo69e4bkXn2c6XeA8XHvqKTY2N7l0aZ/p6Rlbm1vcu3cP7S3f/O0vc/DeHY7uPgAF5WKKzzT4mvfefI133/wWNz79XTIuBU5H5iRFob79r0+AxDCeXgFKExS9VKlFlB1tju8SSnRGtOwE3RlBwsz2bdGjT0Ja9AopR/p7Z49dzM2k8Eky21i3SxDr4H8WzEEDs9fVcAW/HucQMCfMRRruV3yQHE6HoBzOBwTrGuQZJzYa5ZJXQGsypXHGgXJUZcF0dkZZzNje3OC5Z65xee8yRmXMFzOmZ6c8fHiXxWJOWZaUi4JiXnB2dsbpyQnT6YyzszPOzs5YTE8o5vPGjDUGjAA65qUNgxoRns4YjXLyXDSteQivPJlMmIyFKdrd22Vvb4/9/X22NrfY2t5kvJGztb3J3v4+e7viE/TCc0/z4PEB9x4eMZvN8T4gVC++Vcr7sNYigHDK45QgTmdFeyfBLQzaajIXTO6chGX1ufgbZRk4VaOc4vKlfY5PTyUYizYNEeGJPgPLphbxbA9pkprzNHBzBHR1/eVajdnFS3p2VQM/o3CgfacT+Bfr9qXFDSPU/y551zzXuuNw3laTRWsD4wwzkatKzAejUGCCb6b3XL56hTzLqW1NWSwoizkuJEXuAB4fzFHCHdGxLWm0GUeWZdR1FQRnfQY3VA6lvcthjGtt5tebcXVqrmCuO/CqR8Qas2yas679Tp3evDol4Um6e7TcxrnTS7juiM9XVVvJcKoes8fwei0xTbTfrcYlw4MZGsfQ6g5qf5rlSYmHeCfbMwZdP5O4JfGu5iFUtNaStNUYw3g8pigWrf+tUjgbTV4FLn3xV3+Ncj4nyzTOVigsZTlnsZjRFcCs38Alxnuoru/KZRotbrOGq314lE/hnCYzrel3SranQgD5XjrWSuMD/Ac6mo4Wl8e/WwuTdeUiAo22TqvJWl6D4FPrLVVdgsoxWcyLF019fSPoTbVCcaFTGBTzEIqGUPqs6ypEUIbHDw955ZOvYIy4eRRFweHBEZvXrwmtmeeSU9M6zs6mZCHdSBTaAm3whdC+NuGsapgtZhweH7K/u085GWOtZTIZSQJ3k+GcxdS6SSei0SijgwZfaM17D+9j793BOcfm668FhtFRzM5w5ZzJzibGWXj/3C0A/jVgkGrnqVwqORMpv62dED5KEXOoNOo67zCIb5AOSeg0utFQeCVoEFSb3K4hZoEgcW6YAA/EnDQ+lRyGSxVxrW8vWVMnJJ0MsZqxIWywLatGFe69Zzwec3JygjGGciF5nJoIY4mNdm1y9p65yUsvPkcWkPWktDx8+Ijbb77GlStX+KHnn8EZh93cZGt0g52NDRau4sVPfIJ//Pd/FuNz/MKjvMVoKKqCr/72l/l3P/M9FN5hQwZAbfpSny5xddEypJI/r953akKSthMJynX24P2+V42zT9B263WRSdo3BG1ikATL3rr2bPnUjy36CXmsLTvR0BpmCon4Ij60AXiF/jJtIPjCSNAFHfIpOWbTI+bzM0Z5zsdu3mRnexvvHKcnJ9x+911mZzPKcsHJyTHHJ0c8fvSIo+NDzo6OWcxmzGazYKJaUtcWW9dYWzbrEAFaGx2vlcqnkgRBjroJfx9NCFQIkGC0Ic9Fo5Rn4q81Go/Y3d3j0qXL3Lhxg/3Ll9m/tM/2rjBR125c58aNZzg6PuXO3bvM5kUI+S+XWSWJITNjog6g0TzHcccx6SB9FAFN4o9lg7mkztna2uLs7CwkzIyh4xvgseZkJozJBfw/IlHRP1ND53Dd3VnnsD4ULSmtO5SEcajeqn71wLga7a4Sv7OU+TuvT49vYsk00vVAJO7t7TGeTJjO52JmV1ZUwYeoz/zF76MkPxXSxLFvbW3hHrRwJI5rVbjoVWZp/brOroaHQ98PWSp0++pq9fr1n4y4++Bl3fn8oP19kLkMlc73F1v+J+rvg4xLwOOydUoXfyWHs+0MpRQbGxI8ZDQa4Zzj5OSk4TtdCMiTZzm50bz/7m2+9do3MVrh6grvLM7VFMUCH/DO7wcu7g47CSbhfef+pkIl5ZdxcfQ3Txcjhi+Xspwwtnmv+sxRglNdy8R023+CQ5KUIbjQF4al9EDMnZaZfGV7kVlK2xwSAiilOvRlVZacTWdMNjYYjcegoCxKHj8+hJ0CrzT5aIzJM0bjEUW5YGtvl42dTbJo8h/Sy5jgE299BYHhlkTuBozjeHqEGnm2R5sYY9jQk2ZMu2YPHwRS0azeBMGpxUNI8u69ZzKZcO36NbY2N9nc3mRza4Pt7W2U0fydn/znF9qDjzyDVNQVpiobiYmiZYRiksjImLSXQjX1ffDR0CFutQpx/F1dE42lbBPGUnUAVdReeITbd9a240je9w8+BImvcminggbKS2QvRIpTl2WToylelLIsGY1GlGXJeDzuBH6I5gDjbMy1a0+TjTfJcs18UeDHGdnmBjrPeP/uHY7OTtFGU7qaEsfMW/Ir+2ztX0JvbeCcRRsxe5G8II53vvkNqpNT/HgTqw1N/lvtE63Ek5chxLgkiY67llzwLhDQDWAYQo4pwbgKeZ4nSRySXq8a/6p+/aCUqGsekWrGvBfTIo9NmOA2Wp1kl68GxxDvgVYKjEF5LzmxUCHBaiZhdq2lLgqOD49wznH9+lU++8lXKBYFjx4+4tGdx8zOppwcn3B8fMC9e+9xdHjAyckxZ9NTFgthiOpijvKeuqqW9sd622TXVnWcX5RqNYvVSL5aQl9RhKhK3su3zR551YRCl/xEOUorRtmEzEzI85zJ5gb7ly5x/cYNbjzzFM/efJb9y5e4cvUqn3jhebzyvPPObR4fHmNCgj6lxQyOILVCBe2FawURkehUSgQFckeFacoyg/cSeVCbjMlkEiJORm2w7I5PNIMpMbDq/KVnZuj3dWXwfJzTT//dk9zvFN6lks2hd3Axn500qW789rwxCfgdnvtksiHM62zGYrGgqkpsDITTMLFt/c7ces+9l7QQ07OzhgFLCbl+G+vWfGis6VzPg08XbXPVt6vW9EnaP6+tVXWH4P7QvC+y90NliHC8yD27SLno/q5jZGFYwRK+bOBi/D6uQxpWPgotY726rqgq0zBGUYtvrW1UNDE4jVYKase//Ge/SVUUjEZizuXwYimwWAR654MxBOvK0h1L7w5dAUcDM9Wyf1zrD26IGhhZN02aV0clSeUjPJe2W6uR/pnMzAiPMAKtNl1sf9L21pXz4G37ftlnKe6fVhl9udCqdlO/6k5AnF5gB1s7To5PcNazt7uP0RlFWfHgwQPMpROybIxSYU21QmWan/gPf4Ly7Cy4iIgmSbkoYPIoLTg0yzImG4KTUaqhcxuBm2otFURIKdZfefDFlv0xIQCZGNjqIMzMcrGAsa5uzvLh8dGF9gL+dWCQqhpdttGDYkQ3Zx1YsLYkzzO8J2Qd9kFzFDREKfKGRn1tY8hXpZrAACnw6muVVNA6KRXDFraHWi60bqSk1kn/XgNlAHJaNwlelVK4qtUgpdLruq4pq5LNzc1mLM3F8jDGQGUZ5yMuXdkHFCfHJ6iixuzNefPxIUf3H3Lj6ackD5LSWO/xkwm5ytjc2+N0NpMM3CF/TaZg/ugxB+/fZf/jL2G1wkBrthjKk0gFzyvpfgKdfVqqm9pzPEHpMzt9IJ2+G5IE979ZNd8W8C0DvVQ6H5GcD9KpxvlVOZyrO4Cy//0Q0dBklfeezGSYgFSyXHLyzM6mHDx+jNGaT7z0EjvbO9y/e5/Xv/YGR0eHnBwd8+jhIw4fP+bRw0ccnzxkNj8SH6OyxLoa70OQkWAnkTKSXWJA5hWZ/XaoQZvSfNN9roJGJ/4M00HcQeSMAlRVKT/zipEpObMOfZLx+OAht997h8k3Nrh69RpPXb/Bcx/7GE898wxXr13n6aee4rmbN7l1+30eHRwwmUzI8kwSfvpoy63xqpu8Nd7NCo3kgLBEn8ZIJJNVkjcsJK72gSBRSoV8ai2WGyKkh87Y0N/9MzwkSLiIdid+E7/XA+Mb6jPtd+gOdH37ls9H/DtKtPvt4kFooe5ZP4+IjUg3ra+1DsShY3d3l3sPH1AUBUVRUJZlc5bTaXSDtyxLYiORqkP6h/Tdk8CMZSENXBSwDQrhEsLvIt9epN75Y/7gMP8iY7hoO+vKOqHcEgPv19/NdW2uGs8QYxbnLkLYgbVU3W/7d0fonsBY6DYSrnOeoig65tdN/yoIf71YFRileHD3Lm+/8QZGgbcW78UKQJgjh1Fi+s05a7xq/OtYwKEijFD7TRQYR4Fj6leUnh951vfv1Akj0zI+nf0dgH3N/UeYAB1NspUE9JIpDZs+X3SOq0qcT9y7SA8qlTVb0Pned/FKWjqmmOGb2G5dW4qiYjabk2WSD7SuLEdHJ1xalGxtT0CLj6eQuo6Pf+LFJu9mPLcaoQsFbNdASPvhXQPPGsEtyforFegHiMJEay35aNT4iXkB6NKf0cHg1Qn9amu0c+RKkfUSia8rH3kGqaxrVFlBsOuvi1IW30twBrxHOzlEZch10Uggolo12paGf4SL5pWiCoks+wRGK6mQr6K5Hr5LSGVZhgsSGwNgJOlsUVaQWKyY6HEXmKc8YT5icljvBdgpWnOwCPyAQHTBvJyjc83+ZfE1yo1mI8upxxN8bTl8fMAzzz5LZWsyk1FaS5aP0F5z+eoVzu7fF8LQS5Qug4L5gge3bnH5pU9QJQyJ9x8MsZ0HFD6IhPC8vvoMRfw5JLkcGlNa/6LjS4mo1FpmiMgSxlqcvIT/Ft8X76vGJK3tH85DNvFtnuWCALXGKM3Z9JSDx4/JTMZ3feazlGXBw3v3+dq9r/D44SMeP3jI4cFjDg8ec3Ym2dKLxYK6LnCuxtoaEN8drUF5S3RnDqvUELsqPItaUsK82gVKR6q6j9cgnMgvxX48cu9KX+KU5EtCK5grlNFMJhNOjg+4e/sW3/zG13nq6Wd4/vkX+dgLL3D12nWeefY5nnr6Bm+9/Q5Hhwfs7e+TGR3yRjlMCFuemrNCyB3lBK5kzuCMmCvmo2DepTWj0Yi6XiRnYvnOpGZZH1a5yBkdqhPHFSV8q4j8VURxn4D+MO/y0DhW9d0cjN575xxYy6VL+0CIZFeWlFUpJiz5aOlqNfs1wNxEhjgLQg5YDls9RBD3x7WOOR6CsSsFRgmReBG4nMLA38+9umjpr9sy4du+6z/rv1vV3u9nWbe3q/ZQBLCqpUEGSsqcp+1WlfiR6MbPUUmgEdemUIjMf3csrb92ZjJypfnd3/4dyvmCLAtMlnPUZUFVFhKExZ9v7tufe5hp/P/gOg35h6k4j6SteNe8D0lxk9KkOwGUMqhIYEWGrsE1nib6bl/4hF7agEbo4drvlWpN+ZyL+O/Du0cpMxEZ4Ai7xNzOYky2dMd9+HidcCalI5p2vZjUzaYzNje3INCY9aJgviiZbMJ4siEBM5TkA3z06AHjyqN9sDby4J3kO5zNZqAVuVYYpamd+DWP8hFAk3C+jsGcnAvMLty8eZPJZMKv/dqvAbC5tYWta4ySHIzWSi7HqqqbNdnb2eKpa1c4PT3lG6+/fuF1/sgzSLVzqJAFPc9zvAaT57iqpizF90EFUxwbOWitsXXdCRkNCDMVGCdoTdcccgB8R11pAiNUEznesiw7Go8+4Z0SuaIVCupq7zHBST5GFirriio40BljmM6mKKUpyhLt4fDwsNGcROBntEZpS0FFQY3ZzKnPJOeRN555WaBHOY+OD3FGUVlZs2pekemMalFw7dp13lPfbCQt3nvxU/Fw591bfEZB7SyZVx273ijpTSVdF5W4rUMgqxu4yPtlSWd/DP09Sh1yV411bbc9SWQnupRXS8A3zj0y1RDN7IK/m/d4xJyu1b5EINf/u52zAnRAfJkxaMBWlvfffw+l4BMff4myKHn161/n3t07PLh/n8cPHzE9O+b0+DEnp8fU1YJyMQNfo5QX3xpywGFMmFfIA+OVamVovpWCipBHdRCIShiklFnqMn0+zH3gHAVEF9cHpRqGCe+DZsuDC3bLFuq6wOiMKp+zmI+Znp5y+9Ytrr3+Gi+/8goff3zAtaef4fmPfQzrLK9/61soo9nY2kKHHEkpAo4aZO+CjyIxClGIJugdPvMhoW5Xs6yUIBKnWs3hEzHe3kdJTufxKuLjIox/+k16JofaW9euohvM4cMtA3eY1Bl7uV66tvFvrTU6y7h+/QYKKIqFMEil+PP5FJAxNPcuPIh3vApa/z5zdN5UltYx/BmFdavGMUxsD8+9K2UeGM6HzDisYlA+SEnn0Y/WF3q7cDthcE/wVbdmf6vWTa0R+yjVoCOlImyMm5w2EH3mWhje4qFhpisySB3NJWKRQiDeXeIXnX4vIMxhjIRtPnx8wDe/8SqZyYhhvJ2zzOfzMKaoeVKDN3FticKFOO00GmcD8hOcAR1Ym96pbl6sFsdC6msUglk8ISxKE3Evf6t6d0zy/VmrQFmxamiYj96XK/D+ueNRrXa3daWo0TonyP9ji8SFG4ILfUFDCheiBmmxKJjPF2xtbTGblQHNaGazKbv7lyVwGeI3X9WW3/jir/Hgq6+zk08oFgW2rvDWi9l+XUuOzSyjKIul426dxRiJpOtsGyPAGM2N61f5whe+wOGbt3jvvfcavKu9RlvdBqMIzL3Wmo3NMe9sCLtz686dc9c1lo88g4Rz4kRoLZVQTEyLEo2Y2W1MJhSLkkuXLjEeSQ4hbTTHJ8dMp9OE6AzROLxDOdu1d9U6mDvFJI+SWLMsapTWaBX8QqKEI0TVq2uLw4oMPQCHqihCbqRcrrD3VGWN0xozmYhWyzm8NizqGmNrnPJMF3OckkhZm6OMxWzGaCz5lY6np5yenqK95/LeFqPMoGqHKj25HmG9p/KOCti9dJnReENC19YLJnrEaXGK3h5xZiu2b1xltL0FjzJqW2GtIke0SA/ev40vpmTjMU4ZrKJRO4t46HyJZv9ZLJHBisCtIbJ9RBKKVHjV+donfcT/REAlnSVVgyNjBDbRN8a375vxqXjEVkvN+gxWv3TN4bpO7gLsQHmPrevQcxuIwTdAxXcAXEQWznt8SNrbMCpewi1rAzrTEsXIKQ4ePeLg0QEvvfBxvHe8+pVXef/920FT9JCjw0fMpmfM56eUxZyyLFpkEO+GVoAEP/HQRvlNJbzxP6q7jtGHREztVpwTEsYKGsS+YuVJEULcP+ttULx3TWKtdThbgffMqwVGZ+TznNn8hIcP7/DG69/ic5/9PGevfIKrN67zPZ/9NPcePeD9u3fY3b1ErnNqZ6VdY3AovFIYBTrADTGjE4BdIzmVsjxvRiKrZpuFi/s7lBtoGHm2iDll45d4pQT5DbXSjRanOj+b8yUPu80uDWeIyOu232/jwyDBPdHOPf4VV8EvaY0UXpzNnW8EyEYrMBnXnn4Gj6cq5thyTlXOkTDdaVh0D86CUsHWvj137TRF62/rSkKKI/6kzRqqGJwjWYMOLdhlXjrQKtznJUYPFUKR+w74FRToAj5z6F7zsLyP7RiGCN/kux7T0ye6fj/KkKZVZhBhw/CJ6vCDIY+VaEDiaQl+gEszbuGW8u1969dp6P2OgKcV7nRcUlLYSDcCHY3QJEZuS3ZfpYch4K0gmFFK8h4qBE+L9UpoKwR80q49G+n4mn3DYhTkuQEsr732KmdnJ0yMJPfEiU9pVReCktHyT0KidpfGL5+rqM1qS6IhT6LPRWYmoPuoowk5e5QE0IrMSaAH+qZiy/DSkW6CUvEshXfN1nRH7XvWWe0J0ImZr4ywhdvRB1qiCrtgddDC8eH70YfzfSFxOr/0uXUW7Qq8b4OJJZXa7+LcEholauR0EGgGLhmPBDybzefB70dMKnEVujjFKQXZmExZ0DkLm+HnJacP7rGIwlxausqFMxgj6nrXCju1Nm2WEVqzeqU0tob3b035ubvvi69XVeG1FgGsA+VaBklrja2DhrQ64/BEmPqqLgfXe6h89Bkknck/NFXQ0GQ6w6PRuaKohVR6dHgM3gtToUWd6AFjcuoYHMEY8GKXi5KEnaLtqZkXJSBRtUQa7EDnlFUFhOSRmUQXsYDOJN9KZctEcqwoQnJN5cC5WvIvoamth7Imy1VIGGspq4rJZIy1NScnJziVMdnYpNYIV66F+y7LEhvyyDgnyTVtWUv4cuuYnk05PTujtDVOiVrdJgEetBLTK4tj5/I+k81NRuMJZwtRkyoMWWY4PjpgfnqCyi6hsiwEwoiXOezHKpyypjRAIL3TfaDiu8/6gC1lYpa0QokUJn3nnEuQYJ+BcwEJLn8b+0slmmnffY1Y/NckXqVljpLpdcaWIsxVgDICuYAXpQ+lwllUoEU6/s4bb3N1/zKfevll3nrjTd5++y0eP37A0dEBR4ePmZ4dUyxmVNWCslgQI7J11zisQyQgSN6nuHLlvrffrZacLZMr60tKdSbrDY2GN60afQudc1RUVHVJUSzEAXlecvDwgDfefI3Pffd387EXX+DGzWf41Muf4q233ibLR0y2tihr0SQrYwCF9oEgid14T13XwiiFcbXRJqMWSjRq8QxGhjeGOz9fspgQ6c1/SP5eRrSdrwcI2iVCoddWr5umztLzVWNfcc8uVJY69oHxSJvtcyDLfXakzlnG1evXMSajrkuqqqCqysTfqNuESsbdvvOdOpPxuCGsmxw1ocKSCVH6W4c/8p13/b46jtbBDy7UDP3QrE3bUQo7hq5oS5x+kLJuP5dMmHpwsf9unaayJTY73AcXQjbet4GZEkKx7bztq4G5gxEIpbLWLbGefN6tm3wUz0Mf7kecp5Y+GphaBPQqhaNd7UoUKgb9CUYpXI/xisVZK4GsECuU3/u938UYMeuOcysK0R75INjyXuFT5iNpdhW8aTV36fOWsI+Mi1KqiViWttn4ZUV8nN6QgfMyhK99lI6wzJisLkHX14EjbXvpfH0SNEjgfb0ES9KxDZV1wtau5kdMzGpbYQLtq7oXvgP70r5TbVQcnPeCF6uqYlEUjEaSVqNWNXiPreaYzOCVwjvLoijJjbiz4CzWtdZRrdav9RPzeKy3LQzDopXk6PIJbPSuRgGZMrhSfJs8iKtHOC/KS3oTpST8fBQAlJWnchUosYy6aPnIM0gnsxJTtup3pWDhZGONyYRx8IJYnHcwL1BGJYdXtWYRAegtFgsmE4m6UVUVRVlQLCqMycnzHAXM53O2t7ex1jJfLBjlOSYLOWwSfwLrrTBUXhJ1GiOM13Q+xQTzpziWytXU0wXeWsbac2V/D1vX5Mbw6U9+klkpTnQjDffvvMfWxoTC1uxub1GXkrwtApjFojUbkTDDHpMZNjY32d/fD9JURW2t4EclEputzQ2yUc5kMkEdg/dChO9sb3FQLJidnbJ35ao4xrk2IzcMAZJhINRHhOchzaHnfaZkqKzy6Yj9DYUtTgkRl/ze7y91eOxLVvvE2JOU1rctBBvp+bwsl6D1BLLMkJsshNlU3Htwj3t37vLySy8xPTnln/3TL/Lw/kMOHj/g4OgBR0eHLOZTymIOXkK5etcGFFm35kN/fxgmOv21TJ8NnbNV/Q6tWSptjIxM/GcrS1HMmRWn3Htwjxc+/gk++13fw3PPv8BLL36c9+7c4eToiN39S9SROA2aZ62Xz2hd12RKYVXMON9GKexL32MUqTjuD9sX6SLloszUk+7x78cZeZI+UgFD/Ke1xnjY39sjH+XYqqCqQhLjAA/7bQ0R7+nfWmvyPO/s5Ydd+jBzlUb+IoEZhopPtJpDfV64nYQA/f3Y7w9aLqLtWsWk9YsIo6IEX0pjWjrAhK7r94No4VLhnAhtl3GR1hJcJppnx3lFvOK8Z5TlKKW4f/cu9+7cZayN5IKsK2ortE808Rahjo66rKXx9xmPZU3IMDPTPyt9TdcHLatoiScpq+bWryOMlPwdGaV2DBfvMxW69Lvrj11gTTZIZbX08DI8X0U7WedYLBZsbExaGgeNDQGGWvNFT1mJUF4p8VWL8+4ycolAKpw5eUnzPP3ZTDzSXaqXeiUsilhBCqNkjOBgSfsr7I67YERB+NeAQSpqGAUmw9XtYoo0tm5/D+JGuZACVDwxtr0H6lYyoQyzssbY8B6DGcnhqII2KJtssKhFAjPe3KSqaqrKYowQmK4OGh0roX+V0ljnqa2oHFEjFkWFD2MUMz4JCKHw2HJBUZbk2R4aS55vso2Cq1fwdcWVS/t455hOp+zu7jAej/DOkmsh/KuqoigKshAq0dY1DqhsjVfiR+QV1FaYSRUA7SQfkY1GZHmGtw6vNY4QXc/VHD16xN5zL8TglhJ2MgnTGwGKRnU06tACvFWSch9FaQNSqaHyQd6lwC4FeksARQUjDN/a6fY1PP3f03opgFhFwHW+DdMeIu76viCd7zVNLp7cGPIso64qvvXat9BG8/Hnn+fNb3+Lu7dvc3TwmMcPH3Jw+JDj08cUizm2roLk2SGmOabDAH4QQqu/3kPPVjE76fr0319kLENr229jiJmZ2TPKas6kLijqgrPplHt37/HJT3+WT37qkzz3/HNc1vu8fesWV67faPtA7nvaZ0QG1llc1bX/jwxRHGsq0ZtMJiwWiw+NMOiHxe2v0br1O6/ti47vwz5DqwQyFy3tmjvG4zF7e3scPHxAWRYS7jskMY4+dkPCnK7kuN3XCHMjcTQUvvxJ97YPV9N+L26auR4etpO7+HfpONa9X9feBzvjXQSxClak42vWMNFY9Oukf/f3ddUcbEwFcM58OmPo4ZLzGLJVc1p1nztCoES7FeFS+q1EMwXtPF/73a+gQ5CniIfLcoG1Eqm07WA5efpFzoCMe/mZjGvZ9Lz5fkBbNHT/+uuwClcP9bEOx6xLv5DuaWayJSa0wQO2WjoDF+0/fZ7SA5GO8s5JEnKWlUZ9PJeuWXo3xKRaYWsb4FcYt6+bflxIjO49GCMhwLXS1LUl6+Hc2OdQXsl0HSPeS99pJQaVMbkxwVJDKdF0Gh+Ft0KBZpm8t96hgw+p7ROea8pHnkGaTmcUVVDfxbOgaFTQ6WUSwkRs0mNSQNnUuGHyMxL9HYK/+bv1p4jPQSIhRXOZGO1KQsZqieGehGksCwm1XFtLbduxN33gcV7UhVlm0E4cwLWRjMfajJhkGUorrly61ETIy0Y5RrUJtgDyPGi9wsWqQ0S8+WLRBK6QsOitFm28OaG2lswYKocwdmXF1uaYR/fu8LyzVFZyz3TNX3sXfeCunwdMFUoyJyeAIL5bx2gMtTXU9xAyaRBeGh5SqWZufWanf8n7ffSft+cISGyX+2PRvX5inSZqTwJMom2vcp7JKGeUS8LU6ekpX//q13j25k2quua3v/QlDh8/5PDRQw4fPeDk+IjZ/IxFOcV5K+FKW9JB7gDtGM4rFyFyziPchhjlPvHXJ1rO6/c8hqC/986L2WlRLnBeEnQ+vF9zdnLMydEjjg4+yfMvvsgrL32cN995l8vXruFRkpDaLNvDGyP5IiLhHE0vVo0RuuZT3ymjsuqurCoXWaN++6va7D//Tpi9/vfrxnnRb7VSwQxTs721zcP7d7HWUYXcc866JsJouqfn9RfvaF8DKOt3cYZy1d99on0IhgzBpFVjXlfvvLu/av/XEfxPMq7zYH1/X9YxMvI7REuJVQx3/Oa84CRKCAb6mqL4+9DK9eHDKhwyhE+GxgkQ8+KkbXXhptArSmlMD4/GuiYzzM6mvP2tb5OhJCKZEjO6xWIeglAJUyQR3johJgbXaB3s6e9bpKni6qU0VzN3htduqKw7t0PM0tD+X7S97nfLFiux7dbszi8xDavbXz6n/XvvfUxZMzyudcxWH06JskDM7DY3d1FKBRowo6prhBnJmlDy0T9Y7kLbdhtsarnPdB5DP7MsI1OaEcIEGW0aqzBtJIKd7tDiCpOJBq2uaxZlKVGei2LFmi6XjzyDND87Ix/X4rim5MIbbXDRmTxsVp7naCVhBW3wBTBaLrwn+JS4oK42ojYU3xwhdDJjWBRBugshT1Jw1DaG3Cgs4ldkKxukkCZEppOxqBCWcTTKAYU1mqLsHmKRPnoyJABDnucYB8pLGy5ovUzQ7iskkt50egreM8qzxnfKOZGSaq0Zj8fUdU2e58znc05PT7ly5Qp1XUtQiVrG67ViNJk0jtrGiO1pWRSQwaP794IjnhImghXA8Bx6IL0Y68pFiPQPo/SJj1XvPyhwjt82kpKBsPEpAB0ys1pCHAomoxGjTPb88aOHvPbN13jhY89z/+49br37DkePH3Pw+AFHB484PTmkWswlxGaQDjWBMGJQjDg9dXGC5jspfcTX18ClwH6IWF1XhhBMH1C3ReMcIdBCIWe8LrH1gte+UfL48WMePXrEJz/zaV5++RVuvfc+W9vbTVbviAjrkGw0SuVi4saqqtYSjt57CY36IZW+cOiDlCf5fhWR8WGdlw/93AEmy9gPob6tFelpVZZrYdcqIgsQs2TVFZx1e/zOSmqmsm69+wR3HPO5zKWoGla286+yPOkdP1cAsKLtoe+HYMVQi10DOxpUqFAMxaF5EoY09U1cNQatNXVdMx6PO7CzO0rxlxqybNBGhLnvvPcexweHkosxaL6rug6BetLJRd+kVoh2nnCiC79153nKDKVM0lK7Fzx6Q8zNEBxs5t/TsK/a7/Pu0NA40rZ0CNrVx2vfCUwT5shitSXL2ui3abmooKIdrwjztre3JJWHF8G6DcqEaMppjJGgCz76h0np0zAprIzwMAp7vWpzlsb3WZaRacM4aIliX9FkUSua/FMt8ynv8twwGhvqjQwzvfi6fuQZpOtXLzPZ2KQsikYbIra3Njn0kom3ripspsV50RhGeY42ptngKOG1QbNjgl25bEBGWY3Bt+9jmMGNjQ1G4zHeORaLIpi2BRGkU3jnRXUYslWPJ2OqqsbhqOqoQhRJT5ZlIdOwRJgx4RBgW22XUjQOpMLJSwAAEE56Mpk0yQ93d3fJ8jwAXMPWlhB1RVFgtKG0EpveVTXKQG0tu7u7GCOMZl076loYL1vVHDx6hK1qVDbBOkeWqSXEfS4iTkpHWpcS6L1yvhQ7UR/260Wo3vBzq+s2QHRFn0P1+wh1delqh9L6UcLUbzuuj3M2JIhrAcp4NGaUi7Pj7du3ufPe+zz79E2+8eo3uHf3DmfHR5wcH/L44X3m01OqYgZeHGyVVmhlOoEq/OplObdclJlaBcRTQBqBbLq269pcJ3FOS4oQh6VcQYPmPXVdihMoNbV1VJXl7GzKydkp80XBZ7/7u7l1+zZb23sYnQnsMC3At9aSmTYEewcB041klZ6fVfOT7+IH3Xfdb1pio4vwPyiBO7SHyVlZWXpnqvf9BxlDXziQdLNmGMk6J9U9ko39xo2n0Er2qSxL5vO5aJDyFSPpEVLp+ud53qR6iMRDbwrNKHxvXBcpUesl0/LN3IYk+r6R8EJqAhrfx3fynM7z2FdnXL0z1t3XJzlbfSCz4tv+wYlyt2Q+0MMf6cHsE9zed7paOsPJ2jRwN1ZYcdhVJA6baq1KSe54W28VbohtOLp3OQo503retfvtQt2yLMnzvO2/nZCsj/cSrCmdRzg7MfLdN7/xDbwVWia6ABTFPJyhxOwu4Od1jLNW3TxCS8FJBoSEcV5p1Tak9bKGZyXj6v1K4KJUwv97mnDeKZ5Jx9ifX7M3a/rwCW2hlA4BLzwxZUrUJA3h+n7fF5VHxIBKqoksqJqfq3BiimtSE0J57hiNxmQmo67ke+uiD5LgubqqSM8XTnJSRcsfWSdZJh38mY0xIW+iwTlLZSWJeqSJCXYs3jkq51BO/s7EwSVYWjl0MicdmaMsJ880mVbk4xHK/B9sYvf+++/zX/6X/yW/8Au/wHw+55VXXuFnfuZn+L7v+z5ANuAv/+W/zE//9E9zeHjIH/yDf5D/4X/4H/jsZz/btFEUBX/hL/wF/u7f/bvM53P+yB/5I/yP/+P/yLPPPvtEY9nbgMnYwWTUgtxwiCOzFLPeGzMO51uFSFNp4i1NPolYMQ+q68ggaZyr0CODUmCyMSBRYJwLNrmuYLQxpsoyqnHwtaktGxsbnQsoCLQi38zxzmJ0RhYCP9TWNvbC2Jqt3KN9BSjQBm/j5esiBWMMo9FI5qJgPB5TLhaUi5KqqhlNNqitY2wN2oxR6gxbe8b5mKoo2RiNKWzFeDKmPjtjZ3OLDM32aJPMlxRe7JBt5VgcH0ExQ43GoDK8N52LGAFOptuD3yJzubzOt+Sh4C0fAGuLBM5jNpaQRwOYAnJKgXgI8dvgzIgnGllYG4IyQuk0ul0Hh3v6mLWRpnjvJepfDxC1kkAfJD/LEfc0Kjj9J98G5sV7G46AOMgqFPk4YzzJMa7m29/6Fmcnc/a2L/PbX/pdTk+POD58wPT4PgcHj1nMZ9RVJfMNWCKuUcoQNoD/gwu2Bss65BP/7iOqvoPpeQjsvH5j3VXMlvdyBpwHH+GF1lDVaDfHek/tK+y3Kuq6pK5LPvdd38WdO/fZ272EMZIMD61BSwLBqqzIs4ztzS0JzDKdUhRFcta644pnxNoapbKlcbZEa/fb1qwmnVuKx5+EgO1vfrfdZgRLtEmf4bwoB7Ou73YMLVE0wIinZyA9JwmcCYOUUM9KzIgwhivXr1FZi/KKuqiwixpXWZi0Z3CZsVg+VwAaQzErBLkrApzw7fhjSP7+XiVkZH92fQIzrrNuW5YzEGp3mK+GqWjNmPp9t9J7OsSf8lGwFM6mbg9Ue4eejOmWdAVNz+GMprBaNfXW+lkn73QE6JE4TUM7h8oCM7tnVojv5MRFIUaMnAWNGKPByUTwH9ZFmSgGkHox7Hporzv3lhvt7GgYd4yE6cPAl+Ba5ztJgeqsoy5KMZ9TTghyErP02GLLtyERxRyZVmQaymLGO++8ic4UXouvifUV82IKOmgCGl3Z8PnvFt0ZZ8swggpWCpHpUZ28Rwrl217EsiGc1VVCtbAuzXqroNVrIhTGe9fTWOl2D3Vmwt7LdwTi3oVz0AS0CGejaae3hS1z1GrEtDZJSHR5lmU65EuKeQ27jEu31VXMXls/+jhlWR6+afWaFxFQtsJCuTvW1oxGuTDFODw2RF0N+UCdoixrmYt26HB2FUjIeiUnwGhFhjBIWVBE6JDMuKwrVKZ45plneHD/PraqRNMZxl2oCPc8lFUcqDQfYXIQjBhtMMYw0Zrt0Tho8VdItwbKh84gHR4e8iM/8iP84T/8h/mFX/gFrl+/zptvvsn+/n5T56/9tb/Gf/ff/Xf8rb/1t3jllVf4b/6b/4Y/9sf+GK+//jo7OzsA/Nk/+2f5uZ/7Of7e3/t7XLlyhT//5/88P/7jP86Xv/zlJ4oCtLORs7E1br4RyYP47ETHMm3GKMSO0VmPQlPVFXVV47xDq3HD4aZSaxsYoCzTaD0iMy3xJodxRG0l8petLVrVjCYGvTVqpFBZlgWJgcM5L3lplMJ7Sz6S7OuN1NyYRhOkvCHPJJdFRH8uSMFE+tGVbEwaszhPtZDEXJf29sB7RqOccrFgWi5YzGecnh2ztblBrRwq19iRYoFiNFZMD+YSKrr27OgctMUZSVyL1xSzGa6W0OYRAA1JYQQwR+TVRai+R7ho1SbXhfMA8HCJezbUhkhKEi1PShDAwNgRNiRhpFKQmKLzLm2mOvPqt71OCzZEMDf/8E2+Ju8kVH2e5+Dhm6++xtnpFE3Gb33pS5ydHnN6csDx4X1mJ4+YzWYt8d2H6u0gB1b0YvtwHiO7ikkZXPOBtjqSRt+et4tEensS5mnVGJ0HrKNGkk4777BviAmKd47v+74f4I033uby1asSsj8SyyE8vguBVKL5FSCRAs0qR/CWyP0g92DVfC4+//P6bYmAdeWDDf0ic+5rFFv4Ad259kmMhpAKBL9zjitXroZkjxbvPGVRNsKzQYaoB7tU07CMw7uWWFfhZeMPS8wPsjzv7nUcvqgpfGvOf0I4B9quEQQ1UDekLBgsvcmkhHTEMsNwrfvzIiXhv8K3fROsZEw9cDU43oYxSgjtDjxOBXEpHmr7H262y2R1YFXDfCZMZ6jXaKgHtNNJpx2GpdfdQPVuv6p9gdaasizZ3NyQR4lLQWfeaV9BMxYl9w/u3+fw4ICR0UIQByI5DQPd+Ul/7P017dZIA2MYbToCsLROX8jar9PXWqVMklaSoiXFK1HY1LdsaZibgYPrA33V19iryHglWr2+VUDU6GndP9Pir5P6ETd5DNedk3NKwxwpi3WWTGUJQTLMUPYFhL0GG5rXGMN4NKJYzIOvswhojRbmyzmhQ8YbG3gTlAZK4eu6scLSISy4VYpKe/TYYMYjnFbUpceXlnfv3BXf/FrOZGSQNT1z+zA+5wL8DCVGacR7MqU4y3I2Nzcw48mF1/FDZ5D+6l/9qzz33HP8T//T/9Q8e+GFF5rfvff89b/+1/mv/+v/mj/xJ/4EAP/z//w/c+PGDf7O3/k7/Ok//ac5Pj7mZ37mZ/jbf/tv80f/6B8F4H/9X/9XnnvuOf7JP/kn/NiP/diFx7O5lbO1NUoISo1HGBCtR2HDo4+AwmpRI2ZZhhtrqrIiy8XRy1qHNqIcl0MlDmpZMMvThPwHsa4WJzFrK8Z5JkBL6YaBkuB5Fm1iRLBu9CGFlsutgs2yd400Q+uINsV8zloXpKfrD7/Hs7Ozw/vv3mZrYwPrLUZ5Mhy2mDE/PcYWc/avXaGcTXFVSaUdVbUAW/PovTvog2NYFBjr0K6XxbqqmZ2esX35BjYFhEvEbov2L6INGiKY15Uu0zEcLCH92aqQv7OoUqvG0bRFwkCpLsM9pPKODEBMODhUxwdgoYDxaCSmoUrx6le+xtnhCYt5wTtvvcVsdsrZ6SHHJ485OXyErYolYD3UR38eFykftI0hBqmP/Prtrav/JGMfqtdK24PMqnderLV4Fdau9hQLz4lXvPnGt0XLjOKVT36G9+/e5cZTzwCeOiQWjcmnXQif2uTOMiYIcboEeHtW3Ae6E4Pz4oOvV7+sOz//KktH+AEDDMdyvaHinGNvb6/xzwQ6uZCGwmWL5LvbTjThzdLodUTJMYNEVypkWVVWwbT4+6CEGPDhLKuB7wb7WfMunkGXMHHnCTb6MHHo+VA/T1x8jydK0KNK3q+d4AXGMsQotwTyMAE6tEZLTNZ3WJRSTKdTtne2O8/W3f3oRD8aiRD3zTffFBgXmSsfzPIuuGjLazXsOxt9MjuMyhr4n74fYiRUoJvwXV+Wfp003HaKi+Pv0Xoo1veBwViFe/rft6aA7fgjjZdlGePxiEWxaNa9K8gP1iEfoJy3Pqu+SUtnTWiZLq3FDcQdTYPAx0kQsGBCrrRYS2T5CJ3laBeEQ0qjjYSPt87hFDgFJZ5PfvoVXvz0p6iM4tG9B3zrN77EYj7D1sEKy0meQJwnU4lvUjM2gruKJIH3IVm99aIR1TjObEVmC9Ti/0AN0j/8h/+QH/uxH+NP/sk/yRe/+EVu3rzJn/kzf4b/9D/9TwF4++23uXfvHv/Wv/VvNd+Mx2N+9Ed/lN/8zd/kT//pP82Xv/xlqqrq1HnmmWf43Oc+x2/+5m8OMkjRpyaWk5MTALRyKIIJko5EqsI5hVJiP6uiFsNbjI4HSknW81yhtBxWYzySCqm9pLkBpVzIoi7EiwRJ0I0QQmcGCGF9vaVNGhalGIIsW+lCkCxUrlH9a6UkGZePzFz0l3CB2ROU532Xi+5LXrSWkJOPHtzn67/zZa5cuczZ2Smzsyn2wRGcnXFtdxv14DEHDx/jrGWUGeZnU4qy4Ntf/Tr23gF2NhefJETVbIzBekdVVhw8fMzOcx9H6VYUll6+FBgOjfM8BDv0bBWDE95CsmerGKGhvuO7JUAvlTtt9sfSHx8Q8k60oZxXheLtIwlh8to6LcMr59F7j9GGcTbCoPnGV1/lwZ0HVLOSt956k2JxxtnZAdOzQ87OjqiKBVq1uZzOI0BWMSYrJekDc79Im6vK0Jr2CY5+yNVVbV+ISOl93yG0QmmQWLj/MeRoyRznHe++/RZYMZn8ru/+Hm7fucP1G08HM5UAJ4zpINLGX2/FmZI6q8fdYQ46vy9N64nKuvvRL2sZzTXtXUQY0ZfMnnvmziE2hwgd773kcfOeUTDLqCoJxVsUBWVZdqIxdcbQ606gsgi4YoLFKLlWKn4bfDlQLUx5QngY61307EeGv1+n33KXuOPChPEHKf09TaOUdseZmij24Aiq9Zkkrv+qohp6QLQrXRwx+EVvPEPncIhgjqXjI5bMuz2voa1B/NFd/U7fSnVNScP7siwZjcZrYVt/rpGhqGvL22+/TZaJ0z1KtJxRgzpUVsH0/j1Lf49jiJqUofkNBSDpr2FsLwpsYyCptK/4jdZtm0NrM9S2wF5zYViafhd9wmNfkRm6fPkKVV1yeHgoEdZUq/nL84y69o0LSHeMw3sZf6Z1I6yS1DXQjxCyDu5297nFt9FtoKorXF2hlW/86q3zFJWlsh5qB1byfTprKcqyOds1DpcZ/GTEjWeeZe/aNRbKM55sMXvzHl/73d8L50IETM5ahA+yEhAiBlLzYUqZwiqNzgx6nKPyjNFkjPWeycRw5ZJE37vx1FPwm796oT380Bmkt956i7/5N/8mf+7P/Tn+q//qv+JLX/oS/9l/9p8xHo/5yZ/8Se7duwfAjRs3Ot/duHGDd999F4B79+4xGo24dOnSUp34fb/8t//tf8tf/st/eem58k4YoBiu2osGxiiR4mU62mR6fMw/khJeQBb8X8TMovUDUtCYs2mlG5t2pYSr1s2FDJx3853YU/oQDjyiqsYZLRxWY3TzLibbUkp8H6KtufMWQwpUaJi0JUCNxyJ2mfW84F/82q9zfW+PjfGIN179JpwWYGW9FB7lJbRtVkNeeVymqOuSkfVoZym0x2YKFwJEOBzeWg4fPeYlFM73iXzCXCQgRLNHAwi6pW+WCaJ1DFNEQKukJn2E1lHlD4w1BXKd5G4eUiC1LnTl0ngj49zzo2kTrXX7jh+lDFKnH0BpzWg0QivFW99+gzdefx2c4t23bjGbnVAWp8xmR8ymx9iy7A5nDVF1HnO6ypRtFSEx9PeqfuLzixDlsW665v3zv6q9VQzqqr67+xIJGot3CqcsWPClZ3rmee+92+T5iO3tba5ev8HJyRHbu3so77G2lSambWutJYdKMv60z74ZQWccvd9TonKVqHzd+q4i9IbKKqFA+vtqIcb68ZzX9+qi4v/P7TfN7RUJgCwz7O/vU4b8UymDBL3z71uGKHbt20vLeDzm+Pi4gc0twSGWBfE78QVcTbD0hSh9QUGst8rBXOCXT5iktK9z9njlG9UMed056M9l3bMhBiQybJGZXIaFvsW7DT6OwpPEvK03JK00Tg2bcPdhSX+sQ4KidFjn3Z3O3QiWKAzMvT/sLmO43E9kkDY3Nzv1Vwkl4pkxwSxqenbG3bt3UUosZDwSydcFTdKTFoGT3b87MJrV4xqaW38NoGXu1sEt2a+WcUjxeoS//d9bxjaZy0D/aZ/tuRNNRj8PUpZlzOYz9vZ22dnZ4eHDhxwdHTXfRZpjKOrb0JWKY0kZqsiM1XUt8xwKn5iMex3sjWsW10bm4yVXopOgQ9EFpLKOs/mceVliFxKh1VqLrWvRHjmHVxqrwZUV3/jqN1hYT6ng9W98k8evvUVR1VhvAQVaCZOlFX5kUMozHkvUvKIouX79Gh976SWu3Hyam889x5Ub15hsbqCMxgGbGyPyYLZ+Np3C//OvrFyHtHzoDJJzju///u/np37qpwD4/Oc/z6uvvsrf/Jt/k5/8yZ9s6q0CQuvKujp/8S/+Rf7cn/tzzd8nJyc899xzwowohY+RMpxDKY2zLUcaL0Br+tBKSKIMok80xYOXOthDVPmp5Pdwqbxusvo2RLx3Mr5EItxFCtKGUgQmKEianQvOfVDXVeNvAtH8LiCDJcAAFsgUbGQ5jw5PePed2+i6ZnZwRF4ijKQSdanCkynIvCb3GWXl2DAaagmbbrXMQfwtLNpI8IX7d++ivTgcorpALv7u/cVlkem3F8lBMYTQhiRvF+07AoP0b6F+1n+7/A1ClOjWdG5oHun4lvIR9AikyCiP8px8lPPo7gN+44tf5PLePm+98SaL2ZyqnFEUp0zPjiiLhZiCesN6T+fV65GWIYlov+7ymNcTC6uQ35MS1U9Sr0/gDBGfq4p3Dudj3mNxVgWoK8Vsesa7777NeDzmB37oB8nHGxSLBeONCULD+caMowlx6j1K6ebd0LhXIbMPWs5b2ye9Mxdlui5S/19V6RI9ghNGoxE3b97krTfeYLFYsFgUjTYJVkS5ij+DEMVDIx2fzWYCEntzT8UMqke9O5bvQV+4ko4//p4KXDpCDbUadvX7Tqs5lUwureMDZ7jWIPD8MsTMNe13+ut21WeeBPvJM0m3IQKtznL59qdqGqVz55eFDHTq9Mf2nZ7jzrcXuG799Un3LhL+aWLipT5iV731FQZJ8+DBA2azGZOQ5BREmh+jrF1kpn2YCv2/2/Os0R3LirQMRXZLmZjYVtTIAw1tN3ymWi1SH8an+ej6DLtY+XQjqQ4JKPo/Ix5P6Ym6riX5dFVy9epVnnnmGYwxHBwcJJYlumGSVsHgoXWJa6iU6kQ8lEAhauW3q0ojVAlzESZMcF9dFdRVSRYsn0yWCTOF5qAqQjLhoDlTTgQRRmCiB2xZ8jtf+Spfff11KhcwaF0y2szRZszm1hbPvfAxXvj4x7l6/Rq7ly+htWZ3dxetNcdHR3zs+ecZb25hJpOGNFNKYZ2jLAvOTk9YLCrR4j8B2fOhM0hPP/00n/nMZzrPPv3pT/P3//7fB+Cpp54CREv09NNPN3UePHjQaJWeeuopylLUjqkW6cGDB/zwD//wYL/j8ZjxeLz0XGGwtZMADA68CxJFpXHOJpdWyz/v0CoDFSJ3IBoSMYNQGC2HzYe2RHofL4FtmBMC8SoO9IaRkUNTVRV5HhNZtapgiJeZ7u8qRHULfzcasNBee5lbiB99mZakbwgQVcDe3h5HozFqvMmzH7vGq0e/BwQg5ER75J0NznQwpQIlDFkW/I60l5zERhvQUIfEpIvpLMS/j867sWdAdVXfafGByeu+9MncWka0BXrdv6MvThfAtuZpnfUYIH7T8K3Nm/i3C7bXPh1PW6fPhEXfAyCEzVZYF5F1z445yOi0cLgNEM2zTPbftRK+VsLkcNZhRhnj0Yj52Yxf/IVfQKN46403mZ2d4WzJYnHKfH6Ks3WIYTPM1DwJERxLlFatKg2SSttvDznQ1aR1GGnSHX8y8quV4kYH+Pb7PtJbGr9SiXQ9yh5Wr40LvoE+nHlFHQQolmIxR6F5+603ycY5P/xv/CEODx/x1ORmMxYPounuEdmeVsgi0lffrGdDoAwQynRme7Gymhhd1sqd10Ynal5v89rPu8yyit+rOH46H4bptyZTqt2SlMCNlTsmagA+MZMc2EuV7DdKCZwFsizn2rVrfOu115hOp9i6oioLbF3DaNyG6g53WMbXrn8UcmmVkRmDtXUgeNp8WC7xLe0sVqe0cC7OV4I76M4XKVj0jRNza74Xv2/v4/BaxGVqppe2nY4qtKHSDVmxxkv90DtvKu6Rj4ADFI1GBe8Ds+hRmA7RJlc2zM9DjDCXNSF9Vfhu9dmKc/feNbq89FsfxtT4cKnkTKUtRnjXn2vcwzi6pM+obUxIV5b2ps8YLtdo5tL4MStJDL9cTw3ea6VUk+/o1rvvyppbG+6TxzuLtzGqGWtKlykZYo7iOvgQ3czjBxn//rjjmDuwIzWnCybKAveXGRdnhVkZjUdMJhO2t7fZ3NxkPB43UYmVUlRVRVEUWOs4PT3l+PiYxaKgtnXrSx7W24VQ16kgMGWe0sAL8VlZSmAfaxT379/n5s2bPPP0MzjrODo+CrheIiXHJK3ybboiq+9vuk6pL9Q6PN+nGZMXche1pA2xwaLJe0dVW6ytUDhqD9f2L/Hw8SFOa9jf4ukbNxiPxzzzzDNoJbm5yrLk0aPHlEWJNhmTyQbjySZXrlxhvDlh9+ouO5f2uHTpEpPNTUyeYX0bmU7GYjBaM76yy1ldMZufMnaVJJANw65rCZK2M9kUq43acnR2tHL+/fKhM0g/8iM/wuuvv9559q1vfYvnn38egBdffJGnnnqKX/qlX+Lzn/88IKGtv/jFL/JX/+pfBeD7vu/7yPOcX/qlX+InfuInALh79y5f//rX+Wt/7a890Xhqb8kRbZHWEkVIaUWmFd7XSAKtKO2X32tbLV3EVgojfj8Ao1EWfDi8ELVofBqwwAsCa8Fo9B9wDXMkVducFLGPhhlq4KjCGGHc5KLJIa5rhwwn1TJ0HftSYGKsIrOOvWtXmZiMnd09Nq9dQo8z6nkZDr5HkaF1JnNSCrRDeyS8o9GI7k0zIUi6tUa5CtAUJ8d4LCobBWYLtArIzBNyS3XT6HWBSQu48e13KImG0q5bnB9hD6PzqALXXYP0usdQreCbiCgRiEQzxzgmQLR1PiISLfbqvqsyJzkvLs6HZJyeQNBEraBuAHW0fTdRo6Q1dVWhEX+x2lqssmhFAzTB4ZwlzwwboxHGen7lF/8J9azi9PCAYnYG9Zy6PqMsz3CuCmrtoHVagn/LQLPPSCy972/AQGnWdQXi89AQo0lnw20NPu2OtYuMxR9PKxoCUUoS5CCJ9tWfb2rznhLWfeLCo0BJ8BeNnFHrLL72kGnmiyn+yPPu22+ztbXF937+8xw+fMD+9adFAOLaxNGNj2M8T1o3GmanWhqp0Swl/7pEVcpwBUZ05epdjJlap7XqP9dhLClR2K0bxqpo7mLDRK0YWgof253yyfzjo+5JkXHLM+fbfhKqGIVvNDVaeTQOtEQyvn7lGraqcbbC2jmuXqBsjbJ1m6cjwqTIxIU+oj8rKLQSZ2HlbVgfmaAh5obpm97FtUyXI2VCwLu6mY5WmlY67Nv/+dRvLa57wpD1hDtJZ51fdb/v+LcPu5DC5KTu0v4wXC+aNTbfqZToDTAyToG631DDMYjPl48D680nPYc+UaKHfG/OCc4NMKnZDyC4+SZjlM1uj2V3HTv3rQN7WE4TERlCApJtwJVvp+E9JsXzvXvQUAphjNa6EKlXUffSY3Tom7AqdV2jnEd70B7uv/c+I6UCTBIfautqbF02670sWAkuBNo0Zz8yQ/Gyy3vd+V3Rtfzoa+bWCW7wwec7rnDEJwp0FkzcnAUvyZqfuvoU169fZ2tzM5i6GVBBi9UwVdJ+DBJw/ZmrKBRV5Tg5PuXe3XscHB6062k02nsyoynLCu8jXgl3HwlOYAKsiLRNXZaYPMc7uHvnHlevXuXpp5+hKErmi3mH/rQ2WhSs1ibFkgqIO1qycK7SpLz9nIIpo9d8m8B+6yy2qhCf/QxnJhigqi2LfJvnXn6Z9957k5ufeJE/9Mf/EPuX93HOBWbTsrm5iVJCL8d+syAIVojWxzorzA0J3JatxnoCXVTjPJIz1DmMhnJ6hrOWsizJsiwEG9Hsbe6S5zmZh0tPwPV86AzSf/6f/+f88A//MD/1Uz/FT/zET/ClL32Jn/7pn+anf/qnAdmQP/tn/yw/9VM/xcsvv8zLL7/MT/3UT7G5ucl/9B/9R4BoN/7Un/pT/Pk//+e5cuUKly9f5i/8hb/Ad33XdzVR7S5aYiju2LfzkmSqDnbgUaInQKU130qJ31RF3cS997KJIg3sXuaUuMrznLqsIEgRYnAFGQ+dKClR0hDHGn/Gi19VVWIvGxmkGogHux3DkAYpUKMYk7G5s402Gb4ug1Yrp9aCWJQPZh2hvhAMhJj2oIzCAYYQKUYJ0W60wWM4PTrG1hVabdCuKN2xsAzw5H1LJLT1uxIRidonodFTzVE3W/SABCS+WeE3s1J6FTHIirkMSt9V75MEead1G3OFBFC5XhhhfDyrNnGglTrj0ZhMab76la/yzVdfJVOaxdkZWlmqumQ+n1KWRdextkP4PJn5VMq8XISsjkB2iLhOex1ixi5Gtp/fd/9ZNJHoI+NVUtV+6a+XUi3zFRnCeE/rukKFpNSHB49584032N7e4WPPfwxblZh8hLeCR51PGHQBSB1ecSjvTl/rtGqccUUH56TavVgnveUCdfrav6EyuH7yYmlgqQ66c716xO7yX50OE3YkqZ/A3a70Pomq5R2XLl8GJXtZ16X4INmgZfCeqJ1av3KSTkGk092R9Ge9tD5rWh3WHnbfdWCuDz/VGljVDmTlOIJYIHnR7nh3LbsM0hPNDd9hrAc/WgLzwuj2pfnpyGNJ8Xa3qUggh+fhLkYibZXfZf/Md+fql571Jf3t2IaZgyigWxqzCsewx1TUdcV4POr0tYSrwtzS+WulKYuSe3fvNvwaaLyvmkSmXQHlefhDToBSCh3TlCSHrg9rz2sv3Vcd6aMgpOvseWB2rLVcvnyZmzdvcuXKFUajEcZkGGPIsoyqEuFhXVUUtkYpoTEkytxEfHsDzl3MS/JM/OOrquLhw4fcvn2bxWLBOMsaX5/+2eqvffq7BNIYURQFp6enaK157rnnuHXrFotivmReJ7+uh8HpmYkCSmstRufNt8vwgcHn8fxLmwprHZW1aJ2hTMZkYxvvPcViASg2NycYA/l4jAXeuXULlOLq1atsbktExTowTAeHh9Qh/PdisZA8gN5z9epVrly5QlVVjZYvyzKU0jgvNPlisWA+nzOZTJr5Nv6hSlFWFZtbWwAcn5w0DNOiqtaer7R86AzSD/zAD/CzP/uz/MW/+Bf5K3/lr/Diiy/y1//6X+c//o//46bOf/Ff/BfM53P+zJ/5M02i2H/8j/9xkwMJ4L//7/97sizjJ37iJ5pEsX/rb/2tJ8qBBC0RER3yREugQxQp18R1Tw9slM5G282U6EkJvqj6jBmQYz1oGawYtShVc2ZZ1vQVfw4R2X3g3pcKxLnIGOP33QO/RJQqMYUbbW2QTUZUsxlPP/00b2QZCx1NBrVoeXxEUF6SfXmRkIs0L5gVEdbSO4xW6BCavFwUjLfWAAnoSkMJsBrVwOxBjYNfdnYEkX71fbnScl6oyy6jNTDegT0aCvN7XonSvXQ+/fGmGpfINEtwkBh5R3KyjMcS0vvk6Ih/9PO/iLeORVXgvaWyC+aLKYti0SCA/6PK0hkcmPO/yr7TMNlDDFRKXK1qJ937PnKPMEGkhA6lFbaumE2nHDx6xJvf/jaX9vc5mc556ROvoDNNUVYSmtSYlQTrkNBjHSO3jnnqjHnw6++w+GgCNoyEh+/NMjncCSOQ3h2f8OpPxN8vr9WQ1DoyEs5Ztra2yDNJDl7X9RPdpz6xUpblEhx5UuJw1XyGpOwp/utObnU7F+3vg5TzzsHvJ0x4Elgd121IILFqzVfhq6F368ayak28Wu8P2e+jKAqyLAvE8bKWAHpwQQmuB/HhPjubNqbckTiOjJxSGtSyJkP4Er0k6I3zcknwmaWxXETwkvRnQo5IVvglW2vZ3trmxRdf5PqN6+R5Tp7nlGXJ9GxKXQv9Jznoorm4RilPVZUUxSnz2ZyyEm3Ezs4uVy9f4cqVKwCcnp4CcPXqVR4+fMid27eDqZ0LGrRkjZVG665ANJaYQmA0GnF6espoNGJnZ4crVy7z6PEjqqoaDDd+kTOU0kvOuY5gYx0uSX+2Z17OR1WLpVA+3kCbEcpIMt2qmDMZGbKgnd7b3+flVz5JURUNzIy+tlVVMZ54dvf2OhEKxcTRNHR4HHtZllhryTIVkt7K/ud5jgjNJSHsxsZGY4qYBt/QWjfPUx+z88qHziAB/PiP/zg//uM/vvK9Uoq/9Jf+En/pL/2llXUmkwl/42/8Df7G3/gb39FY4iLHhYkXnkTikmqZZIC0BypBMn1Ek0YyUko1Uvr+4W2DP7ROhJF5UkqcaYXA1y3jk9gQx766Uc5ayWpVSabkKEVKmbEOQFdASIA42drEjEdMi4JHDx6CDSMM9yB+25XyizO6V+EfIkn0IViDciKZrKuKw8cHPHP1GnXiyNj3C+qJIDu/rQKWQxq2eBnaSxzHu1obsAq49BHiqvLBEbkAmX4fQ8R4x0HfegxK8gFUlkwrRiZDA7/8j36JYjqXTNU4qrqgqubM5medvC3fSWnOEl3Sah2Btm6NVmlB0r76bV6UuArCxBVIWN6vCy4BXbOEPuJI7+7QeCKsGY1G2EqYWbSmqgrmszPu37vL6699k89+93fz8P4drly9htHiUO6VQoUIj/2xDwlPurAmCDN6Z8mHBenksO8LEPzFkjBf9NwPSiHXtCO+g6oFC4FY8wOS2OT1IOO3ap0uWrwXX0AbvptMJkwmExaVmIjUVSXmSAME89AYYptlKdqnZr4JPll13vptLK/DasZmKIBES+AOt7lqn4bOYKc/eTn4bmgOQ/ty3tlaVXdorS8y9lWM6qo1GHLo79cbErDEZ0Mw/iLwbamdFdqDBmeHvT89PWVra2vpHKThrX34m/BNFnzi7t69KwSpTgUzMYVIyyitWu80QNHQ/FKB87ozv2qtUquEhiLyreAS4OMvfpybzz3Hzs4Oxhim0ymPHx1QVXXDMNa15fDwKESWFPpsPB4xGuWMxxO2r+3iPcznM46PTnj/9vvkec6NGzd4+umnuXTpEoeHh+R5ztM3rnPr3XfD2qVrrvDYBu+k916pYF4WfIyyLOPw8BBrLbt7u+xWuzx8+BDoWjKlOH3Vee+/t1bMDKPP/Dq4mNJrcubETNd7mC8K8skGSo9xLmOyuY0xmpOjA7bGOdiana0ttMko65r5YhECiYlWdhHS8TjrGi2eUqph6OMeRmFUnudMJpPg8yXmm+lZqaqqob/n83kzt7IsG7o6z3OyLCPLMiYDsQpWld8XBun/n8oQIPWIv0d6yKIWCNUCwqgujQclHmDoElhxg+Ilb9XQckFyk4XD1QUWEVDFduP3TWjI0EYK0OK3IqVpL1qWZdS1pR/eO503KKx3WAV6PMKMcvLRiLffeou6LEEryW4cfHGUEtvcBqlqGssMLybEYkLndQhxFJwAreP05IS6qiX5VFJSQjWOqTWrW96zdfuZFlmzZQbpIkR6v/11yHdoDOdJcjoMildLgD6VavQRcGSKVehXko/CZDQmz3LeevNNXv3K18i0wXlPbWu8r5nNT3C+/o6Yo0EAqlRHAnVRAqffznkR/FJkvw5xDq99a4qSvpff1xNNUUgxNLYUAa1CRrFeXHejhSNztkZ5x/TsDIXivdu3uHHjOspkXLt6hcwYvLeSYFoN59oYWochAmOICF1FVK0q5xErF2hgjbnR8hgbX7zI3cb3Ke+Xfh9yx5HOccXYBqNJsnzX0zqedp+1an3VqqqiSjLC99vow460z8ViwWg0WsojMzTu9N4OaTmH/l51x2KddbBs1dlKy9rv/XKQmqaOVFzbznntr3u+fMcvVvciJYUTEQevMrG7SFnF+J7H6Dbf+u67VedZKcVisWB7e1toAqM73zVnJpgjxVaiE/69e3exdY3JWym8CJLFvM651s9naY5r5pQKgYfG3WknOdd95ieFbWlI7CiY+tSnPsX1GzfIxqKVOTo6oixLTk9POTs7Yz4vQvCtIPgOEYWds2SZaaJObm5usr+/z9WrV7l8+SpbW5vcu3uXV199lTfeeIOPfexjvPzyy8IoPXrE888/z6VLl3n99W9RFGUIzOLQpju/Zg0CfIE27HhRFA2suHTpEtPplPl8viSUW4eD0vf9NRy61+v2QCnJu+lqoScePnzM5vYOSk8oa8XlK1cBxcnhYzLleO3Vr2LrisnGJrNFwbyoOJstKMuyETZJ/qKqY02llATGgFY7GGke731IYyKMU2SwR6MRWZY1dFKEr1UQYh0dHXF8fMz169ebuovFYnC9hspHnkFaRyAOAiXfJlpNFz5ewFRC08mLQ0vs9k2kus13CbPIIPXVgX1NVV/i3SSGdS0AFQDmO313kIeCGBwC49m9vM/sndvUVS1OjkHT5b3Dq6whPhxQIc6bEfg5L89qFCEwW4MElVIcPj5oxtsH3PJ7yyiti1xzHkLrEobd52nfF0GMfYJmFdBIx3We9Au6hIoLi9X/Pm0jEth9k0q8xztHnueMRiOctfzSP/pHaC9Mqa0tHsd0ekptS6q6PHdsv59lFeGyiqBf9S2cTyilgoX+fne/9Q0iGuormgCct2ZpH/25xL/LomCc5QGe2LCvFWVZcPj4MW9++zWee/553n37bV546RM4L8hUgrN853uWni0xz1k1me58PpTz4rsu5OvGCDQRoWJADRWYpCbEtY9AhmaMKjJhHupz4Ox5Z2yIaHDBYT8mJPReMsBbW4sPas88e6ik56Ku607I3XQe/Wd9IvxJCfv+mFL8IUFalu/mReHkUIkM5RN9c8E7ftH3q+oMCTaepKQ44SL46IOu4VBZ3pPVzF6Kr4wxnJ6ecvXq1Y6wpX/HVWCOfBIdVSnF0dFxl6nFdy1tLiwn6eH9ge8i0zQET/tzS4UhTdthvs45dnd3+dznPsf+/j6Vddy794DDw0MODw+ZTqcNAT4ebzAejdDaNPTW/4+9/2qyJDnzvLGfe4gj86SsytJV3dXdQEMDA4zaWfHu8n3N+AVIfgaSVy8/CnlDMxp5w4sljUbjxd7SuLuz2AEGOxAD3WhZulLnyaNCuDsvXIRH5Mms6hmukcSst1Vn5jkRHh4un/8j/o+vU+s6rP/VsuD57AXPn71kNBqytbVBmqYsl0vOzs64uLjg8PCQDz74gHt3bnPu8hh997vf5Xe/+z3nZ1MrQ16Kxo76JlK+2ASxmQNIGXkvY29vj9evX4c2dkH7m+Z06xyI+i6+dx1IasCRTTGT5xlgODk95cG7H3ByuiCtYDzZRGnFfHbBKCmZlSWjwZDdnR2yLCfJLMzohphIGsp4Pw98KcsyuBZmWRZiqGql0LoZbyFEqCOOzc6yDCEEN2/eZH9/P9TtXfHetvzRAySfNM4nZfWlNVHcwQcWQCCMo0iFulZ47nsrWLXZc+I6Y8ErHmylVUNVCiFZnaGZhD5QL26bH0ivOYg3X2vCte0tyoLBYBjAhl84NgNxo5H126lJnP/maEhva8Jmvs2LizlmubCGoERSu2SzYAkbjHAsPwg0htoJJRWeIUejpWXxK+uanZ1tEmGvEW4cWmZxx6Tm6cDbwg9cdRDERBpx/zeQq3s42TZffWh1D5n2s2MgF3yKRfyd6VxrtfXG/Wl/N74ZSCHQzhJnh8cx/cimD5q5GSWvxFmHgF6eIwT8/Kc/5fmzZ/RJ3SZRsypmFOXSbfJt7eAlCVlEmno/N/1X4ZLLjELx71cBjXV/Xy4NWF4nnHc/Ww+s/AFMcHtCCLI0ax34OPcQL6heRU/uhQCfMmDdO9g+odXu+Ds/ZlVdkwiJRIS1KLWhLFcUZcrBwQHbO7v8/Oc/496Dh8g0RxqolbJUpk1PXOq3a3s1OuhC+0wHrriuswqPNc+J12PrPtMsNgdm2sU06/CK8b/U78Y4Rkdv7aJZM1FDLmlPRXPoEuYRwY5s3B/2PsGlfjR+rXXb54QumUACWZ4xGA2Zzi4wylCVlWPCtHvwpXxirt3CA0/XbdIlFffJFLvCjn+2Z5+LWkTYa7rdLeL36s7Fhm7drlMT5q0QXFpXVwmmVymMWgJtp01rejvU3xWGu9fEz173+7r7um32pevqdZ1SJm7juvquuvdNdbqr3APCTfEf7bUi/NkQt0eEs2Lde8bt96UoCgaDgRufaH6ZqAnx2ShAJpal9eTkiCxNiOfWpVCE8D6XKm2/ebwfaYMWeu18u+qsEUI4i0NEttKZi7VSbG1t8Y1vfIPNzS3mszkvXr3i+OSE09MzpLBsdYONEUmSk6R9kiQjTSVVVfLs6RNqVbI5mXBz/5ZNYlpVKFVTlQWr1YLFYs7F9NTG0JRVYF2cz+f8/Oc/5/TkmK9+5SvkvR6vXh3w1Q8/5NNPP+Xw4AhiL5mwbzp5qqOgqFWNqASFi5Xa3tlmc3MzKNPt5Y13yJsUG6EfvSwSjWlzbyNzNOPhc0UJsjQly3sIIbl16zbvPn6fk5/+hs29HXq9PtVyRrGckSQl9/b3GPRzNiZDahyRmVLOmqacq58lA1PKM0LnrFYrLi4uKFycpneFU0q58xO359vPPNiVUro4Mnv2ZlkWlFGDwaDlnbN2Dl9T/ugBklY6nF9SCEc5LS1rlNIgGyCijWVrS2VKpSrHQ2BQtSbPk3C4xEQM3WR88aIPBA5aox0Q8EjXH+7KmQt9AFmv12u59cXPartMKDwxg1IVWtdOwBBUyukrnLBo3MZiXIxQqTW5ksidCVtfe5f9jQmzckV+0OfVy5cslwVlbf1V7dYqLNW0ASWgNIZaCmpjSIQhxZAlOVqkFFKi+j3uPryDFDZuJhFWWyBpwJKQSbRw24epiASj7nf+KPbvhFvcJrxvdKXw46I7tTRCta8r/tseIl5o8J+JUB/g3BtN5ObY1OGFRu1dDk0zftpZ6HwzWkIKXqCLm2k/r7Sh1jW9PCdNBcVyxl//+/8nmTQYDZoaxZJlcYahQGnvxhO5xNEu8eei6YzQCWFe086g/TZa2EuARxgCvb3PYYJVPFwWUNeDY7hMYBFfZzdCa1UtiiIoDLyWvxuLGGvq/TO7ZCvNd12hCbf+1gsEvlRakaWZy2VmBRyjKlarBefnKS9fvmZV1nz8h094/6tfs1Zad60XqC8LZLI1Fl1hoduG5vcIzOJmhmgY2LygK4VoYpKgRUlsovhI465d0wENQHHP11EdIgAagusoGNDO0hUBH+lZuxxw8LM5UO+7gGCfpDdmtgwAJeS1adoXHu4xtttHQnJvrREYlFaIJGG0MUG8PkRiKfhrVdp6pX9mXH/8GCvp2rkIAo3waR1olCDXl86+EO018ZhGOSF8b7S/w+5hWmu6rr7rSlf7/ybrSLxtva016qpYwOsA0VVzvWvpidfGdcJ3d61cF5/YsrxcJ+A74CDwdNaEsTFe2PMrr9Wu5rogRIe/W0Trl4BZ9/29uxkijiU2zbx0LZBg5ZNEQAJVvWJ+cW7FaaNA+L1L41OItBQjrXfhEkhqAUwH+rquzNeOLyCVa7cQmKTZ//yZOpps8LVvfpPJ5jYnp+c8efKUg6PXVHVNnvTZHm8gE4MRKb3hTUS2QdLPGfQzElFzcj7j5Ogl59Nz7rzzVcaTm9RliVErDl8/4/TVc4aDhEE+QisQJiUREq3gxt5NDIYvnj5jOpvzrW99izv37/H06VMePX4HI23eTykkaZJQ18rSfRs74MLHcEsC6FK6ZrVaYrSh3x8wHIyYzxZUpc3BhEzRurzUV+v6z5ckTbCxRH6eeBr2Zi+Jp7zWCpBIaRDpiM2tG9y6vc8//+f/nMOLFaO9mww29unJjOPnnyFFyWjUJ01T7t67Sa1nrAqF1k3cridb8JakLMvoyR7HJ2fO5TAhTXMWiwUgKYqC0WhEVS/J85xer4cQJpBreKI0Ywyr1aoViuItUHGi3aqqArnG25Q/eoAElzdBX7wg5DfF2PoCTZB80HgZExJkxdcG0LPmWVdtYp4RSSJC4iywLB7QBkRtcgbfboOUaRC2rQ/tZdNhe+OBWltApkvDeGPCi08/YzIYYfKMRVmhRAJJijYKVTuBTkqqJKPGMC9LCq2phaQGUilJBeRJQp73GW/v8M/+6p/x8viED/bvkJjLNM9rtXRXjN21h2ykUQoaucg1qRX03b4xPPFanV+njd1A6qvvswddS2a6YtPqUniuO9Ab4R+yJEEA/+Un/4Xj4yPyNKWuLUBeLufUdek2NitIX/WCXYtRt8RA4e00pFcJ5e6dJAjR5L9qAFISAYH1h2WzRq3lxmu8fFmnmPCPj+MBfYnB0TphSykV4g9jYTvMmY5wdZVw12gEVYt8xT+jWBUcHR2S9Qb87d/+iMcffIVESipVW+XNGyxFV5V43JpxNlfWFuEDX4FdNyKIPaHeVj9esa+u7ZM1z1zbluhebyW68kbRCGqi8+XbCt6xVC9l22JvhRdLbrGzs8NnfEJVVjYOqapo2BAvv1Gjb7isPFt3JjhUcW3frC8melj35ug7on2pA3iuOrPWlauAi3AP6O6NbwLy/pqu50W3ni4IehuFwJvWZrcNfk/oKnfi53Xru+677nXX/R2tskvtba5ta/i79cTnk4+L9soJH5j/JswqpAj0yV7ZYNCtcypuj29zt1+74621tsH48vJ13ff1zwh1uG4JOMxfJyyxTa/f4xvf+Cabm1scHh7z+edfcHx8AolhuDFi3JtQrJa8fvmEWht2b5Z843v/HNkbgVGoasVXvvldDl/vo+uKfLBBqRRJmpHlGTdu3eXo+BWzxRmJ7LMx2rRKY22V2tPplDt37rC1vcWTJ0/4u7/7O77zne9w584dXr58yTvvvENd1xwdHVHj5Lk4xlBIlFN0eGFeKWUpr7VlFByPx4zHY8qypCiKlrx63XztFm2aEJA45Ywfo7gYY+fU1tYW9x6+x/e+/33ee/9ddm/e5Pnf/5a9G3uYZIBcVbx49gWJMNy/d5fz4xO++93voVRNXWuSJAvty/M8yDP+52w2A6DX64XzemtrK7zLarViOBySJAnL5TJYjOLSTZfiPUC8q14c6+RZA9+m/JMASN2JZKLP43if+NC4RLPcmYT+ZwyMWsGUtDf+uO54MOuqcffpPufyhkT4TnY2msV8wXA4CiwzcQkLRgiyRKKVwtQVea/P2XTGr86nDIcjxOYGwzxHLJYM0wwpJHnepzcakg779Dc2SHo9TJ4h+z2y4YDxxoTBYEA/7dHP+owmE3SacnwxQyapO6PXB0OvK28+SNr9ENdr7A1r6uxovFpfXg+S1rWheyBeJ1D469YJRV1hKbYQdp+plaaXWwrM5WLBj3/0I6uNqmoEkrouWSxmVgtlDIlMbBLhIMBd387YQtQV6q4iLXhTaR2SwgvcHpQIIM7K3QCWqqqC5ce2zbjv0/C9MZCmCcbR9QvRsAH5rOZdq2s8Xn7trxtLrXUASM29EM+U9j5wNWC2gE7RBdcWhJUsZjNypZlezPjis8945/H7JEJSmxr0+vxRb1PWARQvnKwrEU6w7sjYNRPD6Eug9wph900C7HVtXSfUvk3x93X3525b3rQGukW6+TCZTEK7PIBet1d3n+1/2jGvL60vX/yufd2e8qZ9qH3d9cK0v/+q/viyY9A9r9YBsHXv1rXUruvHde256rr473XvF5/b6+Zdd6++SgHQBWzdEj65Ysyuepd173ZVP1w3RmVZ0uv1HPnJ5XndANvuJIHFcoGKc/GZy+dDvN7W9Xe33TaG1ipqYitSt0+uej+DO0Nwa8Upy9Is4Wtf+zo7OzscH5/w8cefcHp6xmAwJB/0yYZjBv0JRl5QmGcYqTm7OKWmdqyiC6piiakrBpM9hDEoJKouMcpgtMKoitsP3uH89ICNwZA8yUiyjKoqKIolR0cnCCF58Og+7777Lp9//jm/+MUv+N73vse9e/d48uQJH3zwAVVVMZvNLo83l+ejtfKCko3VZTweB6tITAL2ZVzG4tFetx90AfdgMEBKyXw+4+Wrl9x5cJdssaA36DOUUFaKs7NDjg+e8/DeDlubG+xtbnJzf59Xrw+YTpdMJlsMBgPGLgdSfAYKIYJLnJ9P3oMqSRL6/b6lBHdz2ecyOj095ezsjPF4HGT4OCbJGxx2dnZYLBYhX9J18e7ryh89QIo7zv8NkOZZ0LS08iDpJjDYC1LgEapG6Tpwr68DRf4ZfvJ6E5+/xlN6+7/TNCWJhLX43njT9htKvJC8Sx3AyekJJydn3LlzN7Tj0kYrJT766bNPP2V/7wYffutbjAYD8iTB/OmfUJUVWZYzGo1JkhQpU2SS2FxHCGpASYnIMnQiLS1xkpLLFKFBaYPWMNnawmCzcMdteFO57pDsvtN1n7XqXHdNZ1O+6iB8m/qvurZ7eNrDxm/xtqwDxc34usDwWiHA+gGnKT/5m59xenxCniYICaquKasliMbCaTqa4nUlPvTWvc86Ld91fRIrFrqW2fZzDVImpEmGEDL4IVvwZE36lhrbi+k2z4LWjhUOCc4FTet17WkUCOusfusO5K6A5PeNhjzFuge+qXTnkq/f0436Z2itqauSqiysVk8k/PKXP+edd9+1bmu60bque0b3PZq/rxB4uZqkQQhrJTE4ec4LusI/J66/Lfiv+7zdnsu/X3VvV+Ba187u329qy7o1sO6ZV7XfCAHGsDG2yRBViE2tLz37qn3EF1U3KScuKd2+PAZ+w/MMxlwGSb50g7uvWutdkBeXqwTbdWBoLShcAwS66/EqJUe37kvCJZfH5aq5Fp+5XRDQLevm1/q2wdsO6lXgcd3zrmtPfI1nsFNKOfKlK9pPM/f8d7OLGUVZkiaO3U3Y/S8+t64D6933CbKX0Ugus+mt6+uWoB4H1XsLoxAgDPcfPODOnbucnZ7zh48+5vT0nMFgaHOX9Sf0xvuMx7vcujdgvLPP0dELRqMtzi+WLNSCyXjEzvY+g16PLJGkUmJQgEbXBlXX1NWK8/MjxhtDitkMCWwOB9RVyWw6ZbGYc3pyhkHz/gfv8/jxYz7++GN+8Ytf8P3vf5/bt2/z5MkT3n//ff7+7/8eVdeW/EU0yrVuSAWut70ypqoqxuMxk8mE1WrFYrlw3XGZer6738VnjroCTF2lTCjLkvl8jhYnfPbpZww3xnzvT/+U0XBEWS8oF0t+/5ufISj58CuPGfVybt+8y9bWNpVeMJlkjmHZekx5oOPPNi97e5na//P5kPx+6ddor9fDGMPW1hZZlnF8fBzY7PI8ZzabBQXnjRs3yLKMnZ2dVrqf/waQohJrgWO3uNh9zpcgHEUuOSF2RGvquiLNktb18eSON4V1GipfZ0zpLZxAYowJh27MnBcfXM2mjtOSe1lGMByOmF3MrGk9aSeiDQeG1qS9DKOshunGjZvcuXOX5WqFRlPrCqWsAGCEQIsEZaCJecBFAdgYolRItLAxStppLGVqE7clxjKfBBeYKzb+q/5eV67bULtFAG3T0GVN2XXPXic8dDU1XaGie/CuA7pWFnoLjZlp2NaUVmRJQpak6Frxkx//mDRJEMZalmplg0gNlmGrqcvFX0SCgK+3K7C8SbPyNt/FdXSBkZ3TAA58KDDBCmqDgY0Boyzdql0W9gCxrk+mUV6EblofF2EVEY0rW7we43/r4pm85sp/36YhJfo9XpfrNehxWfdcYwxKVTbRnoBP/vAHTk9P2JhsWssFV4GG64Sm65QLLb1AGwRHt4to3KycdzXAXPf3mz6/qly3ttcJAa3vubTCv3TpCnkCAoHPwLl4YEzLgnTdXtQVFOvOXAzPw2vGr94L1rUzbmt8XfP926/ndUDzKhDlr1s3/64TdN907VXXrKvzbcB3t56rzuN1Zd0ZfF25dF2DSC616bp+u66/4uviErOw+Wvm8znD4XAt8PV/G2PcxGvv2ati5c4NK6cY/JnR3Nt93rV9QSRndeTztwHqxpgA8gwgEkmtFMPxiEfvvMN8tuSTTz7j5MRajjbGG4zHE/LRNv3NO0w2b1Fpxe6djGy0RbWqGA83ub0zYTzqk0qoiiWrxTmrsqQorbIxlT2bC2k0YLJxn7LaYzY95/TohOViRpblCJlwfnbO5mSD46NjhBR87Wtf45133uHjjz/m7//+7/nud7/LzZs3ef36Ne+//z6//c1vrGTk3lEZjSRp7Q0NqYoJ7HZ+TPv9PsvloklN0+m7dXOoWa+X9/HWObBmLMqypD49oyqs9Yok5YP3P0SvVnz6218xPX7Fhx884uH925SLJe+/95jt7W1ktoUQCQcHhxwfHzOZTAJ5QtyudW78XWCUJIl1OYzk416vx7179yjLJtfjZGI9mvI8DwBPCNEidPj/eqLY/18qcVZen5QqSRKKqrxE2+0nSkzFGgtONtFUYw2K7+1akq7bQHx9jdAlAkDybkIx8Irr7Gq7pGyTREgpw4a2jjdfOYaszckmz5495+GjdyDLUI7kAampa4WQCVJaVI9M0NIKDJkRGAVC02iD/OR2GmdL1Wsi4HS1Fveqv+PyJm1dGOsO8AhXvMEA9KZ631S6c2XdBhSehUCK9iZ1lbDhx1trzbA3JEtTfvOrX3N8dIw0Bo2dJ0W5sjmPVIUNohWtQ6/L3BLP1fjfuv646l2713etRN5M3mhz7dtbWlUsQDL+MG0rKZrPhLsn1q6JsGb8RufXTFyH7zd/WMcHTxfMdN8nVnjEhA7r+sHfe5UgFX/m12QQvrSiLiuSNMcA89kFv/n1L/kX//K/Q5UGrZXLHn79Nt0Odr720la7GnDU+PkDyLgO8cbl848uX1ar9//JctW+JDodORqNwnyuqoaC9jrg2C0m8o2/9rp/wB70ZUq3nesUP28DDq4DHG/bJ936YmVOvDddu6e+JdD6MoAnFtiuu6fbrn/s2F0FCLvtuuo+LwPM53Pu3r1rrxe0NCPr3sfukXafXCwWbs+ze7TWJlj5/7HlqnP/TWPiny6EjTsSUvDBV75CmuV88eQJr18f0O8P2NzcxGg4Pj7m1miT7b0JlYaL5ZKz8yOyRHP3wQNG/QFaF5y8fsnJ0SvmFyeoconA5npCSLSWpNmAPO+xMRlx8+YeW9vb7OztcXp4xMHLl+R5DykSZtMZG9tjjo+P+eyzz3jvvfe4d+8en332GZ988glf+cpXWCwWpGnKzZs3OTw4wOEVBO353nhhNAq1qqpYLi1RgSUnyIOc96a+a899eYkgozvn4vPOn+M9mZHIhNn5BR//9g9sj3d4+sVTnnz0e+7dvsF3vv11pNCMR0MeP36XJM0wsqYqK4bDIQcHB5ydnTEajVrP9HtPrJDwFiDfhi6o8R5Y3o1uNBoFa1NRFK38pUI0Mf7ei6P+bwCpXWIfR18uaSiiDc5TLsdo1QvcwR1ICMsK4hK2KmXdoBKXsNMY3OeaPM8a5o5A8SpxDwFhtbap425XTsO8zqGncTloDg1LmyiYTDaoqpKs17cLxxASKjp9BInTCu1s7/Cjv/kR9+7fp5dnUGhODw7Z3t0jSTKMsHmR0CaAHEtZLJEShDIIIxHCapgQoDz6NzaGwbK4+XiGtva6bcLp6DojNiYT/m/1rI1Fyn0XoyDheH+8pquh/+EqtGSwLD4mrqx7yHmQHB6Kc1kyTfXGhJ9WMxe/kRf0vVbavZExYKyvsc9DgYja7Z6dCEGeZQgEf/vjH9l5lkhUrWwgZFUG8GvwVMnWUVRVl60abTbE64kGrgJvDVCxc9le7/M3JUHJYIVKFQCSjW9pXODs/U191mrkSRQaUgavRfLXGyOQIfmh18HbQ923y2bclm6JyaChT2Tixi+m5fdKCBO1pVF0dC3CsTDnXZmuE45izZivTxtDrRW51ohEYpTi13//9/zlX/yl3dxNAwLbe1QMFJu//eSUwq+48FGrX3DfejcPEX8YKxaidv/XAjBdgLFO4GwO8MtzxtUSvYDAdNZ4E0ll514zTNEZEALN/bVuAmA7Ls0zCxaNYx5VyqWCaD/btrn99LjR/cHAVdtmPDPi6rnj+6Mr5Mc/u33WfvN2cY+Pro2uj+qQsvk91Clo/4zeLe7/uFti9RCdoE8T9nX/QVNP/E7d+XBVMYDpKlxwyhh3ogrj91m35ptTxbXI2G3ZCDCyWRetudg8L9QZnnO5/42fTuFEM2Eg/LnR9FJ7TYTntQ+8Zg0IHPNrs79rrRmPxw1Aim2sIvrTNHtDIiWJFJRF2RrfdSCx+TV+W//d5fEJoQ2ybT14E6CUUrpUII0UALC3e4ObN/Y5Pzvn2bNnJEnKaLxBVWuePnmKMXBwes745h2UGXJ2NmM4HnD31i6p0ZwcPOPV8ycs5lNAIU1FIrX93U0/I0HoimpVcXBxxtHrF4w2Jjx48JCbN2+yMRjw8vlzitWSxXzOaDygKFe8evWK7a1t9m/uM5/P+eSTT7hx4wY3btzg888/5/79+5ycnKAq+ywvZ4X5FdZho+T2pBsXFxeO/S2lrIpwLsb5hK4r/vo476adY9bJWghL9Z7IBCFlcF27vX+Xu3cfsLmzy3hzi08/+ZTf/urXPLx9h69/6332djeZTY/4zg/+FJkKalWDsWCzKApu3rzBalUwn89aStM8zymKAqUUw+GQPM+pXFx+kvjQEytDWBDUvKNPi7NarSyI6/UYDAZorZlOp+GzNE3p9/uhf6bT6Rv7yZd/EgDJbzJbW1ssl8u1LBZhI5aOktZJ9NoJ/0iBMNLtacIdcPanNgYhEptsVQhEoMhNyNIUgd14WkKncocbJlDqyjRxAM0+p6ptAldouwpazbg/IL37i0ZpePHqGUWl2Nu5wag3RiKpVIlRFWkvRasaISSDYZ/ZxTn/+//d/5Y7+7d4/uQpJ4dH/LN/8z/hL/7Nf8+8qkmM48VTGrTASKiN1WiL1G5ZMhJggwbaWbUEhgRD4Flfe1zTxUrNWUv3wPGHqxsrv3GHe6PrWmMbCwTRY42xmmLTtMzT/LZySLk6W/dibM4o1yYThEv7uZetWnd4GcPVbWILhwfL4RXse1V1xaA/IE0kR69f8/SLz0mkQBuFNopVWVA7xpu6tvMAoRHCCc2mrZVq+qQ9Fl2QdPVm264vntOJTNFGo5VBCfDky74Nvv89+AkCfadtzbO9kHb5O3uQRJowr2MU0e/g1pYdDGM0UljLKBjqukIIFYAYdIOOm41c666A0BZG/XR5mxIszlJijKaoSoZpCkZzdnTIk08/5t77H4R4xdglz7avOz5OsDMGoQ0iSTrKiNAFTvQUCGOFjsR9biJFRxDefNVryjqN+VUMZK1mvG0nXX4i7XkQgyQPanyzRes+Zfz89q6bHUDhrvNgvHmeRAuNTgS90YDesE9dlqBrUDU4JZRwCQw0gPZ7WEvCRAjQ2lpQDdIprnwfxtr5q8bXfmYBduzSLVp7W9wn6+GRfeMGsKtLa68NnpqeNS2UbSACIjYWsFnH2qnkGpprX1dnrXvrVQsoCJuKgwiAGg9820DJdPZpjUC3AJJLtuDOjaAo0ZHLvZs4UsowdbyyD2Ec5HP1aNOeXp1e9lu4bVLnHPLftwByF7S7dBiuq6184PfJBih5sG8VlPZv6XI1GtN4o1ih0sZPt5Rh0rZIu7ZIZciEIAWM80TQphsTG++NnXPZfxiNcdcCuM4i6MfyWkWMEM7lWGCni+D+/UfoWvD0yQuKVcF4sk1/uIlGQHIAqkaIlGJWcLFcsLW5y61bN9DlBU+e/Jajl0/BKc210lRatdolpSJNM5IE0iSh18+p65rF2Rm/PT3lzt27vPPoEQ8ePkRIyenZKUkqSBYzLs7O+OKzz5mMN7hz5w4n0zN+9/vf85d/8Zfs7OxyfHzM7Tt3efLFE6RntBN+TxZuzrmoULcXxOeABTlpYDwWwsaK1VUdXMni/ozPjiSRIUxECIHRFhzZmGDo9zIGgyFp3iPNcpQ2/OVf/jP+8l/8K45Pz3j57Dl/97f/hRdPnrA12eSbH37AzvaQxXzK5tYu481tjs9PEMIy1mVZxmQypiiKEFe0WCyYzxeAIMtyNjc3A/HScrly3lpZME403iRWOe/ZQ/154z1IyrIM+ZC2t7cBAvjyIDNJEqr/xmLXFD+5tra2gk/uxcVFmDCxG47WOvCq+3vXkTDEJWjjO+e+vzc2D7Zpg92Gbi4TSfh7k+iZuqWt9JsKQbMABKaTT375a37/m4/IRM7DB4+49+AuYKiqGqQNRBRK89577/F//usf8uuf/70T2iV//cMf8dXv/Rmi16fGII0BozFCkKYJSZoijQ+ml0gBiWiY+VqB/8Iuds/tv85y8abSkTPsZ75PrtDqde974zOi32OrUFyJx8zxXSIGTW301CrrDoN4A4sF4Phzv7DTNEEKwW9+/Su70SQC7RixiqJwm0Pl41fxuYUsiGhTv1/X52+yGjUHXJv9TQgbZGnzGwnnSqZbIKoLgroueVeVtxGou+QlLatvJPB6zZxdk86iLHx/mRYIWrc/vF25/rqw7ltxSMq6jWDdB375y1/x6MOvUVeNy21XsFivzRWBuvVym801AjOt601nsl8Hpq/qly9jcWqD0svAy34efrvi8zc+hQCF3rAGWtp8N196vR5ZlrIqVlRVHQ5eb6HoPqNbn3VTUpfoafF3NP+L6uq2nwgcxde0AaFoNePyuwpwFg0LcnRnD3UyaARHPLyJlRyN+6z9RkZCP+73+B2j+Rp/IUQQ8EFYdjIakBFV0OodP0W7+68BjNDdLThUovEESR7EgVfg6Uix0ljWROgfiBRnofmi0y4Tfl5ag/5a95FwiZF1lLvLW9AC7qPJIRaE6bg+V2HiBEfvauSFUaUUSSpZNw/A5zzzlP32My9k03mXbrlyGXX2Dm/RsmvFDmQD9teDpuvKeGOD3d1dzs+nvHx9QK8/Ih9t0p/ssLV7g73bj5hNzxiNN1jVhs3NTW7dvkm5uuDj3/2C8+OnCF2jKmuVsGt70ArH8HTaxphggej1evSkzc3z8sULlosF7777Lg8ePEBKwfHZCb3egHpQsVzMePnyJfce3rckDZ894dmzZ9y5c4fpdMr+/j6vXr2yynqnwEE0e9NlYiPbVx4E9Ho9VsWSNE3Z3t7mX//rf82//bf/NnhKtRVq7XPXM8HZeNs0knMNlVIsz85R2ipEbt26w72HD1ksF/z217/iZ3/3d9TFincf3+Mr77/P7vYGmhJj4PbtO5ydnbfk5izL2N7eZjwes7FhyRpGozFKvWI2WyClChYg734ezxcvBwEsFgtWqxVCGJcPSbiwlzS84/n5OcPhkKqqrEwirIudl5OSJKH28/styj8JgJTnefB9TNO05YPZHYzY9xGaAy7+F3/vwY3X3vgBjgW2+KePaWjurWncO2zxoEfKpAWM4gnv2etiYGVjrDL292/zh9M/8PzZE/72R/+Fnb0d/vv/6b9h68Y2MkvJ04xcSO4/fMD+/j7PvnhCVSlIU37wZ3/BcDRhVTkaSayLnTaGolJIZVAasjQjzxOr7RBXg8iuleFNY/VlSpcc46r63iSsrROA32bDjufGpTqjE32ddWSdoNy91s8nIW2271/84mdkaWKpRzFUVYkQRHEyhiTJ7Hj4zdZcZrmJ29F9n+ve1R92IFrtb+pvE5bEwOjLAKKugHpVu2MA0wVIfl10AYYVPkzrHtu29QQc8TOva9fbvlu8XtsEMDVpYl1xP/v0Uy4uZvSGw5ay5ss8900KiDZ0evO6u0oR8Tb3vXWbrtknvL7lTc/4MnW+TRFCIJFkWUaW5awQwW1EK/XGnms00k3Sw5b1A64FM64RkUzeWE2sW4xo3d9qTwS6Yi2+cft5UAi4zxCidY/9zaItD5D8ekuShNiCFMDTFe/fUr6sfcWOh0Wk2CC0oAFoHjzEz/DPv2pMrKO5Dn1AlLC7cyGN14PtZ6P9c7rxiH5vsN3X7EPt9ttbXF+675sk2THoAXTjWiSEsPGm2lrmMVZGqas6WJHquqKqK8AKxz4RppdD7LbXVRg17bc4uUmoXVV1/GZ4V+S33cNbtUfjL6UMqLsty1ytkFtX7ty5A8CzZ8/QBvLRJvl4i8HWDfqTPbb3h+xrxatXr0lqw917d1jOp3z8219ydvQc6qUDColNwjpswJE9c2WIV69r6+7lPY/6g0Ggnj47O+Ojjz7igw8+4PbtOyxWK5aLOf1en2q14tWr19y4tc/uzh4HL17z2Wef2XxJW1scHh5y584dvvjii1ay7W5fxLJJCzwJazmpqoqTkxM++uijACTite5/T9OUyWTCZDJBCMFsNnP04XU0XwVpmjEYjtiYTNiYbDEcb/D7jz7ixz/+MUeHr9maDPjqh9+mnyfcv3OLW7f2ef7iJQ8efI3JZOIAV5TjSUrAurkrZcgy60K3v3+bLDvl/Oyc2cWCVbFCCsnGxjj0r1e8yiShl+eMhpKNjQ3SVEbWJeHc8irquma5XHJxcYFSil6vx3A4DFTlxlhjSeFil96m/NEDpDRNKYqC58+fMx6PLUFDWYZFGgMlD2C6Gm8PnPzvvrQFr2YDiuuKk7wGE59Dt/6wiAFT/MxEtrXBMZpOkoYW0QtRWmvyvMfO9g693oB+b0BZVnzyyae8//l77NcFG1sTNkZjEAlZnvPt736Hg9ev2d7d5c/++b/ir/7Vv8AA/V7PnZVWw6VpFq7WhrKqEFIiU5uzZZ0g1N3yusDzKhByWXvs9ZiEQ/lNQlF3k1lX/5uEp641wf9ujHH9cVljaL9ra0zjurpuSF3BIP7djmeOFILnz59xdHxoD3lj4x/KssAfYFmW0iAyJ+tE7VvHtNh996v6YZ17Xgx8YguoFGlrDvtrvoyA331W97vuvAdah0OX2ce/e+N37dtE0N4a0wCWGHhB+/Or2hu/X3ctxH93rYX2HSxAyjNLYTpfLPjii8/56te/canut+2/q68Tl38ztHIkXXXvlwVpb2pjt96rAGgz5uvvv650lUtvuj+sy3gpCJvEcDwecXZ0iFLKHciBaeQSeuu+k5+XV7mTtolKIBa87f8aDX8Tp2PncvNO4bbwzK4HhHFt1QbrMheDDN3EQERv4qweLrYIe58yphXr1mC02FLku6/5zAuecYZ73zdhPXcE5xapjK9bNnPCAkUbvC8T2Zonrb5QmiRtBKuATx2Fv/1MO4sRljXT+DjKWPlj39gqoITrAgekjMGYdW5kNh1D7VhGPe2xf+/VakVRFNZiVFnPgOVyGaxA8/mcNEmQNIku89yC9jzPkYkgy2zyz+fPn3P79u0gi9i+avbHuG99PKjFybb9RVFE11mPAUtOFe1xa6ylzRi13evivTQRdg/ugi0/Jl3CEP+dFeENSZJy88YNiqLg5OQkxJ2MRiNG4w0Gow3SPOf0+IT5suS9d99F6JovPvkNZ0dPMfWS0aCH1obFYkVZFtR1wWAwCPOpKEoXv+pz7tl+LMsSpW1ONJ9Tx8cYffWrX+Xe/Xt8/umnXMznTKcXDEYDDg+PuP/oAXs3bvD82TNevXrFjRs3ODo6Ynd3l6dPn9qQ66tknjU/kyQhyzKUqplOp2it+elPfxrmUvds98Dg9PSUk5NTdz7a8Izt7U2yPEMIyPMevWxA1ushJcxm5yxXC36na3Z3NvnK+w+5efMG4/GI8aBHUS44PH7Nw4cPuXFjP8ypujbBKmTPupLp9MLJJLaPpRSsViVJYuWF0XDk1oXi4mIWzsjlcmVl9jwnTVN6/RytDWnajqXy+8pwOMQYw2Kx4OjoiNevXweAmOc5g8GA07OzS3PsqvJHD5A8zZ+UkqOjI2t2k3bRt7XflwVU/3OdMBuXLjBaxxjmBSPPpLH24HIlz/OQDKzbNn+vFwJjDbMQgvliQZL2GY02gAO3sCvKokZiXet0VVNJjTI1f/rP/oJ7jx5w6/Zttm/sAxlC9FCRv75BBXc2/37GGIyuUUo4LcTlhSmkuFLt+zaa5Mtf+A5v9338/uvKus2nO07xtesEwe488SCp+71xwswbPK1C/V0Nmv8Zk4okacrvfvcbqqq05AyqtpZHo1G66rT1ep32OuGs+3t8XQwwmne87PLlr/XjECsG4vquEoKval88VuvA0nVA++rnNADJqPZciMFfvFbXAcv42VfNvbeZY15AtHEDGXVV8dFHH/HhN74ZXA/eFhytf991FwGmrcR4033XAT//fRfwX9e+t2mnHxsb17i+Pdf1/3Xv8BZX23Y6ITLPrWBVOVakqqrs3HCXWo3+1UAujg9YNyfjDW5dM2MwZP9uz684Xs8KI233cf9MpXUAR15BFwPCRgN8ef8W0dYWB0ybLn9zfE9kUPHzHAj0vFYjbsGJiACX5x7xyaX9niudIE8EmBDOPc+I8CxvYfHvIkRCw5YgLCCUgsS59WEMxjTkSMYo6qpCUCNlwqooLHuhVmilgxuWf6+LiwtqVVvna6NZLJaAZ5rVbj+xOVu8p4l3l7LWFcFwNKKX9UhkEgRhsEmxhRAkwrJbYmwwfbMH2niyNLUuYwcHB1cqxeIzxyBsHJ1pLEXL5bJ9Hhk/HxtZw3q9xPMi/qP9+VV75HWKpe51CEvwM5lMGA6HPH3+nFrVbGyMGOUJ417KxiBn1M9ZFiWz6Rl7O9uMehnPPvsdJ6+fIPSSwTCnPxhxenLKcrUEoK7LwBAnE+kUEJZW2id29daIqq65uLhge3s7KLnPz8958uQJ7773mL3dG8zOpqyKkv5wyOHhETfv3GF7e5uXL17w9OlTbt++zcbGBtPplM3NTc5OTlt9s06xGMsXPglwnueMx2POzs4uyYvdMWnON2vF08aCjDRL2dqakKaCQd5n2B+S9XrkvT7D0Zid3RvcunWLQT/BqIqtrTFSJpyfW2B2+/Y9er0B52cXri/rsNf4Z/b7fYy2MfXaWFdyKROkSEP8r2XsswvepwRZLJYURcF4PMIYWC4LzqfngGIw6DMcDoPRozuHBoMBd+/eDX0Xe259mRPgjx4gQWMd8kJbIpJLWipoC4Pdxet/dhd5mJDGhI2hYcZqC1dxTEZTj3BBlI2/pacp1O7A8JMtBkVSpi1A5idJXdUkaIaDIVneQ4jE0RxWoA3DvM8w77FYLBBSoDB8+0+/7w4oMCZB14pMZC5dmgEjSJ2i1GqOnKZMW2vGkydfOD/cjglefLnJ+GVLd/O4/N16ALAWMF3hEhfX//aC+BpgaJqv3lRCED/Wh1crxe9+97uW+4YxGpkIdHGFYNJ5l3Wgb33b25/Fh1tjXTEti03znRNAOvWtFQhF1EjW9/dVAng8HnFbu6DlqmKv8ZrmJKi62+/YpkLvApp/SInbHluStHa5n4zTVgNPnjxlsVgEX/G4jv8apWsli0fmumd2R/DKXhfta2M2vzfVGapYI0RcNc5vquMqhYD/3YILt4yFdUnb3d3hI62pXV6SoiwaBZW3KOuu7bgp69JHhPZ6lMX6/az9VuvbDaLzHlf0k4jAmGjHcrbb1gZqEfa43LJr1puMrV+2Vjv3tXFJQBuiolZEl3d78gK8+zwQJ3ldlIuj8i7p/j200pRV5XLD2bN1tbJuRUppyqLgYjZzc9C4GIcixDTYGBTLQmtlB3uWe2avJLFpP4bDIb1ej8lkQppmjhxBRu78iQOsbUZG6WOQQqyyde9CtZU1XlEDBonCk3xIaUGht+RpRzyxt7fHy5cv18owzRSIgCPgmWMNsFwuA+gCnDumDvPpv0ZZp5gMshJN321ubgFwdHiEQDAc9FCrKb/5xXM2nj7n29//SzQSVa64eecO5XzKq2efQr1gPMpJM6v8LZUlORICyqqmrApkssEgH9LvDUmSLIAQ77KV5zn9wYDZbMbFxQUbGxvkeY7WmoODA7Z2dtjd3eXs+ISjgwMwguVixfnZOTf3dtjc3OTk5IT5fM7W1hbn51Nu3rzJydHxGla59lr2Qr4QgtVqRX9gYxm7ir3rQKYQzuVcGMpyhVKaqiz45je/zmQyYpjlTEZjRuMN+oMBQqYYIej3BxhdUlaG89NjjJFMNnd49PBd8l6f1bIK4MaSvsjIWk6whFomOcI1jdXWhM9jucKy9eWMRsNQd5r2SVKrJAjhLc5SFfdhLEPFTLAAuVM6vE35owdIUlpGOOV8S43TAMqw4TlsIzwQsQJo1y0ogBunHfLJFEPQpGm7z8TamzRNLRhRjlHLEEznNijVCyXCtcXGedS1E1qce5tyZn6ZpGjVBkyJ80cFiS4LRqMBw1GfJLMqv6qqMAp6WQ9Va4qysodKUnF+MePWzX0EgtWqgjyhVpqQms0YUD6jtvdbt/okrQy3bt1qCTotYfKasQnaKbr6Uzq/2dOwAT3e3aM5yHEa0+Yk7goZ62sN1zutWEsD4773Bn6tTdA0ItzB5K8XXgtpmZS87tnXG+oz7U3PUqJHWndvcTSGLE3JZMLZ8QkHr17Zt9YKjEY5piGfGLZdRNRrkSQR90YEoLoCWXyIN4eVjIIeHdWxhjSxdPB2nsrQn7Fm27g5JATWeksEbsG+U/RsYyyzkDYay46Fe4bXevuxcvPGs+QJ0X3N1rtGnyCEA30u+aeRYFmnBKpW1Mq63dgktdquedEG5W1hYf1cs5dctqr4A8/uFVZY0rqmVhUguDg54vT1K+7cv+fyXWE16VZfcf264orvTQPcPRmXnx3aaRVlR8h2UpEdQ9GayRih22vWDmzzfCdQN+2xdyvjnh993zxORHU0Y+qJA8KbdYBG2HMu9YV/Zzd3TGzxwa09t/eGQHKHmYXAIEEYtFLs7NzAYN1AlosVi8WSWilyY+M0HZq07+Tb5cg3EIL+aIgy1goSzoK4n0ObvYa+vVesFXZF+90FMggrHRHYnXX27NJR/S0LqbTrubm3Dd5sPdr1ZfNceannm33HaPtMfy7WqnbWIXtnWEtuovjAda3rQIihHZFJURSsVitqo1Fas1gsqCqbw6+uFavVgjSVlj1WSuqqIklT+v0eqcwwWtDLe+S9HkmSsDnZYjAcWgKiKE+iBzWJTEhTiTb+LAal2+72RkOckqA5h4ybgSIAOeH7JSh0PDscZKl145IYkjQJe0+a+f3csvtBkxi22ek9vb9VXEoH8v0cJBD0uf3TWz6dsk3VCkyK0ZrZYubmsMagMI6uOU1j1tBrSrQBXRLYY6C8RphfC5K8G7cx7O7tUpQls9mMLE1JheDw1QvOpgsOj06ZbG5z++4Dtjc2GA9yPn3yEbPZOXneYzzeYLEqmM3mof+8XJWmGYYEZMLW3g1u7t9mZ3cXjGE2n/PF559zdnZGJhM2NmwMT5omjEYjelnGrKh48fw5H37tQ/b2bzJbLFB1xWxxwcnhMTd299ja3uPo+JzXB8e88847JGnOaDwhSdM2+QaNzNmNQa1ry1S3XCzp9fsIYSm5lVSkSYJRKrAwX94v3LmqXQwhNXmasbe1xb17d5BG089ysjyj1x8yGA5YLFecnh6zKlYIKch7A+7cusOt2/coC2Up4RGBLdHLyXluwWXMGF2WysV+5SSJpKpq2ueptGvNydE2Z5yIlAyZnYOJYrVSrffTWoc4pNhAESuI/PXrvDquKn/0AMlrmzw7DgLH9AQmaLsl2u1p/jCI44e89UZKSeJMlN5f24MrrxWLEW3QZomIOhy/KDW1UiSBZcweENrYDS2TiQMgbRe9sqrD4qmcpSnLcytouwB6lGY86jMc9THC0T6DpTjOeiRS0KsVi8WCYlXx+sVrbm7fJJMZgyyhrGuU8Juo9vjD9VVbI+ndBPwkjbXCbfraNcUBpLhu+9MdLYZwiNjL/e/u4Omc4t7iZq+IARStjbkDFaKfbbeB+HM7d0yQ0URzvDR9IQRG2ESb2gm+nvXI1+vzVRhiF0bbwdKAcv2gtKKXZmQy4fOPPkaXFVmaWD/7aENo+sPV2z2PAtDpHkgmzN3u9W2XOv+zjqxEEp9EWAiba8i7a4X3jLWXCIS0oKTx8W8Aoc0B5RUQzv9faMt2JYwDKu5zo4MmN4ykf0eci40QGJOE79ubpCYk0w2zwYHdqI+sd6hAkCCFRJkq3B9bq9pzaF1pgFG8L/h9xbZPARJtaoyxmjBZFTz9+CMePbyPMVjAZqyrmY7W1WWLloMckYDhn28DzGP3Xve9kCDsXlcTi9ZOKAsgo/u2wu4v/qm6UWJ4Mo91PWOMwDhlk6USbr6TiCiPXLstAdnQYbD0Ymekfb30PK8J8wBQOOtIpNgwjRRp9z1t40mUEZhas7u3j0xSlNKsllbQqmpNpQz+WGjmvQgAEez+vbG5xfT8nNorWrqgyLTduV2mvMsHundtEzh9S7MWpGdjlB4sGWvVEo4FzeXsstpoP/445ZtdC1pbNzYhmripjrOaW8MOMCiFri1REW5e10o5V2BDVVfMZjNw1y6WS7t3Iahrw2w2R0rp3MlsDEye5+RpFtzTLNNYRpKmJElOmuekWcbujcZak8iENJOkqQ7r1wMTjLFAxjQeIrEAemmO4hVX9i8JGFXb/V3glCkakJCIoECxfejpRD0IvnyueIsMNPTdxkCeW1eyJAEhdKt9YQq70TZaW3nBrzlHi75cXFCVS1Rl3f+EBKNVAOpCNMoAIcCoClXV5OmQxWrJdHpm9yejMMa7cysnIzXArlsa63MzW/xnIWZ1bXbHTt93BXshEIkkFZLRaMRysaBYFQyHI5I0R6Z9DEvSJGE06LFazNm/c5/Vas7R8UtqpdjY2CTNhqjZClWVDHo9hgMntyBIs4z+YMSHX/86jz/4KsgksNj1t3a49/h9zs7O+O3Pfs5iekZZFqyWC/p5Ripz8iRnenbG+XTKZGeX3uExxXJOXhXMp1Oq2rCxuUuSPuPw+JxH76b0hxNWywXD4ZC5c+XrekJcPmvsGlquVqR5LyiA/NmJ0XYPjfqvpQgRCqMhlZI0yRn1+ozyPjlW3ixrxWJVwPmsiQsUgsFowqPH73Hnzl2MMZydnbFYLJlMNlguVkhpXTs9i2pVldYVFEs2liSNFdVacH0CWB2us3mPfA5FmzvREuIsybKM168PuHFjj8nmiO3t7XCGrlarcL76ueb7KZZTuyE1b1P++AFSJ9bHlkZoiBFsvPC9wB93pgUp9gJPyxtP4Bjt+8klpbTWG3cAejNgHJQaCzH+QDo/P2cxX7C3uxsWipSS1WoViB78s71vuw361AzyIXWt2NradP6bhtWqAGEn63A0ZFVUjEZJMFWenp9z5+ZtRxctocYeKpGQ2+1T13i8GPCPMb/HE1gk7U00Bp0xEPN9Ftfhy2UNP63vuuVN1wqPjMLnrF1s/jIpGm1b65q1AAYnnFsho3IuIUmSoJXmoz98ZLVDTjBWznxc11fFNHz5cei6Nfjf43icLhGDEKJFXR+3I14Pvnizd7yZaa3BGFwYQquvLO5pqKsvb3ARSKIRFIwRGN18niRW69oIK22AH/ddN6bP/27/jtsWa6XePgYmJmjwdft+icklMIYvPv+cf+HbqJ2gm4jWWnz7cnm9XL5EtBiVfNFX3mM687ntOnz1Y/xY+L/b4Mt/b0xQTVx+8jXvEX/XKFkuv393/Xatx6FVTqmxs7tDnvdQdUVZlUynU1bLFcPxRkQa0N4z43He2trh+fNXVLU9B6yQezmeI35srOm+FIdnBFp5d1GrZNHKgJBILZ2VTgagoJVPnmxnkNYO1DjAVJY1AmXdz8qKura5Seq6CqkxtLbCz3w+D+dZXSvqsiJLMrI8s0K8MeS9nH5/QJJaUD8YDMn6PXbHG2RZ7lzQhIuvse1MUyuSSCFCXFQQHP2YCIHprKOGSAGkUM0gegAhQKYCE1x4IEnBGHnpnLffe1uPRyXexcwrIELjwoPsb03yamPqMH999WG844e5PciCFgvMNc24xxqELhW59sqiluxwxKCfgrExconwrk9u37IbrTvfBcVqRVlWCODFi+ecn54G90cVKXJ8v/i5+Ta7UCz0hzF5i9K2nlohPc6jY4y2FM95jw+/8wPKqmY4mrC/f5vnL14xGQ84PHjJxXRKlqUMhwOUqpnP5gggkSlpZt3oZJbT6w/4+je+yYNH76AMVLUilZlLvm5YLQpGgxF//hd/wc/+y49RuuT05JjlcsnGOCdJU9RyyevXr/nqhzesO11l3TXLVUlZFIwnm/T6fc7OzqiqmsFgwMX0nMnGhPlstvYMWfeZMQZVq0BdbYzLgYXPCeT3Tn894Q8hDIm0+YSkFPTynPl8wdnZud3/k5SqqtFa0e8PuHPnPg8fPuT2vfukeR7O7t3d3SCTGQ3Hxye8fPnSWX2s8sSy7EoGgyFK1S2Z2ssWPo7Ln9v2pyWRSBLp3O7s/Lt1a5/J5gZ5noQcgdPp1Cr6iyKw9HnmWmNMiBUty9Iy4knJ0fHx201C/ikApAiINIvVaX5oDsnG+tGYpqGNvoUQKP+dsSpO5TQzaZZTV3UDGIyzYdTWjc77G3vSiMZ/0h8OjbBkjKVEnM1njDfGVpg0sCxWLkbBUFR2wDHCajqxW7TGsCpLMILt3V1u3r7F7z/+mKIswz7b6/VJkjS8X54nzGZzVlulda9wWmyDAbUGBMSaCQPKbdqelSguXeH9KsHmktZozfP8OMTj1tWStMf5suB6FTj6MlqFbl3r3tELN1cJZ20BMdY+E6yXWZZRq5qXL15GAoCkKFZtt5h/BDBt2nC5Dr8BrvNtjgFr/G7dvgxCjLFsbf67WPCTa+7xdfmkw3Eb1/dndK/xsRP2uuCqGr1jV+iMAU8MDH2dXnizWvF18UnNXLiqP+O64uf5vSAeU601h0dHzOdz8tHQufdYweYfOt5XrQkPRuL2de+7plZ3n62nAUn+nsv7R6MxX7+3tMHn1e8CDcBOxHr/ey9fXjlXrvzb4JOT2rUJo9GImzdv8PL5c+q65nw6ZTafMdneJk+b/TQe13g+ySTjwYNH/Pa3H/GVr3yFNE2Crt27/jXj4OdnA8CFSELbrHBs3YNAhCBtK5RYK9B8vgzXrpwQ7M+holg6QN4wtGZZ5uJt0sAQ1e/3HUXwZrDi+Pf0Lml+vH1uPC9/++sai6cTgLSxwfAA1DY5u3dPdwDC+E3R96lL5Cmks7D4xMauWilBGWUtnpH+xINDu0Q1QihrVcLW6fP/GGL37OZ+TOzhYP9pXfsv7TjpaDdvAVh/XTO/2sDLfa4jJYRxM0K5ZLXGRHUajI4oit05Y5pJjlKGF8+/YG/vBqvVhYthMoBLCCwEWikLMrVGa5hOL9AaiuWSv/vJTyiLFWmaUVWaulYtZe+6mMHW+znL6aXP3Xheb/2m9V1ImSKss79PPGotBu7vXp+Nm7fZ272BFJL5xZQ8T8kSwXx6RlEsGQ8HCKM5Pz9DVZW9L01JM5f/KMt59PBdHj18F4SL+RISjXbx6mCkCW6e3/nud/nxj/4z5+enFGXJUGvSLCWpEs7OrHVpc3OTo8PXpElKYayb3ub2DqPRiIODA2azGb1ez1qWJxNevnxxZb/6/midF45Qw8oJOZUpSLOM8XjsGBINtTur/B7iHQsEhkQK9vZ2+Oa3v8X27g7D4ZA06zMabjAej5lsTiyznIB+r898vkC5eD2lFMfHxxhjySt2d3eZTMbc3P8W8/mco6NDyrJkMBySpTm9Xo/T0zOMMSyXC5RSDAYDF2Zi10ie92y+RykcWZFEJglJKtgYbzB0Z6B9/zrsTzdu3MAYS20/m82YzWaMx2OAQKLT6/VcPJOND/xviWI7xR98DbWo1Qj54Me2O11bkx0LMDJJUFG8hHTAREpJrRWkNq6oLMqwwJVSpEJSl3aDUkoFhGtNgGC0Iu/lKGU3pCyzwtjO3h610UirAKM2msS4wyKxQEmr2m12Vgup3GdJkpJIwf0HD3j0zjts7myzsbmJVpYifHdnz1oFjCZLbXLci8WCzc0JqqpJk8RtpBYExgRKXctIzFjWZl5p7rkKhKwTxmLGvPiatnXhagG0K1x9WQC0TpC+Spi76h28UHZJ+HYCUQwjPaDy8W3QWDam02nQ3gIuqahqtWm90PvlS2zd8HMVCJuRdq5wUjTgKF4n3cPTGGdJNRr7lW7NGf9M9BXCsO83J+TF864LeOJ22zgn4Wh6Gxewxu0jpvptPzt2jfV94IGajRVIWwyDl5vcnWuNK2PXAuD7O1aYeC1wKlJmsxnHx8fcGY9su5Trv3/AWHfvaM+XdS6YX75W/57N3+vHte021KzxSxaSTj3XgfGur367XJ6XV9Udv5sQIoB3ZQypENy+fYfnT564gH8bgL1/+zZB+F+zV4Q5n6Zs7uygheB3f/iIGzf2LC2tNuDiCYuiDL78WmlWThni99iyKKNuF+CICbxrigXyVunmaW39WrFxNjZPiRAElxaI/fUJfduMV9vCFWT5yBLiXZqD63G0hpLuGst8bp7wBKSwQjypW6c28ZCT6xwhgRDB2mZvN9blTVpAmLj4xgBBjHXZDPcIg9FRElR7eFsCCK+woa1g00q59tj2ejdhIfy8SRxLn3WbRzTO1558Ke5Pr0zx7on2rNNBwWC0iwOM5qYOhCG2/TG5g9+jlLIAe7lc8vnnn3Lr1g0OD16hjbYss9rm7PLU4nVlY5DLsqIoKpIk5eD1Ib/4xc9dOxRGmKAQurT2Oqsl9BnGxe+1P/e/d4HWurXogVEz/zRGWEAEsFqt3FyWZFlOkvUQSYrAUNcV/X6OFIr5bIrQCqMVZ6cnzC8uMFqTJnZ+ZKm1KEwmG7z3/vuh0WmaWNcvITDSnT1CgBaIJCFLBI/eecSLl0+tdaiqyDObuNTHyI1GY9IkYb5aobWlaRdCMBgMQl4ln5vTWrdU8MaI96R4L2kpWxyQSxKbBsRguHPnDv+b//F/5PTkhIODA54/f87R0RGHh4eWYbGurdu6Uezs7vC//F//r/jmN7/Op599Spqm7O3sk6R5GKMsTandPrcoVyAIIGNra6s1TkVZoI0mTRPu3LlD4Rkfa8v2OBz20dowGPTwKQ08gLEgvqLXyx0BSkaatdkeGw8Lr5gwYS4YY4lTdnZ2wnVaN8mS4wTASimy9O1hzz8JgNSwxMSJtERLQPFFShGBl/ZB7BdqEGik9W2uVQ1GksjM3et9vm2siVb2IPFuSkmSUJYleZ5TFDUYYTVbAEZQFpbC2aa7ECFnAoBJrXlVa03e69lN1gnNNqGdQZoUbWqWVcHN/X3u37/Pe++/x3hjbM2vGvr9AUKIJmM0hun8gt6gR55mqLomlRJlDEbKa0hcLwMKuFoTvQ7kXCdsrvsZl+vuXQd01tVznbZ63XdX1dN83363GDwSxWb7w9gt+eZvY8izFJlInj59ilI1UrQp4+2Cv8ws1BVQ36bE98TAxc95P/eklCQyxWuyrwOhfv0YL4jQgMV4PK3A3/TJ5TY1h4J/5rp5sx5gNcKSEP454to5Go9Vc0Bp50KlwxqOD3BbRRssxtYD7wK1zoLTnSfBjRAbm/Xy5UvuvfMwhJ0YY1oxQW+jdPB9EX8Xg8B1970d4L68vv3nplFqR9/739avp+7v8WdXKQGsZvrLArvLbb60V/l56TTiUkpUVXH//n3+7id/G1xczqfnLBZL8v4wsoq06/ZrXJkaZTQ7e1tsbk+4uJhycnaCUjWJEWSeAc0RBuSjnN1sL8y1WCDx8ZZa4jTszZ5khWvw7G7GzdeY5QlnMbGv2Ky1IIA0b0GSxukrTDOXovFPRDTYxhC6QnilT2OlFDpSOlFd2sOMew+jbDyFFNYKpU1DRKBMQ31vlAig0iqhon3IeIWeU5Y4q057Tnmbjv2pHf230TZ/TCKkY2w17kw3FtAJ6+nhlapaW8tXs5bVpbmtTWMxj2MR4391VYXE857VtnJsfLoqQy4lYxoXIgPMFytOT0/pZRmf/OHj1hzURlFXdu8qyoLT01OKVeGShdp41tPTc8rK5mPDATMPqLxS6joLUpgxovu3WwedM2Ddur/q/BJCBOtl7ZRIUiZgNKmoyRODMBpVFwz7OVpVlMUSIQwCS11erFZgFKuFYbCxgUHR66Xs7m0zHPZcWpLEzS0csYi3aCToVFBXJWmWcefeXfqDAcvliqIsSFPrMlqq2sXmbJKmGQdHB0xGQxbzuc1159zUZrMZN27ccKAofat+9X0ihAgU7wAPHz3kxYvnlEXB9vY29+/dQQpL9CGEoCxKphdTm0h1PkcYw2g84t3HjxlujPn2d77DRx99xMeffcLt/Ttsbm3axNhpRg5kWcqqWKG0CvmEVqsVVVU5ubqmLFdhnEajkaNFHyAckIvPm3geddlw/boBg1IVSlXhHg9yvFteVVWcn5/T7/cD2Iyp8b1S1wMmb/H2ISlvU/7oAVIjpIjgMjAYDDg7m1KVVUiQ6H2f7UA2G2gc96K1RqRWv1OrGlNH2l8lSJLGupQmKUXpBkLYA6Msy+A7CQ5BK7sh13XttEFOu5emGGkDXJVS1kIkBaqq3IDbQ6x2mZCNMZZlDIk2julMGxKtePDOI/Zu3qDWGokO5A6LxSJQepZ1RWVqDg4Pub1/ywWKJla7L/8h2mXWboRf5t5/THkT+PqH1tkIwf8gRX67P4KQAIi29csLM59//nmwpAAhi7pPcPqP6aZ4bfi/vfDvNyU/v5v2r7eSuNdpvcOVvv1CBFksFtCuA57rNLGxO0cs+NvN0LfV52vxet2Gorx5f/+MdjvBA0WBZXNqhCqvlbL/XM0RGG7aCcZczkLfFfr9fY37ng3KevbsGT9wFh6vTY8UtF9inawHQzYAvy0SX1X8e7ZrjVitfBuN1/wTfvoxd2/ZriMCjusUKM3zr5of160DLwBf8e0Vz75UjD1wb93at64x7rBezBcslwvG9SaZbNzP1j/LIKShVvYc2JiMmGyOrVVZCWRwoWva5uuy+c8a0J25NBAIBdQuRtHPQWtNkCK1hAKJXyTWGiJk7Frq+yh60dYYWzcysMQPzVcmuIUJsABCN6QCLYWGjtGyCMBMYIK7mp8jQkTfG2+haiJ7vC5FGhXaIqRAGo3QlsFP60YAMsbQnMoGoxqA1NXW+9J1ddUhd4zGFMqSqThrj095YIFNbXNMORcoY2zajaIsgtVZ1YqqrsDgFKu2H+uqDvTjRqlAoqAcMPNtlb7njLWqFYUFSIdHR7x49Zp33nmHnZ0dFvNF0KB7EiqjJfOLOU+ePuX58+fOOm5BXyITEJIEm9BWC+FSRrUtGI3l/rr9oq0Iiz51OSjbSomuIvqS3CCaZxtjrXrGKSGlMFAuUMUFWZohVEXay9FaUZYFibRKhbJYYYxCq5rlokJhrEdOKtjd3caYmuFggDKGqlJoIZDaWoSK1ZKLsuT8/IxePrCMb9LSqZ+cnNoxj9o5n8+wrK8pGNy4qta5URRFsBh5OvjrFLXd/tJKI5x16+DggLqqmYw3KVYrenlCmihUbUizjCyT7O1uWtmzqKjKiuFoyHK5QKQJeb/P+1/9Cg8flbx88YLlakaWT6iUU6xkgp7IHQ29dYvb3NyMgI61HFkLdhNbbNe2vGQF8nH08WfeWtQYEXwiWEss5Pe1LEudjGz7Ym9vL/RdDIh8SoVYljLGsFwuOT8/v2butssfPUAqa0NSWeLlne0dJhsbqKpGDTSnxSkoMEKgtbP2GEtraRxjVF1b03RZlGgEiUob61IkqdS1AioSt4GUVY3AMgfVRiPQFFVF5n2kjQc/VgullCJNUrv9CQF1CVIjZBT/VFpgZQETlIXCqwutuVyTyAQjLNteVSkqpbhxc48sTyiKFWR9iqpiuaoC1WdVlXZDV4ZlveTw4JDNjQ1kZoNohXKuCkHRGGl+vGYufNUIol6bGd/SHJz2KiBQpvuDQHR81uOEhLIVDN2M83WAxZhooKK/vfDcFfoDfqEtAAshGvEhelZLnDCN48k6Yc+7omAaJrEgPDhhOgF6iSRB8/LFUwR2Pgph54zBbj5CxM9Y//LefcP3Wzxpu5YU3wdKaTwltBCCRGZOi25ZsBohHpr8GVZL7N3AhJT28BWRQO/mm+jGA2EfF8Y/ABYnnJk1Gn66wM0J/EYQKMXinvF/G+PY4tzm6WIAG2tTM79iK5Jll3QuNXZlu/ljtV2x1bkLPOO58CbhIhySRoMWHL9+Dc4n3ucBtePejoNyr9buoxYgagsgbReXWEANU9HNTdm09lLTRZjr7a9tnf5v3//RKm4q8g/EWtqEuBzDGN4TGlBsIlAnfGNp1Wmw01OuGYfoxqZut76J1rAQwgr4EjCC8da2pb+9uGB2fsZiPmW5mLv1mCFEElw7u30hhe8zwAXPewuYkH58RdhggiDp9guJAb//aat0S4SrXStbDyKseWEqu2/79WOarhLOVVxI0RoK2+JGILbcWF4ZYYUUf94Ir6Tx1lMHaOzvzZ5uSRMiNtaAtARC+tiraO+NhtUrN7wiqXmOU4iE9dIAGoQOe2mzHiUYjVZVUKJAk7jWKyG11iiXj8V7biilUUpTFCuKcgVYSn6tDaquMcqT5tShDVVVIZKEOlLG+D3FuvaLYNWLlS1aa3Stgrukj88SWCtamqRBaVWUmsVizscff8L5+ZS93T3yLOP48Ig0S5lMJkghkImgqiqqqqIoC6QQJN6qpu1e7/PSlHWFlO5MFn4OEPo+nBOXN4Po++hvonMm7LHd/bC9N4XVGT5zOeuMdaVsznrD9PSEX//y5/T7A37wZ39OkvXxVPZCSpLE9rEyGuXqUkZTKUVa1VxML+zaEnZMy9KSr1RVyfn5NLCvVVWNqiuGk01SIRB5j0F/aOVHx4zq217XijTN6PeHbG/toOrCxpMJq2QR0irYkzQhzTLL7CclxjFLhvenrUBrK9Q0qq4wScKqKKnqigcP7nPvwR2qckVdrWzcYV0zHo+RiX1WmmbkA23fp6qQqxVJmpKmlsji3r17zGYzBoNBmIN+XgopSdyc9W2x68gmdrUKHDt3V6sVRWFJGkajUSsHnHf7tX2WBvdCgCxLMEZycXFOsVqxubVlZWoHpjCQpj3AxkbZ+eYIIRIRrOlVWbo4utK5ka7CPjg9n/K25Y8eIFkTn+LurdsMewMykZAkgq3RmI3hiEopLhYLprMFtVZOfFWW1UWIwL5hjAGRUNSN36QPnLYbogVhOvFdan3K/YJQniPeCFRlfYABqlpTO4CUp5A64UUrS8mYpn4ygj/Q7WZsXeq8q54V9A2VqcGxfkkEdVGjTxWbkwlVXWIQLIqSfj5E6xql7VlmNKRpTlUWvHj2HHH3DqNBn14vx8sk4fCjs3HSbHax1scLRfb8arsb2fTd611SRBQz0AU+JkInbe2n3zyiE3b9jAj9GYDQGgHcf26ClaIBRwGSRMDO90lL/FqjlTYYTGSh9LDJNQ2jFakQJBhUseLs5ABtLMOQdvmPhLBaKZ+PhFZ/mfbTWqDP/+3Hq50nwP9ulZZ2bvucXPY/Sdz3Qni457O7e396S4srBMH62Bqq8M4OADvBQaaO5CNI1pbJ6tLhgGXtSZPUzS0vCDXxcMbX4x4lRPNnYKLCgHDaXo2jNm4H2wdLmnFCt5t/RntNs3A5zpokd14Q8vU079uaLpcAanCv82Omak6PjlhdzBhuTKy7KwY7Oy7PcTsmyaXPoRFM1lkFtQdeshHYQ/tMG2xegcNbbbATOcyUS9e05qgTmpr36YBbv/GAdXvzddAIyYAN6g/f2RtDUyIFi4grtDe2n2W8IIeb+zZ+zpImCHqjEaPxiNnpIYcHZ/R6GY/fe5eqXNLrWRpqI0RQrtj4FePmoEHiBDzXUuH3HqGw6RiERVLWv9rlWnPgx1UVZpS28zfqSvtMD3pVNEscQNIYpDEI09A++3tF9LvBgGMyswJS1//fWzOa8TBeuaA1OG2uVgpFjXCsbForB5AIi9Ja4yxzZ5gbxvaNchTA1jJkPS20Uu47HdZd/E9rRV0rFwNhQY4BqxBUtbNAWBedWlmihlVR2PvrOgAog2ObNdalz1vwvDUoTVNMrWgAvu251Amc2qHRPM8RQoT4DWPyYN2J3Y28hcGSZHiteIrWDWCq64okkaxWBYvFiu3dHc6mF0wvLtjd2yHLUgaDfrBODAZ9hqMR04sLlktQ05rRxpCNzTGHh0c2Jo0ERILSNtbOhLhnv+eZZo5Eq+eqU1a48TPu8A7KJtYDpK4i6ZJVz4DRgqpSLudUkzD94OAV8/NTpqen/PynP+VP/vTP7bhrQ5LavkuShNFozIW+sGPr0ilgBGmaU5d2PBOZMJ/NqKuS5WxOKgTj0YjxeETq4rQHvT66VmSO2lsg3LlBWBsmpC5I2NrapVjOqIyhVopa136jtmtLADLBkrRrd26aQJK4zrJpLWeCVAprZTX2ZP7DR7/n5atn3L61j9YZSeqUdki0EZydX9Dr9RiNx0y2h62YJ62sRbbfHzAYDIPCr4lNbGLMmrPVpwawFsrmWoGUKXW9JEkIFqIsy8LvgLXUaU+iZt1F0zQjTRI2NzY5qRSHrw/Z2tqi3+9zfHTMcDgiTTXT6ZTNzU1WK+vaNxhYd77jo0MWiwV1XZNlWaBqx69lrTk5Obli5l4uf/QAKQMeP3zIjb09To6OUZVF+lkiEVojZUa+tcXFxYyL81N6wwEGQVk5gSeS0NNMYoQErGVJOLc5aIQBT7vprT4ae/Yp45NB2hwRxcreJ5PEZarGfpZl4HK9SAmVqkCIoJ3WSllNPgJVlzbfRLVCJo5O3Bi0sAGNaZJQFSWlKKmqmnffeZez8wtWywWS1PnyAkJaocAIlssVv/jlL20QnpvwSZqwTgjvmsbXCbKY5rqYuQwZW5euAzTt4oU7v1l0iQViAfO6Or7MM32J/WC79XWeQAPExNpru+4FvnhfWSFsMrrVatUQGXSSAzd14ebcZY1T3PdXtcP/a+KP7IEUM9V1r3cZxpzmyGqIY4E1fo7dWJtcR7YtogWEfGLcjcmE8XjMxsaGy1SeNcBNaZarJbPZjMViwfR8Ztl1HNOiTARK10HgiNvQAPfIWhD1jZ+TkqvdrWxAuwhzILgj0iZ36PaxL5etS+3Dz9dpjItTwO4vx8cnjCabVuhyYDMkdBWdXr/m+QLRWi9hfKL7uoKLw9VfukhBOOTdw5t39RMnLsZr2DsAprnAgtToXZpv8IbM8CgBgayjoTW7VGW7kX5K+rngE+M6ZjXt5vju7g6vn37BcNBnOj1juZhRrRaofo8ssXulQCFdzi+0JwqxeeVihRHhX6MECyBTgM9ltKYb3bg1jEyx8gOj2++mCaBNG4OI9uKuddNb7PWaa/z3QrTJGzzxg8VVDvC72NgE44gCTEMy46xslQMkPmC7diDFEwl4IFOVVThXtVEuPqZqrZu4H3xdWjXxT1VVOgHUW0zc/uUAT1lVeIBjXX5sLK92YKjXs4kvh0PL7Nfv90mlJWKSDgAhCHTCBkGapeR5z0+20D4p06jfbVtiV6Esy8J5bhlnLb16kkiGwwH9fsVoZOWIra0tbt26zfb2NkBw3/LWqsXC5tp59eoVq9UqyhsTxwO1z/XLcZbtfUX7TNNrinGbxlXWj6tKFyjFf0spQ+yIj+NRSjEcjjg0BiMk440N0iylLFfIRNDr5QE0e3KEoijIsox+r89oNGY4GnJ4eMiDhw8RwO72jq17r6bf7zvrnY17EkK4+GwLvqfTaVCg2fbYOZimiVs/VsG9MsbF7nrgZN/B5g1KrfX3LUq8FpVWJNoDa9tftarJs5ytrS3voAI0cT7j8djOzcS3r0lDY98DoJED/E/7nQihHUkiO2MpkUHnYftjNBoxGAyCVXW1aph3KxcmYl0P7fq38fgF0+lrBoMBw/6Aoih5+fIV8/mCra0tlNIcHx+zWKxCfavVisViEZ7R62Usl8ugxFgul0wmE8qy5PDwkJOTk2DVfZvyRw+QdrYmCK04OzlGYM3ned5DVVZjpbVGZhnD0ZCTs1NWRYkWKUKkKC+Au81c19Z/3Gs8tbb86j6pnjYNo47f6ATW0tMwzljw42ONDBU+RkFoKwQIpyEvy8qaUY1lCfGTC6xbnmdG8ZMhBH2iGQ4HbIw3SJOEVCb84XcfcevmLYaDAa+nRyglyfLcCh3usEswnJ5PQUjOzs/JezYZnxGSJGkL8+HQ7Gxqcemaz+PPzJrrrwIta5/bqXfddfHzrmvjVXXEn8WWlque0TyrEfKueqfuYRD/TKQNCj48PGwJ3TocXG3BugFIl4UEn9g0jg3wfunN/U377aHYBFN2NXxNn3l0461G2rltXPWuzmXDfhC+6w8GbG1tce/ePW7cuMFoNHIUrBn9fo8ktQKjjftT1HVl867UNVWpWSyWnBwf8/zFC16/ekVRlgFExOXS/OsAeikby0U8zlfNFw/Ok8TSwGayARUt60wHmMYH/1oLowdfqQWRxhheH7zm0ePHdjyjmIQwNhEyCL92xs2yDzY0/O2Afa8tjt7XaQhblV4q1+iSjY+WCrWHEmMgK5jpxr3s8sg198n16yrc2rFoWK+uzlyI+1sY2i59dk4bR8MlnVuWEJpaV6TSuuFsbIzIepk9yMdjlosLiuUMMx4gTOaSfyubs8ZrSEV4Wcua5sfQa9alDu/QLCPj8hQlnTb69zAYUzV95Oo3/mckeDXz3V6nXRyMJyMw0XW+Dm898ZYgY3DuZlbosbmSHIGQj3sNbmkqZLbHmAB2/OfQKAWgobm28TvNO9RKhfbEwiGJHeOGlc97J1g3cxG5sNW1tcDnMmM0dEKiEAyGw6ZX3TkqpXQeGY0LnhQ2kahXzGXudzsn29Yfvy8IIVgsFjYPVCKpqtpRqFt3JClSfKL6mBDHrnFDIjNEZtuuasVqVVDVJTjvlqIoKcua+WzJcllw8+Y+r14dYLTh/Pw8BMgnyUvKqqRSitPTU6fQ9Vaw9l4VK/5a6+sa4LKuxNbdeB/yloI4R2Drvmhf67KUQsNY5sFOWZbs7u7y/T/7CwaDEQ8ePkQjuJidIgRMNsYBrHqw4xWQ/V6fNJEkQrh8RnP6/Zzh2LLPGT8Wbmx6zgrodRDn5+ccHR21xg839j7hcVBYa8sUbN2zJQLJcDBCYPMcSnHlBntpLLzSMngCOIIN7xJ6dHzErdv7SGSTU8z1db/ft/d1zvWmPv+vod33Y+i9JSxRkXcndGezkGFd93q9QByyXC4ja1EVwExZllYhURacn09d7iQL7MuyZDGf08t7VFXF0dERxhh2d3e4uLDJph8+fMhgMOA//If/4Ag00lBfmiZhf0lTm67g5OSE0WhEnufBsvS25Y8eINkzymCkzRZe1hpNhVaautYkWUpVa/L+EJHmzBdLZJ5gD58Ev2dUlaFeLu0ixh042OBKK/TZg0wbT1uokKK2blhu8uZ5TlkUjh3FInFL76rIshStFPNqQekmSu0mmgCkM7l7RpuqsubP0XDU0kThzJ7LRUGxKMnTFCkEF/kFv/7Vb3j3vccURcHxyZTxxhYyS1yQpiQTilcHhwxHY86nFwyHQ/r9vvPlvcxcBlZLclUiSe8zGguEPlhx3R2xwHoVeHoTqGriYy4LUuvquKredfXHzIb+u+va+qYSb1K+LqUUZHYzPjg4sIepc9H0rkJeKImFhk6rXZ3r31W6+CCft8RbP7wAI0QSwE58eLcPLa+yXw+C/UEU3i1xrhYu4e329jbvPX6PGzdu0B8MmGxsMBgOAj1qrWpUrVC6xMcDDtMmP4uUCVrDfD5ncfc2j99/zMXFBa9evuKTTz7h+OjYCn7RGBljSIQIMmbXMnYVqI3fq2tts/W362tcQNaXt5njITbOGA4PDsL1Qoh2XI2OlBQOz6y3YjWgrtsORARgBCHmoVFmdAWky/1lwnzwzzW0cgN0DvlmEGx8V9TK1j2dHmrq6Fbd6UurDPXB/pHioFN/lzDCCjru/bR7Xwx5KjBGgalJU8FoPCJJJKPRAHTNYnZGtTHA5ClJ1kMKg0KhhaI2nqG0WTLeTRRjY1nsmFrAgrJxNFKCqQ2QrrUOa61A1EHw9PF/fjy0VkFIs2eHCgJ3XddNnhSlnUXGfu/rUUpRlEUQflTtQJJzkxNS2HuqCnTtwKQVIuM4HwxBqDKx6460Ap6UMgSqx+tbJkmgMM6zhvK3rCqSnqUB9pTH0n1n6/aaersg6rqm18vtvuZdCD4aLwABAABJREFU4I0VoIqyQCsfKG6Zv7wLoO033QJhfqbafrXvNxqNAKtkLKvSESvY/Gurle3/5XJhA+uFsDkJtb3e5qcqqGtFUdhcVVaAtDFNPn6iLG2ciY11k6RphtGR5ceAxFoxtFYcH4tmnQqbv1FrD8K8K7A3YJpLZ9s6paZ9R33purBu3qLELnaxQqlb5yUForACtLXgDQOgrZXixs4e+/v7DEajACR1XbK5uUGeWyuS90h48uQJRVGAse6WdV0iS8HHH/2e7/3JnyBcChUDZKnNQ2jjVL18BapWfPrpp8xcXiDv/uhTwIzHY3zKFjv/FMMsD3NCyoQ877FaFSht0DTJUFuY0DT7VReY+lyeXt7MZMY777zDe4/fYzgcsFoULBaWqMMzvAkhSNImybuXN3z/p6l9Py9b1I7Myya2taAjy7yrnJ+bhYvPczKrIxsbDoecnJxQVRVJklAUBbPZjPl8HrnCWotQIhOmFxfMZhcs5gsQMBqMHKCaU5YVWZayv7/P+++/zxdffBFyHvlUBtaNcki/32MymWCMCQoaYwzHx8dUVUWWZZyenr7VXIV/CgBJZFzMloikIEkTaq2p5ivsdiLQhWK2WPDF06dUjvs+Mda6pJRlnLHaBIPNzdBovQIQcgGN2k1nrStLty2lNfmuVhhVUzqf6MTlQUqSJNS5mC+pq8qyEzkSBoEgEYlzS7Cba5bmJCKjl7kNuagpiiK4BWijMcLGQ9VaUyxWYAypTPjNr37NZHOCMZKXL17SG16QDwZk/Zw0lQySlFevDtjd2eb14RF53mM4HDEaWQBlE2U2rkXBShF39xXapcai4SwdkaB6VekK92/ahNdtrFeBruu0YF8W8Kyry9+yzoJ1lUbOA8oksQf+8fEx0BxeXrAondASBOqIp0kI0ejrvZEn/q4jPMcaqaDRNTowX8X9F9qIDwQ32BiIzoOgJYiH5yWSza1Nvv71r3Pr1i36/T47Ozv0ej2WyyXT6ZTDoyNHPatCYlbrLpoipAjf2Q09ZW9vl929HdI0ZbFYsLW9yYOH9zk+OOE3v/kNrw9euz5t3Ao8Vf91c/W6EoMggw3UXufad9W9fi7GlkD/3KqqnObPIKXNZO+1aDY2saE37hhNMP6d1rxbDHgu/QxuXbbIbr9E+KsZ5gbV+Aii9vpyIIl1QF2FSq1Pexwrc437Q4xtMM0a44qtxDXYmPV1iu60FcGG5F7PWy+ty2OtFUoV9Hs5t2/f5uLinDyzXgLz6SlHiUEaxcZoYgU/t0ZEcLETNq5HShs/E7myec00OAWerhxhkCfgaSwz2pEIWAtO6YTexmLswUldV1S1s+JgXd5s7jv7XK+sCu7g2uDjGu0as/Uqx5SKcNZttx/4PH8WpAhkYpVxSZpYgT2xucPSzLp7J1EyXT/HsqQtsFmvC0flnNlgbwFWyywEdVWz3ctRqnGvvri4IBcpg+HIenQEy5Htr/lqSZ716A+HJEnW7GlSkmqBEgptKqqyBCEwbp/RWjsBUFEsV0EIVEoxX8wtm6ib+5X7vCgKlAOdZVFhdBP7EMcYelDrJ6ofP2g0+jZwfhiESq0VBkcooLAWiRAj6s8cr9lvyJAsqI0siI4kRbgN5Dol4SWg4tqnTRM3uA5I+RK/0zpPhO59Vyl3fF3L5dJSUGdZAEjLoqCsa4ZO3krThKJYMplsMBqNmE6nzOdzdnd32dnZ4fXr19bd0vj4Qs3Tp0/Y2dm2OSM3t53FR9DL8vCOQthYtc8++4zf/va3VC7prJeLPDnDYDCgKAoWywWnR4cINMPBEBDBJXw02rAU4asCQe3OO7+24l5ar8yVLpa7rupwtm1ONtne2Q4MiMvlkouLC4CQKFW5PvTu+9PplMFgwP7+PovFEmNgNptRlmVwk5vNZi5hsObVq1dhTL3FyNefpqkFn+Dc5aacnZ1R1zWz2YylMzD4ZL+CJLj3ZVlGmuSkac3p6SlG4cBWQlkuqGvFalXyy1/+mtVqwWQywVN+B1dIIcgy6566sbERvp9OLdnGbDYjTW1+wbctf/QASRsbM1QsS7SoKCobj5Olfcq65vzigpcHBxRVTZb3rD5xVTtaVWXZPgJrnQVJsTkV7ORQRlPW1u+5SdgnUaW7v66sCTBL0XVF7QQhoxSj0ZDRaMTF+ZS6LO3n2mCQJCgqU6G0QteW4cZrsqRzW+rnNvmWMAJphKXoNDZmRStFVZSsjKEoVvy7f/fv2L99l15/wrw4o1/VDNSQLJXUwMHhAVJC5rT14/HIugZ0zLL+MI6F46s2wHgD9YJ4G1a17/HCVhdMIERLU2zln/YG0rUgtX5395tIMy24fAg0wGM9MHuT1mwdKIrdt1qfuxKsOM5tJJGSs7MzrHumChqdYGUiYgS8ygfKtJ/XNc/HANL3deasV/7eriuJFZrsT+MC2MN3jnHK/+03LSkleb/HV7/2Ie+++y6TzQnb29uoWnFyesLR4RGj0SgkLpZCsipWiNIe/rWCXk/Q7/eZbI7p9XoIAUpVHB4d8vkXX5D3ch48eMDd+/dYLVdsjCbs7+/z5MkTfvnLX4aDojvOLSue/aIzlv76Zvxa428IgMS73nRj1K4qnm3sUjviOWcMJycnGGNddpW27mjJOlAuBCasUxqLBY3mNgZo0Z2wJr7HBKndA6mmH7x2vakz9GDUYc07tBSjutPHcTtoGM+aNnSvc39HHyRrIJJvv5RRfbFCx3Sgle7sO0G7re2XApazKb1exp07d3jxwjJLalWxWs45qlZMT04YjzcYDW3m94vZjJPjY6bTC5bL0grYjupZa03l3aUKB2xMDUKxuTVmf3+PJJWIpHGNjFk3Va0gilFqKaAAy3bZrEHfJ+Eow8fL2JjHPE9I0yy4p2Vp2hDRAGkq6fWyAE4TKcN+pQ0kSRpyo3hAb4yhqosAypr4DDufU+d+vpjP0UYzSAakWUqSuChHCUVVkZAhhUQJz0aWUNf2/dK0B0aymBcsl0vKyoKkuqpZLBcsFwueP3+FMTbQvyjtOHhXIFXXrAprSfJpMwLhirZz14PJZt454givNfDnoo95AqSxrJ8EMG/nm1IKl102nOUWHMqO9VkEBj2tTYiB9HuM72OfvDuVlrXNuhxF+wvGOn0665ExzRpHiBCL1LWU+9+b9Xb587dVWsbXr1PUXauwpGGAPD095datW4zHY6bTKWVZsSwKlkXBxGjSNGU0GnJ+dsr9u/fY399nOp0ynU5dbh7rVlmsVmgpHPuZJFmm/OQnf8uqWPLe4w/Y2tpx54xwSsmS1WrFJ59+xk9/+lOOj48tgHIAwVsqxhtjRqMRp6fnHB4ccHF2xvZkg/F4DAiWiyVpmjIYDh0xiKauVkExIOJN9qr+MMYRNtnrU5FhjOLzLz7n9evXbG1OKIqCfr/Pcrnk9PSUuq6ZTCYsFguUc98fDAZsbm5ycnLiwNIFxliL6Hw+5+OPbS6tfr/P1tYOT58+xRjD6ekp9+/fD0DDGOvW6am6e70e0+mU4+NjLi4uEEIwn89DP3kF+3KxcvPBpjCw1qChnetCUBYVec8SkGmlmLuUCmlqXSZ9vVVVObfLmqqy7fVgCAh94c/nmzdvXtu/cfmjB0i/e/KC8WSC1pqycoxvxqCWc8qyZL5cUnoSBeNoQ3VjqrabZY0xDtHKFIE9nOzhqMhSg0wkeZbS7+VYEgXLCmfVXxkyMZYpLkmQIgl+uPYM12xORkwmEy7Oz5jP5lRFhRESkTpKTpcNu6pKhIRUC5cAUCCEpwm1h2FCCsKgpGXM09gM0waDWsHnX3xOlvUZjje4IfbJZA1JikhTZrMLyu0t6qrg1cslW5MRW5MN8iRBC0mWZ2hh3SyME4YvR3zYYsD680dCpjEGLUz43ZeWGd9p5m3XeB0dDtxcN9oR7XCjkg4/jKMINYhIAe41Npfb5CVM4X8KwoLWrgIjGu2530yFoWF48kKq+7N5GxPV5wUcZYXf1M6N8+mUhhJZoVzOkKDRjbT4Vxd7Texf7H82Vj1vjbCWK2FvCC4rELnaKTvvjbN8NqxCWEYd77csHeudgBv7N/ne97/Pzs4OOzvb5FnO7373O86nc7I8JwGOFyccHhxyMZ1yMZtS1aXtn9ogk9StO0kvzxkN++xsb7J3Z58bN2+wt3eDuq75/LMvWK1+z8MH97l77zbz2Yysn7B3c5df/v2v+PTTTxGJdGNtHG03AQNYzjIVNHTtQ7vN9AdRHI8WwS2o6dfLAD8GpV1rU0sw90KXS+a5WM6YLy7Y2NrC1BqDBHE5BhAI1MsyXNN6A9cG3TqEA0zoKB98slQRqNydtchOePe9I08wTV2teec+jJUOQaESPyv8XYdPvAXD9p19Nx/fEPdlLHz5w9UrQQR+zUVQ0elZYsuNNh4I2b0Smpi/2hEIVHXJ+dkZWZqQb2Tcvn2L169fsVisrBAnJcJMefLsCxaLFYeHRxwcHLNaFu68IIyvcbEIgHOvqe2+nkp2d3esm/TSku9kfavFljJxuWnsXtHv920/OYFDJtY1ViYJaZogpAnv64UXrTWJTINCJigzjAlKMaV1cK9BiAAQvCeDVope3iMNeV4UZ6en9Pt9+v2+9Z6IXN6ytE9R2LiDSvs5CGjH7FXVKJWwXJacn54gsKDo9PwsWF68+46NaYKqVIG6OrbQaGXjoPyc80oM7fZtonkTF62108rrAI79lJGO7MOThARlqbekY90wwVtfnWJJ2M9DvigX7yUcc2Z7b2hWhFdMxQni/XNNeK5dOcZYEgFtNDWCxFnk/AnqLZCeitr+ax4qgVRaVkx/NoQ2uXiV2HIU9j/TbnPrN7/23PvFCjP/nVcqmfj66P39+zZKQIWQgvPpCffv32VnZ5uzszNWyzn1as5qNuUEazEY5AmvXx9jbu+zv3+Tp0+fMJ1OOT09YTQaunAGwXR2jtJur5eS/mDAdDrlyedP+cpXP2R3dzfErbx+/ZqnT5/y6vUhi+WK1aogTTN6+RCMRNUCLfvs7d8jTTPOjg+pChuDkw0GDDY3qZVmMZsxGA5J+z2Oz86RImM2nVnSHdcH3jPB96pXRPk+afrfWkr3b93g7OwErS2IKIuSYlVQlZV1+awVi8Ui5G/b2d3lwYOHVFUVSIBOTk5YLpcslyveffddlFJMpza5rAc3vbzH3t4N3nn4kF6/x6DfpyhWnJ2d8/rlSzenJcfHRyFGTEqby6jX61kZuiox2p6V/b51M0wze472+zm9fp9aVeRJai3gZUmWpdSVZe+9dWufqi6RiSDLc1Jl66rq0uZmEiK4Yvp9wVuNhsNhcDl+2/JHD5B+//Q5w+EZibAasSzLLIrPM/JeD6UMGkmSJs6tLQmCf0Pz2pj/ZWIBUuI1P8ZaWzB2ARusn6cGaudfbUSGTEHXNueAxFIUN0KYQtUly8UFUsLe7i7nJ1Pm84VdKMKQ9jKElojMUn3q0gav2g2xiSGxOYlckiwEaZ5TqRplSpfMz5CnEq0rZtMTqmLO48eP2djaYTq9YDGfopX1gV4slzx/9pTdrU362W0SYRBZQsy4BNg4CNOxnAgrmKiQK8MqbI0/METjPy28xCLsYe9BROv/pqGZbBUT/yqI2fbittjmeqGyK8g5cNQNljQiaK5wbTCO0coIE5IM+w3fYLV0KbJNjtWctTZA238Wa9ndYScTqwWsa7vZ+Rfw94Q3fUNgZ/Nu9jcZNIVNMKaV751oGnJhpG4cdUuA8u+Py0yOscHLwo0ZWI2y9gcg1tXm8XuP+fo3vsHGZMLe3i6vXrzg4NWB05rXHLw45PDogLIoMVpYgobegNFoiJTQkxKtBYoEre3cPz+bc3p8xkeffExv0Of+/fs8uH+fRw8eopTm2bMnPHv6GR9++CHvvPOQ168PSJKE3b09fvbzn7Msli63gp9XVqErnOBvBWzVFhaExM+ty9pO24eNC4tHXfEcaGtpPdBuLFRRkHYAMLZRZbliOj1jtDnG4NSGkgAU2sXFD4V5QgPS0QhjCUDiu6QQJGHvuPxmAdb7NejWcbMPuGe4tdXoMxoB1YMW/15NXxGEVo1BmDpU0HLdES7eUTdUtxAxrQXriV1HXqC3oL5hSfNjoLQKnxts4sbVasVsdkFV1lR1RZomkSurVRjs7+/T7w9Ik4wss9rqw8MDzs/PrZuNWlFVJa9eHXB4eILRkrKoUcrGMfl8ZFYIrpAiIctS8r6kP8jZ29tjZ2eHyWQS3KaH43FwG1t3uMeeDIF5SlqXOq8Y8ox+VV3Z88snanQueamwwnvl4pY8oDLGun0maYJIUkypqFVJUdbMlzanzmpVsFqWXEyt0rERUGpWq4KysPFORhsWi0WYFzbO1sVARTFL3pJuMZRpxUj60lWutdxF8XHC8Ry3Spx1c9wYR1nu1kUD1l1xoNtfGwP7hlzEz/c4Aa92Z40HK8268fXF6791fAqILbTNvGmnZrBnvqMtrzVQhHne7af47waQSBJ3LnjvAv9MpAXIwSIZKQFDX1+joPPP8m6YgJ1Hvm1BXdKAgbi0LN2OOGU6PaeqSzYmY8f0V1IuZ7x8tuI//uEjtNZ88MEHPH78mKPDA27fusvt27dYLhecn59Zwg4BaWaVDUkq7Z7hmRWV5uWrVxwdn5Dn1uKwWq04Ozuz4Gs05mK+IklSBs5trq40dW0Yb22zubXDarlkubhgZ3uTi9mc3mhMNhgwPTyhLmt29/fRUrJYrcizjNn51FqvXdqMMCeiPvfnQ3MeC/fsmhcvXqB1iUzGfPT73zPoD5jPFszn8+CSWBSFte4mCeONMbdv3w79fHZ2jhCSra1tBoOC58+fB7bD+XzOxcUFRmvu3L7DyfFxsGB6JjnvztpYNIULE9FOzWfzEiVJQubcEbO8hxGCvJcGIofFck6aJQwHPabTC7IsIc+HVHVJXZdUVYHWQza3JiyXS8DGDHplTlGsyLM8xB71+/2wzwhhSVOEEP+N5jsuw+EWvf4AXddkeZ8sTRnsTBCJdV3SxtDLRyQ+B4uxPutW2eRjAixpA0aTCcjSJNDs9vOeHXRtCR9OT8+xDhmCXpaBkOSZoCgXpL3UBqtq66uepZLhMEepwlqiBrmd2GdnDPI+wmjOzs+RiUQYm1tGIsmSnKSPpX32gbCOkSURAqUds6zQqFpbkJJKWw8SNMEfvFxV/OH3HyOEYDqdoqqKjz/6PTf3dinLgjxLePViwrAv2N3dA1Ej0x4+YabXZDVaXy8dWaFIyMQJaMbx+0LiBM1I8exV2FhNfuM6ZoRxLII2ZiM8zn8fb646+jIuAV1YwHPpitgdPNaIxfgFcIgOY5w2MRzKjQDotT/dZ7SxYyP4dd2qUqcFXswXrJbLgHH8AeoT5XWaurb4jdULmKJlFXEaVTz7kwhWTfucZnNuW5saATd22xFCBGEGIUiylG984xu898H73Lh5EyMMv/vdb8mSjPl8wcvnLzk7PkHVmnQ0oL8xIcuGJGmfJMvo9TJevXzG65PXZHnGnfuPyQYb1Mqg65qqWGDqM6pyycd/+AN/+Ogj7t69y9e//nXeeeddynLBr371W27e3OcrH3yVfm9EnvcZbYz56//011YjhSO/8J0ZCeRdLXMsyHRdUeJ4vDgX0rpr19UdFy/kN0xeoFVNVa6QaPLEWuZSZ8km0lDbMfVsaE07/FTx75A4joSgscVag3ysTKdFFhjhlBvaRPO8tXACSPLWmwiZOYDTuHy1ro37StngdxvMb1lHcVrwsqqdm5AOgr2njg6sU15odLlwrFbV+tsLIRgMB+TO1cLH52hlAVJRVLx88ZrT0ylKVbz3/jtsbm4yGAwYj8dMJhOyLLdxNWlCpiVJCll+iyxPXKxDRa83YGdnjzwbcnJyznJ5Gqj7m7WjGI36jDdGbG9vsbU9ZnNzQpomlj46tYxnNomjjbcYj4cBsMR07caYwNbl3YFUqclz6yKksTGtNj8MaKXQusRA0CKvVjYeIs1T5vM588UCrRRlZQOxq7JCKUNZVqxW9rzyGlqlPbtiA/TjfcbHRfl1ELvgCd2QnVyyCHqLWzSPu0L65dlqaHbly2usC6z+MUVASKjqi1//8Trv7gFxX1zl2hbvvUDQhHfBSrw3d0Gkr9N/F7tWN983rtcxvbNV7Fqygnhd/WP6zRNxXFW6VuHWuzgF7Gq14uLigslkM4ovWiKEtbgiBJ9//oQPP/wap6dn7Gzv8ejRI46PjynLktlsRr/fp9fr0+/1KauKfi8jz/vked+BccN8vmC1Sri4uKCuK0saIg2L+RKUoT8a0e8P0NpQVBVplnH37h16ec7By2cYY+j1+yxWJTu7e0gpbWwNsHvjBksHPgYI5rO587y4rASI531c7F6sERLnHaT55je/ycXFBdPzKVVp2d12dna4uLhgNLJkXiunCDo8PGQ8HgcAsVgsKIqC8/PzAKh8zBBY19FPP/3UugcOBq252SaCsSxxeZ6zubkZYmo90NzY2HDxwssw3+L97Pj4mF6eU1cV8/mcXq8XKMD9vi+lDPWsVqtA+66dAsazUfqQhNFoxGg04uzsDFgfH33lnH3rK///tIzHG0iZkfRsFmAphMsnUltXN2kPEFVrsjTF951yNKiDXkZRrABDnsAHD+/z3nvvsTEa03cMGv1eH4Sk1Jof/vA/8/TZc2SakqQWIPUyCWbI9vYOG6MNikWB0YY7t29z+/YeeS9B65r5bM6PfvQTPv/0KZ999oTjkxMOT4545913LfuOEORJ6gTpFIyhcjSqsStU1stQuqKqoNYleT8n0YkNdK8M0tiEtEppjFJUtSJJJONhj36+Q1UWnB4fUpUFial5/ixnMIA0MWxu7dFPUnwuJit4OUGchskFmkiCRpMcOeN5QBTK5Q3AfyaEIUn8gWdYCwwcMHE4bS1Oil0ALn3jFVVt8xKxedsDDM+s1FTjBMdW60OFIadN+J9p3stbr2xyOJvjSgCr1YK6LEJ7bb6Ay2xDbyptH3CnxTTWAqW1ciZwn+yuHZvkN63WYe825dhX3m84/j2lFPzgBz/g4cOH7N28wenZGbPFjFVR8Icnn/P82QsSLRj3xwzyjDJLyEabpL0JMhvTH4zJsoRPvnjJdFkhVhU3RM7dB+9RVJrVqqBezXn96RHzCxuwKQQ8f/6aV6+OePToId/97jf51je/w8cff8J/+k8/5Hvf+x4PHz4gy1P+5b/8l/znH/6Q5WLputc4AGn1z20Bzxbt/Pn99V5ISxKJMAk+iWRXcOkKOu2D30/WtiAYtOcelmiDckHFxhjSVJBJPze9NdvPIx9j4/5288daEuqmDdo4fYJ1A/KUzn6+uQvDumoBGWel0Q7I+DbHQpp3d/KxNtAEq9s52FgN4nty4ee7A67GumFprVFOYPMlgNK6DkHLsdbaB7bPFzOXK8NQlINoLIyzSoEUGVoZPv74M2YXc5JEcPv2Le7cvsdkMrE5S2QWkkJa6uYSm0gcdnd3GI2GnJ2ds1wWqFqwXJQtrbzSlgq/1+8xGg24/+AOG+MRZVUyHo/o9XrOyyEPh36apOR5HyFqqko7q46kKi14Wa1W1JW1eHlAM5/NWBUlq2XpYmhrVqtVyA/SJgwgGocmriWOPdTGA11ByKIbTV8fUxbAibDeGMadEB2R3SrvEKhak0R7TnfNdWMZ/dztCs+xogbTpphuzZWwINYUpzCIBdJ/CCDwVrd1AKm7n8Z1X7WnD4dDvvWtb/Gzn/0saMBjoBHXHwOkLhBs9VHnxbsubb5+IROMENFaWm/Fe1P/xOPXjWntNKUFsv11VvnW5D188eIFW1vb3Lp1i+l0ynK5ZHNzk52dXYqi5N69e2xtblOViqdPn/L48WPef//9QEqwWq0YjQZsbEwoipKey4mUpjkYK8OUznXTu2XZ+C/LuDkej22qGGNYlQUKw639fba3NymKJUdHBwhtLSwyzdjdu0GxWHJ6ckqvP2T3xj6zxYKyXGKq2lm1YtKLdrf4+Le2S6YIngIGSGTC+fm5y/OUBzA4nU4pigIhbLzOYDhAGwskDg4OAqCZzWah35fLZQAevv/LVRHmb+ni5D0Jgp8vnrAiniO9Xi8AoDS1yhdPPqIRCM/u7EghlsslZVmFmCHfvs3NTcbjMTKRlFURQFwMhLI8w6gm/5en8xaiIV3Jsoxev3/tfI3LHz1A6mduwbuNYNDrWQ2asZmMe70eUggq18nlaoHRiv64j5CCRAoG+ztsbm4wGQ0QdUk5O8dkkkoVvPjiU8qy5N79+9y+d58P3rmPKpcsi4I87zMYDplsjMAoVosl/4//6/+FTz/+FK0Muzu7/A//w7/mr/7qz+j1M7Y3NsgTyS9+/lM+/+wph0evQQpm52d87RvfZDDcQCub8RkBaZaR5jY7cVWWNukeBqFrG7ScSHSaslLWxaGqKqSGft6nlyaYNKEsLVNPVdsYl8ViwXI+o1guyfOUYtDj5PiQp5lGGEOe9+j1+giZIH3qGGchUlpbtzNjwk/tDy1D8LtuABVOca9bm4LwcT0O8HgBzV3eKQ2U8okd/Yba1m432tv2id3oG4V7NpGveeyS0RxErt26yTEkpYzkhgjIGQLjjz/Am5Y3vxhjc5PIzJIPnBwdolXt6LbbWti16G9NiQ/m4OEsGvcaH8vkAYJnVWuDwnaPe41i/N7x84QQ/Mn3/oTHjx+zu7vL68MDLmYzFvM5f//LX3N2saI/2GAy3iQTiqeff8ZFVbL/4DHf/sF3GWzcJMn6SDQ/yHr88if/L5TWvPPhN9m5cRcjElStKJcXfPyrH3F2OmM6s37TIulTlhUff/IFh4cH/Omf/oD33nufV69e8sMf/pA///M/5/79+1RK8Vd/9Vf8x//wH6lcpm2ff0XIhHXF50KLXVBiYOXfP849YbX94lIcQXO97fOuC5Gdd7EF0IBRCF2hK8uW1UtGkUUxYjDD5sXxYBoaoUSbCu+Wp1yEr3J06mCc8GyBkqptMs6qrMLcDeOuGkui0rWLa0nCuovfRdUq9NE64dML6ElilVeLukZAq8+sBVTaZI1R/4f5lyYIQ4i/8LEviaOUX64WHB0dMZ1OefToER988IEDLp4yXyFIWS5KjFHOm0C5OqwLc+PKDGhFLSVCJM6qYnPj5Xmffq+mKmEwMPR6S6ScBsIfgWAy2WBvb5c0k5RFyWldo1XNalWSJuehn+u6tvu50lRKRPE2muXSut52wU6MXGzOEtd/tsPQyubTiQWfIJiZkM8WjECQhHkY6nWPaASzWElEhx7b7ZX4hKRJsGBaIO/uQbbWT1hbUT1XKR3Wldg6GZd1wnwMzKSw7vJd4HXVs4Lgjm5crTvAyN8fW5Vja0z379Y7uFKWJcvlMtAs+3nvhcL4Pg/O1oGtdUDMXqtbc8FbOqWLP9LuXh+/1pxBb1dCXR3LWRcoSinbZ6MxxGu8uUdycnLCdHrBZLLB5uYmZ2dnFL2K+/cesrW1xYMHD8iyHru7ezx58oQXL15w69Ytlsslv//977m4uGA+n9t438Qm8g3gSClqZeNybXMlaZqjlFXCjAbWwwGgrEqKqmJnd4/9u3dIEsGTz57YfWy5pChLdm7cJkkzDg8PKZYFDx6+Q3845uXzQ9JEcPjshX/jTs9FZCuuX2IXWztnHYETJoCoi4sLy66oGpc8f8/GxgZVbcHKYrEIVsPpdNpSUklpSRB8HJHve+8K5+N7/PeeSt27unkg4uesH19/zi2XS8va5yzc3iXOxxlqrSlczivA5e6ylqY0SzHY+vf29jg6OgpncuVykvo1ELt2ggVrvV4vxFS9TfmjB0gZNf0sJRGWIneQZYjMrkWlKjLsIb7luPU3bt5iezJic2vC5uaE3d1tl7FbcX56QiIEn37yCSdHB2RJSuYQ8+nhARfnZ4wnG3zl8SOOT07I8h7z+YJEGFKZ8X//d/83/vZvfgwItDIcHx7zxSefcPTqNX/6539CkiX84Ad/wkd/+Jha1WQ5LkhQ89nHH3Pn7gO2tnecr3QatPUSSZKlGHcIqqJAK7uBJgYyoKprymKFKitmWvHyRe023tJNUJfh3JFTSGFdCbcnY7YmG/z5n/0FP//5z9na2mU4HAE6CJPe1c44gGSMwSjRkC1E4EZ4a432kASn9WuKd+gJG2YANZcPPWMagT4WEsylOu3/1wOkWNNI+NvpxVvXhrPLOIlCuPiHIDbQkGO55zQuUDYotnvceouBUjWDfp88TShWS/teRthM754eWKmWgPGmEmtem0Sb9l+TMLZrZVrXz5GAYdZrfIUQPH7vPR4/fszW5hYvX71ivpjz+uCA3/7yt2hSxpM9BpNdhhtjjl8952S5AgOqhsnmDS5KuDg/oVxdoMoZD97/EIPg4GTK61Pr+50mCZNBzv1H77NY2MSHm7u3UUozn89YzGcs5hf8+3//13znO+d84xtfJ0lS/uZv/oY/+4s/49GjR0gh+Is//3N++MMfWnc7KUFdoxEV3srWiftxIKlxoWoONEt9moS4i1iA8YJDS1C9JEARLJBZIlnNLyirAmMEiWxyVRjjqMaVolKldYfSNvjdx974g6d2WeBtfokSow2D4cBq36Jx9u/jBT0fj+XniJ1DLkBdCGpRO2uBBZtNMkGbw83fm8gEI4yLtUtCUk//TBvX1hxqiSMcAEFZV2HO+jwxHuwJIE0zlPss9IvSrJYFv/vtR86FWvLNb3ybLMuYz+cUq5okyRBSkvcysixhuVwiBBRFxdnZOWVZMR5X4fC3fWopnYuyZOVoc5cueHsxX1oa79qyqPkxreqK4+MjLi6mbkszQZnklUQCG2QsE6uRFQhI0gCEodm/bAC9doH/8ZwVeHfI5hM73zwDpF/n8VK3+42zZotmrzNNJTQ0OHYe+NlgRHRl1BSBCDFQjbXC50dqFEdrgU9HZrwOSIS/O3tXGxABcr17jdXIN8+JrS5xPX49hNQLxrLLxu+w7p+/N/4Z/961dvmyXC756U9/GrT1UsqQMPWSRSHa679s6d5vXezs+HkXKb/e/NxYg8OuLV0FSQye7Lncfp+rilKKg4PXvP/eVxxL3QXL5Yo0nZNlOaenNl6o1+tz48YNXr58SZqmPHr0CICPPvqI5WKOUjVFUVKsKmbZksaDAqpaBYBi9/HcWpFTa1EtyhUKwc6NXR48fMRwPOLZ55/z8sUze5gJMEZw+849KmV4/uwZaZpx5+59FquS07NzNrKE4+NXQSrw+6fto3ZOx1ZsGDjFqw7XykQymUxCf6Zp0lK8eQCSZlkrDsy70XkyCm9p8eDUWyyNsxR7S5Hfdz1NtydS8QrC+Bzz1iYPdPx4x8BICMsC66+RUraUAmVZBqtRksngMh27bfs2ZVkWAJIHfH7+xgqLtyl/9ADpwb1dxqMxGIsw0yRlMh6TSOP8vQehIweDPlVZ0ssSHt65yXK14Nnnf2A6PSdLE4RMOD4+QyvLdw9eo244Oz9lYzwkkZqq1kzGQwpX18X5lIODI37/+z8gk4yqqKir2tKQKs2P/ua/8PVvfoO0l/CrX/+a7Z0tKlXS6+XW7U9oFos5z589o1hV3Lp9G4M9cKHR6B+fHDM9O6OeLyhXLrlcVaBUZTXF2mY8N45txMfNaOOSDOJ2PffPKEOepfwv/mf/c3b3dlHa8Nlnn7K1vcVmJkhl6tiZHDsQBh1l5/bAQBvtQJQFFBYkpQiSxkWjdS5GLmxhF27cw3yxFh83CgYQVqshkwQhzKWDaN0BFQMG92V0LrfhEZ1nm+i9jGncf9owSESHigDVaNfjz42xcSBpIsnShPPzMzCWccrIJkjXGG+du2bS+ycL0XmGeydtLQa+79I0xTNq+eEPJBDRSRjq0d764WPFbNnb2+Ob3/wme3t7HJ8cM5vNePnqJb/97e/oJQP6wy3627fIJzts7mxx6+5dsn6P5ekZDx++x+efP2NWCfJ+zuakx4272wx6PZI0pzYJq6KiLktm52dcnJ+QDzb46je+y8Zkwq39WxRlyfHREWl+gppLFosLfv6zX7BarfjTP/0+xmh+8rd/yw/+7M+5d+8eVVnyne98h5/99KfO0nH1oSy8uTMSLuPv7IEjiCnZbaB7Q90ba5VjS57f6FtClXP30UqR9hIW8wtOTyTL5ZyyKpnPJuFQ8v7ixWplWYtUozCw+TWsC6DAAh2vxfP501Rd0xvkdk+Q9kBMsgwBJGlGmubNnBONRllg17YNvs2aQPLY5dI0cWlWPnXfJUlYO8Jpj429Ca+jqKqGUEC7+E/vxmlMe02AJbMwbi+z1hdFUVQYk7B/8w55L+fhw4fMZyuUnjO7mAVL1apY8fLFCy5mU4Q0aC34+ONPePr0WQCufg1Y2lnnRhiBlsaVyL6XbbdzD1QKoRVa+ID4FDyTnbakIP59srTv+jsJ7HqNFUU2oEK1XZ4uz9n2HDUYYgOp77cAfiHEEgnh9mbTMLTF7nWWnc06gUohmmg0uzG2l5Lrk0bn9XaCsCeWiPfLqywk0V0NsOsCLiFatOVdsCWiZ8TXeOVGLJAFYcs4S5/WrXvXAb6rQNC6v+PPfUxFDPzjPaV7f2w1eNsSg5Xmn4TICtZWDkUawC/xjKs+v6SEu6ZIKXn9+jUPHzxie3ub3d09Dg8PWS5X9HpLzs7OybKc3d2E4XDIjRs3ePbsKUorHj58SJ7nfPrpJ5ydniKlpcq34QHaYneRNG6GTnD3bbLWE7vH3bhxg3uPHjIYjXn6/BlPvviM50+fMhoNGPQH7N+5R97r8eLVaxaLBfdvP2R7e48vDl9SK8Xx+TG6rhEyd2/WWM2UalvQY0sauPnlrbPA7du32drassxxQmK0afIDuXV0cnKC0prMZAHke1CxWq3CvOn1emG/80o9KUSI9/SAQwjBYDAIFiM/L70yLna3a1sBhYtbNMGK5fdIX0+eZoFm3a+5LMsYj8dsbk1I0iTkcvIeVZ4IwjjFte8rb723hDKrSGH95vJHD5D+xZ/9Cb085+zszG42VR3cK8qyJE0kg+GYlfO7vHt7H4TmN7/7lUPXPilWiUxsfgErJNj6jbQbeZZmPHr8mDzPmc8XHJ2cOL72IXs393j58jm93AavVaUFK1VtTY0ff/EHXh+/4t69O5wenXByfESWpGFyrpYLMBVGwcnxc7ResLV7k/5wiKqt//nnn3/BwcEBaZLQS1PQ1jVHYBlEtK6dQNNodr1dR7iF6QOjMQaTWMH48ydP+T/8H/9PfPjhe7z77rscHx3y4tlT8lQyGAztgSPdoQqWvEBZs6+XKaWzLnmrjhX+qqCZFhhMIFhwpuMIHAVrkDGB9DoIVKbRYgqjkcYgffSGiVzuvBtKCwDhhIFog3aAwDOw+Jwgl60ojonItVNGWeSlcBPDPcBbzYRDdFI0gkajETaAppelYDTnp2dWmDWEgH1Pr+1ZkZoDKgJ4l4oVwjw4EgK0roEkHPreXB+uQTuhyAlh0RiEp4hwRXBV/e73v8/t23coy4qz03MODg753W8/op8PmGxsMtjYId+5webtR2xu7SLrFe9/IDk7P6NGsn9ji2/fu8fuzha91KBVQVFVVHWNQSBFjzzLSORtVF1xfnHOi+cvefbsJZ8/e8H25i57Nx8y7E/4+NfPqCvFYNjnV7/+FUmW8O1vf5tVWfKTH/2Yf/mv/hV3795DK83x0RGffvoZlqHvcj964ckQxxnJQNuLSYAUjD/UYtc2E+Z+nMUcouSJxusPAWlzmgh8zI6iKBW//OWv2JgMUap26Qa6xBmW5a2uC+e2l4SD3Wr8JGma2bknJUWl+Pkvfsv29g7vPn7I5s4kxMHkeU6W5xitbU4upxX0h1sQ/GI3LhnFOXjLhSHsnwab4kBIidEivLdXniSpTY2gjXaJdzWmtukEKpR1D1sWTmBULBZzphcXrJYrli7AuChWwfVj6ViYyqJiuVyFQ/vF09f8zX/6cbCu2Bgkg9Y2xk8I615n3ec08/mitT8IIQLboV+X0sW2CnQwjiRSYnQdODWFEGhPFy26Vho375wQIlJPYuPmmTHBQmMZCF0cpNFB6d6dtXYnDTYRRJRgwO/3eD2Ln89CooRAITFIvMrHz0eEtHl+LCoiEQaExlAjyIEUg3LvRgB20s1uKa17ZlcAbvbXCLQhwt/NVmmac0aIwH5nHCCT0gpHynilhQzz06E9TCR4xv3mr4tlJ6sEcgl0lUEkPv5U00DCy/vF2wr61xV/b6zNL8vyEgCLr133Xbe+Nz2zNc8Tm8LD97EXqqV0ID0+w1r94FScxlukvGtYYx2OwWPjtuevv/w+9lv/vY0R//yzT/jqV7/GrVu3WCwsa5t157TXLJcznjz9gqoquXXrFi9fPqMsV9y5c5vhaMjLl695/vy5Y0SDBOei6wiokE44R6GUnZ+ShMFowt7tfXb290myhM8+/5zZdArOfS3tjcjHW9y885C6rnn1/ClJf8itR++wrCqmhyds5n0+evoKKVN0SPTu533kSu/mW/iJV5JYAhttBEIYRqOBDYkolgxHQwb9AaenZy0F3GI1R8qEXn87WI68BccrAWJrSwySJD55tL3OW3vKsrSxam7ctBAgE3q9Pt51F1zS8yiXWK0UibQJs/uDAWCtWUJasrE8SUmTlFrViESgezXVckUy3mB6em5DY4wgJcHUhvl8jpCCRblA1TakpImJt2tfa7t3lUX5xrXgyx89QHr2xWe8+/9m78+Cbcmu81zsm9nn6tfu99mnbwrVF6vQFFoCIgmKFEWqpURJlnwl3XtD98UOx/Wj7Sc/3Ag/6M3hB79YoRt2uBNFWRQlkUFKIgGQAApAAVWFqjr92Wef3a5+rexz+mHOmSvXPqeAosRrUrCyYtfZzVq5MmfOOeYY//jHP65fp9UIVJfrrCBazCvd90ajwc7ODpubm3iex5PDJ5ycHlcRukGcbN1Zua5YpayytttCMJpM6HQ6NNstWp02YRiS5znT+Vw5t7oLtm1bCKHkOfMiJU4j0iwGUXJycqSjYsVRFwKyPENKVUwss5zxSG3q3V6fZqPJwZNDFuMxLd9XvOFSF9ILG7fRwBIwn09BlrrYcPVQ/VuWzk+hObjCspnOF/zuv/s9vv6N32dzc5O/9/f+Hvv7+7RaDba2tnFdB8teRcaWm1atALRc9kAwRkAtbv2l3X5FyRC6ELwWIOmgpFwxmnXDrAyYBjBrmy3a8VhF6OvH+Y1hieJLyoKq8a65BkVtWUrHSimxdFNFEyTK2rntFVqHWB0XKZVjLmwcSygxCikZjYYY1FeyrKmSqNevHqubXz0NL7RBNb+rKyHVU/fL52fOYcaqrP5gjGCFDhjES5a8/MrLXLl6Bc/zefhwn9FoynvvvY/vNej1esSLBYWY89xzW7TXNxlPFgyPD8jnU/YuX+H6zes0A58kmnOyf5vjw8dMxiPiJK5UcoyCTrfTZWNrk7WtDV588RbXr13l0aND7t/fZzqP6LWbDIcj5vMxO7s7BEGT7333bRw34NatG0xGI77z1lt89rOfZTQc8uprr3FyespsOqtizvOotXr29f5Biv5ohkIIpXIkdRNds87KmqqVGe+VMa/mnQoyKC2ddVqiX5ZtcffePZqtAM/z8Dy36oxurtPzPHw/oNVu6P44VJQcU6waBk11ncJhOo2ZTGYMRzOiOGJn94v4fqhoJLYNUmBZDk6NTqFsoQoVVZbWpsjLJWqo10KaJBXlr9BUlSzPlr1sMqVoZJSTiqKoZLZznREzsrT1Pjd5Jklqm5uhd5zPyJpxO4+0n7dJq9kCBdSg1zW1wFWZGpP51gsL46hTyd1bqKBZGGRIzyMLibQElGYOGCl9iZR6fminc5mhMSBWyfJsdUdXVoH7M49apsbYwwr1FzYCQ4+mZstUtspxXQ3ylNhGDMTYOY3WloUSeLEsGylt1UdQCMDWWSplm23bwndC8lz3RXHsihKoGAeQ50UVTNWphMa2PpXpEU+DVki9fkzgWK3Nehanvk+tDBSwKiax/EyB69hcuXKJbrfLu+++S1GAtF0l0CGLyqbWFex+3PFxg6dnZZ1+VCZm9do/zueeZxnU1kptvFazR0sp9uXfDHBYP7ehcD77es4DRWUhn/pb7VTVN2ZPPTo6ZGdnl25vg729PR48eMB8PsfWrTI+vP0+R0eqxmcyGfP5z3+e4fCM2WzChb3L3Lx1i43NTc7OzhgMBkynU0X7KpVAV1ko4EAKge26tFptNvqbdNfW8Roh8zji7r3blKkq1XAsi163D07A1evP4XohD+7dpixKLly+wtrGJo8fPIIiZ3o6IpnPsa1yZT83e64BW83+a1rNqNeYImgD0EiazSYg8QNfN6NWpRJxHOO6Dq1Wm+3tbUajMUmiVCtlKSu6nSxV3allL+uTzLNR2Su7ygqZbJbZX5I0A6n6SVkaOBRS7T+q0XGBkBog08Gao0UvHFuJ0riOgwUkcUKe5QhbAXR5llEWJWmsWBKe67FYLHTdql01ezbtGgzgbTJnBpAxgZ2UsqJ8f5zjJz5AWizmPHnyBN/3aDSatNttfN+n2+1WHXbb7TZnZ2cMhwPVUVtvWoYmY5wz4/zX09HmKIqc4+Nj9vf3sW2V2jWISyFlxdcsyyVn3LYsStumlJI4TrVTkJOlOWmakaZJNSFNmltKVRScZpIwDPE9l3azwTwMSLN0pYhSFW2jO5PbqGB61Vidr4Ewmvbq/bLi7c/nKVl2zG//9u/SaPhIChzPptNuqaaEYolU6DhCo3q6/8SzgiYp9UYmzv19tdjUGGupEUQTvEDdkVj+H7WfV8bFvLf+30ryxXDhUdQZc+5SKgUtMzZVAb60NLJUQ8CEg43JyLiArqewLBA10QOsakMBXZ9ReyRK+lIynU60k6ycBUPdqs6zsoF8xGYrVl9XD4jO76FLBHd5OfWA8dnnV+fe2NjghRdeoN3u8PDeI7Ks4L333gdh0+72GE1nHD7aBydg58bLWGGXw8MjAhs+/eYb7GyuMZtN+P5b3+H+3dtEs6lCp2VZZekEUAhBPBoyfPKYBx8I7MBj+8Iez7/wEs8/d5Xd3W2+/857HA+esLa5zmQyYjiYcPnKJbKs4LtvfZd2q8GNGzf4zne+w927d7l06RJxHPPaa6/xB7//B0+NiRm382NQpw6Y8VeKX0bIQT+32hwuanNp6QTKlWdh1k9RLPn+zWaLIAi1epKH49hq7fs+cRyv1CcoRFAFFIrCqoJ8KSHPFyqAKxWne/fCBQZnQ0BwcjxACEerNqkslaoptIiTpQKa2lwjLbsKaaIy2NEiqjapNFFCEmVN+aoOnBjBkboYSIVWWpbOQJlxM9NMgLR0tmeVzlT/vp5RWx3r5RpYnfNLB3rlb1iYLHAt3lHXqDGC80GXlLWssvm8WgF/aemsdFVj9R+fYfh4hwGQzJf+TGmDdFHOpkRQ6MxUjmNLLEtlssMgoNUMCTyXJE2ZzyIFGJUSy/KQEsKwSavVxvVc1tb7tNtt1tf7rK2vacesyfVrt7j/4CG/8Ru/waNHj1gsFtVelheK7i2l0AHv6l5h1tESCVdofpEv1121H6P69lhW3TE352BlHMycWtYvsFKzBcvam06vyX//3/8vWN/Y5H/7v/nf8eDBvgK9pEVJiqEy1oVF/jSPP1bmSq/L8zWU1XqFp9aI1EBlhcNJ+SM/s77+DPBjzlX/+qhA6vxRV8X74IMP+OSn1uj1uqTpLo8fP2Y+j1D9nZR8d1EUtFodut0eYdjk5OSY999/n15vnY0NJQN+4cKFqm9QkihmT4lA2Cob4vkBfhDi2Upa+tGD20wmE6VKKWwmgyFJklNaNjeuX6fZUiDd8emAXneNm9dvMRoMGY+GtEKX2z+8C5ZqJ7MauJ+jTQqesmHotxiAxrZtNjc3mE6nNJohUkotcgCNMFQ07DjmNM3I0ozFIiLXynJZqhsr69EvypLA9xG6b6ipDzpPMZVS6gCowLXsqkVIWZYUssARFjIroNSsHv14y7JYaRFgWRZZnFR+Z6ZroKb5tPKZVUZIASxPjg6BpZx+XQil2WzSarXodXt4vldJjPd6PTqdDmEY0mg0cF2X33/rmx9rrv3EB0iKX6mahNWVwIwBCMOwKghTSipLeUkzAeqpYGMETaEamIVukSSGApJXBWuWpXi8zWZz+cC1FLbpWi5LyXg0RkqLdqtDHOVIiVYsmlYyjea68yzHjnMeS7h0+bKmhFD17amyLZpm59gOruOS1O7lWY6B2WhMMLg0kgJwyAvBN7/9Ft1uk+liihs43LxxnUYQKBlyfSgu/fJ7M3bqMww6vrpR1Sc8LNO76lqhbjzP/3zeeD+VmhfL+pu6o1Z/rznqn1tKleXTxBhKqVXfWNaRqMtXCn4SXXSvX2MJC0uqsTM1HCrUs6qCf9t2EECaZZQyx7YFi2ihayGonMXqumrP7aMCl/OI+mogv0TWV99j3idqWbinz7/iECKRQvDaa6/RbLUYjycsoogPbt9mtojob2zht3ocnY2RlosQNmkccbD/gP5ajzdee5nAhTs/fJsffO+7TCZjTe6RShilUKIhhqdsWzaWrednWZIvZtz/4IfsP7jPjVvP8+Krr/HJN17k/R/+EOIzPD8kDFQGtSgki/mY737nO2z87M9w48YN3n77bba3t9nd3UUIwb0L93i8//gj7/mjxnl56CxDzV7UM1JKMS2vBalFFSAZBNbQ6nw/wHM9Op0uQaC6kMdxpv+FNC1x3UQ12EVqRaOSPE9BaIUzLbOdabGGPC/IMy0ukCgQBgRxEvFv/+1RFdjVex0px5RlYKLXhcmEKOUyWYEg55FoIYTmwKuhULWOhXYq6w6ZGj8VwJnA6Dzyr85iNuwKwNFH/fPrh+G4Q53OtawtqT/jau3odbo0MzqTiK7zF6v2x3ymbduKZqxhFtuq1SZaint//hosLbZQlssMpSWW5/hR8+7HOabLa9P3g2IvWORAASIHkdNselzY2+WFF5/jypU9ms0G165eYXdnmzSJ2N/fxxKu+rIV6NUMO3S7G7Savar421DBYq1UdXJ6ynB4Srfb4td+7VcJw5DZfEaapIxGI45PTnn0aJ+DJwcMBgPiOCZLM/JCta/I82KFTmpZqs+fLK0KMNMziyobJ8y9gy4q0WOmM//akTONh+tZ3fMBb7PZ5B/+w7/PZ958A7D5b/6b/5r/4X/4PzAezbGEjW055DJ7av78uGzPn5XDOJd1wRgwa6nAUOzqrANYDe2f8ikqm/bRma5nZa3OX9f5eV4fY7OGFos5H374Q27deo6NjTWyLOPo6IjZbE4YBuxd2MP1PHZ3d5nPFoRhyM7OLtPpgpPTM05PT2k0FMuh3W7TarVAlFg25EVJmikaW5aXjMYTpsMzkmhBmieMBgPWemtMo5xoEZPkkstXb9Bf32CxWHD/wUP8sMmt51+izAsOHz+mGXo8uPc+eR4jKMCytL1YHR9V37hs6F0fj7pgg2UJ2u02lmWRphFFqQSdbOGqGkyhbM58uqjVBdU+rFRrSGXdHJ0V1VlxlPJbp91WmXtBRaszta+u61Km2kcSS8XWFNWixARChglg7sO8rmpsrZ9rnuc4roNlaN1IXN+n6Xk4tk2j2cBxHC5c2CMMA/Isx9UqeleuXGFzc5NGo4Gps3ddJdyDEDi2qi0bDIcfuR7OHz/xAZKwVA8ZofpwLZ2HsiTL0qqIWvk1FqrL9dNqVSZQMrru5zdj09CwvniXahmikla1nirMFoDFeDxlsYhIs5zbt+9wdHSifk7jajKZz8qyDJGp1P7pyRFh2CDPU6TM1UYrbSxh5J1l1cxu1bl5Wl7V/K5e5GmyPPrdlLJkMp3yw/c/UJz/rGRjfZ1Wq6XodqaGwrKqjd8UhtedGeP0mmDJIOqO7VTPqEK1pCaaiaUDJ8zzUle5cu6POupOW1kuFcDqBmclMEPrdOmUt2tZmlJiUGz1OpVpdKugFiGxLK0qKC1kUWBT42EjAFsjNooypGhJGVmWMp9PmUwm+lmqq6hQc56V0Vh1Nqu5f24TqnPJ1b+1jW0F9jbnXO3BUHceSqk46Fubm2xsbOB7HvcfP2Q0GvP48QGNdgc3bNLqrfNT2xc4e3JIEDZAwM5mj1dfe5Uii/mjr32Dhx+8B3mGKFTBsyxVq2WzXmxhIcuSJMt0Rg88z8f1bBxhUcQR737/uxwdPuFzX/wiL794i9CFB3cf0QzbCAl5mpPnKcPBiHfffY/XXnuVTqfDd7/7Xb7whS8wHA557bXXOHh88JEB0fk5ssyYloAxMGoAi0L1nxGca5BKXeGnqMbZBAhCZ49mswm+HwAFw2FRrQ/12vNAQKk3onPF8XpuLIOweiZhiZgvAYQKtlDnEYIyf7pmUf3fUrLItXljLSFlDRyYn5dxhhFsWN6Luq+6UIBBep71DM4H9uft9FPBWe1v57P+z3pv9TtpEFqhi35rzqGlGm2v2nhFVbYRuLa9HE9Td2QJpGUoeEtnwMyFMl/y9JdCHjqQEueAm9r3deTfjKcJAMCq2TbI8xTLsumvtbh4YY2yzOh0G3i+xZd++k16/TaeZxOEjrKvcsjJyUCr6Z2xd+EapbSYzabaCVkgGWFZLkgfpKLmOXZJGDqkiWRzo0+z1aLRaq04tVKiVcFchLAqNVVFVSs5PT1hNlOI/nw+YzAYEkUR4/GI73//Bxwdnmn6jton8qLAcvScKsGya0wFbcMVTcnSgKKi/yk7o+a7AfaEEDiOkhH+m7/2N/krf/VXKMsSz/f56ld/ju+89T3+2a//BnlWYGNRFst9E1iZE3/Wj7osvxJ0WVUho1Yz9BR75qMySM+I6is/Sc/vOjBR/V2sSsWf37dg2Wy2/pmHR09otZtc2N1ja2sTEJyeDpjPFpQNH8tyGI8m5Jnqa9RqtVSdYhRpSp7N4eEh+/v7ANh2icJ7LeIkp5Q2eSERlk1gW9gWPL5/jyiaQ15QlA5SuFy6fovdC3skcczDhw+RAq7evEWj0+Pg3l3yJGK8WHB2dohtSQS2ysbWh+6Z4MfTA2pslGVZXL9xgyiKCAKXJI216Is6l5HmdnTrAs/3oW63bMhAgftOWWVdiiJWAH5RsJjOyIqcpFYDl6Zp5RvY2JVgTBzHWLpGyXWcyt45+nsjw23sVqfTYWt7m3a7xdbWNkEQsLW9BfayvxEoup6rG3wb9oQQSvHTjIdRuTPkz8BXSQyVj5BkZUFZgOt7fNzjJz5AUjudKsIFcBylIKQWXYGkxDIqGmWunMZazcazNlzjGNWlcBVtaxU9qt6HUlaKoog8y5eLHrXgw0aDr3/9G3zta4rikyQ5ZUHF2a/ryds6CrYthzxLmU2neK6LY1lklTMLysmRFRJp1N2ME/es7MKzvlf3po6yFNqphyhKee+Hd3i8f4TvOro2wqsCQdM0zHEcHFcV3KE56aYY0NyP7/vVa9X7NNJnqzSv7Tha2EDREiu0y7IqikAdDa7fX/0wohAVxUfdoHpGllXVKdVmjpK31M/ecdwKdZQmcKk1sDNF8UJYWPYSkTPXq+7b1tdikBM1B0ajEWmcM51OODk5Jk0TTPPRvDA9Wayl0/Yx2AgG1TEoaR2tUc+z1HUEFrI8985zwVV9TIuiwHYcSiTPP/88nU6HyXRKmqTcv38fy3Zotru0u33avTUuXbnG1es3efDgPrbj8cbrr5JGC77+73+X44NHiCJVQiSA7wdKRUeWrK2tY7uudnAkUiNRo9GI8WgMi0L1NghCXEswPD7i3/2bf8Pnv/RFbj33HFkG+/cP6bY6dLtr5FlEnnl88MH7XLy4x9WrV/nWt77FZDJhfX2d+WzO3t4ejx8//shsxEcdS5BBVLU4lqWaUxfFs7Nx4tzGaHoiKec5xbYFo1FSO7/xSFQGBb1JGhpGLU6uPee681wPrlezC2YCyOr/JjuqRUdYZmE1pKG/VqZNdZSrn7zqxLNqZ+rjoCCdVbDj/Dw041EPEn/cczoP/tRBofPKglLKSnRBAFKuyrG7gW6OqG2Z2bw9z2Ozv0a71cbWrIFmq4WrbVvQatJfXyMIAvI85+joiEePHjEcjvjhO+9zcnSC57i6V4lyaKQQlGKJ9NfnpVmL9aBxZVykkmZQmaICx4Wr1y7x3/2jv88Ln7jMwcE+3V6TvEgYDI959Og+Dx/eJww8rl27hmMJkiSi3Wri2g6NwKcobRxLMp2NGY9m9Hs7FEmBEC4S6PX6BEEDSkm/18axXUrbwTKqr3rcTc1OlhvquWtSP1i2zfb2RrVAiiKnETZA3/P9e/f5wQ/e5cGDB1V/qyiOaLfa3Llzl8FgWGVqbdvWapJKlMb3/Ir9EQQBQRDQ6/UIGyGO7bC2vka73ebChQu8+OKL7O7usphPGM6mCCHI0oJf/pVf4ttvfYuHDx8hCxDlaq+f84DI/1THn8T5zRyq90wzjqhEqAxHzRf4KIU8s8eYtWNe//Q1Llkcq/ehWgCY85wH5J61DwmhgIe8yHjw4B6NsEm73WVnewfXCTg9O2E2G5NlGWmaMZ8vaDabTKczHjx4yJOjQyzL4ubNm9y6dauqZ5FFRCN0CBtt8gJOT8dYlktZWqRJxMMHD0mimF63S5Ln2EGDS1c/QXdti1mUcO/2bYo848bNW2xtX+Dg+JT5fIxtJdy/+yFC5CjraGii5cq41APCFWCmFpwKDWLZtk0jDAnDENsGiYcsJGUiybMCWapm1UoWG6aTGQqjWgp/xElCqllKlT0Ryv5XzB69D5v11G6rGvtWq0Wn3SWOYsaTMZZl0+12uXr1ChcvXtKiJpJWq0Wp2SBFWeA6LkEYVD6f6bfneR4IwXgy1gCexPcD3eoBBoMhjuuwiBY4jkOz1SRNM+I4IghDimhBv79GksTYjiotsRxb9YYqC90o9+P38vqJD5CKwqT3chVY2EtELQyDanMxEqvUuJTngyBYGgKTLqzQ4FJWxbp1pEWlSqVKGwpBnmcUuRJCsISF4/o0G02SJCZJIk25ssiL5SZSXzCWpXpZ+K4PQJLEDM5OVSdo4zShVFgsHSDF8aKib5iI/sc5f3XKgZSKaqbEGBRfXNUJWsymMTOtjCd4Oji0bFttevVNHC0NW0PiTWbEGApQDrHjuFWD2aXzYq04Y+Z3quDQ/FasfO+6qtGjCbSEQaueYXwrxEa/1hhqs3EIIXBcG9e1q9oPUzTvui62Y2kEU/cksG0838e2LK0ItizCV4iYzXQ6xfcC9h895s6du0hp+gaIKkh2nKCGDq8ez9q06qh9PUBavmf5XllL8y+zCKvzxATLxpA2Wk0uXb5MGDa4d+8e4+mEo+Njuv11wkaDdqfL1u4FHM/n4PiIRZbz05/9LGWe8q1v/AFH+w8hT5ktJnieSyNsYFk2URyTZhlSWHTX1/GDAEdzlG3bYWOngeOHzEYjZFEwn81pNVs4lk0yW/C13/v3fOHnf5bnPvE8k2HMbDyj22ozGXm4jsd8EfHhhx/y6U9/ms3NTb7//e/z5ptv4gc+V69d5fHBwR8vOKp9p1QZjZOhqa/lOXGXcyMNy8BFiSGIKnC3bQPmLJ8XOlNpHATKOgWjfs5nAwUGeVxCH8vslABkRX8T1bo2WcVlJssEX08HZk8dNdBG/V9Q73lS8e8EKltYC/zq1yxWodYlyIEWeDnnVNWDhTrt1wA0as06uJ5Hp93m+vXrBEGI53l02g167RZra31c162U8BqNkGa3TX+tj+s61T6heidZvP7KqwqhzZVjUZRL+hKOrcAeS5DpDVsIizRNefft93nvnfe4e/ce9+7dZTweE8cxURxTCuMciWoumOdr7GqViSqlfp0S6XEsoQI5R/DmZ9/g7/7dv8XGeodHj+6zv/+QjcUae3u7WFh87rNfZD6NKPKC06MJn//cmyAl+w8fslikJHNBo9Hi5OxU2SPhcf/2Q1qtDS7sXUAIycP7d/H8gDhKOT4+5fr1mzT7a3T767iuw2KhgJBGo6nskWXheu5KkGpZS9l3x3EoNTU9LwpOT47Z2trky1/+Aknyqaq4/rlPfILA9zk6OubRwyeA6gXTbDZZX1/H83yKIlMSzI1GRRc1PYUkUtdGlQqEs23Ozo6ZzcYEfsDgbMBsNmM6mVBS8rnPfZrB8JjxZIFjO9RpQ/Vs0scN4P80j4pJYSv2h8kiCFu1NjkPPJpeNc8KYFzHAbnMcp7fl+qCQeZQ51kGYPXPqv9b9y2W7zUiMRnvvvcuL7/0Gr4XKtECAc1mwNnZGZPJlCRJWSwi5dR3O5wNzzA2bjgc6j1SNYP91OuvkBeCw8MTDvePyPKYOM5J4hjX9uj3+sRZQnejz86V5/Bb60wXCY8fPgDg+o0bbO3scjocMhgM6PoWd96/TVkkCFGi6gBtwAKRsRQpWQZIlmXYJhh0ayXoFEIxk5IkYX44wbYhy1OyOAPdoLwOqqiSjgIpiwpo7/V6WOlS+MZxHC5fvkyr3cZ1HBrNJoHvY3uqV1yn0+HKlStcvHgR3/eRUuIHIX4YViAmwtDZlJBJludVHZGUkjiKq/YQZaFA5jRR1Nq0LGiEIY1msypViZMYp1CBVLPVJMtzwkYD27bJtGLdaDxGWBZho4GpIo9iVRdr7H1RFLBYkH8MtlE1Hh/7lf+ZHo5jFqSq9zAoaLVZC0sXOS+b8dX7xCj1OBMcLXXjFaKqNjgpSyVbKJbGxAg8GAem4vJLpf4jUCpQlhC0Wg3mM0k0j0iiGM8LFF2GVSOxNLYWtuMhLBurlCzimHkyxnE9fN/Dkig5ZFswm02J4lj7IwLb+hHB0Qpra+nMCKEdDKg2rup6JAhUBsI4KzquUKfRtPqyNNxvdf9pXW5cooqEddAkizodr05XWNLr6nbXOF9SGDpFzUkqy6eoR5bu4VI5/HpJLcUc9Hn1V3WftXoqZZwcJWdpCvO1Y2euwaChjuPgua76XnP1bZ1BkkidEVSB+Hg8YWNjU2VlJo+rMbFtUGIP9YdUpxs9XXBuWyrjZBvnvCgqZMiyHGzb0XNRUMil1Kj5qp+v+t5kKmybi5evEDbbJGlBmhQ82n+E7QmaDZ9W4LG13qPXbTKcTTk6PuOTr71OOwh5+9t/yP79D0DmzOMZzU6Pzc0tJuMRs/GY6XSMLAuyOGIRxTRbLSxhEfg+mRYAiBcLZFHQarXUBrGY0261EZYgWsz49h/8e778s7/AJ168xR/94TeJ85Injx+RZQs2trfZf3TEzRszLl28zre//W1ef12ytrbFaDIhbDZY6G7bZoOqZN/FM2TfzczXlahVLaAEISW2EOS6EaiShjaqRI62NTqYUXr46slaDnluaJDWcg3WgBoplWCIqGbwuUCnonOuKnQJjepVs6gEp7AoLMh1fY2UujuIQM0NeQ4RtqhsilLnRK8hnc21DLCkheaFhSVcyhJsIbEcsyDVlSppf1X3YJrB2o4CV8w8zbKSIi+rBoOKwqqeS2melQFexDK+ElokxXZsHNui1Wrx/Asv8NM//UWuXrtCEAR0Oh263a62GVJlscol5cRkh4uiYDKdcHh0xOHhEzoaSQ18n26vx3AyxnM9irIkjiLyvNA0bhuJ6l3neT5lWWhUe05Zlmxur3HrE79U8fUXiwVnZ2fcu3eP/cf7HB0dMZ1Omc3mTCYT0iQhy1WfKIPydjod/CAgiWPKPOPizhaf/dyb9HpNXM/ixRefYxFNiWYDvMChv9YHCSdHp6yvbfP4/iGff/MrIBR15eDJiDxNuXTxOb739ttM5gleAIOBoiVtb++yu9uC0iKXMBiO8b0G3V4fy5qxteMQNhu4Xohle0gpaHf6Wj1LzX3HUSyBxWJB4CkFrv37D4kWC4JAKc96nqfvOWV7Z5toOuPJkydMJlM8z2Mxn/N//x//r3S7XV595TXu377D3t4FuhsbBK7LYjrFbilq3+BESTtvbm5y69Ytjg8PK9pOkqR8+9vfYmNjg3t379HtdZnP58ymc3q9fsWMEAJ67R6//It/kX/+L/4V01mk5psFmczBUaCICZL/LB9q7Wifx0igm6AHFVy7rqsadDs2cZyT5amyHkL5BgCWKPE8jzff/Az37z/i0f6BXoN6F9XbeSmeFrmqbIpW+j0PyqkE97J2ZcnGMG0S1D6YpznvvvMDbty4QRTFJEmKH/js7Owym00rlbo4jnE9j83NrUoc4PT0tOrtVvTanA5mDIdjPvzgNqPxlDwvybOCtJCkRUHY6HDt2gU2tncphcvJYMCT/ScEYYPL12/SX1vjdHDKeHBCyy65+8E7JJHaU5ZBoAR0WUQtMKoHnbbGiqSUei8xbSD0e8k4eLKP6qNmKSpdXuKgQGTHcQj9gEbYoN/vkxU57W6HTqeD67p85StfYTKZ8PDhQzqdDmtra2xtbSGlks4OgkA9L9uuFEQty2I6XzDXrXE6nS7OdKpATSlpNBo4tk2SpuSZqpFtNpvkWaZZMUr8J9MZOyOWJnxBnmVVJs8ENe12+6kapjiOcRxHyX1bFtvb2yRJUpVmZPo8dcpo1UD2jyGm8hMfIBVFiesuJ2VdaabOcVxVR6rzvZco5Co/dHkOM6kNAmDQSasq0FUFoVKbI2Gpv5WFUulq6GIzy7IJAg/bcojjaKVGoJ6yz/OcrMgJPRfP9nD8gDTLKEpJnGY4QBxFSFmojs+FKv43ErQfFSA9IwlR3axxjmXt3o0DZNK9P+4wlC4zXmXlDFq68lnWDO+SQrIcg/Mpe4MooYPP5c8rFCmxDCvUOesFp7ISzaD2mup7/WYppeqFoYMgIRS3Nc+KSiFQiGW9iUFvnsrAiNp41j6vnkEYDIYrtMGyLBVNsSbRbQLR8wjdihMrltQcM38VNUg5nkJ3KFm5d549CarNTAMMErh85QpB2ODJwRPyvGR0NqTZCKEsuf3+D4mSnJ2LFxkOTuh1Wlzau8Dh/j3e/cH3KIucxWJBp9dnd/citm1xenrCaDyiyDME4Lg5i/mcMGxQypIoK0jimCxNFdorFY98d3eXNE2ZzqZ0u1082+Hs5Ii3v/sWn/zsT3Px0gUe3vmQKF6QRDPWNrdJ05x79x7wyU9+kjBs8uD+Iz7x/C1aZ20uXb7ED997zzz96v6fmaXTc88E02ZuGHVGWagMsrIjqr+UsLTUaFlgCSVH6vsujWZIv9dh7+IlDg+G3LlzT1MrtWR4zQbUr8cERiajYDLPlmUhtHhKFejXI4fq3gqkVSCFi0BJfAtKHFFiSYGU7hLd1/WENmrOq94zKmi2WGY1hFXguEJTMZqEfohr+4Rhg163RavVwnZsRaG1Ha3Q5+L5CkgKwoBWs0UYhvT7fXw/4Ox0zP37D7j/4D737t3jycEBw+GINEvJtES1cupEFWQ5jo3jKkTzypWrfObNz/DZN9/kwoULuJ5LFEdVhqcs84oCUpYljm0r6pWuN7IsCz/w6XQ7XLp0CSklURSRpimTyYQsy3hyeMh4PAGUkI9yLg2t1qo2f/OMfN9XlLsi42xwguu6DAYDgiDg8pWL3Lh5DZO9Bqr3GsfCSPKWZclisaicyCJNkXnE5Ut75EXKYjHjd377d/jpL/80hcyZjBesr29z9coV7t+9R5nbjAZzNjf2GE0mWJbFJ557GT8I+M633+LgyRm3noM4K7l87RbNZhtZQqvVptloIYVDf31Gq9ViPl/QaPUBaLc72H4D2/H0nlco0l+hwMUsVSpVruNQFAVnJ6cMTk8ZDUdMp1NS3cw5S1LKoiCNlRx8r9Oj1WgRBCpD8MB9yNGTI37n6Lf5wQ/epd/vs7e3x6NHj1SNpO9j2xYbG+tsb29zenxCEqlm6o8fKyDqC1/4Av1uj5OjY65eucJwOCT0A8bDMXmWEQQBk/GYdrtNEsdsbmzyqTde5/e/9nWVGbFcJIJClsgy18HDx0er/zSOlf209r1ijaheO4biX2XFylILdag1ZwlwHZfPvvlpXnzhebZ3djn9zd8imkdoBFUDkct9sC7KZOiP9WMVgGJlz1z1xRSAZLJQWZby4YcfcPnyZRzXZjabq4xwp0ur1WY+n2u1uvipzzcZtPFkwsHhsVISTpRTr4AOl6DdYXtri/X1TRzXY7pIOD07ZjKdsb62zoW9S/hhyOHhE6LpCEemPLj9PsliVqvR1OMulkDkCq3u/DPS929AVQW8CjY2lbx5EAS0Wi2uXbvG7u4uo+GQXrtDs9GsQBNPM10KJF6g1E/LsqTf7xM2G3R63aosAsBzPbzAr7XYcAkbTYQGT4QQuk1Dynw+r3opCSEYDYcEQUCkaXAgOT05qRg4/X4f33EoUEG1EX2QUlZBjAlcHU2TW/UDlY89m82wLCWOZmy4UXV1dM/ByWRSnSMMw5XWGB/n+IkPkIQQFepgVDxMcaxxPswGaH6uL1YTRJkHYB6UmdTGCbZqqIh5sCaaRXM3W60mZydn+J6DZStFJ2FZuK5DEHg0GiFFYWgS1gq3efWeoCwU7cpzPdbW15VKV5wQLRYUaYrnOjpbI0jSWGe7fnSAxIqtFCu/r//48SdYuXQgRZWPAU1BWt9Y5+bNm5ycHHP//gPNk1XOY4VOS+P8CYSo1XzJEhMrVPdlqCeGrqH/JmsBpuLEasdW31y9T9Eyw2gCv9VNw4xdPeCu/75OGzyPkhlKUN3A14Nv81qz8I0D5XkelqO4v3Wp6B9/PLuWSP/pP+owAWcYhLSaLfJcBTrDwZAsy+h22oxGI05PzxiMZly9cZPxZMobn/o0WTrlB2+/RRJHZElGELTY2b5Er9djMDhlOp1VNUeO7WDZLo7v4wUBoe8jEDSyBqPhiMFwSCnzSjRle3ubgydPWCwWtFpNHNvj7t27XLv5AteuXObw0T22dnY42H+oaDS2zcOHD3nuuedYX1/n/v37vPDS81rlaIf33n2vCkDNPFoGGT9qfMy4g5SCElvNU1XMhmWXOI6g2fK5sLvJpUt77F3YZX1jnbX1PoHvM5lM+b/8k99YmVeFljk1KLY5zlN46mpt54GVp+kuGnARJdIuELhY2NiixCpjWp5UVF6ptgnHdlTwUAjSLKPREwg7psilDgRUUOO6PmGry1/91b+p6BrNNq12G2UHLUI/wBKiApFOTk6YTqe4rkuWp7iuS6PRULx6DTJlWcbOhV1+6o1X1KaYppwcH/Pk8JD33nuP8XSiaKx6I1xfW8N2HMIwoNFQtSb9Xo+w0dDjp7JkjUajWqtGZdB13copNA6BCUZgycs3KKdBLJMkIS/KqoDYrOtKIRWqFhPmHKb+0hKyclB6vV71/tPTU4bDIYvFgo2NDVqtVsX/932/Op95vQHnFvMpSTRVqlzAeDyku/aQrHC5dPk6Fy8/RyMIsW2bTmuOkJJPf+pLpFnK48enPPeJ57CwaQQdXn/9M8ymCTduvMTW1rYCoqTAdTwNLEoWUcT65g627TCeRnzwwR3SNOXTn/o0nl2QpRFlWVS1c4vFgtlsSpyoQvl+r8fWxgZlnrO+ts721jYbGxsV4GT2042NDS0QsVQnvHjpEi+9/DLz2Yxvf/vbdLs9jo9P2Nzc4Pr1a9i2w7Vr1/jWt/6ITqfD9evXuXPnDsPhkGazybVr17Asi/F4TKfTAWA+n5PnOePxGMexOTh4rNeVzdnZGa7rcnBwQKPhcf3aHvcfPqKMSzzXJssFJSXSslj2dfrP/6jbGrWTK4DXc21uXLvKtatXCAOf7c0Nrl66yO3bd8h0Fu1ZI1BRT3m6DchT/gerPftWbdoqcFoUBffu3WNza4ter894rOqQTL3Z2tqaEmlIEt1kOiXLcopEX6tlMV4keJ6P44U0g5BOp0er1cLxQyzbZZYkjI8HTGdz/CDk6o0brPXWiKOIw4NHFFlMHk94+OA2ebqofI1njWm9jOIpv0yP9fkxEULwq7/6N3jxxRcQQugaJOXLFnnObDpjY2ODIAiI45jZbEYWZzRbTSaTSSV3bbKnxkZZlkUQBMxmMwDCMNTArPJ14jjm5OREi0IEFcXP+NRmf1Ly4Kan37KnUpZlTCYT4lgxpYIgoNFoVIkL9Xv1fvN9WZaMRiOCIEBKZSejKMJ13QogqoJcSzEEFotFVR9qlKorVek/xvETHyDZtk2SJDSbzRVn9XzBuolabY1kmd+bSLYe4ZtNr+6UFLLEtUU1QUxvkiRNKCVMZ1MVCIUermNTlrmWH7QIG6HiaRYZtm0K/VW9kI1dZQDM9bqui22p9GuWpRRZRrffV02+5jM90RVn3XVtbMciiuYfbYCgzjqsjuVrnoWc19AdxLMXNxIpcyzbAVkSBAGXLl/mueee4+atG2zv7qgFphfd/fv3+da3vs3J0akq2pMSRUdfjns9A2TbBk3Qz4qaiIGJxUAhyzogshTEjeFJK1oQFX2KysFdDXiqz9e84HrwUy+Uro/tM9F+jcw9ayzrzu0ym6kpeDqDdB5p+6jDZMdMHd2KoIhcFtI+6zosazkGz9zcJbQ7beIk5sMPP0SWypGzsfG9kHExRwhVA5BGEaFjc2FrjXt3P+Rg/z6UBWVRsr29S7e3jpQ5w8EA27ZZW99UYhRC4Loez734Ajdv3qTT6eDZDkmS8PjRI77+ta/xwfs/VIo5nken2yWKY4W+hwGO47JIM77/9vf48le+wqWLe2TRlN3dHcbjCdPplCiKmE6ntNttHjx4UKlzNRoNLYihMkA/9qitHQMImCBJCAvHdUCo2gbXlXzpi5/hF//CV2m1fRoNnzwvqoapeZ7ze9/+JvsPH1HvOl8Jk5zbJOvP7VnP81nrsp7xBiiljbRcLGlhixSXBS883+fX/sZXaLYkpVRCEb7vV4DA2dkZnX6HNM8ZDSeUhUW3s44sba5cuUlv7RX2Lr5CtIixXR9hq8xOKQvyMsWxHabzBScnx0gJa2trVSNg1bhaZ+/1+vRtG6XWl1OWgrxIWNvos7W7yfMvPIfjqfq/2WzGeDwm0K0HkiSuaNZSSkajAY1GA9/3VYNCz68aD4ZhqBDQ0YjT01OyWkBq1rXJDBu0MwiCWv1PSbPZJNCBR6vVotvtVo7LYrHg4OBAt56ISZKE6XRKr9dVKphakSmO4yrouXDhAru7u9X6NUGBsQ+qQSQVmGIOx/NotrcxzRR3Ll7jV/7KJRXMpSme42BblgIihApEy9yhyAvu3HnECy+8Rr/fR5Yltz94j0cPD1nMUzyvSRQnnJ6cEUUx7VZHOTiF4N/+y3+FEIJPf/rTBI0W3/jD3yVstsnzDKmR47OzM05OTrh165YOKFWdQTSb4yBotzssNEBkGrkvFgs2NzeVkE2a6qDS5e7duyrA63TI85z9/X1u3LiBlJJr167ieR5vvfUW7XYbKXN+7ud+locPH/BH3/xDkJLJdMLG+kYlYPR4f5/5fI7jOLTbbeaLBdFiQZ4XZFmuUfEY3/dJU9XAukwz9va26PU6vPfDD5nOYspCqvEsxVO2v348E6T8M3ScBwdXMtioPcR1bH7q1Ve5cf0aZVEwGg6Yzxe8+ak3OH7yhOlsTiEV3Re9J51nVdT3yfMA5HL/lSuvWf5NsBQ8Wh3Po8ND5vOI7e0tsiwjihakaYLnubh+QKPdobe2jmXbyLKkKFWdQGnZCMfF9wNtc+0qQBjNIsbTE9I0pdFscvXadTqdNlLC2ckRSbTAomB0esjp4T4UCRYFiKczZPVxPe/jVM+AVTtv9oArV6/ymc98ugKETFbZBDfNVotGs4nnuURxDELQaDZxXZdOt1sFFXmeVwGLybBEUVRljubzubZHPcpS2bx+v0+3260YLSYgMRlsY6NUBt1mPB5Xwg4mo66EalRAOh6Pmc1mFXAkhOqJ1myqOsXzwRosxcqklFX2ybZtTk9PK7th9nZDiTfU7I/rQ8H/HwRIha5TqBsnk7Izi2+lSVrNCJgHXRdtMANsBrkKpiQrD2LZp0NRuPr9Ln/77/waw8EQWZT80R99kx++8x5FWdJohFUPJSUPXJDl6VKenCXaYvjw/W5PKXNISZrGjAYDJtMZZZEjpAkiVCGECtiyqhHfswKkZ8RHK3/jmfQiXVQpV4OApZETdHsd9vb2eOWVV7h58ybr6+u6OFd9YJblBKHHxUu7bG2v8/IrL7KYxRwdHvPo4SMGwwHz+ZwiLxQPPUvJUhWcplmqZCPRRdGlMh5FudTlN3K30jrX9FSgVf1A9WV5VkZtqVZYGWqpGu/W7/P8xlcPaM3PJmB2bIuiXAZW9bE87/A+S5yjTperv/ZZGSwpVS1IXYGr/h7z/bMcbJUBecYmqWlMjUaDOIqZLZR61HQ2xfMDHMfjhZdeZpEmXLl6nbKErc0tyHMe3rtHkWVkSUS73aXf74GQPDk4YDwe0Ww2lYpdGHJh7yKf+9znWNteV9TOUvULa0nJxuYmn3jxRX7vd/4t3/yjb1KUJUmasraxzniiejF1Wh1cGw4fP2I8OGPv4h4PH9zDDXxaRlEyzxkMBly7pmhMR0fHbG6v4bquuqfJdDV1+tQY6fFEy6TLJd3NFN0qdcQC17NZ67b563/1l/nUp14hDBxKVD2VLAosCbbt4jser7/2U/zO73yHOFmVxTcI2vm58qygqR4MP+vn1cNBSBfXSmi5M776My/zF3/pFdqthLJMKKVSfxSWahpdliWdZoe0LDkZjBAiZn1tgygak2dwfHSP8cSi198lzyyyKCJoNmi2GwgBnu0hpMoSN1otHMeh0+vq+7QrW5emaUXpM33EHK0c54dBVcQ7GA0ZjUY4jlOjUykVuFarhRBKEVQ5eBaPHu2zt7dHFKUIa8HDhw9ZX1+vxtlxHBUA1pSaDFvAjJ+hz9WzeQCj8aTK/pqMkHFebNtmd3e3WqPGWZBSNWs0j7Gu4mXsT13+2+xXJmAzdjhJEp1dURnY2SyqrnU+V13o1c8+siiJohTXKbmwe5F//Vv/irX+GnES8+ILr/D9t99ld3eX8XjMd77zHVqtHmmScefOXRZzJSa0mEf80Te+ydVr12g0Qq5dvca9e/f4vd/9PZWZuXqNu7fvUBY5zWaD48ND0jSl3W7zR9/4hqZvKXbH9uYWRZpx+dIlijzn8ePHKgPYaDCfz9ne3mZvb4/hcMg777zDzs5uNVbvvPNOlQnMsoyXXnqRb37zmxpNbvJTP/UaUkrefvt7PDl8wmIxx7Is3Xcpqp756dkJWZaxtrbGaDzUtWyKutVutxiPx3p9eZRlQRj6WJZHUeQgBa+9+jIf3r7Hk6MThOUg0xJpWxUt8j/3wwQuUkqQJa5j8dzNG9y8cR0hwHUssiTBpiR0bV5/7WV+/2t/qAMPS9VEWk/bocqP4GnfpAIKa8O3asM0mPwsEMiymM0mzOdTNjc3CQKf2WxGUWQs4gTbC1dUDqvDE5R5RpIrHzFNiipT6vgBW9s7tJoKZMmzlMHpMUkU41qQR1MOHz8gnk+wRKEyiayyUer7+7IGdfVvRqikfhhb0Ov1+Lmf/VkdwKkMt/FJ5/M5/bU1AOIsYTybIBD01vsqA25ZVebF932mU6XOuL6+rpt/KyC77suq7O0SFDI+qLGJJpvT7XYJtVhDlmU6Cy9pt9uVnTLJCmVLqQIeQ30za/Hy5ctMp9MKOPR9v6IRm3losvsmUFosFnS7XcqyJI7jKsCqU0STJFlRhf5xx098gGTbNhsbG0gpGY/HKwGPQQbrFIjzTVLNxDWbnpmI51Va6hS++mLrtNt0+z0Oj45ot1v0u12EgCtXLhL9hZ/n/v19Ll+5xr/5N/+WoswpipKyUEWTpujbnM98dp7nuLbD3/qbv0aUxPzu7/47RpMJWZqqJqUVTcwITlgVRcEESH8Sh0q1q0DIZFdsy2Z9fZ2LFy/y0kvPcfnqHs2G6n+jKDo5UhZgCYpymZkrypIsTVXGrci5efMGzz13iyiKqmDTFDRPJhMGg0HFOVUoaUY0jZlqzunZ2UDz/VNms/mKcTefqZ51iaQeiCzVriSKGWU4sq7rEidx9d7zwZFZ4L7vV46K4ukm1T1kWY4pnoTaBvAMx7UuBmBQFzMH69Ks5x1kcz5R25B+FNXq/CEllYP/1P3pkVlbW6tQqKIoiKOYVquF32iwvrvNF195lTzP+fY3v8Vrr77KfDpl/8E+SodA0uu1lVTwkwNGZ8dKTcxVyN6ly9f4ys/8OWVIrYI8S/VYqSZ4ipoq+PO/+Iu0Ox3efvttpKVq0JqdNoOzMwK/geM6xLMZD+/f5eVXXiVoNsn0czSy8sPhkGvXrtFoNDg7O+PKtYs0m032Ll7kvXffXeHN14ONcysBkzEyM0c9A5AyB6vk4sVd/rv/9h9w6cIWlCmebRMlOUVeYuExG89RjfokvdYmly5e5vbdByu1dObrj9Nn5aMyxqt0mRzXStleE/yNv/IlPvupPWwxQerst+UEIHNkvlTWTKKI0gqZTwWu3SXPPGRpM5vOaDVgtjjkW9/89/TXLjKepOxdvsIFf4+CHGkLPMdTSkT6GURRTKPRqK4ty3OKUimNuToIyXMFjERRzHwxZ39/n729i3R7Pba2trAsu+rPsQS1JEmc4LgunudjCQdr06HbXdOUvoznn3++svsmQ//mZz5TKZwZFckgCFao08ZRME6B53lsbm0jZQ04Y7XmwthwM/7GflnaJuR5XjkF5n2e51UUFOMkGdofQKvVUlmYKKqANsMjLoqC+WSG6zkcjAZaedMjXqhGj3maMRwM+fDDD2i3Wnx45zZJmvDlL3+Fs7NTjo9PuHPnDjdv3uDevTscHDwmjhNeeukViryg3Qo4PT4gzTJG4zHNRgNZ5BwePGZjY4N+p60ArCInbDVZRAsEks2NdYXqxzH9/hrz6Ywfvvce8+mUbqfLzZs3+ea3vsV8sWA6nWp55ikPHjzgW9/6Nt2uCqi/9KUv8dJLL1UZwOl0QlHmvPHJ17l9+zYXL+1xeHjAcDjk+OQY27Yoipz5PKbZDIki5XCFYcjzz39CN8CMK4dwsVjgtXx8P6CUhfrZdwGFtDeCANd3VX1K2ODixYt8cPtD3v7+O5Q4pNmSon/ejvzndiwDdoswCHjxhU9w7epVdH6INImJ5nNcWzAdD7hy6SIPLu5z78GBItaXJXbF11gNhs6PSX2/Ur2CrGe+zvzumfunAIVFSo6Pj0AoR7zVbOGGHnGaYts2UZKqEgnbwvN8wiAgDBuAau3huT627eI4qtl5lmVEiwWz0YAiT/EcG1EmPH5wn3g2giLFF5ICSQGUwsFmCUAbIPOj9u/69yaDDkuQ/C//5b/ML/3SLzGeDLC1iEb9/mfzGaX2RTJtu2aLOVtbW4yGw2oPNPS0ZrNJGIYAVV8oY+NMdsb3gypjbehv5j6iKMJxHAWaxjHT6RTLMtelsuWGfWBamqhslV/5441Go7JnlmVxcHCwQpEzFHMT8DiOozJlzSZSyqqmyvhlxl83X0IoWm9DK1h+3OMnPkAyKDEs5V3N4jM8SuOs5nm+UswPyxRvXTXFRKBms1Lobsh6f53T09Pqb0YG8ezsTHHpNSKVpim2ZXHt+jVeevlV8lwVO25ubvLOD95hf/8xloXOIIkVKWnjKNsOnJwc8d/+o3/Ez331q/yTf/JP+d3f+z3yolTpXLm8j1Iuleeo3V+1kWojQr1E3yDTtWBBvxNlEEW1gF3XY2Njg5dffolbN2+xsbHB7u4uhUwYDE+0XG1UjZdyhD3iRBXJZmlGmqUUec7ZYEC6yNne3kUAI80Lz/O8QjayLMN1XXq9XlUvEMcxRafAvXoVgEwHNVEUKc5sHOE6ahwRgkQ7JrZtEzYDhRQ3mwRhyPr6Op1OG9fz2dza4q233uKf/tN/yqc//WnyPOcP//APq0Vo5ofnefR6XdbW1pVQgOchpeT4+Jj79+/j+z5nZ2cVrc88AxMEPQvxr3+5jk2r2UQgcB0XIxzxow6JzvjIVfljAGnJ6lXPfOe5wGiZLVGB7ubmJlOdUlepdbWmCgHNTgcv8JkO5kgBzXaDh/ceES8iiqwgDALKomA2GzMYDknjmEbY08W0HT7/hc/TbLa0ulKJsC3ViE4IpFCS+XmasogWfOazb3J6dspwOELKgm6vy+BsoMRZHLVJHj55wms/9Rpr6+s8fPCAru9XXbWHw2E1t87OzrAsG9tx6XQ6y/s+N86rAa1RfAKjf1B/uaSgyFOCwGUwOGMyOCFezIijOaPRmDTNmU5mnJ4OiOOEslBCK0fjGYYiKSxRPcOPeubnM0nnr7NO2pBC6d4JAbYFrp1z40qLv/03vsD1yw6WPEWUkjyzEY5NLtUmaebtbD6jLCRpqRoQjkcDSjkkDJqkWcTxyT67ey8xGp3w+S/8DJ3uLgUOpZBIUWJJiyIrybICzwtZW1Md3EfjCbPZFKCiY8znc+0gtypgSm3QCRsb29i2ku+3LRfbccjSHNsS+EFQ2WvaSsEUVODS62nhEi2PbjI9JjNTliVJHLOxto6wRFV7ZYKWip6iwQ4DEkjDMigK8qLA9zwWWv47z3NcT9G3U63EKNFOTJoxm04oy6KyZ1mWEQYhc53tcBynynqCCooMwup5HgcHB/i+TxRFrK2tIRDMxjMcx8LzXaTMiZM5BwePabZaSCnUdeQF0XzOyckTPG+PKJozX8R85ztv0el0GA6HtNsKiV3Mp3RbIeu9DuPBMWv9dS5cucjDh484Hpwymkw4evKYZrNJq9UmjRecTiYgoChLBoMzbfPUOCu6XMhitlDIfp4zn874zKc/zeHBE7a2t5lOp5yenvKbv/mbvPrqq7z33nuEYcjNmzdoNlscHx8hJezu7lAUSu1uvpgyHA7IspQ//MOvk6appm4KVeeb55WK1+npKY1Gg8lkonqnaOcwS7NK1CjL1PPq93sIoZpqdrsdzJ4Zxyme76lm7XnCrZvX2Njo8c23fsDBwTGWtSx2N2ItSpt/uTCfXqV/+kd9X6pnpjudDm9+5g06rSZRtKAsHALPw3Eder0uo7MBhS0ZTY7otNu4nkuZlVWfwfP40rOy4iu1IlL+yAFZrftVYJXJ66v9tVQbgZRkWcrZ4AzHj1jb2KHX71HkBUmakMQJaZogZxatdhvHcQFBWWQKEJKSPM2RElzbQmYx0WzCw+MjosUcT6ZYZYaQRvxLILEplfSNuZlKya26Nqj2jpU5YFkI/Toj8d/ptLn13E2GowFZFjMejytWjus6Cuy3LRLtI81ncx34eZyenuI6y9YEJkkwGo7Islxln/o9HYC4eJ5b1TAJYXF2Nqh8Z8dRn2WSDJZlVWvJtu2qNjjL0so+TiaTqu7P932SJNH0V5W8mM/nSnZc27vzvbnMnDDglAGo6vVLjUaD2WxGkiRVlitN06rnmck4fdzjJz5AMpveeTqTLKHIVTdjZQRKHNtDYFU1IkbCGyzy3MhQFxSFRIhlJkkIJZV8dnZWPSRT0JamaVWTZFmW+kxhkeUFtutRUHA2PKXdbfC5L3yKNz71KtPphMPDQwbDAdeuXa14lEDVWEvKjNOTU/4f/+x/5Jf+wq/wd/+rv8UffvPrLBYxuclKIFjKddtoZQhtjVX2RwpNDZJUjqjJDNm2jSWVDKjtOLiei2Pb2LaqFRCWYL3X5fLFXba3tul2lVN5dnJENJ+QlTmzSCHjs/kMqdGGIAxphiGWpYQqzDjZjk0zaNHyBWkc0e50uHblsqY6CLrdLo1mq+qGDVQFeJZl4Vk2caRU+5IkIdLfTyYTptMZi8WilgoOVHZxc4OgFdBut5WKludXvN80yUijjOHpCbtbm/zar/510jTj7gcfELYa2LrmYW19XRdOB7iOqygFqJqTsBHg+Q5hw8ed2qRFRpEvsz8GaalvRCZLZBw827bwbYtes4ErbBxscnKU7p9u6FczrFU2TGj6o7WsuYNlrVVZZsss2rm6qGdRuFSAZSEtmyjNyCVIKYgWCbKQWJaNb7sEwsJHUMYx7UYIQjIenkKRUZY5vt8my3LOzgZE87mW5p4ThiEvvfgc62stsDIoSxzLQkoLaQuysqRA0Tscx9aUSnjtp36K3/7t38Z1fUXDcjySZEEQKGn1yVQ1klxf7/Pw7h0oHLI0w7ZsFlFEVuS4gc/hyRFZKSiFTZTmFKXKKCKVEl19Z18GnAAK+FjWHQnKUgcnpcCzfB7ce8z/6f/4f4ayxBY2Ra6yI7Zt02g0aDZbdLubbG9v4boeQTdk/8lj/sN/+H3SLMbyA03Xk1gsM4v1wPc8HbL+s4VEiIJM+pTSx7bBExFNL+Jzb+zyy7/4Aq1mRhHNKWSmuqALicxzyiJHWiUFBUmWM5lHJElJmuSMhimLRU6cpLhuSdhogddElj1eevF1okVJEOY4noPAxrY8RFlie0tVN1M8q8CF3kpRvhA2/f4aUqr6QVkq6kyeZ9WctiwLS0CuqVymebPjWMoZEVCi5oztqE3TlgqwCkKVfRFAWcv6eLZLPE9wHZtcpDi2TVnkpGmirKmtlCCjGjpqWRbTyZS8KJjPlRz3xYsXyXMlJpJYVCjpbDarHMHBYFB9r+qs1HocSPX60WhAURTMZrPKUVBOgs1oMGJzc4Ner8/BwWMVaMQLwiCg2+3heS7z2YyyLOi3e7h7Nqdnqg5rd3OXLM/Zf/QIx/PJS8kbb3yKyXTK5uYG8/mcLFOUFN+3sS1BXkpG4yHRIubOnTvs7OySZTkPHj5E1aKWdLsd8jzR9gWms5kWzugxn88Zj8cVUIYsOD05QgiLyWRMs9nkm299k8997vO88OKLOK7LxtYm/+E//Af6G32++gs/R6vZYj6ZMpuOOTw85ODggFdeeYU8zzk8PGAeTUm1I2SJkmbDZ7GY0W63cRyLRthStDgknY6iT671+mRJziKL6K+tsZgrdD0PcoSw8Fyf0WhEt92jlCWu45DGGevrXRBlJYk8m02J5xPajYCf+dLnuH3vAW+//Q4LWZLlhipagoxRstbWueBomWH5s3DUbb/rurTbbV54/nkaQUAaL0jjmNkkJUtT8kL5FklWkmYFJUpFuNlqk2n2jqmrhKcBvmcFSbZtI+zlmBiGh2E4IAVSZvq8gqKoiTlIC0oLC0eDhWqndATILOfsySNGxweEoba/YUNlisKAWFPPbNsBHdimWUoRJ0SzqQ6oI30dBbYEKQQFDgij1GthC7AkCLHMZBRFfm7/qNBsDPtAjUGJhaXvTWI78MlPvcpzz11mMp1QFDmdTpvFYsaDBw/odrv0en2dKWliuRahH4KA2WTG9vY2hd6DhYAoihmPZriuj+8LbNsjy3LdM6xESgVmFIUECsIwrISChBBVkGOCku3t7SrbbvyZxSJmsViws7OD59n0++uEYViBEovFgrluqbG5uUme5wyHwyqwMQkJE9yoNexUGawoimi3lTCUuR6gasZtMlSTyYTA1wyA9L9Q7Kojz5/uPl4WqiDPIIcK2QEzOeuovkEUDdXCLNx60GVSfFLLUJsJUq8XqSveqQjc5ejomP5aj/5aj8l0jEQSNnx6/YtsbW9WkXedaymE0EWvsLWxxWIR889//dd59ZXX+eTrn+TrX/+GQhukCXRsSinJClWYixRVLYtxwj3PIwgCEKp/UrPRoNfpKjRAWLieh+Upp9Jxner6iyKn02yQxTFlkTMajaome3nuUsiShpZi3d29UKk7OY5DsxHQCEOlLNPv49W60QshVFo2CNT5rKUSiqKvlCvPxFBSPMtiba2vUBRtZFTjVkePf47ruJRSEi0WSMB2naoni0njmmJtRE6cJKytrxOEIbdu3WJza4s7d+/w01/5Mu1uh3/8j/8xk8mkuu5cqwumaUq329UNakVl7BVVYFn/BlQZSYOM1OecEEplr9nwaTeb+F6j6m9UUa7KZ9eg1AUlDGpSzyio5IgJjp4lsrE8lkZbNb+Mooju2hpxnDKfKpTbtlQjXiGhzHPm0ymeRnHm8xnUqIyj4YjxaKTWklSGLs9Srl+/RtU8C9OA2aIEAs8jzy3VPLMosIQgjWP2dnfxXY/5fE673cGxLbIkXqbWNfWoqesUPrx9m+PjY3b3LlAm6l6EZbFYRGRFieN4dLs97QQv77/KuZ1HOKuMW51qp5+dcHAci3arwWc/84YK7iyHRqjUyF566UVu3LypOPK+j+04zOdzEpnw8OFDvve97zIYqq7ijqXqVbI8x7LEiq2qFzXXqZnVsxMCWVio/lcgxIL1fslf+Lk3ePP1bSxOSHOFbivxExshFJ0xz3MKCVluMVtIhiPJwZMJ86lkMk5wvQDXC2i1e2xuXeVzn/sCl6++RJLZ2vGdYjkxtusRBA2EplyBAkg836uuvVJi0vbJ9xVgYVk2FkZYBYStEMQsz1XdqGPjaTTRZPSKfKn46NhLsRsjU5smCa5rV6/zPI/hSEnUFklGkRq1OkVbs+zlmnMcl2azSVnkzKaJLuTPeHJwgLAEQRAwHJyBLGk0GprKBYvFnOFwSLfbZbGICMOgossp6WElHS6lpN3uEMcRnueymM/I0gTHVo1VkzRhNo1Z6/ewLZhORuxsbxF12pWy1MHjR5RlSbfbJU1TxuMxURRxenpGs90mWqg+TaoOQcnmpmnO/fsPODo64ubN61X/kdOzU/IsodttV7YsSRLeeusttcdia+fKYTqdcOHCLghF8ZZS0miEZJmir21ubmjEeEYjFBW663kuo9GIw8ND/uAPfp+8lOR5waVLF/lrf+2v8sGH7/PkyYjhYEAaJSwWEbPZDN/3+f7338bzXKbTCc1WoPZIHaypgMxSn9doMptOq6BcCMF8ppzE+UwxSg4eH1T23ChoSVmSasfKdV2aTUXBevLkMb1+D8tSjm+v19O0cIfpbMYrz9/EpuDe/X3OzkYsogTbFhQFWl1VUkp+ZK3jn9Zh7t84w+vr6wRBwPHJCWcnT1RWRGg1XsOAQFHKSkRVs23ZSm4/z5fKkB/1ec8Ceup/P/8zQlG2q7YGQrce0cHGsi5UAY1UwFKBZan3LmYzZpNpFbxJS1BqZoBtW5oRoDI/Vmn2JjQYps6luRXIClQ3ALXElDso5V2TQfw4D0AL1RQFti344he+wF/6S79MkigQ/mwxpN0OdUb1pgKRLIHvBxRFqflAJYEfYFttVK2WJNd17r1eV7GXShgMTnVz+xYnJ6cVs6rRaOA4DtPptPJj4ziu2Dym3lNKyWQyqfzfNE0rRdC9vT1Nf51WNZtSSlqtVpUt7/V61W0bgQVDl+50OhRFUdUZmmDdiOIYv9z3/UpsAqjqP+M4pt1uV4I26X+pQVoextGUUjKbzapBBqreEivUo1I18zNOuan/MN2jzwdN5igKJQygCvcylY7NMp1NgELT9zqdDhsbGxwfHxMGAUWe8+jokDAM2dzYYLFYcKqpWGmieOKtdlupupWyQht7zTbjyYTRaMLZ2Yjf+H/+79nZ2aXX7DCP55hqEcex2djYpNVsIoGwGVST3jhYo9GIXrdDI/RIE4WWBp5PoxHi2g7NTptmr0u31yXwAwKdfbGERTNs4DnuSq8Qw8l3PBcpltkLg7JaKnWEbasaIlMDZgIjPdBVM7A4iijKgiAIcR2vWpT1ZyyEaorbbDaxHLsWTOYgFa3NchySXCmZCN2E0rKN4p+FbTukqdL1V0XfHs1Wm1yWXLt5g6OzUzZ3d/jrv/Y3iBaq/06322U2m60EPIaXawJPE4j6vs8iipeFrix53WZO1QvBTQ2C63r0+322ttZZxLnejBVF5/yGUg+QVEM5sTJGldNs/QiVuh9zCGHR7/exXZc4SvT6UgHe8fExd+/e5Qs//SXCZqO6rzhe3rcJCF3XXakX8TyPMFRc51Kv2SLPKzpnmisEP00zyrIgmk91IGOxvrbGwwcPSJO0wmBV5lFtkGma0mi1kEimsxlJlpLq4uk4ikFArpUT3ZpioKw1e6ycgHNI50ePkzBkVPr9Pr/yl3+ZS3s7bKyv0wgaWK4LUrCIFuR5znQRk+Vqw5jNpzQbDf7cl7/M//vX/wVlIZG2hYVFXioJ1fMUz3qWvD7HlCNhY1kOlm1hOwXdts1f+0uf48VbHUo5QgpXByUuua4VMOIn83nKaBSzWFgsFj4PHow5O02wHR/XbxNnFl13g4uXX+fWrRdpd28SNDYIkGR5get5FKXEsh2MNHCpr8vzPR3kq2yhnqwqWyQlud4kXdfFkoqqlaYpvuchhdBaL4Lh2RlhGK7U6liWpexwURJFx6pmpNlE6vmYJDF5mhDHCaenp2xubjCbzbFti8APCVyfKFI1hEkaq98HyvaYwmQjDb6kicyq7EgQBNy9ewZIkiRFyqJS0Hv8eF/TUFRhtJHK7XQ6ZFmqA96SPM9Is0iJCsmCvEhJUgUaeJ7D229/j+3t7aodgFF9MntWp9Ph6OhJJQjx6NEDJtMZF1zVn0TRFSNsW9DrdTk+PuXixYuEYcA777yLZUF/rV/tn9PpmDRNaTU7GgkOmc8jKIW2dar31WKxoNlqVLVBx8fH9Pt97XSqQvNGo0GRg+8HlcjGZDKpmj//81//daRUAglvfvYz3L79AUkSAaqhpOu4CCHxfVdnpob0+t2VRpPGaTNg2ng8rvb9xWJBlmVMp1NarQ5pklfSwqZeWe1HJXmR4+igXMqcKFL9dVz92iUFSNDpdImjiM21Hov5nFvXr7C7tcl8EfPh7bs8fPiIKLXJCgNO/dkLjmDp67TbbdrtNqDGLM9zKFQjZUMZN/tYWZbkpdRdDVZruZVN+vGfa3yup5Xsni4TMMBV/TVLm1yCqKs76PcKlY0WQmoQTWV7DE1cZfk1xyZf2n3bgGBCZUpL3QPKBDxSlFUWSH9gNTYqa1SuXP9HjfmK3UZdW6PR4Fd+5S9x/fotzs5OCAKfMGwihGri6/swGAwVvVYIoKx81tPT00oVLo4X+IFHlmU0wgwpYX19g0U0rVRAYVnv9Pjx44qxY4IWY6dGoxHj8biixZnaIqP0afoTmt5EvV6vAr9MraR5zqaXUhAEFSgTBEEVCFmWkuE3FD7XdavacCEEOzs7zGYz+v1+JaZSF2pwXJeF9smOj45+/CTUx098gOQ4qmbn9PSU2WymMiU16hksN9M67cksduPgGW642cyM82smQ5Zl+K6nu6erTXM+n9NqtSigolm0Wy2KPGd9bY12u83R0RN2tncUIhcnTCdTSp31cl2f6WRG6qYcHR5X+u+z6YyD+DGLWUSaZggsmmFIIwj55M++wSJdEEcRQRiw1l+j0+1gCYu8yCmFZHNri729PTzPqxSaHj64RzRXfFYLoVSF8pxm2MD2PXIbzfn0SFPVrM91XTzHxxYOlqZxqbSmFpawVL8UE+jUa7iKLAWUKkqr1apQiPF4TLOjNtg4jsESBM0lciBKsVKMZ3qBlGVJI1S9cuIkJs2UHKztOLpOQHFmEdBqt/RzVTU6huqjJDMbOpgJKaV638ngjLTImUcLHM+l3e3S7nRWeK5m3ph/fd/n8PCQzc1NPM/jxRdf5MMPP2Q8nWHrpsRm7pnDzBs1b50q69Nut9jcXGNja43FIsfzXcSMGkK1pCfU+dgqg7EsVjRG71lG+rzD/5SDXf1NZUjKoqRI0upZG1rq48ePieKI3/qt3+Jnfu5nCXQ2znbsleyrkfRMkgQpVPftXq8HUo1JkqWcnJxQ5jkNfY40y5QQiVD1C0WWsNbr4VgC33MZD4fEi0jRLs09SkWdAvA9D8u22djeJM2z6v5yXR+AlLpju6JrWZZFKRRtpJ5Bqm9i58fI/K4+jnle0GiEPP+J5wh8mzieMxqeYbu+Cka0wZdSUsiCoBEQBD4SwV//a7/K7/zOf2A8WSgao20otkv71Gq16PV6VZbAzOV6FhIhsV2whCT0bXqtBt/4g+9x912H9bUGnTWFsAWhr1FTpdwURZLZ1OP0JGIwmGvhggaet4btWzieoJQ280Ty9T/6Hm+9fYdWs8OlK7v80i/9efb29pBCaLqbmtuFvmc1jxRgketnlCQJaHQxzVQT0clkgu8HONoum+LcSmGpKEiThNFwyMOHD9nZ2a0cN9u2SdKkKjY+OjwgLwq63S7j0QjXsbUTLSnyDM9zSOKEs+kMSggCn7PBGa1Wg9FoUqkopWlKq9Wq0EoD9LiuQ6MZ6saSgrChenpc2NhRTVKl1NnljspazudEcVRlsQ4Pnyh1vizVAQAkaaKog6VSw1PCLzGddkfLlbvV/DH3aZy2yWTCwcGBrtlRvaVcz1PXmOWcDU5phA0ajZBWu8mdO3cpCgUSqMLpGEsI1tf75GlKWeacnZ6xmMdcuLDH5uYWcbzPeDLBcT3abSUtHIYBfuBRlkEVnJn+VmmasrW1pajPE1Xgvbe3x/7+Pmtra8RxXFHUsywlTSN+8P3v4fmqRQaAbam99OTkhDiJsG2LbrdDWRQURUmv1+P4+LiqBXUcR1Mhi5X6CyOoAxauk1fqtsaR6/V6PHnymDSLtRJaUFEqjdy4URxr6ILxJE5xHBebEtcShM0maRTRXO9zce/zHB6d8P7t+9x7uM9My2DrXst/LHbdjwO3fpwz/nHOb1lKbt1IOJvfi1JRuM0+aj5PCCiFrGiu569DCPsjr7vO2jnfb9LsG8+6p7oC3tPnVgGointqzeN1j0ZD2ROWokOXZYklSiwhlz6iBCFV7ZQUNlIqypvKHkn9vQmUnt4HzD0YVsFT91D78XyGzFBBr1+/zr/+rX/NgwcPGJyd0el0eOGFl8jzKe12R1+3w2KuMpSOq6j7YRDy6NG+lubuEYYK4B6PRtqOJQwGp8ByPzFMnizLqvIOsxYODw+rTI2phz87O8NxHK5cuVIBEdPplNFoVNWSCiE4ODig0+mwvr6uGCjdLlmWcfHiRc7Ozmg2mxX12GSXTOsCkxkKw5A4VrQ906TbiFctFosqsDKvM+Of53lFk+/UslU/7viJD5CAyuDt7u7qrEJRaaKfV/cyznKappXihQmYjNNRV/cwjme/16fTbOl6lwmdfp9uu00YNmi320ymE7qdruIvuy6ddoeDJwdMJ1M8xyFJUqbTqVIES2LSJGF//wn7jx/zeP8xruuytrbGjZs3WF9bZ3Ntk63NbYVGNxo4juIHu56rSo1qjrK5N6E55BIqZSSD7GxsrGFRcHJ8TEurfXW6Hcq8ULLZOkNk0M2qQ7wlSLTTOp1O1aREZczSNCXR49hqtSotfMdxcGyLJI44Pj7mwoULKhjQizNNUsJGQ4tRaAENjSoUeVE9N7P5maACSz0/LwhUb6ksI8tU1qHMs6ouIIoibMum2Woq4QZhk6YJrutVKE8UxSi6kcN4oIQENjc2WMzmaowtC7fRYH19nbt3767QJ82GbFRdwjDkH/2jf8Rv/uZvcnh8wnQ6W+HHmqNuOOvZJIW0W3iehcRTRC5LICpaF0+do8p0yrJy8OuHokM83ZPio47636RUafo8l4SNkPl0XgVqnudVRs6gOI5tEwbhSnC2tbWF4zgcHh4SNhUnWanuZPgEOpPjIgV42ilqhp2Vxry2KHFdB89zSBPVtV2W6ncGoTKBm+M4unYl59KVK7iux3g00kXzqs7CQtEHy7JkMVf9TyydvUI8u9fXszbsp7j1KFrX44MD4sUM33NpNds4uUXYaGLbFmmcVvzuLMnJ4pzxZMrR0QnNRovJeEaZpwrZFEsaibFt9QJWcw3GbqlxlzQaFq+8/DLrvT5CFvg2ZPGc2SIhKRxOzyzKMqeUmWoXYIEsbWTZw7Y36a85JGmmKa45WAWZzBEl5LpOME7nzBZT/IZNp7dGmhf88Ac/oNNqsb6mqLSO4xLrzU3x1JfN/gyi77pu1SuoKAp83a/IjHYURTSbzcqOTSbT6v7v3b1b0TxmsxnT+bSiYhgHwGScfc+tCvYfPFD0rl6vR+CHFFlhAGOEaNDrddna2tCBzJHqc5fEtR4iDlmecHys+n5Mpwr06Pf7nJ4eURSlptJF7OxsU5ZLWooBfDpdBQ7ZjlUBWKrWQRKGysFvt5vM53Nc1+HC3g6BHxDFChiIoxg/MN3olQLUIppxcLBPq/UcnW6L8WTKcHhGu91he3uLoii08xHQ6XQYj2esrfX13qjaIUgJW9vb2Bbs7OxwfHRKluWMx5MKzBodHdPvd8nznK2tDaJoges5VS8V0/zX932ePHlSieQYIYbT09Oq1uDDDz5EAEEQksuCxWxKEqkMXp7neEHA2eCUsBFUKncIqd8fV/UKJphWwWVeBUrnKfNFLun3+wyHw8ohNCDoCy+8yHwxxXVc8qIgDIOqjjTww8oPOD46qTKBQghchQAwnc2xXYeT03vYjkuaqwL6CxcuMB5PmMzmpFlGmilVS54263/ix8cNnlb21/rvMfYQqBSpy5Uo76Ns44/67LrtND7MMsB4en8DKhq5ee0qvXjVXi6p0BKEiUqFCnoEimYmlw3qV45SatluDUBosQ2zl6qA6tn0QDNWT1EEq++XrzPgpPIt1R/effc9bt++zde+9g2klHi+z80b36XX69LpdAkCpbToug7r6+ukqaLB5UVB4PuUheD+/X18z6PbE3heE9sSRFHCfB4RBL4GJPKVRqtCCObzOb7vs7+/T7vdXvFx1tfXq9ok01PJKOEZ0GY0GtFoNOh0OrqHWFplpIbDYQWsKwZJyGKxYDwe09X9mgyryIBTxme3bZu1tbVKACUIgiqQNzTAJEmq8XRcFyxBnCYfOf/OHz/xAVKWZpXaBqAllx2FEudZhVQYSo1RRIOlct2S6qQ2HJNBStOUZrOpAoCwwWw8Ya3f59LeHmmWcXJywtnpqeo27bj4+j2zyZTjwyOOjo7wfY9Hs1nVRHA2m6noPc0QlsNrr7zGL/z8L/Dqq6/S7/cJAkVB8jyfvFA0tbzIEJrzW5Y5SIkQiu+aF4VqUOm5Ko1cLPX3XdepFqxtO0gsdi9dVophOlMmHJtWI0Tq/iOWZVHqHktmHGVDGaRGs7kiOmAWkgnCDDpnKHaNMKg2yytXrlTPQVqiCiDmNaU0IQR5otSdTOamjgRKa5mhKMsS1/NwXJf5Yl41kCyKAoQgK3LGkwmWsCiyoto4YVkL0W62sYVFww8okpSN3hqLyZT19XVc16OQJZtbW7Xiy2WRt8kiGbnMKIr44he/yP/rn/06cZxUTuGPQtPMBp5lip7jupYqANU1TBKU0s0zYEdbi2nIQmKUtsx5QW0qP2oTrqN/z8qWNBtNzsYjwtDCdRxdzFlw4cIFNjc3+alPvkGz3eLw6EjRbBrhinNq2zabm5tKoCNLKzrLZDKl2Wrh+75qkClLSk3NrFMUpJRKxrUscBybwydPcB2bZjPEtpZdtYUscRy3yvbmmqKVl0ppTAjBZDxhMZvpjhUKCVTqY2qcykI5//UM0sc/ZCVs0mn36Ld7TKczOu1NHFtliZIoZjiY8ejRPvfu3ePevXs8eXLEaDRiOBoSJRG+71GUBZ5vk5dKBMJQVpMkYT6fV3PGrDcTdHiex8Zanyt7O3iOw2w+Jc9ypBRkqZJOl0QgFZVIILEd1UfI9DBpNtu4jk+epwhRYNmq75gtfEqNlFuOQ5zE+L7LL/3yX8T1QxbRnDt37tDvtGkEL3A2n6Ga57qcnJ4wOBvQbDWxdaYtzzMsy2Y6VcIqUkrdc8ghXkTMZora1Gq3ODs9Ub100hzH8YgWC0DV9PXX+tVc67QVGtnttomjmCD06XV7jHxX9aBCspgr0GJjfY0iL1jMZwhh42kw5OzsFNdTa2g+X+jgy3DqhVIwA4JAZVFUE0Spriua67meMB6PKrlbKSW7uztVxqSp7WcQKDs0m821KITEslTdUxjaOtgIcR0HR9uWNE2Yz5UYQrPZrJya0ViJOKRpWjUrv3XrBkUJi0XE48eP8TyXLLORUmUux+MZjx49oixVMKYk1FVDxzRRdT+XLl7Bth0OD48Yjcb0+n36a+tMJmOkLBUVnJJer1vZb1NMrWiEma7bXSpcFUVRKc5atkWRZiALhHBUY3TLYjIek2YpgabKO45dUY5N8TtIzRSBZrPJ8fExm5ubuv5jGZAb+zsej7GETZ4rJ9wg1J7nMdIgiqmVMA63qdmKomjFeVegmo0sJRRKVKSUEiyIkhhLO51JkZNkRWUL7VJiFaVSevyYaaT/1AzRjzt+LP1aLOsu1fc//jwf95LrQY7512TE62qTUoJtrdLuVoOkpRiCUU9Ur9PjrKIWVB2Rbs4I+vvz18MyOEJ/CZA6+6JJefr11D5bzQsTpK0Ed6JOyVtevwlKTV84EwSo31lkWcHtD+8o+6PvQSnYqWCj0NRw27GxLbvK3EgpuXz5IlevXmVre5N2u4WwBL4fkqYzBoNBlRU3iYLpdFqVTRj1YCMao0RkRopyb1SBdW256VNnQHHbtitbBwo8evToEdvb21UwY4JhU5cohKj6M/m+X/nHnU6HbrfLkyeKPmyEX0wmSUrFSmm328o2lCW5XNbTfdzjJz5AApQijUax4zgh1spqpj9No9Go6iLiOEFo9a/5fEEjbJClGfP5giROq9S6mbS+HzAeTTg7OsGSkEQx8SLivffeI0lSms0Go+EIWKo2WZaSdk3SBMdRFBDP89jc3OT5TzxPr9fjwoULNJptNtY3VVYlU1r9png5yRMQkCQ5pVSbg+d52I6FLAW2sBFlieO5WLa6HynBtpepat/3qFRlVCW0+l5KbCEIGk1VGK1Tk6ZJqOMsDU2dlmhQUBVMOhj1OTXpl9mQsiyUU68na6PRYP/xY/YuXMCybUqkVqxayqhX9QW5MhLm5yRJGA6Hqj8REs9XWaq1tTVsy6aQBd1Ol3a7zc7ODkVe4LiOdnohiROieVTxVk9OTnj06JFCRecLrFJxeOeTGb/727/D+vqaEvfwffxGyHg0Ar2oZVEiLAvLXqWn+b7PcDjkzc+8yYvPv8AfffOblXxmtXeci1fqdIOqt4tt41kejqMUrHIdyOoS0Oq9xkG2bVWDYX53/t+V7GJt5/pxtDFZqkJys0n5gY9lKwqn63l84vlPcOPGDQajYZUp6PV6VcbPGHvHceh0OhwcHmA7quj7/r17bG1tYGlqpAru1OCYDvV5USgMUI/76ekpaZawvb2N47oMB2Mc2wFUw2Qv8Gm1WpwNBqBFDlTwqWo87t67o8QeLl/BsVU2N1rMKctCiWr8mLE5f6xSPUzYJVTDwbxgMo55751vcPvDuzx8+JDTkxPGk7HaPLKMvFBN9NTmWxD4Ht1+lxu3rvGZN9/kn/6P/zdOT1XrgKIsybOMyXiixFmKHM91eP755/nyV77Me+++y5e+9NN87rNvMhkMeOfdt/mD3//3xNmCJEtx/UAJYZSlFn6gog0LIclkihCSWZzTCFqaMpjhWgIhHaRUNLC8UPQrR9js7uxgC8G77/wAgeTq5cu8/d3vYgGuYzMYDgnDBotowWQ8odVqEpsgT6tFGVtiGqyWRVkJtsRxxKNHD7l54waB5xEGAfNZRKvV1PQ5VZg/GA5xXYdCKmSzLEomkzGepuQGnkeSRDSbLTY3NxmPRiRpyvramlK7K1SdgeNYhI0QiVtRi5vNRhXIJHFcNY+eTsc0m40KwTS8e9e1aTbWkKUSCPJ9j0jX5RlRGAPuNMIGj/YfKfpzUYAoiWOVIY2TiE63o2pFHQfLIPnAwZMnOlOWVTai2+1oEEnVFwyHI84GA5qtDlEUEccRnU67yja6rmqSmySK+tZf6zI4O6WUJXmW0tA1goPhkFZTBZ65DmyCMARUvzrPcxEWxLEq7t9Y31CS+lnOaDgiiiImkwmytGi12vS6PbqdDnGccHJyrNTGXIssT9nYXNd0Spt2u4OwIMkyWq0Wk7FSqIyiaAWo6na6TKcTSikJg5D79+6TFzlpmpEk6VLcJ8/pdLtMJjNtTyVJEusAFl3LZCNLqv2vKIsahdUIi7jkuu5pOpsq2lnpqD5ntkVeFkxnE/KyoCwKkjyl0GyO3LQwkCCw/ljJo4+Twf6f5jDUcJM1McEMnP/4Z7EXPupYvlY+tS+ZwwCtyxOeyyhZolL6VD6IMqZLCpypcdWBLUL7QUZcoR4oUe2vZZUlM/TBUr9s2RLlfNZoNZv1EXuuePp9BqQFdD84ofdyQZGVCCGZTmfV3x3b1mApCGFRlqKidxdFymg048mTE4QQvPfDD/FcJR60sbHO9RvXaDYbXLx0gX6/W2XnG41GRd9VQIqnlS0V+6rdbi8bU+v6QVjuHwZkMOCP8WNarRaj0ahSFDbtdgyoncQxtj6fyQAZG+kHAZPJpPL/Op2Ospma9WTEVXq9nhZ+UUJsWEo8TGX2Jx85/84fP/EBUp5KQq3a1uuskcQ5FBbD4YDZTBXjr6+vV7rti8UCx/UrIYCJUChRkRca3WvgWiGFLEnTgvcefqD6qHgu/XaL0/wUx7FZTBXtYDaZIS2BFFbFl2w2mzTbHS70+vT6XdbW+uxs79DutPE9H1Cy2uPxhLTMyTNZadyrFGgTodFh112qglh6EthVZmLZxLXQEpyW1JkHUZfDlNoI6LoYKclL/ZtqQWvjJZYbhaHtqUWJLusukRQUusfIUiQAjBEtymXTRGyLTr/HD3/4Q+49uM/VK1e4uHdRGZOAyqFWnawzoiQGBEdHR0SR6hRv6H62ZWNjkywSRnKkUGTHxfFr2v+27kxv6b4mjsTt2ISh6ir9/POf4PLli5yennJyeMTZk2OKLOGN117l9OiQFz/xHHt7exTA6XiEZzm4llLCUnNE1bFgKTU3IVF1QFmOJQS/8As/zzvv/IA0Scgdhywv9Niquo/zQZJlWaRZhhAOsgDLtXB1gbXaxAWFzFcMsXmvuudlkWxd2EJRh6wVg22M9rM2sDqFwThIV25cJy0KkjTDa4QkRU4uc6I0IU5jPM8FJNF8TqfXxWsExFlCIQvGkxFbm1u0Wk3Vhdx2SKIFH7z3LtevXmRraxNKm9JRHCdLWGBJMpmBUMX/NqoHzre+/R0kFmsbm0zGU5I0o9XsICyHgoz1Xo8gDJmNxgTCpuW6DJEISjzPxvVccpkStJsIyyOJE5phgEALWwg9i89tdD/OEZFS1RRRSh48esT/8n/1v6bMC7IkI0sz8rIkK3Jdm1JW608IycWLO3zyk68zGo/5tV/7m1y/fp12u4XvB+zubJMmGf/8N/4FP/j+uxRFie8H+L5Hq+3y0ksv8Q/+4d9nb2+PXDeqLMuSXnePqzcukaQR3/r2t2A6U1nZXBWgK+qmi+e7mg4WY+MpBzv0cSwVdDquLpJNMqSlm6bqImfbdmg3WwxOjmk2mwqdtwTvvvMus8mUl15+Gc/xGOlGzmWWUaQZDoL1bh/XtSvHNssyus0ek+mENInwm00m0zG2ZdEIfY4On1SZxzRJ6fV7VRPhLMvIk4T5VBUqy1LiOQ6baxs8fPhQgVSeA5RkScx8qvqtxdGc05OsogMDWHhYsqTMMrI4Vqqdui41CAJVrJ5l9Pt9Dh8/Jk90zzbpIEoL1/LxnIAw8Ll54xpZnjOZTnF9RdnK0oxO2EFgaRAs48bVqzw+2FcIsA1h4OrCZEkazZVojdfCsXzyrGCt36fRCDk6OiAMeshS9RlrtzvM53Mtm55ycnLIYtFWiHGSVmh1Esf0e/1KHGI+n7Gzu4VlSTzfIS8ybNunRNBs91jMF9y995A4VrWerXaTOFZgSFn6pKlSXfVcn8BdEAmVHTbzTUpJmUqiaMZ8MsXXGR/bsgj9oEpIpFnK2dkQx7bxgwDHUSwPz3c4PDgkS1OkVHVatqWbcecZT8QTkKqXYKHpylleIrEqUMkEU+PxgoJSNfXUznfVg0VKykKAtClLqZ08pVZ3dnaG43mqDYZGx4Gq9gFD1ar8YFEV7WOyF4BqE0LVcuM/9fiTCpp+VGBjgNWyXM3S6Ff+iLP+KAU3FWgtP6b+/bJ2qc7WUEIsBUIKvafpLI72VSxbVPWI6Npzae5FCg0KWdXnoWuLTLbRBB0K1IVKXrx2q5VvdO7eV/ZjVLsYk4FUN2Koec8OpixLM0aMf1ZTC7SEBo1sG2HZyk/THyfLUrfdsFRrCKk+PdPlIJRq7iVZzmL/kEePla22rZz1jTabm5uEYchXvvIVtre3K1EVlcl3K/qyuRbD5jGUYJMlMiCqKa8YDAbs7e2xWCyUSp7n0dP+ja2FUzxdN27EbIwYgwnUKl/PZMp0wsAIr5gaVUOjFbZNCUoAqCxYzGaMdcLi4xw/8QHS2toaSqpzzPe+93alLua6Li+88ALXr1+v6iGUcVcLydDtjCKOotc1VLGwRjellDx8+JCTkxPyLMV1RJUmdB2XIFSKccPRmFa7o/udNCslDoWEFJUUtJSSNMs4ODhgMBjwU2+8vlKsD0t0up7Bqaf463UHykleShALC2Shk0U6LQsq4kcISrTCmFB0BtCOsTBBTlmhB6buwbbtFafeOI+GWmYCk3pGpJ4GN59x+fJlHj16BEJwcPCY05NTNjc3qwzKUlFQKUeZTsvb29tVb6i6WEKSJMxnqreO53nV+xVqGleIY1EUOM4yq5amKqt48eJFbt24idCS4o6t6ENV7xUJGxsb/PxXv8r9u/e4c/s2th7TPE2VSp4xZpZVpaA/++abXLt2lSh6T22+Mq1kyy1YSSOZZ1ylpIuCZsPBdmyyLKWUSkYZa3VDOm9oTbBUpztKloFT/bPOqzOeP0xQdXp2yq0Xn8eWMJ8uCMKQ6WRMmqQs5nPihepP0AhCDp884eatW/T6feZaYevsbECvq4QFjDJSkgSUsuRrf/AH/NzP/axCtgtrOSZSKvqnlBoUlLz//m329x/TbLaxLVs7gyrNn+m5d/HiJZVpOj5RUsCzEciCLE9ZW1vj4qUr3Llzl83NHfKiYDqbksZJNZZIqbKrH5GNOz9e9TVg3j6fRUTzGEqp6poQFBQVAmk7NpYAz3PZvbDDn//qz/AP/+t/QLRY4Lpq/uZFzmQ85I03XkMg+MRzt3j4aJ84UrThTqfN5nZPIZxlwcnREZZWilzM5zSbLZI04ZVXXmF9fZ1vfeubPNrfx9LZSyXTrh2fIsOxHdJUIei+51MWJY1GqBHKEj/wyPMCRf9UxctBEHL1yhWixVzJracJiyhiNBrywgsvMBicUeYZsiiIo4Uqxk1TBsMhWZbSbreqzVdRVFMcLROcpgmObVeF1eqRqF42hndu246uCQqQUim/CWHT0NRoIQSeq1oUIEuERUWfNvUnwvd1XaRa6waIMdeUJAm+71c0Etd1q8aJL7/8MmmW8ujhQw4Pn3D9+nVt422KwiaOS+UQOA6O5dJqt1Xn+dLCc1wirTJVljnXr19Vqqanx0tFSklVo3U0OeLqlRvMT2bIUgXIF3YvMJ6MAYHnBQwGQ6WKOJuxvr7O66+/oWhxA9WYeGtrk2SxUKIrWcb62hq7F3ZpNhusrfdwHJfT0zOKMifLCuIoIQxDnhwcUpSKZl2UEoGmtHQ6xFGsg9ScgydPGJwOK+nxKIro9/rM5io4d12PslDtFIyYT1kqiqtl2wpM0/uXotMppw+pqGtKpVRWKHZeFEtEXmo6uXZqkzQjSYtK/KMsy2U9cqlUZleETYwtLQVCOCv71nSuaDwyjtXeWatRPk/vquyyybKo2VSBh/XfqF/9yVPn/mPoeM/aB370eVSAUweP6lmTj/GJrGRuVj7/6Wbq5jXm2Zuxf5bA0Mol6o9S+yIIsaoutzIHdA+8SqnuxwSaH8XKEEKJ0iylyFcDovrPlSJiLSNVzzDXa5NL829RDzx1r6iyVBTimi/X7Te5dGmbVqvFlStXiCIlPuL7Aa5j0et2dA1TWvmpeZ6ztra2UkNsVFMdx6lKF4wqpPE3jM9j/Ma9vT2kVLV+vV4PdE1wEASVzRdCVEwTQxM2IlymTqkoiopeHukWHqZWydFtMoRY1nXGcVztb57nESf/pQapOoIgYD6fs7u7S6vVYnd3F9d1KzpOpJuH1qNQyzId2x1cV3EnPd/TzoNV0cmklDz33C1u3rxBUWQoNo7uBVOro8kzlSUwDjmwsphNMGGuY319HYDRcMjm5qbKSGjjYTuKVmP6HFlCpZQVymCKB6VW7pLqtUVZLU5LqrokE2kbNC8IQ7zArwrkOp1OpRsvpVTcaVjKNMYxpZT4OoVpFq5BEgxqXV/45qsKrMRS077b7dLpdNTYZDntVruSkWxqdSDzPoArV65UC9SgBWbhGMffBKtSyipIMuosRltfFdWm1aI2jSsNR95CICzloPmeTyGVEECudfmDbof/2d/9O/yrf/mbyhCkKbksSXWz2tlsRrfb5fLlS6pGIPD5yk//NA8fPCDPC5IkpfwICnfFR86Uo22cMd/3VfBlsnx6TpnxNEGpoR6cVwA6LwxRD7pNDYv52agLmkPq+x+PxxR5QaBpQJ1Om8HpqaKPaIe83WrR7/c5Pj7mEy88z87ONk8eK8GR2XTGcKgcp36/z2w2ZTgcMJ2OmUxGRNGCn/mZP8fuhV1dRF1WioNSz+l33vshb731HYIgJE0L4jhRtNhGQ61hDWzcunWLJI4ZDAY8fHif46N9tnd2kLKk3ekThC2kcNneuUiWZrp2T2X8TEWAtQToPtax3CAtTM+iMlM9ewzRw/Ys2u0WrueQpgl/9+/+HV597VX6/S6dVoPR4Iz5fF4VjBsFzaJQc3w4HOPYAt+D2WyM65Tc/uCI9fU1xmNF2TO9S6bTKWenJ3Q6HcUTDzzyLCWNlWCJZVHRYM26sm0PI4FvPrMojGJagaez3er3ud78ulgW5EXGyelxJRCxvrFGUWaMxkNsKZXSWSNAUrKI5jQaAb7fZbGYYVmwvb1VSe6ruppGtbaHw2HVtNKsccVZVxtps6noGFG00LU7NnmhMo/CEnS6LW7cuMHu7g6DwRn37t3j9PS0mue+7zOfzypbZ2yUaYJoPtPUpozH4wqkCUJPUco21+j22pSywPVsonhBXuTaVoHtKVGaNMvwg4AizkjzhLzIKFHBWzReUJYFm5tb3LlzR+8Rqt4PhLLZnottC8bjkZLF9RwODp6wWCzwgwZh2GA+n9NsNomiRLVJ8AJkXmBJiSMErlZ8Gp6eMJzMOT45UXVNecbFi3tIKZhMI5CSyWTKbB7hh03SNMNzPcpS08ezgjxSzkqRqawowibOcuZRgrAEjhdwMhhi6M2gFCPrTl6qhYBELWttaobU3qkyC5YO1E2tg7CUXHycqaalpi7J2MK8KAFbr+dzzeOVNURKtWbNb6U02eMacCRURlIBTSrrZPbxj2MXzjvPf9LHHz+w+ZM//lM/7+l7eBqcMnu2AYXN+54F/D0VqIlaePqMoMsc5vz1z/i4R/3zLPtppkb9PqrX1ee5LEGugsrL4G31XlfuTQd+pu6nDhz7vsNLLz/HL/zCn8e2bRaLBWEYMpvNiKOUVkP5XbOZaqxsao/MHmSkw1UyYZnRMtkeYxsNBc5kc3Z2duj3+0t/MwgopKTZbGphs2nV88gEWsPhsAp8jU9mfEvj58Cyj6QRmzF+oKI2u7phtCBLUw2czz72M/yJD5Bee+01Wq0WjuNU9SqgJk+qi7/MpF02ds20M+3g+66mpylXySRUCt1sy6T4JArVznVfC2O4VbNJwWw6q+pp6sGQKjy3K2e0LFVjP9tWaPhcFyXXgw+1edTT3XXUZXWB+55bvVdKWUlTO45Dr9erIv7pdEpeKqffIAfmvMZJNhPSsW0lmVxtWEulMFNQZ5wLc311etf5BV13xItC9U0xtWEmAzgYDBiPxywWR7rx7G6VXTINXoGVzzTPBqgcmyiKKnngVqtFs9lYyg/r15sF5vkeuUZGJtMpY90T4M6dO8ymU7rtDs8/9xybmxv8/J//qpLERFAgQdd6mTmg1K4iZFnymU9/kn/5L/8/quDQdZCpCq7PbwrGOJv5B2Bbikerze0z54A5VLHw8rzLrOVSNrX+/vPom5pPT2dHJFSKgI12B9tx6HS72LZFEsdkacrXv/Y14jjm1q1bWLbNaDjkyuUr3Lt7j+PDQzzf48mTJ+zu7lbFo0ruOWexWPDuu+8SxxHPPfcJLl++Qq/XrbK69+/f5+23v8/J6ZBLly4xn0dYwuL4+ATX9Wg221AqcODCpQv0ej32Hz5gvpirbK8WH3Bcj/WNTUqpuohv7+yyiBa4jsNsNl8a4Y/YQH+UE7BSuI1yyKQA2xHYlqqv6/ea/Fd//39OWRQsojmf/ORrXLq0x2g04uz0hOl0qjOdOdPpjMlE9WZZLOa6p0WkN70Az/WQMmVne4fA93DX18izXNEzkayv9ZlOppyenJDnql3BZDzG91zNelHginr+pkGixLZ9TWXIdXGr1LYvJ02Xgiag1EE7nTaj0RCEYDafEYYBzWYL3/dYW1MUrtl4RLfbQUpJu90milQNie/7dLvtygHIspS1NdUZXsnJq0Ct3+9re1RUdrjX6+q1nWo1Ssnm5oamUE1VDVJZ0mq1uH79GvP5jMeP97l06RK3bt3inXfe4e233656CcVxgmZ9URSFUlvUgYQRvjAytO12m5OTE65fv850NuHg4HGlgFbPgDfCFgKLIAzwG0r5qTR21bZI0xhhW1iOhRA2RVoyn02Zjmc8//wL3L59m8VC9RSZz5Wk7YWdXQaDM+I4Vg2GdZZsPJ5QyindrlLoezQ4IIkTWu0Wi/kcWeRKDns85saNmwzOBriuy2QRMxyOyfKMZrPFvfuPlGy8ZfP6668jZcm3v/0WrushhJKs9/2QPE+0mIzUGfFCqxQCiKqZpxCK/pSlKdrLo6yBW2Z9qcyPAifMM146liqQkaU0SQvdb1Cdq9Rz19CXhDD2Teh9XNHBzdvVdZh1XXesdVa9Kso3BkCJSJTSnH+prHXeZv7/Klh5lp3+OK87/9r638+f4zwz4Vmv+ZMPjJa/P3+d9S+jJgxLOW8T1DzruVQ0vGd85kfdUz2Y+XF7wPlr/ajrqN9b/fqrPVsLS9TB5eX34pnXJiwLUXtWil6r6m7PTof8q9/8HU5PxvyFv/CLpGmMs+URBCGB18C2nIpiF4ZhBVJbllUxesx1gKnRs6prNkCS2c+Nzbx//z5PnjxhZ2enysBHRj1WM37MOqoaeesgzARAvu9X6nr1en7jt5n7NEJhic4Uebq9R6vVIoqiFYD4xx0/8QGSGTgjyHDhwoWqEdXh4SFRFHH58mWOj4+ZzWZsbm4QNkIQkqLMK5TBdlR2SVYBleKjFkWmgyZBlmakSbpE8IHxcESz1a6ux0wCVciqZKWTJK6i7iiKKofREkI1SdWFyghBbDJejqgcF+P0mgyMQSEEgiJTqUsbVS5usj3mWkzA1l/rQ82gGKMjhHLmFL1DXZNdFVDnKpNVQ1hMUZxZ9OeNwfnJ+SxExdZZAiFEpexk7m0+j7hz5w6Hh4eVdO98Pq+0+EEpF5nAaj6fV3Q6pUK0pE8eHh7qDOGy75VZ2MPhUKHQccTh4RFXrlzm0qVLhGGoGqOhHLhkEXH7zofEiwWj0QBZSnJZEqdJpZ4CVNmqIsu4ce06X/jcZzk6PtYLGuIkpSiojDYsDWWqm5oalNStPUM9q1bGs3qvdgWqTJRG4Os9qc5nkOrGrn6+5fxVn5dnGSenJ2zs7NAIQ7I4VjUci4h4EXHv7j3SNOXs5JQ3P/tZ3v/hD/nsZz/HjRs3GJ6dIRCkccLx8TEbGxs6M+YRxwVxrJTQBoMB3/7Wt/nB2z+orvH07FQrXpXs7F6kLBWddX//MVlW0G53lNBCluC6Dm+88QZlWfL+++/TbDZptzukmYtEyby32h1OTge0Om26vQ5379+lKEtGo6G6d+0zfdTG/aMOtTFmgMCxHTxfcVybDZ+1fpf1jXXu3Hmfr37153Aci92dDR7cv02eF4R+gG1Bu9XUNUI2O9tbCCG4d+8+6+tr7O8/YjIZEy3m7O3tkaYRDx7cw7IUpSzVdFhTH2GkWM0GZJTbXMdFceuXnd6zLF25D0er2uW5sakq6Hc1XU2t1Qa2rXqE5HlW9d1xHJtWqwlIms0GWbxYBmmTsVY1U5kxhG4kKyWlLJjNJghLMJnMcGyboiwJwwDLVhldx1E0i5PTYwA8z6HRDEiSlCD0EcKi2Wyxs7Nd2f92p8PZ6SlPnjypVNO++MUvcuXKFb7+9a8zHo9ZX1+rACvLsmi323ieR7PZZDQakWUZQRDQarXodDrs7OyoZqe2g3BMuwCH/UcHXLsWIGWJ7ylbZjsuvkT1lKspo1m2hyxLokXEbD5lNlPS5aIoefz4gN3dC7z77ruMxxNmsxm7u9uEDZ/5Ysb62jr9fp/33vshruvhOAFRnDIaTbUyoMXW1hYHBweaoqcykiUW2Qd36HQ6nA2HTOYL8kJlY0/PhsgSWq02n//8FxiPR3zjD7/ObDYHKbBth0ajyfHxoNoL87yeudEMibKkKFZBhvNUtvP2C5byJuoPICs7V2J67zy15tB9eQxVTWd+pfmfrE63/DypX18H4euf/QzaWynlyp+fBTZ93OOjApQ/zvEfG5jUwcofFTz9aWal1Ef/aJCqHrw969/6XqfOxgrw9VH3Ut8P60FO/To+KlhcGTNJrf5MHXUW0fnnUN+Xn3W/5qgHT9X3paoFNeBwHRAtCsF8lvONr3+HvQtX+KnXXyOOCjxPZVlms9mKHLcBguqMnyiKaLVUL8n5fF7R5MqypN1uV7Q841MZ1ovnebqnnY/jOMzOzipaXFEUVQAEivlleqYBNBqNKoNlfLhOp1M1tjV1SkEQVOUy5tkFQYAAIq2k+ceZtz/xARKoDd3IfhonUAjBlStXNMf7tJLrjOKYKF5UEt4q+2ScgwJLNworiqLipidJgmvbqgeLbVNIiNKEk5MT7t69w4svv8KtW88xGA558OABnU6nokttbm6QZcs6DMuyODw8VMiw52HXImXDe1f8eYljCZCqiPDh48ckccz62hqJRuKMzPR0OkF1Ww5wHLcKfEwmRXVpL5nO51U/GmMQKklEKcnTTBW0p0aC0lJUA1t3mtYL8jzKUP9d3RH/KDTK0AfrC7z+LK9cuYLn+8ph0shRludEi0Ul4VoPBA36WC/s832ftbU1iiIny9IqaHIcRzXMDALGozG2sNjb2cXGYnQ2YIRavOsbazQaGxw9OeSb3/wjhFT9QdZ6fda3Nil10GyCRbNwizRFSOh0O7z/wQe8+977LKK4QjvqRx2lybKMOIoQwiJsND5yrpvxNRlMi/O8+Gejf/W/PZWyrx1lKRG2QuCODo+4ces5ut0uw7Mztre2uf3BB0RRxJqm1pWyZGtzk+PBGYPBgJs3b3J8eMjtD29XgMB0OqXdblOWOUHg43qOkjF2HWQhSBJ176WULGYRZQGWsAmCkGgRc3JywmIR0el0aTSaJLFCpp5/+SV2dnZ4tP+I8XjE1to6V6/dYDgaMRwOuXTlIp7vMxwPuHT5MkWp1OBmM9Ux3BZqU1PF1WoN1DnkP+oZmLG0HaWe1Ou3+Nu/9qt897vf5s99+UtsbW7g2A6Bph8sFjPGozN8X9HXTk6OsS1LKepFC0BwdnZKEIScnoyYTxeMxyMW0ZywEaj+Ze22RrQtooVay5PxmKPjI3rdHv7/l70/jbVty84CwW/O1e/+9Oee2754TTTvvegNOGwcJsIYqLRNka4yjUSZrmThP1jYYJAAYclhyyClyUIghJKSXaZJyllAJVVUJc4khXGEsXE4IhzvRcTrb3/vafbZ/erXnPVjzDHX3Puee98NiEqjEEt679xz9l5rzTXXbMY3xje+EUeoygJZusJqlZJekwSAhpSfBMxaR0amMN55mPka+L5xiAAaDTxfQHqA71M9oKtX34OkQxSy0KcaGMvl3FR318iyFTyP1DN9J7K7WMxtbqjSJNvueQSqeHPn+jknJyd2HueFidQ3NaQRZiiKHEVZoNfrmTpHEQaDEa5du4ZOp4NXXnkVn//8b5DS2XCIoqC1dz6f44UXXsCnP/1p/Pqv/zrSNEW3K21kuyxLqp/meXazL8sSZ2dn6PV6mEwmZs3zMJlQHaT65Bx1XeO3futVWxwWIIWly9euQnoetne2sbd/gCCIAA30Bz3UdYUHD+7i9u1bWMznGAz62N7awvb2Dr73e5+z9MkkiaGaAt/93d+F4XALe7v7+J/+p1/CzZu3MR5PMJsTVSXwA3zHd3yC5Hnr30Cns0KlFB48eICzyQModQ+XL1+G7/t44613UFQlqrrBdL6EgIckWeEX/x//T8wXU6imglI0Jihoc0b4Qj5a50UpZaI8ngU37jokjcAPn0J/NwYhTaDH0lq1y0nWFwhjP+ZE1z23bnDDrR3/6PG4z54SzzxthOcbdTzufu923yd9/r8VIHrccRFA4cMdV/y7yyYBsA5EtDZr3OOvt2mj8FrwpCiXe7iOx6Zp1gASX9u1R93z2khpq2i3/hwtFc+1t4Sg3DYhTEFlB9hQLUCBIPBw7dplbG2NICVQ1RnCqAMhPOzv79s2MnOJC8ByPTEhhAUjTP1mQMWRH6bCcY04TtfgdzIej60QDkeAWB6cHHK+fW52LvNnDLxcu57by8EBjkxxygC0XksRedrjmx4guQlqACwFjJNG2ZgOw5BySuIYSlegWg5j+L5n1TiSuAvfD+wgWS6XlgbTjRMMen30el34QYA8TXH3zh00VY3FbI6FqZmwu7uDMIzMi6eKw0WRW3471+7QWhNnsiHPbl03KMLChDArCFSIwhBRFCKOIrzn+lUAGk1VYDIek1fYDGhVVVhMJ0jiDgam+NYqz3Hr1h1cuXqNOPdZhjAKrWEUhjTgi6JEWZSo8gJ1XWExn+P05AQnJw/xgfe/H8++8AKUashzanmxjTXSAdp8qOCZiUZJUmy7aKHRWgMm6Zf5E9rQJKQktblOJ0GaZlCqMQp3DTjkzNK71Id036YxtZccwCKEQJplxuiFDQu7ogHSk4ijmIzzpoGqScZagOproZNguDXEH/qv/xBOj0/w4MED7OzvQkiBOKactUY1kNJDkefIlMJ8OsP0fIKsKPDBlz+It9++iVWamUVtfb9tF3NqX5bnqKuaEtCpt5z/Lo4iuYDYDX9rTTknNsKEVnFw3SsHZ7E2bBSlIITE6ckJ5tM5Dg+O0OkNICGRxAmyNMPWcIStrS1cvXYVl69fQdFU+MIXvoBPfvKT+OAHP4jZfIbjh8cIIqpfk2cZpBQ4unwZW1tDdDoJ5SZoD6vlkirO1wq+F2I2n5nxWeH27TtQSpsq4R2oRqFuFHYOD/HBj3wERVni1S+/gm6nizwrUOQl8qxEEMQ4PLyE5XKJsqrwvve/H6t0BSE0Th4egzcnfiFPLzDlGGuCosxJHOL3ftd3Yjjs4pOf/AS0LvHw4R1AkYfr/v078APfRHQqpFmGumxs4eI0zSz/mgz+AVHGtMLR0RHiOESvR4pxYRiZHCIPZZnj4GAfk8k5gsCDLyWU7+PS4SFu37lj6odIhKagH0QrrR8ElEuplLJRTSEFfOEbr2AIKQSk5wFaoWMKjXIep22v8QpSVHZK3kMhUDYNFXBWGtdv3EBzcookjhFEHknDVyWkojU7yzKk6QpCSNRVbTe4MIjgSVIyTJII83ltiwsCxjiQAtPJBHVVwQ981HWF4ZA8j1ma2SLXb7/9tokoh9jf38frr79O9X1GI+QGcAshcH4+QWnKMmRZZhOJgyAw3lRga2sX4/EJraWSjAQpJFarDH7go6wqXLl+HZ/+9Kcx2t4iSlpNToeiLOB5Eof7h3j+2RcAAHVeYH9/H+fntK5fuXwDVVXh+PghlCrxyiuvoqokqhJ4//tfxunpFEWh8PxwF57vYzqZ4LP//tfx+uuvo5MkWGYFVs4zKdXgwemYRBEaYldIQw3XWiArKqLqooEURnZYSBOVEeAaaGvgx64h5PCySpCCZ0nrxTfu9Y1ZRHPHWQ3bFU5oA5AcMLUW0dnwssMNErkhJMegvnAuO1ew6Gnzm4+nWOk14Mbehva42M42e557q6fBJRc9j9kWXBDKbRNr74EXOfqf+6TtWiYe25TH9h1fVrzL9/gWfNoFX3wSkOGrt3Xy1sUcNiMzBE4MCILj8BLcjvUcl3cDhm6UZ43G5ziFlR3LrpPYo7IrdlTSC2upc0QD5H23BWlsz7aCXJt2Lqs+9rpd9AcDYkbFCS5fuYT3vOc69vZ2EIQeut2YhHACCYHA1pvk52cKNOWZNqYgNq11XOjbFchiZzBANHwAlpLMdkhRFBiNRugPh/YaNrcabEMqC3S4v9h248/42q44BOddAS0Yq6oKnSSB73nk+PSfHvZ80wMk7lC30BajUh68w+HQ/k5eTB/bWzGGw200dY1ud0A5BJqNTt9SFg4PD+nleZ7ZVIh6NdzdwUe/5eP2XnmeIkliU5y2RtNQe7hdDIz4BXN4UOkSSdKzcoeqadDt9+DrCuOzE5ysVoBq0NQVxmen2N7axq/9+m9glab4Pd/5e7Czs4MwCjGMYyyXM0zTKaKkg6LS2O73UeUVorCHfj9BEPqo6hpSSBRlAykCohB6MbqDLvJ0hTAI0YlC3HnnTXzty19Ali7w8sc+jqYu4PsBhCZpTFbDqmpt87DoEFRTAsQZ36R4aU3yyzTpPft3oBW5yPMM8zkl9cVxDCkp/4EXfv5Jk5oKtDVaYrGgyAAXQiOObAyYgn08VtgzIoSAahpcvnoZYRhRDpoQRmKdJDsjUOjWi0Js7e9BmsKSnvBsFFAIiUU5x2w6Q6MU9i4dYTad4Ts/9V149auvI/vSl1EWDXSZo3bqRdnFVQJl1aBsFIqqgvA9iuKYXVBsuD7Zm9Q0CtprPUtASxkQQqA2FBhPetagAUBeX2McsVecuSoaVIPBg4QuGpzdP8XW6ADD7X3kWYVLh1dx8603UYUlhrsj+EmAymuwd7iLu+/cxpe+9AV87OMfwwc/8iH8+1//NTKadYimqlCVJR7ef4DlcoHR9hBRHEFpAUiBeDBAWTToyhBhdwt5XmA+n0MIicGgj063h6qukBcF4l4XH/72b0PQ7+E3f/WzqIsCO6MtjMdTovBVNZ59z3vQ6w3x2htvYG//CFu7h3j7zTfgQ+H+3bv0/MbfLBxp/IsO3svM8IYA0GiivXoAukmMQS/BfHKCbjdBFIYIo8TUpTnHajnD5cuXAa0RhhHNP4/UDX0/QLdLUqm8RnV7IQbD2Dp3eMMgcEK5N5y3p1SDF1/8AE5PT7G/s4vZbEYbiRCIgsBuIpImDskWOxsQ/wzCEDDrVFVV8D0PtRln7Akc9Psk0lEWKMqSag6FIeUgBSHKvES6zBBHEulqiZ2dPXzsY98CP4zwgZc+jDu3b+P+8S3kteGOBz4W0xmJLJQVRcHDAIBEVdWYzxaAFuj1u1CNQhxHpoaNB5I+jzCbTdGUDfJ0RbXHigJJHKMbx5Cej/Q4Q56SeMH47BxKKTy4/5DGlvRwejqBUgplRXk4FOUXaNQCUkhoCJK6jhNcuXoND47PcDI+Q9JJkC6WaFRjI5ocRb5y7QY+8OLLiOMuxicTarPwsJwvcHp6itVqib39fSzmC9y5cwdVpTGdTDEajXDr5i0sjYBEWVW49/AhTk9PSRnP8ww9bwkNoBItrcdGkSeLdtBarGAAStMAgvKf6kYbpwmJKFjajzaDnI1/odGwgIG1DblmDB1KNzbng6WuaZ1qjeF3D06sR3vsZOP/b1xgLTIFJ/pg19WNyz9tdOQiK184k3/tqw7EEFgLUT0eLLSLCffTOuDTG9+98NYWEbZrloO2xGOu98g13D+8W/882i7t3vPC77nHeoRxk9rnguVNOho5ZNf/3l7zUZraWmRJaprHa+N3YywZCXEAjmDWBT2gqZ1K8dwRxmYwinRoAZqrStmYsil2zV2L/gSo6wahKeeitLLlM4SJ+AdBiOFwiCRJcPnyZWxtDXDpaM8qz+3v71shK3b2VmWOTjJEXSoEQQLdKKP+3doJVVXZQsmsLMesqbIsbZ44O1qYEsc5Rjzner1WmbTX62F7e5uYUMYZx2wq92A7xRVbYCDITl4ANqjAII2jSm4Eke2fsiwhfR/C+y85SPZgABJF0VqY2Sa/m98ZRLnS1AEAEYs1hA604VGXK8rXEVIgECFGRhue5Q/TNLWJyBb8GH4mt5OTVH3ft+ohottFnlNibhAESAYDUsKqBaJOH+fTBZI4xnQyR9kAN+/cx7PPP4/ZbI7P/uqv4ujoCO9973tJslZrLJdL7EYJirJAAx+h5yHLM6iMjCMKQVZYLhc22dn3fciOD/g+wpCU/b7n+/4gxmen+Hef+yzi3gBHly9DqRWGwyGgyGOidAt+3CRb2qhooXHD3tazIki+umnUI/3s+yF63RBh2MHx8TEePKAcltWKqtWnaYooogVDKcpnaJRCrUj8gt8fT96mruF7wk4yS/NzeMdac62QAkVRmBByAKUak+sFDIZD9AcDC54rA8TIiy0RRCFG29vwfTI0lzsr3L//AH/8j/9x/LfnfxtVfRMKCo3JR3O9X0qRhHFRFMjyjIxo6QGocZFn0nrE9Hoiq5sb5/apO34vpg+sGwAasLVE3nzjdVx75j1I+gMoCAy2t5Gc9DFbLuFFPoKzMzy43cUz155Bub+PO3fv4tVXX8V73/c+QACf//xvYnIyReB3IJsadVNicj7HdDKD9CS8wDd+NSq6qLUHpQDP8xEnCaIohBcEWBU56qZBb9TH7/rWT2B/bxuvf/XLuHv7JmLPw3Ixw3w2QZrlCOMubjz3XtQAJosVvu0jH0WalSSEMB4jS1NSyNK19e6xcfV4kNT+nfuZqKs1dne2ceXyEbQuEfk+mrpGHNPYuHz5CLdu3YIQAoPBwHK+i4I2J65C7q4Ny+USZVkaimhjQUuv1yMjOcvWpPjjmEQcqqrCcDh8RKq/BeOwwgM0DgQ4OZ2VEgHYNbJ1WBS4fv06EiNK0NU9KCgslkuUZUX5llGM05MxDg8vIc8yLFcp+v0aR5ev4P7DY6Tn57hy5Qq2dgf4whd+E3maodQVpOejKCqTM1MjL0qEQQSWFo/jDuq6Ql2SqEoU+agrooFNJjNopaEVcD49BRVbVOgkHaxWS1S1Ql5UqOsKcZwAmvL9iAdP4IfnDtcSEmY9aWwxXaLP3D8+Q7/fx+7uPp599r3QWmMcjnHznZtYrVakFtVJEAYhfuvLX8Xdew8RBiEBoLqCL8m7maWZNSwpb6wCtBEBkCRSIUyejYZGZWhCjVmL7HwH0IiLDE7zEp15vub1dvJ2nHiL/d4TowDikX+s3XvzXjRvnuJyF/793QGNEK2D1D6/+W8tnGWv++7XfPT5HUDzxLXzqZpMX3M6ZXO92YwEvds1NoGG84W2SY+9jHj0gS+6p10rLm7D09ILH/e9x1HanDPxuHfwuLHn2iVPolxd1NYntWctisS2hGaxqNaWdJ2WntlXmA7n5pD7foQgoFy/OI6xt7eL7e1tbG1tIY5DDEdD3Lhxw6q/jUYjZFkKIcjJtVwubYoIAGJmmOg+5w6xbcZAhOlyAGyOupuPKYSwdDteA5m6xuIITLNjKjLnvlZVhd3dXaK+meflqBQLbjFVjvNnWcGuY1ILeH/dfId8X6bVsU3H74WL2r5bRNA9vukBkjVSHWNwU3GD81h4A+QQ3WaI1jUugUcFB2zexwaA4orEnEjG92RJaQ5L8mZrJaaNV9g3hbN4UIRxDCE7CDsD7OxfApTGlevPYDIeoywLVEWB3YMSl65cQ7/fQ6M0oCU8CXhRgrPJFF4QIYwDpFkGP9QAJIQXoMgzQABh2LEVjYX0sUwzipIFAbwwgi+Bw04Xv2+4hf/wG/8BUZzg0qVLYJY3hYW5snPbp7wAQLS1BYQgwQmWl4QCmrpBr9c1Sd4+VWqPY0CTwhRTEqtS4ex0giiO8OD+iTUkF/OU6EdJBxAatart+3IXxKIooJvK1rxKksS+Pw7/Mp2SFw3P82wi+Wq5RK/Xw/jsDJ7nYTgcIopjhHFMct8lKbkkvS7KooCAwGq5gtYKzz33LKT0kGX/Z/zNv/k30egGZV3ZcdACxwZlWSFNc6SrzAhXeND60YXdBTkM+rmQm6vuw+PYNSCeauEQBMiUoS2mqyXu3rmJ97zwXhxdvoQ7t+/i6MZ1vP6VV7FcrRDOAhzfuY/Ei9Dt9tDrdvHOW29DKYUXX3oJnU4Xv/VbX8Wdm3cgpEQURohUA23y4lTTQKsGwlPwBNVNkR7RqkRA9ZzSMocWAgdHl/CRj38Uuzs7eOf1r+GNV76MuzffQrZa4cb1Z5GmGdI8w4sf+RZ0Btv40qtfRn97D5cuX8OD+/cBCLz+2mvkTTMeyTWTULdKjo9TwnHnved5CATw3heexdZoACEaCEXSrdqosmVZhhs3btgNiCqMh7YeF485jj4IQbV4lqaeFL9bu9ma6DM7gLguxP7BPoqUCnlybTeek7x5t1652m5KvHbahPu6sV5CLvhH1cwlFoul3eQ6vS4yk1s3nc5RVw2m0ylee+0N7O1tIYo6mC+W+Kf//X+Pj37s43j+hffhta99FTeeu4Hv/u7fj//ll34JeUr5ZtDkNKlUASGALE/NXKD1iWu3NY2y7c+LHKppEEUx8rwwc4rEJ5TSKMoCda0QxQlUo3A2JuU2fn913ZigSmPXDHLa1GgaDa5srzUlRZPozznefPMu6lrBN7Xt0hUlHSutsFqVlC917/iRuQrZznlmKrAjx8SBiL3gUcFSAisaWrTjsFaNEyh4NKrs/nw3I3Vzz7vIWH0S8PnP5WADj4Wavl4DafN4GlDyn3q4zsLNddnNx33Smu1+712PxyRftXWZ3r29j/v71zMu3H3pSde94E4mekNAqT3tSdGeC6CuPbE9b7Mbn9Smtt30u+e1dD4egwwwGHQwOBiNRja6cnR0hPPzc3S7Xbz//e9HHFO+dF1TgeKyLBFFIYoyR57l8HwPRblCGHmYzs7QSTqAoQ5KKe05ZVkiCkMkcWyUQZVpJ1HglsulpcK59YaY3cRRpMViYSNHSikkSWLZOVznk21sztviQEWWZTg9PUWv36eisCan0wWWLm2QxxDvRzyfAdi9j/vezTFncS+OePG7E0JY6t/THN/0AIkV64A2WZ8VNgBYY4HRrqsbvzkZGKxsbgyMdqfT6ZqnUQhhFaMGg4EtWuouHO4g4t83Jy+HJznaJIREAw8KGtKP4EmBThihNxjR5FQEEphiVpaFrSkynY6xd7CHOOmiboAgiKE0IKUPwBieDpCUUkILjQZ0TU8KKogKohoORh6+9Vs/gU6nY+mB1D/G4274sm6lY3oGvbb48sRrmgahF2K6mBqe/YlN5o+iCMtFbmk9RFtJTMjaQ5pn9j3N5wvMZl8zeWURhN8amTx5yOPhQ4l2YvL44InI/TAYDNZAhoBGVeQUCFMag14f/X7fGld+6AMaKAui5vi+R95sTfckvX4CUL/rd/0O/IE/8PvxT3/xF40qWCvLzmOB8iAaZFkOKQMISAhIkJd3HSjx+FJarYXH+Xnc8cUAyU1IbY3lNrInqcgXtCa5agqoEAntza99FVeuXEZvaxf9rSGEJ3B49Soe3LmFYLaELyRez76GV7/yFSitce3GdQgpkOU5PvzhD+Nbfue34PK1q3jtq69hcjqG0gK+FyDwqT6PNpWDtNnIPUkFIYuqRK0ajLa28Ox7n8dzL7wAL/Dx6iuv4M5rX4OnFWaTc3zogx9EmtdY5RkOL1/Bs8+/H5PlAmfTOb7jO343yqbB+GyMdDrF+ckZpOTcGzNGhfGkq4uNQ+uNdX7SOAHCyMeVy5ewXMwwGvZI3bBUCMMYtaoxn89tHRcuR1CWBTodqmEzn88RBAFOTqjaebfbtRsfg/jpdIper2edPnmeI45jeJ5nVX6qskQ36SBNU6xWq7X3XZYVKKlXQwguxEpJ+FRHyEdV1VaZbLFYYjqdmdw/YHd3H0J40FogimJMJhMsVivESYzz8RQPHjzE1tY2ZrMFDg+PMJub+6sGX/3KV/CF3/wSPvWd34nv+NSn8Nlf/hVcunwZ73/fi/jVz30Oxw8eIktTyleSRK0oihIaAnVFjoOmUdCg5+Eab8p4FekRSUYWTgSEJOUbNM2E5r7WqEoGSEQ3qZU2c1ajqqk+llZcxYqNMlai4jIGRKdReWH3BlZca8y6XCui1JCDSANCoNbK1rNjkKSbxlC7lZ2Pjalpx2NSQxlnlCCvlCnYpbW6sLbaRcfTesk3//Y0xu83CjQ87XFRG9kBt9mex4Glp414PO68zejNo1Ggp+u3i753kWNmM+LUjqG2PY8Hs20EbL3NF9si7vXdaz1NZP2i40nP6H626XRev4i0c0Kptv4fNvrQFUbY3PM2n6ttf0vPpP8ACKrBRZ+ts2B834fv+8Zx1Ee/P7AO8iSh+mxJkuD5559Hz9QJ7HQ6VpHX932kKdXyS9MVpCRBpyzLUOU50mxh1HFLBIGPwbCHPM/R7faMPeOZtynt3s+0aiEEqrpGaBzu3CdcIqDf76/VgeToS+uobYvec7QmSRIow87hXCVWpuMo1cqIf/H5TdNgMZ+j0+utOfhWq5W1hZlpxQcLa7l9fH5+buoCtoBOKVIu5n2P/8YCXFJK5EX+xDHpHt/0AImVhzYnAv/OyJ4nDQBTCJELIJLxufnCgLbzOXTIIUu+LkeGWNZwc9Fyr8PHpteIjR53kiqt0ChACyfBUJP9RrWazGv1AN8XEH4IP2rQHW5h79IhcVrDCFVFVeYbpSCFD62lUR2irZfVqoQAPEO18YxXk4pv0UAdDvq0NzthYyGAqm6sC4aV2BiAaCgEgW+V9hhI5nmOCjUEPERRgjhKoDXgezUCP8LuLk3sMAwxnU4BaJL6zVfIC6oNEviB8cwQXTDLasjAW5v8bh2qwGvDyK0Xt1WPYU+uGwIXWkOGEeKI3vdm4iC9OyDyfQilUWYE7JiXzhGrsqjg+wH+6B/7I3j9zTfx2V/9VRs9ZEAvpcBqlaIoCpt7RQatGUMXMBzIYMIjbXLD0vzMm4cUAloaGg975sz1qXK3pMJ3DclYp4s53nz9a3jhpQ/jYO8Qi/kCu4eHKIoMk+NjSFCkLktJQvjO7dt46aWXMD49w//6b/4N3v/yy3jhhWdxeLiH4/snuHf3Hk6PT5EXBVTd5g62+QsVkk4HBzv7uHrtGq5fv45ul0QKfuPXfh2zyTn2RiPUZYHr128giBKc3LsFGSd4+SMfg++H+OpXX8O1Gzewf2kfD+/chgeFV7/4RUgIw8WmO0Ks6ditzVGen3y46wqtJw36vT6euXEFnU6EPFshCiJoTyBLCyzShaXPpWlqo8dRFCHLctR1Y/nW/HwkgR5ZpwFvxryOMb1Oa43JZGLnalmWyKIUWmu89tobGI0Glv7rjgkey7z21XWNPKd1NM9zA1AK60SYTmeI41PkeQ5oIAgD+m5RQEHDkz7m8yX29y+hrmscH59glaZ4+UMv4/T4BFlW4Llnn8fZ6SnyxQJ/4A98D+bLBSQEPvnJGLfeeQe/+H//RcwXM2RlBumRU8CTPg4ODtHv7+Cdd97BMs1MQWWaXywwQzV2yFGkGio2zO9Jm/HM/14z1kxRcD5cj/y6eAAdvG5IwM4ZrfUaNdGlcCkYhTczzCBa/O22g2rtODQuaLOkakuPEnSSAfFOO/Gogfr1GqxPG3n6RhxP8ur/x4IW17j7es552s/XhswFe7feOO9p2vG0kTn32Tav67bl3QFa+/m6o8yp5aYvlp7maz8pov6kaOVamy9onwuWLgKcDITs79K9l3bo0a0z2taudJzTrtNJCI0gYFtAQnoS3U7HqAD7GAwGGI2G6PV66PV6CEMqDg0Aw+HIfj4wlPu6bqwdStR8V4SAi2FHWC7n6PV66Pd7hl4mESck1JNlKeI4QpalJr+nQVkoW19zVs/Q65KDTUAizwsLUvI8tzmsMHZNmqZGxbOwQIbXuCzLrGy3rV1mDgYpYRhaO9llK7hjiB37HHio63pN0KEsClt+h98D12taLpfWRmKnMV+naRrb30Txzm1EjKNGTNNz2RK8Fzb1f1GxsweHAV3vgbugsGHBf9uUTmSAwl54BkRubR1WxWPKC9O/AFghgcctVnyvTfqe+2++L7dXSgmpDAWioarrqqkRhaamD6QTNTf8Vp9pYR5kQJ4PSLKsfd+HJz0CTJ4wtUzc3CsCSVISvYMqnBujqlbI88ICCDa0yrIETF/WdY3ZbGYpa2VZIunE2N2lQT6fz5EkCQ4PD5GlGVQNTCdTZGmOVqlOGeoP5XmtUmq750mEkYdOd4j9/R1TWd43oNSz3lXlbAJuPSCYSBAnGgKw71oIYUEw0C7WeZ5D1w1CjyteaxRGzrJuakrmrmqTMB6gRoEip0ryDRS01PA8H74fom4qW1Dxz/yZP4Pj01O89dZbawsWACzmC2Rphtl0jrLWhmL37uOfaZ9uwVwe5zy+HtnchDCiDQyQhK37YTcpurr1P7752ldxeOk6Ar+LK5ev4M6927h09SpUXdlE+xvXb+B8eo7RaAs72zsYjUa4c+cOfuPX/z22d3fw/PPvxY1nr+H6s9eRZjlm0zlWi5Wt1+P7PqIoRL/fw2A4RDIYkaLOcokvf+m3cPuddxD6AQ5GO9BNifPzKSB8vP7WO6hFgI985FvQGYzw6pdfQRwG+MhHPoTlYobp+RjHN29iOZuAYSd3rQbZp5ve+PV1BHY+cOSPNlwP733vc4jCAFIAvU4HUnhoaqDyGzR1Y9WNOI+Ia+2cno4tn3symWAwGFivX1FQ/Sj+G3vput0utCahmePjY2itrThAHMeQghwBH/3oh/G6kWNfLBaoa4WmpjFZlAUaQ6Nrk2G1pXXR8zF9AhC+RFNrnJ2e2/Fa1zWSboJVukKWFQAk7t9/gPlsgXSVYZFlyIoSN65dxXd9+vfixrVr+Pef+yz+r//df4dv/c5P4RPf/u3wfR95fg/37j7AC8+/Dzdv3USpani+j6IosZgvMF/keHg8wTs37xGlzgA7fgcs9FI2auO9mTcruJ6Na2CycbtOztHO+kH04PV1mqaLgIQyJzL4cYxdbBxr007DEWbbGGzStssgOeccB7ivXW3jD1/H8TQRjt/u43FUsyeBj/8UcPf4cx8FoE8CJP+xbXic7fC467r2zjro2TxPgiWh+bx2DVt/BrcNmyybJ0WQngSQ1r6LdZvHve7jonP0odt+NyJGJQlcJ6FSyqpOcrH4OI6xvb1tKWL7+7vodjskFjQaYXd3FwcHBzYKwjXR3OdIkgT37t1bU0dmoQMhBBYLYgrs7u6iKAobzfA8D1tbW/A8z5S6UBY0dbtdFEWBwWBgc2w6nY6JkEl4XgDPC42YjwfyxQhUVWFsD98wERLrFM6zDJ7DwOFnGg6H5OQyB4MSBjaughzbY7QfRxZouukqURTZotr8vjjaxO1ZmpIsACy4YrDERWJHo5FVzXPtsNlsZscW29rswGbhInaCx3FsI16cvvK0xzc9QNpUY2KDd9Ojzt9xDR8eAHlOtVZcNO16NDhq1DQNVil5abvGcAFgteA5kc2NaLmLF/90qVzuS+fPoMhgaZqaePZhgDgi2V1BFwUrkAkhaH+193PqAHgBecwNgPQDz4lGKUipIYVGXVWoiwwaVPNEgWhkiwXJLy/mC1vstmkUtFIIoxCpkevlKFon6aDX7yGKSJa42+kAQqCTJEb6XCLwA9SlsknlnPgXhCHVaQlIwlJrTUn8UqLe8HKwPDF5X41UNTwT/aBoABejrasS49NTlGVpi4g1cYI8zwDzXSFowlN7akjpwfckFikBQ6IoAXlOtXrqskLg+SbcTLVQpJDI8gxJr4O0SI3cPIFOouAFCDyBP/2D/yf8rf/Lf4vSeOvrmqJIs8UKk8kCUTyHH0QWwKwZ85teXkHvsW5Ishy8YZqIGXSrDMjAu50PLX+aNkzYfDKtFRpN0UTdNETyq2t84fOfx7d/agdRN8H27j5OHj7A5WvP4G7zFqbzOQb9Hi5dOsJgOMLtW7exf3CI69duYDY/x9nkHP/+c59FbzDEweERdg/2sXuwi6NLR5TEavncygKCh/fu4s7tO5iOx1jO5uh1u9ja2cZiucRkfIb5YoFlmkHJCB94+cPYPTjCrbv3cDod4/f83k8BqsHdWzdRpUu89dqr8CWsV5+jVQKOPWqOR7zyhvfOjClooh4GgY/3Pv+8fUMaAsvVCp4I4Hsm9zCMaA50OphMJiir0shap5aawBzyTocodwICw8EAgR+gzAtIIRCHEXzpIS9KTCcznJ6eYLFcWmNAmnzAoiTAtVqukGYZqFB1hTKv4QcBedcE9YOQZv2ByRmEkWsWvqFzSVRK4+z8HFoDdU25QGEQoFINtCBJaK0VojjEweEB7t67j+Uqw+T1N/Haa69j0OviyuEhmrpClq3w2u37+P/+0v+CXreHt958E2+8/gZgnCpFU6E2tDkBAj8M4hQM/c2CHFLLrFXTghsh7PvVWtP6IA2dzZBW+dCgQrXuO3ZtYQVl55OCMkpXrMJpT7Jqnu785Ou7YEgIYTKNHjG5CTiJ9e/aG+hNem37728EzBFiMzLgAsPNL2985YLfHwFx6zdzTmv7gz5yDV+DGbFuVFvXhr0vR93a6J3Z+pxrbq6bTtTBXvXxoGfjzgYbt+DYVHraAEmPRkW4leyQ4LXHLuVCbBj/HKVkUADrDBRmwHBwRQi55uFxn4SfTWv+rrD9QHksrQP3IrBzMfgRj/nctNMIi1iVPv6O1jYCJFlpzMxTj+04j/Yk6USzXOczR3T8wMeg30fSiW1eD0faO50Otre3EIYRhABGo621+o91XWK5XFB9yyCwQCUIAuzu7mK5XBLTxbCGhsMhTk9PEUURut2uVXBzDfder4coiiyNTQhh1/Q8z9ecsf1+3xEo6GC1zCCFDwFi0XBxbyE8QDcI4ggwIGW5XNp1K4ljNHWNIEkoF1+SDSCFsLlFbF/N53MbfYnj2Nqs/H7c3HkGS2VZ2hqGDMStOJFxzHKJHLIxSEBHQKBuGvjMejCU8Lqq4BnRqzAIcTY7Q1mWGA1HBCblei2opmlQN7VTqxNkYyWUV1qVdK88IwohR8u0evqV8ZseIDGNBGhBkltQCoDlNG4uBHVNOQKc/8LnMRXFzVGJE5KQ1QL2hXV65NGtysp6DVwqlhvJcq/JoUtuswug7O9NBQlQtMfcg0FfrcgT7UlKKuawZZZlENLkRTkRIo6QzRczNE1tvSMEEGNUeYHVfGWMqtQmjgPsqaSq6tBAkoTodjoYDAZYpUtk2cp6AnqGc8qTCYr4/kkUW+DnSwk/lohjGprUv+TNqusavG5y3ldpDHzuM45ecSiYPRFMS6nrBoCG7/loVAMojdj3sZhOkYQh0tUKp2VJtZoUhbbZMGU5+OVySeppvrTekzRNURYl/IBCuKslVW0ej8cWgNCCWmE8JkEHz/eQZzn8IEBZlBDQiJMEv+PDH0SZ5zg+PYcG5Upkhcbtu8fo9EZIEhIs8LQCJMB2+UWHBuVTQBCg1E1jNlRtk9qBFpSvU+5YSEPbjVIz3UeY+0pJSetSYjI9w2/91ufx4oc/iuFoF2WtMRuf4sZ7P4AHN9/G+ekJkkqhaoA8r5BmVNCzE0W49/Y9zJcLXL1+DcUyxxtffY3oY2FoACTNz6omD1BZlJAQ6CUdFOfnePMrX0WFBu//0EtotEK+SDFfFRBhFy9+5GM4OLyMB8fHePv2LXzwd3wUUb+DB7dvQqUZvvTrvwZV5rQZm2gQjT0zB+1Ahx27Lp2OjF3qF6EBoQAhGmwPB9jb3cbkfIo0pXo5YRSim0jM5xOEYYDRcISqrkm+NY5RNzWKujI5fQEmk3NkWY7RaIjz83NUZYVslaKpSbWNPaFZmiLNcmjt4fz8nOibVYWqOge0RhTHWKQrs2lrS/uA1pS/Aw+qVgCIZisgILQg+9v2iVF2M0NAmYhU2VBNLaL/axRVDqUbhFGIwnjrFDTG03Ok+QppnkGZCHg5XeJs9qYdceLBOfxXvgqAhFoaRQ4XosKpdqALDah67T3AgMA1I5Xfj/O3Nc+1ct/xJhJ2f7SAZs0gN2CNv4I1MibQaEY3j0MTzqntndbAhcZmprhj3Kv1a+uN6379h2jRh7lya+OuG+dc0Lv9lNuv1353m+SCj3Vw4/ybDd7Nlj0mmiClWJ+LNifTzEnh/iQA4lKm18aQ01wXiGq0+y8/iRAC0tnDwdexBrsT+XOgr3b6ja5J36U2KFpAtJmDTBnTDFhaSpgQ63Ll0lDu+f5tn1+cS0QfMnOFa1upFlQZ5yD18Tp9XgjPuUcL7oQQ8GRgnZPCGLzK5O4JLQCvLY5qi7UKQzjR1F7KKxbGqQn4vkQYBvANfV5KiZ2dHRwdHWJvfwsHB/umUHhiGT2+5yOMQnimRmNRlMaGE5jPF7h37y6uXr2KokhRVQRSmFrG9kS/30dRFOj3+1gsSHafBb2ANuLBqm4MLObzua2VxnbParWydiXnHTGoOD09Rb/fX1MZ5WiL7/tYLBY2GlWWJYJ+gKYmeppuyMaRIsBw0F1jRVVVRXbMagVmF3BkhYVLpJQ2P8q1g9lpymOH7Sx2qnJKCTOmpJS2rxhIhUFkx4aUEmVBCq1kI2ukpo4clLJOawgyLoQWUJUCFCAh15Tuer0elrkpd5C1gFMIgdALMV/NLT1PKYXQDxGH5KifTCZ42uObHiANh8QVLYoCy+XSVk1nsMN0qqIorOIdg5WyLJHnOfb399Htdm1Eww2vKqUwm80gTfiWeZacNEaDMoAnWw5kuzgoG2XiScFArKWIsJJR63GSUiIw4UqKpHioeYCWJWbzuSlaWNt7xHFs84BY2YMnSRAElA8U0+RZLBa2ncvlElVRQTcKUSdGfzQw5/h2EW1zWchrxaHs7e0hmqaVom5DpO0ir7UysrxEoQvDwOQMNGg3MirwCNBWVZUlgjCwiwA0sMqp5kiWUqLggwf34QcBeqY+Dif5zedzlEWBbq9nFPMo4tPpdHDv7l0sFgscHBzgjTfewOHhAaQncHxCAPPuvTsQQmA8HmO1WhoqIo2xyWSCLMuoL73ACDCUdkFiYAjQeOj3+zY3jXNKBv0eyqrBt/zOb8X7X3wJ/+Dn/2+4//AEKiePzPn5GG+88QaeeeYGedLRbmSPunPp4HHmUkp54YQWa39rwfujtSNaI0Je4FU2n6sat956Db1eD1eeeQ4H+3uQAGaTMa4++wLizgB379xGVk5JMj3PsOp0MD47xTvvvAMlaKP+zk99igztokCepqirGqcPKXfl6pWr2Or3IUYeVOBBlRXuffkhyrrCaDCAWuZYZhnmRYWt/Ut430sfQXe0jeOzMW7evIUPfuiDuHb1Mm6//TbS6TneePUVLOYzBKDcPhhvK70p0fatM/fcfrmoH1TTALrGpcNDnJ2cwJcKy+USw+EQWlNkSWtge7SN89NzTGcz5GWBoipRWGobqI/KEk1d4+2337EGiaobq8jFa1AQBJjNF1BKGPGBxnhqQUaKoOfjOai0NlLmyiT++5SjY+Rg3aik+6x0XWUNRmXmqFbaeg7ZO8yeQikEfvnf/ao1iKqGPd/r5RPMHeyayPfeHIvml0fewdNSwzYpT5uGOj//pkHuHq75/nSQ5IJvPdn5/tjfHzHoL/Ti/0ceYv3Z3OMiI3vTi7/5Of9027wGqlxD/oLPL7reRX9zgYtFdJrplC5YIYcPK4y14LWljLmsEjqYdt9+7iqA8drpOpfoO20EkYATfeZJnyKURnBDQBgRHGq/RmMjOi1KJ7AiINcjXGtdIi98D9Q/7b/dyBUV/abnl1I+8u4Z7Eop4Jl9m5/FXgN6fRxAmCLU9GwaGoEX2P2e+yQ0ZUWkQUa9XtdGYXZ3d7G1tYXRaISmqXH12hVo42jtdLtomhpbW9soihyr1dzachy5aZoGs9UMW1tbtsQKR3Lo3TRUrLTft5QrYoOUtsRKFEUWQFBeaGbPZ4ZRYfJomHbGQgXsrGUK2mAwWBMZYDDF0ZnRaGQFvFyQRvlJFMnhnKJOpwMBoDSO736/j+l0aul0nMrAUZ7BYIDZbGbV5ZIksUpu7GDjHHBX8OD+/fsYDocWDAJYEztgm4b7i69hbYyyQl4WSJIOhBBWUIEpdNITtt85T8jSE4VvKYfL5dKCUpfFxf3O96yqyubmMhjk/uZc28flXT/u+KYHSJwEvVgsMJ1OLcrkpC5XK93lOPJLZvnDNE1tdIe/ywvCwcEBtNYINFHWtNJYzheoTVIcAMvN5PNGo5G9Dod3m6bBbDZDHMd20PHi624yWhOlyTMhUY6SVFWFNEuNlC0VLBMwNBdFIc0kDrG3QwXEIFpFEPJiwebtuB4vKozY5lt4nm8Hpi88NHVN4Uzj9dHaQ5amBGDM4kmCBAWiOLbhWsozyOF7HiAEsixDlpHiljbep6KgopFaU5i9NDWhlsuFUQzksPcCSimMx2Ocnp4CgJHdHqEocghBBQ97vR6WiyXqujL0oxASpORy585t3L17D1/84hfw4Q9/GCenJ/B9iV6vh+PjY8wN8EySBHFMlEDOydjaGmE0GlLC5vYuAj+wIXYehxy+Z24yT1bfJwWx88mE8kBmc1y9ehn/x+//Q/gf/tk/x507d6FMxGxyfgYhNEW/PGlyWB5v0LiLyRqFTtDmvUkrZY+mcsLQroEqxKNGEn/ugeS5X3v1i7R4SoHtnR1Iz8dkPMb+letI+gPcvfUOJvMpyrJAnmfme9vI8gy9XhezybndKDpJjLtnVGBWa8oXe+GFF1CsKqRVhaIo0e8PEEfkaTudzCGDEC+89FFceeY5wA9x6+4DnJ2e4sMf/SiuXTnC/Tu3kM2nuPXOWzh+cB/QCo15boOFrDHEVFUYKsumQU/ziIoVNg3RF6hCusBgOMBqvkTgAR96+UNQSuHk5AR37tzBYrFAN+kjCCKUVQkFYL5cYDafoaobo8amqXCzbCuma5t70TojPM9DWWtABKhVhapW5v3RJmUV1kRLqXTzhbSmSIQyEV2mVDLdx3nbEMZAV4pKPbPogZSSvL12XBgPqwzIow2grEoy8kyyDV/novH0buDHXX9dg9W9zkXH04IoBiKPO97NmH/SsXnu07Rp8z7uNZ50/tffvsd/fxPMuMb440CP274ngaiL/r2ZZ/Skf69Fe5w1kSM5RBNdXxPXwZBcuy6PLwBwnUZtPou0RpsbXeExTY4SrF2DDU2eg2HQSvIzE4HYHo2NirX/8TMa1424aEwAHDXb7OO1QuCCI14APF7vOYLUznnbdwI20sPL32YUbD1i1cD3aV/zPFKNZUO83+tgd3cXcRThxo0buHR0hEG/j0Y12NneQqOYoRHD8yR8n+rypOnCPmNVV4iTAPPFuTWYWwcUOf8YZHE5AlaSYwOdqWRsTHNUh+0+NqrTNEUYhlZhl5k4dV2j2+2iLEvT3sg+J99DCGGdoIvFAkEQ2GKqQghrWwJAr9cDACwW9JxsH3ZNHUxWibM57mUJT0orBMG0OAYprCrMeaxurhCr2rkOKrYxWTQLAHZ2dixd0J0XPG+4PfwMPJcYoOZFQXahYRq4gmmNapAulmsS39xWrQHpt04PplGvVivbL/z+eO6wyBEr6Lk0wDiOLfuH85Ce9hD6qXeMpzvqusZf/+t/Hf/oH/0jPHz4EJcuXcKf+BN/An/lr/yVNe/rT/zET+Dv//2/j8lkgt/5O38n/s7f+Tt48cUX7XWKosCP/diP4Z/8k3+CLMvw6U9/Gn/37/5dXLly5anawZSoe3fuIo5j3Lt3z04mRuRsfDPSZGTJxohrPPJgYy+y673lAUMFHgv7Qvn6jOgB2ITpnZ0d6z1wBQJY8hdo0T3fg2lQZVmiMJ4KKaUVgbDKLMKAPN+zHh1+DmW8vwIEGKSkArIaGnVT2vydpmkgpERd1QjCAJWh6gkh4DmRLQkBVZkilkGAyWQCrbVF8pyfxXKSWZYhNFWRldaYTiYYjUaYzecWSPmeh8lkQgUZjRCGEAK7Ozt4+OABsiyF53tI4gSLxRxFWSIwoeH79x/g+vVrePnlD+LevbtYLJbkJa8rKK2ws70N1v2fzebI8gzPPfsCfvM3fxOf//znIQRw/dp1vOfZZ7FcLhCGAcqyMB6Q0irRkGJYijAM7AKwWJAks1Yk7831ijyPoi5VVSGMAggBjMdn2NvbRxD4CENSpZGCAKMGUFY1hPTw4OEx/vm/+Bd45+ZNlGWFsqL30mgBLYyHVLcRRgCPbFz89yiKbC6VEJIoVLqliPAmTptnO174WvQ9Dy69wjWUhG7I+yl8eEGMD3zoY9i7fBVR0kfVaJwcHyP0BEJPYHxyHw/v3UGWLhH6ARJDKeO+jQy1ToCigffu34Pnebhy+Qol1BYlVKGQVxWypsGqqqCkxP7lIzzznucwGB1gmeZ46+ZtaAAf+8iHsD3o4fTBHcwnJ7hz8ybe+NpXIVQDI2tivJ2t/LKQbOxQFIQBpdvXpEyo0DQ1mlpZ8fXBoIPD/R0c7o3w+3/vp/HKK6+iY+gSOzs7eOvtt3F6fI5v+7bfjcOjS+h0u1ikK/wPv/iLmM3mSIvcOD7a+1gDUxD9rKoq1E1NdFGzIdXNeoHjdp0i8KOdd9k+h3Dkq9E61XmlcJ3VG2BaOQBqzWCGB+bicTTJjhe5Hhl6Eph53Pb0OJBx0bXWn/XxxrZ7qJaYt3bu5nV+OwHSpmH6NOe9+yGeSNN7XJvXxifWlVldY+zd3unXEz3iv7tApxUP4PZIQLeRIrckAq95dG/bCvs5U7sB6eyh7XeoqGZMHn1B12dHFIOVuiawwPVuqqrC6ekZlFI2X4btjtlsZo1wKtJOjgra/x8FxUzNYzuAnmcdINl6es6Tra1dhlLHv1vHgyS7wfd9SE+iqRtr4F++fBlKNZhMx+j1iBa2v7+P4WAAISU8D9jd3bUMlWeeecbkcmdYLmZGECCzTmGKcMwhhFm7NEXjfM+nIus+0eHZGbxcLhHHsZHGbhCF8Zo6WpbRtUejkaW4u1QwV5SLRXHKssTOzg7u3LkDALbwtsvmYSc3jzOmbz18+BC7u7vWwGf7gt8J23Nso9mC0+Z6bqoHAwxWKHULevPzhWFogo2kksz35XM8z7MiDwDW8oms2qYJAkgprbovq7+5dPtN0FGWpe2D4XCI+XxuIzssvsCqeJR77VtGkVLKRseqqkJRZjbyx3OYCqGvEIWxZVwxeOVUF+5/7l9mU7h2exRF1hHIdD/+/snJCb7v+/8gZrMZBoPBheuKXSO+0QDpM5/5DH72Z38WP//zP48XX3wRv/Ebv4E/+Sf/JH7yJ38Sf+7P/TkAwM/8zM/gM5/5DH7u534OL7zwAn7yJ38Sv/zLv4zXXnsN/X4fAPBn/+yfxb/8l/8SP/dzP4ednR386I/+KM7Pz/H5z3/+qUJkLkBKkmQtesQDmwcfL748IFxhB3cy8QB3vZz88gVACWiC6rsUeY7CRE22trfR6/ftYHDD8jwhmPPKA4U3GL6XuxFAc/J4G9pnEEXEIFr0mtpMBhPpKPLCcoN93zceXYp4lRXp6gMw0Q2JLMtNvwgoM8gYwLGiSrpaWf5pv99HmqaoygpVVSLLCtTGkCN5xRp1UxsZawJBrWdDGu8IedeiMMTCFMOUUmIyOUen00G/10UcRYgM8OJwcFVVGA6H8DwP9+/fo2hUmiHpdDAcDqB1jTzLUTeUnHh2dmbAZYJeb4Asy3F8fAxpvDJJkiCKQgwGfaxWS8xmM5ydjS1vOI4p6sgRxdVqCd6YwiCEVrAc/V6vh7IgPmwYBVCqsQtL34yLsiwgpA8BgTTL0Ol07MR+88038cv/7rP40pdfwXKVQQsJouaajV83T0XzcbnG0ISr3OJrvBF4HrWDx9e6l7qVP94cl1S4VlMpFiGhhI9n3/cSdg+vQAQJ0jyHqksEnkC/24HQCicPH+JsfIbZbAYohcAPEPg+fM+DJyQgGkg4hTNNm5qqhi6AGoDodTA6PMSV9zyH7mAAQODkwSlOxhNcOryEF557Fh5qnD24i2xxjttvv4633njDzCMNwYAIAoC0AAlQJkJH/GjL8UdroFKbGoog1aQqFvoBdnYG+NDLH8C3f+J34f/z//p/4+TkBKPRCN/2bd+G27dv4+Mf/zjCMEEcJzg5O0MUR5C+j5//+Z/DZDKl+j6GukbKiGR0aUMhquoKnqTIK1HejOIgWgeO0srW8QGwNka0dgDAhtG6FiUT65SrNdDFIEI4X3ZPfMyoFJryirj/NiMTX+9x0Tmb13S/8zTAhLDdu3/vaQHIRcDg3eiaX+89vp7j8aAUawDpUQrk46/nAqTN6M/jIl7uXvhubXxSP7h7srDzVBBQN/OaRuymqIVDzVv/hNgQ0FCNe006p40m8WewUVuAo9BcU8y9H33G0SK2Mdhrz9/hdZdBEF2T80GF3fO578IohFK100aTGM+J7c56xVQvgEQPpEfRjMPDS8aAbbC1tYXd3V10uySqJITE9vYWOp2uoYSHSNNWORMAprMZ4ihClqfwPQ9BGBpwQGUKiryA7+Rtc8Si3+/j3r27tk4YO31938fDhw9RVRX29vahNdVKjMKQyq54Hu7ffwBoWAobOSopCsPqb9oACaaX8efcFxw1YXlp2vvJLmEhBXaAM/jhaAQb/ryvMuDgtAa2EW0NHqPsxiIJLuBy1WaZ6sc1h+bz+SMUTs+8Qxbi4fu6jnRuL9uJeZ5bG9tN6+BnlVJadpVbLJbpfQyA2P7jZ+Oi4Ryt4uepSqqdl2WZtXWsom5dWNDFTAgWRasrZcW92DHPeeccsHCDEFq3lEi+Fke4lFJWoKHT6WC5XOLTv/+7fnsA0vd8z/fg4OAA/+Af/AP7t+///u9Hp9PBL/zCL0BrjaOjI/zIj/wIfvzHfxwARXIODg7wMz/zM/ihH/ohzGYz7O3t4Rd+4Rfwh//wHwYA3L9/H1evXsW/+lf/Cr/v9/2+d20HA6T7d+9ZlY26rnH79m1Lmdvb20MURTapn/NF0jS1UR9GpBxBkg6tjV8268s3dQ3P86G1QhzFiOIIvW4XwvNItcNEg1xwxIuFJyUqZyC4Gwd/Zy1q5ZzbgiOQmlxdIgwC1HVlIxdCkAJdWZWIQooiLBYLQu+SABBHN87PJ2hUg9VySVGkskRdkGLbZDqF59E1Z7O58aDT4pskCZ599lmcnZ2ZGkUCaZqZQUjgzc2r4tpSu3t7NmeiriuUZWW4tDT48zy3k3W1XCDwfeRFQX1mDPyyLOEHpgZLlmM6nVBhzV4PvieRdCK7GFC/0QJXlBWSpGcXwAcPHuDSpSN4nkSSxGga8jpNJhMsFktIKUwdJ9oMkyTBtavXLJWQomrGU2qMWwZRAMymKKyXJ0kSszkKeIZOMJ/N0OuR90drjdl0iqpp8KXfegX/8//6y5itUjRaomqMj181jyhdtRv+uqHIHicAaCplFx9XGMSTnqVlbXrfAQkp/Qv+Dmh2NAgFKTQaDSgR4tLVZ3D12feibKgYZl01gKYFuNPtwPMEsjTFYjbHfDrFcr5AkZJqohSNNSSAtuhwp9NFd2cP2/sH6G3tIOn0UOY1pqcTHD88QTxM8Ozzz2NnewurxQwP7rwNUWT46pe/gPPThxzPQGsOmg7UTAGDyQUgZ4MUTB99NJLwKEDykSQBLh8d4Px8gjwrTGSWx79Ap9MlR0Jd052lQFGZ37WGbtoX6ubxeZ5Hif/GJewahhoawmvf29o70jDZC9z29bHytDY4OYMoqqRFG11yPesAG8zumY7RDawBt//U4yIA9I0ESP9btPFJxzcCID39NdYjSF9PGy/qY/ezi/p9E4A9Da3ucffnCA/9dCKZTg7RJhBaB1aPAjIWYOHrrtGUnWdxo1jrY2/9Pm79Hfbmu3YBgaPa0tM9y0AQgBCWoj0cDs01lSlKOsBw2LdiUDs7O/a6165dgzZ5vd1ux+bReL6POIlshGi0NUKSJFbEKc9zzBdzjEYjLJfLNdrSaNSH5wkrolIaYSPf87FYLJEkVJKAoxqc3w2lkaYZwrDNdR4OR5hOJ6jrCsPRELPZzDprpfTQNApJ3EFpCjkfHx8jCAJcuXIFSincvXsb/X4fg8HAvkvOpVGKCocOBgMrTsBAhKnubNcJIWzNIJcFxG1nKhmDUwZATCljtTv+LM9zC0gYRHGfMFBiAODmKS+XS8znc+zt7a2NKY6U9Pt9Etwy48h1rvPzc3FxlvpmYOCmkfC+vzm/+DlZcY/7wiqiGqDiRtcY7C4WlPrA4KYsWuDP4Ir7rCxzRHFk28+5UJ70kGWFVQTkZ8tNegX3u9ba9h23k6NmLAvOkS/Ou+92u5hOp/je//r7ngogfcNzkL79278df+/v/T28/vrreOGFF/ClL30Jv/Irv4K/9bf+FgDgnXfewcOHD/Hd3/3d9pwoivDJT34Sn/vc5/BDP/RD+PznP4+qqta+c3R0hJdeegmf+9znLgRInCzHx3w+BwDjAebwm8DhpUPkWYaqLFHVNaqqhFaK8lRAeQajrRGlcypFIWOHPqC1Rq/bgZRtJElKAd1QLaLxeIzPfe7X8Mwz78GlS4eIwwBCkWJZmWeQTONriPvfNBUAAe15lDApJcoiR9OQUpk7ODjMm2U5umaylWUBKjzWIIpiKN1gMaUCkVxUjPqF5HensxmkJB35KIqxXC6xt7uL88k58iJDWZB0Y1EWSGIy3gW0LdnRhlEDNFWN4c4OwijEKk0x6Pdx/PAYURThyuUrdhMoyhKxGeiNUuiYJMGqrgFojM9OkKUZTYg4QrpaIEuXGG2NMJ1MUVYViiIzwgIKWbZCmqbYGo1QNxWkEtBQOD15aL0fWmkURQatGgRhgG43RrdDfaaUQhgEUE2DChWapkIYxrh//wFNSNXg4clD+L6HKI6Q54VJrFdYrXIDnhIslwsICNy/fx++76HfHyCKIzSqQVXWKIsCjVIYn51RzYV+H51OAoCSNrvdrg3vp2kK35PI0hVGW0OslktEYYiqKFFVBRqt8LGPfgiHh4f4N//2V/DOzXtYpSVRwqBJahimros1BDii2C5+LrBnsAGs0/Eaw0dnI1cpbUEweSLdBdUBYix1DJPDpDWkVLh/+x1MZjO8+MEPoxP3UEcA4CEvS5xOFvCkQBSF2Dm8gqOrz0BoQzUtCjRG4c3zPPiBD894ssI4RhNEUBqYTue4e+8mVpMltrpDfPADH8TwcISmqXDrrTdwfO82pqcPcHbvNlSVwwN7Y02zwREkCQhln4tsEmkAEhtcDvvMHmwk0c9GK+RFgTfffgeABERrDEETGJqnnBjcQJgIL707A7LZwBPreWZNXUFTiA5ar1eDpy8YmW4Divi9ta2/+HBzzlyjddOwN0181OHu0Hjp+8o5rzWMydAjz/vTwaNHkP+jx4Zhq7EJAA2gM21vZb+fdM2Lfl/vRY7arf8Nj/aNGUetdLNwBhI/gmNUb8xZvrd7Co9HMuA3ruN8kdrDamLtcwvnM63bvz0ZIAnzOoRr+ZvuN2uNkFSGATS2aTwoa+SzUdNedx00tREd0ebO2ad3OtcBO1rDOh60NnMDPN48uEI2biFRVknjOcQKcfY9aEWCCvaltqUGWJSonYMkJtQ0tQEmLXByj5aqRcVIgyC0tO0rV65ie3sLgILnUxTl6OgIOzs7WC6X6Pf7tC9EEXq9HvIit0AImnaBbrdrc5waRTnNnThB6Ae20DOrrjZNhdWyhB/6SOIYVVmgrmj/73Q7qMrSfGdBRUrTDGmWUoTflzg7O4Xn+1AmmtLtduFJgdFwiCRJ0CiFssjBdRrJidwQVbwsMRiQuBFRqLrY2hpBCIF7d++j1+uh2+2aNbPC+fnEAIoYV69eBSAwn8/R6SS4ceMGmqaxggJnZ2cAyLHKOTrsIHejK1tbW5aSxqDFNaZZ4nsymdgoB+cU8fX5O/P53BroaZra+nNkr7W5Q1pTNI3zrznSx8CTHZgsLAbAyn5zzTs+b293F1q3+TcALN2tKArbfxz5YYc0t53BBwNFBj1EXaRcq52dHcznc3suK/HxNQFY6iKDULYp3MKwHPXUzoIpPUqTaJRhbNAUpVpPghSY84L+GEYR4jhCUZZ2uddao6orQJC8NzRsYVzebzmq1TQkysE5/097fMMB0o//+I9jNpvhfe97nzWQP/OZz+CP/tE/CgB4+PAhAODg4GDtvIODA9y6dct+JwxDbG1tPfIdPn/z+Omf/mn8xE/8xCN/r+sKjaKJMZ/PcXJyQiE8I/lIFKwh+r0eGY2eB8WRDq6jYCQwq6qEx7kkhjfpFlOsqxK9fgef+vR3Yjab4dbtd7C3t4/RaIiqIjDGOTmWFicplJ8XbWFQnjSrLIeEwGq1In5oFCGJYyzmc6wEkKYrsHIKQCHuwA+MKIGwYWPfcHhn0xnm86WNnLFKSmkiNKEXojPo4OjwyE4kIQRWqyXKsrDRF6bZdXsDrFYrhGGEOIzR1Aqz2ZyUYbICQmgEIYVg33nnTRu1qwcD2nSM96soqOZSUaRomhJa1xBC4nx8ivPzc/LAQEKr2hjsDXq9DsqqQBgGVs0EqoEnSCUsjhP4PoWUo8BHFLTylgxIet0eyrxE4Es0VYmt4RYmkwlOymNA0LMEKYXz07KAJwUCnyI/CwPA8yy31Mq93T34no+T8Sn6/QEmkyka1SBOEqQp1T7a29sFA3YGvrxo0iYdojaLK+UjaYy2tjGdnSEMBW5cO8L3f9/34JUvv4bPfvbXMJ3OUUmJ2hjYUpgaLfZY36CZ/xv4ARq09EyX0imlMKDB0GaMfUGHhhDrqopM7aRPmQomrSEl0SBfTPCbv/rvcO09z+LGe56HH4cIoxBxohCG5Hkan0/Jq+UHCMIAURgjSqhOEIQg50FdYZlVSM/HKPMSTVUhTjrY293D+9/3AfQ6PeRZipMHd5EtFzi+ewu33vwa8tUMUtemfziaY9qtSXxCSpOnw8Ya1mlDhG4cW1m7BRMBzzdABwpFTe/D8wQEGmgQaKcBvLFINWxItsZZwwBuo86NBtbOJwU6BlMmf4qNY/sc5t1bg/ICT78zTHj82PNtnRVtbGQ+vzXOqZ/aJG8CI+tAi/tcrRm5jz9ao9n98mNOctt/oWCDcOz7iyNIbjSAkMxmtJDH+KP3deDlY9pnMtycd+W2eQ3obrRLOuphm1Q2rRp7HY5wtO19nB4d7NzUPKbtNYWZEpyX2L5XAQ9BEFmKDNNrmHINwDItqqpCA2OQavYiE5iS7dKw9iyKDSUICEhIEbQdzA4acJ5NW9xAgyO8LMTwKMBjJ4GU7XvWWtuaehASSsNQcngMS7M+tiCKCnJSVDkMI1R1bq4p4XkCgQ4Qxa3Xu6kbdLpdbG9vo5MkkJ6H3d0d7O0Osbe3g6OjI3iebxkyLFMdxzEWi4U1RpkSnsQ+hsMhRV88gSxdkq2wogjP3t6eNaB5X6wNeCmr0qrxBlEAXVK0mxocTqwAAQAASURBVGn/q+XSGurKGJRlUaKuKvieh+lkgu1tEngq8xp7OwfWVsnyHFVZosxrFLqGakxkAVS/BlqYeomFpcKdn49Nkj9Fku7evYswDLG/v2+jVU1DdD+mbRGglAYcEcNjMNix++hqtbKOP2aWuFEVrbUVxWIDn6NcDI4YILCBzwCk1+uBmTIMjsqytHLSHJ1iga3VamXZL0II45Rux0an08H5+bkFFFwTiW1KphqmabqmGEf7t2+d5kzfYwDIezoDOs7h5rwc/j2O4zVAwe1nVV22k1jIzK2RxLLnbh1FZnporW39o04i1sAbt9mTHuarOaAFATkIVFWDPCvQ1Aph5CMIDT2uLNCoNh3GD8iuo/HgIc9TBAHNU24Tr1FpmrbRS7PGLJaLx62KjxzfcID0T//pP8U//If/EP/4H/9jvPjii/jiF7+IH/mRH8HR0RF+8Ad/0H5v07NyUYh783jSd/7yX/7L+PN//s/b3+fzOa5evWonsFJUgKvf7xujTRjp7Wbde940EL5nJZo59J3nOZaLJQZmkhRFYcOxSin4noQnBKaTKUajEVaLJT77K78C3/Px8W/5OLZGW5ZXGicxqrJEr9fHYrnAeHyGe3fvwvN89HpdBEGIhw8fQigypKfTKYbDoU3EU0qh36NJ3ul0LEKOogie8S4ppSyfloFmukrh+x5u3LgO3/dQ1w2SJLYLA3FJVyjLnOh4ZuPTmiQ43QWIvVA8AZmnyyid9PC7UIrA1N7eHn7lV37FemKUUnay8MLF3h2mCcRxjK2tLes9S5IEk8m5pZ+5i+1qtcJwOLSJrgSk1FpYuKoqG8rlxaLb7aJsapRVhdVqhdVqhf2DPaxWcwghMZlMMRwOkCQdSwlg3nJRuNTMEHfu3MVoNILnSSyXcwyGfbPIRYiiy/A8aQCyQFHkAGjR3NnZRpJ0TK6WZ6mP3W4Xd+7cMcn4DbK0MEIRAT784Rdx6dI+vvDFL+HV11/DbF6Qwa4puVZzfhL0miEmhLALuoRnx7ybvLlJOblorl70d2DTx+78XRFd7eabr+H+vbt45tnncXh0Bb7wIBqJwz1K6s2zDGmWIcsyTJcLNA3QKPKAK1CYP0pi7O6MEPWGFHUMfVRlgSxd4a1bd9DkOZrFFJOzE7zz1ptoqgK+oLpPwnDgiRYAAMa7boxhN0F17ZnZveX0I/Os29ohraFn6XdODhdH4B53rHuvH/+9i/p98525zpYnnX/RfVwD8klUsM1z3ee+6Htrxuq7rPX/KcdFVKmv/3scKvs6Dg0ATu0aB2DzeGmh1KMUROmc50bOpGyTnDcBEoMNl2LWvrcn5w9d/HgUufF9H7Gh/5RlCQ2NIIjg+4GNQvD4d8caO+zaXF4B6fmQpo0G4hjFQydqpNvaN1JKU4eubT+NLWX2njYPcv15eL3CI/3Oc567WKkGa7k97MlHBSGFzaGpqhrDXh9RFGLbCPwEQYid7W3MFwscHe3j8uWjtXyM3b1dVBUZdOOzM3JIxjGKPEfP0P2LbGXYH7RHHR4eANDWMGfDuyxLnJ+f22hHWZZ48OABer0emqaG1j7CMEAc71ilMwYTe3t7tgAo77MsV822g+/7VHxatApko9GIas05+TfL5RJXr15FFEU4Pz+3hj/loyzhez4QwEYIWfWXAfNyubSREc/zEMexfT7e6zlCwcDAzfNpc4f0mnBBr9dDmqY2f8jNucmyzOYGcdoE1zXiwtxMV2SFNQYrXM9Ha43d3V0LtLgMCstKu/k/vLdyhIqpZvz8XNOIo20sNsGCD5zHPZ/PbUkWfpfL5dLKonMf+7JVeev1evbaDILCMLRy3oPBANPpFEopm0fNYIyDBxwB4jEIwAIldoAwsOO6TDx+OTefKXAATO51bfuBaYVsj+/s7Fhbmu2Pfr8PDY1OJ7brCM9/nsdZltk8MhY9Y9DI+zbblpzOwdGroijQSX4bI0h/4S/8Bfylv/SX8Ef+yB8BALz88su4desWfvqnfxo/+IM/iMPDQwCwCnd8nJyc2KjS4eEhyrLEZDJZiyKdnJzgE5/4xIX3ZaN18yizDLVB8Fq3dTxUva7uwkVht7a3oAqFpq5RZDTJoUmi9v69O3gnL/Ge97wHDx48wO7uLubzOQGSukK6WCLNUmRpiulkgmeu38Crr74KVTeoqwpaNVjMZzg9ObYJ+KvVCp1OB7s7u5hMJrhz+w5WqxWuX78OKPJ27O/vU3VgsxHNZjOMhn0bKlwazw/r+XNC3Wg0wtnZmQUcL730EoqiwMOHD+2iw/SA4ZC4mCwvypQyKYX1INR1jePjY2RZhmeffdZya3lB4oUCAIbDAcbjM1R1YfnBH/jAB/Daa69BSomDgwML5LTW1lPCCYAMZBg8sacjimJr1LN0Z5ZlmEwmCKRnJyIv7lxjgOUyefKz5r4f+PAAzGfnUErD8wJkaWYWagJ8tDimNrTu++QtGg5HtqYAq7gAwPb2Nooiw3A4somdUkpMp1PTfoUoSiAEcH5+bnjWBFLyLIfWwHy+wGw2N3lgAmdnp+j1+tgxEu0UwQvw8Y+/jKs3LuPhwxN88UuvYLHKUFWaIjkQgGjWDFKXshmHiX0mXuD4O64xBjxqVG8az27o3P2dvmcoQUpDSg91usTXfusLePv1r2G0vQ0vSDAYbqPf79k+jD2KPmZ5hcATULqBFkAvDnB0dADpB1g1wGR8gny1RFNmCIRGtpzj4d3bmNy7jaauIJSGxxENKa3ZyeMe1mBqawS5Xnj7nGgNShfM81gE9CN9tNYv38DjSdfcfG//MQcbS08CRo8DzY/7m+uE4p+b93ya48Lo17u07UnH465BEZ/2Myndd7t5Dffcx1wPj4oyXATQLm67gBAeuO7a5rksP890NgL/8pF2XnRsjllSavQBTaqdfH++Pu8x7rtkQ8RdN9wokysuAK3h+YE1kjbXnbYx61RPN8LWOjfaZ+C56nkSvudGnQAZGM+zKWdR15R32ul0bG5rGAbY3tnCwcEB+oMBBv0+9vf3UVUVtre3oTXlfrh0raZp4AfCGrJRRNGGxWKJwJcAahwd7SNJYkMlaxBFEnXdYLlY2j2d83u4lg0bkp1OB4eHh7hy5QrSNLX0Od4XWWWNPfvn5+c4ODiwBqPneej3++j1epjP5yiKAru7uxgOhwBagZ4wDG1eC7MZ5vM5Tk9PceXKFWtDcFtZNZcjMJbSZ0Ax5+1MJhMb0RoMBmvveTKZoNPprO39DJ5YoIglmdn52+/3rQHP+4SbZ8RjkRXoOBLDtC+uhRkEgQVgDObG47FVGuTIXRAEFiTFcWyZTZwDw20uisICNQaUDBI4Ytfr9ayyHAMCpv/dv38fgVEA5hwaFlTyfd86dpn9IYRAnmWk7mdyraqqsmp1SikMh0MLXjjawuIMAGyUi+0hpuO5thbbSfwuNsVEeE66Yg+c98N2m+d52N7etnsls5DSNLXgBSD7fTweO/nGrQorO7N5neFoHgcx+F0BAmVRmbw2ytFbrVZ2LSqKwlLunvb4hgMkfnD3YG8TADzzzDM4PDzEL/3SL+EjH/kIAMqL+Lf/9t/iZ37mZwAAH/vYxxAEAX7pl34JP/ADPwAAePDgAV555RX8jb/xN76u9hRZjrkGApOwyJ6HpqqhlcJ0NsWgP6BQpfRQ5jlqVWE8HlvvAR9Hh4d4+51bOD09RVmWmE6nODk5wdWrV+ELykUJfB+L2RyXjo4Q+AG6SQe33nkHl69cwv7+ASooBL5Er9uh6FXTIPB8xGGEwPNx6fAQywUl03E+TdM0WCwWNuxaFDmUqi0w4gWyKAqrw8+LA8tNMkgCNPb3d6E1cHZ2agZ9Ca2V4eNSgbumqSElEAQhalOYEiDjnycub+jdbtcurpx0GIYRDg4O0KgaWZZiuVziYx//OOI4xnQ6te1yAQ0nj3IYn41QBmK0yFHkhYuesfekqiocP3iA5ckSly9ftgWCeXF1w8iujHqjFFSjABDn9dbN27h67TI8j+5V1xmJFgRc6DdAHJMntdNJEIYhojCxC0XgR6irBqPRNrIsNZ6ciBT4+gMURYnAD9FoBU962N7aRZ6VmM/PkKYrs7H5JE+qGhQ5AUzVSEynC6xWKaQUWCzmODw8QByH6HcS9J99BkeHR3h4fIY33nwHp2cT5GWJRkiopn4EzKimLc7GC4gbRb3ImGMPsAsG1qIjNm/n0WKLWisIaDS1ET2ARJ0rjB9maLSHE59kvA8PD3H58mWifQgBeKS0lJt8rmxxjrs3UwKaCJAtlxifHmN2foYyWyBfLUi6W1QQWkNqoDG5UcIWY2wNacqvUmuR5IvUxQhyttEZ12tOm0htv/8kcLRpkG724+bvT3NsgoL/1AjN5n0fF9ly2/j13O9J7bvoudvfHx8N47XRjdhtfse990X3d6OnsPENjki0Y8IFS5ugj6Mmm22n2wnn348+R/ssJO/cOvAIELhxJftdC/D5Op69l4B0ADNL0oMKCEuB9YQprnFD67/BMtwz4EKr7vh0+5mdQGxws+eXDNza7EutF5hqv7Fjhp9hvayAK1bkrjP0LHTfMArR77UKsYDGoN/D9evXAADPvudZ+AHt/aPREFVdoiiIyfHMMzfg+wEmk3PEUQQ/IJpSXZHXOwgDbG9vY7FYQiuFxXwKP6Ck98APQLLUCrs721gsFjg5fmgMwhGausZyuUJTV7hz+wTbO9sYDCh6EUcRyiK0463X69ln3dvbQ6/Xs5QqorCHtqbPbDajCJSj3sUqsfzfxJTOYKYA2z0Mptiu6Pf72N7etuBob28P5+fnlD/UUD2i27dvo9vtYmdnZ82OYPYFR5c4OiWEsDVnrl69ulbDxy2VwgCF9584jh8RTuAoUF3X2N/ft6CD82dchyeDKqYLMvWMgZzneTg4OLDGPEd0ut2ujYTxd3ms7e7uWpnruq6xvb1tbRXf97G1tWXH5mKxsCptDEh5b+D32O/3cXZ2hk6nYw13t/QGzyUGGu7aykCN5olC1O9DG9vGsofMXGHQx0CewRWPj6qqrO3I843zoABYWh1T09gZzZFNdtDyOGVw5AI5zsOrmwZVWdk9k/uT+43tMc/zLOOLIoKlfYfsLGc7hYEbR8x4LKlGWec1O19Co3jI84QFJZ72+IYDpO/93u/FZz7zGVy7dg0vvvgivvCFL+C/+W/+G/ypP/WnANDL/pEf+RH81E/9FJ5//nk8//zz+Kmf+il0Oh38sT/2xwAAw+EQf/pP/2n86I/+KHZ2drC9vY0f+7Efw8svv4zv+q7v+rrac+vmLXi+B2VQZ5wk6HV7qIx04mKxQLaVYX9/H+lqhZs330YQ0SD62mtfw5UrV7CzQ+Hr1YpC0+PxGFevXsVXvvIVzOdzPPPMM8hXqZWw3NnZwT//Z/8MV65cwUsvvoj9/V1keYrx+JQmy5IW326na+vx8AK5OCP559jUEWI+Mqt10MZDmxvnEPFCwGFrXsh4wnCUgBZSibqpIUDt5GiN1pQYKhqNpjEJexEtGkkSo6oa9Pt9vOc978FkMiFZZrS6+oPBwC5WpPJXoCwL+EErBHDn9m3s7+9b4MIJh+xJAmCL5AJEk2yaxikgpu0kZa+bVYDxfTz37HNrWv2s65+mqX1+XgTZa5VmGU7HE8xnK3gywGw2Q3/aw9bWAFFINLoiL2zxOiBDnkeQUuD0tLGTLs8Lu9lrrdHpJCirCp6UZsx1DTecaARUL8OzNItOp4M0zQBocIJvVZUASAmwqhrjIYoBENf21q17xA+XHiAkpOdjONrB+55/DsP+MRarFZZlgYVRr+HwNC8Qbc2NtvgxL6ibHmKgVb7h3BsGU7whbXrP2dujNaenU84DGWANhFKAlhBaQakaWgjcu/kWju/eMptpiDjpIIyIhiADHxpAaepE6LJBkWVoqgpCN4Bu4HsSummgfEALwdk01CDBYgfCtK02AggAU57cxfOiKAlvAK3H2jMUqMYahe53L1qMLwJDFx0XetYf07Z3+/vjjqcBYk/6/KIoyEXnbBrWLiBwc4bWI4+banTr7+ZikE7HppPuovOe+ExiPYLEf6dxzr8TiHDzdDa76nHt2KTYtXk+gJCeMz4EhPvc9lwjoHJBNEsIqtmlFJz2apPLBCihANu/rSgC328TiLpgyPOE0w/rfc5GiUvRoT7wUNccWVo3Oaj/OMeqWXs2oN1feK7ROQ08X+O5557Ff/U934MbN24QQAoCVFWNyGuT6MfjM8RxjP39AwgJCE/g+PihyQ9JTSSGSx9oBEYsaXdnG8vlkoQKTO5OXZXI0xQw7AvP8+BJibqsMJ1MEfg+yqJEkVIOUBSEkJ6H/d09UtkSFXStMFvNEMaxWfNTS2967rnnKH/X1Aucz+fWGclGJfctr9UseDEej5HnOQ4ODjCbzSxFjIGDlNJGoZhNoZTCbDZDnueoqgrz+dzSrbQmteF79+4Z2riH8Xhsk//Z5mCaO3vsmdblylLzPrFcLnF4eGiNYGZ5cDSJaYD8TKy+xn3B1DR2HvP84v3LzX/Ruq3FyMY+G9ur1QplWWJ/f9+CJb4vG/HcbxydAigaxFS1nZ0dTKdTa5dwqkOv1yORK1MP6/z83NIv3XpDHH1zy3lwVGo0Ghm7cEaCGZ0OoihaU2bjsitMc2P2Dc9JjgINBgNrYzE45H2c5yvT97gPeN/m9grR5hFxDhQ7rdn+4fvzWGOqZxSGNpdXa23pjd1uF57n2XIn7r2VaiyQZvU9dpjwfbjgLgBLuaxUtUaNd7/HUTsA9rynOb7hAOlv/+2/jb/6V/8qfviHfxgnJyc4OjrCD/3QD+Gv/bW/Zr/zF//iX0SWZfjhH/5hWyj2X//rf70WAvzZn/1Z+L6PH/iBH7CFYn/u537ukdyAdzvm0yniOCZwFEbQdQMoheVigdl0Sh6aIEDW66Gpa4RBgCjysTUa4Hy8g14ngS8Fbr79FrTwEEYJZrMZ5vM5ZrMZTk9PzUtPcPONMba2tvD6117D7/j4t+ArX/kK7t29h1W6QKcTASCwsTUiD0tdlRBCod/r0WRTCsfHx9jb20OSJDg8PMR8PrchyoODAywWC8MlbazKCNAWR2PQxN6MlalTxNGjRtWghHSBpqkhpEZiag6UZYlev2sXY4AGX1EW6Hb6Vu6aQ5us2HN+fm6rFTM3+cGDByjLHAeHexb88aLM0SAOM7PRyd9hyhovkKwKQ96teK1i9WKxQF3XWC2XUDUZ61tbWzYkzlKZy+WSnsXxgAEksnA+PoeAj/5oBK2B44cnkBKIogB1rUwUjTyCBEKX1ltFnoncLMy5meAaWVoiM6o40BMIw/kVQltJSu5foK0Hwt4uAMiLwhTwBKqKpJ7rmjwlyiT8N3WNQArESYyDS9u4dLCP0dYWqrrCw9Mz3Ds9xypNcXJyAiEEbt682XrZFdYWTKCtN+Qaumu1wUSr6MhHy/HnQqsXGNRCAvBAEgltMrrWiuh3GvAEebibsoKqBKpcIl0u0Jg2NZo836zUJ7UyggkakAJKA4VuIDxAGPUpotGxepjxwut1TjNEC/4eB2yEUayygM8xDuu63Shc2oA99zEg6SKA9LjIx/8/j3e7z0VtelIk5uu55rqxDXB08lGgJR7ps80IxpNA6UXtv+iz9h6uduf6QYCIo621cYyYmjgbAONJ73YTjLP4BY9J+/cLaHs2KuS00XVosMPCFV+hnwRI3u3ZL/qc6dd8Dx7nlM9jwJy5RhiFaGuptY6Fi/rk0TY+Cqbd36M4wie/81vw6U9/CnESY7GYGHlqBSjAT8jxOBh0oZoKRVlgNj3H7v4ubt+7g6ossb+/h/5ggAcPHlCU3w8ArdFJyFO+WiwxXywACKiGov2BH6DyiB0hhMR0OoMnPSxkikGX6NQIPJRFAykk8oJylKtSQTUCi3lqx2jckda5ycXV7969a3NxeB/liA8Dg+3tbcsYYYOe81eYXsXRhLqucXJyYg13pRTu379vJZin06ml+fPey0Ys5+MwEGPDlvO4p9MpJpOJtS1ms5k18l0wG5paSGwQn56eomkaK3xwcHBgPfzn5+dgZysDCldCm4EcO+QYWHA/8RrO0QZ+Po4ysJDBaDSyeUt8cE4WACwWC8RxbMWvuK+Z/j8YDGzeNbNgOKpCEuWBtcXctA8e/wyK+L5VVeHg4MD2Y1mWFkQx5Y6B6NrzV5WN9rHjl3OueJ6ywhzR+yM7j9xcID74XTKFksU+2CHIYMyNFgOwZXKEIKobO+SFEFitUjuWOF+bHdtuDUb+nfu7UbWJCMMqMfMawuPT3bNp/WxtER47PG+YAsrCE097fMPrIP3ncnAdpF/6F/8jGYxVBa204e0GGBt62XJFyV6NpkUNAMqqLXbFtLWyLMlLH0Y4Oxtbzfbbt2/jwx/+MJ5/9lncufkOJfovlrh58yaiKMJHP/pREBe6tpsI5ztxYdM8b6MjX/7yl/He974XdV2T+tlqhUuXLmG1StHpElho6hrSI48Ze0sA2HZ2OnFrCADwpESWZ1CNQhS1SZO+H5gCXzRwqWAsSXi7CXVBECIM2iJcQkgzAEs0SiOKI/T7A1QV0Rco5BqirkvUdbnGFeXQqO8HOD4+saIO/V6fwGoUrtU2Yq7sfD5HU9fw/ZaHyovo6ekpZtMZmrqxNLjDQ1LYuXfvPqIoRBhTKDuJY5pIZsKMx+c4P5thtcowGm3hi1/6ErRWJJYRBkjiBMrxyNQVFfAsS6JKNKqBNLUqtAbKokCtgCwrANGG7AUERe6M/eEaar7nwfcDAqyC1NpU00DpFkwFZlMuy9pM8gxlWaOuKoqcBCQeAUmLaVmVaJRCpQTqpqWtaK2ce3NujbCLOoMht31uXsFawUQzvlTDIgVG+taZh3bxElyPxAgVuMYiDG3JMZLpXGNyGuAE0SbOk1fKUN0EgSO4NCZzL62NXIWAvTcDJKYEQoCSt4WJcrFbnkMEgu7HUSKltQWuFB0jj7ybJ8GfbR7rxuH633mRp3s6wsZaO8px1JbHy5O599wQR9btvyW9sLYR4tGzbFvWHwCc28GniUeuJdabItyWtL9bIKABV676YoDkXBdOPxoqGiUTk4dU2LGkaYwLae/uKvIxXYu708pBU5YatJa2rZSTyTcEPI+UmLTWZl7THK+axgzv9XwawZNlrSvdl8g1cniT5/YA2uQTcXtt1AoCEiw17dnP4phq8GXpCnVN4igt4mr7TEqBumlMTp5nvqJb+Wt+r/adKPi+hygKbQ6r7wcYDAcoixIHB/s4ODhEHEfY2trCtWvXMJ3O8Morr+Ff/+v/2VkL3aEh4Kr0Ub+Z4qsQVCAZCkLQPLty5TK+7/v+K3zwQy9gla6sw2Iw6BMVrtE43D/E+fnYXreuKpRVhcFwQDLVYWApOUFI+Sm60YAmcYbFYoHK0IbqusZiscDBwQHCIMDZeGzycSuKEgiJbo+En+q6QRRHWC1XCMMAGkBkPPOVKVtQGGNyMOxBSIkHDx5YWpabT8RCACxQxAwNZoewwQ7AGoDS8wDH2eh5HmYmKrRZTJQZFJwoz2wVVmPTus0NcnOiOP+HAQm3gT38HD1ggMeMDyEon/fk5BTT6RSHh4fWMOY8Yq6HyYCt2+3i5OSE2uV76HS7RmLct8/COUhVXaMyIgyc/0T5csIa5d1OF0VZ2HHY6SQWdLCoAgBr83EaAEdRVmmKjqlb6IIdu14JYWsNsVObHdScp+ZGZDg3ajweY3d3F3merykXCiFI/ELD0u2jOLKgUNUNqSCbfq+rmlRzHcqZLc9hxklVlhBSWLDCNExuY11RLc/haGgBUhRFWC2X6PX6SDNKofGkZ2pqhtAaqBsa33Eco24axFEMz/dQla2SMoMazt/jqBH3CY8hrTXKiuohNapBr9slnYCqRlmVaw513gtpHGgUhskDwN7DpftGUYSyKvGp7/70b08dpP/cjk63B2iBplY4PT3F6ekZdra34ElgPqdIDITGKlshjiMEfgBPeKhLWoy6CSHQVbFCGCfwPB+DwcB6EyaTCQAScegO+4iSGHmRo6orbG9vodcjz0cc9yzSp4N43Z7nIwjIsJ9OJ6iqEt0uhVQpITJCVZXIshWCwANMTYk0TTHJc5uvUxSFNWLn8xnCKLADkCdBEPgoshxQGlFAk6Nnwr9akZc9NlGRbtKxoevVagVPguSwmxoQAlpVqMocGgJN7aGuSlCtC6rJVBYFOp0Iua7hxzGCMLAeRRgDM4kSCC0gIZGlGZq6gQiAXtJFmmWAAhYzipjdvn0bAHB0dAgBqv80m80RBOR1qWtWJRKoa4WHD0+sJ2s6nWOVpYiT2AhTeBifndECqgWq3MjRNgqqrrG1NcLO7i7Oz89xfk7KL3meI4pjeFIi6SS4dHgZ29tbiJMYR0eX0e10IMzmUCuNKO5aLnBZlpjP57h79y6+9MqXKUpiE1tru2C2lbm18W4ZoABAgcAJGwQWnBjvjqgbIC9pg+Q+BkdNXMNyowCs+XfdNFRS0aFFuOCoNWDaZEkbBZCwn1nD197PGL/KNXoBGIqR1jCAw/GeO8Ycm+r2ilrbXiHsIin6ZK9rnsrIY5Px11IKlFLQipXnhDXE3D5qawe5NLCG6jUwcDEe9EZRRIye5WJa2eZB/cmP2YKR9nTTHnv+OqDEY67LxvvGX9prrtG4pH3WCxpoTtaP3MuNRLTvv+3fNrJD13YjRO41hPPsLt57NAL0LhE1A2zLqjaRRZMDJNrxJJxtrgVBgBAmurjhsAAArUisgMd+Xev2uTQVBReisY2g7qRaOjZMudFm+QRXJHU15+MoZ8w2xknQqtwJIag2mPQgDUiSUkJIal9d1VSXTzcIfIokUR0kQ1kz70NKiTgK0VjKmMRoNES3R4yLwCdqUn/Qx2g4wmDYw87OFvr9nk3Al5Jq6iUJGZsp17OLIiRJjPG4i1/7td+AJz1Umhxftn8YEJs6guSRNkaS0kCj4Qmqb7SzN8LHPvohfOLbfhd6vS6qokJTNrbuzOx8bj3G09mU6vMohbzIbY4GU5+83LMGooCAqpVVZB2fn5lnJrXaIPQRRgHKqkCarRCEPnp9yrlNsxTD7aGlV4URlSeo6gKHhwfI8xyz2QxVQxGMUAQoqgIajY0SRWGIwWBgGCUVoDUqUzewqipsGUW5qakXxEdd11iB0hHqurZiT2EYUn1BM0ZDQ9efGZEGZmUwjY+L3ru5MAyCzs7OrHob1f1bWqEIjiBKKTGZTMjwNHYGR3GYoqW1xnA4NJGkno3AsZ+M7aBeL8RkMjWOzwpJ0kGvR8VK4zBAU9fwwhBSCNRVBSkEojC0NS9Zjny1XAJaY29n11LThRaYz+Z2jTo42AeEYUcIAsZakeBUbMYG21SNAZQ9kwogBRXZLYoC47MzuzfGSQLfRFAmkwn6/b4dZxwJY9o/v0MAtm9ZLIFtvyiKkIRUuFcrjTAIoRuqeThbzSxQKorU5nB50kddNTYviMCpXMv94lIWbr0lSjtI4HdN2ZSyRp4VBNqbHFoLLBZL6+BobI0wWnsCIQFN+Yu+R1L5y+n8kXHFoJOjk+6+wUCJwDLnOIWYTGYmT8+DVkAUrSscdrtdunfTUvP43bn3cNfapz2+6QHSe1/8AAaDARarFb74m79JnoWmgqwVtBDwbBJjAK7a7HshJhMqtsqJelVVYZXl2DETqNvpYrVc4tLhIYb9AXyfio01FSFmLoBGam8syalNfoyHJInB6kNFQVSs973vfQBgAQ8rpPHhvmTmtfLfOcJRliWyfIW+7EMpwJMe4rhjOMZL5Cl5fobDAai2UwPPC6BMrag8L6BUYwYWJ9zWNspFko3kQZrNZoAGOp0uVEWUOA8SSUCet9l4hm4nRl1XCEzkqsiJw5wXBZqyxCxLTXKdD8/3MD45wdkZKbtlWYaiLFAWJe7cuY3T01N0Ogl293Yx6A+sNwMaWKUrBL6Pynj2wjCE75kcpTBEEEQ43L+El156CUopnE8mqMoSk8kMX/jCl0jxJ47x8oc+hKPLl1FXFS5fu4HrN27g8tFlSE9iMBigY7xQnTjBfL6wUqHj8RjT6RRpmuHs/Bzn0wlOT08xnc1QVzVmsymyLMMqp4gfiUO0YXY2LN1NEGiBCoSEFi0tjIHNJl3FGqJmIXNzBdYMTNfjD6zlz3DonMEYL1yPo4653vyLDhNzAKDsb+vXWb/eux0CT47ObH7kgr1WkvtRIQb3IdzQvVIKtUNHcEUtxEWhgad8CJeO9W6B/E061uMv6kaw2vclBGzE48k0tBYAXSQTvQZsNtrD1DP6Vdr7cF++e/sBjuC0/XrxeW4kEwC0IiAkBSc3Sxtl2ZSE5uK/3F8cBbMRJQjAo3fD84eNQaWUAZje2vi3mzG5gQxQg/P8m8Wv2iiOdWGYOcmJ3dx/vsn74UhUoxRIe1PDlzDCOjR/PU8iDkIo1SD2KHqxvb2NbreL0WhkjZRr165hZ2cHB4cHaOrGCt14vkQYeYgjymetmxpZmhl6TYPBgKjbW1tbKIoCx8fHODo6sv0zm03R7XYwnU5wfq4wny/w8OF9eL5AGPnW88vPT3lHtcltMmq0cYxet4skiHHjxnW8973P4/qNK4jjALP5BNPZuaUBFUWB4XCIhw8fWrEgzjthytTOzg48z8PZ2ZlVbGPaDkfOmQbEogjswed9mNdmpj4ppTAYDKCUssCAqVaLxQJf+9rXsL+/v5bLwvsuO8xYnIAVvzjHhNkrnBfCUSR2TPGYYjloVgpz6x5yhMX3fcO8oDym6XRKRWZNLRu+D+dlsNw1G7HM+JBS2j5jR+9iscByucT29jYA2Fxhl/rGarEs3+37oZWCZioUt5mENEZr6nJMHQw8gbKk+cBqcG40hiNO/Mw0Fijnl1X9+P0wrbAo28KwpEo7hJTS5j5zMVmureNGflwqGucZKbR5t65KHr8vlxrIQgIcafM8D71ez+ZWc+5VkRZr75zBFVP8OTLD39da27+naWqFGbivIaiWF9MmmUYopYTvtUJmvAalaWrX7iRJ7LxgUM3UO7dUC9+P8vZXa6IbTKllZwbPJabdMVDi7/Jc5r7kNZgjj9yf7j7DoB5oi9XyUVUVmg0b60nHNz1AqgWgfYlOv4uP/I6P4/U33kC6XELWtaEw5dBKwZMBfI8WhDwrbGiXebfXr1+HFwTIqwqNahCEHrZGQ5yfn6PX68A3Ie0yL7Czs2MnziuvvIL3v/99CMPADgauaVTXFShRv7ReGTfkywORubes9sFUN87V4U1nsViY2gI5tra20Ol0KPfKFATrjDqYqHMIAaRphjhOUBQ50pRU5ipTC4j5m57noakb+EGAqiJgV9eV5St3u10sF0tILXE/isjLYpRt5vM5VFMjCH1EYYTBcABO7l+tVijrEtPZFKvliowSIexmUFdAWVbIsxxlVSIIQgx6Q5R5hf5ggPl0icl4Dg0gMYuUkAKlqhFFMZK4C8/3IIVEksQIoxjf+onfjd/9nb8Hs/E5xuMzvO99HYy2tvD2W2/j6OoNaA3s7e0ZT0dlqHtjfO21t/DlV76K8RkBIKUVCXukObIst7xZfrdKayjdQEGtGeGcvwJplKQ00c6EEFQUVJH3n34ISzni2IlWygIXN6KzaYBuGm18/82Dzc82QuLk5KA1gXlRB9qEaTdSsGak2v/hgvtq5ycbomTwKXWxRPZFx5OM6016G/++rlwj1gxQV72H2+few+Vpu32+ljeh10HS5ru48DnM/y3V6EnfveCzi/6mHa88fcdbj4w9EaS82z02wPAFoJCBB0fmlGpBmhRO3osNUF3UT07kZwOgudTCNtJIgEorzp9rI6Dtdx4FdO0927Hs5sgI4dlokTsHuOwDTL0x1zngCYrsQAqbq2fvKS/eajnC5ktpPbsu/QxCQKOGH5CRKgBEcYROp4t+r4vd3RGyLMPloyPESYL9/X1IIXBweGALPF+7dg2podsxvYqNn6Zp4CUBosiDRoU7d97Bzu4W3h6PkcQxlNY2yftg/7CNWDvG7d27d7G9TaIGHBkBCOyMRhKf/q7fg4cPTzGdThAEIZYm8Xxvfw9BIBBHEYQU2N7aRq/XI0p0EGHQ6wPQSNMV8iKltVFoAI01+JVSuHv3LgCiFp6dndkcm36/j9lstkbrGQ6HVsqZBXw4H4cN0F6vZ3NJ2Hhmo5/lrt3SDQxyuG+YdsWRGFdxy61ZyIY7G5ss9czvhg3dy5cvYzKZ2HWLa+gwSGiaxuZouAqCnLfSKGXlyvm6DIjdXJXJZGJBGds+XDiVhYXcXB6eN+Px2FIEeb6wGFBL02d57s6ayi5T6Dn5nw3g8/Nz25dBEKAqMvtsnBPEfcmy5Bw1YWVcpTQEpLWbANj6QIC20S1ez3nccvQIaBXT+J27OTYMrlh9V6tWpIRpj9xWpvXxWOBCs5wmwHlirkMzDELIQK7l3bdRIWEBvJu7xO+MxwcDb+7/MApsfSimKLpqh3le2CgMF8xl4MPiWdwnDK44QsbXYPEGBnE8Lpiy51Lt2JZwpbd5DLiMK1eBjgUg+HwecwAsDdS9N9+DAd1vq0jDf3aHRwaoFkDc7eCDH/4Q8jRDtlji7OQYDx/ch6orMqbjBALAakm6+IeHh9jd3bWDeDqbYTAaYj6fI9zeRlNXkNA4H5/h7PQEWZEjDiOqeaQ18izDgwcPsL09wnDYNxM3w2q1sPze+bytXu15Hk5OTqwePy/gly5dskW+XNWVqqqsF4wXOb5Wmj5Yk93M85z+W7X1fPI8x9nZGba2Rkg6BPKUao1GHqRhSBrzjWrAyepFUWI6mUMKCd1Q2Jm9FY1SOD05QRyF8KQ0FBzyYAtjRDRaIc0zcFVygHjwvu8hiXuQjUCUeOgNRoiiCFtbI7z08odRKYUwiuB7HvZ29xAZpb1OpwPp02bLi8FyucRiscAqy/GFL76Kz/3q5zGbk7BGnuXo9XuYTmcoqhKrVWoXXa01lFaoqhoa62pN2uSoaGXoAZKKG7qRGwg28xpLd4MQENIjAMX5FhbAoP2eQ71SujWVXC+Sa1iuRY2c46KEaPfYPMcFQcT59h/5G9Ny3Pu6IM36/C8EOBqkIsf/bsEbAyT3+Z4I7jaeidvB7eXvbIKu9m8Xg0t6T+1326gTFZN1F273mm6bNtv+bkDVbR+3/6IIz0XXv7hfNqMl64BmE9jSc2LtPHesue/b3bDad792t/baWsCTDpVuA1e5wPwCHGb/IT1pZPhN2yxNkNtBUUkCRhfXgHpcfzIQ4ufkd90+y3p/2HGhlEmHE9bJwO9PQxhjerNuDwE1bXJ8qP8A3ydjod9LoA1dpNfvY3d316qDxUmIS0eXyNMsSRUzCALs7e6gqjIoTUI/J0YwiESJKLl7NpvhbHyMwWAI6QnMFxNIj8Z1GAVI08I4/BJAhLh2/Qpmsyn29natJ7fbPYIQAkncxWKxsiAhDEPs7+9juVxiNBrZqIjneTg+PsbOzg6WyyWGwz5eeOF5+D7R0+/cuYMoisjwXk4xGg2sN3s2n2Nn9wrKosByNaUImwFFStWmKGpk81NZrS2OY0szY6BzdnaGnZ0dS0Hr9/tralu+72OxWFijmvfetlRFaIGLq/jJQMstlgrAqsgKIbC1tYXxeIzZbGZLUrB3fLlcWlpekiRWNc5N6ueoFxuPLKnNCnVsiHMtHtfYtjlJZkyPtrbgG2cqzwPOlWGjn20OtkO4gOnZ2ZnNKzo7O8NgMLDvlb/DkTueV9PpFIPBwKqPcc4UKam1dXfYqOVnYzltjmCwgZ6mKfrdZE1cialaHEEYjUZr+9NsNkOW5ShyKlLPhjIzbSA0mpLuxZERNqx5DLPhzXYUgxw2sNnwt3uCbJVzeU1xozoMNjm/jIEZ5ybx2OQIiPQkIj+y9+Rxyf3AEScA1gZkYMDUObYL+TmFBOI4svYBRzaJ7hnbNcsVUOA+Y/DDwMSNgnmehzRNrVogA94kSZDnuWXaMMBjmXabQ1a1RXFZAXAzh4j7maNWrqOT/2MJe3ZIaK3tOGVn9pOckZvHN71Iw70H9zEajdYAhCclPGMQ3Hr7Ldy7fRtVkSMKA6wWS7z1ztsoigJXr161k2c0GuFsfIbMDJTlaoXpZIJr167h5PQEu3v7SLodDPsDrJZLfOHzv4k333wTvufh+effAyG0UZJplY/quoFSGmlKk4gT9DhZkhcrRvs8eVerlY008aDlwbxYLGzBNSk9O0mkFEhXKQLD7x0MBk4SZmZseIE8z+xkbRXVxIZHRRrvE3uNKY9DSgnP95DE1GdFntkCcVEYYTQawg8CeFJie3cPSa9nB/BwMDDh9I7JwaGidFopxEls3908yzA+P0dZFJjPZkjTDEWR4/T0DMvFAvPFAqvV0kTeCnuehis5zfkrhlrGRh+wRrdpjFIcG80WmKA1cFpwQN8TQkALZXM7Hs3heTTrY91z3Rrma4dj5G1+ZsHbuwCLzXPcCNJa9MUsfq4qEW+urnoNn8d/kyb/46J7awgnQd52OUjhihClC/xco3TzOR8Hoi4yjteeV1P0Soq2/9b6UnBES61tIIBRSQPseNp8Oi6Nw/3hetovbJMxmF2qmDtW1qIPzvjZzOdxo2PUbx44j83exvbBxQCC2uD6ylqaG1O/tKa8QeG+l4vGGAMrZyxovSl3bb0AeJRuuf7c63OHr+Mm9NMz83NfDEof7U/njo/0iTBRTe7fzfchoYmBZ1wCSjngVihAakRhhE6HqFm+KUPgmcKT169ftzSv3Z0d9Ad99JIYeZYi6XTQ63Ux6JOS12QyQV4VazK1ZMAR/bkoyYPNteXG4zH6/b4FSkyLcp0dTPvinAj2FtP5ZwBA9LFGoTF06zCMUJU1lCJDng0mdopFUYTTUxI+2t3dxXg8tgbdytBktre30TQNptOpKaZdoCmodEGSJDg9PcXOzg4mkwkGgwHG4zEGg4FVZ+OIB9G/ImvQMg2Lc1F4/jHounz5snXyuAwNNmRHoxGWy6U1+tnDzTaEW2aCjTY2+vlgkMTqZr1ez6q7su3Bn3N7mQJ1enpq80EY6LgUo7qmGjxBEFgQen5+juFwaKN2bMQzqOUIj1IK/cEARVlaqhhF6ShqxmILvPawU5YLmrpOm52dHShFDAqmF65WKyslzZQ4KaUFSExF42hWXStbhoOjVWzXuOsYO4GllMRegbYFWhkIuEXk3YhUURQmKhICWqzR3di2CcIAeZ5aqpeU0rBvSguSOaLEFEOupZjnpE7IoJZB4mK1slFBrltVmZIUvJ/y+3cd2wwkeG7XdY3pdEoiXoYF4Ebl2NDnechgPjECElYMxbxPrbWNHFNOo7K2H9MN6R34CIPIvgtur0sJ5HHJ9EO3XzhixmOJ28EHR5k8z7O2K0efeMwwhZUpkdwfLpPDjUqyTcfgyr0PjxO2Y2xOWdPgD3zf/+6pRBq+6QHS7Tu3sbW1RUnJWlNUQCuEQQwWcp2dj3Hv1i08uHcPy8UCe4f7uHXrJi4dXsLxw4eAWUTH52OcjU9R1zVeffVVpGmKF198EZ0O0bXibg9REGI6mUArhX/5P/5LXLt6FYeX9tDtxnaD4yS8uq7R1GQg8mBZrVZtyNYgZSGEDb/zYkRVuUOsVqmRgmSjmpLpKIKVk5KIGYxVVcGDsGCLBAukXdi4fbzpaa3h+T6kIOnNra0tuykOh0P4vo9ev4+9g0N0ul0kSYIkiRH4PuqqBjRQ1TWWqxVqQ8cQgkKk81WGZZphMV9gOp1gfDYmTnVd2WTaPMssPQAA6rpBpSmK0xqBZCgR15rEBow5DsBwghXRXtzvsxKZFBLQam0jcA0ya9hb9hIreDnV4fEoXcjEKS6kj61POAZarr15wZTciDC493IjHWv3uWBq23Oc7110Lx5/LNTBhohLAXA95AyQXIN/rT3AmoIYP4tSCtAtnYnbyJEH9xpu9IOBrgus1ql0aw8ElhomWXH1mP7RdmzxZ7zYSykp+ndhZE5DYL12lAtoLopo0EXWi4rydZ8UJaLT5NpZ6wDJf6Qv6Z4KWrdc7fWjBUhtZKZ91k2wuhmp22w/fffiCNhmH6wDp0cBkvt9F6S7/UT/ftJ92vFGnzsqfGId7K/1j5mYfA6NfcBDgyj0KVodBrh06QiDfh8aGu99/3OQnsTu7q5Jmg8ghKFOqwa9Xhe+H+DOnTt2PYXWCDxSFmzqGqt0hTgicLNcpRifj3F0+QoWi4Wl+OR5hjiKIX0fVVkCQiDwTfV6n+rzLIzBv5jPkecFrl69QnK/cUx5n4FP9XtCAnB+EKAsSpvk3+32sLu3i7ffets6GdmwZ2EGzidgA4c9/nEcW6/x7t4OprMp8jzHtWvXbG0XrTWyxRJxFFvDBcawqqsKe/t7FhSRkUTqdmVRoKoJiLhRBQY/nD/C0SU29tjoTdMUe3t7GI/HWBmVWKClX/G+y15yPp+FC5g25+b5uDLEQEs7YrloBmc8Dpnm1Ov1MB6PUVUV9vb2UNc1jo+PMZvNsL29besNcpFSl7LExUBdByrncjAwU0pBAyjM5y5tikFDFEU20sX2BTtdXelr12aYzWZrdRf7/b59D2wEM40qTVObK8ZCShyZ4fQBBrg8hhj4eZ6HJI4RBj7KsrBtZsAFwBrSDH7430VewPdD2w5ODSCnr4bnk+3DUUdeH9zn5vEEtKUEgFYciaNMnuehrCrkJrrK+TcM4lxqHJ/PwMPNGePnK8sS3U4XVVkBGjbSwucxS4b3Ze5v12HJAIJ/KqVQVoWNWvNYtXt83aYFxHGM5XJpx9Rmng+PDx7PPO74+zwuGKiw3QfA1iZiQMv2HTsteH3nNvN85r/zmsAAm+ctPyfPSW4b9w/XXCrL8qkB0jc9xa6pa1RGMlB6Ep4EtKZiVL4J8W7vHWC4tYPrzz6P115/De+89TXUTYXXvvoqhFbG2i3w4MEDzGYL1HWD0A8gkg7KooCqGxRlQcpWhg8ceD5qVWE6nyCOA2g1gJACdVVjPl9h0O8D8FAVOZraDHLpY9gfoihLCy6ouniDDCV8L8Tduw9QlRV0I6AaGqhleWwmT2NoHuRF0GC1JUPXMoZ4FIXWi5EkCS5duoTDoyNcvX4NSmv0+31b0bvb7SKJEviSPFOz+Rzz2Rzj8Rl5etIMJ6cTrG7ew71796z3ZjqZYDyZYGm4yxyWbjhMKiSqprES0WTUtTQurclb3dQNtAE8WmtAtgIF1lOvFKpGGaufjGtlqtGDjV0jaU3eXmNoG+UaDSqO25qHxpASgFYNOMIhJRtiinINNEMmtOhGCHhCAkalh/OuXMOLLm0WSvZwWwlsgBO81zz0UmyEA+hZWQBaep7pS9NPQkM3BOKskcfABszie5S2tWmgutFJd3HkZ+JNQykFGIEqjiauNVZrSqQ3tCutzLyChhCKqDQWhLoRshZEbPYhwHOT+74NTa3BF34RBtS2ILXtC34PDUch2k6gM5WylMdHAYaw3zNPRJQ8fk0Qa33NF5dC2rHsGvH0XWVzUNpxTRv7ZnkeF+Zpvd5P7WN4UPDWzhEG/HvCA/QmdROgfA/6puetgyKO3qxHgjh6JIwch1ujgj73PQ8azC+HofsSbZeNk6qqYcULBPd3m9cjDa1VOfLVPH9Jbr91kNA1DPAxfStIdhFCAJ4vraeaPfHdbgdhGNkaLUHgY29vz471o0sH6HUSUq3yW3Df1DU6vRjCo9o5paFAV3UOzxdo6gZl7kEHNZ65fhWTyRSr5ZLASRIjiEJAa3ieDwiS4B4M+mgaBQmBwPNx6eCQIg5eQGp1Nc3F5XKJa9euIUkSvPXWW9ja2sLhwSUcHx8jjjvY3t7FapWhKCoMBiMTcYrg+wEC38hFNxpBEKJpFMqKinEzHaYoCqSrlaUy7e3uYjqdQkppyhT4mJyfGyGeHJ6UuHx0RDTwvMDWcITuETn+qrDCcr6kfFVIG+mPohjdboJet4/x+RhC0Gej0QhlWaEsKzvSGMBwkUs2rgCaq5wXxB791WqFk5MTa+wtl0tLH2JPe57nVuCH95YHDx5gsVjg6tWrNnmenZaTyQQjozLH3vyqqmxESut1VojrUWeDdjwe2yjOnTt3rCPGDwIsVytAMiCs4QcBZvMFlssVul0SaZrN57hkiq/mRYVEekizHFlO4PD09BT9fh9ZnmFrawtlWeLk5MQaq9xWXns4QtXr9bBarSxFbDgcGhGi1AJT3hN4f+BIxGg0QlWR3RHHMebzBbrdPuKYpPg5osYGL0f7yrxAk1QIgxCB5yNJYipnUZQo8hzDwQDT2QwCAp2ka2jPtC7WVYNO0sW0mKFROTpJB0EQQkpvjcolpTQOYPp7FMUIwxhZmkJIcjIoDeSmXAmD8263hyzP7FhpNKUMKKVQ1Q2K0kh5x8x24bVHIs+pJIeUrahImuZO6QBBc1x68P0QZTlHFMVYpSnyVWZFQra2tmw/V1WF4WgECALbzGCpqsru95wjyEwgAmOBjUBzREVrAklSKEuBBGCBGwMSAPZ3Bi9cNJbBEPcX0ApWuEISfC47HdhpLmVbT4qdLq4yMzvsmZLI49V9No6cMiB2wSjXzuRo49Me3/QRpLfefM1GZEgpzYeERFlUaOqKvG0+5dhEcQRA4/jhfbz2W1/Gr3/2sxh0E2ihkZU5lmmK1YJEE7h+BKNXP/AhPQ9lWSDPKBltPB7D8zzsbG9DNZXZbGkRp6iNgG4IWTd1Y0ANgToIQCkCSUCrUtQ0hv6j2fiQ8NizDxhFFMD3fPQHfYRhiG6ni16vh/2Dfewd7GEwHCKOqODqcDRCp9uB9HyczSaYTCZYLJY4n5wjyzLcu3cPJw9OkC4yTCYTSgYsShtWBRf3VI211OqGokdKALVuvfE8+JuG9Pt54LEh1ajWG+KG0l2AIcSjUQ822CTWoxvuBLGRD2hb74p/104bN73+m5EZ/s5FRxs18GyuxGYEROl15To+zzUC+b5r9C+5bvC6kQy37Zxf1n5nPdrjGsFuv/L1bHud/ttsq5uP5PbvZu4K3+eiKNtmvzyuf7Xm+kkbhr/JS7gw2gb+86PevosiRzzO2JHgRk422+Oec1GkbPNzjqjxd11xh4v6pT3Im07Amf4TIAlpLZR9ts3omXRAkCtqAADKierYdyUlhIat6+RGCKlN5KzgOjsUEaWr813sdwFTl4OAHPkzpKWbwoA3DVITdGk1LZWN8a62tDm+nyeF2ew6dpNrAXqFra0tkuk1lJr+YAApgP3dHfT7AwhB6+eVK5cBDQyGfQipKKditUInIXobe+2Hw5HZRwaYTCZrNU/CMLTr4f7+fpsXomqEUWhrwVBUPcHJyQk6cccYboFNmp9MJphOp7hy5TLu3r1nveFcl2U0GgGgfNHBYGCNBo5OCPNdTtDmuj1Jktgik+xR5ajHfD63RcbdHBx3zWXQwdGFLMsQ+L6lILPh4o5l1zCh0hYUxeDEcS6wyTQtzr1gGg3LVE+nU/t++Vy+B1MHGfhIKXF6eoqtrS34vo/5fI4gCKzw0AsvvGDp4tzW3d1dnJ2dWREHzhthkJUkCabTKZ5//nn7OQsVuB5ujjCxgAFLibMS23w+t0XLJ5MJ5vM5Ll26BCGEzZdioQzOwaAxtE6Rc6MDYRhbrzrT3tM0tcVVx+OxjZYxQJxOpzg42LOy40zh2t3dtYIPvV4PWZbZNm1vb9v9hO9/fHyMpmmwv79vxSl6vZ6lCHIO1WKxRNMo+z55njIQ6vf7FnzzPhLHMR7ef4AwCGwkjdXdGFi675Aly/m6HI1xc1e01jahn+W2mcLFESXu99VqZaN0WZFDelyv0bdRIgJUbRFeAFZkgpk/nJ/DESguUsv7Ju8zLITFz88CAvy+wzBEWRRWFZn7goFInudo0JbhcKNEUkh4BoTyZ6xwyG1xI/LunsVRLlaiA1olOI6Kcg4i94279zNVkccfKzPyfOVIGtvN/K4AWJEuty3MYuH7cqSII5/c55x75+4l/G+m/gEkXDKdTvEH/w//+/9CsRsOh3j91VftJkPGNyX9zuZzBIFnKA8SRZWjqSskSQ9CxNBVg//wuc/ia1/9CjxPoKhKnJ9P0ZQUAq3qGqvlClVdtWH9nDaiMAoBDezs0AaYZksUZd6iZylM0dYIAhLaLIie54MNC6UVPI8KqgZBgOFwiKTTQRiEGAz7kEGLrLu9Hnq9HjpJgu2tHSRJH9ACo60teL6HIs9RFiWyPMfZdILpZIqT01OcnZ1hNptRVezZFFOT2Ng0DeqmJg8lb5p1W9OkNt4hiuhINJp4oFK0hRXJmFKom/YagGMMGkAHcB6KsosAS6LzhruZFM+GFBugdkJcULHdNYrdieMCIIh1ALFJt3Pb7v7tomgLe6+55ox7L44eCLGeCM9HUzdgw/MRULYRtHBBivWqy7aoXdsHLT2Jf7pG+kVt1GqdgnZRRGKzHZvGEh/u4rn2PE8ASO7v2jH4OaLg3B2PA0j6MaDoIoDEz0HGOx4BgJuAbrONDG43P+e/u33SClK0z0Tfx9o1bL4QJEhIgOrZAICQGizn7ebKCCEgtBvpWb8mHDrZI9S2jb8z6F0bI2h7230GGwEzYIj6mOq1eZ600TPyuDZg3Ob5/FwacRQhiiNEEQkMBEGAra1t9Pt9aG3ECzoJrl2/jh1jvFV1jSSOqQi2arCzvY3FcmFqrdCmq5WCbtr1qjRG9XKxQBRH1uAGyLk0m80scHMpV2wsTqdTW52dC1K6xk3STbBKV9YrPxgMoLUmeeVOD570rOHCKmWu1DFH25m+xHQuzktlZTQ2eBRg6WSXLl3CZDKxKqlMOWIDjqMnOzs7Vr3N/bm/v78mtLBYLLC3t4fj42P0ej34xpBlw4WNGzZ0oiiyRheDO/4uJ3Fz/lOSJLY2DpeyCMPQAiemoLPiHFP73IgNGz9nZ2e20KYrTMTS0uxlZnDGgHA+n9t56QIB7mfuw36/b6NQfB1+Xn4eduxx+3k9YTGGpmms559rN+V5bnN0ptOpNbZjph9mmVU8i6II29s7mM3mVp1sNptZo5UNePb6c94PSYTTfDg7O8Px8TGef/55LBYkFMUKcJybxvkfbOiORqM1kQqOGHBEJssyO04YNPl+gLKkNnGelOvpZ2ObwQsDjrpo1dncfR+ABTzc/25EiEEJ0/9YlY3FMJRSNm+IgRSPKTbW2eEQBAG0IKYLr3MsWsHn8TjivDF2hvM4YeOdxwQAq/rLOT/srHApYbzu8rlaa3honcTc1q2tLQJXUqxJwFuRC89DFNC6xWDGlWznnCgGQq5jw50vm7/zGsXvwbUp+J0xdZbfl+vQcYUv3D3WjTQx8OS1hd8NjwdXFZLnGOd68T7FYwOABYTcnqqqMJvNnhogfdNT7OqiQpWV8AMfeUk8YelJFHkO30tQZCssygxBIFHXBeoiRbe7izhK8K3f8QnsX9rFW2+9BQ1gbz/Fw3sPsFqtcH5+DogGEAoQigql+hJK1Whq8lJduXKZ6vNE22iMccwDDAD29/axs7OD/Z1dSN9HbLxXjeENC0n1KDrdLvwggNIK08kEqzTFcjXHfDFHnuc4HU/xtdffIq5zo1CWGmenZ0TTqGtkhgNcNQ0yE/5trHgBGStNs24Qk6EjTKSKKDoU6RGotWqpYFpTFEloVEpBaGM4aaKn2etpbScIAYzWaJbMLlMA2XdEuWGjxF0EudBja69ySFdAbBjd65GIRyMndLYpuuqAns1oi3tN99+bURV6Fgk3L8YFWG5bNiNj9H2xFt1qn/DitvAiyt67dUOcwUProd+MGLlAci23pnXjP9oWB5RtAqhNQOJedzPCtNkfa6DIfVbRPgNMLlHbK4/WlrHXUG1CqXvdTeDnttM+Ox4FEE/KlTF/sT/bekCci9PmVElpyGc8SVwamfPYCp7N6aJ7t/1r1SCVgpRtW+x8EsxFF/Y8CMAzm/AmwBfC0OKUqeMjBODBgh3PJ2DDlEBWYROmBk8cx0g6CcIgtAbB9s4QcRxaqloYBhgMh/A9H91e16w/TPf1MBqNEMeRoZNI62AhQ5TUPwGTT5EX6Pe6RmY/hxANtrb7KKoUW1t93Lx1CwcHB7RxA5hNzhEEPqIoNu9GIop91HVpDUk25JIkobwdk+/DidoAFczmxGSWhY7j2BrjaZpisVhAafIgr1YrJEmCO3fu2E29k3RsHgtTVDod+hsbMbxpM+3L9br2+31rAGqtETr1R/i9sqf25ORkjS7FBsfdu3etQXt4eGhBUp5TeQiOTuzt7VkjnI1bAIZCVdnICQMLjrIwCGDlVH4uPn84HOLs7Aynp6c2j2R7exur1Qr379/HcDi0oKvbJfYDq2NxjRuOvLChH5qCofzZYkEFxtmTz+skR/Y4asIOIxY+4kgDgzYppY0ccWI91/hhEQo2Tt0EdVbVWywWa3WZONrHII4NOy4QGkURKtMmpjYCsDlOdr5KiV6vh8ViYQ1MHmMMUPldEZ2MIng940xlw5/3AVdpj0E9e+fZCGUwzGCMoxP7+/s0Fk2frFYpyrLNqeIxwAcbwUyJcnOi+v2+BYYM6Dmqwtdh4Sz3HfH+x4Yzvw8eEwBs/hH3P+9NDNaZFilkq1bKNojWJDnO74htElckoaoqG71z5eH5nbEjRAhhQTX3OUfH2JHAczuJYttnPK7yPIdn2sTOHQYpRJVr9zUeIwDWwBs7OhjAbNYU4nfEFFTeK3m+cNv5c+4Hvh5HffmZeJ3l89xcMY7m8X3d98T2H4uG8Ltw3yELZ/T7fQs0eY3kue+ycR7n7L3o+KaPIL3y+S+g1+1BComyosUpiiN0+j1AaHTiEE2VYT6boCozExqMqfaPQazsyVWNQp7mCMMAjVLI0tSEbGtkeQEpAsTdLnHjQHkNUkh0On1UtRlcdY3VKqUCqGWF+WyGPE1RNw2m5+f/P/b+5Fm2LEvvw769T+ftdb/N6yKyMiurCqURTBNpogYgUARIAaQk0x+nxkwGjsSBTBySEkhRlIgCChIpUBIMTQGoykJmRkbEe++2fr057dZgn98+yz2iUIlpWLpZxHvvXvfj5+y99lrf+lanh4cHff78WbvdTs+7vfaHg/q+S513ouB5Dc6r63uFIYwT1Kdwa0zHmxLYEth1Xl2wzkV8JdAYJmxMbY5CfI4EYt00AybzXvJSP8TDdO40DNLQy7tpsCbC3ve9hkCLXSJIJjrkI7inMI+DGSMk5ag0e/X9eVqY01Q78n0OknUerMPGRG0Mz/cV4v95jo5dv+n3F2DXpPjJKdVJXBY+Eiayiip9l2JjCeuU8dlLx21yBL6/293337Ndx++PtFw6RPZlv9euHf+2ztElOP/zFJZxd9OfAOd47+fO2dlnjYGzz/t9z3D2c28jMJdRIqVnOb/OmAL3fetyUUNlX9bpsuTB+Fsx0yc+X68s8+p75vKcO/KcMR+UomB5lmkY0+O6vpPcFGmMKRJZzNN3Tpn3iXl7//5DSr358MU7ZZnT7c2tpDiD5/2792NnyVaHw6s+fPiQQD1Gr6pyBcUuaczIAUwfDkcd9jGNRyOo8S52e/v86ZMWi2WKfMM413Wt2aJKgHM+n2sxX6hu6gQILeikwLgoChXjWsHmk6ZxOtXKsjIx0m3bpkY4tiAaow/bXVWVvv3229SeGFBX17XqtpbzLhXgO+f0+fNnbbdbXS2v9PXXXyfwT+oQ6Sp853K51FdffZXaE9/c3OjP/uzP0nBWoh2S9PTykpwMIlqAWF7b7VbffPNNAnakBwKCY7TuOrUffnl5SSkrb968SWvW1LW8c+n+uW+K7ImwkT7onEvRCWbweO9TcwGAGg4K4GsYhtSJFQeB+oSyLPX6+ipYe5h9WHm6vRGJI5UQcG3X3nazQ3YB2XT4kpTSufgsdoL7JS3tzyOKSFmEXAIMS9Mwbtutaz86COwrkcUos42uxo6v2HuiOyGE5FS9vLxovV6nFLg8Py+ox3F+fX3V/f29hmHQ9fW1Pn36pNVqlbJu6AiGA2Y7kVnmP4SQopuvr4fk3BIxYV3onmZlMZFuw1TfSmQXoAxRsN3GuV9ERC3hh5PE7yQZTBRBOTJA5zoiSeiHWFPUKbipFfpUt5Qn/WZrZZAH9tU6dtyHTXdjr9HfOCM2otK2rbxzmlez5NhnWZaix0MIcplP0WbWB8dKQ0gRW/bGpprj2HBftkECWAv5tg4OTg6RT84uDgt2HkeMtYCgsLVLkCZEuZAHbCOfs9gQYsieJe6R6Bt2lkgwe0Rk9+Hh4TcRJF4+L5VXsXAur2Yq5nNdX1/r0DQ6HPZaXy1UlQstZlt9/dUvddgdtN//Stu7O62vr9X0sWi4OR1VH2s9Pj1rv9/rdb+Pzs3ppM3VRt3gtNvXSbgexoLVw/EgDZkOhynFbvey0x//iz+OCro+qWunwVU29NinSIQTDQL6fpw+7pyk6GCoo54m5v5bHHYerQipTS8KlutLUlBsSBDC2IRAUyqNG+JXOo0O2RAdoNStarxMcJNj5jRG1MyBkzQCtqAQunT9iVXXeF1qrvq0HpLGgw7zPqZAjTeQ+e8CbpgyrsVhsQ7lpaNgjdyfB+D/PLBtozSX1+NerdPA71kzywSffZ/5XvaPl1V8vKaow7mT9G9iT9LvLpycX+dllRj7ZZ/Fgnn7/j/Pybt87vE36ach0P3v3EFKTNEFa3S5Pnb/rbNm32vruS7v69zhM3VSks4HnNrnmqJo53sV63bO1yQbFf9Yd6dBWeaUh9hUIdb/jUajLBTr3qTMxdS19XothRBn6tzeSs7p5u5Wfd/rd373d/T0+KTVeqW/9Jf+kjQELeZztU2rtmv1/v37ZMTLstDxGNOdSLf5Z//8n+t6e63ZfCbvY0QnzhZxautei+VSp2OtzeZKmXN6vL9XCEH16STvvdbLtXKfaRiB3eexe1VVVVqvVnEP+l6HMeqRZ5lms0r1KepXWuhGkimmcC2Xa3nn9auvvlbfxzk1t7dv1NS19ruXcR5HM0aHKNCv0kDRp6enlJIDCN5sNimSBIN/d3eXOowhDzh+MKrb620CZZ8/f9abN28SULi5uUnpRc655JRYMsg5pw8fPuh0Omm32ynP83Eg+SqB5f1+n1L9aIdNTREA9Pd+7/f0Z3/2Z/rZz36WipQh/Pb7vd68eZNANc9sZ+5Y8LLb7WJ30pGtJZJDa21AunNTOg1ACkAEwMERITKG4wTzvt1uE4tOdMh7nyJvsMnUOXRdlxxjIkFEWN69e5dk+fn5OdkA0gaZxxNCSO2tsyw7a/0MYGdPifoB5olmoX/QHTYNTFLaF8Aujt5ms0kA1nuvn/zkJ2n4KqM+cEJ3u31i2WlpzRgSnEKc+inS0app+gTsX15ezhzf2F0uyvLt7W2qUbMtmVkj1tXKI1EAauSI+BAxYf9oWc79A+idi6zsrKyS00K0k2gS9U7UqBDp5RyRquacS1FD0tLYu8Vikbqm8Tvun2jW8XhUlufqw3CW6mmBfwjhbDCvpNT0AhIF+4fzg15nPYk24bgzH4k6IZoJcK+cMWlsFZ/Fpl7IG/KIw7KYxbbhtg08KXLoGuSXaAvXx+GwaXREsHkG5B47ZlOFmUtmo56JjB5tJ2ef6NGl3cVho1si0SCcTus0UoPEd6BruE+wJ7Jy2Uf43/T6wUeQfvHzX+nm5kbeOXXdWIitoDYM6rtB6oO6U6PmeNQv/uzn+rv/2X+q+0/fan61Vrlc6VA3et3ttX9+1f5w1KFutXt9Vdd2Y95qpd/5nd/Rhy9/W8c2gq7jYQovRtZnr76tU6iXVIKH+3v1Q6vBFGQ658aIEOAwFo7n2cSIDEMvhU7eKRWVn0cibHqWAfkh5rFxqM4jJTGSws/PQpIhyJnaifiesaBaXkO4iLAkp6fXECYFYcFhP3h5PzEx9iBledAwEIq27bRHp1AuMQnTs0th6CRNz8VzWHDM7yagPiQMexntsA7Q5TGxDpFN2Ys7dp6OZe8zrvO5U5Uci4FQ+ffU7Ljwnfv5vijQ5f39eQ7S9zks5obP3v9viiZdXtveo3WQbNTl+xzL73VCv/OT774uWdv02QtH0/7e3svldWTu5/vS7FJu+AjykoMUJgPA7+J1SJnkmhM5EdcoSKmDnTPvcWPBeCW5oOVyLrmg1Wqp9WKjzGe6u7tT3/f66U9/qs1mIynoarVQ3TR6//59Ykc3Vxs9vzypG7oRFC4SGxjlLigfwTCsL10nq6rQy0scUu2zeFZXy5Ve9696ed7pxz/+idq2keR0OBxGQBCU54WqaqbN5mrspDZX2zYqy0pZlms+X+hf/It/oevrrZkAf0oDREMIqftUTNt5VVbGJjtVWeqrr77S1dWVHh4ftJgvtFxuUuE3Bt45pzzzmlWF6vqkEJQYVe+9vvnmm9QZKssyPT8/J2BL9InhjdRlkOrDzBwAUhqMuJyLVvGktAAqDq8HVeU05NQOO+37XldXVyktDzDPLCOY/i+//DIBwhCCsrE+ldQxgDMNEU6nUwIPFJLj8F1dXZ05AFGGlAAu+4kz048MeQghDUptmiYNCrW1N7DWRBJIKeRPOuABmrkX5spM7ZjPwWUIIdWBtG2rDx8+pD2HCFwulykC2ve97u/vU/RtPp+naJVl0amLOZ1OqRbHgiyijEQzcDSJoOGs2fVmSCZnCblumiY5P33fp1RG52LksR3vmeYBzMiJDniMwEhTXQ7RNfaK9acRRN93aprI/BPpo+aG+iBas/uxzgxSk+J6ZAkdgXPDfrGeMUJ4SnqSmhFbZ8az4Gx4H7v/FlnEA4vFIqaqDlODCktAIBuAcOuQSEoyDRDHKWbNJKXn4jt41XWtoiqVjU4fDoyNvNjUWwA7tma322mz2aTnAugjY0QCbU0c94QDg+3suk4+nBOpCbN5r6ZrU0TLOihEnhgiDKlCTR/6Q5q6wJGiah0enGRk4fr6OsmpvZdhGFIa6mKxSAQLBACOFtFH2vGj49lfziKfpSkO92z3+LxcwyXH3H4PpE1VVUnvsJZ/8O//u7+JIEnSP/j7/62KYq5j3Wi/O2j38qLX3V7HNtbmNE2tw+tej/f3en561mH/qkxBTd/L57mci3N0vGLbVbqyRTBVaH/o9M//+E/1+fGkL378Uznn1Q1OdX1SUZbqQ6e269XUtQ7Ho9yYYjBbLFQdj9o9P8ZUmiFIii1jvcYDP7aTznxsfpBYqjAodkW2oG9sDDAMKUrhXUw6c2OEhrbf3lNjNLbYHqQ4vDbW9/iQcGI0xJoiQ4m1zzQ6Qk4+TM5C/E6N96m0hiHE2q8o5EGZy5UXZVKOGFafZdJ4X0SlhiG28maGggW3ANAhNWjoYurh6GGSJkjRO+mCNJOQMSjBOmLOT8M/0/+na6bImxvZf+pJuG0X/z4M42+8H68/dQmj3oN7lfOxDbbc2A7bpfexDtwv3xPTKcf27sHcbfhuKiU//76XVT7JSZgWOX2fdTwuHcfLv9uo4fc6Yjof1GodpjOHh6ikdcQd9xXrtoZgnXo3+qFjW/aRGxjitsvLawh9MuIozSAn76ZarvMoUUwp9ZmPaa3OKc/jEGWfZWmvLz+b526s1clTe33aBZdlVNy3NzfK8iyBnZ/85CdaLOa6vb1TVZUj69VrMYKN+kjUJLLdT8/PUgh69+6dmuak4/Gojx8/ajvO2en6WqvVQk1bqypzHQ/RmIUiV9t22lxv9OnzJ53qk5qmVl7kOh6O2l5v1bzWqmazNKdk6GPV3nZzrTyPQOIwsqfX1zGqsnvdSSE+c920WizjVPd+iI0VvM+0e92prMq4dsi5k9q20enkU7va4dTLZ15t18VaqK5XE5o4SNVnutlEB+fbr7+Ks+jKyPzjBHR9l1Ly9vvYPAGDSd0BIMKCc0DQ1dWVXl5ekvHf7XZ6+/ZtAgWn00k3Nzd6enqSFEHO/rDXer3WfD7XerlOwGVWzZKzFcb9enp60vX1dXIwSOMhtYzC8/mY+cA5SYSLi7OTijzXcpyb45xT18ZZQmVRaP/6qmq51GI2i7YkxDSc0/EYUyuLQmEY1Jh6oTyL82cg3PaHg4oxCvbmzZvUJY7uUrDYpHg1TZPAR9M0+vrrr88AOalWd3d3en19TWlROBM4U7YQHIBzd3eXahhgnDkPnGXSo+o6tgmnKJ40MAq7cYzQOaR1Adyis5PLyatre7VNp/kstrmeVfG9tY/Pn2eFFvMstY1+GVuZz2eL5Az2fZ+uk/lcbdPp06d79WPa1NAHBe80ny/Sd2dZrq6LKeUuxNmNONN5lqutax2PJ/k8S45K38eBvLvdTu/fvx8dgVx13Wq3ex11fiQqnKu1WCwVhwDnCezGCFSmrqvHdF+vzWarsqzGPR+U56Vi5kPQaWwtXlXl+F1RholOAvqpXyL6e3d3l+xcCIP2x4NkQHNQkLxT07R6enmJXSzbXm0/lhKMjqb3Pp077z0WW8f6pGpWJdB/Op3UDbG2seta9SOmY88HBXnTOY4oRZZl6ZwTKZIiGU/qG8QHtXDUrJHuyXkhncw6mLOxRTgzkUIIclmmIcS60yEMaro22cZ+dM4smcd33T8+qq5PY/fbaL29z1RmXl5Onz590mazSWlqz8/PicglOmdt+Wq1Smmk1qHk9xBN6E/0ATWJROuw79QN8Rmi0ThLi8Ui1ZARoeSsE42nbhIyC2d3uVymZ1osFulaOLW2rvQvev3gI0j/zl/5n8vlcw0hk3e53BCFTS7TsY7zGobQjczOKToQLsSZFW2cR+TGTWu7Vt3QJkU9q6roPHmvarZUVkwtOFEIeZ6rqQ/q2qgUYBUBeplz8poMHsDWOa9BXWoLDdCU4iEo83ggEFYbjg1dHC4W7efYWWoYa4rGFrvDaPgGs/1p1I6LAJPW3f3QKzhY/svuVU6Zz82/p4hJlmem7e80+yF+PjpKgFoApfdeGqZ2lzCRyYHy5y24+c97pyE0GoYpb/XyP/s560z8eVESfmffY58z/j1e57KG5DKSEj8/fSdpk8xl8t7LKc6Esa/LNLDL5++HTsPQipoU+/2X0SXrjNh7+4vWwL5ixOH7Cx4vP/t9tVSXa0MEg+iJI9BJ5FNmzdwU3AohpMGz6UO86Xu+i99558a+eHFmjq2PC/LKslJ451mWJ4OQ+UgxEDWYz+Yqq1KzaqaizPXu3VtVVZXSqZqm0RdffKGiyLS+isDt6uoqMc+cV4wpYBsWH4bWAkcY5k+fPqXzcXt7G4cqj2lPNlce4Hl3d5eMN+w5zC6jCFarVUppIC3k7du3CSRSDN62bWopDLCktbI1VLvdLhXY73a7lBrDQEmKhAGjsPTWQEpTYbEUnQXLaA7DkMDJfr/Xu3fvohM3TB2fKMRfLpdn9UTsA84P7ZlJO2rbVp8/f9ZPf/rTNO/m4eEhtfXebrf69ttvE+Npzw7OFHti01U4v0Qpvv32W33xxReJYee9Dw8PSTYB9G/evEmMLzVd2+02RTZsWhVgA7AAc825Y3gpjR+IoL179y6BJUCejYLh7K1WqwSCYOdJ44G5vb+/1+3tbVpnHCjqnGazma6urlJbdZ7fe5/Y51R/NpulcwBLDCjlmVhfGHuAPo4ShegALPtMgFP2HkctdsGSvDtPNyIlilbTRGUAuDwLUQebVkdtkU2rhFVfX12pbpu0N4C52SwOYC+y/MxZZG/kYqddrgW4pAsg+gQnHHDKXsDS41yxTpJSZJb26qzlMAy6urrS6+trugbzkqx+sl3xbD0Ttoe1w16hF4hI8V50IeQHUdvCT2lrNiIiF7OFLKi39TBEcfi9cy6Be5yiYRjS2hHhoe6Khh6kb3FdnodIBpGsw+GQnETsiO3Qhg6xHfvQ59iyEEK6Lg6YfT9pgjgV3Dt6KAxBL09PyekpyzJFJInQ8bLZNlk2DfZFvyKXlnxg/dg3XmeY15wLsI2N2NqMIp6D9bbphqzPVLrhz84aDrrVD1IcevybGqTx9ZOf/o6e963qRqrrTn3bKDintu/kyzI6AS6P4GvoNF8u1NQnuSwuJu2u3dj4YJCTfBYjHUWpTLGrU9116k+7szQcBLMoy+joOKeh66Usj0yQ93EwWderKGJL3H7o0iHpukZ9PzEK0rnQAnAxANN3jjmdQ2QZhiE2c4iRpxjhiayYU+i7qdFDGu/o1A+9+h4QGo165rORqYhOXIzEjMA/SBF0TvmkeTkOYBy7X7kxajOkoZDfTYHq+15e5+l4Np3JvmzUI65HrMEaxjXkHnHkJob/slMcTl84f7bxd/yZAhpEmWwbAYZ4pggOtWTZeP9K13XG2fSewsw4rPMS4KNMbCqfrVXIfCbaXeNg2PW0DsJ5bYzO3p+c6TBo6hRnn9eshj+Pgk2/DGd5cSGM6YY4hs7+zjhyY70aa8x+xfs1dUSD/Swd4sZLOx+DSiktLkZQ5WhMEK+XZ5ly58YBgWUCeb/927+ttuu0ulprPovOw9XmKsneernUarnQj3/84xhxKPIpPSIEra9WCkPQfozOHPZjutkY+aM7mHMutfSVlHLHbXcoAAhgxgIDcvdtlINUCuoe1+t1SqlYr9fJ8MMKv7y86NOnT7q6utJ6vY6zP47H9B+FzA8PD8mRApCS8kAXKYCdcy51eaP9LfU8ND3geQCZrAFF3ETWYHoBrLvdTl988YVCCGmmzXq9lqRUS7HZbDQMQ3K8uq7T9fV1cgYeHx+/kx7EPdECmJRQQDwMK2vsnEstdUmloR6Fjm44MewPAIe6ivv7e/V9r/fv32s2m+mLL75IzhkAg/oEmkqEEBIQBXzhENFa9+HhIT3LcrlM9yYpzfnBgQXE4DzAnhdFoW+//VZt26aoi6QEmK+urhITC/igtunp6SnNI+KMrlarBEIA4Dg4gNxf/epXcs7p3bt3+vz5c3peZOXp6Sk9Mw0R0H3UfQBYkVGbNkjHM+qWiLwA4HHGcTQgNQHavGxHua7rEqgEMEpKNV7oadK/bCF5XdepeQfgWZrA6fPTk4J3KRqGTeS5cAg5z/yJ08/9sZ+QA3zfMAwJQLIG6/U6gV6cbOwtjhFg3taXee/T++/u7lI9GXtEmib3wn84y8gSoJ9r8nMcf9u4g72FMJKkYqzftK2sowUIasdRJdw3n8MpyfM8dT97fn5Oe0JE1+4BzjBkD9Goq6ur9J2Q1MgSMobets4R0REcemw8cod9wqmybbZtowwcSkg39o/vRraqqlI96jpk2t4PUVkbvbZEHs9EpA4yje/gvixeAMdxT33fJz2GA8fZo0aO67GOOGDgaZvWaHEEtWLIPc4TThfEhHWc/qLXD95B+p3f+139//7Jz1TlM2VVbCyQZU7lrFIIg5omhop95nV//1lD36saa4XKrhWd0uIraBj6NAh1UnAhFifPy7NIAxucF5V85jUMMCchGazFbKE8i5Od27bREHrleaaua9U2tZr6lFpLckhi4XKEpuTqoqwkUovCmddOSllwUp4Xms3m6rpW6txZh5PMk77klReZQhiUl/nZgeu62EXOey/n47+Lcsxt7frE7me+EPU0w+ioOR+LC70PY0RgmrxshR2liJLgZZWjZRviWg/qhz6l27EWpNJJYyRnzLWi7gPPxzk3doobW4/HLTdFfS5dE88iZj+681qv8VoiHdFN71eIjTJCCBr6uB5hkLqhlx+7+tm1YO34t2Wa4vf79P3nr6leLP0kOaM4RJdOpxv367Ku6bsOUxidHv6Mb3NpT92Y1un82JrbTc9jny++yyfnLTYycOO1J4c2Gtwpysi92/8mI5xruZprNptrtVpqu71Wnmf68OGDvvziy1hTN4KyeC2v3/7tn+h4OqoLncpiBJNlocxnI8Md5EXziU55XiiE2La77zs9Pn5OoH+/P451JYO6blBRlKm7F+ldpMJgfKwxxaGgEJU0oMVioZeXF61WqwQgYAaLokipRzgyX331VXI2AFi73S4V0WJ4MThEba6urtT3cb4MNSsYMNoUczafnp6+02CATmMAeElpECXGGNCEcwX4wbkDaHZdp7/8l/+yPn36lNo248Cg7xhwSdSKiBj1Qxhn5pRwprquS0ND5/O51uu1drtdeg66W8U25eUZ+dV1cSArc3x4LtaGfbAZBaSHAbABiUTViG4xYBUZ4Rn57jzP0wBaUkkATKTySEqgjWuhJ2FaWe++j+2VKXYGjEhKjiK2hEYI6/VaDw8PCSDR7pz3vb6+arlcJifZzkMCiD8+Purdu3eaz+f65ptvtNlsdH19rc+fP585HbDHnB/0IMCT6CDRLMsoIwM4xt7HJgM4j9wreol9hrjIskhcAaiJCtKy29a4cDZwGgBs7MUwxKHE7IMUo684g8MwqJrN1HRtcqhxzL799ltJ0nZ9leSJP5umUd00yusiERrow8PhkPYUZ2O/3yeHm722ZBFnNsuys6YeOMNckwwBrmH1Mk41z06hvXMupahaXc65pIPgdrs9c9IgKmw6GlG2vmnPnIyUkqWpYN/WVyWsMzoirAu6FJwFgUCECHngZ+wbUSD0MM7/YrFIkRW+k2e1qZA4OpxVnpcGC9LUxRG5tveHvkB+0Rd2mC7yWYwOGVkHrDn3b4kC2/2NdYxz6cJZ3R12iHXiHlkPIlq8qNFiPZEj5BaSDmeH9UbOaOFu9xD5res66V7WE1vBdxHh/XVeP3gH6b/3+7+rf/LHP1fXScoyuUwaQq/94Si5aGy6ttPQdypGQzor4wTgeTUVK0YH5KT5vJJzXqfTUX0/qCwLhSFoPpey4M82LSlhxfobFEcqsHVeTRd0amr1XavYbrpV29UR3LaNhr5P6RIY5q7rlLkYpeBAAEZCCOraIc08kaZoU3Ax3a3teg3HY4wU9UOsfRkjCBoBaZaNQp5n0gjIsywfIyJewxC70cn58dlGpVxOIU8cyqgEKejsFcZ5Jn03pcrgEA3DoDIv0vqxXtIErnkfTDBOat/HKIN32ZnDgCIGHAxuOPvZWfpidj4kVqnc5fvn/sR/6yySMgyxDowfUYifolfBjT7F+Pfxrdwf17h8XUbbkAkqa+yLlDuuZ1/2vqdrICdeeX4exTpzasI0B8deKzni5jsm5e/U9518dr5uUw1QpjDE7mthiBHQCASL6ID6WGdUVmUqMp7NZrq9udV2u1GeF3r//p2qstLv/O7vjOC20nw+U1GUyotcXduO83ROqopC2dgZzCmCsI+fPirPvRbrhU517Kp1fNknwDqrKj3exxb8Hz58UJ5n2u0OI7s70+Gw13y+UJ5n2u9fR0NXq6oWyaDjJGB4um6aG4FhfR47YwL0MdqPj4+JQSa1xTmXGFrAIWkfz8/Pur29TSCOdQ8hpHSYLIuNHgCSgElIiJubm+Q0AIItuPvVr36l7XabZt5wD58+fUpghLk91KwAlDHgrAtRpbquU7ME0qT6vtf19XVKmXp+fj6L/PAdtvsa0TPABvcPOLbGm+Y5XdelhgMYaOdcAsOQU/x8u93q9fU1OaufPn1Kzq/tUAWjCcggDZPuZ09PTyktiHUALACA0HfPz8+pix9nEx2GbXh4eEjOMo4e98S+26gkMg7QoynEarVKYIZ7BUzTWAPQQgSNKAOOA893d3f3naL4u7u7M6eVNKXZbJYcKyKCnBdJyRki5Ybn4XuJitIOu+u65IhYh5W0PVI0uS/sC4x9bAefp5oRuh7O5/P0J444ZAagFF3IM7L+gFNSMYk4Oe/VhyE5lRAOzjlVI7CFMCWrJDW62Mb0XGSesyadz7PB8eI5bftqgL91ggDN6CpsCtgInWAdCuSZOVUAfwYt2w58AFqcZ/QPeop7ZNYO33k6naQRRx2PR11fX180Vphq+NC1Nl2NtEqeg/2iFomuZ8hY0zTJwbV6gDPDmeXarClrZjv3YS9xnHBisaGAefQZ7+N6DLS2kW/wAw4+jkvChWMDh2wkrWkggbNGBAldwNpDSkBoYbtJX+bc8172D+cQssBiBBtVZT/R28iqlT30ADqKjpCQipwv5AqC8XJ/iKD+uq8fvIP0k9/6oLe3t/rm8z6mmQ296vqoPKsiKCsz1eNskSIrVDe1QpZrMVvIeaeujcXB3vsoWCEWuLZqNJ9HByqM6Vl916cC1Lqup7aCboqSNG2rYTQcWZ5LIRZ65+OQ2aBePstU10epbZQ5nwAOgtS2rQ6vL2d5m5IB8bA4RgFaMO19PkZZYuEmB0AaklHDyy6KQs3ofTs3CSzGwme5apMHPgyDirGtehg6zWbFBOzaQUMfozzSFK5FUacoSTsNjrMA30ZnEPwJcE+pVCGc19SQpjZFIGxLZSnPvzuP5mw99d3ud6zXeGdnMmdT2axSuIwwOTc9U/xZlCPpomnCX/By3qc7wImJjR401vecrx+NK+ytJ2fQ3GMIYRpcS72WlJpLSDrbI2TU7mlUmqVia/rJqbMGJMu8vvjig/IsVzWLdTw/+tGPtFqtVFVRya7Wa11vt3FAp3daLVe6Gtv0Ho+wunM1daP94aCXl2eFoVeRe/Vdq75r9XKK0ZgheHV1qzz3KWXCe6+m7VS0RXIiYJOHYVAYpO32Rm3b69tvPirPC11trhQ7MebK81J5Xuj5eae+DyrLQlU1H2tAIrD++uuvU3rWdrtNKVw2zxynAYcAo4GRYdjjL37xiwQw7+/vU3oRwA6m19YFAXqIChwOhxSVsQ4NMlsURRpg2nWdnp6eUt3IMAz64osvzoAHQIu0M1JsJJ21ty6KQtvt9iyaQdeiEELqpEcNDLoUoMWQU+4L48zzk7ZDehFgB4YTgM3+0iabVEVb7A/wX61WaXYQxtrOHyI6xLnYbrfJoH/8+FG3t7dpb15eXs6MuaQUmXh6ekqk12azSUADnWCjduh1SQkMIR/UocCc2rbUeZ7rmgYeo+5mXWBh6cB2Op30r//1v9bv//7vn6UKkT6HvSNCcXt7m0AwgAsHerVa6dtvv1VZllqtVqlpA0NiAUTWzqGPcAxwRgFhMMI8nzTNlWHd6NCGviIShHN7c3OT0hph2vn+tm1VFlWKDgGkWR/uZb/fJ5BKuhhkDvcE2AWs4uBYwiRI8nmW0jbtZ+/u3mj3/JzknDWfzeLcRiKznHGcaxxafsb1iA7wGUAvTiPODudmtVolJwn90HVdqiHjPHEtvh/HASeSPeHszWazRIoQkbgsU8ApIf2ONK+2aVL3O9aSaPn66kp9mGqX6Nxp18Q6EOwXe2zT5bgH6ojYF9aHfZAmB9u+bMogL/Q0epD9AUfZdDIcEewypAmyyvUvs2pwyFLEfYizIvm+tm1Td0UcfOSX9WcfLTnGc2K/LGlsa0QvyR5bb0bUzxJIvDiPdv04v+g6CCVsHMQHHTYhOMAlrP2/TQTpB9+k4V/8sz/Rf/0P/qn+qz/8R2q6QY/Pn5XnTlWxlhRGY9qrKMYwZNuoKmIqydAPqV5mv9+ra1sNXasgqcjzsYvYoLKIc0jatlGeF2PaXkwtKstCwTmVVYw85UWe2Ieu65X5Ut7ncn5QlkmHw05Ne9Tj44NC08gNUz6zJA197CqnYXIOSJNAyIoqgpW2aeV8PNx0QgvBqyhQ2r3CMCgv8jFKNDJnY92TwpjPfDxG5iE5GlOOqM8LdUPsruaNkm+bRkPfKM+kjgiQKK6PkZMQbDRjAuXZOCMFxwdnAkaFl2Vb+Cx1NDHz67w1NmyJc44GeSlyZa9pD3s6Hk5RHpIjNzW4iBEmPzkbwxR9stlp1sm9fO7498kRs87ZeNUz+bbRodEnTj+XZH733eN9GQlDebG+lw4iEbQ4Q+s8WhbC5Gij8Cj+vLm50fPzk66vb/TTn/6W3r1/q+12o4eHR/3oRz/SbDYbC+P3WiznaW9I/6jKUmVV6uO332q1XquuT6ng/Gq91nF/0KyqlI+GU1IaxLxaXcn76IA9PT5qCNTAFKpmVQLYGIqmabTZbvT88pLYravNlWbVTM8vL6ryUq+7KcJimeiqynWqT0n58/y73U43Nzd6fHyUcy797nA46Pn5WdfX18rzXC8vL2muDkAB1nKz2eiXv/xliniwR3SV22w2ms1mqaifFBmMB6kxpOrBSuMkrNdr/exnP0v3wjNIGltvT8X8GG9AC5EJ8vWRO5wam56H4SXqtdlszkCxnQWDM2jTUgAxPCOy9/LykiJuRD5o6YzxBQTyeRxPokkwqYAg3k+bZcuao/u4HnN7AF4M6LTpkbvd7owYwTHo+163t7eJSAOE0yoXprNtYwrRbDZLUTSifewlgIn6teVyqfv7+++ALP6NEwZYARgShWKvSR+kFo6hqPasE4HHCYckIT3MOu+WRbddvp6fn7Xdbsc5VbGpALUypPhQL2FrmHhuvp8ICOcHZ4kIRN/3Wq1WopjdNj8B9LOOpP6s11eqT9NcFklJ1qy+JgoiKTnHNjoDmLWAlyYaMPZ5nmt/OqZ7tCRgkecauv47qYvDEDNGmm6amWOHpbIPnKXVanV2BkjfhGXHWcYxyPNc3377bToHNk2VCAaOPecHx957n1q4kyIH629tn62HwwGg3tDWneEYIkd1XWuzWqf7xpE6HA5yPnakI1Jk7TzyC/mMPkGWi6JI0WvWIO2DSQ3Gcbd1Wuge9CbgHZDunNPt7e3ZeyzZYc+HtdGkdrLWOKJEfHgGntOmYeMIDiPm4/5xqjgzyCfXtyQ85xAHDFtE9Jqzgz3FhpDSx30R/UE+0Bm2to2XdfbIULBpgOgxzip/shZlWcVyFVM7dzgc9Df+1t/8tZo0/OAdpH/1L/9Uv/p6p//9f/R/1PPLUU3Xqh86tXWrvp8iH7GuKKiuj+raUwTZ1J+EoK7rYzjXa8qhz4vYxc7FFpR5HgfpwYAoRN49hEyb7Y1Wm43m67VUZCoWM/UhKBu8cpfpdDrq+flpHLg4qO86hfpVzw+fVR9PyryTD0F9G++71zBGtkjNGNT3sUNdWeSaVZXarjtrBx2FeUjOUt/H5g0+G0PQp1pOLj1HlmXqR4aWDC6iD1meJWZ9dGPks1iQ3w99bD86tOrGhgnOm+YClOSPjgyOU3w5hRDrQpyLDR1iy+DYBCHzIRXjp8jF+P+hj7U+fR+79OVlObYCHZS7SfkCcoo8Krp+iJGSaABiHVJU4FLft2eh9Hj4gvq+G1tyT6/kWPhMXrmCgoZ+SM6S914+fH/6HM/uTIMKugjGRhlDijjx+bMUtXHALwomhfFDkM+mIX84QlG+h3HmVmxokOX52AY+tosv8lzFmEK6Xq+1WC50e7OVz6TFfKFqVikMQT/57Z+ktsnL5VKzaqZqFqee03FtfbXWqT6Zrj8naazpyczcH85jGGLb9d3rq0II2l5vtXuJc33u7+/jwMjFQq+vr8m41vVJq9VaXdupPp0igzjKsSRVZexsRM45BkBSmrlwOp1UN41mVaXFcnnWUel0OEXyYEwBzceWuCEMen19TZPrmZFjI2k2neDjx4+xI9sIKLquU8lckdGQzUfmFeMDYH58fDwDGQApwBXNEySleS38Hofp+vo6GXrn3Nn8oM1mI5oKEMGwgIi1kCaQwbNxPQt8rczZhgaACmp4yOfHgXDOpS5hyAzPBfsoTfNUACG2ThNgjkxh7AHRgK7Pnz8nwMmfgBaY5/l8rtfX15QOFMLUPObl5UXL5TLdJ+lpAG7W2tYUkJ6F0SeyaDv12XMuRZD/8PCQIjasN6DARlVsobut+bKpVzjSgKfIJq/U1O0YqXqUc15VVY5y9aqiLFIUEMctfjbW5VVldG7rpsYwRAd0dGjKspL3YwODPNd6BOh9T5p1r+VyJeek5+eX5Lgfjwc1TatiJC8Xy+VIcMU6YuTySDF822o2m8uNJaZlGUEz6Wiz2UwFIHk8M+v11aize/XDoP3rq1brOIDYzr2xHSNZC2QM0IxTimNk602GYUi5B33fabFcKs8yHY5HhSHoar2KzQWGQX506Lfbjfq+1+F4UpZnCXTOxnk3zkm5z9QPvYo8yoZGsumbb75RH4aUdsqZtemVPB+RBqK9sPFtO9VF4QSd1UCNHRWJsOAYIpvIKUDfploiu6yXBbik+n7+/Dk5cLZpx2w20wBWGWcm5kUhJ+kwpjPnea67u7uUymrJQKJGRMJxwCAIuA/0GOtF8xCipdgPHFabYUGzFSLl3DfPzznE1lhHwRJDOASQFehLZI/vZX9Za6LvVVWp73pljvppnwgEotOW7LBRGSJIRHitjrcztHgeapqIYEOQYG+t7kpk++gkxejtVPbAOamqmer6JO+npl3YSEoKnPOjPsgVU/tNO/c+1qc3Ta2/+bd/PQfpB59i13a1qipT7gc19UFN12sIg+rT1NqQwxpC0PGwV9cczowOB8R7r67p1LVjq9Fm6vMvOTVNBKn16ZCMr3NeZTHT7iXWvmTzKrYD73q5PFOWxzlBLs+1vNpoebUZc+9fVfet5HPVXa8sBPkQFIboqHVhUB+CpE6nuh5rXiLQ7LpW++NRU0eykLzu1FVNpFU5hXqswxlGIHGqx4MX/ytns7ENQBxYy4GVpD4EhRBrgFiLeVHKOSlrTvJEKMLIQo8RuThGaapfGcaoDP/3Xsqz2I49vif+O37vpAySspPTEHoFhXESdnRKiiKPzRBCbGMenJT5TE5D7ACXO+VhbE7gIzCvxg5lsTDfjQ02grwbla/3CqFX2zWpwYMdaJv7ImKDILksdvDLYEWo3VKsE9LYUAMZjN8bD37upuHAIUTHjPocom/IWNzQKdXNudgUpBhbVfuxFiimIjoVRa67u1vd3FyrLCtVVam7N2/0utvpxz/+LV2NKTCRWeo1n8d5Atc3W7VtHWuKxrxq8r4j27oeU7kGffPNfgRlTk9PDyqryObEWpvHyTiNjkFZjFGPw34qXh8ioH16fB4V81G/9Vs/joZmCJLzqmbzlMYTjepOQ5hSZFDuxzFliGJtOqbBCkOqAN5Pp5NWy6WGYUhOT+XLpA/6vtfbt2/0+PiYIgHUQticc3QH0SBa30rScrXSn/3Zn6nrOv3u7/6uHh8fdXd3p9MIxPf7uIaPj4/68Y9/nPQUDh4MILVNy/F+SXeixga2FHniea6vrxPDenNzk9oQM5tnu91KmsYISDpzZK+urnR/f58iCxTrW2dFUkrdaZomDTYlbWe73Z51eWJeEd21Pn78eFavhQMgKT0TUToiAbZ21AIJ9u7x8TFFinBycN5Il8GpAvj0fa/tdnsWdSTVygII1qaqqtS1brvdJgeF/SEVyNZZEBmBwSZdDqBHWqYtvLa1QNbZQU6YS8TPt9ttYun5Oeu6f421dJE5J2Lp1bbRiY9z16ahllPtaNDpeFIYpkGonL/FqDtIRXPO6c2bt7q/v9d+f0jd5Z6enkawGNccR/75+TmlZ8XvLeTHdO9hGHR9fZPSp+az+fjcE2nR952en49j7eJ8cj6DNKtm41rYQZJObdOqKErtX/fJ6SdTg8gdOvsy6wAyA8ICucNh3u/38kV0BpjPY9uQO+fVj3vSd502V1eR9KlrDWFQmU2ty1OXt75XmRep8QeRv8eHh9jBb2wBzn2zBpBCRLZxfMFFpMiGENLwYc7UlJo/Nawigo1OssQJEYtLbGXXDrnifHEmOcOcA5qyaEQe+zEKgZxACLD+RGPQUc655LhwZi0hgeNr6/cgaKhn4ywReeYMcT0pRmhpAoIOYvSBc+5MXycMapqs4IxipyCs0H+QHTgLfI9trIAd996rnBfSEFJGknXEiMjh5BO9Yd1x1th/Sd+pP8JxlpSiTugkUkJtfaIdSjxlJp03pIp7mKeIkI1q0ZHSOnNRzhgeHM4+ZwnTX+f1g3eQvv7V15Jm+q3feq9/9ac/U1AEuD7L1IyMAQseDXr7nWGYHOT4u9g6O4Q4fV6S5ouFBjpW5UWcGzTO8un7Tk13VH9odOoPqtXq/Y9+ovl8Jvk8gmiMPhOzxwN5UK/6dFJwmQ4vLzGqFAZJ/jvpUNPLq1eMsuTlLM2s8dk0j6YoS+VZHECWZ1lK3gp9jEaMPcRGgQ1x1liIjQdsvZMUq3D6EJKylaYwb5EX8mFqKtC6OJ+p7Vo5Dcrz+F1Eq8IQUxrD2PTNZ5mKfHISnPfq2kxB48DOEOTcEAeAOikrYjRsCEHq+xglG8ZBuyMh4RSkwcm7TAp+/M54HRfG6FnbJUOZ+VLDmLc7Kys5pzHC5OVdrW6sl3JZSPU+mc/k3Hm7cD86XzSIyLJsnMEV5woNPjayyLM8RXWmeqjRWXWxQUJeFBr6XvNUO1JpuYwg6ebmVtfXW2U+tqleLaMRePf27RRuLwplmdf19UZ9z0DGqMAen55UFYVWywgYXl5iZ7CqLLV7fdXz88MZG20ZJ1v0jIGBPZdzakeQ632cz0InrL7rtBrrTh4fHyVNc79QkoAN2FoUJ+lWAM+2jQNGN5tNMq4wXUQCbB44ypX7lpRYfWourDHACMEYDsOQmFNANi2Wu65L9T1d1+nx8TGxafP5XIfRuby5uUmAcj6fqx2jKlIEHu/evVNd14nBhYEFvBB1gE17//59YjIBmDbNxHuvN2/enKUyAARIdeO7LwkigI+kBESYZUNKEywvLDcGkb3n+47Ho15fX7Xb7ZJxI3pDu2gcCMD4fD7XbrfTy8uLrq6u0vfBcgLgYFJfXl4S6LIsJ86BpBTpombn9vZWu90upQ4SMXp8fEz76ZxLTilROgy+rfnEObW593Rtw2lA1ngPMvfhwwcdj0c9PDzo5uYmrYUFZl999VXqiGcZ2MfHx+QsIuN8N99BdMZ7n+ZHSU4aU8/oDoczXRR5YqUBS6z30+Oj7u7enNW38D6KqrGxRC1Zc2SXFurYD2rCbBSQCJAFVc/Pz6nt+7fffpucdBqbAOiQZ863rVHCCUavAdxwggDVrCP4gHPFmbRRANL/cAz4zGKxkMsydX18DoC+TaNsRwcaPbJarXQ4HpXlWQLofDfdydCDkA38u+sisYF+uDyT2BocJKK99p5xDO3zD8OQ9DxRD4AwAB4HBCKB70ano4OpKwL0SrGOj0gX+4FTaJuyhBBSWi2RZjuegL3FsaX26Fe/+lVy4CBj0Hc03kDubPT6EgPZZhg4IpwPW5/D/qKPWWf0ArrGppBh/3AsbFMCWy/N3733qSaUtQkhJJko8lxN25zJKdfmZ5xpfo8zYwlp1ooINeSLjQYim+hn7tk6xkQfkTvmc3XddH3OO/qD66Nj+B06FB3CubQpw+CDX/f1g3eQlou5bm4/6N/5q/8j/b0//Afa7Xdqu0Ft0yXhYvFgCrIypsfhlU551RHokd5Q183EymdB1SwKX9e1ognCIMkNnZr6KN83qpYLdXWt6rqUsnJMSesUDCvYD4O6MKiYL3Tz5r1u3wQ9Pzzo87ffyDupyLIUfen7WASPgvbeqxu6sX30MLbVjlGM3Aqnjy25s9FR8s6NnjtRqF5Z5hM7Ffp+dD7GhR3rk9q+V0gh2GEEkbnC2FJ8GOtqgoKGvFBVzaS2iY0rxsPki1L92N7ZhTijifsILs6HCv04JXmxVl5QHD1TkRdjGmOIn81zOS/leaanp0cNQ6/meNDr5086jDUP6ZCH2FhjfJxx7eIDRmNZyuWFQs8wvli3Rbe/IqtUZFPuLqy5G9c6KqcIOvjOPBujX4qOW+a8vBu73fig1Wo5Ojo3Wi6XqZPYfD5TXuT6nZ/+dEwhHJRnucqqUFnmKRd8GPpRSceI0e71Ra+7nW7v7uRdnMEzjBGbp+f7saajVF44ve6f5dygx8dPauqFdruXUbn36rpSWeZSiB32n7VieCjhd1jJtCajbBdFoevr6wRe3r59q3rsRgSbTz0OraK9jwW5+/0+dTCzoBxD9PDwoBCC1quVXsa0ENK3GMYKYwf44l5w5IqiSIMOmVVjwT9Gkee8ubnRL37xC5VlmYpdbQoKz49zend3pyzL9PXXXysfARrFsYCn6KRPRup4PKaBqNTvnE4nffnllyldDOBPuhz/3m63Z0WxdMeLuqNPz0zhPPUHtm6CMxNCzIvP8zg7hHQ+G83leWezWWpeYB0K2E0MIR3uAHTcK0AYg/c8FqfzHKQLElFBf8PWAqyJFDGzA6MKwIIBBYDhUD89PaX1xzG+vb1NDRdI1+G+bToh8vJ2JCVYN+twkpJHHc3rmEpaFHHYZlmW+vz5s+7u7iQpgR7WmAYJtsbLe5/qum5ubhLwRPew35bFBUCSAliNERUIA56laRoFDSqKKXKAgxRCSA4+EU3OCP+29R7UauEc2RRAgA61HDixpG4RxQE0Amb3+31KRQU4W9lAtgBoREwsOCTNyXYGw/GCYMEJJlppa43sM7Mvkqb1G/UCGRXDEAcq4+ijl8osT/oOGYypWZ1OTZ0ceZ6Ttvx90yZ5IvWJtKqTiVguFoukS3C4U9rhiHkgTHCu0ZusO/dlibI8z7XdbrXf79M54lzZKIdNGeOaOKvoI9Ycx4n32JRiziiyyHmyKWAAfBu9pQaLayFfOKSAa84BUT90NBETmlQQPUm2zk0dLtGNFrTTNRMHjGtcko72uXkfESGb5ovzYjsjcn45/9aZIJvB1kDZuiAbtWIdqZ2yKaNWt1DHZskE9sY6VjwD2Js/eU987ohvbe0Uzp6tl+KebB0ZpCYyzJ6zRosxLf/Xff3gHSQnqamPulrP9T/8H/z39Z//F/8Pdb0b05ymWg0W3znF3F/vpwLIEfgNIajrB3VDo8OJRgzjprrY+hpB8NnUuUPOa+jiINPj4aSvv/paq6trbTZz+SJXcNME8rwcp0QvF9IwqK0b1ceD5DJleanT4aAyzxWGOIsJ5RwbE8R5TnlWxNqd0YmSzzR0fXRCskJhiI0eXJZpGLudDUGSj80LgvdS1muQU1ZWcn2voWujQzH0yTAfmwiksjxTcF5BTnk5l/dOeV7EjK8sdikjEpKNbbSdHzuSVKWqslI+Gt7j4agw5JrPFinvfwgh1o9Imi2r+GzjDB4O9NAHta1TnhVyPsQI1amVFJXj8fGT5rMRoCgTtURVGWuFIiicyAXvnZzGEG+RjfvTKjaBaNW1MW0tggGUIINye2U+pgsuFqupK9diobLINJ9Xevfuvb788guFIC0Wc/32b/+2dq8vYzQgT58p8kKH4yGBFeeirG22mzRw7enpQYf9LoGMoe9SkXM1r9R2jYah09PLi6SYIvDu3Tu5UwQPdVObdAgiTLEYPrbTnen1NbZrjYXNU5QA1gcFSSQJ5V8UMe1jN9ZlABYxWs/Pz3pze3tWkEqrZ9vq+o//+I91d3eX8r+LokhpObvdTm/fvk1AYb5Y6GHMWafj1ldffZXmb1RVrI/6+c9/noAUba6ZD7HZbNJ8HECupARMYL4eHh4SIKITGu1pMahTjVSdHIqrqyt1fa+mbVNKWlmWurq60ul00vb6Wpurq8S2Mkj0zZs3CVADIDDUgB/LfJPeRSc42FwML449s5VsVyOcYZs6BNiDUcX4WJaeCMVut0sAHJno+14PDw+pHglDyqwm6zTRYtqm88AWf/z48Yy9twDMdsuyoJBojk1JtAXUgMS6rlPUTlJinEMIev/+fYq04ZjSrQ3Qx0BVG1kgooXcwLADeJjfQbocDoFl9en0B0hBtiQlx9WCTuSPOhSah8Dopu6NY+RkPp+PHdvaFLG09XROLqVssmY8x3y2UNt2KTpjgTROBXUY7AckBylApN8RHcGpjgRRdIJoyMHnrMNDd0AbMeMFmMfOWvBq2XiiBTwzjjiyTsSa6CLXsfVuyCzpb+hBKYLcLM+1f92pms309u3bBPh3u13UJXmRcIWVq/X6Sv0wqKyivmEthmHQYNKaONN8rihioyhYd+6XrnQ2hdOmiBHtJoXU1uYhQ6SEIo/U8aA/IMpoiDGfz1MqF7qD82qL/Fljzh1Rg+fn5yTbfB4HF11DVMbKAc/E+ceZQv+gm9CLnFUaL9iMIqIlONeQX5BBEBOWLEIOWH/2hvPC2APGJViiAJtKgwYiaURveAZkE3mzQ3qliLdmVSU36iwcCH4PiUiDDGQPu4EjYp0osDP3wj7YKCF2xNaXWb1tu+dFHTa1iZeUmtJwBpEvZBCZ5U8cOVLCkfeUhuh//QjSD75Jw//nv/1vNF8s5bJKf/T//O/0v/7f/B297htl+TwtNiCgbVt5F9R3dUon4D1ZlqntOvUhOhHe+dT6OISgIi9TWlZZxjS+oR90PB40n8/GznClsqLS4Ly2N3f68OWXqlYruTwWGHoXK32aJg77y3yhrmk1dJ2a40FPjw867ffyUqxJ8mY+zRhxGYagMA5hjUoyFt7TTSAedmpzhtQ8IMti3Q6HJfNTa9iu72JK2RhRulpfxeL9EJsANPVpPAAUq46efNtqUOxu57xXPoLdYRjU1kwCPyofGYrj4SA5p9eXV1VFedb9ZLlcqus7nU6vOh6nzlLOxXbmMSJYjHNtvE6HV52OO3knDV2jbGwWQTe+fuhVlZWcdxqGblQGU251UZSKc7LGNrpFOaayrVQ3tfKs0G63l8+icp3P5iNDMddyOdft3Y22263evHmjN3dv5LzT/nWvIo9jXbuu02xOmg/FhGUCl1OaUCvJqRi//+Vlp6apR0MzE8WJy+Uysee2zXFWZimUDdsa0/syDUPcpzhnK1PT1GPNQ6nTYT8atVZ5Pk2iPp1qLRbLszSah4eHxJhmWZaYeYC1c07LcXYISo3v3Ww2ypzT119/nZxCDE1ZljqNIGM2m+n+/l4fPnxIhbF3d3fa7XbJ4YIdur291WEcVGmBowVP1LTAONFR7+XlJYG/tm1TRz0YevQFhfm0aiY1ZLVaJQMH8Lq+vj5L1WOtumFIBnsY/w7ork8nffvNN/riiy+SDrKsKOxpCOGs/gcAzjpLSg6RZXJJW8MAW4a77/vUgQ19QMSQ9AuGheJkAPKci3n+GCwcZBxo8v77vtfj42Pq+CZNjCkpWTCF1P0QdYQBZwYN6XwvIwFAmglGmbXAOEtKrbwxqKRA2ogBQ2N5RpwTzijOO+cAncRe4yQABgC10jTnxAJVfsbfAUGcFwAZ4I06OZjWw+GQ2sOz74AvagNZU8AK+yRFIDGfL+Rdls6UbVbhvVPbTek5D2N9SwSIpU7HU0pjRSbQZZwdgK2VG5w0GHCIDewVqUXW2SDCRBohL5s2Cmi3qZ/WoQd4kZLF93GP6CLq/SQlmWefuV6K/I4phfYsAzqt49CHoPlifhaZCiE2hKqKqcMgz9M0jXyWqQ+TM4bzH8aMgNB/t7FAtDNzDZrOtq2TI/KDQwWQ5NmY00R0iee1HeOso8MZttFvnDYi6ayFbf5iMZiNXlvnBzIB3U36FanH1vm3+8ce2nRB9C3PDrnGPREd57lxRm2KFzqNsw8JwO82m036Hr4b3WLTna0eYg+QfyK4rAkkJfrEnmHW3q6VbRhSlqWKLI+p/aNN4b04YVwXshBnFhuMPsAZxqahP3CYGCpMkAF5YP3RuayHlXcyiKxscsZsOiNyjk2XppR8SYm8wVFiv0/1SX/w7/313zRpkKT962vsNpb3+tEX73VzvdVu93Us3HeXNUadpKA8G2eiFIXqplGW5SqyTLfbG7lsWjLLBoQQB4KGITYJYMNiOpVT5nNpBEV9kIa218evv1axWqlcLhKbKsXam34Yc13dIOeDsqLS+mqrIi81dJ18GNQmb302CkkuOakN40HPvfoQVI0hXQWpnAXNZzP5zGtWzdIA2yzL1TRtbMSQF2edT7q+03w5T6zCy8uLjqMh6ZtaXrEAvm5q9V2fDrcbpLZuRFOCaJgic9k3TezJn5zQflKaapTn00HyzuteQZn3cmPnszzL5IvYNaksS2Xr2BXMO+n9uzdq64OqMtPmaoxCVAvJxWF7s9lMd3d3KqtKV1frGH0LMb1tGIaxc5xXkWdy2dTiGOciDs7MVeSl2pYuMKQoBjVtLVq9395s1fVH+eD1/PxJm6uV8syrbk6Si7U3p9NJ1Twycd55LecxzWV/OKQaMe+CmtNRToOKLIsNQoZhrCcr1LW9vMvknddiHh0Dp6DMx3qj/etR79691eF41NCHsc6tUtcNqqpFGqpZ17VeXw96eXoaJ9vf6he/+IXW67Vms7mKokoAgjST5+dn/ehHP0rg+DCyqzZ94zQaSpoHMC19s9moaxr95Cc/0cePH1P6FsDCOae7u7uzVIPNZpPYZxhhImZ0XuN8Eu3h8y8vL8qyWO9AET3PAxhFMWP8N5uN6rpObbFJDURnvHv3Lp11mC8YxCgbPjko1sA3JsUBQIIRapsmDYYkXcV7n5j21WqVgB/XhbEjIgSIIIWMdZiaWTQpCgXTCftJtAHnyRoxGENJCayTUgIoxKBnWRxye3V1NcrWawIBgGuMJY6GpLQ/j4+PyVlDpoiu2BRoW4uG/gGYAIjozMU64aCQDo3cMfuL54cNRYdJSoAD3YDx5b3Wmeb7IAwAOi8vL+N+1Irz+eoUKYzd+yr1fZf0swV5pLeu12vlea7X19cYlWy72MCgn1hbmF8iuKw3wGc+n8fPZV5d2+np6f5s1g5piMvVIgF2QCXnDKeJ4azo9dlsliKeVn82zTQIdL1ep8iZddCJauFASRMgsk7j6XTSer3W8/NzihTCOOd5nuqxcE7tOeE7ccBPp1NKa+S5AJ+cQ4AcNoHIq+2ChvwQnQNkI+NFnqWoOe9v2zam9o8jE2wNSIomyGm9Xqfztdvt4r30vdbLVSKEqqoy7eVDitxboA5gBcT2fZ86X6LDeD8YCbCKQ3lZ10Nkgue0KaqcC84mtScQC+wr8pXn+dkAXwvGiZR779NMLc6jJWesw2UdRKJ7dm9x8NDFRPb5bgvCqb1EDlkfnB+yDmwEB/Kj7/sUhUWuIaOxZRBOpFpCtFgSSZoik9wHzhMvGoYkfDk2LWBtOLecB3QZ8mFlHbzMs3BmLekwpc9N9fHIsCWs7P1b8i6ub6cQXCJzsF3oWl7WJiFTOLe2tsvKPNkuv+7rBx9B+nv/9f9ds/lC3hc67Fv9x/+H/0T/73/0j9UNsZ5mGIKC8/JZPjokTr6IraKzbDKg+dha07swdkwb1Hexa1rXdZpVc+XZCLC6QXleji2wner6pPlypr7rU8tlSZovl6o1qFgs9MUXX6goch1oGxqk5thpVs51f/9JTX1SWRYqilwvuxcNPtahLFfLMUI1ApeiUC6fijbrsYCzburYya2Pgrt7eVFZxe5lT49POuz3ymjG4JzyPNPhuNcwdBr6IA1jgVw/6FSf0hpo6FSOzkyWR4Ae62mcqtwr87FTEEZuNp9puVgoDL2qWan5fJ6YgfUqDqmsZl6bq5XmoxLcbDbKvNd8sVBZTB1xQgi6vb2VNLaGVhhnUjntX19FR7/Veq2X3atms1jgnXkvNzKI8fDEuU3e5CtPLNTYwWk8dFEhBbVtBKbr9Vq73S4pRoxA28Yapvl8PkZYcoUQD//xeBjvX2N0LToBh8NJ3keHMA7+K3Q4xDk0h/1eVSpojvfUj+ka1FNE0LnSbveitmm1XE1KuG4a3d7cqG6asS19vL+6bjSbTfMvIoicwusof8BqWZYpP9129prNZnrZ7fQ6srGAh3ZU7GHcI5hVSQlwAPqJYJCCVBSFZuU0u6oZ2SsYzdl8ro8fPyYZwVAvFgu9jG1YLcuHMufvAITHx8dU71VUVUpNmI2h/6IsY7fCMHXwIYJDPrMtSoaFxiHDCADkbBceC8IwsPv9fnTQ89SUAEYQsMAcJIAHho36CRw9jM9lG1kbGeJ9NtJjawQwQKw9TgD3zbPzd4gUjBmAwg6htUXyvAcHk1QcO08Eh63rusRKkxZDdI3hrOT5UztaVVVKhQS8Rzk/KfOZsnFAcdtEMNX1sc364XAc92WQc4wVCKqqUrvXXXL6rANmIzKw0VJ0npt6LMaezdKMvVk1U993Wq3W0XnMM7XN5NDOZpX6PqZV7V/3CdAVRa6u68/qF7puGqzqnE9AJLbVbtS2kxOz3x+0WMxV1400kmIw6IAi2GJ0YggxM8E5l9K7qVe1dV3WqVgsFmNqsDvrkgWgpPZJmua/2KJqdLSt9QDAIvOkH7He9kxxDixhCWB/fn5ORAPACaebM4YuQl5tnYiNEOK0WAfPFr4ji0RhhyHoeIzOCNkMXR/ta57HaD4gH5CHoxTGofTOOXV9H229c0k/oSNoZjCbx5Ei1FfxTLYWjfW2NR/oDdYPgIpDY9PySEe1ESxrp8kCwWGhBg0SQYr1k6wxOtJ7n+wN8gx+wiYDwJEPG81Hz3Iu0UWWcEG2Ikm6SoO3pSnSTDQSPUwEHeeAqB7XQ6dZBxn9xhpBquAk4RRiH3G8bfqhpNSUxBL76GDsq03jYx8i+SxlzidC8f7+PjUVWq3WKstKTVPrdKrPnK84kmOKlONkxsjdzNS9hpHM6cZMpeysTo/OqvH8TmMayrIYMfd0z/wdGWTvLSZB/yJz/HzSg+e6ADLy3/sPfjMHSZvNRv/kn/zjMeXFS6HUz/7kl/rf/u/+Iz3vO/WDxqhOr/liLp/lsevbMOhwHNkWxRoNpwi2qd9BMIuiUFPX6jXIFZm8y1SWlbIs1zBI9alRXmYqqyKyes6rms10Oh51c3Or9fWdytlCq+VKzsPExTxMnxUKcqOw9ZrNKp3qsX1oEYvomraJTo2Xuq5V1zZq9rH4tG1atV2buscMXafuuE+DwqyyCX0v9XHOREync8oyN+ZEew1dq+1moy+//DKxp1mW6eZmI6nXYhEdtevttTYjUOnbRnfXN2raJqUBbTdbtWNXnWpWqm3as9z6+Xymh4fPatpY6/GrX/1Kb968SUCQw8Ykd2oCUJTUBNgUjbaN3/H27dvUMcjmaJ9Op8Tcfvvtt7E+x7kzxs9OEUfRotybptHDw4PevXunvo8F1HwX75WmA0y6EmkspJaRjgLrSqqSZcvZM2peALTcD/NXAJpt26acZSI+KF0UOAqbgX6klAE6aHQAOHDO6dtvvz3LeV4ul2N+fJXC76TFAW7ZB64BeAC4zmZx4KlNd1iMNUfv37/Xz3/+81SDNF8s9Du/+7t6fn5O6TJPT096eXnRhw8f1I1RnuSYmwLky/Qk6h36YVA3fi8Oy/F4jLU0TaNgGHGcxTzP9enTJ7158yYxsfyc77LNCkhdAPyxDjc3N2rbNnUsI6+fgtxhGFLdEbrH7hdOD99PSkiMdmYpwoTMAyowVvwe9pKapaqqUlocbDcOKw4VKYUWRAAEKDS3KTc07wBEYcCowwAUAQjYD7o84TDCbPIsgEicbhws9oJaixRxPMR6OsBGXdfJgeW8AjZ55qhXgoKGVKMAO0p3MRtV4MzE94WxfXRx5igAZImYWKaa9FCelecljYuILO+1KUAPDw/JaSFyCfjkGW3uP2w/LDp6BWBB1MieX3QbUS8iCcgKtVXoJNKmsT82Ekn6VQhxADBEhk29kqbIDnrDzlhBlmjCIikNHOWs8yzWAZCmTpykFjHPCiKHiCI2k7MAIcI1OAMWWpGSOXX8mggBe93owMb7toNokUe+C3nHySBVz6bPkY6V5bl8PqUXssdEQLg2Mma/z6aAcV5tXQ7DVAHoEDA4O6RGUbvJsOHHx8ck29h+Pk8zFxvt5X6wX0Q9kB1LbKLzkSf7zFYfhxCSTrXfj1OO7sRJRadd6m/2nb1FPokqkm4GuWZT69A71uGUlOw6OpVoDVEdznsIsWU3OpUsDtY9yQCRSO9VjvMfpakza5T1tebzRcIONpoXx3OcDxJH5iSfmjrYdMqYAjtFgZDLiUiehidzPUgOu7forunsTDWD1pmWpqHIrDNRTb7/dDrpcDjob/0v/me/cZA2m43+5E/+5QiAMoUh19e/utff+Tv/sf75n36rbhyS6rI4KHa2qDR0vfLgVJ/irBdAcpZl6odBxzFMj3DDmPWuVzYrJI3zG2LXg3hIFzMtFrNRmY9tYuki5yq97mKBMoeZuofOSd3Qq20bnU5HHQ6vOhxeY9i2j40XjqeDhqFX37eSgvquU+6koZ/CjafTKTaNCL0K3ygvct3c3OjtmzdyLk51rspSeea0Xq30o9/6cuyAJ33xxXsd968a+ph6YBmXzXYT61fyCMAenx419FMB4aJaqmti97vjIQLN9dVVbDXZtIpTjqfUksgM9nIuDsHlIK3Xa33+/DkBH8sISTprdwoQK4pCDw8PCRwBVqwBsOkCIcR2yLbzEUqWQ2zbRMM27vd73d7eJsO6WCz0y1/+MhUeotAwJKQV2Vzj3W6XQPpms0kg8dOnT7q5udFisdBXX301Mr+RGaOJADUu+/0+pRphoFHWpEMQpbHsOuCTdDQLjrMsSzUesDG23gMFfnNzE2eVjKk0d3d3qfYB4Hxzc5McBtIwAN+k0pEWQ6Ska1s1YxoGYAinbTab6WrsmERBNcxyCEEvT08JDOHUMEeC50IGpKg4P33+LI3GCKMahzRudToetR6dT9bDMs2WzcNxx8khZQXlzrNauWPfqqrSy8tLcnQB8zDVwxBTwe7v79PZwEAgO3Vdp5lGV1dXKooiDTaUlJjSYRjSjBkLlGznvsti91SjaFJncO5tSgtgnnQ8WzuDLA3DkMDnzc2NPn36lEAVzj9gAplmHTF+gHj+s86YTbngM/wXgWVQ20wsLnqEdccJBYzgAA9Dr6KcUuZYD4w4ck9KGY5bfWrGbIT8rAYHMJ3neTqbrLvN47e6C/vDmvA90lSUjGzRqAQg+/HjR7Vtq5ubm/RcACiIIZs2CLBj/zlrpJGytwBLAJSkdFZgrG26C/eIPBDVAJBLSsXfOASsC/tF5IK9555t/Q6fBQjaonnIJ8grvhOdgHxZZ8juKbJpHUDk1uoFwOz0fe5M93Ivcc2mzlt8J9E1nDppSvG3Tjpn+CxtzXsFN9W+sN/IDhEMHFK+73Q6naVgoQ/QRaS/sW+kdwFSbUMO7DXOBffHntmUQmwPDjjfiS2jMQVOFGSiHamAPsU+QFiQQmj3D7kl8oIDQuQKmUKXICO2Fgp5Zg6ejQTR2Y8IP+tEJIoumzhfRMJ3u53W6/XZeWGdeH7rWINPeHGG0UUhBGUuZiZwRm262zDEeUGXEcuoD9t0ziEqIFyen1+0WEzdUa2cVVW0F7Z5y3QuzzvY2XPPMyPD/M7qPZ4RWbF60EaoORNWBv7D/9V/8JsaJIl2zbny3CvLneR6yfc61rWenl80X85ja+6+k7wUwhDTtJxXURaab9daLJbK80ynutaHzUZSHCwXa3nirJ6iLDV0AIeTQoiHqG5qSYMOh71Ox1ZDHw//breLHcqedwrdIOfi5Oeu7ZTncf7Qfr9T2zbq+lbeOXV9bCueead5VWi5mOuLm5VCyLTZ3On29lpNU2texlax19trXd9ca1ZVms3nyjKn3E+pC947zapZbI0tSWFQ20ZjXJSZTqejirxTnw/a3LxRMQ5QdWFQ19R6fniIndx8puPhqL7tddgftN1u1HadjqeDMue138Vn3mzX8l4qSq8Q8jENrdBstlbft/JesabHDWrGYbwYHBQ3w//W63ViGemWhQPFwEFJyQBtNps4c2cECURbMJQABsLhKNa+jzUORCOYv4KCBxSjtD9//pwUB0XTRVEk5/Lx8TFFCoh+McPBe6/7+/t0fzjNu90uzYU5nU56+/ZtisRsNpuUokJ4mXuyhp40EOp2cMJpqCDpbK1ZGwwjzg0AwRp/jAR1Rk9PT2nI48vLS1L4GA3rcAH4Qwh69+6dmqZJxa3DMKgbIyRd1511QGrbNtVw0N0Kp+Z4PGplOgHtdrv0HhQs+4syTdE3k4aR57nevXsXAeZyObbCn9K52rZNbN3V1VVqVnF7e5siW4Duvp86Z61WqzTvSZLevHmT8v1fXl4Se2hThNgXoiw2hQ0AhtNL6hcR1Y8fP6ZBrdQoEEWwoA7WDVAAA8m9bzabZARJx2StLOiBNbWtvNlr/iOawwsyg2gU+817LMiB8U2Ox8gYsmb8nOvR+QrHyfuxHbbzms9jlM0CWQyxNfSkdZ5OJ5VVkcAkZIakKY1lNO6w5QCNPD9P8ZTOaxtgV0nPtE4Rz0b6CNE91h39YaNoklIraNYNsoI6LkkpmkyaEDKAA4IjCFANISTAQ+Qa/YHjQjQH58iSF+gCSCIL9khd4pmx4YB+G423joAFWZwDG+mxES1b02BBF2DQe59qpvg8Tqp1LLgH9KPVowBP+xlkMK7BxHpbHRvvaUjpf/Z6fJ71u76+TjKCA8dZQOa8j0PEW5MxAUHQNFM3St6LM0n0A+eItaLOCifeOgJcxzqOyC2RQ0jC1WqVokwWcPOMkGcQbS8vL6lWDVyQZvsURUoT49zadEsigjhgRGRItWNPqFHL82kmHnKPY2AdeM4aEW2ceUsio1sB5hAwXBsnhmu+efMmnaftdpvkmnMaQqwlI0uBqDVn8vn5OdleSF5kuKoquXA+W86mQMZU+2leG7V58TnbtKbWAcEugT/Q3TjOcR4kM0Y74zi3yvMiES+QtVaP2Ai+zU6wpBjnDP3PtexZQ4/T/MXK61/0+sFHkP7RP/pH2mxWKspSXTvo5fmg/9N/8p/q//vPfqGuC1pvN1oslmq7Tm3fqagqZVUhn/lUH/P6+iqF2HVGmfS6e03Aqx+icxNOQS8fnzWE6Ay9vu5GIxcN3uPT4xTmJM3Fe7muVu7jIaStsnOxmcDb7Vab1VLVrNJ8PtNyuVCWeX344r1W25kG9VosYrpeXsYubN45zUfDmflsVFTM73HquiExQNvtVrE5Q6PT8agiz3U6HbVaLdX1rbqOmoVC9WnKDw0hTk0fhkE+y3RsYpG2QtDzy4uGvtdiuVTmg/p+GtSHlx+BTq8iL5UXufIsU9PGtr/1qdbL7jmlhWCEpXO2AEV+dXWV6jw48J8+fVJd1/ryyy+T4rHGGJYoy7IUnbKtNa2i+/TpU1K8RAOkqfhRmgxc18XBlDDypDnRNpk0Q9aB1BMiSADqyMo8JzCFQQDAYAAAFjZVkCndzCXCmFkjjNEjl9lGX66vr88MFwqdv/N5FI812C+7ne7GVDNbp8RMFhQcKUgoV8AyzGs11gEdDgfNqirOizIAIjUTqGtlJjUIINH3vcoR7NtnOBwOyZE9Ho+pZer19XWsDeg69SYyASDL8zzVIElKzs2lUcRgWbYbg8ye3d/fJwcUmUQFE53EsQWYc23rEEk6Mw42J5xrdl2n6+vrZCQx1pLSGg/DkNL7eNmUJiKWwzDo9vY2AYr7+/vkDJOyglNjnSYYYXL5Me7svWVBpSn1FQAHyLCOlk2zyLKpayKRLH7OM+PwcT91HRuolEXsvkYKFvqAFFNLsrAu8X7yWD80yuEwDGntAemQLERcuq7TYr7U6VQnht5Gu0n9tZFaIn/oBvaf++JlwTfRWNYWnQb7DLih7bJNyyNSy77bdDgikTYliXW1+sA6LjhaNlWH72NfONOAUmwTugu9hf5nPZFXZJ3ntLV1dPa0TTsA6zhAgEjOEvqTPedlATfrwj0ReecZ0c2WEbdMdlwbnYE6wHZ8xizJM/YIWQGUkl6LXLDf2EYi1LNZ7KD7OhI5VlbQSXGUQ4yeIG9ZliUHhDW29UmWIOS5cX66rktEJnoZ4oS/W8af9cVGEL1B3nD+2V+7nzaawr2he9kDdBBnjnNFuqhNdWMfOEOWcCDiLinNULLkQpZlZ/eGzkYG7DBc/uRFqjJny3YitamxvBc9eXd3l5wPdBX7y7qcRRq7Ts1pqqvj+yKxsTiTO/AGpL9zSnKPHonpoUHOTREeZDDinv4sBZPzHTGdTzaUZ7Q2wabj2WiW1QXsG7KEc2VTXyHrOHuHw15/8O//wW9S7Dabjf70T/9U2+u1yrJQGJzaNuhf/auf6+/90X+nl91B+2Otpu7V9lJRVPrqV7/SoZ66RO33e72+vo4GP0iuVdd2OtW1hr7XicPbSzNfKA4GHXR1FaMlcnFw5XrstFJVpX7845/o7du30QjmQZmPyvLNmzd69+5dBLX7vZZlJQ1BdX3UEOLg1jDON+pDr65vtVzMVTcnFWWhtm00q2YK/TRRezar1LadYupaprqdDk2WxSGx3Xi4J0CSSxrGWqGgqprrsK8TaGzbseCx61SOitWP842GftB8MQ6hy3PVdQTCQdJquVTTtppVlU71KeUmo8CIlpxOJ3348CGBLcADjsp8Pk8dy/g5zgZAEUapLMtk7C2rRl0W/0apWIcjyzKtViv943/8j3V7e5sAJ0P2SG8i5YzPeu/18PCQUrtQ6sMw6OHhIYWqUYQAi+12m4CLTRdkvzAc1JxgtHB4uB7OmGWk7dwcjCPpNkS5LHuLEsMhsUP0UECwWCgq572WY6TC5knDBrFPrK8FM6zhbDZL0cG2bXU1RrKoKUApO+dUN01qtoHswt45E0UjjQHnFlCLsgdwBSl14MNAArjbptHc1PHYlCzWLs/zNIwWwHVzc5PSkfg9hpHIIA6JZRetUYalZU4KjCF7w55jFPs+Ng95fHxMaXYWXPR9nxo84LxD+JDOhwy8efPmjJEHvDPYdBiGlELDebXGqSxL7Xa7lP7y9PR0Bo74j7Ntnxc5tyksFM7bmjyb9sfeME8EVhiZgoGO65Upz6a6NAwz32FZcKJHEaA7HU+HBCCpYbJAG5mE7cyzXEVRJbnjzAMIaBsPcx+7RsZaN2qTWG/Auk1XwtnhPegL/kTWqeGyzgf3yMuCQ0Cx/R3/2TRnPs/zsB/sIfLBNaRp+C33yf4hw8gEoB6Ay++BLvye/2wKrE0rRf8ToUWG+r5PHcsgOfgM5wASgMgrRA/1MqRzAfK4b/aKc0dKInwLayMp6R3svHVoALSAYVKyiXwQdeGsolPn87mGENT2U5MCyDsiatvtNu0dZ4o9xS5z5lh7PovMAd6t48V6scdXV1dp/XBUbZMIzrglSbBp7BGRtctUL/YAh87qFZ6NM806U6tsHVmiTZZcI9sgReSkpMcvoy2sF3aGe0S20NlkAUCu8RzIHKCf+jSIQ6Kb3A/kEl38GHvBQGWb/no6ndS3nYp8qrtFPuP6x+Yu6BL0bVzfOK/Rpj3z2a6bomA2HTBijKm5FfI6nd2JeLLRRktqW9myBIm1kxMunWq4LBnCPvGZx8fH3zRpwEH6l//yX6osY2ciJ6++k2bzlX72r3+hP/uzX+qXX32rz5+f9fnzsz59ftTrbqfQRWNZjKBzv9+rKEuVhVdVRYD+4f17rddrffr8WV98+KDb262Wi1Kz+UyHw06//ds/UQixU9vVaqX6VCsMU+cNgHMaeqo4qgigVhaFui5uft1ENrYsMr3sXqKxzWfKs0KvrzvleSbvFRWrJB+KWN+UxTQ257zyYgw751OkY7VaJcFtu+j0bTZXY5e6oCz3Oh4Oms1nGoZesWvS1FLxdDrp/du36vtWTi6xVfWpjjVPytW24UzJTQe6UN3UCVxK0ViQ+kUraBQuc3IA5ZaZw0DgdMAa2nQ6FItNkSD1A+WJMnIu1mV98803Z/nOgDHbupNUJNKQiBIBfvb7vYbRGpK2hoGBOcGB22w2qVYJtv3u7u4sxczO9kHxeR/3vRnXGCXCmqJgSFPAyeH7qFchmkTtDGCY50XBSZNTgeLP81xd32sgLB6C8pEBBHQTvbCOHfuG03p1dZX2sBiNbzmCNRhhANDezOrpuu6snWk5Ah2UJ/dOWh1O9/X1tY7HY2KYcgPqZ1Wlz/f38qM8hDEaR+E2KVywrjiLAF7vvd68eaOnp6fE/NIWvRmjrjgSRABhqDHirDPOGOBoNpvr+ekpnWuIg1/96le6u7tLhowz0vf9OMsrntGUelLG72na2OHqm2++kXNOy8VSeRGfh1RW7qMsKx0O+zFClqvr2hQVOh1PyvJM2+21uq5NLayraqYQhpSiA7scp6ZPw1jzPM5ny/IsgZ0I7OIYAyJacZoYdXZedT1NjKc9Ni/bdW9ysMfcdjd1rqTmgqJquzdZFmenhSGomsX0ZPY0viYjH0FkN8plL+fiHuxe9mfgmfOL8SYSBvlgU3HQPUR0LPOOjrROghQZ6/1+r/1+n1pXw4JfXV2lc8i5wlm2AAVdZOuUbDG6dZovI1w2GmezAGzUEECEbbBpUBLOFN8zNTtgzXBIJlZ6csC4V0lpfThDFkjZdaagG2BmIzvc62KxSFkLRCTRX9w7qanIAyQEfw5DTLPjvOd5bmp2pq557DF/Wt2JwzIBa6e2bWLjqa5TNdqGfhjUjucBMI9zTRYBs7JIjaSuBztmHQf23Hb44zWfzVOnw6qqFIYQz2kboy1t1yY9GIaQIrFVValrO3X9lGaHLnUu1msjP9WsSvcwkR2TnvSjAyhJuSFPbA1MlB8vZvxxniIOqBTGeVM2Ukozpfv72JUWJ5S9sqRl3w8KYUrbdM6nCAypcNSmcn3Ohm2ikfks2QVJcSalj50M2UvOZRiCur6bCMTRqbWkV5nHWZHoU/AI92jbc0/P0ivP/YgNGlXVNJ8y3nP8nHXk2Kth6DWfz9LaWGe270NyYiCFOR+Qwzajwjqc9oUeQo9Y0oYzwufyPNfT85P+6h/8ld84SJvNRn/3P/s/a7vdKM8L1fVJXdtpe32tEOLhWa3Wqk+NPo1NAPqu1+PDQwo7O+d0f3+vIQy6ub5WNjIQQxgSKFsul2MHn9N4YKKS8qOQkusKo8PhhU28uro6YyAxNk0zDX2Mc4uqcdYQGx476+VZrt3rLkVUmrpNTEY0DIPqesw7LUip6OV9FNrDfq/5fKEhhPHgTylZ5LguV8vo+Hin4+kkPx5428lMigKYZ7Fdet8PyrNcbjS4T0+xnXjT0qkkS1EBW/9gO6LQAtqmSnAAPn/+nJw8HCKYUkAhKWa3t7dnRbesP8qStUZpcUABChh1DCzOGukqAJ/D4aBhNKp0dLMpZOvVSvUoDzDMthkAufF8383NTVKeAKvT6aTNZpMY58eRVQNkJSa7bZX5acI2QIKIEkoFg8ggTkA46Xy2MxRpKTbNy6bdsUZDCKrKMnV8bMYoR9f3qsoytaW1ICyC0Jh6ejweVTeN+jHthXtkv2ARqcXquthKnPbcOFmkLWDMMD44hLPZTMvlMqXbzWYzVbOZnp+eJiAxOh+vo1PFMwIyD4eDXl9fvzMPqe9jR0OMOGmj7EOe52nIIdFqSWf1QtQ/sH9t22pWzVUUpXzm9fwU01EXy4WOh6Ne969aLpYJNGMAvfd6eXlW1/Uqq9JEw/KzegMbSWUujpy0mC/Ge81Hx6FSnscW2af6pMV8kaKyfd+nobu02u26Vrvdq5qm1mw212q9knfkknfK85F5z7zcOPR6StsYW+J7W6jbqyim+VVDGGIEcmTn2SPn3MiCtvI+U9sy08mlNbV1Nqyb807OZyqKPDr93msYQiSovI9dR9007NC2m80yCDmNOrpTPg7mRqfZNCAAkq2H4J7YF8CaTXuTzgvyrYPAc9zf3ydAYoeLcl3q+i5rUVg7QI91Koic8P1W1vgZ10JvcQa4vo16WhBKem4EZ9EBb5p6bOpTjiltCx2Ph6RDAak4SThqlp2nnsWSS0SpLqNn3JdNN7IRM+swQiDhOFxdXaU0UhxZnAvOb3Sgpk5qT0+PaS5OUZQKQWeRXe+9drsXE3kvk2OMvj+ejho0RceLotAwRkYXi4XkwpkNI6obMUirEOJ143Of5JzXbBYHptM8aRjnFoYQi/2r8dx1bZeiIc45lVUZx4AMQ2phfqpjS30c2rqu1Q8RwOZZrqBINqyWq2TjhmE4Sy9lP4qiUNM2KtJg+ihvTRtrtbMxdbFtGjkfa6RP9Ul5NqZd5zGlvx9lk86ayESMqm1UFIUeHh7TeVytVskOsvdWLiC/2FdwDPaq7zvV9RSpTs2Iuu7MztoW5X3D8NQxMl2VSY8MYUgD6vu+V9u1sf27d5rP5slxy/Ooo8M4rga9isPLmbVpfci5vdfowMRIE85Myr4Ik3xZwkaSlsuFmmaKBkfZjTqdelUILAgSG0myEWGr53B6rGNko1HYTe5tOks7/ZU/+J/+pkmDJA19r6EftDu8JO92Pxa1z+dzPdaROV7OR2avcFp8+XYErFH5/PjHH0agU6vvpSG0I+u2183N9bj4UlHEaeOSdByVZN/3urm5SSx1URSJmbEdSWDGYWKpf0I5pHSRMCnwGFaMrIE0TcYuylynWuqHTmVWaD5fyfk4bKw9NslZ2+/3urq60nK5SHUVZZmfARGU+ul4SiAax4tOUwhlVL69Xl/3KQJBS89YcBpiNCoMiTG/vr5OYXfnXBx2aNgUQBaKH8XUtq22221SVhhXIka0UyZtjLXHGaNLDOB0uVym4sbPnz+nQ/n4+Ji61HEgeTbSULgu1ytGx8Cm3JA2M4wGnCgNQB3Hg30n2gFoncLgsR33ZrNJRvhquz2rdaBxQ55lsb5rVBSAgd1ul+qeMOKXaSe2hbTNgQZwsAeshc3Ll6S+6xSKIrX+7dop8sh3zWaz5IDjEIQQUgpBVVVajkQFNVucGRwjUg5xLIdhSJ9hj1DwtBAn7YAIQNM0qdvXer3Wz//1v06fYU/60Rm7vr7W4+NjYl0BuKSPYDAxuOv1OqVVck/IFvKOE/Thwwc9Pj4m40BEj3sglarvopGpfCQMqNdaLBZar9bJ0PPnfr/XmzdvNJ8vkizWrjlLk7FsLU49+1TXtfoysozH4yn9rCiik+DkkjxjzEMIZ3V9pK6Rtmrbfef5NFB4upc46oDzY1PHMMDILc7EfDY/Y5Qt2ZRlear5AASQiodRRz7zPDpFzemori8S2MEZozrFAhf2p+tifdR8FtPJkuM5TPVqlrWFVUVvdV2XGn8AMEjxAZQQ1SB6y17TARGdygBNiA0LMkkDQmfYyJY0RV9sSp+kRAjxc2lqoGHTLbFbnA/eTxomOoQ/IZ44s9EGBHVdnUgq9vJ0qs+axFgZwDmw6XNEd4jOJwdiPOO2Ff3EbvcJrOEEIXN277AnYcwQ4B5ZZ9aaM87Zl4Kapk6kjE1VLssqMeusd4yKEomdmHfsUVM36jWlF0IYZnmufBzdcTgcUoc4Sbq/v9fV1SalfFXVZO+jHB5VFF2KlFv2v8wLVSZSwXdio1gfC5RJg+L8dqcuRobCCGI1Rf5shMimo9uOr/mSqOCUwo08RrvkVRQufYfdq6qsdGrqJH+cK57hdKoVwtSh1PtpALWt38RpwhGB/ACQc3100Ha7SKmLVl/YqCdkZ1VW6v2kX9iXlJqsadgwn8dWcu6IIqGLIiESZcSm26PrJSVnhfPOn4nEHM8URIatG+J5IHyITIEPwGvonuVymWSD++Zc8R6bfp0IMePYke5obas0tWwH46Bj/m1eP3gHiYNtWxujXLIsS20fMZZ2QvTPf/5zXV9fp4Nxc3OTlC1FnrZfPcqHgwJTYw28LcbFqFFA2PdT9ywUD8oBhs9OfJaiEKzX65QiQl0Dny3LMoE/Usr4XoTn48ePKTWJyBOHrus63d7eJmYD0J5lWWpuQNGgrYexqQuwlG3bntUu5Hmub7/9VsMwpO4tMPKAHwsSYecAfkSVMM6AdnLs+X4+TxSBdYOlsc0TMNDOuQTUcVpvb28T40F0DXDz+Piou7u72C2trkVY9ttvv9Vms9H79++jEzeyiX3f6+7uLnUGw4hiFNgfFA9gAjlNbbq7TsdxT6krkaKROOz3ysdrs7YUasNswqrMxhbdyBMtt6ndQSlynjCkGC5b9G2V5vF4TO19WX+ACY4w0TXAy93dnT5//qxhGPTx40f91m/9ligm/vz583fqpPgulG2WZfr06ZPyPE/vhfH++uuvdX19rZubG+12O338+DHJZ2QMH7Rer5POeHp6SqCWPdhsNunc4uBw1kmVpEPU27dvExngnEtOgc2V5ywDrDh/tgkILdrrutZ8tkwMPMCW7oiwadwrr8+fP581AgFko1MAckQnMPK8AL2LxSIZIxziw+GQIprIq3XokUmiaOhfALmVc8AGz8H7uCeuTVMEPmcjE6RInqXdjDoasMHfAaaAMuQ3OlRTwwELbsMQUooMa2Ofh5eNFl6SCxYkoXfYC54f8I7zbZ1enBLb2rcYCQmIAtI8OQOsHalg6E3Lutr0Nc4WRBDnnp8jRzw3KTE4xOheOweJFwAawhASgD0DTFuiAyAX7yM7I2boLMpnsdOkTHLWsPvIXd9Pc+hsSs/Dw0OK9mHvWEOAOAQAQ5Bt9Gm1WiUdAVi16YtEjNgvzkfTtMqyIulEGw2z4wwsaPY+djPtNTm3EBbRcfA6nY6JGERW2G/A/3K5TI0d2Bv+BPQSdfKa5jJxLm2qnc3q4Owh05e6CYfTzlvDHvE7vsfWNXIOsuw8jZE1Y03RJfwuyzIdTydlxRQdBFwTwQTMg5twXpHju7u7dA7Qg5CJdB/EkeIzFtRbQhO550z2fZ8GCPP8nDHbotxmwiC74CPsC84twYG+78+a0oAbwQXYaYhjq4vZVxwN3me7N9rIGM9q5R+dndLyu2lOHWuGneO7UtTSnENLZLB2khJJjh61OBYZsXbtL3r94FPs/uEf/n1tt9sknLvdLnVagUEHuGEcOQgpIlAUSRisM4VzgNEC2GdZluag2CJVaWLbAMYcfOk8P9iG5mHHUfQWhAK+cMJwjCSlNr04DeT9A1IeHx8T84NRJSqz2+3SwEpShe7u7vTzn/9cP/rRj9IzYdAAfUTC+H4cHQatAmKmdIEi/SlNjAGHmQO83W716dOntE43Nzd6enpKh+Cbb75R3/f64osvUgqZNLVoxTFCqVuWkT23ufWPjzHtoaqmGQUAO2QghKDr62s9PDyk9pqkiT08Pqbhs6SrSdJ8NLhFUSTwbeWD7yGqRg4/oPn+/j4V1lZVpaZtlY3yQDQG8BpC0MowuqSDErG8u7tLChH2FwUDIFqv18mpRwHa7kkhhARG+TzG1g5ozPM4uNKmFmJ4IAYwKChYzh3yhBKkG52ksxbw0lToChhCAduoJGAIRhnmnQHBkBEoWtYDQ3d9fZ26DAK01+u1np6e0gBgmgI8PT3pzZs3CXyj9AEfNJAAYFMgjo54fHw864y0XC7VdyF1SGS/siw2WSDNA6ePz9kaDJs6Ydk2zomt1bHpDxhZAL+NFlkG/tJBw8Dh1ANsbXdFIrwWsPEdkE+QGTCJNvID2EXekCPkmmfnu3l+HC27JyHEmtBu6FPUCd0Ra6R6NWMUA/lFd0nnbXQx4HSXww4hjxaY2NoiCzrRYYA5GxGyzCvPbtNeLekjTQAfpvxSFojmQlbwnkv2ms/xYm2RB+4fPUFWBOsM+LRrwnMjL7PZIpGOFsjGMxJ0dbVOJAJg2Ubd0EWXup4mOwBNfmbZa5xISFAioIBaupVa3ICsQ/7YZ+HcAS4hJ+17sd+S03y+OMsUgXi0nQcBzUVRqB96dcPUwhvCKd5X0Ha7UQhB9/f3yvOpjXXbdur7uP40puIeOd+WxEpEQN/LO39mK5Av9hjy0jpKyIwlNqwTxF6DedARrDWfZc/LspA0ZQKwh0R0hyFmTjDPLBG33mkwpB3XtnXP2BELwK0+Y9/ACDYSbyOY3D840UZ8wAfYOs5hURQKw6C2bpIO4vzxQu/wJ3aY+2vbNhHMPAt64FJncX9gMfaDs4PjZiO52HVSdqm7s2tmCQv2hu/kZxDbnBdIgClTYequybMgb1bfcE2+l7VHb7MfdV3/2il2P3gH6f/1D/4oLSIbhJBikJnbAqiktkCausFZZQdQYK4OQgS4YE4KhgC2Y7lc6vb2Vn/6p3+qDx8+JCV5PB5T3qntVMLm8jscISJUMM4oSRsVsykSKDkK4XC2drtYt2Tn1tgwJtciSsAwV5vDTb0B3ZZgWWGlAaSPj48JtMKaYags0MLwSEoHHOBjDTP7VxSxFTeHAFAI8McQcJi5NxwGlBdsIbN1Xl5eEpsGWIMt8d6nrnYcWK7djg5LNh5IQBed35wx4IBcWCbrKNoUMGpEPnz4kPaHORKLxUK78Xmsg0+kZ+im7kU2MkikBOUnTTUuRNNCiMNHaUxweXZgjvI8Tx3Q2G/LjLJ3KEsAJHuVZVkaTHoZebQsv6TEusNKETmq6zpFslgLIhqsJcYIcIZRwIm3gI10sP1+f+ZYIfO830ZqWXfrbOz3e202m7Q3RGg4QxhFa1xsuoBlC2FQD/vTmRFumial8aKv0C0W4BOVYi1xZtBbnBH+A5ySHkx9HnvPumD0cIos8SFNDgP7hcyw9jaaBiMP0CR6hr7FyLMmgC+ua/Uzsm4NKHvDPrFW2AZ0vvNe8tMe8Nm+7xX6QYNJs7pkJTH2AA3SGnmx7jDF9rzb6CxOJ2tCpIA1ts/NfvAslunluugo9C2OF9fBWQPMor/s2WUtbMQMQobrcg6JtLPX3CeOi03XwemmQUlcp2k0A/eKk9w0Uz2pBV3YCn5ndUlRFElPYTttdIPoZQghpT5jc7GhdCblvpEXvpdzyb5bAnK326UIxZQueEqyyJlrmlaz2TzZdmTGOvmr1SrpatpVd2GagwdxE1O2lqKpyGU3uqqaKc8nObDE0mXUDMLWe6+h7VKapsVA7CO6hzN4mSYKoWGjOqwJZ9iCYGy/Td+MhNEiNQ2wWC3uZam2nc6Ajdq0fZfqY3lmzi3/4eDZ1FwbFbe2ivUD33B/1iGy6W4W8zCew+IVdER7qhMpCxnAvdoukDg89pxz7+ApvpO9teQIZ4515L3IgE175P7Q+ZDRlkjkGjgq6CUcIpx+dAsYyJ4nGzXiXtl75JK9sNlWnBWrv2wQomka/U/+2v/4NzVIktKBIS+b0BsbsFgsEvC3oAPnhsOKoWSjL5k76+Gu1+vkFaMQpQjumB/SdV1ih0nxohnBZrP5TtoXdQ8AIsuGAOwZwEYdDwrfzioBEFtPG4AJeOi6LoEIBBNFDHjDyLBOi8XizMDC2mI4UDg2rMqMA9YRAGtZYtt5bmo6EZLxoMgeZoBBqzbVyTI7Nm2De9lsNunAsra///u/r7KqUgt0aZp9hEGFoaBIn33NiyJ2rtPkYCMfnz9+TBEWwDqGQlICD3Vdp/uyDjeAAWZtt9upN2wt94bc9OMe3d/fK4Sgu7u75BAxeJPUKqKhDJRFKeMgLxaLM5bdhssxqgwvBAixxrPZTJvNRl9//fV3IoNcd7fbqa7r5KwjI0RRaVmNESW9BuYL5Y7zZeWOM4wxBPyQooSMQEa0bZsifDwj6SkYPL6f1BRpSodDmfOczrn0PTbCQo2dlSnmUElK542aJ5wXoskYJSJPpOFYsGOJFGqjJKWzAKCwDCEOC04IRtbu7cTSnhssjDu6I6Xl+CmfnOeTpi5tFgAi6xjYpmnOpsqjuwAtrKd1lCw7bckKwKutCQLsAvKyLNNsMT/7fAJIxvGykQIAHkacdCGiMZfRAO6P9EnuA8Bio7XIITbJssrSlJpyCWCsgwWgtQCVyB3ZELZwXJoiXcgK6TvWObSOCLodPYC+Rr6IhqPfcdQsM841m+aoqpqdRTXt+eM8WjBmIy3YDe4X3YpzQLoScmxTC6kFJTLrfayXIKqLg8h+cuZXq5Wen59T5BXnmHPC+edeaCIzpfHfqm0nuy5Ns2eI4pGRgE0vy1LOOw2jU8KecsaojUaX2P1AL6GTLNFh5YuobqrvGX9n07cB7XzOPi8ZJLy4rh0hQDSONeUs4JjaIn7ksO+HFC3DuWZ/uS+cD/ajaRr5LFPbnc9ewsbhCCKb2D/0A+fcrpF1+KwTir2uqiqlV0OCIMvoHJvlwrOyVmAlsizYJ0sUWWcuhJD0JcQYUXtky5IdNjqDPQeTcQ0b3eZs8d32ZXUQesjWT3INbKi9H9bVkgzWmbS2DJ2NnQczXzrT7Dt7TUbMr/P6wUeQ/uEf/v0U/QCMPz09JeHabDZnhatd1+nz58+p1SueOeARZUqaHsw1h5uDYQEPTDhhfxTGYrFICtU5l8Lf1iGwuZi0Y73Md+fQXAL5S9a6KAotl8uUm348nbRcLOKAzO68EJDneXl50du3b7XbvSZHrO+nsHBsVzp1RIoAKJP3EzPB7JPNZnPGxmIgMUKkQ/A7mjt0XZcGwkpKwIbwPUYRIxiNYmwrHLvoxQP4+PiYnD6rDFlfFCsNAFgrlB+gzRbP4iQS9XMuzueZzedaLZfRcI7Rqqqq9DrWCQGubb0KssXf7T5YxUCzgrIs1Q+Dur6Xdy4O7TWtr51z0jCcKRquRx0dbbWZ32M72VgnkudEyXI2bBSBfH8ANrVSWRZTTiEM2EOiBzh8FlCQxkjrbtg1vhc2DacWJxSDj1KmfoPnYN0toLp8Vv5O8SfOmzSxZOgCIsMo76ZuYpt8dz4XBZBKkbU0KfhhGNLZpv6L+glqwLyfOvu87va6vr4+Mx6kM1rgb51ZABwdDVlj7+NsIggNZAQAgwEk6spZQ9atUbdpXPyMZ5Qm8GzBGddHxvgM0U6bfiVN9YSWwSXiB/iwzHc01k50ygthAkR2fawhd87JZV7eZ6qbOrUqds5p6Ac1da0yRXmmltRRB53PM+F31onEpgBEyCLguXDuWX90/aUjirwjB5bF5n2AGc61XUMbbecc8G/W1UbVuG90AedlAoSxFTf337adZBoHcFbZF2o9sJXsBWC474OyzItxF6xXCDR/6BJJcC4fsQNbCBqL6adoH3LIGqHDbPSdyCJRZAilSz3C9yHjrCH7aVN/bep8xA+rMcLXJnl/edmNJGJxth9EhskacD4OhEfWT6eTjqejytFZkZTqYOL9NAn45nmUgf3+oNjRsVDfn7P6zsUh8/3Qy0nyWab6VCvPM4VxHTJN6ZTgJ/YIEhGnab1ep1RtnAP0I3IA4ZLAsIK8izqaLpJcL8uyOP9u1DdB47nop1lDck55Fttty0lOI1mY5+r6TqdTraKa2s8jC9hazgqZLuhvbAMgHD2Es2Cjac/PzyqK4qwVOp+D/MEe83P0LPqorRvl2dRUxnuvru+T/snyXNWo6192O3kfm+aAUQ+Hg+aL2FIdXQIWBHshG5wN60jzGTATssxzW0KI88Uasq84jtTPW52I7UWH2SgR5DK29jISBNFgZReHGlvDs1iSeve601//m3/tNyl2m81Gf/T3/lB5nqfIjQUPbDyC771PwypRMLYIkQN6KVwYN4QMJwmlD+C2rBUGyNYeWKAEULEvmDTLjDFQEyCHEHD4KBz/9ttvdXNzI/lpirVNRanKUi+jo/by8qKrq6sklMMQ5PNCx+NB3mfpmvv9Xsv5QkWeq+1a03aT7mFxrgFzgnDybMoCzMHpdDpLr+MaGE3qITh4HFieJc9zvTy/JkWNsn58fJSclGVTqgWFjABfGBbS6V5fX9M+W2aDNBYLDuhs1jTN6Dz2qTj3yx/9SA/395KiIcUBKsvYOOMymoWyBiTa9uEwUawPawPQRFEAypEly/DwXqJ9u90uGSlJiQ20dS0ALyKb7DuNDLhHCvUtm+WcSwQC6VmAU+SYSAj3zfOjlmwKDiAeJ9EyezyXVbb2Wpbh5j4A9TaygaNoa2wu0ytxRhjyKjl5l6lpG/Xd1Dp4sVik6ECU0UxVNbHn1Gyx37vdTnJxlghEzjAM2mw26UyyLgBDnqtt27O0Rp4dI2R1Dc7NZW0OwABZQs9ko+NtDRS6iMgE+goZJkIvTYaQM8+eIvPoXpvKYvUr+4ehZb+sscS4AnKQg2iAs3QP/KzrSNk9b9XNfbRtdKhspASZsNEwztnEmGeKIxQm0GCjGJfrYokdngGZxn7wTPzd2hbW3DLmyIRNpWKd+D50r2WC+ZytSYU8svvIOiBzEZDEuXtE5Kldjc87pYdy37DhdR07jUJcYSPRBaRncqYmomp6H/bI6kQLWom6xlbVfbIdzIEjywQnGxuDXuEsw77bWj2ug8yzb0RXrNMKCRSdoClyyXoga5wDuz9N02gIg8L4nvl8nhrMcG6RC9tRMerBIOqMpKlJSD8Sa5nzZ9HPHqLCOMmcUXQN9hmiaiIjlCL+pPOv1+vvOMecZc5VWVVqxojObDZLQ+ibseERZwPyhgjffr9P98P6kUJtnV50P3Jma1QuiQRkDF3BGeN7yJTBNtp/c39gHJxqzhe4C73PfiDnrA3XGoZBfdfLZ9OAZDAAoN/aOeTAEn3Z6FRJcb6ml0spq5aks/fJ2nHW0RmWFOOc8ayLxSLVuludw382Ui9NJDkyhVPKXnL+bbQJktjaNWvrsa3IJ3bH1sbXda2/+bf/xm9S7CQlMHN7eytpyuVls2zY3XufDrM0FYDVdZ2iFTAdOCc2tYMZRxgUroG3bQcQooSpdbGd3phsjZLHaUNJ8XPS0XgODjBCTciVNLqiLJWNUS6iJ9Qf7UflDRiUlDoonepa7X4qnic15ObmRpn3cmM/+9lqlgRSkj59elSWx4NDkaR15mCiYVyur6/lvU/d6ihG5tmYKcNzMYCzqir1Xa+6nlqI0qGpLMu4xouY1vbVV1+dpRZgyCTp4eEhOWGswW63S6Hx5XKph4eHM8N7c3OTDi+dlLIsdsirx9xiSWfpNkQjaUXO819dXSWWjX3koANYberm27dvdTwe05BH3g8jjyOCg8p6EJnj323bpvu0ha6AZ5xJurrBiKGAQggpOohzyLVYZ0AKQJvvhWXj/mwuO8DcAl6MMsoVIwIo4CxedoMkN9xGLi3gZo+GYUiGh4YHXdedtXMlCoYxf3neabuNshvKkAq8AYVFUSS2nJ9zZnHIaDpBsav3PnXaCyGktEPAHXUQ6ARrLEjZYR9sap5NQ6RVLcaQ9ePZuCbXZf1xYmhiwppj5HE8LZgHCHK//B1ZtY6RNcDSlDtvnQGbYmZ1unX4+LPvp0Jh0oDis8ZInnXCMLJFUZ5dA6DDOcRBs1GrKFPTrJzvA5U2MmDX2b7Hgj1+zhrbtFHAGGDPgh2eB33B9SyDy9oCUpApy+ZyRrCTklKNqz1P2Lq+7/Xw8JBkOMrWeQoe94+c3NzcpOdDH9JB1DZkwQZ2Xa+imIZf2toW1pi1CSEk4iRGWON3o69wSHhOG41C1gCS7B26hu/iczR1opssQJ10MPRmvI/oUFrH8TKaxV6zv0VRaHBTfe2HDx+UZVmqm7WkhY0IWABpzyJA2ucTUch3837kCWcWmSeDg9+zflbOLKFr7S3XY32Px2PsKpdnmo+kjcazU5pUMEvi0ADLEqK8D2KQ5yHChU2HWLXpcBBfyDx1QawlpBE6yeIsu868F70H6Yj8c49gJO7T6lHsVtJpmnQlNhWbZ+0018W5tvpnOZ6RIi/kDVFD6iFrANlssRp7zM9t1oi1Z0Tb0CHoF1vjZ53CPJ9m/dnzRJkHOsiSRqwXa8NekilidSpn0Tph2Klf9/WDd5Dm87nevHmjh4eHM8EBRC+Xy5SzexkytYYTpbhYLFKEAQbLhtO5/nK51OPjYzqYfCcCYw0hXWcQNISDv69WqzNmYhqkF9mP5+fnZKyIPtkiTg5q17YqRi8c56ht25RqdRqdwbdv36a0muVyqbKqVJSzpGxgCLIsU5Hn0jANyMOIEXGoZlUq0GRoIeFWFCeRL/YEVgiw9fr6mupVUHK//OUvJSmlqDVNo/3rUdvtNtWtsE6L5UIh9PrZz36mzWaT2HvLgM7nc33zzTdnoALl8PnzZ93d3enp6enMUEpTvrCtBSAqY9OErNNA97GyLFPXM5xBmC8b2QCkSUppfVmW6ePHjwk0ooTevXuXGFXALVEdnH3SPVH22+02yRr/cU3Lqn78+PGMWcNQOudSMwMr67DEKEOYT+qu2F/WzSp/lD5F0eSZWzAKuLNglX8jVxNjm6eaIwwCKTu0nscY8uwAcUDI8XhM9YGsbV3XyvJzRw5jB2NJ3jtyZdMWKWSHxcaZRffQaAJjbgd0WjAC4OG+IXUwkPv9Puk1QLVlHK3zYlPA+LwFWfb3OIlWh7L+AAZpSmWyYAH9x14gI+g/5InnsmCE5+W+L6Mnkwz1cm5Kg8JY8kxZNoFFnj0WomcKQWkveQ72PLGzJqo2gZtpbohldrm+ZWpt9Ie0EMAXoARgTYE8QMACZ1LzbC2tBR2sDesIEcH6Qtjg5Nh6LUlne2vJCByTmA7epPtEduLzT90muSeeGQfCyoONfsPwWqBWFLkOh6MYeMna2jQvCEnItfgsE0DlWSCbIMCwwaw917ekmD0DRDGen58lKaUYQ6jYznpZliU7s1yukx7F2bUjNgDuOOnJiRqHq0rRUYVs416tDmPPeF7W3erZpm7kNUVJeL8948gq32v1U9/3qSW3jbxZecYGWb1rz9N2u43vc1O6p3WkOQvcn32uy/P83fM/7Z9Nu7JAHXsH5sL+WqIQWYYgs2TUpS0jVd1mB1giC+zm/ZR6b5/PkjycQewyz4l8WNnnrPNZqy95dV2nzE8ZE9h38Ks9n9ZZ5rxYR4m14T5sBMc64FZ38czIIEQ+eJKsBtYEmwOW43kZQ0IKIw67xdtkaFw6qDzTr/P6wTtIx5HNtaye9VwxMrDRlr0DUF6mQpDGZoEpB9d+5vPnz6n2IuYYTylk3Mt6vU5K2hoxGBjpnKHBs7+7u0uAnvQl0pSIKs3n81RwD9CsRwb19vY2fTfKcTmf6/3798mYvXv3TpJU9oOCm4Yz0gzh8+fP+uLDBzWnOgE56oSiI1clYEukiMN6fX2donus2dPT0xmw5RBjgPu+H41LNJ7r9ToNwfz06ZM2m9jNB6cJYHW1vtKpPqT5NdI5W0toeLvdJqcAw1PXte7u7s72mSgfjhkOH84Oe4bDulwudXNzkyITXRebGcCwkZJiWRZAEUoTQ8Nn+LeVKZx1wD7f9/r6mpqAkA6E3Ly+vqaUK0CWjQDgVCAzPL+NjEpK0S0YzM1mk86XZUitwsLQYWxsOiPPiTPCmlCfMwzDWQtTHD/WAWBk62IwTDgjEB2wVgATIqWcOZ6PBhGAGqKbznm1zVSsb8E9ZxlnEofaFrYSmfk+Q26bZPC9GB4L3KwjzXNa48Z92ZlrNg8ex5915sV9op8uI2A4etYgs3bfZ2AvU2AlJXlFpuzZZF/5tzX2nGNADt/NmkbDPFOWTVEU1oJaFucmYH4JSLzPkm1ArtApfBefmwiVKZXPpguz/9yndaosA8x7OTvoQeTBMtiQPOhVgARZDMgIupNr4NDaTqNEdLk2e875silSnHUGj8bnnGabcB45886FRBhZ8AbgQh4A2IAs66BwFqIjcVKsJ5ucGV7IJDbHAqO+71K7a2wipIU9o977s/oYzieOAvWIPOtl5MkCPPQQ8v7w8DCuRaVYv9ucOTaSUtdIzir3JDcNgCXlF4CI/iaVDx1hdTM6Ftkoy1LzaiZn7huZQ69AGFiSzqZPsQZkXnD22FPea89130/dG0nn7vuxjjb3KfImxawOiGgbCZj2dHIYWWd0qXReD2nPa57nyc5jpyFqIaGRC/YRGbZ4D53Hutj0WNad72d9OWPcDwQauBL9z+eQPXRTnufJjmMvL9PT7LU5u3meq5xVGrqpgQ66Gx1ARI/v5jm5ttX71q5YPcm6c26xg+g6W6aCXba1mHzW6mXI1uVymRpOWTxaVVVqjkb9LnJkhxxf2o+/6PWDr0H6L//uf57m/qB0Ufrr9TotZNu2qVUuwmhTtWB6Cf0jkJKSQcCgAGh4D4IGG0jUh7Ag77N56GwLrLJVLnmeJ+8Yo2lTERBKFP4vfvEL/fSnP40hccMEA6hh6P14DxxilFrTtgry2m63+pM/+RNtNpup85ZzausmMUlWsWaZV92c0n2lSNboeGB4OBCkIhAS5+Aw34colAVzOJTz+Vyn4wRGGY748PAQQYAPZ46qBamkzm2322S0F4tFYmNPp5Pev3+vl5eXVEQPwL65uUm56KQI4qjyXcgC4MU2l7DAEkAF6MQAYgRsaiXKmL0m3UGaUr+Q9f1+nxynzWaTngvlz/wc5mRZw2/ZfF44Qva+UOrcLwqJzwHuLSmB8uV5McYobQvyLMAE1KHMUXp0U8RAXDL2lt1CfjjnGAFkiz/ZFwuKeZ7pnDr13XD2rJx5wI0lQFhDew+smY04sMbsqa0psY6HZe/Qbay9ZUYB2rYJALoAo4WuYfilBT2AQD6LnAAgbNqbNZDsn2UdbRTQsnrcr3VwbSqn1bF2wCGAL6UimWhV308MPmcuRjxOyjL/nfMV5T2kCIW9JrJmo102BSnPM+X51Kad9bVMap7nqWEPa4wDfMnkEpmzzykppW3yXuuoUtQPILZMLbU0NrKJ/Nm9enx81Gw2SxEc9hfZsnIc9VA+Rt3C2VmOzx9rsziLOObYIGTFgkF0DkQIenSq98hTTQJ6k/ljdBS1Mh733+v6epvOE+3j+R6YeWTYgsNhGFKkWVIibrhf9KbV5zZqY9ctkjCLtK6w5tS/WEiGQ7ZareS8V1bkyXbYyIqNitjvZ3/s9XieEEJsABCmejfkEKcNzISMWznAgSU6wD3wndghm9LE90tKTahYd2UTaLeygMyg1/m7xXLsE7JsdaG1H/xpm1qhG61DBwFAhIW1thE/9IG9Z3Q+Oo21tNkW3KfFesgG63t+tiYcyfu4F65jiUWrl2xKmnNORZ6ryKZzzJ+sETrw8r7s2ly6DDwX98p7OUfYMj5rv5P15wzZPUNX2z2Xpmg00Uj0icUfl5Fp6/wPYdBf/YO/8psaJGnq0W/DoDYkDWvFvCKMMYbFKlrvfaqlQTEDNjB8lonjMwitVZYYD4rbbe0BihbwB3Nmc74B+ABmyxJaxgcwTiTgNAqqTfE5nU7q2laDATAcwPl8rrpplI0G7osvvkj5yt57aQiJkfTe6/PnzwkIF0WhvJjuF+EHLHHPOHqkGaCUpSm86r1PwzlhHzjUtGlnTVFqpPx57zWbl0kx2rAsbMmXX36ZgCFMKN3kyInHmMLioUQZsMdzsZ8YCfaBWjBau9JVj8NPh0Lk1Crr2WyW2k7bttHS1BocxwNAjzNqAZYF0LBWb968SQYFh8s66RbwHo9HXV1dnQETlKJtwmBrr+w92fQKDBw1XhgcFB/7j/KDTODeMIw4IXZmFmfAMuisqWWwbYoOCpX6LNbi6enpLLpjWcO4v5mGfmLWkW2Al03HYP35Powd77F57RYkYjSs88hZQE7tmvA+GxVnj6wM44TYSBfRMuoqpPOmIN/nCJEayHvtnxa8IzNWp+Jw2+6F6OXL6BhrhJMDM2mjK8jLpG+naCW6PerWVs5NYIgzFRnSXmVZpXVHzm3004JH59wYuZ4yFrgvnsdGNjlb/B0Hj7PJutjoGXaD9cIRsk54lmUpFRnnFt1MB0ML+qWJzbZONBEBWyMJAEMm2dsoS1PzDs7ppR63Og09JynNCey6LkW50R2SEilmGevYRCNPOqNpGj0+PiZ9jf6zYJEIDnKDzDG+Ah1BbSg2Cd1kgSlpQZw3Osdyf1ZWrINNxEIi/XNKacKe40xzdm2kIvipDs1GCWzExmYUkM7MC8fCRnM1TOmq1CaTKsgaWT1siQ50FLJn9Y/VCeh19lA6x2b8G3uNQ2JJRBxD6wTw3Dh3yDj7ZnU+th5yin2idhJQb+8NJwMChfeBASEabIqbdWpsRI/75r6sg4CDgX20JAINiWzWwSX5znpZW8EzctazLFORFxpMpoHVAzaKb9fO2iK7dqwDZ9A6ZLa2K4RYRw+BDKa2a231KzqGKCn3auUWGboky9kjrodesfcymIYlf9HrB+8gEYWY8pBJs/Da7V5VloWcJiVE6L4oyhEMTAaurhtlWWQXF4ulpMgyOjl551XMzttzxsM0pEhN0zQqi1Kr9WqMwLR6++atstGxcs5pVs1UlIWGfhz22XYaQpCTlOURiPX9oOPhqCzLdToeNYQQu9DtXlSVY8eylKYQD9Jhf1CW53p5jm2mszxT13Yqi1Jt10pBOh4OWiyWattpQN3+da+6aeR8p6Hvtdvt1Ladrsdoy9V6rdPhmACwVayPj4+6ulprCIOqaqZmNHgMwQP80MLYGtSYtlTr9XWv+Xym/f6gEIbxcE2zUsqyUua9juGk0/GgOPguT2zi/f29iiJGkPi+t2/fqh1TDWkjnmXZWaQo1ifFMPZ6vRplaZxbs1zJyY3pIEFN02q1Wuqbr79Jhr/rOm2326TgcG45tDDAvMqyTAad0LBViNSzYYBI/cBBjmvm0nMVRZHSSCzzjhJHwcZ1nea/DP2g192rZnO62GXyfpBzsRtSHBgrrZYrtV2nYZjan4agM0XmXFReVRWN3nK5kkLQ8XRUnjG410uaiuOpZZDOW4HaSJaNlnDGQ1BSrlVZaQiDsix2rpKkoR9UlFM0ou+H2HXRxWJ9771eXnbyzo9RvlqzqtI+HNQ0rfJ8ylm3jmbX9SqL7KzeDEaU75ImlswCfQCuBV4W9FnHBtKBSA6/g5TBOcNI2sYz6C8MmDSl3eCEAEwAmxg5yxzigFtSwzKaNmJnjSXrACjlniWlCAlyfTweU0t36toAZTwvoI99QGfYRjbTi4LeKZoWAd8w6p4pNcxG3TC6NiXaMpJxDf24vkT3OznnE6iczeajIxjOrsX6WwaYLIeqqrTb7dIz4awj9zbjwIJke01LSLVtBC7RAfRaLheKHc2mgmbkBeeRFtd8n12XrutShIbGMFEuQiIFrHMUdVKnLPPJHsZzWo16J9fd3RudTscUEez7uDf7/UGrVWxsRLOS6LgWCmFysgG+s9kspQ2SWgORQ0ZDlNdJ58xmcx2PJxVFrq6bBm5a3Rt1aIyERfkfdDyeFNuMd2NX0ELO4axOzT3QTXXdaBh6xbba8RoQWJZYIL3L6gSbjtT0nQrOw3h/RRExDE4CssE55nzZszg5A07OjUPCu079MMhn542giqI4m/0VQebYPEBOWT6y/d5pGIKKstRhjJCiHzibfR87skkuDmPWCObzXEWenRFkvP8yA4B7sxkHnAX0F1EGoqzobZzjy7OI3FoCmzRHzglkEntDRpHVv1ZXouOjDZz0MfqEzA1LwnN2rc1G5/CclhCykSSrczl/2KmpVrmOe5Z5KUx26VTXMRvIRMrsf54shTCo70ZiRZLz0/y8fjBdLrNp9AqDZG3UzEaFwJrYBfbV1nDZKKD9DPLB87OGVjci69iPupkiqn/R6wfvINEFBWGJ/2Xq2l5hkJyi4r5aj93l+qCymOl4OE1h7F4q8iIxU0Pfa/cSD0afT4zJw/2D8jyySWUxS7VBx+NRVSnd3rxJh7jvexX5GG4dgrp2GIFlr+OxPjOKCLk0TQ2ez5dq6lZVNR+NVK/V8iqxDZnPlWdS3w86HWMDg8Vioaqo0qEIfVBbT45jnpUqi0qnYx0dmqZRkFOeFSllbTGLnfeO+4Ny53V4jX37bZ95cqeDgrKsUO4k7zOVxchaN52cvOrTNOQzsgbdGWvgvZeTl1Om1XKd9pR5EPWpVb4sVdetyqJSVc5SxI1DI0nz+UKn+vCd1q2XxtCG3odh0GI+135/VFNHllRBKvJKRRFnKhFhqKpKXTtou71JjgEpiNIEqmGdbEpCvL95+j2KDZBi09dsyhvvtaF1QCsGls5plk22zFbfBd3eRAexqZuz1BwFr74LijNHphaaXTsB4aTYsil32zmvpqY1aK/5fKG+66XMqVNUZkVeJeNhmfHMezV1J+8yhYEIk5NzRaoJAPBEGcnUtWNjCPlR6Xs1JtKk4HU8jNGu4PX6GhuGxBkf7iyCEoFMq+VyrTC4qCeaRkVeJkOVZ2MUuo2M/2I+1ZlIU4oYbDBMp43GSROAIZoIQQPAsalgGD6UPvrMRqa49iVriExYNhTZuXSquF/2+pLtxIGdnKFBeV6cybIUHYbIkJ++915tPYeklB7Ketg6C4BmqlMYI46WJYWxtesMAGKOFj5TWeYJtJblefoeBApnuCyrZPytzsjzLF0vkjZ0oWvkfa6uG+R9TDkrS58cMSJoNoobwYgfn8GpaTplWaGuG9J6xtbhU5t2W5uKfbBRgri/QXle6HSqEzlYlqWen3fjfecj+MHBomlBnF2ErGA30T9tGzteAoRwbLNsqlGglsBGGmHqY2S3V9sexnVdqG1r1XWrrhvkXJ/s42q1Hgmjk0JwyrJc8/kiMcxEhz99+pR0OM5ECFONYd/HZg9N045y58X8n1g7VSjPC+12j6NDMGVgANSYV1RVM0lORVGeEQRR/rvxWrmyrDfAbDp/EZxPHdzatk3NeWznSRtJsOfQDUHd6JC3XZP2ta5rDZqiEjaSxZm2wN4C9mEYdGzq6VlGomJQUFkWKv1MQxhSOj5njLONTh6GQYWPXW2J5KA3cNKyshgdFtIxx654s2mYN9e2ZAf6NJ6zKUNAmmZREXkLYWrZbvXpMAwpmkodDU4N948dI43e6hnOnJ01CPlEmiT3Y51JIhq2gQajZMg0ITrGvRBRZi3400bs2Eue3UazeZ91QoiUBQUJeQiDBicVVanmVKcsIp4BW1FUpfowKHe5+mHUiZCEfS8NvYZxxlzw475lXn3bpQj0JXaZghUuOdPgHeyWxRqWCLKNRmwqvo0i8R02W+Lf9vWDr0H6b/7oH6YGAjaUOKumblHSNB8FQbMsJBtKiBTBZJNQZhgpy+xStI+iQrBRwBxcnIxhmAZsofgAWaQeWNBsczM5jJcgre/71DWO+0NhUcQvTWHpCexoZPJiTc12u00KgkPJVG+6ZKF8yC9G6dH9js8BkHlGpl3bnGaYTYwu6Snb7Tbl8ONc2JxgjDmHrGkaHU97kfplU4e4J+41Ro4WKSLS1G1q4gCrhBG2qT6z2UwPDw9jHcdcp/p4Fi0AzFgnkr0npQH2hiJrolt5nqd28DRvsAaQZ4Rdt4whRgzlzPsPh6PaZmqv/Pz8nJxgq7hsGgIyaiMjyLCNGqCY7HA3W7xqO/dwJjlDfA9GpWlPSc6Q+8TCuWhM+QzRh8qQIn0/1aOROmMnx3O+OPMAFZx0WwRvjQZOB+sA0MFoY4zsubcRFgtc+A6AM8qc82z3krXlvLOmNpp2meLGmeQ+rV6zf0cvsCf8HL3Da0ohiTOgrOPFvUkTm8f3ELHiXPB7zp+t87KRSNIoYWXRT+yDTavAQLK2XM/qWoym1Z1FUaSZTlFv5BqG719fapeQAZsGIrkUEQfos67R8ZjSqrh2lOvhjF21ut25kBoOkGJnibMp8tInPRafqUryxWdookLnSvaX33VdOw5ZnRhYdMrlPrDGcUbXRrFuyyWSin2hnoX0dEkp6k2dJufn5eUlyTx1PpdZGfbMQJrwzKwL+27r5WyWgmXJL51xy8Cjoy7Bs01351o2ZYrXpX222IF7tmnQOMDoHlsQP2VXTNGlJCPepfpiUutIzQYPWFLQngsblbC1Pugi/s7zWPCOs2KBqTMpe3wveKAdzpsNcG+WTGINk1NldKXNwJCU6jQtCWOjnZASds1YS54FGSTyaGcD2sgba0cqWrT1i7Ru6B0GuWO/+d66rlMEGPmFcLLn2T6vJaf4vW20gWxfyr+1bZwB5NeuIcRqVVXqmlbhghjhmn0YkkOEnNqMAn6OTCQb1/Vpn2zWjLULRJnAcJxBuxbeyDYv5JNnsSQl68S6YE/Zn7/xt/7d39Qg8bKKEMeDAw/zZL1VhNgCUDZDmphaDhRAHIDN32kLTapKnud6fn5OyhFgTWoawmzBHcYZAG1TZCxg431449wzCgFFae/79fU1GUjui3xsFLMk3dzcqKoqPT09nYXA6ViHcoAJpmNblmVprhSsomWhL/+EhWEN7Z7YuhWbH86BsozDJWjI81zr9ToBIArQYSps7YwF4V3b6Xis07rirGDo7CBQZKLrOtVNndaI99u6A+QBuczzaR4Uh/v29lbPz89JmeEo2hA+azIMQ8qBt/nnU5rNedtlAJ09F4Bgq4j5HkLdvGC5bNE+94lssgc2cguwYr1RzkVRpDoknETusR+m2o1LpTqEiT1iT2wdBwb26ekprQ/OGvduUxXYGxuJ4P0YXuug0BiF9DR7zjFaNi0SGcIJ5vrsCfLNXnVdl/LSLUuKgUIe7F5asMOaYETQSxZo8cwA32EY9Pj4mIZfSjojUNAL8Xm85vNp8CLt5eM9TqbFPjt1ItwD90UTHNaKNDXu0+plnsvui2UK+Qy6zjK6gEIAmM1ph5iKr+8WPSMv8WdTfYkFnUVxnvKDYY8ydt5gBNAUaxmW33u+48+mQcXW8WN4sE2LnO5Pen3dp/sAEC6Xy0Rm5fk4SFtTxC3W8U3PatdWmrrEIfPOuZGA9BqGNj0X9oy1g3hADohEDMOQ5s2EEBsnsT840+gbC/pYJ2sbqdO0RIJNu2E/kRPW0epdzquVE9aBs0W3UrvenGX0mT2TfJb3ACZZG4gUdIO1sXZv8nzq6kctKj/zLpMfI0PW/tsBz5eyam3TJQi/dI6sfeTMWdKIc1PXdRxEatK70JWsl20aASmFTmQkCPoZwsam1dkoE/oQEtBGWHifBeysge06yIt1A4eQSnrpTGOzIeWsjPb9+aDj/X6f9DUOFc6RjeYjo9ZOXhIzllgm84Da5nMdNM3a5OxbZ549Yc2TUzeSILaeKWUwdJNjj71g7y1hg35Ldmj8NzJOhg77ZAlz/m4JenuGsefYTrtG7Kl1Ntkv6gUtRv51Xz94B8kqHcvYeKezjUTIEQIKDC37ySGFkc2yTLvdLoF5GAsiHTYahSAzFNYOo7RhRDqJWRbdhoKl8zaseOUASoQaxc5zoVgsIy1N4WCb90x6BEzZ4XBI0ZqyLFP0huJ+DNLd3d3ZNblX730qJLftTBFYjL+NYDk3NZiQploySWf7QjErBpVmFJZpdM5psZzWhPdzsAEPpO5gIOfzhebz/jspbjb6QDMC6wBFR/k8RYl1sF24UIY8n3Oxda+de3IZMbAFurbLnTUOVklYhUVOdezC6OWrafCxFNORlsvl2QBblKh1vCyTxn3BmtlQN/vMiygJ68m94SxxDcCeNLHAAHjus8gL5flk/GCRkEWMMwBhv99LUkplgDHm7zaSwV5Yp4P1JD0lhJAaffAcvPg3+oZ9h+3EaNloGYQC64nTAPi1oMs6XDj43Bt6h++7dEw5kzatwzq3wzCMdXtF0k+AEAgOoi+LxVQzhx7KsizJlxvnAS0Wi8TQXk5ZZ19ZF+SDtaUQmiHR7DMAmxRXHEHWwYIzQA3OIetm9/nyHoYhJLBgGfx4LaUuooBZvpt6Iz7H+sZ1jPKKg47sRPJjAhkW/LNfgEt7/gDJgGnkn3pX7MJqtUrd3Yqi0GazSefv6uoqRaWIwHo/kQsAWNYR5xV55RlxWLkv7plUYWQQPQgw5GfoBus4W3kHtCGjnEmcWhsxQI+hRyEa6D6GfrHfYZ1Z9hvbwPeR/obTgJNzGZ2xEUyAM7YOObXOlT3H1unApqM7QwhJ7tAJOH11M2WasK7IEPdi9THXHYZpKDp6ypJhdg3Q05AFOLxgjmRzTxOxcWYDvVdnahdZc/AS8miJUTvbhvTXophSrtH3/GftqSU4rLMQQkjkLtkT3JPVn5xdG91HnyH31nFBN/Z9n+ooScUD0+FgQEpydu7v79P4EvaCNeG8rddrFWP6I3iJNbBRdGTSRtW5JnV54BnqnUOIzQv6oJTlgk6M69LpYJqQcJ5tDZglGqYzPNUosTdckzXj3oZhyp7CBvI8VudZos5mG5CBZZth2PWwjtmv+/rBp9j90d/7w7NQLc5SVU7TsJ+fn3Vzc3PGpFhnxHrtWRaHRgKSURgIAR65NOXNEgG4ZBY5WCg6hI4DxqA7rg2wtK0z7WDWPM/PGBgUGQcbY0ykwbJaMHD7/T6lQuBccFBRAESfSG9jMNrl+tk0Bdbr8gDxfThHKCPLimBwyGsehuFsrhIGD0OPk4uB9t6rrKbuaRhRyzJzP9LUxto5r74bzhQPDQkAcdYRoDA0y71CmFLquGcMIal6NhXARpRsu0vk5zL1AeVtr33pTCGf7L+kCdT4TH1/XsdhQ+U8p3VQbASK69p7hJWzTB+GjTVDkWLIbQjdhv+RI7khrSvrnIyDsjPHlD2VplA864vS5/m5J2v8LgEpAMCCCc4r98s5B3BZptlGnOzv+Cx7xH1Y55t9wJhZkG+BPCm83CfPYgEp32n/Yx2QDcsQQwZZ4M8zwZLHKFAslmc9kceoYw6SQgIIlqHnXF5GK21EFNnp+/47aZDIl2UwOVPoXEse8GI/7PpbZ501Bcg4N0VeLJs7DL2qaooasK/xbEyzWBjGDKAahigvXA8iK8p3lUDvOUMaZ+ZAUlhQyNpg33B4eTXNNPOD36FrbMSdtYlOfxULuY2u4axgi2gcYeXEOR/Tdk06n10zq5fJNiAF6RLMWv3AWUZv2n3kPHNfNprBOuD4sT6sq9WFVmdZosE6Gfz9Upb4vQV/ZCnYqAzny9oa63whM4wcgXHnfizJdknsSoqNFYapK6FNg7TsuSWQ0Bk0NSAqYckSiBiLFSwYBa9YJ9DLqR5Ze+em+VchBJ2aWhqJQKu3eB4b7cTedl0sL8DRuLq6SkPncdosycyZGobhO04HjizgfrfbnUU0AdCsE+cGXWGzIpBzax8glsFF2FHS+Nh3Uvlx4i0GZZ/+6T/9p8qyTO/fv09ngHOEXr0cDG1xK6NKDodD2hsyP9ADVl4UgnI/tddGTw7DoEEhOUgWD3AGub6Vtb7vVWS5WmqfxjW0Opl1s5+VJszH9VlD5AHS5dIO2OeyuAc975xT0zb663/zr/0mxU6STmPNAYqEPzkEePKwTxhVqxSkqT6HjbMHx4J+UgSsMyRNXjwbxcZhJKlxsUACICQpOVikLuBMXEbGmEpN6gaG2LJePP9sNku57Rhfew/2/QBUBBzgG0JIU7Ttc7NWrLcF+awL9SAAPZ6Vz3HwcAr5PhQagMIqe77fMkAoHHLVrcN8CdTPFIcmVo3PYsSpB4KlR5k651QWuV73r8nR4LsAyaQsWWBuUwJQGBbIcX/IEutvHSXuw75XmhSFZaebtolNLgzotGlspApgEHCgrSPF+9lTO0MMsoA0SIwHe8j+IBM4pTaiMQyDnJ9yifkugEHmzxsj2DxkC6DYc/sctijXOZeIBQtaLMuMMbRsoa3nsZE79tQ6FRh/6xBihKwhskqd99ko5KVTjcG03/F90UfOMAQRxIFl4Yj+AFItcIXVg+yJTOZJRTGlp3o/1RIVZl4LINY6fAAh1oGoB/V/tmbKAifrPHE/AGSMt5V1C2JYY85AURQpPcsC5Shj0+cB8+cRpvO6AVJRqyrWt9EgALA23U+W5vrY0QF9P6UKWTKJ/aPtNnsjTQDf+5ihQPRXok6iSOcCp8s2iUCv4ThE3d7pcNinZhlWF7Fv1pYxGmM+X55FBtgX7AcOg42eoDst0Lc2lRfrbm0jZ886p5yny6i6HTvBWbZdVLFFyD66CHmxEWZISOqjOD88qyUyOX+AYfsM/NySTRANEE1EatH5yK4ldZAT7zMVox3gDFALbM+9XV/O0Gw2O+uSaTMHbBc2S+DZaDQOHfeUOXf2TBbHXM0qNe003NnaVCsH6BT0UlVVKdpBquhlyq3V2YB3u3a2syhrsVgsEmHMvhKdQqfzO6LVrLs0jQgg7ZIoOaSOjbyB9cAPx+PxrBbJYg/vvX7v934vXQ/HACeH5yXrxZ5xzgdpjehL0vTZf2SC5ynyXKGfup2i5zijRGCx4Zbc4jOQX7PZTF3byo11iUSXrRxabMI62Sg40Wf0ymWU2dol1oO1s7qD9bf659d9/eAdJIUxpeQUmwQ0oVFmGFoLpOq6TjN+YCVtvQj5vdaIW6FnI19fX1NjiEugioFl+NvDw8PZz1Hc0nn7SRTw/f29yrLUer0+S+NI4Hx05AAWgD6Yrd1ul9IHUY7ci2W1bJcUnqvv+zQ8zeYEo9T4HhgLnt/mmKKoABV2DhDfiyFGmLuuOxvSB5vCi8/a6NVkOMZ0jpHpzrzX6YSi9CrLTGEIyrIRmGS56jpGn8pZqePhmAw54IIiSdabQ899vL7WGkLsdDYMg4Kkvo8zV9q2UxhiukGeFwrDoCHErlp932tsMCNmZEhOmc80hKC+6ySN9QAhdqlSCMrHwW/9EFvAe58ptsMt1Xc4706Hw3FUtkfZ1uo2FRQ2NBvXIQRpGIKyzKvrejVNKzlpfzgoFmXHPfDeKyRQ49R1/QiQczk3qCicYjvk2HqY9wGwpSmVA4Oa57mGENvZHk/HBCCzbEx3HSb2n/NiwQcyELuYTc4jAI2zAUOJo2ojvjZ6i/NjU19sEbUFh1mWnUUeWSPSY4hwWsbWkiKcD86APZvWWOBc2DRV1gTSxRr7y8geANCm82LQbCQP2bCpq/FeYJpjx0HaSGfZVDdk9ZTdG+7DMv6w0bxg0qUpXQ/jH9vsD2kfrBOLk8gzAQi892fNQ1hPO3SV58LBwYGJ122MQ9sn8qnv42ceHx+TY2iLhNFTpHvhyAJIy3KqEQWMQdyFMEU4wnjuP336NO5HrpubG3mfabd7VV2fRHt95ANHDFmxESQG1aZC7a4ba0hrNU075vVPkUUcj67rR/JtrqapVdcnMdICeYdsY8+tY8TeOufSvDrska0948zaCKrNqkAubDTHRq2sE2ZBNACas27TquwZRP5DmDIY+JlNWeMagE0iCDaqwGclxU6beaZ8KOQzryyL9zqbz9WP92KJP7CIdST4Xfx3q2AITsAsgNI69Mi0JXG7rkt2e7/fJ1KAbBQIIRsJ4MzYPffeK/PRpiKvNqoRNDl+2BoclLQ2zTSrEb10Op1SKp4F+ZzhS9LE2hKbFsi+s77IPfv2/PwsSSm1V4o1mJxJSEOyZzinEM6X6WE4c7YWjDRJ0uWsTbJ4jugQTrJN+bUpbpxLuzeWHORe0a8Wk1r5apsm4mXF9t1yTnJS13fy2dTkxN4r92efmTOJ7bMOEDaEe7HzkmwUcr1ep7lJnCvIA5uxAPGJXeb5L7+Ltfi3ff3gU+z+y//L/zU5EzgP3vsEmjAalpnGmOA0WeaahebfCABKgANKGgJMGssMk0AOLE4RytZGugBQHEq+i1A4QmINuTUaFJETEQDUWWWUZVNHNdYIZYjS4TmtEPJZWCCUlj0oKED+jhGxxo39sODPMrJ8D+ua5jJocjjX67XKstTDw0PaL/bWKhfLtqMQuR/LPrEH1vlB4VglwDqQXsj62pC+NLFhljWzRrvrupRSyTqy/wAdrnkZNQJo86wYGyKHNmUKYI2xZY0Bq3xPZM+/O1MgyzJ1Q69qbFGKwvr8+XM0ykWZZnMgb/bc8OJ7JoZ/iojY8DyssGWH7P5hoCzwt5EW7sNGp6QpLdWyXnZPrWwCAGwTAxheij9hsmyKp3WqWEfLBtuIhVXBXdedzb6xLB0ya8ENBv+SQbOGEgPKzyCHbISPdeP5MVxW7i2ry/1wHc46AJdrWTbPnjGK5S1wwFkLIZx1igLM2PMfwlREzctGE9hb/rQNYYie2EJ3q//sNSwRhN6R4hwt9BN6EWeDc2WZTxo7UKN0OcQ0y4qzek9SiQCT1kGxaTzcO3Wj9j1t2yZwZOWxaRptNpuzulDsngU9khIbbq91OBwSeGVdi6JIJBa1FVzHpuRALHImrW4AaNH2ummas/QgnsO2EAcIWVmHtMDR3u/3qqoq1cJBHOIMEA1iTbH/yFxsET45xpxZq2c4C6wTezabzZJOtk5eN/TKxv3iXhMb3nZnzRZsdJkzMQxTNgmf25+OZ5ktyAfrin7ECfy+rA7OA1jCNsew9o2159lttCFOhjy3A+i2rJzqpqz94ZrWeWJPh2HQ8/NzwkKfP3/WZrNJOAEihMwA1t5G/7gXslNsAxDkJsuyJMP8DvtlZZC/g+XQ5yGEs3Rw1pnnsw4u94Pes7iSZ7H2bb/fJ5zEPdgaOIsRLO7i35awZn047zwX38/+oFMt0c/nSfHjrIFVcT7BLTTsoH6PtbIEhTR14oO8t+fPro80dVHFsbK6357NS/lE9zw/P+tv/y//1m9S7HjhRXMwLMiQTF1GNk3ORtHBzFqhRqlbUGGLmFFaKKZL5gYWj/QGQqjWUUBAAbMoKkDaMAxJwDmAPCOGEjCB0NPAgCiOdD6/xXr2l6DIGgIOLhEUroUjyjUtuLIgzLLaVnjtPgDOLp1RSWcKv2kaPT09JWOMwmV9LeCl1TltnjncGD8Uiz1o7IGNhtnZCtbBA0hxLyho1tAqUrsu9qBbg0IInvu9dKqswUOJ0K3wMgVCOndM2AsLYCe2vFccIHgu58jIMMQOjZK+w5jxnewPxaoWHGA0JuU21aAAFmw4HSBqjQBrbc8VBtA6jDYNFaPL3gDSWXeuZR0b9oL3s76XKTV26OOlM8jnkXUMlP28NboQEzwT6X/ItAUuyLA9m6z/5ZniDNuURXvWuYYFtDaVifddpo3yJ3qF99D8o23bs9bS7KdN6bTsoGU8kQNr4GBWifwQ7YJkAmQD/JF3zoQFC9TBsLboD/SCPU+sWYxgDKnjYVEUKbUaoGFTgJ+fn/XhwwfV9UllWaSWti8vLyNLPUXJGRKLk4gMwnIjj3meJzABSLRppJwhC4A5C2QL4GgCTK2DYdvxeu/PiLYsy3R1dZUcJWym7WxnIzqcdXQr582CW+yfjayxjpxZ1sVmD1xdXZ2dc6vHkVk7W4t1AQxzTVKZkBVL8jGk2xIwllhETtkrbCxEw6UuKMtSfuglQzzu9/u0V1VeJH3BOg7DkMZsIP+vr69pvS0pYMGsNAFKa8OtbbVEHvVylhjFkbU6AHmSdKYv27aVhji43jo5VtY4x6wb+4d9tLa076eGB9TTWPnlGuwnqfi73S6RNeiAsiy12+1S3XNd17q5uUlnGscZG4MeQtf3fZ+iRZZstlFNW/OLPvLep+HWthEH9+6cSx0p7V6yJ+h6KWIC8KK1hZDhNlqD7kcPoXOJrCDLlmBHz3AW67pOeNU662BJXhab8grDIJdNJSIWO4MteUaL8fI8TwQZtW7cF/bJEoi2ex33aAlhZB0bNZtNQ6D/otcP3kFCKeOMkB/J4eVlvX3+bkN1tkkDQNlGVmwKySXrI005mrwHwOWcS46VZTMsg7larRJDhxLBIcvzafowylmKArvdbpNyQMClKeqA4rJdyiwriXKw0SYOD4CDa/A9HEYAwmUvekAu17XKHAFHYVpjifHn3i2Tx17wYu0lpXWCGSQ/3TpeKC2r+DhkKD8YEfaJbiowTvawWyb80iGy0Ry+7xJAoEBhUjnodu0lJUBtZR1AiVKwkSj+tHUgvIfzEP/skwG0TklZxmFxFujghBZFobpt5d05ILLrjQxZp6wsSx0O5wXROGPsNfeAzNkaMsAIBvMyGsBeShMJwPnhHi8V9OXfMXiw4ZZ5teCCfbdnxYIKa5Q4t+yBPe824mxrJGzqCOeYZ7Ygxd4vegGZtfrLgiDrWNoIra1LIKcd44TusGwm+036WwghRUZYM7pG4aD2fZ+IIt7Hd1LXxl5Z/cl8Mhw2IgGQNvYc2mgkMojjxv1ybVvbmGVZcgSwG6dTndprt20ssEZXAtBsGvJ2ux0dzXh+GYNAqna83+YsCkkEAX1ux0Bwvi4j25bMs5Fwm8bJWbS1BFaukE0+a88f54rfX0bqLBCx6Y7I2OFw0DAMMdXdRL6tLgOM2kZCOOmc/+12m/SObR/Nd1qHiJfVxegOu8fYeH7HGtqMCc4nxCSpqezFpV2x5A3PgRz1Ou8kCwAuikJ9MxEorAfDeblf9g2SwWdT10sb/bGg3ZJrrA3X5F6w3dwve27XEtm0zpsl3PJsSoFjbVmbYSRALtOhrGzaZ7DRPAvoAcfoIq5DSQR2ou/7s9olOstZwkNSOov8G/yCbKHPLJGEk4p8Wgecl7X36IioQ07prIEvcDxIj7RnDl2NPcMmoMdwJrHhNkJIfaKtvbW6wWZAcEZsjSpnCp2B3ICrIXCwr+xDTOx3Z3jBOkg2omZtI3Jh7SLrw5nCsbb3aJ0mu17WxhZFoa6fcP9f9PrBp9j9V//F/03r1TSgVNKZA1JVlR4fHxNzkPq+91PLbw4aShMBRDAYbodjZGtkrOMlnbecRVAsQ34JomyKh3XI2HQrNDa8CaBEiVjAB0tqhcyCG54Pg2O/8/vuwRpTPs/LhoR5futQWEbOrg9/x0EBTGFkLFNvmQnu3UZNLCjintt2qn+yioHPct88A9Ep61gOwxT6ls7naVxdXWkYhsQIS+ezi7h/G1mw68F3W/YfoMf68zOUnjUQ1nhYp4prABgxxIDMCIh72SGZPKtzTqf6pGxM0wOcJPDt/f+fvX8LtW1b73rRf6mXdu2XcZlrzpmly5jsnYi3E0UwxvOg7hgjmxgEIUIgiIgGBCUYUXxTkEjyoC+++BYQwTeFbDzZxvOgwjJKAm6PEgVPoqzEzKzMOcbovbd7vZTzUNqvlH+tY8Q11zmew2acNJizj956a7WWWsp3+X//7ytf0f5plx2Kg3bAG4DCGZ90eGYpg5w7Xg+qvWTI96egez6Xzl7zrN49EF1gvvmeyznjJWjBQc+DI0ALYNSzMm4vWA/GQnt9wDZzxDozF77fxnXMzbez9ugQjgFAi64CfhyMcF3mgqwBe0Rw1j5nXjbrJRYEoOgvZyS9ePEiB3uUGrPhmvkFoDkYAvAxdzzHOI56fHzUy5cvJxlOAAaf47lYG94nKKMUijXDaaMXMUbd39/r4eFBfT+o78vetsPhkA8jpvyNIM2dfzpfqGzwdvkYx6hhmLYl5gwVB5KeWc2g0xhjJwYgfvy5+Y53XnRwgQ0BEL+rNBm9QBacnPCskKRMSAKGyAg52GPNCAyxK6wZsnu5XPLeXphtnw/AoRM7BPjYDEjFYRhyBs4ZZ3QHO7rf73O2jiDMCQLkHRDLM5Ddcb/NMy8WC5370hnWsUVdVQqxHDyNf2VNXQd53kzGhHJIveMCdMvHyHUBucgyuuG4wYkkX28nLdzWxGFUZeAUAN8Pg5rF9JBxD3SciKH6hcwyf3eAjh1CR8A+vE95qGMy92eMl/njOuiIz4cHVB4Q4deQGyeMkTMnNhkL32eOkE9kwTHinMRy/8lcIF/YNSe4IV3Asj422mrznNhA2oHzqus6rwP3pPTWu9cic6yBxpgPnwWDuW310nX+5kHTXE6cpHYSElvlL7d3rM0wDHp4ePj1g2J5NXWT2UG6h+UIN07T5YA6ByLStHYeo4fzyYKgt7vtYERxiAjcfBMi93OgwcuN7DxwwTiwAdQP7pSmwQnK7iyUND21HtbA2WsXXD/nAoDNd3EeGFCADIrsissYPHXv5yAxfxgN368gTdtCMnfMv2c8WBf+TaAhaZKZwfAApsnsOXDwcROkMU8edHENUuqMFyX1OXcGi3kiyPIAz52egxQMqRt0Z76cJUbO/Ho4BWSK8qVUbjN9TpxEVMmG8n5+fiu3w5nNAxnK7XzvR1UFVVVpl+5BnWfAaKrg13RAi7yhx34YJVla5gxwdeD8AAEAAElEQVS5mwct6AZrN2c4YTUBvxh9goL5ejqRQKaE8TAPHuwvl8sMKD3rxH8eHLFuLmcxlvayzCdjI+Dwkk63b7yapsklKDCr2ALk6tmzZ3p4eMh/4z+6UhI8PT4+KsZ08LGXe2CTYS9dXn39vTSDrBBA+3K55IOouTZ2BFDG+rA2ACUOJwXQE5jC7FJy5raXNQ8hTEq1Wf/1ev0WW41MMoZhKOROjKkUMDXTWCtGA5nXvyHbTkzNZcJtCkEkDL93H0O+Mxiv395TyX/oe9d1ev36dZYDAKaDJ8bpQS3j9AwevsqzATDu+B63eYAh1g7w62DYyyqdqAAEMn88F+81TZPX3DOZvl+JucV/up7BgDspx3UfHx9zkOy2KsaY9X00/+Sl8lVVKeYGN3Uus5wfu1BVVS47kqSo0iocf8C+Vl8nJxM9e8KcOwBm3rA5yDFz4oEUtuh4PGrZLnKnOrdXq+VSo2L2IR5U8JxkGSmlizHm0jnwEsAaXwzugeQmsPRGM27H8eVOILp99xI/bIHPA5VIlLS5PGOfIAnY344vcn3zChv3MeiqEznokmfzmHP3a47L/H3mA/vCT9e3ENIhyP4+5MM8SEPWGQdjRM9y4D6MaozA9WwaGXnG4jiG8eAfmFeXX8c+vmfKA3ZeU8L417vY5ZfXUDqYq6pq0nrUU9yUIHmbRBYIYfNSLyLrOSjFYMFQcW8Uax40SFNjzt+kEp3DcvC715e64nvNqTTt9MP7CJeDODccUjEajI1/e9kSisR8zoNIZ02cucLAOQDiHp7h8O9wb8+quMISYDm75Rk9xoI84ND9Xqy57yOaM/EeDHtg5uvq4/XAwZ2QG+85y+TOxQ2dB82sgYMbXyPWw+fw2bNnE+DnRoh7n8/dpCsP/x6HUQurBwZkVVWl0DTarjc5OOLvjBeAA/CmxarLKc/cNE3eAMtc4mQ8G+O66cErjpRrSmUPADLpBpk1wbEwHsAw95oHmFwb0OJgFMfPHMFWe5DA/dyAIzcEOcgiZAssn7PmDmJoN+s6E2PM73vgwNjRCeYRUAJLCpnw+PiY9ZrveKviEEI+Xf7169eTvXCMB2YXuQX8MD5aLj88POR9oAQz6AcA3I8YYJ6x29vtNre29bIiuki5vXUbLqXOVYBc3/i+WCy02aQOmDhoxkd2EjDjdi7ZsyipzgwtAbIDK8C/B6wALmSQYBR/w/uuQ7ywLawfnQ29vNJ9icsCduzFixdZ3gHz7O8i6EC2+T66TyWHB42AxjnpwzN5xQYy6uw+6+X2E91El/AryAl2F2YceSSgRZ4BoZCAyGOMpYQTAst1xu1cVVW5JM6zmDxbCEGqUke3ue/t+15VLGXiXBd7wnwjz8zd4XjUEMfcdY61wh56YOV+zu8xDEOuLsEXECDw+xzAY0O9pL47n9XUpdSZZ5ekfihHMiCrnEVEpg6bRJklgSzj9iwYARQgGf3w4IyyV4J6b6/vZBQYgjUlS8f9PFOKbDJucAQ6QMMlz/Y4QeUAXyp7sdBj5BjZcPIcXMJ42rbNlSzce71e5857HlRQFXA+n/NerjmudVKC53RZw944+THPhmG3+D7X9/2vHsC5H+P62FTma05++zi5H2sx96VOoFQWpH6t13sfIFV1NTGUDqhROlg2qUyyZwN4IeBsGMTRuAHxrBBtw7kG1/bFw1jTR//58+c5WHDlQ1G87tQjbwwAApc74oylvM4ZB14YSO6FgHtpgLOgOA8H+YA+jIwDWGe+XdmdzZsL9rnrdT6dtGlaRY06x17jKC2bhcJ17rprHWmVqDgNSm2uGxtDXde6v7+XpHymA2AFp9X3fZ43B8AJhDbqOsoQOLk8qu/LOTMpuOiuGZBWqVNVUNtyjglGdNoFzANPN8rjOOYOccw/z+NG0wNlX3NP8c8BAvXXrEP6L+p43Oe1TwF+/5YDBCBtNhtduovadqHVYqmoa6nfOKoKRT6QYV44Sw9USwBXgjgyEOM4TvYHuXPEeUnKZIYTAcmwnie6m2QurU0pkasVY2q9DrM0n1eujZ75eVi+oZ/nhXn28iPWHYfqJUBkG2EZPXsIcEXPir4NappWqZFGCcq22xt1XSE+yPD4OXBVRYa7VwglIMLx8PyHwyHvyUglcL1S6/jldcxHK3lKY9zt9tdgZ8it8ql6SJkkZcdNxgUGn/svl6urTV7pxYtGl8tZwzBqvd5ouRyv36tykJaPKBhHKQT1w6Cb2zsN46BhjBrGKFXKfw9Vpa7v1S6WCte1SDbgch3L8pqtjNdyrFaXS6e6TnNe1831vVJ6jWzSpMbJMs8ypVJSMiNRXddfD9lO7fSdnfV9CdhgbxjhmU4HosgIfwPQUV5EqSSBpJN4+ALPcCF/PANlbWR+sCOUrhHs8h5j5nPsr0BmsYGANQdAzBXVHc4uAzDRZe6BL2PcXsYG+MOesReFQLiqqqwrVJXgmyBasWUEOv5c2G+CHn7yXA4Yh/46x+Ooy+mcyZAYoyoDf54Rc3+O/+XZqqrS4npkwulwVN3U6rqUBVy0Cx1Px7xeHpAxP9gqL2Gn/Ao58vG7DWR8kBGVgvqrPHqJLAQD2ULHOQR0BCtk8eYy6aWcZHPmxDBjw4aiU2TQ+Dz3JwPKczthRiCAnDHn3N8zaMgGwJ71c1vOe3MyHb1wHIa84zNYf3SM+/l79/f3mbRifX2PuSSNQyrlPB4OOh1PUpBWq7W6S6eo0swGEsIzR14i78Qw2ND91TiOWrStQhWuvioFyCEkcoDrME7WqPi5ck/Wg2DX7YfbPyf/PNHAGjh59Hle732A1F06XZqy2ZsJ9cyPVBwSe0ZYYGennMXHGHgZhlQWiJplDMw8OHBAzKbHjz76KAdBgCYW1ZkfN46+BwBhQHGlaXmgM4nu/Pg872FopFLHidNxBkEqiuB7GjxlPWernE1gbJ4ZUAjqq1GLcdSNBtUapWWrGIM0SJdBehwHnauoEIPq0EiqFKtrytmMjzvuEEqHHndkzAfAF+eYlG+8ApcErFPr66C6bq6B0nDNBLRWJlCpqoKGISoEHFGlEIoxcOaWeWJuWEecAM/CmPu+zydww+QiCxgPAlbfOEkpTwgh76s4nUr3vtPpnE/epqEJjD5g6HK5aOh7hSj11+BAMZ2+nWS8ymvuAbBUwB/OK92fks5RTVNPnCnz4nsgGFtmYVW68rh8p3WtMpBNMlcpXs+aooW5pFkb/2bSgcrZdfTAHTtBqQMzgBzkCSw1+91w0l5awT4YHKHX3aOfPEfKwNxd11CSgsZRWixSYILOE2xig1IQX+ty6SfPlZz0RU1Tyq08yE1ZhbQnbRyjzudTlokYpdev3+jTTz/Vl770Jd3fP7uylDfa7XbZGSXZ3GRHWNdFDiidBUzu94fre5QUtVd5rHW5dOLcrK5LTO39/bOkR1Wtpk3r1ywW2bGFENT1g0J9JZqusoNDvVwSEbZet9fMTXUNnNM8p/kfJoFOXde5+x3r7HX87mPcUccohVCLM8ZSZqKQBeg82UHkCODhhJIDL7KU2DnstpNq8/1Vvp8S+UV/sEdeiglgARQzD87MAkwgWtB7AkknEwgG/JmQOeaDa7NPb04IeSbCfSU644y3j8mBraRJIALD7+y5lxixrk44ejaEazIOL4Hyio31+gpIY1TVBtVVJcWo1TVIQA89U4WNA/BiH+u6Vrff63RImbmbmxs9PT2lZ+97HWKUqlItALj2cTthiH3CDnkm3ln+rutym3Svnhi7UvXhZGRd11IsZdnzQJZMNOsDTsGGYPvnmS5klXXCP7ht9mwHHe4gItE5ruVHhTBGJwp8LMgZc0jQDe7x4Abfvt/vc0MN9MDxB2MngEL2nPBzOUMOvKEIn8VGIXdD30tjVIjS5XRWBWk7DFosWnXX53XC0QNp35/tgZPjSvxy0zR2fqNVIwUpBpn/jxO7JSmXKHJtD9TmFRDz+UHXkG+3w13XqbsUMulrvd77AElSNiaAFN/IiiAz6R65A6pheaVSe4tBRSj5jlTa2jqgwrj5xlmMK4D1k08+yUAMh0vWwzvvcd0EbE8Th8H9PKDzaHxu2HHCHlkjrBg3xuQODYWDrZCKspK1wAgSxJH98kyVVAAdY1qee8XP3ugLYam7eqGqiarXG52fDvp/dmfdvLxTP5y1XK5Ux0rnYdBi2WjVNIpDqSPm3swlG4ad2cfgOTPJHKT1fHsDP+OGuXDwhDPgOx5ApveK8+QeLguUdQA0uK83/bhcLpnNdMPrYyFIctbXnfb5fM4H2N3e3urx8THPCcwRgczcMPMeJUWspwf/PB9jc3Dpe1XSmKt8Pz5PIOHGGLn0FvVuyB1shTA9w4l58gyUr2nTNNfa8sME6Lms8jzYAQiVm5ub/Cw4W5ywB+mwzOv1Ord4Ridvbm4yg+rlHx4kOUAdhiFnnVlv9nKR6fKum8vlShzQy7WYY6nV5XLOJSXYw/TszURPYWz3+31m/V+8eJHnmZIIQIrbGdapbUtHRSeJku0t+wMBZnTx9MzDq1evdHd3V+xgd5nsG2LMAGy38WQ2ukunuirdpWi/jc1A3jxjgZPG3k2DrXIuEkQFJUJkk/ieBwT4KGTIwQ567iXHrJ+X/M4JK3yEg378m8tzkYHyIrjmWXjPwZL7FoJdXx8ISI4DwMb5dRz013XZ0+PXwIa6vvq6oIcOkrzblmduuD8ygu4j87x4pvm9yLT42jtgc5KGuZ+3N/asG+uLb8TeYPOQM9cdromd4Swr9M0zaU62XPoiV4yXQJSxEigih1wL/YHIBNgyHnQl32sYtVpMz1dzH8JaeJDHWiAn2HiapziJizxDCPp95kG7Z43IkJHld4zmeI41Z86dAEAG8I34dR8HtsqxoW/J8BJlyBWem/nEprNHEplD1t2mum12W+ByCq5pmkaq0jW8u+8wDGqXC40W4DDnfk/uxX+OVZzU8LJEvuN6h1/wTKtjJuSEe/O7Nwhyu+TrznXAowuTxaZpVDe/XmKXXwAygKU7NyaZheccjKqqMoAjBcxCYTykYqBdMBz8IsQAHRwJxtY3prpispBu3J214wWodOeIgHmdrqQri1ycjLMtLlAOVtwZu2PE4WMknDFFsanzxahRisizA1h5Hj53OZ/VHC/6+Z/5f+hnfvr/0MsxaNkutH12r/sPP9L4bb9Vqxd3aqtai6ZVHKJi32m5WGq1aLW7bgpnTp114r25UjnD4/JSVUExlsDTN0jiCH2jrDPmvMfaYEiHoWzYRvaQG9bSs3YezMEUu1Ojnv/m5iaPnftivHw/EHOSsmBJHjk80dkgZ+ZwAO7oIAy2222WK4w+zgEH5qxWVVV5D0gpRyrGEeaQ+xMAMGeezXSH7oaR7/r8z9lP5JE9CazZYrGYOFUvdXL2lXkgCPGyI8ZWVWkvz4sXL7It8uADWUHvvSsgY2YcMcY8nylrNOZzsshUJfuQ1oj9NzQLiTGVynm77ZK5KY1ZnDFOzn3QcrmeBA9sZAcEA8Q8iwkL3TTpdPr7+3sNw6Bnz54phJhPrHcAkYBfeWYO3KQcDx3gvg6WXM55oU9OVD09PZnNC+qv5aQAEeYVIINt48WaMK+svYN3gCh67F3dkFHPFjlBBDCmFTLjQnYh9zwABVA6yLhcLnnzulc4sO6QbsiA+w/XdWTeZdLtdd6bOJZGQ07qFJkteyLRb/dRXiLohApjxzZgJ30/Gn7JCRgvw+J6nslAHlhnz6I4aGZsrmfsnaR7LTaQ61Iux1w4QHYybB44ICdUBiBXfmjy09NTDprv7+91d3eX9+D4WVngl8Uigd51Ww579fPJPIvhfoX5Qf88iOD72H8PDE+nUzooNkxbmGdAe21AEWPM+2YciKOL2EcOTvbAmblAxjxL5yAZW0aJ6TwoxV/NiU3kkjny7KLbZ/+8y6YT2FRv4FsOh0MOjLD/c7IeuwkmQTdo0409Z508gHGCkvI6J7frulaoSmWQ6xv3G8cxd3dDTx0XeJYL2zTHxv5vlyfkK0gKVWkm4TbFfSL34V4eBLvt5PPIgpN6lOE73vy8r/c+QMKheSvGOVByw4lyoVAIhlT268B+8D0HYSwgi4SBxVjt9/t8LdgoXhgpwIcbD4+mvRU5js7vx3Uw+igkBghFcsHxe+FAMerOfjBPDh59jqVpu2Z34iiYO32v1Q0h6Hg6ad8s9Nn2Rt03f6O+ejoprBb65t/x27T9n/9n9aHSue801FHny0VVDKpCpfPppP50VJgFqmTeMDSArvm8evaFwGIcS+bCZcMZpsVioW/+5m/WL/3SL+nVq1cTw+NyQ2Cc9irVuRMOcgSLg+EGRHtXQOaPwN2bB8ybdWBs/LsAKcAh1+L7GF/knLG70+B6lBZ5ZmSz2ej58+fabDZ69epVPu/Gs1+sjQdNPscYfA8ikD3kycswGCvOHEYsxrLZ20sVybgydgKkAq6nh/tSG+5z5HpGgBBjzMGHs65kI5B/3ofVKuWOp0k5lduVsk/mPAFczBH3lKTT6ZD1jRJJ9pqcTlOml3XtuosOh0M+XwZdaNtW+/1B53M3acnqAJY9RYAAZJ4294C+pmlyQDcMXd4LMyeZnHF3lt9bT/MeNlchKIbCbCJD8xJPB/fn81kaoxZXu+hBzuVyyfsfWJPL5ZKzhVI5bNdtN//BijOP8yyqpAnY4j0vPeIa830oLufYN78+36N0MYQwadmL7mJrAPJ8n7lA5gCxnoXlc2SmnfTy5+ffDnaYT+bA7RNr5gE6QAh9xz6iw+6LxrEcgM54sP2e4QH0uj2drwOAdrVa5ZbdbdtmGYZIcRKO+aWsFjl1ID6OqS09beGdpHl4eMg2B5/kJfQExszNnATieXgPX9suWlVGvGFjvDwZOcPG+bzO8RHzzwtdyPplMops4u/6ayMJ9iFChJxOp0yEOH5ym+J6gL9hPJ6tgEDxvSr+Pff32BGIXwJA/BI2zzGer4VnM5A9AhzXGWTSq3KwNayBy7M3QfE1duLSy8PxCXzO8anP4zikbrOemQE79MPwls44DnAyGzzL83lmleDOCXvHROkZouIYr/uTijxPgqhQ9hjOgyPPkPN9J7GdNHU5dB/yeV7vfYDkexYw9L5oDkZQJibR9xF4KtSZJgRyuVzq4eFhwlx5sIRi4Zi4F8LmJR3ch7GSrfEMkDOOrrQeACIcXjMN+0V5lHdzQnjd0HAfVxCMCcaYsfK8sL3zVLELqpdjkSlYrVaKY1RdrfR/+b3/V21/z6CuP+vYRGm11GcxqLlcFJugoQ7q+07D4azlaq12vdRwuWgcy5p6CQuBwJwN4XfWCTnAUfR92TfG55hv5vlyuegbvuHj7Cw9Cwdzw1wtFk12oF1Xzr2Zlwo4CPbsyJxFpWwUWXcAA9jBaPA3ZzZxMDh2ro8cZ9YplFIgZ2TnrN3r16/11a9+ddKgwcsRWAcvH2vbKavDswEQMOAe1CKjOEnfU7FarXQ4nLIeScplKJ6NOp/Pea9M0e+y78mzvASPkAesK89V13XO0Mx1GnCFY+R8G67N39HrcUyNOnDYAKSu63R3d6cY+wmgdwaN8kieEzlO9ioFdAAaAMpischlgjSVYdz39/fXJgVJlh4fH/O8E2yyFtgnD5hhv7uuM7a5NJlhjEW+p6VY2BeAI7aE8V0uF92uVhpiaWADOGDeWCtk3gN09wN8hnv2fT8pT6XtMHIEiGDukSHWhOvwnJ4t4DvohK8boHoeXLldRl6c4faA/nA45IADcsB9jjPRz549y+vqWQTWxfda8Az7/T7vrZsHG4A3ArS2bXV7e5tthoNe3zzOPXkePudZArdbzIfvG3RCk+fh2rxcJvBxHqTPMwZN0+RSQS9rHccxl+BDcECCbLfbbFuwv2RgHVNQbYE+4EOYZ9efw+GQ58XJHwJ+jgjAr0jlmBBVJTPoRKuX0jlOYZ74OwEvwB0f7yA265mCFMs+aw5SbttWTZX8zXa7zQ0F5iSXpIkOOLGKnmKLCWLQBdaaTA1nzTnpBbFFJQO+CDLVnxn5dbkjM0vGH3vQNE2u5OAYB8g1/CzBv1SqPBwPgFGwJ06uQmqzXmSNIQxZH+7ndo0uoGM1KFoQ7UFQGxaq+7KWzJUTIOi3lwJ7ZgYb6piAn57lHMdRqtK+O09WeNmw660Tp+4T8MFuLzzA9qAqB9FmC77W670/KPZ//99+UtvtNk8SAu1GyIGnOzJnv5h0Jh4WDmbZBQGnhRNyQZun7T1zg3Ji/FDIu7u7ye+MC6ZwHMdsLJwBcGPH95zxc4fAGD0F6QwpxtA3byNwniHyQAMWk5IAXi7AsLOSCrsYFgpdr7E7amyitGpUqVHbS2Mc1C9q9U2lZpCWQ6W6bnTsO419p9WqsDdeqjXffM+6OyuFYhemNyjGacDqMsHnvQyN+XYA4mx/CFKMZc+TM0fzwBOZYE39AEUyEBgWAm9/vnmJnQdXMUqp0UQpNcBAI/+UUmK4cBSejUWOmTPmyUsqD4dDLhkhqEN/0ph6rdep3AdA788O0PNnQQY9y8AzJ/krgZw7RubBZRgQFIIEtgKwEsBeLpf83H5AJbrhHZhcZxxMPz095b8NQ2rxTD28t8lFxvidYIpAcbEoDprPkxk9HHaTZ0Rn+35QfW1UwJ4o5Kdpag1Dn9cWp7lcLrVcrnQ8lg6QAG+yDB5YkKHEqSPXlPJA+JxORw1DkVVnUA+HoxaLZc60OfDCwR4Oh5yhyjJQT6sDHAQ78cV8DMOg5WKh7lw2crtuuzx7JogXgAnZY/xOjnFPv5YHbsiB2xEP5D0IQN89Y4g8My/4Feyw67JnAbi+72/ievOMjANVL9vjmk6OOZvr3/dAj7li7Z3p5x6MyQNDfr9cLpNjMryMzoEh33USDDCNHiOb/GT8HoC6ryTrif4gu65vBO3zkjuCVweDXkLHM2LH3YYzR/hHSDnG+PT0pGfPnuVs7jzAVJCqqyx4wEgFCcGOE6ueWZyTAk4IeBaDDoDd+aL6ei8ysdLVj1beSCdk2+qkHPcj0814AcWsB/OH7DgBjR32sk70xv3L6XSadFB0ksKzKOgtsoKN8OCFZ/IKEMbOT0hqJ2kJbBj7MAzZH7gOY3eYe8ZEa3THLvMMXF63MWq82hSydVn/JakqGUK3cU4cOfnPnHIdxu9YkLnCT3L9OqQSZyf6uCbvOfHumBX9xecwJ7wc8/j9hyGddfbrB8VeX+v1OrPQGACcvxtFlFWaHoQ5B/MYH9gcFz43lhgxaXoQl6RcOoEAS8qGGyeXFDzocul0PJ6upTKnK4jDcR6zAnsnKFpKS28fPuuOg7Hx/AQ6GF+u4d9HwD1AxKEyH84qwZjwvoMD1sEzc03bqg9RnQapbRSaSpehU31t9b1arqQ6aOgHdZdeldI8aRzVLlKL6su1SwnMUnFk0ulUDvejFbdUWj3ztxhLVyI3nG5AubZnxwogLRtvCRbS3A+iux0d8U6n8zXIKp0JMco4tWEYtVgs8xqnlsuLqzMubaurqhj2JD9T9rUY25Ju5jo4HAIb1niz2by1dwUGjjlOQejJSqpqnc8pS5kOmtyK0lQcK46lqkqgBcjBOMPwAoa93nsYRjUNYJIyyn4C8LyUDWdGByHmIrenrSodj4creEzPsl7z7L2aJnV8a9tysCTAAb3GcKc5aK6yGHQ+X3JAmsiDcl4JbDvlNrvdTq9evdKzZ89yYFIClqV2u0MuD/Q9FItFq6q6yc9NiR2BzXJZOqHhWJlzWNQ3bx50e3ur1N2t1es3bxRClTbvxqi7+3udL2dtNlsd9vtc4uXHHlAigj2FzWWsUVKzSO2H66ZWuMppVFTdttpc93TwfOM4KtSV4jiqH9Nm4mEcdemuJcphuqmfYM0BngcGBL2Z/azqVF6iqEpBw3gtTW6uzLGiqqZO59YotWFmzPNSO3QXm+bsqgcu6JcHJnMyhe/A5Lt+eHDpRBvyVNd11jEPUvBJGaTUZfO5Az23GVzLM3CeiedFUMD1DoeDbm9vJ89A+ZEHnciPZ7mQXV7+ec7HIlMCmPWypkJylfIkMj8EK/My+kIeTdtpYwMBsPg1yA30j3JWStQIRvyQUHTAA1nfq9s0jc6Xs87XYLSukixVdTq/pQ1B9TiqXS7UX23d7d2dqqZWqCqtt0mnF02b5TiEoLEfFGql/UFVUF3V0hhVN2X/0byaou/73BEPfJLXZIy6nM6pq2kIqppK+91Ofdcn4qErZy55oDsMJeOD/yTT4/s9PYBAh8BG6AyyTGaa9XFykiAIGUJPvCEINtnxIfIDSYYu+hYFng+5nAfL+DH8I1kqJ7h9DiA60XE/9J114bmQVWwLeDb742FQFSqN8Yplr+SKYlS4BqWu83V97S4XpX6MCnVQkFRXqSX9EKf7d95FprPWc5/I57E7pYKllON65o65xrY4tkIm/J7orpN7bg89QeHB5ud5vfcBUtf3WhijJCnX/eNEpVKzjCFncXBkCDQMkS8SzsSZX++k4wZcKh2LPNJ2B8i1079TO+nLpZsY1arCiCWgmNrTBsVYSmdQaBgrd+YIiwdtnllxhs1ZL88eECgQXPF9Ps88+334HClfnJorR11VCqo0jlJ/6NWGa4mSeh2O16CjqjSqSsCpCqpipXEYxfkrySkpO+MEcBstFoVZOhyOeS6Y5xilupb6fshlfx5cIC+uvMgOz4JCYig8mCIoksp5C7SgxlmmOatVVTBmdM1r8udTWRrn4JQAPxlwzklJ/yGvyHsyitO9c5y4nlj8spHUjd1ut5uU9pCl5Tpps/94DQ5P2fjd3t5ouVxnOem6Tjc3N3p4eNBisbgGUJtcenZ3dzdhbj3L5kxxjEXuErAEqJZ9BuyhQCZ9IzOBLc4/XT/N8zAMOh7TeUqAnr6nfCq1vE7n/5TyAQAFzjrpP7KfzimiRGUcoxaLZnImEMRL3/e5Q5tnrjH8z58/z2UZrltJF5Wd6na7zcHver3RalW65wEEsFFPT7srUBpUVbWeP79LTHnbqqprrTbr7Ng27VZjjNre3KTNtlfZ2+12We4hDhgj4LGua0UFLVepzj/vXwpJlm+va79crxSDru2JU3BSt42i0plGx7PZlroECQAdL1l24obnzhvzh0GjUi38xbJJp/NZ1VUeFII6K/eproDTy0nQPalkXgFEDpIgoPius/ZuRz3z40ERcut7eTzbwVgICAmSkMd5Ngdg4t9HVwi0ea52Bqw8CGH8EBmSchtlfB7laO43POvme1m8oQ/zQrkZ4zyfz9mOsKfHgdU8I47u932fmx14UB9jzPuNkBH8OPNBFoEMZiF5yjo6oOZanuljjtFhdFeSmraRQtBiudQYo86ncljq/s3rPKaqqtQNvaqmVrtcTI54CCFcZbpSu1goKDVHwNZrkFRHtU2jUFWqLRhHviCUCVRd5kIIClGKYzr/rm4abVbpAOA6lOyFVDbro3dNU87pw27jNwmQWX/PJDB2AnjPQLFdgBdyw3oh8wQgXv5NkOt77bgG8oFdkTTZk0VQ5IQY+A9dhyxCf7GNlE8zLl6u/9wTG48cQlDju8B2WTfGUf1YAtFhGKQxqu9KIEiFjycFCKiWV9sVx6uvraXe8DHySqaUsXFdD57QQeacOQkhSLHICetUkgOlizKy47jUcZeve/G70wofJ+ir+vMHSe99gHQ6HlPUHMJE4FB+TwlKb3fcYFKdgXSj66wbyuGpX0lvlYv4TxaUQMaZbRyQH/rlQYikfIieZ3667jxhTXleyve4rztiBB4WArbLgxie19kxjJ87V2dDPcOA88RJuQP066bsTppr2CGChhDKZk1/Fg8qmXfW1hkiPuepexSNg/E4M4T5fhd7Q8DI35kbGCZnWnmPNWPuGMc8fQxji1Hged0oEbhxL597T7/f3d2qrmu9efMmd2RM3XBahVBPysyQX1LvGCuehbm5vb3V3d2dXr16Jc7sSiCj0v39nR4fHyebx9Mzd/rww4/0+vXrLL84CrrGPX/+XG3b5u5DLos4gq7rcrbC95ow/5Le6vJFqQZrAFtNxhT9q6pqcmggQJuM736/nwBw9I5smTtgHE9dVzkDLBWHnMabZI69Lb6nA1kl28V3LpeL7u42b5EwxbGU9qc458S4b7LtwrHzvCEE7XY7NU2TAzPAfd02aq7zDjCD/AhVChQAzswNsuRsNGPquk63t7eKSvvVbm9vFWOcsMDYm8I0Ts8P8xp41p3vYWMgSZg7/sb8MyZkL8aYP++2jkwdIAdbGYcxj53vF6Ig6e/t7W22QR68sfeB+aAMySsR0GX8Ql3XORPhRAc2B5vqvgi92O12WV6dofb9dE6G8Blsp5MS2Kx50O5+i3lk7dq2zTaV75B9Q794duSMOWVzv4NJ5o3vEeB4EIWt2263OeMEuPXMA8/LT9bJ55V7N00zKX/3IBa7wx5fQOA86wG4xe44YG6aRnGM6sbiL/D/3JuggOf2qg8P/lMW/NqtcLlUtO6pTh5Ipbzc7S2yJpVyUmTl+rX8nMfjcRLIkt1gTlerVSZWYzVtuOBBDPsuWU/mjOf2syF5Di9pRW9Lp9Y6VzR4h0CqYNxWeOYB3XD/TvdL5nteZo59cOIB/OQkH+vkpaFOXpPhdllGzjwj6sFPIvOOBVdKuZOgYxueGbuPDDKPkBKOP5GJse80jGXPsjfzQP7RG9bdAyrG73arqaa2w/24Ey9zEpr3GCO/s/74Npdb9y1fz+u9D5CYOAfldV1n5gZBc4VFCaTCRjhTBGh1YWCBXaD5jjNZGEvJWz+Xzb6U7hQhqN7psEMI2u/3Rdiash+oqt7uVgSYmLOWjA9HBUDEYflZAxgEHARGxY08Si2VlsEA2jnzyE/Gx/UYgwcV6TmVM2cOetzJMfcwXawZjpXgYbfbTdZqfy0XYqPrfr+fMIMOlpGreaaI+87T5c4KZfZEpcyF+WYuXN48y8j8w/wwN16Tj9FNhmzMG2RxVNw/HYapSZc1Z/53u90ElJKRcIBPpufZs2fXPTinDGqQjwJ4EjtL29aqqnR7e6tf+ZVf0WLRaLvd5uf85JNP8pqxXnwnM5ghaL8/ZOeKk3GHyTyydrw8yHRAyZ4M12MMK7omKW+IZl8SmTM+T5lO6nJVznzyYDqtqyb65BltlzHGn8YqPTw8TLoI+jpVVWr57uD5+fPnappW/bXFLoAfeYV9ZD5ub2+zY5eU94Y1TTM5cHTsBy2ucgjoeHx8zPI+J3RwqIfjQev1Ws+ePcvz7/qGnjmj6EAYkCOVQBCgwnMAGp1NdlvB3wHW6E5q8HHIa+ABOH7idDpp0ZRDCOf+AUAHSzwv/aK0j0CejdWMj+9QdgRxIU2zvui/kynYOP7GNR2A4QP8oEzsNwQF93XykM/FGCeA0Z8bGfesA37Wn4113m63+bvOxrPmAE/3y27bHSj655bL5SQTjRxCrBAQ+1o4weV2xoNKsmp81gNzAjkH44wRGfC9WfzE3gBi2+saH49H3d/fTwIiJ3JZB57TbYkTFJfLRXWYZiEyZhhHRZVMju/d4L7cGzlL81kO3/XMlbP+DoizD551m+T7yANjcFnFz87PM0P2PIjG9hfyanpuoweIMcZcCkymyIMuJ7GRCfSX6z179izrHGPgefHJ+F4nUiGmfGzIvesC64ju4++wJeAODw7qqlaIZQ8ba+aBNMEbc4JNwA54wJE+2ytUJclAkOZkGGPjGb06q1R9lIxR3ZRGEi4TdFL1vVpO2iD/3MeDKV58zgk7z4Z/3td7HyAhRBgxFsP30EhlIp0pw5B4jSQgfn5tFh+BlUr3LoQRZXe2jX+7Yygb1cPE+XF9Tslm4Z0JSWMsrZt5tjkAdyPKNRin9Hb3EJwae1F8TGSRMCQYBy8JdAPgGS2p9Pf3Oed7i8VCT09PE0bC07c4I67njLOva9u22u/3E6DEZwG8yAgGzMfjQJzn4X1+J1hiLjz7wnpLpQSR+2Gk6rp+K9UPuO77flKCwPXO57MeHx9z9xyf7+R8k0GkrKoE72nfVd/3ur29zc00vPwAkOLsHgaJGnxJufXtarXMzsAzRICKw+E4YcwJUpqmHMDHvNzf3+f7+QGFDgqdcQeMkJHy/SY4HQ+gXH6ZU8CKt9Fv29S5zZsOMH4YzbT3Z5rFKDpZTRwHDHX63JCBBusQYznbinnjZ5pTiS5wrJGzbJdLATjIdwpsDgohBaw0i4BEOZ/Pev78eZZFAPtisVBn2fN5Frk7X3S52jeArneE8mCQ+wF0CKggUHw8njVhfbCjMZZ9OqwhOuL77Shza5omZ1B8rwhr4pkqD0T9+z6G4/GYzl+7zhPve4baCStfB88EOsuKzFKCCCPs16CMCLmAUGAvrBNMgIC7u7tMLDA387FWVepUKCWSCNvk92K+AJHDMLwVhOMDuLY3MjkcDjl7NWeGPTgEnALcPCNJ1o11x9Y5CPNsJcGGAz3u6wCTIM9JTb7nhAz3wd8g1+i1Z5bcv7OefP9dvgIiCBCnKnWL2W632edhzxxMQmzQDdN9fLExo4KmB8XzSnMV1XUl2+eEg/tJfH6et7pRvSpZFyf7nGTl82RN2uVC0e4/95k8gxOCc8yEncEWYFN9fd0fONnLZ3hhrzzjOb8vMsXco5dO3IxjyShzffy62zKuw7XmGSL8GZjFgyeu4/MzDyJ5sR7z8j1JWY94LnRA0ls4NXfIU9TQlwOuh6HsZ+SFHDDWKbn9NjHO8yArvNAF1xlsKtfzOfE5c7zleu74etGWPXBf6/XeB0jUgDuzB3OBg5tHoC6ILnx81gMNvofCIJAIoLOermh8xjMFzo6lzxbA4Uz+nEXid8BF33diL4gbTs+iOCOF8Pj1ADKu1DB+jBFHhpHyvVU8K4wdh7N5zbGXfMBM8zssgrMkaSyFJfV78/J9Vr4G+/0+Pz/GEiMIEPK1YS4cpHFNZMQNiFT2q0llw/LcYM7bfGIsKIGh46KDLUq8kMu2bXMmxjMZviYJnJSDNclseXlOVZU2o5xFAQvGnPj1+T4BF+w6AC4FXSX17gYa50fwwvw8e/ZMbdsoxjHvQQK08/IsAutIwwJYIfSP/TkAXebS92J4ySvrCZg7nU6T0iOeD73b7/eTds9J3lI2CHIDgJL0OO3pQe4Zb7p3Ca7nawgIZHwwn8MwZpnlsEoyeH3f6+Zmk0EB675er7Xb7dW2iwwiAeV8dl424kFJuywsvWduNpu1dk+7SYacfSapmcQuAzl0aLlcaohjzibe3Nzo/v4+AwEHBpA+zBf3gdxyIIf9YWyAI8pVvO2xl3RwbW/TTDDsjpyxpaMICovN9x1UH4/HSYMYvu+ssAfg6JQH/MwnthswjBxgzwEBfJfvd12nTz/9NMsFn2F+vHwHO0L2hPFiF0IoG9XRG+STbF8IIevc3d3dhETDzhPY+Vlk3mTBgdS8wQ7PQBb8zZs3EyJIUg4wz+dz1kPk1YmdOXhzsgn9dJuP7UM+0A0P0iDrIMx83Tyrg11xwtPtfN/3Oh5Pk8CKZwCHsL6ljLfOgBW8c7lccilxFabdQHn2ruvU9b1imB6J4dkz5pN/M8amqdXURa8gKfBRXZeOJCB4Yz7Ha2WfEwnYRtfpd2VIXL55XrcTTkx4kAQGcEKDoNSbBc2JAa6DnEP+zW0CJMJ2u52c7+TkiZMyrKWTaa7ryBzy72VlHsyznjxfqfQJCldfw/eQHeSHuXJdYK55ZsYzjqPaplU/TPEk8smzse7ut5hnnhkcE0JQ7AuhDjmCnHpgyD3c5vqcOe520tmJci/fG8bPfxbSex8g1U2TwTBG14EShpkyM4TRN1piFKTpRDuIdbaJa9Z1nU+55j2pbGh1dtEFopR8FFCAQCCIzibxKpvTSjDljoLnBADwHIzJHbVnaZqmyfu15qdwe6DDc3BtnBkMrqTsaFxxUSZXylwacDUCzIFUjK0Hi/7CCPA8OFxPTxNgeYkG6+e/A8Jd6dzgdF05y8jvPzfmfJ6AyoENssjvflCig7u6rrOBhn1HztzpEQB3XSqtgSHGKAJaYkzy8erVqyuI3k3KIcex1LHjHKRyThHyx5rWddDlkubCzw05nU7abm9yxoln4R6Pj4+qqmLIAIOsg6TMSBOYpaDn8s6sjxMKzso5+8tzcm3mxUkC7k02C0Dq+1HSNaIul2Iv/MRzmoN4JkACxI9aLNoM7Fg7Wo8yDzHGrENJpwhQNpMSyePxoPP5+BbLeTqdrkC0yXucUjB1o8fHx8zw+/MDHtbrtZrFtHQKWTifTnr58mXOpGG7IA88EGX/WtsmRyspky2AfUgU30w/juMki+ogkbEC0uu6zh0WKQvkebGTBIEALAd+PANAAVvm906ArlPblGY6+BayOU4QeHbdgRW2kDkjoMbmuV9BX5fLZS6vxN9kAGNVDlzbx41NnbPh7M/zzAHzit1zMsEJQuQOW4WPQca9ZAq7QUCOfHtQOC/3xfawFsMwZOLM5Yw19M50zDHBJDbDnymz45aNgZxB/r1Cwe8lKTcwYD3ABmTJ3Q8xB/h2XwfWiLFiwwCLBDrYZTLMzBs+nq6ijPH29jbNr4L6YcjNZjy4q6pro6NYzkz0gMYJK4KB9XqtOqSuks7m89MDH37PQXxdK+ptG+u+GczlQTfvO3gGGzg+c31w4oKxEdgTTHANZMr9MrLOGCB/8Cn4ESdbGIMTBMjv3H7h35ygZk0cn/FMnmVxG8E88QohKLzjvozNA2EPurDt3NOzcKEuWS73sayF4wrsJC/m3kv50jhL1scDP5chl0d0xtfUbZaTmZ58cHkJIeh8KuTr13q99wFSd+k0LEcNw/X03shm2sI8eYeNpISNQkgdV9L3Uqc4gAXK55klVzhnN3BU7ljcoACK+B4Lme5RKUaEcFRdN+r7qK67yPfiZENnAoFxxyl4FmceXKA8DjZgXJxNktKZC4B5B+YoEkLu9eo4HDIuzMHhcMgsKvNDAAcYBxD7fNGpLr2imob9Ven3tp22wk1rW9giNz5uFDD+zibBDuJEWSNnk9xpwsB6Ot9LEzDGbZu6fiFPnhLHMBCEUsI0jqm8EuMIwE1tmS968+aNPvjgg0l3mmHodXt7p/V6rcfHJx0Oe10uXdKFkM4gCnWtS9/p5u5W29tbdZeLgqbAzQ0Y4IIAkj0tY5RWm7Wqutbt/Z2enp70tN/rzZvXijHo/v4+O403b94Yw9aqbTlnhf1niwKo+/4q71HL5VrbbX0FcKvsTJhjZ+bQQWeJcZo8D2DFmT7WzR2zM+tk+ZwNXK04mT61YO/7kxaLNmdtaKndtm0+jDVl0jotFmRlSne8ZE9OWi5XqqpwPR9oodOpHH4LoHJZBeQlG9RpGE6qqnIOCIB9vV7r4eFB2+32rW5GHBmw2Wx0OB50upwz8GUfGnqLTfHzJLgHgQJZgpub1IJ8jKXsAbDy9PQ0OR8LcObgwDMGDmTGcZRiVHe+pO5aw6BF0+p4OCgOo5p2ofX2JgGFrtfF2P3D4SA1UYOkTqUTYAhBIUZ1l4vaplFbpz17x/1Bj4+PiqbjnKWCbCMrnoGSNAFQsLQ0OEGnWCdkF7/kWVeuSRXEcrmcZDaRX/wUMo+8zxscYJecYScQ4RVCyP7EzwHCbnIPJzBc57gHmfJoMuCZFe7ve2FYe2TFx4R/cwDFT4JjL5dkbubBKlk/91vYYeSQfxOooTcOwCDesOdeSeBzcTyfpHgt8VJUpZLZRW6YUwLOua0CuPOe71ljbc/ns1bLlZqqmhA32S+2bc4geSkx92MOkJfM8A+jur5T3dSqlLpMKgSNMWoYU1BG0OGBklTWibV1osMxxTiOEzlALr0yA13yIIxg13URO+2Bj5PWPJ/LFrLJOjOvfj3PWDgedBlAzj04wT6AG1hzD0CkaYkmzzvPXDOXZRyjmmq6r4e/MU70CftyPp+VWpJW0jiqbmrFoOsRB42iopq6lKWzVu43kD3HCU5k8TsYZ4yDhgj5MqaW4mPBk57hZx49OHI5RTd4LrdJzKWTV5/39fU1BZf0L/7Fv9Af+2N/TF/84hcVQtA//sf/ePL3GKP++l//6/riF7+o9XqtP/gH/6D+w3/4D5PPnM9n/YW/8Bf0wQcfaLvd6nu/93v1i7/4i5PPvH79Wj/wAz+g+/t73d/f6wd+4AcyK/31vGJMLZsTE9Dl/4YhlXns93s9Pj5OzutJf09tftlHwCZnHBuKIJXMDQzPdrvV7e3thL10J4Gjadt2khHwxU8CRV1uOkhzGHotFq2Wy9QK+e7uLt/DMz4AJ0/TPj095fKCrutyKRFlKGQuCO4o+XGWBSdDAEVDgzmQ5jkxOLe3tzno4V4EHOv1OndN8xIfjLKXjFE20HUXxTiqbekq1KvrLleAWEmKV5AjSWnjet+XEjPS5XNlv7m5ycYEY7VYLHKnMUpclsulbm5SRoSObuv1epKNgrV32XBmA2AE4MRAezDIeChzQdGl6Yn3x+NRm81GX/ziF/M8ATLqutHxeNJnn73SMAzabNL9FCpVdaVQVbq9u9ViuUznbCxabW+S/NLCmwDEgwIMNd3dACkKQUMc9bTb6c3Dg5arpb75f/qf9MEXPsjfAaDf399npi3pVqvUqjsZzBDSWQkpyGjUNJw/Uauum2zsXP5wMDi0XId/Bfh+3gbz6N170Gme1zOP6L4HsWQqOIg1BSOLqyyErNeHw2GiayW4GtV1qeX8cpla0+/3Bx2PJ71+/UaPj496etoptfEv98bZUz4qSS9evNRqtdY4xjyO5TLp583NrVarVS5x5PvOiuPYttttHt96vcklnhxGyXfHYcwyjMNn7vzgQ2xOJo2acvDwZ599lp04dsdZYdYOQO6AGBvWNq1Wi6UWbavlIp0P09S1Xj5/obqq1F0u2j09pXOblkttNxutVyvVVaVn9/cKkpaLhRTjtXVxlKLSOU0hHR/w6rNX+vRXf1VPj49ar1b64IMPsn3lIG/Oszqfz3rz5s2EVWbNPYPMfDBXBJvVFcw6qwv4cBvKXlRKGSG0kAcylvgVZNUzNsj8fr/P4/XgDD3xQAt7jV3woICDSodrxsJtmssC+ukBIWXDbBLHFnM9SA3PDEjKtpaxY2fwF+5TmRf0gIwL9hv54rk9QPf5lKZ7OU6nU9ZxWogfDgcdj0c9PDxov99nuTifz7lV/e6w16XrdDge1fW9WmPZGSM+wjNe3kTFs0boMfbq9vY22y0qOvhbVVVq2iZnbG9ubial3Owl9aoSCL5hHBSqKp/PlAIk6Xw563Q6TxohuF3tu3KwOE2deAYnROiOyjjAUI5B2DeMfNKpVCqZEw+8uQ/zUTBiabSEHAP4wSpc14MMB/7IsO+h495OogLSmX/sm+MGzyCS/cEXOVnNy4OT9Fxl3xdj9rF41tKrG9p2oTGOGhU1jOMVDyy0WpejClzmWC/sMzbaM0L+3zxTfu4uUghZdrqhV9f36Uw6e3b0GrlnvMwXgfN83ZhbSEMnqz7v6+vOIO33e33bt32b/vSf/tP6E3/iT7z19x/7sR/T3/7bf1s//uM/rm/91m/V3/ybf1Pf9V3fpf/0n/5TPqvgh37oh/QTP/ET+of/8B/q5cuX+uEf/mF9z/d8j372Z382C+73f//36xd/8Rf1kz/5k5KkP/fn/px+4Ad+QD/xEz/xdY3XmQXYkzTBpbzMN40nA1uMhu8xWSxaxTjmsz08nYdQICDUI/vnvCwJAUMAHNQBALg238eZANqcuXCwA7NJVy5AOQwzHaO8BhXB96gfR4oQInAYAQwm12ZcgB3u62NDyVNJ0HHyWQwXjtxTz3yGDBelDw7spBIgEtigWE2z0GJRDuRDAfm+G23q/N2w8uxkoVij3W6XHT7A08u1+Mm9SulVOeOBjnE4MEBi3/f5oEVnkJg/MgDO0tP5Zc68YCgyK3gdAywZ67rZbFSFoNWidElzucVY40TJknZdp/VmrWEs7TRvbm7yZm53BJSKAYoYg8sz8g6okcqZVshuCCGf6+NMIuuI7MyzEL5GHtRyX2coAbWuC96yGLvibCiAk+dCv8dxzK28GWPf97q/v1dVVbksh/HR+MBZaGwUNmQYhrxhnzXZ7/c56CZgdn1hX5KkPAfMUdkHkfY39eOgp91OL168yMw/pVJVFfIRCsgVY6BDGhvN0ZnD4aAhRikkZ/vBBx9IUt6b4vLI+qMnnp1GHsg+O/nDMyI7HhBSriQp72d7F2vL/dBtytCaJjV92G63+vDDD3PZkt/HgRvZFpd9HDpOG1nykmAHpZIm+/JijPm+AIbkn0omhcYpXNvHh83DLhMQEDDA/POazxH2iz1s2O05CPHAAv1B9o/H44TtnzdNcpKM58NG84xeVoTfYAxOOno5Hn6Ze2FLvTPZvKyHNWDeCh6YliCyhu5Dua5n3C6Xi6q2ycDfCUevKHFilTb8rAfjwV6xRp65IAjuu04hahJEsmbD6aTVZj2ZRwf9HqAiA/z0DLpkhw9XqZMaf2OckjRGqbH1YV0d/+ADuaZnKjzj4hgFPIS9Y4xzDFJV0wYi+ERwg5fmMQfuv5Bl5IZnRmaxcZ6Rdf8GNuOZua77amyDk0M+ZvyS64PP8eV8UR3KlhJsGXLFczJv+LquL+XAdPQF6/lzerXGnDR1ffX15PsQSegSY+c7Ppf+b2wJ2Nqxmeu1Y1qw/zxY/HpeIX693/Avh6B/9I/+kf74H//j+eZf/OIX9UM/9EP6q3/1r0pKSvnRRx/pR3/0R/WDP/iDenh40Be+8AX9/b//9/Un/+SflCT9t//23/SlL31J/+Sf/BN993d/t37u535Ov+23/Tb99E//tL79279dkvTTP/3T+o7v+A79x//4H/VbfstveWsssLO8Hh8f9aUvfUn/+//2T3MNu6f/2nYK7pzFjLEsjJdXJeNfSqFYII9sm6bJqWxaA2MkcewImF/DmRRXWi9JQ5iSsNUTYfNmDlWlCaj19eJ+KCGGycEjJXHuAHwDKA4QYfS5ciOEY4B1pLMQAu0O14MylAnA6JkBNyawPsfjMXdDQkkxDrD7bVuYI+6BwwBosjZu+FFOwBpjRo5w+J6N6vs+G0LAmgM+mE3OuGF9nc0hK1Qab/STmnjkDoMKyMCRSNJXvvKVScmdp7n7YVDdTvecISuLdqFgjhYgMd/XwOf5d7NodLk2aQDIA74vp4tOx5OeP38+2cjp5RbugAAKyB8OwxkqvoezwOGiI+iLgx7mbW64HTgxH4BpOvgxJmTGATnz6uNwBhw7M9dBrsfz8R1kiOD3dDrp448/1n6/11e+8hU9f/48Z754Tpzf8XjUzc3N5Iw0bBPzic3yunDWERkex1HtMnWy416Mab/fS+Oopi514u7kGTeZY+zBfr/X9vZWm+0m22nKelgLSjd5NqkQFMwrIC/buGFafsF3sJ+A/vP5nIM3Z96xwS53vLbbbZ4TJ4SQLXR8Xp7pZBKBJc8UY8yliowFx0/mBR8BEMJ+EtSi++gkBAyBOPKA/3OQXlVVzvw62eGgD1nxEiZkPsY46R6IjGOLnFhiTuZ6iQ1gvTwwZeweJCJP6BHrit/ET2OTmCsH+NyX9s5zsMnL55mM2vl8nth7QJ77A2REUt6f6sFTDparoKquc+bqzZs3OYAna0Q2AT/EGvpGd+bJyVQfU4xRVQiqQ5VlDd9dJZZYqkoJptsBJ/g8yHMCizHzzJfLRYu6NAjwsYQQFKtkI3g25A65dBwhaRLIOMk19xdgFCcmwR0eSHjQG2PpoujZFAIdCFleHojiA1hn133GxX0dLzrW8bn05+LZ0SHmnTVjfbiHBxtt214Phe0mfhKygesip4whxqiqqTWYvnMvb9KBTfAMnAevjJ919Ln1rK5jOHTHg5gwTveoud/kWeeHI7u8omusn/uE0+mk7/pf/7AeHh4mpeHvev0P3YP0C7/wC/rkk0/0R/7IH8nvLZdL/YE/8Af05S9/WT/4gz+on/3Zn1XXdZPPfPGLX9Tv+B2/Q1/+8pf13d/93fpX/+pf6f7+PgdHkvT7ft/v0/39vb785S+/M0D6W3/rb+lv/I2/8db7TDITjcJ2XZh0PKOeOxnDctgfgRGsQN9fJhGsswvOPlAGgcNgLCgfAQxnBcGoS8rtqLuuy6VYOG0cOE5iDjSTwI8ToAxrhcJvNpsssHPDgWDNGdXnz59LKuw/jC3fR1hRdMrDPPPhztZZDMBH16XON77pdc4WYCylwlASkPp8sO6pDDDodLro6ekpf465pIGHK3A2NNcXKXE2ZOKEvXmDB04ASEAeisocoxfMIUYedm+73eru7m4iN9zPgQyGCpnCSSCPH374YXaeGESM0XKx0BhU6tStLbNiVFs3mbX0QAi5ZQ6RYUBe0xZWH3YYEIfcIOeMh7H5hmjfTFxIjTaDUdaKTmjOOuLIvHzMwYMHFRhUd/pkJpBbb8qA3B2PxwycIUBwmj7PjMefCdsAUEe3nVFjnMjAdrvV4+OjQgj6hm/4hjy/2DayOjHGXPLGfPsck7Hivg4gPZMMezj0ZS8RDpO1juOoEIuN6ft0vtjt7W0GepTz0uwklR0dtN6sM2BmjBAC2D3Ghgwy59gQb29cWytjD2SwibD1ACZnyp14IWB04OEsNnKEY2bdnehxsEKAxljmAN6BOplkQDzzRxbOg3zsDlkcZIt70XIYQM1cYyMIXAFxzrgjj8xJycKnrCZ76BiHNA0oCHCx/dhAACtyCjjlPtg/fAWEnLPyriN0/ZwHKL7mHoAhL24b8AOuBw4sGTv34Pt+DeyJlxoy57e3t9lPTvxXFdRd15/SwmEYMqHx9PSU58Z1DznzoNV9JTiHe8WY9tHFatoeerfbpfltap1P0woOzwr6PcmGSppkD9EZfBPZIyeIkJW+H6QreHZS10Eu8u9z7hmEORFDwE6gDrgnyKmqavJM6CyfQ7Y9CPTGSMwxto/1hIDxcjJ027/Lc3kGmPlB3xxPegkg9+F5PThCNjx46PtedVXICmyXy5KvDeRgjFExSLWmGXdso3+PeeM97BQ2jTl3+0hjEb6DnoBj5/Zaw7QlOLrI8yMLfj3m2e0JY2UenLz5PK//oQHSJ598Ikn66KOPJu9/9NFH+q//9b/mzywWiwy4/TN8/5NPPtGHH3741vU//PDD/Jn566/9tb+mv/SX/lL+nQzSnFkrTEwRQI+Yk7CmCQTkS7CC0wXx77Ztm52dOwZK6dq2tGbu+z4ftIdgsaCLxUK3t7c5sPKa2ZL27dW21eSeBGdJ4YfJqfAILVkGaXruE1kvFNaDF/6DfYVtw9AidBg2gi+cL/cAaBLBI6gEODFGvXjxYpKFgmFnTM6aesnLYrHQbrebPJdUOr2kphuFbeOEey9fg2Fxw8R1fB8Rz+msl382hJBZajdozJEbYwIIrl9V6fBGDIzLJdeGJQVceAaD+yALq9VKu91uUmrFetV1reqaQQKAAWKGYZDqcojsu8Ax8+QB7Hq11qjCwuJE1+u1LqeLxiGBs5ubmyzLOA7m3rMP85ISz3qyMZ759o5vHiAXQqTLOrvZbPTs2bNJh0kCSc/ieLDNmhEMOVhGj/05qEf3rKXLFQwb+jXP1iE3zJ87SuwCwTDjRYawNzhkACt/c6CNo8FWoNMxpj0Jwzgq1OU9ApO+76Uxndru7POLFy+yXfLnIBhYLBZqlkmPP/300wyIHeTgUD0jilyzx8fZ/rqqFGIJ9sm0eM0+Y0emmXN+EhR4eZODrWFIe2pg9pnzeWvo5XKZx47NklKAiw6zfoBSZJvPMscuD5AMTdO8VZnA9R08OyPNdx1w4sOwpXO7yTiqqmSzY4z5DC1JWWekEpgA9iD5fP8Ieu1+mHlCFrGHfA8ygnFBBniWlvXxIIFAGzuLHkNG4psBs1yPOWItPCtNsEjDFbKCcxvizRvmtnwYBkWV0l/mD1IGWSVT6yDQy85Zdx8vfoQX2GAcoy79ZcKwgz+iyv44xkP1g1c08Lv7I88yOdgNV9LTs9MEBelHIUxZG2wldogxYc95Prdd+D/KTfm8Z/ZYO9ZzTrBmX1iVLsSebeDvPkf4TN/L7LbZSVPGy7WlaXMHB+3uo+eEJt/juSm7c1+cvxOCNMaM4dwnerDn45GUS59ZV+6Fvjl5wL/n18beorNl3UtQyE/fZjAfX13XimPpRsg1fq2sI9/xzzp+RQ5ijDocy0HgX+v1/5UudvMIzTMAv9Zr/pl3ff6/d53lcjkpP+K12W60XCwnyt33naqqOL4MCoWBL6wZC5K+L6UuaSW74AETAu7AmSyCs9kYZxQPgeQZvKyP7IMD6uR80hxxGr3v45GUgxAE009uxuAAjDygQ7AwVjgInIkbZq5b9m6V9PBnn302YQ6lohSr1Ur7/X7CvMA0w2wD2MnCUSO82WwmLcf5nhstN4B8b7VKDp4AjmeHtWMczBtBRVVVenh4eCsTw/WRq8yqX4Gll+Qhz8jI4XCYdBQC2JGd8xI/STlYBvQRFCHvnsFknpGpm5ubXI7kGa8xjhr7tzvgOIvmm3SdbeJ5mDeyA/3YS6GUNiFvp9NJYz9KsQAoZ9kBd+ybuLu7e6usB/1h/1yMpRRJ0iTYYO74XF2XElTm4OHhIcs3cwcjPzey6AcOmY3XNzc32m63WR6dZXvXfg9nprEpBIqAJ89kAR6QUwKMuq4zwAEE+YtOh+gPJA0yDMCTSo3/vOVyztb1nYZYSojRiaZpNHSd6qoE3ATjDw8PWq/XWq1W2m63evXq1eR+N3d3Ol/Oed2RAR8PGUXAE+sD6EJPMyhSOajXy+A8s8LzedYW/cG+FlA5ZtCGTEGYoYt93+eN4VVVTUgp1tqzGHOGGZuNrWGtXB593ggabm9vsx1Fxubky5xB5Xk9m4X99hfz7QAI+eW52cy/2+3ys3qAzjz6XHsAwpj4O/cAiHkpooPsdBxAlf0n13QgKpWuXE4eMC4PyJzA8ECE5+S+6Jrr6RyMOZPv2Qzu7yVnMUYNKiWHrDV/95Iw7ot/xQZ59sqBOnPspBKZXmx613X66KOPks+vK20XRffm5V3YJNbCK134u4PYJAelQzDvg2HGoNQERcVPeZWNg1pIMsbs2SX8gDStrHAyGB32AMTXGLnxgAQdcTxIwM0cMwb0HjlGFhgXsuh2Cdnw7D1z70ScB2o8y3xN/L7MXdM0GvpBQz89K8hlc07u5qAlSL35GSfL+LzLvgdvXBd5JeD3jNucrHLin3twjUXd5Iw15KNXrzjp63Lv8zQfp2cpP+/rf2iA9PHHH0tKGaBv+IZvyO9/9atfzVmljz/+WJfLRa9fv55kkb761a/q9//+358/8yu/8itvXf9Xf/VX38pOfa3X7mmnfjVlZxHWh4cHte2020bfl5IzZ6ySYJ9VVbVC8I1y/dWRXSTF7Li91IB6cOrMify94w4LOS1BatV1g8Yxqq7TuS+XS6flcqGmqa6GE4a5VtNUattSQ864YTwJ0DCyCKwzH3yO7zvwnwN3SRNhdwYNJXEj6CAcA+hMIYYyPeNS2+3mOh+9Vqu1YhxVVbWePXuum5vb6zMoM5b7/eHaCSyq78fr2tIhrdSkYtRhHNNcN5N2sJ6F8PG64nuWjbnAEWPQUUgPSgko2J8AOAAYePCAo+V7jA3wBKCHtWGOvTbfAyBfu7pKZxHEYdDYD6k+PErVtVzJz7fx9XbHDnN6Pp+1XC3zZ4ZhUNu0CjEoxKA4lj0YODgMNJkuDPxut8uMn5c2+r4T5sn3BUlTB4vBJL3vMo9jYTyScrANsHGmzPWUcQCY0WvuOS8ZZC2c+QI0ImfYBe6LrPo+BuQCp4P8OuMJ0CNLii0D8HmmDTIHGUMvuq7TbrdLzS+ujP5ysZBCVKWgqKCmqjRWlS7DdQzjqNVmrRiCFqultut0YO3j42PWAzIrHCbMng5sSghBVeo1q8VyqbpuVCuora9rfpWt1Ma7UQiV+ksCuV3fZeeKbPr+GjqdzQNvWH6em3XzDAL22Et4lstlPmeGrAdyRRMHsi/ch8yyz7mTCJ5Ndv3FDjVNk89awsZ66QjBtmd+AQxk/bw7nmd4eEaewecNkMV+Jrrt0VHPCRHPYqBryL7LIuNEx5EB9Ah55jv4IcpG3R85Q4xOeRaP52VM6CljmgcpDmIhmQDHznAzZn9+B8rcg/E4CKZTV13X6oayd3pQ6gLKNT0wQB4d1HI91yGeKZctVZVSJ95Go2I6iyglGRT7XpXJCfPKmmPj5zrj2dmJT6nTPpau69UqSkPyMf0wKFSlVJy58ABlHnSyP8vxE/NP1YNXUGDj3Ed4dgi/5Jk9J8md7PYDdx0Pvov0BE+hl773yGUFfOSBjhNcjo34zLt+uv56YM9zduxJGweFOEohaLspZ8mNkmIVdOl7hZC6PA9DKn308Tox6bLHmJE71sGJRM/YOcYk0HYy3jHKOKZ23+AtruGfxea6XWENfc4ZD3bk/53X/9AA6Zu+6Zv08ccf66d+6qf0u3/375aUhOKf//N/rh/90R+VJP2e3/N71Latfuqnfkrf933fJ0n65V/+Zf37f//v9WM/9mOSpO/4ju/Qw8OD/s2/+Tf6vb/390qS/vW//td6eHjIQdTnfVGyRmRLHS2CCBg/n8vGZKl0TnNWKrXtTe18E5g658VLYLKkZVFcjDmMG62xyaAAkCnfILBIJVStUrveVKqU2n1ur8ocrk61ugYJpcMcETbCjGBhgHDGjMfL7nB87lgxZJKyYvA37oeQu7NBSWAkHORzdo4bM7JAIdTXoOqixWKp5XKVsywAPIBLCCHvLVqvNxMDm1ojBx2PpxyQugMm0OBZAaY4AIwpLLGz3AA7B7vO2PC+G0pYNEAcRh8w46CYwHYcx3xf9ImxYhjcmR0Oh8kzYmgAGDgF5C7qmsHqz4XhsqypP4c7HVhNPidJVajySeJt2+rx4TEZf8scITsuc2TOvvrVr+YAdb/f5+CPOQFU875vAnXAwt+cbSZA5Z7IG/9mX4RUmCdnxt3Azvc9kJFhHRgna82ccV0CYxgyAkzfuzVfax+zB0ToKwek4sixcx5Qc28nTBaLhZ6eniZZZPa9EKA3dTmANY6jgqTz6ax+HNRfgTtlXIvlUlHpLBRn5uu61m/8jb8x2YxxkGYlWejJer1WZ5utM5C5yoHr1Bj7tJduRuywdjHGySHAZEs8WMOZOsnhABt5Yh5ZtxhjLuulwxy2g9JP7DiOm/ddNxkjsulAHPuAPXf/xHWdifYyKwd7/M2zunyX/XDIGuWcrA0y7cCS5hIEf/NnYpwASc+ueHbDQQ7rgvz5dRkPpZN8Hh3wjAOlgL4GzAM+2Rl1z7Y52YLMeJmyX48Xuujly8yVA1fkhBLSPGdGhHHfqFI9wfzM/Qo/PSCYvxLZWNq6xxg19CWT3fWXvD+Q6/FZPs96YdeRVQgtDxy8aqJpG43XOc8AWSVI9vXw8monsbGLEEReCiuV8i/0kYwEc4k943q+R8sDQpdfJ6ux6R5cMz78LZiS63k5q2MpgjfPQjJnVVUOhvWgzUk8ntcrlHifNXK/Ute1muscV1Wl/TXIRM7dtzdNo7opJX9kbbguMo7fdp300jtsh9tiD3x4Bp935MfxTAipqYj7XAJP1siDnnlPAJ6fqhy3Ab+Wrvxar687QNrtdvrP//k/599/4Rd+Qf/23/5bvXjxQr/pN/0m/dAP/ZB+5Ed+RN/yLd+ib/mWb9GP/MiPaLPZ6Pu///slpVPp/8yf+TP64R/+Yb18+VIvXrzQX/7Lf1m/83f+Tv3hP/yHJUm/9bf+Vv3RP/pH9Wf/7J/V3/t7f09SavP9Pd/zPe9s0PDfeznQR0kQEpgWXxivHXahRpiZeMq6UNK6brRcpuCBNsDOfnv3JO86A0CmyxtZp7SPYKGqqvN7XmZ2Ph+zQUZpnD3HacMkeyCDoM+ZVjcAGHoPpGBtKKljXhhbCCEzLzClzhzS2pV7UWfNewDDvu+02WxzgOfGzw0LinF/fz+5lzMgPOv5fM7nlbC+Hgg68+a16b4vwOXEQZ1UwAT3hSXzzzhAxllS752eu58YZQc+MNDuTDC8MK9zB+MBrztWngH59hbJlJPA1nkds7NWDqowpo+Pj1kO3KljNJFf5oH54YwuMmIYPoJyxsHemuyEzXFxPYw0a4SeIz/8zsvBgJcROeM1D74oIeL5Ca68VAoZWCwWk/PFeHb2QTw8PExApXfTgy3HKTE+nj3GqNevX2u1Wunu7i6DF56H+x0OB93d3U0cM7oLQAGQYTscYABQ0CP+VteNFMo5E8hc27Yar2UezMVms8lAkxfyxH0ul3TQa28y5A7YMw5OYDmLi67xN2cqeV4y1U4aOBgg+EeevIyI65Ht8+yR738CKDmIRe64L3pEGTZ+At2pqiq3i5+DYWQRG+YMNvLmNomfHhQAOGDMsXXzbBJrRgMBJxNcblgnABjXwJY6MOEaDrq8TA4/7AQFc8NcevZqThhxLQ8yPNOArccuQOixpq7zTjy4z52DbPywfxcf4mOFbackk6APG7G6Vk54STRz6+BvbiOdPKH03YmeebCQSMX1xPa4jCIL/j1Jb+1/ndtEZNLnD1n3gB9/wD29THACluvSgh6/+vj4mLGXYzRkh2fg+2BAl0MH6e/ydW7zsVWQBcgSthgf6PbJgz4Pap0wQDZZJ353++zrygsZnmcusXnoLXbIcZcHVu8KtD3AZA6Qe+aK77v94HfG4SSHYwcn7twfZyJQQdXsGj5uLz9G15EFcLuPG9vt9uTzvr7uAOlnfuZn9If+0B/Kv9MY4U/9qT+lH//xH9df+St/RcfjUX/+z/95vX79Wt/+7d+uf/pP/2k+A0mS/s7f+Ttqmkbf933fp+PxqO/8zu/Uj//4j0+YoX/wD/6B/uJf/Iu52933fu/36u/+3b/79Q5X7SIpC8YHgAmox8E504vDYhO3l7Ss1+sJYMGprdcr3d7e5O5LfA/AMAxD7v7jKVmpdEnDcJY++FExKrOP+/0+36/vB1HS50IoTc+Lwdk5uMHYwB7SKQ/F9HbSlDUQrPDMOLD5/DnwZY4IOp49e5aNPmeQ4DhwfGkcjbAHTdNk5henNC/tGIZBz58/12effTYpe+P6KGTbtrncyp2Pl8vhDFlz1oSAzA0xxl8q6W2yYxhnBygY13lQDrjychmMgaRJW0zGg7Nxx+9gxQ0LhoIx8gwYRwcmfigtskbw4lkznzPkSCqG25l5N4yu48zRZrPJQJ9reIkIJS7ok8s6z4FDoNsVThK9xRF7cO6y6Xt0mAt3KoCiqqryHh93iM5uOkhzhvFyuej+/n4yHmdevZU4AMj3TvFysoB2397U5fHxMQdgADHmjuYWOF1kFpmiBJXADpn0IIQzOVabdWbcN5tNLsskQBqv+uE67qQTwRh6vVgs0qGtM9uDTEAkeZaFw385/4s190ATQMi+BeYbGXDZ9BIxqZRglqCwZCCYFyfgfvVXf3WSmfKyScAHNhbbxJzwd54B+cLm0ZabZ3dwiJ4jo/yH3GHvnSkniPJ9Wp71QxZ9vh1QcT8AoWeoPHvi9sZBUYxlj6QHUQ5a0SO/3jyQ8qwHz8ja4PNZS74DiELPnXRy9h774HbPfRHAzO0ttoJAmGAHWWG+nHSQlJl7fy4Hpg7ImSfG7aQUfgvfwbVijFmG3Fajax6EMl8EUMgBOoB/JWOH3m82m8m+ZD9jkqAIv4XMeBkXJA/Pgyyx5qyB7+1zEsdlDluJbPHdue3GFnmQgQzwN/czrvf8Pg/AuQbzyn353UkFdNwDMdaEMmr3fW5L5xkS1o219ooi1zG3Z1yPF++zhvPAlfthX/066CLkHPPm5ZHYAMbp9oIxLpvSFp17uV10EtX1wgkFxskaonf7w16f9/X/0TlI/2d+PT4+6v7+Xj/1T/6Z7u/vJ2DIF8ODGAf1AJumafJZHQROLAjCkBZ61GIxPW1ZSovz+vXrDPJZYASmrsuJ1XOjH0KpK4edRlCapsosnLMSCMXxeMydRLy0iud2tpC9QFI5m4X5gF32LkoASWcaMZqS3tqfw5gBLc6qY8Bg1tLclLp45phAyDuZMc8ANLoteXkVZXUOCJ29xWjPGVZnWZEZxu/BsQeOw1DOhPA5xFA424es3dzc5Pti4JELB6du4Hguz3QBeHi5M3EGBobPA2MAoz874A3j5m123cH780jFaHpdNWN2p8BPWnTzvcPhoM8++0z39/fabDa51IsDeZPslzQ9oJ05cMbONxOz3q4Pc5aPtXWWnTWG+PCgm2fygIYg1MGNA2B030EoOsmaMjfoO0Grrz9678Enz+ggB5kFGGEj3IEwduRgzmii81wzg+G6UnP9DllI1iEOg+JYupfxjOM4qm4bLUyXADcxRi2aVkNfOsJht8mEVVU1aSzgASn3ooSYNXRihGd3thjdZezOantGlnHwPeQ9xqhXr15lO0wwMrfNzjZ7IwPkxuWNucH24BMA2wAnZ5IhWTjkkbUlAEBn+Tfj4ZmRLfZiYv+d7ICgcX3DniMbXgblGRYnZ+ZAh3Waj9PtGoEvOsO1PYD4tWypA17/m5MUfAbfiN5TOcELWXCA7/LmeuDr6TZ4GEpzIGf8u66T6pIB5m9ucz0olkq2B/vkwajbMQeRBUc02ccNw5DJD+Zxu93mChICHQ8aHLPUdT1pSe+dzBi/k7PoswfCbCVwmcB2cC23u9hafDjrgA3wc6uYd7f1LhP4NicWHaB7QIougI88C/f09JQJKs9muc7wLPMAjAokt8eeJeLzvHw+sOeM1/VkHozgf+bP5EHgnMjl+8is4w1ejjVc7tAvZJa/I9uUmcaYOqMumnaiR+5DkC/3h44/5xiEv7HeD48P+s7v/l/+f38O0v8ZX1Vtm7/q6b4KZ+5RLibzckntoMl0SAV0wqi4ga3rUvbAfVDeb/qmb9KbN28mdeYurCiBVAB6GkdptOBAKrE20xp/Z5FxCtvtNr/Hf7Avbdvq1atXWUAxVlKp7ZWm3VVQHp4RA+XgwdkYZ1HYaAk4f1epTZn/oHHUBGjAlACW27bNYAHgIJXyEEATYIqsxHa7nYAuBw+S3npGz1agsJ4hYNM0fyPg4PPOSmIw1uv1xFAiWx4oYLxwKi4bzCnG3Ev5MBqsDbLo2SoHldwHWXXgEkI5jRt98SyQO2YMKTLtQMGNoLO7dOwDCPDdDz74IANA5gIZ43M8E3PmRAKA21P5fI9584Yb8wDegyycpDtq1tF1petS5y/GjDymPXXTPWNv3rzJe+m4JrpBlyjYXN875w5ysVhk0M9cs96AFA9KyXzznGSrQijlfnWduuN5BsSZcV7OziuUkjMA3zAMGvoh7w9iXAQ9zaLV8To/nnFkPoOmgQ9zQIkl9pd1Z0xe8lPIltKCm2d1NhL7zTM62z53zMyR2w8pZfk4msEDECcpfD8TcuBVDDHG3ACBM9R8jAAISkjn+40AgwSqHjQyJuRhHEsWHrl14gM9dSDHnDFH6OKc8CA48s/P7Q/ve/ZkvkfUAxLPWAGEHfS6bSBAdKJzPgYn7dyvYdsAv074ISN+L4IdD/TdbvMsc//hoHoOJEMIulyD0LquJ/vT5o020E8Hrg74sRHoC7LiWwBcfhgz2Ib5Zb54Fn53P4ENZ3zINzqKnHn1A6Qma0GQ5eN1G8/zIgfcn/XyPcI+Tg8WkS3GPCfG53OKjjuum8sm43cilbUgg+yBrTcA8WAMW8nz83kPBOaZTw9+CLCkgoPmOMPlk/chv+eY1kl7nwteLrvoCfaCvzuR5LbPvzsvJU0XuR7fEKadMLkG4/WM/xx7zm1AloX+7cDu13q99wGSzPhh4NjMxv4WDBbOzA18VVW6u7tT13UThq6qqiz84zhqvb5VCMqOEDZBKh2KPEvizImzyjGmsoPdbqfLpexHwXggpH1fUpsO5BEYr9fmfozNFW673Wq9Xk+6FbEJ18s9qqq01sU4ki2BpWQ87mjSEsSJMM8DAHdK6W+ldTYBHSwnGZC+7yfRv4MPr1/3LIrvxaHeugSjJcOGXAC4na1jHlBQzjtyQ1TX5ZBhrsV9N5tNBicOSrguRtMduzMtno1w4+AMqaQMsgEeGDsHljgJxuxgCjDKWpE95T4eQGCYHUTzzOgA1yKA4TuMCwOIbA7DoE8//TSX9fkeOphGrk/WhYDaSwAckHkpUQhpoz6OvW3bSevY5XI56RCIbAzDdO+aM97ekZGyLmR2u91mB45OIq+Mn3Ix9mLxftGLcoAs+kfTF0CPgwnsAfrjpX2AFPTKbRZr53t8vJSKOUkyXGt/2Gewj9yEOAXByNf5fNYwY/W5R9u2qkJQH8v+EIJNLwH2IJ81OR6PWdeRc7JpzubOyyg51wj7wXiQU+YdGfJyQGelnTmu6zqXAnJdAmLPgqDf+Bv0f7vdTmQX0IStRQ+9YY1nfJhLSAgAKu9xlpSDNOQCYOaZBGeisRWAXQ9sfaxuZyVNZBxdh2RDT9A35htd5dmdwJkHqciXkzVuM5nncRxzh0a/F2P2IK1pGj1//nxC9KCv7h9ZDyfbXA69uZBXXyAXlPKTXXFbSvULcu9krc89pdDYCyeoIA88YODZuS5rzDoh+06MIR9go5ubm0zo1HWdtwAQNHrlBveeE5quy75GDpYBtzSKcRvg9tb3S82DC/yNE2v8zjwtjNBhbt2OojvMC+SwA3OCt3nZrwfUvMd8+jx71Qj6Na9A8OdxDMPY+d3JXr8OL+yQl4ZzbcbMPZEZ7K3bAydn3VbyWQ86uWYIQbq2nsfWZCJYqZNuVddSnGafHRcxZseS3Bs5Q/7859fzeu9L7P7Z/+2nZgcHps5m6RVEz/4E9sbr31LJXBJ8ouZUZwvgQAhJCVdVrWEoxgeHjiEAWDmghD1kbBiSuq6v3chW6rpS9uMAFIDjZVtc/3jcZ4PHs8+ZfxTZBRMwiuH3TeVupN2YSdPuQ4ARHBgAybNmCVzXSl36Kp3PqWNfXaPEhYEFHDEvzqo7wAJ8O2uFA/LncAeCsyBIcwCdlViaOOKqShun7+/vM0Al2HHWxR0Qvzurj1Mr81HYP0/nu1IzNi8P8uATptoZyXmWBTlgft3AsJ6sFYbT2XDGLxVGyQNIru3ziry74/PgGfnBeeFEaPoAKHz58mXOGgK8aHwC8PTMIPLHmgFumCdnuf05kCXGfDgcNI6p0cDDw4Nub28ne2e8vMOdEqfVs6Z+xhIvBx0AVmeanYHj34zPg+Q5E853yBa4nXD5JPDxbKODFGyY7/VgrhSSDe0uFy2WSw19r1Clc+bGmMzrYrFITq4qe2xCjLpcOjV1rVBRs57Ov9pfSykBag4mPAvAOhMsU1rLXAM0nRxDJvn3PAuC3Dhp47LhesF8YQP4jBMB2FjWAf1zG85/BLrOxDuwcV1hvABwJ+M8AzYnqHgG1xUHV4yfPWPz7Cv3xhZ4Btk34jvj6/PtNgyd51nRN5flecdZZBZ7xd+8JNJZbpdVZAC/4fbV7di71tj9G/92xh+5w4bS5h0CZ7vd5vvnoLttFKqKk1OvfvBKSChO/GuMcXIYKnbN/ZhnSpxkgzjCx/g+NN+n6ACXa3AdryKQlJ95Hjz5y2UAuWLOd7tdtoVkwfFbzA9y6VkbSCBseLKbg6owLeUvGYSgMY5v6YPrCL7Ys9FO9CBf2cdF6Xy+XO/dqbnulem6yzXgL2XvqXFNOrbEg/uqTo0I0oG5tfqhT0disBZV0DhMS1GnWEtXHFtpHAdESKG6VhMM1tigCqrCNZBuG/XdtYFMXapH+r6/fqbseZdSF+AQlN/r+k5tQzVL+l8a56gQqjz21Ka7zmvSNLViTJ2WR1uHuqoVgpT2nF+zViPltIOCiq/rh+EaUI1q61LezgtZQYd9X2CS3aLj+/1e3/W/fuevl9hJyo6n1Hsnzz03COkVlRojlD1Eh8M+OwVvn+wlHc5m3d/f58wSzg6l9oica0glwHDmLBnYMFlYv+8801BAbznJm4AC8OblHDhvxnY6nXLpGowjRsoBFMEf93Sg4sZ1nh3hOXGsbVvOCCkAX5ltY07ICjgbhUNk8znzwjpRjkNGwsc1B0vO5jszSSCB83aDzxx5LTpAyEslHWg5Q41R4j2yIc7sIX8eCDk7SWBLMMd7vsfIWSfWDfnymmBkBPl0xs9Bm7PCXB/d8Xn0rBSfcdDN/ABcPAiWEmtKEMr4+77PjUo++eQTxRj1jd/4jddM60U3Nze5HIXsCoAApg8ZYI2QEZw4c8OGfnTI5873fXjZm5+1BAvNuuLQCezZd8VzOQhFz5g7n0v2jDHHXjaDfjur6tkZ7uf2zgNy9BwyAfDlcsFaZOZZZQ1pza0YVYVKTdvo0pUDpOX2pm7UXtd9s9loHEYt2laHa8c2DzQZO3qH3fZx3d7e5s/GGDNzy/1YGzLlXlLoYNCZYPQfe+DE1xxAcm/2cTKnZNwBdre3txMdJlhALnx9nMTA/vL86CWBzDAMk8NMPfOLrnOmGTbZM6c8N2vFs7vOFqAxPesLmfT9fsicpLcyj+5Hse9OVHkASNBEAIlf8qwo6+gZMS9pI7jYbDaTOYX4GIZhssdoDq5YI168h71jTXwP1mazeWdp/GT/jqQ4lvLJGGMCs3XJyvs887t3YJzbU3w5vzuxxtoz72R5PKvCmB1YEgT6AeruI7BZ7uOdLHJCBtn1knoC8mwnri8v4/TgxYMgSQpKQQA2lHtmMiJMu615UOn+jvG5f/AGCKxhInYJ2st+RMhe7BVzfbl0OQhjrc6n0qkyhEpVKM2sQgjSUI6LYe743Qk2l8tMDMQZSTlE6TqtQ18yZ/7ZMVB1UjBE+lylcbRgO5Rqp7TmJdEAEed+jDETYMUYNF4DnRCC6rbOuuvkRAhBYyyNYXojXaowPUYAGzKXM5dR9yWO7T/P670PkIZxmHQBk8rhexh3JnzOFGEo+bsz7LBwpJURDmfyPb2PkEmlLtQVSiplSYzTmUNnPTAq3vZ5+t3p9505ciPBfbwrl4N02BNKhHAI+/1ex+NRz58/nzyjj2G322UB5nr8TKVnlwnI3263k0YA0rSzC0bYnQVKgkHGcQDmPHBj3gnGPOAhoOJ+3hDCAwhSwZRaMjbmaR6UOZgFCOEo3VF76Q9lWlJxcoAm/s4zAeTYB8N6OgDm2TEiDlZ4Bs8O+BrCbvNyw+TgAPlGDpB95J21QJbdGc3nqmlKm3wH5Kzl8+fPdXt7q3/37/6dHh4edH9/r/P5rPv7ezVNow8//DAHKO4oF4tFLikDaEjKwRPrBPD0sgoHYYC3pmnyWWboP0HTbrdTCCEHQmyadwDjgaUD9NTC/zxhunkGWqCzboAliALXMZjszWaT9YH5dDDBc2O70Gf2sxDY8XcH1+gQ9ocgZhgGnbtO7aKdOC2eBVny77odI2PH3AJasGPOxiNn7Ntx0OY64DbSS4Nx7H4/7Cq6jAzzjMimM/OQFE5mwE4ydsaNLO92u3xN/qN824EgcoXN4PvoE8/n4HlOsngwzbOgA6wLa+d76pzkcTn1gNIzWtgYMnoEW5QxegmnE0u8eD4Hpthc7CC2bL/f5/Ej89K0xTv3DCHkMkw+3zSl2yVzRcm0l6J68xTvpEjQyrN7yaP7DdZ1s9mUjehVKedu27KfFt/M2jA2rwzgjC7sh5drMy5Kjz2jh/wcDodsM12mIV18TtABLxH1jByAGZsGXsKvMB8eiHgJPP7WCRyel9JhbJYTrh7sIsvMD5/r+15jLHZWKh0y0W+eGRn3sfLTKyku51Kq6hkybJRjN2wROsJnvXSa9ecabn98zvCR2ATmwzEWc4o98Ewp10e+GJcH0cyR+945OeKEgWMFr1pwG5EDN3sxRreX7iPS2JUPFnZ7EcJUjubVNXOb5JidQM2DzK/1ev8DpH7QzbaU4UjKG+ulYqCkt1kXGB93El53ilJwanrf93nTIcLJdxAUFpAXQAUGCuVLjFzQYlH2Dbky+AZfroHBSZ97dztH7okguvIA8F2hAUsIG2DM2UwMMkYKFn0OCmj7mRzmJRtmQBFK4OPg2ZzZdMaRlysmLYdRXEA4xpt54xk9YJ07WeSB58cQYcSlwuA7e8a4vXTJ9wwhe/4ZMjPOyPqa8dMdFNdhjGRLAezIjAc6c2bPAxh3XMw1LCK/EwQ5IHLgyBjdsc5LIpkT7kfAz8ZimmlICWBSroa8/Pbf/tuzngCWHh4etN/v8yGRBGo8F4G4y/i7mEP0kLNhaDDiQSmNNujwxBzg8FPr/3LQLbqCY5h/nvna7/c5APd9EoAdB4/IKE6XkiTYSHSVefTMp2cqIA0c3DsjDnjjfowFUMj8QkR1XadRUa0KePQsGZk1Mi6+Rr6PwB2/yzKfRWYZk5Mh2Ch0DlvBc6M/XlLFM7jdZN1oR+32l/GQufQ5wV65XrpPgTDhc9hP7+75LlLNHT6fQz/5CeDj5bLoQAxSD/nhefy8GOadQNB9maQJa4wd9mfAJr+L8PDMlmedeAayyw7efF8UhAbXdvuFXCHPT09P+ZnwCw68fG8jgTfjdT9EJotxcE/WARvne92wb56ZqWOly3XNOITV9zTxzARlkGPDMOSyeewILyeC0UOXVdchrsG9kBmyyGQRvEEMcoje5IyHNDmQmTXwFvdOunlHTCcWnbRzvOZBkZOfCWsNCkrrSVOqQpwGtc0yg2lkEZIX4sCrXpgf5Jw1Q47W61ohlL10jhnme/UcsHMPfJxXJ7gczv2j22HWk59OYLA23NeDM/TK7aOPDXuEzmBf8IeOtXg272rI55mrvi+l7eAZJ1UYqxPakFbJ9naq6rdL8NMz97ks1XXlXTrj5FbX9RM78nlf732ARNmYR/iwBp7KRgg8tS9NuylJ5UBQHByC4EJJ8CCVA8+kwuJL0177sEUOXNNnpvsBpNJBbc7cInjJeKe0JgENTkaaCjhnGPDMlC7AGgNiPFuw3++zsNIxKYSgN2/eZPCBw+DfGDPmNDnnpch0OVODQSWb5IEdzBn3dAfMOAEhOI8Yp7XsBGiAPRweCubsujM1DiwcuGFg/Hn5O3KBPPFdnoXvcRgk3QUBPwAo3vOygHexxAALGE7AMLLm68MzSpoEbn4P5NA32c+75zmrxPy40/Bzupzx4W/uLHECPhYP1L3jlWfGFouF7u7uMjB7/fp1PnML4Md8eXDi6+q6yjydTqfcyMHLGclyuUMABOOMmQd3pMgjAZbPmTu9Z8+eZeCI/vh1mA+Cbge4nglBV1hHD4g8YFgul5M9KYwDmZyzkcgbgQ42DOfdNI2G6x5ODrFlLqV0xoVnVAF7rn9kRr0pAX/nGee2z5lMAmGAiDtMfncwDakACJsDKoCAA0Bk8nQ65UNUWRMnaJwA8rIdghSeA9afe2K3AEHYCXSDn8gQ8s48eUAybyk+Z6ddl5FRgmvGNGVjS5kZJYzMmwfwrg9eVujrBoBivdwWI7v4bbI+/lkYd3TY7Qc/PRh0YoXrs15uD7Cv7g8Yu8+tzwt7aPy5PNDMc3yNKbkn4y8gsRwgi71CN+lmx/ew+/grsonYR9cdlwsHxjQzwcfSLdEzJFyXeffsuxMXjIXnYe1ct8Ac2EUP9pgLx1TzPZKMq6mbXBpGRpbPObDHRvOeZzKRVeYFGzPPHg9DOrog7TcvjQrmNtPXybNeYDbPfqEXvpcUXfLGKS6fzJ1jQie80QPHtu7PkQPXVSdjvIoH+fdslX+H+zkZ4O95EOUyPLfd3GsYBo0xajR75PcLIWi5KuSUy938HvPs6TjGicx/ntd7HyABOnnFGHM3KdgGBI30MkBQ0kQYWVhn3PmuB1IuuA6qHXzDtAE8PaKXKIsodeHcw5l+B+UYhjT+0mgAY3pzc5OZaVpfu5PjmhgsHFBVVTkocoXm+oAwWvwCJkMoB+1SYoTRT4HYWqdTKVVzZSI4gl1wQwVI8LpTZwwceDpY8gAYI7/f7zMo4sUhd4BwgLCDOYyWVPaEwaTvdjtJyo7cy3B4/tvb20nJBkEsJU3IAxvAkRsHAe60MRKc+eBMisudB3eUZbD/zMvH+Duyydx7wwEyQwBY5MdLw1yu0CeCb9huQCBAlvK64/GYnaIDcWSg6zrd3t5m3ZBKq+Bf+qVf0he+8IXsBFlLgCDP40AYZzTXb9aS35Ep9mZ56QZAmgAI3a2qKpeosj673W6SdSPokkqb2hjj5Pw0uqTxdxwUQTZg+/Xr15M24gBTwIuXSLAOHmyzfk7oeDDNMzkYJ+NHCdLxfMrjfvXq1SQwDuO1Fv9qj8ZxzFk4Pz/IASi654CP35lHLyPlb+gKz+VdpBaLhfb7vTabTQZtrqvSdN8kOsgcMPfefGOeActArikds9BD5IHPzjMInjUE1GHvsbFO4rA+2E5Ktpgr5BibwL2QCZ7Z9RdA5MyvE0PDMORGKXNgxBo4CeIgMu9Bs2dgbrxVP/YGEmK/3+frk+HgurzmzD7XIrD36gWCPGQQufE9hB6IekbGgavrBf6R8bnO8ZN/40O9QmL+bw6RDSHkdvDICXpPCZ8TYh7IcD3W3rPYzAtZXXw+a+alkD5u7JbjBrcfvn+VdcFOUgXjgTByjL54lgrsgZ8JIagfpvtRUpZnXfZoqTTRcRznpCh67LLJejqeSd8tY3AinPVznQGLsA4eRGBjnPBGvtBxfIxvifCGJE4so3ce/Pp4/LmQacbgwSDzw3rgq90/OE51e+GyAc7zuSn4dFoS6OseQlAdal268+SaGZvr2uHOCEfH3azNPJuZxlv2p33e13sfIG2320mnKwc/c7YNBcEYwK7y3hyQOmCTSvpeSqlADypilMYxSpouKPXHybG3Wiza65kal4mTYNG5L0ru+zUkatLDdcypgwjfQVFTI4rCuiYhKuCIlwNzNywYm67r8n4BxsAze4DGfQCqMBiwbZSX3N/f5/IkZ4kx7CGEHIhwTQylOwDAkhsEZ6klTU6Z5qwagLLLB/Pg5UXO2I5jKenDgc+bR/g6Eji600AOmF+UmxIrN3w4MYy3AyKYOzKBzAX7asj4wTQ7+9r3vZ6eniaOCsCKYeP+PNOcWey6LjOPztbxPDxD13X5Osw9zh1nxXvOxLnO+h6jObstSa9evVLbtnr27FmWnXmA50E5suWOk/cczPEM3Je5vlwuk0Du4eEhl0h41yXGisNCt73sl2flegBnSl096GWvEBkHSn4vl8uk5A37hKwBNnhWL7HBHqGnPC9rPS+RkZTBKoHh+XxWqN4+GDeEoFBVigaK+TzX8rIqtykeTIzjqJubmzzfHoC7fYK8gOhiLZj/u7u7CevrWWsPFBxw+3ic+JHKGTfMn4MNX29neJl3Z2nd0c8rAPhvTnAxJl+nOWOOTWLN6FwKuPDncyaf7/MZzr1iPh3sOTHlwMuBMLb6cDjkezpJiF9wwAUY9wAYP4Nf9zXDXvl8sU74HOYP3aSUl/XCpvA5xg0hstvtCkNtQW2MUYfjUXVVZcIP0Ov6OA9CsFP8zQMp5FNS3o+IvfCAlGAJ/XWyijVlzBxUzrM5CegZG8/4XLoun3FGgMb3WGPmnECddcceck3WGjvDXHhmicZZAX96mRJmdT0t8cZeK0h1XQhnx25u/zxLhm9H5iiHy+SFSjYlzZl3k5tWc/BvD4qwDdzPx+O+14NZcAVyhz6xRo5b3Ua4r2FN/XsenHjGzbFcND3gXk5geXDltin9nmYsP2eQ2kUrRanrLhqHUe1Vnge7B3YNPUe3MyERNVkDXnyWNQPflOC5BN+f9/XeB0jOFjpb4o6UBaaGlRIl2GAmPn2u0jBEVVXZA7Jcrg00vH1QoG+uS4a+1jgOappWUqXFYqW6JlM1ar8/KIRKdNsjCALEMD6Aowt/GmfUMFDjHhQj5Xps/E3BWHJaUXXdqqpq1XWrEGrR+jwpaFSMNH6oJFU509F1pY2wVKnrhuu4Lte5LiwVig04vrlpMrABsGBk+Y40PUEbBXFHy5w4kMQgM/8O8qldZ0284x9MIi+MiyTd3d1NsnXuNGIsJTRN0+jx8THLHorK8zvTxfOeTifd3t5KKkbIQboDWeYQpw1o4Tq8x+vTTz/Vy5cv9fr1a93d3eVOXjgpP8D37u5O2+1WDw8PObOE8cVQA3QoUXLWkMCYueTwYeSeYEhSzjgyFinJCvPMXDGnHqAR3DFP6Cm//67f9bsUY9TP/dzP6XQ66e7uLhMdMMW+YZuW3ciOA0Ev35k7AN+b54AYuQTgMzYAGV3XaN7hzGXf97k+PoSQ25O7LLIeZOvc2c+ZPLcX2DHmDgDkG9LJantQhNNBBp1ocn2FAOm6Tst2oX7oVcWUMQpjVIyDQlVptdmq77pMnjh76IDCA2y3CU3TXBu9lL0yrJVnH2CpkVVng509xQ4R6PhzIyNOanltPjoOqGDMEELIsOsvawngxJZ4hsMzaL4G+CHkBtA3D+bcfiDPzg6jY968xwNwX2tJOXPPNTyTic673PCcztajX16axbpDsOCTeSaYeCdBWE+ux7+dEKP0nb8h376uLneenWdsPBc+C1sPcXQ8HtWPg5rreNs6Bf6hqdVUlfqY7NVysdDmNlVwjEEKTepIeblc8jlLTiLh5xxYE0h2l059P0zWorv0qpt6otvuM1gb9xnuI+Z+CXuFfJwvF1VXmejJaEoT++9AuQrXRjinixSDFIMu51KVMS8ZL+um1NFy0apuGsUxNUVAXgMtp1WpriuNoWQy0EF8YwqogsYhqut6KV5tXlMrXDFad+nVtI3aptX5fFEcU4ZoHFIZnUJQHJWe4yonqqLGOKqqEwleNwuNMZWFKaYzHBfLNpNTbdNqHKNCDG/p5DxYwb4jqx5EOSZwTMrfHQO5LeIFOeUErGOruq7V9b2iouI4aOyv2yzGKAUaJsT03P2gqk6yMMZRCvQZT03R6qpO7dWVAqyqDlK4Nsg5HtUPg+KYgqNxHJJsxWsCII7pb3WTOt4No6SgS3fKz+yNh/ycSebAsSDygf1L86DP/XrvAyQ38p5mxIFKpVEDThRQ41kliQAnKaV3G+MVQnHsXloxB7yF5Ss19hiPvi+b7jD+vo8IoMV1MGiercHBFNarycbRGWE3oLDVOEdnd7zspTjaKgcXsH0hpLNrMIKLRTnbCIBdWNzCDJBqdwDhQu41zcwJTtWZI+9QQ1ervi+16oBfMinMj3cDhFnks1y7bUv3Omd7PXtIVgVFdRBUVVU+ZA5DlWQmZMaSZ8fgsRZN0+jp6SkzS4yPrAYBEo6dNVmtVvrwww81jqO+8IUvZMa2bdssU/OMz+Pj48QAU4I4H9cwDHkPnmdRnIn0Q3Gl0vrTGRxYYT9klfnBgJOpYe18QzwOA0PIdy+Xy6RLE6CG7wAivMSBuW6a1IXr6enpLZBB4ASg9vV2GcPeuCN78eJFzpYwJt+MzHwzV4zHjynoutJyFxlgfQDudV3n7IqTCqydpLfOrZk7Ws8sYTNZG0gHDzKYP9+X2He9mroEiYzn8eEhr5UHdm5vY4y5RMXnx20SQNsDRGwGdiCz+8aketDhWRoPxCmzJTM3X3/3C86mupOmhJQyWyezfM4Y4zxThB55uSRr53uosEXut7AVXqIrKQeAPA+y7fqJLuE/eV7kkXlmbSAV0Sf2tBHwME7slGdonKRgPGT9nFxgzsZxzH+r6zqTMPhD/JrLAfLh5V/+bMwT3wFkuh90MIrNiTFKQ8h2TJJGfGgVsv0bhiHLEPN9c3MzuTYyztiZS9Ypb2IP5ZgOD2KPx6PaRTMpYUK3vWzNZdTtPnOIv2QsusohWxXOu911HEHddU2dcAghTJraeMCy3+9VN1MCAHs5jqP6bih7EQ/HSTMD1gudcJnEJvhz4BdPp1PO+uDD0QNJqkLKQMV4zvPK2mMLuG66Z58CBptjZDo9a6/zebrPUSKoqzPBgj13QgXbMA+knPhwmeZ3dIH/eJ/1dUzgPsDn7nK5qB+GfC4dNpDPDUOvxjJRZPx9fib37krWPZHuVyweU9atbhst28Vb+uV2Ad103cceOCHk5BO40clMJwL57ud9/f9FgOQC7uwJykd9Lx3ovEMXQpQUt3zfN/V5apeFQ2FhZzFSHoQgCNTqOpsACyaV7iMYIJRyzggTTHkWgvdJE7tjZg48Lc48oQiAxfmY2rbVdrvVbrfL50nw98KMlgPTpJINSkCuHCbm/zEG5ssZcuag7/tJeR7z2jTNpB7+fD5rv9/r5cuXk+cli+QOA4acuWLvFobOS2kcBHlQgkNwNmdeHywVwO9OhDI3jGdVVRMZ4/q+2ZjsAGtI8JAZIQPZADVYaMCLB83+N3c+GCfWFaNTVVU+L6jv+8n+LzrAMV+spRvncRxzUP/q1avJ+UqS8t41snpt2+ZAnLHudrtJcO8A4MWLF9putxm0f+UrX8nBogdU7owIOJAxZMP3t/h8ECAz35Qe4XT4GwbavytpAmgJmviOr6Okt3TEGXycOY6We2CTXGY980BGCxtJEM93uLekCQGADZtnpzzTNJdVSA3ujawxtwRzPKuXR2DLfH24tpeCenYCYOr7JD1LBMjFoWKfkG3sBzpbVVVuxsAczrMkyAdkETLNGDxo8CYGbv/dZswBgAf1HgBho7xFsGe6WH/PoCFL7q8IrLAdvO/7lPCNMZbD072hjJfm4Ytc3lwX3gVwV6vVpGyMwI2xstZOdkBysqbMnc+fE53onK8l+uJZHHyq70dy8NWERoPZscJSvx2QYt+xX8gq9wEHSMp6hf56i3AnG1jH1Wqlpi02wwMuB8bYIvybYyKfU+b1XYRe13WSkVledl7XtRTDxO7wfgo8C6jn2ZPuVNnHeUCMbjPfTmAzvyXDFLIOkIVnzKyBB0kE406YMj88r+Oxpml0Op+0WBTSk3V7V0BNGWRVVRqHUefztNLBM6aOEXzusFPIJXPjwZyPkzn08lpsjX+fOSmB3/W64/Ta2VaEMsdcZ17W7NUrjit5MYdZXkJZH/6TlKs7mAtk0ufGAyXG4/jYSSPWdG5LP8/rvQ+QfK+BNBV4V37fNM/f5mDRmU4UeV7y5eyGd5ZyZ4GgzLNQGAPfY+IBECU60tsHiXFfd6q+mZixozQYRAcaPk/+WZyUMxUwh84kOIhJ+0eirjI/YTjSMxdnjVMnm+FMJo4Z59c0zeSAP54fUMCaANDoZsVnCEYcnAFQeC4HpBhVDCHzSiaL9aPJwvz7rBPAgTWWUtkKZW/zciuA5NwZuJOs61qvXr3K90SWyY4QrLKWvgkXZ+MBaQghN4YAhMyzCw4mXDYkZYLB5xSZ6LpO2+12InNdlxotzPfCsJeFeeXZn56esqFsmtL9z4OD3W6nm5ub7HTIpA3DoLu7O/3Lf/kv9S3f8i36+OOPcztwZIH5njt+9K3rusxM+h4zd1KencAm4LAYtxMqrK07XLdDzAn2gXv4/PpmXkCcEwqMgzV21tKdjAccgDXGhQ10MEBARctzB/8u+w5oq6qaHKKLHDHnrjdus5AZJ1vc4ZGxQEewrdhySvIAzQ4sPVNJtodAzMkmroX++fuSso4NQ2oWQ9msB2HYFK7F+mCT3Iew3ozN15K1ZTxesso958GpByB8l0wZNkEqLbT5ngdGdAH1vYuQcp7N45rIPPIMKcL4IaqQTV9T7nG5XCb7mdyWz0tEmXu3HcwHGVtsomcY3f4wZwRH7gsAXxlDxFHBAk0AHOvE2qKbzDegHyDu+/pYA67ppFdTl/GxDovFIrdFRh7Yq+X7frmPN0fw58bG45u6rkslUldd8cY9owFWSrGdsOT66BMyGjWtXmH+pVFjnO79RO75ybxT+eABAD5pGEpHXtbKdZdn9DV3cM41kTPmLft0myO3o4yTNfe1HcdRiqVJCPqLHfKge05g8X3He35fnoXss5MiPLtXKnBtlzOeNYSQSuzMnvm8hDDNliM33gTJ7bbLo5MG2V9q2jyC9fLnBDejM25LyQjhQ+bzj+3hWtgrf/6v9XrvAyQvwXID5c4AwCpp8jcm3Y1djIWRcEAwV0iplGGw6LCYHjwhhAgxwkMKFsPgrCcL7OlCBB/hcVbHjQOpYAQcwcYAunPwwMDBsZeKYPgys6SidGmPVWnrSiYqXbt0USFIYv6kYhh9DwFKhPEnq4ORZI3c4GBA+BxG/C1lraeNGVgXzwhIxTlzcj2gHGfL3Hjp2jx7iMI70+rO2QEcjtVB1DxAAGCQYUI2YZNYd9YGw09gBqAhEAUYl2B2ep4CsuBzQpYKGcGZMi7fN4SD9lI71msOlHgWnA0btx8fH7XZbPImc57Ts65+/fP5rMPhoC984Qt5rmk9vV6vcxkbgSzg1c8ywk44WPVAyMuPeB7mw+eOZ5uUsqi05GXszD8lKawpZMk8oEeuAYwhhFyvzZ4H7ueBJuvoAMiBpKTcOYtgyZlSAmACM88oOBPKevqcOcvMnLgNQn/4vIMhgBefQ86xGS6782yBs+PYcf5NkO/klxMofI9rO3nmoN+/M45jXh/G7DLqwYzbBc+yuP1xJw9A8Gyc6yX2xYNwl09k12262xTu4d9lnmlAQFc1xgqYIUjxwJb78D7y4WQOQa8TXw7mfF6RB9aRe1ANgp/1jLzvSZgHph6Q+xlz7jeYM42DBiOy0D8nQyEF0Enkw5sAQeT52vl6DUPa2xHCtJU5sh76oNV6mbvPIUesH7qPz/RGCcwt8pBl1ID4HIPU1fQgUf59Pp/VNu2k2sDLUtmL4iA8y7aCmrZk1Hwt0N15BYfbMicC+C4y6WvvpIHr1DxD8y5Coh96tWOb7ydNy6HRRSdRlsulgkrQ5AGLE1Gu8z4G5JS/8T38AH+fk0bII/LA8zn288C+qmsNYyGAeO70/SpnDX1enWB3DOOkE9jBqy9CCHRxyL4dOQWTOunLM/ATe+H2kmvjL3we0U9sxed9heja9h69Hh8fdX9/r//7T/6zzHACzKTpAZzuQAHtl8sls/oYjuT0+typjPILqSg6gi4pl1ghvM4cu2F3wfdAByacMRIxe92vX5/r8BwImm+oR6k9M8ELYfQ9HtLb9fYoplTKFClJ8qxL113UdckwU25DLTVNKFBUZywxDM5CSiXwgrV0UE7whQGaszr+fDhwjBlZBkm5pIu1pDQNsITRA+ADdsgMOZBgDhkrL2QKII1cEJw4m+gZxXnWwo2PM0+svwNYnpVAtKxRyYY4iUBDBTJK3n2Jz3iZkrNfMcZ8pgYME/Pvjo2/8ze/hssa+wyYS2cst9ttXh90zgE2zo9W5m3b6uHhIcvc6XTSN37jN04YbRho9GzO3gEuMLjO/HqZAfu9nDTBuQHSvMTG7Qhygk5gJ1x3sT8+b4ASD8DnLChZMJcfZ4J9P4jLk4MTngUQ6oEWQYYH7W/evJl0KmT+0QtAgwcN2DXmi2CZ5/Qz0bCHPBMyz9w6y+iZWg9uvKzL7Sky7DrrmQefYz5f1+XsIc++sL5e8uK6yHUdYLPGDjzcxnmmx7Mb3MvJEmwY52wRcPta8z5BJiCNbIDLIfPJ7x6g+D4kt+1OQLq/ZU0dHHkg58DaSUJsLkEaxAz6xbU2m83kQHIPcr3MDR3i+h4YYNvy3pqQQD3PR4DhZVwe/POe2yfWE//JdZh3l7e6mu6lZOzr9UpVPS2hYk4hLgjwHJyz7nOiQZL64e3KmAy+LYviJGzf9woqASHf43WtqsrXnX4mSLHsw0WWvZzK7SRz7l0y0V2uOQfDORioSlbcy6+cMIGwnn621mK5mFzLMx/ougd2IaRmD8gdftjXfhimRxA4VnTM6Oc9Mo+MDTnxoMqJY3TB8YLb1tSMoZqsafbvSh0B0U3Gx/PyDNhung9/4SVvuZQ+Fl8xfx4PCgmGsMFt22aMxhog205yIGPoAbKw2+/0nd/9v+jh4UF3d3f6773e+wxS1/VarUpzgwROKzVNcUrubFJN6lmXS9m0lwQvtc3mPT+zBIfv4EhSPm+pGMnUyAEBRXic1fTMCcbaMzA4wvl3EBY/bBBDTXDIfXluFO7x8VGLxSIDcb8niuDCJk0bXjjLi4Ij+CiFO7xhGFVVydg4Q+oKhzIQUDlY8A3mKBxjYr6lYmi9exXPPQdM7N8hU4A8ULPsc+ABgZcqcE3+5sESc4JBlgqYdicFqHMWyGXUQYizyjgSZ57YSwfLxPtkSTwzwzOw5vO59c337qCdgcrlHhacORPH+nI/5Mf1gd+RJYAlARm15d7YA2CE4XbmDEaLAHqxWOiDDz7I8n1/f6/lcqnPPvtMz5490zAMuYTD18ADI8/M8QzIBUSLVDr10VAAXfeSM0gPB0zMhaTsCHg2nNlisZiUNDrY82CKz3tmDDvI+wA+so/85ywp8sVzIieUsvmevflY+NycocfZIuMAG/TIOxShI8y/gxxnlMnu1nUqP5qXWCPXXM/1iYDNbToZONbOm6Ign1wXO+j2GPnlBThyMMpaOTjipxMmni0GxHm7e/SD7Dpj9j1x6K8HYfg934PoBAh+Al3rul5tCxscVddlv5OD/hSQh8n8l6Cu2AMPfLjnnORyPXOyz9lrGHwfQ7vgoOhepys5VLLbqYkQstC2gMXh6p+aHCRUVaWqrlJntGgNa+paobru+xvG3E3Ou+uljEvqEtZdLqrqEgSnDFWQdLpes5EUM9HjALaqSrczMkHFdkSFqjD/c/KqudoE7E6S4dJ5dBgGVXUtDm8/XnXPyRdv2DNeQXNMi6rKAo+qKtkOD6gJFPqevTeL7KeSXF4mWXJ0YxxTS/+u73Uye9j1V5sYRw3jNJvC+qCb2Aj3oWADtwvMneuag+26rnKlgZMZ6Lb7WYjEdN8kO046LBatpOkxDFyraRuNw9WnK2rZJhJqjKPqWGeS1mUdX+AZZroYSyVQkFJXOuaCn3WTuhOybQJChODIiXLfY+RnIjoG5HO8PNhN81Se2QkuPzyba8wxqAdG2F4nMZumHLHBGhIQzhMD/73Xex8ghVAptbgeNQxRpxM1z4V98HKlZJSWoiNd111U141CkNq21PYDwEo70cIidN0lOyjqrTHCIeh67wKKfc9R6WbXZ4aboEdyBrpPRjG44LVqmtSy2xlEWOy+7/PBpwQ0fk6LM8QhhAxqMKLOtPI88zS0G4ikKJXO504Syl8YbgeNKJKDl8xIGfNIkIgBWK/XE8DBdclYDebgJE3Gzr4Er2emfG/OUPLvuSNHQZOsldIPZ4CcseR6jKtpmtw5jvXFCDHf3BPGD8PopUfuDN04e5bDszXOpAKePTDhvtzb9xx5Zg9nxWcw2OzJwvD53hXmljHxee+WSMCDw7y5ucnlI8iMg3HPDDN/NLxg7Ojkfr/Xxx9/rDdv3qiua71+/Vrr9To3fMDJzIE4usq/CTD4O/IKGEVGCJLQKd/wDfEhFfLBy66wL13XvSUnIYScQZszkABhsmYeRKMrrO30rIiSmUS3sQu8kDPmh+DI9zG4vLNn0B2Yyy3PAkvo8u7y73oJQPRyTQ9SPHAhEGBukBHPuKGLXvrsgSYvdMuZ/qqq9IUvfEGPj4/5b86s8jlfH67lpU6ud2RNPbh02yIpz7sTDYA69pp4FYQDFAgN1gNWFj3263DtNCY2PEcNQyFlhmHMwamTR8vlQglGl3N1kr5A7inbBp7Xg2Fk2UusCIS4D89S17V2+71ClbrbLZdLXbrHBKxPJy0vC8Vro6VRo2IY1A3Dtcoh6NIPE9lcRKmuG9V1pVBXqi0wjWNUvC5lHKLGIQG+7tJrrEuJVSm5kuIY1feD6igNGtWFXnVVyu/Hcbx+ptc4RC0Wy9Q2XOmQy7oqlQM0HyHj2/e9+m6QNGgYr2A/SovFUlJQ36d1PJ1pfxx0vDYDSnNcqe8LaVVV03K1tl1cpTZkfJHHPYzqh0FSVKhq9dcxhWHUqChVJYvaVLUUKg1jlK62cnUN/OJYSKiCl676cDyktttBuvTdNVhNktV15XDypimEbndJAbrrPLrmoNzJL4gI5NCDrCS35XxMxzlzotPLrEsQKHX9tQ13nfb7EKDHKDVtnQPwYbhmgapaIabMW9PWOp160fyqbkpWKIR0gGr6sErrbV4haoyDOvb81Y2aRbLfTY0NTnK2XLRqmzQHQW/vPwb3eRDL8zpG4PPYgnlg4tktfLonGdD7OTE3D/7xj6wXaze3m/jpr+f13gdILsR9z4bSwiBI09PhEyNWgJyDFy9dcmBPRmmzWYsDwyj9kkqnFV4heM1s2deEYDAmACT3kAorG2Opy0aIiuMZjSUqLY73+33+nRdgAEcOGLq5uckC7CDBBT89S8hBG2y4B0/L5TLvdUHgF4uFDoeD9vu9nj17psPhoN1ul9laDAvAGmDPNQDOKN6bN29ypzoArjt7npPnc8MIK+ZZD2dl2BzsneX4nisw12WDOA7bg2nGQV17Xde5/avLK2Nzo8MaovxSAcnMmWdqfB9ECGUvFWDdyz0k6f7+Xg8PD/l3Z5hdTjwo8rp6GGiCL/YPMD+M1YNdAI/v32Psno0oYKuUVOHMCI7ZB0aQ7PvmyJB0XTrYGPlwwPn69etMHmw2Gz1//jyTJ4zHs47Ik4NtqZyb5FmAeUmCB7KQFF5iA+CjNBNCwfdXcV/sEAEgf2fOkDHsDxkYZBcQj/Nwe+LZTAcr6JU3MvBrOnB150bmqqqq3Iac+1L+hA2hbTTz6CQHxBPPA8GE8+aefN7tNXLt2VaeH+fL+8iMywCg1LNP6DtzxL5WiK55ZlMqDQScQGOeARU8H6DOg1oHYF6qi012XUUmCOYJuhibEzr4F2l6AGn6fgnm/CdzDIFBpjzJz6B0WHlpyOF74cZRudSWa5Y9rGUvwTxrx/r0fZ8Z7/S+Utmb2b6c7bkCQJ4VnV0uC2Pdtu1kX1QIQefLRavVMs+TZxmHYVTbtBO7zJy63LNOAPFxHHNnMHwZn2cOPUgliC0BzPSMp6Zpss+D/AvV1cYOvY6z5h0eEHjTlDmR5rLOXAeb3+mapDbYOYMRpLZpp76jrrWoyl6YEIr9Y00ghjxT2g/Tsmsnejjk3vU2hKCmbuQVO9hcl28AtGfwIWmcUHFMQsaQZ2JNHIT7/k2fQyoTuG4h30aB3Slv9XuWzP+UoOdaLj9cl/nC5vtzoNNT4qxk2rAVzBO40QMSbBJZJJ616Fvx92ABxjj0vdbLtE3FCVNsH34J+869GI9nohzTOpnpBBb2nz3Xn/f13gdIbdtMQFOJ8oswuQAvFm1WWBYOg46BIOPAgakAIuqOKVVw5pIF9b8npSqHtrKofgCiKzPCm8DLSn3fvQU+peJo+a4DNMbB97qum+y58BIOB0WAcL7D+w7ECWg8OILBhyn3Mh6CTzfIzKGDFmcGAC2AgcslHbYHY8oaAcRwdvOSOpSfEjoHCM7sOggMIeS5WiwW2u12k8AZ5+kyQZkM2QNkAKPkoIn5ZZ0Iypzx97Xg+QDZBOrIilRqvD3I9vdx9G/evMnGybuiOTPvZXnOJPEe+7ecVeOe7riYawyY3wfGmrXwIMxZS+QTlnyz2Wi73U7YZEk5qOI/An82lwOEGPd2u9Xj46Oenp7yWSUYYGTZ59EDVJ7Hr4u8skbuuJyFc5CKHDG/nrHj+j6vPo6mafKhki4HnpVylg075t3E3Nm63vh9sI0euEN+kJEq7Hk5eNfLdLgnNg/74U7YnRxjIhj0IMIDG/TJgc9cDh1EsMbIF2vghAQ2ZE5o8Dmvc0e3AWPcz4kJ1htb5vs33e7yLGTGyR6wFpJyMFmyAaVRgq8BY/A1chDjWW8HSMhqWvMCyrClPDN23c9tSnNRq+87UdoeQsm4prUs5/z5Pefjc/KIIBSb4fudqqus8z76iQ54gOe6RIc+gpnc2GA1rTTwYLQ24gF9waaO4zg5J4//IC288sDBMPLjfho9c8KMcnDsg8t6CWpbdf20GYiDb35HPlh75LFpmkmDH2RB0gRfMHZ0DJ31YNgBMPdCv0MIGiwzgDxlIsOAM2uYfk+nx3r2mjnq+yGdLKviWx2oI++A/fn8THHVdX7jKA2lDM9L9v35/awv7udkBLrDdX09XP8dL2ArmFvG7jbKiTzWxEvryDhin7i/6ysyil3ifhDh2BbmgPtKynLpY3BblcmfUErkkCs+Ax5w3Ob2CQKBAB2/AXbgPrx8vhjf53299wFS07ZarxKonbKLpeWq1+aez2edTme9fPlSfd/nzawYcAfUOPd00GVKocJW45x8fwCOxBe9MjZCKpvMnb2OMeY9FHxGiuq6siHdHVVdTzupYGxg03EWlO9JxYBR9uP7bgAzLrAEVoz76ekpM7mwMH1fuoDB2nOIHGOAPSNYclbMBZvn5HsEZzhSlL5tW+12u5wtSWs9PYjPwSjGWJoePOeBCh28YCu8HHFeL03mghfGywEfxs+ZQzeIMcac6eDvm80my6BnrNg/xnUpOcSQPT09qe973dzc5PE4U8scUGrpm7YxPsjS5XLJpT+LxSLv9bi5uclpdu4BKQGDyjz7M1EmwvO7A2Ct0Q0PWqlRZj6RfQf3AIxghhg9ubm5yWNivL/hN/wGHQ6HLPuPj4+SNGmL7iyxB6AetLpjxPnzGW8R7oe9cl2ch3fV5LsepPleQQe/Tg4g954VcNljXglYpGkHTz7Hs3iWg/tuNpsMiNEf70bpzB3BE+uBLeKZHWQ5i4m9ZSxe9ggYdb1l7A5ysCNcl3HwXdbPiRBkCgDOs2DLHLx7qbBUyj3RGRw+dtQJMuQFvXaAjJyxJm7H+Am4I8Pq5y2RGXDg5aQGeiwp20/PFqDPU6BYyj792mSQOSfKwXbXddpub6/Ztz6Xudd1OUgccsMJJ3wQPseDWm8nzedDCOr60vHK95+5TfbMBPOFX+JZvaqEuadkmOBuGAY1daOu871AZV3K/pOQ9W++b8Tl0WXKM/zO3COvfm2u9ebNG63X60weYAOrENRaeTYBsmdB3E653AFUvSMj13VSgvlwW+bVKdK0lTlz4EShxjiRcWz++fqsbsvwzSkAKuAZOUi+ZaFxmJbwl8zVdI29BMznwcmhcRxTTV8oRIsHN04U8+xgB2Sbe+Nb3Z66Xvt75diUaYk+68T9nLx2e+FkCTrB9Z2UmpNjyCq4ALtGBtozMTwPNhU/ThUEMoKtBMN4aZwHQu73PJDlPtjOh4eHrB8ErJ5MwGdgC5OdL9VcX+v13gdIOFo3Psko9llhcaBeurLb7bKSLxaLfJiipFwihpKmBUq1sCwgAN07NXkWBCXpul7LZSkBAmShUJSf9H2fWdYk8JViLHW1BFBVVedytf1+n6/lCoLC4FjY5J5bhlq0fz6f9fz58wwe50wXAaS3U3Ulwxkxz9K0ixt/Q9kcyDrwAXxJb7PggBZJ12B1lR2f19nDiGBoGAvGxdkXZ274DPegpOb58+eTIMSDIX9GgkXGgGHGOeDsnGly+Q0h5AMZJWm32+n29jaX9Tw8PGT208shWReM67xMhUxRYnGXk3Hx8o2XfMfXyQ2pZ12ZN385E3W5XHR3dzcJKjGazhSii+iA69Q8cEYWITQc2OEIHDCj7/5cj4+P+uCDDyZBmG989Yywr7WvF5kmPoczAfj4fOJQPZvggBAZnYNwZBYQWsiROgcOAEAcNDIgKY8Lp+rBjMui3w8w5/oMU819WTfkj2s5e+zMIhkZ5gSnyLN4s5a6rnMZl2e2HIzxHuPwbCZ6jEP1wAhZIuAgMHPCxpl8QKqvFdd0uzQM5VweD3xYAw/oPespKZNkkDPuw3yOPRj3ueNZsf08GzafQ5S5BqU9ZOKlskG6lOiV/TLMI+NiPp3QcDlJQUOnGK+BYrvQ6dQrBE1sMPeGCPB9ocynM92+RkHTDnnYNq8S4bnwKbzPcwBkcxmPpjLAK1UKLKRYqjSQcWTUMyaSJngADMB4AHcuq14axTh9nw6fh3TxTCNB7/lyUbxiCdbZM3gewLv8YJ+8ZBQ8xdj5rI8RwnLuT7DTYB1s+jiO6i4XdZdydp+TdHVVabEsm+s9MByHVMLJfADOM9mhUhHh84nd9fXEJs/JTPQyyULKGnItt/8enKDvyBc4wAkjlykP7rCzJcOafLgfw+AkDTrGvLvPc4KA53VSD/vglSz+ea8ImmfN2H6An8Hv4Ks8yHe/m21YLPPO/PH3efUD8+IBK77ZM6j4Pbet6F3GCtWvB0j5dTqe1DZlQyfC1LYJkO12u0n2KDEUhQEn6g0hlX95JgdBSMI4VSRn+XEi3voTI5M6DKXrwRSgWDhIV2hOgx/H6cGzbLpHAQj+UBQA5by0iCj/dDrlPSg4t91ul50LWRkidp4BRXPQhNICvhkXrZ+lKUM9V3TGitI7czIHLB74uFNmPjC2Dthx8KydrxPzQtkK7zu4xMj98i//cj5sdLPZ5Fp6wAT3o87WlZ61dkDAXPBMOA9nwx0A87m7u7tJcP3w8JCDKM8OMK88xxzkkF1E7qVSSsA8znXF2SsCcOaHEktAQYylCQaMrLOjDmrQP+TGDTtr4KlygMEcJHhzEw9umF9kgGeHVPj000/18ccfZ5lg3jDilFA6uGQe562KXc5YY2fy3GbwAmB1XTfpVgdgZA54PnSt2Ldp2S5rzRoBWv2eHri4M8OBuxN0BtU7ahV7WICyO+wYYw5yPHsolSMDPLO62+0yUJZKZpBnc2ePzHrgzOdZCxp3OEBg7phHdB8bzHNIpaRuTtBQVuvsLffLYO6qy76nEl3w0mO+w9r7mvATncPPsLbMja8hjC/6iH1zNn8Yhsn+PL+2g8R5loe/A0wIuJDNx8dHXc5dajgQo4ahV9O2aQN9DOr6ki1lzpxcYgweMPuhmIBz1ulyOep4BXwAWHTXsxw8M3LX930+0gHghWwO/ZA7zzEuxno+nRTCtPzTZQO9xn45aESe3Ubw4lnnARdEqc8ZusGxIAQrGW8oaozF/zAOCAF0jrEybg8SnRR1QmyeBfHME/MNweoNVQDSzH+6RrkP1+Ceo6b7bsexNIdZr5bZdiD3SRY6BYWJHWYvLjLN/Zzc5OX+tmRPSrdgJ38kTWwdL+bu9vZ2Mm7P/LDergPDMOSjNjyrxLz4C/vmARDXRPbw6fNKACcRkUfHVVQl4Fd8jl3OCaQZb9/3mWSc462maRTHqLqaZkWxx1wXGcQeOUHF2rEOTv7wn/vdyZ7N86+X2OXX/rC/Zk2ClsuVUgvNs7pOmXl1ECCFHFF3XZeNzuvXr7NSe6QNWGJzIsINWy8pXzfGwtZ6xA5L4FkPZ3ARdAemkq6sCGNoJF379F+Nu0fRlMRJhblyxtgFyEsQENAYo87nU77m6XRUCOkwWAfKCKak7Gx534O4u7s7xVg6jeAECfYwBl4a4QabEgYP0hzMYPT8vCZn+TAMGAHvOsicOOCaB0ycDcT19vv9RFFZV34HnMSY0u5eZuOG00EzjojvkBXhmR2ceqABMEAOWHPmjednjG5YswEz1tOZRWQS+fNrOcPN/jwMmQe7PifoiINk7s0aI1uefRmGaTtuX0c3jsX5Ft0BgPvc0+Thgw8+UFVVevbsmT777LOsixhlQApzAihiXpkvL6mZ6y+fcSfNes/L3FhPB3w8G7aBdXXWj++jiwS6ACfWnYwBzsgDNQ8qWWcH7DhAdJQgwWWFtYOwwQHyPnPKPZyo4P7cE2Don/GsIfaYUkQAr/8N3cYeQBh5mYbbHOwIpMfhcLg+d9rfOo5Rfd9lX4IuYYs8Y+ElRm7TPDMmaRJApu5h8Up8POb3563a89kiVa26rq62caHUIGHaPpuXk08F3FWqKkoxUyl6sg1SjGkPUvIFpbzOyQK3kdi7EEJqyXztfnY6nXKDgnEc1bStGss0ra4k4Pl0up7LUg5ph2isqtQiO0par1ape16VNtCzlsiTj4O1xM7xc94xkfmKMSpUQaMFiqxpXdcah3Gik+/qvOh+Hvklg0uGwffGYJ+wUZTV4kfd5roNgsA4n88axlIBERS0WJUyPj7v5c5VVamuqlRBBngepgGL6yVtvdum0fpK6qKLdV1N9rgdDwc1V1/c1LUGeevmJHPoDHbSfVPTpFbXTlKk7JCujSBKRs4DVLrYNdf21eH6b2zpGK8+cBw1jm9nQus6Ne2iw3FVpz3qc1vtBAHrgUzxu2evITKcYCfQYPxcy0uPkSfk1/2uE140inJf4OvuQRLjwXY7JuEzHGIMzvNKD28g8q5MchXSAbNcK1zXoaorNVU55oL1Z+2cGHbc4JlAXo5hCPy9FA+5cdn6vK/3PkBatI2aBoWjtXLpvOROYrvd6nKhDKAwg77/iIXxnwmYLZQORi3sE4t1Op0VQnVVmLOqKioEzpgoXd94ETjMSzhglZIiLNS2xeClNHNU35cacpQQB8o9UDhP6eJIUFYChuTUK1VVKhO6vb29Bk+6OuXCxEnKTBFCy/W9dIqMgLM2BD7Ocnk2hXE7m+gMCV2/HFxiYOZnsGCAeO6bm5sJIKPU0DNZnGXDPDpolzQpo/K6eP4mKRsswD/BGHNPORTzw/klVVVNSos8lexA2x0f84Ax3e/3k+AaYOWpfubcA0kAtWch0RcnCGiRvd1u1bZtDsbJPMz3G/n+DrKNgIntdjupCXcw486aPXAAd19fZAdGXiosHTLk1+TlmaWXL1/qzZs3Ez3n3h48A6KZDw82CSI8IMBgO9PnwJLrzo27VDa5wmjyLE56+L8Zu5MfrgtequHBDHqYwdJYykT4m5MJrCGsLgCB8aBbzBFjY+z+vF5i5cSNj9s3rCPHDvQ9m0UWck56IKesHXPlpBJBt6/NarXW5XLW5cKmdcBOOsIBkscBKGvlrHEp06xF62Sul8qnB7MP3hp+UOooF1VV6ftJDuL1b6kUjkARwBajdD5fJraVIJk1nO9Zwi7w8izsnPhwW+1MbrsImRDy8uGmaXQ4XXW6rqUorTZX0iRUWm9Tk5RFaxntcVSoGynQYztqiNLpcgWsVa2mmpZh+dr52PEb+J2maTKRxTNhV5HVS19so2Jqee06BLkI+YNcQdwg51I544w1cAzgwNubSznB52XtXddp1KjjOVUxxN4CiqbWcCVI6iokubnqK/Ke1nJMADYEtU2tLo4KilJM3eaC2NNcMoSSFMe0z2mzXl39xShVQd2lv241SEETDbOWi1bjOC35qptaTV0O1Qa/jOOo5Sq1O5diHk8V6rTHyDKBvlZ936fA9hrcD/2g9SaRnP3QTQilKlSq6qAqBg1Dr37osw8KIWi5SrKrWPY3evWAZ1ywbfg/bJX7UvwfMumEkVdFuP33/ZaeafFsJnJyd3c3wTdgJQ+ukSeCEs+ikenb7XaTjLaPExsCxnVs6T5nvMpdFdN2sRCC+uv87Q0nMVb3cawjxJj7dgh8t9++NwsbxLXwn+4XP8/rvQ+Qbm5vJg0PmDCCCJxf7lizXGoc3z7xGAfrjhkjyKKx9wAngCBvNtscdAHqAPYomrPQrugAQRhDL7nzTk4xlk2QGGbAiIMLd3rcEyNJSZZ/Jilmp3Ec9MEHH2TBvLu7s2xO6XfvWSqezefZMwoObKqqlFyxVg7SHcS4MmJUYM6ckR/HMR/WixN0J7bdbidntDjz56yfG0JvL8xPAjNP8wIcvISiruucLuY1d8SbzWbSBVEqraMz8zWWAwQ9WMPhz7M2zKk7egwT4JxMCXMx7yaHHNLdibHAJm02m0l63AMGqTCG6A1gzJuG+HwzPrrTuFwCqF3XHAShe3yfABE74HuxCKw8Y8vfHh4e9JWvfEVVVen+/l5SqZMex7Tvz8fD3/kdJ+LAExvja0Gw7PvfXC48eOD+peSj2ApfZwIYZI8197VxGefaTqbgyMkg81lsHXLM9z1LhHyiO8yPB8Vc3+WMfZbYJsbvGS6eG/lFbtGH8/k8OeeL7Cslcw5ksLcAkKenJ223W9V1rfv7+zxG77JHWTYEEiSAVLKqTdPk/VXomNfHO+N7PpegsoDVYcKWIj9e5sucpbkqLXch8rBH+It32UBnX/np4N5tldssruVNRlgX9I9yV0i9qqpyZ0juzTi8tMuDN9aQf3N9votsOsHmQZGDOs+eeadYdI0ghveddOL63uADGWc87FXk+3Obgm9EhpkH1hvf7viCcQDsnGBgjaoqHfLp6+ABKvaReWS82GSe39cXOwWo99JA9AW5Rk5cr6RU8sfv3kmNcThxAhmCHjiu6i6dur5gN3SVShMaATBPh8MhP5uvue+jQt5ijOrHkplq21bN0OR7e/A/DINCH7JtxXchA75fx/0kz8u9vdyN+fB59Col9J+19iCAnx4E8R+2QCrd8nJWpyoNVrwiwPGok7GuD+4nXG4dX3vioW2aa4auyLUTNFwLO+64ZbFYTLpAOrHkmIHxe/DjgSyf8+ZHn/f13gdIi7Zs7nsXmGDS2B+zWJQMAZ1t2BdU13XOBlCmgtNaLNoc/KCYfi6Ed0IiOJsHNA6KUCxvNzoP9KTCxmO03fA468zfMcLOKqOE842VXCcBummJDYZzsSjMGGUkzDFCDrjkPTeMJQNWntfLlXCgzLkbfebTU90YBgKX+XNihPq+1263y+OC5cEw+z4cAC/172TY3rx5k9cZRSeD6Bk4NxpuwNzRtm2rzWajh4eHSYqdWl6el58esFFm5kECpY0Ep9zT97UhGzCnGCHvbCUVJv18Pme5J4VPxmAemOJIPWBw9tRZLHRLKkaOueGzyALjcAfiNfG8RxDgBpjvMo+w/M6W49SaptHd3Z2+7du+TeM46tWrV9lJbLfbPHZkeLVaTdpre4B7OBxyNpB5QVcZLyW2zhhih5wUwQn7PVy2PROFXDhBwX44zw65XXId8YAF+wnIwCa5w/Xgnr9j29ijhz0BFLuseAMIwEoIpUEJOs38sCfMSQiAi5fbYl+QA59XssPY3Zubm7wOLod0/koOflRVtZM59PI/7DcZAg/syVpB6jDfdV1NAgmfY67n4BFZQt6cZGE88xIeZIt5Zv24BzaC+XBQxO+MwckvZ3yxH66DvofAg/MYo/ohbXzHDzvo9ywwZISz3A7GnVSb7x2mIgBiiXWclw0xJmTFs7fum7F34AeCBychAZJ8H5vsNpA5R8c8wCNAZq6Yfw90PNBVeDu77DIT7He3G87cu3/xtWft2rbNZfOQW7ywhzy/l5A7ueK230sdL5dOitNzwXwuuD9Zasc5qfPwKcsWtsPvze/8G5xBAI/tx/f5PVxWfF+sB1ysN9/3bRL4QccuHgw7sCeAcLKWMuD5OJlnr4iAbJvjF8/68JOSTAIlZBAsN187z35hf92P85qQxHHqwx2jub1wMpGXZ4kKDi0kCfLMe3MM7MQl+uvz+nle732ANAyD3rx5kzepMnkAGzblAigPh72kEq2u1+sJG0fJB+ltmA8/sdm/K0mXS5cdHkqD8XbABqDhd69Rdme8Xq+1Xq+12+0mYASDAhODkvDMXNcBAODYW/W6s0mGMp0gHULQ3d3d9VDczVUx+G/MwRElbQgwLxwLJVfMpz8DY2WOnOHk5SyZNGVuMEKuEPOSJsrLGI8bcloxM36M1RwgsF5VVenh4SEbT67pzSg82MEh8JwOnAE1pPdhpnk+DB+GEAPJs2E4MGIwXDzDfM+QVBhfskE4Re+i4+vhjo/P4ehZD+QIuUUf5mvr+/HmGSyu4Qww88cc8zmfB2QA/UJWATdcl5+QA/yNYHUcx6yPh8NBL1++1Ol00qtXr3R3dyepgDpIEJ9/xuW2AuDnoHI+38wL3yM4pQSTMW82mwlIRv6YU+bdM0VumwB18znnOcgieVdLvhdCyAFyXZd9QFyfn05IoQ+eSWZsBF4+nwQB7tAdqGIvOB7AM0MhhNwAxVltgAr3c3bXv3c+n3P2B3IB3RvHUS9ffpBlYL/f5zI29saMYyltdtCNDYcVZd3S2pVyKR8Pesb30XVkHsDEXDvznVnvGXniDW34rNsEXxe3q54ZdPDMOB2AOKnHOnhWILPJmgYgTjDyPtlKD5wl9uAOGTzhk+Z+AKISO1D2v5RmHsgseuRA9V1+he+ir6yNB2Juj+aEiAN/rs38IE9OEPE76+xnEzEfo97ejJ99jUp5oZftu13G7vg8+jWcYHXi0n09ctB1XbZRTlB5FossL8+oWDr4OklDcIRtQ479jLt5pQHEie+xdPn2YI6x0ShlHtw46Pbgw+eIsYJZ3pX5cRuJTXI9I/B3wM8zYseQT/AkY3Y9ZAzI4nxOPUBhzKyxE2aMA53lJ3pX1/WkQyfP7XguhLLXl/l0e+w+CH3xLJCfbYjt42+uk25PmTvXSScY3J99rdd7HyABON1ZexYHAQsh2MGQpWMG4M0FhozQNPovbDjODGFJ9yqOikDG708GwReb1tqAdmfEQwi6v7/Pe2O4J06f3x2IMTaP8HkurnM+n98KCtmY6A4FwB+jxAnrGF8UyMufpNK1yR3pfGMzQaPPE8LtYJzMBfNEOdF2u51sVvUgi7XjXnPg6qVX7vBZV4JZP0h4uqG6zZ1nqqrS4+NjzmKRefMgknHgmPq+nAfC+mHk2JiN7PmeJ2cSXX6cxXUn7MbJy0HngSefI4j2ta/rehJMZsATS6cmQC5s3Lw8CJ10ZpV5doDhQTbfwcHBQAH4COBYV/TBdVOaGnScGfMEOPZgAR29ubmZsP/oGtd3xhGgM46jXrx4kUuz6rqcI0GGGJBRVVUu5yXLXFVVPrsJh4ccuaN3B4iDJKOCfDDP+/0+BxFeZuug3MEjgA79cWfK33kW1pdg3nXVCQb/3UkEz8Z6UMe9kFUACPOAriN/yArO2jPRfMezOIwVQM4asr7Is5Q2bh+PR10uFz0+PmY5iLFsnodAQ7edgKCBRGHoy9w7eHGWlOfmfXf0yLyvlxNLHuTMn8dBswNlZIb1cfvAvbE/AB/Atc8z68ezQ1SmfTOarKXrj8sG9s+DZcY7933IohMJniEva1U6sDoR4N3zGDcy5LLv5N27fB3fnZM+jgvcfnMd5NaBLKCPZ/V9stI1K9KXMrZ5ZjVIE1/n88hzIa9uR/BRlKmi3y576BIBCfbLZRaCi7XwLF0OWkMB407gxBg1xiHbTN8TSADsJZvIO99nbZxUQS88yPGzody38jkntfkJwenn33mJnNsjt9XDkDK1Nzc3E3l07MHco5v8HVvJ+vA35Mt9MXqPvoM30Svf4+mEyDwT68E9dhPSzvH0PPCO47SrKXJMwJOSEuXIHEg55MDPJ3N9ma8R9/Z9r8whtoG5YT0+z+u9D5A8azBnEhAgQDqOcRyV2VtAszMfLABAJS1eqcfkWhgDMizOijkrIJU6ZQ+COKvCg7thGDIb60AZQcfA4QwwNl7r7OwVAIK5CCHo6elpVgIzarVKGabD4ZDP4EmGppQloXAYsaZp8h4fXjwfTsNZIWfZyFZ4WYAD33kpDw4UpXanjoGUNHFuzkySdfOgAGfFesKEOZNzPp8nCg34BXD7Pivq5buuewu0+p4N1oTsxel00gcffJBbZIcQcgbODZKkLDfMicscMgRI5x7cGzCOQXYGHJlhDZEtxu815lJpbe4MDwCfMSHXfI9Ocsi566hf25khZMczgr4mzJcHMdK0wxuZWmTpdDrp7u5uMn4C8K7r9PM///P68MMPcydG34vkYJ17EuwRFBa9ihPDj06tVqvcYh9d8EDWM5z8jhOB/cTR8aw+97xHcD4HZh4EzW0pMu2gEOLCgTD3R8b4uzsv5gjghR2WSgmKBxXOwDpoRQ6dPfY1x775nhHsIuUrVVUOU6R0xtfJM8CffvqpQqgmmZhS5txlQsHtEfrPGBk/tggb6oDH/Q6+i+94dcB6vc5+AJu53+8zcHPw5YwwIJX5hgDyslMPCBi777PxgNoZbOyFZ1SQQ+SirmtVBn58PlxWfU+D65pvXPfsOnPmZZiAWve9njnimjzHnCxDD3l2Jxv4nJNHBAXzoNdtmmc8HTzyGQ+aWCsfi9vm8/msui16h+z0fa/1aqXlVT7mBBNrDxHr4yHgwSYyf/gTZKbruhxAoc/YLiemXPY8+MCvXs4lGEcPmVuFkp2D9MF++z445god3O12uSSNwMBLtLGZ/M3JMtaYdfdDnX0eXa8YE4ET8ub7q25vbzMe5Sd4EVlHPsjOM0/Ik9sIZJ559fI7t52M/12kITrkvsPxIjLlpBJ2FPvL/ZCXtm0Vxuk+T9ddfIWvJSQLZJs/H+uKfmP/GB+BM9fyIJ/PVVWl6tcPii2vqp4ynSxoyujo6mhSp7kU5KSOLY+Pj7q7u5soszNdDppQIpyYg7FkGKOkZqJQcwYQoXNgSLSP4mA4pLJ3xOvnUVAERipldwgTRhhj4kwPgomwXS6XXHKSxl3pxYuXGsdB4xj16aefqapS3T6GBgcHK4Di+sZvng+HiFF0JpfPOLB3hkUq6VgMvJ+X40EbjpD19xIN5sedHcwx15MSoGS9pbJnLcaYz6biWVxmkA0Ue7VaZVbZQbez8IAz3+eBPDrodDaSNQwhZUJ9/pydYT6QIQcJyI/X1XtphKeuXZYdFPD9ObvtgABDSeaI+fXPoSPewcbl3tlVDwaoRUdfkHPWyvVvHMv+CAdmlA0wByGURinjmLJB9/f3+XvL5VKvX7/WdrvV69evc2kefyeryjNiO3zOYLy9wYDPtZfbeGZ1TgbwPvfys4sAKcjk09PThKRx8uL58+eZQHBW2TM8rAMvZMTPwpqDPndkAAPWBFIKuUa3WU/0Qiot8LFxnlVwUOCAH3n2oN4DH16AKwfszFPJ6LaKsb3OPQfXjoqxdLfyA5rnZJXrVbF3ZPsrVRWVC6PW69tsUwBVAB50xufjfD7nYwjobnq5nNX3w9V2l/NlXMcBOF6ClTqPjTkDw3d8czc6QhDochtj6pgWY9D5fFGoKnVd6lIXFaQwLTviHsh436f9WSUgkEJIh6Rj19yGFZKzjBm22svGHTwBPKlMcF/JM7iNmAevHujMSSH3X7znlRBt2yhK105qqbU2HQslXZsvpGNCsj8dy7gL8TCqCo3i1bcje/hv7FlVVZODo/GXTm54IyLGyfjRfQeuZLe9kQm2krl0mXUMQgnrOI6KiqrqSpeOTou92natUAWt18nf7na7CdHBPXx/MgHR09OTpLKlwIM0J0L5LnIDWYPdwK663WCunHBFLqhEcvyzekeAiu2BLPGSWeabNfJsOXLP/dwGO1HlL7DFHAtIemtOnTDgfugkcgl2dOKO+6ITfd8rxJjPIWVOPIPjRL1nyyXlPf0eVPOdOfnp2Nx9o9sm93Wf9/XeB0i8mHSMYVURdUdJgy4XIuBmkl4E+DoriuOVCpPTNE0um/AsR1qs6Snqzn5iUACWzhBLpYTB9yM4yEXp+Q6HucLqOJCG1UHgvH7Uo3qUCAPszO04pjlrmlbL5Uq3t7cTo8F5QCgX2RTPGDhA9aCC53HlhQ1lHiaO1xykp4NdEVF8NtBLemt9eHF9MnObzUa73S4HQZ7p4fseXLA+UjI6zD1ZtO12m2WALI+P0xlmGBRnZedODZkgE8C1AWI8D9fzPUk0dyCLxXrzDN7Jag5wnf1jHVgTsmO8cEh+3gffJwhEn9jf5oYQEMg6OPuFjDgo5tmRI0kZNDVNk7M+ZBT8mswj8sP6epaPcpPlcqnHx0c9PDzo9evXevbsWQ4snVCBOCEY8YAcufeyAAdtrDMBmpMffv6PO/15diKEkm28vb2dBImetUSmmcv9fj8pNeJz3N9b6uP4HUg5keHOyjfkIv9kWpgT3sdRAxq8+x+fpXGKB2TYR+yeB2mso+ugZzTJOHiJktuHOWuZnjPocjnn62Oj3Tc4iGY83jWtsOF0qWyVWoaXTcqw506mlQqGkonCT9V1pb73ToilcUbTFPuADib5kvoe5pxmQOkMvBiLr3D/5cHpPMMRQlDXR+32T1qvV4qDFGNl2aOSgQNgA2b7vjD86Ofp1GkYesVIK/Ry/hL6T/k0Jc5TX1yaaBAsMH7KeTxjBhBEpllX9MT9owfSrDv/dtaddY4qJftjHDRey/DXm9Uk4K2tCkChUhPqyXNXdaWqbrOdXV0rB5Ddpp4eLYBfJxgEpDMHzJPPGWuCj3CC1cvApdJABf10oo89oeg5+pcChFanU9LJ0zn5wvPldA30yrERnt3zNXUQ7c23fL2l6R4k1p7PkEWp67S/5unpaRL8YuN8XbF/6AVjQFc8SJ9XJFECOie1HCO6nuFfPHMDKYWdRg/5vAf52AbGzzjnfpv3sXtzn8JY5z7YCb3NZiONYz7zjO/Ng3Jk0jNuHsi5bPF59JxxOpnpPoLP4zv5zOd9vfcB0nWuJgx53w/iwDucK/9tt9vctMHZbKmUfUllYzhKVdeV1uubCTOMwhyPJ20227c63qAkXhPqY5FKeYxH8xgsgh7AVNM0enh4yAeb8Qw0YSBj4cwQQk+Qg2FwR+hGBAOCw0KJUETPROFkSGE7iEKoUSbvnDTPRmHsAfsAJQy8b05nzkIIeT+Br6Gz+cw7hp715Kez855hcgXG+LkzZU7YTwQb7cwbhtFbwxNEXS4X7Xa73C3NA3ICDW8B61kZmCiALc+N3DIOz7Zxba43Z+A9iHGA4GDTwSzgiOsAQqQSnHvjBOYLGWEsrD+yQckgDCnzCrBiPO4onTVy5gqDjhzA/OEseCZnAkMI+uijj/TZZ59Jkm5vb7VarfRf/st/yftQXrx4IUl6/vx51g9khfFLiR3zoB1nSEC43+8zgYE98ZJPL53ytcJREohgp/gc3x+GVLtPeUPZVDs9K8nX9fHxcVKGw8vbF+Pg3Dk72+v703DUvg9RmjK+XlLB3x2cuGPmOzyHl+I8PT1ZY5l0MDBzSHabgGnOpmKLCN6HYdBut8t2ied1IgUnjl2B/OBZPAM3J724rzfOcQLJ9dhlgHlIcpWqFgDR6ESylSWQ8Kx90qVSxuuBfhpfpfN5yIQUcsE4+d3HK0lj5BDTRQbqCZDWGoYxl5ViV/AfDsS4fgKYnUKIed3S2NI5T+v1Rhxui032Z4As8qyAs/PMI2uKjXLmGR1xmwi+8ACL+zmjjq/u+3ROj3+HuePf6AVyxJjwWeiRP4tfx8t8+Q5A2JtnQKY+PDzkuSKwIcPi+3Ul5f2L7EMFB6Hf+IUQQj6XBz/JuJkjPxheKuV8yEnf93r16tXE96IX2HDk2QG8l2ZBFHmw4/eAaPE96dhEfKljBZ6ZteEaYBf+DY6bN9XwgMEzMy7vXN+xEXPMujuZDj51Uox7IFfMk9tkumq673XSnc/yN4hLfCXf5x5O1ipKw1j24CH7rkf8zpq63Wf90H/ujy7Ngy5JE38yt89f7+u9D5D6rnSZwagktq8w9wgkbAPChVNEuDEemdm5GlLKpk6nwyTLhCGG/cXxe2kRQYrXNDuIJCDBsM2zPTh23sdgMFbv7uMvBMfr/jFizlLMWTECrRhj3mA4P9uHPQUesHgg5enueUmVX9+DN2cB+Mn8+7OjoCg5c8X6syY8LwbAO+7gXBzIOEPlTHnTNDlT9+LFi3wgq5ekeGDoayMV9pr5hY3jADSXM4I3ZJNAiQAT44dsOBjjmWBXAQ4EZ344InNC6aA7E57b74+h9BInQGXbtlmevLSRvTLexc8NnWez1uu1bm9v83x7iaEDKZfBruv0/PnzfB93GgQYzp57QOmOZx5AoodeDvCbf/NvzvvDfv7nfz43ZGCMgBSAC8/GeJF13z/E+y4vHoxmdvj6bAAYZxql0ixAKnXxHrx67biTFpAqLhO3t7dZH5wRx5ETADhYdPZSKp2rcFrMpQctnNzOnMG+OlAZhiE3SWiaZhLwo0cObPk7sv/ixQsNw5Dn5Hg85n0PDpQBHJ4Vg7hgvnHg+BeehyAHdhp75HPi9hebw7ok3R4m13a7jl1EDvFFt7e3Vx8TJmN0oC1pMq/YAwefDsTTs6yy/XZyxrMo3A97Wde1okY1bTmsGR0+n3tJo16/fq0YU1k2Pi91Jyzz7gx4sk+DmoY9DVExSn0/qGlSG3Zn6bGZ85cHIe47PSDjd9c3J56cCef5sJ9OpmLLMvhTaQKC/3CcgtzNswLYJc8IIZMQhZ79nANixvb09JQJEg8e+DxHWABEAed85nA45L2bEDI3Nzf5Ow5Q3X87ieifYa4JvMFl7hfwIZ6lQUe9xN2zwZ6ZINiAhOI/vz/6z7oxRveB7D1HzxkT84tMeHDJGvI3D0jQb/dTzAU65OvtwSX6CG7g6ArXY8c7jsnw1/5vZJR5cAKT6zBuD1h4OW7ebDYautKp1n0qY/RgEayEzLnvdftUVSmrThDvfyOb5hjdyYQ5Tvhar/c+QIKdAoTgFGMsoMMzGc7IMpHeLQbBgTFBsKuqKBmK5MJd180ErDjDgHHG8PlPqQgiSkgQM2ciACQwuhgKj6ydEXbDw3NLhd1xocOwAZAI+rquy6CHezFHfIZrumHs+z6z7Q6037x5o7pOdfA3NzeTVLPvz9nv9xnowrgDDOfMo5cmSgUs4Wxvb2+z8SVAgxnz7nNca74ngzU6nU56fHzMWT2fN+TBwW1hdAv4mc/7nMl1B8xcE3S7s+N5uBbz7uvshp9sDMaS9z1L5rLqLDKZCwATWUuyWTgRZ4dw/ARS/A25Z22Qd2d2PYhDhgEJq9VKr1+/1jAMOVDn2Z1h9GyAZ1ucseLvyDVdJQmoPRPH83/rt35rngPGT3lYVaW9jRzIjB2AFfQSSHf6kCRt2046W6J/BKSu2y5nDqrQYZyvN3VBVjzY8KMH0B/2EDmbx70ccDggx5HRQWpOUiBDXubozzCO46TUFkBJ2SPP7R3zfFM7YNNLZpqm0ePjY54jJwzmG/75HVn2c2DmOsp7XnbjWRkACPaD50K3eSHzkDoAPmSDe3nQQwvxBGaLDeDvySdMz0Fjzwj38s3WtFFHRlzGYI+RPR+/y16oWtvHWqoSVitIpJAzYn6f83nIgSL+L9mepYah1zCM1/8gm1oNw6i2nWZ73M8ic/O/43NYHydA3P47aHMbis677+R5kT0nDxgHds5tPddEtj0L5uCPuWaNAMe+LwRw+vT0lMuDIQQAmU6CQsIR/BC0Ou6h8QG4ah5IIH+3t7d5ztx3cR0CevTNZcDb6mN/b25u8jMy9wQ9zCmYx//txKnLJteQlEvaWUfmCZ/pZKtUKhOo1kGH5seyxBj18PCQZQvd5d7oAtjFSWCfN/83GGiegeeaEMHzPdQ+JieauK5U/NE80GMdPeDlPQKaeSb8dDyqCoVc4JrYMcet2FR0B//u5LATFTwnMuvlf5CPzAtzlQO78dfPQcovByelzr3V4+PThEF2hpf/PLsBAHXj5qz7+XzReD2RGUMOa1/XrcaxRLEYJbq9eJCC0ZCKEnp3EUkZSLlhkKadb9xY7Xa7ycb9uRHf7/eTFHJVVfkUbByGlAww4F+adtdCiQFClBPt9/tJWQjGTVIOhtw4OPs6rY0vPwHgfBYlcEbDGSJS+D5uslWsNWuKs/eMDHLB3NBWtKqqXArHmDHwbCj1EgACmsfHx+z8CDi5no+TcXkGCOV31odrMTcEZ8g0AIs1otwH+cd5eDA7Z8VZY8blDBXOHxDhwJRxM1bmFkfk7KKX37jhpvRpu91mGXa5w9Dv9/tc6uZ7CKUCkAko0A8nC+aEg6RJ50sCaL9vjDE7+b7v9fz5c9V1aUNP4MRY7u/vs31wVppxcR9eIZTN+ARiyDky4+cHMT88L86BLlMESASUzh4jD76myBnXm7PxOCFnHJnHeQBOR050iwDBdZvuh74uPDv2wY9dgM1l/PMyHHTGnStAjAMv587XQa0Hh54ZYQ6cTeVZmUcYXQ8a+Rt7PVhPZ6g9UEv3ffvgTIAAuu7rTnOHGEvtP89NsBajcjCMDCYwVIg37B9/l4LquqyZl4th292W5nUcJIVSisazJFvWq2nqHPixtuln/ZZNK+VIlKQPSnuSouqqlkJUCCVz4ftDPMjAp3r2yP0BNtP9Ef7fMQI2k/u4XHjpttugvu/VtCW76c1A3P46CefjxLYyXifNeJ+/YZd5VsgHZIlyMDLg2Gts+e3tbZZXqZwTR/UI50uid571cHCNTIQQckkWOuJ2Hd0leOP5kTEPft61hu/at+O21u2i+xwnm/x7HggAxF1XnVxnDefZF9dRz16wPtgVz165/XEbzHXBAR68+BxDLiNPbgN5BmTY7QjNjJxQZl08iOb7c3vLnGCn2rpWXZU9l76Hzf0fz4IMQRp6YOtBI/MDrnff7frCfHkWz+Ljr/l67wMkjAUdiwoQLHtKWFAUEcPkNfR3d3e5FAmhhfVMSpH2IXl5k6SrAtcTRYDBgSkBLDp7hKI5Y+jlAFIBznwXo+HBlhtoZ4qkpIRPT085A3R3d5fny9O4XN/ZKwChl205o4HzZH6cvYCBdkADK+6bjFmDvu9zGSMAjODJmQ/PXAHimRc3/HwP4O4G7eHhIT+nt1l25sXBJsy+VJojeBkfa4xSo6ysK+NmPRwE+foSXLjjB5gy126Ij8fjpNQQY+xz5qwU60RQC0jgul664GQCMoL8eKZRKpvpPePjxpTrecDjDJuD5OPxmIN0N67IKM8CcTDPBHNfN/DMNWP0eXGHPWGgrrrpmZC5zgEgC1u+yutC5oy19vIK7vGutUPOXd48MGeMyCjXgBHme8wZ6+b7ERgnukMbX77D33gubKHLgs+7B22UOGJXAW9ka72cyxlP9M8BGGvKfLLuZLz4D5DlgIjPtm2b91Iwv1LaP1ZVVc42oc/IAjJN8AOgQgYgztAdyiedGXbAje9xEIUMzdlXzzhIZe+as9vYhxCUG4pw7lUJrvpM5PA83rmPF5nQYUgldtyLz7lu+T08uIghdcJzuUrzkzrJOjkwjmOWB6mUJvr6jOOocUhMcBylpi4ZRMk74E33/GDH3LehIy5TDrp97ahaYE3cpsyzeugA9g3SBJmUpufUeJmjE5LMP2Mnu8ccemaDoAiAybgYjz8b8gX5AnbgGtvtNgdNnsXDLzqgRZ7d5nJYPATpPDMz91d+mO9c/rA/Tqb4PiWCc98DxZq7//CsBXPNs72rYQ36zhzTbIm1dqLL992wh9tLAT2wme/fc1lEP50URZe84QXy4GSDX3+xWOSmI/Mg2vEYssWcI69U/8yJLtdf/EWyDctJBQhVOGM/KF4xEGtPIOZjYT18b7LjHewOMubZPTC0Z+C8BLzYm/GKNcqep6/1eu8DpPP5omF4MIYhOY6qanKJAMGTszNSmVhnWngR8ABYkiD7qeWVQugUoxSCNI6l85pU2iRiNOZpRIwWn4WJBlh52RkGywELqXYECOPOmAH21Kx75O0Mk6QJ8HKHMwcxGGEHsF4q5ELrYMOdH+V7zsa5YYsxZsXn+dhX4GDWHR9z7saW+TidTjmjcXd3lwGi1ykz/4wHo+/MIEDTAQaGy0sDcQzMN0bC2adxHHPDgDmbT6CAMfI9ST63ONp5KQ4/WVMHtIBYl3+CRBwH42S+Ad6ASG8G4Z/zAN6dCftIHMx6uR/jRTddfp1MYK2k6Tk885IL/w6lKU9PTwawpi3HcWaeAfN7oZ8eZPFs6KlnLh1UOuiWSsCDzDMmnDTPhC4ikwCNp6enCdu+2WwmjpAxsaZ9X7rhwQi77jhbB8ij8QvyK5VSHoIJ3huvz3c6n9TUdera1TTqh9JdTpJCVauqo4bz+VqSUWm73VyzcDDylEam0rE0j6nhTtfZPcdRKdNBMJv+K8RYselSEEcWJJ+QDvQehjdKDQUGDcMlX2ez2Wq9TmdUlWsXGUd+CX7cfrlsOtlRWGM/30TXrMiYx9Z1hchjXbz5hoP2EErXQ2f+/1/tnWuwZWlZ3//rti/n3jNtT9OiCBVTJIJIwGgiCalckEQk0aokIo5U5UtIiTImRaDKWKZSlYCmYi4SpUhZqUqZFH4ZKU0q6JjgBEoUa4ZRFA1UgqDGoZnuPufsfS57r8ubD2v/3vVfqxt6ZpxmsFlv1anuc/baa73v8z7X//O8z2riSxsz5Xn3vifKmdpuj+2LcJHh9ZrgZUPr9QZEDEFFnqlpNtm/NFOWT1RVQVXVKE2lqto0FUol2lSX67XSLFOSpCqKXCGUqqquFA2d1cqqJLXtnvOccsVVS6sQFJpmQ6tNMBYaZWlXigeN3eF1OfTzITEYTaSmqWMlRF7kmm/NdbI8UZ5vGoEAgjZBVV1pUhSqqlpJmmiStcFP3TQq8kKTSaGzTaOgPM+Upps1JDfbGA820b3wkTuNtwJhd3d3tVgsop5C5j0LCo8O9Q2AFrp7Pp/H6gd0GfdAH2HHuAd6eTqdxuoJdJQH9Q7S+RltbAh/41me5XXAGh5xx9gBGg/AXGaGoBudPSlNPT4+1v7+fvQJHBD0jAx600FD5IlyMf4Ob3kwCZ3dvmJnnG/ZTwIgfncaYX/QMw6WwUv84FcObZjrcc+AU2WAXyKpNy8HdbkHFSrT6VShaFSu11H3Ewgr6UBqnuVnXr1kzsFPp5UDg14C6zRhXr4P3OfJjLs+QCrL9vxPi5adRwecrInXMUpda2FHsqUONXOEZ4jMF8V0I7xr5XmqqupemucRNoKCAiHAcgeX68ickGnw8g4MoaP0q9UqMqikqLRQUO4wOnKHwLlCZc1SlwnIsqx3eN/TrNCCYA66EGC4onCHj3bTBHt+8N5T3qyZffOghPuC9HjpmCNf7B+KgMOE7PVsNovfp2yNEioM062QauZIvTYHYD0TNURD2EsQDxd06r+lrpEDioIMnQf2XqbHGr20zZFP9sV5AOXnqXVo7bQ4PT2NdHZnD0fLjRT3woCyZ+4AuIFFMaNwQZycP6GNZ03ZO0+tO2LI3nEvz4qAuEEPV/TIuBsZ7yLkNOQ5jqIiF/Cnzw16++8OUmD8CMgwSkND5+//2N/f76FxBDvoHi9B4D44MGQxMUTsIesJIWh/f1/z+VxXr16Nego95kG/JFVNrcbmv9rMKU2CghJNprM4z6Pj45YHg5SmiZq61ulp9z6dDiwhA93KHuV4OP5SW+rc8kK6uea0xyvoDkmaTjugpg3EWv44P69EpzfWiM7gNQbdnrXBRvv/VHmeaT6fdQ62IfZDHmVtreMWlKZZTx7rOmg246xY3qMxutnRYP5PW/uWj9v3INV1uy+AK1J3thaasHd1TWlzC+7Bv1UpNU2iJMlVV6XSRMrzTQOANFGebBzVjJJnaTqdaLVaK0sp583bzE9o31FYh66k1Ev7Wl6sI82KYi6pDRazrA3ykLl2fzLlIVXbojzr0QTd4rrIAY+6rlVMCp2vzqJ+TdNUSoII0La25xFISbPN/fI0BlV5kW2yC5uKhw0PrMu18rwt/ZvOplHn1HXbCRFwFB5zMM0BJ95VQ1ah3acuWEK2/TwbuhF7DA0cVOQe0NEd/SRpK0xw5D0zgE5Ad2LLKdFyUIB9aAGOs16XStYLj2FDfA3uswwDA+wl83DHuKqqXsMkP5tCaeDZ2VmUaWw1suoBhAfcnkVCVh0slRSrTBxgZQ0eiCwWi+hjuL/ZlZJ2JeueycG+Ehxyb/wSnsO8fS3ur6DH2W8y6549ctvl+olzTvhR8BX0CiGoqmudb/RMHRqtN68QSM12Qht8P0B9QB54h/l5tYDbVOjnQSh2j73L83x8UawPd5xJ/0uKWQqYmGukrrlCmqa9tKCkKHSgpSCvlCtUVdU7YMgG4/CS2SH4QWEiZDhVLlBlWUanDsZjDggmf/czM+5AXbhwQXme90qvXFBRLI6Eu4NNvW9d1z0ji2Jar9fxnUjQEGVEVypnduYIygmCRcDFnPwgqDfLKMsyvk9hvV5HAT0+Po70y/NcBwcHWiwW0aiQ8UBBE8Ts7Oz0glIUIW3CEUAUNgHharXS3t5ezxEmRQ2ahOKXuja9joBwbwxLCEHL5TJ+1wNmL7GAX1gH60LhuYPm2ZDhPuDMebMCUDB42LMedV1rZ2cn8h50Qra4F5+hxJiXl5Z5kA844UGsBy4YCkcAod8QLWKNzAlegz+3t7djQMtcPNvVOa79N4d7NnM6ncbyG3d2CcS51zALhFODYwBfOBrKMygnQJbhAdbqem6IEuMItmUFLf/7uUUHgTww4h1IXAP9jo+Pe0G5d1DEseoyT90ZOgx9p1u7OXlWpCgKJSEo2/A4c3a9dytEsZcBUHcubrhf/nd0FjLozibXwivsgWfWuDcyzlrTtHsBtAM07pQ4Es4+1XWru6eTzXt7ig1SXtdtRiZJlGgz/0RKlKhuGjV1rXUo25xPCMqLvM2slXXUDSfLUxUbeSzXa62TrpQrNFKaZGrqRryUtc0iKeoZaDwp2peWtvvRvt+vLNfKslQhtKiwNi/7rOtaWZ5ptV6pqivlSa4kCcrydIMoK77wtA1C+k1XPOPQLxPreGn40tFWz3Ul7A4soEPTNI3OqJ/FhC+64LR1gk9OTmI5k4N2lK16IOyONXzhmW/kkf2nRJPvuOwCbqBbd3Z2YhCLbvNmKR4sU92AswuNPEiA/5At1uVdHAFvKYNl3S5D3Asb7M8n+Ol4vGsD7VUEXkrFXnBfvov9Ya4OFLk98xJK7g1t3AZAy2GDEQ8qnffZfwJZ/EbowLXMfwgOsreuK9Ch3twBXnfAyoN5bCg/lEK6Xodn3K91HQxPemYMH8V9CtbsATx+TlVV0Tdj/VRjcU/PYMJTnsFkLQTrdBKlqmRnZ6fnrzrd/d2G8O6QT6CFV31MJhOdnnWv17jduOsDpBC6g7wESTAxBIOJUF4enaN8YB6MOpkPHGicZa6nk5wLDs8AkfeSPTfKKCqMKZkVz3g5QhNCVxrl0bIb9KEQYgRYF3Nwhea1rcwPBThEh2B+nAM3MHTccucYh+/o6KjnPJL5Ia1OtoAA6vz8PHYrQ8FTxsQz2E/S/JIiWuQoHAJ/q0AZ4+w1zdzfFYA7u9AfukALeMYzAjigXjsM3SXFLBFBg2eIuAYD5iWX8BuGlD1zZxsnHXqz574O50sccpQlc/CAEKfDSx8ceeLerN+DBn97PUYMhwY+Yg2eOfPMK0YSGXaHy42qpJ7ceTDF2QdHWvnxLBvBEc6bn/3AyKK8fU3Mn7+5vPM79AeEgN7n5+exUYyjxsgp6/TsgTuPnu11hHGYAcJwsk4cCTdK/roBD7z5Xut418qU9fiN65MkUaKuWxhrSZJkU65U9XgN2jk45GtDB3I9+0oAirPGHsesQVH0aOWoI/KFcwhvwePQk0ALvpe6pjDcx8Go9Xqtk5OTyDc4smVZq666Dm7wJXM6X3cdvuBJR4XbjEequmrMQUw29fZJ+z6Suo7ZrpbGbeYsovVZ3paR1V1QC43bDF97nqA9O9QGSEXRnsWazPqgQBskce4lUwiNkjSR1P6bJsmmxK47h+YvKXUQijm4PqbMiT2Frg7OuBPo8ulZhwgKJl2GAp4H1HI+x3agA7DF6CF4Hd04LM1Hj3A/l5mTkxNtb29H2y8pApPwHD9e+stzyQJhTxaLRWyygI1gr10X+NlAKiDYR+gPEORAF/dAPtGJ8ClznM1m8fUXHiixv5QSSq3do3IC24OfgjxxRhb94ef7XPd6EIROJ3uEHEuKFTH8HTqy76wV3ezgnPtTnn1xHqZ1fQghyj50dxCTwJgAzrMf+JzwdVVVsZEWWUjmgV5x8ItKAXQka/CGOdgRPh+CSQQ3VPxwPAGaetMybDdBNWCU1JXfZ0miNEljt1n8wyzLtLe3F+2U8z8NsbzM3vUUw+cOWIHOHIKLn2/c9QGSd6PBcYKYvKuETZS6YMOVMgJLVod092q16nVWgfFRUDwXpBUj7i9WdEccB5g0KelyapWLoogvkHTECiFGkeFUuMPg5U0EIQRHjmh4yQUKyR1LDL87XR7AoVT5ITAji4QTh1EkQ4exQxA9KEMp01KWdYMwD5EE/o6yRzny/hJQC5Qsw5F1aAOiQVbKDYfUvXh2mKnAIfSzOO7UcYjeS8ocBWHN8A/KsnPE1CtngHfZq6Fjyz46z6VpGsslOKALfZgLTj1744is85m/1NSDCx9OI4YbW5QdxgKZ8DIOaA4Psn5Hqhwl9fIMD5JwPr3c0p0HR/+Yp/99iGB6tsDblXJvroE/GA56+EHvqqoi72xvbyvP8157cL4DmOLZKHdkkHvo62vyefB37g+PwW8YfPaFZ7CXGC+MY5FOFNSVH/q6k815FF8vdKjzPL7de5iFdzl1hBc9Ca/4+hzQQSfx3GGg57pEUnRu3TnxvfTSJA/qh8Ab+4l8erlqWZatTp/t6Ox8Fe/ZlpPRRhg93PT04WSSb3gyES9Kbb+bKEkyuR8QgtR2U22UZbnSFDCwPSfbNO1PVVWq6rZ5ArYCuxmaNvuXZTgubRC2s7MlpanW5VqzWXvWyfcrTckCt2dwQmg2wdlaUlemhZ1DxuE9bx7hJT7IuXc19KwRus4DB3SLZ41iIKVuXz3z4/LpABX6hXIfeIB9psrBS2+9PBg5ovU9WXt4FP2O3sdR9rNLZMu8iQLzgJcAwqAlOmNtQbf7OwQ16BLXfTjxriMAfZIk0fHxcbTzfJ/mBlwDYE0LcUkRZPAADDAE/YP9YY3DqhTOEPtrR7BVrke8sYs7zARcbr9ZMzoMerE+5jiZTOKrH7wSoWmaCHa5TkBn4xvixMNzTlPmh+5g/9zGD4Fa+KuVvzTaE6lf1YHd92dgZ70Swkv3oHHTNLGVO5lUgk30vZc8kmhg3QpSEzoQ07N/DkTzuwdwDtAD8Dk4zTp9n6T+y+ifzLjrAySPNBEqGNjP4EidkOIII7CeSkzTttYf599bXHobbASNtOPx8XGvDAon3lO3HijxXJ5Ja1LuTQQvdR1zcAZhOOaFg+sONELuyKw7ru64SYpZDFAoShj8nqwry7ruJh69o2CKooglaJ4hkhQRAgRKUrwHSl3qd0NCqNwo8j13aodjiKhI/YwfAuvGl8Dagx2UH2vk+fCKO83uKKF4PDuEEaZNNPvoL9/1kgC/l6OSjr55VsRLmqTOgeWsGrR0dJrMAftIiYQHUo7UQlNHeFxR+Rkzng/t4EcCNww/yjKEfjMMnnErYILn+V6xB6zZgx3nIw8+uBfBvAcFQ2UMcOIOIs/G0WBe7pjwN0e80El+T0eS3fh7WYqj79zDjT9z9/mxXmQBWvnf0U/MHZpQOtxDprNMSdplFKBbCCGWViHjzCvLMoWmK28igHXHF7lyHrtVIE62X1KvDNJRXmQSHYyOdWcCnmJPeDZyhEMLALdadS/2xklxZHWoZ/luWVUKQVqvSzVNl2Wdz7d6fBuDzA2/zudbapsK+KsbkKtUkwlZp7YCrigmapp+xkFqS+MIlBJ15y8ATWazmYqc7l00B2ibLTRNo+l8pnSVqK4bzeczhdBsSmYmaluOS5wh2t7e2swnUdN0Og75hr+QJ/aHd1Yhn+6ETiYTbW9vxzMd6GQHS3BAh+dgXEe5jkQ/o0M9UK7rOr4D51aZ57Istb293WsdDg8hI0OnD/l3p1hSlC2AOOwlOsBBAXQqOo/7U64OoOvPd36GlugTpwfrhdcd+UeOoBP2DL/Ig1P4GNrgA3iDI3fUh9lEB24dYPQyaeaDDfcAi/1DlijxvpVPiE/hQNnQdmC30Qc0mwDY8jl7ZRHz9S62DtrCM8MsmYMxTgvW6cEza/BmTl5BdHBw0LPn6FfWQ6ZtOp1qsVjEfefsEACF87DPa7FYaDabxdfM5HneBVt1Z6OYE0C0+57IsfOEPw9edwByWD3hfurQXny+cdcHSDhkOA7uiLljdO3atRhgUP7m5wMwtihJHDUMrwvYtWvX4oFmBPXChQs9h4r7SuoJvyO5zIf63tPTU+3u7kaFjcJEEbjA+jknVyQ8DyWOwOEcch4I444SBiEgCOKAngcYlAC5g0LbcK/ZdacHA0O6NssyHR4eKkmSmzrMsJ+8jBXmpzxhZ2cnKtmmaSI65c4vguPpbeaE8CGkoDQ4RlwP4ueKdBhkeeDmQQ085whXXde9kkFHF1EaQ0de6pwrAhXm42cr3NixLgalFDgmOBf8uMJzA46iHYILHrA4muMKnUyGG8Otra0YCHE405Ee9gHZ9bOE/Mv1HojBG8zBjYQ7D/Awc8N4+fA96BzRfmcs/wx+QWfAI9ATmntwwkDJT6dT7ezsRKPK/eAj1ukOF5+xJrKPfIbsw5c4LP4+IBwJdwrYU5wzB3Rcx/CcYjpRYug7739ar9darzrjytxiwFfXvXIbXljJdSCS0JtyNuQJGvnrGOBD/9f3giDUwRDPvtZ13WtM47rI7QrOBMGXI6Dwo+sg9i5NMylQlouz1GZrWl3AC5nbLEwH7LQNGMqya5LS7nemqmoDpCQh6GiUZZPNc0HT28YWHT/VSpJG6eZ1FZ6RbJpGp2cncc6z2UxplqiqNo5V0gY/SRJU1xz+z1XXldbrjre6zoGUauY9hwq9gJ7DHkndSyzRb1xHYM58fWxvb2tvb6+XhWGvaD4QaaIu8M+yrhVxknRZHg/e+AEgwMbyjNPT09jx1PUSz2e96Hr2wbMUri9dBtgT7Lyk2MzJ5Ql+d1uO/DtowvzQp352jowMe3Z+fh6BBKo0UpN1ZG8+n8fKEc/yESTyHQIEeMQrHpwnoAdyDU2cXtj+YVBE1gJ6OsDqthx5dv0OL/qzuZbhASXnWhmedUaPuo+Jnoav2QP0hoM50M4zf55F5H4OtpE1Q8a8nbuDhSQC/FUx0ML9FujldPaqAioJ6rqOr44hyOJaSWpURT/LQW0HgYZAFjLOv26PuN5p7zR0nnqy464PkIieCZI8dekOaghBBwcHvfI6rkWA3UlzlNeR/Pl8HhUyDOjBAGicIwmOILChUv99LHVd9wINhBHBBQEg8IER6ro7sEeG4fT0NCpGrkP55HkeU/hDlAea+fsGcGC41lExrsNx8trsNE1jW1LPdKRpGtt+uiPrSAUDdGp/f7+3Ln+vihselEdVdW+vB33zlC7/L4qi16bUgyAcSbJhUie0KDCfJ4GuK3mUkTsIWZZFBcP92W8CdYIcnu0BiAcqBLPQcJi1cKXt33EDMHQKKSVABjxwI8jgmQQaDgzQyGQ6ncb28o4WuZPrRsede3dk3SB4OQay78YJHeDn7uAtLxHwDAnzAHX1vYN2KHeMggeuPMP3xjNhvhcexDjvHB4e9uYEHbg3BhY5GjoLOEJu5Hx/uJcjdk5r9sWdepwHQAMvd12tVipjEF9pa2dXWZIqCdKkmGhaTJRluaqq1Gw6UQicUwlS4JB7ez6mfYnoTgQqkkRar4OahkCdstG8x9vDANCdTxrrwAs4JDi6OMTwH3vPfR10w5Hib3XdZTPbXIyUJKm2tmaxW1aeF6JteYue07hBmky684ISwFmX6Q4KSpKgNJPyIlVZnm8+CyommdogJ1VRMOdKTVOqbholZf+ll+4AtTIoSaFHK+dPLwEKoQP1yrJU3dQqy0pNaBSatuPg/v6+Tk5OdF7XqkJX/59sslUrmhnVtdZWeoaOTC0jCV8iI9Npy+vTTTDdNG3DimLSlkRvbbfg2NnpmdIsjc59fVZpZ6erUjg7O227lFaV0jSJ6+Y58DoBM89C3sm4ODiArZa6QAP9g7OIXnebgg6gkqV7H1T/PBXfwSHmXugqgEwvvWavCErwCdhrnGJk2QFV9BqZbL7jLxl2sMsBMG99jR2FJoAbbnOlrvkGOg+fwZ1xB31YL4G762D4iTlPp1Mdb7pm+vVOZ87CMZ9b2XIP2LyiAH2D7KBPPGvsmVIqarCn6PLMeJ/AyO0MPw5WwoMeuLHf7h86mOblkpT7OtjDXjh9eB7zchvo/l3TNG1tb9P+JGkLqKjZ8PDmfh7sIU+szQN45uR0cDsHPbkP6x/a2DQZu9jFMZlOeiVMUlcz64THUXZGlLpD9M7k7nCyQZTboXx2d3d7tbdS53C6YoOZvN00jgeKlzIsykO8TAdBGqaXUeoEX6S9SXGCrLjDBPOR/fGSOVeCoK2OZPM7jowjYBgCFwJ3NjBeGEBHc4ui6HW5g/Y8lxIM1g7toI0jdu70bW9v95B7/vVUrzv+nslhTUVRxLeAo2ikrkMiAt40XYdD1owiha4EFey3ByrunHmgMwyw3LFzlMmzWFKnNN2JHCp6HEDWWxRF7yV+KGEMrxtjkB6+y17AY/v7+9re3u6Vk/p3MejQzrMyPidoDl1u5SA7Ukug4GcMCO4dbfNglHvxHNcLTgsO+GLo0BMeWA2Nigc6oHweqDqdHSjxzDV8jaMKLzvA4WfkPDPk+ozAEB7FcLlBRQcOacX+4lB5AJGmqdarleqqkkKI5VuhqZVnXWfQplaPBztEt9kAAV0bXGQG/djNuSs/Qee5Y8ge0unSy7dOT09jGTHXQk/kEt3DfTxQ9/1dr8tetrKua52cnKquOyACeWnf65Qqy1oePV+1jWSCGtX1Roc2LS1Ds8lQRiR6pSY09GDY7EmtoE1AlSVqQqm8SDRJi00JIEF6oroOStOguqk0nVFO3J05hH4AU3meqa671sZN0+mIpNo4omkX5J+eLJVI2t6a92TF7V5VtXxwvulgVVWV1jQEsAACMIiXp67LFjVfnix72R3kuSg23U6rtUIJAJCorEo1wbrKFbmqulJVlTEbQiUEttqdR8A7dAb6DX01BCq9Exx6BxtIdUUIQdeuXYuygA103QRYRzAzBGHgNbd3rgPdxtCZj+8QzHk21s+l8GzsjINryAA2gnJs7J/L4XQ6jeXpZD0AlIuiiN3YuN51DXYVPe+2QlI8X4y84YsNbS0ACee70CkeELnjXdd17HgKf+ErOoDi//J99Dx77wAqvg5BCDrG7R7ywv/xCVynpWkaQWqvCHGbwR44+IaN86AJ24Tf47xKcOn+IjRi7swRu17XdVtGl2ZtEW/TKAktbFQMns1wH90DZ+jP5w7wuH/mgJgDecy7qipV9fii2DhgUN5l44PNR3kMmZ3rYWBHW1EIXpNPEIPhdqeM7xM0ITzc0zt98DxS2wxSz5J6CtoFAyR5uVxGRByjTjYEhUf2DKEhm+POH4wFwzm6yEHL+XwehRSFz8sUYXJ+uD8tQR2dccdwKKSOknhtsSsTAhNoQ+CKgGEIaJDgtEWovc7aBdPp7kEdjro363CUDUWfZV2nPPYe/vSMAcEyCgeD6+l9FACKwYNdd+oc5fKsgfP5MPvoNeWS4tkq0DC+gwzwLzwMzT17xz5Hp8RKRbw8wwNPd8CRKfgaZxaHlYDBnTDfJ187z+F6529HBJ1mONMcnIavHQFGjqCDO884GjyToAXAwh0NNzquo+B9jLSjo+yBI4j8nQ5V7CXP8dJDd3j4Hs6Eo3geMBGgOFqHDhyWDLkBdP511HFodB3k4d7sHfy0Wq02OrHffMSvd4eB/7NW16844Dg9rBG+4ToCJQ+CHZga8h284rrz/Pw8ZruRgyEC6qWqZDt9v/K8s09d2VSi9boDJPx77N1s1h6OrpugPEuVF4WyQJOMVQxEk6SrbGBd2AW3o+6weDYeWQZ8dLlkHW57kCHKfLIsU2Ly2DSNrl27FvdjXXbNDJz3d3Z2op2kGoS9Z789wGYveacW9osGAlRgYFvdtsArntlaLBYxy+4gg+sIzicR6GCLsMfYbRx0D/QJQLAPzjfIJrbOGwQ50Alg5zZ1Op3G8787OzvRp4AXeAa/QxNozfM8w+AAI/KM3c6ytlJiMpno8PCwZ88JHMj0U9YHn7G3/jJmeJUsMPLL9fCqA0rMyzNwXiaJzeMZ8L0DOQ6aOXAET3INZbzsHcEo9tXfI4QfRec7AhOXK57BfNgfbCWfo7/d13M9hVyi6/CRHIT34euDF6CjN8SKQHbTPw8MH6LX/T4e8HpiwfU6voHT1ocHxQ4iwjueyXsy464PkKqykqb9bkpeX+uZDZjYS75QTu5kIeBsOAxD8INBwOD6WSZXFAjjcrmMio+3WNd13escQkYJVIr5rNdrXbhwIQokgZVH/H7Icm9vrxdYuGPkBowA4FZOpQsNAuUIAs4AaAJKwbMNPJP226zPBVdqBYCAhueh8N0RcVphYDE6Xmvt8+N3jBbfd0WKcHvgxv66Ywdt/D5SV46RpmmvRSX3xwmCx0AhuScBhN/byxY9u0SQ7U6pIyuu3LzkygMLD4D52zBY9MwQ17hcQRcPaD1TV1WVjo+PI/3hRUcoudYdeEo29/f3YzDHujwz4k47MujBtxtvd4DhJeTGg0131t3o4Bjh5GKEPKvpjq2keKYO2SCo8jng5EDnrsSsM0guuwT46DV3PF0X8Hc/P+iBKY4PxsXbwzoQ4wG7/421UyaCM+aopme4WDvzR7f5/cn0oItZAzqT9fnzfe1DeRxm7hzoiIbdAmr2yufrpR7sI3PwslgPMOE9B31wormOe8Bn3MvXRIDsDqs7JlzvzgM8U9d12z2q2bTGb7r34SjQdbO5iX9a/uuvA55xgGEITlAWQ1kUzmBXcVD1MnnYwCRNVW5spzu07OGtADP2bjabxXMg6AlsuuvvoTNGxsj5xB1q19ucmfTGCdDLARAPZtI0je/uA0CktM0zxgCt/B/neVj6JrU+BY40NPY9GQIV2B/sBWtz/sdGOnjDwFYhs8vlMsoE+9A03flfmpjgQDsgBY05g+z8FELQ3t5eXOv29nYEVqElVSXDJhDuR3iABx86uM1nBCU0skCnAfah6wmGAfe88yJ0Qpc5aM67Ek9OTnrAXAjt2d/lchnfaeQyRiYR3vOqCtbmsjqbzWLw6PLvFSnwFXzuoBq6hz3B/iDPDiyj/9Bj2Ar3MdAlrhscGBtmOtE3BPPMETne29uLdm8IRrk9gh7YBM+euT243bjrAyTPEGGAPNWJg0nkjoAgJC7EBC4oESJSjDob5wrVHYcsa7u70Qrz6Ogodskh84Pxo9yjaZqoRHkXAMzjaCjKByWPMoTBcXakzqB76tydA5zsIQKLs+AC4hkcgjovIXJHDqHhftyT+Q1TraxnsVj0shqedUERO6rtQuwOB91XHL1mfvwNYfMMjTuirMPLFrxkRuqQekcGMZzQmX1HsQzLF4ZKCIXOfTFeOA5eIsp8Hbljr1xRwufuUKEEMYB8BycDRUaQhvGr6zp2avRgGkfZla8HosPSOJ4JPzkKy/XDc01DVAhae0DJPg8bhqBQ/SwNez08eO3vAuG5HlzBP+gU1gjAQNMFHB2uYU89k+DlOuwvskdwfHBwEEssT05Oem3a77333vj34+PjuF8e8DJ3lynnGXdufK+gj+s29sNRReiD48TwoM/5nft7Joy5UGaLTkMmOj3VBUcYaF/LsCzRs+XMx884Dh1y9h3+BnXnWtcf/B1d7o648xxAjcse1+K8or8cBHBbw766vDFn5zsAhnZ9ifJ8sgkwqw0Ptq23FW5uPoEeQu6hgXdZHaLbOKQ4565HkUn2y/ec85xnp6c6Oz9XYsEh95jNZmpCF0Qiq2SBsIf8//T0NDrb6FB/bwu87cATzZA8sHJ+xblj7wAX+Mw7rnqzIHgS/SQp6gOCDC8Rwl46iDWdTnuVKDizDpp54DiZTGJJc5Ik8eyvnyvxbLADu+7DkPX04MrLMNH1VVX1rsVeus7gemRiKO/wATznWYO6rnvtwfGXho0s2BfX78jB0E/geq8yAYB2PoO/PSPEHHHICYqw+35GGaAKu4DtGFb6ODjvzSjQbf5c9pF9Ya6u1/kbtpHne3Dk59KinG2+D3BGwA5NmLP7d56MyLPOH3b/jsAee+/22GnuwTsZWn+m61MPvvm/22tovV7d3Mzlc427PkDKi34TAATBEXzQAEk9xerKihajvin8jkJGMFEEIDFlWcbDblJ3eHw2m8WGDY4So+hwzOjeA/rkh9hI5ZOd8IAGZYrD5qlzd374F8TEFRAG1lGJPM9jithRc0fEhw4G/7JOBJYgDfrN53MdHR31UKdbOXMIuddss484O74+RyeGJWru0PIsR8kZjqaw7/42d3+Z3RDl4P+OcLtDRWnJ0JFAIXoGhmd7uQ8OyVB5uNPO39kPqTXeKHp3LHgmysoDQObm57Xm87muX7+uEELPIfC0ts/N5wD9CAK8u48jTqyF9XI9vDnMCvGMLMviW7qbpom0JrOBckXunKbsuytg3x9H6TwYY53Mn+/gRLkzzP5g0KG9y6VnPnkG76Lw9fK8q1evxvXjFDjCKfXfcbRet++o2NnZiSix3xu551qXE9bnmVCcEBBTdJaDS26kvfTPM/aOjsPvrNV1TQj9jBEyMgQMXM69zMide8+w+p44cAEN/SwH13hW2sujPHBzPkFXOQhA0Owyx7Wsm/30QI+zMx4Ec+9ujYnqio5jK7XNCdKbeHuYFSyKudrOdF22i7l5JsvBAXgdZ955vssGdu8i9JeJBkkXNo2TeOF464hKq3U7Jxq9MEfkCL4rikIXL16MZ1ycDxw8SpK2qcTZ2ZmOj497QRnXIU/e0MB9CfQhepM1ceYXuajrWjs7O3H/dnZ2ev4B8xuWprKfXsrEuvlBXpgfug/bjrwOzxGjW3iud5JDdzRNo6Ojo9gtlrlCL3+1h9t61uMVKcgG2R72nMDDz7FgD7CVABVuI5xvvaoHnmQtkuLrVghksSUOenNPADUaUuCLeSYJfcIz8IWch5grPE7Ahiz589ErDo6y1+gHzmqxd54twc45X0pdEIhu4L6U5jHwD92OuS13cNZ1Mfaid75tMu3ZEtcP8DD74n6H389BOc+aOSA29MtcJ3nlRUvj8T1IcUwn03j2B8Xgjo5naTBqWZZFxxFDVdd1rOen9S7CInUoDIqC1LOkGADgICFg99xzj9br9h1J6/VaBwcH8V4uZMPgxB0LZzpQMe+K54b4xo0bEVVG2cNgOF4EbtyTTjoICEobZYdw+lks/vXyEAKtpmliQwoUDIqauRF4VFUVFRF7g6AwQAQRNHciXaB9DuwrAu7OG8LI8AyDB4COqHuWjr3xLMetjJDUlQn4GqCdO0FOZxQsa2R+8LaXMzF/D6rcGYQuPIf5cS8CCalz4jw76HMIIcTsIfsCf/mBX9YJ3Rzpd0eTeXv5EbLBfV1eoRt7SamCB5qSohxLrbEj0M6yTI8//rgWi4UuXboUnRzfH1fg3AtnxHkTXuNzlxucfC+V8f3BCXBnCTQamiBHZERBH7kW48kz3IBJ6iGAoISglhxE9yygB/Y4DaDArBn5cZQYYMgDSTfMOHDoK88kUCbMcFADertc4GT7/hAEDQEP/o8NoLQPXcszusCg6PG9B6KS4jrhe4IYZAMHD7nyjCY6jufhzCC7BJR+pg9HFJvlqC7z8yCW53TOaqp0c99WRnIlSSptOuQF1artILODI57dgFa8IwW+cHlzZx05BrBq59K+nwl+gs5ZlmlrE9R4th79jL1xlN351J14ZHm5XPY6hKE74DX4dHd3N37Xg3K3gR7kw8foxyzLdPHixfhdz+J5dtTLXPO8fVXF9vZ2D1Ah6+BOtjcySJIklusNdQlBPu+gQZ/BC1QEuA2r6zpmnJum0WKxiDoDPkVeqLjBprpuZsATdGbFPgEKswav3oHnJfUaKhBoooccIIUfsTc4+ezbfD6P60Rm3T643wKoTKCN/ONbDfUAfDPMqBAEuc/gQL3zD3vrQS/8zl6zX5Ii/Xi2B9A8E/1EVZGDilyLDcX2uo11e4oO8M/Qb9yPvXagoG76WXEP5pgbe0vW0nWr+3bYUvbd99Bp4fvCGplnXddK0v65pc837voA6fDwsJe+ROF4KZDUj5KlDq1mM2Aesj4wAQLpSpcNg/FPTk56qc+dnR1tb29HBCPLMt1zzz3RAFKvCjO6sx9CiO9W4Pn87jWjzuwEVzhvCKLUP2DPvXCWeTYBIwgI34UhadAATUIIvUYFrjS4BwaO63gmBtedZwwqiCWBlaMYrAVaDTNPlCJwX3fqvH4cI+ZBg/OHG1I+Z83USQ8NBfdiLTyL//O+DUc1ff2OjHpZBIJPIEM7aGgAiuRlGo6GOmIILw1RNw88UJKOpDNX5oXx9gDUnQwPnHB02FPq9pE9jCb7yDzhY4wjBoS5QyscGUesoINnpZCB7e3t+DyXI3gFR4QyXTcO0BheZj3QBmfl8ccf13q91u7ubq98xeeIbsJI4uR7MIpTB5jhoIrvjZdNuJy5k+2It9QCKW6A3Vh5adTQAPHvMIPA7z7YS5cn3zPnzVYH5RvZLA2txxlX3AdkzNu14wQM0Ub4kOc7EAR90pT39gRJOFTp5lD7lg4PjzSbTeN+EcA62sm+8iwPBDlfyf6SrYcnkCt4UOqcO/jj5GQZg7T1eqW67s61tY4XZaCUlEt5VquuGxVFrqbpmj00oVaSdCXHk0kRs4vofl6R4Cg3/OZZSUf80akewLZ82T5rsVgoTduXyp6cLJWmqQ6Pj1sHDduVZDo5PVGR58rrftdOdC2AGzqdANxl41aglQfqfjDfZZz98Gdi85x3mIc/h+BkCF5xH3iOl6bu7Oz0wAaXIXQ4TiJ20QFbsiQHBwdaLpe6ceNGXO+tQKY0bV+74bqWvcM3kBS7tvr5Zs5tI3vY9LOzMx0cHES5Qy45c723txfPH2Oz/Dw1egZ5d/vngIUH5g5kwofOd55FJ9giaPAgCdlFhgjsHOjxwAxdzmDfHYjHnnkFkYMFACjYDGwSNgz+ZW7MgTlL3Xk6txfs+TDD5rYCXeIBhwPAXs7M/EMI8Z1KBKXYZYJ/NZ2uZa2ekfc5ASQMAQi36Q74wKtF0XY69n3xZ/r6nuq46wMkgiHKn6qq0uOPPx5f5IqSlRRTyZ4JwMnJ81wHBwfa3t6ONaW80NS7y6VpquVyqSRJdHR0FJUDwnx+fq79/f2Y8id6RumhWIdnc3BsEA5qoCmJkbpULE4na/CDjc74OCdbW1sRbXXHh7WhQGD8YWoboYRWHrH7Idbd3d1eXStCUFVVL1Ml9V/YRqAJ2spaCJS4djKZxFatOPsII99xB4418X0PFlCO7B0KzTsjueFCiEnHO2KN4nXjNEQ/pS7wc9SR74IA+dkaV2AExyD2vl4/U+YZGs9OsZfOX2QIUNoeoPBceBYasLcoUak7w+HGHqSdzoNHR0eRHnzfeREkH8M5RA7JELvjxrxdCc9mM52envZKYj1IRpHCJ03T6Pr162qaJmaNqZ8/ODjQYrGIc3J+IvBwpyFJEt13331RfshsOyLKdzFo8DmBO46Kd3ByxA0aAIw4yLJer3sBp790Gv2DM+yZZw924Vd0pj/fSy7hd+7jmT7fC+5/KzmF57rzAkmUidZQF3H+Pl/mBD0988Tf4EPnE3hYkhaL417ABc87H1dVqbquVFXITqk0zSJaDS94yelqtdLOzs5NmVv2aLlcRh0O8OUyxB514B1nIHOt16uek7Zed+8W4h4t4NK+SLZtD56oCRuwRLXahuFtEwcp6Oys75yxP5J67+RzJ8eDRT+Ujk5F1snkS9Lu7m7UKezXZNrqX4VGaZqrCY3Ozk5VbN4z6Nc7EMQeOZDiWTv2B5n080CuG/k+uhH+4RoP6kHa0eV7e3s6PT2NmTV3NLHnALfQBqAiSRIdHx9HPnAAg0wG32O9nnlAR6/Xax0eHka9TTDRNE3sBIrNInNFgI7d5uwW2Rv20svJ0c0O8pRlqQsXLkR7Cc3yPI+v2EAHsw+r1ap3VMBBKZ53dHSke++9N87Pyzy9PJn7b21tRTDaz3khQwCaDqDDM94wBn+C9cLnyMYwEPNsKPf3OTigx5wI7gnomRPPdx+W583n81g+ToDvcupg+N7eXuRPBvdxP4vvcy10gH/gObKQQ/uFHJ6dnSlVosySDO7PEgANS/+wWdzPwSt0r6Re5Q9r6bLinZ73QFVq38X3ZMddHyCh6CRFx+bSpUvxd69t393djUESjA9zSO15Der+cUi9PMqZ0ZkMY4BAoZRQkO5ISh3aiGC4oyKpF7BQYuPr8QCJOaCoUE7DrAEK2x09qQu6uA6nDmFZr9exnAFauJJG8HAwQFs8OzdcuwuTK6Uh4iappyChAfvnAQ/Pc+XkB5oJ8DBCzEXqsj3D++L8zOdzHR8fx71wRArhxIDwXQaZGYQ/z/OIqkFHVzwEAKyXa6bTaezc5DT1DIsHMpKiY4kiZ7hDhcJyxAclTnCKjFGu5inuYZaP+yJzlNBgjHGi4QN4FINH/T/zRWEDFPAsEHSnXZqmunHjRgx2AUDIsDJX5Il5uOPlQXf34s9+ptDLfzBolMNJ6p0FxPlHTjzghgboD7Ki/jnGBbpzBsFLO9y4wzMYZK/3T9O2vAiUmPXgAFFmyT7DE575czSfgf7wICOENgvmAY+vhft1c0+UJB2SD5/glDtCit7yjnxuVBkeTMBrPDsE9fjY0c+hjnSQAAcPcAG6IQuUUQ9RYGSa68go4Hyv1+uYbSKwbPe/dWJYK/fxgN8zsi39ukYmOIKAK6enJ5pOZ1F+0HNkuZFH12fwsNMVXQWPkMFZLpfRsfHAO0mS3rmhuql7gSv74tUagHfsXV3XsRkBOnQoj1XVNTBA5pgzjq0HRq4HnD+YAyVo8B38enx8rPl83pMVvxcBAfT0MlPPTjCQRWyY34smDfA3JfSARw7YICvQQVLsCoodStNU+/v72trairIN0MQeELixj/hKyB6grGdTvXsne+tAkvMofpkHn3Vd6/Lly1FnY8ex3QCt8CzyfX5+ruVy2fNL2HMHr+Ajr0BA1h1ogj+wwdgz9gUb4eAxgRB2wJ14D+6GIBGva3Gb78/1M8PwWgihd6YI/nIwlvVhRzy4daDJwUhkmO8iO0MQ2H2TPM1UbII7eBddBn29mogs4/CVNtiFW8nlkN4OiDioF/e8efKtvu/6AAkHwc9BeDodJAXl6RGwO9gYY0kx00KUzGawqaBhjnJ7QMW1BCygGShLSsFgnqZpojOG0pO6+kwyQD5vf/9Jmqax5IR74ExKiuVxkiJShPKTum5RODeegXLUGMPhZXhS16Ico+0HjelOxXpQ3DhOoHJudD17hMBBCzfI0M9RF0fmHZGAHkOkzI2qP9uRehpWSP1MkhtHR8XIBHnQJ/UPc3Kex1PH0N6dn2Hg54dGPfsgKdIRJcreQnvuh4ywd9Skewcvnu/8EULolahxTxxfeNFRWEmRFiBkQ3RNUuzSxD34LogXBstlyxH/EIKOj4+1Wq20u7sb372BQvc22PywnyhvaI2yZ00u4+gJjBbOI0aFtXuA4lkOR2JxPqE5MuDggjuivnceUEMzqStz8Gsp1/OMC3LkDij0BHGHv7iv7wO8gJM/DGygAzQj2wW93OHm+rbMTXHfJfUO9vv5Dl8v98C58fUPwQHAhrquVJb9dsTDkpRhINcCJu1ZC5xLeBieGIIU0MztjJdiwZM4jB40htBvCICc7O/vR5TfEV9Kt1tgrQtOoAPBAevzzBl86GWht8quANoN6c/cPPDn/ujY6XTae38ecu7OrqSYdQXI4JrhO8qweegpB6Zcxr1Eze2wB9aOrqMP0FnQMc/zOLetra3Y2Kksy15jBXiI6hbW7bqMPfEMo9S97we5Wq1WOjo6igAVND8+Po6/ExDyd+SSebCvrH13dzeeMWI+2CzfQ8oNPQBz+SIIhHcAD8lsIQ+ceSRIcpAZH8xt3+7ubtxP9KwH+g7gsm/wCGAUcuD39X1Btpum6zBHcIUdwU/D72JvPbjDWce2AW7gnzmIAK8gT/D2ZDKJoJoHE0mSxPdQUrnipd8MByOd/922OaDmwRv3IlAlw+jAA3YNOXFdn6rfXATd5uATMsvekJ1y+Xdf3HkDO4wdYG0Elf59z9o/2XHXB0ggz2wIzjsbA2FhMIQF9Enq0rvucCD4oM8wi0e5Q2cZtJ5gCbSnLEvt7u7GkgrfUATVHTk/4IeBh6EckZa69CzKR+rQZxzwNE2jcBFcOCoN/QhaOI9E+Z4rNJSUMyQKib+78KdpGtH/qqpiVz93wBAab7BB4ITwuwLjPkOUFsfbUWRHB/kuf4d+KBPo7YLrjr476cOD9tDUnY5hpxie586GOyquIFCsHgBJ3aFYfjDM0MwRIndCoB8Oe5IkPZQOpeXKnHkwf5QPChfZYe0YK997N4ge4Hr3RfhgePaHe8IfyBbZWTIjrAdEl/0kY4qO8IDSHTkCeeTEW9CSlZbUk0XfQ+cfSkPYP9cXIXSHuD3A8oymZ/U82GQu0MnLHqA74EWet+Wojk7i3HgXrGHw5gErnzEvzwQgm6zByzbcIce5Bz2FHzwTgh5p79F3uLmeZzIPd6rYK/Sggya+L/Ar+rMoitghDWQTGYcmyA1OX/vcNvtE8LuzsxN5B8eJMzwcbqcJh+tobBBZEWiHHMIvUueQcS0OG2dD8jyPncb87BpVA47Anp6eRLqj62ncgZ5m72n6wzrPzs56bfS9rNIDEviJMl5oPplMYslt3TTKs1xBobe//sJ3By09eB3e08EqL7PyjnnuXHqXMRxe+I3n+Rkc+DiEEF+aToDq84FfvESO4AbbgZxCZ77jyDx2h3nw3dPT015pvp9tQ9/wr9to5zcHSQh8aFpB5sYzXgQ4yBuAMQEWPpADzg5iwvOeaaH02jN22HRsCvf0TBh8hc7EFhBMHx0dxQwTtPBskts1KmXIhnkJJQAIPuHR0VG0TegS9ge5ZO7sp2cIoT3zIUD3DOrJyUn0NbGP6CP0HHLioAX8Ap/7+gCDPUPlGTYHzdCh3qwJ+eNsFfvhFQRB6tnD1Wql5XLZaywk9btFEuwif/6Zzwv/B12M3YCf3Dfj+yEEyfb5duOuD5Bc8TjTuPN+cnKi/f39nqLhjcxbW1s6ODjoRddsAveCoTHCbvQxRihGHKbd3d14XgZUBkXsXbZgCowHDR+yLIudYVDmwxSy1ArH7u6uqqqKwYwHb9zDOyINnX+QEAQNgXCHyRUt/0ep4nhLXWaCYNWVhZc0enDDc4fOsXfSgwZ85nvN5wgUCoH98kCFNXlgKnVd+6ALCtUzbf45gYobHA+yUKY891Y85SiKGyUcea+b94yLB8sECdwb44QScqXIs+B1ZAY+9ADNUWLmNAx8PRj0tbH/nlXhO+5ko+iGSDXPYG18l7GzsxM/Wy6X2t/f7wVXGBiMDg4O+wGdcCYJOuAZnGwcAK7F6USOvEYb2WCee3t7Wq1WOj097QXw1OF74AftbhVMelkIWRj4AuPhGRKu9/cKIVs8B73AM6ALATOG0Ut9XNYcSJC6rDQ0cR5GLhyNpIzMS/fYB3iKucH3Hty7/Lte9v1FXzh9AbDacrJa16+fxuoD1sM8hw5Vl6XunNimabuAwZ+Uffl+E0zgZOJ48yyMvpeiss6Wph1tOMvqgWAI4RZlYF255FB34qBCcwfbOOuHbnFQyTupecmwOz3+d/bHA3AQanRbWnR2ZAhUoV9d7x4cHEQn1vmCIBC5Rxe4fHi50d7eXu88FdkA9hjZAExwXoYuDgZiq+Fvt7GATPtmFQAAUVVJREFUElnWlgkDDEFXB1WgNw63Z0ddptxv8GoXz2jAI8MfZA+6YKs8OzOdTqP+ILiHhnVdx3LMuq514cKFeN3Z2VkvW0RWyQGZoZPPntOJj8wTPtbR0VE8S4lv5uAotnwIRjoIgJxBR/SOZ+RYF7Tz4Bqedb+LfUBvLhaLmLXzzsee1WbvXUagKXLkIAO8hw1yveP208EhX79X3gwBLs/MMW8HEQlA4UnmAMDMdXVoVK47nefnaJkPegq9hswxL7fd2BDWQ/Ds+p3PuAd6tssKd/bwduOuD5BwDr1swRGEum7PPgzf++NdWdxAo1gxdDs7O5F5uDdKWmpr0o+OjmKGCoMvda2TPUsAs3RlFF3al9Q9DFKWZXzxpDsDzG0+n8f3JGFY+QznaChErNNLXHA+yDi50+LCBbM6AgCNUbju6EArShEILj3yRxE43ZgT9wTFcEQII+MlauwvQojwu3PvDtwQZfJ5cU9HfaAH/DVE/fyMhRspsow95CV0GTgPuuAZjJA7zkNnGjrhoKK8uBdrBAGX1FNCrAVFA8+ghH2+/l0Phvn/yclJNLSugKE1Bp/7EpB7Sp25My+e6WUoHtzhdDRN97JlrnGjC4KLI0HGlGs7dP00ghOUidC4g/NMKHnu7fPm+TgdXjKIk+gOPvMaZmBY3xBhhU/d8OAc+LlF5B9+hWYe9LMedyLdCUDXIN/QTupKqeB19Jo73HzmuqHLinRvhUc+Wrp2sk9A43Lm9yE74qCOOzw4I+50rlar2MUTR9n3Dl3jQFLTNPFcG7KeZTe/hBMbQVaHeXp2kL3JsiyeGaWzGQ6CX9+e5ShuorXrTHgHWk2nUx0eHsV5uIPVylHVO+eDI+pBtwfNHuyQcaqq9kWh2AFJvW5cbjNwGLG5Xl4Z0iTqSfgN3sa5ns/nsbV9WZbxfUkODtE+++zsLAY/7hMgQwQqHnzwLzLr2daqqmKJ2ZCn0QEEVPCB21rPLKLTkX32eyhfXuHgesSDLvgaRxz9yLsU3aa57XZas398hsxzf/iL55KZZw9ZCw0fOHcCDyPDZIvIJjIv1oTjDN/v7u5GvcjrT6qq3/Vva2srPpcAwEEqwFhozXPd3qPzWRs8DM2QBX4nE8z3oRslyt4ch5/9/f04P9e/HrDyHAfE4Cn2h3VJXbbSAUjfP6/i8HI9aMy+wo/IM/fh3h4cNU3XSdC7XSZJomrdZcYcEHawELqzdvid0QE7XQUX+tPBHK716iNspFeABY0ZpDggDBtKlkJSRARA8XCMQOL4nA3w1CbGnLMRKD+YX+pqWSXFDE5dd2UQfnDy0qVLsfyiaZp4mFXq3uLs6CpOyNBweaCEooZJHJni/x0jBrUtYjsHCsd5Op1IVkvq2Teeg+LFUKzX64i0EQgMywNx/EGsUIJ+3gNljGPtzQFIneN8eSkCAuQv4MNguvOBsoYmUuuw8qbyDhnu1u/onn/HU/GusDwLgvJxpBjnwo0/tMWgsL/O19CPPXZnBZ6R1KM5z3dk3ekNb2Msh5kX5uGpbEcs4UWcSP6lBMczI26oWZOjXB6sYlC9C5Nnjdy4SOrxIteBKnlpkwefRVHo+Pi4h1LTIne5XOrg4KCH3h8cHGh3d1c3btyI6DodKouiiCVsnEl0RFBSDLZ8b8hao5MADNAF0MTlF8fEMz3uaBDQsRfwNLxD9oG99hKmYbYIOnqwQxCNMXXAwYM53inipbI4iB4Qs479/f2I1Lf0k9I00XpdKcvadtshNMrzLvtJBgccAz6YTicbh5JDze05HFp0VxWZkqCtrdaROz9fqe34Vmu14mxMqrpuIjCWJFKSpBvAgg5pIeoo7EoH8qSScoXA2cGWnw8PD7W/vx/3aJj9w7Fxx8sBEUA5/m0dzCIeTi9Lzjg1kgBdKmXZVNPpRGdn5+JME/KB7MG3OILoloODg8gvTdP0Xp0BbwPquM6CF8iqDAEPbFy1LpWn7TmGoijUZI2Wy4USqZctRUbm83mkIXTk7C485C9qTpKk13wC3mavZrNZPEsG4IjuT5K2zMoz0IBx2BpklH2iYQLOLU6609iBBGwB9HYACZ2LrvG5AzI44ImdRe5cluE1LzH0UutbgW98XhSFtre3YyYcfcAcWf/W1paWy2U8SuDZRHde8UfgI/wHr17ArhOAuu3DjnvmyG2CdxR0P4HKHA/Y0aXYF3wP11mAZjzDn+3AhGeCCLTR5x4AOQiADYJPucaBS/YZUMUzOvgOnhxwe+OBC7wH/fzcmiRlaaaQNkqCFOr25c6pWr1arUslQSqyXDvzLeWb+zRl9/Jz+IJ/4X1ow/wcGET/YT9YK9d5Wa37iugUB6ABxCaTydikwUdedMgTyghicuAMAXTnFocJJcxGoNxBSlx5IVQIDRE1TAqSwHV+GPDo6Ch2YAO5w0mC2ckIuQNDZxhnDIQTlAUHkfUQKHDvVrGl2tnZ7hncEBolCVkXRScKY+YlOtCYNTF3lCFz8IO1GFaEABQXx410POvzLEGWZTG1juHBkNH4whWPI4QocC+zYz7cp3Ve2pKPxWIR1wa9EFAPVl25uwMKXzlKI3Uv33PeQmGzZvbDERwUmx9UdyOBUsLQO1qLkWC/CO7JpNFlxxWqGx6cBuQCXoCv4D13OnCQQHtdObNe5ocBYE9QrtABumAE4BsClCtXrsTvcD/2H9lg3dwHlA8DQ7Zrf39fUota7u3t9QJIUHZK66bTaTxIi7NLEOcvZgUthV7r9TpmgT17g3LHoYP+GG2XOXe0PDg5OjqKf8M5lbozlejBk5OTmw4Rw9/+TJwnB4LYR88+8Xx4AUfHDTUyJinSydHyJOla/Hrg1Oq1DizwTDYDGciyVGnaNbBo32fUtYbOsjSelyMbgG46PT2LZ9ncyUzTTHXdROQS/t/e3okAmDuzBAhtqWUWUVn0s4Mb7jhQYih1bdB3d3fjfNnXs7PTXqYawI/vkcl0AIT3tbUg0XSTXWvfQQRKTokRsse+8dz1eh1fho6OA1BE5rg+y7J47gpdhTwNu50RmGAXV5vzutWGFxMlSja8vbu7GwEvdwzhNfQr90KPOmruMtm9+yqNdMCZhTexnTiQ169fjzqF+0EP6OSyga5irpzZcv0IX7jMzmaz2FiG53nA4hlM6O5217PbrN8zZZ7B9rMu7I2XKDkQhR11QIt3HQGwuv07PDyM16Nb6PgHaIUMcfbNnV+vTvC21vAlpdPu+FO+6oEF1TzMi3XiG3gWyOXU6QvYzf8dpGVO7A+yiSx7wIyORPcMM0muE5m7A4B8hywV++aZde6Pj+mljsgR80Tu3a+d5LnWq1Y+66pSswHtqrLS7s6OEkkNoGzozvlAQ2wdTZIcoEQu4VXWDx3df8EuQdchQAw/u68wlA/fx9uNuz5AWq/WyrPupZI4wAgCGSEcpTRtWwE7yuIleY5cuAIaZnNw/thcqW0T7pvP5kpdKY47F0PmJnDC4eF7bD5KHMMsqYdm4IxSh833u2i+335SSjSZ8OKuOip0PyhLIOFpfhQNgQMlD56qZR8cyXEkkLnC9CgthE1qjSTrYy7QiZbRPBMaQmMcfK5HiNxRdbSDfWQuKNujo6N4T38Git1LXIZBqxtO9meYbYM+HgAzT+c95wf4ygNKlIk7tLPZLDYJobsRQZ9nqKAhPMs6KaPESXRkCqQOBMezFa6sMUDsVQjtmTnP4Dn/O0pKGYgkHR8fx4DGDRS047mOVEGf5XLZ44ELFy7oxo0b8R1Hly9fjkEM9AA5Zk9B0t0RQ1+wT9AX/sABfuKJJ6LxZ7+n02k8WM9ZpZ2dnXimiYAf58ODXujtug19B594gMU8eE8Gz/CMtdSVZBAIeEkvfOlZUA8oPXBnTcg7PE8W2fWto4A830EjN3yus+lk5VlynCPv5oQu86xadM5Xq1iiCRLtegAHhczZ7u5uPG9BYIK+ghdA1QEXPKPsgAXOBKAU51dxZKBDWZZR1+BYst7Vat1zrDxb4Giuz5N9lLrW4Tii3DuEEGUmhBABE2gLeAjoQFe9YVmm6xB0S57nunHjhvb29mJ5Ifvr5UGTSdfgCNo63/mZBdftOJgekHEWEfvK9QSVBJQAfp7NABBEp8MTlN56pod9dcccGaTxEXYFeUO/O93QOcxhvV7HzIwDTKzBAx704NCB536AGMwT2sPjnt1AnrAP8BoywXrYW2SY/ZO689bocwJsso8OQriz7eec0GvDAMZ5naCEzD9rQ7fwfQIHaOHBvgcx8BY2y8Fmz7a67nNeRje3YMxpzFwRaHu1j1ddoJvRdd7IwwFHDz7gB8A2Bz+dp6iuQNdx77qutaobyfjO/T4vm4Ze0NbtTJqmMQvrYC321H011ueJC+Qb/eaZUUBddJgfGXD/MMsyZWlXtXK7cdcHSDDndDq96XClC5VnDhAsjA+bgsDwXW8SQMYEIz5EeFBQZKykrg4T5vBzGFJ38B3GwYjicEv9HvQoLQyZOx8wM4yLUMF4bbCQbrIv9NBPNjXuqarq7Cakw5W2Ow5eSuWOh0f1nllB8XowgpIkiHFkBdQbNA/jiLC6U4Xy9+f4s9048znZQzIwCKsbFA824B2CbM+euLPljt1QqbBvjiazT9CdNDp86egKn8GbzNuDJJQJtHXDQbMO/3GHwQNtDBb3ZY6uCJ23siwbHJKsb3qWZ5L8gKbLCegq+3t8fKytra1ovJFvnCNvLuEOPHKBw+SOI7Kwt7cX51MURe+wPY6BB3U4tnwPeZW6VwngYDEPL8sFuc2yTIeHhzFb6CW8nhGVpIODg5jx8/uAvl68eDHukTve0Bq59PefEeTVdXsgmyztcrmMmVlk0ks/k6QrV8L5gXZJ0pXt+HtOPKtHqY6XreAcADI4T6HHhgATARZOusvG7/7u7+pTn/qU0jTVc5/7XB0cHCjLsniGiHeatXROeuc34Se3KU3TlnpSagWogWPvWVCfL4fVkyTpdeTi7NJyuYx6C15m33FMyFy3pYZJjz58V+oCBubhegAHDl3BjzshOBoegIQQ4otFnZdYN+ebiqJ92S1O8NbWVi/D5XNjjd74hB9/WXld1yomE602wRsOHZken6cHotAN3YwDjH6C56ATcoC+HFaZAAYhXw6YsK/u1OIIQifszmKxiOXp8DCIPnvjTSU8CMAhvHHjRnyxrLc2p3rFg/QkaTOzgCGeleFZ0M4BCJxnMh9OE3wqaCx1jjv6wZF/6E7Gg/1A5tETzMf3jGvcFqCHsEX4bGR4WCO8zbqd5gA3AFf+EmTfY5cV9JYH96zdAV/2w4Mjz8r6WSX+hYedVxwYd9/CwXq3o57NormGv6LCgXPX41Qp9fw1SZOiuEk/cI1ncB00RBehF5ijl4q7T+A6BRpzf4JA5JrrPdiFX7201+9Z17XKqjtff7tx1wdIk+kklisQGbPBGKsQQq+XPJ3UeMEYgszmwFg4HR5t+0F6qWtxWNd1L61fFEWsY18sFpHBEXoyMiChBGN53r6JmjIgnFI35I4Geb3/sMsWjjpObVs+UqssWwHNskzL5Ymm0845Rim44uP7HiCgPJmjpFhq6E4pc0TxgMCiRHFAhxkzhM0VI2g285LUy+JIig64B79839P3/N/P36CQhii2K3/P3riC8WARY8MamR984e+f8AOsUr/NMbQ8PT3tIW2enXLnkh/2DFo62sPvjq4ND5h6WR3nHeBrPyAKf+V5Hp3NjtfSnlL1ufEMBy2KoojGG3rj2CdJooODg2io3Zny0jQMEPvogZsj8GdnZ9rZ2YmHfbkWucKRx2iF0J1NdLTbedzXDirvMu86hXeoMN+LFy9Gx9FRavaD8051XcegaGdnJ2YAPKvk/Ai9vGyXdTRN22nTg206blKOhtF1UIg9RIb8++508LmXucKryCp6mPee4IijC4bZRze8Tif29fLly7p06VLcN7JD8/lc165d0yc+8Ql91Vd9VXxJZpomvU5lXlLMc9Cp3vnUwRkc/FaG+sBXnuexGqF9EW6/fAkZdnlhb7py7CryuwNlntFnn3g2ziDywZw9YEF+3PFyVB0bQHDnuhXeL8uuiRAOLw4RMs690Y1klCTFckTu7boefcDzmCcBAU4oNgc7h/wfHx/3ArvhOR6e4/RAlvEjhuAdfM1nBOsErfgC6Cjo73a0adrOh+hDbIrLDL6EA4noaPcffO6ur+EPsj/QH100zES5D4P9IhiDF7e2tnplx86LyLcDjD53z7oQFLouctljD7gff4PHkUd07BBkQ18BQgz5HD1DFg0aeOULw7NL2P+h7XS5cECVZzswtrOzE+0U98LPwl/x0nCehS5wHmW+XOeZH2TLm9g4+O0ywGfa6H+A3iGPuW5yQIa5c43bcHjA+XwIfDldobmDy+gK30vABL7PXmBbmrGLXX/gNMNgMBB15I4e4zB4OljqGArB9IO3lFTg4CCMIGigvyBrKAAcBs9wYLh4No4UjkeaphHBQdEMX1SKUXDkYSh4KElnrJOTM5XrUlVdSUFqQqNEieqm0u7uTs/x9cyBZz88WJPUU76ewUEYh2caECKuxwFyg3p+fh5rqD1j4MgXhscDAXfW/NA5DgLXwS8oM4QVheNBBcKepm0mkHQ5+y/137EAwsW8QT/9b863XrblSouDpDgUrgSGZ62YM3NA4cMrKOFhKYMHjXyP/QVc4F0TjkiyB1LXHp3vARIMAyV4ABpLXYbCn+33AsUG6V0ul6qqSnt7e6qqrvVwnucRKPAgxR0mnC34wZ09R6mg62Kx0HQ6je/K4bvO3zhDHlxhIKLh2XyHrCtZCObDHvI3nu+vCKBshJIj7uEG3x1A/i61zgfdlHxewyw090H+uR45ZK8JMJ2n3Gl255r36hAs4NDAc8PnerDMOoZBN8/00q2yLGOWR2pLGv37SZLoOc95jra2tnTx4sXNHnblHThblLx5VgK9wzyXy2WcozuSyJwUokOO3g9Bapo66hr0JfvkJUCsp5XpVEnSPwAtcc6za9fNs1y20fmOxg7v4890Zy06Gk2X5arrOmZz2X8CZ5wqP/+Ajse+QUMHozwL4sj/dDrV6Un3MlB0BFk17IGfRYX3kQ/kECeRcyrMb7lc9nRrXXevbnCeY77YwaZpIojA3E5OTiKAAc+R+UWX+/cl9UAB5g3fAJa5bJOZw75R3ucyzX4gIw7OsA4aGOEDSYodIOu662zGvF3PeeADH5Mx97JdD6rd/rjcuD71s2JuB+AHziqhJ93P6GUOLEDlnI3LOM9M0zRm790vcJCAwMwBxyFggGyxBvw96A/tXA9hO6Gv0wv96MGI+yQ8x+04QJbbeuSR70N7qTsbCk/HwC5JlSZdK3ZkF33ttHY/xkEN/GXW4PeC/znHy5ywDz4X9xvgB3jBQQH3T3vBbz6W2MXR1P2ONsN0pKc5syy76fwLjqe37XWmRekQsed53kuLUu9KuhSHA2FGWD0aR5BAHh2hlfp1mDiKMIPUnXVC6I+OjuLzaJPpSMPh4aFu3DhUCIkUCCgr5Xmb5drb3VNQh5jBlFJX4+mIAkKOEUFoHTFwxMydGw86PJByJG6Y+WCfhu1pEQrPbKEsXVihI89HIXt2zffZS9PgCZwjR58cFZO6F5tyD4TfM1eu/BjQFAUBP6HgfD/gIfYAJJN7Q2dfv7e4HwZiONse+LriyrL2zA68gFHAIee7nsIf1vkPjYSjSBgx5JNyNQALrjs9PY1d4yiRkxSzQdyT93FcuHAhPm+IXg3LdNgjl/m9vb2esoav3IB6QOM0gB9wDuGnum7L2jwQwIHBkYM3aM5CMMhLOv0ZoPbwtGdnCUjI7DkyCj08kwsdKA0bngHw5i2eDSRAkrrzPt4tDx5Adgh6h3wI/agGoCEA/ItskDnAUWSPkBmcky/7si/TZDKJZ9eqqtKlS5diQD+dziLK7IEQe+ngmXdIcsSejCp83863OxNI9ULrbPSNv5e24ojh7PO8NrDsyi7dsSnLdaS/1D+k7/zqyLY7bvyOrUzTNJ75gwfQYwAzLgs42u7MwIM0R8L2klEnE75YLFSWpba3tyMQCbAynU61LtdRDrk3++6Zl6G9ILjyTKaXRRGwUDbpetaBImyag4WUScEbDno6H3uWlOwoskwQ4TYfu80zkH+yq9DYHVtkzHU9wMXJyUn0SbgHn7M2dAEluQRgtM6Gth5Awsus2ffLgS70K0E/8+eeBLbwE3oDOh8eHkaZktQLnpE5B7o804becdvl4C46iXWzd8ic87LPG1kis0gJKdcSJFZVpYsXL8aGJewZcjmZTFRWlULTB5g9iPD9ImuGreh0Qhf0os89cHG97zoNHYMewzdtbdW5tre2Iz+hi+A3eIZ5O6iLnm5sXa57+ByfmcG93S/CNnvQ5YEjwLrrTOQozqEau9jFAZKDwk2SJB4AB9nEwFLShkBj5IftgD2KZ4MxwgiFlx6dn5/30qeuQAimHNXP8zxmiXi+o9g7Ozs95JLfOSjqjJFl7Uvq6rqtx0VpLBaLGGQcHBxsyk3a1q+SlKbZBtVs0c0sm0RHiCwF82ag+HAmcWJYc3vfzpgPDTMG3ZUPQsX6pe68AQIE+ocThdLw+zviRWtwD3DcmHvg4soF9EVSLJMYOhaeLcMwogAkMnUnMYh15NbPh3igxDOghQc4XmbkCDTrgi8dHYP/cALIYrlTC02GBtRT+/yf9TtaTSDjjoU7vfCOO63wiu9v5/CV8SwDjrs7Luv1Or5oEYciSRJduHChF1ikadrrYISjO3RmWAc0YSDflO3gQDDPNE1jiQo8xfwwkugXygTRLx6gDTNAyJ0bGHSAl0Cyn+wZ5UeOHhPUOL97tmIyaVuNI3fMCb3E/dBhzAcj7kGn1NX3A/jgNADYJElbIokD4+CA7+dQd7gTijPrmTK+44BKkiT6si/7shhkOI0cNGjX0bYVb9c/NcexiDpyOm11HZmLsqw0mZDxb7RaUSUwlZSoKHIdHy8iL2VZ3tMRaZrG4JPmNp5JZs3wbIdSB4XAC4zX8R0xdOHk/lyf513FAT/t2rogzx3XWwUFAA7oMXQQ8u/Biwde7B3yTqYXR/jg4CCi3lXVni0kaEiSRMWkUGP6gf1GZhyMI7PF75SvcfYHvXr9+vWINFdVFfWX2ze3H/C86zVkG/rAWwAVALCu16GJBybIoutAQE3miIyzD+wRz0eOKbXFIff5wf88n7khSx5oksHhfVKsHTuP3+HyPqxeIKjqeD+LWUWe7620XTbRd+ho9LLbCniGjBzgycnJSXyOA9+UxGL7vOyRAIm5u5O+Xq/jMQcCR/aMDBY6Hl3q2fidnZ3YGCTPc6UmE1LbDe58tdLcmiV5WRky7B02HQRwoM6zv27btre3dXx8HP0laHq+6Rrpfk+apsqms6gXhnbabRF7CaDLPdDRnq1jPrz2huASucDPwV8gAHUA04NHfB1kAv51EIh1Pdlx1wdI7jy6kZUUWxZDOP6GYsWphiGcwBgKR/sc2UXpoChIa56cnEQhgZndsUHJ8QNDcC8CHBwTFJ6X8pAaZp3MlUDFkXNXLK0BLHpGoc0GzONauS/KBCVF+Ymk6DB49xynE4yMk4lCZI/cMeZ6lCmOjNQJCLQmK4PCgn4eAOEUI8jQAgHl/RxSl8VxhN0Fj/MpzN8PPsN3/l0MNzznqWSv7fVDngRHQ34KIURkyulEcOhGk2AW/kVhw3ceiCIflIOCzngg4QYcHkPxeXofejp640AChhjHz1FNvicpyiDG0vfLS2igi4MW8D/7gKPncszesF/MSep3YGMN0BMkmsHc3UFDzi5cuBB1gwc+gDW+Z8g1tGV/cXCgN/LHWkMIsfMdPA4vwSfuoKGnkiS5KUjzYErqWtIPHWacABwAD/Dhe3SZyyBnUxzQgG+gAcikzx9ZoEzKAyGG8xm0BR0m+MQpunjxYswU8c4cfxb0crQYB8j3yA/vb29vRxR5a2tLi8Viw1OVzs+7tt1S232NF2V7+153rgECPJvmZ+nW635jGhxEglHmfnp6GjuyFUUbIC+Xy2iLkB3KJpnP0dFRDxhhjsgt+s7PfFDeBg+QwcBB9qB0d3c36rbT01Plea6DgwNJXYbUgZZWZ3UNJDzL6Z332BMct9lsFs8a7+7uRqcNGWKfvQWyN1WC9jiH8J+35uYa7LbLtGcL3MfwPQWgpbzKy8rQK/AxmTf0Kc9xoMfPfiHr+ADoagcXWbPTGtsKv/F/ZHFvby/SgH3m2XQwhDeGHXAdDDk8POzZJ/iH67nPyclJbLoFLWkQIrXNa7DTnnH1/ztQ5GALgDYZlltluuB7ZBw9kGX9smFoOqwMATRgvyix5/OwoU1V9883DbM2LkPofw+yCQw6menO+rI2fCsvCeZ+AG/SprHEhs7Qz0sNPfhC72GX4LOi6M6bA2qjiz0L7foWW+4+ofuSyP1wT9k35NR52HnuduOuD5AQ/hBCrCsm9YvxcdQT4hZFEevJvSTs6tWrUdD9gLwzEo7yZNK+G4I+/wiGBwO+uZyRIlPlQQKK9ezsrLcmST2HzpEXmAvFglBBF2p3XUG4g4PQu4PvSEYIoZeZcCWNUnCl7sbAkb577rlHk8lER0dH0UBIXStmd/79rAhr5Rrm6N/luZ52l/rt1D3AdUc+TdP48j9Hbzyj6N+jrASkrK7rXhMAqUMOJfUUjJdT+ecYIlcO0MBR/Vuhj+ylNxBhXShED3yhG/vq6LKj3DieZApxjpEjOrl5mSMoJryJYuQeOCmLxaIHYlRVW96Ks8EZL+hOcE4bbG8aMES7nSehFYaV66ATjhUOCMYJefZW6ARzzBsdAj2bpul1JuMaz/R4yZDX9rus8AzQSXj49PT0Jv0C0o2O4m+egWF+jhh7VpB5OXrrSBw6iXmii1x+0AXoUcAcMlE4/pIindl79KZnz7gnfA//ehkyIIkHbZ4dYC2Uqfj5CacJzpTP3XUftEDG4RtkiIw9jn9VdQEt8nJ8fBz3TerOALjsuhOxWCxicwnmzcDeSF2HMGjPPiDXyAN6ihIhrsMh85IXgmEyUk3TxCwMQSF05VwiAZuXwMFTPI8KDmQGOwXf+nvX6rrWdDZVsvkegwxelmWxsdKtwDCCOfiTeaOH4Hv+ZTi/oms9ACRw9g5l8J6DU8wR3kF/ZVkWzysTXHEtgZADV+4oouvhPed3zoTB87xLCzvDd/y8nJ8BhHfgBfgXenB2lucTJKCvPOvlZZ7MsSzLmOUZ0oAfHOqmad+fdfHixZjFwndhrdAF8Ap/hdd+SF1WGqDSZcMzVlIH2MA38Dz2F3tBoA0IxZ7C5x4MeRY1yzLVG1mG7oDH6/W6fb9Q0+86SkDoGXpfKzZ66Gu43kE/UXKHPKBr3Q9kHZWVeCIfABJDmXHQ220Vv/M5NIXuyDw/nu3hjCu8Bi9xz2GZN7rTweLWzj75sOeuD5Am004gt7e3exsO0yyXy/h/WuzO5/PeoWN3IheLhU5OTqKjwWZIXYDE5vJ/hAzD4iitZyVwIEC8QNQw2l6TyRpc+cIwfAdmZG7OkDAfSLDUlSq44XXmhnHpsgVNSVXDtCgF70KEouZ5jk45ks6acEJwahguCFF4zYGEvjgnTmvuy3Nd4Ny5Zr0YZ9bA/aGXH2BlH6Ah+xBT502/yxG8wz7xfxQha8bQoxAJ6OApDJJnHFCkODXuILPnzNGRRYwZCpc1cAYGUIEyPfbAAyccdnckKcniTBBroNTAS5zc0WSvF4tFRClZQ9M00aDnea6rV6/qvvvu6xlVnu0KG+PjwYHvn5dvQDMP9ra2tqJzyruJPEhH7uFH6Iqzyr/eRQgd5GUeXhoHT0IXspfInweVHhhkWRbPHTqIAp96gAu/eUYLHnC0HCTcDS7fAXhyGUL+HIHGmWKOOCfwk6SYWWCODu54ADsMzjygc+DGgSNo6e3nuS8OIHLiJbLs3xBFdyTaQSH2rnXUOx5z5wG9A594iQvAFnRpmiYi9I74Ig/oA9ZH1tID4LIsI/otKfIydKqqqhe4+TkP5uLgBvvCuiTFw9YEeQAs6IGiKKKT53y5t7en09PT2GUuTdv3EpLVJzDOJ11bZJ6LDT44OOjZLL6HQ4YOYx993z2jhNNG5hbb5JlGdxI5G8ke+7u4XHYdJMSPcMcUm8VewNOSYukk8uZBCDyHjWSvWYs7/WRp0IHwH2sBpOR+lKgxt6IoYudQaAidJfVkRlJ8JQNr8TI4XsiMHRiuDfCAufPD9S6LnnWYz+dxr1wHEKQ4yOyAFmv3hkL4XayRYNiBRIA+ADPmtb+/H+WVYBc5Zw74MS5ffv/9/f0YyHrGHdkegnxeweLrQ1a4xgNA1zNOj7Ista5XKvK8F+DBB26z+A70wba6znP95nIE7f137uVzYa9Zq4OJ6BH+NtznEIJkfHm7cdcHSOW6jEaC4ARjAZNBTMojQDa8TS6Cj9MlSfv7+z0DDZPCQDiEoGnelhSBwciz4TAp6WNXZDhV1IkOy6B4D8wQpUYZS/2XhtKpxrNEjhwQALnw4+RwgJb54xjRScyDMe+a4rXwzI/7OA1wUqA1a0BouBbnH0Pkjg5/cwfMswlS/23TKCBHzRF+lIkbS/bHA0p4h3tjtB1hc3QHQ+GBriszggKMGkEyShyjh6FylB0l5MgfBh/auQLy4HVYQslz3cDzLD8bgWHgGVzjDv+NGzfinJ944gndd999krqzNl7Ox945OsXZPWhzfn4ez5XAxyCW8JnX5kudIfe1ez25OyegpQ56gPB6uZ6j014yCZqIYoe3yEbixPv64Nv5fB4bUjhNp9NpLMvwLJeXFjIvN2bsITTww9V+ngjH1mUJdJTgmH3HUQfZ5BkEia4DCIBZqxt6N3xe8uLgh5dOUAnggAjAg9PW+Qqa+MtNvXQQviA4w4DDn/wLTVyO0JmerXb5JJh1PnHQCWcQ/eGlLL6HknqdqeBF7NVyuYz3uueee2JTC+bBOkDsHUXH8YTPvPzKy8OYN3bPG9TAk+yD61cCTs9eoF9wtOnOCL/gpFZVFTNWXnbr+tUDVM56OmDowbvbdObhQAM85I023H9w0JP9cgB2b28vZjrcWUVu8jyP5yn4P+CoI+esjzmSjWafvCubZ8IAV6C9A0bsCS/DTpIkyjYyBS9TEeOgqQOfAGVN0+izn/1sLCfm2AA+wXOe8xxdv349Am7YNGyE85QDG64v+Yw5eYYTGUb2blXtgh7wTq/wC2tkPTwb3nLnnGcxX+iOvWB//VwxfOiZ8hi8pf2KHPQMNKBygvJPdBr87DwDKO8+G3Losu8ZUA/MPRiE3qt1qTTpvzbBgWeu45kA5l7+6f7AcM/wQ9EL0N15jmdBA+g/9G+HwRif8/NUxl0fINHlh01DKLIsi06+l9A1TRO7g2D4CVL4Ho5qmqZaLBYxVQkSBHLQPj/X7u5uVDowk0fcGEQCHkerYWyP7qlzBW1xB0JSr+THAxGYm8OFOEiOFHo61J1IdxI8++ClZJ6xkPpnPxyhPj8/j6WJGEbowDXUGpMlQNlgMFH83qUGwUA5IexD1J41u5PiQaAfRL8VGg0duPcww4SB5McdZowqAwPpZXsoUi9T4LPPhZy4s8E+eOp7qCyg2bDUjh8Uu2fc+N4wc8Da3QA4zaEXSOC9996rk5MTnZ+f69KlS/E8itPM0Smei1NOmak7F/CTZwTc4LJfngmQFMtoHVFzWiVJEjMwGNOqqnR0dBQDMgIglzlqtTmAjlMEv7gTArCBM4acYnQ9+4N8gpyy59DLARD22EEEnDmXAc6cuH6SFLPXzA1QxQNnHHUMLnzijjx6l+e74edeyJiDGOyxG1ecAZw36MDwYMPRUeRgiHoS4JCNQJ+ii+EZ6OJIupfroqORG5x5z5aFoCgf6AzWUNfd2UmnFaV7XIsDx5zhQS+7QjcfHR3dVFrTyWWXPUMWnL8ddWX93giF0hwvmaMSwAEr9BhoP7YHfUfQ7uh7CCE2F4A3AAXKstTW9rZ0ftajPY6l60EHe7gPIIgHDsgjZVdeEogNmc/nMSMEjd2Z5+9eKg1IwEumpS6zQqBINg0Z94ARfsSGYNsAm9gP7rm3txcDnmGWc3iGi/0lW9c0TWyYQrdCSt+ROYIywDIyInt7ez1bKynqia2trfg+taZpdHBwEB1c6OSgjZc380NDIexaWZZRX6GjWQ/rBURkb+Avt8PDAJff/dUs7Ct8SVCHbmB+7Du6A0DWQbFhk4zoc4agUHUNYzy4dbskde+ygndcN8F//l0H7LGD8Bf08yzkEEBmX+bzuZq6K3tH5l3W0WnYAnjNQTvuj6/gz2DOyI4HSPh07G2apj0/lP1zYJN7uL3PskwagA+fb9z1AVK2YVKyHChylCqGw9EYCExak0yGMxJIV5K0h1jplHN4eBgREoQBRM8DBhT3MOXrRsWNl9Q50cPaSg9kfC2ejcFJOjo66iFpIEduLDDifj7DgwQcSJQ8ZQJS/yVrRVHo+Pi4VysqqVcXTZel9Xoda4IdhfV1ozhQGMwZpelpWhdE1iSptz6pOwjugZXUfwcLSoi/Sepl8dgvD3yYqx+u9qwHmSYU0b333huRU55FdsTLL0AH4Q13CFCOOE5Sl4Vx5efGgefwOe+YkhQRetaPM++HPynHwHi5w86zWSPPBeEik+B75YBAWZY6Pj6O6/VyVZwKN0iLxSLyFwE/jiQ8xDOQZQ9A3cC7YwUfeDkRraFdGWNo2X+cOvjMMyA4uk5/6Oy8SAbY+R2U0vcenvTy3mEQDa/jfKOPHJTBCQdNdkPGvbzcE4cbvnPE3403ARuGfShfGH4Pur28lTmyZzjPHoxSNsYZAHS7I89Dh4O9QM94iRXBn6PG6BBk0VFsX6Mj4shAWfbfF9c66k2PXmQ70e8eQOEcIN/obWwM9sODBs8q8pzlchE/d/5PkraDHLLgAdBQv2ODGH6ovaVDqhA6R4jvOVDo/2dPnLboTHgGJ16ha4Tiuoj9Yl/n87kU+qVSgCtul13HpWkaAQsyH0nSlb3DR+54oWNYO9dSfUG2Qure++POG/ILf2JXnb88y3f9+vWY4aTxhYOlOMgEwGXZdous67pXGn90dKTZbKbd3V2VZRk799HhzwETr1hwsJDnwrc8H/ABfoT+yJbbY/wyaILsoHM9E+a21jNLQ/txK33K766jkH+/n5/VdcDSAWFkZJiFYT51XUegHTvvVQzQJk3TWPKFTYy6umlUWGbK/UMHDeALB8WQTS9ZdLAU4Ac6Y29CCDHryjVlWWqSd/s/DELcDwE4Qg6kzi/FhrDH0M4z+9BxyD8O0HIdAarrDvaadUJX7tvyyhggxVHXtdJpGhUeSt+zEWQiUIAoI0e2uJ5sEptF6QJOOcYDZpO6EhiUAkrHmdIFm45Rbri4NwYBg0m9uZfKEFyQ6UL4UWgY3rOzs9gC3JEY7lFVVXxPCIjzzs5OVEgYBkk94eWQ+XK5jEYc2iFYXpayv7/fQyJRLFzDPR0ddrog1KA0zAfBQll6ds+DGfYChYwzC3LEZyh6dwRBnBzp5Zk4Jh7ssc90kyKgBZVBAXMtaNWwbAcnYtgFLc+7tvWOQEmdc+OIvJckcX9vG816Qrh1BzQPKLkXdGCvHbF35yLPcz3++OPa3d2NBpj9hAdpYU/whiGG7+k4BK9Jiug2tHbH3gOeEEJ0huB3DxIwiJ7Sh8az2UzXr1+P9Kyqqtd5D+fOA2w3/u6wksHBCRxe50gausUbPLA+HCUQTXQehoRMEHoH+eDFmOgH/o5eQV5x3JFTz7zwfAcR4BE3Vt6e2LMlyBa6A/mDj+AlB0k4i8G9MYwOaLnT6DrZnWYCGJwv+MOBIfYCZxr6OmAEr7H/brBBrquq7SDXOkGZpFRSkNRv0w5tKKF2niS7hb4DVOFZyIAHqKy93f9CITTa3t7Z6JW2/Xhd+/v/grKsVtMEVVV33qiuG5Vl53xmWfuOlPl8S21L9KAsy3sOELrdy5dxTF1Puv1EHzroh7yzP+6gk0lZna9Ul6WyJJHqJmZDrp89IaWJckPYvcScfec8FqVl3gAHHcm8oWld1/HdNg4U0EYZPirLUjs7O5Ev1+t1zK64fyFJn/nMZ2KQjh3g/mQaOdtCYA9ogP0h+zqZTPSZz3xGW1tbMVuPbmM/dnd3dfny5Zghga+rqnuxrx878GzHdDqNL6klCOUa503mhR7yoIfh4B+6Fv7wYN5tm4PFnk0GaHUb4OXv8Kb7PnmeazqZqqpq1VWjNM2UqH2Zc5qkSpJUWZ6qboLqJigvJqrrSkGJymrzjse6UVCiNM3UBGldVirUdVRel6WSTWAE/eEvB66TtOsAhy5Ed2MPpA648qwOgDhjOp32XiTuwA/PC3WjarVWpo1+yzcBcd1oXXdVEh54+kuw0eW3KinHX2It2AsPMN3Pxdbxd/YTEJjhgDi8jr8Onzjw4Lb0yYy7PkAq8iJmJjD2UufkeRDEpvP+CDYG40S5CE4sCpZrCYIcseeZWdbVezua4sg1TiCK0j9z5AkG8PKSzrA1Oj4+jkbAA0KYGcVHVi1J2rQ6z82yLJ55wPDAtJJuympwf0c63XFEQFgTShJmBXU4OjpS0zSxxeswC+b7JnXnR1iz1AWf7hA5WpUkScx4uVH2tC5zR3l4Ng8lTyaF/fGzQkVRxBdektXAWLhj6KlqHE+65nk7Yl83SoSsCPSWFJUifIKC9IyLO+DOCwzvkIUB8owOytidwahgQ1fnzf+HDSWQNb4L4CApOhnwKV3AQNWSJIn0xDEnK4ohd4Pih9/ZUw+4kVmMi5d/uZ4gcMFYIysENtCYFzJnWfvyXPQJa8a5KYru/UeU7aBfqM0HHfaSOy8v8KyGo7roARBknr27uxudPuSD/XWDBq8iw/v7+7E9NPeCltC5zUosY1mOI4HoPeYH8OHZAujkWVdHTaEN8sUes588h/1zMMN1sgdmQ1TadRl2wevnoT26ivt44OuOFp+5bN0cTLXvUmpt03mvisGz5Ogf5NxBBK824CwN+pjvp2m/q5fvMbw0m81jYBBClxkeItfIu4Mk0+ksPtNReQfjnGehRZqmN5Uhotu8xMwDJubdlOt4f/R+u/ZM042MLlfLXqYtLwplRecQA0ogW8wB++fVBX4uDH5AR+MXuJ11PwFdwXzJXCVJW3ECWJnnuZ73vOdF2eNf3lNYlqUWi0VsUQ4Q4O9Sc1SdMrzJZBLtOXwKOAT/ohc8s4Ae5r09ru+gk6Re0wvXCx7s++foAug+BITYS2wIsozulNQDUuAp9sazQtgr9w0cCL7Vv3XVAgMO4NRVrWQTjOVWItrSoF+2zfwJglt9UcfAkf1h8Fxki/uuLYPrPiXPQt5cvzho43pQUtT/8K0HLlVVqdkEu1xDWeF0E3QAkBGksXeswWngsoOsIV9eSopecUDbZdF1zVDfY1v5zIN6B8D5DvzkOvl2464PkGAkFD5RK5E07RWpO4VBQgi9lqkEER4ceKkICvP09DQKtUe7ZK/oxoPxRvHeuHGjl+J1h9SVhqPHGBMQA0d8cKBhQHfembsfunXEB8SY79yqNMcNrKfHuQ805N7e3ELqMil+lsMdXE9De9bDa6mhO/dDyHhOZ/C7Q9reoIDv5Hl3WJ2yExxIOq5RwoMCaJomInCOTnlJxLA0whFVRwtRRH7uAefMETuCPhBElBaHtB0R805rjpoxJ5xUBvvnZacePGF8oc0QiWEuGEAvK3BFCQ/xPIJh0FSUMMgk9ON6DCBZKtYLr+LAMheMN+tnTWQacBKgJ/dh/ZQbeqCDLON4QW8yfU47L0Vype2BjmdtPBMxRN0JsDCaGCmnEV0V2QP0DY6mB8nMd+iIM086EXLm0UsZnEeQL9BwnBcPTn1PcOwcmHA9gUwzD9Bz75roOsL3DjDFASt38nkWtMPxQob5cT7g/sg9a0BW0NmeiWffcD64n2eufI7Mk7lxb2SeMzDYBG9N7edH2WvaIoPgc++maWJGgN95FnrOZZn9hV8Ag7wygSAYenqQ3jYY6Zwj+G+9XsczpshWURTxRa4MOnQyH/Zd6hxLsmjI2TTvgl/2p6VxpiaE2LzBbR0OPfPm3vfcc4+SJImt/eHrz372sz07wZ5Pp9N4Dmsyad9r9MQTT/QyFAQ1165d03Q61f7+vra3t2NbdIASP1fGHpHxZo4ER/ASQQ00JbBKkiSCJNgV+MZlDn+FeXrTEGSY6zzIIlPn84bX+V4IofeeSQeX2UfkAp3L/fHFmD9rJKPowTl7yD3gY37nX/gXHwqdE5rQZo6SpKdjfJ+5n/srng3CjnX+StLTZ9iHIVjp4GHY3Be7gO/nskRQ6BUa8LLLL7YIQAX+8IC+KkslofMTHBRm3e5Doj+84QV+hftAng30wMeBVfwCp6HbS/S+y6br/i5YVdSf3APdh250v+V2464PkFbrfhcaFzB3ZrzDy3K5jKU7KCg6EGH0PV2Ms4Nji+H3w7SUBXGdp+c9m0KQxjkdsjKk6ynNwmiyLqmPbsGMoAG8rJDPPcDzbAABZF13hw9dIRNAMAcUDPRzx84RYJwFqUMXpC7T4/MG2XA0CIfPjZLU79SFQ06JFc+q67r3AkEPKthjnH5Kd1BEnOk4PT2N12KMWCOoNzT14NPPNPgbtVkDDos7SOwTvERpw8WLFyNft8htP0jAyXGFSIDkNdc4IPAo3xk6FO48ucPEc3y4guN35AynyIMk3zfkh+ukfjDHD3vn++9zg//hLdaCEXNUq2na8wzsEfSDN7zk1t/rFEJ7gHx/f19SFxjCmwT7rIkzD8gQBp19wHmg1a3zdNN0HZEI5pz/2TMP2nkmugy94FmSodPgIIPzK7rSeQcd5/rKMyXoAAeHyC67DsG58qwP8uPGzlFC5sp9bmVs4QsAMJBDN57cj/W6Y8WaXa48KwtPeTDswIuXgTAXL+1w20MGf4iKOljkehP+9uyNI7Z13VY10IiGTm/ulLJH0NUDR7ItrNOdXGiD/nIdgRygf/kudnMyKaJ+cLlFtzjoR+ACzRaLRS/zQJCC7pIU11qWpbbmc4XJtGfXmGtRFGqkCMq4E8ca07R9hQV6CR2LfYOOdV3r8PAwVhJwH0DUCxcuSGrtG0EWDj36+Cu+4ivicwnsAT6Yi+svnE9vAMDee6DPnmBXkWHm70ECtgD5cHR/2Doaveayh/xwxolsGGewnb5DoIogHXlgf9FVZKD8es9iuq/lABa+AuCT77U7x26zenrBKk68bHy9XiskXdMS1zXYjxjchK7xTKsPckkh+hd0pfOggQoKRp7nagwwQ095hpl9xKZ52Rq6BN0Arzrv4EOen58rS/rNK7i3+yEkGeAj1yFDkJE1OB9iJ92XYP3Q0vfU+dkDHff5COC5JyXZ7rt6ltoBmNuNuz5AKtdlL8qFcRAWFDuEZgO9pARFQmpbUk/ZgOghcNwHFI+DlF7ecH5+HlEcSZHpUQ7Xrl3rIZGguARKOO2OxqMMWBMH7mFiBAUEPaaP6+6APYwMsoSQIzA4xjAZAsPapO5Qngcd3MezTTwLIcFJdBTBgyVeKOcoCwLu7YXZXy834PmOsEFHrkORk2Fk1HUdz8J4TTrZAYJmHCf2ADRfUnxnBPs5RE7YH3+/FnRmP2mly1kXzpi5UxZCiE4re0SQjvODLHgJBAoWI8W6PTMJbeEzjBDBNOseInnwNYrbU+aOVLpjhnOHjDJc2VVVpfvuu0+LxaIXHCVJEoMpP0SPIwuvuCNM+cPJyUl8LQBoLXwrdecJm6aJTh2yj8PrGR5X8tAPPkd/EDSxB9AGxNUBFAyeZx08owNCxrOZJ+gfiLOX2uEQQQv4wIEXAkT2DEPGZzgw8CK6wuWd+Xg2BP5ABzv66WcbhnN1/nY0GJ5B/w3r1aGvG2IvhXIEG53gThY6C/r52j1YoFubI7tlWcazc5Rquhx4WSH87c4eoIg3HEGnoG8cnILf0RHsDY4vvOfZLbLa7qxCU2wbetdBGe4BqNQPstbKsrQHUk2nU128eDFmW3iOdHPgBz8SkOd5riwE5UUef+ccZ5qkmm8yI/5d5luZrKC3vUwWfQlPkdEiWM3zXMvlUpPJ5CbAYm9vL+6lpFhyy/o8CEdP4MShI7g3NtA7GDKHvb29CAI2TRP3yUtCHZBAtrEx2FjAPK6FBg7+esYZvYIcedbGz82RmfBqE/gQHUagy/DXFjgIh3x6MA6fAVJCXw942SsHX9BvXirMWlgHMt7UXVMbt9NVXasJHVAOv3IvZMx9ylae+xko/AbPXPk5aoKjyaZJCXLigDrD5wEg5zbbAznXs8hMtK9J975K9g89hI8ACAA/sSbsH/vkQaTTcBhcu5+DXwQ9eAaDvfY1IcueNWT/HRzx7Lrz3e3GXR8gsWGOnOPsgKp44OLOmH/HDxtCYGrGqTOVuq42GNwsy2IWB4XEc7z1KalmlJujWk888US8J0gA83Lkkuc7gyKIrtSGaW8/2MbA+JK94DqpCw5xevkd5eClGD4vRwwdAXADicOE8cUw0mmHEkgCzCFiKXXGG+Sc80l7e3vx3syR/1+/fj0KE8YHZwFh9lKWYRMAlI2jq+wxCO4TTzwRz7fBJ5SrUOPNuRMUEfyIo0ygDg+6w4OCd2UGHxFcwccoZwwwgSHOFs9HuaDM4CEv4STbCtqNkfQMA6CBZz8cSYI/3Nlnb3H6oTVBJKWxfm4LnoKuvtcgrDgSlNWiYLe2tiLP+kFWHBpkwY0I66A8Brpyf3gS2XZQAMeT+8NrHoSy/8gVa3O6eDYSh8nlx+nnPOLGijn5XJEVR7f5HOfbjRpzqOu2JIYgAb5gzQBGPIf5sV4PzKLD0vTL5HDiCDShIXqHe3hg5lkcPuOZ7mR4IOSGFR2ATJBFIHOIk8AzTk5OYokYawHU8kColZe65+TCj8gRepuACf1WVVXvReYAFR5sUMo2DC5DCPGsJHRfLBYRGCOQwQ4wB2yJB1rQiIwPTnvLp5XqurMbzMvnubW1FfU/mSjAxbIse6BGkiSaTLuXm0sdcKG0e80FAeV63Xa4vH7jhrIivylznCRJbGbiLycGhLxw4ULP9lEJsre3F0vvcDzhfYJS+Bwa7+zsRN2GzvemCJ4BybIs3t9LpqA3Ohvb4w1n4GHnY0AHwEZ4C5vjCD187uWn7uN4AIN8ksnyDFg/SOi/I9Kzow5Ye9bAwTt38Fk//I5OAux0ORwCRvCY8yPfb+W0Ul11gQr2jDVVdXesgX1xGeJ58LeDY/AWVUJ5nsfyVPcdNpPqBbjoIEqf8VudNp75gUbcG9n3kl/m44GJBz2eefEsHd/1a1z/O1DN3J3uDtJ7MOR6G7vAPsJrDo4BuLFHzmMOXEOTIeB6u5GEp3L1H6NxdHSkg4MDve/nfj4a8vnWXHW1SdfOOsRvvereoZCmqfKiezFrud6k79LOwamrOjqsaZrqfHWu9WqtYlLEDayrjaOabRR6MVFVV1qdr5TlbS/21QqUpe1YkqapsrRDz9blWk1da73Jgs1mU9V1o+XJUtNJ1yLZkfokkRIYqerKUTxT4M6ZM1/MEEy6oKOpuzIfZ74sb/89WZ7EQDMvWtSjNfat0ccQebtLz6RE5DFNpBAUgnqofFmulReFtuZtsJKmqZI0UWi8rEZar0tNpt1b1derrotblqVxTut1uSn5CErTROt1qdl8FoXm7PQsGurpbKqqbGvC1+Va21vb8RlVWUUEdntnW3VVR8MGGhQV7OZdXOdn591ByflMiZKI7pdVqaos1TQtMlquS+VFrtm0RTdn85mKvFATGuVZ+96flt7dObR4fmrDh03doS/FpIi8XFWVikmhYtO6k3viECRpq1CWi2U8BxTLfaYT5Vmusiq1Xq11etY2Gdjf249ZIJR63WzOOKzW8W+t4so1m7Vo4fn5mdIsi/Ixm850eHgY9y3NMmVppqqu4t5++lOf1mq9UpEXWq1Xuvfee1WVlaazqdIk1fb2lrRZP7y6Ol+1fF1tunoVG35OM9VNrfOzrhUvKB8Bmiv1sixVN7WKvLiJf0MTlOXdfNvnt7zpJQroCBR2Memc2aZpVK5LTWdTrVdr5UWu9Wqt89W55puD9Bgiz45keaaT5Ulb2jLpZLsqq1huGTNPG/0WmqA0S+N+KmzeMh5ClI+imKgJXXCSJl0nzNZJWsU98sxGCEHnq3NNiskGKGh1FwFD3dTtezWSLnsSjWlVqSxbHj07PYs8PSk4bNw+M4SgSTFR3dRKlPT0mw+yK94pED3P5+5YeACZpRuUOfQDtEkx0dnZ6SY7kkVZZC8kRXnDmcE4102t+Wx+UyMUjH5V1SomhULTqK6bGCBFNFYbRDsvdH5+pvPzVQxmi6JoMyvFRPOtuU5PTjcBVBL3VEmi0DRtR7e8A8+SJFFZdeXPRkBtBKPlnyzXar3S7k5XVpUkSaRRCEFF3p3NaZqWnuuyvW5vd1fT6SwGbjhs86155Nm8yHV22jU48nLHJE1VN7Wmm/38nd/5nfjepK2tLV28cI8kxWCOTFBQUB2CptOJ9vb2I0DSAj6l1uv2XGjd1O0aNzwDaOayK3n5kDbAS9Vz4txRxtY4wEommTl6yWbrmKdxb5lLlnUIellWKopcVVUryzY+w7orN5pOaQZ18/tn8iLX6nyls/OzKNOAC2W5jnybZXkMWFuaFqrNrhA4t90Zu+DIgx1kCj7L81xn52eaz+YxqHJZdfAVnoYn4QUc39PTE6VpF2i5XnWwxfdF6prUYDu5rg1S0mg7uAYbWTe1zjdlp9AZmazKtoFDlnbnjrGdq9WZsixvebCiY+0srpvAjExTBMbyVjdnaaaqKjf0mESwxO0tABpZYebA3sOzyJSXLqdp2nbfazp/QpLqpo66RFLbrGLjH+CDsW9+xKMFXyeq665MEUArzdL2PkbztrNniLqzvX+jpunWF0JQMSlioKYQVNdN97eyjC3Tm83fQwjKs1zr9SqWTi4WC33rt71Gh4eHsVT+c427NoN07do1SdKrv/Wbn+WZjGMc4xjHOMYxjnGMYxzj+GIYi8XiSzdAuueeFkX69Kc/fVsijOOpjePjY33FV3yFfu/3fk97e3vP9nTumjHS9c6Mka53Zox0vTNjpOudGSNd78wY6XpnxkjXOzPIKF+5cuW21961ARIpwv39/ZG57tDY29sbaXsHxkjXOzNGut6ZMdL1zoyRrndmjHS9M2Ok650ZI12f+fFkkybp7S8ZxzjGMY5xjGMc4xjHOMYxji+NMQZI4xjHOMYxjnGMYxzjGMc4xrEZd22ANJ1O9UM/9EOxfe44nrkx0vbOjJGud2aMdL0zY6TrnRkjXe/MGOl6Z8ZI1zszRro+++OubfM9jnGMYxzjGMc4xjGOcYxjHE913LUZpHGMYxzjGMc4xjGOcYxjHON4qmMMkMYxjnGMYxzjGMc4xjGOcYxjM8YAaRzjGMc4xjGOcYxjHOMYxzg2YwyQxjGOcYxjHOMYxzjGMY5xjGMzxgBpHOMYxzjGMY5xjGMc4xjHODbjrg2QfvzHf1zPf/7zNZvN9LKXvUwf+MAHnu0pfdGOt7/97fr6r/967e7u6tKlS/pbf+tv6X//7//duyaEoH/6T/+prly5ovl8rr/0l/6Sfuu3fqt3zWq10vd+7/fq4sWL2t7e1mtf+1r9/u///hdyKV/U4+1vf7uSJNEDDzwQ/zbS9emNP/iDP9B3fdd36d5779XW1pa+7uu+To888kj8fKTr0xtVVemf/JN/ouc///maz+d6wQteoH/2z/6ZmqaJ14y0vf34X//rf+lbv/VbdeXKFSVJove+9729z58pGt64cUP333+/9vf3tb+/r/vvv1+Hh4d3eHXP3vh8dC3LUm9961v14he/WNvb27py5Yq++7u/W//v//2/3j1Gut48bsevPv7+3//7SpJE/+bf/Jve30e63jyeDF1/+7d/W6997Wu1v7+v3d1dfeM3fqM+/elPx89Huj57464MkH76p39aDzzwgH7gB35AH/nIR/QX/sJf0F//63+9x3Tj6MbDDz+s7/me79Gv/Mqv6KGHHlJVVXrVq16lk5OTeM2P/MiP6Ed/9Ef1zne+U7/2a7+my5cv66/9tb+mxWIRr3nggQf0Mz/zM3rPe96jD37wg1oul3rNa16juq6fjWV9UY1f+7Vf07vf/W597dd+be/vI12f+rhx44a+6Zu+SUVR6L//9/+uj33sY/pX/+pf6eDgIF4z0vXpjR/+4R/Wu971Lr3zne/Ub//2b+tHfuRH9C//5b/Uj/3Yj8VrRtrefpycnOglL3mJ3vnOd97y82eKht/5nd+pxx57TO973/v0vve9T4899pjuv//+O76+Z2t8Prqenp7q0Ucf1Q/+4A/q0Ucf1YMPPqiPf/zjeu1rX9u7bqTrzeN2/Mp473vfq1/91V/VlStXbvpspOvN43Z0/T//5//oFa94hV74whfql37pl/Trv/7r+sEf/EHNZrN4zUjXZ3GEu3D82T/7Z8Mb3/jG3t9e+MIXhre97W3P0oz+eI2rV68GSeHhhx8OIYTQNE24fPlyeMc73hGvOT8/D/v7++Fd73pXCCGEw8PDUBRFeM973hOv+YM/+IOQpml43/ve94VdwBfZWCwW4au/+qvDQw89FF75yleGN7/5zSGEka5Pd7z1rW8Nr3jFKz7n5yNdn/74lm/5lvD3/t7f6/3t27/928N3fdd3hRBG2j6dISn8zM/8TPz9maLhxz72sSAp/Mqv/Eq85kMf+lCQFH7nd37nDq/q2R9Dut5qfPjDHw6Swqc+9akQwkjXJzM+F11///d/P3z5l395+M3f/M3wvOc9L/zrf/2v42cjXW8/bkXXv/t3/27UrbcaI12f3XHXZZDW67UeeeQRvepVr+r9/VWvepV++Zd/+Vma1R+vcXR0JEm65557JEmf/OQn9fjjj/doOp1O9cpXvjLS9JFHHlFZlr1rrly5ohe96EVf8nT/nu/5Hn3Lt3yL/upf/au9v490fXrjZ3/2Z/Xyl79cf/tv/21dunRJL33pS/Uf/sN/iJ+PdH364xWveIX+x//4H/r4xz8uSfr1X/91ffCDH9Tf+Bt/Q9JI22diPFM0/NCHPqT9/X19wzd8Q7zmG7/xG7W/vz/SeTOOjo6UJEnMLo90fXqjaRrdf//9estb3qKv+Zqvuenzka5PfTRNo//23/6b/uSf/JP65m/+Zl26dEnf8A3f0CvDG+n67I67LkB64oknVNe17rvvvt7f77vvPj3++OPP0qz++IwQgv7hP/yHesUrXqEXvehFkhTp9vlo+vjjj2symejChQuf85ovxfGe97xHjz76qN7+9rff9NlI16c3/u///b/6iZ/4CX31V3+1fv7nf15vfOMb9X3f9336T//pP0ka6fpHGW9961v1ute9Ti984QtVFIVe+tKX6oEHHtDrXvc6SSNtn4nxTNHw8ccf16VLl266/6VLl0Y6Szo/P9fb3vY2fed3fqf29vYkjXR9uuOHf/iHlee5vu/7vu+Wn490ferj6tWrWi6Xesc73qFXv/rV+oVf+AV927d9m779279dDz/8sKSRrs/2yJ/tCdypkSRJ7/cQwk1/G8fN401vepN+4zd+Qx/84Adv+uzp0PRLme6/93u/pze/+c36hV/4hV5N8XCMdH1qo2kavfzlL9e/+Bf/QpL00pe+VL/1W7+ln/iJn9B3f/d3x+tGuj718dM//dP6qZ/6Kf2X//Jf9DVf8zV67LHH9MADD+jKlSt6wxveEK8baftHH88EDW91/UjntmHDd3zHd6hpGv34j//4ba8f6fq5xyOPPKJ/+2//rR599NGnvP6Rrp970Pjmb/7Nv6nv//7vlyR93dd9nX75l39Z73rXu/TKV77yc353pOsXZtx1GaSLFy8qy7KbIuerV6/ehNiNoz++93u/Vz/7sz+r97///Xruc58b/3758mVJ+rw0vXz5stbrtW7cuPE5r/lSG4888oiuXr2ql73sZcrzXHme6+GHH9a/+3f/TnmeR7qMdH1q4znPeY7+9J/+072//ak/9adiE5aRX5/+eMtb3qK3ve1t+o7v+A69+MUv1v3336/v//7vjxnQkbZ/9PFM0fDy5cv6zGc+c9P9P/vZz35J07ksS/2dv/N39MlPflIPPfRQzB5JI12fzvjABz6gq1ev6iu/8iujHfvUpz6lf/SP/pG+6qu+StJI16czLl68qDzPb2vLRro+e+OuC5Amk4le9rKX6aGHHur9/aGHHtKf//N//lma1Rf3CCHoTW96kx588EH9z//5P/X85z+/9/nzn/98Xb58uUfT9Xqthx9+ONL0ZS97mYqi6F3zh3/4h/rN3/zNL1m6/5W/8lf00Y9+VI899lj8efnLX67Xv/71euyxx/SCF7xgpOvTGN/0Td90Uxv6j3/843re854naeTXP8o4PT1VmvbNQpZlEe0caftHH88UDf/cn/tzOjo60oc//OF4za/+6q/q6OjoS5bOBEef+MQn9Iu/+Iu69957e5+PdH3q4/7779dv/MZv9OzYlStX9Ja3vEU///M/L2mk69MZk8lEX//1X/95bdlI12d5fGF7Qnxhxnve855QFEX4yZ/8yfCxj30sPPDAA2F7ezv87u/+7rM9tS/K8Q/+wT8I+/v74Zd+6ZfCH/7hH8af09PTeM073vGOsL+/Hx588MHw0Y9+NLzuda8Lz3nOc8Lx8XG85o1vfGN47nOfG37xF38xPProo+Ev/+W/HF7ykpeEqqqejWV9UQ7vYhfCSNenMz784Q+HPM/DP//n/zx84hOfCP/5P//nsLW1FX7qp34qXjPS9emNN7zhDeHLv/zLw3/9r/81fPKTnwwPPvhguHjxYvjH//gfx2tG2t5+LBaL8JGPfCR85CMfCZLCj/7oj4aPfOQjsZvaM0XDV7/61eFrv/Zrw4c+9KHwoQ99KLz4xS8Or3nNa77g6/1Cjc9H17Isw2tf+9rw3Oc+Nzz22GM9W7ZareI9RrrePG7Hr8Mx7GIXwkjXW43b0fXBBx8MRVGEd7/73eETn/hE+LEf+7GQZVn4wAc+EO8x0vXZG3dlgBRCCP/+3//78LznPS9MJpPwZ/7Mn4ktq8dx85B0y5//+B//Y7ymaZrwQz/0Q+Hy5cthOp2Gv/gX/2L46Ec/2rvP2dlZeNOb3hTuueeeMJ/Pw2te85rw6U9/+gu8mi/uMQyQRro+vfFzP/dz4UUvelGYTqfhhS98YXj3u9/d+3yk69Mbx8fH4c1vfnP4yq/8yjCbzcILXvCC8AM/8AM9B3Ok7e3H+9///lvq1De84Q0hhGeOhteuXQuvf/3rw+7ubtjd3Q2vf/3rw40bN75Aq/zCj89H109+8pOf05a9//3vj/cY6XrzuB2/DsetAqSRrjePJ0PXn/zJnwx/4k/8iTCbzcJLXvKS8N73vrd3j5Guz95IQgjhzuaoxjGOcYxjHOMYxzjGMY5xjOOPx7jrziCNYxzjGMc4xjGOcYxjHOMYx9MdY4A0jnGMYxzjGMc4xjGOcYxjHJsxBkjjGMc4xjGOcYxjHOMYxzjGsRljgDSOcYxjHOMYxzjGMY5xjGMcmzEGSOMYxzjGMY5xjGMc4xjHOMaxGWOANI5xjGMc4xjHOMYxjnGMYxybMQZI4xjHOMYxjnGMYxzjGMc4xrEZY4A0jnGMYxzjGMc4xjGOcYxjHJsxBkjjGMc4xjGOcYxjHOMYxzjGsRljgDSOcYxjHOMYxzjGMY5xjGMcmzEGSOMYxzjGMY5xjGMc4xjHOMaxGf8fvAt79JbPX/MAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10,10))\\n\",\n \"plt.imshow(image)\\n\",\n \"show_points(input_point, input_label, plt.gca())\\n\",\n \"plt.axis('on')\\n\",\n \"plt.show() \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c765e952\",\n \"metadata\": {},\n \"source\": [\n \"Predict with `SamPredictor.predict`. The model returns masks, quality predictions for those masks, and low resolution mask logits that can be passed to the next iteration of prediction.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"5373fd68\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"masks, scores, logits = predictor.predict(\\n\",\n \" point_coords=input_point,\\n\",\n \" point_labels=input_label,\\n\",\n \" multimask_output=True,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c7f0e938\",\n \"metadata\": {},\n \"source\": [\n \"With `multimask_output=True` (the default setting), SAM outputs 3 masks, where `scores` gives the model's own estimation of the quality of these masks. This setting is intended for ambiguous input prompts, and helps the model disambiguate different objects consistent with the prompt. When `False`, it will return a single mask. For ambiguous prompts such as a single point, it is recommended to use `multimask_output=True` even if only a single mask is desired; the best single mask can be chosen by picking the one with the highest score returned in `scores`. This will often result in a better mask.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"47821187\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(3, 1200, 1800)\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"masks.shape # (number_of_masks) x H x W\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"e9c227a6\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"for i, (mask, score) in enumerate(zip(masks, scores)):\\n\",\n \" plt.figure(figsize=(10,10))\\n\",\n \" plt.imshow(image)\\n\",\n \" show_mask(mask, plt.gca())\\n\",\n \" show_points(input_point, input_label, plt.gca())\\n\",\n \" plt.title(f\\\"Mask {i+1}, Score: {score:.3f}\\\", fontsize=18)\\n\",\n \" plt.axis('off')\\n\",\n \" plt.show() \\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3fa31f7c\",\n \"metadata\": {},\n \"source\": [\n \"## Specifying a specific object with additional points\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"88d6d29a\",\n \"metadata\": {},\n \"source\": [\n \"The single input point is ambiguous, and the model has returned multiple objects consistent with it. To obtain a single object, multiple points can be provided. If available, a mask from a previous iteration can also be supplied to the model to aid in prediction. When specifying a single object with multiple prompts, a single mask can be requested by setting `multimask_output=False`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"f6923b94\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"input_point = np.array([[500, 375], [1125, 625]])\\n\",\n \"input_label = np.array([1, 1])\\n\",\n \"\\n\",\n \"mask_input = logits[np.argmax(scores), :, :] # Choose the model's best mask\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"d98f96a1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"masks, _, _ = predictor.predict(\\n\",\n \" point_coords=input_point,\\n\",\n \" point_labels=input_label,\\n\",\n \" mask_input=mask_input[None, :, :],\\n\",\n \" multimask_output=False,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"0ce8b82f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(1, 1200, 1800)\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"masks.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"e06d5c8d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10,10))\\n\",\n \"plt.imshow(image)\\n\",\n \"show_mask(masks, plt.gca())\\n\",\n \"show_points(input_point, input_label, plt.gca())\\n\",\n \"plt.axis('off')\\n\",\n \"plt.show() \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c93e2087\",\n \"metadata\": {},\n \"source\": [\n \"To exclude the car and specify just the window, a background point (with label 0, here shown in red) can be supplied.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"9a196f68\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"input_point = np.array([[500, 375], [1125, 625]])\\n\",\n \"input_label = np.array([1, 0])\\n\",\n \"\\n\",\n \"mask_input = logits[np.argmax(scores), :, :] # Choose the model's best mask\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"81a52282\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"masks, _, _ = predictor.predict(\\n\",\n \" point_coords=input_point,\\n\",\n \" point_labels=input_label,\\n\",\n \" mask_input=mask_input[None, :, :],\\n\",\n \" multimask_output=False,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"bfca709f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10, 10))\\n\",\n \"plt.imshow(image)\\n\",\n \"show_mask(masks, plt.gca())\\n\",\n \"show_points(input_point, input_label, plt.gca())\\n\",\n \"plt.axis('off')\\n\",\n \"plt.show() \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"41e2d5a9\",\n \"metadata\": {},\n \"source\": [\n \"## Specifying a specific object with a box\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d61ca7ac\",\n \"metadata\": {},\n \"source\": [\n \"The model can also take a box as input, provided in xyxy format.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"8ea92a7b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"input_box = np.array([425, 600, 700, 875])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"b35a8814\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"masks, _, _ = predictor.predict(\\n\",\n \" point_coords=None,\\n\",\n \" point_labels=None,\\n\",\n \" box=input_box[None, :],\\n\",\n \" multimask_output=False,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"984b79c1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10, 10))\\n\",\n \"plt.imshow(image)\\n\",\n \"show_mask(masks[0], plt.gca())\\n\",\n \"show_box(input_box, plt.gca())\\n\",\n \"plt.axis('off')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c1ed9f0a\",\n \"metadata\": {},\n \"source\": [\n \"## Combining points and boxes\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8455d1c5\",\n \"metadata\": {},\n \"source\": [\n \"Points and boxes may be combined, just by including both types of prompts to the predictor. Here this can be used to select just the trucks's tire, instead of the entire wheel.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"90e2e547\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"input_box = np.array([425, 600, 700, 875])\\n\",\n \"input_point = np.array([[575, 750]])\\n\",\n \"input_label = np.array([0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"6956d8c4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"masks, _, _ = predictor.predict(\\n\",\n \" point_coords=input_point,\\n\",\n \" point_labels=input_label,\\n\",\n \" box=input_box,\\n\",\n \" multimask_output=False,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"8e13088a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10, 10))\\n\",\n \"plt.imshow(image)\\n\",\n \"show_mask(masks[0], plt.gca())\\n\",\n \"show_box(input_box, plt.gca())\\n\",\n \"show_points(input_point, input_label, plt.gca())\\n\",\n \"plt.axis('off')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"45ddbca3\",\n \"metadata\": {},\n \"source\": [\n \"## Batched prompt inputs\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"df6f18a0\",\n \"metadata\": {},\n \"source\": [\n \"SamPredictor can take multiple input prompts for the same image, using `predict_torch` method. This method assumes input points are already torch tensors and have already been transformed to the input frame. For example, imagine we have several box outputs from an object detector.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"0a06681b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"input_boxes = torch.tensor([\\n\",\n \" [75, 275, 1725, 850],\\n\",\n \" [425, 600, 700, 875],\\n\",\n \" [1375, 550, 1650, 800],\\n\",\n \" [1240, 675, 1400, 750],\\n\",\n \"], device=predictor.device)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bf957d16\",\n \"metadata\": {},\n \"source\": [\n \"Transform the boxes to the input frame, then predict masks. `SamPredictor` stores the necessary transform as the `transform` field for easy access, though it can also be instantiated directly for use in e.g. a dataloader (see `segment_anything.utils.transforms`).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"117521a3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"transformed_boxes = predictor.transform.apply_boxes_torch(input_boxes, image.shape[:2])\\n\",\n \"masks, _, _ = predictor.predict_torch(\\n\",\n \" point_coords=None,\\n\",\n \" point_labels=None,\\n\",\n \" boxes=transformed_boxes,\\n\",\n \" multimask_output=False,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"6a8f5d49\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"torch.Size([4, 1, 1200, 1800])\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"masks.shape # (batch_size) x (num_predicted_masks_per_input) x H x W\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"c00c3681\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10, 10))\\n\",\n \"plt.imshow(image)\\n\",\n \"for mask in masks:\\n\",\n \" show_mask(mask.cpu().numpy(), plt.gca(), random_color=True)\\n\",\n \"for box in input_boxes:\\n\",\n \" show_box(box.cpu().numpy(), plt.gca())\\n\",\n \"plt.axis('off')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8bea70c0\",\n \"metadata\": {},\n \"source\": [\n \"## End-to-end batched inference\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"89c3ba52\",\n \"metadata\": {},\n \"source\": [\n \"If all prompts are available in advance, it is possible to run SAM directly in an end-to-end fashion. This also allows batching over images.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"45c01ae4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"image1 = image # truck.jpg from above\\n\",\n \"image1_boxes = torch.tensor([\\n\",\n \" [75, 275, 1725, 850],\\n\",\n \" [425, 600, 700, 875],\\n\",\n \" [1375, 550, 1650, 800],\\n\",\n \" [1240, 675, 1400, 750],\\n\",\n \"], device=sam.device)\\n\",\n \"\\n\",\n \"image2 = cv2.imread('images/groceries.jpg')\\n\",\n \"image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2RGB)\\n\",\n \"image2_boxes = torch.tensor([\\n\",\n \" [450, 170, 520, 350],\\n\",\n \" [350, 190, 450, 350],\\n\",\n \" [500, 170, 580, 350],\\n\",\n \" [580, 170, 640, 350],\\n\",\n \"], device=sam.device)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ce56c57d\",\n \"metadata\": {},\n \"source\": [\n \"Both images and prompts are input as PyTorch tensors that are already transformed to the correct frame. Inputs are packaged as a list over images, which each element is a dict that takes the following keys:\\n\",\n \"* `image`: The input image as a PyTorch tensor in CHW format.\\n\",\n \"* `original_size`: The size of the image before transforming for input to SAM, in (H, W) format.\\n\",\n \"* `point_coords`: Batched coordinates of point prompts.\\n\",\n \"* `point_labels`: Batched labels of point prompts.\\n\",\n \"* `boxes`: Batched input boxes.\\n\",\n \"* `mask_inputs`: Batched input masks.\\n\",\n \"\\n\",\n \"If a prompt is not present, the key can be excluded.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"79f908ca\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from segment_anything.utils.transforms import ResizeLongestSide\\n\",\n \"resize_transform = ResizeLongestSide(sam.image_encoder.img_size)\\n\",\n \"\\n\",\n \"def prepare_image(image, transform, device):\\n\",\n \" image = transform.apply_image(image)\\n\",\n \" image = torch.as_tensor(image, device=device.device) \\n\",\n \" return image.permute(2, 0, 1).contiguous()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"23f63723\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"batched_input = [\\n\",\n \" {\\n\",\n \" 'image': prepare_image(image1, resize_transform, sam),\\n\",\n \" 'boxes': resize_transform.apply_boxes_torch(image1_boxes, image1.shape[:2]),\\n\",\n \" 'original_size': image1.shape[:2]\\n\",\n \" },\\n\",\n \" {\\n\",\n \" 'image': prepare_image(image2, resize_transform, sam),\\n\",\n \" 'boxes': resize_transform.apply_boxes_torch(image2_boxes, image2.shape[:2]),\\n\",\n \" 'original_size': image2.shape[:2]\\n\",\n \" }\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6fbeb831\",\n \"metadata\": {},\n \"source\": [\n \"Run the model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"f3b311b1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"batched_output = sam(batched_input, multimask_output=False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"27bb50fd\",\n \"metadata\": {},\n \"source\": [\n \"The output is a list over results for each input image, where list elements are dictionaries with the following keys:\\n\",\n \"* `masks`: A batched torch tensor of predicted binary masks, the size of the original image.\\n\",\n \"* `iou_predictions`: The model's prediction of the quality for each mask.\\n\",\n \"* `low_res_logits`: Low res logits for each mask, which can be passed back to the model as mask input on a later iteration.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"eb3dba0f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"dict_keys(['masks', 'iou_predictions', 'low_res_logits'])\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"batched_output[0].keys()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"id\": \"e1108f48\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, ax = plt.subplots(1, 2, figsize=(20, 20))\\n\",\n \"\\n\",\n \"ax[0].imshow(image1)\\n\",\n \"for mask in batched_output[0]['masks']:\\n\",\n \" show_mask(mask.cpu().numpy(), ax[0], random_color=True)\\n\",\n \"for box in image1_boxes:\\n\",\n \" show_box(box.cpu().numpy(), ax[0])\\n\",\n \"ax[0].axis('off')\\n\",\n \"\\n\",\n \"ax[1].imshow(image2)\\n\",\n \"for mask in batched_output[1]['masks']:\\n\",\n \" show_mask(mask.cpu().numpy(), ax[1], random_color=True)\\n\",\n \"for box in image2_boxes:\\n\",\n \" show_box(box.cpu().numpy(), ax[1])\\n\",\n \"ax[1].axis('off')\\n\",\n \"\\n\",\n \"plt.tight_layout()\\n\",\n \"plt.show()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/scripts/amg.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport cv2 # type: ignore\n\nfrom segment_anything import SamAutomaticMaskGenerator, sam_model_registry\n\nimport argparse\nimport json\nimport os\nfrom typing import Any, Dict, List\n\nparser = argparse.ArgumentParser(\n description=(\n \"Runs automatic mask generation on an input image or directory of images, \"\n \"and outputs masks as either PNGs or COCO-style RLEs. Requires open-cv, \"\n \"as well as pycocotools if saving in RLE format.\"\n )\n)\n\nparser.add_argument(\n \"--input\",\n type=str,\n required=True,\n help=\"Path to either a single input image or folder of images.\",\n)\n\nparser.add_argument(\n \"--output\",\n type=str,\n required=True,\n help=(\n \"Path to the directory where masks will be output. Output will be either a folder \"\n \"of PNGs per image or a single json with COCO-style masks.\"\n ),\n)\n\nparser.add_argument(\n \"--model-type\",\n type=str,\n default=\"default\",\n help=\"The type of model to load, in ['default', 'vit_l', 'vit_b']\",\n)\n\nparser.add_argument(\n \"--checkpoint\",\n type=str,\n required=True,\n help=\"The path to the SAM checkpoint to use for mask generation.\",\n)\n\nparser.add_argument(\"--device\", type=str, default=\"cuda\", help=\"The device to run generation on.\")\n\nparser.add_argument(\n \"--convert-to-rle\",\n action=\"store_true\",\n help=(\n \"Save masks as COCO RLEs in a single json instead of as a folder of PNGs. \"\n \"Requires pycocotools.\"\n ),\n)\n\namg_settings = parser.add_argument_group(\"AMG Settings\")\n\namg_settings.add_argument(\n \"--points-per-side\",\n type=int,\n default=None,\n help=\"Generate masks by sampling a grid over the image with this many points to a side.\",\n)\n\namg_settings.add_argument(\n \"--points-per-batch\",\n type=int,\n default=None,\n help=\"How many input points to process simultaneously in one batch.\",\n)\n\namg_settings.add_argument(\n \"--pred-iou-thresh\",\n type=float,\n default=None,\n help=\"Exclude masks with a predicted score from the model that is lower than this threshold.\",\n)\n\namg_settings.add_argument(\n \"--stability-score-thresh\",\n type=float,\n default=None,\n help=\"Exclude masks with a stability score lower than this threshold.\",\n)\n\namg_settings.add_argument(\n \"--stability-score-offset\",\n type=float,\n default=None,\n help=\"Larger values perturb the mask more when measuring stability score.\",\n)\n\namg_settings.add_argument(\n \"--box-nms-thresh\",\n type=float,\n default=None,\n help=\"The overlap threshold for excluding a duplicate mask.\",\n)\n\namg_settings.add_argument(\n \"--crop-n-layers\",\n type=int,\n default=None,\n help=(\n \"If >0, mask generation is run on smaller crops of the image to generate more masks. \"\n \"The value sets how many different scales to crop at.\"\n ),\n)\n\namg_settings.add_argument(\n \"--crop-nms-thresh\",\n type=float,\n default=None,\n help=\"The overlap threshold for excluding duplicate masks across different crops.\",\n)\n\namg_settings.add_argument(\n \"--crop-overlap-ratio\",\n type=int,\n default=None,\n help=\"Larger numbers mean image crops will overlap more.\",\n)\n\namg_settings.add_argument(\n \"--crop-n-points-downscale-factor\",\n type=int,\n default=None,\n help=\"The number of points-per-side in each layer of crop is reduced by this factor.\",\n)\n\namg_settings.add_argument(\n \"--min-mask-region-area\",\n type=int,\n default=None,\n help=(\n \"Disconnected mask regions or holes with area smaller than this value \"\n \"in pixels are removed by postprocessing.\"\n ),\n)\n\n\ndef write_masks_to_folder(masks: List[Dict[str, Any]], path: str) -> None:\n header = \"id,area,bbox_x0,bbox_y0,bbox_w,bbox_h,point_input_x,point_input_y,predicted_iou,stability_score,crop_box_x0,crop_box_y0,crop_box_w,crop_box_h\" # noqa\n metadata = [header]\n for i, mask_data in enumerate(masks):\n mask = mask_data[\"segmentation\"]\n filename = f\"{i}.png\"\n cv2.imwrite(os.path.join(path, filename), mask * 255)\n mask_metadata = [\n str(i),\n str(mask_data[\"area\"]),\n *[str(x) for x in mask_data[\"bbox\"]],\n *[str(x) for x in mask_data[\"point_coords\"][0]],\n str(mask_data[\"predicted_iou\"]),\n str(mask_data[\"stability_score\"]),\n *[str(x) for x in mask_data[\"crop_box\"]],\n ]\n row = \",\".join(mask_metadata)\n metadata.append(row)\n metadata_path = os.path.join(path, \"metadata.csv\")\n with open(metadata_path, \"w\") as f:\n f.write(\"\\n\".join(metadata))\n\n return\n\n\ndef get_amg_kwargs(args):\n amg_kwargs = {\n \"points_per_side\": args.points_per_side,\n \"points_per_batch\": args.points_per_batch,\n \"pred_iou_thresh\": args.pred_iou_thresh,\n \"stability_score_thresh\": args.stability_score_thresh,\n \"stability_score_offset\": args.stability_score_offset,\n \"box_nms_thresh\": args.box_nms_thresh,\n \"crop_n_layers\": args.crop_n_layers,\n \"crop_nms_thresh\": args.crop_nms_thresh,\n \"crop_overlap_ratio\": args.crop_overlap_ratio,\n \"crop_n_points_downscale_factor\": args.crop_n_points_downscale_factor,\n \"min_mask_region_area\": args.min_mask_region_area,\n }\n amg_kwargs = {k: v for k, v in amg_kwargs.items() if v is not None}\n return amg_kwargs\n\n\ndef main(args: argparse.Namespace) -> None:\n print(\"Loading model...\")\n sam = sam_model_registry[args.model_type](checkpoint=args.checkpoint)\n _ = sam.to(device=args.device)\n output_mode = \"coco_rle\" if args.convert_to_rle else \"binary_mask\"\n amg_kwargs = get_amg_kwargs(args)\n generator = SamAutomaticMaskGenerator(sam, output_mode=output_mode, **amg_kwargs)\n\n if not os.path.isdir(args.input):\n targets = [args.input]\n else:\n targets = [\n f for f in os.listdir(args.input) if not os.path.isdir(os.path.join(args.input, f))\n ]\n targets = [os.path.join(args.input, f) for f in targets]\n\n os.makedirs(args.output, exist_ok=True)\n\n for t in targets:\n print(f\"Processing '{t}'...\")\n image = cv2.imread(t)\n if image is None:\n print(f\"Could not load '{t}' as an image, skipping...\")\n continue\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n\n masks = generator.generate(image)\n\n base = os.path.basename(t)\n base = os.path.splitext(base)[0]\n save_base = os.path.join(args.output, base)\n if output_mode == \"binary_mask\":\n os.makedirs(save_base, exist_ok=False)\n write_masks_to_folder(masks, save_base)\n else:\n save_file = save_base + \".json\"\n with open(save_file, \"w\") as f:\n json.dump(masks, f)\n print(\"Done!\")\n\n\nif __name__ == \"__main__\":\n args = parser.parse_args()\n main(args)\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/scripts/export_onnx_model.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\n\nfrom segment_anything import build_sam, build_sam_vit_b, build_sam_vit_l\nfrom segment_anything.utils.onnx import SamOnnxModel\n\nimport argparse\nimport warnings\n\ntry:\n import onnxruntime # type: ignore\n\n onnxruntime_exists = True\nexcept ImportError:\n onnxruntime_exists = False\n\nparser = argparse.ArgumentParser(\n description=\"Export the SAM prompt encoder and mask decoder to an ONNX model.\"\n)\n\nparser.add_argument(\n \"--checkpoint\", type=str, required=True, help=\"The path to the SAM model checkpoint.\"\n)\n\nparser.add_argument(\n \"--output\", type=str, required=True, help=\"The filename to save the ONNX model to.\"\n)\n\nparser.add_argument(\n \"--model-type\",\n type=str,\n default=\"default\",\n help=\"In ['default', 'vit_b', 'vit_l']. Which type of SAM model to export.\",\n)\n\nparser.add_argument(\n \"--return-single-mask\",\n action=\"store_true\",\n help=(\n \"If true, the exported ONNX model will only return the best mask, \"\n \"instead of returning multiple masks. For high resolution images \"\n \"this can improve runtime when upscaling masks is expensive.\"\n ),\n)\n\nparser.add_argument(\n \"--opset\",\n type=int,\n default=17,\n help=\"The ONNX opset version to use. Must be >=11\",\n)\n\nparser.add_argument(\n \"--quantize-out\",\n type=str,\n default=None,\n help=(\n \"If set, will quantize the model and save it with this name. \"\n \"Quantization is performed with quantize_dynamic from onnxruntime.quantization.quantize.\"\n ),\n)\n\nparser.add_argument(\n \"--gelu-approximate\",\n action=\"store_true\",\n help=(\n \"Replace GELU operations with approximations using tanh. Useful \"\n \"for some runtimes that have slow or unimplemented erf ops, used in GELU.\"\n ),\n)\n\nparser.add_argument(\n \"--use-stability-score\",\n action=\"store_true\",\n help=(\n \"Replaces the model's predicted mask quality score with the stability \"\n \"score calculated on the low resolution masks using an offset of 1.0. \"\n ),\n)\n\nparser.add_argument(\n \"--return-extra-metrics\",\n action=\"store_true\",\n help=(\n \"The model will return five results: (masks, scores, stability_scores, \"\n \"areas, low_res_logits) instead of the usual three. This can be \"\n \"significantly slower for high resolution outputs.\"\n ),\n)\n\n\ndef run_export(\n model_type: str,\n checkpoint: str,\n output: str,\n opset: int,\n return_single_mask: bool,\n gelu_approximate: bool = False,\n use_stability_score: bool = False,\n return_extra_metrics=False,\n):\n print(\"Loading model...\")\n if model_type == \"vit_b\":\n sam = build_sam_vit_b(checkpoint)\n elif model_type == \"vit_l\":\n sam = build_sam_vit_l(checkpoint)\n else:\n sam = build_sam(checkpoint)\n\n onnx_model = SamOnnxModel(\n model=sam,\n return_single_mask=return_single_mask,\n use_stability_score=use_stability_score,\n return_extra_metrics=return_extra_metrics,\n )\n\n if gelu_approximate:\n for n, m in onnx_model.named_modules():\n if isinstance(m, torch.nn.GELU):\n m.approximate = \"tanh\"\n\n dynamic_axes = {\n \"point_coords\": {1: \"num_points\"},\n \"point_labels\": {1: \"num_points\"},\n }\n\n embed_dim = sam.prompt_encoder.embed_dim\n embed_size = sam.prompt_encoder.image_embedding_size\n mask_input_size = [4 * x for x in embed_size]\n dummy_inputs = {\n \"image_embeddings\": torch.randn(1, embed_dim, *embed_size, dtype=torch.float),\n \"point_coords\": torch.randint(low=0, high=1024, size=(1, 5, 2), dtype=torch.float),\n \"point_labels\": torch.randint(low=0, high=4, size=(1, 5), dtype=torch.float),\n \"mask_input\": torch.randn(1, 1, *mask_input_size, dtype=torch.float),\n \"has_mask_input\": torch.tensor([1], dtype=torch.float),\n \"orig_im_size\": torch.tensor([1500, 2250], dtype=torch.float),\n }\n\n _ = onnx_model(**dummy_inputs)\n\n output_names = [\"masks\", \"iou_predictions\", \"low_res_masks\"]\n\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\", category=torch.jit.TracerWarning)\n warnings.filterwarnings(\"ignore\", category=UserWarning)\n with open(output, \"wb\") as f:\n print(f\"Exporing onnx model to {output}...\")\n torch.onnx.export(\n onnx_model,\n tuple(dummy_inputs.values()),\n f,\n export_params=True,\n verbose=False,\n opset_version=opset,\n do_constant_folding=True,\n input_names=list(dummy_inputs.keys()),\n output_names=output_names,\n dynamic_axes=dynamic_axes,\n )\n\n if onnxruntime_exists:\n ort_inputs = {k: to_numpy(v) for k, v in dummy_inputs.items()}\n ort_session = onnxruntime.InferenceSession(output)\n _ = ort_session.run(None, ort_inputs)\n print(\"Model has successfully been run with ONNXRuntime.\")\n\n\ndef to_numpy(tensor):\n return tensor.cpu().numpy()\n\n\nif __name__ == \"__main__\":\n args = parser.parse_args()\n run_export(\n model_type=args.model_type,\n checkpoint=args.checkpoint,\n output=args.output,\n opset=args.opset,\n return_single_mask=args.return_single_mask,\n gelu_approximate=args.gelu_approximate,\n use_stability_score=args.use_stability_score,\n return_extra_metrics=args.return_extra_metrics,\n )\n\n if args.quantize_out is not None:\n assert onnxruntime_exists, \"onnxruntime is required to quantize the model.\"\n from onnxruntime.quantization import QuantType # type: ignore\n from onnxruntime.quantization.quantize import quantize_dynamic # type: ignore\n\n print(f\"Quantizing model and writing to {args.quantize_out}...\")\n quantize_dynamic(\n model_input=args.output,\n model_output=args.quantize_out,\n optimize_model=True,\n per_channel=False,\n reduce_range=False,\n weight_type=QuantType.QUInt8,\n )\n print(\"Done!\")\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/segment_anything/__init__.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom .build_sam import (\n build_sam,\n build_sam_vit_h,\n build_sam_vit_l,\n build_sam_vit_b,\n sam_model_registry,\n)\nfrom .build_sam_hq import (\n build_sam_hq,\n build_sam_hq_vit_h,\n build_sam_hq_vit_l,\n build_sam_hq_vit_b,\n sam_hq_model_registry,\n)\nfrom .predictor import SamPredictor\nfrom .automatic_mask_generator import SamAutomaticMaskGenerator\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/segment_anything/automatic_mask_generator.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport numpy as np\nimport torch\nfrom torchvision.ops.boxes import batched_nms, box_area # type: ignore\n\nfrom typing import Any, Dict, List, Optional, Tuple\n\nfrom .modeling import Sam\nfrom .predictor import SamPredictor\nfrom .utils.amg import (\n MaskData,\n area_from_rle,\n batch_iterator,\n batched_mask_to_box,\n box_xyxy_to_xywh,\n build_all_layer_point_grids,\n calculate_stability_score,\n coco_encode_rle,\n generate_crop_boxes,\n is_box_near_crop_edge,\n mask_to_rle_pytorch,\n remove_small_regions,\n rle_to_mask,\n uncrop_boxes_xyxy,\n uncrop_masks,\n uncrop_points,\n)\n\n\nclass SamAutomaticMaskGenerator:\n def __init__(\n self,\n model: Sam,\n points_per_side: Optional[int] = 32,\n points_per_batch: int = 64,\n pred_iou_thresh: float = 0.88,\n stability_score_thresh: float = 0.95,\n stability_score_offset: float = 1.0,\n box_nms_thresh: float = 0.7,\n crop_n_layers: int = 0,\n crop_nms_thresh: float = 0.7,\n crop_overlap_ratio: float = 512 / 1500,\n crop_n_points_downscale_factor: int = 1,\n point_grids: Optional[List[np.ndarray]] = None,\n min_mask_region_area: int = 0,\n output_mode: str = \"binary_mask\",\n ) -> None:\n \"\"\"\n Using a SAM model, generates masks for the entire image.\n Generates a grid of point prompts over the image, then filters\n low quality and duplicate masks. The default settings are chosen\n for SAM with a ViT-H backbone.\n\n Arguments:\n model (Sam): The SAM model to use for mask prediction.\n points_per_side (int or None): The number of points to be sampled\n along one side of the image. The total number of points is\n points_per_side**2. If None, 'point_grids' must provide explicit\n point sampling.\n points_per_batch (int): Sets the number of points run simultaneously\n by the model. Higher numbers may be faster but use more GPU memory.\n pred_iou_thresh (float): A filtering threshold in [0,1], using the\n model's predicted mask quality.\n stability_score_thresh (float): A filtering threshold in [0,1], using\n the stability of the mask under changes to the cutoff used to binarize\n the model's mask predictions.\n stability_score_offset (float): The amount to shift the cutoff when\n calculated the stability score.\n box_nms_thresh (float): The box IoU cutoff used by non-maximal\n suppression to filter duplicate masks.\n crops_n_layers (int): If >0, mask prediction will be run again on\n crops of the image. Sets the number of layers to run, where each\n layer has 2**i_layer number of image crops.\n crops_nms_thresh (float): The box IoU cutoff used by non-maximal\n suppression to filter duplicate masks between different crops.\n crop_overlap_ratio (float): Sets the degree to which crops overlap.\n In the first crop layer, crops will overlap by this fraction of\n the image length. Later layers with more crops scale down this overlap.\n crop_n_points_downscale_factor (int): The number of points-per-side\n sampled in layer n is scaled down by crop_n_points_downscale_factor**n.\n point_grids (list(np.ndarray) or None): A list over explicit grids\n of points used for sampling, normalized to [0,1]. The nth grid in the\n list is used in the nth crop layer. Exclusive with points_per_side.\n min_mask_region_area (int): If >0, postprocessing will be applied\n to remove disconnected regions and holes in masks with area smaller\n than min_mask_region_area. Requires opencv.\n output_mode (str): The form masks are returned in. Can be 'binary_mask',\n 'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.\n For large resolutions, 'binary_mask' may consume large amounts of\n memory.\n \"\"\"\n\n assert (points_per_side is None) != (\n point_grids is None\n ), \"Exactly one of points_per_side or point_grid must be provided.\"\n if points_per_side is not None:\n self.point_grids = build_all_layer_point_grids(\n points_per_side,\n crop_n_layers,\n crop_n_points_downscale_factor,\n )\n elif point_grids is not None:\n self.point_grids = point_grids\n else:\n raise ValueError(\"Can't have both points_per_side and point_grid be None.\")\n\n assert output_mode in [\n \"binary_mask\",\n \"uncompressed_rle\",\n \"coco_rle\",\n ], f\"Unknown output_mode {output_mode}.\"\n if output_mode == \"coco_rle\":\n from pycocotools import mask as mask_utils # type: ignore # noqa: F401\n\n if min_mask_region_area > 0:\n import cv2 # type: ignore # noqa: F401\n\n self.predictor = SamPredictor(model)\n self.points_per_batch = points_per_batch\n self.pred_iou_thresh = pred_iou_thresh\n self.stability_score_thresh = stability_score_thresh\n self.stability_score_offset = stability_score_offset\n self.box_nms_thresh = box_nms_thresh\n self.crop_n_layers = crop_n_layers\n self.crop_nms_thresh = crop_nms_thresh\n self.crop_overlap_ratio = crop_overlap_ratio\n self.crop_n_points_downscale_factor = crop_n_points_downscale_factor\n self.min_mask_region_area = min_mask_region_area\n self.output_mode = output_mode\n\n @torch.no_grad()\n def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:\n \"\"\"\n Generates masks for the given image.\n\n Arguments:\n image (np.ndarray): The image to generate masks for, in HWC uint8 format.\n\n Returns:\n list(dict(str, any)): A list over records for masks. Each record is\n a dict containing the following keys:\n segmentation (dict(str, any) or np.ndarray): The mask. If\n output_mode='binary_mask', is an array of shape HW. Otherwise,\n is a dictionary containing the RLE.\n bbox (list(float)): The box around the mask, in XYWH format.\n area (int): The area in pixels of the mask.\n predicted_iou (float): The model's own prediction of the mask's\n quality. This is filtered by the pred_iou_thresh parameter.\n point_coords (list(list(float))): The point coordinates input\n to the model to generate this mask.\n stability_score (float): A measure of the mask's quality. This\n is filtered on using the stability_score_thresh parameter.\n crop_box (list(float)): The crop of the image used to generate\n the mask, given in XYWH format.\n \"\"\"\n\n # Generate masks\n mask_data = self._generate_masks(image)\n\n # Filter small disconnected regions and holes in masks\n if self.min_mask_region_area > 0:\n mask_data = self.postprocess_small_regions(\n mask_data,\n self.min_mask_region_area,\n max(self.box_nms_thresh, self.crop_nms_thresh),\n )\n\n # Encode masks\n if self.output_mode == \"coco_rle\":\n mask_data[\"segmentations\"] = [coco_encode_rle(rle) for rle in mask_data[\"rles\"]]\n elif self.output_mode == \"binary_mask\":\n mask_data[\"segmentations\"] = [rle_to_mask(rle) for rle in mask_data[\"rles\"]]\n else:\n mask_data[\"segmentations\"] = mask_data[\"rles\"]\n\n # Write mask records\n curr_anns = []\n for idx in range(len(mask_data[\"segmentations\"])):\n ann = {\n \"segmentation\": mask_data[\"segmentations\"][idx],\n \"area\": area_from_rle(mask_data[\"rles\"][idx]),\n \"bbox\": box_xyxy_to_xywh(mask_data[\"boxes\"][idx]).tolist(),\n \"predicted_iou\": mask_data[\"iou_preds\"][idx].item(),\n \"point_coords\": [mask_data[\"points\"][idx].tolist()],\n \"stability_score\": mask_data[\"stability_score\"][idx].item(),\n \"crop_box\": box_xyxy_to_xywh(mask_data[\"crop_boxes\"][idx]).tolist(),\n }\n curr_anns.append(ann)\n\n return curr_anns\n\n def _generate_masks(self, image: np.ndarray) -> MaskData:\n orig_size = image.shape[:2]\n crop_boxes, layer_idxs = generate_crop_boxes(\n orig_size, self.crop_n_layers, self.crop_overlap_ratio\n )\n\n # Iterate over image crops\n data = MaskData()\n for crop_box, layer_idx in zip(crop_boxes, layer_idxs):\n crop_data = self._process_crop(image, crop_box, layer_idx, orig_size)\n data.cat(crop_data)\n\n # Remove duplicate masks between crops\n if len(crop_boxes) > 1:\n # Prefer masks from smaller crops\n scores = 1 / box_area(data[\"crop_boxes\"])\n scores = scores.to(data[\"boxes\"].device)\n keep_by_nms = batched_nms(\n data[\"boxes\"].float(),\n scores,\n torch.zeros(len(data[\"boxes\"])), # categories\n iou_threshold=self.crop_nms_thresh,\n )\n data.filter(keep_by_nms)\n\n data.to_numpy()\n return data\n\n def _process_crop(\n self,\n image: np.ndarray,\n crop_box: List[int],\n crop_layer_idx: int,\n orig_size: Tuple[int, ...],\n ) -> MaskData:\n # Crop the image and calculate embeddings\n x0, y0, x1, y1 = crop_box\n cropped_im = image[y0:y1, x0:x1, :]\n cropped_im_size = cropped_im.shape[:2]\n self.predictor.set_image(cropped_im)\n\n # Get points for this crop\n points_scale = np.array(cropped_im_size)[None, ::-1]\n points_for_image = self.point_grids[crop_layer_idx] * points_scale\n\n # Generate masks for this crop in batches\n data = MaskData()\n for (points,) in batch_iterator(self.points_per_batch, points_for_image):\n batch_data = self._process_batch(points, cropped_im_size, crop_box, orig_size)\n data.cat(batch_data)\n del batch_data\n self.predictor.reset_image()\n\n # Remove duplicates within this crop.\n keep_by_nms = batched_nms(\n data[\"boxes\"].float(),\n data[\"iou_preds\"],\n torch.zeros(len(data[\"boxes\"])), # categories\n iou_threshold=self.box_nms_thresh,\n )\n data.filter(keep_by_nms)\n\n # Return to the original image frame\n data[\"boxes\"] = uncrop_boxes_xyxy(data[\"boxes\"], crop_box)\n data[\"points\"] = uncrop_points(data[\"points\"], crop_box)\n data[\"crop_boxes\"] = torch.tensor([crop_box for _ in range(len(data[\"rles\"]))])\n\n return data\n\n def _process_batch(\n self,\n points: np.ndarray,\n im_size: Tuple[int, ...],\n crop_box: List[int],\n orig_size: Tuple[int, ...],\n ) -> MaskData:\n orig_h, orig_w = orig_size\n\n # Run model on this batch\n transformed_points = self.predictor.transform.apply_coords(points, im_size)\n in_points = torch.as_tensor(transformed_points, device=self.predictor.device)\n in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device)\n masks, iou_preds, _ = self.predictor.predict_torch(\n in_points[:, None, :],\n in_labels[:, None],\n multimask_output=True,\n return_logits=True,\n )\n\n # Serialize predictions and store in MaskData\n data = MaskData(\n masks=masks.flatten(0, 1),\n iou_preds=iou_preds.flatten(0, 1),\n points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),\n )\n del masks\n\n # Filter by predicted IoU\n if self.pred_iou_thresh > 0.0:\n keep_mask = data[\"iou_preds\"] > self.pred_iou_thresh\n data.filter(keep_mask)\n\n # Calculate stability score\n data[\"stability_score\"] = calculate_stability_score(\n data[\"masks\"], self.predictor.model.mask_threshold, self.stability_score_offset\n )\n if self.stability_score_thresh > 0.0:\n keep_mask = data[\"stability_score\"] >= self.stability_score_thresh\n data.filter(keep_mask)\n\n # Threshold masks and calculate boxes\n data[\"masks\"] = data[\"masks\"] > self.predictor.model.mask_threshold\n data[\"boxes\"] = batched_mask_to_box(data[\"masks\"])\n\n # Filter boxes that touch crop boundaries\n keep_mask = ~is_box_near_crop_edge(data[\"boxes\"], crop_box, [0, 0, orig_w, orig_h])\n if not torch.all(keep_mask):\n data.filter(keep_mask)\n\n # Compress to RLE\n data[\"masks\"] = uncrop_masks(data[\"masks\"], crop_box, orig_h, orig_w)\n data[\"rles\"] = mask_to_rle_pytorch(data[\"masks\"])\n del data[\"masks\"]\n\n return data\n\n @staticmethod\n def postprocess_small_regions(\n mask_data: MaskData, min_area: int, nms_thresh: float\n ) -> MaskData:\n \"\"\"\n Removes small disconnected regions and holes in masks, then reruns\n box NMS to remove any new duplicates.\n\n Edits mask_data in place.\n\n Requires open-cv as a dependency.\n \"\"\"\n if len(mask_data[\"rles\"]) == 0:\n return mask_data\n\n # Filter small disconnected regions and holes\n new_masks = []\n scores = []\n for rle in mask_data[\"rles\"]:\n mask = rle_to_mask(rle)\n\n mask, changed = remove_small_regions(mask, min_area, mode=\"holes\")\n unchanged = not changed\n mask, changed = remove_small_regions(mask, min_area, mode=\"islands\")\n unchanged = unchanged and not changed\n\n new_masks.append(torch.as_tensor(mask).unsqueeze(0))\n # Give score=0 to changed masks and score=1 to unchanged masks\n # so NMS will prefer ones that didn't need postprocessing\n scores.append(float(unchanged))\n\n # Recalculate boxes and remove any new duplicates\n masks = torch.cat(new_masks, dim=0)\n boxes = batched_mask_to_box(masks)\n keep_by_nms = batched_nms(\n boxes.float(),\n torch.as_tensor(scores),\n torch.zeros(len(boxes)), # categories\n iou_threshold=nms_thresh,\n )\n\n # Only recalculate RLEs for masks that have changed\n for i_mask in keep_by_nms:\n if scores[i_mask] == 0.0:\n mask_torch = masks[i_mask].unsqueeze(0)\n mask_data[\"rles\"][i_mask] = mask_to_rle_pytorch(mask_torch)[0]\n mask_data[\"boxes\"][i_mask] = boxes[i_mask] # update res directly\n mask_data.filter(keep_by_nms)\n\n return mask_data\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/segment_anything/build_sam.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\n\nfrom functools import partial\n\nfrom .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWayTransformer\n\n\ndef build_sam_vit_h(checkpoint=None):\n return _build_sam(\n encoder_embed_dim=1280,\n encoder_depth=32,\n encoder_num_heads=16,\n encoder_global_attn_indexes=[7, 15, 23, 31],\n checkpoint=checkpoint,\n )\n\n\nbuild_sam = build_sam_vit_h\n\n\ndef build_sam_vit_l(checkpoint=None):\n return _build_sam(\n encoder_embed_dim=1024,\n encoder_depth=24,\n encoder_num_heads=16,\n encoder_global_attn_indexes=[5, 11, 17, 23],\n checkpoint=checkpoint,\n )\n\n\ndef build_sam_vit_b(checkpoint=None):\n return _build_sam(\n encoder_embed_dim=768,\n encoder_depth=12,\n encoder_num_heads=12,\n encoder_global_attn_indexes=[2, 5, 8, 11],\n checkpoint=checkpoint,\n )\n\n\nsam_model_registry = {\n \"default\": build_sam,\n \"vit_h\": build_sam,\n \"vit_l\": build_sam_vit_l,\n \"vit_b\": build_sam_vit_b,\n}\n\n\ndef _build_sam(\n encoder_embed_dim,\n encoder_depth,\n encoder_num_heads,\n encoder_global_attn_indexes,\n checkpoint=None,\n):\n prompt_embed_dim = 256\n image_size = 1024\n vit_patch_size = 16\n image_embedding_size = image_size // vit_patch_size\n sam = Sam(\n image_encoder=ImageEncoderViT(\n depth=encoder_depth,\n embed_dim=encoder_embed_dim,\n img_size=image_size,\n mlp_ratio=4,\n norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),\n num_heads=encoder_num_heads,\n patch_size=vit_patch_size,\n qkv_bias=True,\n use_rel_pos=True,\n global_attn_indexes=encoder_global_attn_indexes,\n window_size=14,\n out_chans=prompt_embed_dim,\n ),\n prompt_encoder=PromptEncoder(\n embed_dim=prompt_embed_dim,\n image_embedding_size=(image_embedding_size, image_embedding_size),\n input_image_size=(image_size, image_size),\n mask_in_chans=16,\n ),\n mask_decoder=MaskDecoder(\n num_multimask_outputs=3,\n transformer=TwoWayTransformer(\n depth=2,\n embedding_dim=prompt_embed_dim,\n mlp_dim=2048,\n num_heads=8,\n ),\n transformer_dim=prompt_embed_dim,\n iou_head_depth=3,\n iou_head_hidden_dim=256,\n ),\n pixel_mean=[123.675, 116.28, 103.53],\n pixel_std=[58.395, 57.12, 57.375],\n )\n sam.eval()\n if checkpoint is not None:\n with open(checkpoint, \"rb\") as f:\n state_dict = torch.load(f)\n sam.load_state_dict(state_dict)\n return sam\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/segment_anything/build_sam_hq.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\n\nfrom functools import partial\n\nfrom .modeling import ImageEncoderViT, MaskDecoderHQ, PromptEncoder, Sam, TwoWayTransformer\n\n\ndef build_sam_hq_vit_h(checkpoint=None):\n return _build_sam(\n encoder_embed_dim=1280,\n encoder_depth=32,\n encoder_num_heads=16,\n encoder_global_attn_indexes=[7, 15, 23, 31],\n checkpoint=checkpoint,\n )\n\n\nbuild_sam_hq = build_sam_hq_vit_h\n\n\ndef build_sam_hq_vit_l(checkpoint=None):\n return _build_sam(\n encoder_embed_dim=1024,\n encoder_depth=24,\n encoder_num_heads=16,\n encoder_global_attn_indexes=[5, 11, 17, 23],\n checkpoint=checkpoint,\n )\n\n\ndef build_sam_hq_vit_b(checkpoint=None):\n return _build_sam(\n encoder_embed_dim=768,\n encoder_depth=12,\n encoder_num_heads=12,\n encoder_global_attn_indexes=[2, 5, 8, 11],\n checkpoint=checkpoint,\n )\n\n\nsam_hq_model_registry = {\n \"default\": build_sam_hq_vit_h,\n \"vit_h\": build_sam_hq_vit_h,\n \"vit_l\": build_sam_hq_vit_l,\n \"vit_b\": build_sam_hq_vit_b,\n}\n\n\ndef _build_sam(\n encoder_embed_dim,\n encoder_depth,\n encoder_num_heads,\n encoder_global_attn_indexes,\n checkpoint=None,\n):\n prompt_embed_dim = 256\n image_size = 1024\n vit_patch_size = 16\n image_embedding_size = image_size // vit_patch_size\n sam = Sam(\n image_encoder=ImageEncoderViT(\n depth=encoder_depth,\n embed_dim=encoder_embed_dim,\n img_size=image_size,\n mlp_ratio=4,\n norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),\n num_heads=encoder_num_heads,\n patch_size=vit_patch_size,\n qkv_bias=True,\n use_rel_pos=True,\n global_attn_indexes=encoder_global_attn_indexes,\n window_size=14,\n out_chans=prompt_embed_dim,\n ),\n prompt_encoder=PromptEncoder(\n embed_dim=prompt_embed_dim,\n image_embedding_size=(image_embedding_size, image_embedding_size),\n input_image_size=(image_size, image_size),\n mask_in_chans=16,\n ),\n mask_decoder=MaskDecoderHQ(\n num_multimask_outputs=3,\n transformer=TwoWayTransformer(\n depth=2,\n embedding_dim=prompt_embed_dim,\n mlp_dim=2048,\n num_heads=8,\n ),\n transformer_dim=prompt_embed_dim,\n iou_head_depth=3,\n iou_head_hidden_dim=256,\n vit_dim=encoder_embed_dim,\n ),\n pixel_mean=[123.675, 116.28, 103.53],\n pixel_std=[58.395, 57.12, 57.375],\n )\n # sam.eval()\n if checkpoint is not None:\n with open(checkpoint, \"rb\") as f:\n device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n state_dict = torch.load(f, map_location=device)\n info = sam.load_state_dict(state_dict, strict=False)\n print(info)\n for n, p in sam.named_parameters():\n if 'hf_token' not in n and 'hf_mlp' not in n and 'compress_vit_feat' not in n and 'embedding_encoder' not in n and 'embedding_maskfeature' not in n:\n p.requires_grad = False\n\n return sam\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/segment_anything/modeling/__init__.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom .sam import Sam\nfrom .image_encoder import ImageEncoderViT\nfrom .mask_decoder_hq import MaskDecoderHQ\nfrom .mask_decoder import MaskDecoder\nfrom .prompt_encoder import PromptEncoder\nfrom .transformer import TwoWayTransformer\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/segment_anything/modeling/common.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nimport torch.nn as nn\n\nfrom typing import Type\n\n\nclass MLPBlock(nn.Module):\n def __init__(\n self,\n embedding_dim: int,\n mlp_dim: int,\n act: Type[nn.Module] = nn.GELU,\n ) -> None:\n super().__init__()\n self.lin1 = nn.Linear(embedding_dim, mlp_dim)\n self.lin2 = nn.Linear(mlp_dim, embedding_dim)\n self.act = act()\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return self.lin2(self.act(self.lin1(x)))\n\n\n# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa\n# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa\nclass LayerNorm2d(nn.Module):\n def __init__(self, num_channels: int, eps: float = 1e-6) -> None:\n super().__init__()\n self.weight = nn.Parameter(torch.ones(num_channels))\n self.bias = nn.Parameter(torch.zeros(num_channels))\n self.eps = eps\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n u = x.mean(1, keepdim=True)\n s = (x - u).pow(2).mean(1, keepdim=True)\n x = (x - u) / torch.sqrt(s + self.eps)\n x = self.weight[:, None, None] * x + self.bias[:, None, None]\n return x\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/segment_anything/modeling/image_encoder.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom typing import Optional, Tuple, Type\n\nfrom .common import LayerNorm2d, MLPBlock\n\n\n# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa\nclass ImageEncoderViT(nn.Module):\n def __init__(\n self,\n img_size: int = 1024,\n patch_size: int = 16,\n in_chans: int = 3,\n embed_dim: int = 768,\n depth: int = 12,\n num_heads: int = 12,\n mlp_ratio: float = 4.0,\n out_chans: int = 256,\n qkv_bias: bool = True,\n norm_layer: Type[nn.Module] = nn.LayerNorm,\n act_layer: Type[nn.Module] = nn.GELU,\n use_abs_pos: bool = True,\n use_rel_pos: bool = False,\n rel_pos_zero_init: bool = True,\n window_size: int = 0,\n global_attn_indexes: Tuple[int, ...] = (),\n ) -> None:\n \"\"\"\n Args:\n img_size (int): Input image size.\n patch_size (int): Patch size.\n in_chans (int): Number of input image channels.\n embed_dim (int): Patch embedding dimension.\n depth (int): Depth of ViT.\n num_heads (int): Number of attention heads in each ViT block.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n qkv_bias (bool): If True, add a learnable bias to query, key, value.\n norm_layer (nn.Module): Normalization layer.\n act_layer (nn.Module): Activation layer.\n use_abs_pos (bool): If True, use absolute positional embeddings.\n use_rel_pos (bool): If True, add relative positional embeddings to the attention map.\n rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.\n window_size (int): Window size for window attention blocks.\n global_attn_indexes (list): Indexes for blocks using global attention.\n \"\"\"\n super().__init__()\n self.img_size = img_size\n\n self.patch_embed = PatchEmbed(\n kernel_size=(patch_size, patch_size),\n stride=(patch_size, patch_size),\n in_chans=in_chans,\n embed_dim=embed_dim,\n )\n\n self.pos_embed: Optional[nn.Parameter] = None\n if use_abs_pos:\n # Initialize absolute positional embedding with pretrain image size.\n self.pos_embed = nn.Parameter(\n torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim)\n )\n\n self.blocks = nn.ModuleList()\n for i in range(depth):\n block = Block(\n dim=embed_dim,\n num_heads=num_heads,\n mlp_ratio=mlp_ratio,\n qkv_bias=qkv_bias,\n norm_layer=norm_layer,\n act_layer=act_layer,\n use_rel_pos=use_rel_pos,\n rel_pos_zero_init=rel_pos_zero_init,\n window_size=window_size if i not in global_attn_indexes else 0,\n input_size=(img_size // patch_size, img_size // patch_size),\n )\n self.blocks.append(block)\n\n self.neck = nn.Sequential(\n nn.Conv2d(\n embed_dim,\n out_chans,\n kernel_size=1,\n bias=False,\n ),\n LayerNorm2d(out_chans),\n nn.Conv2d(\n out_chans,\n out_chans,\n kernel_size=3,\n padding=1,\n bias=False,\n ),\n LayerNorm2d(out_chans),\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n x = self.patch_embed(x)\n if self.pos_embed is not None:\n x = x + self.pos_embed\n\n interm_embeddings=[]\n for blk in self.blocks:\n x = blk(x)\n if blk.window_size == 0:\n interm_embeddings.append(x)\n\n x = self.neck(x.permute(0, 3, 1, 2))\n\n return x, interm_embeddings\n\n\nclass Block(nn.Module):\n \"\"\"Transformer blocks with support of window attention and residual propagation blocks\"\"\"\n\n def __init__(\n self,\n dim: int,\n num_heads: int,\n mlp_ratio: float = 4.0,\n qkv_bias: bool = True,\n norm_layer: Type[nn.Module] = nn.LayerNorm,\n act_layer: Type[nn.Module] = nn.GELU,\n use_rel_pos: bool = False,\n rel_pos_zero_init: bool = True,\n window_size: int = 0,\n input_size: Optional[Tuple[int, int]] = None,\n ) -> None:\n \"\"\"\n Args:\n dim (int): Number of input channels.\n num_heads (int): Number of attention heads in each ViT block.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n qkv_bias (bool): If True, add a learnable bias to query, key, value.\n norm_layer (nn.Module): Normalization layer.\n act_layer (nn.Module): Activation layer.\n use_rel_pos (bool): If True, add relative positional embeddings to the attention map.\n rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.\n window_size (int): Window size for window attention blocks. If it equals 0, then\n use global attention.\n input_size (tuple(int, int) or None): Input resolution for calculating the relative\n positional parameter size.\n \"\"\"\n super().__init__()\n self.norm1 = norm_layer(dim)\n self.attn = Attention(\n dim,\n num_heads=num_heads,\n qkv_bias=qkv_bias,\n use_rel_pos=use_rel_pos,\n rel_pos_zero_init=rel_pos_zero_init,\n input_size=input_size if window_size == 0 else (window_size, window_size),\n )\n\n self.norm2 = norm_layer(dim)\n self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)\n\n self.window_size = window_size\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n shortcut = x\n x = self.norm1(x)\n # Window partition\n if self.window_size > 0:\n H, W = x.shape[1], x.shape[2]\n x, pad_hw = window_partition(x, self.window_size)\n\n x = self.attn(x)\n # Reverse window partition\n if self.window_size > 0:\n x = window_unpartition(x, self.window_size, pad_hw, (H, W))\n\n x = shortcut + x\n x = x + self.mlp(self.norm2(x))\n\n return x\n\n\nclass Attention(nn.Module):\n \"\"\"Multi-head Attention block with relative position embeddings.\"\"\"\n\n def __init__(\n self,\n dim: int,\n num_heads: int = 8,\n qkv_bias: bool = True,\n use_rel_pos: bool = False,\n rel_pos_zero_init: bool = True,\n input_size: Optional[Tuple[int, int]] = None,\n ) -> None:\n \"\"\"\n Args:\n dim (int): Number of input channels.\n num_heads (int): Number of attention heads.\n qkv_bias (bool): If True, add a learnable bias to query, key, value.\n rel_pos (bool): If True, add relative positional embeddings to the attention map.\n rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.\n input_size (tuple(int, int) or None): Input resolution for calculating the relative\n positional parameter size.\n \"\"\"\n super().__init__()\n self.num_heads = num_heads\n head_dim = dim // num_heads\n self.scale = head_dim**-0.5\n\n self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n self.proj = nn.Linear(dim, dim)\n\n self.use_rel_pos = use_rel_pos\n if self.use_rel_pos:\n assert (\n input_size is not None\n ), \"Input size must be provided if using relative positional encoding.\"\n # initialize relative positional embeddings\n self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))\n self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n B, H, W, _ = x.shape\n # qkv with shape (3, B, nHead, H * W, C)\n qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)\n # q, k, v with shape (B * nHead, H * W, C)\n q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)\n\n attn = (q * self.scale) @ k.transpose(-2, -1)\n\n if self.use_rel_pos:\n attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))\n\n attn = attn.softmax(dim=-1)\n x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)\n x = self.proj(x)\n\n return x\n\n\ndef window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:\n \"\"\"\n Partition into non-overlapping windows with padding if needed.\n Args:\n x (tensor): input tokens with [B, H, W, C].\n window_size (int): window size.\n\n Returns:\n windows: windows after partition with [B * num_windows, window_size, window_size, C].\n (Hp, Wp): padded height and width before partition\n \"\"\"\n B, H, W, C = x.shape\n\n pad_h = (window_size - H % window_size) % window_size\n pad_w = (window_size - W % window_size) % window_size\n if pad_h > 0 or pad_w > 0:\n x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))\n Hp, Wp = H + pad_h, W + pad_w\n\n x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)\n windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)\n return windows, (Hp, Wp)\n\n\ndef window_unpartition(\n windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]\n) -> torch.Tensor:\n \"\"\"\n Window unpartition into original sequences and removing padding.\n Args:\n windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].\n window_size (int): window size.\n pad_hw (Tuple): padded height and width (Hp, Wp).\n hw (Tuple): original height and width (H, W) before padding.\n\n Returns:\n x: unpartitioned sequences with [B, H, W, C].\n \"\"\"\n Hp, Wp = pad_hw\n H, W = hw\n B = windows.shape[0] // (Hp * Wp // window_size // window_size)\n x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)\n x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)\n\n if Hp > H or Wp > W:\n x = x[:, :H, :W, :].contiguous()\n return x\n\n\ndef get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Get relative positional embeddings according to the relative positions of\n query and key sizes.\n Args:\n q_size (int): size of query q.\n k_size (int): size of key k.\n rel_pos (Tensor): relative position embeddings (L, C).\n\n Returns:\n Extracted positional embeddings according to relative positions.\n \"\"\"\n max_rel_dist = int(2 * max(q_size, k_size) - 1)\n # Interpolate rel pos if needed.\n if rel_pos.shape[0] != max_rel_dist:\n # Interpolate rel pos.\n rel_pos_resized = F.interpolate(\n rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),\n size=max_rel_dist,\n mode=\"linear\",\n )\n rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)\n else:\n rel_pos_resized = rel_pos\n\n # Scale the coords with short length if shapes for q and k are different.\n q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)\n k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)\n relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)\n\n return rel_pos_resized[relative_coords.long()]\n\n\ndef add_decomposed_rel_pos(\n attn: torch.Tensor,\n q: torch.Tensor,\n rel_pos_h: torch.Tensor,\n rel_pos_w: torch.Tensor,\n q_size: Tuple[int, int],\n k_size: Tuple[int, int],\n) -> torch.Tensor:\n \"\"\"\n Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.\n https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950\n Args:\n attn (Tensor): attention map.\n q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).\n rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.\n rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.\n q_size (Tuple): spatial sequence size of query q with (q_h, q_w).\n k_size (Tuple): spatial sequence size of key k with (k_h, k_w).\n\n Returns:\n attn (Tensor): attention map with added relative positional embeddings.\n \"\"\"\n q_h, q_w = q_size\n k_h, k_w = k_size\n Rh = get_rel_pos(q_h, k_h, rel_pos_h)\n Rw = get_rel_pos(q_w, k_w, rel_pos_w)\n\n B, _, dim = q.shape\n r_q = q.reshape(B, q_h, q_w, dim)\n rel_h = torch.einsum(\"bhwc,hkc->bhwk\", r_q, Rh)\n rel_w = torch.einsum(\"bhwc,wkc->bhwk\", r_q, Rw)\n\n attn = (\n attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]\n ).view(B, q_h * q_w, k_h * k_w)\n\n return attn\n\n\nclass PatchEmbed(nn.Module):\n \"\"\"\n Image to Patch Embedding.\n \"\"\"\n\n def __init__(\n self,\n kernel_size: Tuple[int, int] = (16, 16),\n stride: Tuple[int, int] = (16, 16),\n padding: Tuple[int, int] = (0, 0),\n in_chans: int = 3,\n embed_dim: int = 768,\n ) -> None:\n \"\"\"\n Args:\n kernel_size (Tuple): kernel size of the projection layer.\n stride (Tuple): stride of the projection layer.\n padding (Tuple): padding size of the projection layer.\n in_chans (int): Number of input image channels.\n embed_dim (int): Patch embedding dimension.\n \"\"\"\n super().__init__()\n\n self.proj = nn.Conv2d(\n in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n x = self.proj(x)\n # B C H W -> B H W C\n x = x.permute(0, 2, 3, 1)\n return x" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/segment_anything/modeling/mask_decoder.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom typing import List, Tuple, Type\n\nfrom .common import LayerNorm2d\n\n\nclass MaskDecoder(nn.Module):\n def __init__(\n self,\n *,\n transformer_dim: int,\n transformer: nn.Module,\n num_multimask_outputs: int = 3,\n activation: Type[nn.Module] = nn.GELU,\n iou_head_depth: int = 3,\n iou_head_hidden_dim: int = 256,\n ) -> None:\n \"\"\"\n Predicts masks given an image and prompt embeddings, using a\n transformer architecture.\n\n Arguments:\n transformer_dim (int): the channel dimension of the transformer\n transformer (nn.Module): the transformer used to predict masks\n num_multimask_outputs (int): the number of masks to predict\n when disambiguating masks\n activation (nn.Module): the type of activation to use when\n upscaling masks\n iou_head_depth (int): the depth of the MLP used to predict\n mask quality\n iou_head_hidden_dim (int): the hidden dimension of the MLP\n used to predict mask quality\n \"\"\"\n super().__init__()\n self.transformer_dim = transformer_dim\n self.transformer = transformer\n\n self.num_multimask_outputs = num_multimask_outputs\n\n self.iou_token = nn.Embedding(1, transformer_dim)\n self.num_mask_tokens = num_multimask_outputs + 1\n self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)\n\n self.output_upscaling = nn.Sequential(\n nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),\n LayerNorm2d(transformer_dim // 4),\n activation(),\n nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),\n activation(),\n )\n self.output_hypernetworks_mlps = nn.ModuleList(\n [\n MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)\n for i in range(self.num_mask_tokens)\n ]\n )\n\n self.iou_prediction_head = MLP(\n transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth\n )\n\n def forward(\n self,\n image_embeddings: torch.Tensor,\n image_pe: torch.Tensor,\n sparse_prompt_embeddings: torch.Tensor,\n dense_prompt_embeddings: torch.Tensor,\n multimask_output: bool,\n hq_token_only: bool,\n interm_embeddings: torch.Tensor,\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"\n Predict masks given image and prompt embeddings.\n\n Arguments:\n image_embeddings (torch.Tensor): the embeddings from the image encoder\n image_pe (torch.Tensor): positional encoding with the shape of image_embeddings\n sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes\n dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs\n multimask_output (bool): Whether to return multiple masks or a single\n mask.\n\n Returns:\n torch.Tensor: batched predicted masks\n torch.Tensor: batched predictions of mask quality\n \"\"\"\n masks, iou_pred = self.predict_masks(\n image_embeddings=image_embeddings,\n image_pe=image_pe,\n sparse_prompt_embeddings=sparse_prompt_embeddings,\n dense_prompt_embeddings=dense_prompt_embeddings,\n )\n\n # Select the correct mask or masks for output\n if multimask_output:\n mask_slice = slice(1, None)\n else:\n mask_slice = slice(0, 1)\n masks = masks[:, mask_slice, :, :]\n iou_pred = iou_pred[:, mask_slice]\n\n # Prepare output\n return masks, iou_pred\n\n def predict_masks(\n self,\n image_embeddings: torch.Tensor,\n image_pe: torch.Tensor,\n sparse_prompt_embeddings: torch.Tensor,\n dense_prompt_embeddings: torch.Tensor,\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Predicts masks. See 'forward' for more details.\"\"\"\n # Concatenate output tokens\n output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)\n output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)\n tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)\n\n # Expand per-image data in batch direction to be per-mask\n src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)\n src = src + dense_prompt_embeddings\n pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)\n b, c, h, w = src.shape\n\n # Run the transformer\n hs, src = self.transformer(src, pos_src, tokens)\n iou_token_out = hs[:, 0, :]\n mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :]\n\n # Upscale mask embeddings and predict masks using the mask tokens\n src = src.transpose(1, 2).view(b, c, h, w)\n upscaled_embedding = self.output_upscaling(src)\n hyper_in_list: List[torch.Tensor] = []\n for i in range(self.num_mask_tokens):\n hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))\n hyper_in = torch.stack(hyper_in_list, dim=1)\n b, c, h, w = upscaled_embedding.shape\n masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)\n\n # Generate mask quality predictions\n iou_pred = self.iou_prediction_head(iou_token_out)\n\n return masks, iou_pred\n\n\n# Lightly adapted from\n# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa\nclass MLP(nn.Module):\n def __init__(\n self,\n input_dim: int,\n hidden_dim: int,\n output_dim: int,\n num_layers: int,\n sigmoid_output: bool = False,\n ) -> None:\n super().__init__()\n self.num_layers = num_layers\n h = [hidden_dim] * (num_layers - 1)\n self.layers = nn.ModuleList(\n nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])\n )\n self.sigmoid_output = sigmoid_output\n\n def forward(self, x):\n for i, layer in enumerate(self.layers):\n x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)\n if self.sigmoid_output:\n x = F.sigmoid(x)\n return x" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/segment_anything/modeling/mask_decoder_hq.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# Modified by HQ-SAM team\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom typing import List, Tuple, Type\n\nfrom .common import LayerNorm2d\n\n\nclass MaskDecoderHQ(nn.Module):\n def __init__(\n self,\n *,\n transformer_dim: int,\n transformer: nn.Module,\n num_multimask_outputs: int = 3,\n activation: Type[nn.Module] = nn.GELU,\n iou_head_depth: int = 3,\n iou_head_hidden_dim: int = 256,\n vit_dim: int = 1024,\n ) -> None:\n \"\"\"\n Predicts masks given an image and prompt embeddings, using a\n transformer architecture.\n\n Arguments:\n transformer_dim (int): the channel dimension of the transformer\n transformer (nn.Module): the transformer used to predict masks\n num_multimask_outputs (int): the number of masks to predict\n when disambiguating masks\n activation (nn.Module): the type of activation to use when\n upscaling masks\n iou_head_depth (int): the depth of the MLP used to predict\n mask quality\n iou_head_hidden_dim (int): the hidden dimension of the MLP\n used to predict mask quality\n \"\"\"\n super().__init__()\n self.transformer_dim = transformer_dim\n self.transformer = transformer\n\n self.num_multimask_outputs = num_multimask_outputs\n\n self.iou_token = nn.Embedding(1, transformer_dim)\n self.num_mask_tokens = num_multimask_outputs + 1\n self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)\n\n self.output_upscaling = nn.Sequential(\n nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),\n LayerNorm2d(transformer_dim // 4),\n activation(),\n nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),\n activation(),\n )\n self.output_hypernetworks_mlps = nn.ModuleList(\n [\n MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)\n for i in range(self.num_mask_tokens)\n ]\n )\n\n self.iou_prediction_head = MLP(\n transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth\n )\n\n # HQ-SAM parameters\n self.hf_token = nn.Embedding(1, transformer_dim) # HQ-Ouptput-Token\n self.hf_mlp = MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3) # corresponding new MLP layer for HQ-Ouptput-Token\n self.num_mask_tokens = self.num_mask_tokens + 1\n \n # three conv fusion layers for obtaining HQ-Feature\n self.compress_vit_feat = nn.Sequential(\n nn.ConvTranspose2d(vit_dim, transformer_dim, kernel_size=2, stride=2),\n LayerNorm2d(transformer_dim),\n nn.GELU(), \n nn.ConvTranspose2d(transformer_dim, transformer_dim // 8, kernel_size=2, stride=2))\n \n self.embedding_encoder = nn.Sequential(\n nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),\n LayerNorm2d(transformer_dim // 4),\n nn.GELU(),\n nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),\n )\n self.embedding_maskfeature = nn.Sequential(\n nn.Conv2d(transformer_dim // 8, transformer_dim // 4, 3, 1, 1), \n LayerNorm2d(transformer_dim // 4),\n nn.GELU(),\n nn.Conv2d(transformer_dim // 4, transformer_dim // 8, 3, 1, 1))\n\n\n\n def forward(\n self,\n image_embeddings: torch.Tensor,\n image_pe: torch.Tensor,\n sparse_prompt_embeddings: torch.Tensor,\n dense_prompt_embeddings: torch.Tensor,\n multimask_output: bool,\n hq_token_only: bool,\n interm_embeddings: torch.Tensor,\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"\n Predict masks given image and prompt embeddings.\n\n Arguments:\n image_embeddings (torch.Tensor): the embeddings from the ViT image encoder\n image_pe (torch.Tensor): positional encoding with the shape of image_embeddings\n sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes\n dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs\n multimask_output (bool): Whether to return multiple masks or a single\n mask.\n\n Returns:\n torch.Tensor: batched predicted masks\n torch.Tensor: batched predictions of mask quality\n \"\"\"\n vit_features = interm_embeddings[0].permute(0, 3, 1, 2) # early-layer ViT feature, after 1st global attention block in ViT\n hq_features = self.embedding_encoder(image_embeddings) + self.compress_vit_feat(vit_features)\n\n masks, iou_pred = self.predict_masks(\n image_embeddings=image_embeddings,\n image_pe=image_pe,\n sparse_prompt_embeddings=sparse_prompt_embeddings,\n dense_prompt_embeddings=dense_prompt_embeddings,\n hq_features=hq_features,\n )\n\n # Select the correct mask or masks for output\n if multimask_output:\n # mask with highest score\n mask_slice = slice(1,self.num_mask_tokens-1)\n iou_pred = iou_pred[:, mask_slice]\n iou_pred, max_iou_idx = torch.max(iou_pred,dim=1)\n iou_pred = iou_pred.unsqueeze(1)\n masks_multi = masks[:, mask_slice, :, :]\n masks_sam = masks_multi[torch.arange(masks_multi.size(0)),max_iou_idx].unsqueeze(1)\n else:\n # singale mask output, default\n mask_slice = slice(0, 1)\n iou_pred = iou_pred[:,mask_slice]\n masks_sam = masks[:,mask_slice]\n\n masks_hq = masks[:,slice(self.num_mask_tokens-1, self.num_mask_tokens)]\n if hq_token_only:\n masks = masks_hq\n else:\n masks = masks_sam + masks_hq\n # Prepare output\n return masks, iou_pred\n\n def predict_masks(\n self,\n image_embeddings: torch.Tensor,\n image_pe: torch.Tensor,\n sparse_prompt_embeddings: torch.Tensor,\n dense_prompt_embeddings: torch.Tensor,\n hq_features: torch.Tensor,\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Predicts masks. See 'forward' for more details.\"\"\"\n # Concatenate output tokens\n output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight, self.hf_token.weight], dim=0)\n output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)\n tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)\n\n # Expand per-image data in batch direction to be per-mask\n src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)\n src = src + dense_prompt_embeddings\n pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)\n b, c, h, w = src.shape\n\n # Run the transformer\n hs, src = self.transformer(src, pos_src, tokens)\n iou_token_out = hs[:, 0, :]\n mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :]\n\n # Upscale mask embeddings and predict masks using the mask tokens\n src = src.transpose(1, 2).view(b, c, h, w)\n\n upscaled_embedding_sam = self.output_upscaling(src)\n upscaled_embedding_hq = self.embedding_maskfeature(upscaled_embedding_sam) + hq_features.repeat(b,1,1,1)\n\n hyper_in_list: List[torch.Tensor] = []\n for i in range(self.num_mask_tokens):\n if i < self.num_mask_tokens - 1:\n hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))\n else:\n hyper_in_list.append(self.hf_mlp(mask_tokens_out[:, i, :]))\n\n hyper_in = torch.stack(hyper_in_list, dim=1)\n b, c, h, w = upscaled_embedding_sam.shape\n\n masks_sam = (hyper_in[:,:self.num_mask_tokens-1] @ upscaled_embedding_sam.view(b, c, h * w)).view(b, -1, h, w)\n masks_sam_hq = (hyper_in[:,self.num_mask_tokens-1:] @ upscaled_embedding_hq.view(b, c, h * w)).view(b, -1, h, w)\n masks = torch.cat([masks_sam,masks_sam_hq],dim=1)\n # Generate mask quality predictions\n iou_pred = self.iou_prediction_head(iou_token_out)\n\n return masks, iou_pred\n\n\n# Lightly adapted from\n# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa\nclass MLP(nn.Module):\n def __init__(\n self,\n input_dim: int,\n hidden_dim: int,\n output_dim: int,\n num_layers: int,\n sigmoid_output: bool = False,\n ) -> None:\n super().__init__()\n self.num_layers = num_layers\n h = [hidden_dim] * (num_layers - 1)\n self.layers = nn.ModuleList(\n nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])\n )\n self.sigmoid_output = sigmoid_output\n\n def forward(self, x):\n for i, layer in enumerate(self.layers):\n x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)\n if self.sigmoid_output:\n x = F.sigmoid(x)\n return x" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/segment_anything/modeling/prompt_encoder.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport numpy as np\nimport torch\nfrom torch import nn\n\nfrom typing import Any, Optional, Tuple, Type\n\nfrom .common import LayerNorm2d\n\n\nclass PromptEncoder(nn.Module):\n def __init__(\n self,\n embed_dim: int,\n image_embedding_size: Tuple[int, int],\n input_image_size: Tuple[int, int],\n mask_in_chans: int,\n activation: Type[nn.Module] = nn.GELU,\n ) -> None:\n \"\"\"\n Encodes prompts for input to SAM's mask decoder.\n\n Arguments:\n embed_dim (int): The prompts' embedding dimension\n image_embedding_size (tuple(int, int)): The spatial size of the\n image embedding, as (H, W).\n input_image_size (int): The padded size of the image as input\n to the image encoder, as (H, W).\n mask_in_chans (int): The number of hidden channels used for\n encoding input masks.\n activation (nn.Module): The activation to use when encoding\n input masks.\n \"\"\"\n super().__init__()\n self.embed_dim = embed_dim\n self.input_image_size = input_image_size\n self.image_embedding_size = image_embedding_size\n self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)\n\n self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners\n point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)]\n self.point_embeddings = nn.ModuleList(point_embeddings)\n self.not_a_point_embed = nn.Embedding(1, embed_dim)\n\n self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1])\n self.mask_downscaling = nn.Sequential(\n nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),\n LayerNorm2d(mask_in_chans // 4),\n activation(),\n nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),\n LayerNorm2d(mask_in_chans),\n activation(),\n nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),\n )\n self.no_mask_embed = nn.Embedding(1, embed_dim)\n\n def get_dense_pe(self) -> torch.Tensor:\n \"\"\"\n Returns the positional encoding used to encode point prompts,\n applied to a dense set of points the shape of the image encoding.\n\n Returns:\n torch.Tensor: Positional encoding with shape\n 1x(embed_dim)x(embedding_h)x(embedding_w)\n \"\"\"\n return self.pe_layer(self.image_embedding_size).unsqueeze(0)\n\n def _embed_points(\n self,\n points: torch.Tensor,\n labels: torch.Tensor,\n pad: bool,\n ) -> torch.Tensor:\n \"\"\"Embeds point prompts.\"\"\"\n points = points + 0.5 # Shift to center of pixel\n if pad:\n padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)\n padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)\n points = torch.cat([points, padding_point], dim=1)\n labels = torch.cat([labels, padding_label], dim=1)\n point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)\n point_embedding[labels == -1] = 0.0\n point_embedding[labels == -1] += self.not_a_point_embed.weight\n point_embedding[labels == 0] += self.point_embeddings[0].weight\n point_embedding[labels == 1] += self.point_embeddings[1].weight\n return point_embedding\n\n def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:\n \"\"\"Embeds box prompts.\"\"\"\n boxes = boxes + 0.5 # Shift to center of pixel\n coords = boxes.reshape(-1, 2, 2)\n corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)\n corner_embedding[:, 0, :] += self.point_embeddings[2].weight\n corner_embedding[:, 1, :] += self.point_embeddings[3].weight\n return corner_embedding\n\n def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:\n \"\"\"Embeds mask inputs.\"\"\"\n mask_embedding = self.mask_downscaling(masks)\n return mask_embedding\n\n def _get_batch_size(\n self,\n points: Optional[Tuple[torch.Tensor, torch.Tensor]],\n boxes: Optional[torch.Tensor],\n masks: Optional[torch.Tensor],\n ) -> int:\n \"\"\"\n Gets the batch size of the output given the batch size of the input prompts.\n \"\"\"\n if points is not None:\n return points[0].shape[0]\n elif boxes is not None:\n return boxes.shape[0]\n elif masks is not None:\n return masks.shape[0]\n else:\n return 1\n\n def _get_device(self) -> torch.device:\n return self.point_embeddings[0].weight.device\n\n def forward(\n self,\n points: Optional[Tuple[torch.Tensor, torch.Tensor]],\n boxes: Optional[torch.Tensor],\n masks: Optional[torch.Tensor],\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"\n Embeds different types of prompts, returning both sparse and dense\n embeddings.\n\n Arguments:\n points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates\n and labels to embed.\n boxes (torch.Tensor or none): boxes to embed\n masks (torch.Tensor or none): masks to embed\n\n Returns:\n torch.Tensor: sparse embeddings for the points and boxes, with shape\n BxNx(embed_dim), where N is determined by the number of input points\n and boxes.\n torch.Tensor: dense embeddings for the masks, in the shape\n Bx(embed_dim)x(embed_H)x(embed_W)\n \"\"\"\n bs = self._get_batch_size(points, boxes, masks)\n sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())\n if points is not None:\n coords, labels = points\n point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))\n sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)\n if boxes is not None:\n box_embeddings = self._embed_boxes(boxes)\n sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)\n\n if masks is not None:\n dense_embeddings = self._embed_masks(masks)\n else:\n dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(\n bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]\n )\n\n return sparse_embeddings, dense_embeddings\n\n\nclass PositionEmbeddingRandom(nn.Module):\n \"\"\"\n Positional encoding using random spatial frequencies.\n \"\"\"\n\n def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:\n super().__init__()\n if scale is None or scale <= 0.0:\n scale = 1.0\n self.register_buffer(\n \"positional_encoding_gaussian_matrix\",\n scale * torch.randn((2, num_pos_feats)),\n )\n\n def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:\n \"\"\"Positionally encode points that are normalized to [0,1].\"\"\"\n # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape\n coords = 2 * coords - 1\n coords = coords @ self.positional_encoding_gaussian_matrix\n coords = 2 * np.pi * coords\n # outputs d_1 x ... x d_n x C shape\n return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)\n\n def forward(self, size: Tuple[int, int]) -> torch.Tensor:\n \"\"\"Generate positional encoding for a grid of the specified size.\"\"\"\n h, w = size\n device: Any = self.positional_encoding_gaussian_matrix.device\n grid = torch.ones((h, w), device=device, dtype=torch.float32)\n y_embed = grid.cumsum(dim=0) - 0.5\n x_embed = grid.cumsum(dim=1) - 0.5\n y_embed = y_embed / h\n x_embed = x_embed / w\n\n pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))\n return pe.permute(2, 0, 1) # C x H x W\n\n def forward_with_coords(\n self, coords_input: torch.Tensor, image_size: Tuple[int, int]\n ) -> torch.Tensor:\n \"\"\"Positionally encode points that are not normalized to [0,1].\"\"\"\n coords = coords_input.clone()\n coords[:, :, 0] = coords[:, :, 0] / image_size[1]\n coords[:, :, 1] = coords[:, :, 1] / image_size[0]\n return self._pe_encoding(coords.to(torch.float)) # B x N x C\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/segment_anything/modeling/sam.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom typing import Any, Dict, List, Tuple\n\nfrom .image_encoder import ImageEncoderViT\nfrom .mask_decoder import MaskDecoder\nfrom .prompt_encoder import PromptEncoder\n\n\nclass Sam(nn.Module):\n mask_threshold: float = 0.0\n image_format: str = \"RGB\"\n\n def __init__(\n self,\n image_encoder: ImageEncoderViT,\n prompt_encoder: PromptEncoder,\n mask_decoder: MaskDecoder,\n pixel_mean: List[float] = [123.675, 116.28, 103.53],\n pixel_std: List[float] = [58.395, 57.12, 57.375],\n ) -> None:\n \"\"\"\n SAM predicts object masks from an image and input prompts.\n\n Arguments:\n image_encoder (ImageEncoderViT): The backbone used to encode the\n image into image embeddings that allow for efficient mask prediction.\n prompt_encoder (PromptEncoder): Encodes various types of input prompts.\n mask_decoder (MaskDecoder): Predicts masks from the image embeddings\n and encoded prompts.\n pixel_mean (list(float)): Mean values for normalizing pixels in the input image.\n pixel_std (list(float)): Std values for normalizing pixels in the input image.\n \"\"\"\n super().__init__()\n self.image_encoder = image_encoder\n self.prompt_encoder = prompt_encoder\n self.mask_decoder = mask_decoder\n self.register_buffer(\"pixel_mean\", torch.Tensor(pixel_mean).view(-1, 1, 1), False)\n self.register_buffer(\"pixel_std\", torch.Tensor(pixel_std).view(-1, 1, 1), False)\n\n @property\n def device(self) -> Any:\n return self.pixel_mean.device\n\n @torch.no_grad()\n def forward(\n self,\n batched_input: List[Dict[str, Any]],\n multimask_output: bool,\n ) -> List[Dict[str, torch.Tensor]]:\n \"\"\"\n Predicts masks end-to-end from provided images and prompts.\n If prompts are not known in advance, using SamPredictor is\n recommended over calling the model directly.\n\n Arguments:\n batched_input (list(dict)): A list over input images, each a\n dictionary with the following keys. A prompt key can be\n excluded if it is not present.\n 'image': The image as a torch tensor in 3xHxW format,\n already transformed for input to the model.\n 'original_size': (tuple(int, int)) The original size of\n the image before transformation, as (H, W).\n 'point_coords': (torch.Tensor) Batched point prompts for\n this image, with shape BxNx2. Already transformed to the\n input frame of the model.\n 'point_labels': (torch.Tensor) Batched labels for point prompts,\n with shape BxN.\n 'boxes': (torch.Tensor) Batched box inputs, with shape Bx4.\n Already transformed to the input frame of the model.\n 'mask_inputs': (torch.Tensor) Batched mask inputs to the model,\n in the form Bx1xHxW.\n multimask_output (bool): Whether the model should predict multiple\n disambiguating masks, or return a single mask.\n\n Returns:\n (list(dict)): A list over input images, where each element is\n as dictionary with the following keys.\n 'masks': (torch.Tensor) Batched binary mask predictions,\n with shape BxCxHxW, where B is the number of input promts,\n C is determiend by multimask_output, and (H, W) is the\n original size of the image.\n 'iou_predictions': (torch.Tensor) The model's predictions\n of mask quality, in shape BxC.\n 'low_res_logits': (torch.Tensor) Low resolution logits with\n shape BxCxHxW, where H=W=256. Can be passed as mask input\n to subsequent iterations of prediction.\n \"\"\"\n input_images = torch.stack([self.preprocess(x[\"image\"]) for x in batched_input], dim=0)\n image_embeddings = self.image_encoder(input_images)\n\n outputs = []\n for image_record, curr_embedding in zip(batched_input, image_embeddings):\n if \"point_coords\" in image_record:\n points = (image_record[\"point_coords\"], image_record[\"point_labels\"])\n else:\n points = None\n sparse_embeddings, dense_embeddings = self.prompt_encoder(\n points=points,\n boxes=image_record.get(\"boxes\", None),\n masks=image_record.get(\"mask_inputs\", None),\n )\n low_res_masks, iou_predictions = self.mask_decoder(\n image_embeddings=curr_embedding.unsqueeze(0),\n image_pe=self.prompt_encoder.get_dense_pe(),\n sparse_prompt_embeddings=sparse_embeddings,\n dense_prompt_embeddings=dense_embeddings,\n multimask_output=multimask_output,\n )\n masks = self.postprocess_masks(\n low_res_masks,\n input_size=image_record[\"image\"].shape[-2:],\n original_size=image_record[\"original_size\"],\n )\n masks = masks > self.mask_threshold\n outputs.append(\n {\n \"masks\": masks,\n \"iou_predictions\": iou_predictions,\n \"low_res_logits\": low_res_masks,\n }\n )\n return outputs\n\n def postprocess_masks(\n self,\n masks: torch.Tensor,\n input_size: Tuple[int, ...],\n original_size: Tuple[int, ...],\n ) -> torch.Tensor:\n \"\"\"\n Remove padding and upscale masks to the original image size.\n\n Arguments:\n masks (torch.Tensor): Batched masks from the mask_decoder,\n in BxCxHxW format.\n input_size (tuple(int, int)): The size of the image input to the\n model, in (H, W) format. Used to remove padding.\n original_size (tuple(int, int)): The original size of the image\n before resizing for input to the model, in (H, W) format.\n\n Returns:\n (torch.Tensor): Batched masks in BxCxHxW format, where (H, W)\n is given by original_size.\n \"\"\"\n masks = F.interpolate(\n masks,\n (self.image_encoder.img_size, self.image_encoder.img_size),\n mode=\"bilinear\",\n align_corners=False,\n )\n masks = masks[..., : input_size[0], : input_size[1]]\n masks = F.interpolate(masks, original_size, mode=\"bilinear\", align_corners=False)\n return masks\n\n def preprocess(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"Normalize pixel values and pad to a square input.\"\"\"\n # Normalize colors\n x = (x - self.pixel_mean) / self.pixel_std\n\n # Pad\n h, w = x.shape[-2:]\n padh = self.image_encoder.img_size - h\n padw = self.image_encoder.img_size - w\n x = F.pad(x, (0, padw, 0, padh))\n return x\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/segment_anything/modeling/transformer.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nfrom torch import Tensor, nn\n\nimport math\nfrom typing import Tuple, Type\n\nfrom .common import MLPBlock\n\n\nclass TwoWayTransformer(nn.Module):\n def __init__(\n self,\n depth: int,\n embedding_dim: int,\n num_heads: int,\n mlp_dim: int,\n activation: Type[nn.Module] = nn.ReLU,\n attention_downsample_rate: int = 2,\n ) -> None:\n \"\"\"\n A transformer decoder that attends to an input image using\n queries whose positional embedding is supplied.\n\n Args:\n depth (int): number of layers in the transformer\n embedding_dim (int): the channel dimension for the input embeddings\n num_heads (int): the number of heads for multihead attention. Must\n divide embedding_dim\n mlp_dim (int): the channel dimension internal to the MLP block\n activation (nn.Module): the activation to use in the MLP block\n \"\"\"\n super().__init__()\n self.depth = depth\n self.embedding_dim = embedding_dim\n self.num_heads = num_heads\n self.mlp_dim = mlp_dim\n self.layers = nn.ModuleList()\n\n for i in range(depth):\n self.layers.append(\n TwoWayAttentionBlock(\n embedding_dim=embedding_dim,\n num_heads=num_heads,\n mlp_dim=mlp_dim,\n activation=activation,\n attention_downsample_rate=attention_downsample_rate,\n skip_first_layer_pe=(i == 0),\n )\n )\n\n self.final_attn_token_to_image = Attention(\n embedding_dim, num_heads, downsample_rate=attention_downsample_rate\n )\n self.norm_final_attn = nn.LayerNorm(embedding_dim)\n\n def forward(\n self,\n image_embedding: Tensor,\n image_pe: Tensor,\n point_embedding: Tensor,\n ) -> Tuple[Tensor, Tensor]:\n \"\"\"\n Args:\n image_embedding (torch.Tensor): image to attend to. Should be shape\n B x embedding_dim x h x w for any h and w.\n image_pe (torch.Tensor): the positional encoding to add to the image. Must\n have the same shape as image_embedding.\n point_embedding (torch.Tensor): the embedding to add to the query points.\n Must have shape B x N_points x embedding_dim for any N_points.\n\n Returns:\n torch.Tensor: the processed point_embedding\n torch.Tensor: the processed image_embedding\n \"\"\"\n # BxCxHxW -> BxHWxC == B x N_image_tokens x C\n bs, c, h, w = image_embedding.shape\n image_embedding = image_embedding.flatten(2).permute(0, 2, 1)\n image_pe = image_pe.flatten(2).permute(0, 2, 1)\n\n # Prepare queries\n queries = point_embedding\n keys = image_embedding\n\n # Apply transformer blocks and final layernorm\n for layer in self.layers:\n queries, keys = layer(\n queries=queries,\n keys=keys,\n query_pe=point_embedding,\n key_pe=image_pe,\n )\n\n # Apply the final attenion layer from the points to the image\n q = queries + point_embedding\n k = keys + image_pe\n attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)\n queries = queries + attn_out\n queries = self.norm_final_attn(queries)\n\n return queries, keys\n\n\nclass TwoWayAttentionBlock(nn.Module):\n def __init__(\n self,\n embedding_dim: int,\n num_heads: int,\n mlp_dim: int = 2048,\n activation: Type[nn.Module] = nn.ReLU,\n attention_downsample_rate: int = 2,\n skip_first_layer_pe: bool = False,\n ) -> None:\n \"\"\"\n A transformer block with four layers: (1) self-attention of sparse\n inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp\n block on sparse inputs, and (4) cross attention of dense inputs to sparse\n inputs.\n\n Arguments:\n embedding_dim (int): the channel dimension of the embeddings\n num_heads (int): the number of heads in the attention layers\n mlp_dim (int): the hidden dimension of the mlp block\n activation (nn.Module): the activation of the mlp block\n skip_first_layer_pe (bool): skip the PE on the first layer\n \"\"\"\n super().__init__()\n self.self_attn = Attention(embedding_dim, num_heads)\n self.norm1 = nn.LayerNorm(embedding_dim)\n\n self.cross_attn_token_to_image = Attention(\n embedding_dim, num_heads, downsample_rate=attention_downsample_rate\n )\n self.norm2 = nn.LayerNorm(embedding_dim)\n\n self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)\n self.norm3 = nn.LayerNorm(embedding_dim)\n\n self.norm4 = nn.LayerNorm(embedding_dim)\n self.cross_attn_image_to_token = Attention(\n embedding_dim, num_heads, downsample_rate=attention_downsample_rate\n )\n\n self.skip_first_layer_pe = skip_first_layer_pe\n\n def forward(\n self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor\n ) -> Tuple[Tensor, Tensor]:\n # Self attention block\n if self.skip_first_layer_pe:\n queries = self.self_attn(q=queries, k=queries, v=queries)\n else:\n q = queries + query_pe\n attn_out = self.self_attn(q=q, k=q, v=queries)\n queries = queries + attn_out\n queries = self.norm1(queries)\n\n # Cross attention block, tokens attending to image embedding\n q = queries + query_pe\n k = keys + key_pe\n attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)\n queries = queries + attn_out\n queries = self.norm2(queries)\n\n # MLP block\n mlp_out = self.mlp(queries)\n queries = queries + mlp_out\n queries = self.norm3(queries)\n\n # Cross attention block, image embedding attending to tokens\n q = queries + query_pe\n k = keys + key_pe\n attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)\n keys = keys + attn_out\n keys = self.norm4(keys)\n\n return queries, keys\n\n\nclass Attention(nn.Module):\n \"\"\"\n An attention layer that allows for downscaling the size of the embedding\n after projection to queries, keys, and values.\n \"\"\"\n\n def __init__(\n self,\n embedding_dim: int,\n num_heads: int,\n downsample_rate: int = 1,\n ) -> None:\n super().__init__()\n self.embedding_dim = embedding_dim\n self.internal_dim = embedding_dim // downsample_rate\n self.num_heads = num_heads\n assert self.internal_dim % num_heads == 0, \"num_heads must divide embedding_dim.\"\n\n self.q_proj = nn.Linear(embedding_dim, self.internal_dim)\n self.k_proj = nn.Linear(embedding_dim, self.internal_dim)\n self.v_proj = nn.Linear(embedding_dim, self.internal_dim)\n self.out_proj = nn.Linear(self.internal_dim, embedding_dim)\n\n def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:\n b, n, c = x.shape\n x = x.reshape(b, n, num_heads, c // num_heads)\n return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head\n\n def _recombine_heads(self, x: Tensor) -> Tensor:\n b, n_heads, n_tokens, c_per_head = x.shape\n x = x.transpose(1, 2)\n return x.reshape(b, n_tokens, n_heads * c_per_head) # B x N_tokens x C\n\n def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:\n # Input projections\n q = self.q_proj(q)\n k = self.k_proj(k)\n v = self.v_proj(v)\n\n # Separate into heads\n q = self._separate_heads(q, self.num_heads)\n k = self._separate_heads(k, self.num_heads)\n v = self._separate_heads(v, self.num_heads)\n\n # Attention\n _, _, _, c_per_head = q.shape\n attn = q @ k.permute(0, 1, 3, 2) # B x N_heads x N_tokens x N_tokens\n attn = attn / math.sqrt(c_per_head)\n attn = torch.softmax(attn, dim=-1)\n\n # Get output\n out = attn @ v\n out = self._recombine_heads(out)\n out = self.out_proj(out)\n\n return out\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/segment_anything/predictor.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport numpy as np\nimport torch\n\nfrom .modeling import Sam\n\nfrom typing import Optional, Tuple\n\nfrom .utils.transforms import ResizeLongestSide\n\n\nclass SamPredictor:\n def __init__(\n self,\n sam_model: Sam,\n ) -> None:\n \"\"\"\n Uses SAM to calculate the image embedding for an image, and then\n allow repeated, efficient mask prediction given prompts.\n\n Arguments:\n sam_model (Sam): The model to use for mask prediction.\n \"\"\"\n super().__init__()\n self.model = sam_model\n self.transform = ResizeLongestSide(sam_model.image_encoder.img_size)\n self.reset_image()\n\n def set_image(\n self,\n image: np.ndarray,\n image_format: str = \"RGB\",\n ) -> None:\n \"\"\"\n Calculates the image embeddings for the provided image, allowing\n masks to be predicted with the 'predict' method.\n\n Arguments:\n image (np.ndarray): The image for calculating masks. Expects an\n image in HWC uint8 format, with pixel values in [0, 255].\n image_format (str): The color format of the image, in ['RGB', 'BGR'].\n \"\"\"\n assert image_format in [\n \"RGB\",\n \"BGR\",\n ], f\"image_format must be in ['RGB', 'BGR'], is {image_format}.\"\n # import pdb;pdb.set_trace()\n if image_format != self.model.image_format:\n image = image[..., ::-1]\n\n # Transform the image to the form expected by the model\n # import pdb;pdb.set_trace()\n input_image = self.transform.apply_image(image)\n input_image_torch = torch.as_tensor(input_image, device=self.device)\n input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]\n\n self.set_torch_image(input_image_torch, image.shape[:2])\n\n @torch.no_grad()\n def set_torch_image(\n self,\n transformed_image: torch.Tensor,\n original_image_size: Tuple[int, ...],\n ) -> None:\n \"\"\"\n Calculates the image embeddings for the provided image, allowing\n masks to be predicted with the 'predict' method. Expects the input\n image to be already transformed to the format expected by the model.\n\n Arguments:\n transformed_image (torch.Tensor): The input image, with shape\n 1x3xHxW, which has been transformed with ResizeLongestSide.\n original_image_size (tuple(int, int)): The size of the image\n before transformation, in (H, W) format.\n \"\"\"\n assert (\n len(transformed_image.shape) == 4\n and transformed_image.shape[1] == 3\n and max(*transformed_image.shape[2:]) == self.model.image_encoder.img_size\n ), f\"set_torch_image input must be BCHW with long side {self.model.image_encoder.img_size}.\"\n self.reset_image()\n\n self.original_size = original_image_size\n self.input_size = tuple(transformed_image.shape[-2:])\n input_image = self.model.preprocess(transformed_image)\n self.features, self.interm_features = self.model.image_encoder(input_image)\n self.is_image_set = True\n\n def predict(\n self,\n point_coords: Optional[np.ndarray] = None,\n point_labels: Optional[np.ndarray] = None,\n box: Optional[np.ndarray] = None,\n mask_input: Optional[np.ndarray] = None,\n multimask_output: bool = True,\n return_logits: bool = False,\n hq_token_only: bool =False,\n ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n \"\"\"\n Predict masks for the given input prompts, using the currently set image.\n\n Arguments:\n point_coords (np.ndarray or None): A Nx2 array of point prompts to the\n model. Each point is in (X,Y) in pixels.\n point_labels (np.ndarray or None): A length N array of labels for the\n point prompts. 1 indicates a foreground point and 0 indicates a\n background point.\n box (np.ndarray or None): A length 4 array given a box prompt to the\n model, in XYXY format.\n mask_input (np.ndarray): A low resolution mask input to the model, typically\n coming from a previous prediction iteration. Has form 1xHxW, where\n for SAM, H=W=256.\n multimask_output (bool): If true, the model will return three masks.\n For ambiguous input prompts (such as a single click), this will often\n produce better masks than a single prediction. If only a single\n mask is needed, the model's predicted quality score can be used\n to select the best mask. For non-ambiguous prompts, such as multiple\n input prompts, multimask_output=False can give better results.\n return_logits (bool): If true, returns un-thresholded masks logits\n instead of a binary mask.\n\n Returns:\n (np.ndarray): The output masks in CxHxW format, where C is the\n number of masks, and (H, W) is the original image size.\n (np.ndarray): An array of length C containing the model's\n predictions for the quality of each mask.\n (np.ndarray): An array of shape CxHxW, where C is the number\n of masks and H=W=256. These low resolution logits can be passed to\n a subsequent iteration as mask input.\n \"\"\"\n if not self.is_image_set:\n raise RuntimeError(\"An image must be set with .set_image(...) before mask prediction.\")\n\n # Transform input prompts\n coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None\n if point_coords is not None:\n assert (\n point_labels is not None\n ), \"point_labels must be supplied if point_coords is supplied.\"\n point_coords = self.transform.apply_coords(point_coords, self.original_size)\n coords_torch = torch.as_tensor(point_coords, dtype=torch.float, device=self.device)\n labels_torch = torch.as_tensor(point_labels, dtype=torch.int, device=self.device)\n coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :]\n if box is not None:\n box = self.transform.apply_boxes(box, self.original_size)\n box_torch = torch.as_tensor(box, dtype=torch.float, device=self.device)\n box_torch = box_torch[None, :]\n if mask_input is not None:\n mask_input_torch = torch.as_tensor(mask_input, dtype=torch.float, device=self.device)\n mask_input_torch = mask_input_torch[None, :, :, :]\n\n masks, iou_predictions, low_res_masks = self.predict_torch(\n coords_torch,\n labels_torch,\n box_torch,\n mask_input_torch,\n multimask_output,\n return_logits=return_logits,\n hq_token_only=hq_token_only,\n )\n\n masks_np = masks[0].detach().cpu().numpy()\n iou_predictions_np = iou_predictions[0].detach().cpu().numpy()\n low_res_masks_np = low_res_masks[0].detach().cpu().numpy()\n return masks_np, iou_predictions_np, low_res_masks_np\n\n @torch.no_grad()\n def predict_torch(\n self,\n point_coords: Optional[torch.Tensor],\n point_labels: Optional[torch.Tensor],\n boxes: Optional[torch.Tensor] = None,\n mask_input: Optional[torch.Tensor] = None,\n multimask_output: bool = True,\n return_logits: bool = False,\n hq_token_only: bool =False,\n ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"\n Predict masks for the given input prompts, using the currently set image.\n Input prompts are batched torch tensors and are expected to already be\n transformed to the input frame using ResizeLongestSide.\n\n Arguments:\n point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the\n model. Each point is in (X,Y) in pixels.\n point_labels (torch.Tensor or None): A BxN array of labels for the\n point prompts. 1 indicates a foreground point and 0 indicates a\n background point.\n boxes (np.ndarray or None): A Bx4 array given a box prompt to the\n model, in XYXY format.\n mask_input (np.ndarray): A low resolution mask input to the model, typically\n coming from a previous prediction iteration. Has form Bx1xHxW, where\n for SAM, H=W=256. Masks returned by a previous iteration of the\n predict method do not need further transformation.\n multimask_output (bool): If true, the model will return three masks.\n For ambiguous input prompts (such as a single click), this will often\n produce better masks than a single prediction. If only a single\n mask is needed, the model's predicted quality score can be used\n to select the best mask. For non-ambiguous prompts, such as multiple\n input prompts, multimask_output=False can give better results.\n return_logits (bool): If true, returns un-thresholded masks logits\n instead of a binary mask.\n\n Returns:\n (torch.Tensor): The output masks in BxCxHxW format, where C is the\n number of masks, and (H, W) is the original image size.\n (torch.Tensor): An array of shape BxC containing the model's\n predictions for the quality of each mask.\n (torch.Tensor): An array of shape BxCxHxW, where C is the number\n of masks and H=W=256. These low res logits can be passed to\n a subsequent iteration as mask input.\n \"\"\"\n if not self.is_image_set:\n raise RuntimeError(\"An image must be set with .set_image(...) before mask prediction.\")\n\n if point_coords is not None:\n points = (point_coords, point_labels)\n else:\n points = None\n\n # Embed prompts\n sparse_embeddings, dense_embeddings = self.model.prompt_encoder(\n points=points,\n boxes=boxes,\n masks=mask_input,\n )\n\n # Predict masks\n low_res_masks, iou_predictions = self.model.mask_decoder(\n image_embeddings=self.features,\n image_pe=self.model.prompt_encoder.get_dense_pe(),\n sparse_prompt_embeddings=sparse_embeddings,\n dense_prompt_embeddings=dense_embeddings,\n multimask_output=multimask_output,\n hq_token_only=hq_token_only,\n interm_embeddings=self.interm_features,\n )\n\n # Upscale the masks to the original image resolution\n masks = self.model.postprocess_masks(low_res_masks, self.input_size, self.original_size)\n\n if not return_logits:\n masks = masks > self.model.mask_threshold\n\n return masks, iou_predictions, low_res_masks\n\n def get_image_embedding(self) -> torch.Tensor:\n \"\"\"\n Returns the image embeddings for the currently set image, with\n shape 1xCxHxW, where C is the embedding dimension and (H,W) are\n the embedding spatial dimension of SAM (typically C=256, H=W=64).\n \"\"\"\n if not self.is_image_set:\n raise RuntimeError(\n \"An image must be set with .set_image(...) to generate an embedding.\"\n )\n assert self.features is not None, \"Features must exist if an image has been set.\"\n return self.features\n\n @property\n def device(self) -> torch.device:\n return self.model.device\n\n def reset_image(self) -> None:\n \"\"\"Resets the currently set image.\"\"\"\n self.is_image_set = False\n self.features = None\n self.orig_h = None\n self.orig_w = None\n self.input_h = None\n self.input_w = None" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/segment_anything/utils/__init__.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/segment_anything/utils/amg.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport numpy as np\nimport torch\n\nimport math\nfrom copy import deepcopy\nfrom itertools import product\nfrom typing import Any, Dict, Generator, ItemsView, List, Tuple\n\n\nclass MaskData:\n \"\"\"\n A structure for storing masks and their related data in batched format.\n Implements basic filtering and concatenation.\n \"\"\"\n\n def __init__(self, **kwargs) -> None:\n for v in kwargs.values():\n assert isinstance(\n v, (list, np.ndarray, torch.Tensor)\n ), \"MaskData only supports list, numpy arrays, and torch tensors.\"\n self._stats = dict(**kwargs)\n\n def __setitem__(self, key: str, item: Any) -> None:\n assert isinstance(\n item, (list, np.ndarray, torch.Tensor)\n ), \"MaskData only supports list, numpy arrays, and torch tensors.\"\n self._stats[key] = item\n\n def __delitem__(self, key: str) -> None:\n del self._stats[key]\n\n def __getitem__(self, key: str) -> Any:\n return self._stats[key]\n\n def items(self) -> ItemsView[str, Any]:\n return self._stats.items()\n\n def filter(self, keep: torch.Tensor) -> None:\n for k, v in self._stats.items():\n if v is None:\n self._stats[k] = None\n elif isinstance(v, torch.Tensor):\n self._stats[k] = v[torch.as_tensor(keep, device=v.device)]\n elif isinstance(v, np.ndarray):\n self._stats[k] = v[keep.detach().cpu().numpy()]\n elif isinstance(v, list) and keep.dtype == torch.bool:\n self._stats[k] = [a for i, a in enumerate(v) if keep[i]]\n elif isinstance(v, list):\n self._stats[k] = [v[i] for i in keep]\n else:\n raise TypeError(f\"MaskData key {k} has an unsupported type {type(v)}.\")\n\n def cat(self, new_stats: \"MaskData\") -> None:\n for k, v in new_stats.items():\n if k not in self._stats or self._stats[k] is None:\n self._stats[k] = deepcopy(v)\n elif isinstance(v, torch.Tensor):\n self._stats[k] = torch.cat([self._stats[k], v], dim=0)\n elif isinstance(v, np.ndarray):\n self._stats[k] = np.concatenate([self._stats[k], v], axis=0)\n elif isinstance(v, list):\n self._stats[k] = self._stats[k] + deepcopy(v)\n else:\n raise TypeError(f\"MaskData key {k} has an unsupported type {type(v)}.\")\n\n def to_numpy(self) -> None:\n for k, v in self._stats.items():\n if isinstance(v, torch.Tensor):\n self._stats[k] = v.detach().cpu().numpy()\n\n\ndef is_box_near_crop_edge(\n boxes: torch.Tensor, crop_box: List[int], orig_box: List[int], atol: float = 20.0\n) -> torch.Tensor:\n \"\"\"Filter masks at the edge of a crop, but not at the edge of the original image.\"\"\"\n crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device)\n orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device)\n boxes = uncrop_boxes_xyxy(boxes, crop_box).float()\n near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0)\n near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0)\n near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge)\n return torch.any(near_crop_edge, dim=1)\n\n\ndef box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor:\n box_xywh = deepcopy(box_xyxy)\n box_xywh[2] = box_xywh[2] - box_xywh[0]\n box_xywh[3] = box_xywh[3] - box_xywh[1]\n return box_xywh\n\n\ndef batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]:\n assert len(args) > 0 and all(\n len(a) == len(args[0]) for a in args\n ), \"Batched iteration must have inputs of all the same size.\"\n n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0)\n for b in range(n_batches):\n yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args]\n\n\ndef mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:\n \"\"\"\n Encodes masks to an uncompressed RLE, in the format expected by\n pycoco tools.\n \"\"\"\n # Put in fortran order and flatten h,w\n b, h, w = tensor.shape\n tensor = tensor.permute(0, 2, 1).flatten(1)\n\n # Compute change indices\n diff = tensor[:, 1:] ^ tensor[:, :-1]\n change_indices = diff.nonzero()\n\n # Encode run length\n out = []\n for i in range(b):\n cur_idxs = change_indices[change_indices[:, 0] == i, 1]\n cur_idxs = torch.cat(\n [\n torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device),\n cur_idxs + 1,\n torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device),\n ]\n )\n btw_idxs = cur_idxs[1:] - cur_idxs[:-1]\n counts = [] if tensor[i, 0] == 0 else [0]\n counts.extend(btw_idxs.detach().cpu().tolist())\n out.append({\"size\": [h, w], \"counts\": counts})\n return out\n\n\ndef rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:\n \"\"\"Compute a binary mask from an uncompressed RLE.\"\"\"\n h, w = rle[\"size\"]\n mask = np.empty(h * w, dtype=bool)\n idx = 0\n parity = False\n for count in rle[\"counts\"]:\n mask[idx : idx + count] = parity\n idx += count\n parity ^= True\n mask = mask.reshape(w, h)\n return mask.transpose() # Put in C order\n\n\ndef area_from_rle(rle: Dict[str, Any]) -> int:\n return sum(rle[\"counts\"][1::2])\n\n\ndef calculate_stability_score(\n masks: torch.Tensor, mask_threshold: float, threshold_offset: float\n) -> torch.Tensor:\n \"\"\"\n Computes the stability score for a batch of masks. The stability\n score is the IoU between the binary masks obtained by thresholding\n the predicted mask logits at high and low values.\n \"\"\"\n # One mask is always contained inside the other.\n # Save memory by preventing unnecesary cast to torch.int64\n intersections = (\n (masks > (mask_threshold + threshold_offset))\n .sum(-1, dtype=torch.int16)\n .sum(-1, dtype=torch.int32)\n )\n unions = (\n (masks > (mask_threshold - threshold_offset))\n .sum(-1, dtype=torch.int16)\n .sum(-1, dtype=torch.int32)\n )\n return intersections / unions\n\n\ndef build_point_grid(n_per_side: int) -> np.ndarray:\n \"\"\"Generates a 2D grid of points evenly spaced in [0,1]x[0,1].\"\"\"\n offset = 1 / (2 * n_per_side)\n points_one_side = np.linspace(offset, 1 - offset, n_per_side)\n points_x = np.tile(points_one_side[None, :], (n_per_side, 1))\n points_y = np.tile(points_one_side[:, None], (1, n_per_side))\n points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2)\n return points\n\n\ndef build_all_layer_point_grids(\n n_per_side: int, n_layers: int, scale_per_layer: int\n) -> List[np.ndarray]:\n \"\"\"Generates point grids for all crop layers.\"\"\"\n points_by_layer = []\n for i in range(n_layers + 1):\n n_points = int(n_per_side / (scale_per_layer**i))\n points_by_layer.append(build_point_grid(n_points))\n return points_by_layer\n\n\ndef generate_crop_boxes(\n im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float\n) -> Tuple[List[List[int]], List[int]]:\n \"\"\"\n Generates a list of crop boxes of different sizes. Each layer\n has (2**i)**2 boxes for the ith layer.\n \"\"\"\n crop_boxes, layer_idxs = [], []\n im_h, im_w = im_size\n short_side = min(im_h, im_w)\n\n # Original image\n crop_boxes.append([0, 0, im_w, im_h])\n layer_idxs.append(0)\n\n def crop_len(orig_len, n_crops, overlap):\n return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops))\n\n for i_layer in range(n_layers):\n n_crops_per_side = 2 ** (i_layer + 1)\n overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side))\n\n crop_w = crop_len(im_w, n_crops_per_side, overlap)\n crop_h = crop_len(im_h, n_crops_per_side, overlap)\n\n crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)]\n crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)]\n\n # Crops in XYWH format\n for x0, y0 in product(crop_box_x0, crop_box_y0):\n box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)]\n crop_boxes.append(box)\n layer_idxs.append(i_layer + 1)\n\n return crop_boxes, layer_idxs\n\n\ndef uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor:\n x0, y0, _, _ = crop_box\n offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device)\n # Check if boxes has a channel dimension\n if len(boxes.shape) == 3:\n offset = offset.unsqueeze(1)\n return boxes + offset\n\n\ndef uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor:\n x0, y0, _, _ = crop_box\n offset = torch.tensor([[x0, y0]], device=points.device)\n # Check if points has a channel dimension\n if len(points.shape) == 3:\n offset = offset.unsqueeze(1)\n return points + offset\n\n\ndef uncrop_masks(\n masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int\n) -> torch.Tensor:\n x0, y0, x1, y1 = crop_box\n if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h:\n return masks\n # Coordinate transform masks\n pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0)\n pad = (x0, pad_x - x0, y0, pad_y - y0)\n return torch.nn.functional.pad(masks, pad, value=0)\n\n\ndef remove_small_regions(\n mask: np.ndarray, area_thresh: float, mode: str\n) -> Tuple[np.ndarray, bool]:\n \"\"\"\n Removes small disconnected regions and holes in a mask. Returns the\n mask and an indicator of if the mask has been modified.\n \"\"\"\n import cv2 # type: ignore\n\n assert mode in [\"holes\", \"islands\"]\n correct_holes = mode == \"holes\"\n working_mask = (correct_holes ^ mask).astype(np.uint8)\n n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)\n sizes = stats[:, -1][1:] # Row 0 is background label\n small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]\n if len(small_regions) == 0:\n return mask, False\n fill_labels = [0] + small_regions\n if not correct_holes:\n fill_labels = [i for i in range(n_labels) if i not in fill_labels]\n # If every region is below threshold, keep largest\n if len(fill_labels) == 0:\n fill_labels = [int(np.argmax(sizes)) + 1]\n mask = np.isin(regions, fill_labels)\n return mask, True\n\n\ndef coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]:\n from pycocotools import mask as mask_utils # type: ignore\n\n h, w = uncompressed_rle[\"size\"]\n rle = mask_utils.frPyObjects(uncompressed_rle, h, w)\n rle[\"counts\"] = rle[\"counts\"].decode(\"utf-8\") # Necessary to serialize with json\n return rle\n\n\ndef batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Calculates boxes in XYXY format around masks. Return [0,0,0,0] for\n an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4.\n \"\"\"\n # torch.max below raises an error on empty inputs, just skip in this case\n if torch.numel(masks) == 0:\n return torch.zeros(*masks.shape[:-2], 4, device=masks.device)\n\n # Normalize shape to CxHxW\n shape = masks.shape\n h, w = shape[-2:]\n if len(shape) > 2:\n masks = masks.flatten(0, -3)\n else:\n masks = masks.unsqueeze(0)\n\n # Get top and bottom edges\n in_height, _ = torch.max(masks, dim=-1)\n in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :]\n bottom_edges, _ = torch.max(in_height_coords, dim=-1)\n in_height_coords = in_height_coords + h * (~in_height)\n top_edges, _ = torch.min(in_height_coords, dim=-1)\n\n # Get left and right edges\n in_width, _ = torch.max(masks, dim=-2)\n in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :]\n right_edges, _ = torch.max(in_width_coords, dim=-1)\n in_width_coords = in_width_coords + w * (~in_width)\n left_edges, _ = torch.min(in_width_coords, dim=-1)\n\n # If the mask is empty the right edge will be to the left of the left edge.\n # Replace these boxes with [0, 0, 0, 0]\n empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges)\n out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1)\n out = out * (~empty_filter).unsqueeze(-1)\n\n # Return to original shape\n if len(shape) > 2:\n out = out.reshape(*shape[:-2], 4)\n else:\n out = out[0]\n\n return out\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/segment_anything/utils/onnx.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nimport torch.nn as nn\nfrom torch.nn import functional as F\n\nfrom typing import Tuple\n\nfrom ..modeling import Sam\nfrom .amg import calculate_stability_score\n\n\nclass SamOnnxModel(nn.Module):\n \"\"\"\n This model should not be called directly, but is used in ONNX export.\n It combines the prompt encoder, mask decoder, and mask postprocessing of Sam,\n with some functions modified to enable model tracing. Also supports extra\n options controlling what information. See the ONNX export script for details.\n \"\"\"\n\n def __init__(\n self,\n model: Sam,\n return_single_mask: bool,\n use_stability_score: bool = False,\n return_extra_metrics: bool = False,\n ) -> None:\n super().__init__()\n self.mask_decoder = model.mask_decoder\n self.model = model\n self.img_size = model.image_encoder.img_size\n self.return_single_mask = return_single_mask\n self.use_stability_score = use_stability_score\n self.stability_score_offset = 1.0\n self.return_extra_metrics = return_extra_metrics\n\n @staticmethod\n def resize_longest_image_size(\n input_image_size: torch.Tensor, longest_side: int\n ) -> torch.Tensor:\n input_image_size = input_image_size.to(torch.float32)\n scale = longest_side / torch.max(input_image_size)\n transformed_size = scale * input_image_size\n transformed_size = torch.floor(transformed_size + 0.5).to(torch.int64)\n return transformed_size\n\n def _embed_points(self, point_coords: torch.Tensor, point_labels: torch.Tensor) -> torch.Tensor:\n point_coords = point_coords + 0.5\n point_coords = point_coords / self.img_size\n point_embedding = self.model.prompt_encoder.pe_layer._pe_encoding(point_coords)\n point_labels = point_labels.unsqueeze(-1).expand_as(point_embedding)\n\n point_embedding = point_embedding * (point_labels != -1)\n point_embedding = point_embedding + self.model.prompt_encoder.not_a_point_embed.weight * (\n point_labels == -1\n )\n\n for i in range(self.model.prompt_encoder.num_point_embeddings):\n point_embedding = point_embedding + self.model.prompt_encoder.point_embeddings[\n i\n ].weight * (point_labels == i)\n\n return point_embedding\n\n def _embed_masks(self, input_mask: torch.Tensor, has_mask_input: torch.Tensor) -> torch.Tensor:\n mask_embedding = has_mask_input * self.model.prompt_encoder.mask_downscaling(input_mask)\n mask_embedding = mask_embedding + (\n 1 - has_mask_input\n ) * self.model.prompt_encoder.no_mask_embed.weight.reshape(1, -1, 1, 1)\n return mask_embedding\n\n def mask_postprocessing(self, masks: torch.Tensor, orig_im_size: torch.Tensor) -> torch.Tensor:\n masks = F.interpolate(\n masks,\n size=(self.img_size, self.img_size),\n mode=\"bilinear\",\n align_corners=False,\n )\n\n prepadded_size = self.resize_longest_image_size(orig_im_size, self.img_size)\n masks = masks[..., : int(prepadded_size[0]), : int(prepadded_size[1])]\n\n orig_im_size = orig_im_size.to(torch.int64)\n h, w = orig_im_size[0], orig_im_size[1]\n masks = F.interpolate(masks, size=(h, w), mode=\"bilinear\", align_corners=False)\n return masks\n\n def select_masks(\n self, masks: torch.Tensor, iou_preds: torch.Tensor, num_points: int\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n # Determine if we should return the multiclick mask or not from the number of points.\n # The reweighting is used to avoid control flow.\n score_reweight = torch.tensor(\n [[1000] + [0] * (self.model.mask_decoder.num_mask_tokens - 1)]\n ).to(iou_preds.device)\n score = iou_preds + (num_points - 2.5) * score_reweight\n best_idx = torch.argmax(score, dim=1)\n masks = masks[torch.arange(masks.shape[0]), best_idx, :, :].unsqueeze(1)\n iou_preds = iou_preds[torch.arange(masks.shape[0]), best_idx].unsqueeze(1)\n\n return masks, iou_preds\n\n @torch.no_grad()\n def forward(\n self,\n image_embeddings: torch.Tensor,\n point_coords: torch.Tensor,\n point_labels: torch.Tensor,\n mask_input: torch.Tensor,\n has_mask_input: torch.Tensor,\n orig_im_size: torch.Tensor,\n ):\n sparse_embedding = self._embed_points(point_coords, point_labels)\n dense_embedding = self._embed_masks(mask_input, has_mask_input)\n\n masks, scores = self.model.mask_decoder.predict_masks(\n image_embeddings=image_embeddings,\n image_pe=self.model.prompt_encoder.get_dense_pe(),\n sparse_prompt_embeddings=sparse_embedding,\n dense_prompt_embeddings=dense_embedding,\n )\n\n if self.use_stability_score:\n scores = calculate_stability_score(\n masks, self.model.mask_threshold, self.stability_score_offset\n )\n\n if self.return_single_mask:\n masks, scores = self.select_masks(masks, scores, point_coords.shape[1])\n\n upscaled_masks = self.mask_postprocessing(masks, orig_im_size)\n\n if self.return_extra_metrics:\n stability_scores = calculate_stability_score(\n upscaled_masks, self.model.mask_threshold, self.stability_score_offset\n )\n areas = (upscaled_masks > self.model.mask_threshold).sum(-1).sum(-1)\n return upscaled_masks, scores, stability_scores, areas, masks\n\n return upscaled_masks, scores, masks\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/segment_anything/utils/transforms.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport numpy as np\nimport torch\nfrom torch.nn import functional as F\nfrom torchvision.transforms.functional import resize, to_pil_image # type: ignore\n\nfrom copy import deepcopy\nfrom typing import Tuple\n\n\nclass ResizeLongestSide:\n \"\"\"\n Resizes images to longest side 'target_length', as well as provides\n methods for resizing coordinates and boxes. Provides methods for\n transforming both numpy array and batched torch tensors.\n \"\"\"\n\n def __init__(self, target_length: int) -> None:\n self.target_length = target_length\n\n def apply_image(self, image: np.ndarray) -> np.ndarray:\n \"\"\"\n Expects a numpy array with shape HxWxC in uint8 format.\n \"\"\"\n target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)\n return np.array(resize(to_pil_image(image), target_size))\n\n def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:\n \"\"\"\n Expects a numpy array of length 2 in the final dimension. Requires the\n original image size in (H, W) format.\n \"\"\"\n old_h, old_w = original_size\n new_h, new_w = self.get_preprocess_shape(\n original_size[0], original_size[1], self.target_length\n )\n coords = deepcopy(coords).astype(float)\n coords[..., 0] = coords[..., 0] * (new_w / old_w)\n coords[..., 1] = coords[..., 1] * (new_h / old_h)\n return coords\n\n def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:\n \"\"\"\n Expects a numpy array shape Bx4. Requires the original image size\n in (H, W) format.\n \"\"\"\n boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size)\n return boxes.reshape(-1, 4)\n\n def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Expects batched images with shape BxCxHxW and float format. This\n transformation may not exactly match apply_image. apply_image is\n the transformation expected by the model.\n \"\"\"\n # Expects an image in BCHW format. May not exactly match apply_image.\n target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)\n return F.interpolate(\n image, target_size, mode=\"bilinear\", align_corners=False, antialias=True\n )\n\n def apply_coords_torch(\n self, coords: torch.Tensor, original_size: Tuple[int, ...]\n ) -> torch.Tensor:\n \"\"\"\n Expects a torch tensor with length 2 in the last dimension. Requires the\n original image size in (H, W) format.\n \"\"\"\n old_h, old_w = original_size\n new_h, new_w = self.get_preprocess_shape(\n original_size[0], original_size[1], self.target_length\n )\n coords = deepcopy(coords).to(torch.float)\n coords[..., 0] = coords[..., 0] * (new_w / old_w)\n coords[..., 1] = coords[..., 1] * (new_h / old_h)\n return coords\n\n def apply_boxes_torch(\n self, boxes: torch.Tensor, original_size: Tuple[int, ...]\n ) -> torch.Tensor:\n \"\"\"\n Expects a torch tensor with shape Bx4. Requires the original image\n size in (H, W) format.\n \"\"\"\n boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size)\n return boxes.reshape(-1, 4)\n\n @staticmethod\n def get_preprocess_shape(oldh: int, oldw: int, long_side_length: int) -> Tuple[int, int]:\n \"\"\"\n Compute the output size given input size and target long side length.\n \"\"\"\n scale = long_side_length * 1.0 / max(oldh, oldw)\n newh, neww = oldh * scale, oldw * scale\n neww = int(neww + 0.5)\n newh = int(newh + 0.5)\n return (newh, neww)\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/setup.cfg", "content": "[isort]\nline_length=100\nmulti_line_output=3\ninclude_trailing_comma=True\nknown_standard_library=numpy,setuptools\nskip_glob=*/__init__.py\nknown_myself=segment_anything\nknown_third_party=matplotlib,cv2,torch,torchvision,pycocotools,onnx,black,isort\nno_lines_before=STDLIB,THIRDPARTY\nsections=FUTURE,STDLIB,THIRDPARTY,MYSELF,FIRSTPARTY,LOCALFOLDER\ndefault_section=FIRSTPARTY\n" }, { "path": "data_generation/Grounded-Segment-Anything/segment_anything/setup.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom setuptools import find_packages, setup\n\nsetup(\n name=\"segment_anything\",\n version=\"1.0\",\n install_requires=[],\n packages=find_packages(exclude=\"notebooks\"),\n extras_require={\n \"all\": [\"matplotlib\", \"pycocotools\", \"opencv-python\", \"onnx\", \"onnxruntime\"],\n \"dev\": [\"flake8\", \"isort\", \"black\", \"mypy\"],\n },\n)\n" }, { "path": "data_generation/Grounded-Segment-Anything/voxelnext_3d_box/README.md", "content": "# 3D-Box via Segment Anything\n\nWe extend [Segment Anything](https://github.com/facebookresearch/segment-anything) to 3D perception by combining it with [VoxelNeXt](https://github.com/dvlab-research/VoxelNeXt). Note that this project is still in progress. We are improving it and developing more examples. Any issue or pull request is welcome!\n\n

\n\n## Why this project?\n[Segment Anything](https://github.com/facebookresearch/segment-anything) and its following projects\nfocus on 2D images. In this project, we extend the scope to 3D world by combining [Segment Anything](https://github.com/facebookresearch/segment-anything) and [VoxelNeXt](https://github.com/dvlab-research/VoxelNeXt). When we provide a prompt (e.g., a point / box), the result is not only 2D segmentation mask, but also 3D boxes.\n\nThe core idea is that [VoxelNeXt](https://github.com/dvlab-research/VoxelNeXt) is a fully sparse 3D detector. It predicts 3D object upon each sparse voxel. We project 3D sparse voxels onto 2D images. And then 3D boxes can be generated for voxels in the SAM mask. \n\n- This project makes 3D object detection to be promptable.\n- VoxelNeXt is based on sparse voxels that are easy to be related to the mask generated from segment anything.\n- This project could facilitate 3D box labeling. 3D box can be obtained via a simple click on image. It might largely save human efforts, especially on autonuous driving scenes.\n\n## Installation\n1. Basic requirements\n`pip install -r requirements.txt\n`\n2. Segment anything\n`pip install git+https://github.com/facebookresearch/segment-anything.git\n`\n3. spconv\n`pip install spconv\n`\nor cuda version spconv `pip install spconv-cu111` based on your cuda version. Please use spconv 2.2 / 2.3 version, for example spconv==2.3.5\n\n\n## Getting Started\nPlease try it via [seg_anything_and_3D.ipynb](seg_anything_and_3D.ipynb).\nWe provide this example on nuScenes dataset. You can use other image-points pairs. \n\n- The demo point for one frame is provided here [points_demo.npy](https://drive.google.com/file/d/1br0VDamameu7B1G1p4HEjj6LshGs5dHB/view?usp=share_link).\n- The point to image translation infos on nuScenes val can be download [here](https://drive.google.com/file/d/1nJqdfs0gMTIo4fjOwytSbM0fdBOJ4IGb/view?usp=share_link).\n- The weight in the demo is [voxelnext_nuscenes_kernel1.pth](https://drive.google.com/file/d/17mQRXXUsaD0dlRzAKep3MQjfj8ugDsp9/view?usp=share_link).\n- The nuScenes info file is [nuscenes_infos_10sweeps_val.pkl](https://drive.google.com/file/d/1Kaxtubzr1GofcoFz97S6qwAIG2wzhPo_/view?usp=share_link). This is generated from [OpenPCDet](https://github.com/open-mmlab/OpenPCDet) codebase.\n\n\n

\n

\n

\n

\n\n## TODO List\n- - [ ] Zero-shot version VoxelNeXt.\n- - [ ] Examples on more datasets.\n- - [ ] Indoor scenes.\n\n## Citation \nIf you find this project useful in your research, please consider citing:\n```\n@article{kirillov2023segany,\n title={Segment Anything}, \n author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\\'a}r, Piotr and Girshick, Ross},\n journal={arXiv:2304.02643},\n year={2023}\n}\n\n@inproceedings{chen2023voxenext,\n title={VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking},\n author={Yukang Chen and Jianhui Liu and Xiangyu Zhang and Xiaojuan Qi and Jiaya Jia},\n booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},\n year={2023}\n}\n\n```\n\n## Acknowledgement\n- [Segment Anything](https://github.com/facebookresearch/segment-anything)\n- [VoxelNeXt](https://github.com/dvlab-research/VoxelNeXt)\n- [UVTR](https://github.com/dvlab-research/UVTR) for 3D to 2D translation.\n" }, { "path": "data_generation/Grounded-Segment-Anything/voxelnext_3d_box/__init__.py", "content": "" }, { "path": "data_generation/Grounded-Segment-Anything/voxelnext_3d_box/config.yaml", "content": "SAM_TYPE: \"vit_h\"\nSAM_CHECKPOINT: \"sam_vit_h_4b8939.pth\"\n\nPOINT_CLOUD_RANGE: [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]\nUSED_FEATURE_LIST: ['x', 'y', 'z', 'intensity', 'timestamp']\nDATA_PROCESSOR:\n - NAME: mask_points_and_boxes_outside_range\n REMOVE_OUTSIDE_BOXES: True\n\n - NAME: shuffle_points\n SHUFFLE_ENABLED: {\n 'train': True,\n 'test': True\n }\n\n - NAME: transform_points_to_voxels\n VOXEL_SIZE: [0.075, 0.075, 0.2]\n MAX_POINTS_PER_VOXEL: 10\n MAX_NUMBER_OF_VOXELS: {\n 'train': 120000,\n 'test': 160000\n }\n\nVOXELNEXT_CHECKPOINT: \"voxelnext_nuscenes_kernel1.pth\"\nINPUT_CHANNELS: 5\nGRID_SIZE: [1440, 1440, 40]\n\nCLASS_NAMES: ['car','truck', 'construction_vehicle', 'bus', 'trailer',\n 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone']\n\nKERNEL_SIZE_HEAD: 1\n\nVOXEL_SIZE: [0.075, 0.075, 0.2]\nCLASS_NAMES_EACH_HEAD: [\n ['car'],\n ['truck', 'construction_vehicle'],\n ['bus', 'trailer'],\n ['barrier'],\n ['motorcycle', 'bicycle'],\n ['pedestrian', 'traffic_cone'],\n]\n\nSEPARATE_HEAD_CFG:\n HEAD_ORDER: ['center', 'center_z', 'dim', 'rot', 'vel']\n HEAD_DICT: {\n 'center': {'out_channels': 2, 'num_conv': 2},\n 'center_z': {'out_channels': 1, 'num_conv': 2},\n 'dim': {'out_channels': 3, 'num_conv': 2},\n 'rot': {'out_channels': 2, 'num_conv': 2},\n 'vel': {'out_channels': 2, 'num_conv': 2},\n }\n\nPOST_PROCESSING:\n SCORE_THRESH: 0\n POST_CENTER_LIMIT_RANGE: [-61.2, -61.2, -10.0, 61.2, 61.2, 10.0]\n MAX_OBJ_PER_SAMPLE: 500\n" }, { "path": "data_generation/Grounded-Segment-Anything/voxelnext_3d_box/model.py", "content": "import numpy as np\nimport torch\nimport torch.nn as nn\nfrom .models.data_processor import DataProcessor\nfrom .models.mean_vfe import MeanVFE\nfrom .models.spconv_backbone_voxelnext import VoxelResBackBone8xVoxelNeXt\nfrom .models.voxelnext_head import VoxelNeXtHead\n\nfrom .utils.image_projection import _proj_voxel_image\nfrom segment_anything import SamPredictor, sam_model_registry\n\nclass VoxelNeXt(nn.Module):\n def __init__(self, model_cfg):\n super().__init__()\n\n point_cloud_range = np.array(model_cfg.POINT_CLOUD_RANGE, dtype=np.float32)\n\n self.data_processor = DataProcessor(\n model_cfg.DATA_PROCESSOR, point_cloud_range=point_cloud_range,\n training=False, num_point_features=len(model_cfg.USED_FEATURE_LIST)\n )\n\n input_channels = model_cfg.get('INPUT_CHANNELS', 5)\n grid_size = np.array(model_cfg.get('GRID_SIZE', [1440, 1440, 40]))\n\n class_names = model_cfg.get('CLASS_NAMES')\n kernel_size_head = model_cfg.get('KERNEL_SIZE_HEAD', 1)\n self.point_cloud_range = torch.Tensor(model_cfg.get('POINT_CLOUD_RANGE', [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]))\n self.voxel_size = torch.Tensor(model_cfg.get('VOXEL_SIZE', [0.075, 0.075, 0.2]))\n CLASS_NAMES_EACH_HEAD = model_cfg.get('CLASS_NAMES_EACH_HEAD')\n SEPARATE_HEAD_CFG = model_cfg.get('SEPARATE_HEAD_CFG')\n POST_PROCESSING = model_cfg.get('POST_PROCESSING')\n self.voxelization = MeanVFE()\n self.backbone_3d = VoxelResBackBone8xVoxelNeXt(input_channels, grid_size)\n self.dense_head = VoxelNeXtHead(class_names, self.point_cloud_range, self.voxel_size, kernel_size_head,\n CLASS_NAMES_EACH_HEAD, SEPARATE_HEAD_CFG, POST_PROCESSING)\n\n\nclass Model(nn.Module):\n def __init__(self, model_cfg, device=\"cuda\"):\n super().__init__()\n\n sam_type = model_cfg.get('SAM_TYPE', \"vit_b\")\n sam_checkpoint = model_cfg.get('SAM_CHECKPOINT', \"/data/sam_vit_b_01ec64.pth\")\n\n sam = sam_model_registry[sam_type](checkpoint=sam_checkpoint).to(device=device)\n self.sam_predictor = SamPredictor(sam)\n\n voxelnext_checkpoint = model_cfg.get('VOXELNEXT_CHECKPOINT', \"/data/voxelnext_nuscenes_kernel1.pth\")\n model_dict = torch.load(voxelnext_checkpoint)\n self.voxelnext = VoxelNeXt(model_cfg).to(device=device)\n self.voxelnext.load_state_dict(model_dict)\n self.point_features = {}\n self.device = device\n\n def image_embedding(self, image):\n self.sam_predictor.set_image(image)\n\n def point_embedding(self, data_dict, image_id):\n data_dict = self.voxelnext.data_processor.forward(\n data_dict=data_dict\n )\n data_dict['voxels'] = torch.Tensor(data_dict['voxels']).to(self.device)\n data_dict['voxel_num_points'] = torch.Tensor(data_dict['voxel_num_points']).to(self.device)\n data_dict['voxel_coords'] = torch.Tensor(data_dict['voxel_coords']).to(self.device)\n\n data_dict = self.voxelnext.voxelization(data_dict)\n n_voxels = data_dict['voxel_coords'].shape[0]\n device = data_dict['voxel_coords'].device\n dtype = data_dict['voxel_coords'].dtype\n data_dict['voxel_coords'] = torch.cat([torch.zeros((n_voxels, 1), device=device, dtype=dtype), data_dict['voxel_coords']], dim=1)\n data_dict['batch_size'] = 1\n\n if not image_id in self.point_features:\n data_dict = self.voxelnext.backbone_3d(data_dict)\n self.point_features[image_id] = data_dict\n else:\n data_dict = self.point_features[image_id]\n pred_dicts = self.voxelnext.dense_head(data_dict)\n\n voxel_coords = data_dict['out_voxels'][pred_dicts[0]['voxel_ids'].squeeze(-1)] * self.voxelnext.dense_head.feature_map_stride\n\n return pred_dicts, voxel_coords\n\n def generate_3D_box(self, lidar2img_rt, mask, voxel_coords, pred_dicts, quality_score=0.1):\n device = voxel_coords.device\n points_image, depth = _proj_voxel_image(voxel_coords, lidar2img_rt, self.voxelnext.voxel_size.to(device), self.voxelnext.point_cloud_range.to(device))\n points = points_image.permute(1, 0).int().cpu().numpy()\n selected_voxels = torch.zeros_like(depth).squeeze(0)\n\n for i in range(points.shape[0]):\n point = points[i]\n if point[0] < 0 or point[1] < 0 or point[0] >= mask.shape[1] or point[1] >= mask.shape[0]:\n continue\n if mask[point[1], point[0]]:\n selected_voxels[i] = 1\n\n mask_extra = (pred_dicts[0]['pred_scores'] > quality_score)\n if mask_extra.sum() == 0:\n print(\"no high quality 3D box related.\")\n return None\n\n selected_voxels *= mask_extra\n if selected_voxels.sum() > 0:\n selected_box_id = pred_dicts[0]['pred_scores'][selected_voxels.bool()].argmax()\n selected_box = pred_dicts[0]['pred_boxes'][selected_voxels.bool()][selected_box_id]\n else:\n grid_x, grid_y = torch.meshgrid(torch.arange(mask.shape[0]), torch.arange(mask.shape[1]))\n mask_x, mask_y = grid_x[mask], grid_y[mask]\n mask_center = torch.Tensor([mask_y.float().mean(), mask_x.float().mean()]).to(\n pred_dicts[0]['pred_boxes'].device).unsqueeze(1)\n\n dist = ((points_image - mask_center) ** 2).sum(0)\n selected_id = dist[mask_extra].argmin()\n selected_box = pred_dicts[0]['pred_boxes'][mask_extra][selected_id]\n return selected_box\n\n def forward(self, image, point_dict, prompt_point, lidar2img_rt, image_id, quality_score=0.1):\n self.image_embedding(image)\n pred_dicts, voxel_coords = self.point_embedding(point_dict, image_id)\n\n masks, scores, _ = self.sam_predictor.predict(point_coords=prompt_point, point_labels=np.array([1]))\n mask = masks[0]\n\n box3d = self.generate_3D_box(lidar2img_rt, mask, voxel_coords, pred_dicts, quality_score=quality_score)\n return mask, box3d\n\n\nif __name__ == '__main__':\n cfg_dataset = 'nuscenes_dataset.yaml'\n cfg_model = 'config.yaml'\n\n dataset_cfg = cfg_from_yaml_file(cfg_dataset, cfg)\n model_cfg = cfg_from_yaml_file(cfg_model, cfg)\n\n nuscenes_dataset = NuScenesDataset(dataset_cfg)\n model = Model(model_cfg)\n\n index = 0\n data_dict = nuscenes_dataset._get_points(index)\n model.point_embedding(data_dict)\n\n" }, { "path": "data_generation/Grounded-Segment-Anything/voxelnext_3d_box/models/__init__.py", "content": "" }, { "path": "data_generation/Grounded-Segment-Anything/voxelnext_3d_box/models/data_processor.py", "content": "from functools import partial\n\nimport numpy as np\n\ntv = None\ntry:\n import cumm.tensorview as tv\nexcept:\n pass\n\n\ndef mask_points_by_range(points, limit_range):\n mask = (points[:, 0] >= limit_range[0]) & (points[:, 0] <= limit_range[3]) \\\n & (points[:, 1] >= limit_range[1]) & (points[:, 1] <= limit_range[4])\n return mask\n\n\nclass VoxelGeneratorWrapper():\n def __init__(self, vsize_xyz, coors_range_xyz, num_point_features, max_num_points_per_voxel, max_num_voxels):\n try:\n from spconv.utils import VoxelGeneratorV2 as VoxelGenerator\n self.spconv_ver = 1\n except:\n try:\n from spconv.utils import VoxelGenerator\n self.spconv_ver = 1\n except:\n from spconv.utils import Point2VoxelCPU3d as VoxelGenerator\n self.spconv_ver = 2\n\n if self.spconv_ver == 1:\n self._voxel_generator = VoxelGenerator(\n voxel_size=vsize_xyz,\n point_cloud_range=coors_range_xyz,\n max_num_points=max_num_points_per_voxel,\n max_voxels=max_num_voxels\n )\n else:\n self._voxel_generator = VoxelGenerator(\n vsize_xyz=vsize_xyz,\n coors_range_xyz=coors_range_xyz,\n num_point_features=num_point_features,\n max_num_points_per_voxel=max_num_points_per_voxel,\n max_num_voxels=max_num_voxels\n )\n\n def generate(self, points):\n if self.spconv_ver == 1:\n voxel_output = self._voxel_generator.generate(points)\n if isinstance(voxel_output, dict):\n voxels, coordinates, num_points = \\\n voxel_output['voxels'], voxel_output['coordinates'], voxel_output['num_points_per_voxel']\n else:\n voxels, coordinates, num_points = voxel_output\n else:\n assert tv is not None, f\"Unexpected error, library: 'cumm' wasn't imported properly.\"\n voxel_output = self._voxel_generator.point_to_voxel(tv.from_numpy(points))\n tv_voxels, tv_coordinates, tv_num_points = voxel_output\n # make copy with numpy(), since numpy_view() will disappear as soon as the generator is deleted\n voxels = tv_voxels.numpy()\n coordinates = tv_coordinates.numpy()\n num_points = tv_num_points.numpy()\n return voxels, coordinates, num_points\n\n\nclass DataProcessor(object):\n def __init__(self, processor_configs, point_cloud_range, training, num_point_features):\n self.point_cloud_range = point_cloud_range\n self.training = training\n self.num_point_features = num_point_features\n self.mode = 'train' if training else 'test'\n self.grid_size = self.voxel_size = None\n self.data_processor_queue = []\n\n self.voxel_generator = None\n\n for cur_cfg in processor_configs:\n cur_processor = getattr(self, cur_cfg.NAME)(config=cur_cfg)\n self.data_processor_queue.append(cur_processor)\n\n def mask_points_and_boxes_outside_range(self, data_dict=None, config=None):\n if data_dict is None:\n return partial(self.mask_points_and_boxes_outside_range, config=config)\n\n if data_dict.get('points', None) is not None:\n mask = mask_points_by_range(data_dict['points'], self.point_cloud_range)\n data_dict['points'] = data_dict['points'][mask]\n\n return data_dict\n\n def shuffle_points(self, data_dict=None, config=None):\n if data_dict is None:\n return partial(self.shuffle_points, config=config)\n\n if config.SHUFFLE_ENABLED[self.mode]:\n points = data_dict['points']\n shuffle_idx = np.random.permutation(points.shape[0])\n points = points[shuffle_idx]\n data_dict['points'] = points\n\n return data_dict\n\n def transform_points_to_voxels_placeholder(self, data_dict=None, config=None):\n # just calculate grid size\n if data_dict is None:\n grid_size = (self.point_cloud_range[3:6] - self.point_cloud_range[0:3]) / np.array(config.VOXEL_SIZE)\n self.grid_size = np.round(grid_size).astype(np.int64)\n self.voxel_size = config.VOXEL_SIZE\n return partial(self.transform_points_to_voxels_placeholder, config=config)\n \n return data_dict\n\n def double_flip(self, points):\n # y flip\n points_yflip = points.copy()\n points_yflip[:, 1] = -points_yflip[:, 1]\n\n # x flip\n points_xflip = points.copy()\n points_xflip[:, 0] = -points_xflip[:, 0]\n\n # x y flip\n points_xyflip = points.copy()\n points_xyflip[:, 0] = -points_xyflip[:, 0]\n points_xyflip[:, 1] = -points_xyflip[:, 1]\n\n return points_yflip, points_xflip, points_xyflip\n\n def transform_points_to_voxels(self, data_dict=None, config=None):\n if data_dict is None:\n grid_size = (self.point_cloud_range[3:6] - self.point_cloud_range[0:3]) / np.array(config.VOXEL_SIZE)\n self.grid_size = np.round(grid_size).astype(np.int64)\n self.voxel_size = config.VOXEL_SIZE\n # just bind the config, we will create the VoxelGeneratorWrapper later,\n # to avoid pickling issues in multiprocess spawn\n return partial(self.transform_points_to_voxels, config=config)\n\n if self.voxel_generator is None:\n self.voxel_generator = VoxelGeneratorWrapper(\n vsize_xyz=config.VOXEL_SIZE,\n coors_range_xyz=self.point_cloud_range,\n num_point_features=self.num_point_features,\n max_num_points_per_voxel=config.MAX_POINTS_PER_VOXEL,\n max_num_voxels=config.MAX_NUMBER_OF_VOXELS[self.mode],\n )\n\n points = data_dict['points']\n voxel_output = self.voxel_generator.generate(points)\n voxels, coordinates, num_points = voxel_output\n\n data_dict['voxels'] = voxels\n data_dict['voxel_coords'] = coordinates\n data_dict['voxel_num_points'] = num_points\n return data_dict\n\n def sample_points(self, data_dict=None, config=None):\n if data_dict is None:\n return partial(self.sample_points, config=config)\n\n num_points = config.NUM_POINTS[self.mode]\n if num_points == -1:\n return data_dict\n\n points = data_dict['points']\n if num_points < len(points):\n pts_depth = np.linalg.norm(points[:, 0:3], axis=1)\n pts_near_flag = pts_depth < 40.0\n far_idxs_choice = np.where(pts_near_flag == 0)[0]\n near_idxs = np.where(pts_near_flag == 1)[0]\n choice = []\n if num_points > len(far_idxs_choice):\n near_idxs_choice = np.random.choice(near_idxs, num_points - len(far_idxs_choice), replace=False)\n choice = np.concatenate((near_idxs_choice, far_idxs_choice), axis=0) \\\n if len(far_idxs_choice) > 0 else near_idxs_choice\n else: \n choice = np.arange(0, len(points), dtype=np.int32)\n choice = np.random.choice(choice, num_points, replace=False)\n np.random.shuffle(choice)\n else:\n choice = np.arange(0, len(points), dtype=np.int32)\n if num_points > len(points):\n extra_choice = np.random.choice(choice, num_points - len(points), replace=False)\n choice = np.concatenate((choice, extra_choice), axis=0)\n np.random.shuffle(choice)\n data_dict['points'] = points[choice]\n return data_dict\n\n def calculate_grid_size(self, data_dict=None, config=None):\n if data_dict is None:\n grid_size = (self.point_cloud_range[3:6] - self.point_cloud_range[0:3]) / np.array(config.VOXEL_SIZE)\n self.grid_size = np.round(grid_size).astype(np.int64)\n self.voxel_size = config.VOXEL_SIZE\n return partial(self.calculate_grid_size, config=config)\n return data_dict\n\n def forward(self, data_dict):\n \"\"\"\n Args:\n data_dict:\n points: (N, 3 + C_in)\n gt_boxes: optional, (N, 7 + C) [x, y, z, dx, dy, dz, heading, ...]\n gt_names: optional, (N), string\n ...\n\n Returns:\n \"\"\"\n\n for cur_processor in self.data_processor_queue:\n data_dict = cur_processor(data_dict=data_dict)\n\n return data_dict\n" }, { "path": "data_generation/Grounded-Segment-Anything/voxelnext_3d_box/models/mean_vfe.py", "content": "import torch\nimport torch.nn as nn\n\nclass MeanVFE(nn.Module):\n def __init__(self):\n super().__init__()\n\n def forward(self, batch_dict, **kwargs):\n \"\"\"\n Args:\n batch_dict:\n voxels: (num_voxels, max_points_per_voxel, C)\n voxel_num_points: optional (num_voxels)\n **kwargs:\n\n Returns:\n vfe_features: (num_voxels, C)\n \"\"\"\n voxel_features, voxel_num_points = batch_dict['voxels'], batch_dict['voxel_num_points']\n points_mean = voxel_features[:, :, :].sum(dim=1, keepdim=False)\n normalizer = torch.clamp_min(voxel_num_points.view(-1, 1), min=1.0).type_as(voxel_features)\n points_mean = points_mean / normalizer\n batch_dict['voxel_features'] = points_mean.contiguous()\n\n return batch_dict\n" }, { "path": "data_generation/Grounded-Segment-Anything/voxelnext_3d_box/models/spconv_backbone_voxelnext.py", "content": "from functools import partial\nimport torch\nimport torch.nn as nn\n\nimport spconv.pytorch as spconv\nfrom spconv.core import ConvAlgo\n\n\ndef replace_feature(out, new_features):\n return out.replace_feature(new_features)\n\n\ndef post_act_block(in_channels, out_channels, kernel_size, indice_key=None, stride=1, padding=0,\n conv_type='subm', norm_fn=None):\n\n if conv_type == 'subm':\n conv = spconv.SubMConv3d(in_channels, out_channels, kernel_size, bias=False, indice_key=indice_key, algo=ConvAlgo.Native)\n elif conv_type == 'spconv':\n conv = spconv.SparseConv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding,\n bias=False, indice_key=indice_key, algo=ConvAlgo.Native)\n elif conv_type == 'inverseconv':\n conv = spconv.SparseInverseConv3d(in_channels, out_channels, kernel_size, indice_key=indice_key, bias=False, algo=ConvAlgo.Native)\n else:\n raise NotImplementedError\n\n m = spconv.SparseSequential(\n conv,\n norm_fn(out_channels),\n nn.ReLU(),\n )\n\n return m\n\n\nclass SparseBasicBlock(spconv.SparseModule):\n expansion = 1\n\n def __init__(self, inplanes, planes, stride=1, norm_fn=None, downsample=None, indice_key=None):\n super(SparseBasicBlock, self).__init__()\n\n assert norm_fn is not None\n bias = norm_fn is not None\n self.conv1 = spconv.SubMConv3d(\n inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key, algo=ConvAlgo.Native\n )\n self.bn1 = norm_fn(planes)\n self.relu = nn.ReLU()\n self.conv2 = spconv.SubMConv3d(\n planes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key, algo=ConvAlgo.Native\n )\n self.bn2 = norm_fn(planes)\n self.downsample = downsample\n self.stride = stride\n\n def forward(self, x):\n identity = x\n\n out = self.conv1(x)\n out = replace_feature(out, self.bn1(out.features))\n out = replace_feature(out, self.relu(out.features))\n\n out = self.conv2(out)\n out = replace_feature(out, self.bn2(out.features))\n\n if self.downsample is not None:\n identity = self.downsample(x)\n\n out = replace_feature(out, out.features + identity.features)\n out = replace_feature(out, self.relu(out.features))\n\n return out\n\n\nclass VoxelResBackBone8xVoxelNeXt(nn.Module):\n def __init__(self, input_channels, grid_size, **kwargs):\n super().__init__()\n norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)\n\n spconv_kernel_sizes = [3, 3, 3, 3]\n channels = [16, 32, 64, 128, 128]\n out_channel = 128\n\n self.sparse_shape = grid_size[::-1] + [1, 0, 0]\n\n self.conv_input = spconv.SparseSequential(\n spconv.SubMConv3d(input_channels, channels[0], 3, padding=1, bias=False, indice_key='subm1', algo=ConvAlgo.Native),\n norm_fn(channels[0]),\n nn.ReLU(),\n )\n block = post_act_block\n\n self.conv1 = spconv.SparseSequential(\n SparseBasicBlock(channels[0], channels[0], norm_fn=norm_fn, indice_key='res1'),\n SparseBasicBlock(channels[0], channels[0], norm_fn=norm_fn, indice_key='res1'),\n )\n\n self.conv2 = spconv.SparseSequential(\n # [1600, 1408, 41] <- [800, 704, 21]\n block(channels[0], channels[1], spconv_kernel_sizes[0], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[0]//2), indice_key='spconv2', conv_type='spconv'),\n SparseBasicBlock(channels[1], channels[1], norm_fn=norm_fn, indice_key='res2'),\n SparseBasicBlock(channels[1], channels[1], norm_fn=norm_fn, indice_key='res2'),\n )\n\n self.conv3 = spconv.SparseSequential(\n # [800, 704, 21] <- [400, 352, 11]\n block(channels[1], channels[2], spconv_kernel_sizes[1], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[1]//2), indice_key='spconv3', conv_type='spconv'),\n SparseBasicBlock(channels[2], channels[2], norm_fn=norm_fn, indice_key='res3'),\n SparseBasicBlock(channels[2], channels[2], norm_fn=norm_fn, indice_key='res3'),\n )\n\n self.conv4 = spconv.SparseSequential(\n # [400, 352, 11] <- [200, 176, 6]\n block(channels[2], channels[3], spconv_kernel_sizes[2], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[2]//2), indice_key='spconv4', conv_type='spconv'),\n SparseBasicBlock(channels[3], channels[3], norm_fn=norm_fn, indice_key='res4'),\n SparseBasicBlock(channels[3], channels[3], norm_fn=norm_fn, indice_key='res4'),\n )\n\n self.conv5 = spconv.SparseSequential(\n # [200, 176, 6] <- [100, 88, 3]\n block(channels[3], channels[4], spconv_kernel_sizes[3], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[3]//2), indice_key='spconv5', conv_type='spconv'),\n SparseBasicBlock(channels[4], channels[4], norm_fn=norm_fn, indice_key='res5'),\n SparseBasicBlock(channels[4], channels[4], norm_fn=norm_fn, indice_key='res5'),\n )\n \n self.conv6 = spconv.SparseSequential(\n # [200, 176, 6] <- [100, 88, 3]\n block(channels[4], channels[4], spconv_kernel_sizes[3], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[3]//2), indice_key='spconv6', conv_type='spconv'),\n SparseBasicBlock(channels[4], channels[4], norm_fn=norm_fn, indice_key='res6'),\n SparseBasicBlock(channels[4], channels[4], norm_fn=norm_fn, indice_key='res6'),\n )\n self.conv_out = spconv.SparseSequential(\n # [200, 150, 5] -> [200, 150, 2]\n spconv.SparseConv2d(channels[3], out_channel, 3, stride=1, padding=1, bias=False, indice_key='spconv_down2', algo=ConvAlgo.Native),\n norm_fn(out_channel),\n nn.ReLU(),\n )\n\n self.shared_conv = spconv.SparseSequential(\n spconv.SubMConv2d(out_channel, out_channel, 3, stride=1, padding=1, bias=True, algo=ConvAlgo.Native),\n nn.BatchNorm1d(out_channel),\n nn.ReLU(True),\n )\n\n self.forward_ret_dict = {}\n self.num_point_features = out_channel\n self.backbone_channels = {\n 'x_conv1': channels[0],\n 'x_conv2': channels[1],\n 'x_conv3': channels[2],\n 'x_conv4': channels[3]\n }\n\n def bev_out(self, x_conv, index):\n features_cat = x_conv.features\n indices_cat = x_conv.indices[:, [0, 2, 3]]\n spatial_shape = x_conv.spatial_shape[1:]\n\n indices_unique, _inv = torch.unique(indices_cat, dim=0, return_inverse=True)\n features_unique = features_cat.new_zeros((indices_unique.shape[0], features_cat.shape[1]))\n features_unique.index_add_(0, _inv, features_cat)\n\n perm = torch.arange(_inv.size(0), dtype=_inv.dtype, device=_inv.device)\n perm = _inv.new_empty(indices_unique.size(0)).scatter_(0, _inv, perm)\n index_out = index[perm]\n\n x_out = spconv.SparseConvTensor(\n features=features_unique,\n indices=indices_unique,\n spatial_shape=spatial_shape,\n batch_size=x_conv.batch_size\n )\n return x_out, index_out\n\n def track_voxels_2d(self, x, x_downsample, index, kernel_size=3):\n _step = int(kernel_size//2)\n kernel_offsets = [[i, j] for i in range(-_step, _step+1) for j in range(-_step, _step+1)]\n #kernel_offsets.remove([0, 0])\n kernel_offsets = torch.Tensor(kernel_offsets).to(x.indices.device)\n\n batch_size = x.batch_size\n index_batch = []\n indices_batch = []\n\n for b in range(batch_size):\n batch_index = x.indices[:, 0]==b\n indices_ori = x.indices[batch_index]\n features_ori = index[batch_index]\n\n features_fore = features_ori\n coords_fore = indices_ori\n\n voxel_kerels_imp = kernel_offsets.unsqueeze(0).repeat(features_fore.shape[0],1, 1)\n indices_fore_kernels = coords_fore[:, 1:].unsqueeze(1).repeat(1, kernel_offsets.shape[0], 1)\n indices_with_imp = indices_fore_kernels + voxel_kerels_imp\n features_fore = features_fore.repeat(1, kernel_offsets.shape[0])\n\n selected_indices = indices_with_imp\n spatial_indices = (selected_indices[:, :, 0] >=0) * (selected_indices[:, :, 1] >=0) * \\\n (selected_indices[:, :, 0] < x.spatial_shape[0]) * (selected_indices[:, :, 1] < x.spatial_shape[1])\n selected_indices = selected_indices[spatial_indices]\n features_fore = features_fore[spatial_indices].view(-1, 1)\n\n selected_indices = torch.cat([torch.ones((selected_indices.shape[0], 1), device=features_fore.device)*b, selected_indices], dim=1)\n\n features_fore, coords_fore = features_fore, selected_indices\n index_batch.append(features_fore)\n indices_batch.append(coords_fore)\n\n index_batch = torch.cat(index_batch)\n indices_batch = torch.cat(indices_batch)\n\n return self.index_from_sparse(index_batch, indices_batch, x_downsample, True)\n\n def index_from_sparse(self, feature, indices, x_target, _2d=False):\n sparse_index = spconv.SparseConvTensor(\n features=feature,\n indices=indices.int(),\n spatial_shape=x_target.spatial_shape,\n batch_size=x_target.batch_size\n )\n dense_index = sparse_index.dense()\n indices_downsample = x_target.indices.long()\n if _2d:\n index_downsample = dense_index[indices_downsample[:, 0], :, indices_downsample[:, 1], indices_downsample[:, 2]]\n else:\n index_downsample = dense_index[indices_downsample[:, 0], :, indices_downsample[:, 1], indices_downsample[:, 2], indices_downsample[:, 3]]\n return index_downsample\n\n def forward(self, batch_dict):\n \"\"\"\n Args:\n batch_dict:\n batch_size: int\n vfe_features: (num_voxels, C)\n voxel_coords: (num_voxels, 4), [batch_idx, z_idx, y_idx, x_idx]\n Returns:\n batch_dict:\n encoded_spconv_tensor: sparse tensor\n \"\"\"\n voxel_features, voxel_coords = batch_dict['voxel_features'], batch_dict['voxel_coords']\n batch_size = batch_dict['batch_size']\n input_sp_tensor = spconv.SparseConvTensor(\n features=voxel_features,\n indices=voxel_coords.int(),\n spatial_shape=self.sparse_shape,\n batch_size=batch_size\n )\n x = self.conv_input(input_sp_tensor)\n\n x_conv1 = self.conv1(x)\n x_conv2 = self.conv2(x_conv1)\n x_conv3 = self.conv3(x_conv2)\n x_conv4 = self.conv4(x_conv3)\n x_conv5 = self.conv5(x_conv4)\n x_conv6 = self.conv6(x_conv5)\n\n x_conv5.indices[:, 1:] *= 2\n x_conv6.indices[:, 1:] *= 4\n x_conv4 = x_conv4.replace_feature(torch.cat([x_conv4.features, x_conv5.features, x_conv6.features]))\n x_conv4.indices = torch.cat([x_conv4.indices, x_conv5.indices, x_conv6.indices])\n\n index6_out = torch.arange(x_conv4.indices.shape[0], device=x_conv4.indices.device).unsqueeze(-1)\n out_bevout, index_bevout = self.bev_out(x_conv4, index6_out)\n\n out = self.conv_out(out_bevout)\n index_out = self.track_voxels_2d(out_bevout, out, index_bevout)\n\n out = self.shared_conv(out)\n\n batch_dict.update({\n 'encoded_spconv_tensor': out,\n 'encoded_spconv_tensor_stride': 8,\n 'out_voxels': x_conv4.indices[index_out.squeeze(-1)],\n })\n batch_dict.update({\n 'multi_scale_3d_features': {\n 'x_conv1': x_conv1,\n 'x_conv2': x_conv2,\n 'x_conv3': x_conv3,\n 'x_conv4': x_conv4,\n }\n })\n batch_dict.update({\n 'multi_scale_3d_strides': {\n 'x_conv1': 1,\n 'x_conv2': 2,\n 'x_conv3': 4,\n 'x_conv4': 8,\n }\n })\n \n return batch_dict\n" }, { "path": "data_generation/Grounded-Segment-Anything/voxelnext_3d_box/models/voxelnext_head.py", "content": "import numpy as np\nimport torch\nimport torch.nn as nn\nfrom voxelnext_3d_box.utils import centernet_utils\nimport spconv.pytorch as spconv\nimport copy\nfrom spconv.core import ConvAlgo\n\n\nclass SeparateHead(nn.Module):\n def __init__(self, input_channels, sep_head_dict, kernel_size, use_bias=False):\n super().__init__()\n self.sep_head_dict = sep_head_dict\n\n for cur_name in self.sep_head_dict:\n output_channels = self.sep_head_dict[cur_name]['out_channels']\n num_conv = self.sep_head_dict[cur_name]['num_conv']\n\n fc_list = []\n for k in range(num_conv - 1):\n fc_list.append(spconv.SparseSequential(\n spconv.SubMConv2d(input_channels, input_channels, kernel_size, padding=int(kernel_size//2), bias=use_bias, indice_key=cur_name, algo=ConvAlgo.Native),\n nn.BatchNorm1d(input_channels),\n nn.ReLU()\n ))\n fc_list.append(spconv.SubMConv2d(input_channels, output_channels, 1, bias=True, indice_key=cur_name+'out', algo=ConvAlgo.Native))\n fc = nn.Sequential(*fc_list)\n self.__setattr__(cur_name, fc)\n\n def forward(self, x):\n ret_dict = {}\n for cur_name in self.sep_head_dict:\n ret_dict[cur_name] = self.__getattr__(cur_name)(x).features\n\n return ret_dict\n\n\nclass VoxelNeXtHead(nn.Module):\n def __init__(self, class_names, point_cloud_range, voxel_size, kernel_size_head,\n CLASS_NAMES_EACH_HEAD, SEPARATE_HEAD_CFG, POST_PROCESSING):\n super().__init__()\n self.point_cloud_range = torch.Tensor(point_cloud_range)\n self.voxel_size = torch.Tensor(voxel_size)\n self.feature_map_stride = 8\n\n self.class_names = class_names\n self.class_names_each_head = []\n self.class_id_mapping_each_head = []\n self.POST_PROCESSING = POST_PROCESSING\n\n for cur_class_names in CLASS_NAMES_EACH_HEAD:\n self.class_names_each_head.append([x for x in cur_class_names if x in class_names])\n cur_class_id_mapping = torch.from_numpy(np.array(\n [self.class_names.index(x) for x in cur_class_names if x in class_names]\n ))\n self.class_id_mapping_each_head.append(cur_class_id_mapping)\n\n total_classes = sum([len(x) for x in self.class_names_each_head])\n assert total_classes == len(self.class_names), f'class_names_each_head={self.class_names_each_head}'\n\n self.heads_list = nn.ModuleList()\n self.separate_head_cfg = SEPARATE_HEAD_CFG\n for idx, cur_class_names in enumerate(self.class_names_each_head):\n cur_head_dict = copy.deepcopy(self.separate_head_cfg.HEAD_DICT)\n cur_head_dict['hm'] = dict(out_channels=len(cur_class_names), num_conv=2)\n self.heads_list.append(\n SeparateHead(\n input_channels=128,\n sep_head_dict=cur_head_dict,\n kernel_size=kernel_size_head,\n use_bias=True,\n )\n )\n self.forward_ret_dict = {}\n\n def generate_predicted_boxes(self, batch_size, pred_dicts, voxel_indices, spatial_shape):\n device = pred_dicts[0]['hm'].device\n post_process_cfg = self.POST_PROCESSING\n post_center_limit_range = torch.tensor(post_process_cfg.POST_CENTER_LIMIT_RANGE).float().to(device)\n\n ret_dict = [{\n 'pred_boxes': [],\n 'pred_scores': [],\n 'pred_labels': [],\n 'pred_ious': [],\n 'voxel_ids': []\n } for k in range(batch_size)]\n for idx, pred_dict in enumerate(pred_dicts):\n batch_hm = pred_dict['hm'].sigmoid()\n batch_center = pred_dict['center']\n batch_center_z = pred_dict['center_z']\n batch_dim = pred_dict['dim'].exp()\n batch_rot_cos = pred_dict['rot'][:, 0].unsqueeze(dim=1)\n batch_rot_sin = pred_dict['rot'][:, 1].unsqueeze(dim=1)\n batch_iou = None\n batch_vel = pred_dict['vel'] if 'vel' in self.separate_head_cfg.HEAD_ORDER else None\n voxel_indices_ = voxel_indices\n\n final_pred_dicts = centernet_utils.decode_bbox_from_voxels_nuscenes(\n batch_size=batch_size, indices=voxel_indices_,\n obj=batch_hm, \n rot_cos=batch_rot_cos,\n rot_sin=batch_rot_sin,\n center=batch_center, center_z=batch_center_z,\n dim=batch_dim, vel=batch_vel, iou=batch_iou,\n point_cloud_range=self.point_cloud_range.to(device), voxel_size=self.voxel_size.to(device),\n feature_map_stride=self.feature_map_stride,\n K=post_process_cfg.MAX_OBJ_PER_SAMPLE,\n score_thresh=post_process_cfg.SCORE_THRESH,\n post_center_limit_range=post_center_limit_range,\n add_features=torch.arange(voxel_indices_.shape[0], device=voxel_indices_.device).unsqueeze(-1)\n )\n\n for k, final_dict in enumerate(final_pred_dicts):\n class_id_mapping_each_head = self.class_id_mapping_each_head[idx].to(device)\n final_dict['pred_labels'] = class_id_mapping_each_head[final_dict['pred_labels'].long()]\n\n ret_dict[k]['pred_boxes'].append(final_dict['pred_boxes'])\n ret_dict[k]['pred_scores'].append(final_dict['pred_scores'])\n ret_dict[k]['pred_labels'].append(final_dict['pred_labels'])\n ret_dict[k]['pred_ious'].append(final_dict['pred_ious'])\n ret_dict[k]['voxel_ids'].append(final_dict['add_features'])\n\n for k in range(batch_size):\n pred_boxes = torch.cat(ret_dict[k]['pred_boxes'], dim=0)\n pred_scores = torch.cat(ret_dict[k]['pred_scores'], dim=0)\n pred_labels = torch.cat(ret_dict[k]['pred_labels'], dim=0)\n voxel_ids = torch.cat(ret_dict[k]['voxel_ids'], dim=0)\n\n ret_dict[k]['pred_boxes'] = pred_boxes\n ret_dict[k]['pred_scores'] = pred_scores\n ret_dict[k]['pred_labels'] = pred_labels + 1\n ret_dict[k]['voxel_ids'] = voxel_ids\n\n return ret_dict\n\n def _get_voxel_infos(self, x):\n spatial_shape = x.spatial_shape\n voxel_indices = x.indices\n spatial_indices = []\n num_voxels = []\n batch_size = x.batch_size\n batch_index = voxel_indices[:, 0]\n\n for bs_idx in range(batch_size):\n batch_inds = batch_index==bs_idx\n spatial_indices.append(voxel_indices[batch_inds][:, [2, 1]])\n num_voxels.append(batch_inds.sum())\n\n return spatial_shape, batch_index, voxel_indices, spatial_indices, num_voxels\n\n def forward(self, data_dict):\n x = data_dict['encoded_spconv_tensor']\n spatial_shape, batch_index, voxel_indices, spatial_indices, num_voxels = self._get_voxel_infos(x)\n\n pred_dicts = []\n for idx, head in enumerate(self.heads_list):\n pred_dict = head(x)\n pred_dicts.append(pred_dict)\n\n pred_dicts = self.generate_predicted_boxes(\n data_dict['batch_size'],\n pred_dicts, voxel_indices, spatial_shape\n )\n\n return pred_dicts\n" }, { "path": "data_generation/Grounded-Segment-Anything/voxelnext_3d_box/requirements.txt", "content": "numpy\ntorch\ntorchvision\neasydict\npyyaml\nopencv-python\npycocotools\nmatplotlib\nonnxruntime\nonnx" }, { "path": "data_generation/Grounded-Segment-Anything/voxelnext_3d_box/utils/centernet_utils.py", "content": "# This file is modified from https://github.com/tianweiy/CenterPoint\n\nimport torch\n\n\ndef _topk_1d(scores, batch_size, batch_idx, obj, K=40, nuscenes=False):\n # scores: (N, num_classes)\n topk_score_list = []\n topk_inds_list = []\n topk_classes_list = []\n\n for bs_idx in range(batch_size):\n batch_inds = batch_idx==bs_idx\n if obj.shape[-1] == 1 and not nuscenes:\n score = scores[batch_inds].permute(1, 0)\n topk_scores, topk_inds = torch.topk(score, K)\n topk_score, topk_ind = torch.topk(obj[topk_inds.view(-1)].squeeze(-1), K) #torch.topk(topk_scores.view(-1), K)\n else:\n score = obj[batch_inds].permute(1, 0)\n topk_scores, topk_inds = torch.topk(score, min(K, score.shape[-1]))\n topk_score, topk_ind = torch.topk(topk_scores.view(-1), min(K, topk_scores.view(-1).shape[-1]))\n\n topk_classes = (topk_ind // K).int()\n topk_inds = topk_inds.view(-1).gather(0, topk_ind)\n #print('topk_inds', topk_inds)\n\n if not obj is None and obj.shape[-1] == 1:\n topk_score_list.append(obj[batch_inds][topk_inds])\n else:\n topk_score_list.append(topk_score)\n topk_inds_list.append(topk_inds)\n topk_classes_list.append(topk_classes)\n\n topk_score = torch.stack(topk_score_list)\n topk_inds = torch.stack(topk_inds_list)\n topk_classes = torch.stack(topk_classes_list)\n\n return topk_score, topk_inds, topk_classes\n\ndef gather_feat_idx(feats, inds, batch_size, batch_idx):\n feats_list = []\n dim = feats.size(-1)\n _inds = inds.unsqueeze(-1).expand(inds.size(0), inds.size(1), dim)\n\n for bs_idx in range(batch_size):\n batch_inds = batch_idx==bs_idx\n feat = feats[batch_inds]\n feats_list.append(feat.gather(0, _inds[bs_idx]))\n feats = torch.stack(feats_list)\n return feats\n\n\ndef decode_bbox_from_voxels_nuscenes(batch_size, indices, obj, rot_cos, rot_sin,\n center, center_z, dim, vel=None, iou=None, point_cloud_range=None, voxel_size=None, voxels_3d=None,\n feature_map_stride=None, K=100, score_thresh=None, post_center_limit_range=None, add_features=None):\n batch_idx = indices[:, 0]\n spatial_indices = indices[:, 1:]\n scores, inds, class_ids = _topk_1d(None, batch_size, batch_idx, obj, K=K, nuscenes=True)\n\n center = gather_feat_idx(center, inds, batch_size, batch_idx)\n rot_sin = gather_feat_idx(rot_sin, inds, batch_size, batch_idx)\n rot_cos = gather_feat_idx(rot_cos, inds, batch_size, batch_idx)\n center_z = gather_feat_idx(center_z, inds, batch_size, batch_idx)\n dim = gather_feat_idx(dim, inds, batch_size, batch_idx)\n spatial_indices = gather_feat_idx(spatial_indices, inds, batch_size, batch_idx)\n\n if not add_features is None:\n add_features = gather_feat_idx(add_features, inds, batch_size, batch_idx) #for add_feature in add_features]\n\n if not isinstance(feature_map_stride, int):\n feature_map_stride = gather_feat_idx(feature_map_stride.unsqueeze(-1), inds, batch_size, batch_idx)\n\n angle = torch.atan2(rot_sin, rot_cos)\n xs = (spatial_indices[:, :, -1:] + center[:, :, 0:1]) * feature_map_stride * voxel_size[0] + point_cloud_range[0]\n ys = (spatial_indices[:, :, -2:-1] + center[:, :, 1:2]) * feature_map_stride * voxel_size[1] + point_cloud_range[1]\n\n box_part_list = [xs, ys, center_z, dim, angle]\n\n if not vel is None:\n vel = gather_feat_idx(vel, inds, batch_size, batch_idx)\n box_part_list.append(vel)\n\n if not iou is None:\n iou = gather_feat_idx(iou, inds, batch_size, batch_idx)\n iou = torch.clamp(iou, min=0, max=1.)\n\n final_box_preds = torch.cat((box_part_list), dim=-1)\n final_scores = scores.view(batch_size, K)\n final_class_ids = class_ids.view(batch_size, K)\n if not add_features is None:\n add_features = add_features.view(batch_size, K, add_features.shape[-1]) #for add_feature in add_features]\n\n assert post_center_limit_range is not None\n mask = (final_box_preds[..., :3] >= post_center_limit_range[:3]).all(2)\n mask &= (final_box_preds[..., :3] <= post_center_limit_range[3:]).all(2)\n\n if score_thresh is not None:\n mask &= (final_scores > score_thresh)\n\n ret_pred_dicts = []\n for k in range(batch_size):\n cur_mask = mask[k]\n cur_boxes = final_box_preds[k, cur_mask]\n cur_scores = final_scores[k, cur_mask]\n cur_labels = final_class_ids[k, cur_mask]\n cur_add_features = add_features[k, cur_mask] if not add_features is None else None\n cur_iou = iou[k, cur_mask] if not iou is None else None\n\n ret_pred_dicts.append({\n 'pred_boxes': cur_boxes,\n 'pred_scores': cur_scores,\n 'pred_labels': cur_labels,\n 'pred_ious': cur_iou,\n 'add_features': cur_add_features,\n })\n return ret_pred_dicts\n" }, { "path": "data_generation/Grounded-Segment-Anything/voxelnext_3d_box/utils/config.py", "content": "import yaml\nfrom easydict import EasyDict\n\ndef merge_new_config(config, new_config):\n if '_BASE_CONFIG_' in new_config:\n with open(new_config['_BASE_CONFIG_'], 'r') as f:\n try:\n yaml_config = yaml.safe_load(f, Loader=yaml.FullLoader)\n except:\n yaml_config = yaml.safe_load(f)\n config.update(EasyDict(yaml_config))\n\n for key, val in new_config.items():\n if not isinstance(val, dict):\n config[key] = val\n continue\n if key not in config:\n config[key] = EasyDict()\n merge_new_config(config[key], val)\n\n return config\n\ndef cfg_from_yaml_file(cfg_file, config):\n with open(cfg_file, 'r') as f:\n try:\n new_config = yaml.safe_load(f, Loader=yaml.FullLoader)\n except:\n new_config = yaml.safe_load(f)\n\n merge_new_config(config=config, new_config=new_config)\n\n return config\n\ncfg = EasyDict()\n" }, { "path": "data_generation/Grounded-Segment-Anything/voxelnext_3d_box/utils/image_projection.py", "content": "import torch\nimport numpy as np\nimport cv2\n\ndef get_data_info(info, cam_type):\n\n cam_info = info[cam_type]\n\n lidar2cam_r = np.linalg.inv(cam_info['sensor2lidar_rotation'])\n lidar2cam_t = cam_info[\n 'sensor2lidar_translation'] @ lidar2cam_r.T\n lidar2cam_rt = np.eye(4)\n lidar2cam_rt[:3, :3] = lidar2cam_r.T\n lidar2cam_rt[3, :3] = -lidar2cam_t\n intrinsic = cam_info['cam_intrinsic']\n viewpad = np.eye(4)\n viewpad[:intrinsic.shape[0], :intrinsic.shape[1]] = intrinsic\n lidar2img_rt = (viewpad @ lidar2cam_rt.T)\n\n return lidar2img_rt\n\n\ndef _proj_voxel_image(voxel_coords, lidar2img_rt, voxel_size, point_cloud_range):\n # voxel_coords [n ,4]\n # lidar2img_rt [4, 4]\n #x_input.indices [n, 4] [[0, Z, Y, Z]xn]\n\n voxel_coords = voxel_coords[:, [3,2,1]]\n device = voxel_coords.device\n lidar2img_rt = torch.Tensor(lidar2img_rt).to(device)\n # point_cloud_rangetensor([-51.2000, -51.2000, -5.0000, 51.2000, 51.2000, 3.0000])\n voxel_coords = voxel_coords * voxel_size.unsqueeze(0) + point_cloud_range[:3].unsqueeze(0)\n # (n, 4)\n voxel_coords = torch.cat([voxel_coords, torch.ones((voxel_coords.shape[0], 1), device=device)], dim=-1)\n points_image = torch.matmul(lidar2img_rt, voxel_coords.permute(1, 0)) #(voxel_coords @ lidar2img_rt).T\n # (4, n)\n depth = points_image[2:3] # (1, n)\n points_image = points_image[:2] / torch.maximum(depth, torch.ones_like(depth*1e-4))\n return points_image, depth\n\ndef _draw_image(points_image, image_path, depth):\n image = cv2.imread(image_path)\n points_image = points_image.int().cpu().numpy()\n for i in range(points_image.shape[1]):\n _point = points_image[:, i]\n if _point[0] > 0 and _point[1] > 0 and depth[0][i] >0:\n cv2.circle(image, tuple(_point), 1, (0,255,0), -1)\n #cv2.imwrite(\"image.png\", image)\n return image\n\ndef _draw_mask(image_path, mask, color=None):\n image = cv2.imread(image_path)\n h, w, _ = image.shape\n\n if color is None:\n color = np.random.random(3)\n\n image[mask] = image[mask] * color\n #cv2.imwrite(\"image_mask.png\", image)\n return image\n\ndef _draw_3dbox(box, lidar2img_rt, image, mask=None, color=None, output_path=\"image_box.png\"):\n #image = cv2.imread(image_path)\n h, w, _ = image.shape\n\n if color is None:\n color = np.random.random(3)\n if not mask is None:\n image[mask] = image[mask] * color\n\n center_x, center_y, center_z, H, W, Z, angle = box[:7]\n sin_angle, cos_angle = torch.sin(angle), torch.cos(angle)\n top1 = [center_x - (H/2 * cos_angle + W/2 * sin_angle), center_y - (H/2 * sin_angle + W/2 * cos_angle), center_z + Z/2]\n top2 = [center_x - (H/2 * cos_angle + W/2 * sin_angle), center_y + (H/2 * sin_angle + W/2 * cos_angle), center_z + Z/2]\n top3 = [center_x + (H/2 * cos_angle + W/2 * sin_angle), center_y + (H/2 * sin_angle + W/2 * cos_angle), center_z + Z/2]\n top4 = [center_x + (H/2 * cos_angle + W/2 * sin_angle), center_y - (H/2 * sin_angle + W/2 * cos_angle), center_z + Z/2]\n\n down1 = [center_x - (H/2 * cos_angle + W/2 * sin_angle), center_y - (H/2 * sin_angle + W/2 * cos_angle), center_z - Z/2]\n down2 = [center_x - (H/2 * cos_angle + W/2 * sin_angle), center_y + (H/2 * sin_angle + W/2 * cos_angle), center_z - Z/2]\n down3 = [center_x + (H/2 * cos_angle + W/2 * sin_angle), center_y + (H/2 * sin_angle + W/2 * cos_angle), center_z - Z/2]\n down4 = [center_x + (H/2 * cos_angle + W/2 * sin_angle), center_y - (H/2 * sin_angle + W/2 * cos_angle), center_z - Z/2]\n points = torch.Tensor([top1, top2, top3, top4, down1, down2, down3, down4, [center_x, center_y, center_z]]) # (8, 3)\n points = torch.cat([points, torch.ones((points.shape[0], 1), device=points.device)], dim=-1)\n points_image = torch.matmul(torch.Tensor(lidar2img_rt).to(points.device), points.permute(1, 0))\n depth = points_image[2:3] # (1, n)\n points_image = points_image[:2] / torch.maximum(depth, torch.ones_like(depth*1e-4))\n points_image = points_image.permute(1, 0).int().cpu().numpy() #(voxel_coords @ lidar2img_rt).T\n lines = [[0, 1], [1, 2], [2, 3], [3, 0], [4, 5], [5, 6], [6, 7], [7, 4], [0, 4], [1, 5], [2, 6], [3, 7]]\n\n cv2.circle(image, tuple(points_image[-1]), 3, (0, 255, 0), -1)\n\n for line in lines:\n cv2.line(image, tuple(points_image[line[0]]), tuple(points_image[line[1]]), tuple(color * 255), 2)\n #cv2.imwrite(output_path, image)\n return image\n\n\n\n\n\n\n\n\n" }, { "path": "data_generation/data_generation.py", "content": "\nimport base64\n\nimport io\nimport multiprocessing\nimport os\nimport random\nimport traceback\nfrom argparse import ArgumentParser\nfrom PIL import Image, ImageOps\nfrom multiprocessing import Process\nfrom typing import Tuple, List\nimport numpy as np\nimport requests\nimport time\nimport torch\nimport torch.nn.functional as F\nfrom PIL import Image\nfrom scipy.ndimage import label, find_objects, grey_dilation\nfrom torch.utils.data import Dataset, DataLoader\nfrom tqdm import tqdm\nfrom metrics.clip_similarity import ClipSimilarity\nfrom sdxl_p2p_pipeline import Prompt2PromptPipeline,Prompt2PromptImg2ImgPipeline,Prompt2PromptInpaintPipeline\nfrom PIL import Image, ImageDraw, ImageFont\nimport argparse\nimport copy\n\nfrom torchvision.ops import box_convert\nimport os, sys\nimport pandas as pd\nImage.MAX_IMAGE_PIXELS = 1000000000\nfrom PIL import Image\nfrom einops import rearrange, repeat\nimport numpy as np\n\n\nimport supervision as sv\n\n# segment anything\nfrom segment_anything import build_sam, SamPredictor \nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n# set to ignore the warning\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n# get INPAINTING for os.environ\ndo_inpainting = os.environ.get('INPAINTING', 'False')\n# convert to boolean\ndo_inpainting = do_inpainting.lower() in ['true', '1', 't', 'y', 'yes']\nif do_inpainting:\n import groundingdino.datasets.transforms as T\n from groundingdino.models import build_model\n from groundingdino.util import box_ops\n from groundingdino.util.slconfig import SLConfig\n from groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap\n from groundingdino.util.inference import annotate, predict\n\ndef generate_Contours_mask(pil_img):\n # Convert PIL image to OpenCV image (RGB to BGR)\n img_np = np.array(pil_img)\n \n if img_np.max() <= 1:\n img_np = (img_np * 255).astype('uint8')\n \n img_cv = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)\n img_cv = cv2.fastNlMeansDenoisingColored(img_cv, None, 60, 60, 7, 15)\n img_gray = cv2.cvtColor(img_cv, cv2.COLOR_BGR2GRAY)\n\n # Threshold the image to get binary image\n _, binary_mask = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY)\n\n # Find contours\n contours, _ = cv2.findContours(binary_mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n\n # Create an empty image to draw the contours\n mask = np.zeros((binary_mask.shape[0], binary_mask.shape[1]), dtype=np.uint8)\n\n # Fill each contour\n for contour in contours:\n # Calculate convex hull \n hull = cv2.convexHull(contour)\n \n # Draw it (filled) on the mask image\n cv2.drawContours(mask, [hull], -1, (255), -1)\n \n # Convert mask to PIL image\n mask_pil = Image.fromarray(mask)\n\n return mask_pil\n\ndef find_contours_number(mask_image: np.array):\n contours, _ = cv2.findContours(mask_image.astype('uint8'), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n return len(contours)\n\ndef check_mask_size(mask, min_size=0.01, max_size=0.9):\n total_pixels = mask.shape[0] * mask.shape[1]\n mask_pixels = np.count_nonzero(mask)\n percent = (mask_pixels / total_pixels)\n if min_size < percent < max_size:\n return True\n else:\n return False\n\ndef create_mask_image(image_source, detected_boxes):\n # Get the height and width of the source image\n h, w, _ = image_source.shape\n\n # Create meshgrid\n x = torch.arange(w).reshape(1, -1).expand(h, -1)\n y = torch.arange(h).reshape(-1, 1).expand(-1, w)\n\n # Convert the detected_boxes to the actual pixel values\n boxes = (detected_boxes * torch.Tensor([w, h, w, h])).type(torch.int)\n\n # Convert to 'xyxy' format\n xyxy = box_convert(boxes=boxes, in_fmt=\"cxcywh\", out_fmt=\"xyxy\")\n\n # Create a mask for each box without for-loop.\n masks = [((y >= box[1]) & (y <= box[3]) & (x >= box[0]) & (x <= box[2])).numpy() for box in xyxy]\n\n # Aggregate all masks\n mask = np.logical_or.reduce(masks, axis=0)\n\n # Convert numpy array to PIL image\n try:\n mask_img = Image.fromarray(mask)\n except:\n mask_img = None\n print(\"CAN NOT SAM\")\n\n return mask_img\ndef to_pil(image: torch.Tensor) -> Image.Image:\n image = 255.0 * rearrange(image.cpu().numpy(), \"c h w -> h w c\")\n image = Image.fromarray(image.astype(np.uint8))\n return image\n\ndef gd_load_image(image_path) -> Tuple[np.array, torch.Tensor]:\n transform = T.Compose(\n [\n T.RandomResize([800], max_size=1333),\n T.ToTensor(),\n T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n ]\n )\n if isinstance(image_path, str):\n image_source = Image.open(image_path).convert(\"RGB\")\n elif isinstance(image_path, Image.Image):\n image_source = image_path\n elif image_path is None:\n return None, None\n else:\n raise ValueError(\"image_path must be a string or a PIL Image\")\n image = np.asarray(image_source)\n image_transformed, _ = transform(image_source, None)\n return image, image_transformed\ndef load_image(image_path, target_size=512):\n # Load the image.\n # try:\n img = Image.open(image_path).convert('RGB')\n \n # Resize while maintaining aspect ratio.\n width, height = img.size\n if width > height:\n ratio = target_size / height\n new_width = int(ratio * width)\n img = img.resize((new_width, target_size))\n else:\n ratio = target_size / width\n new_height = int(ratio * height)\n img = img.resize((target_size, new_height))\n \n # Center-crop the image to 512x512.\n width, height = img.size\n left = (width - target_size) / 2\n top = (height - target_size) / 2\n right = (width + target_size) / 2\n bottom = (height + target_size) / 2\n img = img.crop((left, top, right, bottom))\n # except Exception as e:\n # print(e)\n # print(f'Image {image_path} cannot be opened')\n # # image = Image.new('RGB', (image_size, image_size), (255, 255, 255))\n # image = Image.open('1.jpg').convert('RGB') # temporal solution\n # image = image.resize((target_size, target_size), Image.Resampling.LANCZOS)\n # image = ImageOps.fit(image, (target_size, target_size), Image.Resampling.LANCZOS, centering=(0.5, 0.5))\n return img\ndef compare_prompts(prompt1, prompt2):\n words1 = prompt1.split(' ')\n words2 = prompt2.split(' ')\n difference = [str2 for str1, str2 in zip(words1, words2) if str1 != str2]\n return difference\n\ndef generate_images(args,pipe, prompts, init_image, cross_attention_kwargs,cond_mask_img=[],cond_whole_mask_img=[],cond_countor_mask_img=[],countor_number_list=[]):\n\n\n try:\n if args.img2img:\n # random choice num_inference_steps in 2 and 4\n num_inference_steps = random.choice([3, 5, 8])\n float_guidance_scale = random.uniform(0.4, 0.8)\n image = pipe(prompts, image=init_image, num_inference_steps=num_inference_steps, strength=0.6, guidance_scale=float_guidance_scale,cross_attention_kwargs=cross_attention_kwargs, output_type = 'pt' ).images\n elif args.do_inpainting and len(cond_mask_img) == len(init_image) and len(cond_mask_img) == len(prompts) :\n # step to random from 6 to 10\n num_inference_steps = random.choice([10,14])\n float_guidance_scale = random.choice([0.0, 0.2,0.4,0.6])\n soft_mask = random.choice([0.0, 0.1,0.3,0.5,0.7,0.8])\n # soft_mask = 0.1\n # soft_mask = None\n # use cond_mask_img or cond_whole_mask_img\n # mask_list =random.choice([cond_mask_img,cond_whole_mask_img])\n # get True in 50% randomly\n if len(countor_number_list) > 0:\n mask_image = [ ]\n temp_image = [ ]\n for idx, each in enumerate(countor_number_list):\n if each< 150 and each > 0 :\n mask_image.append(cond_mask_img[idx])\n temp_image.append(cond_countor_mask_img[idx])\n elif each >=150 and each < 500:\n mask_image.append(cond_countor_mask_img[idx])\n temp_image.append(cond_whole_mask_img[idx])\n else:\n mask_image.append(cond_mask_img[idx])\n temp_image.append(cond_mask_img[idx])\n\n using_soft_mask = random.choice([True, False])\n if using_soft_mask:\n image = pipe(prompts, image=init_image, mask_image=mask_image, num_inference_steps=num_inference_steps, guidance_scale=float_guidance_scale, temp_mask = temp_image ,cross_attention_kwargs=cross_attention_kwargs,soft_mask=soft_mask,output_type = 'pt' ).images\n else:\n image = pipe(prompts, image=init_image, mask_image=mask_image, num_inference_steps=num_inference_steps, guidance_scale=float_guidance_scale ,cross_attention_kwargs=cross_attention_kwargs,output_type = 'pt' ).images\n\n\n \n else:\n use_contour = random.choice([True, False])\n if use_contour:\n image = pipe(prompts, image=init_image, mask_image=cond_countor_mask_img, num_inference_steps=num_inference_steps, guidance_scale=float_guidance_scale ,cross_attention_kwargs=cross_attention_kwargs,output_type = 'pt' ).images\n else:\n temp_mask_type = random.choice([True, False])\n if temp_mask_type:\n temp_mask = cond_whole_mask_img\n else: \n temp_mask = cond_countor_mask_img\n\n image = pipe(prompts, image=init_image, mask_image=cond_mask_img, num_inference_steps=num_inference_steps, guidance_scale=float_guidance_scale, temp_mask = temp_mask ,cross_attention_kwargs=cross_attention_kwargs,soft_mask=soft_mask,output_type = 'pt' ).images\n\n\n else:\n # step to random from 1 to 4\n num_inference_steps = random.randint(2, 4)\n float_guidance_scale = random.uniform(0.4, 1.0)\n image = pipe(prompts, num_inference_steps=num_inference_steps, guidance_scale=float_guidance_scale,cross_attention_kwargs=cross_attention_kwargs, output_type = 'pt' ).images\n except Exception as e:\n traceback.print_exc()\n print(\"cross_attention_kwargs:\", cross_attention_kwargs)\n print(\"prompt:\", prompts)\n return None\n return image\ndef generate_contour_mask(mask):\n # Convert the mask to grayscale and then to uint8 format\n mask_gray = cv2.cvtColor(mask, cv2.COLOR_RGB2GRAY)\n mask_uint8 = mask_gray.astype('uint8')\n\n # Find contours using OpenCV\n contours, _ = cv2.findContours(mask_uint8, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\n # Create an empty mask to fill the contours\n contour_mask = np.zeros_like(mask_uint8)\n\n # Fill the contours on the mask\n for contour in contours:\n cv2.drawContours(contour_mask, [contour], 0, (255), thickness=cv2.FILLED)\n\n return contour_mask \n# detect object using grounding DINO\ndef detect(image, text_prompt, model, box_threshold = 0.3, text_threshold = 0.25):\n boxes, logits, phrases = predict(\n model=model, \n image=image, \n caption=text_prompt,\n box_threshold=box_threshold,\n text_threshold=text_threshold\n )\n return boxes \n\ndef segment(image, sam_model, boxes,device):\n sam_model.set_image(image)\n H, W, _ = image.shape\n boxes_xyxy = box_ops.box_cxcywh_to_xyxy(boxes) * torch.Tensor([W, H, W, H])\n\n transformed_boxes = sam_model.transform.apply_boxes_torch(boxes_xyxy.to(device), image.shape[:2])\n masks, _, _ = sam_model.predict_torch(\n point_coords = None,\n point_labels = None,\n boxes = transformed_boxes,\n multimask_output = False,\n )\n return masks.cpu()\n \n@torch.no_grad()\ndef save_tsv(args, shard_id, shard, device, global_data):\n random.seed(args.seed)\n torch.manual_seed(args.seed)\n torch.cuda.set_device(device)\n image_size = args.image_size\n # background_images = load_json(args.background_images)\n # CLip score\n clip_similarity = ClipSimilarity(args.clip_model).cuda(device)\n # SDXL_ P2P Pipeline:\n groundingdino_model = None\n sam_predictor = None\n if args.do_inpainting:\n print('Using Prompt2PromptInpaintingPipeline')\n pipe = Prompt2PromptInpaintPipeline.from_pretrained(args.pipeline_ckpt,torch_dtype=torch.float16, variant=\"fp16\").to(device)\n groundingdino_model = load_groundingdino_model(args.groundingdino_config_file, args.groundingdino_checkpoint, device)\n sam_predictor = SamPredictor(build_sam(checkpoint=args.sam_checkpoint).to(device))\n elif args.img2img:\n print('Using Prompt2PromptImg2ImgPipeline')\n pipe = Prompt2PromptImg2ImgPipeline.from_pretrained(args.pipeline_ckpt,torch_dtype=torch.float16, variant=\"fp16\").to(device)\n else:\n print('Using Prompt2PromptPipeline')\n pipe = Prompt2PromptPipeline.from_pretrained(args.pipeline_ckpt,torch_dtype=torch.float16, variant=\"fp16\").to(device)\n pipe.unet.config.addition_embed_type =None\n pipe.set_progress_bar_config(disable=True)\n try:\n pipe.safety_checker = lambda images, clip_input: (images, False)\n except Exception as e:\n print(\"Error: \",e)\n pass\n # pipe.eval()\n\n\n cnt = 0\n image_paths = []\n prompt = []\n new_prompt = []\n edit= []\n data_path= []\n print(\"=====================Strat training========================\")\n for s_data in tqdm(shard):\n if args.do_inpainting:\n init_image,global_idx,gd_image,gd_image_transformed= s_data\n else:\n init_image,global_idx = s_data\n if init_image is None and global_idx is None:\n continue\n cond_img=[]\n cond_mask_img=[]\n cond_whole_mask_img=[]\n cond_countor_mask_img=[]\n countor_number_list=[]\n replace_countor_number_list = []\n prompts=[]\n edit_instruction = []\n replace_prompt=[]\n replace_text_prompt =[]\n cond_text_prompt = []\n replace_cond_img = [] # batch generate do not support the replace edit\n replace_cond_mask_img = [] # batch generate do not support the replace edit\n replace_cond_whole_mask_img = [] # batch generate do not support the replace edit\n replace_cond_countor_mask_img = [] # batch generate do not support the replace edit\n replace_edit_instruction =[]\n for num, (img,idx) in enumerate(zip(init_image,global_idx)):\n if args.img2img or args.do_inpainting:\n if img is None:\n continue\n data_temp = global_data[idx]\n input_text_length = len(data_temp['input_text'].split(' '))\n output_text_length = len(data_temp['output_text'].split(' '))\n if input_text_length<=60 and output_text_length <= 60:\n if args.do_replace:\n if input_text_length == output_text_length:\n replace_prompt.append([data_temp['input_text'], data_temp['output_text']])\n if args.img2img:\n replace_cond_img.append(img)\n elif args.do_inpainting: \n # (image, text_prompt, model, box_threshold = 0.3, text_threshold = 0.25)\n text_prompt = data_temp['edit_object']\n # if text_prompt[-1] != '.':\n # text_prompt += '.'\n try:\n if'NONE' in text_prompt:\n # make mask_img as the mask of whole image in size of args.image_size\n mask_img = Image.new('L', (args.image_size, args.image_size), color=255)\n mask_contour = mask_img\n mask_box = mask_img\n contour_number = 0\n else:\n detect_boxes = detect(gd_image_transformed[num], text_prompt, groundingdino_model,box_threshold = args.gd_box_threshold, text_threshold = args.gd_text_threshold)\n mask_box = create_mask_image(gd_image[num], detect_boxes)\n if mask_box is None:\n prompts = prompts[:-2]\n edit_instruction = edit_instruction[:-1]\n continue\n segmented_frame_masks = segment(gd_image[num], sam_predictor, boxes=detect_boxes,device=device)\n merged_mask = torch.max(segmented_frame_masks, 0)[0][0]\n mask = merged_mask.cpu().numpy()\n # inverted_mask = ((1 - mask) * 255).astype(np.uint8)\n # generate_contour_mask(mask)\n mask_img = Image.fromarray(mask)\n if not check_mask_size(mask, min_size=0.01, max_size=0.9):\n replace_prompt = replace_prompt[:-2]\n continue\n contour_number = find_contours_number(mask)\n if contour_number > 500:\n replace_prompt = replace_prompt[:-2]\n continue\n mask_contour = generate_Contours_mask(mask_img)\n except Exception as e:\n # wipe out the append prompts and edit_instruction\n replace_prompt = replace_prompt[:-2]\n print(e)\n traceback.print_exc()\n continue\n \n replace_cond_mask_img.append(mask_img)\n replace_cond_whole_mask_img.append(mask_box)\n replace_cond_countor_mask_img.append(mask_contour)\n replace_cond_img.append(img)\n replace_text_prompt.append(text_prompt)\n replace_countor_number_list.append(contour_number)\n replace_countor_number_list.append(contour_number)\n else:\n replace_cond_img.append(None)\n replace_edit_instruction.append(data_temp['edit_instruction'])\n\n else:\n prompts.extend([data_temp['input_text'], data_temp['output_text']])\n edit_instruction.append(data_temp['edit_instruction'])\n if args.img2img:\n cond_img.append(img)\n cond_img.append(img)\n elif args.do_inpainting:\n text_prompt = data_temp['edit_object']\n # if text_prompt[-1] != '.':\n # text_prompt += '.'\n try:\n if'NONE' in text_prompt:\n # make mask_img as the mask of whole image in size of args.image_size\n mask_img = Image.new('L', (args.image_size, args.image_size), color=255)\n mask_contour = mask_img\n mask_box = mask_img\n contour_number = 0\n else:\n detect_boxes = detect(gd_image_transformed[num], text_prompt, groundingdino_model,box_threshold = args.gd_box_threshold, text_threshold = args.gd_text_threshold)\n mask_box = create_mask_image(gd_image[num], detect_boxes)\n if mask_box is None:\n prompts = prompts[:-2]\n edit_instruction = edit_instruction[:-1]\n continue\n segmented_frame_masks = segment(gd_image[num], sam_predictor, boxes=detect_boxes,device=device)\n merged_mask = torch.max(segmented_frame_masks, 0)[0][0]\n mask = merged_mask.cpu().numpy()\n # inverted_mask = ((1 - mask) * 255).astype(np.uint8)\n mask_img = Image.fromarray(mask)\n if not check_mask_size(mask, min_size=0.01, max_size=0.9):\n prompts = prompts[:-2]\n edit_instruction = edit_instruction[:-1]\n continue\n contour_number = find_contours_number(mask)\n if contour_number > 500:\n prompts = prompts[:-2]\n edit_instruction = edit_instruction[:-1]\n continue\n mask_contour = generate_Contours_mask(mask_img)\n except Exception as e:\n # wipe out the append prompts and edit_instruction\n prompts = prompts[:-2]\n edit_instruction = edit_instruction[:-1]\n print(e)\n traceback.print_exc()\n continue\n cond_mask_img.append(mask_img)\n cond_mask_img.append(mask_img)\n cond_whole_mask_img.append(mask_box)\n cond_whole_mask_img.append(mask_box)\n cond_countor_mask_img.append(mask_contour)\n cond_countor_mask_img.append(mask_contour)\n cond_img.append(img)\n cond_img.append(img)\n cond_text_prompt.append(text_prompt)\n countor_number_list.append(contour_number)\n countor_number_list.append(contour_number)\n \n else:\n prompts.extend([data_temp['input_text'], data_temp['output_text']])\n edit_instruction.append(data_temp['edit_instruction'])\n if args.img2img:\n cond_img.append(img)\n cond_img.append(img)\n elif args.do_inpainting:\n text_prompt = data_temp['edit_object']\n # if text_prompt[-1] != '.':\n # text_prompt += '.'\n try:\n if'NONE' in text_prompt:\n # make mask_img as the mask of whole image in size of args.image_size\n mask_img = Image.new('L', (args.image_size, args.image_size), color=255)\n mask_contour = mask_img\n mask_box = mask_img\n contour_number = 0\n else:\n detect_boxes = detect(gd_image_transformed[num], text_prompt, groundingdino_model,box_threshold = args.gd_box_threshold, text_threshold = args.gd_text_threshold)\n mask_box = create_mask_image(gd_image[num], detect_boxes)\n if mask_box is None:\n prompts = prompts[:-2]\n edit_instruction = edit_instruction[:-1]\n continue\n segmented_frame_masks = segment(gd_image[num], sam_predictor, boxes=detect_boxes,device=device)\n merged_mask = torch.max(segmented_frame_masks, 0)[0][0]\n mask = merged_mask.cpu().numpy()\n # inverted_mask = ((1 - mask) * 255).astype(np.uint8)\n mask_img = Image.fromarray(mask)\n if not check_mask_size(mask, min_size=0.01, max_size=0.9):\n prompts = prompts[:-2]\n edit_instruction = edit_instruction[:-1] \n continue\n contour_number = find_contours_number(mask)\n if contour_number > 500 or contour_number< 4:\n prompts = prompts[:-2]\n edit_instruction = edit_instruction[:-1]\n continue\n mask_contour = generate_Contours_mask(mask_img)\n except Exception as e:\n # wipe out the append prompts and edit_instruction\n prompts = prompts[:-2]\n edit_instruction = edit_instruction[:-1]\n print(e)\n traceback.print_exc()\n continue\n\n cond_mask_img.append(mask_img)\n cond_mask_img.append(mask_img)\n cond_whole_mask_img.append(mask_box)\n cond_whole_mask_img.append(mask_box)\n cond_countor_mask_img.append(mask_contour)\n cond_countor_mask_img.append(mask_contour)\n cond_img.append(img)\n cond_img.append(img)\n cond_text_prompt.append(text_prompt)\n countor_number_list.append(contour_number)\n countor_number_list.append(contour_number)\n else:\n print(\"length input text: \", input_text_length)\n print(\"length output text: \", output_text_length)\n\n if cnt % 1000 == 0:\n # close previous file if any\n # if cnt > 0:\n # f.close()\n # f = open(os.path.join(args.output_dir, f\"cnt_{args.data_split_start}_{args.data_split_end}_{args.machine_id}_{shard_id}_{cnt // 1000}.tsv\"), \"w\", encoding='utf-8')\n folder = os.path.join(args.output_dir, f\"cnt_{args.data_split_start}_{args.data_split_end}_{args.machine_id}_{shard_id}_{cnt // 1000}\")\n if not os.path.exists(folder):\n os.makedirs(folder,exist_ok=True)\n\n if cnt > 0:\n pd.DataFrame(\n {\n # 'image_paths': image_paths,\n 'data_path': data_path, \n 'prompt': prompt,\n 'new_prompt': new_prompt,\n 'edit': edit\n }\n ).to_csv(os.path.join(args.output_dir, f\"cnt_{args.data_split_start}_{args.data_split_end}_{args.machine_id}_{shard_id}_{cnt // 1000}.csv\"), index=False)\n # image_paths = []\n data_path= []\n prompt = []\n new_prompt = []\n edit = []\n # f = open(os.path.join(args.output_dir, f\"cnt_{args.machine_id}_{shard_id}_{cnt // 1000}.tsv\"), \"w\", encoding='utf-8')\n \n \n # cnt += 1\n # results = [{}]*args.batch_size\n new_results ={\n i:{}\n for i in range(args.batch_size)\n }\n # throw error if the length of edit_instruction + replace_edit_instruction != batch_size\n # assert len(edit_instruction) + len(replace_edit_instruction) == args.batch_size\n start_count = 0\n if len(replace_edit_instruction) > 0:\n start_count = len(replace_edit_instruction)\n # cnt+= args.batch_size\n while len(new_results[0]) < args.n_samples:\n seed = torch.randint(1 << 32, ()).item()\n if seed in new_results:\n continue\n # set random seed\n torch.manual_seed(seed)\n torch.cuda.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n random.seed(seed)\n np.random.seed(seed)\n\n\n p2p_threshold = args.min_p2p + torch.rand(()).item() * (args.max_p2p - args.min_p2p)\n cfg_scale = args.min_cfg + torch.rand(()).item() * (args.max_cfg - args.min_cfg)\n\n # image = pipe(prompts, num_inference_steps=2, guidance_scale=0.0,cross_attention_kwargs=cross_attention_kwargs,)\n\n # do replace one by one:\n if len(replace_prompt)>0 and args.do_replace:\n # for replace_p, replace_img, replace_edit in zip(replace_prompt, replace_cond_img, replace_edit_instruction):# make it enumerate\n\n for idx, (replace_p, replace_img, replace_edit) in enumerate(zip(replace_prompt, replace_cond_img, replace_edit_instruction)):\n cross_replace = compare_prompts(replace_p[0],replace_p[1])\n if len(cross_replace)<=5:\n cross_replace = ' '.join(cross_replace)\n n_cross_replace = {\"default_\": 1.0, str(cross_replace): p2p_threshold}\n else:\n n_cross_replace = p2p_threshold\n cross_attention_kwargs = {\n \"edit_type\": \"replace\",\n \"n_self_replace\": p2p_threshold,\n \"n_cross_replace\": n_cross_replace ,\n }\n if args.do_inpainting and len(replace_cond_mask_img) == 0 :\n continue\n image = generate_images(args,pipe, replace_p, replace_img, cross_attention_kwargs,replace_cond_mask_img,cond_whole_mask_img,replace_cond_countor_mask_img,replace_countor_number_list)\n if image is None:\n print(\"replace_p\",replace_p)\n continue\n original_image,edit_image = image[0],image[1]\n clip_sim_0, clip_sim_1, clip_sim_dir, clip_sim_image,dinov2_sim,ssim = clip_similarity(\n original_image.unsqueeze(0), edit_image.unsqueeze(0), [replace_p[0]], [replace_p[1]]\n )\n new_results[idx][seed] = dict(\n image_0=to_pil(original_image),\n image_1=to_pil(edit_image),\n p2p_threshold=p2p_threshold,\n clip_sim_0=clip_sim_0[0].item(),\n clip_sim_1=clip_sim_1[0].item(),\n clip_sim_dir=clip_sim_dir[0].item(),\n clip_sim_image=clip_sim_image[0].item(),\n dinov2_sim = dinov2_sim[0].item(),\n ssim = ssim,\n caption=replace_p[0],\n new_caption=replace_p[1],\n edit_instance= replace_edit\n )\n if args.do_inpainting:\n new_results[num_idx+start_count][seed]['img_mask'] = replace_cond_mask_img[0]\n new_results[num_idx+start_count][seed]['edit_object'] = replace_text_prompt[idx]\n # batch refine:\n \n cross_attention_kwargs = {\n \"edit_type\": \"refine\",\n \"n_self_replace\": p2p_threshold,\n \"n_cross_replace\": p2p_threshold\n }\n if len(prompts)>0:\n if args.do_inpainting and len(cond_mask_img) == 0 :\n break\n # if args.img2img:\n # if isinstance(cond_img, list):\n # if cond_img[0].shape[0] == 3:\n # cond_img = torch.stack(cond_img)\n # else:\n # cond_img = torch.cat(cond_img)\n image = generate_images(args,pipe, prompts, cond_img, cross_attention_kwargs,cond_mask_img,cond_whole_mask_img,cond_countor_mask_img,countor_number_list)\n # spilt image into pairs, the first is original image, the second is edited image\n if image is None:\n break\n else:\n break\n # make it enumerate\n # for i in range(0,len(image),2):\n for num_idx, i in enumerate(range(0,len(image),2)):\n original_image,edit_image = image[i],image[i+1]\n clip_sim_0, clip_sim_1, clip_sim_dir, clip_sim_image,dinov2_sim,ssim = clip_similarity(\n original_image.unsqueeze(0), edit_image.unsqueeze(0), [prompts[i]], [prompts[i+1]]\n )\n new_results[num_idx+start_count][seed] = dict(\n image_0=to_pil(original_image),\n image_1=to_pil(edit_image),\n p2p_threshold=p2p_threshold,\n clip_sim_0=clip_sim_0[0].item(),\n clip_sim_1=clip_sim_1[0].item(),\n clip_sim_dir=clip_sim_dir[0].item(),\n clip_sim_image=clip_sim_image[0].item(),\n dinov2_sim = dinov2_sim[0].item(),\n ssim = ssim,\n caption=prompts[i],\n new_caption=prompts[i+1],\n edit_instance= edit_instruction[i//2],\n )\n if args.do_inpainting:\n new_results[num_idx+start_count][seed]['img_mask'] = cond_mask_img[i]\n new_results[num_idx+start_count][seed]['edit_object'] = cond_text_prompt[i//2]\n\n\n for re_idx,(k,result) in enumerate(new_results.items()):\n cnt+=1\n metadata = [\n (result_sample[\"clip_sim_dir\"], seed)\n for seed, result_sample in result.items()\n if result_sample[\"clip_sim_image\"] >= args.clip_img_threshold\n and result_sample[\"clip_sim_dir\"] >= args.clip_dir_threshold\n and result_sample[\"clip_sim_0\"] >= args.clip_threshold\n and result_sample[\"clip_sim_1\"] >= args.clip_threshold\n and result_sample[\"dinov2_sim\"] >= args.dinov2_sim_threshold\n # and (result_sample[\"dinov2_sim\"] <= args.max_dinov2_sim_threshold or result_sample[\"clip_sim_dir\"]>=args.clip_dir_threshold+0.05 )\n # and (result_sample[\"ssim\"] <= args.max_ssim_threshold or result_sample[\"clip_sim_dir\"]>=args.clip_dir_threshold+0.1 )\n\n ] \n metadata.sort(reverse=True)\n\n if len(metadata) > 0:\n samples = metadata[: args.max_out_samples]\n if args.do_inpainting:\n if len(samples) < args.max_out_samples:\n continue\n prompt_dir = os.path.join(folder, str(cnt).zfill(7))\n prompt.append(result[metadata[0][1]]['caption'])\n new_prompt.append(result[metadata[0][1]]['new_caption'])\n edit.append(result[metadata[0][1]]['edit_instance'])\n # image_paths.append(metadata[0]['input_image'])\n data_path.append(prompt_dir)\n os.makedirs(prompt_dir, exist_ok=True)\n\n for _, seed in samples:\n result_sample = result[seed]\n image_0 = result_sample.pop(\"image_0\")\n image_1 = result_sample.pop(\"image_1\")\n if args.do_inpainting:\n img_mask = result_sample.pop(\"img_mask\")\n try:\n image_0.save(os.path.join(prompt_dir, f\"{seed}_0.jpg\"), quality=100)\n image_1.save(os.path.join(prompt_dir, f\"{seed}_1.jpg\"), quality=100)\n if args.do_inpainting:\n img_mask.save(os.path.join(prompt_dir, f\"{seed}_mask.jpg\"), quality=100)\n except Exception as e:\n print(e)\n print(f'Image cannot be saved, sleep 5s')\n time.sleep(5)\n try: \n image_0.save(os.path.join(prompt_dir, f\"{seed}_0.jpg\"), quality=100)\n image_1.save(os.path.join(prompt_dir, f\"{seed}_1.jpg\"), quality=100)\n if args.do_inpainting:\n img_mask.save(os.path.join(prompt_dir, f\"{seed}_mask.jpg\"), quality=100)\n except Exception as e:\n print(e)\n print('Image cannot be saved again, skipping...')\n continue \n with open(os.path.join(prompt_dir, f\"metadata.jsonl\"), \"a\") as fp:\n fp.write(f\"{json.dumps(dict(seed=seed, **result_sample))}\\n\")\n\n# class OpenImageDataset(Dataset):\n# def __init__(self, url_data):\n# self.data = url_data\n\n# def __len__(self):\n# return len(self.data)\n\n# def __getitem__(self, idx):\n# try:\n# # items = self.data[idx].split(',')\n# # image = Image.open(requests.get(items[2], stream=True).raw).convert('RGB')\n# data_temp = self.data[idx]\n# img_path = data_temp['input_image']\n# global_idx = data_temp['global_idx']\n# # try:\n# image = Image.open(img_path).convert('RGB')\n# # except Exception as e:\n# # print(e)\n# # print(f'Image {img_path} cannot be opened')\n# # # image = Image.new('RGB', (224, 224), (255, 255, 255))\n# # image = Image.open('1.jpg ').convert('RGB') # temporal solution\n \n\n# # caption\n# width, height = image.size\n# shortest_side = min(width, height)\n# left = (width - shortest_side) // 2\n# top = (height - shortest_side) // 2\n# right = left + shortest_side\n# bottom = top + shortest_side\n# image = image.crop((left, top, right, bottom))\n# return image,global_idx\n# except:\n# return None\n\nimport requests\nfrom PIL import Image\nfrom torch.utils.data import Dataset\nclass NoneImageDataset(Dataset):\n def __init__(self, url_data):\n self.data = url_data\n\n def __len__(self):\n return len(self.data)\n\n def __getitem__(self, idx):\n data_temp = self.data[idx]\n img_path = data_temp['input_image']\n global_idx = data_temp['global_idx']\n \n return img_path, global_idx\n\nclass OpenImageDataset(Dataset):\n def __init__(self, url_data):\n self.data = url_data\n\n def __len__(self):\n return len(self.data)\n\n def __getitem__(self, idx):\n data_temp = self.data[idx]\n img_path = data_temp['input_image']\n global_idx = data_temp['global_idx']\n\n try:\n image = load_image(img_path)\n except Exception as e:\n print(e)\n print(f'Image {img_path} cannot be opened')\n image = None\n \n if image is not None:\n # caption\n width, height = image.size\n shortest_side = min(width, height)\n left = (width - shortest_side) // 2\n top = (height - shortest_side) // 2\n right = left + shortest_side\n bottom = top + shortest_side\n image = image.crop((left, top, right, bottom))\n \n return image, global_idx\n\nclass RegionDataset(Dataset):\n def __init__(self, url_data):\n self.data = url_data\n\n def __len__(self):\n return len(self.data)\n\n def __getitem__(self, idx):\n data_temp = self.data[idx]\n img_path = data_temp['input_image']\n global_idx = data_temp['global_idx']\n\n try:\n image = load_image(img_path)\n except Exception as e:\n print(e)\n print(f'Image {img_path} cannot be opened')\n image = None\n \n if image is not None:\n # caption\n width, height = image.size\n shortest_side = min(width, height)\n left = (width - shortest_side) // 2\n top = (height - shortest_side) // 2\n right = left + shortest_side\n bottom = top + shortest_side\n image = image.crop((left, top, right, bottom))\n gd_image_source, image_transformed = gd_load_image(image)\n \n return image, global_idx,gd_image_source, image_transformed\n\n\n# def load_images(image_path, image_size=384):\n# try:\n# image = Image.open(image_path).convert('RGB')\n# image = image.resize((image_size, image_size), Image.Resampling.LANCZOS)\n# image = ImageOps.fit(image, (image_size, image_size), Image.Resampling.LANCZOS, centering=(0.5, 0.5))\n# except Exception as e:\n# print(e)\n# print(f'Image {image_path} cannot be opened')\n# # image = Image.new('RGB', (image_size, image_size), (255, 255, 255))\n# image = Image.open('1.jpg').convert('RGB') # temporal solution\n# image = image.resize((image_size, image_size), Image.Resampling.LANCZOS)\n# image = ImageOps.fit(image, (image_size, image_size), Image.Resampling.LANCZOS, centering=(0.5, 0.5))\n \n# return np.array(image)\n\n# def collate_fn(batch):\n# return batch[0] if batch is not None else None\n\ndef collate_fn(batch):\n images = []\n labels = []\n gd_image_source = []\n image_transformed = []\n if batch is None:\n return None,None\n # elif batch is not None and len(batch) ==1:\n # return batch[0]\n else:\n for item in batch:\n if item is None:\n continue\n if len(item) == 2:\n img, lbl = item\n images.append(img)\n labels.append(lbl)\n else:\n img, lbl, gd_img, gd_img_tran = item\n images.append(img)\n labels.append(lbl)\n gd_image_source.append(gd_img)\n image_transformed.append(gd_img_tran)\n if len(gd_image_source) == 0:\n return images, labels\n else:\n return images, labels,gd_image_source,image_transformed\n \n# def collate_fn(batch):\n # # Handle the condition where getitem returns None\n # batch = [item for item in batch if item is not None]\n \n # # separate the images and the labels \n # images, labels = zip(*batch)\n\n # # pad images if they are not of same size and stack all images & labels\n # images = torch.stack(images)\n # labels = torch.stack(labels)\n\n # return images, labels\n\n\nimport json\nimport random\n\ndef load_jsonl(file_path):\n data = []\n with open(file_path, 'r', encoding='utf8') as f:\n for line in f:\n data.append(json.loads(line))\n return data\ndef load_json(file_path):\n with open(file_path, 'r', encoding='utf8') as f:\n return json.load(f)\ndef load_groundingdino_model(model_config_path, model_checkpoint_path, device):\n args = SLConfig.fromfile(model_config_path)\n args.device = device\n model = build_model(args)\n checkpoint = torch.load(model_checkpoint_path)\n load_res = model.load_state_dict(clean_state_dict(checkpoint[\"model\"]), strict=False)\n # print(load_res)\n _ = model.eval()\n model = model.to(device)\n return model\ndef main():\n \"\"\"Parse commandline arguments.\"\"\"\n parser = ArgumentParser()\n parser.add_argument('--data-dir', type=str,\n default='/path/to/image_ids_and_rotation.csv')\n parser.add_argument('--background_images', type=str, default='image.csv')\n parser.add_argument('--img2img',action='store_true')\n parser.add_argument('--do_replace',action='store_true')\n parser.add_argument('--do_inpainting',action='store_true')\n parser.add_argument('--groundingdino_checkpoint',type=str, default='../../model2/grounding_dino/groundingdino_swinb_cogcoor.pthc')\n parser.add_argument('--groundingdino_config_file',type=str, default='Grounded-Segment-Anything/GroundingDINO/groundingdino/config/GroundingDINO_SwinB.py')\n parser.add_argument('--sam_checkpoint',type=str, default='../../model2/sam/sam_vit_h_4b8939.pth')\n parser.add_argument('--gd_box_threshold',type=float, default=0.3)\n parser.add_argument('--gd_text_threshold',type=float, default=0.25)\n\n\n parser.add_argument('--output-dir', type=str, default='/path/to/output-dir/')\n parser.add_argument('--num-process', type=int, default=8)\n parser.add_argument('--cuda-device', nargs='+', type=int,default=[0, 1, 2, 3,4,5,6,7] )\n parser.add_argument('--num-machine', type=int, default=1)\n parser.add_argument('--machine-id', type=int, default=0)\n\n parser.add_argument('--max-seq-len', type=int, default=None)\n parser.add_argument('--max-new-tokens', type=int, default=10)\n\n parser.add_argument('--pipeline_ckpt', type=str, default=\"sdxl-turbo\")\n parser.add_argument('--clip_model', type=str, default=\"ViT-L-14.pt\")\n\n \n parser.add_argument('--top-k', type=int, default=50)\n parser.add_argument('--top-p', type=float, default=0.95)\n parser.add_argument('--batch_size', type=int, default=4)\n parser.add_argument('--repetition-penalty', type=float, default=1.0)\n parser.add_argument('--no-repeat-ngram-size', type=int, default=0)\n parser.add_argument('--n_samples', type=float, default=10)\n parser.add_argument(\n \"--min-p2p\",\n type=float,\n default=0.1,\n help=\"Min prompt2prompt threshold (portion of denoising for which to fix self attention maps).\",\n )\n parser.add_argument(\n \"--max-p2p\",\n type=float,\n default=0.9,\n help=\"Max prompt2prompt threshold (portion of denoising for which to fix self attention maps).\",\n )\n parser.add_argument(\n \"--max-out-samples\",\n type=int,\n default=4,\n help=\"Max number of output samples to save per prompt (after CLIP filtering).\",\n )\n parser.add_argument(\n \"--clip-threshold\",\n type=float,\n default=0.2,\n help=\"CLIP threshold for text-image similarity of each image.\",\n )\n parser.add_argument(\n \"--clip-dir-threshold\",\n type=float,\n default=0.2,\n help=\"Directional CLIP threshold for similarity of change between pairs of text and pairs of images.\",\n )\n parser.add_argument(\n \"--min-cfg\",\n type=float,\n default=7.5,\n help=\"Min classifier free guidance scale.\",\n )\n parser.add_argument(\n \"--max-cfg\",\n type=float,\n default=15,\n help=\"Max classifier free guidance scale.\",\n )\n parser.add_argument(\n \"--clip-img-threshold\",\n type=float,\n default=0.8,\n help=\"CLIP threshold for image-image similarity.\",\n )\n parser.add_argument(\n \"--dinov2-sim-threshold\",\n type=float,\n default=0.3,\n help=\"DINOv2 threshold for image-image similarity.\",\n )\n # max_dinov2_sim_threshold\n parser.add_argument(\n \"--max-dinov2-sim-threshold\",\n type=float,\n default=0.9,\n help=\"Max DINOv2 threshold for image-image similarity.\",\n )\n parser.add_argument(\n \"--max-ssim-threshold\",\n type=float,\n default=0.5,\n help=\"max SSIM threshold for image-image similarity.\",\n )\n parser.add_argument('--seed', type=int, default=324)\n parser.add_argument('--do-sample', type=bool, default=True)\n parser.add_argument('--use-cache', type=bool, default=True)\n parser.add_argument('--trust-remote-code', type=bool, default=True)\n parser.add_argument('--attn-impl', type=str, default='torch')\n parser.add_argument('--threshold', type=float, default=0.3)\n parser.add_argument('--data_split_start', type=int, default=0)\n parser.add_argument('--data_split_end', type=int, default=1000000000)\n parser.add_argument('--image_size', type=int, default=384)\n args = parser.parse_args()\n\n\n\n\n os.environ['TOKENIZERS_PARALLELISM'] = 'false'\n\n # with open(args.data_dir, 'r', encoding='utf8') as f:\n # url_data = f.read().strip().split('\\n')\n url_data = load_jsonl(args.data_dir)\n import copy\n # global global_data \n global_data = copy.deepcopy(url_data)\n # random.shuffle(url_data)\n global_data = {\n each['global_idx']: each\n for each in global_data\n }\n url_data = url_data[args.data_split_start:args.data_split_end]\n # split into 8 machine, and pick the part of machine_id\n url_data = url_data[args.machine_id::args.num_machine]\n print(f'Processing {len(url_data)} images')\n # split url data into shards\n url_data = [url_data[i::args.num_process] for i in range(args.num_process)]\n if args.img2img:\n dataloaders = [\n DataLoader(\n OpenImageDataset(url_data[i]),\n batch_size=args.batch_size,\n shuffle=False,\n num_workers=12,\n pin_memory=True,\n persistent_workers=True,\n drop_last=False,\n prefetch_factor=4,\n collate_fn=collate_fn\n )\n for i in range(args.num_process)\n ]\n elif args.do_inpainting:\n dataloaders = [\n DataLoader(\n RegionDataset(url_data[i]),\n batch_size=args.batch_size,\n shuffle=False,\n num_workers=12,\n pin_memory=True,\n persistent_workers=True,\n drop_last=False,\n prefetch_factor=4,\n collate_fn=collate_fn\n )\n for i in range(args.num_process)\n ]\n else:\n dataloaders = [\n DataLoader(\n NoneImageDataset(url_data[i]),\n batch_size=args.batch_size,\n shuffle=False,\n num_workers=12,\n pin_memory=True,\n persistent_workers=True,\n drop_last=False,\n prefetch_factor=4,\n collate_fn=collate_fn\n )\n for i in range(args.num_process)\n ]\n\n multiprocessing.set_start_method('spawn')\n processes = []\n # cuda_device = [int(x) for x in args.cuda_device.split(',')]\n cuda_device = args.cuda_device\n \n for shard_id, shard in enumerate(dataloaders):\n p = Process(\n target=save_tsv,\n args=(\n args,\n shard_id,\n shard,\n torch.device('cuda:{}'.format(cuda_device[shard_id % len(cuda_device)])),\n global_data\n )\n )\n p.start()\n processes.append(p)\n\n for p in processes:\n p.join()\n\n print('Done!')\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "data_generation/ldm/data/__init__.py", "content": "" }, { "path": "data_generation/ldm/data/base.py", "content": "from abc import abstractmethod\nfrom torch.utils.data import Dataset, ConcatDataset, ChainDataset, IterableDataset\n\n\nclass Txt2ImgIterableBaseDataset(IterableDataset):\n '''\n Define an interface to make the IterableDatasets for text2img data chainable\n '''\n def __init__(self, num_records=0, valid_ids=None, size=256):\n super().__init__()\n self.num_records = num_records\n self.valid_ids = valid_ids\n self.sample_ids = valid_ids\n self.size = size\n\n print(f'{self.__class__.__name__} dataset contains {self.__len__()} examples.')\n\n def __len__(self):\n return self.num_records\n\n @abstractmethod\n def __iter__(self):\n pass" }, { "path": "data_generation/ldm/data/imagenet.py", "content": "import os, yaml, pickle, shutil, tarfile, glob\nimport cv2\nimport albumentations\nimport PIL\nimport numpy as np\nimport torchvision.transforms.functional as TF\nfrom omegaconf import OmegaConf\nfrom functools import partial\nfrom PIL import Image\nfrom tqdm import tqdm\nfrom torch.utils.data import Dataset, Subset\n\nimport taming.data.utils as tdu\nfrom taming.data.imagenet import str_to_indices, give_synsets_from_indices, download, retrieve\nfrom taming.data.imagenet import ImagePaths\n\nfrom ldm.modules.image_degradation import degradation_fn_bsr, degradation_fn_bsr_light\n\n\ndef synset2idx(path_to_yaml=\"data/index_synset.yaml\"):\n with open(path_to_yaml) as f:\n di2s = yaml.load(f)\n return dict((v,k) for k,v in di2s.items())\n\n\nclass ImageNetBase(Dataset):\n def __init__(self, config=None):\n self.config = config or OmegaConf.create()\n if not type(self.config)==dict:\n self.config = OmegaConf.to_container(self.config)\n self.keep_orig_class_label = self.config.get(\"keep_orig_class_label\", False)\n self.process_images = True # if False we skip loading & processing images and self.data contains filepaths\n self._prepare()\n self._prepare_synset_to_human()\n self._prepare_idx_to_synset()\n self._prepare_human_to_integer_label()\n self._load()\n\n def __len__(self):\n return len(self.data)\n\n def __getitem__(self, i):\n return self.data[i]\n\n def _prepare(self):\n raise NotImplementedError()\n\n def _filter_relpaths(self, relpaths):\n ignore = set([\n \"n06596364_9591.JPEG\",\n ])\n relpaths = [rpath for rpath in relpaths if not rpath.split(\"/\")[-1] in ignore]\n if \"sub_indices\" in self.config:\n indices = str_to_indices(self.config[\"sub_indices\"])\n synsets = give_synsets_from_indices(indices, path_to_yaml=self.idx2syn) # returns a list of strings\n self.synset2idx = synset2idx(path_to_yaml=self.idx2syn)\n files = []\n for rpath in relpaths:\n syn = rpath.split(\"/\")[0]\n if syn in synsets:\n files.append(rpath)\n return files\n else:\n return relpaths\n\n def _prepare_synset_to_human(self):\n SIZE = 2655750\n URL = \"https://heibox.uni-heidelberg.de/f/9f28e956cd304264bb82/?dl=1\"\n self.human_dict = os.path.join(self.root, \"synset_human.txt\")\n if (not os.path.exists(self.human_dict) or\n not os.path.getsize(self.human_dict)==SIZE):\n download(URL, self.human_dict)\n\n def _prepare_idx_to_synset(self):\n URL = \"https://heibox.uni-heidelberg.de/f/d835d5b6ceda4d3aa910/?dl=1\"\n self.idx2syn = os.path.join(self.root, \"index_synset.yaml\")\n if (not os.path.exists(self.idx2syn)):\n download(URL, self.idx2syn)\n\n def _prepare_human_to_integer_label(self):\n URL = \"https://heibox.uni-heidelberg.de/f/2362b797d5be43b883f6/?dl=1\"\n self.human2integer = os.path.join(self.root, \"imagenet1000_clsidx_to_labels.txt\")\n if (not os.path.exists(self.human2integer)):\n download(URL, self.human2integer)\n with open(self.human2integer, \"r\") as f:\n lines = f.read().splitlines()\n assert len(lines) == 1000\n self.human2integer_dict = dict()\n for line in lines:\n value, key = line.split(\":\")\n self.human2integer_dict[key] = int(value)\n\n def _load(self):\n with open(self.txt_filelist, \"r\") as f:\n self.relpaths = f.read().splitlines()\n l1 = len(self.relpaths)\n self.relpaths = self._filter_relpaths(self.relpaths)\n print(\"Removed {} files from filelist during filtering.\".format(l1 - len(self.relpaths)))\n\n self.synsets = [p.split(\"/\")[0] for p in self.relpaths]\n self.abspaths = [os.path.join(self.datadir, p) for p in self.relpaths]\n\n unique_synsets = np.unique(self.synsets)\n class_dict = dict((synset, i) for i, synset in enumerate(unique_synsets))\n if not self.keep_orig_class_label:\n self.class_labels = [class_dict[s] for s in self.synsets]\n else:\n self.class_labels = [self.synset2idx[s] for s in self.synsets]\n\n with open(self.human_dict, \"r\") as f:\n human_dict = f.read().splitlines()\n human_dict = dict(line.split(maxsplit=1) for line in human_dict)\n\n self.human_labels = [human_dict[s] for s in self.synsets]\n\n labels = {\n \"relpath\": np.array(self.relpaths),\n \"synsets\": np.array(self.synsets),\n \"class_label\": np.array(self.class_labels),\n \"human_label\": np.array(self.human_labels),\n }\n\n if self.process_images:\n self.size = retrieve(self.config, \"size\", default=256)\n self.data = ImagePaths(self.abspaths,\n labels=labels,\n size=self.size,\n random_crop=self.random_crop,\n )\n else:\n self.data = self.abspaths\n\n\nclass ImageNetTrain(ImageNetBase):\n NAME = \"ILSVRC2012_train\"\n URL = \"http://www.image-net.org/challenges/LSVRC/2012/\"\n AT_HASH = \"a306397ccf9c2ead27155983c254227c0fd938e2\"\n FILES = [\n \"ILSVRC2012_img_train.tar\",\n ]\n SIZES = [\n 147897477120,\n ]\n\n def __init__(self, process_images=True, data_root=None, **kwargs):\n self.process_images = process_images\n self.data_root = data_root\n super().__init__(**kwargs)\n\n def _prepare(self):\n if self.data_root:\n self.root = os.path.join(self.data_root, self.NAME)\n else:\n cachedir = os.environ.get(\"XDG_CACHE_HOME\", os.path.expanduser(\"~/.cache\"))\n self.root = os.path.join(cachedir, \"autoencoders/data\", self.NAME)\n\n self.datadir = os.path.join(self.root, \"data\")\n self.txt_filelist = os.path.join(self.root, \"filelist.txt\")\n self.expected_length = 1281167\n self.random_crop = retrieve(self.config, \"ImageNetTrain/random_crop\",\n default=True)\n if not tdu.is_prepared(self.root):\n # prep\n print(\"Preparing dataset {} in {}\".format(self.NAME, self.root))\n\n datadir = self.datadir\n if not os.path.exists(datadir):\n path = os.path.join(self.root, self.FILES[0])\n if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]:\n import academictorrents as at\n atpath = at.get(self.AT_HASH, datastore=self.root)\n assert atpath == path\n\n print(\"Extracting {} to {}\".format(path, datadir))\n os.makedirs(datadir, exist_ok=True)\n with tarfile.open(path, \"r:\") as tar:\n tar.extractall(path=datadir)\n\n print(\"Extracting sub-tars.\")\n subpaths = sorted(glob.glob(os.path.join(datadir, \"*.tar\")))\n for subpath in tqdm(subpaths):\n subdir = subpath[:-len(\".tar\")]\n os.makedirs(subdir, exist_ok=True)\n with tarfile.open(subpath, \"r:\") as tar:\n tar.extractall(path=subdir)\n\n filelist = glob.glob(os.path.join(datadir, \"**\", \"*.JPEG\"))\n filelist = [os.path.relpath(p, start=datadir) for p in filelist]\n filelist = sorted(filelist)\n filelist = \"\\n\".join(filelist)+\"\\n\"\n with open(self.txt_filelist, \"w\") as f:\n f.write(filelist)\n\n tdu.mark_prepared(self.root)\n\n\nclass ImageNetValidation(ImageNetBase):\n NAME = \"ILSVRC2012_validation\"\n URL = \"http://www.image-net.org/challenges/LSVRC/2012/\"\n AT_HASH = \"5d6d0df7ed81efd49ca99ea4737e0ae5e3a5f2e5\"\n VS_URL = \"https://heibox.uni-heidelberg.de/f/3e0f6e9c624e45f2bd73/?dl=1\"\n FILES = [\n \"ILSVRC2012_img_val.tar\",\n \"validation_synset.txt\",\n ]\n SIZES = [\n 6744924160,\n 1950000,\n ]\n\n def __init__(self, process_images=True, data_root=None, **kwargs):\n self.data_root = data_root\n self.process_images = process_images\n super().__init__(**kwargs)\n\n def _prepare(self):\n if self.data_root:\n self.root = os.path.join(self.data_root, self.NAME)\n else:\n cachedir = os.environ.get(\"XDG_CACHE_HOME\", os.path.expanduser(\"~/.cache\"))\n self.root = os.path.join(cachedir, \"autoencoders/data\", self.NAME)\n self.datadir = os.path.join(self.root, \"data\")\n self.txt_filelist = os.path.join(self.root, \"filelist.txt\")\n self.expected_length = 50000\n self.random_crop = retrieve(self.config, \"ImageNetValidation/random_crop\",\n default=False)\n if not tdu.is_prepared(self.root):\n # prep\n print(\"Preparing dataset {} in {}\".format(self.NAME, self.root))\n\n datadir = self.datadir\n if not os.path.exists(datadir):\n path = os.path.join(self.root, self.FILES[0])\n if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]:\n import academictorrents as at\n atpath = at.get(self.AT_HASH, datastore=self.root)\n assert atpath == path\n\n print(\"Extracting {} to {}\".format(path, datadir))\n os.makedirs(datadir, exist_ok=True)\n with tarfile.open(path, \"r:\") as tar:\n tar.extractall(path=datadir)\n\n vspath = os.path.join(self.root, self.FILES[1])\n if not os.path.exists(vspath) or not os.path.getsize(vspath)==self.SIZES[1]:\n download(self.VS_URL, vspath)\n\n with open(vspath, \"r\") as f:\n synset_dict = f.read().splitlines()\n synset_dict = dict(line.split() for line in synset_dict)\n\n print(\"Reorganizing into synset folders\")\n synsets = np.unique(list(synset_dict.values()))\n for s in synsets:\n os.makedirs(os.path.join(datadir, s), exist_ok=True)\n for k, v in synset_dict.items():\n src = os.path.join(datadir, k)\n dst = os.path.join(datadir, v)\n shutil.move(src, dst)\n\n filelist = glob.glob(os.path.join(datadir, \"**\", \"*.JPEG\"))\n filelist = [os.path.relpath(p, start=datadir) for p in filelist]\n filelist = sorted(filelist)\n filelist = \"\\n\".join(filelist)+\"\\n\"\n with open(self.txt_filelist, \"w\") as f:\n f.write(filelist)\n\n tdu.mark_prepared(self.root)\n\n\n\nclass ImageNetSR(Dataset):\n def __init__(self, size=None,\n degradation=None, downscale_f=4, min_crop_f=0.5, max_crop_f=1.,\n random_crop=True):\n \"\"\"\n Imagenet Superresolution Dataloader\n Performs following ops in order:\n 1. crops a crop of size s from image either as random or center crop\n 2. resizes crop to size with cv2.area_interpolation\n 3. degrades resized crop with degradation_fn\n\n :param size: resizing to size after cropping\n :param degradation: degradation_fn, e.g. cv_bicubic or bsrgan_light\n :param downscale_f: Low Resolution Downsample factor\n :param min_crop_f: determines crop size s,\n where s = c * min_img_side_len with c sampled from interval (min_crop_f, max_crop_f)\n :param max_crop_f: \"\"\n :param data_root:\n :param random_crop:\n \"\"\"\n self.base = self.get_base()\n assert size\n assert (size / downscale_f).is_integer()\n self.size = size\n self.LR_size = int(size / downscale_f)\n self.min_crop_f = min_crop_f\n self.max_crop_f = max_crop_f\n assert(max_crop_f <= 1.)\n self.center_crop = not random_crop\n\n self.image_rescaler = albumentations.SmallestMaxSize(max_size=size, interpolation=cv2.INTER_AREA)\n\n self.pil_interpolation = False # gets reset later if incase interp_op is from pillow\n\n if degradation == \"bsrgan\":\n self.degradation_process = partial(degradation_fn_bsr, sf=downscale_f)\n\n elif degradation == \"bsrgan_light\":\n self.degradation_process = partial(degradation_fn_bsr_light, sf=downscale_f)\n\n else:\n interpolation_fn = {\n \"cv_nearest\": cv2.INTER_NEAREST,\n \"cv_bilinear\": cv2.INTER_LINEAR,\n \"cv_bicubic\": cv2.INTER_CUBIC,\n \"cv_area\": cv2.INTER_AREA,\n \"cv_lanczos\": cv2.INTER_LANCZOS4,\n \"pil_nearest\": PIL.Image.NEAREST,\n \"pil_bilinear\": PIL.Image.BILINEAR,\n \"pil_bicubic\": PIL.Image.BICUBIC,\n \"pil_box\": PIL.Image.BOX,\n \"pil_hamming\": PIL.Image.HAMMING,\n \"pil_lanczos\": PIL.Image.LANCZOS,\n }[degradation]\n\n self.pil_interpolation = degradation.startswith(\"pil_\")\n\n if self.pil_interpolation:\n self.degradation_process = partial(TF.resize, size=self.LR_size, interpolation=interpolation_fn)\n\n else:\n self.degradation_process = albumentations.SmallestMaxSize(max_size=self.LR_size,\n interpolation=interpolation_fn)\n\n def __len__(self):\n return len(self.base)\n\n def __getitem__(self, i):\n example = self.base[i]\n image = Image.open(example[\"file_path_\"])\n\n if not image.mode == \"RGB\":\n image = image.convert(\"RGB\")\n\n image = np.array(image).astype(np.uint8)\n\n min_side_len = min(image.shape[:2])\n crop_side_len = min_side_len * np.random.uniform(self.min_crop_f, self.max_crop_f, size=None)\n crop_side_len = int(crop_side_len)\n\n if self.center_crop:\n self.cropper = albumentations.CenterCrop(height=crop_side_len, width=crop_side_len)\n\n else:\n self.cropper = albumentations.RandomCrop(height=crop_side_len, width=crop_side_len)\n\n image = self.cropper(image=image)[\"image\"]\n image = self.image_rescaler(image=image)[\"image\"]\n\n if self.pil_interpolation:\n image_pil = PIL.Image.fromarray(image)\n LR_image = self.degradation_process(image_pil)\n LR_image = np.array(LR_image).astype(np.uint8)\n\n else:\n LR_image = self.degradation_process(image=image)[\"image\"]\n\n example[\"image\"] = (image/127.5 - 1.0).astype(np.float32)\n example[\"LR_image\"] = (LR_image/127.5 - 1.0).astype(np.float32)\n\n return example\n\n\nclass ImageNetSRTrain(ImageNetSR):\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\n def get_base(self):\n with open(\"data/imagenet_train_hr_indices.p\", \"rb\") as f:\n indices = pickle.load(f)\n dset = ImageNetTrain(process_images=False,)\n return Subset(dset, indices)\n\n\nclass ImageNetSRValidation(ImageNetSR):\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\n def get_base(self):\n with open(\"data/imagenet_val_hr_indices.p\", \"rb\") as f:\n indices = pickle.load(f)\n dset = ImageNetValidation(process_images=False,)\n return Subset(dset, indices)\n" }, { "path": "data_generation/ldm/data/lsun.py", "content": "import os\nimport numpy as np\nimport PIL\nfrom PIL import Image\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\n\n\nclass LSUNBase(Dataset):\n def __init__(self,\n txt_file,\n data_root,\n size=None,\n interpolation=\"bicubic\",\n flip_p=0.5\n ):\n self.data_paths = txt_file\n self.data_root = data_root\n with open(self.data_paths, \"r\") as f:\n self.image_paths = f.read().splitlines()\n self._length = len(self.image_paths)\n self.labels = {\n \"relative_file_path_\": [l for l in self.image_paths],\n \"file_path_\": [os.path.join(self.data_root, l)\n for l in self.image_paths],\n }\n\n self.size = size\n self.interpolation = {\"linear\": PIL.Image.LINEAR,\n \"bilinear\": PIL.Image.BILINEAR,\n \"bicubic\": PIL.Image.BICUBIC,\n \"lanczos\": PIL.Image.LANCZOS,\n }[interpolation]\n self.flip = transforms.RandomHorizontalFlip(p=flip_p)\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, i):\n example = dict((k, self.labels[k][i]) for k in self.labels)\n image = Image.open(example[\"file_path_\"])\n if not image.mode == \"RGB\":\n image = image.convert(\"RGB\")\n\n # default to score-sde preprocessing\n img = np.array(image).astype(np.uint8)\n crop = min(img.shape[0], img.shape[1])\n h, w, = img.shape[0], img.shape[1]\n img = img[(h - crop) // 2:(h + crop) // 2,\n (w - crop) // 2:(w + crop) // 2]\n\n image = Image.fromarray(img)\n if self.size is not None:\n image = image.resize((self.size, self.size), resample=self.interpolation)\n\n image = self.flip(image)\n image = np.array(image).astype(np.uint8)\n example[\"image\"] = (image / 127.5 - 1.0).astype(np.float32)\n return example\n\n\nclass LSUNChurchesTrain(LSUNBase):\n def __init__(self, **kwargs):\n super().__init__(txt_file=\"data/lsun/church_outdoor_train.txt\", data_root=\"data/lsun/churches\", **kwargs)\n\n\nclass LSUNChurchesValidation(LSUNBase):\n def __init__(self, flip_p=0., **kwargs):\n super().__init__(txt_file=\"data/lsun/church_outdoor_val.txt\", data_root=\"data/lsun/churches\",\n flip_p=flip_p, **kwargs)\n\n\nclass LSUNBedroomsTrain(LSUNBase):\n def __init__(self, **kwargs):\n super().__init__(txt_file=\"data/lsun/bedrooms_train.txt\", data_root=\"data/lsun/bedrooms\", **kwargs)\n\n\nclass LSUNBedroomsValidation(LSUNBase):\n def __init__(self, flip_p=0.0, **kwargs):\n super().__init__(txt_file=\"data/lsun/bedrooms_val.txt\", data_root=\"data/lsun/bedrooms\",\n flip_p=flip_p, **kwargs)\n\n\nclass LSUNCatsTrain(LSUNBase):\n def __init__(self, **kwargs):\n super().__init__(txt_file=\"data/lsun/cat_train.txt\", data_root=\"data/lsun/cats\", **kwargs)\n\n\nclass LSUNCatsValidation(LSUNBase):\n def __init__(self, flip_p=0., **kwargs):\n super().__init__(txt_file=\"data/lsun/cat_val.txt\", data_root=\"data/lsun/cats\",\n flip_p=flip_p, **kwargs)\n" }, { "path": "data_generation/ldm/lr_scheduler.py", "content": "import numpy as np\n\n\nclass LambdaWarmUpCosineScheduler:\n \"\"\"\n note: use with a base_lr of 1.0\n \"\"\"\n def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0):\n self.lr_warm_up_steps = warm_up_steps\n self.lr_start = lr_start\n self.lr_min = lr_min\n self.lr_max = lr_max\n self.lr_max_decay_steps = max_decay_steps\n self.last_lr = 0.\n self.verbosity_interval = verbosity_interval\n\n def schedule(self, n, **kwargs):\n if self.verbosity_interval > 0:\n if n % self.verbosity_interval == 0: print(f\"current step: {n}, recent lr-multiplier: {self.last_lr}\")\n if n < self.lr_warm_up_steps:\n lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start\n self.last_lr = lr\n return lr\n else:\n t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps)\n t = min(t, 1.0)\n lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (\n 1 + np.cos(t * np.pi))\n self.last_lr = lr\n return lr\n\n def __call__(self, n, **kwargs):\n return self.schedule(n,**kwargs)\n\n\nclass LambdaWarmUpCosineScheduler2:\n \"\"\"\n supports repeated iterations, configurable via lists\n note: use with a base_lr of 1.0.\n \"\"\"\n def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0):\n assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths)\n self.lr_warm_up_steps = warm_up_steps\n self.f_start = f_start\n self.f_min = f_min\n self.f_max = f_max\n self.cycle_lengths = cycle_lengths\n self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths))\n self.last_f = 0.\n self.verbosity_interval = verbosity_interval\n\n def find_in_interval(self, n):\n interval = 0\n for cl in self.cum_cycles[1:]:\n if n <= cl:\n return interval\n interval += 1\n\n def schedule(self, n, **kwargs):\n cycle = self.find_in_interval(n)\n n = n - self.cum_cycles[cycle]\n if self.verbosity_interval > 0:\n if n % self.verbosity_interval == 0: print(f\"current step: {n}, recent lr-multiplier: {self.last_f}, \"\n f\"current cycle {cycle}\")\n if n < self.lr_warm_up_steps[cycle]:\n f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]\n self.last_f = f\n return f\n else:\n t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle])\n t = min(t, 1.0)\n f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * (\n 1 + np.cos(t * np.pi))\n self.last_f = f\n return f\n\n def __call__(self, n, **kwargs):\n return self.schedule(n, **kwargs)\n\n\nclass LambdaLinearScheduler(LambdaWarmUpCosineScheduler2):\n\n def schedule(self, n, **kwargs):\n cycle = self.find_in_interval(n)\n n = n - self.cum_cycles[cycle]\n if self.verbosity_interval > 0:\n if n % self.verbosity_interval == 0: print(f\"current step: {n}, recent lr-multiplier: {self.last_f}, \"\n f\"current cycle {cycle}\")\n\n if n < self.lr_warm_up_steps[cycle]:\n f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]\n self.last_f = f\n return f\n else:\n f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / (self.cycle_lengths[cycle])\n self.last_f = f\n return f\n\n" }, { "path": "data_generation/ldm/models/autoencoder.py", "content": "import torch\nimport pytorch_lightning as pl\nimport torch.nn.functional as F\nfrom contextlib import contextmanager\n\nfrom taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer\n\nfrom ldm.modules.diffusionmodules.model import Encoder, Decoder\nfrom ldm.modules.distributions.distributions import DiagonalGaussianDistribution\n\nfrom ldm.util import instantiate_from_config\n\n\nclass VQModel(pl.LightningModule):\n def __init__(self,\n ddconfig,\n lossconfig,\n n_embed,\n embed_dim,\n ckpt_path=None,\n ignore_keys=[],\n image_key=\"image\",\n colorize_nlabels=None,\n monitor=None,\n batch_resize_range=None,\n scheduler_config=None,\n lr_g_factor=1.0,\n remap=None,\n sane_index_shape=False, # tell vector quantizer to return indices as bhw\n use_ema=False\n ):\n super().__init__()\n self.embed_dim = embed_dim\n self.n_embed = n_embed\n self.image_key = image_key\n self.encoder = Encoder(**ddconfig)\n self.decoder = Decoder(**ddconfig)\n self.loss = instantiate_from_config(lossconfig)\n self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,\n remap=remap,\n sane_index_shape=sane_index_shape)\n self.quant_conv = torch.nn.Conv2d(ddconfig[\"z_channels\"], embed_dim, 1)\n self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig[\"z_channels\"], 1)\n if colorize_nlabels is not None:\n assert type(colorize_nlabels)==int\n self.register_buffer(\"colorize\", torch.randn(3, colorize_nlabels, 1, 1))\n if monitor is not None:\n self.monitor = monitor\n self.batch_resize_range = batch_resize_range\n if self.batch_resize_range is not None:\n print(f\"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.\")\n\n self.use_ema = use_ema\n if self.use_ema:\n self.model_ema = LitEma(self)\n print(f\"Keeping EMAs of {len(list(self.model_ema.buffers()))}.\")\n\n if ckpt_path is not None:\n self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)\n self.scheduler_config = scheduler_config\n self.lr_g_factor = lr_g_factor\n\n @contextmanager\n def ema_scope(self, context=None):\n if self.use_ema:\n self.model_ema.store(self.parameters())\n self.model_ema.copy_to(self)\n if context is not None:\n print(f\"{context}: Switched to EMA weights\")\n try:\n yield None\n finally:\n if self.use_ema:\n self.model_ema.restore(self.parameters())\n if context is not None:\n print(f\"{context}: Restored training weights\")\n\n def init_from_ckpt(self, path, ignore_keys=list()):\n sd = torch.load(path, map_location=\"cpu\")[\"state_dict\"]\n keys = list(sd.keys())\n for k in keys:\n for ik in ignore_keys:\n if k.startswith(ik):\n print(\"Deleting key {} from state_dict.\".format(k))\n del sd[k]\n missing, unexpected = self.load_state_dict(sd, strict=False)\n print(f\"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys\")\n if len(missing) > 0:\n print(f\"Missing Keys: {missing}\")\n print(f\"Unexpected Keys: {unexpected}\")\n\n def on_train_batch_end(self, *args, **kwargs):\n if self.use_ema:\n self.model_ema(self)\n\n def encode(self, x):\n h = self.encoder(x)\n h = self.quant_conv(h)\n quant, emb_loss, info = self.quantize(h)\n return quant, emb_loss, info\n\n def encode_to_prequant(self, x):\n h = self.encoder(x)\n h = self.quant_conv(h)\n return h\n\n def decode(self, quant):\n quant = self.post_quant_conv(quant)\n dec = self.decoder(quant)\n return dec\n\n def decode_code(self, code_b):\n quant_b = self.quantize.embed_code(code_b)\n dec = self.decode(quant_b)\n return dec\n\n def forward(self, input, return_pred_indices=False):\n quant, diff, (_,_,ind) = self.encode(input)\n dec = self.decode(quant)\n if return_pred_indices:\n return dec, diff, ind\n return dec, diff\n\n def get_input(self, batch, k):\n x = batch[k]\n if len(x.shape) == 3:\n x = x[..., None]\n x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()\n if self.batch_resize_range is not None:\n lower_size = self.batch_resize_range[0]\n upper_size = self.batch_resize_range[1]\n if self.global_step <= 4:\n # do the first few batches with max size to avoid later oom\n new_resize = upper_size\n else:\n new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))\n if new_resize != x.shape[2]:\n x = F.interpolate(x, size=new_resize, mode=\"bicubic\")\n x = x.detach()\n return x\n\n def training_step(self, batch, batch_idx, optimizer_idx):\n # https://github.com/pytorch/pytorch/issues/37142\n # try not to fool the heuristics\n x = self.get_input(batch, self.image_key)\n xrec, qloss, ind = self(x, return_pred_indices=True)\n\n if optimizer_idx == 0:\n # autoencode\n aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,\n last_layer=self.get_last_layer(), split=\"train\",\n predicted_indices=ind)\n\n self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)\n return aeloss\n\n if optimizer_idx == 1:\n # discriminator\n discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,\n last_layer=self.get_last_layer(), split=\"train\")\n self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)\n return discloss\n\n def validation_step(self, batch, batch_idx):\n log_dict = self._validation_step(batch, batch_idx)\n with self.ema_scope():\n log_dict_ema = self._validation_step(batch, batch_idx, suffix=\"_ema\")\n return log_dict\n\n def _validation_step(self, batch, batch_idx, suffix=\"\"):\n x = self.get_input(batch, self.image_key)\n xrec, qloss, ind = self(x, return_pred_indices=True)\n aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,\n self.global_step,\n last_layer=self.get_last_layer(),\n split=\"val\"+suffix,\n predicted_indices=ind\n )\n\n discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,\n self.global_step,\n last_layer=self.get_last_layer(),\n split=\"val\"+suffix,\n predicted_indices=ind\n )\n rec_loss = log_dict_ae[f\"val{suffix}/rec_loss\"]\n self.log(f\"val{suffix}/rec_loss\", rec_loss,\n prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)\n self.log(f\"val{suffix}/aeloss\", aeloss,\n prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)\n if version.parse(pl.__version__) >= version.parse('1.4.0'):\n del log_dict_ae[f\"val{suffix}/rec_loss\"]\n self.log_dict(log_dict_ae)\n self.log_dict(log_dict_disc)\n return self.log_dict\n\n def configure_optimizers(self):\n lr_d = self.learning_rate\n lr_g = self.lr_g_factor*self.learning_rate\n print(\"lr_d\", lr_d)\n print(\"lr_g\", lr_g)\n opt_ae = torch.optim.Adam(list(self.encoder.parameters())+\n list(self.decoder.parameters())+\n list(self.quantize.parameters())+\n list(self.quant_conv.parameters())+\n list(self.post_quant_conv.parameters()),\n lr=lr_g, betas=(0.5, 0.9))\n opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),\n lr=lr_d, betas=(0.5, 0.9))\n\n if self.scheduler_config is not None:\n scheduler = instantiate_from_config(self.scheduler_config)\n\n print(\"Setting up LambdaLR scheduler...\")\n scheduler = [\n {\n 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),\n 'interval': 'step',\n 'frequency': 1\n },\n {\n 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),\n 'interval': 'step',\n 'frequency': 1\n },\n ]\n return [opt_ae, opt_disc], scheduler\n return [opt_ae, opt_disc], []\n\n def get_last_layer(self):\n return self.decoder.conv_out.weight\n\n def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):\n log = dict()\n x = self.get_input(batch, self.image_key)\n x = x.to(self.device)\n if only_inputs:\n log[\"inputs\"] = x\n return log\n xrec, _ = self(x)\n if x.shape[1] > 3:\n # colorize with random projection\n assert xrec.shape[1] > 3\n x = self.to_rgb(x)\n xrec = self.to_rgb(xrec)\n log[\"inputs\"] = x\n log[\"reconstructions\"] = xrec\n if plot_ema:\n with self.ema_scope():\n xrec_ema, _ = self(x)\n if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)\n log[\"reconstructions_ema\"] = xrec_ema\n return log\n\n def to_rgb(self, x):\n assert self.image_key == \"segmentation\"\n if not hasattr(self, \"colorize\"):\n self.register_buffer(\"colorize\", torch.randn(3, x.shape[1], 1, 1).to(x))\n x = F.conv2d(x, weight=self.colorize)\n x = 2.*(x-x.min())/(x.max()-x.min()) - 1.\n return x\n\n\nclass VQModelInterface(VQModel):\n def __init__(self, embed_dim, *args, **kwargs):\n super().__init__(embed_dim=embed_dim, *args, **kwargs)\n self.embed_dim = embed_dim\n\n def encode(self, x):\n h = self.encoder(x)\n h = self.quant_conv(h)\n return h\n\n def decode(self, h, force_not_quantize=False):\n # also go through quantization layer\n if not force_not_quantize:\n quant, emb_loss, info = self.quantize(h)\n else:\n quant = h\n quant = self.post_quant_conv(quant)\n dec = self.decoder(quant)\n return dec\n\n\nclass AutoencoderKL(pl.LightningModule):\n def __init__(self,\n ddconfig,\n lossconfig,\n embed_dim,\n ckpt_path=None,\n ignore_keys=[],\n image_key=\"image\",\n colorize_nlabels=None,\n monitor=None,\n ):\n super().__init__()\n self.image_key = image_key\n self.encoder = Encoder(**ddconfig)\n self.decoder = Decoder(**ddconfig)\n self.loss = instantiate_from_config(lossconfig)\n assert ddconfig[\"double_z\"]\n self.quant_conv = torch.nn.Conv2d(2*ddconfig[\"z_channels\"], 2*embed_dim, 1)\n self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig[\"z_channels\"], 1)\n self.embed_dim = embed_dim\n if colorize_nlabels is not None:\n assert type(colorize_nlabels)==int\n self.register_buffer(\"colorize\", torch.randn(3, colorize_nlabels, 1, 1))\n if monitor is not None:\n self.monitor = monitor\n if ckpt_path is not None:\n self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)\n\n def init_from_ckpt(self, path, ignore_keys=list()):\n sd = torch.load(path, map_location=\"cpu\")[\"state_dict\"]\n keys = list(sd.keys())\n for k in keys:\n for ik in ignore_keys:\n if k.startswith(ik):\n print(\"Deleting key {} from state_dict.\".format(k))\n del sd[k]\n self.load_state_dict(sd, strict=False)\n print(f\"Restored from {path}\")\n\n def encode(self, x):\n h = self.encoder(x)\n moments = self.quant_conv(h)\n posterior = DiagonalGaussianDistribution(moments)\n return posterior\n\n def decode(self, z):\n z = self.post_quant_conv(z)\n dec = self.decoder(z)\n return dec\n\n def forward(self, input, sample_posterior=True):\n posterior = self.encode(input)\n if sample_posterior:\n z = posterior.sample()\n else:\n z = posterior.mode()\n dec = self.decode(z)\n return dec, posterior\n\n def get_input(self, batch, k):\n x = batch[k]\n if len(x.shape) == 3:\n x = x[..., None]\n x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()\n return x\n\n def training_step(self, batch, batch_idx, optimizer_idx):\n inputs = self.get_input(batch, self.image_key)\n reconstructions, posterior = self(inputs)\n\n if optimizer_idx == 0:\n # train encoder+decoder+logvar\n aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,\n last_layer=self.get_last_layer(), split=\"train\")\n self.log(\"aeloss\", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)\n self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)\n return aeloss\n\n if optimizer_idx == 1:\n # train the discriminator\n discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,\n last_layer=self.get_last_layer(), split=\"train\")\n\n self.log(\"discloss\", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)\n self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)\n return discloss\n\n def validation_step(self, batch, batch_idx):\n inputs = self.get_input(batch, self.image_key)\n reconstructions, posterior = self(inputs)\n aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,\n last_layer=self.get_last_layer(), split=\"val\")\n\n discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,\n last_layer=self.get_last_layer(), split=\"val\")\n\n self.log(\"val/rec_loss\", log_dict_ae[\"val/rec_loss\"])\n self.log_dict(log_dict_ae)\n self.log_dict(log_dict_disc)\n return self.log_dict\n\n def configure_optimizers(self):\n lr = self.learning_rate\n opt_ae = torch.optim.Adam(list(self.encoder.parameters())+\n list(self.decoder.parameters())+\n list(self.quant_conv.parameters())+\n list(self.post_quant_conv.parameters()),\n lr=lr, betas=(0.5, 0.9))\n opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),\n lr=lr, betas=(0.5, 0.9))\n return [opt_ae, opt_disc], []\n\n def get_last_layer(self):\n return self.decoder.conv_out.weight\n\n @torch.no_grad()\n def log_images(self, batch, only_inputs=False, **kwargs):\n log = dict()\n x = self.get_input(batch, self.image_key)\n x = x.to(self.device)\n if not only_inputs:\n xrec, posterior = self(x)\n if x.shape[1] > 3:\n # colorize with random projection\n assert xrec.shape[1] > 3\n x = self.to_rgb(x)\n xrec = self.to_rgb(xrec)\n log[\"samples\"] = self.decode(torch.randn_like(posterior.sample()))\n log[\"reconstructions\"] = xrec\n log[\"inputs\"] = x\n return log\n\n def to_rgb(self, x):\n assert self.image_key == \"segmentation\"\n if not hasattr(self, \"colorize\"):\n self.register_buffer(\"colorize\", torch.randn(3, x.shape[1], 1, 1).to(x))\n x = F.conv2d(x, weight=self.colorize)\n x = 2.*(x-x.min())/(x.max()-x.min()) - 1.\n return x\n\n\nclass IdentityFirstStage(torch.nn.Module):\n def __init__(self, *args, vq_interface=False, **kwargs):\n self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff\n super().__init__()\n\n def encode(self, x, *args, **kwargs):\n return x\n\n def decode(self, x, *args, **kwargs):\n return x\n\n def quantize(self, x, *args, **kwargs):\n if self.vq_interface:\n return x, None, [None, None, None]\n return x\n\n def forward(self, x, *args, **kwargs):\n return x\n" }, { "path": "data_generation/ldm/models/diffusion/__init__.py", "content": "" }, { "path": "data_generation/ldm/models/diffusion/classifier.py", "content": "import os\nimport torch\nimport pytorch_lightning as pl\nfrom omegaconf import OmegaConf\nfrom torch.nn import functional as F\nfrom torch.optim import AdamW\nfrom torch.optim.lr_scheduler import LambdaLR\nfrom copy import deepcopy\nfrom einops import rearrange\nfrom glob import glob\nfrom natsort import natsorted\n\nfrom ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel\nfrom ldm.util import log_txt_as_img, default, ismap, instantiate_from_config\n\n__models__ = {\n 'class_label': EncoderUNetModel,\n 'segmentation': UNetModel\n}\n\n\ndef disabled_train(self, mode=True):\n \"\"\"Overwrite model.train with this function to make sure train/eval mode\n does not change anymore.\"\"\"\n return self\n\n\nclass NoisyLatentImageClassifier(pl.LightningModule):\n\n def __init__(self,\n diffusion_path,\n num_classes,\n ckpt_path=None,\n pool='attention',\n label_key=None,\n diffusion_ckpt_path=None,\n scheduler_config=None,\n weight_decay=1.e-2,\n log_steps=10,\n monitor='val/loss',\n *args,\n **kwargs):\n super().__init__(*args, **kwargs)\n self.num_classes = num_classes\n # get latest config of diffusion model\n diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1]\n self.diffusion_config = OmegaConf.load(diffusion_config).model\n self.diffusion_config.params.ckpt_path = diffusion_ckpt_path\n self.load_diffusion()\n\n self.monitor = monitor\n self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1\n self.log_time_interval = self.diffusion_model.num_timesteps // log_steps\n self.log_steps = log_steps\n\n self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \\\n else self.diffusion_model.cond_stage_key\n\n assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params'\n\n if self.label_key not in __models__:\n raise NotImplementedError()\n\n self.load_classifier(ckpt_path, pool)\n\n self.scheduler_config = scheduler_config\n self.use_scheduler = self.scheduler_config is not None\n self.weight_decay = weight_decay\n\n def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):\n sd = torch.load(path, map_location=\"cpu\")\n if \"state_dict\" in list(sd.keys()):\n sd = sd[\"state_dict\"]\n keys = list(sd.keys())\n for k in keys:\n for ik in ignore_keys:\n if k.startswith(ik):\n print(\"Deleting key {} from state_dict.\".format(k))\n del sd[k]\n missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(\n sd, strict=False)\n print(f\"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys\")\n if len(missing) > 0:\n print(f\"Missing Keys: {missing}\")\n if len(unexpected) > 0:\n print(f\"Unexpected Keys: {unexpected}\")\n\n def load_diffusion(self):\n model = instantiate_from_config(self.diffusion_config)\n self.diffusion_model = model.eval()\n self.diffusion_model.train = disabled_train\n for param in self.diffusion_model.parameters():\n param.requires_grad = False\n\n def load_classifier(self, ckpt_path, pool):\n model_config = deepcopy(self.diffusion_config.params.unet_config.params)\n model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels\n model_config.out_channels = self.num_classes\n if self.label_key == 'class_label':\n model_config.pool = pool\n\n self.model = __models__[self.label_key](**model_config)\n if ckpt_path is not None:\n print('#####################################################################')\n print(f'load from ckpt \"{ckpt_path}\"')\n print('#####################################################################')\n self.init_from_ckpt(ckpt_path)\n\n @torch.no_grad()\n def get_x_noisy(self, x, t, noise=None):\n noise = default(noise, lambda: torch.randn_like(x))\n continuous_sqrt_alpha_cumprod = None\n if self.diffusion_model.use_continuous_noise:\n continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1)\n # todo: make sure t+1 is correct here\n\n return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise,\n continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod)\n\n def forward(self, x_noisy, t, *args, **kwargs):\n return self.model(x_noisy, t)\n\n @torch.no_grad()\n def get_input(self, batch, k):\n x = batch[k]\n if len(x.shape) == 3:\n x = x[..., None]\n x = rearrange(x, 'b h w c -> b c h w')\n x = x.to(memory_format=torch.contiguous_format).float()\n return x\n\n @torch.no_grad()\n def get_conditioning(self, batch, k=None):\n if k is None:\n k = self.label_key\n assert k is not None, 'Needs to provide label key'\n\n targets = batch[k].to(self.device)\n\n if self.label_key == 'segmentation':\n targets = rearrange(targets, 'b h w c -> b c h w')\n for down in range(self.numd):\n h, w = targets.shape[-2:]\n targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest')\n\n # targets = rearrange(targets,'b c h w -> b h w c')\n\n return targets\n\n def compute_top_k(self, logits, labels, k, reduction=\"mean\"):\n _, top_ks = torch.topk(logits, k, dim=1)\n if reduction == \"mean\":\n return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()\n elif reduction == \"none\":\n return (top_ks == labels[:, None]).float().sum(dim=-1)\n\n def on_train_epoch_start(self):\n # save some memory\n self.diffusion_model.model.to('cpu')\n\n @torch.no_grad()\n def write_logs(self, loss, logits, targets):\n log_prefix = 'train' if self.training else 'val'\n log = {}\n log[f\"{log_prefix}/loss\"] = loss.mean()\n log[f\"{log_prefix}/acc@1\"] = self.compute_top_k(\n logits, targets, k=1, reduction=\"mean\"\n )\n log[f\"{log_prefix}/acc@5\"] = self.compute_top_k(\n logits, targets, k=5, reduction=\"mean\"\n )\n\n self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True)\n self.log('loss', log[f\"{log_prefix}/loss\"], prog_bar=True, logger=False)\n self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True)\n lr = self.optimizers().param_groups[0]['lr']\n self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True)\n\n def shared_step(self, batch, t=None):\n x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key)\n targets = self.get_conditioning(batch)\n if targets.dim() == 4:\n targets = targets.argmax(dim=1)\n if t is None:\n t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long()\n else:\n t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long()\n x_noisy = self.get_x_noisy(x, t)\n logits = self(x_noisy, t)\n\n loss = F.cross_entropy(logits, targets, reduction='none')\n\n self.write_logs(loss.detach(), logits.detach(), targets.detach())\n\n loss = loss.mean()\n return loss, logits, x_noisy, targets\n\n def training_step(self, batch, batch_idx):\n loss, *_ = self.shared_step(batch)\n return loss\n\n def reset_noise_accs(self):\n self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in\n range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)}\n\n def on_validation_start(self):\n self.reset_noise_accs()\n\n @torch.no_grad()\n def validation_step(self, batch, batch_idx):\n loss, *_ = self.shared_step(batch)\n\n for t in self.noisy_acc:\n _, logits, _, targets = self.shared_step(batch, t)\n self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean'))\n self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean'))\n\n return loss\n\n def configure_optimizers(self):\n optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)\n\n if self.use_scheduler:\n scheduler = instantiate_from_config(self.scheduler_config)\n\n print(\"Setting up LambdaLR scheduler...\")\n scheduler = [\n {\n 'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule),\n 'interval': 'step',\n 'frequency': 1\n }]\n return [optimizer], scheduler\n\n return optimizer\n\n @torch.no_grad()\n def log_images(self, batch, N=8, *args, **kwargs):\n log = dict()\n x = self.get_input(batch, self.diffusion_model.first_stage_key)\n log['inputs'] = x\n\n y = self.get_conditioning(batch)\n\n if self.label_key == 'class_label':\n y = log_txt_as_img((x.shape[2], x.shape[3]), batch[\"human_label\"])\n log['labels'] = y\n\n if ismap(y):\n log['labels'] = self.diffusion_model.to_rgb(y)\n\n for step in range(self.log_steps):\n current_time = step * self.log_time_interval\n\n _, logits, x_noisy, _ = self.shared_step(batch, t=current_time)\n\n log[f'inputs@t{current_time}'] = x_noisy\n\n pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes)\n pred = rearrange(pred, 'b h w c -> b c h w')\n\n log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred)\n\n for key in log:\n log[key] = log[key][:N]\n\n return log\n" }, { "path": "data_generation/ldm/models/diffusion/ddim.py", "content": "\"\"\"SAMPLING ONLY.\"\"\"\n\nimport torch\nimport numpy as np\nfrom tqdm import tqdm\nfrom functools import partial\n\nfrom ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, \\\n extract_into_tensor\n\n\nclass DDIMSampler(object):\n def __init__(self, model, schedule=\"linear\", **kwargs):\n super().__init__()\n self.model = model\n self.ddpm_num_timesteps = model.num_timesteps\n self.schedule = schedule\n\n def register_buffer(self, name, attr):\n if type(attr) == torch.Tensor:\n if attr.device != torch.device(\"cuda\"):\n attr = attr.to(torch.device(\"cuda\"))\n setattr(self, name, attr)\n\n def make_schedule(self, ddim_num_steps, ddim_discretize=\"uniform\", ddim_eta=0., verbose=True):\n self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,\n num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)\n alphas_cumprod = self.model.alphas_cumprod\n assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'\n to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)\n\n self.register_buffer('betas', to_torch(self.model.betas))\n self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))\n self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))\n\n # calculations for diffusion q(x_t | x_{t-1}) and others\n self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))\n self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))\n self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))\n self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))\n self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))\n\n # ddim sampling parameters\n ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),\n ddim_timesteps=self.ddim_timesteps,\n eta=ddim_eta,verbose=verbose)\n self.register_buffer('ddim_sigmas', ddim_sigmas)\n self.register_buffer('ddim_alphas', ddim_alphas)\n self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)\n self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))\n sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(\n (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (\n 1 - self.alphas_cumprod / self.alphas_cumprod_prev))\n self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)\n\n @torch.no_grad()\n def sample(self,\n S,\n batch_size,\n shape,\n conditioning=None,\n callback=None,\n normals_sequence=None,\n img_callback=None,\n quantize_x0=False,\n eta=0.,\n mask=None,\n x0=None,\n temperature=1.,\n noise_dropout=0.,\n score_corrector=None,\n corrector_kwargs=None,\n verbose=True,\n x_T=None,\n log_every_t=100,\n unconditional_guidance_scale=1.,\n unconditional_conditioning=None,\n # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...\n **kwargs\n ):\n if conditioning is not None:\n if isinstance(conditioning, dict):\n cbs = conditioning[list(conditioning.keys())[0]].shape[0]\n if cbs != batch_size:\n print(f\"Warning: Got {cbs} conditionings but batch-size is {batch_size}\")\n else:\n if conditioning.shape[0] != batch_size:\n print(f\"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}\")\n\n self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)\n # sampling\n C, H, W = shape\n size = (batch_size, C, H, W)\n print(f'Data shape for DDIM sampling is {size}, eta {eta}')\n\n samples, intermediates = self.ddim_sampling(conditioning, size,\n callback=callback,\n img_callback=img_callback,\n quantize_denoised=quantize_x0,\n mask=mask, x0=x0,\n ddim_use_original_steps=False,\n noise_dropout=noise_dropout,\n temperature=temperature,\n score_corrector=score_corrector,\n corrector_kwargs=corrector_kwargs,\n x_T=x_T,\n log_every_t=log_every_t,\n unconditional_guidance_scale=unconditional_guidance_scale,\n unconditional_conditioning=unconditional_conditioning,\n )\n return samples, intermediates\n\n @torch.no_grad()\n def ddim_sampling(self, cond, shape,\n x_T=None, ddim_use_original_steps=False,\n callback=None, timesteps=None, quantize_denoised=False,\n mask=None, x0=None, img_callback=None, log_every_t=100,\n temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,\n unconditional_guidance_scale=1., unconditional_conditioning=None,):\n device = self.model.betas.device\n b = shape[0]\n if x_T is None:\n img = torch.randn(shape, device=device)\n else:\n img = x_T\n\n if timesteps is None:\n timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps\n elif timesteps is not None and not ddim_use_original_steps:\n subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1\n timesteps = self.ddim_timesteps[:subset_end]\n\n intermediates = {'x_inter': [img], 'pred_x0': [img]}\n time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)\n total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]\n print(f\"Running DDIM Sampling with {total_steps} timesteps\")\n\n iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)\n\n for i, step in enumerate(iterator):\n index = total_steps - i - 1\n ts = torch.full((b,), step, device=device, dtype=torch.long)\n\n if mask is not None:\n assert x0 is not None\n img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?\n img = img_orig * mask + (1. - mask) * img\n\n outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,\n quantize_denoised=quantize_denoised, temperature=temperature,\n noise_dropout=noise_dropout, score_corrector=score_corrector,\n corrector_kwargs=corrector_kwargs,\n unconditional_guidance_scale=unconditional_guidance_scale,\n unconditional_conditioning=unconditional_conditioning)\n img, pred_x0 = outs\n if callback: callback(i)\n if img_callback: img_callback(pred_x0, i)\n\n if index % log_every_t == 0 or index == total_steps - 1:\n intermediates['x_inter'].append(img)\n intermediates['pred_x0'].append(pred_x0)\n\n return img, intermediates\n\n @torch.no_grad()\n def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,\n temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,\n unconditional_guidance_scale=1., unconditional_conditioning=None):\n b, *_, device = *x.shape, x.device\n\n if unconditional_conditioning is None or unconditional_guidance_scale == 1.:\n e_t = self.model.apply_model(x, t, c)\n else:\n x_in = torch.cat([x] * 2)\n t_in = torch.cat([t] * 2)\n c_in = torch.cat([unconditional_conditioning, c])\n e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)\n e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)\n\n if score_corrector is not None:\n assert self.model.parameterization == \"eps\"\n e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)\n\n alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas\n alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev\n sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas\n sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas\n # select parameters corresponding to the currently considered timestep\n a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)\n a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)\n sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)\n sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)\n\n # current prediction for x_0\n pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()\n if quantize_denoised:\n pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)\n # direction pointing to x_t\n dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t\n noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature\n if noise_dropout > 0.:\n noise = torch.nn.functional.dropout(noise, p=noise_dropout)\n x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise\n return x_prev, pred_x0\n\n @torch.no_grad()\n def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):\n # fast, but does not allow for exact reconstruction\n # t serves as an index to gather the correct alphas\n if use_original_steps:\n sqrt_alphas_cumprod = self.sqrt_alphas_cumprod\n sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod\n else:\n sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)\n sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas\n\n if noise is None:\n noise = torch.randn_like(x0)\n return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +\n extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)\n\n @torch.no_grad()\n def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,\n use_original_steps=False):\n\n timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps\n timesteps = timesteps[:t_start]\n\n time_range = np.flip(timesteps)\n total_steps = timesteps.shape[0]\n print(f\"Running DDIM Sampling with {total_steps} timesteps\")\n\n iterator = tqdm(time_range, desc='Decoding image', total=total_steps)\n x_dec = x_latent\n for i, step in enumerate(iterator):\n index = total_steps - i - 1\n ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)\n x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,\n unconditional_guidance_scale=unconditional_guidance_scale,\n unconditional_conditioning=unconditional_conditioning)\n return x_dec" }, { "path": "data_generation/ldm/models/diffusion/ddpm.py", "content": "\"\"\"\nwild mixture of\nhttps://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py\nhttps://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py\nhttps://github.com/CompVis/taming-transformers\n-- merci\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport numpy as np\nimport pytorch_lightning as pl\nfrom torch.optim.lr_scheduler import LambdaLR\nfrom einops import rearrange, repeat\nfrom contextlib import contextmanager\nfrom functools import partial\nfrom tqdm import tqdm\nfrom torchvision.utils import make_grid\nfrom pytorch_lightning.utilities.distributed import rank_zero_only\n\nfrom ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config\nfrom ldm.modules.ema import LitEma\nfrom ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution\nfrom ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL\nfrom ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like\nfrom ldm.models.diffusion.ddim import DDIMSampler\n\n\n__conditioning_keys__ = {'concat': 'c_concat',\n 'crossattn': 'c_crossattn',\n 'adm': 'y'}\n\n\ndef disabled_train(self, mode=True):\n \"\"\"Overwrite model.train with this function to make sure train/eval mode\n does not change anymore.\"\"\"\n return self\n\n\ndef uniform_on_device(r1, r2, shape, device):\n return (r1 - r2) * torch.rand(*shape, device=device) + r2\n\n\nclass DDPM(pl.LightningModule):\n # classic DDPM with Gaussian diffusion, in image space\n def __init__(self,\n unet_config,\n timesteps=1000,\n beta_schedule=\"linear\",\n loss_type=\"l2\",\n ckpt_path=None,\n ignore_keys=[],\n load_only_unet=False,\n monitor=\"val/loss\",\n use_ema=True,\n first_stage_key=\"image\",\n image_size=256,\n channels=3,\n log_every_t=100,\n clip_denoised=True,\n linear_start=1e-4,\n linear_end=2e-2,\n cosine_s=8e-3,\n given_betas=None,\n original_elbo_weight=0.,\n v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta\n l_simple_weight=1.,\n conditioning_key=None,\n parameterization=\"eps\", # all assuming fixed variance schedules\n scheduler_config=None,\n use_positional_encodings=False,\n learn_logvar=False,\n logvar_init=0.,\n ):\n super().__init__()\n assert parameterization in [\"eps\", \"x0\"], 'currently only supporting \"eps\" and \"x0\"'\n self.parameterization = parameterization\n print(f\"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode\")\n self.cond_stage_model = None\n self.clip_denoised = clip_denoised\n self.log_every_t = log_every_t\n self.first_stage_key = first_stage_key\n self.image_size = image_size # try conv?\n self.channels = channels\n self.use_positional_encodings = use_positional_encodings\n self.model = DiffusionWrapper(unet_config, conditioning_key)\n count_params(self.model, verbose=True)\n self.use_ema = use_ema\n if self.use_ema:\n self.model_ema = LitEma(self.model)\n print(f\"Keeping EMAs of {len(list(self.model_ema.buffers()))}.\")\n\n self.use_scheduler = scheduler_config is not None\n if self.use_scheduler:\n self.scheduler_config = scheduler_config\n\n self.v_posterior = v_posterior\n self.original_elbo_weight = original_elbo_weight\n self.l_simple_weight = l_simple_weight\n\n if monitor is not None:\n self.monitor = monitor\n if ckpt_path is not None:\n self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)\n\n self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,\n linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)\n\n self.loss_type = loss_type\n\n self.learn_logvar = learn_logvar\n self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))\n if self.learn_logvar:\n self.logvar = nn.Parameter(self.logvar, requires_grad=True)\n\n\n def register_schedule(self, given_betas=None, beta_schedule=\"linear\", timesteps=1000,\n linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):\n if exists(given_betas):\n betas = given_betas\n else:\n betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,\n cosine_s=cosine_s)\n alphas = 1. - betas\n alphas_cumprod = np.cumprod(alphas, axis=0)\n alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])\n\n timesteps, = betas.shape\n self.num_timesteps = int(timesteps)\n self.linear_start = linear_start\n self.linear_end = linear_end\n assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'\n\n to_torch = partial(torch.tensor, dtype=torch.float32)\n\n self.register_buffer('betas', to_torch(betas))\n self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))\n self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))\n\n # calculations for diffusion q(x_t | x_{t-1}) and others\n self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))\n self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))\n self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))\n self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))\n self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))\n\n # calculations for posterior q(x_{t-1} | x_t, x_0)\n posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (\n 1. - alphas_cumprod) + self.v_posterior * betas\n # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)\n self.register_buffer('posterior_variance', to_torch(posterior_variance))\n # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain\n self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))\n self.register_buffer('posterior_mean_coef1', to_torch(\n betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))\n self.register_buffer('posterior_mean_coef2', to_torch(\n (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))\n\n if self.parameterization == \"eps\":\n lvlb_weights = self.betas ** 2 / (\n 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))\n elif self.parameterization == \"x0\":\n lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))\n else:\n raise NotImplementedError(\"mu not supported\")\n # TODO how to choose this term\n lvlb_weights[0] = lvlb_weights[1]\n self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)\n assert not torch.isnan(self.lvlb_weights).all()\n\n @contextmanager\n def ema_scope(self, context=None):\n if self.use_ema:\n self.model_ema.store(self.model.parameters())\n self.model_ema.copy_to(self.model)\n if context is not None:\n print(f\"{context}: Switched to EMA weights\")\n try:\n yield None\n finally:\n if self.use_ema:\n self.model_ema.restore(self.model.parameters())\n if context is not None:\n print(f\"{context}: Restored training weights\")\n\n def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):\n sd = torch.load(path, map_location=\"cpu\")\n if \"state_dict\" in list(sd.keys()):\n sd = sd[\"state_dict\"]\n keys = list(sd.keys())\n for k in keys:\n for ik in ignore_keys:\n if k.startswith(ik):\n print(\"Deleting key {} from state_dict.\".format(k))\n del sd[k]\n missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(\n sd, strict=False)\n print(f\"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys\")\n if len(missing) > 0:\n print(f\"Missing Keys: {missing}\")\n if len(unexpected) > 0:\n print(f\"Unexpected Keys: {unexpected}\")\n\n def q_mean_variance(self, x_start, t):\n \"\"\"\n Get the distribution q(x_t | x_0).\n :param x_start: the [N x C x ...] tensor of noiseless inputs.\n :param t: the number of diffusion steps (minus 1). Here, 0 means one step.\n :return: A tuple (mean, variance, log_variance), all of x_start's shape.\n \"\"\"\n mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)\n variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)\n log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)\n return mean, variance, log_variance\n\n def predict_start_from_noise(self, x_t, t, noise):\n return (\n extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -\n extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise\n )\n\n def q_posterior(self, x_start, x_t, t):\n posterior_mean = (\n extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +\n extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t\n )\n posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)\n posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)\n return posterior_mean, posterior_variance, posterior_log_variance_clipped\n\n def p_mean_variance(self, x, t, clip_denoised: bool):\n model_out = self.model(x, t)\n if self.parameterization == \"eps\":\n x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)\n elif self.parameterization == \"x0\":\n x_recon = model_out\n if clip_denoised:\n x_recon.clamp_(-1., 1.)\n\n model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)\n return model_mean, posterior_variance, posterior_log_variance\n\n @torch.no_grad()\n def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):\n b, *_, device = *x.shape, x.device\n model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)\n noise = noise_like(x.shape, device, repeat_noise)\n # no noise when t == 0\n nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))\n return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise\n\n @torch.no_grad()\n def p_sample_loop(self, shape, return_intermediates=False):\n device = self.betas.device\n b = shape[0]\n img = torch.randn(shape, device=device)\n intermediates = [img]\n for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):\n img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),\n clip_denoised=self.clip_denoised)\n if i % self.log_every_t == 0 or i == self.num_timesteps - 1:\n intermediates.append(img)\n if return_intermediates:\n return img, intermediates\n return img\n\n @torch.no_grad()\n def sample(self, batch_size=16, return_intermediates=False):\n image_size = self.image_size\n channels = self.channels\n return self.p_sample_loop((batch_size, channels, image_size, image_size),\n return_intermediates=return_intermediates)\n\n def q_sample(self, x_start, t, noise=None):\n noise = default(noise, lambda: torch.randn_like(x_start))\n return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +\n extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)\n\n def get_loss(self, pred, target, mean=True):\n if self.loss_type == 'l1':\n loss = (target - pred).abs()\n if mean:\n loss = loss.mean()\n elif self.loss_type == 'l2':\n if mean:\n loss = torch.nn.functional.mse_loss(target, pred)\n else:\n loss = torch.nn.functional.mse_loss(target, pred, reduction='none')\n else:\n raise NotImplementedError(\"unknown loss type '{loss_type}'\")\n\n return loss\n\n def p_losses(self, x_start, t, noise=None):\n noise = default(noise, lambda: torch.randn_like(x_start))\n x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)\n model_out = self.model(x_noisy, t)\n\n loss_dict = {}\n if self.parameterization == \"eps\":\n target = noise\n elif self.parameterization == \"x0\":\n target = x_start\n else:\n raise NotImplementedError(f\"Paramterization {self.parameterization} not yet supported\")\n\n loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])\n\n log_prefix = 'train' if self.training else 'val'\n\n loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})\n loss_simple = loss.mean() * self.l_simple_weight\n\n loss_vlb = (self.lvlb_weights[t] * loss).mean()\n loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})\n\n loss = loss_simple + self.original_elbo_weight * loss_vlb\n\n loss_dict.update({f'{log_prefix}/loss': loss})\n\n return loss, loss_dict\n\n def forward(self, x, *args, **kwargs):\n # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size\n # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'\n t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()\n return self.p_losses(x, t, *args, **kwargs)\n\n def get_input(self, batch, k):\n x = batch[k]\n if len(x.shape) == 3:\n x = x[..., None]\n x = rearrange(x, 'b h w c -> b c h w')\n x = x.to(memory_format=torch.contiguous_format).float()\n return x\n\n def shared_step(self, batch):\n x = self.get_input(batch, self.first_stage_key)\n loss, loss_dict = self(x)\n return loss, loss_dict\n\n def training_step(self, batch, batch_idx):\n loss, loss_dict = self.shared_step(batch)\n\n self.log_dict(loss_dict, prog_bar=True,\n logger=True, on_step=True, on_epoch=True)\n\n self.log(\"global_step\", self.global_step,\n prog_bar=True, logger=True, on_step=True, on_epoch=False)\n\n if self.use_scheduler:\n lr = self.optimizers().param_groups[0]['lr']\n self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)\n\n return loss\n\n @torch.no_grad()\n def validation_step(self, batch, batch_idx):\n _, loss_dict_no_ema = self.shared_step(batch)\n with self.ema_scope():\n _, loss_dict_ema = self.shared_step(batch)\n loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}\n self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)\n self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)\n\n def on_train_batch_end(self, *args, **kwargs):\n if self.use_ema:\n self.model_ema(self.model)\n\n def _get_rows_from_list(self, samples):\n n_imgs_per_row = len(samples)\n denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')\n denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')\n denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)\n return denoise_grid\n\n @torch.no_grad()\n def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):\n log = dict()\n x = self.get_input(batch, self.first_stage_key)\n N = min(x.shape[0], N)\n n_row = min(x.shape[0], n_row)\n x = x.to(self.device)[:N]\n log[\"inputs\"] = x\n\n # get diffusion row\n diffusion_row = list()\n x_start = x[:n_row]\n\n for t in range(self.num_timesteps):\n if t % self.log_every_t == 0 or t == self.num_timesteps - 1:\n t = repeat(torch.tensor([t]), '1 -> b', b=n_row)\n t = t.to(self.device).long()\n noise = torch.randn_like(x_start)\n x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)\n diffusion_row.append(x_noisy)\n\n log[\"diffusion_row\"] = self._get_rows_from_list(diffusion_row)\n\n if sample:\n # get denoise row\n with self.ema_scope(\"Plotting\"):\n samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)\n\n log[\"samples\"] = samples\n log[\"denoise_row\"] = self._get_rows_from_list(denoise_row)\n\n if return_keys:\n if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:\n return log\n else:\n return {key: log[key] for key in return_keys}\n return log\n\n def configure_optimizers(self):\n lr = self.learning_rate\n params = list(self.model.parameters())\n if self.learn_logvar:\n params = params + [self.logvar]\n opt = torch.optim.AdamW(params, lr=lr)\n return opt\n\n\nclass LatentDiffusion(DDPM):\n \"\"\"main class\"\"\"\n def __init__(self,\n first_stage_config,\n cond_stage_config,\n num_timesteps_cond=None,\n cond_stage_key=\"image\",\n cond_stage_trainable=False,\n concat_mode=True,\n cond_stage_forward=None,\n conditioning_key=None,\n scale_factor=1.0,\n scale_by_std=False,\n *args, **kwargs):\n self.num_timesteps_cond = default(num_timesteps_cond, 1)\n self.scale_by_std = scale_by_std\n assert self.num_timesteps_cond <= kwargs['timesteps']\n # for backwards compatibility after implementation of DiffusionWrapper\n if conditioning_key is None:\n conditioning_key = 'concat' if concat_mode else 'crossattn'\n if cond_stage_config == '__is_unconditional__':\n conditioning_key = None\n ckpt_path = kwargs.pop(\"ckpt_path\", None)\n ignore_keys = kwargs.pop(\"ignore_keys\", [])\n super().__init__(conditioning_key=conditioning_key, *args, **kwargs)\n self.concat_mode = concat_mode\n self.cond_stage_trainable = cond_stage_trainable\n self.cond_stage_key = cond_stage_key\n try:\n self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1\n except:\n self.num_downs = 0\n if not scale_by_std:\n self.scale_factor = scale_factor\n else:\n self.register_buffer('scale_factor', torch.tensor(scale_factor))\n self.instantiate_first_stage(first_stage_config)\n self.instantiate_cond_stage(cond_stage_config)\n self.cond_stage_forward = cond_stage_forward\n self.clip_denoised = False\n self.bbox_tokenizer = None \n\n self.restarted_from_ckpt = False\n if ckpt_path is not None:\n self.init_from_ckpt(ckpt_path, ignore_keys)\n self.restarted_from_ckpt = True\n\n def make_cond_schedule(self, ):\n self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)\n ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()\n self.cond_ids[:self.num_timesteps_cond] = ids\n\n @rank_zero_only\n @torch.no_grad()\n def on_train_batch_start(self, batch, batch_idx, dataloader_idx):\n # only for very first batch\n if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:\n assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'\n # set rescale weight to 1./std of encodings\n print(\"### USING STD-RESCALING ###\")\n x = super().get_input(batch, self.first_stage_key)\n x = x.to(self.device)\n encoder_posterior = self.encode_first_stage(x)\n z = self.get_first_stage_encoding(encoder_posterior).detach()\n del self.scale_factor\n self.register_buffer('scale_factor', 1. / z.flatten().std())\n print(f\"setting self.scale_factor to {self.scale_factor}\")\n print(\"### USING STD-RESCALING ###\")\n\n def register_schedule(self,\n given_betas=None, beta_schedule=\"linear\", timesteps=1000,\n linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):\n super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)\n\n self.shorten_cond_schedule = self.num_timesteps_cond > 1\n if self.shorten_cond_schedule:\n self.make_cond_schedule()\n\n def instantiate_first_stage(self, config):\n model = instantiate_from_config(config)\n self.first_stage_model = model.eval()\n self.first_stage_model.train = disabled_train\n for param in self.first_stage_model.parameters():\n param.requires_grad = False\n\n def instantiate_cond_stage(self, config):\n if not self.cond_stage_trainable:\n if config == \"__is_first_stage__\":\n print(\"Using first stage also as cond stage.\")\n self.cond_stage_model = self.first_stage_model\n elif config == \"__is_unconditional__\":\n print(f\"Training {self.__class__.__name__} as an unconditional model.\")\n self.cond_stage_model = None\n # self.be_unconditional = True\n else:\n model = instantiate_from_config(config)\n self.cond_stage_model = model.eval()\n self.cond_stage_model.train = disabled_train\n for param in self.cond_stage_model.parameters():\n param.requires_grad = False\n else:\n assert config != '__is_first_stage__'\n assert config != '__is_unconditional__'\n model = instantiate_from_config(config)\n self.cond_stage_model = model\n\n def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):\n denoise_row = []\n for zd in tqdm(samples, desc=desc):\n denoise_row.append(self.decode_first_stage(zd.to(self.device),\n force_not_quantize=force_no_decoder_quantization))\n n_imgs_per_row = len(denoise_row)\n denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W\n denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')\n denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')\n denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)\n return denoise_grid\n\n def get_first_stage_encoding(self, encoder_posterior):\n if isinstance(encoder_posterior, DiagonalGaussianDistribution):\n z = encoder_posterior.sample()\n elif isinstance(encoder_posterior, torch.Tensor):\n z = encoder_posterior\n else:\n raise NotImplementedError(f\"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented\")\n return self.scale_factor * z\n\n def get_learned_conditioning(self, c):\n if self.cond_stage_forward is None:\n if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):\n c = self.cond_stage_model.encode(c)\n if isinstance(c, DiagonalGaussianDistribution):\n c = c.mode()\n else:\n c = self.cond_stage_model(c)\n else:\n assert hasattr(self.cond_stage_model, self.cond_stage_forward)\n c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)\n return c\n\n def meshgrid(self, h, w):\n y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)\n x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)\n\n arr = torch.cat([y, x], dim=-1)\n return arr\n\n def delta_border(self, h, w):\n \"\"\"\n :param h: height\n :param w: width\n :return: normalized distance to image border,\n wtith min distance = 0 at border and max dist = 0.5 at image center\n \"\"\"\n lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)\n arr = self.meshgrid(h, w) / lower_right_corner\n dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]\n dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]\n edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]\n return edge_dist\n\n def get_weighting(self, h, w, Ly, Lx, device):\n weighting = self.delta_border(h, w)\n weighting = torch.clip(weighting, self.split_input_params[\"clip_min_weight\"],\n self.split_input_params[\"clip_max_weight\"], )\n weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)\n\n if self.split_input_params[\"tie_braker\"]:\n L_weighting = self.delta_border(Ly, Lx)\n L_weighting = torch.clip(L_weighting,\n self.split_input_params[\"clip_min_tie_weight\"],\n self.split_input_params[\"clip_max_tie_weight\"])\n\n L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)\n weighting = weighting * L_weighting\n return weighting\n\n def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code\n \"\"\"\n :param x: img of size (bs, c, h, w)\n :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])\n \"\"\"\n bs, nc, h, w = x.shape\n\n # number of crops in image\n Ly = (h - kernel_size[0]) // stride[0] + 1\n Lx = (w - kernel_size[1]) // stride[1] + 1\n\n if uf == 1 and df == 1:\n fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)\n unfold = torch.nn.Unfold(**fold_params)\n\n fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)\n\n weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)\n normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap\n weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))\n\n elif uf > 1 and df == 1:\n fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)\n unfold = torch.nn.Unfold(**fold_params)\n\n fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),\n dilation=1, padding=0,\n stride=(stride[0] * uf, stride[1] * uf))\n fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)\n\n weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)\n normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap\n weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))\n\n elif df > 1 and uf == 1:\n fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)\n unfold = torch.nn.Unfold(**fold_params)\n\n fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),\n dilation=1, padding=0,\n stride=(stride[0] // df, stride[1] // df))\n fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)\n\n weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)\n normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap\n weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))\n\n else:\n raise NotImplementedError\n\n return fold, unfold, normalization, weighting\n\n @torch.no_grad()\n def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,\n cond_key=None, return_original_cond=False, bs=None):\n x = super().get_input(batch, k)\n if bs is not None:\n x = x[:bs]\n x = x.to(self.device)\n encoder_posterior = self.encode_first_stage(x)\n z = self.get_first_stage_encoding(encoder_posterior).detach()\n\n if self.model.conditioning_key is not None:\n if cond_key is None:\n cond_key = self.cond_stage_key\n if cond_key != self.first_stage_key:\n if cond_key in ['caption', 'coordinates_bbox']:\n xc = batch[cond_key]\n elif cond_key == 'class_label':\n xc = batch\n else:\n xc = super().get_input(batch, cond_key).to(self.device)\n else:\n xc = x\n if not self.cond_stage_trainable or force_c_encode:\n if isinstance(xc, dict) or isinstance(xc, list):\n # import pudb; pudb.set_trace()\n c = self.get_learned_conditioning(xc)\n else:\n c = self.get_learned_conditioning(xc.to(self.device))\n else:\n c = xc\n if bs is not None:\n c = c[:bs]\n\n if self.use_positional_encodings:\n pos_x, pos_y = self.compute_latent_shifts(batch)\n ckey = __conditioning_keys__[self.model.conditioning_key]\n c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}\n\n else:\n c = None\n xc = None\n if self.use_positional_encodings:\n pos_x, pos_y = self.compute_latent_shifts(batch)\n c = {'pos_x': pos_x, 'pos_y': pos_y}\n out = [z, c]\n if return_first_stage_outputs:\n xrec = self.decode_first_stage(z)\n out.extend([x, xrec])\n if return_original_cond:\n out.append(xc)\n return out\n\n @torch.no_grad()\n def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):\n if predict_cids:\n if z.dim() == 4:\n z = torch.argmax(z.exp(), dim=1).long()\n z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)\n z = rearrange(z, 'b h w c -> b c h w').contiguous()\n\n z = 1. / self.scale_factor * z\n\n if hasattr(self, \"split_input_params\"):\n if self.split_input_params[\"patch_distributed_vq\"]:\n ks = self.split_input_params[\"ks\"] # eg. (128, 128)\n stride = self.split_input_params[\"stride\"] # eg. (64, 64)\n uf = self.split_input_params[\"vqf\"]\n bs, nc, h, w = z.shape\n if ks[0] > h or ks[1] > w:\n ks = (min(ks[0], h), min(ks[1], w))\n print(\"reducing Kernel\")\n\n if stride[0] > h or stride[1] > w:\n stride = (min(stride[0], h), min(stride[1], w))\n print(\"reducing stride\")\n\n fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)\n\n z = unfold(z) # (bn, nc * prod(**ks), L)\n # 1. Reshape to img shape\n z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )\n\n # 2. apply model loop over last dim\n if isinstance(self.first_stage_model, VQModelInterface):\n output_list = [self.first_stage_model.decode(z[:, :, :, :, i],\n force_not_quantize=predict_cids or force_not_quantize)\n for i in range(z.shape[-1])]\n else:\n\n output_list = [self.first_stage_model.decode(z[:, :, :, :, i])\n for i in range(z.shape[-1])]\n\n o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)\n o = o * weighting\n # Reverse 1. reshape to img shape\n o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)\n # stitch crops together\n decoded = fold(o)\n decoded = decoded / normalization # norm is shape (1, 1, h, w)\n return decoded\n else:\n if isinstance(self.first_stage_model, VQModelInterface):\n return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)\n else:\n return self.first_stage_model.decode(z)\n\n else:\n if isinstance(self.first_stage_model, VQModelInterface):\n return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)\n else:\n return self.first_stage_model.decode(z)\n\n # same as above but without decorator\n def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):\n if predict_cids:\n if z.dim() == 4:\n z = torch.argmax(z.exp(), dim=1).long()\n z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)\n z = rearrange(z, 'b h w c -> b c h w').contiguous()\n\n z = 1. / self.scale_factor * z\n\n if hasattr(self, \"split_input_params\"):\n if self.split_input_params[\"patch_distributed_vq\"]:\n ks = self.split_input_params[\"ks\"] # eg. (128, 128)\n stride = self.split_input_params[\"stride\"] # eg. (64, 64)\n uf = self.split_input_params[\"vqf\"]\n bs, nc, h, w = z.shape\n if ks[0] > h or ks[1] > w:\n ks = (min(ks[0], h), min(ks[1], w))\n print(\"reducing Kernel\")\n\n if stride[0] > h or stride[1] > w:\n stride = (min(stride[0], h), min(stride[1], w))\n print(\"reducing stride\")\n\n fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)\n\n z = unfold(z) # (bn, nc * prod(**ks), L)\n # 1. Reshape to img shape\n z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )\n\n # 2. apply model loop over last dim\n if isinstance(self.first_stage_model, VQModelInterface): \n output_list = [self.first_stage_model.decode(z[:, :, :, :, i],\n force_not_quantize=predict_cids or force_not_quantize)\n for i in range(z.shape[-1])]\n else:\n\n output_list = [self.first_stage_model.decode(z[:, :, :, :, i])\n for i in range(z.shape[-1])]\n\n o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)\n o = o * weighting\n # Reverse 1. reshape to img shape\n o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)\n # stitch crops together\n decoded = fold(o)\n decoded = decoded / normalization # norm is shape (1, 1, h, w)\n return decoded\n else:\n if isinstance(self.first_stage_model, VQModelInterface):\n return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)\n else:\n return self.first_stage_model.decode(z)\n\n else:\n if isinstance(self.first_stage_model, VQModelInterface):\n return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)\n else:\n return self.first_stage_model.decode(z)\n\n @torch.no_grad()\n def encode_first_stage(self, x):\n if hasattr(self, \"split_input_params\"):\n if self.split_input_params[\"patch_distributed_vq\"]:\n ks = self.split_input_params[\"ks\"] # eg. (128, 128)\n stride = self.split_input_params[\"stride\"] # eg. (64, 64)\n df = self.split_input_params[\"vqf\"]\n self.split_input_params['original_image_size'] = x.shape[-2:]\n bs, nc, h, w = x.shape\n if ks[0] > h or ks[1] > w:\n ks = (min(ks[0], h), min(ks[1], w))\n print(\"reducing Kernel\")\n\n if stride[0] > h or stride[1] > w:\n stride = (min(stride[0], h), min(stride[1], w))\n print(\"reducing stride\")\n\n fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)\n z = unfold(x) # (bn, nc * prod(**ks), L)\n # Reshape to img shape\n z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )\n\n output_list = [self.first_stage_model.encode(z[:, :, :, :, i])\n for i in range(z.shape[-1])]\n\n o = torch.stack(output_list, axis=-1)\n o = o * weighting\n\n # Reverse reshape to img shape\n o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)\n # stitch crops together\n decoded = fold(o)\n decoded = decoded / normalization\n return decoded\n\n else:\n return self.first_stage_model.encode(x)\n else:\n return self.first_stage_model.encode(x)\n\n def shared_step(self, batch, **kwargs):\n x, c = self.get_input(batch, self.first_stage_key)\n loss = self(x, c)\n return loss\n\n def forward(self, x, c, *args, **kwargs):\n t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()\n if self.model.conditioning_key is not None:\n assert c is not None\n if self.cond_stage_trainable:\n c = self.get_learned_conditioning(c)\n if self.shorten_cond_schedule: # TODO: drop this option\n tc = self.cond_ids[t].to(self.device)\n c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))\n return self.p_losses(x, c, t, *args, **kwargs)\n\n def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset\n def rescale_bbox(bbox):\n x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])\n y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])\n w = min(bbox[2] / crop_coordinates[2], 1 - x0)\n h = min(bbox[3] / crop_coordinates[3], 1 - y0)\n return x0, y0, w, h\n\n return [rescale_bbox(b) for b in bboxes]\n\n def apply_model(self, x_noisy, t, cond, return_ids=False):\n\n if isinstance(cond, dict):\n # hybrid case, cond is exptected to be a dict\n pass\n else:\n if not isinstance(cond, list):\n cond = [cond]\n key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'\n cond = {key: cond}\n\n if hasattr(self, \"split_input_params\"):\n assert len(cond) == 1 # todo can only deal with one conditioning atm\n assert not return_ids \n ks = self.split_input_params[\"ks\"] # eg. (128, 128)\n stride = self.split_input_params[\"stride\"] # eg. (64, 64)\n\n h, w = x_noisy.shape[-2:]\n\n fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)\n\n z = unfold(x_noisy) # (bn, nc * prod(**ks), L)\n # Reshape to img shape\n z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )\n z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]\n\n if self.cond_stage_key in [\"image\", \"LR_image\", \"segmentation\",\n 'bbox_img'] and self.model.conditioning_key: # todo check for completeness\n c_key = next(iter(cond.keys())) # get key\n c = next(iter(cond.values())) # get value\n assert (len(c) == 1) # todo extend to list with more than one elem\n c = c[0] # get element\n\n c = unfold(c)\n c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L )\n\n cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]\n\n elif self.cond_stage_key == 'coordinates_bbox':\n assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'\n\n # assuming padding of unfold is always 0 and its dilation is always 1\n n_patches_per_row = int((w - ks[0]) / stride[0] + 1)\n full_img_h, full_img_w = self.split_input_params['original_image_size']\n # as we are operating on latents, we need the factor from the original image size to the\n # spatial latent size to properly rescale the crops for regenerating the bbox annotations\n num_downs = self.first_stage_model.encoder.num_resolutions - 1\n rescale_latent = 2 ** (num_downs)\n\n # get top left postions of patches as conforming for the bbbox tokenizer, therefore we\n # need to rescale the tl patch coordinates to be in between (0,1)\n tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,\n rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)\n for patch_nr in range(z.shape[-1])]\n\n # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)\n patch_limits = [(x_tl, y_tl,\n rescale_latent * ks[0] / full_img_w,\n rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]\n # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]\n\n # tokenize crop coordinates for the bounding boxes of the respective patches\n patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)\n for bbox in patch_limits] # list of length l with tensors of shape (1, 2)\n print(patch_limits_tknzd[0].shape)\n # cut tknzd crop position from conditioning\n assert isinstance(cond, dict), 'cond must be dict to be fed into model'\n cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)\n print(cut_cond.shape)\n\n adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])\n adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')\n print(adapted_cond.shape)\n adapted_cond = self.get_learned_conditioning(adapted_cond)\n print(adapted_cond.shape)\n adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])\n print(adapted_cond.shape)\n\n cond_list = [{'c_crossattn': [e]} for e in adapted_cond]\n\n else:\n cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient\n\n # apply model by loop over crops\n output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]\n assert not isinstance(output_list[0],\n tuple) # todo cant deal with multiple model outputs check this never happens\n\n o = torch.stack(output_list, axis=-1)\n o = o * weighting\n # Reverse reshape to img shape\n o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)\n # stitch crops together\n x_recon = fold(o) / normalization\n\n else:\n x_recon = self.model(x_noisy, t, **cond)\n\n if isinstance(x_recon, tuple) and not return_ids:\n return x_recon[0]\n else:\n return x_recon\n\n def _predict_eps_from_xstart(self, x_t, t, pred_xstart):\n return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \\\n extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)\n\n def _prior_bpd(self, x_start):\n \"\"\"\n Get the prior KL term for the variational lower-bound, measured in\n bits-per-dim.\n This term can't be optimized, as it only depends on the encoder.\n :param x_start: the [N x C x ...] tensor of inputs.\n :return: a batch of [N] KL values (in bits), one per batch element.\n \"\"\"\n batch_size = x_start.shape[0]\n t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)\n qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)\n kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)\n return mean_flat(kl_prior) / np.log(2.0)\n\n def p_losses(self, x_start, cond, t, noise=None):\n noise = default(noise, lambda: torch.randn_like(x_start))\n x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)\n model_output = self.apply_model(x_noisy, t, cond)\n\n loss_dict = {}\n prefix = 'train' if self.training else 'val'\n\n if self.parameterization == \"x0\":\n target = x_start\n elif self.parameterization == \"eps\":\n target = noise\n else:\n raise NotImplementedError()\n\n loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])\n loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})\n\n logvar_t = self.logvar[t].to(self.device)\n loss = loss_simple / torch.exp(logvar_t) + logvar_t\n # loss = loss_simple / torch.exp(self.logvar) + self.logvar\n if self.learn_logvar:\n loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})\n loss_dict.update({'logvar': self.logvar.data.mean()})\n\n loss = self.l_simple_weight * loss.mean()\n\n loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))\n loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()\n loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})\n loss += (self.original_elbo_weight * loss_vlb)\n loss_dict.update({f'{prefix}/loss': loss})\n\n return loss, loss_dict\n\n def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,\n return_x0=False, score_corrector=None, corrector_kwargs=None):\n t_in = t\n model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)\n\n if score_corrector is not None:\n assert self.parameterization == \"eps\"\n model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)\n\n if return_codebook_ids:\n model_out, logits = model_out\n\n if self.parameterization == \"eps\":\n x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)\n elif self.parameterization == \"x0\":\n x_recon = model_out\n else:\n raise NotImplementedError()\n\n if clip_denoised:\n x_recon.clamp_(-1., 1.)\n if quantize_denoised:\n x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)\n model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)\n if return_codebook_ids:\n return model_mean, posterior_variance, posterior_log_variance, logits\n elif return_x0:\n return model_mean, posterior_variance, posterior_log_variance, x_recon\n else:\n return model_mean, posterior_variance, posterior_log_variance\n\n @torch.no_grad()\n def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,\n return_codebook_ids=False, quantize_denoised=False, return_x0=False,\n temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):\n b, *_, device = *x.shape, x.device\n outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,\n return_codebook_ids=return_codebook_ids,\n quantize_denoised=quantize_denoised,\n return_x0=return_x0,\n score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)\n if return_codebook_ids:\n raise DeprecationWarning(\"Support dropped.\")\n model_mean, _, model_log_variance, logits = outputs\n elif return_x0:\n model_mean, _, model_log_variance, x0 = outputs\n else:\n model_mean, _, model_log_variance = outputs\n\n noise = noise_like(x.shape, device, repeat_noise) * temperature\n if noise_dropout > 0.:\n noise = torch.nn.functional.dropout(noise, p=noise_dropout)\n # no noise when t == 0\n nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))\n\n if return_codebook_ids:\n return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)\n if return_x0:\n return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0\n else:\n return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise\n\n @torch.no_grad()\n def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,\n img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,\n score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,\n log_every_t=None):\n if not log_every_t:\n log_every_t = self.log_every_t\n timesteps = self.num_timesteps\n if batch_size is not None:\n b = batch_size if batch_size is not None else shape[0]\n shape = [batch_size] + list(shape)\n else:\n b = batch_size = shape[0]\n if x_T is None:\n img = torch.randn(shape, device=self.device)\n else:\n img = x_T\n intermediates = []\n if cond is not None:\n if isinstance(cond, dict):\n cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else\n list(map(lambda x: x[:batch_size], cond[key])) for key in cond}\n else:\n cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]\n\n if start_T is not None:\n timesteps = min(timesteps, start_T)\n iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',\n total=timesteps) if verbose else reversed(\n range(0, timesteps))\n if type(temperature) == float:\n temperature = [temperature] * timesteps\n\n for i in iterator:\n ts = torch.full((b,), i, device=self.device, dtype=torch.long)\n if self.shorten_cond_schedule:\n assert self.model.conditioning_key != 'hybrid'\n tc = self.cond_ids[ts].to(cond.device)\n cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))\n\n img, x0_partial = self.p_sample(img, cond, ts,\n clip_denoised=self.clip_denoised,\n quantize_denoised=quantize_denoised, return_x0=True,\n temperature=temperature[i], noise_dropout=noise_dropout,\n score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)\n if mask is not None:\n assert x0 is not None\n img_orig = self.q_sample(x0, ts)\n img = img_orig * mask + (1. - mask) * img\n\n if i % log_every_t == 0 or i == timesteps - 1:\n intermediates.append(x0_partial)\n if callback: callback(i)\n if img_callback: img_callback(img, i)\n return img, intermediates\n\n @torch.no_grad()\n def p_sample_loop(self, cond, shape, return_intermediates=False,\n x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,\n mask=None, x0=None, img_callback=None, start_T=None,\n log_every_t=None):\n\n if not log_every_t:\n log_every_t = self.log_every_t\n device = self.betas.device\n b = shape[0]\n if x_T is None:\n img = torch.randn(shape, device=device)\n else:\n img = x_T\n\n intermediates = [img]\n if timesteps is None:\n timesteps = self.num_timesteps\n\n if start_T is not None:\n timesteps = min(timesteps, start_T)\n iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(\n range(0, timesteps))\n\n if mask is not None:\n assert x0 is not None\n assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match\n\n for i in iterator:\n ts = torch.full((b,), i, device=device, dtype=torch.long)\n if self.shorten_cond_schedule:\n assert self.model.conditioning_key != 'hybrid'\n tc = self.cond_ids[ts].to(cond.device)\n cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))\n\n img = self.p_sample(img, cond, ts,\n clip_denoised=self.clip_denoised,\n quantize_denoised=quantize_denoised)\n if mask is not None:\n img_orig = self.q_sample(x0, ts)\n img = img_orig * mask + (1. - mask) * img\n\n if i % log_every_t == 0 or i == timesteps - 1:\n intermediates.append(img)\n if callback: callback(i)\n if img_callback: img_callback(img, i)\n\n if return_intermediates:\n return img, intermediates\n return img\n\n @torch.no_grad()\n def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,\n verbose=True, timesteps=None, quantize_denoised=False,\n mask=None, x0=None, shape=None,**kwargs):\n if shape is None:\n shape = (batch_size, self.channels, self.image_size, self.image_size)\n if cond is not None:\n if isinstance(cond, dict):\n cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else\n list(map(lambda x: x[:batch_size], cond[key])) for key in cond}\n else:\n cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]\n return self.p_sample_loop(cond,\n shape,\n return_intermediates=return_intermediates, x_T=x_T,\n verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,\n mask=mask, x0=x0)\n\n @torch.no_grad()\n def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):\n\n if ddim:\n ddim_sampler = DDIMSampler(self)\n shape = (self.channels, self.image_size, self.image_size)\n samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,\n shape,cond,verbose=False,**kwargs)\n\n else:\n samples, intermediates = self.sample(cond=cond, batch_size=batch_size,\n return_intermediates=True,**kwargs)\n\n return samples, intermediates\n\n\n @torch.no_grad()\n def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,\n quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,\n plot_diffusion_rows=True, **kwargs):\n\n use_ddim = ddim_steps is not None\n\n log = dict()\n z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,\n return_first_stage_outputs=True,\n force_c_encode=True,\n return_original_cond=True,\n bs=N)\n N = min(x.shape[0], N)\n n_row = min(x.shape[0], n_row)\n log[\"inputs\"] = x\n log[\"reconstruction\"] = xrec\n if self.model.conditioning_key is not None:\n if hasattr(self.cond_stage_model, \"decode\"):\n xc = self.cond_stage_model.decode(c)\n log[\"conditioning\"] = xc\n elif self.cond_stage_key in [\"caption\"]:\n xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[\"caption\"])\n log[\"conditioning\"] = xc\n elif self.cond_stage_key == 'class_label':\n xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[\"human_label\"])\n log['conditioning'] = xc\n elif isimage(xc):\n log[\"conditioning\"] = xc\n if ismap(xc):\n log[\"original_conditioning\"] = self.to_rgb(xc)\n\n if plot_diffusion_rows:\n # get diffusion row\n diffusion_row = list()\n z_start = z[:n_row]\n for t in range(self.num_timesteps):\n if t % self.log_every_t == 0 or t == self.num_timesteps - 1:\n t = repeat(torch.tensor([t]), '1 -> b', b=n_row)\n t = t.to(self.device).long()\n noise = torch.randn_like(z_start)\n z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)\n diffusion_row.append(self.decode_first_stage(z_noisy))\n\n diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W\n diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')\n diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')\n diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])\n log[\"diffusion_row\"] = diffusion_grid\n\n if sample:\n # get denoise row\n with self.ema_scope(\"Plotting\"):\n samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,\n ddim_steps=ddim_steps,eta=ddim_eta)\n # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)\n x_samples = self.decode_first_stage(samples)\n log[\"samples\"] = x_samples\n if plot_denoise_rows:\n denoise_grid = self._get_denoise_row_from_list(z_denoise_row)\n log[\"denoise_row\"] = denoise_grid\n\n if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(\n self.first_stage_model, IdentityFirstStage):\n # also display when quantizing x0 while sampling\n with self.ema_scope(\"Plotting Quantized Denoised\"):\n samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,\n ddim_steps=ddim_steps,eta=ddim_eta,\n quantize_denoised=True)\n # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,\n # quantize_denoised=True)\n x_samples = self.decode_first_stage(samples.to(self.device))\n log[\"samples_x0_quantized\"] = x_samples\n\n if inpaint:\n # make a simple center square\n b, h, w = z.shape[0], z.shape[2], z.shape[3]\n mask = torch.ones(N, h, w).to(self.device)\n # zeros will be filled in\n mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.\n mask = mask[:, None, ...]\n with self.ema_scope(\"Plotting Inpaint\"):\n\n samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,\n ddim_steps=ddim_steps, x0=z[:N], mask=mask)\n x_samples = self.decode_first_stage(samples.to(self.device))\n log[\"samples_inpainting\"] = x_samples\n log[\"mask\"] = mask\n\n # outpaint\n with self.ema_scope(\"Plotting Outpaint\"):\n samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,\n ddim_steps=ddim_steps, x0=z[:N], mask=mask)\n x_samples = self.decode_first_stage(samples.to(self.device))\n log[\"samples_outpainting\"] = x_samples\n\n if plot_progressive_rows:\n with self.ema_scope(\"Plotting Progressives\"):\n img, progressives = self.progressive_denoising(c,\n shape=(self.channels, self.image_size, self.image_size),\n batch_size=N)\n prog_row = self._get_denoise_row_from_list(progressives, desc=\"Progressive Generation\")\n log[\"progressive_row\"] = prog_row\n\n if return_keys:\n if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:\n return log\n else:\n return {key: log[key] for key in return_keys}\n return log\n\n def configure_optimizers(self):\n lr = self.learning_rate\n params = list(self.model.parameters())\n if self.cond_stage_trainable:\n print(f\"{self.__class__.__name__}: Also optimizing conditioner params!\")\n params = params + list(self.cond_stage_model.parameters())\n if self.learn_logvar:\n print('Diffusion model optimizing logvar')\n params.append(self.logvar)\n opt = torch.optim.AdamW(params, lr=lr)\n if self.use_scheduler:\n assert 'target' in self.scheduler_config\n scheduler = instantiate_from_config(self.scheduler_config)\n\n print(\"Setting up LambdaLR scheduler...\")\n scheduler = [\n {\n 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),\n 'interval': 'step',\n 'frequency': 1\n }]\n return [opt], scheduler\n return opt\n\n @torch.no_grad()\n def to_rgb(self, x):\n x = x.float()\n if not hasattr(self, \"colorize\"):\n self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)\n x = nn.functional.conv2d(x, weight=self.colorize)\n x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.\n return x\n\n\nclass DiffusionWrapper(pl.LightningModule):\n def __init__(self, diff_model_config, conditioning_key):\n super().__init__()\n self.diffusion_model = instantiate_from_config(diff_model_config)\n self.conditioning_key = conditioning_key\n assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm']\n\n def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):\n if self.conditioning_key is None:\n out = self.diffusion_model(x, t)\n elif self.conditioning_key == 'concat':\n xc = torch.cat([x] + c_concat, dim=1)\n out = self.diffusion_model(xc, t)\n elif self.conditioning_key == 'crossattn':\n cc = torch.cat(c_crossattn, 1)\n out = self.diffusion_model(x, t, context=cc)\n elif self.conditioning_key == 'hybrid':\n xc = torch.cat([x] + c_concat, dim=1)\n cc = torch.cat(c_crossattn, 1)\n out = self.diffusion_model(xc, t, context=cc)\n elif self.conditioning_key == 'adm':\n cc = c_crossattn[0]\n out = self.diffusion_model(x, t, y=cc)\n else:\n raise NotImplementedError()\n\n return out\n\n\nclass Layout2ImgDiffusion(LatentDiffusion):\n # TODO: move all layout-specific hacks to this class\n def __init__(self, cond_stage_key, *args, **kwargs):\n assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key=\"coordinates_bbox\"'\n super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)\n\n def log_images(self, batch, N=8, *args, **kwargs):\n logs = super().log_images(batch=batch, N=N, *args, **kwargs)\n\n key = 'train' if self.training else 'validation'\n dset = self.trainer.datamodule.datasets[key]\n mapper = dset.conditional_builders[self.cond_stage_key]\n\n bbox_imgs = []\n map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno))\n for tknzd_bbox in batch[self.cond_stage_key][:N]:\n bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256))\n bbox_imgs.append(bboximg)\n\n cond_img = torch.stack(bbox_imgs, dim=0)\n logs['bbox_image'] = cond_img\n return logs\n" }, { "path": "data_generation/ldm/models/diffusion/dpm_solver/__init__.py", "content": "from .sampler import DPMSolverSampler" }, { "path": "data_generation/ldm/models/diffusion/dpm_solver/dpm_solver.py", "content": "import torch\nimport torch.nn.functional as F\nimport math\n\n\nclass NoiseScheduleVP:\n def __init__(\n self,\n schedule='discrete',\n betas=None,\n alphas_cumprod=None,\n continuous_beta_0=0.1,\n continuous_beta_1=20.,\n ):\n \"\"\"Create a wrapper class for the forward SDE (VP type).\n\n ***\n Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.\n We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.\n ***\n\n The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).\n We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).\n Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:\n\n log_alpha_t = self.marginal_log_mean_coeff(t)\n sigma_t = self.marginal_std(t)\n lambda_t = self.marginal_lambda(t)\n\n Moreover, as lambda(t) is an invertible function, we also support its inverse function:\n\n t = self.inverse_lambda(lambda_t)\n\n ===============================================================\n\n We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).\n\n 1. For discrete-time DPMs:\n\n For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:\n t_i = (i + 1) / N\n e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.\n We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.\n\n Args:\n betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)\n alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)\n\n Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.\n\n **Important**: Please pay special attention for the args for `alphas_cumprod`:\n The `alphas_cumprod` is the \\hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that\n q_{t_n | 0}(x_{t_n} | x_0) = N ( \\sqrt{\\hat{alpha_n}} * x_0, (1 - \\hat{alpha_n}) * I ).\n Therefore, the notation \\hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have\n alpha_{t_n} = \\sqrt{\\hat{alpha_n}},\n and\n log(alpha_{t_n}) = 0.5 * log(\\hat{alpha_n}).\n\n\n 2. For continuous-time DPMs:\n\n We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise\n schedule are the default settings in DDPM and improved-DDPM:\n\n Args:\n beta_min: A `float` number. The smallest beta for the linear schedule.\n beta_max: A `float` number. The largest beta for the linear schedule.\n cosine_s: A `float` number. The hyperparameter in the cosine schedule.\n cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.\n T: A `float` number. The ending time of the forward process.\n\n ===============================================================\n\n Args:\n schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,\n 'linear' or 'cosine' for continuous-time DPMs.\n Returns:\n A wrapper object of the forward SDE (VP type).\n \n ===============================================================\n\n Example:\n\n # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):\n >>> ns = NoiseScheduleVP('discrete', betas=betas)\n\n # For discrete-time DPMs, given alphas_cumprod (the \\hat{alpha_n} array for n = 0, 1, ..., N - 1):\n >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)\n\n # For continuous-time DPMs (VPSDE), linear schedule:\n >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)\n\n \"\"\"\n\n if schedule not in ['discrete', 'linear', 'cosine']:\n raise ValueError(\"Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'\".format(schedule))\n\n self.schedule = schedule\n if schedule == 'discrete':\n if betas is not None:\n log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)\n else:\n assert alphas_cumprod is not None\n log_alphas = 0.5 * torch.log(alphas_cumprod)\n self.total_N = len(log_alphas)\n self.T = 1.\n self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))\n self.log_alpha_array = log_alphas.reshape((1, -1,))\n else:\n self.total_N = 1000\n self.beta_0 = continuous_beta_0\n self.beta_1 = continuous_beta_1\n self.cosine_s = 0.008\n self.cosine_beta_max = 999.\n self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s\n self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))\n self.schedule = schedule\n if schedule == 'cosine':\n # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.\n # Note that T = 0.9946 may be not the optimal setting. However, we find it works well.\n self.T = 0.9946\n else:\n self.T = 1.\n\n def marginal_log_mean_coeff(self, t):\n \"\"\"\n Compute log(alpha_t) of a given continuous-time label t in [0, T].\n \"\"\"\n if self.schedule == 'discrete':\n return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1))\n elif self.schedule == 'linear':\n return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0\n elif self.schedule == 'cosine':\n log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))\n log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0\n return log_alpha_t\n\n def marginal_alpha(self, t):\n \"\"\"\n Compute alpha_t of a given continuous-time label t in [0, T].\n \"\"\"\n return torch.exp(self.marginal_log_mean_coeff(t))\n\n def marginal_std(self, t):\n \"\"\"\n Compute sigma_t of a given continuous-time label t in [0, T].\n \"\"\"\n return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))\n\n def marginal_lambda(self, t):\n \"\"\"\n Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].\n \"\"\"\n log_mean_coeff = self.marginal_log_mean_coeff(t)\n log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))\n return log_mean_coeff - log_std\n\n def inverse_lambda(self, lamb):\n \"\"\"\n Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.\n \"\"\"\n if self.schedule == 'linear':\n tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))\n Delta = self.beta_0**2 + tmp\n return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)\n elif self.schedule == 'discrete':\n log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)\n t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1]))\n return t.reshape((-1,))\n else:\n log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))\n t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s\n t = t_fn(log_alpha)\n return t\n\n\ndef model_wrapper(\n model,\n noise_schedule,\n model_type=\"noise\",\n model_kwargs={},\n guidance_type=\"uncond\",\n condition=None,\n unconditional_condition=None,\n guidance_scale=1.,\n classifier_fn=None,\n classifier_kwargs={},\n):\n \"\"\"Create a wrapper function for the noise prediction model.\n\n DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to\n firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.\n\n We support four types of the diffusion model by setting `model_type`:\n\n 1. \"noise\": noise prediction model. (Trained by predicting noise).\n\n 2. \"x_start\": data prediction model. (Trained by predicting the data x_0 at time 0).\n\n 3. \"v\": velocity prediction model. (Trained by predicting the velocity).\n The \"v\" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].\n\n [1] Salimans, Tim, and Jonathan Ho. \"Progressive distillation for fast sampling of diffusion models.\"\n arXiv preprint arXiv:2202.00512 (2022).\n [2] Ho, Jonathan, et al. \"Imagen Video: High Definition Video Generation with Diffusion Models.\"\n arXiv preprint arXiv:2210.02303 (2022).\n \n 4. \"score\": marginal score function. (Trained by denoising score matching).\n Note that the score function and the noise prediction model follows a simple relationship:\n ```\n noise(x_t, t) = -sigma_t * score(x_t, t)\n ```\n\n We support three types of guided sampling by DPMs by setting `guidance_type`:\n 1. \"uncond\": unconditional sampling by DPMs.\n The input `model` has the following format:\n ``\n model(x, t_input, **model_kwargs) -> noise | x_start | v | score\n ``\n\n 2. \"classifier\": classifier guidance sampling [3] by DPMs and another classifier.\n The input `model` has the following format:\n ``\n model(x, t_input, **model_kwargs) -> noise | x_start | v | score\n `` \n\n The input `classifier_fn` has the following format:\n ``\n classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)\n ``\n\n [3] P. Dhariwal and A. Q. Nichol, \"Diffusion models beat GANs on image synthesis,\"\n in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.\n\n 3. \"classifier-free\": classifier-free guidance sampling by conditional DPMs.\n The input `model` has the following format:\n ``\n model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score\n `` \n And if cond == `unconditional_condition`, the model output is the unconditional DPM output.\n\n [4] Ho, Jonathan, and Tim Salimans. \"Classifier-free diffusion guidance.\"\n arXiv preprint arXiv:2207.12598 (2022).\n \n\n The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)\n or continuous-time labels (i.e. epsilon to T).\n\n We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:\n ``\n def model_fn(x, t_continuous) -> noise:\n t_input = get_model_input_time(t_continuous)\n return noise_pred(model, x, t_input, **model_kwargs) \n ``\n where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.\n\n ===============================================================\n\n Args:\n model: A diffusion model with the corresponding format described above.\n noise_schedule: A noise schedule object, such as NoiseScheduleVP.\n model_type: A `str`. The parameterization type of the diffusion model.\n \"noise\" or \"x_start\" or \"v\" or \"score\".\n model_kwargs: A `dict`. A dict for the other inputs of the model function.\n guidance_type: A `str`. The type of the guidance for sampling.\n \"uncond\" or \"classifier\" or \"classifier-free\".\n condition: A pytorch tensor. The condition for the guided sampling.\n Only used for \"classifier\" or \"classifier-free\" guidance type.\n unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.\n Only used for \"classifier-free\" guidance type.\n guidance_scale: A `float`. The scale for the guided sampling.\n classifier_fn: A classifier function. Only used for the classifier guidance.\n classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.\n Returns:\n A noise prediction model that accepts the noised data and the continuous time as the inputs.\n \"\"\"\n\n def get_model_input_time(t_continuous):\n \"\"\"\n Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.\n For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].\n For continuous-time DPMs, we just use `t_continuous`.\n \"\"\"\n if noise_schedule.schedule == 'discrete':\n return (t_continuous - 1. / noise_schedule.total_N) * 1000.\n else:\n return t_continuous\n\n def noise_pred_fn(x, t_continuous, cond=None):\n if t_continuous.reshape((-1,)).shape[0] == 1:\n t_continuous = t_continuous.expand((x.shape[0]))\n t_input = get_model_input_time(t_continuous)\n if cond is None:\n output = model(x, t_input, **model_kwargs)\n else:\n output = model(x, t_input, cond, **model_kwargs)\n if model_type == \"noise\":\n return output\n elif model_type == \"x_start\":\n alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)\n dims = x.dim()\n return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)\n elif model_type == \"v\":\n alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)\n dims = x.dim()\n return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x\n elif model_type == \"score\":\n sigma_t = noise_schedule.marginal_std(t_continuous)\n dims = x.dim()\n return -expand_dims(sigma_t, dims) * output\n\n def cond_grad_fn(x, t_input):\n \"\"\"\n Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).\n \"\"\"\n with torch.enable_grad():\n x_in = x.detach().requires_grad_(True)\n log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)\n return torch.autograd.grad(log_prob.sum(), x_in)[0]\n\n def model_fn(x, t_continuous):\n \"\"\"\n The noise predicition model function that is used for DPM-Solver.\n \"\"\"\n if t_continuous.reshape((-1,)).shape[0] == 1:\n t_continuous = t_continuous.expand((x.shape[0]))\n if guidance_type == \"uncond\":\n return noise_pred_fn(x, t_continuous)\n elif guidance_type == \"classifier\":\n assert classifier_fn is not None\n t_input = get_model_input_time(t_continuous)\n cond_grad = cond_grad_fn(x, t_input)\n sigma_t = noise_schedule.marginal_std(t_continuous)\n noise = noise_pred_fn(x, t_continuous)\n return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad\n elif guidance_type == \"classifier-free\":\n if guidance_scale == 1. or unconditional_condition is None:\n return noise_pred_fn(x, t_continuous, cond=condition)\n else:\n x_in = torch.cat([x] * 2)\n t_in = torch.cat([t_continuous] * 2)\n c_in = torch.cat([unconditional_condition, condition])\n noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)\n return noise_uncond + guidance_scale * (noise - noise_uncond)\n\n assert model_type in [\"noise\", \"x_start\", \"v\"]\n assert guidance_type in [\"uncond\", \"classifier\", \"classifier-free\"]\n return model_fn\n\n\nclass DPM_Solver:\n def __init__(self, model_fn, noise_schedule, predict_x0=False, thresholding=False, max_val=1.):\n \"\"\"Construct a DPM-Solver. \n\n We support both the noise prediction model (\"predicting epsilon\") and the data prediction model (\"predicting x0\").\n If `predict_x0` is False, we use the solver for the noise prediction model (DPM-Solver).\n If `predict_x0` is True, we use the solver for the data prediction model (DPM-Solver++).\n In such case, we further support the \"dynamic thresholding\" in [1] when `thresholding` is True.\n The \"dynamic thresholding\" can greatly improve the sample quality for pixel-space DPMs with large guidance scales.\n\n Args:\n model_fn: A noise prediction model function which accepts the continuous-time input (t in [epsilon, T]):\n ``\n def model_fn(x, t_continuous):\n return noise\n ``\n noise_schedule: A noise schedule object, such as NoiseScheduleVP.\n predict_x0: A `bool`. If true, use the data prediction model; else, use the noise prediction model.\n thresholding: A `bool`. Valid when `predict_x0` is True. Whether to use the \"dynamic thresholding\" in [1].\n max_val: A `float`. Valid when both `predict_x0` and `thresholding` are True. The max value for thresholding.\n \n [1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b.\n \"\"\"\n self.model = model_fn\n self.noise_schedule = noise_schedule\n self.predict_x0 = predict_x0\n self.thresholding = thresholding\n self.max_val = max_val\n\n def noise_prediction_fn(self, x, t):\n \"\"\"\n Return the noise prediction model.\n \"\"\"\n return self.model(x, t)\n\n def data_prediction_fn(self, x, t):\n \"\"\"\n Return the data prediction model (with thresholding).\n \"\"\"\n noise = self.noise_prediction_fn(x, t)\n dims = x.dim()\n alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)\n x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)\n if self.thresholding:\n p = 0.995 # A hyperparameter in the paper of \"Imagen\" [1].\n s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)\n s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)\n x0 = torch.clamp(x0, -s, s) / s\n return x0\n\n def model_fn(self, x, t):\n \"\"\"\n Convert the model to the noise prediction model or the data prediction model. \n \"\"\"\n if self.predict_x0:\n return self.data_prediction_fn(x, t)\n else:\n return self.noise_prediction_fn(x, t)\n\n def get_time_steps(self, skip_type, t_T, t_0, N, device):\n \"\"\"Compute the intermediate time steps for sampling.\n\n Args:\n skip_type: A `str`. The type for the spacing of the time steps. We support three types:\n - 'logSNR': uniform logSNR for the time steps.\n - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)\n - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)\n t_T: A `float`. The starting time of the sampling (default is T).\n t_0: A `float`. The ending time of the sampling (default is epsilon).\n N: A `int`. The total number of the spacing of the time steps.\n device: A torch device.\n Returns:\n A pytorch tensor of the time steps, with the shape (N + 1,).\n \"\"\"\n if skip_type == 'logSNR':\n lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))\n lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))\n logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)\n return self.noise_schedule.inverse_lambda(logSNR_steps)\n elif skip_type == 'time_uniform':\n return torch.linspace(t_T, t_0, N + 1).to(device)\n elif skip_type == 'time_quadratic':\n t_order = 2\n t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device)\n return t\n else:\n raise ValueError(\"Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'\".format(skip_type))\n\n def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):\n \"\"\"\n Get the order of each step for sampling by the singlestep DPM-Solver.\n\n We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as \"DPM-Solver-fast\".\n Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is:\n - If order == 1:\n We take `steps` of DPM-Solver-1 (i.e. DDIM).\n - If order == 2:\n - Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling.\n - If steps % 2 == 0, we use K steps of DPM-Solver-2.\n - If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1.\n - If order == 3:\n - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.\n - If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1.\n - If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1.\n - If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2.\n\n ============================================\n Args:\n order: A `int`. The max order for the solver (2 or 3).\n steps: A `int`. The total number of function evaluations (NFE).\n skip_type: A `str`. The type for the spacing of the time steps. We support three types:\n - 'logSNR': uniform logSNR for the time steps.\n - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)\n - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)\n t_T: A `float`. The starting time of the sampling (default is T).\n t_0: A `float`. The ending time of the sampling (default is epsilon).\n device: A torch device.\n Returns:\n orders: A list of the solver order of each step.\n \"\"\"\n if order == 3:\n K = steps // 3 + 1\n if steps % 3 == 0:\n orders = [3,] * (K - 2) + [2, 1]\n elif steps % 3 == 1:\n orders = [3,] * (K - 1) + [1]\n else:\n orders = [3,] * (K - 1) + [2]\n elif order == 2:\n if steps % 2 == 0:\n K = steps // 2\n orders = [2,] * K\n else:\n K = steps // 2 + 1\n orders = [2,] * (K - 1) + [1]\n elif order == 1:\n K = 1\n orders = [1,] * steps\n else:\n raise ValueError(\"'order' must be '1' or '2' or '3'.\")\n if skip_type == 'logSNR':\n # To reproduce the results in DPM-Solver paper\n timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)\n else:\n timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders)).to(device)]\n return timesteps_outer, orders\n\n def denoise_to_zero_fn(self, x, s):\n \"\"\"\n Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. \n \"\"\"\n return self.data_prediction_fn(x, s)\n\n def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False):\n \"\"\"\n DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`.\n\n Args:\n x: A pytorch tensor. The initial value at time `s`.\n s: A pytorch tensor. The starting time, with the shape (x.shape[0],).\n t: A pytorch tensor. The ending time, with the shape (x.shape[0],).\n model_s: A pytorch tensor. The model function evaluated at time `s`.\n If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.\n return_intermediate: A `bool`. If true, also return the model value at time `s`.\n Returns:\n x_t: A pytorch tensor. The approximated solution at time `t`.\n \"\"\"\n ns = self.noise_schedule\n dims = x.dim()\n lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)\n h = lambda_t - lambda_s\n log_alpha_s, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(t)\n sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t)\n alpha_t = torch.exp(log_alpha_t)\n\n if self.predict_x0:\n phi_1 = torch.expm1(-h)\n if model_s is None:\n model_s = self.model_fn(x, s)\n x_t = (\n expand_dims(sigma_t / sigma_s, dims) * x\n - expand_dims(alpha_t * phi_1, dims) * model_s\n )\n if return_intermediate:\n return x_t, {'model_s': model_s}\n else:\n return x_t\n else:\n phi_1 = torch.expm1(h)\n if model_s is None:\n model_s = self.model_fn(x, s)\n x_t = (\n expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x\n - expand_dims(sigma_t * phi_1, dims) * model_s\n )\n if return_intermediate:\n return x_t, {'model_s': model_s}\n else:\n return x_t\n\n def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s=None, return_intermediate=False, solver_type='dpm_solver'):\n \"\"\"\n Singlestep solver DPM-Solver-2 from time `s` to time `t`.\n\n Args:\n x: A pytorch tensor. The initial value at time `s`.\n s: A pytorch tensor. The starting time, with the shape (x.shape[0],).\n t: A pytorch tensor. The ending time, with the shape (x.shape[0],).\n r1: A `float`. The hyperparameter of the second-order solver.\n model_s: A pytorch tensor. The model function evaluated at time `s`.\n If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.\n return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time).\n solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.\n The type slightly impacts the performance. We recommend to use 'dpm_solver' type.\n Returns:\n x_t: A pytorch tensor. The approximated solution at time `t`.\n \"\"\"\n if solver_type not in ['dpm_solver', 'taylor']:\n raise ValueError(\"'solver_type' must be either 'dpm_solver' or 'taylor', got {}\".format(solver_type))\n if r1 is None:\n r1 = 0.5\n ns = self.noise_schedule\n dims = x.dim()\n lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)\n h = lambda_t - lambda_s\n lambda_s1 = lambda_s + r1 * h\n s1 = ns.inverse_lambda(lambda_s1)\n log_alpha_s, log_alpha_s1, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(t)\n sigma_s, sigma_s1, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(t)\n alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t)\n\n if self.predict_x0:\n phi_11 = torch.expm1(-r1 * h)\n phi_1 = torch.expm1(-h)\n\n if model_s is None:\n model_s = self.model_fn(x, s)\n x_s1 = (\n expand_dims(sigma_s1 / sigma_s, dims) * x\n - expand_dims(alpha_s1 * phi_11, dims) * model_s\n )\n model_s1 = self.model_fn(x_s1, s1)\n if solver_type == 'dpm_solver':\n x_t = (\n expand_dims(sigma_t / sigma_s, dims) * x\n - expand_dims(alpha_t * phi_1, dims) * model_s\n - (0.5 / r1) * expand_dims(alpha_t * phi_1, dims) * (model_s1 - model_s)\n )\n elif solver_type == 'taylor':\n x_t = (\n expand_dims(sigma_t / sigma_s, dims) * x\n - expand_dims(alpha_t * phi_1, dims) * model_s\n + (1. / r1) * expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * (model_s1 - model_s)\n )\n else:\n phi_11 = torch.expm1(r1 * h)\n phi_1 = torch.expm1(h)\n\n if model_s is None:\n model_s = self.model_fn(x, s)\n x_s1 = (\n expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x\n - expand_dims(sigma_s1 * phi_11, dims) * model_s\n )\n model_s1 = self.model_fn(x_s1, s1)\n if solver_type == 'dpm_solver':\n x_t = (\n expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x\n - expand_dims(sigma_t * phi_1, dims) * model_s\n - (0.5 / r1) * expand_dims(sigma_t * phi_1, dims) * (model_s1 - model_s)\n )\n elif solver_type == 'taylor':\n x_t = (\n expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x\n - expand_dims(sigma_t * phi_1, dims) * model_s\n - (1. / r1) * expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * (model_s1 - model_s)\n )\n if return_intermediate:\n return x_t, {'model_s': model_s, 'model_s1': model_s1}\n else:\n return x_t\n\n def singlestep_dpm_solver_third_update(self, x, s, t, r1=1./3., r2=2./3., model_s=None, model_s1=None, return_intermediate=False, solver_type='dpm_solver'):\n \"\"\"\n Singlestep solver DPM-Solver-3 from time `s` to time `t`.\n\n Args:\n x: A pytorch tensor. The initial value at time `s`.\n s: A pytorch tensor. The starting time, with the shape (x.shape[0],).\n t: A pytorch tensor. The ending time, with the shape (x.shape[0],).\n r1: A `float`. The hyperparameter of the third-order solver.\n r2: A `float`. The hyperparameter of the third-order solver.\n model_s: A pytorch tensor. The model function evaluated at time `s`.\n If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.\n model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`).\n If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it.\n return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).\n solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.\n The type slightly impacts the performance. We recommend to use 'dpm_solver' type.\n Returns:\n x_t: A pytorch tensor. The approximated solution at time `t`.\n \"\"\"\n if solver_type not in ['dpm_solver', 'taylor']:\n raise ValueError(\"'solver_type' must be either 'dpm_solver' or 'taylor', got {}\".format(solver_type))\n if r1 is None:\n r1 = 1. / 3.\n if r2 is None:\n r2 = 2. / 3.\n ns = self.noise_schedule\n dims = x.dim()\n lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)\n h = lambda_t - lambda_s\n lambda_s1 = lambda_s + r1 * h\n lambda_s2 = lambda_s + r2 * h\n s1 = ns.inverse_lambda(lambda_s1)\n s2 = ns.inverse_lambda(lambda_s2)\n log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(s2), ns.marginal_log_mean_coeff(t)\n sigma_s, sigma_s1, sigma_s2, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(s2), ns.marginal_std(t)\n alpha_s1, alpha_s2, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_s2), torch.exp(log_alpha_t)\n\n if self.predict_x0:\n phi_11 = torch.expm1(-r1 * h)\n phi_12 = torch.expm1(-r2 * h)\n phi_1 = torch.expm1(-h)\n phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1.\n phi_2 = phi_1 / h + 1.\n phi_3 = phi_2 / h - 0.5\n\n if model_s is None:\n model_s = self.model_fn(x, s)\n if model_s1 is None:\n x_s1 = (\n expand_dims(sigma_s1 / sigma_s, dims) * x\n - expand_dims(alpha_s1 * phi_11, dims) * model_s\n )\n model_s1 = self.model_fn(x_s1, s1)\n x_s2 = (\n expand_dims(sigma_s2 / sigma_s, dims) * x\n - expand_dims(alpha_s2 * phi_12, dims) * model_s\n + r2 / r1 * expand_dims(alpha_s2 * phi_22, dims) * (model_s1 - model_s)\n )\n model_s2 = self.model_fn(x_s2, s2)\n if solver_type == 'dpm_solver':\n x_t = (\n expand_dims(sigma_t / sigma_s, dims) * x\n - expand_dims(alpha_t * phi_1, dims) * model_s\n + (1. / r2) * expand_dims(alpha_t * phi_2, dims) * (model_s2 - model_s)\n )\n elif solver_type == 'taylor':\n D1_0 = (1. / r1) * (model_s1 - model_s)\n D1_1 = (1. / r2) * (model_s2 - model_s)\n D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)\n D2 = 2. * (D1_1 - D1_0) / (r2 - r1)\n x_t = (\n expand_dims(sigma_t / sigma_s, dims) * x\n - expand_dims(alpha_t * phi_1, dims) * model_s\n + expand_dims(alpha_t * phi_2, dims) * D1\n - expand_dims(alpha_t * phi_3, dims) * D2\n )\n else:\n phi_11 = torch.expm1(r1 * h)\n phi_12 = torch.expm1(r2 * h)\n phi_1 = torch.expm1(h)\n phi_22 = torch.expm1(r2 * h) / (r2 * h) - 1.\n phi_2 = phi_1 / h - 1.\n phi_3 = phi_2 / h - 0.5\n\n if model_s is None:\n model_s = self.model_fn(x, s)\n if model_s1 is None:\n x_s1 = (\n expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x\n - expand_dims(sigma_s1 * phi_11, dims) * model_s\n )\n model_s1 = self.model_fn(x_s1, s1)\n x_s2 = (\n expand_dims(torch.exp(log_alpha_s2 - log_alpha_s), dims) * x\n - expand_dims(sigma_s2 * phi_12, dims) * model_s\n - r2 / r1 * expand_dims(sigma_s2 * phi_22, dims) * (model_s1 - model_s)\n )\n model_s2 = self.model_fn(x_s2, s2)\n if solver_type == 'dpm_solver':\n x_t = (\n expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x\n - expand_dims(sigma_t * phi_1, dims) * model_s\n - (1. / r2) * expand_dims(sigma_t * phi_2, dims) * (model_s2 - model_s)\n )\n elif solver_type == 'taylor':\n D1_0 = (1. / r1) * (model_s1 - model_s)\n D1_1 = (1. / r2) * (model_s2 - model_s)\n D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)\n D2 = 2. * (D1_1 - D1_0) / (r2 - r1)\n x_t = (\n expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x\n - expand_dims(sigma_t * phi_1, dims) * model_s\n - expand_dims(sigma_t * phi_2, dims) * D1\n - expand_dims(sigma_t * phi_3, dims) * D2\n )\n\n if return_intermediate:\n return x_t, {'model_s': model_s, 'model_s1': model_s1, 'model_s2': model_s2}\n else:\n return x_t\n\n def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type=\"dpm_solver\"):\n \"\"\"\n Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`.\n\n Args:\n x: A pytorch tensor. The initial value at time `s`.\n model_prev_list: A list of pytorch tensor. The previous computed model values.\n t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)\n t: A pytorch tensor. The ending time, with the shape (x.shape[0],).\n solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.\n The type slightly impacts the performance. We recommend to use 'dpm_solver' type.\n Returns:\n x_t: A pytorch tensor. The approximated solution at time `t`.\n \"\"\"\n if solver_type not in ['dpm_solver', 'taylor']:\n raise ValueError(\"'solver_type' must be either 'dpm_solver' or 'taylor', got {}\".format(solver_type))\n ns = self.noise_schedule\n dims = x.dim()\n model_prev_1, model_prev_0 = model_prev_list\n t_prev_1, t_prev_0 = t_prev_list\n lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)\n log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)\n sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)\n alpha_t = torch.exp(log_alpha_t)\n\n h_0 = lambda_prev_0 - lambda_prev_1\n h = lambda_t - lambda_prev_0\n r0 = h_0 / h\n D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)\n if self.predict_x0:\n if solver_type == 'dpm_solver':\n x_t = (\n expand_dims(sigma_t / sigma_prev_0, dims) * x\n - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0\n - 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0\n )\n elif solver_type == 'taylor':\n x_t = (\n expand_dims(sigma_t / sigma_prev_0, dims) * x\n - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0\n + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0\n )\n else:\n if solver_type == 'dpm_solver':\n x_t = (\n expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x\n - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0\n - 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0\n )\n elif solver_type == 'taylor':\n x_t = (\n expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x\n - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0\n - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0\n )\n return x_t\n\n def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev_list, t, solver_type='dpm_solver'):\n \"\"\"\n Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`.\n\n Args:\n x: A pytorch tensor. The initial value at time `s`.\n model_prev_list: A list of pytorch tensor. The previous computed model values.\n t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)\n t: A pytorch tensor. The ending time, with the shape (x.shape[0],).\n solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.\n The type slightly impacts the performance. We recommend to use 'dpm_solver' type.\n Returns:\n x_t: A pytorch tensor. The approximated solution at time `t`.\n \"\"\"\n ns = self.noise_schedule\n dims = x.dim()\n model_prev_2, model_prev_1, model_prev_0 = model_prev_list\n t_prev_2, t_prev_1, t_prev_0 = t_prev_list\n lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_2), ns.marginal_lambda(t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)\n log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)\n sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)\n alpha_t = torch.exp(log_alpha_t)\n\n h_1 = lambda_prev_1 - lambda_prev_2\n h_0 = lambda_prev_0 - lambda_prev_1\n h = lambda_t - lambda_prev_0\n r0, r1 = h_0 / h, h_1 / h\n D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)\n D1_1 = expand_dims(1. / r1, dims) * (model_prev_1 - model_prev_2)\n D1 = D1_0 + expand_dims(r0 / (r0 + r1), dims) * (D1_0 - D1_1)\n D2 = expand_dims(1. / (r0 + r1), dims) * (D1_0 - D1_1)\n if self.predict_x0:\n x_t = (\n expand_dims(sigma_t / sigma_prev_0, dims) * x\n - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0\n + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1\n - expand_dims(alpha_t * ((torch.exp(-h) - 1. + h) / h**2 - 0.5), dims) * D2\n )\n else:\n x_t = (\n expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x\n - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0\n - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1\n - expand_dims(sigma_t * ((torch.exp(h) - 1. - h) / h**2 - 0.5), dims) * D2\n )\n return x_t\n\n def singlestep_dpm_solver_update(self, x, s, t, order, return_intermediate=False, solver_type='dpm_solver', r1=None, r2=None):\n \"\"\"\n Singlestep DPM-Solver with the order `order` from time `s` to time `t`.\n\n Args:\n x: A pytorch tensor. The initial value at time `s`.\n s: A pytorch tensor. The starting time, with the shape (x.shape[0],).\n t: A pytorch tensor. The ending time, with the shape (x.shape[0],).\n order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.\n return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).\n solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.\n The type slightly impacts the performance. We recommend to use 'dpm_solver' type.\n r1: A `float`. The hyperparameter of the second-order or third-order solver.\n r2: A `float`. The hyperparameter of the third-order solver.\n Returns:\n x_t: A pytorch tensor. The approximated solution at time `t`.\n \"\"\"\n if order == 1:\n return self.dpm_solver_first_update(x, s, t, return_intermediate=return_intermediate)\n elif order == 2:\n return self.singlestep_dpm_solver_second_update(x, s, t, return_intermediate=return_intermediate, solver_type=solver_type, r1=r1)\n elif order == 3:\n return self.singlestep_dpm_solver_third_update(x, s, t, return_intermediate=return_intermediate, solver_type=solver_type, r1=r1, r2=r2)\n else:\n raise ValueError(\"Solver order must be 1 or 2 or 3, got {}\".format(order))\n\n def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list, t, order, solver_type='dpm_solver'):\n \"\"\"\n Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`.\n\n Args:\n x: A pytorch tensor. The initial value at time `s`.\n model_prev_list: A list of pytorch tensor. The previous computed model values.\n t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)\n t: A pytorch tensor. The ending time, with the shape (x.shape[0],).\n order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.\n solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.\n The type slightly impacts the performance. We recommend to use 'dpm_solver' type.\n Returns:\n x_t: A pytorch tensor. The approximated solution at time `t`.\n \"\"\"\n if order == 1:\n return self.dpm_solver_first_update(x, t_prev_list[-1], t, model_s=model_prev_list[-1])\n elif order == 2:\n return self.multistep_dpm_solver_second_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)\n elif order == 3:\n return self.multistep_dpm_solver_third_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)\n else:\n raise ValueError(\"Solver order must be 1 or 2 or 3, got {}\".format(order))\n\n def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.0078, rtol=0.05, theta=0.9, t_err=1e-5, solver_type='dpm_solver'):\n \"\"\"\n The adaptive step size solver based on singlestep DPM-Solver.\n\n Args:\n x: A pytorch tensor. The initial value at time `t_T`.\n order: A `int`. The (higher) order of the solver. We only support order == 2 or 3.\n t_T: A `float`. The starting time of the sampling (default is T).\n t_0: A `float`. The ending time of the sampling (default is epsilon).\n h_init: A `float`. The initial step size (for logSNR).\n atol: A `float`. The absolute tolerance of the solver. For image data, the default setting is 0.0078, followed [1].\n rtol: A `float`. The relative tolerance of the solver. The default setting is 0.05.\n theta: A `float`. The safety hyperparameter for adapting the step size. The default setting is 0.9, followed [1].\n t_err: A `float`. The tolerance for the time. We solve the diffusion ODE until the absolute error between the \n current time and `t_0` is less than `t_err`. The default setting is 1e-5.\n solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.\n The type slightly impacts the performance. We recommend to use 'dpm_solver' type.\n Returns:\n x_0: A pytorch tensor. The approximated solution at time `t_0`.\n\n [1] A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas, \"Gotta go fast when generating data with score-based models,\" arXiv preprint arXiv:2105.14080, 2021.\n \"\"\"\n ns = self.noise_schedule\n s = t_T * torch.ones((x.shape[0],)).to(x)\n lambda_s = ns.marginal_lambda(s)\n lambda_0 = ns.marginal_lambda(t_0 * torch.ones_like(s).to(x))\n h = h_init * torch.ones_like(s).to(x)\n x_prev = x\n nfe = 0\n if order == 2:\n r1 = 0.5\n lower_update = lambda x, s, t: self.dpm_solver_first_update(x, s, t, return_intermediate=True)\n higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, solver_type=solver_type, **kwargs)\n elif order == 3:\n r1, r2 = 1. / 3., 2. / 3.\n lower_update = lambda x, s, t: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, return_intermediate=True, solver_type=solver_type)\n higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_third_update(x, s, t, r1=r1, r2=r2, solver_type=solver_type, **kwargs)\n else:\n raise ValueError(\"For adaptive step size solver, order must be 2 or 3, got {}\".format(order))\n while torch.abs((s - t_0)).mean() > t_err:\n t = ns.inverse_lambda(lambda_s + h)\n x_lower, lower_noise_kwargs = lower_update(x, s, t)\n x_higher = higher_update(x, s, t, **lower_noise_kwargs)\n delta = torch.max(torch.ones_like(x).to(x) * atol, rtol * torch.max(torch.abs(x_lower), torch.abs(x_prev)))\n norm_fn = lambda v: torch.sqrt(torch.square(v.reshape((v.shape[0], -1))).mean(dim=-1, keepdim=True))\n E = norm_fn((x_higher - x_lower) / delta).max()\n if torch.all(E <= 1.):\n x = x_higher\n s = t\n x_prev = x_lower\n lambda_s = ns.marginal_lambda(s)\n h = torch.min(theta * h * torch.float_power(E, -1. / order).float(), lambda_0 - lambda_s)\n nfe += order\n print('adaptive solver nfe', nfe)\n return x\n\n def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform',\n method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',\n atol=0.0078, rtol=0.05,\n ):\n \"\"\"\n Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`.\n\n =====================================================\n\n We support the following algorithms for both noise prediction model and data prediction model:\n - 'singlestep':\n Singlestep DPM-Solver (i.e. \"DPM-Solver-fast\" in the paper), which combines different orders of singlestep DPM-Solver. \n We combine all the singlestep solvers with order <= `order` to use up all the function evaluations (steps).\n The total number of function evaluations (NFE) == `steps`.\n Given a fixed NFE == `steps`, the sampling procedure is:\n - If `order` == 1:\n - Denote K = steps. We use K steps of DPM-Solver-1 (i.e. DDIM).\n - If `order` == 2:\n - Denote K = (steps // 2) + (steps % 2). We take K intermediate time steps for sampling.\n - If steps % 2 == 0, we use K steps of singlestep DPM-Solver-2.\n - If steps % 2 == 1, we use (K - 1) steps of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.\n - If `order` == 3:\n - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.\n - If steps % 3 == 0, we use (K - 2) steps of singlestep DPM-Solver-3, and 1 step of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.\n - If steps % 3 == 1, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of DPM-Solver-1.\n - If steps % 3 == 2, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of singlestep DPM-Solver-2.\n - 'multistep':\n Multistep DPM-Solver with the order of `order`. The total number of function evaluations (NFE) == `steps`.\n We initialize the first `order` values by lower order multistep solvers.\n Given a fixed NFE == `steps`, the sampling procedure is:\n Denote K = steps.\n - If `order` == 1:\n - We use K steps of DPM-Solver-1 (i.e. DDIM).\n - If `order` == 2:\n - We firstly use 1 step of DPM-Solver-1, then use (K - 1) step of multistep DPM-Solver-2.\n - If `order` == 3:\n - We firstly use 1 step of DPM-Solver-1, then 1 step of multistep DPM-Solver-2, then (K - 2) step of multistep DPM-Solver-3.\n - 'singlestep_fixed':\n Fixed order singlestep DPM-Solver (i.e. DPM-Solver-1 or singlestep DPM-Solver-2 or singlestep DPM-Solver-3).\n We use singlestep DPM-Solver-`order` for `order`=1 or 2 or 3, with total [`steps` // `order`] * `order` NFE.\n - 'adaptive':\n Adaptive step size DPM-Solver (i.e. \"DPM-Solver-12\" and \"DPM-Solver-23\" in the paper).\n We ignore `steps` and use adaptive step size DPM-Solver with a higher order of `order`.\n You can adjust the absolute tolerance `atol` and the relative tolerance `rtol` to balance the computatation costs\n (NFE) and the sample quality.\n - If `order` == 2, we use DPM-Solver-12 which combines DPM-Solver-1 and singlestep DPM-Solver-2.\n - If `order` == 3, we use DPM-Solver-23 which combines singlestep DPM-Solver-2 and singlestep DPM-Solver-3.\n\n =====================================================\n\n Some advices for choosing the algorithm:\n - For **unconditional sampling** or **guided sampling with small guidance scale** by DPMs:\n Use singlestep DPM-Solver (\"DPM-Solver-fast\" in the paper) with `order = 3`.\n e.g.\n >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=False)\n >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3,\n skip_type='time_uniform', method='singlestep')\n - For **guided sampling with large guidance scale** by DPMs:\n Use multistep DPM-Solver with `predict_x0 = True` and `order = 2`.\n e.g.\n >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=True)\n >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=2,\n skip_type='time_uniform', method='multistep')\n\n We support three types of `skip_type`:\n - 'logSNR': uniform logSNR for the time steps. **Recommended for low-resolutional images**\n - 'time_uniform': uniform time for the time steps. **Recommended for high-resolutional images**.\n - 'time_quadratic': quadratic time for the time steps.\n\n =====================================================\n Args:\n x: A pytorch tensor. The initial value at time `t_start`\n e.g. if `t_start` == T, then `x` is a sample from the standard normal distribution.\n steps: A `int`. The total number of function evaluations (NFE).\n t_start: A `float`. The starting time of the sampling.\n If `T` is None, we use self.noise_schedule.T (default is 1.0).\n t_end: A `float`. The ending time of the sampling.\n If `t_end` is None, we use 1. / self.noise_schedule.total_N.\n e.g. if total_N == 1000, we have `t_end` == 1e-3.\n For discrete-time DPMs:\n - We recommend `t_end` == 1. / self.noise_schedule.total_N.\n For continuous-time DPMs:\n - We recommend `t_end` == 1e-3 when `steps` <= 15; and `t_end` == 1e-4 when `steps` > 15.\n order: A `int`. The order of DPM-Solver.\n skip_type: A `str`. The type for the spacing of the time steps. 'time_uniform' or 'logSNR' or 'time_quadratic'.\n method: A `str`. The method for sampling. 'singlestep' or 'multistep' or 'singlestep_fixed' or 'adaptive'.\n denoise_to_zero: A `bool`. Whether to denoise to time 0 at the final step.\n Default is `False`. If `denoise_to_zero` is `True`, the total NFE is (`steps` + 1).\n\n This trick is firstly proposed by DDPM (https://arxiv.org/abs/2006.11239) and\n score_sde (https://arxiv.org/abs/2011.13456). Such trick can improve the FID\n for diffusion models sampling by diffusion SDEs for low-resolutional images\n (such as CIFAR-10). However, we observed that such trick does not matter for\n high-resolutional images. As it needs an additional NFE, we do not recommend\n it for high-resolutional images.\n lower_order_final: A `bool`. Whether to use lower order solvers at the final steps.\n Only valid for `method=multistep` and `steps < 15`. We empirically find that\n this trick is a key to stabilizing the sampling by DPM-Solver with very few steps\n (especially for steps <= 10). So we recommend to set it to be `True`.\n solver_type: A `str`. The taylor expansion type for the solver. `dpm_solver` or `taylor`. We recommend `dpm_solver`.\n atol: A `float`. The absolute tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.\n rtol: A `float`. The relative tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.\n Returns:\n x_end: A pytorch tensor. The approximated solution at time `t_end`.\n\n \"\"\"\n t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end\n t_T = self.noise_schedule.T if t_start is None else t_start\n device = x.device\n if method == 'adaptive':\n with torch.no_grad():\n x = self.dpm_solver_adaptive(x, order=order, t_T=t_T, t_0=t_0, atol=atol, rtol=rtol, solver_type=solver_type)\n elif method == 'multistep':\n assert steps >= order\n timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)\n assert timesteps.shape[0] - 1 == steps\n with torch.no_grad():\n vec_t = timesteps[0].expand((x.shape[0]))\n model_prev_list = [self.model_fn(x, vec_t)]\n t_prev_list = [vec_t]\n # Init the first `order` values by lower order multistep DPM-Solver.\n for init_order in range(1, order):\n vec_t = timesteps[init_order].expand(x.shape[0])\n x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, init_order, solver_type=solver_type)\n model_prev_list.append(self.model_fn(x, vec_t))\n t_prev_list.append(vec_t)\n # Compute the remaining values by `order`-th order multistep DPM-Solver.\n for step in range(order, steps + 1):\n vec_t = timesteps[step].expand(x.shape[0])\n if lower_order_final and steps < 15:\n step_order = min(order, steps + 1 - step)\n else:\n step_order = order\n x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, step_order, solver_type=solver_type)\n for i in range(order - 1):\n t_prev_list[i] = t_prev_list[i + 1]\n model_prev_list[i] = model_prev_list[i + 1]\n t_prev_list[-1] = vec_t\n # We do not need to evaluate the final model value.\n if step < steps:\n model_prev_list[-1] = self.model_fn(x, vec_t)\n elif method in ['singlestep', 'singlestep_fixed']:\n if method == 'singlestep':\n timesteps_outer, orders = self.get_orders_and_timesteps_for_singlestep_solver(steps=steps, order=order, skip_type=skip_type, t_T=t_T, t_0=t_0, device=device)\n elif method == 'singlestep_fixed':\n K = steps // order\n orders = [order,] * K\n timesteps_outer = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=K, device=device)\n for i, order in enumerate(orders):\n t_T_inner, t_0_inner = timesteps_outer[i], timesteps_outer[i + 1]\n timesteps_inner = self.get_time_steps(skip_type=skip_type, t_T=t_T_inner.item(), t_0=t_0_inner.item(), N=order, device=device)\n lambda_inner = self.noise_schedule.marginal_lambda(timesteps_inner)\n vec_s, vec_t = t_T_inner.tile(x.shape[0]), t_0_inner.tile(x.shape[0])\n h = lambda_inner[-1] - lambda_inner[0]\n r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h\n r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h\n x = self.singlestep_dpm_solver_update(x, vec_s, vec_t, order, solver_type=solver_type, r1=r1, r2=r2)\n if denoise_to_zero:\n x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)\n return x\n\n\n\n#############################################################\n# other utility functions\n#############################################################\n\ndef interpolate_fn(x, xp, yp):\n \"\"\"\n A piecewise linear function y = f(x), using xp and yp as keypoints.\n We implement f(x) in a differentiable way (i.e. applicable for autograd).\n The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)\n\n Args:\n x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).\n xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.\n yp: PyTorch tensor with shape [C, K].\n Returns:\n The function values f(x), with shape [N, C].\n \"\"\"\n N, K = x.shape[0], xp.shape[1]\n all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)\n sorted_all_x, x_indices = torch.sort(all_x, dim=2)\n x_idx = torch.argmin(x_indices, dim=2)\n cand_start_idx = x_idx - 1\n start_idx = torch.where(\n torch.eq(x_idx, 0),\n torch.tensor(1, device=x.device),\n torch.where(\n torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,\n ),\n )\n end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)\n start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)\n end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)\n start_idx2 = torch.where(\n torch.eq(x_idx, 0),\n torch.tensor(0, device=x.device),\n torch.where(\n torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,\n ),\n )\n y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)\n start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)\n end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)\n cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)\n return cand\n\n\ndef expand_dims(v, dims):\n \"\"\"\n Expand the tensor `v` to the dim `dims`.\n\n Args:\n `v`: a PyTorch tensor with shape [N].\n `dim`: a `int`.\n Returns:\n a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.\n \"\"\"\n return v[(...,) + (None,)*(dims - 1)]" }, { "path": "data_generation/ldm/models/diffusion/dpm_solver/sampler.py", "content": "\"\"\"SAMPLING ONLY.\"\"\"\n\nimport torch\n\nfrom .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver\n\n\nclass DPMSolverSampler(object):\n def __init__(self, model, **kwargs):\n super().__init__()\n self.model = model\n to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)\n self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))\n\n def register_buffer(self, name, attr):\n if type(attr) == torch.Tensor:\n if attr.device != torch.device(\"cuda\"):\n attr = attr.to(torch.device(\"cuda\"))\n setattr(self, name, attr)\n\n @torch.no_grad()\n def sample(self,\n S,\n batch_size,\n shape,\n conditioning=None,\n callback=None,\n normals_sequence=None,\n img_callback=None,\n quantize_x0=False,\n eta=0.,\n mask=None,\n x0=None,\n temperature=1.,\n noise_dropout=0.,\n score_corrector=None,\n corrector_kwargs=None,\n verbose=True,\n x_T=None,\n log_every_t=100,\n unconditional_guidance_scale=1.,\n unconditional_conditioning=None,\n # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...\n **kwargs\n ):\n if conditioning is not None:\n if isinstance(conditioning, dict):\n cbs = conditioning[list(conditioning.keys())[0]].shape[0]\n if cbs != batch_size:\n print(f\"Warning: Got {cbs} conditionings but batch-size is {batch_size}\")\n else:\n if conditioning.shape[0] != batch_size:\n print(f\"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}\")\n\n # sampling\n C, H, W = shape\n size = (batch_size, C, H, W)\n\n # print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {S}')\n\n device = self.model.betas.device\n if x_T is None:\n img = torch.randn(size, device=device)\n else:\n img = x_T\n\n ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)\n\n model_fn = model_wrapper(\n lambda x, t, c: self.model.apply_model(x, t, c),\n ns,\n model_type=\"noise\",\n guidance_type=\"classifier-free\",\n condition=conditioning,\n unconditional_condition=unconditional_conditioning,\n guidance_scale=unconditional_guidance_scale,\n )\n\n dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False)\n x = dpm_solver.sample(img, steps=S, skip_type=\"time_uniform\", method=\"multistep\", order=2, lower_order_final=True)\n\n return x.to(device), None\n" }, { "path": "data_generation/ldm/models/diffusion/plms.py", "content": "\"\"\"SAMPLING ONLY.\"\"\"\n\nimport torch\nimport numpy as np\nfrom tqdm import tqdm\nfrom functools import partial\n\nfrom ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like\n\n\nclass PLMSSampler(object):\n def __init__(self, model, schedule=\"linear\", **kwargs):\n super().__init__()\n self.model = model\n self.ddpm_num_timesteps = model.num_timesteps\n self.schedule = schedule\n\n def register_buffer(self, name, attr):\n if type(attr) == torch.Tensor:\n if attr.device != torch.device(\"cuda\"):\n attr = attr.to(torch.device(\"cuda\"))\n setattr(self, name, attr)\n\n def make_schedule(self, ddim_num_steps, ddim_discretize=\"uniform\", ddim_eta=0., verbose=True):\n if ddim_eta != 0:\n raise ValueError('ddim_eta must be 0 for PLMS')\n self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,\n num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)\n alphas_cumprod = self.model.alphas_cumprod\n assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'\n to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)\n\n self.register_buffer('betas', to_torch(self.model.betas))\n self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))\n self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))\n\n # calculations for diffusion q(x_t | x_{t-1}) and others\n self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))\n self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))\n self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))\n self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))\n self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))\n\n # ddim sampling parameters\n ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),\n ddim_timesteps=self.ddim_timesteps,\n eta=ddim_eta,verbose=verbose)\n self.register_buffer('ddim_sigmas', ddim_sigmas)\n self.register_buffer('ddim_alphas', ddim_alphas)\n self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)\n self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))\n sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(\n (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (\n 1 - self.alphas_cumprod / self.alphas_cumprod_prev))\n self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)\n\n @torch.no_grad()\n def sample(self,\n S,\n batch_size,\n shape,\n conditioning=None,\n callback=None,\n normals_sequence=None,\n img_callback=None,\n quantize_x0=False,\n eta=0.,\n mask=None,\n x0=None,\n temperature=1.,\n noise_dropout=0.,\n score_corrector=None,\n corrector_kwargs=None,\n verbose=True,\n x_T=None,\n log_every_t=100,\n unconditional_guidance_scale=1.,\n unconditional_conditioning=None,\n # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...\n **kwargs\n ):\n if conditioning is not None:\n if isinstance(conditioning, dict):\n cbs = conditioning[list(conditioning.keys())[0]].shape[0]\n if cbs != batch_size:\n print(f\"Warning: Got {cbs} conditionings but batch-size is {batch_size}\")\n else:\n if conditioning.shape[0] != batch_size:\n print(f\"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}\")\n\n self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)\n # sampling\n C, H, W = shape\n size = (batch_size, C, H, W)\n print(f'Data shape for PLMS sampling is {size}')\n\n samples, intermediates = self.plms_sampling(conditioning, size,\n callback=callback,\n img_callback=img_callback,\n quantize_denoised=quantize_x0,\n mask=mask, x0=x0,\n ddim_use_original_steps=False,\n noise_dropout=noise_dropout,\n temperature=temperature,\n score_corrector=score_corrector,\n corrector_kwargs=corrector_kwargs,\n x_T=x_T,\n log_every_t=log_every_t,\n unconditional_guidance_scale=unconditional_guidance_scale,\n unconditional_conditioning=unconditional_conditioning,\n )\n return samples, intermediates\n\n @torch.no_grad()\n def plms_sampling(self, cond, shape,\n x_T=None, ddim_use_original_steps=False,\n callback=None, timesteps=None, quantize_denoised=False,\n mask=None, x0=None, img_callback=None, log_every_t=100,\n temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,\n unconditional_guidance_scale=1., unconditional_conditioning=None,):\n device = self.model.betas.device\n b = shape[0]\n if x_T is None:\n img = torch.randn(shape, device=device)\n else:\n img = x_T\n\n if timesteps is None:\n timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps\n elif timesteps is not None and not ddim_use_original_steps:\n subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1\n timesteps = self.ddim_timesteps[:subset_end]\n\n intermediates = {'x_inter': [img], 'pred_x0': [img]}\n time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps)\n total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]\n print(f\"Running PLMS Sampling with {total_steps} timesteps\")\n\n iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)\n old_eps = []\n\n for i, step in enumerate(iterator):\n index = total_steps - i - 1\n ts = torch.full((b,), step, device=device, dtype=torch.long)\n ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)\n\n if mask is not None:\n assert x0 is not None\n img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?\n img = img_orig * mask + (1. - mask) * img\n\n outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,\n quantize_denoised=quantize_denoised, temperature=temperature,\n noise_dropout=noise_dropout, score_corrector=score_corrector,\n corrector_kwargs=corrector_kwargs,\n unconditional_guidance_scale=unconditional_guidance_scale,\n unconditional_conditioning=unconditional_conditioning,\n old_eps=old_eps, t_next=ts_next)\n img, pred_x0, e_t = outs\n old_eps.append(e_t)\n if len(old_eps) >= 4:\n old_eps.pop(0)\n if callback: callback(i)\n if img_callback: img_callback(pred_x0, i)\n\n if index % log_every_t == 0 or index == total_steps - 1:\n intermediates['x_inter'].append(img)\n intermediates['pred_x0'].append(pred_x0)\n\n return img, intermediates\n\n @torch.no_grad()\n def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,\n temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,\n unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None):\n b, *_, device = *x.shape, x.device\n\n def get_model_output(x, t):\n if unconditional_conditioning is None or unconditional_guidance_scale == 1.:\n e_t = self.model.apply_model(x, t, c)\n else:\n x_in = torch.cat([x] * 2)\n t_in = torch.cat([t] * 2)\n c_in = torch.cat([unconditional_conditioning, c])\n e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)\n e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)\n\n if score_corrector is not None:\n assert self.model.parameterization == \"eps\"\n e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)\n\n return e_t\n\n alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas\n alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev\n sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas\n sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas\n\n def get_x_prev_and_pred_x0(e_t, index):\n # select parameters corresponding to the currently considered timestep\n a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)\n a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)\n sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)\n sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)\n\n # current prediction for x_0\n pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()\n if quantize_denoised:\n pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)\n # direction pointing to x_t\n dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t\n noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature\n if noise_dropout > 0.:\n noise = torch.nn.functional.dropout(noise, p=noise_dropout)\n x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise\n return x_prev, pred_x0\n\n e_t = get_model_output(x, t)\n if len(old_eps) == 0:\n # Pseudo Improved Euler (2nd order)\n x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)\n e_t_next = get_model_output(x_prev, t_next)\n e_t_prime = (e_t + e_t_next) / 2\n elif len(old_eps) == 1:\n # 2nd order Pseudo Linear Multistep (Adams-Bashforth)\n e_t_prime = (3 * e_t - old_eps[-1]) / 2\n elif len(old_eps) == 2:\n # 3nd order Pseudo Linear Multistep (Adams-Bashforth)\n e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12\n elif len(old_eps) >= 3:\n # 4nd order Pseudo Linear Multistep (Adams-Bashforth)\n e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24\n\n x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)\n\n return x_prev, pred_x0, e_t\n" }, { "path": "data_generation/ldm/modules/attention.py", "content": "from inspect import isfunction\nimport math\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn, einsum\nfrom einops import rearrange, repeat\n\nfrom ldm.modules.diffusionmodules.util import checkpoint\n\n\ndef exists(val):\n return val is not None\n\n\ndef uniq(arr):\n return{el: True for el in arr}.keys()\n\n\ndef default(val, d):\n if exists(val):\n return val\n return d() if isfunction(d) else d\n\n\ndef max_neg_value(t):\n return -torch.finfo(t.dtype).max\n\n\ndef init_(tensor):\n dim = tensor.shape[-1]\n std = 1 / math.sqrt(dim)\n tensor.uniform_(-std, std)\n return tensor\n\n\n# feedforward\nclass GEGLU(nn.Module):\n def __init__(self, dim_in, dim_out):\n super().__init__()\n self.proj = nn.Linear(dim_in, dim_out * 2)\n\n def forward(self, x):\n x, gate = self.proj(x).chunk(2, dim=-1)\n return x * F.gelu(gate)\n\n\nclass FeedForward(nn.Module):\n def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):\n super().__init__()\n inner_dim = int(dim * mult)\n dim_out = default(dim_out, dim)\n project_in = nn.Sequential(\n nn.Linear(dim, inner_dim),\n nn.GELU()\n ) if not glu else GEGLU(dim, inner_dim)\n\n self.net = nn.Sequential(\n project_in,\n nn.Dropout(dropout),\n nn.Linear(inner_dim, dim_out)\n )\n\n def forward(self, x):\n return self.net(x)\n\n\ndef zero_module(module):\n \"\"\"\n Zero out the parameters of a module and return it.\n \"\"\"\n for p in module.parameters():\n p.detach().zero_()\n return module\n\n\ndef Normalize(in_channels):\n return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)\n\n\nclass LinearAttention(nn.Module):\n def __init__(self, dim, heads=4, dim_head=32):\n super().__init__()\n self.heads = heads\n hidden_dim = dim_head * heads\n self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)\n self.to_out = nn.Conv2d(hidden_dim, dim, 1)\n\n def forward(self, x):\n b, c, h, w = x.shape\n qkv = self.to_qkv(x)\n q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)\n k = k.softmax(dim=-1) \n context = torch.einsum('bhdn,bhen->bhde', k, v)\n out = torch.einsum('bhde,bhdn->bhen', context, q)\n out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)\n return self.to_out(out)\n\n\nclass SpatialSelfAttention(nn.Module):\n def __init__(self, in_channels):\n super().__init__()\n self.in_channels = in_channels\n\n self.norm = Normalize(in_channels)\n self.q = torch.nn.Conv2d(in_channels,\n in_channels,\n kernel_size=1,\n stride=1,\n padding=0)\n self.k = torch.nn.Conv2d(in_channels,\n in_channels,\n kernel_size=1,\n stride=1,\n padding=0)\n self.v = torch.nn.Conv2d(in_channels,\n in_channels,\n kernel_size=1,\n stride=1,\n padding=0)\n self.proj_out = torch.nn.Conv2d(in_channels,\n in_channels,\n kernel_size=1,\n stride=1,\n padding=0)\n\n def forward(self, x):\n h_ = x\n h_ = self.norm(h_)\n q = self.q(h_)\n k = self.k(h_)\n v = self.v(h_)\n\n # compute attention\n b,c,h,w = q.shape\n q = rearrange(q, 'b c h w -> b (h w) c')\n k = rearrange(k, 'b c h w -> b c (h w)')\n w_ = torch.einsum('bij,bjk->bik', q, k)\n\n w_ = w_ * (int(c)**(-0.5))\n w_ = torch.nn.functional.softmax(w_, dim=2)\n\n # attend to values\n v = rearrange(v, 'b c h w -> b c (h w)')\n w_ = rearrange(w_, 'b i j -> b j i')\n h_ = torch.einsum('bij,bjk->bik', v, w_)\n h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)\n h_ = self.proj_out(h_)\n\n return x+h_\n\n\nclass CrossAttention(nn.Module):\n def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):\n super().__init__()\n inner_dim = dim_head * heads\n context_dim = default(context_dim, query_dim)\n\n self.scale = dim_head ** -0.5\n self.heads = heads\n\n self.to_q = nn.Linear(query_dim, inner_dim, bias=False)\n self.to_k = nn.Linear(context_dim, inner_dim, bias=False)\n self.to_v = nn.Linear(context_dim, inner_dim, bias=False)\n\n self.to_out = nn.Sequential(\n nn.Linear(inner_dim, query_dim),\n nn.Dropout(dropout)\n )\n\n def forward(self, x, context=None, mask=None):\n h = self.heads\n\n q = self.to_q(x)\n context = default(context, x)\n k = self.to_k(context)\n v = self.to_v(context)\n\n q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))\n\n sim = einsum('b i d, b j d -> b i j', q, k) * self.scale\n\n if exists(mask):\n mask = rearrange(mask, 'b ... -> b (...)')\n max_neg_value = -torch.finfo(sim.dtype).max\n mask = repeat(mask, 'b j -> (b h) () j', h=h)\n sim.masked_fill_(~mask, max_neg_value)\n\n # attention, what we cannot get enough of\n attn = sim.softmax(dim=-1)\n\n out = einsum('b i j, b j d -> b i d', attn, v)\n out = rearrange(out, '(b h) n d -> b n (h d)', h=h)\n return self.to_out(out)\n\n\nclass BasicTransformerBlock(nn.Module):\n def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True):\n super().__init__()\n self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout) # is a self-attention\n self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)\n self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,\n heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none\n self.norm1 = nn.LayerNorm(dim)\n self.norm2 = nn.LayerNorm(dim)\n self.norm3 = nn.LayerNorm(dim)\n self.checkpoint = checkpoint\n\n def forward(self, x, context=None):\n return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)\n\n def _forward(self, x, context=None):\n x = self.attn1(self.norm1(x)) + x\n x = self.attn2(self.norm2(x), context=context) + x\n x = self.ff(self.norm3(x)) + x\n return x\n\n\nclass SpatialTransformer(nn.Module):\n \"\"\"\n Transformer block for image-like data.\n First, project the input (aka embedding)\n and reshape to b, t, d.\n Then apply standard transformer action.\n Finally, reshape to image\n \"\"\"\n def __init__(self, in_channels, n_heads, d_head,\n depth=1, dropout=0., context_dim=None):\n super().__init__()\n self.in_channels = in_channels\n inner_dim = n_heads * d_head\n self.norm = Normalize(in_channels)\n\n self.proj_in = nn.Conv2d(in_channels,\n inner_dim,\n kernel_size=1,\n stride=1,\n padding=0)\n\n self.transformer_blocks = nn.ModuleList(\n [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)\n for d in range(depth)]\n )\n\n self.proj_out = zero_module(nn.Conv2d(inner_dim,\n in_channels,\n kernel_size=1,\n stride=1,\n padding=0))\n\n def forward(self, x, context=None):\n # note: if no context is given, cross-attention defaults to self-attention\n b, c, h, w = x.shape\n x_in = x\n x = self.norm(x)\n x = self.proj_in(x)\n x = rearrange(x, 'b c h w -> b (h w) c')\n for block in self.transformer_blocks:\n x = block(x, context=context)\n x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)\n x = self.proj_out(x)\n return x + x_in" }, { "path": "data_generation/ldm/modules/diffusionmodules/__init__.py", "content": "" }, { "path": "data_generation/ldm/modules/diffusionmodules/model.py", "content": "# pytorch_diffusion + derived encoder decoder\nimport math\nimport torch\nimport torch.nn as nn\nimport numpy as np\nfrom einops import rearrange\n\nfrom ldm.util import instantiate_from_config\nfrom ldm.modules.attention import LinearAttention\n\n\ndef get_timestep_embedding(timesteps, embedding_dim):\n \"\"\"\n This matches the implementation in Denoising Diffusion Probabilistic Models:\n From Fairseq.\n Build sinusoidal embeddings.\n This matches the implementation in tensor2tensor, but differs slightly\n from the description in Section 3.5 of \"Attention Is All You Need\".\n \"\"\"\n assert len(timesteps.shape) == 1\n\n half_dim = embedding_dim // 2\n emb = math.log(10000) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)\n emb = emb.to(device=timesteps.device)\n emb = timesteps.float()[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0,1,0,0))\n return emb\n\n\ndef nonlinearity(x):\n # swish\n return x*torch.sigmoid(x)\n\n\ndef Normalize(in_channels, num_groups=32):\n return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)\n\n\nclass Upsample(nn.Module):\n def __init__(self, in_channels, with_conv):\n super().__init__()\n self.with_conv = with_conv\n if self.with_conv:\n self.conv = torch.nn.Conv2d(in_channels,\n in_channels,\n kernel_size=3,\n stride=1,\n padding=1)\n\n def forward(self, x):\n x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode=\"nearest\")\n if self.with_conv:\n x = self.conv(x)\n return x\n\n\nclass Downsample(nn.Module):\n def __init__(self, in_channels, with_conv):\n super().__init__()\n self.with_conv = with_conv\n if self.with_conv:\n # no asymmetric padding in torch conv, must do it ourselves\n self.conv = torch.nn.Conv2d(in_channels,\n in_channels,\n kernel_size=3,\n stride=2,\n padding=0)\n\n def forward(self, x):\n if self.with_conv:\n pad = (0,1,0,1)\n x = torch.nn.functional.pad(x, pad, mode=\"constant\", value=0)\n x = self.conv(x)\n else:\n x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)\n return x\n\n\nclass ResnetBlock(nn.Module):\n def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,\n dropout, temb_channels=512):\n super().__init__()\n self.in_channels = in_channels\n out_channels = in_channels if out_channels is None else out_channels\n self.out_channels = out_channels\n self.use_conv_shortcut = conv_shortcut\n\n self.norm1 = Normalize(in_channels)\n self.conv1 = torch.nn.Conv2d(in_channels,\n out_channels,\n kernel_size=3,\n stride=1,\n padding=1)\n if temb_channels > 0:\n self.temb_proj = torch.nn.Linear(temb_channels,\n out_channels)\n self.norm2 = Normalize(out_channels)\n self.dropout = torch.nn.Dropout(dropout)\n self.conv2 = torch.nn.Conv2d(out_channels,\n out_channels,\n kernel_size=3,\n stride=1,\n padding=1)\n if self.in_channels != self.out_channels:\n if self.use_conv_shortcut:\n self.conv_shortcut = torch.nn.Conv2d(in_channels,\n out_channels,\n kernel_size=3,\n stride=1,\n padding=1)\n else:\n self.nin_shortcut = torch.nn.Conv2d(in_channels,\n out_channels,\n kernel_size=1,\n stride=1,\n padding=0)\n\n def forward(self, x, temb):\n h = x\n h = self.norm1(h)\n h = nonlinearity(h)\n h = self.conv1(h)\n\n if temb is not None:\n h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]\n\n h = self.norm2(h)\n h = nonlinearity(h)\n h = self.dropout(h)\n h = self.conv2(h)\n\n if self.in_channels != self.out_channels:\n if self.use_conv_shortcut:\n x = self.conv_shortcut(x)\n else:\n x = self.nin_shortcut(x)\n\n return x+h\n\n\nclass LinAttnBlock(LinearAttention):\n \"\"\"to match AttnBlock usage\"\"\"\n def __init__(self, in_channels):\n super().__init__(dim=in_channels, heads=1, dim_head=in_channels)\n\n\nclass AttnBlock(nn.Module):\n def __init__(self, in_channels):\n super().__init__()\n self.in_channels = in_channels\n\n self.norm = Normalize(in_channels)\n self.q = torch.nn.Conv2d(in_channels,\n in_channels,\n kernel_size=1,\n stride=1,\n padding=0)\n self.k = torch.nn.Conv2d(in_channels,\n in_channels,\n kernel_size=1,\n stride=1,\n padding=0)\n self.v = torch.nn.Conv2d(in_channels,\n in_channels,\n kernel_size=1,\n stride=1,\n padding=0)\n self.proj_out = torch.nn.Conv2d(in_channels,\n in_channels,\n kernel_size=1,\n stride=1,\n padding=0)\n\n\n def forward(self, x):\n h_ = x\n h_ = self.norm(h_)\n q = self.q(h_)\n k = self.k(h_)\n v = self.v(h_)\n\n # compute attention\n b,c,h,w = q.shape\n q = q.reshape(b,c,h*w)\n q = q.permute(0,2,1) # b,hw,c\n k = k.reshape(b,c,h*w) # b,c,hw\n w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]\n w_ = w_ * (int(c)**(-0.5))\n w_ = torch.nn.functional.softmax(w_, dim=2)\n\n # attend to values\n v = v.reshape(b,c,h*w)\n w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q)\n h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]\n h_ = h_.reshape(b,c,h,w)\n\n h_ = self.proj_out(h_)\n\n return x+h_\n\n\ndef make_attn(in_channels, attn_type=\"vanilla\"):\n assert attn_type in [\"vanilla\", \"linear\", \"none\"], f'attn_type {attn_type} unknown'\n print(f\"making attention of type '{attn_type}' with {in_channels} in_channels\")\n if attn_type == \"vanilla\":\n return AttnBlock(in_channels)\n elif attn_type == \"none\":\n return nn.Identity(in_channels)\n else:\n return LinAttnBlock(in_channels)\n\n\nclass Model(nn.Module):\n def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,\n attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,\n resolution, use_timestep=True, use_linear_attn=False, attn_type=\"vanilla\"):\n super().__init__()\n if use_linear_attn: attn_type = \"linear\"\n self.ch = ch\n self.temb_ch = self.ch*4\n self.num_resolutions = len(ch_mult)\n self.num_res_blocks = num_res_blocks\n self.resolution = resolution\n self.in_channels = in_channels\n\n self.use_timestep = use_timestep\n if self.use_timestep:\n # timestep embedding\n self.temb = nn.Module()\n self.temb.dense = nn.ModuleList([\n torch.nn.Linear(self.ch,\n self.temb_ch),\n torch.nn.Linear(self.temb_ch,\n self.temb_ch),\n ])\n\n # downsampling\n self.conv_in = torch.nn.Conv2d(in_channels,\n self.ch,\n kernel_size=3,\n stride=1,\n padding=1)\n\n curr_res = resolution\n in_ch_mult = (1,)+tuple(ch_mult)\n self.down = nn.ModuleList()\n for i_level in range(self.num_resolutions):\n block = nn.ModuleList()\n attn = nn.ModuleList()\n block_in = ch*in_ch_mult[i_level]\n block_out = ch*ch_mult[i_level]\n for i_block in range(self.num_res_blocks):\n block.append(ResnetBlock(in_channels=block_in,\n out_channels=block_out,\n temb_channels=self.temb_ch,\n dropout=dropout))\n block_in = block_out\n if curr_res in attn_resolutions:\n attn.append(make_attn(block_in, attn_type=attn_type))\n down = nn.Module()\n down.block = block\n down.attn = attn\n if i_level != self.num_resolutions-1:\n down.downsample = Downsample(block_in, resamp_with_conv)\n curr_res = curr_res // 2\n self.down.append(down)\n\n # middle\n self.mid = nn.Module()\n self.mid.block_1 = ResnetBlock(in_channels=block_in,\n out_channels=block_in,\n temb_channels=self.temb_ch,\n dropout=dropout)\n self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)\n self.mid.block_2 = ResnetBlock(in_channels=block_in,\n out_channels=block_in,\n temb_channels=self.temb_ch,\n dropout=dropout)\n\n # upsampling\n self.up = nn.ModuleList()\n for i_level in reversed(range(self.num_resolutions)):\n block = nn.ModuleList()\n attn = nn.ModuleList()\n block_out = ch*ch_mult[i_level]\n skip_in = ch*ch_mult[i_level]\n for i_block in range(self.num_res_blocks+1):\n if i_block == self.num_res_blocks:\n skip_in = ch*in_ch_mult[i_level]\n block.append(ResnetBlock(in_channels=block_in+skip_in,\n out_channels=block_out,\n temb_channels=self.temb_ch,\n dropout=dropout))\n block_in = block_out\n if curr_res in attn_resolutions:\n attn.append(make_attn(block_in, attn_type=attn_type))\n up = nn.Module()\n up.block = block\n up.attn = attn\n if i_level != 0:\n up.upsample = Upsample(block_in, resamp_with_conv)\n curr_res = curr_res * 2\n self.up.insert(0, up) # prepend to get consistent order\n\n # end\n self.norm_out = Normalize(block_in)\n self.conv_out = torch.nn.Conv2d(block_in,\n out_ch,\n kernel_size=3,\n stride=1,\n padding=1)\n\n def forward(self, x, t=None, context=None):\n #assert x.shape[2] == x.shape[3] == self.resolution\n if context is not None:\n # assume aligned context, cat along channel axis\n x = torch.cat((x, context), dim=1)\n if self.use_timestep:\n # timestep embedding\n assert t is not None\n temb = get_timestep_embedding(t, self.ch)\n temb = self.temb.dense[0](temb)\n temb = nonlinearity(temb)\n temb = self.temb.dense[1](temb)\n else:\n temb = None\n\n # downsampling\n hs = [self.conv_in(x)]\n for i_level in range(self.num_resolutions):\n for i_block in range(self.num_res_blocks):\n h = self.down[i_level].block[i_block](hs[-1], temb)\n if len(self.down[i_level].attn) > 0:\n h = self.down[i_level].attn[i_block](h)\n hs.append(h)\n if i_level != self.num_resolutions-1:\n hs.append(self.down[i_level].downsample(hs[-1]))\n\n # middle\n h = hs[-1]\n h = self.mid.block_1(h, temb)\n h = self.mid.attn_1(h)\n h = self.mid.block_2(h, temb)\n\n # upsampling\n for i_level in reversed(range(self.num_resolutions)):\n for i_block in range(self.num_res_blocks+1):\n h = self.up[i_level].block[i_block](\n torch.cat([h, hs.pop()], dim=1), temb)\n if len(self.up[i_level].attn) > 0:\n h = self.up[i_level].attn[i_block](h)\n if i_level != 0:\n h = self.up[i_level].upsample(h)\n\n # end\n h = self.norm_out(h)\n h = nonlinearity(h)\n h = self.conv_out(h)\n return h\n\n def get_last_layer(self):\n return self.conv_out.weight\n\n\nclass Encoder(nn.Module):\n def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,\n attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,\n resolution, z_channels, double_z=True, use_linear_attn=False, attn_type=\"vanilla\",\n **ignore_kwargs):\n super().__init__()\n if use_linear_attn: attn_type = \"linear\"\n self.ch = ch\n self.temb_ch = 0\n self.num_resolutions = len(ch_mult)\n self.num_res_blocks = num_res_blocks\n self.resolution = resolution\n self.in_channels = in_channels\n\n # downsampling\n self.conv_in = torch.nn.Conv2d(in_channels,\n self.ch,\n kernel_size=3,\n stride=1,\n padding=1)\n\n curr_res = resolution\n in_ch_mult = (1,)+tuple(ch_mult)\n self.in_ch_mult = in_ch_mult\n self.down = nn.ModuleList()\n for i_level in range(self.num_resolutions):\n block = nn.ModuleList()\n attn = nn.ModuleList()\n block_in = ch*in_ch_mult[i_level]\n block_out = ch*ch_mult[i_level]\n for i_block in range(self.num_res_blocks):\n block.append(ResnetBlock(in_channels=block_in,\n out_channels=block_out,\n temb_channels=self.temb_ch,\n dropout=dropout))\n block_in = block_out\n if curr_res in attn_resolutions:\n attn.append(make_attn(block_in, attn_type=attn_type))\n down = nn.Module()\n down.block = block\n down.attn = attn\n if i_level != self.num_resolutions-1:\n down.downsample = Downsample(block_in, resamp_with_conv)\n curr_res = curr_res // 2\n self.down.append(down)\n\n # middle\n self.mid = nn.Module()\n self.mid.block_1 = ResnetBlock(in_channels=block_in,\n out_channels=block_in,\n temb_channels=self.temb_ch,\n dropout=dropout)\n self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)\n self.mid.block_2 = ResnetBlock(in_channels=block_in,\n out_channels=block_in,\n temb_channels=self.temb_ch,\n dropout=dropout)\n\n # end\n self.norm_out = Normalize(block_in)\n self.conv_out = torch.nn.Conv2d(block_in,\n 2*z_channels if double_z else z_channels,\n kernel_size=3,\n stride=1,\n padding=1)\n\n def forward(self, x):\n # timestep embedding\n temb = None\n\n # downsampling\n hs = [self.conv_in(x)]\n for i_level in range(self.num_resolutions):\n for i_block in range(self.num_res_blocks):\n h = self.down[i_level].block[i_block](hs[-1], temb)\n if len(self.down[i_level].attn) > 0:\n h = self.down[i_level].attn[i_block](h)\n hs.append(h)\n if i_level != self.num_resolutions-1:\n hs.append(self.down[i_level].downsample(hs[-1]))\n\n # middle\n h = hs[-1]\n h = self.mid.block_1(h, temb)\n h = self.mid.attn_1(h)\n h = self.mid.block_2(h, temb)\n\n # end\n h = self.norm_out(h)\n h = nonlinearity(h)\n h = self.conv_out(h)\n return h\n\n\nclass Decoder(nn.Module):\n def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,\n attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,\n resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,\n attn_type=\"vanilla\", **ignorekwargs):\n super().__init__()\n if use_linear_attn: attn_type = \"linear\"\n self.ch = ch\n self.temb_ch = 0\n self.num_resolutions = len(ch_mult)\n self.num_res_blocks = num_res_blocks\n self.resolution = resolution\n self.in_channels = in_channels\n self.give_pre_end = give_pre_end\n self.tanh_out = tanh_out\n\n # compute in_ch_mult, block_in and curr_res at lowest res\n in_ch_mult = (1,)+tuple(ch_mult)\n block_in = ch*ch_mult[self.num_resolutions-1]\n curr_res = resolution // 2**(self.num_resolutions-1)\n self.z_shape = (1,z_channels,curr_res,curr_res)\n print(\"Working with z of shape {} = {} dimensions.\".format(\n self.z_shape, np.prod(self.z_shape)))\n\n # z to block_in\n self.conv_in = torch.nn.Conv2d(z_channels,\n block_in,\n kernel_size=3,\n stride=1,\n padding=1)\n\n # middle\n self.mid = nn.Module()\n self.mid.block_1 = ResnetBlock(in_channels=block_in,\n out_channels=block_in,\n temb_channels=self.temb_ch,\n dropout=dropout)\n self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)\n self.mid.block_2 = ResnetBlock(in_channels=block_in,\n out_channels=block_in,\n temb_channels=self.temb_ch,\n dropout=dropout)\n\n # upsampling\n self.up = nn.ModuleList()\n for i_level in reversed(range(self.num_resolutions)):\n block = nn.ModuleList()\n attn = nn.ModuleList()\n block_out = ch*ch_mult[i_level]\n for i_block in range(self.num_res_blocks+1):\n block.append(ResnetBlock(in_channels=block_in,\n out_channels=block_out,\n temb_channels=self.temb_ch,\n dropout=dropout))\n block_in = block_out\n if curr_res in attn_resolutions:\n attn.append(make_attn(block_in, attn_type=attn_type))\n up = nn.Module()\n up.block = block\n up.attn = attn\n if i_level != 0:\n up.upsample = Upsample(block_in, resamp_with_conv)\n curr_res = curr_res * 2\n self.up.insert(0, up) # prepend to get consistent order\n\n # end\n self.norm_out = Normalize(block_in)\n self.conv_out = torch.nn.Conv2d(block_in,\n out_ch,\n kernel_size=3,\n stride=1,\n padding=1)\n\n def forward(self, z):\n #assert z.shape[1:] == self.z_shape[1:]\n self.last_z_shape = z.shape\n\n # timestep embedding\n temb = None\n\n # z to block_in\n h = self.conv_in(z)\n\n # middle\n h = self.mid.block_1(h, temb)\n h = self.mid.attn_1(h)\n h = self.mid.block_2(h, temb)\n\n # upsampling\n for i_level in reversed(range(self.num_resolutions)):\n for i_block in range(self.num_res_blocks+1):\n h = self.up[i_level].block[i_block](h, temb)\n if len(self.up[i_level].attn) > 0:\n h = self.up[i_level].attn[i_block](h)\n if i_level != 0:\n h = self.up[i_level].upsample(h)\n\n # end\n if self.give_pre_end:\n return h\n\n h = self.norm_out(h)\n h = nonlinearity(h)\n h = self.conv_out(h)\n if self.tanh_out:\n h = torch.tanh(h)\n return h\n\n\nclass SimpleDecoder(nn.Module):\n def __init__(self, in_channels, out_channels, *args, **kwargs):\n super().__init__()\n self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),\n ResnetBlock(in_channels=in_channels,\n out_channels=2 * in_channels,\n temb_channels=0, dropout=0.0),\n ResnetBlock(in_channels=2 * in_channels,\n out_channels=4 * in_channels,\n temb_channels=0, dropout=0.0),\n ResnetBlock(in_channels=4 * in_channels,\n out_channels=2 * in_channels,\n temb_channels=0, dropout=0.0),\n nn.Conv2d(2*in_channels, in_channels, 1),\n Upsample(in_channels, with_conv=True)])\n # end\n self.norm_out = Normalize(in_channels)\n self.conv_out = torch.nn.Conv2d(in_channels,\n out_channels,\n kernel_size=3,\n stride=1,\n padding=1)\n\n def forward(self, x):\n for i, layer in enumerate(self.model):\n if i in [1,2,3]:\n x = layer(x, None)\n else:\n x = layer(x)\n\n h = self.norm_out(x)\n h = nonlinearity(h)\n x = self.conv_out(h)\n return x\n\n\nclass UpsampleDecoder(nn.Module):\n def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,\n ch_mult=(2,2), dropout=0.0):\n super().__init__()\n # upsampling\n self.temb_ch = 0\n self.num_resolutions = len(ch_mult)\n self.num_res_blocks = num_res_blocks\n block_in = in_channels\n curr_res = resolution // 2 ** (self.num_resolutions - 1)\n self.res_blocks = nn.ModuleList()\n self.upsample_blocks = nn.ModuleList()\n for i_level in range(self.num_resolutions):\n res_block = []\n block_out = ch * ch_mult[i_level]\n for i_block in range(self.num_res_blocks + 1):\n res_block.append(ResnetBlock(in_channels=block_in,\n out_channels=block_out,\n temb_channels=self.temb_ch,\n dropout=dropout))\n block_in = block_out\n self.res_blocks.append(nn.ModuleList(res_block))\n if i_level != self.num_resolutions - 1:\n self.upsample_blocks.append(Upsample(block_in, True))\n curr_res = curr_res * 2\n\n # end\n self.norm_out = Normalize(block_in)\n self.conv_out = torch.nn.Conv2d(block_in,\n out_channels,\n kernel_size=3,\n stride=1,\n padding=1)\n\n def forward(self, x):\n # upsampling\n h = x\n for k, i_level in enumerate(range(self.num_resolutions)):\n for i_block in range(self.num_res_blocks + 1):\n h = self.res_blocks[i_level][i_block](h, None)\n if i_level != self.num_resolutions - 1:\n h = self.upsample_blocks[k](h)\n h = self.norm_out(h)\n h = nonlinearity(h)\n h = self.conv_out(h)\n return h\n\n\nclass LatentRescaler(nn.Module):\n def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):\n super().__init__()\n # residual block, interpolate, residual block\n self.factor = factor\n self.conv_in = nn.Conv2d(in_channels,\n mid_channels,\n kernel_size=3,\n stride=1,\n padding=1)\n self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,\n out_channels=mid_channels,\n temb_channels=0,\n dropout=0.0) for _ in range(depth)])\n self.attn = AttnBlock(mid_channels)\n self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,\n out_channels=mid_channels,\n temb_channels=0,\n dropout=0.0) for _ in range(depth)])\n\n self.conv_out = nn.Conv2d(mid_channels,\n out_channels,\n kernel_size=1,\n )\n\n def forward(self, x):\n x = self.conv_in(x)\n for block in self.res_block1:\n x = block(x, None)\n x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))\n x = self.attn(x)\n for block in self.res_block2:\n x = block(x, None)\n x = self.conv_out(x)\n return x\n\n\nclass MergedRescaleEncoder(nn.Module):\n def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,\n attn_resolutions, dropout=0.0, resamp_with_conv=True,\n ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):\n super().__init__()\n intermediate_chn = ch * ch_mult[-1]\n self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,\n z_channels=intermediate_chn, double_z=False, resolution=resolution,\n attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,\n out_ch=None)\n self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,\n mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)\n\n def forward(self, x):\n x = self.encoder(x)\n x = self.rescaler(x)\n return x\n\n\nclass MergedRescaleDecoder(nn.Module):\n def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),\n dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):\n super().__init__()\n tmp_chn = z_channels*ch_mult[-1]\n self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,\n resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,\n ch_mult=ch_mult, resolution=resolution, ch=ch)\n self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,\n out_channels=tmp_chn, depth=rescale_module_depth)\n\n def forward(self, x):\n x = self.rescaler(x)\n x = self.decoder(x)\n return x\n\n\nclass Upsampler(nn.Module):\n def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):\n super().__init__()\n assert out_size >= in_size\n num_blocks = int(np.log2(out_size//in_size))+1\n factor_up = 1.+ (out_size % in_size)\n print(f\"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}\")\n self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,\n out_channels=in_channels)\n self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,\n attn_resolutions=[], in_channels=None, ch=in_channels,\n ch_mult=[ch_mult for _ in range(num_blocks)])\n\n def forward(self, x):\n x = self.rescaler(x)\n x = self.decoder(x)\n return x\n\n\nclass Resize(nn.Module):\n def __init__(self, in_channels=None, learned=False, mode=\"bilinear\"):\n super().__init__()\n self.with_conv = learned\n self.mode = mode\n if self.with_conv:\n print(f\"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode\")\n raise NotImplementedError()\n assert in_channels is not None\n # no asymmetric padding in torch conv, must do it ourselves\n self.conv = torch.nn.Conv2d(in_channels,\n in_channels,\n kernel_size=4,\n stride=2,\n padding=1)\n\n def forward(self, x, scale_factor=1.0):\n if scale_factor==1.0:\n return x\n else:\n x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)\n return x\n\nclass FirstStagePostProcessor(nn.Module):\n\n def __init__(self, ch_mult:list, in_channels,\n pretrained_model:nn.Module=None,\n reshape=False,\n n_channels=None,\n dropout=0.,\n pretrained_config=None):\n super().__init__()\n if pretrained_config is None:\n assert pretrained_model is not None, 'Either \"pretrained_model\" or \"pretrained_config\" must not be None'\n self.pretrained_model = pretrained_model\n else:\n assert pretrained_config is not None, 'Either \"pretrained_model\" or \"pretrained_config\" must not be None'\n self.instantiate_pretrained(pretrained_config)\n\n self.do_reshape = reshape\n\n if n_channels is None:\n n_channels = self.pretrained_model.encoder.ch\n\n self.proj_norm = Normalize(in_channels,num_groups=in_channels//2)\n self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3,\n stride=1,padding=1)\n\n blocks = []\n downs = []\n ch_in = n_channels\n for m in ch_mult:\n blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout))\n ch_in = m * n_channels\n downs.append(Downsample(ch_in, with_conv=False))\n\n self.model = nn.ModuleList(blocks)\n self.downsampler = nn.ModuleList(downs)\n\n\n def instantiate_pretrained(self, config):\n model = instantiate_from_config(config)\n self.pretrained_model = model.eval()\n # self.pretrained_model.train = False\n for param in self.pretrained_model.parameters():\n param.requires_grad = False\n\n\n @torch.no_grad()\n def encode_with_pretrained(self,x):\n c = self.pretrained_model.encode(x)\n if isinstance(c, DiagonalGaussianDistribution):\n c = c.mode()\n return c\n\n def forward(self,x):\n z_fs = self.encode_with_pretrained(x)\n z = self.proj_norm(z_fs)\n z = self.proj(z)\n z = nonlinearity(z)\n\n for submodel, downmodel in zip(self.model,self.downsampler):\n z = submodel(z,temb=None)\n z = downmodel(z)\n\n if self.do_reshape:\n z = rearrange(z,'b c h w -> b (h w) c')\n return z\n\n" }, { "path": "data_generation/ldm/modules/diffusionmodules/openaimodel.py", "content": "from abc import abstractmethod\nfrom functools import partial\nimport math\nfrom typing import Iterable\n\nimport numpy as np\nimport torch as th\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom ldm.modules.diffusionmodules.util import (\n checkpoint,\n conv_nd,\n linear,\n avg_pool_nd,\n zero_module,\n normalization,\n timestep_embedding,\n)\nfrom ldm.modules.attention import SpatialTransformer\n\n\n# dummy replace\ndef convert_module_to_f16(x):\n pass\n\ndef convert_module_to_f32(x):\n pass\n\n\n## go\nclass AttentionPool2d(nn.Module):\n \"\"\"\n Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py\n \"\"\"\n\n def __init__(\n self,\n spacial_dim: int,\n embed_dim: int,\n num_heads_channels: int,\n output_dim: int = None,\n ):\n super().__init__()\n self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)\n self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)\n self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)\n self.num_heads = embed_dim // num_heads_channels\n self.attention = QKVAttention(self.num_heads)\n\n def forward(self, x):\n b, c, *_spatial = x.shape\n x = x.reshape(b, c, -1) # NC(HW)\n x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1)\n x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1)\n x = self.qkv_proj(x)\n x = self.attention(x)\n x = self.c_proj(x)\n return x[:, :, 0]\n\n\nclass TimestepBlock(nn.Module):\n \"\"\"\n Any module where forward() takes timestep embeddings as a second argument.\n \"\"\"\n\n @abstractmethod\n def forward(self, x, emb):\n \"\"\"\n Apply the module to `x` given `emb` timestep embeddings.\n \"\"\"\n\n\nclass TimestepEmbedSequential(nn.Sequential, TimestepBlock):\n \"\"\"\n A sequential module that passes timestep embeddings to the children that\n support it as an extra input.\n \"\"\"\n\n def forward(self, x, emb, context=None):\n for layer in self:\n if isinstance(layer, TimestepBlock):\n x = layer(x, emb)\n elif isinstance(layer, SpatialTransformer):\n x = layer(x, context)\n else:\n x = layer(x)\n return x\n\n\nclass Upsample(nn.Module):\n \"\"\"\n An upsampling layer with an optional convolution.\n :param channels: channels in the inputs and outputs.\n :param use_conv: a bool determining if a convolution is applied.\n :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then\n upsampling occurs in the inner-two dimensions.\n \"\"\"\n\n def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):\n super().__init__()\n self.channels = channels\n self.out_channels = out_channels or channels\n self.use_conv = use_conv\n self.dims = dims\n if use_conv:\n self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)\n\n def forward(self, x):\n assert x.shape[1] == self.channels\n if self.dims == 3:\n x = F.interpolate(\n x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode=\"nearest\"\n )\n else:\n x = F.interpolate(x, scale_factor=2, mode=\"nearest\")\n if self.use_conv:\n x = self.conv(x)\n return x\n\nclass TransposedUpsample(nn.Module):\n 'Learned 2x upsampling without padding'\n def __init__(self, channels, out_channels=None, ks=5):\n super().__init__()\n self.channels = channels\n self.out_channels = out_channels or channels\n\n self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)\n\n def forward(self,x):\n return self.up(x)\n\n\nclass Downsample(nn.Module):\n \"\"\"\n A downsampling layer with an optional convolution.\n :param channels: channels in the inputs and outputs.\n :param use_conv: a bool determining if a convolution is applied.\n :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then\n downsampling occurs in the inner-two dimensions.\n \"\"\"\n\n def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):\n super().__init__()\n self.channels = channels\n self.out_channels = out_channels or channels\n self.use_conv = use_conv\n self.dims = dims\n stride = 2 if dims != 3 else (1, 2, 2)\n if use_conv:\n self.op = conv_nd(\n dims, self.channels, self.out_channels, 3, stride=stride, padding=padding\n )\n else:\n assert self.channels == self.out_channels\n self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)\n\n def forward(self, x):\n assert x.shape[1] == self.channels\n return self.op(x)\n\n\nclass ResBlock(TimestepBlock):\n \"\"\"\n A residual block that can optionally change the number of channels.\n :param channels: the number of input channels.\n :param emb_channels: the number of timestep embedding channels.\n :param dropout: the rate of dropout.\n :param out_channels: if specified, the number of out channels.\n :param use_conv: if True and out_channels is specified, use a spatial\n convolution instead of a smaller 1x1 convolution to change the\n channels in the skip connection.\n :param dims: determines if the signal is 1D, 2D, or 3D.\n :param use_checkpoint: if True, use gradient checkpointing on this module.\n :param up: if True, use this block for upsampling.\n :param down: if True, use this block for downsampling.\n \"\"\"\n\n def __init__(\n self,\n channels,\n emb_channels,\n dropout,\n out_channels=None,\n use_conv=False,\n use_scale_shift_norm=False,\n dims=2,\n use_checkpoint=False,\n up=False,\n down=False,\n ):\n super().__init__()\n self.channels = channels\n self.emb_channels = emb_channels\n self.dropout = dropout\n self.out_channels = out_channels or channels\n self.use_conv = use_conv\n self.use_checkpoint = use_checkpoint\n self.use_scale_shift_norm = use_scale_shift_norm\n\n self.in_layers = nn.Sequential(\n normalization(channels),\n nn.SiLU(),\n conv_nd(dims, channels, self.out_channels, 3, padding=1),\n )\n\n self.updown = up or down\n\n if up:\n self.h_upd = Upsample(channels, False, dims)\n self.x_upd = Upsample(channels, False, dims)\n elif down:\n self.h_upd = Downsample(channels, False, dims)\n self.x_upd = Downsample(channels, False, dims)\n else:\n self.h_upd = self.x_upd = nn.Identity()\n\n self.emb_layers = nn.Sequential(\n nn.SiLU(),\n linear(\n emb_channels,\n 2 * self.out_channels if use_scale_shift_norm else self.out_channels,\n ),\n )\n self.out_layers = nn.Sequential(\n normalization(self.out_channels),\n nn.SiLU(),\n nn.Dropout(p=dropout),\n zero_module(\n conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)\n ),\n )\n\n if self.out_channels == channels:\n self.skip_connection = nn.Identity()\n elif use_conv:\n self.skip_connection = conv_nd(\n dims, channels, self.out_channels, 3, padding=1\n )\n else:\n self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)\n\n def forward(self, x, emb):\n \"\"\"\n Apply the block to a Tensor, conditioned on a timestep embedding.\n :param x: an [N x C x ...] Tensor of features.\n :param emb: an [N x emb_channels] Tensor of timestep embeddings.\n :return: an [N x C x ...] Tensor of outputs.\n \"\"\"\n return checkpoint(\n self._forward, (x, emb), self.parameters(), self.use_checkpoint\n )\n\n\n def _forward(self, x, emb):\n if self.updown:\n in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]\n h = in_rest(x)\n h = self.h_upd(h)\n x = self.x_upd(x)\n h = in_conv(h)\n else:\n h = self.in_layers(x)\n emb_out = self.emb_layers(emb).type(h.dtype)\n while len(emb_out.shape) < len(h.shape):\n emb_out = emb_out[..., None]\n if self.use_scale_shift_norm:\n out_norm, out_rest = self.out_layers[0], self.out_layers[1:]\n scale, shift = th.chunk(emb_out, 2, dim=1)\n h = out_norm(h) * (1 + scale) + shift\n h = out_rest(h)\n else:\n h = h + emb_out\n h = self.out_layers(h)\n return self.skip_connection(x) + h\n\n\nclass AttentionBlock(nn.Module):\n \"\"\"\n An attention block that allows spatial positions to attend to each other.\n Originally ported from here, but adapted to the N-d case.\n https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.\n \"\"\"\n\n def __init__(\n self,\n channels,\n num_heads=1,\n num_head_channels=-1,\n use_checkpoint=False,\n use_new_attention_order=False,\n ):\n super().__init__()\n self.channels = channels\n if num_head_channels == -1:\n self.num_heads = num_heads\n else:\n assert (\n channels % num_head_channels == 0\n ), f\"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}\"\n self.num_heads = channels // num_head_channels\n self.use_checkpoint = use_checkpoint\n self.norm = normalization(channels)\n self.qkv = conv_nd(1, channels, channels * 3, 1)\n if use_new_attention_order:\n # split qkv before split heads\n self.attention = QKVAttention(self.num_heads)\n else:\n # split heads before split qkv\n self.attention = QKVAttentionLegacy(self.num_heads)\n\n self.proj_out = zero_module(conv_nd(1, channels, channels, 1))\n\n def forward(self, x):\n return checkpoint(self._forward, (x,), self.parameters(), True) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!\n #return pt_checkpoint(self._forward, x) # pytorch\n\n def _forward(self, x):\n b, c, *spatial = x.shape\n x = x.reshape(b, c, -1)\n qkv = self.qkv(self.norm(x))\n h = self.attention(qkv)\n h = self.proj_out(h)\n return (x + h).reshape(b, c, *spatial)\n\n\ndef count_flops_attn(model, _x, y):\n \"\"\"\n A counter for the `thop` package to count the operations in an\n attention operation.\n Meant to be used like:\n macs, params = thop.profile(\n model,\n inputs=(inputs, timestamps),\n custom_ops={QKVAttention: QKVAttention.count_flops},\n )\n \"\"\"\n b, c, *spatial = y[0].shape\n num_spatial = int(np.prod(spatial))\n # We perform two matmuls with the same number of ops.\n # The first computes the weight matrix, the second computes\n # the combination of the value vectors.\n matmul_ops = 2 * b * (num_spatial ** 2) * c\n model.total_ops += th.DoubleTensor([matmul_ops])\n\n\nclass QKVAttentionLegacy(nn.Module):\n \"\"\"\n A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping\n \"\"\"\n\n def __init__(self, n_heads):\n super().__init__()\n self.n_heads = n_heads\n\n def forward(self, qkv):\n \"\"\"\n Apply QKV attention.\n :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.\n :return: an [N x (H * C) x T] tensor after attention.\n \"\"\"\n bs, width, length = qkv.shape\n assert width % (3 * self.n_heads) == 0\n ch = width // (3 * self.n_heads)\n q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)\n scale = 1 / math.sqrt(math.sqrt(ch))\n weight = th.einsum(\n \"bct,bcs->bts\", q * scale, k * scale\n ) # More stable with f16 than dividing afterwards\n weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)\n a = th.einsum(\"bts,bcs->bct\", weight, v)\n return a.reshape(bs, -1, length)\n\n @staticmethod\n def count_flops(model, _x, y):\n return count_flops_attn(model, _x, y)\n\n\nclass QKVAttention(nn.Module):\n \"\"\"\n A module which performs QKV attention and splits in a different order.\n \"\"\"\n\n def __init__(self, n_heads):\n super().__init__()\n self.n_heads = n_heads\n\n def forward(self, qkv):\n \"\"\"\n Apply QKV attention.\n :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.\n :return: an [N x (H * C) x T] tensor after attention.\n \"\"\"\n bs, width, length = qkv.shape\n assert width % (3 * self.n_heads) == 0\n ch = width // (3 * self.n_heads)\n q, k, v = qkv.chunk(3, dim=1)\n scale = 1 / math.sqrt(math.sqrt(ch))\n weight = th.einsum(\n \"bct,bcs->bts\",\n (q * scale).view(bs * self.n_heads, ch, length),\n (k * scale).view(bs * self.n_heads, ch, length),\n ) # More stable with f16 than dividing afterwards\n weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)\n a = th.einsum(\"bts,bcs->bct\", weight, v.reshape(bs * self.n_heads, ch, length))\n return a.reshape(bs, -1, length)\n\n @staticmethod\n def count_flops(model, _x, y):\n return count_flops_attn(model, _x, y)\n\n\nclass UNetModel(nn.Module):\n \"\"\"\n The full UNet model with attention and timestep embedding.\n :param in_channels: channels in the input Tensor.\n :param model_channels: base channel count for the model.\n :param out_channels: channels in the output Tensor.\n :param num_res_blocks: number of residual blocks per downsample.\n :param attention_resolutions: a collection of downsample rates at which\n attention will take place. May be a set, list, or tuple.\n For example, if this contains 4, then at 4x downsampling, attention\n will be used.\n :param dropout: the dropout probability.\n :param channel_mult: channel multiplier for each level of the UNet.\n :param conv_resample: if True, use learned convolutions for upsampling and\n downsampling.\n :param dims: determines if the signal is 1D, 2D, or 3D.\n :param num_classes: if specified (as an int), then this model will be\n class-conditional with `num_classes` classes.\n :param use_checkpoint: use gradient checkpointing to reduce memory usage.\n :param num_heads: the number of attention heads in each attention layer.\n :param num_heads_channels: if specified, ignore num_heads and instead use\n a fixed channel width per attention head.\n :param num_heads_upsample: works with num_heads to set a different number\n of heads for upsampling. Deprecated.\n :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.\n :param resblock_updown: use residual blocks for up/downsampling.\n :param use_new_attention_order: use a different attention pattern for potentially\n increased efficiency.\n \"\"\"\n\n def __init__(\n self,\n image_size,\n in_channels,\n model_channels,\n out_channels,\n num_res_blocks,\n attention_resolutions,\n dropout=0,\n channel_mult=(1, 2, 4, 8),\n conv_resample=True,\n dims=2,\n num_classes=None,\n use_checkpoint=False,\n use_fp16=False,\n num_heads=-1,\n num_head_channels=-1,\n num_heads_upsample=-1,\n use_scale_shift_norm=False,\n resblock_updown=False,\n use_new_attention_order=False,\n use_spatial_transformer=False, # custom transformer support\n transformer_depth=1, # custom transformer support\n context_dim=None, # custom transformer support\n n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model\n legacy=True,\n ):\n super().__init__()\n if use_spatial_transformer:\n assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'\n\n if context_dim is not None:\n assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'\n from omegaconf.listconfig import ListConfig\n if type(context_dim) == ListConfig:\n context_dim = list(context_dim)\n\n if num_heads_upsample == -1:\n num_heads_upsample = num_heads\n\n if num_heads == -1:\n assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'\n\n if num_head_channels == -1:\n assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'\n\n self.image_size = image_size\n self.in_channels = in_channels\n self.model_channels = model_channels\n self.out_channels = out_channels\n self.num_res_blocks = num_res_blocks\n self.attention_resolutions = attention_resolutions\n self.dropout = dropout\n self.channel_mult = channel_mult\n self.conv_resample = conv_resample\n self.num_classes = num_classes\n self.use_checkpoint = use_checkpoint\n self.dtype = th.float16 if use_fp16 else th.float32\n self.num_heads = num_heads\n self.num_head_channels = num_head_channels\n self.num_heads_upsample = num_heads_upsample\n self.predict_codebook_ids = n_embed is not None\n\n time_embed_dim = model_channels * 4\n self.time_embed = nn.Sequential(\n linear(model_channels, time_embed_dim),\n nn.SiLU(),\n linear(time_embed_dim, time_embed_dim),\n )\n\n if self.num_classes is not None:\n self.label_emb = nn.Embedding(num_classes, time_embed_dim)\n\n self.input_blocks = nn.ModuleList(\n [\n TimestepEmbedSequential(\n conv_nd(dims, in_channels, model_channels, 3, padding=1)\n )\n ]\n )\n self._feature_size = model_channels\n input_block_chans = [model_channels]\n ch = model_channels\n ds = 1\n for level, mult in enumerate(channel_mult):\n for _ in range(num_res_blocks):\n layers = [\n ResBlock(\n ch,\n time_embed_dim,\n dropout,\n out_channels=mult * model_channels,\n dims=dims,\n use_checkpoint=use_checkpoint,\n use_scale_shift_norm=use_scale_shift_norm,\n )\n ]\n ch = mult * model_channels\n if ds in attention_resolutions:\n if num_head_channels == -1:\n dim_head = ch // num_heads\n else:\n num_heads = ch // num_head_channels\n dim_head = num_head_channels\n if legacy:\n #num_heads = 1\n dim_head = ch // num_heads if use_spatial_transformer else num_head_channels\n layers.append(\n AttentionBlock(\n ch,\n use_checkpoint=use_checkpoint,\n num_heads=num_heads,\n num_head_channels=dim_head,\n use_new_attention_order=use_new_attention_order,\n ) if not use_spatial_transformer else SpatialTransformer(\n ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim\n )\n )\n self.input_blocks.append(TimestepEmbedSequential(*layers))\n self._feature_size += ch\n input_block_chans.append(ch)\n if level != len(channel_mult) - 1:\n out_ch = ch\n self.input_blocks.append(\n TimestepEmbedSequential(\n ResBlock(\n ch,\n time_embed_dim,\n dropout,\n out_channels=out_ch,\n dims=dims,\n use_checkpoint=use_checkpoint,\n use_scale_shift_norm=use_scale_shift_norm,\n down=True,\n )\n if resblock_updown\n else Downsample(\n ch, conv_resample, dims=dims, out_channels=out_ch\n )\n )\n )\n ch = out_ch\n input_block_chans.append(ch)\n ds *= 2\n self._feature_size += ch\n\n if num_head_channels == -1:\n dim_head = ch // num_heads\n else:\n num_heads = ch // num_head_channels\n dim_head = num_head_channels\n if legacy:\n #num_heads = 1\n dim_head = ch // num_heads if use_spatial_transformer else num_head_channels\n self.middle_block = TimestepEmbedSequential(\n ResBlock(\n ch,\n time_embed_dim,\n dropout,\n dims=dims,\n use_checkpoint=use_checkpoint,\n use_scale_shift_norm=use_scale_shift_norm,\n ),\n AttentionBlock(\n ch,\n use_checkpoint=use_checkpoint,\n num_heads=num_heads,\n num_head_channels=dim_head,\n use_new_attention_order=use_new_attention_order,\n ) if not use_spatial_transformer else SpatialTransformer(\n ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim\n ),\n ResBlock(\n ch,\n time_embed_dim,\n dropout,\n dims=dims,\n use_checkpoint=use_checkpoint,\n use_scale_shift_norm=use_scale_shift_norm,\n ),\n )\n self._feature_size += ch\n\n self.output_blocks = nn.ModuleList([])\n for level, mult in list(enumerate(channel_mult))[::-1]:\n for i in range(num_res_blocks + 1):\n ich = input_block_chans.pop()\n layers = [\n ResBlock(\n ch + ich,\n time_embed_dim,\n dropout,\n out_channels=model_channels * mult,\n dims=dims,\n use_checkpoint=use_checkpoint,\n use_scale_shift_norm=use_scale_shift_norm,\n )\n ]\n ch = model_channels * mult\n if ds in attention_resolutions:\n if num_head_channels == -1:\n dim_head = ch // num_heads\n else:\n num_heads = ch // num_head_channels\n dim_head = num_head_channels\n if legacy:\n #num_heads = 1\n dim_head = ch // num_heads if use_spatial_transformer else num_head_channels\n layers.append(\n AttentionBlock(\n ch,\n use_checkpoint=use_checkpoint,\n num_heads=num_heads_upsample,\n num_head_channels=dim_head,\n use_new_attention_order=use_new_attention_order,\n ) if not use_spatial_transformer else SpatialTransformer(\n ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim\n )\n )\n if level and i == num_res_blocks:\n out_ch = ch\n layers.append(\n ResBlock(\n ch,\n time_embed_dim,\n dropout,\n out_channels=out_ch,\n dims=dims,\n use_checkpoint=use_checkpoint,\n use_scale_shift_norm=use_scale_shift_norm,\n up=True,\n )\n if resblock_updown\n else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)\n )\n ds //= 2\n self.output_blocks.append(TimestepEmbedSequential(*layers))\n self._feature_size += ch\n\n self.out = nn.Sequential(\n normalization(ch),\n nn.SiLU(),\n zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),\n )\n if self.predict_codebook_ids:\n self.id_predictor = nn.Sequential(\n normalization(ch),\n conv_nd(dims, model_channels, n_embed, 1),\n #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits\n )\n\n def convert_to_fp16(self):\n \"\"\"\n Convert the torso of the model to float16.\n \"\"\"\n self.input_blocks.apply(convert_module_to_f16)\n self.middle_block.apply(convert_module_to_f16)\n self.output_blocks.apply(convert_module_to_f16)\n\n def convert_to_fp32(self):\n \"\"\"\n Convert the torso of the model to float32.\n \"\"\"\n self.input_blocks.apply(convert_module_to_f32)\n self.middle_block.apply(convert_module_to_f32)\n self.output_blocks.apply(convert_module_to_f32)\n\n def forward(self, x, timesteps=None, context=None, y=None,**kwargs):\n \"\"\"\n Apply the model to an input batch.\n :param x: an [N x C x ...] Tensor of inputs.\n :param timesteps: a 1-D batch of timesteps.\n :param context: conditioning plugged in via crossattn\n :param y: an [N] Tensor of labels, if class-conditional.\n :return: an [N x C x ...] Tensor of outputs.\n \"\"\"\n assert (y is not None) == (\n self.num_classes is not None\n ), \"must specify y if and only if the model is class-conditional\"\n hs = []\n t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)\n emb = self.time_embed(t_emb)\n\n if self.num_classes is not None:\n assert y.shape == (x.shape[0],)\n emb = emb + self.label_emb(y)\n\n h = x.type(self.dtype)\n for module in self.input_blocks:\n h = module(h, emb, context)\n hs.append(h)\n h = self.middle_block(h, emb, context)\n for module in self.output_blocks:\n h = th.cat([h, hs.pop()], dim=1)\n h = module(h, emb, context)\n h = h.type(x.dtype)\n if self.predict_codebook_ids:\n return self.id_predictor(h)\n else:\n return self.out(h)\n\n\nclass EncoderUNetModel(nn.Module):\n \"\"\"\n The half UNet model with attention and timestep embedding.\n For usage, see UNet.\n \"\"\"\n\n def __init__(\n self,\n image_size,\n in_channels,\n model_channels,\n out_channels,\n num_res_blocks,\n attention_resolutions,\n dropout=0,\n channel_mult=(1, 2, 4, 8),\n conv_resample=True,\n dims=2,\n use_checkpoint=False,\n use_fp16=False,\n num_heads=1,\n num_head_channels=-1,\n num_heads_upsample=-1,\n use_scale_shift_norm=False,\n resblock_updown=False,\n use_new_attention_order=False,\n pool=\"adaptive\",\n *args,\n **kwargs\n ):\n super().__init__()\n\n if num_heads_upsample == -1:\n num_heads_upsample = num_heads\n\n self.in_channels = in_channels\n self.model_channels = model_channels\n self.out_channels = out_channels\n self.num_res_blocks = num_res_blocks\n self.attention_resolutions = attention_resolutions\n self.dropout = dropout\n self.channel_mult = channel_mult\n self.conv_resample = conv_resample\n self.use_checkpoint = use_checkpoint\n self.dtype = th.float16 if use_fp16 else th.float32\n self.num_heads = num_heads\n self.num_head_channels = num_head_channels\n self.num_heads_upsample = num_heads_upsample\n\n time_embed_dim = model_channels * 4\n self.time_embed = nn.Sequential(\n linear(model_channels, time_embed_dim),\n nn.SiLU(),\n linear(time_embed_dim, time_embed_dim),\n )\n\n self.input_blocks = nn.ModuleList(\n [\n TimestepEmbedSequential(\n conv_nd(dims, in_channels, model_channels, 3, padding=1)\n )\n ]\n )\n self._feature_size = model_channels\n input_block_chans = [model_channels]\n ch = model_channels\n ds = 1\n for level, mult in enumerate(channel_mult):\n for _ in range(num_res_blocks):\n layers = [\n ResBlock(\n ch,\n time_embed_dim,\n dropout,\n out_channels=mult * model_channels,\n dims=dims,\n use_checkpoint=use_checkpoint,\n use_scale_shift_norm=use_scale_shift_norm,\n )\n ]\n ch = mult * model_channels\n if ds in attention_resolutions:\n layers.append(\n AttentionBlock(\n ch,\n use_checkpoint=use_checkpoint,\n num_heads=num_heads,\n num_head_channels=num_head_channels,\n use_new_attention_order=use_new_attention_order,\n )\n )\n self.input_blocks.append(TimestepEmbedSequential(*layers))\n self._feature_size += ch\n input_block_chans.append(ch)\n if level != len(channel_mult) - 1:\n out_ch = ch\n self.input_blocks.append(\n TimestepEmbedSequential(\n ResBlock(\n ch,\n time_embed_dim,\n dropout,\n out_channels=out_ch,\n dims=dims,\n use_checkpoint=use_checkpoint,\n use_scale_shift_norm=use_scale_shift_norm,\n down=True,\n )\n if resblock_updown\n else Downsample(\n ch, conv_resample, dims=dims, out_channels=out_ch\n )\n )\n )\n ch = out_ch\n input_block_chans.append(ch)\n ds *= 2\n self._feature_size += ch\n\n self.middle_block = TimestepEmbedSequential(\n ResBlock(\n ch,\n time_embed_dim,\n dropout,\n dims=dims,\n use_checkpoint=use_checkpoint,\n use_scale_shift_norm=use_scale_shift_norm,\n ),\n AttentionBlock(\n ch,\n use_checkpoint=use_checkpoint,\n num_heads=num_heads,\n num_head_channels=num_head_channels,\n use_new_attention_order=use_new_attention_order,\n ),\n ResBlock(\n ch,\n time_embed_dim,\n dropout,\n dims=dims,\n use_checkpoint=use_checkpoint,\n use_scale_shift_norm=use_scale_shift_norm,\n ),\n )\n self._feature_size += ch\n self.pool = pool\n if pool == \"adaptive\":\n self.out = nn.Sequential(\n normalization(ch),\n nn.SiLU(),\n nn.AdaptiveAvgPool2d((1, 1)),\n zero_module(conv_nd(dims, ch, out_channels, 1)),\n nn.Flatten(),\n )\n elif pool == \"attention\":\n assert num_head_channels != -1\n self.out = nn.Sequential(\n normalization(ch),\n nn.SiLU(),\n AttentionPool2d(\n (image_size // ds), ch, num_head_channels, out_channels\n ),\n )\n elif pool == \"spatial\":\n self.out = nn.Sequential(\n nn.Linear(self._feature_size, 2048),\n nn.ReLU(),\n nn.Linear(2048, self.out_channels),\n )\n elif pool == \"spatial_v2\":\n self.out = nn.Sequential(\n nn.Linear(self._feature_size, 2048),\n normalization(2048),\n nn.SiLU(),\n nn.Linear(2048, self.out_channels),\n )\n else:\n raise NotImplementedError(f\"Unexpected {pool} pooling\")\n\n def convert_to_fp16(self):\n \"\"\"\n Convert the torso of the model to float16.\n \"\"\"\n self.input_blocks.apply(convert_module_to_f16)\n self.middle_block.apply(convert_module_to_f16)\n\n def convert_to_fp32(self):\n \"\"\"\n Convert the torso of the model to float32.\n \"\"\"\n self.input_blocks.apply(convert_module_to_f32)\n self.middle_block.apply(convert_module_to_f32)\n\n def forward(self, x, timesteps):\n \"\"\"\n Apply the model to an input batch.\n :param x: an [N x C x ...] Tensor of inputs.\n :param timesteps: a 1-D batch of timesteps.\n :return: an [N x K] Tensor of outputs.\n \"\"\"\n emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))\n\n results = []\n h = x.type(self.dtype)\n for module in self.input_blocks:\n h = module(h, emb)\n if self.pool.startswith(\"spatial\"):\n results.append(h.type(x.dtype).mean(dim=(2, 3)))\n h = self.middle_block(h, emb)\n if self.pool.startswith(\"spatial\"):\n results.append(h.type(x.dtype).mean(dim=(2, 3)))\n h = th.cat(results, axis=-1)\n return self.out(h)\n else:\n h = h.type(x.dtype)\n return self.out(h)\n\n" }, { "path": "data_generation/ldm/modules/diffusionmodules/util.py", "content": "# adopted from\n# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py\n# and\n# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py\n# and\n# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py\n#\n# thanks!\n\n\nimport os\nimport math\nimport torch\nimport torch.nn as nn\nimport numpy as np\nfrom einops import repeat\n\nfrom ldm.util import instantiate_from_config\n\n\ndef make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):\n if schedule == \"linear\":\n betas = (\n torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2\n )\n\n elif schedule == \"cosine\":\n timesteps = (\n torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s\n )\n alphas = timesteps / (1 + cosine_s) * np.pi / 2\n alphas = torch.cos(alphas).pow(2)\n alphas = alphas / alphas[0]\n betas = 1 - alphas[1:] / alphas[:-1]\n betas = np.clip(betas, a_min=0, a_max=0.999)\n\n elif schedule == \"sqrt_linear\":\n betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)\n elif schedule == \"sqrt\":\n betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5\n else:\n raise ValueError(f\"schedule '{schedule}' unknown.\")\n return betas.numpy()\n\n\ndef make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):\n if ddim_discr_method == 'uniform':\n c = num_ddpm_timesteps // num_ddim_timesteps\n ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))\n elif ddim_discr_method == 'quad':\n ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)\n else:\n raise NotImplementedError(f'There is no ddim discretization method called \"{ddim_discr_method}\"')\n\n # assert ddim_timesteps.shape[0] == num_ddim_timesteps\n # add one to get the final alpha values right (the ones from first scale to data during sampling)\n steps_out = ddim_timesteps + 1\n if verbose:\n print(f'Selected timesteps for ddim sampler: {steps_out}')\n return steps_out\n\n\ndef make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):\n # select alphas for computing the variance schedule\n alphas = alphacums[ddim_timesteps]\n alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())\n\n # according the the formula provided in https://arxiv.org/abs/2010.02502\n sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))\n if verbose:\n print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')\n print(f'For the chosen value of eta, which is {eta}, '\n f'this results in the following sigma_t schedule for ddim sampler {sigmas}')\n return sigmas, alphas, alphas_prev\n\n\ndef betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function,\n which defines the cumulative product of (1-beta) over time from t = [0,1].\n :param num_diffusion_timesteps: the number of betas to produce.\n :param alpha_bar: a lambda that takes an argument t from 0 to 1 and\n produces the cumulative product of (1-beta) up to that\n part of the diffusion process.\n :param max_beta: the maximum beta to use; use values lower than 1 to\n prevent singularities.\n \"\"\"\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))\n return np.array(betas)\n\n\ndef extract_into_tensor(a, t, x_shape):\n b, *_ = t.shape\n out = a.gather(-1, t)\n return out.reshape(b, *((1,) * (len(x_shape) - 1)))\n\n\ndef checkpoint(func, inputs, params, flag):\n \"\"\"\n Evaluate a function without caching intermediate activations, allowing for\n reduced memory at the expense of extra compute in the backward pass.\n :param func: the function to evaluate.\n :param inputs: the argument sequence to pass to `func`.\n :param params: a sequence of parameters `func` depends on but does not\n explicitly take as arguments.\n :param flag: if False, disable gradient checkpointing.\n \"\"\"\n if flag:\n args = tuple(inputs) + tuple(params)\n return CheckpointFunction.apply(func, len(inputs), *args)\n else:\n return func(*inputs)\n\n\nclass CheckpointFunction(torch.autograd.Function):\n @staticmethod\n def forward(ctx, run_function, length, *args):\n ctx.run_function = run_function\n ctx.input_tensors = list(args[:length])\n ctx.input_params = list(args[length:])\n\n with torch.no_grad():\n output_tensors = ctx.run_function(*ctx.input_tensors)\n return output_tensors\n\n @staticmethod\n def backward(ctx, *output_grads):\n ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]\n with torch.enable_grad():\n # Fixes a bug where the first op in run_function modifies the\n # Tensor storage in place, which is not allowed for detach()'d\n # Tensors.\n shallow_copies = [x.view_as(x) for x in ctx.input_tensors]\n output_tensors = ctx.run_function(*shallow_copies)\n input_grads = torch.autograd.grad(\n output_tensors,\n ctx.input_tensors + ctx.input_params,\n output_grads,\n allow_unused=True,\n )\n del ctx.input_tensors\n del ctx.input_params\n del output_tensors\n return (None, None) + input_grads\n\n\ndef timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):\n \"\"\"\n Create sinusoidal timestep embeddings.\n :param timesteps: a 1-D Tensor of N indices, one per batch element.\n These may be fractional.\n :param dim: the dimension of the output.\n :param max_period: controls the minimum frequency of the embeddings.\n :return: an [N x dim] Tensor of positional embeddings.\n \"\"\"\n if not repeat_only:\n half = dim // 2\n freqs = torch.exp(\n -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half\n ).to(device=timesteps.device)\n args = timesteps[:, None].float() * freqs[None]\n embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)\n if dim % 2:\n embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)\n else:\n embedding = repeat(timesteps, 'b -> b d', d=dim)\n return embedding\n\n\ndef zero_module(module):\n \"\"\"\n Zero out the parameters of a module and return it.\n \"\"\"\n for p in module.parameters():\n p.detach().zero_()\n return module\n\n\ndef scale_module(module, scale):\n \"\"\"\n Scale the parameters of a module and return it.\n \"\"\"\n for p in module.parameters():\n p.detach().mul_(scale)\n return module\n\n\ndef mean_flat(tensor):\n \"\"\"\n Take the mean over all non-batch dimensions.\n \"\"\"\n return tensor.mean(dim=list(range(1, len(tensor.shape))))\n\n\ndef normalization(channels):\n \"\"\"\n Make a standard normalization layer.\n :param channels: number of input channels.\n :return: an nn.Module for normalization.\n \"\"\"\n return GroupNorm32(32, channels)\n\n\n# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.\nclass SiLU(nn.Module):\n def forward(self, x):\n return x * torch.sigmoid(x)\n\n\nclass GroupNorm32(nn.GroupNorm):\n def forward(self, x):\n return super().forward(x.float()).type(x.dtype)\n\ndef conv_nd(dims, *args, **kwargs):\n \"\"\"\n Create a 1D, 2D, or 3D convolution module.\n \"\"\"\n if dims == 1:\n return nn.Conv1d(*args, **kwargs)\n elif dims == 2:\n return nn.Conv2d(*args, **kwargs)\n elif dims == 3:\n return nn.Conv3d(*args, **kwargs)\n raise ValueError(f\"unsupported dimensions: {dims}\")\n\n\ndef linear(*args, **kwargs):\n \"\"\"\n Create a linear module.\n \"\"\"\n return nn.Linear(*args, **kwargs)\n\n\ndef avg_pool_nd(dims, *args, **kwargs):\n \"\"\"\n Create a 1D, 2D, or 3D average pooling module.\n \"\"\"\n if dims == 1:\n return nn.AvgPool1d(*args, **kwargs)\n elif dims == 2:\n return nn.AvgPool2d(*args, **kwargs)\n elif dims == 3:\n return nn.AvgPool3d(*args, **kwargs)\n raise ValueError(f\"unsupported dimensions: {dims}\")\n\n\nclass HybridConditioner(nn.Module):\n\n def __init__(self, c_concat_config, c_crossattn_config):\n super().__init__()\n self.concat_conditioner = instantiate_from_config(c_concat_config)\n self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)\n\n def forward(self, c_concat, c_crossattn):\n c_concat = self.concat_conditioner(c_concat)\n c_crossattn = self.crossattn_conditioner(c_crossattn)\n return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}\n\n\ndef noise_like(shape, device, repeat=False):\n repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))\n noise = lambda: torch.randn(shape, device=device)\n return repeat_noise() if repeat else noise()" }, { "path": "data_generation/ldm/modules/distributions/__init__.py", "content": "" }, { "path": "data_generation/ldm/modules/distributions/distributions.py", "content": "import torch\nimport numpy as np\n\n\nclass AbstractDistribution:\n def sample(self):\n raise NotImplementedError()\n\n def mode(self):\n raise NotImplementedError()\n\n\nclass DiracDistribution(AbstractDistribution):\n def __init__(self, value):\n self.value = value\n\n def sample(self):\n return self.value\n\n def mode(self):\n return self.value\n\n\nclass DiagonalGaussianDistribution(object):\n def __init__(self, parameters, deterministic=False):\n self.parameters = parameters\n self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)\n self.logvar = torch.clamp(self.logvar, -30.0, 20.0)\n self.deterministic = deterministic\n self.std = torch.exp(0.5 * self.logvar)\n self.var = torch.exp(self.logvar)\n if self.deterministic:\n self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)\n\n def sample(self):\n x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)\n return x\n\n def kl(self, other=None):\n if self.deterministic:\n return torch.Tensor([0.])\n else:\n if other is None:\n return 0.5 * torch.sum(torch.pow(self.mean, 2)\n + self.var - 1.0 - self.logvar,\n dim=[1, 2, 3])\n else:\n return 0.5 * torch.sum(\n torch.pow(self.mean - other.mean, 2) / other.var\n + self.var / other.var - 1.0 - self.logvar + other.logvar,\n dim=[1, 2, 3])\n\n def nll(self, sample, dims=[1,2,3]):\n if self.deterministic:\n return torch.Tensor([0.])\n logtwopi = np.log(2.0 * np.pi)\n return 0.5 * torch.sum(\n logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,\n dim=dims)\n\n def mode(self):\n return self.mean\n\n\ndef normal_kl(mean1, logvar1, mean2, logvar2):\n \"\"\"\n source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12\n Compute the KL divergence between two gaussians.\n Shapes are automatically broadcasted, so batches can be compared to\n scalars, among other use cases.\n \"\"\"\n tensor = None\n for obj in (mean1, logvar1, mean2, logvar2):\n if isinstance(obj, torch.Tensor):\n tensor = obj\n break\n assert tensor is not None, \"at least one argument must be a Tensor\"\n\n # Force variances to be Tensors. Broadcasting helps convert scalars to\n # Tensors, but it does not work for torch.exp().\n logvar1, logvar2 = [\n x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)\n for x in (logvar1, logvar2)\n ]\n\n return 0.5 * (\n -1.0\n + logvar2\n - logvar1\n + torch.exp(logvar1 - logvar2)\n + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)\n )\n" }, { "path": "data_generation/ldm/modules/ema.py", "content": "import torch\nfrom torch import nn\n\n\nclass LitEma(nn.Module):\n def __init__(self, model, decay=0.9999, use_num_upates=True):\n super().__init__()\n if decay < 0.0 or decay > 1.0:\n raise ValueError('Decay must be between 0 and 1')\n\n self.m_name2s_name = {}\n self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))\n self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates\n else torch.tensor(-1,dtype=torch.int))\n\n for name, p in model.named_parameters():\n if p.requires_grad:\n #remove as '.'-character is not allowed in buffers\n s_name = name.replace('.','')\n self.m_name2s_name.update({name:s_name})\n self.register_buffer(s_name,p.clone().detach().data)\n\n self.collected_params = []\n\n def forward(self,model):\n decay = self.decay\n\n if self.num_updates >= 0:\n self.num_updates += 1\n decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates))\n\n one_minus_decay = 1.0 - decay\n\n with torch.no_grad():\n m_param = dict(model.named_parameters())\n shadow_params = dict(self.named_buffers())\n\n for key in m_param:\n if m_param[key].requires_grad:\n sname = self.m_name2s_name[key]\n shadow_params[sname] = shadow_params[sname].type_as(m_param[key])\n shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))\n else:\n assert not key in self.m_name2s_name\n\n def copy_to(self, model):\n m_param = dict(model.named_parameters())\n shadow_params = dict(self.named_buffers())\n for key in m_param:\n if m_param[key].requires_grad:\n m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)\n else:\n assert not key in self.m_name2s_name\n\n def store(self, parameters):\n \"\"\"\n Save the current parameters for restoring later.\n Args:\n parameters: Iterable of `torch.nn.Parameter`; the parameters to be\n temporarily stored.\n \"\"\"\n self.collected_params = [param.clone() for param in parameters]\n\n def restore(self, parameters):\n \"\"\"\n Restore the parameters stored with the `store` method.\n Useful to validate the model with EMA parameters without affecting the\n original optimization process. Store the parameters before the\n `copy_to` method. After validation (or model saving), use this to\n restore the former parameters.\n Args:\n parameters: Iterable of `torch.nn.Parameter`; the parameters to be\n updated with the stored parameters.\n \"\"\"\n for c_param, param in zip(self.collected_params, parameters):\n param.data.copy_(c_param.data)\n" }, { "path": "data_generation/ldm/modules/encoders/__init__.py", "content": "" }, { "path": "data_generation/ldm/modules/encoders/modules.py", "content": "import torch\nimport torch.nn as nn\nfrom functools import partial\nimport clip\nfrom einops import rearrange, repeat\nfrom transformers import CLIPTokenizer, CLIPTextModel\nimport kornia\n\nfrom ldm.modules.x_transformer import Encoder, TransformerWrapper # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test\n\n\nclass AbstractEncoder(nn.Module):\n def __init__(self):\n super().__init__()\n\n def encode(self, *args, **kwargs):\n raise NotImplementedError\n\n\n\nclass ClassEmbedder(nn.Module):\n def __init__(self, embed_dim, n_classes=1000, key='class'):\n super().__init__()\n self.key = key\n self.embedding = nn.Embedding(n_classes, embed_dim)\n\n def forward(self, batch, key=None):\n if key is None:\n key = self.key\n # this is for use in crossattn\n c = batch[key][:, None]\n c = self.embedding(c)\n return c\n\n\nclass TransformerEmbedder(AbstractEncoder):\n \"\"\"Some transformer encoder layers\"\"\"\n def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device=\"cuda\"):\n super().__init__()\n self.device = device\n self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,\n attn_layers=Encoder(dim=n_embed, depth=n_layer))\n\n def forward(self, tokens):\n tokens = tokens.to(self.device) # meh\n z = self.transformer(tokens, return_embeddings=True)\n return z\n\n def encode(self, x):\n return self(x)\n\n\nclass BERTTokenizer(AbstractEncoder):\n \"\"\" Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)\"\"\"\n def __init__(self, device=\"cuda\", vq_interface=True, max_length=77):\n super().__init__()\n from transformers import BertTokenizerFast # TODO: add to reuquirements\n self.tokenizer = BertTokenizerFast.from_pretrained(\"bert-base-uncased\")\n self.device = device\n self.vq_interface = vq_interface\n self.max_length = max_length\n\n def forward(self, text):\n batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,\n return_overflowing_tokens=False, padding=\"max_length\", return_tensors=\"pt\")\n tokens = batch_encoding[\"input_ids\"].to(self.device)\n return tokens\n\n @torch.no_grad()\n def encode(self, text):\n tokens = self(text)\n if not self.vq_interface:\n return tokens\n return None, None, [None, None, tokens]\n\n def decode(self, text):\n return text\n\n\nclass BERTEmbedder(AbstractEncoder):\n \"\"\"Uses the BERT tokenizr model and add some transformer encoder layers\"\"\"\n def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,\n device=\"cuda\",use_tokenizer=True, embedding_dropout=0.0):\n super().__init__()\n self.use_tknz_fn = use_tokenizer\n if self.use_tknz_fn:\n self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len)\n self.device = device\n self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,\n attn_layers=Encoder(dim=n_embed, depth=n_layer),\n emb_dropout=embedding_dropout)\n\n def forward(self, text):\n if self.use_tknz_fn:\n tokens = self.tknz_fn(text)#.to(self.device)\n else:\n tokens = text\n z = self.transformer(tokens, return_embeddings=True)\n return z\n\n def encode(self, text):\n # output of length 77\n return self(text)\n\n\nclass SpatialRescaler(nn.Module):\n def __init__(self,\n n_stages=1,\n method='bilinear',\n multiplier=0.5,\n in_channels=3,\n out_channels=None,\n bias=False):\n super().__init__()\n self.n_stages = n_stages\n assert self.n_stages >= 0\n assert method in ['nearest','linear','bilinear','trilinear','bicubic','area']\n self.multiplier = multiplier\n self.interpolator = partial(torch.nn.functional.interpolate, mode=method)\n self.remap_output = out_channels is not None\n if self.remap_output:\n print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.')\n self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias)\n\n def forward(self,x):\n for stage in range(self.n_stages):\n x = self.interpolator(x, scale_factor=self.multiplier)\n\n\n if self.remap_output:\n x = self.channel_mapper(x)\n return x\n\n def encode(self, x):\n return self(x)\n\nclass FrozenCLIPEmbedder(AbstractEncoder):\n \"\"\"Uses the CLIP transformer encoder for text (from Hugging Face)\"\"\"\n def __init__(self, version=\"/home/zhaohaozhe/model2/clip-vit-large-patch14\", device=\"cuda\", max_length=77):\n super().__init__()\n self.tokenizer = CLIPTokenizer.from_pretrained(version)\n self.transformer = CLIPTextModel.from_pretrained(version)\n self.device = device\n self.max_length = max_length\n self.freeze()\n\n def freeze(self):\n self.transformer = self.transformer.eval()\n for param in self.parameters():\n param.requires_grad = False\n\n def forward(self, text):\n batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,\n return_overflowing_tokens=False, padding=\"max_length\", return_tensors=\"pt\")\n tokens = batch_encoding[\"input_ids\"].to(self.device)\n outputs = self.transformer(input_ids=tokens)\n\n z = outputs.last_hidden_state\n return z\n\n def encode(self, text):\n return self(text)\n\n\nclass FrozenCLIPTextEmbedder(nn.Module):\n \"\"\"\n Uses the CLIP transformer encoder for text.\n \"\"\"\n def __init__(self, version='ViT-L/14', device=\"cuda\", max_length=77, n_repeat=1, normalize=True):\n super().__init__()\n self.model, _ = clip.load(version, jit=False, device=\"cpu\")\n self.device = device\n self.max_length = max_length\n self.n_repeat = n_repeat\n self.normalize = normalize\n\n def freeze(self):\n self.model = self.model.eval()\n for param in self.parameters():\n param.requires_grad = False\n\n def forward(self, text):\n tokens = clip.tokenize(text).to(self.device)\n z = self.model.encode_text(tokens)\n if self.normalize:\n z = z / torch.linalg.norm(z, dim=1, keepdim=True)\n return z\n\n def encode(self, text):\n z = self(text)\n if z.ndim==2:\n z = z[:, None, :]\n z = repeat(z, 'b 1 d -> b k d', k=self.n_repeat)\n return z\n\n\nclass FrozenClipImageEmbedder(nn.Module):\n \"\"\"\n Uses the CLIP image encoder.\n \"\"\"\n def __init__(\n self,\n model,\n jit=False,\n device='cuda' if torch.cuda.is_available() else 'cpu',\n antialias=False,\n ):\n super().__init__()\n self.model, _ = clip.load(name=model, device=device, jit=jit)\n\n self.antialias = antialias\n\n self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)\n self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)\n\n def preprocess(self, x):\n # normalize to [0,1]\n x = kornia.geometry.resize(x, (224, 224),\n interpolation='bicubic',align_corners=True,\n antialias=self.antialias)\n x = (x + 1.) / 2.\n # renormalize according to clip\n x = kornia.enhance.normalize(x, self.mean, self.std)\n return x\n\n def forward(self, x):\n # x is assumed to be in range [-1,1]\n return self.model.encode_image(self.preprocess(x))\n\n\nif __name__ == \"__main__\":\n from ldm.util import count_params\n model = FrozenCLIPEmbedder()\n count_params(model, verbose=True)" }, { "path": "data_generation/ldm/modules/image_degradation/__init__.py", "content": "from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr\nfrom ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light\n" }, { "path": "data_generation/ldm/modules/image_degradation/bsrgan.py", "content": "# -*- coding: utf-8 -*-\n\"\"\"\n# --------------------------------------------\n# Super-Resolution\n# --------------------------------------------\n#\n# Kai Zhang (cskaizhang@gmail.com)\n# https://github.com/cszn\n# From 2019/03--2021/08\n# --------------------------------------------\n\"\"\"\n\nimport numpy as np\nimport cv2\nimport torch\n\nfrom functools import partial\nimport random\nfrom scipy import ndimage\nimport scipy\nimport scipy.stats as ss\nfrom scipy.interpolate import interp2d\nfrom scipy.linalg import orth\nimport albumentations\n\nimport ldm.modules.image_degradation.utils_image as util\n\n\ndef modcrop_np(img, sf):\n '''\n Args:\n img: numpy image, WxH or WxHxC\n sf: scale factor\n Return:\n cropped image\n '''\n w, h = img.shape[:2]\n im = np.copy(img)\n return im[:w - w % sf, :h - h % sf, ...]\n\n\n\"\"\"\n# --------------------------------------------\n# anisotropic Gaussian kernels\n# --------------------------------------------\n\"\"\"\n\n\ndef analytic_kernel(k):\n \"\"\"Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)\"\"\"\n k_size = k.shape[0]\n # Calculate the big kernels size\n big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))\n # Loop over the small kernel to fill the big one\n for r in range(k_size):\n for c in range(k_size):\n big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k\n # Crop the edges of the big kernel to ignore very small values and increase run time of SR\n crop = k_size // 2\n cropped_big_k = big_k[crop:-crop, crop:-crop]\n # Normalize to 1\n return cropped_big_k / cropped_big_k.sum()\n\n\ndef anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):\n \"\"\" generate an anisotropic Gaussian kernel\n Args:\n ksize : e.g., 15, kernel size\n theta : [0, pi], rotation angle range\n l1 : [0.1,50], scaling of eigenvalues\n l2 : [0.1,l1], scaling of eigenvalues\n If l1 = l2, will get an isotropic Gaussian kernel.\n Returns:\n k : kernel\n \"\"\"\n\n v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))\n V = np.array([[v[0], v[1]], [v[1], -v[0]]])\n D = np.array([[l1, 0], [0, l2]])\n Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))\n k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)\n\n return k\n\n\ndef gm_blur_kernel(mean, cov, size=15):\n center = size / 2.0 + 0.5\n k = np.zeros([size, size])\n for y in range(size):\n for x in range(size):\n cy = y - center + 1\n cx = x - center + 1\n k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)\n\n k = k / np.sum(k)\n return k\n\n\ndef shift_pixel(x, sf, upper_left=True):\n \"\"\"shift pixel for super-resolution with different scale factors\n Args:\n x: WxHxC or WxH\n sf: scale factor\n upper_left: shift direction\n \"\"\"\n h, w = x.shape[:2]\n shift = (sf - 1) * 0.5\n xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)\n if upper_left:\n x1 = xv + shift\n y1 = yv + shift\n else:\n x1 = xv - shift\n y1 = yv - shift\n\n x1 = np.clip(x1, 0, w - 1)\n y1 = np.clip(y1, 0, h - 1)\n\n if x.ndim == 2:\n x = interp2d(xv, yv, x)(x1, y1)\n if x.ndim == 3:\n for i in range(x.shape[-1]):\n x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)\n\n return x\n\n\ndef blur(x, k):\n '''\n x: image, NxcxHxW\n k: kernel, Nx1xhxw\n '''\n n, c = x.shape[:2]\n p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2\n x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')\n k = k.repeat(1, c, 1, 1)\n k = k.view(-1, 1, k.shape[2], k.shape[3])\n x = x.view(1, -1, x.shape[2], x.shape[3])\n x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)\n x = x.view(n, c, x.shape[2], x.shape[3])\n\n return x\n\n\ndef gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):\n \"\"\"\"\n # modified version of https://github.com/assafshocher/BlindSR_dataset_generator\n # Kai Zhang\n # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var\n # max_var = 2.5 * sf\n \"\"\"\n # Set random eigen-vals (lambdas) and angle (theta) for COV matrix\n lambda_1 = min_var + np.random.rand() * (max_var - min_var)\n lambda_2 = min_var + np.random.rand() * (max_var - min_var)\n theta = np.random.rand() * np.pi # random theta\n noise = -noise_level + np.random.rand(*k_size) * noise_level * 2\n\n # Set COV matrix using Lambdas and Theta\n LAMBDA = np.diag([lambda_1, lambda_2])\n Q = np.array([[np.cos(theta), -np.sin(theta)],\n [np.sin(theta), np.cos(theta)]])\n SIGMA = Q @ LAMBDA @ Q.T\n INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]\n\n # Set expectation position (shifting kernel for aligned image)\n MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2)\n MU = MU[None, None, :, None]\n\n # Create meshgrid for Gaussian\n [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))\n Z = np.stack([X, Y], 2)[:, :, :, None]\n\n # Calcualte Gaussian for every pixel of the kernel\n ZZ = Z - MU\n ZZ_t = ZZ.transpose(0, 1, 3, 2)\n raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)\n\n # shift the kernel so it will be centered\n # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)\n\n # Normalize the kernel and return\n # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)\n kernel = raw_kernel / np.sum(raw_kernel)\n return kernel\n\n\ndef fspecial_gaussian(hsize, sigma):\n hsize = [hsize, hsize]\n siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]\n std = sigma\n [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))\n arg = -(x * x + y * y) / (2 * std * std)\n h = np.exp(arg)\n h[h < scipy.finfo(float).eps * h.max()] = 0\n sumh = h.sum()\n if sumh != 0:\n h = h / sumh\n return h\n\n\ndef fspecial_laplacian(alpha):\n alpha = max([0, min([alpha, 1])])\n h1 = alpha / (alpha + 1)\n h2 = (1 - alpha) / (alpha + 1)\n h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]\n h = np.array(h)\n return h\n\n\ndef fspecial(filter_type, *args, **kwargs):\n '''\n python code from:\n https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py\n '''\n if filter_type == 'gaussian':\n return fspecial_gaussian(*args, **kwargs)\n if filter_type == 'laplacian':\n return fspecial_laplacian(*args, **kwargs)\n\n\n\"\"\"\n# --------------------------------------------\n# degradation models\n# --------------------------------------------\n\"\"\"\n\n\ndef bicubic_degradation(x, sf=3):\n '''\n Args:\n x: HxWxC image, [0, 1]\n sf: down-scale factor\n Return:\n bicubicly downsampled LR image\n '''\n x = util.imresize_np(x, scale=1 / sf)\n return x\n\n\ndef srmd_degradation(x, k, sf=3):\n ''' blur + bicubic downsampling\n Args:\n x: HxWxC image, [0, 1]\n k: hxw, double\n sf: down-scale factor\n Return:\n downsampled LR image\n Reference:\n @inproceedings{zhang2018learning,\n title={Learning a single convolutional super-resolution network for multiple degradations},\n author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},\n booktitle={IEEE Conference on Computer Vision and Pattern Recognition},\n pages={3262--3271},\n year={2018}\n }\n '''\n x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror'\n x = bicubic_degradation(x, sf=sf)\n return x\n\n\ndef dpsr_degradation(x, k, sf=3):\n ''' bicubic downsampling + blur\n Args:\n x: HxWxC image, [0, 1]\n k: hxw, double\n sf: down-scale factor\n Return:\n downsampled LR image\n Reference:\n @inproceedings{zhang2019deep,\n title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},\n author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},\n booktitle={IEEE Conference on Computer Vision and Pattern Recognition},\n pages={1671--1681},\n year={2019}\n }\n '''\n x = bicubic_degradation(x, sf=sf)\n x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')\n return x\n\n\ndef classical_degradation(x, k, sf=3):\n ''' blur + downsampling\n Args:\n x: HxWxC image, [0, 1]/[0, 255]\n k: hxw, double\n sf: down-scale factor\n Return:\n downsampled LR image\n '''\n x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')\n # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))\n st = 0\n return x[st::sf, st::sf, ...]\n\n\ndef add_sharpening(img, weight=0.5, radius=50, threshold=10):\n \"\"\"USM sharpening. borrowed from real-ESRGAN\n Input image: I; Blurry image: B.\n 1. K = I + weight * (I - B)\n 2. Mask = 1 if abs(I - B) > threshold, else: 0\n 3. Blur mask:\n 4. Out = Mask * K + (1 - Mask) * I\n Args:\n img (Numpy array): Input image, HWC, BGR; float32, [0, 1].\n weight (float): Sharp weight. Default: 1.\n radius (float): Kernel size of Gaussian blur. Default: 50.\n threshold (int):\n \"\"\"\n if radius % 2 == 0:\n radius += 1\n blur = cv2.GaussianBlur(img, (radius, radius), 0)\n residual = img - blur\n mask = np.abs(residual) * 255 > threshold\n mask = mask.astype('float32')\n soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)\n\n K = img + weight * residual\n K = np.clip(K, 0, 1)\n return soft_mask * K + (1 - soft_mask) * img\n\n\ndef add_blur(img, sf=4):\n wd2 = 4.0 + sf\n wd = 2.0 + 0.2 * sf\n if random.random() < 0.5:\n l1 = wd2 * random.random()\n l2 = wd2 * random.random()\n k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)\n else:\n k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random())\n img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')\n\n return img\n\n\ndef add_resize(img, sf=4):\n rnum = np.random.rand()\n if rnum > 0.8: # up\n sf1 = random.uniform(1, 2)\n elif rnum < 0.7: # down\n sf1 = random.uniform(0.5 / sf, 1)\n else:\n sf1 = 1.0\n img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))\n img = np.clip(img, 0.0, 1.0)\n\n return img\n\n\n# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):\n# noise_level = random.randint(noise_level1, noise_level2)\n# rnum = np.random.rand()\n# if rnum > 0.6: # add color Gaussian noise\n# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)\n# elif rnum < 0.4: # add grayscale Gaussian noise\n# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)\n# else: # add noise\n# L = noise_level2 / 255.\n# D = np.diag(np.random.rand(3))\n# U = orth(np.random.rand(3, 3))\n# conv = np.dot(np.dot(np.transpose(U), D), U)\n# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)\n# img = np.clip(img, 0.0, 1.0)\n# return img\n\ndef add_Gaussian_noise(img, noise_level1=2, noise_level2=25):\n noise_level = random.randint(noise_level1, noise_level2)\n rnum = np.random.rand()\n if rnum > 0.6: # add color Gaussian noise\n img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)\n elif rnum < 0.4: # add grayscale Gaussian noise\n img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)\n else: # add noise\n L = noise_level2 / 255.\n D = np.diag(np.random.rand(3))\n U = orth(np.random.rand(3, 3))\n conv = np.dot(np.dot(np.transpose(U), D), U)\n img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)\n img = np.clip(img, 0.0, 1.0)\n return img\n\n\ndef add_speckle_noise(img, noise_level1=2, noise_level2=25):\n noise_level = random.randint(noise_level1, noise_level2)\n img = np.clip(img, 0.0, 1.0)\n rnum = random.random()\n if rnum > 0.6:\n img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)\n elif rnum < 0.4:\n img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)\n else:\n L = noise_level2 / 255.\n D = np.diag(np.random.rand(3))\n U = orth(np.random.rand(3, 3))\n conv = np.dot(np.dot(np.transpose(U), D), U)\n img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)\n img = np.clip(img, 0.0, 1.0)\n return img\n\n\ndef add_Poisson_noise(img):\n img = np.clip((img * 255.0).round(), 0, 255) / 255.\n vals = 10 ** (2 * random.random() + 2.0) # [2, 4]\n if random.random() < 0.5:\n img = np.random.poisson(img * vals).astype(np.float32) / vals\n else:\n img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])\n img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.\n noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray\n img += noise_gray[:, :, np.newaxis]\n img = np.clip(img, 0.0, 1.0)\n return img\n\n\ndef add_JPEG_noise(img):\n quality_factor = random.randint(30, 95)\n img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)\n result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])\n img = cv2.imdecode(encimg, 1)\n img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)\n return img\n\n\ndef random_crop(lq, hq, sf=4, lq_patchsize=64):\n h, w = lq.shape[:2]\n rnd_h = random.randint(0, h - lq_patchsize)\n rnd_w = random.randint(0, w - lq_patchsize)\n lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]\n\n rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)\n hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]\n return lq, hq\n\n\ndef degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):\n \"\"\"\n This is the degradation model of BSRGAN from the paper\n \"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution\"\n ----------\n img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)\n sf: scale factor\n isp_model: camera ISP model\n Returns\n -------\n img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]\n hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]\n \"\"\"\n isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25\n sf_ori = sf\n\n h1, w1 = img.shape[:2]\n img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop\n h, w = img.shape[:2]\n\n if h < lq_patchsize * sf or w < lq_patchsize * sf:\n raise ValueError(f'img size ({h1}X{w1}) is too small!')\n\n hq = img.copy()\n\n if sf == 4 and random.random() < scale2_prob: # downsample1\n if np.random.rand() < 0.5:\n img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),\n interpolation=random.choice([1, 2, 3]))\n else:\n img = util.imresize_np(img, 1 / 2, True)\n img = np.clip(img, 0.0, 1.0)\n sf = 2\n\n shuffle_order = random.sample(range(7), 7)\n idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)\n if idx1 > idx2: # keep downsample3 last\n shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]\n\n for i in shuffle_order:\n\n if i == 0:\n img = add_blur(img, sf=sf)\n\n elif i == 1:\n img = add_blur(img, sf=sf)\n\n elif i == 2:\n a, b = img.shape[1], img.shape[0]\n # downsample2\n if random.random() < 0.75:\n sf1 = random.uniform(1, 2 * sf)\n img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),\n interpolation=random.choice([1, 2, 3]))\n else:\n k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))\n k_shifted = shift_pixel(k, sf)\n k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel\n img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')\n img = img[0::sf, 0::sf, ...] # nearest downsampling\n img = np.clip(img, 0.0, 1.0)\n\n elif i == 3:\n # downsample3\n img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))\n img = np.clip(img, 0.0, 1.0)\n\n elif i == 4:\n # add Gaussian noise\n img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)\n\n elif i == 5:\n # add JPEG noise\n if random.random() < jpeg_prob:\n img = add_JPEG_noise(img)\n\n elif i == 6:\n # add processed camera sensor noise\n if random.random() < isp_prob and isp_model is not None:\n with torch.no_grad():\n img, hq = isp_model.forward(img.copy(), hq)\n\n # add final JPEG compression noise\n img = add_JPEG_noise(img)\n\n # random crop\n img, hq = random_crop(img, hq, sf_ori, lq_patchsize)\n\n return img, hq\n\n\n# todo no isp_model?\ndef degradation_bsrgan_variant(image, sf=4, isp_model=None):\n \"\"\"\n This is the degradation model of BSRGAN from the paper\n \"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution\"\n ----------\n sf: scale factor\n isp_model: camera ISP model\n Returns\n -------\n img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]\n hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]\n \"\"\"\n image = util.uint2single(image)\n isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25\n sf_ori = sf\n\n h1, w1 = image.shape[:2]\n image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop\n h, w = image.shape[:2]\n\n hq = image.copy()\n\n if sf == 4 and random.random() < scale2_prob: # downsample1\n if np.random.rand() < 0.5:\n image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),\n interpolation=random.choice([1, 2, 3]))\n else:\n image = util.imresize_np(image, 1 / 2, True)\n image = np.clip(image, 0.0, 1.0)\n sf = 2\n\n shuffle_order = random.sample(range(7), 7)\n idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)\n if idx1 > idx2: # keep downsample3 last\n shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]\n\n for i in shuffle_order:\n\n if i == 0:\n image = add_blur(image, sf=sf)\n\n elif i == 1:\n image = add_blur(image, sf=sf)\n\n elif i == 2:\n a, b = image.shape[1], image.shape[0]\n # downsample2\n if random.random() < 0.75:\n sf1 = random.uniform(1, 2 * sf)\n image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),\n interpolation=random.choice([1, 2, 3]))\n else:\n k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))\n k_shifted = shift_pixel(k, sf)\n k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel\n image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')\n image = image[0::sf, 0::sf, ...] # nearest downsampling\n image = np.clip(image, 0.0, 1.0)\n\n elif i == 3:\n # downsample3\n image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))\n image = np.clip(image, 0.0, 1.0)\n\n elif i == 4:\n # add Gaussian noise\n image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25)\n\n elif i == 5:\n # add JPEG noise\n if random.random() < jpeg_prob:\n image = add_JPEG_noise(image)\n\n # elif i == 6:\n # # add processed camera sensor noise\n # if random.random() < isp_prob and isp_model is not None:\n # with torch.no_grad():\n # img, hq = isp_model.forward(img.copy(), hq)\n\n # add final JPEG compression noise\n image = add_JPEG_noise(image)\n image = util.single2uint(image)\n example = {\"image\":image}\n return example\n\n\n# TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc...\ndef degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None):\n \"\"\"\n This is an extended degradation model by combining\n the degradation models of BSRGAN and Real-ESRGAN\n ----------\n img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)\n sf: scale factor\n use_shuffle: the degradation shuffle\n use_sharp: sharpening the img\n Returns\n -------\n img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]\n hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]\n \"\"\"\n\n h1, w1 = img.shape[:2]\n img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop\n h, w = img.shape[:2]\n\n if h < lq_patchsize * sf or w < lq_patchsize * sf:\n raise ValueError(f'img size ({h1}X{w1}) is too small!')\n\n if use_sharp:\n img = add_sharpening(img)\n hq = img.copy()\n\n if random.random() < shuffle_prob:\n shuffle_order = random.sample(range(13), 13)\n else:\n shuffle_order = list(range(13))\n # local shuffle for noise, JPEG is always the last one\n shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6)))\n shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13)))\n\n poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1\n\n for i in shuffle_order:\n if i == 0:\n img = add_blur(img, sf=sf)\n elif i == 1:\n img = add_resize(img, sf=sf)\n elif i == 2:\n img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)\n elif i == 3:\n if random.random() < poisson_prob:\n img = add_Poisson_noise(img)\n elif i == 4:\n if random.random() < speckle_prob:\n img = add_speckle_noise(img)\n elif i == 5:\n if random.random() < isp_prob and isp_model is not None:\n with torch.no_grad():\n img, hq = isp_model.forward(img.copy(), hq)\n elif i == 6:\n img = add_JPEG_noise(img)\n elif i == 7:\n img = add_blur(img, sf=sf)\n elif i == 8:\n img = add_resize(img, sf=sf)\n elif i == 9:\n img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)\n elif i == 10:\n if random.random() < poisson_prob:\n img = add_Poisson_noise(img)\n elif i == 11:\n if random.random() < speckle_prob:\n img = add_speckle_noise(img)\n elif i == 12:\n if random.random() < isp_prob and isp_model is not None:\n with torch.no_grad():\n img, hq = isp_model.forward(img.copy(), hq)\n else:\n print('check the shuffle!')\n\n # resize to desired size\n img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])),\n interpolation=random.choice([1, 2, 3]))\n\n # add final JPEG compression noise\n img = add_JPEG_noise(img)\n\n # random crop\n img, hq = random_crop(img, hq, sf, lq_patchsize)\n\n return img, hq\n\n\nif __name__ == '__main__':\n\tprint(\"hey\")\n\timg = util.imread_uint('utils/test.png', 3)\n\tprint(img)\n\timg = util.uint2single(img)\n\tprint(img)\n\timg = img[:448, :448]\n\th = img.shape[0] // 4\n\tprint(\"resizing to\", h)\n\tsf = 4\n\tdeg_fn = partial(degradation_bsrgan_variant, sf=sf)\n\tfor i in range(20):\n\t\tprint(i)\n\t\timg_lq = deg_fn(img)\n\t\tprint(img_lq)\n\t\timg_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)[\"image\"]\n\t\tprint(img_lq.shape)\n\t\tprint(\"bicubic\", img_lq_bicubic.shape)\n\t\tprint(img_hq.shape)\n\t\tlq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),\n\t\t interpolation=0)\n\t\tlq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),\n\t\t interpolation=0)\n\t\timg_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)\n\t\tutil.imsave(img_concat, str(i) + '.png')\n\n\n" }, { "path": "data_generation/ldm/modules/image_degradation/bsrgan_light.py", "content": "# -*- coding: utf-8 -*-\nimport numpy as np\nimport cv2\nimport torch\n\nfrom functools import partial\nimport random\nfrom scipy import ndimage\nimport scipy\nimport scipy.stats as ss\nfrom scipy.interpolate import interp2d\nfrom scipy.linalg import orth\nimport albumentations\n\nimport ldm.modules.image_degradation.utils_image as util\n\n\"\"\"\n# --------------------------------------------\n# Super-Resolution\n# --------------------------------------------\n#\n# Kai Zhang (cskaizhang@gmail.com)\n# https://github.com/cszn\n# From 2019/03--2021/08\n# --------------------------------------------\n\"\"\"\n\n\ndef modcrop_np(img, sf):\n '''\n Args:\n img: numpy image, WxH or WxHxC\n sf: scale factor\n Return:\n cropped image\n '''\n w, h = img.shape[:2]\n im = np.copy(img)\n return im[:w - w % sf, :h - h % sf, ...]\n\n\n\"\"\"\n# --------------------------------------------\n# anisotropic Gaussian kernels\n# --------------------------------------------\n\"\"\"\n\n\ndef analytic_kernel(k):\n \"\"\"Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)\"\"\"\n k_size = k.shape[0]\n # Calculate the big kernels size\n big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))\n # Loop over the small kernel to fill the big one\n for r in range(k_size):\n for c in range(k_size):\n big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k\n # Crop the edges of the big kernel to ignore very small values and increase run time of SR\n crop = k_size // 2\n cropped_big_k = big_k[crop:-crop, crop:-crop]\n # Normalize to 1\n return cropped_big_k / cropped_big_k.sum()\n\n\ndef anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):\n \"\"\" generate an anisotropic Gaussian kernel\n Args:\n ksize : e.g., 15, kernel size\n theta : [0, pi], rotation angle range\n l1 : [0.1,50], scaling of eigenvalues\n l2 : [0.1,l1], scaling of eigenvalues\n If l1 = l2, will get an isotropic Gaussian kernel.\n Returns:\n k : kernel\n \"\"\"\n\n v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))\n V = np.array([[v[0], v[1]], [v[1], -v[0]]])\n D = np.array([[l1, 0], [0, l2]])\n Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))\n k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)\n\n return k\n\n\ndef gm_blur_kernel(mean, cov, size=15):\n center = size / 2.0 + 0.5\n k = np.zeros([size, size])\n for y in range(size):\n for x in range(size):\n cy = y - center + 1\n cx = x - center + 1\n k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)\n\n k = k / np.sum(k)\n return k\n\n\ndef shift_pixel(x, sf, upper_left=True):\n \"\"\"shift pixel for super-resolution with different scale factors\n Args:\n x: WxHxC or WxH\n sf: scale factor\n upper_left: shift direction\n \"\"\"\n h, w = x.shape[:2]\n shift = (sf - 1) * 0.5\n xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)\n if upper_left:\n x1 = xv + shift\n y1 = yv + shift\n else:\n x1 = xv - shift\n y1 = yv - shift\n\n x1 = np.clip(x1, 0, w - 1)\n y1 = np.clip(y1, 0, h - 1)\n\n if x.ndim == 2:\n x = interp2d(xv, yv, x)(x1, y1)\n if x.ndim == 3:\n for i in range(x.shape[-1]):\n x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)\n\n return x\n\n\ndef blur(x, k):\n '''\n x: image, NxcxHxW\n k: kernel, Nx1xhxw\n '''\n n, c = x.shape[:2]\n p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2\n x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')\n k = k.repeat(1, c, 1, 1)\n k = k.view(-1, 1, k.shape[2], k.shape[3])\n x = x.view(1, -1, x.shape[2], x.shape[3])\n x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)\n x = x.view(n, c, x.shape[2], x.shape[3])\n\n return x\n\n\ndef gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):\n \"\"\"\"\n # modified version of https://github.com/assafshocher/BlindSR_dataset_generator\n # Kai Zhang\n # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var\n # max_var = 2.5 * sf\n \"\"\"\n # Set random eigen-vals (lambdas) and angle (theta) for COV matrix\n lambda_1 = min_var + np.random.rand() * (max_var - min_var)\n lambda_2 = min_var + np.random.rand() * (max_var - min_var)\n theta = np.random.rand() * np.pi # random theta\n noise = -noise_level + np.random.rand(*k_size) * noise_level * 2\n\n # Set COV matrix using Lambdas and Theta\n LAMBDA = np.diag([lambda_1, lambda_2])\n Q = np.array([[np.cos(theta), -np.sin(theta)],\n [np.sin(theta), np.cos(theta)]])\n SIGMA = Q @ LAMBDA @ Q.T\n INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]\n\n # Set expectation position (shifting kernel for aligned image)\n MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2)\n MU = MU[None, None, :, None]\n\n # Create meshgrid for Gaussian\n [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))\n Z = np.stack([X, Y], 2)[:, :, :, None]\n\n # Calcualte Gaussian for every pixel of the kernel\n ZZ = Z - MU\n ZZ_t = ZZ.transpose(0, 1, 3, 2)\n raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)\n\n # shift the kernel so it will be centered\n # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)\n\n # Normalize the kernel and return\n # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)\n kernel = raw_kernel / np.sum(raw_kernel)\n return kernel\n\n\ndef fspecial_gaussian(hsize, sigma):\n hsize = [hsize, hsize]\n siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]\n std = sigma\n [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))\n arg = -(x * x + y * y) / (2 * std * std)\n h = np.exp(arg)\n h[h < scipy.finfo(float).eps * h.max()] = 0\n sumh = h.sum()\n if sumh != 0:\n h = h / sumh\n return h\n\n\ndef fspecial_laplacian(alpha):\n alpha = max([0, min([alpha, 1])])\n h1 = alpha / (alpha + 1)\n h2 = (1 - alpha) / (alpha + 1)\n h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]\n h = np.array(h)\n return h\n\n\ndef fspecial(filter_type, *args, **kwargs):\n '''\n python code from:\n https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py\n '''\n if filter_type == 'gaussian':\n return fspecial_gaussian(*args, **kwargs)\n if filter_type == 'laplacian':\n return fspecial_laplacian(*args, **kwargs)\n\n\n\"\"\"\n# --------------------------------------------\n# degradation models\n# --------------------------------------------\n\"\"\"\n\n\ndef bicubic_degradation(x, sf=3):\n '''\n Args:\n x: HxWxC image, [0, 1]\n sf: down-scale factor\n Return:\n bicubicly downsampled LR image\n '''\n x = util.imresize_np(x, scale=1 / sf)\n return x\n\n\ndef srmd_degradation(x, k, sf=3):\n ''' blur + bicubic downsampling\n Args:\n x: HxWxC image, [0, 1]\n k: hxw, double\n sf: down-scale factor\n Return:\n downsampled LR image\n Reference:\n @inproceedings{zhang2018learning,\n title={Learning a single convolutional super-resolution network for multiple degradations},\n author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},\n booktitle={IEEE Conference on Computer Vision and Pattern Recognition},\n pages={3262--3271},\n year={2018}\n }\n '''\n x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror'\n x = bicubic_degradation(x, sf=sf)\n return x\n\n\ndef dpsr_degradation(x, k, sf=3):\n ''' bicubic downsampling + blur\n Args:\n x: HxWxC image, [0, 1]\n k: hxw, double\n sf: down-scale factor\n Return:\n downsampled LR image\n Reference:\n @inproceedings{zhang2019deep,\n title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},\n author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},\n booktitle={IEEE Conference on Computer Vision and Pattern Recognition},\n pages={1671--1681},\n year={2019}\n }\n '''\n x = bicubic_degradation(x, sf=sf)\n x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')\n return x\n\n\ndef classical_degradation(x, k, sf=3):\n ''' blur + downsampling\n Args:\n x: HxWxC image, [0, 1]/[0, 255]\n k: hxw, double\n sf: down-scale factor\n Return:\n downsampled LR image\n '''\n x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')\n # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))\n st = 0\n return x[st::sf, st::sf, ...]\n\n\ndef add_sharpening(img, weight=0.5, radius=50, threshold=10):\n \"\"\"USM sharpening. borrowed from real-ESRGAN\n Input image: I; Blurry image: B.\n 1. K = I + weight * (I - B)\n 2. Mask = 1 if abs(I - B) > threshold, else: 0\n 3. Blur mask:\n 4. Out = Mask * K + (1 - Mask) * I\n Args:\n img (Numpy array): Input image, HWC, BGR; float32, [0, 1].\n weight (float): Sharp weight. Default: 1.\n radius (float): Kernel size of Gaussian blur. Default: 50.\n threshold (int):\n \"\"\"\n if radius % 2 == 0:\n radius += 1\n blur = cv2.GaussianBlur(img, (radius, radius), 0)\n residual = img - blur\n mask = np.abs(residual) * 255 > threshold\n mask = mask.astype('float32')\n soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)\n\n K = img + weight * residual\n K = np.clip(K, 0, 1)\n return soft_mask * K + (1 - soft_mask) * img\n\n\ndef add_blur(img, sf=4):\n wd2 = 4.0 + sf\n wd = 2.0 + 0.2 * sf\n\n wd2 = wd2/4\n wd = wd/4\n\n if random.random() < 0.5:\n l1 = wd2 * random.random()\n l2 = wd2 * random.random()\n k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)\n else:\n k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())\n img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')\n\n return img\n\n\ndef add_resize(img, sf=4):\n rnum = np.random.rand()\n if rnum > 0.8: # up\n sf1 = random.uniform(1, 2)\n elif rnum < 0.7: # down\n sf1 = random.uniform(0.5 / sf, 1)\n else:\n sf1 = 1.0\n img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))\n img = np.clip(img, 0.0, 1.0)\n\n return img\n\n\n# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):\n# noise_level = random.randint(noise_level1, noise_level2)\n# rnum = np.random.rand()\n# if rnum > 0.6: # add color Gaussian noise\n# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)\n# elif rnum < 0.4: # add grayscale Gaussian noise\n# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)\n# else: # add noise\n# L = noise_level2 / 255.\n# D = np.diag(np.random.rand(3))\n# U = orth(np.random.rand(3, 3))\n# conv = np.dot(np.dot(np.transpose(U), D), U)\n# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)\n# img = np.clip(img, 0.0, 1.0)\n# return img\n\ndef add_Gaussian_noise(img, noise_level1=2, noise_level2=25):\n noise_level = random.randint(noise_level1, noise_level2)\n rnum = np.random.rand()\n if rnum > 0.6: # add color Gaussian noise\n img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)\n elif rnum < 0.4: # add grayscale Gaussian noise\n img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)\n else: # add noise\n L = noise_level2 / 255.\n D = np.diag(np.random.rand(3))\n U = orth(np.random.rand(3, 3))\n conv = np.dot(np.dot(np.transpose(U), D), U)\n img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)\n img = np.clip(img, 0.0, 1.0)\n return img\n\n\ndef add_speckle_noise(img, noise_level1=2, noise_level2=25):\n noise_level = random.randint(noise_level1, noise_level2)\n img = np.clip(img, 0.0, 1.0)\n rnum = random.random()\n if rnum > 0.6:\n img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)\n elif rnum < 0.4:\n img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)\n else:\n L = noise_level2 / 255.\n D = np.diag(np.random.rand(3))\n U = orth(np.random.rand(3, 3))\n conv = np.dot(np.dot(np.transpose(U), D), U)\n img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)\n img = np.clip(img, 0.0, 1.0)\n return img\n\n\ndef add_Poisson_noise(img):\n img = np.clip((img * 255.0).round(), 0, 255) / 255.\n vals = 10 ** (2 * random.random() + 2.0) # [2, 4]\n if random.random() < 0.5:\n img = np.random.poisson(img * vals).astype(np.float32) / vals\n else:\n img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])\n img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.\n noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray\n img += noise_gray[:, :, np.newaxis]\n img = np.clip(img, 0.0, 1.0)\n return img\n\n\ndef add_JPEG_noise(img):\n quality_factor = random.randint(80, 95)\n img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)\n result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])\n img = cv2.imdecode(encimg, 1)\n img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)\n return img\n\n\ndef random_crop(lq, hq, sf=4, lq_patchsize=64):\n h, w = lq.shape[:2]\n rnd_h = random.randint(0, h - lq_patchsize)\n rnd_w = random.randint(0, w - lq_patchsize)\n lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]\n\n rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)\n hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]\n return lq, hq\n\n\ndef degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):\n \"\"\"\n This is the degradation model of BSRGAN from the paper\n \"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution\"\n ----------\n img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)\n sf: scale factor\n isp_model: camera ISP model\n Returns\n -------\n img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]\n hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]\n \"\"\"\n isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25\n sf_ori = sf\n\n h1, w1 = img.shape[:2]\n img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop\n h, w = img.shape[:2]\n\n if h < lq_patchsize * sf or w < lq_patchsize * sf:\n raise ValueError(f'img size ({h1}X{w1}) is too small!')\n\n hq = img.copy()\n\n if sf == 4 and random.random() < scale2_prob: # downsample1\n if np.random.rand() < 0.5:\n img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),\n interpolation=random.choice([1, 2, 3]))\n else:\n img = util.imresize_np(img, 1 / 2, True)\n img = np.clip(img, 0.0, 1.0)\n sf = 2\n\n shuffle_order = random.sample(range(7), 7)\n idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)\n if idx1 > idx2: # keep downsample3 last\n shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]\n\n for i in shuffle_order:\n\n if i == 0:\n img = add_blur(img, sf=sf)\n\n elif i == 1:\n img = add_blur(img, sf=sf)\n\n elif i == 2:\n a, b = img.shape[1], img.shape[0]\n # downsample2\n if random.random() < 0.75:\n sf1 = random.uniform(1, 2 * sf)\n img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),\n interpolation=random.choice([1, 2, 3]))\n else:\n k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))\n k_shifted = shift_pixel(k, sf)\n k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel\n img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')\n img = img[0::sf, 0::sf, ...] # nearest downsampling\n img = np.clip(img, 0.0, 1.0)\n\n elif i == 3:\n # downsample3\n img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))\n img = np.clip(img, 0.0, 1.0)\n\n elif i == 4:\n # add Gaussian noise\n img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8)\n\n elif i == 5:\n # add JPEG noise\n if random.random() < jpeg_prob:\n img = add_JPEG_noise(img)\n\n elif i == 6:\n # add processed camera sensor noise\n if random.random() < isp_prob and isp_model is not None:\n with torch.no_grad():\n img, hq = isp_model.forward(img.copy(), hq)\n\n # add final JPEG compression noise\n img = add_JPEG_noise(img)\n\n # random crop\n img, hq = random_crop(img, hq, sf_ori, lq_patchsize)\n\n return img, hq\n\n\n# todo no isp_model?\ndef degradation_bsrgan_variant(image, sf=4, isp_model=None):\n \"\"\"\n This is the degradation model of BSRGAN from the paper\n \"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution\"\n ----------\n sf: scale factor\n isp_model: camera ISP model\n Returns\n -------\n img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]\n hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]\n \"\"\"\n image = util.uint2single(image)\n isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25\n sf_ori = sf\n\n h1, w1 = image.shape[:2]\n image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop\n h, w = image.shape[:2]\n\n hq = image.copy()\n\n if sf == 4 and random.random() < scale2_prob: # downsample1\n if np.random.rand() < 0.5:\n image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),\n interpolation=random.choice([1, 2, 3]))\n else:\n image = util.imresize_np(image, 1 / 2, True)\n image = np.clip(image, 0.0, 1.0)\n sf = 2\n\n shuffle_order = random.sample(range(7), 7)\n idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)\n if idx1 > idx2: # keep downsample3 last\n shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]\n\n for i in shuffle_order:\n\n if i == 0:\n image = add_blur(image, sf=sf)\n\n # elif i == 1:\n # image = add_blur(image, sf=sf)\n\n if i == 0:\n pass\n\n elif i == 2:\n a, b = image.shape[1], image.shape[0]\n # downsample2\n if random.random() < 0.8:\n sf1 = random.uniform(1, 2 * sf)\n image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),\n interpolation=random.choice([1, 2, 3]))\n else:\n k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))\n k_shifted = shift_pixel(k, sf)\n k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel\n image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')\n image = image[0::sf, 0::sf, ...] # nearest downsampling\n\n image = np.clip(image, 0.0, 1.0)\n\n elif i == 3:\n # downsample3\n image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))\n image = np.clip(image, 0.0, 1.0)\n\n elif i == 4:\n # add Gaussian noise\n image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2)\n\n elif i == 5:\n # add JPEG noise\n if random.random() < jpeg_prob:\n image = add_JPEG_noise(image)\n #\n # elif i == 6:\n # # add processed camera sensor noise\n # if random.random() < isp_prob and isp_model is not None:\n # with torch.no_grad():\n # img, hq = isp_model.forward(img.copy(), hq)\n\n # add final JPEG compression noise\n image = add_JPEG_noise(image)\n image = util.single2uint(image)\n example = {\"image\": image}\n return example\n\n\n\n\nif __name__ == '__main__':\n print(\"hey\")\n img = util.imread_uint('utils/test.png', 3)\n img = img[:448, :448]\n h = img.shape[0] // 4\n print(\"resizing to\", h)\n sf = 4\n deg_fn = partial(degradation_bsrgan_variant, sf=sf)\n for i in range(20):\n print(i)\n img_hq = img\n img_lq = deg_fn(img)[\"image\"]\n img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)\n print(img_lq)\n img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)[\"image\"]\n print(img_lq.shape)\n print(\"bicubic\", img_lq_bicubic.shape)\n print(img_hq.shape)\n lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),\n interpolation=0)\n lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic),\n (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),\n interpolation=0)\n img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)\n util.imsave(img_concat, str(i) + '.png')\n" }, { "path": "data_generation/ldm/modules/image_degradation/utils_image.py", "content": "import os\nimport math\nimport random\nimport numpy as np\nimport torch\nimport cv2\nfrom torchvision.utils import make_grid\nfrom datetime import datetime\n#import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py\n\n\nos.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\"\n\n\n'''\n# --------------------------------------------\n# Kai Zhang (github: https://github.com/cszn)\n# 03/Mar/2019\n# --------------------------------------------\n# https://github.com/twhui/SRGAN-pyTorch\n# https://github.com/xinntao/BasicSR\n# --------------------------------------------\n'''\n\n\nIMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif']\n\n\ndef is_image_file(filename):\n return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)\n\n\ndef get_timestamp():\n return datetime.now().strftime('%y%m%d-%H%M%S')\n\n\ndef imshow(x, title=None, cbar=False, figsize=None):\n plt.figure(figsize=figsize)\n plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')\n if title:\n plt.title(title)\n if cbar:\n plt.colorbar()\n plt.show()\n\n\ndef surf(Z, cmap='rainbow', figsize=None):\n plt.figure(figsize=figsize)\n ax3 = plt.axes(projection='3d')\n\n w, h = Z.shape[:2]\n xx = np.arange(0,w,1)\n yy = np.arange(0,h,1)\n X, Y = np.meshgrid(xx, yy)\n ax3.plot_surface(X,Y,Z,cmap=cmap)\n #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)\n plt.show()\n\n\n'''\n# --------------------------------------------\n# get image pathes\n# --------------------------------------------\n'''\n\n\ndef get_image_paths(dataroot):\n paths = None # return None if dataroot is None\n if dataroot is not None:\n paths = sorted(_get_paths_from_images(dataroot))\n return paths\n\n\ndef _get_paths_from_images(path):\n assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)\n images = []\n for dirpath, _, fnames in sorted(os.walk(path)):\n for fname in sorted(fnames):\n if is_image_file(fname):\n img_path = os.path.join(dirpath, fname)\n images.append(img_path)\n assert images, '{:s} has no valid image file'.format(path)\n return images\n\n\n'''\n# --------------------------------------------\n# split large images into small images \n# --------------------------------------------\n'''\n\n\ndef patches_from_image(img, p_size=512, p_overlap=64, p_max=800):\n w, h = img.shape[:2]\n patches = []\n if w > p_max and h > p_max:\n w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int))\n h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int))\n w1.append(w-p_size)\n h1.append(h-p_size)\n# print(w1)\n# print(h1)\n for i in w1:\n for j in h1:\n patches.append(img[i:i+p_size, j:j+p_size,:])\n else:\n patches.append(img)\n\n return patches\n\n\ndef imssave(imgs, img_path):\n \"\"\"\n imgs: list, N images of size WxHxC\n \"\"\"\n img_name, ext = os.path.splitext(os.path.basename(img_path))\n\n for i, img in enumerate(imgs):\n if img.ndim == 3:\n img = img[:, :, [2, 1, 0]]\n new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png')\n cv2.imwrite(new_path, img)\n\n\ndef split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000):\n \"\"\"\n split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),\n and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)\n will be splitted.\n Args:\n original_dataroot:\n taget_dataroot:\n p_size: size of small images\n p_overlap: patch size in training is a good choice\n p_max: images with smaller size than (p_max)x(p_max) keep unchanged.\n \"\"\"\n paths = get_image_paths(original_dataroot)\n for img_path in paths:\n # img_name, ext = os.path.splitext(os.path.basename(img_path))\n img = imread_uint(img_path, n_channels=n_channels)\n patches = patches_from_image(img, p_size, p_overlap, p_max)\n imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path)))\n #if original_dataroot == taget_dataroot:\n #del img_path\n\n'''\n# --------------------------------------------\n# makedir\n# --------------------------------------------\n'''\n\n\ndef mkdir(path):\n if not os.path.exists(path):\n os.makedirs(path)\n\n\ndef mkdirs(paths):\n if isinstance(paths, str):\n mkdir(paths)\n else:\n for path in paths:\n mkdir(path)\n\n\ndef mkdir_and_rename(path):\n if os.path.exists(path):\n new_name = path + '_archived_' + get_timestamp()\n print('Path already exists. Rename it to [{:s}]'.format(new_name))\n os.rename(path, new_name)\n os.makedirs(path)\n\n\n'''\n# --------------------------------------------\n# read image from path\n# opencv is fast, but read BGR numpy image\n# --------------------------------------------\n'''\n\n\n# --------------------------------------------\n# get uint8 image of size HxWxn_channles (RGB)\n# --------------------------------------------\ndef imread_uint(path, n_channels=3):\n # input: path\n # output: HxWx3(RGB or GGG), or HxWx1 (G)\n if n_channels == 1:\n img = cv2.imread(path, 0) # cv2.IMREAD_GRAYSCALE\n img = np.expand_dims(img, axis=2) # HxWx1\n elif n_channels == 3:\n img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # BGR or G\n if img.ndim == 2:\n img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) # GGG\n else:\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB\n return img\n\n\n# --------------------------------------------\n# matlab's imwrite\n# --------------------------------------------\ndef imsave(img, img_path):\n img = np.squeeze(img)\n if img.ndim == 3:\n img = img[:, :, [2, 1, 0]]\n cv2.imwrite(img_path, img)\n\ndef imwrite(img, img_path):\n img = np.squeeze(img)\n if img.ndim == 3:\n img = img[:, :, [2, 1, 0]]\n cv2.imwrite(img_path, img)\n\n\n\n# --------------------------------------------\n# get single image of size HxWxn_channles (BGR)\n# --------------------------------------------\ndef read_img(path):\n # read image by cv2\n # return: Numpy float32, HWC, BGR, [0,1]\n img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # cv2.IMREAD_GRAYSCALE\n img = img.astype(np.float32) / 255.\n if img.ndim == 2:\n img = np.expand_dims(img, axis=2)\n # some images have 4 channels\n if img.shape[2] > 3:\n img = img[:, :, :3]\n return img\n\n\n'''\n# --------------------------------------------\n# image format conversion\n# --------------------------------------------\n# numpy(single) <---> numpy(unit)\n# numpy(single) <---> tensor\n# numpy(unit) <---> tensor\n# --------------------------------------------\n'''\n\n\n# --------------------------------------------\n# numpy(single) [0, 1] <---> numpy(unit)\n# --------------------------------------------\n\n\ndef uint2single(img):\n\n return np.float32(img/255.)\n\n\ndef single2uint(img):\n\n return np.uint8((img.clip(0, 1)*255.).round())\n\n\ndef uint162single(img):\n\n return np.float32(img/65535.)\n\n\ndef single2uint16(img):\n\n return np.uint16((img.clip(0, 1)*65535.).round())\n\n\n# --------------------------------------------\n# numpy(unit) (HxWxC or HxW) <---> tensor\n# --------------------------------------------\n\n\n# convert uint to 4-dimensional torch tensor\ndef uint2tensor4(img):\n if img.ndim == 2:\n img = np.expand_dims(img, axis=2)\n return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0)\n\n\n# convert uint to 3-dimensional torch tensor\ndef uint2tensor3(img):\n if img.ndim == 2:\n img = np.expand_dims(img, axis=2)\n return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.)\n\n\n# convert 2/3/4-dimensional torch tensor to uint\ndef tensor2uint(img):\n img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()\n if img.ndim == 3:\n img = np.transpose(img, (1, 2, 0))\n return np.uint8((img*255.0).round())\n\n\n# --------------------------------------------\n# numpy(single) (HxWxC) <---> tensor\n# --------------------------------------------\n\n\n# convert single (HxWxC) to 3-dimensional torch tensor\ndef single2tensor3(img):\n return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()\n\n\n# convert single (HxWxC) to 4-dimensional torch tensor\ndef single2tensor4(img):\n return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)\n\n\n# convert torch tensor to single\ndef tensor2single(img):\n img = img.data.squeeze().float().cpu().numpy()\n if img.ndim == 3:\n img = np.transpose(img, (1, 2, 0))\n\n return img\n\n# convert torch tensor to single\ndef tensor2single3(img):\n img = img.data.squeeze().float().cpu().numpy()\n if img.ndim == 3:\n img = np.transpose(img, (1, 2, 0))\n elif img.ndim == 2:\n img = np.expand_dims(img, axis=2)\n return img\n\n\ndef single2tensor5(img):\n return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)\n\n\ndef single32tensor5(img):\n return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)\n\n\ndef single42tensor4(img):\n return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()\n\n\n# from skimage.io import imread, imsave\ndef tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):\n '''\n Converts a torch Tensor into an image Numpy array of BGR channel order\n Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order\n Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)\n '''\n tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # squeeze first, then clamp\n tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1]\n n_dim = tensor.dim()\n if n_dim == 4:\n n_img = len(tensor)\n img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()\n img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR\n elif n_dim == 3:\n img_np = tensor.numpy()\n img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR\n elif n_dim == 2:\n img_np = tensor.numpy()\n else:\n raise TypeError(\n 'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))\n if out_type == np.uint8:\n img_np = (img_np * 255.0).round()\n # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.\n return img_np.astype(out_type)\n\n\n'''\n# --------------------------------------------\n# Augmentation, flipe and/or rotate\n# --------------------------------------------\n# The following two are enough.\n# (1) augmet_img: numpy image of WxHxC or WxH\n# (2) augment_img_tensor4: tensor image 1xCxWxH\n# --------------------------------------------\n'''\n\n\ndef augment_img(img, mode=0):\n '''Kai Zhang (github: https://github.com/cszn)\n '''\n if mode == 0:\n return img\n elif mode == 1:\n return np.flipud(np.rot90(img))\n elif mode == 2:\n return np.flipud(img)\n elif mode == 3:\n return np.rot90(img, k=3)\n elif mode == 4:\n return np.flipud(np.rot90(img, k=2))\n elif mode == 5:\n return np.rot90(img)\n elif mode == 6:\n return np.rot90(img, k=2)\n elif mode == 7:\n return np.flipud(np.rot90(img, k=3))\n\n\ndef augment_img_tensor4(img, mode=0):\n '''Kai Zhang (github: https://github.com/cszn)\n '''\n if mode == 0:\n return img\n elif mode == 1:\n return img.rot90(1, [2, 3]).flip([2])\n elif mode == 2:\n return img.flip([2])\n elif mode == 3:\n return img.rot90(3, [2, 3])\n elif mode == 4:\n return img.rot90(2, [2, 3]).flip([2])\n elif mode == 5:\n return img.rot90(1, [2, 3])\n elif mode == 6:\n return img.rot90(2, [2, 3])\n elif mode == 7:\n return img.rot90(3, [2, 3]).flip([2])\n\n\ndef augment_img_tensor(img, mode=0):\n '''Kai Zhang (github: https://github.com/cszn)\n '''\n img_size = img.size()\n img_np = img.data.cpu().numpy()\n if len(img_size) == 3:\n img_np = np.transpose(img_np, (1, 2, 0))\n elif len(img_size) == 4:\n img_np = np.transpose(img_np, (2, 3, 1, 0))\n img_np = augment_img(img_np, mode=mode)\n img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))\n if len(img_size) == 3:\n img_tensor = img_tensor.permute(2, 0, 1)\n elif len(img_size) == 4:\n img_tensor = img_tensor.permute(3, 2, 0, 1)\n\n return img_tensor.type_as(img)\n\n\ndef augment_img_np3(img, mode=0):\n if mode == 0:\n return img\n elif mode == 1:\n return img.transpose(1, 0, 2)\n elif mode == 2:\n return img[::-1, :, :]\n elif mode == 3:\n img = img[::-1, :, :]\n img = img.transpose(1, 0, 2)\n return img\n elif mode == 4:\n return img[:, ::-1, :]\n elif mode == 5:\n img = img[:, ::-1, :]\n img = img.transpose(1, 0, 2)\n return img\n elif mode == 6:\n img = img[:, ::-1, :]\n img = img[::-1, :, :]\n return img\n elif mode == 7:\n img = img[:, ::-1, :]\n img = img[::-1, :, :]\n img = img.transpose(1, 0, 2)\n return img\n\n\ndef augment_imgs(img_list, hflip=True, rot=True):\n # horizontal flip OR rotate\n hflip = hflip and random.random() < 0.5\n vflip = rot and random.random() < 0.5\n rot90 = rot and random.random() < 0.5\n\n def _augment(img):\n if hflip:\n img = img[:, ::-1, :]\n if vflip:\n img = img[::-1, :, :]\n if rot90:\n img = img.transpose(1, 0, 2)\n return img\n\n return [_augment(img) for img in img_list]\n\n\n'''\n# --------------------------------------------\n# modcrop and shave\n# --------------------------------------------\n'''\n\n\ndef modcrop(img_in, scale):\n # img_in: Numpy, HWC or HW\n img = np.copy(img_in)\n if img.ndim == 2:\n H, W = img.shape\n H_r, W_r = H % scale, W % scale\n img = img[:H - H_r, :W - W_r]\n elif img.ndim == 3:\n H, W, C = img.shape\n H_r, W_r = H % scale, W % scale\n img = img[:H - H_r, :W - W_r, :]\n else:\n raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))\n return img\n\n\ndef shave(img_in, border=0):\n # img_in: Numpy, HWC or HW\n img = np.copy(img_in)\n h, w = img.shape[:2]\n img = img[border:h-border, border:w-border]\n return img\n\n\n'''\n# --------------------------------------------\n# image processing process on numpy image\n# channel_convert(in_c, tar_type, img_list):\n# rgb2ycbcr(img, only_y=True):\n# bgr2ycbcr(img, only_y=True):\n# ycbcr2rgb(img):\n# --------------------------------------------\n'''\n\n\ndef rgb2ycbcr(img, only_y=True):\n '''same as matlab rgb2ycbcr\n only_y: only return Y channel\n Input:\n uint8, [0, 255]\n float, [0, 1]\n '''\n in_img_type = img.dtype\n img.astype(np.float32)\n if in_img_type != np.uint8:\n img *= 255.\n # convert\n if only_y:\n rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0\n else:\n rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],\n [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]\n if in_img_type == np.uint8:\n rlt = rlt.round()\n else:\n rlt /= 255.\n return rlt.astype(in_img_type)\n\n\ndef ycbcr2rgb(img):\n '''same as matlab ycbcr2rgb\n Input:\n uint8, [0, 255]\n float, [0, 1]\n '''\n in_img_type = img.dtype\n img.astype(np.float32)\n if in_img_type != np.uint8:\n img *= 255.\n # convert\n rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],\n [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]\n if in_img_type == np.uint8:\n rlt = rlt.round()\n else:\n rlt /= 255.\n return rlt.astype(in_img_type)\n\n\ndef bgr2ycbcr(img, only_y=True):\n '''bgr version of rgb2ycbcr\n only_y: only return Y channel\n Input:\n uint8, [0, 255]\n float, [0, 1]\n '''\n in_img_type = img.dtype\n img.astype(np.float32)\n if in_img_type != np.uint8:\n img *= 255.\n # convert\n if only_y:\n rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0\n else:\n rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],\n [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]\n if in_img_type == np.uint8:\n rlt = rlt.round()\n else:\n rlt /= 255.\n return rlt.astype(in_img_type)\n\n\ndef channel_convert(in_c, tar_type, img_list):\n # conversion among BGR, gray and y\n if in_c == 3 and tar_type == 'gray': # BGR to gray\n gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]\n return [np.expand_dims(img, axis=2) for img in gray_list]\n elif in_c == 3 and tar_type == 'y': # BGR to y\n y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]\n return [np.expand_dims(img, axis=2) for img in y_list]\n elif in_c == 1 and tar_type == 'RGB': # gray/y to BGR\n return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]\n else:\n return img_list\n\n\n'''\n# --------------------------------------------\n# metric, PSNR and SSIM\n# --------------------------------------------\n'''\n\n\n# --------------------------------------------\n# PSNR\n# --------------------------------------------\ndef calculate_psnr(img1, img2, border=0):\n # img1 and img2 have range [0, 255]\n #img1 = img1.squeeze()\n #img2 = img2.squeeze()\n if not img1.shape == img2.shape:\n raise ValueError('Input images must have the same dimensions.')\n h, w = img1.shape[:2]\n img1 = img1[border:h-border, border:w-border]\n img2 = img2[border:h-border, border:w-border]\n\n img1 = img1.astype(np.float64)\n img2 = img2.astype(np.float64)\n mse = np.mean((img1 - img2)**2)\n if mse == 0:\n return float('inf')\n return 20 * math.log10(255.0 / math.sqrt(mse))\n\n\n# --------------------------------------------\n# SSIM\n# --------------------------------------------\ndef calculate_ssim(img1, img2, border=0):\n '''calculate SSIM\n the same outputs as MATLAB's\n img1, img2: [0, 255]\n '''\n #img1 = img1.squeeze()\n #img2 = img2.squeeze()\n if not img1.shape == img2.shape:\n raise ValueError('Input images must have the same dimensions.')\n h, w = img1.shape[:2]\n img1 = img1[border:h-border, border:w-border]\n img2 = img2[border:h-border, border:w-border]\n\n if img1.ndim == 2:\n return ssim(img1, img2)\n elif img1.ndim == 3:\n if img1.shape[2] == 3:\n ssims = []\n for i in range(3):\n ssims.append(ssim(img1[:,:,i], img2[:,:,i]))\n return np.array(ssims).mean()\n elif img1.shape[2] == 1:\n return ssim(np.squeeze(img1), np.squeeze(img2))\n else:\n raise ValueError('Wrong input image dimensions.')\n\n\ndef ssim(img1, img2):\n C1 = (0.01 * 255)**2\n C2 = (0.03 * 255)**2\n\n img1 = img1.astype(np.float64)\n img2 = img2.astype(np.float64)\n kernel = cv2.getGaussianKernel(11, 1.5)\n window = np.outer(kernel, kernel.transpose())\n\n mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid\n mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]\n mu1_sq = mu1**2\n mu2_sq = mu2**2\n mu1_mu2 = mu1 * mu2\n sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq\n sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq\n sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2\n\n ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *\n (sigma1_sq + sigma2_sq + C2))\n return ssim_map.mean()\n\n\n'''\n# --------------------------------------------\n# matlab's bicubic imresize (numpy and torch) [0, 1]\n# --------------------------------------------\n'''\n\n\n# matlab 'imresize' function, now only support 'bicubic'\ndef cubic(x):\n absx = torch.abs(x)\n absx2 = absx**2\n absx3 = absx**3\n return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \\\n (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx))\n\n\ndef calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):\n if (scale < 1) and (antialiasing):\n # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width\n kernel_width = kernel_width / scale\n\n # Output-space coordinates\n x = torch.linspace(1, out_length, out_length)\n\n # Input-space coordinates. Calculate the inverse mapping such that 0.5\n # in output space maps to 0.5 in input space, and 0.5+scale in output\n # space maps to 1.5 in input space.\n u = x / scale + 0.5 * (1 - 1 / scale)\n\n # What is the left-most pixel that can be involved in the computation?\n left = torch.floor(u - kernel_width / 2)\n\n # What is the maximum number of pixels that can be involved in the\n # computation? Note: it's OK to use an extra pixel here; if the\n # corresponding weights are all zero, it will be eliminated at the end\n # of this function.\n P = math.ceil(kernel_width) + 2\n\n # The indices of the input pixels involved in computing the k-th output\n # pixel are in row k of the indices matrix.\n indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(\n 1, P).expand(out_length, P)\n\n # The weights used to compute the k-th output pixel are in row k of the\n # weights matrix.\n distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices\n # apply cubic kernel\n if (scale < 1) and (antialiasing):\n weights = scale * cubic(distance_to_center * scale)\n else:\n weights = cubic(distance_to_center)\n # Normalize the weights matrix so that each row sums to 1.\n weights_sum = torch.sum(weights, 1).view(out_length, 1)\n weights = weights / weights_sum.expand(out_length, P)\n\n # If a column in weights is all zero, get rid of it. only consider the first and last column.\n weights_zero_tmp = torch.sum((weights == 0), 0)\n if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):\n indices = indices.narrow(1, 1, P - 2)\n weights = weights.narrow(1, 1, P - 2)\n if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):\n indices = indices.narrow(1, 0, P - 2)\n weights = weights.narrow(1, 0, P - 2)\n weights = weights.contiguous()\n indices = indices.contiguous()\n sym_len_s = -indices.min() + 1\n sym_len_e = indices.max() - in_length\n indices = indices + sym_len_s - 1\n return weights, indices, int(sym_len_s), int(sym_len_e)\n\n\n# --------------------------------------------\n# imresize for tensor image [0, 1]\n# --------------------------------------------\ndef imresize(img, scale, antialiasing=True):\n # Now the scale should be the same for H and W\n # input: img: pytorch tensor, CHW or HW [0,1]\n # output: CHW or HW [0,1] w/o round\n need_squeeze = True if img.dim() == 2 else False\n if need_squeeze:\n img.unsqueeze_(0)\n in_C, in_H, in_W = img.size()\n out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)\n kernel_width = 4\n kernel = 'cubic'\n\n # Return the desired dimension order for performing the resize. The\n # strategy is to perform the resize first along the dimension with the\n # smallest scale factor.\n # Now we do not support this.\n\n # get weights and indices\n weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(\n in_H, out_H, scale, kernel, kernel_width, antialiasing)\n weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(\n in_W, out_W, scale, kernel, kernel_width, antialiasing)\n # process H dimension\n # symmetric copying\n img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)\n img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)\n\n sym_patch = img[:, :sym_len_Hs, :]\n inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()\n sym_patch_inv = sym_patch.index_select(1, inv_idx)\n img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)\n\n sym_patch = img[:, -sym_len_He:, :]\n inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()\n sym_patch_inv = sym_patch.index_select(1, inv_idx)\n img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)\n\n out_1 = torch.FloatTensor(in_C, out_H, in_W)\n kernel_width = weights_H.size(1)\n for i in range(out_H):\n idx = int(indices_H[i][0])\n for j in range(out_C):\n out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])\n\n # process W dimension\n # symmetric copying\n out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)\n out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)\n\n sym_patch = out_1[:, :, :sym_len_Ws]\n inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()\n sym_patch_inv = sym_patch.index_select(2, inv_idx)\n out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)\n\n sym_patch = out_1[:, :, -sym_len_We:]\n inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()\n sym_patch_inv = sym_patch.index_select(2, inv_idx)\n out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)\n\n out_2 = torch.FloatTensor(in_C, out_H, out_W)\n kernel_width = weights_W.size(1)\n for i in range(out_W):\n idx = int(indices_W[i][0])\n for j in range(out_C):\n out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])\n if need_squeeze:\n out_2.squeeze_()\n return out_2\n\n\n# --------------------------------------------\n# imresize for numpy image [0, 1]\n# --------------------------------------------\ndef imresize_np(img, scale, antialiasing=True):\n # Now the scale should be the same for H and W\n # input: img: Numpy, HWC or HW [0,1]\n # output: HWC or HW [0,1] w/o round\n img = torch.from_numpy(img)\n need_squeeze = True if img.dim() == 2 else False\n if need_squeeze:\n img.unsqueeze_(2)\n\n in_H, in_W, in_C = img.size()\n out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)\n kernel_width = 4\n kernel = 'cubic'\n\n # Return the desired dimension order for performing the resize. The\n # strategy is to perform the resize first along the dimension with the\n # smallest scale factor.\n # Now we do not support this.\n\n # get weights and indices\n weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(\n in_H, out_H, scale, kernel, kernel_width, antialiasing)\n weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(\n in_W, out_W, scale, kernel, kernel_width, antialiasing)\n # process H dimension\n # symmetric copying\n img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)\n img_aug.narrow(0, sym_len_Hs, in_H).copy_(img)\n\n sym_patch = img[:sym_len_Hs, :, :]\n inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()\n sym_patch_inv = sym_patch.index_select(0, inv_idx)\n img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv)\n\n sym_patch = img[-sym_len_He:, :, :]\n inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()\n sym_patch_inv = sym_patch.index_select(0, inv_idx)\n img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)\n\n out_1 = torch.FloatTensor(out_H, in_W, in_C)\n kernel_width = weights_H.size(1)\n for i in range(out_H):\n idx = int(indices_H[i][0])\n for j in range(out_C):\n out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])\n\n # process W dimension\n # symmetric copying\n out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C)\n out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1)\n\n sym_patch = out_1[:, :sym_len_Ws, :]\n inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()\n sym_patch_inv = sym_patch.index_select(1, inv_idx)\n out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv)\n\n sym_patch = out_1[:, -sym_len_We:, :]\n inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()\n sym_patch_inv = sym_patch.index_select(1, inv_idx)\n out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)\n\n out_2 = torch.FloatTensor(out_H, out_W, in_C)\n kernel_width = weights_W.size(1)\n for i in range(out_W):\n idx = int(indices_W[i][0])\n for j in range(out_C):\n out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i])\n if need_squeeze:\n out_2.squeeze_()\n\n return out_2.numpy()\n\n\nif __name__ == '__main__':\n print('---')\n# img = imread_uint('test.bmp', 3)\n# img = uint2single(img)\n# img_bicubic = imresize_np(img, 1/4)" }, { "path": "data_generation/ldm/modules/losses/__init__.py", "content": "from ldm.modules.losses.contperceptual import LPIPSWithDiscriminator" }, { "path": "data_generation/ldm/modules/losses/contperceptual.py", "content": "import torch\nimport torch.nn as nn\n\nfrom taming.modules.losses.vqperceptual import * # TODO: taming dependency yes/no?\n\n\nclass LPIPSWithDiscriminator(nn.Module):\n def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0,\n disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,\n perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,\n disc_loss=\"hinge\"):\n\n super().__init__()\n assert disc_loss in [\"hinge\", \"vanilla\"]\n self.kl_weight = kl_weight\n self.pixel_weight = pixelloss_weight\n self.perceptual_loss = LPIPS().eval()\n self.perceptual_weight = perceptual_weight\n # output log variance\n self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)\n\n self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,\n n_layers=disc_num_layers,\n use_actnorm=use_actnorm\n ).apply(weights_init)\n self.discriminator_iter_start = disc_start\n self.disc_loss = hinge_d_loss if disc_loss == \"hinge\" else vanilla_d_loss\n self.disc_factor = disc_factor\n self.discriminator_weight = disc_weight\n self.disc_conditional = disc_conditional\n\n def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):\n if last_layer is not None:\n nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]\n g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]\n else:\n nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]\n g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]\n\n d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)\n d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()\n d_weight = d_weight * self.discriminator_weight\n return d_weight\n\n def forward(self, inputs, reconstructions, posteriors, optimizer_idx,\n global_step, last_layer=None, cond=None, split=\"train\",\n weights=None):\n rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())\n if self.perceptual_weight > 0:\n p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())\n rec_loss = rec_loss + self.perceptual_weight * p_loss\n\n nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar\n weighted_nll_loss = nll_loss\n if weights is not None:\n weighted_nll_loss = weights*nll_loss\n weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]\n nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]\n kl_loss = posteriors.kl()\n kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]\n\n # now the GAN part\n if optimizer_idx == 0:\n # generator update\n if cond is None:\n assert not self.disc_conditional\n logits_fake = self.discriminator(reconstructions.contiguous())\n else:\n assert self.disc_conditional\n logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))\n g_loss = -torch.mean(logits_fake)\n\n if self.disc_factor > 0.0:\n try:\n d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)\n except RuntimeError:\n assert not self.training\n d_weight = torch.tensor(0.0)\n else:\n d_weight = torch.tensor(0.0)\n\n disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)\n loss = weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss\n\n log = {\"{}/total_loss\".format(split): loss.clone().detach().mean(), \"{}/logvar\".format(split): self.logvar.detach(),\n \"{}/kl_loss\".format(split): kl_loss.detach().mean(), \"{}/nll_loss\".format(split): nll_loss.detach().mean(),\n \"{}/rec_loss\".format(split): rec_loss.detach().mean(),\n \"{}/d_weight\".format(split): d_weight.detach(),\n \"{}/disc_factor\".format(split): torch.tensor(disc_factor),\n \"{}/g_loss\".format(split): g_loss.detach().mean(),\n }\n return loss, log\n\n if optimizer_idx == 1:\n # second pass for discriminator update\n if cond is None:\n logits_real = self.discriminator(inputs.contiguous().detach())\n logits_fake = self.discriminator(reconstructions.contiguous().detach())\n else:\n logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))\n logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))\n\n disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)\n d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)\n\n log = {\"{}/disc_loss\".format(split): d_loss.clone().detach().mean(),\n \"{}/logits_real\".format(split): logits_real.detach().mean(),\n \"{}/logits_fake\".format(split): logits_fake.detach().mean()\n }\n return d_loss, log\n\n" }, { "path": "data_generation/ldm/modules/losses/vqperceptual.py", "content": "import torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom einops import repeat\n\nfrom taming.modules.discriminator.model import NLayerDiscriminator, weights_init\nfrom taming.modules.losses.lpips import LPIPS\nfrom taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss\n\n\ndef hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights):\n assert weights.shape[0] == logits_real.shape[0] == logits_fake.shape[0]\n loss_real = torch.mean(F.relu(1. - logits_real), dim=[1,2,3])\n loss_fake = torch.mean(F.relu(1. + logits_fake), dim=[1,2,3])\n loss_real = (weights * loss_real).sum() / weights.sum()\n loss_fake = (weights * loss_fake).sum() / weights.sum()\n d_loss = 0.5 * (loss_real + loss_fake)\n return d_loss\n\ndef adopt_weight(weight, global_step, threshold=0, value=0.):\n if global_step < threshold:\n weight = value\n return weight\n\n\ndef measure_perplexity(predicted_indices, n_embed):\n # src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py\n # eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally\n encodings = F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed)\n avg_probs = encodings.mean(0)\n perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp()\n cluster_use = torch.sum(avg_probs > 0)\n return perplexity, cluster_use\n\ndef l1(x, y):\n return torch.abs(x-y)\n\n\ndef l2(x, y):\n return torch.pow((x-y), 2)\n\n\nclass VQLPIPSWithDiscriminator(nn.Module):\n def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0,\n disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,\n perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,\n disc_ndf=64, disc_loss=\"hinge\", n_classes=None, perceptual_loss=\"lpips\",\n pixel_loss=\"l1\"):\n super().__init__()\n assert disc_loss in [\"hinge\", \"vanilla\"]\n assert perceptual_loss in [\"lpips\", \"clips\", \"dists\"]\n assert pixel_loss in [\"l1\", \"l2\"]\n self.codebook_weight = codebook_weight\n self.pixel_weight = pixelloss_weight\n if perceptual_loss == \"lpips\":\n print(f\"{self.__class__.__name__}: Running with LPIPS.\")\n self.perceptual_loss = LPIPS().eval()\n else:\n raise ValueError(f\"Unknown perceptual loss: >> {perceptual_loss} <<\")\n self.perceptual_weight = perceptual_weight\n\n if pixel_loss == \"l1\":\n self.pixel_loss = l1\n else:\n self.pixel_loss = l2\n\n self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,\n n_layers=disc_num_layers,\n use_actnorm=use_actnorm,\n ndf=disc_ndf\n ).apply(weights_init)\n self.discriminator_iter_start = disc_start\n if disc_loss == \"hinge\":\n self.disc_loss = hinge_d_loss\n elif disc_loss == \"vanilla\":\n self.disc_loss = vanilla_d_loss\n else:\n raise ValueError(f\"Unknown GAN loss '{disc_loss}'.\")\n print(f\"VQLPIPSWithDiscriminator running with {disc_loss} loss.\")\n self.disc_factor = disc_factor\n self.discriminator_weight = disc_weight\n self.disc_conditional = disc_conditional\n self.n_classes = n_classes\n\n def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):\n if last_layer is not None:\n nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]\n g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]\n else:\n nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]\n g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]\n\n d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)\n d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()\n d_weight = d_weight * self.discriminator_weight\n return d_weight\n\n def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx,\n global_step, last_layer=None, cond=None, split=\"train\", predicted_indices=None):\n if not exists(codebook_loss):\n codebook_loss = torch.tensor([0.]).to(inputs.device)\n #rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())\n rec_loss = self.pixel_loss(inputs.contiguous(), reconstructions.contiguous())\n if self.perceptual_weight > 0:\n p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())\n rec_loss = rec_loss + self.perceptual_weight * p_loss\n else:\n p_loss = torch.tensor([0.0])\n\n nll_loss = rec_loss\n #nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]\n nll_loss = torch.mean(nll_loss)\n\n # now the GAN part\n if optimizer_idx == 0:\n # generator update\n if cond is None:\n assert not self.disc_conditional\n logits_fake = self.discriminator(reconstructions.contiguous())\n else:\n assert self.disc_conditional\n logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))\n g_loss = -torch.mean(logits_fake)\n\n try:\n d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)\n except RuntimeError:\n assert not self.training\n d_weight = torch.tensor(0.0)\n\n disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)\n loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean()\n\n log = {\"{}/total_loss\".format(split): loss.clone().detach().mean(),\n \"{}/quant_loss\".format(split): codebook_loss.detach().mean(),\n \"{}/nll_loss\".format(split): nll_loss.detach().mean(),\n \"{}/rec_loss\".format(split): rec_loss.detach().mean(),\n \"{}/p_loss\".format(split): p_loss.detach().mean(),\n \"{}/d_weight\".format(split): d_weight.detach(),\n \"{}/disc_factor\".format(split): torch.tensor(disc_factor),\n \"{}/g_loss\".format(split): g_loss.detach().mean(),\n }\n if predicted_indices is not None:\n assert self.n_classes is not None\n with torch.no_grad():\n perplexity, cluster_usage = measure_perplexity(predicted_indices, self.n_classes)\n log[f\"{split}/perplexity\"] = perplexity\n log[f\"{split}/cluster_usage\"] = cluster_usage\n return loss, log\n\n if optimizer_idx == 1:\n # second pass for discriminator update\n if cond is None:\n logits_real = self.discriminator(inputs.contiguous().detach())\n logits_fake = self.discriminator(reconstructions.contiguous().detach())\n else:\n logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))\n logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))\n\n disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)\n d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)\n\n log = {\"{}/disc_loss\".format(split): d_loss.clone().detach().mean(),\n \"{}/logits_real\".format(split): logits_real.detach().mean(),\n \"{}/logits_fake\".format(split): logits_fake.detach().mean()\n }\n return d_loss, log\n" }, { "path": "data_generation/ldm/modules/x_transformer.py", "content": "\"\"\"shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers\"\"\"\nimport torch\nfrom torch import nn, einsum\nimport torch.nn.functional as F\nfrom functools import partial\nfrom inspect import isfunction\nfrom collections import namedtuple\nfrom einops import rearrange, repeat, reduce\n\n# constants\n\nDEFAULT_DIM_HEAD = 64\n\nIntermediates = namedtuple('Intermediates', [\n 'pre_softmax_attn',\n 'post_softmax_attn'\n])\n\nLayerIntermediates = namedtuple('Intermediates', [\n 'hiddens',\n 'attn_intermediates'\n])\n\n\nclass AbsolutePositionalEmbedding(nn.Module):\n def __init__(self, dim, max_seq_len):\n super().__init__()\n self.emb = nn.Embedding(max_seq_len, dim)\n self.init_()\n\n def init_(self):\n nn.init.normal_(self.emb.weight, std=0.02)\n\n def forward(self, x):\n n = torch.arange(x.shape[1], device=x.device)\n return self.emb(n)[None, :, :]\n\n\nclass FixedPositionalEmbedding(nn.Module):\n def __init__(self, dim):\n super().__init__()\n inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))\n self.register_buffer('inv_freq', inv_freq)\n\n def forward(self, x, seq_dim=1, offset=0):\n t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset\n sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq)\n emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)\n return emb[None, :, :]\n\n\n# helpers\n\ndef exists(val):\n return val is not None\n\n\ndef default(val, d):\n if exists(val):\n return val\n return d() if isfunction(d) else d\n\n\ndef always(val):\n def inner(*args, **kwargs):\n return val\n return inner\n\n\ndef not_equals(val):\n def inner(x):\n return x != val\n return inner\n\n\ndef equals(val):\n def inner(x):\n return x == val\n return inner\n\n\ndef max_neg_value(tensor):\n return -torch.finfo(tensor.dtype).max\n\n\n# keyword argument helpers\n\ndef pick_and_pop(keys, d):\n values = list(map(lambda key: d.pop(key), keys))\n return dict(zip(keys, values))\n\n\ndef group_dict_by_key(cond, d):\n return_val = [dict(), dict()]\n for key in d.keys():\n match = bool(cond(key))\n ind = int(not match)\n return_val[ind][key] = d[key]\n return (*return_val,)\n\n\ndef string_begins_with(prefix, str):\n return str.startswith(prefix)\n\n\ndef group_by_key_prefix(prefix, d):\n return group_dict_by_key(partial(string_begins_with, prefix), d)\n\n\ndef groupby_prefix_and_trim(prefix, d):\n kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)\n kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))\n return kwargs_without_prefix, kwargs\n\n\n# classes\nclass Scale(nn.Module):\n def __init__(self, value, fn):\n super().__init__()\n self.value = value\n self.fn = fn\n\n def forward(self, x, **kwargs):\n x, *rest = self.fn(x, **kwargs)\n return (x * self.value, *rest)\n\n\nclass Rezero(nn.Module):\n def __init__(self, fn):\n super().__init__()\n self.fn = fn\n self.g = nn.Parameter(torch.zeros(1))\n\n def forward(self, x, **kwargs):\n x, *rest = self.fn(x, **kwargs)\n return (x * self.g, *rest)\n\n\nclass ScaleNorm(nn.Module):\n def __init__(self, dim, eps=1e-5):\n super().__init__()\n self.scale = dim ** -0.5\n self.eps = eps\n self.g = nn.Parameter(torch.ones(1))\n\n def forward(self, x):\n norm = torch.norm(x, dim=-1, keepdim=True) * self.scale\n return x / norm.clamp(min=self.eps) * self.g\n\n\nclass RMSNorm(nn.Module):\n def __init__(self, dim, eps=1e-8):\n super().__init__()\n self.scale = dim ** -0.5\n self.eps = eps\n self.g = nn.Parameter(torch.ones(dim))\n\n def forward(self, x):\n norm = torch.norm(x, dim=-1, keepdim=True) * self.scale\n return x / norm.clamp(min=self.eps) * self.g\n\n\nclass Residual(nn.Module):\n def forward(self, x, residual):\n return x + residual\n\n\nclass GRUGating(nn.Module):\n def __init__(self, dim):\n super().__init__()\n self.gru = nn.GRUCell(dim, dim)\n\n def forward(self, x, residual):\n gated_output = self.gru(\n rearrange(x, 'b n d -> (b n) d'),\n rearrange(residual, 'b n d -> (b n) d')\n )\n\n return gated_output.reshape_as(x)\n\n\n# feedforward\n\nclass GEGLU(nn.Module):\n def __init__(self, dim_in, dim_out):\n super().__init__()\n self.proj = nn.Linear(dim_in, dim_out * 2)\n\n def forward(self, x):\n x, gate = self.proj(x).chunk(2, dim=-1)\n return x * F.gelu(gate)\n\n\nclass FeedForward(nn.Module):\n def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):\n super().__init__()\n inner_dim = int(dim * mult)\n dim_out = default(dim_out, dim)\n project_in = nn.Sequential(\n nn.Linear(dim, inner_dim),\n nn.GELU()\n ) if not glu else GEGLU(dim, inner_dim)\n\n self.net = nn.Sequential(\n project_in,\n nn.Dropout(dropout),\n nn.Linear(inner_dim, dim_out)\n )\n\n def forward(self, x):\n return self.net(x)\n\n\n# attention.\nclass Attention(nn.Module):\n def __init__(\n self,\n dim,\n dim_head=DEFAULT_DIM_HEAD,\n heads=8,\n causal=False,\n mask=None,\n talking_heads=False,\n sparse_topk=None,\n use_entmax15=False,\n num_mem_kv=0,\n dropout=0.,\n on_attn=False\n ):\n super().__init__()\n if use_entmax15:\n raise NotImplementedError(\"Check out entmax activation instead of softmax activation!\")\n self.scale = dim_head ** -0.5\n self.heads = heads\n self.causal = causal\n self.mask = mask\n\n inner_dim = dim_head * heads\n\n self.to_q = nn.Linear(dim, inner_dim, bias=False)\n self.to_k = nn.Linear(dim, inner_dim, bias=False)\n self.to_v = nn.Linear(dim, inner_dim, bias=False)\n self.dropout = nn.Dropout(dropout)\n\n # talking heads\n self.talking_heads = talking_heads\n if talking_heads:\n self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads))\n self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads))\n\n # explicit topk sparse attention\n self.sparse_topk = sparse_topk\n\n # entmax\n #self.attn_fn = entmax15 if use_entmax15 else F.softmax\n self.attn_fn = F.softmax\n\n # add memory key / values\n self.num_mem_kv = num_mem_kv\n if num_mem_kv > 0:\n self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))\n self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))\n\n # attention on attention\n self.attn_on_attn = on_attn\n self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim)\n\n def forward(\n self,\n x,\n context=None,\n mask=None,\n context_mask=None,\n rel_pos=None,\n sinusoidal_emb=None,\n prev_attn=None,\n mem=None\n ):\n b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device\n kv_input = default(context, x)\n\n q_input = x\n k_input = kv_input\n v_input = kv_input\n\n if exists(mem):\n k_input = torch.cat((mem, k_input), dim=-2)\n v_input = torch.cat((mem, v_input), dim=-2)\n\n if exists(sinusoidal_emb):\n # in shortformer, the query would start at a position offset depending on the past cached memory\n offset = k_input.shape[-2] - q_input.shape[-2]\n q_input = q_input + sinusoidal_emb(q_input, offset=offset)\n k_input = k_input + sinusoidal_emb(k_input)\n\n q = self.to_q(q_input)\n k = self.to_k(k_input)\n v = self.to_v(v_input)\n\n q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v))\n\n input_mask = None\n if any(map(exists, (mask, context_mask))):\n q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool())\n k_mask = q_mask if not exists(context) else context_mask\n k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool())\n q_mask = rearrange(q_mask, 'b i -> b () i ()')\n k_mask = rearrange(k_mask, 'b j -> b () () j')\n input_mask = q_mask * k_mask\n\n if self.num_mem_kv > 0:\n mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v))\n k = torch.cat((mem_k, k), dim=-2)\n v = torch.cat((mem_v, v), dim=-2)\n if exists(input_mask):\n input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True)\n\n dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale\n mask_value = max_neg_value(dots)\n\n if exists(prev_attn):\n dots = dots + prev_attn\n\n pre_softmax_attn = dots\n\n if talking_heads:\n dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous()\n\n if exists(rel_pos):\n dots = rel_pos(dots)\n\n if exists(input_mask):\n dots.masked_fill_(~input_mask, mask_value)\n del input_mask\n\n if self.causal:\n i, j = dots.shape[-2:]\n r = torch.arange(i, device=device)\n mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j')\n mask = F.pad(mask, (j - i, 0), value=False)\n dots.masked_fill_(mask, mask_value)\n del mask\n\n if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]:\n top, _ = dots.topk(self.sparse_topk, dim=-1)\n vk = top[..., -1].unsqueeze(-1).expand_as(dots)\n mask = dots < vk\n dots.masked_fill_(mask, mask_value)\n del mask\n\n attn = self.attn_fn(dots, dim=-1)\n post_softmax_attn = attn\n\n attn = self.dropout(attn)\n\n if talking_heads:\n attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous()\n\n out = einsum('b h i j, b h j d -> b h i d', attn, v)\n out = rearrange(out, 'b h n d -> b n (h d)')\n\n intermediates = Intermediates(\n pre_softmax_attn=pre_softmax_attn,\n post_softmax_attn=post_softmax_attn\n )\n\n return self.to_out(out), intermediates\n\n\nclass AttentionLayers(nn.Module):\n def __init__(\n self,\n dim,\n depth,\n heads=8,\n causal=False,\n cross_attend=False,\n only_cross=False,\n use_scalenorm=False,\n use_rmsnorm=False,\n use_rezero=False,\n rel_pos_num_buckets=32,\n rel_pos_max_distance=128,\n position_infused_attn=False,\n custom_layers=None,\n sandwich_coef=None,\n par_ratio=None,\n residual_attn=False,\n cross_residual_attn=False,\n macaron=False,\n pre_norm=True,\n gate_residual=False,\n **kwargs\n ):\n super().__init__()\n ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs)\n attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs)\n\n dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD)\n\n self.dim = dim\n self.depth = depth\n self.layers = nn.ModuleList([])\n\n self.has_pos_emb = position_infused_attn\n self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None\n self.rotary_pos_emb = always(None)\n\n assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance'\n self.rel_pos = None\n\n self.pre_norm = pre_norm\n\n self.residual_attn = residual_attn\n self.cross_residual_attn = cross_residual_attn\n\n norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm\n norm_class = RMSNorm if use_rmsnorm else norm_class\n norm_fn = partial(norm_class, dim)\n\n norm_fn = nn.Identity if use_rezero else norm_fn\n branch_fn = Rezero if use_rezero else None\n\n if cross_attend and not only_cross:\n default_block = ('a', 'c', 'f')\n elif cross_attend and only_cross:\n default_block = ('c', 'f')\n else:\n default_block = ('a', 'f')\n\n if macaron:\n default_block = ('f',) + default_block\n\n if exists(custom_layers):\n layer_types = custom_layers\n elif exists(par_ratio):\n par_depth = depth * len(default_block)\n assert 1 < par_ratio <= par_depth, 'par ratio out of range'\n default_block = tuple(filter(not_equals('f'), default_block))\n par_attn = par_depth // par_ratio\n depth_cut = par_depth * 2 // 3 # 2 / 3 attention layer cutoff suggested by PAR paper\n par_width = (depth_cut + depth_cut // par_attn) // par_attn\n assert len(default_block) <= par_width, 'default block is too large for par_ratio'\n par_block = default_block + ('f',) * (par_width - len(default_block))\n par_head = par_block * par_attn\n layer_types = par_head + ('f',) * (par_depth - len(par_head))\n elif exists(sandwich_coef):\n assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth'\n layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef\n else:\n layer_types = default_block * depth\n\n self.layer_types = layer_types\n self.num_attn_layers = len(list(filter(equals('a'), layer_types)))\n\n for layer_type in self.layer_types:\n if layer_type == 'a':\n layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs)\n elif layer_type == 'c':\n layer = Attention(dim, heads=heads, **attn_kwargs)\n elif layer_type == 'f':\n layer = FeedForward(dim, **ff_kwargs)\n layer = layer if not macaron else Scale(0.5, layer)\n else:\n raise Exception(f'invalid layer type {layer_type}')\n\n if isinstance(layer, Attention) and exists(branch_fn):\n layer = branch_fn(layer)\n\n if gate_residual:\n residual_fn = GRUGating(dim)\n else:\n residual_fn = Residual()\n\n self.layers.append(nn.ModuleList([\n norm_fn(),\n layer,\n residual_fn\n ]))\n\n def forward(\n self,\n x,\n context=None,\n mask=None,\n context_mask=None,\n mems=None,\n return_hiddens=False\n ):\n hiddens = []\n intermediates = []\n prev_attn = None\n prev_cross_attn = None\n\n mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers\n\n for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)):\n is_last = ind == (len(self.layers) - 1)\n\n if layer_type == 'a':\n hiddens.append(x)\n layer_mem = mems.pop(0)\n\n residual = x\n\n if self.pre_norm:\n x = norm(x)\n\n if layer_type == 'a':\n out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos,\n prev_attn=prev_attn, mem=layer_mem)\n elif layer_type == 'c':\n out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn)\n elif layer_type == 'f':\n out = block(x)\n\n x = residual_fn(out, residual)\n\n if layer_type in ('a', 'c'):\n intermediates.append(inter)\n\n if layer_type == 'a' and self.residual_attn:\n prev_attn = inter.pre_softmax_attn\n elif layer_type == 'c' and self.cross_residual_attn:\n prev_cross_attn = inter.pre_softmax_attn\n\n if not self.pre_norm and not is_last:\n x = norm(x)\n\n if return_hiddens:\n intermediates = LayerIntermediates(\n hiddens=hiddens,\n attn_intermediates=intermediates\n )\n\n return x, intermediates\n\n return x\n\n\nclass Encoder(AttentionLayers):\n def __init__(self, **kwargs):\n assert 'causal' not in kwargs, 'cannot set causality on encoder'\n super().__init__(causal=False, **kwargs)\n\n\n\nclass TransformerWrapper(nn.Module):\n def __init__(\n self,\n *,\n num_tokens,\n max_seq_len,\n attn_layers,\n emb_dim=None,\n max_mem_len=0.,\n emb_dropout=0.,\n num_memory_tokens=None,\n tie_embedding=False,\n use_pos_emb=True\n ):\n super().__init__()\n assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'\n\n dim = attn_layers.dim\n emb_dim = default(emb_dim, dim)\n\n self.max_seq_len = max_seq_len\n self.max_mem_len = max_mem_len\n self.num_tokens = num_tokens\n\n self.token_emb = nn.Embedding(num_tokens, emb_dim)\n self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if (\n use_pos_emb and not attn_layers.has_pos_emb) else always(0)\n self.emb_dropout = nn.Dropout(emb_dropout)\n\n self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()\n self.attn_layers = attn_layers\n self.norm = nn.LayerNorm(dim)\n\n self.init_()\n\n self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t()\n\n # memory tokens (like [cls]) from Memory Transformers paper\n num_memory_tokens = default(num_memory_tokens, 0)\n self.num_memory_tokens = num_memory_tokens\n if num_memory_tokens > 0:\n self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim))\n\n # let funnel encoder know number of memory tokens, if specified\n if hasattr(attn_layers, 'num_memory_tokens'):\n attn_layers.num_memory_tokens = num_memory_tokens\n\n def init_(self):\n nn.init.normal_(self.token_emb.weight, std=0.02)\n\n def forward(\n self,\n x,\n return_embeddings=False,\n mask=None,\n return_mems=False,\n return_attn=False,\n mems=None,\n **kwargs\n ):\n b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens\n x = self.token_emb(x)\n x += self.pos_emb(x)\n x = self.emb_dropout(x)\n\n x = self.project_emb(x)\n\n if num_mem > 0:\n mem = repeat(self.memory_tokens, 'n d -> b n d', b=b)\n x = torch.cat((mem, x), dim=1)\n\n # auto-handle masking after appending memory tokens\n if exists(mask):\n mask = F.pad(mask, (num_mem, 0), value=True)\n\n x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs)\n x = self.norm(x)\n\n mem, x = x[:, :num_mem], x[:, num_mem:]\n\n out = self.to_logits(x) if not return_embeddings else x\n\n if return_mems:\n hiddens = intermediates.hiddens\n new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens\n new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems))\n return out, new_mems\n\n if return_attn:\n attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))\n return out, attn_maps\n\n return out\n\n" }, { "path": "data_generation/ldm/util.py", "content": "import importlib\n\nimport torch\nimport numpy as np\nfrom collections import abc\nfrom einops import rearrange\nfrom functools import partial\n\nimport multiprocessing as mp\nfrom threading import Thread\nfrom queue import Queue\n\nfrom inspect import isfunction\nfrom PIL import Image, ImageDraw, ImageFont\n\n\ndef log_txt_as_img(wh, xc, size=10):\n # wh a tuple of (width, height)\n # xc a list of captions to plot\n b = len(xc)\n txts = list()\n for bi in range(b):\n txt = Image.new(\"RGB\", wh, color=\"white\")\n draw = ImageDraw.Draw(txt)\n font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)\n nc = int(40 * (wh[0] / 256))\n lines = \"\\n\".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))\n\n try:\n draw.text((0, 0), lines, fill=\"black\", font=font)\n except UnicodeEncodeError:\n print(\"Cant encode string for logging. Skipping.\")\n\n txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0\n txts.append(txt)\n txts = np.stack(txts)\n txts = torch.tensor(txts)\n return txts\n\n\ndef ismap(x):\n if not isinstance(x, torch.Tensor):\n return False\n return (len(x.shape) == 4) and (x.shape[1] > 3)\n\n\ndef isimage(x):\n if not isinstance(x, torch.Tensor):\n return False\n return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)\n\n\ndef exists(x):\n return x is not None\n\n\ndef default(val, d):\n if exists(val):\n return val\n return d() if isfunction(d) else d\n\n\ndef mean_flat(tensor):\n \"\"\"\n https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86\n Take the mean over all non-batch dimensions.\n \"\"\"\n return tensor.mean(dim=list(range(1, len(tensor.shape))))\n\n\ndef count_params(model, verbose=False):\n total_params = sum(p.numel() for p in model.parameters())\n if verbose:\n print(f\"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.\")\n return total_params\n\n\ndef instantiate_from_config(config):\n if not \"target\" in config:\n if config == '__is_first_stage__':\n return None\n elif config == \"__is_unconditional__\":\n return None\n raise KeyError(\"Expected key `target` to instantiate.\")\n return get_obj_from_str(config[\"target\"])(**config.get(\"params\", dict()))\n\n\ndef get_obj_from_str(string, reload=False):\n module, cls = string.rsplit(\".\", 1)\n if reload:\n module_imp = importlib.import_module(module)\n importlib.reload(module_imp)\n return getattr(importlib.import_module(module, package=None), cls)\n\n\ndef _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False):\n # create dummy dataset instance\n\n # run prefetching\n if idx_to_fn:\n res = func(data, worker_id=idx)\n else:\n res = func(data)\n Q.put([idx, res])\n Q.put(\"Done\")\n\n\ndef parallel_data_prefetch(\n func: callable, data, n_proc, target_data_type=\"ndarray\", cpu_intensive=True, use_worker_id=False\n):\n # if target_data_type not in [\"ndarray\", \"list\"]:\n # raise ValueError(\n # \"Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray.\"\n # )\n if isinstance(data, np.ndarray) and target_data_type == \"list\":\n raise ValueError(\"list expected but function got ndarray.\")\n elif isinstance(data, abc.Iterable):\n if isinstance(data, dict):\n print(\n f'WARNING:\"data\" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.'\n )\n data = list(data.values())\n if target_data_type == \"ndarray\":\n data = np.asarray(data)\n else:\n data = list(data)\n else:\n raise TypeError(\n f\"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}.\"\n )\n\n if cpu_intensive:\n Q = mp.Queue(1000)\n proc = mp.Process\n else:\n Q = Queue(1000)\n proc = Thread\n # spawn processes\n if target_data_type == \"ndarray\":\n arguments = [\n [func, Q, part, i, use_worker_id]\n for i, part in enumerate(np.array_split(data, n_proc))\n ]\n else:\n step = (\n int(len(data) / n_proc + 1)\n if len(data) % n_proc != 0\n else int(len(data) / n_proc)\n )\n arguments = [\n [func, Q, part, i, use_worker_id]\n for i, part in enumerate(\n [data[i: i + step] for i in range(0, len(data), step)]\n )\n ]\n processes = []\n for i in range(n_proc):\n p = proc(target=_do_parallel_data_prefetch, args=arguments[i])\n processes += [p]\n\n # start processes\n print(f\"Start prefetching...\")\n import time\n\n start = time.time()\n gather_res = [[] for _ in range(n_proc)]\n try:\n for p in processes:\n p.start()\n\n k = 0\n while k < n_proc:\n # get result\n res = Q.get()\n if res == \"Done\":\n k += 1\n else:\n gather_res[res[0]] = res[1]\n\n except Exception as e:\n print(\"Exception: \", e)\n for p in processes:\n p.terminate()\n\n raise e\n finally:\n for p in processes:\n p.join()\n print(f\"Prefetching complete. [{time.time() - start} sec.]\")\n\n if target_data_type == 'ndarray':\n if not isinstance(gather_res[0], np.ndarray):\n return np.concatenate([np.asarray(r) for r in gather_res], axis=0)\n\n # order outputs\n return np.concatenate(gather_res, axis=0)\n elif target_data_type == 'list':\n out = []\n for r in gather_res:\n out.extend(r)\n return out\n else:\n return gather_res\n" }, { "path": "data_generation/metrics/clip_similarity.py", "content": "from __future__ import annotations\n\nimport clip\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom einops import rearrange\nfrom skimage.metrics import structural_similarity as ssim\nimport numpy as np\n\n\n\n\nclass ClipSimilarity(nn.Module):\n def __init__(self, name: str = \"ViT-L/14\"):\n super().__init__()\n # assert name in (\"RN50\", \"RN101\", \"RN50x4\", \"RN50x16\", \"RN50x64\", \"ViT-B/32\", \"ViT-B/16\", \"ViT-L-14.pt\", \"ViT-L/14\", \"ViT-L/14@336px\") # fmt: skip\n self.size = {\"RN50x4\": 288, \"RN50x16\": 384, \"RN50x64\": 448, \"ViT-L/14@336px\": 336}.get(name, 224)\n\n self.model, _ = clip.load(name, device=\"cpu\", download_root=\"./\")\n self.model.eval().requires_grad_(False)\n self.dinov2_model = torch.hub.load('/home/zhaohaozhe/projects/comput_dinov2/dinov2', 'dinov2_vitl14_reg', source='local', pretrained=False).cuda()\n self.dinov2_model.load_state_dict(torch.load('/home/zhaohaozhe/model2/dinov2_vitl14_reg4_pretrain.pth'))\n self.dinov2_model.eval().requires_grad_(False)\n self.register_buffer(\"mean\", torch.tensor((0.48145466, 0.4578275, 0.40821073)))\n self.register_buffer(\"std\", torch.tensor((0.26862954, 0.26130258, 0.27577711)))\n\n def encode_text(self, text: list[str]) -> torch.Tensor:\n text = clip.tokenize(text, truncate=True).to(next(self.parameters()).device)\n text_features = self.model.encode_text(text)\n text_features = text_features / text_features.norm(dim=1, keepdim=True)\n return text_features\n\n def encode_image(self, image: torch.Tensor) -> torch.Tensor: # Input images in range [0, 1].\n image = F.interpolate(image.float(), size=self.size, mode=\"bicubic\", align_corners=False)\n image = image - rearrange(self.mean, \"c -> 1 c 1 1\")\n image = image / rearrange(self.std, \"c -> 1 c 1 1\")\n image_features = self.model.encode_image(image)\n image_features = image_features / image_features.norm(dim=1, keepdim=True)\n return image_features\n def get_dinov2_embedding(self, image: torch.Tensor) -> torch.Tensor:\n image = F.interpolate(image, size=(518, 518))\n img_embedding = self.dinov2_model(image)\n img_embedding = img_embedding/img_embedding.norm(dim=1, keepdim=True)\n return img_embedding\n \n def get_ssim_sim(self, image1,image2):\n if len(image1.shape) == 4:\n image1 = image1.squeeze(0).permute(1, 2, 0)\n image2 = image2.squeeze(0).permute(1, 2, 0)\n image1= image1.cpu().detach().numpy()\n image2= image2.cpu().detach().numpy()\n \n\n ssim_values = [\n ssim(image1[:, :, i], image2[:, :, i], data_range=1.0) \n for i in range(3)\n ] \n ssim_average = np.mean(ssim_values)\n return ssim_average \n \n def forward(\n self, image_0: torch.Tensor, image_1: torch.Tensor, text_0: list[str], text_1: list[str]\n ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:\n \n ssim = self.get_ssim_sim(image_0,image_1)\n image_features_0 = self.encode_image(image_0)\n image_features_1 = self.encode_image(image_1)\n text_features_0 = self.encode_text(text_0)\n text_features_1 = self.encode_text(text_1)\n sim_0 = F.cosine_similarity(image_features_0, text_features_0)\n sim_1 = F.cosine_similarity(image_features_1, text_features_1)\n sim_direction = F.cosine_similarity(image_features_1 - image_features_0, text_features_1 - text_features_0)\n sim_image = F.cosine_similarity(image_features_0, image_features_1)\n \n img1_embedding = self.get_dinov2_embedding(image_0)\n img2_embedding = self.get_dinov2_embedding(image_1)\n\n dinov2_sim = F.cosine_similarity(img1_embedding,img2_embedding)\n \n return sim_0, sim_1, sim_direction, sim_image,dinov2_sim,ssim\n" }, { "path": "data_generation/metrics/compute_metrics.py", "content": "from __future__ import annotations\n\nimport math\nimport random\nimport sys\nfrom argparse import ArgumentParser\n\nimport einops\nimport k_diffusion as K\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom tqdm.auto import tqdm\nfrom einops import rearrange\nfrom omegaconf import OmegaConf\nfrom PIL import Image, ImageOps\nfrom torch import autocast\n\nimport json\nimport matplotlib.pyplot as plt\nimport seaborn\nfrom pathlib import Path\n\nsys.path.append(\"./\")\n\nfrom clip_similarity import ClipSimilarity\nfrom edit_dataset import EditDatasetEval\n\nsys.path.append(\"./stable_diffusion\")\n\nfrom ldm.util import instantiate_from_config\n\n\nclass CFGDenoiser(nn.Module):\n def __init__(self, model):\n super().__init__()\n self.inner_model = model\n\n def forward(self, z, sigma, cond, uncond, text_cfg_scale, image_cfg_scale):\n cfg_z = einops.repeat(z, \"1 ... -> n ...\", n=3)\n cfg_sigma = einops.repeat(sigma, \"1 ... -> n ...\", n=3)\n cfg_cond = {\n \"c_crossattn\": [torch.cat([cond[\"c_crossattn\"][0], uncond[\"c_crossattn\"][0], uncond[\"c_crossattn\"][0]])],\n \"c_concat\": [torch.cat([cond[\"c_concat\"][0], cond[\"c_concat\"][0], uncond[\"c_concat\"][0]])],\n }\n out_cond, out_img_cond, out_uncond = self.inner_model(cfg_z, cfg_sigma, cond=cfg_cond).chunk(3)\n return out_uncond + text_cfg_scale * (out_cond - out_img_cond) + image_cfg_scale * (out_img_cond - out_uncond)\n\n\ndef load_model_from_config(config, ckpt, vae_ckpt=None, verbose=False):\n print(f\"Loading model from {ckpt}\")\n pl_sd = torch.load(ckpt, map_location=\"cpu\")\n if \"global_step\" in pl_sd:\n print(f\"Global Step: {pl_sd['global_step']}\")\n sd = pl_sd[\"state_dict\"]\n if vae_ckpt is not None:\n print(f\"Loading VAE from {vae_ckpt}\")\n vae_sd = torch.load(vae_ckpt, map_location=\"cpu\")[\"state_dict\"]\n sd = {\n k: vae_sd[k[len(\"first_stage_model.\") :]] if k.startswith(\"first_stage_model.\") else v\n for k, v in sd.items()\n }\n model = instantiate_from_config(config.model)\n m, u = model.load_state_dict(sd, strict=False)\n if len(m) > 0 and verbose:\n print(\"missing keys:\")\n print(m)\n if len(u) > 0 and verbose:\n print(\"unexpected keys:\")\n print(u)\n return model\n\nclass ImageEditor(nn.Module):\n def __init__(self, config, ckpt, vae_ckpt=None):\n super().__init__()\n \n config = OmegaConf.load(config)\n self.model = load_model_from_config(config, ckpt, vae_ckpt)\n self.model.eval().cuda()\n self.model_wrap = K.external.CompVisDenoiser(self.model)\n self.model_wrap_cfg = CFGDenoiser(self.model_wrap)\n self.null_token = self.model.get_learned_conditioning([\"\"])\n\n def forward(\n self,\n image: torch.Tensor,\n edit: str,\n scale_txt: float = 7.5,\n scale_img: float = 1.0,\n steps: int = 100,\n ) -> torch.Tensor:\n assert image.dim() == 3\n assert image.size(1) % 64 == 0\n assert image.size(2) % 64 == 0\n with torch.no_grad(), autocast(\"cuda\"), self.model.ema_scope():\n cond = {\n \"c_crossattn\": [self.model.get_learned_conditioning([edit])],\n \"c_concat\": [self.model.encode_first_stage(image[None]).mode()],\n }\n uncond = {\n \"c_crossattn\": [self.model.get_learned_conditioning([\"\"])],\n \"c_concat\": [torch.zeros_like(cond[\"c_concat\"][0])],\n }\n extra_args = {\n \"uncond\": uncond,\n \"cond\": cond,\n \"image_cfg_scale\": scale_img,\n \"text_cfg_scale\": scale_txt,\n }\n sigmas = self.model_wrap.get_sigmas(steps)\n x = torch.randn_like(cond[\"c_concat\"][0]) * sigmas[0]\n x = K.sampling.sample_euler_ancestral(self.model_wrap_cfg, x, sigmas, extra_args=extra_args)\n x = self.model.decode_first_stage(x)[0]\n return x\n\n\ndef compute_metrics(config,\n model_path, \n vae_ckpt,\n data_path,\n output_path, \n scales_img, \n scales_txt, \n num_samples = 5000, \n split = \"test\", \n steps = 50, \n res = 512, \n seed = 0):\n editor = ImageEditor(config, model_path, vae_ckpt).cuda()\n clip_similarity = ClipSimilarity().cuda()\n\n\n\n outpath = Path(output_path, f\"n={num_samples}_p={split}_s={steps}_r={res}_e={seed}.jsonl\")\n Path(output_path).mkdir(parents=True, exist_ok=True)\n\n for scale_txt in scales_txt:\n for scale_img in scales_img:\n dataset = EditDatasetEval(\n path=data_path, \n split=split, \n res=res\n )\n assert num_samples <= len(dataset)\n print(f'Processing t={scale_txt}, i={scale_img}')\n torch.manual_seed(seed)\n perm = torch.randperm(len(dataset))\n count = 0\n i = 0\n\n sim_0_avg = 0\n sim_1_avg = 0\n sim_direction_avg = 0\n sim_image_avg = 0\n count = 0\n\n pbar = tqdm(total=num_samples)\n while count < num_samples:\n \n idx = perm[i].item()\n sample = dataset[idx]\n i += 1\n\n gen = editor(sample[\"image_0\"].cuda(), sample[\"edit\"], scale_txt=scale_txt, scale_img=scale_img, steps=steps)\n\n sim_0, sim_1, sim_direction, sim_image = clip_similarity(\n sample[\"image_0\"][None].cuda(), gen[None].cuda(), [sample[\"input_prompt\"]], [sample[\"output_prompt\"]]\n )\n sim_0_avg += sim_0.item()\n sim_1_avg += sim_1.item()\n sim_direction_avg += sim_direction.item()\n sim_image_avg += sim_image.item()\n count += 1\n pbar.update(count)\n pbar.close()\n\n sim_0_avg /= count\n sim_1_avg /= count\n sim_direction_avg /= count\n sim_image_avg /= count\n\n with open(outpath, \"a\") as f:\n f.write(f\"{json.dumps(dict(sim_0=sim_0_avg, sim_1=sim_1_avg, sim_direction=sim_direction_avg, sim_image=sim_image_avg, num_samples=num_samples, split=split, scale_txt=scale_txt, scale_img=scale_img, steps=steps, res=res, seed=seed))}\\n\")\n return outpath\n\ndef plot_metrics(metrics_file, output_path):\n \n with open(metrics_file, 'r') as f:\n data = [json.loads(line) for line in f]\n \n plt.rcParams.update({'font.size': 11.5})\n seaborn.set_style(\"darkgrid\")\n plt.figure(figsize=(20.5* 0.7, 10.8* 0.7), dpi=200)\n\n x = [d[\"sim_direction\"] for d in data]\n y = [d[\"sim_image\"] for d in data]\n\n plt.plot(x, y, marker='o', linewidth=2, markersize=4)\n\n plt.xlabel(\"CLIP Text-Image Direction Similarity\", labelpad=10)\n plt.ylabel(\"CLIP Image Similarity\", labelpad=10)\n\n plt.savefig(Path(output_path) / Path(\"plot.pdf\"), bbox_inches=\"tight\")\n\ndef main():\n parser = ArgumentParser()\n parser.add_argument(\"--resolution\", default=512, type=int)\n parser.add_argument(\"--steps\", default=100, type=int)\n parser.add_argument(\"--config\", default=\"configs/generate.yaml\", type=str)\n parser.add_argument(\"--output_path\", default=\"analysis/\", type=str)\n parser.add_argument(\"--ckpt\", default=\"checkpoints/instruct-pix2pix-00-22000.ckpt\", type=str)\n parser.add_argument(\"--dataset\", default=\"data/clip-filtered-dataset/\", type=str)\n parser.add_argument(\"--vae-ckpt\", default=None, type=str)\n args = parser.parse_args()\n\n scales_img = [1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.2]\n scales_txt = [7.5]\n \n metrics_file = compute_metrics(\n args.config,\n args.ckpt, \n args.vae_ckpt,\n args.dataset, \n args.output_path, \n scales_img, \n scales_txt,\n steps = args.steps,\n )\n \n plot_metrics(metrics_file, args.output_path)\n \n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "data_generation/processors.py", "content": "from __future__ import annotations\n\nimport abc\nfrom typing import Dict, List, Optional, Tuple, Union\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom diffusers.models.attention import Attention\n\n\nclass P2PCrossAttnProcessor:\n def __init__(self, controller, place_in_unet):\n super().__init__()\n self.controller = controller\n self.place_in_unet = place_in_unet\n\n def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):\n batch_size, sequence_length, _ = hidden_states.shape\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n\n query = attn.to_q(hidden_states)\n\n is_cross = encoder_hidden_states is not None\n encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n attention_probs = attn.get_attention_scores(query, key, attention_mask)\n\n # one line change\n self.controller(attention_probs, is_cross, self.place_in_unet)\n\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n return hidden_states\n\n\ndef create_controller(\n prompts: List[str], cross_attention_kwargs: Dict, num_inference_steps: int, tokenizer, device, attn_res\n) -> AttentionControl:\n edit_type = cross_attention_kwargs.get(\"edit_type\", None)\n local_blend_words = cross_attention_kwargs.get(\"local_blend_words\", None)\n equalizer_words = cross_attention_kwargs.get(\"equalizer_words\", None)\n equalizer_strengths = cross_attention_kwargs.get(\"equalizer_strengths\", None)\n n_cross_replace = cross_attention_kwargs.get(\"n_cross_replace\", 0.4)\n n_self_replace = cross_attention_kwargs.get(\"n_self_replace\", 0.4)\n\n # only replace\n if edit_type == \"replace\" and local_blend_words is None:\n return AttentionReplace(\n prompts, num_inference_steps, n_cross_replace, n_self_replace, tokenizer=tokenizer, device=device, attn_res=attn_res\n )\n\n # replace + localblend\n if edit_type == \"replace\" and local_blend_words is not None:\n lb = LocalBlend(prompts, local_blend_words, tokenizer=tokenizer, device=device, attn_res=attn_res)\n return AttentionReplace(\n prompts, num_inference_steps, n_cross_replace, n_self_replace, lb, tokenizer=tokenizer, device=device, attn_res=attn_res\n )\n\n # only refine\n if edit_type == \"refine\" and local_blend_words is None:\n return AttentionRefine(\n prompts, num_inference_steps, n_cross_replace, n_self_replace, tokenizer=tokenizer, device=device, attn_res=attn_res\n )\n\n # refine + localblend\n if edit_type == \"refine\" and local_blend_words is not None:\n lb = LocalBlend(prompts, local_blend_words, tokenizer=tokenizer, device=device, attn_res=attn_res)\n return AttentionRefine(\n prompts, num_inference_steps, n_cross_replace, n_self_replace, lb, tokenizer=tokenizer, device=device, attn_res=attn_res\n )\n\n # only reweight\n if edit_type == \"reweight\" and local_blend_words is None:\n assert (\n equalizer_words is not None and equalizer_strengths is not None\n ), \"To use reweight edit, please specify equalizer_words and equalizer_strengths.\"\n assert len(equalizer_words) == len(\n equalizer_strengths\n ), \"equalizer_words and equalizer_strengths must be of same length.\"\n equalizer = get_equalizer(prompts[1], equalizer_words, equalizer_strengths, tokenizer=tokenizer)\n return AttentionReweight(\n prompts,\n num_inference_steps,\n n_cross_replace,\n n_self_replace,\n tokenizer=tokenizer,\n device=device,\n equalizer=equalizer,\n attn_res=attn_res,\n )\n\n # reweight and localblend\n if edit_type == \"reweight\" and local_blend_words:\n assert (\n equalizer_words is not None and equalizer_strengths is not None\n ), \"To use reweight edit, please specify equalizer_words and equalizer_strengths.\"\n assert len(equalizer_words) == len(\n equalizer_strengths\n ), \"equalizer_words and equalizer_strengths must be of same length.\"\n equalizer = get_equalizer(prompts[1], equalizer_words, equalizer_strengths, tokenizer=tokenizer)\n lb = LocalBlend(prompts, local_blend_words, tokenizer=tokenizer, device=device, attn_res=attn_res)\n return AttentionReweight(\n prompts,\n num_inference_steps,\n n_cross_replace,\n n_self_replace,\n tokenizer=tokenizer,\n device=device,\n equalizer=equalizer,\n attn_res=attn_res,\n local_blend=lb,\n )\n\n raise ValueError(f\"Edit type {edit_type} not recognized. Use one of: replace, refine, reweight.\")\n\n\nclass AttentionControl(abc.ABC):\n def step_callback(self, x_t):\n return x_t\n\n def between_steps(self):\n return\n\n @property\n def num_uncond_att_layers(self):\n return 0\n\n @abc.abstractmethod\n def forward(self, attn, is_cross: bool, place_in_unet: str):\n raise NotImplementedError\n\n def __call__(self, attn, is_cross: bool, place_in_unet: str):\n if self.cur_att_layer >= self.num_uncond_att_layers:\n h = attn.shape[0]\n attn[h // 2 :] = self.forward(attn[h // 2 :], is_cross, place_in_unet)\n self.cur_att_layer += 1\n if self.cur_att_layer == self.num_att_layers + self.num_uncond_att_layers:\n self.cur_att_layer = 0\n self.cur_step += 1\n self.between_steps()\n return attn\n\n def reset(self):\n self.cur_step = 0\n self.cur_att_layer = 0\n\n def __init__(self, attn_res=None):\n self.cur_step = 0\n self.num_att_layers = -1\n self.cur_att_layer = 0\n self.attn_res = attn_res\n\n\nclass EmptyControl(AttentionControl):\n def forward(self, attn, is_cross: bool, place_in_unet: str):\n return attn\n\n\nclass AttentionStore(AttentionControl):\n @staticmethod\n def get_empty_store():\n return {\"down_cross\": [], \"mid_cross\": [], \"up_cross\": [], \"down_self\": [], \"mid_self\": [], \"up_self\": []}\n\n def forward(self, attn, is_cross: bool, place_in_unet: str):\n key = f\"{place_in_unet}_{'cross' if is_cross else 'self'}\"\n if attn.shape[1] <= 32**2: # avoid memory overhead\n self.step_store[key].append(attn)\n return attn\n\n def between_steps(self):\n if len(self.attention_store) == 0:\n self.attention_store = self.step_store\n else:\n for key in self.attention_store:\n for i in range(len(self.attention_store[key])):\n self.attention_store[key][i] += self.step_store[key][i]\n self.step_store = self.get_empty_store()\n\n def get_average_attention(self):\n average_attention = {\n key: [item / self.cur_step for item in self.attention_store[key]] for key in self.attention_store\n }\n return average_attention\n\n def reset(self):\n super(AttentionStore, self).reset()\n self.step_store = self.get_empty_store()\n self.attention_store = {}\n\n def __init__(self, attn_res=None):\n super(AttentionStore, self).__init__(attn_res)\n self.step_store = self.get_empty_store()\n self.attention_store = {}\n\n\n\nclass LocalBlend:\n def __call__(self, x_t, attention_store):\n # note that this code works on the latent level!\n k = 1\n # maps = attention_store[\"down_cross\"][2:4] + attention_store[\"up_cross\"][:3] # These are the numbers because we want to take layers that are 256 x 256, I think this can be changed to something smarter...like, get all attentions where thesecond dim is self.attn_res[0] * self.attn_res[1] in up and down cross.\n maps = [m for m in attention_store[\"down_cross\"] + attention_store[\"mid_cross\"] + attention_store[\"up_cross\"] if m.shape[1] == self.attn_res[0] * self.attn_res[1]]\n maps = [item.reshape(self.alpha_layers.shape[0], -1, 1, self.attn_res[0], self.attn_res[1], self.max_num_words) for item in maps]\n maps = torch.cat(maps, dim=1)\n maps = (maps * self.alpha_layers).sum(-1).mean(1) # since alpha_layers is all 0s except where we edit, the product zeroes out all but what we change. Then, the sum adds the values of the original and what we edit. Then, we average across dim=1, which is the number of layers.\n mask = F.max_pool2d(maps, (k * 2 + 1, k * 2 + 1), (1, 1), padding=(k, k))\n mask = F.interpolate(mask, size=(x_t.shape[2:]))\n mask = mask / mask.max(2, keepdims=True)[0].max(3, keepdims=True)[0]\n mask = mask.gt(self.threshold)\n\n mask = mask[:1] + mask[1:]\n mask = mask.to(torch.float16)\n\n x_t = x_t[:1] + mask * (x_t - x_t[:1]) # x_t[:1] is the original image. mask*(x_t - x_t[:1]) zeroes out the original image and removes the difference between the original and each image we are generating (mostly just one). Then, it applies the mask on the image. That is, it's only keeping the cells we want to generate.\n return x_t\n\n def __init__(\n self, prompts: List[str], words: [List[List[str]]], tokenizer, device, threshold=0.3, attn_res=None\n ):\n self.max_num_words = 77\n self.attn_res = attn_res\n\n alpha_layers = torch.zeros(len(prompts), 1, 1, 1, 1, self.max_num_words)\n for i, (prompt, words_) in enumerate(zip(prompts, words)):\n if isinstance(words_, str):\n words_ = [words_]\n for word in words_:\n ind = get_word_inds(prompt, word, tokenizer)\n alpha_layers[i, :, :, :, :, ind] = 1\n self.alpha_layers = alpha_layers.to(device) # a one-hot vector where the 1s are the words we modify (source and target)\n self.threshold = threshold\n\n\nclass AttentionControlEdit(AttentionStore, abc.ABC):\n def step_callback(self, x_t):\n if self.local_blend is not None:\n x_t = self.local_blend(x_t, self.attention_store)\n return x_t\n\n def replace_self_attention(self, attn_base, att_replace):\n if att_replace.shape[2] <= self.attn_res[0]**2:\n return attn_base.unsqueeze(0).expand(att_replace.shape[0], *attn_base.shape)\n else:\n return att_replace\n\n @abc.abstractmethod\n def replace_cross_attention(self, attn_base, att_replace):\n raise NotImplementedError\n\n def forward(self, attn, is_cross: bool, place_in_unet: str):\n super(AttentionControlEdit, self).forward(attn, is_cross, place_in_unet)\n if is_cross or (self.num_self_replace[0] <= self.cur_step < self.num_self_replace[1]):\n h = attn.shape[0] // (self.batch_size)\n attn = attn.reshape(self.batch_size, h, *attn.shape[1:])\n attn_base, attn_replace = attn[0], attn[1:]\n if is_cross:\n alpha_words = self.cross_replace_alpha[self.cur_step]\n attn_replace_new = (\n self.replace_cross_attention(attn_base, attn_replace) * alpha_words\n + (1 - alpha_words) * attn_replace\n )\n attn[1:] = attn_replace_new\n else:\n attn[1:] = self.replace_self_attention(attn_base, attn_replace)\n attn = attn.reshape(self.batch_size * h, *attn.shape[2:])\n return attn\n\n def __init__(\n self,\n prompts,\n num_steps: int,\n cross_replace_steps: Union[float, Tuple[float, float], Dict[str, Tuple[float, float]]],\n self_replace_steps: Union[float, Tuple[float, float]],\n local_blend: Optional[LocalBlend],\n tokenizer,\n device,\n attn_res=None,\n ):\n super(AttentionControlEdit, self).__init__(attn_res=attn_res)\n # add tokenizer and device here\n\n self.tokenizer = tokenizer\n self.device = device\n\n self.batch_size = len(prompts)\n self.cross_replace_alpha = get_time_words_attention_alpha(\n prompts, num_steps, cross_replace_steps, self.tokenizer\n ).to(self.device)\n if isinstance(self_replace_steps, float):\n self_replace_steps = 0, self_replace_steps\n self.num_self_replace = int(num_steps * self_replace_steps[0]), int(num_steps * self_replace_steps[1])\n self.local_blend = local_blend\n\n\nclass AttentionReplace(AttentionControlEdit):\n def replace_cross_attention(self, attn_base, att_replace):\n return torch.einsum(\"hpw,bwn->bhpn\", attn_base, self.mapper)\n\n def __init__(\n self,\n prompts,\n num_steps: int,\n cross_replace_steps: float,\n self_replace_steps: float,\n local_blend: Optional[LocalBlend] = None,\n tokenizer=None,\n device=None,\n attn_res=None,\n ):\n super(AttentionReplace, self).__init__(\n prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend, tokenizer, device, attn_res\n )\n self.mapper = get_replacement_mapper(prompts, self.tokenizer).to(self.device)\n\n\nclass AttentionRefine(AttentionControlEdit):\n def replace_cross_attention(self, attn_base, att_replace):\n attn_base_replace = attn_base[:, :, self.mapper].permute(2, 0, 1, 3)\n attn_replace = attn_base_replace * self.alphas + att_replace * (1 - self.alphas)\n return attn_replace\n\n def __init__(\n self,\n prompts,\n num_steps: int,\n cross_replace_steps: float,\n self_replace_steps: float,\n local_blend: Optional[LocalBlend] = None,\n tokenizer=None,\n device=None,\n attn_res=None\n ):\n super(AttentionRefine, self).__init__(\n prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend, tokenizer, device, attn_res\n )\n self.mapper, alphas = get_refinement_mapper(prompts, self.tokenizer)\n self.mapper, alphas = self.mapper.to(self.device), alphas.to(self.device)\n self.alphas = alphas.reshape(alphas.shape[0], 1, 1, alphas.shape[1])\n\n\nclass AttentionReweight(AttentionControlEdit):\n def replace_cross_attention(self, attn_base, att_replace):\n if self.prev_controller is not None:\n attn_base = self.prev_controller.replace_cross_attention(attn_base, att_replace)\n attn_replace = attn_base[None, :, :, :] * self.equalizer[:, None, None, :]\n return attn_replace\n\n def __init__(\n self,\n prompts,\n num_steps: int,\n cross_replace_steps: float,\n self_replace_steps: float,\n equalizer,\n local_blend: Optional[LocalBlend] = None,\n controller: Optional[AttentionControlEdit] = None,\n tokenizer=None,\n device=None,\n attn_res=None,\n ):\n super(AttentionReweight, self).__init__(\n prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend, tokenizer, device, attn_res\n )\n self.equalizer = equalizer.to(self.device)\n self.prev_controller = controller\n\n\n### util functions for all Edits\ndef update_alpha_time_word(\n alpha, bounds: Union[float, Tuple[float, float]], prompt_ind: int, word_inds: Optional[torch.Tensor] = None\n):\n if isinstance(bounds, float):\n bounds = 0, bounds\n start, end = int(bounds[0] * alpha.shape[0]), int(bounds[1] * alpha.shape[0])\n if word_inds is None:\n word_inds = torch.arange(alpha.shape[2])\n alpha[:start, prompt_ind, word_inds] = 0\n alpha[start:end, prompt_ind, word_inds] = 1\n alpha[end:, prompt_ind, word_inds] = 0\n return alpha\n\n\ndef get_time_words_attention_alpha(\n prompts, num_steps, cross_replace_steps: Union[float, Dict[str, Tuple[float, float]]], tokenizer, max_num_words=77\n):\n if not isinstance(cross_replace_steps, dict):\n cross_replace_steps = {\"default_\": cross_replace_steps}\n if \"default_\" not in cross_replace_steps:\n cross_replace_steps[\"default_\"] = (0.0, 1.0)\n alpha_time_words = torch.zeros(num_steps + 1, len(prompts) - 1, max_num_words)\n for i in range(len(prompts) - 1):\n alpha_time_words = update_alpha_time_word(alpha_time_words, cross_replace_steps[\"default_\"], i)\n for key, item in cross_replace_steps.items():\n if key != \"default_\":\n inds = [get_word_inds(prompts[i], key, tokenizer) for i in range(1, len(prompts))]\n for i, ind in enumerate(inds):\n if len(ind) > 0:\n alpha_time_words = update_alpha_time_word(alpha_time_words, item, i, ind)\n alpha_time_words = alpha_time_words.reshape(num_steps + 1, len(prompts) - 1, 1, 1, max_num_words)\n return alpha_time_words\n\n\n### util functions for LocalBlend and ReplacementEdit\ndef get_word_inds(text: str, word_place: int, tokenizer):\n split_text = text.split(\" \")\n if isinstance(word_place, str):\n word_place = [i for i, word in enumerate(split_text) if word_place == word]\n elif isinstance(word_place, int):\n word_place = [word_place]\n out = []\n if len(word_place) > 0:\n words_encode = [tokenizer.decode([item]).strip(\"#\") for item in tokenizer.encode(text)][1:-1]\n cur_len, ptr = 0, 0\n\n for i in range(len(words_encode)):\n cur_len += len(words_encode[i])\n if ptr in word_place:\n out.append(i + 1)\n if cur_len >= len(split_text[ptr]):\n ptr += 1\n cur_len = 0\n return np.array(out)\n\n\n### util functions for ReplacementEdit\ndef get_replacement_mapper_(x: str, y: str, tokenizer, max_len=77):\n words_x = x.split(\" \")\n words_y = y.split(\" \")\n if len(words_x) != len(words_y):\n raise ValueError(\n f\"attention replacement edit can only be applied on prompts with the same length\"\n f\" but prompt A has {len(words_x)} words and prompt B has {len(words_y)} words.\"\n )\n inds_replace = [i for i in range(len(words_y)) if words_y[i] != words_x[i]]\n inds_source = [get_word_inds(x, i, tokenizer) for i in inds_replace]\n inds_target = [get_word_inds(y, i, tokenizer) for i in inds_replace]\n mapper = np.zeros((max_len, max_len))\n i = j = 0\n cur_inds = 0\n while i < max_len and j < max_len:\n if cur_inds < len(inds_source) and inds_source[cur_inds][0] == i:\n inds_source_, inds_target_ = inds_source[cur_inds], inds_target[cur_inds]\n if len(inds_source_) == len(inds_target_):\n mapper[inds_source_, inds_target_] = 1\n else:\n ratio = 1 / len(inds_target_)\n for i_t in inds_target_:\n mapper[inds_source_, i_t] = ratio\n cur_inds += 1\n i += len(inds_source_)\n j += len(inds_target_)\n elif cur_inds < len(inds_source):\n mapper[i, j] = 1\n i += 1\n j += 1\n else:\n mapper[j, j] = 1\n i += 1\n j += 1\n\n # return torch.from_numpy(mapper).float()\n return torch.from_numpy(mapper).to(torch.float16)\n\n\ndef get_replacement_mapper(prompts, tokenizer, max_len=77):\n x_seq = prompts[0]\n mappers = []\n for i in range(1, len(prompts)):\n mapper = get_replacement_mapper_(x_seq, prompts[i], tokenizer, max_len)\n mappers.append(mapper)\n return torch.stack(mappers)\n\n\n### util functions for ReweightEdit\ndef get_equalizer(\n text: str, word_select: Union[int, Tuple[int, ...]], values: Union[List[float], Tuple[float, ...]], tokenizer\n):\n if isinstance(word_select, (int, str)):\n word_select = (word_select,)\n equalizer = torch.ones(len(values), 77)\n values = torch.tensor(values, dtype=torch.float32)\n for i, word in enumerate(word_select):\n inds = get_word_inds(text, word, tokenizer)\n equalizer[:, inds] = torch.FloatTensor(values[i])\n return equalizer\n\n\n### util functions for RefinementEdit\nclass ScoreParams:\n def __init__(self, gap, match, mismatch):\n self.gap = gap\n self.match = match\n self.mismatch = mismatch\n\n def mis_match_char(self, x, y):\n if x != y:\n return self.mismatch\n else:\n return self.match\n\n\ndef get_matrix(size_x, size_y, gap):\n matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32)\n matrix[0, 1:] = (np.arange(size_y) + 1) * gap\n matrix[1:, 0] = (np.arange(size_x) + 1) * gap\n return matrix\n\n\ndef get_traceback_matrix(size_x, size_y):\n matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32)\n matrix[0, 1:] = 1\n matrix[1:, 0] = 2\n matrix[0, 0] = 4\n return matrix\n\n\ndef global_align(x, y, score):\n matrix = get_matrix(len(x), len(y), score.gap)\n trace_back = get_traceback_matrix(len(x), len(y))\n for i in range(1, len(x) + 1):\n for j in range(1, len(y) + 1):\n left = matrix[i, j - 1] + score.gap\n up = matrix[i - 1, j] + score.gap\n diag = matrix[i - 1, j - 1] + score.mis_match_char(x[i - 1], y[j - 1])\n matrix[i, j] = max(left, up, diag)\n if matrix[i, j] == left:\n trace_back[i, j] = 1\n elif matrix[i, j] == up:\n trace_back[i, j] = 2\n else:\n trace_back[i, j] = 3\n return matrix, trace_back\n\n\ndef get_aligned_sequences(x, y, trace_back):\n x_seq = []\n y_seq = []\n i = len(x)\n j = len(y)\n mapper_y_to_x = []\n while i > 0 or j > 0:\n if trace_back[i, j] == 3:\n x_seq.append(x[i - 1])\n y_seq.append(y[j - 1])\n i = i - 1\n j = j - 1\n mapper_y_to_x.append((j, i))\n elif trace_back[i][j] == 1:\n x_seq.append(\"-\")\n y_seq.append(y[j - 1])\n j = j - 1\n mapper_y_to_x.append((j, -1))\n elif trace_back[i][j] == 2:\n x_seq.append(x[i - 1])\n y_seq.append(\"-\")\n i = i - 1\n elif trace_back[i][j] == 4:\n break\n mapper_y_to_x.reverse()\n return x_seq, y_seq, torch.tensor(mapper_y_to_x, dtype=torch.int64)\n\n\ndef get_mapper(x: str, y: str, tokenizer, max_len=77):\n x_seq = tokenizer.encode(x)\n y_seq = tokenizer.encode(y)\n score = ScoreParams(0, 1, -1)\n matrix, trace_back = global_align(x_seq, y_seq, score)\n mapper_base = get_aligned_sequences(x_seq, y_seq, trace_back)[-1]\n alphas = torch.ones(max_len)\n alphas[: mapper_base.shape[0]] = mapper_base[:, 1].ne(-1).float()\n mapper = torch.zeros(max_len, dtype=torch.int64)\n mapper[: mapper_base.shape[0]] = mapper_base[:, 1]\n mapper[mapper_base.shape[0] :] = len(y_seq) + torch.arange(max_len - len(y_seq))\n return mapper, alphas\n\n\ndef get_refinement_mapper(prompts, tokenizer, max_len=77):\n x_seq = prompts[0]\n mappers, alphas = [], []\n for i in range(1, len(prompts)):\n mapper, alpha = get_mapper(x_seq, prompts[i], tokenizer, max_len)\n mappers.append(mapper)\n alphas.append(alpha)\n return torch.stack(mappers), torch.stack(alphas)\n" }, { "path": "data_generation/prompt_to_prompt_pipeline.py", "content": "from typing import Any, Callable, List\nfrom diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput\nfrom diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipeline\nfrom processors import *\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\nclass Prompt2PromptPipeline(StableDiffusionXLPipeline):\n r\"\"\"\n Args:\n Prompt-to-Prompt-Pipeline for text-to-image generation using Stable Diffusion. This model inherits from\n [`StableDiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for\n all the pipelines (such as downloading or saving, running on a particular device, etc.)\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler\n ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPFeatureExtractor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n\n def check_inputs(\n self,\n prompt,\n prompt_2,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n negative_prompt_2=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n pooled_prompt_embeds=None,\n negative_pooled_prompt_embeds=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if prompt_embeds is not None and pooled_prompt_embeds is None:\n raise ValueError(\n \"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.\"\n )\n\n if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:\n raise ValueError(\n \"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.\"\n )\n\n def _aggregate_and_get_attention_maps_per_token(self, with_softmax):\n attention_maps = self.controller.aggregate_attention(\n from_where=(\"up_cross\", \"down_cross\", \"mid_cross\"),\n # from_where=(\"up\", \"down\"),\n # from_where=(\"down\",)\n )\n attention_maps_list = self._get_attention_maps_list(\n attention_maps=attention_maps, with_softmax=with_softmax\n )\n return attention_maps_list\n\n @staticmethod\n def _get_attention_maps_list(\n attention_maps: torch.Tensor, with_softmax\n ) -> List[torch.Tensor]:\n attention_maps *= 100\n\n if with_softmax:\n attention_maps = torch.nn.functional.softmax(attention_maps, dim=-1)\n\n attention_maps_list = [\n attention_maps[:, :, i] for i in range(attention_maps.shape[2])\n ]\n return attention_maps_list\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n prompt_2: Optional[Union[str, List[str]]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,\n callback_steps: Optional[int] = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Optional[Tuple[int, int]] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Optional[Tuple[int, int]] = None,\n negative_original_size: Optional[Tuple[int, int]] = None,\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\n negative_target_size: Optional[Tuple[int, int]] = None,\n attn_res=None,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.FloatTensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n\n The keyword arguments to configure the edit are:\n - edit_type (`str`). The edit type to apply. Can be either of `replace`, `refine`, `reweight`.\n - n_cross_replace (`int`): Number of diffusion steps in which cross attention should be replaced\n - n_self_replace (`int`): Number of diffusion steps in which self attention should be replaced\n - local_blend_words(`List[str]`, *optional*, default to `None`): Determines which area should be\n changed. If None, then the whole image can be changed.\n - equalizer_words(`List[str]`, *optional*, default to `None`): Required for edit type `reweight`.\n Determines which words should be enhanced.\n - equalizer_strengths (`List[float]`, *optional*, default to `None`) Required for edit type `reweight`.\n Determines which how much the words in `equalizer_words` should be enhanced.\n\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when\n using zero terminal SNR.\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n if attn_res is None:\n attn_res = int(np.ceil(width / 32)), int(np.ceil(height / 32))\n self.attn_res = attn_res\n\n self.controller = create_controller(\n prompt, cross_attention_kwargs, num_inference_steps, tokenizer=self.tokenizer, device=self.device, attn_res=self.attn_res\n )\n self.register_attention_control(self.controller) # add attention controller\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n height,\n width,\n callback_steps,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n latents[1] = latents[0]\n\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Prepare added time ids & embeddings\n add_text_embeds = pooled_prompt_embeds\n add_time_ids = self._get_add_time_ids(\n original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=self.text_encoder_2.config.projection_dim # if none should be changed to enc1\n )\n if negative_original_size is not None and negative_target_size is not None:\n negative_add_time_ids = self._get_add_time_ids(\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype=prompt_embeds.dtype,\n )\n else:\n negative_add_time_ids = add_time_ids\n\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)\n\n # 8. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n\n # 7.1 Apply denoising_end\n if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1:\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds,\n added_cond_kwargs=added_cond_kwargs, ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if do_classifier_free_guidance and guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # step callback\n latents = self.controller.step_callback(latents)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # 8. Post-processing\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n image = latents\n\n if not output_type == \"latent\":\n # apply watermark if available\n if self.watermark is not None:\n image = self.watermark.apply_watermark(image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)\n\n\n def register_attention_control(self, controller):\n attn_procs = {}\n cross_att_count = 0\n for name in self.unet.attn_processors.keys():\n None if name.endswith(\"attn1.processor\") else self.unet.config.cross_attention_dim\n if name.startswith(\"mid_block\"):\n self.unet.config.block_out_channels[-1]\n place_in_unet = \"mid\"\n elif name.startswith(\"up_blocks\"):\n block_id = int(name[len(\"up_blocks.\")])\n list(reversed(self.unet.config.block_out_channels))[block_id]\n place_in_unet = \"up\"\n elif name.startswith(\"down_blocks\"):\n block_id = int(name[len(\"down_blocks.\")])\n self.unet.config.block_out_channels[block_id]\n place_in_unet = \"down\"\n else:\n continue\n cross_att_count += 1\n attn_procs[name] = P2PCrossAttnProcessor(controller=controller, place_in_unet=place_in_unet)\n\n self.unet.set_attn_processor(attn_procs)\n controller.num_att_layers = cross_att_count\n" }, { "path": "data_generation/run_inpainting_multiiple_objects.sh", "content": "#!/bin/bash\nexport NCCL_DEBUG=INFO\nexport PYTHONFAULTHANDLER=1\nexport CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7\nexport INPAINTING=True\ngroundingdino_checkpoint=model/grounding_dino/groundingdino_swinb_cogcoor.pth\ngroundingdino_config_file=.Grounded-Segment-Anything/GroundingDINO/groundingdino/config/GroundingDINO_SwinB.py\nsam_checkpoint=model/sam/sam_vit_h_4b8939.pth\ngd_box_threshold=0.3\ngd_text_threshold=0.25\npython data_generation.py \\\n--data-dircaption_list_w_edit_oblect.jsonl \\\n--output-dir mask_based_sdxl_turbo_p2p_img \\\n--num-process 16 \\\n--image_size 512 \\\n--cuda-device 0 1 2 3 4 5 6 7 \\\n--n_samples 100 \\\n--pipeline_ckpt model/sdxl-turbo \\\n--clip_model model/ViT-L-14.pt \\\n--batch_size 6 \\\n--data_split_start 0 \\\n--data_split_end 100000 \\\n--clip-img-threshold 0.7 \\\n--clip-dir-threshold 0.22 \\\n--dinov2-sim-threshold 0.4 \\\n--max-dinov2-sim-threshold 0.9 \\\n--max-ssim-threshold 0.9 \\\n--max-p2p 0.9 \\\n--do_inpainting \\\n--groundingdino_checkpoint ${groundingdino_checkpoint} \\\n--groundingdino_config_file ${groundingdino_config_file} \\\n--sam_checkpoint ${sam_checkpoint} \\\n--gd_box_threshold ${gd_box_threshold} \\\n--gd_text_threshold ${gd_text_threshold} \\\n--max-out-samples 3 \\\n--seed 4324 \\\n" }, { "path": "data_generation/run_sdxl_turbo_p2p_i2i_8gpu.sh", "content": "#!/bin/bash\nexport NCCL_DEBUG=INFO\nexport PYTHONFAULTHANDLER=1\nexport CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7\npython data_generation.py \\\n--data-dir caption_data.jsonl \\\n--output-dir sdxl_turbo_p2p_img \\\n--num-process 16 \\\n--image_size 512 \\\n--cuda-device 0 1 2 3 4 5 6 7 \\\n--n_samples 30 \\\n--pipeline_ckpt model/sdxl-turbo \\\n--clip_model model/ViT-L-14.pt \\\n--batch_size 7 \\\n--data_split_start 0 \\\n--data_split_end 100000 \\\n--clip-img-threshold 0.75 \\\n--clip-dir-threshold 0.22 \\\n--dinov2-sim-threshold 0.4 \\\n--max-p2p 0.9 \\\n--img2img \\\n\n\n\n" }, { "path": "data_generation/sdxl_p2p_pipeline.py", "content": "from typing import Any, Callable, List\nfrom diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput\nfrom diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipeline, StableDiffusionXLInpaintPipeline\nimport inspect\nimport torch\nfrom diffusers.image_processor import PipelineImageInput\nfrom diffusers.pipelines import StableDiffusionXLImg2ImgPipeline\nfrom util import *\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\nclass Prompt2PromptPipeline(StableDiffusionXLPipeline):\n r\"\"\"\n Args:\n Prompt-to-Prompt-Pipeline for text-to-image generation using Stable Diffusion. This model inherits from\n [`StableDiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for\n all the pipelines (such as downloading or saving, running on a particular device, etc.)\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler\n ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPFeatureExtractor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n\n def check_inputs(\n self,\n prompt,\n prompt_2,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n negative_prompt_2=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n pooled_prompt_embeds=None,\n negative_pooled_prompt_embeds=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if prompt_embeds is not None and pooled_prompt_embeds is None:\n raise ValueError(\n \"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.\"\n )\n\n if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:\n raise ValueError(\n \"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.\"\n )\n\n def _aggregate_and_get_attention_maps_per_token(self, with_softmax):\n attention_maps = self.controller.aggregate_attention(\n from_where=(\"up_cross\", \"down_cross\", \"mid_cross\"),\n # from_where=(\"up\", \"down\"),\n # from_where=(\"down\",)\n )\n attention_maps_list = self._get_attention_maps_list(\n attention_maps=attention_maps, with_softmax=with_softmax\n )\n return attention_maps_list\n\n @staticmethod\n def _get_attention_maps_list(\n attention_maps: torch.Tensor, with_softmax\n ) -> List[torch.Tensor]:\n attention_maps *= 100\n\n if with_softmax:\n attention_maps = torch.nn.functional.softmax(attention_maps, dim=-1)\n\n attention_maps_list = [\n attention_maps[:, :, i] for i in range(attention_maps.shape[2])\n ]\n return attention_maps_list\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n prompt_2: Optional[Union[str, List[str]]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,\n callback_steps: Optional[int] = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Optional[Tuple[int, int]] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Optional[Tuple[int, int]] = None,\n negative_original_size: Optional[Tuple[int, int]] = None,\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\n negative_target_size: Optional[Tuple[int, int]] = None,\n attn_res=None,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.FloatTensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n\n The keyword arguments to configure the edit are:\n - edit_type (`str`). The edit type to apply. Can be either of `replace`, `refine`, `reweight`.\n - n_cross_replace (`int`): Number of diffusion steps in which cross attention should be replaced\n - n_self_replace (`int`): Number of diffusion steps in which self attention should be replaced\n - local_blend_words(`List[str]`, *optional*, default to `None`): Determines which area should be\n changed. If None, then the whole image can be changed.\n - equalizer_words(`List[str]`, *optional*, default to `None`): Required for edit type `reweight`.\n Determines which words should be enhanced.\n - equalizer_strengths (`List[float]`, *optional*, default to `None`) Required for edit type `reweight`.\n Determines which how much the words in `equalizer_words` should be enhanced.\n\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when\n using zero terminal SNR.\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n if attn_res is None:\n attn_res = int(np.ceil(width / 32)), int(np.ceil(height / 32))\n self.attn_res = attn_res\n\n self.controller = create_controller(\n prompt, cross_attention_kwargs, num_inference_steps, tokenizer=self.tokenizer, device=self.device, attn_res=self.attn_res,torch_dtype=self.dtype\n )\n self.register_attention_control(self.controller) # add attention controller\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n height,\n width,\n callback_steps,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n # latents[1] = latents[0]\n latents[1::2] = latents[::2]\n\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Prepare added time ids & embeddings\n add_text_embeds = pooled_prompt_embeds\n add_time_ids = self._get_add_time_ids(\n original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=self.text_encoder_2.config.projection_dim # if none should be changed to enc1\n )\n if negative_original_size is not None and negative_target_size is not None:\n negative_add_time_ids = self._get_add_time_ids(\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype=prompt_embeds.dtype,\n )\n else:\n negative_add_time_ids = add_time_ids\n\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)\n\n # 8. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n\n # 7.1 Apply denoising_end\n if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1:\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds,\n added_cond_kwargs=added_cond_kwargs, ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if do_classifier_free_guidance and guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # step callback\n latents = self.controller.step_callback(latents)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # 8. Post-processing\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n image = latents\n\n if not output_type == \"latent\":\n # apply watermark if available\n if self.watermark is not None:\n image = self.watermark.apply_watermark(image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)\n\n\n def register_attention_control(self, controller):\n attn_procs = {}\n cross_att_count = 0\n for name in self.unet.attn_processors.keys():\n None if name.endswith(\"attn1.processor\") else self.unet.config.cross_attention_dim\n if name.startswith(\"mid_block\"):\n self.unet.config.block_out_channels[-1]\n place_in_unet = \"mid\"\n elif name.startswith(\"up_blocks\"):\n block_id = int(name[len(\"up_blocks.\")])\n list(reversed(self.unet.config.block_out_channels))[block_id]\n place_in_unet = \"up\"\n elif name.startswith(\"down_blocks\"):\n block_id = int(name[len(\"down_blocks.\")])\n self.unet.config.block_out_channels[block_id]\n place_in_unet = \"down\"\n else:\n continue\n cross_att_count += 1\n attn_procs[name] = P2PCrossAttnProcessor(controller=controller, place_in_unet=place_in_unet)\n\n self.unet.set_attn_processor(attn_procs)\n controller.num_att_layers = cross_att_count\n\n\n\n\n\n# Img2Img Pipeline\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used,\n `timesteps` must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default\n timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`\n must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\nclass Prompt2PromptImg2ImgPipeline(StableDiffusionXLImg2ImgPipeline):\n r\"\"\"\n Args:\n Prompt-to-Prompt-Pipeline for text-to-image generation using Stable Diffusion. This model inherits from\n [`StableDiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for\n all the pipelines (such as downloading or saving, running on a particular device, etc.)\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler\n ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPFeatureExtractor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n\n def check_inputs(\n self,\n prompt,\n prompt_2,\n strength,\n num_inference_steps,\n callback_steps,\n negative_prompt=None,\n negative_prompt_2=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n if num_inference_steps is None:\n raise ValueError(\"`num_inference_steps` cannot be None.\")\n elif not isinstance(num_inference_steps, int) or num_inference_steps <= 0:\n raise ValueError(\n f\"`num_inference_steps` has to be a positive integer but is {num_inference_steps} of type\"\n f\" {type(num_inference_steps)}.\"\n )\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n def _aggregate_and_get_attention_maps_per_token(self, with_softmax):\n attention_maps = self.controller.aggregate_attention(\n from_where=(\"up_cross\", \"down_cross\", \"mid_cross\"),\n # from_where=(\"up\", \"down\"),\n # from_where=(\"down\",)\n )\n attention_maps_list = self._get_attention_maps_list(\n attention_maps=attention_maps, with_softmax=with_softmax\n )\n return attention_maps_list\n\n @staticmethod\n def _get_attention_maps_list(\n attention_maps: torch.Tensor, with_softmax\n ) -> List[torch.Tensor]:\n attention_maps *= 100\n\n if with_softmax:\n attention_maps = torch.nn.functional.softmax(attention_maps, dim=-1)\n\n attention_maps_list = [\n attention_maps[:, :, i] for i in range(attention_maps.shape[2])\n ]\n return attention_maps_list\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n prompt_2: Optional[Union[str, List[str]]] = None,\n image: PipelineImageInput = None,\n strength: float = 0.3,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n denoising_start: Optional[float] = None,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,\n callback_steps: Optional[int] = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Optional[Tuple[int, int]] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Optional[Tuple[int, int]] = None,\n negative_original_size: Optional[Tuple[int, int]] = None,\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\n negative_target_size: Optional[Tuple[int, int]] = None,\n attn_res=None,\n aesthetic_score: float = 6.0,\n negative_aesthetic_score: float = 2.5,\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.FloatTensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n\n The keyword arguments to configure the edit are:\n - edit_type (`str`). The edit type to apply. Can be either of `replace`, `refine`, `reweight`.\n - n_cross_replace (`int`): Number of diffusion steps in which cross attention should be replaced\n - n_self_replace (`int`): Number of diffusion steps in which self attention should be replaced\n - local_blend_words(`List[str]`, *optional*, default to `None`): Determines which area should be\n changed. If None, then the whole image can be changed.\n - equalizer_words(`List[str]`, *optional*, default to `None`): Required for edit type `reweight`.\n Determines which words should be enhanced.\n - equalizer_strengths (`List[float]`, *optional*, default to `None`) Required for edit type `reweight`.\n Determines which how much the words in `equalizer_words` should be enhanced.\n\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when\n using zero terminal SNR.\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n\n # 0. Default height and width to unet\n # height = height or self.unet.config.sample_size * self.vae_scale_factor\n # width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # original_size = original_size or (height, width)\n # target_size = target_size or (height, width)\n\n\n\n\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n strength,\n num_inference_steps,\n callback_steps,\n negative_prompt=None,\n negative_prompt_2=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n )\n\n self._guidance_scale = guidance_scale\n self._guidance_rescale = guidance_rescale\n self._cross_attention_kwargs = cross_attention_kwargs\n self._denoising_end = denoising_end\n self._denoising_start = denoising_start\n self._interrupt = False\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n # image = self.image_processor.preprocess(image)\n\n # 4. Preprocess image\n # self.scheduler.set_timesteps(num_inference_steps, device=device)\n # 4. Preprocess image\n try:\n image = self.image_processor.preprocess(image)\n except Exception as e:\n print(e)\n print(\"retry image preprocess\")\n if isinstance(image,list):\n img_list = []\n for img in image:\n img_list.append(self.image_processor.preprocess(img))\n image = torch.concat(img_list)\n \n\n # 5. Prepare timesteps\n # timesteps = self.scheduler.timesteps\n def denoising_value_valid(dnv):\n return isinstance(self.denoising_end, float) and 0 < dnv < 1\n timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)\n timesteps, num_inference_steps = self.get_timesteps(\n num_inference_steps,\n strength,\n device,\n denoising_start=self.denoising_start if denoising_value_valid else None,\n )\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n\n add_noise = True if self.denoising_start is None else False\n\n # 6. Prepare latent variables\n latents = self.prepare_latents(\n image,\n latent_timestep,\n batch_size,\n num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n generator,\n add_noise,\n )\n # latents[1] = latents[0]\n latents[1::2] = latents[::2]\n \n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n \n height, width = latents.shape[-2:]\n height = height * self.vae_scale_factor\n width = width * self.vae_scale_factor\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n if attn_res is None:\n attn_res = int(np.ceil(width / 32)), int(np.ceil(height / 32))\n self.attn_res = attn_res\n \n self.controller = create_controller(\n prompt, cross_attention_kwargs, num_inference_steps, tokenizer=self.tokenizer, device=self.device, attn_res=self.attn_res\n )\n self.register_attention_control(self.controller) # add attention controller \n\n # 8. Prepare added time ids & embeddings\n if negative_original_size is None:\n negative_original_size = original_size\n if negative_target_size is None:\n negative_target_size = target_size\n add_text_embeds = pooled_prompt_embeds\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n add_time_ids, add_neg_time_ids = self._get_add_time_ids(\n\n original_size,\n crops_coords_top_left,\n target_size,\n aesthetic_score,\n negative_aesthetic_score,\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)\n\n # if negative_original_size is not None and negative_target_size is not None:\n # negative_add_time_ids = self._get_add_time_ids(\n # negative_original_size,\n # negative_crops_coords_top_left,\n # negative_target_size,\n # dtype=prompt_embeds.dtype,\n # )\n # else:\n # negative_add_time_ids = add_time_ids\n\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1)\n add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)\n\n # 9. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n\n # 7.1 Apply denoising_end\n if (\n self.denoising_end is not None\n and self.denoising_start is not None\n and denoising_value_valid(self.denoising_end)\n and denoising_value_valid(self.denoising_start)\n and self.denoising_start >= self.denoising_end\n ):\n raise ValueError(\n f\"`denoising_start`: {self.denoising_start} cannot be larger than or equal to `denoising_end`: \"\n + f\" {self.denoising_end} when using type float.\"\n )\n elif self.denoising_end is not None and denoising_value_valid(self.denoising_end):\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (self.denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n # print(\"latent_model_input\", latent_model_input.shape)\n # print(\"t\", t)\n # print(\"prompt_embeds\", prompt_embeds.shape)\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs = cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs, ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if do_classifier_free_guidance and guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # step callback\n latents = self.controller.step_callback(latents)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # 8. Post-processing\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n image = latents\n\n if not output_type == \"latent\":\n # apply watermark if available\n if self.watermark is not None:\n image = self.watermark.apply_watermark(image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)\n\n\n def register_attention_control(self, controller):\n attn_procs = {}\n cross_att_count = 0\n for name in self.unet.attn_processors.keys():\n None if name.endswith(\"attn1.processor\") else self.unet.config.cross_attention_dim\n if name.startswith(\"mid_block\"):\n self.unet.config.block_out_channels[-1]\n place_in_unet = \"mid\"\n elif name.startswith(\"up_blocks\"):\n block_id = int(name[len(\"up_blocks.\")])\n list(reversed(self.unet.config.block_out_channels))[block_id]\n place_in_unet = \"up\"\n elif name.startswith(\"down_blocks\"):\n block_id = int(name[len(\"down_blocks.\")])\n self.unet.config.block_out_channels[block_id]\n place_in_unet = \"down\"\n else:\n continue\n cross_att_count += 1\n attn_procs[name] = P2PCrossAttnProcessor(controller=controller, place_in_unet=place_in_unet)\n\n self.unet.set_attn_processor(attn_procs)\n controller.num_att_layers = cross_att_count\n\n\n\n\n\nclass Prompt2PromptInpaintPipeline(StableDiffusionXLInpaintPipeline):\n r\"\"\"\n Args:\n Prompt-to-Prompt-Pipeline for text-to-image generation using Stable Diffusion. This model inherits from\n [`StableDiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for\n all the pipelines (such as downloading or saving, running on a particular device, etc.)\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler\n ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPFeatureExtractor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n\n def check_inputs(\n self,\n prompt,\n prompt_2,\n image,\n mask_image,\n height,\n width,\n strength,\n callback_steps,\n output_type,\n negative_prompt=None,\n negative_prompt_2=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n padding_mask_crop=None,\n ):\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n if padding_mask_crop is not None:\n if not isinstance(image, PIL.Image.Image):\n raise ValueError(\n f\"The image should be a PIL image when inpainting mask crop, but is of type\" f\" {type(image)}.\"\n )\n if not isinstance(mask_image, PIL.Image.Image):\n raise ValueError(\n f\"The mask image should be a PIL image when inpainting mask crop, but is of type\"\n f\" {type(mask_image)}.\"\n )\n if output_type != \"pil\":\n raise ValueError(f\"The output type should be PIL when inpainting mask crop, but is\" f\" {output_type}.\")\n\n def _aggregate_and_get_attention_maps_per_token(self, with_softmax):\n attention_maps = self.controller.aggregate_attention(\n from_where=(\"up_cross\", \"down_cross\", \"mid_cross\"),\n # from_where=(\"up\", \"down\"),\n # from_where=(\"down\",)\n )\n attention_maps_list = self._get_attention_maps_list(\n attention_maps=attention_maps, with_softmax=with_softmax\n )\n return attention_maps_list\n\n @staticmethod\n def _get_attention_maps_list(\n attention_maps: torch.Tensor, with_softmax\n ) -> List[torch.Tensor]:\n attention_maps *= 100\n\n if with_softmax:\n attention_maps = torch.nn.functional.softmax(attention_maps, dim=-1)\n\n attention_maps_list = [\n attention_maps[:, :, i] for i in range(attention_maps.shape[2])\n ]\n return attention_maps_list\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n prompt_2: Optional[Union[str, List[str]]] = None,\n image: PipelineImageInput = None,\n mask_image: PipelineImageInput = None,\n masked_image_latents: torch.FloatTensor = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n padding_mask_crop: Optional[int] = None,\n strength: float = 0.3,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n denoising_start: Optional[float] = None,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,\n callback_steps: Optional[int] = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Optional[Tuple[int, int]] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Optional[Tuple[int, int]] = None,\n negative_original_size: Optional[Tuple[int, int]] = None,\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\n negative_target_size: Optional[Tuple[int, int]] = None,\n attn_res=None,\n aesthetic_score: float = 6.0,\n negative_aesthetic_score: float = 2.5,\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.FloatTensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n\n The keyword arguments to configure the edit are:\n - edit_type (`str`). The edit type to apply. Can be either of `replace`, `refine`, `reweight`.\n - n_cross_replace (`int`): Number of diffusion steps in which cross attention should be replaced\n - n_self_replace (`int`): Number of diffusion steps in which self attention should be replaced\n - local_blend_words(`List[str]`, *optional*, default to `None`): Determines which area should be\n changed. If None, then the whole image can be changed.\n - equalizer_words(`List[str]`, *optional*, default to `None`): Required for edit type `reweight`.\n Determines which words should be enhanced.\n - equalizer_strengths (`List[float]`, *optional*, default to `None`) Required for edit type `reweight`.\n Determines which how much the words in `equalizer_words` should be enhanced.\n\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when\n using zero terminal SNR.\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # original_size = original_size or (height, width)\n # target_size = target_size or (height, width)\n\n\n\n\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n image,\n mask_image,\n height,\n width,\n strength,\n callback_steps,\n output_type,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n padding_mask_crop,\n )\n\n self._guidance_scale = guidance_scale\n self._guidance_rescale = guidance_rescale\n self._cross_attention_kwargs = cross_attention_kwargs\n self._denoising_end = denoising_end\n self._denoising_start = denoising_start\n self._interrupt = False\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n # image = self.image_processor.preprocess(image)\n\n # 4. Preprocess image\n # self.scheduler.set_timesteps(num_inference_steps, device=device)\n # 4. Preprocess image\n \n # 5. Prepare timesteps\n # timesteps = self.scheduler.timesteps\n def denoising_value_valid(dnv):\n return isinstance(self.denoising_end, float) and 0 < dnv < 1\n timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)\n timesteps, num_inference_steps = self.get_timesteps(\n num_inference_steps,\n strength,\n device,\n denoising_start=self.denoising_start if denoising_value_valid else None,\n )\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n\n is_strength_max = strength == 1.0\n if padding_mask_crop is not None:\n crops_coords = self.mask_processor.get_crop_region(mask_image, width, height, pad=padding_mask_crop)\n resize_mode = \"fill\"\n else:\n crops_coords = None\n resize_mode = \"default\"\n \n \n original_image = image\n # init_image = self.image_processor.preprocess(\n # image, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode\n # )\n \n try:\n init_image = self.image_processor.preprocess(\n image, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode\n )\n except Exception as e:\n print(e)\n print(\"retry image preprocess\")\n if isinstance(image,list):\n img_list = []\n for img in image:\n img_list.append(self.image_processor.preprocess(\n img, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode\n ))\n init_image = torch.concat(img_list)\n \n\n init_image = init_image.to(dtype=torch.float32)\n # mask = self.mask_processor.preprocess(\n # mask_image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords\n # )\n \n try:\n mask = self.mask_processor.preprocess(\n mask_image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords\n )\n except Exception as e:\n print(e)\n print(\"retry mask image preprocess\")\n if isinstance(mask_image,list):\n mask_list = []\n for mask in mask_image:\n mask_list.append(self.mask_processor.preprocess(\n mask, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode\n ))\n mask = torch.concat(mask_list)\n \n if masked_image_latents is not None:\n masked_image = masked_image_latents\n elif init_image.shape[1] == 4:\n # if images are in latent space, we can't mask it\n masked_image = None\n else:\n masked_image = init_image * (mask < 0.5)\n \n\n # 6. Prepare latent variables\n num_channels_latents = self.vae.config.latent_channels\n num_channels_unet = self.unet.config.in_channels\n return_image_latents = num_channels_unet == 4\n add_noise = True if self.denoising_start is None else False\n \n latents_outputs = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n image=init_image,\n timestep=latent_timestep,\n is_strength_max=is_strength_max,\n add_noise=add_noise,\n return_noise=True,\n return_image_latents=return_image_latents, \n )\n \n if return_image_latents:\n latents, noise, image_latents = latents_outputs\n else:\n latents, noise = latents_outputs\n \n # latents[1] = latents[0]\n latents[1::2] = latents[::2]\n \n # 7. Prepare mask latent variables\n mask, masked_image_latents = self.prepare_mask_latents(\n mask,\n masked_image,\n batch_size * num_images_per_prompt,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n self.do_classifier_free_guidance,\n )\n mask[1::2] = mask[::2]\n \n if num_channels_unet == 9:\n # default case for runwayml/stable-diffusion-inpainting\n num_channels_mask = mask.shape[1]\n num_channels_masked_image = masked_image_latents.shape[1]\n if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:\n raise ValueError(\n f\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\"\n f\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\"\n f\" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}\"\n f\" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of\"\n \" `pipeline.unet` or your `mask_image` or `image` input.\"\n )\n elif num_channels_unet != 4:\n raise ValueError(\n f\"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}.\"\n )\n \n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n \n height, width = latents.shape[-2:]\n \n height = height * self.vae_scale_factor\n width = width * self.vae_scale_factor\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n \n \n \n if attn_res is None:\n attn_res = int(np.ceil(width / 32)), int(np.ceil(height / 32))\n self.attn_res = attn_res\n \n self.controller = create_controller(\n prompt, cross_attention_kwargs, num_inference_steps, tokenizer=self.tokenizer, device=self.device, attn_res=self.attn_res\n )\n self.register_attention_control(self.controller) # add attention controller \n\n # 8. Prepare added time ids & embeddings\n if negative_original_size is None:\n negative_original_size = original_size\n if negative_target_size is None:\n negative_target_size = target_size\n add_text_embeds = pooled_prompt_embeds\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n add_time_ids, add_neg_time_ids = self._get_add_time_ids(\n\n original_size,\n crops_coords_top_left,\n target_size,\n aesthetic_score,\n negative_aesthetic_score,\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)\n\n # if negative_original_size is not None and negative_target_size is not None:\n # negative_add_time_ids = self._get_add_time_ids(\n # negative_original_size,\n # negative_crops_coords_top_left,\n # negative_target_size,\n # dtype=prompt_embeds.dtype,\n # )\n # else:\n # negative_add_time_ids = add_time_ids\n\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1)\n add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)\n\n # 9. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n\n # 7.1 Apply denoising_end\n if (\n self.denoising_end is not None\n and self.denoising_start is not None\n and denoising_value_valid(self.denoising_end)\n and denoising_value_valid(self.denoising_start)\n and self.denoising_start >= self.denoising_end\n ):\n raise ValueError(\n f\"`denoising_start`: {self.denoising_start} cannot be larger than or equal to `denoising_end`: \"\n + f\" {self.denoising_end} when using type float.\"\n )\n elif self.denoising_end is not None and denoising_value_valid(self.denoising_end):\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (self.denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n self._num_timesteps = len(timesteps)\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n\n temp_mask = kwargs.get(\"temp_mask\", None)\n if temp_mask is not None:\n temp_mask = self.mask_processor.preprocess(\n temp_mask, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords\n )\n temp_masked_image = init_image * (temp_mask < 0.5)\n temp_mask, temp_masked_image_latents = self.prepare_mask_latents(\n temp_mask,\n temp_masked_image,\n batch_size * num_images_per_prompt,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n self.do_classifier_free_guidance,\n )\n temp_mask[1::2] = temp_mask[::2]\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n if self.interrupt:\n continue\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n # print(\"latent_model_input\", latent_model_input.shape)\n # print(\"t\", t)\n # print(\"prompt_embeds\", prompt_embeds.shape)\n if num_channels_unet == 9:\n latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)\n \n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs = cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs, ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if do_classifier_free_guidance and guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n if num_channels_unet == 4:\n init_latents_proper = image_latents\n if do_classifier_free_guidance:\n init_mask, _ = mask.chunk(2)\n else:\n init_mask = mask\n \n if temp_mask is not None:\n if do_classifier_free_guidance:\n init_temp_mask, _ = temp_mask.chunk(2)\n else:\n init_temp_mask = temp_mask\n \n\n if i < len(timesteps) - 1:\n noise_timestep = timesteps[i + 1]\n init_latents_proper = self.scheduler.add_noise(\n init_latents_proper, noise, torch.tensor([noise_timestep])\n )\n # 50% to do the mask:\n # import random\n # mask_choice = mask_choice = random.random() > 0.5\n # print(init_mask.shape)\n # if mask_choice:\n # if i %2 == 1:\n # get soft_mask from kwargs\n soft_mask = kwargs.get(\"soft_mask\", None)\n mask_choice = kwargs.get(\"mask_choice\", None)\n if mask_choice is None:\n if i % 2 == 1 :\n if temp_mask is not None and soft_mask is not None :\n init_mask[~(init_mask== init_temp_mask)] = soft_mask\n latents = (1 - init_mask) * init_latents_proper + init_mask * latents\n elif mask_choice == \"wo_final_layer\":\n if i != len(timesteps) - 1:\n if temp_mask is not None and soft_mask is not None :\n init_mask[~(init_mask== init_temp_mask)] = soft_mask\n latents = (1 - init_mask) * init_latents_proper + init_mask * latents\n elif mask_choice == \"wo_final_two_layer\":\n if i < len(timesteps) - 2:\n if temp_mask is not None and soft_mask is not None :\n init_mask[~(init_mask== init_temp_mask)] = soft_mask\n latents = (1 - init_mask) * init_latents_proper + init_mask * latents\n elif mask_choice == \"mask_first_layer\":\n if i ==0 or i == len(timesteps) - 2:\n if temp_mask is not None and soft_mask is not None :\n init_mask[~(init_mask== init_temp_mask)] = soft_mask\n latents = (1 - init_mask) * init_latents_proper + init_mask * latents\n \n \n # step callback\n latents = self.controller.step_callback(latents)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # 8. Post-processing\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n image = latents\n\n if not output_type == \"latent\":\n # apply watermark if available\n if self.watermark is not None:\n image = self.watermark.apply_watermark(image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n \n if padding_mask_crop is not None:\n image = [self.image_processor.apply_overlay(mask_image, original_image, i, crops_coords) for i in image]\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)\n\n\n def register_attention_control(self, controller):\n attn_procs = {}\n cross_att_count = 0\n for name in self.unet.attn_processors.keys():\n None if name.endswith(\"attn1.processor\") else self.unet.config.cross_attention_dim\n if name.startswith(\"mid_block\"):\n self.unet.config.block_out_channels[-1]\n place_in_unet = \"mid\"\n elif name.startswith(\"up_blocks\"):\n block_id = int(name[len(\"up_blocks.\")])\n list(reversed(self.unet.config.block_out_channels))[block_id]\n place_in_unet = \"up\"\n elif name.startswith(\"down_blocks\"):\n block_id = int(name[len(\"down_blocks.\")])\n self.unet.config.block_out_channels[block_id]\n place_in_unet = \"down\"\n else:\n continue\n cross_att_count += 1\n attn_procs[name] = P2PCrossAttnProcessor(controller=controller, place_in_unet=place_in_unet)\n\n self.unet.set_attn_processor(attn_procs)\n controller.num_att_layers = cross_att_count\n" }, { "path": "data_generation/util.py", "content": "from __future__ import annotations\n\nimport abc\nfrom typing import Dict, List, Optional, Tuple, Union\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom diffusers.models.attention import Attention\n\n\nclass P2PCrossAttnProcessor:\n def __init__(self, controller, place_in_unet):\n super().__init__()\n self.controller = controller\n self.place_in_unet = place_in_unet\n\n def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, **kwargs):\n batch_size, sequence_length, _ = hidden_states.shape\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n\n query = attn.to_q(hidden_states)\n\n is_cross = encoder_hidden_states is not None\n encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n attention_probs = attn.get_attention_scores(query, key, attention_mask)\n\n # one line change\n self.controller(attention_probs, is_cross, self.place_in_unet)\n\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n return hidden_states\n\n\ndef create_controller(\n prompts: List[str], cross_attention_kwargs: Dict, num_inference_steps: int, tokenizer, device, attn_res,torch_dtype=torch.float32\n) -> AttentionControl:\n edit_type = cross_attention_kwargs.get(\"edit_type\", None)\n local_blend_words = cross_attention_kwargs.get(\"local_blend_words\", None)\n equalizer_words = cross_attention_kwargs.get(\"equalizer_words\", None)\n equalizer_strengths = cross_attention_kwargs.get(\"equalizer_strengths\", None)\n n_cross_replace = cross_attention_kwargs.get(\"n_cross_replace\", 0.4)\n n_self_replace = cross_attention_kwargs.get(\"n_self_replace\", 0.4)\n\n # only replace\n if edit_type == \"replace\" and local_blend_words is None:\n return AttentionReplace(\n prompts, num_inference_steps, n_cross_replace, n_self_replace, tokenizer=tokenizer, device=device, attn_res=attn_res,torch_dtype=torch_dtype\n )\n\n # replace + localblend\n if edit_type == \"replace\" and local_blend_words is not None:\n lb = LocalBlend(prompts, local_blend_words, tokenizer=tokenizer, device=device, attn_res=attn_res)\n return AttentionReplace(\n prompts, num_inference_steps, n_cross_replace, n_self_replace, lb, tokenizer=tokenizer, device=device, attn_res=attn_res,torch_dtype=torch_dtype\n )\n\n # only refine\n if edit_type == \"refine\" and local_blend_words is None:\n return AttentionRefine(\n prompts, num_inference_steps, n_cross_replace, n_self_replace, tokenizer=tokenizer, device=device, attn_res=attn_res\n )\n\n # refine + localblend\n if edit_type == \"refine\" and local_blend_words is not None:\n lb = LocalBlend(prompts, local_blend_words, tokenizer=tokenizer, device=device, attn_res=attn_res)\n return AttentionRefine(\n prompts, num_inference_steps, n_cross_replace, n_self_replace, lb, tokenizer=tokenizer, device=device, attn_res=attn_res\n )\n\n # only reweight\n if edit_type == \"reweight\" and local_blend_words is None:\n assert (\n equalizer_words is not None and equalizer_strengths is not None\n ), \"To use reweight edit, please specify equalizer_words and equalizer_strengths.\"\n assert len(equalizer_words) == len(\n equalizer_strengths\n ), \"equalizer_words and equalizer_strengths must be of same length.\"\n equalizer = get_equalizer(prompts[1], equalizer_words, equalizer_strengths, tokenizer=tokenizer)\n return AttentionReweight(\n prompts,\n num_inference_steps,\n n_cross_replace,\n n_self_replace,\n tokenizer=tokenizer,\n device=device,\n equalizer=equalizer,\n attn_res=attn_res,\n )\n\n # reweight and localblend\n if edit_type == \"reweight\" and local_blend_words:\n assert (\n equalizer_words is not None and equalizer_strengths is not None\n ), \"To use reweight edit, please specify equalizer_words and equalizer_strengths.\"\n assert len(equalizer_words) == len(\n equalizer_strengths\n ), \"equalizer_words and equalizer_strengths must be of same length.\"\n equalizer = get_equalizer(prompts[1], equalizer_words, equalizer_strengths, tokenizer=tokenizer)\n lb = LocalBlend(prompts, local_blend_words, tokenizer=tokenizer, device=device, attn_res=attn_res)\n return AttentionReweight(\n prompts,\n num_inference_steps,\n n_cross_replace,\n n_self_replace,\n tokenizer=tokenizer,\n device=device,\n equalizer=equalizer,\n attn_res=attn_res,\n local_blend=lb,\n )\n\n raise ValueError(f\"Edit type {edit_type} not recognized. Use one of: replace, refine, reweight.\")\n\n\nclass AttentionControl(abc.ABC):\n def step_callback(self, x_t):\n return x_t\n\n def between_steps(self):\n return\n\n @property\n def num_uncond_att_layers(self):\n return 0\n\n @abc.abstractmethod\n def forward(self, attn, is_cross: bool, place_in_unet: str):\n raise NotImplementedError\n\n def __call__(self, attn, is_cross: bool, place_in_unet: str):\n if self.cur_att_layer >= self.num_uncond_att_layers:\n h = attn.shape[0]\n attn[h // 2 :] = self.forward(attn[h // 2 :], is_cross, place_in_unet)\n self.cur_att_layer += 1\n if self.cur_att_layer == self.num_att_layers + self.num_uncond_att_layers:\n self.cur_att_layer = 0\n self.cur_step += 1\n self.between_steps()\n return attn\n\n def reset(self):\n self.cur_step = 0\n self.cur_att_layer = 0\n\n def __init__(self, attn_res=None):\n self.cur_step = 0\n self.num_att_layers = -1\n self.cur_att_layer = 0\n self.attn_res = attn_res\n\n\nclass EmptyControl(AttentionControl):\n def forward(self, attn, is_cross: bool, place_in_unet: str):\n return attn\n\n\nclass AttentionStore(AttentionControl):\n @staticmethod\n def get_empty_store():\n return {\"down_cross\": [], \"mid_cross\": [], \"up_cross\": [], \"down_self\": [], \"mid_self\": [], \"up_self\": []}\n\n def forward(self, attn, is_cross: bool, place_in_unet: str):\n key = f\"{place_in_unet}_{'cross' if is_cross else 'self'}\"\n if attn.shape[1] <= 32**2: # avoid memory overhead\n self.step_store[key].append(attn)\n return attn\n\n def between_steps(self):\n if len(self.attention_store) == 0:\n self.attention_store = self.step_store\n else:\n for key in self.attention_store:\n for i in range(len(self.attention_store[key])):\n self.attention_store[key][i] += self.step_store[key][i]\n self.step_store = self.get_empty_store()\n\n def get_average_attention(self):\n average_attention = {\n key: [item / self.cur_step for item in self.attention_store[key]] for key in self.attention_store\n }\n return average_attention\n\n def reset(self):\n super(AttentionStore, self).reset()\n self.step_store = self.get_empty_store()\n self.attention_store = {}\n\n def __init__(self, attn_res=None):\n super(AttentionStore, self).__init__(attn_res)\n self.step_store = self.get_empty_store()\n self.attention_store = {}\n\n\n\nclass LocalBlend:\n def __call__(self, x_t, attention_store):\n # note that this code works on the latent level!\n k = 1\n # maps = attention_store[\"down_cross\"][2:4] + attention_store[\"up_cross\"][:3] # These are the numbers because we want to take layers that are 256 x 256, I think this can be changed to something smarter...like, get all attentions where thesecond dim is self.attn_res[0] * self.attn_res[1] in up and down cross.\n maps = [m for m in attention_store[\"down_cross\"] + attention_store[\"mid_cross\"] + attention_store[\"up_cross\"] if m.shape[1] == self.attn_res[0] * self.attn_res[1]]\n maps = [item.reshape(self.alpha_layers.shape[0], -1, 1, self.attn_res[0], self.attn_res[1], self.max_num_words) for item in maps]\n maps = torch.cat(maps, dim=1)\n maps = (maps * self.alpha_layers).sum(-1).mean(1) # since alpha_layers is all 0s except where we edit, the product zeroes out all but what we change. Then, the sum adds the values of the original and what we edit. Then, we average across dim=1, which is the number of layers.\n mask = F.max_pool2d(maps, (k * 2 + 1, k * 2 + 1), (1, 1), padding=(k, k))\n mask = F.interpolate(mask, size=(x_t.shape[2:]))\n mask = mask / mask.max(2, keepdims=True)[0].max(3, keepdims=True)[0]\n mask = mask.gt(self.threshold)\n\n mask = mask[:1] + mask[1:]\n mask = mask.to(torch.float)\n\n x_t = x_t[:1] + mask * (x_t - x_t[:1]) # x_t[:1] is the original image. mask*(x_t - x_t[:1]) zeroes out the original image and removes the difference between the original and each image we are generating (mostly just one). Then, it applies the mask on the image. That is, it's only keeping the cells we want to generate.\n return x_t\n\n def __init__(\n self, prompts: List[str], words: [List[List[str]]], tokenizer, device, threshold=0.3, attn_res=None\n ):\n self.max_num_words = 77\n self.attn_res = attn_res\n\n alpha_layers = torch.zeros(len(prompts), 1, 1, 1, 1, self.max_num_words)\n for i, (prompt, words_) in enumerate(zip(prompts, words)):\n if isinstance(words_, str):\n words_ = [words_]\n for word in words_:\n ind = get_word_inds(prompt, word, tokenizer)\n alpha_layers[i, :, :, :, :, ind] = 1\n self.alpha_layers = alpha_layers.to(device) # a one-hot vector where the 1s are the words we modify (source and target)\n self.threshold = threshold\n\n\nclass AttentionControlEdit(AttentionStore, abc.ABC):\n def step_callback(self, x_t):\n if self.local_blend is not None:\n x_t = self.local_blend(x_t, self.attention_store)\n return x_t\n\n def replace_self_attention(self, attn_base, att_replace):\n if att_replace.shape[2] <= self.attn_res[0]**2:\n return attn_base.unsqueeze(0).expand(att_replace.shape[0], *attn_base.shape)\n else:\n return att_replace\n\n @abc.abstractmethod\n def replace_cross_attention(self, attn_base, att_replace):\n raise NotImplementedError\n\n def forward(self, attn, is_cross: bool, place_in_unet: str):\n super(AttentionControlEdit, self).forward(attn, is_cross, place_in_unet)\n if is_cross or (self.num_self_replace[0] <= self.cur_step < self.num_self_replace[1]):\n h = attn.shape[0] // (self.batch_size)\n attn = attn.reshape(self.batch_size, h, *attn.shape[1:])\n attn_base, attn_replace = attn[0], attn[1:]\n if is_cross:\n alpha_words = self.cross_replace_alpha[self.cur_step]\n attn_replace_new = (\n self.replace_cross_attention(attn_base, attn_replace) * alpha_words\n + (1 - alpha_words) * attn_replace\n )\n attn[1:] = attn_replace_new\n else:\n attn[1:] = self.replace_self_attention(attn_base, attn_replace)\n attn = attn.reshape(self.batch_size * h, *attn.shape[2:])\n return attn\n\n def __init__(\n self,\n prompts,\n num_steps: int,\n cross_replace_steps: Union[float, Tuple[float, float], Dict[str, Tuple[float, float]]],\n self_replace_steps: Union[float, Tuple[float, float]],\n local_blend: Optional[LocalBlend],\n tokenizer,\n device,\n attn_res=None,\n ):\n super(AttentionControlEdit, self).__init__(attn_res=attn_res)\n # add tokenizer and device here\n\n self.tokenizer = tokenizer\n self.device = device\n\n self.batch_size = len(prompts)\n self.cross_replace_alpha = get_time_words_attention_alpha(\n prompts, num_steps, cross_replace_steps, self.tokenizer\n ).to(self.device)\n if isinstance(self_replace_steps, float):\n self_replace_steps = 0, self_replace_steps\n self.num_self_replace = int(num_steps * self_replace_steps[0]), int(num_steps * self_replace_steps[1])\n self.local_blend = local_blend\n\n\nclass AttentionReplace(AttentionControlEdit):\n def replace_cross_attention(self, attn_base, att_replace):\n # print(attn_base.dtype, att_replace.dtype, self.mapper.dtype)\n if self.mapper.dtype != attn_base.dtype:\n self.mapper = self.mapper.to(attn_base.dtype)\n return torch.einsum(\"hpw,bwn->bhpn\", attn_base, self.mapper)\n\n def __init__(\n self,\n prompts,\n num_steps: int,\n cross_replace_steps: float,\n self_replace_steps: float,\n local_blend: Optional[LocalBlend] = None,\n tokenizer=None,\n device=None,\n attn_res=None,\n torch_dtype=torch.float32\n ):\n super(AttentionReplace, self).__init__(\n prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend, tokenizer, device, attn_res\n )\n self.mapper = get_replacement_mapper(prompts, self.tokenizer,torch_dtype=torch_dtype).to(self.device)\n\n\nclass AttentionRefine(AttentionControlEdit):\n def replace_cross_attention(self, attn_base, att_replace):\n attn_base_replace = attn_base[:, :, self.mapper].permute(2, 0, 1, 3)\n attn_replace = attn_base_replace * self.alphas + att_replace * (1 - self.alphas)\n return attn_replace\n\n def __init__(\n self,\n prompts,\n num_steps: int,\n cross_replace_steps: float,\n self_replace_steps: float,\n local_blend: Optional[LocalBlend] = None,\n tokenizer=None,\n device=None,\n attn_res=None\n ):\n super(AttentionRefine, self).__init__(\n prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend, tokenizer, device, attn_res\n )\n self.mapper, alphas = get_refinement_mapper(prompts, self.tokenizer)\n self.mapper, alphas = self.mapper.to(self.device), alphas.to(self.device)\n self.alphas = alphas.reshape(alphas.shape[0], 1, 1, alphas.shape[1])\n\n\nclass AttentionReweight(AttentionControlEdit):\n def replace_cross_attention(self, attn_base, att_replace):\n if self.prev_controller is not None:\n attn_base = self.prev_controller.replace_cross_attention(attn_base, att_replace)\n attn_replace = attn_base[None, :, :, :] * self.equalizer[:, None, None, :]\n return attn_replace\n\n def __init__(\n self,\n prompts,\n num_steps: int,\n cross_replace_steps: float,\n self_replace_steps: float,\n equalizer,\n local_blend: Optional[LocalBlend] = None,\n controller: Optional[AttentionControlEdit] = None,\n tokenizer=None,\n device=None,\n attn_res=None,\n ):\n super(AttentionReweight, self).__init__(\n prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend, tokenizer, device, attn_res\n )\n self.equalizer = equalizer.to(self.device)\n self.prev_controller = controller\n\n\n### util functions for all Edits\ndef update_alpha_time_word(\n alpha, bounds: Union[float, Tuple[float, float]], prompt_ind: int, word_inds: Optional[torch.Tensor] = None\n):\n if isinstance(bounds, float):\n bounds = 0, bounds\n start, end = int(bounds[0] * alpha.shape[0]), int(bounds[1] * alpha.shape[0])\n if word_inds is None:\n word_inds = torch.arange(alpha.shape[2])\n alpha[:start, prompt_ind, word_inds] = 0\n alpha[start:end, prompt_ind, word_inds] = 1\n alpha[end:, prompt_ind, word_inds] = 0\n return alpha\n\n\ndef get_time_words_attention_alpha(\n prompts, num_steps, cross_replace_steps: Union[float, Dict[str, Tuple[float, float]]], tokenizer, max_num_words=77\n):\n if not isinstance(cross_replace_steps, dict):\n cross_replace_steps = {\"default_\": cross_replace_steps}\n if \"default_\" not in cross_replace_steps:\n cross_replace_steps[\"default_\"] = (0.0, 1.0)\n alpha_time_words = torch.zeros(num_steps + 1, len(prompts) - 1, max_num_words)\n for i in range(len(prompts) - 1):\n alpha_time_words = update_alpha_time_word(alpha_time_words, cross_replace_steps[\"default_\"], i)\n for key, item in cross_replace_steps.items():\n if key != \"default_\":\n inds = [get_word_inds(prompts[i], key, tokenizer) for i in range(1, len(prompts))]\n for i, ind in enumerate(inds):\n if len(ind) > 0:\n alpha_time_words = update_alpha_time_word(alpha_time_words, item, i, ind)\n alpha_time_words = alpha_time_words.reshape(num_steps + 1, len(prompts) - 1, 1, 1, max_num_words)\n return alpha_time_words\n\n\n### util functions for LocalBlend and ReplacementEdit\ndef get_word_inds(text: str, word_place: int, tokenizer):\n split_text = text.split(\" \")\n if isinstance(word_place, str):\n word_place = [i for i, word in enumerate(split_text) if word_place == word]\n elif isinstance(word_place, int):\n word_place = [word_place]\n out = []\n if len(word_place) > 0:\n words_encode = [tokenizer.decode([item]).strip(\"#\") for item in tokenizer.encode(text)][1:-1]\n cur_len, ptr = 0, 0\n\n for i in range(len(words_encode)):\n cur_len += len(words_encode[i])\n if ptr in word_place:\n out.append(i + 1)\n if cur_len >= len(split_text[ptr]):\n ptr += 1\n cur_len = 0\n return np.array(out)\n\n\n### util functions for ReplacementEdit\ndef get_replacement_mapper_(x: str, y: str, tokenizer, max_len=77):\n words_x = x.split(\" \")\n words_y = y.split(\" \")\n # print(words_x, words_y)\n if len(words_x) != len(words_y):\n raise ValueError(\n f\"attention replacement edit can only be applied on prompts with the same length\"\n f\" but prompt A has {len(words_x)} words and prompt B has {len(words_y)} words.\"\n )\n inds_replace = [i for i in range(len(words_y)) if words_y[i] != words_x[i]]\n inds_source = [get_word_inds(x, i, tokenizer) for i in inds_replace]\n inds_target = [get_word_inds(y, i, tokenizer) for i in inds_replace]\n mapper = np.zeros((max_len, max_len))\n i = j = 0\n cur_inds = 0\n while i < max_len and j < max_len:\n if cur_inds < len(inds_source) and inds_source[cur_inds][0] == i:\n inds_source_, inds_target_ = inds_source[cur_inds], inds_target[cur_inds]\n if len(inds_source_) == len(inds_target_):\n mapper[inds_source_, inds_target_] = 1\n else:\n ratio = 1 / len(inds_target_)\n for i_t in inds_target_:\n mapper[inds_source_, i_t] = ratio\n cur_inds += 1\n i += len(inds_source_)\n j += len(inds_target_)\n elif cur_inds < len(inds_source):\n mapper[i, j] = 1\n i += 1\n j += 1\n else:\n mapper[j, j] = 1\n i += 1\n j += 1\n\n # return torch.from_numpy(mapper).float()\n return torch.from_numpy(mapper).to(torch.float)\n\n\ndef get_replacement_mapper(prompts, tokenizer, max_len=77,torch_dtype=torch.float32):\n x_seq = prompts[0]\n mappers = []\n for i in range(1, len(prompts)):\n mapper = get_replacement_mapper_(x_seq, prompts[i], tokenizer, max_len)\n mappers.append(mapper)\n return torch.stack(mappers).to(torch_dtype)\n# def get_replacement_mapper(prompts, tokenizer, max_len=77,torch_dtype=torch.float32): # modify to get replacement_mapper every two prompts\n# mappers = []\n# for i in range(0, len(prompts), 2): # start from 0 and increment by 2 [0, 2, 4, 6, ...]\n# if i+1 < len(prompts): # ensure that there always exists a pair of tokens\n# mapper = get_replacement_mapper_(prompts[i], prompts[i+1], tokenizer, max_len)\n# mappers.append(mapper)\n# return torch.stack(mappers).to(torch_dtype)\n\n\n\n### util functions for ReweightEdit\ndef get_equalizer(\n text: str, word_select: Union[int, Tuple[int, ...]], values: Union[List[float], Tuple[float, ...]], tokenizer\n):\n if isinstance(word_select, (int, str)):\n word_select = (word_select,)\n equalizer = torch.ones(len(values), 77)\n values = torch.tensor(values, dtype=torch.float32)\n for i, word in enumerate(word_select):\n inds = get_word_inds(text, word, tokenizer)\n equalizer[:, inds] = torch.FloatTensor(values[i])\n return equalizer\n\n\n### util functions for RefinementEdit\nclass ScoreParams:\n def __init__(self, gap, match, mismatch):\n self.gap = gap\n self.match = match\n self.mismatch = mismatch\n\n def mis_match_char(self, x, y):\n if x != y:\n return self.mismatch\n else:\n return self.match\n\n\ndef get_matrix(size_x, size_y, gap):\n matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32)\n matrix[0, 1:] = (np.arange(size_y) + 1) * gap\n matrix[1:, 0] = (np.arange(size_x) + 1) * gap\n return matrix\n\n\ndef get_traceback_matrix(size_x, size_y):\n matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32)\n matrix[0, 1:] = 1\n matrix[1:, 0] = 2\n matrix[0, 0] = 4\n return matrix\n\n\ndef global_align(x, y, score):\n matrix = get_matrix(len(x), len(y), score.gap)\n trace_back = get_traceback_matrix(len(x), len(y))\n for i in range(1, len(x) + 1):\n for j in range(1, len(y) + 1):\n left = matrix[i, j - 1] + score.gap\n up = matrix[i - 1, j] + score.gap\n diag = matrix[i - 1, j - 1] + score.mis_match_char(x[i - 1], y[j - 1])\n matrix[i, j] = max(left, up, diag)\n if matrix[i, j] == left:\n trace_back[i, j] = 1\n elif matrix[i, j] == up:\n trace_back[i, j] = 2\n else:\n trace_back[i, j] = 3\n return matrix, trace_back\n\n\ndef get_aligned_sequences(x, y, trace_back):\n x_seq = []\n y_seq = []\n i = len(x)\n j = len(y)\n mapper_y_to_x = []\n while i > 0 or j > 0:\n if trace_back[i, j] == 3:\n x_seq.append(x[i - 1])\n y_seq.append(y[j - 1])\n i = i - 1\n j = j - 1\n mapper_y_to_x.append((j, i))\n elif trace_back[i][j] == 1:\n x_seq.append(\"-\")\n y_seq.append(y[j - 1])\n j = j - 1\n mapper_y_to_x.append((j, -1))\n elif trace_back[i][j] == 2:\n x_seq.append(x[i - 1])\n y_seq.append(\"-\")\n i = i - 1\n elif trace_back[i][j] == 4:\n break\n mapper_y_to_x.reverse()\n return x_seq, y_seq, torch.tensor(mapper_y_to_x, dtype=torch.int64)\n\n\ndef get_mapper(x: str, y: str, tokenizer, max_len=77):\n x_seq = tokenizer.encode(x)\n y_seq = tokenizer.encode(y)\n score = ScoreParams(0, 1, -1)\n matrix, trace_back = global_align(x_seq, y_seq, score)\n mapper_base = get_aligned_sequences(x_seq, y_seq, trace_back)[-1]\n alphas = torch.ones(max_len)\n alphas[: mapper_base.shape[0]] = mapper_base[:, 1].ne(-1).float()\n mapper = torch.zeros(max_len, dtype=torch.int64)\n mapper[: mapper_base.shape[0]] = mapper_base[:, 1]\n mapper[mapper_base.shape[0] :] = len(y_seq) + torch.arange(max_len - len(y_seq))\n return mapper, alphas\n\n\ndef get_refinement_mapper(prompts, tokenizer, max_len=77):\n x_seq = prompts[0]\n mappers, alphas = [], []\n for i in range(1, len(prompts)):\n mapper, alpha = get_mapper(x_seq, prompts[i], tokenizer, max_len)\n mappers.append(mapper)\n alphas.append(alpha)\n return torch.stack(mappers), torch.stack(alphas)\n\n\n\n" }, { "path": "diffusers/.github/ISSUE_TEMPLATE/bug-report.yml", "content": "name: \"\\U0001F41B Bug Report\"\ndescription: Report a bug on Diffusers\nlabels: [ \"bug\" ]\nbody:\n - type: markdown\n attributes:\n value: |\n Thanks a lot for taking the time to file this issue 🤗.\n Issues do not only help to improve the library, but also publicly document common problems, questions, workflows for the whole community!\n Thus, issues are of the same importance as pull requests when contributing to this library ❤️.\n In order to make your issue as **useful for the community as possible**, let's try to stick to some simple guidelines:\n - 1. Please try to be as precise and concise as possible.\n *Give your issue a fitting title. Assume that someone which very limited knowledge of Diffusers can understand your issue. Add links to the source code, documentation other issues, pull requests etc...*\n - 2. If your issue is about something not working, **always** provide a reproducible code snippet. The reader should be able to reproduce your issue by **only copy-pasting your code snippet into a Python shell**.\n *The community cannot solve your issue if it cannot reproduce it. If your bug is related to training, add your training script and make everything needed to train public. Otherwise, just add a simple Python code snippet.*\n - 3. Add the **minimum** amount of code / context that is needed to understand, reproduce your issue.\n *Make the life of maintainers easy. `diffusers` is getting many issues every day. Make sure your issue is about one bug and one bug only. Make sure you add only the context, code needed to understand your issues - nothing more. Generally, every issue is a way of documenting this library, try to make it a good documentation entry.*\n - 4. For issues related to community pipelines (i.e., the pipelines located in the `examples/community` folder), please tag the author of the pipeline in your issue thread as those pipelines are not maintained.\n - type: markdown\n attributes:\n value: |\n For more in-detail information on how to write good issues you can have a look [here](https://huggingface.co/course/chapter8/5?fw=pt).\n - type: textarea\n id: bug-description\n attributes:\n label: Describe the bug\n description: A clear and concise description of what the bug is. If you intend to submit a pull request for this issue, tell us in the description. Thanks!\n placeholder: Bug description\n validations:\n required: true\n - type: textarea\n id: reproduction\n attributes:\n label: Reproduction\n description: Please provide a minimal reproducible code which we can copy/paste and reproduce the issue.\n placeholder: Reproduction\n validations:\n required: true\n - type: textarea\n id: logs\n attributes:\n label: Logs\n description: \"Please include the Python logs if you can.\"\n render: shell\n - type: textarea\n id: system-info\n attributes:\n label: System Info\n description: Please share your system info with us. You can run the command `diffusers-cli env` and copy-paste its output below.\n placeholder: Diffusers version, platform, Python version, ...\n validations:\n required: true\n - type: textarea\n id: who-can-help\n attributes:\n label: Who can help?\n description: |\n Your issue will be replied to more quickly if you can figure out the right person to tag with @.\n If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.\n \n All issues are read by one of the core maintainers, so if you don't know who to tag, just leave this blank and\n a core maintainer will ping the right person.\n \n Please tag a maximum of 2 people.\n\n Questions on DiffusionPipeline (Saving, Loading, From pretrained, ...):\n\n Questions on pipelines:\n - Stable Diffusion @yiyixuxu @DN6 @sayakpaul \n - Stable Diffusion XL @yiyixuxu @sayakpaul @DN6 \n - Kandinsky @yiyixuxu \n - ControlNet @sayakpaul @yiyixuxu @DN6 \n - T2I Adapter @sayakpaul @yiyixuxu @DN6 \n - IF @DN6 \n - Text-to-Video / Video-to-Video @DN6 @sayakpaul \n - Wuerstchen @DN6 \n - Other: @yiyixuxu @DN6\n\n Questions on models:\n - UNet @DN6 @yiyixuxu @sayakpaul \n - VAE @sayakpaul @DN6 @yiyixuxu \n - Transformers/Attention @DN6 @yiyixuxu @sayakpaul @DN6 \n\n Questions on Schedulers: @yiyixuxu \n\n Questions on LoRA: @sayakpaul \n\n Questions on Textual Inversion: @sayakpaul \n\n Questions on Training: \n - DreamBooth @sayakpaul \n - Text-to-Image Fine-tuning @sayakpaul \n - Textual Inversion @sayakpaul \n - ControlNet @sayakpaul \n\n Questions on Tests: @DN6 @sayakpaul @yiyixuxu \n\n Questions on Documentation: @stevhliu\n\n Questions on JAX- and MPS-related things: @pcuenca\n\n Questions on audio pipelines: @DN6 \n \n\n \n placeholder: \"@Username ...\"\n" }, { "path": "diffusers/.github/ISSUE_TEMPLATE/config.yml", "content": "contact_links:\n - name: Questions / Discussions\n url: https://github.com/huggingface/diffusers/discussions\n about: General usage questions and community discussions\n" }, { "path": "diffusers/.github/ISSUE_TEMPLATE/feature_request.md", "content": "---\nname: \"\\U0001F680 Feature Request\"\nabout: Suggest an idea for this project\ntitle: ''\nlabels: ''\nassignees: ''\n\n---\n\n**Is your feature request related to a problem? Please describe.**\nA clear and concise description of what the problem is. Ex. I'm always frustrated when [...].\n\n**Describe the solution you'd like.**\nA clear and concise description of what you want to happen.\n\n**Describe alternatives you've considered.**\nA clear and concise description of any alternative solutions or features you've considered.\n\n**Additional context.**\nAdd any other context or screenshots about the feature request here.\n" }, { "path": "diffusers/.github/ISSUE_TEMPLATE/feedback.md", "content": "---\nname: \"💬 Feedback about API Design\"\nabout: Give feedback about the current API design\ntitle: ''\nlabels: ''\nassignees: ''\n\n---\n\n**What API design would you like to have changed or added to the library? Why?**\n\n**What use case would this enable or better enable? Can you give us a code example?**\n" }, { "path": "diffusers/.github/ISSUE_TEMPLATE/new-model-addition.yml", "content": "name: \"\\U0001F31F New Model/Pipeline/Scheduler Addition\"\ndescription: Submit a proposal/request to implement a new diffusion model/pipeline/scheduler\nlabels: [ \"New model/pipeline/scheduler\" ]\n\nbody:\n - type: textarea\n id: description-request\n validations:\n required: true\n attributes:\n label: Model/Pipeline/Scheduler description\n description: |\n Put any and all important information relative to the model/pipeline/scheduler\n\n - type: checkboxes\n id: information-tasks\n attributes:\n label: Open source status\n description: |\n Please note that if the model implementation isn't available or if the weights aren't open-source, we are less likely to implement it in `diffusers`.\n options:\n - label: \"The model implementation is available.\"\n - label: \"The model weights are available (Only relevant if addition is not a scheduler).\"\n\n - type: textarea\n id: additional-info\n attributes:\n label: Provide useful links for the implementation\n description: |\n Please provide information regarding the implementation, the weights, and the authors.\n Please mention the authors by @gh-username if you're aware of their usernames.\n" }, { "path": "diffusers/.github/ISSUE_TEMPLATE/translate.md", "content": "---\nname: 🌐 Translating a New Language?\nabout: Start a new translation effort in your language\ntitle: '[] Translating docs to '\nlabels: WIP\nassignees: ''\n\n---\n\n\n\nHi!\n\nLet's bring the documentation to all the -speaking community 🌐.\n\nWho would want to translate? Please follow the 🤗 [TRANSLATING guide](https://github.com/huggingface/diffusers/blob/main/docs/TRANSLATING.md). Here is a list of the files ready for translation. Let us know in this issue if you'd like to translate any, and we'll add your name to the list.\n\nSome notes:\n\n* Please translate using an informal tone (imagine you are talking with a friend about Diffusers 🤗).\n* Please translate in a gender-neutral way.\n* Add your translations to the folder called `` inside the [source folder](https://github.com/huggingface/diffusers/tree/main/docs/source).\n* Register your translation in `/_toctree.yml`; please follow the order of the [English version](https://github.com/huggingface/diffusers/blob/main/docs/source/en/_toctree.yml).\n* Once you're finished, open a pull request and tag this issue by including #issue-number in the description, where issue-number is the number of this issue. Please ping @stevhliu for review.\n* 🙋 If you'd like others to help you with the translation, you can also post in the 🤗 [forums](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63).\n\nThank you so much for your help! 🤗\n" }, { "path": "diffusers/.github/PULL_REQUEST_TEMPLATE.md", "content": "# What does this PR do?\n\n\n\n\n\nFixes # (issue)\n\n\n## Before submitting\n- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).\n- [ ] Did you read the [contributor guideline](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md)?\n- [ ] Did you read our [philosophy doc](https://github.com/huggingface/diffusers/blob/main/PHILOSOPHY.md) (important for complex PRs)?\n- [ ] Was this discussed/approved via a GitHub issue or the [forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63)? Please add a link to it if that's the case.\n- [ ] Did you make sure to update the documentation with your changes? Here are the\n [documentation guidelines](https://github.com/huggingface/diffusers/tree/main/docs), and\n [here are tips on formatting docstrings](https://github.com/huggingface/diffusers/tree/main/docs#writing-source-documentation).\n- [ ] Did you write any new necessary tests?\n\n\n## Who can review?\n\nAnyone in the community is free to review the PR once the tests have passed. Feel free to tag\nmembers/contributors who may be interested in your PR.\n\n\n" }, { "path": "diffusers/.github/actions/setup-miniconda/action.yml", "content": "name: Set up conda environment for testing\n\ndescription: Sets up miniconda in your ${RUNNER_TEMP} environment and gives you the ${CONDA_RUN} environment variable so you don't have to worry about polluting non-empeheral runners anymore\n\ninputs:\n python-version:\n description: If set to any value, don't use sudo to clean the workspace\n required: false\n type: string\n default: \"3.9\"\n miniconda-version:\n description: Miniconda version to install\n required: false\n type: string\n default: \"4.12.0\"\n environment-file:\n description: Environment file to install dependencies from\n required: false\n type: string\n default: \"\"\n\nruns:\n using: composite\n steps:\n # Use the same trick from https://github.com/marketplace/actions/setup-miniconda\n # to refresh the cache daily. This is kind of optional though\n - name: Get date\n id: get-date\n shell: bash\n run: echo \"today=$(/bin/date -u '+%Y%m%d')d\" >> $GITHUB_OUTPUT\n - name: Setup miniconda cache\n id: miniconda-cache\n uses: actions/cache@v2\n with:\n path: ${{ runner.temp }}/miniconda\n key: miniconda-${{ runner.os }}-${{ runner.arch }}-${{ inputs.python-version }}-${{ steps.get-date.outputs.today }}\n - name: Install miniconda (${{ inputs.miniconda-version }})\n if: steps.miniconda-cache.outputs.cache-hit != 'true'\n env:\n MINICONDA_VERSION: ${{ inputs.miniconda-version }}\n shell: bash -l {0}\n run: |\n MINICONDA_INSTALL_PATH=\"${RUNNER_TEMP}/miniconda\"\n mkdir -p \"${MINICONDA_INSTALL_PATH}\"\n case ${RUNNER_OS}-${RUNNER_ARCH} in\n Linux-X64)\n MINICONDA_ARCH=\"Linux-x86_64\"\n ;;\n macOS-ARM64)\n MINICONDA_ARCH=\"MacOSX-arm64\"\n ;;\n macOS-X64)\n MINICONDA_ARCH=\"MacOSX-x86_64\"\n ;;\n *)\n echo \"::error::Platform ${RUNNER_OS}-${RUNNER_ARCH} currently unsupported using this action\"\n exit 1\n ;;\n esac\n MINICONDA_URL=\"https://repo.anaconda.com/miniconda/Miniconda3-py39_${MINICONDA_VERSION}-${MINICONDA_ARCH}.sh\"\n curl -fsSL \"${MINICONDA_URL}\" -o \"${MINICONDA_INSTALL_PATH}/miniconda.sh\"\n bash \"${MINICONDA_INSTALL_PATH}/miniconda.sh\" -b -u -p \"${MINICONDA_INSTALL_PATH}\"\n rm -rf \"${MINICONDA_INSTALL_PATH}/miniconda.sh\"\n - name: Update GitHub path to include miniconda install\n shell: bash\n run: |\n MINICONDA_INSTALL_PATH=\"${RUNNER_TEMP}/miniconda\"\n echo \"${MINICONDA_INSTALL_PATH}/bin\" >> $GITHUB_PATH\n - name: Setup miniconda env cache (with env file)\n id: miniconda-env-cache-env-file\n if: ${{ runner.os }} == 'macOS' && ${{ inputs.environment-file }} != ''\n uses: actions/cache@v2\n with:\n path: ${{ runner.temp }}/conda-python-${{ inputs.python-version }}\n key: miniconda-env-${{ runner.os }}-${{ runner.arch }}-${{ inputs.python-version }}-${{ steps.get-date.outputs.today }}-${{ hashFiles(inputs.environment-file) }}\n - name: Setup miniconda env cache (without env file)\n id: miniconda-env-cache\n if: ${{ runner.os }} == 'macOS' && ${{ inputs.environment-file }} == ''\n uses: actions/cache@v2\n with:\n path: ${{ runner.temp }}/conda-python-${{ inputs.python-version }}\n key: miniconda-env-${{ runner.os }}-${{ runner.arch }}-${{ inputs.python-version }}-${{ steps.get-date.outputs.today }}\n - name: Setup conda environment with python (v${{ inputs.python-version }})\n if: steps.miniconda-env-cache-env-file.outputs.cache-hit != 'true' && steps.miniconda-env-cache.outputs.cache-hit != 'true'\n shell: bash\n env:\n PYTHON_VERSION: ${{ inputs.python-version }}\n ENV_FILE: ${{ inputs.environment-file }}\n run: |\n CONDA_BASE_ENV=\"${RUNNER_TEMP}/conda-python-${PYTHON_VERSION}\"\n ENV_FILE_FLAG=\"\"\n if [[ -f \"${ENV_FILE}\" ]]; then\n ENV_FILE_FLAG=\"--file ${ENV_FILE}\"\n elif [[ -n \"${ENV_FILE}\" ]]; then\n echo \"::warning::Specified env file (${ENV_FILE}) not found, not going to include it\"\n fi\n conda create \\\n --yes \\\n --prefix \"${CONDA_BASE_ENV}\" \\\n \"python=${PYTHON_VERSION}\" \\\n ${ENV_FILE_FLAG} \\\n cmake=3.22 \\\n conda-build=3.21 \\\n ninja=1.10 \\\n pkg-config=0.29 \\\n wheel=0.37\n - name: Clone the base conda environment and update GitHub env\n shell: bash\n env:\n PYTHON_VERSION: ${{ inputs.python-version }}\n CONDA_BASE_ENV: ${{ runner.temp }}/conda-python-${{ inputs.python-version }}\n run: |\n CONDA_ENV=\"${RUNNER_TEMP}/conda_environment_${GITHUB_RUN_ID}\"\n conda create \\\n --yes \\\n --prefix \"${CONDA_ENV}\" \\\n --clone \"${CONDA_BASE_ENV}\"\n # TODO: conda-build could not be cloned because it hardcodes the path, so it\n # could not be cached\n conda install --yes -p ${CONDA_ENV} conda-build=3.21\n echo \"CONDA_ENV=${CONDA_ENV}\" >> \"${GITHUB_ENV}\"\n echo \"CONDA_RUN=conda run -p ${CONDA_ENV} --no-capture-output\" >> \"${GITHUB_ENV}\"\n echo \"CONDA_BUILD=conda run -p ${CONDA_ENV} conda-build\" >> \"${GITHUB_ENV}\"\n echo \"CONDA_INSTALL=conda install -p ${CONDA_ENV}\" >> \"${GITHUB_ENV}\"\n - name: Get disk space usage and throw an error for low disk space\n shell: bash\n run: |\n echo \"Print the available disk space for manual inspection\"\n df -h\n # Set the minimum requirement space to 4GB\n MINIMUM_AVAILABLE_SPACE_IN_GB=4\n MINIMUM_AVAILABLE_SPACE_IN_KB=$(($MINIMUM_AVAILABLE_SPACE_IN_GB * 1024 * 1024))\n # Use KB to avoid floating point warning like 3.1GB\n df -k | tr -s ' ' | cut -d' ' -f 4,9 | while read -r LINE;\n do\n AVAIL=$(echo $LINE | cut -f1 -d' ')\n MOUNT=$(echo $LINE | cut -f2 -d' ')\n if [ \"$MOUNT\" = \"/\" ]; then\n if [ \"$AVAIL\" -lt \"$MINIMUM_AVAILABLE_SPACE_IN_KB\" ]; then\n echo \"There is only ${AVAIL}KB free space left in $MOUNT, which is less than the minimum requirement of ${MINIMUM_AVAILABLE_SPACE_IN_KB}KB. Please help create an issue to PyTorch Release Engineering via https://github.com/pytorch/test-infra/issues and provide the link to the workflow run.\"\n exit 1;\n else\n echo \"There is ${AVAIL}KB free space left in $MOUNT, continue\"\n fi\n fi\n done\n" }, { "path": "diffusers/.github/workflows/benchmark.yml", "content": "name: Benchmarking tests\n\non:\n workflow_dispatch:\n schedule:\n - cron: \"30 1 1,15 * *\" # every 2 weeks on the 1st and the 15th of every month at 1:30 AM\n\nenv:\n DIFFUSERS_IS_CI: yes\n HF_HOME: /mnt/cache\n OMP_NUM_THREADS: 8\n MKL_NUM_THREADS: 8\n\njobs:\n torch_pipelines_cuda_benchmark_tests:\n name: Torch Core Pipelines CUDA Benchmarking Tests\n strategy:\n fail-fast: false\n max-parallel: 1\n runs-on: [single-gpu, nvidia-gpu, a10, ci]\n container:\n image: diffusers/diffusers-pytorch-cuda\n options: --shm-size \"16gb\" --ipc host -v /mnt/hf_cache:/mnt/cache/ --gpus 0\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n - name: NVIDIA-SMI\n run: |\n nvidia-smi\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install -e [quality,test]\n python -m uv pip install pandas peft\n - name: Environment\n run: |\n python utils/print_env.py\n - name: Diffusers Benchmarking\n env:\n HF_TOKEN: ${{ secrets.DIFFUSERS_BOT_TOKEN }}\n BASE_PATH: benchmark_outputs\n run: |\n export TOTAL_GPU_MEMORY=$(python -c \"import torch; print(torch.cuda.get_device_properties(0).total_memory / (1024**3))\")\n cd benchmarks && mkdir ${BASE_PATH} && python run_all.py && python push_results.py\n\n - name: Test suite reports artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v2\n with:\n name: benchmark_test_reports\n path: benchmarks/benchmark_outputs" }, { "path": "diffusers/.github/workflows/build_docker_images.yml", "content": "name: Test, build, and push Docker images\n\non:\n pull_request: # During PRs, we just check if the changes Dockerfiles can be successfully built\n branches:\n - main\n paths:\n - \"docker/**\"\n workflow_dispatch:\n schedule:\n - cron: \"0 0 * * *\" # every day at midnight\n\nconcurrency:\n group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}\n cancel-in-progress: true\n\nenv:\n REGISTRY: diffusers\n CI_SLACK_CHANNEL: ${{ secrets.CI_DOCKER_CHANNEL }}\n\njobs:\n test-build-docker-images:\n runs-on: [ self-hosted, intel-cpu, 8-cpu, ci ]\n if: github.event_name == 'pull_request'\n steps:\n - name: Set up Docker Buildx\n uses: docker/setup-buildx-action@v1\n\n - name: Check out code\n uses: actions/checkout@v3\n\n - name: Find Changed Dockerfiles\n id: file_changes\n uses: jitterbit/get-changed-files@v1\n with:\n format: 'space-delimited'\n token: ${{ secrets.GITHUB_TOKEN }}\n\n - name: Build Changed Docker Images\n run: |\n CHANGED_FILES=\"${{ steps.file_changes.outputs.all }}\"\n for FILE in $CHANGED_FILES; do\n if [[ \"$FILE\" == docker/*Dockerfile ]]; then\n DOCKER_PATH=\"${FILE%/Dockerfile}\"\n DOCKER_TAG=$(basename \"$DOCKER_PATH\")\n echo \"Building Docker image for $DOCKER_TAG\"\n docker build -t \"$DOCKER_TAG\" \"$DOCKER_PATH\"\n fi\n done\n if: steps.file_changes.outputs.all != ''\n\n build-and-push-docker-images:\n runs-on: [ self-hosted, intel-cpu, 8-cpu, ci ]\n if: github.event_name != 'pull_request'\n\n permissions:\n contents: read\n packages: write\n\n strategy:\n fail-fast: false\n matrix:\n image-name:\n - diffusers-pytorch-cpu\n - diffusers-pytorch-cuda\n - diffusers-pytorch-compile-cuda\n - diffusers-pytorch-xformers-cuda\n - diffusers-flax-cpu\n - diffusers-flax-tpu\n - diffusers-onnxruntime-cpu\n - diffusers-onnxruntime-cuda\n - diffusers-doc-builder\n\n steps:\n - name: Checkout repository\n uses: actions/checkout@v3\n - name: Set up Docker Buildx\n uses: docker/setup-buildx-action@v1\n - name: Login to Docker Hub\n uses: docker/login-action@v2\n with:\n username: ${{ env.REGISTRY }}\n password: ${{ secrets.DOCKERHUB_TOKEN }}\n - name: Build and push\n uses: docker/build-push-action@v3\n with:\n no-cache: true\n context: ./docker/${{ matrix.image-name }}\n push: true\n tags: ${{ env.REGISTRY }}/${{ matrix.image-name }}:latest\n\n - name: Post to a Slack channel\n id: slack\n uses: huggingface/hf-workflows/.github/actions/post-slack@main\n with:\n # Slack channel id, channel name, or user id to post message.\n # See also: https://api.slack.com/methods/chat.postMessage#channels\n slack_channel: ${{ env.CI_SLACK_CHANNEL }}\n title: \"🤗 Results of the ${{ matrix.image-name }} Docker Image build\"\n status: ${{ job.status }}\n slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}" }, { "path": "diffusers/.github/workflows/build_documentation.yml", "content": "name: Build documentation\n\non:\n push:\n branches:\n - main\n - doc-builder*\n - v*-release\n - v*-patch\n paths:\n - \"src/diffusers/**.py\"\n - \"examples/**\"\n - \"docs/**\"\n\njobs:\n build:\n uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@main\n with:\n commit_sha: ${{ github.sha }}\n install_libgl1: true\n package: diffusers\n notebook_folder: diffusers_doc\n languages: en ko zh ja pt\n custom_container: diffusers/diffusers-doc-builder\n secrets:\n token: ${{ secrets.HUGGINGFACE_PUSH }}\n hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}\n" }, { "path": "diffusers/.github/workflows/build_pr_documentation.yml", "content": "name: Build PR Documentation\n\non:\n pull_request:\n paths:\n - \"src/diffusers/**.py\"\n - \"examples/**\"\n - \"docs/**\"\n\nconcurrency:\n group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}\n cancel-in-progress: true\n\njobs:\n build:\n uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main\n with:\n commit_sha: ${{ github.event.pull_request.head.sha }}\n pr_number: ${{ github.event.number }}\n install_libgl1: true\n package: diffusers\n languages: en ko zh ja pt\n custom_container: diffusers/diffusers-doc-builder\n" }, { "path": "diffusers/.github/workflows/mirror_community_pipeline.yml", "content": "name: Mirror Community Pipeline\n\non:\n # Push changes on the main branch\n push:\n branches:\n - main\n paths:\n - 'examples/community/**.py'\n\n # And on tag creation (e.g. `v0.28.1`)\n tags:\n - '*'\n\n # Manual trigger with ref input\n workflow_dispatch:\n inputs:\n ref:\n description: \"Either 'main' or a tag ref\"\n required: true\n default: 'main'\n\njobs:\n mirror_community_pipeline:\n runs-on: ubuntu-latest\n steps:\n # Checkout to correct ref\n # If workflow dispatch\n # If ref is 'main', set:\n # CHECKOUT_REF=refs/heads/main\n # PATH_IN_REPO=main\n # Else it must be a tag. Set:\n # CHECKOUT_REF=refs/tags/{tag}\n # PATH_IN_REPO={tag}\n # If not workflow dispatch\n # If ref is 'refs/heads/main' => set 'main'\n # Else it must be a tag => set {tag}\n - name: Set checkout_ref and path_in_repo\n run: | \n if [ \"${{ github.event_name }}\" == \"workflow_dispatch\" ]; then\n if [ -z \"${{ github.event.inputs.ref }}\" ]; then\n echo \"Error: Missing ref input\"\n exit 1\n elif [ \"${{ github.event.inputs.ref }}\" == \"main\" ]; then\n echo \"CHECKOUT_REF=refs/heads/main\" >> $GITHUB_ENV\n echo \"PATH_IN_REPO=main\" >> $GITHUB_ENV\n else\n echo \"CHECKOUT_REF=refs/tags/${{ github.event.inputs.ref }}\" >> $GITHUB_ENV\n echo \"PATH_IN_REPO=${{ github.event.inputs.ref }}\" >> $GITHUB_ENV\n fi\n elif [ \"${{ github.ref }}\" == \"refs/heads/main\" ]; then\n echo \"CHECKOUT_REF=${{ github.ref }}\" >> $GITHUB_ENV\n echo \"PATH_IN_REPO=main\" >> $GITHUB_ENV\n else\n # e.g. refs/tags/v0.28.1 -> v0.28.1\n echo \"CHECKOUT_REF=${{ github.ref }}\" >> $GITHUB_ENV\n echo \"PATH_IN_REPO=$(echo ${{ github.ref }} | sed 's/^refs\\/tags\\///')\" >> $GITHUB_ENV\n fi\n - name: Print env vars\n run: |\n echo \"CHECKOUT_REF: ${{ env.CHECKOUT_REF }}\"\n echo \"PATH_IN_REPO: ${{ env.PATH_IN_REPO }}\"\n - uses: actions/checkout@v3\n with:\n ref: ${{ env.CHECKOUT_REF }}\n\n # Setup + install dependencies\n - name: Set up Python\n uses: actions/setup-python@v4\n with:\n python-version: \"3.10\"\n - name: Install dependencies\n run: |\n python -m pip install --upgrade pip\n pip install --upgrade huggingface_hub\n\n # Check secret is set\n - name: whoami\n run: huggingface-cli whoami\n env:\n HF_TOKEN: ${{ secrets.HF_TOKEN_MIRROR_COMMUNITY_PIPELINES }}\n\n # Push to HF! (under subfolder based on checkout ref)\n # https://huggingface.co/datasets/diffusers/community-pipelines-mirror\n - name: Mirror community pipeline to HF\n run: huggingface-cli upload diffusers/community-pipelines-mirror ./examples/community ${PATH_IN_REPO} --repo-type dataset\n env:\n PATH_IN_REPO: ${{ env.PATH_IN_REPO }}\n HF_TOKEN: ${{ secrets.HF_TOKEN_MIRROR_COMMUNITY_PIPELINES }}" }, { "path": "diffusers/.github/workflows/nightly_tests.yml", "content": "name: Nightly and release tests on main/release branch\n\non:\n workflow_dispatch:\n schedule:\n - cron: \"0 0 * * *\" # every day at midnight\n\nenv:\n DIFFUSERS_IS_CI: yes\n HF_HOME: /mnt/cache\n OMP_NUM_THREADS: 8\n MKL_NUM_THREADS: 8\n PYTEST_TIMEOUT: 600\n RUN_SLOW: yes\n RUN_NIGHTLY: yes\n PIPELINE_USAGE_CUTOFF: 5000\n SLACK_API_TOKEN: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}\n\njobs:\n setup_torch_cuda_pipeline_matrix:\n name: Setup Torch Pipelines Matrix\n runs-on: diffusers/diffusers-pytorch-cpu\n outputs:\n pipeline_test_matrix: ${{ steps.fetch_pipeline_matrix.outputs.pipeline_test_matrix }}\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n - name: Set up Python\n uses: actions/setup-python@v4\n with:\n python-version: \"3.8\"\n - name: Install dependencies\n run: |\n pip install -e .\n pip install huggingface_hub\n - name: Fetch Pipeline Matrix\n id: fetch_pipeline_matrix\n run: |\n matrix=$(python utils/fetch_torch_cuda_pipeline_test_matrix.py)\n echo $matrix\n echo \"pipeline_test_matrix=$matrix\" >> $GITHUB_OUTPUT\n\n - name: Pipeline Tests Artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v2\n with:\n name: test-pipelines.json\n path: reports\n\n run_nightly_tests_for_torch_pipelines:\n name: Torch Pipelines CUDA Nightly Tests\n needs: setup_torch_cuda_pipeline_matrix\n strategy:\n fail-fast: false\n matrix:\n module: ${{ fromJson(needs.setup_torch_cuda_pipeline_matrix.outputs.pipeline_test_matrix) }}\n runs-on: [single-gpu, nvidia-gpu, t4, ci]\n container:\n image: diffusers/diffusers-pytorch-cuda\n options: --shm-size \"16gb\" --ipc host -v /mnt/cache/.cache/huggingface/diffusers:/mnt/cache/ --gpus 0\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n - name: NVIDIA-SMI\n run: nvidia-smi\n\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install -e [quality,test]\n python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git\n python -m uv pip install pytest-reportlog\n\n - name: Environment\n run: |\n python utils/print_env.py\n\n - name: Nightly PyTorch CUDA checkpoint (pipelines) tests\n env:\n HF_TOKEN: ${{ secrets.HF_TOKEN }}\n # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms\n CUBLAS_WORKSPACE_CONFIG: :16:8\n run: |\n python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \\\n -s -v -k \"not Flax and not Onnx\" \\\n --make-reports=tests_pipeline_${{ matrix.module }}_cuda \\\n --report-log=tests_pipeline_${{ matrix.module }}_cuda.log \\\n tests/pipelines/${{ matrix.module }}\n\n - name: Failure short reports\n if: ${{ failure() }}\n run: |\n cat reports/tests_pipeline_${{ matrix.module }}_cuda_stats.txt\n cat reports/tests_pipeline_${{ matrix.module }}_cuda_failures_short.txt\n\n - name: Test suite reports artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v2\n with:\n name: pipeline_${{ matrix.module }}_test_reports\n path: reports\n\n - name: Generate Report and Notify Channel\n if: always()\n run: |\n pip install slack_sdk tabulate\n python scripts/log_reports.py >> $GITHUB_STEP_SUMMARY\n\n run_nightly_tests_for_other_torch_modules:\n name: Torch Non-Pipelines CUDA Nightly Tests\n runs-on: [single-gpu, nvidia-gpu, t4, ci]\n container:\n image: diffusers/diffusers-pytorch-cuda\n options: --shm-size \"16gb\" --ipc host -v /mnt/hf_cache:/mnt/cache/ --gpus 0\n defaults:\n run:\n shell: bash\n strategy:\n matrix:\n module: [models, schedulers, others, examples]\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install -e [quality,test]\n python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git\n python -m uv pip install pytest-reportlog\n\n - name: Environment\n run: python utils/print_env.py\n\n - name: Run nightly PyTorch CUDA tests for non-pipeline modules\n if: ${{ matrix.module != 'examples'}}\n env:\n HF_TOKEN: ${{ secrets.HF_TOKEN }}\n # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms\n CUBLAS_WORKSPACE_CONFIG: :16:8\n run: |\n python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \\\n -s -v -k \"not Flax and not Onnx\" \\\n --make-reports=tests_torch_${{ matrix.module }}_cuda \\\n --report-log=tests_torch_${{ matrix.module }}_cuda.log \\\n tests/${{ matrix.module }}\n\n - name: Run nightly example tests with Torch\n if: ${{ matrix.module == 'examples' }}\n env:\n HF_TOKEN: ${{ secrets.HF_TOKEN }}\n # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms\n CUBLAS_WORKSPACE_CONFIG: :16:8\n run: |\n python -m uv pip install peft@git+https://github.com/huggingface/peft.git\n python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \\\n -s -v --make-reports=examples_torch_cuda \\\n --report-log=examples_torch_cuda.log \\\n examples/\n\n - name: Failure short reports\n if: ${{ failure() }}\n run: |\n cat reports/tests_torch_${{ matrix.module }}_cuda_stats.txt\n cat reports/tests_torch_${{ matrix.module }}_cuda_failures_short.txt\n\n - name: Test suite reports artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v2\n with:\n name: torch_${{ matrix.module }}_cuda_test_reports\n path: reports\n\n - name: Generate Report and Notify Channel\n if: always()\n run: |\n pip install slack_sdk tabulate\n python scripts/log_reports.py >> $GITHUB_STEP_SUMMARY\n\n run_lora_nightly_tests:\n name: Nightly LoRA Tests with PEFT and TORCH\n runs-on: [single-gpu, nvidia-gpu, t4, ci]\n container:\n image: diffusers/diffusers-pytorch-cuda\n options: --shm-size \"16gb\" --ipc host -v /mnt/hf_cache:/mnt/cache/ --gpus 0\n defaults:\n run:\n shell: bash\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install -e [quality,test]\n python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git\n python -m uv pip install peft@git+https://github.com/huggingface/peft.git\n python -m uv pip install pytest-reportlog\n\n - name: Environment\n run: python utils/print_env.py\n\n - name: Run nightly LoRA tests with PEFT and Torch\n env:\n HF_TOKEN: ${{ secrets.HF_TOKEN }}\n # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms\n CUBLAS_WORKSPACE_CONFIG: :16:8\n run: |\n python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \\\n -s -v -k \"not Flax and not Onnx\" \\\n --make-reports=tests_torch_lora_cuda \\\n --report-log=tests_torch_lora_cuda.log \\\n tests/lora\n\n - name: Failure short reports\n if: ${{ failure() }}\n run: |\n cat reports/tests_torch_lora_cuda_stats.txt\n cat reports/tests_torch_lora_cuda_failures_short.txt\n\n - name: Test suite reports artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v2\n with:\n name: torch_lora_cuda_test_reports\n path: reports\n\n - name: Generate Report and Notify Channel\n if: always()\n run: |\n pip install slack_sdk tabulate\n python scripts/log_reports.py >> $GITHUB_STEP_SUMMARY\n\n run_flax_tpu_tests:\n name: Nightly Flax TPU Tests\n runs-on: docker-tpu\n if: github.event_name == 'schedule'\n\n container:\n image: diffusers/diffusers-flax-tpu\n options: --shm-size \"16gb\" --ipc host -v /mnt/hf_cache:/mnt/cache/ --privileged\n defaults:\n run:\n shell: bash\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install -e [quality,test]\n python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git\n python -m uv pip install pytest-reportlog\n\n - name: Environment\n run: python utils/print_env.py\n\n - name: Run nightly Flax TPU tests\n env:\n HF_TOKEN: ${{ secrets.HF_TOKEN }}\n run: |\n python -m pytest -n 0 \\\n -s -v -k \"Flax\" \\\n --make-reports=tests_flax_tpu \\\n --report-log=tests_flax_tpu.log \\\n tests/\n\n - name: Failure short reports\n if: ${{ failure() }}\n run: |\n cat reports/tests_flax_tpu_stats.txt\n cat reports/tests_flax_tpu_failures_short.txt\n\n - name: Test suite reports artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v2\n with:\n name: flax_tpu_test_reports\n path: reports\n\n - name: Generate Report and Notify Channel\n if: always()\n run: |\n pip install slack_sdk tabulate\n python scripts/log_reports.py >> $GITHUB_STEP_SUMMARY\n\n run_nightly_onnx_tests:\n name: Nightly ONNXRuntime CUDA tests on Ubuntu\n runs-on: [single-gpu, nvidia-gpu, t4, ci]\n container:\n image: diffusers/diffusers-onnxruntime-cuda\n options: --gpus 0 --shm-size \"16gb\" --ipc host -v /mnt/hf_cache:/mnt/cache/\n\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n\n - name: NVIDIA-SMI\n run: nvidia-smi\n\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install -e [quality,test]\n python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git\n python -m uv pip install pytest-reportlog\n\n - name: Environment\n run: python utils/print_env.py\n\n - name: Run nightly ONNXRuntime CUDA tests\n env:\n HF_TOKEN: ${{ secrets.HF_TOKEN }}\n run: |\n python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \\\n -s -v -k \"Onnx\" \\\n --make-reports=tests_onnx_cuda \\\n --report-log=tests_onnx_cuda.log \\\n tests/\n\n - name: Failure short reports\n if: ${{ failure() }}\n run: |\n cat reports/tests_onnx_cuda_stats.txt\n cat reports/tests_onnx_cuda_failures_short.txt\n\n - name: Test suite reports artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v2\n with:\n name: ${{ matrix.config.report }}_test_reports\n path: reports\n\n - name: Generate Report and Notify Channel\n if: always()\n run: |\n pip install slack_sdk tabulate\n python scripts/log_reports.py >> $GITHUB_STEP_SUMMARY\n\n run_nightly_tests_apple_m1:\n name: Nightly PyTorch MPS tests on MacOS\n runs-on: [ self-hosted, apple-m1 ]\n if: github.event_name == 'schedule'\n\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n\n - name: Clean checkout\n shell: arch -arch arm64 bash {0}\n run: |\n git clean -fxd\n\n - name: Setup miniconda\n uses: ./.github/actions/setup-miniconda\n with:\n python-version: 3.9\n\n - name: Install dependencies\n shell: arch -arch arm64 bash {0}\n run: |\n ${CONDA_RUN} python -m pip install --upgrade pip uv\n ${CONDA_RUN} python -m uv pip install -e [quality,test]\n ${CONDA_RUN} python -m uv pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu\n ${CONDA_RUN} python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate\n ${CONDA_RUN} python -m uv pip install pytest-reportlog\n\n - name: Environment\n shell: arch -arch arm64 bash {0}\n run: |\n ${CONDA_RUN} python utils/print_env.py\n\n - name: Run nightly PyTorch tests on M1 (MPS)\n shell: arch -arch arm64 bash {0}\n env:\n HF_HOME: /System/Volumes/Data/mnt/cache\n HF_TOKEN: ${{ secrets.HF_TOKEN }}\n run: |\n ${CONDA_RUN} python -m pytest -n 1 -s -v --make-reports=tests_torch_mps \\\n --report-log=tests_torch_mps.log \\\n tests/\n\n - name: Failure short reports\n if: ${{ failure() }}\n run: cat reports/tests_torch_mps_failures_short.txt\n\n - name: Test suite reports artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v2\n with:\n name: torch_mps_test_reports\n path: reports\n\n - name: Generate Report and Notify Channel\n if: always()\n run: |\n pip install slack_sdk tabulate\n python scripts/log_reports.py >> $GITHUB_STEP_SUMMARY\n" }, { "path": "diffusers/.github/workflows/notify_slack_about_release.yml", "content": "name: Notify Slack about a release\n\non:\n workflow_dispatch:\n release:\n types: [published]\n\njobs:\n build:\n runs-on: ubuntu-latest\n\n steps:\n - uses: actions/checkout@v3\n \n - name: Setup Python\n uses: actions/setup-python@v4\n with:\n python-version: '3.8'\n \n - name: Notify Slack about the release\n env:\n SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL }}\n run: pip install requests && python utils/notify_slack_about_release.py\n" }, { "path": "diffusers/.github/workflows/pr_dependency_test.yml", "content": "name: Run dependency tests\n\non:\n pull_request:\n branches:\n - main\n paths:\n - \"src/diffusers/**.py\"\n push:\n branches:\n - main\n\nconcurrency:\n group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}\n cancel-in-progress: true\n\njobs:\n check_dependencies:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v3\n - name: Set up Python\n uses: actions/setup-python@v4\n with:\n python-version: \"3.8\"\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m pip install --upgrade pip uv\n python -m uv pip install -e .\n python -m uv pip install pytest\n - name: Check for soft dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n pytest tests/others/test_dependencies.py\n " }, { "path": "diffusers/.github/workflows/pr_flax_dependency_test.yml", "content": "name: Run Flax dependency tests\n\non:\n pull_request:\n branches:\n - main\n paths:\n - \"src/diffusers/**.py\"\n push:\n branches:\n - main\n\nconcurrency:\n group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}\n cancel-in-progress: true\n\njobs:\n check_flax_dependencies:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v3\n - name: Set up Python\n uses: actions/setup-python@v4\n with:\n python-version: \"3.8\"\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m pip install --upgrade pip uv\n python -m uv pip install -e .\n python -m uv pip install \"jax[cpu]>=0.2.16,!=0.3.2\"\n python -m uv pip install \"flax>=0.4.1\"\n python -m uv pip install \"jaxlib>=0.1.65\"\n python -m uv pip install pytest\n - name: Check for soft dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n pytest tests/others/test_dependencies.py\n" }, { "path": "diffusers/.github/workflows/pr_test_fetcher.yml", "content": "name: Fast tests for PRs - Test Fetcher\n\non: workflow_dispatch\n\nenv:\n DIFFUSERS_IS_CI: yes\n OMP_NUM_THREADS: 4\n MKL_NUM_THREADS: 4\n PYTEST_TIMEOUT: 60\n\nconcurrency:\n group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}\n cancel-in-progress: true\n\njobs:\n setup_pr_tests:\n name: Setup PR Tests\n runs-on: [ self-hosted, intel-cpu, 8-cpu, ci ]\n container:\n image: diffusers/diffusers-pytorch-cpu\n options: --shm-size \"16gb\" --ipc host -v /mnt/hf_cache:/mnt/cache/\n defaults:\n run:\n shell: bash\n outputs:\n matrix: ${{ steps.set_matrix.outputs.matrix }}\n test_map: ${{ steps.set_matrix.outputs.test_map }}\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 0\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install -e [quality,test]\n - name: Environment\n run: |\n python utils/print_env.py\n echo $(git --version)\n - name: Fetch Tests\n run: |\n python utils/tests_fetcher.py | tee test_preparation.txt\n - name: Report fetched tests\n uses: actions/upload-artifact@v3\n with:\n name: test_fetched\n path: test_preparation.txt\n - id: set_matrix\n name: Create Test Matrix\n # The `keys` is used as GitHub actions matrix for jobs, i.e. `models`, `pipelines`, etc.\n # The `test_map` is used to get the actual identified test files under each key.\n # If no test to run (so no `test_map.json` file), create a dummy map (empty matrix will fail)\n run: |\n if [ -f test_map.json ]; then\n keys=$(python3 -c 'import json; fp = open(\"test_map.json\"); test_map = json.load(fp); fp.close(); d = list(test_map.keys()); print(json.dumps(d))')\n test_map=$(python3 -c 'import json; fp = open(\"test_map.json\"); test_map = json.load(fp); fp.close(); print(json.dumps(test_map))')\n else\n keys=$(python3 -c 'keys = [\"dummy\"]; print(keys)')\n test_map=$(python3 -c 'test_map = {\"dummy\": []}; print(test_map)')\n fi\n echo $keys\n echo $test_map\n echo \"matrix=$keys\" >> $GITHUB_OUTPUT\n echo \"test_map=$test_map\" >> $GITHUB_OUTPUT\n\n run_pr_tests:\n name: Run PR Tests\n needs: setup_pr_tests\n if: contains(fromJson(needs.setup_pr_tests.outputs.matrix), 'dummy') != true\n strategy:\n fail-fast: false\n max-parallel: 2\n matrix:\n modules: ${{ fromJson(needs.setup_pr_tests.outputs.matrix) }}\n runs-on: [ self-hosted, intel-cpu, 8-cpu, ci ]\n container:\n image: diffusers/diffusers-pytorch-cpu\n options: --shm-size \"16gb\" --ipc host -v /mnt/hf_cache:/mnt/cache/\n defaults:\n run:\n shell: bash\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m pip install -e [quality,test]\n python -m pip install accelerate\n\n - name: Environment\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python utils/print_env.py\n\n - name: Run all selected tests on CPU\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m pytest -n 2 --dist=loadfile -v --make-reports=${{ matrix.modules }}_tests_cpu ${{ fromJson(needs.setup_pr_tests.outputs.test_map)[matrix.modules] }}\n\n - name: Failure short reports\n if: ${{ failure() }}\n continue-on-error: true\n run: |\n cat reports/${{ matrix.modules }}_tests_cpu_stats.txt\n cat reports/${{ matrix.modules }}_tests_cpu_failures_short.txt\n\n - name: Test suite reports artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v3\n with:\n name: ${{ matrix.modules }}_test_reports\n path: reports\n\n run_staging_tests:\n strategy:\n fail-fast: false\n matrix:\n config:\n - name: Hub tests for models, schedulers, and pipelines\n framework: hub_tests_pytorch\n runner: [ self-hosted, intel-cpu, 8-cpu, ci ]\n image: diffusers/diffusers-pytorch-cpu\n report: torch_hub\n\n name: ${{ matrix.config.name }}\n runs-on: ${{ matrix.config.runner }}\n container:\n image: ${{ matrix.config.image }}\n options: --shm-size \"16gb\" --ipc host -v /mnt/hf_cache:/mnt/cache/\n\n defaults:\n run:\n shell: bash\n\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m pip install -e [quality,test]\n\n - name: Environment\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python utils/print_env.py\n\n - name: Run Hub tests for models, schedulers, and pipelines on a staging env\n if: ${{ matrix.config.framework == 'hub_tests_pytorch' }}\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n HUGGINGFACE_CO_STAGING=true python -m pytest \\\n -m \"is_staging_test\" \\\n --make-reports=tests_${{ matrix.config.report }} \\\n tests\n\n - name: Failure short reports\n if: ${{ failure() }}\n run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt\n\n - name: Test suite reports artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v2\n with:\n name: pr_${{ matrix.config.report }}_test_reports\n path: reports\n" }, { "path": "diffusers/.github/workflows/pr_test_peft_backend.yml", "content": "name: Fast tests for PRs - PEFT backend\n\non:\n pull_request:\n branches:\n - main\n paths:\n - \"src/diffusers/**.py\"\n - \"tests/**.py\"\n\nconcurrency:\n group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}\n cancel-in-progress: true\n\nenv:\n DIFFUSERS_IS_CI: yes\n OMP_NUM_THREADS: 4\n MKL_NUM_THREADS: 4\n PYTEST_TIMEOUT: 60\n\njobs:\n check_code_quality:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v3\n - name: Set up Python\n uses: actions/setup-python@v4\n with:\n python-version: \"3.8\"\n - name: Install dependencies\n run: |\n python -m pip install --upgrade pip\n pip install .[quality]\n - name: Check quality\n run: make quality\n - name: Check if failure\n if: ${{ failure() }}\n run: |\n echo \"Quality check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make style && make quality'\" >> $GITHUB_STEP_SUMMARY\n\n check_repository_consistency:\n needs: check_code_quality\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v3\n - name: Set up Python\n uses: actions/setup-python@v4\n with:\n python-version: \"3.8\"\n - name: Install dependencies\n run: |\n python -m pip install --upgrade pip\n pip install .[quality]\n - name: Check repo consistency\n run: |\n python utils/check_copies.py\n python utils/check_dummies.py\n make deps_table_check_updated\n - name: Check if failure\n if: ${{ failure() }}\n run: |\n echo \"Repo consistency check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make fix-copies'\" >> $GITHUB_STEP_SUMMARY\n\n run_fast_tests:\n needs: [check_code_quality, check_repository_consistency]\n strategy:\n fail-fast: false\n matrix:\n lib-versions: [\"main\", \"latest\"]\n\n\n name: LoRA - ${{ matrix.lib-versions }}\n\n runs-on: [ self-hosted, intel-cpu, 8-cpu, ci ]\n\n container:\n image: diffusers/diffusers-pytorch-cpu\n options: --shm-size \"16gb\" --ipc host -v /mnt/hf_cache:/mnt/cache/\n\n defaults:\n run:\n shell: bash\n\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install -e [quality,test]\n if [ \"${{ matrix.lib-versions }}\" == \"main\" ]; then\n python -m pip install -U peft@git+https://github.com/huggingface/peft.git\n python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git\n python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git\n else\n python -m uv pip install -U peft transformers accelerate\n fi\n\n - name: Environment\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python utils/print_env.py\n\n - name: Run fast PyTorch LoRA CPU tests with PEFT backend\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \\\n -s -v \\\n --make-reports=tests_${{ matrix.config.report }} \\\n tests/lora/\n python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \\\n -s -v \\\n --make-reports=tests_models_lora_${{ matrix.config.report }} \\\n tests/models/ -k \"lora\"\n \n \n - name: Failure short reports\n if: ${{ failure() }}\n run: |\n cat reports/tests_${{ matrix.config.report }}_failures_short.txt\n cat reports/tests_models_lora_${{ matrix.config.report }}_failures_short.txt\n \n - name: Test suite reports artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v2\n with:\n name: pr_${{ matrix.config.report }}_test_reports\n path: reports" }, { "path": "diffusers/.github/workflows/pr_tests.yml", "content": "name: Fast tests for PRs\n\non:\n pull_request:\n branches:\n - main\n paths:\n - \"src/diffusers/**.py\"\n - \"benchmarks/**.py\"\n - \"examples/**.py\"\n - \"scripts/**.py\"\n - \"tests/**.py\"\n - \".github/**.yml\"\n - \"utils/**.py\"\n push:\n branches:\n - ci-*\n\nconcurrency:\n group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}\n cancel-in-progress: true\n\nenv:\n DIFFUSERS_IS_CI: yes\n OMP_NUM_THREADS: 4\n MKL_NUM_THREADS: 4\n PYTEST_TIMEOUT: 60\n\njobs:\n check_code_quality:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v3\n - name: Set up Python\n uses: actions/setup-python@v4\n with:\n python-version: \"3.8\"\n - name: Install dependencies\n run: |\n python -m pip install --upgrade pip\n pip install .[quality]\n - name: Check quality\n run: make quality\n - name: Check if failure\n if: ${{ failure() }}\n run: |\n echo \"Quality check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make style && make quality'\" >> $GITHUB_STEP_SUMMARY\n\n check_repository_consistency:\n needs: check_code_quality\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v3\n - name: Set up Python\n uses: actions/setup-python@v4\n with:\n python-version: \"3.8\"\n - name: Install dependencies\n run: |\n python -m pip install --upgrade pip\n pip install .[quality]\n - name: Check repo consistency\n run: |\n python utils/check_copies.py\n python utils/check_dummies.py\n make deps_table_check_updated\n - name: Check if failure\n if: ${{ failure() }}\n run: |\n echo \"Repo consistency check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make fix-copies'\" >> $GITHUB_STEP_SUMMARY\n\n run_fast_tests:\n needs: [check_code_quality, check_repository_consistency]\n strategy:\n fail-fast: false\n matrix:\n config:\n - name: Fast PyTorch Pipeline CPU tests\n framework: pytorch_pipelines\n runner: [ self-hosted, intel-cpu, 32-cpu, 256-ram, ci ]\n image: diffusers/diffusers-pytorch-cpu\n report: torch_cpu_pipelines\n - name: Fast PyTorch Models & Schedulers CPU tests\n framework: pytorch_models\n runner: [ self-hosted, intel-cpu, 8-cpu, ci ]\n image: diffusers/diffusers-pytorch-cpu\n report: torch_cpu_models_schedulers\n - name: Fast Flax CPU tests\n framework: flax\n runner: [ self-hosted, intel-cpu, 8-cpu, ci ]\n image: diffusers/diffusers-flax-cpu\n report: flax_cpu\n - name: PyTorch Example CPU tests\n framework: pytorch_examples\n runner: [ self-hosted, intel-cpu, 8-cpu, ci ]\n image: diffusers/diffusers-pytorch-cpu\n report: torch_example_cpu\n\n name: ${{ matrix.config.name }}\n\n runs-on: ${{ matrix.config.runner }}\n\n container:\n image: ${{ matrix.config.image }}\n options: --shm-size \"16gb\" --ipc host -v /mnt/hf_cache:/mnt/cache/\n\n defaults:\n run:\n shell: bash\n\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install -e [quality,test]\n python -m uv pip install accelerate\n\n - name: Environment\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python utils/print_env.py\n\n - name: Run fast PyTorch Pipeline CPU tests\n if: ${{ matrix.config.framework == 'pytorch_pipelines' }}\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m pytest -n 8 --max-worker-restart=0 --dist=loadfile \\\n -s -v -k \"not Flax and not Onnx\" \\\n --make-reports=tests_${{ matrix.config.report }} \\\n tests/pipelines\n\n - name: Run fast PyTorch Model Scheduler CPU tests\n if: ${{ matrix.config.framework == 'pytorch_models' }}\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \\\n -s -v -k \"not Flax and not Onnx and not Dependency\" \\\n --make-reports=tests_${{ matrix.config.report }} \\\n tests/models tests/schedulers tests/others\n\n - name: Run fast Flax TPU tests\n if: ${{ matrix.config.framework == 'flax' }}\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \\\n -s -v -k \"Flax\" \\\n --make-reports=tests_${{ matrix.config.report }} \\\n tests\n\n - name: Run example PyTorch CPU tests\n if: ${{ matrix.config.framework == 'pytorch_examples' }}\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install peft timm\n python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \\\n --make-reports=tests_${{ matrix.config.report }} \\\n examples\n\n - name: Failure short reports\n if: ${{ failure() }}\n run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt\n\n - name: Test suite reports artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v2\n with:\n name: pr_${{ matrix.config.report }}_test_reports\n path: reports\n\n run_staging_tests:\n needs: [check_code_quality, check_repository_consistency]\n strategy:\n fail-fast: false\n matrix:\n config:\n - name: Hub tests for models, schedulers, and pipelines\n framework: hub_tests_pytorch\n runner: [ self-hosted, intel-cpu, 8-cpu, ci ]\n image: diffusers/diffusers-pytorch-cpu\n report: torch_hub\n\n name: ${{ matrix.config.name }}\n\n runs-on: ${{ matrix.config.runner }}\n\n container:\n image: ${{ matrix.config.image }}\n options: --shm-size \"16gb\" --ipc host -v /mnt/hf_cache:/mnt/cache/\n\n defaults:\n run:\n shell: bash\n\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install -e [quality,test]\n\n - name: Environment\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python utils/print_env.py\n\n - name: Run Hub tests for models, schedulers, and pipelines on a staging env\n if: ${{ matrix.config.framework == 'hub_tests_pytorch' }}\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n HUGGINGFACE_CO_STAGING=true python -m pytest \\\n -m \"is_staging_test\" \\\n --make-reports=tests_${{ matrix.config.report }} \\\n tests\n\n - name: Failure short reports\n if: ${{ failure() }}\n run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt\n\n - name: Test suite reports artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v2\n with:\n name: pr_${{ matrix.config.report }}_test_reports\n path: reports\n" }, { "path": "diffusers/.github/workflows/pr_torch_dependency_test.yml", "content": "name: Run Torch dependency tests\n\non:\n pull_request:\n branches:\n - main\n paths:\n - \"src/diffusers/**.py\"\n push:\n branches:\n - main\n\nconcurrency:\n group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}\n cancel-in-progress: true\n\njobs:\n check_torch_dependencies:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v3\n - name: Set up Python\n uses: actions/setup-python@v4\n with:\n python-version: \"3.8\"\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m pip install --upgrade pip uv\n python -m uv pip install -e .\n python -m uv pip install torch torchvision torchaudio\n python -m uv pip install pytest\n - name: Check for soft dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n pytest tests/others/test_dependencies.py\n" }, { "path": "diffusers/.github/workflows/push_tests.yml", "content": "name: Slow Tests on main\n\non:\n push:\n branches:\n - main\n paths:\n - \"src/diffusers/**.py\"\n - \"examples/**.py\"\n - \"tests/**.py\"\n\nenv:\n DIFFUSERS_IS_CI: yes\n HF_HOME: /mnt/cache\n OMP_NUM_THREADS: 8\n MKL_NUM_THREADS: 8\n PYTEST_TIMEOUT: 600\n RUN_SLOW: yes\n PIPELINE_USAGE_CUTOFF: 50000\n\njobs:\n setup_torch_cuda_pipeline_matrix:\n name: Setup Torch Pipelines CUDA Slow Tests Matrix\n runs-on: [ self-hosted, intel-cpu, 8-cpu, ci ]\n container:\n image: diffusers/diffusers-pytorch-cpu\n outputs:\n pipeline_test_matrix: ${{ steps.fetch_pipeline_matrix.outputs.pipeline_test_matrix }}\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install -e [quality,test]\n - name: Environment\n run: |\n python utils/print_env.py\n - name: Fetch Pipeline Matrix\n id: fetch_pipeline_matrix\n run: |\n matrix=$(python utils/fetch_torch_cuda_pipeline_test_matrix.py)\n echo $matrix\n echo \"pipeline_test_matrix=$matrix\" >> $GITHUB_OUTPUT\n - name: Pipeline Tests Artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v2\n with:\n name: test-pipelines.json\n path: reports\n\n torch_pipelines_cuda_tests:\n name: Torch Pipelines CUDA Slow Tests\n needs: setup_torch_cuda_pipeline_matrix\n strategy:\n fail-fast: false\n max-parallel: 8\n matrix:\n module: ${{ fromJson(needs.setup_torch_cuda_pipeline_matrix.outputs.pipeline_test_matrix) }}\n runs-on: [single-gpu, nvidia-gpu, t4, ci]\n container:\n image: diffusers/diffusers-pytorch-cuda\n options: --shm-size \"16gb\" --ipc host -v /mnt/cache/.cache/huggingface/diffusers:/mnt/cache/ --gpus 0\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n - name: NVIDIA-SMI\n run: |\n nvidia-smi\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install -e [quality,test]\n python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git\n - name: Environment\n run: |\n python utils/print_env.py\n - name: Slow PyTorch CUDA checkpoint tests on Ubuntu\n env:\n HF_TOKEN: ${{ secrets.HF_TOKEN }}\n # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms\n CUBLAS_WORKSPACE_CONFIG: :16:8\n run: |\n python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \\\n -s -v -k \"not Flax and not Onnx\" \\\n --make-reports=tests_pipeline_${{ matrix.module }}_cuda \\\n tests/pipelines/${{ matrix.module }}\n - name: Failure short reports\n if: ${{ failure() }}\n run: |\n cat reports/tests_pipeline_${{ matrix.module }}_cuda_stats.txt\n cat reports/tests_pipeline_${{ matrix.module }}_cuda_failures_short.txt\n - name: Test suite reports artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v2\n with:\n name: pipeline_${{ matrix.module }}_test_reports\n path: reports\n\n torch_cuda_tests:\n name: Torch CUDA Tests\n runs-on: [single-gpu, nvidia-gpu, t4, ci]\n container:\n image: diffusers/diffusers-pytorch-cuda\n options: --shm-size \"16gb\" --ipc host -v /mnt/cache/.cache/huggingface/diffusers:/mnt/cache/ --gpus 0\n defaults:\n run:\n shell: bash\n strategy:\n matrix:\n module: [models, schedulers, lora, others, single_file]\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install -e [quality,test]\n python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git\n\n - name: Environment\n run: |\n python utils/print_env.py\n\n - name: Run slow PyTorch CUDA tests\n env:\n HF_TOKEN: ${{ secrets.HF_TOKEN }}\n # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms\n CUBLAS_WORKSPACE_CONFIG: :16:8\n run: |\n python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \\\n -s -v -k \"not Flax and not Onnx\" \\\n --make-reports=tests_torch_cuda \\\n tests/${{ matrix.module }}\n\n - name: Failure short reports\n if: ${{ failure() }}\n run: |\n cat reports/tests_torch_cuda_stats.txt\n cat reports/tests_torch_cuda_failures_short.txt\n\n - name: Test suite reports artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v2\n with:\n name: torch_cuda_test_reports\n path: reports\n\n peft_cuda_tests:\n name: PEFT CUDA Tests\n runs-on: [single-gpu, nvidia-gpu, t4, ci]\n container:\n image: diffusers/diffusers-pytorch-cuda\n options: --shm-size \"16gb\" --ipc host -v /mnt/cache/.cache/huggingface/diffusers:/mnt/cache/ --gpus 0\n defaults:\n run:\n shell: bash\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install -e [quality,test]\n python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git\n python -m pip install -U peft@git+https://github.com/huggingface/peft.git\n\n - name: Environment\n run: |\n python utils/print_env.py\n\n - name: Run slow PEFT CUDA tests\n env:\n HF_TOKEN: ${{ secrets.HF_TOKEN }}\n # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms\n CUBLAS_WORKSPACE_CONFIG: :16:8\n run: |\n python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \\\n -s -v -k \"not Flax and not Onnx and not PEFTLoRALoading\" \\\n --make-reports=tests_peft_cuda \\\n tests/lora/\n python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \\\n -s -v -k \"lora and not Flax and not Onnx and not PEFTLoRALoading\" \\\n --make-reports=tests_peft_cuda_models_lora \\\n tests/models/\n\n - name: Failure short reports\n if: ${{ failure() }}\n run: |\n cat reports/tests_peft_cuda_stats.txt\n cat reports/tests_peft_cuda_failures_short.txt\n cat reports/tests_peft_cuda_models_lora_failures_short.txt\n\n - name: Test suite reports artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v2\n with:\n name: torch_peft_test_reports\n path: reports\n\n flax_tpu_tests:\n name: Flax TPU Tests\n runs-on: docker-tpu\n container:\n image: diffusers/diffusers-flax-tpu\n options: --shm-size \"16gb\" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ --privileged\n defaults:\n run:\n shell: bash\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install -e [quality,test]\n python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git\n\n - name: Environment\n run: |\n python utils/print_env.py\n\n - name: Run slow Flax TPU tests\n env:\n HF_TOKEN: ${{ secrets.HF_TOKEN }}\n run: |\n python -m pytest -n 0 \\\n -s -v -k \"Flax\" \\\n --make-reports=tests_flax_tpu \\\n tests/\n\n - name: Failure short reports\n if: ${{ failure() }}\n run: |\n cat reports/tests_flax_tpu_stats.txt\n cat reports/tests_flax_tpu_failures_short.txt\n\n - name: Test suite reports artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v2\n with:\n name: flax_tpu_test_reports\n path: reports\n\n onnx_cuda_tests:\n name: ONNX CUDA Tests\n runs-on: [single-gpu, nvidia-gpu, t4, ci]\n container:\n image: diffusers/diffusers-onnxruntime-cuda\n options: --shm-size \"16gb\" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ --gpus 0\n defaults:\n run:\n shell: bash\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install -e [quality,test]\n python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git\n\n - name: Environment\n run: |\n python utils/print_env.py\n\n - name: Run slow ONNXRuntime CUDA tests\n env:\n HF_TOKEN: ${{ secrets.HF_TOKEN }}\n run: |\n python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \\\n -s -v -k \"Onnx\" \\\n --make-reports=tests_onnx_cuda \\\n tests/\n\n - name: Failure short reports\n if: ${{ failure() }}\n run: |\n cat reports/tests_onnx_cuda_stats.txt\n cat reports/tests_onnx_cuda_failures_short.txt\n\n - name: Test suite reports artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v2\n with:\n name: onnx_cuda_test_reports\n path: reports\n\n run_torch_compile_tests:\n name: PyTorch Compile CUDA tests\n\n runs-on: [single-gpu, nvidia-gpu, t4, ci]\n\n container:\n image: diffusers/diffusers-pytorch-compile-cuda\n options: --gpus 0 --shm-size \"16gb\" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/\n\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n\n - name: NVIDIA-SMI\n run: |\n nvidia-smi\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install -e [quality,test,training]\n - name: Environment\n run: |\n python utils/print_env.py\n - name: Run example tests on GPU\n env:\n HF_TOKEN: ${{ secrets.HF_TOKEN }}\n run: |\n python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k \"compile\" --make-reports=tests_torch_compile_cuda tests/\n - name: Failure short reports\n if: ${{ failure() }}\n run: cat reports/tests_torch_compile_cuda_failures_short.txt\n\n - name: Test suite reports artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v2\n with:\n name: torch_compile_test_reports\n path: reports\n\n run_xformers_tests:\n name: PyTorch xformers CUDA tests\n\n runs-on: [single-gpu, nvidia-gpu, t4, ci]\n\n container:\n image: diffusers/diffusers-pytorch-xformers-cuda\n options: --gpus 0 --shm-size \"16gb\" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/\n\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n\n - name: NVIDIA-SMI\n run: |\n nvidia-smi\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install -e [quality,test,training]\n - name: Environment\n run: |\n python utils/print_env.py\n - name: Run example tests on GPU\n env:\n HF_TOKEN: ${{ secrets.HF_TOKEN }}\n run: |\n python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v -k \"xformers\" --make-reports=tests_torch_xformers_cuda tests/\n - name: Failure short reports\n if: ${{ failure() }}\n run: cat reports/tests_torch_xformers_cuda_failures_short.txt\n\n - name: Test suite reports artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v2\n with:\n name: torch_xformers_test_reports\n path: reports\n\n run_examples_tests:\n name: Examples PyTorch CUDA tests on Ubuntu\n\n runs-on: [single-gpu, nvidia-gpu, t4, ci]\n\n container:\n image: diffusers/diffusers-pytorch-cuda\n options: --gpus 0 --shm-size \"16gb\" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/\n\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n\n - name: NVIDIA-SMI\n run: |\n nvidia-smi\n\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install -e [quality,test,training]\n\n - name: Environment\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python utils/print_env.py\n\n - name: Run example tests on GPU\n env:\n HF_TOKEN: ${{ secrets.HF_TOKEN }}\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install timm\n python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/\n\n - name: Failure short reports\n if: ${{ failure() }}\n run: |\n cat reports/examples_torch_cuda_stats.txt\n cat reports/examples_torch_cuda_failures_short.txt\n\n - name: Test suite reports artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v2\n with:\n name: examples_test_reports\n path: reports\n" }, { "path": "diffusers/.github/workflows/push_tests_fast.yml", "content": "name: Fast tests on main\n\non:\n push:\n branches:\n - main\n paths:\n - \"src/diffusers/**.py\"\n - \"examples/**.py\"\n - \"tests/**.py\"\n\nconcurrency:\n group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}\n cancel-in-progress: true\n\nenv:\n DIFFUSERS_IS_CI: yes\n HF_HOME: /mnt/cache\n OMP_NUM_THREADS: 8\n MKL_NUM_THREADS: 8\n PYTEST_TIMEOUT: 600\n RUN_SLOW: no\n\njobs:\n run_fast_tests:\n strategy:\n fail-fast: false\n matrix:\n config:\n - name: Fast PyTorch CPU tests on Ubuntu\n framework: pytorch\n runner: [ self-hosted, intel-cpu, 8-cpu, ci ]\n image: diffusers/diffusers-pytorch-cpu\n report: torch_cpu\n - name: Fast Flax CPU tests on Ubuntu\n framework: flax\n runner: [ self-hosted, intel-cpu, 8-cpu, ci ]\n image: diffusers/diffusers-flax-cpu\n report: flax_cpu\n - name: Fast ONNXRuntime CPU tests on Ubuntu\n framework: onnxruntime\n runner: [ self-hosted, intel-cpu, 8-cpu, ci ]\n image: diffusers/diffusers-onnxruntime-cpu\n report: onnx_cpu\n - name: PyTorch Example CPU tests on Ubuntu\n framework: pytorch_examples\n runner: [ self-hosted, intel-cpu, 8-cpu, ci ]\n image: diffusers/diffusers-pytorch-cpu\n report: torch_example_cpu\n\n name: ${{ matrix.config.name }}\n\n runs-on: ${{ matrix.config.runner }}\n\n container:\n image: ${{ matrix.config.image }}\n options: --shm-size \"16gb\" --ipc host -v /mnt/hf_cache:/mnt/cache/\n\n defaults:\n run:\n shell: bash\n\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n\n - name: Install dependencies\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install -e [quality,test]\n\n - name: Environment\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python utils/print_env.py\n\n - name: Run fast PyTorch CPU tests\n if: ${{ matrix.config.framework == 'pytorch' }}\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \\\n -s -v -k \"not Flax and not Onnx\" \\\n --make-reports=tests_${{ matrix.config.report }} \\\n tests/\n\n - name: Run fast Flax TPU tests\n if: ${{ matrix.config.framework == 'flax' }}\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \\\n -s -v -k \"Flax\" \\\n --make-reports=tests_${{ matrix.config.report }} \\\n tests/\n\n - name: Run fast ONNXRuntime CPU tests\n if: ${{ matrix.config.framework == 'onnxruntime' }}\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \\\n -s -v -k \"Onnx\" \\\n --make-reports=tests_${{ matrix.config.report }} \\\n tests/\n\n - name: Run example PyTorch CPU tests\n if: ${{ matrix.config.framework == 'pytorch_examples' }}\n run: |\n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install peft timm\n python -m pytest -n 4 --max-worker-restart=0 --dist=loadfile \\\n --make-reports=tests_${{ matrix.config.report }} \\\n examples\n\n - name: Failure short reports\n if: ${{ failure() }}\n run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt\n\n - name: Test suite reports artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v2\n with:\n name: pr_${{ matrix.config.report }}_test_reports\n path: reports\n" }, { "path": "diffusers/.github/workflows/push_tests_mps.yml", "content": "name: Fast mps tests on main\n\non:\n push:\n branches:\n - main\n paths:\n - \"src/diffusers/**.py\"\n - \"tests/**.py\"\n\nenv:\n DIFFUSERS_IS_CI: yes\n HF_HOME: /mnt/cache\n OMP_NUM_THREADS: 8\n MKL_NUM_THREADS: 8\n PYTEST_TIMEOUT: 600\n RUN_SLOW: no\n\nconcurrency:\n group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}\n cancel-in-progress: true\n\njobs:\n run_fast_tests_apple_m1:\n name: Fast PyTorch MPS tests on MacOS\n runs-on: macos-13-xlarge\n\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n\n - name: Clean checkout\n shell: arch -arch arm64 bash {0}\n run: |\n git clean -fxd\n\n - name: Setup miniconda\n uses: ./.github/actions/setup-miniconda\n with:\n python-version: 3.9\n\n - name: Install dependencies\n shell: arch -arch arm64 bash {0}\n run: |\n ${CONDA_RUN} python -m pip install --upgrade pip uv\n ${CONDA_RUN} python -m uv pip install -e [quality,test]\n ${CONDA_RUN} python -m uv pip install torch torchvision torchaudio\n ${CONDA_RUN} python -m uv pip install accelerate@git+https://github.com/huggingface/accelerate.git\n ${CONDA_RUN} python -m uv pip install transformers --upgrade\n\n - name: Environment\n shell: arch -arch arm64 bash {0}\n run: |\n ${CONDA_RUN} python utils/print_env.py\n\n - name: Run fast PyTorch tests on M1 (MPS)\n shell: arch -arch arm64 bash {0}\n env:\n HF_HOME: /System/Volumes/Data/mnt/cache\n HF_TOKEN: ${{ secrets.HF_TOKEN }}\n run: |\n ${CONDA_RUN} python -m pytest -n 0 -s -v --make-reports=tests_torch_mps tests/\n\n - name: Failure short reports\n if: ${{ failure() }}\n run: cat reports/tests_torch_mps_failures_short.txt\n\n - name: Test suite reports artifacts\n if: ${{ always() }}\n uses: actions/upload-artifact@v2\n with:\n name: pr_torch_mps_test_reports\n path: reports\n" }, { "path": "diffusers/.github/workflows/pypi_publish.yaml", "content": "# Adapted from https://blog.deepjyoti30.dev/pypi-release-github-action\n\nname: PyPI release\n\non:\n workflow_dispatch:\n push:\n tags:\n - \"*\"\n\njobs:\n find-and-checkout-latest-branch:\n runs-on: ubuntu-latest\n outputs:\n latest_branch: ${{ steps.set_latest_branch.outputs.latest_branch }}\n steps:\n - name: Checkout Repo\n uses: actions/checkout@v3\n\n - name: Set up Python\n uses: actions/setup-python@v4\n with:\n python-version: '3.8'\n\n - name: Fetch latest branch\n id: fetch_latest_branch\n run: |\n pip install -U requests packaging\n LATEST_BRANCH=$(python utils/fetch_latest_release_branch.py)\n echo \"Latest branch: $LATEST_BRANCH\"\n echo \"latest_branch=$LATEST_BRANCH\" >> $GITHUB_ENV\n \n - name: Set latest branch output\n id: set_latest_branch\n run: echo \"::set-output name=latest_branch::${{ env.latest_branch }}\"\n\n release:\n needs: find-and-checkout-latest-branch\n runs-on: ubuntu-latest\n\n steps:\n - name: Checkout Repo\n uses: actions/checkout@v3\n with:\n ref: ${{ needs.find-and-checkout-latest-branch.outputs.latest_branch }}\n \n - name: Setup Python\n uses: actions/setup-python@v4\n with:\n python-version: \"3.8\"\n \n - name: Install dependencies\n run: |\n python -m pip install --upgrade pip\n pip install -U setuptools wheel twine\n pip install -U torch --index-url https://download.pytorch.org/whl/cpu\n pip install -U transformers\n \n - name: Build the dist files\n run: python setup.py bdist_wheel && python setup.py sdist\n \n - name: Publish to the test PyPI\n env:\n TWINE_USERNAME: ${{ secrets.TEST_PYPI_USERNAME }}\n TWINE_PASSWORD: ${{ secrets.TEST_PYPI_PASSWORD }}\n run: twine upload dist/* -r pypitest --repository-url=https://test.pypi.org/legacy/ \n\n - name: Test installing diffusers and importing\n run: |\n pip install diffusers && pip uninstall diffusers -y\n pip install -i https://testpypi.python.org/pypi diffusers\n python -c \"from diffusers import __version__; print(__version__)\"\n python -c \"from diffusers import DiffusionPipeline; pipe = DiffusionPipeline.from_pretrained('fusing/unet-ldm-dummy-update'); pipe()\"\n python -c \"from diffusers import DiffusionPipeline; pipe = DiffusionPipeline.from_pretrained('hf-internal-testing/tiny-stable-diffusion-pipe', safety_checker=None); pipe('ah suh du')\"\n python -c \"from diffusers import *\"\n\n - name: Publish to PyPI\n env:\n TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}\n TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}\n run: twine upload dist/* -r pypi\n" }, { "path": "diffusers/.github/workflows/run_tests_from_a_pr.yml", "content": "name: Check running SLOW tests from a PR (only GPU)\n\non:\n workflow_dispatch:\n inputs:\n docker_image:\n default: 'diffusers/diffusers-pytorch-cuda'\n description: 'Name of the Docker image'\n required: true\n branch: \n description: 'PR Branch to test on'\n required: true\n test:\n description: 'Tests to run (e.g.: `tests/models`).'\n required: true\n\nenv:\n DIFFUSERS_IS_CI: yes\n IS_GITHUB_CI: \"1\"\n HF_HOME: /mnt/cache\n OMP_NUM_THREADS: 8\n MKL_NUM_THREADS: 8\n PYTEST_TIMEOUT: 600\n RUN_SLOW: yes\n\njobs:\n run_tests:\n name: \"Run a test on our runner from a PR\"\n runs-on: [single-gpu, nvidia-gpu, t4, ci]\n container:\n image: ${{ github.event.inputs.docker_image }}\n options: --gpus 0 --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/\n\n steps:\n - name: Validate test files input\n id: validate_test_files\n env: \n PY_TEST: ${{ github.event.inputs.test }}\n run: |\n if [[ ! \"$PY_TEST\" =~ ^tests/ ]]; then\n echo \"Error: The input string must start with 'tests/'.\"\n exit 1\n fi\n \n if [[ ! \"$PY_TEST\" =~ ^tests/(models|pipelines) ]]; then\n echo \"Error: The input string must contain either 'models' or 'pipelines' after 'tests/'.\"\n exit 1\n fi\n \n if [[ \"$PY_TEST\" == *\";\"* ]]; then\n echo \"Error: The input string must not contain ';'.\"\n exit 1\n fi\n echo \"$PY_TEST\"\n\n - name: Checkout PR branch\n uses: actions/checkout@v4\n with:\n ref: ${{ github.event.inputs.branch }}\n repository: ${{ github.event.pull_request.head.repo.full_name }}\n\n\n - name: Install pytest \n run: | \n python -m venv /opt/venv && export PATH=\"/opt/venv/bin:$PATH\"\n python -m uv pip install -e [quality,test]\n python -m uv pip install peft\n \n - name: Run tests\n env: \n PY_TEST: ${{ github.event.inputs.test }}\n run: |\n pytest \"$PY_TEST\"" }, { "path": "diffusers/.github/workflows/ssh-runner.yml", "content": "name: SSH into runners\n\non:\n workflow_dispatch:\n inputs:\n runner_type:\n description: 'Type of runner to test (a10 or t4)'\n required: true\n docker_image:\n description: 'Name of the Docker image'\n required: true\n\nenv:\n IS_GITHUB_CI: \"1\"\n HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}\n HF_HOME: /mnt/cache\n DIFFUSERS_IS_CI: yes\n OMP_NUM_THREADS: 8\n MKL_NUM_THREADS: 8\n RUN_SLOW: yes\n\njobs:\n ssh_runner:\n name: \"SSH\"\n runs-on: [single-gpu, nvidia-gpu, \"${{ github.event.inputs.runner_type }}\", ci]\n container:\n image: ${{ github.event.inputs.docker_image }}\n options: --shm-size \"16gb\" --ipc host -v /mnt/cache/.cache/huggingface/diffusers:/mnt/cache/ --gpus 0 --privileged\n\n steps:\n - name: Checkout diffusers\n uses: actions/checkout@v3\n with:\n fetch-depth: 2\n\n - name: NVIDIA-SMI\n run: |\n nvidia-smi\n\n - name: Tailscale # In order to be able to SSH when a test fails\n uses: huggingface/tailscale-action@main\n with:\n authkey: ${{ secrets.TAILSCALE_SSH_AUTHKEY }}\n slackChannel: ${{ secrets.SLACK_CIFEEDBACK_CHANNEL }}\n slackToken: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}\n waitForSSH: true\n" }, { "path": "diffusers/.github/workflows/stale.yml", "content": "name: Stale Bot\n\non:\n schedule:\n - cron: \"0 15 * * *\"\n\njobs:\n close_stale_issues:\n name: Close Stale Issues\n if: github.repository == 'huggingface/diffusers'\n runs-on: ubuntu-latest\n env:\n GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}\n steps:\n - uses: actions/checkout@v2\n\n - name: Setup Python\n uses: actions/setup-python@v1\n with:\n python-version: 3.8\n\n - name: Install requirements\n run: |\n pip install PyGithub\n - name: Close stale issues\n run: |\n python utils/stale.py\n" }, { "path": "diffusers/.github/workflows/trufflehog.yml", "content": "on:\n push:\n\nname: Secret Leaks\n\njobs:\n trufflehog:\n runs-on: ubuntu-latest\n steps:\n - name: Checkout code\n uses: actions/checkout@v4\n with:\n fetch-depth: 0\n - name: Secret Scanning\n uses: trufflesecurity/trufflehog@main\n" }, { "path": "diffusers/.github/workflows/typos.yml", "content": "name: Check typos\n\non:\n workflow_dispatch:\n\njobs:\n build:\n runs-on: ubuntu-latest\n\n steps:\n - uses: actions/checkout@v3\n\n - name: typos-action\n uses: crate-ci/typos@v1.12.4\n" }, { "path": "diffusers/.github/workflows/update_metadata.yml", "content": "name: Update Diffusers metadata\n\non:\n workflow_dispatch:\n push:\n branches:\n - main\n - update_diffusers_metadata*\n\njobs:\n update_metadata:\n runs-on: ubuntu-22.04\n defaults:\n run:\n shell: bash -l {0}\n\n steps:\n - uses: actions/checkout@v3\n\n - name: Setup environment\n run: |\n pip install --upgrade pip\n pip install datasets pandas\n pip install .[torch]\n\n - name: Update metadata\n env:\n HF_TOKEN: ${{ secrets.SAYAK_HF_TOKEN }}\n run: |\n python utils/update_metadata.py --commit_sha ${{ github.sha }}\n" }, { "path": "diffusers/.github/workflows/upload_pr_documentation.yml", "content": "name: Upload PR Documentation\n\non:\n workflow_run:\n workflows: [\"Build PR Documentation\"]\n types:\n - completed\n\njobs:\n build:\n uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@main\n with:\n package_name: diffusers\n secrets:\n hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}\n comment_bot_token: ${{ secrets.COMMENT_BOT_TOKEN }}\n" }, { "path": "diffusers/.gitignore", "content": "# Initially taken from GitHub's Python gitignore file\n\n# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# tests and logs\ntests/fixtures/cached_*_text.txt\nlogs/\nlightning_logs/\nlang_code_data/\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a Python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n.hypothesis/\n.pytest_cache/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# vscode\n.vs\n.vscode\n\n# Pycharm\n.idea\n\n# TF code\ntensorflow_code\n\n# Models\nproc_data\n\n# examples\nruns\n/runs_old\n/wandb\n/examples/runs\n/examples/**/*.args\n/examples/rag/sweep\n\n# data\n/data\nserialization_dir\n\n# emacs\n*.*~\ndebug.env\n\n# vim\n.*.swp\n\n# ctags\ntags\n\n# pre-commit\n.pre-commit*\n\n# .lock\n*.lock\n\n# DS_Store (MacOS)\n.DS_Store\n\n# RL pipelines may produce mp4 outputs\n*.mp4\n\n# dependencies\n/transformers\n\n# ruff\n.ruff_cache\n\n# wandb\nwandb\n" }, { "path": "diffusers/CITATION.cff", "content": "cff-version: 1.2.0\ntitle: 'Diffusers: State-of-the-art diffusion models'\nmessage: >-\n If you use this software, please cite it using the\n metadata from this file.\ntype: software\nauthors:\n - given-names: Patrick\n family-names: von Platen\n - given-names: Suraj\n family-names: Patil\n - given-names: Anton\n family-names: Lozhkov\n - given-names: Pedro\n family-names: Cuenca\n - given-names: Nathan\n family-names: Lambert\n - given-names: Kashif\n family-names: Rasul\n - given-names: Mishig\n family-names: Davaadorj\n - given-names: Dhruv\n family-names: Nair\n - given-names: Sayak\n family-names: Paul\n - given-names: Steven\n family-names: Liu\n - given-names: William\n family-names: Berman\n - given-names: Yiyi\n family-names: Xu\n - given-names: Thomas\n family-names: Wolf\nrepository-code: 'https://github.com/huggingface/diffusers'\nabstract: >-\n Diffusers provides pretrained diffusion models across\n multiple modalities, such as vision and audio, and serves\n as a modular toolbox for inference and training of\n diffusion models.\nkeywords:\n - deep-learning\n - pytorch\n - image-generation\n - hacktoberfest\n - diffusion\n - text2image\n - image2image\n - score-based-generative-modeling\n - stable-diffusion\n - stable-diffusion-diffusers\nlicense: Apache-2.0\nversion: 0.12.1\n" }, { "path": "diffusers/CODE_OF_CONDUCT.md", "content": "\n# Contributor Covenant Code of Conduct\n\n## Our Pledge\n\nWe as members, contributors, and leaders pledge to make participation in our\ncommunity a harassment-free experience for everyone, regardless of age, body\nsize, visible or invisible disability, ethnicity, sex characteristics, gender\nidentity and expression, level of experience, education, socio-economic status,\nnationality, personal appearance, race, caste, color, religion, or sexual identity\nand orientation.\n\nWe pledge to act and interact in ways that contribute to an open, welcoming,\ndiverse, inclusive, and healthy community.\n\n## Our Standards\n\nExamples of behavior that contributes to a positive environment for our\ncommunity include:\n\n* Demonstrating empathy and kindness toward other people\n* Being respectful of differing opinions, viewpoints, and experiences\n* Giving and gracefully accepting constructive feedback\n* Accepting responsibility and apologizing to those affected by our mistakes,\n and learning from the experience\n* Focusing on what is best not just for us as individuals, but for the\n overall Diffusers community\n\nExamples of unacceptable behavior include:\n\n* The use of sexualized language or imagery, and sexual attention or\n advances of any kind\n* Trolling, insulting or derogatory comments, and personal or political attacks\n* Public or private harassment\n* Publishing others' private information, such as a physical or email\n address, without their explicit permission\n* Spamming issues or PRs with links to projects unrelated to this library\n* Other conduct which could reasonably be considered inappropriate in a\n professional setting\n\n## Enforcement Responsibilities\n\nCommunity leaders are responsible for clarifying and enforcing our standards of\nacceptable behavior and will take appropriate and fair corrective action in\nresponse to any behavior that they deem inappropriate, threatening, offensive,\nor harmful.\n\nCommunity leaders have the right and responsibility to remove, edit, or reject\ncomments, commits, code, wiki edits, issues, and other contributions that are\nnot aligned to this Code of Conduct, and will communicate reasons for moderation\ndecisions when appropriate.\n\n## Scope\n\nThis Code of Conduct applies within all community spaces, and also applies when\nan individual is officially representing the community in public spaces.\nExamples of representing our community include using an official e-mail address,\nposting via an official social media account, or acting as an appointed\nrepresentative at an online or offline event.\n\n## Enforcement\n\nInstances of abusive, harassing, or otherwise unacceptable behavior may be\nreported to the community leaders responsible for enforcement at\nfeedback@huggingface.co.\nAll complaints will be reviewed and investigated promptly and fairly.\n\nAll community leaders are obligated to respect the privacy and security of the\nreporter of any incident.\n\n## Enforcement Guidelines\n\nCommunity leaders will follow these Community Impact Guidelines in determining\nthe consequences for any action they deem in violation of this Code of Conduct:\n\n### 1. Correction\n\n**Community Impact**: Use of inappropriate language or other behavior deemed\nunprofessional or unwelcome in the community.\n\n**Consequence**: A private, written warning from community leaders, providing\nclarity around the nature of the violation and an explanation of why the\nbehavior was inappropriate. A public apology may be requested.\n\n### 2. Warning\n\n**Community Impact**: A violation through a single incident or series\nof actions.\n\n**Consequence**: A warning with consequences for continued behavior. No\ninteraction with the people involved, including unsolicited interaction with\nthose enforcing the Code of Conduct, for a specified period of time. This\nincludes avoiding interactions in community spaces as well as external channels\nlike social media. Violating these terms may lead to a temporary or\npermanent ban.\n\n### 3. Temporary Ban\n\n**Community Impact**: A serious violation of community standards, including\nsustained inappropriate behavior.\n\n**Consequence**: A temporary ban from any sort of interaction or public\ncommunication with the community for a specified period of time. No public or\nprivate interaction with the people involved, including unsolicited interaction\nwith those enforcing the Code of Conduct, is allowed during this period.\nViolating these terms may lead to a permanent ban.\n\n### 4. Permanent Ban\n\n**Community Impact**: Demonstrating a pattern of violation of community\nstandards, including sustained inappropriate behavior, harassment of an\nindividual, or aggression toward or disparagement of classes of individuals.\n\n**Consequence**: A permanent ban from any sort of public interaction within\nthe community.\n\n## Attribution\n\nThis Code of Conduct is adapted from the [Contributor Covenant][homepage],\nversion 2.1, available at\nhttps://www.contributor-covenant.org/version/2/1/code_of_conduct.html.\n\nCommunity Impact Guidelines were inspired by [Mozilla's code of conduct\nenforcement ladder](https://github.com/mozilla/diversity).\n\n[homepage]: https://www.contributor-covenant.org\n\nFor answers to common questions about this code of conduct, see the FAQ at\nhttps://www.contributor-covenant.org/faq. Translations are available at\nhttps://www.contributor-covenant.org/translations.\n" }, { "path": "diffusers/CONTRIBUTING.md", "content": "\n\n# How to contribute to Diffusers 🧨\n\nWe ❤️ contributions from the open-source community! Everyone is welcome, and all types of participation –not just code– are valued and appreciated. Answering questions, helping others, reaching out, and improving the documentation are all immensely valuable to the community, so don't be afraid and get involved if you're up for it!\n\nEveryone is encouraged to start by saying 👋 in our public Discord channel. We discuss the latest trends in diffusion models, ask questions, show off personal projects, help each other with contributions, or just hang out ☕. \"Join\n\nWhichever way you choose to contribute, we strive to be part of an open, welcoming, and kind community. Please, read our [code of conduct](https://github.com/huggingface/diffusers/blob/main/CODE_OF_CONDUCT.md) and be mindful to respect it during your interactions. We also recommend you become familiar with the [ethical guidelines](https://huggingface.co/docs/diffusers/conceptual/ethical_guidelines) that guide our project and ask you to adhere to the same principles of transparency and responsibility.\n\nWe enormously value feedback from the community, so please do not be afraid to speak up if you believe you have valuable feedback that can help improve the library - every message, comment, issue, and pull request (PR) is read and considered.\n\n## Overview\n\nYou can contribute in many ways ranging from answering questions on issues to adding new diffusion models to\nthe core library.\n\nIn the following, we give an overview of different ways to contribute, ranked by difficulty in ascending order. All of them are valuable to the community.\n\n* 1. Asking and answering questions on [the Diffusers discussion forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers) or on [Discord](https://discord.gg/G7tWnz98XR).\n* 2. Opening new issues on [the GitHub Issues tab](https://github.com/huggingface/diffusers/issues/new/choose).\n* 3. Answering issues on [the GitHub Issues tab](https://github.com/huggingface/diffusers/issues).\n* 4. Fix a simple issue, marked by the \"Good first issue\" label, see [here](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22).\n* 5. Contribute to the [documentation](https://github.com/huggingface/diffusers/tree/main/docs/source).\n* 6. Contribute a [Community Pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3Acommunity-examples).\n* 7. Contribute to the [examples](https://github.com/huggingface/diffusers/tree/main/examples).\n* 8. Fix a more difficult issue, marked by the \"Good second issue\" label, see [here](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22Good+second+issue%22).\n* 9. Add a new pipeline, model, or scheduler, see [\"New Pipeline/Model\"](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22) and [\"New scheduler\"](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22) issues. For this contribution, please have a look at [Design Philosophy](https://github.com/huggingface/diffusers/blob/main/PHILOSOPHY.md).\n\nAs said before, **all contributions are valuable to the community**.\nIn the following, we will explain each contribution a bit more in detail.\n\nFor all contributions 4-9, you will need to open a PR. It is explained in detail how to do so in [Opening a pull request](#how-to-open-a-pr).\n\n### 1. Asking and answering questions on the Diffusers discussion forum or on the Diffusers Discord\n\nAny question or comment related to the Diffusers library can be asked on the [discussion forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/) or on [Discord](https://discord.gg/G7tWnz98XR). Such questions and comments include (but are not limited to):\n- Reports of training or inference experiments in an attempt to share knowledge\n- Presentation of personal projects\n- Questions to non-official training examples\n- Project proposals\n- General feedback\n- Paper summaries\n- Asking for help on personal projects that build on top of the Diffusers library\n- General questions\n- Ethical questions regarding diffusion models\n- ...\n\nEvery question that is asked on the forum or on Discord actively encourages the community to publicly\nshare knowledge and might very well help a beginner in the future that has the same question you're\nhaving. Please do pose any questions you might have.\nIn the same spirit, you are of immense help to the community by answering such questions because this way you are publicly documenting knowledge for everybody to learn from.\n\n**Please** keep in mind that the more effort you put into asking or answering a question, the higher\nthe quality of the publicly documented knowledge. In the same way, well-posed and well-answered questions create a high-quality knowledge database accessible to everybody, while badly posed questions or answers reduce the overall quality of the public knowledge database.\nIn short, a high quality question or answer is *precise*, *concise*, *relevant*, *easy-to-understand*, *accessible*, and *well-formated/well-posed*. For more information, please have a look through the [How to write a good issue](#how-to-write-a-good-issue) section.\n\n**NOTE about channels**:\n[*The forum*](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) is much better indexed by search engines, such as Google. Posts are ranked by popularity rather than chronologically. Hence, it's easier to look up questions and answers that we posted some time ago.\nIn addition, questions and answers posted in the forum can easily be linked to.\nIn contrast, *Discord* has a chat-like format that invites fast back-and-forth communication.\nWhile it will most likely take less time for you to get an answer to your question on Discord, your\nquestion won't be visible anymore over time. Also, it's much harder to find information that was posted a while back on Discord. We therefore strongly recommend using the forum for high-quality questions and answers in an attempt to create long-lasting knowledge for the community. If discussions on Discord lead to very interesting answers and conclusions, we recommend posting the results on the forum to make the information more available for future readers.\n\n### 2. Opening new issues on the GitHub issues tab\n\nThe 🧨 Diffusers library is robust and reliable thanks to the users who notify us of\nthe problems they encounter. So thank you for reporting an issue.\n\nRemember, GitHub issues are reserved for technical questions directly related to the Diffusers library, bug reports, feature requests, or feedback on the library design.\n\nIn a nutshell, this means that everything that is **not** related to the **code of the Diffusers library** (including the documentation) should **not** be asked on GitHub, but rather on either the [forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) or [Discord](https://discord.gg/G7tWnz98XR).\n\n**Please consider the following guidelines when opening a new issue**:\n- Make sure you have searched whether your issue has already been asked before (use the search bar on GitHub under Issues).\n- Please never report a new issue on another (related) issue. If another issue is highly related, please\nopen a new issue nevertheless and link to the related issue.\n- Make sure your issue is written in English. Please use one of the great, free online translation services, such as [DeepL](https://www.deepl.com/translator) to translate from your native language to English if you are not comfortable in English.\n- Check whether your issue might be solved by updating to the newest Diffusers version. Before posting your issue, please make sure that `python -c \"import diffusers; print(diffusers.__version__)\"` is higher or matches the latest Diffusers version.\n- Remember that the more effort you put into opening a new issue, the higher the quality of your answer will be and the better the overall quality of the Diffusers issues.\n\nNew issues usually include the following.\n\n#### 2.1. Reproducible, minimal bug reports\n\nA bug report should always have a reproducible code snippet and be as minimal and concise as possible.\nThis means in more detail:\n- Narrow the bug down as much as you can, **do not just dump your whole code file**.\n- Format your code.\n- Do not include any external libraries except for Diffusers depending on them.\n- **Always** provide all necessary information about your environment; for this, you can run: `diffusers-cli env` in your shell and copy-paste the displayed information to the issue.\n- Explain the issue. If the reader doesn't know what the issue is and why it is an issue, she cannot solve it.\n- **Always** make sure the reader can reproduce your issue with as little effort as possible. If your code snippet cannot be run because of missing libraries or undefined variables, the reader cannot help you. Make sure your reproducible code snippet is as minimal as possible and can be copy-pasted into a simple Python shell.\n- If in order to reproduce your issue a model and/or dataset is required, make sure the reader has access to that model or dataset. You can always upload your model or dataset to the [Hub](https://huggingface.co) to make it easily downloadable. Try to keep your model and dataset as small as possible, to make the reproduction of your issue as effortless as possible.\n\nFor more information, please have a look through the [How to write a good issue](#how-to-write-a-good-issue) section.\n\nYou can open a bug report [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=bug&projects=&template=bug-report.yml).\n\n#### 2.2. Feature requests\n\nA world-class feature request addresses the following points:\n\n1. Motivation first:\n* Is it related to a problem/frustration with the library? If so, please explain\nwhy. Providing a code snippet that demonstrates the problem is best.\n* Is it related to something you would need for a project? We'd love to hear\nabout it!\n* Is it something you worked on and think could benefit the community?\nAwesome! Tell us what problem it solved for you.\n2. Write a *full paragraph* describing the feature;\n3. Provide a **code snippet** that demonstrates its future use;\n4. In case this is related to a paper, please attach a link;\n5. Attach any additional information (drawings, screenshots, etc.) you think may help.\n\nYou can open a feature request [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feature_request.md&title=).\n\n#### 2.3 Feedback\n\nFeedback about the library design and why it is good or not good helps the core maintainers immensely to build a user-friendly library. To understand the philosophy behind the current design philosophy, please have a look [here](https://huggingface.co/docs/diffusers/conceptual/philosophy). If you feel like a certain design choice does not fit with the current design philosophy, please explain why and how it should be changed. If a certain design choice follows the design philosophy too much, hence restricting use cases, explain why and how it should be changed.\nIf a certain design choice is very useful for you, please also leave a note as this is great feedback for future design decisions.\n\nYou can open an issue about feedback [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=).\n\n#### 2.4 Technical questions\n\nTechnical questions are mainly about why certain code of the library was written in a certain way, or what a certain part of the code does. Please make sure to link to the code in question and please provide detail on\nwhy this part of the code is difficult to understand.\n\nYou can open an issue about a technical question [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=bug&template=bug-report.yml).\n\n#### 2.5 Proposal to add a new model, scheduler, or pipeline\n\nIf the diffusion model community released a new model, pipeline, or scheduler that you would like to see in the Diffusers library, please provide the following information:\n\n* Short description of the diffusion pipeline, model, or scheduler and link to the paper or public release.\n* Link to any of its open-source implementation.\n* Link to the model weights if they are available.\n\nIf you are willing to contribute to the model yourself, let us know so we can best guide you. Also, don't forget\nto tag the original author of the component (model, scheduler, pipeline, etc.) by GitHub handle if you can find it.\n\nYou can open a request for a model/pipeline/scheduler [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=New+model%2Fpipeline%2Fscheduler&template=new-model-addition.yml).\n\n### 3. Answering issues on the GitHub issues tab\n\nAnswering issues on GitHub might require some technical knowledge of Diffusers, but we encourage everybody to give it a try even if you are not 100% certain that your answer is correct.\nSome tips to give a high-quality answer to an issue:\n- Be as concise and minimal as possible.\n- Stay on topic. An answer to the issue should concern the issue and only the issue.\n- Provide links to code, papers, or other sources that prove or encourage your point.\n- Answer in code. If a simple code snippet is the answer to the issue or shows how the issue can be solved, please provide a fully reproducible code snippet.\n\nAlso, many issues tend to be simply off-topic, duplicates of other issues, or irrelevant. It is of great\nhelp to the maintainers if you can answer such issues, encouraging the author of the issue to be\nmore precise, provide the link to a duplicated issue or redirect them to [the forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) or [Discord](https://discord.gg/G7tWnz98XR).\n\nIf you have verified that the issued bug report is correct and requires a correction in the source code,\nplease have a look at the next sections.\n\nFor all of the following contributions, you will need to open a PR. It is explained in detail how to do so in the [Opening a pull request](#how-to-open-a-pr) section.\n\n### 4. Fixing a \"Good first issue\"\n\n*Good first issues* are marked by the [Good first issue](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) label. Usually, the issue already\nexplains how a potential solution should look so that it is easier to fix.\nIf the issue hasn't been closed and you would like to try to fix this issue, you can just leave a message \"I would like to try this issue.\". There are usually three scenarios:\n- a.) The issue description already proposes a fix. In this case and if the solution makes sense to you, you can open a PR or draft PR to fix it.\n- b.) The issue description does not propose a fix. In this case, you can ask what a proposed fix could look like and someone from the Diffusers team should answer shortly. If you have a good idea of how to fix it, feel free to directly open a PR.\n- c.) There is already an open PR to fix the issue, but the issue hasn't been closed yet. If the PR has gone stale, you can simply open a new PR and link to the stale PR. PRs often go stale if the original contributor who wanted to fix the issue suddenly cannot find the time anymore to proceed. This often happens in open-source and is very normal. In this case, the community will be very happy if you give it a new try and leverage the knowledge of the existing PR. If there is already a PR and it is active, you can help the author by giving suggestions, reviewing the PR or even asking whether you can contribute to the PR.\n\n\n### 5. Contribute to the documentation\n\nA good library **always** has good documentation! The official documentation is often one of the first points of contact for new users of the library, and therefore contributing to the documentation is a **highly\nvaluable contribution**.\n\nContributing to the library can have many forms:\n\n- Correcting spelling or grammatical errors.\n- Correct incorrect formatting of the docstring. If you see that the official documentation is weirdly displayed or a link is broken, we are very happy if you take some time to correct it.\n- Correct the shape or dimensions of a docstring input or output tensor.\n- Clarify documentation that is hard to understand or incorrect.\n- Update outdated code examples.\n- Translating the documentation to another language.\n\nAnything displayed on [the official Diffusers doc page](https://huggingface.co/docs/diffusers/index) is part of the official documentation and can be corrected, adjusted in the respective [documentation source](https://github.com/huggingface/diffusers/tree/main/docs/source).\n\nPlease have a look at [this page](https://github.com/huggingface/diffusers/tree/main/docs) on how to verify changes made to the documentation locally.\n\n\n### 6. Contribute a community pipeline\n\n[Pipelines](https://huggingface.co/docs/diffusers/api/pipelines/overview) are usually the first point of contact between the Diffusers library and the user.\nPipelines are examples of how to use Diffusers [models](https://huggingface.co/docs/diffusers/api/models/overview) and [schedulers](https://huggingface.co/docs/diffusers/api/schedulers/overview).\nWe support two types of pipelines:\n\n- Official Pipelines\n- Community Pipelines\n\nBoth official and community pipelines follow the same design and consist of the same type of components.\n\nOfficial pipelines are tested and maintained by the core maintainers of Diffusers. Their code\nresides in [src/diffusers/pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines).\nIn contrast, community pipelines are contributed and maintained purely by the **community** and are **not** tested.\nThey reside in [examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) and while they can be accessed via the [PyPI diffusers package](https://pypi.org/project/diffusers/), their code is not part of the PyPI distribution.\n\nThe reason for the distinction is that the core maintainers of the Diffusers library cannot maintain and test all\npossible ways diffusion models can be used for inference, but some of them may be of interest to the community.\nOfficially released diffusion pipelines,\nsuch as Stable Diffusion are added to the core src/diffusers/pipelines package which ensures\nhigh quality of maintenance, no backward-breaking code changes, and testing.\nMore bleeding edge pipelines should be added as community pipelines. If usage for a community pipeline is high, the pipeline can be moved to the official pipelines upon request from the community. This is one of the ways we strive to be a community-driven library.\n\nTo add a community pipeline, one should add a .py file to [examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) and adapt the [examples/community/README.md](https://github.com/huggingface/diffusers/tree/main/examples/community/README.md) to include an example of the new pipeline.\n\nAn example can be seen [here](https://github.com/huggingface/diffusers/pull/2400).\n\nCommunity pipeline PRs are only checked at a superficial level and ideally they should be maintained by their original authors.\n\nContributing a community pipeline is a great way to understand how Diffusers models and schedulers work. Having contributed a community pipeline is usually the first stepping stone to contributing an official pipeline to the\ncore package.\n\n### 7. Contribute to training examples\n\nDiffusers examples are a collection of training scripts that reside in [examples](https://github.com/huggingface/diffusers/tree/main/examples).\n\nWe support two types of training examples:\n\n- Official training examples\n- Research training examples\n\nResearch training examples are located in [examples/research_projects](https://github.com/huggingface/diffusers/tree/main/examples/research_projects) whereas official training examples include all folders under [examples](https://github.com/huggingface/diffusers/tree/main/examples) except the `research_projects` and `community` folders.\nThe official training examples are maintained by the Diffusers' core maintainers whereas the research training examples are maintained by the community.\nThis is because of the same reasons put forward in [6. Contribute a community pipeline](#6-contribute-a-community-pipeline) for official pipelines vs. community pipelines: It is not feasible for the core maintainers to maintain all possible training methods for diffusion models.\nIf the Diffusers core maintainers and the community consider a certain training paradigm to be too experimental or not popular enough, the corresponding training code should be put in the `research_projects` folder and maintained by the author.\n\nBoth official training and research examples consist of a directory that contains one or more training scripts, a requirements.txt file, and a README.md file. In order for the user to make use of the\ntraining examples, it is required to clone the repository:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\n```\n\nas well as to install all additional dependencies required for training:\n\n```bash\npip install -r /examples//requirements.txt\n```\n\nTherefore when adding an example, the `requirements.txt` file shall define all pip dependencies required for your training example so that once all those are installed, the user can run the example's training script. See, for example, the [DreamBooth `requirements.txt` file](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/requirements.txt).\n\nTraining examples of the Diffusers library should adhere to the following philosophy:\n- All the code necessary to run the examples should be found in a single Python file.\n- One should be able to run the example from the command line with `python .py --args`.\n- Examples should be kept simple and serve as **an example** on how to use Diffusers for training. The purpose of example scripts is **not** to create state-of-the-art diffusion models, but rather to reproduce known training schemes without adding too much custom logic. As a byproduct of this point, our examples also strive to serve as good educational materials.\n\nTo contribute an example, it is highly recommended to look at already existing examples such as [dreambooth](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py) to get an idea of how they should look like.\nWe strongly advise contributors to make use of the [Accelerate library](https://github.com/huggingface/accelerate) as it's tightly integrated\nwith Diffusers.\nOnce an example script works, please make sure to add a comprehensive `README.md` that states how to use the example exactly. This README should include:\n- An example command on how to run the example script as shown [here e.g.](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth#running-locally-with-pytorch).\n- A link to some training results (logs, models, ...) that show what the user can expect as shown [here e.g.](https://api.wandb.ai/report/patrickvonplaten/xm6cd5q5).\n- If you are adding a non-official/research training example, **please don't forget** to add a sentence that you are maintaining this training example which includes your git handle as shown [here](https://github.com/huggingface/diffusers/tree/main/examples/research_projects/intel_opts#diffusers-examples-with-intel-optimizations).\n\nIf you are contributing to the official training examples, please also make sure to add a test to [examples/test_examples.py](https://github.com/huggingface/diffusers/blob/main/examples/test_examples.py). This is not necessary for non-official training examples.\n\n### 8. Fixing a \"Good second issue\"\n\n*Good second issues* are marked by the [Good second issue](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22Good+second+issue%22) label. Good second issues are\nusually more complicated to solve than [Good first issues](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22).\nThe issue description usually gives less guidance on how to fix the issue and requires\na decent understanding of the library by the interested contributor.\nIf you are interested in tackling a good second issue, feel free to open a PR to fix it and link the PR to the issue. If you see that a PR has already been opened for this issue but did not get merged, have a look to understand why it wasn't merged and try to open an improved PR.\nGood second issues are usually more difficult to get merged compared to good first issues, so don't hesitate to ask for help from the core maintainers. If your PR is almost finished the core maintainers can also jump into your PR and commit to it in order to get it merged.\n\n### 9. Adding pipelines, models, schedulers\n\nPipelines, models, and schedulers are the most important pieces of the Diffusers library.\nThey provide easy access to state-of-the-art diffusion technologies and thus allow the community to\nbuild powerful generative AI applications.\n\nBy adding a new model, pipeline, or scheduler you might enable a new powerful use case for any of the user interfaces relying on Diffusers which can be of immense value for the whole generative AI ecosystem.\n\nDiffusers has a couple of open feature requests for all three components - feel free to gloss over them\nif you don't know yet what specific component you would like to add:\n- [Model or pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22)\n- [Scheduler](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22)\n\nBefore adding any of the three components, it is strongly recommended that you give the [Philosophy guide](https://github.com/huggingface/diffusers/blob/main/PHILOSOPHY.md) a read to better understand the design of any of the three components. Please be aware that\nwe cannot merge model, scheduler, or pipeline additions that strongly diverge from our design philosophy\nas it will lead to API inconsistencies. If you fundamentally disagree with a design choice, please\nopen a [Feedback issue](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=) instead so that it can be discussed whether a certain design\npattern/design choice shall be changed everywhere in the library and whether we shall update our design philosophy. Consistency across the library is very important for us.\n\nPlease make sure to add links to the original codebase/paper to the PR and ideally also ping the\noriginal author directly on the PR so that they can follow the progress and potentially help with questions.\n\nIf you are unsure or stuck in the PR, don't hesitate to leave a message to ask for a first review or help.\n\n## How to write a good issue\n\n**The better your issue is written, the higher the chances that it will be quickly resolved.**\n\n1. Make sure that you've used the correct template for your issue. You can pick between *Bug Report*, *Feature Request*, *Feedback about API Design*, *New model/pipeline/scheduler addition*, *Forum*, or a blank issue. Make sure to pick the correct one when opening [a new issue](https://github.com/huggingface/diffusers/issues/new/choose).\n2. **Be precise**: Give your issue a fitting title. Try to formulate your issue description as simple as possible. The more precise you are when submitting an issue, the less time it takes to understand the issue and potentially solve it. Make sure to open an issue for one issue only and not for multiple issues. If you found multiple issues, simply open multiple issues. If your issue is a bug, try to be as precise as possible about what bug it is - you should not just write \"Error in diffusers\".\n3. **Reproducibility**: No reproducible code snippet == no solution. If you encounter a bug, maintainers **have to be able to reproduce** it. Make sure that you include a code snippet that can be copy-pasted into a Python interpreter to reproduce the issue. Make sure that your code snippet works, *i.e.* that there are no missing imports or missing links to images, ... Your issue should contain an error message **and** a code snippet that can be copy-pasted without any changes to reproduce the exact same error message. If your issue is using local model weights or local data that cannot be accessed by the reader, the issue cannot be solved. If you cannot share your data or model, try to make a dummy model or dummy data.\n4. **Minimalistic**: Try to help the reader as much as you can to understand the issue as quickly as possible by staying as concise as possible. Remove all code / all information that is irrelevant to the issue. If you have found a bug, try to create the easiest code example you can to demonstrate your issue, do not just dump your whole workflow into the issue as soon as you have found a bug. E.g., if you train a model and get an error at some point during the training, you should first try to understand what part of the training code is responsible for the error and try to reproduce it with a couple of lines. Try to use dummy data instead of full datasets.\n5. Add links. If you are referring to a certain naming, method, or model make sure to provide a link so that the reader can better understand what you mean. If you are referring to a specific PR or issue, make sure to link it to your issue. Do not assume that the reader knows what you are talking about. The more links you add to your issue the better.\n6. Formatting. Make sure to nicely format your issue by formatting code into Python code syntax, and error messages into normal code syntax. See the [official GitHub formatting docs](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax) for more information.\n7. Think of your issue not as a ticket to be solved, but rather as a beautiful entry to a well-written encyclopedia. Every added issue is a contribution to publicly available knowledge. By adding a nicely written issue you not only make it easier for maintainers to solve your issue, but you are helping the whole community to better understand a certain aspect of the library.\n\n## How to write a good PR\n\n1. Be a chameleon. Understand existing design patterns and syntax and make sure your code additions flow seamlessly into the existing code base. Pull requests that significantly diverge from existing design patterns or user interfaces will not be merged.\n2. Be laser focused. A pull request should solve one problem and one problem only. Make sure to not fall into the trap of \"also fixing another problem while we're adding it\". It is much more difficult to review pull requests that solve multiple, unrelated problems at once.\n3. If helpful, try to add a code snippet that displays an example of how your addition can be used.\n4. The title of your pull request should be a summary of its contribution.\n5. If your pull request addresses an issue, please mention the issue number in\nthe pull request description to make sure they are linked (and people\nconsulting the issue know you are working on it);\n6. To indicate a work in progress please prefix the title with `[WIP]`. These\nare useful to avoid duplicated work, and to differentiate it from PRs ready\nto be merged;\n7. Try to formulate and format your text as explained in [How to write a good issue](#how-to-write-a-good-issue).\n8. Make sure existing tests pass;\n9. Add high-coverage tests. No quality testing = no merge.\n- If you are adding new `@slow` tests, make sure they pass using\n`RUN_SLOW=1 python -m pytest tests/test_my_new_model.py`.\nCircleCI does not run the slow tests, but GitHub Actions does every night!\n10. All public methods must have informative docstrings that work nicely with markdown. See [`pipeline_latent_diffusion.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py) for an example.\n11. Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted `dataset` like\n[`hf-internal-testing`](https://huggingface.co/hf-internal-testing) or [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images) to place these files.\nIf an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images\nto this dataset.\n\n## How to open a PR\n\nBefore writing code, we strongly advise you to search through the existing PRs or\nissues to make sure that nobody is already working on the same thing. If you are\nunsure, it is always a good idea to open an issue to get some feedback.\n\nYou will need basic `git` proficiency to be able to contribute to\n🧨 Diffusers. `git` is not the easiest tool to use but it has the greatest\nmanual. Type `git --help` in a shell and enjoy. If you prefer books, [Pro\nGit](https://git-scm.com/book/en/v2) is a very good reference.\n\nFollow these steps to start contributing ([supported Python versions](https://github.com/huggingface/diffusers/blob/42f25d601a910dceadaee6c44345896b4cfa9928/setup.py#L270)):\n\n1. Fork the [repository](https://github.com/huggingface/diffusers) by\nclicking on the 'Fork' button on the repository's page. This creates a copy of the code\nunder your GitHub user account.\n\n2. Clone your fork to your local disk, and add the base repository as a remote:\n\n ```bash\n $ git clone git@github.com:/diffusers.git\n $ cd diffusers\n $ git remote add upstream https://github.com/huggingface/diffusers.git\n ```\n\n3. Create a new branch to hold your development changes:\n\n ```bash\n $ git checkout -b a-descriptive-name-for-my-changes\n ```\n\n**Do not** work on the `main` branch.\n\n4. Set up a development environment by running the following command in a virtual environment:\n\n ```bash\n $ pip install -e \".[dev]\"\n ```\n\nIf you have already cloned the repo, you might need to `git pull` to get the most recent changes in the\nlibrary.\n\n5. Develop the features on your branch.\n\nAs you work on the features, you should make sure that the test suite\npasses. You should run the tests impacted by your changes like this:\n\n ```bash\n $ pytest tests/.py\n ```\n\nBefore you run the tests, please make sure you install the dependencies required for testing. You can do so\nwith this command:\n\n ```bash\n $ pip install -e \".[test]\"\n ```\n\nYou can also run the full test suite with the following command, but it takes\na beefy machine to produce a result in a decent amount of time now that\nDiffusers has grown a lot. Here is the command for it:\n\n ```bash\n $ make test\n ```\n\n🧨 Diffusers relies on `ruff` and `isort` to format its source code\nconsistently. After you make changes, apply automatic style corrections and code verifications\nthat can't be automated in one go with:\n\n ```bash\n $ make style\n ```\n\n🧨 Diffusers also uses `ruff` and a few custom scripts to check for coding mistakes. Quality\ncontrol runs in CI, however, you can also run the same checks with:\n\n ```bash\n $ make quality\n ```\n\nOnce you're happy with your changes, add changed files using `git add` and\nmake a commit with `git commit` to record your changes locally:\n\n ```bash\n $ git add modified_file.py\n $ git commit -m \"A descriptive message about your changes.\"\n ```\n\nIt is a good idea to sync your copy of the code with the original\nrepository regularly. This way you can quickly account for changes:\n\n ```bash\n $ git pull upstream main\n ```\n\nPush the changes to your account using:\n\n ```bash\n $ git push -u origin a-descriptive-name-for-my-changes\n ```\n\n6. Once you are satisfied, go to the\nwebpage of your fork on GitHub. Click on 'Pull request' to send your changes\nto the project maintainers for review.\n\n7. It's ok if maintainers ask you for changes. It happens to core contributors\ntoo! So everyone can see the changes in the Pull request, work in your local\nbranch and push the changes to your fork. They will automatically appear in\nthe pull request.\n\n### Tests\n\nAn extensive test suite is included to test the library behavior and several examples. Library tests can be found in\nthe [tests folder](https://github.com/huggingface/diffusers/tree/main/tests).\n\nWe like `pytest` and `pytest-xdist` because it's faster. From the root of the\nrepository, here's how to run tests with `pytest` for the library:\n\n```bash\n$ python -m pytest -n auto --dist=loadfile -s -v ./tests/\n```\n\nIn fact, that's how `make test` is implemented!\n\nYou can specify a smaller set of tests in order to test only the feature\nyou're working on.\n\nBy default, slow tests are skipped. Set the `RUN_SLOW` environment variable to\n`yes` to run them. This will download many gigabytes of models — make sure you\nhave enough disk space and a good Internet connection, or a lot of patience!\n\n```bash\n$ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/\n```\n\n`unittest` is fully supported, here's how to run tests with it:\n\n```bash\n$ python -m unittest discover -s tests -t . -v\n$ python -m unittest discover -s examples -t examples -v\n```\n\n### Syncing forked main with upstream (HuggingFace) main\n\nTo avoid pinging the upstream repository which adds reference notes to each upstream PR and sends unnecessary notifications to the developers involved in these PRs,\nwhen syncing the main branch of a forked repository, please, follow these steps:\n1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead, merge directly into the forked main.\n2. If a PR is absolutely necessary, use the following steps after checking out your branch:\n```bash\n$ git checkout -b your-branch-for-syncing\n$ git pull --squash --no-commit upstream main\n$ git commit -m ''\n$ git push --set-upstream origin your-branch-for-syncing\n```\n\n### Style guide\n\nFor documentation strings, 🧨 Diffusers follows the [Google style](https://google.github.io/styleguide/pyguide.html).\n" }, { "path": "diffusers/LICENSE", "content": " Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n\n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n 1. 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We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n\n Copyright [yyyy] [name of copyright owner]\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n" }, { "path": "diffusers/MANIFEST.in", "content": "include LICENSE\ninclude src/diffusers/utils/model_card_template.md\n" }, { "path": "diffusers/Makefile", "content": ".PHONY: deps_table_update modified_only_fixup extra_style_checks quality style fixup fix-copies test test-examples\n\n# make sure to test the local checkout in scripts and not the pre-installed one (don't use quotes!)\nexport PYTHONPATH = src\n\ncheck_dirs := examples scripts src tests utils benchmarks\n\nmodified_only_fixup:\n\t$(eval modified_py_files := $(shell python utils/get_modified_files.py $(check_dirs)))\n\t@if test -n \"$(modified_py_files)\"; then \\\n\t\techo \"Checking/fixing $(modified_py_files)\"; \\\n\t\truff check $(modified_py_files) --fix; \\\n\t\truff format $(modified_py_files);\\\n\telse \\\n\t\techo \"No library .py files were modified\"; \\\n\tfi\n\n# Update src/diffusers/dependency_versions_table.py\n\ndeps_table_update:\n\t@python setup.py deps_table_update\n\ndeps_table_check_updated:\n\t@md5sum src/diffusers/dependency_versions_table.py > md5sum.saved\n\t@python setup.py deps_table_update\n\t@md5sum -c --quiet md5sum.saved || (printf \"\\nError: the version dependency table is outdated.\\nPlease run 'make fixup' or 'make style' and commit the changes.\\n\\n\" && exit 1)\n\t@rm md5sum.saved\n\n# autogenerating code\n\nautogenerate_code: deps_table_update\n\n# Check that the repo is in a good state\n\nrepo-consistency:\n\tpython utils/check_dummies.py\n\tpython utils/check_repo.py\n\tpython utils/check_inits.py\n\n# this target runs checks on all files\n\nquality:\n\truff check $(check_dirs) setup.py\n\truff format --check $(check_dirs) setup.py\n\tdoc-builder style src/diffusers docs/source --max_len 119 --check_only\n\tpython utils/check_doc_toc.py\n\n# Format source code automatically and check is there are any problems left that need manual fixing\n\nextra_style_checks:\n\tpython utils/custom_init_isort.py\n\tpython utils/check_doc_toc.py --fix_and_overwrite\n\n# this target runs checks on all files and potentially modifies some of them\n\nstyle:\n\truff check $(check_dirs) setup.py --fix\n\truff format $(check_dirs) setup.py\n\tdoc-builder style src/diffusers docs/source --max_len 119\n\t${MAKE} autogenerate_code\n\t${MAKE} extra_style_checks\n\n# Super fast fix and check target that only works on relevant modified files since the branch was made\n\nfixup: modified_only_fixup extra_style_checks autogenerate_code repo-consistency\n\n# Make marked copies of snippets of codes conform to the original\n\nfix-copies:\n\tpython utils/check_copies.py --fix_and_overwrite\n\tpython utils/check_dummies.py --fix_and_overwrite\n\n# Run tests for the library\n\ntest:\n\tpython -m pytest -n auto --dist=loadfile -s -v ./tests/\n\n# Run tests for examples\n\ntest-examples:\n\tpython -m pytest -n auto --dist=loadfile -s -v ./examples/\n\n\n# Release stuff\n\npre-release:\n\tpython utils/release.py\n\npre-patch:\n\tpython utils/release.py --patch\n\npost-release:\n\tpython utils/release.py --post_release\n\npost-patch:\n\tpython utils/release.py --post_release --patch\n" }, { "path": "diffusers/PHILOSOPHY.md", "content": "\n\n# Philosophy\n\n🧨 Diffusers provides **state-of-the-art** pretrained diffusion models across multiple modalities.\nIts purpose is to serve as a **modular toolbox** for both inference and training.\n\nWe aim at building a library that stands the test of time and therefore take API design very seriously.\n\nIn a nutshell, Diffusers is built to be a natural extension of PyTorch. Therefore, most of our design choices are based on [PyTorch's Design Principles](https://pytorch.org/docs/stable/community/design.html#pytorch-design-philosophy). Let's go over the most important ones:\n\n## Usability over Performance\n\n- While Diffusers has many built-in performance-enhancing features (see [Memory and Speed](https://huggingface.co/docs/diffusers/optimization/fp16)), models are always loaded with the highest precision and lowest optimization. Therefore, by default diffusion pipelines are always instantiated on CPU with float32 precision if not otherwise defined by the user. This ensures usability across different platforms and accelerators and means that no complex installations are required to run the library.\n- Diffusers aims to be a **light-weight** package and therefore has very few required dependencies, but many soft dependencies that can improve performance (such as `accelerate`, `safetensors`, `onnx`, etc...). We strive to keep the library as lightweight as possible so that it can be added without much concern as a dependency on other packages.\n- Diffusers prefers simple, self-explainable code over condensed, magic code. This means that short-hand code syntaxes such as lambda functions, and advanced PyTorch operators are often not desired.\n\n## Simple over easy\n\nAs PyTorch states, **explicit is better than implicit** and **simple is better than complex**. This design philosophy is reflected in multiple parts of the library:\n- We follow PyTorch's API with methods like [`DiffusionPipeline.to`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.to) to let the user handle device management.\n- Raising concise error messages is preferred to silently correct erroneous input. Diffusers aims at teaching the user, rather than making the library as easy to use as possible.\n- Complex model vs. scheduler logic is exposed instead of magically handled inside. Schedulers/Samplers are separated from diffusion models with minimal dependencies on each other. This forces the user to write the unrolled denoising loop. However, the separation allows for easier debugging and gives the user more control over adapting the denoising process or switching out diffusion models or schedulers.\n- Separately trained components of the diffusion pipeline, *e.g.* the text encoder, the UNet, and the variational autoencoder, each has their own model class. This forces the user to handle the interaction between the different model components, and the serialization format separates the model components into different files. However, this allows for easier debugging and customization. DreamBooth or Textual Inversion training\nis very simple thanks to Diffusers' ability to separate single components of the diffusion pipeline.\n\n## Tweakable, contributor-friendly over abstraction\n\nFor large parts of the library, Diffusers adopts an important design principle of the [Transformers library](https://github.com/huggingface/transformers), which is to prefer copy-pasted code over hasty abstractions. This design principle is very opinionated and stands in stark contrast to popular design principles such as [Don't repeat yourself (DRY)](https://en.wikipedia.org/wiki/Don%27t_repeat_yourself).\nIn short, just like Transformers does for modeling files, Diffusers prefers to keep an extremely low level of abstraction and very self-contained code for pipelines and schedulers.\nFunctions, long code blocks, and even classes can be copied across multiple files which at first can look like a bad, sloppy design choice that makes the library unmaintainable.\n**However**, this design has proven to be extremely successful for Transformers and makes a lot of sense for community-driven, open-source machine learning libraries because:\n- Machine Learning is an extremely fast-moving field in which paradigms, model architectures, and algorithms are changing rapidly, which therefore makes it very difficult to define long-lasting code abstractions.\n- Machine Learning practitioners like to be able to quickly tweak existing code for ideation and research and therefore prefer self-contained code over one that contains many abstractions.\n- Open-source libraries rely on community contributions and therefore must build a library that is easy to contribute to. The more abstract the code, the more dependencies, the harder to read, and the harder to contribute to. Contributors simply stop contributing to very abstract libraries out of fear of breaking vital functionality. If contributing to a library cannot break other fundamental code, not only is it more inviting for potential new contributors, but it is also easier to review and contribute to multiple parts in parallel.\n\nAt Hugging Face, we call this design the **single-file policy** which means that almost all of the code of a certain class should be written in a single, self-contained file. To read more about the philosophy, you can have a look\nat [this blog post](https://huggingface.co/blog/transformers-design-philosophy).\n\nIn Diffusers, we follow this philosophy for both pipelines and schedulers, but only partly for diffusion models. The reason we don't follow this design fully for diffusion models is because almost all diffusion pipelines, such\nas [DDPM](https://huggingface.co/docs/diffusers/api/pipelines/ddpm), [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview#stable-diffusion-pipelines), [unCLIP (DALL·E 2)](https://huggingface.co/docs/diffusers/api/pipelines/unclip) and [Imagen](https://imagen.research.google/) all rely on the same diffusion model, the [UNet](https://huggingface.co/docs/diffusers/api/models/unet2d-cond).\n\nGreat, now you should have generally understood why 🧨 Diffusers is designed the way it is 🤗.\nWe try to apply these design principles consistently across the library. Nevertheless, there are some minor exceptions to the philosophy or some unlucky design choices. If you have feedback regarding the design, we would ❤️ to hear it [directly on GitHub](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=).\n\n## Design Philosophy in Details\n\nNow, let's look a bit into the nitty-gritty details of the design philosophy. Diffusers essentially consists of three major classes: [pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines), [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models), and [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers).\nLet's walk through more detailed design decisions for each class.\n\n### Pipelines\n\nPipelines are designed to be easy to use (therefore do not follow [*Simple over easy*](#simple-over-easy) 100%), are not feature complete, and should loosely be seen as examples of how to use [models](#models) and [schedulers](#schedulers) for inference.\n\nThe following design principles are followed:\n- Pipelines follow the single-file policy. All pipelines can be found in individual directories under src/diffusers/pipelines. One pipeline folder corresponds to one diffusion paper/project/release. Multiple pipeline files can be gathered in one pipeline folder, as it’s done for [`src/diffusers/pipelines/stable-diffusion`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/stable_diffusion). If pipelines share similar functionality, one can make use of the [#Copied from mechanism](https://github.com/huggingface/diffusers/blob/125d783076e5bd9785beb05367a2d2566843a271/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py#L251).\n- Pipelines all inherit from [`DiffusionPipeline`].\n- Every pipeline consists of different model and scheduler components, that are documented in the [`model_index.json` file](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/model_index.json), are accessible under the same name as attributes of the pipeline and can be shared between pipelines with [`DiffusionPipeline.components`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.components) function.\n- Every pipeline should be loadable via the [`DiffusionPipeline.from_pretrained`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained) function.\n- Pipelines should be used **only** for inference.\n- Pipelines should be very readable, self-explanatory, and easy to tweak.\n- Pipelines should be designed to build on top of each other and be easy to integrate into higher-level APIs.\n- Pipelines are **not** intended to be feature-complete user interfaces. For future complete user interfaces one should rather have a look at [InvokeAI](https://github.com/invoke-ai/InvokeAI), [Diffuzers](https://github.com/abhishekkrthakur/diffuzers), and [lama-cleaner](https://github.com/Sanster/lama-cleaner).\n- Every pipeline should have one and only one way to run it via a `__call__` method. The naming of the `__call__` arguments should be shared across all pipelines.\n- Pipelines should be named after the task they are intended to solve.\n- In almost all cases, novel diffusion pipelines shall be implemented in a new pipeline folder/file.\n\n### Models\n\nModels are designed as configurable toolboxes that are natural extensions of [PyTorch's Module class](https://pytorch.org/docs/stable/generated/torch.nn.Module.html). They only partly follow the **single-file policy**.\n\nThe following design principles are followed:\n- Models correspond to **a type of model architecture**. *E.g.* the [`UNet2DConditionModel`] class is used for all UNet variations that expect 2D image inputs and are conditioned on some context.\n- All models can be found in [`src/diffusers/models`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and every model architecture shall be defined in its file, e.g. [`unet_2d_condition.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py), [`transformer_2d.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformer_2d.py), etc...\n- Models **do not** follow the single-file policy and should make use of smaller model building blocks, such as [`attention.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py), [`resnet.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py), [`embeddings.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/embeddings.py), etc... **Note**: This is in stark contrast to Transformers' modeling files and shows that models do not really follow the single-file policy.\n- Models intend to expose complexity, just like PyTorch's `Module` class, and give clear error messages.\n- Models all inherit from `ModelMixin` and `ConfigMixin`.\n- Models can be optimized for performance when it doesn’t demand major code changes, keep backward compatibility, and give significant memory or compute gain.\n- Models should by default have the highest precision and lowest performance setting.\n- To integrate new model checkpoints whose general architecture can be classified as an architecture that already exists in Diffusers, the existing model architecture shall be adapted to make it work with the new checkpoint. One should only create a new file if the model architecture is fundamentally different.\n- Models should be designed to be easily extendable to future changes. This can be achieved by limiting public function arguments, configuration arguments, and \"foreseeing\" future changes, *e.g.* it is usually better to add `string` \"...type\" arguments that can easily be extended to new future types instead of boolean `is_..._type` arguments. Only the minimum amount of changes shall be made to existing architectures to make a new model checkpoint work.\n- The model design is a difficult trade-off between keeping code readable and concise and supporting many model checkpoints. For most parts of the modeling code, classes shall be adapted for new model checkpoints, while there are some exceptions where it is preferred to add new classes to make sure the code is kept concise and\nreadable long-term, such as [UNet blocks](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py) and [Attention processors](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n\n### Schedulers\n\nSchedulers are responsible to guide the denoising process for inference as well as to define a noise schedule for training. They are designed as individual classes with loadable configuration files and strongly follow the **single-file policy**.\n\nThe following design principles are followed:\n- All schedulers are found in [`src/diffusers/schedulers`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers).\n- Schedulers are **not** allowed to import from large utils files and shall be kept very self-contained.\n- One scheduler Python file corresponds to one scheduler algorithm (as might be defined in a paper).\n- If schedulers share similar functionalities, we can make use of the `#Copied from` mechanism.\n- Schedulers all inherit from `SchedulerMixin` and `ConfigMixin`.\n- Schedulers can be easily swapped out with the [`ConfigMixin.from_config`](https://huggingface.co/docs/diffusers/main/en/api/configuration#diffusers.ConfigMixin.from_config) method as explained in detail [here](./docs/source/en/using-diffusers/schedulers.md).\n- Every scheduler has to have a `set_num_inference_steps`, and a `step` function. `set_num_inference_steps(...)` has to be called before every denoising process, *i.e.* before `step(...)` is called.\n- Every scheduler exposes the timesteps to be \"looped over\" via a `timesteps` attribute, which is an array of timesteps the model will be called upon.\n- The `step(...)` function takes a predicted model output and the \"current\" sample (x_t) and returns the \"previous\", slightly more denoised sample (x_t-1).\n- Given the complexity of diffusion schedulers, the `step` function does not expose all the complexity and can be a bit of a \"black box\".\n- In almost all cases, novel schedulers shall be implemented in a new scheduling file.\n" }, { "path": "diffusers/README.md", "content": "\n\n

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\n \n \"GitHub\"\n \n \n \"GitHub\n \n \n \"GitHub\n \n \n \"Contributor\n \n \n \"X\n \n

\n\n🤗 Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or training your own diffusion models, 🤗 Diffusers is a modular toolbox that supports both. Our library is designed with a focus on [usability over performance](https://huggingface.co/docs/diffusers/conceptual/philosophy#usability-over-performance), [simple over easy](https://huggingface.co/docs/diffusers/conceptual/philosophy#simple-over-easy), and [customizability over abstractions](https://huggingface.co/docs/diffusers/conceptual/philosophy#tweakable-contributorfriendly-over-abstraction).\n\n🤗 Diffusers offers three core components:\n\n- State-of-the-art [diffusion pipelines](https://huggingface.co/docs/diffusers/api/pipelines/overview) that can be run in inference with just a few lines of code.\n- Interchangeable noise [schedulers](https://huggingface.co/docs/diffusers/api/schedulers/overview) for different diffusion speeds and output quality.\n- Pretrained [models](https://huggingface.co/docs/diffusers/api/models/overview) that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems.\n\n## Installation\n\nWe recommend installing 🤗 Diffusers in a virtual environment from PyPI or Conda. For more details about installing [PyTorch](https://pytorch.org/get-started/locally/) and [Flax](https://flax.readthedocs.io/en/latest/#installation), please refer to their official documentation.\n\n### PyTorch\n\nWith `pip` (official package):\n\n```bash\npip install --upgrade diffusers[torch]\n```\n\nWith `conda` (maintained by the community):\n\n```sh\nconda install -c conda-forge diffusers\n```\n\n### Flax\n\nWith `pip` (official package):\n\n```bash\npip install --upgrade diffusers[flax]\n```\n\n### Apple Silicon (M1/M2) support\n\nPlease refer to the [How to use Stable Diffusion in Apple Silicon](https://huggingface.co/docs/diffusers/optimization/mps) guide.\n\n## Quickstart\n\nGenerating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the `from_pretrained` method to load any pretrained diffusion model (browse the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) for 25.000+ checkpoints):\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipeline = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16)\npipeline.to(\"cuda\")\npipeline(\"An image of a squirrel in Picasso style\").images[0]\n```\n\nYou can also dig into the models and schedulers toolbox to build your own diffusion system:\n\n```python\nfrom diffusers import DDPMScheduler, UNet2DModel\nfrom PIL import Image\nimport torch\n\nscheduler = DDPMScheduler.from_pretrained(\"google/ddpm-cat-256\")\nmodel = UNet2DModel.from_pretrained(\"google/ddpm-cat-256\").to(\"cuda\")\nscheduler.set_timesteps(50)\n\nsample_size = model.config.sample_size\nnoise = torch.randn((1, 3, sample_size, sample_size), device=\"cuda\")\ninput = noise\n\nfor t in scheduler.timesteps:\n with torch.no_grad():\n noisy_residual = model(input, t).sample\n prev_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample\n input = prev_noisy_sample\n\nimage = (input / 2 + 0.5).clamp(0, 1)\nimage = image.cpu().permute(0, 2, 3, 1).numpy()[0]\nimage = Image.fromarray((image * 255).round().astype(\"uint8\"))\nimage\n```\n\nCheck out the [Quickstart](https://huggingface.co/docs/diffusers/quicktour) to launch your diffusion journey today!\n\n## How to navigate the documentation\n\n| **Documentation** | **What can I learn?** |\n|---------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [Tutorial](https://huggingface.co/docs/diffusers/tutorials/tutorial_overview) | A basic crash course for learning how to use the library's most important features like using models and schedulers to build your own diffusion system, and training your own diffusion model. |\n| [Loading](https://huggingface.co/docs/diffusers/using-diffusers/loading_overview) | Guides for how to load and configure all the components (pipelines, models, and schedulers) of the library, as well as how to use different schedulers. |\n| [Pipelines for inference](https://huggingface.co/docs/diffusers/using-diffusers/pipeline_overview) | Guides for how to use pipelines for different inference tasks, batched generation, controlling generated outputs and randomness, and how to contribute a pipeline to the library. |\n| [Optimization](https://huggingface.co/docs/diffusers/optimization/opt_overview) | Guides for how to optimize your diffusion model to run faster and consume less memory. |\n| [Training](https://huggingface.co/docs/diffusers/training/overview) | Guides for how to train a diffusion model for different tasks with different training techniques. |\n## Contribution\n\nWe ❤️ contributions from the open-source community!\nIf you want to contribute to this library, please check out our [Contribution guide](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md).\nYou can look out for [issues](https://github.com/huggingface/diffusers/issues) you'd like to tackle to contribute to the library.\n- See [Good first issues](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) for general opportunities to contribute\n- See [New model/pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22) to contribute exciting new diffusion models / diffusion pipelines\n- See [New scheduler](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22)\n\nAlso, say 👋 in our public Discord channel \"Join. We discuss the hottest trends about diffusion models, help each other with contributions, personal projects or just hang out ☕.\n\n\n## Popular Tasks & Pipelines\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
TaskPipeline🤗 Hub
Unconditional Image Generation DDPM google/ddpm-ema-church-256
Text-to-ImageStable Diffusion Text-to-Image runwayml/stable-diffusion-v1-5
Text-to-ImageunCLIP kakaobrain/karlo-v1-alpha
Text-to-ImageDeepFloyd IF DeepFloyd/IF-I-XL-v1.0
Text-to-ImageKandinsky kandinsky-community/kandinsky-2-2-decoder
Text-guided Image-to-ImageControlNet lllyasviel/sd-controlnet-canny
Text-guided Image-to-ImageInstructPix2Pix timbrooks/instruct-pix2pix
Text-guided Image-to-ImageStable Diffusion Image-to-Image runwayml/stable-diffusion-v1-5
Text-guided Image InpaintingStable Diffusion Inpainting runwayml/stable-diffusion-inpainting
Image VariationStable Diffusion Image Variation lambdalabs/sd-image-variations-diffusers
Super ResolutionStable Diffusion Upscale stabilityai/stable-diffusion-x4-upscaler
Super ResolutionStable Diffusion Latent Upscale stabilityai/sd-x2-latent-upscaler
\n\n## Popular libraries using 🧨 Diffusers\n\n- https://github.com/microsoft/TaskMatrix\n- https://github.com/invoke-ai/InvokeAI\n- https://github.com/apple/ml-stable-diffusion\n- https://github.com/Sanster/lama-cleaner\n- https://github.com/IDEA-Research/Grounded-Segment-Anything\n- https://github.com/ashawkey/stable-dreamfusion\n- https://github.com/deep-floyd/IF\n- https://github.com/bentoml/BentoML\n- https://github.com/bmaltais/kohya_ss\n- +11.000 other amazing GitHub repositories 💪\n\nThank you for using us ❤️.\n\n## Credits\n\nThis library concretizes previous work by many different authors and would not have been possible without their great research and implementations. We'd like to thank, in particular, the following implementations which have helped us in our development and without which the API could not have been as polished today:\n\n- @CompVis' latent diffusion models library, available [here](https://github.com/CompVis/latent-diffusion)\n- @hojonathanho original DDPM implementation, available [here](https://github.com/hojonathanho/diffusion) as well as the extremely useful translation into PyTorch by @pesser, available [here](https://github.com/pesser/pytorch_diffusion)\n- @ermongroup's DDIM implementation, available [here](https://github.com/ermongroup/ddim)\n- @yang-song's Score-VE and Score-VP implementations, available [here](https://github.com/yang-song/score_sde_pytorch)\n\nWe also want to thank @heejkoo for the very helpful overview of papers, code and resources on diffusion models, available [here](https://github.com/heejkoo/Awesome-Diffusion-Models) as well as @crowsonkb and @rromb for useful discussions and insights.\n\n## Citation\n\n```bibtex\n@misc{von-platen-etal-2022-diffusers,\n author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Dhruv Nair and Sayak Paul and William Berman and Yiyi Xu and Steven Liu and Thomas Wolf},\n title = {Diffusers: State-of-the-art diffusion models},\n year = {2022},\n publisher = {GitHub},\n journal = {GitHub repository},\n howpublished = {\\url{https://github.com/huggingface/diffusers}}\n}\n```\n" }, { "path": "diffusers/_typos.toml", "content": "# Files for typos\n# Instruction: https://github.com/marketplace/actions/typos-action#getting-started\n\n[default.extend-identifiers]\n\n[default.extend-words]\nNIN=\"NIN\" # NIN is used in scripts/convert_ncsnpp_original_checkpoint_to_diffusers.py\nnd=\"np\" # nd may be np (numpy)\nparms=\"parms\" # parms is used in scripts/convert_original_stable_diffusion_to_diffusers.py\n\n\n[files]\nextend-exclude = [\"_typos.toml\"]\n" }, { "path": "diffusers/benchmarks/base_classes.py", "content": "import os\nimport sys\n\nimport torch\n\nfrom diffusers import (\n AutoPipelineForImage2Image,\n AutoPipelineForInpainting,\n AutoPipelineForText2Image,\n ControlNetModel,\n LCMScheduler,\n StableDiffusionAdapterPipeline,\n StableDiffusionControlNetPipeline,\n StableDiffusionXLAdapterPipeline,\n StableDiffusionXLControlNetPipeline,\n T2IAdapter,\n WuerstchenCombinedPipeline,\n)\nfrom diffusers.utils import load_image\n\n\nsys.path.append(\".\")\n\nfrom utils import ( # noqa: E402\n BASE_PATH,\n PROMPT,\n BenchmarkInfo,\n benchmark_fn,\n bytes_to_giga_bytes,\n flush,\n generate_csv_dict,\n write_to_csv,\n)\n\n\nRESOLUTION_MAPPING = {\n \"runwayml/stable-diffusion-v1-5\": (512, 512),\n \"lllyasviel/sd-controlnet-canny\": (512, 512),\n \"diffusers/controlnet-canny-sdxl-1.0\": (1024, 1024),\n \"TencentARC/t2iadapter_canny_sd14v1\": (512, 512),\n \"TencentARC/t2i-adapter-canny-sdxl-1.0\": (1024, 1024),\n \"stabilityai/stable-diffusion-2-1\": (768, 768),\n \"stabilityai/stable-diffusion-xl-base-1.0\": (1024, 1024),\n \"stabilityai/stable-diffusion-xl-refiner-1.0\": (1024, 1024),\n \"stabilityai/sdxl-turbo\": (512, 512),\n}\n\n\nclass BaseBenchmak:\n pipeline_class = None\n\n def __init__(self, args):\n super().__init__()\n\n def run_inference(self, args):\n raise NotImplementedError\n\n def benchmark(self, args):\n raise NotImplementedError\n\n def get_result_filepath(self, args):\n pipeline_class_name = str(self.pipe.__class__.__name__)\n name = (\n args.ckpt.replace(\"/\", \"_\")\n + \"_\"\n + pipeline_class_name\n + f\"-bs@{args.batch_size}-steps@{args.num_inference_steps}-mco@{args.model_cpu_offload}-compile@{args.run_compile}.csv\"\n )\n filepath = os.path.join(BASE_PATH, name)\n return filepath\n\n\nclass TextToImageBenchmark(BaseBenchmak):\n pipeline_class = AutoPipelineForText2Image\n\n def __init__(self, args):\n pipe = self.pipeline_class.from_pretrained(args.ckpt, torch_dtype=torch.float16)\n pipe = pipe.to(\"cuda\")\n\n if args.run_compile:\n if not isinstance(pipe, WuerstchenCombinedPipeline):\n pipe.unet.to(memory_format=torch.channels_last)\n print(\"Run torch compile\")\n pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n\n if hasattr(pipe, \"movq\") and getattr(pipe, \"movq\", None) is not None:\n pipe.movq.to(memory_format=torch.channels_last)\n pipe.movq = torch.compile(pipe.movq, mode=\"reduce-overhead\", fullgraph=True)\n else:\n print(\"Run torch compile\")\n pipe.decoder = torch.compile(pipe.decoder, mode=\"reduce-overhead\", fullgraph=True)\n pipe.vqgan = torch.compile(pipe.vqgan, mode=\"reduce-overhead\", fullgraph=True)\n\n pipe.set_progress_bar_config(disable=True)\n self.pipe = pipe\n\n def run_inference(self, pipe, args):\n _ = pipe(\n prompt=PROMPT,\n num_inference_steps=args.num_inference_steps,\n num_images_per_prompt=args.batch_size,\n )\n\n def benchmark(self, args):\n flush()\n\n print(f\"[INFO] {self.pipe.__class__.__name__}: Running benchmark with: {vars(args)}\\n\")\n\n time = benchmark_fn(self.run_inference, self.pipe, args) # in seconds.\n memory = bytes_to_giga_bytes(torch.cuda.max_memory_allocated()) # in GBs.\n benchmark_info = BenchmarkInfo(time=time, memory=memory)\n\n pipeline_class_name = str(self.pipe.__class__.__name__)\n flush()\n csv_dict = generate_csv_dict(\n pipeline_cls=pipeline_class_name, ckpt=args.ckpt, args=args, benchmark_info=benchmark_info\n )\n filepath = self.get_result_filepath(args)\n write_to_csv(filepath, csv_dict)\n print(f\"Logs written to: {filepath}\")\n flush()\n\n\nclass TurboTextToImageBenchmark(TextToImageBenchmark):\n def __init__(self, args):\n super().__init__(args)\n\n def run_inference(self, pipe, args):\n _ = pipe(\n prompt=PROMPT,\n num_inference_steps=args.num_inference_steps,\n num_images_per_prompt=args.batch_size,\n guidance_scale=0.0,\n )\n\n\nclass LCMLoRATextToImageBenchmark(TextToImageBenchmark):\n lora_id = \"latent-consistency/lcm-lora-sdxl\"\n\n def __init__(self, args):\n super().__init__(args)\n self.pipe.load_lora_weights(self.lora_id)\n self.pipe.fuse_lora()\n self.pipe.unload_lora_weights()\n self.pipe.scheduler = LCMScheduler.from_config(self.pipe.scheduler.config)\n\n def get_result_filepath(self, args):\n pipeline_class_name = str(self.pipe.__class__.__name__)\n name = (\n self.lora_id.replace(\"/\", \"_\")\n + \"_\"\n + pipeline_class_name\n + f\"-bs@{args.batch_size}-steps@{args.num_inference_steps}-mco@{args.model_cpu_offload}-compile@{args.run_compile}.csv\"\n )\n filepath = os.path.join(BASE_PATH, name)\n return filepath\n\n def run_inference(self, pipe, args):\n _ = pipe(\n prompt=PROMPT,\n num_inference_steps=args.num_inference_steps,\n num_images_per_prompt=args.batch_size,\n guidance_scale=1.0,\n )\n\n def benchmark(self, args):\n flush()\n\n print(f\"[INFO] {self.pipe.__class__.__name__}: Running benchmark with: {vars(args)}\\n\")\n\n time = benchmark_fn(self.run_inference, self.pipe, args) # in seconds.\n memory = bytes_to_giga_bytes(torch.cuda.max_memory_allocated()) # in GBs.\n benchmark_info = BenchmarkInfo(time=time, memory=memory)\n\n pipeline_class_name = str(self.pipe.__class__.__name__)\n flush()\n csv_dict = generate_csv_dict(\n pipeline_cls=pipeline_class_name, ckpt=self.lora_id, args=args, benchmark_info=benchmark_info\n )\n filepath = self.get_result_filepath(args)\n write_to_csv(filepath, csv_dict)\n print(f\"Logs written to: {filepath}\")\n flush()\n\n\nclass ImageToImageBenchmark(TextToImageBenchmark):\n pipeline_class = AutoPipelineForImage2Image\n url = \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/1665_Girl_with_a_Pearl_Earring.jpg\"\n image = load_image(url).convert(\"RGB\")\n\n def __init__(self, args):\n super().__init__(args)\n self.image = self.image.resize(RESOLUTION_MAPPING[args.ckpt])\n\n def run_inference(self, pipe, args):\n _ = pipe(\n prompt=PROMPT,\n image=self.image,\n num_inference_steps=args.num_inference_steps,\n num_images_per_prompt=args.batch_size,\n )\n\n\nclass TurboImageToImageBenchmark(ImageToImageBenchmark):\n def __init__(self, args):\n super().__init__(args)\n\n def run_inference(self, pipe, args):\n _ = pipe(\n prompt=PROMPT,\n image=self.image,\n num_inference_steps=args.num_inference_steps,\n num_images_per_prompt=args.batch_size,\n guidance_scale=0.0,\n strength=0.5,\n )\n\n\nclass InpaintingBenchmark(ImageToImageBenchmark):\n pipeline_class = AutoPipelineForInpainting\n mask_url = \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/overture-creations-5sI6fQgYIuo_mask.png\"\n mask = load_image(mask_url).convert(\"RGB\")\n\n def __init__(self, args):\n super().__init__(args)\n self.image = self.image.resize(RESOLUTION_MAPPING[args.ckpt])\n self.mask = self.mask.resize(RESOLUTION_MAPPING[args.ckpt])\n\n def run_inference(self, pipe, args):\n _ = pipe(\n prompt=PROMPT,\n image=self.image,\n mask_image=self.mask,\n num_inference_steps=args.num_inference_steps,\n num_images_per_prompt=args.batch_size,\n )\n\n\nclass IPAdapterTextToImageBenchmark(TextToImageBenchmark):\n url = \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/load_neg_embed.png\"\n image = load_image(url)\n\n def __init__(self, args):\n pipe = self.pipeline_class.from_pretrained(args.ckpt, torch_dtype=torch.float16).to(\"cuda\")\n pipe.load_ip_adapter(\n args.ip_adapter_id[0],\n subfolder=\"models\" if \"sdxl\" not in args.ip_adapter_id[1] else \"sdxl_models\",\n weight_name=args.ip_adapter_id[1],\n )\n\n if args.run_compile:\n pipe.unet.to(memory_format=torch.channels_last)\n print(\"Run torch compile\")\n pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n\n pipe.set_progress_bar_config(disable=True)\n self.pipe = pipe\n\n def run_inference(self, pipe, args):\n _ = pipe(\n prompt=PROMPT,\n ip_adapter_image=self.image,\n num_inference_steps=args.num_inference_steps,\n num_images_per_prompt=args.batch_size,\n )\n\n\nclass ControlNetBenchmark(TextToImageBenchmark):\n pipeline_class = StableDiffusionControlNetPipeline\n aux_network_class = ControlNetModel\n root_ckpt = \"runwayml/stable-diffusion-v1-5\"\n\n url = \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/canny_image_condition.png\"\n image = load_image(url).convert(\"RGB\")\n\n def __init__(self, args):\n aux_network = self.aux_network_class.from_pretrained(args.ckpt, torch_dtype=torch.float16)\n pipe = self.pipeline_class.from_pretrained(self.root_ckpt, controlnet=aux_network, torch_dtype=torch.float16)\n pipe = pipe.to(\"cuda\")\n\n pipe.set_progress_bar_config(disable=True)\n self.pipe = pipe\n\n if args.run_compile:\n pipe.unet.to(memory_format=torch.channels_last)\n pipe.controlnet.to(memory_format=torch.channels_last)\n\n print(\"Run torch compile\")\n pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n pipe.controlnet = torch.compile(pipe.controlnet, mode=\"reduce-overhead\", fullgraph=True)\n\n self.image = self.image.resize(RESOLUTION_MAPPING[args.ckpt])\n\n def run_inference(self, pipe, args):\n _ = pipe(\n prompt=PROMPT,\n image=self.image,\n num_inference_steps=args.num_inference_steps,\n num_images_per_prompt=args.batch_size,\n )\n\n\nclass ControlNetSDXLBenchmark(ControlNetBenchmark):\n pipeline_class = StableDiffusionXLControlNetPipeline\n root_ckpt = \"stabilityai/stable-diffusion-xl-base-1.0\"\n\n def __init__(self, args):\n super().__init__(args)\n\n\nclass T2IAdapterBenchmark(ControlNetBenchmark):\n pipeline_class = StableDiffusionAdapterPipeline\n aux_network_class = T2IAdapter\n root_ckpt = \"CompVis/stable-diffusion-v1-4\"\n\n url = \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/canny_for_adapter.png\"\n image = load_image(url).convert(\"L\")\n\n def __init__(self, args):\n aux_network = self.aux_network_class.from_pretrained(args.ckpt, torch_dtype=torch.float16)\n pipe = self.pipeline_class.from_pretrained(self.root_ckpt, adapter=aux_network, torch_dtype=torch.float16)\n pipe = pipe.to(\"cuda\")\n\n pipe.set_progress_bar_config(disable=True)\n self.pipe = pipe\n\n if args.run_compile:\n pipe.unet.to(memory_format=torch.channels_last)\n pipe.adapter.to(memory_format=torch.channels_last)\n\n print(\"Run torch compile\")\n pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n pipe.adapter = torch.compile(pipe.adapter, mode=\"reduce-overhead\", fullgraph=True)\n\n self.image = self.image.resize(RESOLUTION_MAPPING[args.ckpt])\n\n\nclass T2IAdapterSDXLBenchmark(T2IAdapterBenchmark):\n pipeline_class = StableDiffusionXLAdapterPipeline\n root_ckpt = \"stabilityai/stable-diffusion-xl-base-1.0\"\n\n url = \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/canny_for_adapter_sdxl.png\"\n image = load_image(url)\n\n def __init__(self, args):\n super().__init__(args)\n" }, { "path": "diffusers/benchmarks/benchmark_controlnet.py", "content": "import argparse\nimport sys\n\n\nsys.path.append(\".\")\nfrom base_classes import ControlNetBenchmark, ControlNetSDXLBenchmark # noqa: E402\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--ckpt\",\n type=str,\n default=\"lllyasviel/sd-controlnet-canny\",\n choices=[\"lllyasviel/sd-controlnet-canny\", \"diffusers/controlnet-canny-sdxl-1.0\"],\n )\n parser.add_argument(\"--batch_size\", type=int, default=1)\n parser.add_argument(\"--num_inference_steps\", type=int, default=50)\n parser.add_argument(\"--model_cpu_offload\", action=\"store_true\")\n parser.add_argument(\"--run_compile\", action=\"store_true\")\n args = parser.parse_args()\n\n benchmark_pipe = (\n ControlNetBenchmark(args) if args.ckpt == \"lllyasviel/sd-controlnet-canny\" else ControlNetSDXLBenchmark(args)\n )\n benchmark_pipe.benchmark(args)\n" }, { "path": "diffusers/benchmarks/benchmark_ip_adapters.py", "content": "import argparse\nimport sys\n\n\nsys.path.append(\".\")\nfrom base_classes import IPAdapterTextToImageBenchmark # noqa: E402\n\n\nIP_ADAPTER_CKPTS = {\n \"runwayml/stable-diffusion-v1-5\": (\"h94/IP-Adapter\", \"ip-adapter_sd15.bin\"),\n \"stabilityai/stable-diffusion-xl-base-1.0\": (\"h94/IP-Adapter\", \"ip-adapter_sdxl.bin\"),\n}\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--ckpt\",\n type=str,\n default=\"runwayml/stable-diffusion-v1-5\",\n choices=list(IP_ADAPTER_CKPTS.keys()),\n )\n parser.add_argument(\"--batch_size\", type=int, default=1)\n parser.add_argument(\"--num_inference_steps\", type=int, default=50)\n parser.add_argument(\"--model_cpu_offload\", action=\"store_true\")\n parser.add_argument(\"--run_compile\", action=\"store_true\")\n args = parser.parse_args()\n\n args.ip_adapter_id = IP_ADAPTER_CKPTS[args.ckpt]\n benchmark_pipe = IPAdapterTextToImageBenchmark(args)\n args.ckpt = f\"{args.ckpt} (IP-Adapter)\"\n benchmark_pipe.benchmark(args)\n" }, { "path": "diffusers/benchmarks/benchmark_sd_img.py", "content": "import argparse\nimport sys\n\n\nsys.path.append(\".\")\nfrom base_classes import ImageToImageBenchmark, TurboImageToImageBenchmark # noqa: E402\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--ckpt\",\n type=str,\n default=\"runwayml/stable-diffusion-v1-5\",\n choices=[\n \"runwayml/stable-diffusion-v1-5\",\n \"stabilityai/stable-diffusion-2-1\",\n \"stabilityai/stable-diffusion-xl-refiner-1.0\",\n \"stabilityai/sdxl-turbo\",\n ],\n )\n parser.add_argument(\"--batch_size\", type=int, default=1)\n parser.add_argument(\"--num_inference_steps\", type=int, default=50)\n parser.add_argument(\"--model_cpu_offload\", action=\"store_true\")\n parser.add_argument(\"--run_compile\", action=\"store_true\")\n args = parser.parse_args()\n\n benchmark_pipe = ImageToImageBenchmark(args) if \"turbo\" not in args.ckpt else TurboImageToImageBenchmark(args)\n benchmark_pipe.benchmark(args)\n" }, { "path": "diffusers/benchmarks/benchmark_sd_inpainting.py", "content": "import argparse\nimport sys\n\n\nsys.path.append(\".\")\nfrom base_classes import InpaintingBenchmark # noqa: E402\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--ckpt\",\n type=str,\n default=\"runwayml/stable-diffusion-v1-5\",\n choices=[\n \"runwayml/stable-diffusion-v1-5\",\n \"stabilityai/stable-diffusion-2-1\",\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n ],\n )\n parser.add_argument(\"--batch_size\", type=int, default=1)\n parser.add_argument(\"--num_inference_steps\", type=int, default=50)\n parser.add_argument(\"--model_cpu_offload\", action=\"store_true\")\n parser.add_argument(\"--run_compile\", action=\"store_true\")\n args = parser.parse_args()\n\n benchmark_pipe = InpaintingBenchmark(args)\n benchmark_pipe.benchmark(args)\n" }, { "path": "diffusers/benchmarks/benchmark_t2i_adapter.py", "content": "import argparse\nimport sys\n\n\nsys.path.append(\".\")\nfrom base_classes import T2IAdapterBenchmark, T2IAdapterSDXLBenchmark # noqa: E402\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--ckpt\",\n type=str,\n default=\"TencentARC/t2iadapter_canny_sd14v1\",\n choices=[\"TencentARC/t2iadapter_canny_sd14v1\", \"TencentARC/t2i-adapter-canny-sdxl-1.0\"],\n )\n parser.add_argument(\"--batch_size\", type=int, default=1)\n parser.add_argument(\"--num_inference_steps\", type=int, default=50)\n parser.add_argument(\"--model_cpu_offload\", action=\"store_true\")\n parser.add_argument(\"--run_compile\", action=\"store_true\")\n args = parser.parse_args()\n\n benchmark_pipe = (\n T2IAdapterBenchmark(args)\n if args.ckpt == \"TencentARC/t2iadapter_canny_sd14v1\"\n else T2IAdapterSDXLBenchmark(args)\n )\n benchmark_pipe.benchmark(args)\n" }, { "path": "diffusers/benchmarks/benchmark_t2i_lcm_lora.py", "content": "import argparse\nimport sys\n\n\nsys.path.append(\".\")\nfrom base_classes import LCMLoRATextToImageBenchmark # noqa: E402\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--ckpt\",\n type=str,\n default=\"stabilityai/stable-diffusion-xl-base-1.0\",\n )\n parser.add_argument(\"--batch_size\", type=int, default=1)\n parser.add_argument(\"--num_inference_steps\", type=int, default=4)\n parser.add_argument(\"--model_cpu_offload\", action=\"store_true\")\n parser.add_argument(\"--run_compile\", action=\"store_true\")\n args = parser.parse_args()\n\n benchmark_pipe = LCMLoRATextToImageBenchmark(args)\n benchmark_pipe.benchmark(args)\n" }, { "path": "diffusers/benchmarks/benchmark_text_to_image.py", "content": "import argparse\nimport sys\n\n\nsys.path.append(\".\")\nfrom base_classes import TextToImageBenchmark, TurboTextToImageBenchmark # noqa: E402\n\n\nALL_T2I_CKPTS = [\n \"runwayml/stable-diffusion-v1-5\",\n \"segmind/SSD-1B\",\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n \"kandinsky-community/kandinsky-2-2-decoder\",\n \"warp-ai/wuerstchen\",\n \"stabilityai/sdxl-turbo\",\n]\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--ckpt\",\n type=str,\n default=\"runwayml/stable-diffusion-v1-5\",\n choices=ALL_T2I_CKPTS,\n )\n parser.add_argument(\"--batch_size\", type=int, default=1)\n parser.add_argument(\"--num_inference_steps\", type=int, default=50)\n parser.add_argument(\"--model_cpu_offload\", action=\"store_true\")\n parser.add_argument(\"--run_compile\", action=\"store_true\")\n args = parser.parse_args()\n\n benchmark_cls = None\n if \"turbo\" in args.ckpt:\n benchmark_cls = TurboTextToImageBenchmark\n else:\n benchmark_cls = TextToImageBenchmark\n\n benchmark_pipe = benchmark_cls(args)\n benchmark_pipe.benchmark(args)\n" }, { "path": "diffusers/benchmarks/push_results.py", "content": "import glob\nimport sys\n\nimport pandas as pd\nfrom huggingface_hub import hf_hub_download, upload_file\nfrom huggingface_hub.utils._errors import EntryNotFoundError\n\n\nsys.path.append(\".\")\nfrom utils import BASE_PATH, FINAL_CSV_FILE, GITHUB_SHA, REPO_ID, collate_csv # noqa: E402\n\n\ndef has_previous_benchmark() -> str:\n csv_path = None\n try:\n csv_path = hf_hub_download(repo_id=REPO_ID, repo_type=\"dataset\", filename=FINAL_CSV_FILE)\n except EntryNotFoundError:\n csv_path = None\n return csv_path\n\n\ndef filter_float(value):\n if isinstance(value, str):\n return float(value.split()[0])\n return value\n\n\ndef push_to_hf_dataset():\n all_csvs = sorted(glob.glob(f\"{BASE_PATH}/*.csv\"))\n collate_csv(all_csvs, FINAL_CSV_FILE)\n\n # If there's an existing benchmark file, we should report the changes.\n csv_path = has_previous_benchmark()\n if csv_path is not None:\n current_results = pd.read_csv(FINAL_CSV_FILE)\n previous_results = pd.read_csv(csv_path)\n\n numeric_columns = current_results.select_dtypes(include=[\"float64\", \"int64\"]).columns\n numeric_columns = [\n c for c in numeric_columns if c not in [\"batch_size\", \"num_inference_steps\", \"actual_gpu_memory (gbs)\"]\n ]\n\n for column in numeric_columns:\n previous_results[column] = previous_results[column].map(lambda x: filter_float(x))\n\n # Calculate the percentage change\n current_results[column] = current_results[column].astype(float)\n previous_results[column] = previous_results[column].astype(float)\n percent_change = ((current_results[column] - previous_results[column]) / previous_results[column]) * 100\n\n # Format the values with '+' or '-' sign and append to original values\n current_results[column] = current_results[column].map(str) + percent_change.map(\n lambda x: f\" ({'+' if x > 0 else ''}{x:.2f}%)\"\n )\n # There might be newly added rows. So, filter out the NaNs.\n current_results[column] = current_results[column].map(lambda x: x.replace(\" (nan%)\", \"\"))\n\n # Overwrite the current result file.\n current_results.to_csv(FINAL_CSV_FILE, index=False)\n\n commit_message = f\"upload from sha: {GITHUB_SHA}\" if GITHUB_SHA is not None else \"upload benchmark results\"\n upload_file(\n repo_id=REPO_ID,\n path_in_repo=FINAL_CSV_FILE,\n path_or_fileobj=FINAL_CSV_FILE,\n repo_type=\"dataset\",\n commit_message=commit_message,\n )\n\n\nif __name__ == \"__main__\":\n push_to_hf_dataset()\n" }, { "path": "diffusers/benchmarks/run_all.py", "content": "import glob\nimport subprocess\nimport sys\nfrom typing import List\n\n\nsys.path.append(\".\")\nfrom benchmark_text_to_image import ALL_T2I_CKPTS # noqa: E402\n\n\nPATTERN = \"benchmark_*.py\"\n\n\nclass SubprocessCallException(Exception):\n pass\n\n\n# Taken from `test_examples_utils.py`\ndef run_command(command: List[str], return_stdout=False):\n \"\"\"\n Runs `command` with `subprocess.check_output` and will potentially return the `stdout`. Will also properly capture\n if an error occurred while running `command`\n \"\"\"\n try:\n output = subprocess.check_output(command, stderr=subprocess.STDOUT)\n if return_stdout:\n if hasattr(output, \"decode\"):\n output = output.decode(\"utf-8\")\n return output\n except subprocess.CalledProcessError as e:\n raise SubprocessCallException(\n f\"Command `{' '.join(command)}` failed with the following error:\\n\\n{e.output.decode()}\"\n ) from e\n\n\ndef main():\n python_files = glob.glob(PATTERN)\n\n for file in python_files:\n print(f\"****** Running file: {file} ******\")\n\n # Run with canonical settings.\n if file != \"benchmark_text_to_image.py\":\n command = f\"python {file}\"\n run_command(command.split())\n\n command += \" --run_compile\"\n run_command(command.split())\n\n # Run variants.\n for file in python_files:\n if file == \"benchmark_text_to_image.py\":\n for ckpt in ALL_T2I_CKPTS:\n command = f\"python {file} --ckpt {ckpt}\"\n\n if \"turbo\" in ckpt:\n command += \" --num_inference_steps 1\"\n\n run_command(command.split())\n\n command += \" --run_compile\"\n run_command(command.split())\n\n elif file == \"benchmark_sd_img.py\":\n for ckpt in [\"stabilityai/stable-diffusion-xl-refiner-1.0\", \"stabilityai/sdxl-turbo\"]:\n command = f\"python {file} --ckpt {ckpt}\"\n\n if ckpt == \"stabilityai/sdxl-turbo\":\n command += \" --num_inference_steps 2\"\n\n run_command(command.split())\n command += \" --run_compile\"\n run_command(command.split())\n\n elif file in [\"benchmark_sd_inpainting.py\", \"benchmark_ip_adapters.py\"]:\n sdxl_ckpt = \"stabilityai/stable-diffusion-xl-base-1.0\"\n command = f\"python {file} --ckpt {sdxl_ckpt}\"\n run_command(command.split())\n\n command += \" --run_compile\"\n run_command(command.split())\n\n elif file in [\"benchmark_controlnet.py\", \"benchmark_t2i_adapter.py\"]:\n sdxl_ckpt = (\n \"diffusers/controlnet-canny-sdxl-1.0\"\n if \"controlnet\" in file\n else \"TencentARC/t2i-adapter-canny-sdxl-1.0\"\n )\n command = f\"python {file} --ckpt {sdxl_ckpt}\"\n run_command(command.split())\n\n command += \" --run_compile\"\n run_command(command.split())\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/benchmarks/utils.py", "content": "import argparse\nimport csv\nimport gc\nimport os\nfrom dataclasses import dataclass\nfrom typing import Dict, List, Union\n\nimport torch\nimport torch.utils.benchmark as benchmark\n\n\nGITHUB_SHA = os.getenv(\"GITHUB_SHA\", None)\nBENCHMARK_FIELDS = [\n \"pipeline_cls\",\n \"ckpt_id\",\n \"batch_size\",\n \"num_inference_steps\",\n \"model_cpu_offload\",\n \"run_compile\",\n \"time (secs)\",\n \"memory (gbs)\",\n \"actual_gpu_memory (gbs)\",\n \"github_sha\",\n]\n\nPROMPT = \"ghibli style, a fantasy landscape with castles\"\nBASE_PATH = os.getenv(\"BASE_PATH\", \".\")\nTOTAL_GPU_MEMORY = float(os.getenv(\"TOTAL_GPU_MEMORY\", torch.cuda.get_device_properties(0).total_memory / (1024**3)))\n\nREPO_ID = \"diffusers/benchmarks\"\nFINAL_CSV_FILE = \"collated_results.csv\"\n\n\n@dataclass\nclass BenchmarkInfo:\n time: float\n memory: float\n\n\ndef flush():\n \"\"\"Wipes off memory.\"\"\"\n gc.collect()\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n\ndef bytes_to_giga_bytes(bytes):\n return f\"{(bytes / 1024 / 1024 / 1024):.3f}\"\n\n\ndef benchmark_fn(f, *args, **kwargs):\n t0 = benchmark.Timer(\n stmt=\"f(*args, **kwargs)\",\n globals={\"args\": args, \"kwargs\": kwargs, \"f\": f},\n num_threads=torch.get_num_threads(),\n )\n return f\"{(t0.blocked_autorange().mean):.3f}\"\n\n\ndef generate_csv_dict(\n pipeline_cls: str, ckpt: str, args: argparse.Namespace, benchmark_info: BenchmarkInfo\n) -> Dict[str, Union[str, bool, float]]:\n \"\"\"Packs benchmarking data into a dictionary for latter serialization.\"\"\"\n data_dict = {\n \"pipeline_cls\": pipeline_cls,\n \"ckpt_id\": ckpt,\n \"batch_size\": args.batch_size,\n \"num_inference_steps\": args.num_inference_steps,\n \"model_cpu_offload\": args.model_cpu_offload,\n \"run_compile\": args.run_compile,\n \"time (secs)\": benchmark_info.time,\n \"memory (gbs)\": benchmark_info.memory,\n \"actual_gpu_memory (gbs)\": f\"{(TOTAL_GPU_MEMORY):.3f}\",\n \"github_sha\": GITHUB_SHA,\n }\n return data_dict\n\n\ndef write_to_csv(file_name: str, data_dict: Dict[str, Union[str, bool, float]]):\n \"\"\"Serializes a dictionary into a CSV file.\"\"\"\n with open(file_name, mode=\"w\", newline=\"\") as csvfile:\n writer = csv.DictWriter(csvfile, fieldnames=BENCHMARK_FIELDS)\n writer.writeheader()\n writer.writerow(data_dict)\n\n\ndef collate_csv(input_files: List[str], output_file: str):\n \"\"\"Collates multiple identically structured CSVs into a single CSV file.\"\"\"\n with open(output_file, mode=\"w\", newline=\"\") as outfile:\n writer = csv.DictWriter(outfile, fieldnames=BENCHMARK_FIELDS)\n writer.writeheader()\n\n for file in input_files:\n with open(file, mode=\"r\") as infile:\n reader = csv.DictReader(infile)\n for row in reader:\n writer.writerow(row)\n" }, { "path": "diffusers/docker/diffusers-doc-builder/Dockerfile", "content": "FROM ubuntu:20.04\nLABEL maintainer=\"Hugging Face\"\nLABEL repository=\"diffusers\"\n\nENV DEBIAN_FRONTEND=noninteractive\n\nRUN apt-get -y update \\\n && apt-get install -y software-properties-common \\\n && add-apt-repository ppa:deadsnakes/ppa\n\nRUN apt install -y bash \\\n build-essential \\\n git \\\n git-lfs \\\n curl \\\n ca-certificates \\\n libsndfile1-dev \\\n python3.10 \\\n python3-pip \\\n libgl1 \\\n zip \\\n wget \\\n python3.10-venv && \\\n rm -rf /var/lib/apt/lists\n\n# make sure to use venv\nRUN python3.10 -m venv /opt/venv\nENV PATH=\"/opt/venv/bin:$PATH\"\n\n# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)\nRUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \\\n python3.10 -m uv pip install --no-cache-dir \\\n torch \\\n torchvision \\\n torchaudio \\\n invisible_watermark \\\n --extra-index-url https://download.pytorch.org/whl/cpu && \\\n python3.10 -m uv pip install --no-cache-dir \\\n accelerate \\\n datasets \\\n hf-doc-builder \\\n huggingface-hub \\\n Jinja2 \\\n librosa \\\n numpy==1.26.4 \\\n scipy \\\n tensorboard \\\n transformers \\\n matplotlib \\\n setuptools==69.5.1\n\nCMD [\"/bin/bash\"]\n" }, { "path": "diffusers/docker/diffusers-flax-cpu/Dockerfile", "content": "FROM ubuntu:20.04\nLABEL maintainer=\"Hugging Face\"\nLABEL repository=\"diffusers\"\n\nENV DEBIAN_FRONTEND=noninteractive\n\nRUN apt-get -y update \\\n && apt-get install -y software-properties-common \\\n && add-apt-repository ppa:deadsnakes/ppa\n\nRUN apt install -y bash \\\n build-essential \\\n git \\\n git-lfs \\\n curl \\\n ca-certificates \\\n libsndfile1-dev \\\n libgl1 \\\n python3.10 \\\n python3-pip \\\n python3.10-venv && \\\n rm -rf /var/lib/apt/lists\n\n# make sure to use venv\nRUN python3.10 -m venv /opt/venv\nENV PATH=\"/opt/venv/bin:$PATH\"\n\n# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)\n# follow the instructions here: https://cloud.google.com/tpu/docs/run-in-container#train_a_jax_model_in_a_docker_container\nRUN python3 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \\\n python3 -m uv pip install --upgrade --no-cache-dir \\\n clu \\\n \"jax[cpu]>=0.2.16,!=0.3.2\" \\\n \"flax>=0.4.1\" \\\n \"jaxlib>=0.1.65\" && \\\n python3 -m uv pip install --no-cache-dir \\\n accelerate \\\n datasets \\\n hf-doc-builder \\\n huggingface-hub \\\n Jinja2 \\\n librosa \\\n numpy==1.26.4 \\\n scipy \\\n tensorboard \\\n transformers\n\nCMD [\"/bin/bash\"]" }, { "path": "diffusers/docker/diffusers-flax-tpu/Dockerfile", "content": "FROM ubuntu:20.04\nLABEL maintainer=\"Hugging Face\"\nLABEL repository=\"diffusers\"\n\nENV DEBIAN_FRONTEND=noninteractive\n\nRUN apt-get -y update \\\n && apt-get install -y software-properties-common \\\n && add-apt-repository ppa:deadsnakes/ppa\n\nRUN apt install -y bash \\\n build-essential \\\n git \\\n git-lfs \\\n curl \\\n ca-certificates \\\n libsndfile1-dev \\\n libgl1 \\\n python3.10 \\\n python3-pip \\\n python3.10-venv && \\\n rm -rf /var/lib/apt/lists\n\n# make sure to use venv\nRUN python3.10 -m venv /opt/venv\nENV PATH=\"/opt/venv/bin:$PATH\"\n\n# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)\n# follow the instructions here: https://cloud.google.com/tpu/docs/run-in-container#train_a_jax_model_in_a_docker_container\nRUN python3 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \\\n python3 -m pip install --no-cache-dir \\\n \"jax[tpu]>=0.2.16,!=0.3.2\" \\\n -f https://storage.googleapis.com/jax-releases/libtpu_releases.html && \\\n python3 -m uv pip install --upgrade --no-cache-dir \\\n clu \\\n \"flax>=0.4.1\" \\\n \"jaxlib>=0.1.65\" && \\\n python3 -m uv pip install --no-cache-dir \\\n accelerate \\\n datasets \\\n hf-doc-builder \\\n huggingface-hub \\\n Jinja2 \\\n librosa \\\n numpy==1.26.4 \\\n scipy \\\n tensorboard \\\n transformers\n\nCMD [\"/bin/bash\"]" }, { "path": "diffusers/docker/diffusers-onnxruntime-cpu/Dockerfile", "content": "FROM ubuntu:20.04\nLABEL maintainer=\"Hugging Face\"\nLABEL repository=\"diffusers\"\n\nENV DEBIAN_FRONTEND=noninteractive\n\nRUN apt-get -y update \\\n && apt-get install -y software-properties-common \\\n && add-apt-repository ppa:deadsnakes/ppa\n\nRUN apt install -y bash \\\n build-essential \\\n git \\\n git-lfs \\\n curl \\\n ca-certificates \\\n libsndfile1-dev \\\n libgl1 \\\n python3.10 \\\n python3-pip \\\n python3.10-venv && \\\n rm -rf /var/lib/apt/lists\n\n# make sure to use venv\nRUN python3.10 -m venv /opt/venv\nENV PATH=\"/opt/venv/bin:$PATH\"\n\n# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)\nRUN python3 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \\\n python3 -m uv pip install --no-cache-dir \\\n torch==2.1.2 \\\n torchvision==0.16.2 \\\n torchaudio==2.1.2 \\\n onnxruntime \\\n --extra-index-url https://download.pytorch.org/whl/cpu && \\\n python3 -m uv pip install --no-cache-dir \\\n accelerate \\\n datasets \\\n hf-doc-builder \\\n huggingface-hub \\\n Jinja2 \\\n librosa \\\n numpy==1.26.4 \\\n scipy \\\n tensorboard \\\n transformers\n\nCMD [\"/bin/bash\"]" }, { "path": "diffusers/docker/diffusers-onnxruntime-cuda/Dockerfile", "content": "FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04\nLABEL maintainer=\"Hugging Face\"\nLABEL repository=\"diffusers\"\n\nENV DEBIAN_FRONTEND=noninteractive\n\nRUN apt-get -y update \\\n && apt-get install -y software-properties-common \\\n && add-apt-repository ppa:deadsnakes/ppa\n\nRUN apt install -y bash \\\n build-essential \\\n git \\\n git-lfs \\\n curl \\\n ca-certificates \\\n libsndfile1-dev \\\n libgl1 \\\n python3.10 \\\n python3-pip \\\n python3.10-venv && \\\n rm -rf /var/lib/apt/lists\n\n# make sure to use venv\nRUN python3.10 -m venv /opt/venv\nENV PATH=\"/opt/venv/bin:$PATH\"\n\n# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)\nRUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \\\n python3.10 -m uv pip install --no-cache-dir \\\n torch \\\n torchvision \\\n torchaudio \\\n \"onnxruntime-gpu>=1.13.1\" \\\n --extra-index-url https://download.pytorch.org/whl/cu117 && \\\n python3.10 -m uv pip install --no-cache-dir \\\n accelerate \\\n datasets \\\n hf-doc-builder \\\n huggingface-hub \\\n Jinja2 \\\n librosa \\\n numpy==1.26.4 \\\n scipy \\\n tensorboard \\\n transformers\n\nCMD [\"/bin/bash\"]" }, { "path": "diffusers/docker/diffusers-pytorch-compile-cuda/Dockerfile", "content": "FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04\nLABEL maintainer=\"Hugging Face\"\nLABEL repository=\"diffusers\"\n\nENV DEBIAN_FRONTEND=noninteractive\n\nRUN apt-get -y update \\\n && apt-get install -y software-properties-common \\\n && add-apt-repository ppa:deadsnakes/ppa\n\nRUN apt install -y bash \\\n build-essential \\\n git \\\n git-lfs \\\n curl \\\n ca-certificates \\\n libsndfile1-dev \\\n libgl1 \\\n python3.10 \\\n python3-pip \\\n python3.10-venv && \\\n rm -rf /var/lib/apt/lists\n\n# make sure to use venv\nRUN python3.10 -m venv /opt/venv\nENV PATH=\"/opt/venv/bin:$PATH\"\n\n# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)\nRUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \\\n python3.10 -m uv pip install --no-cache-dir \\\n torch \\\n torchvision \\\n torchaudio \\\n invisible_watermark && \\\n python3.10 -m pip install --no-cache-dir \\\n accelerate \\\n datasets \\\n hf-doc-builder \\\n huggingface-hub \\\n Jinja2 \\\n librosa \\\n numpy==1.26.4 \\\n scipy \\\n tensorboard \\\n transformers\n\nCMD [\"/bin/bash\"]\n" }, { "path": "diffusers/docker/diffusers-pytorch-cpu/Dockerfile", "content": "FROM ubuntu:20.04\nLABEL maintainer=\"Hugging Face\"\nLABEL repository=\"diffusers\"\n\nENV DEBIAN_FRONTEND=noninteractive\n\nRUN apt-get -y update \\\n && apt-get install -y software-properties-common \\\n && add-apt-repository ppa:deadsnakes/ppa\n\nRUN apt install -y bash \\\n build-essential \\\n git \\\n git-lfs \\\n curl \\\n ca-certificates \\\n libsndfile1-dev \\\n python3.10 \\\n python3-pip \\\n libgl1 \\\n python3.10-venv && \\\n rm -rf /var/lib/apt/lists\n\n# make sure to use venv\nRUN python3.10 -m venv /opt/venv\nENV PATH=\"/opt/venv/bin:$PATH\"\n\n# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)\nRUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \\\n python3.10 -m uv pip install --no-cache-dir \\\n torch \\\n torchvision \\\n torchaudio \\\n invisible_watermark \\\n --extra-index-url https://download.pytorch.org/whl/cpu && \\\n python3.10 -m uv pip install --no-cache-dir \\\n accelerate \\\n datasets \\\n hf-doc-builder \\\n huggingface-hub \\\n Jinja2 \\\n librosa \\\n numpy==1.26.4 \\\n scipy \\\n tensorboard \\\n transformers matplotlib\n\nCMD [\"/bin/bash\"]\n" }, { "path": "diffusers/docker/diffusers-pytorch-cuda/Dockerfile", "content": "FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04\nLABEL maintainer=\"Hugging Face\"\nLABEL repository=\"diffusers\"\n\nENV DEBIAN_FRONTEND=noninteractive\n\nRUN apt-get -y update \\\n && apt-get install -y software-properties-common \\\n && add-apt-repository ppa:deadsnakes/ppa\n\nRUN apt install -y bash \\\n build-essential \\\n git \\\n git-lfs \\\n curl \\\n ca-certificates \\\n libsndfile1-dev \\\n libgl1 \\\n python3.10 \\\n python3-pip \\\n python3.10-venv && \\\n rm -rf /var/lib/apt/lists\n\n# make sure to use venv\nRUN python3.10 -m venv /opt/venv\nENV PATH=\"/opt/venv/bin:$PATH\"\n\n# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)\nRUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \\\n python3.10 -m uv pip install --no-cache-dir \\\n torch \\\n torchvision \\\n torchaudio \\\n invisible_watermark && \\\n python3.10 -m pip install --no-cache-dir \\\n accelerate \\\n datasets \\\n hf-doc-builder \\\n huggingface-hub \\\n Jinja2 \\\n librosa \\\n numpy==1.26.4 \\\n scipy \\\n tensorboard \\\n transformers \\\n pytorch-lightning\n\nCMD [\"/bin/bash\"]\n" }, { "path": "diffusers/docker/diffusers-pytorch-xformers-cuda/Dockerfile", "content": "FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04\nLABEL maintainer=\"Hugging Face\"\nLABEL repository=\"diffusers\"\n\nENV DEBIAN_FRONTEND=noninteractive\n\nRUN apt-get -y update \\\n && apt-get install -y software-properties-common \\\n && add-apt-repository ppa:deadsnakes/ppa\n\nRUN apt install -y bash \\\n build-essential \\\n git \\\n git-lfs \\\n curl \\\n ca-certificates \\\n libsndfile1-dev \\\n libgl1 \\\n python3.10 \\\n python3-pip \\\n python3.10-venv && \\\n rm -rf /var/lib/apt/lists\n\n# make sure to use venv\nRUN python3.10 -m venv /opt/venv\nENV PATH=\"/opt/venv/bin:$PATH\"\n\n# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)\nRUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \\\n python3.10 -m pip install --no-cache-dir \\\n torch \\\n torchvision \\\n torchaudio \\\n invisible_watermark && \\\n python3.10 -m uv pip install --no-cache-dir \\\n accelerate \\\n datasets \\\n hf-doc-builder \\\n huggingface-hub \\\n Jinja2 \\\n librosa \\\n numpy==1.26.4 \\\n scipy \\\n tensorboard \\\n transformers \\\n xformers\n\nCMD [\"/bin/bash\"]\n" }, { "path": "diffusers/docs/README.md", "content": "\n\n# Generating the documentation\n\nTo generate the documentation, you first have to build it. Several packages are necessary to build the doc,\nyou can install them with the following command, at the root of the code repository:\n\n```bash\npip install -e \".[docs]\"\n```\n\nThen you need to install our open source documentation builder tool:\n\n```bash\npip install git+https://github.com/huggingface/doc-builder\n```\n\n---\n**NOTE**\n\nYou only need to generate the documentation to inspect it locally (if you're planning changes and want to\ncheck how they look before committing for instance). You don't have to commit the built documentation.\n\n---\n\n## Previewing the documentation\n\nTo preview the docs, first install the `watchdog` module with:\n\n```bash\npip install watchdog\n```\n\nThen run the following command:\n\n```bash\ndoc-builder preview {package_name} {path_to_docs}\n```\n\nFor example:\n\n```bash\ndoc-builder preview diffusers docs/source/en\n```\n\nThe docs will be viewable at [http://localhost:3000](http://localhost:3000). You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives.\n\n---\n**NOTE**\n\nThe `preview` command only works with existing doc files. When you add a completely new file, you need to update `_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again).\n\n---\n\n## Adding a new element to the navigation bar\n\nAccepted files are Markdown (.md).\n\nCreate a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting\nthe filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/diffusers/blob/main/docs/source/en/_toctree.yml) file.\n\n## Renaming section headers and moving sections\n\nIt helps to keep the old links working when renaming the section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums, and Social media and it'd make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information.\n\nTherefore, we simply keep a little map of moved sections at the end of the document where the original section was. The key is to preserve the original anchor.\n\nSo if you renamed a section from: \"Section A\" to \"Section B\", then you can add at the end of the file:\n\n```md\nSections that were moved:\n\n[ Section A ]\n```\nand of course, if you moved it to another file, then:\n\n```md\nSections that were moved:\n\n[ Section A ]\n```\n\nUse the relative style to link to the new file so that the versioned docs continue to work.\n\nFor an example of a rich moved section set please see the very end of [the transformers Trainer doc](https://github.com/huggingface/transformers/blob/main/docs/source/en/main_classes/trainer.md).\n\n\n## Writing Documentation - Specification\n\nThe `huggingface/diffusers` documentation follows the\n[Google documentation](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) style for docstrings,\nalthough we can write them directly in Markdown.\n\n### Adding a new tutorial\n\nAdding a new tutorial or section is done in two steps:\n\n- Add a new Markdown (.md) file under `docs/source/`.\n- Link that file in `docs/source//_toctree.yml` on the correct toc-tree.\n\nMake sure to put your new file under the proper section. It's unlikely to go in the first section (*Get Started*), so\ndepending on the intended targets (beginners, more advanced users, or researchers) it should go in sections two, three, or four.\n\n### Adding a new pipeline/scheduler\n\nWhen adding a new pipeline:\n\n- Create a file `xxx.md` under `docs/source//api/pipelines` (don't hesitate to copy an existing file as template).\n- Link that file in (*Diffusers Summary*) section in `docs/source/api/pipelines/overview.md`, along with the link to the paper, and a colab notebook (if available).\n- Write a short overview of the diffusion model:\n - Overview with paper & authors\n - Paper abstract\n - Tips and tricks and how to use it best\n - Possible an end-to-end example of how to use it\n- Add all the pipeline classes that should be linked in the diffusion model. These classes should be added using our Markdown syntax. By default as follows:\n\n```\n[[autodoc]] XXXPipeline\n - all\n\t- __call__\n```\n\nThis will include every public method of the pipeline that is documented, as well as the `__call__` method that is not documented by default. If you just want to add additional methods that are not documented, you can put the list of all methods to add in a list that contains `all`.\n\n```\n[[autodoc]] XXXPipeline\n - all\n\t- __call__\n\t- enable_attention_slicing\n\t- disable_attention_slicing\n - enable_xformers_memory_efficient_attention\n - disable_xformers_memory_efficient_attention\n```\n\nYou can follow the same process to create a new scheduler under the `docs/source//api/schedulers` folder.\n\n### Writing source documentation\n\nValues that should be put in `code` should either be surrounded by backticks: \\`like so\\`. Note that argument names\nand objects like True, None, or any strings should usually be put in `code`.\n\nWhen mentioning a class, function, or method, it is recommended to use our syntax for internal links so that our tool\nadds a link to its documentation with this syntax: \\[\\`XXXClass\\`\\] or \\[\\`function\\`\\]. This requires the class or\nfunction to be in the main package.\n\nIf you want to create a link to some internal class or function, you need to\nprovide its path. For instance: \\[\\`pipelines.ImagePipelineOutput\\`\\]. This will be converted into a link with\n`pipelines.ImagePipelineOutput` in the description. To get rid of the path and only keep the name of the object you are\nlinking to in the description, add a ~: \\[\\`~pipelines.ImagePipelineOutput\\`\\] will generate a link with `ImagePipelineOutput` in the description.\n\nThe same works for methods so you can either use \\[\\`XXXClass.method\\`\\] or \\[\\`~XXXClass.method\\`\\].\n\n#### Defining arguments in a method\n\nArguments should be defined with the `Args:` (or `Arguments:` or `Parameters:`) prefix, followed by a line return and\nan indentation. The argument should be followed by its type, with its shape if it is a tensor, a colon, and its\ndescription:\n\n```\n Args:\n n_layers (`int`): The number of layers of the model.\n```\n\nIf the description is too long to fit in one line, another indentation is necessary before writing the description\nafter the argument.\n\nHere's an example showcasing everything so far:\n\n```\n Args:\n input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n Indices of input sequence tokens in the vocabulary.\n\n Indices can be obtained using [`AlbertTokenizer`]. See [`~PreTrainedTokenizer.encode`] and\n [`~PreTrainedTokenizer.__call__`] for details.\n\n [What are input IDs?](../glossary#input-ids)\n```\n\nFor optional arguments or arguments with defaults we follow the following syntax: imagine we have a function with the\nfollowing signature:\n\n```py\ndef my_function(x: str=None, a: float=3.14):\n```\n\nthen its documentation should look like this:\n\n```\n Args:\n x (`str`, *optional*):\n This argument controls ...\n a (`float`, *optional*, defaults to `3.14`):\n This argument is used to ...\n```\n\nNote that we always omit the \"defaults to \\`None\\`\" when None is the default for any argument. Also note that even\nif the first line describing your argument type and its default gets long, you can't break it on several lines. You can\nhowever write as many lines as you want in the indented description (see the example above with `input_ids`).\n\n#### Writing a multi-line code block\n\nMulti-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown:\n\n\n````\n```\n# first line of code\n# second line\n# etc\n```\n````\n\n#### Writing a return block\n\nThe return block should be introduced with the `Returns:` prefix, followed by a line return and an indentation.\nThe first line should be the type of the return, followed by a line return. No need to indent further for the elements\nbuilding the return.\n\nHere's an example of a single value return:\n\n```\n Returns:\n `List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token.\n```\n\nHere's an example of a tuple return, comprising several objects:\n\n```\n Returns:\n `tuple(torch.Tensor)` comprising various elements depending on the configuration ([`BertConfig`]) and inputs:\n - ** loss** (*optional*, returned when `masked_lm_labels` is provided) `torch.Tensor` of shape `(1,)` --\n Total loss is the sum of the masked language modeling loss and the next sequence prediction (classification) loss.\n - **prediction_scores** (`torch.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`) --\n Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).\n```\n\n#### Adding an image\n\nDue to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted `dataset` like\nthe ones hosted on [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) in which to place these files and reference\nthem by URL. We recommend putting them in the following dataset: [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images).\nIf an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images\nto this dataset.\n\n## Styling the docstring\n\nWe have an automatic script running with the `make style` command that will make sure that:\n- the docstrings fully take advantage of the line width\n- all code examples are formatted using black, like the code of the Transformers library\n\nThis script may have some weird failures if you made a syntax mistake or if you uncover a bug. Therefore, it's\nrecommended to commit your changes before running `make style`, so you can revert the changes done by that script\neasily.\n" }, { "path": "diffusers/docs/TRANSLATING.md", "content": "\n\n### Translating the Diffusers documentation into your language\n\nAs part of our mission to democratize machine learning, we'd love to make the Diffusers library available in many more languages! Follow the steps below if you want to help translate the documentation into your language 🙏.\n\n**🗞️ Open an issue**\n\nTo get started, navigate to the [Issues](https://github.com/huggingface/diffusers/issues) page of this repo and check if anyone else has opened an issue for your language. If not, open a new issue by selecting the \"🌐 Translating a New Language?\" from the \"New issue\" button.\n\nOnce an issue exists, post a comment to indicate which chapters you'd like to work on, and we'll add your name to the list.\n\n\n**🍴 Fork the repository**\n\nFirst, you'll need to [fork the Diffusers repo](https://docs.github.com/en/get-started/quickstart/fork-a-repo). You can do this by clicking on the **Fork** button on the top-right corner of this repo's page.\n\nOnce you've forked the repo, you'll want to get the files on your local machine for editing. You can do that by cloning the fork with Git as follows:\n\n```bash\ngit clone https://github.com//diffusers.git\n```\n\n**📋 Copy-paste the English version with a new language code**\n\nThe documentation files are in one leading directory:\n\n- [`docs/source`](https://github.com/huggingface/diffusers/tree/main/docs/source): All the documentation materials are organized here by language.\n\nYou'll only need to copy the files in the [`docs/source/en`](https://github.com/huggingface/diffusers/tree/main/docs/source/en) directory, so first navigate to your fork of the repo and run the following:\n\n```bash\ncd ~/path/to/diffusers/docs\ncp -r source/en source/\n```\n\nHere, `` should be one of the ISO 639-1 or ISO 639-2 language codes -- see [here](https://www.loc.gov/standards/iso639-2/php/code_list.php) for a handy table.\n\n**✍️ Start translating**\n\nThe fun part comes - translating the text!\n\nThe first thing we recommend is translating the part of the `_toctree.yml` file that corresponds to your doc chapter. This file is used to render the table of contents on the website.\n\n> 🙋 If the `_toctree.yml` file doesn't yet exist for your language, you can create one by copy-pasting from the English version and deleting the sections unrelated to your chapter. Just make sure it exists in the `docs/source//` directory!\n\nThe fields you should add are `local` (with the name of the file containing the translation; e.g. `autoclass_tutorial`), and `title` (with the title of the doc in your language; e.g. `Load pretrained instances with an AutoClass`) -- as a reference, here is the `_toctree.yml` for [English](https://github.com/huggingface/diffusers/blob/main/docs/source/en/_toctree.yml):\n\n```yaml\n- sections:\n - local: pipeline_tutorial # Do not change this! Use the same name for your .md file\n title: Pipelines for inference # Translate this!\n ...\n title: Tutorials # Translate this!\n```\n\nOnce you have translated the `_toctree.yml` file, you can start translating the [MDX](https://mdxjs.com/) files associated with your docs chapter.\n\n> 🙋 If you'd like others to help you with the translation, you should [open an issue](https://github.com/huggingface/diffusers/issues) and tag @patrickvonplaten.\n" }, { "path": "diffusers/docs/source/_config.py", "content": "# docstyle-ignore\nINSTALL_CONTENT = \"\"\"\n# Diffusers installation\n! pip install diffusers transformers datasets accelerate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/diffusers.git\n\"\"\"\n\nnotebook_first_cells = [{\"type\": \"code\", \"content\": INSTALL_CONTENT}]\n" }, { "path": "diffusers/docs/source/en/_toctree.yml", "content": "- sections:\n - local: index\n title: 🧨 Diffusers\n - local: quicktour\n title: Quicktour\n - local: stable_diffusion\n title: Effective and efficient diffusion\n - local: installation\n title: Installation\n title: Get started\n- sections:\n - local: tutorials/tutorial_overview\n title: Overview\n - local: using-diffusers/write_own_pipeline\n title: Understanding pipelines, models and schedulers\n - local: tutorials/autopipeline\n title: AutoPipeline\n - local: tutorials/basic_training\n title: Train a diffusion model\n - local: tutorials/using_peft_for_inference\n title: Load LoRAs for inference\n - local: tutorials/fast_diffusion\n title: Accelerate inference of text-to-image diffusion models\n title: Tutorials\n- sections:\n - local: using-diffusers/loading\n title: Load pipelines\n - local: using-diffusers/custom_pipeline_overview\n title: Load community pipelines and components\n - local: using-diffusers/schedulers\n title: Load schedulers and models\n - local: using-diffusers/other-formats\n title: Model files and layouts\n - local: using-diffusers/loading_adapters\n title: Load adapters\n - local: using-diffusers/push_to_hub\n title: Push files to the Hub\n title: Load pipelines and adapters\n- sections:\n - local: using-diffusers/unconditional_image_generation\n title: Unconditional image generation\n - local: using-diffusers/conditional_image_generation\n title: Text-to-image\n - local: using-diffusers/img2img\n title: Image-to-image\n - local: using-diffusers/inpaint\n title: Inpainting\n - local: using-diffusers/text-img2vid\n title: Text or image-to-video\n - local: using-diffusers/depth2img\n title: Depth-to-image\n title: Generative tasks\n- sections:\n - local: using-diffusers/overview_techniques\n title: Overview\n - local: training/distributed_inference\n title: Distributed inference with multiple GPUs\n - local: using-diffusers/merge_loras\n title: Merge LoRAs\n - local: using-diffusers/scheduler_features\n title: Scheduler features\n - local: using-diffusers/callback\n title: Pipeline callbacks\n - local: using-diffusers/reusing_seeds\n title: Reproducible pipelines\n - local: using-diffusers/image_quality\n title: Controlling image quality\n - local: using-diffusers/weighted_prompts\n title: Prompt techniques\n title: Inference techniques\n- sections:\n - local: advanced_inference/outpaint\n title: Outpainting\n title: Advanced inference\n- sections:\n - local: using-diffusers/sdxl\n title: Stable Diffusion XL\n - local: using-diffusers/sdxl_turbo\n title: SDXL Turbo\n - local: using-diffusers/kandinsky\n title: Kandinsky\n - local: using-diffusers/ip_adapter\n title: IP-Adapter\n - local: using-diffusers/controlnet\n title: ControlNet\n - local: using-diffusers/t2i_adapter\n title: T2I-Adapter\n - local: using-diffusers/inference_with_lcm\n title: Latent Consistency Model\n - local: using-diffusers/textual_inversion_inference\n title: Textual inversion\n - local: using-diffusers/shap-e\n title: Shap-E\n - local: using-diffusers/diffedit\n title: DiffEdit\n - local: using-diffusers/inference_with_tcd_lora\n title: Trajectory Consistency Distillation-LoRA\n - local: using-diffusers/svd\n title: Stable Video Diffusion\n - local: using-diffusers/marigold_usage\n title: Marigold Computer Vision\n title: Specific pipeline examples\n- sections:\n - local: training/overview\n title: Overview\n - local: training/create_dataset\n title: Create a dataset for training\n - local: training/adapt_a_model\n title: Adapt a model to a new task\n - isExpanded: false\n sections:\n - local: training/unconditional_training\n title: Unconditional image generation\n - local: training/text2image\n title: Text-to-image\n - local: training/sdxl\n title: Stable Diffusion XL\n - local: training/kandinsky\n title: Kandinsky 2.2\n - local: training/wuerstchen\n title: Wuerstchen\n - local: training/controlnet\n title: ControlNet\n - local: training/t2i_adapters\n title: T2I-Adapters\n - local: training/instructpix2pix\n title: InstructPix2Pix\n title: Models\n - isExpanded: false\n sections:\n - local: training/text_inversion\n title: Textual Inversion\n - local: training/dreambooth\n title: DreamBooth\n - local: training/lora\n title: LoRA\n - local: training/custom_diffusion\n title: Custom Diffusion\n - local: training/lcm_distill\n title: Latent Consistency Distillation\n - local: training/ddpo\n title: Reinforcement learning training with DDPO\n title: Methods\n title: Training\n- sections:\n - local: optimization/fp16\n title: Speed up inference\n - local: optimization/memory\n title: Reduce memory usage\n - local: optimization/torch2.0\n title: PyTorch 2.0\n - local: optimization/xformers\n title: xFormers\n - local: optimization/tome\n title: Token merging\n - local: optimization/deepcache\n title: DeepCache\n - local: optimization/tgate\n title: TGATE\n - sections:\n - local: using-diffusers/stable_diffusion_jax_how_to\n title: JAX/Flax\n - local: optimization/onnx\n title: ONNX\n - local: optimization/open_vino\n title: OpenVINO\n - local: optimization/coreml\n title: Core ML\n title: Optimized model formats\n - sections:\n - local: optimization/mps\n title: Metal Performance Shaders (MPS)\n - local: optimization/habana\n title: Habana Gaudi\n title: Optimized hardware\n title: Accelerate inference and reduce memory\n- sections:\n - local: conceptual/philosophy\n title: Philosophy\n - local: using-diffusers/controlling_generation\n title: Controlled generation\n - local: conceptual/contribution\n title: How to contribute?\n - local: conceptual/ethical_guidelines\n title: Diffusers' Ethical Guidelines\n - local: conceptual/evaluation\n title: Evaluating Diffusion Models\n title: Conceptual Guides\n- sections:\n - isExpanded: false\n sections:\n - local: api/configuration\n title: Configuration\n - local: api/logging\n title: Logging\n - local: api/outputs\n title: Outputs\n title: Main Classes\n - isExpanded: false\n sections:\n - local: api/loaders/ip_adapter\n title: IP-Adapter\n - local: api/loaders/lora\n title: LoRA\n - local: api/loaders/single_file\n title: Single files\n - local: api/loaders/textual_inversion\n title: Textual Inversion\n - local: api/loaders/unet\n title: UNet\n - local: api/loaders/peft\n title: PEFT\n title: Loaders\n - isExpanded: false\n sections:\n - local: api/models/overview\n title: Overview\n - local: api/models/unet\n title: UNet1DModel\n - local: api/models/unet2d\n title: UNet2DModel\n - local: api/models/unet2d-cond\n title: UNet2DConditionModel\n - local: api/models/unet3d-cond\n title: UNet3DConditionModel\n - local: api/models/unet-motion\n title: UNetMotionModel\n - local: api/models/uvit2d\n title: UViT2DModel\n - local: api/models/vq\n title: VQModel\n - local: api/models/autoencoderkl\n title: AutoencoderKL\n - local: api/models/asymmetricautoencoderkl\n title: AsymmetricAutoencoderKL\n - local: api/models/autoencoder_tiny\n title: Tiny AutoEncoder\n - local: api/models/consistency_decoder_vae\n title: ConsistencyDecoderVAE\n - local: api/models/transformer2d\n title: Transformer2DModel\n - local: api/models/pixart_transformer2d\n title: PixArtTransformer2DModel\n - local: api/models/dit_transformer2d\n title: DiTTransformer2DModel\n - local: api/models/hunyuan_transformer2d\n title: HunyuanDiT2DModel\n - local: api/models/transformer_temporal\n title: TransformerTemporalModel\n - local: api/models/sd3_transformer2d\n title: SD3Transformer2DModel\n - local: api/models/prior_transformer\n title: PriorTransformer\n - local: api/models/controlnet\n title: ControlNetModel\n title: Models\n - isExpanded: false\n sections:\n - local: api/pipelines/overview\n title: Overview\n - local: api/pipelines/amused\n title: aMUSEd\n - local: api/pipelines/animatediff\n title: AnimateDiff\n - local: api/pipelines/attend_and_excite\n title: Attend-and-Excite\n - local: api/pipelines/audioldm\n title: AudioLDM\n - local: api/pipelines/audioldm2\n title: AudioLDM 2\n - local: api/pipelines/auto_pipeline\n title: AutoPipeline\n - local: api/pipelines/blip_diffusion\n title: BLIP-Diffusion\n - local: api/pipelines/consistency_models\n title: Consistency Models\n - local: api/pipelines/controlnet\n title: ControlNet\n - local: api/pipelines/controlnet_sdxl\n title: ControlNet with Stable Diffusion XL\n - local: api/pipelines/controlnetxs\n title: ControlNet-XS\n - local: api/pipelines/controlnetxs_sdxl\n title: ControlNet-XS with Stable Diffusion XL\n - local: api/pipelines/dance_diffusion\n title: Dance Diffusion\n - local: api/pipelines/ddim\n title: DDIM\n - local: api/pipelines/ddpm\n title: DDPM\n - local: api/pipelines/deepfloyd_if\n title: DeepFloyd IF\n - local: api/pipelines/diffedit\n title: DiffEdit\n - local: api/pipelines/dit\n title: DiT\n - local: api/pipelines/hunyuandit\n title: Hunyuan-DiT\n - local: api/pipelines/i2vgenxl\n title: I2VGen-XL\n - local: api/pipelines/pix2pix\n title: InstructPix2Pix\n - local: api/pipelines/kandinsky\n title: Kandinsky 2.1\n - local: api/pipelines/kandinsky_v22\n title: Kandinsky 2.2\n - local: api/pipelines/kandinsky3\n title: Kandinsky 3\n - local: api/pipelines/latent_consistency_models\n title: Latent Consistency Models\n - local: api/pipelines/latent_diffusion\n title: Latent Diffusion\n - local: api/pipelines/ledits_pp\n title: LEDITS++\n - local: api/pipelines/marigold\n title: Marigold\n - local: api/pipelines/panorama\n title: MultiDiffusion\n - local: api/pipelines/musicldm\n title: MusicLDM\n - local: api/pipelines/paint_by_example\n title: Paint by Example\n - local: api/pipelines/pia\n title: Personalized Image Animator (PIA)\n - local: api/pipelines/pixart\n title: PixArt-α\n - local: api/pipelines/pixart_sigma\n title: PixArt-Σ\n - local: api/pipelines/self_attention_guidance\n title: Self-Attention Guidance\n - local: api/pipelines/semantic_stable_diffusion\n title: Semantic Guidance\n - local: api/pipelines/shap_e\n title: Shap-E\n - local: api/pipelines/stable_cascade\n title: Stable Cascade\n - sections:\n - local: api/pipelines/stable_diffusion/overview\n title: Overview\n - local: api/pipelines/stable_diffusion/text2img\n title: Text-to-image\n - local: api/pipelines/stable_diffusion/img2img\n title: Image-to-image\n - local: api/pipelines/stable_diffusion/svd\n title: Image-to-video\n - local: api/pipelines/stable_diffusion/inpaint\n title: Inpainting\n - local: api/pipelines/stable_diffusion/depth2img\n title: Depth-to-image\n - local: api/pipelines/stable_diffusion/image_variation\n title: Image variation\n - local: api/pipelines/stable_diffusion/stable_diffusion_safe\n title: Safe Stable Diffusion\n - local: api/pipelines/stable_diffusion/stable_diffusion_2\n title: Stable Diffusion 2\n - local: api/pipelines/stable_diffusion/stable_diffusion_3\n title: Stable Diffusion 3\n - local: api/pipelines/stable_diffusion/stable_diffusion_xl\n title: Stable Diffusion XL\n - local: api/pipelines/stable_diffusion/sdxl_turbo\n title: SDXL Turbo\n - local: api/pipelines/stable_diffusion/latent_upscale\n title: Latent upscaler\n - local: api/pipelines/stable_diffusion/upscale\n title: Super-resolution\n - local: api/pipelines/stable_diffusion/k_diffusion\n title: K-Diffusion\n - local: api/pipelines/stable_diffusion/ldm3d_diffusion\n title: LDM3D Text-to-(RGB, Depth), Text-to-(RGB-pano, Depth-pano), LDM3D Upscaler\n - local: api/pipelines/stable_diffusion/adapter\n title: T2I-Adapter\n - local: api/pipelines/stable_diffusion/gligen\n title: GLIGEN (Grounded Language-to-Image Generation)\n title: Stable Diffusion\n - local: api/pipelines/stable_unclip\n title: Stable unCLIP\n - local: api/pipelines/text_to_video\n title: Text-to-video\n - local: api/pipelines/text_to_video_zero\n title: Text2Video-Zero\n - local: api/pipelines/unclip\n title: unCLIP\n - local: api/pipelines/unidiffuser\n title: UniDiffuser\n - local: api/pipelines/value_guided_sampling\n title: Value-guided sampling\n - local: api/pipelines/wuerstchen\n title: Wuerstchen\n title: Pipelines\n - isExpanded: false\n sections:\n - local: api/schedulers/overview\n title: Overview\n - local: api/schedulers/cm_stochastic_iterative\n title: CMStochasticIterativeScheduler\n - local: api/schedulers/consistency_decoder\n title: ConsistencyDecoderScheduler\n - local: api/schedulers/ddim_inverse\n title: DDIMInverseScheduler\n - local: api/schedulers/ddim\n title: DDIMScheduler\n - local: api/schedulers/ddpm\n title: DDPMScheduler\n - local: api/schedulers/deis\n title: DEISMultistepScheduler\n - local: api/schedulers/multistep_dpm_solver_inverse\n title: DPMSolverMultistepInverse\n - local: api/schedulers/multistep_dpm_solver\n title: DPMSolverMultistepScheduler\n - local: api/schedulers/dpm_sde\n title: DPMSolverSDEScheduler\n - local: api/schedulers/singlestep_dpm_solver\n title: DPMSolverSinglestepScheduler\n - local: api/schedulers/edm_multistep_dpm_solver\n title: EDMDPMSolverMultistepScheduler\n - local: api/schedulers/edm_euler\n title: EDMEulerScheduler\n - local: api/schedulers/euler_ancestral\n title: EulerAncestralDiscreteScheduler\n - local: api/schedulers/euler\n title: EulerDiscreteScheduler\n - local: api/schedulers/flow_match_euler_discrete\n title: FlowMatchEulerDiscreteScheduler\n - local: api/schedulers/heun\n title: HeunDiscreteScheduler\n - local: api/schedulers/ipndm\n title: IPNDMScheduler\n - local: api/schedulers/stochastic_karras_ve\n title: KarrasVeScheduler\n - local: api/schedulers/dpm_discrete_ancestral\n title: KDPM2AncestralDiscreteScheduler\n - local: api/schedulers/dpm_discrete\n title: KDPM2DiscreteScheduler\n - local: api/schedulers/lcm\n title: LCMScheduler\n - local: api/schedulers/lms_discrete\n title: LMSDiscreteScheduler\n - local: api/schedulers/pndm\n title: PNDMScheduler\n - local: api/schedulers/repaint\n title: RePaintScheduler\n - local: api/schedulers/score_sde_ve\n title: ScoreSdeVeScheduler\n - local: api/schedulers/score_sde_vp\n title: ScoreSdeVpScheduler\n - local: api/schedulers/tcd\n title: TCDScheduler\n - local: api/schedulers/unipc\n title: UniPCMultistepScheduler\n - local: api/schedulers/vq_diffusion\n title: VQDiffusionScheduler\n title: Schedulers\n - isExpanded: false\n sections:\n - local: api/internal_classes_overview\n title: Overview\n - local: api/attnprocessor\n title: Attention Processor\n - local: api/activations\n title: Custom activation functions\n - local: api/normalization\n title: Custom normalization layers\n - local: api/utilities\n title: Utilities\n - local: api/image_processor\n title: VAE Image Processor\n - local: api/video_processor\n title: Video Processor\n title: Internal classes\n title: API\n" }, { "path": "diffusers/docs/source/en/advanced_inference/outpaint.md", "content": "\n\n# Outpainting\n\nOutpainting extends an image beyond its original boundaries, allowing you to add, replace, or modify visual elements in an image while preserving the original image. Like [inpainting](../using-diffusers/inpaint), you want to fill the white area (in this case, the area outside of the original image) with new visual elements while keeping the original image (represented by a mask of black pixels). There are a couple of ways to outpaint, such as with a [ControlNet](https://hf.co/blog/OzzyGT/outpainting-controlnet) or with [Differential Diffusion](https://hf.co/blog/OzzyGT/outpainting-differential-diffusion).\n\nThis guide will show you how to outpaint with an inpainting model, ControlNet, and a ZoeDepth estimator.\n\nBefore you begin, make sure you have the [controlnet_aux](https://github.com/huggingface/controlnet_aux) library installed so you can use the ZoeDepth estimator.\n\n```py\n!pip install -q controlnet_aux\n```\n\n## Image preparation\n\nStart by picking an image to outpaint with and remove the background with a Space like [BRIA-RMBG-1.4](https://hf.co/spaces/briaai/BRIA-RMBG-1.4).\n\n\n\nFor example, remove the background from this image of a pair of shoes.\n\n
\n
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original image
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\n \n
background removed
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\n\n[Stable Diffusion XL (SDXL)](../using-diffusers/sdxl) models work best with 1024x1024 images, but you can resize the image to any size as long as your hardware has enough memory to support it. The transparent background in the image should also be replaced with a white background. Create a function (like the one below) that scales and pastes the image onto a white background.\n\n```py\nimport random\n\nimport requests\nimport torch\nfrom controlnet_aux import ZoeDetector\nfrom PIL import Image, ImageOps\n\nfrom diffusers import (\n AutoencoderKL,\n ControlNetModel,\n StableDiffusionXLControlNetPipeline,\n StableDiffusionXLInpaintPipeline,\n)\n\ndef scale_and_paste(original_image):\n aspect_ratio = original_image.width / original_image.height\n\n if original_image.width > original_image.height:\n new_width = 1024\n new_height = round(new_width / aspect_ratio)\n else:\n new_height = 1024\n new_width = round(new_height * aspect_ratio)\n\n resized_original = original_image.resize((new_width, new_height), Image.LANCZOS)\n white_background = Image.new(\"RGBA\", (1024, 1024), \"white\")\n x = (1024 - new_width) // 2\n y = (1024 - new_height) // 2\n white_background.paste(resized_original, (x, y), resized_original)\n\n return resized_original, white_background\n\noriginal_image = Image.open(\n requests.get(\n \"https://huggingface.co/datasets/stevhliu/testing-images/resolve/main/no-background-jordan.png\",\n stream=True,\n ).raw\n).convert(\"RGBA\")\nresized_img, white_bg_image = scale_and_paste(original_image)\n```\n\nTo avoid adding unwanted extra details, use the ZoeDepth estimator to provide additional guidance during generation and to ensure the shoes remain consistent with the original image.\n\n```py\nzoe = ZoeDetector.from_pretrained(\"lllyasviel/Annotators\")\nimage_zoe = zoe(white_bg_image, detect_resolution=512, image_resolution=1024)\nimage_zoe\n```\n\n
\n \n
\n\n## Outpaint\n\nOnce your image is ready, you can generate content in the white area around the shoes with [controlnet-inpaint-dreamer-sdxl](https://hf.co/destitech/controlnet-inpaint-dreamer-sdxl), a SDXL ControlNet trained for inpainting.\n\nLoad the inpainting ControlNet, ZoeDepth model, VAE and pass them to the [`StableDiffusionXLControlNetPipeline`]. Then you can create an optional `generate_image` function (for convenience) to outpaint an initial image.\n\n```py\ncontrolnets = [\n ControlNetModel.from_pretrained(\n \"destitech/controlnet-inpaint-dreamer-sdxl\", torch_dtype=torch.float16, variant=\"fp16\"\n ),\n ControlNetModel.from_pretrained(\n \"diffusers/controlnet-zoe-depth-sdxl-1.0\", torch_dtype=torch.float16\n ),\n]\nvae = AutoencoderKL.from_pretrained(\"madebyollin/sdxl-vae-fp16-fix\", torch_dtype=torch.float16).to(\"cuda\")\npipeline = StableDiffusionXLControlNetPipeline.from_pretrained(\n \"SG161222/RealVisXL_V4.0\", torch_dtype=torch.float16, variant=\"fp16\", controlnet=controlnets, vae=vae\n).to(\"cuda\")\n\ndef generate_image(prompt, negative_prompt, inpaint_image, zoe_image, seed: int = None):\n if seed is None:\n seed = random.randint(0, 2**32 - 1)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(seed)\n\n image = pipeline(\n prompt,\n negative_prompt=negative_prompt,\n image=[inpaint_image, zoe_image],\n guidance_scale=6.5,\n num_inference_steps=25,\n generator=generator,\n controlnet_conditioning_scale=[0.5, 0.8],\n control_guidance_end=[0.9, 0.6],\n ).images[0]\n\n return image\n\nprompt = \"nike air jordans on a basketball court\"\nnegative_prompt = \"\"\n\ntemp_image = generate_image(prompt, negative_prompt, white_bg_image, image_zoe, 908097)\n```\n\nPaste the original image over the initial outpainted image. You'll improve the outpainted background in a later step.\n\n```py\nx = (1024 - resized_img.width) // 2\ny = (1024 - resized_img.height) // 2\ntemp_image.paste(resized_img, (x, y), resized_img)\ntemp_image\n```\n\n
\n \n
\n\n> [!TIP]\n> Now is a good time to free up some memory if you're running low!\n>\n> ```py\n> pipeline=None\n> torch.cuda.empty_cache()\n> ```\n\nNow that you have an initial outpainted image, load the [`StableDiffusionXLInpaintPipeline`] with the [RealVisXL](https://hf.co/SG161222/RealVisXL_V4.0) model to generate the final outpainted image with better quality.\n\n```py\npipeline = StableDiffusionXLInpaintPipeline.from_pretrained(\n \"OzzyGT/RealVisXL_V4.0_inpainting\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n vae=vae,\n).to(\"cuda\")\n```\n\nPrepare a mask for the final outpainted image. To create a more natural transition between the original image and the outpainted background, blur the mask to help it blend better.\n\n```py\nmask = Image.new(\"L\", temp_image.size)\nmask.paste(resized_img.split()[3], (x, y))\nmask = ImageOps.invert(mask)\nfinal_mask = mask.point(lambda p: p > 128 and 255)\nmask_blurred = pipeline.mask_processor.blur(final_mask, blur_factor=20)\nmask_blurred\n```\n\n
\n \n
\n\nCreate a better prompt and pass it to the `generate_outpaint` function to generate the final outpainted image. Again, paste the original image over the final outpainted background.\n\n```py\ndef generate_outpaint(prompt, negative_prompt, image, mask, seed: int = None):\n if seed is None:\n seed = random.randint(0, 2**32 - 1)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(seed)\n\n image = pipeline(\n prompt,\n negative_prompt=negative_prompt,\n image=image,\n mask_image=mask,\n guidance_scale=10.0,\n strength=0.8,\n num_inference_steps=30,\n generator=generator,\n ).images[0]\n\n return image\n\nprompt = \"high quality photo of nike air jordans on a basketball court, highly detailed\"\nnegative_prompt = \"\"\n\nfinal_image = generate_outpaint(prompt, negative_prompt, temp_image, mask_blurred, 7688778)\nx = (1024 - resized_img.width) // 2\ny = (1024 - resized_img.height) // 2\nfinal_image.paste(resized_img, (x, y), resized_img)\nfinal_image\n```\n\n
\n \n
\n" }, { "path": "diffusers/docs/source/en/api/activations.md", "content": "\n\n# Activation functions\n\nCustomized activation functions for supporting various models in 🤗 Diffusers.\n\n## GELU\n\n[[autodoc]] models.activations.GELU\n\n## GEGLU\n\n[[autodoc]] models.activations.GEGLU\n\n## ApproximateGELU\n\n[[autodoc]] models.activations.ApproximateGELU\n" }, { "path": "diffusers/docs/source/en/api/attnprocessor.md", "content": "\n\n# Attention Processor\n\nAn attention processor is a class for applying different types of attention mechanisms.\n\n## AttnProcessor\n[[autodoc]] models.attention_processor.AttnProcessor\n\n## AttnProcessor2_0\n[[autodoc]] models.attention_processor.AttnProcessor2_0\n\n## AttnAddedKVProcessor\n[[autodoc]] models.attention_processor.AttnAddedKVProcessor\n\n## AttnAddedKVProcessor2_0\n[[autodoc]] models.attention_processor.AttnAddedKVProcessor2_0\n\n## CrossFrameAttnProcessor\n[[autodoc]] pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.CrossFrameAttnProcessor\n\n## CustomDiffusionAttnProcessor\n[[autodoc]] models.attention_processor.CustomDiffusionAttnProcessor\n\n## CustomDiffusionAttnProcessor2_0\n[[autodoc]] models.attention_processor.CustomDiffusionAttnProcessor2_0\n\n## CustomDiffusionXFormersAttnProcessor\n[[autodoc]] models.attention_processor.CustomDiffusionXFormersAttnProcessor\n\n## FusedAttnProcessor2_0\n[[autodoc]] models.attention_processor.FusedAttnProcessor2_0\n\n## LoRAAttnAddedKVProcessor\n[[autodoc]] models.attention_processor.LoRAAttnAddedKVProcessor\n\n## LoRAXFormersAttnProcessor\n[[autodoc]] models.attention_processor.LoRAXFormersAttnProcessor\n\n## SlicedAttnProcessor\n[[autodoc]] models.attention_processor.SlicedAttnProcessor\n\n## SlicedAttnAddedKVProcessor\n[[autodoc]] models.attention_processor.SlicedAttnAddedKVProcessor\n\n## XFormersAttnProcessor\n[[autodoc]] models.attention_processor.XFormersAttnProcessor\n\n## AttnProcessorNPU\n[[autodoc]] models.attention_processor.AttnProcessorNPU\n" }, { "path": "diffusers/docs/source/en/api/configuration.md", "content": "\n\n# Configuration\n\nSchedulers from [`~schedulers.scheduling_utils.SchedulerMixin`] and models from [`ModelMixin`] inherit from [`ConfigMixin`] which stores all the parameters that are passed to their respective `__init__` methods in a JSON-configuration file.\n\n\n\nTo use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `huggingface-cli login`.\n\n\n\n## ConfigMixin\n\n[[autodoc]] ConfigMixin\n\t- load_config\n\t- from_config\n\t- save_config\n\t- to_json_file\n\t- to_json_string\n" }, { "path": "diffusers/docs/source/en/api/image_processor.md", "content": "\n\n# VAE Image Processor\n\nThe [`VaeImageProcessor`] provides a unified API for [`StableDiffusionPipeline`]s to prepare image inputs for VAE encoding and post-processing outputs once they're decoded. This includes transformations such as resizing, normalization, and conversion between PIL Image, PyTorch, and NumPy arrays.\n\nAll pipelines with [`VaeImageProcessor`] accept PIL Image, PyTorch tensor, or NumPy arrays as image inputs and return outputs based on the `output_type` argument by the user. You can pass encoded image latents directly to the pipeline and return latents from the pipeline as a specific output with the `output_type` argument (for example `output_type=\"latent\"`). This allows you to take the generated latents from one pipeline and pass it to another pipeline as input without leaving the latent space. It also makes it much easier to use multiple pipelines together by passing PyTorch tensors directly between different pipelines.\n\n## VaeImageProcessor\n\n[[autodoc]] image_processor.VaeImageProcessor\n\n## VaeImageProcessorLDM3D\n\nThe [`VaeImageProcessorLDM3D`] accepts RGB and depth inputs and returns RGB and depth outputs.\n\n[[autodoc]] image_processor.VaeImageProcessorLDM3D\n\n## PixArtImageProcessor\n\n[[autodoc]] image_processor.PixArtImageProcessor\n\n## IPAdapterMaskProcessor\n\n[[autodoc]] image_processor.IPAdapterMaskProcessor\n" }, { "path": "diffusers/docs/source/en/api/internal_classes_overview.md", "content": "\n\n# Overview\n\nThe APIs in this section are more experimental and prone to breaking changes. Most of them are used internally for development, but they may also be useful to you if you're interested in building a diffusion model with some custom parts or if you're interested in some of our helper utilities for working with 🤗 Diffusers.\n" }, { "path": "diffusers/docs/source/en/api/loaders/ip_adapter.md", "content": "\n\n# IP-Adapter\n\n[IP-Adapter](https://hf.co/papers/2308.06721) is a lightweight adapter that enables prompting a diffusion model with an image. This method decouples the cross-attention layers of the image and text features. The image features are generated from an image encoder.\n\n\n\nLearn how to load an IP-Adapter checkpoint and image in the IP-Adapter [loading](../../using-diffusers/loading_adapters#ip-adapter) guide, and you can see how to use it in the [usage](../../using-diffusers/ip_adapter) guide.\n\n\n\n## IPAdapterMixin\n\n[[autodoc]] loaders.ip_adapter.IPAdapterMixin\n\n## IPAdapterMaskProcessor\n\n[[autodoc]] image_processor.IPAdapterMaskProcessor" }, { "path": "diffusers/docs/source/en/api/loaders/lora.md", "content": "\n\n# LoRA\n\nLoRA is a fast and lightweight training method that inserts and trains a significantly smaller number of parameters instead of all the model parameters. This produces a smaller file (~100 MBs) and makes it easier to quickly train a model to learn a new concept. LoRA weights are typically loaded into the UNet, text encoder or both. There are two classes for loading LoRA weights:\n\n- [`LoraLoaderMixin`] provides functions for loading and unloading, fusing and unfusing, enabling and disabling, and more functions for managing LoRA weights. This class can be used with any model.\n- [`StableDiffusionXLLoraLoaderMixin`] is a [Stable Diffusion (SDXL)](../../api/pipelines/stable_diffusion/stable_diffusion_xl) version of the [`LoraLoaderMixin`] class for loading and saving LoRA weights. It can only be used with the SDXL model.\n\n\n\nTo learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.\n\n\n\n## LoraLoaderMixin\n\n[[autodoc]] loaders.lora.LoraLoaderMixin\n\n## StableDiffusionXLLoraLoaderMixin\n\n[[autodoc]] loaders.lora.StableDiffusionXLLoraLoaderMixin" }, { "path": "diffusers/docs/source/en/api/loaders/peft.md", "content": "\n\n# PEFT\n\nDiffusers supports loading adapters such as [LoRA](../../using-diffusers/loading_adapters) with the [PEFT](https://huggingface.co/docs/peft/index) library with the [`~loaders.peft.PeftAdapterMixin`] class. This allows modeling classes in Diffusers like [`UNet2DConditionModel`] to load an adapter.\n\n\n\nRefer to the [Inference with PEFT](../../tutorials/using_peft_for_inference.md) tutorial for an overview of how to use PEFT in Diffusers for inference.\n\n\n\n## PeftAdapterMixin\n\n[[autodoc]] loaders.peft.PeftAdapterMixin\n" }, { "path": "diffusers/docs/source/en/api/loaders/single_file.md", "content": "\n\n# Single files\n\nThe [`~loaders.FromSingleFileMixin.from_single_file`] method allows you to load:\n\n* a model stored in a single file, which is useful if you're working with models from the diffusion ecosystem, like Automatic1111, and commonly rely on a single-file layout to store and share models\n* a model stored in their originally distributed layout, which is useful if you're working with models finetuned with other services, and want to load it directly into Diffusers model objects and pipelines\n\n> [!TIP]\n> Read the [Model files and layouts](../../using-diffusers/other-formats) guide to learn more about the Diffusers-multifolder layout versus the single-file layout, and how to load models stored in these different layouts.\n\n## Supported pipelines\n\n- [`StableDiffusionPipeline`]\n- [`StableDiffusionImg2ImgPipeline`]\n- [`StableDiffusionInpaintPipeline`]\n- [`StableDiffusionControlNetPipeline`]\n- [`StableDiffusionControlNetImg2ImgPipeline`]\n- [`StableDiffusionControlNetInpaintPipeline`]\n- [`StableDiffusionUpscalePipeline`]\n- [`StableDiffusionXLPipeline`]\n- [`StableDiffusionXLImg2ImgPipeline`]\n- [`StableDiffusionXLInpaintPipeline`]\n- [`StableDiffusionXLInstructPix2PixPipeline`]\n- [`StableDiffusionXLControlNetPipeline`]\n- [`StableDiffusionXLKDiffusionPipeline`]\n- [`StableDiffusion3Pipeline`]\n- [`LatentConsistencyModelPipeline`]\n- [`LatentConsistencyModelImg2ImgPipeline`]\n- [`StableDiffusionControlNetXSPipeline`]\n- [`StableDiffusionXLControlNetXSPipeline`]\n- [`LEditsPPPipelineStableDiffusion`]\n- [`LEditsPPPipelineStableDiffusionXL`]\n- [`PIAPipeline`]\n\n## Supported models\n\n- [`UNet2DConditionModel`]\n- [`StableCascadeUNet`]\n- [`AutoencoderKL`]\n- [`ControlNetModel`]\n- [`SD3Transformer2DModel`]\n\n## FromSingleFileMixin\n\n[[autodoc]] loaders.single_file.FromSingleFileMixin\n\n## FromOriginalModelMixin\n\n[[autodoc]] loaders.single_file_model.FromOriginalModelMixin\n" }, { "path": "diffusers/docs/source/en/api/loaders/textual_inversion.md", "content": "\n\n# Textual Inversion\n\nTextual Inversion is a training method for personalizing models by learning new text embeddings from a few example images. The file produced from training is extremely small (a few KBs) and the new embeddings can be loaded into the text encoder.\n\n[`TextualInversionLoaderMixin`] provides a function for loading Textual Inversion embeddings from Diffusers and Automatic1111 into the text encoder and loading a special token to activate the embeddings.\n\n\n\nTo learn more about how to load Textual Inversion embeddings, see the [Textual Inversion](../../using-diffusers/loading_adapters#textual-inversion) loading guide.\n\n\n\n## TextualInversionLoaderMixin\n\n[[autodoc]] loaders.textual_inversion.TextualInversionLoaderMixin" }, { "path": "diffusers/docs/source/en/api/loaders/unet.md", "content": "\n\n# UNet\n\nSome training methods - like LoRA and Custom Diffusion - typically target the UNet's attention layers, but these training methods can also target other non-attention layers. Instead of training all of a model's parameters, only a subset of the parameters are trained, which is faster and more efficient. This class is useful if you're *only* loading weights into a UNet. If you need to load weights into the text encoder or a text encoder and UNet, try using the [`~loaders.LoraLoaderMixin.load_lora_weights`] function instead.\n\nThe [`UNet2DConditionLoadersMixin`] class provides functions for loading and saving weights, fusing and unfusing LoRAs, disabling and enabling LoRAs, and setting and deleting adapters.\n\n\n\nTo learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.\n\n\n\n## UNet2DConditionLoadersMixin\n\n[[autodoc]] loaders.unet.UNet2DConditionLoadersMixin" }, { "path": "diffusers/docs/source/en/api/logging.md", "content": "\n\n# Logging\n\n🤗 Diffusers has a centralized logging system to easily manage the verbosity of the library. The default verbosity is set to `WARNING`.\n\nTo change the verbosity level, use one of the direct setters. For instance, to change the verbosity to the `INFO` level.\n\n```python\nimport diffusers\n\ndiffusers.logging.set_verbosity_info()\n```\n\nYou can also use the environment variable `DIFFUSERS_VERBOSITY` to override the default verbosity. You can set it\nto one of the following: `debug`, `info`, `warning`, `error`, `critical`. For example:\n\n```bash\nDIFFUSERS_VERBOSITY=error ./myprogram.py\n```\n\nAdditionally, some `warnings` can be disabled by setting the environment variable\n`DIFFUSERS_NO_ADVISORY_WARNINGS` to a true value, like `1`. This disables any warning logged by\n[`logger.warning_advice`]. For example:\n\n```bash\nDIFFUSERS_NO_ADVISORY_WARNINGS=1 ./myprogram.py\n```\n\nHere is an example of how to use the same logger as the library in your own module or script:\n\n```python\nfrom diffusers.utils import logging\n\nlogging.set_verbosity_info()\nlogger = logging.get_logger(\"diffusers\")\nlogger.info(\"INFO\")\nlogger.warning(\"WARN\")\n```\n\n\nAll methods of the logging module are documented below. The main methods are\n[`logging.get_verbosity`] to get the current level of verbosity in the logger and\n[`logging.set_verbosity`] to set the verbosity to the level of your choice.\n\nIn order from the least verbose to the most verbose:\n\n| Method | Integer value | Description |\n|----------------------------------------------------------:|--------------:|----------------------------------------------------:|\n| `diffusers.logging.CRITICAL` or `diffusers.logging.FATAL` | 50 | only report the most critical errors |\n| `diffusers.logging.ERROR` | 40 | only report errors |\n| `diffusers.logging.WARNING` or `diffusers.logging.WARN` | 30 | only report errors and warnings (default) |\n| `diffusers.logging.INFO` | 20 | only report errors, warnings, and basic information |\n| `diffusers.logging.DEBUG` | 10 | report all information |\n\nBy default, `tqdm` progress bars are displayed during model download. [`logging.disable_progress_bar`] and [`logging.enable_progress_bar`] are used to enable or disable this behavior.\n\n## Base setters\n\n[[autodoc]] utils.logging.set_verbosity_error\n\n[[autodoc]] utils.logging.set_verbosity_warning\n\n[[autodoc]] utils.logging.set_verbosity_info\n\n[[autodoc]] utils.logging.set_verbosity_debug\n\n## Other functions\n\n[[autodoc]] utils.logging.get_verbosity\n\n[[autodoc]] utils.logging.set_verbosity\n\n[[autodoc]] utils.logging.get_logger\n\n[[autodoc]] utils.logging.enable_default_handler\n\n[[autodoc]] utils.logging.disable_default_handler\n\n[[autodoc]] utils.logging.enable_explicit_format\n\n[[autodoc]] utils.logging.reset_format\n\n[[autodoc]] utils.logging.enable_progress_bar\n\n[[autodoc]] utils.logging.disable_progress_bar\n" }, { "path": "diffusers/docs/source/en/api/models/asymmetricautoencoderkl.md", "content": "\n\n# AsymmetricAutoencoderKL\n\nImproved larger variational autoencoder (VAE) model with KL loss for inpainting task: [Designing a Better Asymmetric VQGAN for StableDiffusion](https://arxiv.org/abs/2306.04632) by Zixin Zhu, Xuelu Feng, Dongdong Chen, Jianmin Bao, Le Wang, Yinpeng Chen, Lu Yuan, Gang Hua.\n\nThe abstract from the paper is:\n\n*StableDiffusion is a revolutionary text-to-image generator that is causing a stir in the world of image generation and editing. Unlike traditional methods that learn a diffusion model in pixel space, StableDiffusion learns a diffusion model in the latent space via a VQGAN, ensuring both efficiency and quality. It not only supports image generation tasks, but also enables image editing for real images, such as image inpainting and local editing. However, we have observed that the vanilla VQGAN used in StableDiffusion leads to significant information loss, causing distortion artifacts even in non-edited image regions. To this end, we propose a new asymmetric VQGAN with two simple designs. Firstly, in addition to the input from the encoder, the decoder contains a conditional branch that incorporates information from task-specific priors, such as the unmasked image region in inpainting. Secondly, the decoder is much heavier than the encoder, allowing for more detailed recovery while only slightly increasing the total inference cost. The training cost of our asymmetric VQGAN is cheap, and we only need to retrain a new asymmetric decoder while keeping the vanilla VQGAN encoder and StableDiffusion unchanged. Our asymmetric VQGAN can be widely used in StableDiffusion-based inpainting and local editing methods. Extensive experiments demonstrate that it can significantly improve the inpainting and editing performance, while maintaining the original text-to-image capability. The code is available at https://github.com/buxiangzhiren/Asymmetric_VQGAN*\n\nEvaluation results can be found in section 4.1 of the original paper.\n\n## Available checkpoints\n\n* [https://huggingface.co/cross-attention/asymmetric-autoencoder-kl-x-1-5](https://huggingface.co/cross-attention/asymmetric-autoencoder-kl-x-1-5)\n* [https://huggingface.co/cross-attention/asymmetric-autoencoder-kl-x-2](https://huggingface.co/cross-attention/asymmetric-autoencoder-kl-x-2)\n\n## Example Usage\n\n```python\nfrom diffusers import AsymmetricAutoencoderKL, StableDiffusionInpaintPipeline\nfrom diffusers.utils import load_image, make_image_grid\n\n\nprompt = \"a photo of a person with beard\"\nimg_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/celeba_hq_256.png\"\nmask_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png\"\n\noriginal_image = load_image(img_url).resize((512, 512))\nmask_image = load_image(mask_url).resize((512, 512))\n\npipe = StableDiffusionInpaintPipeline.from_pretrained(\"runwayml/stable-diffusion-inpainting\")\npipe.vae = AsymmetricAutoencoderKL.from_pretrained(\"cross-attention/asymmetric-autoencoder-kl-x-1-5\")\npipe.to(\"cuda\")\n\nimage = pipe(prompt=prompt, image=original_image, mask_image=mask_image).images[0]\nmake_image_grid([original_image, mask_image, image], rows=1, cols=3)\n```\n\n## AsymmetricAutoencoderKL\n\n[[autodoc]] models.autoencoders.autoencoder_asym_kl.AsymmetricAutoencoderKL\n\n## AutoencoderKLOutput\n\n[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput\n\n## DecoderOutput\n\n[[autodoc]] models.autoencoders.vae.DecoderOutput\n" }, { "path": "diffusers/docs/source/en/api/models/autoencoder_tiny.md", "content": "\n\n# Tiny AutoEncoder\n\nTiny AutoEncoder for Stable Diffusion (TAESD) was introduced in [madebyollin/taesd](https://github.com/madebyollin/taesd) by Ollin Boer Bohan. It is a tiny distilled version of Stable Diffusion's VAE that can quickly decode the latents in a [`StableDiffusionPipeline`] or [`StableDiffusionXLPipeline`] almost instantly.\n\nTo use with Stable Diffusion v-2.1:\n\n```python\nimport torch\nfrom diffusers import DiffusionPipeline, AutoencoderTiny\n\npipe = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-1-base\", torch_dtype=torch.float16\n)\npipe.vae = AutoencoderTiny.from_pretrained(\"madebyollin/taesd\", torch_dtype=torch.float16)\npipe = pipe.to(\"cuda\")\n\nprompt = \"slice of delicious New York-style berry cheesecake\"\nimage = pipe(prompt, num_inference_steps=25).images[0]\nimage\n```\n\nTo use with Stable Diffusion XL 1.0\n\n```python\nimport torch\nfrom diffusers import DiffusionPipeline, AutoencoderTiny\n\npipe = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16\n)\npipe.vae = AutoencoderTiny.from_pretrained(\"madebyollin/taesdxl\", torch_dtype=torch.float16)\npipe = pipe.to(\"cuda\")\n\nprompt = \"slice of delicious New York-style berry cheesecake\"\nimage = pipe(prompt, num_inference_steps=25).images[0]\nimage\n```\n\n## AutoencoderTiny\n\n[[autodoc]] AutoencoderTiny\n\n## AutoencoderTinyOutput\n\n[[autodoc]] models.autoencoders.autoencoder_tiny.AutoencoderTinyOutput\n" }, { "path": "diffusers/docs/source/en/api/models/autoencoderkl.md", "content": "\n\n# AutoencoderKL\n\nThe variational autoencoder (VAE) model with KL loss was introduced in [Auto-Encoding Variational Bayes](https://arxiv.org/abs/1312.6114v11) by Diederik P. Kingma and Max Welling. The model is used in 🤗 Diffusers to encode images into latents and to decode latent representations into images.\n\nThe abstract from the paper is:\n\n*How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions are two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.*\n\n## Loading from the original format\n\nBy default the [`AutoencoderKL`] should be loaded with [`~ModelMixin.from_pretrained`], but it can also be loaded\nfrom the original format using [`FromOriginalVAEMixin.from_single_file`] as follows:\n\n```py\nfrom diffusers import AutoencoderKL\n\nurl = \"https://huggingface.co/stabilityai/sd-vae-ft-mse-original/blob/main/vae-ft-mse-840000-ema-pruned.safetensors\" # can also be a local file\nmodel = AutoencoderKL.from_single_file(url)\n```\n\n## AutoencoderKL\n\n[[autodoc]] AutoencoderKL\n - decode\n - encode\n - all\n\n## AutoencoderKLOutput\n\n[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput\n\n## DecoderOutput\n\n[[autodoc]] models.autoencoders.vae.DecoderOutput\n\n## FlaxAutoencoderKL\n\n[[autodoc]] FlaxAutoencoderKL\n\n## FlaxAutoencoderKLOutput\n\n[[autodoc]] models.vae_flax.FlaxAutoencoderKLOutput\n\n## FlaxDecoderOutput\n\n[[autodoc]] models.vae_flax.FlaxDecoderOutput\n" }, { "path": "diffusers/docs/source/en/api/models/consistency_decoder_vae.md", "content": "\n\n# Consistency Decoder\n\nConsistency decoder can be used to decode the latents from the denoising UNet in the [`StableDiffusionPipeline`]. This decoder was introduced in the [DALL-E 3 technical report](https://openai.com/dall-e-3).\n\nThe original codebase can be found at [openai/consistencydecoder](https://github.com/openai/consistencydecoder).\n\n\n\nInference is only supported for 2 iterations as of now.\n\n\n\nThe pipeline could not have been contributed without the help of [madebyollin](https://github.com/madebyollin) and [mrsteyk](https://github.com/mrsteyk) from [this issue](https://github.com/openai/consistencydecoder/issues/1).\n\n## ConsistencyDecoderVAE\n[[autodoc]] ConsistencyDecoderVAE\n - all\n - decode\n" }, { "path": "diffusers/docs/source/en/api/models/controlnet.md", "content": "\n\n# ControlNetModel\n\nThe ControlNet model was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, Maneesh Agrawala. It provides a greater degree of control over text-to-image generation by conditioning the model on additional inputs such as edge maps, depth maps, segmentation maps, and keypoints for pose detection.\n\nThe abstract from the paper is:\n\n*We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with \"zero convolutions\" (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.*\n\n## Loading from the original format\n\nBy default the [`ControlNetModel`] should be loaded with [`~ModelMixin.from_pretrained`], but it can also be loaded\nfrom the original format using [`FromOriginalControlnetMixin.from_single_file`] as follows:\n\n```py\nfrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel\n\nurl = \"https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth\" # can also be a local path\ncontrolnet = ControlNetModel.from_single_file(url)\n\nurl = \"https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned.safetensors\" # can also be a local path\npipe = StableDiffusionControlNetPipeline.from_single_file(url, controlnet=controlnet)\n```\n\n## ControlNetModel\n\n[[autodoc]] ControlNetModel\n\n## ControlNetOutput\n\n[[autodoc]] models.controlnet.ControlNetOutput\n\n## FlaxControlNetModel\n\n[[autodoc]] FlaxControlNetModel\n\n## FlaxControlNetOutput\n\n[[autodoc]] models.controlnet_flax.FlaxControlNetOutput\n" }, { "path": "diffusers/docs/source/en/api/models/dit_transformer2d.md", "content": "\n\n# DiTTransformer2DModel\n\nA Transformer model for image-like data from [DiT](https://huggingface.co/papers/2212.09748).\n\n## DiTTransformer2DModel\n\n[[autodoc]] DiTTransformer2DModel\n" }, { "path": "diffusers/docs/source/en/api/models/hunyuan_transformer2d.md", "content": "\n\n# HunyuanDiT2DModel\n\nA Diffusion Transformer model for 2D data from [Hunyuan-DiT](https://github.com/Tencent/HunyuanDiT).\n\n## HunyuanDiT2DModel\n\n[[autodoc]] HunyuanDiT2DModel\n\n" }, { "path": "diffusers/docs/source/en/api/models/overview.md", "content": "\n\n# Models\n\n🤗 Diffusers provides pretrained models for popular algorithms and modules to create custom diffusion systems. The primary function of models is to denoise an input sample as modeled by the distribution \\\\(p_{\\theta}(x_{t-1}|x_{t})\\\\).\n\nAll models are built from the base [`ModelMixin`] class which is a [`torch.nn.Module`](https://pytorch.org/docs/stable/generated/torch.nn.Module.html) providing basic functionality for saving and loading models, locally and from the Hugging Face Hub.\n\n## ModelMixin\n[[autodoc]] ModelMixin\n\n## FlaxModelMixin\n\n[[autodoc]] FlaxModelMixin\n\n## PushToHubMixin\n\n[[autodoc]] utils.PushToHubMixin\n" }, { "path": "diffusers/docs/source/en/api/models/pixart_transformer2d.md", "content": "\n\n# PixArtTransformer2DModel\n\nA Transformer model for image-like data from [PixArt-Alpha](https://huggingface.co/papers/2310.00426) and [PixArt-Sigma](https://huggingface.co/papers/2403.04692). \n\n## PixArtTransformer2DModel\n\n[[autodoc]] PixArtTransformer2DModel\n" }, { "path": "diffusers/docs/source/en/api/models/prior_transformer.md", "content": "\n\n# PriorTransformer\n\nThe Prior Transformer was originally introduced in [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://huggingface.co/papers/2204.06125) by Ramesh et al. It is used to predict CLIP image embeddings from CLIP text embeddings; image embeddings are predicted through a denoising diffusion process.\n\nThe abstract from the paper is:\n\n*Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a two-stage model: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding. We show that explicitly generating image representations improves image diversity with minimal loss in photorealism and caption similarity. Our decoders conditioned on image representations can also produce variations of an image that preserve both its semantics and style, while varying the non-essential details absent from the image representation. Moreover, the joint embedding space of CLIP enables language-guided image manipulations in a zero-shot fashion. We use diffusion models for the decoder and experiment with both autoregressive and diffusion models for the prior, finding that the latter are computationally more efficient and produce higher-quality samples.*\n\n## PriorTransformer\n\n[[autodoc]] PriorTransformer\n\n## PriorTransformerOutput\n\n[[autodoc]] models.transformers.prior_transformer.PriorTransformerOutput\n" }, { "path": "diffusers/docs/source/en/api/models/sd3_transformer2d.md", "content": "\n\n# SD3 Transformer Model\n\nThe Transformer model introduced in [Stable Diffusion 3](https://hf.co/papers/2403.03206). Its novelty lies in the MMDiT transformer block. \n\n## SD3Transformer2DModel\n\n[[autodoc]] SD3Transformer2DModel" }, { "path": "diffusers/docs/source/en/api/models/transformer2d.md", "content": "\n\n# Transformer2DModel\n\nA Transformer model for image-like data from [CompVis](https://huggingface.co/CompVis) that is based on the [Vision Transformer](https://huggingface.co/papers/2010.11929) introduced by Dosovitskiy et al. The [`Transformer2DModel`] accepts discrete (classes of vector embeddings) or continuous (actual embeddings) inputs.\n\nWhen the input is **continuous**:\n\n1. Project the input and reshape it to `(batch_size, sequence_length, feature_dimension)`.\n2. Apply the Transformer blocks in the standard way.\n3. Reshape to image.\n\nWhen the input is **discrete**:\n\n\n\nIt is assumed one of the input classes is the masked latent pixel. The predicted classes of the unnoised image don't contain a prediction for the masked pixel because the unnoised image cannot be masked.\n\n\n\n1. Convert input (classes of latent pixels) to embeddings and apply positional embeddings.\n2. Apply the Transformer blocks in the standard way.\n3. Predict classes of unnoised image.\n\n## Transformer2DModel\n\n[[autodoc]] Transformer2DModel\n\n## Transformer2DModelOutput\n\n[[autodoc]] models.transformers.transformer_2d.Transformer2DModelOutput\n" }, { "path": "diffusers/docs/source/en/api/models/transformer_temporal.md", "content": "\n\n# TransformerTemporalModel\n\nA Transformer model for video-like data.\n\n## TransformerTemporalModel\n\n[[autodoc]] models.transformers.transformer_temporal.TransformerTemporalModel\n\n## TransformerTemporalModelOutput\n\n[[autodoc]] models.transformers.transformer_temporal.TransformerTemporalModelOutput\n" }, { "path": "diffusers/docs/source/en/api/models/unet-motion.md", "content": "\n\n# UNetMotionModel\n\nThe [UNet](https://huggingface.co/papers/1505.04597) model was originally introduced by Ronneberger et al for biomedical image segmentation, but it is also commonly used in 🤗 Diffusers because it outputs images that are the same size as the input. It is one of the most important components of a diffusion system because it facilitates the actual diffusion process. There are several variants of the UNet model in 🤗 Diffusers, depending on it's number of dimensions and whether it is a conditional model or not. This is a 2D UNet model.\n\nThe abstract from the paper is:\n\n*There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.*\n\n## UNetMotionModel\n[[autodoc]] UNetMotionModel\n\n## UNet3DConditionOutput\n[[autodoc]] models.unets.unet_3d_condition.UNet3DConditionOutput\n" }, { "path": "diffusers/docs/source/en/api/models/unet.md", "content": "\n\n# UNet1DModel\n\nThe [UNet](https://huggingface.co/papers/1505.04597) model was originally introduced by Ronneberger et al. for biomedical image segmentation, but it is also commonly used in 🤗 Diffusers because it outputs images that are the same size as the input. It is one of the most important components of a diffusion system because it facilitates the actual diffusion process. There are several variants of the UNet model in 🤗 Diffusers, depending on it's number of dimensions and whether it is a conditional model or not. This is a 1D UNet model.\n\nThe abstract from the paper is:\n\n*There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.*\n\n## UNet1DModel\n[[autodoc]] UNet1DModel\n\n## UNet1DOutput\n[[autodoc]] models.unets.unet_1d.UNet1DOutput\n" }, { "path": "diffusers/docs/source/en/api/models/unet2d-cond.md", "content": "\n\n# UNet2DConditionModel\n\nThe [UNet](https://huggingface.co/papers/1505.04597) model was originally introduced by Ronneberger et al. for biomedical image segmentation, but it is also commonly used in 🤗 Diffusers because it outputs images that are the same size as the input. It is one of the most important components of a diffusion system because it facilitates the actual diffusion process. There are several variants of the UNet model in 🤗 Diffusers, depending on it's number of dimensions and whether it is a conditional model or not. This is a 2D UNet conditional model.\n\nThe abstract from the paper is:\n\n*There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.*\n\n## UNet2DConditionModel\n[[autodoc]] UNet2DConditionModel\n\n## UNet2DConditionOutput\n[[autodoc]] models.unets.unet_2d_condition.UNet2DConditionOutput\n\n## FlaxUNet2DConditionModel\n[[autodoc]] models.unets.unet_2d_condition_flax.FlaxUNet2DConditionModel\n\n## FlaxUNet2DConditionOutput\n[[autodoc]] models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput\n" }, { "path": "diffusers/docs/source/en/api/models/unet2d.md", "content": "\n\n# UNet2DModel\n\nThe [UNet](https://huggingface.co/papers/1505.04597) model was originally introduced by Ronneberger et al. for biomedical image segmentation, but it is also commonly used in 🤗 Diffusers because it outputs images that are the same size as the input. It is one of the most important components of a diffusion system because it facilitates the actual diffusion process. There are several variants of the UNet model in 🤗 Diffusers, depending on it's number of dimensions and whether it is a conditional model or not. This is a 2D UNet model.\n\nThe abstract from the paper is:\n\n*There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.*\n\n## UNet2DModel\n[[autodoc]] UNet2DModel\n\n## UNet2DOutput\n[[autodoc]] models.unets.unet_2d.UNet2DOutput\n" }, { "path": "diffusers/docs/source/en/api/models/unet3d-cond.md", "content": "\n\n# UNet3DConditionModel\n\nThe [UNet](https://huggingface.co/papers/1505.04597) model was originally introduced by Ronneberger et al. for biomedical image segmentation, but it is also commonly used in 🤗 Diffusers because it outputs images that are the same size as the input. It is one of the most important components of a diffusion system because it facilitates the actual diffusion process. There are several variants of the UNet model in 🤗 Diffusers, depending on it's number of dimensions and whether it is a conditional model or not. This is a 3D UNet conditional model.\n\nThe abstract from the paper is:\n\n*There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.*\n\n## UNet3DConditionModel\n[[autodoc]] UNet3DConditionModel\n\n## UNet3DConditionOutput\n[[autodoc]] models.unets.unet_3d_condition.UNet3DConditionOutput\n" }, { "path": "diffusers/docs/source/en/api/models/uvit2d.md", "content": "\n\n# UVit2DModel\n\nThe [U-ViT](https://hf.co/papers/2301.11093) model is a vision transformer (ViT) based UNet. This model incorporates elements from ViT (considers all inputs such as time, conditions and noisy image patches as tokens) and a UNet (long skip connections between the shallow and deep layers). The skip connection is important for predicting pixel-level features. An additional 3x3 convolutional block is applied prior to the final output to improve image quality.\n\nThe abstract from the paper is:\n\n*Currently, applying diffusion models in pixel space of high resolution images is difficult. Instead, existing approaches focus on diffusion in lower dimensional spaces (latent diffusion), or have multiple super-resolution levels of generation referred to as cascades. The downside is that these approaches add additional complexity to the diffusion framework. This paper aims to improve denoising diffusion for high resolution images while keeping the model as simple as possible. The paper is centered around the research question: How can one train a standard denoising diffusion models on high resolution images, and still obtain performance comparable to these alternate approaches? The four main findings are: 1) the noise schedule should be adjusted for high resolution images, 2) It is sufficient to scale only a particular part of the architecture, 3) dropout should be added at specific locations in the architecture, and 4) downsampling is an effective strategy to avoid high resolution feature maps. Combining these simple yet effective techniques, we achieve state-of-the-art on image generation among diffusion models without sampling modifiers on ImageNet.*\n\n## UVit2DModel\n\n[[autodoc]] UVit2DModel\n\n## UVit2DConvEmbed\n\n[[autodoc]] models.unets.uvit_2d.UVit2DConvEmbed\n\n## UVitBlock\n\n[[autodoc]] models.unets.uvit_2d.UVitBlock\n\n## ConvNextBlock\n\n[[autodoc]] models.unets.uvit_2d.ConvNextBlock\n\n## ConvMlmLayer\n\n[[autodoc]] models.unets.uvit_2d.ConvMlmLayer\n" }, { "path": "diffusers/docs/source/en/api/models/vq.md", "content": "\n\n# VQModel\n\nThe VQ-VAE model was introduced in [Neural Discrete Representation Learning](https://huggingface.co/papers/1711.00937) by Aaron van den Oord, Oriol Vinyals and Koray Kavukcuoglu. The model is used in 🤗 Diffusers to decode latent representations into images. Unlike [`AutoencoderKL`], the [`VQModel`] works in a quantized latent space.\n\nThe abstract from the paper is:\n\n*Learning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector Quantised-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways: the encoder network outputs discrete, rather than continuous, codes; and the prior is learnt rather than static. In order to learn a discrete latent representation, we incorporate ideas from vector quantisation (VQ). Using the VQ method allows the model to circumvent issues of \"posterior collapse\" -- where the latents are ignored when they are paired with a powerful autoregressive decoder -- typically observed in the VAE framework. Pairing these representations with an autoregressive prior, the model can generate high quality images, videos, and speech as well as doing high quality speaker conversion and unsupervised learning of phonemes, providing further evidence of the utility of the learnt representations.*\n\n## VQModel\n\n[[autodoc]] VQModel\n\n## VQEncoderOutput\n\n[[autodoc]] models.autoencoders.vq_model.VQEncoderOutput\n" }, { "path": "diffusers/docs/source/en/api/normalization.md", "content": "\n\n# Normalization layers\n\nCustomized normalization layers for supporting various models in 🤗 Diffusers.\n\n## AdaLayerNorm\n\n[[autodoc]] models.normalization.AdaLayerNorm\n\n## AdaLayerNormZero\n\n[[autodoc]] models.normalization.AdaLayerNormZero\n\n## AdaLayerNormSingle\n\n[[autodoc]] models.normalization.AdaLayerNormSingle\n\n## AdaGroupNorm\n\n[[autodoc]] models.normalization.AdaGroupNorm\n" }, { "path": "diffusers/docs/source/en/api/outputs.md", "content": "\n\n# Outputs\n\nAll model outputs are subclasses of [`~utils.BaseOutput`], data structures containing all the information returned by the model. The outputs can also be used as tuples or dictionaries.\n\nFor example:\n\n```python\nfrom diffusers import DDIMPipeline\n\npipeline = DDIMPipeline.from_pretrained(\"google/ddpm-cifar10-32\")\noutputs = pipeline()\n```\n\nThe `outputs` object is a [`~pipelines.ImagePipelineOutput`] which means it has an image attribute.\n\nYou can access each attribute as you normally would or with a keyword lookup, and if that attribute is not returned by the model, you will get `None`:\n\n```python\noutputs.images\noutputs[\"images\"]\n```\n\nWhen considering the `outputs` object as a tuple, it only considers the attributes that don't have `None` values.\nFor instance, retrieving an image by indexing into it returns the tuple `(outputs.images)`:\n\n```python\noutputs[:1]\n```\n\n\n\nTo check a specific pipeline or model output, refer to its corresponding API documentation.\n\n\n\n## BaseOutput\n\n[[autodoc]] utils.BaseOutput\n - to_tuple\n\n## ImagePipelineOutput\n\n[[autodoc]] pipelines.ImagePipelineOutput\n\n## FlaxImagePipelineOutput\n\n[[autodoc]] pipelines.pipeline_flax_utils.FlaxImagePipelineOutput\n\n## AudioPipelineOutput\n\n[[autodoc]] pipelines.AudioPipelineOutput\n\n## ImageTextPipelineOutput\n\n[[autodoc]] ImageTextPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/amused.md", "content": "\n\n# aMUSEd\n\naMUSEd was introduced in [aMUSEd: An Open MUSE Reproduction](https://huggingface.co/papers/2401.01808) by Suraj Patil, William Berman, Robin Rombach, and Patrick von Platen.\n\nAmused is a lightweight text to image model based off of the [MUSE](https://arxiv.org/abs/2301.00704) architecture. Amused is particularly useful in applications that require a lightweight and fast model such as generating many images quickly at once.\n\nAmused is a vqvae token based transformer that can generate an image in fewer forward passes than many diffusion models. In contrast with muse, it uses the smaller text encoder CLIP-L/14 instead of t5-xxl. Due to its small parameter count and few forward pass generation process, amused can generate many images quickly. This benefit is seen particularly at larger batch sizes.\n\nThe abstract from the paper is:\n\n*We present aMUSEd, an open-source, lightweight masked image model (MIM) for text-to-image generation based on MUSE. With 10 percent of MUSE's parameters, aMUSEd is focused on fast image generation. We believe MIM is under-explored compared to latent diffusion, the prevailing approach for text-to-image generation. Compared to latent diffusion, MIM requires fewer inference steps and is more interpretable. Additionally, MIM can be fine-tuned to learn additional styles with only a single image. We hope to encourage further exploration of MIM by demonstrating its effectiveness on large-scale text-to-image generation and releasing reproducible training code. We also release checkpoints for two models which directly produce images at 256x256 and 512x512 resolutions.*\n\n| Model | Params |\n|-------|--------|\n| [amused-256](https://huggingface.co/amused/amused-256) | 603M |\n| [amused-512](https://huggingface.co/amused/amused-512) | 608M |\n\n## AmusedPipeline\n\n[[autodoc]] AmusedPipeline\n\t- __call__\n\t- all\n\t- enable_xformers_memory_efficient_attention\n\t- disable_xformers_memory_efficient_attention\n\n[[autodoc]] AmusedImg2ImgPipeline\n\t- __call__\n\t- all\n\t- enable_xformers_memory_efficient_attention\n\t- disable_xformers_memory_efficient_attention\n\n[[autodoc]] AmusedInpaintPipeline\n\t- __call__\n\t- all\n\t- enable_xformers_memory_efficient_attention\n\t- disable_xformers_memory_efficient_attention" }, { "path": "diffusers/docs/source/en/api/pipelines/animatediff.md", "content": "\n\n# Text-to-Video Generation with AnimateDiff\n\n## Overview\n\n[AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://arxiv.org/abs/2307.04725) by Yuwei Guo, Ceyuan Yang, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, Bo Dai.\n\nThe abstract of the paper is the following:\n\n*With the advance of text-to-image models (e.g., Stable Diffusion) and corresponding personalization techniques such as DreamBooth and LoRA, everyone can manifest their imagination into high-quality images at an affordable cost. Subsequently, there is a great demand for image animation techniques to further combine generated static images with motion dynamics. In this report, we propose a practical framework to animate most of the existing personalized text-to-image models once and for all, saving efforts in model-specific tuning. At the core of the proposed framework is to insert a newly initialized motion modeling module into the frozen text-to-image model and train it on video clips to distill reasonable motion priors. Once trained, by simply injecting this motion modeling module, all personalized versions derived from the same base T2I readily become text-driven models that produce diverse and personalized animated images. We conduct our evaluation on several public representative personalized text-to-image models across anime pictures and realistic photographs, and demonstrate that our proposed framework helps these models generate temporally smooth animation clips while preserving the domain and diversity of their outputs. Code and pre-trained weights will be publicly available at [this https URL](https://animatediff.github.io/).*\n\n## Available Pipelines\n\n| Pipeline | Tasks | Demo\n|---|---|:---:|\n| [AnimateDiffPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/animatediff/pipeline_animatediff.py) | *Text-to-Video Generation with AnimateDiff* |\n| [AnimateDiffVideoToVideoPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py) | *Video-to-Video Generation with AnimateDiff* |\n\n## Available checkpoints\n\nMotion Adapter checkpoints can be found under [guoyww](https://huggingface.co/guoyww/). These checkpoints are meant to work with any model based on Stable Diffusion 1.4/1.5.\n\n## Usage example\n\n### AnimateDiffPipeline\n\nAnimateDiff works with a MotionAdapter checkpoint and a Stable Diffusion model checkpoint. The MotionAdapter is a collection of Motion Modules that are responsible for adding coherent motion across image frames. These modules are applied after the Resnet and Attention blocks in Stable Diffusion UNet.\n\nThe following example demonstrates how to use a *MotionAdapter* checkpoint with Diffusers for inference based on StableDiffusion-1.4/1.5.\n\n```python\nimport torch\nfrom diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter\nfrom diffusers.utils import export_to_gif\n\n# Load the motion adapter\nadapter = MotionAdapter.from_pretrained(\"guoyww/animatediff-motion-adapter-v1-5-2\", torch_dtype=torch.float16)\n# load SD 1.5 based finetuned model\nmodel_id = \"SG161222/Realistic_Vision_V5.1_noVAE\"\npipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16)\nscheduler = DDIMScheduler.from_pretrained(\n model_id,\n subfolder=\"scheduler\",\n clip_sample=False,\n timestep_spacing=\"linspace\",\n beta_schedule=\"linear\",\n steps_offset=1,\n)\npipe.scheduler = scheduler\n\n# enable memory savings\npipe.enable_vae_slicing()\npipe.enable_model_cpu_offload()\n\noutput = pipe(\n prompt=(\n \"masterpiece, bestquality, highlydetailed, ultradetailed, sunset, \"\n \"orange sky, warm lighting, fishing boats, ocean waves seagulls, \"\n \"rippling water, wharf, silhouette, serene atmosphere, dusk, evening glow, \"\n \"golden hour, coastal landscape, seaside scenery\"\n ),\n negative_prompt=\"bad quality, worse quality\",\n num_frames=16,\n guidance_scale=7.5,\n num_inference_steps=25,\n generator=torch.Generator(\"cpu\").manual_seed(42),\n)\nframes = output.frames[0]\nexport_to_gif(frames, \"animation.gif\")\n\n```\n\nHere are some sample outputs:\n\n\n \n \n \n
\n masterpiece, bestquality, sunset.\n
\n \"masterpiece,\n
\n\n\n\nAnimateDiff tends to work better with finetuned Stable Diffusion models. If you plan on using a scheduler that can clip samples, make sure to disable it by setting `clip_sample=False` in the scheduler as this can also have an adverse effect on generated samples. Additionally, the AnimateDiff checkpoints can be sensitive to the beta schedule of the scheduler. We recommend setting this to `linear`.\n\n\n\n### AnimateDiffSDXLPipeline\n\nAnimateDiff can also be used with SDXL models. This is currently an experimental feature as only a beta release of the motion adapter checkpoint is available.\n\n```python\nimport torch\nfrom diffusers.models import MotionAdapter\nfrom diffusers import AnimateDiffSDXLPipeline, DDIMScheduler\nfrom diffusers.utils import export_to_gif\n\nadapter = MotionAdapter.from_pretrained(\"guoyww/animatediff-motion-adapter-sdxl-beta\", torch_dtype=torch.float16)\n\nmodel_id = \"stabilityai/stable-diffusion-xl-base-1.0\"\nscheduler = DDIMScheduler.from_pretrained(\n model_id,\n subfolder=\"scheduler\",\n clip_sample=False,\n timestep_spacing=\"linspace\",\n beta_schedule=\"linear\",\n steps_offset=1,\n)\npipe = AnimateDiffSDXLPipeline.from_pretrained(\n model_id,\n motion_adapter=adapter,\n scheduler=scheduler,\n torch_dtype=torch.float16,\n variant=\"fp16\",\n).to(\"cuda\")\n\n# enable memory savings\npipe.enable_vae_slicing()\npipe.enable_vae_tiling()\n\noutput = pipe(\n prompt=\"a panda surfing in the ocean, realistic, high quality\",\n negative_prompt=\"low quality, worst quality\",\n num_inference_steps=20,\n guidance_scale=8,\n width=1024,\n height=1024,\n num_frames=16,\n)\n\nframes = output.frames[0]\nexport_to_gif(frames, \"animation.gif\")\n```\n\n### AnimateDiffVideoToVideoPipeline\n\nAnimateDiff can also be used to generate visually similar videos or enable style/character/background or other edits starting from an initial video, allowing you to seamlessly explore creative possibilities.\n\n```python\nimport imageio\nimport requests\nimport torch\nfrom diffusers import AnimateDiffVideoToVideoPipeline, DDIMScheduler, MotionAdapter\nfrom diffusers.utils import export_to_gif\nfrom io import BytesIO\nfrom PIL import Image\n\n# Load the motion adapter\nadapter = MotionAdapter.from_pretrained(\"guoyww/animatediff-motion-adapter-v1-5-2\", torch_dtype=torch.float16)\n# load SD 1.5 based finetuned model\nmodel_id = \"SG161222/Realistic_Vision_V5.1_noVAE\"\npipe = AnimateDiffVideoToVideoPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16)\nscheduler = DDIMScheduler.from_pretrained(\n model_id,\n subfolder=\"scheduler\",\n clip_sample=False,\n timestep_spacing=\"linspace\",\n beta_schedule=\"linear\",\n steps_offset=1,\n)\npipe.scheduler = scheduler\n\n# enable memory savings\npipe.enable_vae_slicing()\npipe.enable_model_cpu_offload()\n\n# helper function to load videos\ndef load_video(file_path: str):\n images = []\n\n if file_path.startswith(('http://', 'https://')):\n # If the file_path is a URL\n response = requests.get(file_path)\n response.raise_for_status()\n content = BytesIO(response.content)\n vid = imageio.get_reader(content)\n else:\n # Assuming it's a local file path\n vid = imageio.get_reader(file_path)\n\n for frame in vid:\n pil_image = Image.fromarray(frame)\n images.append(pil_image)\n\n return images\n\nvideo = load_video(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-vid2vid-input-1.gif\")\n\noutput = pipe(\n video = video,\n prompt=\"panda playing a guitar, on a boat, in the ocean, high quality\",\n negative_prompt=\"bad quality, worse quality\",\n guidance_scale=7.5,\n num_inference_steps=25,\n strength=0.5,\n generator=torch.Generator(\"cpu\").manual_seed(42),\n)\nframes = output.frames[0]\nexport_to_gif(frames, \"animation.gif\")\n```\n\nHere are some sample outputs:\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n
Source VideoOutput Video
\n raccoon playing a guitar\n
\n \"racoon\n 		\n panda playing a guitar\n
\n \"panda\n
\n closeup of margot robbie, fireworks in the background, high quality\n
\n \"closeup\n 		\n closeup of tony stark, robert downey jr, fireworks\n
\n \"closeup\n
\n\n## Using Motion LoRAs\n\nMotion LoRAs are a collection of LoRAs that work with the `guoyww/animatediff-motion-adapter-v1-5-2` checkpoint. These LoRAs are responsible for adding specific types of motion to the animations.\n\n```python\nimport torch\nfrom diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter\nfrom diffusers.utils import export_to_gif\n\n# Load the motion adapter\nadapter = MotionAdapter.from_pretrained(\"guoyww/animatediff-motion-adapter-v1-5-2\", torch_dtype=torch.float16)\n# load SD 1.5 based finetuned model\nmodel_id = \"SG161222/Realistic_Vision_V5.1_noVAE\"\npipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16)\npipe.load_lora_weights(\n \"guoyww/animatediff-motion-lora-zoom-out\", adapter_name=\"zoom-out\"\n)\n\nscheduler = DDIMScheduler.from_pretrained(\n model_id,\n subfolder=\"scheduler\",\n clip_sample=False,\n beta_schedule=\"linear\",\n timestep_spacing=\"linspace\",\n steps_offset=1,\n)\npipe.scheduler = scheduler\n\n# enable memory savings\npipe.enable_vae_slicing()\npipe.enable_model_cpu_offload()\n\noutput = pipe(\n prompt=(\n \"masterpiece, bestquality, highlydetailed, ultradetailed, sunset, \"\n \"orange sky, warm lighting, fishing boats, ocean waves seagulls, \"\n \"rippling water, wharf, silhouette, serene atmosphere, dusk, evening glow, \"\n \"golden hour, coastal landscape, seaside scenery\"\n ),\n negative_prompt=\"bad quality, worse quality\",\n num_frames=16,\n guidance_scale=7.5,\n num_inference_steps=25,\n generator=torch.Generator(\"cpu\").manual_seed(42),\n)\nframes = output.frames[0]\nexport_to_gif(frames, \"animation.gif\")\n\n```\n\n\n \n \n \n
\n masterpiece, bestquality, sunset.\n
\n \"masterpiece,\n
\n\n## Using Motion LoRAs with PEFT\n\nYou can also leverage the [PEFT](https://github.com/huggingface/peft) backend to combine Motion LoRA's and create more complex animations.\n\nFirst install PEFT with\n\n```shell\npip install peft\n```\n\nThen you can use the following code to combine Motion LoRAs.\n\n```python\nimport torch\nfrom diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter\nfrom diffusers.utils import export_to_gif\n\n# Load the motion adapter\nadapter = MotionAdapter.from_pretrained(\"guoyww/animatediff-motion-adapter-v1-5-2\", torch_dtype=torch.float16)\n# load SD 1.5 based finetuned model\nmodel_id = \"SG161222/Realistic_Vision_V5.1_noVAE\"\npipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16)\n\npipe.load_lora_weights(\n \"diffusers/animatediff-motion-lora-zoom-out\", adapter_name=\"zoom-out\",\n)\npipe.load_lora_weights(\n \"diffusers/animatediff-motion-lora-pan-left\", adapter_name=\"pan-left\",\n)\npipe.set_adapters([\"zoom-out\", \"pan-left\"], adapter_weights=[1.0, 1.0])\n\nscheduler = DDIMScheduler.from_pretrained(\n model_id,\n subfolder=\"scheduler\",\n clip_sample=False,\n timestep_spacing=\"linspace\",\n beta_schedule=\"linear\",\n steps_offset=1,\n)\npipe.scheduler = scheduler\n\n# enable memory savings\npipe.enable_vae_slicing()\npipe.enable_model_cpu_offload()\n\noutput = pipe(\n prompt=(\n \"masterpiece, bestquality, highlydetailed, ultradetailed, sunset, \"\n \"orange sky, warm lighting, fishing boats, ocean waves seagulls, \"\n \"rippling water, wharf, silhouette, serene atmosphere, dusk, evening glow, \"\n \"golden hour, coastal landscape, seaside scenery\"\n ),\n negative_prompt=\"bad quality, worse quality\",\n num_frames=16,\n guidance_scale=7.5,\n num_inference_steps=25,\n generator=torch.Generator(\"cpu\").manual_seed(42),\n)\nframes = output.frames[0]\nexport_to_gif(frames, \"animation.gif\")\n\n```\n\n\n \n \n \n
\n masterpiece, bestquality, sunset.\n
\n \"masterpiece,\n
\n\n## Using FreeInit\n\n[FreeInit: Bridging Initialization Gap in Video Diffusion Models](https://arxiv.org/abs/2312.07537) by Tianxing Wu, Chenyang Si, Yuming Jiang, Ziqi Huang, Ziwei Liu.\n\nFreeInit is an effective method that improves temporal consistency and overall quality of videos generated using video-diffusion-models without any addition training. It can be applied to AnimateDiff, ModelScope, VideoCrafter and various other video generation models seamlessly at inference time, and works by iteratively refining the latent-initialization noise. More details can be found it the paper.\n\nThe following example demonstrates the usage of FreeInit.\n\n```python\nimport torch\nfrom diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler\nfrom diffusers.utils import export_to_gif\n\nadapter = MotionAdapter.from_pretrained(\"guoyww/animatediff-motion-adapter-v1-5-2\")\nmodel_id = \"SG161222/Realistic_Vision_V5.1_noVAE\"\npipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16).to(\"cuda\")\npipe.scheduler = DDIMScheduler.from_pretrained(\n model_id,\n subfolder=\"scheduler\",\n beta_schedule=\"linear\",\n clip_sample=False,\n timestep_spacing=\"linspace\",\n steps_offset=1\n)\n\n# enable memory savings\npipe.enable_vae_slicing()\npipe.enable_vae_tiling()\n\n# enable FreeInit\n# Refer to the enable_free_init documentation for a full list of configurable parameters\npipe.enable_free_init(method=\"butterworth\", use_fast_sampling=True)\n\n# run inference\noutput = pipe(\n prompt=\"a panda playing a guitar, on a boat, in the ocean, high quality\",\n negative_prompt=\"bad quality, worse quality\",\n num_frames=16,\n guidance_scale=7.5,\n num_inference_steps=20,\n generator=torch.Generator(\"cpu\").manual_seed(666),\n)\n\n# disable FreeInit\npipe.disable_free_init()\n\nframes = output.frames[0]\nexport_to_gif(frames, \"animation.gif\")\n```\n\n\n\nFreeInit is not really free - the improved quality comes at the cost of extra computation. It requires sampling a few extra times depending on the `num_iters` parameter that is set when enabling it. Setting the `use_fast_sampling` parameter to `True` can improve the overall performance (at the cost of lower quality compared to when `use_fast_sampling=False` but still better results than vanilla video generation models).\n\n\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n\n \n \n \n \n \n \n \n \n
Without FreeInit enabledWith FreeInit enabled
\n panda playing a guitar\n
\n \"panda\n 		\n panda playing a guitar\n
\n \"panda\n
\n\n## Using AnimateLCM\n\n[AnimateLCM](https://animatelcm.github.io/) is a motion module checkpoint and an [LCM LoRA](https://huggingface.co/docs/diffusers/using-diffusers/inference_with_lcm_lora) that have been created using a consistency learning strategy that decouples the distillation of the image generation priors and the motion generation priors.\n\n```python\nimport torch\nfrom diffusers import AnimateDiffPipeline, LCMScheduler, MotionAdapter\nfrom diffusers.utils import export_to_gif\n\nadapter = MotionAdapter.from_pretrained(\"wangfuyun/AnimateLCM\")\npipe = AnimateDiffPipeline.from_pretrained(\"emilianJR/epiCRealism\", motion_adapter=adapter)\npipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, beta_schedule=\"linear\")\n\npipe.load_lora_weights(\"wangfuyun/AnimateLCM\", weight_name=\"sd15_lora_beta.safetensors\", adapter_name=\"lcm-lora\")\n\npipe.enable_vae_slicing()\npipe.enable_model_cpu_offload()\n\noutput = pipe(\n prompt=\"A space rocket with trails of smoke behind it launching into space from the desert, 4k, high resolution\",\n negative_prompt=\"bad quality, worse quality, low resolution\",\n num_frames=16,\n guidance_scale=1.5,\n num_inference_steps=6,\n generator=torch.Generator(\"cpu\").manual_seed(0),\n)\nframes = output.frames[0]\nexport_to_gif(frames, \"animatelcm.gif\")\n```\n\n\n \n \n \n
\n A space rocket, 4K.\n
\n \"A\n
\n\nAnimateLCM is also compatible with existing [Motion LoRAs](https://huggingface.co/collections/dn6/animatediff-motion-loras-654cb8ad732b9e3cf4d3c17e).\n\n```python\nimport torch\nfrom diffusers import AnimateDiffPipeline, LCMScheduler, MotionAdapter\nfrom diffusers.utils import export_to_gif\n\nadapter = MotionAdapter.from_pretrained(\"wangfuyun/AnimateLCM\")\npipe = AnimateDiffPipeline.from_pretrained(\"emilianJR/epiCRealism\", motion_adapter=adapter)\npipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, beta_schedule=\"linear\")\n\npipe.load_lora_weights(\"wangfuyun/AnimateLCM\", weight_name=\"sd15_lora_beta.safetensors\", adapter_name=\"lcm-lora\")\npipe.load_lora_weights(\"guoyww/animatediff-motion-lora-tilt-up\", adapter_name=\"tilt-up\")\n\npipe.set_adapters([\"lcm-lora\", \"tilt-up\"], [1.0, 0.8])\npipe.enable_vae_slicing()\npipe.enable_model_cpu_offload()\n\noutput = pipe(\n prompt=\"A space rocket with trails of smoke behind it launching into space from the desert, 4k, high resolution\",\n negative_prompt=\"bad quality, worse quality, low resolution\",\n num_frames=16,\n guidance_scale=1.5,\n num_inference_steps=6,\n generator=torch.Generator(\"cpu\").manual_seed(0),\n)\nframes = output.frames[0]\nexport_to_gif(frames, \"animatelcm-motion-lora.gif\")\n```\n\n\n \n \n \n
\n A space rocket, 4K.\n
\n \"A\n
\n\n\n## AnimateDiffPipeline\n\n[[autodoc]] AnimateDiffPipeline\n - all\n - __call__\n\n## AnimateDiffSDXLPipeline\n\n[[autodoc]] AnimateDiffSDXLPipeline\n - all\n - __call__\n\n## AnimateDiffVideoToVideoPipeline\n\n[[autodoc]] AnimateDiffVideoToVideoPipeline\n - all\n - __call__\n\n## AnimateDiffPipelineOutput\n\n[[autodoc]] pipelines.animatediff.AnimateDiffPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/attend_and_excite.md", "content": "\n\n# Attend-and-Excite\n\nAttend-and-Excite for Stable Diffusion was proposed in [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://attendandexcite.github.io/Attend-and-Excite/) and provides textual attention control over image generation.\n\nThe abstract from the paper is:\n\n*Recent text-to-image generative models have demonstrated an unparalleled ability to generate diverse and creative imagery guided by a target text prompt. While revolutionary, current state-of-the-art diffusion models may still fail in generating images that fully convey the semantics in the given text prompt. We analyze the publicly available Stable Diffusion model and assess the existence of catastrophic neglect, where the model fails to generate one or more of the subjects from the input prompt. Moreover, we find that in some cases the model also fails to correctly bind attributes (e.g., colors) to their corresponding subjects. To help mitigate these failure cases, we introduce the concept of Generative Semantic Nursing (GSN), where we seek to intervene in the generative process on the fly during inference time to improve the faithfulness of the generated images. Using an attention-based formulation of GSN, dubbed Attend-and-Excite, we guide the model to refine the cross-attention units to attend to all subject tokens in the text prompt and strengthen - or excite - their activations, encouraging the model to generate all subjects described in the text prompt. We compare our approach to alternative approaches and demonstrate that it conveys the desired concepts more faithfully across a range of text prompts.*\n\nYou can find additional information about Attend-and-Excite on the [project page](https://attendandexcite.github.io/Attend-and-Excite/), the [original codebase](https://github.com/AttendAndExcite/Attend-and-Excite), or try it out in a [demo](https://huggingface.co/spaces/AttendAndExcite/Attend-and-Excite).\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## StableDiffusionAttendAndExcitePipeline\n\n[[autodoc]] StableDiffusionAttendAndExcitePipeline\n\t- all\n\t- __call__\n\n## StableDiffusionPipelineOutput\n\n[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/audioldm.md", "content": "\n\n# AudioLDM\n\nAudioLDM was proposed in [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://huggingface.co/papers/2301.12503) by Haohe Liu et al. Inspired by [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview), AudioLDM\nis a text-to-audio _latent diffusion model (LDM)_ that learns continuous audio representations from [CLAP](https://huggingface.co/docs/transformers/main/model_doc/clap)\nlatents. AudioLDM takes a text prompt as input and predicts the corresponding audio. It can generate text-conditional\nsound effects, human speech and music.\n\nThe abstract from the paper is:\n\n*Text-to-audio (TTA) system has recently gained attention for its ability to synthesize general audio based on text descriptions. However, previous studies in TTA have limited generation quality with high computational costs. In this study, we propose AudioLDM, a TTA system that is built on a latent space to learn the continuous audio representations from contrastive language-audio pretraining (CLAP) latents. The pretrained CLAP models enable us to train LDMs with audio embedding while providing text embedding as a condition during sampling. By learning the latent representations of audio signals and their compositions without modeling the cross-modal relationship, AudioLDM is advantageous in both generation quality and computational efficiency. Trained on AudioCaps with a single GPU, AudioLDM achieves state-of-the-art TTA performance measured by both objective and subjective metrics (e.g., frechet distance). Moreover, AudioLDM is the first TTA system that enables various text-guided audio manipulations (e.g., style transfer) in a zero-shot fashion. Our implementation and demos are available at [this https URL](https://audioldm.github.io/).*\n\nThe original codebase can be found at [haoheliu/AudioLDM](https://github.com/haoheliu/AudioLDM).\n\n## Tips\n\nWhen constructing a prompt, keep in mind:\n\n* Descriptive prompt inputs work best; you can use adjectives to describe the sound (for example, \"high quality\" or \"clear\") and make the prompt context specific (for example, \"water stream in a forest\" instead of \"stream\").\n* It's best to use general terms like \"cat\" or \"dog\" instead of specific names or abstract objects the model may not be familiar with.\n\nDuring inference:\n\n* The _quality_ of the predicted audio sample can be controlled by the `num_inference_steps` argument; higher steps give higher quality audio at the expense of slower inference.\n* The _length_ of the predicted audio sample can be controlled by varying the `audio_length_in_s` argument.\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## AudioLDMPipeline\n[[autodoc]] AudioLDMPipeline\n\t- all\n\t- __call__\n\n## AudioPipelineOutput\n[[autodoc]] pipelines.AudioPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/audioldm2.md", "content": "\n\n# AudioLDM 2\n\nAudioLDM 2 was proposed in [AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining](https://arxiv.org/abs/2308.05734) by Haohe Liu et al. AudioLDM 2 takes a text prompt as input and predicts the corresponding audio. It can generate text-conditional sound effects, human speech and music.\n\nInspired by [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview), AudioLDM 2 is a text-to-audio _latent diffusion model (LDM)_ that learns continuous audio representations from text embeddings. Two text encoder models are used to compute the text embeddings from a prompt input: the text-branch of [CLAP](https://huggingface.co/docs/transformers/main/en/model_doc/clap) and the encoder of [Flan-T5](https://huggingface.co/docs/transformers/main/en/model_doc/flan-t5). These text embeddings are then projected to a shared embedding space by an [AudioLDM2ProjectionModel](https://huggingface.co/docs/diffusers/main/api/pipelines/audioldm2#diffusers.AudioLDM2ProjectionModel). A [GPT2](https://huggingface.co/docs/transformers/main/en/model_doc/gpt2) _language model (LM)_ is used to auto-regressively predict eight new embedding vectors, conditional on the projected CLAP and Flan-T5 embeddings. The generated embedding vectors and Flan-T5 text embeddings are used as cross-attention conditioning in the LDM. The [UNet](https://huggingface.co/docs/diffusers/main/en/api/pipelines/audioldm2#diffusers.AudioLDM2UNet2DConditionModel) of AudioLDM 2 is unique in the sense that it takes **two** cross-attention embeddings, as opposed to one cross-attention conditioning, as in most other LDMs.\n\nThe abstract of the paper is the following:\n\n*Although audio generation shares commonalities across different types of audio, such as speech, music, and sound effects, designing models for each type requires careful consideration of specific objectives and biases that can significantly differ from those of other types. To bring us closer to a unified perspective of audio generation, this paper proposes a framework that utilizes the same learning method for speech, music, and sound effect generation. Our framework introduces a general representation of audio, called \"language of audio\" (LOA). Any audio can be translated into LOA based on AudioMAE, a self-supervised pre-trained representation learning model. In the generation process, we translate any modalities into LOA by using a GPT-2 model, and we perform self-supervised audio generation learning with a latent diffusion model conditioned on LOA. The proposed framework naturally brings advantages such as in-context learning abilities and reusable self-supervised pretrained AudioMAE and latent diffusion models. Experiments on the major benchmarks of text-to-audio, text-to-music, and text-to-speech demonstrate state-of-the-art or competitive performance against previous approaches. Our code, pretrained model, and demo are available at [this https URL](https://audioldm.github.io/audioldm2).*\n\nThis pipeline was contributed by [sanchit-gandhi](https://huggingface.co/sanchit-gandhi) and [Nguyễn Công Tú Anh](https://github.com/tuanh123789). The original codebase can be \nfound at [haoheliu/audioldm2](https://github.com/haoheliu/audioldm2). \n\n## Tips\n\n### Choosing a checkpoint\n\nAudioLDM2 comes in three variants. Two of these checkpoints are applicable to the general task of text-to-audio generation. The third checkpoint is trained exclusively on text-to-music generation.\n\nAll checkpoints share the same model size for the text encoders and VAE. They differ in the size and depth of the UNet.\nSee table below for details on the three checkpoints:\n\n| Checkpoint | Task | UNet Model Size | Total Model Size | Training Data / h |\n|-----------------------------------------------------------------|---------------|-----------------|------------------|-------------------|\n| [audioldm2](https://huggingface.co/cvssp/audioldm2) | Text-to-audio | 350M | 1.1B | 1150k |\n| [audioldm2-large](https://huggingface.co/cvssp/audioldm2-large) | Text-to-audio | 750M | 1.5B | 1150k |\n| [audioldm2-music](https://huggingface.co/cvssp/audioldm2-music) | Text-to-music | 350M | 1.1B | 665k |\n| [audioldm2-gigaspeech](https://huggingface.co/anhnct/audioldm2_gigaspeech) | Text-to-speech | 350M | 1.1B |10k |\n| [audioldm2-ljspeech](https://huggingface.co/anhnct/audioldm2_ljspeech) | Text-to-speech | 350M | 1.1B | |\n\n### Constructing a prompt\n\n* Descriptive prompt inputs work best: use adjectives to describe the sound (e.g. \"high quality\" or \"clear\") and make the prompt context specific (e.g. \"water stream in a forest\" instead of \"stream\").\n* It's best to use general terms like \"cat\" or \"dog\" instead of specific names or abstract objects the model may not be familiar with.\n* Using a **negative prompt** can significantly improve the quality of the generated waveform, by guiding the generation away from terms that correspond to poor quality audio. Try using a negative prompt of \"Low quality.\"\n\n### Controlling inference\n\n* The _quality_ of the predicted audio sample can be controlled by the `num_inference_steps` argument; higher steps give higher quality audio at the expense of slower inference.\n* The _length_ of the predicted audio sample can be controlled by varying the `audio_length_in_s` argument.\n\n### Evaluating generated waveforms:\n\n* The quality of the generated waveforms can vary significantly based on the seed. Try generating with different seeds until you find a satisfactory generation.\n* Multiple waveforms can be generated in one go: set `num_waveforms_per_prompt` to a value greater than 1. Automatic scoring will be performed between the generated waveforms and prompt text, and the audios ranked from best to worst accordingly.\n\nThe following example demonstrates how to construct good music and speech generation using the aforementioned tips: [example](https://huggingface.co/docs/diffusers/main/en/api/pipelines/audioldm2#diffusers.AudioLDM2Pipeline.__call__.example).\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## AudioLDM2Pipeline\n[[autodoc]] AudioLDM2Pipeline\n\t- all\n\t- __call__\n\n## AudioLDM2ProjectionModel\n[[autodoc]] AudioLDM2ProjectionModel\n\t- forward\n\n## AudioLDM2UNet2DConditionModel\n[[autodoc]] AudioLDM2UNet2DConditionModel\n\t- forward\n\n## AudioPipelineOutput\n[[autodoc]] pipelines.AudioPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/auto_pipeline.md", "content": "\n\n# AutoPipeline\n\nThe `AutoPipeline` is designed to make it easy to load a checkpoint for a task without needing to know the specific pipeline class. Based on the task, the `AutoPipeline` automatically retrieves the correct pipeline class from the checkpoint `model_index.json` file.\n\n> [!TIP]\n> Check out the [AutoPipeline](../../tutorials/autopipeline) tutorial to learn how to use this API!\n\n## AutoPipelineForText2Image\n\n[[autodoc]] AutoPipelineForText2Image\n\t- all\n\t- from_pretrained\n\t- from_pipe\n\n## AutoPipelineForImage2Image\n\n[[autodoc]] AutoPipelineForImage2Image\n\t- all\n\t- from_pretrained\n\t- from_pipe\n\n## AutoPipelineForInpainting\n\n[[autodoc]] AutoPipelineForInpainting\n\t- all\n\t- from_pretrained\n\t- from_pipe\n" }, { "path": "diffusers/docs/source/en/api/pipelines/blip_diffusion.md", "content": "\n\n# BLIP-Diffusion\n\nBLIP-Diffusion was proposed in [BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing](https://arxiv.org/abs/2305.14720). It enables zero-shot subject-driven generation and control-guided zero-shot generation. \n\n\nThe abstract from the paper is:\n\n*Subject-driven text-to-image generation models create novel renditions of an input subject based on text prompts. Existing models suffer from lengthy fine-tuning and difficulties preserving the subject fidelity. To overcome these limitations, we introduce BLIP-Diffusion, a new subject-driven image generation model that supports multimodal control which consumes inputs of subject images and text prompts. Unlike other subject-driven generation models, BLIP-Diffusion introduces a new multimodal encoder which is pre-trained to provide subject representation. We first pre-train the multimodal encoder following BLIP-2 to produce visual representation aligned with the text. Then we design a subject representation learning task which enables a diffusion model to leverage such visual representation and generates new subject renditions. Compared with previous methods such as DreamBooth, our model enables zero-shot subject-driven generation, and efficient fine-tuning for customized subject with up to 20x speedup. We also demonstrate that BLIP-Diffusion can be flexibly combined with existing techniques such as ControlNet and prompt-to-prompt to enable novel subject-driven generation and editing applications. Project page at [this https URL](https://dxli94.github.io/BLIP-Diffusion-website/).*\n\nThe original codebase can be found at [salesforce/LAVIS](https://github.com/salesforce/LAVIS/tree/main/projects/blip-diffusion). You can find the official BLIP-Diffusion checkpoints under the [hf.co/SalesForce](https://hf.co/SalesForce) organization.\n\n`BlipDiffusionPipeline` and `BlipDiffusionControlNetPipeline` were contributed by [`ayushtues`](https://github.com/ayushtues/).\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n\n## BlipDiffusionPipeline\n[[autodoc]] BlipDiffusionPipeline\n - all\n - __call__\n\n## BlipDiffusionControlNetPipeline\n[[autodoc]] BlipDiffusionControlNetPipeline\n - all\n - __call__\n" }, { "path": "diffusers/docs/source/en/api/pipelines/consistency_models.md", "content": "\n\n# Consistency Models\n\nConsistency Models were proposed in [Consistency Models](https://huggingface.co/papers/2303.01469) by Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever.\n\nThe abstract from the paper is:\n\n*Diffusion models have significantly advanced the fields of image, audio, and video generation, but they depend on an iterative sampling process that causes slow generation. To overcome this limitation, we propose consistency models, a new family of models that generate high quality samples by directly mapping noise to data. They support fast one-step generation by design, while still allowing multistep sampling to trade compute for sample quality. They also support zero-shot data editing, such as image inpainting, colorization, and super-resolution, without requiring explicit training on these tasks. Consistency models can be trained either by distilling pre-trained diffusion models, or as standalone generative models altogether. Through extensive experiments, we demonstrate that they outperform existing distillation techniques for diffusion models in one- and few-step sampling, achieving the new state-of-the-art FID of 3.55 on CIFAR-10 and 6.20 on ImageNet 64x64 for one-step generation. When trained in isolation, consistency models become a new family of generative models that can outperform existing one-step, non-adversarial generative models on standard benchmarks such as CIFAR-10, ImageNet 64x64 and LSUN 256x256.*\n\nThe original codebase can be found at [openai/consistency_models](https://github.com/openai/consistency_models), and additional checkpoints are available at [openai](https://huggingface.co/openai).\n\nThe pipeline was contributed by [dg845](https://github.com/dg845) and [ayushtues](https://huggingface.co/ayushtues). ❤️\n\n## Tips\n\nFor an additional speed-up, use `torch.compile` to generate multiple images in <1 second:\n\n```diff\n import torch\n from diffusers import ConsistencyModelPipeline\n\n device = \"cuda\"\n # Load the cd_bedroom256_lpips checkpoint.\n model_id_or_path = \"openai/diffusers-cd_bedroom256_lpips\"\n pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n pipe.to(device)\n\n+ pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n\n # Multistep sampling\n # Timesteps can be explicitly specified; the particular timesteps below are from the original GitHub repo:\n # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L83\n for _ in range(10):\n image = pipe(timesteps=[17, 0]).images[0]\n image.show()\n```\n\n\n## ConsistencyModelPipeline\n[[autodoc]] ConsistencyModelPipeline\n - all\n - __call__\n\n## ImagePipelineOutput\n[[autodoc]] pipelines.ImagePipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/controlnet.md", "content": "\n\n# ControlNet\n\nControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.\n\nWith a ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, if you provide a depth map, the ControlNet model generates an image that'll preserve the spatial information from the depth map. It is a more flexible and accurate way to control the image generation process.\n\nThe abstract from the paper is:\n\n*We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with \"zero convolutions\" (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.*\n\nThis model was contributed by [takuma104](https://huggingface.co/takuma104). ❤️\n\nThe original codebase can be found at [lllyasviel/ControlNet](https://github.com/lllyasviel/ControlNet), and you can find official ControlNet checkpoints on [lllyasviel's](https://huggingface.co/lllyasviel) Hub profile.\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## StableDiffusionControlNetPipeline\n[[autodoc]] StableDiffusionControlNetPipeline\n\t- all\n\t- __call__\n\t- enable_attention_slicing\n\t- disable_attention_slicing\n\t- enable_vae_slicing\n\t- disable_vae_slicing\n\t- enable_xformers_memory_efficient_attention\n\t- disable_xformers_memory_efficient_attention\n\t- load_textual_inversion\n\n## StableDiffusionControlNetImg2ImgPipeline\n[[autodoc]] StableDiffusionControlNetImg2ImgPipeline\n\t- all\n\t- __call__\n\t- enable_attention_slicing\n\t- disable_attention_slicing\n\t- enable_vae_slicing\n\t- disable_vae_slicing\n\t- enable_xformers_memory_efficient_attention\n\t- disable_xformers_memory_efficient_attention\n\t- load_textual_inversion\n\n## StableDiffusionControlNetInpaintPipeline\n[[autodoc]] StableDiffusionControlNetInpaintPipeline\n\t- all\n\t- __call__\n\t- enable_attention_slicing\n\t- disable_attention_slicing\n\t- enable_vae_slicing\n\t- disable_vae_slicing\n\t- enable_xformers_memory_efficient_attention\n\t- disable_xformers_memory_efficient_attention\n\t- load_textual_inversion\n\n## StableDiffusionPipelineOutput\n[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput\n\n## FlaxStableDiffusionControlNetPipeline\n[[autodoc]] FlaxStableDiffusionControlNetPipeline\n\t- all\n\t- __call__\n\n## FlaxStableDiffusionControlNetPipelineOutput\n[[autodoc]] pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/controlnet_sdxl.md", "content": "\n\n# ControlNet with Stable Diffusion XL\n\nControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.\n\nWith a ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, if you provide a depth map, the ControlNet model generates an image that'll preserve the spatial information from the depth map. It is a more flexible and accurate way to control the image generation process.\n\nThe abstract from the paper is:\n\n*We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with \"zero convolutions\" (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.*\n\nYou can find additional smaller Stable Diffusion XL (SDXL) ControlNet checkpoints from the 🤗 [Diffusers](https://huggingface.co/diffusers) Hub organization, and browse [community-trained](https://huggingface.co/models?other=stable-diffusion-xl&other=controlnet) checkpoints on the Hub.\n\n\n\n🧪 Many of the SDXL ControlNet checkpoints are experimental, and there is a lot of room for improvement. Feel free to open an [Issue](https://github.com/huggingface/diffusers/issues/new/choose) and leave us feedback on how we can improve!\n\n\n\nIf you don't see a checkpoint you're interested in, you can train your own SDXL ControlNet with our [training script](../../../../../examples/controlnet/README_sdxl).\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## StableDiffusionXLControlNetPipeline\n[[autodoc]] StableDiffusionXLControlNetPipeline\n\t- all\n\t- __call__\n\n## StableDiffusionXLControlNetImg2ImgPipeline\n[[autodoc]] StableDiffusionXLControlNetImg2ImgPipeline\n\t- all\n\t- __call__\n\n## StableDiffusionXLControlNetInpaintPipeline\n[[autodoc]] StableDiffusionXLControlNetInpaintPipeline\n\t- all\n\t- __call__\n\n## StableDiffusionPipelineOutput\n[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/controlnetxs.md", "content": "\n\n# ControlNet-XS\n\nControlNet-XS was introduced in [ControlNet-XS](https://vislearn.github.io/ControlNet-XS/) by Denis Zavadski and Carsten Rother. It is based on the observation that the control model in the [original ControlNet](https://huggingface.co/papers/2302.05543) can be made much smaller and still produce good results.\n\nLike the original ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, if you provide a depth map, the ControlNet model generates an image that'll preserve the spatial information from the depth map. It is a more flexible and accurate way to control the image generation process.\n\nControlNet-XS generates images with comparable quality to a regular ControlNet, but it is 20-25% faster ([see benchmark](https://github.com/UmerHA/controlnet-xs-benchmark/blob/main/Speed%20Benchmark.ipynb) with StableDiffusion-XL) and uses ~45% less memory.\n\nHere's the overview from the [project page](https://vislearn.github.io/ControlNet-XS/):\n\n*With increasing computing capabilities, current model architectures appear to follow the trend of simply upscaling all components without validating the necessity for doing so. In this project we investigate the size and architectural design of ControlNet [Zhang et al., 2023] for controlling the image generation process with stable diffusion-based models. We show that a new architecture with as little as 1% of the parameters of the base model achieves state-of-the art results, considerably better than ControlNet in terms of FID score. Hence we call it ControlNet-XS. We provide the code for controlling StableDiffusion-XL [Podell et al., 2023] (Model B, 48M Parameters) and StableDiffusion 2.1 [Rombach et al. 2022] (Model B, 14M Parameters), all under openrail license.*\n\nThis model was contributed by [UmerHA](https://twitter.com/UmerHAdil). ❤️\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## StableDiffusionControlNetXSPipeline\n[[autodoc]] StableDiffusionControlNetXSPipeline\n\t- all\n\t- __call__\n\n## StableDiffusionPipelineOutput\n[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/controlnetxs_sdxl.md", "content": "\n\n# ControlNet-XS with Stable Diffusion XL\n\nControlNet-XS was introduced in [ControlNet-XS](https://vislearn.github.io/ControlNet-XS/) by Denis Zavadski and Carsten Rother. It is based on the observation that the control model in the [original ControlNet](https://huggingface.co/papers/2302.05543) can be made much smaller and still produce good results.\n\nLike the original ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, if you provide a depth map, the ControlNet model generates an image that'll preserve the spatial information from the depth map. It is a more flexible and accurate way to control the image generation process.\n\nControlNet-XS generates images with comparable quality to a regular ControlNet, but it is 20-25% faster ([see benchmark](https://github.com/UmerHA/controlnet-xs-benchmark/blob/main/Speed%20Benchmark.ipynb)) and uses ~45% less memory.\n\nHere's the overview from the [project page](https://vislearn.github.io/ControlNet-XS/):\n\n*With increasing computing capabilities, current model architectures appear to follow the trend of simply upscaling all components without validating the necessity for doing so. In this project we investigate the size and architectural design of ControlNet [Zhang et al., 2023] for controlling the image generation process with stable diffusion-based models. We show that a new architecture with as little as 1% of the parameters of the base model achieves state-of-the art results, considerably better than ControlNet in terms of FID score. Hence we call it ControlNet-XS. We provide the code for controlling StableDiffusion-XL [Podell et al., 2023] (Model B, 48M Parameters) and StableDiffusion 2.1 [Rombach et al. 2022] (Model B, 14M Parameters), all under openrail license.*\n\nThis model was contributed by [UmerHA](https://twitter.com/UmerHAdil). ❤️\n\n\n\n🧪 Many of the SDXL ControlNet checkpoints are experimental, and there is a lot of room for improvement. Feel free to open an [Issue](https://github.com/huggingface/diffusers/issues/new/choose) and leave us feedback on how we can improve!\n\n\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## StableDiffusionXLControlNetXSPipeline\n[[autodoc]] StableDiffusionXLControlNetXSPipeline\n\t- all\n\t- __call__\n\n## StableDiffusionPipelineOutput\n[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/dance_diffusion.md", "content": "\n\n# Dance Diffusion\n\n[Dance Diffusion](https://github.com/Harmonai-org/sample-generator) is by Zach Evans.\n\nDance Diffusion is the first in a suite of generative audio tools for producers and musicians released by [Harmonai](https://github.com/Harmonai-org).\n\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## DanceDiffusionPipeline\n[[autodoc]] DanceDiffusionPipeline\n\t- all\n\t- __call__\n\n## AudioPipelineOutput\n[[autodoc]] pipelines.AudioPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/ddim.md", "content": "\n\n# DDIM\n\n[Denoising Diffusion Implicit Models](https://huggingface.co/papers/2010.02502) (DDIM) by Jiaming Song, Chenlin Meng and Stefano Ermon.\n\nThe abstract from the paper is:\n\n*Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a Markovian diffusion process. We construct a class of non-Markovian diffusion processes that lead to the same training objective, but whose reverse process can be much faster to sample from. We empirically demonstrate that DDIMs can produce high quality samples 10× to 50× faster in terms of wall-clock time compared to DDPMs, allow us to trade off computation for sample quality, and can perform semantically meaningful image interpolation directly in the latent space.*\n\nThe original codebase can be found at [ermongroup/ddim](https://github.com/ermongroup/ddim).\n\n## DDIMPipeline\n[[autodoc]] DDIMPipeline\n\t- all\n\t- __call__\n\n## ImagePipelineOutput\n[[autodoc]] pipelines.ImagePipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/ddpm.md", "content": "\n\n# DDPM\n\n[Denoising Diffusion Probabilistic Models](https://huggingface.co/papers/2006.11239) (DDPM) by Jonathan Ho, Ajay Jain and Pieter Abbeel proposes a diffusion based model of the same name. In the 🤗 Diffusers library, DDPM refers to the *discrete denoising scheduler* from the paper as well as the pipeline.\n\nThe abstract from the paper is:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.*\n\nThe original codebase can be found at [hohonathanho/diffusion](https://github.com/hojonathanho/diffusion).\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n# DDPMPipeline\n[[autodoc]] DDPMPipeline\n\t- all\n\t- __call__\n\n## ImagePipelineOutput\n[[autodoc]] pipelines.ImagePipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/deepfloyd_if.md", "content": "\n\n# DeepFloyd IF\n\n## Overview\n\nDeepFloyd IF is a novel state-of-the-art open-source text-to-image model with a high degree of photorealism and language understanding.\nThe model is a modular composed of a frozen text encoder and three cascaded pixel diffusion modules:\n- Stage 1: a base model that generates 64x64 px image based on text prompt,\n- Stage 2: a 64x64 px => 256x256 px super-resolution model, and\n- Stage 3: a 256x256 px => 1024x1024 px super-resolution model\nStage 1 and Stage 2 utilize a frozen text encoder based on the T5 transformer to extract text embeddings, which are then fed into a UNet architecture enhanced with cross-attention and attention pooling.\nStage 3 is [Stability AI's x4 Upscaling model](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler).\nThe result is a highly efficient model that outperforms current state-of-the-art models, achieving a zero-shot FID score of 6.66 on the COCO dataset.\nOur work underscores the potential of larger UNet architectures in the first stage of cascaded diffusion models and depicts a promising future for text-to-image synthesis.\n\n## Usage\n\nBefore you can use IF, you need to accept its usage conditions. To do so:\n1. Make sure to have a [Hugging Face account](https://huggingface.co/join) and be logged in.\n2. Accept the license on the model card of [DeepFloyd/IF-I-XL-v1.0](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0). Accepting the license on the stage I model card will auto accept for the other IF models.\n3. Make sure to login locally. Install `huggingface_hub`:\n```sh\npip install huggingface_hub --upgrade\n```\n\nrun the login function in a Python shell:\n\n```py\nfrom huggingface_hub import login\n\nlogin()\n```\n\nand enter your [Hugging Face Hub access token](https://huggingface.co/docs/hub/security-tokens#what-are-user-access-tokens).\n\nNext we install `diffusers` and dependencies:\n\n```sh\npip install -q diffusers accelerate transformers\n```\n\nThe following sections give more in-detail examples of how to use IF. Specifically:\n\n- [Text-to-Image Generation](#text-to-image-generation)\n- [Image-to-Image Generation](#text-guided-image-to-image-generation)\n- [Inpainting](#text-guided-inpainting-generation)\n- [Reusing model weights](#converting-between-different-pipelines)\n- [Speed optimization](#optimizing-for-speed)\n- [Memory optimization](#optimizing-for-memory)\n\n**Available checkpoints**\n- *Stage-1*\n - [DeepFloyd/IF-I-XL-v1.0](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0)\n - [DeepFloyd/IF-I-L-v1.0](https://huggingface.co/DeepFloyd/IF-I-L-v1.0)\n - [DeepFloyd/IF-I-M-v1.0](https://huggingface.co/DeepFloyd/IF-I-M-v1.0)\n\n- *Stage-2*\n - [DeepFloyd/IF-II-L-v1.0](https://huggingface.co/DeepFloyd/IF-II-L-v1.0)\n - [DeepFloyd/IF-II-M-v1.0](https://huggingface.co/DeepFloyd/IF-II-M-v1.0)\n\n- *Stage-3*\n - [stabilityai/stable-diffusion-x4-upscaler](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler)\n\n\n**Google Colab**\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/deepfloyd_if_free_tier_google_colab.ipynb)\n\n### Text-to-Image Generation\n\nBy default diffusers makes use of [model cpu offloading](../../optimization/memory#model-offloading) to run the whole IF pipeline with as little as 14 GB of VRAM.\n\n```python\nfrom diffusers import DiffusionPipeline\nfrom diffusers.utils import pt_to_pil, make_image_grid\nimport torch\n\n# stage 1\nstage_1 = DiffusionPipeline.from_pretrained(\"DeepFloyd/IF-I-XL-v1.0\", variant=\"fp16\", torch_dtype=torch.float16)\nstage_1.enable_model_cpu_offload()\n\n# stage 2\nstage_2 = DiffusionPipeline.from_pretrained(\n \"DeepFloyd/IF-II-L-v1.0\", text_encoder=None, variant=\"fp16\", torch_dtype=torch.float16\n)\nstage_2.enable_model_cpu_offload()\n\n# stage 3\nsafety_modules = {\n \"feature_extractor\": stage_1.feature_extractor,\n \"safety_checker\": stage_1.safety_checker,\n \"watermarker\": stage_1.watermarker,\n}\nstage_3 = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-x4-upscaler\", **safety_modules, torch_dtype=torch.float16\n)\nstage_3.enable_model_cpu_offload()\n\nprompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says \"very deep learning\"'\ngenerator = torch.manual_seed(1)\n\n# text embeds\nprompt_embeds, negative_embeds = stage_1.encode_prompt(prompt)\n\n# stage 1\nstage_1_output = stage_1(\n prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, generator=generator, output_type=\"pt\"\n).images\n#pt_to_pil(stage_1_output)[0].save(\"./if_stage_I.png\")\n\n# stage 2\nstage_2_output = stage_2(\n image=stage_1_output,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_embeds,\n generator=generator,\n output_type=\"pt\",\n).images\n#pt_to_pil(stage_2_output)[0].save(\"./if_stage_II.png\")\n\n# stage 3\nstage_3_output = stage_3(prompt=prompt, image=stage_2_output, noise_level=100, generator=generator).images\n#stage_3_output[0].save(\"./if_stage_III.png\")\nmake_image_grid([pt_to_pil(stage_1_output)[0], pt_to_pil(stage_2_output)[0], stage_3_output[0]], rows=1, rows=3)\n```\n\n### Text Guided Image-to-Image Generation\n\nThe same IF model weights can be used for text-guided image-to-image translation or image variation.\nIn this case just make sure to load the weights using the [`IFImg2ImgPipeline`] and [`IFImg2ImgSuperResolutionPipeline`] pipelines.\n\n**Note**: You can also directly move the weights of the text-to-image pipelines to the image-to-image pipelines\nwithout loading them twice by making use of the [`~DiffusionPipeline.components`] argument as explained [here](#converting-between-different-pipelines).\n\n```python\nfrom diffusers import IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline, DiffusionPipeline\nfrom diffusers.utils import pt_to_pil, load_image, make_image_grid\nimport torch\n\n# download image\nurl = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\noriginal_image = load_image(url)\noriginal_image = original_image.resize((768, 512))\n\n# stage 1\nstage_1 = IFImg2ImgPipeline.from_pretrained(\"DeepFloyd/IF-I-XL-v1.0\", variant=\"fp16\", torch_dtype=torch.float16)\nstage_1.enable_model_cpu_offload()\n\n# stage 2\nstage_2 = IFImg2ImgSuperResolutionPipeline.from_pretrained(\n \"DeepFloyd/IF-II-L-v1.0\", text_encoder=None, variant=\"fp16\", torch_dtype=torch.float16\n)\nstage_2.enable_model_cpu_offload()\n\n# stage 3\nsafety_modules = {\n \"feature_extractor\": stage_1.feature_extractor,\n \"safety_checker\": stage_1.safety_checker,\n \"watermarker\": stage_1.watermarker,\n}\nstage_3 = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-x4-upscaler\", **safety_modules, torch_dtype=torch.float16\n)\nstage_3.enable_model_cpu_offload()\n\nprompt = \"A fantasy landscape in style minecraft\"\ngenerator = torch.manual_seed(1)\n\n# text embeds\nprompt_embeds, negative_embeds = stage_1.encode_prompt(prompt)\n\n# stage 1\nstage_1_output = stage_1(\n image=original_image,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_embeds,\n generator=generator,\n output_type=\"pt\",\n).images\n#pt_to_pil(stage_1_output)[0].save(\"./if_stage_I.png\")\n\n# stage 2\nstage_2_output = stage_2(\n image=stage_1_output,\n original_image=original_image,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_embeds,\n generator=generator,\n output_type=\"pt\",\n).images\n#pt_to_pil(stage_2_output)[0].save(\"./if_stage_II.png\")\n\n# stage 3\nstage_3_output = stage_3(prompt=prompt, image=stage_2_output, generator=generator, noise_level=100).images\n#stage_3_output[0].save(\"./if_stage_III.png\")\nmake_image_grid([original_image, pt_to_pil(stage_1_output)[0], pt_to_pil(stage_2_output)[0], stage_3_output[0]], rows=1, rows=4)\n```\n\n### Text Guided Inpainting Generation\n\nThe same IF model weights can be used for text-guided image-to-image translation or image variation.\nIn this case just make sure to load the weights using the [`IFInpaintingPipeline`] and [`IFInpaintingSuperResolutionPipeline`] pipelines.\n\n**Note**: You can also directly move the weights of the text-to-image pipelines to the image-to-image pipelines\nwithout loading them twice by making use of the [`~DiffusionPipeline.components()`] function as explained [here](#converting-between-different-pipelines).\n\n```python\nfrom diffusers import IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline, DiffusionPipeline\nfrom diffusers.utils import pt_to_pil, load_image, make_image_grid\nimport torch\n\n# download image\nurl = \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/person.png\"\noriginal_image = load_image(url)\n\n# download mask\nurl = \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/glasses_mask.png\"\nmask_image = load_image(url)\n\n# stage 1\nstage_1 = IFInpaintingPipeline.from_pretrained(\"DeepFloyd/IF-I-XL-v1.0\", variant=\"fp16\", torch_dtype=torch.float16)\nstage_1.enable_model_cpu_offload()\n\n# stage 2\nstage_2 = IFInpaintingSuperResolutionPipeline.from_pretrained(\n \"DeepFloyd/IF-II-L-v1.0\", text_encoder=None, variant=\"fp16\", torch_dtype=torch.float16\n)\nstage_2.enable_model_cpu_offload()\n\n# stage 3\nsafety_modules = {\n \"feature_extractor\": stage_1.feature_extractor,\n \"safety_checker\": stage_1.safety_checker,\n \"watermarker\": stage_1.watermarker,\n}\nstage_3 = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-x4-upscaler\", **safety_modules, torch_dtype=torch.float16\n)\nstage_3.enable_model_cpu_offload()\n\nprompt = \"blue sunglasses\"\ngenerator = torch.manual_seed(1)\n\n# text embeds\nprompt_embeds, negative_embeds = stage_1.encode_prompt(prompt)\n\n# stage 1\nstage_1_output = stage_1(\n image=original_image,\n mask_image=mask_image,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_embeds,\n generator=generator,\n output_type=\"pt\",\n).images\n#pt_to_pil(stage_1_output)[0].save(\"./if_stage_I.png\")\n\n# stage 2\nstage_2_output = stage_2(\n image=stage_1_output,\n original_image=original_image,\n mask_image=mask_image,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_embeds,\n generator=generator,\n output_type=\"pt\",\n).images\n#pt_to_pil(stage_1_output)[0].save(\"./if_stage_II.png\")\n\n# stage 3\nstage_3_output = stage_3(prompt=prompt, image=stage_2_output, generator=generator, noise_level=100).images\n#stage_3_output[0].save(\"./if_stage_III.png\")\nmake_image_grid([original_image, mask_image, pt_to_pil(stage_1_output)[0], pt_to_pil(stage_2_output)[0], stage_3_output[0]], rows=1, rows=5)\n```\n\n### Converting between different pipelines\n\nIn addition to being loaded with `from_pretrained`, Pipelines can also be loaded directly from each other.\n\n```python\nfrom diffusers import IFPipeline, IFSuperResolutionPipeline\n\npipe_1 = IFPipeline.from_pretrained(\"DeepFloyd/IF-I-XL-v1.0\")\npipe_2 = IFSuperResolutionPipeline.from_pretrained(\"DeepFloyd/IF-II-L-v1.0\")\n\n\nfrom diffusers import IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline\n\npipe_1 = IFImg2ImgPipeline(**pipe_1.components)\npipe_2 = IFImg2ImgSuperResolutionPipeline(**pipe_2.components)\n\n\nfrom diffusers import IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline\n\npipe_1 = IFInpaintingPipeline(**pipe_1.components)\npipe_2 = IFInpaintingSuperResolutionPipeline(**pipe_2.components)\n```\n\n### Optimizing for speed\n\nThe simplest optimization to run IF faster is to move all model components to the GPU.\n\n```py\npipe = DiffusionPipeline.from_pretrained(\"DeepFloyd/IF-I-XL-v1.0\", variant=\"fp16\", torch_dtype=torch.float16)\npipe.to(\"cuda\")\n```\n\nYou can also run the diffusion process for a shorter number of timesteps.\n\nThis can either be done with the `num_inference_steps` argument:\n\n```py\npipe(\"\", num_inference_steps=30)\n```\n\nOr with the `timesteps` argument:\n\n```py\nfrom diffusers.pipelines.deepfloyd_if import fast27_timesteps\n\npipe(\"\", timesteps=fast27_timesteps)\n```\n\nWhen doing image variation or inpainting, you can also decrease the number of timesteps\nwith the strength argument. The strength argument is the amount of noise to add to the input image which also determines how many steps to run in the denoising process.\nA smaller number will vary the image less but run faster.\n\n```py\npipe = IFImg2ImgPipeline.from_pretrained(\"DeepFloyd/IF-I-XL-v1.0\", variant=\"fp16\", torch_dtype=torch.float16)\npipe.to(\"cuda\")\n\nimage = pipe(image=image, prompt=\"\", strength=0.3).images\n```\n\nYou can also use [`torch.compile`](../../optimization/torch2.0). Note that we have not exhaustively tested `torch.compile`\nwith IF and it might not give expected results.\n\n```py\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipe = DiffusionPipeline.from_pretrained(\"DeepFloyd/IF-I-XL-v1.0\", variant=\"fp16\", torch_dtype=torch.float16)\npipe.to(\"cuda\")\n\npipe.text_encoder = torch.compile(pipe.text_encoder, mode=\"reduce-overhead\", fullgraph=True)\npipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n```\n\n### Optimizing for memory\n\nWhen optimizing for GPU memory, we can use the standard diffusers CPU offloading APIs.\n\nEither the model based CPU offloading,\n\n```py\npipe = DiffusionPipeline.from_pretrained(\"DeepFloyd/IF-I-XL-v1.0\", variant=\"fp16\", torch_dtype=torch.float16)\npipe.enable_model_cpu_offload()\n```\n\nor the more aggressive layer based CPU offloading.\n\n```py\npipe = DiffusionPipeline.from_pretrained(\"DeepFloyd/IF-I-XL-v1.0\", variant=\"fp16\", torch_dtype=torch.float16)\npipe.enable_sequential_cpu_offload()\n```\n\nAdditionally, T5 can be loaded in 8bit precision\n\n```py\nfrom transformers import T5EncoderModel\n\ntext_encoder = T5EncoderModel.from_pretrained(\n \"DeepFloyd/IF-I-XL-v1.0\", subfolder=\"text_encoder\", device_map=\"auto\", load_in_8bit=True, variant=\"8bit\"\n)\n\nfrom diffusers import DiffusionPipeline\n\npipe = DiffusionPipeline.from_pretrained(\n \"DeepFloyd/IF-I-XL-v1.0\",\n text_encoder=text_encoder, # pass the previously instantiated 8bit text encoder\n unet=None,\n device_map=\"auto\",\n)\n\nprompt_embeds, negative_embeds = pipe.encode_prompt(\"\")\n```\n\nFor CPU RAM constrained machines like Google Colab free tier where we can't load all model components to the CPU at once, we can manually only load the pipeline with\nthe text encoder or UNet when the respective model components are needed.\n\n```py\nfrom diffusers import IFPipeline, IFSuperResolutionPipeline\nimport torch\nimport gc\nfrom transformers import T5EncoderModel\nfrom diffusers.utils import pt_to_pil, make_image_grid\n\ntext_encoder = T5EncoderModel.from_pretrained(\n \"DeepFloyd/IF-I-XL-v1.0\", subfolder=\"text_encoder\", device_map=\"auto\", load_in_8bit=True, variant=\"8bit\"\n)\n\n# text to image\npipe = DiffusionPipeline.from_pretrained(\n \"DeepFloyd/IF-I-XL-v1.0\",\n text_encoder=text_encoder, # pass the previously instantiated 8bit text encoder\n unet=None,\n device_map=\"auto\",\n)\n\nprompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says \"very deep learning\"'\nprompt_embeds, negative_embeds = pipe.encode_prompt(prompt)\n\n# Remove the pipeline so we can re-load the pipeline with the unet\ndel text_encoder\ndel pipe\ngc.collect()\ntorch.cuda.empty_cache()\n\npipe = IFPipeline.from_pretrained(\n \"DeepFloyd/IF-I-XL-v1.0\", text_encoder=None, variant=\"fp16\", torch_dtype=torch.float16, device_map=\"auto\"\n)\n\ngenerator = torch.Generator().manual_seed(0)\nstage_1_output = pipe(\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_embeds,\n output_type=\"pt\",\n generator=generator,\n).images\n\n#pt_to_pil(stage_1_output)[0].save(\"./if_stage_I.png\")\n\n# Remove the pipeline so we can load the super-resolution pipeline\ndel pipe\ngc.collect()\ntorch.cuda.empty_cache()\n\n# First super resolution\n\npipe = IFSuperResolutionPipeline.from_pretrained(\n \"DeepFloyd/IF-II-L-v1.0\", text_encoder=None, variant=\"fp16\", torch_dtype=torch.float16, device_map=\"auto\"\n)\n\ngenerator = torch.Generator().manual_seed(0)\nstage_2_output = pipe(\n image=stage_1_output,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_embeds,\n output_type=\"pt\",\n generator=generator,\n).images\n\n#pt_to_pil(stage_2_output)[0].save(\"./if_stage_II.png\")\nmake_image_grid([pt_to_pil(stage_1_output)[0], pt_to_pil(stage_2_output)[0]], rows=1, rows=2)\n```\n\n## Available Pipelines:\n\n| Pipeline | Tasks | Colab\n|---|---|:---:|\n| [pipeline_if.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py) | *Text-to-Image Generation* | - |\n| [pipeline_if_superresolution.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py) | *Text-to-Image Generation* | - |\n| [pipeline_if_img2img.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py) | *Image-to-Image Generation* | - |\n| [pipeline_if_img2img_superresolution.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py) | *Image-to-Image Generation* | - |\n| [pipeline_if_inpainting.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py) | *Image-to-Image Generation* | - |\n| [pipeline_if_inpainting_superresolution.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting_superresolution.py) | *Image-to-Image Generation* | - |\n\n## IFPipeline\n[[autodoc]] IFPipeline\n\t- all\n\t- __call__\n\n## IFSuperResolutionPipeline\n[[autodoc]] IFSuperResolutionPipeline\n\t- all\n\t- __call__\n\n## IFImg2ImgPipeline\n[[autodoc]] IFImg2ImgPipeline\n\t- all\n\t- __call__\n\n## IFImg2ImgSuperResolutionPipeline\n[[autodoc]] IFImg2ImgSuperResolutionPipeline\n\t- all\n\t- __call__\n\n## IFInpaintingPipeline\n[[autodoc]] IFInpaintingPipeline\n\t- all\n\t- __call__\n\n## IFInpaintingSuperResolutionPipeline\n[[autodoc]] IFInpaintingSuperResolutionPipeline\n\t- all\n\t- __call__\n" }, { "path": "diffusers/docs/source/en/api/pipelines/diffedit.md", "content": "\n\n# DiffEdit\n\n[DiffEdit: Diffusion-based semantic image editing with mask guidance](https://huggingface.co/papers/2210.11427) is by Guillaume Couairon, Jakob Verbeek, Holger Schwenk, and Matthieu Cord.\n\nThe abstract from the paper is:\n\n*Image generation has recently seen tremendous advances, with diffusion models allowing to synthesize convincing images for a large variety of text prompts. In this article, we propose DiffEdit, a method to take advantage of text-conditioned diffusion models for the task of semantic image editing, where the goal is to edit an image based on a text query. Semantic image editing is an extension of image generation, with the additional constraint that the generated image should be as similar as possible to a given input image. Current editing methods based on diffusion models usually require to provide a mask, making the task much easier by treating it as a conditional inpainting task. In contrast, our main contribution is able to automatically generate a mask highlighting regions of the input image that need to be edited, by contrasting predictions of a diffusion model conditioned on different text prompts. Moreover, we rely on latent inference to preserve content in those regions of interest and show excellent synergies with mask-based diffusion. DiffEdit achieves state-of-the-art editing performance on ImageNet. In addition, we evaluate semantic image editing in more challenging settings, using images from the COCO dataset as well as text-based generated images.*\n\nThe original codebase can be found at [Xiang-cd/DiffEdit-stable-diffusion](https://github.com/Xiang-cd/DiffEdit-stable-diffusion), and you can try it out in this [demo](https://blog.problemsolversguild.com/technical/research/2022/11/02/DiffEdit-Implementation.html).\n\nThis pipeline was contributed by [clarencechen](https://github.com/clarencechen). ❤️\n\n## Tips\n\n* The pipeline can generate masks that can be fed into other inpainting pipelines.\n* In order to generate an image using this pipeline, both an image mask (source and target prompts can be manually specified or generated, and passed to [`~StableDiffusionDiffEditPipeline.generate_mask`])\nand a set of partially inverted latents (generated using [`~StableDiffusionDiffEditPipeline.invert`]) _must_ be provided as arguments when calling the pipeline to generate the final edited image.\n* The function [`~StableDiffusionDiffEditPipeline.generate_mask`] exposes two prompt arguments, `source_prompt` and `target_prompt`\nthat let you control the locations of the semantic edits in the final image to be generated. Let's say,\nyou wanted to translate from \"cat\" to \"dog\". In this case, the edit direction will be \"cat -> dog\". To reflect\nthis in the generated mask, you simply have to set the embeddings related to the phrases including \"cat\" to\n`source_prompt` and \"dog\" to `target_prompt`.\n* When generating partially inverted latents using `invert`, assign a caption or text embedding describing the\noverall image to the `prompt` argument to help guide the inverse latent sampling process. In most cases, the\nsource concept is sufficiently descriptive to yield good results, but feel free to explore alternatives.\n* When calling the pipeline to generate the final edited image, assign the source concept to `negative_prompt`\nand the target concept to `prompt`. Taking the above example, you simply have to set the embeddings related to\nthe phrases including \"cat\" to `negative_prompt` and \"dog\" to `prompt`.\n* If you wanted to reverse the direction in the example above, i.e., \"dog -> cat\", then it's recommended to:\n * Swap the `source_prompt` and `target_prompt` in the arguments to `generate_mask`.\n * Change the input prompt in [`~StableDiffusionDiffEditPipeline.invert`] to include \"dog\".\n * Swap the `prompt` and `negative_prompt` in the arguments to call the pipeline to generate the final edited image.\n* The source and target prompts, or their corresponding embeddings, can also be automatically generated. Please refer to the [DiffEdit](../../using-diffusers/diffedit) guide for more details.\n\n## StableDiffusionDiffEditPipeline\n[[autodoc]] StableDiffusionDiffEditPipeline\n - all\n - generate_mask\n - invert\n - __call__\n\n## StableDiffusionPipelineOutput\n[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/dit.md", "content": "\n\n# DiT\n\n[Scalable Diffusion Models with Transformers](https://huggingface.co/papers/2212.09748) (DiT) is by William Peebles and Saining Xie.\n\nThe abstract from the paper is:\n\n*We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Diffusion Transformers (DiTs) through the lens of forward pass complexity as measured by Gflops. We find that DiTs with higher Gflops -- through increased transformer depth/width or increased number of input tokens -- consistently have lower FID. In addition to possessing good scalability properties, our largest DiT-XL/2 models outperform all prior diffusion models on the class-conditional ImageNet 512x512 and 256x256 benchmarks, achieving a state-of-the-art FID of 2.27 on the latter.*\n\nThe original codebase can be found at [facebookresearch/dit](https://github.com/facebookresearch/dit).\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## DiTPipeline\n[[autodoc]] DiTPipeline\n\t- all\n\t- __call__\n\n## ImagePipelineOutput\n[[autodoc]] pipelines.ImagePipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/hunyuandit.md", "content": "\n\n# Hunyuan-DiT\n![chinese elements understanding](https://github.com/gnobitab/diffusers-hunyuan/assets/1157982/39b99036-c3cb-4f16-bb1a-40ec25eda573)\n\n[Hunyuan-DiT : A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding](https://arxiv.org/abs/2405.08748) from Tencent Hunyuan.\n\nThe abstract from the paper is:\n\n*We present Hunyuan-DiT, a text-to-image diffusion transformer with fine-grained understanding of both English and Chinese. To construct Hunyuan-DiT, we carefully design the transformer structure, text encoder, and positional encoding. We also build from scratch a whole data pipeline to update and evaluate data for iterative model optimization. For fine-grained language understanding, we train a Multimodal Large Language Model to refine the captions of the images. Finally, Hunyuan-DiT can perform multi-turn multimodal dialogue with users, generating and refining images according to the context. Through our holistic human evaluation protocol with more than 50 professional human evaluators, Hunyuan-DiT sets a new state-of-the-art in Chinese-to-image generation compared with other open-source models.*\n\n\nYou can find the original codebase at [Tencent/HunyuanDiT](https://github.com/Tencent/HunyuanDiT) and all the available checkpoints at [Tencent-Hunyuan](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT).\n\n**Highlights**: HunyuanDiT supports Chinese/English-to-image, multi-resolution generation.\n\nHunyuanDiT has the following components:\n* It uses a diffusion transformer as the backbone\n* It combines two text encoders, a bilingual CLIP and a multilingual T5 encoder\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers.md) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## Optimization\n\nYou can optimize the pipeline's runtime and memory consumption with torch.compile and feed-forward chunking. To learn about other optimization methods, check out the [Speed up inference](../../optimization/fp16) and [Reduce memory usage](../../optimization/memory) guides. \n\n### Inference\n\nUse [`torch.compile`](https://huggingface.co/docs/diffusers/main/en/tutorials/fast_diffusion#torchcompile) to reduce the inference latency.\n\nFirst, load the pipeline:\n\n```python\nfrom diffusers import HunyuanDiTPipeline\nimport torch \n\npipeline = HunyuanDiTPipeline.from_pretrained(\n\t\"Tencent-Hunyuan/HunyuanDiT-Diffusers\", torch_dtype=torch.float16\n).to(\"cuda\")\n```\n\nThen change the memory layout of the pipelines `transformer` and `vae` components to `torch.channels-last`:\n\n```python\npipeline.transformer.to(memory_format=torch.channels_last)\npipeline.vae.to(memory_format=torch.channels_last)\n```\n\nFinally, compile the components and run inference:\n\n```python\npipeline.transformer = torch.compile(pipeline.transformer, mode=\"max-autotune\", fullgraph=True)\npipeline.vae.decode = torch.compile(pipeline.vae.decode, mode=\"max-autotune\", fullgraph=True)\n\nimage = pipeline(prompt=\"一个宇航员在骑马\").images[0]\n```\n\nThe [benchmark](https://gist.github.com/sayakpaul/29d3a14905cfcbf611fe71ebd22e9b23) results on a 80GB A100 machine are:\n\n```bash\nWith torch.compile(): Average inference time: 12.470 seconds.\nWithout torch.compile(): Average inference time: 20.570 seconds.\n```\n\n### Memory optimization\n\nBy loading the T5 text encoder in 8 bits, you can run the pipeline in just under 6 GBs of GPU VRAM. Refer to [this script](https://gist.github.com/sayakpaul/3154605f6af05b98a41081aaba5ca43e) for details. \n\nFurthermore, you can use the [`~HunyuanDiT2DModel.enable_forward_chunking`] method to reduce memory usage. Feed-forward chunking runs the feed-forward layers in a transformer block in a loop instead of all at once. This gives you a trade-off between memory consumption and inference runtime.\n\n```diff\n+ pipeline.transformer.enable_forward_chunking(chunk_size=1, dim=1)\n```\n\n\n## HunyuanDiTPipeline\n\n[[autodoc]] HunyuanDiTPipeline\n\t- all\n\t- __call__\n\t\n" }, { "path": "diffusers/docs/source/en/api/pipelines/i2vgenxl.md", "content": "\n\n# I2VGen-XL\n\n[I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models](https://hf.co/papers/2311.04145.pdf) by Shiwei Zhang, Jiayu Wang, Yingya Zhang, Kang Zhao, Hangjie Yuan, Zhiwu Qin, Xiang Wang, Deli Zhao, and Jingren Zhou.\n\nThe abstract from the paper is:\n\n*Video synthesis has recently made remarkable strides benefiting from the rapid development of diffusion models. However, it still encounters challenges in terms of semantic accuracy, clarity and spatio-temporal continuity. They primarily arise from the scarcity of well-aligned text-video data and the complex inherent structure of videos, making it difficult for the model to simultaneously ensure semantic and qualitative excellence. In this report, we propose a cascaded I2VGen-XL approach that enhances model performance by decoupling these two factors and ensures the alignment of the input data by utilizing static images as a form of crucial guidance. I2VGen-XL consists of two stages: i) the base stage guarantees coherent semantics and preserves content from input images by using two hierarchical encoders, and ii) the refinement stage enhances the video's details by incorporating an additional brief text and improves the resolution to 1280×720. To improve the diversity, we collect around 35 million single-shot text-video pairs and 6 billion text-image pairs to optimize the model. By this means, I2VGen-XL can simultaneously enhance the semantic accuracy, continuity of details and clarity of generated videos. Through extensive experiments, we have investigated the underlying principles of I2VGen-XL and compared it with current top methods, which can demonstrate its effectiveness on diverse data. The source code and models will be publicly available at [this https URL](https://i2vgen-xl.github.io/).*\n\nThe original codebase can be found [here](https://github.com/ali-vilab/i2vgen-xl/). The model checkpoints can be found [here](https://huggingface.co/ali-vilab/).\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines. Also, to know more about reducing the memory usage of this pipeline, refer to the [\"Reduce memory usage\"] section [here](../../using-diffusers/svd#reduce-memory-usage).\n\n\n\nSample output with I2VGenXL:\n\n\n \n \n \n
\n library.\n
\n \"library\"\n\n
\n\n## Notes\n\n* I2VGenXL always uses a `clip_skip` value of 1. This means it leverages the penultimate layer representations from the text encoder of CLIP.\n* It can generate videos of quality that is often on par with [Stable Video Diffusion](../../using-diffusers/svd) (SVD).\n* Unlike SVD, it additionally accepts text prompts as inputs.\n* It can generate higher resolution videos.\n* When using the [`DDIMScheduler`] (which is default for this pipeline), less than 50 steps for inference leads to bad results.\n* This implementation is 1-stage variant of I2VGenXL. The main figure in the [I2VGen-XL](https://arxiv.org/abs/2311.04145) paper shows a 2-stage variant, however, 1-stage variant works well. See [this discussion](https://github.com/huggingface/diffusers/discussions/7952) for more details.\n\n## I2VGenXLPipeline\n[[autodoc]] I2VGenXLPipeline\n\t- all\n\t- __call__\n\n## I2VGenXLPipelineOutput\n[[autodoc]] pipelines.i2vgen_xl.pipeline_i2vgen_xl.I2VGenXLPipelineOutput" }, { "path": "diffusers/docs/source/en/api/pipelines/kandinsky.md", "content": "\n\n# Kandinsky 2.1\n\nKandinsky 2.1 is created by [Arseniy Shakhmatov](https://github.com/cene555), [Anton Razzhigaev](https://github.com/razzant), [Aleksandr Nikolich](https://github.com/AlexWortega), [Vladimir Arkhipkin](https://github.com/oriBetelgeuse), [Igor Pavlov](https://github.com/boomb0om), [Andrey Kuznetsov](https://github.com/kuznetsoffandrey), and [Denis Dimitrov](https://github.com/denndimitrov).\n\nThe description from it's GitHub page is:\n\n*Kandinsky 2.1 inherits best practicies from Dall-E 2 and Latent diffusion, while introducing some new ideas. As text and image encoder it uses CLIP model and diffusion image prior (mapping) between latent spaces of CLIP modalities. This approach increases the visual performance of the model and unveils new horizons in blending images and text-guided image manipulation.*\n\nThe original codebase can be found at [ai-forever/Kandinsky-2](https://github.com/ai-forever/Kandinsky-2).\n\n\n\nCheck out the [Kandinsky Community](https://huggingface.co/kandinsky-community) organization on the Hub for the official model checkpoints for tasks like text-to-image, image-to-image, and inpainting.\n\n\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## KandinskyPriorPipeline\n\n[[autodoc]] KandinskyPriorPipeline\n\t- all\n\t- __call__\n\t- interpolate\n\n## KandinskyPipeline\n\n[[autodoc]] KandinskyPipeline\n\t- all\n\t- __call__\n\n## KandinskyCombinedPipeline\n\n[[autodoc]] KandinskyCombinedPipeline\n\t- all\n\t- __call__\n\n## KandinskyImg2ImgPipeline\n\n[[autodoc]] KandinskyImg2ImgPipeline\n\t- all\n\t- __call__\n\n## KandinskyImg2ImgCombinedPipeline\n\n[[autodoc]] KandinskyImg2ImgCombinedPipeline\n\t- all\n\t- __call__\n\n## KandinskyInpaintPipeline\n\n[[autodoc]] KandinskyInpaintPipeline\n\t- all\n\t- __call__\n\n## KandinskyInpaintCombinedPipeline\n\n[[autodoc]] KandinskyInpaintCombinedPipeline\n\t- all\n\t- __call__\n" }, { "path": "diffusers/docs/source/en/api/pipelines/kandinsky3.md", "content": "\n\n# Kandinsky 3\n\nKandinsky 3 is created by [Vladimir Arkhipkin](https://github.com/oriBetelgeuse),[Anastasia Maltseva](https://github.com/NastyaMittseva),[Igor Pavlov](https://github.com/boomb0om),[Andrei Filatov](https://github.com/anvilarth),[Arseniy Shakhmatov](https://github.com/cene555),[Andrey Kuznetsov](https://github.com/kuznetsoffandrey),[Denis Dimitrov](https://github.com/denndimitrov), [Zein Shaheen](https://github.com/zeinsh)\n\nThe description from it's Github page:\n\n*Kandinsky 3.0 is an open-source text-to-image diffusion model built upon the Kandinsky2-x model family. In comparison to its predecessors, enhancements have been made to the text understanding and visual quality of the model, achieved by increasing the size of the text encoder and Diffusion U-Net models, respectively.*\n\nIts architecture includes 3 main components:\n1. [FLAN-UL2](https://huggingface.co/google/flan-ul2), which is an encoder decoder model based on the T5 architecture.\n2. New U-Net architecture featuring BigGAN-deep blocks doubles depth while maintaining the same number of parameters.\n3. Sber-MoVQGAN is a decoder proven to have superior results in image restoration.\n\n\n\nThe original codebase can be found at [ai-forever/Kandinsky-3](https://github.com/ai-forever/Kandinsky-3).\n\n\n\nCheck out the [Kandinsky Community](https://huggingface.co/kandinsky-community) organization on the Hub for the official model checkpoints for tasks like text-to-image, image-to-image, and inpainting.\n\n\n\n\n\nMake sure to check out the schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## Kandinsky3Pipeline\n\n[[autodoc]] Kandinsky3Pipeline\n\t- all\n\t- __call__\n\n## Kandinsky3Img2ImgPipeline\n\n[[autodoc]] Kandinsky3Img2ImgPipeline\n\t- all\n\t- __call__\n" }, { "path": "diffusers/docs/source/en/api/pipelines/kandinsky_v22.md", "content": "\n\n# Kandinsky 2.2\n\nKandinsky 2.2 is created by [Arseniy Shakhmatov](https://github.com/cene555), [Anton Razzhigaev](https://github.com/razzant), [Aleksandr Nikolich](https://github.com/AlexWortega), [Vladimir Arkhipkin](https://github.com/oriBetelgeuse), [Igor Pavlov](https://github.com/boomb0om), [Andrey Kuznetsov](https://github.com/kuznetsoffandrey), and [Denis Dimitrov](https://github.com/denndimitrov).\n\nThe description from it's GitHub page is:\n\n*Kandinsky 2.2 brings substantial improvements upon its predecessor, Kandinsky 2.1, by introducing a new, more powerful image encoder - CLIP-ViT-G and the ControlNet support. The switch to CLIP-ViT-G as the image encoder significantly increases the model's capability to generate more aesthetic pictures and better understand text, thus enhancing the model's overall performance. The addition of the ControlNet mechanism allows the model to effectively control the process of generating images. This leads to more accurate and visually appealing outputs and opens new possibilities for text-guided image manipulation.*\n\nThe original codebase can be found at [ai-forever/Kandinsky-2](https://github.com/ai-forever/Kandinsky-2).\n\n\n\nCheck out the [Kandinsky Community](https://huggingface.co/kandinsky-community) organization on the Hub for the official model checkpoints for tasks like text-to-image, image-to-image, and inpainting.\n\n\n\n\n\nMake sure to check out the schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## KandinskyV22PriorPipeline\n\n[[autodoc]] KandinskyV22PriorPipeline\n\t- all\n\t- __call__\n\t- interpolate\n\n## KandinskyV22Pipeline\n\n[[autodoc]] KandinskyV22Pipeline\n\t- all\n\t- __call__\n\n## KandinskyV22CombinedPipeline\n\n[[autodoc]] KandinskyV22CombinedPipeline\n\t- all\n\t- __call__\n\n## KandinskyV22ControlnetPipeline\n\n[[autodoc]] KandinskyV22ControlnetPipeline\n\t- all\n\t- __call__\n\n## KandinskyV22PriorEmb2EmbPipeline\n\n[[autodoc]] KandinskyV22PriorEmb2EmbPipeline\n\t- all\n\t- __call__\n\t- interpolate\n\n## KandinskyV22Img2ImgPipeline\n\n[[autodoc]] KandinskyV22Img2ImgPipeline\n\t- all\n\t- __call__\n\n## KandinskyV22Img2ImgCombinedPipeline\n\n[[autodoc]] KandinskyV22Img2ImgCombinedPipeline\n\t- all\n\t- __call__\n\n## KandinskyV22ControlnetImg2ImgPipeline\n\n[[autodoc]] KandinskyV22ControlnetImg2ImgPipeline\n\t- all\n\t- __call__\n\n## KandinskyV22InpaintPipeline\n\n[[autodoc]] KandinskyV22InpaintPipeline\n\t- all\n\t- __call__\n\n## KandinskyV22InpaintCombinedPipeline\n\n[[autodoc]] KandinskyV22InpaintCombinedPipeline\n\t- all\n\t- __call__\n" }, { "path": "diffusers/docs/source/en/api/pipelines/latent_consistency_models.md", "content": "\n\n# Latent Consistency Models\n\nLatent Consistency Models (LCMs) were proposed in [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://huggingface.co/papers/2310.04378) by Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao.\n\nThe abstract of the paper is as follows:\n\n*Latent Diffusion models (LDMs) have achieved remarkable results in synthesizing high-resolution images. However, the iterative sampling process is computationally intensive and leads to slow generation. Inspired by Consistency Models (song et al.), we propose Latent Consistency Models (LCMs), enabling swift inference with minimal steps on any pre-trained LDMs, including Stable Diffusion (rombach et al). Viewing the guided reverse diffusion process as solving an augmented probability flow ODE (PF-ODE), LCMs are designed to directly predict the solution of such ODE in latent space, mitigating the need for numerous iterations and allowing rapid, high-fidelity sampling. Efficiently distilled from pre-trained classifier-free guided diffusion models, a high-quality 768 x 768 2~4-step LCM takes only 32 A100 GPU hours for training. Furthermore, we introduce Latent Consistency Fine-tuning (LCF), a novel method that is tailored for fine-tuning LCMs on customized image datasets. Evaluation on the LAION-5B-Aesthetics dataset demonstrates that LCMs achieve state-of-the-art text-to-image generation performance with few-step inference. Project Page: [this https URL](https://latent-consistency-models.github.io/).*\n\nA demo for the [SimianLuo/LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7) checkpoint can be found [here](https://huggingface.co/spaces/SimianLuo/Latent_Consistency_Model).\n\nThe pipelines were contributed by [luosiallen](https://luosiallen.github.io/), [nagolinc](https://github.com/nagolinc), and [dg845](https://github.com/dg845).\n\n\n## LatentConsistencyModelPipeline\n\n[[autodoc]] LatentConsistencyModelPipeline\n - all\n - __call__\n - enable_freeu\n - disable_freeu\n - enable_vae_slicing\n - disable_vae_slicing\n - enable_vae_tiling\n - disable_vae_tiling\n\n## LatentConsistencyModelImg2ImgPipeline\n\n[[autodoc]] LatentConsistencyModelImg2ImgPipeline\n - all\n - __call__\n - enable_freeu\n - disable_freeu\n - enable_vae_slicing\n - disable_vae_slicing\n - enable_vae_tiling\n - disable_vae_tiling\n\n## StableDiffusionPipelineOutput\n\n[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/latent_diffusion.md", "content": "\n\n# Latent Diffusion\n\nLatent Diffusion was proposed in [High-Resolution Image Synthesis with Latent Diffusion Models](https://huggingface.co/papers/2112.10752) by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer.\n\nThe abstract from the paper is:\n\n*By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. However, since these models typically operate directly in pixel space, optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations. To enable DM training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Our latent diffusion models (LDMs) achieve a new state of the art for image inpainting and highly competitive performance on various tasks, including unconditional image generation, semantic scene synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs.*\n\nThe original codebase can be found at [CompVis/latent-diffusion](https://github.com/CompVis/latent-diffusion).\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## LDMTextToImagePipeline\n[[autodoc]] LDMTextToImagePipeline\n\t- all\n\t- __call__\n\n## LDMSuperResolutionPipeline\n[[autodoc]] LDMSuperResolutionPipeline\n\t- all\n\t- __call__\n\n## ImagePipelineOutput\n[[autodoc]] pipelines.ImagePipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/ledits_pp.md", "content": "\n\n# LEDITS++\n\nLEDITS++ was proposed in [LEDITS++: Limitless Image Editing using Text-to-Image Models](https://huggingface.co/papers/2311.16711) by Manuel Brack, Felix Friedrich, Katharina Kornmeier, Linoy Tsaban, Patrick Schramowski, Kristian Kersting, Apolinário Passos.\n\nThe abstract from the paper is:\n\n*Text-to-image diffusion models have recently received increasing interest for their astonishing ability to produce high-fidelity images from solely text inputs. Subsequent research efforts aim to exploit and apply their capabilities to real image editing. However, existing image-to-image methods are often inefficient, imprecise, and of limited versatility. They either require time-consuming fine-tuning, deviate unnecessarily strongly from the input image, and/or lack support for multiple, simultaneous edits. To address these issues, we introduce LEDITS++, an efficient yet versatile and precise textual image manipulation technique. LEDITS++'s novel inversion approach requires no tuning nor optimization and produces high-fidelity results with a few diffusion steps. Second, our methodology supports multiple simultaneous edits and is architecture-agnostic. Third, we use a novel implicit masking technique that limits changes to relevant image regions. We propose the novel TEdBench++ benchmark as part of our exhaustive evaluation. Our results demonstrate the capabilities of LEDITS++ and its improvements over previous methods. The project page is available at https://leditsplusplus-project.static.hf.space .*\n\n\n\nYou can find additional information about LEDITS++ on the [project page](https://leditsplusplus-project.static.hf.space/index.html) and try it out in a [demo](https://huggingface.co/spaces/editing-images/leditsplusplus).\n\n\n\n\nDue to some backward compatability issues with the current diffusers implementation of [`~schedulers.DPMSolverMultistepScheduler`] this implementation of LEdits++ can no longer guarantee perfect inversion.\nThis issue is unlikely to have any noticeable effects on applied use-cases. However, we provide an alternative implementation that guarantees perfect inversion in a dedicated [GitHub repo](https://github.com/ml-research/ledits_pp).\n\n\nWe provide two distinct pipelines based on different pre-trained models.\n\n## LEditsPPPipelineStableDiffusion\n[[autodoc]] pipelines.ledits_pp.LEditsPPPipelineStableDiffusion\n\t- all\n\t- __call__\n\t- invert\n\n## LEditsPPPipelineStableDiffusionXL\n[[autodoc]] pipelines.ledits_pp.LEditsPPPipelineStableDiffusionXL\n\t- all\n\t- __call__\n\t- invert\n\n\n\n## LEditsPPDiffusionPipelineOutput\n[[autodoc]] pipelines.ledits_pp.pipeline_output.LEditsPPDiffusionPipelineOutput\n\t- all\n\n## LEditsPPInversionPipelineOutput\n[[autodoc]] pipelines.ledits_pp.pipeline_output.LEditsPPInversionPipelineOutput\n\t- all\n" }, { "path": "diffusers/docs/source/en/api/pipelines/marigold.md", "content": "\n\n# Marigold Pipelines for Computer Vision Tasks\n\n![marigold](https://marigoldmonodepth.github.io/images/teaser_collage_compressed.jpg)\n\nMarigold was proposed in [Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation](https://huggingface.co/papers/2312.02145), a CVPR 2024 Oral paper by [Bingxin Ke](http://www.kebingxin.com/), [Anton Obukhov](https://www.obukhov.ai/), [Shengyu Huang](https://shengyuh.github.io/), [Nando Metzger](https://nandometzger.github.io/), [Rodrigo Caye Daudt](https://rcdaudt.github.io/), and [Konrad Schindler](https://scholar.google.com/citations?user=FZuNgqIAAAAJ&hl=en).\nThe idea is to repurpose the rich generative prior of Text-to-Image Latent Diffusion Models (LDMs) for traditional computer vision tasks.\nInitially, this idea was explored to fine-tune Stable Diffusion for Monocular Depth Estimation, as shown in the teaser above.\nLater,\n- [Tianfu Wang](https://tianfwang.github.io/) trained the first Latent Consistency Model (LCM) of Marigold, which unlocked fast single-step inference;\n- [Kevin Qu](https://www.linkedin.com/in/kevin-qu-b3417621b/?locale=en_US) extended the approach to Surface Normals Estimation;\n- [Anton Obukhov](https://www.obukhov.ai/) contributed the pipelines and documentation into diffusers (enabled and supported by [YiYi Xu](https://yiyixuxu.github.io/) and [Sayak Paul](https://sayak.dev/)).\n\nThe abstract from the paper is:\n\n*Monocular depth estimation is a fundamental computer vision task. Recovering 3D depth from a single image is geometrically ill-posed and requires scene understanding, so it is not surprising that the rise of deep learning has led to a breakthrough. The impressive progress of monocular depth estimators has mirrored the growth in model capacity, from relatively modest CNNs to large Transformer architectures. Still, monocular depth estimators tend to struggle when presented with images with unfamiliar content and layout, since their knowledge of the visual world is restricted by the data seen during training, and challenged by zero-shot generalization to new domains. This motivates us to explore whether the extensive priors captured in recent generative diffusion models can enable better, more generalizable depth estimation. We introduce Marigold, a method for affine-invariant monocular depth estimation that is derived from Stable Diffusion and retains its rich prior knowledge. The estimator can be fine-tuned in a couple of days on a single GPU using only synthetic training data. It delivers state-of-the-art performance across a wide range of datasets, including over 20% performance gains in specific cases. Project page: https://marigoldmonodepth.github.io.*\n\n## Available Pipelines\n\nEach pipeline supports one Computer Vision task, which takes an input RGB image as input and produces a *prediction* of the modality of interest, such as a depth map of the input image.\nCurrently, the following tasks are implemented:\n\n| Pipeline | Predicted Modalities | Demos |\n|---------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------:|\n| [MarigoldDepthPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/marigold/pipeline_marigold_depth.py) | [Depth](https://en.wikipedia.org/wiki/Depth_map), [Disparity](https://en.wikipedia.org/wiki/Binocular_disparity) | [Fast Demo (LCM)](https://huggingface.co/spaces/prs-eth/marigold-lcm), [Slow Original Demo (DDIM)](https://huggingface.co/spaces/prs-eth/marigold) |\n| [MarigoldNormalsPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/marigold/pipeline_marigold_normals.py) | [Surface normals](https://en.wikipedia.org/wiki/Normal_mapping) | [Fast Demo (LCM)](https://huggingface.co/spaces/prs-eth/marigold-normals-lcm) |\n\n\n## Available Checkpoints\n\nThe original checkpoints can be found under the [PRS-ETH](https://huggingface.co/prs-eth/) Hugging Face organization.\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines. Also, to know more about reducing the memory usage of this pipeline, refer to the [\"Reduce memory usage\"] section [here](../../using-diffusers/svd#reduce-memory-usage).\n\n\n\n\n\nMarigold pipelines were designed and tested only with `DDIMScheduler` and `LCMScheduler`.\nDepending on the scheduler, the number of inference steps required to get reliable predictions varies, and there is no universal value that works best across schedulers.\nBecause of that, the default value of `num_inference_steps` in the `__call__` method of the pipeline is set to `None` (see the API reference).\nUnless set explicitly, its value will be taken from the checkpoint configuration `model_index.json`.\nThis is done to ensure high-quality predictions when calling the pipeline with just the `image` argument.\n\n\n\nSee also Marigold [usage examples](marigold_usage).\n\n## MarigoldDepthPipeline\n[[autodoc]] MarigoldDepthPipeline\n\t- all\n\t- __call__\n\n## MarigoldNormalsPipeline\n[[autodoc]] MarigoldNormalsPipeline\n\t- all\n\t- __call__\n\n## MarigoldDepthOutput\n[[autodoc]] pipelines.marigold.pipeline_marigold_depth.MarigoldDepthOutput\n\n## MarigoldNormalsOutput\n[[autodoc]] pipelines.marigold.pipeline_marigold_normals.MarigoldNormalsOutput" }, { "path": "diffusers/docs/source/en/api/pipelines/musicldm.md", "content": "\n\n# MusicLDM\n\nMusicLDM was proposed in [MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies](https://huggingface.co/papers/2308.01546) by Ke Chen, Yusong Wu, Haohe Liu, Marianna Nezhurina, Taylor Berg-Kirkpatrick, Shlomo Dubnov.\nMusicLDM takes a text prompt as input and predicts the corresponding music sample.\n\nInspired by [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview) and [AudioLDM](https://huggingface.co/docs/diffusers/api/pipelines/audioldm),\nMusicLDM is a text-to-music _latent diffusion model (LDM)_ that learns continuous audio representations from [CLAP](https://huggingface.co/docs/transformers/main/model_doc/clap)\nlatents.\n\nMusicLDM is trained on a corpus of 466 hours of music data. Beat-synchronous data augmentation strategies are applied to the music samples, both in the time domain and in the latent space. Using beat-synchronous data augmentation strategies encourages the model to interpolate between the training samples, but stay within the domain of the training data. The result is generated music that is more diverse while staying faithful to the corresponding style.\n\nThe abstract of the paper is the following:\n\n*Diffusion models have shown promising results in cross-modal generation tasks, including text-to-image and text-to-audio generation. However, generating music, as a special type of audio, presents unique challenges due to limited availability of music data and sensitive issues related to copyright and plagiarism. In this paper, to tackle these challenges, we first construct a state-of-the-art text-to-music model, MusicLDM, that adapts Stable Diffusion and AudioLDM architectures to the music domain. We achieve this by retraining the contrastive language-audio pretraining model (CLAP) and the Hifi-GAN vocoder, as components of MusicLDM, on a collection of music data samples. Then, to address the limitations of training data and to avoid plagiarism, we leverage a beat tracking model and propose two different mixup strategies for data augmentation: beat-synchronous audio mixup and beat-synchronous latent mixup, which recombine training audio directly or via a latent embeddings space, respectively. Such mixup strategies encourage the model to interpolate between musical training samples and generate new music within the convex hull of the training data, making the generated music more diverse while still staying faithful to the corresponding style. In addition to popular evaluation metrics, we design several new evaluation metrics based on CLAP score to demonstrate that our proposed MusicLDM and beat-synchronous mixup strategies improve both the quality and novelty of generated music, as well as the correspondence between input text and generated music.*\n\nThis pipeline was contributed by [sanchit-gandhi](https://huggingface.co/sanchit-gandhi).\n\n## Tips\n\nWhen constructing a prompt, keep in mind:\n\n* Descriptive prompt inputs work best; use adjectives to describe the sound (for example, \"high quality\" or \"clear\") and make the prompt context specific where possible (e.g. \"melodic techno with a fast beat and synths\" works better than \"techno\").\n* Using a *negative prompt* can significantly improve the quality of the generated audio. Try using a negative prompt of \"low quality, average quality\".\n\nDuring inference:\n\n* The _quality_ of the generated audio sample can be controlled by the `num_inference_steps` argument; higher steps give higher quality audio at the expense of slower inference.\n* Multiple waveforms can be generated in one go: set `num_waveforms_per_prompt` to a value greater than 1 to enable. Automatic scoring will be performed between the generated waveforms and prompt text, and the audios ranked from best to worst accordingly.\n* The _length_ of the generated audio sample can be controlled by varying the `audio_length_in_s` argument.\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## MusicLDMPipeline\n[[autodoc]] MusicLDMPipeline\n\t- all\n\t- __call__\n" }, { "path": "diffusers/docs/source/en/api/pipelines/overview.md", "content": "\n\n# Pipelines\n\nPipelines provide a simple way to run state-of-the-art diffusion models in inference by bundling all of the necessary components (multiple independently-trained models, schedulers, and processors) into a single end-to-end class. Pipelines are flexible and they can be adapted to use different schedulers or even model components.\n\nAll pipelines are built from the base [`DiffusionPipeline`] class which provides basic functionality for loading, downloading, and saving all the components. Specific pipeline types (for example [`StableDiffusionPipeline`]) loaded with [`~DiffusionPipeline.from_pretrained`] are automatically detected and the pipeline components are loaded and passed to the `__init__` function of the pipeline.\n\n\n\nYou shouldn't use the [`DiffusionPipeline`] class for training. Individual components (for example, [`UNet2DModel`] and [`UNet2DConditionModel`]) of diffusion pipelines are usually trained individually, so we suggest directly working with them instead.\n\n
\n\nPipelines do not offer any training functionality. You'll notice PyTorch's autograd is disabled by decorating the [`~DiffusionPipeline.__call__`] method with a [`torch.no_grad`](https://pytorch.org/docs/stable/generated/torch.no_grad.html) decorator because pipelines should not be used for training. If you're interested in training, please take a look at the [Training](../../training/overview) guides instead!\n\n\n\nThe table below lists all the pipelines currently available in 🤗 Diffusers and the tasks they support. Click on a pipeline to view its abstract and published paper.\n\n| Pipeline | Tasks |\n|---|---|\n| [AltDiffusion](alt_diffusion) | image2image |\n| [AnimateDiff](animatediff) | text2video |\n| [Attend-and-Excite](attend_and_excite) | text2image |\n| [Audio Diffusion](audio_diffusion) | image2audio |\n| [AudioLDM](audioldm) | text2audio |\n| [AudioLDM2](audioldm2) | text2audio |\n| [BLIP Diffusion](blip_diffusion) | text2image |\n| [Consistency Models](consistency_models) | unconditional image generation |\n| [ControlNet](controlnet) | text2image, image2image, inpainting |\n| [ControlNet with Stable Diffusion XL](controlnet_sdxl) | text2image |\n| [ControlNet-XS](controlnetxs) | text2image |\n| [ControlNet-XS with Stable Diffusion XL](controlnetxs_sdxl) | text2image |\n| [Cycle Diffusion](cycle_diffusion) | image2image |\n| [Dance Diffusion](dance_diffusion) | unconditional audio generation |\n| [DDIM](ddim) | unconditional image generation |\n| [DDPM](ddpm) | unconditional image generation |\n| [DeepFloyd IF](deepfloyd_if) | text2image, image2image, inpainting, super-resolution |\n| [DiffEdit](diffedit) | inpainting |\n| [DiT](dit) | text2image |\n| [GLIGEN](stable_diffusion/gligen) | text2image |\n| [InstructPix2Pix](pix2pix) | image editing |\n| [Kandinsky 2.1](kandinsky) | text2image, image2image, inpainting, interpolation |\n| [Kandinsky 2.2](kandinsky_v22) | text2image, image2image, inpainting |\n| [Kandinsky 3](kandinsky3) | text2image, image2image |\n| [Latent Consistency Models](latent_consistency_models) | text2image |\n| [Latent Diffusion](latent_diffusion) | text2image, super-resolution |\n| [LDM3D](stable_diffusion/ldm3d_diffusion) | text2image, text-to-3D, text-to-pano, upscaling |\n| [LEDITS++](ledits_pp) | image editing |\n| [MultiDiffusion](panorama) | text2image |\n| [MusicLDM](musicldm) | text2audio |\n| [Paint by Example](paint_by_example) | inpainting |\n| [ParaDiGMS](paradigms) | text2image |\n| [Pix2Pix Zero](pix2pix_zero) | image editing |\n| [PixArt-α](pixart) | text2image |\n| [PNDM](pndm) | unconditional image generation |\n| [RePaint](repaint) | inpainting |\n| [Score SDE VE](score_sde_ve) | unconditional image generation |\n| [Self-Attention Guidance](self_attention_guidance) | text2image |\n| [Semantic Guidance](semantic_stable_diffusion) | text2image |\n| [Shap-E](shap_e) | text-to-3D, image-to-3D |\n| [Spectrogram Diffusion](spectrogram_diffusion) | |\n| [Stable Diffusion](stable_diffusion/overview) | text2image, image2image, depth2image, inpainting, image variation, latent upscaler, super-resolution |\n| [Stable Diffusion Model Editing](model_editing) | model editing |\n| [Stable Diffusion XL](stable_diffusion/stable_diffusion_xl) | text2image, image2image, inpainting |\n| [Stable Diffusion XL Turbo](stable_diffusion/sdxl_turbo) | text2image, image2image, inpainting |\n| [Stable unCLIP](stable_unclip) | text2image, image variation |\n| [Stochastic Karras VE](stochastic_karras_ve) | unconditional image generation |\n| [T2I-Adapter](stable_diffusion/adapter) | text2image |\n| [Text2Video](text_to_video) | text2video, video2video |\n| [Text2Video-Zero](text_to_video_zero) | text2video |\n| [unCLIP](unclip) | text2image, image variation |\n| [Unconditional Latent Diffusion](latent_diffusion_uncond) | unconditional image generation |\n| [UniDiffuser](unidiffuser) | text2image, image2text, image variation, text variation, unconditional image generation, unconditional audio generation |\n| [Value-guided planning](value_guided_sampling) | value guided sampling |\n| [Versatile Diffusion](versatile_diffusion) | text2image, image variation |\n| [VQ Diffusion](vq_diffusion) | text2image |\n| [Wuerstchen](wuerstchen) | text2image |\n\n## DiffusionPipeline\n\n[[autodoc]] DiffusionPipeline\n\t- all\n\t- __call__\n\t- device\n\t- to\n\t- components\n\n\n[[autodoc]] pipelines.StableDiffusionMixin.enable_freeu\n\n[[autodoc]] pipelines.StableDiffusionMixin.disable_freeu\n\n## FlaxDiffusionPipeline\n\n[[autodoc]] pipelines.pipeline_flax_utils.FlaxDiffusionPipeline\n\n## PushToHubMixin\n\n[[autodoc]] utils.PushToHubMixin\n" }, { "path": "diffusers/docs/source/en/api/pipelines/paint_by_example.md", "content": "\n\n# Paint by Example\n\n[Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://huggingface.co/papers/2211.13227) is by Binxin Yang, Shuyang Gu, Bo Zhang, Ting Zhang, Xuejin Chen, Xiaoyan Sun, Dong Chen, Fang Wen.\n\nThe abstract from the paper is:\n\n*Language-guided image editing has achieved great success recently. In this paper, for the first time, we investigate exemplar-guided image editing for more precise control. We achieve this goal by leveraging self-supervised training to disentangle and re-organize the source image and the exemplar. However, the naive approach will cause obvious fusing artifacts. We carefully analyze it and propose an information bottleneck and strong augmentations to avoid the trivial solution of directly copying and pasting the exemplar image. Meanwhile, to ensure the controllability of the editing process, we design an arbitrary shape mask for the exemplar image and leverage the classifier-free guidance to increase the similarity to the exemplar image. The whole framework involves a single forward of the diffusion model without any iterative optimization. We demonstrate that our method achieves an impressive performance and enables controllable editing on in-the-wild images with high fidelity.*\n\nThe original codebase can be found at [Fantasy-Studio/Paint-by-Example](https://github.com/Fantasy-Studio/Paint-by-Example), and you can try it out in a [demo](https://huggingface.co/spaces/Fantasy-Studio/Paint-by-Example).\n\n## Tips\n\nPaint by Example is supported by the official [Fantasy-Studio/Paint-by-Example](https://huggingface.co/Fantasy-Studio/Paint-by-Example) checkpoint. The checkpoint is warm-started from [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) to inpaint partly masked images conditioned on example and reference images.\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## PaintByExamplePipeline\n[[autodoc]] PaintByExamplePipeline\n\t- all\n\t- __call__\n\n## StableDiffusionPipelineOutput\n[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/panorama.md", "content": "\n\n# MultiDiffusion\n\n[MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation](https://huggingface.co/papers/2302.08113) is by Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel.\n\nThe abstract from the paper is:\n\n*Recent advances in text-to-image generation with diffusion models present transformative capabilities in image quality. However, user controllability of the generated image, and fast adaptation to new tasks still remains an open challenge, currently mostly addressed by costly and long re-training and fine-tuning or ad-hoc adaptations to specific image generation tasks. In this work, we present MultiDiffusion, a unified framework that enables versatile and controllable image generation, using a pre-trained text-to-image diffusion model, without any further training or finetuning. At the center of our approach is a new generation process, based on an optimization task that binds together multiple diffusion generation processes with a shared set of parameters or constraints. We show that MultiDiffusion can be readily applied to generate high quality and diverse images that adhere to user-provided controls, such as desired aspect ratio (e.g., panorama), and spatial guiding signals, ranging from tight segmentation masks to bounding boxes.*\n\nYou can find additional information about MultiDiffusion on the [project page](https://multidiffusion.github.io/), [original codebase](https://github.com/omerbt/MultiDiffusion), and try it out in a [demo](https://huggingface.co/spaces/weizmannscience/MultiDiffusion).\n\n## Tips\n\nWhile calling [`StableDiffusionPanoramaPipeline`], it's possible to specify the `view_batch_size` parameter to be > 1.\nFor some GPUs with high performance, this can speedup the generation process and increase VRAM usage.\n\nTo generate panorama-like images make sure you pass the width parameter accordingly. We recommend a width value of 2048 which is the default.\n\nCircular padding is applied to ensure there are no stitching artifacts when working with panoramas to ensure a seamless transition from the rightmost part to the leftmost part. By enabling circular padding (set `circular_padding=True`), the operation applies additional crops after the rightmost point of the image, allowing the model to \"see” the transition from the rightmost part to the leftmost part. This helps maintain visual consistency in a 360-degree sense and creates a proper “panorama” that can be viewed using 360-degree panorama viewers. When decoding latents in Stable Diffusion, circular padding is applied to ensure that the decoded latents match in the RGB space.\n\nFor example, without circular padding, there is a stitching artifact (default):\n![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/indoor_%20no_circular_padding.png)\n\nBut with circular padding, the right and the left parts are matching (`circular_padding=True`):\n![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/indoor_%20circular_padding.png)\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## StableDiffusionPanoramaPipeline\n[[autodoc]] StableDiffusionPanoramaPipeline\n\t- __call__\n\t- all\n\n## StableDiffusionPipelineOutput\n[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/pia.md", "content": "\n\n# Image-to-Video Generation with PIA (Personalized Image Animator)\n\n## Overview\n\n[PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Models](https://arxiv.org/abs/2312.13964) by Yiming Zhang, Zhening Xing, Yanhong Zeng, Youqing Fang, Kai Chen\n\nRecent advancements in personalized text-to-image (T2I) models have revolutionized content creation, empowering non-experts to generate stunning images with unique styles. While promising, adding realistic motions into these personalized images by text poses significant challenges in preserving distinct styles, high-fidelity details, and achieving motion controllability by text. In this paper, we present PIA, a Personalized Image Animator that excels in aligning with condition images, achieving motion controllability by text, and the compatibility with various personalized T2I models without specific tuning. To achieve these goals, PIA builds upon a base T2I model with well-trained temporal alignment layers, allowing for the seamless transformation of any personalized T2I model into an image animation model. A key component of PIA is the introduction of the condition module, which utilizes the condition frame and inter-frame affinity as input to transfer appearance information guided by the affinity hint for individual frame synthesis in the latent space. This design mitigates the challenges of appearance-related image alignment within and allows for a stronger focus on aligning with motion-related guidance.\n\n[Project page](https://pi-animator.github.io/)\n\n## Available Pipelines\n\n| Pipeline | Tasks | Demo\n|---|---|:---:|\n| [PIAPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pia/pipeline_pia.py) | *Image-to-Video Generation with PIA* |\n\n## Available checkpoints\n\nMotion Adapter checkpoints for PIA can be found under the [OpenMMLab org](https://huggingface.co/openmmlab/PIA-condition-adapter). These checkpoints are meant to work with any model based on Stable Diffusion 1.5\n\n## Usage example\n\nPIA works with a MotionAdapter checkpoint and a Stable Diffusion 1.5 model checkpoint. The MotionAdapter is a collection of Motion Modules that are responsible for adding coherent motion across image frames. These modules are applied after the Resnet and Attention blocks in the Stable Diffusion UNet. In addition to the motion modules, PIA also replaces the input convolution layer of the SD 1.5 UNet model with a 9 channel input convolution layer.\n\nThe following example demonstrates how to use PIA to generate a video from a single image.\n\n```python\nimport torch\nfrom diffusers import (\n EulerDiscreteScheduler,\n MotionAdapter,\n PIAPipeline,\n)\nfrom diffusers.utils import export_to_gif, load_image\n\nadapter = MotionAdapter.from_pretrained(\"openmmlab/PIA-condition-adapter\")\npipe = PIAPipeline.from_pretrained(\"SG161222/Realistic_Vision_V6.0_B1_noVAE\", motion_adapter=adapter, torch_dtype=torch.float16)\n\npipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)\npipe.enable_model_cpu_offload()\npipe.enable_vae_slicing()\n\nimage = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png?download=true\"\n)\nimage = image.resize((512, 512))\nprompt = \"cat in a field\"\nnegative_prompt = \"wrong white balance, dark, sketches,worst quality,low quality\"\n\ngenerator = torch.Generator(\"cpu\").manual_seed(0)\noutput = pipe(image=image, prompt=prompt, generator=generator)\nframes = output.frames[0]\nexport_to_gif(frames, \"pia-animation.gif\")\n```\n\nHere are some sample outputs:\n\n\n \n \n \n
\n cat in a field.\n
\n \"cat\n
\n\n\n\n\nIf you plan on using a scheduler that can clip samples, make sure to disable it by setting `clip_sample=False` in the scheduler as this can also have an adverse effect on generated samples. Additionally, the PIA checkpoints can be sensitive to the beta schedule of the scheduler. We recommend setting this to `linear`.\n\n\n\n## Using FreeInit\n\n[FreeInit: Bridging Initialization Gap in Video Diffusion Models](https://arxiv.org/abs/2312.07537) by Tianxing Wu, Chenyang Si, Yuming Jiang, Ziqi Huang, Ziwei Liu.\n\nFreeInit is an effective method that improves temporal consistency and overall quality of videos generated using video-diffusion-models without any addition training. It can be applied to PIA, AnimateDiff, ModelScope, VideoCrafter and various other video generation models seamlessly at inference time, and works by iteratively refining the latent-initialization noise. More details can be found it the paper.\n\nThe following example demonstrates the usage of FreeInit.\n\n```python\nimport torch\nfrom diffusers import (\n DDIMScheduler,\n MotionAdapter,\n PIAPipeline,\n)\nfrom diffusers.utils import export_to_gif, load_image\n\nadapter = MotionAdapter.from_pretrained(\"openmmlab/PIA-condition-adapter\")\npipe = PIAPipeline.from_pretrained(\"SG161222/Realistic_Vision_V6.0_B1_noVAE\", motion_adapter=adapter)\n\n# enable FreeInit\n# Refer to the enable_free_init documentation for a full list of configurable parameters\npipe.enable_free_init(method=\"butterworth\", use_fast_sampling=True)\n\n# Memory saving options\npipe.enable_model_cpu_offload()\npipe.enable_vae_slicing()\n\npipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\nimage = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png?download=true\"\n)\nimage = image.resize((512, 512))\nprompt = \"cat in a field\"\nnegative_prompt = \"wrong white balance, dark, sketches,worst quality,low quality\"\n\ngenerator = torch.Generator(\"cpu\").manual_seed(0)\n\noutput = pipe(image=image, prompt=prompt, generator=generator)\nframes = output.frames[0]\nexport_to_gif(frames, \"pia-freeinit-animation.gif\")\n```\n\n\n \n \n \n
\n cat in a field.\n
\n \"cat\n
\n\n\n\n\nFreeInit is not really free - the improved quality comes at the cost of extra computation. It requires sampling a few extra times depending on the `num_iters` parameter that is set when enabling it. Setting the `use_fast_sampling` parameter to `True` can improve the overall performance (at the cost of lower quality compared to when `use_fast_sampling=False` but still better results than vanilla video generation models).\n\n\n\n## PIAPipeline\n\n[[autodoc]] PIAPipeline\n\t- all\n\t- __call__\n - enable_freeu\n - disable_freeu\n - enable_free_init\n - disable_free_init\n - enable_vae_slicing\n - disable_vae_slicing\n - enable_vae_tiling\n - disable_vae_tiling\n\n## PIAPipelineOutput\n\n[[autodoc]] pipelines.pia.PIAPipelineOutput" }, { "path": "diffusers/docs/source/en/api/pipelines/pix2pix.md", "content": "\n\n# InstructPix2Pix\n\n[InstructPix2Pix: Learning to Follow Image Editing Instructions](https://huggingface.co/papers/2211.09800) is by Tim Brooks, Aleksander Holynski and Alexei A. Efros.\n\nThe abstract from the paper is:\n\n*We propose a method for editing images from human instructions: given an input image and a written instruction that tells the model what to do, our model follows these instructions to edit the image. To obtain training data for this problem, we combine the knowledge of two large pretrained models -- a language model (GPT-3) and a text-to-image model (Stable Diffusion) -- to generate a large dataset of image editing examples. Our conditional diffusion model, InstructPix2Pix, is trained on our generated data, and generalizes to real images and user-written instructions at inference time. Since it performs edits in the forward pass and does not require per example fine-tuning or inversion, our model edits images quickly, in a matter of seconds. We show compelling editing results for a diverse collection of input images and written instructions.*\n\nYou can find additional information about InstructPix2Pix on the [project page](https://www.timothybrooks.com/instruct-pix2pix), [original codebase](https://github.com/timothybrooks/instruct-pix2pix), and try it out in a [demo](https://huggingface.co/spaces/timbrooks/instruct-pix2pix).\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## StableDiffusionInstructPix2PixPipeline\n[[autodoc]] StableDiffusionInstructPix2PixPipeline\n\t- __call__\n\t- all\n\t- load_textual_inversion\n\t- load_lora_weights\n\t- save_lora_weights\n\n## StableDiffusionXLInstructPix2PixPipeline\n[[autodoc]] StableDiffusionXLInstructPix2PixPipeline\n\t- __call__\n\t- all\n" }, { "path": "diffusers/docs/source/en/api/pipelines/pixart.md", "content": "\n\n# PixArt-α\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/pixart/header_collage.png)\n\n[PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis](https://huggingface.co/papers/2310.00426) is Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li.\n\nThe abstract from the paper is:\n\n*The most advanced text-to-image (T2I) models require significant training costs (e.g., millions of GPU hours), seriously hindering the fundamental innovation for the AIGC community while increasing CO2 emissions. This paper introduces PIXART-α, a Transformer-based T2I diffusion model whose image generation quality is competitive with state-of-the-art image generators (e.g., Imagen, SDXL, and even Midjourney), reaching near-commercial application standards. Additionally, it supports high-resolution image synthesis up to 1024px resolution with low training cost, as shown in Figure 1 and 2. To achieve this goal, three core designs are proposed: (1) Training strategy decomposition: We devise three distinct training steps that separately optimize pixel dependency, text-image alignment, and image aesthetic quality; (2) Efficient T2I Transformer: We incorporate cross-attention modules into Diffusion Transformer (DiT) to inject text conditions and streamline the computation-intensive class-condition branch; (3) High-informative data: We emphasize the significance of concept density in text-image pairs and leverage a large Vision-Language model to auto-label dense pseudo-captions to assist text-image alignment learning. As a result, PIXART-α's training speed markedly surpasses existing large-scale T2I models, e.g., PIXART-α only takes 10.8% of Stable Diffusion v1.5's training time (675 vs. 6,250 A100 GPU days), saving nearly $300,000 ($26,000 vs. $320,000) and reducing 90% CO2 emissions. Moreover, compared with a larger SOTA model, RAPHAEL, our training cost is merely 1%. Extensive experiments demonstrate that PIXART-α excels in image quality, artistry, and semantic control. We hope PIXART-α will provide new insights to the AIGC community and startups to accelerate building their own high-quality yet low-cost generative models from scratch.*\n\nYou can find the original codebase at [PixArt-alpha/PixArt-alpha](https://github.com/PixArt-alpha/PixArt-alpha) and all the available checkpoints at [PixArt-alpha](https://huggingface.co/PixArt-alpha).\n\nSome notes about this pipeline:\n\n* It uses a Transformer backbone (instead of a UNet) for denoising. As such it has a similar architecture as [DiT](./dit).\n* It was trained using text conditions computed from T5. This aspect makes the pipeline better at following complex text prompts with intricate details.\n* It is good at producing high-resolution images at different aspect ratios. To get the best results, the authors recommend some size brackets which can be found [here](https://github.com/PixArt-alpha/PixArt-alpha/blob/08fbbd281ec96866109bdd2cdb75f2f58fb17610/diffusion/data/datasets/utils.py).\n* It rivals the quality of state-of-the-art text-to-image generation systems (as of this writing) such as Stable Diffusion XL, Imagen, and DALL-E 2, while being more efficient than them.\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers.md) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## Inference with under 8GB GPU VRAM\n\nRun the [`PixArtAlphaPipeline`] with under 8GB GPU VRAM by loading the text encoder in 8-bit precision. Let's walk through a full-fledged example.\n\nFirst, install the [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) library:\n\n```bash\npip install -U bitsandbytes\n```\n\nThen load the text encoder in 8-bit:\n\n```python\nfrom transformers import T5EncoderModel\nfrom diffusers import PixArtAlphaPipeline\nimport torch\n\ntext_encoder = T5EncoderModel.from_pretrained(\n \"PixArt-alpha/PixArt-XL-2-1024-MS\",\n subfolder=\"text_encoder\",\n load_in_8bit=True,\n device_map=\"auto\",\n\n)\npipe = PixArtAlphaPipeline.from_pretrained(\n \"PixArt-alpha/PixArt-XL-2-1024-MS\",\n text_encoder=text_encoder,\n transformer=None,\n device_map=\"auto\"\n)\n```\n\nNow, use the `pipe` to encode a prompt:\n\n```python\nwith torch.no_grad():\n prompt = \"cute cat\"\n prompt_embeds, prompt_attention_mask, negative_embeds, negative_prompt_attention_mask = pipe.encode_prompt(prompt)\n```\n\nSince text embeddings have been computed, remove the `text_encoder` and `pipe` from the memory, and free up some GPU VRAM:\n\n```python\nimport gc\n\ndef flush():\n gc.collect()\n torch.cuda.empty_cache()\n\ndel text_encoder\ndel pipe\nflush()\n```\n\nThen compute the latents with the prompt embeddings as inputs:\n\n```python\npipe = PixArtAlphaPipeline.from_pretrained(\n \"PixArt-alpha/PixArt-XL-2-1024-MS\",\n text_encoder=None,\n torch_dtype=torch.float16,\n).to(\"cuda\")\n\nlatents = pipe(\n negative_prompt=None,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_embeds,\n prompt_attention_mask=prompt_attention_mask,\n negative_prompt_attention_mask=negative_prompt_attention_mask,\n num_images_per_prompt=1,\n output_type=\"latent\",\n).images\n\ndel pipe.transformer\nflush()\n```\n\n\n\nNotice that while initializing `pipe`, you're setting `text_encoder` to `None` so that it's not loaded.\n\n\n\nOnce the latents are computed, pass it off to the VAE to decode into a real image:\n\n```python\nwith torch.no_grad():\n image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor, return_dict=False)[0]\nimage = pipe.image_processor.postprocess(image, output_type=\"pil\")[0]\nimage.save(\"cat.png\")\n```\n\nBy deleting components you aren't using and flushing the GPU VRAM, you should be able to run [`PixArtAlphaPipeline`] with under 8GB GPU VRAM.\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/pixart/8bits_cat.png)\n\nIf you want a report of your memory-usage, run this [script](https://gist.github.com/sayakpaul/3ae0f847001d342af27018a96f467e4e).\n\n\n\nText embeddings computed in 8-bit can impact the quality of the generated images because of the information loss in the representation space caused by the reduced precision. It's recommended to compare the outputs with and without 8-bit.\n\n\n\nWhile loading the `text_encoder`, you set `load_in_8bit` to `True`. You could also specify `load_in_4bit` to bring your memory requirements down even further to under 7GB.\n\n## PixArtAlphaPipeline\n\n[[autodoc]] PixArtAlphaPipeline\n\t- all\n\t- __call__\n" }, { "path": "diffusers/docs/source/en/api/pipelines/pixart_sigma.md", "content": "\n\n# PixArt-Σ\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/pixart/header_collage_sigma.jpg)\n\n[PixArt-Σ: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation](https://huggingface.co/papers/2403.04692) is Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li.\n\nThe abstract from the paper is:\n\n*In this paper, we introduce PixArt-Σ, a Diffusion Transformer model (DiT) capable of directly generating images at 4K resolution. PixArt-Σ represents a significant advancement over its predecessor, PixArt-α, offering images of markedly higher fidelity and improved alignment with text prompts. A key feature of PixArt-Σ is its training efficiency. Leveraging the foundational pre-training of PixArt-α, it evolves from the ‘weaker’ baseline to a ‘stronger’ model via incorporating higher quality data, a process we term “weak-to-strong training”. The advancements in PixArt-Σ are twofold: (1) High-Quality Training Data: PixArt-Σ incorporates superior-quality image data, paired with more precise and detailed image captions. (2) Efficient Token Compression: we propose a novel attention module within the DiT framework that compresses both keys and values, significantly improving efficiency and facilitating ultra-high-resolution image generation. Thanks to these improvements, PixArt-Σ achieves superior image quality and user prompt adherence capabilities with significantly smaller model size (0.6B parameters) than existing text-to-image diffusion models, such as SDXL (2.6B parameters) and SD Cascade (5.1B parameters). Moreover, PixArt-Σ’s capability to generate 4K images supports the creation of high-resolution posters and wallpapers, efficiently bolstering the production of highquality visual content in industries such as film and gaming.*\n\nYou can find the original codebase at [PixArt-alpha/PixArt-sigma](https://github.com/PixArt-alpha/PixArt-sigma) and all the available checkpoints at [PixArt-alpha](https://huggingface.co/PixArt-alpha).\n\nSome notes about this pipeline:\n\n* It uses a Transformer backbone (instead of a UNet) for denoising. As such it has a similar architecture as [DiT](https://hf.co/docs/transformers/model_doc/dit).\n* It was trained using text conditions computed from T5. This aspect makes the pipeline better at following complex text prompts with intricate details.\n* It is good at producing high-resolution images at different aspect ratios. To get the best results, the authors recommend some size brackets which can be found [here](https://github.com/PixArt-alpha/PixArt-sigma/blob/master/diffusion/data/datasets/utils.py).\n* It rivals the quality of state-of-the-art text-to-image generation systems (as of this writing) such as PixArt-α, Stable Diffusion XL, Playground V2.0 and DALL-E 3, while being more efficient than them.\n* It shows the ability of generating super high resolution images, such as 2048px or even 4K.\n* It shows that text-to-image models can grow from a weak model to a stronger one through several improvements (VAEs, datasets, and so on.)\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## Inference with under 8GB GPU VRAM\n\nRun the [`PixArtSigmaPipeline`] with under 8GB GPU VRAM by loading the text encoder in 8-bit precision. Let's walk through a full-fledged example.\n\nFirst, install the [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) library:\n\n```bash\npip install -U bitsandbytes\n```\n\nThen load the text encoder in 8-bit:\n\n```python\nfrom transformers import T5EncoderModel\nfrom diffusers import PixArtSigmaPipeline\nimport torch\n\ntext_encoder = T5EncoderModel.from_pretrained(\n \"PixArt-alpha/PixArt-Sigma-XL-2-1024-MS\",\n subfolder=\"text_encoder\",\n load_in_8bit=True,\n device_map=\"auto\",\n)\npipe = PixArtSigmaPipeline.from_pretrained(\n \"PixArt-alpha/PixArt-Sigma-XL-2-1024-MS\",\n text_encoder=text_encoder,\n transformer=None,\n device_map=\"balanced\"\n)\n```\n\nNow, use the `pipe` to encode a prompt:\n\n```python\nwith torch.no_grad():\n prompt = \"cute cat\"\n prompt_embeds, prompt_attention_mask, negative_embeds, negative_prompt_attention_mask = pipe.encode_prompt(prompt)\n```\n\nSince text embeddings have been computed, remove the `text_encoder` and `pipe` from the memory, and free up some GPU VRAM:\n\n```python\nimport gc\n\ndef flush():\n gc.collect()\n torch.cuda.empty_cache()\n\ndel text_encoder\ndel pipe\nflush()\n```\n\nThen compute the latents with the prompt embeddings as inputs:\n\n```python\npipe = PixArtSigmaPipeline.from_pretrained(\n \"PixArt-alpha/PixArt-Sigma-XL-2-1024-MS\",\n text_encoder=None,\n torch_dtype=torch.float16,\n).to(\"cuda\")\n\nlatents = pipe(\n negative_prompt=None,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_embeds,\n prompt_attention_mask=prompt_attention_mask,\n negative_prompt_attention_mask=negative_prompt_attention_mask,\n num_images_per_prompt=1,\n output_type=\"latent\",\n).images\n\ndel pipe.transformer\nflush()\n```\n\n\n\nNotice that while initializing `pipe`, you're setting `text_encoder` to `None` so that it's not loaded.\n\n\n\nOnce the latents are computed, pass it off to the VAE to decode into a real image:\n\n```python\nwith torch.no_grad():\n image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor, return_dict=False)[0]\nimage = pipe.image_processor.postprocess(image, output_type=\"pil\")[0]\nimage.save(\"cat.png\")\n```\n\nBy deleting components you aren't using and flushing the GPU VRAM, you should be able to run [`PixArtSigmaPipeline`] with under 8GB GPU VRAM.\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/pixart/8bits_cat.png)\n\nIf you want a report of your memory-usage, run this [script](https://gist.github.com/sayakpaul/3ae0f847001d342af27018a96f467e4e).\n\n\n\nText embeddings computed in 8-bit can impact the quality of the generated images because of the information loss in the representation space caused by the reduced precision. It's recommended to compare the outputs with and without 8-bit.\n\n\n\nWhile loading the `text_encoder`, you set `load_in_8bit` to `True`. You could also specify `load_in_4bit` to bring your memory requirements down even further to under 7GB.\n\n## PixArtSigmaPipeline\n\n[[autodoc]] PixArtSigmaPipeline\n\t- all\n\t- __call__\n" }, { "path": "diffusers/docs/source/en/api/pipelines/self_attention_guidance.md", "content": "\n\n# Self-Attention Guidance\n\n[Improving Sample Quality of Diffusion Models Using Self-Attention Guidance](https://huggingface.co/papers/2210.00939) is by Susung Hong et al.\n\nThe abstract from the paper is:\n\n*Denoising diffusion models (DDMs) have attracted attention for their exceptional generation quality and diversity. This success is largely attributed to the use of class- or text-conditional diffusion guidance methods, such as classifier and classifier-free guidance. In this paper, we present a more comprehensive perspective that goes beyond the traditional guidance methods. From this generalized perspective, we introduce novel condition- and training-free strategies to enhance the quality of generated images. As a simple solution, blur guidance improves the suitability of intermediate samples for their fine-scale information and structures, enabling diffusion models to generate higher quality samples with a moderate guidance scale. Improving upon this, Self-Attention Guidance (SAG) uses the intermediate self-attention maps of diffusion models to enhance their stability and efficacy. Specifically, SAG adversarially blurs only the regions that diffusion models attend to at each iteration and guides them accordingly. Our experimental results show that our SAG improves the performance of various diffusion models, including ADM, IDDPM, Stable Diffusion, and DiT. Moreover, combining SAG with conventional guidance methods leads to further improvement.*\n\nYou can find additional information about Self-Attention Guidance on the [project page](https://ku-cvlab.github.io/Self-Attention-Guidance), [original codebase](https://github.com/KU-CVLAB/Self-Attention-Guidance), and try it out in a [demo](https://huggingface.co/spaces/susunghong/Self-Attention-Guidance) or [notebook](https://colab.research.google.com/github/SusungHong/Self-Attention-Guidance/blob/main/SAG_Stable.ipynb).\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## StableDiffusionSAGPipeline\n[[autodoc]] StableDiffusionSAGPipeline\n\t- __call__\n\t- all\n\n## StableDiffusionOutput\n[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/semantic_stable_diffusion.md", "content": "\n\n# Semantic Guidance\n\nSemantic Guidance for Diffusion Models was proposed in [SEGA: Instructing Text-to-Image Models using Semantic Guidance](https://huggingface.co/papers/2301.12247) and provides strong semantic control over image generation.\nSmall changes to the text prompt usually result in entirely different output images. However, with SEGA a variety of changes to the image are enabled that can be controlled easily and intuitively, while staying true to the original image composition.\n\nThe abstract from the paper is:\n\n*Text-to-image diffusion models have recently received a lot of interest for their astonishing ability to produce high-fidelity images from text only. However, achieving one-shot generation that aligns with the user's intent is nearly impossible, yet small changes to the input prompt often result in very different images. This leaves the user with little semantic control. To put the user in control, we show how to interact with the diffusion process to flexibly steer it along semantic directions. This semantic guidance (SEGA) generalizes to any generative architecture using classifier-free guidance. More importantly, it allows for subtle and extensive edits, changes in composition and style, as well as optimizing the overall artistic conception. We demonstrate SEGA's effectiveness on both latent and pixel-based diffusion models such as Stable Diffusion, Paella, and DeepFloyd-IF using a variety of tasks, thus providing strong evidence for its versatility, flexibility, and improvements over existing methods.*\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## SemanticStableDiffusionPipeline\n[[autodoc]] SemanticStableDiffusionPipeline\n\t- all\n\t- __call__\n\n## SemanticStableDiffusionPipelineOutput\n[[autodoc]] pipelines.semantic_stable_diffusion.pipeline_output.SemanticStableDiffusionPipelineOutput\n\t- all\n" }, { "path": "diffusers/docs/source/en/api/pipelines/shap_e.md", "content": "\n\n# Shap-E\n\nThe Shap-E model was proposed in [Shap-E: Generating Conditional 3D Implicit Functions](https://huggingface.co/papers/2305.02463) by Alex Nichol and Heewoo Jun from [OpenAI](https://github.com/openai).\n\nThe abstract from the paper is:\n\n*We present Shap-E, a conditional generative model for 3D assets. Unlike recent work on 3D generative models which produce a single output representation, Shap-E directly generates the parameters of implicit functions that can be rendered as both textured meshes and neural radiance fields. We train Shap-E in two stages: first, we train an encoder that deterministically maps 3D assets into the parameters of an implicit function; second, we train a conditional diffusion model on outputs of the encoder. When trained on a large dataset of paired 3D and text data, our resulting models are capable of generating complex and diverse 3D assets in a matter of seconds. When compared to Point-E, an explicit generative model over point clouds, Shap-E converges faster and reaches comparable or better sample quality despite modeling a higher-dimensional, multi-representation output space.*\n\nThe original codebase can be found at [openai/shap-e](https://github.com/openai/shap-e).\n\n\n\nSee the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## ShapEPipeline\n[[autodoc]] ShapEPipeline\n\t- all\n\t- __call__\n\n## ShapEImg2ImgPipeline\n[[autodoc]] ShapEImg2ImgPipeline\n\t- all\n\t- __call__\n\n## ShapEPipelineOutput\n[[autodoc]] pipelines.shap_e.pipeline_shap_e.ShapEPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/stable_cascade.md", "content": "\n\n# Stable Cascade\n\nThis model is built upon the [Würstchen](https://openreview.net/forum?id=gU58d5QeGv) architecture and its main\ndifference to other models like Stable Diffusion is that it is working at a much smaller latent space. Why is this\nimportant? The smaller the latent space, the **faster** you can run inference and the **cheaper** the training becomes.\nHow small is the latent space? Stable Diffusion uses a compression factor of 8, resulting in a 1024x1024 image being\nencoded to 128x128. Stable Cascade achieves a compression factor of 42, meaning that it is possible to encode a\n1024x1024 image to 24x24, while maintaining crisp reconstructions. The text-conditional model is then trained in the\nhighly compressed latent space. Previous versions of this architecture, achieved a 16x cost reduction over Stable\nDiffusion 1.5.\n\nTherefore, this kind of model is well suited for usages where efficiency is important. Furthermore, all known extensions\nlike finetuning, LoRA, ControlNet, IP-Adapter, LCM etc. are possible with this method as well.\n\nThe original codebase can be found at [Stability-AI/StableCascade](https://github.com/Stability-AI/StableCascade).\n\n## Model Overview\nStable Cascade consists of three models: Stage A, Stage B and Stage C, representing a cascade to generate images,\nhence the name \"Stable Cascade\".\n\nStage A & B are used to compress images, similar to what the job of the VAE is in Stable Diffusion.\nHowever, with this setup, a much higher compression of images can be achieved. While the Stable Diffusion models use a\nspatial compression factor of 8, encoding an image with resolution of 1024 x 1024 to 128 x 128, Stable Cascade achieves\na compression factor of 42. This encodes a 1024 x 1024 image to 24 x 24, while being able to accurately decode the\nimage. This comes with the great benefit of cheaper training and inference. Furthermore, Stage C is responsible\nfor generating the small 24 x 24 latents given a text prompt.\n\nThe Stage C model operates on the small 24 x 24 latents and denoises the latents conditioned on text prompts. The model is also the largest component in the Cascade pipeline and is meant to be used with the `StableCascadePriorPipeline`\n\nThe Stage B and Stage A models are used with the `StableCascadeDecoderPipeline` and are responsible for generating the final image given the small 24 x 24 latents.\n\n\n\nThere are some restrictions on data types that can be used with the Stable Cascade models. The official checkpoints for the `StableCascadePriorPipeline` do not support the `torch.float16` data type. Please use `torch.bfloat16` instead.\n\nIn order to use the `torch.bfloat16` data type with the `StableCascadeDecoderPipeline` you need to have PyTorch 2.2.0 or higher installed. This also means that using the `StableCascadeCombinedPipeline` with `torch.bfloat16` requires PyTorch 2.2.0 or higher, since it calls the `StableCascadeDecoderPipeline` internally.\n\nIf it is not possible to install PyTorch 2.2.0 or higher in your environment, the `StableCascadeDecoderPipeline` can be used on its own with the `torch.float16` data type. You can download the full precision or `bf16` variant weights for the pipeline and cast the weights to `torch.float16`.\n\n\n\n## Usage example\n\n```python\nimport torch\nfrom diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline\n\nprompt = \"an image of a shiba inu, donning a spacesuit and helmet\"\nnegative_prompt = \"\"\n\nprior = StableCascadePriorPipeline.from_pretrained(\"stabilityai/stable-cascade-prior\", variant=\"bf16\", torch_dtype=torch.bfloat16)\ndecoder = StableCascadeDecoderPipeline.from_pretrained(\"stabilityai/stable-cascade\", variant=\"bf16\", torch_dtype=torch.float16)\n\nprior.enable_model_cpu_offload()\nprior_output = prior(\n prompt=prompt,\n height=1024,\n width=1024,\n negative_prompt=negative_prompt,\n guidance_scale=4.0,\n num_images_per_prompt=1,\n num_inference_steps=20\n)\n\ndecoder.enable_model_cpu_offload()\ndecoder_output = decoder(\n image_embeddings=prior_output.image_embeddings.to(torch.float16),\n prompt=prompt,\n negative_prompt=negative_prompt,\n guidance_scale=0.0,\n output_type=\"pil\",\n num_inference_steps=10\n).images[0]\ndecoder_output.save(\"cascade.png\")\n```\n\n## Using the Lite Versions of the Stage B and Stage C models\n\n```python\nimport torch\nfrom diffusers import (\n StableCascadeDecoderPipeline,\n StableCascadePriorPipeline,\n StableCascadeUNet,\n)\n\nprompt = \"an image of a shiba inu, donning a spacesuit and helmet\"\nnegative_prompt = \"\"\n\nprior_unet = StableCascadeUNet.from_pretrained(\"stabilityai/stable-cascade-prior\", subfolder=\"prior_lite\")\ndecoder_unet = StableCascadeUNet.from_pretrained(\"stabilityai/stable-cascade\", subfolder=\"decoder_lite\")\n\nprior = StableCascadePriorPipeline.from_pretrained(\"stabilityai/stable-cascade-prior\", prior=prior_unet)\ndecoder = StableCascadeDecoderPipeline.from_pretrained(\"stabilityai/stable-cascade\", decoder=decoder_unet)\n\nprior.enable_model_cpu_offload()\nprior_output = prior(\n prompt=prompt,\n height=1024,\n width=1024,\n negative_prompt=negative_prompt,\n guidance_scale=4.0,\n num_images_per_prompt=1,\n num_inference_steps=20\n)\n\ndecoder.enable_model_cpu_offload()\ndecoder_output = decoder(\n image_embeddings=prior_output.image_embeddings,\n prompt=prompt,\n negative_prompt=negative_prompt,\n guidance_scale=0.0,\n output_type=\"pil\",\n num_inference_steps=10\n).images[0]\ndecoder_output.save(\"cascade.png\")\n```\n\n## Loading original checkpoints with `from_single_file`\n\nLoading the original format checkpoints is supported via `from_single_file` method in the StableCascadeUNet.\n\n```python\nimport torch\nfrom diffusers import (\n StableCascadeDecoderPipeline,\n StableCascadePriorPipeline,\n StableCascadeUNet,\n)\n\nprompt = \"an image of a shiba inu, donning a spacesuit and helmet\"\nnegative_prompt = \"\"\n\nprior_unet = StableCascadeUNet.from_single_file(\n \"https://huggingface.co/stabilityai/stable-cascade/resolve/main/stage_c_bf16.safetensors\",\n torch_dtype=torch.bfloat16\n)\ndecoder_unet = StableCascadeUNet.from_single_file(\n \"https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b_bf16.safetensors\",\n torch_dtype=torch.bfloat16\n)\n\nprior = StableCascadePriorPipeline.from_pretrained(\"stabilityai/stable-cascade-prior\", prior=prior_unet, torch_dtype=torch.bfloat16)\ndecoder = StableCascadeDecoderPipeline.from_pretrained(\"stabilityai/stable-cascade\", decoder=decoder_unet, torch_dtype=torch.bfloat16)\n\nprior.enable_model_cpu_offload()\nprior_output = prior(\n prompt=prompt,\n height=1024,\n width=1024,\n negative_prompt=negative_prompt,\n guidance_scale=4.0,\n num_images_per_prompt=1,\n num_inference_steps=20\n)\n\ndecoder.enable_model_cpu_offload()\ndecoder_output = decoder(\n image_embeddings=prior_output.image_embeddings,\n prompt=prompt,\n negative_prompt=negative_prompt,\n guidance_scale=0.0,\n output_type=\"pil\",\n num_inference_steps=10\n).images[0]\ndecoder_output.save(\"cascade-single-file.png\")\n```\n\n## Uses\n\n### Direct Use\n\nThe model is intended for research purposes for now. Possible research areas and tasks include\n\n- Research on generative models.\n- Safe deployment of models which have the potential to generate harmful content.\n- Probing and understanding the limitations and biases of generative models.\n- Generation of artworks and use in design and other artistic processes.\n- Applications in educational or creative tools.\n\nExcluded uses are described below.\n\n### Out-of-Scope Use\n\nThe model was not trained to be factual or true representations of people or events,\nand therefore using the model to generate such content is out-of-scope for the abilities of this model.\nThe model should not be used in any way that violates Stability AI's [Acceptable Use Policy](https://stability.ai/use-policy).\n\n## Limitations and Bias\n\n### Limitations\n- Faces and people in general may not be generated properly.\n- The autoencoding part of the model is lossy.\n\n\n## StableCascadeCombinedPipeline\n\n[[autodoc]] StableCascadeCombinedPipeline\n\t- all\n\t- __call__\n\n## StableCascadePriorPipeline\n\n[[autodoc]] StableCascadePriorPipeline\n\t- all\n\t- __call__\n\n## StableCascadePriorPipelineOutput\n\n[[autodoc]] pipelines.stable_cascade.pipeline_stable_cascade_prior.StableCascadePriorPipelineOutput\n\n## StableCascadeDecoderPipeline\n\n[[autodoc]] StableCascadeDecoderPipeline\n\t- all\n\t- __call__\n\n" }, { "path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/adapter.md", "content": "\n\n# T2I-Adapter\n\n[T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.08453) by Chong Mou, Xintao Wang, Liangbin Xie, Jian Zhang, Zhongang Qi, Ying Shan, Xiaohu Qie.\n\nUsing the pretrained models we can provide control images (for example, a depth map) to control Stable Diffusion text-to-image generation so that it follows the structure of the depth image and fills in the details.\n\nThe abstract of the paper is the following:\n\n*The incredible generative ability of large-scale text-to-image (T2I) models has demonstrated strong power of learning complex structures and meaningful semantics. However, relying solely on text prompts cannot fully take advantage of the knowledge learned by the model, especially when flexible and accurate controlling (e.g., color and structure) is needed. In this paper, we aim to ``dig out\" the capabilities that T2I models have implicitly learned, and then explicitly use them to control the generation more granularly. Specifically, we propose to learn simple and lightweight T2I-Adapters to align internal knowledge in T2I models with external control signals, while freezing the original large T2I models. In this way, we can train various adapters according to different conditions, achieving rich control and editing effects in the color and structure of the generation results. Further, the proposed T2I-Adapters have attractive properties of practical value, such as composability and generalization ability. Extensive experiments demonstrate that our T2I-Adapter has promising generation quality and a wide range of applications.*\n\nThis model was contributed by the community contributor [HimariO](https://github.com/HimariO) ❤️ .\n\n## StableDiffusionAdapterPipeline\n\n[[autodoc]] StableDiffusionAdapterPipeline\n - all\n - __call__\n - enable_attention_slicing\n - disable_attention_slicing\n - enable_vae_slicing\n - disable_vae_slicing\n - enable_xformers_memory_efficient_attention\n - disable_xformers_memory_efficient_attention\n\n## StableDiffusionXLAdapterPipeline\n\n[[autodoc]] StableDiffusionXLAdapterPipeline\n - all\n - __call__\n - enable_attention_slicing\n - disable_attention_slicing\n - enable_vae_slicing\n - disable_vae_slicing\n - enable_xformers_memory_efficient_attention\n - disable_xformers_memory_efficient_attention\n" }, { "path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/depth2img.md", "content": "\n\n# Depth-to-image\n\nThe Stable Diffusion model can also infer depth based on an image using [MiDaS](https://github.com/isl-org/MiDaS). This allows you to pass a text prompt and an initial image to condition the generation of new images as well as a `depth_map` to preserve the image structure.\n\n\n\nMake sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!\n\nIf you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!\n\n\n\n## StableDiffusionDepth2ImgPipeline\n\n[[autodoc]] StableDiffusionDepth2ImgPipeline\n\t- all\n\t- __call__\n\t- enable_attention_slicing\n\t- disable_attention_slicing\n\t- enable_xformers_memory_efficient_attention\n\t- disable_xformers_memory_efficient_attention\n\t- load_textual_inversion\n\t- load_lora_weights\n\t- save_lora_weights\n\n## StableDiffusionPipelineOutput\n\n[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/gligen.md", "content": "\n\n# GLIGEN (Grounded Language-to-Image Generation)\n\nThe GLIGEN model was created by researchers and engineers from [University of Wisconsin-Madison, Columbia University, and Microsoft](https://github.com/gligen/GLIGEN). The [`StableDiffusionGLIGENPipeline`] and [`StableDiffusionGLIGENTextImagePipeline`] can generate photorealistic images conditioned on grounding inputs. Along with text and bounding boxes with [`StableDiffusionGLIGENPipeline`], if input images are given, [`StableDiffusionGLIGENTextImagePipeline`] can insert objects described by text at the region defined by bounding boxes. Otherwise, it'll generate an image described by the caption/prompt and insert objects described by text at the region defined by bounding boxes. It's trained on COCO2014D and COCO2014CD datasets, and the model uses a frozen CLIP ViT-L/14 text encoder to condition itself on grounding inputs.\n\nThe abstract from the [paper](https://huggingface.co/papers/2301.07093) is:\n\n*Large-scale text-to-image diffusion models have made amazing advances. However, the status quo is to use text input alone, which can impede controllability. In this work, we propose GLIGEN, Grounded-Language-to-Image Generation, a novel approach that builds upon and extends the functionality of existing pre-trained text-to-image diffusion models by enabling them to also be conditioned on grounding inputs. To preserve the vast concept knowledge of the pre-trained model, we freeze all of its weights and inject the grounding information into new trainable layers via a gated mechanism. Our model achieves open-world grounded text2img generation with caption and bounding box condition inputs, and the grounding ability generalizes well to novel spatial configurations and concepts. GLIGEN’s zeroshot performance on COCO and LVIS outperforms existing supervised layout-to-image baselines by a large margin.*\n\n\n\nMake sure to check out the Stable Diffusion [Tips](https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality and how to reuse pipeline components efficiently!\n\nIf you want to use one of the official checkpoints for a task, explore the [gligen](https://huggingface.co/gligen) Hub organizations!\n\n\n\n[`StableDiffusionGLIGENPipeline`] was contributed by [Nikhil Gajendrakumar](https://github.com/nikhil-masterful) and [`StableDiffusionGLIGENTextImagePipeline`] was contributed by [Nguyễn Công Tú Anh](https://github.com/tuanh123789).\n\n## StableDiffusionGLIGENPipeline\n\n[[autodoc]] StableDiffusionGLIGENPipeline\n\t- all\n\t- __call__\n\t- enable_vae_slicing\n\t- disable_vae_slicing\n\t- enable_vae_tiling\n\t- disable_vae_tiling\n\t- enable_model_cpu_offload\n\t- prepare_latents\n\t- enable_fuser\n\n## StableDiffusionGLIGENTextImagePipeline\n\n[[autodoc]] StableDiffusionGLIGENTextImagePipeline\n\t- all\n\t- __call__\n\t- enable_vae_slicing\n\t- disable_vae_slicing\n\t- enable_vae_tiling\n\t- disable_vae_tiling\n\t- enable_model_cpu_offload\n\t- prepare_latents\n\t- enable_fuser\n\n## StableDiffusionPipelineOutput\n\n[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/image_variation.md", "content": "\n\n# Image variation\n\nThe Stable Diffusion model can also generate variations from an input image. It uses a fine-tuned version of a Stable Diffusion model by [Justin Pinkney](https://www.justinpinkney.com/) from [Lambda](https://lambdalabs.com/).\n\nThe original codebase can be found at [LambdaLabsML/lambda-diffusers](https://github.com/LambdaLabsML/lambda-diffusers#stable-diffusion-image-variations) and additional official checkpoints for image variation can be found at [lambdalabs/sd-image-variations-diffusers](https://huggingface.co/lambdalabs/sd-image-variations-diffusers).\n\n\n\nMake sure to check out the Stable Diffusion [Tips](./overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!\n\n\n\n## StableDiffusionImageVariationPipeline\n\n[[autodoc]] StableDiffusionImageVariationPipeline\n\t- all\n\t- __call__\n\t- enable_attention_slicing\n\t- disable_attention_slicing\n\t- enable_xformers_memory_efficient_attention\n\t- disable_xformers_memory_efficient_attention\n\n## StableDiffusionPipelineOutput\n\n[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/img2img.md", "content": "\n\n# Image-to-image\n\nThe Stable Diffusion model can also be applied to image-to-image generation by passing a text prompt and an initial image to condition the generation of new images.\n\nThe [`StableDiffusionImg2ImgPipeline`] uses the diffusion-denoising mechanism proposed in [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://huggingface.co/papers/2108.01073) by Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon.\n\nThe abstract from the paper is:\n\n*Guided image synthesis enables everyday users to create and edit photo-realistic images with minimum effort. The key challenge is balancing faithfulness to the user input (e.g., hand-drawn colored strokes) and realism of the synthesized image. Existing GAN-based methods attempt to achieve such balance using either conditional GANs or GAN inversions, which are challenging and often require additional training data or loss functions for individual applications. To address these issues, we introduce a new image synthesis and editing method, Stochastic Differential Editing (SDEdit), based on a diffusion model generative prior, which synthesizes realistic images by iteratively denoising through a stochastic differential equation (SDE). Given an input image with user guide of any type, SDEdit first adds noise to the input, then subsequently denoises the resulting image through the SDE prior to increase its realism. SDEdit does not require task-specific training or inversions and can naturally achieve the balance between realism and faithfulness. SDEdit significantly outperforms state-of-the-art GAN-based methods by up to 98.09% on realism and 91.72% on overall satisfaction scores, according to a human perception study, on multiple tasks, including stroke-based image synthesis and editing as well as image compositing.*\n\n\n\nMake sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!\n\n\n\n## StableDiffusionImg2ImgPipeline\n\n[[autodoc]] StableDiffusionImg2ImgPipeline\n\t- all\n\t- __call__\n\t- enable_attention_slicing\n\t- disable_attention_slicing\n\t- enable_xformers_memory_efficient_attention\n\t- disable_xformers_memory_efficient_attention\n\t- load_textual_inversion\n\t- from_single_file\n\t- load_lora_weights\n\t- save_lora_weights\n\n## StableDiffusionPipelineOutput\n\n[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput\n\n## FlaxStableDiffusionImg2ImgPipeline\n\n[[autodoc]] FlaxStableDiffusionImg2ImgPipeline\n\t- all\n\t- __call__\n\n## FlaxStableDiffusionPipelineOutput\n\n[[autodoc]] pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/inpaint.md", "content": "\n\n# Inpainting\n\nThe Stable Diffusion model can also be applied to inpainting which lets you edit specific parts of an image by providing a mask and a text prompt using Stable Diffusion.\n\n## Tips\n\nIt is recommended to use this pipeline with checkpoints that have been specifically fine-tuned for inpainting, such\nas [runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting). Default\ntext-to-image Stable Diffusion checkpoints, such as\n[runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) are also compatible but they might be less performant.\n\n\n\nMake sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!\n\nIf you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!\n\n\n\n## StableDiffusionInpaintPipeline\n\n[[autodoc]] StableDiffusionInpaintPipeline\n\t- all\n\t- __call__\n\t- enable_attention_slicing\n\t- disable_attention_slicing\n\t- enable_xformers_memory_efficient_attention\n\t- disable_xformers_memory_efficient_attention\n\t- load_textual_inversion\n\t- load_lora_weights\n\t- save_lora_weights\n\n## StableDiffusionPipelineOutput\n\n[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput\n\n## FlaxStableDiffusionInpaintPipeline\n\n[[autodoc]] FlaxStableDiffusionInpaintPipeline\n\t- all\n\t- __call__\n\n## FlaxStableDiffusionPipelineOutput\n\n[[autodoc]] pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/k_diffusion.md", "content": "\n\n# K-Diffusion\n\n[k-diffusion](https://github.com/crowsonkb/k-diffusion) is a popular library created by [Katherine Crowson](https://github.com/crowsonkb/). We provide `StableDiffusionKDiffusionPipeline` and `StableDiffusionXLKDiffusionPipeline` that allow you to run Stable DIffusion with samplers from k-diffusion. \n\nNote that most the samplers from k-diffusion are implemented in Diffusers and we recommend using existing schedulers. You can find a mapping between k-diffusion samplers and schedulers in Diffusers [here](https://huggingface.co/docs/diffusers/api/schedulers/overview)\n\n\n## StableDiffusionKDiffusionPipeline\n\n[[autodoc]] StableDiffusionKDiffusionPipeline\n\n\n## StableDiffusionXLKDiffusionPipeline\n\n[[autodoc]] StableDiffusionXLKDiffusionPipeline" }, { "path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/latent_upscale.md", "content": "\n\n# Latent upscaler\n\nThe Stable Diffusion latent upscaler model was created by [Katherine Crowson](https://github.com/crowsonkb/k-diffusion) in collaboration with [Stability AI](https://stability.ai/). It is used to enhance the output image resolution by a factor of 2 (see this demo [notebook](https://colab.research.google.com/drive/1o1qYJcFeywzCIdkfKJy7cTpgZTCM2EI4) for a demonstration of the original implementation).\n\n\n\nMake sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!\n\nIf you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!\n\n\n\n## StableDiffusionLatentUpscalePipeline\n\n[[autodoc]] StableDiffusionLatentUpscalePipeline\n\t- all\n\t- __call__\n\t- enable_sequential_cpu_offload\n\t- enable_attention_slicing\n\t- disable_attention_slicing\n\t- enable_xformers_memory_efficient_attention\n\t- disable_xformers_memory_efficient_attention\n\n## StableDiffusionPipelineOutput\n\n[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/ldm3d_diffusion.md", "content": "\n\n# Text-to-(RGB, depth)\n\nLDM3D was proposed in [LDM3D: Latent Diffusion Model for 3D](https://huggingface.co/papers/2305.10853) by Gabriela Ben Melech Stan, Diana Wofk, Scottie Fox, Alex Redden, Will Saxton, Jean Yu, Estelle Aflalo, Shao-Yen Tseng, Fabio Nonato, Matthias Muller, and Vasudev Lal. LDM3D generates an image and a depth map from a given text prompt unlike the existing text-to-image diffusion models such as [Stable Diffusion](./overview) which only generates an image. With almost the same number of parameters, LDM3D achieves to create a latent space that can compress both the RGB images and the depth maps. \n\nTwo checkpoints are available for use:\n- [ldm3d-original](https://huggingface.co/Intel/ldm3d). The original checkpoint used in the [paper](https://arxiv.org/pdf/2305.10853.pdf)\n- [ldm3d-4c](https://huggingface.co/Intel/ldm3d-4c). The new version of LDM3D using 4 channels inputs instead of 6-channels inputs and finetuned on higher resolution images. \n\n\nThe abstract from the paper is:\n\n*This research paper proposes a Latent Diffusion Model for 3D (LDM3D) that generates both image and depth map data from a given text prompt, allowing users to generate RGBD images from text prompts. The LDM3D model is fine-tuned on a dataset of tuples containing an RGB image, depth map and caption, and validated through extensive experiments. We also develop an application called DepthFusion, which uses the generated RGB images and depth maps to create immersive and interactive 360-degree-view experiences using TouchDesigner. This technology has the potential to transform a wide range of industries, from entertainment and gaming to architecture and design. Overall, this paper presents a significant contribution to the field of generative AI and computer vision, and showcases the potential of LDM3D and DepthFusion to revolutionize content creation and digital experiences. A short video summarizing the approach can be found at [this url](https://t.ly/tdi2).*\n\n\n\nMake sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!\n\n\n\n## StableDiffusionLDM3DPipeline\n\n[[autodoc]] pipelines.stable_diffusion_ldm3d.pipeline_stable_diffusion_ldm3d.StableDiffusionLDM3DPipeline\n\t- all\n\t- __call__\n\n\n## LDM3DPipelineOutput\n\n[[autodoc]] pipelines.stable_diffusion_ldm3d.pipeline_stable_diffusion_ldm3d.LDM3DPipelineOutput\n\t- all\n\t- __call__\n\n# Upscaler\n\n[LDM3D-VR](https://arxiv.org/pdf/2311.03226.pdf) is an extended version of LDM3D. \n\nThe abstract from the paper is:\n*Latent diffusion models have proven to be state-of-the-art in the creation and manipulation of visual outputs. However, as far as we know, the generation of depth maps jointly with RGB is still limited. We introduce LDM3D-VR, a suite of diffusion models targeting virtual reality development that includes LDM3D-pano and LDM3D-SR. These models enable the generation of panoramic RGBD based on textual prompts and the upscaling of low-resolution inputs to high-resolution RGBD, respectively. Our models are fine-tuned from existing pretrained models on datasets containing panoramic/high-resolution RGB images, depth maps and captions. Both models are evaluated in comparison to existing related methods*\n\nTwo checkpoints are available for use:\n- [ldm3d-pano](https://huggingface.co/Intel/ldm3d-pano). This checkpoint enables the generation of panoramic images and requires the StableDiffusionLDM3DPipeline pipeline to be used.\n- [ldm3d-sr](https://huggingface.co/Intel/ldm3d-sr). This checkpoint enables the upscaling of RGB and depth images. Can be used in cascade after the original LDM3D pipeline using the StableDiffusionUpscaleLDM3DPipeline from communauty pipeline.\n\n" }, { "path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/overview.md", "content": "\n\n# Stable Diffusion pipelines\n\nStable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/) and [LAION](https://laion.ai/). Latent diffusion applies the diffusion process over a lower dimensional latent space to reduce memory and compute complexity. This specific type of diffusion model was proposed in [High-Resolution Image Synthesis with Latent Diffusion Models](https://huggingface.co/papers/2112.10752) by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer.\n\nStable Diffusion is trained on 512x512 images from a subset of the LAION-5B dataset. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and can run on consumer GPUs.\n\nFor more details about how Stable Diffusion works and how it differs from the base latent diffusion model, take a look at the Stability AI [announcement](https://stability.ai/blog/stable-diffusion-announcement) and our own [blog post](https://huggingface.co/blog/stable_diffusion#how-does-stable-diffusion-work) for more technical details.\n\nYou can find the original codebase for Stable Diffusion v1.0 at [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion) and Stable Diffusion v2.0 at [Stability-AI/stablediffusion](https://github.com/Stability-AI/stablediffusion) as well as their original scripts for various tasks. Additional official checkpoints for the different Stable Diffusion versions and tasks can be found on the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations. Explore these organizations to find the best checkpoint for your use-case!\n\nThe table below summarizes the available Stable Diffusion pipelines, their supported tasks, and an interactive demo:\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n Pipeline\n \n Supported tasks\n \n 🤗 Space\n
\n StableDiffusion\n text-to-image\n
\n StableDiffusionImg2Img\n image-to-image\n
\n StableDiffusionInpaint\n inpainting\n
\n StableDiffusionDepth2Img\n depth-to-image\n
\n StableDiffusionImageVariation\n image variation\n
\n StableDiffusionPipelineSafe\n filtered text-to-image\n
\n StableDiffusion2\n text-to-image, inpainting, depth-to-image, super-resolution\n
\n StableDiffusionXL\n text-to-image, image-to-image\n
\n StableDiffusionLatentUpscale\n super-resolution\n
\n StableDiffusionUpscale\n super-resolution
\n StableDiffusionLDM3D\n text-to-rgb, text-to-depth, text-to-pano\n
\n StableDiffusionUpscaleLDM3D\n ldm3d super-resolution
\n
\n
\n\n## Tips\n\nTo help you get the most out of the Stable Diffusion pipelines, here are a few tips for improving performance and usability. These tips are applicable to all Stable Diffusion pipelines.\n\n### Explore tradeoff between speed and quality\n\n[`StableDiffusionPipeline`] uses the [`PNDMScheduler`] by default, but 🤗 Diffusers provides many other schedulers (some of which are faster or output better quality) that are compatible. For example, if you want to use the [`EulerDiscreteScheduler`] instead of the default:\n\n```py\nfrom diffusers import StableDiffusionPipeline, EulerDiscreteScheduler\n\npipeline = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\")\npipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)\n\n# or\neuler_scheduler = EulerDiscreteScheduler.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"scheduler\")\npipeline = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", scheduler=euler_scheduler)\n```\n\n### Reuse pipeline components to save memory\n\nTo save memory and use the same components across multiple pipelines, use the `.components` method to avoid loading weights into RAM more than once.\n\n```py\nfrom diffusers import (\n StableDiffusionPipeline,\n StableDiffusionImg2ImgPipeline,\n StableDiffusionInpaintPipeline,\n)\n\ntext2img = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\")\nimg2img = StableDiffusionImg2ImgPipeline(**text2img.components)\ninpaint = StableDiffusionInpaintPipeline(**text2img.components)\n\n# now you can use text2img(...), img2img(...), inpaint(...) just like the call methods of each respective pipeline\n```\n\n### Create web demos using `gradio`\n\nThe Stable Diffusion pipelines are automatically supported in [Gradio](https://github.com/gradio-app/gradio/), a library that makes creating beautiful and user-friendly machine learning apps on the web a breeze. First, make sure you have Gradio installed:\n\n```sh\npip install -U gradio\n```\n\nThen, create a web demo around any Stable Diffusion-based pipeline. For example, you can create an image generation pipeline in a single line of code with Gradio's [`Interface.from_pipeline`](https://www.gradio.app/docs/interface#interface-from-pipeline) function:\n\n```py\nfrom diffusers import StableDiffusionPipeline\nimport gradio as gr\n\npipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\")\n\ngr.Interface.from_pipeline(pipe).launch()\n```\n\nwhich opens an intuitive drag-and-drop interface in your browser:\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/gradio-panda.png)\n\nSimilarly, you could create a demo for an image-to-image pipeline with:\n\n```py\nfrom diffusers import StableDiffusionImg2ImgPipeline\nimport gradio as gr\n\n\npipe = StableDiffusionImg2ImgPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n\ngr.Interface.from_pipeline(pipe).launch()\n```\n\nBy default, the web demo runs on a local server. If you'd like to share it with others, you can generate a temporary public\nlink by setting `share=True` in `launch()`. Or, you can host your demo on [Hugging Face Spaces](https://huggingface.co/spaces)https://huggingface.co/spaces for a permanent link." }, { "path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/sdxl_turbo.md", "content": "\n\n# SDXL Turbo\n\nStable Diffusion XL (SDXL) Turbo was proposed in [Adversarial Diffusion Distillation](https://stability.ai/research/adversarial-diffusion-distillation) by Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach.\n\nThe abstract from the paper is:\n\n*We introduce Adversarial Diffusion Distillation (ADD), a novel training approach that efficiently samples large-scale foundational image diffusion models in just 1–4 steps while maintaining high image quality. We use score distillation to leverage large-scale off-the-shelf image diffusion models as a teacher signal in combination with an adversarial loss to ensure high image fidelity even in the low-step regime of one or two sampling steps. Our analyses show that our model clearly outperforms existing few-step methods (GANs,Latent Consistency Models) in a single step and reaches the performance of state-of-the-art diffusion models (SDXL) in only four steps. ADD is the first method to unlock single-step, real-time image synthesis with foundation models.*\n\n## Tips\n\n- SDXL Turbo uses the exact same architecture as [SDXL](./stable_diffusion_xl), which means it also has the same API. Please refer to the [SDXL](./stable_diffusion_xl) API reference for more details.\n- SDXL Turbo should disable guidance scale by setting `guidance_scale=0.0`.\n- SDXL Turbo should use `timestep_spacing='trailing'` for the scheduler and use between 1 and 4 steps.\n- SDXL Turbo has been trained to generate images of size 512x512.\n- SDXL Turbo is open-access, but not open-source meaning that one might have to buy a model license in order to use it for commercial applications. Make sure to read the [official model card](https://huggingface.co/stabilityai/sdxl-turbo) to learn more.\n\n\n\nTo learn how to use SDXL Turbo for various tasks, how to optimize performance, and other usage examples, take a look at the [SDXL Turbo](../../../using-diffusers/sdxl_turbo) guide.\n\nCheck out the [Stability AI](https://huggingface.co/stabilityai) Hub organization for the official base and refiner model checkpoints!\n\n\n" }, { "path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_2.md", "content": "\n\n# Stable Diffusion 2\n\nStable Diffusion 2 is a text-to-image _latent diffusion_ model built upon the work of the original [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release), and it was led by Robin Rombach and Katherine Crowson from [Stability AI](https://stability.ai/) and [LAION](https://laion.ai/).\n\n*The Stable Diffusion 2.0 release includes robust text-to-image models trained using a brand new text encoder (OpenCLIP), developed by LAION with support from Stability AI, which greatly improves the quality of the generated images compared to earlier V1 releases. The text-to-image models in this release can generate images with default resolutions of both 512x512 pixels and 768x768 pixels.\nThese models are trained on an aesthetic subset of the [LAION-5B dataset](https://laion.ai/blog/laion-5b/) created by the DeepFloyd team at Stability AI, which is then further filtered to remove adult content using [LAION’s NSFW filter](https://openreview.net/forum?id=M3Y74vmsMcY).*\n\nFor more details about how Stable Diffusion 2 works and how it differs from the original Stable Diffusion, please refer to the official [announcement post](https://stability.ai/blog/stable-diffusion-v2-release).\n\nThe architecture of Stable Diffusion 2 is more or less identical to the original [Stable Diffusion model](./text2img) so check out it's API documentation for how to use Stable Diffusion 2. We recommend using the [`DPMSolverMultistepScheduler`] as it gives a reasonable speed/quality trade-off and can be run with as little as 20 steps.\n\nStable Diffusion 2 is available for tasks like text-to-image, inpainting, super-resolution, and depth-to-image:\n\n| Task | Repository |\n|-------------------------|---------------------------------------------------------------------------------------------------------------|\n| text-to-image (512x512) | [stabilityai/stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base) |\n| text-to-image (768x768) | [stabilityai/stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) |\n| inpainting | [stabilityai/stable-diffusion-2-inpainting](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting) |\n| super-resolution | [stable-diffusion-x4-upscaler](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler) |\n| depth-to-image | [stabilityai/stable-diffusion-2-depth](https://huggingface.co/stabilityai/stable-diffusion-2-depth) |\n\nHere are some examples for how to use Stable Diffusion 2 for each task:\n\n\n\nMake sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!\n\nIf you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!\n\n\n\n## Text-to-image\n\n```py\nfrom diffusers import DiffusionPipeline, DPMSolverMultistepScheduler\nimport torch\n\nrepo_id = \"stabilityai/stable-diffusion-2-base\"\npipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision=\"fp16\")\n\npipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\npipe = pipe.to(\"cuda\")\n\nprompt = \"High quality photo of an astronaut riding a horse in space\"\nimage = pipe(prompt, num_inference_steps=25).images[0]\nimage\n```\n\n## Inpainting\n\n```py\nimport torch\nfrom diffusers import DiffusionPipeline, DPMSolverMultistepScheduler\nfrom diffusers.utils import load_image, make_image_grid\n\nimg_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\nmask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\n\ninit_image = load_image(img_url).resize((512, 512))\nmask_image = load_image(mask_url).resize((512, 512))\n\nrepo_id = \"stabilityai/stable-diffusion-2-inpainting\"\npipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision=\"fp16\")\n\npipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\npipe = pipe.to(\"cuda\")\n\nprompt = \"Face of a yellow cat, high resolution, sitting on a park bench\"\nimage = pipe(prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=25).images[0]\nmake_image_grid([init_image, mask_image, image], rows=1, cols=3)\n```\n\n## Super-resolution\n\n```py\nfrom diffusers import StableDiffusionUpscalePipeline\nfrom diffusers.utils import load_image, make_image_grid\nimport torch\n\n# load model and scheduler\nmodel_id = \"stabilityai/stable-diffusion-x4-upscaler\"\npipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)\npipeline = pipeline.to(\"cuda\")\n\n# let's download an image\nurl = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png\"\nlow_res_img = load_image(url)\nlow_res_img = low_res_img.resize((128, 128))\nprompt = \"a white cat\"\nupscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]\nmake_image_grid([low_res_img.resize((512, 512)), upscaled_image.resize((512, 512))], rows=1, cols=2)\n```\n\n## Depth-to-image\n\n```py\nimport torch\nfrom diffusers import StableDiffusionDepth2ImgPipeline\nfrom diffusers.utils import load_image, make_image_grid\n\npipe = StableDiffusionDepth2ImgPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-depth\",\n torch_dtype=torch.float16,\n).to(\"cuda\")\n\n\nurl = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\ninit_image = load_image(url)\nprompt = \"two tigers\"\nnegative_prompt = \"bad, deformed, ugly, bad anotomy\"\nimage = pipe(prompt=prompt, image=init_image, negative_prompt=negative_prompt, strength=0.7).images[0]\nmake_image_grid([init_image, image], rows=1, cols=2)\n```\n" }, { "path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_3.md", "content": "\n\n# Stable Diffusion 3\n\nStable Diffusion 3 (SD3) was proposed in [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://arxiv.org/pdf/2403.03206.pdf) by Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Muller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, Kyle Lacey, Alex Goodwin, Yannik Marek, and Robin Rombach.\n\nThe abstract from the paper is:\n\n*Diffusion models create data from noise by inverting the forward paths of data towards noise and have emerged as a powerful generative modeling technique for high-dimensional, perceptual data such as images and videos. Rectified flow is a recent generative model formulation that connects data and noise in a straight line. Despite its better theoretical properties and conceptual simplicity, it is not yet decisively established as standard practice. In this work, we improve existing noise sampling techniques for training rectified flow models by biasing them towards perceptually relevant scales. Through a large-scale study, we demonstrate the superior performance of this approach compared to established diffusion formulations for high-resolution text-to-image synthesis. Additionally, we present a novel transformer-based architecture for text-to-image generation that uses separate weights for the two modalities and enables a bidirectional flow of information between image and text tokens, improving text comprehension typography, and human preference ratings. We demonstrate that this architecture follows predictable scaling trends and correlates lower validation loss to improved text-to-image synthesis as measured by various metrics and human evaluations.*\n\n\n## Usage Example\n\n_As the model is gated, before using it with diffusers you first need to go to the [Stable Diffusion 3 Medium Hugging Face page](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers), fill in the form and accept the gate. Once you are in, you need to login so that your system knows you’ve accepted the gate._\n\nUse the command below to log in:\n\n```bash\nhuggingface-cli login\n```\n\n\n\nThe SD3 pipeline uses three text encoders to generate an image. Model offloading is necessary in order for it to run on most commodity hardware. Please use the `torch.float16` data type for additional memory savings.\n\n\n\n\n```python\nimport torch\nfrom diffusers import StableDiffusion3Pipeline\n\npipe = StableDiffusion3Pipeline.from_pretrained(\"stabilityai/stable-diffusion-3-medium-diffusers\", torch_dtype=torch.float16)\npipe.to(\"cuda\")\n\nimage = pipe(\n prompt=\"a photo of a cat holding a sign that says hello world\",\n negative_prompt=\"\",\n num_inference_steps=28,\n height=1024,\n width=1024,\n guidance_scale=7.0,\n).images[0]\n\nimage.save(\"sd3_hello_world.png\")\n```\n\n## Memory Optimisations for SD3\n\nSD3 uses three text encoders, one if which is the very large T5-XXL model. This makes it challenging to run the model on GPUs with less than 24GB of VRAM, even when using `fp16` precision. The following section outlines a few memory optimizations in Diffusers that make it easier to run SD3 on low resource hardware.\n\n### Running Inference with Model Offloading\n\nThe most basic memory optimization available in Diffusers allows you to offload the components of the model to CPU during inference in order to save memory, while seeing a slight increase in inference latency. Model offloading will only move a model component onto the GPU when it needs to be executed, while keeping the remaining components on the CPU.\n\n```python\nimport torch\nfrom diffusers import StableDiffusion3Pipeline\n\npipe = StableDiffusion3Pipeline.from_pretrained(\"stabilityai/stable-diffusion-3-medium-diffusers\", torch_dtype=torch.float16)\npipe.enable_model_cpu_offload()\n\nimage = pipe(\n prompt=\"a photo of a cat holding a sign that says hello world\",\n negative_prompt=\"\",\n num_inference_steps=28,\n height=1024,\n width=1024,\n guidance_scale=7.0,\n).images[0]\n\nimage.save(\"sd3_hello_world.png\")\n```\n\n### Dropping the T5 Text Encoder during Inference\n\nRemoving the memory-intensive 4.7B parameter T5-XXL text encoder during inference can significantly decrease the memory requirements for SD3 with only a slight loss in performance.\n\n```python\nimport torch\nfrom diffusers import StableDiffusion3Pipeline\n\npipe = StableDiffusion3Pipeline.from_pretrained(\n \"stabilityai/stable-diffusion-3-medium-diffusers\",\n text_encoder_3=None,\n tokenizer_3=None,\n torch_dtype=torch.float16\n)\npipe.to(\"cuda\")\n\nimage = pipe(\n prompt=\"a photo of a cat holding a sign that says hello world\",\n negative_prompt=\"\",\n num_inference_steps=28,\n height=1024,\n width=1024,\n guidance_scale=7.0,\n).images[0]\n\nimage.save(\"sd3_hello_world-no-T5.png\")\n```\n\n### Using a Quantized Version of the T5 Text Encoder\n\nWe can leverage the `bitsandbytes` library to load and quantize the T5-XXL text encoder to 8-bit precision. This allows you to keep using all three text encoders while only slightly impacting performance.\n\nFirst install the `bitsandbytes` library.\n\n```shell\npip install bitsandbytes\n```\n\nThen load the T5-XXL model using the `BitsAndBytesConfig`.\n\n```python\nimport torch\nfrom diffusers import StableDiffusion3Pipeline\nfrom transformers import T5EncoderModel, BitsAndBytesConfig\n\nquantization_config = BitsAndBytesConfig(load_in_8bit=True)\n\nmodel_id = \"stabilityai/stable-diffusion-3-medium-diffusers\"\ntext_encoder = T5EncoderModel.from_pretrained(\n model_id,\n subfolder=\"text_encoder_3\",\n quantization_config=quantization_config,\n)\npipe = StableDiffusion3Pipeline.from_pretrained(\n model_id,\n text_encoder_3=text_encoder,\n device_map=\"balanced\",\n torch_dtype=torch.float16\n)\n\nimage = pipe(\n prompt=\"a photo of a cat holding a sign that says hello world\",\n negative_prompt=\"\",\n num_inference_steps=28,\n height=1024,\n width=1024,\n guidance_scale=7.0,\n).images[0]\n\nimage.save(\"sd3_hello_world-8bit-T5.png\")\n```\n\nYou can find the end-to-end script [here](https://gist.github.com/sayakpaul/82acb5976509851f2db1a83456e504f1).\n\n## Performance Optimizations for SD3\n\n### Using Torch Compile to Speed Up Inference\n\nUsing compiled components in the SD3 pipeline can speed up inference by as much as 4X. The following code snippet demonstrates how to compile the Transformer and VAE components of the SD3 pipeline.\n\n```python\nimport torch\nfrom diffusers import StableDiffusion3Pipeline\n\ntorch.set_float32_matmul_precision(\"high\")\n\ntorch._inductor.config.conv_1x1_as_mm = True\ntorch._inductor.config.coordinate_descent_tuning = True\ntorch._inductor.config.epilogue_fusion = False\ntorch._inductor.config.coordinate_descent_check_all_directions = True\n\npipe = StableDiffusion3Pipeline.from_pretrained(\n \"stabilityai/stable-diffusion-3-medium-diffusers\",\n torch_dtype=torch.float16\n).to(\"cuda\")\npipe.set_progress_bar_config(disable=True)\n\npipe.transformer.to(memory_format=torch.channels_last)\npipe.vae.to(memory_format=torch.channels_last)\n\npipe.transformer = torch.compile(pipe.transformer, mode=\"max-autotune\", fullgraph=True)\npipe.vae.decode = torch.compile(pipe.vae.decode, mode=\"max-autotune\", fullgraph=True)\n\n# Warm Up\nprompt = \"a photo of a cat holding a sign that says hello world\"\nfor _ in range(3):\n _ = pipe(prompt=prompt, generator=torch.manual_seed(1))\n\n# Run Inference\nimage = pipe(prompt=prompt, generator=torch.manual_seed(1)).images[0]\nimage.save(\"sd3_hello_world.png\")\n```\n\nCheck out the full script [here](https://gist.github.com/sayakpaul/508d89d7aad4f454900813da5d42ca97).\n\n## Loading the original checkpoints via `from_single_file`\n\nThe `SD3Transformer2DModel` and `StableDiffusion3Pipeline` classes support loading the original checkpoints via the `from_single_file` method. This method allows you to load the original checkpoint files that were used to train the models.\n\n## Loading the original checkpoints for the `SD3Transformer2DModel`\n\n```python\nfrom diffusers import SD3Transformer2DModel\n\nmodel = SD3Transformer2DModel.from_single_file(\"https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/sd3_medium.safetensors\")\n```\n\n## Loading the single checkpoint for the `StableDiffusion3Pipeline`\n\n### Loading the single file checkpoint without T5\n\n```python\nimport torch\nfrom diffusers import StableDiffusion3Pipeline\n\npipe = StableDiffusion3Pipeline.from_single_file(\n \"https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/sd3_medium_incl_clips.safetensors\",\n torch_dtype=torch.float16,\n text_encoder_3=None\n)\npipe.enable_model_cpu_offload()\n\nimage = pipe(\"a picture of a cat holding a sign that says hello world\").images[0]\nimage.save('sd3-single-file.png')\n```\n\n### Loading the single file checkpoint without T5\n\n```python\nimport torch\nfrom diffusers import StableDiffusion3Pipeline\n\npipe = StableDiffusion3Pipeline.from_single_file(\n \"https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/sd3_medium_incl_clips_t5xxlfp8.safetensors\",\n torch_dtype=torch.float16,\n)\npipe.enable_model_cpu_offload()\n\nimage = pipe(\"a picture of a cat holding a sign that says hello world\").images[0]\nimage.save('sd3-single-file-t5-fp8.png')\n```\n\n## StableDiffusion3Pipeline\n\n[[autodoc]] StableDiffusion3Pipeline\n\t- all\n\t- __call__\n" }, { "path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_safe.md", "content": "\n\n# Safe Stable Diffusion\n\nSafe Stable Diffusion was proposed in [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://huggingface.co/papers/2211.05105) and mitigates inappropriate degeneration from Stable Diffusion models because they're trained on unfiltered web-crawled datasets. For instance Stable Diffusion may unexpectedly generate nudity, violence, images depicting self-harm, and otherwise offensive content. Safe Stable Diffusion is an extension of Stable Diffusion that drastically reduces this type of content.\n\nThe abstract from the paper is:\n\n*Text-conditioned image generation models have recently achieved astonishing results in image quality and text alignment and are consequently employed in a fast-growing number of applications. Since they are highly data-driven, relying on billion-sized datasets randomly scraped from the internet, they also suffer, as we demonstrate, from degenerated and biased human behavior. In turn, they may even reinforce such biases. To help combat these undesired side effects, we present safe latent diffusion (SLD). Specifically, to measure the inappropriate degeneration due to unfiltered and imbalanced training sets, we establish a novel image generation test bed-inappropriate image prompts (I2P)-containing dedicated, real-world image-to-text prompts covering concepts such as nudity and violence. As our exhaustive empirical evaluation demonstrates, the introduced SLD removes and suppresses inappropriate image parts during the diffusion process, with no additional training required and no adverse effect on overall image quality or text alignment.*\n\n## Tips\n\nUse the `safety_concept` property of [`StableDiffusionPipelineSafe`] to check and edit the current safety concept:\n\n```python\n>>> from diffusers import StableDiffusionPipelineSafe\n\n>>> pipeline = StableDiffusionPipelineSafe.from_pretrained(\"AIML-TUDA/stable-diffusion-safe\")\n>>> pipeline.safety_concept\n'an image showing hate, harassment, violence, suffering, humiliation, harm, suicide, sexual, nudity, bodily fluids, blood, obscene gestures, illegal activity, drug use, theft, vandalism, weapons, child abuse, brutality, cruelty'\n```\nFor each image generation the active concept is also contained in [`StableDiffusionSafePipelineOutput`].\n\nThere are 4 configurations (`SafetyConfig.WEAK`, `SafetyConfig.MEDIUM`, `SafetyConfig.STRONG`, and `SafetyConfig.MAX`) that can be applied:\n\n```python\n>>> from diffusers import StableDiffusionPipelineSafe\n>>> from diffusers.pipelines.stable_diffusion_safe import SafetyConfig\n\n>>> pipeline = StableDiffusionPipelineSafe.from_pretrained(\"AIML-TUDA/stable-diffusion-safe\")\n>>> prompt = \"the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c. leyendecker\"\n>>> out = pipeline(prompt=prompt, **SafetyConfig.MAX)\n```\n\n\n\nMake sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!\n\n\n\n## StableDiffusionPipelineSafe\n\n[[autodoc]] StableDiffusionPipelineSafe\n\t- all\n\t- __call__\n\n## StableDiffusionSafePipelineOutput\n\n[[autodoc]] pipelines.stable_diffusion_safe.StableDiffusionSafePipelineOutput\n\t- all\n\t- __call__\n" }, { "path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md", "content": "\n\n# Stable Diffusion XL\n\nStable Diffusion XL (SDXL) was proposed in [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://huggingface.co/papers/2307.01952) by Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach.\n\nThe abstract from the paper is:\n\n*We present SDXL, a latent diffusion model for text-to-image synthesis. Compared to previous versions of Stable Diffusion, SDXL leverages a three times larger UNet backbone: The increase of model parameters is mainly due to more attention blocks and a larger cross-attention context as SDXL uses a second text encoder. We design multiple novel conditioning schemes and train SDXL on multiple aspect ratios. We also introduce a refinement model which is used to improve the visual fidelity of samples generated by SDXL using a post-hoc image-to-image technique. We demonstrate that SDXL shows drastically improved performance compared the previous versions of Stable Diffusion and achieves results competitive with those of black-box state-of-the-art image generators.*\n\n## Tips\n\n- Using SDXL with a DPM++ scheduler for less than 50 steps is known to produce [visual artifacts](https://github.com/huggingface/diffusers/issues/5433) because the solver becomes numerically unstable. To fix this issue, take a look at this [PR](https://github.com/huggingface/diffusers/pull/5541) which recommends for ODE/SDE solvers:\n\t- set `use_karras_sigmas=True` or `lu_lambdas=True` to improve image quality\n\t- set `euler_at_final=True` if you're using a solver with uniform step sizes (DPM++2M or DPM++2M SDE)\n- Most SDXL checkpoints work best with an image size of 1024x1024. Image sizes of 768x768 and 512x512 are also supported, but the results aren't as good. Anything below 512x512 is not recommended and likely won't be for default checkpoints like [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0).\n- SDXL can pass a different prompt for each of the text encoders it was trained on. We can even pass different parts of the same prompt to the text encoders.\n- SDXL output images can be improved by making use of a refiner model in an image-to-image setting.\n- SDXL offers `negative_original_size`, `negative_crops_coords_top_left`, and `negative_target_size` to negatively condition the model on image resolution and cropping parameters.\n\n\n\nTo learn how to use SDXL for various tasks, how to optimize performance, and other usage examples, take a look at the [Stable Diffusion XL](../../../using-diffusers/sdxl) guide.\n\nCheck out the [Stability AI](https://huggingface.co/stabilityai) Hub organization for the official base and refiner model checkpoints!\n\n\n\n## StableDiffusionXLPipeline\n\n[[autodoc]] StableDiffusionXLPipeline\n\t- all\n\t- __call__\n\n## StableDiffusionXLImg2ImgPipeline\n\n[[autodoc]] StableDiffusionXLImg2ImgPipeline\n\t- all\n\t- __call__\n\n## StableDiffusionXLInpaintPipeline\n\n[[autodoc]] StableDiffusionXLInpaintPipeline\n\t- all\n\t- __call__\n" }, { "path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/svd.md", "content": "\n\n# Stable Video Diffusion\n\nStable Video Diffusion was proposed in [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://hf.co/papers/2311.15127) by Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, Varun Jampani, Robin Rombach.\n\nThe abstract from the paper is:\n\n*We present Stable Video Diffusion - a latent video diffusion model for high-resolution, state-of-the-art text-to-video and image-to-video generation. Recently, latent diffusion models trained for 2D image synthesis have been turned into generative video models by inserting temporal layers and finetuning them on small, high-quality video datasets. However, training methods in the literature vary widely, and the field has yet to agree on a unified strategy for curating video data. In this paper, we identify and evaluate three different stages for successful training of video LDMs: text-to-image pretraining, video pretraining, and high-quality video finetuning. Furthermore, we demonstrate the necessity of a well-curated pretraining dataset for generating high-quality videos and present a systematic curation process to train a strong base model, including captioning and filtering strategies. We then explore the impact of finetuning our base model on high-quality data and train a text-to-video model that is competitive with closed-source video generation. We also show that our base model provides a powerful motion representation for downstream tasks such as image-to-video generation and adaptability to camera motion-specific LoRA modules. Finally, we demonstrate that our model provides a strong multi-view 3D-prior and can serve as a base to finetune a multi-view diffusion model that jointly generates multiple views of objects in a feedforward fashion, outperforming image-based methods at a fraction of their compute budget. We release code and model weights at this https URL.*\n\n\n\nTo learn how to use Stable Video Diffusion, take a look at the [Stable Video Diffusion](../../../using-diffusers/svd) guide.\n\n
\n\nCheck out the [Stability AI](https://huggingface.co/stabilityai) Hub organization for the [base](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid) and [extended frame](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt) checkpoints!\n\n\n\n## Tips\n\nVideo generation is memory-intensive and one way to reduce your memory usage is to set `enable_forward_chunking` on the pipeline's UNet so you don't run the entire feedforward layer at once. Breaking it up into chunks in a loop is more efficient.\n\nCheck out the [Text or image-to-video](text-img2vid) guide for more details about how certain parameters can affect video generation and how to optimize inference by reducing memory usage.\n\n## StableVideoDiffusionPipeline\n\n[[autodoc]] StableVideoDiffusionPipeline\n\n## StableVideoDiffusionPipelineOutput\n\n[[autodoc]] pipelines.stable_video_diffusion.StableVideoDiffusionPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/text2img.md", "content": "\n\n# Text-to-image\n\nThe Stable Diffusion model was created by researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/), [Runway](https://github.com/runwayml), and [LAION](https://laion.ai/). The [`StableDiffusionPipeline`] is capable of generating photorealistic images given any text input. It's trained on 512x512 images from a subset of the LAION-5B dataset. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and can run on consumer GPUs. Latent diffusion is the research on top of which Stable Diffusion was built. It was proposed in [High-Resolution Image Synthesis with Latent Diffusion Models](https://huggingface.co/papers/2112.10752) by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer.\n\nThe abstract from the paper is:\n\n*By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. However, since these models typically operate directly in pixel space, optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations. To enable DM training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Our latent diffusion models (LDMs) achieve a new state of the art for image inpainting and highly competitive performance on various tasks, including unconditional image generation, semantic scene synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs. Code is available at https://github.com/CompVis/latent-diffusion.*\n\n\n\nMake sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!\n\nIf you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!\n\n\n\n## StableDiffusionPipeline\n\n[[autodoc]] StableDiffusionPipeline\n\t- all\n\t- __call__\n\t- enable_attention_slicing\n\t- disable_attention_slicing\n\t- enable_vae_slicing\n\t- disable_vae_slicing\n\t- enable_xformers_memory_efficient_attention\n\t- disable_xformers_memory_efficient_attention\n\t- enable_vae_tiling\n\t- disable_vae_tiling\n\t- load_textual_inversion\n\t- from_single_file\n\t- load_lora_weights\n\t- save_lora_weights\n\n## StableDiffusionPipelineOutput\n\n[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput\n\n## FlaxStableDiffusionPipeline\n\n[[autodoc]] FlaxStableDiffusionPipeline\n\t- all\n\t- __call__\n\n## FlaxStableDiffusionPipelineOutput\n\n[[autodoc]] pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/upscale.md", "content": "\n\n# Super-resolution\n\nThe Stable Diffusion upscaler diffusion model was created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/), and [LAION](https://laion.ai/). It is used to enhance the resolution of input images by a factor of 4.\n\n\n\nMake sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!\n\nIf you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!\n\n\n\n## StableDiffusionUpscalePipeline\n\n[[autodoc]] StableDiffusionUpscalePipeline\n\t- all\n\t- __call__\n\t- enable_attention_slicing\n\t- disable_attention_slicing\n\t- enable_xformers_memory_efficient_attention\n\t- disable_xformers_memory_efficient_attention\n\n## StableDiffusionPipelineOutput\n\n[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/stable_unclip.md", "content": "\n\n# Stable unCLIP\n\nStable unCLIP checkpoints are finetuned from [Stable Diffusion 2.1](./stable_diffusion/stable_diffusion_2) checkpoints to condition on CLIP image embeddings.\nStable unCLIP still conditions on text embeddings. Given the two separate conditionings, stable unCLIP can be used\nfor text guided image variation. When combined with an unCLIP prior, it can also be used for full text to image generation.\n\nThe abstract from the paper is:\n\n*Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a two-stage model: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding. We show that explicitly generating image representations improves image diversity with minimal loss in photorealism and caption similarity. Our decoders conditioned on image representations can also produce variations of an image that preserve both its semantics and style, while varying the non-essential details absent from the image representation. Moreover, the joint embedding space of CLIP enables language-guided image manipulations in a zero-shot fashion. We use diffusion models for the decoder and experiment with both autoregressive and diffusion models for the prior, finding that the latter are computationally more efficient and produce higher-quality samples.*\n\n## Tips\n\nStable unCLIP takes `noise_level` as input during inference which determines how much noise is added to the image embeddings. A higher `noise_level` increases variation in the final un-noised images. By default, we do not add any additional noise to the image embeddings (`noise_level = 0`).\n\n### Text-to-Image Generation\nStable unCLIP can be leveraged for text-to-image generation by pipelining it with the prior model of KakaoBrain's open source DALL-E 2 replication [Karlo](https://huggingface.co/kakaobrain/karlo-v1-alpha):\n\n```python\nimport torch\nfrom diffusers import UnCLIPScheduler, DDPMScheduler, StableUnCLIPPipeline\nfrom diffusers.models import PriorTransformer\nfrom transformers import CLIPTokenizer, CLIPTextModelWithProjection\n\nprior_model_id = \"kakaobrain/karlo-v1-alpha\"\ndata_type = torch.float16\nprior = PriorTransformer.from_pretrained(prior_model_id, subfolder=\"prior\", torch_dtype=data_type)\n\nprior_text_model_id = \"openai/clip-vit-large-patch14\"\nprior_tokenizer = CLIPTokenizer.from_pretrained(prior_text_model_id)\nprior_text_model = CLIPTextModelWithProjection.from_pretrained(prior_text_model_id, torch_dtype=data_type)\nprior_scheduler = UnCLIPScheduler.from_pretrained(prior_model_id, subfolder=\"prior_scheduler\")\nprior_scheduler = DDPMScheduler.from_config(prior_scheduler.config)\n\nstable_unclip_model_id = \"stabilityai/stable-diffusion-2-1-unclip-small\"\n\npipe = StableUnCLIPPipeline.from_pretrained(\n stable_unclip_model_id,\n torch_dtype=data_type,\n variant=\"fp16\",\n prior_tokenizer=prior_tokenizer,\n prior_text_encoder=prior_text_model,\n prior=prior,\n prior_scheduler=prior_scheduler,\n)\n\npipe = pipe.to(\"cuda\")\nwave_prompt = \"dramatic wave, the Oceans roar, Strong wave spiral across the oceans as the waves unfurl into roaring crests; perfect wave form; perfect wave shape; dramatic wave shape; wave shape unbelievable; wave; wave shape spectacular\"\n\nimage = pipe(prompt=wave_prompt).images[0]\nimage\n```\n\n\nFor text-to-image we use `stabilityai/stable-diffusion-2-1-unclip-small` as it was trained on CLIP ViT-L/14 embedding, the same as the Karlo model prior. [stabilityai/stable-diffusion-2-1-unclip](https://hf.co/stabilityai/stable-diffusion-2-1-unclip) was trained on OpenCLIP ViT-H, so we don't recommend its use.\n\n\n\n### Text guided Image-to-Image Variation\n\n```python\nfrom diffusers import StableUnCLIPImg2ImgPipeline\nfrom diffusers.utils import load_image\nimport torch\n\npipe = StableUnCLIPImg2ImgPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-1-unclip\", torch_dtype=torch.float16, variation=\"fp16\"\n)\npipe = pipe.to(\"cuda\")\n\nurl = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/tarsila_do_amaral.png\"\ninit_image = load_image(url)\n\nimages = pipe(init_image).images\nimages[0].save(\"variation_image.png\")\n```\n\nOptionally, you can also pass a prompt to `pipe` such as:\n\n```python\nprompt = \"A fantasy landscape, trending on artstation\"\n\nimage = pipe(init_image, prompt=prompt).images[0]\nimage\n```\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## StableUnCLIPPipeline\n\n[[autodoc]] StableUnCLIPPipeline\n\t- all\n\t- __call__\n\t- enable_attention_slicing\n\t- disable_attention_slicing\n\t- enable_vae_slicing\n\t- disable_vae_slicing\n\t- enable_xformers_memory_efficient_attention\n\t- disable_xformers_memory_efficient_attention\n\n## StableUnCLIPImg2ImgPipeline\n\n[[autodoc]] StableUnCLIPImg2ImgPipeline\n\t- all\n\t- __call__\n\t- enable_attention_slicing\n\t- disable_attention_slicing\n\t- enable_vae_slicing\n\t- disable_vae_slicing\n\t- enable_xformers_memory_efficient_attention\n\t- disable_xformers_memory_efficient_attention\n\n## ImagePipelineOutput\n[[autodoc]] pipelines.ImagePipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/text_to_video.md", "content": "\n\n\n\n🧪 This pipeline is for research purposes only.\n\n\n\n# Text-to-video\n\n[ModelScope Text-to-Video Technical Report](https://arxiv.org/abs/2308.06571) is by Jiuniu Wang, Hangjie Yuan, Dayou Chen, Yingya Zhang, Xiang Wang, Shiwei Zhang.\n\nThe abstract from the paper is:\n\n*This paper introduces ModelScopeT2V, a text-to-video synthesis model that evolves from a text-to-image synthesis model (i.e., Stable Diffusion). ModelScopeT2V incorporates spatio-temporal blocks to ensure consistent frame generation and smooth movement transitions. The model could adapt to varying frame numbers during training and inference, rendering it suitable for both image-text and video-text datasets. ModelScopeT2V brings together three components (i.e., VQGAN, a text encoder, and a denoising UNet), totally comprising 1.7 billion parameters, in which 0.5 billion parameters are dedicated to temporal capabilities. The model demonstrates superior performance over state-of-the-art methods across three evaluation metrics. The code and an online demo are available at https://modelscope.cn/models/damo/text-to-video-synthesis/summary.*\n\nYou can find additional information about Text-to-Video on the [project page](https://modelscope.cn/models/damo/text-to-video-synthesis/summary), [original codebase](https://github.com/modelscope/modelscope/), and try it out in a [demo](https://huggingface.co/spaces/damo-vilab/modelscope-text-to-video-synthesis). Official checkpoints can be found at [damo-vilab](https://huggingface.co/damo-vilab) and [cerspense](https://huggingface.co/cerspense).\n\n## Usage example\n\n### `text-to-video-ms-1.7b`\n\nLet's start by generating a short video with the default length of 16 frames (2s at 8 fps):\n\n```python\nimport torch\nfrom diffusers import DiffusionPipeline\nfrom diffusers.utils import export_to_video\n\npipe = DiffusionPipeline.from_pretrained(\"damo-vilab/text-to-video-ms-1.7b\", torch_dtype=torch.float16, variant=\"fp16\")\npipe = pipe.to(\"cuda\")\n\nprompt = \"Spiderman is surfing\"\nvideo_frames = pipe(prompt).frames[0]\nvideo_path = export_to_video(video_frames)\nvideo_path\n```\n\nDiffusers supports different optimization techniques to improve the latency\nand memory footprint of a pipeline. Since videos are often more memory-heavy than images,\nwe can enable CPU offloading and VAE slicing to keep the memory footprint at bay.\n\nLet's generate a video of 8 seconds (64 frames) on the same GPU using CPU offloading and VAE slicing:\n\n```python\nimport torch\nfrom diffusers import DiffusionPipeline\nfrom diffusers.utils import export_to_video\n\npipe = DiffusionPipeline.from_pretrained(\"damo-vilab/text-to-video-ms-1.7b\", torch_dtype=torch.float16, variant=\"fp16\")\npipe.enable_model_cpu_offload()\n\n# memory optimization\npipe.enable_vae_slicing()\n\nprompt = \"Darth Vader surfing a wave\"\nvideo_frames = pipe(prompt, num_frames=64).frames[0]\nvideo_path = export_to_video(video_frames)\nvideo_path\n```\n\nIt just takes **7 GBs of GPU memory** to generate the 64 video frames using PyTorch 2.0, \"fp16\" precision and the techniques mentioned above.\n\nWe can also use a different scheduler easily, using the same method we'd use for Stable Diffusion:\n\n```python\nimport torch\nfrom diffusers import DiffusionPipeline, DPMSolverMultistepScheduler\nfrom diffusers.utils import export_to_video\n\npipe = DiffusionPipeline.from_pretrained(\"damo-vilab/text-to-video-ms-1.7b\", torch_dtype=torch.float16, variant=\"fp16\")\npipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\npipe.enable_model_cpu_offload()\n\nprompt = \"Spiderman is surfing\"\nvideo_frames = pipe(prompt, num_inference_steps=25).frames[0]\nvideo_path = export_to_video(video_frames)\nvideo_path\n```\n\nHere are some sample outputs:\n\n\n \n \n \n \n
\n An astronaut riding a horse.\n
\n \"An\n
\n Darth vader surfing in waves.\n
\n \"Darth\n
\n\n### `cerspense/zeroscope_v2_576w` & `cerspense/zeroscope_v2_XL`\n\nZeroscope are watermark-free model and have been trained on specific sizes such as `576x320` and `1024x576`.\nOne should first generate a video using the lower resolution checkpoint [`cerspense/zeroscope_v2_576w`](https://huggingface.co/cerspense/zeroscope_v2_576w) with [`TextToVideoSDPipeline`],\nwhich can then be upscaled using [`VideoToVideoSDPipeline`] and [`cerspense/zeroscope_v2_XL`](https://huggingface.co/cerspense/zeroscope_v2_XL).\n\n\n```py\nimport torch\nfrom diffusers import DiffusionPipeline, DPMSolverMultistepScheduler\nfrom diffusers.utils import export_to_video\nfrom PIL import Image\n\npipe = DiffusionPipeline.from_pretrained(\"cerspense/zeroscope_v2_576w\", torch_dtype=torch.float16)\npipe.enable_model_cpu_offload()\n\n# memory optimization\npipe.unet.enable_forward_chunking(chunk_size=1, dim=1)\npipe.enable_vae_slicing()\n\nprompt = \"Darth Vader surfing a wave\"\nvideo_frames = pipe(prompt, num_frames=24).frames[0]\nvideo_path = export_to_video(video_frames)\nvideo_path\n```\n\nNow the video can be upscaled:\n\n```py\npipe = DiffusionPipeline.from_pretrained(\"cerspense/zeroscope_v2_XL\", torch_dtype=torch.float16)\npipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\npipe.enable_model_cpu_offload()\n\n# memory optimization\npipe.unet.enable_forward_chunking(chunk_size=1, dim=1)\npipe.enable_vae_slicing()\n\nvideo = [Image.fromarray(frame).resize((1024, 576)) for frame in video_frames]\n\nvideo_frames = pipe(prompt, video=video, strength=0.6).frames[0]\nvideo_path = export_to_video(video_frames)\nvideo_path\n```\n\nHere are some sample outputs:\n\n\n \n \n \n
\n Darth vader surfing in waves.\n
\n \"Darth\n
\n\n## Tips\n\nVideo generation is memory-intensive and one way to reduce your memory usage is to set `enable_forward_chunking` on the pipeline's UNet so you don't run the entire feedforward layer at once. Breaking it up into chunks in a loop is more efficient.\n\nCheck out the [Text or image-to-video](text-img2vid) guide for more details about how certain parameters can affect video generation and how to optimize inference by reducing memory usage.\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## TextToVideoSDPipeline\n[[autodoc]] TextToVideoSDPipeline\n\t- all\n\t- __call__\n\n## VideoToVideoSDPipeline\n[[autodoc]] VideoToVideoSDPipeline\n\t- all\n\t- __call__\n\n## TextToVideoSDPipelineOutput\n[[autodoc]] pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/text_to_video_zero.md", "content": "\n\n# Text2Video-Zero\n\n[Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators](https://huggingface.co/papers/2303.13439) is by Levon Khachatryan, Andranik Movsisyan, Vahram Tadevosyan, Roberto Henschel, [Zhangyang Wang](https://www.ece.utexas.edu/people/faculty/atlas-wang), Shant Navasardyan, [Humphrey Shi](https://www.humphreyshi.com).\n\nText2Video-Zero enables zero-shot video generation using either:\n1. A textual prompt\n2. A prompt combined with guidance from poses or edges\n3. Video Instruct-Pix2Pix (instruction-guided video editing)\n\nResults are temporally consistent and closely follow the guidance and textual prompts.\n\n![teaser-img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/t2v_zero_teaser.png)\n\nThe abstract from the paper is:\n\n*Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets. In this paper, we introduce a new task of zero-shot text-to-video generation and propose a low-cost approach (without any training or optimization) by leveraging the power of existing text-to-image synthesis methods (e.g., Stable Diffusion), making them suitable for the video domain.\nOur key modifications include (i) enriching the latent codes of the generated frames with motion dynamics to keep the global scene and the background time consistent; and (ii) reprogramming frame-level self-attention using a new cross-frame attention of each frame on the first frame, to preserve the context, appearance, and identity of the foreground object.\nExperiments show that this leads to low overhead, yet high-quality and remarkably consistent video generation. Moreover, our approach is not limited to text-to-video synthesis but is also applicable to other tasks such as conditional and content-specialized video generation, and Video Instruct-Pix2Pix, i.e., instruction-guided video editing.\nAs experiments show, our method performs comparably or sometimes better than recent approaches, despite not being trained on additional video data.*\n\nYou can find additional information about Text2Video-Zero on the [project page](https://text2video-zero.github.io/), [paper](https://arxiv.org/abs/2303.13439), and [original codebase](https://github.com/Picsart-AI-Research/Text2Video-Zero).\n\n## Usage example\n\n### Text-To-Video\n\nTo generate a video from prompt, run the following Python code:\n```python\nimport torch\nfrom diffusers import TextToVideoZeroPipeline\n\nmodel_id = \"runwayml/stable-diffusion-v1-5\"\npipe = TextToVideoZeroPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(\"cuda\")\n\nprompt = \"A panda is playing guitar on times square\"\nresult = pipe(prompt=prompt).images\nresult = [(r * 255).astype(\"uint8\") for r in result]\nimageio.mimsave(\"video.mp4\", result, fps=4)\n```\nYou can change these parameters in the pipeline call:\n* Motion field strength (see the [paper](https://arxiv.org/abs/2303.13439), Sect. 3.3.1):\n * `motion_field_strength_x` and `motion_field_strength_y`. Default: `motion_field_strength_x=12`, `motion_field_strength_y=12`\n* `T` and `T'` (see the [paper](https://arxiv.org/abs/2303.13439), Sect. 3.3.1)\n * `t0` and `t1` in the range `{0, ..., num_inference_steps}`. Default: `t0=45`, `t1=48`\n* Video length:\n * `video_length`, the number of frames video_length to be generated. Default: `video_length=8`\n\nWe can also generate longer videos by doing the processing in a chunk-by-chunk manner:\n```python\nimport torch\nfrom diffusers import TextToVideoZeroPipeline\nimport numpy as np\n\nmodel_id = \"runwayml/stable-diffusion-v1-5\"\npipe = TextToVideoZeroPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(\"cuda\")\nseed = 0\nvideo_length = 24 #24 ÷ 4fps = 6 seconds\nchunk_size = 8\nprompt = \"A panda is playing guitar on times square\"\n\n# Generate the video chunk-by-chunk\nresult = []\nchunk_ids = np.arange(0, video_length, chunk_size - 1)\ngenerator = torch.Generator(device=\"cuda\")\nfor i in range(len(chunk_ids)):\n print(f\"Processing chunk {i + 1} / {len(chunk_ids)}\")\n ch_start = chunk_ids[i]\n ch_end = video_length if i == len(chunk_ids) - 1 else chunk_ids[i + 1]\n # Attach the first frame for Cross Frame Attention\n frame_ids = [0] + list(range(ch_start, ch_end))\n # Fix the seed for the temporal consistency\n generator.manual_seed(seed)\n output = pipe(prompt=prompt, video_length=len(frame_ids), generator=generator, frame_ids=frame_ids)\n result.append(output.images[1:])\n\n# Concatenate chunks and save\nresult = np.concatenate(result)\nresult = [(r * 255).astype(\"uint8\") for r in result]\nimageio.mimsave(\"video.mp4\", result, fps=4)\n```\n\n\n- #### SDXL Support\nIn order to use the SDXL model when generating a video from prompt, use the `TextToVideoZeroSDXLPipeline` pipeline:\n\n```python\nimport torch\nfrom diffusers import TextToVideoZeroSDXLPipeline\n\nmodel_id = \"stabilityai/stable-diffusion-xl-base-1.0\"\npipe = TextToVideoZeroSDXLPipeline.from_pretrained(\n model_id, torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n).to(\"cuda\")\n```\n\n### Text-To-Video with Pose Control\nTo generate a video from prompt with additional pose control\n\n1. Download a demo video\n\n ```python\n from huggingface_hub import hf_hub_download\n\n filename = \"__assets__/poses_skeleton_gifs/dance1_corr.mp4\"\n repo_id = \"PAIR/Text2Video-Zero\"\n video_path = hf_hub_download(repo_type=\"space\", repo_id=repo_id, filename=filename)\n ```\n\n\n2. Read video containing extracted pose images\n ```python\n from PIL import Image\n import imageio\n\n reader = imageio.get_reader(video_path, \"ffmpeg\")\n frame_count = 8\n pose_images = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)]\n ```\n To extract pose from actual video, read [ControlNet documentation](controlnet).\n\n3. Run `StableDiffusionControlNetPipeline` with our custom attention processor\n\n ```python\n import torch\n from diffusers import StableDiffusionControlNetPipeline, ControlNetModel\n from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor\n\n model_id = \"runwayml/stable-diffusion-v1-5\"\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-openpose\", torch_dtype=torch.float16)\n pipe = StableDiffusionControlNetPipeline.from_pretrained(\n model_id, controlnet=controlnet, torch_dtype=torch.float16\n ).to(\"cuda\")\n\n # Set the attention processor\n pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))\n pipe.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))\n\n # fix latents for all frames\n latents = torch.randn((1, 4, 64, 64), device=\"cuda\", dtype=torch.float16).repeat(len(pose_images), 1, 1, 1)\n\n prompt = \"Darth Vader dancing in a desert\"\n result = pipe(prompt=[prompt] * len(pose_images), image=pose_images, latents=latents).images\n imageio.mimsave(\"video.mp4\", result, fps=4)\n ```\n- #### SDXL Support\n\t\n\tSince our attention processor also works with SDXL, it can be utilized to generate a video from prompt using ControlNet models powered by SDXL:\n\t```python\n\timport torch\n\tfrom diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel\n\tfrom diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor\n\t\n\tcontrolnet_model_id = 'thibaud/controlnet-openpose-sdxl-1.0'\n\tmodel_id = 'stabilityai/stable-diffusion-xl-base-1.0'\n\t\n\tcontrolnet = ControlNetModel.from_pretrained(controlnet_model_id, torch_dtype=torch.float16)\n\tpipe = StableDiffusionControlNetPipeline.from_pretrained(\n\t\tmodel_id, controlnet=controlnet, torch_dtype=torch.float16\n\t).to('cuda')\n\t\n\t# Set the attention processor\n\tpipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))\n\tpipe.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))\n\t\n\t# fix latents for all frames\n\tlatents = torch.randn((1, 4, 128, 128), device=\"cuda\", dtype=torch.float16).repeat(len(pose_images), 1, 1, 1)\n\t\n\tprompt = \"Darth Vader dancing in a desert\"\n\tresult = pipe(prompt=[prompt] * len(pose_images), image=pose_images, latents=latents).images\n\timageio.mimsave(\"video.mp4\", result, fps=4)\n\t```\n\n### Text-To-Video with Edge Control\n\nTo generate a video from prompt with additional Canny edge control, follow the same steps described above for pose-guided generation using [Canny edge ControlNet model](https://huggingface.co/lllyasviel/sd-controlnet-canny).\n\n\n### Video Instruct-Pix2Pix\n\nTo perform text-guided video editing (with [InstructPix2Pix](pix2pix)):\n\n1. Download a demo video\n\n ```python\n from huggingface_hub import hf_hub_download\n\n filename = \"__assets__/pix2pix video/camel.mp4\"\n repo_id = \"PAIR/Text2Video-Zero\"\n video_path = hf_hub_download(repo_type=\"space\", repo_id=repo_id, filename=filename)\n ```\n\n2. Read video from path\n ```python\n from PIL import Image\n import imageio\n\n reader = imageio.get_reader(video_path, \"ffmpeg\")\n frame_count = 8\n video = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)]\n ```\n\n3. Run `StableDiffusionInstructPix2PixPipeline` with our custom attention processor\n ```python\n import torch\n from diffusers import StableDiffusionInstructPix2PixPipeline\n from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor\n\n model_id = \"timbrooks/instruct-pix2pix\"\n pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(\"cuda\")\n pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=3))\n\n prompt = \"make it Van Gogh Starry Night style\"\n result = pipe(prompt=[prompt] * len(video), image=video).images\n imageio.mimsave(\"edited_video.mp4\", result, fps=4)\n ```\n\n\n### DreamBooth specialization\n\nMethods **Text-To-Video**, **Text-To-Video with Pose Control** and **Text-To-Video with Edge Control**\ncan run with custom [DreamBooth](../../training/dreambooth) models, as shown below for\n[Canny edge ControlNet model](https://huggingface.co/lllyasviel/sd-controlnet-canny) and\n[Avatar style DreamBooth](https://huggingface.co/PAIR/text2video-zero-controlnet-canny-avatar) model:\n\n1. Download a demo video\n\n ```python\n from huggingface_hub import hf_hub_download\n\n filename = \"__assets__/canny_videos_mp4/girl_turning.mp4\"\n repo_id = \"PAIR/Text2Video-Zero\"\n video_path = hf_hub_download(repo_type=\"space\", repo_id=repo_id, filename=filename)\n ```\n\n2. Read video from path\n ```python\n from PIL import Image\n import imageio\n\n reader = imageio.get_reader(video_path, \"ffmpeg\")\n frame_count = 8\n canny_edges = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)]\n ```\n\n3. Run `StableDiffusionControlNetPipeline` with custom trained DreamBooth model\n ```python\n import torch\n from diffusers import StableDiffusionControlNetPipeline, ControlNetModel\n from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor\n\n # set model id to custom model\n model_id = \"PAIR/text2video-zero-controlnet-canny-avatar\"\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\", torch_dtype=torch.float16)\n pipe = StableDiffusionControlNetPipeline.from_pretrained(\n model_id, controlnet=controlnet, torch_dtype=torch.float16\n ).to(\"cuda\")\n\n # Set the attention processor\n pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))\n pipe.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))\n\n # fix latents for all frames\n latents = torch.randn((1, 4, 64, 64), device=\"cuda\", dtype=torch.float16).repeat(len(canny_edges), 1, 1, 1)\n\n prompt = \"oil painting of a beautiful girl avatar style\"\n result = pipe(prompt=[prompt] * len(canny_edges), image=canny_edges, latents=latents).images\n imageio.mimsave(\"video.mp4\", result, fps=4)\n ```\n\nYou can filter out some available DreamBooth-trained models with [this link](https://huggingface.co/models?search=dreambooth).\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## TextToVideoZeroPipeline\n[[autodoc]] TextToVideoZeroPipeline\n\t- all\n\t- __call__\n\n## TextToVideoZeroSDXLPipeline\n[[autodoc]] TextToVideoZeroSDXLPipeline\n\t- all\n\t- __call__\n\n## TextToVideoPipelineOutput\n[[autodoc]] pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/unclip.md", "content": "\n\n# unCLIP\n\n[Hierarchical Text-Conditional Image Generation with CLIP Latents](https://huggingface.co/papers/2204.06125) is by Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen. The unCLIP model in 🤗 Diffusers comes from kakaobrain's [karlo](https://github.com/kakaobrain/karlo).\n\nThe abstract from the paper is following:\n\n*Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a two-stage model: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding. We show that explicitly generating image representations improves image diversity with minimal loss in photorealism and caption similarity. Our decoders conditioned on image representations can also produce variations of an image that preserve both its semantics and style, while varying the non-essential details absent from the image representation. Moreover, the joint embedding space of CLIP enables language-guided image manipulations in a zero-shot fashion. We use diffusion models for the decoder and experiment with both autoregressive and diffusion models for the prior, finding that the latter are computationally more efficient and produce higher-quality samples.*\n\nYou can find lucidrains' DALL-E 2 recreation at [lucidrains/DALLE2-pytorch](https://github.com/lucidrains/DALLE2-pytorch).\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## UnCLIPPipeline\n[[autodoc]] UnCLIPPipeline\n\t- all\n\t- __call__\n\n## UnCLIPImageVariationPipeline\n[[autodoc]] UnCLIPImageVariationPipeline\n\t- all\n\t- __call__\n\n## ImagePipelineOutput\n[[autodoc]] pipelines.ImagePipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/unidiffuser.md", "content": "\n\n# UniDiffuser\n\nThe UniDiffuser model was proposed in [One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale](https://huggingface.co/papers/2303.06555) by Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, Jun Zhu.\n\nThe abstract from the paper is:\n\n*This paper proposes a unified diffusion framework (dubbed UniDiffuser) to fit all distributions relevant to a set of multi-modal data in one model. Our key insight is -- learning diffusion models for marginal, conditional, and joint distributions can be unified as predicting the noise in the perturbed data, where the perturbation levels (i.e. timesteps) can be different for different modalities. Inspired by the unified view, UniDiffuser learns all distributions simultaneously with a minimal modification to the original diffusion model -- perturbs data in all modalities instead of a single modality, inputs individual timesteps in different modalities, and predicts the noise of all modalities instead of a single modality. UniDiffuser is parameterized by a transformer for diffusion models to handle input types of different modalities. Implemented on large-scale paired image-text data, UniDiffuser is able to perform image, text, text-to-image, image-to-text, and image-text pair generation by setting proper timesteps without additional overhead. In particular, UniDiffuser is able to produce perceptually realistic samples in all tasks and its quantitative results (e.g., the FID and CLIP score) are not only superior to existing general-purpose models but also comparable to the bespoken models (e.g., Stable Diffusion and DALL-E 2) in representative tasks (e.g., text-to-image generation).*\n\nYou can find the original codebase at [thu-ml/unidiffuser](https://github.com/thu-ml/unidiffuser) and additional checkpoints at [thu-ml](https://huggingface.co/thu-ml).\n\n\n\nThere is currently an issue on PyTorch 1.X where the output images are all black or the pixel values become `NaNs`. This issue can be mitigated by switching to PyTorch 2.X.\n\n\n\nThis pipeline was contributed by [dg845](https://github.com/dg845). ❤️\n\n## Usage Examples\n\nBecause the UniDiffuser model is trained to model the joint distribution of (image, text) pairs, it is capable of performing a diverse range of generation tasks:\n\n### Unconditional Image and Text Generation\n\nUnconditional generation (where we start from only latents sampled from a standard Gaussian prior) from a [`UniDiffuserPipeline`] will produce a (image, text) pair:\n\n```python\nimport torch\n\nfrom diffusers import UniDiffuserPipeline\n\ndevice = \"cuda\"\nmodel_id_or_path = \"thu-ml/unidiffuser-v1\"\npipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\npipe.to(device)\n\n# Unconditional image and text generation. The generation task is automatically inferred.\nsample = pipe(num_inference_steps=20, guidance_scale=8.0)\nimage = sample.images[0]\ntext = sample.text[0]\nimage.save(\"unidiffuser_joint_sample_image.png\")\nprint(text)\n```\n\nThis is also called \"joint\" generation in the UniDiffuser paper, since we are sampling from the joint image-text distribution.\n\nNote that the generation task is inferred from the inputs used when calling the pipeline.\nIt is also possible to manually specify the unconditional generation task (\"mode\") manually with [`UniDiffuserPipeline.set_joint_mode`]:\n\n```python\n# Equivalent to the above.\npipe.set_joint_mode()\nsample = pipe(num_inference_steps=20, guidance_scale=8.0)\n```\n\nWhen the mode is set manually, subsequent calls to the pipeline will use the set mode without attempting to infer the mode.\nYou can reset the mode with [`UniDiffuserPipeline.reset_mode`], after which the pipeline will once again infer the mode.\n\nYou can also generate only an image or only text (which the UniDiffuser paper calls \"marginal\" generation since we sample from the marginal distribution of images and text, respectively):\n\n```python\n# Unlike other generation tasks, image-only and text-only generation don't use classifier-free guidance\n# Image-only generation\npipe.set_image_mode()\nsample_image = pipe(num_inference_steps=20).images[0]\n# Text-only generation\npipe.set_text_mode()\nsample_text = pipe(num_inference_steps=20).text[0]\n```\n\n### Text-to-Image Generation\n\nUniDiffuser is also capable of sampling from conditional distributions; that is, the distribution of images conditioned on a text prompt or the distribution of texts conditioned on an image.\nHere is an example of sampling from the conditional image distribution (text-to-image generation or text-conditioned image generation):\n\n```python\nimport torch\n\nfrom diffusers import UniDiffuserPipeline\n\ndevice = \"cuda\"\nmodel_id_or_path = \"thu-ml/unidiffuser-v1\"\npipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\npipe.to(device)\n\n# Text-to-image generation\nprompt = \"an elephant under the sea\"\n\nsample = pipe(prompt=prompt, num_inference_steps=20, guidance_scale=8.0)\nt2i_image = sample.images[0]\nt2i_image\n```\n\nThe `text2img` mode requires that either an input `prompt` or `prompt_embeds` be supplied. You can set the `text2img` mode manually with [`UniDiffuserPipeline.set_text_to_image_mode`].\n\n### Image-to-Text Generation\n\nSimilarly, UniDiffuser can also produce text samples given an image (image-to-text or image-conditioned text generation):\n\n```python\nimport torch\n\nfrom diffusers import UniDiffuserPipeline\nfrom diffusers.utils import load_image\n\ndevice = \"cuda\"\nmodel_id_or_path = \"thu-ml/unidiffuser-v1\"\npipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\npipe.to(device)\n\n# Image-to-text generation\nimage_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unidiffuser/unidiffuser_example_image.jpg\"\ninit_image = load_image(image_url).resize((512, 512))\n\nsample = pipe(image=init_image, num_inference_steps=20, guidance_scale=8.0)\ni2t_text = sample.text[0]\nprint(i2t_text)\n```\n\nThe `img2text` mode requires that an input `image` be supplied. You can set the `img2text` mode manually with [`UniDiffuserPipeline.set_image_to_text_mode`].\n\n### Image Variation\n\nThe UniDiffuser authors suggest performing image variation through a \"round-trip\" generation method, where given an input image, we first perform an image-to-text generation, and then perform a text-to-image generation on the outputs of the first generation.\nThis produces a new image which is semantically similar to the input image:\n\n```python\nimport torch\n\nfrom diffusers import UniDiffuserPipeline\nfrom diffusers.utils import load_image\n\ndevice = \"cuda\"\nmodel_id_or_path = \"thu-ml/unidiffuser-v1\"\npipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\npipe.to(device)\n\n# Image variation can be performed with an image-to-text generation followed by a text-to-image generation:\n# 1. Image-to-text generation\nimage_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unidiffuser/unidiffuser_example_image.jpg\"\ninit_image = load_image(image_url).resize((512, 512))\n\nsample = pipe(image=init_image, num_inference_steps=20, guidance_scale=8.0)\ni2t_text = sample.text[0]\nprint(i2t_text)\n\n# 2. Text-to-image generation\nsample = pipe(prompt=i2t_text, num_inference_steps=20, guidance_scale=8.0)\nfinal_image = sample.images[0]\nfinal_image.save(\"unidiffuser_image_variation_sample.png\")\n```\n\n### Text Variation\n\nSimilarly, text variation can be performed on an input prompt with a text-to-image generation followed by a image-to-text generation:\n\n```python\nimport torch\n\nfrom diffusers import UniDiffuserPipeline\n\ndevice = \"cuda\"\nmodel_id_or_path = \"thu-ml/unidiffuser-v1\"\npipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\npipe.to(device)\n\n# Text variation can be performed with a text-to-image generation followed by a image-to-text generation:\n# 1. Text-to-image generation\nprompt = \"an elephant under the sea\"\n\nsample = pipe(prompt=prompt, num_inference_steps=20, guidance_scale=8.0)\nt2i_image = sample.images[0]\nt2i_image.save(\"unidiffuser_text2img_sample_image.png\")\n\n# 2. Image-to-text generation\nsample = pipe(image=t2i_image, num_inference_steps=20, guidance_scale=8.0)\nfinal_prompt = sample.text[0]\nprint(final_prompt)\n```\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## UniDiffuserPipeline\n[[autodoc]] UniDiffuserPipeline\n\t- all\n\t- __call__\n\n## ImageTextPipelineOutput\n[[autodoc]] pipelines.ImageTextPipelineOutput\n" }, { "path": "diffusers/docs/source/en/api/pipelines/value_guided_sampling.md", "content": "\n\n# Value-guided planning\n\n\n\n🧪 This is an experimental pipeline for reinforcement learning!\n\n\n\nThis pipeline is based on the [Planning with Diffusion for Flexible Behavior Synthesis](https://huggingface.co/papers/2205.09991) paper by Michael Janner, Yilun Du, Joshua B. Tenenbaum, Sergey Levine.\n\nThe abstract from the paper is:\n\n*Model-based reinforcement learning methods often use learning only for the purpose of estimating an approximate dynamics model, offloading the rest of the decision-making work to classical trajectory optimizers. While conceptually simple, this combination has a number of empirical shortcomings, suggesting that learned models may not be well-suited to standard trajectory optimization. In this paper, we consider what it would look like to fold as much of the trajectory optimization pipeline as possible into the modeling problem, such that sampling from the model and planning with it become nearly identical. The core of our technical approach lies in a diffusion probabilistic model that plans by iteratively denoising trajectories. We show how classifier-guided sampling and image inpainting can be reinterpreted as coherent planning strategies, explore the unusual and useful properties of diffusion-based planning methods, and demonstrate the effectiveness of our framework in control settings that emphasize long-horizon decision-making and test-time flexibility.*\n\nYou can find additional information about the model on the [project page](https://diffusion-planning.github.io/), the [original codebase](https://github.com/jannerm/diffuser), or try it out in a demo [notebook](https://colab.research.google.com/drive/1rXm8CX4ZdN5qivjJ2lhwhkOmt_m0CvU0#scrollTo=6HXJvhyqcITc&uniqifier=1).\n\nThe script to run the model is available [here](https://github.com/huggingface/diffusers/tree/main/examples/reinforcement_learning).\n\n\n\nMake sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.\n\n\n\n## ValueGuidedRLPipeline\n[[autodoc]] diffusers.experimental.ValueGuidedRLPipeline\n" }, { "path": "diffusers/docs/source/en/api/pipelines/wuerstchen.md", "content": "\n\n# Würstchen\n\n\n\n[Wuerstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models](https://huggingface.co/papers/2306.00637) is by Pablo Pernias, Dominic Rampas, Mats L. Richter and Christopher Pal and Marc Aubreville.\n\nThe abstract from the paper is:\n\n*We introduce Würstchen, a novel architecture for text-to-image synthesis that combines competitive performance with unprecedented cost-effectiveness for large-scale text-to-image diffusion models. A key contribution of our work is to develop a latent diffusion technique in which we learn a detailed but extremely compact semantic image representation used to guide the diffusion process. This highly compressed representation of an image provides much more detailed guidance compared to latent representations of language and this significantly reduces the computational requirements to achieve state-of-the-art results. Our approach also improves the quality of text-conditioned image generation based on our user preference study. The training requirements of our approach consists of 24,602 A100-GPU hours - compared to Stable Diffusion 2.1's 200,000 GPU hours. Our approach also requires less training data to achieve these results. Furthermore, our compact latent representations allows us to perform inference over twice as fast, slashing the usual costs and carbon footprint of a state-of-the-art (SOTA) diffusion model significantly, without compromising the end performance. In a broader comparison against SOTA models our approach is substantially more efficient and compares favorably in terms of image quality. We believe that this work motivates more emphasis on the prioritization of both performance and computational accessibility.*\n\n## Würstchen Overview\nWürstchen is a diffusion model, whose text-conditional model works in a highly compressed latent space of images. Why is this important? Compressing data can reduce computational costs for both training and inference by magnitudes. Training on 1024x1024 images is way more expensive than training on 32x32. Usually, other works make use of a relatively small compression, in the range of 4x - 8x spatial compression. Würstchen takes this to an extreme. Through its novel design, we achieve a 42x spatial compression. This was unseen before because common methods fail to faithfully reconstruct detailed images after 16x spatial compression. Würstchen employs a two-stage compression, what we call Stage A and Stage B. Stage A is a VQGAN, and Stage B is a Diffusion Autoencoder (more details can be found in the [paper](https://huggingface.co/papers/2306.00637)). A third model, Stage C, is learned in that highly compressed latent space. This training requires fractions of the compute used for current top-performing models, while also allowing cheaper and faster inference.\n\n## Würstchen v2 comes to Diffusers\n\nAfter the initial paper release, we have improved numerous things in the architecture, training and sampling, making Würstchen competitive to current state-of-the-art models in many ways. We are excited to release this new version together with Diffusers. Here is a list of the improvements.\n\n- Higher resolution (1024x1024 up to 2048x2048)\n- Faster inference\n- Multi Aspect Resolution Sampling\n- Better quality\n\n\nWe are releasing 3 checkpoints for the text-conditional image generation model (Stage C). Those are:\n\n- v2-base\n- v2-aesthetic\n- **(default)** v2-interpolated (50% interpolation between v2-base and v2-aesthetic)\n\nWe recommend using v2-interpolated, as it has a nice touch of both photorealism and aesthetics. Use v2-base for finetunings as it does not have a style bias and use v2-aesthetic for very artistic generations.\nA comparison can be seen here:\n\n\n\n## Text-to-Image Generation\n\nFor the sake of usability, Würstchen can be used with a single pipeline. This pipeline can be used as follows:\n\n```python\nimport torch\nfrom diffusers import AutoPipelineForText2Image\nfrom diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS\n\npipe = AutoPipelineForText2Image.from_pretrained(\"warp-ai/wuerstchen\", torch_dtype=torch.float16).to(\"cuda\")\n\ncaption = \"Anthropomorphic cat dressed as a fire fighter\"\nimages = pipe(\n caption,\n width=1024,\n height=1536,\n prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS,\n prior_guidance_scale=4.0,\n num_images_per_prompt=2,\n).images\n```\n\nFor explanation purposes, we can also initialize the two main pipelines of Würstchen individually. Würstchen consists of 3 stages: Stage C, Stage B, Stage A. They all have different jobs and work only together. When generating text-conditional images, Stage C will first generate the latents in a very compressed latent space. This is what happens in the `prior_pipeline`. Afterwards, the generated latents will be passed to Stage B, which decompresses the latents into a bigger latent space of a VQGAN. These latents can then be decoded by Stage A, which is a VQGAN, into the pixel-space. Stage B & Stage A are both encapsulated in the `decoder_pipeline`. For more details, take a look at the [paper](https://huggingface.co/papers/2306.00637).\n\n```python\nimport torch\nfrom diffusers import WuerstchenDecoderPipeline, WuerstchenPriorPipeline\nfrom diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS\n\ndevice = \"cuda\"\ndtype = torch.float16\nnum_images_per_prompt = 2\n\nprior_pipeline = WuerstchenPriorPipeline.from_pretrained(\n \"warp-ai/wuerstchen-prior\", torch_dtype=dtype\n).to(device)\ndecoder_pipeline = WuerstchenDecoderPipeline.from_pretrained(\n \"warp-ai/wuerstchen\", torch_dtype=dtype\n).to(device)\n\ncaption = \"Anthropomorphic cat dressed as a fire fighter\"\nnegative_prompt = \"\"\n\nprior_output = prior_pipeline(\n prompt=caption,\n height=1024,\n width=1536,\n timesteps=DEFAULT_STAGE_C_TIMESTEPS,\n negative_prompt=negative_prompt,\n guidance_scale=4.0,\n num_images_per_prompt=num_images_per_prompt,\n)\ndecoder_output = decoder_pipeline(\n image_embeddings=prior_output.image_embeddings,\n prompt=caption,\n negative_prompt=negative_prompt,\n guidance_scale=0.0,\n output_type=\"pil\",\n).images[0]\ndecoder_output\n```\n\n## Speed-Up Inference\nYou can make use of `torch.compile` function and gain a speed-up of about 2-3x:\n\n```python\nprior_pipeline.prior = torch.compile(prior_pipeline.prior, mode=\"reduce-overhead\", fullgraph=True)\ndecoder_pipeline.decoder = torch.compile(decoder_pipeline.decoder, mode=\"reduce-overhead\", fullgraph=True)\n```\n\n## Limitations\n\n- Due to the high compression employed by Würstchen, generations can lack a good amount\nof detail. To our human eye, this is especially noticeable in faces, hands etc.\n- **Images can only be generated in 128-pixel steps**, e.g. the next higher resolution\nafter 1024x1024 is 1152x1152\n- The model lacks the ability to render correct text in images\n- The model often does not achieve photorealism\n- Difficult compositional prompts are hard for the model\n\nThe original codebase, as well as experimental ideas, can be found at [dome272/Wuerstchen](https://github.com/dome272/Wuerstchen).\n\n\n## WuerstchenCombinedPipeline\n\n[[autodoc]] WuerstchenCombinedPipeline\n\t- all\n\t- __call__\n\n## WuerstchenPriorPipeline\n\n[[autodoc]] WuerstchenPriorPipeline\n\t- all\n\t- __call__\n\n## WuerstchenPriorPipelineOutput\n\n[[autodoc]] pipelines.wuerstchen.pipeline_wuerstchen_prior.WuerstchenPriorPipelineOutput\n\n## WuerstchenDecoderPipeline\n\n[[autodoc]] WuerstchenDecoderPipeline\n\t- all\n\t- __call__\n\n## Citation\n\n```bibtex\n @misc{pernias2023wuerstchen,\n title={Wuerstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models},\n author={Pablo Pernias and Dominic Rampas and Mats L. Richter and Christopher J. Pal and Marc Aubreville},\n year={2023},\n eprint={2306.00637},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n }\n```\n" }, { "path": "diffusers/docs/source/en/api/schedulers/cm_stochastic_iterative.md", "content": "\n\n# CMStochasticIterativeScheduler\n\n[Consistency Models](https://huggingface.co/papers/2303.01469) by Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever introduced a multistep and onestep scheduler (Algorithm 1) that is capable of generating good samples in one or a small number of steps.\n\nThe abstract from the paper is:\n\n*Diffusion models have significantly advanced the fields of image, audio, and video generation, but they depend on an iterative sampling process that causes slow generation. To overcome this limitation, we propose consistency models, a new family of models that generate high quality samples by directly mapping noise to data. They support fast one-step generation by design, while still allowing multistep sampling to trade compute for sample quality. They also support zero-shot data editing, such as image inpainting, colorization, and super-resolution, without requiring explicit training on these tasks. Consistency models can be trained either by distilling pre-trained diffusion models, or as standalone generative models altogether. Through extensive experiments, we demonstrate that they outperform existing distillation techniques for diffusion models in one- and few-step sampling, achieving the new state-of-the-art FID of 3.55 on CIFAR-10 and 6.20 on ImageNet 64x64 for one-step generation. When trained in isolation, consistency models become a new family of generative models that can outperform existing one-step, non-adversarial generative models on standard benchmarks such as CIFAR-10, ImageNet 64x64 and LSUN 256x256.*\n\nThe original codebase can be found at [openai/consistency_models](https://github.com/openai/consistency_models).\n\n## CMStochasticIterativeScheduler\n[[autodoc]] CMStochasticIterativeScheduler\n\n## CMStochasticIterativeSchedulerOutput\n[[autodoc]] schedulers.scheduling_consistency_models.CMStochasticIterativeSchedulerOutput\n" }, { "path": "diffusers/docs/source/en/api/schedulers/consistency_decoder.md", "content": "\n\n# ConsistencyDecoderScheduler\n\nThis scheduler is a part of the [`ConsistencyDecoderPipeline`] and was introduced in [DALL-E 3](https://openai.com/dall-e-3).\n\nThe original codebase can be found at [openai/consistency_models](https://github.com/openai/consistency_models).\n\n\n## ConsistencyDecoderScheduler\n[[autodoc]] schedulers.scheduling_consistency_decoder.ConsistencyDecoderScheduler\n" }, { "path": "diffusers/docs/source/en/api/schedulers/ddim.md", "content": "\n\n# DDIMScheduler\n\n[Denoising Diffusion Implicit Models](https://huggingface.co/papers/2010.02502) (DDIM) by Jiaming Song, Chenlin Meng and Stefano Ermon.\n\nThe abstract from the paper is:\n\n*Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample.\nTo accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models\nwith the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a Markovian diffusion process.\nWe construct a class of non-Markovian diffusion processes that lead to the same training objective, but whose reverse process can be much faster to sample from.\nWe empirically demonstrate that DDIMs can produce high quality samples 10× to 50× faster in terms of wall-clock time compared to DDPMs, allow us to trade off computation for sample quality, and can perform semantically meaningful image interpolation directly in the latent space.*\n\nThe original codebase of this paper can be found at [ermongroup/ddim](https://github.com/ermongroup/ddim), and you can contact the author on [tsong.me](https://tsong.me/).\n\n## Tips\n\nThe paper [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) claims that a mismatch between the training and inference settings leads to suboptimal inference generation results for Stable Diffusion. To fix this, the authors propose:\n\n\n\n🧪 This is an experimental feature!\n\n\n\n1. rescale the noise schedule to enforce zero terminal signal-to-noise ratio (SNR)\n\n```py\npipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, rescale_betas_zero_snr=True)\n```\n\n2. train a model with `v_prediction` (add the following argument to the [train_text_to_image.py](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) or [train_text_to_image_lora.py](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py) scripts)\n\n```bash\n--prediction_type=\"v_prediction\"\n```\n\n3. change the sampler to always start from the last timestep\n\n```py\npipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing=\"trailing\")\n```\n\n4. rescale classifier-free guidance to prevent over-exposure\n\n```py\nimage = pipe(prompt, guidance_rescale=0.7).images[0]\n```\n\nFor example:\n\n```py\nfrom diffusers import DiffusionPipeline, DDIMScheduler\nimport torch\n\npipe = DiffusionPipeline.from_pretrained(\"ptx0/pseudo-journey-v2\", torch_dtype=torch.float16)\npipe.scheduler = DDIMScheduler.from_config(\n pipe.scheduler.config, rescale_betas_zero_snr=True, timestep_spacing=\"trailing\"\n)\npipe.to(\"cuda\")\n\nprompt = \"A lion in galaxies, spirals, nebulae, stars, smoke, iridescent, intricate detail, octane render, 8k\"\nimage = pipe(prompt, guidance_rescale=0.7).images[0]\nimage\n```\n\n## DDIMScheduler\n[[autodoc]] DDIMScheduler\n\n## DDIMSchedulerOutput\n[[autodoc]] schedulers.scheduling_ddim.DDIMSchedulerOutput\n" }, { "path": "diffusers/docs/source/en/api/schedulers/ddim_inverse.md", "content": "\n\n# DDIMInverseScheduler\n\n`DDIMInverseScheduler` is the inverted scheduler from [Denoising Diffusion Implicit Models](https://huggingface.co/papers/2010.02502) (DDIM) by Jiaming Song, Chenlin Meng and Stefano Ermon.\nThe implementation is mostly based on the DDIM inversion definition from [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://huggingface.co/papers/2211.09794).\n\n## DDIMInverseScheduler\n[[autodoc]] DDIMInverseScheduler\n" }, { "path": "diffusers/docs/source/en/api/schedulers/ddpm.md", "content": "\n\n# DDPMScheduler\n\n[Denoising Diffusion Probabilistic Models](https://huggingface.co/papers/2006.11239) (DDPM) by Jonathan Ho, Ajay Jain and Pieter Abbeel proposes a diffusion based model of the same name. In the context of the 🤗 Diffusers library, DDPM refers to the discrete denoising scheduler from the paper as well as the pipeline.\n\nThe abstract from the paper is:\n\n*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at [this https URL](https://github.com/hojonathanho/diffusion).*\n\n## DDPMScheduler\n[[autodoc]] DDPMScheduler\n\n## DDPMSchedulerOutput\n[[autodoc]] schedulers.scheduling_ddpm.DDPMSchedulerOutput\n" }, { "path": "diffusers/docs/source/en/api/schedulers/deis.md", "content": "\n\n# DEISMultistepScheduler\n\nDiffusion Exponential Integrator Sampler (DEIS) is proposed in [Fast Sampling of Diffusion Models with Exponential Integrator](https://huggingface.co/papers/2204.13902) by Qinsheng Zhang and Yongxin Chen. `DEISMultistepScheduler` is a fast high order solver for diffusion ordinary differential equations (ODEs).\n\nThis implementation modifies the polynomial fitting formula in log-rho space instead of the original linear `t` space in the DEIS paper. The modification enjoys closed-form coefficients for exponential multistep update instead of replying on the numerical solver.\n\nThe abstract from the paper is:\n\n*The past few years have witnessed the great success of Diffusion models~(DMs) in generating high-fidelity samples in generative modeling tasks. A major limitation of the DM is its notoriously slow sampling procedure which normally requires hundreds to thousands of time discretization steps of the learned diffusion process to reach the desired accuracy. Our goal is to develop a fast sampling method for DMs with a much less number of steps while retaining high sample quality. To this end, we systematically analyze the sampling procedure in DMs and identify key factors that affect the sample quality, among which the method of discretization is most crucial. By carefully examining the learned diffusion process, we propose Diffusion Exponential Integrator Sampler~(DEIS). It is based on the Exponential Integrator designed for discretizing ordinary differential equations (ODEs) and leverages a semilinear structure of the learned diffusion process to reduce the discretization error. The proposed method can be applied to any DMs and can generate high-fidelity samples in as few as 10 steps. In our experiments, it takes about 3 minutes on one A6000 GPU to generate 50k images from CIFAR10. Moreover, by directly using pre-trained DMs, we achieve the state-of-art sampling performance when the number of score function evaluation~(NFE) is limited, e.g., 4.17 FID with 10 NFEs, 3.37 FID, and 9.74 IS with only 15 NFEs on CIFAR10. Code is available at [this https URL](https://github.com/qsh-zh/deis).*\n\n## Tips\n\nIt is recommended to set `solver_order` to 2 or 3, while `solver_order=1` is equivalent to [`DDIMScheduler`].\n\nDynamic thresholding from [Imagen](https://huggingface.co/papers/2205.11487) is supported, and for pixel-space\ndiffusion models, you can set `thresholding=True` to use the dynamic thresholding.\n\n## DEISMultistepScheduler\n[[autodoc]] DEISMultistepScheduler\n\n## SchedulerOutput\n[[autodoc]] schedulers.scheduling_utils.SchedulerOutput\n" }, { "path": "diffusers/docs/source/en/api/schedulers/dpm_discrete.md", "content": "\n\n# KDPM2DiscreteScheduler\n\nThe `KDPM2DiscreteScheduler` is inspired by the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper, and the scheduler is ported from and created by [Katherine Crowson](https://github.com/crowsonkb/).\n\nThe original codebase can be found at [crowsonkb/k-diffusion](https://github.com/crowsonkb/k-diffusion).\n\n## KDPM2DiscreteScheduler\n[[autodoc]] KDPM2DiscreteScheduler\n\n## SchedulerOutput\n[[autodoc]] schedulers.scheduling_utils.SchedulerOutput\n" }, { "path": "diffusers/docs/source/en/api/schedulers/dpm_discrete_ancestral.md", "content": "\n\n# KDPM2AncestralDiscreteScheduler\n\nThe `KDPM2DiscreteScheduler` with ancestral sampling is inspired by the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper, and the scheduler is ported from and created by [Katherine Crowson](https://github.com/crowsonkb/).\n\nThe original codebase can be found at [crowsonkb/k-diffusion](https://github.com/crowsonkb/k-diffusion).\n\n## KDPM2AncestralDiscreteScheduler\n[[autodoc]] KDPM2AncestralDiscreteScheduler\n\n## SchedulerOutput\n[[autodoc]] schedulers.scheduling_utils.SchedulerOutput\n" }, { "path": "diffusers/docs/source/en/api/schedulers/dpm_sde.md", "content": "\n\n# DPMSolverSDEScheduler\n\nThe `DPMSolverSDEScheduler` is inspired by the stochastic sampler from the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper, and the scheduler is ported from and created by [Katherine Crowson](https://github.com/crowsonkb/).\n\n## DPMSolverSDEScheduler\n[[autodoc]] DPMSolverSDEScheduler\n\n## SchedulerOutput\n[[autodoc]] schedulers.scheduling_utils.SchedulerOutput\n" }, { "path": "diffusers/docs/source/en/api/schedulers/edm_euler.md", "content": "\n\n# EDMEulerScheduler\n\nThe Karras formulation of the Euler scheduler (Algorithm 2) from the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper by Karras et al. This is a fast scheduler which can often generate good outputs in 20-30 steps. The scheduler is based on the original [k-diffusion](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L51) implementation by [Katherine Crowson](https://github.com/crowsonkb/).\n\n\n## EDMEulerScheduler\n[[autodoc]] EDMEulerScheduler\n\n## EDMEulerSchedulerOutput\n[[autodoc]] schedulers.scheduling_edm_euler.EDMEulerSchedulerOutput\n" }, { "path": "diffusers/docs/source/en/api/schedulers/edm_multistep_dpm_solver.md", "content": "\n\n# EDMDPMSolverMultistepScheduler\n\n`EDMDPMSolverMultistepScheduler` is a [Karras formulation](https://huggingface.co/papers/2206.00364) of `DPMSolverMultistepScheduler`, a multistep scheduler from [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://huggingface.co/papers/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.\n\nDPMSolver (and the improved version DPMSolver++) is a fast dedicated high-order solver for diffusion ODEs with convergence order guarantee. Empirically, DPMSolver sampling with only 20 steps can generate high-quality\nsamples, and it can generate quite good samples even in 10 steps.\n\n## EDMDPMSolverMultistepScheduler\n[[autodoc]] EDMDPMSolverMultistepScheduler\n\n## SchedulerOutput\n[[autodoc]] schedulers.scheduling_utils.SchedulerOutput\n" }, { "path": "diffusers/docs/source/en/api/schedulers/euler.md", "content": "\n\n# EulerDiscreteScheduler\n\nThe Euler scheduler (Algorithm 2) is from the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper by Karras et al. This is a fast scheduler which can often generate good outputs in 20-30 steps. The scheduler is based on the original [k-diffusion](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L51) implementation by [Katherine Crowson](https://github.com/crowsonkb/).\n\n\n## EulerDiscreteScheduler\n[[autodoc]] EulerDiscreteScheduler\n\n## EulerDiscreteSchedulerOutput\n[[autodoc]] schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput\n" }, { "path": "diffusers/docs/source/en/api/schedulers/euler_ancestral.md", "content": "\n\n# EulerAncestralDiscreteScheduler\n\nA scheduler that uses ancestral sampling with Euler method steps. This is a fast scheduler which can often generate good outputs in 20-30 steps. The scheduler is based on the original [k-diffusion](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L72) implementation by [Katherine Crowson](https://github.com/crowsonkb/).\n\n## EulerAncestralDiscreteScheduler\n[[autodoc]] EulerAncestralDiscreteScheduler\n\n## EulerAncestralDiscreteSchedulerOutput\n[[autodoc]] schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteSchedulerOutput\n" }, { "path": "diffusers/docs/source/en/api/schedulers/flow_match_euler_discrete.md", "content": "\n\n# FlowMatchEulerDiscreteScheduler\n\n`FlowMatchEulerDiscreteScheduler` is based on the flow-matching sampling introduced in [Stable Diffusion 3](https://arxiv.org/abs/2403.03206).\n\n## FlowMatchEulerDiscreteScheduler\n[[autodoc]] FlowMatchEulerDiscreteScheduler\n" }, { "path": "diffusers/docs/source/en/api/schedulers/heun.md", "content": "\n\n# HeunDiscreteScheduler\n\nThe Heun scheduler (Algorithm 1) is from the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper by Karras et al. The scheduler is ported from the [k-diffusion](https://github.com/crowsonkb/k-diffusion) library and created by [Katherine Crowson](https://github.com/crowsonkb/).\n\n## HeunDiscreteScheduler\n[[autodoc]] HeunDiscreteScheduler\n\n## SchedulerOutput\n[[autodoc]] schedulers.scheduling_utils.SchedulerOutput\n" }, { "path": "diffusers/docs/source/en/api/schedulers/ipndm.md", "content": "\n\n# IPNDMScheduler\n\n`IPNDMScheduler` is a fourth-order Improved Pseudo Linear Multistep scheduler. The original implementation can be found at [crowsonkb/v-diffusion-pytorch](https://github.com/crowsonkb/v-diffusion-pytorch/blob/987f8985e38208345c1959b0ea767a625831cc9b/diffusion/sampling.py#L296).\n\n## IPNDMScheduler\n[[autodoc]] IPNDMScheduler\n\n## SchedulerOutput\n[[autodoc]] schedulers.scheduling_utils.SchedulerOutput\n" }, { "path": "diffusers/docs/source/en/api/schedulers/lcm.md", "content": "\n\n# Latent Consistency Model Multistep Scheduler\n\n## Overview\n\nMultistep and onestep scheduler (Algorithm 3) introduced alongside latent consistency models in the paper [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://arxiv.org/abs/2310.04378) by Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao.\nThis scheduler should be able to generate good samples from [`LatentConsistencyModelPipeline`] in 1-8 steps.\n\n## LCMScheduler\n[[autodoc]] LCMScheduler\n" }, { "path": "diffusers/docs/source/en/api/schedulers/lms_discrete.md", "content": "\n\n# LMSDiscreteScheduler\n\n`LMSDiscreteScheduler` is a linear multistep scheduler for discrete beta schedules. The scheduler is ported from and created by [Katherine Crowson](https://github.com/crowsonkb/), and the original implementation can be found at [crowsonkb/k-diffusion](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181).\n\n## LMSDiscreteScheduler\n[[autodoc]] LMSDiscreteScheduler\n\n## LMSDiscreteSchedulerOutput\n[[autodoc]] schedulers.scheduling_lms_discrete.LMSDiscreteSchedulerOutput\n" }, { "path": "diffusers/docs/source/en/api/schedulers/multistep_dpm_solver.md", "content": "\n\n# DPMSolverMultistepScheduler\n\n`DPMSolverMultistepScheduler` is a multistep scheduler from [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://huggingface.co/papers/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.\n\nDPMSolver (and the improved version DPMSolver++) is a fast dedicated high-order solver for diffusion ODEs with convergence order guarantee. Empirically, DPMSolver sampling with only 20 steps can generate high-quality\nsamples, and it can generate quite good samples even in 10 steps.\n\n## Tips\n\nIt is recommended to set `solver_order` to 2 for guide sampling, and `solver_order=3` for unconditional sampling.\n\nDynamic thresholding from [Imagen](https://huggingface.co/papers/2205.11487) is supported, and for pixel-space\ndiffusion models, you can set both `algorithm_type=\"dpmsolver++\"` and `thresholding=True` to use the dynamic\nthresholding. This thresholding method is unsuitable for latent-space diffusion models such as\nStable Diffusion.\n\nThe SDE variant of DPMSolver and DPM-Solver++ is also supported, but only for the first and second-order solvers. This is a fast SDE solver for the reverse diffusion SDE. It is recommended to use the second-order `sde-dpmsolver++`.\n\n## DPMSolverMultistepScheduler\n[[autodoc]] DPMSolverMultistepScheduler\n\n## SchedulerOutput\n[[autodoc]] schedulers.scheduling_utils.SchedulerOutput\n" }, { "path": "diffusers/docs/source/en/api/schedulers/multistep_dpm_solver_inverse.md", "content": "\n\n# DPMSolverMultistepInverse\n\n`DPMSolverMultistepInverse` is the inverted scheduler from [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://huggingface.co/papers/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.\n\nThe implementation is mostly based on the DDIM inversion definition of [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://huggingface.co/papers/2211.09794) and notebook implementation of the [`DiffEdit`] latent inversion from [Xiang-cd/DiffEdit-stable-diffusion](https://github.com/Xiang-cd/DiffEdit-stable-diffusion/blob/main/diffedit.ipynb).\n\n## Tips\n\nDynamic thresholding from [Imagen](https://huggingface.co/papers/2205.11487) is supported, and for pixel-space\ndiffusion models, you can set both `algorithm_type=\"dpmsolver++\"` and `thresholding=True` to use the dynamic\nthresholding. This thresholding method is unsuitable for latent-space diffusion models such as\nStable Diffusion.\n\n## DPMSolverMultistepInverseScheduler\n[[autodoc]] DPMSolverMultistepInverseScheduler\n\n## SchedulerOutput\n[[autodoc]] schedulers.scheduling_utils.SchedulerOutput\n" }, { "path": "diffusers/docs/source/en/api/schedulers/overview.md", "content": "\n\n# Schedulers\n\n🤗 Diffusers provides many scheduler functions for the diffusion process. A scheduler takes a model's output (the sample which the diffusion process is iterating on) and a timestep to return a denoised sample. The timestep is important because it dictates where in the diffusion process the step is; data is generated by iterating forward *n* timesteps and inference occurs by propagating backward through the timesteps. Based on the timestep, a scheduler may be *discrete* in which case the timestep is an `int` or *continuous* in which case the timestep is a `float`.\n\nDepending on the context, a scheduler defines how to iteratively add noise to an image or how to update a sample based on a model's output:\n\n- during *training*, a scheduler adds noise (there are different algorithms for how to add noise) to a sample to train a diffusion model\n- during *inference*, a scheduler defines how to update a sample based on a pretrained model's output\n\nMany schedulers are implemented from the [k-diffusion](https://github.com/crowsonkb/k-diffusion) library by [Katherine Crowson](https://github.com/crowsonkb/), and they're also widely used in A1111. To help you map the schedulers from k-diffusion and A1111 to the schedulers in 🤗 Diffusers, take a look at the table below:\n\n| A1111/k-diffusion | 🤗 Diffusers | Usage |\n|---------------------|-------------------------------------|---------------------------------------------------------------------------------------------------------------|\n| DPM++ 2M | [`DPMSolverMultistepScheduler`] | |\n| DPM++ 2M Karras | [`DPMSolverMultistepScheduler`] | init with `use_karras_sigmas=True` |\n| DPM++ 2M SDE | [`DPMSolverMultistepScheduler`] | init with `algorithm_type=\"sde-dpmsolver++\"` |\n| DPM++ 2M SDE Karras | [`DPMSolverMultistepScheduler`] | init with `use_karras_sigmas=True` and `algorithm_type=\"sde-dpmsolver++\"` |\n| DPM++ 2S a | N/A | very similar to `DPMSolverSinglestepScheduler` |\n| DPM++ 2S a Karras | N/A | very similar to `DPMSolverSinglestepScheduler(use_karras_sigmas=True, ...)` |\n| DPM++ SDE | [`DPMSolverSinglestepScheduler`] | |\n| DPM++ SDE Karras | [`DPMSolverSinglestepScheduler`] | init with `use_karras_sigmas=True` |\n| DPM2 | [`KDPM2DiscreteScheduler`] | |\n| DPM2 Karras | [`KDPM2DiscreteScheduler`] | init with `use_karras_sigmas=True` |\n| DPM2 a | [`KDPM2AncestralDiscreteScheduler`] | |\n| DPM2 a Karras | [`KDPM2AncestralDiscreteScheduler`] | init with `use_karras_sigmas=True` |\n| DPM adaptive | N/A | |\n| DPM fast | N/A | |\n| Euler | [`EulerDiscreteScheduler`] | |\n| Euler a | [`EulerAncestralDiscreteScheduler`] | |\n| Heun | [`HeunDiscreteScheduler`] | |\n| LMS | [`LMSDiscreteScheduler`] | |\n| LMS Karras | [`LMSDiscreteScheduler`] | init with `use_karras_sigmas=True` |\n| N/A | [`DEISMultistepScheduler`] | |\n| N/A | [`UniPCMultistepScheduler`] | |\n\nAll schedulers are built from the base [`SchedulerMixin`] class which implements low level utilities shared by all schedulers.\n\n## SchedulerMixin\n[[autodoc]] SchedulerMixin\n\n## SchedulerOutput\n[[autodoc]] schedulers.scheduling_utils.SchedulerOutput\n\n## KarrasDiffusionSchedulers\n\n[`KarrasDiffusionSchedulers`] are a broad generalization of schedulers in 🤗 Diffusers. The schedulers in this class are distinguished at a high level by their noise sampling strategy, the type of network and scaling, the training strategy, and how the loss is weighed.\n\nThe different schedulers in this class, depending on the ordinary differential equations (ODE) solver type, fall into the above taxonomy and provide a good abstraction for the design of the main schedulers implemented in 🤗 Diffusers. The schedulers in this class are given [here](https://github.com/huggingface/diffusers/blob/a69754bb879ed55b9b6dc9dd0b3cf4fa4124c765/src/diffusers/schedulers/scheduling_utils.py#L32).\n\n## PushToHubMixin\n\n[[autodoc]] utils.PushToHubMixin\n" }, { "path": "diffusers/docs/source/en/api/schedulers/pndm.md", "content": "\n\n# PNDMScheduler\n\n`PNDMScheduler`, or pseudo numerical methods for diffusion models, uses more advanced ODE integration techniques like the Runge-Kutta and linear multi-step method. The original implementation can be found at [crowsonkb/k-diffusion](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181).\n\n## PNDMScheduler\n[[autodoc]] PNDMScheduler\n\n## SchedulerOutput\n[[autodoc]] schedulers.scheduling_utils.SchedulerOutput\n" }, { "path": "diffusers/docs/source/en/api/schedulers/repaint.md", "content": "\n\n# RePaintScheduler\n\n`RePaintScheduler` is a DDPM-based inpainting scheduler for unsupervised inpainting with extreme masks. It is designed to be used with the [`RePaintPipeline`], and it is based on the paper [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://huggingface.co/papers/2201.09865) by Andreas Lugmayr et al.\n\nThe abstract from the paper is:\n\n*Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Most existing approaches train for a certain distribution of masks, which limits their generalization capabilities to unseen mask types. Furthermore, training with pixel-wise and perceptual losses often leads to simple textural extensions towards the missing areas instead of semantically meaningful generation. In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks. We employ a pretrained unconditional DDPM as the generative prior. To condition the generation process, we only alter the reverse diffusion iterations by sampling the unmasked regions using the given image information. Since this technique does not modify or condition the original DDPM network itself, the model produces high-quality and diverse output images for any inpainting form. We validate our method for both faces and general-purpose image inpainting using standard and extreme masks. RePaint outperforms state-of-the-art Autoregressive, and GAN approaches for at least five out of six mask distributions. GitHub Repository: [this http URL](http://git.io/RePaint).*\n\nThe original implementation can be found at [andreas128/RePaint](https://github.com/andreas128/).\n\n## RePaintScheduler\n[[autodoc]] RePaintScheduler\n\n## RePaintSchedulerOutput\n[[autodoc]] schedulers.scheduling_repaint.RePaintSchedulerOutput\n" }, { "path": "diffusers/docs/source/en/api/schedulers/score_sde_ve.md", "content": "\n\n# ScoreSdeVeScheduler\n\n`ScoreSdeVeScheduler` is a variance exploding stochastic differential equation (SDE) scheduler. It was introduced in the [Score-Based Generative Modeling through Stochastic Differential Equations](https://huggingface.co/papers/2011.13456) paper by Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole.\n\nThe abstract from the paper is:\n\n*Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reverse-time SDE depends only on the time-dependent gradient field (\\aka, score) of the perturbed data distribution. By leveraging advances in score-based generative modeling, we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples. We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling, allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE. We also derive an equivalent neural ODE that samples from the same distribution as the SDE, but additionally enables exact likelihood computation, and improved sampling efficiency. In addition, we provide a new way to solve inverse problems with score-based models, as demonstrated with experiments on class-conditional generation, image inpainting, and colorization. Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.*\n\n## ScoreSdeVeScheduler\n[[autodoc]] ScoreSdeVeScheduler\n\n## SdeVeOutput\n[[autodoc]] schedulers.scheduling_sde_ve.SdeVeOutput\n" }, { "path": "diffusers/docs/source/en/api/schedulers/score_sde_vp.md", "content": "\n\n# ScoreSdeVpScheduler\n\n`ScoreSdeVpScheduler` is a variance preserving stochastic differential equation (SDE) scheduler. It was introduced in the [Score-Based Generative Modeling through Stochastic Differential Equations](https://huggingface.co/papers/2011.13456) paper by Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole.\n\nThe abstract from the paper is:\n\n*Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reverse-time SDE depends only on the time-dependent gradient field (\\aka, score) of the perturbed data distribution. By leveraging advances in score-based generative modeling, we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples. We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling, allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE. We also derive an equivalent neural ODE that samples from the same distribution as the SDE, but additionally enables exact likelihood computation, and improved sampling efficiency. In addition, we provide a new way to solve inverse problems with score-based models, as demonstrated with experiments on class-conditional generation, image inpainting, and colorization. Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.*\n\n\n\n🚧 This scheduler is under construction!\n\n\n\n## ScoreSdeVpScheduler\n[[autodoc]] schedulers.deprecated.scheduling_sde_vp.ScoreSdeVpScheduler\n" }, { "path": "diffusers/docs/source/en/api/schedulers/singlestep_dpm_solver.md", "content": "\n\n# DPMSolverSinglestepScheduler\n\n`DPMSolverSinglestepScheduler` is a single step scheduler from [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) and [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://huggingface.co/papers/2211.01095) by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.\n\nDPMSolver (and the improved version DPMSolver++) is a fast dedicated high-order solver for diffusion ODEs with convergence order guarantee. Empirically, DPMSolver sampling with only 20 steps can generate high-quality\nsamples, and it can generate quite good samples even in 10 steps.\n\nThe original implementation can be found at [LuChengTHU/dpm-solver](https://github.com/LuChengTHU/dpm-solver).\n\n## Tips\n\nIt is recommended to set `solver_order` to 2 for guide sampling, and `solver_order=3` for unconditional sampling.\n\nDynamic thresholding from [Imagen](https://huggingface.co/papers/2205.11487) is supported, and for pixel-space\ndiffusion models, you can set both `algorithm_type=\"dpmsolver++\"` and `thresholding=True` to use dynamic\nthresholding. This thresholding method is unsuitable for latent-space diffusion models such as\nStable Diffusion.\n\n## DPMSolverSinglestepScheduler\n[[autodoc]] DPMSolverSinglestepScheduler\n\n## SchedulerOutput\n[[autodoc]] schedulers.scheduling_utils.SchedulerOutput\n" }, { "path": "diffusers/docs/source/en/api/schedulers/stochastic_karras_ve.md", "content": "\n\n# KarrasVeScheduler\n\n`KarrasVeScheduler` is a stochastic sampler tailored to variance-expanding (VE) models. It is based on the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) and [Score-based generative modeling through stochastic differential equations](https://huggingface.co/papers/2011.13456) papers.\n\n## KarrasVeScheduler\n[[autodoc]] KarrasVeScheduler\n\n## KarrasVeOutput\n[[autodoc]] schedulers.deprecated.scheduling_karras_ve.KarrasVeOutput" }, { "path": "diffusers/docs/source/en/api/schedulers/tcd.md", "content": "\n\n# TCDScheduler \n\n[Trajectory Consistency Distillation](https://huggingface.co/papers/2402.19159) by Jianbin Zheng, Minghui Hu, Zhongyi Fan, Chaoyue Wang, Changxing Ding, Dacheng Tao and Tat-Jen Cham introduced a Strategic Stochastic Sampling (Algorithm 4) that is capable of generating good samples in a small number of steps. Distinguishing it as an advanced iteration of the multistep scheduler (Algorithm 1) in the [Consistency Models](https://huggingface.co/papers/2303.01469), Strategic Stochastic Sampling specifically tailored for the trajectory consistency function.\n\nThe abstract from the paper is:\n\n*Latent Consistency Model (LCM) extends the Consistency Model to the latent space and leverages the guided consistency distillation technique to achieve impressive performance in accelerating text-to-image synthesis. However, we observed that LCM struggles to generate images with both clarity and detailed intricacy. To address this limitation, we initially delve into and elucidate the underlying causes. Our investigation identifies that the primary issue stems from errors in three distinct areas. Consequently, we introduce Trajectory Consistency Distillation (TCD), which encompasses trajectory consistency function and strategic stochastic sampling. The trajectory consistency function diminishes the distillation errors by broadening the scope of the self-consistency boundary condition and endowing the TCD with the ability to accurately trace the entire trajectory of the Probability Flow ODE. Additionally, strategic stochastic sampling is specifically designed to circumvent the accumulated errors inherent in multi-step consistency sampling, which is meticulously tailored to complement the TCD model. Experiments demonstrate that TCD not only significantly enhances image quality at low NFEs but also yields more detailed results compared to the teacher model at high NFEs.*\n\nThe original codebase can be found at [jabir-zheng/TCD](https://github.com/jabir-zheng/TCD).\n\n## TCDScheduler\n[[autodoc]] TCDScheduler\n\n\n## TCDSchedulerOutput\n[[autodoc]] schedulers.scheduling_tcd.TCDSchedulerOutput\n\n" }, { "path": "diffusers/docs/source/en/api/schedulers/unipc.md", "content": "\n\n# UniPCMultistepScheduler\n\n`UniPCMultistepScheduler` is a training-free framework designed for fast sampling of diffusion models. It was introduced in [UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models](https://huggingface.co/papers/2302.04867) by Wenliang Zhao, Lujia Bai, Yongming Rao, Jie Zhou, Jiwen Lu.\n\nIt consists of a corrector (UniC) and a predictor (UniP) that share a unified analytical form and support arbitrary orders.\nUniPC is by design model-agnostic, supporting pixel-space/latent-space DPMs on unconditional/conditional sampling. It can also be applied to both noise prediction and data prediction models. The corrector UniC can be also applied after any off-the-shelf solvers to increase the order of accuracy.\n\nThe abstract from the paper is:\n\n*Diffusion probabilistic models (DPMs) have demonstrated a very promising ability in high-resolution image synthesis. However, sampling from a pre-trained DPM is time-consuming due to the multiple evaluations of the denoising network, making it more and more important to accelerate the sampling of DPMs. Despite recent progress in designing fast samplers, existing methods still cannot generate satisfying images in many applications where fewer steps (e.g., <10) are favored. In this paper, we develop a unified corrector (UniC) that can be applied after any existing DPM sampler to increase the order of accuracy without extra model evaluations, and derive a unified predictor (UniP) that supports arbitrary order as a byproduct. Combining UniP and UniC, we propose a unified predictor-corrector framework called UniPC for the fast sampling of DPMs, which has a unified analytical form for any order and can significantly improve the sampling quality over previous methods, especially in extremely few steps. We evaluate our methods through extensive experiments including both unconditional and conditional sampling using pixel-space and latent-space DPMs. Our UniPC can achieve 3.87 FID on CIFAR10 (unconditional) and 7.51 FID on ImageNet 256×256 (conditional) with only 10 function evaluations. Code is available at [this https URL](https://github.com/wl-zhao/UniPC).*\n\n## Tips\n\nIt is recommended to set `solver_order` to 2 for guide sampling, and `solver_order=3` for unconditional sampling.\n\nDynamic thresholding from [Imagen](https://huggingface.co/papers/2205.11487) is supported, and for pixel-space\ndiffusion models, you can set both `predict_x0=True` and `thresholding=True` to use dynamic thresholding. This thresholding method is unsuitable for latent-space diffusion models such as Stable Diffusion.\n\n## UniPCMultistepScheduler\n[[autodoc]] UniPCMultistepScheduler\n\n## SchedulerOutput\n[[autodoc]] schedulers.scheduling_utils.SchedulerOutput\n" }, { "path": "diffusers/docs/source/en/api/schedulers/vq_diffusion.md", "content": "\n\n# VQDiffusionScheduler\n\n`VQDiffusionScheduler` converts the transformer model's output into a sample for the unnoised image at the previous diffusion timestep. It was introduced in [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://huggingface.co/papers/2111.14822) by Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, Baining Guo.\n\nThe abstract from the paper is:\n\n*We present the vector quantized diffusion (VQ-Diffusion) model for text-to-image generation. This method is based on a vector quantized variational autoencoder (VQ-VAE) whose latent space is modeled by a conditional variant of the recently developed Denoising Diffusion Probabilistic Model (DDPM). We find that this latent-space method is well-suited for text-to-image generation tasks because it not only eliminates the unidirectional bias with existing methods but also allows us to incorporate a mask-and-replace diffusion strategy to avoid the accumulation of errors, which is a serious problem with existing methods. Our experiments show that the VQ-Diffusion produces significantly better text-to-image generation results when compared with conventional autoregressive (AR) models with similar numbers of parameters. Compared with previous GAN-based text-to-image methods, our VQ-Diffusion can handle more complex scenes and improve the synthesized image quality by a large margin. Finally, we show that the image generation computation in our method can be made highly efficient by reparameterization. With traditional AR methods, the text-to-image generation time increases linearly with the output image resolution and hence is quite time consuming even for normal size images. The VQ-Diffusion allows us to achieve a better trade-off between quality and speed. Our experiments indicate that the VQ-Diffusion model with the reparameterization is fifteen times faster than traditional AR methods while achieving a better image quality.*\n\n## VQDiffusionScheduler\n[[autodoc]] VQDiffusionScheduler\n\n## VQDiffusionSchedulerOutput\n[[autodoc]] schedulers.scheduling_vq_diffusion.VQDiffusionSchedulerOutput\n" }, { "path": "diffusers/docs/source/en/api/utilities.md", "content": "\n\n# Utilities\n\nUtility and helper functions for working with 🤗 Diffusers.\n\n## numpy_to_pil\n\n[[autodoc]] utils.numpy_to_pil\n\n## pt_to_pil\n\n[[autodoc]] utils.pt_to_pil\n\n## load_image\n\n[[autodoc]] utils.load_image\n\n## export_to_gif\n\n[[autodoc]] utils.export_to_gif\n\n## export_to_video\n\n[[autodoc]] utils.export_to_video\n\n## make_image_grid\n\n[[autodoc]] utils.make_image_grid\n\n## randn_tensor\n\n[[autodoc]] utils.torch_utils.randn_tensor\n" }, { "path": "diffusers/docs/source/en/api/video_processor.md", "content": "\n\n# Video Processor\n\nThe [`VideoProcessor`] provides a unified API for video pipelines to prepare inputs for VAE encoding and post-processing outputs once they're decoded. The class inherits [`VaeImageProcessor`] so it includes transformations such as resizing, normalization, and conversion between PIL Image, PyTorch, and NumPy arrays.\n\n## VideoProcessor\n\n[[autodoc]] video_processor.VideoProcessor.preprocess_video\n\n[[autodoc]] video_processor.VideoProcessor.postprocess_video\n" }, { "path": "diffusers/docs/source/en/conceptual/contribution.md", "content": "\n\n# How to contribute to Diffusers 🧨\n\nWe ❤️ contributions from the open-source community! Everyone is welcome, and all types of participation –not just code– are valued and appreciated. Answering questions, helping others, reaching out, and improving the documentation are all immensely valuable to the community, so don't be afraid and get involved if you're up for it!\n\nEveryone is encouraged to start by saying 👋 in our public Discord channel. We discuss the latest trends in diffusion models, ask questions, show off personal projects, help each other with contributions, or just hang out ☕. \"Join\n\nWhichever way you choose to contribute, we strive to be part of an open, welcoming, and kind community. Please, read our [code of conduct](https://github.com/huggingface/diffusers/blob/main/CODE_OF_CONDUCT.md) and be mindful to respect it during your interactions. We also recommend you become familiar with the [ethical guidelines](https://huggingface.co/docs/diffusers/conceptual/ethical_guidelines) that guide our project and ask you to adhere to the same principles of transparency and responsibility.\n\nWe enormously value feedback from the community, so please do not be afraid to speak up if you believe you have valuable feedback that can help improve the library - every message, comment, issue, and pull request (PR) is read and considered.\n\n## Overview\n\nYou can contribute in many ways ranging from answering questions on issues to adding new diffusion models to\nthe core library.\n\nIn the following, we give an overview of different ways to contribute, ranked by difficulty in ascending order. All of them are valuable to the community.\n\n* 1. Asking and answering questions on [the Diffusers discussion forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers) or on [Discord](https://discord.gg/G7tWnz98XR).\n* 2. Opening new issues on [the GitHub Issues tab](https://github.com/huggingface/diffusers/issues/new/choose).\n* 3. Answering issues on [the GitHub Issues tab](https://github.com/huggingface/diffusers/issues).\n* 4. Fix a simple issue, marked by the \"Good first issue\" label, see [here](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22).\n* 5. Contribute to the [documentation](https://github.com/huggingface/diffusers/tree/main/docs/source).\n* 6. Contribute a [Community Pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3Acommunity-examples).\n* 7. Contribute to the [examples](https://github.com/huggingface/diffusers/tree/main/examples).\n* 8. Fix a more difficult issue, marked by the \"Good second issue\" label, see [here](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22Good+second+issue%22).\n* 9. Add a new pipeline, model, or scheduler, see [\"New Pipeline/Model\"](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22) and [\"New scheduler\"](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22) issues. For this contribution, please have a look at [Design Philosophy](https://github.com/huggingface/diffusers/blob/main/PHILOSOPHY.md).\n\nAs said before, **all contributions are valuable to the community**.\nIn the following, we will explain each contribution a bit more in detail.\n\nFor all contributions 4 - 9, you will need to open a PR. It is explained in detail how to do so in [Opening a pull request](#how-to-open-a-pr).\n\n### 1. Asking and answering questions on the Diffusers discussion forum or on the Diffusers Discord\n\nAny question or comment related to the Diffusers library can be asked on the [discussion forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/) or on [Discord](https://discord.gg/G7tWnz98XR). Such questions and comments include (but are not limited to):\n- Reports of training or inference experiments in an attempt to share knowledge\n- Presentation of personal projects\n- Questions to non-official training examples\n- Project proposals\n- General feedback\n- Paper summaries\n- Asking for help on personal projects that build on top of the Diffusers library\n- General questions\n- Ethical questions regarding diffusion models\n- ...\n\nEvery question that is asked on the forum or on Discord actively encourages the community to publicly\nshare knowledge and might very well help a beginner in the future who has the same question you're\nhaving. Please do pose any questions you might have.\nIn the same spirit, you are of immense help to the community by answering such questions because this way you are publicly documenting knowledge for everybody to learn from.\n\n**Please** keep in mind that the more effort you put into asking or answering a question, the higher\nthe quality of the publicly documented knowledge. In the same way, well-posed and well-answered questions create a high-quality knowledge database accessible to everybody, while badly posed questions or answers reduce the overall quality of the public knowledge database.\nIn short, a high quality question or answer is *precise*, *concise*, *relevant*, *easy-to-understand*, *accessible*, and *well-formated/well-posed*. For more information, please have a look through the [How to write a good issue](#how-to-write-a-good-issue) section.\n\n**NOTE about channels**:\n[*The forum*](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) is much better indexed by search engines, such as Google. Posts are ranked by popularity rather than chronologically. Hence, it's easier to look up questions and answers that we posted some time ago.\nIn addition, questions and answers posted in the forum can easily be linked to.\nIn contrast, *Discord* has a chat-like format that invites fast back-and-forth communication.\nWhile it will most likely take less time for you to get an answer to your question on Discord, your\nquestion won't be visible anymore over time. Also, it's much harder to find information that was posted a while back on Discord. We therefore strongly recommend using the forum for high-quality questions and answers in an attempt to create long-lasting knowledge for the community. If discussions on Discord lead to very interesting answers and conclusions, we recommend posting the results on the forum to make the information more available for future readers.\n\n### 2. Opening new issues on the GitHub issues tab\n\nThe 🧨 Diffusers library is robust and reliable thanks to the users who notify us of\nthe problems they encounter. So thank you for reporting an issue.\n\nRemember, GitHub issues are reserved for technical questions directly related to the Diffusers library, bug reports, feature requests, or feedback on the library design.\n\nIn a nutshell, this means that everything that is **not** related to the **code of the Diffusers library** (including the documentation) should **not** be asked on GitHub, but rather on either the [forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) or [Discord](https://discord.gg/G7tWnz98XR).\n\n**Please consider the following guidelines when opening a new issue**:\n- Make sure you have searched whether your issue has already been asked before (use the search bar on GitHub under Issues).\n- Please never report a new issue on another (related) issue. If another issue is highly related, please\nopen a new issue nevertheless and link to the related issue.\n- Make sure your issue is written in English. Please use one of the great, free online translation services, such as [DeepL](https://www.deepl.com/translator) to translate from your native language to English if you are not comfortable in English.\n- Check whether your issue might be solved by updating to the newest Diffusers version. Before posting your issue, please make sure that `python -c \"import diffusers; print(diffusers.__version__)\"` is higher or matches the latest Diffusers version.\n- Remember that the more effort you put into opening a new issue, the higher the quality of your answer will be and the better the overall quality of the Diffusers issues.\n\nNew issues usually include the following.\n\n#### 2.1. Reproducible, minimal bug reports\n\nA bug report should always have a reproducible code snippet and be as minimal and concise as possible.\nThis means in more detail:\n- Narrow the bug down as much as you can, **do not just dump your whole code file**.\n- Format your code.\n- Do not include any external libraries except for Diffusers depending on them.\n- **Always** provide all necessary information about your environment; for this, you can run: `diffusers-cli env` in your shell and copy-paste the displayed information to the issue.\n- Explain the issue. If the reader doesn't know what the issue is and why it is an issue, she cannot solve it.\n- **Always** make sure the reader can reproduce your issue with as little effort as possible. If your code snippet cannot be run because of missing libraries or undefined variables, the reader cannot help you. Make sure your reproducible code snippet is as minimal as possible and can be copy-pasted into a simple Python shell.\n- If in order to reproduce your issue a model and/or dataset is required, make sure the reader has access to that model or dataset. You can always upload your model or dataset to the [Hub](https://huggingface.co) to make it easily downloadable. Try to keep your model and dataset as small as possible, to make the reproduction of your issue as effortless as possible.\n\nFor more information, please have a look through the [How to write a good issue](#how-to-write-a-good-issue) section.\n\nYou can open a bug report [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=bug&projects=&template=bug-report.yml).\n\n#### 2.2. Feature requests\n\nA world-class feature request addresses the following points:\n\n1. Motivation first:\n* Is it related to a problem/frustration with the library? If so, please explain\nwhy. Providing a code snippet that demonstrates the problem is best.\n* Is it related to something you would need for a project? We'd love to hear\nabout it!\n* Is it something you worked on and think could benefit the community?\nAwesome! Tell us what problem it solved for you.\n2. Write a *full paragraph* describing the feature;\n3. Provide a **code snippet** that demonstrates its future use;\n4. In case this is related to a paper, please attach a link;\n5. Attach any additional information (drawings, screenshots, etc.) you think may help.\n\nYou can open a feature request [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feature_request.md&title=).\n\n#### 2.3 Feedback\n\nFeedback about the library design and why it is good or not good helps the core maintainers immensely to build a user-friendly library. To understand the philosophy behind the current design philosophy, please have a look [here](https://huggingface.co/docs/diffusers/conceptual/philosophy). If you feel like a certain design choice does not fit with the current design philosophy, please explain why and how it should be changed. If a certain design choice follows the design philosophy too much, hence restricting use cases, explain why and how it should be changed.\nIf a certain design choice is very useful for you, please also leave a note as this is great feedback for future design decisions.\n\nYou can open an issue about feedback [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=).\n\n#### 2.4 Technical questions\n\nTechnical questions are mainly about why certain code of the library was written in a certain way, or what a certain part of the code does. Please make sure to link to the code in question and please provide details on\nwhy this part of the code is difficult to understand.\n\nYou can open an issue about a technical question [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=bug&template=bug-report.yml).\n\n#### 2.5 Proposal to add a new model, scheduler, or pipeline\n\nIf the diffusion model community released a new model, pipeline, or scheduler that you would like to see in the Diffusers library, please provide the following information:\n\n* Short description of the diffusion pipeline, model, or scheduler and link to the paper or public release.\n* Link to any of its open-source implementation(s).\n* Link to the model weights if they are available.\n\nIf you are willing to contribute to the model yourself, let us know so we can best guide you. Also, don't forget\nto tag the original author of the component (model, scheduler, pipeline, etc.) by GitHub handle if you can find it.\n\nYou can open a request for a model/pipeline/scheduler [here](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=New+model%2Fpipeline%2Fscheduler&template=new-model-addition.yml).\n\n### 3. Answering issues on the GitHub issues tab\n\nAnswering issues on GitHub might require some technical knowledge of Diffusers, but we encourage everybody to give it a try even if you are not 100% certain that your answer is correct.\nSome tips to give a high-quality answer to an issue:\n- Be as concise and minimal as possible.\n- Stay on topic. An answer to the issue should concern the issue and only the issue.\n- Provide links to code, papers, or other sources that prove or encourage your point.\n- Answer in code. If a simple code snippet is the answer to the issue or shows how the issue can be solved, please provide a fully reproducible code snippet.\n\nAlso, many issues tend to be simply off-topic, duplicates of other issues, or irrelevant. It is of great\nhelp to the maintainers if you can answer such issues, encouraging the author of the issue to be\nmore precise, provide the link to a duplicated issue or redirect them to [the forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) or [Discord](https://discord.gg/G7tWnz98XR).\n\nIf you have verified that the issued bug report is correct and requires a correction in the source code,\nplease have a look at the next sections.\n\nFor all of the following contributions, you will need to open a PR. It is explained in detail how to do so in the [Opening a pull request](#how-to-open-a-pr) section.\n\n### 4. Fixing a \"Good first issue\"\n\n*Good first issues* are marked by the [Good first issue](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) label. Usually, the issue already\nexplains how a potential solution should look so that it is easier to fix.\nIf the issue hasn't been closed and you would like to try to fix this issue, you can just leave a message \"I would like to try this issue.\". There are usually three scenarios:\n- a.) The issue description already proposes a fix. In this case and if the solution makes sense to you, you can open a PR or draft PR to fix it.\n- b.) The issue description does not propose a fix. In this case, you can ask what a proposed fix could look like and someone from the Diffusers team should answer shortly. If you have a good idea of how to fix it, feel free to directly open a PR.\n- c.) There is already an open PR to fix the issue, but the issue hasn't been closed yet. If the PR has gone stale, you can simply open a new PR and link to the stale PR. PRs often go stale if the original contributor who wanted to fix the issue suddenly cannot find the time anymore to proceed. This often happens in open-source and is very normal. In this case, the community will be very happy if you give it a new try and leverage the knowledge of the existing PR. If there is already a PR and it is active, you can help the author by giving suggestions, reviewing the PR or even asking whether you can contribute to the PR.\n\n\n### 5. Contribute to the documentation\n\nA good library **always** has good documentation! The official documentation is often one of the first points of contact for new users of the library, and therefore contributing to the documentation is a **highly\nvaluable contribution**.\n\nContributing to the library can have many forms:\n\n- Correcting spelling or grammatical errors.\n- Correct incorrect formatting of the docstring. If you see that the official documentation is weirdly displayed or a link is broken, we would be very happy if you take some time to correct it.\n- Correct the shape or dimensions of a docstring input or output tensor.\n- Clarify documentation that is hard to understand or incorrect.\n- Update outdated code examples.\n- Translating the documentation to another language.\n\nAnything displayed on [the official Diffusers doc page](https://huggingface.co/docs/diffusers/index) is part of the official documentation and can be corrected, adjusted in the respective [documentation source](https://github.com/huggingface/diffusers/tree/main/docs/source).\n\nPlease have a look at [this page](https://github.com/huggingface/diffusers/tree/main/docs) on how to verify changes made to the documentation locally.\n\n### 6. Contribute a community pipeline\n\n> [!TIP]\n> Read the [Community pipelines](../using-diffusers/custom_pipeline_overview#community-pipelines) guide to learn more about the difference between a GitHub and Hugging Face Hub community pipeline. If you're interested in why we have community pipelines, take a look at GitHub Issue [#841](https://github.com/huggingface/diffusers/issues/841) (basically, we can't maintain all the possible ways diffusion models can be used for inference but we also don't want to prevent the community from building them).\n\nContributing a community pipeline is a great way to share your creativity and work with the community. It lets you build on top of the [`DiffusionPipeline`] so that anyone can load and use it by setting the `custom_pipeline` parameter. This section will walk you through how to create a simple pipeline where the UNet only does a single forward pass and calls the scheduler once (a \"one-step\" pipeline).\n\n1. Create a one_step_unet.py file for your community pipeline. This file can contain whatever package you want to use as long as it's installed by the user. Make sure you only have one pipeline class that inherits from [`DiffusionPipeline`] to load model weights and the scheduler configuration from the Hub. Add a UNet and scheduler to the `__init__` function.\n\n You should also add the `register_modules` function to ensure your pipeline and its components can be saved with [`~DiffusionPipeline.save_pretrained`].\n\n```py\nfrom diffusers import DiffusionPipeline\nimport torch\n\nclass UnetSchedulerOneForwardPipeline(DiffusionPipeline):\n def __init__(self, unet, scheduler):\n super().__init__()\n\n self.register_modules(unet=unet, scheduler=scheduler)\n```\n\n1. In the forward pass (which we recommend defining as `__call__`), you can add any feature you'd like. For the \"one-step\" pipeline, create a random image and call the UNet and scheduler once by setting `timestep=1`.\n\n```py\n from diffusers import DiffusionPipeline\n import torch\n\n class UnetSchedulerOneForwardPipeline(DiffusionPipeline):\n def __init__(self, unet, scheduler):\n super().__init__()\n\n self.register_modules(unet=unet, scheduler=scheduler)\n\n def __call__(self):\n image = torch.randn(\n (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),\n )\n timestep = 1\n\n model_output = self.unet(image, timestep).sample\n scheduler_output = self.scheduler.step(model_output, timestep, image).prev_sample\n\n return scheduler_output\n```\n\nNow you can run the pipeline by passing a UNet and scheduler to it or load pretrained weights if the pipeline structure is identical.\n\n```py\nfrom diffusers import DDPMScheduler, UNet2DModel\n\nscheduler = DDPMScheduler()\nunet = UNet2DModel()\n\npipeline = UnetSchedulerOneForwardPipeline(unet=unet, scheduler=scheduler)\noutput = pipeline()\n# load pretrained weights\npipeline = UnetSchedulerOneForwardPipeline.from_pretrained(\"google/ddpm-cifar10-32\", use_safetensors=True)\noutput = pipeline()\n```\n\nYou can either share your pipeline as a GitHub community pipeline or Hub community pipeline.\n\n\n\n\nShare your GitHub pipeline by opening a pull request on the Diffusers [repository](https://github.com/huggingface/diffusers) and add the one_step_unet.py file to the [examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) subfolder.\n\n\n\n\nShare your Hub pipeline by creating a model repository on the Hub and uploading the one_step_unet.py file to it.\n\n\n\n\n### 7. Contribute to training examples\n\nDiffusers examples are a collection of training scripts that reside in [examples](https://github.com/huggingface/diffusers/tree/main/examples).\n\nWe support two types of training examples:\n\n- Official training examples\n- Research training examples\n\nResearch training examples are located in [examples/research_projects](https://github.com/huggingface/diffusers/tree/main/examples/research_projects) whereas official training examples include all folders under [examples](https://github.com/huggingface/diffusers/tree/main/examples) except the `research_projects` and `community` folders.\nThe official training examples are maintained by the Diffusers' core maintainers whereas the research training examples are maintained by the community.\nThis is because of the same reasons put forward in [6. Contribute a community pipeline](#6-contribute-a-community-pipeline) for official pipelines vs. community pipelines: It is not feasible for the core maintainers to maintain all possible training methods for diffusion models.\nIf the Diffusers core maintainers and the community consider a certain training paradigm to be too experimental or not popular enough, the corresponding training code should be put in the `research_projects` folder and maintained by the author.\n\nBoth official training and research examples consist of a directory that contains one or more training scripts, a requirements.txt file, and a README.md file. In order for the user to make use of the\ntraining examples, it is required to clone the repository:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\n```\n\nas well as to install all additional dependencies required for training:\n\n```bash\npip install -r /examples//requirements.txt\n```\n\nTherefore when adding an example, the `requirements.txt` file shall define all pip dependencies required for your training example so that once all those are installed, the user can run the example's training script. See, for example, the [DreamBooth `requirements.txt` file](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/requirements.txt).\n\nTraining examples of the Diffusers library should adhere to the following philosophy:\n- All the code necessary to run the examples should be found in a single Python file.\n- One should be able to run the example from the command line with `python .py --args`.\n- Examples should be kept simple and serve as **an example** on how to use Diffusers for training. The purpose of example scripts is **not** to create state-of-the-art diffusion models, but rather to reproduce known training schemes without adding too much custom logic. As a byproduct of this point, our examples also strive to serve as good educational materials.\n\nTo contribute an example, it is highly recommended to look at already existing examples such as [dreambooth](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py) to get an idea of how they should look like.\nWe strongly advise contributors to make use of the [Accelerate library](https://github.com/huggingface/accelerate) as it's tightly integrated\nwith Diffusers.\nOnce an example script works, please make sure to add a comprehensive `README.md` that states how to use the example exactly. This README should include:\n- An example command on how to run the example script as shown [here](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth#running-locally-with-pytorch).\n- A link to some training results (logs, models, etc.) that show what the user can expect as shown [here](https://api.wandb.ai/report/patrickvonplaten/xm6cd5q5).\n- If you are adding a non-official/research training example, **please don't forget** to add a sentence that you are maintaining this training example which includes your git handle as shown [here](https://github.com/huggingface/diffusers/tree/main/examples/research_projects/intel_opts#diffusers-examples-with-intel-optimizations).\n\nIf you are contributing to the official training examples, please also make sure to add a test to [examples/test_examples.py](https://github.com/huggingface/diffusers/blob/main/examples/test_examples.py). This is not necessary for non-official training examples.\n\n### 8. Fixing a \"Good second issue\"\n\n*Good second issues* are marked by the [Good second issue](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22Good+second+issue%22) label. Good second issues are\nusually more complicated to solve than [Good first issues](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22).\nThe issue description usually gives less guidance on how to fix the issue and requires\na decent understanding of the library by the interested contributor.\nIf you are interested in tackling a good second issue, feel free to open a PR to fix it and link the PR to the issue. If you see that a PR has already been opened for this issue but did not get merged, have a look to understand why it wasn't merged and try to open an improved PR.\nGood second issues are usually more difficult to get merged compared to good first issues, so don't hesitate to ask for help from the core maintainers. If your PR is almost finished the core maintainers can also jump into your PR and commit to it in order to get it merged.\n\n### 9. Adding pipelines, models, schedulers\n\nPipelines, models, and schedulers are the most important pieces of the Diffusers library.\nThey provide easy access to state-of-the-art diffusion technologies and thus allow the community to\nbuild powerful generative AI applications.\n\nBy adding a new model, pipeline, or scheduler you might enable a new powerful use case for any of the user interfaces relying on Diffusers which can be of immense value for the whole generative AI ecosystem.\n\nDiffusers has a couple of open feature requests for all three components - feel free to gloss over them\nif you don't know yet what specific component you would like to add:\n- [Model or pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22)\n- [Scheduler](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22)\n\nBefore adding any of the three components, it is strongly recommended that you give the [Philosophy guide](philosophy) a read to better understand the design of any of the three components. Please be aware that we cannot merge model, scheduler, or pipeline additions that strongly diverge from our design philosophy\nas it will lead to API inconsistencies. If you fundamentally disagree with a design choice, please open a [Feedback issue](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=) instead so that it can be discussed whether a certain design pattern/design choice shall be changed everywhere in the library and whether we shall update our design philosophy. Consistency across the library is very important for us.\n\nPlease make sure to add links to the original codebase/paper to the PR and ideally also ping the original author directly on the PR so that they can follow the progress and potentially help with questions.\n\nIf you are unsure or stuck in the PR, don't hesitate to leave a message to ask for a first review or help.\n\n#### Copied from mechanism\n\nA unique and important feature to understand when adding any pipeline, model or scheduler code is the `# Copied from` mechanism. You'll see this all over the Diffusers codebase, and the reason we use it is to keep the codebase easy to understand and maintain. Marking code with the `# Copied from` mechanism forces the marked code to be identical to the code it was copied from. This makes it easy to update and propagate changes across many files whenever you run `make fix-copies`.\n\nFor example, in the code example below, [`~diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is the original code and `AltDiffusionPipelineOutput` uses the `# Copied from` mechanism to copy it. The only difference is changing the class prefix from `Stable` to `Alt`.\n\n```py\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_output.StableDiffusionPipelineOutput with Stable->Alt\nclass AltDiffusionPipelineOutput(BaseOutput):\n \"\"\"\n Output class for Alt Diffusion pipelines.\n\n Args:\n images (`List[PIL.Image.Image]` or `np.ndarray`)\n List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,\n num_channels)`.\n nsfw_content_detected (`List[bool]`)\n List indicating whether the corresponding generated image contains \"not-safe-for-work\" (nsfw) content or\n `None` if safety checking could not be performed.\n \"\"\"\n```\n\nTo learn more, read this section of the [~Don't~ Repeat Yourself*](https://huggingface.co/blog/transformers-design-philosophy#4-machine-learning-models-are-static) blog post.\n\n## How to write a good issue\n\n**The better your issue is written, the higher the chances that it will be quickly resolved.**\n\n1. Make sure that you've used the correct template for your issue. You can pick between *Bug Report*, *Feature Request*, *Feedback about API Design*, *New model/pipeline/scheduler addition*, *Forum*, or a blank issue. Make sure to pick the correct one when opening [a new issue](https://github.com/huggingface/diffusers/issues/new/choose).\n2. **Be precise**: Give your issue a fitting title. Try to formulate your issue description as simple as possible. The more precise you are when submitting an issue, the less time it takes to understand the issue and potentially solve it. Make sure to open an issue for one issue only and not for multiple issues. If you found multiple issues, simply open multiple issues. If your issue is a bug, try to be as precise as possible about what bug it is - you should not just write \"Error in diffusers\".\n3. **Reproducibility**: No reproducible code snippet == no solution. If you encounter a bug, maintainers **have to be able to reproduce** it. Make sure that you include a code snippet that can be copy-pasted into a Python interpreter to reproduce the issue. Make sure that your code snippet works, *i.e.* that there are no missing imports or missing links to images, ... Your issue should contain an error message **and** a code snippet that can be copy-pasted without any changes to reproduce the exact same error message. If your issue is using local model weights or local data that cannot be accessed by the reader, the issue cannot be solved. If you cannot share your data or model, try to make a dummy model or dummy data.\n4. **Minimalistic**: Try to help the reader as much as you can to understand the issue as quickly as possible by staying as concise as possible. Remove all code / all information that is irrelevant to the issue. If you have found a bug, try to create the easiest code example you can to demonstrate your issue, do not just dump your whole workflow into the issue as soon as you have found a bug. E.g., if you train a model and get an error at some point during the training, you should first try to understand what part of the training code is responsible for the error and try to reproduce it with a couple of lines. Try to use dummy data instead of full datasets.\n5. Add links. If you are referring to a certain naming, method, or model make sure to provide a link so that the reader can better understand what you mean. If you are referring to a specific PR or issue, make sure to link it to your issue. Do not assume that the reader knows what you are talking about. The more links you add to your issue the better.\n6. Formatting. Make sure to nicely format your issue by formatting code into Python code syntax, and error messages into normal code syntax. See the [official GitHub formatting docs](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax) for more information.\n7. Think of your issue not as a ticket to be solved, but rather as a beautiful entry to a well-written encyclopedia. Every added issue is a contribution to publicly available knowledge. By adding a nicely written issue you not only make it easier for maintainers to solve your issue, but you are helping the whole community to better understand a certain aspect of the library.\n\n## How to write a good PR\n\n1. Be a chameleon. Understand existing design patterns and syntax and make sure your code additions flow seamlessly into the existing code base. Pull requests that significantly diverge from existing design patterns or user interfaces will not be merged.\n2. Be laser focused. A pull request should solve one problem and one problem only. Make sure to not fall into the trap of \"also fixing another problem while we're adding it\". It is much more difficult to review pull requests that solve multiple, unrelated problems at once.\n3. If helpful, try to add a code snippet that displays an example of how your addition can be used.\n4. The title of your pull request should be a summary of its contribution.\n5. If your pull request addresses an issue, please mention the issue number in\nthe pull request description to make sure they are linked (and people\nconsulting the issue know you are working on it);\n6. To indicate a work in progress please prefix the title with `[WIP]`. These\nare useful to avoid duplicated work, and to differentiate it from PRs ready\nto be merged;\n7. Try to formulate and format your text as explained in [How to write a good issue](#how-to-write-a-good-issue).\n8. Make sure existing tests pass;\n9. Add high-coverage tests. No quality testing = no merge.\n- If you are adding new `@slow` tests, make sure they pass using\n`RUN_SLOW=1 python -m pytest tests/test_my_new_model.py`.\nCircleCI does not run the slow tests, but GitHub Actions does every night!\n10. All public methods must have informative docstrings that work nicely with markdown. See [`pipeline_latent_diffusion.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py) for an example.\n11. Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted `dataset` like\n[`hf-internal-testing`](https://huggingface.co/hf-internal-testing) or [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images) to place these files.\nIf an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images\nto this dataset.\n\n## How to open a PR\n\nBefore writing code, we strongly advise you to search through the existing PRs or\nissues to make sure that nobody is already working on the same thing. If you are\nunsure, it is always a good idea to open an issue to get some feedback.\n\nYou will need basic `git` proficiency to be able to contribute to\n🧨 Diffusers. `git` is not the easiest tool to use but it has the greatest\nmanual. Type `git --help` in a shell and enjoy. If you prefer books, [Pro\nGit](https://git-scm.com/book/en/v2) is a very good reference.\n\nFollow these steps to start contributing ([supported Python versions](https://github.com/huggingface/diffusers/blob/main/setup.py#L244)):\n\n1. Fork the [repository](https://github.com/huggingface/diffusers) by\nclicking on the 'Fork' button on the repository's page. This creates a copy of the code\nunder your GitHub user account.\n\n2. Clone your fork to your local disk, and add the base repository as a remote:\n\n ```bash\n $ git clone git@github.com:/diffusers.git\n $ cd diffusers\n $ git remote add upstream https://github.com/huggingface/diffusers.git\n ```\n\n3. Create a new branch to hold your development changes:\n\n ```bash\n $ git checkout -b a-descriptive-name-for-my-changes\n ```\n\n**Do not** work on the `main` branch.\n\n4. Set up a development environment by running the following command in a virtual environment:\n\n ```bash\n $ pip install -e \".[dev]\"\n ```\n\nIf you have already cloned the repo, you might need to `git pull` to get the most recent changes in the\nlibrary.\n\n5. Develop the features on your branch.\n\nAs you work on the features, you should make sure that the test suite\npasses. You should run the tests impacted by your changes like this:\n\n ```bash\n $ pytest tests/.py\n ```\n\nBefore you run the tests, please make sure you install the dependencies required for testing. You can do so\nwith this command:\n\n ```bash\n $ pip install -e \".[test]\"\n ```\n\nYou can also run the full test suite with the following command, but it takes\na beefy machine to produce a result in a decent amount of time now that\nDiffusers has grown a lot. Here is the command for it:\n\n ```bash\n $ make test\n ```\n\n🧨 Diffusers relies on `black` and `isort` to format its source code\nconsistently. After you make changes, apply automatic style corrections and code verifications\nthat can't be automated in one go with:\n\n ```bash\n $ make style\n ```\n\n🧨 Diffusers also uses `ruff` and a few custom scripts to check for coding mistakes. Quality\ncontrol runs in CI, however, you can also run the same checks with:\n\n ```bash\n $ make quality\n ```\n\nOnce you're happy with your changes, add changed files using `git add` and\nmake a commit with `git commit` to record your changes locally:\n\n ```bash\n $ git add modified_file.py\n $ git commit -m \"A descriptive message about your changes.\"\n ```\n\nIt is a good idea to sync your copy of the code with the original\nrepository regularly. This way you can quickly account for changes:\n\n ```bash\n $ git pull upstream main\n ```\n\nPush the changes to your account using:\n\n ```bash\n $ git push -u origin a-descriptive-name-for-my-changes\n ```\n\n6. Once you are satisfied, go to the\nwebpage of your fork on GitHub. Click on 'Pull request' to send your changes\nto the project maintainers for review.\n\n7. It's OK if maintainers ask you for changes. It happens to core contributors\ntoo! So everyone can see the changes in the Pull request, work in your local\nbranch and push the changes to your fork. They will automatically appear in\nthe pull request.\n\n### Tests\n\nAn extensive test suite is included to test the library behavior and several examples. Library tests can be found in\nthe [tests folder](https://github.com/huggingface/diffusers/tree/main/tests).\n\nWe like `pytest` and `pytest-xdist` because it's faster. From the root of the\nrepository, here's how to run tests with `pytest` for the library:\n\n```bash\n$ python -m pytest -n auto --dist=loadfile -s -v ./tests/\n```\n\nIn fact, that's how `make test` is implemented!\n\nYou can specify a smaller set of tests in order to test only the feature\nyou're working on.\n\nBy default, slow tests are skipped. Set the `RUN_SLOW` environment variable to\n`yes` to run them. This will download many gigabytes of models — make sure you\nhave enough disk space and a good Internet connection, or a lot of patience!\n\n```bash\n$ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/\n```\n\n`unittest` is fully supported, here's how to run tests with it:\n\n```bash\n$ python -m unittest discover -s tests -t . -v\n$ python -m unittest discover -s examples -t examples -v\n```\n\n### Syncing forked main with upstream (HuggingFace) main\n\nTo avoid pinging the upstream repository which adds reference notes to each upstream PR and sends unnecessary notifications to the developers involved in these PRs,\nwhen syncing the main branch of a forked repository, please, follow these steps:\n1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead, merge directly into the forked main.\n2. If a PR is absolutely necessary, use the following steps after checking out your branch:\n```bash\n$ git checkout -b your-branch-for-syncing\n$ git pull --squash --no-commit upstream main\n$ git commit -m ''\n$ git push --set-upstream origin your-branch-for-syncing\n```\n\n### Style guide\n\nFor documentation strings, 🧨 Diffusers follows the [Google style](https://google.github.io/styleguide/pyguide.html).\n" }, { "path": "diffusers/docs/source/en/conceptual/ethical_guidelines.md", "content": "\n\n# 🧨 Diffusers’ Ethical Guidelines\n\n## Preamble\n\n[Diffusers](https://huggingface.co/docs/diffusers/index) provides pre-trained diffusion models and serves as a modular toolbox for inference and training.\n\nGiven its real case applications in the world and potential negative impacts on society, we think it is important to provide the project with ethical guidelines to guide the development, users’ contributions, and usage of the Diffusers library.\n\nThe risks associated with using this technology are still being examined, but to name a few: copyrights issues for artists; deep-fake exploitation; sexual content generation in inappropriate contexts; non-consensual impersonation; harmful social biases perpetuating the oppression of marginalized groups.\nWe will keep tracking risks and adapt the following guidelines based on the community's responsiveness and valuable feedback.\n\n\n## Scope\n\nThe Diffusers community will apply the following ethical guidelines to the project’s development and help coordinate how the community will integrate the contributions, especially concerning sensitive topics related to ethical concerns.\n\n\n## Ethical guidelines\n\nThe following ethical guidelines apply generally, but we will primarily implement them when dealing with ethically sensitive issues while making a technical choice. Furthermore, we commit to adapting those ethical principles over time following emerging harms related to the state of the art of the technology in question.\n\n- **Transparency**: we are committed to being transparent in managing PRs, explaining our choices to users, and making technical decisions.\n\n- **Consistency**: we are committed to guaranteeing our users the same level of attention in project management, keeping it technically stable and consistent.\n\n- **Simplicity**: with a desire to make it easy to use and exploit the Diffusers library, we are committed to keeping the project’s goals lean and coherent.\n\n- **Accessibility**: the Diffusers project helps lower the entry bar for contributors who can help run it even without technical expertise. Doing so makes research artifacts more accessible to the community.\n\n- **Reproducibility**: we aim to be transparent about the reproducibility of upstream code, models, and datasets when made available through the Diffusers library.\n\n- **Responsibility**: as a community and through teamwork, we hold a collective responsibility to our users by anticipating and mitigating this technology's potential risks and dangers.\n\n\n## Examples of implementations: Safety features and Mechanisms\n\nThe team works daily to make the technical and non-technical tools available to deal with the potential ethical and social risks associated with diffusion technology. Moreover, the community's input is invaluable in ensuring these features' implementation and raising awareness with us.\n\n- [**Community tab**](https://huggingface.co/docs/hub/repositories-pull-requests-discussions): it enables the community to discuss and better collaborate on a project.\n\n- **Bias exploration and evaluation**: the Hugging Face team provides a [space](https://huggingface.co/spaces/society-ethics/DiffusionBiasExplorer) to demonstrate the biases in Stable Diffusion interactively. In this sense, we support and encourage bias explorers and evaluations.\n\n- **Encouraging safety in deployment**\n\n - [**Safe Stable Diffusion**](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_safe): It mitigates the well-known issue that models, like Stable Diffusion, that are trained on unfiltered, web-crawled datasets tend to suffer from inappropriate degeneration. Related paper: [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://arxiv.org/abs/2211.05105).\n\n - [**Safety Checker**](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py): It checks and compares the class probability of a set of hard-coded harmful concepts in the embedding space against an image after it has been generated. The harmful concepts are intentionally hidden to prevent reverse engineering of the checker.\n\n- **Staged released on the Hub**: in particularly sensitive situations, access to some repositories should be restricted. This staged release is an intermediary step that allows the repository’s authors to have more control over its use.\n\n- **Licensing**: [OpenRAILs](https://huggingface.co/blog/open_rail), a new type of licensing, allow us to ensure free access while having a set of restrictions that ensure more responsible use.\n" }, { "path": "diffusers/docs/source/en/conceptual/evaluation.md", "content": "\n\n# Evaluating Diffusion Models\n\n\n \"Open\n\n\nEvaluation of generative models like [Stable Diffusion](https://huggingface.co/docs/diffusers/stable_diffusion) is subjective in nature. But as practitioners and researchers, we often have to make careful choices amongst many different possibilities. So, when working with different generative models (like GANs, Diffusion, etc.), how do we choose one over the other?\n\nQualitative evaluation of such models can be error-prone and might incorrectly influence a decision.\nHowever, quantitative metrics don't necessarily correspond to image quality. So, usually, a combination\nof both qualitative and quantitative evaluations provides a stronger signal when choosing one model\nover the other.\n\nIn this document, we provide a non-exhaustive overview of qualitative and quantitative methods to evaluate Diffusion models. For quantitative methods, we specifically focus on how to implement them alongside `diffusers`.\n\nThe methods shown in this document can also be used to evaluate different [noise schedulers](https://huggingface.co/docs/diffusers/main/en/api/schedulers/overview) keeping the underlying generation model fixed.\n\n## Scenarios\n\nWe cover Diffusion models with the following pipelines:\n\n- Text-guided image generation (such as the [`StableDiffusionPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/text2img)).\n- Text-guided image generation, additionally conditioned on an input image (such as the [`StableDiffusionImg2ImgPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/img2img) and [`StableDiffusionInstructPix2PixPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/pix2pix)).\n- Class-conditioned image generation models (such as the [`DiTPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/dit)).\n\n## Qualitative Evaluation\n\nQualitative evaluation typically involves human assessment of generated images. Quality is measured across aspects such as compositionality, image-text alignment, and spatial relations. Common prompts provide a degree of uniformity for subjective metrics.\nDrawBench and PartiPrompts are prompt datasets used for qualitative benchmarking. DrawBench and PartiPrompts were introduced by [Imagen](https://imagen.research.google/) and [Parti](https://parti.research.google/) respectively.\n\nFrom the [official Parti website](https://parti.research.google/):\n\n> PartiPrompts (P2) is a rich set of over 1600 prompts in English that we release as part of this work. P2 can be used to measure model capabilities across various categories and challenge aspects.\n\n![parti-prompts](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/parti-prompts.png)\n\nPartiPrompts has the following columns:\n\n- Prompt\n- Category of the prompt (such as “Abstract”, “World Knowledge”, etc.)\n- Challenge reflecting the difficulty (such as “Basic”, “Complex”, “Writing & Symbols”, etc.)\n\nThese benchmarks allow for side-by-side human evaluation of different image generation models.\n\nFor this, the 🧨 Diffusers team has built **Open Parti Prompts**, which is a community-driven qualitative benchmark based on Parti Prompts to compare state-of-the-art open-source diffusion models:\n- [Open Parti Prompts Game](https://huggingface.co/spaces/OpenGenAI/open-parti-prompts): For 10 parti prompts, 4 generated images are shown and the user selects the image that suits the prompt best.\n- [Open Parti Prompts Leaderboard](https://huggingface.co/spaces/OpenGenAI/parti-prompts-leaderboard): The leaderboard comparing the currently best open-sourced diffusion models to each other.\n\nTo manually compare images, let’s see how we can use `diffusers` on a couple of PartiPrompts.\n\nBelow we show some prompts sampled across different challenges: Basic, Complex, Linguistic Structures, Imagination, and Writing & Symbols. Here we are using PartiPrompts as a [dataset](https://huggingface.co/datasets/nateraw/parti-prompts).\n\n```python\nfrom datasets import load_dataset\n\n# prompts = load_dataset(\"nateraw/parti-prompts\", split=\"train\")\n# prompts = prompts.shuffle()\n# sample_prompts = [prompts[i][\"Prompt\"] for i in range(5)]\n\n# Fixing these sample prompts in the interest of reproducibility.\nsample_prompts = [\n \"a corgi\",\n \"a hot air balloon with a yin-yang symbol, with the moon visible in the daytime sky\",\n \"a car with no windows\",\n \"a cube made of porcupine\",\n 'The saying \"BE EXCELLENT TO EACH OTHER\" written on a red brick wall with a graffiti image of a green alien wearing a tuxedo. A yellow fire hydrant is on a sidewalk in the foreground.',\n]\n```\n\nNow we can use these prompts to generate some images using Stable Diffusion ([v1-4 checkpoint](https://huggingface.co/CompVis/stable-diffusion-v1-4)):\n\n```python\nimport torch\n\nseed = 0\ngenerator = torch.manual_seed(seed)\n\nimages = sd_pipeline(sample_prompts, num_images_per_prompt=1, generator=generator).images\n```\n\n![parti-prompts-14](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/parti-prompts-14.png)\n\nWe can also set `num_images_per_prompt` accordingly to compare different images for the same prompt. Running the same pipeline but with a different checkpoint ([v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)), yields:\n\n![parti-prompts-15](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/parti-prompts-15.png)\n\nOnce several images are generated from all the prompts using multiple models (under evaluation), these results are presented to human evaluators for scoring. For\nmore details on the DrawBench and PartiPrompts benchmarks, refer to their respective papers.\n\n\n\nIt is useful to look at some inference samples while a model is training to measure the\ntraining progress. In our [training scripts](https://github.com/huggingface/diffusers/tree/main/examples/), we support this utility with additional support for\nlogging to TensorBoard and Weights & Biases.\n\n\n\n## Quantitative Evaluation\n\nIn this section, we will walk you through how to evaluate three different diffusion pipelines using:\n\n- CLIP score\n- CLIP directional similarity\n- FID\n\n### Text-guided image generation\n\n[CLIP score](https://arxiv.org/abs/2104.08718) measures the compatibility of image-caption pairs. Higher CLIP scores imply higher compatibility 🔼. The CLIP score is a quantitative measurement of the qualitative concept \"compatibility\". Image-caption pair compatibility can also be thought of as the semantic similarity between the image and the caption. CLIP score was found to have high correlation with human judgement.\n\nLet's first load a [`StableDiffusionPipeline`]:\n\n```python\nfrom diffusers import StableDiffusionPipeline\nimport torch\n\nmodel_ckpt = \"CompVis/stable-diffusion-v1-4\"\nsd_pipeline = StableDiffusionPipeline.from_pretrained(model_ckpt, torch_dtype=torch.float16).to(\"cuda\")\n```\n\nGenerate some images with multiple prompts:\n\n```python\nprompts = [\n \"a photo of an astronaut riding a horse on mars\",\n \"A high tech solarpunk utopia in the Amazon rainforest\",\n \"A pikachu fine dining with a view to the Eiffel Tower\",\n \"A mecha robot in a favela in expressionist style\",\n \"an insect robot preparing a delicious meal\",\n \"A small cabin on top of a snowy mountain in the style of Disney, artstation\",\n]\n\nimages = sd_pipeline(prompts, num_images_per_prompt=1, output_type=\"np\").images\n\nprint(images.shape)\n# (6, 512, 512, 3)\n```\n\nAnd then, we calculate the CLIP score.\n\n```python\nfrom torchmetrics.functional.multimodal import clip_score\nfrom functools import partial\n\nclip_score_fn = partial(clip_score, model_name_or_path=\"openai/clip-vit-base-patch16\")\n\ndef calculate_clip_score(images, prompts):\n images_int = (images * 255).astype(\"uint8\")\n clip_score = clip_score_fn(torch.from_numpy(images_int).permute(0, 3, 1, 2), prompts).detach()\n return round(float(clip_score), 4)\n\nsd_clip_score = calculate_clip_score(images, prompts)\nprint(f\"CLIP score: {sd_clip_score}\")\n# CLIP score: 35.7038\n```\n\nIn the above example, we generated one image per prompt. If we generated multiple images per prompt, we would have to take the average score from the generated images per prompt.\n\nNow, if we wanted to compare two checkpoints compatible with the [`StableDiffusionPipeline`] we should pass a generator while calling the pipeline. First, we generate images with a\nfixed seed with the [v1-4 Stable Diffusion checkpoint](https://huggingface.co/CompVis/stable-diffusion-v1-4):\n\n```python\nseed = 0\ngenerator = torch.manual_seed(seed)\n\nimages = sd_pipeline(prompts, num_images_per_prompt=1, generator=generator, output_type=\"np\").images\n```\n\nThen we load the [v1-5 checkpoint](https://huggingface.co/runwayml/stable-diffusion-v1-5) to generate images:\n\n```python\nmodel_ckpt_1_5 = \"runwayml/stable-diffusion-v1-5\"\nsd_pipeline_1_5 = StableDiffusionPipeline.from_pretrained(model_ckpt_1_5, torch_dtype=weight_dtype).to(device)\n\nimages_1_5 = sd_pipeline_1_5(prompts, num_images_per_prompt=1, generator=generator, output_type=\"np\").images\n```\n\nAnd finally, we compare their CLIP scores:\n\n```python\nsd_clip_score_1_4 = calculate_clip_score(images, prompts)\nprint(f\"CLIP Score with v-1-4: {sd_clip_score_1_4}\")\n# CLIP Score with v-1-4: 34.9102\n\nsd_clip_score_1_5 = calculate_clip_score(images_1_5, prompts)\nprint(f\"CLIP Score with v-1-5: {sd_clip_score_1_5}\")\n# CLIP Score with v-1-5: 36.2137\n```\n\nIt seems like the [v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) checkpoint performs better than its predecessor. Note, however, that the number of prompts we used to compute the CLIP scores is quite low. For a more practical evaluation, this number should be way higher, and the prompts should be diverse.\n\n\n\nBy construction, there are some limitations in this score. The captions in the training dataset\nwere crawled from the web and extracted from `alt` and similar tags associated an image on the internet.\nThey are not necessarily representative of what a human being would use to describe an image. Hence we\nhad to \"engineer\" some prompts here.\n\n\n\n### Image-conditioned text-to-image generation\n\nIn this case, we condition the generation pipeline with an input image as well as a text prompt. Let's take the [`StableDiffusionInstructPix2PixPipeline`], as an example. It takes an edit instruction as an input prompt and an input image to be edited.\n\nHere is one example:\n\n![edit-instruction](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/edit-instruction.png)\n\nOne strategy to evaluate such a model is to measure the consistency of the change between the two images (in [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) space) with the change between the two image captions (as shown in [CLIP-Guided Domain Adaptation of Image Generators](https://arxiv.org/abs/2108.00946)). This is referred to as the \"**CLIP directional similarity**\".\n\n- Caption 1 corresponds to the input image (image 1) that is to be edited.\n- Caption 2 corresponds to the edited image (image 2). It should reflect the edit instruction.\n\nFollowing is a pictorial overview:\n\n![edit-consistency](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/edit-consistency.png)\n\nWe have prepared a mini dataset to implement this metric. Let's first load the dataset.\n\n```python\nfrom datasets import load_dataset\n\ndataset = load_dataset(\"sayakpaul/instructpix2pix-demo\", split=\"train\")\ndataset.features\n```\n\n```bash\n{'input': Value(dtype='string', id=None),\n 'edit': Value(dtype='string', id=None),\n 'output': Value(dtype='string', id=None),\n 'image': Image(decode=True, id=None)}\n```\n\nHere we have:\n\n- `input` is a caption corresponding to the `image`.\n- `edit` denotes the edit instruction.\n- `output` denotes the modified caption reflecting the `edit` instruction.\n\nLet's take a look at a sample.\n\n```python\nidx = 0\nprint(f\"Original caption: {dataset[idx]['input']}\")\nprint(f\"Edit instruction: {dataset[idx]['edit']}\")\nprint(f\"Modified caption: {dataset[idx]['output']}\")\n```\n\n```bash\nOriginal caption: 2. FAROE ISLANDS: An archipelago of 18 mountainous isles in the North Atlantic Ocean between Norway and Iceland, the Faroe Islands has 'everything you could hope for', according to Big 7 Travel. It boasts 'crystal clear waterfalls, rocky cliffs that seem to jut out of nowhere and velvety green hills'\nEdit instruction: make the isles all white marble\nModified caption: 2. WHITE MARBLE ISLANDS: An archipelago of 18 mountainous white marble isles in the North Atlantic Ocean between Norway and Iceland, the White Marble Islands has 'everything you could hope for', according to Big 7 Travel. It boasts 'crystal clear waterfalls, rocky cliffs that seem to jut out of nowhere and velvety green hills'\n```\n\nAnd here is the image:\n\n```python\ndataset[idx][\"image\"]\n```\n\n![edit-dataset](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/edit-dataset.png)\n\nWe will first edit the images of our dataset with the edit instruction and compute the directional similarity.\n\nLet's first load the [`StableDiffusionInstructPix2PixPipeline`]:\n\n```python\nfrom diffusers import StableDiffusionInstructPix2PixPipeline\n\ninstruct_pix2pix_pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained(\n \"timbrooks/instruct-pix2pix\", torch_dtype=torch.float16\n).to(device)\n```\n\nNow, we perform the edits:\n\n```python\nimport numpy as np\n\n\ndef edit_image(input_image, instruction):\n image = instruct_pix2pix_pipeline(\n instruction,\n image=input_image,\n output_type=\"np\",\n generator=generator,\n ).images[0]\n return image\n\ninput_images = []\noriginal_captions = []\nmodified_captions = []\nedited_images = []\n\nfor idx in range(len(dataset)):\n input_image = dataset[idx][\"image\"]\n edit_instruction = dataset[idx][\"edit\"]\n edited_image = edit_image(input_image, edit_instruction)\n\n input_images.append(np.array(input_image))\n original_captions.append(dataset[idx][\"input\"])\n modified_captions.append(dataset[idx][\"output\"])\n edited_images.append(edited_image)\n```\n\nTo measure the directional similarity, we first load CLIP's image and text encoders:\n\n```python\nfrom transformers import (\n CLIPTokenizer,\n CLIPTextModelWithProjection,\n CLIPVisionModelWithProjection,\n CLIPImageProcessor,\n)\n\nclip_id = \"openai/clip-vit-large-patch14\"\ntokenizer = CLIPTokenizer.from_pretrained(clip_id)\ntext_encoder = CLIPTextModelWithProjection.from_pretrained(clip_id).to(device)\nimage_processor = CLIPImageProcessor.from_pretrained(clip_id)\nimage_encoder = CLIPVisionModelWithProjection.from_pretrained(clip_id).to(device)\n```\n\nNotice that we are using a particular CLIP checkpoint, i.e., `openai/clip-vit-large-patch14`. This is because the Stable Diffusion pre-training was performed with this CLIP variant. For more details, refer to the [documentation](https://huggingface.co/docs/transformers/model_doc/clip).\n\nNext, we prepare a PyTorch `nn.Module` to compute directional similarity:\n\n```python\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass DirectionalSimilarity(nn.Module):\n def __init__(self, tokenizer, text_encoder, image_processor, image_encoder):\n super().__init__()\n self.tokenizer = tokenizer\n self.text_encoder = text_encoder\n self.image_processor = image_processor\n self.image_encoder = image_encoder\n\n def preprocess_image(self, image):\n image = self.image_processor(image, return_tensors=\"pt\")[\"pixel_values\"]\n return {\"pixel_values\": image.to(device)}\n\n def tokenize_text(self, text):\n inputs = self.tokenizer(\n text,\n max_length=self.tokenizer.model_max_length,\n padding=\"max_length\",\n truncation=True,\n return_tensors=\"pt\",\n )\n return {\"input_ids\": inputs.input_ids.to(device)}\n\n def encode_image(self, image):\n preprocessed_image = self.preprocess_image(image)\n image_features = self.image_encoder(**preprocessed_image).image_embeds\n image_features = image_features / image_features.norm(dim=1, keepdim=True)\n return image_features\n\n def encode_text(self, text):\n tokenized_text = self.tokenize_text(text)\n text_features = self.text_encoder(**tokenized_text).text_embeds\n text_features = text_features / text_features.norm(dim=1, keepdim=True)\n return text_features\n\n def compute_directional_similarity(self, img_feat_one, img_feat_two, text_feat_one, text_feat_two):\n sim_direction = F.cosine_similarity(img_feat_two - img_feat_one, text_feat_two - text_feat_one)\n return sim_direction\n\n def forward(self, image_one, image_two, caption_one, caption_two):\n img_feat_one = self.encode_image(image_one)\n img_feat_two = self.encode_image(image_two)\n text_feat_one = self.encode_text(caption_one)\n text_feat_two = self.encode_text(caption_two)\n directional_similarity = self.compute_directional_similarity(\n img_feat_one, img_feat_two, text_feat_one, text_feat_two\n )\n return directional_similarity\n```\n\nLet's put `DirectionalSimilarity` to use now.\n\n```python\ndir_similarity = DirectionalSimilarity(tokenizer, text_encoder, image_processor, image_encoder)\nscores = []\n\nfor i in range(len(input_images)):\n original_image = input_images[i]\n original_caption = original_captions[i]\n edited_image = edited_images[i]\n modified_caption = modified_captions[i]\n\n similarity_score = dir_similarity(original_image, edited_image, original_caption, modified_caption)\n scores.append(float(similarity_score.detach().cpu()))\n\nprint(f\"CLIP directional similarity: {np.mean(scores)}\")\n# CLIP directional similarity: 0.0797976553440094\n```\n\nLike the CLIP Score, the higher the CLIP directional similarity, the better it is.\n\nIt should be noted that the `StableDiffusionInstructPix2PixPipeline` exposes two arguments, namely, `image_guidance_scale` and `guidance_scale` that let you control the quality of the final edited image. We encourage you to experiment with these two arguments and see the impact of that on the directional similarity.\n\nWe can extend the idea of this metric to measure how similar the original image and edited version are. To do that, we can just do `F.cosine_similarity(img_feat_two, img_feat_one)`. For these kinds of edits, we would still want the primary semantics of the images to be preserved as much as possible, i.e., a high similarity score.\n\nWe can use these metrics for similar pipelines such as the [`StableDiffusionPix2PixZeroPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/pix2pix_zero#diffusers.StableDiffusionPix2PixZeroPipeline).\n\n\n\nBoth CLIP score and CLIP direction similarity rely on the CLIP model, which can make the evaluations biased.\n\n\n\n***Extending metrics like IS, FID (discussed later), or KID can be difficult*** when the model under evaluation was pre-trained on a large image-captioning dataset (such as the [LAION-5B dataset](https://laion.ai/blog/laion-5b/)). This is because underlying these metrics is an InceptionNet (pre-trained on the ImageNet-1k dataset) used for extracting intermediate image features. The pre-training dataset of Stable Diffusion may have limited overlap with the pre-training dataset of InceptionNet, so it is not a good candidate here for feature extraction.\n\n***Using the above metrics helps evaluate models that are class-conditioned. For example, [DiT](https://huggingface.co/docs/diffusers/main/en/api/pipelines/dit). It was pre-trained being conditioned on the ImageNet-1k classes.***\n\n### Class-conditioned image generation\n\nClass-conditioned generative models are usually pre-trained on a class-labeled dataset such as [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k). Popular metrics for evaluating these models include Fréchet Inception Distance (FID), Kernel Inception Distance (KID), and Inception Score (IS). In this document, we focus on FID ([Heusel et al.](https://arxiv.org/abs/1706.08500)). We show how to compute it with the [`DiTPipeline`](https://huggingface.co/docs/diffusers/api/pipelines/dit), which uses the [DiT model](https://arxiv.org/abs/2212.09748) under the hood.\n\nFID aims to measure how similar are two datasets of images. As per [this resource](https://mmgeneration.readthedocs.io/en/latest/quick_run.html#fid):\n\n> Fréchet Inception Distance is a measure of similarity between two datasets of images. It was shown to correlate well with the human judgment of visual quality and is most often used to evaluate the quality of samples of Generative Adversarial Networks. FID is calculated by computing the Fréchet distance between two Gaussians fitted to feature representations of the Inception network.\n\nThese two datasets are essentially the dataset of real images and the dataset of fake images (generated images in our case). FID is usually calculated with two large datasets. However, for this document, we will work with two mini datasets.\n\nLet's first download a few images from the ImageNet-1k training set:\n\n```python\nfrom zipfile import ZipFile\nimport requests\n\n\ndef download(url, local_filepath):\n r = requests.get(url)\n with open(local_filepath, \"wb\") as f:\n f.write(r.content)\n return local_filepath\n\ndummy_dataset_url = \"https://hf.co/datasets/sayakpaul/sample-datasets/resolve/main/sample-imagenet-images.zip\"\nlocal_filepath = download(dummy_dataset_url, dummy_dataset_url.split(\"/\")[-1])\n\nwith ZipFile(local_filepath, \"r\") as zipper:\n zipper.extractall(\".\")\n```\n\n```python\nfrom PIL import Image\nimport os\n\ndataset_path = \"sample-imagenet-images\"\nimage_paths = sorted([os.path.join(dataset_path, x) for x in os.listdir(dataset_path)])\n\nreal_images = [np.array(Image.open(path).convert(\"RGB\")) for path in image_paths]\n```\n\nThese are 10 images from the following ImageNet-1k classes: \"cassette_player\", \"chain_saw\" (x2), \"church\", \"gas_pump\" (x3), \"parachute\" (x2), and \"tench\".\n\n

\n \"real-images\"
\n Real images.\n

\n\nNow that the images are loaded, let's apply some lightweight pre-processing on them to use them for FID calculation.\n\n```python\nfrom torchvision.transforms import functional as F\n\n\ndef preprocess_image(image):\n image = torch.tensor(image).unsqueeze(0)\n image = image.permute(0, 3, 1, 2) / 255.0\n return F.center_crop(image, (256, 256))\n\nreal_images = torch.cat([preprocess_image(image) for image in real_images])\nprint(real_images.shape)\n# torch.Size([10, 3, 256, 256])\n```\n\nWe now load the [`DiTPipeline`](https://huggingface.co/docs/diffusers/api/pipelines/dit) to generate images conditioned on the above-mentioned classes.\n\n```python\nfrom diffusers import DiTPipeline, DPMSolverMultistepScheduler\n\ndit_pipeline = DiTPipeline.from_pretrained(\"facebook/DiT-XL-2-256\", torch_dtype=torch.float16)\ndit_pipeline.scheduler = DPMSolverMultistepScheduler.from_config(dit_pipeline.scheduler.config)\ndit_pipeline = dit_pipeline.to(\"cuda\")\n\nwords = [\n \"cassette player\",\n \"chainsaw\",\n \"chainsaw\",\n \"church\",\n \"gas pump\",\n \"gas pump\",\n \"gas pump\",\n \"parachute\",\n \"parachute\",\n \"tench\",\n]\n\nclass_ids = dit_pipeline.get_label_ids(words)\noutput = dit_pipeline(class_labels=class_ids, generator=generator, output_type=\"np\")\n\nfake_images = output.images\nfake_images = torch.tensor(fake_images)\nfake_images = fake_images.permute(0, 3, 1, 2)\nprint(fake_images.shape)\n# torch.Size([10, 3, 256, 256])\n```\n\nNow, we can compute the FID using [`torchmetrics`](https://torchmetrics.readthedocs.io/).\n\n```python\nfrom torchmetrics.image.fid import FrechetInceptionDistance\n\nfid = FrechetInceptionDistance(normalize=True)\nfid.update(real_images, real=True)\nfid.update(fake_images, real=False)\n\nprint(f\"FID: {float(fid.compute())}\")\n# FID: 177.7147216796875\n```\n\nThe lower the FID, the better it is. Several things can influence FID here:\n\n- Number of images (both real and fake)\n- Randomness induced in the diffusion process\n- Number of inference steps in the diffusion process\n- The scheduler being used in the diffusion process\n\nFor the last two points, it is, therefore, a good practice to run the evaluation across different seeds and inference steps, and then report an average result.\n\n\n\nFID results tend to be fragile as they depend on a lot of factors:\n\n* The specific Inception model used during computation.\n* The implementation accuracy of the computation.\n* The image format (not the same if we start from PNGs vs JPGs).\n\nKeeping that in mind, FID is often most useful when comparing similar runs, but it is\nhard to reproduce paper results unless the authors carefully disclose the FID\nmeasurement code.\n\nThese points apply to other related metrics too, such as KID and IS.\n\n\n\nAs a final step, let's visually inspect the `fake_images`.\n\n

\n \"fake-images\"
\n Fake images.\n

\n" }, { "path": "diffusers/docs/source/en/conceptual/philosophy.md", "content": "\n\n# Philosophy\n\n🧨 Diffusers provides **state-of-the-art** pretrained diffusion models across multiple modalities.\nIts purpose is to serve as a **modular toolbox** for both inference and training.\n\nWe aim at building a library that stands the test of time and therefore take API design very seriously.\n\nIn a nutshell, Diffusers is built to be a natural extension of PyTorch. Therefore, most of our design choices are based on [PyTorch's Design Principles](https://pytorch.org/docs/stable/community/design.html#pytorch-design-philosophy). Let's go over the most important ones:\n\n## Usability over Performance\n\n- While Diffusers has many built-in performance-enhancing features (see [Memory and Speed](https://huggingface.co/docs/diffusers/optimization/fp16)), models are always loaded with the highest precision and lowest optimization. Therefore, by default diffusion pipelines are always instantiated on CPU with float32 precision if not otherwise defined by the user. This ensures usability across different platforms and accelerators and means that no complex installations are required to run the library.\n- Diffusers aims to be a **light-weight** package and therefore has very few required dependencies, but many soft dependencies that can improve performance (such as `accelerate`, `safetensors`, `onnx`, etc...). We strive to keep the library as lightweight as possible so that it can be added without much concern as a dependency on other packages.\n- Diffusers prefers simple, self-explainable code over condensed, magic code. This means that short-hand code syntaxes such as lambda functions, and advanced PyTorch operators are often not desired.\n\n## Simple over easy\n\nAs PyTorch states, **explicit is better than implicit** and **simple is better than complex**. This design philosophy is reflected in multiple parts of the library:\n- We follow PyTorch's API with methods like [`DiffusionPipeline.to`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.to) to let the user handle device management.\n- Raising concise error messages is preferred to silently correct erroneous input. Diffusers aims at teaching the user, rather than making the library as easy to use as possible.\n- Complex model vs. scheduler logic is exposed instead of magically handled inside. Schedulers/Samplers are separated from diffusion models with minimal dependencies on each other. This forces the user to write the unrolled denoising loop. However, the separation allows for easier debugging and gives the user more control over adapting the denoising process or switching out diffusion models or schedulers.\n- Separately trained components of the diffusion pipeline, *e.g.* the text encoder, the unet, and the variational autoencoder, each have their own model class. This forces the user to handle the interaction between the different model components, and the serialization format separates the model components into different files. However, this allows for easier debugging and customization. DreamBooth or Textual Inversion training\nis very simple thanks to Diffusers' ability to separate single components of the diffusion pipeline.\n\n## Tweakable, contributor-friendly over abstraction\n\nFor large parts of the library, Diffusers adopts an important design principle of the [Transformers library](https://github.com/huggingface/transformers), which is to prefer copy-pasted code over hasty abstractions. This design principle is very opinionated and stands in stark contrast to popular design principles such as [Don't repeat yourself (DRY)](https://en.wikipedia.org/wiki/Don%27t_repeat_yourself).\nIn short, just like Transformers does for modeling files, Diffusers prefers to keep an extremely low level of abstraction and very self-contained code for pipelines and schedulers.\nFunctions, long code blocks, and even classes can be copied across multiple files which at first can look like a bad, sloppy design choice that makes the library unmaintainable.\n**However**, this design has proven to be extremely successful for Transformers and makes a lot of sense for community-driven, open-source machine learning libraries because:\n- Machine Learning is an extremely fast-moving field in which paradigms, model architectures, and algorithms are changing rapidly, which therefore makes it very difficult to define long-lasting code abstractions.\n- Machine Learning practitioners like to be able to quickly tweak existing code for ideation and research and therefore prefer self-contained code over one that contains many abstractions.\n- Open-source libraries rely on community contributions and therefore must build a library that is easy to contribute to. The more abstract the code, the more dependencies, the harder to read, and the harder to contribute to. Contributors simply stop contributing to very abstract libraries out of fear of breaking vital functionality. If contributing to a library cannot break other fundamental code, not only is it more inviting for potential new contributors, but it is also easier to review and contribute to multiple parts in parallel.\n\nAt Hugging Face, we call this design the **single-file policy** which means that almost all of the code of a certain class should be written in a single, self-contained file. To read more about the philosophy, you can have a look\nat [this blog post](https://huggingface.co/blog/transformers-design-philosophy).\n\nIn Diffusers, we follow this philosophy for both pipelines and schedulers, but only partly for diffusion models. The reason we don't follow this design fully for diffusion models is because almost all diffusion pipelines, such\nas [DDPM](https://huggingface.co/docs/diffusers/api/pipelines/ddpm), [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview#stable-diffusion-pipelines), [unCLIP (DALL·E 2)](https://huggingface.co/docs/diffusers/api/pipelines/unclip) and [Imagen](https://imagen.research.google/) all rely on the same diffusion model, the [UNet](https://huggingface.co/docs/diffusers/api/models/unet2d-cond).\n\nGreat, now you should have generally understood why 🧨 Diffusers is designed the way it is 🤗.\nWe try to apply these design principles consistently across the library. Nevertheless, there are some minor exceptions to the philosophy or some unlucky design choices. If you have feedback regarding the design, we would ❤️ to hear it [directly on GitHub](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=).\n\n## Design Philosophy in Details\n\nNow, let's look a bit into the nitty-gritty details of the design philosophy. Diffusers essentially consists of three major classes: [pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines), [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models), and [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers).\nLet's walk through more in-detail design decisions for each class.\n\n### Pipelines\n\nPipelines are designed to be easy to use (therefore do not follow [*Simple over easy*](#simple-over-easy) 100%), are not feature complete, and should loosely be seen as examples of how to use [models](#models) and [schedulers](#schedulers) for inference.\n\nThe following design principles are followed:\n- Pipelines follow the single-file policy. All pipelines can be found in individual directories under src/diffusers/pipelines. One pipeline folder corresponds to one diffusion paper/project/release. Multiple pipeline files can be gathered in one pipeline folder, as it’s done for [`src/diffusers/pipelines/stable-diffusion`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/stable_diffusion). If pipelines share similar functionality, one can make use of the [#Copied from mechanism](https://github.com/huggingface/diffusers/blob/125d783076e5bd9785beb05367a2d2566843a271/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py#L251).\n- Pipelines all inherit from [`DiffusionPipeline`].\n- Every pipeline consists of different model and scheduler components, that are documented in the [`model_index.json` file](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/model_index.json), are accessible under the same name as attributes of the pipeline and can be shared between pipelines with [`DiffusionPipeline.components`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.components) function.\n- Every pipeline should be loadable via the [`DiffusionPipeline.from_pretrained`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained) function.\n- Pipelines should be used **only** for inference.\n- Pipelines should be very readable, self-explanatory, and easy to tweak.\n- Pipelines should be designed to build on top of each other and be easy to integrate into higher-level APIs.\n- Pipelines are **not** intended to be feature-complete user interfaces. For feature-complete user interfaces one should rather have a look at [InvokeAI](https://github.com/invoke-ai/InvokeAI), [Diffuzers](https://github.com/abhishekkrthakur/diffuzers), and [lama-cleaner](https://github.com/Sanster/lama-cleaner).\n- Every pipeline should have one and only one way to run it via a `__call__` method. The naming of the `__call__` arguments should be shared across all pipelines.\n- Pipelines should be named after the task they are intended to solve.\n- In almost all cases, novel diffusion pipelines shall be implemented in a new pipeline folder/file.\n\n### Models\n\nModels are designed as configurable toolboxes that are natural extensions of [PyTorch's Module class](https://pytorch.org/docs/stable/generated/torch.nn.Module.html). They only partly follow the **single-file policy**.\n\nThe following design principles are followed:\n- Models correspond to **a type of model architecture**. *E.g.* the [`UNet2DConditionModel`] class is used for all UNet variations that expect 2D image inputs and are conditioned on some context.\n- All models can be found in [`src/diffusers/models`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and every model architecture shall be defined in its file, e.g. [`unet_2d_condition.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py), [`transformer_2d.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformer_2d.py), etc...\n- Models **do not** follow the single-file policy and should make use of smaller model building blocks, such as [`attention.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py), [`resnet.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py), [`embeddings.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/embeddings.py), etc... **Note**: This is in stark contrast to Transformers' modeling files and shows that models do not really follow the single-file policy.\n- Models intend to expose complexity, just like PyTorch's `Module` class, and give clear error messages.\n- Models all inherit from `ModelMixin` and `ConfigMixin`.\n- Models can be optimized for performance when it doesn’t demand major code changes, keeps backward compatibility, and gives significant memory or compute gain.\n- Models should by default have the highest precision and lowest performance setting.\n- To integrate new model checkpoints whose general architecture can be classified as an architecture that already exists in Diffusers, the existing model architecture shall be adapted to make it work with the new checkpoint. One should only create a new file if the model architecture is fundamentally different.\n- Models should be designed to be easily extendable to future changes. This can be achieved by limiting public function arguments, configuration arguments, and \"foreseeing\" future changes, *e.g.* it is usually better to add `string` \"...type\" arguments that can easily be extended to new future types instead of boolean `is_..._type` arguments. Only the minimum amount of changes shall be made to existing architectures to make a new model checkpoint work.\n- The model design is a difficult trade-off between keeping code readable and concise and supporting many model checkpoints. For most parts of the modeling code, classes shall be adapted for new model checkpoints, while there are some exceptions where it is preferred to add new classes to make sure the code is kept concise and\nreadable long-term, such as [UNet blocks](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py) and [Attention processors](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n\n### Schedulers\n\nSchedulers are responsible to guide the denoising process for inference as well as to define a noise schedule for training. They are designed as individual classes with loadable configuration files and strongly follow the **single-file policy**.\n\nThe following design principles are followed:\n- All schedulers are found in [`src/diffusers/schedulers`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers).\n- Schedulers are **not** allowed to import from large utils files and shall be kept very self-contained.\n- One scheduler Python file corresponds to one scheduler algorithm (as might be defined in a paper).\n- If schedulers share similar functionalities, we can make use of the `#Copied from` mechanism.\n- Schedulers all inherit from `SchedulerMixin` and `ConfigMixin`.\n- Schedulers can be easily swapped out with the [`ConfigMixin.from_config`](https://huggingface.co/docs/diffusers/main/en/api/configuration#diffusers.ConfigMixin.from_config) method as explained in detail [here](../using-diffusers/schedulers.md).\n- Every scheduler has to have a `set_num_inference_steps`, and a `step` function. `set_num_inference_steps(...)` has to be called before every denoising process, *i.e.* before `step(...)` is called.\n- Every scheduler exposes the timesteps to be \"looped over\" via a `timesteps` attribute, which is an array of timesteps the model will be called upon.\n- The `step(...)` function takes a predicted model output and the \"current\" sample (x_t) and returns the \"previous\", slightly more denoised sample (x_t-1).\n- Given the complexity of diffusion schedulers, the `step` function does not expose all the complexity and can be a bit of a \"black box\".\n- In almost all cases, novel schedulers shall be implemented in a new scheduling file.\n" }, { "path": "diffusers/docs/source/en/index.md", "content": "\n\n

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\n\n# Diffusers\n\n🤗 Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or want to train your own diffusion model, 🤗 Diffusers is a modular toolbox that supports both. Our library is designed with a focus on [usability over performance](conceptual/philosophy#usability-over-performance), [simple over easy](conceptual/philosophy#simple-over-easy), and [customizability over abstractions](conceptual/philosophy#tweakable-contributorfriendly-over-abstraction).\n\nThe library has three main components:\n\n- State-of-the-art diffusion pipelines for inference with just a few lines of code. There are many pipelines in 🤗 Diffusers, check out the table in the pipeline [overview](api/pipelines/overview) for a complete list of available pipelines and the task they solve.\n- Interchangeable [noise schedulers](api/schedulers/overview) for balancing trade-offs between generation speed and quality.\n- Pretrained [models](api/models) that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems.\n\n
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Tutorials
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Learn the fundamental skills you need to start generating outputs, build your own diffusion system, and train a diffusion model. We recommend starting here if you're using 🤗 Diffusers for the first time!

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How-to guides
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Practical guides for helping you load pipelines, models, and schedulers. You'll also learn how to use pipelines for specific tasks, control how outputs are generated, optimize for inference speed, and different training techniques.

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Conceptual guides
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Understand why the library was designed the way it was, and learn more about the ethical guidelines and safety implementations for using the library.

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Reference
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Technical descriptions of how 🤗 Diffusers classes and methods work.

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\n" }, { "path": "diffusers/docs/source/en/installation.md", "content": "\n\n# Installation\n\n🤗 Diffusers is tested on Python 3.8+, PyTorch 1.7.0+, and Flax. Follow the installation instructions below for the deep learning library you are using:\n\n- [PyTorch](https://pytorch.org/get-started/locally/) installation instructions\n- [Flax](https://flax.readthedocs.io/en/latest/) installation instructions\n\n## Install with pip\n\nYou should install 🤗 Diffusers in a [virtual environment](https://docs.python.org/3/library/venv.html).\nIf you're unfamiliar with Python virtual environments, take a look at this [guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).\nA virtual environment makes it easier to manage different projects and avoid compatibility issues between dependencies.\n\nStart by creating a virtual environment in your project directory:\n\n```bash\npython -m venv .env\n```\n\nActivate the virtual environment:\n\n```bash\nsource .env/bin/activate\n```\n\nYou should also install 🤗 Transformers because 🤗 Diffusers relies on its models:\n\n\n\n\nNote - PyTorch only supports Python 3.8 - 3.11 on Windows.\n```bash\npip install diffusers[\"torch\"] transformers\n```\n\n\n```bash\npip install diffusers[\"flax\"] transformers\n```\n\n\n\n## Install with conda\n\nAfter activating your virtual environment, with `conda` (maintained by the community):\n\n```bash\nconda install -c conda-forge diffusers\n```\n\n## Install from source\n\nBefore installing 🤗 Diffusers from source, make sure you have PyTorch and 🤗 Accelerate installed.\n\nTo install 🤗 Accelerate:\n\n```bash\npip install accelerate\n```\n\nThen install 🤗 Diffusers from source:\n\n```bash\npip install git+https://github.com/huggingface/diffusers\n```\n\nThis command installs the bleeding edge `main` version rather than the latest `stable` version.\nThe `main` version is useful for staying up-to-date with the latest developments.\nFor instance, if a bug has been fixed since the last official release but a new release hasn't been rolled out yet.\nHowever, this means the `main` version may not always be stable.\nWe strive to keep the `main` version operational, and most issues are usually resolved within a few hours or a day.\nIf you run into a problem, please open an [Issue](https://github.com/huggingface/diffusers/issues/new/choose) so we can fix it even sooner!\n\n## Editable install\n\nYou will need an editable install if you'd like to:\n\n* Use the `main` version of the source code.\n* Contribute to 🤗 Diffusers and need to test changes in the code.\n\nClone the repository and install 🤗 Diffusers with the following commands:\n\n```bash\ngit clone https://github.com/huggingface/diffusers.git\ncd diffusers\n```\n\n\n\n```bash\npip install -e \".[torch]\"\n```\n\n\n```bash\npip install -e \".[flax]\"\n```\n\n\n\nThese commands will link the folder you cloned the repository to and your Python library paths.\nPython will now look inside the folder you cloned to in addition to the normal library paths.\nFor example, if your Python packages are typically installed in `~/anaconda3/envs/main/lib/python3.10/site-packages/`, Python will also search the `~/diffusers/` folder you cloned to.\n\n\n\nYou must keep the `diffusers` folder if you want to keep using the library.\n\n\n\nNow you can easily update your clone to the latest version of 🤗 Diffusers with the following command:\n\n```bash\ncd ~/diffusers/\ngit pull\n```\n\nYour Python environment will find the `main` version of 🤗 Diffusers on the next run.\n\n## Cache\n\nModel weights and files are downloaded from the Hub to a cache which is usually your home directory. You can change the cache location by specifying the `HF_HOME` or `HUGGINFACE_HUB_CACHE` environment variables or configuring the `cache_dir` parameter in methods like [`~DiffusionPipeline.from_pretrained`].\n\nCached files allow you to run 🤗 Diffusers offline. To prevent 🤗 Diffusers from connecting to the internet, set the `HF_HUB_OFFLINE` environment variable to `True` and 🤗 Diffusers will only load previously downloaded files in the cache.\n\n```shell\nexport HF_HUB_OFFLINE=True\n```\n\nFor more details about managing and cleaning the cache, take a look at the [caching](https://huggingface.co/docs/huggingface_hub/guides/manage-cache) guide.\n\n## Telemetry logging\n\nOur library gathers telemetry information during [`~DiffusionPipeline.from_pretrained`] requests.\nThe data gathered includes the version of 🤗 Diffusers and PyTorch/Flax, the requested model or pipeline class,\nand the path to a pretrained checkpoint if it is hosted on the Hugging Face Hub.\nThis usage data helps us debug issues and prioritize new features.\nTelemetry is only sent when loading models and pipelines from the Hub,\nand it is not collected if you're loading local files.\n\nWe understand that not everyone wants to share additional information,and we respect your privacy.\nYou can disable telemetry collection by setting the `DISABLE_TELEMETRY` environment variable from your terminal:\n\nOn Linux/MacOS:\n```bash\nexport DISABLE_TELEMETRY=YES\n```\n\nOn Windows:\n```bash\nset DISABLE_TELEMETRY=YES\n```\n" }, { "path": "diffusers/docs/source/en/optimization/coreml.md", "content": "\n\n# How to run Stable Diffusion with Core ML\n\n[Core ML](https://developer.apple.com/documentation/coreml) is the model format and machine learning library supported by Apple frameworks. If you are interested in running Stable Diffusion models inside your macOS or iOS/iPadOS apps, this guide will show you how to convert existing PyTorch checkpoints into the Core ML format and use them for inference with Python or Swift.\n\nCore ML models can leverage all the compute engines available in Apple devices: the CPU, the GPU, and the Apple Neural Engine (or ANE, a tensor-optimized accelerator available in Apple Silicon Macs and modern iPhones/iPads). Depending on the model and the device it's running on, Core ML can mix and match compute engines too, so some portions of the model may run on the CPU while others run on GPU, for example.\n\n\n\nYou can also run the `diffusers` Python codebase on Apple Silicon Macs using the `mps` accelerator built into PyTorch. This approach is explained in depth in [the mps guide](mps), but it is not compatible with native apps.\n\n\n\n## Stable Diffusion Core ML Checkpoints\n\nStable Diffusion weights (or checkpoints) are stored in the PyTorch format, so you need to convert them to the Core ML format before we can use them inside native apps.\n\nThankfully, Apple engineers developed [a conversion tool](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml) based on `diffusers` to convert the PyTorch checkpoints to Core ML.\n\nBefore you convert a model, though, take a moment to explore the Hugging Face Hub – chances are the model you're interested in is already available in Core ML format:\n\n- the [Apple](https://huggingface.co/apple) organization includes Stable Diffusion versions 1.4, 1.5, 2.0 base, and 2.1 base\n- [coreml community](https://huggingface.co/coreml-community) includes custom finetuned models\n- use this [filter](https://huggingface.co/models?pipeline_tag=text-to-image&library=coreml&p=2&sort=likes) to return all available Core ML checkpoints\n\nIf you can't find the model you're interested in, we recommend you follow the instructions for [Converting Models to Core ML](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml) by Apple.\n\n## Selecting the Core ML Variant to Use\n\nStable Diffusion models can be converted to different Core ML variants intended for different purposes:\n\n- The type of attention blocks used. The attention operation is used to \"pay attention\" to the relationship between different areas in the image representations and to understand how the image and text representations are related. Attention is compute- and memory-intensive, so different implementations exist that consider the hardware characteristics of different devices. For Core ML Stable Diffusion models, there are two attention variants:\n * `split_einsum` ([introduced by Apple](https://machinelearning.apple.com/research/neural-engine-transformers)) is optimized for ANE devices, which is available in modern iPhones, iPads and M-series computers.\n * The \"original\" attention (the base implementation used in `diffusers`) is only compatible with CPU/GPU and not ANE. It can be *faster* to run your model on CPU + GPU using `original` attention than ANE. See [this performance benchmark](https://huggingface.co/blog/fast-mac-diffusers#performance-benchmarks) as well as some [additional measures provided by the community](https://github.com/huggingface/swift-coreml-diffusers/issues/31) for additional details.\n\n- The supported inference framework.\n * `packages` are suitable for Python inference. This can be used to test converted Core ML models before attempting to integrate them inside native apps, or if you want to explore Core ML performance but don't need to support native apps. For example, an application with a web UI could perfectly use a Python Core ML backend.\n * `compiled` models are required for Swift code. The `compiled` models in the Hub split the large UNet model weights into several files for compatibility with iOS and iPadOS devices. This corresponds to the [`--chunk-unet` conversion option](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml). If you want to support native apps, then you need to select the `compiled` variant.\n\nThe official Core ML Stable Diffusion [models](https://huggingface.co/apple/coreml-stable-diffusion-v1-4/tree/main) include these variants, but the community ones may vary:\n\n```\ncoreml-stable-diffusion-v1-4\n├── README.md\n├── original\n│ ├── compiled\n│ └── packages\n└── split_einsum\n ├── compiled\n └── packages\n```\n\nYou can download and use the variant you need as shown below.\n\n## Core ML Inference in Python\n\nInstall the following libraries to run Core ML inference in Python:\n\n```bash\npip install huggingface_hub\npip install git+https://github.com/apple/ml-stable-diffusion\n```\n\n### Download the Model Checkpoints\n\nTo run inference in Python, use one of the versions stored in the `packages` folders because the `compiled` ones are only compatible with Swift. You may choose whether you want to use `original` or `split_einsum` attention.\n\nThis is how you'd download the `original` attention variant from the Hub to a directory called `models`:\n\n```Python\nfrom huggingface_hub import snapshot_download\nfrom pathlib import Path\n\nrepo_id = \"apple/coreml-stable-diffusion-v1-4\"\nvariant = \"original/packages\"\n\nmodel_path = Path(\"./models\") / (repo_id.split(\"/\")[-1] + \"_\" + variant.replace(\"/\", \"_\"))\nsnapshot_download(repo_id, allow_patterns=f\"{variant}/*\", local_dir=model_path, local_dir_use_symlinks=False)\nprint(f\"Model downloaded at {model_path}\")\n```\n\n### Inference[[python-inference]]\n\nOnce you have downloaded a snapshot of the model, you can test it using Apple's Python script.\n\n```shell\npython -m python_coreml_stable_diffusion.pipeline --prompt \"a photo of an astronaut riding a horse on mars\" -i models/coreml-stable-diffusion-v1-4_original_packages -o --compute-unit CPU_AND_GPU --seed 93\n```\n\nPass the path of the downloaded checkpoint with `-i` flag to the script. `--compute-unit` indicates the hardware you want to allow for inference. It must be one of the following options: `ALL`, `CPU_AND_GPU`, `CPU_ONLY`, `CPU_AND_NE`. You may also provide an optional output path, and a seed for reproducibility.\n\nThe inference script assumes you're using the original version of the Stable Diffusion model, `CompVis/stable-diffusion-v1-4`. If you use another model, you *have* to specify its Hub id in the inference command line, using the `--model-version` option. This works for models already supported and custom models you trained or fine-tuned yourself.\n\nFor example, if you want to use [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5):\n\n```shell\npython -m python_coreml_stable_diffusion.pipeline --prompt \"a photo of an astronaut riding a horse on mars\" --compute-unit ALL -o output --seed 93 -i models/coreml-stable-diffusion-v1-5_original_packages --model-version runwayml/stable-diffusion-v1-5\n```\n\n## Core ML inference in Swift\n\nRunning inference in Swift is slightly faster than in Python because the models are already compiled in the `mlmodelc` format. This is noticeable on app startup when the model is loaded but shouldn’t be noticeable if you run several generations afterward.\n\n### Download\n\nTo run inference in Swift on your Mac, you need one of the `compiled` checkpoint versions. We recommend you download them locally using Python code similar to the previous example, but with one of the `compiled` variants:\n\n```Python\nfrom huggingface_hub import snapshot_download\nfrom pathlib import Path\n\nrepo_id = \"apple/coreml-stable-diffusion-v1-4\"\nvariant = \"original/compiled\"\n\nmodel_path = Path(\"./models\") / (repo_id.split(\"/\")[-1] + \"_\" + variant.replace(\"/\", \"_\"))\nsnapshot_download(repo_id, allow_patterns=f\"{variant}/*\", local_dir=model_path, local_dir_use_symlinks=False)\nprint(f\"Model downloaded at {model_path}\")\n```\n\n### Inference[[swift-inference]]\n\nTo run inference, please clone Apple's repo:\n\n```bash\ngit clone https://github.com/apple/ml-stable-diffusion\ncd ml-stable-diffusion\n```\n\nAnd then use Apple's command line tool, [Swift Package Manager](https://www.swift.org/package-manager/#):\n\n```bash\nswift run StableDiffusionSample --resource-path models/coreml-stable-diffusion-v1-4_original_compiled --compute-units all \"a photo of an astronaut riding a horse on mars\"\n```\n\nYou have to specify in `--resource-path` one of the checkpoints downloaded in the previous step, so please make sure it contains compiled Core ML bundles with the extension `.mlmodelc`. The `--compute-units` has to be one of these values: `all`, `cpuOnly`, `cpuAndGPU`, `cpuAndNeuralEngine`.\n\nFor more details, please refer to the [instructions in Apple's repo](https://github.com/apple/ml-stable-diffusion).\n\n## Supported Diffusers Features\n\nThe Core ML models and inference code don't support many of the features, options, and flexibility of 🧨 Diffusers. These are some of the limitations to keep in mind:\n\n- Core ML models are only suitable for inference. They can't be used for training or fine-tuning.\n- Only two schedulers have been ported to Swift, the default one used by Stable Diffusion and `DPMSolverMultistepScheduler`, which we ported to Swift from our `diffusers` implementation. We recommend you use `DPMSolverMultistepScheduler`, since it produces the same quality in about half the steps.\n- Negative prompts, classifier-free guidance scale, and image-to-image tasks are available in the inference code. Advanced features such as depth guidance, ControlNet, and latent upscalers are not available yet.\n\nApple's [conversion and inference repo](https://github.com/apple/ml-stable-diffusion) and our own [swift-coreml-diffusers](https://github.com/huggingface/swift-coreml-diffusers) repos are intended as technology demonstrators to enable other developers to build upon.\n\nIf you feel strongly about any missing features, please feel free to open a feature request or, better yet, a contribution PR 🙂.\n\n## Native Diffusers Swift app\n\nOne easy way to run Stable Diffusion on your own Apple hardware is to use [our open-source Swift repo](https://github.com/huggingface/swift-coreml-diffusers), based on `diffusers` and Apple's conversion and inference repo. You can study the code, compile it with [Xcode](https://developer.apple.com/xcode/) and adapt it for your own needs. For your convenience, there's also a [standalone Mac app in the App Store](https://apps.apple.com/app/diffusers/id1666309574), so you can play with it without having to deal with the code or IDE. If you are a developer and have determined that Core ML is the best solution to build your Stable Diffusion app, then you can use the rest of this guide to get started with your project. We can't wait to see what you'll build 🙂.\n" }, { "path": "diffusers/docs/source/en/optimization/deepcache.md", "content": "\n\n# DeepCache\n[DeepCache](https://huggingface.co/papers/2312.00858) accelerates [`StableDiffusionPipeline`] and [`StableDiffusionXLPipeline`] by strategically caching and reusing high-level features while efficiently updating low-level features by taking advantage of the U-Net architecture.\n\nStart by installing [DeepCache](https://github.com/horseee/DeepCache):\n```bash\npip install DeepCache\n```\n\nThen load and enable the [`DeepCacheSDHelper`](https://github.com/horseee/DeepCache#usage):\n\n```diff\n import torch\n from diffusers import StableDiffusionPipeline\n pipe = StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5', torch_dtype=torch.float16).to(\"cuda\")\n\n+ from DeepCache import DeepCacheSDHelper\n+ helper = DeepCacheSDHelper(pipe=pipe)\n+ helper.set_params(\n+ cache_interval=3,\n+ cache_branch_id=0,\n+ )\n+ helper.enable()\n\n image = pipe(\"a photo of an astronaut on a moon\").images[0]\n```\n\nThe `set_params` method accepts two arguments: `cache_interval` and `cache_branch_id`. `cache_interval` means the frequency of feature caching, specified as the number of steps between each cache operation. `cache_branch_id` identifies which branch of the network (ordered from the shallowest to the deepest layer) is responsible for executing the caching processes.\nOpting for a lower `cache_branch_id` or a larger `cache_interval` can lead to faster inference speed at the expense of reduced image quality (ablation experiments of these two hyperparameters can be found in the [paper](https://arxiv.org/abs/2312.00858)). Once those arguments are set, use the `enable` or `disable` methods to activate or deactivate the `DeepCacheSDHelper`.\n\n
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\n\nYou can find more generated samples (original pipeline vs DeepCache) and the corresponding inference latency in the [WandB report](https://wandb.ai/horseee/DeepCache/runs/jwlsqqgt?workspace=user-horseee). The prompts are randomly selected from the [MS-COCO 2017](https://cocodataset.org/#home) dataset.\n\n## Benchmark\n\nWe tested how much faster DeepCache accelerates [Stable Diffusion v2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1) with 50 inference steps on an NVIDIA RTX A5000, using different configurations for resolution, batch size, cache interval (I), and cache branch (B).\n\n| **Resolution** | **Batch size** | **Original** | **DeepCache(I=3, B=0)** | **DeepCache(I=5, B=0)** | **DeepCache(I=5, B=1)** |\n|----------------|----------------|--------------|-------------------------|-------------------------|-------------------------|\n| 512| 8| 15.96| 6.88(2.32x)| 5.03(3.18x)| 7.27(2.20x)|\n| | 4| 8.39| 3.60(2.33x)| 2.62(3.21x)| 3.75(2.24x)|\n| | 1| 2.61| 1.12(2.33x)| 0.81(3.24x)| 1.11(2.35x)|\n| 768| 8| 43.58| 18.99(2.29x)| 13.96(3.12x)| 21.27(2.05x)|\n| | 4| 22.24| 9.67(2.30x)| 7.10(3.13x)| 10.74(2.07x)|\n| | 1| 6.33| 2.72(2.33x)| 1.97(3.21x)| 2.98(2.12x)|\n| 1024| 8| 101.95| 45.57(2.24x)| 33.72(3.02x)| 53.00(1.92x)|\n| | 4| 49.25| 21.86(2.25x)| 16.19(3.04x)| 25.78(1.91x)|\n| | 1| 13.83| 6.07(2.28x)| 4.43(3.12x)| 7.15(1.93x)|\n" }, { "path": "diffusers/docs/source/en/optimization/fp16.md", "content": "\n\n# Speed up inference\n\nThere are several ways to optimize Diffusers for inference speed, such as reducing the computational burden by lowering the data precision or using a lightweight distilled model. There are also memory-efficient attention implementations, [xFormers](xformers) and [scaled dot product attention](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html) in PyTorch 2.0, that reduce memory usage which also indirectly speeds up inference. Different speed optimizations can be stacked together to get the fastest inference times.\n\n> [!TIP]\n> Optimizing for inference speed or reduced memory usage can lead to improved performance in the other category, so you should try to optimize for both whenever you can. This guide focuses on inference speed, but you can learn more about lowering memory usage in the [Reduce memory usage](memory) guide.\n\nThe inference times below are obtained from generating a single 512x512 image from the prompt \"a photo of an astronaut riding a horse on mars\" with 50 DDIM steps on a NVIDIA A100.\n\n| setup | latency | speed-up |\n|----------|---------|----------|\n| baseline | 5.27s | x1 |\n| tf32 | 4.14s | x1.27 |\n| fp16 | 3.51s | x1.50 |\n| combined | 3.41s | x1.54 |\n\n## TensorFloat-32\n\nOn Ampere and later CUDA devices, matrix multiplications and convolutions can use the [TensorFloat-32 (tf32)](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/) mode for faster, but slightly less accurate computations. By default, PyTorch enables tf32 mode for convolutions but not matrix multiplications. Unless your network requires full float32 precision, we recommend enabling tf32 for matrix multiplications. It can significantly speed up computations with typically negligible loss in numerical accuracy.\n\n```python\nimport torch\n\ntorch.backends.cuda.matmul.allow_tf32 = True\n```\n\nLearn more about tf32 in the [Mixed precision training](https://huggingface.co/docs/transformers/en/perf_train_gpu_one#tf32) guide.\n\n## Half-precision weights\n\nTo save GPU memory and get more speed, set `torch_dtype=torch.float16` to load and run the model weights directly with half-precision weights.\n\n```Python\nimport torch\nfrom diffusers import DiffusionPipeline\n\npipe = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n torch_dtype=torch.float16,\n use_safetensors=True,\n)\npipe = pipe.to(\"cuda\")\n```\n\n> [!WARNING]\n> Don't use [torch.autocast](https://pytorch.org/docs/stable/amp.html#torch.autocast) in any of the pipelines as it can lead to black images and is always slower than pure float16 precision.\n\n## Distilled model\n\nYou could also use a distilled Stable Diffusion model and autoencoder to speed up inference. During distillation, many of the UNet's residual and attention blocks are shed to reduce the model size by 51% and improve latency on CPU/GPU by 43%. The distilled model is faster and uses less memory while generating images of comparable quality to the full Stable Diffusion model.\n\n> [!TIP]\n> Read the [Open-sourcing Knowledge Distillation Code and Weights of SD-Small and SD-Tiny](https://huggingface.co/blog/sd_distillation) blog post to learn more about how knowledge distillation training works to produce a faster, smaller, and cheaper generative model.\n\nThe inference times below are obtained from generating 4 images from the prompt \"a photo of an astronaut riding a horse on mars\" with 25 PNDM steps on a NVIDIA A100. Each generation is repeated 3 times with the distilled Stable Diffusion v1.4 model by [Nota AI](https://hf.co/nota-ai).\n\n| setup | latency | speed-up |\n|------------------------------|---------|----------|\n| baseline | 6.37s | x1 |\n| distilled | 4.18s | x1.52 |\n| distilled + tiny autoencoder | 3.83s | x1.66 |\n\nLet's load the distilled Stable Diffusion model and compare it against the original Stable Diffusion model.\n\n```py\nfrom diffusers import StableDiffusionPipeline\nimport torch\n\ndistilled = StableDiffusionPipeline.from_pretrained(\n \"nota-ai/bk-sdm-small\", torch_dtype=torch.float16, use_safetensors=True,\n).to(\"cuda\")\nprompt = \"a golden vase with different flowers\"\ngenerator = torch.manual_seed(2023)\nimage = distilled(\"a golden vase with different flowers\", num_inference_steps=25, generator=generator).images[0]\nimage\n```\n\n
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\n\n### Tiny AutoEncoder\n\nTo speed inference up even more, replace the autoencoder with a [distilled version](https://huggingface.co/sayakpaul/taesdxl-diffusers) of it.\n\n```py\nimport torch\nfrom diffusers import AutoencoderTiny, StableDiffusionPipeline\n\ndistilled = StableDiffusionPipeline.from_pretrained(\n \"nota-ai/bk-sdm-small\", torch_dtype=torch.float16, use_safetensors=True,\n).to(\"cuda\")\ndistilled.vae = AutoencoderTiny.from_pretrained(\n \"sayakpaul/taesd-diffusers\", torch_dtype=torch.float16, use_safetensors=True,\n).to(\"cuda\")\n\nprompt = \"a golden vase with different flowers\"\ngenerator = torch.manual_seed(2023)\nimage = distilled(\"a golden vase with different flowers\", num_inference_steps=25, generator=generator).images[0]\nimage\n```\n\n
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distilled Stable Diffusion + Tiny AutoEncoder
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\n" }, { "path": "diffusers/docs/source/en/optimization/habana.md", "content": "\n\n# Habana Gaudi\n\n🤗 Diffusers is compatible with Habana Gaudi through 🤗 [Optimum](https://huggingface.co/docs/optimum/habana/usage_guides/stable_diffusion). Follow the [installation](https://docs.habana.ai/en/latest/Installation_Guide/index.html) guide to install the SynapseAI and Gaudi drivers, and then install Optimum Habana:\n\n```bash\npython -m pip install --upgrade-strategy eager optimum[habana]\n```\n\nTo generate images with Stable Diffusion 1 and 2 on Gaudi, you need to instantiate two instances:\n\n- [`~optimum.habana.diffusers.GaudiStableDiffusionPipeline`], a pipeline for text-to-image generation.\n- [`~optimum.habana.diffusers.GaudiDDIMScheduler`], a Gaudi-optimized scheduler.\n\nWhen you initialize the pipeline, you have to specify `use_habana=True` to deploy it on HPUs and to get the fastest possible generation, you should enable **HPU graphs** with `use_hpu_graphs=True`.\n\nFinally, specify a [`~optimum.habana.GaudiConfig`] which can be downloaded from the [Habana](https://huggingface.co/Habana) organization on the Hub.\n\n```python\nfrom optimum.habana import GaudiConfig\nfrom optimum.habana.diffusers import GaudiDDIMScheduler, GaudiStableDiffusionPipeline\n\nmodel_name = \"stabilityai/stable-diffusion-2-base\"\nscheduler = GaudiDDIMScheduler.from_pretrained(model_name, subfolder=\"scheduler\")\npipeline = GaudiStableDiffusionPipeline.from_pretrained(\n model_name,\n scheduler=scheduler,\n use_habana=True,\n use_hpu_graphs=True,\n gaudi_config=\"Habana/stable-diffusion-2\",\n)\n```\n\nNow you can call the pipeline to generate images by batches from one or several prompts:\n\n```python\noutputs = pipeline(\n prompt=[\n \"High quality photo of an astronaut riding a horse in space\",\n \"Face of a yellow cat, high resolution, sitting on a park bench\",\n ],\n num_images_per_prompt=10,\n batch_size=4,\n)\n```\n\nFor more information, check out 🤗 Optimum Habana's [documentation](https://huggingface.co/docs/optimum/habana/usage_guides/stable_diffusion) and the [example](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion) provided in the official GitHub repository.\n\n## Benchmark\n\nWe benchmarked Habana's first-generation Gaudi and Gaudi2 with the [Habana/stable-diffusion](https://huggingface.co/Habana/stable-diffusion) and [Habana/stable-diffusion-2](https://huggingface.co/Habana/stable-diffusion-2) Gaudi configurations (mixed precision bf16/fp32) to demonstrate their performance.\n\nFor [Stable Diffusion v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) on 512x512 images:\n\n| | Latency (batch size = 1) | Throughput |\n| ---------------------- |:------------------------:|:---------------------------:|\n| first-generation Gaudi | 3.80s | 0.308 images/s (batch size = 8) |\n| Gaudi2 | 1.33s | 1.081 images/s (batch size = 8) |\n\nFor [Stable Diffusion v2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1) on 768x768 images:\n\n| | Latency (batch size = 1) | Throughput |\n| ---------------------- |:------------------------:|:-------------------------------:|\n| first-generation Gaudi | 10.2s | 0.108 images/s (batch size = 4) |\n| Gaudi2 | 3.17s | 0.379 images/s (batch size = 8) |\n" }, { "path": "diffusers/docs/source/en/optimization/memory.md", "content": "\n\n# Reduce memory usage\n\nA barrier to using diffusion models is the large amount of memory required. To overcome this challenge, there are several memory-reducing techniques you can use to run even some of the largest models on free-tier or consumer GPUs. Some of these techniques can even be combined to further reduce memory usage.\n\n\n\nIn many cases, optimizing for memory or speed leads to improved performance in the other, so you should try to optimize for both whenever you can. This guide focuses on minimizing memory usage, but you can also learn more about how to [Speed up inference](fp16).\n\n\n\nThe results below are obtained from generating a single 512x512 image from the prompt a photo of an astronaut riding a horse on mars with 50 DDIM steps on a Nvidia Titan RTX, demonstrating the speed-up you can expect as a result of reduced memory consumption.\n\n| | latency | speed-up |\n| ---------------- | ------- | ------- |\n| original | 9.50s | x1 |\n| fp16 | 3.61s | x2.63 |\n| channels last | 3.30s | x2.88 |\n| traced UNet | 3.21s | x2.96 |\n| memory-efficient attention | 2.63s | x3.61 |\n\n## Sliced VAE\n\nSliced VAE enables decoding large batches of images with limited VRAM or batches with 32 images or more by decoding the batches of latents one image at a time. You'll likely want to couple this with [`~ModelMixin.enable_xformers_memory_efficient_attention`] to reduce memory use further if you have xFormers installed.\n\nTo use sliced VAE, call [`~StableDiffusionPipeline.enable_vae_slicing`] on your pipeline before inference:\n\n```python\nimport torch\nfrom diffusers import StableDiffusionPipeline\n\npipe = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n torch_dtype=torch.float16,\n use_safetensors=True,\n)\npipe = pipe.to(\"cuda\")\n\nprompt = \"a photo of an astronaut riding a horse on mars\"\npipe.enable_vae_slicing()\n#pipe.enable_xformers_memory_efficient_attention()\nimages = pipe([prompt] * 32).images\n```\n\nYou may see a small performance boost in VAE decoding on multi-image batches, and there should be no performance impact on single-image batches.\n\n## Tiled VAE\n\nTiled VAE processing also enables working with large images on limited VRAM (for example, generating 4k images on 8GB of VRAM) by splitting the image into overlapping tiles, decoding the tiles, and then blending the outputs together to compose the final image. You should also used tiled VAE with [`~ModelMixin.enable_xformers_memory_efficient_attention`] to reduce memory use further if you have xFormers installed.\n\nTo use tiled VAE processing, call [`~StableDiffusionPipeline.enable_vae_tiling`] on your pipeline before inference:\n\n```python\nimport torch\nfrom diffusers import StableDiffusionPipeline, UniPCMultistepScheduler\n\npipe = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n torch_dtype=torch.float16,\n use_safetensors=True,\n)\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\npipe = pipe.to(\"cuda\")\nprompt = \"a beautiful landscape photograph\"\npipe.enable_vae_tiling()\n#pipe.enable_xformers_memory_efficient_attention()\n\nimage = pipe([prompt], width=3840, height=2224, num_inference_steps=20).images[0]\n```\n\nThe output image has some tile-to-tile tone variation because the tiles are decoded separately, but you shouldn't see any sharp and obvious seams between the tiles. Tiling is turned off for images that are 512x512 or smaller.\n\n## CPU offloading\n\nOffloading the weights to the CPU and only loading them on the GPU when performing the forward pass can also save memory. Often, this technique can reduce memory consumption to less than 3GB.\n\nTo perform CPU offloading, call [`~StableDiffusionPipeline.enable_sequential_cpu_offload`]:\n\n```Python\nimport torch\nfrom diffusers import StableDiffusionPipeline\n\npipe = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n torch_dtype=torch.float16,\n use_safetensors=True,\n)\n\nprompt = \"a photo of an astronaut riding a horse on mars\"\npipe.enable_sequential_cpu_offload()\nimage = pipe(prompt).images[0]\n```\n\nCPU offloading works on submodules rather than whole models. This is the best way to minimize memory consumption, but inference is much slower due to the iterative nature of the diffusion process. The UNet component of the pipeline runs several times (as many as `num_inference_steps`); each time, the different UNet submodules are sequentially onloaded and offloaded as needed, resulting in a large number of memory transfers.\n\n\n\nConsider using [model offloading](#model-offloading) if you want to optimize for speed because it is much faster. The tradeoff is your memory savings won't be as large.\n\n\n\n\n\nWhen using [`~StableDiffusionPipeline.enable_sequential_cpu_offload`], don't move the pipeline to CUDA beforehand or else the gain in memory consumption will only be minimal (see this [issue](https://github.com/huggingface/diffusers/issues/1934) for more information).\n\n[`~StableDiffusionPipeline.enable_sequential_cpu_offload`] is a stateful operation that installs hooks on the models.\n\n\n\n## Model offloading\n\n\n\nModel offloading requires 🤗 Accelerate version 0.17.0 or higher.\n\n\n\n[Sequential CPU offloading](#cpu-offloading) preserves a lot of memory but it makes inference slower because submodules are moved to GPU as needed, and they're immediately returned to the CPU when a new module runs.\n\nFull-model offloading is an alternative that moves whole models to the GPU, instead of handling each model's constituent *submodules*. There is a negligible impact on inference time (compared with moving the pipeline to `cuda`), and it still provides some memory savings.\n\nDuring model offloading, only one of the main components of the pipeline (typically the text encoder, UNet and VAE)\nis placed on the GPU while the others wait on the CPU. Components like the UNet that run for multiple iterations stay on the GPU until they're no longer needed.\n\nEnable model offloading by calling [`~StableDiffusionPipeline.enable_model_cpu_offload`] on the pipeline:\n\n```Python\nimport torch\nfrom diffusers import StableDiffusionPipeline\n\npipe = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n torch_dtype=torch.float16,\n use_safetensors=True,\n)\n\nprompt = \"a photo of an astronaut riding a horse on mars\"\npipe.enable_model_cpu_offload()\nimage = pipe(prompt).images[0]\n```\n\n\n\nIn order to properly offload models after they're called, it is required to run the entire pipeline and models are called in the pipeline's expected order. Exercise caution if models are reused outside the context of the pipeline after hooks have been installed. See [Removing Hooks](https://huggingface.co/docs/accelerate/en/package_reference/big_modeling#accelerate.hooks.remove_hook_from_module) for more information.\n\n[`~StableDiffusionPipeline.enable_model_cpu_offload`] is a stateful operation that installs hooks on the models and state on the pipeline.\n\n\n\n## Channels-last memory format\n\nThe channels-last memory format is an alternative way of ordering NCHW tensors in memory to preserve dimension ordering. Channels-last tensors are ordered in such a way that the channels become the densest dimension (storing images pixel-per-pixel). Since not all operators currently support the channels-last format, it may result in worst performance but you should still try and see if it works for your model.\n\nFor example, to set the pipeline's UNet to use the channels-last format:\n\n```python\nprint(pipe.unet.conv_out.state_dict()[\"weight\"].stride()) # (2880, 9, 3, 1)\npipe.unet.to(memory_format=torch.channels_last) # in-place operation\nprint(\n pipe.unet.conv_out.state_dict()[\"weight\"].stride()\n) # (2880, 1, 960, 320) having a stride of 1 for the 2nd dimension proves that it works\n```\n\n## Tracing\n\nTracing runs an example input tensor through the model and captures the operations that are performed on it as that input makes its way through the model's layers. The executable or `ScriptFunction` that is returned is optimized with just-in-time compilation.\n\nTo trace a UNet:\n\n```python\nimport time\nimport torch\nfrom diffusers import StableDiffusionPipeline\nimport functools\n\n# torch disable grad\ntorch.set_grad_enabled(False)\n\n# set variables\nn_experiments = 2\nunet_runs_per_experiment = 50\n\n\n# load inputs\ndef generate_inputs():\n sample = torch.randn((2, 4, 64, 64), device=\"cuda\", dtype=torch.float16)\n timestep = torch.rand(1, device=\"cuda\", dtype=torch.float16) * 999\n encoder_hidden_states = torch.randn((2, 77, 768), device=\"cuda\", dtype=torch.float16)\n return sample, timestep, encoder_hidden_states\n\n\npipe = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n torch_dtype=torch.float16,\n use_safetensors=True,\n).to(\"cuda\")\nunet = pipe.unet\nunet.eval()\nunet.to(memory_format=torch.channels_last) # use channels_last memory format\nunet.forward = functools.partial(unet.forward, return_dict=False) # set return_dict=False as default\n\n# warmup\nfor _ in range(3):\n with torch.inference_mode():\n inputs = generate_inputs()\n orig_output = unet(*inputs)\n\n# trace\nprint(\"tracing..\")\nunet_traced = torch.jit.trace(unet, inputs)\nunet_traced.eval()\nprint(\"done tracing\")\n\n\n# warmup and optimize graph\nfor _ in range(5):\n with torch.inference_mode():\n inputs = generate_inputs()\n orig_output = unet_traced(*inputs)\n\n\n# benchmarking\nwith torch.inference_mode():\n for _ in range(n_experiments):\n torch.cuda.synchronize()\n start_time = time.time()\n for _ in range(unet_runs_per_experiment):\n orig_output = unet_traced(*inputs)\n torch.cuda.synchronize()\n print(f\"unet traced inference took {time.time() - start_time:.2f} seconds\")\n for _ in range(n_experiments):\n torch.cuda.synchronize()\n start_time = time.time()\n for _ in range(unet_runs_per_experiment):\n orig_output = unet(*inputs)\n torch.cuda.synchronize()\n print(f\"unet inference took {time.time() - start_time:.2f} seconds\")\n\n# save the model\nunet_traced.save(\"unet_traced.pt\")\n```\n\nReplace the `unet` attribute of the pipeline with the traced model:\n\n```python\nfrom diffusers import StableDiffusionPipeline\nimport torch\nfrom dataclasses import dataclass\n\n\n@dataclass\nclass UNet2DConditionOutput:\n sample: torch.Tensor\n\n\npipe = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n torch_dtype=torch.float16,\n use_safetensors=True,\n).to(\"cuda\")\n\n# use jitted unet\nunet_traced = torch.jit.load(\"unet_traced.pt\")\n\n\n# del pipe.unet\nclass TracedUNet(torch.nn.Module):\n def __init__(self):\n super().__init__()\n self.in_channels = pipe.unet.config.in_channels\n self.device = pipe.unet.device\n\n def forward(self, latent_model_input, t, encoder_hidden_states):\n sample = unet_traced(latent_model_input, t, encoder_hidden_states)[0]\n return UNet2DConditionOutput(sample=sample)\n\n\npipe.unet = TracedUNet()\n\nwith torch.inference_mode():\n image = pipe([prompt] * 1, num_inference_steps=50).images[0]\n```\n\n## Memory-efficient attention\n\nRecent work on optimizing bandwidth in the attention block has generated huge speed-ups and reductions in GPU memory usage. The most recent type of memory-efficient attention is [Flash Attention](https://arxiv.org/abs/2205.14135) (you can check out the original code at [HazyResearch/flash-attention](https://github.com/HazyResearch/flash-attention)).\n\n\n\nIf you have PyTorch >= 2.0 installed, you should not expect a speed-up for inference when enabling `xformers`.\n\n\n\nTo use Flash Attention, install the following:\n\n- PyTorch > 1.12\n- CUDA available\n- [xFormers](xformers)\n\nThen call [`~ModelMixin.enable_xformers_memory_efficient_attention`] on the pipeline:\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipe = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n torch_dtype=torch.float16,\n use_safetensors=True,\n).to(\"cuda\")\n\npipe.enable_xformers_memory_efficient_attention()\n\nwith torch.inference_mode():\n sample = pipe(\"a small cat\")\n\n# optional: You can disable it via\n# pipe.disable_xformers_memory_efficient_attention()\n```\n\nThe iteration speed when using `xformers` should match the iteration speed of PyTorch 2.0 as described [here](torch2.0).\n" }, { "path": "diffusers/docs/source/en/optimization/mps.md", "content": "\n\n# Metal Performance Shaders (MPS)\n\n🤗 Diffusers is compatible with Apple silicon (M1/M2 chips) using the PyTorch [`mps`](https://pytorch.org/docs/stable/notes/mps.html) device, which uses the Metal framework to leverage the GPU on MacOS devices. You'll need to have:\n\n- macOS computer with Apple silicon (M1/M2) hardware\n- macOS 12.6 or later (13.0 or later recommended)\n- arm64 version of Python\n- [PyTorch 2.0](https://pytorch.org/get-started/locally/) (recommended) or 1.13 (minimum version supported for `mps`)\n\nThe `mps` backend uses PyTorch's `.to()` interface to move the Stable Diffusion pipeline on to your M1 or M2 device:\n\n```python\nfrom diffusers import DiffusionPipeline\n\npipe = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\npipe = pipe.to(\"mps\")\n\n# Recommended if your computer has < 64 GB of RAM\npipe.enable_attention_slicing()\n\nprompt = \"a photo of an astronaut riding a horse on mars\"\nimage = pipe(prompt).images[0]\nimage\n```\n\n\n\nGenerating multiple prompts in a batch can [crash](https://github.com/huggingface/diffusers/issues/363) or fail to work reliably. We believe this is related to the [`mps`](https://github.com/pytorch/pytorch/issues/84039) backend in PyTorch. While this is being investigated, you should iterate instead of batching.\n\n\n\nIf you're using **PyTorch 1.13**, you need to \"prime\" the pipeline with an additional one-time pass through it. This is a temporary workaround for an issue where the first inference pass produces slightly different results than subsequent ones. You only need to do this pass once, and after just one inference step you can discard the result.\n\n```diff\n from diffusers import DiffusionPipeline\n\n pipe = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\").to(\"mps\")\n pipe.enable_attention_slicing()\n\n prompt = \"a photo of an astronaut riding a horse on mars\"\n # First-time \"warmup\" pass if PyTorch version is 1.13\n+ _ = pipe(prompt, num_inference_steps=1)\n\n # Results match those from the CPU device after the warmup pass.\n image = pipe(prompt).images[0]\n```\n\n## Troubleshoot\n\nM1/M2 performance is very sensitive to memory pressure. When this occurs, the system automatically swaps if it needs to which significantly degrades performance.\n\nTo prevent this from happening, we recommend *attention slicing* to reduce memory pressure during inference and prevent swapping. This is especially relevant if your computer has less than 64GB of system RAM, or if you generate images at non-standard resolutions larger than 512×512 pixels. Call the [`~DiffusionPipeline.enable_attention_slicing`] function on your pipeline:\n\n```py\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipeline = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True).to(\"mps\")\npipeline.enable_attention_slicing()\n```\n\nAttention slicing performs the costly attention operation in multiple steps instead of all at once. It usually improves performance by ~20% in computers without universal memory, but we've observed *better performance* in most Apple silicon computers unless you have 64GB of RAM or more.\n" }, { "path": "diffusers/docs/source/en/optimization/onnx.md", "content": "\n\n# ONNX Runtime\n\n🤗 [Optimum](https://github.com/huggingface/optimum) provides a Stable Diffusion pipeline compatible with ONNX Runtime. You'll need to install 🤗 Optimum with the following command for ONNX Runtime support:\n\n```bash\npip install -q optimum[\"onnxruntime\"]\n```\n\nThis guide will show you how to use the Stable Diffusion and Stable Diffusion XL (SDXL) pipelines with ONNX Runtime.\n\n## Stable Diffusion\n\nTo load and run inference, use the [`~optimum.onnxruntime.ORTStableDiffusionPipeline`]. If you want to load a PyTorch model and convert it to the ONNX format on-the-fly, set `export=True`:\n\n```python\nfrom optimum.onnxruntime import ORTStableDiffusionPipeline\n\nmodel_id = \"runwayml/stable-diffusion-v1-5\"\npipeline = ORTStableDiffusionPipeline.from_pretrained(model_id, export=True)\nprompt = \"sailing ship in storm by Leonardo da Vinci\"\nimage = pipeline(prompt).images[0]\npipeline.save_pretrained(\"./onnx-stable-diffusion-v1-5\")\n```\n\n\n\nGenerating multiple prompts in a batch seems to take too much memory. While we look into it, you may need to iterate instead of batching.\n\n\n\nTo export the pipeline in the ONNX format offline and use it later for inference,\nuse the [`optimum-cli export`](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli) command:\n\n```bash\noptimum-cli export onnx --model runwayml/stable-diffusion-v1-5 sd_v15_onnx/\n```\n\nThen to perform inference (you don't have to specify `export=True` again):\n\n```python\nfrom optimum.onnxruntime import ORTStableDiffusionPipeline\n\nmodel_id = \"sd_v15_onnx\"\npipeline = ORTStableDiffusionPipeline.from_pretrained(model_id)\nprompt = \"sailing ship in storm by Leonardo da Vinci\"\nimage = pipeline(prompt).images[0]\n```\n\n
\n \n
\n\nYou can find more examples in 🤗 Optimum [documentation](https://huggingface.co/docs/optimum/), and Stable Diffusion is supported for text-to-image, image-to-image, and inpainting.\n\n## Stable Diffusion XL\n\nTo load and run inference with SDXL, use the [`~optimum.onnxruntime.ORTStableDiffusionXLPipeline`]:\n\n```python\nfrom optimum.onnxruntime import ORTStableDiffusionXLPipeline\n\nmodel_id = \"stabilityai/stable-diffusion-xl-base-1.0\"\npipeline = ORTStableDiffusionXLPipeline.from_pretrained(model_id)\nprompt = \"sailing ship in storm by Leonardo da Vinci\"\nimage = pipeline(prompt).images[0]\n```\n\nTo export the pipeline in the ONNX format and use it later for inference, use the [`optimum-cli export`](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli) command:\n\n```bash\noptimum-cli export onnx --model stabilityai/stable-diffusion-xl-base-1.0 --task stable-diffusion-xl sd_xl_onnx/\n```\n\nSDXL in the ONNX format is supported for text-to-image and image-to-image.\n" }, { "path": "diffusers/docs/source/en/optimization/open_vino.md", "content": "\n\n# OpenVINO\n\n🤗 [Optimum](https://github.com/huggingface/optimum-intel) provides Stable Diffusion pipelines compatible with OpenVINO to perform inference on a variety of Intel processors (see the [full list](https://docs.openvino.ai/latest/openvino_docs_OV_UG_supported_plugins_Supported_Devices.html) of supported devices).\n\nYou'll need to install 🤗 Optimum Intel with the `--upgrade-strategy eager` option to ensure [`optimum-intel`](https://github.com/huggingface/optimum-intel) is using the latest version:\n\n```bash\npip install --upgrade-strategy eager optimum[\"openvino\"]\n```\n\nThis guide will show you how to use the Stable Diffusion and Stable Diffusion XL (SDXL) pipelines with OpenVINO.\n\n## Stable Diffusion\n\nTo load and run inference, use the [`~optimum.intel.OVStableDiffusionPipeline`]. If you want to load a PyTorch model and convert it to the OpenVINO format on-the-fly, set `export=True`:\n\n```python\nfrom optimum.intel import OVStableDiffusionPipeline\n\nmodel_id = \"runwayml/stable-diffusion-v1-5\"\npipeline = OVStableDiffusionPipeline.from_pretrained(model_id, export=True)\nprompt = \"sailing ship in storm by Rembrandt\"\nimage = pipeline(prompt).images[0]\n\n# Don't forget to save the exported model\npipeline.save_pretrained(\"openvino-sd-v1-5\")\n```\n\nTo further speed-up inference, statically reshape the model. If you change any parameters such as the outputs height or width, you’ll need to statically reshape your model again.\n\n```python\n# Define the shapes related to the inputs and desired outputs\nbatch_size, num_images, height, width = 1, 1, 512, 512\n\n# Statically reshape the model\npipeline.reshape(batch_size, height, width, num_images)\n# Compile the model before inference\npipeline.compile()\n\nimage = pipeline(\n prompt,\n height=height,\n width=width,\n num_images_per_prompt=num_images,\n).images[0]\n```\n
\n \n
\n\nYou can find more examples in the 🤗 Optimum [documentation](https://huggingface.co/docs/optimum/intel/inference#stable-diffusion), and Stable Diffusion is supported for text-to-image, image-to-image, and inpainting.\n\n## Stable Diffusion XL\n\nTo load and run inference with SDXL, use the [`~optimum.intel.OVStableDiffusionXLPipeline`]:\n\n```python\nfrom optimum.intel import OVStableDiffusionXLPipeline\n\nmodel_id = \"stabilityai/stable-diffusion-xl-base-1.0\"\npipeline = OVStableDiffusionXLPipeline.from_pretrained(model_id)\nprompt = \"sailing ship in storm by Rembrandt\"\nimage = pipeline(prompt).images[0]\n```\n\nTo further speed-up inference, [statically reshape](#stable-diffusion) the model as shown in the Stable Diffusion section.\n\nYou can find more examples in the 🤗 Optimum [documentation](https://huggingface.co/docs/optimum/intel/inference#stable-diffusion-xl), and running SDXL in OpenVINO is supported for text-to-image and image-to-image.\n" }, { "path": "diffusers/docs/source/en/optimization/tgate.md", "content": "# T-GATE\n\n[T-GATE](https://github.com/HaozheLiu-ST/T-GATE/tree/main) accelerates inference for [Stable Diffusion](../api/pipelines/stable_diffusion/overview), [PixArt](../api/pipelines/pixart), and [Latency Consistency Model](../api/pipelines/latent_consistency_models.md) pipelines by skipping the cross-attention calculation once it converges. This method doesn't require any additional training and it can speed up inference from 10-50%. T-GATE is also compatible with other optimization methods like [DeepCache](./deepcache).\n\nBefore you begin, make sure you install T-GATE.\n\n```bash\npip install tgate\npip install -U torch diffusers transformers accelerate DeepCache\n```\n\n\nTo use T-GATE with a pipeline, you need to use its corresponding loader.\n\n| Pipeline | T-GATE Loader |\n|---|---|\n| PixArt | TgatePixArtLoader |\n| Stable Diffusion XL | TgateSDXLLoader |\n| Stable Diffusion XL + DeepCache | TgateSDXLDeepCacheLoader |\n| Stable Diffusion | TgateSDLoader |\n| Stable Diffusion + DeepCache | TgateSDDeepCacheLoader |\n\nNext, create a `TgateLoader` with a pipeline, the gate step (the time step to stop calculating the cross attention), and the number of inference steps. Then call the `tgate` method on the pipeline with a prompt, gate step, and the number of inference steps.\n\nLet's see how to enable this for several different pipelines.\n\n\n\n\nAccelerate `PixArtAlphaPipeline` with T-GATE:\n\n```py\nimport torch\nfrom diffusers import PixArtAlphaPipeline\nfrom tgate import TgatePixArtLoader\n\npipe = PixArtAlphaPipeline.from_pretrained(\"PixArt-alpha/PixArt-XL-2-1024-MS\", torch_dtype=torch.float16)\n\ngate_step = 8\ninference_step = 25\npipe = TgatePixArtLoader(\n pipe,\n gate_step=gate_step,\n num_inference_steps=inference_step,\n).to(\"cuda\")\n\nimage = pipe.tgate(\n \"An alpaca made of colorful building blocks, cyberpunk.\",\n gate_step=gate_step,\n num_inference_steps=inference_step,\n).images[0]\n```\n\n\n\nAccelerate `StableDiffusionXLPipeline` with T-GATE:\n\n```py\nimport torch\nfrom diffusers import StableDiffusionXLPipeline\nfrom diffusers import DPMSolverMultistepScheduler\nfrom tgate import TgateSDXLLoader\n\npipe = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n use_safetensors=True,\n)\npipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n\ngate_step = 10\ninference_step = 25\npipe = TgateSDXLLoader(\n pipe,\n gate_step=gate_step,\n num_inference_steps=inference_step,\n).to(\"cuda\")\n\nimage = pipe.tgate(\n \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k.\",\n gate_step=gate_step,\n num_inference_steps=inference_step\n).images[0]\n```\n\n\n\nAccelerate `StableDiffusionXLPipeline` with [DeepCache](https://github.com/horseee/DeepCache) and T-GATE:\n\n```py\nimport torch\nfrom diffusers import StableDiffusionXLPipeline\nfrom diffusers import DPMSolverMultistepScheduler\nfrom tgate import TgateSDXLDeepCacheLoader\n\npipe = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n use_safetensors=True,\n)\npipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n\ngate_step = 10\ninference_step = 25\npipe = TgateSDXLDeepCacheLoader(\n pipe,\n cache_interval=3,\n cache_branch_id=0,\n).to(\"cuda\")\n\nimage = pipe.tgate(\n \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k.\",\n gate_step=gate_step,\n num_inference_steps=inference_step\n).images[0]\n```\n\n\n\nAccelerate `latent-consistency/lcm-sdxl` with T-GATE:\n\n```py\nimport torch\nfrom diffusers import StableDiffusionXLPipeline\nfrom diffusers import UNet2DConditionModel, LCMScheduler\nfrom diffusers import DPMSolverMultistepScheduler\nfrom tgate import TgateSDXLLoader\n\nunet = UNet2DConditionModel.from_pretrained(\n \"latent-consistency/lcm-sdxl\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n)\npipe = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n unet=unet,\n torch_dtype=torch.float16,\n variant=\"fp16\",\n)\npipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\n\ngate_step = 1\ninference_step = 4\npipe = TgateSDXLLoader(\n pipe,\n gate_step=gate_step,\n num_inference_steps=inference_step,\n lcm=True\n).to(\"cuda\")\n\nimage = pipe.tgate(\n \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k.\",\n gate_step=gate_step,\n num_inference_steps=inference_step\n).images[0]\n```\n\n\n\nT-GATE also supports [`StableDiffusionPipeline`] and [PixArt-alpha/PixArt-LCM-XL-2-1024-MS](https://hf.co/PixArt-alpha/PixArt-LCM-XL-2-1024-MS).\n\n## Benchmarks\n| Model | MACs | Param | Latency | Zero-shot 10K-FID on MS-COCO |\n|-----------------------|----------|-----------|---------|---------------------------|\n| SD-1.5 | 16.938T | 859.520M | 7.032s | 23.927 |\n| SD-1.5 w/ T-GATE | 9.875T | 815.557M | 4.313s | 20.789 |\n| SD-2.1 | 38.041T | 865.785M | 16.121s | 22.609 |\n| SD-2.1 w/ T-GATE | 22.208T | 815.433 M | 9.878s | 19.940 |\n| SD-XL | 149.438T | 2.570B | 53.187s | 24.628 |\n| SD-XL w/ T-GATE | 84.438T | 2.024B | 27.932s | 22.738 |\n| Pixart-Alpha | 107.031T | 611.350M | 61.502s | 38.669 |\n| Pixart-Alpha w/ T-GATE | 65.318T | 462.585M | 37.867s | 35.825 |\n| DeepCache (SD-XL) | 57.888T | - | 19.931s | 23.755 |\n| DeepCache w/ T-GATE | 43.868T | - | 14.666s | 23.999 |\n| LCM (SD-XL) | 11.955T | 2.570B | 3.805s | 25.044 |\n| LCM w/ T-GATE | 11.171T | 2.024B | 3.533s | 25.028 |\n| LCM (Pixart-Alpha) | 8.563T | 611.350M | 4.733s | 36.086 |\n| LCM w/ T-GATE | 7.623T | 462.585M | 4.543s | 37.048 |\n\nThe latency is tested on an NVIDIA 1080TI, MACs and Params are calculated with [calflops](https://github.com/MrYxJ/calculate-flops.pytorch), and the FID is calculated with [PytorchFID](https://github.com/mseitzer/pytorch-fid).\n" }, { "path": "diffusers/docs/source/en/optimization/tome.md", "content": "\n\n# Token merging\n\n[Token merging](https://huggingface.co/papers/2303.17604) (ToMe) merges redundant tokens/patches progressively in the forward pass of a Transformer-based network which can speed-up the inference latency of [`StableDiffusionPipeline`].\n\nInstall ToMe from `pip`:\n\n```bash\npip install tomesd\n```\n\nYou can use ToMe from the [`tomesd`](https://github.com/dbolya/tomesd) library with the [`apply_patch`](https://github.com/dbolya/tomesd?tab=readme-ov-file#usage) function:\n\n```diff\n from diffusers import StableDiffusionPipeline\n import torch\n import tomesd\n\n pipeline = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, use_safetensors=True,\n ).to(\"cuda\")\n+ tomesd.apply_patch(pipeline, ratio=0.5)\n\n image = pipeline(\"a photo of an astronaut riding a horse on mars\").images[0]\n```\n\nThe `apply_patch` function exposes a number of [arguments](https://github.com/dbolya/tomesd#usage) to help strike a balance between pipeline inference speed and the quality of the generated tokens. The most important argument is `ratio` which controls the number of tokens that are merged during the forward pass.\n\nAs reported in the [paper](https://huggingface.co/papers/2303.17604), ToMe can greatly preserve the quality of the generated images while boosting inference speed. By increasing the `ratio`, you can speed-up inference even further, but at the cost of some degraded image quality.\n\nTo test the quality of the generated images, we sampled a few prompts from [Parti Prompts](https://parti.research.google/) and performed inference with the [`StableDiffusionPipeline`] with the following settings:\n\n
\n \n
\n\nWe didn’t notice any significant decrease in the quality of the generated samples, and you can check out the generated samples in this [WandB report](https://wandb.ai/sayakpaul/tomesd-results/runs/23j4bj3i?workspace=). If you're interested in reproducing this experiment, use this [script](https://gist.github.com/sayakpaul/8cac98d7f22399085a060992f411ecbd).\n\n## Benchmarks\n\nWe also benchmarked the impact of `tomesd` on the [`StableDiffusionPipeline`] with [xFormers](https://huggingface.co/docs/diffusers/optimization/xformers) enabled across several image resolutions. The results are obtained from A100 and V100 GPUs in the following development environment:\n\n```bash\n- `diffusers` version: 0.15.1\n- Python version: 3.8.16\n- PyTorch version (GPU?): 1.13.1+cu116 (True)\n- Huggingface_hub version: 0.13.2\n- Transformers version: 4.27.2\n- Accelerate version: 0.18.0\n- xFormers version: 0.0.16\n- tomesd version: 0.1.2\n```\n\nTo reproduce this benchmark, feel free to use this [script](https://gist.github.com/sayakpaul/27aec6bca7eb7b0e0aa4112205850335). The results are reported in seconds, and where applicable we report the speed-up percentage over the vanilla pipeline when using ToMe and ToMe + xFormers.\n\n| **GPU** | **Resolution** | **Batch size** | **Vanilla** | **ToMe** | **ToMe + xFormers** |\n|----------|----------------|----------------|-------------|----------------|---------------------|\n| **A100** | 512 | 10 | 6.88 | 5.26 (+23.55%) | 4.69 (+31.83%) |\n| | 768 | 10 | OOM | 14.71 | 11 |\n| | | 8 | OOM | 11.56 | 8.84 |\n| | | 4 | OOM | 5.98 | 4.66 |\n| | | 2 | 4.99 | 3.24 (+35.07%) | 2.1 (+37.88%) |\n| | | 1 | 3.29 | 2.24 (+31.91%) | 2.03 (+38.3%) |\n| | 1024 | 10 | OOM | OOM | OOM |\n| | | 8 | OOM | OOM | OOM |\n| | | 4 | OOM | 12.51 | 9.09 |\n| | | 2 | OOM | 6.52 | 4.96 |\n| | | 1 | 6.4 | 3.61 (+43.59%) | 2.81 (+56.09%) |\n| **V100** | 512 | 10 | OOM | 10.03 | 9.29 |\n| | | 8 | OOM | 8.05 | 7.47 |\n| | | 4 | 5.7 | 4.3 (+24.56%) | 3.98 (+30.18%) |\n| | | 2 | 3.14 | 2.43 (+22.61%) | 2.27 (+27.71%) |\n| | | 1 | 1.88 | 1.57 (+16.49%) | 1.57 (+16.49%) |\n| | 768 | 10 | OOM | OOM | 23.67 |\n| | | 8 | OOM | OOM | 18.81 |\n| | | 4 | OOM | 11.81 | 9.7 |\n| | | 2 | OOM | 6.27 | 5.2 |\n| | | 1 | 5.43 | 3.38 (+37.75%) | 2.82 (+48.07%) |\n| | 1024 | 10 | OOM | OOM | OOM |\n| | | 8 | OOM | OOM | OOM |\n| | | 4 | OOM | OOM | 19.35 |\n| | | 2 | OOM | 13 | 10.78 |\n| | | 1 | OOM | 6.66 | 5.54 |\n\nAs seen in the tables above, the speed-up from `tomesd` becomes more pronounced for larger image resolutions. It is also interesting to note that with `tomesd`, it is possible to run the pipeline on a higher resolution like 1024x1024. You may be able to speed-up inference even more with [`torch.compile`](torch2.0).\n" }, { "path": "diffusers/docs/source/en/optimization/torch2.0.md", "content": "\n\n# PyTorch 2.0\n\n🤗 Diffusers supports the latest optimizations from [PyTorch 2.0](https://pytorch.org/get-started/pytorch-2.0/) which include:\n\n1. A memory-efficient attention implementation, scaled dot product attention, without requiring any extra dependencies such as xFormers.\n2. [`torch.compile`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html), a just-in-time (JIT) compiler to provide an extra performance boost when individual models are compiled.\n\nBoth of these optimizations require PyTorch 2.0 or later and 🤗 Diffusers > 0.13.0.\n\n```bash\npip install --upgrade torch diffusers\n```\n\n## Scaled dot product attention\n\n[`torch.nn.functional.scaled_dot_product_attention`](https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention) (SDPA) is an optimized and memory-efficient attention (similar to xFormers) that automatically enables several other optimizations depending on the model inputs and GPU type. SDPA is enabled by default if you're using PyTorch 2.0 and the latest version of 🤗 Diffusers, so you don't need to add anything to your code.\n\nHowever, if you want to explicitly enable it, you can set a [`DiffusionPipeline`] to use [`~models.attention_processor.AttnProcessor2_0`]:\n\n```diff\n import torch\n from diffusers import DiffusionPipeline\n+ from diffusers.models.attention_processor import AttnProcessor2_0\n\n pipe = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, use_safetensors=True).to(\"cuda\")\n+ pipe.unet.set_attn_processor(AttnProcessor2_0())\n\n prompt = \"a photo of an astronaut riding a horse on mars\"\n image = pipe(prompt).images[0]\n```\n\nSDPA should be as fast and memory efficient as `xFormers`; check the [benchmark](#benchmark) for more details.\n\nIn some cases - such as making the pipeline more deterministic or converting it to other formats - it may be helpful to use the vanilla attention processor, [`~models.attention_processor.AttnProcessor`]. To revert to [`~models.attention_processor.AttnProcessor`], call the [`~UNet2DConditionModel.set_default_attn_processor`] function on the pipeline:\n\n```diff\n import torch\n from diffusers import DiffusionPipeline\n\n pipe = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, use_safetensors=True).to(\"cuda\")\n+ pipe.unet.set_default_attn_processor()\n\n prompt = \"a photo of an astronaut riding a horse on mars\"\n image = pipe(prompt).images[0]\n```\n\n## torch.compile\n\nThe `torch.compile` function can often provide an additional speed-up to your PyTorch code. In 🤗 Diffusers, it is usually best to wrap the UNet with `torch.compile` because it does most of the heavy lifting in the pipeline.\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipe = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, use_safetensors=True).to(\"cuda\")\npipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\nimages = pipe(prompt, num_inference_steps=steps, num_images_per_prompt=batch_size).images[0]\n```\n\nDepending on GPU type, `torch.compile` can provide an *additional speed-up* of **5-300x** on top of SDPA! If you're using more recent GPU architectures such as Ampere (A100, 3090), Ada (4090), and Hopper (H100), `torch.compile` is able to squeeze even more performance out of these GPUs.\n\nCompilation requires some time to complete, so it is best suited for situations where you prepare your pipeline once and then perform the same type of inference operations multiple times. For example, calling the compiled pipeline on a different image size triggers compilation again which can be expensive.\n\nFor more information and different options about `torch.compile`, refer to the [`torch_compile`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) tutorial.\n\n> [!TIP]\n> Learn more about other ways PyTorch 2.0 can help optimize your model in the [Accelerate inference of text-to-image diffusion models](../tutorials/fast_diffusion) tutorial.\n\n## Benchmark\n\nWe conducted a comprehensive benchmark with PyTorch 2.0's efficient attention implementation and `torch.compile` across different GPUs and batch sizes for five of our most used pipelines. The code is benchmarked on 🤗 Diffusers v0.17.0.dev0 to optimize `torch.compile` usage (see [here](https://github.com/huggingface/diffusers/pull/3313) for more details).\n\nExpand the dropdown below to find the code used to benchmark each pipeline:\n\n
\n\n### Stable Diffusion text-to-image\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\npath = \"runwayml/stable-diffusion-v1-5\"\n\nrun_compile = True # Set True / False\n\npipe = DiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16, use_safetensors=True)\npipe = pipe.to(\"cuda\")\npipe.unet.to(memory_format=torch.channels_last)\n\nif run_compile:\n print(\"Run torch compile\")\n pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n\nprompt = \"ghibli style, a fantasy landscape with castles\"\n\nfor _ in range(3):\n images = pipe(prompt=prompt).images\n```\n\n### Stable Diffusion image-to-image\n\n```python\nfrom diffusers import StableDiffusionImg2ImgPipeline\nfrom diffusers.utils import load_image\nimport torch\n\nurl = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n\ninit_image = load_image(url)\ninit_image = init_image.resize((512, 512))\n\npath = \"runwayml/stable-diffusion-v1-5\"\n\nrun_compile = True # Set True / False\n\npipe = StableDiffusionImg2ImgPipeline.from_pretrained(path, torch_dtype=torch.float16, use_safetensors=True)\npipe = pipe.to(\"cuda\")\npipe.unet.to(memory_format=torch.channels_last)\n\nif run_compile:\n print(\"Run torch compile\")\n pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n\nprompt = \"ghibli style, a fantasy landscape with castles\"\n\nfor _ in range(3):\n image = pipe(prompt=prompt, image=init_image).images[0]\n```\n\n### Stable Diffusion inpainting\n\n```python\nfrom diffusers import StableDiffusionInpaintPipeline\nfrom diffusers.utils import load_image\nimport torch\n\nimg_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\nmask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\n\ninit_image = load_image(img_url).resize((512, 512))\nmask_image = load_image(mask_url).resize((512, 512))\n\npath = \"runwayml/stable-diffusion-inpainting\"\n\nrun_compile = True # Set True / False\n\npipe = StableDiffusionInpaintPipeline.from_pretrained(path, torch_dtype=torch.float16, use_safetensors=True)\npipe = pipe.to(\"cuda\")\npipe.unet.to(memory_format=torch.channels_last)\n\nif run_compile:\n print(\"Run torch compile\")\n pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n\nprompt = \"ghibli style, a fantasy landscape with castles\"\n\nfor _ in range(3):\n image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]\n```\n\n### ControlNet\n\n```python\nfrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel\nfrom diffusers.utils import load_image\nimport torch\n\nurl = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n\ninit_image = load_image(url)\ninit_image = init_image.resize((512, 512))\n\npath = \"runwayml/stable-diffusion-v1-5\"\n\nrun_compile = True # Set True / False\ncontrolnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\", torch_dtype=torch.float16, use_safetensors=True)\npipe = StableDiffusionControlNetPipeline.from_pretrained(\n path, controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True\n)\n\npipe = pipe.to(\"cuda\")\npipe.unet.to(memory_format=torch.channels_last)\npipe.controlnet.to(memory_format=torch.channels_last)\n\nif run_compile:\n print(\"Run torch compile\")\n pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n pipe.controlnet = torch.compile(pipe.controlnet, mode=\"reduce-overhead\", fullgraph=True)\n\nprompt = \"ghibli style, a fantasy landscape with castles\"\n\nfor _ in range(3):\n image = pipe(prompt=prompt, image=init_image).images[0]\n```\n\n### DeepFloyd IF text-to-image + upscaling\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\nrun_compile = True # Set True / False\n\npipe_1 = DiffusionPipeline.from_pretrained(\"DeepFloyd/IF-I-M-v1.0\", variant=\"fp16\", text_encoder=None, torch_dtype=torch.float16, use_safetensors=True)\npipe_1.to(\"cuda\")\npipe_2 = DiffusionPipeline.from_pretrained(\"DeepFloyd/IF-II-M-v1.0\", variant=\"fp16\", text_encoder=None, torch_dtype=torch.float16, use_safetensors=True)\npipe_2.to(\"cuda\")\npipe_3 = DiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-x4-upscaler\", torch_dtype=torch.float16, use_safetensors=True)\npipe_3.to(\"cuda\")\n\n\npipe_1.unet.to(memory_format=torch.channels_last)\npipe_2.unet.to(memory_format=torch.channels_last)\npipe_3.unet.to(memory_format=torch.channels_last)\n\nif run_compile:\n pipe_1.unet = torch.compile(pipe_1.unet, mode=\"reduce-overhead\", fullgraph=True)\n pipe_2.unet = torch.compile(pipe_2.unet, mode=\"reduce-overhead\", fullgraph=True)\n pipe_3.unet = torch.compile(pipe_3.unet, mode=\"reduce-overhead\", fullgraph=True)\n\nprompt = \"the blue hulk\"\n\nprompt_embeds = torch.randn((1, 2, 4096), dtype=torch.float16)\nneg_prompt_embeds = torch.randn((1, 2, 4096), dtype=torch.float16)\n\nfor _ in range(3):\n image_1 = pipe_1(prompt_embeds=prompt_embeds, negative_prompt_embeds=neg_prompt_embeds, output_type=\"pt\").images\n image_2 = pipe_2(image=image_1, prompt_embeds=prompt_embeds, negative_prompt_embeds=neg_prompt_embeds, output_type=\"pt\").images\n image_3 = pipe_3(prompt=prompt, image=image_1, noise_level=100).images\n```\n
\n\nThe graph below highlights the relative speed-ups for the [`StableDiffusionPipeline`] across five GPU families with PyTorch 2.0 and `torch.compile` enabled. The benchmarks for the following graphs are measured in *number of iterations/second*.\n\n![t2i_speedup](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/pt2_benchmarks/t2i_speedup.png)\n\nTo give you an even better idea of how this speed-up holds for the other pipelines, consider the following\ngraph for an A100 with PyTorch 2.0 and `torch.compile`:\n\n![a100_numbers](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/pt2_benchmarks/a100_numbers.png)\n\nIn the following tables, we report our findings in terms of the *number of iterations/second*.\n\n### A100 (batch size: 1)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 21.66 | 23.13 | 44.03 | 49.74 |\n| SD - img2img | 21.81 | 22.40 | 43.92 | 46.32 |\n| SD - inpaint | 22.24 | 23.23 | 43.76 | 49.25 |\n| SD - controlnet | 15.02 | 15.82 | 32.13 | 36.08 |\n| IF | 20.21 /
13.84 /
24.00 | 20.12 /
13.70 /
24.03 | ❌ | 97.34 /
27.23 /
111.66 |\n| SDXL - txt2img | 8.64 | 9.9 | - | - |\n\n### A100 (batch size: 4)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 11.6 | 13.12 | 14.62 | 17.27 |\n| SD - img2img | 11.47 | 13.06 | 14.66 | 17.25 |\n| SD - inpaint | 11.67 | 13.31 | 14.88 | 17.48 |\n| SD - controlnet | 8.28 | 9.38 | 10.51 | 12.41 |\n| IF | 25.02 | 18.04 | ❌ | 48.47 |\n| SDXL - txt2img | 2.44 | 2.74 | - | - |\n\n### A100 (batch size: 16)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 3.04 | 3.6 | 3.83 | 4.68 |\n| SD - img2img | 2.98 | 3.58 | 3.83 | 4.67 |\n| SD - inpaint | 3.04 | 3.66 | 3.9 | 4.76 |\n| SD - controlnet | 2.15 | 2.58 | 2.74 | 3.35 |\n| IF | 8.78 | 9.82 | ❌ | 16.77 |\n| SDXL - txt2img | 0.64 | 0.72 | - | - |\n\n### V100 (batch size: 1)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 18.99 | 19.14 | 20.95 | 22.17 |\n| SD - img2img | 18.56 | 19.18 | 20.95 | 22.11 |\n| SD - inpaint | 19.14 | 19.06 | 21.08 | 22.20 |\n| SD - controlnet | 13.48 | 13.93 | 15.18 | 15.88 |\n| IF | 20.01 /
9.08 /
23.34 | 19.79 /
8.98 /
24.10 | ❌ | 55.75 /
11.57 /
57.67 |\n\n### V100 (batch size: 4)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 5.96 | 5.89 | 6.83 | 6.86 |\n| SD - img2img | 5.90 | 5.91 | 6.81 | 6.82 |\n| SD - inpaint | 5.99 | 6.03 | 6.93 | 6.95 |\n| SD - controlnet | 4.26 | 4.29 | 4.92 | 4.93 |\n| IF | 15.41 | 14.76 | ❌ | 22.95 |\n\n### V100 (batch size: 16)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 1.66 | 1.66 | 1.92 | 1.90 |\n| SD - img2img | 1.65 | 1.65 | 1.91 | 1.89 |\n| SD - inpaint | 1.69 | 1.69 | 1.95 | 1.93 |\n| SD - controlnet | 1.19 | 1.19 | OOM after warmup | 1.36 |\n| IF | 5.43 | 5.29 | ❌ | 7.06 |\n\n### T4 (batch size: 1)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 6.9 | 6.95 | 7.3 | 7.56 |\n| SD - img2img | 6.84 | 6.99 | 7.04 | 7.55 |\n| SD - inpaint | 6.91 | 6.7 | 7.01 | 7.37 |\n| SD - controlnet | 4.89 | 4.86 | 5.35 | 5.48 |\n| IF | 17.42 /
2.47 /
18.52 | 16.96 /
2.45 /
18.69 | ❌ | 24.63 /
2.47 /
23.39 |\n| SDXL - txt2img | 1.15 | 1.16 | - | - |\n\n### T4 (batch size: 4)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 1.79 | 1.79 | 2.03 | 1.99 |\n| SD - img2img | 1.77 | 1.77 | 2.05 | 2.04 |\n| SD - inpaint | 1.81 | 1.82 | 2.09 | 2.09 |\n| SD - controlnet | 1.34 | 1.27 | 1.47 | 1.46 |\n| IF | 5.79 | 5.61 | ❌ | 7.39 |\n| SDXL - txt2img | 0.288 | 0.289 | - | - |\n\n### T4 (batch size: 16)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 2.34s | 2.30s | OOM after 2nd iteration | 1.99s |\n| SD - img2img | 2.35s | 2.31s | OOM after warmup | 2.00s |\n| SD - inpaint | 2.30s | 2.26s | OOM after 2nd iteration | 1.95s |\n| SD - controlnet | OOM after 2nd iteration | OOM after 2nd iteration | OOM after warmup | OOM after warmup |\n| IF * | 1.44 | 1.44 | ❌ | 1.94 |\n| SDXL - txt2img | OOM | OOM | - | - |\n\n### RTX 3090 (batch size: 1)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 22.56 | 22.84 | 23.84 | 25.69 |\n| SD - img2img | 22.25 | 22.61 | 24.1 | 25.83 |\n| SD - inpaint | 22.22 | 22.54 | 24.26 | 26.02 |\n| SD - controlnet | 16.03 | 16.33 | 17.38 | 18.56 |\n| IF | 27.08 /
9.07 /
31.23 | 26.75 /
8.92 /
31.47 | ❌ | 68.08 /
11.16 /
65.29 |\n\n### RTX 3090 (batch size: 4)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 6.46 | 6.35 | 7.29 | 7.3 |\n| SD - img2img | 6.33 | 6.27 | 7.31 | 7.26 |\n| SD - inpaint | 6.47 | 6.4 | 7.44 | 7.39 |\n| SD - controlnet | 4.59 | 4.54 | 5.27 | 5.26 |\n| IF | 16.81 | 16.62 | ❌ | 21.57 |\n\n### RTX 3090 (batch size: 16)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 1.7 | 1.69 | 1.93 | 1.91 |\n| SD - img2img | 1.68 | 1.67 | 1.93 | 1.9 |\n| SD - inpaint | 1.72 | 1.71 | 1.97 | 1.94 |\n| SD - controlnet | 1.23 | 1.22 | 1.4 | 1.38 |\n| IF | 5.01 | 5.00 | ❌ | 6.33 |\n\n### RTX 4090 (batch size: 1)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 40.5 | 41.89 | 44.65 | 49.81 |\n| SD - img2img | 40.39 | 41.95 | 44.46 | 49.8 |\n| SD - inpaint | 40.51 | 41.88 | 44.58 | 49.72 |\n| SD - controlnet | 29.27 | 30.29 | 32.26 | 36.03 |\n| IF | 69.71 /
18.78 /
85.49 | 69.13 /
18.80 /
85.56 | ❌ | 124.60 /
26.37 /
138.79 |\n| SDXL - txt2img | 6.8 | 8.18 | - | - |\n\n### RTX 4090 (batch size: 4)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 12.62 | 12.84 | 15.32 | 15.59 |\n| SD - img2img | 12.61 | 12,.79 | 15.35 | 15.66 |\n| SD - inpaint | 12.65 | 12.81 | 15.3 | 15.58 |\n| SD - controlnet | 9.1 | 9.25 | 11.03 | 11.22 |\n| IF | 31.88 | 31.14 | ❌ | 43.92 |\n| SDXL - txt2img | 2.19 | 2.35 | - | - |\n\n### RTX 4090 (batch size: 16)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 3.17 | 3.2 | 3.84 | 3.85 |\n| SD - img2img | 3.16 | 3.2 | 3.84 | 3.85 |\n| SD - inpaint | 3.17 | 3.2 | 3.85 | 3.85 |\n| SD - controlnet | 2.23 | 2.3 | 2.7 | 2.75 |\n| IF | 9.26 | 9.2 | ❌ | 13.31 |\n| SDXL - txt2img | 0.52 | 0.53 | - | - |\n\n## Notes\n\n* Follow this [PR](https://github.com/huggingface/diffusers/pull/3313) for more details on the environment used for conducting the benchmarks.\n* For the DeepFloyd IF pipeline where batch sizes > 1, we only used a batch size of > 1 in the first IF pipeline for text-to-image generation and NOT for upscaling. That means the two upscaling pipelines received a batch size of 1.\n\n*Thanks to [Horace He](https://github.com/Chillee) from the PyTorch team for their support in improving our support of `torch.compile()` in Diffusers.*\n" }, { "path": "diffusers/docs/source/en/optimization/xformers.md", "content": "\n\n# xFormers\n\nWe recommend [xFormers](https://github.com/facebookresearch/xformers) for both inference and training. In our tests, the optimizations performed in the attention blocks allow for both faster speed and reduced memory consumption.\n\nInstall xFormers from `pip`:\n\n```bash\npip install xformers\n```\n\n\n\nThe xFormers `pip` package requires the latest version of PyTorch. If you need to use a previous version of PyTorch, then we recommend [installing xFormers from the source](https://github.com/facebookresearch/xformers#installing-xformers).\n\n\n\nAfter xFormers is installed, you can use `enable_xformers_memory_efficient_attention()` for faster inference and reduced memory consumption as shown in this [section](memory#memory-efficient-attention).\n\n\n\nAccording to this [issue](https://github.com/huggingface/diffusers/issues/2234#issuecomment-1416931212), xFormers `v0.0.16` cannot be used for training (fine-tune or DreamBooth) in some GPUs. If you observe this problem, please install a development version as indicated in the issue comments.\n\n\n" }, { "path": "diffusers/docs/source/en/quicktour.md", "content": "\n\n[[open-in-colab]]\n\n# Quicktour\n\nDiffusion models are trained to denoise random Gaussian noise step-by-step to generate a sample of interest, such as an image or audio. This has sparked a tremendous amount of interest in generative AI, and you have probably seen examples of diffusion generated images on the internet. 🧨 Diffusers is a library aimed at making diffusion models widely accessible to everyone.\n\nWhether you're a developer or an everyday user, this quicktour will introduce you to 🧨 Diffusers and help you get up and generating quickly! There are three main components of the library to know about:\n\n* The [`DiffusionPipeline`] is a high-level end-to-end class designed to rapidly generate samples from pretrained diffusion models for inference.\n* Popular pretrained [model](./api/models) architectures and modules that can be used as building blocks for creating diffusion systems.\n* Many different [schedulers](./api/schedulers/overview) - algorithms that control how noise is added for training, and how to generate denoised images during inference.\n\nThe quicktour will show you how to use the [`DiffusionPipeline`] for inference, and then walk you through how to combine a model and scheduler to replicate what's happening inside the [`DiffusionPipeline`].\n\n\n\nThe quicktour is a simplified version of the introductory 🧨 Diffusers [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) to help you get started quickly. If you want to learn more about 🧨 Diffusers' goal, design philosophy, and additional details about its core API, check out the notebook!\n\n\n\nBefore you begin, make sure you have all the necessary libraries installed:\n\n```py\n# uncomment to install the necessary libraries in Colab\n#!pip install --upgrade diffusers accelerate transformers\n```\n\n- [🤗 Accelerate](https://huggingface.co/docs/accelerate/index) speeds up model loading for inference and training.\n- [🤗 Transformers](https://huggingface.co/docs/transformers/index) is required to run the most popular diffusion models, such as [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview).\n\n## DiffusionPipeline\n\nThe [`DiffusionPipeline`] is the easiest way to use a pretrained diffusion system for inference. It is an end-to-end system containing the model and the scheduler. You can use the [`DiffusionPipeline`] out-of-the-box for many tasks. Take a look at the table below for some supported tasks, and for a complete list of supported tasks, check out the [🧨 Diffusers Summary](./api/pipelines/overview#diffusers-summary) table.\n\n| **Task** | **Description** | **Pipeline**\n|------------------------------|--------------------------------------------------------------------------------------------------------------|-----------------|\n| Unconditional Image Generation | generate an image from Gaussian noise | [unconditional_image_generation](./using-diffusers/unconditional_image_generation) |\n| Text-Guided Image Generation | generate an image given a text prompt | [conditional_image_generation](./using-diffusers/conditional_image_generation) |\n| Text-Guided Image-to-Image Translation | adapt an image guided by a text prompt | [img2img](./using-diffusers/img2img) |\n| Text-Guided Image-Inpainting | fill the masked part of an image given the image, the mask and a text prompt | [inpaint](./using-diffusers/inpaint) |\n| Text-Guided Depth-to-Image Translation | adapt parts of an image guided by a text prompt while preserving structure via depth estimation | [depth2img](./using-diffusers/depth2img) |\n\nStart by creating an instance of a [`DiffusionPipeline`] and specify which pipeline checkpoint you would like to download.\nYou can use the [`DiffusionPipeline`] for any [checkpoint](https://huggingface.co/models?library=diffusers&sort=downloads) stored on the Hugging Face Hub.\nIn this quicktour, you'll load the [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) checkpoint for text-to-image generation.\n\n\n\nFor [Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion) models, please carefully read the [license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) first before running the model. 🧨 Diffusers implements a [`safety_checker`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) to prevent offensive or harmful content, but the model's improved image generation capabilities can still produce potentially harmful content.\n\n\n\nLoad the model with the [`~DiffusionPipeline.from_pretrained`] method:\n\n```python\n>>> from diffusers import DiffusionPipeline\n\n>>> pipeline = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", use_safetensors=True)\n```\n\nThe [`DiffusionPipeline`] downloads and caches all modeling, tokenization, and scheduling components. You'll see that the Stable Diffusion pipeline is composed of the [`UNet2DConditionModel`] and [`PNDMScheduler`] among other things:\n\n```py\n>>> pipeline\nStableDiffusionPipeline {\n \"_class_name\": \"StableDiffusionPipeline\",\n \"_diffusers_version\": \"0.21.4\",\n ...,\n \"scheduler\": [\n \"diffusers\",\n \"PNDMScheduler\"\n ],\n ...,\n \"unet\": [\n \"diffusers\",\n \"UNet2DConditionModel\"\n ],\n \"vae\": [\n \"diffusers\",\n \"AutoencoderKL\"\n ]\n}\n```\n\nWe strongly recommend running the pipeline on a GPU because the model consists of roughly 1.4 billion parameters.\nYou can move the generator object to a GPU, just like you would in PyTorch:\n\n```python\n>>> pipeline.to(\"cuda\")\n```\n\nNow you can pass a text prompt to the `pipeline` to generate an image, and then access the denoised image. By default, the image output is wrapped in a [`PIL.Image`](https://pillow.readthedocs.io/en/stable/reference/Image.html?highlight=image#the-image-class) object.\n\n```python\n>>> image = pipeline(\"An image of a squirrel in Picasso style\").images[0]\n>>> image\n```\n\n
\n \n
\n\nSave the image by calling `save`:\n\n```python\n>>> image.save(\"image_of_squirrel_painting.png\")\n```\n\n### Local pipeline\n\nYou can also use the pipeline locally. The only difference is you need to download the weights first:\n\n```bash\n!git lfs install\n!git clone https://huggingface.co/runwayml/stable-diffusion-v1-5\n```\n\nThen load the saved weights into the pipeline:\n\n```python\n>>> pipeline = DiffusionPipeline.from_pretrained(\"./stable-diffusion-v1-5\", use_safetensors=True)\n```\n\nNow, you can run the pipeline as you would in the section above.\n\n### Swapping schedulers\n\nDifferent schedulers come with different denoising speeds and quality trade-offs. The best way to find out which one works best for you is to try them out! One of the main features of 🧨 Diffusers is to allow you to easily switch between schedulers. For example, to replace the default [`PNDMScheduler`] with the [`EulerDiscreteScheduler`], load it with the [`~diffusers.ConfigMixin.from_config`] method:\n\n```py\n>>> from diffusers import EulerDiscreteScheduler\n\n>>> pipeline = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", use_safetensors=True)\n>>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)\n```\n\nTry generating an image with the new scheduler and see if you notice a difference!\n\nIn the next section, you'll take a closer look at the components - the model and scheduler - that make up the [`DiffusionPipeline`] and learn how to use these components to generate an image of a cat.\n\n## Models\n\nMost models take a noisy sample, and at each timestep it predicts the *noise residual* (other models learn to predict the previous sample directly or the velocity or [`v-prediction`](https://github.com/huggingface/diffusers/blob/5e5ce13e2f89ac45a0066cb3f369462a3cf1d9ef/src/diffusers/schedulers/scheduling_ddim.py#L110)), the difference between a less noisy image and the input image. You can mix and match models to create other diffusion systems.\n\nModels are initiated with the [`~ModelMixin.from_pretrained`] method which also locally caches the model weights so it is faster the next time you load the model. For the quicktour, you'll load the [`UNet2DModel`], a basic unconditional image generation model with a checkpoint trained on cat images:\n\n```py\n>>> from diffusers import UNet2DModel\n\n>>> repo_id = \"google/ddpm-cat-256\"\n>>> model = UNet2DModel.from_pretrained(repo_id, use_safetensors=True)\n```\n\nTo access the model parameters, call `model.config`:\n\n```py\n>>> model.config\n```\n\nThe model configuration is a 🧊 frozen 🧊 dictionary, which means those parameters can't be changed after the model is created. This is intentional and ensures that the parameters used to define the model architecture at the start remain the same, while other parameters can still be adjusted during inference.\n\nSome of the most important parameters are:\n\n* `sample_size`: the height and width dimension of the input sample.\n* `in_channels`: the number of input channels of the input sample.\n* `down_block_types` and `up_block_types`: the type of down- and upsampling blocks used to create the UNet architecture.\n* `block_out_channels`: the number of output channels of the downsampling blocks; also used in reverse order for the number of input channels of the upsampling blocks.\n* `layers_per_block`: the number of ResNet blocks present in each UNet block.\n\nTo use the model for inference, create the image shape with random Gaussian noise. It should have a `batch` axis because the model can receive multiple random noises, a `channel` axis corresponding to the number of input channels, and a `sample_size` axis for the height and width of the image:\n\n```py\n>>> import torch\n\n>>> torch.manual_seed(0)\n\n>>> noisy_sample = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)\n>>> noisy_sample.shape\ntorch.Size([1, 3, 256, 256])\n```\n\nFor inference, pass the noisy image and a `timestep` to the model. The `timestep` indicates how noisy the input image is, with more noise at the beginning and less at the end. This helps the model determine its position in the diffusion process, whether it is closer to the start or the end. Use the `sample` method to get the model output:\n\n```py\n>>> with torch.no_grad():\n... noisy_residual = model(sample=noisy_sample, timestep=2).sample\n```\n\nTo generate actual examples though, you'll need a scheduler to guide the denoising process. In the next section, you'll learn how to couple a model with a scheduler.\n\n## Schedulers\n\nSchedulers manage going from a noisy sample to a less noisy sample given the model output - in this case, it is the `noisy_residual`.\n\n\n\n🧨 Diffusers is a toolbox for building diffusion systems. While the [`DiffusionPipeline`] is a convenient way to get started with a pre-built diffusion system, you can also choose your own model and scheduler components separately to build a custom diffusion system.\n\n\n\nFor the quicktour, you'll instantiate the [`DDPMScheduler`] with its [`~diffusers.ConfigMixin.from_config`] method:\n\n```py\n>>> from diffusers import DDPMScheduler\n\n>>> scheduler = DDPMScheduler.from_pretrained(repo_id)\n>>> scheduler\nDDPMScheduler {\n \"_class_name\": \"DDPMScheduler\",\n \"_diffusers_version\": \"0.21.4\",\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n \"beta_start\": 0.0001,\n \"clip_sample\": true,\n \"clip_sample_range\": 1.0,\n \"dynamic_thresholding_ratio\": 0.995,\n \"num_train_timesteps\": 1000,\n \"prediction_type\": \"epsilon\",\n \"sample_max_value\": 1.0,\n \"steps_offset\": 0,\n \"thresholding\": false,\n \"timestep_spacing\": \"leading\",\n \"trained_betas\": null,\n \"variance_type\": \"fixed_small\"\n}\n```\n\n\n\n💡 Unlike a model, a scheduler does not have trainable weights and is parameter-free!\n\n\n\nSome of the most important parameters are:\n\n* `num_train_timesteps`: the length of the denoising process or, in other words, the number of timesteps required to process random Gaussian noise into a data sample.\n* `beta_schedule`: the type of noise schedule to use for inference and training.\n* `beta_start` and `beta_end`: the start and end noise values for the noise schedule.\n\nTo predict a slightly less noisy image, pass the following to the scheduler's [`~diffusers.DDPMScheduler.step`] method: model output, `timestep`, and current `sample`.\n\n```py\n>>> less_noisy_sample = scheduler.step(model_output=noisy_residual, timestep=2, sample=noisy_sample).prev_sample\n>>> less_noisy_sample.shape\ntorch.Size([1, 3, 256, 256])\n```\n\nThe `less_noisy_sample` can be passed to the next `timestep` where it'll get even less noisy! Let's bring it all together now and visualize the entire denoising process.\n\nFirst, create a function that postprocesses and displays the denoised image as a `PIL.Image`:\n\n```py\n>>> import PIL.Image\n>>> import numpy as np\n\n\n>>> def display_sample(sample, i):\n... image_processed = sample.cpu().permute(0, 2, 3, 1)\n... image_processed = (image_processed + 1.0) * 127.5\n... image_processed = image_processed.numpy().astype(np.uint8)\n\n... image_pil = PIL.Image.fromarray(image_processed[0])\n... display(f\"Image at step {i}\")\n... display(image_pil)\n```\n\nTo speed up the denoising process, move the input and model to a GPU:\n\n```py\n>>> model.to(\"cuda\")\n>>> noisy_sample = noisy_sample.to(\"cuda\")\n```\n\nNow create a denoising loop that predicts the residual of the less noisy sample, and computes the less noisy sample with the scheduler:\n\n```py\n>>> import tqdm\n\n>>> sample = noisy_sample\n\n>>> for i, t in enumerate(tqdm.tqdm(scheduler.timesteps)):\n... # 1. predict noise residual\n... with torch.no_grad():\n... residual = model(sample, t).sample\n\n... # 2. compute less noisy image and set x_t -> x_t-1\n... sample = scheduler.step(residual, t, sample).prev_sample\n\n... # 3. optionally look at image\n... if (i + 1) % 50 == 0:\n... display_sample(sample, i + 1)\n```\n\nSit back and watch as a cat is generated from nothing but noise! 😻\n\n
\n \n
\n\n## Next steps\n\nHopefully, you generated some cool images with 🧨 Diffusers in this quicktour! For your next steps, you can:\n\n* Train or finetune a model to generate your own images in the [training](./tutorials/basic_training) tutorial.\n* See example official and community [training or finetuning scripts](https://github.com/huggingface/diffusers/tree/main/examples#-diffusers-examples) for a variety of use cases.\n* Learn more about loading, accessing, changing, and comparing schedulers in the [Using different Schedulers](./using-diffusers/schedulers) guide.\n* Explore prompt engineering, speed and memory optimizations, and tips and tricks for generating higher-quality images with the [Stable Diffusion](./stable_diffusion) guide.\n* Dive deeper into speeding up 🧨 Diffusers with guides on [optimized PyTorch on a GPU](./optimization/fp16), and inference guides for running [Stable Diffusion on Apple Silicon (M1/M2)](./optimization/mps) and [ONNX Runtime](./optimization/onnx).\n" }, { "path": "diffusers/docs/source/en/stable_diffusion.md", "content": "\n\n# Effective and efficient diffusion\n\n[[open-in-colab]]\n\nGetting the [`DiffusionPipeline`] to generate images in a certain style or include what you want can be tricky. Often times, you have to run the [`DiffusionPipeline`] several times before you end up with an image you're happy with. But generating something out of nothing is a computationally intensive process, especially if you're running inference over and over again.\n\nThis is why it's important to get the most *computational* (speed) and *memory* (GPU vRAM) efficiency from the pipeline to reduce the time between inference cycles so you can iterate faster.\n\nThis tutorial walks you through how to generate faster and better with the [`DiffusionPipeline`].\n\nBegin by loading the [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) model:\n\n```python\nfrom diffusers import DiffusionPipeline\n\nmodel_id = \"runwayml/stable-diffusion-v1-5\"\npipeline = DiffusionPipeline.from_pretrained(model_id, use_safetensors=True)\n```\n\nThe example prompt you'll use is a portrait of an old warrior chief, but feel free to use your own prompt:\n\n```python\nprompt = \"portrait photo of a old warrior chief\"\n```\n\n## Speed\n\n\n\n💡 If you don't have access to a GPU, you can use one for free from a GPU provider like [Colab](https://colab.research.google.com/)!\n\n\n\nOne of the simplest ways to speed up inference is to place the pipeline on a GPU the same way you would with any PyTorch module:\n\n```python\npipeline = pipeline.to(\"cuda\")\n```\n\nTo make sure you can use the same image and improve on it, use a [`Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) and set a seed for [reproducibility](./using-diffusers/reusing_seeds):\n\n```python\nimport torch\n\ngenerator = torch.Generator(\"cuda\").manual_seed(0)\n```\n\nNow you can generate an image:\n\n```python\nimage = pipeline(prompt, generator=generator).images[0]\nimage\n```\n\n
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\n\nThis process took ~30 seconds on a T4 GPU (it might be faster if your allocated GPU is better than a T4). By default, the [`DiffusionPipeline`] runs inference with full `float32` precision for 50 inference steps. You can speed this up by switching to a lower precision like `float16` or running fewer inference steps.\n\nLet's start by loading the model in `float16` and generate an image:\n\n```python\nimport torch\n\npipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, use_safetensors=True)\npipeline = pipeline.to(\"cuda\")\ngenerator = torch.Generator(\"cuda\").manual_seed(0)\nimage = pipeline(prompt, generator=generator).images[0]\nimage\n```\n\n
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\n\nThis time, it only took ~11 seconds to generate the image, which is almost 3x faster than before!\n\n\n\n💡 We strongly suggest always running your pipelines in `float16`, and so far, we've rarely seen any degradation in output quality.\n\n\n\nAnother option is to reduce the number of inference steps. Choosing a more efficient scheduler could help decrease the number of steps without sacrificing output quality. You can find which schedulers are compatible with the current model in the [`DiffusionPipeline`] by calling the `compatibles` method:\n\n```python\npipeline.scheduler.compatibles\n[\n diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler,\n diffusers.schedulers.scheduling_unipc_multistep.UniPCMultistepScheduler,\n diffusers.schedulers.scheduling_k_dpm_2_discrete.KDPM2DiscreteScheduler,\n diffusers.schedulers.scheduling_deis_multistep.DEISMultistepScheduler,\n diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler,\n diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler,\n diffusers.schedulers.scheduling_ddpm.DDPMScheduler,\n diffusers.schedulers.scheduling_dpmsolver_singlestep.DPMSolverSinglestepScheduler,\n diffusers.schedulers.scheduling_k_dpm_2_ancestral_discrete.KDPM2AncestralDiscreteScheduler,\n diffusers.utils.dummy_torch_and_torchsde_objects.DPMSolverSDEScheduler,\n diffusers.schedulers.scheduling_heun_discrete.HeunDiscreteScheduler,\n diffusers.schedulers.scheduling_pndm.PNDMScheduler,\n diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler,\n diffusers.schedulers.scheduling_ddim.DDIMScheduler,\n]\n```\n\nThe Stable Diffusion model uses the [`PNDMScheduler`] by default which usually requires ~50 inference steps, but more performant schedulers like [`DPMSolverMultistepScheduler`], require only ~20 or 25 inference steps. Use the [`~ConfigMixin.from_config`] method to load a new scheduler:\n\n```python\nfrom diffusers import DPMSolverMultistepScheduler\n\npipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)\n```\n\nNow set the `num_inference_steps` to 20:\n\n```python\ngenerator = torch.Generator(\"cuda\").manual_seed(0)\nimage = pipeline(prompt, generator=generator, num_inference_steps=20).images[0]\nimage\n```\n\n
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\n\nGreat, you've managed to cut the inference time to just 4 seconds! ⚡️\n\n## Memory\n\nThe other key to improving pipeline performance is consuming less memory, which indirectly implies more speed, since you're often trying to maximize the number of images generated per second. The easiest way to see how many images you can generate at once is to try out different batch sizes until you get an `OutOfMemoryError` (OOM).\n\nCreate a function that'll generate a batch of images from a list of prompts and `Generators`. Make sure to assign each `Generator` a seed so you can reuse it if it produces a good result.\n\n```python\ndef get_inputs(batch_size=1):\n generator = [torch.Generator(\"cuda\").manual_seed(i) for i in range(batch_size)]\n prompts = batch_size * [prompt]\n num_inference_steps = 20\n\n return {\"prompt\": prompts, \"generator\": generator, \"num_inference_steps\": num_inference_steps}\n```\n\nStart with `batch_size=4` and see how much memory you've consumed:\n\n```python\nfrom diffusers.utils import make_image_grid\n\nimages = pipeline(**get_inputs(batch_size=4)).images\nmake_image_grid(images, 2, 2)\n```\n\nUnless you have a GPU with more vRAM, the code above probably returned an `OOM` error! Most of the memory is taken up by the cross-attention layers. Instead of running this operation in a batch, you can run it sequentially to save a significant amount of memory. All you have to do is configure the pipeline to use the [`~DiffusionPipeline.enable_attention_slicing`] function:\n\n```python\npipeline.enable_attention_slicing()\n```\n\nNow try increasing the `batch_size` to 8!\n\n```python\nimages = pipeline(**get_inputs(batch_size=8)).images\nmake_image_grid(images, rows=2, cols=4)\n```\n\n
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\n\nWhereas before you couldn't even generate a batch of 4 images, now you can generate a batch of 8 images at ~3.5 seconds per image! This is probably the fastest you can go on a T4 GPU without sacrificing quality.\n\n## Quality\n\nIn the last two sections, you learned how to optimize the speed of your pipeline by using `fp16`, reducing the number of inference steps by using a more performant scheduler, and enabling attention slicing to reduce memory consumption. Now you're going to focus on how to improve the quality of generated images.\n\n### Better checkpoints\n\nThe most obvious step is to use better checkpoints. The Stable Diffusion model is a good starting point, and since its official launch, several improved versions have also been released. However, using a newer version doesn't automatically mean you'll get better results. You'll still have to experiment with different checkpoints yourself, and do a little research (such as using [negative prompts](https://minimaxir.com/2022/11/stable-diffusion-negative-prompt/)) to get the best results.\n\nAs the field grows, there are more and more high-quality checkpoints finetuned to produce certain styles. Try exploring the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) and [Diffusers Gallery](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery) to find one you're interested in!\n\n### Better pipeline components\n\nYou can also try replacing the current pipeline components with a newer version. Let's try loading the latest [autoencoder](https://huggingface.co/stabilityai/stable-diffusion-2-1/tree/main/vae) from Stability AI into the pipeline, and generate some images:\n\n```python\nfrom diffusers import AutoencoderKL\n\nvae = AutoencoderKL.from_pretrained(\"stabilityai/sd-vae-ft-mse\", torch_dtype=torch.float16).to(\"cuda\")\npipeline.vae = vae\nimages = pipeline(**get_inputs(batch_size=8)).images\nmake_image_grid(images, rows=2, cols=4)\n```\n\n
\n \n
\n\n### Better prompt engineering\n\nThe text prompt you use to generate an image is super important, so much so that it is called *prompt engineering*. Some considerations to keep during prompt engineering are:\n\n- How is the image or similar images of the one I want to generate stored on the internet?\n- What additional detail can I give that steers the model towards the style I want?\n\nWith this in mind, let's improve the prompt to include color and higher quality details:\n\n```python\nprompt += \", tribal panther make up, blue on red, side profile, looking away, serious eyes\"\nprompt += \" 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta\"\n```\n\nGenerate a batch of images with the new prompt:\n\n```python\nimages = pipeline(**get_inputs(batch_size=8)).images\nmake_image_grid(images, rows=2, cols=4)\n```\n\n
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\n\nPretty impressive! Let's tweak the second image - corresponding to the `Generator` with a seed of `1` - a bit more by adding some text about the age of the subject:\n\n```python\nprompts = [\n \"portrait photo of the oldest warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta\",\n \"portrait photo of a old warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta\",\n \"portrait photo of a warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta\",\n \"portrait photo of a young warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta\",\n]\n\ngenerator = [torch.Generator(\"cuda\").manual_seed(1) for _ in range(len(prompts))]\nimages = pipeline(prompt=prompts, generator=generator, num_inference_steps=25).images\nmake_image_grid(images, 2, 2)\n```\n\n
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\n\n## Next steps\n\nIn this tutorial, you learned how to optimize a [`DiffusionPipeline`] for computational and memory efficiency as well as improving the quality of generated outputs. If you're interested in making your pipeline even faster, take a look at the following resources:\n\n- Learn how [PyTorch 2.0](./optimization/torch2.0) and [`torch.compile`](https://pytorch.org/docs/stable/generated/torch.compile.html) can yield 5 - 300% faster inference speed. On an A100 GPU, inference can be up to 50% faster!\n- If you can't use PyTorch 2, we recommend you install [xFormers](./optimization/xformers). Its memory-efficient attention mechanism works great with PyTorch 1.13.1 for faster speed and reduced memory consumption.\n- Other optimization techniques, such as model offloading, are covered in [this guide](./optimization/fp16).\n" }, { "path": "diffusers/docs/source/en/training/adapt_a_model.md", "content": "# Adapt a model to a new task\n\nMany diffusion systems share the same components, allowing you to adapt a pretrained model for one task to an entirely different task.\n\nThis guide will show you how to adapt a pretrained text-to-image model for inpainting by initializing and modifying the architecture of a pretrained [`UNet2DConditionModel`].\n\n## Configure UNet2DConditionModel parameters\n\nA [`UNet2DConditionModel`] by default accepts 4 channels in the [input sample](https://huggingface.co/docs/diffusers/v0.16.0/en/api/models#diffusers.UNet2DConditionModel.in_channels). For example, load a pretrained text-to-image model like [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) and take a look at the number of `in_channels`:\n\n```py\nfrom diffusers import StableDiffusionPipeline\n\npipeline = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", use_safetensors=True)\npipeline.unet.config[\"in_channels\"]\n4\n```\n\nInpainting requires 9 channels in the input sample. You can check this value in a pretrained inpainting model like [`runwayml/stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting):\n\n```py\nfrom diffusers import StableDiffusionPipeline\n\npipeline = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-inpainting\", use_safetensors=True)\npipeline.unet.config[\"in_channels\"]\n9\n```\n\nTo adapt your text-to-image model for inpainting, you'll need to change the number of `in_channels` from 4 to 9. \n\nInitialize a [`UNet2DConditionModel`] with the pretrained text-to-image model weights, and change `in_channels` to 9. Changing the number of `in_channels` means you need to set `ignore_mismatched_sizes=True` and `low_cpu_mem_usage=False` to avoid a size mismatch error because the shape is different now.\n\n```py\nfrom diffusers import UNet2DConditionModel\n\nmodel_id = \"runwayml/stable-diffusion-v1-5\"\nunet = UNet2DConditionModel.from_pretrained(\n model_id,\n subfolder=\"unet\",\n in_channels=9,\n low_cpu_mem_usage=False,\n ignore_mismatched_sizes=True,\n use_safetensors=True,\n)\n```\n\nThe pretrained weights of the other components from the text-to-image model are initialized from their checkpoints, but the input channel weights (`conv_in.weight`) of the `unet` are randomly initialized. It is important to finetune the model for inpainting because otherwise the model returns noise.\n" }, { "path": "diffusers/docs/source/en/training/controlnet.md", "content": "\n\n# ControlNet\n\n[ControlNet](https://hf.co/papers/2302.05543) models are adapters trained on top of another pretrained model. It allows for a greater degree of control over image generation by conditioning the model with an additional input image. The input image can be a canny edge, depth map, human pose, and many more.\n\nIf you're training on a GPU with limited vRAM, you should try enabling the `gradient_checkpointing`, `gradient_accumulation_steps`, and `mixed_precision` parameters in the training command. You can also reduce your memory footprint by using memory-efficient attention with [xFormers](../optimization/xformers). JAX/Flax training is also supported for efficient training on TPUs and GPUs, but it doesn't support gradient checkpointing or xFormers. You should have a GPU with >30GB of memory if you want to train faster with Flax.\n\nThis guide will explore the [train_controlnet.py](https://github.com/huggingface/diffusers/blob/main/examples/controlnet/train_controlnet.py) training script to help you become familiar with it, and how you can adapt it for your own use-case.\n\nBefore running the script, make sure you install the library from source:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nThen navigate to the example folder containing the training script and install the required dependencies for the script you're using:\n\n\n\n```bash\ncd examples/controlnet\npip install -r requirements.txt\n```\n\n\n\nIf you have access to a TPU, the Flax training script runs even faster! Let's run the training script on the [Google Cloud TPU VM](https://cloud.google.com/tpu/docs/run-calculation-jax). Create a single TPU v4-8 VM and connect to it:\n\n```bash\nZONE=us-central2-b\nTPU_TYPE=v4-8\nVM_NAME=hg_flax\n\ngcloud alpha compute tpus tpu-vm create $VM_NAME \\\n --zone $ZONE \\\n --accelerator-type $TPU_TYPE \\\n --version tpu-vm-v4-base\n\ngcloud alpha compute tpus tpu-vm ssh $VM_NAME --zone $ZONE -- \\\n```\n\nInstall JAX 0.4.5:\n\n```bash\npip install \"jax[tpu]==0.4.5\" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html\n```\n\nThen install the required dependencies for the Flax script:\n\n```bash\ncd examples/controlnet\npip install -r requirements_flax.txt\n```\n\n\n\n\n\n\n🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co/docs/accelerate/quicktour) to learn more.\n\n\n\nInitialize an 🤗 Accelerate environment:\n\n```bash\naccelerate config\n```\n\nTo setup a default 🤗 Accelerate environment without choosing any configurations:\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell, like a notebook, you can use:\n\n```py\nfrom accelerate.utils import write_basic_config\n\nwrite_basic_config()\n```\n\nLastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.\n\n\n\nThe following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn't cover every aspect of the script in detail. If you're interested in learning more, feel free to read through the [script](https://github.com/huggingface/diffusers/blob/main/examples/controlnet/train_controlnet.py) and let us know if you have any questions or concerns.\n\n\n\n## Script parameters\n\nThe training script provides many parameters to help you customize your training run. All of the parameters and their descriptions are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/controlnet/train_controlnet.py#L231) function. This function provides default values for each parameter, such as the training batch size and learning rate, but you can also set your own values in the training command if you'd like.\n\nFor example, to speedup training with mixed precision using the fp16 format, add the `--mixed_precision` parameter to the training command:\n\n```bash\naccelerate launch train_controlnet.py \\\n --mixed_precision=\"fp16\"\n```\n\nMany of the basic and important parameters are described in the [Text-to-image](text2image#script-parameters) training guide, so this guide just focuses on the relevant parameters for ControlNet:\n\n- `--max_train_samples`: the number of training samples; this can be lowered for faster training, but if you want to stream really large datasets, you'll need to include this parameter and the `--streaming` parameter in your training command\n- `--gradient_accumulation_steps`: number of update steps to accumulate before the backward pass; this allows you to train with a bigger batch size than your GPU memory can typically handle\n\n### Min-SNR weighting\n\nThe [Min-SNR](https://huggingface.co/papers/2303.09556) weighting strategy can help with training by rebalancing the loss to achieve faster convergence. The training script supports predicting `epsilon` (noise) or `v_prediction`, but Min-SNR is compatible with both prediction types. This weighting strategy is only supported by PyTorch and is unavailable in the Flax training script.\n\nAdd the `--snr_gamma` parameter and set it to the recommended value of 5.0:\n\n```bash\naccelerate launch train_controlnet.py \\\n --snr_gamma=5.0\n```\n\n## Training script\n\nAs with the script parameters, a general walkthrough of the training script is provided in the [Text-to-image](text2image#training-script) training guide. Instead, this guide takes a look at the relevant parts of the ControlNet script.\n\nThe training script has a [`make_train_dataset`](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/controlnet/train_controlnet.py#L582) function for preprocessing the dataset with image transforms and caption tokenization. You'll see that in addition to the usual caption tokenization and image transforms, the script also includes transforms for the conditioning image.\n\n\n\nIf you're streaming a dataset on a TPU, performance may be bottlenecked by the 🤗 Datasets library which is not optimized for images. To ensure maximum throughput, you're encouraged to explore other dataset formats like [WebDataset](https://webdataset.github.io/webdataset/), [TorchData](https://github.com/pytorch/data), and [TensorFlow Datasets](https://www.tensorflow.org/datasets/tfless_tfds).\n\n\n\n```py\nconditioning_image_transforms = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(args.resolution),\n transforms.ToTensor(),\n ]\n)\n```\n\nWithin the [`main()`](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/controlnet/train_controlnet.py#L713) function, you'll find the code for loading the tokenizer, text encoder, scheduler and models. This is also where the ControlNet model is loaded either from existing weights or randomly initialized from a UNet:\n\n```py\nif args.controlnet_model_name_or_path:\n logger.info(\"Loading existing controlnet weights\")\n controlnet = ControlNetModel.from_pretrained(args.controlnet_model_name_or_path)\nelse:\n logger.info(\"Initializing controlnet weights from unet\")\n controlnet = ControlNetModel.from_unet(unet)\n```\n\nThe [optimizer](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/controlnet/train_controlnet.py#L871) is set up to update the ControlNet parameters:\n\n```py\nparams_to_optimize = controlnet.parameters()\noptimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n)\n```\n\nFinally, in the [training loop](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/controlnet/train_controlnet.py#L943), the conditioning text embeddings and image are passed to the down and mid-blocks of the ControlNet model:\n\n```py\nencoder_hidden_states = text_encoder(batch[\"input_ids\"])[0]\ncontrolnet_image = batch[\"conditioning_pixel_values\"].to(dtype=weight_dtype)\n\ndown_block_res_samples, mid_block_res_sample = controlnet(\n noisy_latents,\n timesteps,\n encoder_hidden_states=encoder_hidden_states,\n controlnet_cond=controlnet_image,\n return_dict=False,\n)\n```\n\nIf you want to learn more about how the training loop works, check out the [Understanding pipelines, models and schedulers](../using-diffusers/write_own_pipeline) tutorial which breaks down the basic pattern of the denoising process.\n\n## Launch the script\n\nNow you're ready to launch the training script! 🚀\n\nThis guide uses the [fusing/fill50k](https://huggingface.co/datasets/fusing/fill50k) dataset, but remember, you can create and use your own dataset if you want (see the [Create a dataset for training](create_dataset) guide).\n\nSet the environment variable `MODEL_NAME` to a model id on the Hub or a path to a local model and `OUTPUT_DIR` to where you want to save the model.\n\nDownload the following images to condition your training with:\n\n```bash\nwget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png\nwget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png\n```\n\nOne more thing before you launch the script! Depending on the GPU you have, you may need to enable certain optimizations to train a ControlNet. The default configuration in this script requires ~38GB of vRAM. If you're training on more than one GPU, add the `--multi_gpu` parameter to the `accelerate launch` command.\n\n\n\n\nOn a 16GB GPU, you can use bitsandbytes 8-bit optimizer and gradient checkpointing to optimize your training run. Install bitsandbytes:\n\n```py\npip install bitsandbytes\n```\n\nThen, add the following parameter to your training command:\n\n```bash\naccelerate launch train_controlnet.py \\\n --gradient_checkpointing \\\n --use_8bit_adam \\\n```\n\n\n\n\nOn a 12GB GPU, you'll need bitsandbytes 8-bit optimizer, gradient checkpointing, xFormers, and set the gradients to `None` instead of zero to reduce your memory-usage.\n\n```bash\naccelerate launch train_controlnet.py \\\n --use_8bit_adam \\\n --gradient_checkpointing \\\n --enable_xformers_memory_efficient_attention \\\n --set_grads_to_none \\\n```\n\n\n\n\nOn a 8GB GPU, you'll need to use [DeepSpeed](https://www.deepspeed.ai/) to offload some of the tensors from the vRAM to either the CPU or NVME to allow training with less GPU memory.\n\nRun the following command to configure your 🤗 Accelerate environment:\n\n```bash\naccelerate config\n```\n\nDuring configuration, confirm that you want to use DeepSpeed stage 2. Now it should be possible to train on under 8GB vRAM by combining DeepSpeed stage 2, fp16 mixed precision, and offloading the model parameters and the optimizer state to the CPU. The drawback is that this requires more system RAM (~25 GB). See the [DeepSpeed documentation](https://huggingface.co/docs/accelerate/usage_guides/deepspeed) for more configuration options. Your configuration file should look something like:\n\n```bash\ncompute_environment: LOCAL_MACHINE\ndeepspeed_config:\n gradient_accumulation_steps: 4\n offload_optimizer_device: cpu\n offload_param_device: cpu\n zero3_init_flag: false\n zero_stage: 2\ndistributed_type: DEEPSPEED\n```\n\nYou should also change the default Adam optimizer to DeepSpeed’s optimized version of Adam [`deepspeed.ops.adam.DeepSpeedCPUAdam`](https://deepspeed.readthedocs.io/en/latest/optimizers.html#adam-cpu) for a substantial speedup. Enabling `DeepSpeedCPUAdam` requires your system’s CUDA toolchain version to be the same as the one installed with PyTorch.\n\nbitsandbytes 8-bit optimizers don’t seem to be compatible with DeepSpeed at the moment.\n\nThat's it! You don't need to add any additional parameters to your training command.\n\n\n\n\n\n\n\n```bash\nexport MODEL_DIR=\"runwayml/stable-diffusion-v1-5\"\nexport OUTPUT_DIR=\"path/to/save/model\"\n\naccelerate launch train_controlnet.py \\\n --pretrained_model_name_or_path=$MODEL_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --dataset_name=fusing/fill50k \\\n --resolution=512 \\\n --learning_rate=1e-5 \\\n --validation_image \"./conditioning_image_1.png\" \"./conditioning_image_2.png\" \\\n --validation_prompt \"red circle with blue background\" \"cyan circle with brown floral background\" \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --push_to_hub\n```\n\n\n\n\nWith Flax, you can [profile your code](https://jax.readthedocs.io/en/latest/profiling.html) by adding the `--profile_steps==5` parameter to your training command. Install the Tensorboard profile plugin:\n\n```bash\npip install tensorflow tensorboard-plugin-profile\ntensorboard --logdir runs/fill-circle-100steps-20230411_165612/\n```\n\nThen you can inspect the profile at [http://localhost:6006/#profile](http://localhost:6006/#profile).\n\n\n\nIf you run into version conflicts with the plugin, try uninstalling and reinstalling all versions of TensorFlow and Tensorboard. The debugging functionality of the profile plugin is still experimental, and not all views are fully functional. The `trace_viewer` cuts off events after 1M, which can result in all your device traces getting lost if for example, you profile the compilation step by accident.\n\n\n\n```bash\npython3 train_controlnet_flax.py \\\n --pretrained_model_name_or_path=$MODEL_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --dataset_name=fusing/fill50k \\\n --resolution=512 \\\n --learning_rate=1e-5 \\\n --validation_image \"./conditioning_image_1.png\" \"./conditioning_image_2.png\" \\\n --validation_prompt \"red circle with blue background\" \"cyan circle with brown floral background\" \\\n --validation_steps=1000 \\\n --train_batch_size=2 \\\n --revision=\"non-ema\" \\\n --from_pt \\\n --report_to=\"wandb\" \\\n --tracker_project_name=$HUB_MODEL_ID \\\n --num_train_epochs=11 \\\n --push_to_hub \\\n --hub_model_id=$HUB_MODEL_ID\n```\n\n\n\n\nOnce training is complete, you can use your newly trained model for inference!\n\n```py\nfrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel\nfrom diffusers.utils import load_image\nimport torch\n\ncontrolnet = ControlNetModel.from_pretrained(\"path/to/controlnet\", torch_dtype=torch.float16)\npipeline = StableDiffusionControlNetPipeline.from_pretrained(\n \"path/to/base/model\", controlnet=controlnet, torch_dtype=torch.float16\n).to(\"cuda\")\n\ncontrol_image = load_image(\"./conditioning_image_1.png\")\nprompt = \"pale golden rod circle with old lace background\"\n\ngenerator = torch.manual_seed(0)\nimage = pipeline(prompt, num_inference_steps=20, generator=generator, image=control_image).images[0]\nimage.save(\"./output.png\")\n```\n\n## Stable Diffusion XL\n\nStable Diffusion XL (SDXL) is a powerful text-to-image model that generates high-resolution images, and it adds a second text-encoder to its architecture. Use the [`train_controlnet_sdxl.py`](https://github.com/huggingface/diffusers/blob/main/examples/controlnet/train_controlnet_sdxl.py) script to train a ControlNet adapter for the SDXL model.\n\nThe SDXL training script is discussed in more detail in the [SDXL training](sdxl) guide.\n\n## Next steps\n\nCongratulations on training your own ControlNet! To learn more about how to use your new model, the following guides may be helpful:\n\n- Learn how to [use a ControlNet](../using-diffusers/controlnet) for inference on a variety of tasks.\n" }, { "path": "diffusers/docs/source/en/training/create_dataset.md", "content": "# Create a dataset for training\n\nThere are many datasets on the [Hub](https://huggingface.co/datasets?task_categories=task_categories:text-to-image&sort=downloads) to train a model on, but if you can't find one you're interested in or want to use your own, you can create a dataset with the 🤗 [Datasets](hf.co/docs/datasets) library. The dataset structure depends on the task you want to train your model on. The most basic dataset structure is a directory of images for tasks like unconditional image generation. Another dataset structure may be a directory of images and a text file containing their corresponding text captions for tasks like text-to-image generation.\n\nThis guide will show you two ways to create a dataset to finetune on:\n\n- provide a folder of images to the `--train_data_dir` argument\n- upload a dataset to the Hub and pass the dataset repository id to the `--dataset_name` argument\n\n\n\n💡 Learn more about how to create an image dataset for training in the [Create an image dataset](https://huggingface.co/docs/datasets/image_dataset) guide. \n\n\n\n## Provide a dataset as a folder\n\nFor unconditional generation, you can provide your own dataset as a folder of images. The training script uses the [`ImageFolder`](https://huggingface.co/docs/datasets/en/image_dataset#imagefolder) builder from 🤗 Datasets to automatically build a dataset from the folder. Your directory structure should look like:\n\n```bash\ndata_dir/xxx.png\ndata_dir/xxy.png\ndata_dir/[...]/xxz.png\n```\n\nPass the path to the dataset directory to the `--train_data_dir` argument, and then you can start training:\n\n```bash\naccelerate launch train_unconditional.py \\\n --train_data_dir \\\n \n```\n\n## Upload your data to the Hub\n\n\n\n💡 For more details and context about creating and uploading a dataset to the Hub, take a look at the [Image search with 🤗 Datasets](https://huggingface.co/blog/image-search-datasets) post.\n\n\n\nStart by creating a dataset with the [`ImageFolder`](https://huggingface.co/docs/datasets/image_load#imagefolder) feature, which creates an `image` column containing the PIL-encoded images. \n\nYou can use the `data_dir` or `data_files` parameters to specify the location of the dataset. The `data_files` parameter supports mapping specific files to dataset splits like `train` or `test`:\n\n```python\nfrom datasets import load_dataset\n\n# example 1: local folder\ndataset = load_dataset(\"imagefolder\", data_dir=\"path_to_your_folder\")\n\n# example 2: local files (supported formats are tar, gzip, zip, xz, rar, zstd)\ndataset = load_dataset(\"imagefolder\", data_files=\"path_to_zip_file\")\n\n# example 3: remote files (supported formats are tar, gzip, zip, xz, rar, zstd)\ndataset = load_dataset(\n \"imagefolder\",\n data_files=\"https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip\",\n)\n\n# example 4: providing several splits\ndataset = load_dataset(\n \"imagefolder\", data_files={\"train\": [\"path/to/file1\", \"path/to/file2\"], \"test\": [\"path/to/file3\", \"path/to/file4\"]}\n)\n```\n\nThen use the [`~datasets.Dataset.push_to_hub`] method to upload the dataset to the Hub:\n\n```python\n# assuming you have ran the huggingface-cli login command in a terminal\ndataset.push_to_hub(\"name_of_your_dataset\")\n\n# if you want to push to a private repo, simply pass private=True:\ndataset.push_to_hub(\"name_of_your_dataset\", private=True)\n```\n\nNow the dataset is available for training by passing the dataset name to the `--dataset_name` argument:\n\n```bash\naccelerate launch --mixed_precision=\"fp16\" train_text_to_image.py \\\n --pretrained_model_name_or_path=\"runwayml/stable-diffusion-v1-5\" \\\n --dataset_name=\"name_of_your_dataset\" \\\n \n```\n\n## Next steps\n\nNow that you've created a dataset, you can plug it into the `train_data_dir` (if your dataset is local) or `dataset_name` (if your dataset is on the Hub) arguments of a training script.\n\nFor your next steps, feel free to try and use your dataset to train a model for [unconditional generation](unconditional_training) or [text-to-image generation](text2image)!" }, { "path": "diffusers/docs/source/en/training/custom_diffusion.md", "content": "\n\n# Custom Diffusion\n\n[Custom Diffusion](https://huggingface.co/papers/2212.04488) is a training technique for personalizing image generation models. Like Textual Inversion, DreamBooth, and LoRA, Custom Diffusion only requires a few (~4-5) example images. This technique works by only training weights in the cross-attention layers, and it uses a special word to represent the newly learned concept. Custom Diffusion is unique because it can also learn multiple concepts at the same time.\n\nIf you're training on a GPU with limited vRAM, you should try enabling xFormers with `--enable_xformers_memory_efficient_attention` for faster training with lower vRAM requirements (16GB). To save even more memory, add `--set_grads_to_none` in the training argument to set the gradients to `None` instead of zero (this option can cause some issues, so if you experience any, try removing this parameter).\n\nThis guide will explore the [train_custom_diffusion.py](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion/train_custom_diffusion.py) script to help you become more familiar with it, and how you can adapt it for your own use-case.\n\nBefore running the script, make sure you install the library from source:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nNavigate to the example folder with the training script and install the required dependencies:\n\n```bash\ncd examples/custom_diffusion\npip install -r requirements.txt\npip install clip-retrieval\n```\n\n\n\n🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co/docs/accelerate/quicktour) to learn more.\n\n\n\nInitialize an 🤗 Accelerate environment:\n\n```bash\naccelerate config\n```\n\nTo setup a default 🤗 Accelerate environment without choosing any configurations:\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell, like a notebook, you can use:\n\n```py\nfrom accelerate.utils import write_basic_config\n\nwrite_basic_config()\n```\n\nLastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.\n\n\n\nThe following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn't cover every aspect of the script in detail. If you're interested in learning more, feel free to read through the [script](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion/train_custom_diffusion.py) and let us know if you have any questions or concerns.\n\n\n\n## Script parameters\n\nThe training script contains all the parameters to help you customize your training run. These are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/custom_diffusion/train_custom_diffusion.py#L319) function. The function comes with default values, but you can also set your own values in the training command if you'd like.\n\nFor example, to change the resolution of the input image:\n\n```bash\naccelerate launch train_custom_diffusion.py \\\n --resolution=256\n```\n\nMany of the basic parameters are described in the [DreamBooth](dreambooth#script-parameters) training guide, so this guide focuses on the parameters unique to Custom Diffusion:\n\n- `--freeze_model`: freezes the key and value parameters in the cross-attention layer; the default is `crossattn_kv`, but you can set it to `crossattn` to train all the parameters in the cross-attention layer\n- `--concepts_list`: to learn multiple concepts, provide a path to a JSON file containing the concepts\n- `--modifier_token`: a special word used to represent the learned concept\n- `--initializer_token`: a special word used to initialize the embeddings of the `modifier_token`\n\n### Prior preservation loss\n\nPrior preservation loss is a method that uses a model's own generated samples to help it learn how to generate more diverse images. Because these generated sample images belong to the same class as the images you provided, they help the model retain what it has learned about the class and how it can use what it already knows about the class to make new compositions.\n\nMany of the parameters for prior preservation loss are described in the [DreamBooth](dreambooth#prior-preservation-loss) training guide.\n\n### Regularization\n\nCustom Diffusion includes training the target images with a small set of real images to prevent overfitting. As you can imagine, this can be easy to do when you're only training on a few images! Download 200 real images with `clip_retrieval`. The `class_prompt` should be the same category as the target images. These images are stored in `class_data_dir`.\n\n```bash\npython retrieve.py --class_prompt cat --class_data_dir real_reg/samples_cat --num_class_images 200\n```\n\nTo enable regularization, add the following parameters:\n\n- `--with_prior_preservation`: whether to use prior preservation loss\n- `--prior_loss_weight`: controls the influence of the prior preservation loss on the model\n- `--real_prior`: whether to use a small set of real images to prevent overfitting\n\n```bash\naccelerate launch train_custom_diffusion.py \\\n --with_prior_preservation \\\n --prior_loss_weight=1.0 \\\n --class_data_dir=\"./real_reg/samples_cat\" \\\n --class_prompt=\"cat\" \\\n --real_prior=True \\\n```\n\n## Training script\n\n\n\nA lot of the code in the Custom Diffusion training script is similar to the [DreamBooth](dreambooth#training-script) script. This guide instead focuses on the code that is relevant to Custom Diffusion.\n\n\n\nThe Custom Diffusion training script has two dataset classes:\n\n- [`CustomDiffusionDataset`](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/custom_diffusion/train_custom_diffusion.py#L165): preprocesses the images, class images, and prompts for training\n- [`PromptDataset`](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/custom_diffusion/train_custom_diffusion.py#L148): prepares the prompts for generating class images\n\nNext, the `modifier_token` is [added to the tokenizer](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/custom_diffusion/train_custom_diffusion.py#L811), converted to token ids, and the token embeddings are resized to account for the new `modifier_token`. Then the `modifier_token` embeddings are initialized with the embeddings of the `initializer_token`. All parameters in the text encoder are frozen, except for the token embeddings since this is what the model is trying to learn to associate with the concepts.\n\n```py\nparams_to_freeze = itertools.chain(\n text_encoder.text_model.encoder.parameters(),\n text_encoder.text_model.final_layer_norm.parameters(),\n text_encoder.text_model.embeddings.position_embedding.parameters(),\n)\nfreeze_params(params_to_freeze)\n```\n\nNow you'll need to add the [Custom Diffusion weights](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/custom_diffusion/train_custom_diffusion.py#L911C3-L911C3) to the attention layers. This is a really important step for getting the shape and size of the attention weights correct, and for setting the appropriate number of attention processors in each UNet block.\n\n```py\nst = unet.state_dict()\nfor name, _ in unet.attn_processors.items():\n cross_attention_dim = None if name.endswith(\"attn1.processor\") else unet.config.cross_attention_dim\n if name.startswith(\"mid_block\"):\n hidden_size = unet.config.block_out_channels[-1]\n elif name.startswith(\"up_blocks\"):\n block_id = int(name[len(\"up_blocks.\")])\n hidden_size = list(reversed(unet.config.block_out_channels))[block_id]\n elif name.startswith(\"down_blocks\"):\n block_id = int(name[len(\"down_blocks.\")])\n hidden_size = unet.config.block_out_channels[block_id]\n layer_name = name.split(\".processor\")[0]\n weights = {\n \"to_k_custom_diffusion.weight\": st[layer_name + \".to_k.weight\"],\n \"to_v_custom_diffusion.weight\": st[layer_name + \".to_v.weight\"],\n }\n if train_q_out:\n weights[\"to_q_custom_diffusion.weight\"] = st[layer_name + \".to_q.weight\"]\n weights[\"to_out_custom_diffusion.0.weight\"] = st[layer_name + \".to_out.0.weight\"]\n weights[\"to_out_custom_diffusion.0.bias\"] = st[layer_name + \".to_out.0.bias\"]\n if cross_attention_dim is not None:\n custom_diffusion_attn_procs[name] = attention_class(\n train_kv=train_kv,\n train_q_out=train_q_out,\n hidden_size=hidden_size,\n cross_attention_dim=cross_attention_dim,\n ).to(unet.device)\n custom_diffusion_attn_procs[name].load_state_dict(weights)\n else:\n custom_diffusion_attn_procs[name] = attention_class(\n train_kv=False,\n train_q_out=False,\n hidden_size=hidden_size,\n cross_attention_dim=cross_attention_dim,\n )\ndel st\nunet.set_attn_processor(custom_diffusion_attn_procs)\ncustom_diffusion_layers = AttnProcsLayers(unet.attn_processors)\n```\n\nThe [optimizer](https://github.com/huggingface/diffusers/blob/84cd9e8d01adb47f046b1ee449fc76a0c32dc4e2/examples/custom_diffusion/train_custom_diffusion.py#L982) is initialized to update the cross-attention layer parameters:\n\n```py\noptimizer = optimizer_class(\n itertools.chain(text_encoder.get_input_embeddings().parameters(), custom_diffusion_layers.parameters())\n if args.modifier_token is not None\n else custom_diffusion_layers.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n)\n```\n\nIn the [training loop](https://github.com/huggingface/diffusers/blob/84cd9e8d01adb47f046b1ee449fc76a0c32dc4e2/examples/custom_diffusion/train_custom_diffusion.py#L1048), it is important to only update the embeddings for the concept you're trying to learn. This means setting the gradients of all the other token embeddings to zero:\n\n```py\nif args.modifier_token is not None:\n if accelerator.num_processes > 1:\n grads_text_encoder = text_encoder.module.get_input_embeddings().weight.grad\n else:\n grads_text_encoder = text_encoder.get_input_embeddings().weight.grad\n index_grads_to_zero = torch.arange(len(tokenizer)) != modifier_token_id[0]\n for i in range(len(modifier_token_id[1:])):\n index_grads_to_zero = index_grads_to_zero & (\n torch.arange(len(tokenizer)) != modifier_token_id[i]\n )\n grads_text_encoder.data[index_grads_to_zero, :] = grads_text_encoder.data[\n index_grads_to_zero, :\n ].fill_(0)\n```\n\n## Launch the script\n\nOnce you’ve made all your changes or you’re okay with the default configuration, you’re ready to launch the training script! 🚀\n\nIn this guide, you'll download and use these example [cat images](https://www.cs.cmu.edu/~custom-diffusion/assets/data.zip). You can also create and use your own dataset if you want (see the [Create a dataset for training](create_dataset) guide).\n\nSet the environment variable `MODEL_NAME` to a model id on the Hub or a path to a local model, `INSTANCE_DIR` to the path where you just downloaded the cat images to, and `OUTPUT_DIR` to where you want to save the model. You'll use `` as the special word to tie the newly learned embeddings to. The script creates and saves model checkpoints and a pytorch_custom_diffusion_weights.bin file to your repository.\n\nTo monitor training progress with Weights and Biases, add the `--report_to=wandb` parameter to the training command and specify a validation prompt with `--validation_prompt`. This is useful for debugging and saving intermediate results.\n\n\n\nIf you're training on human faces, the Custom Diffusion team has found the following parameters to work well:\n\n- `--learning_rate=5e-6`\n- `--max_train_steps` can be anywhere between 1000 and 2000\n- `--freeze_model=crossattn`\n- use at least 15-20 images to train with\n\n\n\n\n\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport OUTPUT_DIR=\"path-to-save-model\"\nexport INSTANCE_DIR=\"./data/cat\"\n\naccelerate launch train_custom_diffusion.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --class_data_dir=./real_reg/samples_cat/ \\\n --with_prior_preservation \\\n --real_prior \\\n --prior_loss_weight=1.0 \\\n --class_prompt=\"cat\" \\\n --num_class_images=200 \\\n --instance_prompt=\"photo of a cat\" \\\n --resolution=512 \\\n --train_batch_size=2 \\\n --learning_rate=1e-5 \\\n --lr_warmup_steps=0 \\\n --max_train_steps=250 \\\n --scale_lr \\\n --hflip \\\n --modifier_token \"\" \\\n --validation_prompt=\" cat sitting in a bucket\" \\\n --report_to=\"wandb\" \\\n --push_to_hub\n```\n\n\n\n\nCustom Diffusion can also learn multiple concepts if you provide a [JSON](https://github.com/adobe-research/custom-diffusion/blob/main/assets/concept_list.json) file with some details about each concept it should learn.\n\nRun clip-retrieval to collect some real images to use for regularization:\n\n```bash\npip install clip-retrieval\npython retrieve.py --class_prompt {} --class_data_dir {} --num_class_images 200\n```\n\nThen you can launch the script:\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\naccelerate launch train_custom_diffusion.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --output_dir=$OUTPUT_DIR \\\n --concepts_list=./concept_list.json \\\n --with_prior_preservation \\\n --real_prior \\\n --prior_loss_weight=1.0 \\\n --resolution=512 \\\n --train_batch_size=2 \\\n --learning_rate=1e-5 \\\n --lr_warmup_steps=0 \\\n --max_train_steps=500 \\\n --num_class_images=200 \\\n --scale_lr \\\n --hflip \\\n --modifier_token \"+\" \\\n --push_to_hub\n```\n\n\n\n\nOnce training is finished, you can use your new Custom Diffusion model for inference.\n\n\n\n\n```py\nimport torch\nfrom diffusers import DiffusionPipeline\n\npipeline = DiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\", torch_dtype=torch.float16,\n).to(\"cuda\")\npipeline.unet.load_attn_procs(\"path-to-save-model\", weight_name=\"pytorch_custom_diffusion_weights.bin\")\npipeline.load_textual_inversion(\"path-to-save-model\", weight_name=\".bin\")\n\nimage = pipeline(\n \" cat sitting in a bucket\",\n num_inference_steps=100,\n guidance_scale=6.0,\n eta=1.0,\n).images[0]\nimage.save(\"cat.png\")\n```\n\n\n\n\n```py\nimport torch\nfrom huggingface_hub.repocard import RepoCard\nfrom diffusers import DiffusionPipeline\n\npipeline = DiffusionPipeline.from_pretrained(\"sayakpaul/custom-diffusion-cat-wooden-pot\", torch_dtype=torch.float16).to(\"cuda\")\npipeline.unet.load_attn_procs(model_id, weight_name=\"pytorch_custom_diffusion_weights.bin\")\npipeline.load_textual_inversion(model_id, weight_name=\".bin\")\npipeline.load_textual_inversion(model_id, weight_name=\".bin\")\n\nimage = pipeline(\n \"the cat sculpture in the style of a wooden pot\",\n num_inference_steps=100,\n guidance_scale=6.0,\n eta=1.0,\n).images[0]\nimage.save(\"multi-subject.png\")\n```\n\n\n\n\n## Next steps\n\nCongratulations on training a model with Custom Diffusion! 🎉 To learn more:\n\n- Read the [Multi-Concept Customization of Text-to-Image Diffusion](https://www.cs.cmu.edu/~custom-diffusion/) blog post to learn more details about the experimental results from the Custom Diffusion team." }, { "path": "diffusers/docs/source/en/training/ddpo.md", "content": "\n\n# Reinforcement learning training with DDPO\n\nYou can fine-tune Stable Diffusion on a reward function via reinforcement learning with the 🤗 TRL library and 🤗 Diffusers. This is done with the Denoising Diffusion Policy Optimization (DDPO) algorithm introduced by Black et al. in [Training Diffusion Models with Reinforcement Learning](https://arxiv.org/abs/2305.13301), which is implemented in 🤗 TRL with the [`~trl.DDPOTrainer`].\n\nFor more information, check out the [`~trl.DDPOTrainer`] API reference and the [Finetune Stable Diffusion Models with DDPO via TRL](https://huggingface.co/blog/trl-ddpo) blog post." }, { "path": "diffusers/docs/source/en/training/distributed_inference.md", "content": "\n\n# Distributed inference with multiple GPUs\n\nOn distributed setups, you can run inference across multiple GPUs with 🤗 [Accelerate](https://huggingface.co/docs/accelerate/index) or [PyTorch Distributed](https://pytorch.org/tutorials/beginner/dist_overview.html), which is useful for generating with multiple prompts in parallel.\n\nThis guide will show you how to use 🤗 Accelerate and PyTorch Distributed for distributed inference.\n\n## 🤗 Accelerate\n\n🤗 [Accelerate](https://huggingface.co/docs/accelerate/index) is a library designed to make it easy to train or run inference across distributed setups. It simplifies the process of setting up the distributed environment, allowing you to focus on your PyTorch code.\n\nTo begin, create a Python file and initialize an [`accelerate.PartialState`] to create a distributed environment; your setup is automatically detected so you don't need to explicitly define the `rank` or `world_size`. Move the [`DiffusionPipeline`] to `distributed_state.device` to assign a GPU to each process.\n\nNow use the [`~accelerate.PartialState.split_between_processes`] utility as a context manager to automatically distribute the prompts between the number of processes.\n\n```py\nimport torch\nfrom accelerate import PartialState\nfrom diffusers import DiffusionPipeline\n\npipeline = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, use_safetensors=True\n)\ndistributed_state = PartialState()\npipeline.to(distributed_state.device)\n\nwith distributed_state.split_between_processes([\"a dog\", \"a cat\"]) as prompt:\n result = pipeline(prompt).images[0]\n result.save(f\"result_{distributed_state.process_index}.png\")\n```\n\nUse the `--num_processes` argument to specify the number of GPUs to use, and call `accelerate launch` to run the script:\n\n```bash\naccelerate launch run_distributed.py --num_processes=2\n```\n\n\n\nTo learn more, take a look at the [Distributed Inference with 🤗 Accelerate](https://huggingface.co/docs/accelerate/en/usage_guides/distributed_inference#distributed-inference-with-accelerate) guide.\n\n\n\n### Device placement\n\n> [!WARNING]\n> This feature is experimental and its APIs might change in the future. \n\nWith Accelerate, you can use the `device_map` to determine how to distribute the models of a pipeline across multiple devices. This is useful in situations where you have more than one GPU.\n\nFor example, if you have two 8GB GPUs, then using [`~DiffusionPipeline.enable_model_cpu_offload`] may not work so well because:\n\n* it only works on a single GPU\n* a single model might not fit on a single GPU ([`~DiffusionPipeline.enable_sequential_cpu_offload`] might work but it will be extremely slow and it is also limited to a single GPU)\n\nTo make use of both GPUs, you can use the \"balanced\" device placement strategy which splits the models across all available GPUs.\n\n> [!WARNING]\n> Only the \"balanced\" strategy is supported at the moment, and we plan to support additional mapping strategies in the future.\n\n```diff\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipeline = DiffusionPipeline.from_pretrained(\n- \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, use_safetensors=True,\n+ \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, use_safetensors=True, device_map=\"balanced\"\n)\nimage = pipeline(\"a dog\").images[0]\nimage\n```\n\nYou can also pass a dictionary to enforce the maximum GPU memory that can be used on each device:\n\n```diff\nfrom diffusers import DiffusionPipeline\nimport torch\n\nmax_memory = {0:\"1GB\", 1:\"1GB\"}\npipeline = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n torch_dtype=torch.float16, \n use_safetensors=True, \n device_map=\"balanced\",\n+ max_memory=max_memory\n)\nimage = pipeline(\"a dog\").images[0]\nimage\n```\n\nIf a device is not present in `max_memory`, then it will be completely ignored and will not participate in the device placement. \n\nBy default, Diffusers uses the maximum memory of all devices. If the models don't fit on the GPUs, they are offloaded to the CPU. If the CPU doesn't have enough memory, then you might see an error. In that case, you could defer to using [`~DiffusionPipeline.enable_sequential_cpu_offload`] and [`~DiffusionPipeline.enable_model_cpu_offload`].\n\nCall [`~DiffusionPipeline.reset_device_map`] to reset the `device_map` of a pipeline. This is also necessary if you want to use methods like `to()`, [`~DiffusionPipeline.enable_sequential_cpu_offload`], and [`~DiffusionPipeline.enable_model_cpu_offload`] on a pipeline that was device-mapped.\n\n```py\npipeline.reset_device_map()\n```\n\nOnce a pipeline has been device-mapped, you can also access its device map via `hf_device_map`:\n\n```py\nprint(pipeline.hf_device_map)\n```\n\nAn example device map would look like so:\n\n\n```bash\n{'unet': 1, 'vae': 1, 'safety_checker': 0, 'text_encoder': 0}\n```\n\n## PyTorch Distributed\n\nPyTorch supports [`DistributedDataParallel`](https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html) which enables data parallelism.\n\nTo start, create a Python file and import `torch.distributed` and `torch.multiprocessing` to set up the distributed process group and to spawn the processes for inference on each GPU. You should also initialize a [`DiffusionPipeline`]:\n\n```py\nimport torch\nimport torch.distributed as dist\nimport torch.multiprocessing as mp\n\nfrom diffusers import DiffusionPipeline\n\nsd = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, use_safetensors=True\n)\n```\n\nYou'll want to create a function to run inference; [`init_process_group`](https://pytorch.org/docs/stable/distributed.html?highlight=init_process_group#torch.distributed.init_process_group) handles creating a distributed environment with the type of backend to use, the `rank` of the current process, and the `world_size` or the number of processes participating. If you're running inference in parallel over 2 GPUs, then the `world_size` is 2.\n\nMove the [`DiffusionPipeline`] to `rank` and use `get_rank` to assign a GPU to each process, where each process handles a different prompt:\n\n```py\ndef run_inference(rank, world_size):\n dist.init_process_group(\"nccl\", rank=rank, world_size=world_size)\n\n sd.to(rank)\n\n if torch.distributed.get_rank() == 0:\n prompt = \"a dog\"\n elif torch.distributed.get_rank() == 1:\n prompt = \"a cat\"\n\n image = sd(prompt).images[0]\n image.save(f\"./{'_'.join(prompt)}.png\")\n```\n\nTo run the distributed inference, call [`mp.spawn`](https://pytorch.org/docs/stable/multiprocessing.html#torch.multiprocessing.spawn) to run the `run_inference` function on the number of GPUs defined in `world_size`:\n\n```py\ndef main():\n world_size = 2\n mp.spawn(run_inference, args=(world_size,), nprocs=world_size, join=True)\n\n\nif __name__ == \"__main__\":\n main()\n```\n\nOnce you've completed the inference script, use the `--nproc_per_node` argument to specify the number of GPUs to use and call `torchrun` to run the script:\n\n```bash\ntorchrun run_distributed.py --nproc_per_node=2\n```\n" }, { "path": "diffusers/docs/source/en/training/dreambooth.md", "content": "\n\n# DreamBooth\n\n[DreamBooth](https://huggingface.co/papers/2208.12242) is a training technique that updates the entire diffusion model by training on just a few images of a subject or style. It works by associating a special word in the prompt with the example images.\n\nIf you're training on a GPU with limited vRAM, you should try enabling the `gradient_checkpointing` and `mixed_precision` parameters in the training command. You can also reduce your memory footprint by using memory-efficient attention with [xFormers](../optimization/xformers). JAX/Flax training is also supported for efficient training on TPUs and GPUs, but it doesn't support gradient checkpointing or xFormers. You should have a GPU with >30GB of memory if you want to train faster with Flax.\n\nThis guide will explore the [train_dreambooth.py](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py) script to help you become more familiar with it, and how you can adapt it for your own use-case.\n\nBefore running the script, make sure you install the library from source:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nNavigate to the example folder with the training script and install the required dependencies for the script you're using:\n\n\n\n\n```bash\ncd examples/dreambooth\npip install -r requirements.txt\n```\n\n\n\n\n```bash\ncd examples/dreambooth\npip install -r requirements_flax.txt\n```\n\n\n\n\n\n\n🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co/docs/accelerate/quicktour) to learn more.\n\n\n\nInitialize an 🤗 Accelerate environment:\n\n```bash\naccelerate config\n```\n\nTo setup a default 🤗 Accelerate environment without choosing any configurations:\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell, like a notebook, you can use:\n\n```py\nfrom accelerate.utils import write_basic_config\n\nwrite_basic_config()\n```\n\nLastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.\n\n\n\nThe following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn't cover every aspect of the script in detail. If you're interested in learning more, feel free to read through the [script](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py) and let us know if you have any questions or concerns.\n\n\n\n## Script parameters\n\n\n\nDreamBooth is very sensitive to training hyperparameters, and it is easy to overfit. Read the [Training Stable Diffusion with Dreambooth using 🧨 Diffusers](https://huggingface.co/blog/dreambooth) blog post for recommended settings for different subjects to help you choose the appropriate hyperparameters.\n\n\n\nThe training script offers many parameters for customizing your training run. All of the parameters and their descriptions are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L228) function. The parameters are set with default values that should work pretty well out-of-the-box, but you can also set your own values in the training command if you'd like.\n\nFor example, to train in the bf16 format:\n\n```bash\naccelerate launch train_dreambooth.py \\\n --mixed_precision=\"bf16\"\n```\n\nSome basic and important parameters to know and specify are:\n\n- `--pretrained_model_name_or_path`: the name of the model on the Hub or a local path to the pretrained model\n- `--instance_data_dir`: path to a folder containing the training dataset (example images)\n- `--instance_prompt`: the text prompt that contains the special word for the example images\n- `--train_text_encoder`: whether to also train the text encoder\n- `--output_dir`: where to save the trained model\n- `--push_to_hub`: whether to push the trained model to the Hub\n- `--checkpointing_steps`: frequency of saving a checkpoint as the model trains; this is useful if for some reason training is interrupted, you can continue training from that checkpoint by adding `--resume_from_checkpoint` to your training command\n\n### Min-SNR weighting\n\nThe [Min-SNR](https://huggingface.co/papers/2303.09556) weighting strategy can help with training by rebalancing the loss to achieve faster convergence. The training script supports predicting `epsilon` (noise) or `v_prediction`, but Min-SNR is compatible with both prediction types. This weighting strategy is only supported by PyTorch and is unavailable in the Flax training script.\n\nAdd the `--snr_gamma` parameter and set it to the recommended value of 5.0:\n\n```bash\naccelerate launch train_dreambooth.py \\\n --snr_gamma=5.0\n```\n\n### Prior preservation loss\n\nPrior preservation loss is a method that uses a model's own generated samples to help it learn how to generate more diverse images. Because these generated sample images belong to the same class as the images you provided, they help the model retain what it has learned about the class and how it can use what it already knows about the class to make new compositions.\n\n- `--with_prior_preservation`: whether to use prior preservation loss\n- `--prior_loss_weight`: controls the influence of the prior preservation loss on the model\n- `--class_data_dir`: path to a folder containing the generated class sample images\n- `--class_prompt`: the text prompt describing the class of the generated sample images\n\n```bash\naccelerate launch train_dreambooth.py \\\n --with_prior_preservation \\\n --prior_loss_weight=1.0 \\\n --class_data_dir=\"path/to/class/images\" \\\n --class_prompt=\"text prompt describing class\"\n```\n\n### Train text encoder\n\nTo improve the quality of the generated outputs, you can also train the text encoder in addition to the UNet. This requires additional memory and you'll need a GPU with at least 24GB of vRAM. If you have the necessary hardware, then training the text encoder produces better results, especially when generating images of faces. Enable this option by:\n\n```bash\naccelerate launch train_dreambooth.py \\\n --train_text_encoder\n```\n\n## Training script\n\nDreamBooth comes with its own dataset classes:\n\n- [`DreamBoothDataset`](https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L604): preprocesses the images and class images, and tokenizes the prompts for training\n- [`PromptDataset`](https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L738): generates the prompt embeddings to generate the class images\n\nIf you enabled [prior preservation loss](https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L842), the class images are generated here:\n\n```py\nsample_dataset = PromptDataset(args.class_prompt, num_new_images)\nsample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)\n\nsample_dataloader = accelerator.prepare(sample_dataloader)\npipeline.to(accelerator.device)\n\nfor example in tqdm(\n sample_dataloader, desc=\"Generating class images\", disable=not accelerator.is_local_main_process\n):\n images = pipeline(example[\"prompt\"]).images\n```\n\nNext is the [`main()`](https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L799) function which handles setting up the dataset for training and the training loop itself. The script loads the [tokenizer](https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L898), [scheduler and models](https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L912C1-L912C1):\n\n```py\n# Load the tokenizer\nif args.tokenizer_name:\n tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)\nelif args.pretrained_model_name_or_path:\n tokenizer = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n\n# Load scheduler and models\nnoise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\ntext_encoder = text_encoder_cls.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision\n)\n\nif model_has_vae(args):\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision\n )\nelse:\n vae = None\n\nunet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision\n)\n```\n\nThen, it's time to [create the training dataset](https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L1073) and DataLoader from `DreamBoothDataset`:\n\n```py\ntrain_dataset = DreamBoothDataset(\n instance_data_root=args.instance_data_dir,\n instance_prompt=args.instance_prompt,\n class_data_root=args.class_data_dir if args.with_prior_preservation else None,\n class_prompt=args.class_prompt,\n class_num=args.num_class_images,\n tokenizer=tokenizer,\n size=args.resolution,\n center_crop=args.center_crop,\n encoder_hidden_states=pre_computed_encoder_hidden_states,\n class_prompt_encoder_hidden_states=pre_computed_class_prompt_encoder_hidden_states,\n tokenizer_max_length=args.tokenizer_max_length,\n)\n\ntrain_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n batch_size=args.train_batch_size,\n shuffle=True,\n collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),\n num_workers=args.dataloader_num_workers,\n)\n```\n\nLastly, the [training loop](https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L1151) takes care of the remaining steps such as converting images to latent space, adding noise to the input, predicting the noise residual, and calculating the loss.\n\nIf you want to learn more about how the training loop works, check out the [Understanding pipelines, models and schedulers](../using-diffusers/write_own_pipeline) tutorial which breaks down the basic pattern of the denoising process.\n\n## Launch the script\n\nYou're now ready to launch the training script! 🚀\n\nFor this guide, you'll download some images of a [dog](https://huggingface.co/datasets/diffusers/dog-example) and store them in a directory. But remember, you can create and use your own dataset if you want (see the [Create a dataset for training](create_dataset) guide).\n\n```py\nfrom huggingface_hub import snapshot_download\n\nlocal_dir = \"./dog\"\nsnapshot_download(\n \"diffusers/dog-example\",\n local_dir=local_dir,\n repo_type=\"dataset\",\n ignore_patterns=\".gitattributes\",\n)\n```\n\nSet the environment variable `MODEL_NAME` to a model id on the Hub or a path to a local model, `INSTANCE_DIR` to the path where you just downloaded the dog images to, and `OUTPUT_DIR` to where you want to save the model. You'll use `sks` as the special word to tie the training to.\n\nIf you're interested in following along with the training process, you can periodically save generated images as training progresses. Add the following parameters to the training command:\n\n```bash\n--validation_prompt=\"a photo of a sks dog\"\n--num_validation_images=4\n--validation_steps=100\n```\n\nOne more thing before you launch the script! Depending on the GPU you have, you may need to enable certain optimizations to train DreamBooth.\n\n\n\n\nOn a 16GB GPU, you can use bitsandbytes 8-bit optimizer and gradient checkpointing to help you train a DreamBooth model. Install bitsandbytes:\n\n```py\npip install bitsandbytes\n```\n\nThen, add the following parameter to your training command:\n\n```bash\naccelerate launch train_dreambooth.py \\\n --gradient_checkpointing \\\n --use_8bit_adam \\\n```\n\n\n\n\nOn a 12GB GPU, you'll need bitsandbytes 8-bit optimizer, gradient checkpointing, xFormers, and set the gradients to `None` instead of zero to reduce your memory-usage.\n\n```bash\naccelerate launch train_dreambooth.py \\\n --use_8bit_adam \\\n --gradient_checkpointing \\\n --enable_xformers_memory_efficient_attention \\\n --set_grads_to_none \\\n```\n\n\n\n\nOn a 8GB GPU, you'll need [DeepSpeed](https://www.deepspeed.ai/) to offload some of the tensors from the vRAM to either the CPU or NVME to allow training with less GPU memory.\n\nRun the following command to configure your 🤗 Accelerate environment:\n\n```bash\naccelerate config\n```\n\nDuring configuration, confirm that you want to use DeepSpeed. Now it should be possible to train on under 8GB vRAM by combining DeepSpeed stage 2, fp16 mixed precision, and offloading the model parameters and the optimizer state to the CPU. The drawback is that this requires more system RAM (~25 GB). See the [DeepSpeed documentation](https://huggingface.co/docs/accelerate/usage_guides/deepspeed) for more configuration options.\n\nYou should also change the default Adam optimizer to DeepSpeed’s optimized version of Adam [`deepspeed.ops.adam.DeepSpeedCPUAdam`](https://deepspeed.readthedocs.io/en/latest/optimizers.html#adam-cpu) for a substantial speedup. Enabling `DeepSpeedCPUAdam` requires your system’s CUDA toolchain version to be the same as the one installed with PyTorch.\n\nbitsandbytes 8-bit optimizers don’t seem to be compatible with DeepSpeed at the moment.\n\nThat's it! You don't need to add any additional parameters to your training command.\n\n\n\n\n\n\n\n```bash\nexport MODEL_NAME=\"runwayml/stable-diffusion-v1-5\"\nexport INSTANCE_DIR=\"./dog\"\nexport OUTPUT_DIR=\"path_to_saved_model\"\n\naccelerate launch train_dreambooth.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --instance_prompt=\"a photo of sks dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=1 \\\n --learning_rate=5e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --max_train_steps=400 \\\n --push_to_hub\n```\n\n\n\n\n```bash\nexport MODEL_NAME=\"duongna/stable-diffusion-v1-4-flax\"\nexport INSTANCE_DIR=\"./dog\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\npython train_dreambooth_flax.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --instance_prompt=\"a photo of sks dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --learning_rate=5e-6 \\\n --max_train_steps=400 \\\n --push_to_hub\n```\n\n\n\n\nOnce training is complete, you can use your newly trained model for inference!\n\n\n\nCan't wait to try your model for inference before training is complete? 🤭 Make sure you have the latest version of 🤗 Accelerate installed.\n\n```py\nfrom diffusers import DiffusionPipeline, UNet2DConditionModel\nfrom transformers import CLIPTextModel\nimport torch\n\nunet = UNet2DConditionModel.from_pretrained(\"path/to/model/checkpoint-100/unet\")\n\n# if you have trained with `--args.train_text_encoder` make sure to also load the text encoder\ntext_encoder = CLIPTextModel.from_pretrained(\"path/to/model/checkpoint-100/checkpoint-100/text_encoder\")\n\npipeline = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", unet=unet, text_encoder=text_encoder, dtype=torch.float16,\n).to(\"cuda\")\n\nimage = pipeline(\"A photo of sks dog in a bucket\", num_inference_steps=50, guidance_scale=7.5).images[0]\nimage.save(\"dog-bucket.png\")\n```\n\n\n\n\n\n\n```py\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipeline = DiffusionPipeline.from_pretrained(\"path_to_saved_model\", torch_dtype=torch.float16, use_safetensors=True).to(\"cuda\")\nimage = pipeline(\"A photo of sks dog in a bucket\", num_inference_steps=50, guidance_scale=7.5).images[0]\nimage.save(\"dog-bucket.png\")\n```\n\n\n\n\n```py\nimport jax\nimport numpy as np\nfrom flax.jax_utils import replicate\nfrom flax.training.common_utils import shard\nfrom diffusers import FlaxStableDiffusionPipeline\n\npipeline, params = FlaxStableDiffusionPipeline.from_pretrained(\"path-to-your-trained-model\", dtype=jax.numpy.bfloat16)\n\nprompt = \"A photo of sks dog in a bucket\"\nprng_seed = jax.random.PRNGKey(0)\nnum_inference_steps = 50\n\nnum_samples = jax.device_count()\nprompt = num_samples * [prompt]\nprompt_ids = pipeline.prepare_inputs(prompt)\n\n# shard inputs and rng\nparams = replicate(params)\nprng_seed = jax.random.split(prng_seed, jax.device_count())\nprompt_ids = shard(prompt_ids)\n\nimages = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images\nimages = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))\nimage.save(\"dog-bucket.png\")\n```\n\n\n\n\n## LoRA\n\nLoRA is a training technique for significantly reducing the number of trainable parameters. As a result, training is faster and it is easier to store the resulting weights because they are a lot smaller (~100MBs). Use the [train_dreambooth_lora.py](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora.py) script to train with LoRA.\n\nThe LoRA training script is discussed in more detail in the [LoRA training](lora) guide.\n\n## Stable Diffusion XL\n\nStable Diffusion XL (SDXL) is a powerful text-to-image model that generates high-resolution images, and it adds a second text-encoder to its architecture. Use the [train_dreambooth_lora_sdxl.py](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora_sdxl.py) script to train a SDXL model with LoRA.\n\nThe SDXL training script is discussed in more detail in the [SDXL training](sdxl) guide.\n\n## DeepFloyd IF\n\nDeepFloyd IF is a cascading pixel diffusion model with three stages. The first stage generates a base image and the second and third stages progressively upscales the base image into a high-resolution 1024x1024 image. Use the [train_dreambooth_lora.py](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora.py) or [train_dreambooth.py](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py) scripts to train a DeepFloyd IF model with LoRA or the full model.\n\nDeepFloyd IF uses predicted variance, but the Diffusers training scripts uses predicted error so the trained DeepFloyd IF models are switched to a fixed variance schedule. The training scripts will update the scheduler config of the fully trained model for you. However, when you load the saved LoRA weights you must also update the pipeline's scheduler config.\n\n```py\nfrom diffusers import DiffusionPipeline\n\npipe = DiffusionPipeline.from_pretrained(\"DeepFloyd/IF-I-XL-v1.0\", use_safetensors=True)\n\npipe.load_lora_weights(\"\")\n\n# Update scheduler config to fixed variance schedule\npipe.scheduler = pipe.scheduler.__class__.from_config(pipe.scheduler.config, variance_type=\"fixed_small\")\n```\n\nThe stage 2 model requires additional validation images to upscale. You can download and use a downsized version of the training images for this.\n\n```py\nfrom huggingface_hub import snapshot_download\n\nlocal_dir = \"./dog_downsized\"\nsnapshot_download(\n \"diffusers/dog-example-downsized\",\n local_dir=local_dir,\n repo_type=\"dataset\",\n ignore_patterns=\".gitattributes\",\n)\n```\n\nThe code samples below provide a brief overview of how to train a DeepFloyd IF model with a combination of DreamBooth and LoRA. Some important parameters to note are:\n\n* `--resolution=64`, a much smaller resolution is required because DeepFloyd IF is a pixel diffusion model and to work on uncompressed pixels, the input images must be smaller\n* `--pre_compute_text_embeddings`, compute the text embeddings ahead of time to save memory because the [`~transformers.T5Model`] can take up a lot of memory\n* `--tokenizer_max_length=77`, you can use a longer default text length with T5 as the text encoder but the default model encoding procedure uses a shorter text length\n* `--text_encoder_use_attention_mask`, to pass the attention mask to the text encoder\n\n\n\n\nTraining stage 1 of DeepFloyd IF with LoRA and DreamBooth requires ~28GB of memory.\n\n```bash\nexport MODEL_NAME=\"DeepFloyd/IF-I-XL-v1.0\"\nexport INSTANCE_DIR=\"dog\"\nexport OUTPUT_DIR=\"dreambooth_dog_lora\"\n\naccelerate launch train_dreambooth_lora.py \\\n --report_to wandb \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --instance_prompt=\"a sks dog\" \\\n --resolution=64 \\\n --train_batch_size=4 \\\n --gradient_accumulation_steps=1 \\\n --learning_rate=5e-6 \\\n --scale_lr \\\n --max_train_steps=1200 \\\n --validation_prompt=\"a sks dog\" \\\n --validation_epochs=25 \\\n --checkpointing_steps=100 \\\n --pre_compute_text_embeddings \\\n --tokenizer_max_length=77 \\\n --text_encoder_use_attention_mask\n```\n\n\n\n\nFor stage 2 of DeepFloyd IF with LoRA and DreamBooth, pay attention to these parameters:\n\n* `--validation_images`, the images to upscale during validation\n* `--class_labels_conditioning=timesteps`, to additionally conditional the UNet as needed in stage 2\n* `--learning_rate=1e-6`, a lower learning rate is used compared to stage 1\n* `--resolution=256`, the expected resolution for the upscaler\n\n```bash\nexport MODEL_NAME=\"DeepFloyd/IF-II-L-v1.0\"\nexport INSTANCE_DIR=\"dog\"\nexport OUTPUT_DIR=\"dreambooth_dog_upscale\"\nexport VALIDATION_IMAGES=\"dog_downsized/image_1.png dog_downsized/image_2.png dog_downsized/image_3.png dog_downsized/image_4.png\"\n\npython train_dreambooth_lora.py \\\n --report_to wandb \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --instance_prompt=\"a sks dog\" \\\n --resolution=256 \\\n --train_batch_size=4 \\\n --gradient_accumulation_steps=1 \\\n --learning_rate=1e-6 \\ \n --max_train_steps=2000 \\\n --validation_prompt=\"a sks dog\" \\\n --validation_epochs=100 \\\n --checkpointing_steps=500 \\\n --pre_compute_text_embeddings \\\n --tokenizer_max_length=77 \\\n --text_encoder_use_attention_mask \\\n --validation_images $VALIDATION_IMAGES \\\n --class_labels_conditioning=timesteps\n```\n\n\n\n\nFor stage 1 of DeepFloyd IF with DreamBooth, pay attention to these parameters:\n\n* `--skip_save_text_encoder`, to skip saving the full T5 text encoder with the finetuned model\n* `--use_8bit_adam`, to use 8-bit Adam optimizer to save memory due to the size of the optimizer state when training the full model\n* `--learning_rate=1e-7`, a really low learning rate should be used for full model training otherwise the model quality is degraded (you can use a higher learning rate with a larger batch size)\n\nTraining with 8-bit Adam and a batch size of 4, the full model can be trained with ~48GB of memory.\n\n```bash\nexport MODEL_NAME=\"DeepFloyd/IF-I-XL-v1.0\"\nexport INSTANCE_DIR=\"dog\"\nexport OUTPUT_DIR=\"dreambooth_if\"\n\naccelerate launch train_dreambooth.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --instance_prompt=\"a photo of sks dog\" \\\n --resolution=64 \\\n --train_batch_size=4 \\\n --gradient_accumulation_steps=1 \\\n --learning_rate=1e-7 \\\n --max_train_steps=150 \\\n --validation_prompt \"a photo of sks dog\" \\\n --validation_steps 25 \\\n --text_encoder_use_attention_mask \\\n --tokenizer_max_length 77 \\\n --pre_compute_text_embeddings \\\n --use_8bit_adam \\\n --set_grads_to_none \\\n --skip_save_text_encoder \\\n --push_to_hub\n```\n\n\n\n\nFor stage 2 of DeepFloyd IF with DreamBooth, pay attention to these parameters:\n\n* `--learning_rate=5e-6`, use a lower learning rate with a smaller effective batch size\n* `--resolution=256`, the expected resolution for the upscaler\n* `--train_batch_size=2` and `--gradient_accumulation_steps=6`, to effectively train on images wiht faces requires larger batch sizes\n\n```bash\nexport MODEL_NAME=\"DeepFloyd/IF-II-L-v1.0\"\nexport INSTANCE_DIR=\"dog\"\nexport OUTPUT_DIR=\"dreambooth_dog_upscale\"\nexport VALIDATION_IMAGES=\"dog_downsized/image_1.png dog_downsized/image_2.png dog_downsized/image_3.png dog_downsized/image_4.png\"\n\naccelerate launch train_dreambooth.py \\\n --report_to wandb \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --instance_prompt=\"a sks dog\" \\\n --resolution=256 \\\n --train_batch_size=2 \\\n --gradient_accumulation_steps=6 \\\n --learning_rate=5e-6 \\\n --max_train_steps=2000 \\\n --validation_prompt=\"a sks dog\" \\\n --validation_steps=150 \\\n --checkpointing_steps=500 \\\n --pre_compute_text_embeddings \\\n --tokenizer_max_length=77 \\\n --text_encoder_use_attention_mask \\\n --validation_images $VALIDATION_IMAGES \\\n --class_labels_conditioning timesteps \\\n --push_to_hub\n```\n\n\n\n\n### Training tips\n\nTraining the DeepFloyd IF model can be challenging, but here are some tips that we've found helpful:\n\n- LoRA is sufficient for training the stage 1 model because the model's low resolution makes representing finer details difficult regardless.\n- For common or simple objects, you don't necessarily need to finetune the upscaler. Make sure the prompt passed to the upscaler is adjusted to remove the new token from the instance prompt. For example, if your stage 1 prompt is \"a sks dog\" then your stage 2 prompt should be \"a dog\".\n- For finer details like faces, fully training the stage 2 upscaler is better than training the stage 2 model with LoRA. It also helps to use lower learning rates with larger batch sizes.\n- Lower learning rates should be used to train the stage 2 model.\n- The [`DDPMScheduler`] works better than the DPMSolver used in the training scripts.\n\n## Next steps\n\nCongratulations on training your DreamBooth model! To learn more about how to use your new model, the following guide may be helpful:\n\n- Learn how to [load a DreamBooth](../using-diffusers/loading_adapters) model for inference if you trained your model with LoRA." }, { "path": "diffusers/docs/source/en/training/instructpix2pix.md", "content": "\n\n# InstructPix2Pix\n\n[InstructPix2Pix](https://hf.co/papers/2211.09800) is a Stable Diffusion model trained to edit images from human-provided instructions. For example, your prompt can be \"turn the clouds rainy\" and the model will edit the input image accordingly. This model is conditioned on the text prompt (or editing instruction) and the input image.\n\nThis guide will explore the [train_instruct_pix2pix.py](https://github.com/huggingface/diffusers/blob/main/examples/instruct_pix2pix/train_instruct_pix2pix.py) training script to help you become familiar with it, and how you can adapt it for your own use-case.\n\nBefore running the script, make sure you install the library from source:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nThen navigate to the example folder containing the training script and install the required dependencies for the script you're using:\n\n```bash\ncd examples/instruct_pix2pix\npip install -r requirements.txt\n```\n\n\n\n🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co/docs/accelerate/quicktour) to learn more.\n\n\n\nInitialize an 🤗 Accelerate environment:\n\n```bash\naccelerate config\n```\n\nTo setup a default 🤗 Accelerate environment without choosing any configurations:\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell, like a notebook, you can use:\n\n```py\nfrom accelerate.utils import write_basic_config\n\nwrite_basic_config()\n```\n\nLastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.\n\n\n\nThe following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn't cover every aspect of the script in detail. If you're interested in learning more, feel free to read through the [script](https://github.com/huggingface/diffusers/blob/main/examples/instruct_pix2pix/train_instruct_pix2pix.py) and let us know if you have any questions or concerns.\n\n\n\n## Script parameters\n\nThe training script has many parameters to help you customize your training run. All of the parameters and their descriptions are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L65) function. Default values are provided for most parameters that work pretty well, but you can also set your own values in the training command if you'd like.\n\nFor example, to increase the resolution of the input image:\n\n```bash\naccelerate launch train_instruct_pix2pix.py \\\n --resolution=512 \\\n```\n\nMany of the basic and important parameters are described in the [Text-to-image](text2image#script-parameters) training guide, so this guide just focuses on the relevant parameters for InstructPix2Pix:\n\n- `--original_image_column`: the original image before the edits are made\n- `--edited_image_column`: the image after the edits are made\n- `--edit_prompt_column`: the instructions to edit the image\n- `--conditioning_dropout_prob`: the dropout probability for the edited image and edit prompts during training which enables classifier-free guidance (CFG) for one or both conditioning inputs\n\n## Training script\n\nThe dataset preprocessing code and training loop are found in the [`main()`](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L374) function. This is where you'll make your changes to the training script to adapt it for your own use-case.\n\nAs with the script parameters, a walkthrough of the training script is provided in the [Text-to-image](text2image#training-script) training guide. Instead, this guide takes a look at the InstructPix2Pix relevant parts of the script.\n\nThe script begins by modifying the [number of input channels](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L445) in the first convolutional layer of the UNet to account for InstructPix2Pix's additional conditioning image:\n\n```py\nin_channels = 8\nout_channels = unet.conv_in.out_channels\nunet.register_to_config(in_channels=in_channels)\n\nwith torch.no_grad():\n new_conv_in = nn.Conv2d(\n in_channels, out_channels, unet.conv_in.kernel_size, unet.conv_in.stride, unet.conv_in.padding\n )\n new_conv_in.weight.zero_()\n new_conv_in.weight[:, :4, :, :].copy_(unet.conv_in.weight)\n unet.conv_in = new_conv_in\n```\n\nThese UNet parameters are [updated](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L545C1-L551C6) by the optimizer:\n\n```py\noptimizer = optimizer_cls(\n unet.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n)\n```\n\nNext, the edited images and and edit instructions are [preprocessed](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L624) and [tokenized](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L610C24-L610C24). It is important the same image transformations are applied to the original and edited images.\n\n```py\ndef preprocess_train(examples):\n preprocessed_images = preprocess_images(examples)\n\n original_images, edited_images = preprocessed_images.chunk(2)\n original_images = original_images.reshape(-1, 3, args.resolution, args.resolution)\n edited_images = edited_images.reshape(-1, 3, args.resolution, args.resolution)\n\n examples[\"original_pixel_values\"] = original_images\n examples[\"edited_pixel_values\"] = edited_images\n\n captions = list(examples[edit_prompt_column])\n examples[\"input_ids\"] = tokenize_captions(captions)\n return examples\n```\n\nFinally, in the [training loop](https://github.com/huggingface/diffusers/blob/64603389da01082055a901f2883c4810d1144edb/examples/instruct_pix2pix/train_instruct_pix2pix.py#L730), it starts by encoding the edited images into latent space:\n\n```py\nlatents = vae.encode(batch[\"edited_pixel_values\"].to(weight_dtype)).latent_dist.sample()\nlatents = latents * vae.config.scaling_factor\n```\n\nThen, the script applies dropout to the original image and edit instruction embeddings to support CFG. This is what enables the model to modulate the influence of the edit instruction and original image on the edited image.\n\n```py\nencoder_hidden_states = text_encoder(batch[\"input_ids\"])[0]\noriginal_image_embeds = vae.encode(batch[\"original_pixel_values\"].to(weight_dtype)).latent_dist.mode()\n\nif args.conditioning_dropout_prob is not None:\n random_p = torch.rand(bsz, device=latents.device, generator=generator)\n prompt_mask = random_p < 2 * args.conditioning_dropout_prob\n prompt_mask = prompt_mask.reshape(bsz, 1, 1)\n null_conditioning = text_encoder(tokenize_captions([\"\"]).to(accelerator.device))[0]\n encoder_hidden_states = torch.where(prompt_mask, null_conditioning, encoder_hidden_states)\n\n image_mask_dtype = original_image_embeds.dtype\n image_mask = 1 - (\n (random_p >= args.conditioning_dropout_prob).to(image_mask_dtype)\n * (random_p < 3 * args.conditioning_dropout_prob).to(image_mask_dtype)\n )\n image_mask = image_mask.reshape(bsz, 1, 1, 1)\n original_image_embeds = image_mask * original_image_embeds\n```\n\nThat's pretty much it! Aside from the differences described here, the rest of the script is very similar to the [Text-to-image](text2image#training-script) training script, so feel free to check it out for more details. If you want to learn more about how the training loop works, check out the [Understanding pipelines, models and schedulers](../using-diffusers/write_own_pipeline) tutorial which breaks down the basic pattern of the denoising process.\n\n## Launch the script\n\nOnce you're happy with the changes to your script or if you're okay with the default configuration, you're ready to launch the training script! 🚀\n\nThis guide uses the [fusing/instructpix2pix-1000-samples](https://huggingface.co/datasets/fusing/instructpix2pix-1000-samples) dataset, which is a smaller version of the [original dataset](https://huggingface.co/datasets/timbrooks/instructpix2pix-clip-filtered). You can also create and use your own dataset if you'd like (see the [Create a dataset for training](create_dataset) guide).\n\nSet the `MODEL_NAME` environment variable to the name of the model (can be a model id on the Hub or a path to a local model), and the `DATASET_ID` to the name of the dataset on the Hub. The script creates and saves all the components (feature extractor, scheduler, text encoder, UNet, etc.) to a subfolder in your repository.\n\n\n\nFor better results, try longer training runs with a larger dataset. We've only tested this training script on a smaller-scale dataset.\n\n
\n\nTo monitor training progress with Weights and Biases, add the `--report_to=wandb` parameter to the training command and specify a validation image with `--val_image_url` and a validation prompt with `--validation_prompt`. This can be really useful for debugging the model.\n\n\n\nIf you’re training on more than one GPU, add the `--multi_gpu` parameter to the `accelerate launch` command.\n\n```bash\naccelerate launch --mixed_precision=\"fp16\" train_instruct_pix2pix.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --dataset_name=$DATASET_ID \\\n --enable_xformers_memory_efficient_attention \\\n --resolution=256 \\\n --random_flip \\\n --train_batch_size=4 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --max_train_steps=15000 \\\n --checkpointing_steps=5000 \\\n --checkpoints_total_limit=1 \\\n --learning_rate=5e-05 \\\n --max_grad_norm=1 \\\n --lr_warmup_steps=0 \\\n --conditioning_dropout_prob=0.05 \\\n --mixed_precision=fp16 \\\n --seed=42 \\\n --push_to_hub\n```\n\nAfter training is finished, you can use your new InstructPix2Pix for inference:\n\n```py\nimport PIL\nimport requests\nimport torch\nfrom diffusers import StableDiffusionInstructPix2PixPipeline\nfrom diffusers.utils import load_image\n\npipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained(\"your_cool_model\", torch_dtype=torch.float16).to(\"cuda\")\ngenerator = torch.Generator(\"cuda\").manual_seed(0)\n\nimage = load_image(\"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/test_pix2pix_4.png\")\nprompt = \"add some ducks to the lake\"\nnum_inference_steps = 20\nimage_guidance_scale = 1.5\nguidance_scale = 10\n\nedited_image = pipeline(\n prompt,\n image=image,\n num_inference_steps=num_inference_steps,\n image_guidance_scale=image_guidance_scale,\n guidance_scale=guidance_scale,\n generator=generator,\n).images[0]\nedited_image.save(\"edited_image.png\")\n```\n\nYou should experiment with different `num_inference_steps`, `image_guidance_scale`, and `guidance_scale` values to see how they affect inference speed and quality. The guidance scale parameters are especially impactful because they control how much the original image and edit instructions affect the edited image.\n\n## Stable Diffusion XL\n\nStable Diffusion XL (SDXL) is a powerful text-to-image model that generates high-resolution images, and it adds a second text-encoder to its architecture. Use the [`train_instruct_pix2pix_sdxl.py`](https://github.com/huggingface/diffusers/blob/main/examples/instruct_pix2pix/train_instruct_pix2pix_sdxl.py) script to train a SDXL model to follow image editing instructions.\n\nThe SDXL training script is discussed in more detail in the [SDXL training](sdxl) guide.\n\n## Next steps\n\nCongratulations on training your own InstructPix2Pix model! 🥳 To learn more about the model, it may be helpful to:\n\n- Read the [Instruction-tuning Stable Diffusion with InstructPix2Pix](https://huggingface.co/blog/instruction-tuning-sd) blog post to learn more about some experiments we've done with InstructPix2Pix, dataset preparation, and results for different instructions." }, { "path": "diffusers/docs/source/en/training/kandinsky.md", "content": "\n\n# Kandinsky 2.2\n\n\n\nThis script is experimental, and it's easy to overfit and run into issues like catastrophic forgetting. Try exploring different hyperparameters to get the best results on your dataset.\n\n\n\nKandinsky 2.2 is a multilingual text-to-image model capable of producing more photorealistic images. The model includes an image prior model for creating image embeddings from text prompts, and a decoder model that generates images based on the prior model's embeddings. That's why you'll find two separate scripts in Diffusers for Kandinsky 2.2, one for training the prior model and one for training the decoder model. You can train both models separately, but to get the best results, you should train both the prior and decoder models.\n\nDepending on your GPU, you may need to enable `gradient_checkpointing` (⚠️ not supported for the prior model!), `mixed_precision`, and `gradient_accumulation_steps` to help fit the model into memory and to speedup training. You can reduce your memory-usage even more by enabling memory-efficient attention with [xFormers](../optimization/xformers) (version [v0.0.16](https://github.com/huggingface/diffusers/issues/2234#issuecomment-1416931212) fails for training on some GPUs so you may need to install a development version instead).\n\nThis guide explores the [train_text_to_image_prior.py](https://github.com/huggingface/diffusers/blob/main/examples/kandinsky2_2/text_to_image/train_text_to_image_prior.py) and the [train_text_to_image_decoder.py](https://github.com/huggingface/diffusers/blob/main/examples/kandinsky2_2/text_to_image/train_text_to_image_decoder.py) scripts to help you become more familiar with it, and how you can adapt it for your own use-case.\n\nBefore running the scripts, make sure you install the library from source:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nThen navigate to the example folder containing the training script and install the required dependencies for the script you're using:\n\n```bash\ncd examples/kandinsky2_2/text_to_image\npip install -r requirements.txt\n```\n\n\n\n🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co/docs/accelerate/quicktour) to learn more.\n\n\n\nInitialize an 🤗 Accelerate environment:\n\n```bash\naccelerate config\n```\n\nTo setup a default 🤗 Accelerate environment without choosing any configurations:\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell, like a notebook, you can use:\n\n```py\nfrom accelerate.utils import write_basic_config\n\nwrite_basic_config()\n```\n\nLastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.\n\n\n\nThe following sections highlight parts of the training scripts that are important for understanding how to modify it, but it doesn't cover every aspect of the scripts in detail. If you're interested in learning more, feel free to read through the scripts and let us know if you have any questions or concerns.\n\n\n\n## Script parameters\n\nThe training scripts provides many parameters to help you customize your training run. All of the parameters and their descriptions are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/kandinsky2_2/text_to_image/train_text_to_image_prior.py#L190) function. The training scripts provides default values for each parameter, such as the training batch size and learning rate, but you can also set your own values in the training command if you'd like.\n\nFor example, to speedup training with mixed precision using the fp16 format, add the `--mixed_precision` parameter to the training command:\n\n```bash\naccelerate launch train_text_to_image_prior.py \\\n --mixed_precision=\"fp16\"\n```\n\nMost of the parameters are identical to the parameters in the [Text-to-image](text2image#script-parameters) training guide, so let's get straight to a walkthrough of the Kandinsky training scripts!\n\n### Min-SNR weighting\n\nThe [Min-SNR](https://huggingface.co/papers/2303.09556) weighting strategy can help with training by rebalancing the loss to achieve faster convergence. The training script supports predicting `epsilon` (noise) or `v_prediction`, but Min-SNR is compatible with both prediction types. This weighting strategy is only supported by PyTorch and is unavailable in the Flax training script.\n\nAdd the `--snr_gamma` parameter and set it to the recommended value of 5.0:\n\n```bash\naccelerate launch train_text_to_image_prior.py \\\n --snr_gamma=5.0\n```\n\n## Training script\n\nThe training script is also similar to the [Text-to-image](text2image#training-script) training guide, but it's been modified to support training the prior and decoder models. This guide focuses on the code that is unique to the Kandinsky 2.2 training scripts.\n\n\n\n\nThe [`main()`](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/kandinsky2_2/text_to_image/train_text_to_image_prior.py#L441) function contains the code for preparing the dataset and training the model.\n\nOne of the main differences you'll notice right away is that the training script also loads a [`~transformers.CLIPImageProcessor`] - in addition to a scheduler and tokenizer - for preprocessing images and a [`~transformers.CLIPVisionModelWithProjection`] model for encoding the images:\n\n```py\nnoise_scheduler = DDPMScheduler(beta_schedule=\"squaredcos_cap_v2\", prediction_type=\"sample\")\nimage_processor = CLIPImageProcessor.from_pretrained(\n args.pretrained_prior_model_name_or_path, subfolder=\"image_processor\"\n)\ntokenizer = CLIPTokenizer.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder=\"tokenizer\")\n\nwith ContextManagers(deepspeed_zero_init_disabled_context_manager()):\n image_encoder = CLIPVisionModelWithProjection.from_pretrained(\n args.pretrained_prior_model_name_or_path, subfolder=\"image_encoder\", torch_dtype=weight_dtype\n ).eval()\n text_encoder = CLIPTextModelWithProjection.from_pretrained(\n args.pretrained_prior_model_name_or_path, subfolder=\"text_encoder\", torch_dtype=weight_dtype\n ).eval()\n```\n\nKandinsky uses a [`PriorTransformer`] to generate the image embeddings, so you'll want to setup the optimizer to learn the prior mode's parameters.\n\n```py\nprior = PriorTransformer.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder=\"prior\")\nprior.train()\noptimizer = optimizer_cls(\n prior.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n)\n```\n\nNext, the input captions are tokenized, and images are [preprocessed](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/kandinsky2_2/text_to_image/train_text_to_image_prior.py#L632) by the [`~transformers.CLIPImageProcessor`]:\n\n```py\ndef preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n examples[\"clip_pixel_values\"] = image_processor(images, return_tensors=\"pt\").pixel_values\n examples[\"text_input_ids\"], examples[\"text_mask\"] = tokenize_captions(examples)\n return examples\n```\n\nFinally, the [training loop](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/kandinsky2_2/text_to_image/train_text_to_image_prior.py#L718) converts the input images into latents, adds noise to the image embeddings, and makes a prediction:\n\n```py\nmodel_pred = prior(\n noisy_latents,\n timestep=timesteps,\n proj_embedding=prompt_embeds,\n encoder_hidden_states=text_encoder_hidden_states,\n attention_mask=text_mask,\n).predicted_image_embedding\n```\n\nIf you want to learn more about how the training loop works, check out the [Understanding pipelines, models and schedulers](../using-diffusers/write_own_pipeline) tutorial which breaks down the basic pattern of the denoising process.\n\n\n\n\nThe [`main()`](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/kandinsky2_2/text_to_image/train_text_to_image_decoder.py#L440) function contains the code for preparing the dataset and training the model.\n\nUnlike the prior model, the decoder initializes a [`VQModel`] to decode the latents into images and it uses a [`UNet2DConditionModel`]:\n\n```py\nwith ContextManagers(deepspeed_zero_init_disabled_context_manager()):\n vae = VQModel.from_pretrained(\n args.pretrained_decoder_model_name_or_path, subfolder=\"movq\", torch_dtype=weight_dtype\n ).eval()\n image_encoder = CLIPVisionModelWithProjection.from_pretrained(\n args.pretrained_prior_model_name_or_path, subfolder=\"image_encoder\", torch_dtype=weight_dtype\n ).eval()\nunet = UNet2DConditionModel.from_pretrained(args.pretrained_decoder_model_name_or_path, subfolder=\"unet\")\n```\n\nNext, the script includes several image transforms and a [preprocessing](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/kandinsky2_2/text_to_image/train_text_to_image_decoder.py#L622) function for applying the transforms to the images and returning the pixel values:\n\n```py\ndef preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n examples[\"pixel_values\"] = [train_transforms(image) for image in images]\n examples[\"clip_pixel_values\"] = image_processor(images, return_tensors=\"pt\").pixel_values\n return examples\n```\n\nLastly, the [training loop](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/kandinsky2_2/text_to_image/train_text_to_image_decoder.py#L706) handles converting the images to latents, adding noise, and predicting the noise residual.\n\nIf you want to learn more about how the training loop works, check out the [Understanding pipelines, models and schedulers](../using-diffusers/write_own_pipeline) tutorial which breaks down the basic pattern of the denoising process.\n\n```py\nmodel_pred = unet(noisy_latents, timesteps, None, added_cond_kwargs=added_cond_kwargs).sample[:, :4]\n```\n\n\n\n\n## Launch the script\n\nOnce you’ve made all your changes or you’re okay with the default configuration, you’re ready to launch the training script! 🚀\n\nYou'll train on the [Naruto BLIP captions](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions) dataset to generate your own Naruto characters, but you can also create and train on your own dataset by following the [Create a dataset for training](create_dataset) guide. Set the environment variable `DATASET_NAME` to the name of the dataset on the Hub or if you're training on your own files, set the environment variable `TRAIN_DIR` to a path to your dataset.\n\nIf you’re training on more than one GPU, add the `--multi_gpu` parameter to the `accelerate launch` command.\n\n\n\nTo monitor training progress with Weights & Biases, add the `--report_to=wandb` parameter to the training command. You’ll also need to add the `--validation_prompt` to the training command to keep track of results. This can be really useful for debugging the model and viewing intermediate results.\n\n\n\n\n\n\n```bash\nexport DATASET_NAME=\"lambdalabs/naruto-blip-captions\"\n\naccelerate launch --mixed_precision=\"fp16\" train_text_to_image_prior.py \\\n --dataset_name=$DATASET_NAME \\\n --resolution=768 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --max_train_steps=15000 \\\n --learning_rate=1e-05 \\\n --max_grad_norm=1 \\\n --checkpoints_total_limit=3 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --validation_prompts=\"A robot naruto, 4k photo\" \\\n --report_to=\"wandb\" \\\n --push_to_hub \\\n --output_dir=\"kandi2-prior-naruto-model\"\n```\n\n\n\n\n```bash\nexport DATASET_NAME=\"lambdalabs/naruto-blip-captions\"\n\naccelerate launch --mixed_precision=\"fp16\" train_text_to_image_decoder.py \\\n --dataset_name=$DATASET_NAME \\\n --resolution=768 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --max_train_steps=15000 \\\n --learning_rate=1e-05 \\\n --max_grad_norm=1 \\\n --checkpoints_total_limit=3 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --validation_prompts=\"A robot naruto, 4k photo\" \\\n --report_to=\"wandb\" \\\n --push_to_hub \\\n --output_dir=\"kandi2-decoder-naruto-model\"\n```\n\n\n\n\nOnce training is finished, you can use your newly trained model for inference!\n\n\n\n\n```py\nfrom diffusers import AutoPipelineForText2Image, DiffusionPipeline\nimport torch\n\nprior_pipeline = DiffusionPipeline.from_pretrained(output_dir, torch_dtype=torch.float16)\nprior_components = {\"prior_\" + k: v for k,v in prior_pipeline.components.items()}\npipeline = AutoPipelineForText2Image.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\", **prior_components, torch_dtype=torch.float16)\n\npipe.enable_model_cpu_offload()\nprompt=\"A robot naruto, 4k photo\"\nimage = pipeline(prompt=prompt, negative_prompt=negative_prompt).images[0]\n```\n\n\n\nFeel free to replace `kandinsky-community/kandinsky-2-2-decoder` with your own trained decoder checkpoint!\n\n\n\n\n\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\"path/to/saved/model\", torch_dtype=torch.float16)\npipeline.enable_model_cpu_offload()\n\nprompt=\"A robot naruto, 4k photo\"\nimage = pipeline(prompt=prompt).images[0]\n```\n\nFor the decoder model, you can also perform inference from a saved checkpoint which can be useful for viewing intermediate results. In this case, load the checkpoint into the UNet:\n\n```py\nfrom diffusers import AutoPipelineForText2Image, UNet2DConditionModel\n\nunet = UNet2DConditionModel.from_pretrained(\"path/to/saved/model\" + \"/checkpoint-/unet\")\n\npipeline = AutoPipelineForText2Image.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\", unet=unet, torch_dtype=torch.float16)\npipeline.enable_model_cpu_offload()\n\nimage = pipeline(prompt=\"A robot naruto, 4k photo\").images[0]\n```\n\n\n\n\n## Next steps\n\nCongratulations on training a Kandinsky 2.2 model! To learn more about how to use your new model, the following guides may be helpful:\n\n- Read the [Kandinsky](../using-diffusers/kandinsky) guide to learn how to use it for a variety of different tasks (text-to-image, image-to-image, inpainting, interpolation), and how it can be combined with a ControlNet.\n- Check out the [DreamBooth](dreambooth) and [LoRA](lora) training guides to learn how to train a personalized Kandinsky model with just a few example images. These two training techniques can even be combined!\n" }, { "path": "diffusers/docs/source/en/training/lcm_distill.md", "content": "\n\n# Latent Consistency Distillation\n\n[Latent Consistency Models (LCMs)](https://hf.co/papers/2310.04378) are able to generate high-quality images in just a few steps, representing a big leap forward because many pipelines require at least 25+ steps. LCMs are produced by applying the latent consistency distillation method to any Stable Diffusion model. This method works by applying *one-stage guided distillation* to the latent space, and incorporating a *skipping-step* method to consistently skip timesteps to accelerate the distillation process (refer to section 4.1, 4.2, and 4.3 of the paper for more details).\n\nIf you're training on a GPU with limited vRAM, try enabling `gradient_checkpointing`, `gradient_accumulation_steps`, and `mixed_precision` to reduce memory-usage and speedup training. You can reduce your memory-usage even more by enabling memory-efficient attention with [xFormers](../optimization/xformers) and [bitsandbytes'](https://github.com/TimDettmers/bitsandbytes) 8-bit optimizer.\n\nThis guide will explore the [train_lcm_distill_sd_wds.py](https://github.com/huggingface/diffusers/blob/main/examples/consistency_distillation/train_lcm_distill_sd_wds.py) script to help you become more familiar with it, and how you can adapt it for your own use-case.\n\nBefore running the script, make sure you install the library from source:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nThen navigate to the example folder containing the training script and install the required dependencies for the script you're using:\n\n```bash\ncd examples/consistency_distillation\npip install -r requirements.txt\n```\n\n\n\n🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co/docs/accelerate/quicktour) to learn more.\n\n\n\nInitialize an 🤗 Accelerate environment (try enabling `torch.compile` to significantly speedup training):\n\n```bash\naccelerate config\n```\n\nTo setup a default 🤗 Accelerate environment without choosing any configurations:\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell, like a notebook, you can use:\n\n```py\nfrom accelerate.utils import write_basic_config\n\nwrite_basic_config()\n```\n\nLastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.\n\n## Script parameters\n\n\n\nThe following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn't cover every aspect of the script in detail. If you're interested in learning more, feel free to read through the [script](https://github.com/huggingface/diffusers/blob/main/examples/consistency_distillation/train_lcm_distill_sd_wds.py) and let us know if you have any questions or concerns.\n\n\n\nThe training script provides many parameters to help you customize your training run. All of the parameters and their descriptions are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/3b37488fa3280aed6a95de044d7a42ffdcb565ef/examples/consistency_distillation/train_lcm_distill_sd_wds.py#L419) function. This function provides default values for each parameter, such as the training batch size and learning rate, but you can also set your own values in the training command if you'd like.\n\nFor example, to speedup training with mixed precision using the fp16 format, add the `--mixed_precision` parameter to the training command:\n\n```bash\naccelerate launch train_lcm_distill_sd_wds.py \\\n --mixed_precision=\"fp16\"\n```\n\nMost of the parameters are identical to the parameters in the [Text-to-image](text2image#script-parameters) training guide, so you'll focus on the parameters that are relevant to latent consistency distillation in this guide.\n\n- `--pretrained_teacher_model`: the path to a pretrained latent diffusion model to use as the teacher model\n- `--pretrained_vae_model_name_or_path`: path to a pretrained VAE; the SDXL VAE is known to suffer from numerical instability, so this parameter allows you to specify an alternative VAE (like this [VAE]((https://huggingface.co/madebyollin/sdxl-vae-fp16-fix)) by madebyollin which works in fp16)\n- `--w_min` and `--w_max`: the minimum and maximum guidance scale values for guidance scale sampling\n- `--num_ddim_timesteps`: the number of timesteps for DDIM sampling\n- `--loss_type`: the type of loss (L2 or Huber) to calculate for latent consistency distillation; Huber loss is generally preferred because it's more robust to outliers\n- `--huber_c`: the Huber loss parameter\n\n## Training script\n\nThe training script starts by creating a dataset class - [`Text2ImageDataset`](https://github.com/huggingface/diffusers/blob/3b37488fa3280aed6a95de044d7a42ffdcb565ef/examples/consistency_distillation/train_lcm_distill_sd_wds.py#L141) - for preprocessing the images and creating a training dataset.\n\n```py\ndef transform(example):\n image = example[\"image\"]\n image = TF.resize(image, resolution, interpolation=transforms.InterpolationMode.BILINEAR)\n\n c_top, c_left, _, _ = transforms.RandomCrop.get_params(image, output_size=(resolution, resolution))\n image = TF.crop(image, c_top, c_left, resolution, resolution)\n image = TF.to_tensor(image)\n image = TF.normalize(image, [0.5], [0.5])\n\n example[\"image\"] = image\n return example\n```\n\nFor improved performance on reading and writing large datasets stored in the cloud, this script uses the [WebDataset](https://github.com/webdataset/webdataset) format to create a preprocessing pipeline to apply transforms and create a dataset and dataloader for training. Images are processed and fed to the training loop without having to download the full dataset first.\n\n```py\nprocessing_pipeline = [\n wds.decode(\"pil\", handler=wds.ignore_and_continue),\n wds.rename(image=\"jpg;png;jpeg;webp\", text=\"text;txt;caption\", handler=wds.warn_and_continue),\n wds.map(filter_keys({\"image\", \"text\"})),\n wds.map(transform),\n wds.to_tuple(\"image\", \"text\"),\n]\n```\n\nIn the [`main()`](https://github.com/huggingface/diffusers/blob/3b37488fa3280aed6a95de044d7a42ffdcb565ef/examples/consistency_distillation/train_lcm_distill_sd_wds.py#L768) function, all the necessary components like the noise scheduler, tokenizers, text encoders, and VAE are loaded. The teacher UNet is also loaded here and then you can create a student UNet from the teacher UNet. The student UNet is updated by the optimizer during training.\n\n```py\nteacher_unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"unet\", revision=args.teacher_revision\n)\n\nunet = UNet2DConditionModel(**teacher_unet.config)\nunet.load_state_dict(teacher_unet.state_dict(), strict=False)\nunet.train()\n```\n\nNow you can create the [optimizer](https://github.com/huggingface/diffusers/blob/3b37488fa3280aed6a95de044d7a42ffdcb565ef/examples/consistency_distillation/train_lcm_distill_sd_wds.py#L979) to update the UNet parameters:\n\n```py\noptimizer = optimizer_class(\n unet.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n)\n```\n\nCreate the [dataset](https://github.com/huggingface/diffusers/blob/3b37488fa3280aed6a95de044d7a42ffdcb565ef/examples/consistency_distillation/train_lcm_distill_sd_wds.py#L994):\n\n```py\ndataset = Text2ImageDataset(\n train_shards_path_or_url=args.train_shards_path_or_url,\n num_train_examples=args.max_train_samples,\n per_gpu_batch_size=args.train_batch_size,\n global_batch_size=args.train_batch_size * accelerator.num_processes,\n num_workers=args.dataloader_num_workers,\n resolution=args.resolution,\n shuffle_buffer_size=1000,\n pin_memory=True,\n persistent_workers=True,\n)\ntrain_dataloader = dataset.train_dataloader\n```\n\nNext, you're ready to setup the [training loop](https://github.com/huggingface/diffusers/blob/3b37488fa3280aed6a95de044d7a42ffdcb565ef/examples/consistency_distillation/train_lcm_distill_sd_wds.py#L1049) and implement the latent consistency distillation method (see Algorithm 1 in the paper for more details). This section of the script takes care of adding noise to the latents, sampling and creating a guidance scale embedding, and predicting the original image from the noise.\n\n```py\npred_x_0 = predicted_origin(\n noise_pred,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n)\n\nmodel_pred = c_skip_start * noisy_model_input + c_out_start * pred_x_0\n```\n\nIt gets the [teacher model predictions](https://github.com/huggingface/diffusers/blob/3b37488fa3280aed6a95de044d7a42ffdcb565ef/examples/consistency_distillation/train_lcm_distill_sd_wds.py#L1172) and the [LCM predictions](https://github.com/huggingface/diffusers/blob/3b37488fa3280aed6a95de044d7a42ffdcb565ef/examples/consistency_distillation/train_lcm_distill_sd_wds.py#L1209) next, calculates the loss, and then backpropagates it to the LCM.\n\n```py\nif args.loss_type == \"l2\":\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\nelif args.loss_type == \"huber\":\n loss = torch.mean(\n torch.sqrt((model_pred.float() - target.float()) ** 2 + args.huber_c**2) - args.huber_c\n )\n```\n\nIf you want to learn more about how the training loop works, check out the [Understanding pipelines, models and schedulers tutorial](../using-diffusers/write_own_pipeline) which breaks down the basic pattern of the denoising process.\n\n## Launch the script\n\nNow you're ready to launch the training script and start distilling!\n\nFor this guide, you'll use the `--train_shards_path_or_url` to specify the path to the [Conceptual Captions 12M](https://github.com/google-research-datasets/conceptual-12m) dataset stored on the Hub [here](https://huggingface.co/datasets/laion/conceptual-captions-12m-webdataset). Set the `MODEL_DIR` environment variable to the name of the teacher model and `OUTPUT_DIR` to where you want to save the model.\n\n```bash\nexport MODEL_DIR=\"runwayml/stable-diffusion-v1-5\"\nexport OUTPUT_DIR=\"path/to/saved/model\"\n\naccelerate launch train_lcm_distill_sd_wds.py \\\n --pretrained_teacher_model=$MODEL_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --mixed_precision=fp16 \\\n --resolution=512 \\\n --learning_rate=1e-6 --loss_type=\"huber\" --ema_decay=0.95 --adam_weight_decay=0.0 \\\n --max_train_steps=1000 \\\n --max_train_samples=4000000 \\\n --dataloader_num_workers=8 \\\n --train_shards_path_or_url=\"pipe:curl -L -s https://huggingface.co/datasets/laion/conceptual-captions-12m-webdataset/resolve/main/data/{00000..01099}.tar?download=true\" \\\n --validation_steps=200 \\\n --checkpointing_steps=200 --checkpoints_total_limit=10 \\\n --train_batch_size=12 \\\n --gradient_checkpointing --enable_xformers_memory_efficient_attention \\\n --gradient_accumulation_steps=1 \\\n --use_8bit_adam \\\n --resume_from_checkpoint=latest \\\n --report_to=wandb \\\n --seed=453645634 \\\n --push_to_hub\n```\n\nOnce training is complete, you can use your new LCM for inference.\n\n```py\nfrom diffusers import UNet2DConditionModel, DiffusionPipeline, LCMScheduler\nimport torch\n\nunet = UNet2DConditionModel.from_pretrained(\"your-username/your-model\", torch_dtype=torch.float16, variant=\"fp16\")\npipeline = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", unet=unet, torch_dtype=torch.float16, variant=\"fp16\")\n\npipeline.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\npipeline.to(\"cuda\")\n\nprompt = \"sushi rolls in the form of panda heads, sushi platter\"\n\nimage = pipeline(prompt, num_inference_steps=4, guidance_scale=1.0).images[0]\n```\n\n## LoRA\n\nLoRA is a training technique for significantly reducing the number of trainable parameters. As a result, training is faster and it is easier to store the resulting weights because they are a lot smaller (~100MBs). Use the [train_lcm_distill_lora_sd_wds.py](https://github.com/huggingface/diffusers/blob/main/examples/consistency_distillation/train_lcm_distill_lora_sd_wds.py) or [train_lcm_distill_lora_sdxl.wds.py](https://github.com/huggingface/diffusers/blob/main/examples/consistency_distillation/train_lcm_distill_lora_sdxl_wds.py) script to train with LoRA.\n\nThe LoRA training script is discussed in more detail in the [LoRA training](lora) guide.\n\n## Stable Diffusion XL\n\nStable Diffusion XL (SDXL) is a powerful text-to-image model that generates high-resolution images, and it adds a second text-encoder to its architecture. Use the [train_lcm_distill_sdxl_wds.py](https://github.com/huggingface/diffusers/blob/main/examples/consistency_distillation/train_lcm_distill_sdxl_wds.py) script to train a SDXL model with LoRA.\n\nThe SDXL training script is discussed in more detail in the [SDXL training](sdxl) guide.\n\n## Next steps\n\nCongratulations on distilling a LCM model! To learn more about LCM, the following may be helpful:\n\n- Learn how to use [LCMs for inference](../using-diffusers/lcm) for text-to-image, image-to-image, and with LoRA checkpoints.\n- Read the [SDXL in 4 steps with Latent Consistency LoRAs](https://huggingface.co/blog/lcm_lora) blog post to learn more about SDXL LCM-LoRA's for super fast inference, quality comparisons, benchmarks, and more.\n" }, { "path": "diffusers/docs/source/en/training/lora.md", "content": "\n\n# LoRA\n\n\n\nThis is experimental and the API may change in the future.\n\n\n\n[LoRA (Low-Rank Adaptation of Large Language Models)](https://hf.co/papers/2106.09685) is a popular and lightweight training technique that significantly reduces the number of trainable parameters. It works by inserting a smaller number of new weights into the model and only these are trained. This makes training with LoRA much faster, memory-efficient, and produces smaller model weights (a few hundred MBs), which are easier to store and share. LoRA can also be combined with other training techniques like DreamBooth to speedup training.\n\n\n\nLoRA is very versatile and supported for [DreamBooth](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora.py), [Kandinsky 2.2](https://github.com/huggingface/diffusers/blob/main/examples/kandinsky2_2/text_to_image/train_text_to_image_lora_decoder.py), [Stable Diffusion XL](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora_sdxl.py), [text-to-image](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py), and [Wuerstchen](https://github.com/huggingface/diffusers/blob/main/examples/wuerstchen/text_to_image/train_text_to_image_lora_prior.py).\n\n\n\nThis guide will explore the [train_text_to_image_lora.py](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py) script to help you become more familiar with it, and how you can adapt it for your own use-case.\n\nBefore running the script, make sure you install the library from source:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nNavigate to the example folder with the training script and install the required dependencies for the script you're using:\n\n\n\n\n```bash\ncd examples/text_to_image\npip install -r requirements.txt\n```\n\n\n\n\n```bash\ncd examples/text_to_image\npip install -r requirements_flax.txt\n```\n\n\n\n\n\n\n🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co/docs/accelerate/quicktour) to learn more.\n\n\n\nInitialize an 🤗 Accelerate environment:\n\n```bash\naccelerate config\n```\n\nTo setup a default 🤗 Accelerate environment without choosing any configurations:\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell, like a notebook, you can use:\n\n```py\nfrom accelerate.utils import write_basic_config\n\nwrite_basic_config()\n```\n\nLastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.\n\n\n\nThe following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn't cover every aspect of the script in detail. If you're interested in learning more, feel free to read through the [script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/text_to_image_lora.py) and let us know if you have any questions or concerns.\n\n\n\n## Script parameters\n\nThe training script has many parameters to help you customize your training run. All of the parameters and their descriptions are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/dd9a5caf61f04d11c0fa9f3947b69ab0010c9a0f/examples/text_to_image/train_text_to_image_lora.py#L85) function. Default values are provided for most parameters that work pretty well, but you can also set your own values in the training command if you'd like.\n\nFor example, to increase the number of epochs to train:\n\n```bash\naccelerate launch train_text_to_image_lora.py \\\n --num_train_epochs=150 \\\n```\n\nMany of the basic and important parameters are described in the [Text-to-image](text2image#script-parameters) training guide, so this guide just focuses on the LoRA relevant parameters:\n\n- `--rank`: the inner dimension of the low-rank matrices to train; a higher rank means more trainable parameters\n- `--learning_rate`: the default learning rate is 1e-4, but with LoRA, you can use a higher learning rate\n\n## Training script\n\nThe dataset preprocessing code and training loop are found in the [`main()`](https://github.com/huggingface/diffusers/blob/dd9a5caf61f04d11c0fa9f3947b69ab0010c9a0f/examples/text_to_image/train_text_to_image_lora.py#L371) function, and if you need to adapt the training script, this is where you'll make your changes.\n\nAs with the script parameters, a walkthrough of the training script is provided in the [Text-to-image](text2image#training-script) training guide. Instead, this guide takes a look at the LoRA relevant parts of the script.\n\n\n\n\nDiffusers uses [`~peft.LoraConfig`] from the [PEFT](https://hf.co/docs/peft) library to set up the parameters of the LoRA adapter such as the rank, alpha, and which modules to insert the LoRA weights into. The adapter is added to the UNet, and only the LoRA layers are filtered for optimization in `lora_layers`.\n\n```py\nunet_lora_config = LoraConfig(\n r=args.rank,\n lora_alpha=args.rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"to_k\", \"to_q\", \"to_v\", \"to_out.0\"],\n)\n\nunet.add_adapter(unet_lora_config)\nlora_layers = filter(lambda p: p.requires_grad, unet.parameters())\n```\n\n\n\n\nDiffusers also supports finetuning the text encoder with LoRA from the [PEFT](https://hf.co/docs/peft) library when necessary such as finetuning Stable Diffusion XL (SDXL). The [`~peft.LoraConfig`] is used to configure the parameters of the LoRA adapter which are then added to the text encoder, and only the LoRA layers are filtered for training.\n\n```py\ntext_lora_config = LoraConfig(\n r=args.rank,\n lora_alpha=args.rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"out_proj\"],\n)\n\ntext_encoder_one.add_adapter(text_lora_config)\ntext_encoder_two.add_adapter(text_lora_config)\ntext_lora_parameters_one = list(filter(lambda p: p.requires_grad, text_encoder_one.parameters()))\ntext_lora_parameters_two = list(filter(lambda p: p.requires_grad, text_encoder_two.parameters()))\n```\n\n\n\n\nThe [optimizer](https://github.com/huggingface/diffusers/blob/e4b8f173b97731686e290b2eb98e7f5df2b1b322/examples/text_to_image/train_text_to_image_lora.py#L529) is initialized with the `lora_layers` because these are the only weights that'll be optimized:\n\n```py\noptimizer = optimizer_cls(\n lora_layers,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n)\n```\n\nAside from setting up the LoRA layers, the training script is more or less the same as train_text_to_image.py!\n\n## Launch the script\n\nOnce you've made all your changes or you're okay with the default configuration, you're ready to launch the training script! 🚀\n\nLet's train on the [Naruto BLIP captions](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions) dataset to generate your own Naruto characters. Set the environment variables `MODEL_NAME` and `DATASET_NAME` to the model and dataset respectively. You should also specify where to save the model in `OUTPUT_DIR`, and the name of the model to save to on the Hub with `HUB_MODEL_ID`. The script creates and saves the following files to your repository:\n\n- saved model checkpoints\n- `pytorch_lora_weights.safetensors` (the trained LoRA weights)\n\nIf you're training on more than one GPU, add the `--multi_gpu` parameter to the `accelerate launch` command.\n\n\n\nA full training run takes ~5 hours on a 2080 Ti GPU with 11GB of VRAM.\n\n\n\n```bash\nexport MODEL_NAME=\"runwayml/stable-diffusion-v1-5\"\nexport OUTPUT_DIR=\"/sddata/finetune/lora/naruto\"\nexport HUB_MODEL_ID=\"naruto-lora\"\nexport DATASET_NAME=\"lambdalabs/naruto-blip-captions\"\n\naccelerate launch --mixed_precision=\"fp16\" train_text_to_image_lora.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --dataset_name=$DATASET_NAME \\\n --dataloader_num_workers=8 \\\n --resolution=512 \\\n --center_crop \\\n --random_flip \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --max_train_steps=15000 \\\n --learning_rate=1e-04 \\\n --max_grad_norm=1 \\\n --lr_scheduler=\"cosine\" \\\n --lr_warmup_steps=0 \\\n --output_dir=${OUTPUT_DIR} \\\n --push_to_hub \\\n --hub_model_id=${HUB_MODEL_ID} \\\n --report_to=wandb \\\n --checkpointing_steps=500 \\\n --validation_prompt=\"A naruto with blue eyes.\" \\\n --seed=1337\n```\n\nOnce training has been completed, you can use your model for inference:\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16).to(\"cuda\")\npipeline.load_lora_weights(\"path/to/lora/model\", weight_name=\"pytorch_lora_weights.safetensors\")\nimage = pipeline(\"A naruto with blue eyes\").images[0]\n```\n\n## Next steps\n\nCongratulations on training a new model with LoRA! To learn more about how to use your new model, the following guides may be helpful:\n\n- Learn how to [load different LoRA formats](../using-diffusers/loading_adapters#LoRA) trained using community trainers like Kohya and TheLastBen.\n- Learn how to use and [combine multiple LoRA's](../tutorials/using_peft_for_inference) with PEFT for inference.\n" }, { "path": "diffusers/docs/source/en/training/overview.md", "content": "\n\n# Overview\n\n🤗 Diffusers provides a collection of training scripts for you to train your own diffusion models. You can find all of our training scripts in [diffusers/examples](https://github.com/huggingface/diffusers/tree/main/examples).\n\nEach training script is:\n\n- **Self-contained**: the training script does not depend on any local files, and all packages required to run the script are installed from the `requirements.txt` file.\n- **Easy-to-tweak**: the training scripts are an example of how to train a diffusion model for a specific task and won't work out-of-the-box for every training scenario. You'll likely need to adapt the training script for your specific use-case. To help you with that, we've fully exposed the data preprocessing code and the training loop so you can modify it for your own use.\n- **Beginner-friendly**: the training scripts are designed to be beginner-friendly and easy to understand, rather than including the latest state-of-the-art methods to get the best and most competitive results. Any training methods we consider too complex are purposefully left out.\n- **Single-purpose**: each training script is expressly designed for only one task to keep it readable and understandable.\n\nOur current collection of training scripts include:\n\n| Training | SDXL-support | LoRA-support | Flax-support |\n|---|---|---|---|\n| [unconditional image generation](https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) | | | |\n| [text-to-image](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image) | 👍 | 👍 | 👍 |\n| [textual inversion](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb) | | | 👍 |\n| [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb) | 👍 | 👍 | 👍 |\n| [ControlNet](https://github.com/huggingface/diffusers/tree/main/examples/controlnet) | 👍 | | 👍 |\n| [InstructPix2Pix](https://github.com/huggingface/diffusers/tree/main/examples/instruct_pix2pix) | 👍 | | |\n| [Custom Diffusion](https://github.com/huggingface/diffusers/tree/main/examples/custom_diffusion) | | | |\n| [T2I-Adapters](https://github.com/huggingface/diffusers/tree/main/examples/t2i_adapter) | 👍 | | |\n| [Kandinsky 2.2](https://github.com/huggingface/diffusers/tree/main/examples/kandinsky2_2/text_to_image) | | 👍 | |\n| [Wuerstchen](https://github.com/huggingface/diffusers/tree/main/examples/wuerstchen/text_to_image) | | 👍 | |\n\nThese examples are **actively** maintained, so please feel free to open an issue if they aren't working as expected. If you feel like another training example should be included, you're more than welcome to start a [Feature Request](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feature_request.md&title=) to discuss your feature idea with us and whether it meets our criteria of being self-contained, easy-to-tweak, beginner-friendly, and single-purpose.\n\n## Install\n\nMake sure you can successfully run the latest versions of the example scripts by installing the library from source in a new virtual environment:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nThen navigate to the folder of the training script (for example, [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth)) and install the `requirements.txt` file. Some training scripts have a specific requirement file for SDXL, LoRA or Flax. If you're using one of these scripts, make sure you install its corresponding requirements file.\n\n```bash\ncd examples/dreambooth\npip install -r requirements.txt\n# to train SDXL with DreamBooth\npip install -r requirements_sdxl.txt\n```\n\nTo speedup training and reduce memory-usage, we recommend:\n\n- using PyTorch 2.0 or higher to automatically use [scaled dot product attention](../optimization/torch2.0#scaled-dot-product-attention) during training (you don't need to make any changes to the training code)\n- installing [xFormers](../optimization/xformers) to enable memory-efficient attention" }, { "path": "diffusers/docs/source/en/training/sdxl.md", "content": "\n\n# Stable Diffusion XL\n\n\n\nThis script is experimental, and it's easy to overfit and run into issues like catastrophic forgetting. Try exploring different hyperparameters to get the best results on your dataset.\n\n\n\n[Stable Diffusion XL (SDXL)](https://hf.co/papers/2307.01952) is a larger and more powerful iteration of the Stable Diffusion model, capable of producing higher resolution images.\n\nSDXL's UNet is 3x larger and the model adds a second text encoder to the architecture. Depending on the hardware available to you, this can be very computationally intensive and it may not run on a consumer GPU like a Tesla T4. To help fit this larger model into memory and to speedup training, try enabling `gradient_checkpointing`, `mixed_precision`, and `gradient_accumulation_steps`. You can reduce your memory-usage even more by enabling memory-efficient attention with [xFormers](../optimization/xformers) and using [bitsandbytes'](https://github.com/TimDettmers/bitsandbytes) 8-bit optimizer.\n\nThis guide will explore the [train_text_to_image_sdxl.py](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py) training script to help you become more familiar with it, and how you can adapt it for your own use-case.\n\nBefore running the script, make sure you install the library from source:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nThen navigate to the example folder containing the training script and install the required dependencies for the script you're using:\n\n```bash\ncd examples/text_to_image\npip install -r requirements_sdxl.txt\n```\n\n\n\n🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co/docs/accelerate/quicktour) to learn more.\n\n\n\nInitialize an 🤗 Accelerate environment:\n\n```bash\naccelerate config\n```\n\nTo setup a default 🤗 Accelerate environment without choosing any configurations:\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell, like a notebook, you can use:\n\n```py\nfrom accelerate.utils import write_basic_config\n\nwrite_basic_config()\n```\n\nLastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.\n\n## Script parameters\n\n\n\nThe following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn't cover every aspect of the script in detail. If you're interested in learning more, feel free to read through the [script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py) and let us know if you have any questions or concerns.\n\n\n\nThe training script provides many parameters to help you customize your training run. All of the parameters and their descriptions are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/text_to_image/train_text_to_image_sdxl.py#L129) function. This function provides default values for each parameter, such as the training batch size and learning rate, but you can also set your own values in the training command if you'd like.\n\nFor example, to speedup training with mixed precision using the bf16 format, add the `--mixed_precision` parameter to the training command:\n\n```bash\naccelerate launch train_text_to_image_sdxl.py \\\n --mixed_precision=\"bf16\"\n```\n\nMost of the parameters are identical to the parameters in the [Text-to-image](text2image#script-parameters) training guide, so you'll focus on the parameters that are relevant to training SDXL in this guide.\n\n- `--pretrained_vae_model_name_or_path`: path to a pretrained VAE; the SDXL VAE is known to suffer from numerical instability, so this parameter allows you to specify a better [VAE](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix)\n- `--proportion_empty_prompts`: the proportion of image prompts to replace with empty strings\n- `--timestep_bias_strategy`: where (earlier vs. later) in the timestep to apply a bias, which can encourage the model to either learn low or high frequency details\n- `--timestep_bias_multiplier`: the weight of the bias to apply to the timestep\n- `--timestep_bias_begin`: the timestep to begin applying the bias\n- `--timestep_bias_end`: the timestep to end applying the bias\n- `--timestep_bias_portion`: the proportion of timesteps to apply the bias to\n\n### Min-SNR weighting\n\nThe [Min-SNR](https://huggingface.co/papers/2303.09556) weighting strategy can help with training by rebalancing the loss to achieve faster convergence. The training script supports predicting either `epsilon` (noise) or `v_prediction`, but Min-SNR is compatible with both prediction types. This weighting strategy is only supported by PyTorch and is unavailable in the Flax training script.\n\nAdd the `--snr_gamma` parameter and set it to the recommended value of 5.0:\n\n```bash\naccelerate launch train_text_to_image_sdxl.py \\\n --snr_gamma=5.0\n```\n\n## Training script\n\nThe training script is also similar to the [Text-to-image](text2image#training-script) training guide, but it's been modified to support SDXL training. This guide will focus on the code that is unique to the SDXL training script.\n\nIt starts by creating functions to [tokenize the prompts](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/text_to_image/train_text_to_image_sdxl.py#L478) to calculate the prompt embeddings, and to compute the image embeddings with the [VAE](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/text_to_image/train_text_to_image_sdxl.py#L519). Next, you'll a function to [generate the timesteps weights](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/text_to_image/train_text_to_image_sdxl.py#L531) depending on the number of timesteps and the timestep bias strategy to apply.\n\nWithin the [`main()`](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/text_to_image/train_text_to_image_sdxl.py#L572) function, in addition to loading a tokenizer, the script loads a second tokenizer and text encoder because the SDXL architecture uses two of each:\n\n```py\ntokenizer_one = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"tokenizer\", revision=args.revision, use_fast=False\n)\ntokenizer_two = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"tokenizer_2\", revision=args.revision, use_fast=False\n)\n\ntext_encoder_cls_one = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision\n)\ntext_encoder_cls_two = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision, subfolder=\"text_encoder_2\"\n)\n```\n\nThe [prompt and image embeddings](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/text_to_image/train_text_to_image_sdxl.py#L857) are computed first and kept in memory, which isn't typically an issue for a smaller dataset, but for larger datasets it can lead to memory problems. If this is the case, you should save the pre-computed embeddings to disk separately and load them into memory during the training process (see this [PR](https://github.com/huggingface/diffusers/pull/4505) for more discussion about this topic).\n\n```py\ntext_encoders = [text_encoder_one, text_encoder_two]\ntokenizers = [tokenizer_one, tokenizer_two]\ncompute_embeddings_fn = functools.partial(\n encode_prompt,\n text_encoders=text_encoders,\n tokenizers=tokenizers,\n proportion_empty_prompts=args.proportion_empty_prompts,\n caption_column=args.caption_column,\n)\n\ntrain_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint)\ntrain_dataset = train_dataset.map(\n compute_vae_encodings_fn,\n batched=True,\n batch_size=args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps,\n new_fingerprint=new_fingerprint_for_vae,\n)\n```\n\nAfter calculating the embeddings, the text encoder, VAE, and tokenizer are deleted to free up some memory:\n\n```py\ndel text_encoders, tokenizers, vae\ngc.collect()\ntorch.cuda.empty_cache()\n```\n\nFinally, the [training loop](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/text_to_image/train_text_to_image_sdxl.py#L943) takes care of the rest. If you chose to apply a timestep bias strategy, you'll see the timestep weights are calculated and added as noise:\n\n```py\nweights = generate_timestep_weights(args, noise_scheduler.config.num_train_timesteps).to(\n model_input.device\n )\n timesteps = torch.multinomial(weights, bsz, replacement=True).long()\n\nnoisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)\n```\n\nIf you want to learn more about how the training loop works, check out the [Understanding pipelines, models and schedulers](../using-diffusers/write_own_pipeline) tutorial which breaks down the basic pattern of the denoising process.\n\n## Launch the script\n\nOnce you’ve made all your changes or you’re okay with the default configuration, you’re ready to launch the training script! 🚀\n\nLet’s train on the [Naruto BLIP captions](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions) dataset to generate your own Naruto characters. Set the environment variables `MODEL_NAME` and `DATASET_NAME` to the model and the dataset (either from the Hub or a local path). You should also specify a VAE other than the SDXL VAE (either from the Hub or a local path) with `VAE_NAME` to avoid numerical instabilities.\n\n\n\nTo monitor training progress with Weights & Biases, add the `--report_to=wandb` parameter to the training command. You’ll also need to add the `--validation_prompt` and `--validation_epochs` to the training command to keep track of results. This can be really useful for debugging the model and viewing intermediate results.\n\n\n\n```bash\nexport MODEL_NAME=\"stabilityai/stable-diffusion-xl-base-1.0\"\nexport VAE_NAME=\"madebyollin/sdxl-vae-fp16-fix\"\nexport DATASET_NAME=\"lambdalabs/naruto-blip-captions\"\n\naccelerate launch train_text_to_image_sdxl.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --pretrained_vae_model_name_or_path=$VAE_NAME \\\n --dataset_name=$DATASET_NAME \\\n --enable_xformers_memory_efficient_attention \\\n --resolution=512 \\\n --center_crop \\\n --random_flip \\\n --proportion_empty_prompts=0.2 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --max_train_steps=10000 \\\n --use_8bit_adam \\\n --learning_rate=1e-06 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --mixed_precision=\"fp16\" \\\n --report_to=\"wandb\" \\\n --validation_prompt=\"a cute Sundar Pichai creature\" \\\n --validation_epochs 5 \\\n --checkpointing_steps=5000 \\\n --output_dir=\"sdxl-naruto-model\" \\\n --push_to_hub\n```\n\nAfter you've finished training, you can use your newly trained SDXL model for inference!\n\n\n\n\n```py\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipeline = DiffusionPipeline.from_pretrained(\"path/to/your/model\", torch_dtype=torch.float16).to(\"cuda\")\n\nprompt = \"A naruto with green eyes and red legs.\"\nimage = pipeline(prompt, num_inference_steps=30, guidance_scale=7.5).images[0]\nimage.save(\"naruto.png\")\n```\n\n\n\n\n[PyTorch XLA](https://pytorch.org/xla) allows you to run PyTorch on XLA devices such as TPUs, which can be faster. The initial warmup step takes longer because the model needs to be compiled and optimized. However, subsequent calls to the pipeline on an input **with the same length** as the original prompt are much faster because it can reuse the optimized graph.\n\n```py\nfrom diffusers import DiffusionPipeline\nimport torch\nimport torch_xla.core.xla_model as xm\n\ndevice = xm.xla_device()\npipeline = DiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\").to(device)\n\nprompt = \"A naruto with green eyes and red legs.\"\nstart = time()\nimage = pipeline(prompt, num_inference_steps=inference_steps).images[0]\nprint(f'Compilation time is {time()-start} sec')\nimage.save(\"naruto.png\")\n\nstart = time()\nimage = pipeline(prompt, num_inference_steps=inference_steps).images[0]\nprint(f'Inference time is {time()-start} sec after compilation')\n```\n\n\n\n\n## Next steps\n\nCongratulations on training a SDXL model! To learn more about how to use your new model, the following guides may be helpful:\n\n- Read the [Stable Diffusion XL](../using-diffusers/sdxl) guide to learn how to use it for a variety of different tasks (text-to-image, image-to-image, inpainting), how to use it's refiner model, and the different types of micro-conditionings.\n- Check out the [DreamBooth](dreambooth) and [LoRA](lora) training guides to learn how to train a personalized SDXL model with just a few example images. These two training techniques can even be combined!" }, { "path": "diffusers/docs/source/en/training/t2i_adapters.md", "content": "\n\n# T2I-Adapter\n\n[T2I-Adapter](https://hf.co/papers/2302.08453) is a lightweight adapter model that provides an additional conditioning input image (line art, canny, sketch, depth, pose) to better control image generation. It is similar to a ControlNet, but it is a lot smaller (~77M parameters and ~300MB file size) because its only inserts weights into the UNet instead of copying and training it.\n\nThe T2I-Adapter is only available for training with the Stable Diffusion XL (SDXL) model.\n\nThis guide will explore the [train_t2i_adapter_sdxl.py](https://github.com/huggingface/diffusers/blob/main/examples/t2i_adapter/train_t2i_adapter_sdxl.py) training script to help you become familiar with it, and how you can adapt it for your own use-case.\n\nBefore running the script, make sure you install the library from source:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nThen navigate to the example folder containing the training script and install the required dependencies for the script you're using:\n\n```bash\ncd examples/t2i_adapter\npip install -r requirements.txt\n```\n\n\n\n🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co/docs/accelerate/quicktour) to learn more.\n\n\n\nInitialize an 🤗 Accelerate environment:\n\n```bash\naccelerate config\n```\n\nTo setup a default 🤗 Accelerate environment without choosing any configurations:\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell, like a notebook, you can use:\n\n```py\nfrom accelerate.utils import write_basic_config\n\nwrite_basic_config()\n```\n\nLastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.\n\n\n\nThe following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn't cover every aspect of the script in detail. If you're interested in learning more, feel free to read through the [script](https://github.com/huggingface/diffusers/blob/main/examples/t2i_adapter/train_t2i_adapter_sdxl.py) and let us know if you have any questions or concerns.\n\n\n\n## Script parameters\n\nThe training script provides many parameters to help you customize your training run. All of the parameters and their descriptions are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/t2i_adapter/train_t2i_adapter_sdxl.py#L233) function. It provides default values for each parameter, such as the training batch size and learning rate, but you can also set your own values in the training command if you'd like.\n\nFor example, to activate gradient accumulation, add the `--gradient_accumulation_steps` parameter to the training command:\n\n```bash\naccelerate launch train_t2i_adapter_sdxl.py \\\n ----gradient_accumulation_steps=4\n```\n\nMany of the basic and important parameters are described in the [Text-to-image](text2image#script-parameters) training guide, so this guide just focuses on the relevant T2I-Adapter parameters:\n\n- `--pretrained_vae_model_name_or_path`: path to a pretrained VAE; the SDXL VAE is known to suffer from numerical instability, so this parameter allows you to specify a better [VAE](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix)\n- `--crops_coords_top_left_h` and `--crops_coords_top_left_w`: height and width coordinates to include in SDXL's crop coordinate embeddings\n- `--conditioning_image_column`: the column of the conditioning images in the dataset\n- `--proportion_empty_prompts`: the proportion of image prompts to replace with empty strings\n\n## Training script\n\nAs with the script parameters, a walkthrough of the training script is provided in the [Text-to-image](text2image#training-script) training guide. Instead, this guide takes a look at the T2I-Adapter relevant parts of the script.\n\nThe training script begins by preparing the dataset. This incudes [tokenizing](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/t2i_adapter/train_t2i_adapter_sdxl.py#L674) the prompt and [applying transforms](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/t2i_adapter/train_t2i_adapter_sdxl.py#L714) to the images and conditioning images.\n\n```py\nconditioning_image_transforms = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(args.resolution),\n transforms.ToTensor(),\n ]\n)\n```\n\nWithin the [`main()`](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/t2i_adapter/train_t2i_adapter_sdxl.py#L770) function, the T2I-Adapter is either loaded from a pretrained adapter or it is randomly initialized:\n\n```py\nif args.adapter_model_name_or_path:\n logger.info(\"Loading existing adapter weights.\")\n t2iadapter = T2IAdapter.from_pretrained(args.adapter_model_name_or_path)\nelse:\n logger.info(\"Initializing t2iadapter weights.\")\n t2iadapter = T2IAdapter(\n in_channels=3,\n channels=(320, 640, 1280, 1280),\n num_res_blocks=2,\n downscale_factor=16,\n adapter_type=\"full_adapter_xl\",\n )\n```\n\nThe [optimizer](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/t2i_adapter/train_t2i_adapter_sdxl.py#L952) is initialized for the T2I-Adapter parameters:\n\n```py\nparams_to_optimize = t2iadapter.parameters()\noptimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n)\n```\n\nLastly, in the [training loop](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/t2i_adapter/train_t2i_adapter_sdxl.py#L1086), the adapter conditioning image and the text embeddings are passed to the UNet to predict the noise residual:\n\n```py\nt2iadapter_image = batch[\"conditioning_pixel_values\"].to(dtype=weight_dtype)\ndown_block_additional_residuals = t2iadapter(t2iadapter_image)\ndown_block_additional_residuals = [\n sample.to(dtype=weight_dtype) for sample in down_block_additional_residuals\n]\n\nmodel_pred = unet(\n inp_noisy_latents,\n timesteps,\n encoder_hidden_states=batch[\"prompt_ids\"],\n added_cond_kwargs=batch[\"unet_added_conditions\"],\n down_block_additional_residuals=down_block_additional_residuals,\n).sample\n```\n\nIf you want to learn more about how the training loop works, check out the [Understanding pipelines, models and schedulers](../using-diffusers/write_own_pipeline) tutorial which breaks down the basic pattern of the denoising process.\n\n## Launch the script\n\nNow you’re ready to launch the training script! 🚀\n\nFor this example training, you'll use the [fusing/fill50k](https://huggingface.co/datasets/fusing/fill50k) dataset. You can also create and use your own dataset if you want (see the [Create a dataset for training](https://moon-ci-docs.huggingface.co/docs/diffusers/pr_5512/en/training/create_dataset) guide).\n\nSet the environment variable `MODEL_DIR` to a model id on the Hub or a path to a local model and `OUTPUT_DIR` to where you want to save the model.\n\nDownload the following images to condition your training with:\n\n```bash\nwget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png\nwget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png\n```\n\n\n\nTo monitor training progress with Weights & Biases, add the `--report_to=wandb` parameter to the training command. You'll also need to add the `--validation_image`, `--validation_prompt`, and `--validation_steps` to the training command to keep track of results. This can be really useful for debugging the model and viewing intermediate results.\n\n\n\n```bash\nexport MODEL_DIR=\"stabilityai/stable-diffusion-xl-base-1.0\"\nexport OUTPUT_DIR=\"path to save model\"\n\naccelerate launch train_t2i_adapter_sdxl.py \\\n --pretrained_model_name_or_path=$MODEL_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --dataset_name=fusing/fill50k \\\n --mixed_precision=\"fp16\" \\\n --resolution=1024 \\\n --learning_rate=1e-5 \\\n --max_train_steps=15000 \\\n --validation_image \"./conditioning_image_1.png\" \"./conditioning_image_2.png\" \\\n --validation_prompt \"red circle with blue background\" \"cyan circle with brown floral background\" \\\n --validation_steps=100 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --report_to=\"wandb\" \\\n --seed=42 \\\n --push_to_hub\n```\n\nOnce training is complete, you can use your T2I-Adapter for inference:\n\n```py\nfrom diffusers import StableDiffusionXLAdapterPipeline, T2IAdapter, EulerAncestralDiscreteSchedulerTest\nfrom diffusers.utils import load_image\nimport torch\n\nadapter = T2IAdapter.from_pretrained(\"path/to/adapter\", torch_dtype=torch.float16)\npipeline = StableDiffusionXLAdapterPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", adapter=adapter, torch_dtype=torch.float16\n)\n\npipeline.scheduler = EulerAncestralDiscreteSchedulerTest.from_config(pipe.scheduler.config)\npipeline.enable_xformers_memory_efficient_attention()\npipeline.enable_model_cpu_offload()\n\ncontrol_image = load_image(\"./conditioning_image_1.png\")\nprompt = \"pale golden rod circle with old lace background\"\n\ngenerator = torch.manual_seed(0)\nimage = pipeline(\n prompt, image=control_image, generator=generator\n).images[0]\nimage.save(\"./output.png\")\n```\n\n## Next steps\n\nCongratulations on training a T2I-Adapter model! 🎉 To learn more:\n\n- Read the [Efficient Controllable Generation for SDXL with T2I-Adapters](https://huggingface.co/blog/t2i-sdxl-adapters) blog post to learn more details about the experimental results from the T2I-Adapter team.\n" }, { "path": "diffusers/docs/source/en/training/text2image.md", "content": "\n\n# Text-to-image\n\n\n\nThe text-to-image script is experimental, and it's easy to overfit and run into issues like catastrophic forgetting. Try exploring different hyperparameters to get the best results on your dataset.\n\n\n\nText-to-image models like Stable Diffusion are conditioned to generate images given a text prompt.\n\nTraining a model can be taxing on your hardware, but if you enable `gradient_checkpointing` and `mixed_precision`, it is possible to train a model on a single 24GB GPU. If you're training with larger batch sizes or want to train faster, it's better to use GPUs with more than 30GB of memory. You can reduce your memory footprint by enabling memory-efficient attention with [xFormers](../optimization/xformers). JAX/Flax training is also supported for efficient training on TPUs and GPUs, but it doesn't support gradient checkpointing, gradient accumulation or xFormers. A GPU with at least 30GB of memory or a TPU v3 is recommended for training with Flax.\n\nThis guide will explore the [train_text_to_image.py](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) training script to help you become familiar with it, and how you can adapt it for your own use-case.\n\nBefore running the script, make sure you install the library from source:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nThen navigate to the example folder containing the training script and install the required dependencies for the script you're using:\n\n\n\n```bash\ncd examples/text_to_image\npip install -r requirements.txt\n```\n\n\n```bash\ncd examples/text_to_image\npip install -r requirements_flax.txt\n```\n\n\n\n\n\n🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co/docs/accelerate/quicktour) to learn more.\n\n\n\nInitialize an 🤗 Accelerate environment:\n\n```bash\naccelerate config\n```\n\nTo setup a default 🤗 Accelerate environment without choosing any configurations:\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell, like a notebook, you can use:\n\n```py\nfrom accelerate.utils import write_basic_config\n\nwrite_basic_config()\n```\n\nLastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.\n\n## Script parameters\n\n\n\nThe following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn't cover every aspect of the script in detail. If you're interested in learning more, feel free to read through the [script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) and let us know if you have any questions or concerns.\n\n\n\nThe training script provides many parameters to help you customize your training run. All of the parameters and their descriptions are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/8959c5b9dec1c94d6ba482c94a58d2215c5fd026/examples/text_to_image/train_text_to_image.py#L193) function. This function provides default values for each parameter, such as the training batch size and learning rate, but you can also set your own values in the training command if you'd like.\n\nFor example, to speedup training with mixed precision using the fp16 format, add the `--mixed_precision` parameter to the training command:\n\n```bash\naccelerate launch train_text_to_image.py \\\n --mixed_precision=\"fp16\"\n```\n\nSome basic and important parameters include:\n\n- `--pretrained_model_name_or_path`: the name of the model on the Hub or a local path to the pretrained model\n- `--dataset_name`: the name of the dataset on the Hub or a local path to the dataset to train on\n- `--image_column`: the name of the image column in the dataset to train on\n- `--caption_column`: the name of the text column in the dataset to train on\n- `--output_dir`: where to save the trained model\n- `--push_to_hub`: whether to push the trained model to the Hub\n- `--checkpointing_steps`: frequency of saving a checkpoint as the model trains; this is useful if for some reason training is interrupted, you can continue training from that checkpoint by adding `--resume_from_checkpoint` to your training command\n\n### Min-SNR weighting\n\nThe [Min-SNR](https://huggingface.co/papers/2303.09556) weighting strategy can help with training by rebalancing the loss to achieve faster convergence. The training script supports predicting `epsilon` (noise) or `v_prediction`, but Min-SNR is compatible with both prediction types. This weighting strategy is only supported by PyTorch and is unavailable in the Flax training script.\n\nAdd the `--snr_gamma` parameter and set it to the recommended value of 5.0:\n\n```bash\naccelerate launch train_text_to_image.py \\\n --snr_gamma=5.0\n```\n\nYou can compare the loss surfaces for different `snr_gamma` values in this [Weights and Biases](https://wandb.ai/sayakpaul/text2image-finetune-minsnr) report. For smaller datasets, the effects of Min-SNR may not be as obvious compared to larger datasets.\n\n## Training script\n\nThe dataset preprocessing code and training loop are found in the [`main()`](https://github.com/huggingface/diffusers/blob/8959c5b9dec1c94d6ba482c94a58d2215c5fd026/examples/text_to_image/train_text_to_image.py#L490) function. If you need to adapt the training script, this is where you'll need to make your changes.\n\nThe `train_text_to_image` script starts by [loading a scheduler](https://github.com/huggingface/diffusers/blob/8959c5b9dec1c94d6ba482c94a58d2215c5fd026/examples/text_to_image/train_text_to_image.py#L543) and tokenizer. You can choose to use a different scheduler here if you want:\n\n```py\nnoise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\ntokenizer = CLIPTokenizer.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"tokenizer\", revision=args.revision\n)\n```\n\nThen the script [loads the UNet](https://github.com/huggingface/diffusers/blob/8959c5b9dec1c94d6ba482c94a58d2215c5fd026/examples/text_to_image/train_text_to_image.py#L619) model:\n\n```py\nload_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder=\"unet\")\nmodel.register_to_config(**load_model.config)\n\nmodel.load_state_dict(load_model.state_dict())\n```\n\nNext, the text and image columns of the dataset need to be preprocessed. The [`tokenize_captions`](https://github.com/huggingface/diffusers/blob/8959c5b9dec1c94d6ba482c94a58d2215c5fd026/examples/text_to_image/train_text_to_image.py#L724) function handles tokenizing the inputs, and the [`train_transforms`](https://github.com/huggingface/diffusers/blob/8959c5b9dec1c94d6ba482c94a58d2215c5fd026/examples/text_to_image/train_text_to_image.py#L742) function specifies the type of transforms to apply to the image. Both of these functions are bundled into `preprocess_train`:\n\n```py\ndef preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n examples[\"pixel_values\"] = [train_transforms(image) for image in images]\n examples[\"input_ids\"] = tokenize_captions(examples)\n return examples\n```\n\nLastly, the [training loop](https://github.com/huggingface/diffusers/blob/8959c5b9dec1c94d6ba482c94a58d2215c5fd026/examples/text_to_image/train_text_to_image.py#L878) handles everything else. It encodes images into latent space, adds noise to the latents, computes the text embeddings to condition on, updates the model parameters, and saves and pushes the model to the Hub. If you want to learn more about how the training loop works, check out the [Understanding pipelines, models and schedulers](../using-diffusers/write_own_pipeline) tutorial which breaks down the basic pattern of the denoising process.\n\n## Launch the script\n\nOnce you've made all your changes or you're okay with the default configuration, you're ready to launch the training script! 🚀\n\n\n\n\nLet's train on the [Naruto BLIP captions](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions) dataset to generate your own Naruto characters. Set the environment variables `MODEL_NAME` and `dataset_name` to the model and the dataset (either from the Hub or a local path). If you're training on more than one GPU, add the `--multi_gpu` parameter to the `accelerate launch` command.\n\n\n\nTo train on a local dataset, set the `TRAIN_DIR` and `OUTPUT_DIR` environment variables to the path of the dataset and where to save the model to.\n\n\n\n```bash\nexport MODEL_NAME=\"runwayml/stable-diffusion-v1-5\"\nexport dataset_name=\"lambdalabs/naruto-blip-captions\"\n\naccelerate launch --mixed_precision=\"fp16\" train_text_to_image.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --dataset_name=$dataset_name \\\n --use_ema \\\n --resolution=512 --center_crop --random_flip \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --max_train_steps=15000 \\\n --learning_rate=1e-05 \\\n --max_grad_norm=1 \\\n --enable_xformers_memory_efficient_attention\n --lr_scheduler=\"constant\" --lr_warmup_steps=0 \\\n --output_dir=\"sd-naruto-model\" \\\n --push_to_hub\n```\n\n\n\n\nTraining with Flax can be faster on TPUs and GPUs thanks to [@duongna211](https://github.com/duongna21). Flax is more efficient on a TPU, but GPU performance is also great.\n\nSet the environment variables `MODEL_NAME` and `dataset_name` to the model and the dataset (either from the Hub or a local path).\n\n\n\nTo train on a local dataset, set the `TRAIN_DIR` and `OUTPUT_DIR` environment variables to the path of the dataset and where to save the model to.\n\n\n\n```bash\nexport MODEL_NAME=\"runwayml/stable-diffusion-v1-5\"\nexport dataset_name=\"lambdalabs/naruto-blip-captions\"\n\npython train_text_to_image_flax.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --dataset_name=$dataset_name \\\n --resolution=512 --center_crop --random_flip \\\n --train_batch_size=1 \\\n --max_train_steps=15000 \\\n --learning_rate=1e-05 \\\n --max_grad_norm=1 \\\n --output_dir=\"sd-naruto-model\" \\\n --push_to_hub\n```\n\n\n\n\nOnce training is complete, you can use your newly trained model for inference:\n\n\n\n\n```py\nfrom diffusers import StableDiffusionPipeline\nimport torch\n\npipeline = StableDiffusionPipeline.from_pretrained(\"path/to/saved_model\", torch_dtype=torch.float16, use_safetensors=True).to(\"cuda\")\n\nimage = pipeline(prompt=\"yoda\").images[0]\nimage.save(\"yoda-naruto.png\")\n```\n\n\n\n\n```py\nimport jax\nimport numpy as np\nfrom flax.jax_utils import replicate\nfrom flax.training.common_utils import shard\nfrom diffusers import FlaxStableDiffusionPipeline\n\npipeline, params = FlaxStableDiffusionPipeline.from_pretrained(\"path/to/saved_model\", dtype=jax.numpy.bfloat16)\n\nprompt = \"yoda naruto\"\nprng_seed = jax.random.PRNGKey(0)\nnum_inference_steps = 50\n\nnum_samples = jax.device_count()\nprompt = num_samples * [prompt]\nprompt_ids = pipeline.prepare_inputs(prompt)\n\n# shard inputs and rng\nparams = replicate(params)\nprng_seed = jax.random.split(prng_seed, jax.device_count())\nprompt_ids = shard(prompt_ids)\n\nimages = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images\nimages = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))\nimage.save(\"yoda-naruto.png\")\n```\n\n\n\n\n## Next steps\n\nCongratulations on training your own text-to-image model! To learn more about how to use your new model, the following guides may be helpful:\n\n- Learn how to [load LoRA weights](../using-diffusers/loading_adapters#LoRA) for inference if you trained your model with LoRA.\n- Learn more about how certain parameters like guidance scale or techniques such as prompt weighting can help you control inference in the [Text-to-image](../using-diffusers/conditional_image_generation) task guide.\n" }, { "path": "diffusers/docs/source/en/training/text_inversion.md", "content": " \n\n# Textual Inversion\n\n[Textual Inversion](https://hf.co/papers/2208.01618) is a training technique for personalizing image generation models with just a few example images of what you want it to learn. This technique works by learning and updating the text embeddings (the new embeddings are tied to a special word you must use in the prompt) to match the example images you provide.\n\nIf you're training on a GPU with limited vRAM, you should try enabling the `gradient_checkpointing` and `mixed_precision` parameters in the training command. You can also reduce your memory footprint by using memory-efficient attention with [xFormers](../optimization/xformers). JAX/Flax training is also supported for efficient training on TPUs and GPUs, but it doesn't support gradient checkpointing or xFormers. With the same configuration and setup as PyTorch, the Flax training script should be at least ~70% faster!\n\nThis guide will explore the [textual_inversion.py](https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py) script to help you become more familiar with it, and how you can adapt it for your own use-case.\n\nBefore running the script, make sure you install the library from source:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nNavigate to the example folder with the training script and install the required dependencies for the script you're using:\n\n\n\n\n```bash\ncd examples/textual_inversion\npip install -r requirements.txt\n```\n\n\n\n\n```bash\ncd examples/textual_inversion\npip install -r requirements_flax.txt\n```\n\n\n\n\n\n\n🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co/docs/accelerate/quicktour) to learn more.\n\n\n\nInitialize an 🤗 Accelerate environment:\n\n```bash\naccelerate config\n```\n\nTo setup a default 🤗 Accelerate environment without choosing any configurations:\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell, like a notebook, you can use:\n\n```py\nfrom accelerate.utils import write_basic_config\n\nwrite_basic_config()\n```\n\nLastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.\n\n\n\nThe following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn't cover every aspect of the script in detail. If you're interested in learning more, feel free to read through the [script](https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py) and let us know if you have any questions or concerns.\n\n\n\n## Script parameters\n\nThe training script has many parameters to help you tailor the training run to your needs. All of the parameters and their descriptions are listed in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/839c2a5ece0af4e75530cb520d77bc7ed8acf474/examples/textual_inversion/textual_inversion.py#L176) function. Where applicable, Diffusers provides default values for each parameter such as the training batch size and learning rate, but feel free to change these values in the training command if you'd like.\n\nFor example, to increase the number of gradient accumulation steps above the default value of 1:\n\n```bash\naccelerate launch textual_inversion.py \\\n --gradient_accumulation_steps=4\n```\n\nSome other basic and important parameters to specify include:\n\n- `--pretrained_model_name_or_path`: the name of the model on the Hub or a local path to the pretrained model\n- `--train_data_dir`: path to a folder containing the training dataset (example images)\n- `--output_dir`: where to save the trained model\n- `--push_to_hub`: whether to push the trained model to the Hub\n- `--checkpointing_steps`: frequency of saving a checkpoint as the model trains; this is useful if for some reason training is interrupted, you can continue training from that checkpoint by adding `--resume_from_checkpoint` to your training command\n- `--num_vectors`: the number of vectors to learn the embeddings with; increasing this parameter helps the model learn better but it comes with increased training costs\n- `--placeholder_token`: the special word to tie the learned embeddings to (you must use the word in your prompt for inference)\n- `--initializer_token`: a single-word that roughly describes the object or style you're trying to train on\n- `--learnable_property`: whether you're training the model to learn a new \"style\" (for example, Van Gogh's painting style) or \"object\" (for example, your dog)\n\n## Training script\n\nUnlike some of the other training scripts, textual_inversion.py has a custom dataset class, [`TextualInversionDataset`](https://github.com/huggingface/diffusers/blob/b81c69e489aad3a0ba73798c459a33990dc4379c/examples/textual_inversion/textual_inversion.py#L487) for creating a dataset. You can customize the image size, placeholder token, interpolation method, whether to crop the image, and more. If you need to change how the dataset is created, you can modify `TextualInversionDataset`.\n\nNext, you'll find the dataset preprocessing code and training loop in the [`main()`](https://github.com/huggingface/diffusers/blob/839c2a5ece0af4e75530cb520d77bc7ed8acf474/examples/textual_inversion/textual_inversion.py#L573) function.\n\nThe script starts by loading the [tokenizer](https://github.com/huggingface/diffusers/blob/b81c69e489aad3a0ba73798c459a33990dc4379c/examples/textual_inversion/textual_inversion.py#L616), [scheduler and model](https://github.com/huggingface/diffusers/blob/b81c69e489aad3a0ba73798c459a33990dc4379c/examples/textual_inversion/textual_inversion.py#L622):\n\n```py\n# Load tokenizer\nif args.tokenizer_name:\n tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)\nelif args.pretrained_model_name_or_path:\n tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"tokenizer\")\n\n# Load scheduler and models\nnoise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\ntext_encoder = CLIPTextModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision\n)\nvae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision)\nunet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision\n)\n```\n\nThe special [placeholder token](https://github.com/huggingface/diffusers/blob/b81c69e489aad3a0ba73798c459a33990dc4379c/examples/textual_inversion/textual_inversion.py#L632) is added next to the tokenizer, and the embedding is readjusted to account for the new token.\n\nThen, the script [creates a dataset](https://github.com/huggingface/diffusers/blob/b81c69e489aad3a0ba73798c459a33990dc4379c/examples/textual_inversion/textual_inversion.py#L716) from the `TextualInversionDataset`:\n\n```py\ntrain_dataset = TextualInversionDataset(\n data_root=args.train_data_dir,\n tokenizer=tokenizer,\n size=args.resolution,\n placeholder_token=(\" \".join(tokenizer.convert_ids_to_tokens(placeholder_token_ids))),\n repeats=args.repeats,\n learnable_property=args.learnable_property,\n center_crop=args.center_crop,\n set=\"train\",\n)\ntrain_dataloader = torch.utils.data.DataLoader(\n train_dataset, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers\n)\n```\n\nFinally, the [training loop](https://github.com/huggingface/diffusers/blob/b81c69e489aad3a0ba73798c459a33990dc4379c/examples/textual_inversion/textual_inversion.py#L784) handles everything else from predicting the noisy residual to updating the embedding weights of the special placeholder token.\n\nIf you want to learn more about how the training loop works, check out the [Understanding pipelines, models and schedulers](../using-diffusers/write_own_pipeline) tutorial which breaks down the basic pattern of the denoising process.\n\n## Launch the script\n\nOnce you've made all your changes or you're okay with the default configuration, you're ready to launch the training script! 🚀\n\nFor this guide, you'll download some images of a [cat toy](https://huggingface.co/datasets/diffusers/cat_toy_example) and store them in a directory. But remember, you can create and use your own dataset if you want (see the [Create a dataset for training](create_dataset) guide).\n\n```py\nfrom huggingface_hub import snapshot_download\n\nlocal_dir = \"./cat\"\nsnapshot_download(\n \"diffusers/cat_toy_example\", local_dir=local_dir, repo_type=\"dataset\", ignore_patterns=\".gitattributes\"\n)\n```\n\nSet the environment variable `MODEL_NAME` to a model id on the Hub or a path to a local model, and `DATA_DIR` to the path where you just downloaded the cat images to. The script creates and saves the following files to your repository:\n\n- `learned_embeds.bin`: the learned embedding vectors corresponding to your example images\n- `token_identifier.txt`: the special placeholder token\n- `type_of_concept.txt`: the type of concept you're training on (either \"object\" or \"style\")\n\n\n\nA full training run takes ~1 hour on a single V100 GPU.\n\n\n\nOne more thing before you launch the script. If you're interested in following along with the training process, you can periodically save generated images as training progresses. Add the following parameters to the training command:\n\n```bash\n--validation_prompt=\"A train\"\n--num_validation_images=4\n--validation_steps=100\n```\n\n\n\n\n```bash\nexport MODEL_NAME=\"runwayml/stable-diffusion-v1-5\"\nexport DATA_DIR=\"./cat\"\n\naccelerate launch textual_inversion.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_data_dir=$DATA_DIR \\\n --learnable_property=\"object\" \\\n --placeholder_token=\"\" \\\n --initializer_token=\"toy\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --max_train_steps=3000 \\\n --learning_rate=5.0e-04 \\\n --scale_lr \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --output_dir=\"textual_inversion_cat\" \\\n --push_to_hub\n```\n\n\n\n\n```bash\nexport MODEL_NAME=\"duongna/stable-diffusion-v1-4-flax\"\nexport DATA_DIR=\"./cat\"\n\npython textual_inversion_flax.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_data_dir=$DATA_DIR \\\n --learnable_property=\"object\" \\\n --placeholder_token=\"\" \\\n --initializer_token=\"toy\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --max_train_steps=3000 \\\n --learning_rate=5.0e-04 \\\n --scale_lr \\\n --output_dir=\"textual_inversion_cat\" \\\n --push_to_hub\n```\n\n\n\n\nAfter training is complete, you can use your newly trained model for inference like:\n\n\n\n\n```py\nfrom diffusers import StableDiffusionPipeline\nimport torch\n\npipeline = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16).to(\"cuda\")\npipeline.load_textual_inversion(\"sd-concepts-library/cat-toy\")\nimage = pipeline(\"A train\", num_inference_steps=50).images[0]\nimage.save(\"cat-train.png\")\n```\n\n\n\n\nFlax doesn't support the [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] method, but the textual_inversion_flax.py script [saves](https://github.com/huggingface/diffusers/blob/c0f058265161178f2a88849e92b37ffdc81f1dcc/examples/textual_inversion/textual_inversion_flax.py#L636C2-L636C2) the learned embeddings as a part of the model after training. This means you can use the model for inference like any other Flax model:\n\n```py\nimport jax\nimport numpy as np\nfrom flax.jax_utils import replicate\nfrom flax.training.common_utils import shard\nfrom diffusers import FlaxStableDiffusionPipeline\n\nmodel_path = \"path-to-your-trained-model\"\npipeline, params = FlaxStableDiffusionPipeline.from_pretrained(model_path, dtype=jax.numpy.bfloat16)\n\nprompt = \"A train\"\nprng_seed = jax.random.PRNGKey(0)\nnum_inference_steps = 50\n\nnum_samples = jax.device_count()\nprompt = num_samples * [prompt]\nprompt_ids = pipeline.prepare_inputs(prompt)\n\n# shard inputs and rng\nparams = replicate(params)\nprng_seed = jax.random.split(prng_seed, jax.device_count())\nprompt_ids = shard(prompt_ids)\n\nimages = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images\nimages = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))\nimage.save(\"cat-train.png\")\n```\n\n\n\n\n## Next steps\n\nCongratulations on training your own Textual Inversion model! 🎉 To learn more about how to use your new model, the following guides may be helpful:\n\n- Learn how to [load Textual Inversion embeddings](../using-diffusers/loading_adapters) and also use them as negative embeddings.\n- Learn how to use [Textual Inversion](textual_inversion_inference) for inference with Stable Diffusion 1/2 and Stable Diffusion XL." }, { "path": "diffusers/docs/source/en/training/unconditional_training.md", "content": "\n\n# Unconditional image generation\n\nUnconditional image generation models are not conditioned on text or images during training. It only generates images that resemble its training data distribution.\n\nThis guide will explore the [train_unconditional.py](https://github.com/huggingface/diffusers/blob/main/examples/unconditional_image_generation/train_unconditional.py) training script to help you become familiar with it, and how you can adapt it for your own use-case.\n\nBefore running the script, make sure you install the library from source:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nThen navigate to the example folder containing the training script and install the required dependencies:\n\n```bash\ncd examples/unconditional_image_generation\npip install -r requirements.txt\n```\n\n\n\n🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co/docs/accelerate/quicktour) to learn more.\n\n\n\nInitialize an 🤗 Accelerate environment:\n\n```bash\naccelerate config\n```\n\nTo setup a default 🤗 Accelerate environment without choosing any configurations:\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell like a notebook, you can use:\n\n```py\nfrom accelerate.utils import write_basic_config\n\nwrite_basic_config()\n```\n\nLastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.\n\n## Script parameters\n\n\n\nThe following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn't cover every aspect of the script in detail. If you're interested in learning more, feel free to read through the [script](https://github.com/huggingface/diffusers/blob/main/examples/unconditional_image_generation/train_unconditional.py) and let us know if you have any questions or concerns.\n\n\n\nThe training script provides many parameters to help you customize your training run. All of the parameters and their descriptions are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/096f84b05f9514fae9f185cbec0a4d38fbad9919/examples/unconditional_image_generation/train_unconditional.py#L55) function. It provides default values for each parameter, such as the training batch size and learning rate, but you can also set your own values in the training command if you'd like.\n\nFor example, to speedup training with mixed precision using the bf16 format, add the `--mixed_precision` parameter to the training command:\n\n```bash\naccelerate launch train_unconditional.py \\\n --mixed_precision=\"bf16\"\n```\n\nSome basic and important parameters to specify include:\n\n- `--dataset_name`: the name of the dataset on the Hub or a local path to the dataset to train on\n- `--output_dir`: where to save the trained model\n- `--push_to_hub`: whether to push the trained model to the Hub\n- `--checkpointing_steps`: frequency of saving a checkpoint as the model trains; this is useful if training is interrupted, you can continue training from that checkpoint by adding `--resume_from_checkpoint` to your training command\n\nBring your dataset, and let the training script handle everything else!\n\n## Training script\n\nThe code for preprocessing the dataset and the training loop is found in the [`main()`](https://github.com/huggingface/diffusers/blob/096f84b05f9514fae9f185cbec0a4d38fbad9919/examples/unconditional_image_generation/train_unconditional.py#L275) function. If you need to adapt the training script, this is where you'll need to make your changes.\n\nThe `train_unconditional` script [initializes a `UNet2DModel`](https://github.com/huggingface/diffusers/blob/096f84b05f9514fae9f185cbec0a4d38fbad9919/examples/unconditional_image_generation/train_unconditional.py#L356) if you don't provide a model configuration. You can configure the UNet here if you'd like:\n\n```py\nmodel = UNet2DModel(\n sample_size=args.resolution,\n in_channels=3,\n out_channels=3,\n layers_per_block=2,\n block_out_channels=(128, 128, 256, 256, 512, 512),\n down_block_types=(\n \"DownBlock2D\",\n \"DownBlock2D\",\n \"DownBlock2D\",\n \"DownBlock2D\",\n \"AttnDownBlock2D\",\n \"DownBlock2D\",\n ),\n up_block_types=(\n \"UpBlock2D\",\n \"AttnUpBlock2D\",\n \"UpBlock2D\",\n \"UpBlock2D\",\n \"UpBlock2D\",\n \"UpBlock2D\",\n ),\n)\n```\n\nNext, the script initializes a [scheduler](https://github.com/huggingface/diffusers/blob/096f84b05f9514fae9f185cbec0a4d38fbad9919/examples/unconditional_image_generation/train_unconditional.py#L418) and [optimizer](https://github.com/huggingface/diffusers/blob/096f84b05f9514fae9f185cbec0a4d38fbad9919/examples/unconditional_image_generation/train_unconditional.py#L429):\n\n```py\n# Initialize the scheduler\naccepts_prediction_type = \"prediction_type\" in set(inspect.signature(DDPMScheduler.__init__).parameters.keys())\nif accepts_prediction_type:\n noise_scheduler = DDPMScheduler(\n num_train_timesteps=args.ddpm_num_steps,\n beta_schedule=args.ddpm_beta_schedule,\n prediction_type=args.prediction_type,\n )\nelse:\n noise_scheduler = DDPMScheduler(num_train_timesteps=args.ddpm_num_steps, beta_schedule=args.ddpm_beta_schedule)\n\n# Initialize the optimizer\noptimizer = torch.optim.AdamW(\n model.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n)\n```\n\nThen it [loads a dataset](https://github.com/huggingface/diffusers/blob/096f84b05f9514fae9f185cbec0a4d38fbad9919/examples/unconditional_image_generation/train_unconditional.py#L451) and you can specify how to [preprocess](https://github.com/huggingface/diffusers/blob/096f84b05f9514fae9f185cbec0a4d38fbad9919/examples/unconditional_image_generation/train_unconditional.py#L455) it:\n\n```py\ndataset = load_dataset(\"imagefolder\", data_dir=args.train_data_dir, cache_dir=args.cache_dir, split=\"train\")\n\naugmentations = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),\n transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n)\n```\n\nFinally, the [training loop](https://github.com/huggingface/diffusers/blob/096f84b05f9514fae9f185cbec0a4d38fbad9919/examples/unconditional_image_generation/train_unconditional.py#L540) handles everything else such as adding noise to the images, predicting the noise residual, calculating the loss, saving checkpoints at specified steps, and saving and pushing the model to the Hub. If you want to learn more about how the training loop works, check out the [Understanding pipelines, models and schedulers](../using-diffusers/write_own_pipeline) tutorial which breaks down the basic pattern of the denoising process.\n\n## Launch the script\n\nOnce you've made all your changes or you're okay with the default configuration, you're ready to launch the training script! 🚀\n\n\n\nA full training run takes 2 hours on 4xV100 GPUs.\n\n\n\n\n\n\n```bash\naccelerate launch train_unconditional.py \\\n --dataset_name=\"huggan/flowers-102-categories\" \\\n --output_dir=\"ddpm-ema-flowers-64\" \\\n --mixed_precision=\"fp16\" \\\n --push_to_hub\n```\n\n\n\n\nIf you're training with more than one GPU, add the `--multi_gpu` parameter to the training command:\n\n```bash\naccelerate launch --multi_gpu train_unconditional.py \\\n --dataset_name=\"huggan/flowers-102-categories\" \\\n --output_dir=\"ddpm-ema-flowers-64\" \\\n --mixed_precision=\"fp16\" \\\n --push_to_hub\n```\n\n\n\n\nThe training script creates and saves a checkpoint file in your repository. Now you can load and use your trained model for inference:\n\n```py\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipeline = DiffusionPipeline.from_pretrained(\"anton-l/ddpm-butterflies-128\").to(\"cuda\")\nimage = pipeline().images[0]\n```\n" }, { "path": "diffusers/docs/source/en/training/wuerstchen.md", "content": "\n\n# Wuerstchen\n\nThe [Wuerstchen](https://hf.co/papers/2306.00637) model drastically reduces computational costs by compressing the latent space by 42x, without compromising image quality and accelerating inference. During training, Wuerstchen uses two models (VQGAN + autoencoder) to compress the latents, and then a third model (text-conditioned latent diffusion model) is conditioned on this highly compressed space to generate an image.\n\nTo fit the prior model into GPU memory and to speedup training, try enabling `gradient_accumulation_steps`, `gradient_checkpointing`, and `mixed_precision` respectively.\n\nThis guide explores the [train_text_to_image_prior.py](https://github.com/huggingface/diffusers/blob/main/examples/wuerstchen/text_to_image/train_text_to_image_prior.py) script to help you become more familiar with it, and how you can adapt it for your own use-case.\n\nBefore running the script, make sure you install the library from source:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nThen navigate to the example folder containing the training script and install the required dependencies for the script you're using:\n\n```bash\ncd examples/wuerstchen/text_to_image\npip install -r requirements.txt\n```\n\n\n\n🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co/docs/accelerate/quicktour) to learn more.\n\n\n\nInitialize an 🤗 Accelerate environment:\n\n```bash\naccelerate config\n```\n\nTo setup a default 🤗 Accelerate environment without choosing any configurations:\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell, like a notebook, you can use:\n\n```py\nfrom accelerate.utils import write_basic_config\n\nwrite_basic_config()\n```\n\nLastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.\n\n\n\nThe following sections highlight parts of the training scripts that are important for understanding how to modify it, but it doesn't cover every aspect of the [script](https://github.com/huggingface/diffusers/blob/main/examples/wuerstchen/text_to_image/train_text_to_image_prior.py) in detail. If you're interested in learning more, feel free to read through the scripts and let us know if you have any questions or concerns.\n\n\n\n## Script parameters\n\nThe training scripts provides many parameters to help you customize your training run. All of the parameters and their descriptions are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/wuerstchen/text_to_image/train_text_to_image_prior.py#L192) function. It provides default values for each parameter, such as the training batch size and learning rate, but you can also set your own values in the training command if you'd like.\n\nFor example, to speedup training with mixed precision using the fp16 format, add the `--mixed_precision` parameter to the training command:\n\n```bash\naccelerate launch train_text_to_image_prior.py \\\n --mixed_precision=\"fp16\"\n```\n\nMost of the parameters are identical to the parameters in the [Text-to-image](text2image#script-parameters) training guide, so let's dive right into the Wuerstchen training script!\n\n## Training script\n\nThe training script is also similar to the [Text-to-image](text2image#training-script) training guide, but it's been modified to support Wuerstchen. This guide focuses on the code that is unique to the Wuerstchen training script.\n\nThe [`main()`](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/wuerstchen/text_to_image/train_text_to_image_prior.py#L441) function starts by initializing the image encoder - an [EfficientNet](https://github.com/huggingface/diffusers/blob/main/examples/wuerstchen/text_to_image/modeling_efficient_net_encoder.py) - in addition to the usual scheduler and tokenizer.\n\n```py\nwith ContextManagers(deepspeed_zero_init_disabled_context_manager()):\n pretrained_checkpoint_file = hf_hub_download(\"dome272/wuerstchen\", filename=\"model_v2_stage_b.pt\")\n state_dict = torch.load(pretrained_checkpoint_file, map_location=\"cpu\")\n image_encoder = EfficientNetEncoder()\n image_encoder.load_state_dict(state_dict[\"effnet_state_dict\"])\n image_encoder.eval()\n```\n\nYou'll also load the [`WuerstchenPrior`] model for optimization.\n\n```py\nprior = WuerstchenPrior.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder=\"prior\")\n\noptimizer = optimizer_cls(\n prior.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n)\n```\n\nNext, you'll apply some [transforms](https://github.com/huggingface/diffusers/blob/65ef7a0c5c594b4f84092e328fbdd73183613b30/examples/wuerstchen/text_to_image/train_text_to_image_prior.py#L656) to the images and [tokenize](https://github.com/huggingface/diffusers/blob/65ef7a0c5c594b4f84092e328fbdd73183613b30/examples/wuerstchen/text_to_image/train_text_to_image_prior.py#L637) the captions:\n\n```py\ndef preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n examples[\"effnet_pixel_values\"] = [effnet_transforms(image) for image in images]\n examples[\"text_input_ids\"], examples[\"text_mask\"] = tokenize_captions(examples)\n return examples\n```\n\nFinally, the [training loop](https://github.com/huggingface/diffusers/blob/65ef7a0c5c594b4f84092e328fbdd73183613b30/examples/wuerstchen/text_to_image/train_text_to_image_prior.py#L656) handles compressing the images to latent space with the `EfficientNetEncoder`, adding noise to the latents, and predicting the noise residual with the [`WuerstchenPrior`] model.\n\n```py\npred_noise = prior(noisy_latents, timesteps, prompt_embeds)\n```\n\nIf you want to learn more about how the training loop works, check out the [Understanding pipelines, models and schedulers](../using-diffusers/write_own_pipeline) tutorial which breaks down the basic pattern of the denoising process.\n\n## Launch the script\n\nOnce you’ve made all your changes or you’re okay with the default configuration, you’re ready to launch the training script! 🚀\n\nSet the `DATASET_NAME` environment variable to the dataset name from the Hub. This guide uses the [Naruto BLIP captions](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions) dataset, but you can create and train on your own datasets as well (see the [Create a dataset for training](create_dataset) guide).\n\n\n\nTo monitor training progress with Weights & Biases, add the `--report_to=wandb` parameter to the training command. You’ll also need to add the `--validation_prompt` to the training command to keep track of results. This can be really useful for debugging the model and viewing intermediate results.\n\n\n\n```bash\nexport DATASET_NAME=\"lambdalabs/naruto-blip-captions\"\n\naccelerate launch train_text_to_image_prior.py \\\n --mixed_precision=\"fp16\" \\\n --dataset_name=$DATASET_NAME \\\n --resolution=768 \\\n --train_batch_size=4 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --dataloader_num_workers=4 \\\n --max_train_steps=15000 \\\n --learning_rate=1e-05 \\\n --max_grad_norm=1 \\\n --checkpoints_total_limit=3 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --validation_prompts=\"A robot naruto, 4k photo\" \\\n --report_to=\"wandb\" \\\n --push_to_hub \\\n --output_dir=\"wuerstchen-prior-naruto-model\"\n```\n\nOnce training is complete, you can use your newly trained model for inference!\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForText2Image\nfrom diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS\n\npipeline = AutoPipelineForText2Image.from_pretrained(\"path/to/saved/model\", torch_dtype=torch.float16).to(\"cuda\")\n\ncaption = \"A cute bird naruto holding a shield\"\nimages = pipeline(\n caption,\n width=1024,\n height=1536,\n prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS,\n prior_guidance_scale=4.0,\n num_images_per_prompt=2,\n).images\n```\n\n## Next steps\n\nCongratulations on training a Wuerstchen model! To learn more about how to use your new model, the following may be helpful:\n\n- Take a look at the [Wuerstchen](../api/pipelines/wuerstchen#text-to-image-generation) API documentation to learn more about how to use the pipeline for text-to-image generation and its limitations.\n" }, { "path": "diffusers/docs/source/en/tutorials/autopipeline.md", "content": "\n\n# AutoPipeline\n\nDiffusers provides many pipelines for basic tasks like generating images, videos, audio, and inpainting. On top of these, there are specialized pipelines for adapters and features like upscaling, super-resolution, and more. Different pipeline classes can even use the same checkpoint because they share the same pretrained model! With so many different pipelines, it can be overwhelming to know which pipeline class to use.\n\nThe [AutoPipeline](../api/pipelines/auto_pipeline) class is designed to simplify the variety of pipelines in Diffusers. It is a generic *task-first* pipeline that lets you focus on a task ([`AutoPipelineForText2Image`], [`AutoPipelineForImage2Image`], and [`AutoPipelineForInpainting`]) without needing to know the specific pipeline class. The [AutoPipeline](../api/pipelines/auto_pipeline) automatically detects the correct pipeline class to use.\n\nFor example, let's use the [dreamlike-art/dreamlike-photoreal-2.0](https://hf.co/dreamlike-art/dreamlike-photoreal-2.0) checkpoint.\n\nUnder the hood, [AutoPipeline](../api/pipelines/auto_pipeline):\n\n1. Detects a `\"stable-diffusion\"` class from the [model_index.json](https://hf.co/dreamlike-art/dreamlike-photoreal-2.0/blob/main/model_index.json) file.\n2. Depending on the task you're interested in, it loads the [`StableDiffusionPipeline`], [`StableDiffusionImg2ImgPipeline`], or [`StableDiffusionInpaintPipeline`]. Any parameter (`strength`, `num_inference_steps`, etc.) you would pass to these specific pipelines can also be passed to the [AutoPipeline](../api/pipelines/auto_pipeline).\n\n\n\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipe_txt2img = AutoPipelineForText2Image.from_pretrained(\n \"dreamlike-art/dreamlike-photoreal-2.0\", torch_dtype=torch.float16, use_safetensors=True\n).to(\"cuda\")\n\nprompt = \"cinematic photo of Godzilla eating sushi with a cat in a izakaya, 35mm photograph, film, professional, 4k, highly detailed\"\ngenerator = torch.Generator(device=\"cpu\").manual_seed(37)\nimage = pipe_txt2img(prompt, generator=generator).images[0]\nimage\n```\n\n
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\n\n\n\n\n```py\nfrom diffusers import AutoPipelineForImage2Image\nfrom diffusers.utils import load_image\nimport torch\n\npipe_img2img = AutoPipelineForImage2Image.from_pretrained(\n \"dreamlike-art/dreamlike-photoreal-2.0\", torch_dtype=torch.float16, use_safetensors=True\n).to(\"cuda\")\n\ninit_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/autopipeline-text2img.png\")\n\nprompt = \"cinematic photo of Godzilla eating burgers with a cat in a fast food restaurant, 35mm photograph, film, professional, 4k, highly detailed\"\ngenerator = torch.Generator(device=\"cpu\").manual_seed(53)\nimage = pipe_img2img(prompt, image=init_image, generator=generator).images[0]\nimage\n```\n\nNotice how the [dreamlike-art/dreamlike-photoreal-2.0](https://hf.co/dreamlike-art/dreamlike-photoreal-2.0) checkpoint is used for both text-to-image and image-to-image tasks? To save memory and avoid loading the checkpoint twice, use the [`~DiffusionPipeline.from_pipe`] method.\n\n```py\npipe_img2img = AutoPipelineForImage2Image.from_pipe(pipe_txt2img).to(\"cuda\")\nimage = pipeline(prompt, image=init_image, generator=generator).images[0]\nimage\n```\n\nYou can learn more about the [`~DiffusionPipeline.from_pipe`] method in the [Reuse a pipeline](../using-diffusers/loading#reuse-a-pipeline) guide.\n\n
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\n\n\n\n\n```py\nfrom diffusers import AutoPipelineForInpainting\nfrom diffusers.utils import load_image\nimport torch\n\npipeline = AutoPipelineForInpainting.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16, use_safetensors=True\n).to(\"cuda\")\n\ninit_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/autopipeline-img2img.png\")\nmask_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/autopipeline-mask.png\")\n\nprompt = \"cinematic photo of a owl, 35mm photograph, film, professional, 4k, highly detailed\"\ngenerator = torch.Generator(device=\"cpu\").manual_seed(38)\nimage = pipeline(prompt, image=init_image, mask_image=mask_image, generator=generator, strength=0.4).images[0]\nimage\n```\n\n
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\n\n\n\n\n## Unsupported checkpoints\n\nThe [AutoPipeline](../api/pipelines/auto_pipeline) supports [Stable Diffusion](../api/pipelines/stable_diffusion/overview), [Stable Diffusion XL](../api/pipelines/stable_diffusion/stable_diffusion_xl), [ControlNet](../api/pipelines/controlnet), [Kandinsky 2.1](../api/pipelines/kandinsky.md), [Kandinsky 2.2](../api/pipelines/kandinsky_v22), and [DeepFloyd IF](../api/pipelines/deepfloyd_if) checkpoints.\n\nIf you try to load an unsupported checkpoint, you'll get an error.\n\n```py\nfrom diffusers import AutoPipelineForImage2Image\nimport torch\n\npipeline = AutoPipelineForImage2Image.from_pretrained(\n \"openai/shap-e-img2img\", torch_dtype=torch.float16, use_safetensors=True\n)\n\"ValueError: AutoPipeline can't find a pipeline linked to ShapEImg2ImgPipeline for None\"\n```\n" }, { "path": "diffusers/docs/source/en/tutorials/basic_training.md", "content": "\n\n[[open-in-colab]]\n\n# Train a diffusion model\n\nUnconditional image generation is a popular application of diffusion models that generates images that look like those in the dataset used for training. Typically, the best results are obtained from finetuning a pretrained model on a specific dataset. You can find many of these checkpoints on the [Hub](https://huggingface.co/search/full-text?q=unconditional-image-generation&type=model), but if you can't find one you like, you can always train your own!\n\nThis tutorial will teach you how to train a [`UNet2DModel`] from scratch on a subset of the [Smithsonian Butterflies](https://huggingface.co/datasets/huggan/smithsonian_butterflies_subset) dataset to generate your own 🦋 butterflies 🦋.\n\n\n\n💡 This training tutorial is based on the [Training with 🧨 Diffusers](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) notebook. For additional details and context about diffusion models like how they work, check out the notebook!\n\n\n\nBefore you begin, make sure you have 🤗 Datasets installed to load and preprocess image datasets, and 🤗 Accelerate, to simplify training on any number of GPUs. The following command will also install [TensorBoard](https://www.tensorflow.org/tensorboard) to visualize training metrics (you can also use [Weights & Biases](https://docs.wandb.ai/) to track your training).\n\n```py\n# uncomment to install the necessary libraries in Colab\n#!pip install diffusers[training]\n```\n\nWe encourage you to share your model with the community, and in order to do that, you'll need to login to your Hugging Face account (create one [here](https://hf.co/join) if you don't already have one!). You can login from a notebook and enter your token when prompted. Make sure your token has the write role.\n\n```py\n>>> from huggingface_hub import notebook_login\n\n>>> notebook_login()\n```\n\nOr login in from the terminal:\n\n```bash\nhuggingface-cli login\n```\n\nSince the model checkpoints are quite large, install [Git-LFS](https://git-lfs.com/) to version these large files:\n\n```bash\n!sudo apt -qq install git-lfs\n!git config --global credential.helper store\n```\n\n## Training configuration\n\nFor convenience, create a `TrainingConfig` class containing the training hyperparameters (feel free to adjust them):\n\n```py\n>>> from dataclasses import dataclass\n\n>>> @dataclass\n... class TrainingConfig:\n... image_size = 128 # the generated image resolution\n... train_batch_size = 16\n... eval_batch_size = 16 # how many images to sample during evaluation\n... num_epochs = 50\n... gradient_accumulation_steps = 1\n... learning_rate = 1e-4\n... lr_warmup_steps = 500\n... save_image_epochs = 10\n... save_model_epochs = 30\n... mixed_precision = \"fp16\" # `no` for float32, `fp16` for automatic mixed precision\n... output_dir = \"ddpm-butterflies-128\" # the model name locally and on the HF Hub\n\n... push_to_hub = True # whether to upload the saved model to the HF Hub\n... hub_model_id = \"/\" # the name of the repository to create on the HF Hub\n... hub_private_repo = False\n... overwrite_output_dir = True # overwrite the old model when re-running the notebook\n... seed = 0\n\n\n>>> config = TrainingConfig()\n```\n\n## Load the dataset\n\nYou can easily load the [Smithsonian Butterflies](https://huggingface.co/datasets/huggan/smithsonian_butterflies_subset) dataset with the 🤗 Datasets library:\n\n```py\n>>> from datasets import load_dataset\n\n>>> config.dataset_name = \"huggan/smithsonian_butterflies_subset\"\n>>> dataset = load_dataset(config.dataset_name, split=\"train\")\n```\n\n\n\n💡 You can find additional datasets from the [HugGan Community Event](https://huggingface.co/huggan) or you can use your own dataset by creating a local [`ImageFolder`](https://huggingface.co/docs/datasets/image_dataset#imagefolder). Set `config.dataset_name` to the repository id of the dataset if it is from the HugGan Community Event, or `imagefolder` if you're using your own images.\n\n\n\n🤗 Datasets uses the [`~datasets.Image`] feature to automatically decode the image data and load it as a [`PIL.Image`](https://pillow.readthedocs.io/en/stable/reference/Image.html) which we can visualize:\n\n```py\n>>> import matplotlib.pyplot as plt\n\n>>> fig, axs = plt.subplots(1, 4, figsize=(16, 4))\n>>> for i, image in enumerate(dataset[:4][\"image\"]):\n... axs[i].imshow(image)\n... axs[i].set_axis_off()\n>>> fig.show()\n```\n\n
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\n\nThe images are all different sizes though, so you'll need to preprocess them first:\n\n* `Resize` changes the image size to the one defined in `config.image_size`.\n* `RandomHorizontalFlip` augments the dataset by randomly mirroring the images.\n* `Normalize` is important to rescale the pixel values into a [-1, 1] range, which is what the model expects.\n\n```py\n>>> from torchvision import transforms\n\n>>> preprocess = transforms.Compose(\n... [\n... transforms.Resize((config.image_size, config.image_size)),\n... transforms.RandomHorizontalFlip(),\n... transforms.ToTensor(),\n... transforms.Normalize([0.5], [0.5]),\n... ]\n... )\n```\n\nUse 🤗 Datasets' [`~datasets.Dataset.set_transform`] method to apply the `preprocess` function on the fly during training:\n\n```py\n>>> def transform(examples):\n... images = [preprocess(image.convert(\"RGB\")) for image in examples[\"image\"]]\n... return {\"images\": images}\n\n\n>>> dataset.set_transform(transform)\n```\n\nFeel free to visualize the images again to confirm that they've been resized. Now you're ready to wrap the dataset in a [DataLoader](https://pytorch.org/docs/stable/data#torch.utils.data.DataLoader) for training!\n\n```py\n>>> import torch\n\n>>> train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=config.train_batch_size, shuffle=True)\n```\n\n## Create a UNet2DModel\n\nPretrained models in 🧨 Diffusers are easily created from their model class with the parameters you want. For example, to create a [`UNet2DModel`]:\n\n```py\n>>> from diffusers import UNet2DModel\n\n>>> model = UNet2DModel(\n... sample_size=config.image_size, # the target image resolution\n... in_channels=3, # the number of input channels, 3 for RGB images\n... out_channels=3, # the number of output channels\n... layers_per_block=2, # how many ResNet layers to use per UNet block\n... block_out_channels=(128, 128, 256, 256, 512, 512), # the number of output channels for each UNet block\n... down_block_types=(\n... \"DownBlock2D\", # a regular ResNet downsampling block\n... \"DownBlock2D\",\n... \"DownBlock2D\",\n... \"DownBlock2D\",\n... \"AttnDownBlock2D\", # a ResNet downsampling block with spatial self-attention\n... \"DownBlock2D\",\n... ),\n... up_block_types=(\n... \"UpBlock2D\", # a regular ResNet upsampling block\n... \"AttnUpBlock2D\", # a ResNet upsampling block with spatial self-attention\n... \"UpBlock2D\",\n... \"UpBlock2D\",\n... \"UpBlock2D\",\n... \"UpBlock2D\",\n... ),\n... )\n```\n\nIt is often a good idea to quickly check the sample image shape matches the model output shape:\n\n```py\n>>> sample_image = dataset[0][\"images\"].unsqueeze(0)\n>>> print(\"Input shape:\", sample_image.shape)\nInput shape: torch.Size([1, 3, 128, 128])\n\n>>> print(\"Output shape:\", model(sample_image, timestep=0).sample.shape)\nOutput shape: torch.Size([1, 3, 128, 128])\n```\n\nGreat! Next, you'll need a scheduler to add some noise to the image.\n\n## Create a scheduler\n\nThe scheduler behaves differently depending on whether you're using the model for training or inference. During inference, the scheduler generates image from the noise. During training, the scheduler takes a model output - or a sample - from a specific point in the diffusion process and applies noise to the image according to a *noise schedule* and an *update rule*.\n\nLet's take a look at the [`DDPMScheduler`] and use the `add_noise` method to add some random noise to the `sample_image` from before:\n\n```py\n>>> import torch\n>>> from PIL import Image\n>>> from diffusers import DDPMScheduler\n\n>>> noise_scheduler = DDPMScheduler(num_train_timesteps=1000)\n>>> noise = torch.randn(sample_image.shape)\n>>> timesteps = torch.LongTensor([50])\n>>> noisy_image = noise_scheduler.add_noise(sample_image, noise, timesteps)\n\n>>> Image.fromarray(((noisy_image.permute(0, 2, 3, 1) + 1.0) * 127.5).type(torch.uint8).numpy()[0])\n```\n\n
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\n\nThe training objective of the model is to predict the noise added to the image. The loss at this step can be calculated by:\n\n```py\n>>> import torch.nn.functional as F\n\n>>> noise_pred = model(noisy_image, timesteps).sample\n>>> loss = F.mse_loss(noise_pred, noise)\n```\n\n## Train the model\n\nBy now, you have most of the pieces to start training the model and all that's left is putting everything together.\n\nFirst, you'll need an optimizer and a learning rate scheduler:\n\n```py\n>>> from diffusers.optimization import get_cosine_schedule_with_warmup\n\n>>> optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate)\n>>> lr_scheduler = get_cosine_schedule_with_warmup(\n... optimizer=optimizer,\n... num_warmup_steps=config.lr_warmup_steps,\n... num_training_steps=(len(train_dataloader) * config.num_epochs),\n... )\n```\n\nThen, you'll need a way to evaluate the model. For evaluation, you can use the [`DDPMPipeline`] to generate a batch of sample images and save it as a grid:\n\n```py\n>>> from diffusers import DDPMPipeline\n>>> from diffusers.utils import make_image_grid\n>>> import os\n\n>>> def evaluate(config, epoch, pipeline):\n... # Sample some images from random noise (this is the backward diffusion process).\n... # The default pipeline output type is `List[PIL.Image]`\n... images = pipeline(\n... batch_size=config.eval_batch_size,\n... generator=torch.Generator(device='cpu').manual_seed(config.seed), # Use a separate torch generator to avoid rewinding the random state of the main training loop\n... ).images\n\n... # Make a grid out of the images\n... image_grid = make_image_grid(images, rows=4, cols=4)\n\n... # Save the images\n... test_dir = os.path.join(config.output_dir, \"samples\")\n... os.makedirs(test_dir, exist_ok=True)\n... image_grid.save(f\"{test_dir}/{epoch:04d}.png\")\n```\n\nNow you can wrap all these components together in a training loop with 🤗 Accelerate for easy TensorBoard logging, gradient accumulation, and mixed precision training. To upload the model to the Hub, write a function to get your repository name and information and then push it to the Hub.\n\n\n\n💡 The training loop below may look intimidating and long, but it'll be worth it later when you launch your training in just one line of code! If you can't wait and want to start generating images, feel free to copy and run the code below. You can always come back and examine the training loop more closely later, like when you're waiting for your model to finish training. 🤗\n\n\n\n```py\n>>> from accelerate import Accelerator\n>>> from huggingface_hub import create_repo, upload_folder\n>>> from tqdm.auto import tqdm\n>>> from pathlib import Path\n>>> import os\n\n>>> def train_loop(config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler):\n... # Initialize accelerator and tensorboard logging\n... accelerator = Accelerator(\n... mixed_precision=config.mixed_precision,\n... gradient_accumulation_steps=config.gradient_accumulation_steps,\n... log_with=\"tensorboard\",\n... project_dir=os.path.join(config.output_dir, \"logs\"),\n... )\n... if accelerator.is_main_process:\n... if config.output_dir is not None:\n... os.makedirs(config.output_dir, exist_ok=True)\n... if config.push_to_hub:\n... repo_id = create_repo(\n... repo_id=config.hub_model_id or Path(config.output_dir).name, exist_ok=True\n... ).repo_id\n... accelerator.init_trackers(\"train_example\")\n\n... # Prepare everything\n... # There is no specific order to remember, you just need to unpack the\n... # objects in the same order you gave them to the prepare method.\n... model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n... model, optimizer, train_dataloader, lr_scheduler\n... )\n\n... global_step = 0\n\n... # Now you train the model\n... for epoch in range(config.num_epochs):\n... progress_bar = tqdm(total=len(train_dataloader), disable=not accelerator.is_local_main_process)\n... progress_bar.set_description(f\"Epoch {epoch}\")\n\n... for step, batch in enumerate(train_dataloader):\n... clean_images = batch[\"images\"]\n... # Sample noise to add to the images\n... noise = torch.randn(clean_images.shape, device=clean_images.device)\n... bs = clean_images.shape[0]\n\n... # Sample a random timestep for each image\n... timesteps = torch.randint(\n... 0, noise_scheduler.config.num_train_timesteps, (bs,), device=clean_images.device,\n... dtype=torch.int64\n... )\n\n... # Add noise to the clean images according to the noise magnitude at each timestep\n... # (this is the forward diffusion process)\n... noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)\n\n... with accelerator.accumulate(model):\n... # Predict the noise residual\n... noise_pred = model(noisy_images, timesteps, return_dict=False)[0]\n... loss = F.mse_loss(noise_pred, noise)\n... accelerator.backward(loss)\n\n... accelerator.clip_grad_norm_(model.parameters(), 1.0)\n... optimizer.step()\n... lr_scheduler.step()\n... optimizer.zero_grad()\n\n... progress_bar.update(1)\n... logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0], \"step\": global_step}\n... progress_bar.set_postfix(**logs)\n... accelerator.log(logs, step=global_step)\n... global_step += 1\n\n... # After each epoch you optionally sample some demo images with evaluate() and save the model\n... if accelerator.is_main_process:\n... pipeline = DDPMPipeline(unet=accelerator.unwrap_model(model), scheduler=noise_scheduler)\n\n... if (epoch + 1) % config.save_image_epochs == 0 or epoch == config.num_epochs - 1:\n... evaluate(config, epoch, pipeline)\n\n... if (epoch + 1) % config.save_model_epochs == 0 or epoch == config.num_epochs - 1:\n... if config.push_to_hub:\n... upload_folder(\n... repo_id=repo_id,\n... folder_path=config.output_dir,\n... commit_message=f\"Epoch {epoch}\",\n... ignore_patterns=[\"step_*\", \"epoch_*\"],\n... )\n... else:\n... pipeline.save_pretrained(config.output_dir)\n```\n\nPhew, that was quite a bit of code! But you're finally ready to launch the training with 🤗 Accelerate's [`~accelerate.notebook_launcher`] function. Pass the function the training loop, all the training arguments, and the number of processes (you can change this value to the number of GPUs available to you) to use for training:\n\n```py\n>>> from accelerate import notebook_launcher\n\n>>> args = (config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler)\n\n>>> notebook_launcher(train_loop, args, num_processes=1)\n```\n\nOnce training is complete, take a look at the final 🦋 images 🦋 generated by your diffusion model!\n\n```py\n>>> import glob\n\n>>> sample_images = sorted(glob.glob(f\"{config.output_dir}/samples/*.png\"))\n>>> Image.open(sample_images[-1])\n```\n\n
\n \n
\n\n## Next steps\n\nUnconditional image generation is one example of a task that can be trained. You can explore other tasks and training techniques by visiting the [🧨 Diffusers Training Examples](../training/overview) page. Here are some examples of what you can learn:\n\n* [Textual Inversion](../training/text_inversion), an algorithm that teaches a model a specific visual concept and integrates it into the generated image.\n* [DreamBooth](../training/dreambooth), a technique for generating personalized images of a subject given several input images of the subject.\n* [Guide](../training/text2image) to finetuning a Stable Diffusion model on your own dataset.\n* [Guide](../training/lora) to using LoRA, a memory-efficient technique for finetuning really large models faster.\n" }, { "path": "diffusers/docs/source/en/tutorials/fast_diffusion.md", "content": "\n\n# Accelerate inference of text-to-image diffusion models\n\nDiffusion models are slower than their GAN counterparts because of the iterative and sequential reverse diffusion process. There are several techniques that can address this limitation such as progressive timestep distillation ([LCM LoRA](../using-diffusers/inference_with_lcm_lora)), model compression ([SSD-1B](https://huggingface.co/segmind/SSD-1B)), and reusing adjacent features of the denoiser ([DeepCache](../optimization/deepcache)).\n\nHowever, you don't necessarily need to use these techniques to speed up inference. With PyTorch 2 alone, you can accelerate the inference latency of text-to-image diffusion pipelines by up to 3x. This tutorial will show you how to progressively apply the optimizations found in PyTorch 2 to reduce inference latency. You'll use the [Stable Diffusion XL (SDXL)](../using-diffusers/sdxl) pipeline in this tutorial, but these techniques are applicable to other text-to-image diffusion pipelines too.\n\nMake sure you're using the latest version of Diffusers:\n\n```bash\npip install -U diffusers\n```\n\nThen upgrade the other required libraries too:\n\n```bash\npip install -U transformers accelerate peft\n```\n\nInstall [PyTorch nightly](https://pytorch.org/) to benefit from the latest and fastest kernels:\n\n```bash\npip3 install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu121\n```\n\n\n\nThe results reported below are from a 80GB 400W A100 with its clock rate set to the maximum.
\n\nIf you're interested in the full benchmarking code, take a look at [huggingface/diffusion-fast](https://github.com/huggingface/diffusion-fast).\n\n\n\n## Baseline\n\nLet's start with a baseline. Disable reduced precision and the [`scaled_dot_product_attention` (SDPA)](../optimization/torch2.0#scaled-dot-product-attention) function which is automatically used by Diffusers:\n\n```python\nfrom diffusers import StableDiffusionXLPipeline\n\n# Load the pipeline in full-precision and place its model components on CUDA.\npipe = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\"\n).to(\"cuda\")\n\n# Run the attention ops without SDPA.\npipe.unet.set_default_attn_processor()\npipe.vae.set_default_attn_processor()\n\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\nimage = pipe(prompt, num_inference_steps=30).images[0]\n```\n\nThis default setup takes 7.36 seconds.\n\n
\n \n
\n\n## bfloat16\n\nEnable the first optimization, reduced precision or more specifically bfloat16. There are several benefits of using reduced precision:\n\n* Using a reduced numerical precision (such as float16 or bfloat16) for inference doesn’t affect the generation quality but significantly improves latency.\n* The benefits of using bfloat16 compared to float16 are hardware dependent, but modern GPUs tend to favor bfloat16.\n* bfloat16 is much more resilient when used with quantization compared to float16, but more recent versions of the quantization library ([torchao](https://github.com/pytorch-labs/ao)) we used don't have numerical issues with float16.\n\n```python\nfrom diffusers import StableDiffusionXLPipeline\nimport torch\n\npipe = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.bfloat16\n).to(\"cuda\")\n\n# Run the attention ops without SDPA.\npipe.unet.set_default_attn_processor()\npipe.vae.set_default_attn_processor()\n\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\nimage = pipe(prompt, num_inference_steps=30).images[0]\n```\n\nbfloat16 reduces the latency from 7.36 seconds to 4.63 seconds.\n\n
\n \n
\n\n\n\nIn our later experiments with float16, recent versions of torchao do not incur numerical problems from float16.\n\n\n\nTake a look at the [Speed up inference](../optimization/fp16) guide to learn more about running inference with reduced precision.\n\n## SDPA\n\nAttention blocks are intensive to run. But with PyTorch's [`scaled_dot_product_attention`](../optimization/torch2.0#scaled-dot-product-attention) function, it is a lot more efficient. This function is used by default in Diffusers so you don't need to make any changes to the code.\n\n```python\nfrom diffusers import StableDiffusionXLPipeline\nimport torch\n\npipe = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.bfloat16\n).to(\"cuda\")\n\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\nimage = pipe(prompt, num_inference_steps=30).images[0]\n```\n\nScaled dot product attention improves the latency from 4.63 seconds to 3.31 seconds.\n\n
\n \n
\n\n## torch.compile\n\nPyTorch 2 includes `torch.compile` which uses fast and optimized kernels. In Diffusers, the UNet and VAE are usually compiled because these are the most compute-intensive modules. First, configure a few compiler flags (refer to the [full list](https://github.com/pytorch/pytorch/blob/main/torch/_inductor/config.py) for more options):\n\n```python\nfrom diffusers import StableDiffusionXLPipeline\nimport torch\n\ntorch._inductor.config.conv_1x1_as_mm = True\ntorch._inductor.config.coordinate_descent_tuning = True\ntorch._inductor.config.epilogue_fusion = False\ntorch._inductor.config.coordinate_descent_check_all_directions = True\n```\n\nIt is also important to change the UNet and VAE's memory layout to \"channels_last\" when compiling them to ensure maximum speed.\n\n```python\npipe.unet.to(memory_format=torch.channels_last)\npipe.vae.to(memory_format=torch.channels_last)\n```\n\nNow compile and perform inference:\n\n```python\n# Compile the UNet and VAE.\npipe.unet = torch.compile(pipe.unet, mode=\"max-autotune\", fullgraph=True)\npipe.vae.decode = torch.compile(pipe.vae.decode, mode=\"max-autotune\", fullgraph=True)\n\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\n\n# First call to `pipe` is slow, subsequent ones are faster.\nimage = pipe(prompt, num_inference_steps=30).images[0]\n```\n\n`torch.compile` offers different backends and modes. For maximum inference speed, use \"max-autotune\" for the inductor backend. “max-autotune” uses CUDA graphs and optimizes the compilation graph specifically for latency. CUDA graphs greatly reduces the overhead of launching GPU operations by using a mechanism to launch multiple GPU operations through a single CPU operation.\n\nUsing SDPA attention and compiling both the UNet and VAE cuts the latency from 3.31 seconds to 2.54 seconds.\n\n
\n \n
\n\n### Prevent graph breaks\n\nSpecifying `fullgraph=True` ensures there are no graph breaks in the underlying model to take full advantage of `torch.compile` without any performance degradation. For the UNet and VAE, this means changing how you access the return variables.\n\n```diff\n- latents = unet(\n- latents, timestep=timestep, encoder_hidden_states=prompt_embeds\n-).sample\n\n+ latents = unet(\n+ latents, timestep=timestep, encoder_hidden_states=prompt_embeds, return_dict=False\n+)[0]\n```\n\n### Remove GPU sync after compilation\n\nDuring the iterative reverse diffusion process, the `step()` function is [called](https://github.com/huggingface/diffusers/blob/1d686bac8146037e97f3fd8c56e4063230f71751/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py#L1228) on the scheduler each time after the denoiser predicts the less noisy latent embeddings. Inside `step()`, the `sigmas` variable is [indexed](https://github.com/huggingface/diffusers/blob/1d686bac8146037e97f3fd8c56e4063230f71751/src/diffusers/schedulers/scheduling_euler_discrete.py#L476) which when placed on the GPU, causes a communication sync between the CPU and GPU. This introduces latency and it becomes more evident when the denoiser has already been compiled.\n\nBut if the `sigmas` array always [stays on the CPU](https://github.com/huggingface/diffusers/blob/35a969d297cba69110d175ee79c59312b9f49e1e/src/diffusers/schedulers/scheduling_euler_discrete.py#L240), the CPU and GPU sync doesn’t occur and you don't get any latency. In general, any CPU and GPU communication sync should be none or be kept to a bare minimum because it can impact inference latency.\n\n## Combine the attention block's projection matrices\n\nThe UNet and VAE in SDXL use Transformer-like blocks which consists of attention blocks and feed-forward blocks.\n\nIn an attention block, the input is projected into three sub-spaces using three different projection matrices – Q, K, and V. These projections are performed separately on the input. But we can horizontally combine the projection matrices into a single matrix and perform the projection in one step. This increases the size of the matrix multiplications of the input projections and improves the impact of quantization.\n\nYou can combine the projection matrices with just a single line of code:\n\n```python\npipe.fuse_qkv_projections()\n```\n\nThis provides a minor improvement from 2.54 seconds to 2.52 seconds.\n\n
\n \n
\n\n\n\nSupport for [`~StableDiffusionXLPipeline.fuse_qkv_projections`] is limited and experimental. It's not available for many non-Stable Diffusion pipelines such as [Kandinsky](../using-diffusers/kandinsky). You can refer to this [PR](https://github.com/huggingface/diffusers/pull/6179) to get an idea about how to enable this for the other pipelines.\n\n\n\n## Dynamic quantization\n\nYou can also use the ultra-lightweight PyTorch quantization library, [torchao](https://github.com/pytorch-labs/ao) (commit SHA `54bcd5a10d0abbe7b0c045052029257099f83fd9`), to apply [dynamic int8 quantization](https://pytorch.org/tutorials/recipes/recipes/dynamic_quantization.html) to the UNet and VAE. Quantization adds additional conversion overhead to the model that is hopefully made up for by faster matmuls (dynamic quantization). If the matmuls are too small, these techniques may degrade performance.\n\nFirst, configure all the compiler tags:\n\n```python\nfrom diffusers import StableDiffusionXLPipeline\nimport torch \n\n# Notice the two new flags at the end.\ntorch._inductor.config.conv_1x1_as_mm = True\ntorch._inductor.config.coordinate_descent_tuning = True\ntorch._inductor.config.epilogue_fusion = False\ntorch._inductor.config.coordinate_descent_check_all_directions = True\ntorch._inductor.config.force_fuse_int_mm_with_mul = True\ntorch._inductor.config.use_mixed_mm = True\n```\n\nCertain linear layers in the UNet and VAE don’t benefit from dynamic int8 quantization. You can filter out those layers with the [`dynamic_quant_filter_fn`](https://github.com/huggingface/diffusion-fast/blob/0f169640b1db106fe6a479f78c1ed3bfaeba3386/utils/pipeline_utils.py#L16) shown below.\n\n```python\ndef dynamic_quant_filter_fn(mod, *args):\n return (\n isinstance(mod, torch.nn.Linear)\n and mod.in_features > 16\n and (mod.in_features, mod.out_features)\n not in [\n (1280, 640),\n (1920, 1280),\n (1920, 640),\n (2048, 1280),\n (2048, 2560),\n (2560, 1280),\n (256, 128),\n (2816, 1280),\n (320, 640),\n (512, 1536),\n (512, 256),\n (512, 512),\n (640, 1280),\n (640, 1920),\n (640, 320),\n (640, 5120),\n (640, 640),\n (960, 320),\n (960, 640),\n ]\n )\n\n\ndef conv_filter_fn(mod, *args):\n return (\n isinstance(mod, torch.nn.Conv2d) and mod.kernel_size == (1, 1) and 128 in [mod.in_channels, mod.out_channels]\n )\n```\n\nFinally, apply all the optimizations discussed so far:\n\n```python\n# SDPA + bfloat16.\npipe = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.bfloat16\n).to(\"cuda\")\n\n# Combine attention projection matrices.\npipe.fuse_qkv_projections()\n\n# Change the memory layout.\npipe.unet.to(memory_format=torch.channels_last)\npipe.vae.to(memory_format=torch.channels_last)\n```\n\nSince dynamic quantization is only limited to the linear layers, convert the appropriate pointwise convolution layers into linear layers to maximize its benefit.\n\n```python\nfrom torchao import swap_conv2d_1x1_to_linear\n\nswap_conv2d_1x1_to_linear(pipe.unet, conv_filter_fn)\nswap_conv2d_1x1_to_linear(pipe.vae, conv_filter_fn)\n```\n\nApply dynamic quantization:\n\n```python\nfrom torchao import apply_dynamic_quant\n\napply_dynamic_quant(pipe.unet, dynamic_quant_filter_fn)\napply_dynamic_quant(pipe.vae, dynamic_quant_filter_fn)\n```\n\nFinally, compile and perform inference:\n\n```python\npipe.unet = torch.compile(pipe.unet, mode=\"max-autotune\", fullgraph=True)\npipe.vae.decode = torch.compile(pipe.vae.decode, mode=\"max-autotune\", fullgraph=True)\n\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\nimage = pipe(prompt, num_inference_steps=30).images[0]\n```\n\nApplying dynamic quantization improves the latency from 2.52 seconds to 2.43 seconds.\n\n
\n \n
\n" }, { "path": "diffusers/docs/source/en/tutorials/tutorial_overview.md", "content": "\n\n# Overview\n\nWelcome to 🧨 Diffusers! If you're new to diffusion models and generative AI, and want to learn more, then you've come to the right place. These beginner-friendly tutorials are designed to provide a gentle introduction to diffusion models and help you understand the library fundamentals - the core components and how 🧨 Diffusers is meant to be used.\n\nYou'll learn how to use a pipeline for inference to rapidly generate things, and then deconstruct that pipeline to really understand how to use the library as a modular toolbox for building your own diffusion systems. In the next lesson, you'll learn how to train your own diffusion model to generate what you want.\n\nAfter completing the tutorials, you'll have gained the necessary skills to start exploring the library on your own and see how to use it for your own projects and applications.\n\nFeel free to join our community on [Discord](https://discord.com/invite/JfAtkvEtRb) or the [forums](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) to connect and collaborate with other users and developers!\n\nLet's start diffusing! 🧨\n" }, { "path": "diffusers/docs/source/en/tutorials/using_peft_for_inference.md", "content": "\n\n[[open-in-colab]]\n\n# Load LoRAs for inference\n\nThere are many adapter types (with [LoRAs](https://huggingface.co/docs/peft/conceptual_guides/adapter#low-rank-adaptation-lora) being the most popular) trained in different styles to achieve different effects. You can even combine multiple adapters to create new and unique images.\n\nIn this tutorial, you'll learn how to easily load and manage adapters for inference with the 🤗 [PEFT](https://huggingface.co/docs/peft/index) integration in 🤗 Diffusers. You'll use LoRA as the main adapter technique, so you'll see the terms LoRA and adapter used interchangeably.\n\nLet's first install all the required libraries.\n\n```bash\n!pip install -q transformers accelerate peft diffusers\n```\n\nNow, load a pipeline with a [Stable Diffusion XL (SDXL)](../api/pipelines/stable_diffusion/stable_diffusion_xl) checkpoint:\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipe_id = \"stabilityai/stable-diffusion-xl-base-1.0\"\npipe = DiffusionPipeline.from_pretrained(pipe_id, torch_dtype=torch.float16).to(\"cuda\")\n```\n\nNext, load a [CiroN2022/toy-face](https://huggingface.co/CiroN2022/toy-face) adapter with the [`~diffusers.loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] method. With the 🤗 PEFT integration, you can assign a specific `adapter_name` to the checkpoint, which let's you easily switch between different LoRA checkpoints. Let's call this adapter `\"toy\"`.\n\n```python\npipe.load_lora_weights(\"CiroN2022/toy-face\", weight_name=\"toy_face_sdxl.safetensors\", adapter_name=\"toy\")\n```\n\nMake sure to include the token `toy_face` in the prompt and then you can perform inference:\n\n```python\nprompt = \"toy_face of a hacker with a hoodie\"\n\nlora_scale = 0.9\nimage = pipe(\n prompt, num_inference_steps=30, cross_attention_kwargs={\"scale\": lora_scale}, generator=torch.manual_seed(0)\n).images[0]\nimage\n```\n\n![toy-face](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/peft_integration/diffusers_peft_lora_inference_8_1.png)\n\nWith the `adapter_name` parameter, it is really easy to use another adapter for inference! Load the [nerijs/pixel-art-xl](https://huggingface.co/nerijs/pixel-art-xl) adapter that has been fine-tuned to generate pixel art images and call it `\"pixel\"`.\n\nThe pipeline automatically sets the first loaded adapter (`\"toy\"`) as the active adapter, but you can activate the `\"pixel\"` adapter with the [`~diffusers.loaders.UNet2DConditionLoadersMixin.set_adapters`] method:\n\n```python\npipe.load_lora_weights(\"nerijs/pixel-art-xl\", weight_name=\"pixel-art-xl.safetensors\", adapter_name=\"pixel\")\npipe.set_adapters(\"pixel\")\n```\n\nMake sure you include the token `pixel art` in your prompt to generate a pixel art image:\n\n```python\nprompt = \"a hacker with a hoodie, pixel art\"\nimage = pipe(\n prompt, num_inference_steps=30, cross_attention_kwargs={\"scale\": lora_scale}, generator=torch.manual_seed(0)\n).images[0]\nimage\n```\n\n![pixel-art](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/peft_integration/diffusers_peft_lora_inference_12_1.png)\n\n## Merge adapters\n\nYou can also merge different adapter checkpoints for inference to blend their styles together.\n\nOnce again, use the [`~diffusers.loaders.UNet2DConditionLoadersMixin.set_adapters`] method to activate the `pixel` and `toy` adapters and specify the weights for how they should be merged.\n\n```python\npipe.set_adapters([\"pixel\", \"toy\"], adapter_weights=[0.5, 1.0])\n```\n\n\n\nLoRA checkpoints in the diffusion community are almost always obtained with [DreamBooth](https://huggingface.co/docs/diffusers/main/en/training/dreambooth). DreamBooth training often relies on \"trigger\" words in the input text prompts in order for the generation results to look as expected. When you combine multiple LoRA checkpoints, it's important to ensure the trigger words for the corresponding LoRA checkpoints are present in the input text prompts.\n\n\n\nRemember to use the trigger words for [CiroN2022/toy-face](https://hf.co/CiroN2022/toy-face) and [nerijs/pixel-art-xl](https://hf.co/nerijs/pixel-art-xl) (these are found in their repositories) in the prompt to generate an image.\n\n```python\nprompt = \"toy_face of a hacker with a hoodie, pixel art\"\nimage = pipe(\n prompt, num_inference_steps=30, cross_attention_kwargs={\"scale\": 1.0}, generator=torch.manual_seed(0)\n).images[0]\nimage\n```\n\n![toy-face-pixel-art](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/peft_integration/diffusers_peft_lora_inference_16_1.png)\n\nImpressive! As you can see, the model generated an image that mixed the characteristics of both adapters.\n\n> [!TIP]\n> Through its PEFT integration, Diffusers also offers more efficient merging methods which you can learn about in the [Merge LoRAs](../using-diffusers/merge_loras) guide!\n\nTo return to only using one adapter, use the [`~diffusers.loaders.UNet2DConditionLoadersMixin.set_adapters`] method to activate the `\"toy\"` adapter:\n\n```python\npipe.set_adapters(\"toy\")\n\nprompt = \"toy_face of a hacker with a hoodie\"\nlora_scale = 0.9\nimage = pipe(\n prompt, num_inference_steps=30, cross_attention_kwargs={\"scale\": lora_scale}, generator=torch.manual_seed(0)\n).images[0]\nimage\n```\n\nOr to disable all adapters entirely, use the [`~diffusers.loaders.UNet2DConditionLoadersMixin.disable_lora`] method to return the base model.\n\n```python\npipe.disable_lora()\n\nprompt = \"toy_face of a hacker with a hoodie\"\nimage = pipe(prompt, num_inference_steps=30, generator=torch.manual_seed(0)).images[0]\nimage\n```\n\n![no-lora](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/peft_integration/diffusers_peft_lora_inference_20_1.png)\n\n### Customize adapters strength\nFor even more customization, you can control how strongly the adapter affects each part of the pipeline. For this, pass a dictionary with the control strengths (called \"scales\") to [`~diffusers.loaders.UNet2DConditionLoadersMixin.set_adapters`].\n\nFor example, here's how you can turn on the adapter for the `down` parts, but turn it off for the `mid` and `up` parts:\n```python\npipe.enable_lora() # enable lora again, after we disabled it above\nprompt = \"toy_face of a hacker with a hoodie, pixel art\"\nadapter_weight_scales = { \"unet\": { \"down\": 1, \"mid\": 0, \"up\": 0} }\npipe.set_adapters(\"pixel\", adapter_weight_scales)\nimage = pipe(prompt, num_inference_steps=30, generator=torch.manual_seed(0)).images[0]\nimage\n```\n\n![block-lora-text-and-down](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/peft_integration/diffusers_peft_lora_inference_block_down.png)\n\nLet's see how turning off the `down` part and turning on the `mid` and `up` part respectively changes the image.\n```python\nadapter_weight_scales = { \"unet\": { \"down\": 0, \"mid\": 1, \"up\": 0} }\npipe.set_adapters(\"pixel\", adapter_weight_scales)\nimage = pipe(prompt, num_inference_steps=30, generator=torch.manual_seed(0)).images[0]\nimage\n```\n\n![block-lora-text-and-mid](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/peft_integration/diffusers_peft_lora_inference_block_mid.png)\n\n```python\nadapter_weight_scales = { \"unet\": { \"down\": 0, \"mid\": 0, \"up\": 1} }\npipe.set_adapters(\"pixel\", adapter_weight_scales)\nimage = pipe(prompt, num_inference_steps=30, generator=torch.manual_seed(0)).images[0]\nimage\n```\n\n![block-lora-text-and-up](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/peft_integration/diffusers_peft_lora_inference_block_up.png)\n\nLooks cool!\n\nThis is a really powerful feature. You can use it to control the adapter strengths down to per-transformer level. And you can even use it for multiple adapters.\n```python\nadapter_weight_scales_toy = 0.5\nadapter_weight_scales_pixel = {\n \"unet\": {\n \"down\": 0.9, # all transformers in the down-part will use scale 0.9\n # \"mid\" # because, in this example, \"mid\" is not given, all transformers in the mid part will use the default scale 1.0\n \"up\": {\n \"block_0\": 0.6, # all 3 transformers in the 0th block in the up-part will use scale 0.6\n \"block_1\": [0.4, 0.8, 1.0], # the 3 transformers in the 1st block in the up-part will use scales 0.4, 0.8 and 1.0 respectively\n }\n }\n}\npipe.set_adapters([\"toy\", \"pixel\"], [adapter_weight_scales_toy, adapter_weight_scales_pixel])\nimage = pipe(prompt, num_inference_steps=30, generator=torch.manual_seed(0)).images[0]\nimage\n```\n\n![block-lora-mixed](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/peft_integration/diffusers_peft_lora_inference_block_mixed.png)\n\n## Manage active adapters\n\nYou have attached multiple adapters in this tutorial, and if you're feeling a bit lost on what adapters have been attached to the pipeline's components, use the [`~diffusers.loaders.LoraLoaderMixin.get_active_adapters`] method to check the list of active adapters:\n\n```py\nactive_adapters = pipe.get_active_adapters()\nactive_adapters\n[\"toy\", \"pixel\"]\n```\n\nYou can also get the active adapters of each pipeline component with [`~diffusers.loaders.LoraLoaderMixin.get_list_adapters`]:\n\n```py\nlist_adapters_component_wise = pipe.get_list_adapters()\nlist_adapters_component_wise\n{\"text_encoder\": [\"toy\", \"pixel\"], \"unet\": [\"toy\", \"pixel\"], \"text_encoder_2\": [\"toy\", \"pixel\"]}\n```\n" }, { "path": "diffusers/docs/source/en/using-diffusers/callback.md", "content": "\n\n# Pipeline callbacks\n\nThe denoising loop of a pipeline can be modified with custom defined functions using the `callback_on_step_end` parameter. The callback function is executed at the end of each step, and modifies the pipeline attributes and variables for the next step. This is really useful for *dynamically* adjusting certain pipeline attributes or modifying tensor variables. This versatility allows for interesting use-cases such as changing the prompt embeddings at each timestep, assigning different weights to the prompt embeddings, and editing the guidance scale. With callbacks, you can implement new features without modifying the underlying code!\n\n> [!TIP]\n> 🤗 Diffusers currently only supports `callback_on_step_end`, but feel free to open a [feature request](https://github.com/huggingface/diffusers/issues/new/choose) if you have a cool use-case and require a callback function with a different execution point!\n\nThis guide will demonstrate how callbacks work by a few features you can implement with them.\n\n## Official callbacks\n\nWe provide a list of callbacks you can plug into an existing pipeline and modify the denoising loop. This is the current list of official callbacks:\n\n- `SDCFGCutoffCallback`: Disables the CFG after a certain number of steps for all SD 1.5 pipelines, including text-to-image, image-to-image, inpaint, and controlnet.\n- `SDXLCFGCutoffCallback`: Disables the CFG after a certain number of steps for all SDXL pipelines, including text-to-image, image-to-image, inpaint, and controlnet.\n- `IPAdapterScaleCutoffCallback`: Disables the IP Adapter after a certain number of steps for all pipelines supporting IP-Adapter.\n\n> [!TIP]\n> If you want to add a new official callback, feel free to open a [feature request](https://github.com/huggingface/diffusers/issues/new/choose) or [submit a PR](https://huggingface.co/docs/diffusers/main/en/conceptual/contribution#how-to-open-a-pr).\n\nTo set up a callback, you need to specify the number of denoising steps after which the callback comes into effect. You can do so by using either one of these two arguments\n\n- `cutoff_step_ratio`: Float number with the ratio of the steps.\n- `cutoff_step_index`: Integer number with the exact number of the step.\n\n```python\nimport torch\n\nfrom diffusers import DPMSolverMultistepScheduler, StableDiffusionXLPipeline\nfrom diffusers.callbacks import SDXLCFGCutoffCallback\n\n\ncallback = SDXLCFGCutoffCallback(cutoff_step_ratio=0.4)\n# can also be used with cutoff_step_index\n# callback = SDXLCFGCutoffCallback(cutoff_step_ratio=None, cutoff_step_index=10)\n\npipeline = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n).to(\"cuda\")\npipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, use_karras_sigmas=True)\n\nprompt = \"a sports car at the road, best quality, high quality, high detail, 8k resolution\"\n\ngenerator = torch.Generator(device=\"cpu\").manual_seed(2628670641)\n\nout = pipeline(\n prompt=prompt,\n negative_prompt=\"\",\n guidance_scale=6.5,\n num_inference_steps=25,\n generator=generator,\n callback_on_step_end=callback,\n)\n\nout.images[0].save(\"official_callback.png\")\n```\n\n
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\n\n## Dynamic classifier-free guidance\n\nDynamic classifier-free guidance (CFG) is a feature that allows you to disable CFG after a certain number of inference steps which can help you save compute with minimal cost to performance. The callback function for this should have the following arguments:\n\n- `pipeline` (or the pipeline instance) provides access to important properties such as `num_timesteps` and `guidance_scale`. You can modify these properties by updating the underlying attributes. For this example, you'll disable CFG by setting `pipeline._guidance_scale=0.0`.\n- `step_index` and `timestep` tell you where you are in the denoising loop. Use `step_index` to turn off CFG after reaching 40% of `num_timesteps`.\n- `callback_kwargs` is a dict that contains tensor variables you can modify during the denoising loop. It only includes variables specified in the `callback_on_step_end_tensor_inputs` argument, which is passed to the pipeline's `__call__` method. Different pipelines may use different sets of variables, so please check a pipeline's `_callback_tensor_inputs` attribute for the list of variables you can modify. Some common variables include `latents` and `prompt_embeds`. For this function, change the batch size of `prompt_embeds` after setting `guidance_scale=0.0` in order for it to work properly.\n\nYour callback function should look something like this:\n\n```python\ndef callback_dynamic_cfg(pipe, step_index, timestep, callback_kwargs):\n # adjust the batch_size of prompt_embeds according to guidance_scale\n if step_index == int(pipeline.num_timesteps * 0.4):\n prompt_embeds = callback_kwargs[\"prompt_embeds\"]\n prompt_embeds = prompt_embeds.chunk(2)[-1]\n\n # update guidance_scale and prompt_embeds\n pipeline._guidance_scale = 0.0\n callback_kwargs[\"prompt_embeds\"] = prompt_embeds\n return callback_kwargs\n```\n\nNow, you can pass the callback function to the `callback_on_step_end` parameter and the `prompt_embeds` to `callback_on_step_end_tensor_inputs`.\n\n```py\nimport torch\nfrom diffusers import StableDiffusionPipeline\n\npipeline = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16)\npipeline = pipeline.to(\"cuda\")\n\nprompt = \"a photo of an astronaut riding a horse on mars\"\n\ngenerator = torch.Generator(device=\"cuda\").manual_seed(1)\nout = pipeline(\n prompt,\n generator=generator,\n callback_on_step_end=callback_dynamic_cfg,\n callback_on_step_end_tensor_inputs=['prompt_embeds']\n)\n\nout.images[0].save(\"out_custom_cfg.png\")\n```\n\n## Interrupt the diffusion process\n\n> [!TIP]\n> The interruption callback is supported for text-to-image, image-to-image, and inpainting for the [StableDiffusionPipeline](../api/pipelines/stable_diffusion/overview) and [StableDiffusionXLPipeline](../api/pipelines/stable_diffusion/stable_diffusion_xl).\n\nStopping the diffusion process early is useful when building UIs that work with Diffusers because it allows users to stop the generation process if they're unhappy with the intermediate results. You can incorporate this into your pipeline with a callback.\n\nThis callback function should take the following arguments: `pipeline`, `i`, `t`, and `callback_kwargs` (this must be returned). Set the pipeline's `_interrupt` attribute to `True` to stop the diffusion process after a certain number of steps. You are also free to implement your own custom stopping logic inside the callback.\n\nIn this example, the diffusion process is stopped after 10 steps even though `num_inference_steps` is set to 50.\n\n```python\nfrom diffusers import StableDiffusionPipeline\n\npipeline = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\npipeline.enable_model_cpu_offload()\nnum_inference_steps = 50\n\ndef interrupt_callback(pipeline, i, t, callback_kwargs):\n stop_idx = 10\n if i == stop_idx:\n pipeline._interrupt = True\n\n return callback_kwargs\n\npipeline(\n \"A photo of a cat\",\n num_inference_steps=num_inference_steps,\n callback_on_step_end=interrupt_callback,\n)\n```\n\n## Display image after each generation step\n\n> [!TIP]\n> This tip was contributed by [asomoza](https://github.com/asomoza).\n\nDisplay an image after each generation step by accessing and converting the latents after each step into an image. The latent space is compressed to 128x128, so the images are also 128x128 which is useful for a quick preview.\n\n1. Use the function below to convert the SDXL latents (4 channels) to RGB tensors (3 channels) as explained in the [Explaining the SDXL latent space](https://huggingface.co/blog/TimothyAlexisVass/explaining-the-sdxl-latent-space) blog post.\n\n```py\ndef latents_to_rgb(latents):\n weights = (\n (60, -60, 25, -70),\n (60, -5, 15, -50),\n (60, 10, -5, -35)\n )\n\n weights_tensor = torch.t(torch.tensor(weights, dtype=latents.dtype).to(latents.device))\n biases_tensor = torch.tensor((150, 140, 130), dtype=latents.dtype).to(latents.device)\n rgb_tensor = torch.einsum(\"...lxy,lr -> ...rxy\", latents, weights_tensor) + biases_tensor.unsqueeze(-1).unsqueeze(-1)\n image_array = rgb_tensor.clamp(0, 255)[0].byte().cpu().numpy()\n image_array = image_array.transpose(1, 2, 0)\n\n return Image.fromarray(image_array)\n```\n\n2. Create a function to decode and save the latents into an image.\n\n```py\ndef decode_tensors(pipe, step, timestep, callback_kwargs):\n latents = callback_kwargs[\"latents\"]\n\n image = latents_to_rgb(latents)\n image.save(f\"{step}.png\")\n\n return callback_kwargs\n```\n\n3. Pass the `decode_tensors` function to the `callback_on_step_end` parameter to decode the tensors after each step. You also need to specify what you want to modify in the `callback_on_step_end_tensor_inputs` parameter, which in this case are the latents.\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\nfrom PIL import Image\n\npipeline = AutoPipelineForText2Image.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n use_safetensors=True\n).to(\"cuda\")\n\nimage = pipeline(\n prompt=\"A croissant shaped like a cute bear.\",\n negative_prompt=\"Deformed, ugly, bad anatomy\",\n callback_on_step_end=decode_tensors,\n callback_on_step_end_tensor_inputs=[\"latents\"],\n).images[0]\n```\n\n
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\n" }, { "path": "diffusers/docs/source/en/using-diffusers/conditional_image_generation.md", "content": "\n\n# Text-to-image\n\n[[open-in-colab]]\n\nWhen you think of diffusion models, text-to-image is usually one of the first things that come to mind. Text-to-image generates an image from a text description (for example, \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\") which is also known as a *prompt*.\n\nFrom a very high level, a diffusion model takes a prompt and some random initial noise, and iteratively removes the noise to construct an image. The *denoising* process is guided by the prompt, and once the denoising process ends after a predetermined number of time steps, the image representation is decoded into an image.\n\n\n\nRead the [How does Stable Diffusion work?](https://huggingface.co/blog/stable_diffusion#how-does-stable-diffusion-work) blog post to learn more about how a latent diffusion model works.\n\n\n\nYou can generate images from a prompt in 🤗 Diffusers in two steps:\n\n1. Load a checkpoint into the [`AutoPipelineForText2Image`] class, which automatically detects the appropriate pipeline class to use based on the checkpoint:\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\n\t\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, variant=\"fp16\"\n).to(\"cuda\")\n```\n\n2. Pass a prompt to the pipeline to generate an image:\n\n```py\nimage = pipeline(\n\t\"stained glass of darth vader, backlight, centered composition, masterpiece, photorealistic, 8k\"\n).images[0]\nimage\n```\n\n
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\n\n## Popular models\n\nThe most common text-to-image models are [Stable Diffusion v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5), [Stable Diffusion XL (SDXL)](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), and [Kandinsky 2.2](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder). There are also ControlNet models or adapters that can be used with text-to-image models for more direct control in generating images. The results from each model are slightly different because of their architecture and training process, but no matter which model you choose, their usage is more or less the same. Let's use the same prompt for each model and compare their results.\n\n### Stable Diffusion v1.5\n\n[Stable Diffusion v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) is a latent diffusion model initialized from [Stable Diffusion v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4), and finetuned for 595K steps on 512x512 images from the LAION-Aesthetics V2 dataset. You can use this model like:\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\n\t\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, variant=\"fp16\"\n).to(\"cuda\")\ngenerator = torch.Generator(\"cuda\").manual_seed(31)\nimage = pipeline(\"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\", generator=generator).images[0]\nimage\n```\n\n### Stable Diffusion XL\n\nSDXL is a much larger version of the previous Stable Diffusion models, and involves a two-stage model process that adds even more details to an image. It also includes some additional *micro-conditionings* to generate high-quality images centered subjects. Take a look at the more comprehensive [SDXL](sdxl) guide to learn more about how to use it. In general, you can use SDXL like:\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16, variant=\"fp16\"\n).to(\"cuda\")\ngenerator = torch.Generator(\"cuda\").manual_seed(31)\nimage = pipeline(\"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\", generator=generator).images[0]\nimage\n```\n\n### Kandinsky 2.2\n\nThe Kandinsky model is a bit different from the Stable Diffusion models because it also uses an image prior model to create embeddings that are used to better align text and images in the diffusion model.\n\nThe easiest way to use Kandinsky 2.2 is:\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\n\t\"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n).to(\"cuda\")\ngenerator = torch.Generator(\"cuda\").manual_seed(31)\nimage = pipeline(\"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\", generator=generator).images[0]\nimage\n```\n\n### ControlNet\n\nControlNet models are auxiliary models or adapters that are finetuned on top of text-to-image models, such as [Stable Diffusion v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5). Using ControlNet models in combination with text-to-image models offers diverse options for more explicit control over how to generate an image. With ControlNet, you add an additional conditioning input image to the model. For example, if you provide an image of a human pose (usually represented as multiple keypoints that are connected into a skeleton) as a conditioning input, the model generates an image that follows the pose of the image. Check out the more in-depth [ControlNet](controlnet) guide to learn more about other conditioning inputs and how to use them.\n\nIn this example, let's condition the ControlNet with a human pose estimation image. Load the ControlNet model pretrained on human pose estimations:\n\n```py\nfrom diffusers import ControlNetModel, AutoPipelineForText2Image\nfrom diffusers.utils import load_image\nimport torch\n\ncontrolnet = ControlNetModel.from_pretrained(\n\t\"lllyasviel/control_v11p_sd15_openpose\", torch_dtype=torch.float16, variant=\"fp16\"\n).to(\"cuda\")\npose_image = load_image(\"https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/control.png\")\n```\n\nPass the `controlnet` to the [`AutoPipelineForText2Image`], and provide the prompt and pose estimation image:\n\n```py\npipeline = AutoPipelineForText2Image.from_pretrained(\n\t\"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, torch_dtype=torch.float16, variant=\"fp16\"\n).to(\"cuda\")\ngenerator = torch.Generator(\"cuda\").manual_seed(31)\nimage = pipeline(\"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\", image=pose_image, generator=generator).images[0]\nimage\n```\n\n
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\n\n## Configure pipeline parameters\n\nThere are a number of parameters that can be configured in the pipeline that affect how an image is generated. You can change the image's output size, specify a negative prompt to improve image quality, and more. This section dives deeper into how to use these parameters.\n\n### Height and width\n\nThe `height` and `width` parameters control the height and width (in pixels) of the generated image. By default, the Stable Diffusion v1.5 model outputs 512x512 images, but you can change this to any size that is a multiple of 8. For example, to create a rectangular image:\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\n\t\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, variant=\"fp16\"\n).to(\"cuda\")\nimage = pipeline(\n\t\"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\", height=768, width=512\n).images[0]\nimage\n```\n\n
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\n\n\n\nOther models may have different default image sizes depending on the image sizes in the training dataset. For example, SDXL's default image size is 1024x1024 and using lower `height` and `width` values may result in lower quality images. Make sure you check the model's API reference first!\n\n\n\n### Guidance scale\n\nThe `guidance_scale` parameter affects how much the prompt influences image generation. A lower value gives the model \"creativity\" to generate images that are more loosely related to the prompt. Higher `guidance_scale` values push the model to follow the prompt more closely, and if this value is too high, you may observe some artifacts in the generated image.\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\n\t\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16\n).to(\"cuda\")\nimage = pipeline(\n\t\"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\", guidance_scale=3.5\n).images[0]\nimage\n```\n\n
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\n\n### Negative prompt\n\nJust like how a prompt guides generation, a *negative prompt* steers the model away from things you don't want the model to generate. This is commonly used to improve overall image quality by removing poor or bad image features such as \"low resolution\" or \"bad details\". You can also use a negative prompt to remove or modify the content and style of an image.\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\n\t\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16\n).to(\"cuda\")\nimage = pipeline(\n\tprompt=\"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\",\n\tnegative_prompt=\"ugly, deformed, disfigured, poor details, bad anatomy\",\n).images[0]\nimage\n```\n\n
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\n\n### Generator\n\nA [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html#generator) object enables reproducibility in a pipeline by setting a manual seed. You can use a `Generator` to generate batches of images and iteratively improve on an image generated from a seed as detailed in the [Improve image quality with deterministic generation](reusing_seeds) guide.\n\nYou can set a seed and `Generator` as shown below. Creating an image with a `Generator` should return the same result each time instead of randomly generating a new image.\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\n\t\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16\n).to(\"cuda\")\ngenerator = torch.Generator(device=\"cuda\").manual_seed(30)\nimage = pipeline(\n\t\"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\",\n\tgenerator=generator,\n).images[0]\nimage\n```\n\n## Control image generation\n\nThere are several ways to exert more control over how an image is generated outside of configuring a pipeline's parameters, such as prompt weighting and ControlNet models.\n\n### Prompt weighting\n\nPrompt weighting is a technique for increasing or decreasing the importance of concepts in a prompt to emphasize or minimize certain features in an image. We recommend using the [Compel](https://github.com/damian0815/compel) library to help you generate the weighted prompt embeddings.\n\n\n\nLearn how to create the prompt embeddings in the [Prompt weighting](weighted_prompts) guide. This example focuses on how to use the prompt embeddings in the pipeline.\n\n\n\nOnce you've created the embeddings, you can pass them to the `prompt_embeds` (and `negative_prompt_embeds` if you're using a negative prompt) parameter in the pipeline.\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\n\t\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16\n).to(\"cuda\")\nimage = pipeline(\n\tprompt_embeds=prompt_embeds, # generated from Compel\n\tnegative_prompt_embeds=negative_prompt_embeds, # generated from Compel\n).images[0]\n```\n\n### ControlNet\n\nAs you saw in the [ControlNet](#controlnet) section, these models offer a more flexible and accurate way to generate images by incorporating an additional conditioning image input. Each ControlNet model is pretrained on a particular type of conditioning image to generate new images that resemble it. For example, if you take a ControlNet model pretrained on depth maps, you can give the model a depth map as a conditioning input and it'll generate an image that preserves the spatial information in it. This is quicker and easier than specifying the depth information in a prompt. You can even combine multiple conditioning inputs with a [MultiControlNet](controlnet#multicontrolnet)!\n\nThere are many types of conditioning inputs you can use, and 🤗 Diffusers supports ControlNet for Stable Diffusion and SDXL models. Take a look at the more comprehensive [ControlNet](controlnet) guide to learn how you can use these models.\n\n## Optimize\n\nDiffusion models are large, and the iterative nature of denoising an image is computationally expensive and intensive. But this doesn't mean you need access to powerful - or even many - GPUs to use them. There are many optimization techniques for running diffusion models on consumer and free-tier resources. For example, you can load model weights in half-precision to save GPU memory and increase speed or offload the entire model to the GPU to save even more memory.\n\nPyTorch 2.0 also supports a more memory-efficient attention mechanism called [*scaled dot product attention*](../optimization/torch2.0#scaled-dot-product-attention) that is automatically enabled if you're using PyTorch 2.0. You can combine this with [`torch.compile`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) to speed your code up even more:\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, variant=\"fp16\").to(\"cuda\")\npipeline.unet = torch.compile(pipeline.unet, mode=\"reduce-overhead\", fullgraph=True)\n```\n\nFor more tips on how to optimize your code to save memory and speed up inference, read the [Memory and speed](../optimization/fp16) and [Torch 2.0](../optimization/torch2.0) guides.\n" }, { "path": "diffusers/docs/source/en/using-diffusers/controlling_generation.md", "content": "\n\n# Controlled generation\n\nControlling outputs generated by diffusion models has been long pursued by the community and is now an active research topic. In many popular diffusion models, subtle changes in inputs, both images and text prompts, can drastically change outputs. In an ideal world we want to be able to control how semantics are preserved and changed.\n\nMost examples of preserving semantics reduce to being able to accurately map a change in input to a change in output. I.e. adding an adjective to a subject in a prompt preserves the entire image, only modifying the changed subject. Or, image variation of a particular subject preserves the subject's pose.\n\nAdditionally, there are qualities of generated images that we would like to influence beyond semantic preservation. I.e. in general, we would like our outputs to be of good quality, adhere to a particular style, or be realistic.\n\nWe will document some of the techniques `diffusers` supports to control generation of diffusion models. Much is cutting edge research and can be quite nuanced. If something needs clarifying or you have a suggestion, don't hesitate to open a discussion on the [forum](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) or a [GitHub issue](https://github.com/huggingface/diffusers/issues).\n\nWe provide a high level explanation of how the generation can be controlled as well as a snippet of the technicals. For more in depth explanations on the technicals, the original papers which are linked from the pipelines are always the best resources.\n\nDepending on the use case, one should choose a technique accordingly. In many cases, these techniques can be combined. For example, one can combine Textual Inversion with SEGA to provide more semantic guidance to the outputs generated using Textual Inversion.\n\nUnless otherwise mentioned, these are techniques that work with existing models and don't require their own weights.\n\n1. [InstructPix2Pix](#instruct-pix2pix)\n2. [Pix2Pix Zero](#pix2pix-zero)\n3. [Attend and Excite](#attend-and-excite)\n4. [Semantic Guidance](#semantic-guidance-sega)\n5. [Self-attention Guidance](#self-attention-guidance-sag)\n6. [Depth2Image](#depth2image)\n7. [MultiDiffusion Panorama](#multidiffusion-panorama)\n8. [DreamBooth](#dreambooth)\n9. [Textual Inversion](#textual-inversion)\n10. [ControlNet](#controlnet)\n11. [Prompt Weighting](#prompt-weighting)\n12. [Custom Diffusion](#custom-diffusion)\n13. [Model Editing](#model-editing)\n14. [DiffEdit](#diffedit)\n15. [T2I-Adapter](#t2i-adapter)\n16. [FABRIC](#fabric)\n\nFor convenience, we provide a table to denote which methods are inference-only and which require fine-tuning/training.\n\n| **Method** | **Inference only** | **Requires training /
fine-tuning** | **Comments** |\n| :-------------------------------------------------: | :----------------: | :-------------------------------------: | :---------------------------------------------------------------------------------------------: |\n| [InstructPix2Pix](#instruct-pix2pix) | ✅ | ❌ | Can additionally be
fine-tuned for better
performance on specific
edit instructions. |\n| [Pix2Pix Zero](#pix2pix-zero) | ✅ | ❌ | |\n| [Attend and Excite](#attend-and-excite) | ✅ | ❌ | |\n| [Semantic Guidance](#semantic-guidance-sega) | ✅ | ❌ | |\n| [Self-attention Guidance](#self-attention-guidance-sag) | ✅ | ❌ | |\n| [Depth2Image](#depth2image) | ✅ | ❌ | |\n| [MultiDiffusion Panorama](#multidiffusion-panorama) | ✅ | ❌ | |\n| [DreamBooth](#dreambooth) | ❌ | ✅ | |\n| [Textual Inversion](#textual-inversion) | ❌ | ✅ | |\n| [ControlNet](#controlnet) | ✅ | ❌ | A ControlNet can be
trained/fine-tuned on
a custom conditioning. |\n| [Prompt Weighting](#prompt-weighting) | ✅ | ❌ | |\n| [Custom Diffusion](#custom-diffusion) | ❌ | ✅ | |\n| [Model Editing](#model-editing) | ✅ | ❌ | |\n| [DiffEdit](#diffedit) | ✅ | ❌ | |\n| [T2I-Adapter](#t2i-adapter) | ✅ | ❌ | |\n| [Fabric](#fabric) | ✅ | ❌ | |\n## InstructPix2Pix\n\n[Paper](https://arxiv.org/abs/2211.09800)\n\n[InstructPix2Pix](../api/pipelines/pix2pix) is fine-tuned from Stable Diffusion to support editing input images. It takes as inputs an image and a prompt describing an edit, and it outputs the edited image.\nInstructPix2Pix has been explicitly trained to work well with [InstructGPT](https://openai.com/blog/instruction-following/)-like prompts.\n\n## Pix2Pix Zero\n\n[Paper](https://arxiv.org/abs/2302.03027)\n\n[Pix2Pix Zero](../api/pipelines/pix2pix_zero) allows modifying an image so that one concept or subject is translated to another one while preserving general image semantics.\n\nThe denoising process is guided from one conceptual embedding towards another conceptual embedding. The intermediate latents are optimized during the denoising process to push the attention maps towards reference attention maps. The reference attention maps are from the denoising process of the input image and are used to encourage semantic preservation.\n\nPix2Pix Zero can be used both to edit synthetic images as well as real images.\n\n- To edit synthetic images, one first generates an image given a caption.\n Next, we generate image captions for the concept that shall be edited and for the new target concept. We can use a model like [Flan-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5) for this purpose. Then, \"mean\" prompt embeddings for both the source and target concepts are created via the text encoder. Finally, the pix2pix-zero algorithm is used to edit the synthetic image.\n- To edit a real image, one first generates an image caption using a model like [BLIP](https://huggingface.co/docs/transformers/model_doc/blip). Then one applies DDIM inversion on the prompt and image to generate \"inverse\" latents. Similar to before, \"mean\" prompt embeddings for both source and target concepts are created and finally the pix2pix-zero algorithm in combination with the \"inverse\" latents is used to edit the image.\n\n\n\nPix2Pix Zero is the first model that allows \"zero-shot\" image editing. This means that the model\ncan edit an image in less than a minute on a consumer GPU as shown [here](../api/pipelines/pix2pix_zero#usage-example).\n\n\n\nAs mentioned above, Pix2Pix Zero includes optimizing the latents (and not any of the UNet, VAE, or the text encoder) to steer the generation toward a specific concept. This means that the overall\npipeline might require more memory than a standard [StableDiffusionPipeline](../api/pipelines/stable_diffusion/text2img).\n\n\n\nAn important distinction between methods like InstructPix2Pix and Pix2Pix Zero is that the former\ninvolves fine-tuning the pre-trained weights while the latter does not. This means that you can\napply Pix2Pix Zero to any of the available Stable Diffusion models.\n\n\n\n## Attend and Excite\n\n[Paper](https://arxiv.org/abs/2301.13826)\n\n[Attend and Excite](../api/pipelines/attend_and_excite) allows subjects in the prompt to be faithfully represented in the final image.\n\nA set of token indices are given as input, corresponding to the subjects in the prompt that need to be present in the image. During denoising, each token index is guaranteed to have a minimum attention threshold for at least one patch of the image. The intermediate latents are iteratively optimized during the denoising process to strengthen the attention of the most neglected subject token until the attention threshold is passed for all subject tokens.\n\nLike Pix2Pix Zero, Attend and Excite also involves a mini optimization loop (leaving the pre-trained weights untouched) in its pipeline and can require more memory than the usual [StableDiffusionPipeline](../api/pipelines/stable_diffusion/text2img).\n\n## Semantic Guidance (SEGA)\n\n[Paper](https://arxiv.org/abs/2301.12247)\n\n[SEGA](../api/pipelines/semantic_stable_diffusion) allows applying or removing one or more concepts from an image. The strength of the concept can also be controlled. I.e. the smile concept can be used to incrementally increase or decrease the smile of a portrait.\n\nSimilar to how classifier free guidance provides guidance via empty prompt inputs, SEGA provides guidance on conceptual prompts. Multiple of these conceptual prompts can be applied simultaneously. Each conceptual prompt can either add or remove their concept depending on if the guidance is applied positively or negatively.\n\nUnlike Pix2Pix Zero or Attend and Excite, SEGA directly interacts with the diffusion process instead of performing any explicit gradient-based optimization.\n\n## Self-attention Guidance (SAG)\n\n[Paper](https://arxiv.org/abs/2210.00939)\n\n[Self-attention Guidance](../api/pipelines/self_attention_guidance) improves the general quality of images.\n\nSAG provides guidance from predictions not conditioned on high-frequency details to fully conditioned images. The high frequency details are extracted out of the UNet self-attention maps.\n\n## Depth2Image\n\n[Project](https://huggingface.co/stabilityai/stable-diffusion-2-depth)\n\n[Depth2Image](../api/pipelines/stable_diffusion/depth2img) is fine-tuned from Stable Diffusion to better preserve semantics for text guided image variation.\n\nIt conditions on a monocular depth estimate of the original image.\n\n## MultiDiffusion Panorama\n\n[Paper](https://arxiv.org/abs/2302.08113)\n\n[MultiDiffusion Panorama](../api/pipelines/panorama) defines a new generation process over a pre-trained diffusion model. This process binds together multiple diffusion generation methods that can be readily applied to generate high quality and diverse images. Results adhere to user-provided controls, such as desired aspect ratio (e.g., panorama), and spatial guiding signals, ranging from tight segmentation masks to bounding boxes.\nMultiDiffusion Panorama allows to generate high-quality images at arbitrary aspect ratios (e.g., panoramas).\n\n## Fine-tuning your own models\n\nIn addition to pre-trained models, Diffusers has training scripts for fine-tuning models on user-provided data.\n\n## DreamBooth\n\n[Project](https://dreambooth.github.io/)\n\n[DreamBooth](../training/dreambooth) fine-tunes a model to teach it about a new subject. I.e. a few pictures of a person can be used to generate images of that person in different styles.\n\n## Textual Inversion\n\n[Paper](https://arxiv.org/abs/2208.01618)\n\n[Textual Inversion](../training/text_inversion) fine-tunes a model to teach it about a new concept. I.e. a few pictures of a style of artwork can be used to generate images in that style.\n\n## ControlNet\n\n[Paper](https://arxiv.org/abs/2302.05543)\n\n[ControlNet](../api/pipelines/controlnet) is an auxiliary network which adds an extra condition.\nThere are 8 canonical pre-trained ControlNets trained on different conditionings such as edge detection, scribbles,\ndepth maps, and semantic segmentations.\n\n## Prompt Weighting\n\n[Prompt weighting](../using-diffusers/weighted_prompts) is a simple technique that puts more attention weight on certain parts of the text\ninput.\n\n## Custom Diffusion\n\n[Paper](https://arxiv.org/abs/2212.04488)\n\n[Custom Diffusion](../training/custom_diffusion) only fine-tunes the cross-attention maps of a pre-trained\ntext-to-image diffusion model. It also allows for additionally performing Textual Inversion. It supports\nmulti-concept training by design. Like DreamBooth and Textual Inversion, Custom Diffusion is also used to\nteach a pre-trained text-to-image diffusion model about new concepts to generate outputs involving the\nconcept(s) of interest.\n\n## Model Editing\n\n[Paper](https://arxiv.org/abs/2303.08084)\n\nThe [text-to-image model editing pipeline](../api/pipelines/model_editing) helps you mitigate some of the incorrect implicit assumptions a pre-trained text-to-image\ndiffusion model might make about the subjects present in the input prompt. For example, if you prompt Stable Diffusion to generate images for \"A pack of roses\", the roses in the generated images\nare more likely to be red. This pipeline helps you change that assumption.\n\n## DiffEdit\n\n[Paper](https://arxiv.org/abs/2210.11427)\n\n[DiffEdit](../api/pipelines/diffedit) allows for semantic editing of input images along with\ninput prompts while preserving the original input images as much as possible.\n\n## T2I-Adapter\n\n[Paper](https://arxiv.org/abs/2302.08453)\n\n[T2I-Adapter](../api/pipelines/stable_diffusion/adapter) is an auxiliary network which adds an extra condition.\nThere are 8 canonical pre-trained adapters trained on different conditionings such as edge detection, sketch,\ndepth maps, and semantic segmentations.\n\n## Fabric\n\n[Paper](https://arxiv.org/abs/2307.10159)\n\n[Fabric](https://github.com/huggingface/diffusers/tree/442017ccc877279bcf24fbe92f92d3d0def191b6/examples/community#stable-diffusion-fabric-pipeline) is a training-free\napproach applicable to a wide range of popular diffusion models, which exploits\nthe self-attention layer present in the most widely used architectures to condition\nthe diffusion process on a set of feedback images.\n" }, { "path": "diffusers/docs/source/en/using-diffusers/controlnet.md", "content": "\n\n# ControlNet\n\nControlNet is a type of model for controlling image diffusion models by conditioning the model with an additional input image. There are many types of conditioning inputs (canny edge, user sketching, human pose, depth, and more) you can use to control a diffusion model. This is hugely useful because it affords you greater control over image generation, making it easier to generate specific images without experimenting with different text prompts or denoising values as much.\n\n\n\nCheck out Section 3.5 of the [ControlNet](https://huggingface.co/papers/2302.05543) paper v1 for a list of ControlNet implementations on various conditioning inputs. You can find the official Stable Diffusion ControlNet conditioned models on [lllyasviel](https://huggingface.co/lllyasviel)'s Hub profile, and more [community-trained](https://huggingface.co/models?other=stable-diffusion&other=controlnet) ones on the Hub.\n\nFor Stable Diffusion XL (SDXL) ControlNet models, you can find them on the 🤗 [Diffusers](https://huggingface.co/diffusers) Hub organization, or you can browse [community-trained](https://huggingface.co/models?other=stable-diffusion-xl&other=controlnet) ones on the Hub.\n\n\n\nA ControlNet model has two sets of weights (or blocks) connected by a zero-convolution layer:\n\n- a *locked copy* keeps everything a large pretrained diffusion model has learned\n- a *trainable copy* is trained on the additional conditioning input\n\nSince the locked copy preserves the pretrained model, training and implementing a ControlNet on a new conditioning input is as fast as finetuning any other model because you aren't training the model from scratch.\n\nThis guide will show you how to use ControlNet for text-to-image, image-to-image, inpainting, and more! There are many types of ControlNet conditioning inputs to choose from, but in this guide we'll only focus on several of them. Feel free to experiment with other conditioning inputs!\n\nBefore you begin, make sure you have the following libraries installed:\n\n```py\n# uncomment to install the necessary libraries in Colab\n#!pip install -q diffusers transformers accelerate opencv-python\n```\n\n## Text-to-image\n\nFor text-to-image, you normally pass a text prompt to the model. But with ControlNet, you can specify an additional conditioning input. Let's condition the model with a canny image, a white outline of an image on a black background. This way, the ControlNet can use the canny image as a control to guide the model to generate an image with the same outline.\n\nLoad an image and use the [opencv-python](https://github.com/opencv/opencv-python) library to extract the canny image:\n\n```py\nfrom diffusers.utils import load_image, make_image_grid\nfrom PIL import Image\nimport cv2\nimport numpy as np\n\noriginal_image = load_image(\n \"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png\"\n)\n\nimage = np.array(original_image)\n\nlow_threshold = 100\nhigh_threshold = 200\n\nimage = cv2.Canny(image, low_threshold, high_threshold)\nimage = image[:, :, None]\nimage = np.concatenate([image, image, image], axis=2)\ncanny_image = Image.fromarray(image)\n```\n\n
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\n\nNext, load a ControlNet model conditioned on canny edge detection and pass it to the [`StableDiffusionControlNetPipeline`]. Use the faster [`UniPCMultistepScheduler`] and enable model offloading to speed up inference and reduce memory usage.\n\n```py\nfrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\nimport torch\n\ncontrolnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\", torch_dtype=torch.float16, use_safetensors=True)\npipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True\n)\n\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\npipe.enable_model_cpu_offload()\n```\n\nNow pass your prompt and canny image to the pipeline:\n\n```py\noutput = pipe(\n \"the mona lisa\", image=canny_image\n).images[0]\nmake_image_grid([original_image, canny_image, output], rows=1, cols=3)\n```\n\n
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\n\n## Image-to-image\n\nFor image-to-image, you'd typically pass an initial image and a prompt to the pipeline to generate a new image. With ControlNet, you can pass an additional conditioning input to guide the model. Let's condition the model with a depth map, an image which contains spatial information. This way, the ControlNet can use the depth map as a control to guide the model to generate an image that preserves spatial information.\n\nYou'll use the [`StableDiffusionControlNetImg2ImgPipeline`] for this task, which is different from the [`StableDiffusionControlNetPipeline`] because it allows you to pass an initial image as the starting point for the image generation process.\n\nLoad an image and use the `depth-estimation` [`~transformers.Pipeline`] from 🤗 Transformers to extract the depth map of an image:\n\n```py\nimport torch\nimport numpy as np\n\nfrom transformers import pipeline\nfrom diffusers.utils import load_image, make_image_grid\n\nimage = load_image(\n \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-img2img.jpg\"\n)\n\ndef get_depth_map(image, depth_estimator):\n image = depth_estimator(image)[\"depth\"]\n image = np.array(image)\n image = image[:, :, None]\n image = np.concatenate([image, image, image], axis=2)\n detected_map = torch.from_numpy(image).float() / 255.0\n depth_map = detected_map.permute(2, 0, 1)\n return depth_map\n\ndepth_estimator = pipeline(\"depth-estimation\")\ndepth_map = get_depth_map(image, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n```\n\nNext, load a ControlNet model conditioned on depth maps and pass it to the [`StableDiffusionControlNetImg2ImgPipeline`]. Use the faster [`UniPCMultistepScheduler`] and enable model offloading to speed up inference and reduce memory usage.\n\n```py\nfrom diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, UniPCMultistepScheduler\nimport torch\n\ncontrolnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11f1p_sd15_depth\", torch_dtype=torch.float16, use_safetensors=True)\npipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True\n)\n\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\npipe.enable_model_cpu_offload()\n```\n\nNow pass your prompt, initial image, and depth map to the pipeline:\n\n```py\noutput = pipe(\n \"lego batman and robin\", image=image, control_image=depth_map,\n).images[0]\nmake_image_grid([image, output], rows=1, cols=2)\n```\n\n
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\n\n## Inpainting\n\nFor inpainting, you need an initial image, a mask image, and a prompt describing what to replace the mask with. ControlNet models allow you to add another control image to condition a model with. Let’s condition the model with an inpainting mask. This way, the ControlNet can use the inpainting mask as a control to guide the model to generate an image within the mask area.\n\nLoad an initial image and a mask image:\n\n```py\nfrom diffusers.utils import load_image, make_image_grid\n\ninit_image = load_image(\n \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-inpaint.jpg\"\n)\ninit_image = init_image.resize((512, 512))\n\nmask_image = load_image(\n \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-inpaint-mask.jpg\"\n)\nmask_image = mask_image.resize((512, 512))\nmake_image_grid([init_image, mask_image], rows=1, cols=2)\n```\n\nCreate a function to prepare the control image from the initial and mask images. This'll create a tensor to mark the pixels in `init_image` as masked if the corresponding pixel in `mask_image` is over a certain threshold.\n\n```py\nimport numpy as np\nimport torch\n\ndef make_inpaint_condition(image, image_mask):\n image = np.array(image.convert(\"RGB\")).astype(np.float32) / 255.0\n image_mask = np.array(image_mask.convert(\"L\")).astype(np.float32) / 255.0\n\n assert image.shape[0:1] == image_mask.shape[0:1]\n image[image_mask > 0.5] = -1.0 # set as masked pixel\n image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)\n image = torch.from_numpy(image)\n return image\n\ncontrol_image = make_inpaint_condition(init_image, mask_image)\n```\n\n
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\n\nLoad a ControlNet model conditioned on inpainting and pass it to the [`StableDiffusionControlNetInpaintPipeline`]. Use the faster [`UniPCMultistepScheduler`] and enable model offloading to speed up inference and reduce memory usage.\n\n```py\nfrom diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, UniPCMultistepScheduler\n\ncontrolnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11p_sd15_inpaint\", torch_dtype=torch.float16, use_safetensors=True)\npipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True\n)\n\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\npipe.enable_model_cpu_offload()\n```\n\nNow pass your prompt, initial image, mask image, and control image to the pipeline:\n\n```py\noutput = pipe(\n \"corgi face with large ears, detailed, pixar, animated, disney\",\n num_inference_steps=20,\n eta=1.0,\n image=init_image,\n mask_image=mask_image,\n control_image=control_image,\n).images[0]\nmake_image_grid([init_image, mask_image, output], rows=1, cols=3)\n```\n\n
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\n\n## Guess mode\n\n[Guess mode](https://github.com/lllyasviel/ControlNet/discussions/188) does not require supplying a prompt to a ControlNet at all! This forces the ControlNet encoder to do it's best to \"guess\" the contents of the input control map (depth map, pose estimation, canny edge, etc.).\n\nGuess mode adjusts the scale of the output residuals from a ControlNet by a fixed ratio depending on the block depth. The shallowest `DownBlock` corresponds to 0.1, and as the blocks get deeper, the scale increases exponentially such that the scale of the `MidBlock` output becomes 1.0.\n\n\n\nGuess mode does not have any impact on prompt conditioning and you can still provide a prompt if you want.\n\n\n\nSet `guess_mode=True` in the pipeline, and it is [recommended](https://github.com/lllyasviel/ControlNet#guess-mode--non-prompt-mode) to set the `guidance_scale` value between 3.0 and 5.0.\n\n```py\nfrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel\nfrom diffusers.utils import load_image, make_image_grid\nimport numpy as np\nimport torch\nfrom PIL import Image\nimport cv2\n\ncontrolnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\", use_safetensors=True)\npipe = StableDiffusionControlNetPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, use_safetensors=True).to(\"cuda\")\n\noriginal_image = load_image(\"https://huggingface.co/takuma104/controlnet_dev/resolve/main/bird_512x512.png\")\n\nimage = np.array(original_image)\n\nlow_threshold = 100\nhigh_threshold = 200\n\nimage = cv2.Canny(image, low_threshold, high_threshold)\nimage = image[:, :, None]\nimage = np.concatenate([image, image, image], axis=2)\ncanny_image = Image.fromarray(image)\n\nimage = pipe(\"\", image=canny_image, guess_mode=True, guidance_scale=3.0).images[0]\nmake_image_grid([original_image, canny_image, image], rows=1, cols=3)\n```\n\n
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\n\n## ControlNet with Stable Diffusion XL\n\nThere aren't too many ControlNet models compatible with Stable Diffusion XL (SDXL) at the moment, but we've trained two full-sized ControlNet models for SDXL conditioned on canny edge detection and depth maps. We're also experimenting with creating smaller versions of these SDXL-compatible ControlNet models so it is easier to run on resource-constrained hardware. You can find these checkpoints on the [🤗 Diffusers Hub organization](https://huggingface.co/diffusers)!\n\nLet's use a SDXL ControlNet conditioned on canny images to generate an image. Start by loading an image and prepare the canny image:\n\n```py\nfrom diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL\nfrom diffusers.utils import load_image, make_image_grid\nfrom PIL import Image\nimport cv2\nimport numpy as np\nimport torch\n\noriginal_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png\"\n)\n\nimage = np.array(original_image)\n\nlow_threshold = 100\nhigh_threshold = 200\n\nimage = cv2.Canny(image, low_threshold, high_threshold)\nimage = image[:, :, None]\nimage = np.concatenate([image, image, image], axis=2)\ncanny_image = Image.fromarray(image)\nmake_image_grid([original_image, canny_image], rows=1, cols=2)\n```\n\n
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\n\nLoad a SDXL ControlNet model conditioned on canny edge detection and pass it to the [`StableDiffusionXLControlNetPipeline`]. You can also enable model offloading to reduce memory usage.\n\n```py\ncontrolnet = ControlNetModel.from_pretrained(\n \"diffusers/controlnet-canny-sdxl-1.0\",\n torch_dtype=torch.float16,\n use_safetensors=True\n)\nvae = AutoencoderKL.from_pretrained(\"madebyollin/sdxl-vae-fp16-fix\", torch_dtype=torch.float16, use_safetensors=True)\npipe = StableDiffusionXLControlNetPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n controlnet=controlnet,\n vae=vae,\n torch_dtype=torch.float16,\n use_safetensors=True\n)\npipe.enable_model_cpu_offload()\n```\n\nNow pass your prompt (and optionally a negative prompt if you're using one) and canny image to the pipeline:\n\n\n\nThe [`controlnet_conditioning_scale`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/controlnet#diffusers.StableDiffusionControlNetPipeline.__call__.controlnet_conditioning_scale) parameter determines how much weight to assign to the conditioning inputs. A value of 0.5 is recommended for good generalization, but feel free to experiment with this number!\n\n\n\n```py\nprompt = \"aerial view, a futuristic research complex in a bright foggy jungle, hard lighting\"\nnegative_prompt = 'low quality, bad quality, sketches'\n\nimage = pipe(\n prompt,\n negative_prompt=negative_prompt,\n image=canny_image,\n controlnet_conditioning_scale=0.5,\n).images[0]\nmake_image_grid([original_image, canny_image, image], rows=1, cols=3)\n```\n\n
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\n\nYou can use [`StableDiffusionXLControlNetPipeline`] in guess mode as well by setting the parameter to `True`:\n\n```py\nfrom diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL\nfrom diffusers.utils import load_image, make_image_grid\nimport numpy as np\nimport torch\nimport cv2\nfrom PIL import Image\n\nprompt = \"aerial view, a futuristic research complex in a bright foggy jungle, hard lighting\"\nnegative_prompt = \"low quality, bad quality, sketches\"\n\noriginal_image = load_image(\n \"https://hf.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png\"\n)\n\ncontrolnet = ControlNetModel.from_pretrained(\n \"diffusers/controlnet-canny-sdxl-1.0\", torch_dtype=torch.float16, use_safetensors=True\n)\nvae = AutoencoderKL.from_pretrained(\"madebyollin/sdxl-vae-fp16-fix\", torch_dtype=torch.float16, use_safetensors=True)\npipe = StableDiffusionXLControlNetPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", controlnet=controlnet, vae=vae, torch_dtype=torch.float16, use_safetensors=True\n)\npipe.enable_model_cpu_offload()\n\nimage = np.array(original_image)\nimage = cv2.Canny(image, 100, 200)\nimage = image[:, :, None]\nimage = np.concatenate([image, image, image], axis=2)\ncanny_image = Image.fromarray(image)\n\nimage = pipe(\n prompt, negative_prompt=negative_prompt, controlnet_conditioning_scale=0.5, image=canny_image, guess_mode=True,\n).images[0]\nmake_image_grid([original_image, canny_image, image], rows=1, cols=3)\n```\n\n\n\nYou can use a refiner model with `StableDiffusionXLControlNetPipeline` to improve image quality, just like you can with a regular `StableDiffusionXLPipeline`.\nSee the [Refine image quality](./sdxl#refine-image-quality) section to learn how to use the refiner model.\nMake sure to use `StableDiffusionXLControlNetPipeline` and pass `image` and `controlnet_conditioning_scale`.\n\n```py\nbase = StableDiffusionXLControlNetPipeline(...)\nimage = base(\n prompt=prompt,\n controlnet_conditioning_scale=0.5,\n image=canny_image,\n num_inference_steps=40,\n denoising_end=0.8,\n output_type=\"latent\",\n).images\n# rest exactly as with StableDiffusionXLPipeline\n```\n\n\n\n## MultiControlNet\n\n\n\nReplace the SDXL model with a model like [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) to use multiple conditioning inputs with Stable Diffusion models.\n\n\n\nYou can compose multiple ControlNet conditionings from different image inputs to create a *MultiControlNet*. To get better results, it is often helpful to:\n\n1. mask conditionings such that they don't overlap (for example, mask the area of a canny image where the pose conditioning is located)\n2. experiment with the [`controlnet_conditioning_scale`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/controlnet#diffusers.StableDiffusionControlNetPipeline.__call__.controlnet_conditioning_scale) parameter to determine how much weight to assign to each conditioning input\n\nIn this example, you'll combine a canny image and a human pose estimation image to generate a new image.\n\nPrepare the canny image conditioning:\n\n```py\nfrom diffusers.utils import load_image, make_image_grid\nfrom PIL import Image\nimport numpy as np\nimport cv2\n\noriginal_image = load_image(\n \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/landscape.png\"\n)\nimage = np.array(original_image)\n\nlow_threshold = 100\nhigh_threshold = 200\n\nimage = cv2.Canny(image, low_threshold, high_threshold)\n\n# zero out middle columns of image where pose will be overlaid\nzero_start = image.shape[1] // 4\nzero_end = zero_start + image.shape[1] // 2\nimage[:, zero_start:zero_end] = 0\n\nimage = image[:, :, None]\nimage = np.concatenate([image, image, image], axis=2)\ncanny_image = Image.fromarray(image)\nmake_image_grid([original_image, canny_image], rows=1, cols=2)\n```\n\n
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\n\nFor human pose estimation, install [controlnet_aux](https://github.com/patrickvonplaten/controlnet_aux):\n \n```py\n# uncomment to install the necessary library in Colab\n#!pip install -q controlnet-aux\n```\n\nPrepare the human pose estimation conditioning:\n\n```py\nfrom controlnet_aux import OpenposeDetector\n\nopenpose = OpenposeDetector.from_pretrained(\"lllyasviel/ControlNet\")\noriginal_image = load_image(\n \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/person.png\"\n)\nopenpose_image = openpose(original_image)\nmake_image_grid([original_image, openpose_image], rows=1, cols=2)\n```\n\n
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\n\nLoad a list of ControlNet models that correspond to each conditioning, and pass them to the [`StableDiffusionXLControlNetPipeline`]. Use the faster [`UniPCMultistepScheduler`] and enable model offloading to reduce memory usage.\n\n```py\nfrom diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL, UniPCMultistepScheduler\nimport torch\n\ncontrolnets = [\n ControlNetModel.from_pretrained(\n \"thibaud/controlnet-openpose-sdxl-1.0\", torch_dtype=torch.float16\n ),\n ControlNetModel.from_pretrained(\n \"diffusers/controlnet-canny-sdxl-1.0\", torch_dtype=torch.float16, use_safetensors=True\n ),\n]\n\nvae = AutoencoderKL.from_pretrained(\"madebyollin/sdxl-vae-fp16-fix\", torch_dtype=torch.float16, use_safetensors=True)\npipe = StableDiffusionXLControlNetPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", controlnet=controlnets, vae=vae, torch_dtype=torch.float16, use_safetensors=True\n)\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\npipe.enable_model_cpu_offload()\n```\n\nNow you can pass your prompt (an optional negative prompt if you're using one), canny image, and pose image to the pipeline:\n\n```py\nprompt = \"a giant standing in a fantasy landscape, best quality\"\nnegative_prompt = \"monochrome, lowres, bad anatomy, worst quality, low quality\"\n\ngenerator = torch.manual_seed(1)\n\nimages = [openpose_image.resize((1024, 1024)), canny_image.resize((1024, 1024))]\n\nimages = pipe(\n prompt,\n image=images,\n num_inference_steps=25,\n generator=generator,\n negative_prompt=negative_prompt,\n num_images_per_prompt=3,\n controlnet_conditioning_scale=[1.0, 0.8],\n).images\nmake_image_grid([original_image, canny_image, openpose_image,\n images[0].resize((512, 512)), images[1].resize((512, 512)), images[2].resize((512, 512))], rows=2, cols=3)\n```\n\n
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\n" }, { "path": "diffusers/docs/source/en/using-diffusers/custom_pipeline_overview.md", "content": "\n\n# Load community pipelines and components\n\n[[open-in-colab]]\n\n## Community pipelines\n\n> [!TIP] Take a look at GitHub Issue [#841](https://github.com/huggingface/diffusers/issues/841) for more context about why we're adding community pipelines to help everyone easily share their work without being slowed down.\n\nCommunity pipelines are any [`DiffusionPipeline`] class that are different from the original paper implementation (for example, the [`StableDiffusionControlNetPipeline`] corresponds to the [Text-to-Image Generation with ControlNet Conditioning](https://arxiv.org/abs/2302.05543) paper). They provide additional functionality or extend the original implementation of a pipeline.\n\nThere are many cool community pipelines like [Marigold Depth Estimation](https://github.com/huggingface/diffusers/tree/main/examples/community#marigold-depth-estimation) or [InstantID](https://github.com/huggingface/diffusers/tree/main/examples/community#instantid-pipeline), and you can find all the official community pipelines [here](https://github.com/huggingface/diffusers/tree/main/examples/community).\n\nThere are two types of community pipelines, those stored on the Hugging Face Hub and those stored on Diffusers GitHub repository. Hub pipelines are completely customizable (scheduler, models, pipeline code, etc.) while Diffusers GitHub pipelines are only limited to custom pipeline code.\n\n| | GitHub community pipeline | HF Hub community pipeline |\n|----------------|------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------|\n| usage | same | same |\n| review process | open a Pull Request on GitHub and undergo a review process from the Diffusers team before merging; may be slower | upload directly to a Hub repository without any review; this is the fastest workflow |\n| visibility | included in the official Diffusers repository and documentation | included on your HF Hub profile and relies on your own usage/promotion to gain visibility |\n\n\n\n\nTo load a Hugging Face Hub community pipeline, pass the repository id of the community pipeline to the `custom_pipeline` argument and the model repository where you'd like to load the pipeline weights and components from. For example, the example below loads a dummy pipeline from [hf-internal-testing/diffusers-dummy-pipeline](https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py) and the pipeline weights and components from [google/ddpm-cifar10-32](https://huggingface.co/google/ddpm-cifar10-32):\n\n> [!WARNING]\n> By loading a community pipeline from the Hugging Face Hub, you are trusting that the code you are loading is safe. Make sure to inspect the code online before loading and running it automatically!\n\n```py\nfrom diffusers import DiffusionPipeline\n\npipeline = DiffusionPipeline.from_pretrained(\n \"google/ddpm-cifar10-32\", custom_pipeline=\"hf-internal-testing/diffusers-dummy-pipeline\", use_safetensors=True\n)\n```\n\n\n\n\nTo load a GitHub community pipeline, pass the repository id of the community pipeline to the `custom_pipeline` argument and the model repository where you you'd like to load the pipeline weights and components from. You can also load model components directly. The example below loads the community [CLIP Guided Stable Diffusion](https://github.com/huggingface/diffusers/tree/main/examples/community#clip-guided-stable-diffusion) pipeline and the CLIP model components.\n\n```py\nfrom diffusers import DiffusionPipeline\nfrom transformers import CLIPImageProcessor, CLIPModel\n\nclip_model_id = \"laion/CLIP-ViT-B-32-laion2B-s34B-b79K\"\n\nfeature_extractor = CLIPImageProcessor.from_pretrained(clip_model_id)\nclip_model = CLIPModel.from_pretrained(clip_model_id)\n\npipeline = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n custom_pipeline=\"clip_guided_stable_diffusion\",\n clip_model=clip_model,\n feature_extractor=feature_extractor,\n use_safetensors=True,\n)\n```\n\n\n\n\n### Load from a local file\n\nCommunity pipelines can also be loaded from a local file if you pass a file path instead. The path to the passed directory must contain a pipeline.py file that contains the pipeline class.\n\n```py\npipeline = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n custom_pipeline=\"./path/to/pipeline_directory/\",\n clip_model=clip_model,\n feature_extractor=feature_extractor,\n use_safetensors=True,\n)\n```\n\n### Load from a specific version\n\nBy default, community pipelines are loaded from the latest stable version of Diffusers. To load a community pipeline from another version, use the `custom_revision` parameter.\n\n\n\n\nFor example, to load from the main branch:\n\n```py\npipeline = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n custom_pipeline=\"clip_guided_stable_diffusion\",\n custom_revision=\"main\",\n clip_model=clip_model,\n feature_extractor=feature_extractor,\n use_safetensors=True,\n)\n```\n\n\n\n\nFor example, to load from a previous version of Diffusers like v0.25.0:\n\n```py\npipeline = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n custom_pipeline=\"clip_guided_stable_diffusion\",\n custom_revision=\"v0.25.0\",\n clip_model=clip_model,\n feature_extractor=feature_extractor,\n use_safetensors=True,\n)\n```\n\n\n\n\n### Load with from_pipe\n\nCommunity pipelines can also be loaded with the [`~DiffusionPipeline.from_pipe`] method which allows you to load and reuse multiple pipelines without any additional memory overhead (learn more in the [Reuse a pipeline](./loading#reuse-a-pipeline) guide). The memory requirement is determined by the largest single pipeline loaded.\n\nFor example, let's load a community pipeline that supports [long prompts with weighting](https://github.com/huggingface/diffusers/tree/main/examples/community#long-prompt-weighting-stable-diffusion) from a Stable Diffusion pipeline.\n\n```py\nimport torch\nfrom diffusers import DiffusionPipeline\n\npipe_sd = DiffusionPipeline.from_pretrained(\"emilianJR/CyberRealistic_V3\", torch_dtype=torch.float16)\npipe_sd.to(\"cuda\")\n# load long prompt weighting pipeline\npipe_lpw = DiffusionPipeline.from_pipe(\n pipe_sd,\n custom_pipeline=\"lpw_stable_diffusion\",\n).to(\"cuda\")\n\nprompt = \"cat, hiding in the leaves, ((rain)), zazie rainyday, beautiful eyes, macro shot, colorful details, natural lighting, amazing composition, subsurface scattering, amazing textures, filmic, soft light, ultra-detailed eyes, intricate details, detailed texture, light source contrast, dramatic shadows, cinematic light, depth of field, film grain, noise, dark background, hyperrealistic dslr film still, dim volumetric cinematic lighting\"\nneg_prompt = \"(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.4), (deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation\"\ngenerator = torch.Generator(device=\"cpu\").manual_seed(20)\nout_lpw = pipe_lpw(\n prompt, \n negative_prompt=neg_prompt, \n width=512,\n height=512,\n max_embeddings_multiples=3, \n num_inference_steps=50,\n generator=generator,\n ).images[0]\nout_lpw\n```\n\n
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\n\n## Example community pipelines\n\nCommunity pipelines are a really fun and creative way to extend the capabilities of the original pipeline with new and unique features. You can find all community pipelines in the [diffusers/examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) folder with inference and training examples for how to use them.\n\nThis section showcases a couple of the community pipelines and hopefully it'll inspire you to create your own (feel free to open a PR for your community pipeline and ping us for a review)!\n\n> [!TIP]\n> The [`~DiffusionPipeline.from_pipe`] method is particularly useful for loading community pipelines because many of them don't have pretrained weights and add a feature on top of an existing pipeline like Stable Diffusion or Stable Diffusion XL. You can learn more about the [`~DiffusionPipeline.from_pipe`] method in the [Load with from_pipe](custom_pipeline_overview#load-with-from_pipe) section.\n\n\n\n\n[Marigold](https://marigoldmonodepth.github.io/) is a depth estimation diffusion pipeline that uses the rich existing and inherent visual knowledge in diffusion models. It takes an input image and denoises and decodes it into a depth map. Marigold performs well even on images it hasn't seen before.\n\n```py\nimport torch\nfrom PIL import Image\nfrom diffusers import DiffusionPipeline\nfrom diffusers.utils import load_image\n\npipeline = DiffusionPipeline.from_pretrained(\n \"prs-eth/marigold-lcm-v1-0\",\n custom_pipeline=\"marigold_depth_estimation\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n)\n\npipeline.to(\"cuda\")\nimage = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/community-marigold.png\")\noutput = pipeline(\n image,\n denoising_steps=4,\n ensemble_size=5,\n processing_res=768,\n match_input_res=True,\n batch_size=0,\n seed=33,\n color_map=\"Spectral\",\n show_progress_bar=True,\n)\ndepth_colored: Image.Image = output.depth_colored\ndepth_colored.save(\"./depth_colored.png\")\n```\n\n
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\n\n\n\n\n[HD-Painter](https://hf.co/papers/2312.14091) is a high-resolution inpainting pipeline. It introduces a *Prompt-Aware Introverted Attention (PAIntA)* layer to better align a prompt with the area to be inpainted, and *Reweighting Attention Score Guidance (RASG)* to keep the latents more prompt-aligned and within their trained domain to generate realistc images.\n\n```py\nimport torch\nfrom diffusers import DiffusionPipeline, DDIMScheduler\nfrom diffusers.utils import load_image\n\npipeline = DiffusionPipeline.from_pretrained(\n \"Lykon/dreamshaper-8-inpainting\",\n custom_pipeline=\"hd_painter\"\n)\npipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)\ninit_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/hd-painter.jpg\")\nmask_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/hd-painter-mask.png\")\nprompt = \"football\"\nimage = pipeline(prompt, init_image, mask_image, use_rasg=True, use_painta=True, generator=torch.manual_seed(0)).images[0]\nimage\n```\n\n
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\n\n\n\n\n## Community components\n\nCommunity components allow users to build pipelines that may have customized components that are not a part of Diffusers. If your pipeline has custom components that Diffusers doesn't already support, you need to provide their implementations as Python modules. These customized components could be a VAE, UNet, and scheduler. In most cases, the text encoder is imported from the Transformers library. The pipeline code itself can also be customized.\n\nThis section shows how users should use community components to build a community pipeline.\n\nYou'll use the [showlab/show-1-base](https://huggingface.co/showlab/show-1-base) pipeline checkpoint as an example.\n\n1. Import and load the text encoder from Transformers:\n\n```python\nfrom transformers import T5Tokenizer, T5EncoderModel\n\npipe_id = \"showlab/show-1-base\"\ntokenizer = T5Tokenizer.from_pretrained(pipe_id, subfolder=\"tokenizer\")\ntext_encoder = T5EncoderModel.from_pretrained(pipe_id, subfolder=\"text_encoder\")\n```\n\n2. Load a scheduler:\n\n```python\nfrom diffusers import DPMSolverMultistepScheduler\n\nscheduler = DPMSolverMultistepScheduler.from_pretrained(pipe_id, subfolder=\"scheduler\")\n```\n\n3. Load an image processor:\n\n```python\nfrom transformers import CLIPFeatureExtractor\n\nfeature_extractor = CLIPFeatureExtractor.from_pretrained(pipe_id, subfolder=\"feature_extractor\")\n```\n\n\n\nIn steps 4 and 5, the custom [UNet](https://github.com/showlab/Show-1/blob/main/showone/models/unet_3d_condition.py) and [pipeline](https://huggingface.co/sayakpaul/show-1-base-with-code/blob/main/unet/showone_unet_3d_condition.py) implementation must match the format shown in their files for this example to work.\n\n\n\n4. Now you'll load a [custom UNet](https://github.com/showlab/Show-1/blob/main/showone/models/unet_3d_condition.py), which in this example, has already been implemented in [showone_unet_3d_condition.py](https://huggingface.co/sayakpaul/show-1-base-with-code/blob/main/unet/showone_unet_3d_condition.py) for your convenience. You'll notice the [`UNet3DConditionModel`] class name is changed to `ShowOneUNet3DConditionModel` because [`UNet3DConditionModel`] already exists in Diffusers. Any components needed for the `ShowOneUNet3DConditionModel` class should be placed in showone_unet_3d_condition.py.\n\n Once this is done, you can initialize the UNet:\n\n ```python\n from showone_unet_3d_condition import ShowOneUNet3DConditionModel\n\n unet = ShowOneUNet3DConditionModel.from_pretrained(pipe_id, subfolder=\"unet\")\n ```\n\n5. Finally, you'll load the custom pipeline code. For this example, it has already been created for you in [pipeline_t2v_base_pixel.py](https://huggingface.co/sayakpaul/show-1-base-with-code/blob/main/pipeline_t2v_base_pixel.py). This script contains a custom `TextToVideoIFPipeline` class for generating videos from text. Just like the custom UNet, any code needed for the custom pipeline to work should go in pipeline_t2v_base_pixel.py.\n\nOnce everything is in place, you can initialize the `TextToVideoIFPipeline` with the `ShowOneUNet3DConditionModel`:\n\n```python\nfrom pipeline_t2v_base_pixel import TextToVideoIFPipeline\nimport torch\n\npipeline = TextToVideoIFPipeline(\n unet=unet,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n scheduler=scheduler,\n feature_extractor=feature_extractor\n)\npipeline = pipeline.to(device=\"cuda\")\npipeline.torch_dtype = torch.float16\n```\n\nPush the pipeline to the Hub to share with the community!\n\n```python\npipeline.push_to_hub(\"custom-t2v-pipeline\")\n```\n\nAfter the pipeline is successfully pushed, you need to make a few changes:\n\n1. Change the `_class_name` attribute in [model_index.json](https://huggingface.co/sayakpaul/show-1-base-with-code/blob/main/model_index.json#L2) to `\"pipeline_t2v_base_pixel\"` and `\"TextToVideoIFPipeline\"`.\n2. Upload `showone_unet_3d_condition.py` to the [unet](https://huggingface.co/sayakpaul/show-1-base-with-code/blob/main/unet/showone_unet_3d_condition.py) subfolder.\n3. Upload `pipeline_t2v_base_pixel.py` to the pipeline [repository](https://huggingface.co/sayakpaul/show-1-base-with-code/tree/main).\n\nTo run inference, add the `trust_remote_code` argument while initializing the pipeline to handle all the \"magic\" behind the scenes.\n\n> [!WARNING]\n> As an additional precaution with `trust_remote_code=True`, we strongly encourage you to pass a commit hash to the `revision` parameter in [`~DiffusionPipeline.from_pretrained`] to make sure the code hasn't been updated with some malicious new lines of code (unless you fully trust the model owners).\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipeline = DiffusionPipeline.from_pretrained(\n \"/\", trust_remote_code=True, torch_dtype=torch.float16\n).to(\"cuda\")\n\nprompt = \"hello\"\n\n# Text embeds\nprompt_embeds, negative_embeds = pipeline.encode_prompt(prompt)\n\n# Keyframes generation (8x64x40, 2fps)\nvideo_frames = pipeline(\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_embeds,\n num_frames=8,\n height=40,\n width=64,\n num_inference_steps=2,\n guidance_scale=9.0,\n output_type=\"pt\"\n).frames\n```\n\nAs an additional reference, take a look at the repository structure of [stabilityai/japanese-stable-diffusion-xl](https://huggingface.co/stabilityai/japanese-stable-diffusion-xl/) which also uses the `trust_remote_code` feature.\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipeline = DiffusionPipeline.from_pretrained(\n \"stabilityai/japanese-stable-diffusion-xl\", trust_remote_code=True\n)\npipeline.to(\"cuda\")\n```\n" }, { "path": "diffusers/docs/source/en/using-diffusers/depth2img.md", "content": "\n\n# Text-guided depth-to-image generation\n\n[[open-in-colab]]\n\nThe [`StableDiffusionDepth2ImgPipeline`] lets you pass a text prompt and an initial image to condition the generation of new images. In addition, you can also pass a `depth_map` to preserve the image structure. If no `depth_map` is provided, the pipeline automatically predicts the depth via an integrated [depth-estimation model](https://github.com/isl-org/MiDaS).\n\nStart by creating an instance of the [`StableDiffusionDepth2ImgPipeline`]:\n\n```python\nimport torch\nfrom diffusers import StableDiffusionDepth2ImgPipeline\nfrom diffusers.utils import load_image, make_image_grid\n\npipeline = StableDiffusionDepth2ImgPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-depth\",\n torch_dtype=torch.float16,\n use_safetensors=True,\n).to(\"cuda\")\n```\n\nNow pass your prompt to the pipeline. You can also pass a `negative_prompt` to prevent certain words from guiding how an image is generated:\n\n```python\nurl = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\ninit_image = load_image(url)\nprompt = \"two tigers\"\nnegative_prompt = \"bad, deformed, ugly, bad anatomy\"\nimage = pipeline(prompt=prompt, image=init_image, negative_prompt=negative_prompt, strength=0.7).images[0]\nmake_image_grid([init_image, image], rows=1, cols=2)\n```\n\n| Input | Output |\n|---------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------|\n| | |\n" }, { "path": "diffusers/docs/source/en/using-diffusers/diffedit.md", "content": "\n\n# DiffEdit\n\n[[open-in-colab]]\n\nImage editing typically requires providing a mask of the area to be edited. DiffEdit automatically generates the mask for you based on a text query, making it easier overall to create a mask without image editing software. The DiffEdit algorithm works in three steps:\n\n1. the diffusion model denoises an image conditioned on some query text and reference text which produces different noise estimates for different areas of the image; the difference is used to infer a mask to identify which area of the image needs to be changed to match the query text\n2. the input image is encoded into latent space with DDIM\n3. the latents are decoded with the diffusion model conditioned on the text query, using the mask as a guide such that pixels outside the mask remain the same as in the input image\n\nThis guide will show you how to use DiffEdit to edit images without manually creating a mask.\n\nBefore you begin, make sure you have the following libraries installed:\n\n```py\n# uncomment to install the necessary libraries in Colab\n#!pip install -q diffusers transformers accelerate\n```\n\nThe [`StableDiffusionDiffEditPipeline`] requires an image mask and a set of partially inverted latents. The image mask is generated from the [`~StableDiffusionDiffEditPipeline.generate_mask`] function, and includes two parameters, `source_prompt` and `target_prompt`. These parameters determine what to edit in the image. For example, if you want to change a bowl of *fruits* to a bowl of *pears*, then:\n\n```py\nsource_prompt = \"a bowl of fruits\"\ntarget_prompt = \"a bowl of pears\"\n```\n\nThe partially inverted latents are generated from the [`~StableDiffusionDiffEditPipeline.invert`] function, and it is generally a good idea to include a `prompt` or *caption* describing the image to help guide the inverse latent sampling process. The caption can often be your `source_prompt`, but feel free to experiment with other text descriptions!\n\nLet's load the pipeline, scheduler, inverse scheduler, and enable some optimizations to reduce memory usage:\n\n```py\nimport torch\nfrom diffusers import DDIMScheduler, DDIMInverseScheduler, StableDiffusionDiffEditPipeline\n\npipeline = StableDiffusionDiffEditPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-1\",\n torch_dtype=torch.float16,\n safety_checker=None,\n use_safetensors=True,\n)\npipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)\npipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)\npipeline.enable_model_cpu_offload()\npipeline.enable_vae_slicing()\n```\n\nLoad the image to edit:\n\n```py\nfrom diffusers.utils import load_image, make_image_grid\n\nimg_url = \"https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png\"\nraw_image = load_image(img_url).resize((768, 768))\nraw_image\n```\n\nUse the [`~StableDiffusionDiffEditPipeline.generate_mask`] function to generate the image mask. You'll need to pass it the `source_prompt` and `target_prompt` to specify what to edit in the image:\n\n```py\nfrom PIL import Image\n\nsource_prompt = \"a bowl of fruits\"\ntarget_prompt = \"a basket of pears\"\nmask_image = pipeline.generate_mask(\n image=raw_image,\n source_prompt=source_prompt,\n target_prompt=target_prompt,\n)\nImage.fromarray((mask_image.squeeze()*255).astype(\"uint8\"), \"L\").resize((768, 768))\n```\n\nNext, create the inverted latents and pass it a caption describing the image:\n\n```py\ninv_latents = pipeline.invert(prompt=source_prompt, image=raw_image).latents\n```\n\nFinally, pass the image mask and inverted latents to the pipeline. The `target_prompt` becomes the `prompt` now, and the `source_prompt` is used as the `negative_prompt`:\n\n```py\noutput_image = pipeline(\n prompt=target_prompt,\n mask_image=mask_image,\n image_latents=inv_latents,\n negative_prompt=source_prompt,\n).images[0]\nmask_image = Image.fromarray((mask_image.squeeze()*255).astype(\"uint8\"), \"L\").resize((768, 768))\nmake_image_grid([raw_image, mask_image, output_image], rows=1, cols=3)\n```\n\n
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\n\n## Generate source and target embeddings\n\nThe source and target embeddings can be automatically generated with the [Flan-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5) model instead of creating them manually.\n\nLoad the Flan-T5 model and tokenizer from the 🤗 Transformers library:\n\n```py\nimport torch\nfrom transformers import AutoTokenizer, T5ForConditionalGeneration\n\ntokenizer = AutoTokenizer.from_pretrained(\"google/flan-t5-large\")\nmodel = T5ForConditionalGeneration.from_pretrained(\"google/flan-t5-large\", device_map=\"auto\", torch_dtype=torch.float16)\n```\n\nProvide some initial text to prompt the model to generate the source and target prompts.\n\n```py\nsource_concept = \"bowl\"\ntarget_concept = \"basket\"\n\nsource_text = f\"Provide a caption for images containing a {source_concept}. \"\n\"The captions should be in English and should be no longer than 150 characters.\"\n\ntarget_text = f\"Provide a caption for images containing a {target_concept}. \"\n\"The captions should be in English and should be no longer than 150 characters.\"\n```\n\nNext, create a utility function to generate the prompts:\n\n```py\n@torch.no_grad()\ndef generate_prompts(input_prompt):\n input_ids = tokenizer(input_prompt, return_tensors=\"pt\").input_ids.to(\"cuda\")\n\n outputs = model.generate(\n input_ids, temperature=0.8, num_return_sequences=16, do_sample=True, max_new_tokens=128, top_k=10\n )\n return tokenizer.batch_decode(outputs, skip_special_tokens=True)\n\nsource_prompts = generate_prompts(source_text)\ntarget_prompts = generate_prompts(target_text)\nprint(source_prompts)\nprint(target_prompts)\n```\n\n\n\nCheck out the [generation strategy](https://huggingface.co/docs/transformers/main/en/generation_strategies) guide if you're interested in learning more about strategies for generating different quality text.\n\n\n\nLoad the text encoder model used by the [`StableDiffusionDiffEditPipeline`] to encode the text. You'll use the text encoder to compute the text embeddings:\n\n```py\nimport torch\nfrom diffusers import StableDiffusionDiffEditPipeline\n\npipeline = StableDiffusionDiffEditPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-1\", torch_dtype=torch.float16, use_safetensors=True\n)\npipeline.enable_model_cpu_offload()\npipeline.enable_vae_slicing()\n\n@torch.no_grad()\ndef embed_prompts(sentences, tokenizer, text_encoder, device=\"cuda\"):\n embeddings = []\n for sent in sentences:\n text_inputs = tokenizer(\n sent,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n prompt_embeds = text_encoder(text_input_ids.to(device), attention_mask=None)[0]\n embeddings.append(prompt_embeds)\n return torch.concatenate(embeddings, dim=0).mean(dim=0).unsqueeze(0)\n\nsource_embeds = embed_prompts(source_prompts, pipeline.tokenizer, pipeline.text_encoder)\ntarget_embeds = embed_prompts(target_prompts, pipeline.tokenizer, pipeline.text_encoder)\n```\n\nFinally, pass the embeddings to the [`~StableDiffusionDiffEditPipeline.generate_mask`] and [`~StableDiffusionDiffEditPipeline.invert`] functions, and pipeline to generate the image:\n\n```diff\n from diffusers import DDIMInverseScheduler, DDIMScheduler\n from diffusers.utils import load_image, make_image_grid\n from PIL import Image\n\n pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)\n pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)\n\n img_url = \"https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png\"\n raw_image = load_image(img_url).resize((768, 768))\n\n mask_image = pipeline.generate_mask(\n image=raw_image,\n- source_prompt=source_prompt,\n- target_prompt=target_prompt,\n+ source_prompt_embeds=source_embeds,\n+ target_prompt_embeds=target_embeds,\n )\n\n inv_latents = pipeline.invert(\n- prompt=source_prompt,\n+ prompt_embeds=source_embeds,\n image=raw_image,\n ).latents\n\n output_image = pipeline(\n mask_image=mask_image,\n image_latents=inv_latents,\n- prompt=target_prompt,\n- negative_prompt=source_prompt,\n+ prompt_embeds=target_embeds,\n+ negative_prompt_embeds=source_embeds,\n ).images[0]\n mask_image = Image.fromarray((mask_image.squeeze()*255).astype(\"uint8\"), \"L\")\n make_image_grid([raw_image, mask_image, output_image], rows=1, cols=3)\n```\n\n## Generate a caption for inversion\n\nWhile you can use the `source_prompt` as a caption to help generate the partially inverted latents, you can also use the [BLIP](https://huggingface.co/docs/transformers/model_doc/blip) model to automatically generate a caption.\n\nLoad the BLIP model and processor from the 🤗 Transformers library:\n\n```py\nimport torch\nfrom transformers import BlipForConditionalGeneration, BlipProcessor\n\nprocessor = BlipProcessor.from_pretrained(\"Salesforce/blip-image-captioning-base\")\nmodel = BlipForConditionalGeneration.from_pretrained(\"Salesforce/blip-image-captioning-base\", torch_dtype=torch.float16, low_cpu_mem_usage=True)\n```\n\nCreate a utility function to generate a caption from the input image:\n\n```py\n@torch.no_grad()\ndef generate_caption(images, caption_generator, caption_processor):\n text = \"a photograph of\"\n\n inputs = caption_processor(images, text, return_tensors=\"pt\").to(device=\"cuda\", dtype=caption_generator.dtype)\n caption_generator.to(\"cuda\")\n outputs = caption_generator.generate(**inputs, max_new_tokens=128)\n\n # offload caption generator\n caption_generator.to(\"cpu\")\n\n caption = caption_processor.batch_decode(outputs, skip_special_tokens=True)[0]\n return caption\n```\n\nLoad an input image and generate a caption for it using the `generate_caption` function:\n\n```py\nfrom diffusers.utils import load_image\n\nimg_url = \"https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png\"\nraw_image = load_image(img_url).resize((768, 768))\ncaption = generate_caption(raw_image, model, processor)\n```\n\n
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\n\nNow you can drop the caption into the [`~StableDiffusionDiffEditPipeline.invert`] function to generate the partially inverted latents!\n" }, { "path": "diffusers/docs/source/en/using-diffusers/image_quality.md", "content": "\n\n# Controlling image quality\n\nThe components of a diffusion model, like the UNet and scheduler, can be optimized to improve the quality of generated images leading to better details. These techniques are especially useful if you don't have the resources to simply use a larger model for inference. You can enable these techniques during inference without any additional training.\n\nThis guide will show you how to turn these techniques on in your pipeline and how to configure them to improve the quality of your generated images.\n\n## Details\n\n[FreeU](https://hf.co/papers/2309.11497) improves image details by rebalancing the UNet's backbone and skip connection weights. The skip connections can cause the model to overlook some of the backbone semantics which may lead to unnatural image details in the generated image. This technique does not require any additional training and can be applied on the fly during inference for tasks like image-to-image and text-to-video.\n\nUse the [`~pipelines.StableDiffusionMixin.enable_freeu`] method on your pipeline and configure the scaling factors for the backbone (`b1` and `b2`) and skip connections (`s1` and `s2`). The number after each scaling factor corresponds to the stage in the UNet where the factor is applied. Take a look at the [FreeU](https://github.com/ChenyangSi/FreeU#parameters) repository for reference hyperparameters for different models.\n\n\n\n\n```py\nimport torch\nfrom diffusers import DiffusionPipeline\n\npipeline = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, safety_checker=None\n).to(\"cuda\")\npipeline.enable_freeu(s1=0.9, s2=0.2, b1=1.5, b2=1.6)\ngenerator = torch.Generator(device=\"cpu\").manual_seed(33)\nprompt = \"\"\nimage = pipeline(prompt, generator=generator).images[0]\nimage\n```\n\n
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\n\n\n\n\n```py\nimport torch\nfrom diffusers import DiffusionPipeline\n\npipeline = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-1\", torch_dtype=torch.float16, safety_checker=None\n).to(\"cuda\")\npipeline.enable_freeu(s1=0.9, s2=0.2, b1=1.4, b2=1.6)\ngenerator = torch.Generator(device=\"cpu\").manual_seed(80)\nprompt = \"A squirrel eating a burger\"\nimage = pipeline(prompt, generator=generator).images[0]\nimage\n```\n\n
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\n\n\n\n\n```py\nimport torch\nfrom diffusers import DiffusionPipeline\n\npipeline = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16,\n).to(\"cuda\")\npipeline.enable_freeu(s1=0.9, s2=0.2, b1=1.3, b2=1.4)\ngenerator = torch.Generator(device=\"cpu\").manual_seed(13)\nprompt = \"A squirrel eating a burger\"\nimage = pipeline(prompt, generator=generator).images[0]\nimage\n```\n\n
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\n\n\n\n\n```py\nimport torch\nfrom diffusers import DiffusionPipeline\nfrom diffusers.utils import export_to_video\n\npipeline = DiffusionPipeline.from_pretrained(\n \"damo-vilab/text-to-video-ms-1.7b\", torch_dtype=torch.float16\n).to(\"cuda\")\n# values come from https://github.com/lyn-rgb/FreeU_Diffusers#video-pipelines\npipeline.enable_freeu(b1=1.2, b2=1.4, s1=0.9, s2=0.2)\nprompt = \"Confident teddy bear surfer rides the wave in the tropics\"\ngenerator = torch.Generator(device=\"cpu\").manual_seed(47)\nvideo_frames = pipeline(prompt, generator=generator).frames[0]\nexport_to_video(video_frames, \"teddy_bear.mp4\", fps=10)\n```\n\n
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\n\n\n\n\nCall the [`pipelines.StableDiffusionMixin.disable_freeu`] method to disable FreeU.\n\n```py\npipeline.disable_freeu()\n```\n" }, { "path": "diffusers/docs/source/en/using-diffusers/img2img.md", "content": "\n\n# Image-to-image\n\n[[open-in-colab]]\n\nImage-to-image is similar to [text-to-image](conditional_image_generation), but in addition to a prompt, you can also pass an initial image as a starting point for the diffusion process. The initial image is encoded to latent space and noise is added to it. Then the latent diffusion model takes a prompt and the noisy latent image, predicts the added noise, and removes the predicted noise from the initial latent image to get the new latent image. Lastly, a decoder decodes the new latent image back into an image.\n\nWith 🤗 Diffusers, this is as easy as 1-2-3:\n\n1. Load a checkpoint into the [`AutoPipelineForImage2Image`] class; this pipeline automatically handles loading the correct pipeline class based on the checkpoint:\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForImage2Image\nfrom diffusers.utils import load_image, make_image_grid\n\npipeline = AutoPipelineForImage2Image.from_pretrained(\n \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16, use_safetensors=True\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n```\n\n\n\nYou'll notice throughout the guide, we use [`~DiffusionPipeline.enable_model_cpu_offload`] and [`~DiffusionPipeline.enable_xformers_memory_efficient_attention`], to save memory and increase inference speed. If you're using PyTorch 2.0, then you don't need to call [`~DiffusionPipeline.enable_xformers_memory_efficient_attention`] on your pipeline because it'll already be using PyTorch 2.0's native [scaled-dot product attention](../optimization/torch2.0#scaled-dot-product-attention).\n\n\n\n2. Load an image to pass to the pipeline:\n\n```py\ninit_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png\")\n```\n\n3. Pass a prompt and image to the pipeline to generate an image:\n\n```py\nprompt = \"cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k\"\nimage = pipeline(prompt, image=init_image).images[0]\nmake_image_grid([init_image, image], rows=1, cols=2)\n```\n\n
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\n\n## Popular models\n\nThe most popular image-to-image models are [Stable Diffusion v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5), [Stable Diffusion XL (SDXL)](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), and [Kandinsky 2.2](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder). The results from the Stable Diffusion and Kandinsky models vary due to their architecture differences and training process; you can generally expect SDXL to produce higher quality images than Stable Diffusion v1.5. Let's take a quick look at how to use each of these models and compare their results.\n\n### Stable Diffusion v1.5\n\nStable Diffusion v1.5 is a latent diffusion model initialized from an earlier checkpoint, and further finetuned for 595K steps on 512x512 images. To use this pipeline for image-to-image, you'll need to prepare an initial image to pass to the pipeline. Then you can pass a prompt and the image to the pipeline to generate a new image:\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForImage2Image\nfrom diffusers.utils import make_image_grid, load_image\n\npipeline = AutoPipelineForImage2Image.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\n# prepare image\nurl = \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png\"\ninit_image = load_image(url)\n\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\n\n# pass prompt and image to pipeline\nimage = pipeline(prompt, image=init_image).images[0]\nmake_image_grid([init_image, image], rows=1, cols=2)\n```\n\n
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\n\n### Stable Diffusion XL (SDXL)\n\nSDXL is a more powerful version of the Stable Diffusion model. It uses a larger base model, and an additional refiner model to increase the quality of the base model's output. Read the [SDXL](sdxl) guide for a more detailed walkthrough of how to use this model, and other techniques it uses to produce high quality images.\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForImage2Image\nfrom diffusers.utils import make_image_grid, load_image\n\npipeline = AutoPipelineForImage2Image.from_pretrained(\n \"stabilityai/stable-diffusion-xl-refiner-1.0\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\n# prepare image\nurl = \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-sdxl-init.png\"\ninit_image = load_image(url)\n\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\n\n# pass prompt and image to pipeline\nimage = pipeline(prompt, image=init_image, strength=0.5).images[0]\nmake_image_grid([init_image, image], rows=1, cols=2)\n```\n\n
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\n\n### Kandinsky 2.2\n\nThe Kandinsky model is different from the Stable Diffusion models because it uses an image prior model to create image embeddings. The embeddings help create a better alignment between text and images, allowing the latent diffusion model to generate better images.\n\nThe simplest way to use Kandinsky 2.2 is:\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForImage2Image\nfrom diffusers.utils import make_image_grid, load_image\n\npipeline = AutoPipelineForImage2Image.from_pretrained(\n \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16, use_safetensors=True\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\n# prepare image\nurl = \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png\"\ninit_image = load_image(url)\n\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\n\n# pass prompt and image to pipeline\nimage = pipeline(prompt, image=init_image).images[0]\nmake_image_grid([init_image, image], rows=1, cols=2)\n```\n\n
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\n\n## Configure pipeline parameters\n\nThere are several important parameters you can configure in the pipeline that'll affect the image generation process and image quality. Let's take a closer look at what these parameters do and how changing them affects the output.\n\n### Strength\n\n`strength` is one of the most important parameters to consider and it'll have a huge impact on your generated image. It determines how much the generated image resembles the initial image. In other words:\n\n- 📈 a higher `strength` value gives the model more \"creativity\" to generate an image that's different from the initial image; a `strength` value of 1.0 means the initial image is more or less ignored\n- 📉 a lower `strength` value means the generated image is more similar to the initial image\n\nThe `strength` and `num_inference_steps` parameters are related because `strength` determines the number of noise steps to add. For example, if the `num_inference_steps` is 50 and `strength` is 0.8, then this means adding 40 (50 * 0.8) steps of noise to the initial image and then denoising for 40 steps to get the newly generated image.\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForImage2Image\nfrom diffusers.utils import make_image_grid, load_image\n\npipeline = AutoPipelineForImage2Image.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\n# prepare image\nurl = \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png\"\ninit_image = load_image(url)\n\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\n\n# pass prompt and image to pipeline\nimage = pipeline(prompt, image=init_image, strength=0.8).images[0]\nmake_image_grid([init_image, image], rows=1, cols=2)\n```\n\n
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strength = 0.4
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strength = 0.6
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strength = 1.0
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\n\n### Guidance scale\n\nThe `guidance_scale` parameter is used to control how closely aligned the generated image and text prompt are. A higher `guidance_scale` value means your generated image is more aligned with the prompt, while a lower `guidance_scale` value means your generated image has more space to deviate from the prompt.\n\nYou can combine `guidance_scale` with `strength` for even more precise control over how expressive the model is. For example, combine a high `strength + guidance_scale` for maximum creativity or use a combination of low `strength` and low `guidance_scale` to generate an image that resembles the initial image but is not as strictly bound to the prompt.\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForImage2Image\nfrom diffusers.utils import make_image_grid, load_image\n\npipeline = AutoPipelineForImage2Image.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\n# prepare image\nurl = \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png\"\ninit_image = load_image(url)\n\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\n\n# pass prompt and image to pipeline\nimage = pipeline(prompt, image=init_image, guidance_scale=8.0).images[0]\nmake_image_grid([init_image, image], rows=1, cols=2)\n```\n\n
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guidance_scale = 0.1
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guidance_scale = 5.0
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guidance_scale = 10.0
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\n\n### Negative prompt\n\nA negative prompt conditions the model to *not* include things in an image, and it can be used to improve image quality or modify an image. For example, you can improve image quality by including negative prompts like \"poor details\" or \"blurry\" to encourage the model to generate a higher quality image. Or you can modify an image by specifying things to exclude from an image.\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForImage2Image\nfrom diffusers.utils import make_image_grid, load_image\n\npipeline = AutoPipelineForImage2Image.from_pretrained(\n \"stabilityai/stable-diffusion-xl-refiner-1.0\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\n# prepare image\nurl = \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png\"\ninit_image = load_image(url)\n\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\nnegative_prompt = \"ugly, deformed, disfigured, poor details, bad anatomy\"\n\n# pass prompt and image to pipeline\nimage = pipeline(prompt, negative_prompt=negative_prompt, image=init_image).images[0]\nmake_image_grid([init_image, image], rows=1, cols=2)\n```\n\n
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negative_prompt = \"ugly, deformed, disfigured, poor details, bad anatomy\"
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negative_prompt = \"jungle\"
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\n\n## Chained image-to-image pipelines\n\nThere are some other interesting ways you can use an image-to-image pipeline aside from just generating an image (although that is pretty cool too). You can take it a step further and chain it with other pipelines.\n\n### Text-to-image-to-image\n\nChaining a text-to-image and image-to-image pipeline allows you to generate an image from text and use the generated image as the initial image for the image-to-image pipeline. This is useful if you want to generate an image entirely from scratch. For example, let's chain a Stable Diffusion and a Kandinsky model.\n\nStart by generating an image with the text-to-image pipeline:\n\n```py\nfrom diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image\nimport torch\nfrom diffusers.utils import make_image_grid\n\npipeline = AutoPipelineForText2Image.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\ntext2image = pipeline(\"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\").images[0]\ntext2image\n```\n\nNow you can pass this generated image to the image-to-image pipeline:\n\n```py\npipeline = AutoPipelineForImage2Image.from_pretrained(\n \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16, use_safetensors=True\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\nimage2image = pipeline(\"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\", image=text2image).images[0]\nmake_image_grid([text2image, image2image], rows=1, cols=2)\n```\n\n### Image-to-image-to-image\n\nYou can also chain multiple image-to-image pipelines together to create more interesting images. This can be useful for iteratively performing style transfer on an image, generating short GIFs, restoring color to an image, or restoring missing areas of an image.\n\nStart by generating an image:\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForImage2Image\nfrom diffusers.utils import make_image_grid, load_image\n\npipeline = AutoPipelineForImage2Image.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\n# prepare image\nurl = \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png\"\ninit_image = load_image(url)\n\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\n\n# pass prompt and image to pipeline\nimage = pipeline(prompt, image=init_image, output_type=\"latent\").images[0]\n```\n\n\n\nIt is important to specify `output_type=\"latent\"` in the pipeline to keep all the outputs in latent space to avoid an unnecessary decode-encode step. This only works if the chained pipelines are using the same VAE.\n\n\n\nPass the latent output from this pipeline to the next pipeline to generate an image in a [comic book art style](https://huggingface.co/ogkalu/Comic-Diffusion):\n\n```py\npipeline = AutoPipelineForImage2Image.from_pretrained(\n \"ogkalu/Comic-Diffusion\", torch_dtype=torch.float16\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\n# need to include the token \"charliebo artstyle\" in the prompt to use this checkpoint\nimage = pipeline(\"Astronaut in a jungle, charliebo artstyle\", image=image, output_type=\"latent\").images[0]\n```\n\nRepeat one more time to generate the final image in a [pixel art style](https://huggingface.co/kohbanye/pixel-art-style):\n\n```py\npipeline = AutoPipelineForImage2Image.from_pretrained(\n \"kohbanye/pixel-art-style\", torch_dtype=torch.float16\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\n# need to include the token \"pixelartstyle\" in the prompt to use this checkpoint\nimage = pipeline(\"Astronaut in a jungle, pixelartstyle\", image=image).images[0]\nmake_image_grid([init_image, image], rows=1, cols=2)\n```\n\n### Image-to-upscaler-to-super-resolution\n\nAnother way you can chain your image-to-image pipeline is with an upscaler and super-resolution pipeline to really increase the level of details in an image.\n\nStart with an image-to-image pipeline:\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForImage2Image\nfrom diffusers.utils import make_image_grid, load_image\n\npipeline = AutoPipelineForImage2Image.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\n# prepare image\nurl = \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png\"\ninit_image = load_image(url)\n\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\n\n# pass prompt and image to pipeline\nimage_1 = pipeline(prompt, image=init_image, output_type=\"latent\").images[0]\n```\n\n\n\nIt is important to specify `output_type=\"latent\"` in the pipeline to keep all the outputs in *latent* space to avoid an unnecessary decode-encode step. This only works if the chained pipelines are using the same VAE.\n\n\n\nChain it to an upscaler pipeline to increase the image resolution:\n\n```py\nfrom diffusers import StableDiffusionLatentUpscalePipeline\n\nupscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(\n \"stabilityai/sd-x2-latent-upscaler\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n)\nupscaler.enable_model_cpu_offload()\nupscaler.enable_xformers_memory_efficient_attention()\n\nimage_2 = upscaler(prompt, image=image_1, output_type=\"latent\").images[0]\n```\n\nFinally, chain it to a super-resolution pipeline to further enhance the resolution:\n\n```py\nfrom diffusers import StableDiffusionUpscalePipeline\n\nsuper_res = StableDiffusionUpscalePipeline.from_pretrained(\n \"stabilityai/stable-diffusion-x4-upscaler\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n)\nsuper_res.enable_model_cpu_offload()\nsuper_res.enable_xformers_memory_efficient_attention()\n\nimage_3 = super_res(prompt, image=image_2).images[0]\nmake_image_grid([init_image, image_3.resize((512, 512))], rows=1, cols=2)\n```\n\n## Control image generation\n\nTrying to generate an image that looks exactly the way you want can be difficult, which is why controlled generation techniques and models are so useful. While you can use the `negative_prompt` to partially control image generation, there are more robust methods like prompt weighting and ControlNets.\n\n### Prompt weighting\n\nPrompt weighting allows you to scale the representation of each concept in a prompt. For example, in a prompt like \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\", you can choose to increase or decrease the embeddings of \"astronaut\" and \"jungle\". The [Compel](https://github.com/damian0815/compel) library provides a simple syntax for adjusting prompt weights and generating the embeddings. You can learn how to create the embeddings in the [Prompt weighting](weighted_prompts) guide.\n\n[`AutoPipelineForImage2Image`] has a `prompt_embeds` (and `negative_prompt_embeds` if you're using a negative prompt) parameter where you can pass the embeddings which replaces the `prompt` parameter.\n\n```py\nfrom diffusers import AutoPipelineForImage2Image\nimport torch\n\npipeline = AutoPipelineForImage2Image.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\nimage = pipeline(prompt_embeds=prompt_embeds, # generated from Compel\n negative_prompt_embeds=negative_prompt_embeds, # generated from Compel\n image=init_image,\n).images[0]\n```\n\n### ControlNet\n\nControlNets provide a more flexible and accurate way to control image generation because you can use an additional conditioning image. The conditioning image can be a canny image, depth map, image segmentation, and even scribbles! Whatever type of conditioning image you choose, the ControlNet generates an image that preserves the information in it.\n\nFor example, let's condition an image with a depth map to keep the spatial information in the image.\n\n```py\nfrom diffusers.utils import load_image, make_image_grid\n\n# prepare image\nurl = \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png\"\ninit_image = load_image(url)\ninit_image = init_image.resize((958, 960)) # resize to depth image dimensions\ndepth_image = load_image(\"https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/control.png\")\nmake_image_grid([init_image, depth_image], rows=1, cols=2)\n```\n\nLoad a ControlNet model conditioned on depth maps and the [`AutoPipelineForImage2Image`]:\n\n```py\nfrom diffusers import ControlNetModel, AutoPipelineForImage2Image\nimport torch\n\ncontrolnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11f1p_sd15_depth\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True)\npipeline = AutoPipelineForImage2Image.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n```\n\nNow generate a new image conditioned on the depth map, initial image, and prompt:\n\n```py\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\nimage_control_net = pipeline(prompt, image=init_image, control_image=depth_image).images[0]\nmake_image_grid([init_image, depth_image, image_control_net], rows=1, cols=3)\n```\n\n
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\n\nLet's apply a new [style](https://huggingface.co/nitrosocke/elden-ring-diffusion) to the image generated from the ControlNet by chaining it with an image-to-image pipeline:\n\n```py\npipeline = AutoPipelineForImage2Image.from_pretrained(\n \"nitrosocke/elden-ring-diffusion\", torch_dtype=torch.float16,\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\nprompt = \"elden ring style astronaut in a jungle\" # include the token \"elden ring style\" in the prompt\nnegative_prompt = \"ugly, deformed, disfigured, poor details, bad anatomy\"\n\nimage_elden_ring = pipeline(prompt, negative_prompt=negative_prompt, image=image_control_net, strength=0.45, guidance_scale=10.5).images[0]\nmake_image_grid([init_image, depth_image, image_control_net, image_elden_ring], rows=2, cols=2)\n```\n\n
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\n\n## Optimize\n\nRunning diffusion models is computationally expensive and intensive, but with a few optimization tricks, it is entirely possible to run them on consumer and free-tier GPUs. For example, you can use a more memory-efficient form of attention such as PyTorch 2.0's [scaled-dot product attention](../optimization/torch2.0#scaled-dot-product-attention) or [xFormers](../optimization/xformers) (you can use one or the other, but there's no need to use both). You can also offload the model to the GPU while the other pipeline components wait on the CPU.\n\n```diff\n+ pipeline.enable_model_cpu_offload()\n+ pipeline.enable_xformers_memory_efficient_attention()\n```\n\nWith [`torch.compile`](../optimization/torch2.0#torchcompile), you can boost your inference speed even more by wrapping your UNet with it:\n\n```py\npipeline.unet = torch.compile(pipeline.unet, mode=\"reduce-overhead\", fullgraph=True)\n```\n\nTo learn more, take a look at the [Reduce memory usage](../optimization/memory) and [Torch 2.0](../optimization/torch2.0) guides.\n" }, { "path": "diffusers/docs/source/en/using-diffusers/inference_with_lcm.md", "content": "\n\n# Latent Consistency Model\n\n[[open-in-colab]]\n\n[Latent Consistency Models (LCMs)](https://hf.co/papers/2310.04378) enable fast high-quality image generation by directly predicting the reverse diffusion process in the latent rather than pixel space. In other words, LCMs try to predict the noiseless image from the noisy image in contrast to typical diffusion models that iteratively remove noise from the noisy image. By avoiding the iterative sampling process, LCMs are able to generate high-quality images in 2-4 steps instead of 20-30 steps.\n\nLCMs are distilled from pretrained models which requires ~32 hours of A100 compute. To speed this up, [LCM-LoRAs](https://hf.co/papers/2311.05556) train a [LoRA adapter](https://huggingface.co/docs/peft/conceptual_guides/adapter#low-rank-adaptation-lora) which have much fewer parameters to train compared to the full model. The LCM-LoRA can be plugged into a diffusion model once it has been trained.\n\nThis guide will show you how to use LCMs and LCM-LoRAs for fast inference on tasks and how to use them with other adapters like ControlNet or T2I-Adapter.\n\n> [!TIP]\n> LCMs and LCM-LoRAs are available for Stable Diffusion v1.5, Stable Diffusion XL, and the SSD-1B model. You can find their checkpoints on the [Latent Consistency](https://hf.co/collections/latent-consistency/latent-consistency-models-weights-654ce61a95edd6dffccef6a8) Collections.\n\n## Text-to-image\n\n\n\n\nTo use LCMs, you need to load the LCM checkpoint for your supported model into [`UNet2DConditionModel`] and replace the scheduler with the [`LCMScheduler`]. Then you can use the pipeline as usual, and pass a text prompt to generate an image in just 4 steps.\n\nA couple of notes to keep in mind when using LCMs are:\n\n* Typically, batch size is doubled inside the pipeline for classifier-free guidance. But LCM applies guidance with guidance embeddings and doesn't need to double the batch size, which leads to faster inference. The downside is that negative prompts don't work with LCM because they don't have any effect on the denoising process.\n* The ideal range for `guidance_scale` is [3., 13.] because that is what the UNet was trained with. However, disabling `guidance_scale` with a value of 1.0 is also effective in most cases.\n\n```python\nfrom diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, LCMScheduler\nimport torch\n\nunet = UNet2DConditionModel.from_pretrained(\n \"latent-consistency/lcm-sdxl\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n)\npipe = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", unet=unet, torch_dtype=torch.float16, variant=\"fp16\",\n).to(\"cuda\")\npipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\n\nprompt = \"Self-portrait oil painting, a beautiful cyborg with golden hair, 8k\"\ngenerator = torch.manual_seed(0)\nimage = pipe(\n prompt=prompt, num_inference_steps=4, generator=generator, guidance_scale=8.0\n).images[0]\nimage\n```\n\n
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\n\n\n\n\nTo use LCM-LoRAs, you need to replace the scheduler with the [`LCMScheduler`] and load the LCM-LoRA weights with the [`~loaders.LoraLoaderMixin.load_lora_weights`] method. Then you can use the pipeline as usual, and pass a text prompt to generate an image in just 4 steps.\n\nA couple of notes to keep in mind when using LCM-LoRAs are:\n\n* Typically, batch size is doubled inside the pipeline for classifier-free guidance. But LCM applies guidance with guidance embeddings and doesn't need to double the batch size, which leads to faster inference. The downside is that negative prompts don't work with LCM because they don't have any effect on the denoising process.\n* You could use guidance with LCM-LoRAs, but it is very sensitive to high `guidance_scale` values and can lead to artifacts in the generated image. The best values we've found are between [1.0, 2.0].\n* Replace [stabilityai/stable-diffusion-xl-base-1.0](https://hf.co/stabilityai/stable-diffusion-xl-base-1.0) with any finetuned model. For example, try using the [animagine-xl](https://huggingface.co/Linaqruf/animagine-xl) checkpoint to generate anime images with SDXL.\n\n```py\nimport torch\nfrom diffusers import DiffusionPipeline, LCMScheduler\n\npipe = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n variant=\"fp16\",\n torch_dtype=torch.float16\n).to(\"cuda\")\npipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\npipe.load_lora_weights(\"latent-consistency/lcm-lora-sdxl\")\n\nprompt = \"Self-portrait oil painting, a beautiful cyborg with golden hair, 8k\"\ngenerator = torch.manual_seed(42)\nimage = pipe(\n prompt=prompt, num_inference_steps=4, generator=generator, guidance_scale=1.0\n).images[0]\nimage\n```\n\n
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\n\n\n\n\n## Image-to-image\n\n\n\n\nTo use LCMs for image-to-image, you need to load the LCM checkpoint for your supported model into [`UNet2DConditionModel`] and replace the scheduler with the [`LCMScheduler`]. Then you can use the pipeline as usual, and pass a text prompt and initial image to generate an image in just 4 steps.\n\n> [!TIP]\n> Experiment with different values for `num_inference_steps`, `strength`, and `guidance_scale` to get the best results.\n\n```python\nimport torch\nfrom diffusers import AutoPipelineForImage2Image, UNet2DConditionModel, LCMScheduler\nfrom diffusers.utils import load_image\n\nunet = UNet2DConditionModel.from_pretrained(\n \"SimianLuo/LCM_Dreamshaper_v7\",\n subfolder=\"unet\",\n torch_dtype=torch.float16,\n)\n\npipe = AutoPipelineForImage2Image.from_pretrained(\n \"Lykon/dreamshaper-7\",\n unet=unet,\n torch_dtype=torch.float16,\n variant=\"fp16\",\n).to(\"cuda\")\npipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\n\ninit_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png\")\nprompt = \"Astronauts in a jungle, cold color palette, muted colors, detailed, 8k\"\ngenerator = torch.manual_seed(0)\nimage = pipe(\n prompt,\n image=init_image,\n num_inference_steps=4,\n guidance_scale=7.5,\n strength=0.5,\n generator=generator\n).images[0]\nimage\n```\n\n
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initial image
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\n\n\n\n\nTo use LCM-LoRAs for image-to-image, you need to replace the scheduler with the [`LCMScheduler`] and load the LCM-LoRA weights with the [`~loaders.LoraLoaderMixin.load_lora_weights`] method. Then you can use the pipeline as usual, and pass a text prompt and initial image to generate an image in just 4 steps.\n\n> [!TIP]\n> Experiment with different values for `num_inference_steps`, `strength`, and `guidance_scale` to get the best results.\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForImage2Image, LCMScheduler\nfrom diffusers.utils import make_image_grid, load_image\n\npipe = AutoPipelineForImage2Image.from_pretrained(\n \"Lykon/dreamshaper-7\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n).to(\"cuda\")\n\npipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\n\npipe.load_lora_weights(\"latent-consistency/lcm-lora-sdv1-5\")\n\ninit_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png\")\nprompt = \"Astronauts in a jungle, cold color palette, muted colors, detailed, 8k\"\n\ngenerator = torch.manual_seed(0)\nimage = pipe(\n prompt,\n image=init_image,\n num_inference_steps=4,\n guidance_scale=1,\n strength=0.6,\n generator=generator\n).images[0]\nimage\n```\n\n
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\n\n\n\n\n## Inpainting\n\nTo use LCM-LoRAs for inpainting, you need to replace the scheduler with the [`LCMScheduler`] and load the LCM-LoRA weights with the [`~loaders.LoraLoaderMixin.load_lora_weights`] method. Then you can use the pipeline as usual, and pass a text prompt, initial image, and mask image to generate an image in just 4 steps.\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForInpainting, LCMScheduler\nfrom diffusers.utils import load_image, make_image_grid\n\npipe = AutoPipelineForInpainting.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n).to(\"cuda\")\n\npipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\n\npipe.load_lora_weights(\"latent-consistency/lcm-lora-sdv1-5\")\n\ninit_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png\")\nmask_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_mask.png\")\n\nprompt = \"concept art digital painting of an elven castle, inspired by lord of the rings, highly detailed, 8k\"\ngenerator = torch.manual_seed(0)\nimage = pipe(\n prompt=prompt,\n image=init_image,\n mask_image=mask_image,\n generator=generator,\n num_inference_steps=4,\n guidance_scale=4, \n).images[0]\nimage\n```\n\n
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generated image
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\n\n## Adapters\n\nLCMs are compatible with adapters like LoRA, ControlNet, T2I-Adapter, and AnimateDiff. You can bring the speed of LCMs to these adapters to generate images in a certain style or condition the model on another input like a canny image.\n\n### LoRA\n\n[LoRA](../using-diffusers/loading_adapters#lora) adapters can be rapidly finetuned to learn a new style from just a few images and plugged into a pretrained model to generate images in that style.\n\n\n\n\nLoad the LCM checkpoint for your supported model into [`UNet2DConditionModel`] and replace the scheduler with the [`LCMScheduler`]. Then you can use the [`~loaders.LoraLoaderMixin.load_lora_weights`] method to load the LoRA weights into the LCM and generate a styled image in a few steps.\n\n```python\nfrom diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, LCMScheduler\nimport torch\n\nunet = UNet2DConditionModel.from_pretrained(\n \"latent-consistency/lcm-sdxl\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n)\npipe = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", unet=unet, torch_dtype=torch.float16, variant=\"fp16\",\n).to(\"cuda\")\npipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\npipe.load_lora_weights(\"TheLastBen/Papercut_SDXL\", weight_name=\"papercut.safetensors\", adapter_name=\"papercut\")\n\nprompt = \"papercut, a cute fox\"\ngenerator = torch.manual_seed(0)\nimage = pipe(\n prompt=prompt, num_inference_steps=4, generator=generator, guidance_scale=8.0\n).images[0]\nimage\n```\n\n
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\n\n\n\n\nReplace the scheduler with the [`LCMScheduler`]. Then you can use the [`~loaders.LoraLoaderMixin.load_lora_weights`] method to load the LCM-LoRA weights and the style LoRA you want to use. Combine both LoRA adapters with the [`~loaders.UNet2DConditionLoadersMixin.set_adapters`] method and generate a styled image in a few steps.\n\n```py\nimport torch\nfrom diffusers import DiffusionPipeline, LCMScheduler\n\npipe = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n variant=\"fp16\",\n torch_dtype=torch.float16\n).to(\"cuda\")\n\npipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\n\npipe.load_lora_weights(\"latent-consistency/lcm-lora-sdxl\", adapter_name=\"lcm\")\npipe.load_lora_weights(\"TheLastBen/Papercut_SDXL\", weight_name=\"papercut.safetensors\", adapter_name=\"papercut\")\n\npipe.set_adapters([\"lcm\", \"papercut\"], adapter_weights=[1.0, 0.8])\n\nprompt = \"papercut, a cute fox\"\ngenerator = torch.manual_seed(0)\nimage = pipe(prompt, num_inference_steps=4, guidance_scale=1, generator=generator).images[0]\nimage\n```\n\n
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\n\n\n\n\n### ControlNet\n\n[ControlNet](./controlnet) are adapters that can be trained on a variety of inputs like canny edge, pose estimation, or depth. The ControlNet can be inserted into the pipeline to provide additional conditioning and control to the model for more accurate generation.\n\nYou can find additional ControlNet models trained on other inputs in [lllyasviel's](https://hf.co/lllyasviel) repository.\n\n\n\n\nLoad a ControlNet model trained on canny images and pass it to the [`ControlNetModel`]. Then you can load a LCM model into [`StableDiffusionControlNetPipeline`] and replace the scheduler with the [`LCMScheduler`]. Now pass the canny image to the pipeline and generate an image.\n\n> [!TIP]\n> Experiment with different values for `num_inference_steps`, `controlnet_conditioning_scale`, `cross_attention_kwargs`, and `guidance_scale` to get the best results.\n\n```python\nimport torch\nimport cv2\nimport numpy as np\nfrom PIL import Image\n\nfrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel, LCMScheduler\nfrom diffusers.utils import load_image, make_image_grid\n\nimage = load_image(\n \"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png\"\n).resize((512, 512))\n\nimage = np.array(image)\n\nlow_threshold = 100\nhigh_threshold = 200\n\nimage = cv2.Canny(image, low_threshold, high_threshold)\nimage = image[:, :, None]\nimage = np.concatenate([image, image, image], axis=2)\ncanny_image = Image.fromarray(image)\n\ncontrolnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\", torch_dtype=torch.float16)\npipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"SimianLuo/LCM_Dreamshaper_v7\",\n controlnet=controlnet,\n torch_dtype=torch.float16,\n safety_checker=None,\n).to(\"cuda\")\npipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\n\ngenerator = torch.manual_seed(0)\nimage = pipe(\n \"the mona lisa\",\n image=canny_image,\n num_inference_steps=4,\n generator=generator,\n).images[0]\nmake_image_grid([canny_image, image], rows=1, cols=2)\n```\n\n
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\n\n\n\n\nLoad a ControlNet model trained on canny images and pass it to the [`ControlNetModel`]. Then you can load a Stable Diffusion v1.5 model into [`StableDiffusionControlNetPipeline`] and replace the scheduler with the [`LCMScheduler`]. Use the [`~loaders.LoraLoaderMixin.load_lora_weights`] method to load the LCM-LoRA weights, and pass the canny image to the pipeline and generate an image.\n\n> [!TIP]\n> Experiment with different values for `num_inference_steps`, `controlnet_conditioning_scale`, `cross_attention_kwargs`, and `guidance_scale` to get the best results.\n\n```py\nimport torch\nimport cv2\nimport numpy as np\nfrom PIL import Image\n\nfrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel, LCMScheduler\nfrom diffusers.utils import load_image\n\nimage = load_image(\n \"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png\"\n).resize((512, 512))\n\nimage = np.array(image)\n\nlow_threshold = 100\nhigh_threshold = 200\n\nimage = cv2.Canny(image, low_threshold, high_threshold)\nimage = image[:, :, None]\nimage = np.concatenate([image, image, image], axis=2)\ncanny_image = Image.fromarray(image)\n\ncontrolnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\", torch_dtype=torch.float16)\npipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n controlnet=controlnet,\n torch_dtype=torch.float16,\n safety_checker=None,\n variant=\"fp16\"\n).to(\"cuda\")\n\npipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\n\npipe.load_lora_weights(\"latent-consistency/lcm-lora-sdv1-5\")\n\ngenerator = torch.manual_seed(0)\nimage = pipe(\n \"the mona lisa\",\n image=canny_image,\n num_inference_steps=4,\n guidance_scale=1.5,\n controlnet_conditioning_scale=0.8,\n cross_attention_kwargs={\"scale\": 1},\n generator=generator,\n).images[0]\nimage\n```\n\n
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\n\n\n\n\n### T2I-Adapter\n\n[T2I-Adapter](./t2i_adapter) is an even more lightweight adapter than ControlNet, that provides an additional input to condition a pretrained model with. It is faster than ControlNet but the results may be slightly worse.\n\nYou can find additional T2I-Adapter checkpoints trained on other inputs in [TencentArc's](https://hf.co/TencentARC) repository.\n\n\n\n\nLoad a T2IAdapter trained on canny images and pass it to the [`StableDiffusionXLAdapterPipeline`]. Then load a LCM checkpoint into [`UNet2DConditionModel`] and replace the scheduler with the [`LCMScheduler`]. Now pass the canny image to the pipeline and generate an image.\n\n```python\nimport torch\nimport cv2\nimport numpy as np\nfrom PIL import Image\n\nfrom diffusers import StableDiffusionXLAdapterPipeline, UNet2DConditionModel, T2IAdapter, LCMScheduler\nfrom diffusers.utils import load_image, make_image_grid\n\n# detect the canny map in low resolution to avoid high-frequency details\nimage = load_image(\n \"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png\"\n).resize((384, 384))\n\nimage = np.array(image)\n\nlow_threshold = 100\nhigh_threshold = 200\n\nimage = cv2.Canny(image, low_threshold, high_threshold)\nimage = image[:, :, None]\nimage = np.concatenate([image, image, image], axis=2)\ncanny_image = Image.fromarray(image).resize((1024, 1216))\n\nadapter = T2IAdapter.from_pretrained(\"TencentARC/t2i-adapter-canny-sdxl-1.0\", torch_dtype=torch.float16, varient=\"fp16\").to(\"cuda\")\n\nunet = UNet2DConditionModel.from_pretrained(\n \"latent-consistency/lcm-sdxl\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n)\npipe = StableDiffusionXLAdapterPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n unet=unet,\n adapter=adapter,\n torch_dtype=torch.float16,\n variant=\"fp16\", \n).to(\"cuda\")\n\npipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\n\nprompt = \"the mona lisa, 4k picture, high quality\"\nnegative_prompt = \"extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured\"\n\ngenerator = torch.manual_seed(0)\nimage = pipe(\n prompt=prompt,\n negative_prompt=negative_prompt,\n image=canny_image,\n num_inference_steps=4,\n guidance_scale=5,\n adapter_conditioning_scale=0.8, \n adapter_conditioning_factor=1,\n generator=generator,\n).images[0]\n```\n\n
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\n\n\n\n\nLoad a T2IAdapter trained on canny images and pass it to the [`StableDiffusionXLAdapterPipeline`]. Replace the scheduler with the [`LCMScheduler`], and use the [`~loaders.LoraLoaderMixin.load_lora_weights`] method to load the LCM-LoRA weights. Pass the canny image to the pipeline and generate an image.\n\n```py\nimport torch\nimport cv2\nimport numpy as np\nfrom PIL import Image\n\nfrom diffusers import StableDiffusionXLAdapterPipeline, UNet2DConditionModel, T2IAdapter, LCMScheduler\nfrom diffusers.utils import load_image, make_image_grid\n\n# detect the canny map in low resolution to avoid high-frequency details\nimage = load_image(\n \"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png\"\n).resize((384, 384))\n\nimage = np.array(image)\n\nlow_threshold = 100\nhigh_threshold = 200\n\nimage = cv2.Canny(image, low_threshold, high_threshold)\nimage = image[:, :, None]\nimage = np.concatenate([image, image, image], axis=2)\ncanny_image = Image.fromarray(image).resize((1024, 1024))\n\nadapter = T2IAdapter.from_pretrained(\"TencentARC/t2i-adapter-canny-sdxl-1.0\", torch_dtype=torch.float16, varient=\"fp16\").to(\"cuda\")\n\npipe = StableDiffusionXLAdapterPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", \n adapter=adapter,\n torch_dtype=torch.float16,\n variant=\"fp16\", \n).to(\"cuda\")\n\npipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\n\npipe.load_lora_weights(\"latent-consistency/lcm-lora-sdxl\")\n\nprompt = \"the mona lisa, 4k picture, high quality\"\nnegative_prompt = \"extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured\"\n\ngenerator = torch.manual_seed(0)\nimage = pipe(\n prompt=prompt,\n negative_prompt=negative_prompt,\n image=canny_image,\n num_inference_steps=4,\n guidance_scale=1.5, \n adapter_conditioning_scale=0.8, \n adapter_conditioning_factor=1,\n generator=generator,\n).images[0]\n```\n\n
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\n\n\n\n\n### AnimateDiff\n\n[AnimateDiff](../api/pipelines/animatediff) is an adapter that adds motion to an image. It can be used with most Stable Diffusion models, effectively turning them into \"video generation\" models. Generating good results with a video model usually requires generating multiple frames (16-24), which can be very slow with a regular Stable Diffusion model. LCM-LoRA can speed up this process by only taking 4-8 steps for each frame.\n\nLoad a [`AnimateDiffPipeline`] and pass a [`MotionAdapter`] to it. Then replace the scheduler with the [`LCMScheduler`], and combine both LoRA adapters with the [`~loaders.UNet2DConditionLoadersMixin.set_adapters`] method. Now you can pass a prompt to the pipeline and generate an animated image.\n\n```py\nimport torch\nfrom diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler, LCMScheduler\nfrom diffusers.utils import export_to_gif\n\nadapter = MotionAdapter.from_pretrained(\"guoyww/animatediff-motion-adapter-v1-5\")\npipe = AnimateDiffPipeline.from_pretrained(\n \"frankjoshua/toonyou_beta6\",\n motion_adapter=adapter,\n).to(\"cuda\")\n\n# set scheduler\npipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\n\n# load LCM-LoRA\npipe.load_lora_weights(\"latent-consistency/lcm-lora-sdv1-5\", adapter_name=\"lcm\")\npipe.load_lora_weights(\"guoyww/animatediff-motion-lora-zoom-in\", weight_name=\"diffusion_pytorch_model.safetensors\", adapter_name=\"motion-lora\")\n\npipe.set_adapters([\"lcm\", \"motion-lora\"], adapter_weights=[0.55, 1.2])\n\nprompt = \"best quality, masterpiece, 1girl, looking at viewer, blurry background, upper body, contemporary, dress\"\ngenerator = torch.manual_seed(0)\nframes = pipe(\n prompt=prompt,\n num_inference_steps=5,\n guidance_scale=1.25,\n cross_attention_kwargs={\"scale\": 1},\n num_frames=24,\n generator=generator\n).frames[0]\nexport_to_gif(frames, \"animation.gif\")\n```\n\n
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\n" }, { "path": "diffusers/docs/source/en/using-diffusers/inference_with_tcd_lora.md", "content": "\n\n[[open-in-colab]]\n\n# Trajectory Consistency Distillation-LoRA\n\nTrajectory Consistency Distillation (TCD) enables a model to generate higher quality and more detailed images with fewer steps. Moreover, owing to the effective error mitigation during the distillation process, TCD demonstrates superior performance even under conditions of large inference steps.\n\nThe major advantages of TCD are:\n\n- Better than Teacher: TCD demonstrates superior generative quality at both small and large inference steps and exceeds the performance of [DPM-Solver++(2S)](../../api/schedulers/multistep_dpm_solver) with Stable Diffusion XL (SDXL). There is no additional discriminator or LPIPS supervision included during TCD training.\n\n- Flexible Inference Steps: The inference steps for TCD sampling can be freely adjusted without adversely affecting the image quality.\n\n- Freely change detail level: During inference, the level of detail in the image can be adjusted with a single hyperparameter, *gamma*.\n\n> [!TIP]\n> For more technical details of TCD, please refer to the [paper](https://arxiv.org/abs/2402.19159) or official [project page](https://mhh0318.github.io/tcd/)).\n\nFor large models like SDXL, TCD is trained with [LoRA](https://huggingface.co/docs/peft/conceptual_guides/adapter#low-rank-adaptation-lora) to reduce memory usage. This is also useful because you can reuse LoRAs between different finetuned models, as long as they share the same base model, without further training.\n\n\n\nThis guide will show you how to perform inference with TCD-LoRAs for a variety of tasks like text-to-image and inpainting, as well as how you can easily combine TCD-LoRAs with other adapters. Choose one of the supported base model and it's corresponding TCD-LoRA checkpoint from the table below to get started.\n\n| Base model | TCD-LoRA checkpoint |\n|-------------------------------------------------------------------------------------------------|----------------------------------------------------------------|\n| [stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) | [TCD-SD15](https://huggingface.co/h1t/TCD-SD15-LoRA) |\n| [stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base) | [TCD-SD21-base](https://huggingface.co/h1t/TCD-SD21-base-LoRA) |\n| [stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) | [TCD-SDXL](https://huggingface.co/h1t/TCD-SDXL-LoRA) |\n\n\nMake sure you have [PEFT](https://github.com/huggingface/peft) installed for better LoRA support.\n\n```bash\npip install -U peft\n```\n\n## General tasks\n\nIn this guide, let's use the [`StableDiffusionXLPipeline`] and the [`TCDScheduler`]. Use the [`~StableDiffusionPipeline.load_lora_weights`] method to load the SDXL-compatible TCD-LoRA weights.\n\nA few tips to keep in mind for TCD-LoRA inference are to:\n\n- Keep the `num_inference_steps` between 4 and 50\n- Set `eta` (used to control stochasticity at each step) between 0 and 1. You should use a higher `eta` when increasing the number of inference steps, but the downside is that a larger `eta` in [`TCDScheduler`] leads to blurrier images. A value of 0.3 is recommended to produce good results.\n\n\n\n\n```python\nimport torch\nfrom diffusers import StableDiffusionXLPipeline, TCDScheduler\n\ndevice = \"cuda\"\nbase_model_id = \"stabilityai/stable-diffusion-xl-base-1.0\"\ntcd_lora_id = \"h1t/TCD-SDXL-LoRA\"\n\npipe = StableDiffusionXLPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant=\"fp16\").to(device)\npipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)\n\npipe.load_lora_weights(tcd_lora_id)\npipe.fuse_lora()\n\nprompt = \"Painting of the orange cat Otto von Garfield, Count of Bismarck-Schönhausen, Duke of Lauenburg, Minister-President of Prussia. Depicted wearing a Prussian Pickelhaube and eating his favorite meal - lasagna.\"\n\nimage = pipe(\n prompt=prompt,\n num_inference_steps=4,\n guidance_scale=0,\n eta=0.3,\n generator=torch.Generator(device=device).manual_seed(0),\n).images[0]\n```\n\n![](https://github.com/jabir-zheng/TCD/raw/main/assets/demo_image.png)\n\n\n\n\n\n```python\nimport torch\nfrom diffusers import AutoPipelineForInpainting, TCDScheduler\nfrom diffusers.utils import load_image, make_image_grid\n\ndevice = \"cuda\"\nbase_model_id = \"diffusers/stable-diffusion-xl-1.0-inpainting-0.1\"\ntcd_lora_id = \"h1t/TCD-SDXL-LoRA\"\n\npipe = AutoPipelineForInpainting.from_pretrained(base_model_id, torch_dtype=torch.float16, variant=\"fp16\").to(device)\npipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)\n\npipe.load_lora_weights(tcd_lora_id)\npipe.fuse_lora()\n\nimg_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\nmask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\n\ninit_image = load_image(img_url).resize((1024, 1024))\nmask_image = load_image(mask_url).resize((1024, 1024))\n\nprompt = \"a tiger sitting on a park bench\"\n\nimage = pipe(\n prompt=prompt,\n image=init_image,\n mask_image=mask_image,\n num_inference_steps=8,\n guidance_scale=0,\n eta=0.3,\n strength=0.99, # make sure to use `strength` below 1.0\n generator=torch.Generator(device=device).manual_seed(0),\n).images[0]\n\ngrid_image = make_image_grid([init_image, mask_image, image], rows=1, cols=3)\n```\n\n![](https://github.com/jabir-zheng/TCD/raw/main/assets/inpainting_tcd.png)\n\n\n\n\n\n## Community models\n\nTCD-LoRA also works with many community finetuned models and plugins. For example, load the [animagine-xl-3.0](https://huggingface.co/cagliostrolab/animagine-xl-3.0) checkpoint which is a community finetuned version of SDXL for generating anime images.\n\n```python\nimport torch\nfrom diffusers import StableDiffusionXLPipeline, TCDScheduler\n\ndevice = \"cuda\"\nbase_model_id = \"cagliostrolab/animagine-xl-3.0\"\ntcd_lora_id = \"h1t/TCD-SDXL-LoRA\"\n\npipe = StableDiffusionXLPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant=\"fp16\").to(device)\npipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)\n\npipe.load_lora_weights(tcd_lora_id)\npipe.fuse_lora()\n\nprompt = \"A man, clad in a meticulously tailored military uniform, stands with unwavering resolve. The uniform boasts intricate details, and his eyes gleam with determination. Strands of vibrant, windswept hair peek out from beneath the brim of his cap.\"\n\nimage = pipe(\n prompt=prompt,\n num_inference_steps=8,\n guidance_scale=0,\n eta=0.3,\n generator=torch.Generator(device=device).manual_seed(0),\n).images[0]\n```\n\n![](https://github.com/jabir-zheng/TCD/raw/main/assets/animagine_xl.png)\n\nTCD-LoRA also supports other LoRAs trained on different styles. For example, let's load the [TheLastBen/Papercut_SDXL](https://huggingface.co/TheLastBen/Papercut_SDXL) LoRA and fuse it with the TCD-LoRA with the [`~loaders.UNet2DConditionLoadersMixin.set_adapters`] method.\n\n> [!TIP]\n> Check out the [Merge LoRAs](merge_loras) guide to learn more about efficient merging methods.\n\n```python\nimport torch\nfrom diffusers import StableDiffusionXLPipeline\nfrom scheduling_tcd import TCDScheduler\n\ndevice = \"cuda\"\nbase_model_id = \"stabilityai/stable-diffusion-xl-base-1.0\"\ntcd_lora_id = \"h1t/TCD-SDXL-LoRA\"\nstyled_lora_id = \"TheLastBen/Papercut_SDXL\"\n\npipe = StableDiffusionXLPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant=\"fp16\").to(device)\npipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)\n\npipe.load_lora_weights(tcd_lora_id, adapter_name=\"tcd\")\npipe.load_lora_weights(styled_lora_id, adapter_name=\"style\")\npipe.set_adapters([\"tcd\", \"style\"], adapter_weights=[1.0, 1.0])\n\nprompt = \"papercut of a winter mountain, snow\"\n\nimage = pipe(\n prompt=prompt,\n num_inference_steps=4,\n guidance_scale=0,\n eta=0.3,\n generator=torch.Generator(device=device).manual_seed(0),\n).images[0]\n```\n\n![](https://github.com/jabir-zheng/TCD/raw/main/assets/styled_lora.png)\n\n\n## Adapters\n\nTCD-LoRA is very versatile, and it can be combined with other adapter types like ControlNets, IP-Adapter, and AnimateDiff.\n\n\n\n\n### Depth ControlNet\n\n```python\nimport torch\nimport numpy as np\nfrom PIL import Image\nfrom transformers import DPTFeatureExtractor, DPTForDepthEstimation\nfrom diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline\nfrom diffusers.utils import load_image, make_image_grid\nfrom scheduling_tcd import TCDScheduler\n\ndevice = \"cuda\"\ndepth_estimator = DPTForDepthEstimation.from_pretrained(\"Intel/dpt-hybrid-midas\").to(device)\nfeature_extractor = DPTFeatureExtractor.from_pretrained(\"Intel/dpt-hybrid-midas\")\n\ndef get_depth_map(image):\n image = feature_extractor(images=image, return_tensors=\"pt\").pixel_values.to(device)\n with torch.no_grad(), torch.autocast(device):\n depth_map = depth_estimator(image).predicted_depth\n\n depth_map = torch.nn.functional.interpolate(\n depth_map.unsqueeze(1),\n size=(1024, 1024),\n mode=\"bicubic\",\n align_corners=False,\n )\n depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)\n depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)\n depth_map = (depth_map - depth_min) / (depth_max - depth_min)\n image = torch.cat([depth_map] * 3, dim=1)\n\n image = image.permute(0, 2, 3, 1).cpu().numpy()[0]\n image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))\n return image\n\nbase_model_id = \"stabilityai/stable-diffusion-xl-base-1.0\"\ncontrolnet_id = \"diffusers/controlnet-depth-sdxl-1.0\"\ntcd_lora_id = \"h1t/TCD-SDXL-LoRA\"\n\ncontrolnet = ControlNetModel.from_pretrained(\n controlnet_id,\n torch_dtype=torch.float16,\n variant=\"fp16\",\n)\npipe = StableDiffusionXLControlNetPipeline.from_pretrained(\n base_model_id,\n controlnet=controlnet,\n torch_dtype=torch.float16,\n variant=\"fp16\",\n)\npipe.enable_model_cpu_offload()\n\npipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)\n\npipe.load_lora_weights(tcd_lora_id)\npipe.fuse_lora()\n\nprompt = \"stormtrooper lecture, photorealistic\"\n\nimage = load_image(\"https://huggingface.co/lllyasviel/sd-controlnet-depth/resolve/main/images/stormtrooper.png\")\ndepth_image = get_depth_map(image)\n\ncontrolnet_conditioning_scale = 0.5 # recommended for good generalization\n\nimage = pipe(\n prompt,\n image=depth_image,\n num_inference_steps=4,\n guidance_scale=0,\n eta=0.3,\n controlnet_conditioning_scale=controlnet_conditioning_scale,\n generator=torch.Generator(device=device).manual_seed(0),\n).images[0]\n\ngrid_image = make_image_grid([depth_image, image], rows=1, cols=2)\n```\n\n![](https://github.com/jabir-zheng/TCD/raw/main/assets/controlnet_depth_tcd.png)\n\n### Canny ControlNet\n```python\nimport torch\nfrom diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline\nfrom diffusers.utils import load_image, make_image_grid\nfrom scheduling_tcd import TCDScheduler\n\ndevice = \"cuda\"\nbase_model_id = \"stabilityai/stable-diffusion-xl-base-1.0\"\ncontrolnet_id = \"diffusers/controlnet-canny-sdxl-1.0\"\ntcd_lora_id = \"h1t/TCD-SDXL-LoRA\"\n\ncontrolnet = ControlNetModel.from_pretrained(\n controlnet_id,\n torch_dtype=torch.float16,\n variant=\"fp16\",\n)\npipe = StableDiffusionXLControlNetPipeline.from_pretrained(\n base_model_id,\n controlnet=controlnet,\n torch_dtype=torch.float16,\n variant=\"fp16\",\n)\npipe.enable_model_cpu_offload()\n\npipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)\n\npipe.load_lora_weights(tcd_lora_id)\npipe.fuse_lora()\n\nprompt = \"ultrarealistic shot of a furry blue bird\"\n\ncanny_image = load_image(\"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png\")\n\ncontrolnet_conditioning_scale = 0.5 # recommended for good generalization\n\nimage = pipe(\n prompt,\n image=canny_image,\n num_inference_steps=4,\n guidance_scale=0,\n eta=0.3,\n controlnet_conditioning_scale=controlnet_conditioning_scale,\n generator=torch.Generator(device=device).manual_seed(0),\n).images[0]\n\ngrid_image = make_image_grid([canny_image, image], rows=1, cols=2)\n```\n![](https://github.com/jabir-zheng/TCD/raw/main/assets/controlnet_canny_tcd.png)\n\n\nThe inference parameters in this example might not work for all examples, so we recommend you to try different values for `num_inference_steps`, `guidance_scale`, `controlnet_conditioning_scale` and `cross_attention_kwargs` parameters and choose the best one.\n\n\n\n\n\nThis example shows how to use the TCD-LoRA with the [IP-Adapter](https://github.com/tencent-ailab/IP-Adapter/tree/main) and SDXL.\n\n```python\nimport torch\nfrom diffusers import StableDiffusionXLPipeline\nfrom diffusers.utils import load_image, make_image_grid\n\nfrom ip_adapter import IPAdapterXL\nfrom scheduling_tcd import TCDScheduler\n\ndevice = \"cuda\"\nbase_model_path = \"stabilityai/stable-diffusion-xl-base-1.0\"\nimage_encoder_path = \"sdxl_models/image_encoder\"\nip_ckpt = \"sdxl_models/ip-adapter_sdxl.bin\"\ntcd_lora_id = \"h1t/TCD-SDXL-LoRA\"\n\npipe = StableDiffusionXLPipeline.from_pretrained(\n base_model_path,\n torch_dtype=torch.float16,\n variant=\"fp16\"\n)\npipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)\n\npipe.load_lora_weights(tcd_lora_id)\npipe.fuse_lora()\n\nip_model = IPAdapterXL(pipe, image_encoder_path, ip_ckpt, device)\n\nref_image = load_image(\"https://raw.githubusercontent.com/tencent-ailab/IP-Adapter/main/assets/images/woman.png\").resize((512, 512))\n\nprompt = \"best quality, high quality, wearing sunglasses\"\n\nimage = ip_model.generate(\n pil_image=ref_image,\n prompt=prompt,\n scale=0.5,\n num_samples=1,\n num_inference_steps=4,\n guidance_scale=0,\n eta=0.3,\n seed=0,\n)[0]\n\ngrid_image = make_image_grid([ref_image, image], rows=1, cols=2)\n```\n\n![](https://github.com/jabir-zheng/TCD/raw/main/assets/ip_adapter.png)\n\n\n\n\n\n\n[`AnimateDiff`] allows animating images using Stable Diffusion models. TCD-LoRA can substantially accelerate the process without degrading image quality. The quality of animation with TCD-LoRA and AnimateDiff has a more lucid outcome.\n\n```python\nimport torch\nfrom diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler\nfrom scheduling_tcd import TCDScheduler\nfrom diffusers.utils import export_to_gif\n\nadapter = MotionAdapter.from_pretrained(\"guoyww/animatediff-motion-adapter-v1-5\")\npipe = AnimateDiffPipeline.from_pretrained(\n \"frankjoshua/toonyou_beta6\",\n motion_adapter=adapter,\n).to(\"cuda\")\n\n# set TCDScheduler\npipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)\n\n# load TCD LoRA\npipe.load_lora_weights(\"h1t/TCD-SD15-LoRA\", adapter_name=\"tcd\")\npipe.load_lora_weights(\"guoyww/animatediff-motion-lora-zoom-in\", weight_name=\"diffusion_pytorch_model.safetensors\", adapter_name=\"motion-lora\")\n\npipe.set_adapters([\"tcd\", \"motion-lora\"], adapter_weights=[1.0, 1.2])\n\nprompt = \"best quality, masterpiece, 1girl, looking at viewer, blurry background, upper body, contemporary, dress\"\ngenerator = torch.manual_seed(0)\nframes = pipe(\n prompt=prompt,\n num_inference_steps=5,\n guidance_scale=0,\n cross_attention_kwargs={\"scale\": 1},\n num_frames=24,\n eta=0.3,\n generator=generator\n).frames[0]\nexport_to_gif(frames, \"animation.gif\")\n```\n\n![](https://github.com/jabir-zheng/TCD/raw/main/assets/animation_example.gif)\n\n\n" }, { "path": "diffusers/docs/source/en/using-diffusers/inpaint.md", "content": "\n\n# Inpainting\n\n[[open-in-colab]]\n\nInpainting replaces or edits specific areas of an image. This makes it a useful tool for image restoration like removing defects and artifacts, or even replacing an image area with something entirely new. Inpainting relies on a mask to determine which regions of an image to fill in; the area to inpaint is represented by white pixels and the area to keep is represented by black pixels. The white pixels are filled in by the prompt.\n\nWith 🤗 Diffusers, here is how you can do inpainting:\n\n1. Load an inpainting checkpoint with the [`AutoPipelineForInpainting`] class. This'll automatically detect the appropriate pipeline class to load based on the checkpoint:\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForInpainting\nfrom diffusers.utils import load_image, make_image_grid\n\npipeline = AutoPipelineForInpainting.from_pretrained(\n \"kandinsky-community/kandinsky-2-2-decoder-inpaint\", torch_dtype=torch.float16\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n```\n\n\n\nYou'll notice throughout the guide, we use [`~DiffusionPipeline.enable_model_cpu_offload`] and [`~DiffusionPipeline.enable_xformers_memory_efficient_attention`], to save memory and increase inference speed. If you're using PyTorch 2.0, it's not necessary to call [`~DiffusionPipeline.enable_xformers_memory_efficient_attention`] on your pipeline because it'll already be using PyTorch 2.0's native [scaled-dot product attention](../optimization/torch2.0#scaled-dot-product-attention).\n\n\n\n2. Load the base and mask images:\n\n```py\ninit_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png\")\nmask_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_mask.png\")\n```\n\n3. Create a prompt to inpaint the image with and pass it to the pipeline with the base and mask images:\n\n```py\nprompt = \"a black cat with glowing eyes, cute, adorable, disney, pixar, highly detailed, 8k\"\nnegative_prompt = \"bad anatomy, deformed, ugly, disfigured\"\nimage = pipeline(prompt=prompt, negative_prompt=negative_prompt, image=init_image, mask_image=mask_image).images[0]\nmake_image_grid([init_image, mask_image, image], rows=1, cols=3)\n```\n\n
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\n\n## Create a mask image\n\nThroughout this guide, the mask image is provided in all of the code examples for convenience. You can inpaint on your own images, but you'll need to create a mask image for it. Use the Space below to easily create a mask image.\n\nUpload a base image to inpaint on and use the sketch tool to draw a mask. Once you're done, click **Run** to generate and download the mask image.\n\n\n\n### Mask blur\n\nThe [`~VaeImageProcessor.blur`] method provides an option for how to blend the original image and inpaint area. The amount of blur is determined by the `blur_factor` parameter. Increasing the `blur_factor` increases the amount of blur applied to the mask edges, softening the transition between the original image and inpaint area. A low or zero `blur_factor` preserves the sharper edges of the mask.\n\nTo use this, create a blurred mask with the image processor.\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForInpainting\nfrom diffusers.utils import load_image\nfrom PIL import Image\n\npipeline = AutoPipelineForInpainting.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16).to('cuda')\n\nmask = load_image(\"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/seashore_mask.png\")\nblurred_mask = pipeline.mask_processor.blur(mask, blur_factor=33)\nblurred_mask\n```\n\n
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\n\n## Popular models\n\n[Stable Diffusion Inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting), [Stable Diffusion XL (SDXL) Inpainting](https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1), and [Kandinsky 2.2 Inpainting](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder-inpaint) are among the most popular models for inpainting. SDXL typically produces higher resolution images than Stable Diffusion v1.5, and Kandinsky 2.2 is also capable of generating high-quality images.\n\n### Stable Diffusion Inpainting\n\nStable Diffusion Inpainting is a latent diffusion model finetuned on 512x512 images on inpainting. It is a good starting point because it is relatively fast and generates good quality images. To use this model for inpainting, you'll need to pass a prompt, base and mask image to the pipeline:\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForInpainting\nfrom diffusers.utils import load_image, make_image_grid\n\npipeline = AutoPipelineForInpainting.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", torch_dtype=torch.float16, variant=\"fp16\"\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\n# load base and mask image\ninit_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png\")\nmask_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_mask.png\")\n\ngenerator = torch.Generator(\"cuda\").manual_seed(92)\nprompt = \"concept art digital painting of an elven castle, inspired by lord of the rings, highly detailed, 8k\"\nimage = pipeline(prompt=prompt, image=init_image, mask_image=mask_image, generator=generator).images[0]\nmake_image_grid([init_image, mask_image, image], rows=1, cols=3)\n```\n\n### Stable Diffusion XL (SDXL) Inpainting\n\nSDXL is a larger and more powerful version of Stable Diffusion v1.5. This model can follow a two-stage model process (though each model can also be used alone); the base model generates an image, and a refiner model takes that image and further enhances its details and quality. Take a look at the [SDXL](sdxl) guide for a more comprehensive guide on how to use SDXL and configure it's parameters.\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForInpainting\nfrom diffusers.utils import load_image, make_image_grid\n\npipeline = AutoPipelineForInpainting.from_pretrained(\n \"diffusers/stable-diffusion-xl-1.0-inpainting-0.1\", torch_dtype=torch.float16, variant=\"fp16\"\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\n# load base and mask image\ninit_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png\")\nmask_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_mask.png\")\n\ngenerator = torch.Generator(\"cuda\").manual_seed(92)\nprompt = \"concept art digital painting of an elven castle, inspired by lord of the rings, highly detailed, 8k\"\nimage = pipeline(prompt=prompt, image=init_image, mask_image=mask_image, generator=generator).images[0]\nmake_image_grid([init_image, mask_image, image], rows=1, cols=3)\n```\n\n### Kandinsky 2.2 Inpainting\n\nThe Kandinsky model family is similar to SDXL because it uses two models as well; the image prior model creates image embeddings, and the diffusion model generates images from them. You can load the image prior and diffusion model separately, but the easiest way to use Kandinsky 2.2 is to load it into the [`AutoPipelineForInpainting`] class which uses the [`KandinskyV22InpaintCombinedPipeline`] under the hood.\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForInpainting\nfrom diffusers.utils import load_image, make_image_grid\n\npipeline = AutoPipelineForInpainting.from_pretrained(\n \"kandinsky-community/kandinsky-2-2-decoder-inpaint\", torch_dtype=torch.float16\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\n# load base and mask image\ninit_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png\")\nmask_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_mask.png\")\n\ngenerator = torch.Generator(\"cuda\").manual_seed(92)\nprompt = \"concept art digital painting of an elven castle, inspired by lord of the rings, highly detailed, 8k\"\nimage = pipeline(prompt=prompt, image=init_image, mask_image=mask_image, generator=generator).images[0]\nmake_image_grid([init_image, mask_image, image], rows=1, cols=3)\n```\n\n
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\n\n## Non-inpaint specific checkpoints\n\nSo far, this guide has used inpaint specific checkpoints such as [runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting). But you can also use regular checkpoints like [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5). Let's compare the results of the two checkpoints.\n\nThe image on the left is generated from a regular checkpoint, and the image on the right is from an inpaint checkpoint. You'll immediately notice the image on the left is not as clean, and you can still see the outline of the area the model is supposed to inpaint. The image on the right is much cleaner and the inpainted area appears more natural.\n\n\n\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForInpainting\nfrom diffusers.utils import load_image, make_image_grid\n\npipeline = AutoPipelineForInpainting.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, variant=\"fp16\"\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\n# load base and mask image\ninit_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png\")\nmask_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_mask.png\")\n\ngenerator = torch.Generator(\"cuda\").manual_seed(92)\nprompt = \"concept art digital painting of an elven castle, inspired by lord of the rings, highly detailed, 8k\"\nimage = pipeline(prompt=prompt, image=init_image, mask_image=mask_image, generator=generator).images[0]\nmake_image_grid([init_image, image], rows=1, cols=2)\n```\n\n\n\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForInpainting\nfrom diffusers.utils import load_image, make_image_grid\n\npipeline = AutoPipelineForInpainting.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", torch_dtype=torch.float16, variant=\"fp16\"\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\n# load base and mask image\ninit_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png\")\nmask_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_mask.png\")\n\ngenerator = torch.Generator(\"cuda\").manual_seed(92)\nprompt = \"concept art digital painting of an elven castle, inspired by lord of the rings, highly detailed, 8k\"\nimage = pipeline(prompt=prompt, image=init_image, mask_image=mask_image, generator=generator).images[0]\nmake_image_grid([init_image, image], rows=1, cols=2)\n```\n\n\n\n\n
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\n\nHowever, for more basic tasks like erasing an object from an image (like the rocks in the road for example), a regular checkpoint yields pretty good results. There isn't as noticeable of difference between the regular and inpaint checkpoint.\n\n\n\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForInpainting\nfrom diffusers.utils import load_image, make_image_grid\n\npipeline = AutoPipelineForInpainting.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, variant=\"fp16\"\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\n# load base and mask image\ninit_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png\")\nmask_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/road-mask.png\")\n\nimage = pipeline(prompt=\"road\", image=init_image, mask_image=mask_image).images[0]\nmake_image_grid([init_image, image], rows=1, cols=2)\n```\n\n\n\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForInpainting\nfrom diffusers.utils import load_image, make_image_grid\n\npipeline = AutoPipelineForInpainting.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", torch_dtype=torch.float16, variant=\"fp16\"\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\n# load base and mask image\ninit_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png\")\nmask_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/road-mask.png\")\n\nimage = pipeline(prompt=\"road\", image=init_image, mask_image=mask_image).images[0]\nmake_image_grid([init_image, image], rows=1, cols=2)\n```\n\n\n\n\n
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\n\nThe trade-off of using a non-inpaint specific checkpoint is the overall image quality may be lower, but it generally tends to preserve the mask area (that is why you can see the mask outline). The inpaint specific checkpoints are intentionally trained to generate higher quality inpainted images, and that includes creating a more natural transition between the masked and unmasked areas. As a result, these checkpoints are more likely to change your unmasked area.\n\nIf preserving the unmasked area is important for your task, you can use the [`VaeImageProcessor.apply_overlay`] method to force the unmasked area of an image to remain the same at the expense of some more unnatural transitions between the masked and unmasked areas.\n\n```py\nimport PIL\nimport numpy as np\nimport torch\n\nfrom diffusers import AutoPipelineForInpainting\nfrom diffusers.utils import load_image, make_image_grid\n\ndevice = \"cuda\"\npipeline = AutoPipelineForInpainting.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\",\n torch_dtype=torch.float16,\n)\npipeline = pipeline.to(device)\n\nimg_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\nmask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\n\ninit_image = load_image(img_url).resize((512, 512))\nmask_image = load_image(mask_url).resize((512, 512))\n\nprompt = \"Face of a yellow cat, high resolution, sitting on a park bench\"\nrepainted_image = pipeline(prompt=prompt, image=init_image, mask_image=mask_image).images[0]\nrepainted_image.save(\"repainted_image.png\")\n\nunmasked_unchanged_image = pipeline.image_processor.apply_overlay(mask_image, init_image, repainted_image)\nunmasked_unchanged_image.save(\"force_unmasked_unchanged.png\")\nmake_image_grid([init_image, mask_image, repainted_image, unmasked_unchanged_image], rows=2, cols=2)\n```\n\n## Configure pipeline parameters\n\nImage features - like quality and \"creativity\" - are dependent on pipeline parameters. Knowing what these parameters do is important for getting the results you want. Let's take a look at the most important parameters and see how changing them affects the output.\n\n### Strength\n\n`strength` is a measure of how much noise is added to the base image, which influences how similar the output is to the base image.\n\n* 📈 a high `strength` value means more noise is added to an image and the denoising process takes longer, but you'll get higher quality images that are more different from the base image\n* 📉 a low `strength` value means less noise is added to an image and the denoising process is faster, but the image quality may not be as great and the generated image resembles the base image more\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForInpainting\nfrom diffusers.utils import load_image, make_image_grid\n\npipeline = AutoPipelineForInpainting.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", torch_dtype=torch.float16, variant=\"fp16\"\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\n# load base and mask image\ninit_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png\")\nmask_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_mask.png\")\n\nprompt = \"concept art digital painting of an elven castle, inspired by lord of the rings, highly detailed, 8k\"\nimage = pipeline(prompt=prompt, image=init_image, mask_image=mask_image, strength=0.6).images[0]\nmake_image_grid([init_image, mask_image, image], rows=1, cols=3)\n```\n\n
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\n\n### Guidance scale\n\n`guidance_scale` affects how aligned the text prompt and generated image are.\n\n* 📈 a high `guidance_scale` value means the prompt and generated image are closely aligned, so the output is a stricter interpretation of the prompt\n* 📉 a low `guidance_scale` value means the prompt and generated image are more loosely aligned, so the output may be more varied from the prompt\n\nYou can use `strength` and `guidance_scale` together for more control over how expressive the model is. For example, a combination high `strength` and `guidance_scale` values gives the model the most creative freedom.\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForInpainting\nfrom diffusers.utils import load_image, make_image_grid\n\npipeline = AutoPipelineForInpainting.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", torch_dtype=torch.float16, variant=\"fp16\"\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\n# load base and mask image\ninit_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png\")\nmask_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_mask.png\")\n\nprompt = \"concept art digital painting of an elven castle, inspired by lord of the rings, highly detailed, 8k\"\nimage = pipeline(prompt=prompt, image=init_image, mask_image=mask_image, guidance_scale=2.5).images[0]\nmake_image_grid([init_image, mask_image, image], rows=1, cols=3)\n```\n\n
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\n\n### Negative prompt\n\nA negative prompt assumes the opposite role of a prompt; it guides the model away from generating certain things in an image. This is useful for quickly improving image quality and preventing the model from generating things you don't want.\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForInpainting\nfrom diffusers.utils import load_image, make_image_grid\n\npipeline = AutoPipelineForInpainting.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", torch_dtype=torch.float16, variant=\"fp16\"\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\n# load base and mask image\ninit_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png\")\nmask_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_mask.png\")\n\nprompt = \"concept art digital painting of an elven castle, inspired by lord of the rings, highly detailed, 8k\"\nnegative_prompt = \"bad architecture, unstable, poor details, blurry\"\nimage = pipeline(prompt=prompt, negative_prompt=negative_prompt, image=init_image, mask_image=mask_image).images[0]\nmake_image_grid([init_image, mask_image, image], rows=1, cols=3)\n```\n\n
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\n\n### Padding mask crop\n\nA method for increasing the inpainting image quality is to use the [`padding_mask_crop`](https://huggingface.co/docs/diffusers/v0.25.0/en/api/pipelines/stable_diffusion/inpaint#diffusers.StableDiffusionInpaintPipeline.__call__.padding_mask_crop) parameter. When enabled, this option crops the masked area with some user-specified padding and it'll also crop the same area from the original image. Both the image and mask are upscaled to a higher resolution for inpainting, and then overlaid on the original image. This is a quick and easy way to improve image quality without using a separate pipeline like [`StableDiffusionUpscalePipeline`].\n\nAdd the `padding_mask_crop` parameter to the pipeline call and set it to the desired padding value.\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForInpainting\nfrom diffusers.utils import load_image\nfrom PIL import Image\n\ngenerator = torch.Generator(device='cuda').manual_seed(0)\npipeline = AutoPipelineForInpainting.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16).to('cuda')\n\nbase = load_image(\"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/seashore.png\")\nmask = load_image(\"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/seashore_mask.png\")\n\nimage = pipeline(\"boat\", image=base, mask_image=mask, strength=0.75, generator=generator, padding_mask_crop=32).images[0]\nimage\n```\n\n
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\n\n## Chained inpainting pipelines\n\n[`AutoPipelineForInpainting`] can be chained with other 🤗 Diffusers pipelines to edit their outputs. This is often useful for improving the output quality from your other diffusion pipelines, and if you're using multiple pipelines, it can be more memory-efficient to chain them together to keep the outputs in latent space and reuse the same pipeline components.\n\n### Text-to-image-to-inpaint\n\nChaining a text-to-image and inpainting pipeline allows you to inpaint the generated image, and you don't have to provide a base image to begin with. This makes it convenient to edit your favorite text-to-image outputs without having to generate an entirely new image.\n\nStart with the text-to-image pipeline to create a castle:\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForText2Image, AutoPipelineForInpainting\nfrom diffusers.utils import load_image, make_image_grid\n\npipeline = AutoPipelineForText2Image.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\ntext2image = pipeline(\"concept art digital painting of an elven castle, inspired by lord of the rings, highly detailed, 8k\").images[0]\n```\n\nLoad the mask image of the output from above:\n\n```py\nmask_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_text-chain-mask.png\")\n```\n\nAnd let's inpaint the masked area with a waterfall:\n\n```py\npipeline = AutoPipelineForInpainting.from_pretrained(\n \"kandinsky-community/kandinsky-2-2-decoder-inpaint\", torch_dtype=torch.float16\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\nprompt = \"digital painting of a fantasy waterfall, cloudy\"\nimage = pipeline(prompt=prompt, image=text2image, mask_image=mask_image).images[0]\nmake_image_grid([text2image, mask_image, image], rows=1, cols=3)\n```\n\n
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\n\n### Inpaint-to-image-to-image\n\nYou can also chain an inpainting pipeline before another pipeline like image-to-image or an upscaler to improve the quality.\n\nBegin by inpainting an image:\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForInpainting, AutoPipelineForImage2Image\nfrom diffusers.utils import load_image, make_image_grid\n\npipeline = AutoPipelineForInpainting.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", torch_dtype=torch.float16, variant=\"fp16\"\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\n# load base and mask image\ninit_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png\")\nmask_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_mask.png\")\n\nprompt = \"concept art digital painting of an elven castle, inspired by lord of the rings, highly detailed, 8k\"\nimage_inpainting = pipeline(prompt=prompt, image=init_image, mask_image=mask_image).images[0]\n\n# resize image to 1024x1024 for SDXL\nimage_inpainting = image_inpainting.resize((1024, 1024))\n```\n\nNow let's pass the image to another inpainting pipeline with SDXL's refiner model to enhance the image details and quality:\n\n```py\npipeline = AutoPipelineForInpainting.from_pretrained(\n \"stabilityai/stable-diffusion-xl-refiner-1.0\", torch_dtype=torch.float16, variant=\"fp16\"\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\nimage = pipeline(prompt=prompt, image=image_inpainting, mask_image=mask_image, output_type=\"latent\").images[0]\n```\n\n\n\nIt is important to specify `output_type=\"latent\"` in the pipeline to keep all the outputs in latent space to avoid an unnecessary decode-encode step. This only works if the chained pipelines are using the same VAE. For example, in the [Text-to-image-to-inpaint](#text-to-image-to-inpaint) section, Kandinsky 2.2 uses a different VAE class than the Stable Diffusion model so it won't work. But if you use Stable Diffusion v1.5 for both pipelines, then you can keep everything in latent space because they both use [`AutoencoderKL`].\n\n\n\nFinally, you can pass this image to an image-to-image pipeline to put the finishing touches on it. It is more efficient to use the [`~AutoPipelineForImage2Image.from_pipe`] method to reuse the existing pipeline components, and avoid unnecessarily loading all the pipeline components into memory again.\n\n```py\npipeline = AutoPipelineForImage2Image.from_pipe(pipeline)\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\nimage = pipeline(prompt=prompt, image=image).images[0]\nmake_image_grid([init_image, mask_image, image_inpainting, image], rows=2, cols=2)\n```\n\n
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\n\nImage-to-image and inpainting are actually very similar tasks. Image-to-image generates a new image that resembles the existing provided image. Inpainting does the same thing, but it only transforms the image area defined by the mask and the rest of the image is unchanged. You can think of inpainting as a more precise tool for making specific changes and image-to-image has a broader scope for making more sweeping changes.\n\n## Control image generation\n\nGetting an image to look exactly the way you want is challenging because the denoising process is random. While you can control certain aspects of generation by configuring parameters like `negative_prompt`, there are better and more efficient methods for controlling image generation.\n\n### Prompt weighting\n\nPrompt weighting provides a quantifiable way to scale the representation of concepts in a prompt. You can use it to increase or decrease the magnitude of the text embedding vector for each concept in the prompt, which subsequently determines how much of each concept is generated. The [Compel](https://github.com/damian0815/compel) library offers an intuitive syntax for scaling the prompt weights and generating the embeddings. Learn how to create the embeddings in the [Prompt weighting](../using-diffusers/weighted_prompts) guide.\n\nOnce you've generated the embeddings, pass them to the `prompt_embeds` (and `negative_prompt_embeds` if you're using a negative prompt) parameter in the [`AutoPipelineForInpainting`]. The embeddings replace the `prompt` parameter:\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForInpainting\nfrom diffusers.utils import make_image_grid\n\npipeline = AutoPipelineForInpainting.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", torch_dtype=torch.float16,\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\nimage = pipeline(prompt_embeds=prompt_embeds, # generated from Compel\n negative_prompt_embeds=negative_prompt_embeds, # generated from Compel\n image=init_image,\n mask_image=mask_image\n).images[0]\nmake_image_grid([init_image, mask_image, image], rows=1, cols=3)\n```\n\n### ControlNet\n\nControlNet models are used with other diffusion models like Stable Diffusion, and they provide an even more flexible and accurate way to control how an image is generated. A ControlNet accepts an additional conditioning image input that guides the diffusion model to preserve the features in it.\n\nFor example, let's condition an image with a ControlNet pretrained on inpaint images:\n\n```py\nimport torch\nimport numpy as np\nfrom diffusers import ControlNetModel, StableDiffusionControlNetInpaintPipeline\nfrom diffusers.utils import load_image, make_image_grid\n\n# load ControlNet\ncontrolnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11p_sd15_inpaint\", torch_dtype=torch.float16, variant=\"fp16\")\n\n# pass ControlNet to the pipeline\npipeline = StableDiffusionControlNetInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", controlnet=controlnet, torch_dtype=torch.float16, variant=\"fp16\"\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\n# load base and mask image\ninit_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png\")\nmask_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_mask.png\")\n\n# prepare control image\ndef make_inpaint_condition(init_image, mask_image):\n init_image = np.array(init_image.convert(\"RGB\")).astype(np.float32) / 255.0\n mask_image = np.array(mask_image.convert(\"L\")).astype(np.float32) / 255.0\n\n assert init_image.shape[0:1] == mask_image.shape[0:1], \"image and image_mask must have the same image size\"\n init_image[mask_image > 0.5] = -1.0 # set as masked pixel\n init_image = np.expand_dims(init_image, 0).transpose(0, 3, 1, 2)\n init_image = torch.from_numpy(init_image)\n return init_image\n\ncontrol_image = make_inpaint_condition(init_image, mask_image)\n```\n\nNow generate an image from the base, mask and control images. You'll notice features of the base image are strongly preserved in the generated image.\n\n```py\nprompt = \"concept art digital painting of an elven castle, inspired by lord of the rings, highly detailed, 8k\"\nimage = pipeline(prompt=prompt, image=init_image, mask_image=mask_image, control_image=control_image).images[0]\nmake_image_grid([init_image, mask_image, PIL.Image.fromarray(np.uint8(control_image[0][0])).convert('RGB'), image], rows=2, cols=2)\n```\n\nYou can take this a step further and chain it with an image-to-image pipeline to apply a new [style](https://huggingface.co/nitrosocke/elden-ring-diffusion):\n\n```py\nfrom diffusers import AutoPipelineForImage2Image\n\npipeline = AutoPipelineForImage2Image.from_pretrained(\n \"nitrosocke/elden-ring-diffusion\", torch_dtype=torch.float16,\n)\npipeline.enable_model_cpu_offload()\n# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed\npipeline.enable_xformers_memory_efficient_attention()\n\nprompt = \"elden ring style castle\" # include the token \"elden ring style\" in the prompt\nnegative_prompt = \"bad architecture, deformed, disfigured, poor details\"\n\nimage_elden_ring = pipeline(prompt, negative_prompt=negative_prompt, image=image).images[0]\nmake_image_grid([init_image, mask_image, image, image_elden_ring], rows=2, cols=2)\n```\n\n
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\n\n## Optimize\n\nIt can be difficult and slow to run diffusion models if you're resource constrained, but it doesn't have to be with a few optimization tricks. One of the biggest (and easiest) optimizations you can enable is switching to memory-efficient attention. If you're using PyTorch 2.0, [scaled-dot product attention](../optimization/torch2.0#scaled-dot-product-attention) is automatically enabled and you don't need to do anything else. For non-PyTorch 2.0 users, you can install and use [xFormers](../optimization/xformers)'s implementation of memory-efficient attention. Both options reduce memory usage and accelerate inference.\n\nYou can also offload the model to the CPU to save even more memory:\n\n```diff\n+ pipeline.enable_xformers_memory_efficient_attention()\n+ pipeline.enable_model_cpu_offload()\n```\n\nTo speed-up your inference code even more, use [`torch_compile`](../optimization/torch2.0#torchcompile). You should wrap `torch.compile` around the most intensive component in the pipeline which is typically the UNet:\n\n```py\npipeline.unet = torch.compile(pipeline.unet, mode=\"reduce-overhead\", fullgraph=True)\n```\n\nLearn more in the [Reduce memory usage](../optimization/memory) and [Torch 2.0](../optimization/torch2.0) guides.\n" }, { "path": "diffusers/docs/source/en/using-diffusers/ip_adapter.md", "content": "\n\n# IP-Adapter\n\n[IP-Adapter](https://hf.co/papers/2308.06721) is an image prompt adapter that can be plugged into diffusion models to enable image prompting without any changes to the underlying model. Furthermore, this adapter can be reused with other models finetuned from the same base model and it can be combined with other adapters like [ControlNet](../using-diffusers/controlnet). The key idea behind IP-Adapter is the *decoupled cross-attention* mechanism which adds a separate cross-attention layer just for image features instead of using the same cross-attention layer for both text and image features. This allows the model to learn more image-specific features.\n\n> [!TIP]\n> Learn how to load an IP-Adapter in the [Load adapters](../using-diffusers/loading_adapters#ip-adapter) guide, and make sure you check out the [IP-Adapter Plus](../using-diffusers/loading_adapters#ip-adapter-plus) section which requires manually loading the image encoder.\n\nThis guide will walk you through using IP-Adapter for various tasks and use cases.\n\n## General tasks\n\nLet's take a look at how to use IP-Adapter's image prompting capabilities with the [`StableDiffusionXLPipeline`] for tasks like text-to-image, image-to-image, and inpainting. We also encourage you to try out other pipelines such as Stable Diffusion, LCM-LoRA, ControlNet, T2I-Adapter, or AnimateDiff!\n\nIn all the following examples, you'll see the [`~loaders.IPAdapterMixin.set_ip_adapter_scale`] method. This method controls the amount of text or image conditioning to apply to the model. A value of `1.0` means the model is only conditioned on the image prompt. Lowering this value encourages the model to produce more diverse images, but they may not be as aligned with the image prompt. Typically, a value of `0.5` achieves a good balance between the two prompt types and produces good results.\n\n> [!TIP]\n> In the examples below, try adding `low_cpu_mem_usage=True` to the [`~loaders.IPAdapterMixin.load_ip_adapter`] method to speed up the loading time.\n\n\n\n\nCrafting the precise text prompt to generate the image you want can be difficult because it may not always capture what you'd like to express. Adding an image alongside the text prompt helps the model better understand what it should generate and can lead to more accurate results.\n\nLoad a Stable Diffusion XL (SDXL) model and insert an IP-Adapter into the model with the [`~loaders.IPAdapterMixin.load_ip_adapter`] method. Use the `subfolder` parameter to load the SDXL model weights.\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nfrom diffusers.utils import load_image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16).to(\"cuda\")\npipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"sdxl_models\", weight_name=\"ip-adapter_sdxl.bin\")\npipeline.set_ip_adapter_scale(0.6)\n```\n\nCreate a text prompt and load an image prompt before passing them to the pipeline to generate an image.\n\n```py\nimage = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_diner.png\")\ngenerator = torch.Generator(device=\"cpu\").manual_seed(0)\nimages = pipeline(\n prompt=\"a polar bear sitting in a chair drinking a milkshake\",\n ip_adapter_image=image,\n negative_prompt=\"deformed, ugly, wrong proportion, low res, bad anatomy, worst quality, low quality\",\n num_inference_steps=100,\n generator=generator,\n).images\nimages[0]\n```\n\n
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\n\n\n\n\nIP-Adapter can also help with image-to-image by guiding the model to generate an image that resembles the original image and the image prompt.\n\nLoad a Stable Diffusion XL (SDXL) model and insert an IP-Adapter into the model with the [`~loaders.IPAdapterMixin.load_ip_adapter`] method. Use the `subfolder` parameter to load the SDXL model weights.\n\n```py\nfrom diffusers import AutoPipelineForImage2Image\nfrom diffusers.utils import load_image\nimport torch\n\npipeline = AutoPipelineForImage2Image.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16).to(\"cuda\")\npipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"sdxl_models\", weight_name=\"ip-adapter_sdxl.bin\")\npipeline.set_ip_adapter_scale(0.6)\n```\n\nPass the original image and the IP-Adapter image prompt to the pipeline to generate an image. Providing a text prompt to the pipeline is optional, but in this example, a text prompt is used to increase image quality.\n\n```py\nimage = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_bear_1.png\")\nip_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_bear_2.png\")\n\ngenerator = torch.Generator(device=\"cpu\").manual_seed(4)\nimages = pipeline(\n prompt=\"best quality, high quality\",\n image=image,\n ip_adapter_image=ip_image,\n generator=generator,\n strength=0.6,\n).images\nimages[0]\n```\n\n
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\n\n\n\n\nIP-Adapter is also useful for inpainting because the image prompt allows you to be much more specific about what you'd like to generate.\n\nLoad a Stable Diffusion XL (SDXL) model and insert an IP-Adapter into the model with the [`~loaders.IPAdapterMixin.load_ip_adapter`] method. Use the `subfolder` parameter to load the SDXL model weights.\n\n```py\nfrom diffusers import AutoPipelineForInpainting\nfrom diffusers.utils import load_image\nimport torch\n\npipeline = AutoPipelineForInpainting.from_pretrained(\"diffusers/stable-diffusion-xl-1.0-inpainting-0.1\", torch_dtype=torch.float16).to(\"cuda\")\npipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"sdxl_models\", weight_name=\"ip-adapter_sdxl.bin\")\npipeline.set_ip_adapter_scale(0.6)\n```\n\nPass a prompt, the original image, mask image, and the IP-Adapter image prompt to the pipeline to generate an image.\n\n```py\nmask_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_mask.png\")\nimage = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_bear_1.png\")\nip_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_gummy.png\")\n\ngenerator = torch.Generator(device=\"cpu\").manual_seed(4)\nimages = pipeline(\n prompt=\"a cute gummy bear waving\",\n image=image,\n mask_image=mask_image,\n ip_adapter_image=ip_image,\n generator=generator,\n num_inference_steps=100,\n).images\nimages[0]\n```\n\n
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\n\n\n\n\nIP-Adapter can also help you generate videos that are more aligned with your text prompt. For example, let's load [AnimateDiff](../api/pipelines/animatediff) with its motion adapter and insert an IP-Adapter into the model with the [`~loaders.IPAdapterMixin.load_ip_adapter`] method.\n\n> [!WARNING]\n> If you're planning on offloading the model to the CPU, make sure you run it after you've loaded the IP-Adapter. When you call [`~DiffusionPipeline.enable_model_cpu_offload`] before loading the IP-Adapter, it offloads the image encoder module to the CPU and it'll return an error when you try to run the pipeline.\n\n```py\nimport torch\nfrom diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter\nfrom diffusers.utils import export_to_gif\nfrom diffusers.utils import load_image\n\nadapter = MotionAdapter.from_pretrained(\"guoyww/animatediff-motion-adapter-v1-5-2\", torch_dtype=torch.float16)\npipeline = AnimateDiffPipeline.from_pretrained(\"emilianJR/epiCRealism\", motion_adapter=adapter, torch_dtype=torch.float16)\nscheduler = DDIMScheduler.from_pretrained(\n \"emilianJR/epiCRealism\",\n subfolder=\"scheduler\",\n clip_sample=False,\n timestep_spacing=\"linspace\",\n beta_schedule=\"linear\",\n steps_offset=1,\n)\npipeline.scheduler = scheduler\npipeline.enable_vae_slicing()\n\npipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"models\", weight_name=\"ip-adapter_sd15.bin\")\npipeline.enable_model_cpu_offload()\n```\n\nPass a prompt and an image prompt to the pipeline to generate a short video.\n\n```py\nip_adapter_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_inpaint.png\")\n\noutput = pipeline(\n prompt=\"A cute gummy bear waving\",\n negative_prompt=\"bad quality, worse quality, low resolution\",\n ip_adapter_image=ip_adapter_image,\n num_frames=16,\n guidance_scale=7.5,\n num_inference_steps=50,\n generator=torch.Generator(device=\"cpu\").manual_seed(0),\n)\nframes = output.frames[0]\nexport_to_gif(frames, \"gummy_bear.gif\")\n```\n\n
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\n\n\n\n\n## Configure parameters\n\nThere are a couple of IP-Adapter parameters that are useful to know about and can help you with your image generation tasks. These parameters can make your workflow more efficient or give you more control over image generation.\n\n### Image embeddings\n\nIP-Adapter enabled pipelines provide the `ip_adapter_image_embeds` parameter to accept precomputed image embeddings. This is particularly useful in scenarios where you need to run the IP-Adapter pipeline multiple times because you have more than one image. For example, [multi IP-Adapter](#multi-ip-adapter) is a specific use case where you provide multiple styling images to generate a specific image in a specific style. Loading and encoding multiple images each time you use the pipeline would be inefficient. Instead, you can precompute and save the image embeddings to disk (which can save a lot of space if you're using high-quality images) and load them when you need them.\n\n> [!TIP]\n> This parameter also gives you the flexibility to load embeddings from other sources. For example, ComfyUI image embeddings for IP-Adapters are compatible with Diffusers and should work ouf-of-the-box!\n\nCall the [`~StableDiffusionPipeline.prepare_ip_adapter_image_embeds`] method to encode and generate the image embeddings. Then you can save them to disk with `torch.save`.\n\n> [!TIP]\n> If you're using IP-Adapter with `ip_adapter_image_embedding` instead of `ip_adapter_image`', you can set `load_ip_adapter(image_encoder_folder=None,...)` because you don't need to load an encoder to generate the image embeddings.\n\n```py\nimage_embeds = pipeline.prepare_ip_adapter_image_embeds(\n ip_adapter_image=image,\n ip_adapter_image_embeds=None,\n device=\"cuda\",\n num_images_per_prompt=1,\n do_classifier_free_guidance=True,\n)\n\ntorch.save(image_embeds, \"image_embeds.ipadpt\")\n```\n\nNow load the image embeddings by passing them to the `ip_adapter_image_embeds` parameter.\n\n```py\nimage_embeds = torch.load(\"image_embeds.ipadpt\")\nimages = pipeline(\n prompt=\"a polar bear sitting in a chair drinking a milkshake\",\n ip_adapter_image_embeds=image_embeds,\n negative_prompt=\"deformed, ugly, wrong proportion, low res, bad anatomy, worst quality, low quality\",\n num_inference_steps=100,\n generator=generator,\n).images\n```\n\n### IP-Adapter masking\n\nBinary masks specify which portion of the output image should be assigned to an IP-Adapter. This is useful for composing more than one IP-Adapter image. For each input IP-Adapter image, you must provide a binary mask.\n\nTo start, preprocess the input IP-Adapter images with the [`~image_processor.IPAdapterMaskProcessor.preprocess()`] to generate their masks. For optimal results, provide the output height and width to [`~image_processor.IPAdapterMaskProcessor.preprocess()`]. This ensures masks with different aspect ratios are appropriately stretched. If the input masks already match the aspect ratio of the generated image, you don't have to set the `height` and `width`.\n\n```py\nfrom diffusers.image_processor import IPAdapterMaskProcessor\n\nmask1 = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_mask_mask1.png\")\nmask2 = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_mask_mask2.png\")\n\noutput_height = 1024\noutput_width = 1024\n\nprocessor = IPAdapterMaskProcessor()\nmasks = processor.preprocess([mask1, mask2], height=output_height, width=output_width)\n```\n\n
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\n\nWhen there is more than one input IP-Adapter image, load them as a list and provide the IP-Adapter scale list. Each of the input IP-Adapter images here corresponds to one of the masks generated above.\n\n```py\npipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"sdxl_models\", weight_name=[\"ip-adapter-plus-face_sdxl_vit-h.safetensors\"])\npipeline.set_ip_adapter_scale([[0.7, 0.7]]) # one scale for each image-mask pair\n\nface_image1 = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_mask_girl1.png\")\nface_image2 = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_mask_girl2.png\")\n\nip_images = [[face_image1, face_image2]]\n\nmasks = [masks.reshape(1, masks.shape[0], masks.shape[2], masks.shape[3])]\n```\n\n
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\n\nNow pass the preprocessed masks to `cross_attention_kwargs` in the pipeline call.\n\n```py\ngenerator = torch.Generator(device=\"cpu\").manual_seed(0)\nnum_images = 1\n\nimage = pipeline(\n prompt=\"2 girls\",\n ip_adapter_image=ip_images,\n negative_prompt=\"monochrome, lowres, bad anatomy, worst quality, low quality\",\n num_inference_steps=20,\n num_images_per_prompt=num_images,\n generator=generator,\n cross_attention_kwargs={\"ip_adapter_masks\": masks}\n).images[0]\nimage\n```\n\n
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\n\n## Specific use cases\n\nIP-Adapter's image prompting and compatibility with other adapters and models makes it a versatile tool for a variety of use cases. This section covers some of the more popular applications of IP-Adapter, and we can't wait to see what you come up with!\n\n### Face model\n\nGenerating accurate faces is challenging because they are complex and nuanced. Diffusers supports two IP-Adapter checkpoints specifically trained to generate faces from the [h94/IP-Adapter](https://huggingface.co/h94/IP-Adapter) repository:\n\n* [ip-adapter-full-face_sd15.safetensors](https://huggingface.co/h94/IP-Adapter/blob/main/models/ip-adapter-full-face_sd15.safetensors) is conditioned with images of cropped faces and removed backgrounds\n* [ip-adapter-plus-face_sd15.safetensors](https://huggingface.co/h94/IP-Adapter/blob/main/models/ip-adapter-plus-face_sd15.safetensors) uses patch embeddings and is conditioned with images of cropped faces\n\nAdditionally, Diffusers supports all IP-Adapter checkpoints trained with face embeddings extracted by `insightface` face models. Supported models are from the [h94/IP-Adapter-FaceID](https://huggingface.co/h94/IP-Adapter-FaceID) repository.\n\nFor face models, use the [h94/IP-Adapter](https://huggingface.co/h94/IP-Adapter) checkpoint. It is also recommended to use [`DDIMScheduler`] or [`EulerDiscreteScheduler`] for face models.\n\n```py\nimport torch\nfrom diffusers import StableDiffusionPipeline, DDIMScheduler\nfrom diffusers.utils import load_image\n\npipeline = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n torch_dtype=torch.float16,\n).to(\"cuda\")\npipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)\npipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"models\", weight_name=\"ip-adapter-full-face_sd15.bin\")\n\npipeline.set_ip_adapter_scale(0.5)\n\nimage = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_einstein_base.png\")\ngenerator = torch.Generator(device=\"cpu\").manual_seed(26)\n\nimage = pipeline(\n prompt=\"A photo of Einstein as a chef, wearing an apron, cooking in a French restaurant\",\n ip_adapter_image=image,\n negative_prompt=\"lowres, bad anatomy, worst quality, low quality\",\n num_inference_steps=100,\n generator=generator,\n).images[0]\nimage\n```\n\n
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\n\nTo use IP-Adapter FaceID models, first extract face embeddings with `insightface`. Then pass the list of tensors to the pipeline as `ip_adapter_image_embeds`.\n\n```py\nimport torch\nfrom diffusers import StableDiffusionPipeline, DDIMScheduler\nfrom diffusers.utils import load_image\nfrom insightface.app import FaceAnalysis\n\npipeline = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n torch_dtype=torch.float16,\n).to(\"cuda\")\npipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)\npipeline.load_ip_adapter(\"h94/IP-Adapter-FaceID\", subfolder=None, weight_name=\"ip-adapter-faceid_sd15.bin\", image_encoder_folder=None)\npipeline.set_ip_adapter_scale(0.6)\n\nimage = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_mask_girl1.png\")\n\nref_images_embeds = []\napp = FaceAnalysis(name=\"buffalo_l\", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])\napp.prepare(ctx_id=0, det_size=(640, 640))\nimage = cv2.cvtColor(np.asarray(image), cv2.COLOR_BGR2RGB)\nfaces = app.get(image)\nimage = torch.from_numpy(faces[0].normed_embedding)\nref_images_embeds.append(image.unsqueeze(0))\nref_images_embeds = torch.stack(ref_images_embeds, dim=0).unsqueeze(0)\nneg_ref_images_embeds = torch.zeros_like(ref_images_embeds)\nid_embeds = torch.cat([neg_ref_images_embeds, ref_images_embeds]).to(dtype=torch.float16, device=\"cuda\")\n\ngenerator = torch.Generator(device=\"cpu\").manual_seed(42)\n\nimages = pipeline(\n prompt=\"A photo of a girl\",\n ip_adapter_image_embeds=[id_embeds], \n negative_prompt=\"monochrome, lowres, bad anatomy, worst quality, low quality\", \n num_inference_steps=20, num_images_per_prompt=1,\n generator=generator\n).images\n```\n\nBoth IP-Adapter FaceID Plus and Plus v2 models require CLIP image embeddings. You can prepare face embeddings as shown previously, then you can extract and pass CLIP embeddings to the hidden image projection layers.\n\n```py\nfrom insightface.utils import face_align\n\nref_images_embeds = []\nip_adapter_images = []\napp = FaceAnalysis(name=\"buffalo_l\", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])\napp.prepare(ctx_id=0, det_size=(640, 640))\nimage = cv2.cvtColor(np.asarray(image), cv2.COLOR_BGR2RGB)\nfaces = app.get(image)\nip_adapter_images.append(face_align.norm_crop(image, landmark=faces[0].kps, image_size=224))\nimage = torch.from_numpy(faces[0].normed_embedding)\nref_images_embeds.append(image.unsqueeze(0))\nref_images_embeds = torch.stack(ref_images_embeds, dim=0).unsqueeze(0)\nneg_ref_images_embeds = torch.zeros_like(ref_images_embeds)\nid_embeds = torch.cat([neg_ref_images_embeds, ref_images_embeds]).to(dtype=torch.float16, device=\"cuda\")\n\nclip_embeds = pipeline.prepare_ip_adapter_image_embeds(\n [ip_adapter_images], None, torch.device(\"cuda\"), num_images, True)[0]\n\npipeline.unet.encoder_hid_proj.image_projection_layers[0].clip_embeds = clip_embeds.to(dtype=torch.float16)\npipeline.unet.encoder_hid_proj.image_projection_layers[0].shortcut = False # True if Plus v2\n```\n\n### Multi IP-Adapter\n\nMore than one IP-Adapter can be used at the same time to generate specific images in more diverse styles. For example, you can use IP-Adapter-Face to generate consistent faces and characters, and IP-Adapter Plus to generate those faces in a specific style.\n\n> [!TIP]\n> Read the [IP-Adapter Plus](../using-diffusers/loading_adapters#ip-adapter-plus) section to learn why you need to manually load the image encoder.\n\nLoad the image encoder with [`~transformers.CLIPVisionModelWithProjection`].\n\n```py\nimport torch\nfrom diffusers import AutoPipelineForText2Image, DDIMScheduler\nfrom transformers import CLIPVisionModelWithProjection\nfrom diffusers.utils import load_image\n\nimage_encoder = CLIPVisionModelWithProjection.from_pretrained(\n \"h94/IP-Adapter\",\n subfolder=\"models/image_encoder\",\n torch_dtype=torch.float16,\n)\n```\n\nNext, you'll load a base model, scheduler, and the IP-Adapters. The IP-Adapters to use are passed as a list to the `weight_name` parameter:\n\n* [ip-adapter-plus_sdxl_vit-h](https://huggingface.co/h94/IP-Adapter#ip-adapter-for-sdxl-10) uses patch embeddings and a ViT-H image encoder\n* [ip-adapter-plus-face_sdxl_vit-h](https://huggingface.co/h94/IP-Adapter#ip-adapter-for-sdxl-10) has the same architecture but it is conditioned with images of cropped faces\n\n```py\npipeline = AutoPipelineForText2Image.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n torch_dtype=torch.float16,\n image_encoder=image_encoder,\n)\npipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)\npipeline.load_ip_adapter(\n \"h94/IP-Adapter\",\n subfolder=\"sdxl_models\",\n weight_name=[\"ip-adapter-plus_sdxl_vit-h.safetensors\", \"ip-adapter-plus-face_sdxl_vit-h.safetensors\"]\n)\npipeline.set_ip_adapter_scale([0.7, 0.3])\npipeline.enable_model_cpu_offload()\n```\n\nLoad an image prompt and a folder containing images of a certain style you want to use.\n\n```py\nface_image = load_image(\"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/women_input.png\")\nstyle_folder = \"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/style_ziggy\"\nstyle_images = [load_image(f\"{style_folder}/img{i}.png\") for i in range(10)]\n```\n\n
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\n\nPass the image prompt and style images as a list to the `ip_adapter_image` parameter, and run the pipeline!\n\n```py\ngenerator = torch.Generator(device=\"cpu\").manual_seed(0)\n\nimage = pipeline(\n prompt=\"wonderwoman\",\n ip_adapter_image=[style_images, face_image],\n negative_prompt=\"monochrome, lowres, bad anatomy, worst quality, low quality\",\n num_inference_steps=50, num_images_per_prompt=1,\n generator=generator,\n).images[0]\nimage\n```\n\n
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\n\n### Instant generation\n\n[Latent Consistency Models (LCM)](../using-diffusers/inference_with_lcm_lora) are diffusion models that can generate images in as little as 4 steps compared to other diffusion models like SDXL that typically require way more steps. This is why image generation with an LCM feels \"instantaneous\". IP-Adapters can be plugged into an LCM-LoRA model to instantly generate images with an image prompt.\n\nThe IP-Adapter weights need to be loaded first, then you can use [`~StableDiffusionPipeline.load_lora_weights`] to load the LoRA style and weight you want to apply to your image.\n\n```py\nfrom diffusers import DiffusionPipeline, LCMScheduler\nimport torch\nfrom diffusers.utils import load_image\n\nmodel_id = \"sd-dreambooth-library/herge-style\"\nlcm_lora_id = \"latent-consistency/lcm-lora-sdv1-5\"\n\npipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\n\npipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"models\", weight_name=\"ip-adapter_sd15.bin\")\npipeline.load_lora_weights(lcm_lora_id)\npipeline.scheduler = LCMScheduler.from_config(pipeline.scheduler.config)\npipeline.enable_model_cpu_offload()\n```\n\nTry using with a lower IP-Adapter scale to condition image generation more on the [herge_style](https://huggingface.co/sd-dreambooth-library/herge-style) checkpoint, and remember to use the special token `herge_style` in your prompt to trigger and apply the style.\n\n```py\npipeline.set_ip_adapter_scale(0.4)\n\nprompt = \"herge_style woman in armor, best quality, high quality\"\ngenerator = torch.Generator(device=\"cpu\").manual_seed(0)\n\nip_adapter_image = load_image(\"https://user-images.githubusercontent.com/24734142/266492875-2d50d223-8475-44f0-a7c6-08b51cb53572.png\")\nimage = pipeline(\n prompt=prompt,\n ip_adapter_image=ip_adapter_image,\n num_inference_steps=4,\n guidance_scale=1,\n).images[0]\nimage\n```\n\n
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\n\n### Structural control\n\nTo control image generation to an even greater degree, you can combine IP-Adapter with a model like [ControlNet](../using-diffusers/controlnet). A ControlNet is also an adapter that can be inserted into a diffusion model to allow for conditioning on an additional control image. The control image can be depth maps, edge maps, pose estimations, and more.\n\nLoad a [`ControlNetModel`] checkpoint conditioned on depth maps, insert it into a diffusion model, and load the IP-Adapter.\n\n```py\nfrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel\nimport torch\nfrom diffusers.utils import load_image\n\ncontrolnet_model_path = \"lllyasviel/control_v11f1p_sd15_depth\"\ncontrolnet = ControlNetModel.from_pretrained(controlnet_model_path, torch_dtype=torch.float16)\n\npipeline = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, torch_dtype=torch.float16)\npipeline.to(\"cuda\")\npipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"models\", weight_name=\"ip-adapter_sd15.bin\")\n```\n\nNow load the IP-Adapter image and depth map.\n\n```py\nip_adapter_image = load_image(\"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/statue.png\")\ndepth_map = load_image(\"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/depth.png\")\n```\n\n
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\n\nPass the depth map and IP-Adapter image to the pipeline to generate an image.\n\n```py\ngenerator = torch.Generator(device=\"cpu\").manual_seed(33)\nimage = pipeline(\n prompt=\"best quality, high quality\",\n image=depth_map,\n ip_adapter_image=ip_adapter_image,\n negative_prompt=\"monochrome, lowres, bad anatomy, worst quality, low quality\",\n num_inference_steps=50,\n generator=generator,\n).images[0]\nimage\n```\n\n
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\n\n### Style & layout control\n\n[InstantStyle](https://arxiv.org/abs/2404.02733) is a plug-and-play method on top of IP-Adapter, which disentangles style and layout from image prompt to control image generation. This way, you can generate images following only the style or layout from image prompt, with significantly improved diversity. This is achieved by only activating IP-Adapters to specific parts of the model. \n\nBy default IP-Adapters are inserted to all layers of the model. Use the [`~loaders.IPAdapterMixin.set_ip_adapter_scale`] method with a dictionary to assign scales to IP-Adapter at different layers.\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nfrom diffusers.utils import load_image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16).to(\"cuda\")\npipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"sdxl_models\", weight_name=\"ip-adapter_sdxl.bin\")\n\nscale = {\n \"down\": {\"block_2\": [0.0, 1.0]},\n \"up\": {\"block_0\": [0.0, 1.0, 0.0]},\n}\npipeline.set_ip_adapter_scale(scale)\n```\n\nThis will activate IP-Adapter at the second layer in the model's down-part block 2 and up-part block 0. The former is the layer where IP-Adapter injects layout information and the latter injects style. Inserting IP-Adapter to these two layers you can generate images following both the style and layout from image prompt, but with contents more aligned to text prompt.\n\n```py\nstyle_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg\")\n\ngenerator = torch.Generator(device=\"cpu\").manual_seed(26)\nimage = pipeline(\n prompt=\"a cat, masterpiece, best quality, high quality\",\n ip_adapter_image=style_image,\n negative_prompt=\"text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry\",\n guidance_scale=5,\n num_inference_steps=30,\n generator=generator,\n).images[0]\nimage\n```\n\n
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\n\nIn contrast, inserting IP-Adapter to all layers will often generate images that overly focus on image prompt and diminish diversity.\n\nActivate IP-Adapter only in the style layer and then call the pipeline again.\n\n```py\nscale = {\n \"up\": {\"block_0\": [0.0, 1.0, 0.0]},\n}\npipeline.set_ip_adapter_scale(scale)\n\ngenerator = torch.Generator(device=\"cpu\").manual_seed(26)\nimage = pipeline(\n prompt=\"a cat, masterpiece, best quality, high quality\",\n ip_adapter_image=style_image,\n negative_prompt=\"text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry\",\n guidance_scale=5,\n num_inference_steps=30,\n generator=generator,\n).images[0]\nimage\n```\n\n
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\n\nNote that you don't have to specify all layers in the dictionary. Those not included in the dictionary will be set to scale 0 which means disable IP-Adapter by default.\n" }, { "path": "diffusers/docs/source/en/using-diffusers/kandinsky.md", "content": "\n\n# Kandinsky\n\n[[open-in-colab]]\n\nThe Kandinsky models are a series of multilingual text-to-image generation models. The Kandinsky 2.0 model uses two multilingual text encoders and concatenates those results for the UNet.\n\n[Kandinsky 2.1](../api/pipelines/kandinsky) changes the architecture to include an image prior model ([`CLIP`](https://huggingface.co/docs/transformers/model_doc/clip)) to generate a mapping between text and image embeddings. The mapping provides better text-image alignment and it is used with the text embeddings during training, leading to higher quality results. Finally, Kandinsky 2.1 uses a [Modulating Quantized Vectors (MoVQ)](https://huggingface.co/papers/2209.09002) decoder - which adds a spatial conditional normalization layer to increase photorealism - to decode the latents into images.\n\n[Kandinsky 2.2](../api/pipelines/kandinsky_v22) improves on the previous model by replacing the image encoder of the image prior model with a larger CLIP-ViT-G model to improve quality. The image prior model was also retrained on images with different resolutions and aspect ratios to generate higher-resolution images and different image sizes.\n\n[Kandinsky 3](../api/pipelines/kandinsky3) simplifies the architecture and shifts away from the two-stage generation process involving the prior model and diffusion model. Instead, Kandinsky 3 uses [Flan-UL2](https://huggingface.co/google/flan-ul2) to encode text, a UNet with [BigGan-deep](https://hf.co/papers/1809.11096) blocks, and [Sber-MoVQGAN](https://github.com/ai-forever/MoVQGAN) to decode the latents into images. Text understanding and generated image quality are primarily achieved by using a larger text encoder and UNet.\n\nThis guide will show you how to use the Kandinsky models for text-to-image, image-to-image, inpainting, interpolation, and more.\n\nBefore you begin, make sure you have the following libraries installed:\n\n```py\n# uncomment to install the necessary libraries in Colab\n#!pip install -q diffusers transformers accelerate\n```\n\n\n\nKandinsky 2.1 and 2.2 usage is very similar! The only difference is Kandinsky 2.2 doesn't accept `prompt` as an input when decoding the latents. Instead, Kandinsky 2.2 only accepts `image_embeds` during decoding.\n\n
\n\nKandinsky 3 has a more concise architecture and it doesn't require a prior model. This means it's usage is identical to other diffusion models like [Stable Diffusion XL](sdxl).\n\n\n\n## Text-to-image\n\nTo use the Kandinsky models for any task, you always start by setting up the prior pipeline to encode the prompt and generate the image embeddings. The prior pipeline also generates `negative_image_embeds` that correspond to the negative prompt `\"\"`. For better results, you can pass an actual `negative_prompt` to the prior pipeline, but this'll increase the effective batch size of the prior pipeline by 2x.\n\n\n\n\n```py\nfrom diffusers import KandinskyPriorPipeline, KandinskyPipeline\nimport torch\n\nprior_pipeline = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1-prior\", torch_dtype=torch.float16).to(\"cuda\")\npipeline = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\", torch_dtype=torch.float16).to(\"cuda\")\n\nprompt = \"A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting\"\nnegative_prompt = \"low quality, bad quality\" # optional to include a negative prompt, but results are usually better\nimage_embeds, negative_image_embeds = prior_pipeline(prompt, negative_prompt, guidance_scale=1.0).to_tuple()\n```\n\nNow pass all the prompts and embeddings to the [`KandinskyPipeline`] to generate an image:\n\n```py\nimage = pipeline(prompt, image_embeds=image_embeds, negative_prompt=negative_prompt, negative_image_embeds=negative_image_embeds, height=768, width=768).images[0]\nimage\n```\n\n
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\n\n\n\n\n```py\nfrom diffusers import KandinskyV22PriorPipeline, KandinskyV22Pipeline\nimport torch\n\nprior_pipeline = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16).to(\"cuda\")\npipeline = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16).to(\"cuda\")\n\nprompt = \"A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting\"\nnegative_prompt = \"low quality, bad quality\" # optional to include a negative prompt, but results are usually better\nimage_embeds, negative_image_embeds = prior_pipeline(prompt, guidance_scale=1.0).to_tuple()\n```\n\nPass the `image_embeds` and `negative_image_embeds` to the [`KandinskyV22Pipeline`] to generate an image:\n\n```py\nimage = pipeline(image_embeds=image_embeds, negative_image_embeds=negative_image_embeds, height=768, width=768).images[0]\nimage\n```\n\n
\n \n
\n\n\n\n\nKandinsky 3 doesn't require a prior model so you can directly load the [`Kandinsky3Pipeline`] and pass a prompt to generate an image:\n\n```py\nfrom diffusers import Kandinsky3Pipeline\nimport torch\n\npipeline = Kandinsky3Pipeline.from_pretrained(\"kandinsky-community/kandinsky-3\", variant=\"fp16\", torch_dtype=torch.float16)\npipeline.enable_model_cpu_offload()\n\nprompt = \"A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting\"\nimage = pipeline(prompt).images[0]\nimage\n```\n\n\n\n\n🤗 Diffusers also provides an end-to-end API with the [`KandinskyCombinedPipeline`] and [`KandinskyV22CombinedPipeline`], meaning you don't have to separately load the prior and text-to-image pipeline. The combined pipeline automatically loads both the prior model and the decoder. You can still set different values for the prior pipeline with the `prior_guidance_scale` and `prior_num_inference_steps` parameters if you want.\n\nUse the [`AutoPipelineForText2Image`] to automatically call the combined pipelines under the hood:\n\n\n\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\"kandinsky-community/kandinsky-2-1\", torch_dtype=torch.float16)\npipeline.enable_model_cpu_offload()\n\nprompt = \"A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting\"\nnegative_prompt = \"low quality, bad quality\"\n\nimage = pipeline(prompt=prompt, negative_prompt=negative_prompt, prior_guidance_scale=1.0, guidance_scale=4.0, height=768, width=768).images[0]\nimage\n```\n\n\n\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16)\npipeline.enable_model_cpu_offload()\n\nprompt = \"A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting\"\nnegative_prompt = \"low quality, bad quality\"\n\nimage = pipeline(prompt=prompt, negative_prompt=negative_prompt, prior_guidance_scale=1.0, guidance_scale=4.0, height=768, width=768).images[0]\nimage\n```\n\n\n\n\n## Image-to-image\n\nFor image-to-image, pass the initial image and text prompt to condition the image to the pipeline. Start by loading the prior pipeline:\n\n\n\n\n```py\nimport torch\nfrom diffusers import KandinskyImg2ImgPipeline, KandinskyPriorPipeline\n\nprior_pipeline = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1-prior\", torch_dtype=torch.float16, use_safetensors=True).to(\"cuda\")\npipeline = KandinskyImg2ImgPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\", torch_dtype=torch.float16, use_safetensors=True).to(\"cuda\")\n```\n\n\n\n\n```py\nimport torch\nfrom diffusers import KandinskyV22Img2ImgPipeline, KandinskyPriorPipeline\n\nprior_pipeline = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16, use_safetensors=True).to(\"cuda\")\npipeline = KandinskyV22Img2ImgPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16, use_safetensors=True).to(\"cuda\")\n```\n\n\n\n\nKandinsky 3 doesn't require a prior model so you can directly load the image-to-image pipeline:\n\n```py\nfrom diffusers import Kandinsky3Img2ImgPipeline\nfrom diffusers.utils import load_image\nimport torch\n\npipeline = Kandinsky3Img2ImgPipeline.from_pretrained(\"kandinsky-community/kandinsky-3\", variant=\"fp16\", torch_dtype=torch.float16)\npipeline.enable_model_cpu_offload()\n```\n\n\n\n\nDownload an image to condition on:\n\n```py\nfrom diffusers.utils import load_image\n\n# download image\nurl = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\noriginal_image = load_image(url)\noriginal_image = original_image.resize((768, 512))\n```\n\n
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\n\nGenerate the `image_embeds` and `negative_image_embeds` with the prior pipeline:\n\n```py\nprompt = \"A fantasy landscape, Cinematic lighting\"\nnegative_prompt = \"low quality, bad quality\"\n\nimage_embeds, negative_image_embeds = prior_pipeline(prompt, negative_prompt).to_tuple()\n```\n\nNow pass the original image, and all the prompts and embeddings to the pipeline to generate an image:\n\n\n\n\n```py\nfrom diffusers.utils import make_image_grid\n\nimage = pipeline(prompt, negative_prompt=negative_prompt, image=original_image, image_embeds=image_embeds, negative_image_embeds=negative_image_embeds, height=768, width=768, strength=0.3).images[0]\nmake_image_grid([original_image.resize((512, 512)), image.resize((512, 512))], rows=1, cols=2)\n```\n\n
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\n\n\n\n\n```py\nfrom diffusers.utils import make_image_grid\n\nimage = pipeline(image=original_image, image_embeds=image_embeds, negative_image_embeds=negative_image_embeds, height=768, width=768, strength=0.3).images[0]\nmake_image_grid([original_image.resize((512, 512)), image.resize((512, 512))], rows=1, cols=2)\n```\n\n
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\n\n\n\n\n```py\nimage = pipeline(prompt, negative_prompt=negative_prompt, image=image, strength=0.75, num_inference_steps=25).images[0]\nimage\n```\n\n\n\n\n🤗 Diffusers also provides an end-to-end API with the [`KandinskyImg2ImgCombinedPipeline`] and [`KandinskyV22Img2ImgCombinedPipeline`], meaning you don't have to separately load the prior and image-to-image pipeline. The combined pipeline automatically loads both the prior model and the decoder. You can still set different values for the prior pipeline with the `prior_guidance_scale` and `prior_num_inference_steps` parameters if you want.\n\nUse the [`AutoPipelineForImage2Image`] to automatically call the combined pipelines under the hood:\n\n\n\n\n```py\nfrom diffusers import AutoPipelineForImage2Image\nfrom diffusers.utils import make_image_grid, load_image\nimport torch\n\npipeline = AutoPipelineForImage2Image.from_pretrained(\"kandinsky-community/kandinsky-2-1\", torch_dtype=torch.float16, use_safetensors=True)\npipeline.enable_model_cpu_offload()\n\nprompt = \"A fantasy landscape, Cinematic lighting\"\nnegative_prompt = \"low quality, bad quality\"\n\nurl = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\noriginal_image = load_image(url)\n\noriginal_image.thumbnail((768, 768))\n\nimage = pipeline(prompt=prompt, negative_prompt=negative_prompt, image=original_image, strength=0.3).images[0]\nmake_image_grid([original_image.resize((512, 512)), image.resize((512, 512))], rows=1, cols=2)\n```\n\n\n\n\n```py\nfrom diffusers import AutoPipelineForImage2Image\nfrom diffusers.utils import make_image_grid, load_image\nimport torch\n\npipeline = AutoPipelineForImage2Image.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16)\npipeline.enable_model_cpu_offload()\n\nprompt = \"A fantasy landscape, Cinematic lighting\"\nnegative_prompt = \"low quality, bad quality\"\n\nurl = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\noriginal_image = load_image(url)\n\noriginal_image.thumbnail((768, 768))\n\nimage = pipeline(prompt=prompt, negative_prompt=negative_prompt, image=original_image, strength=0.3).images[0]\nmake_image_grid([original_image.resize((512, 512)), image.resize((512, 512))], rows=1, cols=2)\n```\n\n\n\n\n## Inpainting\n\n\n\n⚠️ The Kandinsky models use ⬜️ **white pixels** to represent the masked area now instead of black pixels. If you are using [`KandinskyInpaintPipeline`] in production, you need to change the mask to use white pixels:\n\n```py\n# For PIL input\nimport PIL.ImageOps\nmask = PIL.ImageOps.invert(mask)\n\n# For PyTorch and NumPy input\nmask = 1 - mask\n```\n\n\n\nFor inpainting, you'll need the original image, a mask of the area to replace in the original image, and a text prompt of what to inpaint. Load the prior pipeline:\n\n\n\n\n```py\nfrom diffusers import KandinskyInpaintPipeline, KandinskyPriorPipeline\nfrom diffusers.utils import load_image, make_image_grid\nimport torch\nimport numpy as np\nfrom PIL import Image\n\nprior_pipeline = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1-prior\", torch_dtype=torch.float16, use_safetensors=True).to(\"cuda\")\npipeline = KandinskyInpaintPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1-inpaint\", torch_dtype=torch.float16, use_safetensors=True).to(\"cuda\")\n```\n\n\n\n\n```py\nfrom diffusers import KandinskyV22InpaintPipeline, KandinskyV22PriorPipeline\nfrom diffusers.utils import load_image, make_image_grid\nimport torch\nimport numpy as np\nfrom PIL import Image\n\nprior_pipeline = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16, use_safetensors=True).to(\"cuda\")\npipeline = KandinskyV22InpaintPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder-inpaint\", torch_dtype=torch.float16, use_safetensors=True).to(\"cuda\")\n```\n\n\n\n\nLoad an initial image and create a mask:\n\n```py\ninit_image = load_image(\"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/cat.png\")\nmask = np.zeros((768, 768), dtype=np.float32)\n# mask area above cat's head\nmask[:250, 250:-250] = 1\n```\n\nGenerate the embeddings with the prior pipeline:\n\n```py\nprompt = \"a hat\"\nprior_output = prior_pipeline(prompt)\n```\n\nNow pass the initial image, mask, and prompt and embeddings to the pipeline to generate an image:\n\n\n\n\n```py\noutput_image = pipeline(prompt, image=init_image, mask_image=mask, **prior_output, height=768, width=768, num_inference_steps=150).images[0]\nmask = Image.fromarray((mask*255).astype('uint8'), 'L')\nmake_image_grid([init_image, mask, output_image], rows=1, cols=3)\n```\n\n
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\n\n\n\n\n```py\noutput_image = pipeline(image=init_image, mask_image=mask, **prior_output, height=768, width=768, num_inference_steps=150).images[0]\nmask = Image.fromarray((mask*255).astype('uint8'), 'L')\nmake_image_grid([init_image, mask, output_image], rows=1, cols=3)\n```\n\n
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\n\n\n\n\nYou can also use the end-to-end [`KandinskyInpaintCombinedPipeline`] and [`KandinskyV22InpaintCombinedPipeline`] to call the prior and decoder pipelines together under the hood. Use the [`AutoPipelineForInpainting`] for this:\n\n\n\n\n```py\nimport torch\nimport numpy as np\nfrom PIL import Image\nfrom diffusers import AutoPipelineForInpainting\nfrom diffusers.utils import load_image, make_image_grid\n\npipe = AutoPipelineForInpainting.from_pretrained(\"kandinsky-community/kandinsky-2-1-inpaint\", torch_dtype=torch.float16)\npipe.enable_model_cpu_offload()\n\ninit_image = load_image(\"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/cat.png\")\nmask = np.zeros((768, 768), dtype=np.float32)\n# mask area above cat's head\nmask[:250, 250:-250] = 1\nprompt = \"a hat\"\n\noutput_image = pipe(prompt=prompt, image=init_image, mask_image=mask).images[0]\nmask = Image.fromarray((mask*255).astype('uint8'), 'L')\nmake_image_grid([init_image, mask, output_image], rows=1, cols=3)\n```\n\n\n\n\n```py\nimport torch\nimport numpy as np\nfrom PIL import Image\nfrom diffusers import AutoPipelineForInpainting\nfrom diffusers.utils import load_image, make_image_grid\n\npipe = AutoPipelineForInpainting.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder-inpaint\", torch_dtype=torch.float16)\npipe.enable_model_cpu_offload()\n\ninit_image = load_image(\"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/cat.png\")\nmask = np.zeros((768, 768), dtype=np.float32)\n# mask area above cat's head\nmask[:250, 250:-250] = 1\nprompt = \"a hat\"\n\noutput_image = pipe(prompt=prompt, image=original_image, mask_image=mask).images[0]\nmask = Image.fromarray((mask*255).astype('uint8'), 'L')\nmake_image_grid([init_image, mask, output_image], rows=1, cols=3)\n```\n\n\n\n\n## Interpolation\n\nInterpolation allows you to explore the latent space between the image and text embeddings which is a cool way to see some of the prior model's intermediate outputs. Load the prior pipeline and two images you'd like to interpolate:\n\n\n\n\n```py\nfrom diffusers import KandinskyPriorPipeline, KandinskyPipeline\nfrom diffusers.utils import load_image, make_image_grid\nimport torch\n\nprior_pipeline = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1-prior\", torch_dtype=torch.float16, use_safetensors=True).to(\"cuda\")\nimg_1 = load_image(\"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/cat.png\")\nimg_2 = load_image(\"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/starry_night.jpeg\")\nmake_image_grid([img_1.resize((512,512)), img_2.resize((512,512))], rows=1, cols=2)\n```\n\n\n\n\n```py\nfrom diffusers import KandinskyV22PriorPipeline, KandinskyV22Pipeline\nfrom diffusers.utils import load_image, make_image_grid\nimport torch\n\nprior_pipeline = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16, use_safetensors=True).to(\"cuda\")\nimg_1 = load_image(\"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/cat.png\")\nimg_2 = load_image(\"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/starry_night.jpeg\")\nmake_image_grid([img_1.resize((512,512)), img_2.resize((512,512))], rows=1, cols=2)\n```\n\n\n\n\n
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a cat
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Van Gogh's Starry Night painting
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\n\nSpecify the text or images to interpolate, and set the weights for each text or image. Experiment with the weights to see how they affect the interpolation!\n\n```py\nimages_texts = [\"a cat\", img_1, img_2]\nweights = [0.3, 0.3, 0.4]\n```\n\nCall the `interpolate` function to generate the embeddings, and then pass them to the pipeline to generate the image:\n\n\n\n\n```py\n# prompt can be left empty\nprompt = \"\"\nprior_out = prior_pipeline.interpolate(images_texts, weights)\n\npipeline = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\", torch_dtype=torch.float16, use_safetensors=True).to(\"cuda\")\n\nimage = pipeline(prompt, **prior_out, height=768, width=768).images[0]\nimage\n```\n\n
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\n\n\n\n\n```py\n# prompt can be left empty\nprompt = \"\"\nprior_out = prior_pipeline.interpolate(images_texts, weights)\n\npipeline = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16, use_safetensors=True).to(\"cuda\")\n\nimage = pipeline(prompt, **prior_out, height=768, width=768).images[0]\nimage\n```\n\n
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\n\n\n\n\n## ControlNet\n\n\n\n⚠️ ControlNet is only supported for Kandinsky 2.2!\n\n\n\nControlNet enables conditioning large pretrained diffusion models with additional inputs such as a depth map or edge detection. For example, you can condition Kandinsky 2.2 with a depth map so the model understands and preserves the structure of the depth image.\n\nLet's load an image and extract it's depth map:\n\n```py\nfrom diffusers.utils import load_image\n\nimg = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/cat.png\"\n).resize((768, 768))\nimg\n```\n\n
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\n\nThen you can use the `depth-estimation` [`~transformers.Pipeline`] from 🤗 Transformers to process the image and retrieve the depth map:\n\n```py\nimport torch\nimport numpy as np\n\nfrom transformers import pipeline\n\ndef make_hint(image, depth_estimator):\n image = depth_estimator(image)[\"depth\"]\n image = np.array(image)\n image = image[:, :, None]\n image = np.concatenate([image, image, image], axis=2)\n detected_map = torch.from_numpy(image).float() / 255.0\n hint = detected_map.permute(2, 0, 1)\n return hint\n\ndepth_estimator = pipeline(\"depth-estimation\")\nhint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n```\n\n### Text-to-image [[controlnet-text-to-image]]\n\nLoad the prior pipeline and the [`KandinskyV22ControlnetPipeline`]:\n\n```py\nfrom diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n\nprior_pipeline = KandinskyV22PriorPipeline.from_pretrained(\n \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16, use_safetensors=True\n).to(\"cuda\")\n\npipeline = KandinskyV22ControlnetPipeline.from_pretrained(\n \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n).to(\"cuda\")\n```\n\nGenerate the image embeddings from a prompt and negative prompt:\n\n```py\nprompt = \"A robot, 4k photo\"\nnegative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\ngenerator = torch.Generator(device=\"cuda\").manual_seed(43)\n\nimage_emb, zero_image_emb = prior_pipeline(\n prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n).to_tuple()\n```\n\nFinally, pass the image embeddings and the depth image to the [`KandinskyV22ControlnetPipeline`] to generate an image:\n\n```py\nimage = pipeline(image_embeds=image_emb, negative_image_embeds=zero_image_emb, hint=hint, num_inference_steps=50, generator=generator, height=768, width=768).images[0]\nimage\n```\n\n
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\n\n### Image-to-image [[controlnet-image-to-image]]\n\nFor image-to-image with ControlNet, you'll need to use the:\n\n- [`KandinskyV22PriorEmb2EmbPipeline`] to generate the image embeddings from a text prompt and an image\n- [`KandinskyV22ControlnetImg2ImgPipeline`] to generate an image from the initial image and the image embeddings\n\nProcess and extract a depth map of an initial image of a cat with the `depth-estimation` [`~transformers.Pipeline`] from 🤗 Transformers:\n\n```py\nimport torch\nimport numpy as np\n\nfrom diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\nfrom diffusers.utils import load_image\nfrom transformers import pipeline\n\nimg = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/cat.png\"\n).resize((768, 768))\n\ndef make_hint(image, depth_estimator):\n image = depth_estimator(image)[\"depth\"]\n image = np.array(image)\n image = image[:, :, None]\n image = np.concatenate([image, image, image], axis=2)\n detected_map = torch.from_numpy(image).float() / 255.0\n hint = detected_map.permute(2, 0, 1)\n return hint\n\ndepth_estimator = pipeline(\"depth-estimation\")\nhint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n```\n\nLoad the prior pipeline and the [`KandinskyV22ControlnetImg2ImgPipeline`]:\n\n```py\nprior_pipeline = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16, use_safetensors=True\n).to(\"cuda\")\n\npipeline = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n).to(\"cuda\")\n```\n\nPass a text prompt and the initial image to the prior pipeline to generate the image embeddings:\n\n```py\nprompt = \"A robot, 4k photo\"\nnegative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\ngenerator = torch.Generator(device=\"cuda\").manual_seed(43)\n\nimg_emb = prior_pipeline(prompt=prompt, image=img, strength=0.85, generator=generator)\nnegative_emb = prior_pipeline(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n```\n\nNow you can run the [`KandinskyV22ControlnetImg2ImgPipeline`] to generate an image from the initial image and the image embeddings:\n\n```py\nimage = pipeline(image=img, strength=0.5, image_embeds=img_emb.image_embeds, negative_image_embeds=negative_emb.image_embeds, hint=hint, num_inference_steps=50, generator=generator, height=768, width=768).images[0]\nmake_image_grid([img.resize((512, 512)), image.resize((512, 512))], rows=1, cols=2)\n```\n\n
\n \n
\n\n## Optimizations\n\nKandinsky is unique because it requires a prior pipeline to generate the mappings, and a second pipeline to decode the latents into an image. Optimization efforts should be focused on the second pipeline because that is where the bulk of the computation is done. Here are some tips to improve Kandinsky during inference.\n\n1. Enable [xFormers](../optimization/xformers) if you're using PyTorch < 2.0:\n\n```diff\n from diffusers import DiffusionPipeline\n import torch\n\n pipe = DiffusionPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\", torch_dtype=torch.float16)\n+ pipe.enable_xformers_memory_efficient_attention()\n```\n\n2. Enable `torch.compile` if you're using PyTorch >= 2.0 to automatically use scaled dot-product attention (SDPA):\n\n```diff\n pipe.unet.to(memory_format=torch.channels_last)\n+ pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n```\n\nThis is the same as explicitly setting the attention processor to use [`~models.attention_processor.AttnAddedKVProcessor2_0`]:\n\n```py\nfrom diffusers.models.attention_processor import AttnAddedKVProcessor2_0\n\npipe.unet.set_attn_processor(AttnAddedKVProcessor2_0())\n```\n\n3. Offload the model to the CPU with [`~KandinskyPriorPipeline.enable_model_cpu_offload`] to avoid out-of-memory errors:\n\n```diff\n from diffusers import DiffusionPipeline\n import torch\n\n pipe = DiffusionPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\", torch_dtype=torch.float16)\n+ pipe.enable_model_cpu_offload()\n```\n\n4. By default, the text-to-image pipeline uses the [`DDIMScheduler`] but you can replace it with another scheduler like [`DDPMScheduler`] to see how that affects the tradeoff between inference speed and image quality:\n\n```py\nfrom diffusers import DDPMScheduler\nfrom diffusers import DiffusionPipeline\n\nscheduler = DDPMScheduler.from_pretrained(\"kandinsky-community/kandinsky-2-1\", subfolder=\"ddpm_scheduler\")\npipe = DiffusionPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\", scheduler=scheduler, torch_dtype=torch.float16, use_safetensors=True).to(\"cuda\")\n```\n" }, { "path": "diffusers/docs/source/en/using-diffusers/loading.md", "content": "\n\n# Load pipelines\n\n[[open-in-colab]]\n\nDiffusion systems consist of multiple components like parameterized models and schedulers that interact in complex ways. That is why we designed the [`DiffusionPipeline`] to wrap the complexity of the entire diffusion system into an easy-to-use API. At the same time, the [`DiffusionPipeline`] is entirely customizable so you can modify each component to build a diffusion system for your use case.\n\nThis guide will show you how to load:\n\n- pipelines from the Hub and locally\n- different components into a pipeline\n- multiple pipelines without increasing memory usage\n- checkpoint variants such as different floating point types or non-exponential mean averaged (EMA) weights\n\n## Load a pipeline\n\n> [!TIP]\n> Skip to the [DiffusionPipeline explained](#diffusionpipeline-explained) section if you're interested in an explanation about how the [`DiffusionPipeline`] class works.\n\nThere are two ways to load a pipeline for a task:\n\n1. Load the generic [`DiffusionPipeline`] class and allow it to automatically detect the correct pipeline class from the checkpoint.\n2. Load a specific pipeline class for a specific task.\n\n\n\n\nThe [`DiffusionPipeline`] class is a simple and generic way to load the latest trending diffusion model from the [Hub](https://huggingface.co/models?library=diffusers&sort=trending). It uses the [`~DiffusionPipeline.from_pretrained`] method to automatically detect the correct pipeline class for a task from the checkpoint, downloads and caches all the required configuration and weight files, and returns a pipeline ready for inference.\n\n```python\nfrom diffusers import DiffusionPipeline\n\npipeline = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", use_safetensors=True)\n```\n\nThis same checkpoint can also be used for an image-to-image task. The [`DiffusionPipeline`] class can handle any task as long as you provide the appropriate inputs. For example, for an image-to-image task, you need to pass an initial image to the pipeline.\n\n```py\nfrom diffusers import DiffusionPipeline\n\npipeline = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", use_safetensors=True)\n\ninit_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png\")\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\nimage = pipeline(\"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\", image=init_image).images[0]\n```\n\n\n\n\nCheckpoints can be loaded by their specific pipeline class if you already know it. For example, to load a Stable Diffusion model, use the [`StableDiffusionPipeline`] class.\n\n```python\nfrom diffusers import StableDiffusionPipeline\n\npipeline = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", use_safetensors=True)\n```\n\nThis same checkpoint may also be used for another task like image-to-image. To differentiate what task you want to use the checkpoint for, you have to use the corresponding task-specific pipeline class. For example, to use the same checkpoint for image-to-image, use the [`StableDiffusionImg2ImgPipeline`] class.\n\n```py\nfrom diffusers import StableDiffusionImg2ImgPipeline\n\npipeline = StableDiffusionImg2ImgPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", use_safetensors=True)\n```\n\n\n\n\nUse the Space below to gauge a pipeline's memory requirements before you download and load it to see if it runs on your hardware.\n\n
\n\t\n
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\n \n
\n\n### Local pipeline\n\nTo load a pipeline locally, use [git-lfs](https://git-lfs.github.com/) to manually download a checkpoint to your local disk.\n\n```bash\ngit-lfs install\ngit clone https://huggingface.co/runwayml/stable-diffusion-v1-5\n```\n\nThis creates a local folder, ./stable-diffusion-v1-5, on your disk and you should pass its path to [`~DiffusionPipeline.from_pretrained`].\n\n```python\nfrom diffusers import DiffusionPipeline\n\nstable_diffusion = DiffusionPipeline.from_pretrained(\"./stable-diffusion-v1-5\", use_safetensors=True)\n```\n\nThe [`~DiffusionPipeline.from_pretrained`] method won't download files from the Hub when it detects a local path, but this also means it won't download and cache the latest changes to a checkpoint.\n\n## Customize a pipeline\n\nYou can customize a pipeline by loading different components into it. This is important because you can:\n\n- change to a scheduler with faster generation speed or higher generation quality depending on your needs (call the `scheduler.compatibles` method on your pipeline to see compatible schedulers)\n- change a default pipeline component to a newer and better performing one\n\nFor example, let's customize the default [stabilityai/stable-diffusion-xl-base-1.0](https://hf.co/stabilityai/stable-diffusion-xl-base-1.0) checkpoint with:\n\n- The [`HeunDiscreteScheduler`] to generate higher quality images at the expense of slower generation speed. You must pass the `subfolder=\"scheduler\"` parameter in [`~HeunDiscreteScheduler.from_pretrained`] to load the scheduler configuration into the correct [subfolder](https://hf.co/stabilityai/stable-diffusion-xl-base-1.0/tree/main/scheduler) of the pipeline repository.\n- A more stable VAE that runs in fp16.\n\n```py\nfrom diffusers import StableDiffusionXLPipeline, HeunDiscreteScheduler, AutoencoderKL\nimport torch\n\nscheduler = HeunDiscreteScheduler.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\", subfolder=\"scheduler\")\nvae = AutoencoderKL.from_pretrained(\"madebyollin/sdxl-vae-fp16-fix\", torch_dtype=torch.float16, use_safetensors=True)\n```\n\nNow pass the new scheduler and VAE to the [`StableDiffusionXLPipeline`].\n\n```py\npipeline = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", \n scheduler=scheduler, \n vae=vae, \n torch_dtype=torch.float16, \n variant=\"fp16\", \n use_safetensors=True\n).to(\"cuda\")\n```\n\n## Reuse a pipeline\n\nWhen you load multiple pipelines that share the same model components, it makes sense to reuse the shared components instead of reloading everything into memory again, especially if your hardware is memory-constrained. For example:\n\n1. You generated an image with the [`StableDiffusionPipeline`] but you want to improve its quality with the [`StableDiffusionSAGPipeline`]. Both of these pipelines share the same pretrained model, so it'd be a waste of memory to load the same model twice.\n2. You want to add a model component, like a [`MotionAdapter`](../api/pipelines/animatediff#animatediffpipeline), to [`AnimateDiffPipeline`] which was instantiated from an existing [`StableDiffusionPipeline`]. Again, both pipelines share the same pretrained model, so it'd be a waste of memory to load an entirely new pipeline again.\n\nWith the [`DiffusionPipeline.from_pipe`] API, you can switch between multiple pipelines to take advantage of their different features without increasing memory-usage. It is similar to turning on and off a feature in your pipeline.\n\n> [!TIP]\n> To switch between tasks (rather than features), use the [`~DiffusionPipeline.from_pipe`] method with the [AutoPipeline](../api/pipelines/auto_pipeline) class, which automatically identifies the pipeline class based on the task (learn more in the [AutoPipeline](../tutorials/autopipeline) tutorial).\n\nLet's start with a [`StableDiffusionPipeline`] and then reuse the loaded model components to create a [`StableDiffusionSAGPipeline`] to increase generation quality. You'll use the [`StableDiffusionPipeline`] with an [IP-Adapter](./ip_adapter) to generate a bear eating pizza.\n\n```python\nfrom diffusers import DiffusionPipeline, StableDiffusionSAGPipeline\nimport torch\nimport gc\nfrom diffusers.utils import load_image\nfrom accelerate.utils import compute_module_sizes\n\nimage = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/load_neg_embed.png\")\n\npipe_sd = DiffusionPipeline.from_pretrained(\"SG161222/Realistic_Vision_V6.0_B1_noVAE\", torch_dtype=torch.float16)\npipe_sd.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"models\", weight_name=\"ip-adapter_sd15.bin\")\npipe_sd.set_ip_adapter_scale(0.6)\npipe_sd.to(\"cuda\")\n\ngenerator = torch.Generator(device=\"cpu\").manual_seed(33)\nout_sd = pipe_sd(\n prompt=\"bear eats pizza\",\n negative_prompt=\"wrong white balance, dark, sketches,worst quality,low quality\", \n ip_adapter_image=image,\n num_inference_steps=50,\n generator=generator,\n).images[0]\nout_sd\n```\n\n
\n \n
\n\nFor reference, you can check how much memory this process consumed.\n\n```python\ndef bytes_to_giga_bytes(bytes):\n return bytes / 1024 / 1024 / 1024\nprint(f\"Max memory allocated: {bytes_to_giga_bytes(torch.cuda.max_memory_allocated())} GB\")\n\"Max memory allocated: 4.406213283538818 GB\"\n```\n\nNow, reuse the same pipeline components from [`StableDiffusionPipeline`] in [`StableDiffusionSAGPipeline`] with the [`~DiffusionPipeline.from_pipe`] method.\n\n> [!WARNING]\n> Some pipeline methods may not function properly on new pipelines created with [`~DiffusionPipeline.from_pipe`]. For instance, the [`~DiffusionPipeline.enable_model_cpu_offload`] method installs hooks on the model components based on a unique offloading sequence for each pipeline. If the models are executed in a different order in the new pipeline, the CPU offloading may not work correctly.\n>\n> To ensure everything works as expected, we recommend re-applying a pipeline method on a new pipeline created with [`~DiffusionPipeline.from_pipe`].\n\n```python\npipe_sag = StableDiffusionSAGPipeline.from_pipe(\n pipe_sd\n)\n\ngenerator = torch.Generator(device=\"cpu\").manual_seed(33)\nout_sag = pipe_sag(\n prompt=\"bear eats pizza\",\n negative_prompt=\"wrong white balance, dark, sketches,worst quality,low quality\",\n ip_adapter_image=image,\n num_inference_steps=50,\n generator=generator,\n guidance_scale=1.0,\n sag_scale=0.75\n).images[0]\nout_sag\n```\n\n
\n \n
\n\nIf you check the memory usage, you'll see it remains the same as before because [`StableDiffusionPipeline`] and [`StableDiffusionSAGPipeline`] are sharing the same pipeline components. This allows you to use them interchangeably without any additional memory overhead.\n\n```py\nprint(f\"Max memory allocated: {bytes_to_giga_bytes(torch.cuda.max_memory_allocated())} GB\")\n\"Max memory allocated: 4.406213283538818 GB\"\n```\n\nLet's animate the image with the [`AnimateDiffPipeline`] and also add a [`MotionAdapter`] module to the pipeline. For the [`AnimateDiffPipeline`], you need to unload the IP-Adapter first and reload it *after* you've created your new pipeline (this only applies to the [`AnimateDiffPipeline`]).\n\n```py\nfrom diffusers import AnimateDiffPipeline, MotionAdapter, DDIMScheduler\nfrom diffusers.utils import export_to_gif\n\npipe_sag.unload_ip_adapter()\nadapter = MotionAdapter.from_pretrained(\"guoyww/animatediff-motion-adapter-v1-5-2\", torch_dtype=torch.float16)\n\npipe_animate = AnimateDiffPipeline.from_pipe(pipe_sd, motion_adapter=adapter)\npipe_animate.scheduler = DDIMScheduler.from_config(pipe_animate.scheduler.config, beta_schedule=\"linear\")\n# load IP-Adapter and LoRA weights again\npipe_animate.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"models\", weight_name=\"ip-adapter_sd15.bin\")\npipe_animate.load_lora_weights(\"guoyww/animatediff-motion-lora-zoom-out\", adapter_name=\"zoom-out\")\npipe_animate.to(\"cuda\")\n\ngenerator = torch.Generator(device=\"cpu\").manual_seed(33)\npipe_animate.set_adapters(\"zoom-out\", adapter_weights=0.75)\nout = pipe_animate(\n prompt=\"bear eats pizza\",\n num_frames=16,\n num_inference_steps=50,\n ip_adapter_image=image,\n generator=generator,\n).frames[0]\nexport_to_gif(out, \"out_animate.gif\")\n```\n\n
\n \n
\n\nThe [`AnimateDiffPipeline`] is more memory-intensive and consumes 15GB of memory (see the [Memory-usage of from_pipe](#memory-usage-of-from_pipe) section to learn what this means for your memory-usage).\n\n```py\nprint(f\"Max memory allocated: {bytes_to_giga_bytes(torch.cuda.max_memory_allocated())} GB\")\n\"Max memory allocated: 15.178664207458496 GB\"\n```\n\n### Modify from_pipe components\n\nPipelines loaded with [`~DiffusionPipeline.from_pipe`] can be customized with different model components or methods. However, whenever you modify the *state* of the model components, it affects all the other pipelines that share the same components. For example, if you call [`~diffusers.loaders.IPAdapterMixin.unload_ip_adapter`] on the [`StableDiffusionSAGPipeline`], you won't be able to use IP-Adapter with the [`StableDiffusionPipeline`] because it's been removed from their shared components.\n\n```py\npipe.sag_unload_ip_adapter()\n\ngenerator = torch.Generator(device=\"cpu\").manual_seed(33)\nout_sd = pipe_sd(\n prompt=\"bear eats pizza\",\n negative_prompt=\"wrong white balance, dark, sketches,worst quality,low quality\", \n ip_adapter_image=image,\n num_inference_steps=50,\n generator=generator,\n).images[0]\n\"AttributeError: 'NoneType' object has no attribute 'image_projection_layers'\"\n```\n\n### Memory usage of from_pipe\n\nThe memory requirement of loading multiple pipelines with [`~DiffusionPipeline.from_pipe`] is determined by the pipeline with the highest memory-usage regardless of the number of pipelines you create.\n\n| Pipeline | Memory usage (GB) |\n|---|---|\n| StableDiffusionPipeline | 4.400 |\n| StableDiffusionSAGPipeline | 4.400 |\n| AnimateDiffPipeline | 15.178 |\n\nThe [`AnimateDiffPipeline`] has the highest memory requirement, so the *total memory-usage* is based only on the [`AnimateDiffPipeline`]. Your memory-usage will not increase if you create additional pipelines as long as their memory requirements doesn't exceed that of the [`AnimateDiffPipeline`]. Each pipeline can be used interchangeably without any additional memory overhead.\n\n## Safety checker\n\nDiffusers implements a [safety checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) for Stable Diffusion models which can generate harmful content. The safety checker screens the generated output against known hardcoded not-safe-for-work (NSFW) content. If for whatever reason you'd like to disable the safety checker, pass `safety_checker=None` to the [`~DiffusionPipeline.from_pretrained`] method.\n\n```python\nfrom diffusers import DiffusionPipeline\n\npipeline = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", safety_checker=None, use_safetensors=True)\n\"\"\"\nYou have disabled the safety checker for by passing `safety_checker=None`. Ensure that you abide by the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend keeping the safety filter enabled in all public-facing circumstances, disabling it only for use cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\n\"\"\"\n```\n\n## Checkpoint variants\n\nA checkpoint variant is usually a checkpoint whose weights are:\n\n- Stored in a different floating point type, such as [torch.float16](https://pytorch.org/docs/stable/tensors.html#data-types), because it only requires half the bandwidth and storage to download. You can't use this variant if you're continuing training or using a CPU.\n- Non-exponential mean averaged (EMA) weights which shouldn't be used for inference. You should use this variant to continue finetuning a model.\n\n> [!TIP]\n> When the checkpoints have identical model structures, but they were trained on different datasets and with a different training setup, they should be stored in separate repositories. For example, [stabilityai/stable-diffusion-2](https://hf.co/stabilityai/stable-diffusion-2) and [stabilityai/stable-diffusion-2-1](https://hf.co/stabilityai/stable-diffusion-2-1) are stored in separate repositories.\n\nOtherwise, a variant is **identical** to the original checkpoint. They have exactly the same serialization format (like [safetensors](./using_safetensors)), model structure, and their weights have identical tensor shapes.\n\n| **checkpoint type** | **weight name** | **argument for loading weights** |\n|---------------------|---------------------------------------------|----------------------------------|\n| original | diffusion_pytorch_model.safetensors | |\n| floating point | diffusion_pytorch_model.fp16.safetensors | `variant`, `torch_dtype` |\n| non-EMA | diffusion_pytorch_model.non_ema.safetensors | `variant` |\n\nThere are two important arguments for loading variants:\n\n- `torch_dtype` specifies the floating point precision of the loaded checkpoint. For example, if you want to save bandwidth by loading a fp16 variant, you should set `variant=\"fp16\"` and `torch_dtype=torch.float16` to *convert the weights* to fp16. Otherwise, the fp16 weights are converted to the default fp32 precision.\n\n If you only set `torch_dtype=torch.float16`, the default fp32 weights are downloaded first and then converted to fp16.\n\n- `variant` specifies which files should be loaded from the repository. For example, if you want to load a non-EMA variant of a UNet from [runwayml/stable-diffusion-v1-5](https://hf.co/runwayml/stable-diffusion-v1-5/tree/main/unet), set `variant=\"non_ema\"` to download the `non_ema` file.\n\n\n\n\n```py\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipeline = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", variant=\"fp16\", torch_dtype=torch.float16, use_safetensors=True\n)\n```\n\n\n\n\n```py\npipeline = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", variant=\"non_ema\", use_safetensors=True\n)\n```\n\n\n\n\nUse the `variant` parameter in the [`DiffusionPipeline.save_pretrained`] method to save a checkpoint as a different floating point type or as a non-EMA variant. You should try save a variant to the same folder as the original checkpoint, so you have the option of loading both from the same folder.\n\n\n\n\n```python\nfrom diffusers import DiffusionPipeline\n\npipeline.save_pretrained(\"runwayml/stable-diffusion-v1-5\", variant=\"fp16\")\n```\n\n\n\n\n```py\npipeline.save_pretrained(\"runwayml/stable-diffusion-v1-5\", variant=\"non_ema\")\n```\n\n\n\n\nIf you don't save the variant to an existing folder, you must specify the `variant` argument otherwise it'll throw an `Exception` because it can't find the original checkpoint.\n\n```python\n# 👎 this won't work\npipeline = DiffusionPipeline.from_pretrained(\n \"./stable-diffusion-v1-5\", torch_dtype=torch.float16, use_safetensors=True\n)\n# 👍 this works\npipeline = DiffusionPipeline.from_pretrained(\n \"./stable-diffusion-v1-5\", variant=\"fp16\", torch_dtype=torch.float16, use_safetensors=True\n)\n```\n\n## DiffusionPipeline explained\n\nAs a class method, [`DiffusionPipeline.from_pretrained`] is responsible for two things:\n\n- Download the latest version of the folder structure required for inference and cache it. If the latest folder structure is available in the local cache, [`DiffusionPipeline.from_pretrained`] reuses the cache and won't redownload the files.\n- Load the cached weights into the correct pipeline [class](../api/pipelines/overview#diffusers-summary) - retrieved from the `model_index.json` file - and return an instance of it.\n\nThe pipelines' underlying folder structure corresponds directly with their class instances. For example, the [`StableDiffusionPipeline`] corresponds to the folder structure in [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5).\n\n```python\nfrom diffusers import DiffusionPipeline\n\nrepo_id = \"runwayml/stable-diffusion-v1-5\"\npipeline = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=True)\nprint(pipeline)\n```\n\nYou'll see pipeline is an instance of [`StableDiffusionPipeline`], which consists of seven components:\n\n- `\"feature_extractor\"`: a [`~transformers.CLIPImageProcessor`] from 🤗 Transformers.\n- `\"safety_checker\"`: a [component](https://github.com/huggingface/diffusers/blob/e55687e1e15407f60f32242027b7bb8170e58266/src/diffusers/pipelines/stable_diffusion/safety_checker.py#L32) for screening against harmful content.\n- `\"scheduler\"`: an instance of [`PNDMScheduler`].\n- `\"text_encoder\"`: a [`~transformers.CLIPTextModel`] from 🤗 Transformers.\n- `\"tokenizer\"`: a [`~transformers.CLIPTokenizer`] from 🤗 Transformers.\n- `\"unet\"`: an instance of [`UNet2DConditionModel`].\n- `\"vae\"`: an instance of [`AutoencoderKL`].\n\n```json\nStableDiffusionPipeline {\n \"feature_extractor\": [\n \"transformers\",\n \"CLIPImageProcessor\"\n ],\n \"safety_checker\": [\n \"stable_diffusion\",\n \"StableDiffusionSafetyChecker\"\n ],\n \"scheduler\": [\n \"diffusers\",\n \"PNDMScheduler\"\n ],\n \"text_encoder\": [\n \"transformers\",\n \"CLIPTextModel\"\n ],\n \"tokenizer\": [\n \"transformers\",\n \"CLIPTokenizer\"\n ],\n \"unet\": [\n \"diffusers\",\n \"UNet2DConditionModel\"\n ],\n \"vae\": [\n \"diffusers\",\n \"AutoencoderKL\"\n ]\n}\n```\n\nCompare the components of the pipeline instance to the [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/main) folder structure, and you'll see there is a separate folder for each of the components in the repository:\n\n```\n.\n├── feature_extractor\n│   └── preprocessor_config.json\n├── model_index.json\n├── safety_checker\n│   ├── config.json\n| ├── model.fp16.safetensors\n│ ├── model.safetensors\n│ ├── pytorch_model.bin\n| └── pytorch_model.fp16.bin\n├── scheduler\n│   └── scheduler_config.json\n├── text_encoder\n│   ├── config.json\n| ├── model.fp16.safetensors\n│ ├── model.safetensors\n│ |── pytorch_model.bin\n| └── pytorch_model.fp16.bin\n├── tokenizer\n│   ├── merges.txt\n│   ├── special_tokens_map.json\n│   ├── tokenizer_config.json\n│   └── vocab.json\n├── unet\n│   ├── config.json\n│   ├── diffusion_pytorch_model.bin\n| |── diffusion_pytorch_model.fp16.bin\n│ |── diffusion_pytorch_model.f16.safetensors\n│ |── diffusion_pytorch_model.non_ema.bin\n│ |── diffusion_pytorch_model.non_ema.safetensors\n│ └── diffusion_pytorch_model.safetensors\n|── vae\n. ├── config.json\n. ├── diffusion_pytorch_model.bin\n ├── diffusion_pytorch_model.fp16.bin\n ├── diffusion_pytorch_model.fp16.safetensors\n └── diffusion_pytorch_model.safetensors\n```\n\nYou can access each of the components of the pipeline as an attribute to view its configuration:\n\n```py\npipeline.tokenizer\nCLIPTokenizer(\n name_or_path=\"/root/.cache/huggingface/hub/models--runwayml--stable-diffusion-v1-5/snapshots/39593d5650112b4cc580433f6b0435385882d819/tokenizer\",\n vocab_size=49408,\n model_max_length=77,\n is_fast=False,\n padding_side=\"right\",\n truncation_side=\"right\",\n special_tokens={\n \"bos_token\": AddedToken(\"<|startoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=True),\n \"eos_token\": AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=True),\n \"unk_token\": AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=True),\n \"pad_token\": \"<|endoftext|>\",\n },\n clean_up_tokenization_spaces=True\n)\n```\n\nEvery pipeline expects a [`model_index.json`](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/model_index.json) file that tells the [`DiffusionPipeline`]:\n\n- which pipeline class to load from `_class_name`\n- which version of 🧨 Diffusers was used to create the model in `_diffusers_version`\n- what components from which library are stored in the subfolders (`name` corresponds to the component and subfolder name, `library` corresponds to the name of the library to load the class from, and `class` corresponds to the class name)\n\n```json\n{\n \"_class_name\": \"StableDiffusionPipeline\",\n \"_diffusers_version\": \"0.6.0\",\n \"feature_extractor\": [\n \"transformers\",\n \"CLIPImageProcessor\"\n ],\n \"safety_checker\": [\n \"stable_diffusion\",\n \"StableDiffusionSafetyChecker\"\n ],\n \"scheduler\": [\n \"diffusers\",\n \"PNDMScheduler\"\n ],\n \"text_encoder\": [\n \"transformers\",\n \"CLIPTextModel\"\n ],\n \"tokenizer\": [\n \"transformers\",\n \"CLIPTokenizer\"\n ],\n \"unet\": [\n \"diffusers\",\n \"UNet2DConditionModel\"\n ],\n \"vae\": [\n \"diffusers\",\n \"AutoencoderKL\"\n ]\n}\n```\n" }, { "path": "diffusers/docs/source/en/using-diffusers/loading_adapters.md", "content": "\n\n# Load adapters\n\n[[open-in-colab]]\n\nThere are several [training](../training/overview) techniques for personalizing diffusion models to generate images of a specific subject or images in certain styles. Each of these training methods produces a different type of adapter. Some of the adapters generate an entirely new model, while other adapters only modify a smaller set of embeddings or weights. This means the loading process for each adapter is also different.\n\nThis guide will show you how to load DreamBooth, textual inversion, and LoRA weights.\n\n\n\nFeel free to browse the [Stable Diffusion Conceptualizer](https://huggingface.co/spaces/sd-concepts-library/stable-diffusion-conceptualizer), [LoRA the Explorer](https://huggingface.co/spaces/multimodalart/LoraTheExplorer), and the [Diffusers Models Gallery](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery) for checkpoints and embeddings to use.\n\n\n\n## DreamBooth\n\n[DreamBooth](https://dreambooth.github.io/) finetunes an *entire diffusion model* on just several images of a subject to generate images of that subject in new styles and settings. This method works by using a special word in the prompt that the model learns to associate with the subject image. Of all the training methods, DreamBooth produces the largest file size (usually a few GBs) because it is a full checkpoint model.\n\nLet's load the [herge_style](https://huggingface.co/sd-dreambooth-library/herge-style) checkpoint, which is trained on just 10 images drawn by Hergé, to generate images in that style. For it to work, you need to include the special word `herge_style` in your prompt to trigger the checkpoint:\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\"sd-dreambooth-library/herge-style\", torch_dtype=torch.float16).to(\"cuda\")\nprompt = \"A cute herge_style brown bear eating a slice of pizza, stunning color scheme, masterpiece, illustration\"\nimage = pipeline(prompt).images[0]\nimage\n```\n\n
\n \n
\n\n## Textual inversion\n\n[Textual inversion](https://textual-inversion.github.io/) is very similar to DreamBooth and it can also personalize a diffusion model to generate certain concepts (styles, objects) from just a few images. This method works by training and finding new embeddings that represent the images you provide with a special word in the prompt. As a result, the diffusion model weights stay the same and the training process produces a relatively tiny (a few KBs) file.\n\nBecause textual inversion creates embeddings, it cannot be used on its own like DreamBooth and requires another model.\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16).to(\"cuda\")\n```\n\nNow you can load the textual inversion embeddings with the [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] method and generate some images. Let's load the [sd-concepts-library/gta5-artwork](https://huggingface.co/sd-concepts-library/gta5-artwork) embeddings and you'll need to include the special word `` in your prompt to trigger it:\n\n```py\npipeline.load_textual_inversion(\"sd-concepts-library/gta5-artwork\")\nprompt = \"A cute brown bear eating a slice of pizza, stunning color scheme, masterpiece, illustration, style\"\nimage = pipeline(prompt).images[0]\nimage\n```\n\n
\n \n
\n\nTextual inversion can also be trained on undesirable things to create *negative embeddings* to discourage a model from generating images with those undesirable things like blurry images or extra fingers on a hand. This can be an easy way to quickly improve your prompt. You'll also load the embeddings with [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`], but this time, you'll need two more parameters:\n\n- `weight_name`: specifies the weight file to load if the file was saved in the 🤗 Diffusers format with a specific name or if the file is stored in the A1111 format\n- `token`: specifies the special word to use in the prompt to trigger the embeddings\n\nLet's load the [sayakpaul/EasyNegative-test](https://huggingface.co/sayakpaul/EasyNegative-test) embeddings:\n\n```py\npipeline.load_textual_inversion(\n \"sayakpaul/EasyNegative-test\", weight_name=\"EasyNegative.safetensors\", token=\"EasyNegative\"\n)\n```\n\nNow you can use the `token` to generate an image with the negative embeddings:\n\n```py\nprompt = \"A cute brown bear eating a slice of pizza, stunning color scheme, masterpiece, illustration, EasyNegative\"\nnegative_prompt = \"EasyNegative\"\n\nimage = pipeline(prompt, negative_prompt=negative_prompt, num_inference_steps=50).images[0]\nimage\n```\n\n
\n \n
\n\n## LoRA\n\n[Low-Rank Adaptation (LoRA)](https://huggingface.co/papers/2106.09685) is a popular training technique because it is fast and generates smaller file sizes (a couple hundred MBs). Like the other methods in this guide, LoRA can train a model to learn new styles from just a few images. It works by inserting new weights into the diffusion model and then only the new weights are trained instead of the entire model. This makes LoRAs faster to train and easier to store.\n\n\n\nLoRA is a very general training technique that can be used with other training methods. For example, it is common to train a model with DreamBooth and LoRA. It is also increasingly common to load and merge multiple LoRAs to create new and unique images. You can learn more about it in the in-depth [Merge LoRAs](merge_loras) guide since merging is outside the scope of this loading guide.\n\n\n\nLoRAs also need to be used with another model:\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16).to(\"cuda\")\n```\n\nThen use the [`~loaders.LoraLoaderMixin.load_lora_weights`] method to load the [ostris/super-cereal-sdxl-lora](https://huggingface.co/ostris/super-cereal-sdxl-lora) weights and specify the weights filename from the repository:\n\n```py\npipeline.load_lora_weights(\"ostris/super-cereal-sdxl-lora\", weight_name=\"cereal_box_sdxl_v1.safetensors\")\nprompt = \"bears, pizza bites\"\nimage = pipeline(prompt).images[0]\nimage\n```\n\n
\n \n
\n\nThe [`~loaders.LoraLoaderMixin.load_lora_weights`] method loads LoRA weights into both the UNet and text encoder. It is the preferred way for loading LoRAs because it can handle cases where:\n\n- the LoRA weights don't have separate identifiers for the UNet and text encoder\n- the LoRA weights have separate identifiers for the UNet and text encoder\n\nBut if you only need to load LoRA weights into the UNet, then you can use the [`~loaders.UNet2DConditionLoadersMixin.load_attn_procs`] method. Let's load the [jbilcke-hf/sdxl-cinematic-1](https://huggingface.co/jbilcke-hf/sdxl-cinematic-1) LoRA:\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16).to(\"cuda\")\npipeline.unet.load_attn_procs(\"jbilcke-hf/sdxl-cinematic-1\", weight_name=\"pytorch_lora_weights.safetensors\")\n\n# use cnmt in the prompt to trigger the LoRA\nprompt = \"A cute cnmt eating a slice of pizza, stunning color scheme, masterpiece, illustration\"\nimage = pipeline(prompt).images[0]\nimage\n```\n\n
\n \n
\n\nTo unload the LoRA weights, use the [`~loaders.LoraLoaderMixin.unload_lora_weights`] method to discard the LoRA weights and restore the model to its original weights:\n\n```py\npipeline.unload_lora_weights()\n```\n\n### Adjust LoRA weight scale\n\nFor both [`~loaders.LoraLoaderMixin.load_lora_weights`] and [`~loaders.UNet2DConditionLoadersMixin.load_attn_procs`], you can pass the `cross_attention_kwargs={\"scale\": 0.5}` parameter to adjust how much of the LoRA weights to use. A value of `0` is the same as only using the base model weights, and a value of `1` is equivalent to using the fully finetuned LoRA.\n\nFor more granular control on the amount of LoRA weights used per layer, you can use [`~loaders.LoraLoaderMixin.set_adapters`] and pass a dictionary specifying by how much to scale the weights in each layer by.\n```python\npipe = ... # create pipeline\npipe.load_lora_weights(..., adapter_name=\"my_adapter\") \nscales = {\n \"text_encoder\": 0.5,\n \"text_encoder_2\": 0.5, # only usable if pipe has a 2nd text encoder\n \"unet\": {\n \"down\": 0.9, # all transformers in the down-part will use scale 0.9\n # \"mid\" # in this example \"mid\" is not given, therefore all transformers in the mid part will use the default scale 1.0\n \"up\": {\n \"block_0\": 0.6, # all 3 transformers in the 0th block in the up-part will use scale 0.6\n \"block_1\": [0.4, 0.8, 1.0], # the 3 transformers in the 1st block in the up-part will use scales 0.4, 0.8 and 1.0 respectively\n }\n }\n}\npipe.set_adapters(\"my_adapter\", scales)\n```\n\nThis also works with multiple adapters - see [this guide](https://huggingface.co/docs/diffusers/tutorials/using_peft_for_inference#customize-adapters-strength) for how to do it.\n\n\n\nCurrently, [`~loaders.LoraLoaderMixin.set_adapters`] only supports scaling attention weights. If a LoRA has other parts (e.g., resnets or down-/upsamplers), they will keep a scale of 1.0.\n\n\n\n### Kohya and TheLastBen\n\nOther popular LoRA trainers from the community include those by [Kohya](https://github.com/kohya-ss/sd-scripts/) and [TheLastBen](https://github.com/TheLastBen/fast-stable-diffusion). These trainers create different LoRA checkpoints than those trained by 🤗 Diffusers, but they can still be loaded in the same way.\n\n\n\n\nTo load a Kohya LoRA, let's download the [Blueprintify SD XL 1.0](https://civitai.com/models/150986/blueprintify-sd-xl-10) checkpoint from [Civitai](https://civitai.com/) as an example:\n\n```sh\n!wget https://civitai.com/api/download/models/168776 -O blueprintify-sd-xl-10.safetensors\n```\n\nLoad the LoRA checkpoint with the [`~loaders.LoraLoaderMixin.load_lora_weights`] method, and specify the filename in the `weight_name` parameter:\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16).to(\"cuda\")\npipeline.load_lora_weights(\"path/to/weights\", weight_name=\"blueprintify-sd-xl-10.safetensors\")\n```\n\nGenerate an image:\n\n```py\n# use bl3uprint in the prompt to trigger the LoRA\nprompt = \"bl3uprint, a highly detailed blueprint of the eiffel tower, explaining how to build all parts, many txt, blueprint grid backdrop\"\nimage = pipeline(prompt).images[0]\nimage\n```\n\n\n\nSome limitations of using Kohya LoRAs with 🤗 Diffusers include:\n\n- Images may not look like those generated by UIs - like ComfyUI - for multiple reasons, which are explained [here](https://github.com/huggingface/diffusers/pull/4287/#issuecomment-1655110736).\n- [LyCORIS checkpoints](https://github.com/KohakuBlueleaf/LyCORIS) aren't fully supported. The [`~loaders.LoraLoaderMixin.load_lora_weights`] method loads LyCORIS checkpoints with LoRA and LoCon modules, but Hada and LoKR are not supported.\n\n\n\n\n\n\nLoading a checkpoint from TheLastBen is very similar. For example, to load the [TheLastBen/William_Eggleston_Style_SDXL](https://huggingface.co/TheLastBen/William_Eggleston_Style_SDXL) checkpoint:\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16).to(\"cuda\")\npipeline.load_lora_weights(\"TheLastBen/William_Eggleston_Style_SDXL\", weight_name=\"wegg.safetensors\")\n\n# use by william eggleston in the prompt to trigger the LoRA\nprompt = \"a house by william eggleston, sunrays, beautiful, sunlight, sunrays, beautiful\"\nimage = pipeline(prompt=prompt).images[0]\nimage\n```\n\n\n\n\n## IP-Adapter\n\n[IP-Adapter](https://ip-adapter.github.io/) is a lightweight adapter that enables image prompting for any diffusion model. This adapter works by decoupling the cross-attention layers of the image and text features. All the other model components are frozen and only the embedded image features in the UNet are trained. As a result, IP-Adapter files are typically only ~100MBs.\n\nYou can learn more about how to use IP-Adapter for different tasks and specific use cases in the [IP-Adapter](../using-diffusers/ip_adapter) guide.\n\n> [!TIP]\n> Diffusers currently only supports IP-Adapter for some of the most popular pipelines. Feel free to open a feature request if you have a cool use case and want to integrate IP-Adapter with an unsupported pipeline!\n> Official IP-Adapter checkpoints are available from [h94/IP-Adapter](https://huggingface.co/h94/IP-Adapter).\n\nTo start, load a Stable Diffusion checkpoint.\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\nfrom diffusers.utils import load_image\n\npipeline = AutoPipelineForText2Image.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16).to(\"cuda\")\n```\n\nThen load the IP-Adapter weights and add it to the pipeline with the [`~loaders.IPAdapterMixin.load_ip_adapter`] method.\n\n```py\npipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"models\", weight_name=\"ip-adapter_sd15.bin\")\n```\n\nOnce loaded, you can use the pipeline with an image and text prompt to guide the image generation process.\n\n```py\nimage = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/load_neg_embed.png\")\ngenerator = torch.Generator(device=\"cpu\").manual_seed(33)\nimages = pipeline(\n    prompt='best quality, high quality, wearing sunglasses',\n    ip_adapter_image=image,\n    negative_prompt=\"monochrome, lowres, bad anatomy, worst quality, low quality\",\n    num_inference_steps=50,\n    generator=generator,\n).images[0]\nimages\n```\n\n
\n    \n
\n\n### IP-Adapter Plus\n\nIP-Adapter relies on an image encoder to generate image features. If the IP-Adapter repository contains an `image_encoder` subfolder, the image encoder is automatically loaded and registered to the pipeline. Otherwise, you'll need to explicitly load the image encoder with a [`~transformers.CLIPVisionModelWithProjection`] model and pass it to the pipeline.\n\nThis is the case for *IP-Adapter Plus* checkpoints which use the ViT-H image encoder.\n\n```py\nfrom transformers import CLIPVisionModelWithProjection\n\nimage_encoder = CLIPVisionModelWithProjection.from_pretrained(\n \"h94/IP-Adapter\",\n subfolder=\"models/image_encoder\",\n torch_dtype=torch.float16\n)\n\npipeline = AutoPipelineForText2Image.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n image_encoder=image_encoder,\n torch_dtype=torch.float16\n).to(\"cuda\")\n\npipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"sdxl_models\", weight_name=\"ip-adapter-plus_sdxl_vit-h.safetensors\")\n```\n\n### IP-Adapter Face ID models\n\nThe IP-Adapter FaceID models are experimental IP Adapters that use image embeddings generated by `insightface` instead of CLIP image embeddings. Some of these models also use LoRA to improve ID consistency.\nYou need to install `insightface` and all its requirements to use these models.\n\n\nAs InsightFace pretrained models are available for non-commercial research purposes, IP-Adapter-FaceID models are released exclusively for research purposes and are not intended for commercial use.\n\n\n```py\npipeline = AutoPipelineForText2Image.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n torch_dtype=torch.float16\n).to(\"cuda\")\n\npipeline.load_ip_adapter(\"h94/IP-Adapter-FaceID\", subfolder=None, weight_name=\"ip-adapter-faceid_sdxl.bin\", image_encoder_folder=None)\n```\n\nIf you want to use one of the two IP-Adapter FaceID Plus models, you must also load the CLIP image encoder, as this models use both `insightface` and CLIP image embeddings to achieve better photorealism.\n\n```py\nfrom transformers import CLIPVisionModelWithProjection\n\nimage_encoder = CLIPVisionModelWithProjection.from_pretrained(\n \"laion/CLIP-ViT-H-14-laion2B-s32B-b79K\",\n torch_dtype=torch.float16,\n)\n\npipeline = AutoPipelineForText2Image.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n image_encoder=image_encoder,\n torch_dtype=torch.float16\n).to(\"cuda\")\n\npipeline.load_ip_adapter(\"h94/IP-Adapter-FaceID\", subfolder=None, weight_name=\"ip-adapter-faceid-plus_sd15.bin\")\n```\n" }, { "path": "diffusers/docs/source/en/using-diffusers/marigold_usage.md", "content": "\n\n# Marigold Pipelines for Computer Vision Tasks\n\n[Marigold](../api/pipelines/marigold) is a novel diffusion-based dense prediction approach, and a set of pipelines for various computer vision tasks, such as monocular depth estimation.\n\nThis guide will show you how to use Marigold to obtain fast and high-quality predictions for images and videos.\n\nEach pipeline supports one Computer Vision task, which takes an input RGB image as input and produces a *prediction* of the modality of interest, such as a depth map of the input image.\nCurrently, the following tasks are implemented:\n\n| Pipeline | Predicted Modalities | Demos |\n|---------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------:|\n| [MarigoldDepthPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/marigold/pipeline_marigold_depth.py) | [Depth](https://en.wikipedia.org/wiki/Depth_map), [Disparity](https://en.wikipedia.org/wiki/Binocular_disparity) | [Fast Demo (LCM)](https://huggingface.co/spaces/prs-eth/marigold-lcm), [Slow Original Demo (DDIM)](https://huggingface.co/spaces/prs-eth/marigold) |\n| [MarigoldNormalsPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/marigold/pipeline_marigold_normals.py) | [Surface normals](https://en.wikipedia.org/wiki/Normal_mapping) | [Fast Demo (LCM)](https://huggingface.co/spaces/prs-eth/marigold-normals-lcm) |\n\nThe original checkpoints can be found under the [PRS-ETH](https://huggingface.co/prs-eth/) Hugging Face organization.\nThese checkpoints are meant to work with diffusers pipelines and the [original codebase](https://github.com/prs-eth/marigold).\nThe original code can also be used to train new checkpoints.\n\n| Checkpoint | Modality | Comment |\n|-----------------------------------------------------------------------------------------------|----------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [prs-eth/marigold-v1-0](https://huggingface.co/prs-eth/marigold-v1-0) | Depth | The first Marigold Depth checkpoint, which predicts *affine-invariant depth* maps. The performance of this checkpoint in benchmarks was studied in the original [paper](https://huggingface.co/papers/2312.02145). Designed to be used with the `DDIMScheduler` at inference, it requires at least 10 steps to get reliable predictions. Affine-invariant depth prediction has a range of values in each pixel between 0 (near plane) and 1 (far plane); both planes are chosen by the model as part of the inference process. See the `MarigoldImageProcessor` reference for visualization utilities. |\n| [prs-eth/marigold-depth-lcm-v1-0](https://huggingface.co/prs-eth/marigold-depth-lcm-v1-0) | Depth | The fast Marigold Depth checkpoint, fine-tuned from `prs-eth/marigold-v1-0`. Designed to be used with the `LCMScheduler` at inference, it requires as little as 1 step to get reliable predictions. The prediction reliability saturates at 4 steps and declines after that. |\n| [prs-eth/marigold-normals-v0-1](https://huggingface.co/prs-eth/marigold-normals-v0-1) | Normals | A preview checkpoint for the Marigold Normals pipeline. Designed to be used with the `DDIMScheduler` at inference, it requires at least 10 steps to get reliable predictions. The surface normals predictions are unit-length 3D vectors with values in the range from -1 to 1. *This checkpoint will be phased out after the release of `v1-0` version.* |\n| [prs-eth/marigold-normals-lcm-v0-1](https://huggingface.co/prs-eth/marigold-normals-lcm-v0-1) | Normals | The fast Marigold Normals checkpoint, fine-tuned from `prs-eth/marigold-normals-v0-1`. Designed to be used with the `LCMScheduler` at inference, it requires as little as 1 step to get reliable predictions. The prediction reliability saturates at 4 steps and declines after that. *This checkpoint will be phased out after the release of `v1-0` version.* |\nThe examples below are mostly given for depth prediction, but they can be universally applied with other supported modalities.\nWe showcase the predictions using the same input image of Albert Einstein generated by Midjourney.\nThis makes it easier to compare visualizations of the predictions across various modalities and checkpoints.\n\n
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\n Example input image for all Marigold pipelines\n
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\n\n### Depth Prediction Quick Start\n\nTo get the first depth prediction, load `prs-eth/marigold-depth-lcm-v1-0` checkpoint into `MarigoldDepthPipeline` pipeline, put the image through the pipeline, and save the predictions:\n\n```python\nimport diffusers\nimport torch\n\npipe = diffusers.MarigoldDepthPipeline.from_pretrained(\n \"prs-eth/marigold-depth-lcm-v1-0\", variant=\"fp16\", torch_dtype=torch.float16\n).to(\"cuda\")\n\nimage = diffusers.utils.load_image(\"https://marigoldmonodepth.github.io/images/einstein.jpg\")\ndepth = pipe(image)\n\nvis = pipe.image_processor.visualize_depth(depth.prediction)\nvis[0].save(\"einstein_depth.png\")\n\ndepth_16bit = pipe.image_processor.export_depth_to_16bit_png(depth.prediction)\ndepth_16bit[0].save(\"einstein_depth_16bit.png\")\n```\n\nThe visualization function for depth [`~pipelines.marigold.marigold_image_processing.MarigoldImageProcessor.visualize_depth`] applies one of [matplotlib's colormaps](https://matplotlib.org/stable/users/explain/colors/colormaps.html) (`Spectral` by default) to map the predicted pixel values from a single-channel `[0, 1]` depth range into an RGB image.\nWith the `Spectral` colormap, pixels with near depth are painted red, and far pixels are assigned blue color.\nThe 16-bit PNG file stores the single channel values mapped linearly from the `[0, 1]` range into `[0, 65535]`.\nBelow are the raw and the visualized predictions; as can be seen, dark areas (mustache) are easier to distinguish in the visualization:\n\n
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\n Predicted depth (16-bit PNG)\n
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\n Predicted depth visualization (Spectral)\n
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\n\n### Surface Normals Prediction Quick Start\n\nLoad `prs-eth/marigold-normals-lcm-v0-1` checkpoint into `MarigoldNormalsPipeline` pipeline, put the image through the pipeline, and save the predictions:\n\n```python\nimport diffusers\nimport torch\n\npipe = diffusers.MarigoldNormalsPipeline.from_pretrained(\n \"prs-eth/marigold-normals-lcm-v0-1\", variant=\"fp16\", torch_dtype=torch.float16\n).to(\"cuda\")\n\nimage = diffusers.utils.load_image(\"https://marigoldmonodepth.github.io/images/einstein.jpg\")\nnormals = pipe(image)\n\nvis = pipe.image_processor.visualize_normals(normals.prediction)\nvis[0].save(\"einstein_normals.png\")\n```\n\nThe visualization function for normals [`~pipelines.marigold.marigold_image_processing.MarigoldImageProcessor.visualize_normals`] maps the three-dimensional prediction with pixel values in the range `[-1, 1]` into an RGB image.\nThe visualization function supports flipping surface normals axes to make the visualization compatible with other choices of the frame of reference.\nConceptually, each pixel is painted according to the surface normal vector in the frame of reference, where `X` axis points right, `Y` axis points up, and `Z` axis points at the viewer.\nBelow is the visualized prediction:\n\n
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\n Predicted surface normals visualization\n
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\n\nIn this example, the nose tip almost certainly has a point on the surface, in which the surface normal vector points straight at the viewer, meaning that its coordinates are `[0, 0, 1]`.\nThis vector maps to the RGB `[128, 128, 255]`, which corresponds to the violet-blue color.\nSimilarly, a surface normal on the cheek in the right part of the image has a large `X` component, which increases the red hue.\nPoints on the shoulders pointing up with a large `Y` promote green color.\n\n### Speeding up inference\n\nThe above quick start snippets are already optimized for speed: they load the LCM checkpoint, use the `fp16` variant of weights and computation, and perform just one denoising diffusion step.\nThe `pipe(image)` call completes in 280ms on RTX 3090 GPU.\nInternally, the input image is encoded with the Stable Diffusion VAE encoder, then the U-Net performs one denoising step, and finally, the prediction latent is decoded with the VAE decoder into pixel space.\nIn this case, two out of three module calls are dedicated to converting between pixel and latent space of LDM.\nBecause Marigold's latent space is compatible with the base Stable Diffusion, it is possible to speed up the pipeline call by more than 3x (85ms on RTX 3090) by using a [lightweight replacement of the SD VAE](../api/models/autoencoder_tiny):\n\n```diff\n import diffusers\n import torch\n\n pipe = diffusers.MarigoldDepthPipeline.from_pretrained(\n \"prs-eth/marigold-depth-lcm-v1-0\", variant=\"fp16\", torch_dtype=torch.float16\n ).to(\"cuda\")\n\n+ pipe.vae = diffusers.AutoencoderTiny.from_pretrained(\n+ \"madebyollin/taesd\", torch_dtype=torch.float16\n+ ).cuda()\n\n image = diffusers.utils.load_image(\"https://marigoldmonodepth.github.io/images/einstein.jpg\")\n depth = pipe(image)\n```\n\nAs suggested in [Optimizations](../optimization/torch2.0#torch.compile), adding `torch.compile` may squeeze extra performance depending on the target hardware:\n\n```diff\n import diffusers\n import torch\n\n pipe = diffusers.MarigoldDepthPipeline.from_pretrained(\n \"prs-eth/marigold-depth-lcm-v1-0\", variant=\"fp16\", torch_dtype=torch.float16\n ).to(\"cuda\")\n\n+ pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n\n image = diffusers.utils.load_image(\"https://marigoldmonodepth.github.io/images/einstein.jpg\")\n depth = pipe(image)\n```\n\n## Qualitative Comparison with Depth Anything\n\nWith the above speed optimizations, Marigold delivers predictions with more details and faster than [Depth Anything](https://huggingface.co/docs/transformers/main/en/model_doc/depth_anything) with the largest checkpoint [LiheYoung/depth-anything-large-hf](https://huggingface.co/LiheYoung/depth-anything-large-hf):\n\n
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\n Marigold LCM fp16 with Tiny AutoEncoder\n
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\n Depth Anything Large\n
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\n\n## Maximizing Precision and Ensembling\n\nMarigold pipelines have a built-in ensembling mechanism combining multiple predictions from different random latents.\nThis is a brute-force way of improving the precision of predictions, capitalizing on the generative nature of diffusion.\nThe ensembling path is activated automatically when the `ensemble_size` argument is set greater than `1`.\nWhen aiming for maximum precision, it makes sense to adjust `num_inference_steps` simultaneously with `ensemble_size`.\nThe recommended values vary across checkpoints but primarily depend on the scheduler type.\nThe effect of ensembling is particularly well-seen with surface normals:\n\n```python\nimport diffusers\n\nmodel_path = \"prs-eth/marigold-normals-v1-0\"\n\nmodel_paper_kwargs = {\n\tdiffusers.schedulers.DDIMScheduler: {\n\t\t\"num_inference_steps\": 10,\n\t\t\"ensemble_size\": 10,\n\t},\n\tdiffusers.schedulers.LCMScheduler: {\n\t\t\"num_inference_steps\": 4,\n\t\t\"ensemble_size\": 5,\n\t},\n}\n\nimage = diffusers.utils.load_image(\"https://marigoldmonodepth.github.io/images/einstein.jpg\")\n\npipe = diffusers.MarigoldNormalsPipeline.from_pretrained(model_path).to(\"cuda\")\npipe_kwargs = model_paper_kwargs[type(pipe.scheduler)]\n\ndepth = pipe(image, **pipe_kwargs)\n\nvis = pipe.image_processor.visualize_normals(depth.prediction)\nvis[0].save(\"einstein_normals.png\")\n```\n\n
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\n Surface normals, no ensembling\n
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\n Surface normals, with ensembling\n
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\n\nAs can be seen, all areas with fine-grained structurers, such as hair, got more conservative and on average more correct predictions.\nSuch a result is more suitable for precision-sensitive downstream tasks, such as 3D reconstruction.\n\n## Quantitative Evaluation\n\nTo evaluate Marigold quantitatively in standard leaderboards and benchmarks (such as NYU, KITTI, and other datasets), follow the evaluation protocol outlined in the paper: load the full precision fp32 model and use appropriate values for `num_inference_steps` and `ensemble_size`.\nOptionally seed randomness to ensure reproducibility. Maximizing `batch_size` will deliver maximum device utilization.\n\n```python\nimport diffusers\nimport torch\n\ndevice = \"cuda\"\nseed = 2024\nmodel_path = \"prs-eth/marigold-v1-0\"\n\nmodel_paper_kwargs = {\n\tdiffusers.schedulers.DDIMScheduler: {\n\t\t\"num_inference_steps\": 50,\n\t\t\"ensemble_size\": 10,\n\t},\n\tdiffusers.schedulers.LCMScheduler: {\n\t\t\"num_inference_steps\": 4,\n\t\t\"ensemble_size\": 10,\n\t},\n}\n\nimage = diffusers.utils.load_image(\"https://marigoldmonodepth.github.io/images/einstein.jpg\")\n\ngenerator = torch.Generator(device=device).manual_seed(seed)\npipe = diffusers.MarigoldDepthPipeline.from_pretrained(model_path).to(device)\npipe_kwargs = model_paper_kwargs[type(pipe.scheduler)]\n\ndepth = pipe(image, generator=generator, **pipe_kwargs)\n\n# evaluate metrics\n```\n\n## Using Predictive Uncertainty\n\nThe ensembling mechanism built into Marigold pipelines combines multiple predictions obtained from different random latents.\nAs a side effect, it can be used to quantify epistemic (model) uncertainty; simply specify `ensemble_size` greater than 1 and set `output_uncertainty=True`.\nThe resulting uncertainty will be available in the `uncertainty` field of the output.\nIt can be visualized as follows:\n\n```python\nimport diffusers\nimport torch\n\npipe = diffusers.MarigoldDepthPipeline.from_pretrained(\n \"prs-eth/marigold-depth-lcm-v1-0\", variant=\"fp16\", torch_dtype=torch.float16\n).to(\"cuda\")\n\nimage = diffusers.utils.load_image(\"https://marigoldmonodepth.github.io/images/einstein.jpg\")\ndepth = pipe(\n\timage,\n\tensemble_size=10, # any number greater than 1; higher values yield higher precision\n\toutput_uncertainty=True,\n)\n\nuncertainty = pipe.image_processor.visualize_uncertainty(depth.uncertainty)\nuncertainty[0].save(\"einstein_depth_uncertainty.png\")\n```\n\n
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\n Depth uncertainty\n
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\n Surface normals uncertainty\n
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\n\nThe interpretation of uncertainty is easy: higher values (white) correspond to pixels, where the model struggles to make consistent predictions.\nEvidently, the depth model is the least confident around edges with discontinuity, where the object depth changes drastically.\nThe surface normals model is the least confident in fine-grained structures, such as hair, and dark areas, such as the collar.\n\n## Frame-by-frame Video Processing with Temporal Consistency\n\nDue to Marigold's generative nature, each prediction is unique and defined by the random noise sampled for the latent initialization.\nThis becomes an obvious drawback compared to traditional end-to-end dense regression networks, as exemplified in the following videos:\n\n
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Input video
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Marigold Depth applied to input video frames independently
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\n\nTo address this issue, it is possible to pass `latents` argument to the pipelines, which defines the starting point of diffusion.\nEmpirically, we found that a convex combination of the very same starting point noise latent and the latent corresponding to the previous frame prediction give sufficiently smooth results, as implemented in the snippet below:\n\n```python\nimport imageio\nfrom PIL import Image\nfrom tqdm import tqdm\nimport diffusers\nimport torch\n\ndevice = \"cuda\"\npath_in = \"obama.mp4\"\npath_out = \"obama_depth.gif\"\n\npipe = diffusers.MarigoldDepthPipeline.from_pretrained(\n \"prs-eth/marigold-depth-lcm-v1-0\", variant=\"fp16\", torch_dtype=torch.float16\n).to(device)\npipe.vae = diffusers.AutoencoderTiny.from_pretrained(\n \"madebyollin/taesd\", torch_dtype=torch.float16\n).to(device)\npipe.set_progress_bar_config(disable=True)\n\nwith imageio.get_reader(path_in) as reader:\n size = reader.get_meta_data()['size']\n last_frame_latent = None\n latent_common = torch.randn(\n (1, 4, 768 * size[1] // (8 * max(size)), 768 * size[0] // (8 * max(size)))\n ).to(device=device, dtype=torch.float16)\n\n out = []\n for frame_id, frame in tqdm(enumerate(reader), desc=\"Processing Video\"):\n frame = Image.fromarray(frame)\n latents = latent_common\n if last_frame_latent is not None:\n latents = 0.9 * latents + 0.1 * last_frame_latent\n\n depth = pipe(\n\t\t\tframe, match_input_resolution=False, latents=latents, output_latent=True\n )\n last_frame_latent = depth.latent\n out.append(pipe.image_processor.visualize_depth(depth.prediction)[0])\n\n diffusers.utils.export_to_gif(out, path_out, fps=reader.get_meta_data()['fps'])\n```\n\nHere, the diffusion process starts from the given computed latent.\nThe pipeline sets `output_latent=True` to access `out.latent` and computes its contribution to the next frame's latent initialization.\nThe result is much more stable now:\n\n
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Marigold Depth applied to input video frames independently
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Marigold Depth with forced latents initialization
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\n\n## Marigold for ControlNet\n\nA very common application for depth prediction with diffusion models comes in conjunction with ControlNet.\nDepth crispness plays a crucial role in obtaining high-quality results from ControlNet.\nAs seen in comparisons with other methods above, Marigold excels at that task.\nThe snippet below demonstrates how to load an image, compute depth, and pass it into ControlNet in a compatible format:\n\n```python\nimport torch\nimport diffusers\n\ndevice = \"cuda\"\ngenerator = torch.Generator(device=device).manual_seed(2024)\nimage = diffusers.utils.load_image(\n \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_depth_source.png\"\n)\n\npipe = diffusers.MarigoldDepthPipeline.from_pretrained(\n \"prs-eth/marigold-depth-lcm-v1-0\", torch_dtype=torch.float16, variant=\"fp16\"\n).to(device)\n\ndepth_image = pipe(image, generator=generator).prediction\ndepth_image = pipe.image_processor.visualize_depth(depth_image, color_map=\"binary\")\ndepth_image[0].save(\"motorcycle_controlnet_depth.png\")\n\ncontrolnet = diffusers.ControlNetModel.from_pretrained(\n \"diffusers/controlnet-depth-sdxl-1.0\", torch_dtype=torch.float16, variant=\"fp16\"\n).to(device)\npipe = diffusers.StableDiffusionXLControlNetPipeline.from_pretrained(\n \"SG161222/RealVisXL_V4.0\", torch_dtype=torch.float16, variant=\"fp16\", controlnet=controlnet\n).to(device)\npipe.scheduler = diffusers.DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True)\n\ncontrolnet_out = pipe(\n prompt=\"high quality photo of a sports bike, city\",\n negative_prompt=\"\",\n guidance_scale=6.5,\n num_inference_steps=25,\n image=depth_image,\n controlnet_conditioning_scale=0.7,\n control_guidance_end=0.7,\n generator=generator,\n).images\ncontrolnet_out[0].save(\"motorcycle_controlnet_out.png\")\n```\n\n
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\n Depth in the format compatible with ControlNet\n
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\n ControlNet generation, conditioned on depth and prompt: \"high quality photo of a sports bike, city\"\n
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\n\nHopefully, you will find Marigold useful for solving your downstream tasks, be it a part of a more broad generative workflow, or a perception task, such as 3D reconstruction.\n" }, { "path": "diffusers/docs/source/en/using-diffusers/merge_loras.md", "content": "\n\n# Merge LoRAs\n\nIt can be fun and creative to use multiple [LoRAs]((https://huggingface.co/docs/peft/conceptual_guides/adapter#low-rank-adaptation-lora)) together to generate something entirely new and unique. This works by merging multiple LoRA weights together to produce images that are a blend of different styles. Diffusers provides a few methods to merge LoRAs depending on *how* you want to merge their weights, which can affect image quality.\n\nThis guide will show you how to merge LoRAs using the [`~loaders.UNet2DConditionLoadersMixin.set_adapters`] and [`~peft.LoraModel.add_weighted_adapter`] methods. To improve inference speed and reduce memory-usage of merged LoRAs, you'll also see how to use the [`~loaders.LoraLoaderMixin.fuse_lora`] method to fuse the LoRA weights with the original weights of the underlying model.\n\nFor this guide, load a Stable Diffusion XL (SDXL) checkpoint and the [KappaNeuro/studio-ghibli-style]() and [Norod78/sdxl-chalkboarddrawing-lora]() LoRAs with the [`~loaders.LoraLoaderMixin.load_lora_weights`] method. You'll need to assign each LoRA an `adapter_name` to combine them later.\n\n```py\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipeline = DiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16).to(\"cuda\")\npipeline.load_lora_weights(\"ostris/ikea-instructions-lora-sdxl\", weight_name=\"ikea_instructions_xl_v1_5.safetensors\", adapter_name=\"ikea\")\npipeline.load_lora_weights(\"lordjia/by-feng-zikai\", weight_name=\"fengzikai_v1.0_XL.safetensors\", adapter_name=\"feng\")\n```\n\n## set_adapters\n\nThe [`~loaders.UNet2DConditionLoadersMixin.set_adapters`] method merges LoRA adapters by concatenating their weighted matrices. Use the adapter name to specify which LoRAs to merge, and the `adapter_weights` parameter to control the scaling for each LoRA. For example, if `adapter_weights=[0.5, 0.5]`, then the merged LoRA output is an average of both LoRAs. Try adjusting the adapter weights to see how it affects the generated image!\n\n```py\npipeline.set_adapters([\"ikea\", \"feng\"], adapter_weights=[0.7, 0.8])\n\ngenerator = torch.manual_seed(0)\nprompt = \"A bowl of ramen shaped like a cute kawaii bear, by Feng Zikai\"\nimage = pipeline(prompt, generator=generator, cross_attention_kwargs={\"scale\": 1.0}).images[0]\nimage\n```\n\n
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\n\n## add_weighted_adapter\n\n> [!WARNING]\n> This is an experimental method that adds PEFTs [`~peft.LoraModel.add_weighted_adapter`] method to Diffusers to enable more efficient merging methods. Check out this [issue](https://github.com/huggingface/diffusers/issues/6892) if you're interested in learning more about the motivation and design behind this integration.\n\nThe [`~peft.LoraModel.add_weighted_adapter`] method provides access to more efficient merging method such as [TIES and DARE](https://huggingface.co/docs/peft/developer_guides/model_merging). To use these merging methods, make sure you have the latest stable version of Diffusers and PEFT installed.\n\n```bash\npip install -U diffusers peft\n```\n\nThere are three steps to merge LoRAs with the [`~peft.LoraModel.add_weighted_adapter`] method:\n\n1. Create a [`~peft.PeftModel`] from the underlying model and LoRA checkpoint.\n2. Load a base UNet model and the LoRA adapters.\n3. Merge the adapters using the [`~peft.LoraModel.add_weighted_adapter`] method and the merging method of your choice.\n\nLet's dive deeper into what these steps entail.\n\n1. Load a UNet that corresponds to the UNet in the LoRA checkpoint. In this case, both LoRAs use the SDXL UNet as their base model.\n\n```python\nfrom diffusers import UNet2DConditionModel\nimport torch\n\nunet = UNet2DConditionModel.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n torch_dtype=torch.float16, \n use_safetensors=True,\n variant=\"fp16\",\n subfolder=\"unet\",\n).to(\"cuda\")\n```\n\nLoad the SDXL pipeline and the LoRA checkpoints, starting with the [ostris/ikea-instructions-lora-sdxl](https://huggingface.co/ostris/ikea-instructions-lora-sdxl) LoRA.\n\n```python\nfrom diffusers import DiffusionPipeline\n\npipeline = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n variant=\"fp16\",\n torch_dtype=torch.float16,\n unet=unet\n).to(\"cuda\")\npipeline.load_lora_weights(\"ostris/ikea-instructions-lora-sdxl\", weight_name=\"ikea_instructions_xl_v1_5.safetensors\", adapter_name=\"ikea\")\n```\n\nNow you'll create a [`~peft.PeftModel`] from the loaded LoRA checkpoint by combining the SDXL UNet and the LoRA UNet from the pipeline.\n\n```python\nfrom peft import get_peft_model, LoraConfig\nimport copy\n\nsdxl_unet = copy.deepcopy(unet)\nikea_peft_model = get_peft_model(\n sdxl_unet,\n pipeline.unet.peft_config[\"ikea\"],\n adapter_name=\"ikea\"\n)\n\noriginal_state_dict = {f\"base_model.model.{k}\": v for k, v in pipeline.unet.state_dict().items()}\nikea_peft_model.load_state_dict(original_state_dict, strict=True)\n```\n\n> [!TIP]\n> You can optionally push the ikea_peft_model to the Hub by calling `ikea_peft_model.push_to_hub(\"ikea_peft_model\", token=TOKEN)`.\n\nRepeat this process to create a [`~peft.PeftModel`] from the [lordjia/by-feng-zikai](https://huggingface.co/lordjia/by-feng-zikai) LoRA.\n\n```python\npipeline.delete_adapters(\"ikea\")\nsdxl_unet.delete_adapters(\"ikea\")\n\npipeline.load_lora_weights(\"lordjia/by-feng-zikai\", weight_name=\"fengzikai_v1.0_XL.safetensors\", adapter_name=\"feng\")\npipeline.set_adapters(adapter_names=\"feng\")\n\nfeng_peft_model = get_peft_model(\n sdxl_unet,\n pipeline.unet.peft_config[\"feng\"],\n adapter_name=\"feng\"\n)\n\noriginal_state_dict = {f\"base_model.model.{k}\": v for k, v in pipe.unet.state_dict().items()}\nfeng_peft_model.load_state_dict(original_state_dict, strict=True)\n```\n\n2. Load a base UNet model and then load the adapters onto it.\n\n```python\nfrom peft import PeftModel\n\nbase_unet = UNet2DConditionModel.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n torch_dtype=torch.float16, \n use_safetensors=True,\n variant=\"fp16\",\n subfolder=\"unet\",\n).to(\"cuda\")\n\nmodel = PeftModel.from_pretrained(base_unet, \"stevhliu/ikea_peft_model\", use_safetensors=True, subfolder=\"ikea\", adapter_name=\"ikea\")\nmodel.load_adapter(\"stevhliu/feng_peft_model\", use_safetensors=True, subfolder=\"feng\", adapter_name=\"feng\")\n```\n\n3. Merge the adapters using the [`~peft.LoraModel.add_weighted_adapter`] method and the merging method of your choice (learn more about other merging methods in this [blog post](https://huggingface.co/blog/peft_merging)). For this example, let's use the `\"dare_linear\"` method to merge the LoRAs.\n\n> [!WARNING]\n> Keep in mind the LoRAs need to have the same rank to be merged!\n\n```python\nmodel.add_weighted_adapter(\n adapters=[\"ikea\", \"feng\"],\n weights=[1.0, 1.0],\n combination_type=\"dare_linear\",\n adapter_name=\"ikea-feng\"\n)\nmodel.set_adapters(\"ikea-feng\")\n```\n\nNow you can generate an image with the merged LoRA.\n\n```python\nmodel = model.to(dtype=torch.float16, device=\"cuda\")\n\npipeline = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", unet=model, variant=\"fp16\", torch_dtype=torch.float16,\n).to(\"cuda\")\n\nimage = pipeline(\"A bowl of ramen shaped like a cute kawaii bear, by Feng Zikai\", generator=torch.manual_seed(0)).images[0]\nimage\n```\n\n
\n \n
\n\n## fuse_lora\n\nBoth the [`~loaders.UNet2DConditionLoadersMixin.set_adapters`] and [`~peft.LoraModel.add_weighted_adapter`] methods require loading the base model and the LoRA adapters separately which incurs some overhead. The [`~loaders.LoraLoaderMixin.fuse_lora`] method allows you to fuse the LoRA weights directly with the original weights of the underlying model. This way, you're only loading the model once which can increase inference and lower memory-usage.\n\nYou can use PEFT to easily fuse/unfuse multiple adapters directly into the model weights (both UNet and text encoder) using the [`~loaders.LoraLoaderMixin.fuse_lora`] method, which can lead to a speed-up in inference and lower VRAM usage.\n\nFor example, if you have a base model and adapters loaded and set as active with the following adapter weights:\n\n```py\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipeline = DiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16).to(\"cuda\")\npipeline.load_lora_weights(\"ostris/ikea-instructions-lora-sdxl\", weight_name=\"ikea_instructions_xl_v1_5.safetensors\", adapter_name=\"ikea\")\npipeline.load_lora_weights(\"lordjia/by-feng-zikai\", weight_name=\"fengzikai_v1.0_XL.safetensors\", adapter_name=\"feng\")\n\npipeline.set_adapters([\"ikea\", \"feng\"], adapter_weights=[0.7, 0.8])\n```\n\nFuse these LoRAs into the UNet with the [`~loaders.LoraLoaderMixin.fuse_lora`] method. The `lora_scale` parameter controls how much to scale the output by with the LoRA weights. It is important to make the `lora_scale` adjustments in the [`~loaders.LoraLoaderMixin.fuse_lora`] method because it won’t work if you try to pass `scale` to the `cross_attention_kwargs` in the pipeline.\n\n```py\npipeline.fuse_lora(adapter_names=[\"ikea\", \"feng\"], lora_scale=1.0)\n```\n\nThen you should use [`~loaders.LoraLoaderMixin.unload_lora_weights`] to unload the LoRA weights since they've already been fused with the underlying base model. Finally, call [`~DiffusionPipeline.save_pretrained`] to save the fused pipeline locally or you could call [`~DiffusionPipeline.push_to_hub`] to push the fused pipeline to the Hub.\n\n```py\npipeline.unload_lora_weights()\n# save locally\npipeline.save_pretrained(\"path/to/fused-pipeline\")\n# save to the Hub\npipeline.push_to_hub(\"fused-ikea-feng\")\n```\n\nNow you can quickly load the fused pipeline and use it for inference without needing to separately load the LoRA adapters.\n\n```py\npipeline = DiffusionPipeline.from_pretrained(\n \"username/fused-ikea-feng\", torch_dtype=torch.float16,\n).to(\"cuda\")\n\nimage = pipeline(\"A bowl of ramen shaped like a cute kawaii bear, by Feng Zikai\", generator=torch.manual_seed(0)).images[0]\nimage\n```\n\nYou can call [`~loaders.LoraLoaderMixin.unfuse_lora`] to restore the original model's weights (for example, if you want to use a different `lora_scale` value). However, this only works if you've only fused one LoRA adapter to the original model. If you've fused multiple LoRAs, you'll need to reload the model.\n\n```py\npipeline.unfuse_lora()\n```\n\n### torch.compile\n\n[torch.compile](../optimization/torch2.0#torchcompile) can speed up your pipeline even more, but the LoRA weights must be fused first and then unloaded. Typically, the UNet is compiled because it is such a computationally intensive component of the pipeline.\n\n```py\nfrom diffusers import DiffusionPipeline\nimport torch\n\n# load base model and LoRAs\npipeline = DiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16).to(\"cuda\")\npipeline.load_lora_weights(\"ostris/ikea-instructions-lora-sdxl\", weight_name=\"ikea_instructions_xl_v1_5.safetensors\", adapter_name=\"ikea\")\npipeline.load_lora_weights(\"lordjia/by-feng-zikai\", weight_name=\"fengzikai_v1.0_XL.safetensors\", adapter_name=\"feng\")\n\n# activate both LoRAs and set adapter weights\npipeline.set_adapters([\"ikea\", \"feng\"], adapter_weights=[0.7, 0.8])\n\n# fuse LoRAs and unload weights\npipeline.fuse_lora(adapter_names=[\"ikea\", \"feng\"], lora_scale=1.0)\npipeline.unload_lora_weights()\n\n# torch.compile\npipeline.unet.to(memory_format=torch.channels_last)\npipeline.unet = torch.compile(pipeline.unet, mode=\"reduce-overhead\", fullgraph=True)\n\nimage = pipeline(\"A bowl of ramen shaped like a cute kawaii bear, by Feng Zikai\", generator=torch.manual_seed(0)).images[0]\n```\n\nLearn more about torch.compile in the [Accelerate inference of text-to-image diffusion models](../tutorials/fast_diffusion#torchcompile) guide.\n\n## Next steps\n\nFor more conceptual details about how each merging method works, take a look at the [🤗 PEFT welcomes new merging methods](https://huggingface.co/blog/peft_merging#concatenation-cat) blog post!\n" }, { "path": "diffusers/docs/source/en/using-diffusers/other-formats.md", "content": "\n\n# Model files and layouts\n\n[[open-in-colab]]\n\nDiffusion models are saved in various file types and organized in different layouts. Diffusers stores model weights as safetensors files in *Diffusers-multifolder* layout and it also supports loading files (like safetensors and ckpt files) from a *single-file* layout which is commonly used in the diffusion ecosystem.\n\nEach layout has its own benefits and use cases, and this guide will show you how to load the different files and layouts, and how to convert them.\n\n## Files\n\nPyTorch model weights are typically saved with Python's [pickle](https://docs.python.org/3/library/pickle.html) utility as ckpt or bin files. However, pickle is not secure and pickled files may contain malicious code that can be executed. This vulnerability is a serious concern given the popularity of model sharing. To address this security issue, the [Safetensors](https://hf.co/docs/safetensors) library was developed as a secure alternative to pickle, which saves models as safetensors files.\n\n### safetensors\n\n> [!TIP]\n> Learn more about the design decisions and why safetensor files are preferred for saving and loading model weights in the [Safetensors audited as really safe and becoming the default](https://blog.eleuther.ai/safetensors-security-audit/) blog post.\n\n[Safetensors](https://hf.co/docs/safetensors) is a safe and fast file format for securely storing and loading tensors. Safetensors restricts the header size to limit certain types of attacks, supports lazy loading (useful for distributed setups), and has generally faster loading speeds.\n\nMake sure you have the [Safetensors](https://hf.co/docs/safetensors) library installed.\n\n```py\n!pip install safetensors\n```\n\nSafetensors stores weights in a safetensors file. Diffusers loads safetensors files by default if they're available and the Safetensors library is installed. There are two ways safetensors files can be organized:\n\n1. Diffusers-multifolder layout: there may be several separate safetensors files, one for each pipeline component (text encoder, UNet, VAE), organized in subfolders (check out the [runwayml/stable-diffusion-v1-5](https://hf.co/runwayml/stable-diffusion-v1-5/tree/main) repository as an example)\n2. single-file layout: all the model weights may be saved in a single file (check out the [WarriorMama777/OrangeMixs](https://hf.co/WarriorMama777/OrangeMixs/tree/main/Models/AbyssOrangeMix) repository as an example)\n\n\n\n\nUse the [`~DiffusionPipeline.from_pretrained`] method to load a model with safetensors files stored in multiple folders.\n\n```py\nfrom diffusers import DiffusionPipeline\n\npipeline = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n use_safetensors=True\n)\n```\n\n\n\n\nUse the [`~loaders.FromSingleFileMixin.from_single_file`] method to load a model with all the weights stored in a single safetensors file.\n\n```py\nfrom diffusers import StableDiffusionPipeline\n\npipeline = StableDiffusionPipeline.from_single_file(\n \"https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix/AbyssOrangeMix.safetensors\"\n)\n```\n\n\n\n\n#### LoRA files\n\n[LoRA](https://hf.co/docs/peft/conceptual_guides/adapter#low-rank-adaptation-lora) is a lightweight adapter that is fast and easy to train, making them especially popular for generating images in a certain way or style. These adapters are commonly stored in a safetensors file, and are widely popular on model sharing platforms like [civitai](https://civitai.com/).\n\nLoRAs are loaded into a base model with the [`~loaders.LoraLoaderMixin.load_lora_weights`] method.\n\n```py\nfrom diffusers import StableDiffusionXLPipeline\nimport torch\n\n# base model\npipeline = StableDiffusionXLPipeline.from_pretrained(\n \"Lykon/dreamshaper-xl-1-0\", torch_dtype=torch.float16, variant=\"fp16\"\n).to(\"cuda\")\n\n# download LoRA weights\n!wget https://civitai.com/api/download/models/168776 -O blueprintify.safetensors\n\n# load LoRA weights\npipeline.load_lora_weights(\".\", weight_name=\"blueprintify.safetensors\")\nprompt = \"bl3uprint, a highly detailed blueprint of the empire state building, explaining how to build all parts, many txt, blueprint grid backdrop\"\nnegative_prompt = \"lowres, cropped, worst quality, low quality, normal quality, artifacts, signature, watermark, username, blurry, more than one bridge, bad architecture\"\n\nimage = pipeline(\n prompt=prompt,\n negative_prompt=negative_prompt,\n generator=torch.manual_seed(0),\n).images[0]\nimage\n```\n\n
\n \n
\n\n### ckpt\n\n> [!WARNING]\n> Pickled files may be unsafe because they can be exploited to execute malicious code. It is recommended to use safetensors files instead where possible, or convert the weights to safetensors files.\n\nPyTorch's [torch.save](https://pytorch.org/docs/stable/generated/torch.save.html) function uses Python's [pickle](https://docs.python.org/3/library/pickle.html) utility to serialize and save models. These files are saved as a ckpt file and they contain the entire model's weights.\n\nUse the [`~loaders.FromSingleFileMixin.from_single_file`] method to directly load a ckpt file.\n\n```py\nfrom diffusers import StableDiffusionPipeline\n\npipeline = StableDiffusionPipeline.from_single_file(\n \"https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned.ckpt\"\n)\n```\n\n## Storage layout\n\nThere are two ways model files are organized, either in a Diffusers-multifolder layout or in a single-file layout. The Diffusers-multifolder layout is the default, and each component file (text encoder, UNet, VAE) is stored in a separate subfolder. Diffusers also supports loading models from a single-file layout where all the components are bundled together.\n\n### Diffusers-multifolder\n\nThe Diffusers-multifolder layout is the default storage layout for Diffusers. Each component's (text encoder, UNet, VAE) weights are stored in a separate subfolder. The weights can be stored as safetensors or ckpt files.\n\n
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multifolder layout
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UNet subfolder
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\n\nTo load from Diffusers-multifolder layout, use the [`~DiffusionPipeline.from_pretrained`] method.\n\n```py\nfrom diffusers import DiffusionPipeline\n\npipeline = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n use_safetensors=True,\n).to(\"cuda\")\n```\n\nBenefits of using the Diffusers-multifolder layout include:\n\n1. Faster to load each component file individually or in parallel.\n2. Reduced memory usage because you only load the components you need. For example, models like [SDXL Turbo](https://hf.co/stabilityai/sdxl-turbo), [SDXL Lightning](https://hf.co/ByteDance/SDXL-Lightning), and [Hyper-SD](https://hf.co/ByteDance/Hyper-SD) have the same components except for the UNet. You can reuse their shared components with the [`~DiffusionPipeline.from_pipe`] method without consuming any additional memory (take a look at the [Reuse a pipeline](./loading#reuse-a-pipeline) guide) and only load the UNet. This way, you don't need to download redundant components and unnecessarily use more memory.\n\n ```py\n import torch\n from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler\n\n # download one model\n sdxl_pipeline = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n use_safetensors=True,\n ).to(\"cuda\")\n\n # switch UNet for another model\n unet = UNet2DConditionModel.from_pretrained(\n \"stabilityai/sdxl-turbo\",\n subfolder=\"unet\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n use_safetensors=True\n )\n # reuse all the same components in new model except for the UNet\n turbo_pipeline = StableDiffusionXLPipeline.from_pipe(\n sdxl_pipeline, unet=unet,\n ).to(\"cuda\")\n turbo_pipeline.scheduler = EulerDiscreteScheduler.from_config(\n turbo_pipeline.scheduler.config,\n timestep+spacing=\"trailing\"\n )\n image = turbo_pipeline(\n \"an astronaut riding a unicorn on mars\",\n num_inference_steps=1,\n guidance_scale=0.0,\n ).images[0]\n image\n ```\n\n3. Reduced storage requirements because if a component, such as the SDXL [VAE](https://hf.co/madebyollin/sdxl-vae-fp16-fix), is shared across multiple models, you only need to download and store a single copy of it instead of downloading and storing it multiple times. For 10 SDXL models, this can save ~3.5GB of storage. The storage savings is even greater for newer models like PixArt Sigma, where the [text encoder](https://hf.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS/tree/main/text_encoder) alone is ~19GB!\n4. Flexibility to replace a component in the model with a newer or better version.\n\n ```py\n from diffusers import DiffusionPipeline, AutoencoderKL\n\n vae = AutoencoderKL.from_pretrained(\"madebyollin/sdxl-vae-fp16-fix\", torch_dtype=torch.float16, use_safetensors=True)\n pipeline = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n vae=vae,\n torch_dtype=torch.float16,\n variant=\"fp16\",\n use_safetensors=True,\n ).to(\"cuda\")\n ```\n\n5. More visibility and information about a model's components, which are stored in a [config.json](https://hf.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/unet/config.json) file in each component subfolder.\n\n### Single-file\n\nThe single-file layout stores all the model weights in a single file. All the model components (text encoder, UNet, VAE) weights are kept together instead of separately in subfolders. This can be a safetensors or ckpt file.\n\n
\n \n
\n\nTo load from a single-file layout, use the [`~loaders.FromSingleFileMixin.from_single_file`] method.\n\n```py\nimport torch\nfrom diffusers import StableDiffusionXLPipeline\n\npipeline = StableDiffusionXLPipeline.from_single_file(\n \"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n use_safetensors=True,\n).to(\"cuda\")\n```\n\nBenefits of using a single-file layout include:\n\n1. Easy compatibility with diffusion interfaces such as [ComfyUI](https://github.com/comfyanonymous/ComfyUI) or [Automatic1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) which commonly use a single-file layout.\n2. Easier to manage (download and share) a single file.\n\n## Convert layout and files\n\nDiffusers provides many scripts and methods to convert storage layouts and file formats to enable broader support across the diffusion ecosystem.\n\nTake a look at the [diffusers/scripts](https://github.com/huggingface/diffusers/tree/main/scripts) collection to find a script that fits your conversion needs.\n\n> [!TIP]\n> Scripts that have \"`to_diffusers`\" appended at the end mean they convert a model to the Diffusers-multifolder layout. Each script has their own specific set of arguments for configuring the conversion, so make sure you check what arguments are available!\n\nFor example, to convert a Stable Diffusion XL model stored in Diffusers-multifolder layout to a single-file layout, run the [convert_diffusers_to_original_sdxl.py](https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_sdxl.py) script. Provide the path to the model to convert, and the path to save the converted model to. You can optionally specify whether you want to save the model as a safetensors file and whether to save the model in half-precision.\n\n```bash\npython convert_diffusers_to_original_sdxl.py --model_path path/to/model/to/convert --checkpoint_path path/to/save/model/to --use_safetensors\n```\n\nYou can also save a model to Diffusers-multifolder layout with the [`~DiffusionPipeline.save_pretrained`] method. This creates a directory for you if it doesn't already exist, and it also saves the files as a safetensors file by default.\n\n```py\nfrom diffusers import StableDiffusionXLPipeline\n\npipeline = StableDiffusionXLPipeline.from_single_file(\n \"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors\",\n)\npipeline.save_pretrained()\n```\n\nLastly, there are also Spaces, such as [SD To Diffusers](https://hf.co/spaces/diffusers/sd-to-diffusers) and [SD-XL To Diffusers](https://hf.co/spaces/diffusers/sdxl-to-diffusers), that provide a more user-friendly interface for converting models to Diffusers-multifolder layout. This is the easiest and most convenient option for converting layouts, and it'll open a PR on your model repository with the converted files. However, this option is not as reliable as running a script, and the Space may fail for more complicated models.\n\n## Single-file layout usage\n\nNow that you're familiar with the differences between the Diffusers-multifolder and single-file layout, this section shows you how to load models and pipeline components, customize configuration options for loading, and load local files with the [`~loaders.FromSingleFileMixin.from_single_file`] method.\n\n### Load a pipeline or model\n\nPass the file path of the pipeline or model to the [`~loaders.FromSingleFileMixin.from_single_file`] method to load it.\n\n\n\n\n```py\nfrom diffusers import StableDiffusionXLPipeline\n\nckpt_path = \"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors\"\npipeline = StableDiffusionXLPipeline.from_single_file(ckpt_path)\n```\n\n\n\n\n```py\nfrom diffusers import StableCascadeUNet\n\nckpt_path = \"https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b_lite.safetensors\"\nmodel = StableCascadeUNet.from_single_file(ckpt_path)\n```\n\n\n\n\nCustomize components in the pipeline by passing them directly to the [`~loaders.FromSingleFileMixin.from_single_file`] method. For example, you can use a different scheduler in a pipeline.\n\n```py\nfrom diffusers import StableDiffusionXLPipeline, DDIMScheduler\n\nckpt_path = \"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors\"\nscheduler = DDIMScheduler()\npipeline = StableDiffusionXLPipeline.from_single_file(ckpt_path, scheduler=scheduler)\n```\n\nOr you could use a ControlNet model in the pipeline.\n\n```py\nfrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel\n\nckpt_path = \"https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors\"\ncontrolnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11p_sd15_canny\")\npipeline = StableDiffusionControlNetPipeline.from_single_file(ckpt_path, controlnet=controlnet)\n```\n\n### Customize configuration options\n\nModels have a configuration file that define their attributes like the number of inputs in a UNet. Pipelines configuration options are available in the pipeline's class. For example, if you look at the [`StableDiffusionXLInstructPix2PixPipeline`] class, there is an option to scale the image latents with the `is_cosxl_edit` parameter.\n\nThese configuration files can be found in the models Hub repository or another location from which the configuration file originated (for example, a GitHub repository or locally on your device).\n\n\n\n\n> [!TIP]\n> The [`~loaders.FromSingleFileMixin.from_single_file`] method automatically maps the checkpoint to the appropriate model repository, but there are cases where it is useful to use the `config` parameter. For example, if the model components in the checkpoint are different from the original checkpoint or if a checkpoint doesn't have the necessary metadata to correctly determine the configuration to use for the pipeline.\n\nThe [`~loaders.FromSingleFileMixin.from_single_file`] method automatically determines the configuration to use from the configuration file in the model repository. You could also explicitly specify the configuration to use by providing the repository id to the `config` parameter.\n\n```py\nfrom diffusers import StableDiffusionXLPipeline\n\nckpt_path = \"https://huggingface.co/segmind/SSD-1B/blob/main/SSD-1B.safetensors\"\nrepo_id = \"segmind/SSD-1B\"\n\npipeline = StableDiffusionXLPipeline.from_single_file(ckpt_path, config=repo_id)\n```\n\nThe model loads the configuration file for the [UNet](https://huggingface.co/segmind/SSD-1B/blob/main/unet/config.json), [VAE](https://huggingface.co/segmind/SSD-1B/blob/main/vae/config.json), and [text encoder](https://huggingface.co/segmind/SSD-1B/blob/main/text_encoder/config.json) from their respective subfolders in the repository.\n\n\n\n\nThe [`~loaders.FromSingleFileMixin.from_single_file`] method can also load the original configuration file of a pipeline that is stored elsewhere. Pass a local path or URL of the original configuration file to the `original_config` parameter.\n\n```py\nfrom diffusers import StableDiffusionXLPipeline\n\nckpt_path = \"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors\"\noriginal_config = \"https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml\"\n\npipeline = StableDiffusionXLPipeline.from_single_file(ckpt_path, original_config=original_config)\n```\n\n> [!TIP]\n> Diffusers attempts to infer the pipeline components based on the type signatures of the pipeline class when you use `original_config` with `local_files_only=True`, instead of fetching the configuration files from the model repository on the Hub. This prevents backward breaking changes in code that can't connect to the internet to fetch the necessary configuration files.\n>\n> This is not as reliable as providing a path to a local model repository with the `config` parameter, and might lead to errors during pipeline configuration. To avoid errors, run the pipeline with `local_files_only=False` once to download the appropriate pipeline configuration files to the local cache.\n\n\n\n\nWhile the configuration files specify the pipeline or models default parameters, you can override them by providing the parameters directly to the [`~loaders.FromSingleFileMixin.from_single_file`] method. Any parameter supported by the model or pipeline class can be configured in this way.\n\n\n\n\nFor example, to scale the image latents in [`StableDiffusionXLInstructPix2PixPipeline`] pass the `is_cosxl_edit` parameter.\n\n```python\nfrom diffusers import StableDiffusionXLInstructPix2PixPipeline\n\nckpt_path = \"https://huggingface.co/stabilityai/cosxl/blob/main/cosxl_edit.safetensors\"\npipeline = StableDiffusionXLInstructPix2PixPipeline.from_single_file(ckpt_path, config=\"diffusers/sdxl-instructpix2pix-768\", is_cosxl_edit=True)\n```\n\n\n\n\nFor example, to upcast the attention dimensions in a [`UNet2DConditionModel`] pass the `upcast_attention` parameter.\n\n```python\nfrom diffusers import UNet2DConditionModel\n\nckpt_path = \"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors\"\nmodel = UNet2DConditionModel.from_single_file(ckpt_path, upcast_attention=True)\n```\n\n\n\n\n### Local files\n\nIn Diffusers>=v0.28.0, the [`~loaders.FromSingleFileMixin.from_single_file`] method attempts to configure a pipeline or model by inferring the model type from the keys in the checkpoint file. The inferred model type is used to determine the appropriate model repository on the Hugging Face Hub to configure the model or pipeline.\n\nFor example, any single file checkpoint based on the Stable Diffusion XL base model will use the [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) model repository to configure the pipeline.\n\nBut if you're working in an environment with restricted internet access, you should download the configuration files with the [`~huggingface_hub.snapshot_download`] function, and the model checkpoint with the [`~huggingface_hub.hf_hub_download`] function. By default, these files are downloaded to the Hugging Face Hub [cache directory](https://huggingface.co/docs/huggingface_hub/en/guides/manage-cache), but you can specify a preferred directory to download the files to with the `local_dir` parameter.\n\nPass the configuration and checkpoint paths to the [`~loaders.FromSingleFileMixin.from_single_file`] method to load locally.\n\n\n\n\n```python\nfrom huggingface_hub import hf_hub_download, snapshot_download\n\nmy_local_checkpoint_path = hf_hub_download(\n repo_id=\"segmind/SSD-1B\",\n filename=\"SSD-1B.safetensors\"\n)\n\nmy_local_config_path = snapshot_download(\n repo_id=\"segmind/SSD-1B\",\n allowed_patterns=[\"*.json\", \"**/*.json\", \"*.txt\", \"**/*.txt\"]\n)\n\npipeline = StableDiffusionXLPipeline.from_single_file(my_local_checkpoint_path, config=my_local_config_path, local_files_only=True)\n```\n\n\n\n\n```python\nfrom huggingface_hub import hf_hub_download, snapshot_download\n\nmy_local_checkpoint_path = hf_hub_download(\n repo_id=\"segmind/SSD-1B\",\n filename=\"SSD-1B.safetensors\"\n local_dir=\"my_local_checkpoints\"\n)\n\nmy_local_config_path = snapshot_download(\n repo_id=\"segmind/SSD-1B\",\n allowed_patterns=[\"*.json\", \"**/*.json\", \"*.txt\", \"**/*.txt\"]\n local_dir=\"my_local_config\"\n)\n\npipeline = StableDiffusionXLPipeline.from_single_file(my_local_checkpoint_path, config=my_local_config_path, local_files_only=True)\n```\n\n\n\n\n#### Local files without symlink\n\n> [!TIP]\n> In huggingface_hub>=v0.23.0, the `local_dir_use_symlinks` argument isn't necessary for the [`~huggingface_hub.hf_hub_download`] and [`~huggingface_hub.snapshot_download`] functions.\n\nThe [`~loaders.FromSingleFileMixin.from_single_file`] method relies on the [huggingface_hub](https://hf.co/docs/huggingface_hub/index) caching mechanism to fetch and store checkpoints and configuration files for models and pipelines. If you're working with a file system that does not support symlinking, you should download the checkpoint file to a local directory first, and disable symlinking with the `local_dir_use_symlink=False` parameter in the [`~huggingface_hub.hf_hub_download`] function and [`~huggingface_hub.snapshot_download`] functions.\n\n```python\nfrom huggingface_hub import hf_hub_download, snapshot_download\n\nmy_local_checkpoint_path = hf_hub_download(\n repo_id=\"segmind/SSD-1B\",\n filename=\"SSD-1B.safetensors\"\n local_dir=\"my_local_checkpoints\",\n local_dir_use_symlinks=False\n)\nprint(\"My local checkpoint: \", my_local_checkpoint_path)\n\nmy_local_config_path = snapshot_download(\n repo_id=\"segmind/SSD-1B\",\n allowed_patterns=[\"*.json\", \"**/*.json\", \"*.txt\", \"**/*.txt\"]\n local_dir_use_symlinks=False,\n)\nprint(\"My local config: \", my_local_config_path)\n\n```\n\nThen you can pass the local paths to the `pretrained_model_link_or_path` and `config` parameters.\n\n```python\npipeline = StableDiffusionXLPipeline.from_single_file(my_local_checkpoint_path, config=my_local_config_path, local_files_only=True)\n```\n" }, { "path": "diffusers/docs/source/en/using-diffusers/overview_techniques.md", "content": "\n\n# Overview\n\nThe inference pipeline supports and enables a wide range of techniques that are divided into two categories:\n\n* Pipeline functionality: these techniques modify the pipeline or extend it for other applications. For example, pipeline callbacks add new features to a pipeline and a pipeline can also be extended for distributed inference.\n* Improve inference quality: these techniques increase the visual quality of the generated images. For example, you can enhance your prompts with GPT2 to create better images with lower effort.\n" }, { "path": "diffusers/docs/source/en/using-diffusers/push_to_hub.md", "content": "\n\n# Push files to the Hub\n\n[[open-in-colab]]\n\n🤗 Diffusers provides a [`~diffusers.utils.PushToHubMixin`] for uploading your model, scheduler, or pipeline to the Hub. It is an easy way to store your files on the Hub, and also allows you to share your work with others. Under the hood, the [`~diffusers.utils.PushToHubMixin`]:\n\n1. creates a repository on the Hub\n2. saves your model, scheduler, or pipeline files so they can be reloaded later\n3. uploads folder containing these files to the Hub\n\nThis guide will show you how to use the [`~diffusers.utils.PushToHubMixin`] to upload your files to the Hub.\n\nYou'll need to log in to your Hub account with your access [token](https://huggingface.co/settings/tokens) first:\n\n```py\nfrom huggingface_hub import notebook_login\n\nnotebook_login()\n```\n\n## Models\n\nTo push a model to the Hub, call [`~diffusers.utils.PushToHubMixin.push_to_hub`] and specify the repository id of the model to be stored on the Hub:\n\n```py\nfrom diffusers import ControlNetModel\n\ncontrolnet = ControlNetModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n in_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n cross_attention_dim=32,\n conditioning_embedding_out_channels=(16, 32),\n)\ncontrolnet.push_to_hub(\"my-controlnet-model\")\n```\n\nFor models, you can also specify the [*variant*](loading#checkpoint-variants) of the weights to push to the Hub. For example, to push `fp16` weights:\n\n```py\ncontrolnet.push_to_hub(\"my-controlnet-model\", variant=\"fp16\")\n```\n\nThe [`~diffusers.utils.PushToHubMixin.push_to_hub`] function saves the model's `config.json` file and the weights are automatically saved in the `safetensors` format.\n\nNow you can reload the model from your repository on the Hub:\n\n```py\nmodel = ControlNetModel.from_pretrained(\"your-namespace/my-controlnet-model\")\n```\n\n## Scheduler\n\nTo push a scheduler to the Hub, call [`~diffusers.utils.PushToHubMixin.push_to_hub`] and specify the repository id of the scheduler to be stored on the Hub:\n\n```py\nfrom diffusers import DDIMScheduler\n\nscheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n)\nscheduler.push_to_hub(\"my-controlnet-scheduler\")\n```\n\nThe [`~diffusers.utils.PushToHubMixin.push_to_hub`] function saves the scheduler's `scheduler_config.json` file to the specified repository.\n\nNow you can reload the scheduler from your repository on the Hub:\n\n```py\nscheduler = DDIMScheduler.from_pretrained(\"your-namepsace/my-controlnet-scheduler\")\n```\n\n## Pipeline\n\nYou can also push an entire pipeline with all it's components to the Hub. For example, initialize the components of a [`StableDiffusionPipeline`] with the parameters you want:\n\n```py\nfrom diffusers import (\n UNet2DConditionModel,\n AutoencoderKL,\n DDIMScheduler,\n StableDiffusionPipeline,\n)\nfrom transformers import CLIPTextModel, CLIPTextConfig, CLIPTokenizer\n\nunet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n)\n\nscheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n)\n\nvae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n)\n\ntext_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n)\ntext_encoder = CLIPTextModel(text_encoder_config)\ntokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n```\n\nPass all of the components to the [`StableDiffusionPipeline`] and call [`~diffusers.utils.PushToHubMixin.push_to_hub`] to push the pipeline to the Hub:\n\n```py\ncomponents = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n}\n\npipeline = StableDiffusionPipeline(**components)\npipeline.push_to_hub(\"my-pipeline\")\n```\n\nThe [`~diffusers.utils.PushToHubMixin.push_to_hub`] function saves each component to a subfolder in the repository. Now you can reload the pipeline from your repository on the Hub:\n\n```py\npipeline = StableDiffusionPipeline.from_pretrained(\"your-namespace/my-pipeline\")\n```\n\n## Privacy\n\nSet `private=True` in the [`~diffusers.utils.PushToHubMixin.push_to_hub`] function to keep your model, scheduler, or pipeline files private:\n\n```py\ncontrolnet.push_to_hub(\"my-controlnet-model-private\", private=True)\n```\n\nPrivate repositories are only visible to you, and other users won't be able to clone the repository and your repository won't appear in search results. Even if a user has the URL to your private repository, they'll receive a `404 - Sorry, we can't find the page you are looking for`. You must be [logged in](https://huggingface.co/docs/huggingface_hub/quick-start#login) to load a model from a private repository." }, { "path": "diffusers/docs/source/en/using-diffusers/reusing_seeds.md", "content": "\n\n# Reproducible pipelines\n\nDiffusion models are inherently random which is what allows it to generate different outputs every time it is run. But there are certain times when you want to generate the same output every time, like when you're testing, replicating results, and even [improving image quality](#deterministic-batch-generation). While you can't expect to get identical results across platforms, you can expect reproducible results across releases and platforms within a certain tolerance range (though even this may vary).\n\nThis guide will show you how to control randomness for deterministic generation on a CPU and GPU.\n\n> [!TIP]\n> We strongly recommend reading PyTorch's [statement about reproducibility](https://pytorch.org/docs/stable/notes/randomness.html):\n>\n> \"Completely reproducible results are not guaranteed across PyTorch releases, individual commits, or different platforms. Furthermore, results may not be reproducible between CPU and GPU executions, even when using identical seeds.\"\n\n## Control randomness\n\nDuring inference, pipelines rely heavily on random sampling operations which include creating the\nGaussian noise tensors to denoise and adding noise to the scheduling step.\n\nTake a look at the tensor values in the [`DDIMPipeline`] after two inference steps.\n\n```python\nfrom diffusers import DDIMPipeline\nimport numpy as np\n\nddim = DDIMPipeline.from_pretrained( \"google/ddpm-cifar10-32\", use_safetensors=True)\nimage = ddim(num_inference_steps=2, output_type=\"np\").images\nprint(np.abs(image).sum())\n```\n\nRunning the code above prints one value, but if you run it again you get a different value.\n\nEach time the pipeline is run, [torch.randn](https://pytorch.org/docs/stable/generated/torch.randn.html) uses a different random seed to create the Gaussian noise tensors. This leads to a different result each time it is run and enables the diffusion pipeline to generate a different random image each time.\n\nBut if you need to reliably generate the same image, that depends on whether you're running the pipeline on a CPU or GPU.\n\n> [!TIP]\n> It might seem unintuitive to pass `Generator` objects to a pipeline instead of the integer value representing the seed. However, this is the recommended design when working with probabilistic models in PyTorch because a `Generator` is a *random state* that can be passed to multiple pipelines in a sequence. As soon as the `Generator` is consumed, the *state* is changed in place which means even if you passed the same `Generator` to a different pipeline, it won't produce the same result because the state is already changed.\n\n\n\n\nTo generate reproducible results on a CPU, you'll need to use a PyTorch [Generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) and set a seed. Now when you run the code, it always prints a value of `1491.1711` because the `Generator` object with the seed is passed to all the random functions in the pipeline. You should get a similar, if not the same, result on whatever hardware and PyTorch version you're using.\n\n```python\nimport torch\nimport numpy as np\nfrom diffusers import DDIMPipeline\n\nddim = DDIMPipeline.from_pretrained(\"google/ddpm-cifar10-32\", use_safetensors=True)\ngenerator = torch.Generator(device=\"cpu\").manual_seed(0)\nimage = ddim(num_inference_steps=2, output_type=\"np\", generator=generator).images\nprint(np.abs(image).sum())\n```\n\n\n\n\nWriting a reproducible pipeline on a GPU is a bit trickier, and full reproducibility across different hardware is not guaranteed because matrix multiplication - which diffusion pipelines require a lot of - is less deterministic on a GPU than a CPU. For example, if you run the same code example from the CPU example, you'll get a different result even though the seed is identical. This is because the GPU uses a different random number generator than the CPU.\n\n```python\nimport torch\nimport numpy as np\nfrom diffusers import DDIMPipeline\n\nddim = DDIMPipeline.from_pretrained(\"google/ddpm-cifar10-32\", use_safetensors=True)\nddim.to(\"cuda\")\ngenerator = torch.Generator(device=\"cuda\").manual_seed(0)\nimage = ddim(num_inference_steps=2, output_type=\"np\", generator=generator).images\nprint(np.abs(image).sum())\n```\n\nTo avoid this issue, Diffusers has a [`~utils.torch_utils.randn_tensor`] function for creating random noise on the CPU, and then moving the tensor to a GPU if necessary. The [`~utils.torch_utils.randn_tensor`] function is used everywhere inside the pipeline. Now you can call [torch.manual_seed](https://pytorch.org/docs/stable/generated/torch.manual_seed.html) which automatically creates a CPU `Generator` that can be passed to the pipeline even if it is being run on a GPU.\n\n```python\nimport torch\nimport numpy as np\nfrom diffusers import DDIMPipeline\n\nddim = DDIMPipeline.from_pretrained(\"google/ddpm-cifar10-32\", use_safetensors=True)\nddim.to(\"cuda\")\ngenerator = torch.manual_seed(0)\nimage = ddim(num_inference_steps=2, output_type=\"np\", generator=generator).images\nprint(np.abs(image).sum())\n```\n\n> [!TIP]\n> If reproducibility is important to your use case, we recommend always passing a CPU `Generator`. The performance loss is often negligible and you'll generate more similar values than if the pipeline had been run on a GPU.\n\nFinally, more complex pipelines such as [`UnCLIPPipeline`], are often extremely\nsusceptible to precision error propagation. You'll need to use\nexactly the same hardware and PyTorch version for full reproducibility.\n\n\n\n\n## Deterministic algorithms\n\nYou can also configure PyTorch to use deterministic algorithms to create a reproducible pipeline. The downside is that deterministic algorithms may be slower than non-deterministic ones and you may observe a decrease in performance.\n\nNon-deterministic behavior occurs when operations are launched in more than one CUDA stream. To avoid this, set the environment variable [CUBLAS_WORKSPACE_CONFIG](https://docs.nvidia.com/cuda/cublas/index.html#results-reproducibility) to `:16:8` to only use one buffer size during runtime.\n\nPyTorch typically benchmarks multiple algorithms to select the fastest one, but if you want reproducibility, you should disable this feature because the benchmark may select different algorithms each time. Set Diffusers [enable_full_determinism](https://github.com/huggingface/diffusers/blob/142f353e1c638ff1d20bd798402b68f72c1ebbdd/src/diffusers/utils/testing_utils.py#L861) to enable deterministic algorithms.\n\n```py\nenable_full_determinism()\n```\n\nNow when you run the same pipeline twice, you'll get identical results.\n\n```py\nimport torch\nfrom diffusers import DDIMScheduler, StableDiffusionPipeline\n\npipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", use_safetensors=True).to(\"cuda\")\npipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\ng = torch.Generator(device=\"cuda\")\n\nprompt = \"A bear is playing a guitar on Times Square\"\n\ng.manual_seed(0)\nresult1 = pipe(prompt=prompt, num_inference_steps=50, generator=g, output_type=\"latent\").images\n\ng.manual_seed(0)\nresult2 = pipe(prompt=prompt, num_inference_steps=50, generator=g, output_type=\"latent\").images\n\nprint(\"L_inf dist =\", abs(result1 - result2).max())\n\"L_inf dist = tensor(0., device='cuda:0')\"\n```\n\n## Deterministic batch generation\n\nA practical application of creating reproducible pipelines is *deterministic batch generation*. You generate a batch of images and select one image to improve with a more detailed prompt. The main idea is to pass a list of [Generator's](https://pytorch.org/docs/stable/generated/torch.Generator.html) to the pipeline and tie each `Generator` to a seed so you can reuse it.\n\nLet's use the [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) checkpoint and generate a batch of images.\n\n```py\nimport torch\nfrom diffusers import DiffusionPipeline\nfrom diffusers.utils import make_image_grid\n\npipeline = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, use_safetensors=True\n)\npipeline = pipeline.to(\"cuda\")\n```\n\nDefine four different `Generator`s and assign each `Generator` a seed (`0` to `3`). Then generate a batch of images and pick one to iterate on.\n\n> [!WARNING]\n> Use a list comprehension that iterates over the batch size specified in `range()` to create a unique `Generator` object for each image in the batch. If you multiply the `Generator` by the batch size integer, it only creates *one* `Generator` object that is used sequentially for each image in the batch.\n>\n> ```py\n> [torch.Generator().manual_seed(seed)] * 4\n> ```\n\n```python\ngenerator = [torch.Generator(device=\"cuda\").manual_seed(i) for i in range(4)]\nprompt = \"Labrador in the style of Vermeer\"\nimages = pipeline(prompt, generator=generator, num_images_per_prompt=4).images[0]\nmake_image_grid(images, rows=2, cols=2)\n```\n\n
\n \n
\n\nLet's improve the first image (you can choose any image you want) which corresponds to the `Generator` with seed `0`. Add some additional text to your prompt and then make sure you reuse the same `Generator` with seed `0`. All the generated images should resemble the first image.\n\n```python\nprompt = [prompt + t for t in [\", highly realistic\", \", artsy\", \", trending\", \", colorful\"]]\ngenerator = [torch.Generator(device=\"cuda\").manual_seed(0) for i in range(4)]\nimages = pipeline(prompt, generator=generator).images\nmake_image_grid(images, rows=2, cols=2)\n```\n\n
\n \n
\n" }, { "path": "diffusers/docs/source/en/using-diffusers/scheduler_features.md", "content": "\n\n# Scheduler features\n\nThe scheduler is an important component of any diffusion model because it controls the entire denoising (or sampling) process. There are many types of schedulers, some are optimized for speed and some for quality. With Diffusers, you can modify the scheduler configuration to use custom noise schedules, sigmas, and rescale the noise schedule. Changing these parameters can have profound effects on inference quality and speed.\n\nThis guide will demonstrate how to use these features to improve inference quality.\n\n> [!TIP]\n> Diffusers currently only supports the `timesteps` and `sigmas` parameters for a select list of schedulers and pipelines. Feel free to open a [feature request](https://github.com/huggingface/diffusers/issues/new/choose) if you want to extend these parameters to a scheduler and pipeline that does not currently support it!\n\n## Timestep schedules\n\nThe timestep or noise schedule determines the amount of noise at each sampling step. The scheduler uses this to generate an image with the corresponding amount of noise at each step. The timestep schedule is generated from the scheduler's default configuration, but you can customize the scheduler to use new and optimized sampling schedules that aren't in Diffusers yet.\n\nFor example, [Align Your Steps (AYS)](https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/) is a method for optimizing a sampling schedule to generate a high-quality image in as little as 10 steps. The optimal [10-step schedule](https://github.com/huggingface/diffusers/blob/a7bf77fc284810483f1e60afe34d1d27ad91ce2e/src/diffusers/schedulers/scheduling_utils.py#L51) for Stable Diffusion XL is:\n\n```py\nfrom diffusers.schedulers import AysSchedules\n\nsampling_schedule = AysSchedules[\"StableDiffusionXLTimesteps\"]\nprint(sampling_schedule)\n\"[999, 845, 730, 587, 443, 310, 193, 116, 53, 13]\"\n```\n\nYou can use the AYS sampling schedule in a pipeline by passing it to the `timesteps` parameter.\n\n```py\npipeline = StableDiffusionXLPipeline.from_pretrained(\n \"SG161222/RealVisXL_V4.0\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n).to(\"cuda\")\npipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, algorithm_type=\"sde-dpmsolver++\")\n\nprompt = \"A cinematic shot of a cute little rabbit wearing a jacket and doing a thumbs up\"\ngenerator = torch.Generator(device=\"cpu\").manual_seed(2487854446)\nimage = pipeline(\n prompt=prompt,\n negative_prompt=\"\",\n generator=generator,\n timesteps=sampling_schedule,\n).images[0]\n```\n\n
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\n\n## Timestep spacing\n\nThe way sample steps are selected in the schedule can affect the quality of the generated image, especially with respect to [rescaling the noise schedule](#rescale-noise-schedule), which can enable a model to generate much brighter or darker images. Diffusers provides three timestep spacing methods:\n\n- `leading` creates evenly spaced steps\n- `linspace` includes the first and last steps and evenly selects the remaining intermediate steps\n- `trailing` only includes the last step and evenly selects the remaining intermediate steps starting from the end\n\nIt is recommended to use the `trailing` spacing method because it generates higher quality images with more details when there are fewer sample steps. But the difference in quality is not as obvious for more standard sample step values.\n\n```py\nimport torch\nfrom diffusers import StableDiffusionXLPipeline, DPMSolverMultistepScheduler\n\npipeline = StableDiffusionXLPipeline.from_pretrained(\n \"SG161222/RealVisXL_V4.0\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n).to(\"cuda\")\npipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, timestep_spacing=\"trailing\")\n\nprompt = \"A cinematic shot of a cute little black cat sitting on a pumpkin at night\"\ngenerator = torch.Generator(device=\"cpu\").manual_seed(2487854446)\nimage = pipeline(\n prompt=prompt,\n negative_prompt=\"\",\n generator=generator,\n num_inference_steps=5,\n).images[0]\nimage\n```\n\n
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\n\n## Sigmas\n\nThe `sigmas` parameter is the amount of noise added at each timestep according to the timestep schedule. Like the `timesteps` parameter, you can customize the `sigmas` parameter to control how much noise is added at each step. When you use a custom `sigmas` value, the `timesteps` are calculated from the custom `sigmas` value and the default scheduler configuration is ignored.\n\nFor example, you can manually pass the [sigmas](https://github.com/huggingface/diffusers/blob/6529ee67ec02fcf58d2fd9242164ea002b351d75/src/diffusers/schedulers/scheduling_utils.py#L55) for something like the 10-step AYS schedule from before to the pipeline.\n\n```py\nimport torch\n\nfrom diffusers import DiffusionPipeline, EulerDiscreteScheduler\n\nmodel_id = \"stabilityai/stable-diffusion-xl-base-1.0\"\npipeline = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n).to(\"cuda\")\npipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)\n\nsigmas = [14.615, 6.315, 3.771, 2.181, 1.342, 0.862, 0.555, 0.380, 0.234, 0.113, 0.0]\nprompt = \"anthropomorphic capybara wearing a suit and working with a computer\"\ngenerator = torch.Generator(device='cuda').manual_seed(123)\nimage = pipeline(\n prompt=prompt,\n num_inference_steps=10,\n sigmas=sigmas,\n generator=generator\n).images[0]\n```\n\nWhen you take a look at the scheduler's `timesteps` parameter, you'll see that it is the same as the AYS timestep schedule because the `timestep` schedule is calculated from the `sigmas`.\n\n```py\nprint(f\" timesteps: {pipe.scheduler.timesteps}\")\n\"timesteps: tensor([999., 845., 730., 587., 443., 310., 193., 116., 53., 13.], device='cuda:0')\"\n```\n\n### Karras sigmas\n\n> [!TIP]\n> Refer to the scheduler API [overview](../api/schedulers/overview) for a list of schedulers that support Karras sigmas.\n>\n> Karras sigmas should not be used for models that weren't trained with them. For example, the base Stable Diffusion XL model shouldn't use Karras sigmas but the [DreamShaperXL](https://hf.co/Lykon/dreamshaper-xl-1-0) model can since they are trained with Karras sigmas.\n\nKarras scheduler's use the timestep schedule and sigmas from the [Elucidating the Design Space of Diffusion-Based Generative Models](https://hf.co/papers/2206.00364) paper. This scheduler variant applies a smaller amount of noise per step as it approaches the end of the sampling process compared to other schedulers, and can increase the level of details in the generated image.\n\nEnable Karras sigmas by setting `use_karras_sigmas=True` in the scheduler.\n\n```py\nimport torch\nfrom diffusers import StableDiffusionXLPipeline, DPMSolverMultistepScheduler\n\npipeline = StableDiffusionXLPipeline.from_pretrained(\n \"SG161222/RealVisXL_V4.0\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n).to(\"cuda\")\npipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, algorithm_type=\"sde-dpmsolver++\", use_karras_sigmas=True)\n\nprompt = \"A cinematic shot of a cute little rabbit wearing a jacket and doing a thumbs up\"\ngenerator = torch.Generator(device=\"cpu\").manual_seed(2487854446)\nimage = pipeline(\n prompt=prompt,\n negative_prompt=\"\",\n generator=generator,\n).images[0]\n```\n\n
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\n\n## Rescale noise schedule\n\nIn the [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://hf.co/papers/2305.08891) paper, the authors discovered that common noise schedules allowed some signal to leak into the last timestep. This signal leakage at inference can cause models to only generate images with medium brightness. By enforcing a zero signal-to-noise ratio (SNR) for the timstep schedule and sampling from the last timestep, the model can be improved to generate very bright or dark images.\n\n> [!TIP]\n> For inference, you need a model that has been trained with *v_prediction*. To train your own model with *v_prediction*, add the following flag to the [train_text_to_image.py](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) or [train_text_to_image_lora.py](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py) scripts.\n>\n> ```bash\n> --prediction_type=\"v_prediction\"\n> ```\n\nFor example, load the [ptx0/pseudo-journey-v2](https://hf.co/ptx0/pseudo-journey-v2) checkpoint which was trained with `v_prediction` and the [`DDIMScheduler`]. Configure the following parameters in the [`DDIMScheduler`]:\n\n* `rescale_betas_zero_snr=True` to rescale the noise schedule to zero SNR\n* `timestep_spacing=\"trailing\"` to start sampling from the last timestep\n\nSet `guidance_rescale` in the pipeline to prevent over-exposure. A lower value increases brightness but some of the details may appear washed out.\n\n```py\nfrom diffusers import DiffusionPipeline, DDIMScheduler\n\npipeline = DiffusionPipeline.from_pretrained(\"ptx0/pseudo-journey-v2\", use_safetensors=True)\n\npipeline.scheduler = DDIMScheduler.from_config(\n pipeline.scheduler.config, rescale_betas_zero_snr=True, timestep_spacing=\"trailing\"\n)\npipeline.to(\"cuda\")\nprompt = \"cinematic photo of a snowy mountain at night with the northern lights aurora borealis overhead, 35mm photograph, film, professional, 4k, highly detailed\"\ngenerator = torch.Generator(device=\"cpu\").manual_seed(23)\nimage = pipeline(prompt, guidance_rescale=0.7, generator=generator).images[0]\nimage\n```\n\n
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\n" }, { "path": "diffusers/docs/source/en/using-diffusers/schedulers.md", "content": "\n\n# Load schedulers and models\n\n[[open-in-colab]]\n\nDiffusion pipelines are a collection of interchangeable schedulers and models that can be mixed and matched to tailor a pipeline to a specific use case. The scheduler encapsulates the entire denoising process such as the number of denoising steps and the algorithm for finding the denoised sample. A scheduler is not parameterized or trained so they don't take very much memory. The model is usually only concerned with the forward pass of going from a noisy input to a less noisy sample.\n\nThis guide will show you how to load schedulers and models to customize a pipeline. You'll use the [runwayml/stable-diffusion-v1-5](https://hf.co/runwayml/stable-diffusion-v1-5) checkpoint throughout this guide, so let's load it first.\n\n```py\nimport torch\nfrom diffusers import DiffusionPipeline\n\npipeline = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, use_safetensors=True\n).to(\"cuda\")\n```\n\nYou can see what scheduler this pipeline uses with the `pipeline.scheduler` attribute.\n\n```py\npipeline.scheduler\nPNDMScheduler {\n \"_class_name\": \"PNDMScheduler\",\n \"_diffusers_version\": \"0.21.4\",\n \"beta_end\": 0.012,\n \"beta_schedule\": \"scaled_linear\",\n \"beta_start\": 0.00085,\n \"clip_sample\": false,\n \"num_train_timesteps\": 1000,\n \"set_alpha_to_one\": false,\n \"skip_prk_steps\": true,\n \"steps_offset\": 1,\n \"timestep_spacing\": \"leading\",\n \"trained_betas\": null\n}\n```\n\n## Load a scheduler\n\nSchedulers are defined by a configuration file that can be used by a variety of schedulers. Load a scheduler with the [`SchedulerMixin.from_pretrained`] method, and specify the `subfolder` parameter to load the configuration file into the correct subfolder of the pipeline repository.\n\nFor example, to load the [`DDIMScheduler`]:\n\n```py\nfrom diffusers import DDIMScheduler, DiffusionPipeline\n\nddim = DDIMScheduler.from_pretrained(\"runwayml/stable-diffusion-v1-5\", subfolder=\"scheduler\")\n```\n\nThen you can pass the newly loaded scheduler to the pipeline.\n\n```python\npipeline = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", scheduler=ddim, torch_dtype=torch.float16, use_safetensors=True\n).to(\"cuda\")\n```\n\n## Compare schedulers\n\nSchedulers have their own unique strengths and weaknesses, making it difficult to quantitatively compare which scheduler works best for a pipeline. You typically have to make a trade-off between denoising speed and denoising quality. We recommend trying out different schedulers to find one that works best for your use case. Call the `pipeline.scheduler.compatibles` attribute to see what schedulers are compatible with a pipeline.\n\nLet's compare the [`LMSDiscreteScheduler`], [`EulerDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`], and the [`DPMSolverMultistepScheduler`] on the following prompt and seed.\n\n```py\nimport torch\nfrom diffusers import DiffusionPipeline\n\npipeline = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, use_safetensors=True\n).to(\"cuda\")\n\nprompt = \"A photograph of an astronaut riding a horse on Mars, high resolution, high definition.\"\ngenerator = torch.Generator(device=\"cuda\").manual_seed(8)\n```\n\nTo change the pipelines scheduler, use the [`~ConfigMixin.from_config`] method to load a different scheduler's `pipeline.scheduler.config` into the pipeline.\n\n\n\n\n[`LMSDiscreteScheduler`] typically generates higher quality images than the default scheduler.\n\n```py\nfrom diffusers import LMSDiscreteScheduler\n\npipeline.scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config)\nimage = pipeline(prompt, generator=generator).images[0]\nimage\n```\n\n\n\n\n[`EulerDiscreteScheduler`] can generate higher quality images in just 30 steps.\n\n```py\nfrom diffusers import EulerDiscreteScheduler\n\npipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)\nimage = pipeline(prompt, generator=generator).images[0]\nimage\n```\n\n\n\n\n[`EulerAncestralDiscreteScheduler`] can generate higher quality images in just 30 steps.\n\n```py\nfrom diffusers import EulerAncestralDiscreteScheduler\n\npipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config)\nimage = pipeline(prompt, generator=generator).images[0]\nimage\n```\n\n\n\n\n[`DPMSolverMultistepScheduler`] provides a balance between speed and quality and can generate higher quality images in just 20 steps.\n\n```py\nfrom diffusers import DPMSolverMultistepScheduler\n\npipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)\nimage = pipeline(prompt, generator=generator).images[0]\nimage\n```\n\n\n\n\n
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\n\nMost images look very similar and are comparable in quality. Again, it often comes down to your specific use case so a good approach is to run multiple different schedulers and compare the results.\n\n### Flax schedulers\n\nTo compare Flax schedulers, you need to additionally load the scheduler state into the model parameters. For example, let's change the default scheduler in [`FlaxStableDiffusionPipeline`] to use the super fast [`FlaxDPMSolverMultistepScheduler`].\n\n> [!WARNING]\n> The [`FlaxLMSDiscreteScheduler`] and [`FlaxDDPMScheduler`] are not compatible with the [`FlaxStableDiffusionPipeline`] yet.\n\n```py\nimport jax\nimport numpy as np\nfrom flax.jax_utils import replicate\nfrom flax.training.common_utils import shard\nfrom diffusers import FlaxStableDiffusionPipeline, FlaxDPMSolverMultistepScheduler\n\nscheduler, scheduler_state = FlaxDPMSolverMultistepScheduler.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n subfolder=\"scheduler\"\n)\npipeline, params = FlaxStableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n scheduler=scheduler,\n revision=\"bf16\",\n dtype=jax.numpy.bfloat16,\n)\nparams[\"scheduler\"] = scheduler_state\n```\n\nThen you can take advantage of Flax's compatibility with TPUs to generate a number of images in parallel. You'll need to make a copy of the model parameters for each available device and then split the inputs across them to generate your desired number of images.\n\n```py\n# Generate 1 image per parallel device (8 on TPUv2-8 or TPUv3-8)\nprompt = \"A photograph of an astronaut riding a horse on Mars, high resolution, high definition.\"\nnum_samples = jax.device_count()\nprompt_ids = pipeline.prepare_inputs([prompt] * num_samples)\n\nprng_seed = jax.random.PRNGKey(0)\nnum_inference_steps = 25\n\n# shard inputs and rng\nparams = replicate(params)\nprng_seed = jax.random.split(prng_seed, jax.device_count())\nprompt_ids = shard(prompt_ids)\n\nimages = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images\nimages = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))\n```\n\n## Models\n\nModels are loaded from the [`ModelMixin.from_pretrained`] method, which downloads and caches the latest version of the model weights and configurations. If the latest files are available in the local cache, [`~ModelMixin.from_pretrained`] reuses files in the cache instead of re-downloading them.\n\nModels can be loaded from a subfolder with the `subfolder` argument. For example, the model weights for [runwayml/stable-diffusion-v1-5](https://hf.co/runwayml/stable-diffusion-v1-5) are stored in the [unet](https://hf.co/runwayml/stable-diffusion-v1-5/tree/main/unet) subfolder.\n\n```python\nfrom diffusers import UNet2DConditionModel\n\nunet = UNet2DConditionModel.from_pretrained(\"runwayml/stable-diffusion-v1-5\", subfolder=\"unet\", use_safetensors=True)\n```\n\nThey can also be directly loaded from a [repository](https://huggingface.co/google/ddpm-cifar10-32/tree/main).\n\n```python\nfrom diffusers import UNet2DModel\n\nunet = UNet2DModel.from_pretrained(\"google/ddpm-cifar10-32\", use_safetensors=True)\n```\n\nTo load and save model variants, specify the `variant` argument in [`ModelMixin.from_pretrained`] and [`ModelMixin.save_pretrained`].\n\n```python\nfrom diffusers import UNet2DConditionModel\n\nunet = UNet2DConditionModel.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", subfolder=\"unet\", variant=\"non_ema\", use_safetensors=True\n)\nunet.save_pretrained(\"./local-unet\", variant=\"non_ema\")\n```\n" }, { "path": "diffusers/docs/source/en/using-diffusers/sdxl.md", "content": "\n\n# Stable Diffusion XL\n\n[[open-in-colab]]\n\n[Stable Diffusion XL](https://huggingface.co/papers/2307.01952) (SDXL) is a powerful text-to-image generation model that iterates on the previous Stable Diffusion models in three key ways:\n\n1. the UNet is 3x larger and SDXL combines a second text encoder (OpenCLIP ViT-bigG/14) with the original text encoder to significantly increase the number of parameters\n2. introduces size and crop-conditioning to preserve training data from being discarded and gain more control over how a generated image should be cropped\n3. introduces a two-stage model process; the *base* model (can also be run as a standalone model) generates an image as an input to the *refiner* model which adds additional high-quality details\n\nThis guide will show you how to use SDXL for text-to-image, image-to-image, and inpainting.\n\nBefore you begin, make sure you have the following libraries installed:\n\n```py\n# uncomment to install the necessary libraries in Colab\n#!pip install -q diffusers transformers accelerate invisible-watermark>=0.2.0\n```\n\n\n\nWe recommend installing the [invisible-watermark](https://pypi.org/project/invisible-watermark/) library to help identify images that are generated. If the invisible-watermark library is installed, it is used by default. To disable the watermarker:\n\n```py\npipeline = StableDiffusionXLPipeline.from_pretrained(..., add_watermarker=False)\n```\n\n\n\n## Load model checkpoints\n\nModel weights may be stored in separate subfolders on the Hub or locally, in which case, you should use the [`~StableDiffusionXLPipeline.from_pretrained`] method:\n\n```py\nfrom diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline\nimport torch\n\npipeline = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n).to(\"cuda\")\n\nrefiner = StableDiffusionXLImg2ImgPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-refiner-1.0\", torch_dtype=torch.float16, use_safetensors=True, variant=\"fp16\"\n).to(\"cuda\")\n```\n\nYou can also use the [`~StableDiffusionXLPipeline.from_single_file`] method to load a model checkpoint stored in a single file format (`.ckpt` or `.safetensors`) from the Hub or locally:\n\n```py\nfrom diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline\nimport torch\n\npipeline = StableDiffusionXLPipeline.from_single_file(\n \"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors\", \n torch_dtype=torch.float16\n).to(\"cuda\")\n\nrefiner = StableDiffusionXLImg2ImgPipeline.from_single_file(\n \"https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/blob/main/sd_xl_refiner_1.0.safetensors\", torch_dtype=torch.float16\n).to(\"cuda\")\n```\n\n## Text-to-image\n\nFor text-to-image, pass a text prompt. By default, SDXL generates a 1024x1024 image for the best results. You can try setting the `height` and `width` parameters to 768x768 or 512x512, but anything below 512x512 is not likely to work.\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline_text2image = AutoPipelineForText2Image.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n).to(\"cuda\")\n\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\nimage = pipeline_text2image(prompt=prompt).images[0]\nimage\n```\n\n
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\n\n## Image-to-image\n\nFor image-to-image, SDXL works especially well with image sizes between 768x768 and 1024x1024. Pass an initial image, and a text prompt to condition the image with:\n\n```py\nfrom diffusers import AutoPipelineForImage2Image\nfrom diffusers.utils import load_image, make_image_grid\n\n# use from_pipe to avoid consuming additional memory when loading a checkpoint\npipeline = AutoPipelineForImage2Image.from_pipe(pipeline_text2image).to(\"cuda\")\n\nurl = \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png\"\ninit_image = load_image(url)\nprompt = \"a dog catching a frisbee in the jungle\"\nimage = pipeline(prompt, image=init_image, strength=0.8, guidance_scale=10.5).images[0]\nmake_image_grid([init_image, image], rows=1, cols=2)\n```\n\n
\n \"generated\n
\n\n## Inpainting\n\nFor inpainting, you'll need the original image and a mask of what you want to replace in the original image. Create a prompt to describe what you want to replace the masked area with.\n\n```py\nfrom diffusers import AutoPipelineForInpainting\nfrom diffusers.utils import load_image, make_image_grid\n\n# use from_pipe to avoid consuming additional memory when loading a checkpoint\npipeline = AutoPipelineForInpainting.from_pipe(pipeline_text2image).to(\"cuda\")\n\nimg_url = \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png\"\nmask_url = \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-inpaint-mask.png\"\n\ninit_image = load_image(img_url)\nmask_image = load_image(mask_url)\n\nprompt = \"A deep sea diver floating\"\nimage = pipeline(prompt=prompt, image=init_image, mask_image=mask_image, strength=0.85, guidance_scale=12.5).images[0]\nmake_image_grid([init_image, mask_image, image], rows=1, cols=3)\n```\n\n
\n \"generated\n
\n\n## Refine image quality\n\nSDXL includes a [refiner model](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0) specialized in denoising low-noise stage images to generate higher-quality images from the base model. There are two ways to use the refiner:\n\n1. use the base and refiner models together to produce a refined image\n2. use the base model to produce an image, and subsequently use the refiner model to add more details to the image (this is how SDXL was originally trained)\n\n### Base + refiner model\n\nWhen you use the base and refiner model together to generate an image, this is known as an [*ensemble of expert denoisers*](https://research.nvidia.com/labs/dir/eDiff-I/). The ensemble of expert denoisers approach requires fewer overall denoising steps versus passing the base model's output to the refiner model, so it should be significantly faster to run. However, you won't be able to inspect the base model's output because it still contains a large amount of noise.\n\nAs an ensemble of expert denoisers, the base model serves as the expert during the high-noise diffusion stage and the refiner model serves as the expert during the low-noise diffusion stage. Load the base and refiner model:\n\n```py\nfrom diffusers import DiffusionPipeline\nimport torch\n\nbase = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n).to(\"cuda\")\n\nrefiner = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-refiner-1.0\",\n text_encoder_2=base.text_encoder_2,\n vae=base.vae,\n torch_dtype=torch.float16,\n use_safetensors=True,\n variant=\"fp16\",\n).to(\"cuda\")\n```\n\nTo use this approach, you need to define the number of timesteps for each model to run through their respective stages. For the base model, this is controlled by the [`denoising_end`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLPipeline.__call__.denoising_end) parameter and for the refiner model, it is controlled by the [`denoising_start`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLImg2ImgPipeline.__call__.denoising_start) parameter.\n\n\n\nThe `denoising_end` and `denoising_start` parameters should be a float between 0 and 1. These parameters are represented as a proportion of discrete timesteps as defined by the scheduler. If you're also using the `strength` parameter, it'll be ignored because the number of denoising steps is determined by the discrete timesteps the model is trained on and the declared fractional cutoff.\n\n\n\nLet's set `denoising_end=0.8` so the base model performs the first 80% of denoising the **high-noise** timesteps and set `denoising_start=0.8` so the refiner model performs the last 20% of denoising the **low-noise** timesteps. The base model output should be in **latent** space instead of a PIL image.\n\n```py\nprompt = \"A majestic lion jumping from a big stone at night\"\n\nimage = base(\n prompt=prompt,\n num_inference_steps=40,\n denoising_end=0.8,\n output_type=\"latent\",\n).images\nimage = refiner(\n prompt=prompt,\n num_inference_steps=40,\n denoising_start=0.8,\n image=image,\n).images[0]\nimage\n```\n\n
\n
\n \"generated\n
default base model
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\n \"generated\n
ensemble of expert denoisers
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\n\nThe refiner model can also be used for inpainting in the [`StableDiffusionXLInpaintPipeline`]:\n\n```py\nfrom diffusers import StableDiffusionXLInpaintPipeline\nfrom diffusers.utils import load_image, make_image_grid\nimport torch\n\nbase = StableDiffusionXLInpaintPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n).to(\"cuda\")\n\nrefiner = StableDiffusionXLInpaintPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-refiner-1.0\",\n text_encoder_2=base.text_encoder_2,\n vae=base.vae,\n torch_dtype=torch.float16,\n use_safetensors=True,\n variant=\"fp16\",\n).to(\"cuda\")\n\nimg_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\nmask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\n\ninit_image = load_image(img_url)\nmask_image = load_image(mask_url)\n\nprompt = \"A majestic tiger sitting on a bench\"\nnum_inference_steps = 75\nhigh_noise_frac = 0.7\n\nimage = base(\n prompt=prompt,\n image=init_image,\n mask_image=mask_image,\n num_inference_steps=num_inference_steps,\n denoising_end=high_noise_frac,\n output_type=\"latent\",\n).images\nimage = refiner(\n prompt=prompt,\n image=image,\n mask_image=mask_image,\n num_inference_steps=num_inference_steps,\n denoising_start=high_noise_frac,\n).images[0]\nmake_image_grid([init_image, mask_image, image.resize((512, 512))], rows=1, cols=3)\n```\n\nThis ensemble of expert denoisers method works well for all available schedulers!\n\n### Base to refiner model\n\nSDXL gets a boost in image quality by using the refiner model to add additional high-quality details to the fully-denoised image from the base model, in an image-to-image setting.\n\nLoad the base and refiner models:\n\n```py\nfrom diffusers import DiffusionPipeline\nimport torch\n\nbase = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n).to(\"cuda\")\n\nrefiner = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-refiner-1.0\",\n text_encoder_2=base.text_encoder_2,\n vae=base.vae,\n torch_dtype=torch.float16,\n use_safetensors=True,\n variant=\"fp16\",\n).to(\"cuda\")\n```\n\nGenerate an image from the base model, and set the model output to **latent** space:\n\n```py\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\n\nimage = base(prompt=prompt, output_type=\"latent\").images[0]\n```\n\nPass the generated image to the refiner model:\n\n```py\nimage = refiner(prompt=prompt, image=image[None, :]).images[0]\n```\n\n
\n
\n \"generated\n
base model
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base model + refiner model
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\n\nFor inpainting, load the base and the refiner model in the [`StableDiffusionXLInpaintPipeline`], remove the `denoising_end` and `denoising_start` parameters, and choose a smaller number of inference steps for the refiner.\n\n## Micro-conditioning\n\nSDXL training involves several additional conditioning techniques, which are referred to as *micro-conditioning*. These include original image size, target image size, and cropping parameters. The micro-conditionings can be used at inference time to create high-quality, centered images.\n\n\n\nYou can use both micro-conditioning and negative micro-conditioning parameters thanks to classifier-free guidance. They are available in the [`StableDiffusionXLPipeline`], [`StableDiffusionXLImg2ImgPipeline`], [`StableDiffusionXLInpaintPipeline`], and [`StableDiffusionXLControlNetPipeline`].\n\n\n\n### Size conditioning\n\nThere are two types of size conditioning:\n\n- [`original_size`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLPipeline.__call__.original_size) conditioning comes from upscaled images in the training batch (because it would be wasteful to discard the smaller images which make up almost 40% of the total training data). This way, SDXL learns that upscaling artifacts are not supposed to be present in high-resolution images. During inference, you can use `original_size` to indicate the original image resolution. Using the default value of `(1024, 1024)` produces higher-quality images that resemble the 1024x1024 images in the dataset. If you choose to use a lower resolution, such as `(256, 256)`, the model still generates 1024x1024 images, but they'll look like the low resolution images (simpler patterns, blurring) in the dataset.\n\n- [`target_size`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLPipeline.__call__.target_size) conditioning comes from finetuning SDXL to support different image aspect ratios. During inference, if you use the default value of `(1024, 1024)`, you'll get an image that resembles the composition of square images in the dataset. We recommend using the same value for `target_size` and `original_size`, but feel free to experiment with other options!\n\n🤗 Diffusers also lets you specify negative conditions about an image's size to steer generation away from certain image resolutions:\n\n```py\nfrom diffusers import StableDiffusionXLPipeline\nimport torch\n\npipe = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n).to(\"cuda\")\n\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\nimage = pipe(\n prompt=prompt,\n negative_original_size=(512, 512),\n negative_target_size=(1024, 1024),\n).images[0]\n```\n\n
\n \n
Images negatively conditioned on image resolutions of (128, 128), (256, 256), and (512, 512).
\n
\n\n### Crop conditioning\n\nImages generated by previous Stable Diffusion models may sometimes appear to be cropped. This is because images are actually cropped during training so that all the images in a batch have the same size. By conditioning on crop coordinates, SDXL *learns* that no cropping - coordinates `(0, 0)` - usually correlates with centered subjects and complete faces (this is the default value in 🤗 Diffusers). You can experiment with different coordinates if you want to generate off-centered compositions!\n\n```py\nfrom diffusers import StableDiffusionXLPipeline\nimport torch\n\npipeline = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n).to(\"cuda\")\n\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\nimage = pipeline(prompt=prompt, crops_coords_top_left=(256, 0)).images[0]\nimage\n```\n\n
\n \"generated\n
\n\nYou can also specify negative cropping coordinates to steer generation away from certain cropping parameters:\n\n```py\nfrom diffusers import StableDiffusionXLPipeline\nimport torch\n\npipe = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n).to(\"cuda\")\n\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\nimage = pipe(\n prompt=prompt,\n negative_original_size=(512, 512),\n negative_crops_coords_top_left=(0, 0),\n negative_target_size=(1024, 1024),\n).images[0]\nimage\n```\n\n## Use a different prompt for each text-encoder\n\nSDXL uses two text-encoders, so it is possible to pass a different prompt to each text-encoder, which can [improve quality](https://github.com/huggingface/diffusers/issues/4004#issuecomment-1627764201). Pass your original prompt to `prompt` and the second prompt to `prompt_2` (use `negative_prompt` and `negative_prompt_2` if you're using negative prompts):\n\n```py\nfrom diffusers import StableDiffusionXLPipeline\nimport torch\n\npipeline = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n).to(\"cuda\")\n\n# prompt is passed to OAI CLIP-ViT/L-14\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\n# prompt_2 is passed to OpenCLIP-ViT/bigG-14\nprompt_2 = \"Van Gogh painting\"\nimage = pipeline(prompt=prompt, prompt_2=prompt_2).images[0]\nimage\n```\n\n
\n \"generated\n
\n\nThe dual text-encoders also support textual inversion embeddings that need to be loaded separately as explained in the [SDXL textual inversion](textual_inversion_inference#stable-diffusion-xl) section.\n\n## Optimizations\n\nSDXL is a large model, and you may need to optimize memory to get it to run on your hardware. Here are some tips to save memory and speed up inference.\n\n1. Offload the model to the CPU with [`~StableDiffusionXLPipeline.enable_model_cpu_offload`] for out-of-memory errors:\n\n```diff\n- base.to(\"cuda\")\n- refiner.to(\"cuda\")\n+ base.enable_model_cpu_offload()\n+ refiner.enable_model_cpu_offload()\n```\n\n2. Use `torch.compile` for ~20% speed-up (you need `torch>=2.0`):\n\n```diff\n+ base.unet = torch.compile(base.unet, mode=\"reduce-overhead\", fullgraph=True)\n+ refiner.unet = torch.compile(refiner.unet, mode=\"reduce-overhead\", fullgraph=True)\n```\n\n3. Enable [xFormers](../optimization/xformers) to run SDXL if `torch<2.0`:\n\n```diff\n+ base.enable_xformers_memory_efficient_attention()\n+ refiner.enable_xformers_memory_efficient_attention()\n```\n\n## Other resources\n\nIf you're interested in experimenting with a minimal version of the [`UNet2DConditionModel`] used in SDXL, take a look at the [minSDXL](https://github.com/cloneofsimo/minSDXL) implementation which is written in PyTorch and directly compatible with 🤗 Diffusers.\n" }, { "path": "diffusers/docs/source/en/using-diffusers/sdxl_turbo.md", "content": "\n\n# Stable Diffusion XL Turbo\n\n[[open-in-colab]]\n\nSDXL Turbo is an adversarial time-distilled [Stable Diffusion XL](https://huggingface.co/papers/2307.01952) (SDXL) model capable\nof running inference in as little as 1 step.\n\nThis guide will show you how to use SDXL-Turbo for text-to-image and image-to-image.\n\nBefore you begin, make sure you have the following libraries installed:\n\n```py\n# uncomment to install the necessary libraries in Colab\n#!pip install -q diffusers transformers accelerate\n```\n\n## Load model checkpoints\n\nModel weights may be stored in separate subfolders on the Hub or locally, in which case, you should use the [`~StableDiffusionXLPipeline.from_pretrained`] method:\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\"stabilityai/sdxl-turbo\", torch_dtype=torch.float16, variant=\"fp16\")\npipeline = pipeline.to(\"cuda\")\n```\n\nYou can also use the [`~StableDiffusionXLPipeline.from_single_file`] method to load a model checkpoint stored in a single file format (`.ckpt` or `.safetensors`) from the Hub or locally. For this loading method, you need to set `timestep_spacing=\"trailing\"` (feel free to experiment with the other scheduler config values to get better results):\n\n```py\nfrom diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler\nimport torch\n\npipeline = StableDiffusionXLPipeline.from_single_file(\n \"https://huggingface.co/stabilityai/sdxl-turbo/blob/main/sd_xl_turbo_1.0_fp16.safetensors\",\n torch_dtype=torch.float16, variant=\"fp16\")\npipeline = pipeline.to(\"cuda\")\npipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config, timestep_spacing=\"trailing\")\n```\n\n## Text-to-image\n\nFor text-to-image, pass a text prompt. By default, SDXL Turbo generates a 512x512 image, and that resolution gives the best results. You can try setting the `height` and `width` parameters to 768x768 or 1024x1024, but you should expect quality degradations when doing so.\n\nMake sure to set `guidance_scale` to 0.0 to disable, as the model was trained without it. A single inference step is enough to generate high quality images.\nIncreasing the number of steps to 2, 3 or 4 should improve image quality.\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline_text2image = AutoPipelineForText2Image.from_pretrained(\"stabilityai/sdxl-turbo\", torch_dtype=torch.float16, variant=\"fp16\")\npipeline_text2image = pipeline_text2image.to(\"cuda\")\n\nprompt = \"A cinematic shot of a baby racoon wearing an intricate italian priest robe.\"\n\nimage = pipeline_text2image(prompt=prompt, guidance_scale=0.0, num_inference_steps=1).images[0]\nimage\n```\n\n
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\n\n## Image-to-image\n\nFor image-to-image generation, make sure that `num_inference_steps * strength` is larger or equal to 1.\nThe image-to-image pipeline will run for `int(num_inference_steps * strength)` steps, e.g. `0.5 * 2.0 = 1` step in\nour example below.\n\n```py\nfrom diffusers import AutoPipelineForImage2Image\nfrom diffusers.utils import load_image, make_image_grid\n\n# use from_pipe to avoid consuming additional memory when loading a checkpoint\npipeline_image2image = AutoPipelineForImage2Image.from_pipe(pipeline_text2image).to(\"cuda\")\n\ninit_image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png\")\ninit_image = init_image.resize((512, 512))\n\nprompt = \"cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k\"\n\nimage = pipeline_image2image(prompt, image=init_image, strength=0.5, guidance_scale=0.0, num_inference_steps=2).images[0]\nmake_image_grid([init_image, image], rows=1, cols=2)\n```\n\n
\n \"Image-to-image\n
\n\n## Speed-up SDXL Turbo even more\n\n- Compile the UNet if you are using PyTorch version 2.0 or higher. The first inference run will be very slow, but subsequent ones will be much faster.\n\n```py\npipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n```\n\n- When using the default VAE, keep it in `float32` to avoid costly `dtype` conversions before and after each generation. You only need to do this one before your first generation:\n\n```py\npipe.upcast_vae()\n```\n\nAs an alternative, you can also use a [16-bit VAE](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix) created by community member [`@madebyollin`](https://huggingface.co/madebyollin) that does not need to be upcasted to `float32`.\n" }, { "path": "diffusers/docs/source/en/using-diffusers/shap-e.md", "content": "\n\n# Shap-E\n\n[[open-in-colab]]\n\nShap-E is a conditional model for generating 3D assets which could be used for video game development, interior design, and architecture. It is trained on a large dataset of 3D assets, and post-processed to render more views of each object and produce 16K instead of 4K point clouds. The Shap-E model is trained in two steps:\n\n1. an encoder accepts the point clouds and rendered views of a 3D asset and outputs the parameters of implicit functions that represent the asset\n2. a diffusion model is trained on the latents produced by the encoder to generate either neural radiance fields (NeRFs) or a textured 3D mesh, making it easier to render and use the 3D asset in downstream applications\n\nThis guide will show you how to use Shap-E to start generating your own 3D assets!\n\nBefore you begin, make sure you have the following libraries installed:\n\n```py\n# uncomment to install the necessary libraries in Colab\n#!pip install -q diffusers transformers accelerate trimesh\n```\n\n## Text-to-3D\n\nTo generate a gif of a 3D object, pass a text prompt to the [`ShapEPipeline`]. The pipeline generates a list of image frames which are used to create the 3D object.\n\n```py\nimport torch\nfrom diffusers import ShapEPipeline\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\npipe = ShapEPipeline.from_pretrained(\"openai/shap-e\", torch_dtype=torch.float16, variant=\"fp16\")\npipe = pipe.to(device)\n\nguidance_scale = 15.0\nprompt = [\"A firecracker\", \"A birthday cupcake\"]\n\nimages = pipe(\n prompt,\n guidance_scale=guidance_scale,\n num_inference_steps=64,\n frame_size=256,\n).images\n```\n\nNow use the [`~utils.export_to_gif`] function to turn the list of image frames into a gif of the 3D object.\n\n```py\nfrom diffusers.utils import export_to_gif\n\nexport_to_gif(images[0], \"firecracker_3d.gif\")\nexport_to_gif(images[1], \"cake_3d.gif\")\n```\n\n
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prompt = \"A firecracker\"
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prompt = \"A birthday cupcake\"
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\n\n## Image-to-3D\n\nTo generate a 3D object from another image, use the [`ShapEImg2ImgPipeline`]. You can use an existing image or generate an entirely new one. Let's use the [Kandinsky 2.1](../api/pipelines/kandinsky) model to generate a new image.\n\n```py\nfrom diffusers import DiffusionPipeline\nimport torch\n\nprior_pipeline = DiffusionPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1-prior\", torch_dtype=torch.float16, use_safetensors=True).to(\"cuda\")\npipeline = DiffusionPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\", torch_dtype=torch.float16, use_safetensors=True).to(\"cuda\")\n\nprompt = \"A cheeseburger, white background\"\n\nimage_embeds, negative_image_embeds = prior_pipeline(prompt, guidance_scale=1.0).to_tuple()\nimage = pipeline(\n prompt,\n image_embeds=image_embeds,\n negative_image_embeds=negative_image_embeds,\n).images[0]\n\nimage.save(\"burger.png\")\n```\n\nPass the cheeseburger to the [`ShapEImg2ImgPipeline`] to generate a 3D representation of it.\n\n```py\nfrom PIL import Image\nfrom diffusers import ShapEImg2ImgPipeline\nfrom diffusers.utils import export_to_gif\n\npipe = ShapEImg2ImgPipeline.from_pretrained(\"openai/shap-e-img2img\", torch_dtype=torch.float16, variant=\"fp16\").to(\"cuda\")\n\nguidance_scale = 3.0\nimage = Image.open(\"burger.png\").resize((256, 256))\n\nimages = pipe(\n image,\n guidance_scale=guidance_scale,\n num_inference_steps=64,\n frame_size=256,\n).images\n\ngif_path = export_to_gif(images[0], \"burger_3d.gif\")\n```\n\n
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cheeseburger
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3D cheeseburger
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\n\n## Generate mesh\n\nShap-E is a flexible model that can also generate textured mesh outputs to be rendered for downstream applications. In this example, you'll convert the output into a `glb` file because the 🤗 Datasets library supports mesh visualization of `glb` files which can be rendered by the [Dataset viewer](https://huggingface.co/docs/hub/datasets-viewer#dataset-preview).\n\nYou can generate mesh outputs for both the [`ShapEPipeline`] and [`ShapEImg2ImgPipeline`] by specifying the `output_type` parameter as `\"mesh\"`:\n\n```py\nimport torch\nfrom diffusers import ShapEPipeline\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\npipe = ShapEPipeline.from_pretrained(\"openai/shap-e\", torch_dtype=torch.float16, variant=\"fp16\")\npipe = pipe.to(device)\n\nguidance_scale = 15.0\nprompt = \"A birthday cupcake\"\n\nimages = pipe(prompt, guidance_scale=guidance_scale, num_inference_steps=64, frame_size=256, output_type=\"mesh\").images\n```\n\nUse the [`~utils.export_to_ply`] function to save the mesh output as a `ply` file:\n\n\n\nYou can optionally save the mesh output as an `obj` file with the [`~utils.export_to_obj`] function. The ability to save the mesh output in a variety of formats makes it more flexible for downstream usage!\n\n\n\n```py\nfrom diffusers.utils import export_to_ply\n\nply_path = export_to_ply(images[0], \"3d_cake.ply\")\nprint(f\"Saved to folder: {ply_path}\")\n```\n\nThen you can convert the `ply` file to a `glb` file with the trimesh library:\n\n```py\nimport trimesh\n\nmesh = trimesh.load(\"3d_cake.ply\")\nmesh_export = mesh.export(\"3d_cake.glb\", file_type=\"glb\")\n```\n\nBy default, the mesh output is focused from the bottom viewpoint but you can change the default viewpoint by applying a rotation transform:\n\n```py\nimport trimesh\nimport numpy as np\n\nmesh = trimesh.load(\"3d_cake.ply\")\nrot = trimesh.transformations.rotation_matrix(-np.pi / 2, [1, 0, 0])\nmesh = mesh.apply_transform(rot)\nmesh_export = mesh.export(\"3d_cake.glb\", file_type=\"glb\")\n```\n\nUpload the mesh file to your dataset repository to visualize it with the Dataset viewer!\n\n
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\n" }, { "path": "diffusers/docs/source/en/using-diffusers/stable_diffusion_jax_how_to.md", "content": "\n\n# JAX/Flax\n\n[[open-in-colab]]\n\n🤗 Diffusers supports Flax for super fast inference on Google TPUs, such as those available in Colab, Kaggle or Google Cloud Platform. This guide shows you how to run inference with Stable Diffusion using JAX/Flax.\n\nBefore you begin, make sure you have the necessary libraries installed:\n\n```py\n# uncomment to install the necessary libraries in Colab\n#!pip install -q jax==0.3.25 jaxlib==0.3.25 flax transformers ftfy\n#!pip install -q diffusers\n```\n\nYou should also make sure you're using a TPU backend. While JAX does not run exclusively on TPUs, you'll get the best performance on a TPU because each server has 8 TPU accelerators working in parallel.\n\nIf you are running this guide in Colab, select *Runtime* in the menu above, select the option *Change runtime type*, and then select *TPU* under the *Hardware accelerator* setting. Import JAX and quickly check whether you're using a TPU:\n\n```python\nimport jax\nimport jax.tools.colab_tpu\njax.tools.colab_tpu.setup_tpu()\n\nnum_devices = jax.device_count()\ndevice_type = jax.devices()[0].device_kind\n\nprint(f\"Found {num_devices} JAX devices of type {device_type}.\")\nassert (\n \"TPU\" in device_type,\n \"Available device is not a TPU, please select TPU from Runtime > Change runtime type > Hardware accelerator\"\n)\n# Found 8 JAX devices of type Cloud TPU.\n```\n\nGreat, now you can import the rest of the dependencies you'll need:\n\n```python\nimport jax.numpy as jnp\nfrom jax import pmap\nfrom flax.jax_utils import replicate\nfrom flax.training.common_utils import shard\n\nfrom diffusers import FlaxStableDiffusionPipeline\n```\n\n## Load a model\n\nFlax is a functional framework, so models are stateless and parameters are stored outside of them. Loading a pretrained Flax pipeline returns *both* the pipeline and the model weights (or parameters). In this guide, you'll use `bfloat16`, a more efficient half-float type that is supported by TPUs (you can also use `float32` for full precision if you want).\n\n```python\ndtype = jnp.bfloat16\npipeline, params = FlaxStableDiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n revision=\"bf16\",\n dtype=dtype,\n)\n```\n\n## Inference\n\nTPUs usually have 8 devices working in parallel, so let's use the same prompt for each device. This means you can perform inference on 8 devices at once, with each device generating one image. As a result, you'll get 8 images in the same amount of time it takes for one chip to generate a single image!\n\n\n\nLearn more details in the [How does parallelization work?](#how-does-parallelization-work) section.\n\n\n\nAfter replicating the prompt, get the tokenized text ids by calling the `prepare_inputs` function on the pipeline. The length of the tokenized text is set to 77 tokens as required by the configuration of the underlying CLIP text model.\n\n```python\nprompt = \"A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of field, close up, split lighting, cinematic\"\nprompt = [prompt] * jax.device_count()\nprompt_ids = pipeline.prepare_inputs(prompt)\nprompt_ids.shape\n# (8, 77)\n```\n\nModel parameters and inputs have to be replicated across the 8 parallel devices. The parameters dictionary is replicated with [`flax.jax_utils.replicate`](https://flax.readthedocs.io/en/latest/api_reference/flax.jax_utils.html#flax.jax_utils.replicate) which traverses the dictionary and changes the shape of the weights so they are repeated 8 times. Arrays are replicated using `shard`.\n\n```python\n# parameters\np_params = replicate(params)\n\n# arrays\nprompt_ids = shard(prompt_ids)\nprompt_ids.shape\n# (8, 1, 77)\n```\n\nThis shape means each one of the 8 devices receives as an input a `jnp` array with shape `(1, 77)`, where `1` is the batch size per device. On TPUs with sufficient memory, you could have a batch size larger than `1` if you want to generate multiple images (per chip) at once.\n\nNext, create a random number generator to pass to the generation function. This is standard procedure in Flax, which is very serious and opinionated about random numbers. All functions that deal with random numbers are expected to receive a generator to ensure reproducibility, even when you're training across multiple distributed devices.\n\nThe helper function below uses a seed to initialize a random number generator. As long as you use the same seed, you'll get the exact same results. Feel free to use different seeds when exploring results later in the guide.\n\n```python\ndef create_key(seed=0):\n return jax.random.PRNGKey(seed)\n```\n\nThe helper function, or `rng`, is split 8 times so each device receives a different generator and generates a different image.\n\n```python\nrng = create_key(0)\nrng = jax.random.split(rng, jax.device_count())\n```\n\nTo take advantage of JAX's optimized speed on a TPU, pass `jit=True` to the pipeline to compile the JAX code into an efficient representation and to ensure the model runs in parallel across the 8 devices.\n\n\n\nYou need to ensure all your inputs have the same shape in subsequent calls, otherwise JAX will need to recompile the code which is slower.\n\n\n\nThe first inference run takes more time because it needs to compile the code, but subsequent calls (even with different inputs) are much faster. For example, it took more than a minute to compile on a TPU v2-8, but then it takes about **7s** on a future inference run!\n\n```py\n%%time\nimages = pipeline(prompt_ids, p_params, rng, jit=True)[0]\n\n# CPU times: user 56.2 s, sys: 42.5 s, total: 1min 38s\n# Wall time: 1min 29s\n```\n\nThe returned array has shape `(8, 1, 512, 512, 3)` which should be reshaped to remove the second dimension and get 8 images of `512 × 512 × 3`. Then you can use the [`~utils.numpy_to_pil`] function to convert the arrays into images.\n\n```python\nfrom diffusers.utils import make_image_grid\n\nimages = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])\nimages = pipeline.numpy_to_pil(images)\nmake_image_grid(images, rows=2, cols=4)\n```\n\n![img](https://huggingface.co/datasets/YiYiXu/test-doc-assets/resolve/main/stable_diffusion_jax_how_to_cell_38_output_0.jpeg)\n\n## Using different prompts\n\nYou don't necessarily have to use the same prompt on all devices. For example, to generate 8 different prompts:\n\n```python\nprompts = [\n \"Labrador in the style of Hokusai\",\n \"Painting of a squirrel skating in New York\",\n \"HAL-9000 in the style of Van Gogh\",\n \"Times Square under water, with fish and a dolphin swimming around\",\n \"Ancient Roman fresco showing a man working on his laptop\",\n \"Close-up photograph of young black woman against urban background, high quality, bokeh\",\n \"Armchair in the shape of an avocado\",\n \"Clown astronaut in space, with Earth in the background\",\n]\n\nprompt_ids = pipeline.prepare_inputs(prompts)\nprompt_ids = shard(prompt_ids)\n\nimages = pipeline(prompt_ids, p_params, rng, jit=True).images\nimages = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])\nimages = pipeline.numpy_to_pil(images)\n\nmake_image_grid(images, 2, 4)\n```\n\n![img](https://huggingface.co/datasets/YiYiXu/test-doc-assets/resolve/main/stable_diffusion_jax_how_to_cell_43_output_0.jpeg)\n\n## How does parallelization work?\n\nThe Flax pipeline in 🤗 Diffusers automatically compiles the model and runs it in parallel on all available devices. Let's take a closer look at how that process works.\n\nJAX parallelization can be done in multiple ways. The easiest one revolves around using the [`jax.pmap`](https://jax.readthedocs.io/en/latest/_autosummary/jax.pmap.html) function to achieve single-program multiple-data (SPMD) parallelization. It means running several copies of the same code, each on different data inputs. More sophisticated approaches are possible, and you can go over to the JAX [documentation](https://jax.readthedocs.io/en/latest/index.html) to explore this topic in more detail if you are interested!\n\n`jax.pmap` does two things:\n\n1. Compiles (or \"`jit`s\") the code which is similar to `jax.jit()`. This does not happen when you call `pmap`, and only the first time the `pmap`ped function is called.\n2. Ensures the compiled code runs in parallel on all available devices.\n\nTo demonstrate, call `pmap` on the pipeline's `_generate` method (this is a private method that generates images and may be renamed or removed in future releases of 🤗 Diffusers):\n\n```python\np_generate = pmap(pipeline._generate)\n```\n\nAfter calling `pmap`, the prepared function `p_generate` will:\n\n1. Make a copy of the underlying function, `pipeline._generate`, on each device.\n2. Send each device a different portion of the input arguments (this is why it's necessary to call the *shard* function). In this case, `prompt_ids` has shape `(8, 1, 77, 768)` so the array is split into 8 and each copy of `_generate` receives an input with shape `(1, 77, 768)`.\n\nThe most important thing to pay attention to here is the batch size (1 in this example), and the input dimensions that make sense for your code. You don't have to change anything else to make the code work in parallel.\n\nThe first time you call the pipeline takes more time, but the calls afterward are much faster. The `block_until_ready` function is used to correctly measure inference time because JAX uses asynchronous dispatch and returns control to the Python loop as soon as it can. You don't need to use that in your code; blocking occurs automatically when you want to use the result of a computation that has not yet been materialized.\n\n```py\n%%time\nimages = p_generate(prompt_ids, p_params, rng)\nimages = images.block_until_ready()\n\n# CPU times: user 1min 15s, sys: 18.2 s, total: 1min 34s\n# Wall time: 1min 15s\n```\n\nCheck your image dimensions to see if they're correct:\n\n```python\nimages.shape\n# (8, 1, 512, 512, 3)\n```\n\n## Resources\n\nTo learn more about how JAX works with Stable Diffusion, you may be interested in reading:\n\n* [Accelerating Stable Diffusion XL Inference with JAX on Cloud TPU v5e](https://hf.co/blog/sdxl_jax)\n" }, { "path": "diffusers/docs/source/en/using-diffusers/svd.md", "content": "\n\n# Stable Video Diffusion\n\n[[open-in-colab]]\n\n[Stable Video Diffusion (SVD)](https://huggingface.co/papers/2311.15127) is a powerful image-to-video generation model that can generate 2-4 second high resolution (576x1024) videos conditioned on an input image.\n\nThis guide will show you how to use SVD to generate short videos from images.\n\nBefore you begin, make sure you have the following libraries installed:\n\n```py\n!pip install -q -U diffusers transformers accelerate\n```\n\nThe are two variants of this model, [SVD](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid) and [SVD-XT](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt). The SVD checkpoint is trained to generate 14 frames and the SVD-XT checkpoint is further finetuned to generate 25 frames.\n\nYou'll use the SVD-XT checkpoint for this guide.\n\n```python\nimport torch\n\nfrom diffusers import StableVideoDiffusionPipeline\nfrom diffusers.utils import load_image, export_to_video\n\npipe = StableVideoDiffusionPipeline.from_pretrained(\n \"stabilityai/stable-video-diffusion-img2vid-xt\", torch_dtype=torch.float16, variant=\"fp16\"\n)\npipe.enable_model_cpu_offload()\n\n# Load the conditioning image\nimage = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png\")\nimage = image.resize((1024, 576))\n\ngenerator = torch.manual_seed(42)\nframes = pipe(image, decode_chunk_size=8, generator=generator).frames[0]\n\nexport_to_video(frames, \"generated.mp4\", fps=7)\n```\n\n
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\"source image of a rocket\"
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\"generated video from source image\"
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\n\n## torch.compile\n\nYou can gain a 20-25% speedup at the expense of slightly increased memory by [compiling](../optimization/torch2.0#torchcompile) the UNet.\n\n```diff\n- pipe.enable_model_cpu_offload()\n+ pipe.to(\"cuda\")\n+ pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n```\n\n## Reduce memory usage\n\nVideo generation is very memory intensive because you're essentially generating `num_frames` all at once, similar to text-to-image generation with a high batch size. To reduce the memory requirement, there are multiple options that trade-off inference speed for lower memory requirement:\n\n- enable model offloading: each component of the pipeline is offloaded to the CPU once it's not needed anymore.\n- enable feed-forward chunking: the feed-forward layer runs in a loop instead of running a single feed-forward with a huge batch size.\n- reduce `decode_chunk_size`: the VAE decodes frames in chunks instead of decoding them all together. Setting `decode_chunk_size=1` decodes one frame at a time and uses the least amount of memory (we recommend adjusting this value based on your GPU memory) but the video might have some flickering.\n\n```diff\n- pipe.enable_model_cpu_offload()\n- frames = pipe(image, decode_chunk_size=8, generator=generator).frames[0]\n+ pipe.enable_model_cpu_offload()\n+ pipe.unet.enable_forward_chunking()\n+ frames = pipe(image, decode_chunk_size=2, generator=generator, num_frames=25).frames[0]\n```\n\nUsing all these tricks together should lower the memory requirement to less than 8GB VRAM.\n\n## Micro-conditioning\n\nStable Diffusion Video also accepts micro-conditioning, in addition to the conditioning image, which allows more control over the generated video:\n\n- `fps`: the frames per second of the generated video.\n- `motion_bucket_id`: the motion bucket id to use for the generated video. This can be used to control the motion of the generated video. Increasing the motion bucket id increases the motion of the generated video.\n- `noise_aug_strength`: the amount of noise added to the conditioning image. The higher the values the less the video resembles the conditioning image. Increasing this value also increases the motion of the generated video.\n\nFor example, to generate a video with more motion, use the `motion_bucket_id` and `noise_aug_strength` micro-conditioning parameters:\n\n```python\nimport torch\n\nfrom diffusers import StableVideoDiffusionPipeline\nfrom diffusers.utils import load_image, export_to_video\n\npipe = StableVideoDiffusionPipeline.from_pretrained(\n \"stabilityai/stable-video-diffusion-img2vid-xt\", torch_dtype=torch.float16, variant=\"fp16\"\n)\npipe.enable_model_cpu_offload()\n\n# Load the conditioning image\nimage = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png\")\nimage = image.resize((1024, 576))\n\ngenerator = torch.manual_seed(42)\nframes = pipe(image, decode_chunk_size=8, generator=generator, motion_bucket_id=180, noise_aug_strength=0.1).frames[0]\nexport_to_video(frames, \"generated.mp4\", fps=7)\n```\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/output_rocket_with_conditions.gif)\n" }, { "path": "diffusers/docs/source/en/using-diffusers/t2i_adapter.md", "content": "\n\n# T2I-Adapter\n\n[T2I-Adapter](https://hf.co/papers/2302.08453) is a lightweight adapter for controlling and providing more accurate\nstructure guidance for text-to-image models. It works by learning an alignment between the internal knowledge of the\ntext-to-image model and an external control signal, such as edge detection or depth estimation.\n\nThe T2I-Adapter design is simple, the condition is passed to four feature extraction blocks and three downsample\nblocks. This makes it fast and easy to train different adapters for different conditions which can be plugged into the\ntext-to-image model. T2I-Adapter is similar to [ControlNet](controlnet) except it is smaller (~77M parameters) and\nfaster because it only runs once during the diffusion process. The downside is that performance may be slightly worse\nthan ControlNet.\n\nThis guide will show you how to use T2I-Adapter with different Stable Diffusion models and how you can compose multiple\nT2I-Adapters to impose more than one condition.\n\n> [!TIP]\n> There are several T2I-Adapters available for different conditions, such as color palette, depth, sketch, pose, and\n> segmentation. Check out the [TencentARC](https://hf.co/TencentARC) repository to try them out!\n\nBefore you begin, make sure you have the following libraries installed.\n\n```py\n# uncomment to install the necessary libraries in Colab\n#!pip install -q diffusers accelerate controlnet-aux==0.0.7\n```\n\n## Text-to-image\n\nText-to-image models rely on a prompt to generate an image, but sometimes, text alone may not be enough to provide more\naccurate structural guidance. T2I-Adapter allows you to provide an additional control image to guide the generation\nprocess. For example, you can provide a canny image (a white outline of an image on a black background) to guide the\nmodel to generate an image with a similar structure.\n\n\n\n\nCreate a canny image with the [opencv-library](https://github.com/opencv/opencv-python).\n\n```py\nimport cv2\nimport numpy as np\nfrom PIL import Image\nfrom diffusers.utils import load_image\n\nimage = load_image(\"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png\")\nimage = np.array(image)\n\nlow_threshold = 100\nhigh_threshold = 200\n\nimage = cv2.Canny(image, low_threshold, high_threshold)\nimage = Image.fromarray(image)\n```\n\nNow load a T2I-Adapter conditioned on [canny images](https://hf.co/TencentARC/t2iadapter_canny_sd15v2) and pass it to\nthe [`StableDiffusionAdapterPipeline`].\n\n```py\nimport torch\nfrom diffusers import StableDiffusionAdapterPipeline, T2IAdapter\n\nadapter = T2IAdapter.from_pretrained(\"TencentARC/t2iadapter_canny_sd15v2\", torch_dtype=torch.float16)\npipeline = StableDiffusionAdapterPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n adapter=adapter,\n torch_dtype=torch.float16,\n)\npipeline.to(\"cuda\")\n```\n\nFinally, pass your prompt and control image to the pipeline.\n\n```py\ngenerator = torch.Generator(\"cuda\").manual_seed(0)\n\nimage = pipeline(\n prompt=\"cinematic photo of a plush and soft midcentury style rug on a wooden floor, 35mm photograph, film, professional, 4k, highly detailed\",\n image=image,\n generator=generator,\n).images[0]\nimage\n```\n\n
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\n\n\n\n\nCreate a canny image with the [controlnet-aux](https://github.com/huggingface/controlnet_aux) library.\n\n```py\nfrom controlnet_aux.canny import CannyDetector\nfrom diffusers.utils import load_image\n\ncanny_detector = CannyDetector()\n\nimage = load_image(\"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png\")\nimage = canny_detector(image, detect_resolution=384, image_resolution=1024)\n```\n\nNow load a T2I-Adapter conditioned on [canny images](https://hf.co/TencentARC/t2i-adapter-canny-sdxl-1.0) and pass it\nto the [`StableDiffusionXLAdapterPipeline`].\n\n```py\nimport torch\nfrom diffusers import StableDiffusionXLAdapterPipeline, T2IAdapter, EulerAncestralDiscreteScheduler, AutoencoderKL\n\nscheduler = EulerAncestralDiscreteScheduler.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\", subfolder=\"scheduler\")\nvae = AutoencoderKL.from_pretrained(\"madebyollin/sdxl-vae-fp16-fix\", torch_dtype=torch.float16)\nadapter = T2IAdapter.from_pretrained(\"TencentARC/t2i-adapter-canny-sdxl-1.0\", torch_dtype=torch.float16)\npipeline = StableDiffusionXLAdapterPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n adapter=adapter,\n vae=vae,\n scheduler=scheduler,\n torch_dtype=torch.float16,\n variant=\"fp16\",\n)\npipeline.to(\"cuda\")\n```\n\nFinally, pass your prompt and control image to the pipeline.\n\n```py\ngenerator = torch.Generator(\"cuda\").manual_seed(0)\n\nimage = pipeline(\n prompt=\"cinematic photo of a plush and soft midcentury style rug on a wooden floor, 35mm photograph, film, professional, 4k, highly detailed\",\n image=image,\n generator=generator,\n).images[0]\nimage\n```\n\n
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\n\n\n\n\n## MultiAdapter\n\nT2I-Adapters are also composable, allowing you to use more than one adapter to impose multiple control conditions on an\nimage. For example, you can use a pose map to provide structural control and a depth map for depth control. This is\nenabled by the [`MultiAdapter`] class.\n\nLet's condition a text-to-image model with a pose and depth adapter. Create and place your depth and pose image and in a list.\n\n```py\nfrom diffusers.utils import load_image\n\npose_image = load_image(\n \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_sample_input.png\"\n)\ndepth_image = load_image(\n \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/depth_sample_input.png\"\n)\ncond = [pose_image, depth_image]\nprompt = [\"Santa Claus walking into an office room with a beautiful city view\"]\n```\n\n
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depth image
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pose image
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\n\nLoad the corresponding pose and depth adapters as a list in the [`MultiAdapter`] class.\n\n```py\nimport torch\nfrom diffusers import StableDiffusionAdapterPipeline, MultiAdapter, T2IAdapter\n\nadapters = MultiAdapter(\n [\n T2IAdapter.from_pretrained(\"TencentARC/t2iadapter_keypose_sd14v1\"),\n T2IAdapter.from_pretrained(\"TencentARC/t2iadapter_depth_sd14v1\"),\n ]\n)\nadapters = adapters.to(torch.float16)\n```\n\nFinally, load a [`StableDiffusionAdapterPipeline`] with the adapters, and pass your prompt and conditioned images to\nit. Use the [`adapter_conditioning_scale`] to adjust the weight of each adapter on the image.\n\n```py\npipeline = StableDiffusionAdapterPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n torch_dtype=torch.float16,\n adapter=adapters,\n).to(\"cuda\")\n\nimage = pipeline(prompt, cond, adapter_conditioning_scale=[0.7, 0.7]).images[0]\nimage\n```\n\n
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" }, { "path": "diffusers/docs/source/en/using-diffusers/text-img2vid.md", "content": "\n\n# Text or image-to-video\n\nDriven by the success of text-to-image diffusion models, generative video models are able to generate short clips of video from a text prompt or an initial image. These models extend a pretrained diffusion model to generate videos by adding some type of temporal and/or spatial convolution layer to the architecture. A mixed dataset of images and videos are used to train the model which learns to output a series of video frames based on the text or image conditioning.\n\nThis guide will show you how to generate videos, how to configure video model parameters, and how to control video generation.\n\n## Popular models\n\n> [!TIP]\n> Discover other cool and trending video generation models on the Hub [here](https://huggingface.co/models?pipeline_tag=text-to-video&sort=trending)!\n\n[Stable Video Diffusions (SVD)](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid), [I2VGen-XL](https://huggingface.co/ali-vilab/i2vgen-xl/), [AnimateDiff](https://huggingface.co/guoyww/animatediff), and [ModelScopeT2V](https://huggingface.co/ali-vilab/text-to-video-ms-1.7b) are popular models used for video diffusion. Each model is distinct. For example, AnimateDiff inserts a motion modeling module into a frozen text-to-image model to generate personalized animated images, whereas SVD is entirely pretrained from scratch with a three-stage training process to generate short high-quality videos.\n\n### Stable Video Diffusion\n\n[SVD](../api/pipelines/svd) is based on the Stable Diffusion 2.1 model and it is trained on images, then low-resolution videos, and finally a smaller dataset of high-resolution videos. This model generates a short 2-4 second video from an initial image. You can learn more details about model, like micro-conditioning, in the [Stable Video Diffusion](../using-diffusers/svd) guide.\n\nBegin by loading the [`StableVideoDiffusionPipeline`] and passing an initial image to generate a video from.\n\n```py\nimport torch\nfrom diffusers import StableVideoDiffusionPipeline\nfrom diffusers.utils import load_image, export_to_video\n\npipeline = StableVideoDiffusionPipeline.from_pretrained(\n \"stabilityai/stable-video-diffusion-img2vid-xt\", torch_dtype=torch.float16, variant=\"fp16\"\n)\npipeline.enable_model_cpu_offload()\n\nimage = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png\")\nimage = image.resize((1024, 576))\n\ngenerator = torch.manual_seed(42)\nframes = pipeline(image, decode_chunk_size=8, generator=generator).frames[0]\nexport_to_video(frames, \"generated.mp4\", fps=7)\n```\n\n
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\n\n### I2VGen-XL\n\n[I2VGen-XL](../api/pipelines/i2vgenxl) is a diffusion model that can generate higher resolution videos than SVD and it is also capable of accepting text prompts in addition to images. The model is trained with two hierarchical encoders (detail and global encoder) to better capture low and high-level details in images. These learned details are used to train a video diffusion model which refines the video resolution and details in the generated video.\n\nYou can use I2VGen-XL by loading the [`I2VGenXLPipeline`], and passing a text and image prompt to generate a video.\n\n```py\nimport torch\nfrom diffusers import I2VGenXLPipeline\nfrom diffusers.utils import export_to_gif, load_image\n\npipeline = I2VGenXLPipeline.from_pretrained(\"ali-vilab/i2vgen-xl\", torch_dtype=torch.float16, variant=\"fp16\")\npipeline.enable_model_cpu_offload()\n\nimage_url = \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/i2vgen_xl_images/img_0009.png\"\nimage = load_image(image_url).convert(\"RGB\")\n\nprompt = \"Papers were floating in the air on a table in the library\"\nnegative_prompt = \"Distorted, discontinuous, Ugly, blurry, low resolution, motionless, static, disfigured, disconnected limbs, Ugly faces, incomplete arms\"\ngenerator = torch.manual_seed(8888)\n\nframes = pipeline(\n prompt=prompt,\n image=image,\n num_inference_steps=50,\n negative_prompt=negative_prompt,\n guidance_scale=9.0,\n generator=generator\n).frames[0]\nexport_to_gif(frames, \"i2v.gif\")\n```\n\n
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\n\n### AnimateDiff\n\n[AnimateDiff](../api/pipelines/animatediff) is an adapter model that inserts a motion module into a pretrained diffusion model to animate an image. The adapter is trained on video clips to learn motion which is used to condition the generation process to create a video. It is faster and easier to only train the adapter and it can be loaded into most diffusion models, effectively turning them into \"video models\".\n\nStart by loading a [`MotionAdapter`].\n\n```py\nimport torch\nfrom diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter\nfrom diffusers.utils import export_to_gif\n\nadapter = MotionAdapter.from_pretrained(\"guoyww/animatediff-motion-adapter-v1-5-2\", torch_dtype=torch.float16)\n```\n\nThen load a finetuned Stable Diffusion model with the [`AnimateDiffPipeline`].\n\n```py\npipeline = AnimateDiffPipeline.from_pretrained(\"emilianJR/epiCRealism\", motion_adapter=adapter, torch_dtype=torch.float16)\nscheduler = DDIMScheduler.from_pretrained(\n \"emilianJR/epiCRealism\",\n subfolder=\"scheduler\",\n clip_sample=False,\n timestep_spacing=\"linspace\",\n beta_schedule=\"linear\",\n steps_offset=1,\n)\npipeline.scheduler = scheduler\npipeline.enable_vae_slicing()\npipeline.enable_model_cpu_offload()\n```\n\nCreate a prompt and generate the video.\n\n```py\noutput = pipeline(\n prompt=\"A space rocket with trails of smoke behind it launching into space from the desert, 4k, high resolution\",\n negative_prompt=\"bad quality, worse quality, low resolution\",\n num_frames=16,\n guidance_scale=7.5,\n num_inference_steps=50,\n generator=torch.Generator(\"cpu\").manual_seed(49),\n)\nframes = output.frames[0]\nexport_to_gif(frames, \"animation.gif\")\n```\n\n
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\n\n### ModelscopeT2V\n\n[ModelscopeT2V](../api/pipelines/text_to_video) adds spatial and temporal convolutions and attention to a UNet, and it is trained on image-text and video-text datasets to enhance what it learns during training. The model takes a prompt, encodes it and creates text embeddings which are denoised by the UNet, and then decoded by a VQGAN into a video.\n\n\n\nModelScopeT2V generates watermarked videos due to the datasets it was trained on. To use a watermark-free model, try the [cerspense/zeroscope_v2_76w](https://huggingface.co/cerspense/zeroscope_v2_576w) model with the [`TextToVideoSDPipeline`] first, and then upscale it's output with the [cerspense/zeroscope_v2_XL](https://huggingface.co/cerspense/zeroscope_v2_XL) checkpoint using the [`VideoToVideoSDPipeline`].\n\n\n\nLoad a ModelScopeT2V checkpoint into the [`DiffusionPipeline`] along with a prompt to generate a video.\n\n```py\nimport torch\nfrom diffusers import DiffusionPipeline\nfrom diffusers.utils import export_to_video\n\npipeline = DiffusionPipeline.from_pretrained(\"damo-vilab/text-to-video-ms-1.7b\", torch_dtype=torch.float16, variant=\"fp16\")\npipeline.enable_model_cpu_offload()\npipeline.enable_vae_slicing()\n\nprompt = \"Confident teddy bear surfer rides the wave in the tropics\"\nvideo_frames = pipeline(prompt).frames[0]\nexport_to_video(video_frames, \"modelscopet2v.mp4\", fps=10)\n```\n\n
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\n\n## Configure model parameters\n\nThere are a few important parameters you can configure in the pipeline that'll affect the video generation process and quality. Let's take a closer look at what these parameters do and how changing them affects the output.\n\n### Number of frames\n\nThe `num_frames` parameter determines how many video frames are generated per second. A frame is an image that is played in a sequence of other frames to create motion or a video. This affects video length because the pipeline generates a certain number of frames per second (check a pipeline's API reference for the default value). To increase the video duration, you'll need to increase the `num_frames` parameter.\n\n```py\nimport torch\nfrom diffusers import StableVideoDiffusionPipeline\nfrom diffusers.utils import load_image, export_to_video\n\npipeline = StableVideoDiffusionPipeline.from_pretrained(\n \"stabilityai/stable-video-diffusion-img2vid\", torch_dtype=torch.float16, variant=\"fp16\"\n)\npipeline.enable_model_cpu_offload()\n\nimage = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png\")\nimage = image.resize((1024, 576))\n\ngenerator = torch.manual_seed(42)\nframes = pipeline(image, decode_chunk_size=8, generator=generator, num_frames=25).frames[0]\nexport_to_video(frames, \"generated.mp4\", fps=7)\n```\n\n
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\n\n### Guidance scale\n\nThe `guidance_scale` parameter controls how closely aligned the generated video and text prompt or initial image is. A higher `guidance_scale` value means your generated video is more aligned with the text prompt or initial image, while a lower `guidance_scale` value means your generated video is less aligned which could give the model more \"creativity\" to interpret the conditioning input.\n\n\n\nSVD uses the `min_guidance_scale` and `max_guidance_scale` parameters for applying guidance to the first and last frames respectively.\n\n\n\n```py\nimport torch\nfrom diffusers import I2VGenXLPipeline\nfrom diffusers.utils import export_to_gif, load_image\n\npipeline = I2VGenXLPipeline.from_pretrained(\"ali-vilab/i2vgen-xl\", torch_dtype=torch.float16, variant=\"fp16\")\npipeline.enable_model_cpu_offload()\n\nimage_url = \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/i2vgen_xl_images/img_0009.png\"\nimage = load_image(image_url).convert(\"RGB\")\n\nprompt = \"Papers were floating in the air on a table in the library\"\nnegative_prompt = \"Distorted, discontinuous, Ugly, blurry, low resolution, motionless, static, disfigured, disconnected limbs, Ugly faces, incomplete arms\"\ngenerator = torch.manual_seed(0)\n\nframes = pipeline(\n prompt=prompt,\n image=image,\n num_inference_steps=50,\n negative_prompt=negative_prompt,\n guidance_scale=1.0,\n generator=generator\n).frames[0]\nexport_to_gif(frames, \"i2v.gif\")\n```\n\n
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\n\n### Negative prompt\n\nA negative prompt deters the model from generating things you don’t want it to. This parameter is commonly used to improve overall generation quality by removing poor or bad features such as “low resolution” or “bad details”.\n\n```py\nimport torch\nfrom diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter\nfrom diffusers.utils import export_to_gif\n\nadapter = MotionAdapter.from_pretrained(\"guoyww/animatediff-motion-adapter-v1-5-2\", torch_dtype=torch.float16)\n\npipeline = AnimateDiffPipeline.from_pretrained(\"emilianJR/epiCRealism\", motion_adapter=adapter, torch_dtype=torch.float16)\nscheduler = DDIMScheduler.from_pretrained(\n \"emilianJR/epiCRealism\",\n subfolder=\"scheduler\",\n clip_sample=False,\n timestep_spacing=\"linspace\",\n beta_schedule=\"linear\",\n steps_offset=1,\n)\npipeline.scheduler = scheduler\npipeline.enable_vae_slicing()\npipeline.enable_model_cpu_offload()\n\noutput = pipeline(\n prompt=\"360 camera shot of a sushi roll in a restaurant\",\n negative_prompt=\"Distorted, discontinuous, ugly, blurry, low resolution, motionless, static\",\n num_frames=16,\n guidance_scale=7.5,\n num_inference_steps=50,\n generator=torch.Generator(\"cpu\").manual_seed(0),\n)\nframes = output.frames[0]\nexport_to_gif(frames, \"animation.gif\")\n```\n\n
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\n\n### Model-specific parameters\n\nThere are some pipeline parameters that are unique to each model such as adjusting the motion in a video or adding noise to the initial image.\n\n\n\n\nStable Video Diffusion provides additional micro-conditioning for the frame rate with the `fps` parameter and for motion with the `motion_bucket_id` parameter. Together, these parameters allow for adjusting the amount of motion in the generated video.\n\nThere is also a `noise_aug_strength` parameter that increases the amount of noise added to the initial image. Varying this parameter affects how similar the generated video and initial image are. A higher `noise_aug_strength` also increases the amount of motion. To learn more, read the [Micro-conditioning](../using-diffusers/svd#micro-conditioning) guide.\n\n\n\n\nText2Video-Zero computes the amount of motion to apply to each frame from randomly sampled latents. You can use the `motion_field_strength_x` and `motion_field_strength_y` parameters to control the amount of motion to apply to the x and y-axes of the video. The parameters `t0` and `t1` are the timesteps to apply motion to the latents.\n\n\n\n\n## Control video generation\n\nVideo generation can be controlled similar to how text-to-image, image-to-image, and inpainting can be controlled with a [`ControlNetModel`]. The only difference is you need to use the [`~pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.CrossFrameAttnProcessor`] so each frame attends to the first frame.\n\n### Text2Video-Zero\n\nText2Video-Zero video generation can be conditioned on pose and edge images for even greater control over a subject's motion in the generated video or to preserve the identity of a subject/object in the video. You can also use Text2Video-Zero with [InstructPix2Pix](../api/pipelines/pix2pix) for editing videos with text.\n\n\n\n\nStart by downloading a video and extracting the pose images from it.\n\n```py\nfrom huggingface_hub import hf_hub_download\nfrom PIL import Image\nimport imageio\n\nfilename = \"__assets__/poses_skeleton_gifs/dance1_corr.mp4\"\nrepo_id = \"PAIR/Text2Video-Zero\"\nvideo_path = hf_hub_download(repo_type=\"space\", repo_id=repo_id, filename=filename)\n\nreader = imageio.get_reader(video_path, \"ffmpeg\")\nframe_count = 8\npose_images = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)]\n```\n\nLoad a [`ControlNetModel`] for pose estimation and a checkpoint into the [`StableDiffusionControlNetPipeline`]. Then you'll use the [`~pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.CrossFrameAttnProcessor`] for the UNet and ControlNet.\n\n```py\nimport torch\nfrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel\nfrom diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor\n\nmodel_id = \"runwayml/stable-diffusion-v1-5\"\ncontrolnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-openpose\", torch_dtype=torch.float16)\npipeline = StableDiffusionControlNetPipeline.from_pretrained(\n model_id, controlnet=controlnet, torch_dtype=torch.float16\n).to(\"cuda\")\n\npipeline.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))\npipeline.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))\n```\n\nFix the latents for all the frames, and then pass your prompt and extracted pose images to the model to generate a video.\n\n```py\nlatents = torch.randn((1, 4, 64, 64), device=\"cuda\", dtype=torch.float16).repeat(len(pose_images), 1, 1, 1)\n\nprompt = \"Darth Vader dancing in a desert\"\nresult = pipeline(prompt=[prompt] * len(pose_images), image=pose_images, latents=latents).images\nimageio.mimsave(\"video.mp4\", result, fps=4)\n```\n\n\n\n\nDownload a video and extract the edges from it.\n\n```py\nfrom huggingface_hub import hf_hub_download\nfrom PIL import Image\nimport imageio\n\nfilename = \"__assets__/poses_skeleton_gifs/dance1_corr.mp4\"\nrepo_id = \"PAIR/Text2Video-Zero\"\nvideo_path = hf_hub_download(repo_type=\"space\", repo_id=repo_id, filename=filename)\n\nreader = imageio.get_reader(video_path, \"ffmpeg\")\nframe_count = 8\npose_images = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)]\n```\n\nLoad a [`ControlNetModel`] for canny edge and a checkpoint into the [`StableDiffusionControlNetPipeline`]. Then you'll use the [`~pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.CrossFrameAttnProcessor`] for the UNet and ControlNet.\n\n```py\nimport torch\nfrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel\nfrom diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor\n\nmodel_id = \"runwayml/stable-diffusion-v1-5\"\ncontrolnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\", torch_dtype=torch.float16)\npipeline = StableDiffusionControlNetPipeline.from_pretrained(\n model_id, controlnet=controlnet, torch_dtype=torch.float16\n).to(\"cuda\")\n\npipeline.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))\npipeline.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2))\n```\n\nFix the latents for all the frames, and then pass your prompt and extracted edge images to the model to generate a video.\n\n```py\nlatents = torch.randn((1, 4, 64, 64), device=\"cuda\", dtype=torch.float16).repeat(len(pose_images), 1, 1, 1)\n\nprompt = \"Darth Vader dancing in a desert\"\nresult = pipeline(prompt=[prompt] * len(pose_images), image=pose_images, latents=latents).images\nimageio.mimsave(\"video.mp4\", result, fps=4)\n```\n\n\n\n\nInstructPix2Pix allows you to use text to describe the changes you want to make to the video. Start by downloading and reading a video.\n\n```py\nfrom huggingface_hub import hf_hub_download\nfrom PIL import Image\nimport imageio\n\nfilename = \"__assets__/pix2pix video/camel.mp4\"\nrepo_id = \"PAIR/Text2Video-Zero\"\nvideo_path = hf_hub_download(repo_type=\"space\", repo_id=repo_id, filename=filename)\n\nreader = imageio.get_reader(video_path, \"ffmpeg\")\nframe_count = 8\nvideo = [Image.fromarray(reader.get_data(i)) for i in range(frame_count)]\n```\n\nLoad the [`StableDiffusionInstructPix2PixPipeline`] and set the [`~pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.CrossFrameAttnProcessor`] for the UNet.\n\n```py\nimport torch\nfrom diffusers import StableDiffusionInstructPix2PixPipeline\nfrom diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor\n\npipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained(\"timbrooks/instruct-pix2pix\", torch_dtype=torch.float16).to(\"cuda\")\npipeline.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=3))\n```\n\nPass a prompt describing the change you want to apply to the video.\n\n```py\nprompt = \"make it Van Gogh Starry Night style\"\nresult = pipeline(prompt=[prompt] * len(video), image=video).images\nimageio.mimsave(\"edited_video.mp4\", result, fps=4)\n```\n\n\n\n\n## Optimize\n\nVideo generation requires a lot of memory because you're generating many video frames at once. You can reduce your memory requirements at the expense of some inference speed. Try:\n\n1. offloading pipeline components that are no longer needed to the CPU\n2. feed-forward chunking runs the feed-forward layer in a loop instead of all at once\n3. break up the number of frames the VAE has to decode into chunks instead of decoding them all at once\n\n```diff\n- pipeline.enable_model_cpu_offload()\n- frames = pipeline(image, decode_chunk_size=8, generator=generator).frames[0]\n+ pipeline.enable_model_cpu_offload()\n+ pipeline.unet.enable_forward_chunking()\n+ frames = pipeline(image, decode_chunk_size=2, generator=generator, num_frames=25).frames[0]\n```\n\nIf memory is not an issue and you want to optimize for speed, try wrapping the UNet with [`torch.compile`](../optimization/torch2.0#torchcompile).\n\n```diff\n- pipeline.enable_model_cpu_offload()\n+ pipeline.to(\"cuda\")\n+ pipeline.unet = torch.compile(pipeline.unet, mode=\"reduce-overhead\", fullgraph=True)\n```\n" }, { "path": "diffusers/docs/source/en/using-diffusers/textual_inversion_inference.md", "content": "\n\n# Textual inversion\n\n[[open-in-colab]]\n\nThe [`StableDiffusionPipeline`] supports textual inversion, a technique that enables a model like Stable Diffusion to learn a new concept from just a few sample images. This gives you more control over the generated images and allows you to tailor the model towards specific concepts. You can get started quickly with a collection of community created concepts in the [Stable Diffusion Conceptualizer](https://huggingface.co/spaces/sd-concepts-library/stable-diffusion-conceptualizer).\n\nThis guide will show you how to run inference with textual inversion using a pre-learned concept from the Stable Diffusion Conceptualizer. If you're interested in teaching a model new concepts with textual inversion, take a look at the [Textual Inversion](../training/text_inversion) training guide.\n\nImport the necessary libraries:\n\n```py\nimport torch\nfrom diffusers import StableDiffusionPipeline\nfrom diffusers.utils import make_image_grid\n```\n\n## Stable Diffusion 1 and 2\n\nPick a Stable Diffusion checkpoint and a pre-learned concept from the [Stable Diffusion Conceptualizer](https://huggingface.co/spaces/sd-concepts-library/stable-diffusion-conceptualizer):\n\n```py\npretrained_model_name_or_path = \"runwayml/stable-diffusion-v1-5\"\nrepo_id_embeds = \"sd-concepts-library/cat-toy\"\n```\n\nNow you can load a pipeline, and pass the pre-learned concept to it:\n\n```py\npipeline = StableDiffusionPipeline.from_pretrained(\n pretrained_model_name_or_path, torch_dtype=torch.float16, use_safetensors=True\n).to(\"cuda\")\n\npipeline.load_textual_inversion(repo_id_embeds)\n```\n\nCreate a prompt with the pre-learned concept by using the special placeholder token ``, and choose the number of samples and rows of images you'd like to generate:\n\n```py\nprompt = \"a grafitti in a favela wall with a on it\"\n\nnum_samples_per_row = 2\nnum_rows = 2\n```\n\nThen run the pipeline (feel free to adjust the parameters like `num_inference_steps` and `guidance_scale` to see how they affect image quality), save the generated images and visualize them with the helper function you created at the beginning:\n\n```py\nall_images = []\nfor _ in range(num_rows):\n images = pipeline(prompt, num_images_per_prompt=num_samples_per_row, num_inference_steps=50, guidance_scale=7.5).images\n all_images.extend(images)\n\ngrid = make_image_grid(all_images, num_rows, num_samples_per_row)\ngrid\n```\n\n
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\n\n## Stable Diffusion XL\n\nStable Diffusion XL (SDXL) can also use textual inversion vectors for inference. In contrast to Stable Diffusion 1 and 2, SDXL has two text encoders so you'll need two textual inversion embeddings - one for each text encoder model.\n\nLet's download the SDXL textual inversion embeddings and have a closer look at it's structure:\n\n```py\nfrom huggingface_hub import hf_hub_download\nfrom safetensors.torch import load_file\n\nfile = hf_hub_download(\"dn118/unaestheticXL\", filename=\"unaestheticXLv31.safetensors\")\nstate_dict = load_file(file)\nstate_dict\n```\n\n```\n{'clip_g': tensor([[ 0.0077, -0.0112, 0.0065, ..., 0.0195, 0.0159, 0.0275],\n ...,\n [-0.0170, 0.0213, 0.0143, ..., -0.0302, -0.0240, -0.0362]],\n 'clip_l': tensor([[ 0.0023, 0.0192, 0.0213, ..., -0.0385, 0.0048, -0.0011],\n ...,\n [ 0.0475, -0.0508, -0.0145, ..., 0.0070, -0.0089, -0.0163]],\n```\n\nThere are two tensors, `\"clip_g\"` and `\"clip_l\"`.\n`\"clip_g\"` corresponds to the bigger text encoder in SDXL and refers to\n`pipe.text_encoder_2` and `\"clip_l\"` refers to `pipe.text_encoder`.\n\nNow you can load each tensor separately by passing them along with the correct text encoder and tokenizer\nto [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`]:\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipe = AutoPipelineForText2Image.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\", variant=\"fp16\", torch_dtype=torch.float16)\npipe.to(\"cuda\")\n\npipe.load_textual_inversion(state_dict[\"clip_g\"], token=\"unaestheticXLv31\", text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)\npipe.load_textual_inversion(state_dict[\"clip_l\"], token=\"unaestheticXLv31\", text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)\n\n# the embedding should be used as a negative embedding, so we pass it as a negative prompt\ngenerator = torch.Generator().manual_seed(33)\nimage = pipe(\"a woman standing in front of a mountain\", negative_prompt=\"unaestheticXLv31\", generator=generator).images[0]\nimage\n```\n" }, { "path": "diffusers/docs/source/en/using-diffusers/unconditional_image_generation.md", "content": "\n\n# Unconditional image generation\n\n[[open-in-colab]]\n\nUnconditional image generation generates images that look like a random sample from the training data the model was trained on because the denoising process is not guided by any additional context like text or image.\n\nTo get started, use the [`DiffusionPipeline`] to load the [anton-l/ddpm-butterflies-128](https://huggingface.co/anton-l/ddpm-butterflies-128) checkpoint to generate images of butterflies. The [`DiffusionPipeline`] downloads and caches all the model components required to generate an image.\n\n```py\nfrom diffusers import DiffusionPipeline\n\ngenerator = DiffusionPipeline.from_pretrained(\"anton-l/ddpm-butterflies-128\").to(\"cuda\")\nimage = generator().images[0]\nimage\n```\n\n\n\nWant to generate images of something else? Take a look at the training [guide](../training/unconditional_training) to learn how to train a model to generate your own images.\n\n\n\nThe output image is a [`PIL.Image`](https://pillow.readthedocs.io/en/stable/reference/Image.html?highlight=image#the-image-class) object that can be saved:\n\n```py\nimage.save(\"generated_image.png\")\n```\n\nYou can also try experimenting with the `num_inference_steps` parameter, which controls the number of denoising steps. More denoising steps typically produce higher quality images, but it'll take longer to generate. Feel free to play around with this parameter to see how it affects the image quality.\n\n```py\nimage = generator(num_inference_steps=100).images[0]\nimage\n```\n\nTry out the Space below to generate an image of a butterfly!\n\n\n" }, { "path": "diffusers/docs/source/en/using-diffusers/weighted_prompts.md", "content": "\n\n# Prompt techniques\n\n[[open-in-colab]]\n\nPrompts are important because they describe what you want a diffusion model to generate. The best prompts are detailed, specific, and well-structured to help the model realize your vision. But crafting a great prompt takes time and effort and sometimes it may not be enough because language and words can be imprecise. This is where you need to boost your prompt with other techniques, such as prompt enhancing and prompt weighting, to get the results you want.\n\nThis guide will show you how you can use these prompt techniques to generate high-quality images with lower effort and adjust the weight of certain keywords in a prompt.\n\n## Prompt engineering\n\n> [!TIP]\n> This is not an exhaustive guide on prompt engineering, but it will help you understand the necessary parts of a good prompt. We encourage you to continue experimenting with different prompts and combine them in new ways to see what works best. As you write more prompts, you'll develop an intuition for what works and what doesn't!\n\nNew diffusion models do a pretty good job of generating high-quality images from a basic prompt, but it is still important to create a well-written prompt to get the best results. Here are a few tips for writing a good prompt:\n\n1. What is the image *medium*? Is it a photo, a painting, a 3D illustration, or something else?\n2. What is the image *subject*? Is it a person, animal, object, or scene?\n3. What *details* would you like to see in the image? This is where you can get really creative and have a lot of fun experimenting with different words to bring your image to life. For example, what is the lighting like? What is the vibe and aesthetic? What kind of art or illustration style are you looking for? The more specific and precise words you use, the better the model will understand what you want to generate.\n\n
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\"A photo of a banana-shaped couch in a living room\"
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\"A vibrant yellow banana-shaped couch sits in a cozy living room, its curve cradling a pile of colorful cushions. on the wooden floor, a patterned rug adds a touch of eclectic charm, and a potted plant sits in the corner, reaching towards the sunlight filtering through the windows\"
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\n\n## Prompt enhancing with GPT2\n\nPrompt enhancing is a technique for quickly improving prompt quality without spending too much effort constructing one. It uses a model like GPT2 pretrained on Stable Diffusion text prompts to automatically enrich a prompt with additional important keywords to generate high-quality images.\n\nThe technique works by curating a list of specific keywords and forcing the model to generate those words to enhance the original prompt. This way, your prompt can be \"a cat\" and GPT2 can enhance the prompt to \"cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain quality sharp focus beautiful detailed intricate stunning amazing epic\".\n\n> [!TIP]\n> You should also use a [*offset noise*](https://www.crosslabs.org//blog/diffusion-with-offset-noise) LoRA to improve the contrast in bright and dark images and create better lighting overall. This [LoRA](https://hf.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_offset_example-lora_1.0.safetensors) is available from [stabilityai/stable-diffusion-xl-base-1.0](https://hf.co/stabilityai/stable-diffusion-xl-base-1.0).\n\nStart by defining certain styles and a list of words (you can check out a more comprehensive list of [words](https://hf.co/LykosAI/GPT-Prompt-Expansion-Fooocus-v2/blob/main/positive.txt) and [styles](https://github.com/lllyasviel/Fooocus/tree/main/sdxl_styles) used by Fooocus) to enhance a prompt with.\n\n```py\nimport torch\nfrom transformers import GenerationConfig, GPT2LMHeadModel, GPT2Tokenizer, LogitsProcessor, LogitsProcessorList\nfrom diffusers import StableDiffusionXLPipeline\n\nstyles = {\n \"cinematic\": \"cinematic film still of {prompt}, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain\",\n \"anime\": \"anime artwork of {prompt}, anime style, key visual, vibrant, studio anime, highly detailed\",\n \"photographic\": \"cinematic photo of {prompt}, 35mm photograph, film, professional, 4k, highly detailed\",\n \"comic\": \"comic of {prompt}, graphic illustration, comic art, graphic novel art, vibrant, highly detailed\",\n \"lineart\": \"line art drawing {prompt}, professional, sleek, modern, minimalist, graphic, line art, vector graphics\",\n \"pixelart\": \" pixel-art {prompt}, low-res, blocky, pixel art style, 8-bit graphics\",\n}\n\nwords = [\n \"aesthetic\", \"astonishing\", \"beautiful\", \"breathtaking\", \"composition\", \"contrasted\", \"epic\", \"moody\", \"enhanced\",\n \"exceptional\", \"fascinating\", \"flawless\", \"glamorous\", \"glorious\", \"illumination\", \"impressive\", \"improved\",\n \"inspirational\", \"magnificent\", \"majestic\", \"hyperrealistic\", \"smooth\", \"sharp\", \"focus\", \"stunning\", \"detailed\",\n \"intricate\", \"dramatic\", \"high\", \"quality\", \"perfect\", \"light\", \"ultra\", \"highly\", \"radiant\", \"satisfying\",\n \"soothing\", \"sophisticated\", \"stylish\", \"sublime\", \"terrific\", \"touching\", \"timeless\", \"wonderful\", \"unbelievable\",\n \"elegant\", \"awesome\", \"amazing\", \"dynamic\", \"trendy\",\n]\n```\n\nYou may have noticed in the `words` list, there are certain words that can be paired together to create something more meaningful. For example, the words \"high\" and \"quality\" can be combined to create \"high quality\". Let's pair these words together and remove the words that can't be paired.\n\n```py\nword_pairs = [\"highly detailed\", \"high quality\", \"enhanced quality\", \"perfect composition\", \"dynamic light\"]\n\ndef find_and_order_pairs(s, pairs):\n words = s.split()\n found_pairs = []\n for pair in pairs:\n pair_words = pair.split()\n if pair_words[0] in words and pair_words[1] in words:\n found_pairs.append(pair)\n words.remove(pair_words[0])\n words.remove(pair_words[1])\n\n for word in words[:]:\n for pair in pairs:\n if word in pair.split():\n words.remove(word)\n break\n ordered_pairs = \", \".join(found_pairs)\n remaining_s = \", \".join(words)\n return ordered_pairs, remaining_s\n```\n\nNext, implement a custom [`~transformers.LogitsProcessor`] class that assigns tokens in the `words` list a value of 0 and assigns tokens not in the `words` list a negative value so they aren't picked during generation. This way, generation is biased towards words in the `words` list. After a word from the list is used, it is also assigned a negative value so it isn't picked again.\n\n```py\nclass CustomLogitsProcessor(LogitsProcessor):\n def __init__(self, bias):\n super().__init__()\n self.bias = bias\n\n def __call__(self, input_ids, scores):\n if len(input_ids.shape) == 2:\n last_token_id = input_ids[0, -1]\n self.bias[last_token_id] = -1e10\n return scores + self.bias\n\nword_ids = [tokenizer.encode(word, add_prefix_space=True)[0] for word in words]\nbias = torch.full((tokenizer.vocab_size,), -float(\"Inf\")).to(\"cuda\")\nbias[word_ids] = 0\nprocessor = CustomLogitsProcessor(bias)\nprocessor_list = LogitsProcessorList([processor])\n```\n\nCombine the prompt and the `cinematic` style prompt defined in the `styles` dictionary earlier.\n\n```py\nprompt = \"a cat basking in the sun on a roof in Turkey\"\nstyle = \"cinematic\"\n\nprompt = styles[style].format(prompt=prompt)\nprompt\n\"cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain\"\n```\n\nLoad a GPT2 tokenizer and model from the [Gustavosta/MagicPrompt-Stable-Diffusion](https://huggingface.co/Gustavosta/MagicPrompt-Stable-Diffusion) checkpoint (this specific checkpoint is trained to generate prompts) to enhance the prompt.\n\n```py\ntokenizer = GPT2Tokenizer.from_pretrained(\"Gustavosta/MagicPrompt-Stable-Diffusion\")\nmodel = GPT2LMHeadModel.from_pretrained(\"Gustavosta/MagicPrompt-Stable-Diffusion\", torch_dtype=torch.float16).to(\n \"cuda\"\n)\nmodel.eval()\n\ninputs = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda\")\ntoken_count = inputs[\"input_ids\"].shape[1]\nmax_new_tokens = 50 - token_count\n\ngeneration_config = GenerationConfig(\n penalty_alpha=0.7,\n top_k=50,\n eos_token_id=model.config.eos_token_id,\n pad_token_id=model.config.eos_token_id,\n pad_token=model.config.pad_token_id,\n do_sample=True,\n)\n\nwith torch.no_grad():\n generated_ids = model.generate(\n input_ids=inputs[\"input_ids\"],\n attention_mask=inputs[\"attention_mask\"],\n max_new_tokens=max_new_tokens,\n generation_config=generation_config,\n logits_processor=proccesor_list,\n )\n```\n\nThen you can combine the input prompt and the generated prompt. Feel free to take a look at what the generated prompt (`generated_part`) is, the word pairs that were found (`pairs`), and the remaining words (`words`). This is all packed together in the `enhanced_prompt`.\n\n```py\noutput_tokens = [tokenizer.decode(generated_id, skip_special_tokens=True) for generated_id in generated_ids]\ninput_part, generated_part = output_tokens[0][: len(prompt)], output_tokens[0][len(prompt) :]\npairs, words = find_and_order_pairs(generated_part, word_pairs)\nformatted_generated_part = pairs + \", \" + words\nenhanced_prompt = input_part + \", \" + formatted_generated_part\nenhanced_prompt\n[\"cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain quality sharp focus beautiful detailed intricate stunning amazing epic\"]\n```\n\nFinally, load a pipeline and the offset noise LoRA with a *low weight* to generate an image with the enhanced prompt.\n\n```py\npipeline = StableDiffusionXLPipeline.from_pretrained(\n \"RunDiffusion/Juggernaut-XL-v9\", torch_dtype=torch.float16, variant=\"fp16\"\n).to(\"cuda\")\n\npipeline.load_lora_weights(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n weight_name=\"sd_xl_offset_example-lora_1.0.safetensors\",\n adapter_name=\"offset\",\n)\npipeline.set_adapters([\"offset\"], adapter_weights=[0.2])\n\nimage = pipeline(\n enhanced_prompt,\n width=1152,\n height=896,\n guidance_scale=7.5,\n num_inference_steps=25,\n).images[0]\nimage\n```\n\n
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\"a cat basking in the sun on a roof in Turkey\"
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\"cinematic film still of a cat basking in the sun on a roof in Turkey, highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain\"
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\n\n## Prompt weighting\n\nPrompt weighting provides a way to emphasize or de-emphasize certain parts of a prompt, allowing for more control over the generated image. A prompt can include several concepts, which gets turned into contextualized text embeddings. The embeddings are used by the model to condition its cross-attention layers to generate an image (read the Stable Diffusion [blog post](https://huggingface.co/blog/stable_diffusion) to learn more about how it works).\n\nPrompt weighting works by increasing or decreasing the scale of the text embedding vector that corresponds to its concept in the prompt because you may not necessarily want the model to focus on all concepts equally. The easiest way to prepare the prompt-weighted embeddings is to use [Compel](https://github.com/damian0815/compel), a text prompt-weighting and blending library. Once you have the prompt-weighted embeddings, you can pass them to any pipeline that has a [`prompt_embeds`](https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline.__call__.prompt_embeds) (and optionally [`negative_prompt_embeds`](https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline.__call__.negative_prompt_embeds)) parameter, such as [`StableDiffusionPipeline`], [`StableDiffusionControlNetPipeline`], and [`StableDiffusionXLPipeline`].\n\n\n\nIf your favorite pipeline doesn't have a `prompt_embeds` parameter, please open an [issue](https://github.com/huggingface/diffusers/issues/new/choose) so we can add it!\n\n\n\nThis guide will show you how to weight and blend your prompts with Compel in 🤗 Diffusers.\n\nBefore you begin, make sure you have the latest version of Compel installed:\n\n```py\n# uncomment to install in Colab\n#!pip install compel --upgrade\n```\n\nFor this guide, let's generate an image with the prompt `\"a red cat playing with a ball\"` using the [`StableDiffusionPipeline`]:\n\n```py\nfrom diffusers import StableDiffusionPipeline, UniPCMultistepScheduler\nimport torch\n\npipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", use_safetensors=True)\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\npipe.to(\"cuda\")\n\nprompt = \"a red cat playing with a ball\"\n\ngenerator = torch.Generator(device=\"cpu\").manual_seed(33)\n\nimage = pipe(prompt, generator=generator, num_inference_steps=20).images[0]\nimage\n```\n\n
\n \n
\n\n### Weighting\n\nYou'll notice there is no \"ball\" in the image! Let's use compel to upweight the concept of \"ball\" in the prompt. Create a [`Compel`](https://github.com/damian0815/compel/blob/main/doc/compel.md#compel-objects) object, and pass it a tokenizer and text encoder:\n\n```py\nfrom compel import Compel\n\ncompel_proc = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder)\n```\n\ncompel uses `+` or `-` to increase or decrease the weight of a word in the prompt. To increase the weight of \"ball\":\n\n\n\n`+` corresponds to the value `1.1`, `++` corresponds to `1.1^2`, and so on. Similarly, `-` corresponds to `0.9` and `--` corresponds to `0.9^2`. Feel free to experiment with adding more `+` or `-` in your prompt!\n\n\n\n```py\nprompt = \"a red cat playing with a ball++\"\n```\n\nPass the prompt to `compel_proc` to create the new prompt embeddings which are passed to the pipeline:\n\n```py\nprompt_embeds = compel_proc(prompt)\ngenerator = torch.manual_seed(33)\n\nimage = pipe(prompt_embeds=prompt_embeds, generator=generator, num_inference_steps=20).images[0]\nimage\n```\n\n
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\n\nTo downweight parts of the prompt, use the `-` suffix:\n\n```py\nprompt = \"a red------- cat playing with a ball\"\nprompt_embeds = compel_proc(prompt)\n\ngenerator = torch.manual_seed(33)\n\nimage = pipe(prompt_embeds=prompt_embeds, generator=generator, num_inference_steps=20).images[0]\nimage\n```\n\n
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\n\nYou can even up or downweight multiple concepts in the same prompt:\n\n```py\nprompt = \"a red cat++ playing with a ball----\"\nprompt_embeds = compel_proc(prompt)\n\ngenerator = torch.manual_seed(33)\n\nimage = pipe(prompt_embeds=prompt_embeds, generator=generator, num_inference_steps=20).images[0]\nimage\n```\n\n
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\n\n### Blending\n\nYou can also create a weighted *blend* of prompts by adding `.blend()` to a list of prompts and passing it some weights. Your blend may not always produce the result you expect because it breaks some assumptions about how the text encoder functions, so just have fun and experiment with it!\n\n```py\nprompt_embeds = compel_proc('(\"a red cat playing with a ball\", \"jungle\").blend(0.7, 0.8)')\ngenerator = torch.Generator(device=\"cuda\").manual_seed(33)\n\nimage = pipe(prompt_embeds=prompt_embeds, generator=generator, num_inference_steps=20).images[0]\nimage\n```\n\n
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\n\n### Conjunction\n\nA conjunction diffuses each prompt independently and concatenates their results by their weighted sum. Add `.and()` to the end of a list of prompts to create a conjunction:\n\n```py\nprompt_embeds = compel_proc('[\"a red cat\", \"playing with a\", \"ball\"].and()')\ngenerator = torch.Generator(device=\"cuda\").manual_seed(55)\n\nimage = pipe(prompt_embeds=prompt_embeds, generator=generator, num_inference_steps=20).images[0]\nimage\n```\n\n
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\n\n### Textual inversion\n\n[Textual inversion](../training/text_inversion) is a technique for learning a specific concept from some images which you can use to generate new images conditioned on that concept.\n\nCreate a pipeline and use the [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] function to load the textual inversion embeddings (feel free to browse the [Stable Diffusion Conceptualizer](https://huggingface.co/spaces/sd-concepts-library/stable-diffusion-conceptualizer) for 100+ trained concepts):\n\n```py\nimport torch\nfrom diffusers import StableDiffusionPipeline\nfrom compel import Compel, DiffusersTextualInversionManager\n\npipe = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16,\n use_safetensors=True, variant=\"fp16\").to(\"cuda\")\npipe.load_textual_inversion(\"sd-concepts-library/midjourney-style\")\n```\n\nCompel provides a `DiffusersTextualInversionManager` class to simplify prompt weighting with textual inversion. Instantiate `DiffusersTextualInversionManager` and pass it to the `Compel` class:\n\n```py\ntextual_inversion_manager = DiffusersTextualInversionManager(pipe)\ncompel_proc = Compel(\n tokenizer=pipe.tokenizer,\n text_encoder=pipe.text_encoder,\n textual_inversion_manager=textual_inversion_manager)\n```\n\nIncorporate the concept to condition a prompt with using the `` syntax:\n\n```py\nprompt_embeds = compel_proc('(\"A red cat++ playing with a ball \")')\n\nimage = pipe(prompt_embeds=prompt_embeds).images[0]\nimage\n```\n\n
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\n\n### DreamBooth\n\n[DreamBooth](../training/dreambooth) is a technique for generating contextualized images of a subject given just a few images of the subject to train on. It is similar to textual inversion, but DreamBooth trains the full model whereas textual inversion only fine-tunes the text embeddings. This means you should use [`~DiffusionPipeline.from_pretrained`] to load the DreamBooth model (feel free to browse the [Stable Diffusion Dreambooth Concepts Library](https://huggingface.co/sd-dreambooth-library) for 100+ trained models):\n\n```py\nimport torch\nfrom diffusers import DiffusionPipeline, UniPCMultistepScheduler\nfrom compel import Compel\n\npipe = DiffusionPipeline.from_pretrained(\"sd-dreambooth-library/dndcoverart-v1\", torch_dtype=torch.float16).to(\"cuda\")\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n```\n\nCreate a `Compel` class with a tokenizer and text encoder, and pass your prompt to it. Depending on the model you use, you'll need to incorporate the model's unique identifier into your prompt. For example, the `dndcoverart-v1` model uses the identifier `dndcoverart`:\n\n```py\ncompel_proc = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder)\nprompt_embeds = compel_proc('(\"magazine cover of a dndcoverart dragon, high quality, intricate details, larry elmore art style\").and()')\nimage = pipe(prompt_embeds=prompt_embeds).images[0]\nimage\n```\n\n
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\n\n### Stable Diffusion XL\n\nStable Diffusion XL (SDXL) has two tokenizers and text encoders so it's usage is a bit different. To address this, you should pass both tokenizers and encoders to the `Compel` class:\n\n```py\nfrom compel import Compel, ReturnedEmbeddingsType\nfrom diffusers import DiffusionPipeline\nfrom diffusers.utils import make_image_grid\nimport torch\n\npipeline = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n variant=\"fp16\",\n use_safetensors=True,\n torch_dtype=torch.float16\n).to(\"cuda\")\n\ncompel = Compel(\n tokenizer=[pipeline.tokenizer, pipeline.tokenizer_2] ,\n text_encoder=[pipeline.text_encoder, pipeline.text_encoder_2],\n returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,\n requires_pooled=[False, True]\n)\n```\n\nThis time, let's upweight \"ball\" by a factor of 1.5 for the first prompt, and downweight \"ball\" by 0.6 for the second prompt. The [`StableDiffusionXLPipeline`] also requires [`pooled_prompt_embeds`](https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLInpaintPipeline.__call__.pooled_prompt_embeds) (and optionally [`negative_pooled_prompt_embeds`](https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLInpaintPipeline.__call__.negative_pooled_prompt_embeds)) so you should pass those to the pipeline along with the conditioning tensors:\n\n```py\n# apply weights\nprompt = [\"a red cat playing with a (ball)1.5\", \"a red cat playing with a (ball)0.6\"]\nconditioning, pooled = compel(prompt)\n\n# generate image\ngenerator = [torch.Generator().manual_seed(33) for _ in range(len(prompt))]\nimages = pipeline(prompt_embeds=conditioning, pooled_prompt_embeds=pooled, generator=generator, num_inference_steps=30).images\nmake_image_grid(images, rows=1, cols=2)\n```\n\n
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\"a red cat playing with a (ball)1.5\"
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\"a red cat playing with a (ball)0.6\"
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\n" }, { "path": "diffusers/docs/source/en/using-diffusers/write_own_pipeline.md", "content": "\n\n# Understanding pipelines, models and schedulers\n\n[[open-in-colab]]\n\n🧨 Diffusers is designed to be a user-friendly and flexible toolbox for building diffusion systems tailored to your use-case. At the core of the toolbox are models and schedulers. While the [`DiffusionPipeline`] bundles these components together for convenience, you can also unbundle the pipeline and use the models and schedulers separately to create new diffusion systems.\n\nIn this tutorial, you'll learn how to use models and schedulers to assemble a diffusion system for inference, starting with a basic pipeline and then progressing to the Stable Diffusion pipeline.\n\n## Deconstruct a basic pipeline\n\nA pipeline is a quick and easy way to run a model for inference, requiring no more than four lines of code to generate an image:\n\n```py\n>>> from diffusers import DDPMPipeline\n\n>>> ddpm = DDPMPipeline.from_pretrained(\"google/ddpm-cat-256\", use_safetensors=True).to(\"cuda\")\n>>> image = ddpm(num_inference_steps=25).images[0]\n>>> image\n```\n\n
\n \"Image\n
\n\nThat was super easy, but how did the pipeline do that? Let's breakdown the pipeline and take a look at what's happening under the hood.\n\nIn the example above, the pipeline contains a [`UNet2DModel`] model and a [`DDPMScheduler`]. The pipeline denoises an image by taking random noise the size of the desired output and passing it through the model several times. At each timestep, the model predicts the *noise residual* and the scheduler uses it to predict a less noisy image. The pipeline repeats this process until it reaches the end of the specified number of inference steps.\n\nTo recreate the pipeline with the model and scheduler separately, let's write our own denoising process.\n\n1. Load the model and scheduler:\n\n```py\n>>> from diffusers import DDPMScheduler, UNet2DModel\n\n>>> scheduler = DDPMScheduler.from_pretrained(\"google/ddpm-cat-256\")\n>>> model = UNet2DModel.from_pretrained(\"google/ddpm-cat-256\", use_safetensors=True).to(\"cuda\")\n```\n\n2. Set the number of timesteps to run the denoising process for:\n\n```py\n>>> scheduler.set_timesteps(50)\n```\n\n3. Setting the scheduler timesteps creates a tensor with evenly spaced elements in it, 50 in this example. Each element corresponds to a timestep at which the model denoises an image. When you create the denoising loop later, you'll iterate over this tensor to denoise an image:\n\n```py\n>>> scheduler.timesteps\ntensor([980, 960, 940, 920, 900, 880, 860, 840, 820, 800, 780, 760, 740, 720,\n 700, 680, 660, 640, 620, 600, 580, 560, 540, 520, 500, 480, 460, 440,\n 420, 400, 380, 360, 340, 320, 300, 280, 260, 240, 220, 200, 180, 160,\n 140, 120, 100, 80, 60, 40, 20, 0])\n```\n\n4. Create some random noise with the same shape as the desired output:\n\n```py\n>>> import torch\n\n>>> sample_size = model.config.sample_size\n>>> noise = torch.randn((1, 3, sample_size, sample_size), device=\"cuda\")\n```\n\n5. Now write a loop to iterate over the timesteps. At each timestep, the model does a [`UNet2DModel.forward`] pass and returns the noisy residual. The scheduler's [`~DDPMScheduler.step`] method takes the noisy residual, timestep, and input and it predicts the image at the previous timestep. This output becomes the next input to the model in the denoising loop, and it'll repeat until it reaches the end of the `timesteps` array.\n\n```py\n>>> input = noise\n\n>>> for t in scheduler.timesteps:\n... with torch.no_grad():\n... noisy_residual = model(input, t).sample\n... previous_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample\n... input = previous_noisy_sample\n```\n\nThis is the entire denoising process, and you can use this same pattern to write any diffusion system.\n\n6. The last step is to convert the denoised output into an image:\n\n```py\n>>> from PIL import Image\n>>> import numpy as np\n\n>>> image = (input / 2 + 0.5).clamp(0, 1).squeeze()\n>>> image = (image.permute(1, 2, 0) * 255).round().to(torch.uint8).cpu().numpy()\n>>> image = Image.fromarray(image)\n>>> image\n```\n\nIn the next section, you'll put your skills to the test and breakdown the more complex Stable Diffusion pipeline. The steps are more or less the same. You'll initialize the necessary components, and set the number of timesteps to create a `timestep` array. The `timestep` array is used in the denoising loop, and for each element in this array, the model predicts a less noisy image. The denoising loop iterates over the `timestep`'s, and at each timestep, it outputs a noisy residual and the scheduler uses it to predict a less noisy image at the previous timestep. This process is repeated until you reach the end of the `timestep` array.\n\nLet's try it out!\n\n## Deconstruct the Stable Diffusion pipeline\n\nStable Diffusion is a text-to-image *latent diffusion* model. It is called a latent diffusion model because it works with a lower-dimensional representation of the image instead of the actual pixel space, which makes it more memory efficient. The encoder compresses the image into a smaller representation, and a decoder to convert the compressed representation back into an image. For text-to-image models, you'll need a tokenizer and an encoder to generate text embeddings. From the previous example, you already know you need a UNet model and a scheduler.\n\nAs you can see, this is already more complex than the DDPM pipeline which only contains a UNet model. The Stable Diffusion model has three separate pretrained models.\n\n\n\n💡 Read the [How does Stable Diffusion work?](https://huggingface.co/blog/stable_diffusion#how-does-stable-diffusion-work) blog for more details about how the VAE, UNet, and text encoder models work.\n\n\n\nNow that you know what you need for the Stable Diffusion pipeline, load all these components with the [`~ModelMixin.from_pretrained`] method. You can find them in the pretrained [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) checkpoint, and each component is stored in a separate subfolder:\n\n```py\n>>> from PIL import Image\n>>> import torch\n>>> from transformers import CLIPTextModel, CLIPTokenizer\n>>> from diffusers import AutoencoderKL, UNet2DConditionModel, PNDMScheduler\n\n>>> vae = AutoencoderKL.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"vae\", use_safetensors=True)\n>>> tokenizer = CLIPTokenizer.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"tokenizer\")\n>>> text_encoder = CLIPTextModel.from_pretrained(\n... \"CompVis/stable-diffusion-v1-4\", subfolder=\"text_encoder\", use_safetensors=True\n... )\n>>> unet = UNet2DConditionModel.from_pretrained(\n... \"CompVis/stable-diffusion-v1-4\", subfolder=\"unet\", use_safetensors=True\n... )\n```\n\nInstead of the default [`PNDMScheduler`], exchange it for the [`UniPCMultistepScheduler`] to see how easy it is to plug a different scheduler in:\n\n```py\n>>> from diffusers import UniPCMultistepScheduler\n\n>>> scheduler = UniPCMultistepScheduler.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"scheduler\")\n```\n\nTo speed up inference, move the models to a GPU since, unlike the scheduler, they have trainable weights:\n\n```py\n>>> torch_device = \"cuda\"\n>>> vae.to(torch_device)\n>>> text_encoder.to(torch_device)\n>>> unet.to(torch_device)\n```\n\n### Create text embeddings\n\nThe next step is to tokenize the text to generate embeddings. The text is used to condition the UNet model and steer the diffusion process towards something that resembles the input prompt.\n\n\n\n💡 The `guidance_scale` parameter determines how much weight should be given to the prompt when generating an image.\n\n\n\nFeel free to choose any prompt you like if you want to generate something else!\n\n```py\n>>> prompt = [\"a photograph of an astronaut riding a horse\"]\n>>> height = 512 # default height of Stable Diffusion\n>>> width = 512 # default width of Stable Diffusion\n>>> num_inference_steps = 25 # Number of denoising steps\n>>> guidance_scale = 7.5 # Scale for classifier-free guidance\n>>> generator = torch.manual_seed(0) # Seed generator to create the initial latent noise\n>>> batch_size = len(prompt)\n```\n\nTokenize the text and generate the embeddings from the prompt:\n\n```py\n>>> text_input = tokenizer(\n... prompt, padding=\"max_length\", max_length=tokenizer.model_max_length, truncation=True, return_tensors=\"pt\"\n... )\n\n>>> with torch.no_grad():\n... text_embeddings = text_encoder(text_input.input_ids.to(torch_device))[0]\n```\n\nYou'll also need to generate the *unconditional text embeddings* which are the embeddings for the padding token. These need to have the same shape (`batch_size` and `seq_length`) as the conditional `text_embeddings`:\n\n```py\n>>> max_length = text_input.input_ids.shape[-1]\n>>> uncond_input = tokenizer([\"\"] * batch_size, padding=\"max_length\", max_length=max_length, return_tensors=\"pt\")\n>>> uncond_embeddings = text_encoder(uncond_input.input_ids.to(torch_device))[0]\n```\n\nLet's concatenate the conditional and unconditional embeddings into a batch to avoid doing two forward passes:\n\n```py\n>>> text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n```\n\n### Create random noise\n\nNext, generate some initial random noise as a starting point for the diffusion process. This is the latent representation of the image, and it'll be gradually denoised. At this point, the `latent` image is smaller than the final image size but that's okay though because the model will transform it into the final 512x512 image dimensions later.\n\n\n\n💡 The height and width are divided by 8 because the `vae` model has 3 down-sampling layers. You can check by running the following:\n\n```py\n2 ** (len(vae.config.block_out_channels) - 1) == 8\n```\n\n\n\n```py\n>>> latents = torch.randn(\n... (batch_size, unet.config.in_channels, height // 8, width // 8),\n... generator=generator,\n... device=torch_device,\n... )\n```\n\n### Denoise the image\n\nStart by scaling the input with the initial noise distribution, *sigma*, the noise scale value, which is required for improved schedulers like [`UniPCMultistepScheduler`]:\n\n```py\n>>> latents = latents * scheduler.init_noise_sigma\n```\n\nThe last step is to create the denoising loop that'll progressively transform the pure noise in `latents` to an image described by your prompt. Remember, the denoising loop needs to do three things:\n\n1. Set the scheduler's timesteps to use during denoising.\n2. Iterate over the timesteps.\n3. At each timestep, call the UNet model to predict the noise residual and pass it to the scheduler to compute the previous noisy sample.\n\n```py\n>>> from tqdm.auto import tqdm\n\n>>> scheduler.set_timesteps(num_inference_steps)\n\n>>> for t in tqdm(scheduler.timesteps):\n... # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.\n... latent_model_input = torch.cat([latents] * 2)\n\n... latent_model_input = scheduler.scale_model_input(latent_model_input, timestep=t)\n\n... # predict the noise residual\n... with torch.no_grad():\n... noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample\n\n... # perform guidance\n... noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n... noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n... # compute the previous noisy sample x_t -> x_t-1\n... latents = scheduler.step(noise_pred, t, latents).prev_sample\n```\n\n### Decode the image\n\nThe final step is to use the `vae` to decode the latent representation into an image and get the decoded output with `sample`:\n\n```py\n# scale and decode the image latents with vae\nlatents = 1 / 0.18215 * latents\nwith torch.no_grad():\n image = vae.decode(latents).sample\n```\n\nLastly, convert the image to a `PIL.Image` to see your generated image!\n\n```py\n>>> image = (image / 2 + 0.5).clamp(0, 1).squeeze()\n>>> image = (image.permute(1, 2, 0) * 255).to(torch.uint8).cpu().numpy()\n>>> image = Image.fromarray(image)\n>>> image\n```\n\n
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\n\n## Next steps\n\nFrom basic to complex pipelines, you've seen that all you really need to write your own diffusion system is a denoising loop. The loop should set the scheduler's timesteps, iterate over them, and alternate between calling the UNet model to predict the noise residual and passing it to the scheduler to compute the previous noisy sample.\n\nThis is really what 🧨 Diffusers is designed for: to make it intuitive and easy to write your own diffusion system using models and schedulers.\n\nFor your next steps, feel free to:\n\n* Learn how to [build and contribute a pipeline](../using-diffusers/contribute_pipeline) to 🧨 Diffusers. We can't wait and see what you'll come up with!\n* Explore [existing pipelines](../api/pipelines/overview) in the library, and see if you can deconstruct and build a pipeline from scratch using the models and schedulers separately.\n" }, { "path": "diffusers/docs/source/ja/_toctree.yml", "content": "- sections:\n - local: index\n title: 🧨 Diffusers\n - local: quicktour\n title: クイックツアー\n - local: stable_diffusion\n title: 有効で効率の良い拡散モデル\n - local: installation\n title: インストール\n title: はじめに\n- sections:\n - local: tutorials/tutorial_overview\n title: 概要\n - local: tutorials/autopipeline\n title: AutoPipeline\n title: チュートリアル" }, { "path": "diffusers/docs/source/ja/index.md", "content": "\n\n

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\n\n# Diffusers\n\n🤗 Diffusers は、画像や音声、さらには分子の3D構造を生成するための、最先端の事前学習済みDiffusion Model(拡散モデル)を提供するライブラリです。シンプルな生成ソリューションをお探しの場合でも、独自の拡散モデルをトレーニングしたい場合でも、🤗 Diffusers はその両方をサポートするモジュール式のツールボックスです。私たちのライブラリは、[性能より使いやすさ](conceptual/philosophy#usability-over-performance)、[簡単よりシンプル](conceptual/philosophy#simple-over-easy)、[抽象化よりカスタマイズ性](conceptual/philosophy#tweakable-contributorfriendly-over-abstraction)に重点を置いて設計されています。\n\nこのライブラリには3つの主要コンポーネントがあります:\n\n- 数行のコードで推論可能な最先端の[拡散パイプライン](api/pipelines/overview)。Diffusersには多くのパイプラインがあります。利用可能なパイプラインを網羅したリストと、それらが解決するタスクについては、パイプラインの[概要](https://huggingface.co/docs/diffusers/api/pipelines/overview)の表をご覧ください。\n- 生成速度と品質のトレードオフのバランスを取る交換可能な[ノイズスケジューラ](api/schedulers/overview)\n- ビルディングブロックとして使用することができ、スケジューラと組み合わせることで、エンドツーエンドの拡散モデルを構築可能な事前学習済み[モデル](api/models)\n\n
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チュートリアル
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出力の生成、独自の拡散システムの構築、拡散モデルのトレーニングを開始するために必要な基本的なスキルを学ぶことができます。初めて 🤗Diffusersを使用する場合は、ここから始めることをおすすめします！

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ガイド
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パイプライン、モデル、スケジューラの読み込みに役立つ実践的なガイドです。また、特定のタスクにパイプラインを使用する方法、出力の生成方法を制御する方法、生成速度を最適化する方法、さまざまなトレーニング手法についても学ぶことができます。

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Conceptual guides
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ライブラリがなぜこのように設計されたのかを理解し、ライブラリを利用する際の倫理的ガイドラインや安全対策について詳しく学べます。

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リファレンス
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🤗 Diffusersのクラスとメソッドがどのように機能するかについての技術的な説明です。

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\n
" }, { "path": "diffusers/docs/source/ja/installation.md", "content": "\n\n# インストール\n\nお使いのディープラーニングライブラリに合わせてDiffusersをインストールできます。\n\n🤗 DiffusersはPython 3.8+、PyTorch 1.7.0+、Flaxでテストされています。使用するディープラーニングライブラリの以下のインストール手順に従ってください：\n\n- [PyTorch](https://pytorch.org/get-started/locally/)のインストール手順。\n- [Flax](https://flax.readthedocs.io/en/latest/)のインストール手順。\n\n## pip でインストール\n\nDiffusersは[仮想環境](https://docs.python.org/3/library/venv.html)の中でインストールすることが推奨されています。\nPython の仮想環境についてよく知らない場合は、こちらの [ガイド](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/) を参照してください。\n仮想環境は異なるプロジェクトの管理を容易にし、依存関係間の互換性の問題を回避します。\n\nではさっそく、プロジェクトディレクトリに仮想環境を作ってみます：\n\n```bash\npython -m venv .env\n```\n\n仮想環境をアクティブにします：\n\n```bash\nsource .env/bin/activate\n```\n\n🤗 Diffusers もまた 🤗 Transformers ライブラリに依存しており、以下のコマンドで両方をインストールできます：\n\n\n\n```bash\npip install diffusers[\"torch\"] transformers\n```\n\n\n```bash\npip install diffusers[\"flax\"] transformers\n```\n\n\n\n## ソースからのインストール\n\nソースから🤗 Diffusersをインストールする前に、`torch`と🤗 Accelerateがインストールされていることを確認してください。\n\n`torch`のインストールについては、`torch` [インストール](https://pytorch.org/get-started/locally/#start-locally)ガイドを参照してください。\n\n🤗 Accelerateをインストールするには：\n\n```bash\npip install accelerate\n```\n\n以下のコマンドでソースから🤗 Diffusersをインストールできます：\n\n```bash\npip install git+https://github.com/huggingface/diffusers\n```\n\nこのコマンドは最新の `stable` バージョンではなく、最先端の `main` バージョンをインストールします。\n`main`バージョンは最新の開発に対応するのに便利です。\n例えば、前回の公式リリース以降にバグが修正されたが、新しいリリースがまだリリースされていない場合などには都合がいいです。\nしかし、これは `main` バージョンが常に安定しているとは限らないです。\n私たちは `main` バージョンを運用し続けるよう努力しており、ほとんどの問題は通常数時間から1日以内に解決されます。\nもし問題が発生した場合は、[Issue](https://github.com/huggingface/diffusers/issues/new/choose) を開いてください！\n\n## 編集可能なインストール\n\n以下の場合、編集可能なインストールが必要です：\n\n* ソースコードの `main` バージョンを使用する。\n* 🤗 Diffusers に貢献し、コードの変更をテストする必要がある場合。\n\nリポジトリをクローンし、次のコマンドで 🤗 Diffusers をインストールしてください：\n\n```bash\ngit clone https://github.com/huggingface/diffusers.git\ncd diffusers\n```\n\n\n\n```bash\npip install -e \".[torch]\"\n```\n\n\n```bash\npip install -e \".[flax]\"\n```\n\n\n\nこれらのコマンドは、リポジトリをクローンしたフォルダと Python のライブラリパスをリンクします。\nPython は通常のライブラリパスに加えて、クローンしたフォルダの中を探すようになります。\n例えば、Python パッケージが通常 `~/anaconda3/envs/main/lib/python3.10/site-packages/` にインストールされている場合、Python はクローンした `~/diffusers/` フォルダも同様に参照します。\n\n\n\nライブラリを使い続けたい場合は、`diffusers`フォルダを残しておく必要があります。\n\n\n\nこれで、以下のコマンドで簡単にクローンを最新版の🤗 Diffusersにアップデートできます：\n\n```bash\ncd ~/diffusers/\ngit pull\n```\n\nPython環境は次の実行時に `main` バージョンの🤗 Diffusersを見つけます。\n\n## テレメトリー・ロギングに関するお知らせ\n\nこのライブラリは `from_pretrained()` リクエスト中にデータを収集します。\nこのデータには Diffusers と PyTorch/Flax のバージョン、要求されたモデルやパイプラインクラスが含まれます。\nまた、Hubでホストされている場合は、事前に学習されたチェックポイントへのパスが含まれます。\nこの使用データは問題のデバッグや新機能の優先順位付けに役立ちます。\nテレメトリーはHuggingFace Hubからモデルやパイプラインをロードするときのみ送信されます。ローカルでの使用中は収集されません。\n\n我々は、すべての人が追加情報を共有したくないことを理解し、あなたのプライバシーを尊重します。\nそのため、ターミナルから `DISABLE_TELEMETRY` 環境変数を設定することで、データ収集を無効にすることができます：\n\nLinux/MacOSの場合\n```bash\nexport DISABLE_TELEMETRY=YES\n```\n\nWindows の場合\n```bash\nset DISABLE_TELEMETRY=YES\n```\n" }, { "path": "diffusers/docs/source/ja/quicktour.md", "content": "\n\n[[open-in-colab]]\n\n# 簡単な案内\n\n拡散モデル(Diffusion Model)は、ランダムな正規分布から段階的にノイズ除去するように学習され、画像や音声などの目的のものを生成できます。これは生成AIに多大な関心を呼び起こしました。インターネット上で拡散によって生成された画像の例を見たことがあるでしょう。🧨 Diffusersは、誰もが拡散モデルに広くアクセスできるようにすることを目的としたライブラリです。\n\nこの案内では、開発者または日常的なユーザーに関わらず、🧨 Diffusers を紹介し、素早く目的のものを生成できるようにします！このライブラリには3つの主要コンポーネントがあります:\n\n* [`DiffusionPipeline`]は事前に学習された拡散モデルからサンプルを迅速に生成するために設計された高レベルのエンドツーエンドクラス。\n* 拡散システムを作成するためのビルディングブロックとして使用できる、人気のある事前学習された[モデル](./api/models)アーキテクチャとモジュール。\n* 多くの異なる[スケジューラ](./api/schedulers/overview) - ノイズがどのようにトレーニングのために加えられるか、そして生成中にどのようにノイズ除去された画像を生成するかを制御するアルゴリズム。\n\nこの案内では、[`DiffusionPipeline`]を生成に使用する方法を紹介し、モデルとスケジューラを組み合わせて[`DiffusionPipeline`]の内部で起こっていることを再現する方法を説明します。\n\n\n\nこの案内は🧨 Diffusers [ノートブック](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb)を簡略化したもので、すぐに使い始めることができます。Diffusers 🧨のゴール、設計哲学、コアAPIの詳細についてもっと知りたい方は、ノートブックをご覧ください！\n\n\n\n始める前に必要なライブラリーがすべてインストールされていることを確認してください：\n\n```py\n# uncomment to install the necessary libraries in Colab\n#!pip install --upgrade diffusers accelerate transformers\n```\n\n- [🤗 Accelerate](https://huggingface.co/docs/accelerate/index)生成とトレーニングのためのモデルのロードを高速化します\n- [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview)ような最も一般的な拡散モデルを実行するには、[🤗 Transformers](https://huggingface.co/docs/transformers/index)が必要です。\n\n## 拡散パイプライン\n\n[`DiffusionPipeline`]は事前学習された拡散システムを生成に使用する最も簡単な方法です。これはモデルとスケジューラを含むエンドツーエンドのシステムです。[`DiffusionPipeline`]は多くの作業／タスクにすぐに使用することができます。また、サポートされているタスクの完全なリストについては[🧨Diffusersの概要](./api/pipelines/overview#diffusers-summary)の表を参照してください。\n\n| **タスク** | **説明** | **パイプライン**\n|------------------------------|--------------------------------------------------------------------------------------------------------------|-----------------|\n| Unconditional Image Generation | 正規分布から画像生成 | [unconditional_image_generation](./using-diffusers/unconditional_image_generation) |\n| Text-Guided Image Generation | 文章から画像生成 | [conditional_image_generation](./using-diffusers/conditional_image_generation) |\n| Text-Guided Image-to-Image Translation | 画像と文章から新たな画像生成 | [img2img](./using-diffusers/img2img) |\n| Text-Guided Image-Inpainting | 画像、マスク、および文章が指定された場合に、画像のマスクされた部分を文章をもとに修復 | [inpaint](./using-diffusers/inpaint) |\n| Text-Guided Depth-to-Image Translation | 文章と深度推定によって構造を保持しながら画像生成 | [depth2img](./using-diffusers/depth2img) |\n\nまず、[`DiffusionPipeline`]のインスタンスを作成し、ダウンロードしたいパイプラインのチェックポイントを指定します。\nこの[`DiffusionPipeline`]はHugging Face Hubに保存されている任意の[チェックポイント](https://huggingface.co/models?library=diffusers&sort=downloads)を使用することができます。\nこの案内では、[`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5)チェックポイントでテキストから画像へ生成します。\n\n\n\n[Stable Diffusion]モデルについては、モデルを実行する前にまず[ライセンス](https://huggingface.co/spaces/CompVis/stable-diffusion-license)を注意深くお読みください。🧨 Diffusers は、攻撃的または有害なコンテンツを防ぐために [`safety_checker`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) を実装していますが、モデルの改良された画像生成機能により、潜在的に有害なコンテンツが生成される可能性があります。\n\n\n\nモデルを[`~DiffusionPipeline.from_pretrained`]メソッドでロードします：\n\n```python\n>>> from diffusers import DiffusionPipeline\n\n>>> pipeline = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", use_safetensors=True)\n```\n[`DiffusionPipeline`]は全てのモデリング、トークン化、スケジューリングコンポーネントをダウンロードしてキャッシュします。Stable Diffusionパイプラインは[`UNet2DConditionModel`]と[`PNDMScheduler`]などで構成されています：\n\n```py\n>>> pipeline\nStableDiffusionPipeline {\n \"_class_name\": \"StableDiffusionPipeline\",\n \"_diffusers_version\": \"0.13.1\",\n ...,\n \"scheduler\": [\n \"diffusers\",\n \"PNDMScheduler\"\n ],\n ...,\n \"unet\": [\n \"diffusers\",\n \"UNet2DConditionModel\"\n ],\n \"vae\": [\n \"diffusers\",\n \"AutoencoderKL\"\n ]\n}\n```\n\nこのモデルはおよそ14億個のパラメータで構成されているため、GPU上でパイプラインを実行することを強く推奨します。\nPyTorchと同じように、ジェネレータオブジェクトをGPUに移すことができます：\n\n```python\n>>> pipeline.to(\"cuda\")\n```\n\nこれで、文章を `pipeline` に渡して画像を生成し、ノイズ除去された画像にアクセスできるようになりました。デフォルトでは、画像出力は[`PIL.Image`](https://pillow.readthedocs.io/en/stable/reference/Image.html?highlight=image#the-image-class)オブジェクトでラップされます。\n\n```python\n>>> image = pipeline(\"An image of a squirrel in Picasso style\").images[0]\n>>> image\n```\n\n
\n \n
\n\n`save`関数で画像を保存できます:\n\n```python\n>>> image.save(\"image_of_squirrel_painting.png\")\n```\n\n### ローカルパイプライン\n\nローカルでパイプラインを使用することもできます。唯一の違いは、最初にウェイトをダウンロードする必要があることです：\n\n```bash\n!git lfs install\n!git clone https://huggingface.co/runwayml/stable-diffusion-v1-5\n```\n\n保存したウェイトをパイプラインにロードします：\n\n```python\n>>> pipeline = DiffusionPipeline.from_pretrained(\"./stable-diffusion-v1-5\", use_safetensors=True)\n```\n\nこれで、上のセクションと同じようにパイプラインを動かすことができます。\n\n### スケジューラの交換\n\nスケジューラーによって、ノイズ除去のスピードや品質のトレードオフが異なります。どれが自分に最適かを知る最善の方法は、実際に試してみることです！Diffusers 🧨の主な機能の1つは、スケジューラを簡単に切り替えることができることです。例えば、デフォルトの[`PNDMScheduler`]を[`EulerDiscreteScheduler`]に置き換えるには、[`~diffusers.ConfigMixin.from_config`]メソッドでロードできます：\n\n```py\n>>> from diffusers import EulerDiscreteScheduler\n\n>>> pipeline = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", use_safetensors=True)\n>>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)\n```\n\n新しいスケジューラを使って画像を生成し、その違いに気づくかどうか試してみてください！\n\n次のセクションでは、[`DiffusionPipeline`]を構成するコンポーネント（モデルとスケジューラ）を詳しく見て、これらのコンポーネントを使って猫の画像を生成する方法を学びます。\n\n## モデル\n\nほとんどのモデルはノイズの多いサンプルを取り、各タイムステップで*残りのノイズ*を予測します（他のモデルは前のサンプルを直接予測するか、速度または[`v-prediction`](https://github.com/huggingface/diffusers/blob/5e5ce13e2f89ac45a0066cb3f369462a3cf1d9ef/src/diffusers/schedulers/scheduling_ddim.py#L110)を予測するように学習します）。モデルを混ぜて他の拡散システムを作ることもできます。\n\nモデルは[`~ModelMixin.from_pretrained`]メソッドで開始されます。このメソッドはモデルをローカルにキャッシュするので、次にモデルをロードするときに高速になります。この案内では、[`UNet2DModel`]をロードします。これは基本的な画像生成モデルであり、猫画像で学習されたチェックポイントを使います：\n\n```py\n>>> from diffusers import UNet2DModel\n\n>>> repo_id = \"google/ddpm-cat-256\"\n>>> model = UNet2DModel.from_pretrained(repo_id, use_safetensors=True)\n```\n\nモデルのパラメータにアクセスするには、`model.config` を呼び出せます：\n\n```py\n>>> model.config\n```\n\nモデル構成は🧊凍結🧊されたディクショナリであり、モデル作成後にこれらのパラメー タを変更することはできません。これは意図的なもので、最初にモデル・アーキテクチャを定義するために使用されるパラメータが同じままであることを保証します。他のパラメータは生成中に調整することができます。\n\n最も重要なパラメータは以下の通りです：\n\n* sample_size`: 入力サンプルの高さと幅。\n* `in_channels`: 入力サンプルの入力チャンネル数。\n* down_block_types` と `up_block_types`: UNet アーキテクチャを作成するために使用されるダウンサンプリングブロックとアップサンプリングブロックのタイプ。\n* block_out_channels`: ダウンサンプリングブロックの出力チャンネル数。逆順でアップサンプリングブロックの入力チャンネル数にも使用されます。\n* layer_per_block`: 各 UNet ブロックに含まれる ResNet ブロックの数。\n\nこのモデルを生成に使用するには、ランダムな画像の形の正規分布を作成します。このモデルは複数のランダムな正規分布を受け取ることができるため`batch`軸を入れます。入力チャンネル数に対応する`channel`軸も必要です。画像の高さと幅に対応する`sample_size`軸を持つ必要があります：\n\n```py\n>>> import torch\n\n>>> torch.manual_seed(0)\n\n>>> noisy_sample = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)\n>>> noisy_sample.shape\ntorch.Size([1, 3, 256, 256])\n```\n\n画像生成には、ノイズの多い画像と `timestep` をモデルに渡します。`timestep`は入力画像がどの程度ノイズが多いかを示します。これは、モデルが拡散プロセスにおける自分の位置を決定するのに役立ちます。モデルの出力を得るには `sample` メソッドを使用します：\n\n```py\n>>> with torch.no_grad():\n... noisy_residual = model(sample=noisy_sample, timestep=2).sample\n```\n\nしかし、実際の例を生成するには、ノイズ除去プロセスをガイドするスケジューラが必要です。次のセクションでは、モデルをスケジューラと組み合わせる方法を学びます。\n\n## スケジューラ\n\nスケジューラは、モデルの出力（この場合は `noisy_residual` ）が与えられたときに、ノイズの多いサンプルからノイズの少ないサンプルへの移行を管理します。\n\n\n\n\n🧨 Diffusersは拡散システムを構築するためのツールボックスです。[`DiffusionPipeline`]は事前に構築された拡散システムを使い始めるのに便利な方法ですが、独自のモデルとスケジューラコンポーネントを個別に選択してカスタム拡散システムを構築することもできます。\n\n\n\nこの案内では、[`DDPMScheduler`]を[`~diffusers.ConfigMixin.from_config`]メソッドでインスタンス化します：\n\n```py\n>>> from diffusers import DDPMScheduler\n\n>>> scheduler = DDPMScheduler.from_config(repo_id)\n>>> scheduler\nDDPMScheduler {\n \"_class_name\": \"DDPMScheduler\",\n \"_diffusers_version\": \"0.13.1\",\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n \"beta_start\": 0.0001,\n \"clip_sample\": true,\n \"clip_sample_range\": 1.0,\n \"num_train_timesteps\": 1000,\n \"prediction_type\": \"epsilon\",\n \"trained_betas\": null,\n \"variance_type\": \"fixed_small\"\n}\n```\n\n\n\n💡 スケジューラがどのようにコンフィギュレーションからインスタンス化されるかに注目してください。モデルとは異なり、スケジューラは学習可能な重みを持たず、パラメーターを持ちません！\n\n\n\n最も重要なパラメータは以下の通りです：\n\n* num_train_timesteps`: ノイズ除去処理の長さ、言い換えれば、ランダムな正規分布をデータサンプルに処理するのに必要なタイムステップ数です。\n* `beta_schedule`: 生成とトレーニングに使用するノイズスケジュールのタイプ。\n* `beta_start` と `beta_end`: ノイズスケジュールの開始値と終了値。\n\n少しノイズの少ない画像を予測するには、スケジューラの [`~diffusers.DDPMScheduler.step`] メソッドに以下を渡します: モデルの出力、`timestep`、現在の `sample`。\n\n```py\n>>> less_noisy_sample = scheduler.step(model_output=noisy_residual, timestep=2, sample=noisy_sample).prev_sample\n>>> less_noisy_sample.shape\n```\n\n`less_noisy_sample`は次の`timestep`に渡すことができ、そこでさらにノイズが少なくなります！\n\nでは、すべてをまとめて、ノイズ除去プロセス全体を視覚化してみましょう。\n\nまず、ノイズ除去された画像を後処理して `PIL.Image` として表示する関数を作成します：\n\n```py\n>>> import PIL.Image\n>>> import numpy as np\n\n\n>>> def display_sample(sample, i):\n... image_processed = sample.cpu().permute(0, 2, 3, 1)\n... image_processed = (image_processed + 1.0) * 127.5\n... image_processed = image_processed.numpy().astype(np.uint8)\n\n... image_pil = PIL.Image.fromarray(image_processed[0])\n... display(f\"Image at step {i}\")\n... display(image_pil)\n```\n\nノイズ除去処理を高速化するために入力とモデルをGPUに移します：\n\n```py\n>>> model.to(\"cuda\")\n>>> noisy_sample = noisy_sample.to(\"cuda\")\n```\n\nここで、ノイズが少なくなったサンプルの残りのノイズを予測するノイズ除去ループを作成し、スケジューラを使ってさらにノイズの少ないサンプルを計算します：\n\n```py\n>>> import tqdm\n\n>>> sample = noisy_sample\n\n>>> for i, t in enumerate(tqdm.tqdm(scheduler.timesteps)):\n... # 1. predict noise residual\n... with torch.no_grad():\n... residual = model(sample, t).sample\n\n... # 2. compute less noisy image and set x_t -> x_t-1\n... sample = scheduler.step(residual, t, sample).prev_sample\n\n... # 3. optionally look at image\n... if (i + 1) % 50 == 0:\n... display_sample(sample, i + 1)\n```\n\n何もないところから猫が生成されるのを、座って見てください！😻\n\n
\n \n
\n\n## 次のステップ\n\nこのクイックツアーで、🧨ディフューザーを使ったクールな画像をいくつか作成できたと思います！次のステップとして\n\n* モデルをトレーニングまたは微調整については、[training](./tutorials/basic_training)チュートリアルを参照してください。\n* 様々な使用例については、公式およびコミュニティの[training or finetuning scripts](https://github.com/huggingface/diffusers/tree/main/examples#-diffusers-examples)の例を参照してください。\n* スケジューラのロード、アクセス、変更、比較については[Using different Schedulers](./using-diffusers/schedulers)ガイドを参照してください。\n* プロンプトエンジニアリング、スピードとメモリの最適化、より高品質な画像を生成するためのヒントやトリックについては、[Stable Diffusion](./stable_diffusion)ガイドを参照してください。\n* 🧨 Diffusers の高速化については、最適化された [PyTorch on a GPU](./optimization/fp16)のガイド、[Stable Diffusion on Apple Silicon (M1/M2)](./optimization/mps)と[ONNX Runtime](./optimization/onnx)を参照してください。\n" }, { "path": "diffusers/docs/source/ja/stable_diffusion.md", "content": "\n\n# 効果的で効率的な拡散モデル\n\n[[open-in-colab]]\n\n[`DiffusionPipeline`]を使って特定のスタイルで画像を生成したり、希望する画像を生成したりするのは難しいことです。多くの場合、[`DiffusionPipeline`]を何度か実行してからでないと満足のいく画像は得られません。しかし、何もないところから何かを生成するにはたくさんの計算が必要です。生成を何度も何度も実行する場合、特にたくさんの計算量が必要になります。\n\nそのため、パイプラインから*計算*（速度）と*メモリ*（GPU RAM）の効率を最大限に引き出し、生成サイクル間の時間を短縮することで、より高速な反復処理を行えるようにすることが重要です。\n\nこのチュートリアルでは、[`DiffusionPipeline`]を用いて、より速く、より良い計算を行う方法を説明します。\n\nまず、[`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5)モデルをロードします：\n\n```python\nfrom diffusers import DiffusionPipeline\n\nmodel_id = \"runwayml/stable-diffusion-v1-5\"\npipeline = DiffusionPipeline.from_pretrained(model_id, use_safetensors=True)\n```\n\nここで使用するプロンプトの例は年老いた戦士の長の肖像画ですが、ご自由に変更してください：\n\n```python\nprompt = \"portrait photo of a old warrior chief\"\n```\n\n## Speed\n\n\n\n💡 GPUを利用できない場合は、[Colab](https://colab.research.google.com/)のようなGPUプロバイダーから無料で利用できます！\n\n\n\n画像生成を高速化する最も簡単な方法の1つは、PyTorchモジュールと同じようにGPU上にパイプラインを配置することです：\n\n```python\npipeline = pipeline.to(\"cuda\")\n```\n\n同じイメージを使って改良できるようにするには、[`Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html)を使い、[reproducibility](./using-diffusers/reusing_seeds)の種を設定します：\n\n```python\nimport torch\n\ngenerator = torch.Generator(\"cuda\").manual_seed(0)\n```\n\nこれで画像を生成できます：\n\n```python\nimage = pipeline(prompt, generator=generator).images[0]\nimage\n```\n\n
\n \n
\n\nこの処理にはT4 GPUで~30秒かかりました（割り当てられているGPUがT4より優れている場合はもっと速いかもしれません）。デフォルトでは、[`DiffusionPipeline`]は完全な`float32`精度で生成を50ステップ実行します。float16`のような低い精度に変更するか、推論ステップ数を減らすことで高速化することができます。\n\nまずは `float16` でモデルをロードして画像を生成してみましょう：\n\n```python\nimport torch\n\npipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, use_safetensors=True)\npipeline = pipeline.to(\"cuda\")\ngenerator = torch.Generator(\"cuda\").manual_seed(0)\nimage = pipeline(prompt, generator=generator).images[0]\nimage\n```\n\n
\n \n
\n\n今回、画像生成にかかった時間はわずか11秒で、以前より3倍近く速くなりました！\n\n\n\n💡 パイプラインは常に `float16` で実行することを強くお勧めします。\n\n\n\n生成ステップ数を減らすという方法もあります。より効率的なスケジューラを選択することで、出力品質を犠牲にすることなくステップ数を減らすことができます。`compatibles`メソッドを呼び出すことで、[`DiffusionPipeline`]の現在のモデルと互換性のあるスケジューラを見つけることができます：\n\n```python\npipeline.scheduler.compatibles\n[\n diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler,\n diffusers.schedulers.scheduling_unipc_multistep.UniPCMultistepScheduler,\n diffusers.schedulers.scheduling_k_dpm_2_discrete.KDPM2DiscreteScheduler,\n diffusers.schedulers.scheduling_deis_multistep.DEISMultistepScheduler,\n diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler,\n diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler,\n diffusers.schedulers.scheduling_ddpm.DDPMScheduler,\n diffusers.schedulers.scheduling_dpmsolver_singlestep.DPMSolverSinglestepScheduler,\n diffusers.schedulers.scheduling_k_dpm_2_ancestral_discrete.KDPM2AncestralDiscreteScheduler,\n diffusers.schedulers.scheduling_heun_discrete.HeunDiscreteScheduler,\n diffusers.schedulers.scheduling_pndm.PNDMScheduler,\n diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler,\n diffusers.schedulers.scheduling_ddim.DDIMScheduler,\n]\n```\n\nStable Diffusionモデルはデフォルトで[`PNDMScheduler`]を使用します。このスケジューラは通常~50の推論ステップを必要としますが、[`DPMSolverMultistepScheduler`]のような高性能なスケジューラでは~20または25の推論ステップで済みます。[`ConfigMixin.from_config`]メソッドを使用すると、新しいスケジューラをロードすることができます：\n\n```python\nfrom diffusers import DPMSolverMultistepScheduler\n\npipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)\n```\n\nここで `num_inference_steps` を20に設定します：\n\n```python\ngenerator = torch.Generator(\"cuda\").manual_seed(0)\nimage = pipeline(prompt, generator=generator, num_inference_steps=20).images[0]\nimage\n```\n\n
\n \n
\n\n推論時間をわずか4秒に短縮することに成功した！⚡️\n\n## メモリー\n\nパイプラインのパフォーマンスを向上させるもう1つの鍵は、消費メモリを少なくすることです。一度に生成できる画像の数を確認する最も簡単な方法は、`OutOfMemoryError`（OOM）が発生するまで、さまざまなバッチサイズを試してみることです。\n\n文章と `Generators` のリストから画像のバッチを生成する関数を作成します。各 `Generator` にシードを割り当てて、良い結果が得られた場合に再利用できるようにします。\n\n```python\ndef get_inputs(batch_size=1):\n generator = [torch.Generator(\"cuda\").manual_seed(i) for i in range(batch_size)]\n prompts = batch_size * [prompt]\n num_inference_steps = 20\n\n return {\"prompt\": prompts, \"generator\": generator, \"num_inference_steps\": num_inference_steps}\n```\n\n`batch_size=4`で開始し、どれだけメモリを消費したかを確認します：\n\n```python\nfrom diffusers.utils import make_image_grid \n\nimages = pipeline(**get_inputs(batch_size=4)).images\nmake_image_grid(images, 2, 2)\n```\n\n大容量のRAMを搭載したGPUでない限り、上記のコードはおそらく`OOM`エラーを返したはずです！メモリの大半はクロスアテンションレイヤーが占めています。この処理をバッチで実行する代わりに、逐次実行することでメモリを大幅に節約できます。必要なのは、[`~DiffusionPipeline.enable_attention_slicing`]関数を使用することだけです：\n\n```python\npipeline.enable_attention_slicing()\n```\n\n今度は`batch_size`を8にしてみてください！\n\n```python\nimages = pipeline(**get_inputs(batch_size=8)).images\nmake_image_grid(images, rows=2, cols=4)\n```\n\n
\n \n
\n\n以前は4枚の画像のバッチを生成することさえできませんでしたが、今では8枚の画像のバッチを1枚あたり～3.5秒で生成できます！これはおそらく、品質を犠牲にすることなくT4 GPUでできる最速の処理速度です。\n\n## 品質\n\n前の2つのセクションでは、`fp16` を使ってパイプラインの速度を最適化する方法、よりパフォーマン スなスケジューラーを使って生成ステップ数を減らす方法、アテンションスライスを有効 にしてメモリ消費量を減らす方法について学びました。今度は、生成される画像の品質を向上させる方法に焦点を当てます。\n\n### より良いチェックポイント\n\n最も単純なステップは、より良いチェックポイントを使うことです。Stable Diffusionモデルは良い出発点であり、公式発表以来、いくつかの改良版もリリースされています。しかし、新しいバージョンを使ったからといって、自動的に良い結果が得られるわけではありません。最良の結果を得るためには、自分でさまざまなチェックポイントを試してみたり、ちょっとした研究（[ネガティブプロンプト](https://minimaxir.com/2022/11/stable-diffusion-negative-prompt/)の使用など）をしたりする必要があります。\n\nこの分野が成長するにつれて、特定のスタイルを生み出すために微調整された、より質の高いチェックポイントが増えています。[Hub](https://huggingface.co/models?library=diffusers&sort=downloads)や[Diffusers Gallery](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery)を探索して、興味のあるものを見つけてみてください！\n\n### より良いパイプラインコンポーネント\n\n現在のパイプラインコンポーネントを新しいバージョンに置き換えてみることもできます。Stability AIが提供する最新の[autodecoder](https://huggingface.co/stabilityai/stable-diffusion-2-1/tree/main/vae)をパイプラインにロードし、画像を生成してみましょう：\n\n```python\nfrom diffusers import AutoencoderKL\n\nvae = AutoencoderKL.from_pretrained(\"stabilityai/sd-vae-ft-mse\", torch_dtype=torch.float16).to(\"cuda\")\npipeline.vae = vae\nimages = pipeline(**get_inputs(batch_size=8)).images\nmake_image_grid(images, rows=2, cols=4)\n```\n\n
\n \n
\n\n### より良いプロンプト・エンジニアリング\n\n画像を生成するために使用する文章は、*プロンプトエンジニアリング*と呼ばれる分野を作られるほど、非常に重要です。プロンプト・エンジニアリングで考慮すべき点は以下の通りです：\n\n- 生成したい画像やその類似画像は、インターネット上にどのように保存されているか？\n- 私が望むスタイルにモデルを誘導するために、どのような追加詳細を与えるべきか？\n\nこのことを念頭に置いて、プロンプトに色やより質の高いディテールを含めるように改良してみましょう：\n\n```python\nprompt += \", tribal panther make up, blue on red, side profile, looking away, serious eyes\"\nprompt += \" 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta\"\n```\n\n新しいプロンプトで画像のバッチを生成しましょう：\n\n```python\nimages = pipeline(**get_inputs(batch_size=8)).images\nmake_image_grid(images, rows=2, cols=4)\n```\n\n
\n \n
\n\nかなりいいです！種が`1`の`Generator`に対応する2番目の画像に、被写体の年齢に関するテキストを追加して、もう少し手を加えてみましょう：\n\n```python\nprompts = [\n \"portrait photo of the oldest warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta\",\n \"portrait photo of a old warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta\",\n \"portrait photo of a warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta\",\n \"portrait photo of a young warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta\",\n]\n\ngenerator = [torch.Generator(\"cuda\").manual_seed(1) for _ in range(len(prompts))]\nimages = pipeline(prompt=prompts, generator=generator, num_inference_steps=25).images\nmake_image_grid(images, 2, 2)\n```\n\n
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\n\n## 次のステップ\n\nこのチュートリアルでは、[`DiffusionPipeline`]を最適化して計算効率とメモリ効率を向上させ、生成される出力の品質を向上させる方法を学びました。パイプラインをさらに高速化することに興味があれば、以下のリソースを参照してください：\n\n- [PyTorch 2.0](./optimization/torch2.0)と[`torch.compile`](https://pytorch.org/docs/stable/generated/torch.compile.html)がどのように生成速度を5-300%高速化できるかを学んでください。A100 GPUの場合、画像生成は最大50%速くなります！\n- PyTorch 2が使えない場合は、[xFormers](./optimization/xformers)をインストールすることをお勧めします。このライブラリのメモリ効率の良いアテンションメカニズムは PyTorch 1.13.1 と相性が良く、高速化とメモリ消費量の削減を同時に実現します。\n- モデルのオフロードなど、その他の最適化テクニックは [this guide](./optimization/fp16) でカバーされています。\n" }, { "path": "diffusers/docs/source/ja/tutorials/autopipeline.md", "content": "\n\n# AutoPipeline\n\nDiffusersは様々なタスクをこなすことができ、テキストから画像、画像から画像、画像の修復など、複数のタスクに対して同じように事前学習された重みを再利用することができます。しかし、ライブラリや拡散モデルに慣れていない場合、どのタスクにどのパイプラインを使えばいいのかがわかりにくいかもしれません。例えば、 [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) チェックポイントをテキストから画像に変換するために使用している場合、それぞれ[`StableDiffusionImg2ImgPipeline`]クラスと[`StableDiffusionInpaintPipeline`]クラスでチェックポイントをロードすることで、画像から画像や画像の修復にも使えることを知らない可能性もあります。\n\n`AutoPipeline` クラスは、🤗 Diffusers の様々なパイプラインをよりシンプルするために設計されています。この汎用的でタスク重視のパイプラインによってタスクそのものに集中することができます。`AutoPipeline` は、使用するべき正しいパイプラインクラスを自動的に検出するため、特定のパイプラインクラス名を知らなくても、タスクのチェックポイントを簡単にロードできます。\n\n\n\nどのタスクがサポートされているかは、[AutoPipeline](../api/pipelines/auto_pipeline) のリファレンスをご覧ください。現在、text-to-image、image-to-image、inpaintingをサポートしています。\n\n\n\nこのチュートリアルでは、`AutoPipeline` を使用して、事前に学習された重みが与えられたときに、特定のタスクを読み込むためのパイプラインクラスを自動的に推測する方法を示します。\n\n## タスクに合わせてAutoPipeline を選択する\nまずはチェックポイントを選ぶことから始めましょう。例えば、 [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) チェックポイントでテキストから画像への変換したいなら、[`AutoPipelineForText2Image`]を使います:\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, use_safetensors=True\n).to(\"cuda\")\nprompt = \"peasant and dragon combat, wood cutting style, viking era, bevel with rune\"\n\nimage = pipeline(prompt, num_inference_steps=25).images[0]\nimage\n```\n\n
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\n\n[`AutoPipelineForText2Image`] を具体的に見ていきましょう:\n\n1. [`model_index.json`](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/model_index.json) ファイルから `\"stable-diffusion\"` クラスを自動的に検出します。\n2. `\"stable-diffusion\"` のクラス名に基づいて、テキストから画像へ変換する [`StableDiffusionPipeline`] を読み込みます。\n\n同様に、画像から画像へ変換する場合、[`AutoPipelineForImage2Image`] は `model_index.json` ファイルから `\"stable-diffusion\"` チェックポイントを検出し、対応する [`StableDiffusionImg2ImgPipeline`] を読み込みます。また、入力画像にノイズの量やバリエーションの追加を決めるための強さなど、パイプラインクラスに固有の追加引数を渡すこともできます:\n\n```py\nfrom diffusers import AutoPipelineForImage2Image\nimport torch\nimport requests\nfrom PIL import Image\nfrom io import BytesIO\n\npipeline = AutoPipelineForImage2Image.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n torch_dtype=torch.float16,\n use_safetensors=True,\n).to(\"cuda\")\nprompt = \"a portrait of a dog wearing a pearl earring\"\n\nurl = \"https://upload.wikimedia.org/wikipedia/commons/thumb/0/0f/1665_Girl_with_a_Pearl_Earring.jpg/800px-1665_Girl_with_a_Pearl_Earring.jpg\"\n\nresponse = requests.get(url)\nimage = Image.open(BytesIO(response.content)).convert(\"RGB\")\nimage.thumbnail((768, 768))\n\nimage = pipeline(prompt, image, num_inference_steps=200, strength=0.75, guidance_scale=10.5).images[0]\nimage\n```\n\n
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\n\nまた、画像の修復を行いたい場合は、 [`AutoPipelineForInpainting`] が、同様にベースとなる[`StableDiffusionInpaintPipeline`]クラスを読み込みます：\n\n```py\nfrom diffusers import AutoPipelineForInpainting\nfrom diffusers.utils import load_image\nimport torch\n\npipeline = AutoPipelineForInpainting.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16, use_safetensors=True\n).to(\"cuda\")\n\nimg_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\nmask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\n\ninit_image = load_image(img_url).convert(\"RGB\")\nmask_image = load_image(mask_url).convert(\"RGB\")\n\nprompt = \"A majestic tiger sitting on a bench\"\nimage = pipeline(prompt, image=init_image, mask_image=mask_image, num_inference_steps=50, strength=0.80).images[0]\nimage\n```\n\n
\n \"generated\n
\n\nサポートされていないチェックポイントを読み込もうとすると、エラーになります:\n\n```py\nfrom diffusers import AutoPipelineForImage2Image\nimport torch\n\npipeline = AutoPipelineForImage2Image.from_pretrained(\n \"openai/shap-e-img2img\", torch_dtype=torch.float16, use_safetensors=True\n)\n\"ValueError: AutoPipeline can't find a pipeline linked to ShapEImg2ImgPipeline for None\"\n```\n\n## 複数のパイプラインを使用する\n\nいくつかのワークフローや多くのパイプラインを読み込む場合、不要なメモリを使ってしまう再読み込みをするよりも、チェックポイントから同じコンポーネントを再利用する方がメモリ効率が良いです。たとえば、テキストから画像への変換にチェックポイントを使い、画像から画像への変換にまたチェックポイントを使いたい場合、[from_pipe()](https://huggingface.co/docs/diffusers/v0.25.1/en/api/pipelines/auto_pipeline#diffusers.AutoPipelineForImage2Image.from_pipe) メソッドを使用します。このメソッドは、以前読み込まれたパイプラインのコンポーネントを使うことで追加のメモリを消費することなく、新しいパイプラインを作成します。\n\n[from_pipe()](https://huggingface.co/docs/diffusers/v0.25.1/en/api/pipelines/auto_pipeline#diffusers.AutoPipelineForImage2Image.from_pipe) メソッドは、元のパイプラインクラスを検出し、実行したいタスクに対応する新しいパイプラインクラスにマッピングします。例えば、テキストから画像への`\"stable-diffusion\"` クラスのパイプラインを読み込む場合：\n\n```py\nfrom diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image\nimport torch\n\npipeline_text2img = AutoPipelineForText2Image.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, use_safetensors=True\n)\nprint(type(pipeline_text2img))\n\"\"\n```\n\nそして、[from_pipe()] (https://huggingface.co/docs/diffusers/v0.25.1/en/api/pipelines/auto_pipeline#diffusers.AutoPipelineForImage2Image.from_pipe)は、もとの`\"stable-diffusion\"` パイプラインのクラスである [`StableDiffusionImg2ImgPipeline`] にマップします:\n\n```py\npipeline_img2img = AutoPipelineForImage2Image.from_pipe(pipeline_text2img)\nprint(type(pipeline_img2img))\n\"\"\n```\n元のパイプラインにオプションとして引数（セーフティチェッカーの無効化など）を渡した場合、この引数も新しいパイプラインに渡されます:\n\n```py\nfrom diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image\nimport torch\n\npipeline_text2img = AutoPipelineForText2Image.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n torch_dtype=torch.float16,\n use_safetensors=True,\n requires_safety_checker=False,\n).to(\"cuda\")\n\npipeline_img2img = AutoPipelineForImage2Image.from_pipe(pipeline_text2img)\nprint(pipeline_img2img.config.requires_safety_checker)\n\"False\"\n```\n\n新しいパイプラインの動作を変更したい場合は、元のパイプラインの引数や設定を上書きすることができます。例えば、セーフティチェッカーをオンに戻し、`strength` 引数を追加します:\n\n```py\npipeline_img2img = AutoPipelineForImage2Image.from_pipe(pipeline_text2img, requires_safety_checker=True, strength=0.3)\nprint(pipeline_img2img.config.requires_safety_checker)\n\"True\"\n```\n" }, { "path": "diffusers/docs/source/ja/tutorials/tutorial_overview.md", "content": "\n\n# Overview\n\nようこそ 🧨Diffusersへ！拡散モデル(diffusion models)や生成AIの初心者で、さらに学びたいのであれば、このチュートリアルが最適です。この初心者向けのチュートリアルは、拡散モデルについて丁寧に解説し、ライブラリの基礎（核となるコンポーネントと 🧨Diffusersの使用方法）を理解することを目的としています。\n\nまず、推論のためのパイプラインを使って、素早く生成する方法を学んでいきます。次に、独自の拡散システムを構築するためのモジュラーツールボックスとしてライブラリをどのように使えば良いかを理解するために、そのパイプラインを分解してみましょう。次のレッスンでは、あなたの欲しいものを生成できるように拡散モデルをトレーニングする方法を学びましょう。\n\nこのチュートリアルがすべて完了したら、ライブラリを自分で調べ、自分のプロジェクトやアプリケーションにどのように使えるかを知るために必要なスキルを身につけることができます。\n\nそして、 [Discord](https://discord.com/invite/JfAtkvEtRb) や [forums](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) でDiffusersコミュニティに参加してユーザーや開発者と繋がって協力していきましょう。\n\nさあ、「拡散」をはじめていきましょう！🧨" }, { "path": "diffusers/docs/source/ko/_toctree.yml", "content": "- sections:\n - local: index\n title: \"🧨 Diffusers\"\n - local: quicktour\n title: \"훑어보기\"\n - local: stable_diffusion\n title: Stable Diffusion\n - local: installation\n title: \"설치\"\n title: \"시작하기\"\n- sections:\n - local: tutorials/tutorial_overview\n title: 개요\n - local: using-diffusers/write_own_pipeline\n title: 모델과 스케줄러 이해하기\n - local: in_translation\n title: AutoPipeline\n - local: tutorials/basic_training\n title: Diffusion 모델 학습하기\n title: Tutorials\n- sections:\n - sections:\n - local: using-diffusers/loading_overview\n title: 개요\n - local: using-diffusers/loading\n title: 파이프라인, 모델, 스케줄러 불러오기\n - local: using-diffusers/schedulers\n title: 다른 스케줄러들을 가져오고 비교하기\n - local: using-diffusers/custom_pipeline_overview\n title: 커뮤니티 파이프라인 불러오기\n - local: using-diffusers/using_safetensors\n title: 세이프텐서 불러오기\n - local: using-diffusers/other-formats\n title: 다른 형식의 Stable Diffusion 불러오기\n - local: in_translation\n title: Hub에 파일 push하기\n title: 불러오기 & 허브\n - sections:\n - local: using-diffusers/pipeline_overview\n title: 개요\n - local: using-diffusers/unconditional_image_generation\n title: Unconditional 이미지 생성\n - local: using-diffusers/conditional_image_generation\n title: Text-to-image 생성\n - local: using-diffusers/img2img\n title: Text-guided image-to-image\n - local: using-diffusers/inpaint\n title: Text-guided 이미지 인페인팅\n - local: using-diffusers/depth2img\n title: Text-guided depth-to-image\n - local: using-diffusers/textual_inversion_inference\n title: Textual inversion\n - local: training/distributed_inference\n title: 여러 GPU를 사용한 분산 추론\n - local: in_translation\n title: Distilled Stable Diffusion 추론\n - local: using-diffusers/reusing_seeds\n title: Deterministic 생성으로 이미지 퀄리티 높이기\n - local: using-diffusers/control_brightness\n title: 이미지 밝기 조정하기\n - local: using-diffusers/reproducibility\n title: 재현 가능한 파이프라인 생성하기\n - local: using-diffusers/custom_pipeline_examples\n title: 커뮤니티 파이프라인들\n - local: using-diffusers/contribute_pipeline\n title: 커뮤티니 파이프라인에 기여하는 방법\n - local: using-diffusers/stable_diffusion_jax_how_to\n title: JAX/Flax에서의 Stable Diffusion\n - local: using-diffusers/weighted_prompts\n title: Weighting Prompts\n title: 추론을 위한 파이프라인\n - sections:\n - local: training/overview\n title: 개요\n - local: training/create_dataset\n title: 학습을 위한 데이터셋 생성하기\n - local: training/adapt_a_model\n title: 새로운 태스크에 모델 적용하기\n - local: training/unconditional_training\n title: Unconditional 이미지 생성\n - local: training/text_inversion\n title: Textual Inversion\n - local: training/dreambooth\n title: DreamBooth\n - local: training/text2image\n title: Text-to-image\n - local: training/lora\n title: Low-Rank Adaptation of Large Language Models (LoRA)\n - local: training/controlnet\n title: ControlNet\n - local: training/instructpix2pix\n title: InstructPix2Pix 학습\n - local: training/custom_diffusion\n title: Custom Diffusion\n title: Training\n title: Diffusers 사용하기\n- sections:\n - local: optimization/opt_overview\n title: 개요\n - local: optimization/fp16\n title: 메모리와 속도\n - local: optimization/torch2.0\n title: Torch2.0 지원\n - local: optimization/xformers\n title: xFormers\n - local: optimization/onnx\n title: ONNX\n - local: optimization/open_vino\n title: OpenVINO\n - local: optimization/coreml\n title: Core ML\n - local: optimization/mps\n title: MPS\n - local: optimization/habana\n title: Habana Gaudi\n - local: optimization/tome\n title: Token Merging\n title: 최적화/특수 하드웨어\n- sections:\n - local: conceptual/philosophy\n title: 철학\n - local: using-diffusers/controlling_generation\n title: 제어된 생성\n - local: conceptual/contribution\n title: 어떻게 기여하나요?\n - local: conceptual/ethical_guidelines\n title: Diffusers의 윤리적 가이드라인\n - local: conceptual/evaluation\n title: Diffusion Models 평가하기\n title: 개념 가이드\n- sections:\n - sections:\n - sections:\n - local: api/pipelines/stable_diffusion/stable_diffusion_xl\n title: Stable Diffusion XL\n title: Stable Diffusion\n title: Pipelines\n title: API" }, { "path": "diffusers/docs/source/ko/api/pipelines/stable_diffusion/stable_diffusion_xl.md", "content": "\n\n# Stable diffusion XL\n\nStable Diffusion XL은 Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, Robin Rombach에 의해 [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://arxiv.org/abs/2307.01952)에서 제안되었습니다.\n\n논문 초록은 다음을 따릅니다:\n\n*text-to-image의 latent diffusion 모델인 SDXL을 소개합니다. 이전 버전의 Stable Diffusion과 비교하면, SDXL은 세 배 더큰 규모의 UNet 백본을 포함합니다: 모델 파라미터의 증가는 많은 attention 블럭을 사용하고 더 큰 cross-attention context를 SDXL의 두 번째 텍스트 인코더에 사용하기 때문입니다. 다중 종횡비에 다수의 새로운 conditioning 방법을 구성했습니다. 또한 후에 수정하는 image-to-image 기술을 사용함으로써 SDXL에 의해 생성된 시각적 품질을 향상하기 위해 정제된 모델을 소개합니다. SDXL은 이전 버전의 Stable Diffusion보다 성능이 향상되었고, 이러한 black-box 최신 이미지 생성자와 경쟁력있는 결과를 달성했습니다.*\n\n## 팁\n\n- Stable Diffusion XL은 특히 786과 1024사이의 이미지에 잘 작동합니다.\n- Stable Diffusion XL은 아래와 같이 학습된 각 텍스트 인코더에 대해 서로 다른 프롬프트를 전달할 수 있습니다. 동일한 프롬프트의 다른 부분을 텍스트 인코더에 전달할 수도 있습니다.\n- Stable Diffusion XL 결과 이미지는 아래에 보여지듯이 정제기(refiner)를 사용함으로써 향상될 수 있습니다.\n\n### 이용가능한 체크포인트:\n\n- *Text-to-Image (1024x1024 해상도)*: [`StableDiffusionXLPipeline`]을 사용한 [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n- *Image-to-Image / 정제기(refiner) (1024x1024 해상도)*: [`StableDiffusionXLImg2ImgPipeline`]를 사용한 [stabilityai/stable-diffusion-xl-refiner-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0)\n\n## 사용 예시\n\nSDXL을 사용하기 전에 `transformers`, `accelerate`, `safetensors` 와 `invisible_watermark`를 설치하세요.\n다음과 같이 라이브러리를 설치할 수 있습니다:\n\n```sh\npip install transformers\npip install accelerate\npip install safetensors\npip install invisible-watermark>=0.2.0\n```\n\n### 워터마커\n\nStable Diffusion XL로 이미지를 생성할 때 워터마크가 보이지 않도록 추가하는 것을 권장하는데, 이는 다운스트림(downstream) 어플리케이션에서 기계에 합성되었는지를 식별하는데 도움을 줄 수 있습니다. 그렇게 하려면 [invisible_watermark 라이브러리](https://pypi.org/project/invisible-watermark/)를 통해 설치해주세요:\n\n\n```sh\npip install invisible-watermark>=0.2.0\n```\n\n`invisible-watermark` 라이브러리가 설치되면 워터마커가 **기본적으로** 사용될 것입니다.\n\n생성 또는 안전하게 이미지를 배포하기 위해 다른 규정이 있다면, 다음과 같이 워터마커를 비활성화할 수 있습니다:\n\n```py\npipe = StableDiffusionXLPipeline.from_pretrained(..., add_watermarker=False)\n```\n\n### Text-to-Image\n\n*text-to-image*를 위해 다음과 같이 SDXL을 사용할 수 있습니다:\n\n```py\nfrom diffusers import StableDiffusionXLPipeline\nimport torch\n\npipe = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n)\npipe.to(\"cuda\")\n\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\nimage = pipe(prompt=prompt).images[0]\n```\n\n### Image-to-image\n\n*image-to-image*를 위해 다음과 같이 SDXL을 사용할 수 있습니다:\n\n```py\nimport torch\nfrom diffusers import StableDiffusionXLImg2ImgPipeline\nfrom diffusers.utils import load_image\n\npipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-refiner-1.0\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n)\npipe = pipe.to(\"cuda\")\nurl = \"https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/aa_xl/000000009.png\"\n\ninit_image = load_image(url).convert(\"RGB\")\nprompt = \"a photo of an astronaut riding a horse on mars\"\nimage = pipe(prompt, image=init_image).images[0]\n```\n\n### 인페인팅\n\n*inpainting*를 위해 다음과 같이 SDXL을 사용할 수 있습니다:\n\n```py\nimport torch\nfrom diffusers import StableDiffusionXLInpaintPipeline\nfrom diffusers.utils import load_image\n\npipe = StableDiffusionXLInpaintPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n)\npipe.to(\"cuda\")\n\nimg_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\nmask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\n\ninit_image = load_image(img_url).convert(\"RGB\")\nmask_image = load_image(mask_url).convert(\"RGB\")\n\nprompt = \"A majestic tiger sitting on a bench\"\nimage = pipe(prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=50, strength=0.80).images[0]\n```\n\n### 이미지 결과물을 정제하기\n\n[base 모델 체크포인트](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)에서, StableDiffusion-XL 또한 고주파 품질을 향상시키는 이미지를 생성하기 위해 낮은 노이즈 단계 이미지를 제거하는데 특화된 [refiner 체크포인트](huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0)를 포함하고 있습니다. 이 refiner 체크포인트는 이미지 품질을 향상시키기 위해 base 체크포인트를 실행한 후 \"두 번째 단계\" 파이프라인에 사용될 수 있습니다.\n\nrefiner를 사용할 때, 쉽게 사용할 수 있습니다\n- 1.) base 모델과 refiner을 사용하는데, 이는 *Denoisers의 앙상블*을 위한 첫 번째 제안된 [eDiff-I](https://research.nvidia.com/labs/dir/eDiff-I/)를 사용하거나\n- 2.) base 모델을 거친 후 [SDEdit](https://arxiv.org/abs/2108.01073) 방법으로 단순하게 refiner를 실행시킬 수 있습니다.\n\n**참고**: SD-XL base와 refiner를 앙상블로 사용하는 아이디어는 커뮤니티 기여자들이 처음으로 제안했으며, 이는 다음과 같은 `diffusers`를 구현하는 데도 도움을 주셨습니다.\n- [SytanSD](https://github.com/SytanSD)\n- [bghira](https://github.com/bghira)\n- [Birch-san](https://github.com/Birch-san)\n- [AmericanPresidentJimmyCarter](https://github.com/AmericanPresidentJimmyCarter)\n\n#### 1.) Denoisers의 앙상블\n\nbase와 refiner 모델을 denoiser의 앙상블로 사용할 때, base 모델은 고주파 diffusion 단계를 위한 전문가의 역할을 해야하고, refiner는 낮은 노이즈 diffusion 단계를 위한 전문가의 역할을 해야 합니다.\n\n2.)에 비해 1.)의 장점은 전체적으로 denoising 단계가 덜 필요하므로 속도가 훨씬 더 빨라집니다. 단점은 base 모델의 결과를 검사할 수 없다는 것입니다. 즉, 여전히 노이즈가 심하게 제거됩니다.\n\nbase 모델과 refiner를 denoiser의 앙상블로 사용하기 위해 각각 고노이즈(high-nosise) (*즉* base 모델)와 저노이즈 (*즉* refiner 모델)의 노이즈를 제거하는 단계를 거쳐야하는 타임스텝의 기간을 정의해야 합니다.\nbase 모델의 [`denoising_end`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLPipeline.__call__.denoising_end)와 refiner 모델의 [`denoising_start`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLImg2ImgPipeline.__call__.denoising_start)를 사용해 간격을 정합니다.\n\n`denoising_end`와 `denoising_start` 모두 0과 1사이의 실수 값으로 전달되어야 합니다.\n전달되면 노이즈 제거의 끝과 시작은 모델 스케줄에 의해 정의된 이산적(discrete) 시간 간격의 비율로 정의됩니다.\n노이즈 제거 단계의 수는 모델이 학습된 불연속적인 시간 간격과 선언된 fractional cutoff에 의해 결정되므로 '강도' 또한 선언된 경우 이 값이 '강도'를 재정의합니다.\n\n예시를 들어보겠습니다.\n우선, 두 개의 파이프라인을 가져옵니다. 텍스트 인코더와 variational autoencoder는 동일하므로 refiner를 위해 다시 불러오지 않아도 됩니다.\n\n```py\nfrom diffusers import DiffusionPipeline\nimport torch\n\nbase = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n)\npipe.to(\"cuda\")\n\nrefiner = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-refiner-1.0\",\n text_encoder_2=base.text_encoder_2,\n vae=base.vae,\n torch_dtype=torch.float16,\n use_safetensors=True,\n variant=\"fp16\",\n)\nrefiner.to(\"cuda\")\n```\n\n이제 추론 단계의 수와 고노이즈에서 노이즈를 제거하는 단계(*즉* base 모델)를 거쳐 실행되는 지점을 정의합니다.\n\n```py\nn_steps = 40\nhigh_noise_frac = 0.8\n```\n\nStable Diffusion XL base 모델은 타임스텝 0-999에 학습되며 Stable Diffusion XL refiner는 포괄적인 낮은 노이즈 타임스텝인 0-199에 base 모델로 부터 파인튜닝되어, 첫 800 타임스텝 (높은 노이즈)에 base 모델을 사용하고 마지막 200 타입스텝 (낮은 노이즈)에서 refiner가 사용됩니다. 따라서, `high_noise_frac`는 0.8로 설정하고, 모든 200-999 스텝(노이즈 제거 타임스텝의 첫 80%)은 base 모델에 의해 수행되며 0-199 스텝(노이즈 제거 타임스텝의 마지막 20%)은 refiner 모델에 의해 수행됩니다.\n\n기억하세요, 노이즈 제거 절차는 **높은 값**(높은 노이즈) 타임스텝에서 시작되고, **낮은 값** (낮은 노이즈) 타임스텝에서 끝납니다.\n\n이제 두 파이프라인을 실행해봅시다. `denoising_end`과 `denoising_start`를 같은 값으로 설정하고 `num_inference_steps`는 상수로 유지합니다. 또한 base 모델의 출력은 잠재 공간에 있어야 한다는 점을 기억하세요:\n\n```py\nprompt = \"A majestic lion jumping from a big stone at night\"\n\nimage = base(\n prompt=prompt,\n num_inference_steps=n_steps,\n denoising_end=high_noise_frac,\n output_type=\"latent\",\n).images\nimage = refiner(\n prompt=prompt,\n num_inference_steps=n_steps,\n denoising_start=high_noise_frac,\n image=image,\n).images[0]\n```\n\n이미지를 살펴보겠습니다.\n\n| 원래의 이미지 | Denoiser들의 앙상블 |\n|---|---|\n| ![lion_base](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lion_base.png) | ![lion_ref](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lion_refined.png)\n\n동일한 40 단계에서 base 모델을 실행한다면, 이미지의 디테일(예: 사자의 눈과 코)이 떨어졌을 것입니다:\n\n\n\n앙상블 방식은 사용 가능한 모든 스케줄러에서 잘 작동합니다!\n\n\n\n#### 2.) 노이즈가 완전히 제거된 기본 이미지에서 이미지 출력을 정제하기\n\n일반적인 [`StableDiffusionImg2ImgPipeline`] 방식에서, 기본 모델에서 생성된 완전히 노이즈가 제거된 이미지는 [refiner checkpoint](huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0)를 사용해 더 향상시킬 수 있습니다.\n\n이를 위해, 보통의 \"base\" text-to-image 파이프라인을 수행 후에 image-to-image 파이프라인으로써 refiner를 실행시킬 수 있습니다. base 모델의 출력을 잠재 공간에 남겨둘 수 있습니다.\n\n```py\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipe = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n)\npipe.to(\"cuda\")\n\nrefiner = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-refiner-1.0\",\n text_encoder_2=pipe.text_encoder_2,\n vae=pipe.vae,\n torch_dtype=torch.float16,\n use_safetensors=True,\n variant=\"fp16\",\n)\nrefiner.to(\"cuda\")\n\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\n\nimage = pipe(prompt=prompt, output_type=\"latent\" if use_refiner else \"pil\").images[0]\nimage = refiner(prompt=prompt, image=image[None, :]).images[0]\n```\n\n| 원래의 이미지 | 정제된 이미지 |\n|---|---|\n| ![](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/sd_xl/init_image.png) | ![](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/sd_xl/refined_image.png) |\n\n\n\nrefiner는 또한 인페인팅 설정에 잘 사용될 수 있습니다. 아래에 보여지듯이 [`StableDiffusionXLInpaintPipeline`] 클래스를 사용해서 만들어보세요.\n\n\n\nDenoiser 앙상블 설정에서 인페인팅에 refiner를 사용하려면 다음을 수행하면 됩니다:\n\n```py\nfrom diffusers import StableDiffusionXLInpaintPipeline\nfrom diffusers.utils import load_image\n\npipe = StableDiffusionXLInpaintPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n)\npipe.to(\"cuda\")\n\nrefiner = StableDiffusionXLInpaintPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-refiner-1.0\",\n text_encoder_2=pipe.text_encoder_2,\n vae=pipe.vae,\n torch_dtype=torch.float16,\n use_safetensors=True,\n variant=\"fp16\",\n)\nrefiner.to(\"cuda\")\n\nimg_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\nmask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\n\ninit_image = load_image(img_url).convert(\"RGB\")\nmask_image = load_image(mask_url).convert(\"RGB\")\n\nprompt = \"A majestic tiger sitting on a bench\"\nnum_inference_steps = 75\nhigh_noise_frac = 0.7\n\nimage = pipe(\n prompt=prompt,\n image=init_image,\n mask_image=mask_image,\n num_inference_steps=num_inference_steps,\n denoising_start=high_noise_frac,\n output_type=\"latent\",\n).images\nimage = refiner(\n prompt=prompt,\n image=image,\n mask_image=mask_image,\n num_inference_steps=num_inference_steps,\n denoising_start=high_noise_frac,\n).images[0]\n```\n\n일반적인 SDE 설정에서 인페인팅에 refiner를 사용하기 위해, `denoising_end`와 `denoising_start`를 제거하고 refiner의 추론 단계의 수를 적게 선택하세요.\n\n### 단독 체크포인트 파일 / 원래의 파일 형식으로 불러오기\n\n[`~diffusers.loaders.FromSingleFileMixin.from_single_file`]를 사용함으로써 원래의 파일 형식을 `diffusers` 형식으로 불러올 수 있습니다:\n\n```py\nfrom diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline\nimport torch\n\npipe = StableDiffusionXLPipeline.from_single_file(\n \"./sd_xl_base_1.0.safetensors\", torch_dtype=torch.float16\n)\npipe.to(\"cuda\")\n\nrefiner = StableDiffusionXLImg2ImgPipeline.from_single_file(\n \"./sd_xl_refiner_1.0.safetensors\", torch_dtype=torch.float16\n)\nrefiner.to(\"cuda\")\n```\n\n### 모델 offloading을 통해 메모리 최적화하기\n\nout-of-memory 에러가 난다면, [`StableDiffusionXLPipeline.enable_model_cpu_offload`]을 사용하는 것을 권장합니다.\n\n```diff\n- pipe.to(\"cuda\")\n+ pipe.enable_model_cpu_offload()\n```\n\n그리고\n\n```diff\n- refiner.to(\"cuda\")\n+ refiner.enable_model_cpu_offload()\n```\n\n### `torch.compile`로 추론 속도를 올리기\n\n`torch.compile`를 사용함으로써 추론 속도를 올릴 수 있습니다. 이는 **ca.** 20% 속도 향상이 됩니다.\n\n```diff\n+ pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n+ refiner.unet = torch.compile(refiner.unet, mode=\"reduce-overhead\", fullgraph=True)\n```\n\n### `torch < 2.0`일 때 실행하기\n\n**참고** Stable Diffusion XL을 `torch`가 2.0 버전 미만에서 실행시키고 싶을 때, xformers 어텐션을 사용해주세요:\n\n```sh\npip install xformers\n```\n\n```diff\n+pipe.enable_xformers_memory_efficient_attention()\n+refiner.enable_xformers_memory_efficient_attention()\n```\n\n## StableDiffusionXLPipeline\n\n[[autodoc]] StableDiffusionXLPipeline\n\t- all\n\t- __call__\n\n## StableDiffusionXLImg2ImgPipeline\n\n[[autodoc]] StableDiffusionXLImg2ImgPipeline\n\t- all\n\t- __call__\n\n## StableDiffusionXLInpaintPipeline\n\n[[autodoc]] StableDiffusionXLInpaintPipeline\n\t- all\n\t- __call__\n\n### 각 텍스트 인코더에 다른 프롬프트를 전달하기\n\nStable Diffusion XL는 두 개의 텍스트 인코더에 학습되었습니다. 기본 동작은 각 프롬프트에 동일한 프롬프트를 전달하는 것입니다. 그러나 [일부 사용자](https://github.com/huggingface/diffusers/issues/4004#issuecomment-1627764201)가 품질을 향상시킬 수 있다고 지적한 것처럼 텍스트 인코더마다 다른 프롬프트를 전달할 수 있습니다. 그렇게 하려면, `prompt_2`와 `negative_prompt_2`를 `prompt`와 `negative_prompt`에 전달해야 합니다. 그렇게 함으로써, 원래의 프롬프트들(`prompt`)과 부정 프롬프트들(`negative_prompt`)를 `텍스트 인코더`에 전달할 것입니다.(공식 SDXL 0.9/1.0의 [OpenAI CLIP-ViT/L-14](https://huggingface.co/openai/clip-vit-large-patch14)에서 볼 수 있습니다.) 그리고 `prompt_2`와 `negative_prompt_2`는 `text_encoder_2`에 전달됩니다.(공식 SDXL 0.9/1.0의 [OpenCLIP-ViT/bigG-14](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)에서 볼 수 있습니다.)\n\n```py\nfrom diffusers import StableDiffusionXLPipeline\nimport torch\n\npipe = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-0.9\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n)\npipe.to(\"cuda\")\n\n# OAI CLIP-ViT/L-14에 prompt가 전달됩니다\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\n# OpenCLIP-ViT/bigG-14에 prompt_2가 전달됩니다\nprompt_2 = \"monet painting\"\nimage = pipe(prompt=prompt, prompt_2=prompt_2).images[0]\n```" }, { "path": "diffusers/docs/source/ko/conceptual/contribution.md", "content": "\n\n# Diffusers에 기여하는 방법 🧨\n\n오픈 소스 커뮤니티에서의 기여를 환영합니다! 누구나 참여할 수 있으며, 코드뿐만 아니라 질문에 답변하거나 문서를 개선하는 등 모든 유형의 참여가 가치 있고 감사히 여겨집니다. 질문에 답변하고 다른 사람들을 도와주며 소통하고 문서를 개선하는 것은 모두 커뮤니티에게 큰 도움이 됩니다. 따라서 관심이 있다면 두려워하지 말고 참여해보세요!\n\n누구나 우리의 공개 Discord 채널에서 👋 인사하며 시작할 수 있도록 장려합니다. 우리는 diffusion 모델의 최신 동향을 논의하고 질문을 하며 개인 프로젝트를 자랑하고 기여에 대해 서로 도와주거나 그냥 어울리기 위해 모이는 곳입니다☕. \"Join\n\n어떤 방식으로든 기여하려는 경우, 우리는 개방적이고 환영하며 친근한 커뮤니티의 일부가 되기 위해 노력하고 있습니다. 우리의 [행동 강령](https://github.com/huggingface/diffusers/blob/main/CODE_OF_CONDUCT.md)을 읽고 상호 작용 중에 이를 존중하도록 주의해주시기 바랍니다. 또한 프로젝트를 안내하는 [윤리 지침](https://huggingface.co/docs/diffusers/conceptual/ethical_guidelines)에 익숙해지고 동일한 투명성과 책임성의 원칙을 준수해주시기를 부탁드립니다.\n\n우리는 커뮤니티로부터의 피드백을 매우 중요하게 생각하므로, 라이브러리를 개선하는 데 도움이 될 가치 있는 피드백이 있다고 생각되면 망설이지 말고 의견을 제시해주세요 - 모든 메시지, 댓글, 이슈, 풀 리퀘스트(PR)는 읽히고 고려됩니다.\n\n## 개요\n\n이슈에 있는 질문에 답변하는 것에서부터 코어 라이브러리에 새로운 diffusion 모델을 추가하는 것까지 다양한 방법으로 기여를 할 수 있습니다. \n\n이어지는 부분에서 우리는 다양한 방법의 기여에 대한 개요를 난이도에 따라 오름차순으로 정리하였습니다. 모든 기여는 커뮤니티에게 가치가 있습니다.\n\n1. [Diffusers 토론 포럼](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers)이나 [Discord](https://discord.gg/G7tWnz98XR)에서 질문에 대답하거나 질문을 할 수 있습니다.\n2. [GitHub Issues 탭](https://github.com/huggingface/diffusers/issues/new/choose)에서 새로운 이슈를 열 수 있습니다.\n3. [GitHub Issues 탭](https://github.com/huggingface/diffusers/issues)에서 이슈에 대답할 수 있습니다.\n4. \"Good first issue\" 라벨이 지정된 간단한 이슈를 수정할 수 있습니다. [여기](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)를 참조하세요.\n5. [문서](https://github.com/huggingface/diffusers/tree/main/docs/source)에 기여할 수 있습니다.\n6. [Community Pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3Acommunity-examples)에 기여할 수 있습니다.\n7. [예제](https://github.com/huggingface/diffusers/tree/main/examples)에 기여할 수 있습니다.\n8. \"Good second issue\" 라벨이 지정된 어려운 이슈를 수정할 수 있습니다. [여기](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22Good+second+issue%22)를 참조하세요.\n9. 새로운 파이프라인, 모델 또는 스케줄러를 추가할 수 있습니다. [\"새로운 파이프라인/모델\"](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22) 및 [\"새로운 스케줄러\"](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22) 이슈를 참조하세요. 이 기여에 대해서는 [디자인 철학](https://github.com/huggingface/diffusers/blob/main/PHILOSOPHY.md)을 확인해주세요.\n\n앞서 말한 대로, **모든 기여는 커뮤니티에게 가치가 있습니다**. 이어지는 부분에서 각 기여에 대해 조금 더 자세히 설명하겠습니다.\n\n4부터 9까지의 모든 기여에는 PR을 열어야 합니다. [PR을 열기](#how-to-open-a-pr)에서 자세히 설명되어 있습니다.\n\n### 1. Diffusers 토론 포럼이나 Diffusers Discord에서 질문하고 답변하기\n\nDiffusers 라이브러리와 관련된 모든 질문이나 의견은 [토론 포럼](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63)이나 [Discord](https://discord.gg/G7tWnz98XR)에서 할 수 있습니다. 이러한 질문과 의견에는 다음과 같은 내용이 포함됩니다(하지만 이에 국한되지는 않습니다):\n- 지식을 공유하기 위해서 훈련 또는 추론 실험에 대한 결과 보고\n- 개인 프로젝트 소개\n- 비공식 훈련 예제에 대한 질문\n- 프로젝트 제안\n- 일반적인 피드백\n- 논문 요약\n- Diffusers 라이브러리를 기반으로 하는 개인 프로젝트에 대한 도움 요청\n- 일반적인 질문\n- Diffusion 모델에 대한 윤리적 질문\n- ...\n\n포럼이나 Discord에서 질문을 하면 커뮤니티가 지식을 공개적으로 공유하도록 장려되며, 미래에 동일한 질문을 가진 초보자에게도 도움이 될 수 있습니다. 따라서 궁금한 질문은 언제든지 하시기 바랍니다.\n또한, 이러한 질문에 답변하는 것은 커뮤니티에게 매우 큰 도움이 됩니다. 왜냐하면 이렇게 하면 모두가 학습할 수 있는 공개적인 지식을 문서화하기 때문입니다.\n\n**주의**하십시오. 질문이나 답변에 투자하는 노력이 많을수록 공개적으로 문서화된 지식의 품질이 높아집니다. 마찬가지로, 잘 정의되고 잘 답변된 질문은 모두에게 접근 가능한 고품질 지식 데이터베이스를 만들어줍니다. 반면에 잘못된 질문이나 답변은 공개 지식 데이터베이스의 전반적인 품질을 낮출 수 있습니다.\n간단히 말해서, 고품질의 질문이나 답변은 *명확하고 간결하며 관련성이 있으며 이해하기 쉽고 접근 가능하며 잘 형식화되어 있어야* 합니다. 자세한 내용은 [좋은 이슈 작성 방법](#how-to-write-a-good-issue) 섹션을 참조하십시오.\n\n**채널에 대한 참고사항**:\n[*포럼*](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63)은 구글과 같은 검색 엔진에서 더 잘 색인화됩니다. 게시물은 인기에 따라 순위가 매겨지며, 시간순으로 정렬되지 않습니다. 따라서 이전에 게시한 질문과 답변을 쉽게 찾을 수 있습니다.\n또한, 포럼에 게시된 질문과 답변은 쉽게 링크할 수 있습니다.\n반면 *Discord*는 채팅 형식으로 되어 있어 빠른 대화를 유도합니다.\n질문에 대한 답변을 빠르게 받을 수는 있겠지만, 시간이 지나면 질문이 더 이상 보이지 않습니다. 또한, Discord에서 이전에 게시된 정보를 찾는 것은 훨씬 어렵습니다. 따라서 포럼을 사용하여 고품질의 질문과 답변을 하여 커뮤니티를 위한 오래 지속되는 지식을 만들기를 권장합니다. Discord에서의 토론이 매우 흥미로운 답변과 결론을 이끌어내는 경우, 해당 정보를 포럼에 게시하여 미래 독자들에게 더 쉽게 액세스할 수 있도록 권장합니다.\n\n### 2. GitHub 이슈 탭에서 새로운 이슈 열기\n\n🧨 Diffusers 라이브러리는 사용자들이 마주치는 문제를 알려주는 덕분에 견고하고 신뢰할 수 있습니다. 따라서 이슈를 보고해주셔서 감사합니다.\n\n기억해주세요, GitHub 이슈는 Diffusers 라이브러리와 직접적으로 관련된 기술적인 질문, 버그 리포트, 기능 요청 또는 라이브러리 디자인에 대한 피드백에 사용됩니다.\n\n간단히 말해서, Diffusers 라이브러리의 **코드와 관련되지 않은** 모든 것(문서 포함)은 GitHub가 아닌 [포럼](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63)이나 [Discord](https://discord.gg/G7tWnz98XR)에서 질문해야 합니다.\n\n**새로운 이슈를 열 때 다음 가이드라인을 고려해주세요**:\n- 이미 같은 이슈가 있는지 검색했는지 확인해주세요(GitHub의 이슈 탭에서 검색 기능을 사용하세요).\n- 다른(관련된) 이슈에 새로운 이슈를 보고하지 말아주세요. 다른 이슈와 관련이 높다면, 새로운 이슈를 열고 관련 이슈에 링크를 걸어주세요.\n- 이슈를 영어로 작성해주세요. 영어에 익숙하지 않다면, [DeepL](https://www.deepl.com/translator)과 같은 뛰어난 무료 온라인 번역 서비스를 사용하여 모국어에서 영어로 번역해주세요.\n- 이슈가 최신 Diffusers 버전으로 업데이트하면 해결될 수 있는지 확인해주세요. 이슈를 게시하기 전에 `python -c \"import diffusers; print(diffusers.__version__)\"` 명령을 실행하여 현재 사용 중인 Diffusers 버전이 최신 버전과 일치하거나 더 높은지 확인해주세요.\n- 새로운 이슈를 열 때 투자하는 노력이 많을수록 답변의 품질이 높아지고 Diffusers 이슈 전체의 품질도 향상됩니다.\n\n#### 2.1 재현가능하고 최소한인 버그 리포트\n\n새로운 이슈는 일반적으로 다음과 같은 내용을 포함합니다.\n\n버그 보고서는 항상 재현 가능한 코드 조각을 포함하고 가능한 한 최소한이어야 하며 간결해야 합니다.\n자세히 말하면:\n- 버그를 가능한 한 좁혀야 합니다. **전체 코드 파일을 그냥 던지지 마세요**.\n- 코드의 서식을 지정해야 합니다.\n- Diffusers가 의존하는 외부 라이브러리를 제외한 다른 외부 라이브러리는 포함하지 마십시오.\n- **반드시** 환경에 대한 모든 필요한 정보를 제공해야 합니다. 이를 위해 쉘에서 `diffusers-cli env`를 실행하고 표시된 정보를 이슈에 복사하여 붙여넣을 수 있습니다.\n- 이슈를 설명해야 합니다. 독자가 문제가 무엇이며 왜 문제인지 모르면 해결할 수 없습니다.\n- **항상** 독자가 가능한 한 적은 노력으로 문제를 재현할 수 있도록 해야 합니다. 코드 조각이 라이브러리가 없거나 정의되지 않은 변수 때문에 실행되지 않는 경우 독자가 도움을 줄 수 없습니다. 재현 가능한 코드 조각이 가능한 한 최소화되고 간단한 Python 셸에 복사하여 붙여넣을 수 있도록 해야 합니다.\n- 문제를 재현하기 위해 모델과/또는 데이터셋이 필요한 경우 독자가 해당 모델이나 데이터셋에 접근할 수 있도록 해야 합니다. 모델이나 데이터셋을 [Hub](https://huggingface.co)에 업로드하여 쉽게 다운로드할 수 있도록 할 수 있습니다. 문제 재현을 가능한 한 쉽게하기 위해 모델과 데이터셋을 가능한 한 작게 유지하려고 노력하세요.\n \n자세한 내용은 [좋은 이슈 작성 방법](#how-to-write-a-good-issue) 섹션을 참조하세요.\n\n버그 보고서를 열려면 [여기](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=bug&projects=&template=bug-report.yml)를 클릭하세요.\n\n\n#### 2.2. 기능 요청\n\n세계적인 기능 요청은 다음 사항을 다룹니다:\n\n1. 먼저 동기부여:\n* 라이브러리와 관련된 문제/불만이 있는가요? 그렇다면 왜 그런지 설명해주세요. 문제를 보여주는 코드 조각을 제공하는 것이 가장 좋습니다.\n* 프로젝트에 필요한 기능인가요? 우리는 그에 대해 듣고 싶습니다!\n* 커뮤니티에 도움이 될 수 있는 것을 작업했고 그것에 대해 생각하고 있는가요? 멋지네요! 어떤 문제를 해결했는지 알려주세요.\n2. 기능을 *상세히 설명하는* 문단을 작성해주세요;\n3. 미래 사용을 보여주는 **코드 조각**을 제공해주세요;\n4. 이것이 논문과 관련된 경우 링크를 첨부해주세요;\n5. 도움이 될 수 있는 추가 정보(그림, 스크린샷 등)를 첨부해주세요.\n\n기능 요청은 [여기](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feature_request.md&title=)에서 열 수 있습니다.\n\n#### 2.3 피드백\n\n라이브러리 디자인과 그것이 왜 좋은지 또는 나쁜지에 대한 이유에 대한 피드백은 핵심 메인테이너가 사용자 친화적인 라이브러리를 만드는 데 엄청난 도움이 됩니다. 현재 디자인 철학을 이해하려면 [여기](https://huggingface.co/docs/diffusers/conceptual/philosophy)를 참조해 주세요. 특정 디자인 선택이 현재 디자인 철학과 맞지 않는다고 생각되면, 그 이유와 어떻게 변경되어야 하는지 설명해 주세요. 반대로 특정 디자인 선택이 디자인 철학을 너무 따르기 때문에 사용 사례를 제한한다고 생각되면, 그 이유와 어떻게 변경되어야 하는지 설명해 주세요. 특정 디자인 선택이 매우 유용하다고 생각되면, 미래의 디자인 결정에 큰 도움이 되므로 이에 대한 의견을 남겨 주세요.\n\n피드백에 관한 이슈는 [여기](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=)에서 열 수 있습니다.\n\n#### 2.4 기술적인 질문\n\n기술적인 질문은 주로 라이브러리의 특정 코드가 왜 특정 방식으로 작성되었는지 또는 코드의 특정 부분이 무엇을 하는지에 대한 질문입니다. 질문하신 코드 부분에 대한 링크를 제공하고 해당 코드 부분이 이해하기 어려운 이유에 대한 자세한 설명을 해주시기 바랍니다.\n\n기술적인 질문에 관한 이슈를 [여기](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=bug&template=bug-report.yml)에서 열 수 있습니다.\n\n#### 2.5 새로운 모델, 스케줄러 또는 파이프라인 추가 제안\n\n만약 diffusion 모델 커뮤니티에서 Diffusers 라이브러리에 추가하고 싶은 새로운 모델, 파이프라인 또는 스케줄러가 있다면, 다음 정보를 제공해주세요:\n\n* Diffusion 파이프라인, 모델 또는 스케줄러에 대한 간단한 설명과 논문 또는 공개된 버전의 링크\n* 해당 모델의 오픈 소스 구현에 대한 링크\n* 모델 가중치가 있는 경우, 가중치의 링크\n\n모델에 직접 기여하고자 하는 경우, 최선의 안내를 위해 우리에게 알려주세요. 또한, 가능하다면 구성 요소(모델, 스케줄러, 파이프라인 등)의 원래 저자를 GitHub 핸들로 태그하는 것을 잊지 마세요.\n\n모델/파이프라인/스케줄러에 대한 요청을 [여기](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=New+model%2Fpipeline%2Fscheduler&template=new-model-addition.yml)에서 열 수 있습니다.\n\n### 3. GitHub 이슈 탭에서 문제에 대한 답변하기\n\nGitHub에서 이슈에 대한 답변을 하기 위해서는 Diffusers에 대한 기술적인 지식이 필요할 수 있지만, 정확한 답변이 아니더라도 모두가 시도해기를 권장합니다. 이슈에 대한 고품질 답변을 제공하기 위한 몇 가지 팁:\n- 가능한 한 간결하고 최소한으로 유지합니다.\n- 주제에 집중합니다. 이슈에 대한 답변은 해당 이슈에 관련된 내용에만 집중해야 합니다.\n- 코드, 논문 또는 다른 소스를 제공하여 답변을 증명하거나 지지합니다.\n- 코드로 답변합니다. 간단한 코드 조각이 이슈에 대한 답변이거나 이슈를 해결하는 방법을 보여준다면, 완전히 재현 가능한 코드 조각을 제공해주세요.\n\n또한, 많은 이슈들은 단순히 주제와 무관하거나 다른 이슈의 중복이거나 관련이 없는 경우가 많습니다. 이러한 이슈들에 대한 답변을 제공하고, 이슈 작성자에게 더 정확한 정보를 제공하거나, 중복된 이슈에 대한 링크를 제공하거나, [포럼](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) 이나 [Discord](https://discord.gg/G7tWnz98XR)로 리디렉션하는 것은 메인테이너에게 큰 도움이 됩니다.\n\n이슈가 올바른 버그 보고서이고 소스 코드에서 수정이 필요하다고 확인한 경우, 다음 섹션을 살펴보세요.\n\n다음 모든 기여에 대해서는 PR을 열여야 합니다. [PR 열기](#how-to-open-a-pr) 섹션에서 자세히 설명되어 있습니다. \n\n### 4. \"Good first issue\" 고치기\n\n*Good first issues*는 [Good first issue](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) 라벨로 표시됩니다. 일반적으로, 이슈는 이미 잠재적인 해결책이 어떻게 보이는지 설명하고 있어서 수정하기 쉽습니다.\n만약 이슈가 아직 닫히지 않았고 이 문제를 해결해보고 싶다면, \"이 이슈를 해결해보고 싶습니다.\"라는 메시지를 남기면 됩니다. 일반적으로 세 가지 시나리오가 있습니다:\n- a.) 이슈 설명이 이미 해결책을 제안합니다. 이 경우, 해결책이 이해되고 합리적으로 보인다면, PR 또는 드래프트 PR을 열어서 수정할 수 있습니다.\n- b.) 이슈 설명이 해결책을 제안하지 않습니다. 이 경우, 어떤 해결책이 가능할지 물어볼 수 있고, Diffusers 팀의 누군가가 곧 답변해줄 것입니다. 만약 어떻게 수정할지 좋은 아이디어가 있다면, 직접 PR을 열어도 됩니다.\n- c.) 이미 이 문제를 해결하기 위해 열린 PR이 있지만, 이슈가 아직 닫히지 않았습니다. PR이 더 이상 진행되지 않았다면, 새로운 PR을 열고 이전 PR에 링크를 걸면 됩니다. PR은 종종 원래 기여자가 갑자기 시간을 내지 못해 더 이상 진행하지 못하는 경우에 더 이상 진행되지 않게 됩니다. 이는 오픈 소스에서 자주 발생하는 일이며 매우 정상적인 상황입니다. 이 경우, 커뮤니티는 새로 시도하고 기존 PR의 지식을 활용해주면 매우 기쁠 것입니다. 이미 PR이 있고 활성화되어 있다면, 제안을 해주거나 PR을 검토하거나 PR에 기여할 수 있는지 물어보는 등 작성자를 도와줄 수 있습니다.\n\n\n### 5. 문서에 기여하기\n\n좋은 라이브러리는 항상 좋은 문서를 갖고 있습니다! 공식 문서는 라이브러리를 처음 사용하는 사용자들에게 첫 번째 접점 중 하나이며, 따라서 문서에 기여하는 것은 매우 가치 있는 기여입니다.\n\n라이브러리에 기여하는 방법은 다양합니다:\n\n- 맞춤법이나 문법 오류를 수정합니다.\n- 공식 문서가 이상하게 표시되거나 링크가 깨진 경우, 올바르게 수정하는 데 시간을 내주시면 매우 기쁠 것입니다.\n- 문서의 입력 또는 출력 텐서의 모양이나 차원을 수정합니다.\n- 이해하기 어렵거나 잘못된 문서를 명확하게 합니다.\n- 오래된 코드 예제를 업데이트합니다.\n- 문서를 다른 언어로 번역합니다.\n\n[공식 Diffusers 문서 페이지](https://huggingface.co/docs/diffusers/index)에 표시된 모든 내용은 공식 문서의 일부이며, 해당 [문서 소스](https://github.com/huggingface/diffusers/tree/main/docs/source)에서 수정할 수 있습니다.\n\n문서에 대한 변경 사항을 로컬에서 확인하는 방법은 [이 페이지](https://github.com/huggingface/diffusers/tree/main/docs)를 참조해주세요.\n\n\n### 6. 커뮤니티 파이프라인에 기여하기\n\n> [!TIP]\n> 커뮤니티 파이프라인에 대해 자세히 알아보려면 [커뮤니티 파이프라인](../using-diffusers/custom_pipeline_overview#community-pipelines) 가이드를 읽어보세요. 커뮤니티 파이프라인이 왜 필요한지 궁금하다면 GitHub 이슈 [#841](https://github.com/huggingface/diffusers/issues/841)를 확인해보세요 (기본적으로, 우리는 diffusion 모델이 추론에 사용될 수 있는 모든 방법을 유지할 수 없지만 커뮤니티가 이를 구축하는 것을 방해하고 싶지 않습니다).\n\n커뮤니티 파이프라인에 기여하는 것은 창의성과 작업을 커뮤니티와 공유하는 좋은 방법입니다. [`DiffusionPipeline`]을 기반으로 빌드하여 `custom_pipeline` 매개변수를 설정함으로써 누구나 로드하고 사용할 수 있도록 할 수 있습니다. 이 섹션에서는 UNet이 단일 순방향 패스만 수행하고 스케줄러를 한 번 호출하는 간단한 파이프라인 (단계별 파이프라인)을 만드는 방법을 안내합니다.\n\n1. 커뮤니티 파이프라인을 위한 one_step_unet.py 파일을 생성하세요. 이 파일은 사용자에 의해 설치되는 패키지를 포함할 수 있지만, [`DiffusionPipeline`]에서 모델 가중치와 스케줄러 구성을 로드하기 위해 하나의 파이프라인 클래스만 있어야 합니다. `__init__` 함수에 UNet과 스케줄러를 추가하세요.\n\n 또한 [`~DiffusionPipeline.save_pretrained`]를 사용하여 파이프라인과 그 구성 요소를 저장할 수 있도록 `register_modules` 함수를 추가해야 합니다.\n\n```py\nfrom diffusers import DiffusionPipeline\nimport torch\n\nclass UnetSchedulerOneForwardPipeline(DiffusionPipeline):\n def __init__(self, unet, scheduler):\n super().__init__()\n\n self.register_modules(unet=unet, scheduler=scheduler)\n```\n\n1. forward 패스에서 (`__call__`로 정의하는 것을 추천합니다), 원하는 어떤 기능이든 추가할 수 있습니다. \"one-step\" 파이프라인의 경우, 무작위 이미지를 생성하고 `timestep=1`로 설정하여 UNet과 스케줄러를 한 번 호출합니다.\n\n```py\n from diffusers import DiffusionPipeline\n import torch\n\n class UnetSchedulerOneForwardPipeline(DiffusionPipeline):\n def __init__(self, unet, scheduler):\n super().__init__()\n\n self.register_modules(unet=unet, scheduler=scheduler)\n\n def __call__(self):\n image = torch.randn(\n (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),\n )\n timestep = 1\n\n model_output = self.unet(image, timestep).sample\n scheduler_output = self.scheduler.step(model_output, timestep, image).prev_sample\n\n return scheduler_output\n```\n\n이제 UNet과 스케줄러를 전달하여 파이프라인을 실행하거나, 파이프라인 구조가 동일한 경우 사전 학습된 가중치를 로드할 수 있습니다.\n\n```py\nfrom diffusers import DDPMScheduler, UNet2DModel\n\nscheduler = DDPMScheduler()\nunet = UNet2DModel()\n\npipeline = UnetSchedulerOneForwardPipeline(unet=unet, scheduler=scheduler)\noutput = pipeline()\n# load pretrained weights\npipeline = UnetSchedulerOneForwardPipeline.from_pretrained(\"google/ddpm-cifar10-32\", use_safetensors=True)\noutput = pipeline()\n```\n\n파이프라인을 GitHub 커뮤니티 파이프라인 또는 Hub 커뮤니티 파이프라인으로 공유할 수 있습니다.\n\n\n\n\nGitHub 파이프라인을 공유하려면 Diffusers [저장소](https://github.com/huggingface/diffusers)에서 PR을 열고 one_step_unet.py 파일을 [examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) 하위 폴더에 추가하세요.\n\n\n\n\nHub 파이프라인을 공유하려면, 허브에 모델 저장소를 생성하고 one_step_unet.py 파일을 업로드하세요.\n\n\n\n\n### 7. 훈련 예제에 기여하기\n\nDiffusers 예제는 [examples](https://github.com/huggingface/diffusers/tree/main/examples) 폴더에 있는 훈련 스크립트의 모음입니다.\n\n두 가지 유형의 훈련 예제를 지원합니다:\n\n- 공식 훈련 예제\n- 연구용 훈련 예제\n\n연구용 훈련 예제는 [examples/research_projects](https://github.com/huggingface/diffusers/tree/main/examples/research_projects)에 위치하며, 공식 훈련 예제는 `research_projects` 및 `community` 폴더를 제외한 [examples](https://github.com/huggingface/diffusers/tree/main/examples)의 모든 폴더를 포함합니다.\n공식 훈련 예제는 Diffusers의 핵심 메인테이너가 유지 관리하며, 연구용 훈련 예제는 커뮤니티가 유지 관리합니다.\n이는 공식 파이프라인 vs 커뮤니티 파이프라인에 대한 [6. 커뮤니티 파이프라인 기여하기](#6-contribute-a-community-pipeline)에서 제시한 이유와 동일합니다: 핵심 메인테이너가 diffusion 모델의 모든 가능한 훈련 방법을 유지 관리하는 것은 현실적으로 불가능합니다.\nDiffusers 핵심 메인테잉너와 커뮤니티가 특정 훈련 패러다임을 너무 실험적이거나 충분히 인기 없는 것으로 간주하는 경우, 해당 훈련 코드는 `research_projects` 폴더에 넣고 작성자가 유지 관리해야 합니다.\n\n공식 훈련 및 연구 예제는 하나 이상의 훈련 스크립트, requirements.txt 파일 및 README.md 파일을 포함하는 디렉토리로 구성됩니다. 사용자가 훈련 예제를 사용하려면 리포지토리를 복제해야 합니다:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\n```\n\n그리고 훈련에 필요한 모든 추가적인 의존성도 설치해야 합니다:\n\n```bash\npip install -r /examples//requirements.txt\n```\n\n따라서 예제를 추가할 때, `requirements.txt` 파일은 훈련 예제에 필요한 모든 pip 종속성을 정의해야 합니다. 이렇게 설치된 모든 종속성을 사용하여 사용자가 예제의 훈련 스크립트를 실행할 수 있어야 합니다. 예를 들어, [DreamBooth `requirements.txt` 파일](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/requirements.txt)을 참조하세요.\n\nDiffusers 라이브러리의 훈련 예제는 다음 철학을 따라야 합니다:\n- 예제를 실행하는 데 필요한 모든 코드는 하나의 Python 파일에 있어야 합니다.\n- 사용자는 명령 줄에서 `python .py --args`와 같이 예제를 실행할 수 있어야 합니다.\n- 예제는 간단하게 유지되어야 하며, Diffusers를 사용한 훈련 방법을 보여주는 **예시**로 사용되어야 합니다. 예제 스크립트의 목적은 최첨단 diffusion 모델을 만드는 것이 아니라, 너무 많은 사용자 정의 로직을 추가하지 않고 이미 알려진 훈련 방법을 재현하는 것입니다. 이 점의 부산물로서, 예제는 좋은 교육 자료로써의 역할을 하기 위해 노력합니다.\n\n예제에 기여하기 위해서는, 이미 존재하는 예제인 [dreambooth](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py)와 같은 예제를 참고하여 어떻게 보여야 하는지에 대한 아이디어를 얻는 것이 매우 권장됩니다.\nDiffusers와 긴밀하게 통합되어 있기 때문에, 기여자들이 [Accelerate 라이브러리](https://github.com/huggingface/accelerate)를 사용하는 것을 강력히 권장합니다.\n예제 스크립트가 작동하는 경우, 반드시 예제를 정확하게 사용하는 방법을 설명하는 포괄적인 `README.md`를 추가해야 합니다. 이 README에는 다음이 포함되어야 합니다:\n- [여기](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth#running-locally-with-pytorch)에 표시된 예제 스크립트를 실행하는 방법에 대한 예제 명령어.\n- [여기](https://api.wandb.ai/report/patrickvonplaten/xm6cd5q5)에 표시된 훈련 결과 (로그, 모델 등)에 대한 링크로 사용자가 기대할 수 있는 내용을 보여줍니다.\n- 비공식/연구용 훈련 예제를 추가하는 경우, **반드시** git 핸들을 포함하여 이 훈련 예제를 유지 관리할 것임을 명시하는 문장을 추가해야 합니다. [여기](https://github.com/huggingface/diffusers/tree/main/examples/research_projects/intel_opts#diffusers-examples-with-intel-optimizations)에 표시된 것과 같습니다.\n\n만약 공식 훈련 예제에 기여하는 경우, [examples/test_examples.py](https://github.com/huggingface/diffusers/blob/main/examples/test_examples.py)에 테스트를 추가하는 것도 확인해주세요. 비공식 훈련 예제에는 이 작업이 필요하지 않습니다.\n\n### 8. \"Good second issue\" 고치기\n\n\"Good second issue\"는 [Good second issue](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22Good+second+issue%22) 라벨로 표시됩니다. Good second issue는 [Good first issues](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)보다 해결하기가 더 복잡합니다.\n이슈 설명은 일반적으로 이슈를 해결하는 방법에 대해 덜 구체적이며, 관심 있는 기여자는 라이브러리에 대한 꽤 깊은 이해가 필요합니다.\nGood second issue를 해결하고자 하는 경우, 해당 이슈를 해결하기 위해 PR을 열고 PR을 이슈에 링크하세요. 이미 해당 이슈에 대한 PR이 열려있지만 병합되지 않은 경우, 왜 병합되지 않았는지 이해하기 위해 살펴보고 개선된 PR을 열어보세요.\nGood second issue는 일반적으로 Good first issue 이슈보다 병합하기가 더 어려우므로, 핵심 메인테이너에게 도움을 요청하는 것이 좋습니다. PR이 거의 완료된 경우, 핵심 메인테이너는 PR에 참여하여 커밋하고 병합을 진행할 수 있습니다.\n\n### 9. 파이프라인, 모델, 스케줄러 추가하기\n\n파이프라인, 모델, 스케줄러는 Diffusers 라이브러리에서 가장 중요한 부분입니다.\n이들은 최첨단 diffusion 기술에 쉽게 접근하도록 하며, 따라서 커뮤니티가 강력한 생성형 AI 애플리케이션을 만들 수 있도록 합니다. \n\n새로운 모델, 파이프라인 또는 스케줄러를 추가함으로써, 사용자 인터페이스에 새로운 강력한 사용 사례를 활성화할 수 있으며, 이는 전체 생성형 AI 생태계에 매우 중요한 가치를 제공할 수 있습니다.\n\nDiffusers에는 세 가지 구성 요소에 대한 여러 개발 요청이 있습니다. 특정 구성 요소를 아직 정확히 어떤 것을 추가하고 싶은지 모르는 경우, 다음 링크를 참조하세요:\n- [모델 또는 파이프라인](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22)\n- [스케줄러](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22)\n\n\n세 가지 구성 요소를 추가하기 전에, [철학 가이드](philosophy)를 읽어보는 것을 강력히 권장합니다. 세 가지 구성 요소 중 어느 것을 추가하든, 디자인 철학과 관련된 API 일관성을 유지하기 위해 우리의 디자인 철학과 크게 다른 구성 요소는 병합할 수 없습니다. 디자인 선택에 근본적으로 동의하지 않는 경우, [피드백 이슈](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=)를 열어 해당 디자인 패턴/선택이 라이브러리 전체에서 변경되어야 하는지, 디자인 철학을 업데이트해야 하는지에 대해 논의할 수 있습니다. 라이브러리 전체의 일관성은 우리에게 매우 중요합니다.\n\nPR에 원본 코드베이스/논문 링크를 추가하고, 가능하면 PR에서 원래 작성자에게 직접 알림을 보내어 진행 상황을 따라갈 수 있도록 해주세요.\n\nPR에서 막힌 경우나 도움이 필요한 경우, 첫 번째 리뷰나 도움을 요청하는 메시지를 남기는 것을 주저하지 마세요.\n\n#### Copied from mechanism\n\n`# Copied from mechanism` 은 파이프라인, 모델 또는 스케줄러 코드를 추가할 때 이해해야 할 독특하고 중요한 기능입니다. Diffusers 코드베이스 전체에서 이를 자주 볼 수 있는데, 이를 사용하는 이유는 코드베이스를 이해하기 쉽고 유지 관리하기 쉽게 유지하기 위함입니다. `# Copied from mechanism` 으로 표시된 코드는 복사한 코드와 정확히 동일하도록 강제됩니다. 이를 통해 `make fix-copies`를 실행할 때 많은 파일에 걸쳐 변경 사항을 쉽게 업데이트하고 전파할 수 있습니다.\n\n예를 들어, 아래 코드 예제에서 [`~diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput`]은 원래 코드이며, `AltDiffusionPipelineOutput`은 `# Copied from mechanism`을 사용하여 복사합니다. 유일한 차이점은 클래스 접두사를 `Stable`에서 `Alt`로 변경한 것입니다.\n\n```py\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_output.StableDiffusionPipelineOutput with Stable->Alt\nclass AltDiffusionPipelineOutput(BaseOutput):\n \"\"\"\n Output class for Alt Diffusion pipelines.\n\n Args:\n images (`List[PIL.Image.Image]` or `np.ndarray`)\n List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,\n num_channels)`.\n nsfw_content_detected (`List[bool]`)\n List indicating whether the corresponding generated image contains \"not-safe-for-work\" (nsfw) content or\n `None` if safety checking could not be performed.\n \"\"\"\n```\n\n더 자세히 알고 싶다면 [~Don't~ Repeat Yourself*](https://huggingface.co/blog/transformers-design-philosophy#4-machine-learning-models-are-static) 블로그 포스트의 이 섹션을 읽어보세요.\n\n## 좋은 이슈 작성 방법\n\n**이슈를 잘 작성할수록 빠르게 해결될 가능성이 높아집니다.**\n\n1. 이슈에 적절한 템플릿을 사용했는지 확인하세요. [새 이슈를 열 때](https://github.com/huggingface/diffusers/issues/new/choose) 올바른 템플릿을 선택해야 합니다. *버그 보고서*, *기능 요청*, *API 디자인에 대한 피드백*, *새로운 모델/파이프라인/스케줄러 추가*, *포럼*, 또는 빈 이슈 중에서 선택하세요. 이슈를 열 때 올바른 템플릿을 선택하는 것이 중요합니다.\n2. **명확성**: 이슈에 적합한 제목을 지정하세요. 이슈 설명을 가능한 간단하게 작성하세요. 이슈를 이해하고 해결하는 데 걸리는 시간을 줄이기 위해 가능한 한 명확하게 작성하세요. 하나의 이슈에 대해 여러 문제를 포함하지 않도록 주의하세요. 여러 문제를 발견한 경우, 각각의 이슈를 개별적으로 열어주세요. 버그인 경우, 어떤 버그인지 가능한 한 정확하게 설명해야 합니다. \"diffusers에서 오류\"와 같이 간단히 작성하지 마세요.\n3. **재현 가능성**: 재현 가능한 코드 조각이 없으면 해결할 수 없습니다. 버그를 발견한 경우, 유지 관리자는 그 버그를 재현할 수 있어야 합니다. 이슈에 재현 가능한 코드 조각을 포함해야 합니다. 코드 조각은 Python 인터프리터에 복사하여 붙여넣을 수 있는 형태여야 합니다. 코드 조각이 작동해야 합니다. 즉, 누락된 import나 이미지에 대한 링크가 없어야 합니다. 이슈에는 오류 메시지와 정확히 동일한 오류 메시지를 재현하기 위해 수정하지 않고 복사하여 붙여넣을 수 있는 코드 조각이 포함되어야 합니다. 이슈에 사용자의 로컬 모델 가중치나 로컬 데이터를 사용하는 경우, 독자가 액세스할 수 없는 경우 이슈를 해결할 수 없습니다. 데이터나 모델을 공유할 수 없는 경우, 더미 모델이나 더미 데이터를 만들어 사용해보세요.\n4. **간결성**: 가능한 한 간결하게 유지하여 독자가 문제를 빠르게 이해할 수 있도록 도와주세요. 문제와 관련이 없는 코드나 정보는 모두 제거해주세요. 버그를 발견한 경우, 문제를 설명하는 가장 간단한 코드 예제를 만들어보세요. 버그를 발견한 후에는 작업 흐름 전체를 문제에 던지는 것이 아니라, 에러가 발생하는 훈련 코드의 어느 부분이 문제인지 먼저 이해하고 몇 줄로 재현해보세요. 전체 데이터셋 대신 더미 데이터를 사용해보세요.\n5. 링크 추가하기. 특정한 이름, 메서드, 또는 모델을 참조하는 경우, 독자가 더 잘 이해할 수 있도록 링크를 제공해주세요. 특정 PR이나 이슈를 참조하는 경우, 해당 이슈에 링크를 걸어주세요. 독자가 무엇을 말하는지 알고 있다고 가정하지 마세요. 이슈에 링크를 추가할수록 좋습니다.\n6. 포맷팅. 파이썬 코드 구문으로 코드를 포맷팅하고, 일반 코드 구문으로 에러 메시지를 포맷팅해주세요. 자세한 내용은 [공식 GitHub 포맷팅 문서](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax)를 참조하세요.\n7. 이슈를 해결해야 하는 티켓이 아니라, 잘 작성된 백과사전 항목으로 생각해보세요. 추가된 이슈는 공개적으로 사용 가능한 지식에 기여하는 것입니다. 잘 작성된 이슈를 추가함으로써 메인테이너가 문제를 해결하는 데 도움을 주는 것뿐만 아니라, 전체 커뮤니티가 라이브러리의 특정 측면을 더 잘 이해할 수 있도록 도움을 주는 것입니다.\n\n## 좋은 PR 작성 방법\n\n1. 카멜레온이 되세요. 기존의 디자인 패턴과 구문을 이해하고, 코드 추가가 기존 코드베이스에 매끄럽게 흐르도록 해야 합니다. 기존 디자인 패턴이나 사용자 인터페이스와 크게 다른 PR은 병합되지 않습니다.\n2. 초점을 맞추세요. 하나의 문제만 해결하는 PR을 작성해야 합니다. \"추가하면서 다른 문제도 해결하기\"에 빠지지 않도록 주의하세요. 여러 개의 관련 없는 문제를 해결하는 PR을 작성하는 것은 리뷰하기가 훨씬 어렵습니다.\n3. 도움이 되는 경우, 추가한 내용이 어떻게 사용되는지 예제 코드 조각을 추가해보세요.\n4. PR의 제목은 기여 내용을 요약해야 합니다.\n5. PR이 이슈를 해결하는 경우, PR 설명에 이슈 번호를 언급하여 연결되도록 해주세요 (이슈를 참조하는 사람들이 작업 중임을 알 수 있도록).\n6. 진행 중인 작업을 나타내려면 제목에 `[WIP]`를 접두사로 붙여주세요. 이는 중복 작업을 피하고, 병합 준비가 된 PR과 구분할 수 있도록 도움이 됩니다.\n7. [좋은 이슈를 작성하는 방법](#how-to-write-a-good-issue)에 설명된 대로 텍스트를 구성하고 형식을 지정해보세요.\n8. 기존 테스트가 통과하는지 확인하세요\n9. 높은 커버리지를 가진 테스트를 추가하세요. 품질 테스트가 없으면 병합할 수 없습니다.\n- 새로운 `@slow` 테스트를 추가하는 경우, 다음 명령을 사용하여 통과하는지 확인하세요.\n`RUN_SLOW=1 python -m pytest tests/test_my_new_model.py`.\nCircleCI는 느린 테스트를 실행하지 않지만, GitHub Actions는 매일 실행합니다!\n10. 모든 공개 메서드는 마크다운과 잘 작동하는 정보성 docstring을 가져야 합니다. 예시로 [`pipeline_latent_diffusion.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py)를 참조하세요.\n11. 레포지토리가 빠르게 성장하고 있기 때문에, 레포지토리에 큰 부담을 주는 파일이 추가되지 않도록 주의해야 합니다. 이미지, 비디오 및 기타 텍스트가 아닌 파일을 포함합니다. 이러한 파일을 배치하기 위해 hf.co 호스팅 `dataset`인 [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) 또는 [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images)를 활용하는 것이 우선입니다.\n외부 기여인 경우, 이미지를 PR에 추가하고 Hugging Face 구성원에게 이미지를 이 데이터셋으로 이동하도록 요청하세요.\n\n## PR을 열기 위한 방법\n\n코드를 작성하기 전에, 이미 누군가가 같은 작업을 하고 있는지 확인하기 위해 기존의 PR이나 이슈를 검색하는 것이 좋습니다. 확실하지 않은 경우, 피드백을 받기 위해 이슈를 열어보는 것이 항상 좋은 아이디어입니다.\n\n🧨 Diffusers에 기여하기 위해서는 기본적인 `git` 사용법을 알아야 합니다. `git`은 가장 쉬운 도구는 아니지만, 가장 훌륭한 매뉴얼을 가지고 있습니다. 셸에서 `git --help`을 입력하고 즐기세요. 책을 선호하는 경우, [Pro Git](https://git-scm.com/book/en/v2)은 매우 좋은 참고 자료입니다.\n\n다음 단계를 따라 기여를 시작하세요 ([지원되는 Python 버전](https://github.com/huggingface/diffusers/blob/main/setup.py#L244)):\n\n1. 저장소 페이지에서 'Fork' 버튼을 클릭하여 [저장소](https://github.com/huggingface/diffusers)를 포크합니다. 이렇게 하면 코드의 사본이 GitHub 사용자 계정에 생성됩니다.\n\n2. 포크한 저장소를 로컬 디스크에 클론하고, 기본 저장소를 원격으로 추가하세요:\n\n ```bash\n $ git clone git@github.com:/diffusers.git\n $ cd diffusers\n $ git remote add upstream https://github.com/huggingface/diffusers.git\n ```\n\n3. 개발 변경 사항을 보관할 새로운 브랜치를 생성하세요:\n\n ```bash\n $ git checkout -b a-descriptive-name-for-my-changes\n ```\n\n`main` 브랜치 위에서 **절대** 작업하지 마세요.\n\n1. 가상 환경에서 다음 명령을 실행하여 개발 환경을 설정하세요:\n\n ```bash\n $ pip install -e \".[dev]\"\n ```\n\n만약 저장소를 이미 클론한 경우, 가장 최신 변경 사항을 가져오기 위해 `git pull`을 실행해야 할 수도 있습니다.\n\n5. 기능을 브랜치에서 개발하세요.\n\n기능을 작업하는 동안 테스트 스위트가 통과되는지 확인해야 합니다. 다음과 같이 변경 사항에 영향을 받는 테스트를 실행해야 합니다:\n\n ```bash\n $ pytest tests/.py\n ```\n\n테스트를 실행하기 전에 테스트를 위해 필요한 의존성들을 설치하였는지 확인하세요. 다음의 커맨드를 통해서 확인할 수 있습니다:\n\n ```bash\n $ pip install -e \".[test]\"\n ```\n\n다음 명령어로 전체 테스트 묶음 실행할 수도 있지만, Diffusers가 많이 성장하였기 때문에 결과를 적당한 시간 내에 생성하기 위해서는 강력한 컴퓨터가 필요합니다. 다음은 해당 명령어입니다:\n\n ```bash\n $ make test\n ```\n\n🧨 Diffusers는 소스 코드를 일관되게 포맷팅하기 위해 `black`과 `isort`를 사용합니다. 변경 사항을 적용한 후에는 다음과 같이 자동 스타일 수정 및 코드 검증을 적용할 수 있습니다:\n\n\n ```bash\n $ make style\n ```\n\n🧨 Diffusers `ruff`와 몇개의 커스텀 스크립트를 이용하여 코딩 실수를 확인합니다. 품질 제어는 CI에서 작동하지만, 동일한 검사를 다음을 통해서도 할 수 있습니다:\n\n ```bash\n $ make quality\n ```\n\n변경사항에 대해 만족한다면 `git add`를 사용하여 변경된 파일을 추가하고 `git commit`을 사용하여 변경사항에 대해 로컬상으로 저장한다:\n\n ```bash\n $ git add modified_file.py\n $ git commit -m \"A descriptive message about your changes.\"\n ```\n\n코드를 정기적으로 원본 저장소와 동기화하는 것은 좋은 아이디어입니다. 이렇게 하면 변경 사항을 빠르게 반영할 수 있습니다:\n\n ```bash\n $ git pull upstream main\n ```\n\n변경 사항을 계정에 푸시하려면 다음을 사용하세요:\n\n ```bash\n $ git push -u origin a-descriptive-name-for-my-changes\n ```\n\n6. 만족하셨다면, GitHub에서 포크한 웹페이지로 이동하여 'Pull request'를 클릭하여 변경사항을 프로젝트 메인테이너에게 검토를 요청합니다.\n\n7. 메인테이너가 변경 사항을 요청하는 것은 괜찮습니다. 핵심 기여자들에게도 일어나는 일입니다! 따라서 변경 사항을 Pull request에서 볼 수 있도록 로컬 브랜치에서 작업하고 변경 사항을 포크에 푸시하면 자동으로 Pull request에 나타납니다.\n\n### 테스트\n\n라이브러리 동작과 여러 예제를 테스트하기 위해 포괄적인 테스트 묶음이 포함되어 있습니다. 라이브러리 테스트는 [tests 폴더](https://github.com/huggingface/diffusers/tree/main/tests)에서 찾을 수 있습니다.\n\n`pytest`와 `pytest-xdist`를 선호하는 이유는 더 빠르기 때문입니다. 루트 디렉토리에서 라이브러리를 위해 `pytest`로 테스트를 실행하는 방법은 다음과 같습니다:\n\n```bash\n$ python -m pytest -n auto --dist=loadfile -s -v ./tests/\n```\n\n사실, `make test`는 이렇게 구현되어 있습니다!\n\n작업 중인 기능만 테스트하기 위해 더 작은 테스트 세트를 지정할 수 있습니다.\n\n기본적으로 느린 테스트는 건너뜁니다. `RUN_SLOW` 환경 변수를 `yes`로 설정하여 실행할 수 있습니다. 이는 많은 기가바이트의 모델을 다운로드합니다. 충분한 디스크 공간과 좋은 인터넷 연결 또는 많은 인내심이 필요합니다!\n\n```bash\n$ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/\n```\n\n`unittest`는 완전히 지원됩니다. 다음은 `unittest`를 사용하여 테스트를 실행하는 방법입니다:\n\n```bash\n$ python -m unittest discover -s tests -t . -v\n$ python -m unittest discover -s examples -t examples -v\n```\n\n### upstream(main)과 forked main 동기화하기\n\nupstream 저장소에 불필요한 참조 노트를 추가하고 관련 개발자에게 알림을 보내는 것을 피하기 위해,\nforked 저장소의 main 브랜치를 동기화할 때 다음 단계를 따르세요:\n1. 가능한 경우, forked 저장소에서 브랜치와 PR을 사용하여 upstream과 동기화하는 것을 피하세요. 대신 forked main으로 직접 병합하세요.\n2. PR이 절대적으로 필요한 경우, 브랜치를 체크아웃한 후 다음 단계를 사용하세요:\n```bash\n$ git checkout -b your-branch-for-syncing\n$ git pull --squash --no-commit upstream main\n$ git commit -m ''\n$ git push --set-upstream origin your-branch-for-syncing\n```\n\n### 스타일 가이드\n\nDocumentation string에 대해서는, 🧨 Diffusers는 [Google 스타일](https://google.github.io/styleguide/pyguide.html)을 따릅니다.\n" }, { "path": "diffusers/docs/source/ko/conceptual/ethical_guidelines.md", "content": "\n\n# 🧨 Diffusers의 윤리 지침\n\n## 서문\n\n[Diffusers](https://huggingface.co/docs/diffusers/index)는 사전 훈련된 diffusion 모델을 제공하며 추론 및 훈련을 위한 모듈식 툴박스로 사용됩니다.\n\n이 기술의 실제 적용과 사회에 미칠 수 있는 부정적인 영향을 고려하여 Diffusers 라이브러리의 개발, 사용자 기여 및 사용에 윤리 지침을 제공하는 것이 중요하다고 생각합니다.\n\n이 기술을 사용하는 데 연관된 위험은 아직 조사 중이지만, 몇 가지 예를 들면: 예술가들에 대한 저작권 문제; 딥 페이크의 악용; 부적절한 맥락에서의 성적 콘텐츠 생성; 동의 없는 impersonation; 사회적인 편견으로 인해 억압되는 그룹들에 대한 해로운 영향입니다.\n우리는 위험을 지속적으로 추적하고 커뮤니티의 응답과 소중한 피드백에 따라 다음 지침을 조정할 것입니다.\n\n\n## 범위\n\nDiffusers 커뮤니티는 프로젝트의 개발에 다음과 같은 윤리 지침을 적용하며, 특히 윤리적 문제와 관련된 민감한 주제에 대한 커뮤니티의 기여를 조정하는 데 도움을 줄 것입니다.\n\n\n## 윤리 지침\n\n다음 윤리 지침은 일반적으로 적용되지만, 기술적 선택을 할 때 윤리적으로 민감한 문제를 다룰 때 주로 적용할 것입니다. 또한, 해당 기술의 최신 동향과 관련된 신규 위험에 따라 시간이 지남에 따라 이러한 윤리 원칙을 조정할 것을 약속합니다.\n\n- **투명성**: 우리는 PR을 관리하고, 사용자에게 우리의 선택을 설명하며, 기술적 의사결정을 내릴 때 투명성을 유지할 것을 약속합니다.\n\n- **일관성**: 우리는 프로젝트 관리에서 사용자들에게 동일한 수준의 관심을 보장하고 기술적으로 안정되고 일관된 상태를 유지할 것을 약속합니다.\n\n- **간결성**: Diffusers 라이브러리를 사용하고 활용하기 쉽게 만들기 위해, 프로젝트의 목표를 간결하고 일관성 있게 유지할 것을 약속합니다.\n\n- **접근성**: Diffusers 프로젝트는 기술적 전문 지식 없어도 프로젝트 운영에 참여할 수 있는 기여자의 진입장벽을 낮춥니다. 이를 통해 연구 결과물이 커뮤니티에 더 잘 접근할 수 있게 됩니다. \n\n- **재현성**: 우리는 Diffusers 라이브러리를 통해 제공되는 업스트림(upstream) 코드, 모델 및 데이터셋의 재현성에 대해 투명하게 공개할 것을 목표로 합니다.\n\n- **책임**: 우리는 커뮤니티와 팀워크를 통해, 이 기술의 잠재적인 위험과 위험을 예측하고 완화하는 데 대한 공동 책임을 가지고 있습니다.\n\n\n## 구현 사례: 안전 기능과 메커니즘\n\n팀은 diffusion 기술과 관련된 잠재적인 윤리 및 사회적 위험에 대처하기 위한 기술적 및 비기술적 도구를 제공하고자 하고 있습니다. 또한, 커뮤니티의 참여는 이러한 기능의 구현하고 우리와 함께 인식을 높이는 데 매우 중요합니다.\n\n- [**커뮤니티 탭**](https://huggingface.co/docs/hub/repositories-pull-requests-discussions): 이를 통해 커뮤니티는 프로젝트에 대해 토론하고 더 나은 협력을 할 수 있습니다.\n\n- **편향 탐색 및 평가**: Hugging Face 팀은 Stable Diffusion 모델의 편향성을 대화형으로 보여주는 [space](https://huggingface.co/spaces/society-ethics/DiffusionBiasExplorer)을 제공합니다. 이런 의미에서, 우리는 편향 탐색 및 평가를 지원하고 장려합니다.\n\n- **배포에서의 안전 유도**\n\n - [**안전한 Stable Diffusion**](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_safe): 이는 필터되지 않은 웹 크롤링 데이터셋으로 훈련된 Stable Diffusion과 같은 모델이 부적절한 변질에 취약한 문제를 완화합니다. 관련 논문: [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://arxiv.org/abs/2211.05105).\n\n - [**안전 검사기**](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py): 이미지가 생성된 후에 이미자가 임베딩 공간에서 일련의 하드코딩된 유해 개념의 클래스일 확률을 확인하고 비교합니다. 유해 개념은 역공학을 방지하기 위해 의도적으로 숨겨져 있습니다.\n\n- **Hub에서의 단계적인 배포**: 특히 민감한 상황에서는 일부 리포지토리에 대한 접근을 제한해야 합니다. 이 단계적인 배포는 중간 단계로, 리포지토리 작성자가 사용에 대한 더 많은 통제력을 갖게 합니다. \n\n- **라이선싱**: [OpenRAILs](https://huggingface.co/blog/open_rail)와 같은 새로운 유형의 라이선싱을 통해 자유로운 접근을 보장하면서도 더 책임 있는 사용을 위한 일련의 제한을 둘 수 있습니다.\n" }, { "path": "diffusers/docs/source/ko/conceptual/evaluation.md", "content": "\n\n# Diffusion 모델 평가하기[[evaluating-diffusion-models]]\n\n\n \"Open\n\n\n[Stable Diffusion](https://huggingface.co/docs/diffusers/stable_diffusion)와 같은 생성 모델의 평가는 주관적인 성격을 가지고 있습니다. 그러나 실무자와 연구자로서 우리는 종종 다양한 가능성 중에서 신중한 선택을 해야 합니다. 그래서 다양한 생성 모델 (GAN, Diffusion 등)을 사용할 때 어떻게 선택해야 할까요?\n\n정성적인 평가는 모델의 이미지 품질에 대한 주관적인 평가이므로 오류가 발생할 수 있고 결정에 잘못된 영향을 미칠 수 있습니다. 반면, 정량적인 평가는 이미지 품질과 직접적인 상관관계를 갖지 않을 수 있습니다. 따라서 일반적으로 정성적 평가와 정량적 평가를 모두 고려하는 것이 더 강력한 신호를 제공하여 모델 선택에 도움이 됩니다.\n\n이 문서에서는 Diffusion 모델을 평가하기 위한 정성적 및 정량적 방법에 대해 상세히 설명합니다. 정량적 방법에 대해서는 특히 `diffusers`와 함께 구현하는 방법에 초점을 맞추었습니다.\n\n이 문서에서 보여진 방법들은 기반 생성 모델을 고정시키고 다양한 [노이즈 스케줄러](https://huggingface.co/docs/diffusers/main/en/api/schedulers/overview)를 평가하는 데에도 사용할 수 있습니다.\n\n## 시나리오[[scenarios]]\n다음과 같은 파이프라인을 사용하여 Diffusion 모델을 다룹니다:\n\n- 텍스트로 안내된 이미지 생성 (예: [`StableDiffusionPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/text2img)).\n- 입력 이미지에 추가로 조건을 건 텍스트로 안내된 이미지 생성 (예: [`StableDiffusionImg2ImgPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/img2img) 및 [`StableDiffusionInstructPix2PixPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/pix2pix)).\n- 클래스 조건화된 이미지 생성 모델 (예: [`DiTPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/dit)).\n\n## 정성적 평가[[qualitative-evaluation]]\n\n정성적 평가는 일반적으로 생성된 이미지의 인간 평가를 포함합니다. 품질은 구성성, 이미지-텍스트 일치, 공간 관계 등과 같은 측면에서 측정됩니다. 일반적인 프롬프트는 주관적인 지표에 대한 일정한 기준을 제공합니다.\nDrawBench와 PartiPrompts는 정성적인 벤치마킹에 사용되는 프롬프트 데이터셋입니다. DrawBench와 PartiPrompts는 각각 [Imagen](https://imagen.research.google/)과 [Parti](https://parti.research.google/)에서 소개되었습니다.\n\n[Parti 공식 웹사이트](https://parti.research.google/)에서 다음과 같이 설명하고 있습니다:\n\n> PartiPrompts (P2)는 이 작업의 일부로 공개되는 영어로 된 1600개 이상의 다양한 프롬프트 세트입니다. P2는 다양한 범주와 도전 측면에서 모델의 능력을 측정하는 데 사용할 수 있습니다.\n\n![parti-prompts](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/parti-prompts.png)\n\nPartiPrompts는 다음과 같은 열을 가지고 있습니다:\n\n- 프롬프트 (Prompt)\n- 프롬프트의 카테고리 (예: \"Abstract\", \"World Knowledge\" 등)\n- 난이도를 반영한 챌린지 (예: \"Basic\", \"Complex\", \"Writing & Symbols\" 등)\n\n이러한 벤치마크는 서로 다른 이미지 생성 모델을 인간 평가로 비교할 수 있도록 합니다.\n\n이를 위해 🧨 Diffusers 팀은 **Open Parti Prompts**를 구축했습니다. 이는 Parti Prompts를 기반으로 한 커뮤니티 기반의 질적 벤치마크로, 최첨단 오픈 소스 확산 모델을 비교하는 데 사용됩니다:\n- [Open Parti Prompts 게임](https://huggingface.co/spaces/OpenGenAI/open-parti-prompts): 10개의 parti prompt에 대해 4개의 생성된 이미지가 제시되며, 사용자는 프롬프트에 가장 적합한 이미지를 선택합니다.\n- [Open Parti Prompts 리더보드](https://huggingface.co/spaces/OpenGenAI/parti-prompts-leaderboard): 현재 최고의 오픈 소스 diffusion 모델들을 서로 비교하는 리더보드입니다.\n\n이미지를 수동으로 비교하려면, `diffusers`를 사용하여 몇가지 PartiPrompts를 어떻게 활용할 수 있는지 알아봅시다.\n\n다음은 몇 가지 다른 도전에서 샘플링한 프롬프트를 보여줍니다: Basic, Complex, Linguistic Structures, Imagination, Writing & Symbols. 여기서는 PartiPrompts를 [데이터셋](https://huggingface.co/datasets/nateraw/parti-prompts)으로 사용합니다.\n\n```python\nfrom datasets import load_dataset\n\n# prompts = load_dataset(\"nateraw/parti-prompts\", split=\"train\")\n# prompts = prompts.shuffle()\n# sample_prompts = [prompts[i][\"Prompt\"] for i in range(5)]\n\n# Fixing these sample prompts in the interest of reproducibility.\nsample_prompts = [\n \"a corgi\",\n \"a hot air balloon with a yin-yang symbol, with the moon visible in the daytime sky\",\n \"a car with no windows\",\n \"a cube made of porcupine\",\n 'The saying \"BE EXCELLENT TO EACH OTHER\" written on a red brick wall with a graffiti image of a green alien wearing a tuxedo. A yellow fire hydrant is on a sidewalk in the foreground.',\n]\n```\n이제 이런 프롬프트를 사용하여 Stable Diffusion ([v1-4 checkpoint](https://huggingface.co/CompVis/stable-diffusion-v1-4))를 사용한 이미지 생성을 할 수 있습니다 :\n\n```python\nimport torch\n\nseed = 0\ngenerator = torch.manual_seed(seed)\n\nimages = sd_pipeline(sample_prompts, num_images_per_prompt=1, generator=generator).images\n```\n\n![parti-prompts-14](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/parti-prompts-14.png)\n\n\n`num_images_per_prompt`를 설정하여 동일한 프롬프트에 대해 다른 이미지를 비교할 수도 있습니다. 다른 체크포인트([v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5))로 동일한 파이프라인을 실행하면 다음과 같은 결과가 나옵니다:\n\n![parti-prompts-15](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/parti-prompts-15.png)\n\n\n다양한 모델을 사용하여 모든 프롬프트에서 생성된 여러 이미지들이 생성되면 (평가 과정에서) 이러한 결과물들은 사람 평가자들에게 점수를 매기기 위해 제시됩니다. DrawBench와 PartiPrompts 벤치마크에 대한 자세한 내용은 각각의 논문을 참조하십시오.\n\n\n\n모델이 훈련 중일 때 추론 샘플을 살펴보는 것은 훈련 진행 상황을 측정하는 데 유용합니다. [훈련 스크립트](https://github.com/huggingface/diffusers/tree/main/examples/)에서는 TensorBoard와 Weights & Biases에 대한 추가 지원과 함께 이 유틸리티를 지원합니다.\n\n\n\n## 정량적 평가[[quantitative-evaluation]]\n\n이 섹션에서는 세 가지 다른 확산 파이프라인을 평가하는 방법을 안내합니다:\n\n- CLIP 점수\n- CLIP 방향성 유사도\n- FID\n\n### 텍스트 안내 이미지 생성[[text-guided-image-generation]]\n\n[CLIP 점수](https://arxiv.org/abs/2104.08718)는 이미지-캡션 쌍의 호환성을 측정합니다. 높은 CLIP 점수는 높은 호환성🔼을 나타냅니다. CLIP 점수는 이미지와 캡션 사이의 의미적 유사성으로 생각할 수도 있습니다. CLIP 점수는 인간 판단과 높은 상관관계를 가지고 있습니다.\n\n[`StableDiffusionPipeline`]을 일단 로드해봅시다:\n\n```python\nfrom diffusers import StableDiffusionPipeline\nimport torch\n\nmodel_ckpt = \"CompVis/stable-diffusion-v1-4\"\nsd_pipeline = StableDiffusionPipeline.from_pretrained(model_ckpt, torch_dtype=torch.float16).to(\"cuda\")\n```\n\n여러 개의 프롬프트를 사용하여 이미지를 생성합니다:\n\n```python\nprompts = [\n \"a photo of an astronaut riding a horse on mars\",\n \"A high tech solarpunk utopia in the Amazon rainforest\",\n \"A pikachu fine dining with a view to the Eiffel Tower\",\n \"A mecha robot in a favela in expressionist style\",\n \"an insect robot preparing a delicious meal\",\n \"A small cabin on top of a snowy mountain in the style of Disney, artstation\",\n]\n\nimages = sd_pipeline(prompts, num_images_per_prompt=1, output_type=\"np\").images\n\nprint(images.shape)\n# (6, 512, 512, 3)\n```\n\n그러고 나서 CLIP 점수를 계산합니다. \n\n```python\nfrom torchmetrics.functional.multimodal import clip_score\nfrom functools import partial\n\nclip_score_fn = partial(clip_score, model_name_or_path=\"openai/clip-vit-base-patch16\")\n\ndef calculate_clip_score(images, prompts):\n images_int = (images * 255).astype(\"uint8\")\n clip_score = clip_score_fn(torch.from_numpy(images_int).permute(0, 3, 1, 2), prompts).detach()\n return round(float(clip_score), 4)\n\nsd_clip_score = calculate_clip_score(images, prompts)\nprint(f\"CLIP score: {sd_clip_score}\")\n# CLIP score: 35.7038\n```\n\n위의 예제에서는 각 프롬프트 당 하나의 이미지를 생성했습니다. 만약 프롬프트 당 여러 이미지를 생성한다면, 프롬프트 당 생성된 이미지의 평균 점수를 사용해야 합니다.\n\n이제 [`StableDiffusionPipeline`]과 호환되는 두 개의 체크포인트를 비교하려면, 파이프라인을 호출할 때 generator를 전달해야 합니다. 먼저, 고정된 시드로 [v1-4 Stable Diffusion 체크포인트](https://huggingface.co/CompVis/stable-diffusion-v1-4)를 사용하여 이미지를 생성합니다:\n\n```python\nseed = 0\ngenerator = torch.manual_seed(seed)\n\nimages = sd_pipeline(prompts, num_images_per_prompt=1, generator=generator, output_type=\"np\").images\n```\n\n그런 다음 [v1-5 checkpoint](https://huggingface.co/runwayml/stable-diffusion-v1-5)를 로드하여 이미지를 생성합니다:\n\n```python\nmodel_ckpt_1_5 = \"runwayml/stable-diffusion-v1-5\"\nsd_pipeline_1_5 = StableDiffusionPipeline.from_pretrained(model_ckpt_1_5, torch_dtype=weight_dtype).to(device)\n\nimages_1_5 = sd_pipeline_1_5(prompts, num_images_per_prompt=1, generator=generator, output_type=\"np\").images\n```\n\n그리고 마지막으로 CLIP 점수를 비교합니다:\n\n```python\nsd_clip_score_1_4 = calculate_clip_score(images, prompts)\nprint(f\"CLIP Score with v-1-4: {sd_clip_score_1_4}\")\n# CLIP Score with v-1-4: 34.9102\n\nsd_clip_score_1_5 = calculate_clip_score(images_1_5, prompts)\nprint(f\"CLIP Score with v-1-5: {sd_clip_score_1_5}\")\n# CLIP Score with v-1-5: 36.2137\n```\n\n[v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) 체크포인트가 이전 버전보다 더 나은 성능을 보이는 것 같습니다. 그러나 CLIP 점수를 계산하기 위해 사용한 프롬프트의 수가 상당히 적습니다. 보다 실용적인 평가를 위해서는 이 수를 훨씬 높게 설정하고, 프롬프트를 다양하게 사용해야 합니다.\n\n\n\n이 점수에는 몇 가지 제한 사항이 있습니다. 훈련 데이터셋의 캡션은 웹에서 크롤링되어 이미지와 관련된 `alt` 및 유사한 태그에서 추출되었습니다. 이들은 인간이 이미지를 설명하는 데 사용할 수 있는 것과 일치하지 않을 수 있습니다. 따라서 여기서는 몇 가지 프롬프트를 \"엔지니어링\"해야 했습니다.\n\n\n\n### 이미지 조건화된 텍스트-이미지 생성[[image-conditioned-text-to-image-generation]]\n\n이 경우, 생성 파이프라인을 입력 이미지와 텍스트 프롬프트로 조건화합니다. [`StableDiffusionInstructPix2PixPipeline`]을 예로 들어보겠습니다. 이는 편집 지시문을 입력 프롬프트로 사용하고 편집할 입력 이미지를 사용합니다.\n\n다음은 하나의 예시입니다:\n\n![edit-instruction](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/edit-instruction.png)\n\n모델을 평가하는 한 가지 전략은 두 이미지 캡션 간의 변경과([CLIP-Guided Domain Adaptation of Image Generators](https://arxiv.org/abs/2108.00946)에서 보여줍니다) 함께 두 이미지 사이의 변경의 일관성을 측정하는 것입니다 ([CLIP](https://huggingface.co/docs/transformers/model_doc/clip) 공간에서). 이를 \"**CLIP 방향성 유사성**\"이라고 합니다.\n\n- 캡션 1은 편집할 이미지 (이미지 1)에 해당합니다.\n- 캡션 2는 편집된 이미지 (이미지 2)에 해당합니다. 편집 지시를 반영해야 합니다.\n\n다음은 그림으로 된 개요입니다:\n\n![edit-consistency](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/edit-consistency.png)\n\n우리는 이 측정 항목을 구현하기 위해 미니 데이터 세트를 준비했습니다. 먼저 데이터 세트를 로드해 보겠습니다.\n\n```python\nfrom datasets import load_dataset\n\ndataset = load_dataset(\"sayakpaul/instructpix2pix-demo\", split=\"train\")\ndataset.features\n```\n\n```bash\n{'input': Value(dtype='string', id=None),\n 'edit': Value(dtype='string', id=None),\n 'output': Value(dtype='string', id=None),\n 'image': Image(decode=True, id=None)}\n```\n\n여기에는 다음과 같은 항목이 있습니다:\n\n- `input`은 `image`에 해당하는 캡션입니다.\n- `edit`은 편집 지시사항을 나타냅니다.\n- `output`은 `edit` 지시사항을 반영한 수정된 캡션입니다.\n\n샘플을 살펴보겠습니다.\n\n```python\nidx = 0\nprint(f\"Original caption: {dataset[idx]['input']}\")\nprint(f\"Edit instruction: {dataset[idx]['edit']}\")\nprint(f\"Modified caption: {dataset[idx]['output']}\")\n```\n\n```bash\nOriginal caption: 2. FAROE ISLANDS: An archipelago of 18 mountainous isles in the North Atlantic Ocean between Norway and Iceland, the Faroe Islands has 'everything you could hope for', according to Big 7 Travel. It boasts 'crystal clear waterfalls, rocky cliffs that seem to jut out of nowhere and velvety green hills'\nEdit instruction: make the isles all white marble\nModified caption: 2. WHITE MARBLE ISLANDS: An archipelago of 18 mountainous white marble isles in the North Atlantic Ocean between Norway and Iceland, the White Marble Islands has 'everything you could hope for', according to Big 7 Travel. It boasts 'crystal clear waterfalls, rocky cliffs that seem to jut out of nowhere and velvety green hills'\n```\n\n다음은 이미지입니다:\n\n```python\ndataset[idx][\"image\"]\n```\n\n![edit-dataset](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/evaluation_diffusion_models/edit-dataset.png)\n\n먼저 편집 지시사항을 사용하여 데이터 세트의 이미지를 편집하고 방향 유사도를 계산합니다.\n\n[`StableDiffusionInstructPix2PixPipeline`]를 먼저 로드합니다:\n\n```python\nfrom diffusers import StableDiffusionInstructPix2PixPipeline\n\ninstruct_pix2pix_pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained(\n \"timbrooks/instruct-pix2pix\", torch_dtype=torch.float16\n).to(device)\n```\n\n이제 편집을 수행합니다:\n\n```python\nimport numpy as np\n\n\ndef edit_image(input_image, instruction):\n image = instruct_pix2pix_pipeline(\n instruction,\n image=input_image,\n output_type=\"np\",\n generator=generator,\n ).images[0]\n return image\n\ninput_images = []\noriginal_captions = []\nmodified_captions = []\nedited_images = []\n\nfor idx in range(len(dataset)):\n input_image = dataset[idx][\"image\"]\n edit_instruction = dataset[idx][\"edit\"]\n edited_image = edit_image(input_image, edit_instruction)\n\n input_images.append(np.array(input_image))\n original_captions.append(dataset[idx][\"input\"])\n modified_captions.append(dataset[idx][\"output\"])\n edited_images.append(edited_image)\n```\n방향 유사도를 계산하기 위해서는 먼저 CLIP의 이미지와 텍스트 인코더를 로드합니다:\n\n```python\nfrom transformers import (\n CLIPTokenizer,\n CLIPTextModelWithProjection,\n CLIPVisionModelWithProjection,\n CLIPImageProcessor,\n)\n\nclip_id = \"openai/clip-vit-large-patch14\"\ntokenizer = CLIPTokenizer.from_pretrained(clip_id)\ntext_encoder = CLIPTextModelWithProjection.from_pretrained(clip_id).to(device)\nimage_processor = CLIPImageProcessor.from_pretrained(clip_id)\nimage_encoder = CLIPVisionModelWithProjection.from_pretrained(clip_id).to(device)\n```\n\n주목할 점은 특정한 CLIP 체크포인트인 `openai/clip-vit-large-patch14`를 사용하고 있다는 것입니다. 이는 Stable Diffusion 사전 훈련이 이 CLIP 변형체와 함께 수행되었기 때문입니다. 자세한 내용은 [문서](https://huggingface.co/docs/transformers/model_doc/clip)를 참조하세요.\n\n다음으로, 방향성 유사도를 계산하기 위해 PyTorch의 `nn.Module`을 준비합니다:\n\n```python\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass DirectionalSimilarity(nn.Module):\n def __init__(self, tokenizer, text_encoder, image_processor, image_encoder):\n super().__init__()\n self.tokenizer = tokenizer\n self.text_encoder = text_encoder\n self.image_processor = image_processor\n self.image_encoder = image_encoder\n\n def preprocess_image(self, image):\n image = self.image_processor(image, return_tensors=\"pt\")[\"pixel_values\"]\n return {\"pixel_values\": image.to(device)}\n\n def tokenize_text(self, text):\n inputs = self.tokenizer(\n text,\n max_length=self.tokenizer.model_max_length,\n padding=\"max_length\",\n truncation=True,\n return_tensors=\"pt\",\n )\n return {\"input_ids\": inputs.input_ids.to(device)}\n\n def encode_image(self, image):\n preprocessed_image = self.preprocess_image(image)\n image_features = self.image_encoder(**preprocessed_image).image_embeds\n image_features = image_features / image_features.norm(dim=1, keepdim=True)\n return image_features\n\n def encode_text(self, text):\n tokenized_text = self.tokenize_text(text)\n text_features = self.text_encoder(**tokenized_text).text_embeds\n text_features = text_features / text_features.norm(dim=1, keepdim=True)\n return text_features\n\n def compute_directional_similarity(self, img_feat_one, img_feat_two, text_feat_one, text_feat_two):\n sim_direction = F.cosine_similarity(img_feat_two - img_feat_one, text_feat_two - text_feat_one)\n return sim_direction\n\n def forward(self, image_one, image_two, caption_one, caption_two):\n img_feat_one = self.encode_image(image_one)\n img_feat_two = self.encode_image(image_two)\n text_feat_one = self.encode_text(caption_one)\n text_feat_two = self.encode_text(caption_two)\n directional_similarity = self.compute_directional_similarity(\n img_feat_one, img_feat_two, text_feat_one, text_feat_two\n )\n return directional_similarity\n```\n\n이제 `DirectionalSimilarity`를 사용해 보겠습니다.\n\n```python\ndir_similarity = DirectionalSimilarity(tokenizer, text_encoder, image_processor, image_encoder)\nscores = []\n\nfor i in range(len(input_images)):\n original_image = input_images[i]\n original_caption = original_captions[i]\n edited_image = edited_images[i]\n modified_caption = modified_captions[i]\n\n similarity_score = dir_similarity(original_image, edited_image, original_caption, modified_caption)\n scores.append(float(similarity_score.detach().cpu()))\n\nprint(f\"CLIP directional similarity: {np.mean(scores)}\")\n# CLIP directional similarity: 0.0797976553440094\n```\n\nCLIP 점수와 마찬가지로, CLIP 방향 유사성이 높을수록 좋습니다.\n\n`StableDiffusionInstructPix2PixPipeline`은 `image_guidance_scale`과 `guidance_scale`이라는 두 가지 인자를 노출시킵니다. 이 두 인자를 조정하여 최종 편집된 이미지의 품질을 제어할 수 있습니다. 이 두 인자의 영향을 실험해보고 방향 유사성에 미치는 영향을 확인해보기를 권장합니다.\n\n이러한 메트릭의 개념을 확장하여 원본 이미지와 편집된 버전의 유사성을 측정할 수 있습니다. 이를 위해 `F.cosine_similarity(img_feat_two, img_feat_one)`을 사용할 수 있습니다. 이러한 종류의 편집에서는 이미지의 주요 의미가 최대한 보존되어야 합니다. 즉, 높은 유사성 점수를 얻어야 합니다.\n\n[`StableDiffusionPix2PixZeroPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/pix2pix_zero#diffusers.StableDiffusionPix2PixZeroPipeline)와 같은 유사한 파이프라인에도 이러한 메트릭을 사용할 수 있습니다.\n\n\n\nCLIP 점수와 CLIP 방향 유사성 모두 CLIP 모델에 의존하기 때문에 평가가 편향될 수 있습니다\n\n\n\n***IS, FID (나중에 설명할 예정), 또는 KID와 같은 메트릭을 확장하는 것은 어려울 수 있습니다***. 평가 중인 모델이 대규모 이미지 캡셔닝 데이터셋 (예: [LAION-5B 데이터셋](https://laion.ai/blog/laion-5b/))에서 사전 훈련되었을 때 이는 문제가 될 수 있습니다. 왜냐하면 이러한 메트릭의 기반에는 중간 이미지 특징을 추출하기 위해 ImageNet-1k 데이터셋에서 사전 훈련된 InceptionNet이 사용되기 때문입니다. Stable Diffusion의 사전 훈련 데이터셋은 InceptionNet의 사전 훈련 데이터셋과 겹치는 부분이 제한적일 수 있으므로 따라서 여기에는 좋은 후보가 아닙니다.\n\n***위의 메트릭을 사용하면 클래스 조건이 있는 모델을 평가할 수 있습니다. 예를 들어, [DiT](https://huggingface.co/docs/diffusers/main/en/api/pipelines/dit). 이는 ImageNet-1k 클래스에 조건을 걸고 사전 훈련되었습니다.***\n\n### 클래스 조건화 이미지 생성[[class-conditioned-image-generation]]\n\n클래스 조건화 생성 모델은 일반적으로 [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k)와 같은 클래스 레이블이 지정된 데이터셋에서 사전 훈련됩니다. 이러한 모델을 평가하는 인기있는 지표에는 Fréchet Inception Distance (FID), Kernel Inception Distance (KID) 및 Inception Score (IS)가 있습니다. 이 문서에서는 FID ([Heusel et al.](https://arxiv.org/abs/1706.08500))에 초점을 맞추고 있습니다. [`DiTPipeline`](https://huggingface.co/docs/diffusers/api/pipelines/dit)을 사용하여 FID를 계산하는 방법을 보여줍니다. 이는 내부적으로 [DiT 모델](https://arxiv.org/abs/2212.09748)을 사용합니다.\n\nFID는 두 개의 이미지 데이터셋이 얼마나 유사한지를 측정하는 것을 목표로 합니다. [이 자료](https://mmgeneration.readthedocs.io/en/latest/quick_run.html#fid)에 따르면:\n\n> Fréchet Inception Distance는 두 개의 이미지 데이터셋 간의 유사성을 측정하는 지표입니다. 시각적 품질에 대한 인간 판단과 잘 상관되는 것으로 나타났으며, 주로 생성적 적대 신경망의 샘플 품질을 평가하는 데 사용됩니다. FID는 Inception 네트워크의 특징 표현에 맞게 적합한 두 개의 가우시안 사이의 Fréchet 거리를 계산하여 구합니다.\n\n이 두 개의 데이터셋은 실제 이미지 데이터셋과 가짜 이미지 데이터셋(우리의 경우 생성된 이미지)입니다. FID는 일반적으로 두 개의 큰 데이터셋으로 계산됩니다. 그러나 이 문서에서는 두 개의 미니 데이터셋으로 작업할 것입니다.\n\n먼저 ImageNet-1k 훈련 세트에서 몇 개의 이미지를 다운로드해 봅시다:\n\n```python\nfrom zipfile import ZipFile\nimport requests\n\n\ndef download(url, local_filepath):\n r = requests.get(url)\n with open(local_filepath, \"wb\") as f:\n f.write(r.content)\n return local_filepath\n\ndummy_dataset_url = \"https://hf.co/datasets/sayakpaul/sample-datasets/resolve/main/sample-imagenet-images.zip\"\nlocal_filepath = download(dummy_dataset_url, dummy_dataset_url.split(\"/\")[-1])\n\nwith ZipFile(local_filepath, \"r\") as zipper:\n zipper.extractall(\".\")\n```\n\n```python\nfrom PIL import Image\nimport os\n\ndataset_path = \"sample-imagenet-images\"\nimage_paths = sorted([os.path.join(dataset_path, x) for x in os.listdir(dataset_path)])\n\nreal_images = [np.array(Image.open(path).convert(\"RGB\")) for path in image_paths]\n```\n\n다음은 ImageNet-1k classes의 이미지 10개입니다 : \"cassette_player\", \"chain_saw\" (x2), \"church\", \"gas_pump\" (x3), \"parachute\" (x2), 그리고 \"tench\".\n\n

\n \"real-images\"
\n Real images.\n

\n\n이제 이미지가 로드되었으므로 이미지에 가벼운 전처리를 적용하여 FID 계산에 사용해 보겠습니다.\n\n```python\nfrom torchvision.transforms import functional as F\n\n\ndef preprocess_image(image):\n image = torch.tensor(image).unsqueeze(0)\n image = image.permute(0, 3, 1, 2) / 255.0\n return F.center_crop(image, (256, 256))\n\nreal_images = torch.cat([preprocess_image(image) for image in real_images])\nprint(real_images.shape)\n# torch.Size([10, 3, 256, 256])\n```\n\n이제 위에서 언급한 클래스에 따라 조건화 된 이미지를 생성하기 위해 [`DiTPipeline`](https://huggingface.co/docs/diffusers/api/pipelines/dit)를 로드합니다.\n\n```python\nfrom diffusers import DiTPipeline, DPMSolverMultistepScheduler\n\ndit_pipeline = DiTPipeline.from_pretrained(\"facebook/DiT-XL-2-256\", torch_dtype=torch.float16)\ndit_pipeline.scheduler = DPMSolverMultistepScheduler.from_config(dit_pipeline.scheduler.config)\ndit_pipeline = dit_pipeline.to(\"cuda\")\n\nwords = [\n \"cassette player\",\n \"chainsaw\",\n \"chainsaw\",\n \"church\",\n \"gas pump\",\n \"gas pump\",\n \"gas pump\",\n \"parachute\",\n \"parachute\",\n \"tench\",\n]\n\nclass_ids = dit_pipeline.get_label_ids(words)\noutput = dit_pipeline(class_labels=class_ids, generator=generator, output_type=\"np\")\n\nfake_images = output.images\nfake_images = torch.tensor(fake_images)\nfake_images = fake_images.permute(0, 3, 1, 2)\nprint(fake_images.shape)\n# torch.Size([10, 3, 256, 256])\n```\n\n이제 [`torchmetrics`](https://torchmetrics.readthedocs.io/)를 사용하여 FID를 계산할 수 있습니다.\n\n```python\nfrom torchmetrics.image.fid import FrechetInceptionDistance\n\nfid = FrechetInceptionDistance(normalize=True)\nfid.update(real_images, real=True)\nfid.update(fake_images, real=False)\n\nprint(f\"FID: {float(fid.compute())}\")\n# FID: 177.7147216796875\n```\n\nFID는 낮을수록 좋습니다. 여러 가지 요소가 FID에 영향을 줄 수 있습니다:\n\n- 이미지의 수 (실제 이미지와 가짜 이미지 모두)\n- diffusion 과정에서 발생하는 무작위성\n- diffusion 과정에서의 추론 단계 수\n- diffusion 과정에서 사용되는 스케줄러\n\n마지막 두 가지 요소에 대해서는, 다른 시드와 추론 단계에서 평가를 실행하고 평균 결과를 보고하는 것은 좋은 실천 방법입니다\n\n\n\nFID 결과는 많은 요소에 의존하기 때문에 취약할 수 있습니다:\n\n* 계산 중 사용되는 특정 Inception 모델.\n* 계산의 구현 정확도.\n* 이미지 형식 (PNG 또는 JPG에서 시작하는 경우가 다릅니다).\n\n이러한 사항을 염두에 두면, FID는 유사한 실행을 비교할 때 가장 유용하지만, 저자가 FID 측정 코드를 주의 깊게 공개하지 않는 한 논문 결과를 재현하기는 어렵습니다.\n\n이러한 사항은 KID 및 IS와 같은 다른 관련 메트릭에도 적용됩니다.\n\n\n\n마지막 단계로, `fake_images`를 시각적으로 검사해 봅시다.\n\n

\n \"fake-images\"
\n Fake images.\n

" }, { "path": "diffusers/docs/source/ko/conceptual/philosophy.md", "content": "\n\n# 철학\n\n🧨 Diffusers는 다양한 모달리티에서 **최신의** 사전 훈련된 diffusion 모델을 제공합니다.\n그 목적은 추론과 훈련을 위한 **모듈식 툴박스**로 사용되는 것입니다.\n\n우리는 오랜 시간에 견딜 수 있는 라이브러리를 구축하는 것을 목표로 하고, 따라서 API 설계를 매우 중요시합니다.\n\n간단히 말해서, Diffusers는 PyTorch의 자연스러운 확장이 되도록 구축되었습니다. 따라서 대부분의 설계 선택은 [PyTorch의 설계 원칙](https://pytorch.org/docs/stable/community/design.html#pytorch-design-philosophy)에 기반합니다. 이제 가장 중요한 것들을 살펴보겠습니다:\n\n## 성능보다는 사용성을\n\n- Diffusers는 많은 내장 성능 향상 기능을 갖고 있지만 (자세한 내용은 [메모리와 속도](https://huggingface.co/docs/diffusers/optimization/fp16) 참조), 모델은 항상 가장 높은 정밀도와 최소한의 최적화로 로드됩니다. 따라서 기본적인 diffusion 파이프라인은 따로 정의하지 않는다면 CPU에서 float32 정밀도로 인스턴스화됩니다. 이는 다양한 플랫폼과 가속기에서의 사용성을 보장하며, 라이브러리를 실행하기 위해 복잡한 설치가 필요하지 않음을 의미합니다.\n- Diffusers는 **가벼운** 패키지를 지향하기 때문에 필수 종속성은 거의 없지만 성능을 향상시킬 수 있는 많은 선택적 종속성이 있습니다 (`accelerate`, `safetensors`, `onnx` 등). 저희는 라이브러리를 가능한 한 가볍게 유지하여 다른 패키지에 대한 종속성 걱정이 없도록 노력하고 있습니다.\n- Diffusers는 간결하고 이해하기 쉬운 코드를 선호합니다. 이는 람다 함수나 고급 PyTorch 연산자와 같은 압축된 코드 구문을 자주 사용하지 않는 것을 의미합니다.\n\n## 쉬움보다는 간단함을\n\nPyTorch에서는 **명시적인 것이 암시적인 것보다 낫다**와 **단순한 것이 복잡한 것보다 낫다**라고 말합니다. 이 설계 철학은 라이브러리의 여러 부분에 반영되어 있습니다:\n- [`DiffusionPipeline.to`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.to)와 같은 메서드를 사용하여 사용자가 장치 관리를 할 수 있도록 PyTorch의 API를 따릅니다.\n- 잘못된 입력을 조용히 수정하는 대신 간결한 오류 메시지를 발생시키는 것이 우선입니다. Diffusers는 라이브러리를 가능한 한 쉽게 사용할 수 있도록 하는 것보다 사용자를 가르치는 것을 목표로 합니다.\n- 복잡한 모델과 스케줄러 로직이 내부에서 마법처럼 처리하는 대신 노출됩니다. 스케줄러/샘플러는 서로에게 최소한의 종속성을 가지고 분리되어 있습니다. 이로써 사용자는 언롤된 노이즈 제거 루프를 작성해야 합니다. 그러나 이 분리는 디버깅을 더 쉽게하고 노이즈 제거 과정을 조정하거나 diffusers 모델이나 스케줄러를 교체하는 데 사용자에게 더 많은 제어권을 제공합니다.\n- diffusers 파이프라인의 따로 훈련된 구성 요소인 text encoder, unet 및 variational autoencoder는 각각 자체 모델 클래스를 갖습니다. 이로써 사용자는 서로 다른 모델의 구성 요소 간의 상호 작용을 처리해야 하며, 직렬화 형식은 모델 구성 요소를 다른 파일로 분리합니다. 그러나 이는 디버깅과 커스터마이징을 더 쉽게합니다. DreamBooth나 Textual Inversion 훈련은 Diffusers의 'diffusion 파이프라인의 단일 구성 요소들을 분리할 수 있는 능력' 덕분에 매우 간단합니다.\n\n## 추상화보다는 수정 가능하고 기여하기 쉬움을\n\n라이브러리의 대부분에 대해 Diffusers는 [Transformers 라이브러리](https://github.com/huggingface/transformers)의 중요한 설계 원칙을 채택합니다, 바로 성급한 추상화보다는 copy-pasted 코드를 선호한다는 것입니다. 이 설계 원칙은 [Don't repeat yourself (DRY)](https://en.wikipedia.org/wiki/Don%27t_repeat_yourself)와 같은 인기 있는 설계 원칙과는 대조적으로 매우 의견이 분분한데요.\n간단히 말해서, Transformers가 모델링 파일에 대해 수행하는 것처럼, Diffusers는 매우 낮은 수준의 추상화와 매우 독립적인 코드를 유지하는 것을 선호합니다. 함수, 긴 코드 블록, 심지어 클래스도 여러 파일에 복사할 수 있으며, 이는 처음에는 라이브러리를 유지할 수 없게 만드는 나쁜, 서투른 설계 선택으로 보일 수 있습니다. 하지만 이러한 설계는 매우 성공적이며, 커뮤니티 기반의 오픈 소스 기계 학습 라이브러리에 매우 적합합니다. 그 이유는 다음과 같습니다:\n- 기계 학습은 패러다임, 모델 아키텍처 및 알고리즘이 빠르게 변화하는 매우 빠르게 움직이는 분야이기 때문에 오랜 기간 지속되는 코드 추상화를 정의하기가 매우 어렵습니다.\n- 기계 학습 전문가들은 아이디어와 연구를 위해 기존 코드를 빠르게 조정할 수 있어야 하므로, 많은 추상화보다는 독립적인 코드를 선호합니다.\n- 오픈 소스 라이브러리는 커뮤니티 기여에 의존하므로, 기여하기 쉬운 라이브러리를 구축해야 합니다. 코드가 추상화되면 의존성이 많아지고 읽기 어렵고 기여하기 어려워집니다. 기여자들은 중요한 기능을 망가뜨릴까 두려워하여 매우 추상화된 라이브러리에 기여하지 않게 됩니다. 라이브러리에 기여하는 것이 다른 기본 코드를 망가뜨릴 수 없다면, 잠재적인 새로운 기여자에게 더욱 환영받을 수 있을 뿐만 아니라 여러 부분에 대해 병렬적으로 검토하고 기여하기가 더 쉬워집니다.\n\nHugging Face에서는 이 설계를 **단일 파일 정책**이라고 부르며, 특정 클래스의 대부분의 코드가 단일하고 독립적인 파일에 작성되어야 한다는 의미입니다. 철학에 대해 자세히 알아보려면 [이 블로그 글](https://huggingface.co/blog/transformers-design-philosophy)을 참조할 수 있습니다.\n\nDiffusers에서는 이러한 철학을 파이프라인과 스케줄러에 모두 따르지만, diffusion 모델에 대해서는 일부만 따릅니다. 일부만 따르는 이유는 Diffusion 파이프라인인 [DDPM](https://huggingface.co/docs/diffusers/api/pipelines/ddpm), [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview#stable-diffusion-pipelines), [unCLIP (DALL·E 2)](https://huggingface.co/docs/diffusers/api/pipelines/unclip) 및 [Imagen](https://imagen.research.google/) 등 대부분의 diffusion 파이프라인은 동일한 diffusion 모델인 [UNet](https://huggingface.co/docs/diffusers/api/models/unet2d-cond)에 의존하기 때문입니다.\n\n좋아요, 이제 🧨 Diffusers가 설계된 방식을 대략적으로 이해했을 것입니다 🤗.\n우리는 이러한 설계 원칙을 일관되게 라이브러리 전체에 적용하려고 노력하고 있습니다. 그럼에도 불구하고 철학에 대한 일부 예외 사항이나 불행한 설계 선택이 있을 수 있습니다. 디자인에 대한 피드백이 있다면 [GitHub에서 직접](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=) 알려주시면 감사하겠습니다.\n\n## 디자인 철학 자세히 알아보기\n\n이제 디자인 철학의 세부 사항을 좀 더 자세히 살펴보겠습니다. Diffusers는 주로 세 가지 주요 클래스로 구성됩니다: [파이프라인](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines), [모델](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models), 그리고 [스케줄러](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers). 각 클래스에 대한 더 자세한 설계 결정 사항을 살펴보겠습니다.\n\n### 파이프라인\n\n파이프라인은 사용하기 쉽도록 설계되었으며 (따라서 [*쉬움보다는 간단함을*](#쉬움보다는-간단함을)을 100% 따르지는 않음), feature-complete하지 않으며, 추론을 위한 [모델](#모델)과 [스케줄러](#스케줄러)를 사용하는 방법의 예시로 간주될 수 있습니다.\n\n다음과 같은 설계 원칙을 따릅니다:\n- 파이프라인은 단일 파일 정책을 따릅니다. 모든 파이프라인은 src/diffusers/pipelines의 개별 디렉토리에 있습니다. 하나의 파이프라인 폴더는 하나의 diffusion 논문/프로젝트/릴리스에 해당합니다. 여러 파이프라인 파일은 하나의 파이프라인 폴더에 모을 수 있습니다. 예를 들어 [`src/diffusers/pipelines/stable-diffusion`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/stable_diffusion)에서 그렇게 하고 있습니다. 파이프라인이 유사한 기능을 공유하는 경우, [#Copied from mechanism](https://github.com/huggingface/diffusers/blob/125d783076e5bd9785beb05367a2d2566843a271/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py#L251)을 사용할 수 있습니다.\n- 파이프라인은 모두 [`DiffusionPipeline`]을 상속합니다.\n- 각 파이프라인은 서로 다른 모델 및 스케줄러 구성 요소로 구성되어 있으며, 이는 [`model_index.json` 파일](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/model_index.json)에 문서화되어 있으며, 파이프라인의 속성 이름과 동일한 이름으로 액세스할 수 있으며, [`DiffusionPipeline.components`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.components) 함수를 통해 파이프라인 간에 공유할 수 있습니다.\n- 각 파이프라인은 [`DiffusionPipeline.from_pretrained`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained) 함수를 통해 로드할 수 있어야 합니다.\n- 파이프라인은 추론에**만** 사용되어야 합니다.\n- 파이프라인은 매우 가독성이 좋고, 이해하기 쉽고, 쉽게 조정할 수 있도록 설계되어야 합니다.\n- 파이프라인은 서로 상호작용하고, 상위 수준 API에 쉽게 통합할 수 있도록 설계되어야 합니다.\n- 파이프라인은 사용자 인터페이스가 feature-complete하지 않게 하는 것을 목표로 합니다. future-complete한 사용자 인터페이스를 원한다면 [InvokeAI](https://github.com/invoke-ai/InvokeAI), [Diffuzers](https://github.com/abhishekkrthakur/diffuzers), [lama-cleaner](https://github.com/Sanster/lama-cleaner)를 참조해야 합니다.\n- 모든 파이프라인은 오로지 `__call__` 메서드를 통해 실행할 수 있어야 합니다. `__call__` 인자의 이름은 모든 파이프라인에서 공유되어야 합니다.\n- 파이프라인은 해결하고자 하는 작업의 이름으로 지정되어야 합니다.\n- 대부분의 경우에 새로운 diffusion 파이프라인은 새로운 파이프라인 폴더/파일에 구현되어야 합니다.\n\n### 모델\n\n모델은 [PyTorch의 Module 클래스](https://pytorch.org/docs/stable/generated/torch.nn.Module.html)의 자연스러운 확장이 되도록, 구성 가능한 툴박스로 설계되었습니다. 그리고 모델은 **단일 파일 정책**을 일부만 따릅니다.\n\n다음과 같은 설계 원칙을 따릅니다:\n- 모델은 **모델 아키텍처 유형**에 해당합니다. 예를 들어 [`UNet2DConditionModel`] 클래스는 2D 이미지 입력을 기대하고 일부 context에 의존하는 모든 UNet 변형들에 사용됩니다.\n- 모든 모델은 [`src/diffusers/models`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models)에서 찾을 수 있으며, 각 모델 아키텍처는 해당 파일에 정의되어야 합니다. 예를 들어 [`unet_2d_condition.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py), [`transformer_2d.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformer_2d.py) 등이 있습니다.\n- 모델은 **단일 파일 정책**을 따르지 않으며, [`attention.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py), [`resnet.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py), [`embeddings.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/embeddings.py) 등과 같은 작은 모델 구성 요소를 사용해야 합니다. **참고**: 이는 Transformers의 모델링 파일과는 대조적으로 모델이 실제로 단일 파일 정책을 따르지 않음을 보여줍니다.\n- 모델은 PyTorch의 `Module` 클래스와 마찬가지로 복잡성을 노출하고 명확한 오류 메시지를 제공해야 합니다.\n- 모든 모델은 `ModelMixin`과 `ConfigMixin`을 상속합니다.\n- 모델은 주요 코드 변경이 필요하지 않고, 역호환성을 유지하며, 메모리 또는 컴퓨팅과 관련한 중요한 이득을 제공할 때 성능을 위해 최적화할 수 있습니다.\n- 모델은 기본적으로 가장 높은 정밀도와 가장 낮은 성능 설정을 가져야 합니다.\n- Diffusers에 이미 있는 모델 아키텍처로 분류할 수 있는 새로운 모델 체크포인트를 통합할 때는 기존 모델 아키텍처를 새로운 체크포인트와 호환되도록 수정해야 합니다. 새로운 파일을 만들어야 하는 경우는 모델 아키텍처가 근본적으로 다른 경우에만 해당합니다.\n- 모델은 미래의 변경 사항을 쉽게 확장할 수 있도록 설계되어야 합니다. 이는 공개 함수 인수들과 구성 인수들을 제한하고,미래의 변경 사항을 \"예상\"하는 것을 통해 달성할 수 있습니다. 예를 들어, 불리언 `is_..._type` 인수보다는 새로운 미래 유형에 쉽게 확장할 수 있는 문자열 \"...type\" 인수를 추가하는 것이 일반적으로 더 좋습니다. 새로운 모델 체크포인트가 작동하도록 하기 위해 기존 아키텍처에 최소한의 변경만을 가해야 합니다.\n- 모델 디자인은 코드의 가독성과 간결성을 유지하는 것과 많은 모델 체크포인트를 지원하는 것 사이의 어려운 균형 조절입니다. 모델링 코드의 대부분은 새로운 모델 체크포인트를 위해 클래스를 수정하는 것이 좋지만, [UNet 블록](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py) 및 [Attention 프로세서](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py)와 같이 코드를 장기적으로 간결하고 읽기 쉽게 유지하기 위해 새로운 클래스를 추가하는 예외도 있습니다.\n\n### 스케줄러\n\n스케줄러는 추론을 위한 노이즈 제거 과정을 안내하고 훈련을 위한 노이즈 스케줄을 정의하는 역할을 합니다. 스케줄러는 개별 클래스로 설계되어 있으며, 로드 가능한 구성 파일과 **단일 파일 정책**을 엄격히 따릅니다.\n\n다음과 같은 설계 원칙을 따릅니다:\n- 모든 스케줄러는 [`src/diffusers/schedulers`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers)에서 찾을 수 있습니다.\n- 스케줄러는 큰 유틸리티 파일에서 가져오지 **않아야** 하며, 자체 포함성을 유지해야 합니다.\n- 하나의 스케줄러 Python 파일은 하나의 스케줄러 알고리즘(논문에서 정의된 것과 같은)에 해당합니다.\n- 스케줄러가 유사한 기능을 공유하는 경우, `#Copied from` 메커니즘을 사용할 수 있습니다.\n- 모든 스케줄러는 `SchedulerMixin`과 `ConfigMixin`을 상속합니다.\n- [`ConfigMixin.from_config`](https://huggingface.co/docs/diffusers/main/en/api/configuration#diffusers.ConfigMixin.from_config) 메서드를 사용하여 스케줄러를 쉽게 교체할 수 있습니다. 자세한 내용은 [여기](../using-diffusers/schedulers.md)에서 설명합니다.\n- 모든 스케줄러는 `set_num_inference_steps`와 `step` 함수를 가져야 합니다. `set_num_inference_steps(...)`는 각 노이즈 제거 과정(즉, `step(...)`이 호출되기 전) 이전에 호출되어야 합니다.\n- 각 스케줄러는 모델이 호출될 타임스텝의 배열인 `timesteps` 속성을 통해 루프를 돌 수 있는 타임스텝을 노출합니다.\n- `step(...)` 함수는 예측된 모델 출력과 \"현재\" 샘플(x_t)을 입력으로 받고, \"이전\" 약간 더 노이즈가 제거된 샘플(x_t-1)을 반환합니다.\n- 노이즈 제거 스케줄러의 복잡성을 고려하여, `step` 함수는 모든 복잡성을 노출하지 않으며, \"블랙 박스\"일 수 있습니다.\n- 거의 모든 경우에 새로운 스케줄러는 새로운 스케줄링 파일에 구현되어야 합니다.\n" }, { "path": "diffusers/docs/source/ko/in_translation.md", "content": "\n\n# 번역중\n\n열심히 번역을 진행중입니다. 조금만 기다려주세요.\n감사합니다!" }, { "path": "diffusers/docs/source/ko/index.md", "content": "\n\n

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\n\n\n# Diffusers\n\n🤗 Diffusers는 이미지, 오디오, 심지어 분자의 3D 구조를 생성하기 위한 최첨단 사전 훈련된 diffusion 모델을 위한 라이브러리입니다. 간단한 추론 솔루션을 찾고 있든, 자체 diffusion 모델을 훈련하고 싶든, 🤗 Diffusers는 두 가지 모두를 지원하는 모듈식 툴박스입니다. 저희 라이브러리는 [성능보다 사용성](conceptual/philosophy#usability-over-performance), [간편함보다 단순함](conceptual/philosophy#simple-over-easy), 그리고 [추상화보다 사용자 지정 가능성](conceptual/philosophy#tweakable-contributorfriendly-over-abstraction)에 중점을 두고 설계되었습니다.\n\n이 라이브러리에는 세 가지 주요 구성 요소가 있습니다:\n\n- 몇 줄의 코드만으로 추론할 수 있는 최첨단 [diffusion 파이프라인](api/pipelines/overview).\n- 생성 속도와 품질 간의 균형을 맞추기 위해 상호교환적으로 사용할 수 있는 [노이즈 스케줄러](api/schedulers/overview).\n- 빌딩 블록으로 사용할 수 있고 스케줄러와 결합하여 자체적인 end-to-end diffusion 시스템을 만들 수 있는 사전 학습된 [모델](api/models).\n\n
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Tutorials
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결과물을 생성하고, 나만의 diffusion 시스템을 구축하고, 확산 모델을 훈련하는 데 필요한 기본 기술을 배워보세요. 🤗 Diffusers를 처음 사용하는 경우 여기에서 시작하는 것이 좋습니다!

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How-to guides
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파이프라인, 모델, 스케줄러를 로드하는 데 도움이 되는 실용적인 가이드입니다. 또한 특정 작업에 파이프라인을 사용하고, 출력 생성 방식을 제어하고, 추론 속도에 맞게 최적화하고, 다양한 학습 기법을 사용하는 방법도 배울 수 있습니다.

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Conceptual guides
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라이브러리가 왜 이런 방식으로 설계되었는지 이해하고, 라이브러리 이용에 대한 윤리적 가이드라인과 안전 구현에 대해 자세히 알아보세요.

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Reference
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🤗 Diffusers 클래스 및 메서드의 작동 방식에 대한 기술 설명.

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\n\n## Supported pipelines\n\n| Pipeline | Paper/Repository | Tasks |\n|---|---|:---:|\n| [alt_diffusion](./api/pipelines/alt_diffusion) | [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679) | Image-to-Image Text-Guided Generation |\n| [audio_diffusion](./api/pipelines/audio_diffusion) | [Audio Diffusion](https://github.com/teticio/audio-diffusion.git) | Unconditional Audio Generation |\n| [controlnet](./api/pipelines/stable_diffusion/controlnet) | [Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543) | Image-to-Image Text-Guided Generation |\n| [cycle_diffusion](./api/pipelines/cycle_diffusion) | [Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance](https://arxiv.org/abs/2210.05559) | Image-to-Image Text-Guided Generation |\n| [dance_diffusion](./api/pipelines/dance_diffusion) | [Dance Diffusion](https://github.com/williamberman/diffusers.git) | Unconditional Audio Generation |\n| [ddpm](./api/pipelines/ddpm) | [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) | Unconditional Image Generation |\n| [ddim](./api/pipelines/ddim) | [Denoising Diffusion Implicit Models](https://arxiv.org/abs/2010.02502) | Unconditional Image Generation |\n| [if](./if) | [**IF**](./api/pipelines/if) | Image Generation |\n| [if_img2img](./if) | [**IF**](./api/pipelines/if) | Image-to-Image Generation |\n| [if_inpainting](./if) | [**IF**](./api/pipelines/if) | Image-to-Image Generation |\n| [latent_diffusion](./api/pipelines/latent_diffusion) | [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)| Text-to-Image Generation |\n| [latent_diffusion](./api/pipelines/latent_diffusion) | [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)| Super Resolution Image-to-Image |\n| [latent_diffusion_uncond](./api/pipelines/latent_diffusion_uncond) | [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) | Unconditional Image Generation |\n| [paint_by_example](./api/pipelines/paint_by_example) | [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://arxiv.org/abs/2211.13227) | Image-Guided Image Inpainting |\n| [pndm](./api/pipelines/pndm) | [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778) | Unconditional Image Generation |\n| [score_sde_ve](./api/pipelines/score_sde_ve) | [Score-Based Generative Modeling through Stochastic Differential Equations](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation |\n| [score_sde_vp](./api/pipelines/score_sde_vp) | [Score-Based Generative Modeling through Stochastic Differential Equations](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation |\n| [semantic_stable_diffusion](./api/pipelines/semantic_stable_diffusion) | [Semantic Guidance](https://arxiv.org/abs/2301.12247) | Text-Guided Generation |\n| [stable_diffusion_text2img](./api/pipelines/stable_diffusion/text2img) | [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release) | Text-to-Image Generation |\n| [stable_diffusion_img2img](./api/pipelines/stable_diffusion/img2img) | [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release) | Image-to-Image Text-Guided Generation |\n| [stable_diffusion_inpaint](./api/pipelines/stable_diffusion/inpaint) | [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release) | Text-Guided Image Inpainting |\n| [stable_diffusion_panorama](./api/pipelines/stable_diffusion/panorama) | [MultiDiffusion](https://multidiffusion.github.io/) | Text-to-Panorama Generation |\n| [stable_diffusion_pix2pix](./api/pipelines/stable_diffusion/pix2pix) | [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://arxiv.org/abs/2211.09800) | Text-Guided Image Editing|\n| [stable_diffusion_pix2pix_zero](./api/pipelines/stable_diffusion/pix2pix_zero) | [Zero-shot Image-to-Image Translation](https://pix2pixzero.github.io/) | Text-Guided Image Editing |\n| [stable_diffusion_attend_and_excite](./api/pipelines/stable_diffusion/attend_and_excite) | [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://arxiv.org/abs/2301.13826) | Text-to-Image Generation |\n| [stable_diffusion_self_attention_guidance](./api/pipelines/stable_diffusion/self_attention_guidance) | [Improving Sample Quality of Diffusion Models Using Self-Attention Guidance](https://arxiv.org/abs/2210.00939) | Text-to-Image Generation Unconditional Image Generation |\n| [stable_diffusion_image_variation](./stable_diffusion/image_variation) | [Stable Diffusion Image Variations](https://github.com/LambdaLabsML/lambda-diffusers#stable-diffusion-image-variations) | Image-to-Image Generation |\n| [stable_diffusion_latent_upscale](./stable_diffusion/latent_upscale) | [Stable Diffusion Latent Upscaler](https://twitter.com/StabilityAI/status/1590531958815064065) | Text-Guided Super Resolution Image-to-Image |\n| [stable_diffusion_model_editing](./api/pipelines/stable_diffusion/model_editing) | [Editing Implicit Assumptions in Text-to-Image Diffusion Models](https://time-diffusion.github.io/) | Text-to-Image Model Editing |\n| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Stable Diffusion 2](https://stability.ai/blog/stable-diffusion-v2-release) | Text-to-Image Generation |\n| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Stable Diffusion 2](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Image Inpainting |\n| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Depth-Conditional Stable Diffusion](https://github.com/Stability-AI/stablediffusion#depth-conditional-stable-diffusion) | Depth-to-Image Generation |\n| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Stable Diffusion 2](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Super Resolution Image-to-Image |\n| [stable_diffusion_safe](./api/pipelines/stable_diffusion_safe) | [Safe Stable Diffusion](https://arxiv.org/abs/2211.05105) | Text-Guided Generation |\n| [stable_unclip](./stable_unclip) | Stable unCLIP | Text-to-Image Generation |\n| [stable_unclip](./stable_unclip) | Stable unCLIP | Image-to-Image Text-Guided Generation |\n| [stochastic_karras_ve](./api/pipelines/stochastic_karras_ve) | [Elucidating the Design Space of Diffusion-Based Generative Models](https://arxiv.org/abs/2206.00364) | Unconditional Image Generation |\n| [text_to_video_sd](./api/pipelines/text_to_video) | [Modelscope's Text-to-video-synthesis Model in Open Domain](https://modelscope.cn/models/damo/text-to-video-synthesis/summary) | Text-to-Video Generation |\n| [unclip](./api/pipelines/unclip) | [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://arxiv.org/abs/2204.06125)(implementation by [kakaobrain](https://github.com/kakaobrain/karlo)) | Text-to-Image Generation |\n| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Text-to-Image Generation |\n| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Image Variations Generation |\n| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Dual Image and Text Guided Generation |\n| [vq_diffusion](./api/pipelines/vq_diffusion) | [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://arxiv.org/abs/2111.14822) | Text-to-Image Generation |\n" }, { "path": "diffusers/docs/source/ko/installation.md", "content": "\n\n# 설치\n\n사용하시는 라이브러리에 맞는 🤗 Diffusers를 설치하세요.\n\n🤗 Diffusers는 Python 3.8+, PyTorch 1.7.0+ 및 flax에서 테스트되었습니다. 사용중인 딥러닝 라이브러리에 대한 아래의 설치 안내를 따르세요.\n\n- [PyTorch 설치 안내](https://pytorch.org/get-started/locally/)\n- [Flax 설치 안내](https://flax.readthedocs.io/en/latest/)\n\n## pip를 이용한 설치\n\n[가상 환경](https://docs.python.org/3/library/venv.html)에 🤗 Diffusers를 설치해야 합니다.\nPython 가상 환경에 익숙하지 않은 경우 [가상환경 pip 설치 가이드](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)를 살펴보세요.\n가상 환경을 사용하면 서로 다른 프로젝트를 더 쉽게 관리하고, 종속성간의 호환성 문제를 피할 수 있습니다.\n\n프로젝트 디렉토리에 가상 환경을 생성하는 것으로 시작하세요:\n\n```bash\npython -m venv .env\n```\n\n그리고 가상 환경을 활성화합니다:\n\n```bash\nsource .env/bin/activate\n```\n\n이제 다음의 명령어로 🤗 Diffusers를 설치할 준비가 되었습니다:\n\n**PyTorch의 경우**\n\n```bash\npip install diffusers[\"torch\"]\n```\n\n**Flax의 경우**\n\n```bash\npip install diffusers[\"flax\"]\n```\n\n## 소스로부터 설치\n\n소스에서 `diffusers`를 설치하기 전에, `torch` 및 `accelerate`이 설치되어 있는지 확인하세요.\n\n`torch` 설치에 대해서는 [torch docs](https://pytorch.org/get-started/locally/#start-locally)를 참고하세요.\n\n다음과 같이 `accelerate`을 설치하세요.\n\n```bash\npip install accelerate\n```\n\n다음 명령어를 사용하여 소스에서 🤗 Diffusers를 설치하세요:\n\n```bash\npip install git+https://github.com/huggingface/diffusers\n```\n\n이 명령어는 최신 `stable` 버전이 아닌 최첨단 `main` 버전을 설치합니다.\n`main` 버전은 최신 개발 정보를 최신 상태로 유지하는 데 유용합니다.\n예를 들어 마지막 공식 릴리즈 이후 버그가 수정되었지만, 새 릴리즈가 아직 출시되지 않은 경우입니다.\n그러나 이는 `main` 버전이 항상 안정적이지 않을 수 있음을 의미합니다.\n우리는 `main` 버전이 지속적으로 작동하도록 노력하고 있으며, 대부분의 문제는 보통 몇 시간 또는 하루 안에 해결됩니다.\n문제가 발생하면 더 빨리 해결할 수 있도록 [Issue](https://github.com/huggingface/transformers/issues)를 열어주세요!\n\n\n## 편집가능한 설치\n\n다음을 수행하려면 편집가능한 설치가 필요합니다:\n\n* 소스 코드의 `main` 버전을 사용\n* 🤗 Diffusers에 기여 (코드의 변경 사항을 테스트하기 위해 필요)\n\n저장소를 복제하고 다음 명령어를 사용하여 🤗 Diffusers를 설치합니다:\n\n```bash\ngit clone https://github.com/huggingface/diffusers.git\ncd diffusers\n```\n\n**PyTorch의 경우**\n\n```sh\npip install -e \".[torch]\"\n```\n\n**Flax의 경우**\n\n```sh\npip install -e \".[flax]\"\n```\n\n이러한 명령어들은 저장소를 복제한 폴더와 Python 라이브러리 경로를 연결합니다.\nPython은 이제 일반 라이브러리 경로에 더하여 복제한 폴더 내부를 살펴봅니다.\n예를들어 Python 패키지가 `~/anaconda3/envs/main/lib/python3.10/site-packages/`에 설치되어 있는 경우 Python은 복제한 폴더인 `~/diffusers/`도 검색합니다.\n\n\n\n라이브러리를 계속 사용하려면 `diffusers` 폴더를 유지해야 합니다.\n\n\n\n이제 다음 명령어를 사용하여 최신 버전의 🤗 Diffusers로 쉽게 업데이트할 수 있습니다:\n\n```bash\ncd ~/diffusers/\ngit pull\n```\n\n이렇게 하면, 다음에 실행할 때 Python 환경이 🤗 Diffusers의 `main` 버전을 찾게 됩니다.\n\n## 텔레메트리 로깅에 대한 알림\n\n우리 라이브러리는 `from_pretrained()` 요청 중에 텔레메트리 정보를 원격으로 수집합니다.\n이 데이터에는 Diffusers 및 PyTorch/Flax의 버전, 요청된 모델 또는 파이프라인 클래스, 그리고 허브에서 호스팅되는 경우 사전학습된 체크포인트에 대한 경로를 포함합니다.\n이 사용 데이터는 문제를 디버깅하고 새로운 기능의 우선순위를 지정하는데 도움이 됩니다.\n텔레메트리는 HuggingFace 허브에서 모델과 파이프라인을 불러올 때만 전송되며, 로컬 사용 중에는 수집되지 않습니다.\n\n우리는 추가 정보를 공유하지 않기를 원하는 사람이 있다는 것을 이해하고 개인 정보를 존중하므로, 터미널에서 `DISABLE_TELEMETRY` 환경 변수를 설정하여 텔레메트리 수집을 비활성화할 수 있습니다.\n\nLinux/MacOS에서:\n```bash\nexport DISABLE_TELEMETRY=YES\n```\n\nWindows에서:\n```bash\nset DISABLE_TELEMETRY=YES\n```" }, { "path": "diffusers/docs/source/ko/optimization/coreml.md", "content": "\n\n# Core ML로 Stable Diffusion을 실행하는 방법\n\n[Core ML](https://developer.apple.com/documentation/coreml)은 Apple 프레임워크에서 지원하는 모델 형식 및 머신 러닝 라이브러리입니다. macOS 또는 iOS/iPadOS 앱 내에서 Stable Diffusion 모델을 실행하는 데 관심이 있는 경우, 이 가이드에서는 기존 PyTorch 체크포인트를 Core ML 형식으로 변환하고 이를 Python 또는 Swift로 추론에 사용하는 방법을 설명합니다.\n\nCore ML 모델은 Apple 기기에서 사용할 수 있는 모든 컴퓨팅 엔진들, 즉 CPU, GPU, Apple Neural Engine(또는 Apple Silicon Mac 및 최신 iPhone/iPad에서 사용할 수 있는 텐서 최적화 가속기인 ANE)을 활용할 수 있습니다. 모델과 실행 중인 기기에 따라 Core ML은 컴퓨팅 엔진도 혼합하여 사용할 수 있으므로, 예를 들어 모델의 일부가 CPU에서 실행되는 반면 다른 부분은 GPU에서 실행될 수 있습니다.\n\n\n\nPyTorch에 내장된 `mps` 가속기를 사용하여 Apple Silicon Macs에서 `diffusers` Python 코드베이스를 실행할 수도 있습니다. 이 방법은 [mps 가이드]에 자세히 설명되어 있지만 네이티브 앱과 호환되지 않습니다.\n\n\n\n## Stable Diffusion Core ML 체크포인트\n\nStable Diffusion 가중치(또는 체크포인트)는 PyTorch 형식으로 저장되기 때문에 네이티브 앱에서 사용하기 위해서는 Core ML 형식으로 변환해야 합니다.\n\n다행히도 Apple 엔지니어들이 `diffusers`를 기반으로 한 [변환 툴](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml)을 개발하여 PyTorch 체크포인트를 Core ML로 변환할 수 있습니다.\n\n모델을 변환하기 전에 잠시 시간을 내어 Hugging Face Hub를 살펴보세요. 관심 있는 모델이 이미 Core ML 형식으로 제공되고 있을 가능성이 높습니다:\n\n- [Apple](https://huggingface.co/apple) organization에는 Stable Diffusion 버전 1.4, 1.5, 2.0 base 및 2.1 base가 포함되어 있습니다.\n- [coreml](https://huggingface.co/coreml) organization에는 커스텀 DreamBooth가 적용되거나, 파인튜닝된 모델이 포함되어 있습니다.\n- 이 [필터](https://huggingface.co/models?pipeline_tag=text-to-image&library=coreml&p=2&sort=likes)를 사용하여 사용 가능한 모든 Core ML 체크포인트들을 반환합니다.\n\n원하는 모델을 찾을 수 없는 경우 Apple의 [모델을 Core ML로 변환하기](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml) 지침을 따르는 것이 좋습니다.\n\n## 사용할 Core ML 변형(Variant) 선택하기\n\nStable Diffusion 모델은 다양한 목적에 따라 다른 Core ML 변형으로 변환할 수 있습니다:\n\n- 사용되는 어텐션 블록 유형. 어텐션 연산은 이미지 표현의 여러 영역 간의 관계에 '주의를 기울이고' 이미지와 텍스트 표현이 어떻게 연관되어 있는지 이해하는 데 사용됩니다. 어텐션 연산은 컴퓨팅 및 메모리 집약적이므로 다양한 장치의 하드웨어 특성을 고려한 다양한 구현이 존재합니다. Core ML Stable Diffusion 모델의 경우 두 가지 주의 변형이 있습니다:\n * `split_einsum` ([Apple에서 도입](https://machinelearning.apple.com/research/neural-engine-transformers)은 최신 iPhone, iPad 및 M 시리즈 컴퓨터에서 사용할 수 있는 ANE 장치에 최적화되어 있습니다.\n * \"원본\" 어텐션(`diffusers`에 사용되는 기본 구현)는 CPU/GPU와만 호환되며 ANE와는 호환되지 않습니다. \"원본\" 어텐션을 사용하여 CPU + GPU에서 모델을 실행하는 것이 ANE보다 *더* 빠를 수 있습니다. 자세한 내용은 [이 성능 벤치마크](https://huggingface.co/blog/fast-mac-diffusers#performance-benchmarks)와 커뮤니티에서 제공하는 일부 [추가 측정](https://github.com/huggingface/swift-coreml-diffusers/issues/31)을 참조하십시오.\n\n- 지원되는 추론 프레임워크\n * `packages`는 Python 추론에 적합합니다. 네이티브 앱에 통합하기 전에 변환된 Core ML 모델을 테스트하거나, Core ML 성능을 알고 싶지만 네이티브 앱을 지원할 필요는 없는 경우에 사용할 수 있습니다. 예를 들어, 웹 UI가 있는 애플리케이션은 Python Core ML 백엔드를 완벽하게 사용할 수 있습니다.\n * Swift 코드에는 `컴파일된` 모델이 필요합니다. Hub의 `컴파일된` 모델은 iOS 및 iPadOS 기기와의 호환성을 위해 큰 UNet 모델 가중치를 여러 파일로 분할합니다. 이는 [`--chunk-unet` 변환 옵션](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml)에 해당합니다. 네이티브 앱을 지원하려면 `컴파일된` 변형을 선택해야 합니다.\n\n공식 Core ML Stable Diffusion [모델](https://huggingface.co/apple/coreml-stable-diffusion-v1-4/tree/main)에는 이러한 변형이 포함되어 있지만 커뮤니티 버전은 다를 수 있습니다:\n\n```\ncoreml-stable-diffusion-v1-4\n├── README.md\n├── original\n│ ├── compiled\n│ └── packages\n└── split_einsum\n ├── compiled\n └── packages\n```\n\n아래와 같이 필요한 변형을 다운로드하여 사용할 수 있습니다.\n\n## Python에서 Core ML 추론\n\nPython에서 Core ML 추론을 실행하려면 다음 라이브러리를 설치하세요:\n\n```bash\npip install huggingface_hub\npip install git+https://github.com/apple/ml-stable-diffusion\n```\n\n### 모델 체크포인트 다운로드하기\n\n`컴파일된` 버전은 Swift와만 호환되므로 Python에서 추론을 실행하려면 `packages` 폴더에 저장된 버전 중 하나를 사용하세요. `원본` 또는 `split_einsum` 어텐션 중 어느 것을 사용할지 선택할 수 있습니다.\n\n다음은 Hub에서 'models'라는 디렉토리로 'original' 어텐션 변형을 다운로드하는 방법입니다:\n\n```Python\nfrom huggingface_hub import snapshot_download\nfrom pathlib import Path\n\nrepo_id = \"apple/coreml-stable-diffusion-v1-4\"\nvariant = \"original/packages\"\n\nmodel_path = Path(\"./models\") / (repo_id.split(\"/\")[-1] + \"_\" + variant.replace(\"/\", \"_\"))\nsnapshot_download(repo_id, allow_patterns=f\"{variant}/*\", local_dir=model_path, local_dir_use_symlinks=False)\nprint(f\"Model downloaded at {model_path}\")\n```\n\n\n### 추론[[python-inference]]\n\n모델의 snapshot을 다운로드한 후에는 Apple의 Python 스크립트를 사용하여 테스트할 수 있습니다.\n\n```shell\npython -m python_coreml_stable_diffusion.pipeline --prompt \"a photo of an astronaut riding a horse on mars\" -i models/coreml-stable-diffusion-v1-4_original_packages -o --compute-unit CPU_AND_GPU --seed 93\n```\n\n``는 위 단계에서 다운로드한 체크포인트를 가리켜야 하며, `--compute-unit`은 추론을 허용할 하드웨어를 나타냅니다. 이는 다음 옵션 중 하나이어야 합니다: `ALL`, `CPU_AND_GPU`, `CPU_ONLY`, `CPU_AND_NE`. 선택적 출력 경로와 재현성을 위한 시드를 제공할 수도 있습니다.\n\n추론 스크립트에서는 Stable Diffusion 모델의 원래 버전인 `CompVis/stable-diffusion-v1-4`를 사용한다고 가정합니다. 다른 모델을 사용하는 경우 추론 명령줄에서 `--model-version` 옵션을 사용하여 해당 허브 ID를 *지정*해야 합니다. 이는 이미 지원되는 모델과 사용자가 직접 학습하거나 파인튜닝한 사용자 지정 모델에 적용됩니다.\n\n예를 들어, [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5)를 사용하려는 경우입니다:\n\n```shell\npython -m python_coreml_stable_diffusion.pipeline --prompt \"a photo of an astronaut riding a horse on mars\" --compute-unit ALL -o output --seed 93 -i models/coreml-stable-diffusion-v1-5_original_packages --model-version runwayml/stable-diffusion-v1-5\n```\n\n\n## Swift에서 Core ML 추론하기\n\nSwift에서 추론을 실행하는 것은 모델이 이미 `mlmodelc` 형식으로 컴파일되어 있기 때문에 Python보다 약간 빠릅니다. 이는 앱이 시작될 때 모델이 불러와지는 것이 눈에 띄지만, 이후 여러 번 실행하면 눈에 띄지 않을 것입니다.\n\n### 다운로드\n\nMac에서 Swift에서 추론을 실행하려면 `컴파일된` 체크포인트 버전 중 하나가 필요합니다. 이전 예제와 유사하지만 `컴파일된` 변형 중 하나를 사용하여 Python 코드를 로컬로 다운로드하는 것이 좋습니다:\n\n```Python\nfrom huggingface_hub import snapshot_download\nfrom pathlib import Path\n\nrepo_id = \"apple/coreml-stable-diffusion-v1-4\"\nvariant = \"original/compiled\"\n\nmodel_path = Path(\"./models\") / (repo_id.split(\"/\")[-1] + \"_\" + variant.replace(\"/\", \"_\"))\nsnapshot_download(repo_id, allow_patterns=f\"{variant}/*\", local_dir=model_path, local_dir_use_symlinks=False)\nprint(f\"Model downloaded at {model_path}\")\n```\n\n### 추론[[swift-inference]]\n\n추론을 실행하기 위해서, Apple의 리포지토리를 복제하세요:\n\n```bash\ngit clone https://github.com/apple/ml-stable-diffusion\ncd ml-stable-diffusion\n```\n\n그 다음 Apple의 명령어 도구인 [Swift 패키지 관리자](https://www.swift.org/package-manager/#)를 사용합니다:\n\n```bash\nswift run StableDiffusionSample --resource-path models/coreml-stable-diffusion-v1-4_original_compiled --compute-units all \"a photo of an astronaut riding a horse on mars\"\n```\n\n`--resource-path`에 이전 단계에서 다운로드한 체크포인트 중 하나를 지정해야 하므로 확장자가 `.mlmodelc`인 컴파일된 Core ML 번들이 포함되어 있는지 확인하시기 바랍니다. `--compute-units`는 다음 값 중 하나이어야 합니다: `all`, `cpuOnly`, `cpuAndGPU`, `cpuAndNeuralEngine`.\n\n자세한 내용은 [Apple의 리포지토리 안의 지침](https://github.com/apple/ml-stable-diffusion)을 참고하시기 바랍니다.\n\n\n## 지원되는 Diffusers 기능\n\nCore ML 모델과 추론 코드는 🧨 Diffusers의 많은 기능, 옵션 및 유연성을 지원하지 않습니다. 다음은 유의해야 할 몇 가지 제한 사항입니다:\n\n- Core ML 모델은 추론에만 적합합니다. 학습이나 파인튜닝에는 사용할 수 없습니다.\n- Swift에 포팅된 스케줄러는 Stable Diffusion에서 사용하는 기본 스케줄러와 `diffusers` 구현에서 Swift로 포팅한 `DPMSolverMultistepScheduler` 두 개뿐입니다. 이들 중 약 절반의 스텝으로 동일한 품질을 생성하는 `DPMSolverMultistepScheduler`를 사용하는 것이 좋습니다.\n- 추론 코드에서 네거티브 프롬프트, classifier-free guidance scale 및 image-to-image 작업을 사용할 수 있습니다. depth guidance, ControlNet, latent upscalers와 같은 고급 기능은 아직 사용할 수 없습니다.\n\nApple의 [변환 및 추론 리포지토리](https://github.com/apple/ml-stable-diffusion)와 자체 [swift-coreml-diffusers](https://github.com/huggingface/swift-coreml-diffusers) 리포지토리는 다른 개발자들이 구축할 수 있는 기술적인 데모입니다. \n\n누락된 기능이 있다고 생각되면 언제든지 기능을 요청하거나, 더 좋은 방법은 기여 PR을 열어주세요. :)\n\n\n## 네이티브 Diffusers Swift 앱\n\n자체 Apple 하드웨어에서 Stable Diffusion을 실행하는 쉬운 방법 중 하나는 `diffusers`와 Apple의 변환 및 추론 리포지토리를 기반으로 하는 [자체 오픈 소스 Swift 리포지토리](https://github.com/huggingface/swift-coreml-diffusers)를 사용하는 것입니다. 코드를 공부하고 [Xcode](https://developer.apple.com/xcode/)로 컴파일하여 필요에 맞게 조정할 수 있습니다. 편의를 위해 앱스토어에 [독립형 Mac 앱](https://apps.apple.com/app/diffusers/id1666309574)도 있으므로 코드나 IDE를 다루지 않고도 사용할 수 있습니다. 개발자로서 Core ML이 Stable Diffusion 앱을 구축하는 데 가장 적합한 솔루션이라고 판단했다면, 이 가이드의 나머지 부분을 사용하여 프로젝트를 시작할 수 있습니다. 여러분이 무엇을 빌드할지 기대됩니다. :)" }, { "path": "diffusers/docs/source/ko/optimization/fp16.md", "content": "\n\n# 메모리와 속도\n\n메모리 또는 속도에 대해 🤗 Diffusers *추론*을 최적화하기 위한 몇 가지 기술과 아이디어를 제시합니다.\n일반적으로, memory-efficient attention을 위해 [xFormers](https://github.com/facebookresearch/xformers) 사용을 추천하기 때문에, 추천하는 [설치 방법](xformers)을 보고 설치해 보세요.\n\n다음 설정이 성능과 메모리에 미치는 영향에 대해 설명합니다.\n\n| | 지연시간 | 속도 향상 |\n| ---------------- | ------- | ------- |\n| 별도 설정 없음 | 9.50s | x1 |\n| cuDNN auto-tuner | 9.37s | x1.01 |\n| fp16 | 3.61s | x2.63 |\n| Channels Last 메모리 형식 | 3.30s | x2.88 |\n| traced UNet | 3.21s | x2.96 |\n| memory-efficient attention | 2.63s | x3.61 |\n\n\n NVIDIA TITAN RTX에서 50 DDIM 스텝의 \"a photo of an astronaut riding a horse on mars\" 프롬프트로 512x512 크기의 단일 이미지를 생성하였습니다.\n\n\n## cuDNN auto-tuner 활성화하기\n\n[NVIDIA cuDNN](https://developer.nvidia.com/cudnn)은 컨볼루션을 계산하는 많은 알고리즘을 지원합니다. Autotuner는 짧은 벤치마크를 실행하고 주어진 입력 크기에 대해 주어진 하드웨어에서 최고의 성능을 가진 커널을 선택합니다.\n\n**컨볼루션 네트워크**를 활용하고 있기 때문에 (다른 유형들은 현재 지원되지 않음), 다음 설정을 통해 추론 전에 cuDNN autotuner를 활성화할 수 있습니다:\n\n```python\nimport torch\n\ntorch.backends.cudnn.benchmark = True\n```\n\n### fp32 대신 tf32 사용하기 (Ampere 및 이후 CUDA 장치들에서)\n\nAmpere 및 이후 CUDA 장치에서 행렬곱 및 컨볼루션은 TensorFloat32(TF32) 모드를 사용하여 더 빠르지만 약간 덜 정확할 수 있습니다.\n기본적으로 PyTorch는 컨볼루션에 대해 TF32 모드를 활성화하지만 행렬 곱셈은 활성화하지 않습니다.\n네트워크에 완전한 float32 정밀도가 필요한 경우가 아니면 행렬 곱셈에 대해서도 이 설정을 활성화하는 것이 좋습니다.\n이는 일반적으로 무시할 수 있는 수치의 정확도 손실이 있지만, 계산 속도를 크게 높일 수 있습니다.\n그것에 대해 [여기](https://huggingface.co/docs/transformers/v4.18.0/en/performance#tf32)서 더 읽을 수 있습니다.\n추론하기 전에 다음을 추가하기만 하면 됩니다:\n\n```python\nimport torch\n\ntorch.backends.cuda.matmul.allow_tf32 = True\n```\n\n## 반정밀도 가중치\n\n더 많은 GPU 메모리를 절약하고 더 빠른 속도를 얻기 위해 모델 가중치를 반정밀도(half precision)로 직접 불러오고 실행할 수 있습니다.\n여기에는 `fp16`이라는 브랜치에 저장된 float16 버전의 가중치를 불러오고, 그 때 `float16` 유형을 사용하도록 PyTorch에 지시하는 작업이 포함됩니다.\n\n```Python\npipe = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n\n torch_dtype=torch.float16,\n)\npipe = pipe.to(\"cuda\")\n\nprompt = \"a photo of an astronaut riding a horse on mars\"\nimage = pipe(prompt).images[0]\n```\n\n\n 어떤 파이프라인에서도 [`torch.autocast`](https://pytorch.org/docs/stable/amp.html#torch.autocast) 를 사용하는 것은 검은색 이미지를 생성할 수 있고, 순수한 float16 정밀도를 사용하는 것보다 항상 느리기 때문에 사용하지 않는 것이 좋습니다.\n\n\n## 추가 메모리 절약을 위한 슬라이스 어텐션\n\n추가 메모리 절약을 위해, 한 번에 모두 계산하는 대신 단계적으로 계산을 수행하는 슬라이스 버전의 어텐션(attention)을 사용할 수 있습니다.\n\n\n Attention slicing은 모델이 하나 이상의 어텐션 헤드를 사용하는 한, 배치 크기가 1인 경우에도 유용합니다.\n 하나 이상의 어텐션 헤드가 있는 경우 *QK^T* 어텐션 매트릭스는 상당한 양의 메모리를 절약할 수 있는 각 헤드에 대해 순차적으로 계산될 수 있습니다.\n\n\n각 헤드에 대해 순차적으로 어텐션 계산을 수행하려면, 다음과 같이 추론 전에 파이프라인에서 [`~StableDiffusionPipeline.enable_attention_slicing`]를 호출하면 됩니다:\n\n```Python\nimport torch\nfrom diffusers import StableDiffusionPipeline\n\npipe = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n\n torch_dtype=torch.float16,\n)\npipe = pipe.to(\"cuda\")\n\nprompt = \"a photo of an astronaut riding a horse on mars\"\npipe.enable_attention_slicing()\nimage = pipe(prompt).images[0]\n```\n\n추론 시간이 약 10% 느려지는 약간의 성능 저하가 있지만 이 방법을 사용하면 3.2GB 정도의 작은 VRAM으로도 Stable Diffusion을 사용할 수 있습니다!\n\n\n## 더 큰 배치를 위한 sliced VAE 디코드\n\n제한된 VRAM에서 대규모 이미지 배치를 디코딩하거나 32개 이상의 이미지가 포함된 배치를 활성화하기 위해, 배치의 latent 이미지를 한 번에 하나씩 디코딩하는 슬라이스 VAE 디코드를 사용할 수 있습니다.\n\n이를 [`~StableDiffusionPipeline.enable_attention_slicing`] 또는 [`~StableDiffusionPipeline.enable_xformers_memory_efficient_attention`]과 결합하여 메모리 사용을 추가로 최소화할 수 있습니다.\n\nVAE 디코드를 한 번에 하나씩 수행하려면 추론 전에 파이프라인에서 [`~StableDiffusionPipeline.enable_vae_slicing`]을 호출합니다. 예를 들어:\n\n```Python\nimport torch\nfrom diffusers import StableDiffusionPipeline\n\npipe = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n\n torch_dtype=torch.float16,\n)\npipe = pipe.to(\"cuda\")\n\nprompt = \"a photo of an astronaut riding a horse on mars\"\npipe.enable_vae_slicing()\nimages = pipe([prompt] * 32).images\n```\n\n다중 이미지 배치에서 VAE 디코드가 약간의 성능 향상이 이루어집니다. 단일 이미지 배치에서는 성능 영향은 없습니다.\n\n\n\n## 메모리 절약을 위해 가속 기능을 사용하여 CPU로 오프로딩\n\n추가 메모리 절약을 위해 가중치를 CPU로 오프로드하고 순방향 전달을 수행할 때만 GPU로 로드할 수 있습니다.\n\nCPU 오프로딩을 수행하려면 [`~StableDiffusionPipeline.enable_sequential_cpu_offload`]를 호출하기만 하면 됩니다:\n\n```Python\nimport torch\nfrom diffusers import StableDiffusionPipeline\n\npipe = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n\n torch_dtype=torch.float16,\n)\n\nprompt = \"a photo of an astronaut riding a horse on mars\"\npipe.enable_sequential_cpu_offload()\nimage = pipe(prompt).images[0]\n```\n\n그러면 메모리 소비를 3GB 미만으로 줄일 수 있습니다.\n\n참고로 이 방법은 전체 모델이 아닌 서브모듈 수준에서 작동합니다. 이는 메모리 소비를 최소화하는 가장 좋은 방법이지만 프로세스의 반복적 특성으로 인해 추론 속도가 훨씬 느립니다. 파이프라인의 UNet 구성 요소는 여러 번 실행됩니다('num_inference_steps' 만큼). 매번 UNet의 서로 다른 서브모듈이 순차적으로 온로드된 다음 필요에 따라 오프로드되므로 메모리 이동 횟수가 많습니다.\n\n\n또 다른 최적화 방법인 모델 오프로딩을 사용하는 것을 고려하십시오. 이는 훨씬 빠르지만 메모리 절약이 크지는 않습니다.\n\n\n또한 ttention slicing과 연결해서 최소 메모리(< 2GB)로도 동작할 수 있습니다.\n\n\n```Python\nimport torch\nfrom diffusers import StableDiffusionPipeline\n\npipe = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n\n torch_dtype=torch.float16,\n)\n\nprompt = \"a photo of an astronaut riding a horse on mars\"\npipe.enable_sequential_cpu_offload()\npipe.enable_attention_slicing(1)\n\nimage = pipe(prompt).images[0]\n```\n\n**참고**: 'enable_sequential_cpu_offload()'를 사용할 때, 미리 파이프라인을 CUDA로 이동하지 **않는** 것이 중요합니다.그렇지 않으면 메모리 소비의 이득이 최소화됩니다. 더 많은 정보를 위해 [이 이슈](https://github.com/huggingface/diffusers/issues/1934)를 보세요.\n\n\n## 빠른 추론과 메모리 메모리 절약을 위한 모델 오프로딩\n\n[순차적 CPU 오프로딩](#sequential_offloading)은 이전 섹션에서 설명한 것처럼 많은 메모리를 보존하지만 필요에 따라 서브모듈을 GPU로 이동하고 새 모듈이 실행될 때 즉시 CPU로 반환되기 때문에 추론 속도가 느려집니다.\n\n전체 모델 오프로딩은 각 모델의 구성 요소인 _modules_을 처리하는 대신, 전체 모델을 GPU로 이동하는 대안입니다. 이로 인해 추론 시간에 미치는 영향은 미미하지만(파이프라인을 'cuda'로 이동하는 것과 비교하여) 여전히 약간의 메모리를 절약할 수 있습니다.\n\n이 시나리오에서는 파이프라인의 주요 구성 요소 중 하나만(일반적으로 텍스트 인코더, unet 및 vae) GPU에 있고, 나머지는 CPU에서 대기할 것입니다.\n여러 반복을 위해 실행되는 UNet과 같은 구성 요소는 더 이상 필요하지 않을 때까지 GPU에 남아 있습니다.\n\n이 기능은 아래와 같이 파이프라인에서 `enable_model_cpu_offload()`를 호출하여 활성화할 수 있습니다.\n\n```Python\nimport torch\nfrom diffusers import StableDiffusionPipeline\n\npipe = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n torch_dtype=torch.float16,\n)\n\nprompt = \"a photo of an astronaut riding a horse on mars\"\npipe.enable_model_cpu_offload()\nimage = pipe(prompt).images[0]\n```\n\n이는 추가적인 메모리 절약을 위한 attention slicing과도 호환됩니다.\n\n```Python\nimport torch\nfrom diffusers import StableDiffusionPipeline\n\npipe = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n torch_dtype=torch.float16,\n)\n\nprompt = \"a photo of an astronaut riding a horse on mars\"\npipe.enable_model_cpu_offload()\npipe.enable_attention_slicing(1)\n\nimage = pipe(prompt).images[0]\n```\n\n\n이 기능을 사용하려면 'accelerate' 버전 0.17.0 이상이 필요합니다.\n\n\n## Channels Last 메모리 형식 사용하기\n\nChannels Last 메모리 형식은 차원 순서를 보존하는 메모리에서 NCHW 텐서 배열을 대체하는 방법입니다.\nChannels Last 텐서는 채널이 가장 조밀한 차원이 되는 방식으로 정렬됩니다(일명 픽셀당 이미지를 저장).\n현재 모든 연산자 Channels Last 형식을 지원하는 것은 아니라 성능이 저하될 수 있으므로, 사용해보고 모델에 잘 작동하는지 확인하는 것이 좋습니다.\n\n\n예를 들어 파이프라인의 UNet 모델이 channels Last 형식을 사용하도록 설정하려면 다음을 사용할 수 있습니다:\n\n```python\nprint(pipe.unet.conv_out.state_dict()[\"weight\"].stride()) # (2880, 9, 3, 1)\npipe.unet.to(memory_format=torch.channels_last) # in-place 연산\n# 2번째 차원에서 스트라이드 1을 가지는 (2880, 1, 960, 320)로, 연산이 작동함을 증명합니다.\nprint(pipe.unet.conv_out.state_dict()[\"weight\"].stride())\n```\n\n## 추적(tracing)\n\n추적은 모델을 통해 예제 입력 텐서를 통해 실행되는데, 해당 입력이 모델의 레이어를 통과할 때 호출되는 작업을 캡처하여 실행 파일 또는 'ScriptFunction'이 반환되도록 하고, 이는 just-in-time 컴파일로 최적화됩니다.\n\nUNet 모델을 추적하기 위해 다음을 사용할 수 있습니다:\n\n```python\nimport time\nimport torch\nfrom diffusers import StableDiffusionPipeline\nimport functools\n\n# torch 기울기 비활성화\ntorch.set_grad_enabled(False)\n\n# 변수 설정\nn_experiments = 2\nunet_runs_per_experiment = 50\n\n\n# 입력 불러오기\ndef generate_inputs():\n sample = torch.randn((2, 4, 64, 64), device=\"cuda\", dtype=torch.float16)\n timestep = torch.rand(1, device=\"cuda\", dtype=torch.float16) * 999\n encoder_hidden_states = torch.randn((2, 77, 768), device=\"cuda\", dtype=torch.float16)\n return sample, timestep, encoder_hidden_states\n\n\npipe = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n torch_dtype=torch.float16,\n).to(\"cuda\")\nunet = pipe.unet\nunet.eval()\nunet.to(memory_format=torch.channels_last) # Channels Last 메모리 형식 사용\nunet.forward = functools.partial(unet.forward, return_dict=False) # return_dict=False을 기본값으로 설정\n\n# 워밍업\nfor _ in range(3):\n with torch.inference_mode():\n inputs = generate_inputs()\n orig_output = unet(*inputs)\n\n# 추적\nprint(\"tracing..\")\nunet_traced = torch.jit.trace(unet, inputs)\nunet_traced.eval()\nprint(\"done tracing\")\n\n\n# 워밍업 및 그래프 최적화\nfor _ in range(5):\n with torch.inference_mode():\n inputs = generate_inputs()\n orig_output = unet_traced(*inputs)\n\n\n# 벤치마킹\nwith torch.inference_mode():\n for _ in range(n_experiments):\n torch.cuda.synchronize()\n start_time = time.time()\n for _ in range(unet_runs_per_experiment):\n orig_output = unet_traced(*inputs)\n torch.cuda.synchronize()\n print(f\"unet traced inference took {time.time() - start_time:.2f} seconds\")\n for _ in range(n_experiments):\n torch.cuda.synchronize()\n start_time = time.time()\n for _ in range(unet_runs_per_experiment):\n orig_output = unet(*inputs)\n torch.cuda.synchronize()\n print(f\"unet inference took {time.time() - start_time:.2f} seconds\")\n\n# 모델 저장\nunet_traced.save(\"unet_traced.pt\")\n```\n\n그 다음, 파이프라인의 `unet` 특성을 다음과 같이 추적된 모델로 바꿀 수 있습니다.\n\n```python\nfrom diffusers import StableDiffusionPipeline\nimport torch\nfrom dataclasses import dataclass\n\n\n@dataclass\nclass UNet2DConditionOutput:\n sample: torch.Tensor\n\n\npipe = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n torch_dtype=torch.float16,\n).to(\"cuda\")\n\n# jitted unet 사용\nunet_traced = torch.jit.load(\"unet_traced.pt\")\n\n\n# pipe.unet 삭제\nclass TracedUNet(torch.nn.Module):\n def __init__(self):\n super().__init__()\n self.in_channels = pipe.unet.config.in_channels\n self.device = pipe.unet.device\n\n def forward(self, latent_model_input, t, encoder_hidden_states):\n sample = unet_traced(latent_model_input, t, encoder_hidden_states)[0]\n return UNet2DConditionOutput(sample=sample)\n\n\npipe.unet = TracedUNet()\n\nwith torch.inference_mode():\n image = pipe([prompt] * 1, num_inference_steps=50).images[0]\n```\n\n\n## Memory-efficient attention\n\n어텐션 블록의 대역폭을 최적화하는 최근 작업으로 GPU 메모리 사용량이 크게 향상되고 향상되었습니다.\n@tridao의 가장 최근의 플래시 어텐션: [code](https://github.com/HazyResearch/flash-attention), [paper](https://arxiv.org/pdf/2205.14135.pdf).\n\n배치 크기 1(프롬프트 1개)의 512x512 크기로 추론을 실행할 때 몇 가지 Nvidia GPU에서 얻은 속도 향상은 다음과 같습니다:\n\n| GPU \t| 기준 어텐션 FP16 \t | 메모리 효율적인 어텐션 FP16 \t|\n|------------------\t|---------------------\t|---------------------------------\t|\n| NVIDIA Tesla T4 \t| 3.5it/s \t| 5.5it/s \t|\n| NVIDIA 3060 RTX \t| 4.6it/s \t| 7.8it/s \t|\n| NVIDIA A10G \t| 8.88it/s \t| 15.6it/s \t|\n| NVIDIA RTX A6000 \t| 11.7it/s \t| 21.09it/s \t|\n| NVIDIA TITAN RTX | 12.51it/s \t| 18.22it/s \t|\n| A100-SXM4-40GB \t| 18.6it/s \t| 29.it/s \t|\n| A100-SXM-80GB \t| 18.7it/s \t| 29.5it/s \t|\n\n이를 활용하려면 다음을 만족해야 합니다:\n - PyTorch > 1.12\n - Cuda 사용 가능\n - [xformers 라이브러리를 설치함](xformers)\n```python\nfrom diffusers import StableDiffusionPipeline\nimport torch\n\npipe = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n torch_dtype=torch.float16,\n).to(\"cuda\")\n\npipe.enable_xformers_memory_efficient_attention()\n\nwith torch.inference_mode():\n sample = pipe(\"a small cat\")\n\n# 선택: 이를 비활성화 하기 위해 다음을 사용할 수 있습니다.\n# pipe.disable_xformers_memory_efficient_attention()\n```\n" }, { "path": "diffusers/docs/source/ko/optimization/habana.md", "content": "\n\n# Habana Gaudi에서 Stable Diffusion을 사용하는 방법\n\n🤗 Diffusers는 🤗 [Optimum Habana](https://huggingface.co/docs/optimum/habana/usage_guides/stable_diffusion)를 통해서 Habana Gaudi와 호환됩니다.\n\n## 요구 사항\n\n- Optimum Habana 1.4 또는 이후, [여기](https://huggingface.co/docs/optimum/habana/installation)에 설치하는 방법이 있습니다.\n- SynapseAI 1.8.\n\n\n## 추론 파이프라인\n\nGaudi에서 Stable Diffusion 1 및 2로 이미지를 생성하려면 두 인스턴스를 인스턴스화해야 합니다:\n- [`GaudiStableDiffusionPipeline`](https://huggingface.co/docs/optimum/habana/package_reference/stable_diffusion_pipeline)이 포함된 파이프라인. 이 파이프라인은 *텍스트-이미지 생성*을 지원합니다.\n- [`GaudiDDIMScheduler`](https://huggingface.co/docs/optimum/habana/package_reference/stable_diffusion_pipeline#optimum.habana.diffusers.GaudiDDIMScheduler)이 포함된 스케줄러. 이 스케줄러는 Habana Gaudi에 최적화되어 있습니다.\n\n파이프라인을 초기화할 때, HPU에 배포하기 위해 `use_habana=True`를 지정해야 합니다.\n또한 가능한 가장 빠른 생성을 위해 `use_hpu_graphs=True`로 **HPU 그래프**를 활성화해야 합니다.\n마지막으로, [Hugging Face Hub](https://huggingface.co/Habana)에서 다운로드할 수 있는 [Gaudi configuration](https://huggingface.co/docs/optimum/habana/package_reference/gaudi_config)을 지정해야 합니다.\n\n```python\nfrom optimum.habana import GaudiConfig\nfrom optimum.habana.diffusers import GaudiDDIMScheduler, GaudiStableDiffusionPipeline\n\nmodel_name = \"stabilityai/stable-diffusion-2-base\"\nscheduler = GaudiDDIMScheduler.from_pretrained(model_name, subfolder=\"scheduler\")\npipeline = GaudiStableDiffusionPipeline.from_pretrained(\n model_name,\n scheduler=scheduler,\n use_habana=True,\n use_hpu_graphs=True,\n gaudi_config=\"Habana/stable-diffusion\",\n)\n```\n\n파이프라인을 호출하여 하나 이상의 프롬프트에서 배치별로 이미지를 생성할 수 있습니다.\n\n```python\noutputs = pipeline(\n prompt=[\n \"High quality photo of an astronaut riding a horse in space\",\n \"Face of a yellow cat, high resolution, sitting on a park bench\",\n ],\n num_images_per_prompt=10,\n batch_size=4,\n)\n```\n\n더 많은 정보를 얻기 위해, Optimum Habana의 [문서](https://huggingface.co/docs/optimum/habana/usage_guides/stable_diffusion)와 공식 Github 저장소에 제공된 [예시](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion)를 확인하세요.\n\n\n## 벤치마크\n\n다음은 [Habana/stable-diffusion](https://huggingface.co/Habana/stable-diffusion) Gaudi 구성(혼합 정밀도 bf16/fp32)을 사용하는 Habana first-generation Gaudi 및 Gaudi2의 지연 시간입니다:\n\n| | Latency (배치 크기 = 1) | Throughput (배치 크기 = 8) |\n| ---------------------- |:------------------------:|:---------------------------:|\n| first-generation Gaudi | 4.29s | 0.283 images/s |\n| Gaudi2 | 1.54s | 0.904 images/s |\n" }, { "path": "diffusers/docs/source/ko/optimization/mps.md", "content": "\n\n# Apple Silicon (M1/M2)에서 Stable Diffusion을 사용하는 방법\n\nDiffusers는 Stable Diffusion 추론을 위해 PyTorch `mps`를 사용해 Apple 실리콘과 호환됩니다. 다음은 Stable Diffusion이 있는 M1 또는 M2 컴퓨터를 사용하기 위해 따라야 하는 단계입니다.\n\n## 요구 사항\n\n- Apple silicon (M1/M2) 하드웨어의 Mac 컴퓨터.\n- macOS 12.6 또는 이후 (13.0 또는 이후 추천).\n- Python arm64 버전\n- PyTorch 2.0(추천) 또는 1.13(`mps`를 지원하는 최소 버전). Yhttps://pytorch.org/get-started/locally/의 지침에 따라 `pip` 또는 `conda`로 설치할 수 있습니다.\n\n\n## 추론 파이프라인\n\n아래 코도는 익숙한 `to()` 인터페이스를 사용하여 `mps` 백엔드로 Stable Diffusion 파이프라인을 M1 또는 M2 장치로 이동하는 방법을 보여줍니다.\n\n\n\n\n**PyTorch 1.13을 사용 중일 때 ** 추가 일회성 전달을 사용하여 파이프라인을 \"프라이밍\"하는 것을 추천합니다. 이것은 발견한 이상한 문제에 대한 임시 해결 방법입니다. 첫 번째 추론 전달은 후속 전달와 약간 다른 결과를 생성합니다. 이 전달은 한 번만 수행하면 되며 추론 단계를 한 번만 사용하고 결과를 폐기해도 됩니다.\n\n\n\n이전 팁에서 설명한 것들을 포함한 여러 문제를 해결하므로 PyTorch 2 이상을 사용하는 것이 좋습니다.\n\n\n```python\n# `huggingface-cli login`에 로그인되어 있음을 확인\nfrom diffusers import DiffusionPipeline\n\npipe = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\npipe = pipe.to(\"mps\")\n\n# 컴퓨터가 64GB 이하의 RAM 램일 때 추천\npipe.enable_attention_slicing()\n\nprompt = \"a photo of an astronaut riding a horse on mars\"\n\n# 처음 \"워밍업\" 전달 (위 설명을 보세요)\n_ = pipe(prompt, num_inference_steps=1)\n\n# 결과는 워밍업 전달 후의 CPU 장치의 결과와 일치합니다.\nimage = pipe(prompt).images[0]\n```\n\n## 성능 추천\n\nM1/M2 성능은 메모리 압력에 매우 민감합니다. 시스템은 필요한 경우 자동으로 스왑되지만 스왑할 때 성능이 크게 저하됩니다.\n\n\n특히 컴퓨터의 시스템 RAM이 64GB 미만이거나 512 × 512픽셀보다 큰 비표준 해상도에서 이미지를 생성하는 경우, 추론 중에 메모리 압력을 줄이고 스와핑을 방지하기 위해 *어텐션 슬라이싱*을 사용하는 것이 좋습니다. 어텐션 슬라이싱은 비용이 많이 드는 어텐션 작업을 한 번에 모두 수행하는 대신 여러 단계로 수행합니다. 일반적으로 범용 메모리가 없는 컴퓨터에서 ~20%의 성능 영향을 미치지만 64GB 이상이 아닌 경우 대부분의 Apple Silicon 컴퓨터에서 *더 나은 성능*이 관찰되었습니다.\n\n```python\npipeline.enable_attention_slicing()\n```\n\n## Known Issues\n\n- 여러 프롬프트를 배치로 생성하는 것은 [충돌이 발생하거나 안정적으로 작동하지 않습니다](https://github.com/huggingface/diffusers/issues/363). 우리는 이것이 [PyTorch의 `mps` 백엔드](https://github.com/pytorch/pytorch/issues/84039)와 관련이 있다고 생각합니다. 이 문제는 해결되고 있지만 지금은 배치 대신 반복 방법을 사용하는 것이 좋습니다." }, { "path": "diffusers/docs/source/ko/optimization/onnx.md", "content": "\n\n\n# 추론을 위해 ONNX 런타임을 사용하는 방법\n\n🤗 Diffusers는 ONNX Runtime과 호환되는 Stable Diffusion 파이프라인을 제공합니다. 이를 통해 ONNX(CPU 포함)를 지원하고 PyTorch의 가속 버전을 사용할 수 없는 모든 하드웨어에서 Stable Diffusion을 실행할 수 있습니다.\n\n## 설치\n\n다음 명령어로 ONNX Runtime를 지원하는 🤗 Optimum를 설치합니다:\n\n```sh\npip install optimum[\"onnxruntime\"]\n```\n\n## Stable Diffusion 추론\n\n아래 코드는 ONNX 런타임을 사용하는 방법을 보여줍니다. `StableDiffusionPipeline` 대신 `OnnxStableDiffusionPipeline`을 사용해야 합니다.\nPyTorch 모델을 불러오고 즉시 ONNX 형식으로 변환하려는 경우 `export=True`로 설정합니다.\n\n```python\nfrom optimum.onnxruntime import ORTStableDiffusionPipeline\n\nmodel_id = \"runwayml/stable-diffusion-v1-5\"\npipe = ORTStableDiffusionPipeline.from_pretrained(model_id, export=True)\nprompt = \"a photo of an astronaut riding a horse on mars\"\nimages = pipe(prompt).images[0]\npipe.save_pretrained(\"./onnx-stable-diffusion-v1-5\")\n```\n\n파이프라인을 ONNX 형식으로 오프라인으로 내보내고 나중에 추론에 사용하려는 경우,\n[`optimum-cli export`](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli) 명령어를 사용할 수 있습니다:\n\n```bash\noptimum-cli export onnx --model runwayml/stable-diffusion-v1-5 sd_v15_onnx/\n```\n\n그 다음 추론을 수행합니다:\n\n```python\nfrom optimum.onnxruntime import ORTStableDiffusionPipeline\n\nmodel_id = \"sd_v15_onnx\"\npipe = ORTStableDiffusionPipeline.from_pretrained(model_id)\nprompt = \"a photo of an astronaut riding a horse on mars\"\nimages = pipe(prompt).images[0]\n```\n\nNotice that we didn't have to specify `export=True` above.\n\n[Optimum 문서](https://huggingface.co/docs/optimum/)에서 더 많은 예시를 찾을 수 있습니다.\n\n## 알려진 이슈들\n\n- 여러 프롬프트를 배치로 생성하면 너무 많은 메모리가 사용되는 것 같습니다. 이를 조사하는 동안, 배치 대신 반복 방법이 필요할 수도 있습니다.\n" }, { "path": "diffusers/docs/source/ko/optimization/open_vino.md", "content": "\n\n# 추론을 위한 OpenVINO 사용 방법\n\n🤗 [Optimum](https://github.com/huggingface/optimum-intel)은 OpenVINO와 호환되는 Stable Diffusion 파이프라인을 제공합니다.\n이제 다양한 Intel 프로세서에서 OpenVINO Runtime으로 쉽게 추론을 수행할 수 있습니다. ([여기](https://docs.openvino.ai/latest/openvino_docs_OV_UG_supported_plugins_Supported_Devices.html)서 지원되는 전 기기 목록을 확인하세요).\n\n## 설치\n\n다음 명령어로 🤗 Optimum을 설치합니다:\n\n```sh\npip install optimum[\"openvino\"]\n```\n\n## Stable Diffusion 추론\n\nOpenVINO 모델을 불러오고 OpenVINO 런타임으로 추론을 실행하려면 `StableDiffusionPipeline`을 `OVStableDiffusionPipeline`으로 교체해야 합니다. PyTorch 모델을 불러오고 즉시 OpenVINO 형식으로 변환하려는 경우 `export=True`로 설정합니다.\n\n```python\nfrom optimum.intel.openvino import OVStableDiffusionPipeline\n\nmodel_id = \"runwayml/stable-diffusion-v1-5\"\npipe = OVStableDiffusionPipeline.from_pretrained(model_id, export=True)\nprompt = \"a photo of an astronaut riding a horse on mars\"\nimages = pipe(prompt).images[0]\n```\n\n[Optimum 문서](https://huggingface.co/docs/optimum/intel/inference#export-and-inference-of-stable-diffusion-models)에서 (정적 reshaping과 모델 컴파일 등의) 더 많은 예시들을 찾을 수 있습니다.\n" }, { "path": "diffusers/docs/source/ko/optimization/opt_overview.md", "content": "\n\n# 개요\n\n노이즈가 많은 출력에서 적은 출력으로 만드는 과정으로 고품질 생성 모델의 출력을 만드는 각각의 반복되는 스텝은 많은 계산이 필요합니다. 🧨 Diffuser의 목표 중 하나는 모든 사람이 이 기술을 널리 이용할 수 있도록 하는 것이며, 여기에는 소비자 및 특수 하드웨어에서 빠른 추론을 가능하게 하는 것을 포함합니다. \n\n이 섹션에서는 추론 속도를 최적화하고 메모리 소비를 줄이기 위한 반정밀(half-precision) 가중치 및 sliced attention과 같은 팁과 요령을 다룹니다. 또한 [`torch.compile`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) 또는 [ONNX Runtime](https://onnxruntime.ai/docs/)을 사용하여 PyTorch 코드의 속도를 높이고, [xFormers](https://facebookresearch.github.io/xformers/)를 사용하여 memory-efficient attention을 활성화하는 방법을 배울 수 있습니다. Apple Silicon, Intel 또는 Habana 프로세서와 같은 특정 하드웨어에서 추론을 실행하기 위한 가이드도 있습니다." }, { "path": "diffusers/docs/source/ko/optimization/tome.md", "content": "\n\n# Token Merging (토큰 병합)\n\nToken Merging (introduced in [Token Merging: Your ViT But Faster](https://arxiv.org/abs/2210.09461))은 트랜스포머 기반 네트워크의 forward pass에서 중복 토큰이나 패치를 점진적으로 병합하는 방식으로 작동합니다. 이를 통해 기반 네트워크의 추론 지연 시간을 단축할 수 있습니다.\n\nToken Merging(ToMe)이 출시된 후, 저자들은 [Fast Stable Diffusion을 위한 토큰 병합](https://arxiv.org/abs/2303.17604)을 발표하여 Stable Diffusion과 더 잘 호환되는 ToMe 버전을 소개했습니다. ToMe를 사용하면 [`DiffusionPipeline`]의 추론 지연 시간을 부드럽게 단축할 수 있습니다. 이 문서에서는 ToMe를 [`StableDiffusionPipeline`]에 적용하는 방법, 예상되는 속도 향상, [`StableDiffusionPipeline`]에서 ToMe를 사용할 때의 질적 측면에 대해 설명합니다.\n\n## ToMe 사용하기\n\nToMe의 저자들은 [`tomesd`](https://github.com/dbolya/tomesd)라는 편리한 Python 라이브러리를 공개했는데, 이 라이브러리를 이용하면 [`DiffusionPipeline`]에 ToMe를 다음과 같이 적용할 수 있습니다:\n\n```diff\nfrom diffusers import StableDiffusionPipeline\nimport tomesd\n\npipeline = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16\n).to(\"cuda\")\n+ tomesd.apply_patch(pipeline, ratio=0.5)\n\nimage = pipeline(\"a photo of an astronaut riding a horse on mars\").images[0]\n```\n\n이것이 다입니다!\n\n`tomesd.apply_patch()`는 파이프라인 추론 속도와 생성된 토큰의 품질 사이의 균형을 맞출 수 있도록 [여러 개의 인자](https://github.com/dbolya/tomesd#usage)를 노출합니다. 이러한 인수 중 가장 중요한 것은 `ratio(비율)`입니다. `ratio`은 forward pass 중에 병합될 토큰의 수를 제어합니다. `tomesd`에 대한 자세한 내용은 해당 리포지토리(https://github.com/dbolya/tomesd) 및 [논문](https://arxiv.org/abs/2303.17604)을 참고하시기 바랍니다.\n\n## `StableDiffusionPipeline`으로 `tomesd` 벤치마킹하기\n\nWe benchmarked the impact of using `tomesd` on [`StableDiffusionPipeline`] along with [xformers](https://huggingface.co/docs/diffusers/optimization/xformers) across different image resolutions. We used A100 and V100 as our test GPU devices with the following development environment (with Python 3.8.5):\n다양한 이미지 해상도에서 [xformers](https://huggingface.co/docs/diffusers/optimization/xformers)를 적용한 상태에서, [`StableDiffusionPipeline`]에 `tomesd`를 사용했을 때의 영향을 벤치마킹했습니다. 테스트 GPU 장치로 A100과 V100을 사용했으며 개발 환경은 다음과 같습니다(Python 3.8.5 사용):\n\n```bash\n- `diffusers` version: 0.15.1\n- Python version: 3.8.16\n- PyTorch version (GPU?): 1.13.1+cu116 (True)\n- Huggingface_hub version: 0.13.2\n- Transformers version: 4.27.2\n- Accelerate version: 0.18.0\n- xFormers version: 0.0.16\n- tomesd version: 0.1.2\n```\n\n벤치마킹에는 다음 스크립트를 사용했습니다: [https://gist.github.com/sayakpaul/27aec6bca7eb7b0e0aa4112205850335](https://gist.github.com/sayakpaul/27aec6bca7eb7b0e0aa4112205850335). 결과는 다음과 같습니다:\n\n### A100\n\n| 해상도 | 배치 크기 | Vanilla | ToMe | ToMe + xFormers | ToMe 속도 향상 (%) | ToMe + xFormers 속도 향상 (%) |\n| --- | --- | --- | --- | --- | --- | --- |\n| 512 | 10 | 6.88 | 5.26 | 4.69 | 23.54651163 | 31.83139535 |\n| | | | | | | |\n| 768 | 10 | OOM | 14.71 | 11 | | |\n| | 8 | OOM | 11.56 | 8.84 | | |\n| | 4 | OOM | 5.98 | 4.66 | | |\n| | 2 | 4.99 | 3.24 | 3.1 | 35.07014028 | 37.8757515 |\n| | 1 | 3.29 | 2.24 | 2.03 | 31.91489362 | 38.29787234 |\n| | | | | | | |\n| 1024 | 10 | OOM | OOM | OOM | | |\n| | 8 | OOM | OOM | OOM | | |\n| | 4 | OOM | 12.51 | 9.09 | | |\n| | 2 | OOM | 6.52 | 4.96 | | |\n| | 1 | 6.4 | 3.61 | 2.81 | 43.59375 | 56.09375 |\n\n***결과는 초 단위입니다. 속도 향상은 `Vanilla`과 비교해 계산됩니다.***\n\n### V100\n\n| 해상도 | 배치 크기 | Vanilla | ToMe | ToMe + xFormers | ToMe 속도 향상 (%) | ToMe + xFormers 속도 향상 (%) |\n| --- | --- | --- | --- | --- | --- | --- |\n| 512 | 10 | OOM | 10.03 | 9.29 | | |\n| | 8 | OOM | 8.05 | 7.47 | | |\n| | 4 | 5.7 | 4.3 | 3.98 | 24.56140351 | 30.1754386 |\n| | 2 | 3.14 | 2.43 | 2.27 | 22.61146497 | 27.70700637 |\n| | 1 | 1.88 | 1.57 | 1.57 | 16.4893617 | 16.4893617 |\n| | | | | | | |\n| 768 | 10 | OOM | OOM | 23.67 | | |\n| | 8 | OOM | OOM | 18.81 | | |\n| | 4 | OOM | 11.81 | 9.7 | | |\n| | 2 | OOM | 6.27 | 5.2 | | |\n| | 1 | 5.43 | 3.38 | 2.82 | 37.75322284 | 48.06629834 |\n| | | | | | | |\n| 1024 | 10 | OOM | OOM | OOM | | |\n| | 8 | OOM | OOM | OOM | | |\n| | 4 | OOM | OOM | 19.35 | | |\n| | 2 | OOM | 13 | 10.78 | | |\n| | 1 | OOM | 6.66 | 5.54 | | |\n\n위의 표에서 볼 수 있듯이, 이미지 해상도가 높을수록 `tomesd`를 사용한 속도 향상이 더욱 두드러집니다. 또한 `tomesd`를 사용하면 1024x1024와 같은 더 높은 해상도에서 파이프라인을 실행할 수 있다는 점도 흥미롭습니다. \n\n[`torch.compile()`](https://huggingface.co/docs/diffusers/optimization/torch2.0)을 사용하면 추론 속도를 더욱 높일 수 있습니다. \n\n## 품질\n\nAs reported in [the paper](https://arxiv.org/abs/2303.17604), ToMe can preserve the quality of the generated images to a great extent while speeding up inference. By increasing the `ratio`, it is possible to further speed up inference, but that might come at the cost of a deterioration in the image quality. \n\nTo test the quality of the generated samples using our setup, we sampled a few prompts from the “Parti Prompts” (introduced in [Parti](https://parti.research.google/)) and performed inference with the [`StableDiffusionPipeline`] in the following settings:\n\n[논문](https://arxiv.org/abs/2303.17604)에 보고된 바와 같이, ToMe는 생성된 이미지의 품질을 상당 부분 보존하면서 추론 속도를 높일 수 있습니다. `ratio`을 높이면 추론 속도를 더 높일 수 있지만, 이미지 품질이 저하될 수 있습니다. \n\n해당 설정을 사용하여 생성된 샘플의 품질을 테스트하기 위해, \"Parti 프롬프트\"([Parti](https://parti.research.google/)에서 소개)에서 몇 가지 프롬프트를 샘플링하고 다음 설정에서 [`StableDiffusionPipeline`]을 사용하여 추론을 수행했습니다:\n\n- Vanilla [`StableDiffusionPipeline`]\n- [`StableDiffusionPipeline`] + ToMe\n- [`StableDiffusionPipeline`] + ToMe + xformers\n\n생성된 샘플의 품질이 크게 저하되는 것을 발견하지 못했습니다. 다음은 샘플입니다: \n\n![tome-samples](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/tome/tome_samples.png)\n\n생성된 샘플은 [여기](https://wandb.ai/sayakpaul/tomesd-results/runs/23j4bj3i?workspace=)에서 확인할 수 있습니다. 이 실험을 수행하기 위해 [이 스크립트](https://gist.github.com/sayakpaul/8cac98d7f22399085a060992f411ecbd)를 사용했습니다." }, { "path": "diffusers/docs/source/ko/optimization/torch2.0.md", "content": "\n\n# Diffusers에서의 PyTorch 2.0 가속화 지원\n\n`0.13.0` 버전부터 Diffusers는 [PyTorch 2.0](https://pytorch.org/get-started/pytorch-2.0/)에서의 최신 최적화를 지원합니다. 이는 다음을 포함됩니다.\n1. momory-efficient attention을 사용한 가속화된 트랜스포머 지원 - `xformers`같은 추가적인 dependencies 필요 없음\n2. 추가 성능 향상을 위한 개별 모델에 대한 컴파일 기능 [torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) 지원\n\n\n## 설치\n가속화된 어텐션 구현과 및 `torch.compile()`을 사용하기 위해, pip에서 최신 버전의 PyTorch 2.0을 설치되어 있고 diffusers 0.13.0. 버전 이상인지 확인하세요. 아래 설명된 바와 같이, PyTorch 2.0이 활성화되어 있을 때 diffusers는 최적화된 어텐션 프로세서([`AttnProcessor2_0`](https://github.com/huggingface/diffusers/blob/1a5797c6d4491a879ea5285c4efc377664e0332d/src/diffusers/models/attention_processor.py#L798))를 사용합니다.\n\n```bash\npip install --upgrade torch diffusers\n```\n\n## 가속화된 트랜스포머와 `torch.compile` 사용하기.\n\n\n1. **가속화된 트랜스포머 구현**\n\n PyTorch 2.0에는 [`torch.nn.functional.scaled_dot_product_attention`](https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention) 함수를 통해 최적화된 memory-efficient attention의 구현이 포함되어 있습니다. 이는 입력 및 GPU 유형에 따라 여러 최적화를 자동으로 활성화합니다. 이는 [xFormers](https://github.com/facebookresearch/xformers)의 `memory_efficient_attention`과 유사하지만 기본적으로 PyTorch에 내장되어 있습니다.\n \n 이러한 최적화는 PyTorch 2.0이 설치되어 있고 `torch.nn.functional.scaled_dot_product_attention`을 사용할 수 있는 경우 Diffusers에서 기본적으로 활성화됩니다. 이를 사용하려면 `torch 2.0`을 설치하고 파이프라인을 사용하기만 하면 됩니다. 예를 들어:\n\n ```Python\n import torch\n from diffusers import DiffusionPipeline\n\n pipe = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16)\n pipe = pipe.to(\"cuda\")\n\n prompt = \"a photo of an astronaut riding a horse on mars\"\n image = pipe(prompt).images[0]\n ```\n\n 이를 명시적으로 활성화하려면(필수는 아님) 아래와 같이 수행할 수 있습니다.\n\n ```diff\n import torch\n from diffusers import DiffusionPipeline\n + from diffusers.models.attention_processor import AttnProcessor2_0\n\n pipe = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16).to(\"cuda\")\n + pipe.unet.set_attn_processor(AttnProcessor2_0())\n\n prompt = \"a photo of an astronaut riding a horse on mars\"\n image = pipe(prompt).images[0]\n ```\n\n 이 실행 과정은 `xFormers`만큼 빠르고 메모리적으로 효율적이어야 합니다. 자세한 내용은 [벤치마크](#benchmark)에서 확인하세요.\n\n 파이프라인을 보다 deterministic으로 만들거나 파인 튜닝된 모델을 [Core ML](https://huggingface.co/docs/diffusers/v0.16.0/en/optimization/coreml#how-to-run-stable-diffusion-with-core-ml)과 같은 다른 형식으로 변환해야 하는 경우 바닐라 어텐션 프로세서 ([`AttnProcessor`](https://github.com/huggingface/diffusers/blob/1a5797c6d4491a879ea5285c4efc377664e0332d/src/diffusers/models/attention_processor.py#L402))로 되돌릴 수 있습니다. 일반 어텐션 프로세서를 사용하려면 [`~diffusers.UNet2DConditionModel.set_default_attn_processor`] 함수를 사용할 수 있습니다:\n\n ```Python\n import torch\n from diffusers import DiffusionPipeline\n from diffusers.models.attention_processor import AttnProcessor\n\n pipe = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16).to(\"cuda\")\n pipe.unet.set_default_attn_processor()\n\n prompt = \"a photo of an astronaut riding a horse on mars\"\n image = pipe(prompt).images[0]\n ```\n\n2. **torch.compile**\n\n 추가적인 속도 향상을 위해 새로운 `torch.compile` 기능을 사용할 수 있습니다. 파이프라인의 UNet은 일반적으로 계산 비용이 가장 크기 때문에 나머지 하위 모델(텍스트 인코더와 VAE)은 그대로 두고 `unet`을 `torch.compile`로 래핑합니다. 자세한 내용과 다른 옵션은 [torch 컴파일 문서](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html)를 참조하세요.\n\n ```python\n pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n images = pipe(prompt, num_inference_steps=steps, num_images_per_prompt=batch_size).images\n ```\n\n GPU 유형에 따라 `compile()`은 가속화된 트랜스포머 최적화를 통해 **5% - 300%**의 _추가 성능 향상_을 얻을 수 있습니다. 그러나 컴파일은 Ampere(A100, 3090), Ada(4090) 및 Hopper(H100)와 같은 최신 GPU 아키텍처에서 더 많은 성능 향상을 가져올 수 있음을 참고하세요.\n \n 컴파일은 완료하는 데 약간의 시간이 걸리므로, 파이프라인을 한 번 준비한 다음 동일한 유형의 추론 작업을 여러 번 수행해야 하는 상황에 가장 적합합니다. 다른 이미지 크기에서 컴파일된 파이프라인을 호출하면 시간적 비용이 많이 들 수 있는 컴파일 작업이 다시 트리거됩니다.\n\n\n## 벤치마크\n\nPyTorch 2.0의 효율적인 어텐션 구현과 `torch.compile`을 사용하여 가장 많이 사용되는 5개의 파이프라인에 대해 다양한 GPU와 배치 크기에 걸쳐 포괄적인 벤치마크를 수행했습니다. 여기서는 [`torch.compile()`이 최적으로 활용되도록 하는](https://github.com/huggingface/diffusers/pull/3313) `diffusers 0.17.0.dev0`을 사용했습니다.\n\n### 벤치마킹 코드\n\n#### Stable Diffusion text-to-image \n\n```python \nfrom diffusers import DiffusionPipeline\nimport torch\n\npath = \"runwayml/stable-diffusion-v1-5\"\n\nrun_compile = True # Set True / False\n\npipe = DiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)\npipe = pipe.to(\"cuda\")\npipe.unet.to(memory_format=torch.channels_last)\n\nif run_compile:\n print(\"Run torch compile\")\n pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n\nprompt = \"ghibli style, a fantasy landscape with castles\"\n\nfor _ in range(3):\n images = pipe(prompt=prompt).images\n```\n\n#### Stable Diffusion image-to-image \n\n```python \nfrom diffusers import StableDiffusionImg2ImgPipeline\nimport requests\nimport torch\nfrom PIL import Image\nfrom io import BytesIO\n\nurl = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n\nresponse = requests.get(url)\ninit_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\ninit_image = init_image.resize((512, 512))\n\npath = \"runwayml/stable-diffusion-v1-5\"\n\nrun_compile = True # Set True / False\n\npipe = StableDiffusionImg2ImgPipeline.from_pretrained(path, torch_dtype=torch.float16)\npipe = pipe.to(\"cuda\")\npipe.unet.to(memory_format=torch.channels_last)\n\nif run_compile:\n print(\"Run torch compile\")\n pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n\nprompt = \"ghibli style, a fantasy landscape with castles\"\n\nfor _ in range(3):\n image = pipe(prompt=prompt, image=init_image).images[0]\n```\n\n#### Stable Diffusion - inpainting\n\n```python \nfrom diffusers import StableDiffusionInpaintPipeline\nimport requests\nimport torch\nfrom PIL import Image\nfrom io import BytesIO\n\nurl = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n\ndef download_image(url):\n response = requests.get(url)\n return Image.open(BytesIO(response.content)).convert(\"RGB\")\n\n\nimg_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\nmask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\n\ninit_image = download_image(img_url).resize((512, 512))\nmask_image = download_image(mask_url).resize((512, 512))\n\npath = \"runwayml/stable-diffusion-inpainting\"\n\nrun_compile = True # Set True / False\n\npipe = StableDiffusionInpaintPipeline.from_pretrained(path, torch_dtype=torch.float16)\npipe = pipe.to(\"cuda\")\npipe.unet.to(memory_format=torch.channels_last)\n\nif run_compile:\n print(\"Run torch compile\")\n pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n\nprompt = \"ghibli style, a fantasy landscape with castles\"\n\nfor _ in range(3):\n image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]\n```\n\n#### ControlNet \n\n```python \nfrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel\nimport requests\nimport torch\nfrom PIL import Image\nfrom io import BytesIO\n\nurl = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n\nresponse = requests.get(url)\ninit_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\ninit_image = init_image.resize((512, 512))\n\npath = \"runwayml/stable-diffusion-v1-5\"\n\nrun_compile = True # Set True / False\ncontrolnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\", torch_dtype=torch.float16)\npipe = StableDiffusionControlNetPipeline.from_pretrained(\n path, controlnet=controlnet, torch_dtype=torch.float16\n)\n\npipe = pipe.to(\"cuda\")\npipe.unet.to(memory_format=torch.channels_last)\npipe.controlnet.to(memory_format=torch.channels_last)\n\nif run_compile:\n print(\"Run torch compile\")\n pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n pipe.controlnet = torch.compile(pipe.controlnet, mode=\"reduce-overhead\", fullgraph=True)\n\nprompt = \"ghibli style, a fantasy landscape with castles\"\n\nfor _ in range(3):\n image = pipe(prompt=prompt, image=init_image).images[0]\n```\n\n#### IF text-to-image + upscaling\n\n```python \nfrom diffusers import DiffusionPipeline\nimport torch\n\nrun_compile = True # Set True / False\n\npipe = DiffusionPipeline.from_pretrained(\"DeepFloyd/IF-I-M-v1.0\", variant=\"fp16\", text_encoder=None, torch_dtype=torch.float16)\npipe.to(\"cuda\")\npipe_2 = DiffusionPipeline.from_pretrained(\"DeepFloyd/IF-II-M-v1.0\", variant=\"fp16\", text_encoder=None, torch_dtype=torch.float16)\npipe_2.to(\"cuda\")\npipe_3 = DiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-x4-upscaler\", torch_dtype=torch.float16)\npipe_3.to(\"cuda\")\n\n\npipe.unet.to(memory_format=torch.channels_last)\npipe_2.unet.to(memory_format=torch.channels_last)\npipe_3.unet.to(memory_format=torch.channels_last)\n\nif run_compile:\n pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n pipe_2.unet = torch.compile(pipe_2.unet, mode=\"reduce-overhead\", fullgraph=True)\n pipe_3.unet = torch.compile(pipe_3.unet, mode=\"reduce-overhead\", fullgraph=True)\n\nprompt = \"the blue hulk\"\n\nprompt_embeds = torch.randn((1, 2, 4096), dtype=torch.float16)\nneg_prompt_embeds = torch.randn((1, 2, 4096), dtype=torch.float16)\n\nfor _ in range(3):\n image = pipe(prompt_embeds=prompt_embeds, negative_prompt_embeds=neg_prompt_embeds, output_type=\"pt\").images\n image_2 = pipe_2(image=image, prompt_embeds=prompt_embeds, negative_prompt_embeds=neg_prompt_embeds, output_type=\"pt\").images\n image_3 = pipe_3(prompt=prompt, image=image, noise_level=100).images\n```\n\nPyTorch 2.0 및 `torch.compile()`로 얻을 수 있는 가능한 속도 향상에 대해, [Stable Diffusion text-to-image pipeline](StableDiffusionPipeline)에 대한 상대적인 속도 향상을 보여주는 차트를 5개의 서로 다른 GPU 제품군(배치 크기 4)에 대해 나타냅니다:\n\n![t2i_speedup](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/pt2_benchmarks/t2i_speedup.png)\n\nTo give you an even better idea of how this speed-up holds for the other pipelines presented above, consider the following \nplot that shows the benchmarking numbers from an A100 across three different batch sizes\n(with PyTorch 2.0 nightly and `torch.compile()`):\n이 속도 향상이 위에 제시된 다른 파이프라인에 대해서도 어떻게 유지되는지 더 잘 이해하기 위해, 세 가지의 다른 배치 크기에 걸쳐 A100의 벤치마킹(PyTorch 2.0 nightly 및 `torch.compile() 사용) 수치를 보여주는 차트를 보입니다:\n\n![a100_numbers](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/pt2_benchmarks/a100_numbers.png)\n\n_(위 차트의 벤치마크 메트릭은 **초당 iteration 수(iterations/second)**입니다)_\n\n그러나 투명성을 위해 모든 벤치마킹 수치를 공개합니다!\n\n다음 표들에서는, **_초당 처리되는 iteration_** 수 측면에서의 결과를 보여줍니다.\n\n### A100 (batch size: 1)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 21.66 | 23.13 | 44.03 | 49.74 |\n| SD - img2img | 21.81 | 22.40 | 43.92 | 46.32 |\n| SD - inpaint | 22.24 | 23.23 | 43.76 | 49.25 |\n| SD - controlnet | 15.02 | 15.82 | 32.13 | 36.08 |\n| IF | 20.21 /
13.84 /
24.00 | 20.12 /
13.70 /
24.03 | ❌ | 97.34 /
27.23 /
111.66 |\n\n### A100 (batch size: 4)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 11.6 | 13.12 | 14.62 | 17.27 |\n| SD - img2img | 11.47 | 13.06 | 14.66 | 17.25 |\n| SD - inpaint | 11.67 | 13.31 | 14.88 | 17.48 |\n| SD - controlnet | 8.28 | 9.38 | 10.51 | 12.41 |\n| IF | 25.02 | 18.04 | ❌ | 48.47 |\n\n### A100 (batch size: 16)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 3.04 | 3.6 | 3.83 | 4.68 |\n| SD - img2img | 2.98 | 3.58 | 3.83 | 4.67 |\n| SD - inpaint | 3.04 | 3.66 | 3.9 | 4.76 |\n| SD - controlnet | 2.15 | 2.58 | 2.74 | 3.35 |\n| IF | 8.78 | 9.82 | ❌ | 16.77 |\n\n### V100 (batch size: 1)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 18.99 | 19.14 | 20.95 | 22.17 |\n| SD - img2img | 18.56 | 19.18 | 20.95 | 22.11 |\n| SD - inpaint | 19.14 | 19.06 | 21.08 | 22.20 |\n| SD - controlnet | 13.48 | 13.93 | 15.18 | 15.88 |\n| IF | 20.01 /
9.08 /
23.34 | 19.79 /
8.98 /
24.10 | ❌ | 55.75 /
11.57 /
57.67 |\n\n### V100 (batch size: 4)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 5.96 | 5.89 | 6.83 | 6.86 |\n| SD - img2img | 5.90 | 5.91 | 6.81 | 6.82 |\n| SD - inpaint | 5.99 | 6.03 | 6.93 | 6.95 |\n| SD - controlnet | 4.26 | 4.29 | 4.92 | 4.93 |\n| IF | 15.41 | 14.76 | ❌ | 22.95 |\n\n### V100 (batch size: 16)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 1.66 | 1.66 | 1.92 | 1.90 |\n| SD - img2img | 1.65 | 1.65 | 1.91 | 1.89 |\n| SD - inpaint | 1.69 | 1.69 | 1.95 | 1.93 |\n| SD - controlnet | 1.19 | 1.19 | OOM after warmup | 1.36 |\n| IF | 5.43 | 5.29 | ❌ | 7.06 |\n\n### T4 (batch size: 1)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 6.9 | 6.95 | 7.3 | 7.56 |\n| SD - img2img | 6.84 | 6.99 | 7.04 | 7.55 |\n| SD - inpaint | 6.91 | 6.7 | 7.01 | 7.37 |\n| SD - controlnet | 4.89 | 4.86 | 5.35 | 5.48 |\n| IF | 17.42 /
2.47 /
18.52 | 16.96 /
2.45 /
18.69 | ❌ | 24.63 /
2.47 /
23.39 |\n\n### T4 (batch size: 4)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 1.79 | 1.79 | 2.03 | 1.99 |\n| SD - img2img | 1.77 | 1.77 | 2.05 | 2.04 |\n| SD - inpaint | 1.81 | 1.82 | 2.09 | 2.09 |\n| SD - controlnet | 1.34 | 1.27 | 1.47 | 1.46 |\n| IF | 5.79 | 5.61 | ❌ | 7.39 |\n\n### T4 (batch size: 16)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 2.34s | 2.30s | OOM after 2nd iteration | 1.99s |\n| SD - img2img | 2.35s | 2.31s | OOM after warmup | 2.00s |\n| SD - inpaint | 2.30s | 2.26s | OOM after 2nd iteration | 1.95s |\n| SD - controlnet | OOM after 2nd iteration | OOM after 2nd iteration | OOM after warmup | OOM after warmup |\n| IF * | 1.44 | 1.44 | ❌ | 1.94 |\n\n### RTX 3090 (batch size: 1)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 22.56 | 22.84 | 23.84 | 25.69 |\n| SD - img2img | 22.25 | 22.61 | 24.1 | 25.83 |\n| SD - inpaint | 22.22 | 22.54 | 24.26 | 26.02 |\n| SD - controlnet | 16.03 | 16.33 | 17.38 | 18.56 |\n| IF | 27.08 /
9.07 /
31.23 | 26.75 /
8.92 /
31.47 | ❌ | 68.08 /
11.16 /
65.29 |\n\n### RTX 3090 (batch size: 4)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 6.46 | 6.35 | 7.29 | 7.3 |\n| SD - img2img | 6.33 | 6.27 | 7.31 | 7.26 |\n| SD - inpaint | 6.47 | 6.4 | 7.44 | 7.39 |\n| SD - controlnet | 4.59 | 4.54 | 5.27 | 5.26 |\n| IF | 16.81 | 16.62 | ❌ | 21.57 |\n\n### RTX 3090 (batch size: 16)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 1.7 | 1.69 | 1.93 | 1.91 |\n| SD - img2img | 1.68 | 1.67 | 1.93 | 1.9 |\n| SD - inpaint | 1.72 | 1.71 | 1.97 | 1.94 |\n| SD - controlnet | 1.23 | 1.22 | 1.4 | 1.38 |\n| IF | 5.01 | 5.00 | ❌ | 6.33 |\n\n### RTX 4090 (batch size: 1)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 40.5 | 41.89 | 44.65 | 49.81 |\n| SD - img2img | 40.39 | 41.95 | 44.46 | 49.8 |\n| SD - inpaint | 40.51 | 41.88 | 44.58 | 49.72 |\n| SD - controlnet | 29.27 | 30.29 | 32.26 | 36.03 |\n| IF | 69.71 /
18.78 /
85.49 | 69.13 /
18.80 /
85.56 | ❌ | 124.60 /
26.37 /
138.79 |\n\n### RTX 4090 (batch size: 4)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 12.62 | 12.84 | 15.32 | 15.59 |\n| SD - img2img | 12.61 | 12,.79 | 15.35 | 15.66 |\n| SD - inpaint | 12.65 | 12.81 | 15.3 | 15.58 |\n| SD - controlnet | 9.1 | 9.25 | 11.03 | 11.22 |\n| IF | 31.88 | 31.14 | ❌ | 43.92 |\n\n### RTX 4090 (batch size: 16)\n\n| **Pipeline** | **torch 2.0 -
no compile** | **torch nightly -
no compile** | **torch 2.0 -
compile** | **torch nightly -
compile** |\n|:---:|:---:|:---:|:---:|:---:|\n| SD - txt2img | 3.17 | 3.2 | 3.84 | 3.85 |\n| SD - img2img | 3.16 | 3.2 | 3.84 | 3.85 |\n| SD - inpaint | 3.17 | 3.2 | 3.85 | 3.85 |\n| SD - controlnet | 2.23 | 2.3 | 2.7 | 2.75 |\n| IF | 9.26 | 9.2 | ❌ | 13.31 |\n\n## 참고\n\n* Follow [this PR](https://github.com/huggingface/diffusers/pull/3313) for more details on the environment used for conducting the benchmarks. \n* For the IF pipeline and batch sizes > 1, we only used a batch size of >1 in the first IF pipeline for text-to-image generation and NOT for upscaling. So, that means the two upscaling pipelines received a batch size of 1. \n\n*Thanks to [Horace He](https://github.com/Chillee) from the PyTorch team for their support in improving our support of `torch.compile()` in Diffusers.*\n\n* 벤치마크 수행에 사용된 환경에 대한 자세한 내용은 [이 PR](https://github.com/huggingface/diffusers/pull/3313)을 참조하세요.\n* IF 파이프라인와 배치 크기 > 1의 경우 첫 번째 IF 파이프라인에서 text-to-image 생성을 위한 배치 크기 > 1만 사용했으며 업스케일링에는 사용하지 않았습니다. 즉, 두 개의 업스케일링 파이프라인이 배치 크기 1임을 의미합니다.\n\n*Diffusers에서 `torch.compile()` 지원을 개선하는 데 도움을 준 PyTorch 팀의 [Horace He](https://github.com/Chillee)에게 감사드립니다.*" }, { "path": "diffusers/docs/source/ko/optimization/xformers.md", "content": "\n\n# xFormers 설치하기\n\n추론과 학습 모두에 [xFormers](https://github.com/facebookresearch/xformers)를 사용하는 것이 좋습니다.\n자체 테스트로 어텐션 블록에서 수행된 최적화가 더 빠른 속도와 적은 메모리 소비를 확인했습니다.\n\n2023년 1월에 출시된 xFormers 버전 '0.0.16'부터 사전 빌드된 pip wheel을 사용하여 쉽게 설치할 수 있습니다:\n\n```bash\npip install xformers\n```\n\n\n\nxFormers PIP 패키지에는 최신 버전의 PyTorch(xFormers 0.0.16에 1.13.1)가 필요합니다. 이전 버전의 PyTorch를 사용해야 하는 경우 [프로젝트 지침](https://github.com/facebookresearch/xformers#installing-xformers)의 소스를 사용해 xFormers를 설치하는 것이 좋습니다.\n\n\n\nxFormers를 설치하면, [여기](fp16#memory-efficient-attention)서 설명한 것처럼 'enable_xformers_memory_efficient_attention()'을 사용하여 추론 속도를 높이고 메모리 소비를 줄일 수 있습니다.\n\n\n\n[이 이슈](https://github.com/huggingface/diffusers/issues/2234#issuecomment-1416931212)에 따르면 xFormers `v0.0.16`에서 GPU를 사용한 학습(파인 튜닝 또는 Dreambooth)을 할 수 없습니다. 해당 문제가 발견되면. 해당 코멘트를 참고해 development 버전을 설치하세요.\n\n\n" }, { "path": "diffusers/docs/source/ko/quicktour.md", "content": "\n[[open-in-colab]]\n\n# 훑어보기\n\nDiffusion 모델은 이미지나 오디오와 같은 관심 샘플들을 생성하기 위해 랜덤 가우시안 노이즈를 단계별로 제거하도록 학습됩니다. 이로 인해 생성 AI에 대한 관심이 매우 높아졌으며, 인터넷에서 diffusion 생성 이미지의 예를 본 적이 있을 것입니다. 🧨 Diffusers는 누구나 diffusion 모델들을 널리 이용할 수 있도록 하기 위한 라이브러리입니다.\n\n개발자든 일반 사용자든 이 훑어보기를 통해 🧨 diffusers를 소개하고 빠르게 생성할 수 있도록 도와드립니다! 알아야 할 라이브러리의 주요 구성 요소는 크게 세 가지입니다:\n\n* [`DiffusionPipeline`]은 추론을 위해 사전 학습된 diffusion 모델에서 샘플을 빠르게 생성하도록 설계된 높은 수준의 엔드투엔드 클래스입니다.\n* Diffusion 시스템 생성을 위한 빌딩 블록으로 사용할 수 있는 널리 사용되는 사전 학습된 [model](./api/models) 아키텍처 및 모듈.\n* 다양한 [schedulers](./api/schedulers/overview) - 학습을 위해 노이즈를 추가하는 방법과 추론 중에 노이즈 제거된 이미지를 생성하는 방법을 제어하는 알고리즘입니다.\n\n훑어보기에서는 추론을 위해 [`DiffusionPipeline`]을 사용하는 방법을 보여준 다음, 모델과 스케줄러를 결합하여 [`DiffusionPipeline`] 내부에서 일어나는 일을 복제하는 방법을 안내합니다.\n\n\n\n훑어보기는 간결한 버전의 🧨 Diffusers 소개로서 [노트북](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) 빠르게 시작할 수 있도록 도와드립니다. 디퓨저의 목표, 디자인 철학, 핵심 API에 대한 추가 세부 정보를 자세히 알아보려면 노트북을 확인하세요!\n\n\n\n시작하기 전에 필요한 라이브러리가 모두 설치되어 있는지 확인하세요:\n\n```py\n# 주석 풀어서 Colab에 필요한 라이브러리 설치하기.\n#!pip install --upgrade diffusers accelerate transformers\n```\n\n- [🤗 Accelerate](https://huggingface.co/docs/accelerate/index)는 추론 및 학습을 위한 모델 로딩 속도를 높여줍니다.\n- [🤗 Transformers](https://huggingface.co/docs/transformers/index)는 [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview)과 같이 가장 많이 사용되는 diffusion 모델을 실행하는 데 필요합니다.\n\n## DiffusionPipeline\n\n[`DiffusionPipeline`] 은 추론을 위해 사전 학습된 diffusion 시스템을 사용하는 가장 쉬운 방법입니다. 모델과 스케줄러를 포함하는 엔드 투 엔드 시스템입니다. 다양한 작업에 [`DiffusionPipeline`]을 바로 사용할 수 있습니다. 아래 표에서 지원되는 몇 가지 작업을 살펴보고, 지원되는 작업의 전체 목록은 [🧨 Diffusers Summary](./api/pipelines/overview#diffusers-summary) 표에서 확인할 수 있습니다.\n\n| **Task** | **Description** | **Pipeline**\n|------------------------------|--------------------------------------------------------------------------------------------------------------|-----------------|\n| Unconditional Image Generation | generate an image from Gaussian noise | [unconditional_image_generation](./using-diffusers/unconditional_image_generation) |\n| Text-Guided Image Generation | generate an image given a text prompt | [conditional_image_generation](./using-diffusers/conditional_image_generation) |\n| Text-Guided Image-to-Image Translation | adapt an image guided by a text prompt | [img2img](./using-diffusers/img2img) |\n| Text-Guided Image-Inpainting | fill the masked part of an image given the image, the mask and a text prompt | [inpaint](./using-diffusers/inpaint) |\n| Text-Guided Depth-to-Image Translation | adapt parts of an image guided by a text prompt while preserving structure via depth estimation | [depth2img](./using-diffusers/depth2img) |\n\n먼저 [`DiffusionPipeline`]의 인스턴스를 생성하고 다운로드할 파이프라인 체크포인트를 지정합니다.\n허깅페이스 허브에 저장된 모든 [checkpoint](https://huggingface.co/models?library=diffusers&sort=downloads)에 대해 [`DiffusionPipeline`]을 사용할 수 있습니다.\n이 훑어보기에서는 text-to-image 생성을 위한 [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) 체크포인트를 로드합니다.\n\n\n\n[Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion) 모델의 경우, 모델을 실행하기 전에 [라이선스](https://huggingface.co/spaces/CompVis/stable-diffusion-license)를 먼저 주의 깊게 읽어주세요. 🧨 Diffusers는 불쾌하거나 유해한 콘텐츠를 방지하기 위해 [`safety_checker`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py)를 구현하고 있지만, 모델의 향상된 이미지 생성 기능으로 인해 여전히 잠재적으로 유해한 콘텐츠가 생성될 수 있습니다.\n\n\n\n[`~DiffusionPipeline.from_pretrained`] 방법으로 모델 로드하기:\n\n```python\n>>> from diffusers import DiffusionPipeline\n\n>>> pipeline = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n```\n\nThe [`DiffusionPipeline`]은 모든 모델링, 토큰화, 스케줄링 컴포넌트를 다운로드하고 캐시합니다. Stable Diffusion Pipeline은 무엇보다도 [`UNet2DConditionModel`]과 [`PNDMScheduler`]로 구성되어 있음을 알 수 있습니다:\n\n```py\n>>> pipeline\nStableDiffusionPipeline {\n \"_class_name\": \"StableDiffusionPipeline\",\n \"_diffusers_version\": \"0.13.1\",\n ...,\n \"scheduler\": [\n \"diffusers\",\n \"PNDMScheduler\"\n ],\n ...,\n \"unet\": [\n \"diffusers\",\n \"UNet2DConditionModel\"\n ],\n \"vae\": [\n \"diffusers\",\n \"AutoencoderKL\"\n ]\n}\n```\n\n이 모델은 약 14억 개의 파라미터로 구성되어 있으므로 GPU에서 파이프라인을 실행할 것을 강력히 권장합니다.\nPyTorch에서와 마찬가지로 제너레이터 객체를 GPU로 이동할 수 있습니다:\n\n```python\n>>> pipeline.to(\"cuda\")\n```\n\n이제 `파이프라인`에 텍스트 프롬프트를 전달하여 이미지를 생성한 다음 노이즈가 제거된 이미지에 액세스할 수 있습니다. 기본적으로 이미지 출력은 [`PIL.Image`](https://pillow.readthedocs.io/en/stable/reference/Image.html?highlight=image#the-image-class) 객체로 감싸집니다.\n\n```python\n>>> image = pipeline(\"An image of a squirrel in Picasso style\").images[0]\n>>> image\n```\n\n
\n \n
\n\n`save`를 호출하여 이미지를 저장합니다:\n\n```python\n>>> image.save(\"image_of_squirrel_painting.png\")\n```\n\n### 로컬 파이프라인\n\n파이프라인을 로컬에서 사용할 수도 있습니다. 유일한 차이점은 가중치를 먼저 다운로드해야 한다는 점입니다:\n\n```bash\n!git lfs install\n!git clone https://huggingface.co/runwayml/stable-diffusion-v1-5\n```\n\n그런 다음 저장된 가중치를 파이프라인에 로드합니다:\n\n```python\n>>> pipeline = DiffusionPipeline.from_pretrained(\"./stable-diffusion-v1-5\")\n```\n\n이제 위 섹션에서와 같이 파이프라인을 실행할 수 있습니다.\n\n### 스케줄러 교체\n\n스케줄러마다 노이즈 제거 속도와 품질이 서로 다릅니다. 자신에게 가장 적합한 스케줄러를 찾는 가장 좋은 방법은 직접 사용해 보는 것입니다! 🧨 Diffusers의 주요 기능 중 하나는 스케줄러 간에 쉽게 전환이 가능하다는 것입니다. 예를 들어, 기본 스케줄러인 [`PNDMScheduler`]를 [`EulerDiscreteScheduler`]로 바꾸려면, [`~diffusers.ConfigMixin.from_config`] 메서드를 사용하여 로드하세요:\n\n```py\n>>> from diffusers import EulerDiscreteScheduler\n\n>>> pipeline = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n>>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)\n```\n\n새 스케줄러로 이미지를 생성해보고 어떤 차이가 있는지 확인해 보세요!\n\n다음 섹션에서는 모델과 스케줄러라는 [`DiffusionPipeline`]을 구성하는 컴포넌트를 자세히 살펴보고 이러한 컴포넌트를 사용하여 고양이 이미지를 생성하는 방법을 배워보겠습니다.\n\n## 모델\n\n대부분의 모델은 노이즈가 있는 샘플을 가져와 각 시간 간격마다 노이즈가 적은 이미지와 입력 이미지 사이의 차이인 *노이즈 잔차*(다른 모델은 이전 샘플을 직접 예측하거나 속도 또는 [`v-prediction`](https://github.com/huggingface/diffusers/blob/5e5ce13e2f89ac45a0066cb3f369462a3cf1d9ef/src/diffusers/schedulers/scheduling_ddim.py#L110)을 예측하는 학습을 합니다)을 예측합니다. 모델을 믹스 앤 매치하여 다른 diffusion 시스템을 만들 수 있습니다.\n\n모델은 [`~ModelMixin.from_pretrained`] 메서드로 시작되며, 이 메서드는 모델 가중치를 로컬에 캐시하여 다음에 모델을 로드할 때 더 빠르게 로드할 수 있습니다. 훑어보기에서는 고양이 이미지에 대해 학습된 체크포인트가 있는 기본적인 unconditional 이미지 생성 모델인 [`UNet2DModel`]을 로드합니다:\n\n```py\n>>> from diffusers import UNet2DModel\n\n>>> repo_id = \"google/ddpm-cat-256\"\n>>> model = UNet2DModel.from_pretrained(repo_id)\n```\n\n모델 매개변수에 액세스하려면 `model.config`를 호출합니다:\n\n```py\n>>> model.config\n```\n\n모델 구성은 🧊 고정된 🧊 딕셔너리로, 모델이 생성된 후에는 해당 매개 변수들을 변경할 수 없습니다. 이는 의도적인 것으로, 처음에 모델 아키텍처를 정의하는 데 사용된 매개변수는 동일하게 유지하면서 다른 매개변수는 추론 중에 조정할 수 있도록 하기 위한 것입니다.\n\n가장 중요한 매개변수들은 다음과 같습니다:\n\n* `sample_size`: 입력 샘플의 높이 및 너비 치수입니다.\n* `in_channels`: 입력 샘플의 입력 채널 수입니다.\n* `down_block_types` 및 `up_block_types`: UNet 아키텍처를 생성하는 데 사용되는 다운 및 업샘플링 블록의 유형.\n* `block_out_channels`: 다운샘플링 블록의 출력 채널 수. 업샘플링 블록의 입력 채널 수에 역순으로 사용되기도 합니다.\n* `layers_per_block`: 각 UNet 블록에 존재하는 ResNet 블록의 수입니다.\n\n추론에 모델을 사용하려면 랜덤 가우시안 노이즈로 이미지 모양을 만듭니다. 모델이 여러 개의 무작위 노이즈를 수신할 수 있으므로 'batch' 축, 입력 채널 수에 해당하는 'channel' 축, 이미지의 높이와 너비를 나타내는 'sample_size' 축이 있어야 합니다:\n\n```py\n>>> import torch\n\n>>> torch.manual_seed(0)\n\n>>> noisy_sample = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)\n>>> noisy_sample.shape\ntorch.Size([1, 3, 256, 256])\n```\n\n추론을 위해 모델에 노이즈가 있는 이미지와 `timestep`을 전달합니다. 'timestep'은 입력 이미지의 노이즈 정도를 나타내며, 시작 부분에 더 많은 노이즈가 있고 끝 부분에 더 적은 노이즈가 있습니다. 이를 통해 모델이 diffusion 과정에서 시작 또는 끝에 더 가까운 위치를 결정할 수 있습니다. `sample` 메서드를 사용하여 모델 출력을 얻습니다:\n\n```py\n>>> with torch.no_grad():\n... noisy_residual = model(sample=noisy_sample, timestep=2).sample\n```\n\n하지만 실제 예를 생성하려면 노이즈 제거 프로세스를 안내할 스케줄러가 필요합니다. 다음 섹션에서는 모델을 스케줄러와 결합하는 방법에 대해 알아봅니다.\n\n## 스케줄러\n\n스케줄러는 모델 출력이 주어졌을 때 노이즈가 많은 샘플에서 노이즈가 적은 샘플로 전환하는 것을 관리합니다 - 이 경우 'noisy_residual'.\n\n\n\n🧨 Diffusers는 Diffusion 시스템을 구축하기 위한 툴박스입니다. [`DiffusionPipeline`]을 사용하면 미리 만들어진 Diffusion 시스템을 편리하게 시작할 수 있지만, 모델과 스케줄러 구성 요소를 개별적으로 선택하여 사용자 지정 Diffusion 시스템을 구축할 수도 있습니다.\n\n\n\n훑어보기의 경우, [`~diffusers.ConfigMixin.from_config`] 메서드를 사용하여 [`DDPMScheduler`]를 인스턴스화합니다:\n\n```py\n>>> from diffusers import DDPMScheduler\n\n>>> scheduler = DDPMScheduler.from_config(repo_id)\n>>> scheduler\nDDPMScheduler {\n \"_class_name\": \"DDPMScheduler\",\n \"_diffusers_version\": \"0.13.1\",\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n \"beta_start\": 0.0001,\n \"clip_sample\": true,\n \"clip_sample_range\": 1.0,\n \"num_train_timesteps\": 1000,\n \"prediction_type\": \"epsilon\",\n \"trained_betas\": null,\n \"variance_type\": \"fixed_small\"\n}\n```\n\n\n\n💡 스케줄러가 구성에서 어떻게 인스턴스화되는지 주목하세요. 모델과 달리 스케줄러에는 학습 가능한 가중치가 없으며 매개변수도 없습니다!\n\n\n\n가장 중요한 매개변수는 다음과 같습니다:\n\n* `num_train_timesteps`: 노이즈 제거 프로세스의 길이, 즉 랜덤 가우스 노이즈를 데이터 샘플로 처리하는 데 필요한 타임스텝 수입니다.\n* `beta_schedule`: 추론 및 학습에 사용할 노이즈 스케줄 유형입니다.\n* `beta_start` 및 `beta_end`: 노이즈 스케줄의 시작 및 종료 노이즈 값입니다.\n\n노이즈가 약간 적은 이미지를 예측하려면 스케줄러의 [`~diffusers.DDPMScheduler.step`] 메서드에 모델 출력, `timestep`, 현재 `sample`을 전달하세요.\n\n```py\n>>> less_noisy_sample = scheduler.step(model_output=noisy_residual, timestep=2, sample=noisy_sample).prev_sample\n>>> less_noisy_sample.shape\n```\n\n`less_noisy_sample`을 다음 `timestep`으로 넘기면 노이즈가 더 줄어듭니다! 이제 이 모든 것을 한데 모아 전체 노이즈 제거 과정을 시각화해 보겠습니다. \n\n먼저 노이즈 제거된 이미지를 후처리하여 `PIL.Image`로 표시하는 함수를 만듭니다:\n\n```py\n>>> import PIL.Image\n>>> import numpy as np\n\n\n>>> def display_sample(sample, i):\n... image_processed = sample.cpu().permute(0, 2, 3, 1)\n... image_processed = (image_processed + 1.0) * 127.5\n... image_processed = image_processed.numpy().astype(np.uint8)\n\n... image_pil = PIL.Image.fromarray(image_processed[0])\n... display(f\"Image at step {i}\")\n... display(image_pil)\n```\n\n노이즈 제거 프로세스의 속도를 높이려면 입력과 모델을 GPU로 옮기세요:\n\n```py\n>>> model.to(\"cuda\")\n>>> noisy_sample = noisy_sample.to(\"cuda\")\n```\n\n이제 노이즈가 적은 샘플의 잔차를 예측하고 스케줄러로 노이즈가 적은 샘플을 계산하는 노이즈 제거 루프를 생성합니다:\n\n```py\n>>> import tqdm\n\n>>> sample = noisy_sample\n\n>>> for i, t in enumerate(tqdm.tqdm(scheduler.timesteps)):\n... # 1. predict noise residual\n... with torch.no_grad():\n... residual = model(sample, t).sample\n\n... # 2. compute less noisy image and set x_t -> x_t-1\n... sample = scheduler.step(residual, t, sample).prev_sample\n\n... # 3. optionally look at image\n... if (i + 1) % 50 == 0:\n... display_sample(sample, i + 1)\n```\n\n가만히 앉아서 고양이가 소음으로만 생성되는 것을 지켜보세요!😻\n\n
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\n\n## 다음 단계\n\n이번 훑어보기에서 🧨 Diffusers로 멋진 이미지를 만들어 보셨기를 바랍니다! 다음 단계로 넘어가세요:\n\n* [training](./tutorials/basic_training) 튜토리얼에서 모델을 학습하거나 파인튜닝하여 나만의 이미지를 생성할 수 있습니다.\n* 다양한 사용 사례는 공식 및 커뮤니티 [학습 또는 파인튜닝 스크립트](https://github.com/huggingface/diffusers/tree/main/examples#-diffusers-examples) 예시를 참조하세요.\n* 스케줄러 로드, 액세스, 변경 및 비교에 대한 자세한 내용은 [다른 스케줄러 사용](./using-diffusers/schedulers) 가이드에서 확인하세요.\n* [Stable Diffusion](./stable_diffusion) 가이드에서 프롬프트 엔지니어링, 속도 및 메모리 최적화, 고품질 이미지 생성을 위한 팁과 요령을 살펴보세요.\n* [GPU에서 파이토치 최적화](./optimization/fp16) 가이드와 [애플 실리콘(M1/M2)에서의 Stable Diffusion](./optimization/mps) 및 [ONNX 런타임](./optimization/onnx) 실행에 대한 추론 가이드를 통해 🧨 Diffuser 속도를 높이는 방법을 더 자세히 알아보세요." }, { "path": "diffusers/docs/source/ko/stable_diffusion.md", "content": "\n \n# 효과적이고 효율적인 Diffusion\n\n[[open-in-colab]]\n\n특정 스타일로 이미지를 생성하거나 원하는 내용을 포함하도록[`DiffusionPipeline`]을 설정하는 것은 까다로울 수 있습니다. 종종 만족스러운 이미지를 얻기까지 [`DiffusionPipeline`]을 여러 번 실행해야 하는 경우가 많습니다. 그러나 무에서 유를 창조하는 것은 특히 추론을 반복해서 실행하는 경우 계산 집약적인 프로세스입니다.\n\n그렇기 때문에 파이프라인에서 *계산*(속도) 및 *메모리*(GPU RAM) 효율성을 극대화하여 추론 주기 사이의 시간을 단축하여 더 빠르게 반복할 수 있도록 하는 것이 중요합니다.\n\n이 튜토리얼에서는 [`DiffusionPipeline`]을 사용하여 더 빠르고 효과적으로 생성하는 방법을 안내합니다.\n\n[`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) 모델을 불러와서 시작합니다:\n\n```python\nfrom diffusers import DiffusionPipeline\n\nmodel_id = \"runwayml/stable-diffusion-v1-5\"\npipeline = DiffusionPipeline.from_pretrained(model_id)\n```\n\n예제 프롬프트는 \"portrait of an old warrior chief\" 이지만, 자유롭게 자신만의 프롬프트를 사용해도 됩니다:\n\n```python\nprompt = \"portrait photo of a old warrior chief\"\n```\n\n## 속도\n\n\n\n💡 GPU에 액세스할 수 없는 경우 다음과 같은 GPU 제공업체에서 무료로 사용할 수 있습니다!. [Colab](https://colab.research.google.com/)\n\n\n\n추론 속도를 높이는 가장 간단한 방법 중 하나는 Pytorch 모듈을 사용할 때와 같은 방식으로 GPU에 파이프라인을 배치하는 것입니다:\n\n```python\npipeline = pipeline.to(\"cuda\")\n```\n\n동일한 이미지를 사용하고 개선할 수 있는지 확인하려면 [`Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html)를 사용하고 [재현성](./using-diffusers/reusing_seeds)에 대한 시드를 설정하세요:\n\n```python\nimport torch\n\ngenerator = torch.Generator(\"cuda\").manual_seed(0)\n```\n\n이제 이미지를 생성할 수 있습니다:\n\n```python\nimage = pipeline(prompt, generator=generator).images[0]\nimage\n```\n\n
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\n\n이 프로세스는 T4 GPU에서 약 30초가 소요되었습니다(할당된 GPU가 T4보다 나은 경우 더 빠를 수 있음). 기본적으로 [`DiffusionPipeline`]은 50개의 추론 단계에 대해 전체 `float32` 정밀도로 추론을 실행합니다. `float16`과 같은 더 낮은 정밀도로 전환하거나 추론 단계를 더 적게 실행하여 속도를 높일 수 있습니다. \n\n`float16`으로 모델을 로드하고 이미지를 생성해 보겠습니다:\n\n\n```python\nimport torch\n\npipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\npipeline = pipeline.to(\"cuda\")\ngenerator = torch.Generator(\"cuda\").manual_seed(0)\nimage = pipeline(prompt, generator=generator).images[0]\nimage\n```\n\n
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\n\n이번에는 이미지를 생성하는 데 약 11초밖에 걸리지 않아 이전보다 3배 가까이 빨라졌습니다!\n\n\n\n💡 파이프라인은 항상 `float16`에서 실행할 것을 강력히 권장하며, 지금까지 출력 품질이 저하되는 경우는 거의 없었습니다.\n\n\n\n또 다른 옵션은 추론 단계의 수를 줄이는 것입니다. 보다 효율적인 스케줄러를 선택하면 출력 품질 저하 없이 단계 수를 줄이는 데 도움이 될 수 있습니다. 현재 모델과 호환되는 스케줄러는 `compatibles` 메서드를 호출하여 [`DiffusionPipeline`]에서 찾을 수 있습니다:\n\n```python\npipeline.scheduler.compatibles\n[\n diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler,\n diffusers.schedulers.scheduling_unipc_multistep.UniPCMultistepScheduler,\n diffusers.schedulers.scheduling_k_dpm_2_discrete.KDPM2DiscreteScheduler,\n diffusers.schedulers.scheduling_deis_multistep.DEISMultistepScheduler,\n diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler,\n diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler,\n diffusers.schedulers.scheduling_ddpm.DDPMScheduler,\n diffusers.schedulers.scheduling_dpmsolver_singlestep.DPMSolverSinglestepScheduler,\n diffusers.schedulers.scheduling_k_dpm_2_ancestral_discrete.KDPM2AncestralDiscreteScheduler,\n diffusers.schedulers.scheduling_heun_discrete.HeunDiscreteScheduler,\n diffusers.schedulers.scheduling_pndm.PNDMScheduler,\n diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler,\n diffusers.schedulers.scheduling_ddim.DDIMScheduler,\n]\n```\n\nStable Diffusion 모델은 일반적으로 약 50개의 추론 단계가 필요한 [`PNDMScheduler`]를 기본으로 사용하지만, [`DPMSolverMultistepScheduler`]와 같이 성능이 더 뛰어난 스케줄러는 약 20개 또는 25개의 추론 단계만 필요로 합니다. 새 스케줄러를 로드하려면 [`ConfigMixin.from_config`] 메서드를 사용합니다:\n\n```python\nfrom diffusers import DPMSolverMultistepScheduler\n\npipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)\n```\n\n`num_inference_steps`를 20으로 설정합니다:\n\n```python\ngenerator = torch.Generator(\"cuda\").manual_seed(0)\nimage = pipeline(prompt, generator=generator, num_inference_steps=20).images[0]\nimage\n```\n\n
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\n\n추론시간을 4초로 단축할 수 있었습니다! ⚡️\n\n## 메모리\n\n파이프라인 성능 향상의 또 다른 핵심은 메모리 사용량을 줄이는 것인데, 초당 생성되는 이미지 수를 최대화하려고 하는 경우가 많기 때문에 간접적으로 더 빠른 속도를 의미합니다. 한 번에 생성할 수 있는 이미지 수를 확인하는 가장 쉬운 방법은 `OutOfMemoryError`(OOM)이 발생할 때까지 다양한 배치 크기를 시도해 보는 것입니다.\n\n프롬프트 목록과 `Generators`에서 이미지 배치를 생성하는 함수를 만듭니다. 좋은 결과를 생성하는 경우 재사용할 수 있도록 각 `Generator`에 시드를 할당해야 합니다.\n\n```python\ndef get_inputs(batch_size=1):\n generator = [torch.Generator(\"cuda\").manual_seed(i) for i in range(batch_size)]\n prompts = batch_size * [prompt]\n num_inference_steps = 20\n\n return {\"prompt\": prompts, \"generator\": generator, \"num_inference_steps\": num_inference_steps}\n```\n\n또한 각 이미지 배치를 보여주는 기능이 필요합니다:\n\n```python\nfrom PIL import Image\n\n\ndef image_grid(imgs, rows=2, cols=2):\n w, h = imgs[0].size\n grid = Image.new(\"RGB\", size=(cols * w, rows * h))\n\n for i, img in enumerate(imgs):\n grid.paste(img, box=(i % cols * w, i // cols * h))\n return grid\n```\n \n`batch_size=4`부터 시작해 얼마나 많은 메모리를 소비했는지 확인합니다:\n\n```python\nimages = pipeline(**get_inputs(batch_size=4)).images\nimage_grid(images)\n```\n\nRAM이 더 많은 GPU가 아니라면 위의 코드에서 `OOM` 오류가 반환되었을 것입니다! 대부분의 메모리는 cross-attention 레이어가 차지합니다. 이 작업을 배치로 실행하는 대신 순차적으로 실행하면 상당한 양의 메모리를 절약할 수 있습니다. 파이프라인을 구성하여 [`~DiffusionPipeline.enable_attention_slicing`] 함수를 사용하기만 하면 됩니다:\n\n\n```python\npipeline.enable_attention_slicing()\n```\n\n이제 `batch_size`를 8로 늘려보세요!\n\n```python\nimages = pipeline(**get_inputs(batch_size=8)).images\nimage_grid(images, rows=2, cols=4)\n```\n\n
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\n\n이전에는 4개의 이미지를 배치로 생성할 수도 없었지만, 이제는 이미지당 약 3.5초 만에 8개의 이미지를 배치로 생성할 수 있습니다! 이는 아마도 품질 저하 없이 T4 GPU에서 가장 빠른 속도일 것입니다.\n\n## 품질\n\n지난 두 섹션에서는 `fp16`을 사용하여 파이프라인의 속도를 최적화하고, 더 성능이 좋은 스케줄러를 사용하여 추론 단계의 수를 줄이고, attention slicing을 활성화하여 메모리 소비를 줄이는 방법을 배웠습니다. 이제 생성된 이미지의 품질을 개선하는 방법에 대해 집중적으로 알아보겠습니다.\n\n\n### 더 나은 체크포인트\n\n가장 확실한 단계는 더 나은 체크포인트를 사용하는 것입니다. Stable Diffusion 모델은 좋은 출발점이며, 공식 출시 이후 몇 가지 개선된 버전도 출시되었습니다. 하지만 최신 버전을 사용한다고 해서 자동으로 더 나은 결과를 얻을 수 있는 것은 아닙니다. 여전히 다양한 체크포인트를 직접 실험해보고, [negative prompts](https://minimaxir.com/2022/11/stable-diffusion-negative-prompt/) 사용 등 약간의 조사를 통해 최상의 결과를 얻어야 합니다.\n\n이 분야가 성장함에 따라 특정 스타일을 연출할 수 있도록 세밀하게 조정된 고품질 체크포인트가 점점 더 많아지고 있습니다. [Hub](https://huggingface.co/models?library=diffusers&sort=downloads)와 [Diffusers Gallery](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery)를 둘러보고 관심 있는 것을 찾아보세요!\n\n\n### 더 나은 파이프라인 구성 요소\n\n현재 파이프라인 구성 요소를 최신 버전으로 교체해 볼 수도 있습니다. Stability AI의 최신 [autodecoder](https://huggingface.co/stabilityai/stable-diffusion-2-1/tree/main/vae)를 파이프라인에 로드하고 몇 가지 이미지를 생성해 보겠습니다:\n\n\n```python\nfrom diffusers import AutoencoderKL\n\nvae = AutoencoderKL.from_pretrained(\"stabilityai/sd-vae-ft-mse\", torch_dtype=torch.float16).to(\"cuda\")\npipeline.vae = vae\nimages = pipeline(**get_inputs(batch_size=8)).images\nimage_grid(images, rows=2, cols=4)\n```\n\n
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\n\n### 더 나은 프롬프트 엔지니어링\n\n이미지를 생성하는 데 사용하는 텍스트 프롬프트는 *prompt engineering*이라고 할 정도로 매우 중요합니다. 프롬프트 엔지니어링 시 고려해야 할 몇 가지 사항은 다음과 같습니다:\n\n- 생성하려는 이미지 또는 유사한 이미지가 인터넷에 어떻게 저장되어 있는가?\n- 내가 원하는 스타일로 모델을 유도하기 위해 어떤 추가 세부 정보를 제공할 수 있는가?\n\n이를 염두에 두고 색상과 더 높은 품질의 디테일을 포함하도록 프롬프트를 개선해 봅시다:\n\n\n```python\nprompt += \", tribal panther make up, blue on red, side profile, looking away, serious eyes\"\nprompt += \" 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta\"\n```\n\n새로운 프롬프트로 이미지 배치를 생성합니다:\n\n```python\nimages = pipeline(**get_inputs(batch_size=8)).images\nimage_grid(images, rows=2, cols=4)\n```\n\n
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\n\n꽤 인상적입니다! `1`의 시드를 가진 `Generator`에 해당하는 두 번째 이미지에 피사체의 나이에 대한 텍스트를 추가하여 조금 더 조정해 보겠습니다:\n\n```python\nprompts = [\n \"portrait photo of the oldest warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta\",\n \"portrait photo of a old warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta\",\n \"portrait photo of a warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta\",\n \"portrait photo of a young warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta\",\n]\n\ngenerator = [torch.Generator(\"cuda\").manual_seed(1) for _ in range(len(prompts))]\nimages = pipeline(prompt=prompts, generator=generator, num_inference_steps=25).images\nimage_grid(images)\n```\n\n
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\n\n## 다음 단계\n\n이 튜토리얼에서는 계산 및 메모리 효율을 높이고 생성된 출력의 품질을 개선하기 위해 [`DiffusionPipeline`]을 최적화하는 방법을 배웠습니다. 파이프라인을 더 빠르게 만드는 데 관심이 있다면 다음 리소스를 살펴보세요:\n\n- [PyTorch 2.0](./optimization/torch2.0) 및 [`torch.compile`](https://pytorch.org/docs/stable/generated/torch.compile.html)이 어떻게 추론 속도를 5~300% 향상시킬 수 있는지 알아보세요. A100 GPU에서는 추론 속도가 최대 50%까지 빨라질 수 있습니다!\n- PyTorch 2를 사용할 수 없는 경우, [xFormers](./optimization/xformers)를 설치하는 것이 좋습니다. 메모리 효율적인 어텐션 메커니즘은 PyTorch 1.13.1과 함께 사용하면 속도가 빨라지고 메모리 소비가 줄어듭니다.\n- 모델 오프로딩과 같은 다른 최적화 기법은 [이 가이드](./optimization/fp16)에서 다루고 있습니다." }, { "path": "diffusers/docs/source/ko/training/adapt_a_model.md", "content": "\n\n# 새로운 작업에 대한 모델을 적용하기\n\n많은 diffusion 시스템은 같은 구성 요소들을 공유하므로 한 작업에 대해 사전학습된 모델을 완전히 다른 작업에 적용할 수 있습니다.\n\n이 인페인팅을 위한 가이드는 사전학습된 [`UNet2DConditionModel`]의 아키텍처를 초기화하고 수정하여 사전학습된 text-to-image 모델을 어떻게 인페인팅에 적용하는지를 알려줄 것입니다.\n\n## UNet2DConditionModel 파라미터 구성\n\n[`UNet2DConditionModel`]은 [input sample](https://huggingface.co/docs/diffusers/v0.16.0/en/api/models#diffusers.UNet2DConditionModel.in_channels)에서 4개의 채널을 기본적으로 허용합니다. 예를 들어, [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5)와 같은 사전학습된 text-to-image 모델을 불러오고 `in_channels`의 수를 확인합니다:\n\n```py\nfrom diffusers import StableDiffusionPipeline\n\npipeline = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\npipeline.unet.config[\"in_channels\"]\n4\n```\n\n인페인팅은 입력 샘플에 9개의 채널이 필요합니다. [`runwayml/stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting)와 같은 사전학습된 인페인팅 모델에서 이 값을 확인할 수 있습니다:\n\n```py\nfrom diffusers import StableDiffusionPipeline\n\npipeline = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-inpainting\")\npipeline.unet.config[\"in_channels\"]\n9\n```\n\n인페인팅에 대한 text-to-image 모델을 적용하기 위해, `in_channels` 수를 4에서 9로 수정해야 할 것입니다.\n\n사전학습된 text-to-image 모델의 가중치와 [`UNet2DConditionModel`]을 초기화하고 `in_channels`를 9로 수정해 주세요. `in_channels`의 수를 수정하면 크기가 달라지기 때문에 크기가 안 맞는 오류를 피하기 위해 `ignore_mismatched_sizes=True` 및 `low_cpu_mem_usage=False`를 설정해야 합니다.\n\n```py\nfrom diffusers import UNet2DConditionModel\n\nmodel_id = \"runwayml/stable-diffusion-v1-5\"\nunet = UNet2DConditionModel.from_pretrained(\n model_id, subfolder=\"unet\", in_channels=9, low_cpu_mem_usage=False, ignore_mismatched_sizes=True\n)\n```\n\nText-to-image 모델로부터 다른 구성 요소의 사전학습된 가중치는 체크포인트로부터 초기화되지만 `unet`의 입력 채널 가중치 (`conv_in.weight`)는 랜덤하게 초기화됩니다. 그렇지 않으면 모델이 노이즈를 리턴하기 때문에 인페인팅의 모델을 파인튜닝 할 때 중요합니다.\n" }, { "path": "diffusers/docs/source/ko/training/controlnet.md", "content": "\n\n# ControlNet\n\n[Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543) (ControlNet)은 Lvmin Zhang과 Maneesh Agrawala에 의해 쓰여졌습니다.\n\n이 예시는 [원본 ControlNet 리포지토리에서 예시 학습하기](https://github.com/lllyasviel/ControlNet/blob/main/docs/train.md)에 기반합니다. ControlNet은 원들을 채우기 위해 [small synthetic dataset](https://huggingface.co/datasets/fusing/fill50k)을 사용해서 학습됩니다.\n\n## 의존성 설치하기\n\n아래의 스크립트를 실행하기 전에, 라이브러리의 학습 의존성을 설치해야 합니다.\n\n\n\n가장 최신 버전의 예시 스크립트를 성공적으로 실행하기 위해서는, 소스에서 설치하고 최신 버전의 설치를 유지하는 것을 강력하게 추천합니다. 우리는 예시 스크립트들을 자주 업데이트하고 예시에 맞춘 특정한 요구사항을 설치합니다.\n\n\n\n위 사항을 만족시키기 위해서, 새로운 가상환경에서 다음 일련의 스텝을 실행하세요:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install -e .\n```\n\n그 다음에는 [예시 폴더](https://github.com/huggingface/diffusers/tree/main/examples/controlnet)으로 이동합니다.\n\n```bash\ncd examples/controlnet\n```\n\n이제 실행하세요:\n\n```bash\npip install -r requirements.txt\n```\n\n[🤗Accelerate](https://github.com/huggingface/accelerate/) 환경을 초기화 합니다:\n\n```bash\naccelerate config\n```\n\n혹은 여러분의 환경이 무엇인지 몰라도 기본적인 🤗Accelerate 구성으로 초기화할 수 있습니다:\n\n```bash\naccelerate config default\n```\n\n혹은 당신의 환경이 노트북 같은 상호작용하는 쉘을 지원하지 않는다면, 아래의 코드로 초기화 할 수 있습니다:\n\n```python\nfrom accelerate.utils import write_basic_config\n\nwrite_basic_config()\n```\n\n## 원을 채우는 데이터셋\n\n원본 데이터셋은 ControlNet [repo](https://huggingface.co/lllyasviel/ControlNet/blob/main/training/fill50k.zip)에 올라와있지만, 우리는 [여기](https://huggingface.co/datasets/fusing/fill50k)에 새롭게 다시 올려서 🤗 Datasets 과 호환가능합니다. 그래서 학습 스크립트 상에서 데이터 불러오기를 다룰 수 있습니다.\n\n우리의 학습 예시는 원래 ControlNet의 학습에 쓰였던 [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5)을 사용합니다. 그렇지만 ControlNet은 대응되는 어느 Stable Diffusion 모델([`CompVis/stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4)) 혹은 [`stabilityai/stable-diffusion-2-1`](https://huggingface.co/stabilityai/stable-diffusion-2-1)의 증가를 위해 학습될 수 있습니다.\n\n자체 데이터셋을 사용하기 위해서는 [학습을 위한 데이터셋 생성하기](create_dataset) 가이드를 확인하세요.\n\n## 학습\n\n이 학습에 사용될 다음 이미지들을 다운로드하세요:\n\n```sh\nwget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png\n\nwget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png\n```\n\n`MODEL_NAME` 환경 변수 (Hub 모델 리포지토리 아이디 혹은 모델 가중치가 있는 디렉토리로 가는 주소)를 명시하고 [`pretrained_model_name_or_path`](https://huggingface.co/docs/diffusers/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path) 인자로 환경변수를 보냅니다.\n\n학습 스크립트는 당신의 리포지토리에 `diffusion_pytorch_model.bin` 파일을 생성하고 저장합니다.\n\n```bash\nexport MODEL_DIR=\"runwayml/stable-diffusion-v1-5\"\nexport OUTPUT_DIR=\"path to save model\"\n\naccelerate launch train_controlnet.py \\\n --pretrained_model_name_or_path=$MODEL_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --dataset_name=fusing/fill50k \\\n --resolution=512 \\\n --learning_rate=1e-5 \\\n --validation_image \"./conditioning_image_1.png\" \"./conditioning_image_2.png\" \\\n --validation_prompt \"red circle with blue background\" \"cyan circle with brown floral background\" \\\n --train_batch_size=4 \\\n --push_to_hub\n```\n\n이 기본적인 설정으로는 ~38GB VRAM이 필요합니다.\n\n기본적으로 학습 스크립트는 결과를 텐서보드에 기록합니다. 가중치(weight)와 편향(bias)을 사용하기 위해 `--report_to wandb` 를 전달합니다.\n\n더 작은 batch(배치) 크기로 gradient accumulation(기울기 누적)을 하면 학습 요구사항을 ~20 GB VRAM으로 줄일 수 있습니다.\n\n```bash\nexport MODEL_DIR=\"runwayml/stable-diffusion-v1-5\"\nexport OUTPUT_DIR=\"path to save model\"\n\naccelerate launch train_controlnet.py \\\n --pretrained_model_name_or_path=$MODEL_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --dataset_name=fusing/fill50k \\\n --resolution=512 \\\n --learning_rate=1e-5 \\\n --validation_image \"./conditioning_image_1.png\" \"./conditioning_image_2.png\" \\\n --validation_prompt \"red circle with blue background\" \"cyan circle with brown floral background\" \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --push_to_hub\n```\n\n## 여러개 GPU로 학습하기\n\n`accelerate` 은 seamless multi-GPU 학습을 고려합니다. `accelerate`과 함께 분산된 학습을 실행하기 위해 [여기](https://huggingface.co/docs/accelerate/basic_tutorials/launch)\n의 설명을 확인하세요. 아래는 예시 명령어입니다:\n\n```bash\nexport MODEL_DIR=\"runwayml/stable-diffusion-v1-5\"\nexport OUTPUT_DIR=\"path to save model\"\n\naccelerate launch --mixed_precision=\"fp16\" --multi_gpu train_controlnet.py \\\n --pretrained_model_name_or_path=$MODEL_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --dataset_name=fusing/fill50k \\\n --resolution=512 \\\n --learning_rate=1e-5 \\\n --validation_image \"./conditioning_image_1.png\" \"./conditioning_image_2.png\" \\\n --validation_prompt \"red circle with blue background\" \"cyan circle with brown floral background\" \\\n --train_batch_size=4 \\\n --mixed_precision=\"fp16\" \\\n --tracker_project_name=\"controlnet-demo\" \\\n --report_to=wandb \\\n --push_to_hub\n```\n\n## 예시 결과\n\n#### 배치 사이즈 8로 300 스텝 이후:\n\n| | | \n|-------------------|:-------------------------:|\n| | 푸른 배경과 빨간 원 | \n![conditioning image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png) | ![푸른 배경과 빨간 원](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/red_circle_with_blue_background_300_steps.png) |\n| | 갈색 꽃 배경과 청록색 원 | \n![conditioning image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png) | ![갈색 꽃 배경과 청록색 원](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/cyan_circle_with_brown_floral_background_300_steps.png) |\n\n#### 배치 사이즈 8로 6000 스텝 이후:\n\n| | | \n|-------------------|:-------------------------:|\n| | 푸른 배경과 빨간 원 | \n![conditioning image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png) | ![푸른 배경과 빨간 원](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/red_circle_with_blue_background_6000_steps.png) |\n| | 갈색 꽃 배경과 청록색 원 | \n![conditioning image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png) | ![갈색 꽃 배경과 청록색 원](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/cyan_circle_with_brown_floral_background_6000_steps.png) |\n\n## 16GB GPU에서 학습하기\n\n16GB GPU에서 학습하기 위해 다음의 최적화를 진행하세요:\n\n- 기울기 체크포인트 저장하기\n- bitsandbyte의 [8-bit optimizer](https://github.com/TimDettmers/bitsandbytes#requirements--installation)가 설치되지 않았다면 링크에 연결된 설명서를 보세요.\n\n이제 학습 스크립트를 시작할 수 있습니다:\n\n```bash\nexport MODEL_DIR=\"runwayml/stable-diffusion-v1-5\"\nexport OUTPUT_DIR=\"path to save model\"\n\naccelerate launch train_controlnet.py \\\n --pretrained_model_name_or_path=$MODEL_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --dataset_name=fusing/fill50k \\\n --resolution=512 \\\n --learning_rate=1e-5 \\\n --validation_image \"./conditioning_image_1.png\" \"./conditioning_image_2.png\" \\\n --validation_prompt \"red circle with blue background\" \"cyan circle with brown floral background\" \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --use_8bit_adam \\\n --push_to_hub\n```\n\n## 12GB GPU에서 학습하기\n\n12GB GPU에서 실행하기 위해 다음의 최적화를 진행하세요:\n\n- 기울기 체크포인트 저장하기\n- bitsandbyte의 8-bit [optimizer](https://github.com/TimDettmers/bitsandbytes#requirements--installation)(가 설치되지 않았다면 링크에 연결된 설명서를 보세요)\n- [xFormers](https://huggingface.co/docs/diffusers/training/optimization/xformers)(가 설치되지 않았다면 링크에 연결된 설명서를 보세요)\n- 기울기를 `None`으로 설정\n\n```bash\nexport MODEL_DIR=\"runwayml/stable-diffusion-v1-5\"\nexport OUTPUT_DIR=\"path to save model\"\n\naccelerate launch train_controlnet.py \\\n --pretrained_model_name_or_path=$MODEL_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --dataset_name=fusing/fill50k \\\n --resolution=512 \\\n --learning_rate=1e-5 \\\n --validation_image \"./conditioning_image_1.png\" \"./conditioning_image_2.png\" \\\n --validation_prompt \"red circle with blue background\" \"cyan circle with brown floral background\" \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --use_8bit_adam \\\n --enable_xformers_memory_efficient_attention \\\n --set_grads_to_none \\\n --push_to_hub\n```\n\n`pip install xformers`으로 `xformers`을 확실히 설치하고 `enable_xformers_memory_efficient_attention`을 사용하세요.\n\n## 8GB GPU에서 학습하기\n\n우리는 ControlNet을 지원하기 위한 DeepSpeed를 철저하게 테스트하지 않았습니다. 환경설정이 메모리를 저장할 때,\n그 환경이 성공적으로 학습했는지를 확정하지 않았습니다. 성공한 학습 실행을 위해 설정을 변경해야 할 가능성이 높습니다.\n\n8GB GPU에서 실행하기 위해 다음의 최적화를 진행하세요:\n\n- 기울기 체크포인트 저장하기\n- bitsandbyte의 8-bit [optimizer](https://github.com/TimDettmers/bitsandbytes#requirements--installation)(가 설치되지 않았다면 링크에 연결된 설명서를 보세요)\n- [xFormers](https://huggingface.co/docs/diffusers/training/optimization/xformers)(가 설치되지 않았다면 링크에 연결된 설명서를 보세요)\n- 기울기를 `None`으로 설정\n- DeepSpeed stage 2 변수와 optimizer 없에기\n- fp16 혼합 정밀도(precision)\n\n[DeepSpeed](https://www.deepspeed.ai/)는 CPU 또는 NVME로 텐서를 VRAM에서 오프로드할 수 있습니다.\n이를 위해서 훨씬 더 많은 RAM(약 25 GB)가 필요합니다.\n\nDeepSpeed stage 2를 활성화하기 위해서 `accelerate config`로 환경을 구성해야합니다.\n\n구성(configuration) 파일은 이런 모습이어야 합니다:\n\n```yaml\ncompute_environment: LOCAL_MACHINE\ndeepspeed_config:\n gradient_accumulation_steps: 4\n offload_optimizer_device: cpu\n offload_param_device: cpu\n zero3_init_flag: false\n zero_stage: 2\ndistributed_type: DEEPSPEED\n```\n\n<팁>\n\n[문서](https://huggingface.co/docs/accelerate/usage_guides/deepspeed)를 더 많은 DeepSpeed 설정 옵션을 위해 보세요.\n\n<팁>\n\n기본 Adam optimizer를 DeepSpeed'의 Adam\n`deepspeed.ops.adam.DeepSpeedCPUAdam` 으로 바꾸면 상당한 속도 향상을 이룰수 있지만,\nPytorch와 같은 버전의 CUDA toolchain이 필요합니다. 8-비트 optimizer는 현재 DeepSpeed와\n호환되지 않는 것 같습니다.\n\n```bash\nexport MODEL_DIR=\"runwayml/stable-diffusion-v1-5\"\nexport OUTPUT_DIR=\"path to save model\"\n\naccelerate launch train_controlnet.py \\\n --pretrained_model_name_or_path=$MODEL_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --dataset_name=fusing/fill50k \\\n --resolution=512 \\\n --validation_image \"./conditioning_image_1.png\" \"./conditioning_image_2.png\" \\\n --validation_prompt \"red circle with blue background\" \"cyan circle with brown floral background\" \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --enable_xformers_memory_efficient_attention \\\n --set_grads_to_none \\\n --mixed_precision fp16 \\\n --push_to_hub\n```\n\n## 추론\n\n학습된 모델은 [`StableDiffusionControlNetPipeline`]과 함께 실행될 수 있습니다.\n`base_model_path`와 `controlnet_path` 에 값을 지정하세요 `--pretrained_model_name_or_path` 와\n`--output_dir` 는 학습 스크립트에 개별적으로 지정됩니다.\n\n```py\nfrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\nfrom diffusers.utils import load_image\nimport torch\n\nbase_model_path = \"path to model\"\ncontrolnet_path = \"path to controlnet\"\n\ncontrolnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)\npipe = StableDiffusionControlNetPipeline.from_pretrained(\n base_model_path, controlnet=controlnet, torch_dtype=torch.float16\n)\n\n# 더 빠른 스케줄러와 메모리 최적화로 diffusion 프로세스 속도 올리기\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n# xformers가 설치되지 않으면 아래 줄을 삭제하기\npipe.enable_xformers_memory_efficient_attention()\n\npipe.enable_model_cpu_offload()\n\ncontrol_image = load_image(\"./conditioning_image_1.png\")\nprompt = \"pale golden rod circle with old lace background\"\n\n# 이미지 생성하기\ngenerator = torch.manual_seed(0)\nimage = pipe(prompt, num_inference_steps=20, generator=generator, image=control_image).images[0]\n\nimage.save(\"./output.png\")\n```\n" }, { "path": "diffusers/docs/source/ko/training/create_dataset.md", "content": "# 학습을 위한 데이터셋 만들기\n\n[Hub](https://huggingface.co/datasets?task_categories=task_categories:text-to-image&sort=downloads) 에는 모델 교육을 위한 많은 데이터셋이 있지만,\n관심이 있거나 사용하고 싶은 데이터셋을 찾을 수 없는 경우 🤗 [Datasets](hf.co/docs/datasets) 라이브러리를 사용하여 데이터셋을 만들 수 있습니다.\n데이터셋 구조는 모델을 학습하려는 작업에 따라 달라집니다.\n가장 기본적인 데이터셋 구조는 unconditional 이미지 생성과 같은 작업을 위한 이미지 디렉토리입니다.\n또 다른 데이터셋 구조는 이미지 디렉토리와 text-to-image 생성과 같은 작업에 해당하는 텍스트 캡션이 포함된 텍스트 파일일 수 있습니다.\n\n이 가이드에는 파인 튜닝할 데이터셋을 만드는 두 가지 방법을 소개합니다:\n\n- 이미지 폴더를 `--train_data_dir` 인수에 제공합니다.\n- 데이터셋을 Hub에 업로드하고 데이터셋 리포지토리 id를 `--dataset_name` 인수에 전달합니다.\n\n\n\n💡 학습에 사용할 이미지 데이터셋을 만드는 방법에 대한 자세한 내용은 [이미지 데이터셋 만들기](https://huggingface.co/docs/datasets/image_dataset) 가이드를 참고하세요.\n\n\n\n## 폴더 형태로 데이터셋 구축하기\n\nUnconditional 생성을 위해 이미지 폴더로 자신의 데이터셋을 구축할 수 있습니다.\n학습 스크립트는 🤗 Datasets의 [ImageFolder](https://huggingface.co/docs/datasets/en/image_dataset#imagefolder) 빌더를 사용하여\n자동으로 폴더에서 데이터셋을 구축합니다. 디렉토리 구조는 다음과 같아야 합니다 :\n\n```bash\ndata_dir/xxx.png\ndata_dir/xxy.png\ndata_dir/[...]/xxz.png\n```\n\n데이터셋 디렉터리의 경로를 `--train_data_dir` 인수로 전달한 다음 학습을 시작할 수 있습니다:\n\n```bash\naccelerate launch train_unconditional.py \\\n # argument로 폴더 지정하기 \\\n --train_data_dir \\\n \n```\n\n## Hub에 데이터 올리기\n\n\n\n💡 데이터셋을 만들고 Hub에 업로드하는 것에 대한 자세한 내용은 [🤗 Datasets을 사용한 이미지 검색](https://huggingface.co/blog/image-search-datasets) 게시물을 참고하세요.\n\n\n\nPIL 인코딩된 이미지가 포함된 `이미지` 열을 생성하는 [이미지 폴더](https://huggingface.co/docs/datasets/image_load#imagefolder) 기능을 사용하여 데이터셋 생성을 시작합니다.\n\n`data_dir` 또는 `data_files` 매개 변수를 사용하여 데이터셋의 위치를 지정할 수 있습니다.\n`data_files` 매개변수는 특정 파일을 `train` 이나 `test` 로 분리한 데이터셋에 매핑하는 것을 지원합니다:\n\n```python\nfrom datasets import load_dataset\n\n# 예시 1: 로컬 폴더\ndataset = load_dataset(\"imagefolder\", data_dir=\"path_to_your_folder\")\n\n# 예시 2: 로컬 파일 (지원 포맷 : tar, gzip, zip, xz, rar, zstd)\ndataset = load_dataset(\"imagefolder\", data_files=\"path_to_zip_file\")\n\n# 예시 3: 원격 파일 (지원 포맷 : tar, gzip, zip, xz, rar, zstd)\ndataset = load_dataset(\n \"imagefolder\",\n data_files=\"https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip\",\n)\n\n# 예시 4: 여러개로 분할\ndataset = load_dataset(\n \"imagefolder\", data_files={\"train\": [\"path/to/file1\", \"path/to/file2\"], \"test\": [\"path/to/file3\", \"path/to/file4\"]}\n)\n```\n\n[push_to_hub(https://huggingface.co/docs/datasets/v2.13.1/en/package_reference/main_classes#datasets.Dataset.push_to_hub) 을 사용해서 Hub에 데이터셋을 업로드 합니다:\n\n```python\n# 터미널에서 huggingface-cli login 커맨드를 이미 실행했다고 가정합니다\ndataset.push_to_hub(\"name_of_your_dataset\")\n\n# 개인 repo로 push 하고 싶다면, `private=True` 을 추가하세요:\ndataset.push_to_hub(\"name_of_your_dataset\", private=True)\n```\n\n이제 데이터셋 이름을 `--dataset_name` 인수에 전달하여 데이터셋을 학습에 사용할 수 있습니다:\n\n```bash\naccelerate launch --mixed_precision=\"fp16\" train_text_to_image.py \\\n --pretrained_model_name_or_path=\"runwayml/stable-diffusion-v1-5\" \\\n --dataset_name=\"name_of_your_dataset\" \\\n \n```\n\n## 다음 단계\n\n데이터셋을 생성했으니 이제 학습 스크립트의 `train_data_dir` (데이터셋이 로컬이면) 혹은 `dataset_name` (Hub에 데이터셋을 올렸으면) 인수에 연결할 수 있습니다.\n\n다음 단계에서는 데이터셋을 사용하여 [unconditional 생성](https://huggingface.co/docs/diffusers/v0.18.2/en/training/unconditional_training) 또는 [텍스트-이미지 생성](https://huggingface.co/docs/diffusers/training/text2image)을 위한 모델을 학습시켜보세요!\n" }, { "path": "diffusers/docs/source/ko/training/custom_diffusion.md", "content": "\n\n# 커스텀 Diffusion 학습 예제 \n\n[커스텀 Diffusion](https://arxiv.org/abs/2212.04488)은 피사체의 이미지 몇 장(4~5장)만 주어지면 Stable Diffusion처럼 text-to-image 모델을 커스터마이징하는 방법입니다.\n'train_custom_diffusion.py' 스크립트는 학습 과정을 구현하고 이를 Stable Diffusion에 맞게 조정하는 방법을 보여줍니다.\n\n이 교육 사례는 [Nupur Kumari](https://nupurkmr9.github.io/)가 제공하였습니다. (Custom Diffusion의 저자 중 한명). \n\n## 로컬에서 PyTorch로 실행하기\n\n### Dependencies 설치하기\n\n스크립트를 실행하기 전에 라이브러리의 학습 dependencies를 설치해야 합니다:\n\n**중요**\n\n예제 스크립트의 최신 버전을 성공적으로 실행하려면 **소스로부터 설치**하는 것을 매우 권장하며, 예제 스크립트를 자주 업데이트하는 만큼 일부 예제별 요구 사항을 설치하고 설치를 최신 상태로 유지하는 것이 좋습니다. 이를 위해 새 가상 환경에서 다음 단계를 실행하세요:\n\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install -e .\n```\n\n[example folder](https://github.com/huggingface/diffusers/tree/main/examples/custom_diffusion)로 cd하여 이동하세요.\n\n```\ncd examples/custom_diffusion\n```\n\n이제 실행\n\n```bash\npip install -r requirements.txt\npip install clip-retrieval \n```\n\n그리고 [🤗Accelerate](https://github.com/huggingface/accelerate/) 환경을 초기화:\n\n```bash\naccelerate config\n```\n\n또는 사용자 환경에 대한 질문에 답하지 않고 기본 가속 구성을 사용하려면 다음과 같이 하세요.\n\n```bash\naccelerate config default\n```\n\n또는 사용 중인 환경이 대화형 셸을 지원하지 않는 경우(예: jupyter notebook)\n\n```python\nfrom accelerate.utils import write_basic_config\n\nwrite_basic_config()\n```\n### 고양이 예제 😺\n\n이제 데이터셋을 가져옵니다. [여기](https://www.cs.cmu.edu/~custom-diffusion/assets/data.zip)에서 데이터셋을 다운로드하고 압축을 풉니다. 직접 데이터셋을 사용하려면 [학습용 데이터셋 생성하기](create_dataset) 가이드를 참고하세요.\n\n또한 'clip-retrieval'을 사용하여 200개의 실제 이미지를 수집하고, regularization으로서 이를 학습 데이터셋의 타겟 이미지와 결합합니다. 이렇게 하면 주어진 타겟 이미지에 대한 과적합을 방지할 수 있습니다. 다음 플래그를 사용하면 `prior_loss_weight=1.`로 `prior_preservation`, `real_prior` regularization을 활성화할 수 있습니다. \n클래스_프롬프트`는 대상 이미지와 동일한 카테고리 이름이어야 합니다. 수집된 실제 이미지에는 `class_prompt`와 유사한 텍스트 캡션이 있습니다. 검색된 이미지는 `class_data_dir`에 저장됩니다. 생성된 이미지를 regularization으로 사용하기 위해 `real_prior`를 비활성화할 수 있습니다. 실제 이미지를 수집하려면 훈련 전에 이 명령을 먼저 사용하십시오. \n\n```bash\npip install clip-retrieval\npython retrieve.py --class_prompt cat --class_data_dir real_reg/samples_cat --num_class_images 200\n```\n\n**___참고: [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 모델을 사용하는 경우 '해상도'를 768로 변경하세요.___**\n\n스크립트는 모델 체크포인트와 `pytorch_custom_diffusion_weights.bin` 파일을 생성하여 저장소에 저장합니다.\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport OUTPUT_DIR=\"path-to-save-model\"\nexport INSTANCE_DIR=\"./data/cat\"\n\naccelerate launch train_custom_diffusion.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --class_data_dir=./real_reg/samples_cat/ \\\n --with_prior_preservation --real_prior --prior_loss_weight=1.0 \\\n --class_prompt=\"cat\" --num_class_images=200 \\\n --instance_prompt=\"photo of a cat\" \\\n --resolution=512 \\\n --train_batch_size=2 \\\n --learning_rate=1e-5 \\\n --lr_warmup_steps=0 \\\n --max_train_steps=250 \\\n --scale_lr --hflip \\\n --modifier_token \"\" \\\n --push_to_hub\n```\n\n**더 낮은 VRAM 요구 사항(GPU당 16GB)으로 더 빠르게 훈련하려면 `--enable_xformers_memory_efficient_attention`을 사용하세요. 설치 방법은 [가이드](https://github.com/facebookresearch/xformers)를 따르세요.**\n\n가중치 및 편향(`wandb`)을 사용하여 실험을 추적하고 중간 결과를 저장하려면(강력히 권장합니다) 다음 단계를 따르세요:\n\n* `wandb` 설치: `pip install wandb`.\n* 로그인 : `wandb login`. \n* 그런 다음 트레이닝을 시작하는 동안 `validation_prompt`를 지정하고 `report_to`를 `wandb`로 설정합니다. 다음과 같은 관련 인수를 구성할 수도 있습니다:\n * `num_validation_images`\n * `validation_steps`\n\n```bash\naccelerate launch train_custom_diffusion.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --class_data_dir=./real_reg/samples_cat/ \\\n --with_prior_preservation --real_prior --prior_loss_weight=1.0 \\\n --class_prompt=\"cat\" --num_class_images=200 \\\n --instance_prompt=\"photo of a cat\" \\\n --resolution=512 \\\n --train_batch_size=2 \\\n --learning_rate=1e-5 \\\n --lr_warmup_steps=0 \\\n --max_train_steps=250 \\\n --scale_lr --hflip \\\n --modifier_token \"\" \\\n --validation_prompt=\" cat sitting in a bucket\" \\\n --report_to=\"wandb\" \\\n --push_to_hub\n```\n\n다음은 [Weights and Biases page](https://wandb.ai/sayakpaul/custom-diffusion/runs/26ghrcau)의 예시이며, 여러 학습 세부 정보와 함께 중간 결과들을 확인할 수 있습니다. \n\n`--push_to_hub`를 지정하면 학습된 파라미터가 허깅 페이스 허브의 리포지토리에 푸시됩니다. 다음은 [예제 리포지토리](https://huggingface.co/sayakpaul/custom-diffusion-cat)입니다.\n\n### 멀티 컨셉에 대한 학습 🐱🪵\n\n[this](https://github.com/ShivamShrirao/diffusers/blob/main/examples/dreambooth/train_dreambooth.py)와 유사하게 각 컨셉에 대한 정보가 포함된 [json](https://github.com/adobe-research/custom-diffusion/blob/main/assets/concept_list.json) 파일을 제공합니다.\n\n실제 이미지를 수집하려면 json 파일의 각 컨셉에 대해 이 명령을 실행합니다. \n\n```bash\npip install clip-retrieval\npython retrieve.py --class_prompt {} --class_data_dir {} --num_class_images 200\n```\n\n그럼 우리는 학습시킬 준비가 되었습니다!\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\naccelerate launch train_custom_diffusion.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --output_dir=$OUTPUT_DIR \\\n --concepts_list=./concept_list.json \\\n --with_prior_preservation --real_prior --prior_loss_weight=1.0 \\\n --resolution=512 \\\n --train_batch_size=2 \\\n --learning_rate=1e-5 \\\n --lr_warmup_steps=0 \\\n --max_train_steps=500 \\\n --num_class_images=200 \\\n --scale_lr --hflip \\\n --modifier_token \"+\" \\\n --push_to_hub\n```\n\n다음은 [Weights and Biases page](https://wandb.ai/sayakpaul/custom-diffusion/runs/3990tzkg)의 예시이며, 다른 학습 세부 정보와 함께 중간 결과들을 확인할 수 있습니다.\n\n### 사람 얼굴에 대한 학습\n\n사람 얼굴에 대한 파인튜닝을 위해 다음과 같은 설정이 더 효과적이라는 것을 확인했습니다: `learning_rate=5e-6`, `max_train_steps=1000 to 2000`, `freeze_model=crossattn`을 최소 15~20개의 이미지로 설정합니다.\n\n실제 이미지를 수집하려면 훈련 전에 이 명령을 먼저 사용하십시오.\n\n```bash\npip install clip-retrieval\npython retrieve.py --class_prompt person --class_data_dir real_reg/samples_person --num_class_images 200\n```\n\n이제 학습을 시작하세요!\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport OUTPUT_DIR=\"path-to-save-model\"\nexport INSTANCE_DIR=\"path-to-images\"\n\naccelerate launch train_custom_diffusion.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --class_data_dir=./real_reg/samples_person/ \\\n --with_prior_preservation --real_prior --prior_loss_weight=1.0 \\\n --class_prompt=\"person\" --num_class_images=200 \\\n --instance_prompt=\"photo of a person\" \\\n --resolution=512 \\\n --train_batch_size=2 \\\n --learning_rate=5e-6 \\\n --lr_warmup_steps=0 \\\n --max_train_steps=1000 \\\n --scale_lr --hflip --noaug \\\n --freeze_model crossattn \\\n --modifier_token \"\" \\\n --enable_xformers_memory_efficient_attention \\\n --push_to_hub\n```\n\n## 추론\n\n위 프롬프트를 사용하여 모델을 학습시킨 후에는 아래 프롬프트를 사용하여 추론을 실행할 수 있습니다. 프롬프트에 'modifier token'(예: 위 예제에서는 \\)을 반드시 포함해야 합니다.\n\n```python\nimport torch\nfrom diffusers import DiffusionPipeline\n\npipe = DiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", torch_dtype=torch.float16).to(\"cuda\")\npipe.unet.load_attn_procs(\"path-to-save-model\", weight_name=\"pytorch_custom_diffusion_weights.bin\")\npipe.load_textual_inversion(\"path-to-save-model\", weight_name=\".bin\")\n\nimage = pipe(\n \" cat sitting in a bucket\",\n num_inference_steps=100,\n guidance_scale=6.0,\n eta=1.0,\n).images[0]\nimage.save(\"cat.png\")\n```\n\n허브 리포지토리에서 이러한 매개변수를 직접 로드할 수 있습니다:\n\n```python\nimport torch\nfrom huggingface_hub.repocard import RepoCard\nfrom diffusers import DiffusionPipeline\n\nmodel_id = \"sayakpaul/custom-diffusion-cat\"\ncard = RepoCard.load(model_id)\nbase_model_id = card.data.to_dict()[\"base_model\"]\n\npipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16).to(\"cuda\")\npipe.unet.load_attn_procs(model_id, weight_name=\"pytorch_custom_diffusion_weights.bin\")\npipe.load_textual_inversion(model_id, weight_name=\".bin\")\n\nimage = pipe(\n \" cat sitting in a bucket\",\n num_inference_steps=100,\n guidance_scale=6.0,\n eta=1.0,\n).images[0]\nimage.save(\"cat.png\")\n```\n\n다음은 여러 컨셉으로 추론을 수행하는 예제입니다:\n\n```python\nimport torch\nfrom huggingface_hub.repocard import RepoCard\nfrom diffusers import DiffusionPipeline\n\nmodel_id = \"sayakpaul/custom-diffusion-cat-wooden-pot\"\ncard = RepoCard.load(model_id)\nbase_model_id = card.data.to_dict()[\"base_model\"]\n\npipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16).to(\"cuda\")\npipe.unet.load_attn_procs(model_id, weight_name=\"pytorch_custom_diffusion_weights.bin\")\npipe.load_textual_inversion(model_id, weight_name=\".bin\")\npipe.load_textual_inversion(model_id, weight_name=\".bin\")\n\nimage = pipe(\n \"the cat sculpture in the style of a wooden pot\",\n num_inference_steps=100,\n guidance_scale=6.0,\n eta=1.0,\n).images[0]\nimage.save(\"multi-subject.png\")\n```\n\n여기서 '고양이'와 '나무 냄비'는 여러 컨셉을 말합니다.\n\n### 학습된 체크포인트에서 추론하기\n\n`--checkpointing_steps` 인수를 사용한 경우 학습 과정에서 저장된 전체 체크포인트 중 하나에서 추론을 수행할 수도 있습니다. \n\n## Grads를 None으로 설정\n\n더 많은 메모리를 절약하려면 스크립트에 `--set_grads_to_none` 인수를 전달하세요. 이렇게 하면 성적이 0이 아닌 없음으로 설정됩니다. 그러나 특정 동작이 변경되므로 문제가 발생하면 이 인수를 제거하세요.\n\n자세한 정보: https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html\n\n## 실험 결과\n\n실험에 대한 자세한 내용은 [당사 웹페이지](https://www.cs.cmu.edu/~custom-diffusion/)를 참조하세요. " }, { "path": "diffusers/docs/source/ko/training/distributed_inference.md", "content": "# 여러 GPU를 사용한 분산 추론\n\n분산 설정에서는 여러 개의 프롬프트를 동시에 생성할 때 유용한 🤗 [Accelerate](https://huggingface.co/docs/accelerate/index) 또는 [PyTorch Distributed](https://pytorch.org/tutorials/beginner/dist_overview.html)를 사용하여 여러 GPU에서 추론을 실행할 수 있습니다.\n\n이 가이드에서는 분산 추론을 위해 🤗 Accelerate와 PyTorch Distributed를 사용하는 방법을 보여드립니다.\n\n## 🤗 Accelerate\n\n🤗 [Accelerate](https://huggingface.co/docs/accelerate/index)는 분산 설정에서 추론을 쉽게 훈련하거나 실행할 수 있도록 설계된 라이브러리입니다. 분산 환경 설정 프로세스를 간소화하여 PyTorch 코드에 집중할 수 있도록 해줍니다.\n\n시작하려면 Python 파일을 생성하고 [`accelerate.PartialState`]를 초기화하여 분산 환경을 생성하면, 설정이 자동으로 감지되므로 `rank` 또는 `world_size`를 명시적으로 정의할 필요가 없습니다. ['DiffusionPipeline`]을 `distributed_state.device`로 이동하여 각 프로세스에 GPU를 할당합니다.\n\n이제 컨텍스트 관리자로 [`~accelerate.PartialState.split_between_processes`] 유틸리티를 사용하여 프로세스 수에 따라 프롬프트를 자동으로 분배합니다.\n\n\n```py\nfrom accelerate import PartialState\nfrom diffusers import DiffusionPipeline\n\npipeline = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16)\ndistributed_state = PartialState()\npipeline.to(distributed_state.device)\n\nwith distributed_state.split_between_processes([\"a dog\", \"a cat\"]) as prompt:\n result = pipeline(prompt).images[0]\n result.save(f\"result_{distributed_state.process_index}.png\")\n```\n\nUse the `--num_processes` argument to specify the number of GPUs to use, and call `accelerate launch` to run the script:\n\n```bash\naccelerate launch run_distributed.py --num_processes=2\n```\n\n자세한 내용은 [🤗 Accelerate를 사용한 분산 추론](https://huggingface.co/docs/accelerate/en/usage_guides/distributed_inference#distributed-inference-with-accelerate) 가이드를 참조하세요.\n\n\n\n## Pytoerch 분산\n\nPyTorch는 데이터 병렬 처리를 가능하게 하는 [`DistributedDataParallel`](https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html)을 지원합니다.\n\n시작하려면 Python 파일을 생성하고 `torch.distributed` 및 `torch.multiprocessing`을 임포트하여 분산 프로세스 그룹을 설정하고 각 GPU에서 추론용 프로세스를 생성합니다. 그리고 [`DiffusionPipeline`]도 초기화해야 합니다:\n\n확산 파이프라인을 `rank`로 이동하고 `get_rank`를 사용하여 각 프로세스에 GPU를 할당하면 각 프로세스가 다른 프롬프트를 처리합니다:\n\n```py\nimport torch\nimport torch.distributed as dist\nimport torch.multiprocessing as mp\n\nfrom diffusers import DiffusionPipeline\n\nsd = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16)\n```\n\n사용할 백엔드 유형, 현재 프로세스의 `rank`, `world_size` 또는 참여하는 프로세스 수로 분산 환경 생성을 처리하는 함수[`init_process_group`]를 만들어 추론을 실행해야 합니다.\n\n2개의 GPU에서 추론을 병렬로 실행하는 경우 `world_size`는 2입니다.\n\n```py\ndef run_inference(rank, world_size):\n dist.init_process_group(\"nccl\", rank=rank, world_size=world_size)\n\n sd.to(rank)\n\n if torch.distributed.get_rank() == 0:\n prompt = \"a dog\"\n elif torch.distributed.get_rank() == 1:\n prompt = \"a cat\"\n\n image = sd(prompt).images[0]\n image.save(f\"./{'_'.join(prompt)}.png\")\n```\n\n분산 추론을 실행하려면 [`mp.spawn`](https://pytorch.org/docs/stable/multiprocessing.html#torch.multiprocessing.spawn)을 호출하여 `world_size`에 정의된 GPU 수에 대해 `run_inference` 함수를 실행합니다:\n\n```py\ndef main():\n world_size = 2\n mp.spawn(run_inference, args=(world_size,), nprocs=world_size, join=True)\n\n\nif __name__ == \"__main__\":\n main()\n```\n\n추론 스크립트를 완료했으면 `--nproc_per_node` 인수를 사용하여 사용할 GPU 수를 지정하고 `torchrun`을 호출하여 스크립트를 실행합니다:\n\n```bash\ntorchrun run_distributed.py --nproc_per_node=2\n```" }, { "path": "diffusers/docs/source/ko/training/dreambooth.md", "content": "\n\n# DreamBooth\n\n[DreamBooth](https://arxiv.org/abs/2208.12242)는 한 주제에 대한 적은 이미지(3~5개)만으로도 stable diffusion과 같이 text-to-image 모델을 개인화할 수 있는 방법입니다. 이를 통해 모델은 다양한 장면, 포즈 및 장면(뷰)에서 피사체에 대해 맥락화(contextualized)된 이미지를 생성할 수 있습니다.\n\n![프로젝트 블로그에서의 DreamBooth 예시](https://dreambooth.github.io/DreamBooth_files/teaser_static.jpg)\n에서의 Dreambooth 예시 project's blog.\n\n\n이 가이드는 다양한 GPU, Flax 사양에 대해 [`CompVis/stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) 모델로 DreamBooth를 파인튜닝하는 방법을 보여줍니다. 더 깊이 파고들어 작동 방식을 확인하는 데 관심이 있는 경우, 이 가이드에 사용된 DreamBooth의 모든 학습 스크립트를 [여기](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth)에서 찾을 수 있습니다.\n\n스크립트를 실행하기 전에 라이브러리의 학습에 필요한 dependencies를 설치해야 합니다. 또한 `main` GitHub 브랜치에서 🧨 Diffusers를 설치하는 것이 좋습니다.\n\n```bash\npip install git+https://github.com/huggingface/diffusers\npip install -U -r diffusers/examples/dreambooth/requirements.txt\n```\n\nxFormers는 학습에 필요한 요구 사항은 아니지만, 가능하면 [설치](../optimization/xformers)하는 것이 좋습니다. 학습 속도를 높이고 메모리 사용량을 줄일 수 있기 때문입니다.\n\n모든 dependencies을 설정한 후 다음을 사용하여 [🤗 Accelerate](https://github.com/huggingface/accelerate/) 환경을 다음과 같이 초기화합니다:\n\n```bash\naccelerate config\n```\n\n별도 설정 없이 기본 🤗 Accelerate 환경을 설치하려면 다음을 실행합니다:\n\n```bash\naccelerate config default\n```\n\n또는 현재 환경이 노트북과 같은 대화형 셸을 지원하지 않는 경우 다음을 사용할 수 있습니다:\n\n```py\nfrom accelerate.utils import write_basic_config\n\nwrite_basic_config()\n```\n\n## 파인튜닝\n\n\n\nDreamBooth 파인튜닝은 하이퍼파라미터에 매우 민감하고 과적합되기 쉽습니다. 적절한 하이퍼파라미터를 선택하는 데 도움이 되도록 다양한 권장 설정이 포함된 [심층 분석](https://huggingface.co/blog/dreambooth)을 살펴보는 것이 좋습니다.\n\n\n\n\n\n[몇 장의 강아지 이미지들](https://drive.google.com/drive/folders/1BO_dyz-p65qhBRRMRA4TbZ8qW4rB99JZ)로 DreamBooth를 시도해봅시다. \n이를 다운로드해 디렉터리에 저장한 다음 `INSTANCE_DIR` 환경 변수를 해당 경로로 설정합니다:\n\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport INSTANCE_DIR=\"path_to_training_images\"\nexport OUTPUT_DIR=\"path_to_saved_model\"\n```\n\n그런 다음, 다음 명령을 사용하여 학습 스크립트를 실행할 수 있습니다 (전체 학습 스크립트는 [여기](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py)에서 찾을 수 있습니다):\n\n```bash\naccelerate launch train_dreambooth.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --instance_prompt=\"a photo of sks dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=1 \\\n --learning_rate=5e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --max_train_steps=400\n```\n\n\n\nTPU에 액세스할 수 있거나 더 빠르게 훈련하고 싶다면 [Flax 학습 스크립트](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_flax.py)를 사용해 볼 수 있습니다. Flax 학습 스크립트는 gradient checkpointing 또는 gradient accumulation을 지원하지 않으므로, 메모리가 30GB 이상인 GPU가 필요합니다.\n\n스크립트를 실행하기 전에 요구 사항이 설치되어 있는지 확인하십시오.\n\n```bash\npip install -U -r requirements.txt\n```\n\n그러면 다음 명령어로 학습 스크립트를 실행시킬 수 있습니다:\n\n```bash\nexport MODEL_NAME=\"duongna/stable-diffusion-v1-4-flax\"\nexport INSTANCE_DIR=\"path-to-instance-images\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\npython train_dreambooth_flax.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --instance_prompt=\"a photo of sks dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --learning_rate=5e-6 \\\n --max_train_steps=400\n```\n\n\n\n### Prior-preserving(사전 보존) loss를 사용한 파인튜닝\n\n과적합과 language drift를 방지하기 위해 사전 보존이 사용됩니다(관심이 있는 경우 [논문](https://arxiv.org/abs/2208.12242)을 참조하세요). 사전 보존을 위해 동일한 클래스의 다른 이미지를 학습 프로세스의 일부로 사용합니다. 좋은 점은 Stable Diffusion 모델 자체를 사용하여 이러한 이미지를 생성할 수 있다는 것입니다! 학습 스크립트는 생성된 이미지를 우리가 지정한 로컬 경로에 저장합니다.\n\n저자들에 따르면 사전 보존을 위해 `num_epochs * num_samples`개의 이미지를 생성하는 것이 좋습니다. 200-300개에서 대부분 잘 작동합니다.\n\n\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport INSTANCE_DIR=\"path_to_training_images\"\nexport CLASS_DIR=\"path_to_class_images\"\nexport OUTPUT_DIR=\"path_to_saved_model\"\n\naccelerate launch train_dreambooth.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --class_data_dir=$CLASS_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --with_prior_preservation --prior_loss_weight=1.0 \\\n --instance_prompt=\"a photo of sks dog\" \\\n --class_prompt=\"a photo of dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=1 \\\n --learning_rate=5e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --num_class_images=200 \\\n --max_train_steps=800\n```\n\n\n```bash\nexport MODEL_NAME=\"duongna/stable-diffusion-v1-4-flax\"\nexport INSTANCE_DIR=\"path-to-instance-images\"\nexport CLASS_DIR=\"path-to-class-images\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\npython train_dreambooth_flax.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --class_data_dir=$CLASS_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --with_prior_preservation --prior_loss_weight=1.0 \\\n --instance_prompt=\"a photo of sks dog\" \\\n --class_prompt=\"a photo of dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --learning_rate=5e-6 \\\n --num_class_images=200 \\\n --max_train_steps=800\n```\n\n\n\n## 텍스트 인코더와 and UNet로 파인튜닝하기\n\n해당 스크립트를 사용하면 `unet`과 함께 `text_encoder`를 파인튜닝할 수 있습니다. 실험에서(자세한 내용은 [🧨 Diffusers를 사용해 DreamBooth로 Stable Diffusion 학습하기](https://huggingface.co/blog/dreambooth) 게시물을 확인하세요), 특히 얼굴 이미지를 생성할 때 훨씬 더 나은 결과를 얻을 수 있습니다.\n\n\n\n텍스트 인코더를 학습시키려면 추가 메모리가 필요해 16GB GPU로는 동작하지 않습니다. 이 옵션을 사용하려면 최소 24GB VRAM이 필요합니다.\n\n\n\n`--train_text_encoder` 인수를 학습 스크립트에 전달하여 `text_encoder` 및 `unet`을 파인튜닝할 수 있습니다:\n\n\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport INSTANCE_DIR=\"path_to_training_images\"\nexport CLASS_DIR=\"path_to_class_images\"\nexport OUTPUT_DIR=\"path_to_saved_model\"\n\naccelerate launch train_dreambooth.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_text_encoder \\\n --instance_data_dir=$INSTANCE_DIR \\\n --class_data_dir=$CLASS_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --with_prior_preservation --prior_loss_weight=1.0 \\\n --instance_prompt=\"a photo of sks dog\" \\\n --class_prompt=\"a photo of dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --use_8bit_adam\n --gradient_checkpointing \\\n --learning_rate=2e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --num_class_images=200 \\\n --max_train_steps=800\n```\n\n\n```bash\nexport MODEL_NAME=\"duongna/stable-diffusion-v1-4-flax\"\nexport INSTANCE_DIR=\"path-to-instance-images\"\nexport CLASS_DIR=\"path-to-class-images\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\npython train_dreambooth_flax.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_text_encoder \\\n --instance_data_dir=$INSTANCE_DIR \\\n --class_data_dir=$CLASS_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --with_prior_preservation --prior_loss_weight=1.0 \\\n --instance_prompt=\"a photo of sks dog\" \\\n --class_prompt=\"a photo of dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --learning_rate=2e-6 \\\n --num_class_images=200 \\\n --max_train_steps=800\n```\n\n\n\n## LoRA로 파인튜닝하기\n\nDreamBooth에서 대규모 모델의 학습을 가속화하기 위한 파인튜닝 기술인 LoRA(Low-Rank Adaptation of Large Language Models)를 사용할 수 있습니다. 자세한 내용은 [LoRA 학습](training/lora#dreambooth) 가이드를 참조하세요.\n\n### 학습 중 체크포인트 저장하기\n\nDreambooth로 훈련하는 동안 과적합하기 쉬우므로, 때때로 학습 중에 정기적인 체크포인트를 저장하는 것이 유용합니다. 중간 체크포인트 중 하나가 최종 모델보다 더 잘 작동할 수 있습니다! 체크포인트 저장 기능을 활성화하려면 학습 스크립트에 다음 인수를 전달해야 합니다:\n\n```bash\n --checkpointing_steps=500\n```\n\n이렇게 하면 `output_dir`의 하위 폴더에 전체 학습 상태가 저장됩니다. 하위 폴더 이름은 접두사 `checkpoint-`로 시작하고 지금까지 수행된 step 수입니다. 예시로 `checkpoint-1500`은 1500 학습 step 후에 저장된 체크포인트입니다.\n\n#### 저장된 체크포인트에서 훈련 재개하기\n\n저장된 체크포인트에서 훈련을 재개하려면, `--resume_from_checkpoint` 인수를 전달한 다음 사용할 체크포인트의 이름을 지정하면 됩니다. 특수 문자열 `\"latest\"`를 사용하여 저장된 마지막 체크포인트(즉, step 수가 가장 많은 체크포인트)에서 재개할 수도 있습니다. 예를 들어 다음은 1500 step 후에 저장된 체크포인트에서부터 학습을 재개합니다:\n\n```bash\n --resume_from_checkpoint=\"checkpoint-1500\"\n```\n\n원하는 경우 일부 하이퍼파라미터를 조정할 수 있습니다.\n\n#### 저장된 체크포인트를 사용하여 추론 수행하기\n\n저장된 체크포인트는 훈련 재개에 적합한 형식으로 저장됩니다. 여기에는 모델 가중치뿐만 아니라 옵티마이저, 데이터 로더 및 학습률의 상태도 포함됩니다.\n\n**`\"accelerate>=0.16.0\"`**이 설치된 경우 다음 코드를 사용하여 중간 체크포인트에서 추론을 실행합니다.\n\n```python\nfrom diffusers import DiffusionPipeline, UNet2DConditionModel\nfrom transformers import CLIPTextModel\nimport torch\n\n# 학습에 사용된 것과 동일한 인수(model, revision)로 파이프라인을 불러옵니다.\nmodel_id = \"CompVis/stable-diffusion-v1-4\"\n\nunet = UNet2DConditionModel.from_pretrained(\"/sddata/dreambooth/daruma-v2-1/checkpoint-100/unet\")\n\n# `args.train_text_encoder`로 학습한 경우면 텍스트 인코더를 꼭 불러오세요\ntext_encoder = CLIPTextModel.from_pretrained(\"/sddata/dreambooth/daruma-v2-1/checkpoint-100/text_encoder\")\n\npipeline = DiffusionPipeline.from_pretrained(model_id, unet=unet, text_encoder=text_encoder, dtype=torch.float16)\npipeline.to(\"cuda\")\n\n# 추론을 수행하거나 저장하거나, 허브에 푸시합니다.\npipeline.save_pretrained(\"dreambooth-pipeline\")\n```\n\nIf you have **`\"accelerate<0.16.0\"`** installed, you need to convert it to an inference pipeline first:\n\n```python\nfrom accelerate import Accelerator\nfrom diffusers import DiffusionPipeline\n\n# 학습에 사용된 것과 동일한 인수(model, revision)로 파이프라인을 불러옵니다.\nmodel_id = \"CompVis/stable-diffusion-v1-4\"\npipeline = DiffusionPipeline.from_pretrained(model_id)\n\naccelerator = Accelerator()\n\n# 초기 학습에 `--train_text_encoder`가 사용된 경우 text_encoder를 사용합니다.\nunet, text_encoder = accelerator.prepare(pipeline.unet, pipeline.text_encoder)\n\n# 체크포인트 경로로부터 상태를 복원합니다. 여기서는 절대 경로를 사용해야 합니다.\naccelerator.load_state(\"/sddata/dreambooth/daruma-v2-1/checkpoint-100\")\n\n# unwrapped 모델로 파이프라인을 다시 빌드합니다.(.unet and .text_encoder로의 할당도 작동해야 합니다)\npipeline = DiffusionPipeline.from_pretrained(\n model_id,\n unet=accelerator.unwrap_model(unet),\n text_encoder=accelerator.unwrap_model(text_encoder),\n)\n\n# 추론을 수행하거나 저장하거나, 허브에 푸시합니다.\npipeline.save_pretrained(\"dreambooth-pipeline\")\n```\n\n## 각 GPU 용량에서의 최적화\n\n하드웨어에 따라 16GB에서 8GB까지 GPU에서 DreamBooth를 최적화하는 몇 가지 방법이 있습니다!\n\n### xFormers\n\n[xFormers](https://github.com/facebookresearch/xformers)는 Transformers를 최적화하기 위한 toolbox이며, 🧨 Diffusers에서 사용되는[memory-efficient attention](https://facebookresearch.github.io/xformers/components/ops.html#module-xformers.ops) 메커니즘을 포함하고 있습니다. [xFormers를 설치](./optimization/xformers)한 다음 학습 스크립트에 다음 인수를 추가합니다:\n\n```bash\n --enable_xformers_memory_efficient_attention\n```\n\nxFormers는 Flax에서 사용할 수 없습니다.\n\n### 그래디언트 없음으로 설정\n\n메모리 사용량을 줄일 수 있는 또 다른 방법은 [기울기 설정](https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html)을 0 대신 `None`으로 하는 것입니다. 그러나 이로 인해 특정 동작이 변경될 수 있으므로 문제가 발생하면 이 인수를 제거해 보십시오. 학습 스크립트에 다음 인수를 추가하여 그래디언트를 `None`으로 설정합니다.\n\n```bash\n --set_grads_to_none\n```\n\n### 16GB GPU\n\nGradient checkpointing과 [bitsandbytes](https://github.com/TimDettmers/bitsandbytes)의 8비트 옵티마이저의 도움으로, 16GB GPU에서 dreambooth를 훈련할 수 있습니다. bitsandbytes가 설치되어 있는지 확인하세요:\n\n```bash\npip install bitsandbytes\n```\n\n그 다음, 학습 스크립트에 `--use_8bit_adam` 옵션을 명시합니다:\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport INSTANCE_DIR=\"path_to_training_images\"\nexport CLASS_DIR=\"path_to_class_images\"\nexport OUTPUT_DIR=\"path_to_saved_model\"\n\naccelerate launch train_dreambooth.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --class_data_dir=$CLASS_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --with_prior_preservation --prior_loss_weight=1.0 \\\n --instance_prompt=\"a photo of sks dog\" \\\n --class_prompt=\"a photo of dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=2 --gradient_checkpointing \\\n --use_8bit_adam \\\n --learning_rate=5e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --num_class_images=200 \\\n --max_train_steps=800\n```\n\n### 12GB GPU\n\n12GB GPU에서 DreamBooth를 실행하려면 gradient checkpointing, 8비트 옵티마이저, xFormers를 활성화하고 그래디언트를 `None`으로 설정해야 합니다.\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport INSTANCE_DIR=\"path-to-instance-images\"\nexport CLASS_DIR=\"path-to-class-images\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\naccelerate launch train_dreambooth.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --class_data_dir=$CLASS_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --with_prior_preservation --prior_loss_weight=1.0 \\\n --instance_prompt=\"a photo of sks dog\" \\\n --class_prompt=\"a photo of dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=1 --gradient_checkpointing \\\n --use_8bit_adam \\\n --enable_xformers_memory_efficient_attention \\\n --set_grads_to_none \\\n --learning_rate=2e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --num_class_images=200 \\\n --max_train_steps=800\n```\n\n### 8GB GPU에서 학습하기\n\n8GB GPU에 대해서는 [DeepSpeed](https://www.deepspeed.ai/)를 사용해 일부 텐서를 VRAM에서 CPU 또는 NVME로 오프로드하여 더 적은 GPU 메모리로 학습할 수도 있습니다.\n\n🤗 Accelerate 환경을 구성하려면 다음 명령을 실행하세요:\n\n```bash\naccelerate config\n```\n\n환경 구성 중에 DeepSpeed를 사용할 것을 확인하세요.\n그러면 DeepSpeed stage 2, fp16 혼합 정밀도를 결합하고 모델 매개변수와 옵티마이저 상태를 모두 CPU로 오프로드하면 8GB VRAM 미만에서 학습할 수 있습니다. \n단점은 더 많은 시스템 RAM(약 25GB)이 필요하다는 것입니다. 추가 구성 옵션은 [DeepSpeed 문서](https://huggingface.co/docs/accelerate/usage_guides/deepspeed)를 참조하세요.\n\n또한 기본 Adam 옵티마이저를 DeepSpeed의 최적화된 Adam 버전으로 변경해야 합니다.\n이는 상당한 속도 향상을 위한 Adam인 [`deepspeed.ops.adam.DeepSpeedCPUAdam`](https://deepspeed.readthedocs.io/en/latest/optimizers.html#adam-cpu)입니다. \n`DeepSpeedCPUAdam`을 활성화하려면 시스템의 CUDA toolchain 버전이 PyTorch와 함께 설치된 것과 동일해야 합니다.\n\n8비트 옵티마이저는 현재 DeepSpeed와 호환되지 않는 것 같습니다.\n\n다음 명령으로 학습을 시작합니다:\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport INSTANCE_DIR=\"path_to_training_images\"\nexport CLASS_DIR=\"path_to_class_images\"\nexport OUTPUT_DIR=\"path_to_saved_model\"\n\naccelerate launch train_dreambooth.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --class_data_dir=$CLASS_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --with_prior_preservation --prior_loss_weight=1.0 \\\n --instance_prompt=\"a photo of sks dog\" \\\n --class_prompt=\"a photo of dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --sample_batch_size=1 \\\n --gradient_accumulation_steps=1 --gradient_checkpointing \\\n --learning_rate=5e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --num_class_images=200 \\\n --max_train_steps=800 \\\n --mixed_precision=fp16\n```\n\n## 추론\n\n모델을 학습한 후에는, 모델이 저장된 경로를 지정해 [`StableDiffusionPipeline`]로 추론을 수행할 수 있습니다. 프롬프트에 학습에 사용된 특수 `식별자`(이전 예시의 `sks`)가 포함되어 있는지 확인하세요.\n\n**`\"accelerate>=0.16.0\"`**이 설치되어 있는 경우 다음 코드를 사용하여 중간 체크포인트에서 추론을 실행할 수 있습니다:\n\n```python\nfrom diffusers import StableDiffusionPipeline\nimport torch\n\nmodel_id = \"path_to_saved_model\"\npipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(\"cuda\")\n\nprompt = \"A photo of sks dog in a bucket\"\nimage = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]\n\nimage.save(\"dog-bucket.png\")\n```\n\n[저장된 학습 체크포인트](#inference-from-a-saved-checkpoint)에서도 추론을 실행할 수도 있습니다.\n" }, { "path": "diffusers/docs/source/ko/training/instructpix2pix.md", "content": "\n\n# InstructPix2Pix \n\n[InstructPix2Pix](https://arxiv.org/abs/2211.09800)는 text-conditioned diffusion 모델이 한 이미지에 편집을 따를 수 있도록 파인튜닝하는 방법입니다. 이 방법을 사용하여 파인튜닝된 모델은 다음을 입력으로 사용합니다:\n\n

\n \"instructpix2pix-inputs\"\n

\n\n출력은 입력 이미지에 편집 지시가 반영된 \"수정된\" 이미지입니다:\n\n

\n \"instructpix2pix-output\"\n

\n\n`train_instruct_pix2pix.py` 스크립트([여기](https://github.com/huggingface/diffusers/blob/main/examples/instruct_pix2pix/train_instruct_pix2pix.py)에서 찾을 수 있습니다.)는 학습 절차를 설명하고 Stable Diffusion에 적용할 수 있는 방법을 보여줍니다.\n\n\n*** `train_instruct_pix2pix.py`는 [원래 구현](https://github.com/timothybrooks/instruct-pix2pix)에 충실하면서 InstructPix2Pix 학습 절차를 구현하고 있지만, [소규모 데이터셋](https://huggingface.co/datasets/fusing/instructpix2pix-1000-samples)에서만 테스트를 했습니다. 이는 최종 결과에 영향을 끼칠 수 있습니다. 더 나은 결과를 위해, 더 큰 데이터셋에서 더 길게 학습하는 것을 권장합니다. [여기](https://huggingface.co/datasets/timbrooks/instructpix2pix-clip-filtered)에서 InstructPix2Pix 학습을 위해 큰 데이터셋을 찾을 수 있습니다.\n***\n\n## PyTorch로 로컬에서 실행하기\n\n### 종속성(dependencies) 설치하기\n\n이 스크립트를 실행하기 전에, 라이브러리의 학습 종속성을 설치하세요:\n\n**중요**\n\n최신 버전의 예제 스크립트를 성공적으로 실행하기 위해, **원본으로부터 설치**하는 것과 예제 스크립트를 자주 업데이트하고 예제별 요구사항을 설치하기 때문에 최신 상태로 유지하는 것을 권장합니다. 이를 위해, 새로운 가상 환경에서 다음 스텝을 실행하세요:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install -e .\n```\n\ncd 명령어로 예제 폴더로 이동하세요.\n```bash\ncd examples/instruct_pix2pix\n```\n\n이제 실행하세요.\n```bash\npip install -r requirements.txt\n```\n\n그리고 [🤗Accelerate](https://github.com/huggingface/accelerate/) 환경에서 초기화하세요:\n\n```bash\naccelerate config\n```\n\n혹은 환경에 대한 질문 없이 기본적인 accelerate 구성을 사용하려면 다음을 실행하세요.\n\n```bash\naccelerate config default\n```\n\n혹은 사용 중인 환경이 notebook과 같은 대화형 쉘은 지원하지 않는 경우는 다음 절차를 따라주세요.\n\n```python\nfrom accelerate.utils import write_basic_config\n\nwrite_basic_config()\n```\n\n### 예시\n\n이전에 언급했듯이, 학습을 위해 [작은 데이터셋](https://huggingface.co/datasets/fusing/instructpix2pix-1000-samples)을 사용할 것입니다. 그 데이터셋은 InstructPix2Pix 논문에서 사용된 [원래의 데이터셋](https://huggingface.co/datasets/timbrooks/instructpix2pix-clip-filtered)보다 작은 버전입니다. 자신의 데이터셋을 사용하기 위해, [학습을 위한 데이터셋 만들기](create_dataset) 가이드를 참고하세요.\n\n`MODEL_NAME` 환경 변수(허브 모델 레포지토리 또는 모델 가중치가 포함된 폴더 경로)를 지정하고 [`pretrained_model_name_or_path`](https://huggingface.co/docs/diffusers/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path) 인수에 전달합니다. `DATASET_ID`에 데이터셋 이름을 지정해야 합니다:\n\n\n```bash\nexport MODEL_NAME=\"runwayml/stable-diffusion-v1-5\"\nexport DATASET_ID=\"fusing/instructpix2pix-1000-samples\"\n```\n\n지금, 학습을 실행할 수 있습니다. 스크립트는 레포지토리의 하위 폴더의 모든 구성요소(`feature_extractor`, `scheduler`, `text_encoder`, `unet` 등)를 저장합니다.\n\n```bash\naccelerate launch --mixed_precision=\"fp16\" train_instruct_pix2pix.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --dataset_name=$DATASET_ID \\\n --enable_xformers_memory_efficient_attention \\\n --resolution=256 --random_flip \\\n --train_batch_size=4 --gradient_accumulation_steps=4 --gradient_checkpointing \\\n --max_train_steps=15000 \\\n --checkpointing_steps=5000 --checkpoints_total_limit=1 \\\n --learning_rate=5e-05 --max_grad_norm=1 --lr_warmup_steps=0 \\\n --conditioning_dropout_prob=0.05 \\\n --mixed_precision=fp16 \\\n --seed=42 \\\n --push_to_hub\n```\n\n\n추가적으로, 가중치와 바이어스를 학습 과정에 모니터링하여 검증 추론을 수행하는 것을 지원합니다. `report_to=\"wandb\"`와 이 기능을 사용할 수 있습니다:\n\n```bash\naccelerate launch --mixed_precision=\"fp16\" train_instruct_pix2pix.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --dataset_name=$DATASET_ID \\\n --enable_xformers_memory_efficient_attention \\\n --resolution=256 --random_flip \\\n --train_batch_size=4 --gradient_accumulation_steps=4 --gradient_checkpointing \\\n --max_train_steps=15000 \\\n --checkpointing_steps=5000 --checkpoints_total_limit=1 \\\n --learning_rate=5e-05 --max_grad_norm=1 --lr_warmup_steps=0 \\\n --conditioning_dropout_prob=0.05 \\\n --mixed_precision=fp16 \\\n --val_image_url=\"https://hf.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png\" \\\n --validation_prompt=\"make the mountains snowy\" \\\n --seed=42 \\\n --report_to=wandb \\\n --push_to_hub\n ```\n\n모델 디버깅에 유용한 이 평가 방법 권장합니다. 이를 사용하기 위해 `wandb`를 설치하는 것을 주목해주세요. `pip install wandb`로 실행해 `wandb`를 설치할 수 있습니다.\n\n[여기](https://wandb.ai/sayakpaul/instruct-pix2pix/runs/ctr3kovq), 몇 가지 평가 방법과 학습 파라미터를 포함하는 예시를 볼 수 있습니다.\n\n ***참고: 원본 논문에서, 저자들은 256x256 이미지 해상도로 학습한 모델로 512x512와 같은 더 큰 해상도로 잘 일반화되는 것을 볼 수 있었습니다. 이는 학습에 사용한 큰 데이터셋을 사용했기 때문입니다.***\n\n ## 다수의 GPU로 학습하기\n\n`accelerate`는 원활한 다수의 GPU로 학습을 가능하게 합니다. `accelerate`로 분산 학습을 실행하는 [여기](https://huggingface.co/docs/accelerate/basic_tutorials/launch) 설명을 따라 해 주시기 바랍니다. 예시의 명령어 입니다:\n\n\n```bash \naccelerate launch --mixed_precision=\"fp16\" --multi_gpu train_instruct_pix2pix.py \\\n --pretrained_model_name_or_path=runwayml/stable-diffusion-v1-5 \\\n --dataset_name=sayakpaul/instructpix2pix-1000-samples \\\n --use_ema \\\n --enable_xformers_memory_efficient_attention \\\n --resolution=512 --random_flip \\\n --train_batch_size=4 --gradient_accumulation_steps=4 --gradient_checkpointing \\\n --max_train_steps=15000 \\\n --checkpointing_steps=5000 --checkpoints_total_limit=1 \\\n --learning_rate=5e-05 --lr_warmup_steps=0 \\\n --conditioning_dropout_prob=0.05 \\\n --mixed_precision=fp16 \\\n --seed=42 \\\n --push_to_hub\n```\n\n ## 추론하기\n\n일단 학습이 완료되면, 추론 할 수 있습니다:\n\n ```python\nimport PIL\nimport requests\nimport torch\nfrom diffusers import StableDiffusionInstructPix2PixPipeline\n\nmodel_id = \"your_model_id\" # <- 이를 수정하세요.\npipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(\"cuda\")\ngenerator = torch.Generator(\"cuda\").manual_seed(0)\n\nurl = \"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/test_pix2pix_4.png\"\n\n\ndef download_image(url):\n image = PIL.Image.open(requests.get(url, stream=True).raw)\n image = PIL.ImageOps.exif_transpose(image)\n image = image.convert(\"RGB\")\n return image\n\n\nimage = download_image(url)\nprompt = \"wipe out the lake\"\nnum_inference_steps = 20\nimage_guidance_scale = 1.5\nguidance_scale = 10\n\nedited_image = pipe(\n prompt,\n image=image,\n num_inference_steps=num_inference_steps,\n image_guidance_scale=image_guidance_scale,\n guidance_scale=guidance_scale,\n generator=generator,\n).images[0]\nedited_image.save(\"edited_image.png\")\n```\n\n학습 스크립트를 사용해 얻은 예시의 모델 레포지토리는 여기 [sayakpaul/instruct-pix2pix](https://huggingface.co/sayakpaul/instruct-pix2pix)에서 확인할 수 있습니다.\n\n성능을 위한 속도와 품질을 제어하기 위해 세 가지 파라미터를 사용하는 것이 좋습니다:\n\n* `num_inference_steps`\n* `image_guidance_scale`\n* `guidance_scale`\n\n특히, `image_guidance_scale`와 `guidance_scale`는 생성된(\"수정된\") 이미지에서 큰 영향을 미칠 수 있습니다.([여기](https://twitter.com/RisingSayak/status/1628392199196151808?s=20)예시를 참고해주세요.)\n\n\n만약 InstructPix2Pix 학습 방법을 사용해 몇 가지 흥미로운 방법을 찾고 있다면, 이 블로그 게시물[Instruction-tuning Stable Diffusion with InstructPix2Pix](https://huggingface.co/blog/instruction-tuning-sd)을 확인해주세요." }, { "path": "diffusers/docs/source/ko/training/lora.md", "content": "\n\n# Low-Rank Adaptation of Large Language Models (LoRA)\n\n[[open-in-colab]]\n\n\n\n현재 LoRA는 [`UNet2DConditionalModel`]의 어텐션 레이어에서만 지원됩니다.\n\n\n\n[LoRA(Low-Rank Adaptation of Large Language Models)](https://arxiv.org/abs/2106.09685)는 메모리를 적게 사용하면서 대규모 모델의 학습을 가속화하는 학습 방법입니다. 이는 rank-decomposition weight 행렬 쌍(**업데이트 행렬**이라고 함)을 추가하고 새로 추가된 가중치**만** 학습합니다. 여기에는 몇 가지 장점이 있습니다.\n\n- 이전에 미리 학습된 가중치는 고정된 상태로 유지되므로 모델이 [치명적인 망각](https://www.pnas.org/doi/10.1073/pnas.1611835114) 경향이 없습니다.\n- Rank-decomposition 행렬은 원래 모델보다 파라메터 수가 훨씬 적으므로 학습된 LoRA 가중치를 쉽게 끼워넣을 수 있습니다.\n- LoRA 매트릭스는 일반적으로 원본 모델의 어텐션 레이어에 추가됩니다. 🧨 Diffusers는 [`~diffusers.loaders.UNet2DConditionLoadersMixin.load_attn_procs`] 메서드를 제공하여 LoRA 가중치를 모델의 어텐션 레이어로 불러옵니다. `scale` 매개변수를 통해 모델이 새로운 학습 이미지에 맞게 조정되는 범위를 제어할 수 있습니다.\n- 메모리 효율성이 향상되어 Tesla T4, RTX 3080 또는 RTX 2080 Ti와 같은 소비자용 GPU에서 파인튜닝을 실행할 수 있습니다! T4와 같은 GPU는 무료이며 Kaggle 또는 Google Colab 노트북에서 쉽게 액세스할 수 있습니다.\n\n\n\n\n💡 LoRA는 어텐션 레이어에만 한정되지는 않습니다. 저자는 언어 모델의 어텐션 레이어를 수정하는 것이 매우 효율적으로 죻은 성능을 얻기에 충분하다는 것을 발견했습니다. 이것이 LoRA 가중치를 모델의 어텐션 레이어에 추가하는 것이 일반적인 이유입니다. LoRA 작동 방식에 대한 자세한 내용은 [Using LoRA for effective Stable Diffusion fine-tuning](https://huggingface.co/blog/lora) 블로그를 확인하세요!\n\n\n\n[cloneofsimo](https://github.com/cloneofsimo)는 인기 있는 [lora](https://github.com/cloneofsimo/lora) GitHub 리포지토리에서 Stable Diffusion을 위한 LoRA 학습을 최초로 시도했습니다. 🧨 Diffusers는 [text-to-image 생성](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image#training-with-lora) 및 [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth#training-with-low-rank-adaptation-of-large-language-models-lora)을 지원합니다. 이 가이드는 두 가지를 모두 수행하는 방법을 보여줍니다.\n\n모델을 저장하거나 커뮤니티와 공유하려면 Hugging Face 계정에 로그인하세요(아직 계정이 없는 경우 [생성](hf.co/join)하세요):\n\n```bash\nhuggingface-cli login\n```\n\n## Text-to-image\n\n수십억 개의 파라메터들이 있는 Stable Diffusion과 같은 모델을 파인튜닝하는 것은 느리고 어려울 수 있습니다. LoRA를 사용하면 diffusion 모델을 파인튜닝하는 것이 훨씬 쉽고 빠릅니다. 8비트 옵티마이저와 같은 트릭에 의존하지 않고도 11GB의 GPU RAM으로 하드웨어에서 실행할 수 있습니다.\n\n\n### 학습[[dreambooth-training]]\n\n[Naruto BLIP 캡션](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions) 데이터셋으로 [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5)를 파인튜닝해 나만의 포켓몬을 생성해 보겠습니다.\n\n시작하려면 `MODEL_NAME` 및 `DATASET_NAME` 환경 변수가 설정되어 있는지 확인하십시오. `OUTPUT_DIR` 및 `HUB_MODEL_ID` 변수는 선택 사항이며 허브에서 모델을 저장할 위치를 지정합니다.\n\n```bash\nexport MODEL_NAME=\"runwayml/stable-diffusion-v1-5\"\nexport OUTPUT_DIR=\"/sddata/finetune/lora/naruto\"\nexport HUB_MODEL_ID=\"naruto-lora\"\nexport DATASET_NAME=\"lambdalabs/naruto-blip-captions\"\n```\n\n학습을 시작하기 전에 알아야 할 몇 가지 플래그가 있습니다.\n\n* `--push_to_hub`를 명시하면 학습된 LoRA 임베딩을 허브에 저장합니다.\n* `--report_to=wandb`는 학습 결과를 가중치 및 편향 대시보드에 보고하고 기록합니다(예를 들어, 이 [보고서](https://wandb.ai/pcuenq/text2image-fine-tune/run/b4k1w0tn?workspace=user-pcuenq)를 참조하세요).\n* `--learning_rate=1e-04`, 일반적으로 LoRA에서 사용하는 것보다 더 높은 학습률을 사용할 수 있습니다.\n\n이제 학습을 시작할 준비가 되었습니다 (전체 학습 스크립트는 [여기](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py)에서 찾을 수 있습니다).\n\n```bash\naccelerate launch train_dreambooth_lora.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --instance_prompt=\"a photo of sks dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=1 \\\n --checkpointing_steps=100 \\\n --learning_rate=1e-4 \\\n --report_to=\"wandb\" \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --max_train_steps=500 \\\n --validation_prompt=\"A photo of sks dog in a bucket\" \\\n --validation_epochs=50 \\\n --seed=\"0\" \\\n --push_to_hub\n```\n\n### 추론[[dreambooth-inference]]\n\n이제 [`StableDiffusionPipeline`]에서 기본 모델을 불러와 추론을 위해 모델을 사용할 수 있습니다:\n\n```py\n>>> import torch\n>>> from diffusers import StableDiffusionPipeline\n\n>>> model_base = \"runwayml/stable-diffusion-v1-5\"\n\n>>> pipe = StableDiffusionPipeline.from_pretrained(model_base, torch_dtype=torch.float16)\n```\n\n*기본 모델의 가중치 위에* 파인튜닝된 DreamBooth 모델에서 LoRA 가중치를 불러온 다음, 더 빠른 추론을 위해 파이프라인을 GPU로 이동합니다. LoRA 가중치를 프리징된 사전 훈련된 모델 가중치와 병합할 때, 선택적으로 'scale' 매개변수로 어느 정도의 가중치를 병합할 지 조절할 수 있습니다:\n\n\n\n💡 `0`의 `scale` 값은 LoRA 가중치를 사용하지 않아 원래 모델의 가중치만 사용한 것과 같고, `1`의 `scale` 값은 파인튜닝된 LoRA 가중치만 사용함을 의미합니다. 0과 1 사이의 값들은 두 결과들 사이로 보간됩니다.\n\n\n\n```py\n>>> pipe.unet.load_attn_procs(model_path)\n>>> pipe.to(\"cuda\")\n# LoRA 파인튜닝된 모델의 가중치 절반과 기본 모델의 가중치 절반 사용\n\n>>> image = pipe(\n... \"A picture of a sks dog in a bucket.\",\n... num_inference_steps=25,\n... guidance_scale=7.5,\n... cross_attention_kwargs={\"scale\": 0.5},\n... ).images[0]\n# 완전히 파인튜닝된 LoRA 모델의 가중치 사용\n\n>>> image = pipe(\"A picture of a sks dog in a bucket.\", num_inference_steps=25, guidance_scale=7.5).images[0]\n>>> image.save(\"bucket-dog.png\")\n```" }, { "path": "diffusers/docs/source/ko/training/overview.md", "content": "\n\n# 🧨 Diffusers 학습 예시\n\n이번 챕터에서는 다양한 유즈케이스들에 대한 예제 코드들을 통해 어떻게하면 효과적으로 `diffusers` 라이브러리를 사용할 수 있을까에 대해 알아보도록 하겠습니다. \n\n**Note**: 혹시 오피셜한 예시코드를 찾고 있다면, [여기](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines)를 참고해보세요!\n\n여기서 다룰 예시들은 다음을 지향합니다.\n\n- **손쉬운 디펜던시 설치** (Self-contained) : 여기서 사용될 예시 코드들의 디펜던시 패키지들은 전부 `pip install` 명령어를 통해 설치 가능한 패키지들입니다. 또한 친절하게 `requirements.txt` 파일에 해당 패키지들이 명시되어 있어, `pip install -r requirements.txt`로 간편하게 해당 디펜던시들을 설치할 수 있습니다. 예시: [train_unconditional.py](https://github.com/huggingface/diffusers/blob/main/examples/unconditional_image_generation/train_unconditional.py), [requirements.txt](https://github.com/huggingface/diffusers/blob/main/examples/unconditional_image_generation/requirements.txt) \n- **손쉬운 수정** (Easy-to-tweak) : 저희는 가능하면 많은 유즈 케이스들을 제공하고자 합니다. 하지만 예시는 결국 그저 예시라는 점들 기억해주세요. 여기서 제공되는 예시코드들을 그저 단순히 복사-붙혀넣기하는 식으로는 여러분이 마주한 문제들을 손쉽게 해결할 순 없을 것입니다. 다시 말해 어느 정도는 여러분의 상황과 니즈에 맞춰 코드를 일정 부분 고쳐나가야 할 것입니다. 따라서 대부분의 학습 예시들은 데이터의 전처리 과정과 학습 과정에 대한 코드들을 함께 제공함으로써, 사용자가 니즈에 맞게 손쉬운 수정할 수 있도록 돕고 있습니다.\n- **입문자 친화적인** (Beginner-friendly) : 이번 챕터는 diffusion 모델과 `diffusers` 라이브러리에 대한 전반적인 이해를 돕기 위해 작성되었습니다. 따라서 diffusion 모델에 대한 최신 SOTA (state-of-the-art) 방법론들 가운데서도, 입문자에게는 많이 어려울 수 있다고 판단되면, 해당 방법론들은 여기서 다루지 않으려고 합니다.\n- **하나의 태스크만 포함할 것**(One-purpose-only): 여기서 다룰 예시들은 하나의 태스크만 포함하고 있어야 합니다. 물론 이미지 초해상화(super-resolution)와 이미지 보정(modification)과 같은 유사한 모델링 프로세스를 갖는 태스크들이 존재하겠지만, 하나의 예제에 하나의 태스크만을 담는 것이 더 이해하기 용이하다고 판단했기 때문입니다.\n\n\n\n저희는 diffusion 모델의 대표적인 태스크들을 다루는 공식 예제를 제공하고 있습니다. *공식* 예제는 현재 진행형으로 `diffusers` 관리자들(maintainers)에 의해 관리되고 있습니다. 또한 저희는 앞서 정의한 저희의 철학을 엄격하게 따르고자 노력하고 있습니다. 혹시 여러분께서 이러한 예시가 반드시 필요하다고 생각되신다면, 언제든지 [Feature Request](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feature_request.md&title=) 혹은 직접 [Pull Request](https://github.com/huggingface/diffusers/compare)를 주시기 바랍니다. 저희는 언제나 환영입니다!\n\n학습 예시들은 다양한 태스크들에 대해 diffusion 모델을 사전학습(pretrain)하거나 파인튜닝(fine-tuning)하는 법을 보여줍니다. 현재 다음과 같은 예제들을 지원하고 있습니다.\n\n- [Unconditional Training](./unconditional_training)\n- [Text-to-Image Training](./text2image)\n- [Text Inversion](./text_inversion)\n- [Dreambooth](./dreambooth)\n\nmemory-efficient attention 연산을 수행하기 위해, 가능하면 [xFormers](../optimization/xformers)를 설치해주시기 바랍니다. 이를 통해 학습 속도를 늘리고 메모리에 대한 부담을 줄일 수 있습니다.\n\n| Task | 🤗 Accelerate | 🤗 Datasets | Colab\n|---|---|:---:|:---:|\n| [**Unconditional Image Generation**](./unconditional_training) | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)\n| [**Text-to-Image fine-tuning**](./text2image) | ✅ | ✅ | \n| [**Textual Inversion**](./text_inversion) | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb)\n| [**Dreambooth**](./dreambooth) | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb)\n| [**Training with LoRA**](./lora) | ✅ | - | - |\n| [**ControlNet**](./controlnet) | ✅ | ✅ | - |\n| [**InstructPix2Pix**](./instructpix2pix) | ✅ | ✅ | - |\n| [**Custom Diffusion**](./custom_diffusion) | ✅ | ✅ | - |\n\n\n## 커뮤니티\n\n공식 예제 외에도 **커뮤니티 예제** 역시 제공하고 있습니다. 해당 예제들은 우리의 커뮤니티에 의해 관리됩니다. 커뮤니티 예쩨는 학습 예시나 추론 파이프라인으로 구성될 수 있습니다. 이러한 커뮤니티 예시들의 경우, 앞서 정의했던 철학들을 좀 더 관대하게 적용하고 있습니다. 또한 이러한 커뮤니티 예시들의 경우, 모든 이슈들에 대한 유지보수를 보장할 수는 없습니다.\n\n유용하긴 하지만, 아직은 대중적이지 못하거나 저희의 철학에 부합하지 않는 예제들은 [community examples](https://github.com/huggingface/diffusers/tree/main/examples/community) 폴더에 담기게 됩니다.\n\n**Note**: 커뮤니티 예제는 `diffusers`에 기여(contribution)를 희망하는 분들에게 [아주 좋은 기여 수단](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)이 될 수 있습니다.\n\n## 주목할 사항들\n\n최신 버전의 예시 코드들의 성공적인 구동을 보장하기 위해서는, 반드시 **소스코드를 통해 `diffusers`를 설치해야 하며,** 해당 예시 코드들이 요구하는 디펜던시들 역시 설치해야 합니다. 이를 위해 새로운 가상 환경을 구축하고 다음의 명령어를 실행해야 합니다.\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\n그 다음 `cd` 명령어를 통해 해당 예제 디렉토리에 접근해서 다음 명령어를 실행하면 됩니다.\n\n```bash\npip install -r requirements.txt\n```" }, { "path": "diffusers/docs/source/ko/training/text2image.md", "content": "\n\n\n# Text-to-image\n\n\n\ntext-to-image 파인튜닝 스크립트는 experimental 상태입니다. 과적합하기 쉽고 치명적인 망각과 같은 문제에 부딪히기 쉽습니다. 자체 데이터셋에서 최상의 결과를 얻으려면 다양한 하이퍼파라미터를 탐색하는 것이 좋습니다.\n\n\n\nStable Diffusion과 같은 text-to-image 모델은 텍스트 프롬프트에서 이미지를 생성합니다. 이 가이드는 PyTorch 및 Flax를 사용하여 자체 데이터셋에서 [`CompVis/stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) 모델로 파인튜닝하는 방법을 보여줍니다. 이 가이드에 사용된 text-to-image 파인튜닝을 위한 모든 학습 스크립트에 관심이 있는 경우 이 [리포지토리](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image)에서 자세히 찾을 수 있습니다.\n\n스크립트를 실행하기 전에, 라이브러리의 학습 dependency들을 설치해야 합니다:\n\n```bash\npip install git+https://github.com/huggingface/diffusers.git\npip install -U -r requirements.txt\n```\n\n그리고 [🤗Accelerate](https://github.com/huggingface/accelerate/) 환경을 초기화합니다:\n\n```bash\naccelerate config\n```\n\n리포지토리를 이미 복제한 경우, 이 단계를 수행할 필요가 없습니다. 대신, 로컬 체크아웃 경로를 학습 스크립트에 명시할 수 있으며 거기에서 로드됩니다.\n\n### 하드웨어 요구 사항\n\n`gradient_checkpointing` 및 `mixed_precision`을 사용하면 단일 24GB GPU에서 모델을 파인튜닝할 수 있습니다. 더 높은 `batch_size`와 더 빠른 훈련을 위해서는 GPU 메모리가 30GB 이상인 GPU를 사용하는 것이 좋습니다. TPU 또는 GPU에서 파인튜닝을 위해 JAX나 Flax를 사용할 수도 있습니다. 자세한 내용은 [아래](#flax-jax-finetuning)를 참조하세요.\n\nxFormers로 memory efficient attention을 활성화하여 메모리 사용량 훨씬 더 줄일 수 있습니다. [xFormers가 설치](./optimization/xformers)되어 있는지 확인하고 `--enable_xformers_memory_efficient_attention`를 학습 스크립트에 명시합니다.\n\nxFormers는 Flax에 사용할 수 없습니다.\n\n## Hub에 모델 업로드하기\n\n학습 스크립트에 다음 인수를 추가하여 모델을 허브에 저장합니다:\n\n```bash\n --push_to_hub\n```\n\n\n## 체크포인트 저장 및 불러오기\n\n학습 중 발생할 수 있는 일에 대비하여 정기적으로 체크포인트를 저장해 두는 것이 좋습니다. 체크포인트를 저장하려면 학습 스크립트에 다음 인수를 명시합니다.\n\n```bash\n --checkpointing_steps=500\n```\n\n500스텝마다 전체 학습 state가 'output_dir'의 하위 폴더에 저장됩니다. 체크포인트는 'checkpoint-'에 지금까지 학습된 step 수입니다. 예를 들어 'checkpoint-1500'은 1500 학습 step 후에 저장된 체크포인트입니다.\n\n학습을 재개하기 위해 체크포인트를 불러오려면 '--resume_from_checkpoint' 인수를 학습 스크립트에 명시하고 재개할 체크포인트를 지정하십시오. 예를 들어 다음 인수는 1500개의 학습 step 후에 저장된 체크포인트에서부터 훈련을 재개합니다.\n\n```bash\n --resume_from_checkpoint=\"checkpoint-1500\"\n```\n\n## 파인튜닝\n\n\n\n다음과 같이 [Naruto BLIP 캡션](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions) 데이터셋에서 파인튜닝 실행을 위해 [PyTorch 학습 스크립트](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py)를 실행합니다:\n\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport dataset_name=\"lambdalabs/naruto-blip-captions\"\n\naccelerate launch train_text_to_image.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --dataset_name=$dataset_name \\\n --use_ema \\\n --resolution=512 --center_crop --random_flip \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --mixed_precision=\"fp16\" \\\n --max_train_steps=15000 \\\n --learning_rate=1e-05 \\\n --max_grad_norm=1 \\\n --lr_scheduler=\"constant\" --lr_warmup_steps=0 \\\n --output_dir=\"sd-naruto-model\" \n```\n\n자체 데이터셋으로 파인튜닝하려면 🤗 [Datasets](https://huggingface.co/docs/datasets/index)에서 요구하는 형식에 따라 데이터셋을 준비하세요. [데이터셋을 허브에 업로드](https://huggingface.co/docs/datasets/image_dataset#upload-dataset-to-the-hub)하거나 [파일들이 있는 로컬 폴더를 준비](https ://huggingface.co/docs/datasets/image_dataset#imagefolder)할 수 있습니다.\n\n사용자 커스텀 loading logic을 사용하려면 스크립트를 수정하십시오. 도움이 되도록 코드의 적절한 위치에 포인터를 남겼습니다. 🤗 아래 예제 스크립트는 `TRAIN_DIR`의 로컬 데이터셋으로를 파인튜닝하는 방법과 `OUTPUT_DIR`에서 모델을 저장할 위치를 보여줍니다:\n\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport TRAIN_DIR=\"path_to_your_dataset\"\nexport OUTPUT_DIR=\"path_to_save_model\"\n\naccelerate launch train_text_to_image.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_data_dir=$TRAIN_DIR \\\n --use_ema \\\n --resolution=512 --center_crop --random_flip \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --mixed_precision=\"fp16\" \\\n --max_train_steps=15000 \\\n --learning_rate=1e-05 \\\n --max_grad_norm=1 \\\n --lr_scheduler=\"constant\" --lr_warmup_steps=0 \\\n --output_dir=${OUTPUT_DIR}\n```\n\n\n\n[@duongna211](https://github.com/duongna21)의 기여로, Flax를 사용해 TPU 및 GPU에서 Stable Diffusion 모델을 더 빠르게 학습할 수 있습니다. 이는 TPU 하드웨어에서 매우 효율적이지만 GPU에서도 훌륭하게 작동합니다. Flax 학습 스크립트는 gradient checkpointing나 gradient accumulation과 같은 기능을 아직 지원하지 않으므로 메모리가 30GB 이상인 GPU 또는 TPU v3가 필요합니다.\n\n스크립트를 실행하기 전에 요구 사항이 설치되어 있는지 확인하십시오:\n\n```bash\npip install -U -r requirements_flax.txt\n```\n\n그러면 다음과 같이 [Flax 학습 스크립트](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_flax.py)를 실행할 수 있습니다.\n\n```bash\nexport MODEL_NAME=\"runwayml/stable-diffusion-v1-5\"\nexport dataset_name=\"lambdalabs/naruto-blip-captions\"\n\npython train_text_to_image_flax.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --dataset_name=$dataset_name \\\n --resolution=512 --center_crop --random_flip \\\n --train_batch_size=1 \\\n --max_train_steps=15000 \\\n --learning_rate=1e-05 \\\n --max_grad_norm=1 \\\n --output_dir=\"sd-naruto-model\" \n```\n\n자체 데이터셋으로 파인튜닝하려면 🤗 [Datasets](https://huggingface.co/docs/datasets/index)에서 요구하는 형식에 따라 데이터셋을 준비하세요. [데이터셋을 허브에 업로드](https://huggingface.co/docs/datasets/image_dataset#upload-dataset-to-the-hub)하거나 [파일들이 있는 로컬 폴더를 준비](https ://huggingface.co/docs/datasets/image_dataset#imagefolder)할 수 있습니다.\n\n사용자 커스텀 loading logic을 사용하려면 스크립트를 수정하십시오. 도움이 되도록 코드의 적절한 위치에 포인터를 남겼습니다. 🤗 아래 예제 스크립트는 `TRAIN_DIR`의 로컬 데이터셋으로를 파인튜닝하는 방법을 보여줍니다:\n\n```bash\nexport MODEL_NAME=\"duongna/stable-diffusion-v1-4-flax\"\nexport TRAIN_DIR=\"path_to_your_dataset\"\n\npython train_text_to_image_flax.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_data_dir=$TRAIN_DIR \\\n --resolution=512 --center_crop --random_flip \\\n --train_batch_size=1 \\\n --mixed_precision=\"fp16\" \\\n --max_train_steps=15000 \\\n --learning_rate=1e-05 \\\n --max_grad_norm=1 \\\n --output_dir=\"sd-naruto-model\"\n```\n\n\n\n## LoRA\n\nText-to-image 모델 파인튜닝을 위해, 대규모 모델 학습을 가속화하기 위한 파인튜닝 기술인 LoRA(Low-Rank Adaptation of Large Language Models)를 사용할 수 있습니다. 자세한 내용은 [LoRA 학습](lora#text-to-image) 가이드를 참조하세요.\n\n## 추론\n\n허브의 모델 경로 또는 모델 이름을 [`StableDiffusionPipeline`]에 전달하여 추론을 위해 파인 튜닝된 모델을 불러올 수 있습니다:\n\n\n\n```python\nfrom diffusers import StableDiffusionPipeline\n\nmodel_path = \"path_to_saved_model\"\npipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)\npipe.to(\"cuda\")\n\nimage = pipe(prompt=\"yoda\").images[0]\nimage.save(\"yoda-naruto.png\")\n```\n\n\n```python\nimport jax\nimport numpy as np\nfrom flax.jax_utils import replicate\nfrom flax.training.common_utils import shard\nfrom diffusers import FlaxStableDiffusionPipeline\n\nmodel_path = \"path_to_saved_model\"\npipe, params = FlaxStableDiffusionPipeline.from_pretrained(model_path, dtype=jax.numpy.bfloat16)\n\nprompt = \"yoda naruto\"\nprng_seed = jax.random.PRNGKey(0)\nnum_inference_steps = 50\n\nnum_samples = jax.device_count()\nprompt = num_samples * [prompt]\nprompt_ids = pipeline.prepare_inputs(prompt)\n\n# shard inputs and rng\nparams = replicate(params)\nprng_seed = jax.random.split(prng_seed, jax.device_count())\nprompt_ids = shard(prompt_ids)\n\nimages = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images\nimages = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))\nimage.save(\"yoda-naruto.png\")\n```\n\n" }, { "path": "diffusers/docs/source/ko/training/text_inversion.md", "content": " \n\n\n\n# Textual-Inversion\n\n[[open-in-colab]]\n\n[textual-inversion](https://arxiv.org/abs/2208.01618)은 소수의 예시 이미지에서 새로운 콘셉트를 포착하는 기법입니다. 이 기술은 원래 [Latent Diffusion](https://github.com/CompVis/latent-diffusion)에서 시연되었지만, 이후 [Stable Diffusion](https://huggingface.co/docs/diffusers/main/en/conceptual/stable_diffusion)과 같은 유사한 다른 모델에도 적용되었습니다. 학습된 콘셉트는 text-to-image 파이프라인에서 생성된 이미지를 더 잘 제어하는 데 사용할 수 있습니다. 이 모델은 텍스트 인코더의 임베딩 공간에서 새로운 '단어'를 학습하여 개인화된 이미지 생성을 위한 텍스트 프롬프트 내에서 사용됩니다.\n\n![Textual Inversion example](https://textual-inversion.github.io/static/images/editing/colorful_teapot.JPG)\nBy using just 3-5 images you can teach new concepts to a model such as Stable Diffusion for personalized image generation (image source).\n\n이 가이드에서는 textual-inversion으로 [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) 모델을 학습하는 방법을 설명합니다. 이 가이드에서 사용된 모든 textual-inversion 학습 스크립트는 [여기](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion)에서 확인할 수 있습니다. 내부적으로 어떻게 작동하는지 자세히 살펴보고 싶으시다면 해당 링크를 참조해주시기 바랍니다.\n\n\n\n[Stable Diffusion Textual Inversion Concepts Library](https://huggingface.co/sd-concepts-library)에는 커뮤니티에서 제작한 학습된 textual-inversion 모델들이 있습니다. 시간이 지남에 따라 더 많은 콘셉트들이 추가되어 유용한 리소스로 성장할 것입니다!\n\n\n\n시작하기 전에 학습을 위한 의존성 라이브러리들을 설치해야 합니다:\n\n```bash\npip install diffusers accelerate transformers\n```\n\n의존성 라이브러리들의 설치가 완료되면, [🤗Accelerate](https://github.com/huggingface/accelerate/) 환경을 초기화시킵니다.\n\n```bash\naccelerate config\n```\n\n별도의 설정없이, 기본 🤗Accelerate 환경을 설정하려면 다음과 같이 하세요:\n\n```bash\naccelerate config default\n```\n\n또는 사용 중인 환경이 노트북과 같은 대화형 셸을 지원하지 않는다면, 다음과 같이 사용할 수 있습니다:\n\n```py\nfrom accelerate.utils import write_basic_config\n\nwrite_basic_config()\n```\n\n마지막으로, Memory-Efficient Attention을 통해 메모리 사용량을 줄이기 위해 [xFormers](https://huggingface.co/docs/diffusers/main/en/training/optimization/xformers)를 설치합니다. xFormers를 설치한 후, 학습 스크립트에 `--enable_xformers_memory_efficient_attention` 인자를 추가합니다. xFormers는 Flax에서 지원되지 않습니다.\n\n## 허브에 모델 업로드하기\n\n모델을 허브에 저장하려면, 학습 스크립트에 다음 인자를 추가해야 합니다.\n\n```bash\n--push_to_hub\n```\n\n## 체크포인트 저장 및 불러오기\n\n학습중에 모델의 체크포인트를 정기적으로 저장하는 것이 좋습니다. 이렇게 하면 어떤 이유로든 학습이 중단된 경우 저장된 체크포인트에서 학습을 다시 시작할 수 있습니다. 학습 스크립트에 다음 인자를 전달하면 500단계마다 전체 학습 상태가 `output_dir`의 하위 폴더에 체크포인트로서 저장됩니다.\n\n```bash\n--checkpointing_steps=500\n```\n\n저장된 체크포인트에서 학습을 재개하려면, 학습 스크립트와 재개할 특정 체크포인트에 다음 인자를 전달하세요.\n\n```bash\n--resume_from_checkpoint=\"checkpoint-1500\"\n```\n\n## 파인 튜닝\n\n학습용 데이터셋으로 [고양이 장난감 데이터셋](https://huggingface.co/datasets/diffusers/cat_toy_example)을 다운로드하여 디렉토리에 저장하세요. 여러분만의 고유한 데이터셋을 사용하고자 한다면, [학습용 데이터셋 만들기](https://huggingface.co/docs/diffusers/training/create_dataset) 가이드를 살펴보시기 바랍니다.\n\n```py\nfrom huggingface_hub import snapshot_download\n\nlocal_dir = \"./cat\"\nsnapshot_download(\n \"diffusers/cat_toy_example\", local_dir=local_dir, repo_type=\"dataset\", ignore_patterns=\".gitattributes\"\n)\n```\n\n모델의 리포지토리 ID(또는 모델 가중치가 포함된 디렉터리 경로)를 `MODEL_NAME` 환경 변수에 할당하고, 해당 값을 [`pretrained_model_name_or_path`](https://huggingface.co/docs/diffusers/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path) 인자에 전달합니다. 그리고 이미지가 포함된 디렉터리 경로를 `DATA_DIR` 환경 변수에 할당합니다.\n\n이제 [학습 스크립트](https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py)를 실행할 수 있습니다. 스크립트는 다음 파일을 생성하고 리포지토리에 저장합니다.\n\n- `learned_embeds.bin` \n- `token_identifier.txt`\n- `type_of_concept.txt`.\n\n\n\n💡V100 GPU 1개를 기준으로 전체 학습에는 최대 1시간이 걸립니다. 학습이 완료되기를 기다리는 동안 궁금한 점이 있으면 아래 섹션에서 [textual-inversion이 어떻게 작동하는지](https://huggingface.co/docs/diffusers/training/text_inversion#how-it-works) 자유롭게 확인하세요 ! \n\n\n\n\n\n```bash\nexport MODEL_NAME=\"runwayml/stable-diffusion-v1-5\"\nexport DATA_DIR=\"./cat\"\n\naccelerate launch textual_inversion.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_data_dir=$DATA_DIR \\\n --learnable_property=\"object\" \\\n --placeholder_token=\"\" --initializer_token=\"toy\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --max_train_steps=3000 \\\n --learning_rate=5.0e-04 --scale_lr \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --output_dir=\"textual_inversion_cat\" \\\n --push_to_hub\n```\n\n\n\n💡학습 성능을 올리기 위해, 플레이스홀더 토큰(``)을 (단일한 임베딩 벡터가 아닌) 복수의 임베딩 벡터로 표현하는 것 역시 고려할 있습니다. 이러한 트릭이 모델이 보다 복잡한 이미지의 스타일(앞서 말한 콘셉트)을 더 잘 캡처하는 데 도움이 될 수 있습니다. 복수의 임베딩 벡터 학습을 활성화하려면 다음 옵션을 전달하십시오.\n\n```bash\n--num_vectors=5\n```\n\n\n\n\n\nTPU에 액세스할 수 있는 경우, [Flax 학습 스크립트](https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion_flax.py)를 사용하여 더 빠르게 모델을 학습시켜보세요. (물론 GPU에서도 작동합니다.) 동일한 설정에서 Flax 학습 스크립트는 PyTorch 학습 스크립트보다 최소 70% 더 빨라야 합니다! ⚡️\n\n시작하기 앞서 Flax에 대한 의존성 라이브러리들을 설치해야 합니다.\n\n```bash\npip install -U -r requirements_flax.txt\n```\n\n모델의 리포지토리 ID(또는 모델 가중치가 포함된 디렉터리 경로)를 `MODEL_NAME` 환경 변수에 할당하고, 해당 값을 [`pretrained_model_name_or_path`](https://huggingface.co/docs/diffusers/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path) 인자에 전달합니다.\n\n그런 다음 [학습 스크립트](https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion_flax.py)를 시작할 수 있습니다.\n\n```bash\nexport MODEL_NAME=\"duongna/stable-diffusion-v1-4-flax\"\nexport DATA_DIR=\"./cat\"\n\npython textual_inversion_flax.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_data_dir=$DATA_DIR \\\n --learnable_property=\"object\" \\\n --placeholder_token=\"\" --initializer_token=\"toy\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --max_train_steps=3000 \\\n --learning_rate=5.0e-04 --scale_lr \\\n --output_dir=\"textual_inversion_cat\" \\\n --push_to_hub\n```\n\n\n\n### 중간 로깅\n\n모델의 학습 진행 상황을 추적하는 데 관심이 있는 경우, 학습 과정에서 생성된 이미지를 저장할 수 있습니다. 학습 스크립트에 다음 인수를 추가하여 중간 로깅을 활성화합니다.\n\n- `validation_prompt` : 샘플을 생성하는 데 사용되는 프롬프트(기본값은 `None`으로 설정되며, 이 때 중간 로깅은 비활성화됨)\n- `num_validation_images` : 생성할 샘플 이미지 수\n- `validation_steps` : `validation_prompt`로부터 샘플 이미지를 생성하기 전 스텝의 수\n\n```bash\n--validation_prompt=\"A backpack\"\n--num_validation_images=4\n--validation_steps=100\n```\n\n## 추론\n\n모델을 학습한 후에는, 해당 모델을 [`StableDiffusionPipeline`]을 사용하여 추론에 사용할 수 있습니다.\n\ntextual-inversion 스크립트는 기본적으로 textual-inversion을 통해 얻어진 임베딩 벡터만을 저장합니다. 해당 임베딩 벡터들은 텍스트 인코더의 임베딩 행렬에 추가되어 있습습니다.\n\n\n\n\n\n💡 커뮤니티는 [sd-concepts-library](https://huggingface.co/sd-concepts-library) 라는 대규모의 textual-inversion 임베딩 벡터 라이브러리를 만들었습니다. textual-inversion 임베딩을 밑바닥부터 학습하는 대신, 해당 라이브러리에 본인이 찾는 textual-inversion 임베딩이 이미 추가되어 있지 않은지를 확인하는 것도 좋은 방법이 될 것 같습니다.\n\n\n\ntextual-inversion 임베딩 벡터을 불러오기 위해서는, 먼저 해당 임베딩 벡터를 학습할 때 사용한 모델을 불러와야 합니다. 여기서는 [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/docs/diffusers/training/runwayml/stable-diffusion-v1-5) 모델이 사용되었다고 가정하고 불러오겠습니다.\n\n```python\nfrom diffusers import StableDiffusionPipeline\nimport torch\n\nmodel_id = \"runwayml/stable-diffusion-v1-5\"\npipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(\"cuda\")\n```\n\n다음으로 `TextualInversionLoaderMixin.load_textual_inversion` 함수를 통해, textual-inversion 임베딩 벡터를 불러와야 합니다. 여기서 우리는 이전의 `` 예제의 임베딩을 불러올 것입니다.\n\n```python\npipe.load_textual_inversion(\"sd-concepts-library/cat-toy\")\n```\n\n이제 플레이스홀더 토큰(``)이 잘 동작하는지를 확인하는 파이프라인을 실행할 수 있습니다.\n\n```python\nprompt = \"A backpack\"\n\nimage = pipe(prompt, num_inference_steps=50).images[0]\nimage.save(\"cat-backpack.png\")\n```\n\n`TextualInversionLoaderMixin.load_textual_inversion`은 Diffusers 형식으로 저장된 텍스트 임베딩 벡터를 로드할 수 있을 뿐만 아니라, [Automatic1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) 형식으로 저장된 임베딩 벡터도 로드할 수 있습니다. 이렇게 하려면, 먼저 [civitAI](https://civitai.com/models/3036?modelVersionId=8387)에서 임베딩 벡터를 다운로드한 다음 로컬에서 불러와야 합니다.\n\n```python\npipe.load_textual_inversion(\"./charturnerv2.pt\")\n```\n\n\n\n현재 Flax에 대한 `load_textual_inversion` 함수는 없습니다. 따라서 학습 후 textual-inversion 임베딩 벡터가 모델의 일부로서 저장되었는지를 확인해야 합니다. 그런 다음은 다른 Flax 모델과 마찬가지로 실행할 수 있습니다.\n\n```python\nimport jax\nimport numpy as np\nfrom flax.jax_utils import replicate\nfrom flax.training.common_utils import shard\nfrom diffusers import FlaxStableDiffusionPipeline\n\nmodel_path = \"path-to-your-trained-model\"\npipeline, params = FlaxStableDiffusionPipeline.from_pretrained(model_path, dtype=jax.numpy.bfloat16)\n\nprompt = \"A backpack\"\nprng_seed = jax.random.PRNGKey(0)\nnum_inference_steps = 50\n\nnum_samples = jax.device_count()\nprompt = num_samples * [prompt]\nprompt_ids = pipeline.prepare_inputs(prompt)\n\n# shard inputs and rng\nparams = replicate(params)\nprng_seed = jax.random.split(prng_seed, jax.device_count())\nprompt_ids = shard(prompt_ids)\n\nimages = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images\nimages = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))\nimage.save(\"cat-backpack.png\")\n```\n\n\n\n## 작동 방식\n\n![Diagram from the paper showing overview](https://textual-inversion.github.io/static/images/training/training.JPG)\nArchitecture overview from the Textual Inversion blog post.\n\n일반적으로 텍스트 프롬프트는 모델에 전달되기 전에 임베딩으로 토큰화됩니다. textual-inversion은 비슷한 작업을 수행하지만, 위 다이어그램의 특수 토큰 `S*`로부터 새로운 토큰 임베딩 `v*`를 학습합니다. 모델의 아웃풋은 디퓨전 모델을 조정하는 데 사용되며, 디퓨전 모델이 단 몇 개의 예제 이미지에서 신속하고 새로운 콘셉트를 이해하는 데 도움을 줍니다.\n\n이를 위해 textual-inversion은 제너레이터 모델과 학습용 이미지의 노이즈 버전을 사용합니다. 제너레이터는 노이즈가 적은 버전의 이미지를 예측하려고 시도하며 토큰 임베딩 `v*`은 제너레이터의 성능에 따라 최적화됩니다. 토큰 임베딩이 새로운 콘셉트를 성공적으로 포착하면 디퓨전 모델에 더 유용한 정보를 제공하고 노이즈가 적은 더 선명한 이미지를 생성하는 데 도움이 됩니다. 이러한 최적화 프로세스는 일반적으로 다양한 프롬프트와 이미지에 수천 번에 노출됨으로써 이루어집니다.\n\n" }, { "path": "diffusers/docs/source/ko/training/unconditional_training.md", "content": "\n\n# Unconditional 이미지 생성\n\nunconditional 이미지 생성은 text-to-image 또는 image-to-image 모델과 달리 텍스트나 이미지에 대한 조건이 없이 학습 데이터 분포와 유사한 이미지만을 생성합니다.\n\n\n\n\n이 가이드에서는 기존에 존재하던 데이터셋과 자신만의 커스텀 데이터셋에 대해 unconditional image generation 모델을 훈련하는 방법을 설명합니다. 훈련 세부 사항에 대해 더 자세히 알고 싶다면 unconditional image generation을 위한 모든 학습 스크립트를 [여기](https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation)에서 확인할 수 있습니다.\n\n스크립트를 실행하기 전, 먼저 의존성 라이브러리들을 설치해야 합니다.\n\n```bash\npip install diffusers[training] accelerate datasets\n```\n\n그 다음 🤗 [Accelerate](https://github.com/huggingface/accelerate/) 환경을 초기화합니다.\n\n```bash\naccelerate config\n```\n\n별도의 설정 없이 기본 설정으로 🤗 [Accelerate](https://github.com/huggingface/accelerate/) 환경을 초기화해봅시다.\n\n```bash\naccelerate config default\n```\n\n노트북과 같은 대화형 쉘을 지원하지 않는 환경의 경우, 다음과 같이 사용해볼 수도 있습니다.\n\n```py\nfrom accelerate.utils import write_basic_config\n\nwrite_basic_config()\n```\n\n## 모델을 허브에 업로드하기\n\n학습 스크립트에 다음 인자를 추가하여 허브에 모델을 업로드할 수 있습니다.\n\n```bash\n--push_to_hub\n```\n\n## 체크포인트 저장하고 불러오기\n\n훈련 중 문제가 발생할 경우를 대비하여 체크포인트를 정기적으로 저장하는 것이 좋습니다. 체크포인트를 저장하려면 학습 스크립트에 다음 인자를 전달합니다:\n\n```bash\n--checkpointing_steps=500\n```\n\n전체 훈련 상태는 500스텝마다 `output_dir`의 하위 폴더에 저장되며, 학습 스크립트에 `--resume_from_checkpoint` 인자를 전달함으로써 체크포인트를 불러오고 훈련을 재개할 수 있습니다.\n\n```bash\n--resume_from_checkpoint=\"checkpoint-1500\"\n```\n\n## 파인튜닝\n\n이제 학습 스크립트를 시작할 준비가 되었습니다! `--dataset_name` 인자에 파인튜닝할 데이터셋 이름을 지정한 다음, `--output_dir` 인자에 지정된 경로로 저장합니다. 본인만의 데이터셋를 사용하려면, [학습용 데이터셋 만들기](create_dataset) 가이드를 참조하세요.\n\n학습 스크립트는 `diffusion_pytorch_model.bin` 파일을 생성하고, 그것을 당신의 리포지토리에 저장합니다.\n\n\n\n💡 전체 학습은 V100 GPU 4개를 사용할 경우, 2시간이 소요됩니다.\n\n\n\n예를 들어, [Oxford Flowers](https://huggingface.co/datasets/huggan/flowers-102-categories) 데이터셋을 사용해 파인튜닝할 경우:\n\n```bash\naccelerate launch train_unconditional.py \\\n --dataset_name=\"huggan/flowers-102-categories\" \\\n --resolution=64 \\\n --output_dir=\"ddpm-ema-flowers-64\" \\\n --train_batch_size=16 \\\n --num_epochs=100 \\\n --gradient_accumulation_steps=1 \\\n --learning_rate=1e-4 \\\n --lr_warmup_steps=500 \\\n --mixed_precision=no \\\n --push_to_hub\n```\n\n
\n \n
\n[Naruto](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions) 데이터셋을 사용할 경우:\n\n```bash\naccelerate launch train_unconditional.py \\\n --dataset_name=\"lambdalabs/naruto-blip-captions\" \\\n --resolution=64 \\\n --output_dir=\"ddpm-ema-naruto-64\" \\\n --train_batch_size=16 \\\n --num_epochs=100 \\\n --gradient_accumulation_steps=1 \\\n --learning_rate=1e-4 \\\n --lr_warmup_steps=500 \\\n --mixed_precision=no \\\n --push_to_hub\n```\n\n
\n \n
\n\n### 여러개의 GPU로 훈련하기\n\n`accelerate`을 사용하면 원활한 다중 GPU 훈련이 가능합니다. `accelerate`을 사용하여 분산 훈련을 실행하려면 [여기](https://huggingface.co/docs/accelerate/basic_tutorials/launch) 지침을 따르세요. 다음은 명령어 예제입니다.\n\n```bash\naccelerate launch --mixed_precision=\"fp16\" --multi_gpu train_unconditional.py \\\n --dataset_name=\"lambdalabs/naruto-blip-captions\" \\\n --resolution=64 --center_crop --random_flip \\\n --output_dir=\"ddpm-ema-naruto-64\" \\\n --train_batch_size=16 \\\n --num_epochs=100 \\\n --gradient_accumulation_steps=1 \\\n --use_ema \\\n --learning_rate=1e-4 \\\n --lr_warmup_steps=500 \\\n --mixed_precision=\"fp16\" \\\n --logger=\"wandb\" \\\n --push_to_hub\n```\n" }, { "path": "diffusers/docs/source/ko/tutorials/basic_training.md", "content": "\n\n[[open-in-colab]]\n\n\n# Diffusion 모델을 학습하기\n\nUnconditional 이미지 생성은 학습에 사용된 데이터셋과 유사한 이미지를 생성하는 diffusion 모델에서 인기 있는 어플리케이션입니다. 일반적으로, 가장 좋은 결과는 특정 데이터셋에 사전 훈련된 모델을 파인튜닝하는 것으로 얻을 수 있습니다. 이 [허브](https://huggingface.co/search/full-text?q=unconditional-image-generation&type=model)에서 이러한 많은 체크포인트를 찾을 수 있지만, 만약 마음에 드는 체크포인트를 찾지 못했다면, 언제든지 스스로 학습할 수 있습니다!\n\n이 튜토리얼은 나만의 🦋 나비 🦋를 생성하기 위해 [Smithsonian Butterflies](https://huggingface.co/datasets/huggan/smithsonian_butterflies_subset) 데이터셋의 하위 집합에서 [`UNet2DModel`] 모델을 학습하는 방법을 가르쳐줄 것입니다.\n\n\n\n💡 이 학습 튜토리얼은 [Training with 🧨 Diffusers](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) 노트북 기반으로 합니다. Diffusion 모델의 작동 방식 및 자세한 내용은 노트북을 확인하세요!\n\n\n\n시작 전에, 🤗 Datasets을 불러오고 전처리하기 위해 데이터셋이 설치되어 있는지 다수 GPU에서 학습을 간소화하기 위해 🤗 Accelerate 가 설치되어 있는지 확인하세요. 그 후 학습 메트릭을 시각화하기 위해 [TensorBoard](https://www.tensorflow.org/tensorboard)를 또한 설치하세요. (또한 학습 추적을 위해 [Weights & Biases](https://docs.wandb.ai/)를 사용할 수 있습니다.)\n\n```bash\n!pip install diffusers[training]\n```\n\n커뮤니티에 모델을 공유할 것을 권장하며, 이를 위해서 Hugging Face 계정에 로그인을 해야 합니다. (계정이 없다면 [여기](https://hf.co/join)에서 만들 수 있습니다.) 노트북에서 로그인할 수 있으며 메시지가 표시되면 토큰을 입력할 수 있습니다.\n\n```py\n>>> from huggingface_hub import notebook_login\n\n>>> notebook_login()\n```\n\n또는 터미널로 로그인할 수 있습니다:\n\n```bash\nhuggingface-cli login\n```\n\n모델 체크포인트가 상당히 크기 때문에 [Git-LFS](https://git-lfs.com/)에서 대용량 파일의 버전 관리를 할 수 있습니다.\n\n```bash\n!sudo apt -qq install git-lfs\n!git config --global credential.helper store\n```\n\n\n## 학습 구성\n\n편의를 위해 학습 파라미터들을 포함한 `TrainingConfig` 클래스를 생성합니다 (자유롭게 조정 가능):\n\n```py\n>>> from dataclasses import dataclass\n\n\n>>> @dataclass\n... class TrainingConfig:\n... image_size = 128 # 생성되는 이미지 해상도\n... train_batch_size = 16\n... eval_batch_size = 16 # 평가 동안에 샘플링할 이미지 수\n... num_epochs = 50\n... gradient_accumulation_steps = 1\n... learning_rate = 1e-4\n... lr_warmup_steps = 500\n... save_image_epochs = 10\n... save_model_epochs = 30\n... mixed_precision = \"fp16\" # `no`는 float32, 자동 혼합 정밀도를 위한 `fp16`\n... output_dir = \"ddpm-butterflies-128\" # 로컬 및 HF Hub에 저장되는 모델명\n\n... push_to_hub = True # 저장된 모델을 HF Hub에 업로드할지 여부\n... hub_private_repo = False\n... overwrite_output_dir = True # 노트북을 다시 실행할 때 이전 모델에 덮어씌울지\n... seed = 0\n\n\n>>> config = TrainingConfig()\n```\n\n\n## 데이터셋 불러오기\n\n🤗 Datasets 라이브러리와 [Smithsonian Butterflies](https://huggingface.co/datasets/huggan/smithsonian_butterflies_subset) 데이터셋을 쉽게 불러올 수 있습니다.\n\n```py\n>>> from datasets import load_dataset\n\n>>> config.dataset_name = \"huggan/smithsonian_butterflies_subset\"\n>>> dataset = load_dataset(config.dataset_name, split=\"train\")\n```\n\n💡[HugGan Community Event](https://huggingface.co/huggan) 에서 추가의 데이터셋을 찾거나 로컬의 [`ImageFolder`](https://huggingface.co/docs/datasets/image_dataset#imagefolder)를 만듦으로써 나만의 데이터셋을 사용할 수 있습니다. HugGan Community Event 에 가져온 데이터셋의 경우 리포지토리의 id로 `config.dataset_name` 을 설정하고, 나만의 이미지를 사용하는 경우 `imagefolder` 를 설정합니다.\n\n🤗 Datasets은 [`~datasets.Image`] 기능을 사용해 자동으로 이미지 데이터를 디코딩하고 [`PIL.Image`](https://pillow.readthedocs.io/en/stable/reference/Image.html)로 불러옵니다. 이를 시각화 해보면:\n\n```py\n>>> import matplotlib.pyplot as plt\n\n>>> fig, axs = plt.subplots(1, 4, figsize=(16, 4))\n>>> for i, image in enumerate(dataset[:4][\"image\"]):\n... axs[i].imshow(image)\n... axs[i].set_axis_off()\n>>> fig.show()\n```\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/butterflies_ds.png)\n\n이미지는 모두 다른 사이즈이기 때문에, 우선 전처리가 필요합니다:\n\n- `Resize` 는 `config.image_size` 에 정의된 이미지 사이즈로 변경합니다.\n- `RandomHorizontalFlip` 은 랜덤적으로 이미지를 미러링하여 데이터셋을 보강합니다.\n- `Normalize` 는 모델이 예상하는 [-1, 1] 범위로 픽셀 값을 재조정 하는데 중요합니다.\n\n```py\n>>> from torchvision import transforms\n\n>>> preprocess = transforms.Compose(\n... [\n... transforms.Resize((config.image_size, config.image_size)),\n... transforms.RandomHorizontalFlip(),\n... transforms.ToTensor(),\n... transforms.Normalize([0.5], [0.5]),\n... ]\n... )\n```\n\n 학습 도중에 `preprocess` 함수를 적용하려면 🤗 Datasets의 [`~datasets.Dataset.set_transform`] 방법이 사용됩니다.\n\n```py\n>>> def transform(examples):\n... images = [preprocess(image.convert(\"RGB\")) for image in examples[\"image\"]]\n... return {\"images\": images}\n\n\n>>> dataset.set_transform(transform)\n```\n\n이미지의 크기가 조정되었는지 확인하기 위해 이미지를 다시 시각화해보세요. 이제 [DataLoader](https://pytorch.org/docs/stable/data#torch.utils.data.DataLoader)에 데이터셋을 포함해 학습할 준비가 되었습니다!\n\n```py\n>>> import torch\n\n>>> train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=config.train_batch_size, shuffle=True)\n```\n\n\n## UNet2DModel 생성하기\n\n🧨 Diffusers에 사전학습된 모델들은 모델 클래스에서 원하는 파라미터로 쉽게 생성할 수 있습니다. 예를 들어, [`UNet2DModel`]를 생성하려면:\n\n```py\n>>> from diffusers import UNet2DModel\n\n>>> model = UNet2DModel(\n... sample_size=config.image_size, # 타겟 이미지 해상도\n... in_channels=3, # 입력 채널 수, RGB 이미지에서 3\n... out_channels=3, # 출력 채널 수\n... layers_per_block=2, # UNet 블럭당 몇 개의 ResNet 레이어가 사용되는지\n... block_out_channels=(128, 128, 256, 256, 512, 512), # 각 UNet 블럭을 위한 출력 채널 수\n... down_block_types=(\n... \"DownBlock2D\", # 일반적인 ResNet 다운샘플링 블럭\n... \"DownBlock2D\",\n... \"DownBlock2D\",\n... \"DownBlock2D\",\n... \"AttnDownBlock2D\", # spatial self-attention이 포함된 일반적인 ResNet 다운샘플링 블럭\n... \"DownBlock2D\",\n... ),\n... up_block_types=(\n... \"UpBlock2D\", # 일반적인 ResNet 업샘플링 블럭\n... \"AttnUpBlock2D\", # spatial self-attention이 포함된 일반적인 ResNet 업샘플링 블럭\n... \"UpBlock2D\",\n... \"UpBlock2D\",\n... \"UpBlock2D\",\n... \"UpBlock2D\",\n... ),\n... )\n```\n\n샘플의 이미지 크기와 모델 출력 크기가 맞는지 빠르게 확인하기 위한 좋은 아이디어가 있습니다:\n\n```py\n>>> sample_image = dataset[0][\"images\"].unsqueeze(0)\n>>> print(\"Input shape:\", sample_image.shape)\nInput shape: torch.Size([1, 3, 128, 128])\n\n>>> print(\"Output shape:\", model(sample_image, timestep=0).sample.shape)\nOutput shape: torch.Size([1, 3, 128, 128])\n```\n\n훌륭해요! 다음, 이미지에 약간의 노이즈를 더하기 위해 스케줄러가 필요합니다.\n\n\n## 스케줄러 생성하기\n\n스케줄러는 모델을 학습 또는 추론에 사용하는지에 따라 다르게 작동합니다. 추론시에, 스케줄러는 노이즈로부터 이미지를 생성합니다. 학습시 스케줄러는 diffusion 과정에서의 특정 포인트로부터 모델의 출력 또는 샘플을 가져와 *노이즈 스케줄* 과 *업데이트 규칙*에 따라 이미지에 노이즈를 적용합니다.\n\n`DDPMScheduler`를 보면 이전으로부터 `sample_image`에 랜덤한 노이즈를 더하는 `add_noise` 메서드를 사용합니다:\n\n```py\n>>> import torch\n>>> from PIL import Image\n>>> from diffusers import DDPMScheduler\n\n>>> noise_scheduler = DDPMScheduler(num_train_timesteps=1000)\n>>> noise = torch.randn(sample_image.shape)\n>>> timesteps = torch.LongTensor([50])\n>>> noisy_image = noise_scheduler.add_noise(sample_image, noise, timesteps)\n\n>>> Image.fromarray(((noisy_image.permute(0, 2, 3, 1) + 1.0) * 127.5).type(torch.uint8).numpy()[0])\n```\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/noisy_butterfly.png)\n\n모델의 학습 목적은 이미지에 더해진 노이즈를 예측하는 것입니다. 이 단계에서 손실은 다음과 같이 계산될 수 있습니다:\n\n```py\n>>> import torch.nn.functional as F\n\n>>> noise_pred = model(noisy_image, timesteps).sample\n>>> loss = F.mse_loss(noise_pred, noise)\n```\n\n## 모델 학습하기\n\n지금까지, 모델 학습을 시작하기 위해 많은 부분을 갖추었으며 이제 남은 것은 모든 것을 조합하는 것입니다.\n\n우선 옵티마이저(optimizer)와 학습률 스케줄러(learning rate scheduler)가 필요할 것입니다:\n\n```py\n>>> from diffusers.optimization import get_cosine_schedule_with_warmup\n\n>>> optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate)\n>>> lr_scheduler = get_cosine_schedule_with_warmup(\n... optimizer=optimizer,\n... num_warmup_steps=config.lr_warmup_steps,\n... num_training_steps=(len(train_dataloader) * config.num_epochs),\n... )\n```\n\n그 후, 모델을 평가하는 방법이 필요합니다. 평가를 위해, `DDPMPipeline`을 사용해 배치의 이미지 샘플들을 생성하고 그리드 형태로 저장할 수 있습니다:\n\n```py\n>>> from diffusers import DDPMPipeline\n>>> import math\n>>> import os\n\n\n>>> def make_grid(images, rows, cols):\n... w, h = images[0].size\n... grid = Image.new(\"RGB\", size=(cols * w, rows * h))\n... for i, image in enumerate(images):\n... grid.paste(image, box=(i % cols * w, i // cols * h))\n... return grid\n\n\n>>> def evaluate(config, epoch, pipeline):\n... # 랜덤한 노이즈로 부터 이미지를 추출합니다.(이는 역전파 diffusion 과정입니다.)\n... # 기본 파이프라인 출력 형태는 `List[PIL.Image]` 입니다.\n... images = pipeline(\n... batch_size=config.eval_batch_size,\n... generator=torch.manual_seed(config.seed),\n... ).images\n\n... # 이미지들을 그리드로 만들어줍니다.\n... image_grid = make_grid(images, rows=4, cols=4)\n\n... # 이미지들을 저장합니다.\n... test_dir = os.path.join(config.output_dir, \"samples\")\n... os.makedirs(test_dir, exist_ok=True)\n... image_grid.save(f\"{test_dir}/{epoch:04d}.png\")\n```\n\nTensorBoard에 로깅, 그래디언트 누적 및 혼합 정밀도 학습을 쉽게 수행하기 위해 🤗 Accelerate를 학습 루프에 함께 앞서 말한 모든 구성 정보들을 묶어 진행할 수 있습니다. 허브에 모델을 업로드 하기 위해 리포지토리 이름 및 정보를 가져오기 위한 함수를 작성하고 허브에 업로드할 수 있습니다.\n\n💡아래의 학습 루프는 어렵고 길어 보일 수 있지만, 나중에 한 줄의 코드로 학습을 한다면 그만한 가치가 있을 것입니다! 만약 기다리지 못하고 이미지를 생성하고 싶다면, 아래 코드를 자유롭게 붙여넣고 작동시키면 됩니다. 🤗\n\n```py\n>>> from accelerate import Accelerator\n>>> from huggingface_hub import create_repo, upload_folder\n>>> from tqdm.auto import tqdm\n>>> from pathlib import Path\n>>> import os\n\n\n>>> def train_loop(config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler):\n... # Initialize accelerator and tensorboard logging\n... accelerator = Accelerator(\n... mixed_precision=config.mixed_precision,\n... gradient_accumulation_steps=config.gradient_accumulation_steps,\n... log_with=\"tensorboard\",\n... project_dir=os.path.join(config.output_dir, \"logs\"),\n... )\n... if accelerator.is_main_process:\n... if config.output_dir is not None:\n... os.makedirs(config.output_dir, exist_ok=True)\n... if config.push_to_hub:\n... repo_id = create_repo(\n... repo_id=config.hub_model_id or Path(config.output_dir).name, exist_ok=True\n... ).repo_id\n... accelerator.init_trackers(\"train_example\")\n\n... # 모든 것이 준비되었습니다.\n... # 기억해야 할 특정한 순서는 없으며 준비한 방법에 제공한 것과 동일한 순서로 객체의 압축을 풀면 됩니다.\n... model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n... model, optimizer, train_dataloader, lr_scheduler\n... )\n\n... global_step = 0\n\n... # 이제 모델을 학습합니다.\n... for epoch in range(config.num_epochs):\n... progress_bar = tqdm(total=len(train_dataloader), disable=not accelerator.is_local_main_process)\n... progress_bar.set_description(f\"Epoch {epoch}\")\n\n... for step, batch in enumerate(train_dataloader):\n... clean_images = batch[\"images\"]\n... # 이미지에 더할 노이즈를 샘플링합니다.\n... noise = torch.randn(clean_images.shape, device=clean_images.device)\n... bs = clean_images.shape[0]\n\n... # 각 이미지를 위한 랜덤한 타임스텝(timestep)을 샘플링합니다.\n... timesteps = torch.randint(\n... 0, noise_scheduler.config.num_train_timesteps, (bs,), device=clean_images.device,\n... dtype=torch.int64\n... )\n\n... # 각 타임스텝의 노이즈 크기에 따라 깨끗한 이미지에 노이즈를 추가합니다.\n... # (이는 foward diffusion 과정입니다.)\n... noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)\n\n... with accelerator.accumulate(model):\n... # 노이즈를 반복적으로 예측합니다.\n... noise_pred = model(noisy_images, timesteps, return_dict=False)[0]\n... loss = F.mse_loss(noise_pred, noise)\n... accelerator.backward(loss)\n\n... accelerator.clip_grad_norm_(model.parameters(), 1.0)\n... optimizer.step()\n... lr_scheduler.step()\n... optimizer.zero_grad()\n\n... progress_bar.update(1)\n... logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0], \"step\": global_step}\n... progress_bar.set_postfix(**logs)\n... accelerator.log(logs, step=global_step)\n... global_step += 1\n\n... # 각 에포크가 끝난 후 evaluate()와 몇 가지 데모 이미지를 선택적으로 샘플링하고 모델을 저장합니다.\n... if accelerator.is_main_process:\n... pipeline = DDPMPipeline(unet=accelerator.unwrap_model(model), scheduler=noise_scheduler)\n\n... if (epoch + 1) % config.save_image_epochs == 0 or epoch == config.num_epochs - 1:\n... evaluate(config, epoch, pipeline)\n\n... if (epoch + 1) % config.save_model_epochs == 0 or epoch == config.num_epochs - 1:\n... if config.push_to_hub:\n... upload_folder(\n... repo_id=repo_id,\n... folder_path=config.output_dir,\n... commit_message=f\"Epoch {epoch}\",\n... ignore_patterns=[\"step_*\", \"epoch_*\"],\n... )\n... else:\n... pipeline.save_pretrained(config.output_dir)\n```\n\n휴, 코드가 꽤 많았네요! 하지만 🤗 Accelerate의 [`~accelerate.notebook_launcher`] 함수와 학습을 시작할 준비가 되었습니다. 함수에 학습 루프, 모든 학습 인수, 학습에 사용할 프로세스 수(사용 가능한 GPU의 수를 변경할 수 있음)를 전달합니다:\n\n```py\n>>> from accelerate import notebook_launcher\n\n>>> args = (config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler)\n\n>>> notebook_launcher(train_loop, args, num_processes=1)\n```\n\n한번 학습이 완료되면, diffusion 모델로 생성된 최종 🦋이미지🦋를 확인해보길 바랍니다!\n\n```py\n>>> import glob\n\n>>> sample_images = sorted(glob.glob(f\"{config.output_dir}/samples/*.png\"))\n>>> Image.open(sample_images[-1])\n```\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/butterflies_final.png)\n\n## 다음 단계\n\nUnconditional 이미지 생성은 학습될 수 있는 작업 중 하나의 예시입니다. 다른 작업과 학습 방법은 [🧨 Diffusers 학습 예시](../training/overview) 페이지에서 확인할 수 있습니다. 다음은 학습할 수 있는 몇 가지 예시입니다:\n\n- [Textual Inversion](../training/text_inversion), 특정 시각적 개념을 학습시켜 생성된 이미지에 통합시키는 알고리즘입니다.\n- [DreamBooth](../training/dreambooth), 주제에 대한 몇 가지 입력 이미지들이 주어지면 주제에 대한 개인화된 이미지를 생성하기 위한 기술입니다.\n- [Guide](../training/text2image) 데이터셋에 Stable Diffusion 모델을 파인튜닝하는 방법입니다.\n- [Guide](../training/lora) LoRA를 사용해 매우 큰 모델을 빠르게 파인튜닝하기 위한 메모리 효율적인 기술입니다.\n" }, { "path": "diffusers/docs/source/ko/tutorials/tutorial_overview.md", "content": "\n\n# Overview\n\n🧨 Diffusers에 오신 걸 환영합니다! 여러분이 diffusion 모델과 생성 AI를 처음 접하고, 더 많은 걸 배우고 싶으셨다면 제대로 찾아오셨습니다. 이 튜토리얼은 diffusion model을 여러분에게 젠틀하게 소개하고, 라이브러리의 기본 사항(핵심 구성요소와 🧨 Diffusers 사용법)을 이해하는 데 도움이 되도록 설계되었습니다.\n\n여러분은 이 튜토리얼을 통해 빠르게 생성하기 위해선 추론 파이프라인을 어떻게 사용해야 하는지, 그리고 라이브러리를 modular toolbox처럼 이용해서 여러분만의 diffusion system을 구축할 수 있도록 파이프라인을 분해하는 법을 배울 수 있습니다. 다음 단원에서는 여러분이 원하는 것을 생성하기 위해 자신만의 diffusion model을 학습하는 방법을 배우게 됩니다.\n\n튜토리얼을 완료한다면 여러분은 라이브러리를 직접 탐색하고, 자신의 프로젝트와 애플리케이션에 적용할 스킬들을 습득할 수 있을 겁니다. \n\n[Discord](https://discord.com/invite/JfAtkvEtRb)나 [포럼](https://discuss.huggingface.co/c/discussion-related-to-httpsgithubcomhuggingfacediffusers/63) 커뮤니티에 자유롭게 참여해서 다른 사용자와 개발자들과 교류하고 협업해 보세요!\n\n자 지금부터 diffusing을 시작해 보겠습니다! 🧨" }, { "path": "diffusers/docs/source/ko/using-diffusers/conditional_image_generation.md", "content": "\n\n# 조건부 이미지 생성\n\n[[open-in-colab]]\n\n조건부 이미지 생성을 사용하면 텍스트 프롬프트에서 이미지를 생성할 수 있습니다. 텍스트는 임베딩으로 변환되며, 임베딩은 노이즈에서 이미지를 생성하도록 모델을 조건화하는 데 사용됩니다.\n\n[`DiffusionPipeline`]은 추론을 위해 사전 훈련된 diffusion 시스템을 사용하는 가장 쉬운 방법입니다.\n\n먼저 [`DiffusionPipeline`]의 인스턴스를 생성하고 다운로드할 파이프라인 [체크포인트](https://huggingface.co/models?library=diffusers&sort=downloads)를 지정합니다.\n\n이 가이드에서는 [잠재 Diffusion](https://huggingface.co/CompVis/ldm-text2im-large-256)과 함께 텍스트-이미지 생성에 [`DiffusionPipeline`]을 사용합니다:\n\n```python\n>>> from diffusers import DiffusionPipeline\n\n>>> generator = DiffusionPipeline.from_pretrained(\"CompVis/ldm-text2im-large-256\")\n```\n\n[`DiffusionPipeline`]은 모든 모델링, 토큰화, 스케줄링 구성 요소를 다운로드하고 캐시합니다. \n이 모델은 약 14억 개의 파라미터로 구성되어 있기 때문에 GPU에서 실행할 것을 강력히 권장합니다.\nPyTorch에서와 마찬가지로 생성기 객체를 GPU로 이동할 수 있습니다:\n\n```python\n>>> generator.to(\"cuda\")\n```\n\n이제 텍스트 프롬프트에서 `생성기`를 사용할 수 있습니다:\n\n```python\n>>> image = generator(\"An image of a squirrel in Picasso style\").images[0]\n```\n\n출력값은 기본적으로 [`PIL.Image`](https://pillow.readthedocs.io/en/stable/reference/Image.html?highlight=image#the-image-class) 객체로 래핑됩니다.\n\n호출하여 이미지를 저장할 수 있습니다:\n\n```python\n>>> image.save(\"image_of_squirrel_painting.png\")\n```\n\n아래 스페이스를 사용해보고 안내 배율 매개변수를 자유롭게 조정하여 이미지 품질에 어떤 영향을 미치는지 확인해 보세요!\n\n" }, { "path": "diffusers/docs/source/ko/using-diffusers/contribute_pipeline.md", "content": "\n\n# 커뮤니티 파이프라인에 기여하는 방법\n\n\n\n💡 모든 사람이 속도 저하 없이 쉽게 작업을 공유할 수 있도록 커뮤니티 파이프라인을 추가하는 이유에 대한 자세한 내용은 GitHub 이슈 [#841](https://github.com/huggingface/diffusers/issues/841)를 참조하세요. \n\n\n\n커뮤니티 파이프라인을 사용하면 [`DiffusionPipeline`] 위에 원하는 추가 기능을 추가할 수 있습니다. `DiffusionPipeline` 위에 구축할 때의 가장 큰 장점은 누구나 인수를 하나만 추가하면 파이프라인을 로드하고 사용할 수 있어 커뮤니티가 매우 쉽게 접근할 수 있다는 것입니다.\n\n이번 가이드에서는 커뮤니티 파이프라인을 생성하는 방법과 작동 원리를 설명합니다.\n간단하게 설명하기 위해 `UNet`이 단일 forward pass를 수행하고 스케줄러를 한 번 호출하는 \"one-step\" 파이프라인을 만들겠습니다.\n\n## 파이프라인 초기화\n\n커뮤니티 파이프라인을 위한 `one_step_unet.py` 파일을 생성하는 것으로 시작합니다. 이 파일에서, Hub에서 모델 가중치와 스케줄러 구성을 로드할 수 있도록 [`DiffusionPipeline`]을 상속하는 파이프라인 클래스를 생성합니다. one-step 파이프라인에는 `UNet`과 스케줄러가 필요하므로 이를 `__init__` 함수에 인수로 추가해야합니다:\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\n\nclass UnetSchedulerOneForwardPipeline(DiffusionPipeline):\n def __init__(self, unet, scheduler):\n super().__init__()\n```\n\n파이프라인과 그 구성요소(`unet` and `scheduler`)를 [`~DiffusionPipeline.save_pretrained`]으로 저장할 수 있도록 하려면 `register_modules` 함수에 추가하세요:\n\n```diff\n from diffusers import DiffusionPipeline\n import torch\n\n class UnetSchedulerOneForwardPipeline(DiffusionPipeline):\n def __init__(self, unet, scheduler):\n super().__init__()\n\n+ self.register_modules(unet=unet, scheduler=scheduler)\n```\n\n이제 '초기화' 단계가 완료되었으니 forward pass로 이동할 수 있습니다! 🔥 \n\n## Forward pass 정의\n\nForward pass 에서는(`__call__`로 정의하는 것이 좋습니다) 원하는 기능을 추가할 수 있는 완전한 창작 자유가 있습니다. 우리의 놀라운 one-step 파이프라인의 경우, 임의의 이미지를 생성하고 `timestep=1`을 설정하여 `unet`과 `scheduler`를 한 번만 호출합니다:\n\n```diff\n from diffusers import DiffusionPipeline\n import torch\n\n\n class UnetSchedulerOneForwardPipeline(DiffusionPipeline):\n def __init__(self, unet, scheduler):\n super().__init__()\n\n self.register_modules(unet=unet, scheduler=scheduler)\n\n+ def __call__(self):\n+ image = torch.randn(\n+ (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),\n+ )\n+ timestep = 1\n\n+ model_output = self.unet(image, timestep).sample\n+ scheduler_output = self.scheduler.step(model_output, timestep, image).prev_sample\n\n+ return scheduler_output\n```\n\n끝났습니다! 🚀 이제 이 파이프라인에 `unet`과 `scheduler`를 전달하여 실행할 수 있습니다:\n\n```python\nfrom diffusers import DDPMScheduler, UNet2DModel\n\nscheduler = DDPMScheduler()\nunet = UNet2DModel()\n\npipeline = UnetSchedulerOneForwardPipeline(unet=unet, scheduler=scheduler)\n\noutput = pipeline()\n```\n\n하지만 파이프라인 구조가 동일한 경우 기존 가중치를 파이프라인에 로드할 수 있다는 장점이 있습니다. 예를 들어 one-step 파이프라인에 [`google/ddpm-cifar10-32`](https://huggingface.co/google/ddpm-cifar10-32) 가중치를 로드할 수 있습니다:\n\n```python\npipeline = UnetSchedulerOneForwardPipeline.from_pretrained(\"google/ddpm-cifar10-32\")\n\noutput = pipeline()\n```\n\n## 파이프라인 공유\n\n🧨Diffusers [리포지토리](https://github.com/huggingface/diffusers)에서 Pull Request를 열어 [examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) 하위 폴더에 `one_step_unet.py`의 멋진 파이프라인을 추가하세요.\n\n병합이 되면, `diffusers >= 0.4.0`이 설치된 사용자라면 누구나 `custom_pipeline` 인수에 지정하여 이 파이프라인을 마술처럼 🪄 사용할 수 있습니다:\n\n```python\nfrom diffusers import DiffusionPipeline\n\npipe = DiffusionPipeline.from_pretrained(\"google/ddpm-cifar10-32\", custom_pipeline=\"one_step_unet\")\npipe()\n```\n\n커뮤니티 파이프라인을 공유하는 또 다른 방법은 Hub 에서 선호하는 [모델 리포지토리](https://huggingface.co/docs/hub/models-uploading)에 직접 `one_step_unet.py` 파일을 업로드하는 것입니다. `one_step_unet.py` 파일을 지정하는 대신 모델 저장소 id를 `custom_pipeline` 인수에 전달하세요:\n\n```python\nfrom diffusers import DiffusionPipeline\n\npipeline = DiffusionPipeline.from_pretrained(\"google/ddpm-cifar10-32\", custom_pipeline=\"stevhliu/one_step_unet\")\n```\n\n다음 표에서 두 가지 공유 워크플로우를 비교하여 자신에게 가장 적합한 옵션을 결정하는 데 도움이 되는 정보를 확인하세요:\n\n| | GitHub 커뮤니티 파이프라인 | HF Hub 커뮤니티 파이프라인 |\n|----------------|------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------|\n| 사용법 | 동일 | 동일 |\n| 리뷰 과정 | 병합하기 전에 GitHub에서 Pull Request를 열고 Diffusers 팀의 검토 과정을 거칩니다. 속도가 느릴 수 있습니다. | 검토 없이 Hub 저장소에 바로 업로드합니다. 가장 빠른 워크플로우 입니다. |\n| 가시성 | 공식 Diffusers 저장소 및 문서에 포함되어 있습니다. | HF 허브 프로필에 포함되며 가시성을 확보하기 위해 자신의 사용량/프로모션에 의존합니다. |\n\n\n\n💡 커뮤니티 파이프라인 파일에 원하는 패키지를 사용할 수 있습니다. 사용자가 패키지를 설치하기만 하면 모든 것이 정상적으로 작동합니다. 파이프라인이 자동으로 감지되므로 `DiffusionPipeline`에서 상속하는 파이프라인 클래스가 하나만 있는지 확인하세요.\n\n\n\n## 커뮤니티 파이프라인은 어떻게 작동하나요?\n\n커뮤니티 파이프라인은 [`DiffusionPipeline`]을 상속하는 클래스입니다:\n\n- [`custom_pipeline`] 인수로 로드할 수 있습니다.\n- 모델 가중치 및 스케줄러 구성은 [`pretrained_model_name_or_path`]에서 로드됩니다.\n- 커뮤니티 파이프라인에서 기능을 구현하는 코드는 `pipeline.py` 파일에 정의되어 있습니다.\n\n공식 저장소에서 모든 파이프라인 구성 요소 가중치를 로드할 수 없는 경우가 있습니다. 이 경우 다른 구성 요소는 파이프라인에 직접 전달해야 합니다:\n\n```python\nfrom diffusers import DiffusionPipeline\nfrom transformers import CLIPFeatureExtractor, CLIPModel\n\nmodel_id = \"CompVis/stable-diffusion-v1-4\"\nclip_model_id = \"laion/CLIP-ViT-B-32-laion2B-s34B-b79K\"\n\nfeature_extractor = CLIPFeatureExtractor.from_pretrained(clip_model_id)\nclip_model = CLIPModel.from_pretrained(clip_model_id, torch_dtype=torch.float16)\n\npipeline = DiffusionPipeline.from_pretrained(\n model_id,\n custom_pipeline=\"clip_guided_stable_diffusion\",\n clip_model=clip_model,\n feature_extractor=feature_extractor,\n scheduler=scheduler,\n torch_dtype=torch.float16,\n)\n```\n\n커뮤니티 파이프라인의 마법은 다음 코드에 담겨 있습니다. 이 코드를 통해 커뮤니티 파이프라인을 GitHub 또는 Hub에서 로드할 수 있으며, 모든 🧨 Diffusers 패키지에서 사용할 수 있습니다.\n\n```python\n# 2. 파이프라인 클래스를 로드합니다. 사용자 지정 모듈을 사용하는 경우 Hub에서 로드합니다\n# 명시적 클래스에서 로드하는 경우, 이를 사용해 보겠습니다.\nif custom_pipeline is not None:\n pipeline_class = get_class_from_dynamic_module(\n custom_pipeline, module_file=CUSTOM_PIPELINE_FILE_NAME, cache_dir=custom_pipeline\n )\nelif cls != DiffusionPipeline:\n pipeline_class = cls\nelse:\n diffusers_module = importlib.import_module(cls.__module__.split(\".\")[0])\n pipeline_class = getattr(diffusers_module, config_dict[\"_class_name\"])\n```\n" }, { "path": "diffusers/docs/source/ko/using-diffusers/control_brightness.md", "content": "# 이미지 밝기 조절하기\n\nStable Diffusion 파이프라인은 [일반적인 디퓨전 노이즈 스케줄과 샘플 단계에 결함이 있음](https://huggingface.co/papers/2305.08891) 논문에서 설명한 것처럼 매우 밝거나 어두운 이미지를 생성하는 데는 성능이 평범합니다. 이 논문에서 제안한 솔루션은 현재 [`DDIMScheduler`]에 구현되어 있으며 이미지의 밝기를 개선하는 데 사용할 수 있습니다.\n\n\n\n💡 제안된 솔루션에 대한 자세한 내용은 위에 링크된 논문을 참고하세요!\n\n\n\n해결책 중 하나는 *v 예측값*과 *v 로스*로 모델을 훈련하는 것입니다. 다음 flag를 [`train_text_to_image.py`](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) 또는 [`train_text_to_image_lora.py`](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py) 스크립트에 추가하여 `v_prediction`을 활성화합니다:\n\n```bash\n--prediction_type=\"v_prediction\"\n```\n\n예를 들어, `v_prediction`으로 미세 조정된 [`ptx0/pseudo-journey-v2`](https://huggingface.co/ptx0/pseudo-journey-v2) 체크포인트를 사용해 보겠습니다.\n\n다음으로 [`DDIMScheduler`]에서 다음 파라미터를 설정합니다:\n\n1. rescale_betas_zero_snr=True`, 노이즈 스케줄을 제로 터미널 신호 대 잡음비(SNR)로 재조정합니다.\n2. `timestep_spacing=\"trailing\"`, 마지막 타임스텝부터 샘플링 시작\n\n```py\n>>> from diffusers import DiffusionPipeline, DDIMScheduler\n\n>>> pipeline = DiffusionPipeline.from_pretrained(\"ptx0/pseudo-journey-v2\")\n# switch the scheduler in the pipeline to use the DDIMScheduler\n\n>>> pipeline.scheduler = DDIMScheduler.from_config(\n... pipeline.scheduler.config, rescale_betas_zero_snr=True, timestep_spacing=\"trailing\"\n... )\n>>> pipeline.to(\"cuda\")\n```\n\n마지막으로 파이프라인에 대한 호출에서 `guidance_rescale`을 설정하여 과다 노출을 방지합니다:\n\n```py\nprompt = \"A lion in galaxies, spirals, nebulae, stars, smoke, iridescent, intricate detail, octane render, 8k\"\nimage = pipeline(prompt, guidance_rescale=0.7).images[0]\n```\n\n
\n \n
" }, { "path": "diffusers/docs/source/ko/using-diffusers/controlling_generation.md", "content": "\n\n# 제어된 생성\n\nDiffusion 모델에 의해 생성된 출력을 제어하는 것은 커뮤니티에서 오랫동안 추구해 왔으며 현재 활발한 연구 주제입니다. 널리 사용되는 많은 diffusion 모델에서는 이미지와 텍스트 프롬프트 등 입력의 미묘한 변화로 인해 출력이 크게 달라질 수 있습니다. 이상적인 세계에서는 의미가 유지되고 변경되는 방식을 제어할 수 있기를 원합니다.\n\n의미 보존의 대부분의 예는 입력의 변화를 출력의 변화에 정확하게 매핑하는 것으로 축소됩니다. 즉, 프롬프트에서 피사체에 형용사를 추가하면 전체 이미지가 보존되고 변경된 피사체만 수정됩니다. 또는 특정 피사체의 이미지를 변형하면 피사체의 포즈가 유지됩니다.\n\n추가적으로 생성된 이미지의 품질에는 의미 보존 외에도 영향을 미치고자 하는 품질이 있습니다. 즉, 일반적으로 결과물의 품질이 좋거나 특정 스타일을 고수하거나 사실적이기를 원합니다.\n\ndiffusion 모델 생성을 제어하기 위해 `diffusers`가 지원하는 몇 가지 기술을 문서화합니다. 많은 부분이 최첨단 연구이며 미묘한 차이가 있을 수 있습니다. 명확한 설명이 필요하거나 제안 사항이 있으면 주저하지 마시고 [포럼](https://discuss.huggingface.co/) 또는 [GitHub 이슈](https://github.com/huggingface/diffusers/issues)에서 토론을 시작하세요.\n\n생성 제어 방법에 대한 개략적인 설명과 기술 개요를 제공합니다. 기술에 대한 자세한 설명은 파이프라인에서 링크된 원본 논문을 참조하는 것이 가장 좋습니다.\n\n사용 사례에 따라 적절한 기술을 선택해야 합니다. 많은 경우 이러한 기법을 결합할 수 있습니다. 예를 들어, 텍스트 반전과 SEGA를 결합하여 텍스트 반전을 사용하여 생성된 출력에 더 많은 의미적 지침을 제공할 수 있습니다.\n\n별도의 언급이 없는 한, 이러한 기법은 기존 모델과 함께 작동하며 자체 가중치가 필요하지 않은 기법입니다.\n\n1. [Instruct Pix2Pix](#instruct-pix2pix)\n2. [Pix2Pix Zero](#pix2pixzero)\n3. [Attend and Excite](#attend-and-excite)\n4. [Semantic Guidance](#semantic-guidance)\n5. [Self-attention Guidance](#self-attention-guidance)\n6. [Depth2Image](#depth2image)\n7. [MultiDiffusion Panorama](#multidiffusion-panorama)\n8. [DreamBooth](#dreambooth)\n9. [Textual Inversion](#textual-inversion)\n10. [ControlNet](#controlnet)\n11. [Prompt Weighting](#prompt-weighting)\n12. [Custom Diffusion](#custom-diffusion)\n13. [Model Editing](#model-editing)\n14. [DiffEdit](#diffedit)\n15. [T2I-Adapter](#t2i-adapter)\n\n편의를 위해, 추론만 하거나 파인튜닝/학습하는 방법에 대한 표를 제공합니다.\n\n| **Method** | **Inference only** | **Requires training /
fine-tuning** | **Comments** |\n| :-------------------------------------------------: | :----------------: | :-------------------------------------: | :---------------------------------------------------------------------------------------------: |\n| [Instruct Pix2Pix](#instruct-pix2pix) | ✅ | ❌ | Can additionally be
fine-tuned for better
performance on specific
edit instructions. |\n| [Pix2Pix Zero](#pix2pixzero) | ✅ | ❌ | |\n| [Attend and Excite](#attend-and-excite) | ✅ | ❌ | |\n| [Semantic Guidance](#semantic-guidance) | ✅ | ❌ | |\n| [Self-attention Guidance](#self-attention-guidance) | ✅ | ❌ | |\n| [Depth2Image](#depth2image) | ✅ | ❌ | |\n| [MultiDiffusion Panorama](#multidiffusion-panorama) | ✅ | ❌ | |\n| [DreamBooth](#dreambooth) | ❌ | ✅ | |\n| [Textual Inversion](#textual-inversion) | ❌ | ✅ | |\n| [ControlNet](#controlnet) | ✅ | ❌ | A ControlNet can be
trained/fine-tuned on
a custom conditioning. |\n| [Prompt Weighting](#prompt-weighting) | ✅ | ❌ | |\n| [Custom Diffusion](#custom-diffusion) | ❌ | ✅ | |\n| [Model Editing](#model-editing) | ✅ | ❌ | |\n| [DiffEdit](#diffedit) | ✅ | ❌ | |\n| [T2I-Adapter](#t2i-adapter) | ✅ | ❌ | |\n\n## Pix2Pix Instruct\n\n[Paper](https://arxiv.org/abs/2211.09800)\n\n[Instruct Pix2Pix](../api/pipelines/stable_diffusion/pix2pix) 는 입력 이미지 편집을 지원하기 위해 stable diffusion에서 미세-조정되었습니다. 이미지와 편집을 설명하는 프롬프트를 입력으로 받아 편집된 이미지를 출력합니다.\nInstruct Pix2Pix는 [InstructGPT](https://openai.com/blog/instruction-following/)와 같은 프롬프트와 잘 작동하도록 명시적으로 훈련되었습니다.\n\n사용 방법에 대한 자세한 내용은 [여기](../api/pipelines/stable_diffusion/pix2pix)를 참조하세요.\n\n## Pix2Pix Zero\n\n[Paper](https://arxiv.org/abs/2302.03027)\n\n[Pix2Pix Zero](../api/pipelines/stable_diffusion/pix2pix_zero)를 사용하면 일반적인 이미지 의미를 유지하면서 한 개념이나 피사체가 다른 개념이나 피사체로 변환되도록 이미지를 수정할 수 있습니다.\n\n노이즈 제거 프로세스는 한 개념적 임베딩에서 다른 개념적 임베딩으로 안내됩니다. 중간 잠복(intermediate latents)은 디노이징(denoising?) 프로세스 중에 최적화되어 참조 주의 지도(reference attention maps)를 향해 나아갑니다. 참조 주의 지도(reference attention maps)는 입력 이미지의 노이즈 제거(?) 프로세스에서 나온 것으로 의미 보존을 장려하는 데 사용됩니다.\n\nPix2Pix Zero는 합성 이미지와 실제 이미지를 편집하는 데 모두 사용할 수 있습니다.\n\n- 합성 이미지를 편집하려면 먼저 캡션이 지정된 이미지를 생성합니다.\n 다음으로 편집할 컨셉과 새로운 타겟 컨셉에 대한 이미지 캡션을 생성합니다. 이를 위해 [Flan-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5)와 같은 모델을 사용할 수 있습니다. 그런 다음 텍스트 인코더를 통해 소스 개념과 대상 개념 모두에 대한 \"평균\" 프롬프트 임베딩을 생성합니다. 마지막으로, 합성 이미지를 편집하기 위해 pix2pix-zero 알고리즘을 사용합니다.\n- 실제 이미지를 편집하려면 먼저 [BLIP](https://huggingface.co/docs/transformers/model_doc/blip)과 같은 모델을 사용하여 이미지 캡션을 생성합니다. 그런 다음 프롬프트와 이미지에 ddim 반전을 적용하여 \"역(inverse)\" latents을 생성합니다. 이전과 마찬가지로 소스 및 대상 개념 모두에 대한 \"평균(mean)\" 프롬프트 임베딩이 생성되고 마지막으로 \"역(inverse)\" latents와 결합된 pix2pix-zero 알고리즘이 이미지를 편집하는 데 사용됩니다.\n\n\n\nPix2Pix Zero는 '제로 샷(zero-shot)' 이미지 편집이 가능한 최초의 모델입니다.\n즉, 이 모델은 다음과 같이 일반 소비자용 GPU에서 1분 이내에 이미지를 편집할 수 있습니다(../api/pipelines/stable_diffusion/pix2pix_zero#usage-example).\n\n\n\n위에서 언급했듯이 Pix2Pix Zero에는 특정 개념으로 세대를 유도하기 위해 (UNet, VAE 또는 텍스트 인코더가 아닌) latents을 최적화하는 기능이 포함되어 있습니다.즉, 전체 파이프라인에 표준 [StableDiffusionPipeline](../api/pipelines/stable_diffusion/text2img)보다 더 많은 메모리가 필요할 수 있습니다.\n\n사용 방법에 대한 자세한 내용은 [여기](../api/pipelines/stable_diffusion/pix2pix_zero)를 참조하세요.\n\n## Attend and Excite\n\n[Paper](https://arxiv.org/abs/2301.13826)\n\n[Attend and Excite](../api/pipelines/stable_diffusion/attend_and_excite)를 사용하면 프롬프트의 피사체가 최종 이미지에 충실하게 표현되도록 할 수 있습니다.\n\n이미지에 존재해야 하는 프롬프트의 피사체에 해당하는 일련의 토큰 인덱스가 입력으로 제공됩니다. 노이즈 제거 중에 각 토큰 인덱스는 이미지의 최소 한 패치 이상에 대해 최소 주의 임계값을 갖도록 보장됩니다. 모든 피사체 토큰에 대해 주의 임계값이 통과될 때까지 노이즈 제거 프로세스 중에 중간 잠복기가 반복적으로 최적화되어 가장 소홀히 취급되는 피사체 토큰의 주의력을 강화합니다.\n\nPix2Pix Zero와 마찬가지로 Attend and Excite 역시 파이프라인에 미니 최적화 루프(사전 학습된 가중치를 그대로 둔 채)가 포함되며, 일반적인 'StableDiffusionPipeline'보다 더 많은 메모리가 필요할 수 있습니다.\n\n사용 방법에 대한 자세한 내용은 [여기](../api/pipelines/stable_diffusion/attend_and_excite)를 참조하세요.\n\n## Semantic Guidance (SEGA)\n\n[Paper](https://arxiv.org/abs/2301.12247)\n\n의미유도(SEGA)를 사용하면 이미지에서 하나 이상의 컨셉을 적용하거나 제거할 수 있습니다. 컨셉의 강도도 조절할 수 있습니다. 즉, 스마일 컨셉을 사용하여 인물 사진의 스마일을 점진적으로 늘리거나 줄일 수 있습니다.\n\n분류기 무료 안내(classifier free guidance)가 빈 프롬프트 입력을 통해 안내를 제공하는 방식과 유사하게, SEGA는 개념 프롬프트에 대한 안내를 제공합니다. 이러한 개념 프롬프트는 여러 개를 동시에 적용할 수 있습니다. 각 개념 프롬프트는 안내가 긍정적으로 적용되는지 또는 부정적으로 적용되는지에 따라 해당 개념을 추가하거나 제거할 수 있습니다.\n\nPix2Pix Zero 또는 Attend and Excite와 달리 SEGA는 명시적인 그라데이션 기반 최적화를 수행하는 대신 확산 프로세스와 직접 상호 작용합니다.\n\n사용 방법에 대한 자세한 내용은 [여기](../api/pipelines/semantic_stable_diffusion)를 참조하세요.\n\n## Self-attention Guidance (SAG)\n\n[Paper](https://arxiv.org/abs/2210.00939)\n\n[자기 주의 안내](../api/pipelines/stable_diffusion/self_attention_guidance)는 이미지의 전반적인 품질을 개선합니다.\n\nSAG는 고빈도 세부 정보를 기반으로 하지 않은 예측에서 완전히 조건화된 이미지에 이르기까지 가이드를 제공합니다. 고빈도 디테일은 UNet 자기 주의 맵에서 추출됩니다.\n\n사용 방법에 대한 자세한 내용은 [여기](../api/pipelines/stable_diffusion/self_attention_guidance)를 참조하세요.\n\n## Depth2Image\n\n[Project](https://huggingface.co/stabilityai/stable-diffusion-2-depth)\n\n[Depth2Image](../pipelines/stable_diffusion_2#depthtoimage)는 텍스트 안내 이미지 변화에 대한 시맨틱을 더 잘 보존하도록 안정적 확산에서 미세 조정되었습니다.\n\n원본 이미지의 단안(monocular) 깊이 추정치를 조건으로 합니다.\n\n사용 방법에 대한 자세한 내용은 [여기](../api/pipelines/stable_diffusion_2#depthtoimage)를 참조하세요.\n\n\n\nInstructPix2Pix와 Pix2Pix Zero와 같은 방법의 중요한 차이점은 전자의 경우\n는 사전 학습된 가중치를 미세 조정하는 반면, 후자는 그렇지 않다는 것입니다. 즉, 다음을 수행할 수 있습니다.\n사용 가능한 모든 안정적 확산 모델에 Pix2Pix Zero를 적용할 수 있습니다.\n\n\n\n## MultiDiffusion Panorama\n\n[Paper](https://arxiv.org/abs/2302.08113)\n\nMultiDiffusion은 사전 학습된 diffusion model을 통해 새로운 생성 프로세스를 정의합니다. 이 프로세스는 고품질의 다양한 이미지를 생성하는 데 쉽게 적용할 수 있는 여러 diffusion 생성 방법을 하나로 묶습니다. 결과는 원하는 종횡비(예: 파노라마) 및 타이트한 분할 마스크에서 바운딩 박스에 이르는 공간 안내 신호와 같은 사용자가 제공한 제어를 준수합니다.\n[MultiDiffusion 파노라마](../api/pipelines/stable_diffusion/panorama)를 사용하면 임의의 종횡비(예: 파노라마)로 고품질 이미지를 생성할 수 있습니다.\n\n파노라마 이미지를 생성하는 데 사용하는 방법에 대한 자세한 내용은 [여기](../api/pipelines/stable_diffusion/panorama)를 참조하세요.\n\n## 나만의 모델 파인튜닝\n\n사전 학습된 모델 외에도 Diffusers는 사용자가 제공한 데이터에 대해 모델을 파인튜닝할 수 있는 학습 스크립트가 있습니다.\n\n## DreamBooth\n\n[DreamBooth](../training/dreambooth)는 모델을 파인튜닝하여 새로운 주제에 대해 가르칩니다. 즉, 한 사람의 사진 몇 장을 사용하여 다양한 스타일로 그 사람의 이미지를 생성할 수 있습니다.\n\n사용 방법에 대한 자세한 내용은 [여기](../training/dreambooth)를 참조하세요.\n\n## Textual Inversion\n\n[Textual Inversion](../training/text_inversion)은 모델을 파인튜닝하여 새로운 개념에 대해 학습시킵니다. 즉, 특정 스타일의 아트웍 사진 몇 장을 사용하여 해당 스타일의 이미지를 생성할 수 있습니다.\n\n사용 방법에 대한 자세한 내용은 [여기](../training/text_inversion)를 참조하세요.\n\n## ControlNet\n\n[Paper](https://arxiv.org/abs/2302.05543)\n\n[ControlNet](../api/pipelines/stable_diffusion/controlnet)은 추가 조건을 추가하는 보조 네트워크입니다.\n가장자리 감지, 낙서, 깊이 맵, 의미적 세그먼트와 같은 다양한 조건에 대해 훈련된 8개의 표준 사전 훈련된 ControlNet이 있습니다,\n깊이 맵, 시맨틱 세그먼테이션과 같은 다양한 조건으로 훈련된 8개의 표준 제어망이 있습니다.\n\n사용 방법에 대한 자세한 내용은 [여기](../api/pipelines/stable_diffusion/controlnet)를 참조하세요.\n\n## Prompt Weighting\n\n프롬프트 가중치는 텍스트의 특정 부분에 더 많은 관심 가중치를 부여하는 간단한 기법입니다.\n입력에 가중치를 부여하는 간단한 기법입니다.\n\n자세한 설명과 예시는 [여기](../using-diffusers/weighted_prompts)를 참조하세요.\n\n## Custom Diffusion\n\n[Custom Diffusion](../training/custom_diffusion)은 사전 학습된 text-to-image 간 확산 모델의 교차 관심도 맵만 미세 조정합니다. \n또한 textual inversion을 추가로 수행할 수 있습니다. 설계상 다중 개념 훈련을 지원합니다. \nDreamBooth 및 Textual Inversion 마찬가지로, 사용자 지정 확산은 사전학습된 text-to-image diffusion 모델에 새로운 개념을 학습시켜 관심 있는 개념과 관련된 출력을 생성하는 데에도 사용됩니다. \n\n자세한 설명은 [공식 문서](../training/custom_diffusion)를 참조하세요.\n\n## Model Editing\n\n[Paper](https://arxiv.org/abs/2303.08084)\n\n[텍스트-이미지 모델 편집 파이프라인](../api/pipelines/model_editing)을 사용하면 사전학습된 text-to-image diffusion 모델이 입력 프롬프트에 있는 피사체에 대해 내릴 수 있는 잘못된 암시적 가정을 완화하는 데 도움이 됩니다. \n예를 들어, 안정적 확산에 \"A pack of roses\"에 대한 이미지를 생성하라는 메시지를 표시하면 생성된 이미지의 장미는 빨간색일 가능성이 높습니다. 이 파이프라인은 이러한 가정을 변경하는 데 도움이 됩니다.\n\n자세한 설명은 [공식 문서](../api/pipelines/model_editing)를 참조하세요.\n\n## DiffEdit\n\n[Paper](https://arxiv.org/abs/2210.11427)\n\n[DiffEdit](../api/pipelines/diffedit)를 사용하면 원본 입력 이미지를 최대한 보존하면서 입력 프롬프트와 함께 입력 이미지의 의미론적 편집이 가능합니다.\n\n\n자세한 설명은 [공식 문서](../api/pipelines/diffedit)를 참조하세요.\n\n## T2I-Adapter\n\n[Paper](https://arxiv.org/abs/2302.08453)\n\n[T2I-어댑터](../api/pipelines/stable_diffusion/adapter)는 추가적인 조건을 추가하는 auxiliary 네트워크입니다.\n가장자리 감지, 스케치, depth maps, semantic segmentations와 같은 다양한 조건에 대해 훈련된 8개의 표준 사전훈련된 adapter가 있습니다, \n\n[공식 문서](api/pipelines/stable_diffusion/adapter)에서 사용 방법에 대한 정보를 참조하세요." }, { "path": "diffusers/docs/source/ko/using-diffusers/custom_pipeline_examples.md", "content": "\n\n# 커뮤니티 파이프라인\n\n> **커뮤니티 파이프라인에 대한 자세한 내용은 [이 이슈](https://github.com/huggingface/diffusers/issues/841)를 참조하세요.\n\n**커뮤니티** 예제는 커뮤니티에서 추가한 추론 및 훈련 예제로 구성되어 있습니다.\n다음 표를 참조하여 모든 커뮤니티 예제에 대한 개요를 확인하시기 바랍니다. **코드 예제**를 클릭하면 복사하여 붙여넣기할 수 있는 코드 예제를 확인할 수 있습니다.\n커뮤니티가 예상대로 작동하지 않는 경우 이슈를 개설하고 작성자에게 핑을 보내주세요.\n\n| 예 | 설명 | 코드 예제 | 콜랩 |저자 |\n|:---------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------:|\n| CLIP Guided Stable Diffusion | CLIP 가이드 기반의 Stable Diffusion으로 텍스트에서 이미지로 생성하기 | [CLIP Guided Stable Diffusion](#clip-guided-stable-diffusion) | [![콜랩에서 열기](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/CLIP_Guided_Stable_diffusion_with_diffusers.ipynb) | [Suraj Patil](https://github.com/patil-suraj/) |\n| One Step U-Net (Dummy) | 커뮤니티 파이프라인을 어떻게 사용해야 하는지에 대한 예시(참고 https://github.com/huggingface/diffusers/issues/841) | [One Step U-Net](#one-step-unet) | - | [Patrick von Platen](https://github.com/patrickvonplaten/) |\n| Stable Diffusion Interpolation | 서로 다른 프롬프트/시드 간 Stable Diffusion의 latent space 보간 | [Stable Diffusion Interpolation](#stable-diffusion-interpolation) | - | [Nate Raw](https://github.com/nateraw/) |\n| Stable Diffusion Mega | 모든 기능을 갖춘 **하나의** Stable Diffusion 파이프라인 [Text2Image](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py), [Image2Image](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py) and [Inpainting](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py) | [Stable Diffusion Mega](#stable-diffusion-mega) | - | [Patrick von Platen](https://github.com/patrickvonplaten/) |\n| Long Prompt Weighting Stable Diffusion | 토큰 길이 제한이 없고 프롬프트에서 파싱 가중치 지원을 하는 **하나의** Stable Diffusion 파이프라인, | [Long Prompt Weighting Stable Diffusion](#long-prompt-weighting-stable-diffusion) |- | [SkyTNT](https://github.com/SkyTNT) |\n| Speech to Image | 자동 음성 인식을 사용하여 텍스트를 작성하고 Stable Diffusion을 사용하여 이미지를 생성합니다. | [Speech to Image](#speech-to-image) | - | [Mikail Duzenli](https://github.com/MikailINTech) |\n\n커스텀 파이프라인을 불러오려면 `diffusers/examples/community`에 있는 파일 중 하나로서 `custom_pipeline` 인수를 `DiffusionPipeline`에 전달하기만 하면 됩니다. 자신만의 파이프라인이 있는 PR을 보내주시면 빠르게 병합해드리겠습니다.\n```py\npipe = DiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\", custom_pipeline=\"filename_in_the_community_folder\"\n)\n```\n\n## 사용 예시\n\n### CLIP 가이드 기반의 Stable Diffusion\n\n모든 노이즈 제거 단계에서 추가 CLIP 모델을 통해 Stable Diffusion을 가이드함으로써 CLIP 모델 기반의 Stable Diffusion은 보다 더 사실적인 이미지를 생성을 할 수 있습니다.\n\n다음 코드는 약 12GB의 GPU RAM이 필요합니다.\n\n```python\nfrom diffusers import DiffusionPipeline\nfrom transformers import CLIPImageProcessor, CLIPModel\nimport torch\n\n\nfeature_extractor = CLIPImageProcessor.from_pretrained(\"laion/CLIP-ViT-B-32-laion2B-s34B-b79K\")\nclip_model = CLIPModel.from_pretrained(\"laion/CLIP-ViT-B-32-laion2B-s34B-b79K\", torch_dtype=torch.float16)\n\n\nguided_pipeline = DiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n custom_pipeline=\"clip_guided_stable_diffusion\",\n clip_model=clip_model,\n feature_extractor=feature_extractor,\n torch_dtype=torch.float16,\n)\nguided_pipeline.enable_attention_slicing()\nguided_pipeline = guided_pipeline.to(\"cuda\")\n\nprompt = \"fantasy book cover, full moon, fantasy forest landscape, golden vector elements, fantasy magic, dark light night, intricate, elegant, sharp focus, illustration, highly detailed, digital painting, concept art, matte, art by WLOP and Artgerm and Albert Bierstadt, masterpiece\"\n\ngenerator = torch.Generator(device=\"cuda\").manual_seed(0)\nimages = []\nfor i in range(4):\n image = guided_pipeline(\n prompt,\n num_inference_steps=50,\n guidance_scale=7.5,\n clip_guidance_scale=100,\n num_cutouts=4,\n use_cutouts=False,\n generator=generator,\n ).images[0]\n images.append(image)\n\n# 이미지 로컬에 저장하기\nfor i, img in enumerate(images):\n img.save(f\"./clip_guided_sd/image_{i}.png\")\n```\n\n이미지` 목록에는 로컬에 저장하거나 구글 콜랩에 직접 표시할 수 있는 PIL 이미지 목록이 포함되어 있습니다. 생성된 이미지는 기본적으로 안정적인 확산을 사용하는 것보다 품질이 높은 경향이 있습니다. 예를 들어 위의 스크립트는 다음과 같은 이미지를 생성합니다:\n\n![clip_guidance](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/clip_guidance/merged_clip_guidance.jpg).\n\n### One Step Unet\n\n예시 \"one-step-unet\"는 다음과 같이 실행할 수 있습니다.\n\n```python\nfrom diffusers import DiffusionPipeline\n\npipe = DiffusionPipeline.from_pretrained(\"google/ddpm-cifar10-32\", custom_pipeline=\"one_step_unet\")\npipe()\n```\n\n**참고**: 이 커뮤니티 파이프라인은 기능으로 유용하지 않으며 커뮤니티 파이프라인을 추가할 수 있는 방법의 예시일 뿐입니다(https://github.com/huggingface/diffusers/issues/841 참조).\n\n### Stable Diffusion Interpolation\n\n다음 코드는 최소 8GB VRAM의 GPU에서 실행할 수 있으며 약 5분 정도 소요됩니다.\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipe = DiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n torch_dtype=torch.float16,\n safety_checker=None, # Very important for videos...lots of false positives while interpolating\n custom_pipeline=\"interpolate_stable_diffusion\",\n).to(\"cuda\")\npipe.enable_attention_slicing()\n\nframe_filepaths = pipe.walk(\n prompts=[\"a dog\", \"a cat\", \"a horse\"],\n seeds=[42, 1337, 1234],\n num_interpolation_steps=16,\n output_dir=\"./dreams\",\n batch_size=4,\n height=512,\n width=512,\n guidance_scale=8.5,\n num_inference_steps=50,\n)\n```\n\nwalk(...)` 함수의 출력은 `output_dir`에 정의된 대로 폴더에 저장된 이미지 목록을 반환합니다. 이 이미지를 사용하여 안정적으로 확산되는 동영상을 만들 수 있습니다.\n\n> 안정된 확산을 이용한 동영상 제작 방법과 더 많은 기능에 대한 자세한 내용은 https://github.com/nateraw/stable-diffusion-videos 에서 확인하시기 바랍니다.\n\n### Stable Diffusion Mega\n\nThe Stable Diffusion Mega 파이프라인을 사용하면 Stable Diffusion 파이프라인의 주요 사용 사례를 단일 클래스에서 사용할 수 있습니다.\n```python\n#!/usr/bin/env python3\nfrom diffusers import DiffusionPipeline\nimport PIL\nimport requests\nfrom io import BytesIO\nimport torch\n\n\ndef download_image(url):\n response = requests.get(url)\n return PIL.Image.open(BytesIO(response.content)).convert(\"RGB\")\n\n\npipe = DiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n custom_pipeline=\"stable_diffusion_mega\",\n torch_dtype=torch.float16,\n)\npipe.to(\"cuda\")\npipe.enable_attention_slicing()\n\n\n### Text-to-Image\n\nimages = pipe.text2img(\"An astronaut riding a horse\").images\n\n### Image-to-Image\n\ninit_image = download_image(\n \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n)\n\nprompt = \"A fantasy landscape, trending on artstation\"\n\nimages = pipe.img2img(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images\n\n### Inpainting\n\nimg_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\nmask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\ninit_image = download_image(img_url).resize((512, 512))\nmask_image = download_image(mask_url).resize((512, 512))\n\nprompt = \"a cat sitting on a bench\"\nimages = pipe.inpaint(prompt=prompt, image=init_image, mask_image=mask_image, strength=0.75).images\n```\n\n위에 표시된 것처럼 하나의 파이프라인에서 '텍스트-이미지 변환', '이미지-이미지 변환', '인페인팅'을 모두 실행할 수 있습니다.\n\n### Long Prompt Weighting Stable Diffusion\n\n파이프라인을 사용하면 77개의 토큰 길이 제한 없이 프롬프트를 입력할 수 있습니다. 또한 \"()\"를 사용하여 단어 가중치를 높이거나 \"[]\"를 사용하여 단어 가중치를 낮출 수 있습니다.\n또한 파이프라인을 사용하면 단일 클래스에서 Stable Diffusion 파이프라인의 주요 사용 사례를 사용할 수 있습니다.\n\n#### pytorch\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipe = DiffusionPipeline.from_pretrained(\n \"hakurei/waifu-diffusion\", custom_pipeline=\"lpw_stable_diffusion\", torch_dtype=torch.float16\n)\npipe = pipe.to(\"cuda\")\n\nprompt = \"best_quality (1girl:1.3) bow bride brown_hair closed_mouth frilled_bow frilled_hair_tubes frills (full_body:1.3) fox_ear hair_bow hair_tubes happy hood japanese_clothes kimono long_sleeves red_bow smile solo tabi uchikake white_kimono wide_sleeves cherry_blossoms\"\nneg_prompt = \"lowres, bad_anatomy, error_body, error_hair, error_arm, error_hands, bad_hands, error_fingers, bad_fingers, missing_fingers, error_legs, bad_legs, multiple_legs, missing_legs, error_lighting, error_shadow, error_reflection, text, error, extra_digit, fewer_digits, cropped, worst_quality, low_quality, normal_quality, jpeg_artifacts, signature, watermark, username, blurry\"\n\npipe.text2img(prompt, negative_prompt=neg_prompt, width=512, height=512, max_embeddings_multiples=3).images[0]\n```\n\n#### onnxruntime\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipe = DiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n custom_pipeline=\"lpw_stable_diffusion_onnx\",\n revision=\"onnx\",\n provider=\"CUDAExecutionProvider\",\n)\n\nprompt = \"a photo of an astronaut riding a horse on mars, best quality\"\nneg_prompt = \"lowres, bad anatomy, error body, error hair, error arm, error hands, bad hands, error fingers, bad fingers, missing fingers, error legs, bad legs, multiple legs, missing legs, error lighting, error shadow, error reflection, text, error, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry\"\n\npipe.text2img(prompt, negative_prompt=neg_prompt, width=512, height=512, max_embeddings_multiples=3).images[0]\n```\n\n토큰 인덱스 시퀀스 길이가 이 모델에 지정된 최대 시퀀스 길이보다 길면(*** > 77). 이 시퀀스를 모델에서 실행하면 인덱싱 오류가 발생합니다`. 정상적인 현상이니 걱정하지 마세요.\n### Speech to Image\n\n다음 코드는 사전학습된 OpenAI whisper-small과 Stable Diffusion을 사용하여 오디오 샘플에서 이미지를 생성할 수 있습니다.\n```Python\nimport torch\n\nimport matplotlib.pyplot as plt\nfrom datasets import load_dataset\nfrom diffusers import DiffusionPipeline\nfrom transformers import (\n WhisperForConditionalGeneration,\n WhisperProcessor,\n)\n\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nds = load_dataset(\"hf-internal-testing/librispeech_asr_dummy\", \"clean\", split=\"validation\")\n\naudio_sample = ds[3]\n\ntext = audio_sample[\"text\"].lower()\nspeech_data = audio_sample[\"audio\"][\"array\"]\n\nmodel = WhisperForConditionalGeneration.from_pretrained(\"openai/whisper-small\").to(device)\nprocessor = WhisperProcessor.from_pretrained(\"openai/whisper-small\")\n\ndiffuser_pipeline = DiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n custom_pipeline=\"speech_to_image_diffusion\",\n speech_model=model,\n speech_processor=processor,\n \n torch_dtype=torch.float16,\n)\n\ndiffuser_pipeline.enable_attention_slicing()\ndiffuser_pipeline = diffuser_pipeline.to(device)\n\noutput = diffuser_pipeline(speech_data)\nplt.imshow(output.images[0])\n```\n위 예시는 다음의 결과 이미지를 보입니다.\n\n![image](https://user-images.githubusercontent.com/45072645/196901736-77d9c6fc-63ee-4072-90b0-dc8b903d63e3.png)" }, { "path": "diffusers/docs/source/ko/using-diffusers/custom_pipeline_overview.md", "content": "\n\n# 커스텀 파이프라인 불러오기\n\n[[open-in-colab]]\n\n커뮤니티 파이프라인은 논문에 명시된 원래의 구현체와 다른 형태로 구현된 모든 [`DiffusionPipeline`] 클래스를 의미합니다. (예를 들어, [`StableDiffusionControlNetPipeline`]는 [\"Text-to-Image Generation with ControlNet Conditioning\"](https://arxiv.org/abs/2302.05543) 해당) 이들은 추가 기능을 제공하거나 파이프라인의 원래 구현을 확장합니다.\n\n[Speech to Image](https://github.com/huggingface/diffusers/tree/main/examples/community#speech-to-image) 또는 [Composable Stable Diffusion](https://github.com/huggingface/diffusers/tree/main/examples/community#composable-stable-diffusion) 과 같은 멋진 커뮤니티 파이프라인이 많이 있으며 [여기에서](https://github.com/huggingface/diffusers/tree/main/examples/community) 모든 공식 커뮤니티 파이프라인을 찾을 수 있습니다.\n\n허브에서 커뮤니티 파이프라인을 로드하려면, 커뮤니티 파이프라인의 리포지토리 ID와 (파이프라인 가중치 및 구성 요소를 로드하려는) 모델의 리포지토리 ID를 인자로 전달해야 합니다. 예를 들어, 아래 예시에서는 `hf-internal-testing/diffusers-dummy-pipeline`에서 더미 파이프라인을 불러오고, `google/ddpm-cifar10-32`에서 파이프라인의 가중치와 컴포넌트들을 로드합니다.\n\n\n\n🔒 허깅 페이스 허브에서 커뮤니티 파이프라인을 불러오는 것은 곧 해당 코드가 안전하다고 신뢰하는 것입니다. 코드를 자동으로 불러오고 실행하기 앞서 반드시 온라인으로 해당 코드의 신뢰성을 검사하세요!\n\n\n\n```py\nfrom diffusers import DiffusionPipeline\n\npipeline = DiffusionPipeline.from_pretrained(\n \"google/ddpm-cifar10-32\", custom_pipeline=\"hf-internal-testing/diffusers-dummy-pipeline\"\n)\n```\n\n공식 커뮤니티 파이프라인을 불러오는 것은 비슷하지만, 공식 리포지토리 ID에서 가중치를 불러오는 것과 더불어 해당 파이프라인 내의 컴포넌트를 직접 지정하는 것 역시 가능합니다. 아래 예제를 보면 커뮤니티 [CLIP Guided Stable Diffusion](https://github.com/huggingface/diffusers/tree/main/examples/community#clip-guided-stable-diffusion) 파이프라인을 로드할 때, 해당 파이프라인에서 사용할 `clip_model` 컴포넌트와 `feature_extractor` 컴포넌트를 직접 설정하는 것을 확인할 수 있습니다.\n\n```py\nfrom diffusers import DiffusionPipeline\nfrom transformers import CLIPImageProcessor, CLIPModel\n\nclip_model_id = \"laion/CLIP-ViT-B-32-laion2B-s34B-b79K\"\n\nfeature_extractor = CLIPImageProcessor.from_pretrained(clip_model_id)\nclip_model = CLIPModel.from_pretrained(clip_model_id)\n\npipeline = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n custom_pipeline=\"clip_guided_stable_diffusion\",\n clip_model=clip_model,\n feature_extractor=feature_extractor,\n)\n```\n\n커뮤니티 파이프라인에 대한 자세한 내용은 [커뮤니티 파이프라인](https://github.com/huggingface/diffusers/blob/main/docs/source/en/using-diffusers/custom_pipeline_examples) 가이드를 살펴보세요. 커뮤니티 파이프라인 등록에 관심이 있는 경우 [커뮤니티 파이프라인에 기여하는 방법](https://github.com/huggingface/diffusers/blob/main/docs/source/en/using-diffusers/contribute_pipeline)에 대한 가이드를 확인하세요 !" }, { "path": "diffusers/docs/source/ko/using-diffusers/depth2img.md", "content": "\n\n# Text-guided depth-to-image 생성\n\n[[open-in-colab]]\n\n[`StableDiffusionDepth2ImgPipeline`]을 사용하면 텍스트 프롬프트와 초기 이미지를 전달하여 새 이미지의 생성을 조절할 수 있습니다. 또한 이미지 구조를 보존하기 위해 `depth_map`을 전달할 수도 있습니다. `depth_map`이 제공되지 않으면 파이프라인은 통합된 [depth-estimation model](https://github.com/isl-org/MiDaS)을 통해 자동으로 깊이를 예측합니다.\n\n\n먼저 [`StableDiffusionDepth2ImgPipeline`]의 인스턴스를 생성합니다:\n\n```python\nimport torch\nimport requests\nfrom PIL import Image\n\nfrom diffusers import StableDiffusionDepth2ImgPipeline\n\npipe = StableDiffusionDepth2ImgPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-depth\",\n torch_dtype=torch.float16,\n).to(\"cuda\")\n```\n\n이제 프롬프트를 파이프라인에 전달합니다. 특정 단어가 이미지 생성을 가이드 하는것을 방지하기 위해 `negative_prompt`를 전달할 수도 있습니다:\n\n```python\nurl = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\ninit_image = Image.open(requests.get(url, stream=True).raw)\nprompt = \"two tigers\"\nn_prompt = \"bad, deformed, ugly, bad anatomy\"\nimage = pipe(prompt=prompt, image=init_image, negative_prompt=n_prompt, strength=0.7).images[0]\nimage\n```\n\n| Input | Output |\n|---------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------|\n| | |\n\n아래의 Spaces를 가지고 놀며 depth map이 있는 이미지와 없는 이미지의 차이가 있는지 확인해 보세요!\n\n\n" }, { "path": "diffusers/docs/source/ko/using-diffusers/img2img.md", "content": "\n\n# 텍스트 기반 image-to-image 생성\n\n[[open-in-colab]]\n\n[`StableDiffusionImg2ImgPipeline`]을 사용하면 텍스트 프롬프트와 시작 이미지를 전달하여 새 이미지 생성의 조건을 지정할 수 있습니다.\n\n시작하기 전에 필요한 라이브러리가 모두 설치되어 있는지 확인하세요:\n\n```bash\n!pip install diffusers transformers ftfy accelerate\n```\n\n[`nitrosocke/Ghibli-Diffusion`](https://huggingface.co/nitrosocke/Ghibli-Diffusion)과 같은 사전학습된 stable diffusion 모델로 [`StableDiffusionImg2ImgPipeline`]을 생성하여 시작하세요.\n\n\n```python\nimport torch\nimport requests\nfrom PIL import Image\nfrom io import BytesIO\nfrom diffusers import StableDiffusionImg2ImgPipeline\n\ndevice = \"cuda\"\npipe = StableDiffusionImg2ImgPipeline.from_pretrained(\"nitrosocke/Ghibli-Diffusion\", torch_dtype=torch.float16).to(\n device\n)\n```\n\n초기 이미지를 다운로드하고 사전 처리하여 파이프라인에 전달할 수 있습니다:\n\n```python\nurl = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n\nresponse = requests.get(url)\ninit_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\ninit_image.thumbnail((768, 768))\ninit_image\n```\n\n
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\n\n\n\n💡 `strength`는 입력 이미지에 추가되는 노이즈의 양을 제어하는 0.0에서 1.0 사이의 값입니다. 1.0에 가까운 값은 다양한 변형을 허용하지만 입력 이미지와 의미적으로 일치하지 않는 이미지를 생성합니다.\n\n\n\n프롬프트를 정의하고(지브리 스타일(Ghibli-style)에 맞게 조정된 이 체크포인트의 경우 프롬프트 앞에 `ghibli style` 토큰을 붙여야 합니다) 파이프라인을 실행합니다:\n\n```python\nprompt = \"ghibli style, a fantasy landscape with castles\"\ngenerator = torch.Generator(device=device).manual_seed(1024)\nimage = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5, generator=generator).images[0]\nimage\n```\n\n
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\n\n다른 스케줄러로 실험하여 출력에 어떤 영향을 미치는지 확인할 수도 있습니다:\n\n```python\nfrom diffusers import LMSDiscreteScheduler\n\nlms = LMSDiscreteScheduler.from_config(pipe.scheduler.config)\npipe.scheduler = lms\ngenerator = torch.Generator(device=device).manual_seed(1024)\nimage = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5, generator=generator).images[0]\nimage\n```\n\n
\n \n
\n\n아래 공백을 확인하고 `strength` 값을 다르게 설정하여 이미지를 생성해 보세요. `strength`를 낮게 설정하면 원본 이미지와 더 유사한 이미지가 생성되는 것을 확인할 수 있습니다.\n\n자유롭게 스케줄러를 [`LMSDiscreteScheduler`]로 전환하여 출력에 어떤 영향을 미치는지 확인해 보세요.\n\n" }, { "path": "diffusers/docs/source/ko/using-diffusers/inpaint.md", "content": "\n\n# Text-guided 이미지 인페인팅(inpainting)\n\n[[open-in-colab]]\n\n[`StableDiffusionInpaintPipeline`]은 마스크와 텍스트 프롬프트를 제공하여 이미지의 특정 부분을 편집할 수 있도록 합니다. 이 기능은 인페인팅 작업을 위해 특별히 훈련된 [`runwayml/stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting)과 같은 Stable Diffusion 버전을 사용합니다.\n\n먼저 [`StableDiffusionInpaintPipeline`] 인스턴스를 불러옵니다:\n\n```python\nimport PIL\nimport requests\nimport torch\nfrom io import BytesIO\n\nfrom diffusers import StableDiffusionInpaintPipeline\n\npipeline = StableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\",\n torch_dtype=torch.float16,\n)\npipeline = pipeline.to(\"cuda\")\n```\n\n나중에 교체할 강아지 이미지와 마스크를 다운로드하세요:\n\n```python\ndef download_image(url):\n response = requests.get(url)\n return PIL.Image.open(BytesIO(response.content)).convert(\"RGB\")\n\n\nimg_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\nmask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\n\ninit_image = download_image(img_url).resize((512, 512))\nmask_image = download_image(mask_url).resize((512, 512))\n```\n\n이제 마스크를 다른 것으로 교체하라는 프롬프트를 만들 수 있습니다:\n\n```python\nprompt = \"Face of a yellow cat, high resolution, sitting on a park bench\"\nimage = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]\n```\n\n`image` | `mask_image` | `prompt` | output |\n:-------------------------:|:-------------------------:|:-------------------------:|-------------------------:|\n\"drawing\" | \"drawing\" | ***Face of a yellow cat, high resolution, sitting on a park bench*** | \"drawing\" |\n\n\n\n이전의 실험적인 인페인팅 구현에서는 품질이 낮은 다른 프로세스를 사용했습니다. 이전 버전과의 호환성을 보장하기 위해 새 모델이 포함되지 않은 사전학습된 파이프라인을 불러오면 이전 인페인팅 방법이 계속 적용됩니다.\n\n\n\n아래 Space에서 이미지 인페인팅을 직접 해보세요!\n\n\n" }, { "path": "diffusers/docs/source/ko/using-diffusers/loading.md", "content": "\n\n\n\n# 파이프라인, 모델, 스케줄러 불러오기\n\n기본적으로 diffusion 모델은 다양한 컴포넌트들(모델, 토크나이저, 스케줄러) 간의 복잡한 상호작용을 기반으로 동작합니다. 디퓨저스(Diffusers)는 이러한 diffusion 모델을 보다 쉽고 간편한 API로 제공하는 것을 목표로 설계되었습니다. [`DiffusionPipeline`]은 diffusion 모델이 갖는 복잡성을 하나의 파이프라인 API로 통합하고, 동시에 이를 구성하는 각각의 컴포넌트들을 태스크에 맞춰 유연하게 커스터마이징할 수 있도록 지원하고 있습니다.\n\ndiffusion 모델의 훈련과 추론에 필요한 모든 것은 [`DiffusionPipeline.from_pretrained`] 메서드를 통해 접근할 수 있습니다. (이 말의 의미는 다음 단락에서 보다 자세하게 다뤄보도록 하겠습니다.)\n\n이 문서에서는 설명할 내용은 다음과 같습니다.\n\n* 허브를 통해 혹은 로컬로 파이프라인을 불러오는 법\n\n* 파이프라인에 다른 컴포넌트들을 적용하는 법\n* 오리지널 체크포인트가 아닌 variant를 불러오는 법 (variant란 기본으로 설정된 `fp32`가 아닌 다른 부동 소수점 타입(예: `fp16`)을 사용하거나 Non-EMA 가중치를 사용하는 체크포인트들을 의미합니다.)\n* 모델과 스케줄러를 불러오는 법\n\n\n\n## Diffusion 파이프라인\n\n\n\n💡 [`DiffusionPipeline`] 클래스가 동작하는 방식에 보다 자세한 내용이 궁금하다면, [DiffusionPipeline explained](#diffusionpipeline에-대해-알아보기) 섹션을 확인해보세요.\n\n\n\n[`DiffusionPipeline`] 클래스는 diffusion 모델을 [허브](https://huggingface.co/models?library=diffusers)로부터 불러오는 가장 심플하면서 보편적인 방식입니다. [`DiffusionPipeline.from_pretrained`] 메서드는 적합한 파이프라인 클래스를 자동으로 탐지하고, 필요한 구성요소(configuration)와 가중치(weight) 파일들을 다운로드하고 캐싱한 다음, 해당 파이프라인 인스턴스를 반환합니다.\n\n```python\nfrom diffusers import DiffusionPipeline\n\nrepo_id = \"runwayml/stable-diffusion-v1-5\"\npipe = DiffusionPipeline.from_pretrained(repo_id)\n```\n\n물론 [`DiffusionPipeline`] 클래스를 사용하지 않고, 명시적으로 직접 해당 파이프라인 클래스를 불러오는 것도 가능합니다. 아래 예시 코드는 위 예시와 동일한 인스턴스를 반환합니다. \n\n```python\nfrom diffusers import StableDiffusionPipeline\n\nrepo_id = \"runwayml/stable-diffusion-v1-5\"\npipe = StableDiffusionPipeline.from_pretrained(repo_id)\n```\n\n[CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4)이나 [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) 같은 체크포인트들의 경우, 하나 이상의 다양한 태스크에 활용될 수 있습니다. (예를 들어 위의 두 체크포인트의 경우, text-to-image와 image-to-image에 모두 활용될 수 있습니다.) 만약 이러한 체크포인트들을 기본 설정 태스크가 아닌 다른 태스크에 활용하고자 한다면, 해당 태스크에 대응되는 파이프라인(task-specific pipeline)을 사용해야 합니다.\n\n```python\nfrom diffusers import StableDiffusionImg2ImgPipeline\n\nrepo_id = \"runwayml/stable-diffusion-v1-5\"\npipe = StableDiffusionImg2ImgPipeline.from_pretrained(repo_id)\n```\n\n\n\n### 로컬 파이프라인\n\n파이프라인을 로컬로 불러오고자 한다면, `git-lfs`를 사용하여 직접 체크포인트를 로컬 디스크에 다운로드 받아야 합니다. 아래의 명령어를 실행하면 `./stable-diffusion-v1-5`란 이름으로 폴더가 로컬디스크에 생성됩니다.\n\n```bash\ngit lfs install\ngit clone https://huggingface.co/runwayml/stable-diffusion-v1-5\n```\n\n그런 다음 해당 로컬 경로를 [`~DiffusionPipeline.from_pretrained`] 메서드에 전달합니다.\n\n```python\nfrom diffusers import DiffusionPipeline\n\nrepo_id = \"./stable-diffusion-v1-5\"\nstable_diffusion = DiffusionPipeline.from_pretrained(repo_id)\n```\n\n위의 예시코드처럼 만약 `repo_id`가 로컬 패스(local path)라면, [`~DiffusionPipeline.from_pretrained`] 메서드는 이를 자동으로 감지하여 허브에서 파일을 다운로드하지 않습니다. 만약 로컬 디스크에 저장된 파이프라인 체크포인트가 최신 버전이 아닐 경우에도, 최신 버전을 다운로드하지 않고 기존 로컬 디스크에 저장된 체크포인트를 사용한다는 것을 의미합니다.\n\n\n\n### 파이프라인 내부의 컴포넌트 교체하기\n\n파이프라인 내부의 컴포넌트들은 호환 가능한 다른 컴포넌트로 교체될 수 있습니다. 이와 같은 컴포넌트 교체가 중요한 이유는 다음과 같습니다. \n\n- 어떤 스케줄러를 사용할 것인가는 생성속도와 생성품질 간의 트레이드오프를 정의하는 중요한 요소입니다.\n- diffusion 모델 내부의 컴포넌트들은 일반적으로 각각 독립적으로 훈련되기 때문에, 더 좋은 성능을 보여주는 컴포넌트가 있다면 그걸로 교체하는 식으로 성능을 향상시킬 수 있습니다.\n- 파인 튜닝 단계에서는 일반적으로 UNet 혹은 텍스트 인코더와 같은 일부 컴포넌트들만 훈련하게 됩니다.\n\n어떤 스케줄러들이 호환가능한지는 `compatibles` 속성을 통해 확인할 수 있습니다.\n\n```python\nfrom diffusers import DiffusionPipeline\n\nrepo_id = \"runwayml/stable-diffusion-v1-5\"\nstable_diffusion = DiffusionPipeline.from_pretrained(repo_id)\nstable_diffusion.scheduler.compatibles\n```\n\n이번에는 [`SchedulerMixin.from_pretrained`] 메서드를 사용해서, 기존 기본 스케줄러였던 [`PNDMScheduler`]를 보다 우수한 성능의 [`EulerDiscreteScheduler`]로 바꿔봅시다. 스케줄러를 로드할 때는 `subfolder` 인자를 통해, 해당 파이프라인의 리포지토리에서 [스케줄러에 관한 하위폴더](https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/main/scheduler)를 명시해주어야 합니다. \n\n그 다음 새롭게 생성한 [`EulerDiscreteScheduler`] 인스턴스를 [`DiffusionPipeline`]의 `scheduler` 인자에 전달합니다.\n\n```python\nfrom diffusers import DiffusionPipeline, EulerDiscreteScheduler, DPMSolverMultistepScheduler\n\nrepo_id = \"runwayml/stable-diffusion-v1-5\"\n\nscheduler = EulerDiscreteScheduler.from_pretrained(repo_id, subfolder=\"scheduler\")\n\nstable_diffusion = DiffusionPipeline.from_pretrained(repo_id, scheduler=scheduler)\n```\n\n### 세이프티 체커\n\n스테이블 diffusion과 같은 diffusion 모델들은 유해한 이미지를 생성할 수도 있습니다. 이를 예방하기 위해 디퓨저스는 생성된 이미지의 유해성을 판단하는 [세이프티 체커(safety checker)](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) 기능을 지원하고 있습니다. 만약 세이프티 체커의 사용을 원하지 않는다면, `safety_checker` 인자에 `None`을 전달해주시면 됩니다.\n\n```python\nfrom diffusers import DiffusionPipeline\n\nrepo_id = \"runwayml/stable-diffusion-v1-5\"\nstable_diffusion = DiffusionPipeline.from_pretrained(repo_id, safety_checker=None)\n```\n\n### 컴포넌트 재사용\n\n복수의 파이프라인에 동일한 모델이 반복적으로 사용한다면, 굳이 해당 모델의 동일한 가중치를 중복으로 RAM에 불러올 필요는 없을 것입니다. [`~DiffusionPipeline.components`] 속성을 통해 파이프라인 내부의 컴포넌트들을 참조할 수 있는데, 이번 단락에서는 이를 통해 동일한 모델 가중치를 RAM에 중복으로 불러오는 것을 방지하는 법에 대해 알아보겠습니다.\n\n```python\nfrom diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline\n\nmodel_id = \"runwayml/stable-diffusion-v1-5\"\nstable_diffusion_txt2img = StableDiffusionPipeline.from_pretrained(model_id)\n\ncomponents = stable_diffusion_txt2img.components\n```\n\n그 다음 위 예시 코드에서 선언한 `components` 변수를 다른 파이프라인에 전달함으로써, 모델의 가중치를 중복으로 RAM에 로딩하지 않고, 동일한 컴포넌트를 재사용할 수 있습니다.\n\n```python\nstable_diffusion_img2img = StableDiffusionImg2ImgPipeline(**components)\n```\n\n물론 각각의 컴포넌트들을 따로 따로 파이프라인에 전달할 수도 있습니다. 예를 들어 `stable_diffusion_txt2img` 파이프라인 안의 컴포넌트들 가운데서 세이프티 체커(`safety_checker`)와 피쳐 익스트랙터(`feature_extractor`)를 제외한 컴포넌트들만 `stable_diffusion_img2img` 파이프라인에서 재사용하는 방식 역시 가능합니다. \n\n```python\nfrom diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline\n\nmodel_id = \"runwayml/stable-diffusion-v1-5\"\nstable_diffusion_txt2img = StableDiffusionPipeline.from_pretrained(model_id)\nstable_diffusion_img2img = StableDiffusionImg2ImgPipeline(\n vae=stable_diffusion_txt2img.vae,\n text_encoder=stable_diffusion_txt2img.text_encoder,\n tokenizer=stable_diffusion_txt2img.tokenizer,\n unet=stable_diffusion_txt2img.unet,\n scheduler=stable_diffusion_txt2img.scheduler,\n safety_checker=None,\n feature_extractor=None,\n requires_safety_checker=False,\n)\n```\n\n## Checkpoint variants\n\nVariant란 일반적으로 다음과 같은 체크포인트들을 의미합니다.\n\n- `torch.float16`과 같이 정밀도는 더 낮지만, 용량 역시 더 작은 부동소수점 타입의 가중치를 사용하는 체크포인트. *(다만 이와 같은 variant의 경우, 추가적인 훈련과 CPU환경에서의 구동이 불가능합니다.)* \n- Non-EMA 가중치를 사용하는 체크포인트. *(Non-EMA 가중치의 경우, 파인 튜닝 단계에서 사용하는 것이 권장되는데, 추론 단계에선 사용하지 않는 것이 권장됩니다.)*\n\n\n\n💡 모델 구조는 동일하지만 서로 다른 학습 환경에서 서로 다른 데이터셋으로 학습된 체크포인트들이 있을 경우, 해당 체크포인트들은 variant 단계가 아닌 리포지토리 단계에서 분리되어 관리되어야 합니다. (즉, 해당 체크포인트들은 서로 다른 리포지토리에서 따로 관리되어야 합니다. 예시: [`stable-diffusion-v1-4`], [`stable-diffusion-v1-5`]). \n\n\n\n| **checkpoint type** | **weight name** | **argument for loading weights** |\n| ------------------- | ----------------------------------- | -------------------------------- |\n| original | diffusion_pytorch_model.bin | |\n| floating point | diffusion_pytorch_model.fp16.bin | `variant`, `torch_dtype` |\n| non-EMA | diffusion_pytorch_model.non_ema.bin | `variant` |\n\nvariant를 로드할 때 2개의 중요한 argument가 있습니다.\n\n* `torch_dtype`은 불러올 체크포인트의 부동소수점을 정의합니다. 예를 들어 `torch_dtype=torch.float16`을 명시함으로써 가중치의 부동소수점 타입을 `fl16`으로 변환할 수 있습니다. (만약 따로 설정하지 않을 경우, 기본값으로 `fp32` 타입의 가중치가 로딩됩니다.) 또한 `variant` 인자를 명시하지 않은 채로 체크포인트를 불러온 다음, 해당 체크포인트를 `torch_dtype=torch.float16` 인자를 통해 `fp16` 타입으로 변환하는 것 역시 가능합니다. 이 경우 기본으로 설정된 `fp32` 가중치가 먼저 다운로드되고, 해당 가중치들을 불러온 다음 `fp16` 타입으로 변환하게 됩니다.\n* `variant` 인자는 리포지토리에서 어떤 variant를 불러올 것인가를 정의합니다. 가령 [`diffusers/stable-diffusion-variants`](https://huggingface.co/diffusers/stable-diffusion-variants/tree/main/unet) 리포지토리로부터 `non_ema` 체크포인트를 불러오고자 한다면, `variant=\"non_ema\"` 인자를 전달해야 합니다.\n\n```python\nfrom diffusers import DiffusionPipeline\n\n# load fp16 variant\nstable_diffusion = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", variant=\"fp16\", torch_dtype=torch.float16\n)\n# load non_ema variant\nstable_diffusion = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", variant=\"non_ema\")\n```\n\n다른 부동소수점 타입의 가중치 혹은 non-EMA 가중치를 사용하는 체크포인트를 저장하기 위해서는, [`DiffusionPipeline.save_pretrained`] 메서드를 사용해야 하며, 이 때 `variant` 인자를 명시해줘야 합니다. 원래의 체크포인트와 동일한 폴더에 variant를 저장해야 하며, 이렇게 하면 동일한 폴더에서 오리지널 체크포인트과 variant를 모두 불러올 수 있습니다.\n\n```python\nfrom diffusers import DiffusionPipeline\n\n# save as fp16 variant\nstable_diffusion.save_pretrained(\"runwayml/stable-diffusion-v1-5\", variant=\"fp16\")\n# save as non-ema variant\nstable_diffusion.save_pretrained(\"runwayml/stable-diffusion-v1-5\", variant=\"non_ema\")\n```\n\n만약 variant를 기존 폴더에 저장하지 않을 경우, `variant` 인자를 반드시 명시해야 합니다. 그렇게 하지 않을 경우 원래의 오리지널 체크포인트를 찾을 수 없게 되기 때문에 에러가 발생합니다.\n\n```python\n# 👎 this won't work\nstable_diffusion = DiffusionPipeline.from_pretrained(\"./stable-diffusion-v1-5\", torch_dtype=torch.float16)\n# 👍 this works\nstable_diffusion = DiffusionPipeline.from_pretrained(\n \"./stable-diffusion-v1-5\", variant=\"fp16\", torch_dtype=torch.float16\n)\n```\n\n### 모델 불러오기\n\n모델들은 [`ModelMixin.from_pretrained`] 메서드를 통해 불러올 수 있습니다. 해당 메서드는 최신 버전의 모델 가중치 파일과 설정 파일(configurations)을 다운로드하고 캐싱합니다. 만약 이러한 파일들이 최신 버전으로 로컬 캐시에 저장되어 있다면, [`ModelMixin.from_pretrained`]는 굳이 해당 파일들을 다시 다운로드하지 않으며, 그저 캐시에 있는 최신 파일들을 재사용합니다.\n\n모델은 `subfolder` 인자에 명시된 하위 폴더로부터 로드됩니다. 예를 들어 `runwayml/stable-diffusion-v1-5`의 UNet 모델의 가중치는 [`unet`](https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/main/unet) 폴더에 저장되어 있습니다.\n\n```python\nfrom diffusers import UNet2DConditionModel\n\nrepo_id = \"runwayml/stable-diffusion-v1-5\"\nmodel = UNet2DConditionModel.from_pretrained(repo_id, subfolder=\"unet\")\n```\n\n혹은 [해당 모델의 리포지토리](https://huggingface.co/google/ddpm-cifar10-32/tree/main)로부터 다이렉트로 가져오는 것 역시 가능합니다. \n\n```python\nfrom diffusers import UNet2DModel\n\nrepo_id = \"google/ddpm-cifar10-32\"\nmodel = UNet2DModel.from_pretrained(repo_id)\n```\n\n또한 앞서 봤던 `variant` 인자를 명시함으로써, Non-EMA나 `fp16`의 가중치를 가져오는 것 역시 가능합니다. \n\n```python\nfrom diffusers import UNet2DConditionModel\n\nmodel = UNet2DConditionModel.from_pretrained(\"runwayml/stable-diffusion-v1-5\", subfolder=\"unet\", variant=\"non-ema\")\nmodel.save_pretrained(\"./local-unet\", variant=\"non-ema\")\n```\n\n### 스케줄러\n\n스케줄러들은 [`SchedulerMixin.from_pretrained`] 메서드를 통해 불러올 수 있습니다. 모델과 달리 스케줄러는 별도의 가중치를 갖지 않으며, 따라서 당연히 별도의 학습과정을 요구하지 않습니다. 이러한 스케줄러들은 (해당 스케줄러 하위폴더의) configration 파일을 통해 정의됩니다. \n\n여러개의 스케줄러를 불러온다고 해서 많은 메모리를 소모하는 것은 아니며, 다양한 스케줄러들에 동일한 스케줄러 configration을 적용하는 것 역시 가능합니다. 다음 예시 코드에서 불러오는 스케줄러들은 모두 [`StableDiffusionPipeline`]과 호환되는데, 이는 곧 해당 스케줄러들에 동일한 스케줄러 configration 파일을 적용할 수 있음을 의미합니다.\n\n```python\nfrom diffusers import StableDiffusionPipeline\nfrom diffusers import (\n DDPMScheduler,\n DDIMScheduler,\n PNDMScheduler,\n LMSDiscreteScheduler,\n EulerDiscreteScheduler,\n EulerAncestralDiscreteScheduler,\n DPMSolverMultistepScheduler,\n)\n\nrepo_id = \"runwayml/stable-diffusion-v1-5\"\n\nddpm = DDPMScheduler.from_pretrained(repo_id, subfolder=\"scheduler\")\nddim = DDIMScheduler.from_pretrained(repo_id, subfolder=\"scheduler\")\npndm = PNDMScheduler.from_pretrained(repo_id, subfolder=\"scheduler\")\nlms = LMSDiscreteScheduler.from_pretrained(repo_id, subfolder=\"scheduler\")\neuler_anc = EulerAncestralDiscreteScheduler.from_pretrained(repo_id, subfolder=\"scheduler\")\neuler = EulerDiscreteScheduler.from_pretrained(repo_id, subfolder=\"scheduler\")\ndpm = DPMSolverMultistepScheduler.from_pretrained(repo_id, subfolder=\"scheduler\")\n\n# replace `dpm` with any of `ddpm`, `ddim`, `pndm`, `lms`, `euler_anc`, `euler`\npipeline = StableDiffusionPipeline.from_pretrained(repo_id, scheduler=dpm)\n```\n\n### DiffusionPipeline에 대해 알아보기\n\n클래스 메서드로서 [`DiffusionPipeline.from_pretrained`]은 2가지를 담당합니다.\n\n- 첫째로, `from_pretrained` 메서드는 최신 버전의 파이프라인을 다운로드하고, 캐시에 저장합니다. 이미 로컬 캐시에 최신 버전의 파이프라인이 저장되어 있다면, [`DiffusionPipeline.from_pretrained`]은 해당 파일들을 다시 다운로드하지 않고, 로컬 캐시에 저장되어 있는 파이프라인을 불러옵니다.\n- `model_index.json` 파일을 통해 체크포인트에 대응되는 적합한 파이프라인 클래스로 불러옵니다.\n\n파이프라인의 폴더 구조는 해당 파이프라인 클래스의 구조와 직접적으로 일치합니다. 예를 들어 [`StableDiffusionPipeline`] 클래스는 [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) 리포지토리와 대응되는 구조를 갖습니다.\n\n```python\nfrom diffusers import DiffusionPipeline\n\nrepo_id = \"runwayml/stable-diffusion-v1-5\"\npipeline = DiffusionPipeline.from_pretrained(repo_id)\nprint(pipeline)\n```\n\n위의 코드 출력 결과를 확인해보면, `pipeline`은 [`StableDiffusionPipeline`]의 인스턴스이며, 다음과 같이 총 7개의 컴포넌트로 구성된다는 것을 알 수 있습니다.\n\n- `\"feature_extractor\"`: [`~transformers.CLIPFeatureExtractor`]의 인스턴스\n- `\"safety_checker\"`: 유해한 컨텐츠를 스크리닝하기 위한 [컴포넌트](https://github.com/huggingface/diffusers/blob/e55687e1e15407f60f32242027b7bb8170e58266/src/diffusers/pipelines/stable_diffusion/safety_checker.py#L32)\n- `\"scheduler\"`: [`PNDMScheduler`]의 인스턴스\n- `\"text_encoder\"`: [`~transformers.CLIPTextModel`]의 인스턴스\n- `\"tokenizer\"`: a [`~transformers.CLIPTokenizer`]의 인스턴스\n- `\"unet\"`: [`UNet2DConditionModel`]의 인스턴스\n- `\"vae\"` [`AutoencoderKL`]의 인스턴스\n\n```json\nStableDiffusionPipeline {\n \"feature_extractor\": [\n \"transformers\",\n \"CLIPImageProcessor\"\n ],\n \"safety_checker\": [\n \"stable_diffusion\",\n \"StableDiffusionSafetyChecker\"\n ],\n \"scheduler\": [\n \"diffusers\",\n \"PNDMScheduler\"\n ],\n \"text_encoder\": [\n \"transformers\",\n \"CLIPTextModel\"\n ],\n \"tokenizer\": [\n \"transformers\",\n \"CLIPTokenizer\"\n ],\n \"unet\": [\n \"diffusers\",\n \"UNet2DConditionModel\"\n ],\n \"vae\": [\n \"diffusers\",\n \"AutoencoderKL\"\n ]\n}\n```\n\n파이프라인 인스턴스의 컴포넌트들을 [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5)의 폴더 구조와 비교해볼 경우, 각각의 컴포넌트마다 별도의 폴더가 있음을 확인할 수 있습니다.\n\n```\n.\n├── feature_extractor\n│ └── preprocessor_config.json\n├── model_index.json\n├── safety_checker\n│ ├── config.json\n│ └── pytorch_model.bin\n├── scheduler\n│ └── scheduler_config.json\n├── text_encoder\n│ ├── config.json\n│ └── pytorch_model.bin\n├── tokenizer\n│ ├── merges.txt\n│ ├── special_tokens_map.json\n│ ├── tokenizer_config.json\n│ └── vocab.json\n├── unet\n│ ├── config.json\n│ ├── diffusion_pytorch_model.bin\n└── vae\n ├── config.json\n ├── diffusion_pytorch_model.bin\n```\n\n또한 각각의 컴포넌트들을 파이프라인 인스턴스의 속성으로써 참조할 수 있습니다. \n\n```py\npipeline.tokenizer\n```\n\n```python\nCLIPTokenizer(\n name_or_path=\"/root/.cache/huggingface/hub/models--runwayml--stable-diffusion-v1-5/snapshots/39593d5650112b4cc580433f6b0435385882d819/tokenizer\",\n vocab_size=49408,\n model_max_length=77,\n is_fast=False,\n padding_side=\"right\",\n truncation_side=\"right\",\n special_tokens={\n \"bos_token\": AddedToken(\"<|startoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=True),\n \"eos_token\": AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=True),\n \"unk_token\": AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=True),\n \"pad_token\": \"<|endoftext|>\",\n },\n)\n```\n\n모든 파이프라인은 `model_index.json` 파일을 통해 [`DiffusionPipeline`]에 다음과 같은 정보를 전달합니다.\n\n- `_class_name` 는 어떤 파이프라인 클래스를 사용해야 하는지에 대해 알려줍니다.\n- `_diffusers_version`는 어떤 버전의 디퓨저스로 파이프라인 안의 모델들이 만들어졌는지를 알려줍니다.\n- 그 다음은 각각의 컴포넌트들이 어떤 라이브러리의 어떤 클래스로 만들어졌는지에 대해 알려줍니다. (아래 예시에서 `\"feature_extractor\" : [\"transformers\", \"CLIPImageProcessor\"]`의 경우, `feature_extractor` 컴포넌트는 `transformers` 라이브러리의 `CLIPImageProcessor` 클래스를 통해 만들어졌다는 것을 의미합니다.)\n\n```json\n{\n \"_class_name\": \"StableDiffusionPipeline\",\n \"_diffusers_version\": \"0.6.0\",\n \"feature_extractor\": [\n \"transformers\",\n \"CLIPImageProcessor\"\n ],\n \"safety_checker\": [\n \"stable_diffusion\",\n \"StableDiffusionSafetyChecker\"\n ],\n \"scheduler\": [\n \"diffusers\",\n \"PNDMScheduler\"\n ],\n \"text_encoder\": [\n \"transformers\",\n \"CLIPTextModel\"\n ],\n \"tokenizer\": [\n \"transformers\",\n \"CLIPTokenizer\"\n ],\n \"unet\": [\n \"diffusers\",\n \"UNet2DConditionModel\"\n ],\n \"vae\": [\n \"diffusers\",\n \"AutoencoderKL\"\n ]\n}\n```\n\n" }, { "path": "diffusers/docs/source/ko/using-diffusers/loading_overview.md", "content": "\n\n# Overview\n\n🧨 Diffusers는 생성 작업을 위한 다양한 파이프라인, 모델, 스케줄러를 제공합니다. 이러한 컴포넌트를 최대한 간단하게 로드할 수 있도록 단일 통합 메서드인 `from_pretrained()`를 제공하여 Hugging Face [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) 또는 로컬 머신에서 이러한 컴포넌트를 불러올 수 있습니다. 파이프라인이나 모델을 로드할 때마다, 최신 파일이 자동으로 다운로드되고 캐시되므로, 다음에 파일을 다시 다운로드하지 않고도 빠르게 재사용할 수 있습니다.\n\n이 섹션은 파이프라인 로딩, 파이프라인에서 다양한 컴포넌트를 로드하는 방법, 체크포인트 variants를 불러오는 방법, 그리고 커뮤니티 파이프라인을 불러오는 방법에 대해 알아야 할 모든 것들을 다룹니다. 또한 스케줄러를 불러오는 방법과 서로 다른 스케줄러를 사용할 때 발생하는 속도와 품질간의 트레이드 오프를 비교하는 방법 역시 다룹니다. 그리고 마지막으로 🧨 Diffusers와 함께 파이토치에서 사용할 수 있도록 KerasCV 체크포인트를 변환하고 불러오는 방법을 살펴봅니다.\n\n" }, { "path": "diffusers/docs/source/ko/using-diffusers/other-formats.md", "content": "\n\n# 다양한 Stable Diffusion 포맷 불러오기\n\nStable Diffusion 모델들은 학습 및 저장된 프레임워크와 다운로드 위치에 따라 다양한 형식으로 제공됩니다. 이러한 형식을 🤗 Diffusers에서 사용할 수 있도록 변환하면 추론을 위한 [다양한 스케줄러 사용](schedulers), 사용자 지정 파이프라인 구축, 추론 속도 최적화를 위한 다양한 기법과 방법 등 라이브러리에서 지원하는 모든 기능을 사용할 수 있습니다.\n\n\n\n우리는 `.safetensors` 형식을 추천합니다. 왜냐하면 기존의 pickled 파일은 취약하고 머신에서 코드를 실행할 때 악용될 수 있는 것에 비해 훨씬 더 안전합니다. (safetensors 불러오기 가이드에서 자세히 알아보세요.)\n\n\n\n이 가이드에서는 다른 Stable Diffusion 형식을 🤗 Diffusers와 호환되도록 변환하는 방법을 설명합니다.\n\n## PyTorch .ckpt\n\n체크포인트 또는 `.ckpt` 형식은 일반적으로 모델을 저장하는 데 사용됩니다. `.ckpt` 파일은 전체 모델을 포함하며 일반적으로 크기가 몇 GB입니다. `.ckpt` 파일을 [~StableDiffusionPipeline.from_ckpt] 메서드를 사용하여 직접 불러와서 사용할 수도 있지만, 일반적으로 두 가지 형식을 모두 사용할 수 있도록 `.ckpt` 파일을 🤗 Diffusers로 변환하는 것이 더 좋습니다.\n\n`.ckpt` 파일을 변환하는 두 가지 옵션이 있습니다. Space를 사용하여 체크포인트를 변환하거나 스크립트를 사용하여 `.ckpt` 파일을 변환합니다.\n\n### Space로 변환하기\n\n`.ckpt` 파일을 변환하는 가장 쉽고 편리한 방법은 SD에서 Diffusers로 스페이스를 사용하는 것입니다. Space의 지침에 따라 .ckpt 파일을 변환 할 수 있습니다.\n\n이 접근 방식은 기본 모델에서는 잘 작동하지만 더 많은 사용자 정의 모델에서는 어려움을 겪을 수 있습니다. 빈 pull request나 오류를 반환하면 Space가 실패한 것입니다.\n이 경우 스크립트를 사용하여 `.ckpt` 파일을 변환해 볼 수 있습니다.\n\n### 스크립트로 변환하기\n\n🤗 Diffusers는 `.ckpt`  파일 변환을 위한 변환 스크립트를 제공합니다. 이 접근 방식은 위의 Space보다 더 안정적입니다.\n\n시작하기 전에 스크립트를 실행할 🤗 Diffusers의 로컬 클론(clone)이 있는지 확인하고 Hugging Face 계정에 로그인하여 pull request를 열고 변환된 모델을 허브에 푸시할 수 있도록 하세요.\n\n```bash\nhuggingface-cli login\n```\n\n스크립트를 사용하려면:\n\n1. 변환하려는 `.ckpt`  파일이 포함된 리포지토리를 Git으로 클론(clone)합니다.\n\n이 예제에서는 TemporalNet .ckpt 파일을 변환해 보겠습니다:\n\n```bash\ngit lfs install\ngit clone https://huggingface.co/CiaraRowles/TemporalNet\n```\n\n2. 체크포인트를 변환할 리포지토리에서 pull request를 엽니다:\n\n```bash\ncd TemporalNet && git fetch origin refs/pr/13:pr/13\ngit checkout pr/13\n```\n\n3. 변환 스크립트에서 구성할 입력 인수는 여러 가지가 있지만 가장 중요한 인수는 다음과 같습니다:\n\n- `checkpoint_path`: 변환할 `.ckpt` 파일의 경로를 입력합니다.\n- `original_config_file`: 원래 아키텍처의 구성을 정의하는 YAML 파일입니다. 이 파일을 찾을 수 없는 경우 `.ckpt` 파일을 찾은 GitHub 리포지토리에서 YAML 파일을 검색해 보세요.\n- `dump_path`: 변환된 모델의 경로\n\n예를 들어, TemporalNet 모델은 Stable Diffusion v1.5 및 ControlNet 모델이기 때문에 ControlNet 리포지토리에서 cldm_v15.yaml 파일을 가져올 수 있습니다.\n\n4. 이제 스크립트를 실행하여 .ckpt 파일을 변환할 수 있습니다:\n\n```bash\npython ../diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py --checkpoint_path temporalnetv3.ckpt --original_config_file cldm_v15.yaml --dump_path ./ --controlnet\n```\n\n5. 변환이 완료되면 변환된 모델을 업로드하고 결과물을 pull request [pull request](https://huggingface.co/CiaraRowles/TemporalNet/discussions/13)를 테스트하세요!\n\n```bash\ngit push origin pr/13:refs/pr/13\n```\n\n## **Keras .pb or .h5**\n\n🧪 이 기능은 실험적인 기능입니다. 현재로서는 Stable Diffusion v1 체크포인트만 변환 KerasCV Space에서 지원됩니다.\n\n[KerasCV](https://keras.io/keras_cv/)는 [Stable Diffusion](https://github.com/keras-team/keras-cv/blob/master/keras_cv/models/stable_diffusion)  v1 및 v2에 대한 학습을 지원합니다. 그러나 추론 및 배포를 위한 Stable Diffusion 모델 실험을 제한적으로 지원하는 반면, 🤗 Diffusers는 다양한 [noise schedulers](https://huggingface.co/docs/diffusers/using-diffusers/schedulers), [flash attention](https://huggingface.co/docs/diffusers/optimization/xformers), and [other optimization techniques](https://huggingface.co/docs/diffusers/optimization/fp16) 등 이러한 목적을 위한 보다 완벽한 기능을 갖추고 있습니다.\n\n[Convert KerasCV](https://huggingface.co/spaces/sayakpaul/convert-kerascv-sd-diffusers) Space 변환은 `.pb` 또는 `.h5`을 PyTorch로 변환한 다음, 추론할 수 있도록 [`StableDiffusionPipeline`] 으로 감싸서 준비합니다. 변환된 체크포인트는 Hugging Face Hub의 리포지토리에 저장됩니다.\n\n예제로, textual-inversion으로 학습된 `[sayakpaul/textual-inversion-kerasio](https://huggingface.co/sayakpaul/textual-inversion-kerasio/tree/main)` 체크포인트를 변환해 보겠습니다. 이것은 특수 토큰  ``을 사용하여 고양이로 이미지를 개인화합니다.\n\nKerasCV Space 변환에서는 다음을 입력할 수 있습니다:\n\n- Hugging Face 토큰.\n- UNet 과 텍스트 인코더(text encoder) 가중치를 다운로드하는 경로입니다. 모델을 어떻게 학습할지 방식에 따라, UNet과 텍스트 인코더의 경로를 모두 제공할 필요는 없습니다. 예를 들어, textual-inversion에는 텍스트 인코더의 임베딩만 필요하고 텍스트-이미지(text-to-image) 모델 변환에는 UNet 가중치만 필요합니다.\n- Placeholder 토큰은 textual-inversion 모델에만 적용됩니다.\n- `output_repo_prefix`는 변환된 모델이 저장되는 리포지토리의 이름입니다.\n\n**Submit** (제출) 버튼을 클릭하면 KerasCV 체크포인트가 자동으로 변환됩니다! 체크포인트가 성공적으로 변환되면, 변환된 체크포인트가 포함된 새 리포지토리로 연결되는 링크가 표시됩니다. 새 리포지토리로 연결되는 링크를 따라가면 변환된 모델을 사용해 볼 수 있는 추론 위젯이 포함된 모델 카드가 생성된 KerasCV Space 변환을 확인할 수 있습니다.\n\n코드를 사용하여 추론을 실행하려면 모델 카드의 오른쪽 상단 모서리에 있는 **Use in Diffusers**  버튼을 클릭하여 예시 코드를 복사하여 붙여넣습니다:\n\n```py\nfrom diffusers import DiffusionPipeline\n\npipeline = DiffusionPipeline.from_pretrained(\"sayakpaul/textual-inversion-cat-kerascv_sd_diffusers_pipeline\")\n```\n\n그러면 다음과 같은 이미지를 생성할 수 있습니다:\n\n```py\nfrom diffusers import DiffusionPipeline\n\npipeline = DiffusionPipeline.from_pretrained(\"sayakpaul/textual-inversion-cat-kerascv_sd_diffusers_pipeline\")\npipeline.to(\"cuda\")\n\nplaceholder_token = \"\"\nprompt = f\"two {placeholder_token} getting married, photorealistic, high quality\"\nimage = pipeline(prompt, num_inference_steps=50).images[0]\n```\n\n## **A1111 LoRA files**\n\n[Automatic1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) (A1111)은 Stable Diffusion을 위해 널리 사용되는 웹 UI로, [Civitai](https://civitai.com/) 와 같은 모델 공유 플랫폼을 지원합니다. 특히 LoRA 기법으로 학습된 모델은 학습 속도가 빠르고 완전히 파인튜닝된 모델보다 파일 크기가 훨씬 작기 때문에 인기가 높습니다.\n\n🤗 Diffusers는 [`~loaders.LoraLoaderMixin.load_lora_weights`]:를 사용하여 A1111 LoRA 체크포인트 불러오기를 지원합니다:\n\n```py\nfrom diffusers import DiffusionPipeline, UniPCMultistepScheduler\nimport torch\n\npipeline = DiffusionPipeline.from_pretrained(\n \"andite/anything-v4.0\", torch_dtype=torch.float16, safety_checker=None\n).to(\"cuda\")\npipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)\n```\n\nCivitai에서 LoRA 체크포인트를 다운로드하세요; 이 예제에서는  [Howls Moving Castle,Interior/Scenery LoRA (Ghibli Stlye)](https://civitai.com/models/14605?modelVersionId=19998) 체크포인트를 사용했지만, 어떤 LoRA 체크포인트든 자유롭게 사용해 보세요!\n\n```bash\n!wget https://civitai.com/api/download/models/19998 -O howls_moving_castle.safetensors\n```\n\n메서드를 사용하여 파이프라인에 LoRA 체크포인트를 불러옵니다:\n\n```py\npipeline.load_lora_weights(\".\", weight_name=\"howls_moving_castle.safetensors\")\n```\n\n이제 파이프라인을 사용하여 이미지를 생성할 수 있습니다:\n\n```py\nprompt = \"masterpiece, illustration, ultra-detailed, cityscape, san francisco, golden gate bridge, california, bay area, in the snow, beautiful detailed starry sky\"\nnegative_prompt = \"lowres, cropped, worst quality, low quality, normal quality, artifacts, signature, watermark, username, blurry, more than one bridge, bad architecture\"\n\nimages = pipeline(\n prompt=prompt,\n negative_prompt=negative_prompt,\n width=512,\n height=512,\n num_inference_steps=25,\n num_images_per_prompt=4,\n generator=torch.manual_seed(0),\n).images\n```\n\n마지막으로, 디스플레이에 이미지를 표시하는 헬퍼 함수를 만듭니다:\n\n```py\nfrom PIL import Image\n\n\ndef image_grid(imgs, rows=2, cols=2):\n w, h = imgs[0].size\n grid = Image.new(\"RGB\", size=(cols * w, rows * h))\n\n for i, img in enumerate(imgs):\n grid.paste(img, box=(i % cols * w, i // cols * h))\n return grid\n\n\nimage_grid(images)\n```\n\n
\n \n
\n" }, { "path": "diffusers/docs/source/ko/using-diffusers/pipeline_overview.md", "content": "\n\n# Overview\n\n파이프라인은 독립적으로 훈련된 모델과 스케줄러를 함께 모아서 추론을 위해 diffusion 시스템을 빠르고 쉽게 사용할 수 있는 방법을 제공하는 end-to-end 클래스입니다. 모델과 스케줄러의 특정 조합은 특수한 기능과 함께 [`StableDiffusionPipeline`] 또는 [`StableDiffusionControlNetPipeline`]과 같은 특정 파이프라인 유형을 정의합니다. 모든 파이프라인 유형은 기본 [`DiffusionPipeline`] 클래스에서 상속됩니다. 어느 체크포인트를 전달하면, 파이프라인 유형을 자동으로 감지하고 필요한 구성 요소들을 불러옵니다.\n\n이 섹션에서는 unconditional 이미지 생성, text-to-image 생성의 다양한 테크닉과 변화를 파이프라인에서 지원하는 작업들을 소개합니다. 프롬프트에 있는 특정 단어가 출력에 영향을 미치는 것을 조정하기 위해 재현성을 위한 시드 설정과 프롬프트에 가중치를 부여하는 것으로 생성 프로세스를 더 잘 제어하는 방법에 대해 배울 수 있습니다. 마지막으로 음성에서부터 이미지 생성과 같은 커스텀 작업을 위한 커뮤니티 파이프라인을 만드는 방법을 알 수 있습니다.\n" }, { "path": "diffusers/docs/source/ko/using-diffusers/reproducibility.md", "content": "\n\n# 재현 가능한 파이프라인 생성하기\n\n[[open-in-colab]]\n\n재현성은 테스트, 결과 재현, 그리고 [이미지 퀄리티 높이기](resuing_seeds)에서 중요합니다.\n그러나 diffusion 모델의 무작위성은 매번 모델이 돌아갈 때마다 파이프라인이 다른 이미지를 생성할 수 있도록 하는 이유로 필요합니다.\n플랫폼 간에 정확하게 동일한 결과를 얻을 수는 없지만, 특정 허용 범위 내에서 릴리스 및 플랫폼 간에 결과를 재현할 수는 있습니다.\n그럼에도 diffusion 파이프라인과 체크포인트에 따라 허용 오차가 달라집니다.\n\ndiffusion 모델에서 무작위성의 원천을 제어하거나 결정론적 알고리즘을 사용하는 방법을 이해하는 것이 중요한 이유입니다.\n\n\n\n💡 Pytorch의 [재현성에 대한 선언](https://pytorch.org/docs/stable/notes/randomness.html)를 꼭 읽어보길 추천합니다:\n\n> 완전하게 재현가능한 결과는 Pytorch 배포, 개별적인 커밋, 혹은 다른 플랫폼들에서 보장되지 않습니다.\n> 또한, 결과는 CPU와 GPU 실행간에 심지어 같은 seed를 사용할 때도 재현 가능하지 않을 수 있습니다.\n\n\n\n## 무작위성 제어하기\n\n추론에서, 파이프라인은 노이즈를 줄이기 위해 가우시안 노이즈를 생성하거나 스케줄링 단계에 노이즈를 더하는 등의 랜덤 샘플링 실행에 크게 의존합니다,\n\n[DDIMPipeline](https://huggingface.co/docs/diffusers/v0.18.0/en/api/pipelines/ddim#diffusers.DDIMPipeline)에서 두 추론 단계 이후의 텐서 값을 살펴보세요:\n\n```python\nfrom diffusers import DDIMPipeline\nimport numpy as np\n\nmodel_id = \"google/ddpm-cifar10-32\"\n\n# 모델과 스케줄러를 불러오기\nddim = DDIMPipeline.from_pretrained(model_id)\n\n# 두 개의 단계에 대해서 파이프라인을 실행하고 numpy tensor로 값을 반환하기\nimage = ddim(num_inference_steps=2, output_type=\"np\").images\nprint(np.abs(image).sum())\n```\n\n위의 코드를 실행하면 하나의 값이 나오지만, 다시 실행하면 다른 값이 나옵니다. 무슨 일이 일어나고 있는 걸까요?\n\n파이프라인이 실행될 때마다, [torch.randn](https://pytorch.org/docs/stable/generated/torch.randn.html)은\n단계적으로 노이즈 제거되는 가우시안 노이즈가 생성하기 위한 다른 랜덤 seed를 사용합니다.\n\n그러나 동일한 이미지를 안정적으로 생성해야 하는 경우에는 CPU에서 파이프라인을 실행하는지 GPU에서 실행하는지에 따라 달라집니다.\n\n### CPU\n\nCPU에서 재현 가능한 결과를 생성하려면, PyTorch [Generator](https://pytorch.org/docs/stable/generated/torch.randn.html)로 seed를 고정합니다:\n\n```python\nimport torch\nfrom diffusers import DDIMPipeline\nimport numpy as np\n\nmodel_id = \"google/ddpm-cifar10-32\"\n\n# 모델과 스케줄러 불러오기\nddim = DDIMPipeline.from_pretrained(model_id)\n\n# 재현성을 위해 generator 만들기\ngenerator = torch.Generator(device=\"cpu\").manual_seed(0)\n\n# 두 개의 단계에 대해서 파이프라인을 실행하고 numpy tensor로 값을 반환하기\nimage = ddim(num_inference_steps=2, output_type=\"np\", generator=generator).images\nprint(np.abs(image).sum())\n```\n\n이제 위의 코드를 실행하면 seed를 가진 `Generator` 객체가 파이프라인의 모든 랜덤 함수에 전달되므로 항상 `1491.1711` 값이 출력됩니다.\n\n특정 하드웨어 및 PyTorch 버전에서 이 코드 예제를 실행하면 동일하지는 않더라도 유사한 결과를 얻을 수 있습니다.\n\n\n\n💡 처음에는 시드를 나타내는 정수값 대신에 `Generator` 개체를 파이프라인에 전달하는 것이 약간 비직관적일 수 있지만,\n`Generator`는 순차적으로 여러 파이프라인에 전달될 수 있는 \\랜덤상태\\이기 때문에 PyTorch에서 확률론적 모델을 다룰 때 권장되는 설계입니다.\n\n\n\n### GPU\n\n예를 들면, GPU 상에서 같은 코드 예시를 실행하면:\n\n```python\nimport torch\nfrom diffusers import DDIMPipeline\nimport numpy as np\n\nmodel_id = \"google/ddpm-cifar10-32\"\n\n# 모델과 스케줄러 불러오기\nddim = DDIMPipeline.from_pretrained(model_id)\nddim.to(\"cuda\")\n\n# 재현성을 위한 generator 만들기\ngenerator = torch.Generator(device=\"cuda\").manual_seed(0)\n\n# 두 개의 단계에 대해서 파이프라인을 실행하고 numpy tensor로 값을 반환하기\nimage = ddim(num_inference_steps=2, output_type=\"np\", generator=generator).images\nprint(np.abs(image).sum())\n```\n\nGPU가 CPU와 다른 난수 생성기를 사용하기 때문에 동일한 시드를 사용하더라도 결과가 같지 않습니다.\n\n이 문제를 피하기 위해 🧨 Diffusers는 CPU에 임의의 노이즈를 생성한 다음 필요에 따라 텐서를 GPU로 이동시키는\n[randn_tensor()](https://huggingface.co/docs/diffusers/v0.18.0/en/api/utilities#diffusers.utils.randn_tensor)기능을 가지고 있습니다.\n`randn_tensor` 기능은 파이프라인 내부 어디에서나 사용되므로 파이프라인이 GPU에서 실행되더라도 **항상** CPU `Generator`를 통과할 수 있습니다.\n\n이제 결과에 훨씬 더 다가왔습니다!\n\n```python\nimport torch\nfrom diffusers import DDIMPipeline\nimport numpy as np\n\nmodel_id = \"google/ddpm-cifar10-32\"\n\n# 모델과 스케줄러 불러오기\nddim = DDIMPipeline.from_pretrained(model_id)\nddim.to(\"cuda\")\n\n#재현성을 위한 generator 만들기 (GPU에 올리지 않도록 조심한다!)\ngenerator = torch.manual_seed(0)\n\n# 두 개의 단계에 대해서 파이프라인을 실행하고 numpy tensor로 값을 반환하기\nimage = ddim(num_inference_steps=2, output_type=\"np\", generator=generator).images\nprint(np.abs(image).sum())\n```\n\n\n\n💡 재현성이 중요한 경우에는 항상 CPU generator를 전달하는 것이 좋습니다.\n성능 손실은 무시할 수 없는 경우가 많으며 파이프라인이 GPU에서 실행되었을 때보다 훨씬 더 비슷한 값을 생성할 수 있습니다.\n\n\n\n마지막으로 [UnCLIPPipeline](https://huggingface.co/docs/diffusers/v0.18.0/en/api/pipelines/unclip#diffusers.UnCLIPPipeline)과 같은\n더 복잡한 파이프라인의 경우, 이들은 종종 정밀 오차 전파에 극도로 취약합니다.\n다른 GPU 하드웨어 또는 PyTorch 버전에서 유사한 결과를 기대하지 마세요.\n이 경우 완전한 재현성을 위해 완전히 동일한 하드웨어 및 PyTorch 버전을 실행해야 합니다.\n\n## 결정론적 알고리즘\n\n결정론적 알고리즘을 사용하여 재현 가능한 파이프라인을 생성하도록 PyTorch를 구성할 수도 있습니다.\n그러나 결정론적 알고리즘은 비결정론적 알고리즘보다 느리고 성능이 저하될 수 있습니다.\n하지만 재현성이 중요하다면, 이것이 최선의 방법입니다!\n\n둘 이상의 CUDA 스트림에서 작업이 시작될 때 비결정론적 동작이 발생합니다.\n이 문제를 방지하려면 환경 변수 [CUBLAS_WORKSPACE_CONFIG](https://docs.nvidia.com/cuda/cublas/index.html#results-reproducibility)를 `:16:8`로 설정해서\n런타임 중에 오직 하나의 버퍼 크리만 사용하도록 설정합니다.\n\nPyTorch는 일반적으로 가장 빠른 알고리즘을 선택하기 위해 여러 알고리즘을 벤치마킹합니다.\n하지만 재현성을 원하는 경우, 벤치마크가 매 순간 다른 알고리즘을 선택할 수 있기 때문에 이 기능을 사용하지 않도록 설정해야 합니다.\n마지막으로, [torch.use_deterministic_algorithms](https://pytorch.org/docs/stable/generated/torch.use_deterministic_algorithms.html)에\n`True`를 통과시켜 결정론적 알고리즘이 활성화 되도록 합니다.\n\n```py\nimport os\n\nos.environ[\"CUBLAS_WORKSPACE_CONFIG\"] = \":16:8\"\n\ntorch.backends.cudnn.benchmark = False\ntorch.use_deterministic_algorithms(True)\n```\n\n이제 동일한 파이프라인을 두번 실행하면 동일한 결과를 얻을 수 있습니다.\n\n```py\nimport torch\nfrom diffusers import DDIMScheduler, StableDiffusionPipeline\nimport numpy as np\n\nmodel_id = \"runwayml/stable-diffusion-v1-5\"\npipe = StableDiffusionPipeline.from_pretrained(model_id).to(\"cuda\")\npipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\ng = torch.Generator(device=\"cuda\")\n\nprompt = \"A bear is playing a guitar on Times Square\"\n\ng.manual_seed(0)\nresult1 = pipe(prompt=prompt, num_inference_steps=50, generator=g, output_type=\"latent\").images\n\ng.manual_seed(0)\nresult2 = pipe(prompt=prompt, num_inference_steps=50, generator=g, output_type=\"latent\").images\n\nprint(\"L_inf dist = \", abs(result1 - result2).max())\n\"L_inf dist = tensor(0., device='cuda:0')\"\n```\n" }, { "path": "diffusers/docs/source/ko/using-diffusers/reusing_seeds.md", "content": "\n\n# Deterministic(결정적) 생성을 통한 이미지 품질 개선\n\n생성된 이미지의 품질을 개선하는 일반적인 방법은 *결정적 batch(배치) 생성*을 사용하는 것입니다. 이 방법은 이미지 batch(배치)를 생성하고 두 번째 추론 라운드에서 더 자세한 프롬프트와 함께 개선할 이미지 하나를 선택하는 것입니다. 핵심은 일괄 이미지 생성을 위해 파이프라인에 [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html#generator) 목록을 전달하고, 각 `Generator`를 시드에 연결하여 이미지에 재사용할 수 있도록 하는 것입니다.\n\n예를 들어 [`runwayml/stable-diffusion-v1-5`](runwayml/stable-diffusion-v1-5)를 사용하여 다음 프롬프트의 여러 버전을 생성해 봅시다.\n\n```py\nprompt = \"Labrador in the style of Vermeer\"\n```\n\n(가능하다면) 파이프라인을 [`DiffusionPipeline.from_pretrained`]로 인스턴스화하여 GPU에 배치합니다.\n\n```python\n>>> from diffusers import DiffusionPipeline\n\n>>> pipe = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16)\n>>> pipe = pipe.to(\"cuda\")\n```\n\n이제 네 개의 서로 다른 `Generator`를 정의하고 각 `Generator`에 시드(`0` ~ `3`)를 할당하여 나중에 특정 이미지에 대해 `Generator`를 재사용할 수 있도록 합니다.\n\n```python\n>>> import torch\n\n>>> generator = [torch.Generator(device=\"cuda\").manual_seed(i) for i in range(4)]\n```\n\n이미지를 생성하고 살펴봅니다.\n\n```python\n>>> images = pipe(prompt, generator=generator, num_images_per_prompt=4).images\n>>> images\n```\n\n![img](https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/reusabe_seeds.jpg)\n\n이 예제에서는 첫 번째 이미지를 개선했지만 실제로는 원하는 모든 이미지를 사용할 수 있습니다(심지어 두 개의 눈이 있는 이미지도!). 첫 번째 이미지에서는 시드가 '0'인 '생성기'를 사용했기 때문에 두 번째 추론 라운드에서는 이 '생성기'를 재사용할 것입니다. 이미지의 품질을 개선하려면 프롬프트에 몇 가지 텍스트를 추가합니다:\n\n```python\nprompt = [prompt + t for t in [\", highly realistic\", \", artsy\", \", trending\", \", colorful\"]]\ngenerator = [torch.Generator(device=\"cuda\").manual_seed(0) for i in range(4)]\n```\n\n시드가 `0`인 제너레이터 4개를 생성하고, 이전 라운드의 첫 번째 이미지처럼 보이는 다른 이미지 batch(배치)를 생성합니다!\n\n```python\n>>> images = pipe(prompt, generator=generator).images\n>>> images\n```\n\n![img](https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/reusabe_seeds_2.jpg)\n" }, { "path": "diffusers/docs/source/ko/using-diffusers/schedulers.md", "content": "\n\n# 스케줄러\n\ndiffusion 파이프라인은 diffusion 모델, 스케줄러 등의 컴포넌트들로 구성됩니다. 그리고 파이프라인 안의 일부 컴포넌트를 다른 컴포넌트로 교체하는 식의 커스터마이징 역시 가능합니다. 이와 같은 컴포넌트 커스터마이징의 가장 대표적인 예시가 바로 [스케줄러](../api/schedulers/overview.md)를 교체하는 것입니다.\n\n\n\n스케쥴러는 다음과 같이 diffusion 시스템의 전반적인 디노이징 프로세스를 정의합니다.\n\n- 디노이징 스텝을 얼마나 가져가야 할까?\n- 확률적으로(stochastic) 혹은 확정적으로(deterministic)?\n- 디노이징 된 샘플을 찾아내기 위해 어떤 알고리즘을 사용해야 할까?\n\n이러한 프로세스는 다소 난해하고, 디노이징 속도와 디노이징 퀄리티 사이의 트레이드 오프를 정의해야 하는 문제가 될 수 있습니다. 주어진 파이프라인에 어떤 스케줄러가 가장 적합한지를 정량적으로 판단하는 것은 매우 어려운 일입니다. 이로 인해 일단 해당 스케줄러를 직접 사용하여, 생성되는 이미지를 직접 눈으로 보며, 정성적으로 성능을 판단해보는 것이 추천되곤 합니다.\n\n\n\n\n\n## 파이프라인 불러오기\n\n먼저 스테이블 diffusion 파이프라인을 불러오도록 해보겠습니다. 물론 스테이블 diffusion을 사용하기 위해서는, 허깅페이스 허브에 등록된 사용자여야 하며, 관련 [라이센스](https://huggingface.co/runwayml/stable-diffusion-v1-5)에 동의해야 한다는 점을 잊지 말아주세요. \n\n*역자 주: 다만, 현재 신규로 생성한 허깅페이스 계정에 대해서는 라이센스 동의를 요구하지 않는 것으로 보입니다!*\n\n```python\nfrom huggingface_hub import login\nfrom diffusers import DiffusionPipeline\nimport torch\n\n# first we need to login with our access token\nlogin()\n\n# Now we can download the pipeline\npipeline = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16)\n```\n\n다음으로, GPU로 이동합니다.\n\n```python\npipeline.to(\"cuda\")\n```\n\n\n\n\n\n## 스케줄러 액세스\n\n스케줄러는 언제나 파이프라인의 컴포넌트로서 존재하며, 일반적으로 파이프라인 인스턴스 내에 `scheduler`라는 이름의 속성(property)으로 정의되어 있습니다. \n\n```python\npipeline.scheduler\n```\n\n**Output**:\n\n```\nPNDMScheduler {\n \"_class_name\": \"PNDMScheduler\",\n \"_diffusers_version\": \"0.8.0.dev0\",\n \"beta_end\": 0.012,\n \"beta_schedule\": \"scaled_linear\",\n \"beta_start\": 0.00085,\n \"clip_sample\": false,\n \"num_train_timesteps\": 1000,\n \"set_alpha_to_one\": false,\n \"skip_prk_steps\": true,\n \"steps_offset\": 1,\n \"trained_betas\": null\n}\n```\n\n출력 결과를 통해, 우리는 해당 스케줄러가 [`PNDMScheduler`]의 인스턴스라는 것을 알 수 있습니다. 이제 [`PNDMScheduler`]와 다른 스케줄러들의 성능을 비교해보도록 하겠습니다. 먼저 테스트에 사용할 프롬프트를 다음과 같이 정의해보도록 하겠습니다. \n\n```python\nprompt = \"A photograph of an astronaut riding a horse on Mars, high resolution, high definition.\"\n```\n\n다음으로 유사한 이미지 생성을 보장하기 위해서, 다음과 같이 랜덤시드를 고정해주도록 하겠습니다. \n\n```python\ngenerator = torch.Generator(device=\"cuda\").manual_seed(8)\nimage = pipeline(prompt, generator=generator).images[0]\nimage\n```\n\n

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\n\n\n\n\n## 스케줄러 교체하기\n\n다음으로 파이프라인의 스케줄러를 다른 스케줄러로 교체하는 방법에 대해 알아보겠습니다. 모든 스케줄러는 [`SchedulerMixin.compatibles`]라는 속성(property)을 갖고 있습니다. 해당 속성은 **호환 가능한** 스케줄러들에 대한 정보를 담고 있습니다. \n\n```python\npipeline.scheduler.compatibles\n```\n\n**Output**:\n\n```\n[diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler,\n diffusers.schedulers.scheduling_ddim.DDIMScheduler,\n diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler,\n diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler,\n diffusers.schedulers.scheduling_pndm.PNDMScheduler,\n diffusers.schedulers.scheduling_ddpm.DDPMScheduler,\n diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler]\n```\n\n호환되는 스케줄러들을 살펴보면 아래와 같습니다.\n\n- [`LMSDiscreteScheduler`], \n- [`DDIMScheduler`], \n- [`DPMSolverMultistepScheduler`], \n- [`EulerDiscreteScheduler`], \n- [`PNDMScheduler`], \n- [`DDPMScheduler`], \n- [`EulerAncestralDiscreteScheduler`].\n\n앞서 정의했던 프롬프트를 사용해서 각각의 스케줄러들을 비교해보도록 하겠습니다.\n\n먼저 파이프라인 안의 스케줄러를 바꾸기 위해 [`ConfigMixin.config`] 속성과 [`ConfigMixin.from_config`] 메서드를 활용해보려고 합니다.\n\n\n\n```python\npipeline.scheduler.config\n```\n\n**Output**:\n\n```\nFrozenDict([('num_train_timesteps', 1000),\n ('beta_start', 0.00085),\n ('beta_end', 0.012),\n ('beta_schedule', 'scaled_linear'),\n ('trained_betas', None),\n ('skip_prk_steps', True),\n ('set_alpha_to_one', False),\n ('steps_offset', 1),\n ('_class_name', 'PNDMScheduler'),\n ('_diffusers_version', '0.8.0.dev0'),\n ('clip_sample', False)])\n```\n\n기존 스케줄러의 config를 호환 가능한 다른 스케줄러에 이식하는 것 역시 가능합니다. \n\n다음 예시는 기존 스케줄러(`pipeline.scheduler`)를 다른 종류의 스케줄러(`DDIMScheduler`)로 바꾸는 코드입니다. 기존 스케줄러가 갖고 있던 config를 `.from_config` 메서드의 인자로 전달하는 것을 확인할 수 있습니다.\n\n```python\nfrom diffusers import DDIMScheduler\n\npipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)\n```\n\n\n\n이제 파이프라인을 실행해서 두 스케줄러 사이의 생성된 이미지의 퀄리티를 비교해봅시다.\n\n```python\ngenerator = torch.Generator(device=\"cuda\").manual_seed(8)\nimage = pipeline(prompt, generator=generator).images[0]\nimage\n```\n\n

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\n\n\n\n\n## 스케줄러들 비교해보기\n\n지금까지는 [`PNDMScheduler`]와 [`DDIMScheduler`] 스케줄러를 실행해보았습니다. 아직 비교해볼 스케줄러들이 더 많이 남아있으니 계속 비교해보도록 하겠습니다.\n\n\n\n[`LMSDiscreteScheduler`]을 일반적으로 더 좋은 결과를 보여줍니다.\n\n```python\nfrom diffusers import LMSDiscreteScheduler\n\npipeline.scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config)\n\ngenerator = torch.Generator(device=\"cuda\").manual_seed(8)\nimage = pipeline(prompt, generator=generator).images[0]\nimage\n```\n\n

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\n\n\n[`EulerDiscreteScheduler`]와 [`EulerAncestralDiscreteScheduler`] 고작 30번의 inference step만으로도 높은 퀄리티의 이미지를 생성하는 것을 알 수 있습니다.\n\n```python\nfrom diffusers import EulerDiscreteScheduler\n\npipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)\n\ngenerator = torch.Generator(device=\"cuda\").manual_seed(8)\nimage = pipeline(prompt, generator=generator, num_inference_steps=30).images[0]\nimage\n```\n\n

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\n\n\n```python\nfrom diffusers import EulerAncestralDiscreteScheduler\n\npipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config)\n\ngenerator = torch.Generator(device=\"cuda\").manual_seed(8)\nimage = pipeline(prompt, generator=generator, num_inference_steps=30).images[0]\nimage\n```\n\n

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\n\n\n지금 이 문서를 작성하는 현시점 기준에선, [`DPMSolverMultistepScheduler`]가 시간 대비 가장 좋은 품질의 이미지를 생성하는 것 같습니다. 20번 정도의 스텝만으로도 실행될 수 있습니다.\n\n\n\n```python\nfrom diffusers import DPMSolverMultistepScheduler\n\npipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)\n\ngenerator = torch.Generator(device=\"cuda\").manual_seed(8)\nimage = pipeline(prompt, generator=generator, num_inference_steps=20).images[0]\nimage\n```\n\n

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\n\n\n보시다시피 생성된 이미지들은 매우 비슷하고, 비슷한 퀄리티를 보이는 것 같습니다. 실제로 어떤 스케줄러를 선택할 것인가는 종종 특정 이용 사례에 기반해서 결정되곤 합니다. 결국 여러 종류의 스케줄러를 직접 실행시켜보고 눈으로 직접 비교해서 판단하는 게 좋은 선택일 것 같습니다.\n\n\n\n## Flax에서 스케줄러 교체하기\n\nJAX/Flax 사용자인 경우 기본 파이프라인 스케줄러를 변경할 수도 있습니다. 다음은 Flax Stable Diffusion 파이프라인과 초고속 [DDPM-Solver++ 스케줄러를](../api/schedulers/multistep_dpm_solver) 사용하여 추론을 실행하는 방법에 대한 예시입니다 .\n\n```Python\nimport jax\nimport numpy as np\nfrom flax.jax_utils import replicate\nfrom flax.training.common_utils import shard\n\nfrom diffusers import FlaxStableDiffusionPipeline, FlaxDPMSolverMultistepScheduler\n\nmodel_id = \"runwayml/stable-diffusion-v1-5\"\nscheduler, scheduler_state = FlaxDPMSolverMultistepScheduler.from_pretrained(\n model_id,\n subfolder=\"scheduler\"\n)\npipeline, params = FlaxStableDiffusionPipeline.from_pretrained(\n model_id,\n scheduler=scheduler,\n revision=\"bf16\",\n dtype=jax.numpy.bfloat16,\n)\nparams[\"scheduler\"] = scheduler_state\n\n# Generate 1 image per parallel device (8 on TPUv2-8 or TPUv3-8)\nprompt = \"a photo of an astronaut riding a horse on mars\"\nnum_samples = jax.device_count()\nprompt_ids = pipeline.prepare_inputs([prompt] * num_samples)\n\nprng_seed = jax.random.PRNGKey(0)\nnum_inference_steps = 25\n\n# shard inputs and rng\nparams = replicate(params)\nprng_seed = jax.random.split(prng_seed, jax.device_count())\nprompt_ids = shard(prompt_ids)\n\nimages = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images\nimages = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))\n```\n\n\n\n다음 Flax 스케줄러는 *아직* Flax Stable Diffusion 파이프라인과 호환되지 않습니다.\n\n- `FlaxLMSDiscreteScheduler`\n- `FlaxDDPMScheduler`\n\n\n\n" }, { "path": "diffusers/docs/source/ko/using-diffusers/stable_diffusion_jax_how_to.md", "content": "\n\n# JAX / Flax에서의 🧨 Stable Diffusion!\n\n[[open-in-colab]]\n\n🤗 Hugging Face [Diffusers] (https://github.com/huggingface/diffusers) 는 버전 0.5.1부터 Flax를 지원합니다! 이를 통해 Colab, Kaggle, Google Cloud Platform에서 사용할 수 있는 것처럼 Google TPU에서 초고속 추론이 가능합니다.\n\n이 노트북은 JAX / Flax를 사용해 추론을 실행하는 방법을 보여줍니다. Stable Diffusion의 작동 방식에 대한 자세한 내용을 원하거나 GPU에서 실행하려면 이 [노트북] ](https://huggingface.co/docs/diffusers/stable_diffusion)을 참조하세요.\n\n먼저, TPU 백엔드를 사용하고 있는지 확인합니다. Colab에서 이 노트북을 실행하는 경우, 메뉴에서 런타임을 선택한 다음 \"런타임 유형 변경\" 옵션을 선택한 다음 하드웨어 가속기 설정에서 TPU를 선택합니다.\n\nJAX는 TPU 전용은 아니지만 각 TPU 서버에는 8개의 TPU 가속기가 병렬로 작동하기 때문에 해당 하드웨어에서 더 빛을 발한다는 점은 알아두세요.\n\n\n## Setup\n\n먼저 diffusers가 설치되어 있는지 확인합니다.\n\n```bash\n!pip install jax==0.3.25 jaxlib==0.3.25 flax transformers ftfy\n!pip install diffusers\n```\n\n```python\nimport jax.tools.colab_tpu\n\njax.tools.colab_tpu.setup_tpu()\nimport jax\n```\n\n```python\nnum_devices = jax.device_count()\ndevice_type = jax.devices()[0].device_kind\n\nprint(f\"Found {num_devices} JAX devices of type {device_type}.\")\nassert (\n \"TPU\" in device_type\n), \"Available device is not a TPU, please select TPU from Edit > Notebook settings > Hardware accelerator\"\n```\n\n```python out\nFound 8 JAX devices of type Cloud TPU.\n```\n\n그런 다음 모든 dependencies를 가져옵니다.\n\n```python\nimport numpy as np\nimport jax\nimport jax.numpy as jnp\n\nfrom pathlib import Path\nfrom jax import pmap\nfrom flax.jax_utils import replicate\nfrom flax.training.common_utils import shard\nfrom PIL import Image\n\nfrom huggingface_hub import notebook_login\nfrom diffusers import FlaxStableDiffusionPipeline\n```\n\n## 모델 불러오기\n\nTPU 장치는 효율적인 half-float 유형인 bfloat16을 지원합니다. 테스트에는 이 유형을 사용하지만 대신 float32를 사용하여 전체 정밀도(full precision)를 사용할 수도 있습니다.\n\n```python\ndtype = jnp.bfloat16\n```\n\nFlax는 함수형 프레임워크이므로 모델은 무상태(stateless)형이며 매개변수는 모델 외부에 저장됩니다. 사전학습된 Flax 파이프라인을 불러오면 파이프라인 자체와 모델 가중치(또는 매개변수)가 모두 반환됩니다. 저희는 bf16 버전의 가중치를 사용하고 있으므로 유형 경고가 표시되지만 무시해도 됩니다.\n\n```python\npipeline, params = FlaxStableDiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n revision=\"bf16\",\n dtype=dtype,\n)\n```\n\n## 추론\n\nTPU에는 일반적으로 8개의 디바이스가 병렬로 작동하므로 보유한 디바이스 수만큼 프롬프트를 복제합니다. 그런 다음 각각 하나의 이미지 생성을 담당하는 8개의 디바이스에서 한 번에 추론을 수행합니다. 따라서 하나의 칩이 하나의 이미지를 생성하는 데 걸리는 시간과 동일한 시간에 8개의 이미지를 얻을 수 있습니다.\n\n프롬프트를 복제하고 나면 파이프라인의 `prepare_inputs` 함수를 호출하여 토큰화된 텍스트 ID를 얻습니다. 토큰화된 텍스트의 길이는 기본 CLIP 텍스트 모델의 구성에 따라 77토큰으로 설정됩니다.\n\n```python\nprompt = \"A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of field, close up, split lighting, cinematic\"\nprompt = [prompt] * jax.device_count()\nprompt_ids = pipeline.prepare_inputs(prompt)\nprompt_ids.shape\n```\n\n```python out\n(8, 77)\n```\n\n### 복사(Replication) 및 정렬화\n\n모델 매개변수와 입력값은 우리가 보유한 8개의 병렬 장치에 복사(Replication)되어야 합니다. 매개변수 딕셔너리는 `flax.jax_utils.replicate`(딕셔너리를 순회하며 가중치의 모양을 변경하여 8번 반복하는 함수)를 사용하여 복사됩니다. 배열은 `shard`를 사용하여 복제됩니다.\n\n```python\np_params = replicate(params)\n```\n\n```python\nprompt_ids = shard(prompt_ids)\nprompt_ids.shape\n```\n\n```python out\n(8, 1, 77)\n```\n\n이 shape은 8개의 디바이스 각각이 shape `(1, 77)`의 jnp 배열을 입력값으로 받는다는 의미입니다. 즉 1은 디바이스당 batch(배치) 크기입니다. 메모리가 충분한 TPU에서는 한 번에 여러 이미지(칩당)를 생성하려는 경우 1보다 클 수 있습니다.\n\n이미지를 생성할 준비가 거의 완료되었습니다! 이제 생성 함수에 전달할 난수 생성기만 만들면 됩니다. 이것은 난수를 다루는 모든 함수에 난수 생성기가 있어야 한다는, 난수에 대해 매우 진지하고 독단적인 Flax의 표준 절차입니다. 이렇게 하면 여러 분산된 기기에서 훈련할 때에도 재현성이 보장됩니다.\n\n아래 헬퍼 함수는 시드를 사용하여 난수 생성기를 초기화합니다. 동일한 시드를 사용하는 한 정확히 동일한 결과를 얻을 수 있습니다. 나중에 노트북에서 결과를 탐색할 때엔 다른 시드를 자유롭게 사용하세요.\n\n```python\ndef create_key(seed=0):\n return jax.random.PRNGKey(seed)\n```\n\nrng를 얻은 다음 8번 '분할'하여 각 디바이스가 다른 제너레이터를 수신하도록 합니다. 따라서 각 디바이스마다 다른 이미지가 생성되며 전체 프로세스를 재현할 수 있습니다.\n\n```python\nrng = create_key(0)\nrng = jax.random.split(rng, jax.device_count())\n```\n\nJAX 코드는 매우 빠르게 실행되는 효율적인 표현으로 컴파일할 수 있습니다. 하지만 후속 호출에서 모든 입력이 동일한 모양을 갖도록 해야 하며, 그렇지 않으면 JAX가 코드를 다시 컴파일해야 하므로 최적화된 속도를 활용할 수 없습니다.\n\n`jit = True`를 인수로 전달하면 Flax 파이프라인이 코드를 컴파일할 수 있습니다. 또한 모델이 사용 가능한 8개의 디바이스에서 병렬로 실행되도록 보장합니다.\n\n다음 셀을 처음 실행하면 컴파일하는 데 시간이 오래 걸리지만 이후 호출(입력이 다른 경우에도)은 훨씬 빨라집니다. 예를 들어, 테스트했을 때 TPU v2-8에서 컴파일하는 데 1분 이상 걸리지만 이후 추론 실행에는 약 7초가 걸립니다.\n\n```\n%%time\nimages = pipeline(prompt_ids, p_params, rng, jit=True)[0]\n```\n\n```python out\nCPU times: user 56.2 s, sys: 42.5 s, total: 1min 38s\nWall time: 1min 29s\n```\n\n반환된 배열의 shape은 `(8, 1, 512, 512, 3)`입니다. 이를 재구성하여 두 번째 차원을 제거하고 512 × 512 × 3의 이미지 8개를 얻은 다음 PIL로 변환합니다.\n\n```python\nimages = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])\nimages = pipeline.numpy_to_pil(images)\n```\n\n### 시각화\n\n이미지를 그리드에 표시하는 도우미 함수를 만들어 보겠습니다.\n\n```python\ndef image_grid(imgs, rows, cols):\n w, h = imgs[0].size\n grid = Image.new(\"RGB\", size=(cols * w, rows * h))\n for i, img in enumerate(imgs):\n grid.paste(img, box=(i % cols * w, i // cols * h))\n return grid\n```\n\n```python\nimage_grid(images, 2, 4)\n```\n\n![img](https://huggingface.co/datasets/YiYiXu/test-doc-assets/resolve/main/stable_diffusion_jax_how_to_cell_38_output_0.jpeg)\n\n\n## 다른 프롬프트 사용\n\n모든 디바이스에서 동일한 프롬프트를 복제할 필요는 없습니다. 프롬프트 2개를 각각 4번씩 생성하거나 한 번에 8개의 서로 다른 프롬프트를 생성하는 등 원하는 것은 무엇이든 할 수 있습니다. 한번 해보세요!\n\n먼저 입력 준비 코드를 편리한 함수로 리팩터링하겠습니다:\n\n```python\nprompts = [\n \"Labrador in the style of Hokusai\",\n \"Painting of a squirrel skating in New York\",\n \"HAL-9000 in the style of Van Gogh\",\n \"Times Square under water, with fish and a dolphin swimming around\",\n \"Ancient Roman fresco showing a man working on his laptop\",\n \"Close-up photograph of young black woman against urban background, high quality, bokeh\",\n \"Armchair in the shape of an avocado\",\n \"Clown astronaut in space, with Earth in the background\",\n]\n```\n\n```python\nprompt_ids = pipeline.prepare_inputs(prompts)\nprompt_ids = shard(prompt_ids)\n\nimages = pipeline(prompt_ids, p_params, rng, jit=True).images\nimages = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])\nimages = pipeline.numpy_to_pil(images)\n\nimage_grid(images, 2, 4)\n```\n\n![img](https://huggingface.co/datasets/YiYiXu/test-doc-assets/resolve/main/stable_diffusion_jax_how_to_cell_43_output_0.jpeg)\n\n\n## 병렬화(parallelization)는 어떻게 작동하는가?\n\n앞서 `diffusers` Flax 파이프라인이 모델을 자동으로 컴파일하고 사용 가능한 모든 기기에서 병렬로 실행한다고 말씀드렸습니다. 이제 그 프로세스를 간략하게 살펴보고 작동 방식을 보여드리겠습니다.\n\nJAX 병렬화는 여러 가지 방법으로 수행할 수 있습니다. 가장 쉬운 방법은 jax.pmap 함수를 사용하여 단일 프로그램, 다중 데이터(SPMD) 병렬화를 달성하는 것입니다. 즉, 동일한 코드의 복사본을 각각 다른 데이터 입력에 대해 여러 개 실행하는 것입니다. 더 정교한 접근 방식도 가능하므로 관심이 있으시다면 [JAX 문서](https://jax.readthedocs.io/en/latest/index.html)와 [`pjit` 페이지](https://jax.readthedocs.io/en/latest/jax-101/08-pjit.html?highlight=pjit)에서 이 주제를 살펴보시기 바랍니다!\n\n`jax.pmap`은 두 가지 기능을 수행합니다:\n\n- `jax.jit()`를 호출한 것처럼 코드를 컴파일(또는 `jit`)합니다. 이 작업은 `pmap`을 호출할 때가 아니라 pmapped 함수가 처음 호출될 때 수행됩니다.\n- 컴파일된 코드가 사용 가능한 모든 기기에서 병렬로 실행되도록 합니다.\n\n작동 방식을 보여드리기 위해 이미지 생성을 실행하는 비공개 메서드인 파이프라인의 `_generate` 메서드를 `pmap`합니다. 이 메서드는 향후 `Diffusers` 릴리스에서 이름이 변경되거나 제거될 수 있다는 점에 유의하세요.\n\n```python\np_generate = pmap(pipeline._generate)\n```\n\n`pmap`을 사용한 후 준비된 함수 `p_generate`는 개념적으로 다음을 수행합니다:\n* 각 장치에서 기본 함수 `pipeline._generate`의 복사본을 호출합니다.\n* 각 장치에 입력 인수의 다른 부분을 보냅니다. 이것이 바로 샤딩이 사용되는 이유입니다. 이 경우 `prompt_ids`의 shape은 `(8, 1, 77, 768)`입니다. 이 배열은 8개로 분할되고 `_generate`의 각 복사본은 `(1, 77, 768)`의 shape을 가진 입력을 받게 됩니다.\n\n병렬로 호출된다는 사실을 완전히 무시하고 `_generate`를 코딩할 수 있습니다. batch(배치) 크기(이 예제에서는 `1`)와 코드에 적합한 차원만 신경 쓰면 되며, 병렬로 작동하기 위해 아무것도 변경할 필요가 없습니다.\n\n파이프라인 호출을 사용할 때와 마찬가지로, 다음 셀을 처음 실행할 때는 시간이 걸리지만 그 이후에는 훨씬 빨라집니다.\n\n```\n%%time\nimages = p_generate(prompt_ids, p_params, rng)\nimages = images.block_until_ready()\nimages.shape\n```\n\n```python out\nCPU times: user 1min 15s, sys: 18.2 s, total: 1min 34s\nWall time: 1min 15s\n```\n\n```python\nimages.shape\n```\n\n```python out\n(8, 1, 512, 512, 3)\n```\n\nJAX는 비동기 디스패치를 사용하고 가능한 한 빨리 제어권을 Python 루프에 반환하기 때문에 추론 시간을 정확하게 측정하기 위해 `block_until_ready()`를 사용합니다. 아직 구체화되지 않은 계산 결과를 사용하려는 경우 자동으로 차단이 수행되므로 코드에서 이 함수를 사용할 필요가 없습니다." }, { "path": "diffusers/docs/source/ko/using-diffusers/textual_inversion_inference.md", "content": "# Textual inversion\n\n[[open-in-colab]]\n\n[`StableDiffusionPipeline`]은 textual-inversion을 지원하는데, 이는 몇 개의 샘플 이미지만으로 stable diffusion과 같은 모델이 새로운 컨셉을 학습할 수 있도록 하는 기법입니다. 이를 통해 생성된 이미지를 더 잘 제어하고 특정 컨셉에 맞게 모델을 조정할 수 있습니다. 커뮤니티에서 만들어진 컨셉들의 컬렉션은 [Stable Diffusion Conceptualizer](https://huggingface.co/spaces/sd-concepts-library/stable-diffusion-conceptualizer)를 통해 빠르게 사용해볼 수 있습니다.\n\n이 가이드에서는 Stable Diffusion Conceptualizer에서 사전학습한 컨셉을 사용하여 textual-inversion으로 추론을 실행하는 방법을 보여드립니다. textual-inversion으로 모델에 새로운 컨셉을 학습시키는 데 관심이 있으시다면, [Textual Inversion](./training/text_inversion) 훈련 가이드를 참조하세요.\n\nHugging Face 계정으로 로그인하세요:\n\n```py\nfrom huggingface_hub import notebook_login\n\nnotebook_login()\n```\n\n필요한 라이브러리를 불러오고 생성된 이미지를 시각화하기 위한 도우미 함수 `image_grid`를 만듭니다:\n\n```py\nimport os\nimport torch\n\nimport PIL\nfrom PIL import Image\n\nfrom diffusers import StableDiffusionPipeline\nfrom transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer\n\n\ndef image_grid(imgs, rows, cols):\n assert len(imgs) == rows * cols\n\n w, h = imgs[0].size\n grid = Image.new(\"RGB\", size=(cols * w, rows * h))\n grid_w, grid_h = grid.size\n\n for i, img in enumerate(imgs):\n grid.paste(img, box=(i % cols * w, i // cols * h))\n return grid\n```\n\nStable Diffusion과 [Stable Diffusion Conceptualizer](https://huggingface.co/spaces/sd-concepts-library/stable-diffusion-conceptualizer)에서 사전학습된 컨셉을 선택합니다:\n\n```py\npretrained_model_name_or_path = \"runwayml/stable-diffusion-v1-5\"\nrepo_id_embeds = \"sd-concepts-library/cat-toy\"\n```\n\n이제 파이프라인을 로드하고 사전학습된 컨셉을 파이프라인에 전달할 수 있습니다:\n\n```py\npipeline = StableDiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16).to(\"cuda\")\n\npipeline.load_textual_inversion(repo_id_embeds)\n```\n\n특별한 placeholder token '``'를 사용하여 사전학습된 컨셉으로 프롬프트를 만들고, 생성할 샘플의 수와 이미지 행의 수를 선택합니다:\n\n```py\nprompt = \"a grafitti in a favela wall with a on it\"\n\nnum_samples = 2\nnum_rows = 2\n```\n\n그런 다음 파이프라인을 실행하고, 생성된 이미지들을 저장합니다. 그리고 처음에 만들었던 도우미 함수 `image_grid`를 사용하여 생성 결과들을 시각화합니다. 이 때 `num_inference_steps`와 `guidance_scale`과 같은 매개 변수들을 조정하여, 이것들이 이미지 품질에 어떠한 영향을 미치는지를 자유롭게 확인해보시기 바랍니다.\n\n```py\nall_images = []\nfor _ in range(num_rows):\n images = pipe(prompt, num_images_per_prompt=num_samples, num_inference_steps=50, guidance_scale=7.5).images\n all_images.extend(images)\n\ngrid = image_grid(all_images, num_samples, num_rows)\ngrid\n```\n\n
\n \n
\n" }, { "path": "diffusers/docs/source/ko/using-diffusers/unconditional_image_generation.md", "content": "\n\n# Unconditional 이미지 생성\n\n[[open-in-colab]]\n\nUnconditional 이미지 생성은 비교적 간단한 작업입니다. 모델이 텍스트나 이미지와 같은 추가 조건 없이 이미 학습된 학습 데이터와 유사한 이미지만 생성합니다.\n\n['DiffusionPipeline']은 추론을 위해 미리 학습된 diffusion 시스템을 사용하는 가장 쉬운 방법입니다.\n\n먼저 ['DiffusionPipeline']의 인스턴스를 생성하고 다운로드할 파이프라인의 [체크포인트](https://huggingface.co/models?library=diffusers&sort=downloads)를 지정합니다. 허브의 🧨 diffusion 체크포인트 중 하나를 사용할 수 있습니다(사용할 체크포인트는 나비 이미지를 생성합니다).\n\n\n\n💡 나만의 unconditional 이미지 생성 모델을 학습시키고 싶으신가요? 학습 가이드를 살펴보고 나만의 이미지를 생성하는 방법을 알아보세요.\n\n\n\n\n이 가이드에서는 unconditional 이미지 생성에 ['DiffusionPipeline']과 [DDPM](https://arxiv.org/abs/2006.11239)을 사용합니다:\n\n```python\n >>> from diffusers import DiffusionPipeline\n\n >>> generator = DiffusionPipeline.from_pretrained(\"anton-l/ddpm-butterflies-128\")\n```\n\n[diffusion 파이프라인]은 모든 모델링, 토큰화, 스케줄링 구성 요소를 다운로드하고 캐시합니다. 이 모델은 약 14억 개의 파라미터로 구성되어 있기 때문에 GPU에서 실행할 것을 강력히 권장합니다. PyTorch에서와 마찬가지로 제너레이터 객체를 GPU로 옮길 수 있습니다:\n\n```python\n >>> generator.to(\"cuda\")\n```\n\n이제 제너레이터를 사용하여 이미지를 생성할 수 있습니다:\n\n```python\n >>> image = generator().images[0]\n```\n\n출력은 기본적으로 [PIL.Image](https://pillow.readthedocs.io/en/stable/reference/Image.html?highlight=image#the-image-class) 객체로 감싸집니다.\n\n다음을 호출하여 이미지를 저장할 수 있습니다:\n\n```python\n >>> image.save(\"generated_image.png\")\n```\n\t\n아래 스페이스(데모 링크)를 이용해 보고, 추론 단계의 매개변수를 자유롭게 조절하여 이미지 품질에 어떤 영향을 미치는지 확인해 보세요!\n\n\n" }, { "path": "diffusers/docs/source/ko/using-diffusers/using_safetensors.md", "content": "# 세이프텐서 로드\n\n[safetensors](https://github.com/huggingface/safetensors)는 텐서를 저장하고 로드하기 위한 안전하고 빠른 파일 형식입니다. 일반적으로 PyTorch 모델 가중치는 Python의 [`pickle`](https://docs.python.org/3/library/pickle.html) 유틸리티를 사용하여 `.bin` 파일에 저장되거나 `피클`됩니다. 그러나 `피클`은 안전하지 않으며 피클된 파일에는 실행될 수 있는 악성 코드가 포함될 수 있습니다. 세이프텐서는 `피클`의 안전한 대안으로 모델 가중치를 공유하는 데 이상적입니다.\n\n이 가이드에서는 `.safetensor` 파일을 로드하는 방법과 다른 형식으로 저장된 안정적 확산 모델 가중치를 `.safetensor`로 변환하는 방법을 보여드리겠습니다. 시작하기 전에 세이프텐서가 설치되어 있는지 확인하세요:\n\n```bash\n!pip install safetensors\n```\n\n['runwayml/stable-diffusion-v1-5`] (https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/main) 리포지토리를 보면 `text_encoder`, `unet` 및 `vae` 하위 폴더에 가중치가 `.safetensors` 형식으로 저장되어 있는 것을 볼 수 있습니다. 기본적으로 🤗 디퓨저는 모델 저장소에서 사용할 수 있는 경우 해당 하위 폴더에서 이러한 '.safetensors` 파일을 자동으로 로드합니다.\n\n보다 명시적인 제어를 위해 선택적으로 `사용_세이프텐서=True`를 설정할 수 있습니다(`세이프텐서`가 설치되지 않은 경우 설치하라는 오류 메시지가 표시됨):\n\n```py\nfrom diffusers import DiffusionPipeline\n\npipeline = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", use_safetensors=True)\n```\n\n그러나 모델 가중치가 위의 예시처럼 반드시 별도의 하위 폴더에 저장되는 것은 아닙니다. 모든 가중치가 하나의 '.safetensors` 파일에 저장되는 경우도 있습니다. 이 경우 가중치가 Stable Diffusion 가중치인 경우 [`~diffusers.loaders.FromCkptMixin.from_ckpt`] 메서드를 사용하여 파일을 직접 로드할 수 있습니다:\n\n```py\nfrom diffusers import StableDiffusionPipeline\n\npipeline = StableDiffusionPipeline.from_ckpt(\n \"https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix/AbyssOrangeMix.safetensors\"\n)\n```\n\n## 세이프텐서로 변환\n\n허브의 모든 가중치를 '.safetensors` 형식으로 사용할 수 있는 것은 아니며, '.bin`으로 저장된 가중치가 있을 수 있습니다. 이 경우 [Convert Space](https://huggingface.co/spaces/diffusers/convert)을 사용하여 가중치를 '.safetensors'로 변환하세요. Convert Space는 피클된 가중치를 다운로드하여 변환한 후 풀 리퀘스트를 열어 허브에 새로 변환된 `.safetensors` 파일을 업로드합니다. 이렇게 하면 피클된 파일에 악성 코드가 포함되어 있는 경우, 안전하지 않은 파일과 의심스러운 피클 가져오기를 탐지하는 [보안 스캐너](https://huggingface.co/docs/hub/security-pickle#hubs-security-scanner)가 있는 허브로 업로드됩니다. - 개별 컴퓨터가 아닌.\n\n개정` 매개변수에 풀 리퀘스트에 대한 참조를 지정하여 새로운 '.safetensors` 가중치가 적용된 모델을 사용할 수 있습니다(허브의 [Check PR](https://huggingface.co/spaces/diffusers/check_pr) 공간에서 테스트할 수도 있음)(예: `refs/pr/22`):\n\n```py\nfrom diffusers import DiffusionPipeline\n\npipeline = DiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-1\", revision=\"refs/pr/22\")\n```\n\n## 세이프센서를 사용하는 이유는 무엇인가요?\n\n세이프티 센서를 사용하는 데에는 여러 가지 이유가 있습니다:\n\n- 세이프텐서를 사용하는 가장 큰 이유는 안전입니다.오픈 소스 및 모델 배포가 증가함에 따라 다운로드한 모델 가중치에 악성 코드가 포함되어 있지 않다는 것을 신뢰할 수 있는 것이 중요해졌습니다.세이프센서의 현재 헤더 크기는 매우 큰 JSON 파일을 구문 분석하지 못하게 합니다.\n- 모델 전환 간의 로딩 속도는 텐서의 제로 카피를 수행하는 세이프텐서를 사용해야 하는 또 다른 이유입니다. 가중치를 CPU(기본값)로 로드하는 경우 '피클'에 비해 특히 빠르며, 가중치를 GPU로 직접 로드하는 경우에도 빠르지는 않더라도 비슷하게 빠릅니다. 모델이 이미 로드된 경우에만 성능 차이를 느낄 수 있으며, 가중치를 다운로드하거나 모델을 처음 로드하는 경우에는 성능 차이를 느끼지 못할 것입니다.\n\n\t전체 파이프라인을 로드하는 데 걸리는 시간입니다:\n\n\t```py\n from diffusers import StableDiffusionPipeline\n\n pipeline = StableDiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-1\")\n \"Loaded in safetensors 0:00:02.033658\"\n \"Loaded in PyTorch 0:00:02.663379\"\n\t```\n\n\t하지만 실제로 500MB의 모델 가중치를 로드하는 데 걸리는 시간은 얼마 되지 않습니다:\n\n\t```bash\n\tsafetensors: 3.4873ms\n\tPyTorch: 172.7537ms\n\t```\n\n지연 로딩은 세이프텐서에서도 지원되며, 이는 분산 설정에서 일부 텐서만 로드하는 데 유용합니다. 이 형식을 사용하면 [BLOOM](https://huggingface.co/bigscience/bloom) 모델을 일반 PyTorch 가중치를 사용하여 10분이 걸리던 것을 8개의 GPU에서 45초 만에 로드할 수 있습니다.\n" }, { "path": "diffusers/docs/source/ko/using-diffusers/weighted_prompts.md", "content": "\n\n# 프롬프트에 가중치 부여하기\n\n[[open-in-colab]]\n\n텍스트 가이드 기반의 diffusion 모델은 주어진 텍스트 프롬프트를 기반으로 이미지를 생성합니다.\n텍스트 프롬프트에는 모델이 생성해야 하는 여러 개념이 포함될 수 있으며 프롬프트의 특정 부분에 가중치를 부여하는 것이 바람직한 경우가 많습니다.\n\nDiffusion 모델은 문맥화된 텍스트 임베딩으로 diffusion 모델의 cross attention 레이어를 조절함으로써 작동합니다.\n([더 많은 정보를 위한 Stable Diffusion Guide](https://huggingface.co/docs/optimum-neuron/main/en/package_reference/modeling#stable-diffusion)를 참고하세요).\n따라서 프롬프트의 특정 부분을 강조하는(또는 강조하지 않는) 간단한 방법은 프롬프트의 관련 부분에 해당하는 텍스트 임베딩 벡터의 크기를 늘리거나 줄이는 것입니다.\n이것은 \"프롬프트 가중치 부여\" 라고 하며, 커뮤니티에서 가장 요구하는 기능입니다.([이곳](https://github.com/huggingface/diffusers/issues/2431)의 issue를 보세요 ).\n\n## Diffusers에서 프롬프트 가중치 부여하는 방법\n\n우리는 `diffusers`의 역할이 다른 프로젝트를 가능하게 하는 필수적인 기능을 제공하는 toolbex라고 생각합니다.\n[InvokeAI](https://github.com/invoke-ai/InvokeAI) 나 [diffuzers](https://github.com/abhishekkrthakur/diffuzers) 같은 강력한 UI를 구축할 수 있습니다.\n프롬프트를 조작하는 방법을 지원하기 위해, `diffusers` 는\n[StableDiffusionPipeline](https://huggingface.co/docs/diffusers/v0.18.2/en/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline)와 같은\n많은 파이프라인에 [prompt_embeds](https://huggingface.co/docs/diffusers/v0.14.0/en/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline.__call__.prompt_embeds)\n인수를 노출시켜, \"prompt-weighted\"/축척된 텍스트 임베딩을 파이프라인에 바로 전달할 수 있게 합니다.\n\n[Compel 라이브러리](https://github.com/damian0815/compel)는 프롬프트의 일부를 강조하거나 강조하지 않을 수 있는 쉬운 방법을 제공합니다.\n임베딩을 직접 준비하는 것 대신 이 방법을 사용하는 것을 강력히 추천합니다.\n\n간단한 예제를 살펴보겠습니다.\n다음과 같이 `\"공을 갖고 노는 붉은색 고양이\"` 이미지를 생성하고 싶습니다:\n\n```py\nfrom diffusers import StableDiffusionPipeline, UniPCMultistepScheduler\n\npipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\")\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n\nprompt = \"a red cat playing with a ball\"\n\ngenerator = torch.Generator(device=\"cpu\").manual_seed(33)\n\nimage = pipe(prompt, generator=generator, num_inference_steps=20).images[0]\nimage\n```\n\n생성된 이미지:\n\n![img](https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/compel/forest_0.png)\n\n사진에서 알 수 있듯이, \"공\"은 이미지에 없습니다. 이 부분을 강조해 볼까요!\n\n먼저 `compel` 라이브러리를 설치해야합니다:\n\n```sh\npip install compel\n```\n\n그런 다음에는 `Compel` 오브젝트를 생성합니다:\n\n```py\nfrom compel import Compel\n\ncompel_proc = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder)\n```\n\n이제 `\"++\"` 를 사용해서 \"공\" 을 강조해 봅시다:\n\n```py\nprompt = \"a red cat playing with a ball++\"\n```\n\n그리고 이 프롬프트를 파이프라인에 바로 전달하지 않고, `compel_proc` 를 사용하여 처리해야합니다:\n\n```py\nprompt_embeds = compel_proc(prompt)\n```\n\n파이프라인에 `prompt_embeds` 를 바로 전달할 수 있습니다:\n\n```py\ngenerator = torch.Generator(device=\"cpu\").manual_seed(33)\n\nimages = pipe(prompt_embeds=prompt_embeds, generator=generator, num_inference_steps=20).images[0]\nimage\n```\n\n이제 \"공\"이 있는 그림을 출력할 수 있습니다!\n\n![img](https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/compel/forest_1.png)\n\n마찬가지로 `--` 접미사를 단어에 사용하여 문장의 일부를 강조하지 않을 수 있습니다. 한번 시도해 보세요!\n\n즐겨찾는 파이프라인에 `prompt_embeds` 입력이 없는 경우 issue를 새로 만들어주세요.\nDiffusers 팀은 최대한 대응하려고 노력합니다.\n\nCompel 1.1.6 는 textual inversions을 사용하여 단순화하는 유티릴티 클래스를 추가합니다.\n`DiffusersTextualInversionManager`를 인스턴스화 한 후 이를 Compel init에 전달합니다:\n\n```\ntextual_inversion_manager = DiffusersTextualInversionManager(pipe)\ncompel = Compel(\n tokenizer=pipe.tokenizer,\n text_encoder=pipe.text_encoder,\n textual_inversion_manager=textual_inversion_manager)\n```\n\n더 많은 정보를 얻고 싶다면 [compel](https://github.com/damian0815/compel) 라이브러리 문서를 참고하세요.\n" }, { "path": "diffusers/docs/source/ko/using-diffusers/write_own_pipeline.md", "content": "\n\n# 파이프라인, 모델 및 스케줄러 이해하기\n\n[[open-in-colab]]\n\n🧨 Diffusers는 사용자 친화적이며 유연한 도구 상자로, 사용사례에 맞게 diffusion 시스템을 구축 할 수 있도록 설계되었습니다. 이 도구 상자의 핵심은 모델과 스케줄러입니다. [`DiffusionPipeline`]은 편의를 위해 이러한 구성 요소를 번들로 제공하지만, 파이프라인을 분리하고 모델과 스케줄러를 개별적으로 사용해 새로운 diffusion 시스템을 만들 수도 있습니다.\n\n이 튜토리얼에서는 기본 파이프라인부터 시작해 Stable Diffusion 파이프라인까지 진행하며 모델과 스케줄러를 사용해 추론을 위한 diffusion 시스템을 조립하는 방법을 배웁니다.\n\n## 기본 파이프라인 해체하기\n\n파이프라인은 추론을 위해 모델을 실행하는 빠르고 쉬운 방법으로, 이미지를 생성하는 데 코드가 4줄 이상 필요하지 않습니다:\n\n```py\n>>> from diffusers import DDPMPipeline\n\n>>> ddpm = DDPMPipeline.from_pretrained(\"google/ddpm-cat-256\").to(\"cuda\")\n>>> image = ddpm(num_inference_steps=25).images[0]\n>>> image\n```\n\n
\n \"Image\n
\n\n정말 쉽습니다. 그런데 파이프라인은 어떻게 이렇게 할 수 있었을까요? 파이프라인을 세분화하여 내부에서 어떤 일이 일어나고 있는지 살펴보겠습니다.\n\n위 예시에서 파이프라인에는 [`UNet2DModel`] 모델과 [`DDPMScheduler`]가 포함되어 있습니다. 파이프라인은 원하는 출력 크기의 랜덤 노이즈를 받아 모델을 여러번 통과시켜 이미지의 노이즈를 제거합니다. 각 timestep에서 모델은 *noise residual*을 예측하고 스케줄러는 이를 사용하여 노이즈가 적은 이미지를 예측합니다. 파이프라인은 지정된 추론 스텝수에 도달할 때까지 이 과정을 반복합니다.\n\n모델과 스케줄러를 별도로 사용하여 파이프라인을 다시 생성하기 위해 자체적인 노이즈 제거 프로세스를 작성해 보겠습니다.\n\n1. 모델과 스케줄러를 불러옵니다:\n\n ```py\n >>> from diffusers import DDPMScheduler, UNet2DModel\n\n >>> scheduler = DDPMScheduler.from_pretrained(\"google/ddpm-cat-256\")\n >>> model = UNet2DModel.from_pretrained(\"google/ddpm-cat-256\").to(\"cuda\")\n ```\n\n2. 노이즈 제거 프로세스를 실행할 timestep 수를 설정합니다:\n\n ```py\n >>> scheduler.set_timesteps(50)\n ```\n\n3. 스케줄러의 timestep을 설정하면 균등한 간격의 구성 요소를 가진 텐서가 생성됩니다.(이 예시에서는 50개) 각 요소는 모델이 이미지의 노이즈를 제거하는 시간 간격에 해당합니다. 나중에 노이즈 제거 루프를 만들 때 이 텐서를 반복하여 이미지의 노이즈를 제거합니다:\n\n ```py\n >>> scheduler.timesteps\n tensor([980, 960, 940, 920, 900, 880, 860, 840, 820, 800, 780, 760, 740, 720,\n 700, 680, 660, 640, 620, 600, 580, 560, 540, 520, 500, 480, 460, 440,\n 420, 400, 380, 360, 340, 320, 300, 280, 260, 240, 220, 200, 180, 160,\n 140, 120, 100, 80, 60, 40, 20, 0])\n ```\n\n4. 원하는 출력과 같은 모양을 가진 랜덤 노이즈를 생성합니다:\n\n ```py\n >>> import torch\n\n >>> sample_size = model.config.sample_size\n >>> noise = torch.randn((1, 3, sample_size, sample_size), device=\"cuda\")\n ```\n\n5. 이제 timestep을 반복하는 루프를 작성합니다. 각 timestep에서 모델은 [`UNet2DModel.forward`]를 통해 noisy residual을 반환합니다. 스케줄러의 [`~DDPMScheduler.step`] 메서드는 noisy residual, timestep, 그리고 입력을 받아 이전 timestep에서 이미지를 예측합니다. 이 출력은 노이즈 제거 루프의 모델에 대한 다음 입력이 되며, `timesteps` 배열의 끝에 도달할 때까지 반복됩니다.\n\n ```py\n >>> input = noise\n\n >>> for t in scheduler.timesteps:\n ... with torch.no_grad():\n ... noisy_residual = model(input, t).sample\n ... previous_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample\n ... input = previous_noisy_sample\n ```\n\n 이것이 전체 노이즈 제거 프로세스이며, 동일한 패턴을 사용해 모든 diffusion 시스템을 작성할 수 있습니다.\n\n6. 마지막 단계는 노이즈가 제거된 출력을 이미지로 변환하는 것입니다:\n\n ```py\n >>> from PIL import Image\n >>> import numpy as np\n\n >>> image = (input / 2 + 0.5).clamp(0, 1)\n >>> image = image.cpu().permute(0, 2, 3, 1).numpy()[0]\n >>> image = Image.fromarray((image * 255).round().astype(\"uint8\"))\n >>> image\n ```\n\n다음 섹션에서는 여러분의 기술을 시험해보고 좀 더 복잡한 Stable Diffusion 파이프라인을 분석해 보겠습니다. 방법은 거의 동일합니다. 필요한 구성요소들을 초기화하고 timestep수를 설정하여 `timestep` 배열을 생성합니다. 노이즈 제거 루프에서 `timestep` 배열이 사용되며, 이 배열의 각 요소에 대해 모델은 노이즈가 적은 이미지를 예측합니다. 노이즈 제거 루프는 `timestep`을 반복하고 각 timestep에서 noise residual을 출력하고 스케줄러는 이를 사용하여 이전 timestep에서 노이즈가 덜한 이미지를 예측합니다. 이 프로세스는 `timestep` 배열의 끝에 도달할 때까지 반복됩니다.\n\n한번 사용해 봅시다!\n\n## Stable Diffusion 파이프라인 해체하기\n\nStable Diffusion 은 text-to-image *latent diffusion* 모델입니다. latent diffusion 모델이라고 불리는 이유는 실제 픽셀 공간 대신 이미지의 저차원의 표현으로 작업하기 때문이고, 메모리 효율이 더 높습니다. 인코더는 이미지를 더 작은 표현으로 압축하고, 디코더는 압축된 표현을 다시 이미지로 변환합니다. text-to-image 모델의 경우 텍스트 임베딩을 생성하기 위해 tokenizer와 인코더가 필요합니다. 이전 예제에서 이미 UNet 모델과 스케줄러가 필요하다는 것은 알고 계셨을 것입니다.\n\n보시다시피, 이것은 UNet 모델만 포함된 DDPM 파이프라인보다 더 복잡합니다. Stable Diffusion 모델에는 세 개의 개별 사전학습된 모델이 있습니다.\n\n\n\n💡 VAE, UNet 및 텍스트 인코더 모델의 작동방식에 대한 자세한 내용은 [How does Stable Diffusion work?](https://huggingface.co/blog/stable_diffusion#how-does-stable-diffusion-work) 블로그를 참조하세요.\n\n\n\n이제 Stable Diffusion 파이프라인에 필요한 구성요소들이 무엇인지 알았으니, [`~ModelMixin.from_pretrained`] 메서드를 사용해 모든 구성요소를 불러옵니다. 사전학습된 체크포인트 [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5)에서 찾을 수 있으며, 각 구성요소들은 별도의 하위 폴더에 저장되어 있습니다:\n\n```py\n>>> from PIL import Image\n>>> import torch\n>>> from transformers import CLIPTextModel, CLIPTokenizer\n>>> from diffusers import AutoencoderKL, UNet2DConditionModel, PNDMScheduler\n\n>>> vae = AutoencoderKL.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"vae\")\n>>> tokenizer = CLIPTokenizer.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"tokenizer\")\n>>> text_encoder = CLIPTextModel.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"text_encoder\")\n>>> unet = UNet2DConditionModel.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"unet\")\n```\n\n기본 [`PNDMScheduler`] 대신, [`UniPCMultistepScheduler`]로 교체하여 다른 스케줄러를 얼마나 쉽게 연결할 수 있는지 확인합니다:\n\n```py\n>>> from diffusers import UniPCMultistepScheduler\n\n>>> scheduler = UniPCMultistepScheduler.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"scheduler\")\n```\n\n추론 속도를 높이려면 스케줄러와 달리 학습 가능한 가중치가 있으므로 모델을 GPU로 옮기세요:\n\n```py\n>>> torch_device = \"cuda\"\n>>> vae.to(torch_device)\n>>> text_encoder.to(torch_device)\n>>> unet.to(torch_device)\n```\n\n### 텍스트 임베딩 생성하기\n\n다음 단계는 임베딩을 생성하기 위해 텍스트를 토큰화하는 것입니다. 이 텍스트는 UNet 모델에서 condition으로 사용되고 입력 프롬프트와 유사한 방향으로 diffusion 프로세스를 조정하는 데 사용됩니다.\n\n\n\n💡 `guidance_scale` 매개변수는 이미지를 생성할 때 프롬프트에 얼마나 많은 가중치를 부여할지 결정합니다.\n\n\n\n다른 프롬프트를 생성하고 싶다면 원하는 프롬프트를 자유롭게 선택하세요!\n\n```py\n>>> prompt = [\"a photograph of an astronaut riding a horse\"]\n>>> height = 512 # Stable Diffusion의 기본 높이\n>>> width = 512 # Stable Diffusion의 기본 너비\n>>> num_inference_steps = 25 # 노이즈 제거 스텝 수\n>>> guidance_scale = 7.5 # classifier-free guidance를 위한 scale\n>>> generator = torch.manual_seed(0) # 초기 잠재 노이즈를 생성하는 seed generator\n>>> batch_size = len(prompt)\n```\n\n텍스트를 토큰화하고 프롬프트에서 임베딩을 생성합니다:\n\n```py\n>>> text_input = tokenizer(\n... prompt, padding=\"max_length\", max_length=tokenizer.model_max_length, truncation=True, return_tensors=\"pt\"\n... )\n\n>>> with torch.no_grad():\n... text_embeddings = text_encoder(text_input.input_ids.to(torch_device))[0]\n```\n\n또한 패딩 토큰의 임베딩인 *unconditional 텍스트 임베딩*을 생성해야 합니다. 이 임베딩은 조건부 `text_embeddings`과 동일한 shape(`batch_size` 그리고 `seq_length`)을 가져야 합니다:\n\n```py\n>>> max_length = text_input.input_ids.shape[-1]\n>>> uncond_input = tokenizer([\"\"] * batch_size, padding=\"max_length\", max_length=max_length, return_tensors=\"pt\")\n>>> uncond_embeddings = text_encoder(uncond_input.input_ids.to(torch_device))[0]\n```\n\n두번의 forward pass를 피하기 위해 conditional 임베딩과 unconditional 임베딩을 배치(batch)로 연결하겠습니다:\n\n```py\n>>> text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n```\n\n### 랜덤 노이즈 생성\n\n그다음 diffusion 프로세스의 시작점으로 초기 랜덤 노이즈를 생성합니다. 이것이 이미지의 잠재적 표현이며 점차적으로 노이즈가 제거됩니다. 이 시점에서 `latent` 이미지는 최종 이미지 크기보다 작지만 나중에 모델이 이를 512x512 이미지 크기로 변환하므로 괜찮습니다.\n\n\n\n💡 `vae` 모델에는 3개의 다운 샘플링 레이어가 있기 때문에 높이와 너비가 8로 나뉩니다. 다음을 실행하여 확인할 수 있습니다:\n\n```py\n2 ** (len(vae.config.block_out_channels) - 1) == 8\n```\n\n\n\n```py\n>>> latents = torch.randn(\n... (batch_size, unet.config.in_channels, height // 8, width // 8),\n... generator=generator,\n... device=torch_device,\n... )\n```\n\n### 이미지 노이즈 제거\n\n먼저 [`UniPCMultistepScheduler`]와 같은 향상된 스케줄러에 필요한 노이즈 스케일 값인 초기 노이즈 분포 *sigma* 로 입력을 스케일링 하는 것부터 시작합니다:\n\n```py\n>>> latents = latents * scheduler.init_noise_sigma\n```\n\n마지막 단계는 `latent`의 순수한 노이즈를 점진적으로 프롬프트에 설명된 이미지로 변환하는 노이즈 제거 루프를 생성하는 것입니다. 노이즈 제거 루프는 세 가지 작업을 수행해야 한다는 점을 기억하세요:\n\n1. 노이즈 제거 중에 사용할 스케줄러의 timesteps를 설정합니다.\n2. timestep을 따라 반복합니다.\n3. 각 timestep에서 UNet 모델을 호출하여 noise residual을 예측하고 스케줄러에 전달하여 이전 노이즈 샘플을 계산합니다.\n\n```py\n>>> from tqdm.auto import tqdm\n\n>>> scheduler.set_timesteps(num_inference_steps)\n\n>>> for t in tqdm(scheduler.timesteps):\n... # classifier-free guidance를 수행하는 경우 두번의 forward pass를 수행하지 않도록 latent를 확장.\n... latent_model_input = torch.cat([latents] * 2)\n\n... latent_model_input = scheduler.scale_model_input(latent_model_input, timestep=t)\n\n... # noise residual 예측\n... with torch.no_grad():\n... noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample\n\n... # guidance 수행\n... noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n... noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n... # 이전 노이즈 샘플을 계산 x_t -> x_t-1\n... latents = scheduler.step(noise_pred, t, latents).prev_sample\n```\n\n### 이미지 디코딩\n\n마지막 단계는 `vae`를 이용하여 잠재 표현을 이미지로 디코딩하고 `sample`과 함께 디코딩된 출력을 얻는 것입니다:\n\n```py\n# latent를 스케일링하고 vae로 이미지 디코딩\nlatents = 1 / 0.18215 * latents\nwith torch.no_grad():\n image = vae.decode(latents).sample\n```\n\n마지막으로 이미지를 `PIL.Image`로 변환하면 생성된 이미지를 확인할 수 있습니다!\n\n```py\n>>> image = (image / 2 + 0.5).clamp(0, 1)\n>>> image = image.detach().cpu().permute(0, 2, 3, 1).numpy()\n>>> images = (image * 255).round().astype(\"uint8\")\n>>> pil_images = [Image.fromarray(image) for image in images]\n>>> pil_images[0]\n```\n\n
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\n\n## 다음 단계\n\n기본 파이프라인부터 복잡한 파이프라인까지, 자신만의 diffusion 시스템을 작성하는 데 필요한 것은 노이즈 제거 루프뿐이라는 것을 알 수 있었습니다. 이 루프는 스케줄러의 timesteps를 설정하고, 이를 반복하며, UNet 모델을 호출하여 noise residual을 예측하고 스케줄러에 전달하여 이전 노이즈 샘플을 계산하는 과정을 번갈아 가며 수행해야 합니다.\n\n이것이 바로 🧨 Diffusers가 설계된 목적입니다: 모델과 스케줄러를 사용해 자신만의 diffusion 시스템을 직관적이고 쉽게 작성할 수 있도록 하기 위해서입니다.\n\n다음 단계를 자유롭게 진행하세요:\n\n* 🧨 Diffusers에 [파이프라인 구축 및 기여](using-diffusers/#contribute_pipeline)하는 방법을 알아보세요. 여러분이 어떤 아이디어를 내놓을지 기대됩니다!\n* 라이브러리에서 [기본 파이프라인](./api/pipelines/overview)을 살펴보고, 모델과 스케줄러를 별도로 사용하여 파이프라인을 처음부터 해체하고 빌드할 수 있는지 확인해 보세요.\n" }, { "path": "diffusers/docs/source/pt/_toctree.yml", "content": "- sections:\n - local: index\n title: 🧨 Diffusers\n - local: quicktour\n title: Tour rápido\n - local: installation\n title: Instalação\n title: Primeiros passos\n" }, { "path": "diffusers/docs/source/pt/index.md", "content": "\n\n

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\n\n# Diffusers\n\n🤗 Diffusers é uma biblioteca de modelos de difusão de última geração para geração de imagens, áudio e até mesmo estruturas 3D de moléculas. Se você está procurando uma solução de geração simples ou queira treinar seu próprio modelo de difusão, 🤗 Diffusers é uma modular caixa de ferramentas que suporta ambos. Nossa biblioteca é desenhada com foco em [usabilidade em vez de desempenho](conceptual/philosophy#usability-over-performance), [simples em vez de fácil](conceptual/philosophy#simple-over-easy) e [customizável em vez de abstrações](conceptual/philosophy#tweakable-contributorfriendly-over-abstraction).\n\nA Biblioteca tem três componentes principais:\n\n- Pipelines de última geração para a geração em poucas linhas de código. Têm muitos pipelines no 🤗 Diffusers, veja a tabela no pipeline [Visão geral](api/pipelines/overview) para uma lista completa de pipelines disponíveis e as tarefas que eles resolvem.\n- Intercambiáveis [agendadores de ruído](api/schedulers/overview) para balancear as compensações entre velocidade e qualidade de geração.\n- [Modelos](api/models) pré-treinados que podem ser usados como se fossem blocos de construção, e combinados com agendadores, para criar seu próprio sistema de difusão de ponta a ponta.\n\n
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Tutoriais
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Aprenda as competências fundamentais que precisa para iniciar a gerar saídas, construa seu próprio sistema de difusão, e treine um modelo de difusão. Nós recomendamos começar por aqui se você está utilizando o 🤗 Diffusers pela primeira vez!

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Guias de utilização
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Guias práticos para ajudar você carregar pipelines, modelos, e agendadores. Você também aprenderá como usar os pipelines para tarefas específicas, controlar como as saídas são geradas, otimizar a velocidade de geração, e outras técnicas diferentes de treinamento.

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Guias conceituais
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Compreenda porque a biblioteca foi desenhada da forma que ela é, e aprenda mais sobre as diretrizes éticas e implementações de segurança para o uso da biblioteca.

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Referência
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Descrições técnicas de como funcionam as classes e métodos do 🤗 Diffusers

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\n" }, { "path": "diffusers/docs/source/pt/installation.md", "content": "\n\n# Instalação\n\n🤗 Diffusers é testado no Python 3.8+, PyTorch 1.7.0+, e Flax. Siga as instruções de instalação abaixo para a biblioteca de deep learning que você está utilizando:\n\n- [PyTorch](https://pytorch.org/get-started/locally/) instruções de instalação\n- [Flax](https://flax.readthedocs.io/en/latest/) instruções de instalação\n\n## Instalação com pip\n\nRecomenda-se instalar 🤗 Diffusers em um [ambiente virtual](https://docs.python.org/3/library/venv.html).\nSe você não está familiarizado com ambiente virtuals, veja o [guia](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).\nUm ambiente virtual deixa mais fácil gerenciar diferentes projetos e evitar problemas de compatibilidade entre dependências.\n\nComece criando um ambiente virtual no diretório do projeto:\n\n```bash\npython -m venv .env\n```\n\nAtive o ambiente virtual:\n\n```bash\nsource .env/bin/activate\n```\n\nRecomenda-se a instalação do 🤗 Transformers porque 🤗 Diffusers depende de seus modelos:\n\n\n\n```bash\npip install diffusers[\"torch\"] transformers\n```\n\n\n```bash\npip install diffusers[\"flax\"] transformers\n```\n\n\n\n## Instalação a partir do código fonte\n\nAntes da instalação do 🤗 Diffusers a partir do código fonte, certifique-se de ter o PyTorch e o 🤗 Accelerate instalados.\n\nPara instalar o 🤗 Accelerate:\n\n```bash\npip install accelerate\n```\n\nentão instale o 🤗 Diffusers do código fonte:\n\n```bash\npip install git+https://github.com/huggingface/diffusers\n```\n\nEsse comando instala a última versão em desenvolvimento `main` em vez da última versão estável `stable`.\nA versão `main` é útil para se manter atualizado com os últimos desenvolvimentos.\nPor exemplo, se um bug foi corrigido desde o último lançamento estável, mas um novo lançamento ainda não foi lançado.\nNo entanto, isso significa que a versão `main` pode não ser sempre estável.\nNós nos esforçamos para manter a versão `main` operacional, e a maioria dos problemas geralmente são resolvidos em algumas horas ou um dia.\nSe você encontrar um problema, por favor abra uma [Issue](https://github.com/huggingface/diffusers/issues/new/choose), assim conseguimos arrumar o quanto antes!\n\n## Instalação editável\n\nVocê precisará de uma instalação editável se você:\n\n- Usar a versão `main` do código fonte.\n- Contribuir para o 🤗 Diffusers e precisa testar mudanças no código.\n\nClone o repositório e instale o 🤗 Diffusers com os seguintes comandos:\n\n```bash\ngit clone https://github.com/huggingface/diffusers.git\ncd diffusers\n```\n\n\n\n```bash\npip install -e \".[torch]\"\n```\n\n\n```bash\npip install -e \".[flax]\"\n```\n\n\n\nEsses comandos irá linkar a pasta que você clonou o repositório e os caminhos das suas bibliotecas Python.\nPython então irá procurar dentro da pasta que você clonou além dos caminhos normais das bibliotecas.\nPor exemplo, se o pacote python for tipicamente instalado no `~/anaconda3/envs/main/lib/python3.10/site-packages/`, o Python também irá procurar na pasta `~/diffusers/` que você clonou.\n\n\n\nVocê deve deixar a pasta `diffusers` se você quiser continuar usando a biblioteca.\n\n\n\nAgora você pode facilmente atualizar seu clone para a última versão do 🤗 Diffusers com o seguinte comando:\n\n```bash\ncd ~/diffusers/\ngit pull\n```\n\nSeu ambiente Python vai encontrar a versão `main` do 🤗 Diffusers na próxima execução.\n\n## Cache\n\nOs pesos e os arquivos dos modelos são baixados do Hub para o cache que geralmente é o seu diretório home. Você pode mudar a localização do cache especificando as variáveis de ambiente `HF_HOME` ou `HUGGINFACE_HUB_CACHE` ou configurando o parâmetro `cache_dir` em métodos como [`~DiffusionPipeline.from_pretrained`].\n\nAquivos em cache permitem que você rode 🤗 Diffusers offline. Para prevenir que o 🤗 Diffusers se conecte à internet, defina a variável de ambiente `HF_HUB_OFFLINE` para `True` e o 🤗 Diffusers irá apenas carregar arquivos previamente baixados em cache.\n\n```shell\nexport HF_HUB_OFFLINE=True\n```\n\nPara mais detalhes de como gerenciar e limpar o cache, olhe o guia de [caching](https://huggingface.co/docs/huggingface_hub/guides/manage-cache).\n\n## Telemetria\n\nNossa biblioteca coleta informações de telemetria durante as requisições [`~DiffusionPipeline.from_pretrained`].\nO dado coletado inclui a versão do 🤗 Diffusers e PyTorch/Flax, o modelo ou classe de pipeline requisitado,\ne o caminho para um checkpoint pré-treinado se ele estiver hospedado no Hugging Face Hub.\nEsse dado de uso nos ajuda a debugar problemas e priorizar novas funcionalidades.\nTelemetria é enviada apenas quando é carregado modelos e pipelines do Hub,\ne não é coletado se você estiver carregando arquivos locais.\n\nNos entendemos que nem todo mundo quer compartilhar informações adicionais, e nós respeitamos sua privacidade.\nVocê pode desabilitar a coleta de telemetria definindo a variável de ambiente `DISABLE_TELEMETRY` do seu terminal:\n\nNo Linux/MacOS:\n\n```bash\nexport DISABLE_TELEMETRY=YES\n```\n\nNo Windows:\n\n```bash\nset DISABLE_TELEMETRY=YES\n```\n" }, { "path": "diffusers/docs/source/pt/quicktour.md", "content": "\n\n[[open-in-colab]]\n\n# Tour rápido\n\nModelos de difusão são treinados para remover o ruído Gaussiano aleatório passo a passo para gerar uma amostra de interesse, como uma imagem ou áudio. Isso despertou um tremendo interesse em IA generativa, e você provavelmente já viu exemplos de imagens geradas por difusão na internet. 🧨 Diffusers é uma biblioteca que visa tornar os modelos de difusão amplamente acessíveis a todos.\n\nSeja você um desenvolvedor ou um usuário, esse tour rápido irá introduzir você ao 🧨 Diffusers e ajudar você a começar a gerar rapidamente! Há três componentes principais da biblioteca para conhecer:\n\n- O [`DiffusionPipeline`] é uma classe de alto nível de ponta a ponta desenhada para gerar rapidamente amostras de modelos de difusão pré-treinados para inferência.\n- [Modelos](./api/models) pré-treinados populares e módulos que podem ser usados como blocos de construção para criar sistemas de difusão.\n- Vários [Agendadores](./api/schedulers/overview) diferentes - algoritmos que controlam como o ruído é adicionado para treinamento, e como gerar imagens sem o ruído durante a inferência.\n\nEsse tour rápido mostrará como usar o [`DiffusionPipeline`] para inferência, e então mostrará como combinar um modelo e um agendador para replicar o que está acontecendo dentro do [`DiffusionPipeline`].\n\n\n\nEsse tour rápido é uma versão simplificada da introdução 🧨 Diffusers [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) para ajudar você a começar rápido. Se você quer aprender mais sobre o objetivo do 🧨 Diffusers, filosofia de design, e detalhes adicionais sobre a API principal, veja o notebook!\n\n\n\nAntes de começar, certifique-se de ter todas as bibliotecas necessárias instaladas:\n\n```py\n# uncomment to install the necessary libraries in Colab\n#!pip install --upgrade diffusers accelerate transformers\n```\n\n- [🤗 Accelerate](https://huggingface.co/docs/accelerate/index) acelera o carregamento do modelo para geração e treinamento.\n- [🤗 Transformers](https://huggingface.co/docs/transformers/index) é necessário para executar os modelos mais populares de difusão, como o [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview).\n\n## DiffusionPipeline\n\nO [`DiffusionPipeline`] é a forma mais fácil de usar um sistema de difusão pré-treinado para geração. É um sistema de ponta a ponta contendo o modelo e o agendador. Você pode usar o [`DiffusionPipeline`] pronto para muitas tarefas. Dê uma olhada na tabela abaixo para algumas tarefas suportadas, e para uma lista completa de tarefas suportadas, veja a tabela [Resumo do 🧨 Diffusers](./api/pipelines/overview#diffusers-summary).\n\n| **Tarefa** | **Descrição** | **Pipeline** |\n| -------------------------------------- | ------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------- |\n| Unconditional Image Generation | gera uma imagem a partir do ruído Gaussiano | [unconditional_image_generation](./using-diffusers/unconditional_image_generation) |\n| Text-Guided Image Generation | gera uma imagem a partir de um prompt de texto | [conditional_image_generation](./using-diffusers/conditional_image_generation) |\n| Text-Guided Image-to-Image Translation | adapta uma imagem guiada por um prompt de texto | [img2img](./using-diffusers/img2img) |\n| Text-Guided Image-Inpainting | preenche a parte da máscara da imagem, dado a imagem, a máscara e o prompt de texto | [inpaint](./using-diffusers/inpaint) |\n| Text-Guided Depth-to-Image Translation | adapta as partes de uma imagem guiada por um prompt de texto enquanto preserva a estrutura por estimativa de profundidade | [depth2img](./using-diffusers/depth2img) |\n\nComece criando uma instância do [`DiffusionPipeline`] e especifique qual checkpoint do pipeline você gostaria de baixar.\nVocê pode usar o [`DiffusionPipeline`] para qualquer [checkpoint](https://huggingface.co/models?library=diffusers&sort=downloads) armazenado no Hugging Face Hub.\nNesse quicktour, você carregará o checkpoint [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) para geração de texto para imagem.\n\n\n\nPara os modelos de [Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion), por favor leia cuidadosamente a [licença](https://huggingface.co/spaces/CompVis/stable-diffusion-license) primeiro antes de rodar o modelo. 🧨 Diffusers implementa uma verificação de segurança: [`safety_checker`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) para prevenir conteúdo ofensivo ou nocivo, mas as capacidades de geração de imagem aprimorada do modelo podem ainda produzir conteúdo potencialmente nocivo.\n\n\n\nPara carregar o modelo com o método [`~DiffusionPipeline.from_pretrained`]:\n\n```python\n>>> from diffusers import DiffusionPipeline\n\n>>> pipeline = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", use_safetensors=True)\n```\n\nO [`DiffusionPipeline`] baixa e armazena em cache todos os componentes de modelagem, tokenização, e agendamento. Você verá que o pipeline do Stable Diffusion é composto pelo [`UNet2DConditionModel`] e [`PNDMScheduler`] entre outras coisas:\n\n```py\n>>> pipeline\nStableDiffusionPipeline {\n \"_class_name\": \"StableDiffusionPipeline\",\n \"_diffusers_version\": \"0.13.1\",\n ...,\n \"scheduler\": [\n \"diffusers\",\n \"PNDMScheduler\"\n ],\n ...,\n \"unet\": [\n \"diffusers\",\n \"UNet2DConditionModel\"\n ],\n \"vae\": [\n \"diffusers\",\n \"AutoencoderKL\"\n ]\n}\n```\n\nNós fortemente recomendamos rodar o pipeline em uma placa de vídeo, pois o modelo consiste em aproximadamente 1.4 bilhões de parâmetros.\nVocê pode mover o objeto gerador para uma placa de vídeo, assim como você faria no PyTorch:\n\n```python\n>>> pipeline.to(\"cuda\")\n```\n\nAgora você pode passar o prompt de texto para o `pipeline` para gerar uma imagem, e então acessar a imagem sem ruído. Por padrão, a saída da imagem é embrulhada em um objeto [`PIL.Image`](https://pillow.readthedocs.io/en/stable/reference/Image.html?highlight=image#the-image-class).\n\n```python\n>>> image = pipeline(\"An image of a squirrel in Picasso style\").images[0]\n>>> image\n```\n\n
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\n\nSalve a imagem chamando o `save`:\n\n```python\n>>> image.save(\"image_of_squirrel_painting.png\")\n```\n\n### Pipeline local\n\nVocê também pode utilizar o pipeline localmente. A única diferença é que você precisa baixar os pesos primeiro:\n\n```bash\n!git lfs install\n!git clone https://huggingface.co/runwayml/stable-diffusion-v1-5\n```\n\nAssim carregue os pesos salvos no pipeline:\n\n```python\n>>> pipeline = DiffusionPipeline.from_pretrained(\"./stable-diffusion-v1-5\", use_safetensors=True)\n```\n\nAgora você pode rodar o pipeline como você faria na seção acima.\n\n### Troca dos agendadores\n\nAgendadores diferentes tem diferentes velocidades de retirar o ruído e compensações de qualidade. A melhor forma de descobrir qual funciona melhor para você é testar eles! Uma das principais características do 🧨 Diffusers é permitir que você troque facilmente entre agendadores. Por exemplo, para substituir o [`PNDMScheduler`] padrão com o [`EulerDiscreteScheduler`], carregue ele com o método [`~diffusers.ConfigMixin.from_config`]:\n\n```py\n>>> from diffusers import EulerDiscreteScheduler\n\n>>> pipeline = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", use_safetensors=True)\n>>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)\n```\n\nTente gerar uma imagem com o novo agendador e veja se você nota alguma diferença!\n\nNa próxima seção, você irá dar uma olhada mais de perto nos componentes - o modelo e o agendador - que compõe o [`DiffusionPipeline`] e aprender como usar esses componentes para gerar uma imagem de um gato.\n\n## Modelos\n\nA maioria dos modelos recebe uma amostra de ruído, e em cada _timestep_ ele prevê o _noise residual_ (outros modelos aprendem a prever a amostra anterior diretamente ou a velocidade ou [`v-prediction`](https://github.com/huggingface/diffusers/blob/5e5ce13e2f89ac45a0066cb3f369462a3cf1d9ef/src/diffusers/schedulers/scheduling_ddim.py#L110)), a diferença entre uma imagem menos com ruído e a imagem de entrada. Você pode misturar e combinar modelos para criar outros sistemas de difusão.\n\nModelos são inicializados com o método [`~ModelMixin.from_pretrained`] que também armazena em cache localmente os pesos do modelo para que seja mais rápido na próxima vez que você carregar o modelo. Para o tour rápido, você irá carregar o [`UNet2DModel`], um modelo básico de geração de imagem incondicional com um checkpoint treinado em imagens de gato:\n\n```py\n>>> from diffusers import UNet2DModel\n\n>>> repo_id = \"google/ddpm-cat-256\"\n>>> model = UNet2DModel.from_pretrained(repo_id, use_safetensors=True)\n```\n\nPara acessar os parâmetros do modelo, chame `model.config`:\n\n```py\n>>> model.config\n```\n\nA configuração do modelo é um dicionário 🧊 congelado 🧊, o que significa que esses parâmetros não podem ser mudados depois que o modelo é criado. Isso é intencional e garante que os parâmetros usados para definir a arquitetura do modelo no início permaneçam os mesmos, enquanto outros parâmetros ainda podem ser ajustados durante a geração.\n\nUm dos parâmetros mais importantes são:\n\n- `sample_size`: a dimensão da altura e largura da amostra de entrada.\n- `in_channels`: o número de canais de entrada da amostra de entrada.\n- `down_block_types` e `up_block_types`: o tipo de blocos de downsampling e upsampling usados para criar a arquitetura UNet.\n- `block_out_channels`: o número de canais de saída dos blocos de downsampling; também utilizado como uma order reversa do número de canais de entrada dos blocos de upsampling.\n- `layers_per_block`: o número de blocks ResNet presentes em cada block UNet.\n\nPara usar o modelo para geração, crie a forma da imagem com ruído Gaussiano aleatório. Deve ter um eixo `batch` porque o modelo pode receber múltiplos ruídos aleatórios, um eixo `channel` correspondente ao número de canais de entrada, e um eixo `sample_size` para a altura e largura da imagem:\n\n```py\n>>> import torch\n\n>>> torch.manual_seed(0)\n\n>>> noisy_sample = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)\n>>> noisy_sample.shape\ntorch.Size([1, 3, 256, 256])\n```\n\nPara geração, passe a imagem com ruído para o modelo e um `timestep`. O `timestep` indica o quão ruidosa a imagem de entrada é, com mais ruído no início e menos no final. Isso ajuda o modelo a determinar sua posição no processo de difusão, se está mais perto do início ou do final. Use o método `sample` para obter a saída do modelo:\n\n```py\n>>> with torch.no_grad():\n... noisy_residual = model(sample=noisy_sample, timestep=2).sample\n```\n\nPara geração de exemplos reais, você precisará de um agendador para guiar o processo de retirada do ruído. Na próxima seção, você irá aprender como acoplar um modelo com um agendador.\n\n## Agendadores\n\nAgendadores gerenciam a retirada do ruído de uma amostra ruidosa para uma amostra menos ruidosa dado a saída do modelo - nesse caso, é o `noisy_residual`.\n\n\n\n🧨 Diffusers é uma caixa de ferramentas para construir sistemas de difusão. Enquanto o [`DiffusionPipeline`] é uma forma conveniente de começar com um sistema de difusão pré-construído, você também pode escolher seus próprios modelos e agendadores separadamente para construir um sistema de difusão personalizado.\n\n\n\nPara o tour rápido, você irá instanciar o [`DDPMScheduler`] com o método [`~diffusers.ConfigMixin.from_config`]:\n\n```py\n>>> from diffusers import DDPMScheduler\n\n>>> scheduler = DDPMScheduler.from_config(repo_id)\n>>> scheduler\nDDPMScheduler {\n \"_class_name\": \"DDPMScheduler\",\n \"_diffusers_version\": \"0.13.1\",\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n \"beta_start\": 0.0001,\n \"clip_sample\": true,\n \"clip_sample_range\": 1.0,\n \"num_train_timesteps\": 1000,\n \"prediction_type\": \"epsilon\",\n \"trained_betas\": null,\n \"variance_type\": \"fixed_small\"\n}\n```\n\n\n\n💡 Perceba como o agendador é instanciado de uma configuração. Diferentemente de um modelo, um agendador não tem pesos treináveis e é livre de parâmetros!\n\n\n\nUm dos parâmetros mais importante são:\n\n- `num_train_timesteps`: o tamanho do processo de retirar ruído ou em outras palavras, o número de _timesteps_ necessários para o processo de ruídos Gausianos aleatórios dentro de uma amostra de dados.\n- `beta_schedule`: o tipo de agendados de ruído para o uso de geração e treinamento.\n- `beta_start` e `beta_end`: para começar e terminar os valores de ruído para o agendador de ruído.\n\nPara predizer uma imagem com um pouco menos de ruído, passe o seguinte para o método do agendador [`~diffusers.DDPMScheduler.step`]: saída do modelo, `timestep`, e a atual `amostra`.\n\n```py\n>>> less_noisy_sample = scheduler.step(model_output=noisy_residual, timestep=2, sample=noisy_sample).prev_sample\n>>> less_noisy_sample.shape\n```\n\nO `less_noisy_sample` pode ser passado para o próximo `timestep` onde ele ficará ainda com menos ruído! Vamos juntar tudo agora e visualizar o processo inteiro de retirada de ruído.\n\nComece, criando a função que faça o pós-processamento e mostre a imagem sem ruído como uma `PIL.Image`:\n\n```py\n>>> import PIL.Image\n>>> import numpy as np\n\n\n>>> def display_sample(sample, i):\n... image_processed = sample.cpu().permute(0, 2, 3, 1)\n... image_processed = (image_processed + 1.0) * 127.5\n... image_processed = image_processed.numpy().astype(np.uint8)\n\n... image_pil = PIL.Image.fromarray(image_processed[0])\n... display(f\"Image at step {i}\")\n... display(image_pil)\n```\n\nPara acelerar o processo de retirada de ruído, mova a entrada e o modelo para uma GPU:\n\n```py\n>>> model.to(\"cuda\")\n>>> noisy_sample = noisy_sample.to(\"cuda\")\n```\n\nAgora, crie um loop de retirada de ruído que prediz o residual da amostra menos ruidosa, e computa a amostra menos ruidosa com o agendador:\n\n```py\n>>> import tqdm\n\n>>> sample = noisy_sample\n\n>>> for i, t in enumerate(tqdm.tqdm(scheduler.timesteps)):\n... # 1. predict noise residual\n... with torch.no_grad():\n... residual = model(sample, t).sample\n\n... # 2. compute less noisy image and set x_t -> x_t-1\n... sample = scheduler.step(residual, t, sample).prev_sample\n\n... # 3. optionally look at image\n... if (i + 1) % 50 == 0:\n... display_sample(sample, i + 1)\n```\n\nSente-se e assista o gato ser gerado do nada além de ruído! 😻\n\n
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\n\n## Próximos passos\n\nEsperamos que você tenha gerado algumas imagens legais com o 🧨 Diffusers neste tour rápido! Para suas próximas etapas, você pode\n\n- Treine ou faça a configuração fina de um modelo para gerar suas próprias imagens no tutorial de [treinamento](./tutorials/basic_training).\n- Veja exemplos oficiais e da comunidade de [scripts de treinamento ou configuração fina](https://github.com/huggingface/diffusers/tree/main/examples#-diffusers-examples) para os mais variados casos de uso.\n- Aprenda sobre como carregar, acessar, mudar e comparar agendadores no guia [Usando diferentes agendadores](./using-diffusers/schedulers).\n- Explore engenharia de prompt, otimizações de velocidade e memória, e dicas e truques para gerar imagens de maior qualidade com o guia [Stable Diffusion](./stable_diffusion).\n- Se aprofunde em acelerar 🧨 Diffusers com guias sobre [PyTorch otimizado em uma GPU](./optimization/fp16), e guias de inferência para rodar [Stable Diffusion em Apple Silicon (M1/M2)](./optimization/mps) e [ONNX Runtime](./optimization/onnx).\n" }, { "path": "diffusers/docs/source/zh/_toctree.yml", "content": "- sections:\n - local: index\n title: 🧨 Diffusers\n - local: quicktour\n title: 快速入门\n - local: stable_diffusion\n title: 有效和高效的扩散\n - local: installation\n title: 安装\n title: 开始\n" }, { "path": "diffusers/docs/source/zh/index.md", "content": "\n\n

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\n\n# 🧨 Diffusers\n\n🤗 Diffusers 是一个值得首选用于生成图像、音频甚至 3D 分子结构的，最先进的预训练扩散模型库。\n无论您是在寻找简单的推理解决方案，还是想训练自己的扩散模型，🤗 Diffusers 这一模块化工具箱都能对其提供支持。\n本库的设计更偏重于[可用而非高性能](conceptual/philosophy#usability-over-performance)、[简明而非简单](conceptual/philosophy#simple-over-easy)以及[易用而非抽象](conceptual/philosophy#tweakable-contributorfriendly-over-abstraction)。\n\n\n本库包含三个主要组件：\n\n- 最先进的扩散管道 [diffusion pipelines](api/pipelines/overview)，只需几行代码即可进行推理。\n- 可交替使用的各种噪声调度器 [noise schedulers](api/schedulers/overview)，用于平衡生成速度和质量。\n- 预训练模型 [models](api/models)，可作为构建模块，并与调度程序结合使用，来创建您自己的端到端扩散系统。\n\n
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Tutorials
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Learn the fundamental skills you need to start generating outputs, build your own diffusion system, and train a diffusion model. We recommend starting here if you're using 🤗 Diffusers for the first time!

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How-to guides
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Practical guides for helping you load pipelines, models, and schedulers. You'll also learn how to use pipelines for specific tasks, control how outputs are generated, optimize for inference speed, and different training techniques.

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Conceptual guides
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Understand why the library was designed the way it was, and learn more about the ethical guidelines and safety implementations for using the library.

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Reference
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Technical descriptions of how 🤗 Diffusers classes and methods work.

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\n\n## 🧨 Diffusers pipelines\n\n下表汇总了当前所有官方支持的pipelines及其对应的论文.\n\n| 管道 | 论文/仓库 | 任务 |\n|---|---|:---:|\n| [alt_diffusion](./api/pipelines/alt_diffusion) | [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679) | Image-to-Image Text-Guided Generation |\n| [audio_diffusion](./api/pipelines/audio_diffusion) | [Audio Diffusion](https://github.com/teticio/audio-diffusion.git) | Unconditional Audio Generation |\n| [controlnet](./api/pipelines/stable_diffusion/controlnet) | [Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543) | Image-to-Image Text-Guided Generation |\n| [cycle_diffusion](./api/pipelines/cycle_diffusion) | [Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance](https://arxiv.org/abs/2210.05559) | Image-to-Image Text-Guided Generation |\n| [dance_diffusion](./api/pipelines/dance_diffusion) | [Dance Diffusion](https://github.com/williamberman/diffusers.git) | Unconditional Audio Generation |\n| [ddpm](./api/pipelines/ddpm) | [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) | Unconditional Image Generation |\n| [ddim](./api/pipelines/ddim) | [Denoising Diffusion Implicit Models](https://arxiv.org/abs/2010.02502) | Unconditional Image Generation |\n| [if](./if) | [**IF**](./api/pipelines/if) | Image Generation |\n| [if_img2img](./if) | [**IF**](./api/pipelines/if) | Image-to-Image Generation |\n| [if_inpainting](./if) | [**IF**](./api/pipelines/if) | Image-to-Image Generation |\n| [latent_diffusion](./api/pipelines/latent_diffusion) | [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)| Text-to-Image Generation |\n| [latent_diffusion](./api/pipelines/latent_diffusion) | [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)| Super Resolution Image-to-Image |\n| [latent_diffusion_uncond](./api/pipelines/latent_diffusion_uncond) | [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) | Unconditional Image Generation |\n| [paint_by_example](./api/pipelines/paint_by_example) | [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://arxiv.org/abs/2211.13227) | Image-Guided Image Inpainting |\n| [pndm](./api/pipelines/pndm) | [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778) | Unconditional Image Generation |\n| [score_sde_ve](./api/pipelines/score_sde_ve) | [Score-Based Generative Modeling through Stochastic Differential Equations](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation |\n| [score_sde_vp](./api/pipelines/score_sde_vp) | [Score-Based Generative Modeling through Stochastic Differential Equations](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation |\n| [semantic_stable_diffusion](./api/pipelines/semantic_stable_diffusion) | [Semantic Guidance](https://arxiv.org/abs/2301.12247) | Text-Guided Generation |\n| [stable_diffusion_text2img](./api/pipelines/stable_diffusion/text2img) | [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release) | Text-to-Image Generation |\n| [stable_diffusion_img2img](./api/pipelines/stable_diffusion/img2img) | [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release) | Image-to-Image Text-Guided Generation |\n| [stable_diffusion_inpaint](./api/pipelines/stable_diffusion/inpaint) | [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release) | Text-Guided Image Inpainting |\n| [stable_diffusion_panorama](./api/pipelines/stable_diffusion/panorama) | [MultiDiffusion](https://multidiffusion.github.io/) | Text-to-Panorama Generation |\n| [stable_diffusion_pix2pix](./api/pipelines/stable_diffusion/pix2pix) | [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://arxiv.org/abs/2211.09800) | Text-Guided Image Editing|\n| [stable_diffusion_pix2pix_zero](./api/pipelines/stable_diffusion/pix2pix_zero) | [Zero-shot Image-to-Image Translation](https://pix2pixzero.github.io/) | Text-Guided Image Editing |\n| [stable_diffusion_attend_and_excite](./api/pipelines/stable_diffusion/attend_and_excite) | [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://arxiv.org/abs/2301.13826) | Text-to-Image Generation |\n| [stable_diffusion_self_attention_guidance](./api/pipelines/stable_diffusion/self_attention_guidance) | [Improving Sample Quality of Diffusion Models Using Self-Attention Guidance](https://arxiv.org/abs/2210.00939) | Text-to-Image Generation Unconditional Image Generation |\n| [stable_diffusion_image_variation](./stable_diffusion/image_variation) | [Stable Diffusion Image Variations](https://github.com/LambdaLabsML/lambda-diffusers#stable-diffusion-image-variations) | Image-to-Image Generation |\n| [stable_diffusion_latent_upscale](./stable_diffusion/latent_upscale) | [Stable Diffusion Latent Upscaler](https://twitter.com/StabilityAI/status/1590531958815064065) | Text-Guided Super Resolution Image-to-Image |\n| [stable_diffusion_model_editing](./api/pipelines/stable_diffusion/model_editing) | [Editing Implicit Assumptions in Text-to-Image Diffusion Models](https://time-diffusion.github.io/) | Text-to-Image Model Editing |\n| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Stable Diffusion 2](https://stability.ai/blog/stable-diffusion-v2-release) | Text-to-Image Generation |\n| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Stable Diffusion 2](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Image Inpainting |\n| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Depth-Conditional Stable Diffusion](https://github.com/Stability-AI/stablediffusion#depth-conditional-stable-diffusion) | Depth-to-Image Generation |\n| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Stable Diffusion 2](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Super Resolution Image-to-Image |\n| [stable_diffusion_safe](./api/pipelines/stable_diffusion_safe) | [Safe Stable Diffusion](https://arxiv.org/abs/2211.05105) | Text-Guided Generation |\n| [stable_unclip](./stable_unclip) | Stable unCLIP | Text-to-Image Generation |\n| [stable_unclip](./stable_unclip) | Stable unCLIP | Image-to-Image Text-Guided Generation |\n| [stochastic_karras_ve](./api/pipelines/stochastic_karras_ve) | [Elucidating the Design Space of Diffusion-Based Generative Models](https://arxiv.org/abs/2206.00364) | Unconditional Image Generation |\n| [text_to_video_sd](./api/pipelines/text_to_video) | [Modelscope's Text-to-video-synthesis Model in Open Domain](https://modelscope.cn/models/damo/text-to-video-synthesis/summary) | Text-to-Video Generation |\n| [unclip](./api/pipelines/unclip) | [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://arxiv.org/abs/2204.06125)(implementation by [kakaobrain](https://github.com/kakaobrain/karlo)) | Text-to-Image Generation |\n| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Text-to-Image Generation |\n| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Image Variations Generation |\n| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Dual Image and Text Guided Generation |\n| [vq_diffusion](./api/pipelines/vq_diffusion) | [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://arxiv.org/abs/2111.14822) | Text-to-Image Generation |\n" }, { "path": "diffusers/docs/source/zh/installation.md", "content": "\n\n# 安装\n\n在你正在使用的任意深度学习框架中安装 🤗 Diffusers 。\n\n🤗 Diffusers已在Python 3.8+、PyTorch 1.7.0+和Flax上进行了测试。按照下面的安装说明，针对你正在使用的深度学习框架进行安装：\n\n- [PyTorch](https://pytorch.org/get-started/locally/) installation instructions.\n- [Flax](https://flax.readthedocs.io/en/latest/) installation instructions.\n\n## 使用pip安装\n\n你需要在[虚拟环境](https://docs.python.org/3/library/venv.html)中安装 🤗 Diffusers 。\n\n如果你对 Python 虚拟环境不熟悉，可以看看这个[教程](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).\n\n在虚拟环境中，你可以轻松管理不同的项目，避免依赖项之间的兼容性问题。\n\n首先，在你的项目目录下创建一个虚拟环境：\n\n```bash\npython -m venv .env\n```\n\n激活虚拟环境：\n\n```bash\nsource .env/bin/activate\n```\n\n现在，你就可以安装 🤗 Diffusers了！使用下边这个命令：\n\n**PyTorch**\n\n```bash\npip install diffusers[\"torch\"]\n```\n\n**Flax**\n\n```bash\npip install diffusers[\"flax\"]\n```\n\n## 从源代码安装\n\n在从源代码安装 `diffusers` 之前，确保你已经安装了 `torch` 和 `accelerate`。\n\n`torch`的安装教程可以看 `torch` [文档](https://pytorch.org/get-started/locally/#start-locally).\n\n安装 `accelerate`\n\n```bash\npip install accelerate\n```\n\n从源码安装 🤗 Diffusers 需要使用以下命令:\n\n```bash\npip install git+https://github.com/huggingface/diffusers\n```\n\n这个命令安装的是最新的 `main`版本，而不是最近的`stable`版。\n`main`是一直和最新进展保持一致的。比如，上次发布的正式版中有bug，在`main`中可以看到这个bug被修复了，但是新的正式版此时尚未推出。\n但是这也意味着 `main`版本不保证是稳定的。\n\n我们努力保持`main`版本正常运行，大多数问题都能在几个小时或一天之内解决\n\n如果你遇到了问题，可以提 [Issue](https://github.com/huggingface/transformers/issues)，这样我们就能更快修复问题了。\n\n## 可修改安装\n\n如果你想做以下两件事，那你可能需要一个可修改代码的安装方式：\n\n* 使用 `main`版本的源代码。\n* 为 🤗 Diffusers 贡献，需要测试代码中的变化。\n\n使用以下命令克隆并安装 🤗 Diffusers:\n\n```bash\ngit clone https://github.com/huggingface/diffusers.git\ncd diffusers\n```\n\n**PyTorch**\n\n```sh\npip install -e \".[torch]\"\n```\n\n**Flax**\n\n```sh\npip install -e \".[flax]\"\n```\n\n这些命令将连接到你克隆的版本库和你的 Python 库路径。\n现在，不只是在通常的库路径，Python 还会在你克隆的文件夹内寻找包。\n例如，如果你的 Python 包通常安装在 `~/anaconda3/envs/main/lib/python3.10/Site-packages/`，Python 也会搜索你克隆到的文件夹。`~/diffusers/`。\n\n\n\n如果你想继续使用这个库，你必须保留 `diffusers` 文件夹。\n\n\n\n\n现在你可以用下面的命令轻松地将你克隆的 🤗 Diffusers 库更新到最新版本。\n\n```bash\ncd ~/diffusers/\ngit pull\n```\n\n你的Python环境将在下次运行时找到`main`版本的 🤗 Diffusers。\n\n## 注意 Telemetry 日志\n\n我们的库会在使用`from_pretrained()`请求期间收集 telemetry 信息。这些数据包括Diffusers和PyTorch/Flax的版本，请求的模型或管道类，以及预训练检查点的路径（如果它被托管在Hub上的话）。\n这些使用数据有助于我们调试问题并确定新功能的开发优先级。\nTelemetry 数据仅在从 HuggingFace Hub 中加载模型和管道时发送，而不会在本地使用期间收集。\n\n我们知道，并不是每个人都想分享这些的信息，我们尊重您的隐私，\n因此您可以通过在终端中设置 `DISABLE_TELEMETRY` 环境变量从而禁用 Telemetry 数据收集：\n\n\nLinux/MacOS :\n```bash\nexport DISABLE_TELEMETRY=YES\n```\n\nWindows :\n```bash\nset DISABLE_TELEMETRY=YES\n```" }, { "path": "diffusers/docs/source/zh/quicktour.md", "content": "\n\n[[open-in-colab]]\n\n# 快速上手\n\n训练扩散模型，是为了对随机高斯噪声进行逐步去噪，以生成令人感兴趣的样本，比如图像或者语音。\n\n扩散模型的发展引起了人们对生成式人工智能的极大兴趣，你可能已经在网上见过扩散生成的图像了。🧨 Diffusers库的目的是让大家更易上手扩散模型。\n\n无论你是开发人员还是普通用户，本文将向你介绍🧨 Diffusers 并帮助你快速开始生成内容！\n\n🧨 Diffusers 库的三个主要组件：\n\n\n无论你是开发者还是普通用户，这个快速指南将向你介绍🧨 Diffusers，并帮助你快速使用和生成！该库三个主要部分如下：\n\n* [`DiffusionPipeline`]是一个高级的端到端类，旨在通过预训练的扩散模型快速生成样本进行推理。\n* 作为创建扩散系统做组件的流行的预训练[模型](./api/models)框架和模块。\n* 许多不同的[调度器](./api/schedulers/overview)：控制如何在训练过程中添加噪声的算法，以及如何在推理过程中生成去噪图像的算法。\n\n快速入门将告诉你如何使用[`DiffusionPipeline`]进行推理，然后指导你如何结合模型和调度器以复现[`DiffusionPipeline`]内部发生的事情。\n\n\n\n快速入门是🧨[Diffusers入门](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb)的简化版，可以帮助你快速上手。如果你想了解更多关于🧨 Diffusers的目标、设计理念以及关于它的核心API的更多细节，可以点击🧨[Diffusers入门](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb)查看。\n\n\n\n在开始之前，确认一下你已经安装好了所需要的库：\n\n```bash\npip install --upgrade diffusers accelerate transformers\n```\n\n- [🤗 Accelerate](https://huggingface.co/docs/accelerate/index) 在推理和训练过程中加速模型加载。\n- [🤗 Transformers](https://huggingface.co/docs/transformers/index) 是运行最流行的扩散模型所必须的库，比如[Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/overview).\n\n## 扩散模型管道\n\n[`DiffusionPipeline`]是用预训练的扩散系统进行推理的最简单方法。它是一个包含模型和调度器的端到端系统。你可以直接使用[`DiffusionPipeline`]完成许多任务。请查看下面的表格以了解一些支持的任务，要获取完整的支持任务列表，请查看[🧨 Diffusers 总结](./api/pipelines/overview#diffusers-summary) 。\n\n| **任务** | **描述** | **管道**\n|------------------------------|--------------------------------------------------------------------------------------------------------------|-----------------|\n| Unconditional Image Generation | 从高斯噪声中生成图片 | [unconditional_image_generation](./using-diffusers/unconditional_image_generation) |\n| Text-Guided Image Generation | 给定文本提示生成图像 | [conditional_image_generation](./using-diffusers/conditional_image_generation) |\n| Text-Guided Image-to-Image Translation | 在文本提示的指导下调整图像 | [img2img](./using-diffusers/img2img) |\n| Text-Guided Image-Inpainting | 给出图像、遮罩和文本提示，填充图像的遮罩部分 | [inpaint](./using-diffusers/inpaint) |\n| Text-Guided Depth-to-Image Translation | 在文本提示的指导下调整图像的部分内容，同时通过深度估计保留其结构 | [depth2img](./using-diffusers/depth2img) |\n\n首先创建一个[`DiffusionPipeline`]的实例，并指定要下载的pipeline检查点。\n你可以使用存储在Hugging Face Hub上的任何[`DiffusionPipeline`][检查点](https://huggingface.co/models?library=diffusers&sort=downloads)。\n在教程中，你将加载[`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5)检查点，用于文本到图像的生成。\n\n首先创建一个[DiffusionPipeline]实例，并指定要下载的管道检查点。\n您可以在Hugging Face Hub上使用[DiffusionPipeline]的任何检查点。\n在本快速入门中，您将加载stable-diffusion-v1-5检查点，用于文本到图像生成。\n\n。\n\n对于[Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion)模型，在运行该模型之前，请先仔细阅读[许可证](https://huggingface.co/spaces/CompVis/stable-diffusion-license)。🧨 Diffusers实现了一个[`safety_checker`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py)，以防止有攻击性的或有害的内容，但Stable Diffusion模型改进图像的生成能力仍有可能产生潜在的有害内容。\n\n\n\n用[`~DiffusionPipeline.from_pretrained`]方法加载模型。\n\n```python\n>>> from diffusers import DiffusionPipeline\n\n>>> pipeline = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n```\n[`DiffusionPipeline`]会下载并缓存所有的建模、标记化和调度组件。你可以看到Stable Diffusion的pipeline是由[`UNet2DConditionModel`]和[`PNDMScheduler`]等组件组成的：\n\n```py\n>>> pipeline\nStableDiffusionPipeline {\n \"_class_name\": \"StableDiffusionPipeline\",\n \"_diffusers_version\": \"0.13.1\",\n ...,\n \"scheduler\": [\n \"diffusers\",\n \"PNDMScheduler\"\n ],\n ...,\n \"unet\": [\n \"diffusers\",\n \"UNet2DConditionModel\"\n ],\n \"vae\": [\n \"diffusers\",\n \"AutoencoderKL\"\n ]\n}\n```\n\n我们强烈建议你在GPU上运行这个pipeline，因为该模型由大约14亿个参数组成。\n\n你可以像在Pytorch里那样把生成器对象移到GPU上：\n\n```python\n>>> pipeline.to(\"cuda\")\n```\n\n现在你可以向`pipeline`传递一个文本提示来生成图像，然后获得去噪的图像。默认情况下，图像输出被放在一个[`PIL.Image`](https://pillow.readthedocs.io/en/stable/reference/Image.html?highlight=image#the-image-class)对象中。\n\n```python\n>>> image = pipeline(\"An image of a squirrel in Picasso style\").images[0]\n>>> image\n```\n\n
\n \n
\n\n\n调用`save`保存图像:\n\n```python\n>>> image.save(\"image_of_squirrel_painting.png\")\n```\n\n### 本地管道\n\n你也可以在本地使用管道。唯一的区别是你需提前下载权重：\n\n```\ngit lfs install\ngit clone https://huggingface.co/runwayml/stable-diffusion-v1-5\n```\n\n将下载好的权重加载到管道中:\n\n```python\n>>> pipeline = DiffusionPipeline.from_pretrained(\"./stable-diffusion-v1-5\")\n```\n\n现在你可以像上一节中那样运行管道了。\n\n### 更换调度器\n\n不同的调度器对去噪速度和质量的权衡是不同的。要想知道哪种调度器最适合你，最好的办法就是试用一下。🧨 Diffusers的主要特点之一是允许你轻松切换不同的调度器。例如，要用[`EulerDiscreteScheduler`]替换默认的[`PNDMScheduler`]，用[`~diffusers.ConfigMixin.from_config`]方法加载即可：\n\n```py\n>>> from diffusers import EulerDiscreteScheduler\n\n>>> pipeline = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n>>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)\n```\n\n\n试着用新的调度器生成一个图像，看看你能否发现不同之处。\n\n在下一节中，你将仔细观察组成[`DiffusionPipeline`]的组件——模型和调度器，并学习如何使用这些组件来生成猫咪的图像。\n\n## 模型\n\n大多数模型取一个噪声样本，在每个时间点预测*噪声残差*（其他模型则直接学习预测前一个样本或速度或[`v-prediction`](https://github.com/huggingface/diffusers/blob/5e5ce13e2f89ac45a0066cb3f369462a3cf1d9ef/src/diffusers/schedulers/scheduling_ddim.py#L110)），即噪声较小的图像与输入图像的差异。你可以混搭模型创建其他扩散系统。\n\n模型是用[`~ModelMixin.from_pretrained`]方法启动的，该方法还在本地缓存了模型权重，所以下次加载模型时更快。对于快速入门，你默认加载的是[`UNet2DModel`]，这是一个基础的无条件图像生成模型，该模型有一个在猫咪图像上训练的检查点：\n\n\n```py\n>>> from diffusers import UNet2DModel\n\n>>> repo_id = \"google/ddpm-cat-256\"\n>>> model = UNet2DModel.from_pretrained(repo_id)\n```\n\n想知道模型的参数，调用 `model.config`:\n\n```py\n>>> model.config\n```\n\n模型配置是一个🧊冻结的🧊字典，意思是这些参数在模型创建后就不变了。这是特意设置的，确保在开始时用于定义模型架构的参数保持不变，其他参数仍然可以在推理过程中进行调整。\n\n一些最重要的参数：\n\n* `sample_size`：输入样本的高度和宽度尺寸。\n* `in_channels`：输入样本的输入通道数。\n* `down_block_types`和`up_block_types`：用于创建U-Net架构的下采样和上采样块的类型。\n* `block_out_channels`：下采样块的输出通道数；也以相反的顺序用于上采样块的输入通道数。\n* `layers_per_block`：每个U-Net块中存在的ResNet块的数量。\n\n为了使用该模型进行推理，用随机高斯噪声生成图像形状。它应该有一个`batch`轴，因为模型可以接收多个随机噪声，一个`channel`轴，对应于输入通道的数量，以及一个`sample_size`轴，对应图像的高度和宽度。\n\n\n```py\n>>> import torch\n\n>>> torch.manual_seed(0)\n\n>>> noisy_sample = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)\n>>> noisy_sample.shape\ntorch.Size([1, 3, 256, 256])\n```\n\n对于推理，将噪声图像和一个`timestep`传递给模型。`timestep` 表示输入图像的噪声程度，开始时噪声更多，结束时噪声更少。这有助于模型确定其在扩散过程中的位置，是更接近开始还是结束。使用 `sample` 获得模型输出：\n\n\n```py\n>>> with torch.no_grad():\n... noisy_residual = model(sample=noisy_sample, timestep=2).sample\n```\n\n想生成实际的样本，你需要一个调度器指导去噪过程。在下一节中，你将学习如何把模型与调度器结合起来。\n\n## 调度器\n\n调度器管理一个噪声样本到一个噪声较小的样本的处理过程，给出模型输出 —— 在这种情况下，它是`noisy_residual`。\n\n\n\n\n\n🧨 Diffusers是一个用于构建扩散系统的工具箱。预定义好的扩散系统[`DiffusionPipeline`]能方便你快速试用，你也可以单独选择自己的模型和调度器组件来建立一个自定义的扩散系统。\n\n\n\n在快速入门教程中，你将用它的[`~diffusers.ConfigMixin.from_config`]方法实例化[`DDPMScheduler`]：\n\n```py\n>>> from diffusers import DDPMScheduler\n\n>>> scheduler = DDPMScheduler.from_config(repo_id)\n>>> scheduler\nDDPMScheduler {\n \"_class_name\": \"DDPMScheduler\",\n \"_diffusers_version\": \"0.13.1\",\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n \"beta_start\": 0.0001,\n \"clip_sample\": true,\n \"clip_sample_range\": 1.0,\n \"num_train_timesteps\": 1000,\n \"prediction_type\": \"epsilon\",\n \"trained_betas\": null,\n \"variance_type\": \"fixed_small\"\n}\n```\n\n\n\n\n💡 注意调度器是如何从配置中实例化的。与模型不同，调度器没有可训练的权重，而且是无参数的。\n\n\n\n* `num_train_timesteps`：去噪过程的长度，或者换句话说，将随机高斯噪声处理成数据样本所需的时间步数。\n* `beta_schedule`：用于推理和训练的噪声表。\n* `beta_start`和`beta_end`：噪声表的开始和结束噪声值。\n\n要预测一个噪音稍小的图像，请将 模型输出、`timestep`和当前`sample` 传递给调度器的[`~diffusers.DDPMScheduler.step`]方法：\n\n\n```py\n>>> less_noisy_sample = scheduler.step(model_output=noisy_residual, timestep=2, sample=noisy_sample).prev_sample\n>>> less_noisy_sample.shape\n```\n\n这个 `less_noisy_sample` 去噪样本 可以被传递到下一个`timestep` ，处理后会将变得噪声更小。现在让我们把所有步骤合起来，可视化整个去噪过程。\n\n首先，创建一个函数，对去噪后的图像进行后处理并显示为`PIL.Image`：\n\n```py\n>>> import PIL.Image\n>>> import numpy as np\n\n\n>>> def display_sample(sample, i):\n... image_processed = sample.cpu().permute(0, 2, 3, 1)\n... image_processed = (image_processed + 1.0) * 127.5\n... image_processed = image_processed.numpy().astype(np.uint8)\n\n... image_pil = PIL.Image.fromarray(image_processed[0])\n... display(f\"Image at step {i}\")\n... display(image_pil)\n```\n\n将输入和模型移到GPU上加速去噪过程：\n\n```py\n>>> model.to(\"cuda\")\n>>> noisy_sample = noisy_sample.to(\"cuda\")\n```\n\n现在创建一个去噪循环，该循环预测噪声较少样本的残差，并使用调度程序计算噪声较少的样本：\n\n```py\n>>> import tqdm\n\n>>> sample = noisy_sample\n\n>>> for i, t in enumerate(tqdm.tqdm(scheduler.timesteps)):\n... # 1. predict noise residual\n... with torch.no_grad():\n... residual = model(sample, t).sample\n\n... # 2. compute less noisy image and set x_t -> x_t-1\n... sample = scheduler.step(residual, t, sample).prev_sample\n\n... # 3. optionally look at image\n... if (i + 1) % 50 == 0:\n... display_sample(sample, i + 1)\n```\n\n看！这样就从噪声中生成出一只猫了！😻\n\n
\n \n
\n\n## 下一步\n\n希望你在这次快速入门教程中用🧨Diffuser 生成了一些很酷的图像! 下一步你可以:\n\n* 在[训练](./tutorials/basic_training)教程中训练或微调一个模型来生成你自己的图像。\n* 查看官方和社区的[训练或微调脚本](https://github.com/huggingface/diffusers/tree/main/examples#-diffusers-examples)的例子，了解更多使用情况。\n* 在[使用不同的调度器](./using-diffusers/schedulers)指南中了解更多关于加载、访问、更改和比较调度器的信息。\n* 在[Stable Diffusion](./stable_diffusion)教程中探索提示工程、速度和内存优化，以及生成更高质量图像的技巧。\n* 通过[在GPU上优化PyTorch](./optimization/fp16)指南，以及运行[Apple (M1/M2)上的Stable Diffusion](./optimization/mps)和[ONNX Runtime](./optimization/onnx)的教程，更深入地了解如何加速🧨Diffuser。" }, { "path": "diffusers/docs/source/zh/stable_diffusion.md", "content": "\r\n \r\n# 有效且高效的扩散\r\n\r\n[[open-in-colab]]\r\n\r\n让 [`DiffusionPipeline`] 生成特定风格或包含你所想要的内容的图像可能会有些棘手。 通常情况下，你需要多次运行 [`DiffusionPipeline`] 才能得到满意的图像。但是从无到有生成图像是一个计算密集的过程，特别是如果你要一遍又一遍地进行推理运算。\r\n\r\n这就是为什么从pipeline中获得最高的 *computational* (speed) 和 *memory* (GPU RAM) 非常重要 ，以减少推理周期之间的时间，从而使迭代速度更快。\r\n\r\n\r\n本教程将指导您如何通过 [`DiffusionPipeline`] 更快、更好地生成图像。\r\n\r\n\r\n首先，加载 [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) 模型:\r\n\r\n```python\r\nfrom diffusers import DiffusionPipeline\r\n\r\nmodel_id = \"runwayml/stable-diffusion-v1-5\"\r\npipeline = DiffusionPipeline.from_pretrained(model_id, use_safetensors=True)\r\n```\r\n\r\n本教程将使用的提示词是 [`portrait photo of a old warrior chief`] ，但是你可以随心所欲的想象和构造自己的提示词：\r\n\r\n```python\r\nprompt = \"portrait photo of a old warrior chief\"\r\n```\r\n\r\n## 速度\r\n\r\n\r\n\r\n💡 如果你没有 GPU, 你可以从像 [Colab](https://colab.research.google.com/) 这样的 GPU 提供商获取免费的 GPU !\r\n\r\n\r\n\r\n加速推理的最简单方法之一是将 pipeline 放在 GPU 上 ，就像使用任何 PyTorch 模块一样：\r\n\r\n```python\r\npipeline = pipeline.to(\"cuda\")\r\n```\r\n\r\n为了确保您可以使用相同的图像并对其进行改进，使用 [`Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) 方法，然后设置一个随机数种子 以确保其 [复现性](./using-diffusers/reusing_seeds):\r\n\r\n```python\r\nimport torch\r\n\r\ngenerator = torch.Generator(\"cuda\").manual_seed(0)\r\n```\r\n\r\n现在，你可以生成一个图像：\r\n\r\n```python\r\nimage = pipeline(prompt, generator=generator).images[0]\r\nimage\r\n```\r\n\r\n
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\r\n\r\n在 T4 GPU 上，这个过程大概要30秒（如果你的 GPU 比 T4 好，可能会更快）。在默认情况下，[`DiffusionPipeline`] 使用完整的 `float32` 精度进行 50 步推理。你可以通过降低精度（如 `float16` ）或者减少推理步数来加速整个过程\r\n\r\n\r\n让我们把模型的精度降低至 `float16` ，然后生成一张图像：\r\n\r\n```python\r\nimport torch\r\n\r\npipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, use_safetensors=True)\r\npipeline = pipeline.to(\"cuda\")\r\ngenerator = torch.Generator(\"cuda\").manual_seed(0)\r\nimage = pipeline(prompt, generator=generator).images[0]\r\nimage\r\n```\r\n\r\n
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\r\n\r\n这一次，生成图像只花了约 11 秒，比之前快了近 3 倍！\r\n\r\n\r\n\r\n💡 我们强烈建议把 pipeline 精度降低至 `float16` , 到目前为止, 我们很少看到输出质量有任何下降。\r\n\r\n\r\n\r\n另一个选择是减少推理步数。 你可以选择一个更高效的调度器 (*scheduler*) 可以减少推理步数同时保证输出质量。您可以在 [DiffusionPipeline] 中通过调用compatibles方法找到与当前模型兼容的调度器 (*scheduler*)。 \r\n\r\n```python\r\npipeline.scheduler.compatibles\r\n[\r\n diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler,\r\n diffusers.schedulers.scheduling_unipc_multistep.UniPCMultistepScheduler,\r\n diffusers.schedulers.scheduling_k_dpm_2_discrete.KDPM2DiscreteScheduler,\r\n diffusers.schedulers.scheduling_deis_multistep.DEISMultistepScheduler,\r\n diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler,\r\n diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler,\r\n diffusers.schedulers.scheduling_ddpm.DDPMScheduler,\r\n diffusers.schedulers.scheduling_dpmsolver_singlestep.DPMSolverSinglestepScheduler,\r\n diffusers.schedulers.scheduling_k_dpm_2_ancestral_discrete.KDPM2AncestralDiscreteScheduler,\r\n diffusers.schedulers.scheduling_heun_discrete.HeunDiscreteScheduler,\r\n diffusers.schedulers.scheduling_pndm.PNDMScheduler,\r\n diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler,\r\n diffusers.schedulers.scheduling_ddim.DDIMScheduler,\r\n]\r\n```\r\n\r\nStable Diffusion 模型默认使用的是 [`PNDMScheduler`] ，通常要大概50步推理, 但是像 [`DPMSolverMultistepScheduler`] 这样更高效的调度器只要大概 20 或 25 步推理. 使用 [`ConfigMixin.from_config`] 方法加载新的调度器:\r\n\r\n```python\r\nfrom diffusers import DPMSolverMultistepScheduler\r\n\r\npipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)\r\n```\r\n\r\n现在将 `num_inference_steps` 设置为 20:\r\n\r\n```python\r\ngenerator = torch.Generator(\"cuda\").manual_seed(0)\r\nimage = pipeline(prompt, generator=generator, num_inference_steps=20).images[0]\r\nimage\r\n```\r\n\r\n
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\r\n\r\n太棒了！你成功把推理时间缩短到 4 秒！⚡️\r\n\r\n## 内存\r\n\r\n改善 pipeline 性能的另一个关键是减少内存的使用量，这间接意味着速度更快，因为你经常试图最大化每秒生成的图像数量。要想知道你一次可以生成多少张图片，最简单的方法是尝试不同的batch size，直到出现`OutOfMemoryError` (OOM)。\r\n\r\n创建一个函数，为每一批要生成的图像分配提示词和 `Generators` 。请务必为每个`Generator` 分配一个种子，以便于复现良好的结果。\r\n\r\n\r\n```python\r\ndef get_inputs(batch_size=1):\r\n generator = [torch.Generator(\"cuda\").manual_seed(i) for i in range(batch_size)]\r\n prompts = batch_size * [prompt]\r\n num_inference_steps = 20\r\n\r\n return {\"prompt\": prompts, \"generator\": generator, \"num_inference_steps\": num_inference_steps}\r\n```\r\n\r\n设置 `batch_size=4` ，然后看一看我们消耗了多少内存:\r\n\r\n```python\r\nfrom diffusers.utils import make_image_grid \r\n\r\nimages = pipeline(**get_inputs(batch_size=4)).images\r\nmake_image_grid(images, 2, 2)\r\n```\r\n\r\n除非你有一个更大内存的GPU, 否则上述代码会返回 `OOM` 错误! 大部分内存被 cross-attention 层使用。按顺序运行可以节省大量内存，而不是在批处理中进行。你可以为 pipeline 配置 [`~DiffusionPipeline.enable_attention_slicing`] 函数:\r\n\r\n```python\r\npipeline.enable_attention_slicing()\r\n```\r\n\r\n现在尝试把 `batch_size` 增加到 8!\r\n\r\n```python\r\nimages = pipeline(**get_inputs(batch_size=8)).images\r\nmake_image_grid(images, rows=2, cols=4)\r\n```\r\n\r\n
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\r\n\r\n以前你不能一批生成 4 张图片，而现在你可以在一张图片里面生成八张图片而只需要大概3.5秒！这可能是 T4 GPU 在不牺牲质量的情况运行速度最快的一种方法。\r\n\r\n## 质量\r\n\r\n在最后两节中, 你要学习如何通过 `fp16` 来优化 pipeline 的速度, 通过使用性能更高的调度器来减少推理步数, 使用注意力切片（*enabling attention slicing*）方法来节省内存。现在，你将关注的是如何提高图像的质量。\r\n\r\n### 更好的 checkpoints\r\n\r\n有个显而易见的方法是使用更好的 checkpoints。 Stable Diffusion 模型是一个很好的起点, 自正式发布以来，还发布了几个改进版本。然而, 使用更新的版本并不意味着你会得到更好的结果。你仍然需要尝试不同的 checkpoints ，并做一些研究 (例如使用 [negative prompts](https://minimaxir.com/2022/11/stable-diffusion-negative-prompt/)) 来获得更好的结果。\r\n\r\n随着该领域的发展, 有越来越多经过微调的高质量的 checkpoints 用来生成不一样的风格. 在 [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) 和 [Diffusers Gallery](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery) 寻找你感兴趣的一种!\r\n\r\n### 更好的 pipeline 组件\r\n\r\n也可以尝试用新版本替换当前 pipeline 组件。让我们加载最新的 [autodecoder](https://huggingface.co/stabilityai/stable-diffusion-2-1/tree/main/vae) 从 Stability AI 加载到 pipeline, 并生成一些图像:\r\n\r\n```python\r\nfrom diffusers import AutoencoderKL\r\n\r\nvae = AutoencoderKL.from_pretrained(\"stabilityai/sd-vae-ft-mse\", torch_dtype=torch.float16).to(\"cuda\")\r\npipeline.vae = vae\r\nimages = pipeline(**get_inputs(batch_size=8)).images\r\nmake_image_grid(images, rows=2, cols=4)\r\n```\r\n\r\n
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\r\n\r\n### 更好的提示词工程\r\n\r\n用于生成图像的文本非常重要, 因此被称为 *提示词工程*。 在设计提示词工程应注意如下事项:\r\n\r\n- 我想生成的图像或类似图像如何存储在互联网上？\r\n- 我可以提供哪些额外的细节来引导模型朝着我想要的风格生成？\r\n\r\n考虑到这一点，让我们改进提示词，以包含颜色和更高质量的细节：\r\n\r\n```python\r\nprompt += \", tribal panther make up, blue on red, side profile, looking away, serious eyes\"\r\nprompt += \" 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta\"\r\n```\r\n\r\n使用新的提示词生成一批图像:\r\n\r\n```python\r\nimages = pipeline(**get_inputs(batch_size=8)).images\r\nmake_image_grid(images, rows=2, cols=4)\r\n```\r\n\r\n
\r\n \r\n
\r\n\r\n非常的令人印象深刻! Let's tweak the second image - 把 `Generator` 的种子设置为 `1` - 添加一些关于年龄的主题文本:\r\n\r\n```python\r\nprompts = [\r\n \"portrait photo of the oldest warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta\",\r\n \"portrait photo of a old warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta\",\r\n \"portrait photo of a warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta\",\r\n \"portrait photo of a young warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta\",\r\n]\r\n\r\ngenerator = [torch.Generator(\"cuda\").manual_seed(1) for _ in range(len(prompts))]\r\nimages = pipeline(prompt=prompts, generator=generator, num_inference_steps=25).images\r\nmake_image_grid(images, 2, 2)\r\n```\r\n\r\n
\r\n \r\n
\r\n\r\n## 最后\r\n\r\n在本教程中, 您学习了如何优化[`DiffusionPipeline`]以提高计算和内存效率，以及提高生成输出的质量. 如果你有兴趣让你的 pipeline 更快, 可以看一看以下资源:\r\n\r\n- 学习 [PyTorch 2.0](./optimization/torch2.0) 和 [`torch.compile`](https://pytorch.org/docs/stable/generated/torch.compile.html) 可以让推理速度提高 5 - 300% . 在 A100 GPU 上, 推理速度可以提高 50% !\r\n- 如果你没法用 PyTorch 2, 我们建议你安装 [xFormers](./optimization/xformers)。它的内存高效注意力机制（*memory-efficient attention mechanism*）与PyTorch 1.13.1配合使用，速度更快，内存消耗更少。\r\n- 其他的优化技术, 如：模型卸载（*model offloading*）, 包含在 [这份指南](./optimization/fp16).\r\n" }, { "path": "diffusers/examples/README.md", "content": "\n\n# 🧨 Diffusers Examples\n\nDiffusers examples are a collection of scripts to demonstrate how to effectively use the `diffusers` library\nfor a variety of use cases involving training or fine-tuning.\n\n**Note**: If you are looking for **official** examples on how to use `diffusers` for inference, please have a look at [src/diffusers/pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines).\n\nOur examples aspire to be **self-contained**, **easy-to-tweak**, **beginner-friendly** and for **one-purpose-only**.\nMore specifically, this means:\n\n- **Self-contained**: An example script shall only depend on \"pip-install-able\" Python packages that can be found in a `requirements.txt` file. Example scripts shall **not** depend on any local files. This means that one can simply download an example script, *e.g.* [train_unconditional.py](https://github.com/huggingface/diffusers/blob/main/examples/unconditional_image_generation/train_unconditional.py), install the required dependencies, *e.g.* [requirements.txt](https://github.com/huggingface/diffusers/blob/main/examples/unconditional_image_generation/requirements.txt) and execute the example script.\n- **Easy-to-tweak**: While we strive to present as many use cases as possible, the example scripts are just that - examples. It is expected that they won't work out-of-the box on your specific problem and that you will be required to change a few lines of code to adapt them to your needs. To help you with that, most of the examples fully expose the preprocessing of the data and the training loop to allow you to tweak and edit them as required.\n- **Beginner-friendly**: We do not aim for providing state-of-the-art training scripts for the newest models, but rather examples that can be used as a way to better understand diffusion models and how to use them with the `diffusers` library. We often purposefully leave out certain state-of-the-art methods if we consider them too complex for beginners.\n- **One-purpose-only**: Examples should show one task and one task only. Even if a task is from a modeling point of view very similar, *e.g.* image super-resolution and image modification tend to use the same model and training method, we want examples to showcase only one task to keep them as readable and easy-to-understand as possible.\n\nWe provide **official** examples that cover the most popular tasks of diffusion models.\n*Official* examples are **actively** maintained by the `diffusers` maintainers and we try to rigorously follow our example philosophy as defined above.\nIf you feel like another important example should exist, we are more than happy to welcome a [Feature Request](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feature_request.md&title=) or directly a [Pull Request](https://github.com/huggingface/diffusers/compare) from you!\n\nTraining examples show how to pretrain or fine-tune diffusion models for a variety of tasks. Currently we support:\n\n| Task | 🤗 Accelerate | 🤗 Datasets | Colab\n|---|---|:---:|:---:|\n| [**Unconditional Image Generation**](./unconditional_image_generation) | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)\n| [**Text-to-Image fine-tuning**](./text_to_image) | ✅ | ✅ |\n| [**Textual Inversion**](./textual_inversion) | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb)\n| [**Dreambooth**](./dreambooth) | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb)\n| [**ControlNet**](./controlnet) | ✅ | ✅ | -\n| [**InstructPix2Pix**](./instruct_pix2pix) | ✅ | ✅ | -\n| [**Reinforcement Learning for Control**](./reinforcement_learning) | - | - | coming soon.\n\n## Community\n\nIn addition, we provide **community** examples, which are examples added and maintained by our community.\nCommunity examples can consist of both *training* examples or *inference* pipelines.\nFor such examples, we are more lenient regarding the philosophy defined above and also cannot guarantee to provide maintenance for every issue.\nExamples that are useful for the community, but are either not yet deemed popular or not yet following our above philosophy should go into the [community examples](https://github.com/huggingface/diffusers/tree/main/examples/community) folder. The community folder therefore includes training examples and inference pipelines.\n**Note**: Community examples can be a [great first contribution](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) to show to the community how you like to use `diffusers` 🪄.\n\n## Research Projects\n\nWe also provide **research_projects** examples that are maintained by the community as defined in the respective research project folders. These examples are useful and offer the extended capabilities which are complementary to the official examples. You may refer to [research_projects](https://github.com/huggingface/diffusers/tree/main/examples/research_projects) for details.\n\n## Important note\n\nTo make sure you can successfully run the latest versions of the example scripts, you have to **install the library from source** and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\nThen cd in the example folder of your choice and run\n```bash\npip install -r requirements.txt\n```\n" }, { "path": "diffusers/examples/advanced_diffusion_training/README.md", "content": "# Advanced diffusion training examples\n\n## Train Dreambooth LoRA with Stable Diffusion XL\n> [!TIP]\n> 💡 This example follows the techniques and recommended practices covered in the blog post: [LoRA training scripts of the world, unite!](https://huggingface.co/blog/sdxl_lora_advanced_script). Make sure to check it out before starting 🤗\n\n[DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text2image models like stable diffusion given just a few(3~5) images of a subject.\n\nLoRA - Low-Rank Adaption of Large Language Models, was first introduced by Microsoft in [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) by *Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen*\nIn a nutshell, LoRA allows to adapt pretrained models by adding pairs of rank-decomposition matrices to existing weights and **only** training those newly added weights. This has a couple of advantages:\n- Previous pretrained weights are kept frozen so that the model is not prone to [catastrophic forgetting](https://www.pnas.org/doi/10.1073/pnas.1611835114)\n- Rank-decomposition matrices have significantly fewer parameters than the original model, which means that trained LoRA weights are easily portable.\n- LoRA attention layers allow to control to which extent the model is adapted towards new training images via a `scale` parameter.\n[cloneofsimo](https://github.com/cloneofsimo) was the first to try out LoRA training for Stable Diffusion in \nthe popular [lora](https://github.com/cloneofsimo/lora) GitHub repository.\n\nThe `train_dreambooth_lora_sdxl_advanced.py` script shows how to implement dreambooth-LoRA, combining the training process shown in `train_dreambooth_lora_sdxl.py`, with \nadvanced features and techniques, inspired and built upon contributions by [Nataniel Ruiz](https://twitter.com/natanielruizg): [Dreambooth](https://dreambooth.github.io), [Rinon Gal](https://twitter.com/RinonGal): [Textual Inversion](https://textual-inversion.github.io), [Ron Mokady](https://twitter.com/MokadyRon): [Pivotal Tuning](https://arxiv.org/abs/2106.05744), [Simo Ryu](https://twitter.com/cloneofsimo): [cog-sdxl](https://github.com/replicate/cog-sdxl), \n[Kohya](https://twitter.com/kohya_tech/): [sd-scripts](https://github.com/kohya-ss/sd-scripts), [The Last Ben](https://twitter.com/__TheBen): [fast-stable-diffusion](https://github.com/TheLastBen/fast-stable-diffusion) ❤️\n\n> [!NOTE]\n> 💡If this is your first time training a Dreambooth LoRA, congrats!🥳 \n> You might want to familiarize yourself more with the techniques: [Dreambooth blog](https://huggingface.co/blog/dreambooth), [Using LoRA for Efficient Stable Diffusion Fine-Tuning blog](https://huggingface.co/blog/lora) \n\n📚 Read more about the advanced features and best practices in this community derived blog post: [LoRA training scripts of the world, unite!](https://huggingface.co/blog/sdxl_lora_advanced_script)\n\n\n## Running locally with PyTorch\n\n### Installing the dependencies\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n**Important**\n\nTo make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install -e .\n```\n\nThen cd in the `examples/advanced_diffusion_training` folder and run\n```bash\npip install -r requirements.txt\n```\n\nAnd initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\n\nOr for a default accelerate configuration without answering questions about your environment\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell e.g. a notebook\n\n```python\nfrom accelerate.utils import write_basic_config\nwrite_basic_config()\n```\n\nWhen running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups. \nNote also that we use PEFT library as backend for LoRA training, make sure to have `peft>=0.6.0` installed in your environment.\n\n### Pivotal Tuning\n**Training with text encoder(s)**\n\nAlongside the UNet, LoRA fine-tuning of the text encoders is also supported. In addition to the text encoder optimization \navailable with `train_dreambooth_lora_sdxl_advanced.py`, in the advanced script **pivotal tuning** is also supported.\n[pivotal tuning](https://huggingface.co/blog/sdxl_lora_advanced_script#pivotal-tuning) combines Textual Inversion with regular diffusion fine-tuning - \nwe insert new tokens into the text encoders of the model, instead of reusing existing ones. \nWe then optimize the newly-inserted token embeddings to represent the new concept. \n\nTo do so, just specify `--train_text_encoder_ti` while launching training (for regular text encoder optimizations, use `--train_text_encoder`). \nPlease keep the following points in mind:\n\n* SDXL has two text encoders. So, we fine-tune both using LoRA.\n* When not fine-tuning the text encoders, we ALWAYS precompute the text embeddings to save memory.\n\n### 3D icon example\n\nNow let's get our dataset. For this example we will use some cool images of 3d rendered icons: https://huggingface.co/datasets/linoyts/3d_icon.\n\nLet's first download it locally:\n\n```python\nfrom huggingface_hub import snapshot_download\n\nlocal_dir = \"./3d_icon\"\nsnapshot_download(\n \"LinoyTsaban/3d_icon\",\n local_dir=local_dir, repo_type=\"dataset\",\n ignore_patterns=\".gitattributes\",\n)\n```\n\nLet's review some of the advanced features we're going to be using for this example:\n- **custom captions**:\nTo use custom captioning, first ensure that you have the datasets library installed, otherwise you can install it by \n```bash\npip install datasets\n```\n\nNow we'll simply specify the name of the dataset and caption column (in this case it's \"prompt\")\n\n```\n--dataset_name=./3d_icon\n--caption_column=prompt\n```\n\nYou can also load a dataset straight from by specifying it's name in `dataset_name`. \nLook [here](https://huggingface.co/blog/sdxl_lora_advanced_script#custom-captioning) for more info on creating/loadin your own caption dataset.\n\n- **optimizer**: for this example, we'll use [prodigy](https://huggingface.co/blog/sdxl_lora_advanced_script#adaptive-optimizers) - an adaptive optimizer\n- **pivotal tuning** \n- **min SNR gamma**\n\n**Now, we can launch training:**\n\n```bash\nexport MODEL_NAME=\"stabilityai/stable-diffusion-xl-base-1.0\"\nexport DATASET_NAME=\"./3d_icon\"\nexport OUTPUT_DIR=\"3d-icon-SDXL-LoRA\"\nexport VAE_PATH=\"madebyollin/sdxl-vae-fp16-fix\"\n\naccelerate launch train_dreambooth_lora_sdxl_advanced.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --pretrained_vae_model_name_or_path=$VAE_PATH \\\n --dataset_name=$DATASET_NAME \\\n --instance_prompt=\"3d icon in the style of TOK\" \\\n --validation_prompt=\"a TOK icon of an astronaut riding a horse, in the style of TOK\" \\\n --output_dir=$OUTPUT_DIR \\\n --caption_column=\"prompt\" \\\n --mixed_precision=\"bf16\" \\\n --resolution=1024 \\\n --train_batch_size=3 \\\n --repeats=1 \\\n --report_to=\"wandb\"\\\n --gradient_accumulation_steps=1 \\\n --gradient_checkpointing \\\n --learning_rate=1.0 \\\n --text_encoder_lr=1.0 \\\n --optimizer=\"prodigy\"\\\n --train_text_encoder_ti\\\n --train_text_encoder_ti_frac=0.5\\\n --snr_gamma=5.0 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --rank=8 \\\n --max_train_steps=1000 \\\n --checkpointing_steps=2000 \\\n --seed=\"0\" \\\n --push_to_hub\n```\n\nTo better track our training experiments, we're using the following flags in the command above:\n\n* `report_to=\"wandb` will ensure the training runs are tracked on Weights and Biases. To use it, be sure to install `wandb` with `pip install wandb`.\n* `validation_prompt` and `validation_epochs` to allow the script to do a few validation inference runs. This allows us to qualitatively check if the training is progressing as expected. \n\nOur experiments were conducted on a single 40GB A100 GPU.\n\n\n### Inference\n\nOnce training is done, we can perform inference like so:\n1. starting with loading the unet lora weights\n```python\nimport torch\nfrom huggingface_hub import hf_hub_download, upload_file\nfrom diffusers import DiffusionPipeline\nfrom diffusers.models import AutoencoderKL\nfrom safetensors.torch import load_file\n\nusername = \"linoyts\"\nrepo_id = f\"{username}/3d-icon-SDXL-LoRA\"\n\npipe = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n).to(\"cuda\")\n\n\npipe.load_lora_weights(repo_id, weight_name=\"pytorch_lora_weights.safetensors\")\n```\n2. now we load the pivotal tuning embeddings\n\n```python\ntext_encoders = [pipe.text_encoder, pipe.text_encoder_2]\ntokenizers = [pipe.tokenizer, pipe.tokenizer_2]\n\nembedding_path = hf_hub_download(repo_id=repo_id, filename=\"3d-icon-SDXL-LoRA_emb.safetensors\", repo_type=\"model\")\n\nstate_dict = load_file(embedding_path)\n# load embeddings of text_encoder 1 (CLIP ViT-L/14)\npipe.load_textual_inversion(state_dict[\"clip_l\"], token=[\"\", \"\"], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)\n# load embeddings of text_encoder 2 (CLIP ViT-G/14)\npipe.load_textual_inversion(state_dict[\"clip_g\"], token=[\"\", \"\"], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)\n```\n\n3. let's generate images \n\n```python\ninstance_token = \"\"\nprompt = f\"a {instance_token} icon of an orange llama eating ramen, in the style of {instance_token}\" \n\nimage = pipe(prompt=prompt, num_inference_steps=25, cross_attention_kwargs={\"scale\": 1.0}).images[0]\nimage.save(\"llama.png\")\n```\n\n### Comfy UI / AUTOMATIC1111 Inference\nThe new script fully supports textual inversion loading with Comfy UI and AUTOMATIC1111 formats!\n\n**AUTOMATIC1111 / SD.Next** \\\nIn AUTOMATIC1111/SD.Next we will load a LoRA and a textual embedding at the same time. \n- *LoRA*: Besides the diffusers format, the script will also train a WebUI compatible LoRA. It is generated as `{your_lora_name}.safetensors`. You can then include it in your `models/Lora` directory. \n- *Embedding*: the embedding is the same for diffusers and WebUI. You can download your `{lora_name}_emb.safetensors` file from a trained model, and include it in your `embeddings` directory. \n\nYou can then run inference by prompting `a y2k_emb webpage about the movie Mean Girls `. You can use the `y2k_emb` token normally, including increasing its weight by doing `(y2k_emb:1.2)`. \n\n**ComfyUI** \\\nIn ComfyUI we will load a LoRA and a textual embedding at the same time. \n- *LoRA*: Besides the diffusers format, the script will also train a ComfyUI compatible LoRA. It is generated as `{your_lora_name}.safetensors`. You can then include it in your `models/Lora` directory. Then you will load the LoRALoader node and hook that up with your model and CLIP. [Official guide for loading LoRAs](https://comfyanonymous.github.io/ComfyUI_examples/lora/)\n- *Embedding*: the embedding is the same for diffusers and WebUI. You can download your `{lora_name}_emb.safetensors` file from a trained model, and include it in your `models/embeddings` directory and use it in your prompts like `embedding:y2k_emb`. [Official guide for loading embeddings](https://comfyanonymous.github.io/ComfyUI_examples/textual_inversion_embeddings/). \n- \n### Specifying a better VAE\n\nSDXL's VAE is known to suffer from numerical instability issues. This is why we also expose a CLI argument namely `--pretrained_vae_model_name_or_path` that lets you specify the location of a better VAE (such as [this one](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix)).\n\n### DoRA training \nThe advanced script supports DoRA training too!\n> Proposed in [DoRA: Weight-Decomposed Low-Rank Adaptation](https://arxiv.org/abs/2402.09353), \n**DoRA** is very similar to LoRA, except it decomposes the pre-trained weight into two components, **magnitude** and **direction** and employs LoRA for _directional_ updates to efficiently minimize the number of trainable parameters. \nThe authors found that by using DoRA, both the learning capacity and training stability of LoRA are enhanced without any additional overhead during inference. \n\n> [!NOTE]\n> 💡DoRA training is still _experimental_ \n> and is likely to require different hyperparameter values to perform best compared to a LoRA.\n> Specifically, we've noticed 2 differences to take into account your training: \n> 1. **LoRA seem to converge faster than DoRA** (so a set of parameters that may lead to overfitting when training a LoRA may be working well for a DoRA)\n> 2. **DoRA quality superior to LoRA especially in lower ranks** the difference in quality of DoRA of rank 8 and LoRA of rank 8 appears to be more significant than when training ranks of 32 or 64 for example. \n> This is also aligned with some of the quantitative analysis shown in the paper. \n\n**Usage**\n1. To use DoRA you need to install `peft` from main: \n```bash\npip install git+https://github.com/huggingface/peft.git\n```\n2. Enable DoRA training by adding this flag\n```bash\n--use_dora\n```\n**Inference** \nThe inference is the same as if you train a regular LoRA 🤗\n\n## Conducting EDM-style training\n\nIt's now possible to perform EDM-style training as proposed in [Elucidating the Design Space of Diffusion-Based Generative Models](https://arxiv.org/abs/2206.00364). \n\nsimply set:\n\n```diff\n+ --do_edm_style_training \\\n```\n\nOther SDXL-like models that use the EDM formulation, such as [playgroundai/playground-v2.5-1024px-aesthetic](https://huggingface.co/playgroundai/playground-v2.5-1024px-aesthetic), can also be DreamBooth'd with the script. Below is an example command:\n\n```bash\naccelerate launch train_dreambooth_lora_sdxl_advanced.py \\\n --pretrained_model_name_or_path=\"playgroundai/playground-v2.5-1024px-aesthetic\" \\\n --dataset_name=\"linoyts/3d_icon\" \\\n --instance_prompt=\"3d icon in the style of TOK\" \\\n --validation_prompt=\"a TOK icon of an astronaut riding a horse, in the style of TOK\" \\\n --output_dir=\"3d-icon-SDXL-LoRA\" \\\n --do_edm_style_training \\\n --caption_column=\"prompt\" \\\n --mixed_precision=\"bf16\" \\\n --resolution=1024 \\\n --train_batch_size=3 \\\n --repeats=1 \\\n --report_to=\"wandb\"\\\n --gradient_accumulation_steps=1 \\\n --gradient_checkpointing \\\n --learning_rate=1.0 \\\n --text_encoder_lr=1.0 \\\n --optimizer=\"prodigy\"\\\n --train_text_encoder_ti\\\n --train_text_encoder_ti_frac=0.5\\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --rank=8 \\\n --max_train_steps=1000 \\\n --checkpointing_steps=2000 \\\n --seed=\"0\" \\\n --push_to_hub\n```\n\n> [!CAUTION]\n> Min-SNR gamma is not supported with the EDM-style training yet. When training with the PlaygroundAI model, it's recommended to not pass any \"variant\".\n\n### B-LoRA training \nThe advanced script now supports B-LoRA training too!\n> Proposed in [Implicit Style-Content Separation using B-LoRA](https://arxiv.org/abs/2403.14572), \nB-LoRA is a method that leverages LoRA to implicitly separate the style and content components of a **single** image.\nIt was shown that learning the LoRA weights of two specific blocks (referred to as B-LoRAs) \nachieves style-content separation that cannot be achieved by training each B-LoRA independently. \nOnce trained, the two B-LoRAs can be used as independent components to allow various image stylization tasks\n\n**Usage**\nEnable B-LoRA training by adding this flag\n```bash\n--use_blora\n```\nYou can train a B-LoRA with as little as 1 image, and 1000 steps. Try this default configuration as a start:\n```bash\n!accelerate launch train_dreambooth_b-lora_sdxl.py \\\n --pretrained_model_name_or_path=\"stabilityai/stable-diffusion-xl-base-1.0\" \\\n --instance_data_dir=\"linoyts/B-LoRA_teddy_bear\" \\\n --output_dir=\"B-LoRA_teddy_bear\" \\\n --instance_prompt=\"a [v18]\" \\\n --resolution=1024 \\\n --rank=64 \\\n --train_batch_size=1 \\\n --learning_rate=5e-5 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --max_train_steps=1000 \\\n --checkpointing_steps=2000 \\\n --seed=\"0\" \\\n --gradient_checkpointing \\\n --mixed_precision=\"fp16\"\n```\n**Inference** \nThe inference is a bit different:\n1. we need load *specific* unet layers (as opposed to a regular LoRA/DoRA)\n2. the trained layers we load, changes based on our objective (e.g. style/content)\n\n```python\nimport torch\nfrom diffusers import StableDiffusionXLPipeline, AutoencoderKL\n\n# taken & modified from B-LoRA repo - https://github.com/yardenfren1996/B-LoRA/blob/main/blora_utils.py\ndef is_belong_to_blocks(key, blocks):\n try:\n for g in blocks:\n if g in key:\n return True\n return False\n except Exception as e:\n raise type(e)(f'failed to is_belong_to_block, due to: {e}')\n \ndef lora_lora_unet_blocks(lora_path, alpha, target_blocks): \n state_dict, _ = pipeline.lora_state_dict(lora_path)\n filtered_state_dict = {k: v * alpha for k, v in state_dict.items() if is_belong_to_blocks(k, target_blocks)}\n return filtered_state_dict\n\nvae = AutoencoderKL.from_pretrained(\"madebyollin/sdxl-vae-fp16-fix\", torch_dtype=torch.float16)\npipeline = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n vae=vae,\n torch_dtype=torch.float16,\n).to(\"cuda\")\n\n# pick a blora for content/style (you can also set one to None) \ncontent_B_lora_path = \"lora-library/B-LoRA-teddybear\"\nstyle_B_lora_path= \"lora-library/B-LoRA-pen_sketch\"\n\n\ncontent_B_LoRA = lora_lora_unet_blocks(content_B_lora_path,alpha=1,target_blocks=[\"unet.up_blocks.0.attentions.0\"])\nstyle_B_LoRA = lora_lora_unet_blocks(style_B_lora_path,alpha=1.1,target_blocks=[\"unet.up_blocks.0.attentions.1\"])\ncombined_lora = {**content_B_LoRA, **style_B_LoRA}\n\n# Load both loras\npipeline.load_lora_into_unet(combined_lora, None, pipeline.unet)\n\n#generate\nprompt = \"a [v18] in [v30] style\"\npipeline(prompt, num_images_per_prompt=4).images\n```\n### LoRA training of Targeted U-net Blocks\nThe advanced script now supports custom choice of U-net blocks to train during Dreambooth LoRA tuning. \n> [!NOTE]\n> This feature is still experimental\n\n> Recently, works like B-LoRA showed the potential advantages of learning the LoRA weights of specific U-net blocks, not only in speed & memory, \n> but also in reducing the amount of needed data, improving style manipulation and overcoming overfitting issues. \n> In light of this, we're introducing a new feature to the advanced script to allow for configurable U-net learned blocks. \n\n**Usage**\nConfigure LoRA learned U-net blocks adding a `lora_unet_blocks` flag, with a comma seperated string specifying the targeted blocks. \ne.g:\n```bash\n--lora_unet_blocks=\"unet.up_blocks.0.attentions.0,unet.up_blocks.0.attentions.1\"\n```\n\n> [!NOTE]\n> if you specify both `--use_blora` and `--lora_unet_blocks`, values given in --lora_unet_blocks will be ignored. \n> When enabling --use_blora, targeted U-net blocks are automatically set to be \"unet.up_blocks.0.attentions.0,unet.up_blocks.0.attentions.1\" as discussed in the paper. \n> If you wish to experiment with different blocks, specify `--lora_unet_blocks` only.\n\n**Inference** \nInference is the same as for B-LoRAs, except the input targeted blocks should be modified based on your training configuration. \n```python\nimport torch\nfrom diffusers import StableDiffusionXLPipeline, AutoencoderKL\n\n# taken & modified from B-LoRA repo - https://github.com/yardenfren1996/B-LoRA/blob/main/blora_utils.py\ndef is_belong_to_blocks(key, blocks):\n try:\n for g in blocks:\n if g in key:\n return True\n return False\n except Exception as e:\n raise type(e)(f'failed to is_belong_to_block, due to: {e}')\n \ndef lora_lora_unet_blocks(lora_path, alpha, target_blocks): \n state_dict, _ = pipeline.lora_state_dict(lora_path)\n filtered_state_dict = {k: v * alpha for k, v in state_dict.items() if is_belong_to_blocks(k, target_blocks)}\n return filtered_state_dict\n\nvae = AutoencoderKL.from_pretrained(\"madebyollin/sdxl-vae-fp16-fix\", torch_dtype=torch.float16)\npipeline = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n vae=vae,\n torch_dtype=torch.float16,\n).to(\"cuda\")\n\nlora_path = \"lora-library/B-LoRA-pen_sketch\"\n\nstate_dict = lora_lora_unet_blocks(content_B_lora_path,alpha=1,target_blocks=[\"unet.up_blocks.0.attentions.0\"])\n\n# Load traine dlora layers into the unet\npipeline.load_lora_into_unet(state_dict, None, pipeline.unet)\n\n#generate\nprompt = \"a dog in [v30] style\"\npipeline(prompt, num_images_per_prompt=4).images\n```\n\n\n### Tips and Tricks\nCheck out [these recommended practices](https://huggingface.co/blog/sdxl_lora_advanced_script#additional-good-practices)\n\n## Running on Colab Notebook\nCheck out [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/SDXL_Dreambooth_LoRA_advanced_example.ipynb).\nto train using the advanced features (including pivotal tuning), and [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/SDXL_DreamBooth_LoRA_.ipynb) to train on a free colab, using some of the advanced features (excluding pivotal tuning)\n\n" }, { "path": "diffusers/examples/advanced_diffusion_training/requirements.txt", "content": "accelerate>=0.16.0\ntorchvision\ntransformers>=4.25.1\nftfy\ntensorboard\nJinja2\npeft==0.7.0" }, { "path": "diffusers/examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport gc\nimport hashlib\nimport itertools\nimport logging\nimport math\nimport os\nimport re\nimport shutil\nimport warnings\nfrom contextlib import nullcontext\nfrom pathlib import Path\nfrom typing import List, Optional\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\n\n# imports of the TokenEmbeddingsHandler class\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import DistributedDataParallelKwargs, ProjectConfiguration, set_seed\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom peft import LoraConfig\nfrom peft.utils import get_peft_model_state_dict\nfrom PIL import Image\nfrom PIL.ImageOps import exif_transpose\nfrom safetensors.torch import load_file, save_file\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n DPMSolverMultistepScheduler,\n StableDiffusionPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.loaders import LoraLoaderMixin\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import compute_snr\nfrom diffusers.utils import (\n check_min_version,\n convert_all_state_dict_to_peft,\n convert_state_dict_to_diffusers,\n convert_state_dict_to_kohya,\n is_wandb_available,\n)\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef save_model_card(\n repo_id: str,\n use_dora: bool,\n images=None,\n base_model=str,\n train_text_encoder=False,\n train_text_encoder_ti=False,\n token_abstraction_dict=None,\n instance_prompt=str,\n validation_prompt=str,\n repo_folder=None,\n vae_path=None,\n):\n img_str = \"widget:\\n\"\n lora = \"lora\" if not use_dora else \"dora\"\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n img_str += f\"\"\"\n - text: '{validation_prompt if validation_prompt else ' ' }'\n output:\n url:\n \"image_{i}.png\"\n \"\"\"\n if not images:\n img_str += f\"\"\"\n - text: '{instance_prompt}'\n \"\"\"\n embeddings_filename = f\"{repo_folder}_emb\"\n instance_prompt_webui = re.sub(r\"\", \"\", re.sub(r\"\", embeddings_filename, instance_prompt, count=1))\n ti_keys = \", \".join(f'\"{match}\"' for match in re.findall(r\"\", instance_prompt))\n if instance_prompt_webui != embeddings_filename:\n instance_prompt_sentence = f\"For example, `{instance_prompt_webui}`\"\n else:\n instance_prompt_sentence = \"\"\n trigger_str = f\"You should use {instance_prompt} to trigger the image generation.\"\n diffusers_imports_pivotal = \"\"\n diffusers_example_pivotal = \"\"\n webui_example_pivotal = \"\"\n if train_text_encoder_ti:\n trigger_str = (\n \"To trigger image generation of trained concept(or concepts) replace each concept identifier \"\n \"in you prompt with the new inserted tokens:\\n\"\n )\n diffusers_imports_pivotal = \"\"\"from huggingface_hub import hf_hub_download\nfrom safetensors.torch import load_file\n \"\"\"\n diffusers_example_pivotal = f\"\"\"embedding_path = hf_hub_download(repo_id='{repo_id}', filename='{embeddings_filename}.safetensors', repo_type=\"model\")\nstate_dict = load_file(embedding_path)\npipeline.load_textual_inversion(state_dict[\"clip_l\"], token=[{ti_keys}], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)\n \"\"\"\n webui_example_pivotal = f\"\"\"- *Embeddings*: download **[`{embeddings_filename}.safetensors` here 💾](/{repo_id}/blob/main/{embeddings_filename}.safetensors)**.\n - Place it on it on your `embeddings` folder\n - Use it by adding `{embeddings_filename}` to your prompt. {instance_prompt_sentence}\n (you need both the LoRA and the embeddings as they were trained together for this LoRA)\n \"\"\"\n if token_abstraction_dict:\n for key, value in token_abstraction_dict.items():\n tokens = \"\".join(value)\n trigger_str += f\"\"\"\nto trigger concept `{key}` → use `{tokens}` in your prompt \\n\n\"\"\"\n\n yaml = f\"\"\"---\ntags:\n- stable-diffusion\n- stable-diffusion-diffusers\n- diffusers-training\n- text-to-image\n- diffusers\n- {lora}\n- template:sd-lora\n{img_str}\nbase_model: {base_model}\ninstance_prompt: {instance_prompt}\nlicense: openrail++\n---\n\"\"\"\n\n model_card = f\"\"\"\n# SD1.5 LoRA DreamBooth - {repo_id}\n\n\n\n## Model description\n\n### These are {repo_id} LoRA adaption weights for {base_model}.\n\n## Download model\n\n### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke\n\n- **LoRA**: download **[`{repo_folder}.safetensors` here 💾](/{repo_id}/blob/main/{repo_folder}.safetensors)**.\n - Place it on your `models/Lora` folder.\n - On AUTOMATIC1111, load the LoRA by adding `` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).\n{webui_example_pivotal}\n\n## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n{diffusers_imports_pivotal}\npipeline = AutoPipelineForText2Image.from_pretrained('runwayml/stable-diffusion-v1-5', torch_dtype=torch.float16).to('cuda')\npipeline.load_lora_weights('{repo_id}', weight_name='pytorch_lora_weights.safetensors')\n{diffusers_example_pivotal}\nimage = pipeline('{validation_prompt if validation_prompt else instance_prompt}').images[0]\n```\n\nFor more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)\n\n## Trigger words\n\n{trigger_str}\n\n## Details\nAll [Files & versions](/{repo_id}/tree/main).\n\nThe weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py).\n\nLoRA for the text encoder was enabled. {train_text_encoder}.\n\nPivotal tuning was enabled: {train_text_encoder_ti}.\n\nSpecial VAE used for training: {vae_path}.\n\n\"\"\"\n with open(os.path.join(repo_folder, \"README.md\"), \"w\") as f:\n f.write(yaml + model_card)\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_vae_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) containing the training data of instance images (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.To load the custom captions, the training set directory needs to follow the structure of a \"\n \"datasets ImageFolder, containing both the images and the corresponding caption for each image. see: \"\n \"https://huggingface.co/docs/datasets/image_dataset for more information\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset. In some cases, a dataset may have more than one configuration (for example \"\n \"if it contains different subsets of data within, and you only wish to load a specific subset - in that case specify the desired configuration using --dataset_config_name. Leave as \"\n \"None if there's only one config.\",\n )\n parser.add_argument(\n \"--instance_data_dir\",\n type=str,\n default=None,\n help=\"A path to local folder containing the training data of instance images. Specify this arg instead of \"\n \"--dataset_name if you wish to train using a local folder without custom captions. If you wish to train with custom captions please specify \"\n \"--dataset_name instead.\",\n )\n\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n\n parser.add_argument(\n \"--image_column\",\n type=str,\n default=\"image\",\n help=\"The column of the dataset containing the target image. By \"\n \"default, the standard Image Dataset maps out 'file_name' \"\n \"to 'image'.\",\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=None,\n help=\"The column of the dataset containing the instance prompt for each image\",\n )\n\n parser.add_argument(\"--repeats\", type=int, default=1, help=\"How many times to repeat the training data.\")\n\n parser.add_argument(\n \"--class_data_dir\",\n type=str,\n default=None,\n required=False,\n help=\"A folder containing the training data of class images.\",\n )\n parser.add_argument(\n \"--instance_prompt\",\n type=str,\n default=None,\n required=True,\n help=\"The prompt with identifier specifying the instance, e.g. 'photo of a TOK dog', 'in the style of TOK'\",\n )\n parser.add_argument(\n \"--token_abstraction\",\n type=str,\n default=\"TOK\",\n help=\"identifier specifying the instance(or instances) as used in instance_prompt, validation prompt, \"\n \"captions - e.g. TOK. To use multiple identifiers, please specify them in a comma seperated string - e.g. \"\n \"'TOK,TOK2,TOK3' etc.\",\n )\n\n parser.add_argument(\n \"--num_new_tokens_per_abstraction\",\n type=int,\n default=2,\n help=\"number of new tokens inserted to the tokenizers per token_abstraction identifier when \"\n \"--train_text_encoder_ti = True. By default, each --token_abstraction (e.g. TOK) is mapped to 2 new \"\n \"tokens - \",\n )\n\n parser.add_argument(\n \"--class_prompt\",\n type=str,\n default=None,\n help=\"The prompt to specify images in the same class as provided instance images.\",\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n help=\"A prompt that is used during validation to verify that the model is learning.\",\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=50,\n help=(\n \"Run dreambooth validation every X epochs. Dreambooth validation consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--with_prior_preservation\",\n default=False,\n action=\"store_true\",\n help=\"Flag to add prior preservation loss.\",\n )\n parser.add_argument(\"--prior_loss_weight\", type=float, default=1.0, help=\"The weight of prior preservation loss.\")\n parser.add_argument(\n \"--num_class_images\",\n type=int,\n default=100,\n help=(\n \"Minimal class images for prior preservation loss. If there are not enough images already present in\"\n \" class_data_dir, additional images will be sampled with class_prompt.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"lora-dreambooth-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--train_text_encoder\",\n action=\"store_true\",\n help=\"Whether to train the text encoder. If set, the text encoder should be float32 precision.\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\n \"--sample_batch_size\", type=int, default=4, help=\"Batch size (per device) for sampling images.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final\"\n \" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n\n parser.add_argument(\n \"--text_encoder_lr\",\n type=float,\n default=5e-6,\n help=\"Text encoder learning rate to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n\n parser.add_argument(\n \"--snr_gamma\",\n type=float,\n default=None,\n help=\"SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. \"\n \"More details here: https://arxiv.org/abs/2303.09556.\",\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\"--lr_power\", type=float, default=1.0, help=\"Power factor of the polynomial scheduler.\")\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n\n parser.add_argument(\n \"--train_text_encoder_ti\",\n action=\"store_true\",\n help=(\"Whether to use textual inversion\"),\n )\n\n parser.add_argument(\n \"--train_text_encoder_ti_frac\",\n type=float,\n default=0.5,\n help=(\"The percentage of epochs to perform textual inversion\"),\n )\n\n parser.add_argument(\n \"--train_text_encoder_frac\",\n type=float,\n default=1.0,\n help=(\"The percentage of epochs to perform text encoder tuning\"),\n )\n\n parser.add_argument(\n \"--optimizer\",\n type=str,\n default=\"adamW\",\n help=('The optimizer type to use. Choose between [\"AdamW\", \"prodigy\"]'),\n )\n\n parser.add_argument(\n \"--use_8bit_adam\",\n action=\"store_true\",\n help=\"Whether or not to use 8-bit Adam from bitsandbytes. Ignored if optimizer is not set to AdamW\",\n )\n\n parser.add_argument(\n \"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam and Prodigy optimizers.\"\n )\n parser.add_argument(\n \"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam and Prodigy optimizers.\"\n )\n parser.add_argument(\n \"--prodigy_beta3\",\n type=float,\n default=None,\n help=\"coefficients for computing the Prodidy stepsize using running averages. If set to None, \"\n \"uses the value of square root of beta2. Ignored if optimizer is adamW\",\n )\n parser.add_argument(\"--prodigy_decouple\", type=bool, default=True, help=\"Use AdamW style decoupled weight decay\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-04, help=\"Weight decay to use for unet params\")\n parser.add_argument(\n \"--adam_weight_decay_text_encoder\", type=float, default=None, help=\"Weight decay to use for text_encoder\"\n )\n\n parser.add_argument(\n \"--adam_epsilon\",\n type=float,\n default=1e-08,\n help=\"Epsilon value for the Adam optimizer and Prodigy optimizers.\",\n )\n\n parser.add_argument(\n \"--prodigy_use_bias_correction\",\n type=bool,\n default=True,\n help=\"Turn on Adam's bias correction. True by default. Ignored if optimizer is adamW\",\n )\n parser.add_argument(\n \"--prodigy_safeguard_warmup\",\n type=bool,\n default=True,\n help=\"Remove lr from the denominator of D estimate to avoid issues during warm-up stage. True by default. \"\n \"Ignored if optimizer is adamW\",\n )\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--prior_generation_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp32\", \"fp16\", \"bf16\"],\n help=(\n \"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\"--noise_offset\", type=float, default=0, help=\"The scale of noise offset.\")\n parser.add_argument(\n \"--rank\",\n type=int,\n default=4,\n help=(\"The dimension of the LoRA update matrices.\"),\n )\n parser.add_argument(\n \"--use_dora\",\n action=\"store_true\",\n default=False,\n help=(\n \"Wether to train a DoRA as proposed in- DoRA: Weight-Decomposed Low-Rank Adaptation https://arxiv.org/abs/2402.09353. \"\n \"Note: to use DoRA you need to install peft from main, `pip install git+https://github.com/huggingface/peft.git`\"\n ),\n )\n parser.add_argument(\n \"--cache_latents\",\n action=\"store_true\",\n default=False,\n help=\"Cache the VAE latents\",\n )\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n if args.dataset_name is None and args.instance_data_dir is None:\n raise ValueError(\"Specify either `--dataset_name` or `--instance_data_dir`\")\n\n if args.dataset_name is not None and args.instance_data_dir is not None:\n raise ValueError(\"Specify only one of `--dataset_name` or `--instance_data_dir`\")\n\n if args.train_text_encoder and args.train_text_encoder_ti:\n raise ValueError(\n \"Specify only one of `--train_text_encoder` or `--train_text_encoder_ti. \"\n \"For full LoRA text encoder training check --train_text_encoder, for textual \"\n \"inversion training check `--train_text_encoder_ti`\"\n )\n\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.with_prior_preservation:\n if args.class_data_dir is None:\n raise ValueError(\"You must specify a data directory for class images.\")\n if args.class_prompt is None:\n raise ValueError(\"You must specify prompt for class images.\")\n else:\n # logger is not available yet\n if args.class_data_dir is not None:\n warnings.warn(\"You need not use --class_data_dir without --with_prior_preservation.\")\n if args.class_prompt is not None:\n warnings.warn(\"You need not use --class_prompt without --with_prior_preservation.\")\n\n return args\n\n\n# Taken from https://github.com/replicate/cog-sdxl/blob/main/dataset_and_utils.py\nclass TokenEmbeddingsHandler:\n def __init__(self, text_encoders, tokenizers):\n self.text_encoders = text_encoders\n self.tokenizers = tokenizers\n\n self.train_ids: Optional[torch.Tensor] = None\n self.inserting_toks: Optional[List[str]] = None\n self.embeddings_settings = {}\n\n def initialize_new_tokens(self, inserting_toks: List[str]):\n idx = 0\n for tokenizer, text_encoder in zip(self.tokenizers, self.text_encoders):\n assert isinstance(inserting_toks, list), \"inserting_toks should be a list of strings.\"\n assert all(\n isinstance(tok, str) for tok in inserting_toks\n ), \"All elements in inserting_toks should be strings.\"\n\n self.inserting_toks = inserting_toks\n special_tokens_dict = {\"additional_special_tokens\": self.inserting_toks}\n tokenizer.add_special_tokens(special_tokens_dict)\n text_encoder.resize_token_embeddings(len(tokenizer))\n\n self.train_ids = tokenizer.convert_tokens_to_ids(self.inserting_toks)\n\n # random initialization of new tokens\n std_token_embedding = text_encoder.text_model.embeddings.token_embedding.weight.data.std()\n\n print(f\"{idx} text encodedr's std_token_embedding: {std_token_embedding}\")\n\n text_encoder.text_model.embeddings.token_embedding.weight.data[self.train_ids] = (\n torch.randn(len(self.train_ids), text_encoder.text_model.config.hidden_size)\n .to(device=self.device)\n .to(dtype=self.dtype)\n * std_token_embedding\n )\n self.embeddings_settings[\n f\"original_embeddings_{idx}\"\n ] = text_encoder.text_model.embeddings.token_embedding.weight.data.clone()\n self.embeddings_settings[f\"std_token_embedding_{idx}\"] = std_token_embedding\n\n inu = torch.ones((len(tokenizer),), dtype=torch.bool)\n inu[self.train_ids] = False\n\n self.embeddings_settings[f\"index_no_updates_{idx}\"] = inu\n\n print(self.embeddings_settings[f\"index_no_updates_{idx}\"].shape)\n\n idx += 1\n\n # Copied from train_dreambooth_lora_sdxl_advanced.py\n def save_embeddings(self, file_path: str):\n assert self.train_ids is not None, \"Initialize new tokens before saving embeddings.\"\n tensors = {}\n # text_encoder_0 - CLIP ViT-L/14, text_encoder_1 - CLIP ViT-G/14 - TODO - change for sd\n idx_to_text_encoder_name = {0: \"clip_l\", 1: \"clip_g\"}\n for idx, text_encoder in enumerate(self.text_encoders):\n assert text_encoder.text_model.embeddings.token_embedding.weight.data.shape[0] == len(\n self.tokenizers[0]\n ), \"Tokenizers should be the same.\"\n new_token_embeddings = text_encoder.text_model.embeddings.token_embedding.weight.data[self.train_ids]\n\n # New tokens for each text encoder are saved under \"clip_l\" (for text_encoder 0), \"clip_g\" (for\n # text_encoder 1) to keep compatible with the ecosystem.\n # Note: When loading with diffusers, any name can work - simply specify in inference\n tensors[idx_to_text_encoder_name[idx]] = new_token_embeddings\n # tensors[f\"text_encoders_{idx}\"] = new_token_embeddings\n\n save_file(tensors, file_path)\n\n @property\n def dtype(self):\n return self.text_encoders[0].dtype\n\n @property\n def device(self):\n return self.text_encoders[0].device\n\n @torch.no_grad()\n def retract_embeddings(self):\n for idx, text_encoder in enumerate(self.text_encoders):\n index_no_updates = self.embeddings_settings[f\"index_no_updates_{idx}\"]\n text_encoder.text_model.embeddings.token_embedding.weight.data[index_no_updates] = (\n self.embeddings_settings[f\"original_embeddings_{idx}\"][index_no_updates]\n .to(device=text_encoder.device)\n .to(dtype=text_encoder.dtype)\n )\n\n # for the parts that were updated, we need to normalize them\n # to have the same std as before\n std_token_embedding = self.embeddings_settings[f\"std_token_embedding_{idx}\"]\n\n index_updates = ~index_no_updates\n new_embeddings = text_encoder.text_model.embeddings.token_embedding.weight.data[index_updates]\n off_ratio = std_token_embedding / new_embeddings.std()\n\n new_embeddings = new_embeddings * (off_ratio**0.1)\n text_encoder.text_model.embeddings.token_embedding.weight.data[index_updates] = new_embeddings\n\n\nclass DreamBoothDataset(Dataset):\n \"\"\"\n A dataset to prepare the instance and class images with the prompts for fine-tuning the model.\n It pre-processes the images.\n \"\"\"\n\n def __init__(\n self,\n instance_data_root,\n instance_prompt,\n class_prompt,\n dataset_name,\n dataset_config_name,\n cache_dir,\n image_column,\n caption_column,\n train_text_encoder_ti,\n class_data_root=None,\n class_num=None,\n token_abstraction_dict=None, # token mapping for textual inversion\n size=1024,\n repeats=1,\n center_crop=False,\n ):\n self.size = size\n self.center_crop = center_crop\n\n self.instance_prompt = instance_prompt\n self.custom_instance_prompts = None\n self.class_prompt = class_prompt\n self.token_abstraction_dict = token_abstraction_dict\n self.train_text_encoder_ti = train_text_encoder_ti\n # if --dataset_name is provided or a metadata jsonl file is provided in the local --instance_data directory,\n # we load the training data using load_dataset\n if dataset_name is not None:\n try:\n from datasets import load_dataset\n except ImportError:\n raise ImportError(\n \"You are trying to load your data using the datasets library. If you wish to train using custom \"\n \"captions please install the datasets library: `pip install datasets`. If you wish to load a \"\n \"local folder containing images only, specify --instance_data_dir instead.\"\n )\n # Downloading and loading a dataset from the hub.\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script\n dataset = load_dataset(\n dataset_name,\n dataset_config_name,\n cache_dir=cache_dir,\n )\n # Preprocessing the datasets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n if image_column is None:\n image_column = column_names[0]\n logger.info(f\"image column defaulting to {image_column}\")\n else:\n if image_column not in column_names:\n raise ValueError(\n f\"`--image_column` value '{image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n instance_images = dataset[\"train\"][image_column]\n\n if caption_column is None:\n logger.info(\n \"No caption column provided, defaulting to instance_prompt for all images. If your dataset \"\n \"contains captions/prompts for the images, make sure to specify the \"\n \"column as --caption_column\"\n )\n self.custom_instance_prompts = None\n else:\n if caption_column not in column_names:\n raise ValueError(\n f\"`--caption_column` value '{caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n custom_instance_prompts = dataset[\"train\"][caption_column]\n # create final list of captions according to --repeats\n self.custom_instance_prompts = []\n for caption in custom_instance_prompts:\n self.custom_instance_prompts.extend(itertools.repeat(caption, repeats))\n else:\n self.instance_data_root = Path(instance_data_root)\n if not self.instance_data_root.exists():\n raise ValueError(\"Instance images root doesn't exists.\")\n\n instance_images = [Image.open(path) for path in list(Path(instance_data_root).iterdir())]\n self.custom_instance_prompts = None\n\n self.instance_images = []\n for img in instance_images:\n self.instance_images.extend(itertools.repeat(img, repeats))\n self.num_instance_images = len(self.instance_images)\n self._length = self.num_instance_images\n\n if class_data_root is not None:\n self.class_data_root = Path(class_data_root)\n self.class_data_root.mkdir(parents=True, exist_ok=True)\n self.class_images_path = list(self.class_data_root.iterdir())\n if class_num is not None:\n self.num_class_images = min(len(self.class_images_path), class_num)\n else:\n self.num_class_images = len(self.class_images_path)\n self._length = max(self.num_class_images, self.num_instance_images)\n else:\n self.class_data_root = None\n\n self.image_transforms = transforms.Compose(\n [\n transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, index):\n example = {}\n instance_image = self.instance_images[index % self.num_instance_images]\n instance_image = exif_transpose(instance_image)\n\n if not instance_image.mode == \"RGB\":\n instance_image = instance_image.convert(\"RGB\")\n example[\"instance_images\"] = self.image_transforms(instance_image)\n\n if self.custom_instance_prompts:\n caption = self.custom_instance_prompts[index % self.num_instance_images]\n if caption:\n if self.train_text_encoder_ti:\n # replace instances of --token_abstraction in caption with the new tokens: \"\" etc.\n for token_abs, token_replacement in self.token_abstraction_dict.items():\n caption = caption.replace(token_abs, \"\".join(token_replacement))\n example[\"instance_prompt\"] = caption\n else:\n example[\"instance_prompt\"] = self.instance_prompt\n\n else: # costum prompts were provided, but length does not match size of image dataset\n example[\"instance_prompt\"] = self.instance_prompt\n\n if self.class_data_root:\n class_image = Image.open(self.class_images_path[index % self.num_class_images])\n class_image = exif_transpose(class_image)\n\n if not class_image.mode == \"RGB\":\n class_image = class_image.convert(\"RGB\")\n example[\"class_images\"] = self.image_transforms(class_image)\n example[\"class_prompt\"] = self.class_prompt\n\n return example\n\n\ndef collate_fn(examples, with_prior_preservation=False):\n pixel_values = [example[\"instance_images\"] for example in examples]\n prompts = [example[\"instance_prompt\"] for example in examples]\n\n # Concat class and instance examples for prior preservation.\n # We do this to avoid doing two forward passes.\n if with_prior_preservation:\n pixel_values += [example[\"class_images\"] for example in examples]\n prompts += [example[\"class_prompt\"] for example in examples]\n\n pixel_values = torch.stack(pixel_values)\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n\n batch = {\"pixel_values\": pixel_values, \"prompts\": prompts}\n return batch\n\n\nclass PromptDataset(Dataset):\n \"\"\"A simple dataset to prepare the prompts to generate class images on multiple GPUs.\"\"\"\n\n def __init__(self, prompt, num_samples):\n self.prompt = prompt\n self.num_samples = num_samples\n\n def __len__(self):\n return self.num_samples\n\n def __getitem__(self, index):\n example = {}\n example[\"prompt\"] = self.prompt\n example[\"index\"] = index\n return example\n\n\ndef tokenize_prompt(tokenizer, prompt, add_special_tokens=False):\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n add_special_tokens=add_special_tokens,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n return text_input_ids\n\n\n# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt\ndef encode_prompt(text_encoders, tokenizers, prompt, text_input_ids_list=None):\n for i, text_encoder in enumerate(text_encoders):\n if tokenizers is not None:\n tokenizer = tokenizers[i]\n text_input_ids = tokenize_prompt(tokenizer, prompt)\n else:\n assert text_input_ids_list is not None\n text_input_ids = text_input_ids_list[i]\n\n prompt_embeds = text_encoder(\n text_input_ids.to(text_encoder.device),\n output_hidden_states=True,\n )\n\n return prompt_embeds[0]\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n kwargs_handlers=[kwargs],\n )\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n import wandb\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Generate class images if prior preservation is enabled.\n if args.with_prior_preservation:\n class_images_dir = Path(args.class_data_dir)\n if not class_images_dir.exists():\n class_images_dir.mkdir(parents=True)\n cur_class_images = len(list(class_images_dir.iterdir()))\n\n if cur_class_images < args.num_class_images:\n torch_dtype = torch.float16 if accelerator.device.type == \"cuda\" else torch.float32\n if args.prior_generation_precision == \"fp32\":\n torch_dtype = torch.float32\n elif args.prior_generation_precision == \"fp16\":\n torch_dtype = torch.float16\n elif args.prior_generation_precision == \"bf16\":\n torch_dtype = torch.bfloat16\n pipeline = StableDiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n torch_dtype=torch_dtype,\n revision=args.revision,\n variant=args.variant,\n )\n pipeline.set_progress_bar_config(disable=True)\n\n num_new_images = args.num_class_images - cur_class_images\n logger.info(f\"Number of class images to sample: {num_new_images}.\")\n\n sample_dataset = PromptDataset(args.class_prompt, num_new_images)\n sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)\n\n sample_dataloader = accelerator.prepare(sample_dataloader)\n pipeline.to(accelerator.device)\n\n for example in tqdm(\n sample_dataloader, desc=\"Generating class images\", disable=not accelerator.is_local_main_process\n ):\n images = pipeline(example[\"prompt\"]).images\n\n for i, image in enumerate(images):\n hash_image = hashlib.sha1(image.tobytes()).hexdigest()\n image_filename = class_images_dir / f\"{example['index'][i] + cur_class_images}-{hash_image}.jpg\"\n image.save(image_filename)\n\n del pipeline\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n model_id = args.hub_model_id or Path(args.output_dir).name\n repo_id = None\n if args.push_to_hub:\n repo_id = create_repo(repo_id=model_id, exist_ok=True, token=args.hub_token).repo_id\n\n # Load the tokenizers\n tokenizer_one = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n variant=args.variant,\n use_fast=False,\n )\n\n # import correct text encoder classes\n text_encoder_cls_one = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision\n )\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n text_encoder_one = text_encoder_cls_one.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n vae_path = (\n args.pretrained_model_name_or_path\n if args.pretrained_vae_model_name_or_path is None\n else args.pretrained_vae_model_name_or_path\n )\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n variant=args.variant,\n )\n vae_scaling_factor = vae.config.scaling_factor\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n if args.train_text_encoder_ti:\n # we parse the provided token identifier (or identifiers) into a list. s.t. - \"TOK\" -> [\"TOK\"], \"TOK,\n # TOK2\" -> [\"TOK\", \"TOK2\"] etc.\n token_abstraction_list = \"\".join(args.token_abstraction.split()).split(\",\")\n logger.info(f\"list of token identifiers: {token_abstraction_list}\")\n\n token_abstraction_dict = {}\n token_idx = 0\n for i, token in enumerate(token_abstraction_list):\n token_abstraction_dict[token] = [\n f\"\" for j in range(args.num_new_tokens_per_abstraction)\n ]\n token_idx += args.num_new_tokens_per_abstraction - 1\n\n # replace instances of --token_abstraction in --instance_prompt with the new tokens: \"\" etc.\n for token_abs, token_replacement in token_abstraction_dict.items():\n args.instance_prompt = args.instance_prompt.replace(token_abs, \"\".join(token_replacement))\n if args.with_prior_preservation:\n args.class_prompt = args.class_prompt.replace(token_abs, \"\".join(token_replacement))\n\n # initialize the new tokens for textual inversion\n embedding_handler = TokenEmbeddingsHandler([text_encoder_one], [tokenizer_one])\n inserting_toks = []\n for new_tok in token_abstraction_dict.values():\n inserting_toks.extend(new_tok)\n embedding_handler.initialize_new_tokens(inserting_toks=inserting_toks)\n\n # We only train the additional adapter LoRA layers\n vae.requires_grad_(False)\n text_encoder_one.requires_grad_(False)\n unet.requires_grad_(False)\n\n # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move unet, vae and text_encoder to device and cast to weight_dtype\n unet.to(accelerator.device, dtype=weight_dtype)\n\n # The VAE is always in float32 to avoid NaN losses.\n vae.to(accelerator.device, dtype=torch.float32)\n\n text_encoder_one.to(accelerator.device, dtype=weight_dtype)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, \"\n \"please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n if args.train_text_encoder:\n text_encoder_one.gradient_checkpointing_enable()\n\n # now we will add new LoRA weights to the attention layers\n unet_lora_config = LoraConfig(\n r=args.rank,\n lora_alpha=args.rank,\n use_dora=args.use_dora,\n init_lora_weights=\"gaussian\",\n target_modules=[\"to_k\", \"to_q\", \"to_v\", \"to_out.0\"],\n )\n unet.add_adapter(unet_lora_config)\n\n # The text encoder comes from 🤗 transformers, so we cannot directly modify it.\n # So, instead, we monkey-patch the forward calls of its attention-blocks.\n if args.train_text_encoder:\n text_lora_config = LoraConfig(\n r=args.rank,\n lora_alpha=args.rank,\n use_dora=args.use_dora,\n init_lora_weights=\"gaussian\",\n target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"out_proj\"],\n )\n text_encoder_one.add_adapter(text_lora_config)\n\n # if we use textual inversion, we freeze all parameters except for the token embeddings\n # in text encoder\n elif args.train_text_encoder_ti:\n text_lora_parameters_one = []\n for name, param in text_encoder_one.named_parameters():\n if \"token_embedding\" in name:\n # ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16\n param = param.to(dtype=torch.float32)\n param.requires_grad = True\n text_lora_parameters_one.append(param)\n else:\n param.requires_grad = False\n\n # Make sure the trainable params are in float32.\n if args.mixed_precision == \"fp16\":\n models = [unet]\n if args.train_text_encoder:\n models.extend([text_encoder_one])\n for model in models:\n for param in model.parameters():\n # only upcast trainable parameters (LoRA) into fp32\n if param.requires_grad:\n param.data = param.to(torch.float32)\n\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n # there are only two options here. Either are just the unet attn processor layers\n # or there are the unet and text encoder atten layers\n unet_lora_layers_to_save = None\n text_encoder_one_lora_layers_to_save = None\n\n for model in models:\n if isinstance(model, type(accelerator.unwrap_model(unet))):\n unet_lora_layers_to_save = convert_state_dict_to_diffusers(get_peft_model_state_dict(model))\n elif isinstance(model, type(accelerator.unwrap_model(text_encoder_one))):\n if args.train_text_encoder:\n text_encoder_one_lora_layers_to_save = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(model)\n )\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n StableDiffusionPipeline.save_lora_weights(\n output_dir,\n unet_lora_layers=unet_lora_layers_to_save,\n text_encoder_lora_layers=text_encoder_one_lora_layers_to_save,\n )\n if args.train_text_encoder_ti:\n embedding_handler.save_embeddings(f\"{output_dir}/{args.output_dir}_emb.safetensors\")\n\n def load_model_hook(models, input_dir):\n unet_ = None\n text_encoder_one_ = None\n\n while len(models) > 0:\n model = models.pop()\n\n if isinstance(model, type(accelerator.unwrap_model(unet))):\n unet_ = model\n elif isinstance(model, type(accelerator.unwrap_model(text_encoder_one))):\n text_encoder_one_ = model\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n lora_state_dict, network_alphas = LoraLoaderMixin.lora_state_dict(input_dir)\n LoraLoaderMixin.load_lora_into_unet(lora_state_dict, network_alphas=network_alphas, unet=unet_)\n\n text_encoder_state_dict = {k: v for k, v in lora_state_dict.items() if \"text_encoder.\" in k}\n LoraLoaderMixin.load_lora_into_text_encoder(\n text_encoder_state_dict, network_alphas=network_alphas, text_encoder=text_encoder_one_\n )\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n unet_lora_parameters = list(filter(lambda p: p.requires_grad, unet.parameters()))\n\n if args.train_text_encoder:\n text_lora_parameters_one = list(filter(lambda p: p.requires_grad, text_encoder_one.parameters()))\n\n # If neither --train_text_encoder nor --train_text_encoder_ti, text_encoders remain frozen during training\n freeze_text_encoder = not (args.train_text_encoder or args.train_text_encoder_ti)\n\n # Optimization parameters\n unet_lora_parameters_with_lr = {\"params\": unet_lora_parameters, \"lr\": args.learning_rate}\n if not freeze_text_encoder:\n # different learning rate for text encoder and unet\n text_lora_parameters_one_with_lr = {\n \"params\": text_lora_parameters_one,\n \"weight_decay\": args.adam_weight_decay_text_encoder\n if args.adam_weight_decay_text_encoder\n else args.adam_weight_decay,\n \"lr\": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate,\n }\n params_to_optimize = [\n unet_lora_parameters_with_lr,\n text_lora_parameters_one_with_lr,\n ]\n else:\n params_to_optimize = [unet_lora_parameters_with_lr]\n\n # Optimizer creation\n if not (args.optimizer.lower() == \"prodigy\" or args.optimizer.lower() == \"adamw\"):\n logger.warning(\n f\"Unsupported choice of optimizer: {args.optimizer}.Supported optimizers include [adamW, prodigy].\"\n \"Defaulting to adamW\"\n )\n args.optimizer = \"adamw\"\n\n if args.use_8bit_adam and not args.optimizer.lower() == \"adamw\":\n logger.warning(\n f\"use_8bit_adam is ignored when optimizer is not set to 'AdamW'. Optimizer was \"\n f\"set to {args.optimizer.lower()}\"\n )\n\n if args.optimizer.lower() == \"adamw\":\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n optimizer = optimizer_class(\n params_to_optimize,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n if args.optimizer.lower() == \"prodigy\":\n try:\n import prodigyopt\n except ImportError:\n raise ImportError(\"To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`\")\n\n optimizer_class = prodigyopt.Prodigy\n\n if args.learning_rate <= 0.1:\n logger.warning(\n \"Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0\"\n )\n if args.train_text_encoder and args.text_encoder_lr:\n logger.warning(\n f\"Learning rates were provided both for the unet and the text encoder- e.g. text_encoder_lr:\"\n f\" {args.text_encoder_lr} and learning_rate: {args.learning_rate}. \"\n f\"When using prodigy only learning_rate is used as the initial learning rate.\"\n )\n # changes the learning rate of text_encoder_parameters_one to be\n # --learning_rate\n params_to_optimize[1][\"lr\"] = args.learning_rate\n\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n beta3=args.prodigy_beta3,\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n decouple=args.prodigy_decouple,\n use_bias_correction=args.prodigy_use_bias_correction,\n safeguard_warmup=args.prodigy_safeguard_warmup,\n )\n\n # Dataset and DataLoaders creation:\n train_dataset = DreamBoothDataset(\n instance_data_root=args.instance_data_dir,\n instance_prompt=args.instance_prompt,\n class_prompt=args.class_prompt,\n dataset_name=args.dataset_name,\n dataset_config_name=args.dataset_config_name,\n cache_dir=args.cache_dir,\n image_column=args.image_column,\n train_text_encoder_ti=args.train_text_encoder_ti,\n caption_column=args.caption_column,\n class_data_root=args.class_data_dir if args.with_prior_preservation else None,\n token_abstraction_dict=token_abstraction_dict if args.train_text_encoder_ti else None,\n class_num=args.num_class_images,\n size=args.resolution,\n repeats=args.repeats,\n center_crop=args.center_crop,\n )\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n batch_size=args.train_batch_size,\n shuffle=True,\n collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),\n num_workers=args.dataloader_num_workers,\n )\n\n if not args.train_text_encoder:\n tokenizers = [tokenizer_one]\n text_encoders = [text_encoder_one]\n\n def compute_text_embeddings(prompt, text_encoders, tokenizers):\n with torch.no_grad():\n prompt_embeds = encode_prompt(text_encoders, tokenizers, prompt)\n prompt_embeds = prompt_embeds.to(accelerator.device)\n return prompt_embeds\n\n # If no type of tuning is done on the text_encoder and custom instance prompts are NOT\n # provided (i.e. the --instance_prompt is used for all images), we encode the instance prompt once to avoid\n # the redundant encoding.\n if freeze_text_encoder and not train_dataset.custom_instance_prompts:\n instance_prompt_hidden_states = compute_text_embeddings(args.instance_prompt, text_encoders, tokenizers)\n\n # Handle class prompt for prior-preservation.\n if args.with_prior_preservation:\n if freeze_text_encoder:\n class_prompt_hidden_states = compute_text_embeddings(args.class_prompt, text_encoders, tokenizers)\n\n # Clear the memory here\n if freeze_text_encoder and not train_dataset.custom_instance_prompts:\n del tokenizers, text_encoders\n gc.collect()\n torch.cuda.empty_cache()\n\n # if --train_text_encoder_ti we need add_special_tokens to be True for textual inversion\n add_special_tokens = True if args.train_text_encoder_ti else False\n\n if not train_dataset.custom_instance_prompts:\n if freeze_text_encoder:\n prompt_embeds = instance_prompt_hidden_states\n if args.with_prior_preservation:\n prompt_embeds = torch.cat([prompt_embeds, class_prompt_hidden_states], dim=0)\n\n # if we're optimizing the text encoder (both if instance prompt is used for all images or custom prompts) we need to tokenize and encode the\n # batch prompts on all training steps\n else:\n tokens_one = tokenize_prompt(tokenizer_one, args.instance_prompt, add_special_tokens)\n if args.with_prior_preservation:\n class_tokens_one = tokenize_prompt(tokenizer_one, args.class_prompt, add_special_tokens)\n tokens_one = torch.cat([tokens_one, class_tokens_one], dim=0)\n\n if args.train_text_encoder_ti and args.validation_prompt:\n # replace instances of --token_abstraction in validation prompt with the new tokens: \"\" etc.\n for token_abs, token_replacement in train_dataset.token_abstraction_dict.items():\n args.validation_prompt = args.validation_prompt.replace(token_abs, \"\".join(token_replacement))\n print(\"validation prompt:\", args.validation_prompt)\n\n if args.cache_latents:\n latents_cache = []\n for batch in tqdm(train_dataloader, desc=\"Caching latents\"):\n with torch.no_grad():\n batch[\"pixel_values\"] = batch[\"pixel_values\"].to(\n accelerator.device, non_blocking=True, dtype=torch.float32\n )\n latents_cache.append(vae.encode(batch[\"pixel_values\"]).latent_dist)\n\n if args.validation_prompt is None:\n del vae\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_cycles=args.lr_num_cycles,\n power=args.lr_power,\n )\n\n # Prepare everything with our `accelerator`.\n if not freeze_text_encoder:\n unet, text_encoder_one, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, text_encoder_one, optimizer, train_dataloader, lr_scheduler\n )\n else:\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"dreambooth-lora-sd-15\", config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num batches each epoch = {len(train_dataloader)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the mos recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n if args.train_text_encoder:\n num_train_epochs_text_encoder = int(args.train_text_encoder_frac * args.num_train_epochs)\n elif args.train_text_encoder_ti: # args.train_text_encoder_ti\n num_train_epochs_text_encoder = int(args.train_text_encoder_ti_frac * args.num_train_epochs)\n\n for epoch in range(first_epoch, args.num_train_epochs):\n # if performing any kind of optimization of text_encoder params\n if args.train_text_encoder or args.train_text_encoder_ti:\n if epoch == num_train_epochs_text_encoder:\n print(\"PIVOT HALFWAY\", epoch)\n # stopping optimization of text_encoder params\n # re setting the optimizer to optimize only on unet params\n optimizer.param_groups[1][\"lr\"] = 0.0\n\n else:\n # still optimizng the text encoder\n text_encoder_one.train()\n # set top parameter requires_grad = True for gradient checkpointing works\n if args.train_text_encoder:\n text_encoder_one.text_model.embeddings.requires_grad_(True)\n\n unet.train()\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n prompts = batch[\"prompts\"]\n # encode batch prompts when custom prompts are provided for each image -\n if train_dataset.custom_instance_prompts:\n if freeze_text_encoder:\n prompt_embeds = compute_text_embeddings(prompts, text_encoders, tokenizers)\n\n else:\n tokens_one = tokenize_prompt(tokenizer_one, prompts, add_special_tokens)\n\n if args.cache_latents:\n model_input = latents_cache[step].sample()\n else:\n pixel_values = batch[\"pixel_values\"].to(dtype=vae.dtype)\n model_input = vae.encode(pixel_values).latent_dist.sample()\n\n model_input = model_input * vae_scaling_factor\n if args.pretrained_vae_model_name_or_path is None:\n model_input = model_input.to(weight_dtype)\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(model_input)\n if args.noise_offset:\n # https://www.crosslabs.org//blog/diffusion-with-offset-noise\n noise += args.noise_offset * torch.randn(\n (model_input.shape[0], model_input.shape[1], 1, 1), device=model_input.device\n )\n bsz = model_input.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device\n )\n timesteps = timesteps.long()\n\n # Add noise to the model input according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)\n\n # Calculate the elements to repeat depending on the use of prior-preservation and custom captions.\n if not train_dataset.custom_instance_prompts:\n elems_to_repeat_text_embeds = bsz // 2 if args.with_prior_preservation else bsz\n\n else:\n elems_to_repeat_text_embeds = 1\n\n # Predict the noise residual\n if freeze_text_encoder:\n prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat_text_embeds, 1, 1)\n model_pred = unet(noisy_model_input, timesteps, prompt_embeds_input).sample\n else:\n prompt_embeds = encode_prompt(\n text_encoders=[text_encoder_one],\n tokenizers=None,\n prompt=None,\n text_input_ids_list=[tokens_one],\n )\n prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat_text_embeds, 1, 1)\n model_pred = unet(noisy_model_input, timesteps, prompt_embeds_input).sample\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(model_input, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n if args.with_prior_preservation:\n # Chunk the noise and model_pred into two parts and compute the loss on each part separately.\n model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)\n target, target_prior = torch.chunk(target, 2, dim=0)\n\n # Compute prior loss\n prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction=\"mean\")\n\n if args.snr_gamma is None:\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n else:\n # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.\n # Since we predict the noise instead of x_0, the original formulation is slightly changed.\n # This is discussed in Section 4.2 of the same paper.\n\n if args.with_prior_preservation:\n # if we're using prior preservation, we calc snr for instance loss only -\n # and hence only need timesteps corresponding to instance images\n snr_timesteps, _ = torch.chunk(timesteps, 2, dim=0)\n else:\n snr_timesteps = timesteps\n\n snr = compute_snr(noise_scheduler, snr_timesteps)\n base_weight = (\n torch.stack([snr, args.snr_gamma * torch.ones_like(snr_timesteps)], dim=1).min(dim=1)[0] / snr\n )\n\n if noise_scheduler.config.prediction_type == \"v_prediction\":\n # Velocity objective needs to be floored to an SNR weight of one.\n mse_loss_weights = base_weight + 1\n else:\n # Epsilon and sample both use the same loss weights.\n mse_loss_weights = base_weight\n\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"none\")\n loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights\n loss = loss.mean()\n\n if args.with_prior_preservation:\n # Add the prior loss to the instance loss.\n loss = loss + args.prior_loss_weight * prior_loss\n\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n params_to_clip = (\n itertools.chain(unet_lora_parameters, text_lora_parameters_one)\n if (args.train_text_encoder or args.train_text_encoder_ti)\n else unet_lora_parameters\n )\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # every step, we reset the embeddings to the original embeddings.\n if args.train_text_encoder_ti:\n for idx, text_encoder in enumerate(text_encoders):\n embedding_handler.retract_embeddings()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompt is not None and epoch % args.validation_epochs == 0:\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n # create pipeline\n if freeze_text_encoder:\n text_encoder_one = text_encoder_cls_one.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"text_encoder\",\n revision=args.revision,\n variant=args.variant,\n )\n pipeline = StableDiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n tokenizer=tokenizer_one,\n text_encoder=accelerator.unwrap_model(text_encoder_one),\n unet=accelerator.unwrap_model(unet),\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n\n # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it\n scheduler_args = {}\n\n if \"variance_type\" in pipeline.scheduler.config:\n variance_type = pipeline.scheduler.config.variance_type\n\n if variance_type in [\"learned\", \"learned_range\"]:\n variance_type = \"fixed_small\"\n\n scheduler_args[\"variance_type\"] = variance_type\n\n pipeline.scheduler = DPMSolverMultistepScheduler.from_config(\n pipeline.scheduler.config, **scheduler_args\n )\n\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n pipeline_args = {\"prompt\": args.validation_prompt}\n\n if torch.backends.mps.is_available():\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type)\n\n with autocast_ctx:\n images = [\n pipeline(**pipeline_args, generator=generator).images[0]\n for _ in range(args.num_validation_images)\n ]\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\")\n for i, image in enumerate(images)\n ]\n }\n )\n\n del pipeline\n torch.cuda.empty_cache()\n\n # Save the lora layers\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = accelerator.unwrap_model(unet)\n unet = unet.to(torch.float32)\n unet_lora_layers = convert_state_dict_to_diffusers(get_peft_model_state_dict(unet))\n\n if args.train_text_encoder:\n text_encoder_one = accelerator.unwrap_model(text_encoder_one)\n text_encoder_lora_layers = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(text_encoder_one.to(torch.float32))\n )\n else:\n text_encoder_lora_layers = None\n\n StableDiffusionPipeline.save_lora_weights(\n save_directory=args.output_dir,\n unet_lora_layers=unet_lora_layers,\n text_encoder_lora_layers=text_encoder_lora_layers,\n )\n\n if args.train_text_encoder_ti:\n embeddings_path = f\"{args.output_dir}/{args.output_dir}_emb.safetensors\"\n embedding_handler.save_embeddings(embeddings_path)\n\n images = []\n if args.validation_prompt and args.num_validation_images > 0:\n # Final inference\n # Load previous pipeline\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n pipeline = StableDiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n\n # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it\n scheduler_args = {}\n\n if \"variance_type\" in pipeline.scheduler.config:\n variance_type = pipeline.scheduler.config.variance_type\n\n if variance_type in [\"learned\", \"learned_range\"]:\n variance_type = \"fixed_small\"\n\n scheduler_args[\"variance_type\"] = variance_type\n\n pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, **scheduler_args)\n\n # load attention processors\n pipeline.load_lora_weights(args.output_dir)\n\n # load new tokens\n if args.train_text_encoder_ti:\n state_dict = load_file(embeddings_path)\n all_new_tokens = []\n for key, value in token_abstraction_dict.items():\n all_new_tokens.extend(value)\n pipeline.load_textual_inversion(\n state_dict[\"clip_l\"],\n token=all_new_tokens,\n text_encoder=pipeline.text_encoder,\n tokenizer=pipeline.tokenizer,\n )\n # run inference\n pipeline = pipeline.to(accelerator.device)\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n images = [\n pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]\n for _ in range(args.num_validation_images)\n ]\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"test\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"test\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\")\n for i, image in enumerate(images)\n ]\n }\n )\n\n # Conver to WebUI format\n lora_state_dict = load_file(f\"{args.output_dir}/pytorch_lora_weights.safetensors\")\n peft_state_dict = convert_all_state_dict_to_peft(lora_state_dict)\n kohya_state_dict = convert_state_dict_to_kohya(peft_state_dict)\n save_file(kohya_state_dict, f\"{args.output_dir}/{args.output_dir}.safetensors\")\n\n save_model_card(\n model_id if not args.push_to_hub else repo_id,\n use_dora=args.use_dora,\n images=images,\n base_model=args.pretrained_model_name_or_path,\n train_text_encoder=args.train_text_encoder,\n train_text_encoder_ti=args.train_text_encoder_ti,\n token_abstraction_dict=train_dataset.token_abstraction_dict,\n instance_prompt=args.instance_prompt,\n validation_prompt=args.validation_prompt,\n repo_folder=args.output_dir,\n vae_path=args.pretrained_vae_model_name_or_path,\n )\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport gc\nimport itertools\nimport json\nimport logging\nimport math\nimport os\nimport random\nimport re\nimport shutil\nimport warnings\nfrom contextlib import nullcontext\nfrom pathlib import Path\nfrom typing import List, Optional\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\n\n# imports of the TokenEmbeddingsHandler class\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import DistributedDataParallelKwargs, ProjectConfiguration, set_seed\nfrom huggingface_hub import create_repo, hf_hub_download, upload_folder\nfrom huggingface_hub.utils import insecure_hashlib\nfrom packaging import version\nfrom peft import LoraConfig, set_peft_model_state_dict\nfrom peft.utils import get_peft_model_state_dict\nfrom PIL import Image\nfrom PIL.ImageOps import exif_transpose\nfrom safetensors.torch import load_file, save_file\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom torchvision.transforms.functional import crop\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n DPMSolverMultistepScheduler,\n EDMEulerScheduler,\n EulerDiscreteScheduler,\n StableDiffusionXLPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.loaders import LoraLoaderMixin\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import _set_state_dict_into_text_encoder, cast_training_params, compute_snr\nfrom diffusers.utils import (\n check_min_version,\n convert_all_state_dict_to_peft,\n convert_state_dict_to_diffusers,\n convert_state_dict_to_kohya,\n convert_unet_state_dict_to_peft,\n is_wandb_available,\n)\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef determine_scheduler_type(pretrained_model_name_or_path, revision):\n model_index_filename = \"model_index.json\"\n if os.path.isdir(pretrained_model_name_or_path):\n model_index = os.path.join(pretrained_model_name_or_path, model_index_filename)\n else:\n model_index = hf_hub_download(\n repo_id=pretrained_model_name_or_path, filename=model_index_filename, revision=revision\n )\n\n with open(model_index, \"r\") as f:\n scheduler_type = json.load(f)[\"scheduler\"][1]\n return scheduler_type\n\n\ndef save_model_card(\n repo_id: str,\n use_dora: bool,\n images=None,\n base_model=str,\n train_text_encoder=False,\n train_text_encoder_ti=False,\n token_abstraction_dict=None,\n instance_prompt=str,\n validation_prompt=str,\n repo_folder=None,\n vae_path=None,\n):\n img_str = \"widget:\\n\"\n lora = \"lora\" if not use_dora else \"dora\"\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n img_str += f\"\"\"\n - text: '{validation_prompt if validation_prompt else ' ' }'\n output:\n url:\n \"image_{i}.png\"\n \"\"\"\n if not images:\n img_str += f\"\"\"\n - text: '{instance_prompt}'\n \"\"\"\n embeddings_filename = f\"{repo_folder}_emb\"\n instance_prompt_webui = re.sub(r\"\", \"\", re.sub(r\"\", embeddings_filename, instance_prompt, count=1))\n ti_keys = \", \".join(f'\"{match}\"' for match in re.findall(r\"\", instance_prompt))\n if instance_prompt_webui != embeddings_filename:\n instance_prompt_sentence = f\"For example, `{instance_prompt_webui}`\"\n else:\n instance_prompt_sentence = \"\"\n trigger_str = f\"You should use {instance_prompt} to trigger the image generation.\"\n diffusers_imports_pivotal = \"\"\n diffusers_example_pivotal = \"\"\n webui_example_pivotal = \"\"\n if train_text_encoder_ti:\n trigger_str = (\n \"To trigger image generation of trained concept(or concepts) replace each concept identifier \"\n \"in you prompt with the new inserted tokens:\\n\"\n )\n diffusers_imports_pivotal = \"\"\"from huggingface_hub import hf_hub_download\nfrom safetensors.torch import load_file\n \"\"\"\n diffusers_example_pivotal = f\"\"\"embedding_path = hf_hub_download(repo_id='{repo_id}', filename='{embeddings_filename}.safetensors', repo_type=\"model\")\nstate_dict = load_file(embedding_path)\npipeline.load_textual_inversion(state_dict[\"clip_l\"], token=[{ti_keys}], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)\npipeline.load_textual_inversion(state_dict[\"clip_g\"], token=[{ti_keys}], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)\n \"\"\"\n webui_example_pivotal = f\"\"\"- *Embeddings*: download **[`{embeddings_filename}.safetensors` here 💾](/{repo_id}/blob/main/{embeddings_filename}.safetensors)**.\n - Place it on it on your `embeddings` folder\n - Use it by adding `{embeddings_filename}` to your prompt. {instance_prompt_sentence}\n (you need both the LoRA and the embeddings as they were trained together for this LoRA)\n \"\"\"\n if token_abstraction_dict:\n for key, value in token_abstraction_dict.items():\n tokens = \"\".join(value)\n trigger_str += f\"\"\"\nto trigger concept `{key}` → use `{tokens}` in your prompt \\n\n\"\"\"\n\n yaml = f\"\"\"---\ntags:\n- stable-diffusion-xl\n- stable-diffusion-xl-diffusers\n- diffusers-training\n- text-to-image\n- diffusers\n- {lora}\n- template:sd-lora\n{img_str}\nbase_model: {base_model}\ninstance_prompt: {instance_prompt}\nlicense: openrail++\n---\n\"\"\"\n\n model_card = f\"\"\"\n# SDXL LoRA DreamBooth - {repo_id}\n\n\n\n## Model description\n\n### These are {repo_id} LoRA adaption weights for {base_model}.\n\n## Download model\n\n### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke\n\n- **LoRA**: download **[`{repo_folder}.safetensors` here 💾](/{repo_id}/blob/main/{repo_folder}.safetensors)**.\n - Place it on your `models/Lora` folder.\n - On AUTOMATIC1111, load the LoRA by adding `` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).\n{webui_example_pivotal}\n\n## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)\n\n```py\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n{diffusers_imports_pivotal}\npipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')\npipeline.load_lora_weights('{repo_id}', weight_name='pytorch_lora_weights.safetensors')\n{diffusers_example_pivotal}\nimage = pipeline('{validation_prompt if validation_prompt else instance_prompt}').images[0]\n```\n\nFor more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)\n\n## Trigger words\n\n{trigger_str}\n\n## Details\nAll [Files & versions](/{repo_id}/tree/main).\n\nThe weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).\n\nLoRA for the text encoder was enabled. {train_text_encoder}.\n\nPivotal tuning was enabled: {train_text_encoder_ti}.\n\nSpecial VAE used for training: {vae_path}.\n\n\"\"\"\n with open(os.path.join(repo_folder, \"README.md\"), \"w\") as f:\n f.write(yaml + model_card)\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_vae_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) containing the training data of instance images (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.To load the custom captions, the training set directory needs to follow the structure of a \"\n \"datasets ImageFolder, containing both the images and the corresponding caption for each image. see: \"\n \"https://huggingface.co/docs/datasets/image_dataset for more information\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset. In some cases, a dataset may have more than one configuration (for example \"\n \"if it contains different subsets of data within, and you only wish to load a specific subset - in that case specify the desired configuration using --dataset_config_name. Leave as \"\n \"None if there's only one config.\",\n )\n parser.add_argument(\n \"--instance_data_dir\",\n type=str,\n default=None,\n help=\"A path to local folder containing the training data of instance images. Specify this arg instead of \"\n \"--dataset_name if you wish to train using a local folder without custom captions. If you wish to train with custom captions please specify \"\n \"--dataset_name instead.\",\n )\n\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n\n parser.add_argument(\n \"--image_column\",\n type=str,\n default=\"image\",\n help=\"The column of the dataset containing the target image. By \"\n \"default, the standard Image Dataset maps out 'file_name' \"\n \"to 'image'.\",\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=None,\n help=\"The column of the dataset containing the instance prompt for each image\",\n )\n\n parser.add_argument(\"--repeats\", type=int, default=1, help=\"How many times to repeat the training data.\")\n\n parser.add_argument(\n \"--class_data_dir\",\n type=str,\n default=None,\n required=False,\n help=\"A folder containing the training data of class images.\",\n )\n parser.add_argument(\n \"--instance_prompt\",\n type=str,\n default=None,\n required=True,\n help=\"The prompt with identifier specifying the instance, e.g. 'photo of a TOK dog', 'in the style of TOK'\",\n )\n parser.add_argument(\n \"--token_abstraction\",\n type=str,\n default=\"TOK\",\n help=\"identifier specifying the instance(or instances) as used in instance_prompt, validation prompt, \"\n \"captions - e.g. TOK. To use multiple identifiers, please specify them in a comma seperated string - e.g. \"\n \"'TOK,TOK2,TOK3' etc.\",\n )\n\n parser.add_argument(\n \"--num_new_tokens_per_abstraction\",\n type=int,\n default=2,\n help=\"number of new tokens inserted to the tokenizers per token_abstraction identifier when \"\n \"--train_text_encoder_ti = True. By default, each --token_abstraction (e.g. TOK) is mapped to 2 new \"\n \"tokens - \",\n )\n\n parser.add_argument(\n \"--class_prompt\",\n type=str,\n default=None,\n help=\"The prompt to specify images in the same class as provided instance images.\",\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n help=\"A prompt that is used during validation to verify that the model is learning.\",\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=50,\n help=(\n \"Run dreambooth validation every X epochs. Dreambooth validation consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--do_edm_style_training\",\n action=\"store_true\",\n help=\"Flag to conduct training using the EDM formulation as introduced in https://arxiv.org/abs/2206.00364.\",\n )\n parser.add_argument(\n \"--with_prior_preservation\",\n default=False,\n action=\"store_true\",\n help=\"Flag to add prior preservation loss.\",\n )\n parser.add_argument(\"--prior_loss_weight\", type=float, default=1.0, help=\"The weight of prior preservation loss.\")\n parser.add_argument(\n \"--num_class_images\",\n type=int,\n default=100,\n help=(\n \"Minimal class images for prior preservation loss. If there are not enough images already present in\"\n \" class_data_dir, additional images will be sampled with class_prompt.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"lora-dreambooth-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=1024,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--train_text_encoder\",\n action=\"store_true\",\n help=\"Whether to train the text encoder. If set, the text encoder should be float32 precision.\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\n \"--sample_batch_size\", type=int, default=4, help=\"Batch size (per device) for sampling images.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final\"\n \" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n\n parser.add_argument(\n \"--text_encoder_lr\",\n type=float,\n default=5e-6,\n help=\"Text encoder learning rate to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n\n parser.add_argument(\n \"--snr_gamma\",\n type=float,\n default=None,\n help=\"SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. \"\n \"More details here: https://arxiv.org/abs/2303.09556.\",\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\"--lr_power\", type=float, default=1.0, help=\"Power factor of the polynomial scheduler.\")\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n\n parser.add_argument(\n \"--train_text_encoder_ti\",\n action=\"store_true\",\n help=(\"Whether to use textual inversion\"),\n )\n\n parser.add_argument(\n \"--train_text_encoder_ti_frac\",\n type=float,\n default=0.5,\n help=(\"The percentage of epochs to perform textual inversion\"),\n )\n\n parser.add_argument(\n \"--train_text_encoder_frac\",\n type=float,\n default=1.0,\n help=(\"The percentage of epochs to perform text encoder tuning\"),\n )\n\n parser.add_argument(\n \"--optimizer\",\n type=str,\n default=\"adamW\",\n help=('The optimizer type to use. Choose between [\"AdamW\", \"prodigy\"]'),\n )\n\n parser.add_argument(\n \"--use_8bit_adam\",\n action=\"store_true\",\n help=\"Whether or not to use 8-bit Adam from bitsandbytes. Ignored if optimizer is not set to AdamW\",\n )\n\n parser.add_argument(\n \"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam and Prodigy optimizers.\"\n )\n parser.add_argument(\n \"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam and Prodigy optimizers.\"\n )\n parser.add_argument(\n \"--prodigy_beta3\",\n type=float,\n default=None,\n help=\"coefficients for computing the Prodidy stepsize using running averages. If set to None, \"\n \"uses the value of square root of beta2. Ignored if optimizer is adamW\",\n )\n parser.add_argument(\"--prodigy_decouple\", type=bool, default=True, help=\"Use AdamW style decoupled weight decay\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-04, help=\"Weight decay to use for unet params\")\n parser.add_argument(\n \"--adam_weight_decay_text_encoder\", type=float, default=None, help=\"Weight decay to use for text_encoder\"\n )\n\n parser.add_argument(\n \"--adam_epsilon\",\n type=float,\n default=1e-08,\n help=\"Epsilon value for the Adam optimizer and Prodigy optimizers.\",\n )\n\n parser.add_argument(\n \"--prodigy_use_bias_correction\",\n type=bool,\n default=True,\n help=\"Turn on Adam's bias correction. True by default. Ignored if optimizer is adamW\",\n )\n parser.add_argument(\n \"--prodigy_safeguard_warmup\",\n type=bool,\n default=True,\n help=\"Remove lr from the denominator of D estimate to avoid issues during warm-up stage. True by default. \"\n \"Ignored if optimizer is adamW\",\n )\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--prior_generation_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp32\", \"fp16\", \"bf16\"],\n help=(\n \"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\"--noise_offset\", type=float, default=0, help=\"The scale of noise offset.\")\n parser.add_argument(\n \"--rank\",\n type=int,\n default=4,\n help=(\"The dimension of the LoRA update matrices.\"),\n )\n parser.add_argument(\n \"--use_dora\",\n action=\"store_true\",\n default=False,\n help=(\n \"Wether to train a DoRA as proposed in- DoRA: Weight-Decomposed Low-Rank Adaptation https://arxiv.org/abs/2402.09353. \"\n \"Note: to use DoRA you need to install peft from main, `pip install git+https://github.com/huggingface/peft.git`\"\n ),\n )\n parser.add_argument(\n \"--lora_unet_blocks\",\n type=str,\n default=None,\n help=(\n \"the U-net blocks to tune during training. please specify them in a comma separated string, e.g. `unet.up_blocks.0.attentions.0,unet.up_blocks.0.attentions.1` etc.\"\n \"NOTE: By default (if not specified) - regular LoRA training is performed. \"\n \"if --use_blora is enabled, this arg will be ignored, since in B-LoRA training, targeted U-net blocks are `unet.up_blocks.0.attentions.0` and `unet.up_blocks.0.attentions.1`\"\n ),\n )\n parser.add_argument(\n \"--use_blora\",\n action=\"store_true\",\n help=(\n \"Whether to train a B-LoRA as proposed in- Implicit Style-Content Separation using B-LoRA https://arxiv.org/abs/2403.14572. \"\n ),\n )\n parser.add_argument(\n \"--cache_latents\",\n action=\"store_true\",\n default=False,\n help=\"Cache the VAE latents\",\n )\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n if args.dataset_name is None and args.instance_data_dir is None:\n raise ValueError(\"Specify either `--dataset_name` or `--instance_data_dir`\")\n\n if args.dataset_name is not None and args.instance_data_dir is not None:\n raise ValueError(\"Specify only one of `--dataset_name` or `--instance_data_dir`\")\n\n if args.train_text_encoder and args.train_text_encoder_ti:\n raise ValueError(\n \"Specify only one of `--train_text_encoder` or `--train_text_encoder_ti. \"\n \"For full LoRA text encoder training check --train_text_encoder, for textual \"\n \"inversion training check `--train_text_encoder_ti`\"\n )\n if args.use_blora and args.lora_unet_blocks:\n warnings.warn(\n \"You specified both `--use_blora` and `--lora_unet_blocks`, for B-LoRA training, target unet blocks are: `unet.up_blocks.0.attentions.0` and `unet.up_blocks.0.attentions.1`. \"\n \"If you wish to target different U-net blocks, don't enable `--use_blora`\"\n )\n\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.with_prior_preservation:\n if args.class_data_dir is None:\n raise ValueError(\"You must specify a data directory for class images.\")\n if args.class_prompt is None:\n raise ValueError(\"You must specify prompt for class images.\")\n else:\n # logger is not available yet\n if args.class_data_dir is not None:\n warnings.warn(\"You need not use --class_data_dir without --with_prior_preservation.\")\n if args.class_prompt is not None:\n warnings.warn(\"You need not use --class_prompt without --with_prior_preservation.\")\n\n return args\n\n\n# Taken (and slightly modified) from B-LoRA repo https://github.com/yardenfren1996/B-LoRA/blob/main/blora_utils.py\ndef is_belong_to_blocks(key, blocks):\n try:\n for g in blocks:\n if g in key:\n return True\n return False\n except Exception as e:\n raise type(e)(f\"failed to is_belong_to_block, due to: {e}\")\n\n\ndef get_unet_lora_target_modules(unet, use_blora, target_blocks=None):\n if use_blora:\n content_b_lora_blocks = \"unet.up_blocks.0.attentions.0\"\n style_b_lora_blocks = \"unet.up_blocks.0.attentions.1\"\n target_blocks = [content_b_lora_blocks, style_b_lora_blocks]\n try:\n blocks = [(\".\").join(blk.split(\".\")[1:]) for blk in target_blocks]\n\n attns = [\n attn_processor_name.rsplit(\".\", 1)[0]\n for attn_processor_name, _ in unet.attn_processors.items()\n if is_belong_to_blocks(attn_processor_name, blocks)\n ]\n\n target_modules = [f\"{attn}.{mat}\" for mat in [\"to_k\", \"to_q\", \"to_v\", \"to_out.0\"] for attn in attns]\n return target_modules\n except Exception as e:\n raise type(e)(\n f\"failed to get_target_modules, due to: {e}. \"\n f\"Please check the modules specified in --lora_unet_blocks are correct\"\n )\n\n\n# Taken from https://github.com/replicate/cog-sdxl/blob/main/dataset_and_utils.py\nclass TokenEmbeddingsHandler:\n def __init__(self, text_encoders, tokenizers):\n self.text_encoders = text_encoders\n self.tokenizers = tokenizers\n\n self.train_ids: Optional[torch.Tensor] = None\n self.inserting_toks: Optional[List[str]] = None\n self.embeddings_settings = {}\n\n def initialize_new_tokens(self, inserting_toks: List[str]):\n idx = 0\n for tokenizer, text_encoder in zip(self.tokenizers, self.text_encoders):\n assert isinstance(inserting_toks, list), \"inserting_toks should be a list of strings.\"\n assert all(\n isinstance(tok, str) for tok in inserting_toks\n ), \"All elements in inserting_toks should be strings.\"\n\n self.inserting_toks = inserting_toks\n special_tokens_dict = {\"additional_special_tokens\": self.inserting_toks}\n tokenizer.add_special_tokens(special_tokens_dict)\n text_encoder.resize_token_embeddings(len(tokenizer))\n\n self.train_ids = tokenizer.convert_tokens_to_ids(self.inserting_toks)\n\n # random initialization of new tokens\n std_token_embedding = text_encoder.text_model.embeddings.token_embedding.weight.data.std()\n\n print(f\"{idx} text encodedr's std_token_embedding: {std_token_embedding}\")\n\n text_encoder.text_model.embeddings.token_embedding.weight.data[self.train_ids] = (\n torch.randn(len(self.train_ids), text_encoder.text_model.config.hidden_size)\n .to(device=self.device)\n .to(dtype=self.dtype)\n * std_token_embedding\n )\n self.embeddings_settings[\n f\"original_embeddings_{idx}\"\n ] = text_encoder.text_model.embeddings.token_embedding.weight.data.clone()\n self.embeddings_settings[f\"std_token_embedding_{idx}\"] = std_token_embedding\n\n inu = torch.ones((len(tokenizer),), dtype=torch.bool)\n inu[self.train_ids] = False\n\n self.embeddings_settings[f\"index_no_updates_{idx}\"] = inu\n\n print(self.embeddings_settings[f\"index_no_updates_{idx}\"].shape)\n\n idx += 1\n\n def save_embeddings(self, file_path: str):\n assert self.train_ids is not None, \"Initialize new tokens before saving embeddings.\"\n tensors = {}\n # text_encoder_0 - CLIP ViT-L/14, text_encoder_1 - CLIP ViT-G/14\n idx_to_text_encoder_name = {0: \"clip_l\", 1: \"clip_g\"}\n for idx, text_encoder in enumerate(self.text_encoders):\n assert text_encoder.text_model.embeddings.token_embedding.weight.data.shape[0] == len(\n self.tokenizers[0]\n ), \"Tokenizers should be the same.\"\n new_token_embeddings = text_encoder.text_model.embeddings.token_embedding.weight.data[self.train_ids]\n\n # New tokens for each text encoder are saved under \"clip_l\" (for text_encoder 0), \"clip_g\" (for\n # text_encoder 1) to keep compatible with the ecosystem.\n # Note: When loading with diffusers, any name can work - simply specify in inference\n tensors[idx_to_text_encoder_name[idx]] = new_token_embeddings\n # tensors[f\"text_encoders_{idx}\"] = new_token_embeddings\n\n save_file(tensors, file_path)\n\n @property\n def dtype(self):\n return self.text_encoders[0].dtype\n\n @property\n def device(self):\n return self.text_encoders[0].device\n\n @torch.no_grad()\n def retract_embeddings(self):\n for idx, text_encoder in enumerate(self.text_encoders):\n index_no_updates = self.embeddings_settings[f\"index_no_updates_{idx}\"]\n text_encoder.text_model.embeddings.token_embedding.weight.data[index_no_updates] = (\n self.embeddings_settings[f\"original_embeddings_{idx}\"][index_no_updates]\n .to(device=text_encoder.device)\n .to(dtype=text_encoder.dtype)\n )\n\n # for the parts that were updated, we need to normalize them\n # to have the same std as before\n std_token_embedding = self.embeddings_settings[f\"std_token_embedding_{idx}\"]\n\n index_updates = ~index_no_updates\n new_embeddings = text_encoder.text_model.embeddings.token_embedding.weight.data[index_updates]\n off_ratio = std_token_embedding / new_embeddings.std()\n\n new_embeddings = new_embeddings * (off_ratio**0.1)\n text_encoder.text_model.embeddings.token_embedding.weight.data[index_updates] = new_embeddings\n\n\nclass DreamBoothDataset(Dataset):\n \"\"\"\n A dataset to prepare the instance and class images with the prompts for fine-tuning the model.\n It pre-processes the images.\n \"\"\"\n\n def __init__(\n self,\n instance_data_root,\n instance_prompt,\n class_prompt,\n dataset_name,\n dataset_config_name,\n cache_dir,\n image_column,\n caption_column,\n train_text_encoder_ti,\n class_data_root=None,\n class_num=None,\n token_abstraction_dict=None, # token mapping for textual inversion\n size=1024,\n repeats=1,\n center_crop=False,\n ):\n self.size = size\n self.center_crop = center_crop\n\n self.instance_prompt = instance_prompt\n self.custom_instance_prompts = None\n self.class_prompt = class_prompt\n self.token_abstraction_dict = token_abstraction_dict\n self.train_text_encoder_ti = train_text_encoder_ti\n # if --dataset_name is provided or a metadata jsonl file is provided in the local --instance_data directory,\n # we load the training data using load_dataset\n if dataset_name is not None:\n try:\n from datasets import load_dataset\n except ImportError:\n raise ImportError(\n \"You are trying to load your data using the datasets library. If you wish to train using custom \"\n \"captions please install the datasets library: `pip install datasets`. If you wish to load a \"\n \"local folder containing images only, specify --instance_data_dir instead.\"\n )\n # Downloading and loading a dataset from the hub.\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script\n dataset = load_dataset(\n dataset_name,\n dataset_config_name,\n cache_dir=cache_dir,\n )\n # Preprocessing the datasets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n if image_column is None:\n image_column = column_names[0]\n logger.info(f\"image column defaulting to {image_column}\")\n else:\n if image_column not in column_names:\n raise ValueError(\n f\"`--image_column` value '{image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n instance_images = dataset[\"train\"][image_column]\n\n if caption_column is None:\n logger.info(\n \"No caption column provided, defaulting to instance_prompt for all images. If your dataset \"\n \"contains captions/prompts for the images, make sure to specify the \"\n \"column as --caption_column\"\n )\n self.custom_instance_prompts = None\n else:\n if caption_column not in column_names:\n raise ValueError(\n f\"`--caption_column` value '{caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n custom_instance_prompts = dataset[\"train\"][caption_column]\n # create final list of captions according to --repeats\n self.custom_instance_prompts = []\n for caption in custom_instance_prompts:\n self.custom_instance_prompts.extend(itertools.repeat(caption, repeats))\n else:\n self.instance_data_root = Path(instance_data_root)\n if not self.instance_data_root.exists():\n raise ValueError(\"Instance images root doesn't exists.\")\n\n instance_images = [Image.open(path) for path in list(Path(instance_data_root).iterdir())]\n self.custom_instance_prompts = None\n\n self.instance_images = []\n for img in instance_images:\n self.instance_images.extend(itertools.repeat(img, repeats))\n\n # image processing to prepare for using SD-XL micro-conditioning\n self.original_sizes = []\n self.crop_top_lefts = []\n self.pixel_values = []\n train_resize = transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR)\n train_crop = transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size)\n train_flip = transforms.RandomHorizontalFlip(p=1.0)\n train_transforms = transforms.Compose(\n [\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n # if using B-LoRA for single image. do not use transformations\n single_image = len(self.instance_images) < 2\n for image in self.instance_images:\n if not single_image:\n image = exif_transpose(image)\n if not image.mode == \"RGB\":\n image = image.convert(\"RGB\")\n self.original_sizes.append((image.height, image.width))\n image = train_resize(image)\n\n if not single_image and args.random_flip and random.random() < 0.5:\n # flip\n image = train_flip(image)\n if args.center_crop or single_image:\n y1 = max(0, int(round((image.height - args.resolution) / 2.0)))\n x1 = max(0, int(round((image.width - args.resolution) / 2.0)))\n image = train_crop(image)\n else:\n y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))\n image = crop(image, y1, x1, h, w)\n crop_top_left = (y1, x1)\n self.crop_top_lefts.append(crop_top_left)\n image = train_transforms(image)\n self.pixel_values.append(image)\n\n self.num_instance_images = len(self.instance_images)\n self._length = self.num_instance_images\n\n if class_data_root is not None:\n self.class_data_root = Path(class_data_root)\n self.class_data_root.mkdir(parents=True, exist_ok=True)\n self.class_images_path = list(self.class_data_root.iterdir())\n\n self.original_sizes_class_imgs = []\n self.crop_top_lefts_class_imgs = []\n self.pixel_values_class_imgs = []\n self.class_images = [Image.open(path) for path in self.class_images_path]\n for image in self.class_images:\n image = exif_transpose(image)\n if not image.mode == \"RGB\":\n image = image.convert(\"RGB\")\n self.original_sizes_class_imgs.append((image.height, image.width))\n image = train_resize(image)\n if args.random_flip and random.random() < 0.5:\n # flip\n image = train_flip(image)\n if args.center_crop:\n y1 = max(0, int(round((image.height - args.resolution) / 2.0)))\n x1 = max(0, int(round((image.width - args.resolution) / 2.0)))\n image = train_crop(image)\n else:\n y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))\n image = crop(image, y1, x1, h, w)\n crop_top_left = (y1, x1)\n self.crop_top_lefts_class_imgs.append(crop_top_left)\n image = train_transforms(image)\n self.pixel_values_class_imgs.append(image)\n\n if class_num is not None:\n self.num_class_images = min(len(self.class_images_path), class_num)\n else:\n self.num_class_images = len(self.class_images_path)\n self._length = max(self.num_class_images, self.num_instance_images)\n else:\n self.class_data_root = None\n\n self.image_transforms = transforms.Compose(\n [\n transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, index):\n example = {}\n example[\"instance_images\"] = self.pixel_values[index % self.num_instance_images]\n example[\"original_size\"] = self.original_sizes[index % self.num_instance_images]\n example[\"crop_top_left\"] = self.crop_top_lefts[index % self.num_instance_images]\n\n if self.custom_instance_prompts:\n caption = self.custom_instance_prompts[index % self.num_instance_images]\n if caption:\n if self.train_text_encoder_ti:\n # replace instances of --token_abstraction in caption with the new tokens: \"\" etc.\n for token_abs, token_replacement in self.token_abstraction_dict.items():\n caption = caption.replace(token_abs, \"\".join(token_replacement))\n example[\"instance_prompt\"] = caption\n else:\n example[\"instance_prompt\"] = self.instance_prompt\n\n else: # costum prompts were provided, but length does not match size of image dataset\n example[\"instance_prompt\"] = self.instance_prompt\n\n if self.class_data_root:\n example[\"class_prompt\"] = self.class_prompt\n example[\"class_images\"] = self.pixel_values_class_imgs[index % self.num_class_images]\n example[\"class_original_size\"] = self.original_sizes_class_imgs[index % self.num_class_images]\n example[\"class_crop_top_left\"] = self.crop_top_lefts_class_imgs[index % self.num_class_images]\n\n return example\n\n\ndef collate_fn(examples, with_prior_preservation=False):\n pixel_values = [example[\"instance_images\"] for example in examples]\n prompts = [example[\"instance_prompt\"] for example in examples]\n original_sizes = [example[\"original_size\"] for example in examples]\n crop_top_lefts = [example[\"crop_top_left\"] for example in examples]\n\n # Concat class and instance examples for prior preservation.\n # We do this to avoid doing two forward passes.\n if with_prior_preservation:\n pixel_values += [example[\"class_images\"] for example in examples]\n prompts += [example[\"class_prompt\"] for example in examples]\n original_sizes += [example[\"class_original_size\"] for example in examples]\n crop_top_lefts += [example[\"class_crop_top_left\"] for example in examples]\n\n pixel_values = torch.stack(pixel_values)\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n\n batch = {\n \"pixel_values\": pixel_values,\n \"prompts\": prompts,\n \"original_sizes\": original_sizes,\n \"crop_top_lefts\": crop_top_lefts,\n }\n return batch\n\n\nclass PromptDataset(Dataset):\n \"\"\"A simple dataset to prepare the prompts to generate class images on multiple GPUs.\"\"\"\n\n def __init__(self, prompt, num_samples):\n self.prompt = prompt\n self.num_samples = num_samples\n\n def __len__(self):\n return self.num_samples\n\n def __getitem__(self, index):\n example = {}\n example[\"prompt\"] = self.prompt\n example[\"index\"] = index\n return example\n\n\ndef tokenize_prompt(tokenizer, prompt, add_special_tokens=False):\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n add_special_tokens=add_special_tokens,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n return text_input_ids\n\n\n# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt\ndef encode_prompt(text_encoders, tokenizers, prompt, text_input_ids_list=None):\n prompt_embeds_list = []\n\n for i, text_encoder in enumerate(text_encoders):\n if tokenizers is not None:\n tokenizer = tokenizers[i]\n text_input_ids = tokenize_prompt(tokenizer, prompt)\n else:\n assert text_input_ids_list is not None\n text_input_ids = text_input_ids_list[i]\n\n prompt_embeds = text_encoder(\n text_input_ids.to(text_encoder.device),\n output_hidden_states=True,\n )\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds.hidden_states[-2]\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)\n return prompt_embeds, pooled_prompt_embeds\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n if args.do_edm_style_training and args.snr_gamma is not None:\n raise ValueError(\"Min-SNR formulation is not supported when conducting EDM-style training.\")\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n kwargs_handlers=[kwargs],\n )\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n import wandb\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Generate class images if prior preservation is enabled.\n if args.with_prior_preservation:\n class_images_dir = Path(args.class_data_dir)\n if not class_images_dir.exists():\n class_images_dir.mkdir(parents=True)\n cur_class_images = len(list(class_images_dir.iterdir()))\n\n if cur_class_images < args.num_class_images:\n torch_dtype = torch.float16 if accelerator.device.type == \"cuda\" else torch.float32\n if args.prior_generation_precision == \"fp32\":\n torch_dtype = torch.float32\n elif args.prior_generation_precision == \"fp16\":\n torch_dtype = torch.float16\n elif args.prior_generation_precision == \"bf16\":\n torch_dtype = torch.bfloat16\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n torch_dtype=torch_dtype,\n revision=args.revision,\n variant=args.variant,\n )\n pipeline.set_progress_bar_config(disable=True)\n\n num_new_images = args.num_class_images - cur_class_images\n logger.info(f\"Number of class images to sample: {num_new_images}.\")\n\n sample_dataset = PromptDataset(args.class_prompt, num_new_images)\n sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)\n\n sample_dataloader = accelerator.prepare(sample_dataloader)\n pipeline.to(accelerator.device)\n\n for example in tqdm(\n sample_dataloader, desc=\"Generating class images\", disable=not accelerator.is_local_main_process\n ):\n images = pipeline(example[\"prompt\"]).images\n\n for i, image in enumerate(images):\n hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()\n image_filename = class_images_dir / f\"{example['index'][i] + cur_class_images}-{hash_image}.jpg\"\n image.save(image_filename)\n\n del pipeline\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n model_id = args.hub_model_id or Path(args.output_dir).name\n repo_id = None\n if args.push_to_hub:\n repo_id = create_repo(repo_id=model_id, exist_ok=True, token=args.hub_token).repo_id\n\n # Load the tokenizers\n tokenizer_one = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n variant=args.variant,\n use_fast=False,\n )\n tokenizer_two = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer_2\",\n revision=args.revision,\n variant=args.variant,\n use_fast=False,\n )\n\n # import correct text encoder classes\n text_encoder_cls_one = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision\n )\n text_encoder_cls_two = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision, subfolder=\"text_encoder_2\"\n )\n\n # Load scheduler and models\n scheduler_type = determine_scheduler_type(args.pretrained_model_name_or_path, args.revision)\n if \"EDM\" in scheduler_type:\n args.do_edm_style_training = True\n noise_scheduler = EDMEulerScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n logger.info(\"Performing EDM-style training!\")\n elif args.do_edm_style_training:\n noise_scheduler = EulerDiscreteScheduler.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"scheduler\"\n )\n logger.info(\"Performing EDM-style training!\")\n else:\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n\n text_encoder_one = text_encoder_cls_one.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n text_encoder_two = text_encoder_cls_two.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder_2\", revision=args.revision, variant=args.variant\n )\n vae_path = (\n args.pretrained_model_name_or_path\n if args.pretrained_vae_model_name_or_path is None\n else args.pretrained_vae_model_name_or_path\n )\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n variant=args.variant,\n )\n latents_mean = latents_std = None\n if hasattr(vae.config, \"latents_mean\") and vae.config.latents_mean is not None:\n latents_mean = torch.tensor(vae.config.latents_mean).view(1, 4, 1, 1)\n if hasattr(vae.config, \"latents_std\") and vae.config.latents_std is not None:\n latents_std = torch.tensor(vae.config.latents_std).view(1, 4, 1, 1)\n\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n if args.train_text_encoder_ti:\n # we parse the provided token identifier (or identifiers) into a list. s.t. - \"TOK\" -> [\"TOK\"], \"TOK,\n # TOK2\" -> [\"TOK\", \"TOK2\"] etc.\n token_abstraction_list = \"\".join(args.token_abstraction.split()).split(\",\")\n logger.info(f\"list of token identifiers: {token_abstraction_list}\")\n\n token_abstraction_dict = {}\n token_idx = 0\n for i, token in enumerate(token_abstraction_list):\n token_abstraction_dict[token] = [\n f\"\" for j in range(args.num_new_tokens_per_abstraction)\n ]\n token_idx += args.num_new_tokens_per_abstraction - 1\n\n # replace instances of --token_abstraction in --instance_prompt with the new tokens: \"\" etc.\n for token_abs, token_replacement in token_abstraction_dict.items():\n args.instance_prompt = args.instance_prompt.replace(token_abs, \"\".join(token_replacement))\n if args.with_prior_preservation:\n args.class_prompt = args.class_prompt.replace(token_abs, \"\".join(token_replacement))\n if args.validation_prompt:\n args.validation_prompt = args.validation_prompt.replace(token_abs, \"\".join(token_replacement))\n print(\"validation prompt:\", args.validation_prompt)\n # initialize the new tokens for textual inversion\n embedding_handler = TokenEmbeddingsHandler(\n [text_encoder_one, text_encoder_two], [tokenizer_one, tokenizer_two]\n )\n inserting_toks = []\n for new_tok in token_abstraction_dict.values():\n inserting_toks.extend(new_tok)\n embedding_handler.initialize_new_tokens(inserting_toks=inserting_toks)\n\n # We only train the additional adapter LoRA layers\n vae.requires_grad_(False)\n text_encoder_one.requires_grad_(False)\n text_encoder_two.requires_grad_(False)\n unet.requires_grad_(False)\n\n # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move unet, vae and text_encoder to device and cast to weight_dtype\n unet.to(accelerator.device, dtype=weight_dtype)\n\n # The VAE is always in float32 to avoid NaN losses.\n vae.to(accelerator.device, dtype=torch.float32)\n\n text_encoder_one.to(accelerator.device, dtype=weight_dtype)\n text_encoder_two.to(accelerator.device, dtype=weight_dtype)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, \"\n \"please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n if args.train_text_encoder:\n text_encoder_one.gradient_checkpointing_enable()\n text_encoder_two.gradient_checkpointing_enable()\n\n # now we will add new LoRA weights to the attention layers\n\n if args.use_blora:\n # if using B-LoRA, the targeted blocks to train are automatically set\n target_modules = get_unet_lora_target_modules(unet, use_blora=True)\n elif args.lora_unet_blocks:\n # if training specific unet blocks not in the B-LoRA scheme\n target_blocks_list = \"\".join(args.lora_unet_blocks.split()).split(\",\")\n logger.info(f\"list of unet blocks to train: {target_blocks_list}\")\n target_modules = get_unet_lora_target_modules(unet, use_blora=False, target_blocks=target_blocks_list)\n else:\n target_modules = [\"to_k\", \"to_q\", \"to_v\", \"to_out.0\"]\n\n unet_lora_config = LoraConfig(\n r=args.rank,\n use_dora=args.use_dora,\n lora_alpha=args.rank,\n init_lora_weights=\"gaussian\",\n target_modules=target_modules,\n )\n unet.add_adapter(unet_lora_config)\n\n # The text encoder comes from 🤗 transformers, so we cannot directly modify it.\n # So, instead, we monkey-patch the forward calls of its attention-blocks.\n if args.train_text_encoder:\n text_lora_config = LoraConfig(\n r=args.rank,\n use_dora=args.use_dora,\n lora_alpha=args.rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"out_proj\"],\n )\n text_encoder_one.add_adapter(text_lora_config)\n text_encoder_two.add_adapter(text_lora_config)\n\n # if we use textual inversion, we freeze all parameters except for the token embeddings\n # in text encoder\n elif args.train_text_encoder_ti:\n text_lora_parameters_one = []\n for name, param in text_encoder_one.named_parameters():\n if \"token_embedding\" in name:\n # ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16\n param.data = param.to(dtype=torch.float32)\n param.requires_grad = True\n text_lora_parameters_one.append(param)\n else:\n param.requires_grad = False\n text_lora_parameters_two = []\n for name, param in text_encoder_two.named_parameters():\n if \"token_embedding\" in name:\n # ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16\n param.data = param.to(dtype=torch.float32)\n param.requires_grad = True\n text_lora_parameters_two.append(param)\n else:\n param.requires_grad = False\n\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n # there are only two options here. Either are just the unet attn processor layers\n # or there are the unet and text encoder atten layers\n unet_lora_layers_to_save = None\n text_encoder_one_lora_layers_to_save = None\n text_encoder_two_lora_layers_to_save = None\n\n for model in models:\n if isinstance(model, type(unwrap_model(unet))):\n unet_lora_layers_to_save = convert_state_dict_to_diffusers(get_peft_model_state_dict(model))\n elif isinstance(model, type(unwrap_model(text_encoder_one))):\n if args.train_text_encoder:\n text_encoder_one_lora_layers_to_save = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(model)\n )\n elif isinstance(model, type(unwrap_model(text_encoder_two))):\n if args.train_text_encoder:\n text_encoder_two_lora_layers_to_save = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(model)\n )\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n StableDiffusionXLPipeline.save_lora_weights(\n output_dir,\n unet_lora_layers=unet_lora_layers_to_save,\n text_encoder_lora_layers=text_encoder_one_lora_layers_to_save,\n text_encoder_2_lora_layers=text_encoder_two_lora_layers_to_save,\n )\n if args.train_text_encoder_ti:\n embedding_handler.save_embeddings(f\"{output_dir}/{args.output_dir}_emb.safetensors\")\n\n def load_model_hook(models, input_dir):\n unet_ = None\n text_encoder_one_ = None\n text_encoder_two_ = None\n\n while len(models) > 0:\n model = models.pop()\n\n if isinstance(model, type(unwrap_model(unet))):\n unet_ = model\n elif isinstance(model, type(unwrap_model(text_encoder_one))):\n text_encoder_one_ = model\n elif isinstance(model, type(unwrap_model(text_encoder_two))):\n text_encoder_two_ = model\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n lora_state_dict, network_alphas = LoraLoaderMixin.lora_state_dict(input_dir)\n\n unet_state_dict = {f'{k.replace(\"unet.\", \"\")}': v for k, v in lora_state_dict.items() if k.startswith(\"unet.\")}\n unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict)\n incompatible_keys = set_peft_model_state_dict(unet_, unet_state_dict, adapter_name=\"default\")\n if incompatible_keys is not None:\n # check only for unexpected keys\n unexpected_keys = getattr(incompatible_keys, \"unexpected_keys\", None)\n if unexpected_keys:\n logger.warning(\n f\"Loading adapter weights from state_dict led to unexpected keys not found in the model: \"\n f\" {unexpected_keys}. \"\n )\n\n if args.train_text_encoder:\n _set_state_dict_into_text_encoder(lora_state_dict, prefix=\"text_encoder.\", text_encoder=text_encoder_one_)\n\n _set_state_dict_into_text_encoder(\n lora_state_dict, prefix=\"text_encoder_2.\", text_encoder=text_encoder_two_\n )\n\n # Make sure the trainable params are in float32. This is again needed since the base models\n # are in `weight_dtype`. More details:\n # https://github.com/huggingface/diffusers/pull/6514#discussion_r1449796804\n if args.mixed_precision == \"fp16\":\n models = [unet_]\n if args.train_text_encoder:\n models.extend([text_encoder_one_, text_encoder_two_])\n # only upcast trainable parameters (LoRA) into fp32\n cast_training_params(models)\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Make sure the trainable params are in float32.\n if args.mixed_precision == \"fp16\":\n models = [unet]\n if args.train_text_encoder:\n models.extend([text_encoder_one, text_encoder_two])\n\n # only upcast trainable parameters (LoRA) into fp32\n cast_training_params(models, dtype=torch.float32)\n\n unet_lora_parameters = list(filter(lambda p: p.requires_grad, unet.parameters()))\n\n if args.train_text_encoder:\n text_lora_parameters_one = list(filter(lambda p: p.requires_grad, text_encoder_one.parameters()))\n text_lora_parameters_two = list(filter(lambda p: p.requires_grad, text_encoder_two.parameters()))\n\n # If neither --train_text_encoder nor --train_text_encoder_ti, text_encoders remain frozen during training\n freeze_text_encoder = not (args.train_text_encoder or args.train_text_encoder_ti)\n\n # Optimization parameters\n unet_lora_parameters_with_lr = {\"params\": unet_lora_parameters, \"lr\": args.learning_rate}\n if not freeze_text_encoder:\n # different learning rate for text encoder and unet\n text_lora_parameters_one_with_lr = {\n \"params\": text_lora_parameters_one,\n \"weight_decay\": args.adam_weight_decay_text_encoder\n if args.adam_weight_decay_text_encoder\n else args.adam_weight_decay,\n \"lr\": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate,\n }\n text_lora_parameters_two_with_lr = {\n \"params\": text_lora_parameters_two,\n \"weight_decay\": args.adam_weight_decay_text_encoder\n if args.adam_weight_decay_text_encoder\n else args.adam_weight_decay,\n \"lr\": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate,\n }\n params_to_optimize = [\n unet_lora_parameters_with_lr,\n text_lora_parameters_one_with_lr,\n text_lora_parameters_two_with_lr,\n ]\n else:\n params_to_optimize = [unet_lora_parameters_with_lr]\n\n # Optimizer creation\n if not (args.optimizer.lower() == \"prodigy\" or args.optimizer.lower() == \"adamw\"):\n logger.warning(\n f\"Unsupported choice of optimizer: {args.optimizer}.Supported optimizers include [adamW, prodigy].\"\n \"Defaulting to adamW\"\n )\n args.optimizer = \"adamw\"\n\n if args.use_8bit_adam and not args.optimizer.lower() == \"adamw\":\n logger.warning(\n f\"use_8bit_adam is ignored when optimizer is not set to 'AdamW'. Optimizer was \"\n f\"set to {args.optimizer.lower()}\"\n )\n\n if args.optimizer.lower() == \"adamw\":\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n optimizer = optimizer_class(\n params_to_optimize,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n if args.optimizer.lower() == \"prodigy\":\n try:\n import prodigyopt\n except ImportError:\n raise ImportError(\"To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`\")\n\n optimizer_class = prodigyopt.Prodigy\n\n if args.learning_rate <= 0.1:\n logger.warning(\n \"Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0\"\n )\n if args.train_text_encoder and args.text_encoder_lr:\n logger.warning(\n f\"Learning rates were provided both for the unet and the text encoder- e.g. text_encoder_lr:\"\n f\" {args.text_encoder_lr} and learning_rate: {args.learning_rate}. \"\n f\"When using prodigy only learning_rate is used as the initial learning rate.\"\n )\n # changes the learning rate of text_encoder_parameters_one and text_encoder_parameters_two to be\n # --learning_rate\n params_to_optimize[1][\"lr\"] = args.learning_rate\n params_to_optimize[2][\"lr\"] = args.learning_rate\n\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n beta3=args.prodigy_beta3,\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n decouple=args.prodigy_decouple,\n use_bias_correction=args.prodigy_use_bias_correction,\n safeguard_warmup=args.prodigy_safeguard_warmup,\n )\n\n # Dataset and DataLoaders creation:\n train_dataset = DreamBoothDataset(\n instance_data_root=args.instance_data_dir,\n instance_prompt=args.instance_prompt,\n class_prompt=args.class_prompt,\n dataset_name=args.dataset_name,\n dataset_config_name=args.dataset_config_name,\n cache_dir=args.cache_dir,\n image_column=args.image_column,\n train_text_encoder_ti=args.train_text_encoder_ti,\n caption_column=args.caption_column,\n class_data_root=args.class_data_dir if args.with_prior_preservation else None,\n token_abstraction_dict=token_abstraction_dict if args.train_text_encoder_ti else None,\n class_num=args.num_class_images,\n size=args.resolution,\n repeats=args.repeats,\n center_crop=args.center_crop,\n )\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n batch_size=args.train_batch_size,\n shuffle=True,\n collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),\n num_workers=args.dataloader_num_workers,\n )\n\n # Computes additional embeddings/ids required by the SDXL UNet.\n # regular text embeddings (when `train_text_encoder` is not True)\n # pooled text embeddings\n # time ids\n\n def compute_time_ids(crops_coords_top_left, original_size=None):\n # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids\n if original_size is None:\n original_size = (args.resolution, args.resolution)\n target_size = (args.resolution, args.resolution)\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n add_time_ids = torch.tensor([add_time_ids])\n add_time_ids = add_time_ids.to(accelerator.device, dtype=weight_dtype)\n return add_time_ids\n\n if not args.train_text_encoder:\n tokenizers = [tokenizer_one, tokenizer_two]\n text_encoders = [text_encoder_one, text_encoder_two]\n\n def compute_text_embeddings(prompt, text_encoders, tokenizers):\n with torch.no_grad():\n prompt_embeds, pooled_prompt_embeds = encode_prompt(text_encoders, tokenizers, prompt)\n prompt_embeds = prompt_embeds.to(accelerator.device)\n pooled_prompt_embeds = pooled_prompt_embeds.to(accelerator.device)\n return prompt_embeds, pooled_prompt_embeds\n\n # If no type of tuning is done on the text_encoder and custom instance prompts are NOT\n # provided (i.e. the --instance_prompt is used for all images), we encode the instance prompt once to avoid\n # the redundant encoding.\n if freeze_text_encoder and not train_dataset.custom_instance_prompts:\n instance_prompt_hidden_states, instance_pooled_prompt_embeds = compute_text_embeddings(\n args.instance_prompt, text_encoders, tokenizers\n )\n\n # Handle class prompt for prior-preservation.\n if args.with_prior_preservation:\n if freeze_text_encoder:\n class_prompt_hidden_states, class_pooled_prompt_embeds = compute_text_embeddings(\n args.class_prompt, text_encoders, tokenizers\n )\n\n # Clear the memory here\n if freeze_text_encoder and not train_dataset.custom_instance_prompts:\n del tokenizers, text_encoders\n gc.collect()\n torch.cuda.empty_cache()\n\n # If custom instance prompts are NOT provided (i.e. the instance prompt is used for all images),\n # pack the statically computed variables appropriately here. This is so that we don't\n # have to pass them to the dataloader.\n\n # if --train_text_encoder_ti we need add_special_tokens to be True fo textual inversion\n add_special_tokens = True if args.train_text_encoder_ti else False\n\n if not train_dataset.custom_instance_prompts:\n if freeze_text_encoder:\n prompt_embeds = instance_prompt_hidden_states\n unet_add_text_embeds = instance_pooled_prompt_embeds\n if args.with_prior_preservation:\n prompt_embeds = torch.cat([prompt_embeds, class_prompt_hidden_states], dim=0)\n unet_add_text_embeds = torch.cat([unet_add_text_embeds, class_pooled_prompt_embeds], dim=0)\n # if we're optmizing the text encoder (both if instance prompt is used for all images or custom prompts) we need to tokenize and encode the\n # batch prompts on all training steps\n else:\n tokens_one = tokenize_prompt(tokenizer_one, args.instance_prompt, add_special_tokens)\n tokens_two = tokenize_prompt(tokenizer_two, args.instance_prompt, add_special_tokens)\n if args.with_prior_preservation:\n class_tokens_one = tokenize_prompt(tokenizer_one, args.class_prompt, add_special_tokens)\n class_tokens_two = tokenize_prompt(tokenizer_two, args.class_prompt, add_special_tokens)\n tokens_one = torch.cat([tokens_one, class_tokens_one], dim=0)\n tokens_two = torch.cat([tokens_two, class_tokens_two], dim=0)\n\n if args.cache_latents:\n latents_cache = []\n for batch in tqdm(train_dataloader, desc=\"Caching latents\"):\n with torch.no_grad():\n batch[\"pixel_values\"] = batch[\"pixel_values\"].to(\n accelerator.device, non_blocking=True, dtype=torch.float32\n )\n latents_cache.append(vae.encode(batch[\"pixel_values\"]).latent_dist)\n\n if args.validation_prompt is None:\n del vae\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_cycles=args.lr_num_cycles,\n power=args.lr_power,\n )\n\n # Prepare everything with our `accelerator`.\n if not freeze_text_encoder:\n unet, text_encoder_one, text_encoder_two, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, text_encoder_one, text_encoder_two, optimizer, train_dataloader, lr_scheduler\n )\n else:\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_name = (\n \"dreambooth-lora-sd-xl\"\n if \"playground\" not in args.pretrained_model_name_or_path\n else \"dreambooth-lora-playground\"\n )\n accelerator.init_trackers(tracker_name, config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num batches each epoch = {len(train_dataloader)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the mos recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):\n sigmas = noise_scheduler.sigmas.to(device=accelerator.device, dtype=dtype)\n schedule_timesteps = noise_scheduler.timesteps.to(accelerator.device)\n timesteps = timesteps.to(accelerator.device)\n\n step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]\n\n sigma = sigmas[step_indices].flatten()\n while len(sigma.shape) < n_dim:\n sigma = sigma.unsqueeze(-1)\n return sigma\n\n if args.train_text_encoder:\n num_train_epochs_text_encoder = int(args.train_text_encoder_frac * args.num_train_epochs)\n elif args.train_text_encoder_ti: # args.train_text_encoder_ti\n num_train_epochs_text_encoder = int(args.train_text_encoder_ti_frac * args.num_train_epochs)\n # flag used for textual inversion\n pivoted = False\n for epoch in range(first_epoch, args.num_train_epochs):\n unet.train()\n # if performing any kind of optimization of text_encoder params\n if args.train_text_encoder or args.train_text_encoder_ti:\n if epoch == num_train_epochs_text_encoder:\n print(\"PIVOT HALFWAY\", epoch)\n # stopping optimization of text_encoder params\n # this flag is used to reset the optimizer to optimize only on unet params\n pivoted = True\n\n else:\n # still optimizing the text encoder\n text_encoder_one.train()\n text_encoder_two.train()\n # set top parameter requires_grad = True for gradient checkpointing works\n if args.train_text_encoder:\n text_encoder_one.text_model.embeddings.requires_grad_(True)\n text_encoder_two.text_model.embeddings.requires_grad_(True)\n\n for step, batch in enumerate(train_dataloader):\n if pivoted:\n # stopping optimization of text_encoder params\n # re setting the optimizer to optimize only on unet params\n optimizer.param_groups[1][\"lr\"] = 0.0\n optimizer.param_groups[2][\"lr\"] = 0.0\n\n with accelerator.accumulate(unet):\n prompts = batch[\"prompts\"]\n # encode batch prompts when custom prompts are provided for each image -\n if train_dataset.custom_instance_prompts:\n if freeze_text_encoder:\n prompt_embeds, unet_add_text_embeds = compute_text_embeddings(\n prompts, text_encoders, tokenizers\n )\n\n else:\n tokens_one = tokenize_prompt(tokenizer_one, prompts, add_special_tokens)\n tokens_two = tokenize_prompt(tokenizer_two, prompts, add_special_tokens)\n\n if args.cache_latents:\n model_input = latents_cache[step].sample()\n else:\n pixel_values = batch[\"pixel_values\"].to(dtype=vae.dtype)\n model_input = vae.encode(pixel_values).latent_dist.sample()\n\n if latents_mean is None and latents_std is None:\n model_input = model_input * vae.config.scaling_factor\n if args.pretrained_vae_model_name_or_path is None:\n model_input = model_input.to(weight_dtype)\n else:\n latents_mean = latents_mean.to(device=model_input.device, dtype=model_input.dtype)\n latents_std = latents_std.to(device=model_input.device, dtype=model_input.dtype)\n model_input = (model_input - latents_mean) * vae.config.scaling_factor / latents_std\n model_input = model_input.to(dtype=weight_dtype)\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(model_input)\n if args.noise_offset:\n # https://www.crosslabs.org//blog/diffusion-with-offset-noise\n noise += args.noise_offset * torch.randn(\n (model_input.shape[0], model_input.shape[1], 1, 1), device=model_input.device\n )\n\n bsz = model_input.shape[0]\n\n # Sample a random timestep for each image\n if not args.do_edm_style_training:\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device\n )\n timesteps = timesteps.long()\n else:\n # in EDM formulation, the model is conditioned on the pre-conditioned noise levels\n # instead of discrete timesteps, so here we sample indices to get the noise levels\n # from `scheduler.timesteps`\n indices = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,))\n timesteps = noise_scheduler.timesteps[indices].to(device=model_input.device)\n\n # Add noise to the model input according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)\n # For EDM-style training, we first obtain the sigmas based on the continuous timesteps.\n # We then precondition the final model inputs based on these sigmas instead of the timesteps.\n # Follow: Section 5 of https://arxiv.org/abs/2206.00364.\n if args.do_edm_style_training:\n sigmas = get_sigmas(timesteps, len(noisy_model_input.shape), noisy_model_input.dtype)\n if \"EDM\" in scheduler_type:\n inp_noisy_latents = noise_scheduler.precondition_inputs(noisy_model_input, sigmas)\n else:\n inp_noisy_latents = noisy_model_input / ((sigmas**2 + 1) ** 0.5)\n\n # time ids\n add_time_ids = torch.cat(\n [\n compute_time_ids(original_size=s, crops_coords_top_left=c)\n for s, c in zip(batch[\"original_sizes\"], batch[\"crop_top_lefts\"])\n ]\n )\n\n # Calculate the elements to repeat depending on the use of prior-preservation and custom captions.\n if not train_dataset.custom_instance_prompts:\n elems_to_repeat_text_embeds = bsz // 2 if args.with_prior_preservation else bsz\n\n else:\n elems_to_repeat_text_embeds = 1\n\n # Predict the noise residual\n if freeze_text_encoder:\n unet_added_conditions = {\n \"time_ids\": add_time_ids,\n # \"time_ids\": add_time_ids.repeat(elems_to_repeat_time_ids, 1),\n \"text_embeds\": unet_add_text_embeds.repeat(elems_to_repeat_text_embeds, 1),\n }\n prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat_text_embeds, 1, 1)\n model_pred = unet(\n inp_noisy_latents if args.do_edm_style_training else noisy_model_input,\n timesteps,\n prompt_embeds_input,\n added_cond_kwargs=unet_added_conditions,\n return_dict=False,\n )[0]\n else:\n unet_added_conditions = {\"time_ids\": add_time_ids}\n prompt_embeds, pooled_prompt_embeds = encode_prompt(\n text_encoders=[text_encoder_one, text_encoder_two],\n tokenizers=None,\n prompt=None,\n text_input_ids_list=[tokens_one, tokens_two],\n )\n unet_added_conditions.update(\n {\"text_embeds\": pooled_prompt_embeds.repeat(elems_to_repeat_text_embeds, 1)}\n )\n prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat_text_embeds, 1, 1)\n model_pred = unet(\n inp_noisy_latents if args.do_edm_style_training else noisy_model_input,\n timesteps,\n prompt_embeds_input,\n added_cond_kwargs=unet_added_conditions,\n return_dict=False,\n )[0]\n\n weighting = None\n if args.do_edm_style_training:\n # Similar to the input preconditioning, the model predictions are also preconditioned\n # on noised model inputs (before preconditioning) and the sigmas.\n # Follow: Section 5 of https://arxiv.org/abs/2206.00364.\n if \"EDM\" in scheduler_type:\n model_pred = noise_scheduler.precondition_outputs(noisy_model_input, model_pred, sigmas)\n else:\n if noise_scheduler.config.prediction_type == \"epsilon\":\n model_pred = model_pred * (-sigmas) + noisy_model_input\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n model_pred = model_pred * (-sigmas / (sigmas**2 + 1) ** 0.5) + (\n noisy_model_input / (sigmas**2 + 1)\n )\n # We are not doing weighting here because it tends result in numerical problems.\n # See: https://github.com/huggingface/diffusers/pull/7126#issuecomment-1968523051\n # There might be other alternatives for weighting as well:\n # https://github.com/huggingface/diffusers/pull/7126#discussion_r1505404686\n if \"EDM\" not in scheduler_type:\n weighting = (sigmas**-2.0).float()\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = model_input if args.do_edm_style_training else noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = (\n model_input\n if args.do_edm_style_training\n else noise_scheduler.get_velocity(model_input, noise, timesteps)\n )\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n if args.with_prior_preservation:\n # Chunk the noise and model_pred into two parts and compute the loss on each part separately.\n model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)\n target, target_prior = torch.chunk(target, 2, dim=0)\n\n # Compute prior loss\n if weighting is not None:\n prior_loss = torch.mean(\n (weighting.float() * (model_pred_prior.float() - target_prior.float()) ** 2).reshape(\n target_prior.shape[0], -1\n ),\n 1,\n )\n prior_loss = prior_loss.mean()\n else:\n prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction=\"mean\")\n\n if args.snr_gamma is None:\n if weighting is not None:\n loss = torch.mean(\n (weighting.float() * (model_pred.float() - target.float()) ** 2).reshape(\n target.shape[0], -1\n ),\n 1,\n )\n loss = loss.mean()\n else:\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n else:\n # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.\n # Since we predict the noise instead of x_0, the original formulation is slightly changed.\n # This is discussed in Section 4.2 of the same paper.\n\n if args.with_prior_preservation:\n # if we're using prior preservation, we calc snr for instance loss only -\n # and hence only need timesteps corresponding to instance images\n snr_timesteps, _ = torch.chunk(timesteps, 2, dim=0)\n else:\n snr_timesteps = timesteps\n\n snr = compute_snr(noise_scheduler, snr_timesteps)\n base_weight = (\n torch.stack([snr, args.snr_gamma * torch.ones_like(snr_timesteps)], dim=1).min(dim=1)[0] / snr\n )\n\n if noise_scheduler.config.prediction_type == \"v_prediction\":\n # Velocity objective needs to be floored to an SNR weight of one.\n mse_loss_weights = base_weight + 1\n else:\n # Epsilon and sample both use the same loss weights.\n mse_loss_weights = base_weight\n\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"none\")\n loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights\n loss = loss.mean()\n\n if args.with_prior_preservation:\n # Add the prior loss to the instance loss.\n loss = loss + args.prior_loss_weight * prior_loss\n\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n params_to_clip = (\n itertools.chain(unet_lora_parameters, text_lora_parameters_one, text_lora_parameters_two)\n if (args.train_text_encoder or args.train_text_encoder_ti)\n else unet_lora_parameters\n )\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # every step, we reset the embeddings to the original embeddings.\n if args.train_text_encoder_ti:\n embedding_handler.retract_embeddings()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompt is not None and epoch % args.validation_epochs == 0:\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n # create pipeline\n if freeze_text_encoder:\n text_encoder_one = text_encoder_cls_one.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"text_encoder\",\n revision=args.revision,\n variant=args.variant,\n )\n text_encoder_two = text_encoder_cls_two.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"text_encoder_2\",\n revision=args.revision,\n variant=args.variant,\n )\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n tokenizer=tokenizer_one,\n tokenizer_2=tokenizer_two,\n text_encoder=accelerator.unwrap_model(text_encoder_one),\n text_encoder_2=accelerator.unwrap_model(text_encoder_two),\n unet=accelerator.unwrap_model(unet),\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n\n # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it\n scheduler_args = {}\n\n if not args.do_edm_style_training:\n if \"variance_type\" in pipeline.scheduler.config:\n variance_type = pipeline.scheduler.config.variance_type\n\n if variance_type in [\"learned\", \"learned_range\"]:\n variance_type = \"fixed_small\"\n\n scheduler_args[\"variance_type\"] = variance_type\n\n pipeline.scheduler = DPMSolverMultistepScheduler.from_config(\n pipeline.scheduler.config, **scheduler_args\n )\n\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n pipeline_args = {\"prompt\": args.validation_prompt}\n if torch.backends.mps.is_available() or \"playground\" in args.pretrained_model_name_or_path:\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type)\n\n with autocast_ctx:\n images = [\n pipeline(**pipeline_args, generator=generator).images[0]\n for _ in range(args.num_validation_images)\n ]\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\")\n for i, image in enumerate(images)\n ]\n }\n )\n\n del pipeline\n torch.cuda.empty_cache()\n\n # Save the lora layers\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = accelerator.unwrap_model(unet)\n unet = unet.to(torch.float32)\n unet_lora_layers = convert_state_dict_to_diffusers(get_peft_model_state_dict(unet))\n\n if args.train_text_encoder:\n text_encoder_one = accelerator.unwrap_model(text_encoder_one)\n text_encoder_lora_layers = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(text_encoder_one.to(torch.float32))\n )\n text_encoder_two = accelerator.unwrap_model(text_encoder_two)\n text_encoder_2_lora_layers = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(text_encoder_two.to(torch.float32))\n )\n else:\n text_encoder_lora_layers = None\n text_encoder_2_lora_layers = None\n\n StableDiffusionXLPipeline.save_lora_weights(\n save_directory=args.output_dir,\n unet_lora_layers=unet_lora_layers,\n text_encoder_lora_layers=text_encoder_lora_layers,\n text_encoder_2_lora_layers=text_encoder_2_lora_layers,\n )\n\n if args.train_text_encoder_ti:\n embeddings_path = f\"{args.output_dir}/{args.output_dir}_emb.safetensors\"\n embedding_handler.save_embeddings(embeddings_path)\n\n images = []\n if args.validation_prompt and args.num_validation_images > 0:\n # Final inference\n # Load previous pipeline\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n\n # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it\n scheduler_args = {}\n\n if not args.do_edm_style_training:\n if \"variance_type\" in pipeline.scheduler.config:\n variance_type = pipeline.scheduler.config.variance_type\n\n if variance_type in [\"learned\", \"learned_range\"]:\n variance_type = \"fixed_small\"\n\n scheduler_args[\"variance_type\"] = variance_type\n\n pipeline.scheduler = DPMSolverMultistepScheduler.from_config(\n pipeline.scheduler.config, **scheduler_args\n )\n\n # load attention processors\n pipeline.load_lora_weights(args.output_dir)\n\n # load new tokens\n if args.train_text_encoder_ti:\n state_dict = load_file(embeddings_path)\n all_new_tokens = []\n for key, value in token_abstraction_dict.items():\n all_new_tokens.extend(value)\n pipeline.load_textual_inversion(\n state_dict[\"clip_l\"],\n token=all_new_tokens,\n text_encoder=pipeline.text_encoder,\n tokenizer=pipeline.tokenizer,\n )\n pipeline.load_textual_inversion(\n state_dict[\"clip_g\"],\n token=all_new_tokens,\n text_encoder=pipeline.text_encoder_2,\n tokenizer=pipeline.tokenizer_2,\n )\n\n # run inference\n pipeline = pipeline.to(accelerator.device)\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n images = [\n pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]\n for _ in range(args.num_validation_images)\n ]\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"test\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"test\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\")\n for i, image in enumerate(images)\n ]\n }\n )\n\n # Conver to WebUI format\n lora_state_dict = load_file(f\"{args.output_dir}/pytorch_lora_weights.safetensors\")\n peft_state_dict = convert_all_state_dict_to_peft(lora_state_dict)\n kohya_state_dict = convert_state_dict_to_kohya(peft_state_dict)\n save_file(kohya_state_dict, f\"{args.output_dir}/{args.output_dir}.safetensors\")\n\n save_model_card(\n model_id if not args.push_to_hub else repo_id,\n use_dora=args.use_dora,\n images=images,\n base_model=args.pretrained_model_name_or_path,\n train_text_encoder=args.train_text_encoder,\n train_text_encoder_ti=args.train_text_encoder_ti,\n token_abstraction_dict=train_dataset.token_abstraction_dict,\n instance_prompt=args.instance_prompt,\n validation_prompt=args.validation_prompt,\n repo_folder=args.output_dir,\n vae_path=args.pretrained_vae_model_name_or_path,\n )\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/amused/README.md", "content": "## Amused training\n\nAmused can be finetuned on simple datasets relatively cheaply and quickly. Using 8bit optimizers, lora, and gradient accumulation, amused can be finetuned with as little as 5.5 GB. Here are a set of examples for finetuning amused on some relatively simple datasets. These training recipies are aggressively oriented towards minimal resources and fast verification -- i.e. the batch sizes are quite low and the learning rates are quite high. For optimal quality, you will probably want to increase the batch sizes and decrease learning rates.\n\nAll training examples use fp16 mixed precision and gradient checkpointing. We don't show 8 bit adam + lora as its about the same memory use as just using lora (bitsandbytes uses full precision optimizer states for weights below a minimum size).\n\n### Finetuning the 256 checkpoint\n\nThese examples finetune on this [nouns](https://huggingface.co/datasets/m1guelpf/nouns) dataset.\n\nExample results:\n\n![noun1](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/amused/noun1.png) ![noun2](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/amused/noun2.png) ![noun3](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/amused/noun3.png)\n\n\n#### Full finetuning\n\nBatch size: 8, Learning rate: 1e-4, Gives decent results in 750-1000 steps\n\n| Batch Size | Gradient Accumulation Steps | Effective Total Batch Size | Memory Used |\n|------------|-----------------------------|------------------|-------------|\n| 8 | 1 | 8 | 19.7 GB |\n| 4 | 2 | 8 | 18.3 GB |\n| 1 | 8 | 8 | 17.9 GB |\n\n```sh\naccelerate launch train_amused.py \\\n --output_dir \\\n --train_batch_size \\\n --gradient_accumulation_steps \\\n --learning_rate 1e-4 \\\n --pretrained_model_name_or_path amused/amused-256 \\\n --instance_data_dataset 'm1guelpf/nouns' \\\n --image_key image \\\n --prompt_key text \\\n --resolution 256 \\\n --mixed_precision fp16 \\\n --lr_scheduler constant \\\n --validation_prompts \\\n 'a pixel art character with square red glasses, a baseball-shaped head and a orange-colored body on a dark background' \\\n 'a pixel art character with square orange glasses, a lips-shaped head and a red-colored body on a light background' \\\n 'a pixel art character with square blue glasses, a microwave-shaped head and a purple-colored body on a sunny background' \\\n 'a pixel art character with square red glasses, a baseball-shaped head and a blue-colored body on an orange background' \\\n 'a pixel art character with square red glasses' \\\n 'a pixel art character' \\\n 'square red glasses on a pixel art character' \\\n 'square red glasses on a pixel art character with a baseball-shaped head' \\\n --max_train_steps 10000 \\\n --checkpointing_steps 500 \\\n --validation_steps 250 \\\n --gradient_checkpointing\n```\n\n#### Full finetuning + 8 bit adam\n\nNote that this training config keeps the batch size low and the learning rate high to get results fast with low resources. However, due to 8 bit adam, it will diverge eventually. If you want to train for longer, you will have to up the batch size and lower the learning rate.\n\nBatch size: 16, Learning rate: 2e-5, Gives decent results in ~750 steps\n\n| Batch Size | Gradient Accumulation Steps | Effective Total Batch Size | Memory Used |\n|------------|-----------------------------|------------------|-------------|\n| 16 | 1 | 16 | 20.1 GB |\n| 8 | 2 | 16 | 15.6 GB |\n| 1 | 16 | 16 | 10.7 GB |\n\n```sh\naccelerate launch train_amused.py \\\n --output_dir \\\n --train_batch_size \\\n --gradient_accumulation_steps \\\n --learning_rate 2e-5 \\\n --use_8bit_adam \\\n --pretrained_model_name_or_path amused/amused-256 \\\n --instance_data_dataset 'm1guelpf/nouns' \\\n --image_key image \\\n --prompt_key text \\\n --resolution 256 \\\n --mixed_precision fp16 \\\n --lr_scheduler constant \\\n --validation_prompts \\\n 'a pixel art character with square red glasses, a baseball-shaped head and a orange-colored body on a dark background' \\\n 'a pixel art character with square orange glasses, a lips-shaped head and a red-colored body on a light background' \\\n 'a pixel art character with square blue glasses, a microwave-shaped head and a purple-colored body on a sunny background' \\\n 'a pixel art character with square red glasses, a baseball-shaped head and a blue-colored body on an orange background' \\\n 'a pixel art character with square red glasses' \\\n 'a pixel art character' \\\n 'square red glasses on a pixel art character' \\\n 'square red glasses on a pixel art character with a baseball-shaped head' \\\n --max_train_steps 10000 \\\n --checkpointing_steps 500 \\\n --validation_steps 250 \\\n --gradient_checkpointing\n```\n\n#### Full finetuning + lora\n\nBatch size: 16, Learning rate: 8e-4, Gives decent results in 1000-1250 steps\n\n| Batch Size | Gradient Accumulation Steps | Effective Total Batch Size | Memory Used |\n|------------|-----------------------------|------------------|-------------|\n| 16 | 1 | 16 | 14.1 GB |\n| 8 | 2 | 16 | 10.1 GB |\n| 1 | 16 | 16 | 6.5 GB |\n\n```sh\naccelerate launch train_amused.py \\\n --output_dir \\\n --train_batch_size \\\n --gradient_accumulation_steps \\\n --learning_rate 8e-4 \\\n --use_lora \\\n --pretrained_model_name_or_path amused/amused-256 \\\n --instance_data_dataset 'm1guelpf/nouns' \\\n --image_key image \\\n --prompt_key text \\\n --resolution 256 \\\n --mixed_precision fp16 \\\n --lr_scheduler constant \\\n --validation_prompts \\\n 'a pixel art character with square red glasses, a baseball-shaped head and a orange-colored body on a dark background' \\\n 'a pixel art character with square orange glasses, a lips-shaped head and a red-colored body on a light background' \\\n 'a pixel art character with square blue glasses, a microwave-shaped head and a purple-colored body on a sunny background' \\\n 'a pixel art character with square red glasses, a baseball-shaped head and a blue-colored body on an orange background' \\\n 'a pixel art character with square red glasses' \\\n 'a pixel art character' \\\n 'square red glasses on a pixel art character' \\\n 'square red glasses on a pixel art character with a baseball-shaped head' \\\n --max_train_steps 10000 \\\n --checkpointing_steps 500 \\\n --validation_steps 250 \\\n --gradient_checkpointing\n```\n\n### Finetuning the 512 checkpoint\n\nThese examples finetune on this [minecraft](https://huggingface.co/monadical-labs/minecraft-preview) dataset.\n\nExample results:\n\n![minecraft1](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/amused/minecraft1.png) ![minecraft2](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/amused/minecraft2.png) ![minecraft3](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/amused/minecraft3.png)\n\n#### Full finetuning\n\nBatch size: 8, Learning rate: 8e-5, Gives decent results in 500-1000 steps\n\n| Batch Size | Gradient Accumulation Steps | Effective Total Batch Size | Memory Used |\n|------------|-----------------------------|------------------|-------------|\n| 8 | 1 | 8 | 24.2 GB |\n| 4 | 2 | 8 | 19.7 GB |\n| 1 | 8 | 8 | 16.99 GB |\n\n```sh\naccelerate launch train_amused.py \\\n --output_dir \\\n --train_batch_size \\\n --gradient_accumulation_steps \\\n --learning_rate 8e-5 \\\n --pretrained_model_name_or_path amused/amused-512 \\\n --instance_data_dataset 'monadical-labs/minecraft-preview' \\\n --prompt_prefix 'minecraft ' \\\n --image_key image \\\n --prompt_key text \\\n --resolution 512 \\\n --mixed_precision fp16 \\\n --lr_scheduler constant \\\n --validation_prompts \\\n 'minecraft Avatar' \\\n 'minecraft character' \\\n 'minecraft' \\\n 'minecraft president' \\\n 'minecraft pig' \\\n --max_train_steps 10000 \\\n --checkpointing_steps 500 \\\n --validation_steps 250 \\\n --gradient_checkpointing\n```\n\n#### Full finetuning + 8 bit adam\n\nBatch size: 8, Learning rate: 5e-6, Gives decent results in 500-1000 steps\n\n| Batch Size | Gradient Accumulation Steps | Effective Total Batch Size | Memory Used |\n|------------|-----------------------------|------------------|-------------|\n| 8 | 1 | 8 | 21.2 GB |\n| 4 | 2 | 8 | 13.3 GB |\n| 1 | 8 | 8 | 9.9 GB |\n\n```sh\naccelerate launch train_amused.py \\\n --output_dir \\\n --train_batch_size \\\n --gradient_accumulation_steps \\\n --learning_rate 5e-6 \\\n --pretrained_model_name_or_path amused/amused-512 \\\n --instance_data_dataset 'monadical-labs/minecraft-preview' \\\n --prompt_prefix 'minecraft ' \\\n --image_key image \\\n --prompt_key text \\\n --resolution 512 \\\n --mixed_precision fp16 \\\n --lr_scheduler constant \\\n --validation_prompts \\\n 'minecraft Avatar' \\\n 'minecraft character' \\\n 'minecraft' \\\n 'minecraft president' \\\n 'minecraft pig' \\\n --max_train_steps 10000 \\\n --checkpointing_steps 500 \\\n --validation_steps 250 \\\n --gradient_checkpointing\n```\n\n#### Full finetuning + lora \n\nBatch size: 8, Learning rate: 1e-4, Gives decent results in 500-1000 steps\n\n| Batch Size | Gradient Accumulation Steps | Effective Total Batch Size | Memory Used |\n|------------|-----------------------------|------------------|-------------|\n| 8 | 1 | 8 | 12.7 GB |\n| 4 | 2 | 8 | 9.0 GB |\n| 1 | 8 | 8 | 5.6 GB |\n\n```sh\naccelerate launch train_amused.py \\\n --output_dir \\\n --train_batch_size \\\n --gradient_accumulation_steps \\\n --learning_rate 1e-4 \\\n --use_lora \\\n --pretrained_model_name_or_path amused/amused-512 \\\n --instance_data_dataset 'monadical-labs/minecraft-preview' \\\n --prompt_prefix 'minecraft ' \\\n --image_key image \\\n --prompt_key text \\\n --resolution 512 \\\n --mixed_precision fp16 \\\n --lr_scheduler constant \\\n --validation_prompts \\\n 'minecraft Avatar' \\\n 'minecraft character' \\\n 'minecraft' \\\n 'minecraft president' \\\n 'minecraft pig' \\\n --max_train_steps 10000 \\\n --checkpointing_steps 500 \\\n --validation_steps 250 \\\n --gradient_checkpointing\n```\n\n### Styledrop\n\n[Styledrop](https://arxiv.org/abs/2306.00983) is an efficient finetuning method for learning a new style from just one or very few images. It has an optional first stage to generate human picked additional training samples. The additional training samples can be used to augment the initial images. Our examples exclude the optional additional image selection stage and instead we just finetune on a single image.\n\nThis is our example style image:\n![example](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/amused/A%20mushroom%20in%20%5BV%5D%20style.png)\n\nDownload it to your local directory with\n```sh\nwget https://huggingface.co/datasets/diffusers/docs-images/resolve/main/amused/A%20mushroom%20in%20%5BV%5D%20style.png\n```\n\n#### 256\n\nExample results:\n\n![glowing_256_1](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/amused/glowing_256_1.png) ![glowing_256_2](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/amused/glowing_256_2.png) ![glowing_256_3](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/amused/glowing_256_3.png)\n\nLearning rate: 4e-4, Gives decent results in 1500-2000 steps\n\nMemory used: 6.5 GB \n\n```sh\naccelerate launch train_amused.py \\\n --output_dir \\\n --mixed_precision fp16 \\\n --report_to wandb \\\n --use_lora \\\n --pretrained_model_name_or_path amused/amused-256 \\\n --train_batch_size 1 \\\n --lr_scheduler constant \\\n --learning_rate 4e-4 \\\n --validation_prompts \\\n 'A chihuahua walking on the street in [V] style' \\\n 'A banana on the table in [V] style' \\\n 'A church on the street in [V] style' \\\n 'A tabby cat walking in the forest in [V] style' \\\n --instance_data_image 'A mushroom in [V] style.png' \\\n --max_train_steps 10000 \\\n --checkpointing_steps 500 \\\n --validation_steps 100 \\\n --resolution 256\n```\n\n#### 512\n\nExample results:\n\n![glowing_512_1](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/amused/glowing_512_1.png) ![glowing_512_2](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/amused/glowing_512_2.png) ![glowing_512_3](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/amused/glowing_512_3.png)\n\nLearning rate: 1e-3, Lora alpha 1, Gives decent results in 1500-2000 steps\n\nMemory used: 5.6 GB \n\n```\naccelerate launch train_amused.py \\\n --output_dir \\\n --mixed_precision fp16 \\\n --report_to wandb \\\n --use_lora \\\n --pretrained_model_name_or_path amused/amused-512 \\\n --train_batch_size 1 \\\n --lr_scheduler constant \\\n --learning_rate 1e-3 \\\n --validation_prompts \\\n 'A chihuahua walking on the street in [V] style' \\\n 'A banana on the table in [V] style' \\\n 'A church on the street in [V] style' \\\n 'A tabby cat walking in the forest in [V] style' \\\n --instance_data_image 'A mushroom in [V] style.png' \\\n --max_train_steps 100000 \\\n --checkpointing_steps 500 \\\n --validation_steps 100 \\\n --resolution 512 \\\n --lora_alpha 1\n```" }, { "path": "diffusers/examples/amused/train_amused.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport argparse\nimport copy\nimport logging\nimport math\nimport os\nimport shutil\nfrom contextlib import nullcontext\nfrom pathlib import Path\n\nimport torch\nimport torch.nn.functional as F\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom peft import LoraConfig\nfrom peft.utils import get_peft_model_state_dict\nfrom PIL import Image\nfrom PIL.ImageOps import exif_transpose\nfrom torch.utils.data import DataLoader, Dataset, default_collate\nfrom torchvision import transforms\nfrom transformers import (\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n)\n\nimport diffusers.optimization\nfrom diffusers import AmusedPipeline, AmusedScheduler, EMAModel, UVit2DModel, VQModel\nfrom diffusers.loaders import LoraLoaderMixin\nfrom diffusers.utils import is_wandb_available\n\n\nif is_wandb_available():\n import wandb\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--instance_data_dataset\",\n type=str,\n default=None,\n required=False,\n help=\"A Hugging Face dataset containing the training images\",\n )\n parser.add_argument(\n \"--instance_data_dir\",\n type=str,\n default=None,\n required=False,\n help=\"A folder containing the training data of instance images.\",\n )\n parser.add_argument(\n \"--instance_data_image\", type=str, default=None, required=False, help=\"A single training image\"\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\"--use_ema\", action=\"store_true\", help=\"Whether to use EMA model.\")\n parser.add_argument(\"--ema_decay\", type=float, default=0.9999)\n parser.add_argument(\"--ema_update_after_step\", type=int, default=0)\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"muse_training\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. \"\n \"In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference.\"\n \"Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components.\"\n \"See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step\"\n \"instructions.\"\n ),\n )\n parser.add_argument(\n \"--logging_steps\",\n type=int,\n default=50,\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\n \"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`.\"\n \" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state\"\n \" for more details\"\n ),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=0.0003,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=100,\n help=(\n \"Run validation every X steps. Validation consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`\"\n \" and logging the images.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"wandb\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\"--validation_prompts\", type=str, nargs=\"*\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\"--split_vae_encode\", type=int, required=False, default=None)\n parser.add_argument(\"--min_masking_rate\", type=float, default=0.0)\n parser.add_argument(\"--cond_dropout_prob\", type=float, default=0.0)\n parser.add_argument(\"--max_grad_norm\", default=None, type=float, help=\"Max gradient norm.\", required=False)\n parser.add_argument(\"--use_lora\", action=\"store_true\", help=\"Fine tune the model using LoRa\")\n parser.add_argument(\"--text_encoder_use_lora\", action=\"store_true\", help=\"Fine tune the model using LoRa\")\n parser.add_argument(\"--lora_r\", default=16, type=int)\n parser.add_argument(\"--lora_alpha\", default=32, type=int)\n parser.add_argument(\"--lora_target_modules\", default=[\"to_q\", \"to_k\", \"to_v\"], type=str, nargs=\"+\")\n parser.add_argument(\"--text_encoder_lora_r\", default=16, type=int)\n parser.add_argument(\"--text_encoder_lora_alpha\", default=32, type=int)\n parser.add_argument(\"--text_encoder_lora_target_modules\", default=[\"to_q\", \"to_k\", \"to_v\"], type=str, nargs=\"+\")\n parser.add_argument(\"--train_text_encoder\", action=\"store_true\")\n parser.add_argument(\"--image_key\", type=str, required=False)\n parser.add_argument(\"--prompt_key\", type=str, required=False)\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\"--prompt_prefix\", type=str, required=False, default=None)\n\n args = parser.parse_args()\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n\n num_datasources = sum(\n [x is not None for x in [args.instance_data_dir, args.instance_data_image, args.instance_data_dataset]]\n )\n\n if num_datasources != 1:\n raise ValueError(\n \"provide one and only one of `--instance_data_dir`, `--instance_data_image`, or `--instance_data_dataset`\"\n )\n\n if args.instance_data_dir is not None:\n if not os.path.exists(args.instance_data_dir):\n raise ValueError(f\"Does not exist: `--args.instance_data_dir` {args.instance_data_dir}\")\n\n if args.instance_data_image is not None:\n if not os.path.exists(args.instance_data_image):\n raise ValueError(f\"Does not exist: `--args.instance_data_image` {args.instance_data_image}\")\n\n if args.instance_data_dataset is not None and (args.image_key is None or args.prompt_key is None):\n raise ValueError(\"`--instance_data_dataset` requires setting `--image_key` and `--prompt_key`\")\n\n return args\n\n\nclass InstanceDataRootDataset(Dataset):\n def __init__(\n self,\n instance_data_root,\n tokenizer,\n size=512,\n ):\n self.size = size\n self.tokenizer = tokenizer\n self.instance_images_path = list(Path(instance_data_root).iterdir())\n\n def __len__(self):\n return len(self.instance_images_path)\n\n def __getitem__(self, index):\n image_path = self.instance_images_path[index % len(self.instance_images_path)]\n instance_image = Image.open(image_path)\n rv = process_image(instance_image, self.size)\n\n prompt = os.path.splitext(os.path.basename(image_path))[0]\n rv[\"prompt_input_ids\"] = tokenize_prompt(self.tokenizer, prompt)[0]\n return rv\n\n\nclass InstanceDataImageDataset(Dataset):\n def __init__(\n self,\n instance_data_image,\n train_batch_size,\n size=512,\n ):\n self.value = process_image(Image.open(instance_data_image), size)\n self.train_batch_size = train_batch_size\n\n def __len__(self):\n # Needed so a full batch of the data can be returned. Otherwise will return\n # batches of size 1\n return self.train_batch_size\n\n def __getitem__(self, index):\n return self.value\n\n\nclass HuggingFaceDataset(Dataset):\n def __init__(\n self,\n hf_dataset,\n tokenizer,\n image_key,\n prompt_key,\n prompt_prefix=None,\n size=512,\n ):\n self.size = size\n self.image_key = image_key\n self.prompt_key = prompt_key\n self.tokenizer = tokenizer\n self.hf_dataset = hf_dataset\n self.prompt_prefix = prompt_prefix\n\n def __len__(self):\n return len(self.hf_dataset)\n\n def __getitem__(self, index):\n item = self.hf_dataset[index]\n\n rv = process_image(item[self.image_key], self.size)\n\n prompt = item[self.prompt_key]\n\n if self.prompt_prefix is not None:\n prompt = self.prompt_prefix + prompt\n\n rv[\"prompt_input_ids\"] = tokenize_prompt(self.tokenizer, prompt)[0]\n\n return rv\n\n\ndef process_image(image, size):\n image = exif_transpose(image)\n\n if not image.mode == \"RGB\":\n image = image.convert(\"RGB\")\n\n orig_height = image.height\n orig_width = image.width\n\n image = transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR)(image)\n\n c_top, c_left, _, _ = transforms.RandomCrop.get_params(image, output_size=(size, size))\n image = transforms.functional.crop(image, c_top, c_left, size, size)\n\n image = transforms.ToTensor()(image)\n\n micro_conds = torch.tensor(\n [orig_width, orig_height, c_top, c_left, 6.0],\n )\n\n return {\"image\": image, \"micro_conds\": micro_conds}\n\n\ndef tokenize_prompt(tokenizer, prompt):\n return tokenizer(\n prompt,\n truncation=True,\n padding=\"max_length\",\n max_length=77,\n return_tensors=\"pt\",\n ).input_ids\n\n\ndef encode_prompt(text_encoder, input_ids):\n outputs = text_encoder(input_ids, return_dict=True, output_hidden_states=True)\n encoder_hidden_states = outputs.hidden_states[-2]\n cond_embeds = outputs[0]\n return encoder_hidden_states, cond_embeds\n\n\ndef main(args):\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n if accelerator.is_main_process:\n os.makedirs(args.output_dir, exist_ok=True)\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n\n if accelerator.is_main_process:\n accelerator.init_trackers(\"amused\", config=vars(copy.deepcopy(args)))\n\n if args.seed is not None:\n set_seed(args.seed)\n\n # TODO - will have to fix loading if training text encoder\n text_encoder = CLIPTextModelWithProjection.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n tokenizer = CLIPTokenizer.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"tokenizer\", revision=args.revision, variant=args.variant\n )\n vq_model = VQModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"vqvae\", revision=args.revision, variant=args.variant\n )\n\n if args.train_text_encoder:\n if args.text_encoder_use_lora:\n lora_config = LoraConfig(\n r=args.text_encoder_lora_r,\n lora_alpha=args.text_encoder_lora_alpha,\n target_modules=args.text_encoder_lora_target_modules,\n )\n text_encoder.add_adapter(lora_config)\n text_encoder.train()\n text_encoder.requires_grad_(True)\n else:\n text_encoder.eval()\n text_encoder.requires_grad_(False)\n\n vq_model.requires_grad_(False)\n\n model = UVit2DModel.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"transformer\",\n revision=args.revision,\n variant=args.variant,\n )\n\n if args.use_lora:\n lora_config = LoraConfig(\n r=args.lora_r,\n lora_alpha=args.lora_alpha,\n target_modules=args.lora_target_modules,\n )\n model.add_adapter(lora_config)\n\n model.train()\n\n if args.gradient_checkpointing:\n model.enable_gradient_checkpointing()\n if args.train_text_encoder:\n text_encoder.gradient_checkpointing_enable()\n\n if args.use_ema:\n ema = EMAModel(\n model.parameters(),\n decay=args.ema_decay,\n update_after_step=args.ema_update_after_step,\n model_cls=UVit2DModel,\n model_config=model.config,\n )\n\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n transformer_lora_layers_to_save = None\n text_encoder_lora_layers_to_save = None\n\n for model_ in models:\n if isinstance(model_, type(accelerator.unwrap_model(model))):\n if args.use_lora:\n transformer_lora_layers_to_save = get_peft_model_state_dict(model_)\n else:\n model_.save_pretrained(os.path.join(output_dir, \"transformer\"))\n elif isinstance(model_, type(accelerator.unwrap_model(text_encoder))):\n if args.text_encoder_use_lora:\n text_encoder_lora_layers_to_save = get_peft_model_state_dict(model_)\n else:\n model_.save_pretrained(os.path.join(output_dir, \"text_encoder\"))\n else:\n raise ValueError(f\"unexpected save model: {model_.__class__}\")\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n if transformer_lora_layers_to_save is not None or text_encoder_lora_layers_to_save is not None:\n LoraLoaderMixin.save_lora_weights(\n output_dir,\n transformer_lora_layers=transformer_lora_layers_to_save,\n text_encoder_lora_layers=text_encoder_lora_layers_to_save,\n )\n\n if args.use_ema:\n ema.save_pretrained(os.path.join(output_dir, \"ema_model\"))\n\n def load_model_hook(models, input_dir):\n transformer = None\n text_encoder_ = None\n\n while len(models) > 0:\n model_ = models.pop()\n\n if isinstance(model_, type(accelerator.unwrap_model(model))):\n if args.use_lora:\n transformer = model_\n else:\n load_model = UVit2DModel.from_pretrained(os.path.join(input_dir, \"transformer\"))\n model_.load_state_dict(load_model.state_dict())\n del load_model\n elif isinstance(model, type(accelerator.unwrap_model(text_encoder))):\n if args.text_encoder_use_lora:\n text_encoder_ = model_\n else:\n load_model = CLIPTextModelWithProjection.from_pretrained(os.path.join(input_dir, \"text_encoder\"))\n model_.load_state_dict(load_model.state_dict())\n del load_model\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n if transformer is not None or text_encoder_ is not None:\n lora_state_dict, network_alphas = LoraLoaderMixin.lora_state_dict(input_dir)\n LoraLoaderMixin.load_lora_into_text_encoder(\n lora_state_dict, network_alphas=network_alphas, text_encoder=text_encoder_\n )\n LoraLoaderMixin.load_lora_into_transformer(\n lora_state_dict, network_alphas=network_alphas, transformer=transformer\n )\n\n if args.use_ema:\n load_from = EMAModel.from_pretrained(os.path.join(input_dir, \"ema_model\"), model_cls=UVit2DModel)\n ema.load_state_dict(load_from.state_dict())\n del load_from\n\n accelerator.register_load_state_pre_hook(load_model_hook)\n accelerator.register_save_state_pre_hook(save_model_hook)\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n )\n\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`\"\n )\n\n optimizer_cls = bnb.optim.AdamW8bit\n else:\n optimizer_cls = torch.optim.AdamW\n\n # no decay on bias and layernorm and embedding\n no_decay = [\"bias\", \"layer_norm.weight\", \"mlm_ln.weight\", \"embeddings.weight\"]\n optimizer_grouped_parameters = [\n {\n \"params\": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],\n \"weight_decay\": args.adam_weight_decay,\n },\n {\n \"params\": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],\n \"weight_decay\": 0.0,\n },\n ]\n\n if args.train_text_encoder:\n optimizer_grouped_parameters.append(\n {\"params\": text_encoder.parameters(), \"weight_decay\": args.adam_weight_decay}\n )\n\n optimizer = optimizer_cls(\n optimizer_grouped_parameters,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n logger.info(\"Creating dataloaders and lr_scheduler\")\n\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n if args.instance_data_dir is not None:\n dataset = InstanceDataRootDataset(\n instance_data_root=args.instance_data_dir,\n tokenizer=tokenizer,\n size=args.resolution,\n )\n elif args.instance_data_image is not None:\n dataset = InstanceDataImageDataset(\n instance_data_image=args.instance_data_image,\n train_batch_size=args.train_batch_size,\n size=args.resolution,\n )\n elif args.instance_data_dataset is not None:\n dataset = HuggingFaceDataset(\n hf_dataset=load_dataset(args.instance_data_dataset, split=\"train\"),\n tokenizer=tokenizer,\n image_key=args.image_key,\n prompt_key=args.prompt_key,\n prompt_prefix=args.prompt_prefix,\n size=args.resolution,\n )\n else:\n assert False\n\n train_dataloader = DataLoader(\n dataset,\n batch_size=args.train_batch_size,\n shuffle=True,\n num_workers=args.dataloader_num_workers,\n collate_fn=default_collate,\n )\n train_dataloader.num_batches = len(train_dataloader)\n\n lr_scheduler = diffusers.optimization.get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n )\n\n logger.info(\"Preparing model, optimizer and dataloaders\")\n\n if args.train_text_encoder:\n model, optimizer, lr_scheduler, train_dataloader, text_encoder = accelerator.prepare(\n model, optimizer, lr_scheduler, train_dataloader, text_encoder\n )\n else:\n model, optimizer, lr_scheduler, train_dataloader = accelerator.prepare(\n model, optimizer, lr_scheduler, train_dataloader\n )\n\n train_dataloader.num_batches = len(train_dataloader)\n\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n if not args.train_text_encoder:\n text_encoder.to(device=accelerator.device, dtype=weight_dtype)\n\n vq_model.to(device=accelerator.device)\n\n if args.use_ema:\n ema.to(accelerator.device)\n\n with nullcontext() if args.train_text_encoder else torch.no_grad():\n empty_embeds, empty_clip_embeds = encode_prompt(\n text_encoder, tokenize_prompt(tokenizer, \"\").to(text_encoder.device, non_blocking=True)\n )\n\n # There is a single image, we can just pre-encode the single prompt\n if args.instance_data_image is not None:\n prompt = os.path.splitext(os.path.basename(args.instance_data_image))[0]\n encoder_hidden_states, cond_embeds = encode_prompt(\n text_encoder, tokenize_prompt(tokenizer, prompt).to(text_encoder.device, non_blocking=True)\n )\n encoder_hidden_states = encoder_hidden_states.repeat(args.train_batch_size, 1, 1)\n cond_embeds = cond_embeds.repeat(args.train_batch_size, 1)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / args.gradient_accumulation_steps)\n # Afterwards we recalculate our number of training epochs.\n # Note: We are not doing epoch based training here, but just using this for book keeping and being able to\n # reuse the same training loop with other datasets/loaders.\n num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # Train!\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num training steps = {args.max_train_steps}\")\n logger.info(f\" Instantaneous batch size per device = { args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n\n resume_from_checkpoint = args.resume_from_checkpoint\n if resume_from_checkpoint:\n if resume_from_checkpoint == \"latest\":\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n if len(dirs) > 0:\n resume_from_checkpoint = os.path.join(args.output_dir, dirs[-1])\n else:\n resume_from_checkpoint = None\n\n if resume_from_checkpoint is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n else:\n accelerator.print(f\"Resuming from checkpoint {resume_from_checkpoint}\")\n\n if resume_from_checkpoint is None:\n global_step = 0\n first_epoch = 0\n else:\n accelerator.load_state(resume_from_checkpoint)\n global_step = int(os.path.basename(resume_from_checkpoint).split(\"-\")[1])\n first_epoch = global_step // num_update_steps_per_epoch\n\n # As stated above, we are not doing epoch based training here, but just using this for book keeping and being able to\n # reuse the same training loop with other datasets/loaders.\n for epoch in range(first_epoch, num_train_epochs):\n for batch in train_dataloader:\n with torch.no_grad():\n micro_conds = batch[\"micro_conds\"].to(accelerator.device, non_blocking=True)\n pixel_values = batch[\"image\"].to(accelerator.device, non_blocking=True)\n\n batch_size = pixel_values.shape[0]\n\n split_batch_size = args.split_vae_encode if args.split_vae_encode is not None else batch_size\n num_splits = math.ceil(batch_size / split_batch_size)\n image_tokens = []\n for i in range(num_splits):\n start_idx = i * split_batch_size\n end_idx = min((i + 1) * split_batch_size, batch_size)\n bs = pixel_values.shape[0]\n image_tokens.append(\n vq_model.quantize(vq_model.encode(pixel_values[start_idx:end_idx]).latents)[2][2].reshape(\n bs, -1\n )\n )\n image_tokens = torch.cat(image_tokens, dim=0)\n\n batch_size, seq_len = image_tokens.shape\n\n timesteps = torch.rand(batch_size, device=image_tokens.device)\n mask_prob = torch.cos(timesteps * math.pi * 0.5)\n mask_prob = mask_prob.clip(args.min_masking_rate)\n\n num_token_masked = (seq_len * mask_prob).round().clamp(min=1)\n batch_randperm = torch.rand(batch_size, seq_len, device=image_tokens.device).argsort(dim=-1)\n mask = batch_randperm < num_token_masked.unsqueeze(-1)\n\n mask_id = accelerator.unwrap_model(model).config.vocab_size - 1\n input_ids = torch.where(mask, mask_id, image_tokens)\n labels = torch.where(mask, image_tokens, -100)\n\n if args.cond_dropout_prob > 0.0:\n assert encoder_hidden_states is not None\n\n batch_size = encoder_hidden_states.shape[0]\n\n mask = (\n torch.zeros((batch_size, 1, 1), device=encoder_hidden_states.device).float().uniform_(0, 1)\n < args.cond_dropout_prob\n )\n\n empty_embeds_ = empty_embeds.expand(batch_size, -1, -1)\n encoder_hidden_states = torch.where(\n (encoder_hidden_states * mask).bool(), encoder_hidden_states, empty_embeds_\n )\n\n empty_clip_embeds_ = empty_clip_embeds.expand(batch_size, -1)\n cond_embeds = torch.where((cond_embeds * mask.squeeze(-1)).bool(), cond_embeds, empty_clip_embeds_)\n\n bs = input_ids.shape[0]\n vae_scale_factor = 2 ** (len(vq_model.config.block_out_channels) - 1)\n resolution = args.resolution // vae_scale_factor\n input_ids = input_ids.reshape(bs, resolution, resolution)\n\n if \"prompt_input_ids\" in batch:\n with nullcontext() if args.train_text_encoder else torch.no_grad():\n encoder_hidden_states, cond_embeds = encode_prompt(\n text_encoder, batch[\"prompt_input_ids\"].to(accelerator.device, non_blocking=True)\n )\n\n # Train Step\n with accelerator.accumulate(model):\n codebook_size = accelerator.unwrap_model(model).config.codebook_size\n\n logits = (\n model(\n input_ids=input_ids,\n encoder_hidden_states=encoder_hidden_states,\n micro_conds=micro_conds,\n pooled_text_emb=cond_embeds,\n )\n .reshape(bs, codebook_size, -1)\n .permute(0, 2, 1)\n .reshape(-1, codebook_size)\n )\n\n loss = F.cross_entropy(\n logits,\n labels.view(-1),\n ignore_index=-100,\n reduction=\"mean\",\n )\n\n # Gather the losses across all processes for logging (if we use distributed training).\n avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n avg_masking_rate = accelerator.gather(mask_prob.repeat(args.train_batch_size)).mean()\n\n accelerator.backward(loss)\n\n if args.max_grad_norm is not None and accelerator.sync_gradients:\n accelerator.clip_grad_norm_(model.parameters(), args.max_grad_norm)\n\n optimizer.step()\n lr_scheduler.step()\n\n optimizer.zero_grad(set_to_none=True)\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n if args.use_ema:\n ema.step(model.parameters())\n\n if (global_step + 1) % args.logging_steps == 0:\n logs = {\n \"step_loss\": avg_loss.item(),\n \"lr\": lr_scheduler.get_last_lr()[0],\n \"avg_masking_rate\": avg_masking_rate.item(),\n }\n accelerator.log(logs, step=global_step + 1)\n\n logger.info(\n f\"Step: {global_step + 1} \"\n f\"Loss: {avg_loss.item():0.4f} \"\n f\"LR: {lr_scheduler.get_last_lr()[0]:0.6f}\"\n )\n\n if (global_step + 1) % args.checkpointing_steps == 0:\n save_checkpoint(args, accelerator, global_step + 1)\n\n if (global_step + 1) % args.validation_steps == 0 and accelerator.is_main_process:\n if args.use_ema:\n ema.store(model.parameters())\n ema.copy_to(model.parameters())\n\n with torch.no_grad():\n logger.info(\"Generating images...\")\n\n model.eval()\n\n if args.train_text_encoder:\n text_encoder.eval()\n\n scheduler = AmusedScheduler.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"scheduler\",\n revision=args.revision,\n variant=args.variant,\n )\n\n pipe = AmusedPipeline(\n transformer=accelerator.unwrap_model(model),\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n vqvae=vq_model,\n scheduler=scheduler,\n )\n\n pil_images = pipe(prompt=args.validation_prompts).images\n wandb_images = [\n wandb.Image(image, caption=args.validation_prompts[i])\n for i, image in enumerate(pil_images)\n ]\n\n wandb.log({\"generated_images\": wandb_images}, step=global_step + 1)\n\n model.train()\n\n if args.train_text_encoder:\n text_encoder.train()\n\n if args.use_ema:\n ema.restore(model.parameters())\n\n global_step += 1\n\n # Stop training if max steps is reached\n if global_step >= args.max_train_steps:\n break\n # End for\n\n accelerator.wait_for_everyone()\n\n # Evaluate and save checkpoint at the end of training\n save_checkpoint(args, accelerator, global_step)\n\n # Save the final trained checkpoint\n if accelerator.is_main_process:\n model = accelerator.unwrap_model(model)\n if args.use_ema:\n ema.copy_to(model.parameters())\n model.save_pretrained(args.output_dir)\n\n accelerator.end_training()\n\n\ndef save_checkpoint(args, accelerator, global_step):\n output_dir = args.output_dir\n\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if accelerator.is_main_process and args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = Path(output_dir) / f\"checkpoint-{global_step}\"\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n\nif __name__ == \"__main__\":\n main(parse_args())\n" }, { "path": "diffusers/examples/community/README.md", "content": "# Community Pipeline Examples\n\n> **For more information about community pipelines, please have a look at [this issue](https://github.com/huggingface/diffusers/issues/841).**\n\n**Community pipeline** examples consist pipelines that have been added by the community.\nPlease have a look at the following tables to get an overview of all community examples. Click on the **Code Example** to get a copy-and-paste ready code example that you can try out.\nIf a community pipeline doesn't work as expected, please open an issue and ping the author on it.\n\nPlease also check out our [Community Scripts](https://github.com/huggingface/diffusers/blob/main/examples/community/README_community_scripts.md) examples for tips and tricks that you can use with diffusers without having to run a community pipeline.\n\n| Example | Description | Code Example | Colab | Author |\n|:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------:|\n|Differential Diffusion|[Differential Diffusion](https://github.com/exx8/differential-diffusion) modifies an image according to a text prompt, and according to a map that specifies the amount of change in each region.|[Differential Diffusion](#differential-diffusion)|[![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/exx8/differential-diffusion) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/exx8/differential-diffusion/blob/main/examples/SD2.ipynb)|[Eran Levin](https://github.com/exx8) and [Ohad Fried](https://www.ohadf.com/)|\n| HD-Painter | [HD-Painter](https://github.com/Picsart-AI-Research/HD-Painter) enables prompt-faithfull and high resolution (up to 2k) image inpainting upon any diffusion-based image inpainting method. | [HD-Painter](#hd-painter) | [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/PAIR/HD-Painter) | [Manukyan Hayk](https://github.com/haikmanukyan) and [Sargsyan Andranik](https://github.com/AndranikSargsyan) |\n| Marigold Monocular Depth Estimation | A universal monocular depth estimator, utilizing Stable Diffusion, delivering sharp predictions in the wild. (See the [project page](https://marigoldmonodepth.github.io) and [full codebase](https://github.com/prs-eth/marigold) for more details.) | [Marigold Depth Estimation](#marigold-depth-estimation) | [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/toshas/marigold) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/12G8reD13DdpMie5ZQlaFNo2WCGeNUH-u?usp=sharing) | [Bingxin Ke](https://github.com/markkua) and [Anton Obukhov](https://github.com/toshas) |\n| LLM-grounded Diffusion (LMD+) | LMD greatly improves the prompt following ability of text-to-image generation models by introducing an LLM as a front-end prompt parser and layout planner. [Project page.](https://llm-grounded-diffusion.github.io/) [See our full codebase (also with diffusers).](https://github.com/TonyLianLong/LLM-groundedDiffusion) | [LLM-grounded Diffusion (LMD+)](#llm-grounded-diffusion) | [Huggingface Demo](https://huggingface.co/spaces/longlian/llm-grounded-diffusion) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1SXzMSeAB-LJYISb2yrUOdypLz4OYWUKj) | [Long (Tony) Lian](https://tonylian.com/) |\n| CLIP Guided Stable Diffusion | Doing CLIP guidance for text to image generation with Stable Diffusion | [CLIP Guided Stable Diffusion](#clip-guided-stable-diffusion) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/CLIP_Guided_Stable_diffusion_with_diffusers.ipynb) | [Suraj Patil](https://github.com/patil-suraj/) |\n| One Step U-Net (Dummy) | Example showcasing of how to use Community Pipelines (see ) | [One Step U-Net](#one-step-unet) | - | [Patrick von Platen](https://github.com/patrickvonplaten/) |\n| Stable Diffusion Interpolation | Interpolate the latent space of Stable Diffusion between different prompts/seeds | [Stable Diffusion Interpolation](#stable-diffusion-interpolation) | - | [Nate Raw](https://github.com/nateraw/) |\n| Stable Diffusion Mega | **One** Stable Diffusion Pipeline with all functionalities of [Text2Image](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py), [Image2Image](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py) and [Inpainting](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py) | [Stable Diffusion Mega](#stable-diffusion-mega) | - | [Patrick von Platen](https://github.com/patrickvonplaten/) |\n| Long Prompt Weighting Stable Diffusion | **One** Stable Diffusion Pipeline without tokens length limit, and support parsing weighting in prompt. | [Long Prompt Weighting Stable Diffusion](#long-prompt-weighting-stable-diffusion) | - | [SkyTNT](https://github.com/SkyTNT) |\n| Speech to Image | Using automatic-speech-recognition to transcribe text and Stable Diffusion to generate images | [Speech to Image](#speech-to-image) | - | [Mikail Duzenli](https://github.com/MikailINTech)\n| Wild Card Stable Diffusion | Stable Diffusion Pipeline that supports prompts that contain wildcard terms (indicated by surrounding double underscores), with values instantiated randomly from a corresponding txt file or a dictionary of possible values | [Wildcard Stable Diffusion](#wildcard-stable-diffusion) | - | [Shyam Sudhakaran](https://github.com/shyamsn97) |\n| [Composable Stable Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/) | Stable Diffusion Pipeline that supports prompts that contain \"|\" in prompts (as an AND condition) and weights (separated by \"|\" as well) to positively / negatively weight prompts. | [Composable Stable Diffusion](#composable-stable-diffusion) | - | [Mark Rich](https://github.com/MarkRich) |\n| Seed Resizing Stable Diffusion | Stable Diffusion Pipeline that supports resizing an image and retaining the concepts of the 512 by 512 generation. | [Seed Resizing](#seed-resizing) | - | [Mark Rich](https://github.com/MarkRich) |\n| Imagic Stable Diffusion | Stable Diffusion Pipeline that enables writing a text prompt to edit an existing image | [Imagic Stable Diffusion](#imagic-stable-diffusion) | - | [Mark Rich](https://github.com/MarkRich) |\n| Multilingual Stable Diffusion | Stable Diffusion Pipeline that supports prompts in 50 different languages. | [Multilingual Stable Diffusion](#multilingual-stable-diffusion-pipeline) | - | [Juan Carlos Piñeros](https://github.com/juancopi81) |\n| GlueGen Stable Diffusion | Stable Diffusion Pipeline that supports prompts in different languages using GlueGen adapter. | [GlueGen Stable Diffusion](#gluegen-stable-diffusion-pipeline) | - | [Phạm Hồng Vinh](https://github.com/rootonchair) |\n| Image to Image Inpainting Stable Diffusion | Stable Diffusion Pipeline that enables the overlaying of two images and subsequent inpainting | [Image to Image Inpainting Stable Diffusion](#image-to-image-inpainting-stable-diffusion) | - | [Alex McKinney](https://github.com/vvvm23) |\n| Text Based Inpainting Stable Diffusion | Stable Diffusion Inpainting Pipeline that enables passing a text prompt to generate the mask for inpainting | [Text Based Inpainting Stable Diffusion](#image-to-image-inpainting-stable-diffusion) | - | [Dhruv Karan](https://github.com/unography) |\n| Bit Diffusion | Diffusion on discrete data | [Bit Diffusion](#bit-diffusion) | - | [Stuti R.](https://github.com/kingstut) |\n| K-Diffusion Stable Diffusion | Run Stable Diffusion with any of [K-Diffusion's samplers](https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/sampling.py) | [Stable Diffusion with K Diffusion](#stable-diffusion-with-k-diffusion) | - | [Patrick von Platen](https://github.com/patrickvonplaten/) |\n| Checkpoint Merger Pipeline | Diffusion Pipeline that enables merging of saved model checkpoints | [Checkpoint Merger Pipeline](#checkpoint-merger-pipeline) | - | [Naga Sai Abhinay Devarinti](https://github.com/Abhinay1997/) |\n| Stable Diffusion v1.1-1.4 Comparison | Run all 4 model checkpoints for Stable Diffusion and compare their results together | [Stable Diffusion Comparison](#stable-diffusion-comparisons) | - | [Suvaditya Mukherjee](https://github.com/suvadityamuk) |\n| MagicMix | Diffusion Pipeline for semantic mixing of an image and a text prompt | [MagicMix](#magic-mix) | - | [Partho Das](https://github.com/daspartho) |\n| Stable UnCLIP | Diffusion Pipeline for combining prior model (generate clip image embedding from text, UnCLIPPipeline `\"kakaobrain/karlo-v1-alpha\"`) and decoder pipeline (decode clip image embedding to image, StableDiffusionImageVariationPipeline `\"lambdalabs/sd-image-variations-diffusers\"` ). | [Stable UnCLIP](#stable-unclip) | - | [Ray Wang](https://wrong.wang) |\n| UnCLIP Text Interpolation Pipeline | Diffusion Pipeline that allows passing two prompts and produces images while interpolating between the text-embeddings of the two prompts | [UnCLIP Text Interpolation Pipeline](#unclip-text-interpolation-pipeline) | - | [Naga Sai Abhinay Devarinti](https://github.com/Abhinay1997/) |\n| UnCLIP Image Interpolation Pipeline | Diffusion Pipeline that allows passing two images/image_embeddings and produces images while interpolating between their image-embeddings | [UnCLIP Image Interpolation Pipeline](#unclip-image-interpolation-pipeline) | - | [Naga Sai Abhinay Devarinti](https://github.com/Abhinay1997/) |\n| DDIM Noise Comparative Analysis Pipeline | Investigating how the diffusion models learn visual concepts from each noise level (which is a contribution of [P2 weighting (CVPR 2022)](https://arxiv.org/abs/2204.00227)) | [DDIM Noise Comparative Analysis Pipeline](#ddim-noise-comparative-analysis-pipeline) | - | [Aengus (Duc-Anh)](https://github.com/aengusng8) |\n| CLIP Guided Img2Img Stable Diffusion Pipeline | Doing CLIP guidance for image to image generation with Stable Diffusion | [CLIP Guided Img2Img Stable Diffusion](#clip-guided-img2img-stable-diffusion) | - | [Nipun Jindal](https://github.com/nipunjindal/) |\n| TensorRT Stable Diffusion Text to Image Pipeline | Accelerates the Stable Diffusion Text2Image Pipeline using TensorRT | [TensorRT Stable Diffusion Text to Image Pipeline](#tensorrt-text2image-stable-diffusion-pipeline) | - | [Asfiya Baig](https://github.com/asfiyab-nvidia) |\n| EDICT Image Editing Pipeline | Diffusion pipeline for text-guided image editing | [EDICT Image Editing Pipeline](#edict-image-editing-pipeline) | - | [Joqsan Azocar](https://github.com/Joqsan) |\n| Stable Diffusion RePaint | Stable Diffusion pipeline using [RePaint](https://arxiv.org/abs/2201.0986) for inpainting. | [Stable Diffusion RePaint](#stable-diffusion-repaint ) | - | [Markus Pobitzer](https://github.com/Markus-Pobitzer) |\n| TensorRT Stable Diffusion Image to Image Pipeline | Accelerates the Stable Diffusion Image2Image Pipeline using TensorRT | [TensorRT Stable Diffusion Image to Image Pipeline](#tensorrt-image2image-stable-diffusion-pipeline) | - | [Asfiya Baig](https://github.com/asfiyab-nvidia) |\n| Stable Diffusion IPEX Pipeline | Accelerate Stable Diffusion inference pipeline with BF16/FP32 precision on Intel Xeon CPUs with [IPEX](https://github.com/intel/intel-extension-for-pytorch) | [Stable Diffusion on IPEX](#stable-diffusion-on-ipex) | - | [Yingjie Han](https://github.com/yingjie-han/) |\n| CLIP Guided Images Mixing Stable Diffusion Pipeline | Сombine images using usual diffusion models. | [CLIP Guided Images Mixing Using Stable Diffusion](#clip-guided-images-mixing-with-stable-diffusion) | - | [Karachev Denis](https://github.com/TheDenk) |\n| TensorRT Stable Diffusion Inpainting Pipeline | Accelerates the Stable Diffusion Inpainting Pipeline using TensorRT | [TensorRT Stable Diffusion Inpainting Pipeline](#tensorrt-inpainting-stable-diffusion-pipeline) | - | [Asfiya Baig](https://github.com/asfiyab-nvidia) |\n| IADB Pipeline | Implementation of [Iterative α-(de)Blending: a Minimalist Deterministic Diffusion Model](https://arxiv.org/abs/2305.03486) | [IADB Pipeline](#iadb-pipeline) | - | [Thomas Chambon](https://github.com/tchambon)\n| Zero1to3 Pipeline | Implementation of [Zero-1-to-3: Zero-shot One Image to 3D Object](https://arxiv.org/abs/2303.11328) | [Zero1to3 Pipeline](#zero1to3-pipeline) | - | [Xin Kong](https://github.com/kxhit) |\n| Stable Diffusion XL Long Weighted Prompt Pipeline | A pipeline support unlimited length of prompt and negative prompt, use A1111 style of prompt weighting | [Stable Diffusion XL Long Weighted Prompt Pipeline](#stable-diffusion-xl-long-weighted-prompt-pipeline) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1LsqilswLR40XLLcp6XFOl5nKb_wOe26W?usp=sharing) | [Andrew Zhu](https://xhinker.medium.com/) |\n| FABRIC - Stable Diffusion with feedback Pipeline | pipeline supports feedback from liked and disliked images | [Stable Diffusion Fabric Pipeline](#stable-diffusion-fabric-pipeline) | - | [Shauray Singh](https://shauray8.github.io/about_shauray/) |\n| sketch inpaint - Inpainting with non-inpaint Stable Diffusion | sketch inpaint much like in automatic1111 | [Masked Im2Im Stable Diffusion Pipeline](#stable-diffusion-masked-im2im) | - | [Anatoly Belikov](https://github.com/noskill) |\n| prompt-to-prompt | change parts of a prompt and retain image structure (see [paper page](https://prompt-to-prompt.github.io/)) | [Prompt2Prompt Pipeline](#prompt2prompt-pipeline) | - | [Umer H. Adil](https://twitter.com/UmerHAdil) |\n| Latent Consistency Pipeline | Implementation of [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://arxiv.org/abs/2310.04378) | [Latent Consistency Pipeline](#latent-consistency-pipeline) | - | [Simian Luo](https://github.com/luosiallen) |\n| Latent Consistency Img2img Pipeline | Img2img pipeline for Latent Consistency Models | [Latent Consistency Img2Img Pipeline](#latent-consistency-img2img-pipeline) | - | [Logan Zoellner](https://github.com/nagolinc) |\n| Latent Consistency Interpolation Pipeline | Interpolate the latent space of Latent Consistency Models with multiple prompts | [Latent Consistency Interpolation Pipeline](#latent-consistency-interpolation-pipeline) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1pK3NrLWJSiJsBynLns1K1-IDTW9zbPvl?usp=sharing) | [Aryan V S](https://github.com/a-r-r-o-w) |\n| SDE Drag Pipeline | The pipeline supports drag editing of images using stochastic differential equations | [SDE Drag Pipeline](#sde-drag-pipeline) | - | [NieShen](https://github.com/NieShenRuc) [Fengqi Zhu](https://github.com/Monohydroxides) |\n| Regional Prompting Pipeline | Assign multiple prompts for different regions | [Regional Prompting Pipeline](#regional-prompting-pipeline) | - | [hako-mikan](https://github.com/hako-mikan) |\n| LDM3D-sr (LDM3D upscaler) | Upscale low resolution RGB and depth inputs to high resolution | [StableDiffusionUpscaleLDM3D Pipeline](https://github.com/estelleafl/diffusers/tree/ldm3d_upscaler_community/examples/community#stablediffusionupscaleldm3d-pipeline) | - | [Estelle Aflalo](https://github.com/estelleafl) |\n| AnimateDiff ControlNet Pipeline | Combines AnimateDiff with precise motion control using ControlNets | [AnimateDiff ControlNet Pipeline](#animatediff-controlnet-pipeline) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1SKboYeGjEQmQPWoFC0aLYpBlYdHXkvAu?usp=sharing) | [Aryan V S](https://github.com/a-r-r-o-w) and [Edoardo Botta](https://github.com/EdoardoBotta) |\n| DemoFusion Pipeline | Implementation of [DemoFusion: Democratising High-Resolution Image Generation With No $$$](https://arxiv.org/abs/2311.16973) | [DemoFusion Pipeline](#demofusion) | - | [Ruoyi Du](https://github.com/RuoyiDu) |\n| Instaflow Pipeline | Implementation of [InstaFlow! One-Step Stable Diffusion with Rectified Flow](https://arxiv.org/abs/2309.06380) | [Instaflow Pipeline](#instaflow-pipeline) | - | [Ayush Mangal](https://github.com/ayushtues) |\n| Null-Text Inversion Pipeline | Implement [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://arxiv.org/abs/2211.09794) as a pipeline. | [Null-Text Inversion](https://github.com/google/prompt-to-prompt/) | - | [Junsheng Luan](https://github.com/Junsheng121) |\n| Rerender A Video Pipeline | Implementation of [[SIGGRAPH Asia 2023] Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation](https://arxiv.org/abs/2306.07954) | [Rerender A Video Pipeline](#rerender-a-video) | - | [Yifan Zhou](https://github.com/SingleZombie) |\n| StyleAligned Pipeline | Implementation of [Style Aligned Image Generation via Shared Attention](https://arxiv.org/abs/2312.02133) | [StyleAligned Pipeline](#stylealigned-pipeline) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/15X2E0jFPTajUIjS0FzX50OaHsCbP2lQ0/view?usp=sharing) | [Aryan V S](https://github.com/a-r-r-o-w) |\n| AnimateDiff Image-To-Video Pipeline | Experimental Image-To-Video support for AnimateDiff (open to improvements) | [AnimateDiff Image To Video Pipeline](#animatediff-image-to-video-pipeline) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1TvzCDPHhfFtdcJZe4RLloAwyoLKuttWK/view?usp=sharing) | [Aryan V S](https://github.com/a-r-r-o-w) |\n| IP Adapter FaceID Stable Diffusion | Stable Diffusion Pipeline that supports IP Adapter Face ID | [IP Adapter Face ID](#ip-adapter-face-id) | - | [Fabio Rigano](https://github.com/fabiorigano) |\n| InstantID Pipeline | Stable Diffusion XL Pipeline that supports InstantID | [InstantID Pipeline](#instantid-pipeline) | [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/InstantX/InstantID) | [Haofan Wang](https://github.com/haofanwang) |\n| UFOGen Scheduler | Scheduler for UFOGen Model (compatible with Stable Diffusion pipelines) | [UFOGen Scheduler](#ufogen-scheduler) | - | [dg845](https://github.com/dg845) |\n| Stable Diffusion XL IPEX Pipeline | Accelerate Stable Diffusion XL inference pipeline with BF16/FP32 precision on Intel Xeon CPUs with [IPEX](https://github.com/intel/intel-extension-for-pytorch) | [Stable Diffusion XL on IPEX](#stable-diffusion-xl-on-ipex) | - | [Dan Li](https://github.com/ustcuna/) |\n| Stable Diffusion BoxDiff Pipeline | Training-free controlled generation with bounding boxes using [BoxDiff](https://github.com/showlab/BoxDiff) | [Stable Diffusion BoxDiff Pipeline](#stable-diffusion-boxdiff) | - | [Jingyang Zhang](https://github.com/zjysteven/) |\n| FRESCO V2V Pipeline | Implementation of [[CVPR 2024] FRESCO: Spatial-Temporal Correspondence for Zero-Shot Video Translation](https://arxiv.org/abs/2403.12962) | [FRESCO V2V Pipeline](#fresco) | - | [Yifan Zhou](https://github.com/SingleZombie) |\n\nTo load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly.\n\n```py\npipe = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", custom_pipeline=\"filename_in_the_community_folder\")\n```\n\n## Example usages\n\n### Differential Diffusion\n\n**Eran Levin, Ohad Fried**\n\n**Tel Aviv University, Reichman University**\n\nDiffusion models have revolutionized image generation and editing, producing state-of-the-art results in conditioned and unconditioned image synthesis. While current techniques enable user control over the degree of change in an image edit, the controllability is limited to global changes over an entire edited region. This paper introduces a novel framework that enables customization of the amount of change per pixel or per image region. Our framework can be integrated into any existing diffusion model, enhancing it with this capability. Such granular control on the quantity of change opens up a diverse array of new editing capabilities, such as control of the extent to which individual objects are modified, or the ability to introduce gradual spatial changes. Furthermore, we showcase the framework's effectiveness in soft-inpainting---the completion of portions of an image while subtly adjusting the surrounding areas to ensure seamless integration. Additionally, we introduce a new tool for exploring the effects of different change quantities. Our framework operates solely during inference, requiring no model training or fine-tuning. We demonstrate our method with the current open state-of-the-art models, and validate it via both quantitative and qualitative comparisons, and a user study.\n\n![teaser-img](https://github.com/exx8/differential-diffusion/raw/main/assets/teaser.png)\n\nYou can find additional information about Differential Diffusion in the [paper](https://differential-diffusion.github.io/paper.pdf) or in the [project website](https://differential-diffusion.github.io/).\n\n#### Usage example\n\n```python\nimport torch\nfrom torchvision import transforms\n\nfrom diffusers import DPMSolverMultistepScheduler\nfrom diffusers.utils import load_image\nfrom examples.community.pipeline_stable_diffusion_xl_differential_img2img import (\n StableDiffusionXLDifferentialImg2ImgPipeline,\n)\n\n\npipeline = StableDiffusionXLDifferentialImg2ImgPipeline.from_pretrained(\n \"SG161222/RealVisXL_V4.0\", torch_dtype=torch.float16, variant=\"fp16\"\n).to(\"cuda\")\npipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, use_karras_sigmas=True)\n\n\ndef preprocess_image(image):\n image = image.convert(\"RGB\")\n image = transforms.CenterCrop((image.size[1] // 64 * 64, image.size[0] // 64 * 64))(image)\n image = transforms.ToTensor()(image)\n image = image * 2 - 1\n image = image.unsqueeze(0).to(\"cuda\")\n return image\n\n\ndef preprocess_map(map):\n map = map.convert(\"L\")\n map = transforms.CenterCrop((map.size[1] // 64 * 64, map.size[0] // 64 * 64))(map)\n map = transforms.ToTensor()(map)\n map = map.to(\"cuda\")\n return map\n\n\nimage = preprocess_image(\n load_image(\n \"https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/20240329211129_4024911930.png?download=true\"\n )\n)\n\nmask = preprocess_map(\n load_image(\n \"https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/differential/gradient_mask.png?download=true\"\n )\n)\n\nprompt = \"a green pear\"\nnegative_prompt = \"blurry\"\n\nimage = pipeline(\n prompt=prompt,\n negative_prompt=negative_prompt,\n guidance_scale=7.5,\n num_inference_steps=25,\n original_image=image,\n image=image,\n strength=1.0,\n map=mask,\n).images[0]\n\nimage.save(\"result.png\")\n```\n\n### HD-Painter\n\nImplementation of [HD-Painter: High-Resolution and Prompt-Faithful Text-Guided Image Inpainting with Diffusion Models](https://arxiv.org/abs/2312.14091).\n\n![teaser-img](https://raw.githubusercontent.com/Picsart-AI-Research/HD-Painter/main/__assets__/github/teaser.jpg)\n\nThe abstract from the paper is:\n\nRecent progress in text-guided image inpainting, based on the unprecedented success of text-to-image diffusion models, has led to exceptionally realistic and visually plausible results.\nHowever, there is still significant potential for improvement in current text-to-image inpainting models, particularly in better aligning the inpainted area with user prompts and performing high-resolution inpainting.\nTherefore, in this paper we introduce _HD-Painter_, a completely **training-free** approach that **accurately follows to prompts** and coherently **scales to high-resolution** image inpainting.\nTo this end, we design the _Prompt-Aware Introverted Attention (PAIntA)_ layer enhancing self-attention scores by prompt information and resulting in better text alignment generations.\nTo further improve the prompt coherence we introduce the _Reweighting Attention Score Guidance (RASG)_ mechanism seamlessly integrating a post-hoc sampling strategy into general form of DDIM to prevent out-of-distribution latent shifts.\nMoreover, HD-Painter allows extension to larger scales by introducing a specialized super-resolution technique customized for inpainting, enabling the completion of missing regions in images of up to 2K resolution.\nOur experiments demonstrate that HD-Painter surpasses existing state-of-the-art approaches qualitatively and quantitatively, achieving an impressive generation accuracy improvement of **61.4** vs **51.9**.\nWe will make the codes publicly available.\n\nYou can find additional information about Text2Video-Zero in the [paper](https://arxiv.org/abs/2312.14091) or the [original codebase](https://github.com/Picsart-AI-Research/HD-Painter).\n\n#### Usage example\n\n```python\nimport torch\nfrom diffusers import DiffusionPipeline, DDIMScheduler\nfrom diffusers.utils import load_image, make_image_grid\n\npipe = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-inpainting\",\n custom_pipeline=\"hd_painter\"\n)\npipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\n\nprompt = \"wooden boat\"\ninit_image = load_image(\"https://raw.githubusercontent.com/Picsart-AI-Research/HD-Painter/main/__assets__/samples/images/2.jpg\")\nmask_image = load_image(\"https://raw.githubusercontent.com/Picsart-AI-Research/HD-Painter/main/__assets__/samples/masks/2.png\")\n\nimage = pipe (prompt, init_image, mask_image, use_rasg = True, use_painta = True, generator=torch.manual_seed(12345)).images[0]\n\nmake_image_grid([init_image, mask_image, image], rows=1, cols=3)\n\n```\n\n### Marigold Depth Estimation\n\nMarigold is a universal monocular depth estimator that delivers accurate and sharp predictions in the wild. Based on Stable Diffusion, it is trained exclusively with synthetic depth data and excels in zero-shot adaptation to real-world imagery. This pipeline is an official implementation of the inference process. More details can be found on our [project page](https://marigoldmonodepth.github.io) and [full codebase](https://github.com/prs-eth/marigold) (also implemented with diffusers).\n\n![Marigold Teaser](https://marigoldmonodepth.github.io/images/teaser_collage_compressed.jpg)\n\nThis depth estimation pipeline processes a single input image through multiple diffusion denoising stages to estimate depth maps. These maps are subsequently merged to produce the final output. Below is an example code snippet, including optional arguments:\n\n```python\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom diffusers import DiffusionPipeline\nfrom diffusers.utils import load_image\n\n# Original DDIM version (higher quality)\npipe = DiffusionPipeline.from_pretrained(\n \"prs-eth/marigold-v1-0\",\n custom_pipeline=\"marigold_depth_estimation\"\n # torch_dtype=torch.float16, # (optional) Run with half-precision (16-bit float).\n # variant=\"fp16\", # (optional) Use with `torch_dtype=torch.float16`, to directly load fp16 checkpoint\n)\n\n# (New) LCM version (faster speed)\npipe = DiffusionPipeline.from_pretrained(\n \"prs-eth/marigold-lcm-v1-0\",\n custom_pipeline=\"marigold_depth_estimation\"\n # torch_dtype=torch.float16, # (optional) Run with half-precision (16-bit float).\n # variant=\"fp16\", # (optional) Use with `torch_dtype=torch.float16`, to directly load fp16 checkpoint\n)\n\npipe.to(\"cuda\")\n\nimg_path_or_url = \"https://share.phys.ethz.ch/~pf/bingkedata/marigold/pipeline_example.jpg\"\nimage: Image.Image = load_image(img_path_or_url)\n\npipeline_output = pipe(\n image, # Input image.\n # ----- recommended setting for DDIM version -----\n # denoising_steps=10, # (optional) Number of denoising steps of each inference pass. Default: 10.\n # ensemble_size=10, # (optional) Number of inference passes in the ensemble. Default: 10.\n # ------------------------------------------------\n\n # ----- recommended setting for LCM version ------\n # denoising_steps=4,\n # ensemble_size=5,\n # -------------------------------------------------\n\n # processing_res=768, # (optional) Maximum resolution of processing. If set to 0: will not resize at all. Defaults to 768.\n # match_input_res=True, # (optional) Resize depth prediction to match input resolution.\n # batch_size=0, # (optional) Inference batch size, no bigger than `num_ensemble`. If set to 0, the script will automatically decide the proper batch size. Defaults to 0.\n # seed=2024, # (optional) Random seed can be set to ensure additional reproducibility. Default: None (unseeded). Note: forcing --batch_size 1 helps to increase reproducibility. To ensure full reproducibility, deterministic mode needs to be used.\n # color_map=\"Spectral\", # (optional) Colormap used to colorize the depth map. Defaults to \"Spectral\". Set to `None` to skip colormap generation.\n # show_progress_bar=True, # (optional) If true, will show progress bars of the inference progress.\n)\n\ndepth: np.ndarray = pipeline_output.depth_np # Predicted depth map\ndepth_colored: Image.Image = pipeline_output.depth_colored # Colorized prediction\n\n# Save as uint16 PNG\ndepth_uint16 = (depth * 65535.0).astype(np.uint16)\nImage.fromarray(depth_uint16).save(\"./depth_map.png\", mode=\"I;16\")\n\n# Save colorized depth map\ndepth_colored.save(\"./depth_colored.png\")\n```\n\n### LLM-grounded Diffusion\n\nLMD and LMD+ greatly improves the prompt understanding ability of text-to-image generation models by introducing an LLM as a front-end prompt parser and layout planner. It improves spatial reasoning, the understanding of negation, attribute binding, generative numeracy, etc. in a unified manner without explicitly aiming for each. LMD is completely training-free (i.e., uses SD model off-the-shelf). LMD+ takes in additional adapters for better control. This is a reproduction of LMD+ model used in our work. [Project page.](https://llm-grounded-diffusion.github.io/) [See our full codebase (also with diffusers).](https://github.com/TonyLianLong/LLM-groundedDiffusion)\n\n![Main Image](https://llm-grounded-diffusion.github.io/main_figure.jpg)\n![Visualizations: Enhanced Prompt Understanding](https://llm-grounded-diffusion.github.io/visualizations.jpg)\n\nThis pipeline can be used with an LLM or on its own. We provide a parser that parses LLM outputs to the layouts. You can obtain the prompt to input to the LLM for layout generation [here](https://github.com/TonyLianLong/LLM-groundedDiffusion/blob/main/prompt.py). After feeding the prompt to an LLM (e.g., GPT-4 on ChatGPT website), you can feed the LLM response into our pipeline.\n\nThe following code has been tested on 1x RTX 4090, but it should also support GPUs with lower GPU memory.\n\n#### Use this pipeline with an LLM\n\n```python\nimport torch\nfrom diffusers import DiffusionPipeline\n\npipe = DiffusionPipeline.from_pretrained(\n \"longlian/lmd_plus\",\n custom_pipeline=\"llm_grounded_diffusion\",\n custom_revision=\"main\",\n variant=\"fp16\", torch_dtype=torch.float16\n)\npipe.enable_model_cpu_offload()\n\n# Generate directly from a text prompt and an LLM response\nprompt = \"a waterfall and a modern high speed train in a beautiful forest with fall foliage\"\nphrases, boxes, bg_prompt, neg_prompt = pipe.parse_llm_response(\"\"\"\n[('a waterfall', [71, 105, 148, 258]), ('a modern high speed train', [255, 223, 181, 149])]\nBackground prompt: A beautiful forest with fall foliage\nNegative prompt:\n\"\"\")\n\nimages = pipe(\n prompt=prompt,\n negative_prompt=neg_prompt,\n phrases=phrases,\n boxes=boxes,\n gligen_scheduled_sampling_beta=0.4,\n output_type=\"pil\",\n num_inference_steps=50,\n lmd_guidance_kwargs={}\n).images\n\nimages[0].save(\"./lmd_plus_generation.jpg\")\n```\n\n#### Use this pipeline on its own for layout generation\n\n```python\nimport torch\nfrom diffusers import DiffusionPipeline\n\npipe = DiffusionPipeline.from_pretrained(\n \"longlian/lmd_plus\",\n custom_pipeline=\"llm_grounded_diffusion\",\n variant=\"fp16\", torch_dtype=torch.float16\n)\npipe.enable_model_cpu_offload()\n\n# Generate an image described by the prompt and\n# insert objects described by text at the region defined by bounding boxes\nprompt = \"a waterfall and a modern high speed train in a beautiful forest with fall foliage\"\nboxes = [[0.1387, 0.2051, 0.4277, 0.7090], [0.4980, 0.4355, 0.8516, 0.7266]]\nphrases = [\"a waterfall\", \"a modern high speed train\"]\n\nimages = pipe(\n prompt=prompt,\n phrases=phrases,\n boxes=boxes,\n gligen_scheduled_sampling_beta=0.4,\n output_type=\"pil\",\n num_inference_steps=50,\n lmd_guidance_kwargs={}\n).images\n\nimages[0].save(\"./lmd_plus_generation.jpg\")\n```\n\n### CLIP Guided Stable Diffusion\n\nCLIP guided stable diffusion can help to generate more realistic images\nby guiding stable diffusion at every denoising step with an additional CLIP model.\n\nThe following code requires roughly 12GB of GPU RAM.\n\n```python\nfrom diffusers import DiffusionPipeline\nfrom transformers import CLIPImageProcessor, CLIPModel\nimport torch\n\n\nfeature_extractor = CLIPImageProcessor.from_pretrained(\"laion/CLIP-ViT-B-32-laion2B-s34B-b79K\")\nclip_model = CLIPModel.from_pretrained(\"laion/CLIP-ViT-B-32-laion2B-s34B-b79K\", torch_dtype=torch.float16)\n\n\nguided_pipeline = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n custom_pipeline=\"clip_guided_stable_diffusion\",\n clip_model=clip_model,\n feature_extractor=feature_extractor,\n\n torch_dtype=torch.float16,\n)\nguided_pipeline.enable_attention_slicing()\nguided_pipeline = guided_pipeline.to(\"cuda\")\n\nprompt = \"fantasy book cover, full moon, fantasy forest landscape, golden vector elements, fantasy magic, dark light night, intricate, elegant, sharp focus, illustration, highly detailed, digital painting, concept art, matte, art by WLOP and Artgerm and Albert Bierstadt, masterpiece\"\n\ngenerator = torch.Generator(device=\"cuda\").manual_seed(0)\nimages = []\nfor i in range(4):\n image = guided_pipeline(\n prompt,\n num_inference_steps=50,\n guidance_scale=7.5,\n clip_guidance_scale=100,\n num_cutouts=4,\n use_cutouts=False,\n generator=generator,\n ).images[0]\n images.append(image)\n\n# save images locally\nfor i, img in enumerate(images):\n img.save(f\"./clip_guided_sd/image_{i}.png\")\n```\n\nThe `images` list contains a list of PIL images that can be saved locally or displayed directly in a google colab.\nGenerated images tend to be of higher qualtiy than natively using stable diffusion. E.g. the above script generates the following images:\n\n![clip_guidance](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/clip_guidance/merged_clip_guidance.jpg).\n\n### One Step Unet\n\nThe dummy \"one-step-unet\" can be run as follows:\n\n```python\nfrom diffusers import DiffusionPipeline\n\npipe = DiffusionPipeline.from_pretrained(\"google/ddpm-cifar10-32\", custom_pipeline=\"one_step_unet\")\npipe()\n```\n\n**Note**: This community pipeline is not useful as a feature, but rather just serves as an example of how community pipelines can be added (see ).\n\n### Stable Diffusion Interpolation\n\nThe following code can be run on a GPU of at least 8GB VRAM and should take approximately 5 minutes.\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipe = DiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n revision='fp16',\n torch_dtype=torch.float16,\n safety_checker=None, # Very important for videos...lots of false positives while interpolating\n custom_pipeline=\"interpolate_stable_diffusion\",\n).to('cuda')\npipe.enable_attention_slicing()\n\nframe_filepaths = pipe.walk(\n prompts=['a dog', 'a cat', 'a horse'],\n seeds=[42, 1337, 1234],\n num_interpolation_steps=16,\n output_dir='./dreams',\n batch_size=4,\n height=512,\n width=512,\n guidance_scale=8.5,\n num_inference_steps=50,\n)\n```\n\nThe output of the `walk(...)` function returns a list of images saved under the folder as defined in `output_dir`. You can use these images to create videos of stable diffusion.\n\n> **Please have a look at for more in-detail information on how to create videos using stable diffusion as well as more feature-complete functionality.**\n\n### Stable Diffusion Mega\n\nThe Stable Diffusion Mega Pipeline lets you use the main use cases of the stable diffusion pipeline in a single class.\n\n```python\n#!/usr/bin/env python3\nfrom diffusers import DiffusionPipeline\nimport PIL\nimport requests\nfrom io import BytesIO\nimport torch\n\n\ndef download_image(url):\n response = requests.get(url)\n return PIL.Image.open(BytesIO(response.content)).convert(\"RGB\")\n\npipe = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", custom_pipeline=\"stable_diffusion_mega\", torch_dtype=torch.float16, revision=\"fp16\")\npipe.to(\"cuda\")\npipe.enable_attention_slicing()\n\n\n### Text-to-Image\n\nimages = pipe.text2img(\"An astronaut riding a horse\").images\n\n### Image-to-Image\n\ninit_image = download_image(\"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\")\n\nprompt = \"A fantasy landscape, trending on artstation\"\n\nimages = pipe.img2img(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images\n\n### Inpainting\n\nimg_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\nmask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\ninit_image = download_image(img_url).resize((512, 512))\nmask_image = download_image(mask_url).resize((512, 512))\n\nprompt = \"a cat sitting on a bench\"\nimages = pipe.inpaint(prompt=prompt, image=init_image, mask_image=mask_image, strength=0.75).images\n```\n\nAs shown above this one pipeline can run all both \"text-to-image\", \"image-to-image\", and \"inpainting\" in one pipeline.\n\n### Long Prompt Weighting Stable Diffusion\n\nFeatures of this custom pipeline:\n\n- Input a prompt without the 77 token length limit.\n- Includes tx2img, img2img. and inpainting pipelines.\n- Emphasize/weigh part of your prompt with parentheses as so: `a baby deer with (big eyes)`\n- De-emphasize part of your prompt as so: `a [baby] deer with big eyes`\n- Precisely weigh part of your prompt as so: `a baby deer with (big eyes:1.3)`\n\nPrompt weighting equivalents:\n\n- `a baby deer with` == `(a baby deer with:1.0)`\n- `(big eyes)` == `(big eyes:1.1)`\n- `((big eyes))` == `(big eyes:1.21)`\n- `[big eyes]` == `(big eyes:0.91)`\n\nYou can run this custom pipeline as so:\n\n#### pytorch\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipe = DiffusionPipeline.from_pretrained(\n 'hakurei/waifu-diffusion',\n custom_pipeline=\"lpw_stable_diffusion\",\n\n torch_dtype=torch.float16\n)\npipe=pipe.to(\"cuda\")\n\nprompt = \"best_quality (1girl:1.3) bow bride brown_hair closed_mouth frilled_bow frilled_hair_tubes frills (full_body:1.3) fox_ear hair_bow hair_tubes happy hood japanese_clothes kimono long_sleeves red_bow smile solo tabi uchikake white_kimono wide_sleeves cherry_blossoms\"\nneg_prompt = \"lowres, bad_anatomy, error_body, error_hair, error_arm, error_hands, bad_hands, error_fingers, bad_fingers, missing_fingers, error_legs, bad_legs, multiple_legs, missing_legs, error_lighting, error_shadow, error_reflection, text, error, extra_digit, fewer_digits, cropped, worst_quality, low_quality, normal_quality, jpeg_artifacts, signature, watermark, username, blurry\"\n\npipe.text2img(prompt, negative_prompt=neg_prompt, width=512,height=512,max_embeddings_multiples=3).images[0]\n\n```\n\n#### onnxruntime\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipe = DiffusionPipeline.from_pretrained(\n 'CompVis/stable-diffusion-v1-4',\n custom_pipeline=\"lpw_stable_diffusion_onnx\",\n revision=\"onnx\",\n provider=\"CUDAExecutionProvider\"\n)\n\nprompt = \"a photo of an astronaut riding a horse on mars, best quality\"\nneg_prompt = \"lowres, bad anatomy, error body, error hair, error arm, error hands, bad hands, error fingers, bad fingers, missing fingers, error legs, bad legs, multiple legs, missing legs, error lighting, error shadow, error reflection, text, error, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry\"\n\npipe.text2img(prompt,negative_prompt=neg_prompt, width=512, height=512, max_embeddings_multiples=3).images[0]\n\n```\n\nif you see `Token indices sequence length is longer than the specified maximum sequence length for this model ( *** > 77 ) . Running this sequence through the model will result in indexing errors`. Do not worry, it is normal.\n\n### Speech to Image\n\nThe following code can generate an image from an audio sample using pre-trained OpenAI whisper-small and Stable Diffusion.\n\n```Python\nimport torch\n\nimport matplotlib.pyplot as plt\nfrom datasets import load_dataset\nfrom diffusers import DiffusionPipeline\nfrom transformers import (\n WhisperForConditionalGeneration,\n WhisperProcessor,\n)\n\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nds = load_dataset(\"hf-internal-testing/librispeech_asr_dummy\", \"clean\", split=\"validation\")\n\naudio_sample = ds[3]\n\ntext = audio_sample[\"text\"].lower()\nspeech_data = audio_sample[\"audio\"][\"array\"]\n\nmodel = WhisperForConditionalGeneration.from_pretrained(\"openai/whisper-small\").to(device)\nprocessor = WhisperProcessor.from_pretrained(\"openai/whisper-small\")\n\ndiffuser_pipeline = DiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n custom_pipeline=\"speech_to_image_diffusion\",\n speech_model=model,\n speech_processor=processor,\n\n torch_dtype=torch.float16,\n)\n\ndiffuser_pipeline.enable_attention_slicing()\ndiffuser_pipeline = diffuser_pipeline.to(device)\n\noutput = diffuser_pipeline(speech_data)\nplt.imshow(output.images[0])\n```\n\nThis example produces the following image:\n\n![image](https://user-images.githubusercontent.com/45072645/196901736-77d9c6fc-63ee-4072-90b0-dc8b903d63e3.png)\n\n### Wildcard Stable Diffusion\n\nFollowing the great examples from and , here's a minimal implementation that allows for users to add \"wildcards\", denoted by `__wildcard__` to prompts that are used as placeholders for randomly sampled values given by either a dictionary or a `.txt` file. For example:\n\nSay we have a prompt:\n\n```\nprompt = \"__animal__ sitting on a __object__ wearing a __clothing__\"\n```\n\nWe can then define possible values to be sampled for `animal`, `object`, and `clothing`. These can either be from a `.txt` with the same name as the category.\n\nThe possible values can also be defined / combined by using a dictionary like: `{\"animal\":[\"dog\", \"cat\", mouse\"]}`.\n\nThe actual pipeline works just like `StableDiffusionPipeline`, except the `__call__` method takes in:\n\n`wildcard_files`: list of file paths for wild card replacement\n`wildcard_option_dict`: dict with key as `wildcard` and values as a list of possible replacements\n`num_prompt_samples`: number of prompts to sample, uniformly sampling wildcards\n\nA full example:\n\ncreate `animal.txt`, with contents like:\n\n```\ndog\ncat\nmouse\n```\n\ncreate `object.txt`, with contents like:\n\n```\nchair\nsofa\nbench\n```\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipe = DiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n custom_pipeline=\"wildcard_stable_diffusion\",\n\n torch_dtype=torch.float16,\n)\nprompt = \"__animal__ sitting on a __object__ wearing a __clothing__\"\nout = pipe(\n prompt,\n wildcard_option_dict={\n \"clothing\":[\"hat\", \"shirt\", \"scarf\", \"beret\"]\n },\n wildcard_files=[\"object.txt\", \"animal.txt\"],\n num_prompt_samples=1\n)\n```\n\n### Composable Stable diffusion\n\n[Composable Stable Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/) proposes conjunction and negation (negative prompts) operators for compositional generation with conditional diffusion models.\n\n```python\nimport torch as th\nimport numpy as np\nimport torchvision.utils as tvu\n\nfrom diffusers import DiffusionPipeline\n\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--prompt\", type=str, default=\"mystical trees | A magical pond | dark\",\n help=\"use '|' as the delimiter to compose separate sentences.\")\nparser.add_argument(\"--steps\", type=int, default=50)\nparser.add_argument(\"--scale\", type=float, default=7.5)\nparser.add_argument(\"--weights\", type=str, default=\"7.5 | 7.5 | -7.5\")\nparser.add_argument(\"--seed\", type=int, default=2)\nparser.add_argument(\"--model_path\", type=str, default=\"CompVis/stable-diffusion-v1-4\")\nparser.add_argument(\"--num_images\", type=int, default=1)\nargs = parser.parse_args()\n\nhas_cuda = th.cuda.is_available()\ndevice = th.device('cpu' if not has_cuda else 'cuda')\n\nprompt = args.prompt\nscale = args.scale\nsteps = args.steps\n\npipe = DiffusionPipeline.from_pretrained(\n args.model_path,\n custom_pipeline=\"composable_stable_diffusion\",\n).to(device)\n\npipe.safety_checker = None\n\nimages = []\ngenerator = th.Generator(\"cuda\").manual_seed(args.seed)\nfor i in range(args.num_images):\n image = pipe(prompt, guidance_scale=scale, num_inference_steps=steps,\n weights=args.weights, generator=generator).images[0]\n images.append(th.from_numpy(np.array(image)).permute(2, 0, 1) / 255.)\ngrid = tvu.make_grid(th.stack(images, dim=0), nrow=4, padding=0)\ntvu.save_image(grid, f'{prompt}_{args.weights}' + '.png')\n\n```\n\n### Imagic Stable Diffusion\n\nAllows you to edit an image using stable diffusion.\n\n```python\nimport requests\nfrom PIL import Image\nfrom io import BytesIO\nimport torch\nimport os\nfrom diffusers import DiffusionPipeline, DDIMScheduler\nhas_cuda = torch.cuda.is_available()\ndevice = torch.device('cpu' if not has_cuda else 'cuda')\npipe = DiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n safety_checker=None,\n custom_pipeline=\"imagic_stable_diffusion\",\n scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule=\"scaled_linear\", clip_sample=False, set_alpha_to_one=False)\n).to(device)\ngenerator = torch.Generator(\"cuda\").manual_seed(0)\nseed = 0\nprompt = \"A photo of Barack Obama smiling with a big grin\"\nurl = 'https://www.dropbox.com/s/6tlwzr73jd1r9yk/obama.png?dl=1'\nresponse = requests.get(url)\ninit_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\ninit_image = init_image.resize((512, 512))\nres = pipe.train(\n prompt,\n image=init_image,\n generator=generator)\nres = pipe(alpha=1, guidance_scale=7.5, num_inference_steps=50)\nos.makedirs(\"imagic\", exist_ok=True)\nimage = res.images[0]\nimage.save('./imagic/imagic_image_alpha_1.png')\nres = pipe(alpha=1.5, guidance_scale=7.5, num_inference_steps=50)\nimage = res.images[0]\nimage.save('./imagic/imagic_image_alpha_1_5.png')\nres = pipe(alpha=2, guidance_scale=7.5, num_inference_steps=50)\nimage = res.images[0]\nimage.save('./imagic/imagic_image_alpha_2.png')\n```\n\n### Seed Resizing\n\nTest seed resizing. Originally generate an image in 512 by 512, then generate image with same seed at 512 by 592 using seed resizing. Finally, generate 512 by 592 using original stable diffusion pipeline.\n\n```python\nimport torch as th\nimport numpy as np\nfrom diffusers import DiffusionPipeline\n\nhas_cuda = th.cuda.is_available()\ndevice = th.device('cpu' if not has_cuda else 'cuda')\n\npipe = DiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n custom_pipeline=\"seed_resize_stable_diffusion\"\n).to(device)\n\ndef dummy(images, **kwargs):\n return images, False\n\npipe.safety_checker = dummy\n\n\nimages = []\nth.manual_seed(0)\ngenerator = th.Generator(\"cuda\").manual_seed(0)\n\nseed = 0\nprompt = \"A painting of a futuristic cop\"\n\nwidth = 512\nheight = 512\n\nres = pipe(\n prompt,\n guidance_scale=7.5,\n num_inference_steps=50,\n height=height,\n width=width,\n generator=generator)\nimage = res.images[0]\nimage.save('./seed_resize/seed_resize_{w}_{h}_image.png'.format(w=width, h=height))\n\n\nth.manual_seed(0)\ngenerator = th.Generator(\"cuda\").manual_seed(0)\n\npipe = DiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n custom_pipeline=\"/home/mark/open_source/diffusers/examples/community/\"\n).to(device)\n\nwidth = 512\nheight = 592\n\nres = pipe(\n prompt,\n guidance_scale=7.5,\n num_inference_steps=50,\n height=height,\n width=width,\n generator=generator)\nimage = res.images[0]\nimage.save('./seed_resize/seed_resize_{w}_{h}_image.png'.format(w=width, h=height))\n\npipe_compare = DiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n custom_pipeline=\"/home/mark/open_source/diffusers/examples/community/\"\n).to(device)\n\nres = pipe_compare(\n prompt,\n guidance_scale=7.5,\n num_inference_steps=50,\n height=height,\n width=width,\n generator=generator\n)\n\nimage = res.images[0]\nimage.save('./seed_resize/seed_resize_{w}_{h}_image_compare.png'.format(w=width, h=height))\n```\n\n### Multilingual Stable Diffusion Pipeline\n\nThe following code can generate an images from texts in different languages using the pre-trained [mBART-50 many-to-one multilingual machine translation model](https://huggingface.co/facebook/mbart-large-50-many-to-one-mmt) and Stable Diffusion.\n\n```python\nfrom PIL import Image\n\nimport torch\n\nfrom diffusers import DiffusionPipeline\nfrom transformers import (\n pipeline,\n MBart50TokenizerFast,\n MBartForConditionalGeneration,\n)\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\ndevice_dict = {\"cuda\": 0, \"cpu\": -1}\n\n# helper function taken from: https://huggingface.co/blog/stable_diffusion\ndef image_grid(imgs, rows, cols):\n assert len(imgs) == rows*cols\n\n w, h = imgs[0].size\n grid = Image.new('RGB', size=(cols*w, rows*h))\n grid_w, grid_h = grid.size\n\n for i, img in enumerate(imgs):\n grid.paste(img, box=(i%cols*w, i//cols*h))\n return grid\n\n# Add language detection pipeline\nlanguage_detection_model_ckpt = \"papluca/xlm-roberta-base-language-detection\"\nlanguage_detection_pipeline = pipeline(\"text-classification\",\n model=language_detection_model_ckpt,\n device=device_dict[device])\n\n# Add model for language translation\ntrans_tokenizer = MBart50TokenizerFast.from_pretrained(\"facebook/mbart-large-50-many-to-one-mmt\")\ntrans_model = MBartForConditionalGeneration.from_pretrained(\"facebook/mbart-large-50-many-to-one-mmt\").to(device)\n\ndiffuser_pipeline = DiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n custom_pipeline=\"multilingual_stable_diffusion\",\n detection_pipeline=language_detection_pipeline,\n translation_model=trans_model,\n translation_tokenizer=trans_tokenizer,\n\n torch_dtype=torch.float16,\n)\n\ndiffuser_pipeline.enable_attention_slicing()\ndiffuser_pipeline = diffuser_pipeline.to(device)\n\nprompt = [\"a photograph of an astronaut riding a horse\",\n \"Una casa en la playa\",\n \"Ein Hund, der Orange isst\",\n \"Un restaurant parisien\"]\n\noutput = diffuser_pipeline(prompt)\n\nimages = output.images\n\ngrid = image_grid(images, rows=2, cols=2)\n```\n\nThis example produces the following images:\n![image](https://user-images.githubusercontent.com/4313860/198328706-295824a4-9856-4ce5-8e66-278ceb42fd29.png)\n\n### GlueGen Stable Diffusion Pipeline\n\nGlueGen is a minimal adapter that allow alignment between any encoder (Text Encoder of different language, Multilingual Roberta, AudioClip) and CLIP text encoder used in standard Stable Diffusion model. This method allows easy language adaptation to available english Stable Diffusion checkpoints without the need of an image captioning dataset as well as long training hours.\n\nMake sure you downloaded `gluenet_French_clip_overnorm_over3_noln.ckpt` for French (there are also pre-trained weights for Chinese, Italian, Japanese, Spanish or train your own) at [GlueGen's official repo](https://github.com/salesforce/GlueGen/tree/main)\n\n```python\nfrom PIL import Image\n\nimport torch\n\nfrom transformers import AutoModel, AutoTokenizer\n\nfrom diffusers import DiffusionPipeline\n\nif __name__ == \"__main__\":\n device = \"cuda\"\n\n lm_model_id = \"xlm-roberta-large\"\n token_max_length = 77\n\n text_encoder = AutoModel.from_pretrained(lm_model_id)\n tokenizer = AutoTokenizer.from_pretrained(lm_model_id, model_max_length=token_max_length, use_fast=False)\n\n tensor_norm = torch.Tensor([[43.8203],[28.3668],[27.9345],[28.0084],[28.2958],[28.2576],[28.3373],[28.2695],[28.4097],[28.2790],[28.2825],[28.2807],[28.2775],[28.2708],[28.2682],[28.2624],[28.2589],[28.2611],[28.2616],[28.2639],[28.2613],[28.2566],[28.2615],[28.2665],[28.2799],[28.2885],[28.2852],[28.2863],[28.2780],[28.2818],[28.2764],[28.2532],[28.2412],[28.2336],[28.2514],[28.2734],[28.2763],[28.2977],[28.2971],[28.2948],[28.2818],[28.2676],[28.2831],[28.2890],[28.2979],[28.2999],[28.3117],[28.3363],[28.3554],[28.3626],[28.3589],[28.3597],[28.3543],[28.3660],[28.3731],[28.3717],[28.3812],[28.3753],[28.3810],[28.3777],[28.3693],[28.3713],[28.3670],[28.3691],[28.3679],[28.3624],[28.3703],[28.3703],[28.3720],[28.3594],[28.3576],[28.3562],[28.3438],[28.3376],[28.3389],[28.3433],[28.3191]])\n\n pipeline = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n custom_pipeline=\"gluegen\"\n ).to(device)\n pipeline.load_language_adapter(\"gluenet_French_clip_overnorm_over3_noln.ckpt\", num_token=token_max_length, dim=1024, dim_out=768, tensor_norm=tensor_norm)\n\n prompt = \"une voiture sur la plage\"\n\n generator = torch.Generator(device=device).manual_seed(42)\n image = pipeline(prompt, generator=generator).images[0]\n image.save(\"gluegen_output_fr.png\")\n```\n\nWhich will produce:\n\n![output_image](https://github.com/rootonchair/diffusers/assets/23548268/db43ffb6-8667-47c1-8872-26f85dc0a57f)\n\n### Image to Image Inpainting Stable Diffusion\n\nSimilar to the standard stable diffusion inpainting example, except with the addition of an `inner_image` argument.\n\n`image`, `inner_image`, and `mask` should have the same dimensions. `inner_image` should have an alpha (transparency) channel.\n\nThe aim is to overlay two images, then mask out the boundary between `image` and `inner_image` to allow stable diffusion to make the connection more seamless.\nFor example, this could be used to place a logo on a shirt and make it blend seamlessly.\n\n```python\nimport PIL\nimport torch\n\nfrom diffusers import DiffusionPipeline\n\nimage_path = \"./path-to-image.png\"\ninner_image_path = \"./path-to-inner-image.png\"\nmask_path = \"./path-to-mask.png\"\n\ninit_image = PIL.Image.open(image_path).convert(\"RGB\").resize((512, 512))\ninner_image = PIL.Image.open(inner_image_path).convert(\"RGBA\").resize((512, 512))\nmask_image = PIL.Image.open(mask_path).convert(\"RGB\").resize((512, 512))\n\npipe = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\",\n custom_pipeline=\"img2img_inpainting\",\n\n torch_dtype=torch.float16\n)\npipe = pipe.to(\"cuda\")\n\nprompt = \"Your prompt here!\"\nimage = pipe(prompt=prompt, image=init_image, inner_image=inner_image, mask_image=mask_image).images[0]\n```\n\n![2 by 2 grid demonstrating image to image inpainting.](https://user-images.githubusercontent.com/44398246/203506577-ec303be4-887e-4ebd-a773-c83fcb3dd01a.png)\n\n### Text Based Inpainting Stable Diffusion\n\nUse a text prompt to generate the mask for the area to be inpainted.\nCurrently uses the CLIPSeg model for mask generation, then calls the standard Stable Diffusion Inpainting pipeline to perform the inpainting.\n\n```python\nfrom transformers import CLIPSegProcessor, CLIPSegForImageSegmentation\nfrom diffusers import DiffusionPipeline\n\nfrom PIL import Image\nimport requests\n\nprocessor = CLIPSegProcessor.from_pretrained(\"CIDAS/clipseg-rd64-refined\")\nmodel = CLIPSegForImageSegmentation.from_pretrained(\"CIDAS/clipseg-rd64-refined\")\n\npipe = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\",\n custom_pipeline=\"text_inpainting\",\n segmentation_model=model,\n segmentation_processor=processor\n)\npipe = pipe.to(\"cuda\")\n\n\nurl = \"https://github.com/timojl/clipseg/blob/master/example_image.jpg?raw=true\"\nimage = Image.open(requests.get(url, stream=True).raw).resize((512, 512))\ntext = \"a glass\" # will mask out this text\nprompt = \"a cup\" # the masked out region will be replaced with this\n\nimage = pipe(image=image, text=text, prompt=prompt).images[0]\n```\n\n### Bit Diffusion\n\nBased , this is used for diffusion on discrete data - eg, discreate image data, DNA sequence data. An unconditional discreate image can be generated like this:\n\n```python\nfrom diffusers import DiffusionPipeline\npipe = DiffusionPipeline.from_pretrained(\"google/ddpm-cifar10-32\", custom_pipeline=\"bit_diffusion\")\nimage = pipe().images[0]\n\n```\n\n### Stable Diffusion with K Diffusion\n\nMake sure you have @crowsonkb's installed:\n\n```sh\npip install k-diffusion\n```\n\nYou can use the community pipeline as follows:\n\n```python\nfrom diffusers import DiffusionPipeline\n\npipe = DiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", custom_pipeline=\"sd_text2img_k_diffusion\")\npipe = pipe.to(\"cuda\")\n\nprompt = \"an astronaut riding a horse on mars\"\npipe.set_scheduler(\"sample_heun\")\ngenerator = torch.Generator(device=\"cuda\").manual_seed(seed)\nimage = pipe(prompt, generator=generator, num_inference_steps=20).images[0]\n\nimage.save(\"./astronaut_heun_k_diffusion.png\")\n```\n\nTo make sure that K Diffusion and `diffusers` yield the same results:\n\n**Diffusers**:\n\n```python\nfrom diffusers import DiffusionPipeline, EulerDiscreteScheduler\n\nseed = 33\n\npipe = DiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\")\npipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)\npipe = pipe.to(\"cuda\")\n\ngenerator = torch.Generator(device=\"cuda\").manual_seed(seed)\nimage = pipe(prompt, generator=generator, num_inference_steps=50).images[0]\n```\n\n![diffusers_euler](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/k_diffusion/astronaut_euler.png)\n\n**K Diffusion**:\n\n```python\nfrom diffusers import DiffusionPipeline, EulerDiscreteScheduler\n\nseed = 33\n\npipe = DiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", custom_pipeline=\"sd_text2img_k_diffusion\")\npipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)\npipe = pipe.to(\"cuda\")\n\npipe.set_scheduler(\"sample_euler\")\ngenerator = torch.Generator(device=\"cuda\").manual_seed(seed)\nimage = pipe(prompt, generator=generator, num_inference_steps=50).images[0]\n```\n\n![diffusers_euler](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/k_diffusion/astronaut_euler_k_diffusion.png)\n\n### Checkpoint Merger Pipeline\n\nBased on the AUTOMATIC1111/webui for checkpoint merging. This is a custom pipeline that merges upto 3 pretrained model checkpoints as long as they are in the HuggingFace model_index.json format.\n\nThe checkpoint merging is currently memory intensive as it modifies the weights of a DiffusionPipeline object in place. Expect at least 13GB RAM Usage on Kaggle GPU kernels and\non colab you might run out of the 12GB memory even while merging two checkpoints.\n\nUsage:-\n\n```python\nfrom diffusers import DiffusionPipeline\n\n#Return a CheckpointMergerPipeline class that allows you to merge checkpoints.\n#The checkpoint passed here is ignored. But still pass one of the checkpoints you plan to\n#merge for convenience\npipe = DiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", custom_pipeline=\"checkpoint_merger\")\n\n#There are multiple possible scenarios:\n#The pipeline with the merged checkpoints is returned in all the scenarios\n\n#Compatible checkpoints a.k.a matched model_index.json files. Ignores the meta attributes in model_index.json during comparison.( attrs with _ as prefix )\nmerged_pipe = pipe.merge([\"CompVis/stable-diffusion-v1-4\",\"CompVis/stable-diffusion-v1-2\"], interp = \"sigmoid\", alpha = 0.4)\n\n#Incompatible checkpoints in model_index.json but merge might be possible. Use force = True to ignore model_index.json compatibility\nmerged_pipe_1 = pipe.merge([\"CompVis/stable-diffusion-v1-4\",\"hakurei/waifu-diffusion\"], force = True, interp = \"sigmoid\", alpha = 0.4)\n\n#Three checkpoint merging. Only \"add_difference\" method actually works on all three checkpoints. Using any other options will ignore the 3rd checkpoint.\nmerged_pipe_2 = pipe.merge([\"CompVis/stable-diffusion-v1-4\",\"hakurei/waifu-diffusion\",\"prompthero/openjourney\"], force = True, interp = \"add_difference\", alpha = 0.4)\n\nprompt = \"An astronaut riding a horse on Mars\"\n\nimage = merged_pipe(prompt).images[0]\n\n```\n\nSome examples along with the merge details:\n\n1. \"CompVis/stable-diffusion-v1-4\" + \"hakurei/waifu-diffusion\" ; Sigmoid interpolation; alpha = 0.8\n\n![Stable plus Waifu Sigmoid 0.8](https://huggingface.co/datasets/NagaSaiAbhinay/CheckpointMergerSamples/resolve/main/stability_v1_4_waifu_sig_0.8.png)\n\n2. \"hakurei/waifu-diffusion\" + \"prompthero/openjourney\" ; Inverse Sigmoid interpolation; alpha = 0.8\n\n![Stable plus Waifu Sigmoid 0.8](https://huggingface.co/datasets/NagaSaiAbhinay/CheckpointMergerSamples/resolve/main/waifu_openjourney_inv_sig_0.8.png)\n\n3. \"CompVis/stable-diffusion-v1-4\" + \"hakurei/waifu-diffusion\" + \"prompthero/openjourney\"; Add Difference interpolation; alpha = 0.5\n\n![Stable plus Waifu plus openjourney add_diff 0.5](https://huggingface.co/datasets/NagaSaiAbhinay/CheckpointMergerSamples/resolve/main/stable_waifu_openjourney_add_diff_0.5.png)\n\n### Stable Diffusion Comparisons\n\nThis Community Pipeline enables the comparison between the 4 checkpoints that exist for Stable Diffusion. They can be found through the following links:\n\n1. [Stable Diffusion v1.1](https://huggingface.co/CompVis/stable-diffusion-v1-1)\n2. [Stable Diffusion v1.2](https://huggingface.co/CompVis/stable-diffusion-v1-2)\n3. [Stable Diffusion v1.3](https://huggingface.co/CompVis/stable-diffusion-v1-3)\n4. [Stable Diffusion v1.4](https://huggingface.co/CompVis/stable-diffusion-v1-4)\n\n```python\nfrom diffusers import DiffusionPipeline\nimport matplotlib.pyplot as plt\n\npipe = DiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4', custom_pipeline='suvadityamuk/StableDiffusionComparison')\npipe.enable_attention_slicing()\npipe = pipe.to('cuda')\nprompt = \"an astronaut riding a horse on mars\"\noutput = pipe(prompt)\n\nplt.subplots(2,2,1)\nplt.imshow(output.images[0])\nplt.title('Stable Diffusion v1.1')\nplt.axis('off')\nplt.subplots(2,2,2)\nplt.imshow(output.images[1])\nplt.title('Stable Diffusion v1.2')\nplt.axis('off')\nplt.subplots(2,2,3)\nplt.imshow(output.images[2])\nplt.title('Stable Diffusion v1.3')\nplt.axis('off')\nplt.subplots(2,2,4)\nplt.imshow(output.images[3])\nplt.title('Stable Diffusion v1.4')\nplt.axis('off')\n\nplt.show()\n```\n\nAs a result, you can look at a grid of all 4 generated images being shown together, that captures a difference the advancement of the training between the 4 checkpoints.\n\n### Magic Mix\n\nImplementation of the [MagicMix: Semantic Mixing with Diffusion Models](https://arxiv.org/abs/2210.16056) paper. This is a Diffusion Pipeline for semantic mixing of an image and a text prompt to create a new concept while preserving the spatial layout and geometry of the subject in the image. The pipeline takes an image that provides the layout semantics and a prompt that provides the content semantics for the mixing process.\n\nThere are 3 parameters for the method-\n\n- `mix_factor`: It is the interpolation constant used in the layout generation phase. The greater the value of `mix_factor`, the greater the influence of the prompt on the layout generation process.\n- `kmax` and `kmin`: These determine the range for the layout and content generation process. A higher value of kmax results in loss of more information about the layout of the original image and a higher value of kmin results in more steps for content generation process.\n\nHere is an example usage-\n\n```python\nfrom diffusers import DiffusionPipeline, DDIMScheduler\nfrom PIL import Image\n\npipe = DiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n custom_pipeline=\"magic_mix\",\n scheduler = DDIMScheduler.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"scheduler\"),\n).to('cuda')\n\nimg = Image.open('phone.jpg')\nmix_img = pipe(\n img,\n prompt = 'bed',\n kmin = 0.3,\n kmax = 0.5,\n mix_factor = 0.5,\n )\nmix_img.save('phone_bed_mix.jpg')\n```\n\nThe `mix_img` is a PIL image that can be saved locally or displayed directly in a google colab. Generated image is a mix of the layout semantics of the given image and the content semantics of the prompt.\n\nE.g. the above script generates the following image:\n\n`phone.jpg`\n\n![206903102-34e79b9f-9ed2-4fac-bb38-82871343c655](https://user-images.githubusercontent.com/59410571/209578593-141467c7-d831-4792-8b9a-b17dc5e47816.jpg)\n\n`phone_bed_mix.jpg`\n\n![206903104-913a671d-ef53-4ae4-919d-64c3059c8f67](https://user-images.githubusercontent.com/59410571/209578602-70f323fa-05b7-4dd6-b055-e40683e37914.jpg)\n\nFor more example generations check out this [demo notebook](https://github.com/daspartho/MagicMix/blob/main/demo.ipynb).\n\n### Stable UnCLIP\n\nUnCLIPPipeline(\"kakaobrain/karlo-v1-alpha\") provide a prior model that can generate clip image embedding from text.\nStableDiffusionImageVariationPipeline(\"lambdalabs/sd-image-variations-diffusers\") provide a decoder model than can generate images from clip image embedding.\n\n```python\nimport torch\nfrom diffusers import DiffusionPipeline\n\ndevice = torch.device(\"cpu\" if not torch.cuda.is_available() else \"cuda\")\n\npipeline = DiffusionPipeline.from_pretrained(\n \"kakaobrain/karlo-v1-alpha\",\n torch_dtype=torch.float16,\n custom_pipeline=\"stable_unclip\",\n decoder_pipe_kwargs=dict(\n image_encoder=None,\n ),\n)\npipeline.to(device)\n\nprompt = \"a shiba inu wearing a beret and black turtleneck\"\nrandom_generator = torch.Generator(device=device).manual_seed(1000)\noutput = pipeline(\n prompt=prompt,\n width=512,\n height=512,\n generator=random_generator,\n prior_guidance_scale=4,\n prior_num_inference_steps=25,\n decoder_guidance_scale=8,\n decoder_num_inference_steps=50,\n)\n\nimage = output.images[0]\nimage.save(\"./shiba-inu.jpg\")\n\n# debug\n\n# `pipeline.decoder_pipe` is a regular StableDiffusionImageVariationPipeline instance.\n# It is used to convert clip image embedding to latents, then fed into VAE decoder.\nprint(pipeline.decoder_pipe.__class__)\n# \n\n# this pipeline only use prior module in \"kakaobrain/karlo-v1-alpha\"\n# It is used to convert clip text embedding to clip image embedding.\nprint(pipeline)\n# StableUnCLIPPipeline {\n# \"_class_name\": \"StableUnCLIPPipeline\",\n# \"_diffusers_version\": \"0.12.0.dev0\",\n# \"prior\": [\n# \"diffusers\",\n# \"PriorTransformer\"\n# ],\n# \"prior_scheduler\": [\n# \"diffusers\",\n# \"UnCLIPScheduler\"\n# ],\n# \"text_encoder\": [\n# \"transformers\",\n# \"CLIPTextModelWithProjection\"\n# ],\n# \"tokenizer\": [\n# \"transformers\",\n# \"CLIPTokenizer\"\n# ]\n# }\n\n# pipeline.prior_scheduler is the scheduler used for prior in UnCLIP.\nprint(pipeline.prior_scheduler)\n# UnCLIPScheduler {\n# \"_class_name\": \"UnCLIPScheduler\",\n# \"_diffusers_version\": \"0.12.0.dev0\",\n# \"clip_sample\": true,\n# \"clip_sample_range\": 5.0,\n# \"num_train_timesteps\": 1000,\n# \"prediction_type\": \"sample\",\n# \"variance_type\": \"fixed_small_log\"\n# }\n```\n\n`shiba-inu.jpg`\n\n![shiba-inu](https://user-images.githubusercontent.com/16448529/209185639-6e5ec794-ce9d-4883-aa29-bd6852a2abad.jpg)\n\n### UnCLIP Text Interpolation Pipeline\n\nThis Diffusion Pipeline takes two prompts and interpolates between the two input prompts using spherical interpolation ( slerp ). The input prompts are converted to text embeddings by the pipeline's text_encoder and the interpolation is done on the resulting text_embeddings over the number of steps specified. Defaults to 5 steps.\n\n```python\nimport torch\nfrom diffusers import DiffusionPipeline\n\ndevice = torch.device(\"cpu\" if not torch.cuda.is_available() else \"cuda\")\n\npipe = DiffusionPipeline.from_pretrained(\n \"kakaobrain/karlo-v1-alpha\",\n torch_dtype=torch.float16,\n custom_pipeline=\"unclip_text_interpolation\"\n)\npipe.to(device)\n\nstart_prompt = \"A photograph of an adult lion\"\nend_prompt = \"A photograph of a lion cub\"\n#For best results keep the prompts close in length to each other. Of course, feel free to try out with differing lengths.\ngenerator = torch.Generator(device=device).manual_seed(42)\n\noutput = pipe(start_prompt, end_prompt, steps = 6, generator = generator, enable_sequential_cpu_offload=False)\n\nfor i,image in enumerate(output.images):\n img.save('result%s.jpg' % i)\n```\n\nThe resulting images in order:-\n\n![result_0](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPTextInterpolationSamples/resolve/main/lion_to_cub_0.png)\n![result_1](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPTextInterpolationSamples/resolve/main/lion_to_cub_1.png)\n![result_2](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPTextInterpolationSamples/resolve/main/lion_to_cub_2.png)\n![result_3](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPTextInterpolationSamples/resolve/main/lion_to_cub_3.png)\n![result_4](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPTextInterpolationSamples/resolve/main/lion_to_cub_4.png)\n![result_5](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPTextInterpolationSamples/resolve/main/lion_to_cub_5.png)\n\n### UnCLIP Image Interpolation Pipeline\n\nThis Diffusion Pipeline takes two images or an image_embeddings tensor of size 2 and interpolates between their embeddings using spherical interpolation ( slerp ). The input images/image_embeddings are converted to image embeddings by the pipeline's image_encoder and the interpolation is done on the resulting image_embeddings over the number of steps specified. Defaults to 5 steps.\n\n```python\nimport torch\nfrom diffusers import DiffusionPipeline\nfrom PIL import Image\n\ndevice = torch.device(\"cpu\" if not torch.cuda.is_available() else \"cuda\")\ndtype = torch.float16 if torch.cuda.is_available() else torch.bfloat16\n\npipe = DiffusionPipeline.from_pretrained(\n \"kakaobrain/karlo-v1-alpha-image-variations\",\n torch_dtype=dtype,\n custom_pipeline=\"unclip_image_interpolation\"\n)\npipe.to(device)\n\nimages = [Image.open('./starry_night.jpg'), Image.open('./flowers.jpg')]\n#For best results keep the prompts close in length to each other. Of course, feel free to try out with differing lengths.\ngenerator = torch.Generator(device=device).manual_seed(42)\n\noutput = pipe(image = images ,steps = 6, generator = generator)\n\nfor i,image in enumerate(output.images):\n image.save('starry_to_flowers_%s.jpg' % i)\n```\n\nThe original images:-\n\n![starry](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/starry_night.jpg)\n![flowers](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/flowers.jpg)\n\nThe resulting images in order:-\n\n![result0](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/starry_to_flowers_0.png)\n![result1](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/starry_to_flowers_1.png)\n![result2](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/starry_to_flowers_2.png)\n![result3](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/starry_to_flowers_3.png)\n![result4](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/starry_to_flowers_4.png)\n![result5](https://huggingface.co/datasets/NagaSaiAbhinay/UnCLIPImageInterpolationSamples/resolve/main/starry_to_flowers_5.png)\n\n### DDIM Noise Comparative Analysis Pipeline\n\n#### **Research question: What visual concepts do the diffusion models learn from each noise level during training?**\n\nThe [P2 weighting (CVPR 2022)](https://arxiv.org/abs/2204.00227) paper proposed an approach to answer the above question, which is their second contribution.\nThe approach consists of the following steps:\n\n1. The input is an image x0.\n2. Perturb it to xt using a diffusion process q(xt|x0).\n - `strength` is a value between 0.0 and 1.0, that controls the amount of noise that is added to the input image. Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input.\n3. Reconstruct the image with the learned denoising process pθ(ˆx0|xt).\n4. Compare x0 and ˆx0 among various t to show how each step contributes to the sample.\nThe authors used [openai/guided-diffusion](https://github.com/openai/guided-diffusion) model to denoise images in FFHQ dataset. This pipeline extends their second contribution by investigating DDIM on any input image.\n\n```python\nimport torch\nfrom PIL import Image\nimport numpy as np\n\nimage_path = \"path/to/your/image\" # images from CelebA-HQ might be better\nimage_pil = Image.open(image_path)\nimage_name = image_path.split(\"/\")[-1].split(\".\")[0]\n\ndevice = torch.device(\"cpu\" if not torch.cuda.is_available() else \"cuda\")\npipe = DiffusionPipeline.from_pretrained(\n \"google/ddpm-ema-celebahq-256\",\n custom_pipeline=\"ddim_noise_comparative_analysis\",\n)\npipe = pipe.to(device)\n\nfor strength in np.linspace(0.1, 1, 25):\n denoised_image, latent_timestep = pipe(\n image_pil, strength=strength, return_dict=False\n )\n denoised_image = denoised_image[0]\n denoised_image.save(\n f\"noise_comparative_analysis_{image_name}_{latent_timestep}.png\"\n )\n```\n\nHere is the result of this pipeline (which is DDIM) on CelebA-HQ dataset.\n\n![noise-comparative-analysis](https://user-images.githubusercontent.com/67547213/224677066-4474b2ed-56ab-4c27-87c6-de3c0255eb9c.jpeg)\n\n### CLIP Guided Img2Img Stable Diffusion\n\nCLIP guided Img2Img stable diffusion can help to generate more realistic images with an initial image\nby guiding stable diffusion at every denoising step with an additional CLIP model.\n\nThe following code requires roughly 12GB of GPU RAM.\n\n```python\nfrom io import BytesIO\nimport requests\nimport torch\nfrom diffusers import DiffusionPipeline\nfrom PIL import Image\nfrom transformers import CLIPFeatureExtractor, CLIPModel\nfeature_extractor = CLIPFeatureExtractor.from_pretrained(\n \"laion/CLIP-ViT-B-32-laion2B-s34B-b79K\"\n)\nclip_model = CLIPModel.from_pretrained(\n \"laion/CLIP-ViT-B-32-laion2B-s34B-b79K\", torch_dtype=torch.float16\n)\nguided_pipeline = DiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n # custom_pipeline=\"clip_guided_stable_diffusion\",\n custom_pipeline=\"/home/njindal/diffusers/examples/community/clip_guided_stable_diffusion.py\",\n clip_model=clip_model,\n feature_extractor=feature_extractor,\n torch_dtype=torch.float16,\n)\nguided_pipeline.enable_attention_slicing()\nguided_pipeline = guided_pipeline.to(\"cuda\")\nprompt = \"fantasy book cover, full moon, fantasy forest landscape, golden vector elements, fantasy magic, dark light night, intricate, elegant, sharp focus, illustration, highly detailed, digital painting, concept art, matte, art by WLOP and Artgerm and Albert Bierstadt, masterpiece\"\nurl = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\nresponse = requests.get(url)\ninit_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\nimage = guided_pipeline(\n prompt=prompt,\n num_inference_steps=30,\n image=init_image,\n strength=0.75,\n guidance_scale=7.5,\n clip_guidance_scale=100,\n num_cutouts=4,\n use_cutouts=False,\n).images[0]\ndisplay(image)\n```\n\nInit Image\n\n![img2img_init_clip_guidance](https://huggingface.co/datasets/njindal/images/resolve/main/clip_guided_img2img_init.jpg)\n\nOutput Image\n\n![img2img_clip_guidance](https://huggingface.co/datasets/njindal/images/resolve/main/clip_guided_img2img.jpg)\n\n### TensorRT Text2Image Stable Diffusion Pipeline\n\nThe TensorRT Pipeline can be used to accelerate the Text2Image Stable Diffusion Inference run.\n\nNOTE: The ONNX conversions and TensorRT engine build may take up to 30 minutes.\n\n```python\nimport torch\nfrom diffusers import DDIMScheduler\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipeline\n\n# Use the DDIMScheduler scheduler here instead\nscheduler = DDIMScheduler.from_pretrained(\"stabilityai/stable-diffusion-2-1\",\n subfolder=\"scheduler\")\n\npipe = StableDiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-1\",\n custom_pipeline=\"stable_diffusion_tensorrt_txt2img\",\n revision='fp16',\n torch_dtype=torch.float16,\n scheduler=scheduler,)\n\n# re-use cached folder to save ONNX models and TensorRT Engines\npipe.set_cached_folder(\"stabilityai/stable-diffusion-2-1\", revision='fp16',)\n\npipe = pipe.to(\"cuda\")\n\nprompt = \"a beautiful photograph of Mt. Fuji during cherry blossom\"\nimage = pipe(prompt).images[0]\nimage.save('tensorrt_mt_fuji.png')\n```\n\n### EDICT Image Editing Pipeline\n\nThis pipeline implements the text-guided image editing approach from the paper [EDICT: Exact Diffusion Inversion via Coupled Transformations](https://arxiv.org/abs/2211.12446). You have to pass:\n\n- (`PIL`) `image` you want to edit.\n- `base_prompt`: the text prompt describing the current image (before editing).\n- `target_prompt`: the text prompt describing with the edits.\n\n```python\nfrom diffusers import DiffusionPipeline, DDIMScheduler\nfrom transformers import CLIPTextModel\nimport torch, PIL, requests\nfrom io import BytesIO\nfrom IPython.display import display\n\ndef center_crop_and_resize(im):\n\n width, height = im.size\n d = min(width, height)\n left = (width - d) / 2\n upper = (height - d) / 2\n right = (width + d) / 2\n lower = (height + d) / 2\n\n return im.crop((left, upper, right, lower)).resize((512, 512))\n\ntorch_dtype = torch.float16\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n# scheduler and text_encoder param values as in the paper\nscheduler = DDIMScheduler(\n num_train_timesteps=1000,\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n set_alpha_to_one=False,\n clip_sample=False,\n)\n\ntext_encoder = CLIPTextModel.from_pretrained(\n pretrained_model_name_or_path=\"openai/clip-vit-large-patch14\",\n torch_dtype=torch_dtype,\n)\n\n# initialize pipeline\npipeline = DiffusionPipeline.from_pretrained(\n pretrained_model_name_or_path=\"CompVis/stable-diffusion-v1-4\",\n custom_pipeline=\"edict_pipeline\",\n revision=\"fp16\",\n scheduler=scheduler,\n text_encoder=text_encoder,\n leapfrog_steps=True,\n torch_dtype=torch_dtype,\n).to(device)\n\n# download image\nimage_url = \"https://huggingface.co/datasets/Joqsan/images/resolve/main/imagenet_dog_1.jpeg\"\nresponse = requests.get(image_url)\nimage = PIL.Image.open(BytesIO(response.content))\n\n# preprocess it\ncropped_image = center_crop_and_resize(image)\n\n# define the prompts\nbase_prompt = \"A dog\"\ntarget_prompt = \"A golden retriever\"\n\n# run the pipeline\nresult_image = pipeline(\n base_prompt=base_prompt,\n target_prompt=target_prompt,\n image=cropped_image,\n)\n\ndisplay(result_image)\n```\n\nInit Image\n\n![img2img_init_edict_text_editing](https://huggingface.co/datasets/Joqsan/images/resolve/main/imagenet_dog_1.jpeg)\n\nOutput Image\n\n![img2img_edict_text_editing](https://huggingface.co/datasets/Joqsan/images/resolve/main/imagenet_dog_1_cropped_generated.png)\n\n### Stable Diffusion RePaint\n\nThis pipeline uses the [RePaint](https://arxiv.org/abs/2201.09865) logic on the latent space of stable diffusion. It can\nbe used similarly to other image inpainting pipelines but does not rely on a specific inpainting model. This means you can use\nmodels that are not specifically created for inpainting.\n\nMake sure to use the ```RePaintScheduler``` as shown in the example below.\n\nDisclaimer: The mask gets transferred into latent space, this may lead to unexpected changes on the edge of the masked part.\nThe inference time is a lot slower.\n\n```py\nimport PIL\nimport requests\nimport torch\nfrom io import BytesIO\nfrom diffusers import StableDiffusionPipeline, RePaintScheduler\ndef download_image(url):\n response = requests.get(url)\n return PIL.Image.open(BytesIO(response.content)).convert(\"RGB\")\nimg_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\nmask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\ninit_image = download_image(img_url).resize((512, 512))\nmask_image = download_image(mask_url).resize((512, 512))\nmask_image = PIL.ImageOps.invert(mask_image)\npipe = StableDiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\", torch_dtype=torch.float16, custom_pipeline=\"stable_diffusion_repaint\",\n)\npipe.scheduler = RePaintScheduler.from_config(pipe.scheduler.config)\npipe = pipe.to(\"cuda\")\nprompt = \"Face of a yellow cat, high resolution, sitting on a park bench\"\nimage = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]\n```\n\n### TensorRT Image2Image Stable Diffusion Pipeline\n\nThe TensorRT Pipeline can be used to accelerate the Image2Image Stable Diffusion Inference run.\n\nNOTE: The ONNX conversions and TensorRT engine build may take up to 30 minutes.\n\n```python\nimport requests\nfrom io import BytesIO\nfrom PIL import Image\nimport torch\nfrom diffusers import DDIMScheduler\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionImg2ImgPipeline\n\n# Use the DDIMScheduler scheduler here instead\nscheduler = DDIMScheduler.from_pretrained(\"stabilityai/stable-diffusion-2-1\",\n subfolder=\"scheduler\")\n\n\npipe = StableDiffusionImg2ImgPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-1\",\n custom_pipeline=\"stable_diffusion_tensorrt_img2img\",\n revision='fp16',\n torch_dtype=torch.float16,\n scheduler=scheduler,)\n\n# re-use cached folder to save ONNX models and TensorRT Engines\npipe.set_cached_folder(\"stabilityai/stable-diffusion-2-1\", revision='fp16',)\n\npipe = pipe.to(\"cuda\")\n\nurl = \"https://pajoca.com/wp-content/uploads/2022/09/tekito-yamakawa-1.png\"\nresponse = requests.get(url)\ninput_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n\nprompt = \"photorealistic new zealand hills\"\nimage = pipe(prompt, image=input_image, strength=0.75,).images[0]\nimage.save('tensorrt_img2img_new_zealand_hills.png')\n```\n\n### Stable Diffusion BoxDiff\nBoxDiff is a training-free method for controlled generation with bounding box coordinates. It shoud work with any Stable Diffusion model. Below shows an example with `stable-diffusion-2-1-base`.\n```py\nimport torch\nfrom PIL import Image, ImageDraw\nfrom copy import deepcopy\n\nfrom examples.community.pipeline_stable_diffusion_boxdiff import StableDiffusionBoxDiffPipeline\n\ndef draw_box_with_text(img, boxes, names):\n colors = [\"red\", \"olive\", \"blue\", \"green\", \"orange\", \"brown\", \"cyan\", \"purple\"]\n img_new = deepcopy(img)\n draw = ImageDraw.Draw(img_new)\n\n W, H = img.size\n for bid, box in enumerate(boxes):\n draw.rectangle([box[0] * W, box[1] * H, box[2] * W, box[3] * H], outline=colors[bid % len(colors)], width=4)\n draw.text((box[0] * W, box[1] * H), names[bid], fill=colors[bid % len(colors)])\n return img_new\n\npipe = StableDiffusionBoxDiffPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-1-base\",\n torch_dtype=torch.float16,\n)\npipe.to(\"cuda\")\n\n# example 1\nprompt = \"as the aurora lights up the sky, a herd of reindeer leisurely wanders on the grassy meadow, admiring the breathtaking view, a serene lake quietly reflects the magnificent display, and in the distance, a snow-capped mountain stands majestically, fantasy, 8k, highly detailed\"\nphrases = [\n \"aurora\",\n \"reindeer\",\n \"meadow\",\n \"lake\",\n \"mountain\"\n]\nboxes = [[1,3,512,202], [75,344,421,495], [1,327,508,507], [2,217,507,341], [1,135,509,242]]\n\n# example 2\n# prompt = \"A rabbit wearing sunglasses looks very proud\"\n# phrases = [\"rabbit\", \"sunglasses\"]\n# boxes = [[67,87,366,512], [66,130,364,262]]\n\nboxes = [[x / 512 for x in box] for box in boxes]\n\nimages = pipe(\n prompt,\n boxdiff_phrases=phrases,\n boxdiff_boxes=boxes,\n boxdiff_kwargs={\n \"attention_res\": 16,\n \"normalize_eot\": True\n },\n num_inference_steps=50,\n guidance_scale=7.5,\n generator=torch.manual_seed(42),\n safety_checker=None\n).images\n\ndraw_box_with_text(images[0], boxes, phrases).save(\"output.png\")\n```\n\n\n### Stable Diffusion Reference\n\nThis pipeline uses the Reference Control. Refer to the [sd-webui-controlnet discussion: Reference-only Control](https://github.com/Mikubill/sd-webui-controlnet/discussions/1236)[sd-webui-controlnet discussion: Reference-adain Control](https://github.com/Mikubill/sd-webui-controlnet/discussions/1280).\n\nBased on [this issue](https://github.com/huggingface/diffusers/issues/3566),\n\n- `EulerAncestralDiscreteScheduler` got poor results.\n\n```py\nimport torch\nfrom diffusers import UniPCMultistepScheduler\nfrom diffusers.utils import load_image\n\ninput_image = load_image(\"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png\")\n\npipe = StableDiffusionReferencePipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n safety_checker=None,\n torch_dtype=torch.float16\n ).to('cuda:0')\n\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n\nresult_img = pipe(ref_image=input_image,\n prompt=\"1girl\",\n num_inference_steps=20,\n reference_attn=True,\n reference_adain=True).images[0]\n```\n\nReference Image\n\n![reference_image](https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png)\n\nOutput Image of `reference_attn=True` and `reference_adain=False`\n\n![output_image](https://github.com/huggingface/diffusers/assets/24734142/813b5c6a-6d89-46ba-b7a4-2624e240eea5)\n\nOutput Image of `reference_attn=False` and `reference_adain=True`\n\n![output_image](https://github.com/huggingface/diffusers/assets/24734142/ffc90339-9ef0-4c4d-a544-135c3e5644da)\n\nOutput Image of `reference_attn=True` and `reference_adain=True`\n\n![output_image](https://github.com/huggingface/diffusers/assets/24734142/3c5255d6-867d-4d35-b202-8dfd30cc6827)\n\n### Stable Diffusion ControlNet Reference\n\nThis pipeline uses the Reference Control with ControlNet. Refer to the [sd-webui-controlnet discussion: Reference-only Control](https://github.com/Mikubill/sd-webui-controlnet/discussions/1236)[sd-webui-controlnet discussion: Reference-adain Control](https://github.com/Mikubill/sd-webui-controlnet/discussions/1280).\n\nBased on [this issue](https://github.com/huggingface/diffusers/issues/3566),\n\n- `EulerAncestralDiscreteScheduler` got poor results.\n- `guess_mode=True` works well for ControlNet v1.1\n\n```py\nimport cv2\nimport torch\nimport numpy as np\nfrom PIL import Image\nfrom diffusers import UniPCMultistepScheduler\nfrom diffusers.utils import load_image\n\ninput_image = load_image(\"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png\")\n\n# get canny image\nimage = cv2.Canny(np.array(input_image), 100, 200)\nimage = image[:, :, None]\nimage = np.concatenate([image, image, image], axis=2)\ncanny_image = Image.fromarray(image)\n\ncontrolnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\", torch_dtype=torch.float16)\npipe = StableDiffusionControlNetReferencePipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n controlnet=controlnet,\n safety_checker=None,\n torch_dtype=torch.float16\n ).to('cuda:0')\n\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n\nresult_img = pipe(ref_image=input_image,\n prompt=\"1girl\",\n image=canny_image,\n num_inference_steps=20,\n reference_attn=True,\n reference_adain=True).images[0]\n```\n\nReference Image\n\n![reference_image](https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png)\n\nOutput Image\n\n![output_image](https://github.com/huggingface/diffusers/assets/24734142/7b9a5830-f173-4b92-b0cf-73d0e9c01d60)\n\n### Stable Diffusion on IPEX\n\nThis diffusion pipeline aims to accelarate the inference of Stable-Diffusion on Intel Xeon CPUs with BF16/FP32 precision using [IPEX](https://github.com/intel/intel-extension-for-pytorch).\n\nTo use this pipeline, you need to:\n\n1. Install [IPEX](https://github.com/intel/intel-extension-for-pytorch)\n\n**Note:** For each PyTorch release, there is a corresponding release of the IPEX. Here is the mapping relationship. It is recommended to install Pytorch/IPEX2.0 to get the best performance.\n\n|PyTorch Version|IPEX Version|\n|--|--|\n|[v2.0.\\*](https://github.com/pytorch/pytorch/tree/v2.0.1 \"v2.0.1\")|[v2.0.\\*](https://github.com/intel/intel-extension-for-pytorch/tree/v2.0.100+cpu)|\n|[v1.13.\\*](https://github.com/pytorch/pytorch/tree/v1.13.0 \"v1.13.0\")|[v1.13.\\*](https://github.com/intel/intel-extension-for-pytorch/tree/v1.13.100+cpu)|\n\nYou can simply use pip to install IPEX with the latest version.\n\n```sh\npython -m pip install intel_extension_for_pytorch\n```\n\n**Note:** To install a specific version, run with the following command:\n\n```sh\npython -m pip install intel_extension_for_pytorch== -f https://developer.intel.com/ipex-whl-stable-cpu\n```\n\n2. After pipeline initialization, `prepare_for_ipex()` should be called to enable IPEX accelaration. Supported inference datatypes are Float32 and BFloat16.\n\n**Note:** The setting of generated image height/width for `prepare_for_ipex()` should be same as the setting of pipeline inference.\n\n```python\npipe = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", custom_pipeline=\"stable_diffusion_ipex\")\n# For Float32\npipe.prepare_for_ipex(prompt, dtype=torch.float32, height=512, width=512) #value of image height/width should be consistent with the pipeline inference\n# For BFloat16\npipe.prepare_for_ipex(prompt, dtype=torch.bfloat16, height=512, width=512) #value of image height/width should be consistent with the pipeline inference\n```\n\nThen you can use the ipex pipeline in a similar way to the default stable diffusion pipeline.\n\n```python\n# For Float32\nimage = pipe(prompt, num_inference_steps=20, height=512, width=512).images[0] #value of image height/width should be consistent with 'prepare_for_ipex()'\n# For BFloat16\nwith torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16):\n image = pipe(prompt, num_inference_steps=20, height=512, width=512).images[0] #value of image height/width should be consistent with 'prepare_for_ipex()'\n```\n\nThe following code compares the performance of the original stable diffusion pipeline with the ipex-optimized pipeline.\n\n```python\nimport torch\nimport intel_extension_for_pytorch as ipex\nfrom diffusers import StableDiffusionPipeline\nimport time\n\nprompt = \"sailing ship in storm by Rembrandt\"\nmodel_id = \"runwayml/stable-diffusion-v1-5\"\n# Helper function for time evaluation\ndef elapsed_time(pipeline, nb_pass=3, num_inference_steps=20):\n # warmup\n for _ in range(2):\n images = pipeline(prompt, num_inference_steps=num_inference_steps, height=512, width=512).images\n #time evaluation\n start = time.time()\n for _ in range(nb_pass):\n pipeline(prompt, num_inference_steps=num_inference_steps, height=512, width=512)\n end = time.time()\n return (end - start) / nb_pass\n\n############## bf16 inference performance ###############\n\n# 1. IPEX Pipeline initialization\npipe = DiffusionPipeline.from_pretrained(model_id, custom_pipeline=\"stable_diffusion_ipex\")\npipe.prepare_for_ipex(prompt, dtype=torch.bfloat16, height=512, width=512)\n\n# 2. Original Pipeline initialization\npipe2 = StableDiffusionPipeline.from_pretrained(model_id)\n\n# 3. Compare performance between Original Pipeline and IPEX Pipeline\nwith torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16):\n latency = elapsed_time(pipe)\n print(\"Latency of StableDiffusionIPEXPipeline--bf16\", latency)\n latency = elapsed_time(pipe2)\n print(\"Latency of StableDiffusionPipeline--bf16\",latency)\n\n############## fp32 inference performance ###############\n\n# 1. IPEX Pipeline initialization\npipe3 = DiffusionPipeline.from_pretrained(model_id, custom_pipeline=\"stable_diffusion_ipex\")\npipe3.prepare_for_ipex(prompt, dtype=torch.float32, height=512, width=512)\n\n# 2. Original Pipeline initialization\npipe4 = StableDiffusionPipeline.from_pretrained(model_id)\n\n# 3. Compare performance between Original Pipeline and IPEX Pipeline\nlatency = elapsed_time(pipe3)\nprint(\"Latency of StableDiffusionIPEXPipeline--fp32\", latency)\nlatency = elapsed_time(pipe4)\nprint(\"Latency of StableDiffusionPipeline--fp32\",latency)\n\n```\n\n### Stable Diffusion XL on IPEX\n\nThis diffusion pipeline aims to accelarate the inference of Stable-Diffusion XL on Intel Xeon CPUs with BF16/FP32 precision using [IPEX](https://github.com/intel/intel-extension-for-pytorch).\n\nTo use this pipeline, you need to:\n\n1. Install [IPEX](https://github.com/intel/intel-extension-for-pytorch)\n\n**Note:** For each PyTorch release, there is a corresponding release of IPEX. Here is the mapping relationship. It is recommended to install Pytorch/IPEX2.0 to get the best performance.\n\n|PyTorch Version|IPEX Version|\n|--|--|\n|[v2.0.\\*](https://github.com/pytorch/pytorch/tree/v2.0.1 \"v2.0.1\")|[v2.0.\\*](https://github.com/intel/intel-extension-for-pytorch/tree/v2.0.100+cpu)|\n|[v1.13.\\*](https://github.com/pytorch/pytorch/tree/v1.13.0 \"v1.13.0\")|[v1.13.\\*](https://github.com/intel/intel-extension-for-pytorch/tree/v1.13.100+cpu)|\n\nYou can simply use pip to install IPEX with the latest version.\n\n```sh\npython -m pip install intel_extension_for_pytorch\n```\n\n**Note:** To install a specific version, run with the following command:\n\n```sh\npython -m pip install intel_extension_for_pytorch== -f https://developer.intel.com/ipex-whl-stable-cpu\n```\n\n2. After pipeline initialization, `prepare_for_ipex()` should be called to enable IPEX accelaration. Supported inference datatypes are Float32 and BFloat16.\n\n**Note:** The values of `height` and `width` used during preparation with `prepare_for_ipex()` should be the same when running inference with the prepared pipeline.\n\n```python\npipe = StableDiffusionXLPipelineIpex.from_pretrained(\"stabilityai/sdxl-turbo\", low_cpu_mem_usage=True, use_safetensors=True)\n# value of image height/width should be consistent with the pipeline inference\n# For Float32\npipe.prepare_for_ipex(torch.float32, prompt, height=512, width=512)\n# For BFloat16\npipe.prepare_for_ipex(torch.bfloat16, prompt, height=512, width=512)\n```\n\nThen you can use the ipex pipeline in a similar way to the default stable diffusion xl pipeline.\n\n```python\n# value of image height/width should be consistent with 'prepare_for_ipex()'\n# For Float32\nimage = pipe(prompt, num_inference_steps=num_inference_steps, height=512, width=512, guidance_scale=guidance_scale).images[0]\n# For BFloat16\nwith torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16):\n image = pipe(prompt, num_inference_steps=num_inference_steps, height=512, width=512, guidance_scale=guidance_scale).images[0]\n```\n\nThe following code compares the performance of the original stable diffusion xl pipeline with the ipex-optimized pipeline.\nBy using this optimized pipeline, we can get about 1.4-2 times performance boost with BFloat16 on fourth generation of Intel Xeon CPUs,\ncode-named Sapphire Rapids.\n\n```python\nimport torch\nfrom diffusers import StableDiffusionXLPipeline\nfrom pipeline_stable_diffusion_xl_ipex import StableDiffusionXLPipelineIpex\nimport time\n\nprompt = \"sailing ship in storm by Rembrandt\"\nmodel_id = \"stabilityai/sdxl-turbo\"\nsteps = 4\n\n# Helper function for time evaluation\ndef elapsed_time(pipeline, nb_pass=3, num_inference_steps=1):\n # warmup\n for _ in range(2):\n images = pipeline(prompt, num_inference_steps=num_inference_steps, height=512, width=512, guidance_scale=0.0).images\n #time evaluation\n start = time.time()\n for _ in range(nb_pass):\n pipeline(prompt, num_inference_steps=num_inference_steps, height=512, width=512, guidance_scale=0.0)\n end = time.time()\n return (end - start) / nb_pass\n\n############## bf16 inference performance ###############\n\n# 1. IPEX Pipeline initialization\npipe = StableDiffusionXLPipelineIpex.from_pretrained(model_id, low_cpu_mem_usage=True, use_safetensors=True)\npipe.prepare_for_ipex(torch.bfloat16, prompt, height=512, width=512)\n\n# 2. Original Pipeline initialization\npipe2 = StableDiffusionXLPipeline.from_pretrained(model_id, low_cpu_mem_usage=True, use_safetensors=True)\n\n# 3. Compare performance between Original Pipeline and IPEX Pipeline\nwith torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16):\n latency = elapsed_time(pipe, num_inference_steps=steps)\n print(\"Latency of StableDiffusionXLPipelineIpex--bf16\", latency, \"s for total\", steps, \"steps\")\n latency = elapsed_time(pipe2, num_inference_steps=steps)\n print(\"Latency of StableDiffusionXLPipeline--bf16\", latency, \"s for total\", steps, \"steps\")\n\n############## fp32 inference performance ###############\n\n# 1. IPEX Pipeline initialization\npipe3 = StableDiffusionXLPipelineIpex.from_pretrained(model_id, low_cpu_mem_usage=True, use_safetensors=True)\npipe3.prepare_for_ipex(torch.float32, prompt, height=512, width=512)\n\n# 2. Original Pipeline initialization\npipe4 = StableDiffusionXLPipeline.from_pretrained(model_id, low_cpu_mem_usage=True, use_safetensors=True)\n\n# 3. Compare performance between Original Pipeline and IPEX Pipeline\nlatency = elapsed_time(pipe3, num_inference_steps=steps)\nprint(\"Latency of StableDiffusionXLPipelineIpex--fp32\", latency, \"s for total\", steps, \"steps\")\nlatency = elapsed_time(pipe4, num_inference_steps=steps)\nprint(\"Latency of StableDiffusionXLPipeline--fp32\",latency, \"s for total\", steps, \"steps\")\n\n```\n\n### CLIP Guided Images Mixing With Stable Diffusion\n\n![clip_guided_images_mixing_examples](https://huggingface.co/datasets/TheDenk/images_mixing/resolve/main/main.png)\n\nCLIP guided stable diffusion images mixing pipeline allows to combine two images using standard diffusion models.\nThis approach is using (optional) CoCa model to avoid writing image description.\n[More code examples](https://github.com/TheDenk/images_mixing)\n\n### Stable Diffusion XL Long Weighted Prompt Pipeline\n\nThis SDXL pipeline support unlimited length prompt and negative prompt, compatible with A1111 prompt weighted style.\n\nYou can provide both `prompt` and `prompt_2`. If only one prompt is provided, `prompt_2` will be a copy of the provided `prompt`. Here is a sample code to use this pipeline.\n\n```python\nfrom diffusers import DiffusionPipeline\nfrom diffusers.utils import load_image\nimport torch\n\npipe = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\"\n , torch_dtype = torch.float16\n , use_safetensors = True\n , variant = \"fp16\"\n , custom_pipeline = \"lpw_stable_diffusion_xl\",\n)\n\nprompt = \"photo of a cute (white) cat running on the grass\" * 20\nprompt2 = \"chasing (birds:1.5)\" * 20\nprompt = f\"{prompt},{prompt2}\"\nneg_prompt = \"blur, low quality, carton, animate\"\n\npipe.to(\"cuda\")\n\n# text2img\nt2i_images = pipe(\n prompt=prompt,\n negative_prompt=neg_prompt,\n).images # alternatively, you can call the .text2img() function\n\n# img2img\ninput_image = load_image(\"/path/to/local/image.png\") # or URL to your input image\ni2i_images = pipe.img2img(\n prompt=prompt,\n negative_prompt=neg_prompt,\n image=input_image,\n strength=0.8, # higher strength will result in more variation compared to original image\n).images\n\n# inpaint\ninput_mask = load_image(\"/path/to/local/mask.png\") # or URL to your input inpainting mask\ninpaint_images = pipe.inpaint(\n prompt=\"photo of a cute (black) cat running on the grass\" * 20,\n negative_prompt=neg_prompt,\n image=input_image,\n mask=input_mask,\n strength=0.6, # higher strength will result in more variation compared to original image\n).images\n\npipe.to(\"cpu\")\ntorch.cuda.empty_cache()\n\nfrom IPython.display import display # assuming you are using this code in a notebook\ndisplay(t2i_images[0])\ndisplay(i2i_images[0])\ndisplay(inpaint_images[0])\n```\n\nIn the above code, the `prompt2` is appended to the `prompt`, which is more than 77 tokens. \"birds\" are showing up in the result.\n![Stable Diffusion XL Long Weighted Prompt Pipeline sample](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_long_weighted_prompt.png)\n\nFor more results, checkout [PR #6114](https://github.com/huggingface/diffusers/pull/6114).\n\n### Example Images Mixing (with CoCa)\n\n```python\nimport requests\nfrom io import BytesIO\n\nimport PIL\nimport torch\nimport open_clip\nfrom open_clip import SimpleTokenizer\nfrom diffusers import DiffusionPipeline\nfrom transformers import CLIPFeatureExtractor, CLIPModel\n\n\ndef download_image(url):\n response = requests.get(url)\n return PIL.Image.open(BytesIO(response.content)).convert(\"RGB\")\n\n# Loading additional models\nfeature_extractor = CLIPFeatureExtractor.from_pretrained(\n \"laion/CLIP-ViT-B-32-laion2B-s34B-b79K\"\n)\nclip_model = CLIPModel.from_pretrained(\n \"laion/CLIP-ViT-B-32-laion2B-s34B-b79K\", torch_dtype=torch.float16\n)\ncoca_model = open_clip.create_model('coca_ViT-L-14', pretrained='laion2B-s13B-b90k').to('cuda')\ncoca_model.dtype = torch.float16\ncoca_transform = open_clip.image_transform(\n coca_model.visual.image_size,\n is_train = False,\n mean = getattr(coca_model.visual, 'image_mean', None),\n std = getattr(coca_model.visual, 'image_std', None),\n)\ncoca_tokenizer = SimpleTokenizer()\n\n# Pipeline creating\nmixing_pipeline = DiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n custom_pipeline=\"clip_guided_images_mixing_stable_diffusion\",\n clip_model=clip_model,\n feature_extractor=feature_extractor,\n coca_model=coca_model,\n coca_tokenizer=coca_tokenizer,\n coca_transform=coca_transform,\n torch_dtype=torch.float16,\n)\nmixing_pipeline.enable_attention_slicing()\nmixing_pipeline = mixing_pipeline.to(\"cuda\")\n\n# Pipeline running\ngenerator = torch.Generator(device=\"cuda\").manual_seed(17)\n\ndef download_image(url):\n response = requests.get(url)\n return PIL.Image.open(BytesIO(response.content)).convert(\"RGB\")\n\ncontent_image = download_image(\"https://huggingface.co/datasets/TheDenk/images_mixing/resolve/main/boromir.jpg\")\nstyle_image = download_image(\"https://huggingface.co/datasets/TheDenk/images_mixing/resolve/main/gigachad.jpg\")\n\npipe_images = mixing_pipeline(\n num_inference_steps=50,\n content_image=content_image,\n style_image=style_image,\n noise_strength=0.65,\n slerp_latent_style_strength=0.9,\n slerp_prompt_style_strength=0.1,\n slerp_clip_image_style_strength=0.1,\n guidance_scale=9.0,\n batch_size=1,\n clip_guidance_scale=100,\n generator=generator,\n).images\n```\n\n![image_mixing_result](https://huggingface.co/datasets/TheDenk/images_mixing/resolve/main/boromir_gigachad.png)\n\n### Stable Diffusion Mixture Tiling\n\nThis pipeline uses the Mixture. Refer to the [Mixture](https://arxiv.org/abs/2302.02412) paper for more details.\n\n```python\nfrom diffusers import LMSDiscreteScheduler, DiffusionPipeline\n\n# Creater scheduler and model (similar to StableDiffusionPipeline)\nscheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule=\"scaled_linear\", num_train_timesteps=1000)\npipeline = DiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", scheduler=scheduler, custom_pipeline=\"mixture_tiling\")\npipeline.to(\"cuda\")\n\n# Mixture of Diffusers generation\nimage = pipeline(\n prompt=[[\n \"A charming house in the countryside, by jakub rozalski, sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece\",\n \"A dirt road in the countryside crossing pastures, by jakub rozalski, sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece\",\n \"An old and rusty giant robot lying on a dirt road, by jakub rozalski, dark sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece\"\n ]],\n tile_height=640,\n tile_width=640,\n tile_row_overlap=0,\n tile_col_overlap=256,\n guidance_scale=8,\n seed=7178915308,\n num_inference_steps=50,\n)[\"images\"][0]\n```\n\n![mixture_tiling_results](https://huggingface.co/datasets/kadirnar/diffusers_readme_images/resolve/main/mixture_tiling.png)\n\n### TensorRT Inpainting Stable Diffusion Pipeline\n\nThe TensorRT Pipeline can be used to accelerate the Inpainting Stable Diffusion Inference run.\n\nNOTE: The ONNX conversions and TensorRT engine build may take up to 30 minutes.\n\n```python\nimport requests\nfrom io import BytesIO\nfrom PIL import Image\nimport torch\nfrom diffusers import PNDMScheduler\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline\n\n# Use the PNDMScheduler scheduler here instead\nscheduler = PNDMScheduler.from_pretrained(\"stabilityai/stable-diffusion-2-inpainting\", subfolder=\"scheduler\")\n\n\npipe = StableDiffusionInpaintPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-inpainting\",\n custom_pipeline=\"stable_diffusion_tensorrt_inpaint\",\n revision='fp16',\n torch_dtype=torch.float16,\n scheduler=scheduler,\n )\n\n# re-use cached folder to save ONNX models and TensorRT Engines\npipe.set_cached_folder(\"stabilityai/stable-diffusion-2-inpainting\", revision='fp16',)\n\npipe = pipe.to(\"cuda\")\n\nurl = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\nresponse = requests.get(url)\ninput_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n\nmask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\nresponse = requests.get(mask_url)\nmask_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n\nprompt = \"a mecha robot sitting on a bench\"\nimage = pipe(prompt, image=input_image, mask_image=mask_image, strength=0.75,).images[0]\nimage.save('tensorrt_inpaint_mecha_robot.png')\n```\n\n### Stable Diffusion Mixture Canvas\n\nThis pipeline uses the Mixture. Refer to the [Mixture](https://arxiv.org/abs/2302.02412) paper for more details.\n\n```python\nfrom PIL import Image\nfrom diffusers import LMSDiscreteScheduler, DiffusionPipeline\nfrom diffusers.pipelines.pipeline_utils import Image2ImageRegion, Text2ImageRegion, preprocess_image\n\n\n# Load and preprocess guide image\niic_image = preprocess_image(Image.open(\"input_image.png\").convert(\"RGB\"))\n\n# Creater scheduler and model (similar to StableDiffusionPipeline)\nscheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule=\"scaled_linear\", num_train_timesteps=1000)\npipeline = DiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", scheduler=scheduler).to(\"cuda:0\", custom_pipeline=\"mixture_canvas\")\npipeline.to(\"cuda\")\n\n# Mixture of Diffusers generation\noutput = pipeline(\n canvas_height=800,\n canvas_width=352,\n regions=[\n Text2ImageRegion(0, 800, 0, 352, guidance_scale=8,\n prompt=f\"best quality, masterpiece, WLOP, sakimichan, art contest winner on pixiv, 8K, intricate details, wet effects, rain drops, ethereal, mysterious, futuristic, UHD, HDR, cinematic lighting, in a beautiful forest, rainy day, award winning, trending on artstation, beautiful confident cheerful young woman, wearing a futuristic sleeveless dress, ultra beautiful detailed eyes, hyper-detailed face, complex, perfect, model,  textured, chiaroscuro, professional make-up, realistic, figure in frame, \"),\n Image2ImageRegion(352-800, 352, 0, 352, reference_image=iic_image, strength=1.0),\n ],\n num_inference_steps=100,\n seed=5525475061,\n)[\"images\"][0]\n```\n\n![Input_Image](https://huggingface.co/datasets/kadirnar/diffusers_readme_images/resolve/main/input_image.png)\n![mixture_canvas_results](https://huggingface.co/datasets/kadirnar/diffusers_readme_images/resolve/main/canvas.png)\n\n### IADB pipeline\n\nThis pipeline is the implementation of the [α-(de)Blending: a Minimalist Deterministic Diffusion Model](https://arxiv.org/abs/2305.03486) paper.\nIt is a simple and minimalist diffusion model.\n\nThe following code shows how to use the IADB pipeline to generate images using a pretrained celebahq-256 model.\n\n```python\n\npipeline_iadb = DiffusionPipeline.from_pretrained(\"thomasc4/iadb-celebahq-256\", custom_pipeline='iadb')\n\npipeline_iadb = pipeline_iadb.to('cuda')\n\noutput = pipeline_iadb(batch_size=4,num_inference_steps=128)\nfor i in range(len(output[0])):\n plt.imshow(output[0][i])\n plt.show()\n\n```\n\nSampling with the IADB formulation is easy, and can be done in a few lines (the pipeline already implements it):\n\n```python\n\ndef sample_iadb(model, x0, nb_step):\n x_alpha = x0\n for t in range(nb_step):\n alpha = (t/nb_step)\n alpha_next =((t+1)/nb_step)\n\n d = model(x_alpha, torch.tensor(alpha, device=x_alpha.device))['sample']\n x_alpha = x_alpha + (alpha_next-alpha)*d\n\n return x_alpha\n\n```\n\nThe training loop is also straightforward:\n\n```python\n\n# Training loop\nwhile True:\n x0 = sample_noise()\n x1 = sample_dataset()\n\n alpha = torch.rand(batch_size)\n\n # Blend\n x_alpha = (1-alpha) * x0 + alpha * x1\n\n # Loss\n loss = torch.sum((D(x_alpha, alpha)- (x1-x0))**2)\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n```\n\n### Zero1to3 pipeline\n\nThis pipeline is the implementation of the [Zero-1-to-3: Zero-shot One Image to 3D Object](https://arxiv.org/abs/2303.11328) paper.\nThe original pytorch-lightning [repo](https://github.com/cvlab-columbia/zero123) and a diffusers [repo](https://github.com/kxhit/zero123-hf).\n\nThe following code shows how to use the Zero1to3 pipeline to generate novel view synthesis images using a pretrained stable diffusion model.\n\n```python\nimport os\nimport torch\nfrom pipeline_zero1to3 import Zero1to3StableDiffusionPipeline\nfrom diffusers.utils import load_image\n\nmodel_id = \"kxic/zero123-165000\" # zero123-105000, zero123-165000, zero123-xl\n\npipe = Zero1to3StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\n\npipe.enable_xformers_memory_efficient_attention()\npipe.enable_vae_tiling()\npipe.enable_attention_slicing()\npipe = pipe.to(\"cuda\")\n\nnum_images_per_prompt = 4\n\n# test inference pipeline\n# x y z, Polar angle (vertical rotation in degrees) Azimuth angle (horizontal rotation in degrees) Zoom (relative distance from center)\nquery_pose1 = [-75.0, 100.0, 0.0]\nquery_pose2 = [-20.0, 125.0, 0.0]\nquery_pose3 = [-55.0, 90.0, 0.0]\n\n# load image\n# H, W = (256, 256) # H, W = (512, 512) # zero123 training is 256,256\n\n# for batch input\ninput_image1 = load_image(\"./demo/4_blackarm.png\") #load_image(\"https://cvlab-zero123-live.hf.space/file=/home/user/app/configs/4_blackarm.png\")\ninput_image2 = load_image(\"./demo/8_motor.png\") #load_image(\"https://cvlab-zero123-live.hf.space/file=/home/user/app/configs/8_motor.png\")\ninput_image3 = load_image(\"./demo/7_london.png\") #load_image(\"https://cvlab-zero123-live.hf.space/file=/home/user/app/configs/7_london.png\")\ninput_images = [input_image1, input_image2, input_image3]\nquery_poses = [query_pose1, query_pose2, query_pose3]\n\n# # for single input\n# H, W = (256, 256)\n# input_images = [input_image2.resize((H, W), PIL.Image.NEAREST)]\n# query_poses = [query_pose2]\n\n\n# better do preprocessing\nfrom gradio_new import preprocess_image, create_carvekit_interface\nimport numpy as np\nimport PIL.Image as Image\n\npre_images = []\nmodels = dict()\nprint('Instantiating Carvekit HiInterface...')\nmodels['carvekit'] = create_carvekit_interface()\nif not isinstance(input_images, list):\n input_images = [input_images]\nfor raw_im in input_images:\n input_im = preprocess_image(models, raw_im, True)\n H, W = input_im.shape[:2]\n pre_images.append(Image.fromarray((input_im * 255.0).astype(np.uint8)))\ninput_images = pre_images\n\n# infer pipeline, in original zero123 num_inference_steps=76\nimages = pipe(input_imgs=input_images, prompt_imgs=input_images, poses=query_poses, height=H, width=W,\n guidance_scale=3.0, num_images_per_prompt=num_images_per_prompt, num_inference_steps=50).images\n\n\n# save imgs\nlog_dir = \"logs\"\nos.makedirs(log_dir, exist_ok=True)\nbs = len(input_images)\ni = 0\nfor obj in range(bs):\n for idx in range(num_images_per_prompt):\n images[i].save(os.path.join(log_dir,f\"obj{obj}_{idx}.jpg\"))\n i += 1\n\n```\n\n### Stable Diffusion XL Reference\n\nThis pipeline uses the Reference . Refer to the [stable_diffusion_reference](https://github.com/huggingface/diffusers/blob/main/examples/community/README.md#stable-diffusion-reference).\n\n```py\nimport torch\nfrom PIL import Image\nfrom diffusers.utils import load_image\nfrom diffusers import DiffusionPipeline\nfrom diffusers.schedulers import UniPCMultistepScheduler\ninput_image = load_image(\"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png\")\n\n# pipe = DiffusionPipeline.from_pretrained(\n# \"stabilityai/stable-diffusion-xl-base-1.0\",\n# custom_pipeline=\"stable_diffusion_xl_reference\",\n# torch_dtype=torch.float16,\n# use_safetensors=True,\n# variant=\"fp16\").to('cuda:0')\n\npipe = StableDiffusionXLReferencePipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n torch_dtype=torch.float16,\n use_safetensors=True,\n variant=\"fp16\").to('cuda:0')\n\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n\nresult_img = pipe(ref_image=input_image,\n prompt=\"1girl\",\n num_inference_steps=20,\n reference_attn=True,\n reference_adain=True).images[0]\n```\n\nReference Image\n\n![reference_image](https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png)\n\nOutput Image\n\n`prompt: 1 girl`\n\n`reference_attn=True, reference_adain=True, num_inference_steps=20`\n![Output_image](https://github.com/zideliu/diffusers/assets/34944964/743848da-a215-48f9-ae39-b5e2ae49fb13)\n\nReference Image\n![reference_image](https://github.com/huggingface/diffusers/assets/34944964/449bdab6-e744-4fb2-9620-d4068d9a741b)\n\nOutput Image\n\n`prompt: A dog`\n\n`reference_attn=True, reference_adain=False, num_inference_steps=20`\n![Output_image](https://github.com/huggingface/diffusers/assets/34944964/fff2f16f-6e91-434b-abcc-5259d866c31e)\n\nReference Image\n![reference_image](https://github.com/huggingface/diffusers/assets/34944964/077ed4fe-2991-4b79-99a1-009f056227d1)\n\nOutput Image\n\n`prompt: An astronaut riding a lion`\n\n`reference_attn=True, reference_adain=True, num_inference_steps=20`\n![output_image](https://github.com/huggingface/diffusers/assets/34944964/9b2f1aca-886f-49c3-89ec-d2031c8e3670)\n\n### Stable diffusion fabric pipeline\n\nFABRIC approach applicable to a wide range of popular diffusion models, which exploits\nthe self-attention layer present in the most widely used architectures to condition\nthe diffusion process on a set of feedback images.\n\n```python\nimport requests\nimport torch\nfrom PIL import Image\nfrom io import BytesIO\n\nfrom diffusers import DiffusionPipeline\n\n# load the pipeline\n# make sure you're logged in with `huggingface-cli login`\nmodel_id_or_path = \"runwayml/stable-diffusion-v1-5\"\n#can also be used with dreamlike-art/dreamlike-photoreal-2.0\npipe = DiffusionPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16, custom_pipeline=\"pipeline_fabric\").to(\"cuda\")\n\n# let's specify a prompt\nprompt = \"An astronaut riding an elephant\"\nnegative_prompt = \"lowres, cropped\"\n\n# call the pipeline\nimage = pipe(\n prompt=prompt,\n negative_prompt=negative_prompt,\n num_inference_steps=20,\n generator=torch.manual_seed(12)\n).images[0]\n\nimage.save(\"horse_to_elephant.jpg\")\n\n# let's try another example with feedback\nurl = \"https://raw.githubusercontent.com/ChenWu98/cycle-diffusion/main/data/dalle2/A%20black%20colored%20car.png\"\nresponse = requests.get(url)\ninit_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n\nprompt = \"photo, A blue colored car, fish eye\"\nliked = [init_image]\n## same goes with disliked\n\n# call the pipeline\ntorch.manual_seed(0)\nimage = pipe(\n prompt=prompt,\n negative_prompt=negative_prompt,\n liked = liked,\n num_inference_steps=20,\n).images[0]\n\nimage.save(\"black_to_blue.png\")\n```\n\n*With enough feedbacks you can create very similar high quality images.*\n\nThe original codebase can be found at [sd-fabric/fabric](https://github.com/sd-fabric/fabric), and available checkpoints are [dreamlike-art/dreamlike-photoreal-2.0](https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0), [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5), and [stabilityai/stable-diffusion-2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1) (may give unexpected results).\n\nLet's have a look at the images (_512X512_)\n\n| Without Feedback | With Feedback (1st image) |\n|---------------------|---------------------|\n| ![Image 1](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/fabric_wo_feedback.jpg) | ![Feedback Image 1](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/fabric_w_feedback.png) |\n\n### Masked Im2Im Stable Diffusion Pipeline\n\nThis pipeline reimplements sketch inpaint feature from A1111 for non-inpaint models. The following code reads two images, original and one with mask painted over it. It computes mask as a difference of two images and does the inpainting in the area defined by the mask.\n\n```python\nimg = PIL.Image.open(\"./mech.png\")\n# read image with mask painted over\nimg_paint = PIL.Image.open(\"./mech_painted.png\")\nneq = numpy.any(numpy.array(img) != numpy.array(img_paint), axis=-1)\nmask = neq / neq.max()\n\npipeline = MaskedStableDiffusionImg2ImgPipeline.from_pretrained(\"frankjoshua/icbinpICantBelieveIts_v8\")\n\n# works best with EulerAncestralDiscreteScheduler\npipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config)\ngenerator = torch.Generator(device=\"cpu\").manual_seed(4)\n\nprompt = \"a man wearing a mask\"\nresult = pipeline(prompt=prompt, image=img_paint, mask=mask, strength=0.75,\n generator=generator)\nresult.images[0].save(\"result.png\")\n```\n\noriginal image mech.png\n\n width=\"25%\" >\n\nimage with mask mech_painted.png\n\n width=\"25%\" >\n\nresult:\n\n width=\"25%\" >\n\n### Prompt2Prompt Pipeline\n\nPrompt2Prompt allows the following edits:\n\n- ReplaceEdit (change words in prompt)\n- ReplaceEdit with local blend (change words in prompt, keep image part unrelated to changes constant)\n- RefineEdit (add words to prompt)\n- RefineEdit with local blend (add words to prompt, keep image part unrelated to changes constant)\n- ReweightEdit (modulate importance of words)\n\nHere's a full example for `ReplaceEdit``:\n\n```python\nimport torch\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom diffusers import DiffusionPipeline\n\npipe = DiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", custom_pipeline=\"pipeline_prompt2prompt\").to(\"cuda\")\n\nprompts = [\"A turtle playing with a ball\",\n \"A monkey playing with a ball\"]\n\ncross_attention_kwargs = {\n \"edit_type\": \"replace\",\n \"cross_replace_steps\": 0.4,\n \"self_replace_steps\": 0.4\n}\n\noutputs = pipe(prompt=prompts, height=512, width=512, num_inference_steps=50, cross_attention_kwargs=cross_attention_kwargs)\n```\n\nAnd abbreviated examples for the other edits:\n\n`ReplaceEdit with local blend`\n\n```python\nprompts = [\"A turtle playing with a ball\",\n \"A monkey playing with a ball\"]\n\ncross_attention_kwargs = {\n \"edit_type\": \"replace\",\n \"cross_replace_steps\": 0.4,\n \"self_replace_steps\": 0.4,\n \"local_blend_words\": [\"turtle\", \"monkey\"]\n}\n```\n\n`RefineEdit`\n\n```python\nprompts = [\"A turtle\",\n \"A turtle in a forest\"]\n\ncross_attention_kwargs = {\n \"edit_type\": \"refine\",\n \"cross_replace_steps\": 0.4,\n \"self_replace_steps\": 0.4,\n}\n```\n\n`RefineEdit with local blend`\n\n```python\nprompts = [\"A turtle\",\n \"A turtle in a forest\"]\n\ncross_attention_kwargs = {\n \"edit_type\": \"refine\",\n \"cross_replace_steps\": 0.4,\n \"self_replace_steps\": 0.4,\n \"local_blend_words\": [\"in\", \"a\" , \"forest\"]\n}\n```\n\n`ReweightEdit`\n\n```python\nprompts = [\"A smiling turtle\"] * 2\n\nedit_kcross_attention_kwargswargs = {\n \"edit_type\": \"reweight\",\n \"cross_replace_steps\": 0.4,\n \"self_replace_steps\": 0.4,\n \"equalizer_words\": [\"smiling\"],\n \"equalizer_strengths\": [5]\n}\n```\n\nSide note: See [this GitHub gist](https://gist.github.com/UmerHA/b65bb5fb9626c9c73f3ade2869e36164) if you want to visualize the attention maps.\n\n### Latent Consistency Pipeline\n\nLatent Consistency Models was proposed in [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://arxiv.org/abs/2310.04378) by _Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, Hang Zhao_ from Tsinghua University.\n\nThe abstract of the paper reads as follows:\n\n*Latent Diffusion models (LDMs) have achieved remarkable results in synthesizing high-resolution images. However, the iterative sampling process is computationally intensive and leads to slow generation. Inspired by Consistency Models (song et al.), we propose Latent Consistency Models (LCMs), enabling swift inference with minimal steps on any pre-trained LDMs, including Stable Diffusion (rombach et al). Viewing the guided reverse diffusion process as solving an augmented probability flow ODE (PF-ODE), LCMs are designed to directly predict the solution of such ODE in latent space, mitigating the need for numerous iterations and allowing rapid, high-fidelity sampling. Efficiently distilled from pre-trained classifier-free guided diffusion models, a high-quality 768 x 768 2~4-step LCM takes only 32 A100 GPU hours for training. Furthermore, we introduce Latent Consistency Fine-tuning (LCF), a novel method that is tailored for fine-tuning LCMs on customized image datasets. Evaluation on the LAION-5B-Aesthetics dataset demonstrates that LCMs achieve state-of-the-art text-to-image generation performance with few-step inference. Project Page: [this https URL](https://latent-consistency-models.github.io/)*\n\nThe model can be used with `diffusers` as follows:\n\n- *1. Load the model from the community pipeline.*\n\n```py\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipe = DiffusionPipeline.from_pretrained(\"SimianLuo/LCM_Dreamshaper_v7\", custom_pipeline=\"latent_consistency_txt2img\", custom_revision=\"main\")\n\n# To save GPU memory, torch.float16 can be used, but it may compromise image quality.\npipe.to(torch_device=\"cuda\", torch_dtype=torch.float32)\n```\n\n- 2. Run inference with as little as 4 steps:\n\n```py\nprompt = \"Self-portrait oil painting, a beautiful cyborg with golden hair, 8k\"\n\n# Can be set to 1~50 steps. LCM support fast inference even <= 4 steps. Recommend: 1~8 steps.\nnum_inference_steps = 4\n\nimages = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=8.0, lcm_origin_steps=50, output_type=\"pil\").images\n```\n\nFor any questions or feedback, feel free to reach out to [Simian Luo](https://github.com/luosiallen).\n\nYou can also try this pipeline directly in the [🚀 official spaces](https://huggingface.co/spaces/SimianLuo/Latent_Consistency_Model).\n\n### Latent Consistency Img2img Pipeline\n\nThis pipeline extends the Latent Consistency Pipeline to allow it to take an input image.\n\n```py\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipe = DiffusionPipeline.from_pretrained(\"SimianLuo/LCM_Dreamshaper_v7\", custom_pipeline=\"latent_consistency_img2img\")\n\n# To save GPU memory, torch.float16 can be used, but it may compromise image quality.\npipe.to(torch_device=\"cuda\", torch_dtype=torch.float32)\n```\n\n- 2. Run inference with as little as 4 steps:\n\n```py\nprompt = \"Self-portrait oil painting, a beautiful cyborg with golden hair, 8k\"\n\n\ninput_image=Image.open(\"myimg.png\")\n\nstrength = 0.5 #strength =0 (no change) strength=1 (completely overwrite image)\n\n# Can be set to 1~50 steps. LCM support fast inference even <= 4 steps. Recommend: 1~8 steps.\nnum_inference_steps = 4\n\nimages = pipe(prompt=prompt, image=input_image, strength=strength, num_inference_steps=num_inference_steps, guidance_scale=8.0, lcm_origin_steps=50, output_type=\"pil\").images\n```\n\n### Latent Consistency Interpolation Pipeline\n\nThis pipeline extends the Latent Consistency Pipeline to allow for interpolation of the latent space between multiple prompts. It is similar to the [Stable Diffusion Interpolate](https://github.com/huggingface/diffusers/blob/main/examples/community/interpolate_stable_diffusion.py) and [unCLIP Interpolate](https://github.com/huggingface/diffusers/blob/main/examples/community/unclip_text_interpolation.py) community pipelines.\n\n```py\nimport torch\nimport numpy as np\n\nfrom diffusers import DiffusionPipeline\n\npipe = DiffusionPipeline.from_pretrained(\"SimianLuo/LCM_Dreamshaper_v7\", custom_pipeline=\"latent_consistency_interpolate\")\n\n# To save GPU memory, torch.float16 can be used, but it may compromise image quality.\npipe.to(torch_device=\"cuda\", torch_dtype=torch.float32)\n\nprompts = [\n \"Self-portrait oil painting, a beautiful cyborg with golden hair, Margot Robbie, 8k\",\n \"Self-portrait oil painting, an extremely strong man, body builder, Huge Jackman, 8k\",\n \"An astronaut floating in space, renaissance art, realistic, high quality, 8k\",\n \"Oil painting of a cat, cute, dream-like\",\n \"Hugging face emoji, cute, realistic\"\n]\nnum_inference_steps = 4\nnum_interpolation_steps = 60\nseed = 1337\n\ntorch.manual_seed(seed)\nnp.random.seed(seed)\n\nimages = pipe(\n prompt=prompts,\n height=512,\n width=512,\n num_inference_steps=num_inference_steps,\n num_interpolation_steps=num_interpolation_steps,\n guidance_scale=8.0,\n embedding_interpolation_type=\"lerp\",\n latent_interpolation_type=\"slerp\",\n process_batch_size=4, # Make it higher or lower based on your GPU memory\n generator=torch.Generator(seed),\n)\n\nassert len(images) == (len(prompts) - 1) * num_interpolation_steps\n```\n\n### StableDiffusionUpscaleLDM3D Pipeline\n\n[LDM3D-VR](https://arxiv.org/pdf/2311.03226.pdf) is an extended version of LDM3D.\n\nThe abstract from the paper is:\n*Latent diffusion models have proven to be state-of-the-art in the creation and manipulation of visual outputs. However, as far as we know, the generation of depth maps jointly with RGB is still limited. We introduce LDM3D-VR, a suite of diffusion models targeting virtual reality development that includes LDM3D-pano and LDM3D-SR. These models enable the generation of panoramic RGBD based on textual prompts and the upscaling of low-resolution inputs to high-resolution RGBD, respectively. Our models are fine-tuned from existing pretrained models on datasets containing panoramic/high-resolution RGB images, depth maps and captions. Both models are evaluated in comparison to existing related methods*\n\nTwo checkpoints are available for use:\n\n- [ldm3d-pano](https://huggingface.co/Intel/ldm3d-pano). This checkpoint enables the generation of panoramic images and requires the StableDiffusionLDM3DPipeline pipeline to be used.\n- [ldm3d-sr](https://huggingface.co/Intel/ldm3d-sr). This checkpoint enables the upscaling of RGB and depth images. Can be used in cascade after the original LDM3D pipeline using the StableDiffusionUpscaleLDM3DPipeline pipeline.\n\n'''py\nfrom PIL import Image\nimport os\nimport torch\nfrom diffusers import StableDiffusionLDM3DPipeline, DiffusionPipeline\n\n# Generate a rgb/depth output from LDM3D\n\npipe_ldm3d = StableDiffusionLDM3DPipeline.from_pretrained(\"Intel/ldm3d-4c\")\npipe_ldm3d.to(\"cuda\")\n\nprompt =f\"A picture of some lemons on a table\"\noutput = pipe_ldm3d(prompt)\nrgb_image, depth_image = output.rgb, output.depth\nrgb_image[0].save(f\"lemons_ldm3d_rgb.jpg\")\ndepth_image[0].save(f\"lemons_ldm3d_depth.png\")\n\n# Upscale the previous output to a resolution of (1024, 1024)\n\npipe_ldm3d_upscale = DiffusionPipeline.from_pretrained(\"Intel/ldm3d-sr\", custom_pipeline=\"pipeline_stable_diffusion_upscale_ldm3d\")\n\npipe_ldm3d_upscale.to(\"cuda\")\n\nlow_res_img = Image.open(f\"lemons_ldm3d_rgb.jpg\").convert(\"RGB\")\nlow_res_depth = Image.open(f\"lemons_ldm3d_depth.png\").convert(\"L\")\noutputs = pipe_ldm3d_upscale(prompt=\"high quality high resolution uhd 4k image\", rgb=low_res_img, depth=low_res_depth, num_inference_steps=50, target_res=[1024, 1024])\n\nupscaled_rgb, upscaled_depth =outputs.rgb[0], outputs.depth[0]\nupscaled_rgb.save(f\"upscaled_lemons_rgb.png\")\nupscaled_depth.save(f\"upscaled_lemons_depth.png\")\n'''\n\n### ControlNet + T2I Adapter Pipeline\n\nThis pipelines combines both ControlNet and T2IAdapter into a single pipeline, where the forward pass is executed once.\nIt receives `control_image` and `adapter_image`, as well as `controlnet_conditioning_scale` and `adapter_conditioning_scale`, for the ControlNet and Adapter modules, respectively. Whenever `adapter_conditioning_scale = 0` or `controlnet_conditioning_scale = 0`, it will act as a full ControlNet module or as a full T2IAdapter module, respectively.\n\n```py\nimport cv2\nimport numpy as np\nimport torch\nfrom controlnet_aux.midas import MidasDetector\nfrom PIL import Image\n\nfrom diffusers import AutoencoderKL, ControlNetModel, MultiAdapter, T2IAdapter\nfrom diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel\nfrom diffusers.utils import load_image\nfrom examples.community.pipeline_stable_diffusion_xl_controlnet_adapter import (\n StableDiffusionXLControlNetAdapterPipeline,\n)\n\ncontrolnet_depth = ControlNetModel.from_pretrained(\n \"diffusers/controlnet-depth-sdxl-1.0\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n use_safetensors=True\n)\nadapter_depth = T2IAdapter.from_pretrained(\n \"TencentARC/t2i-adapter-depth-midas-sdxl-1.0\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n)\nvae = AutoencoderKL.from_pretrained(\"madebyollin/sdxl-vae-fp16-fix\", torch_dtype=torch.float16, use_safetensors=True)\n\npipe = StableDiffusionXLControlNetAdapterPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n controlnet=controlnet_depth,\n adapter=adapter_depth,\n vae=vae,\n variant=\"fp16\",\n use_safetensors=True,\n torch_dtype=torch.float16,\n)\npipe = pipe.to(\"cuda\")\npipe.enable_xformers_memory_efficient_attention()\n# pipe.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)\nmidas_depth = MidasDetector.from_pretrained(\n \"valhalla/t2iadapter-aux-models\", filename=\"dpt_large_384.pt\", model_type=\"dpt_large\"\n).to(\"cuda\")\n\nprompt = \"a tiger sitting on a park bench\"\nimg_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\n\nimage = load_image(img_url).resize((1024, 1024))\n\ndepth_image = midas_depth(\n image, detect_resolution=512, image_resolution=1024\n)\n\nstrength = 0.5\n\nimages = pipe(\n prompt,\n control_image=depth_image,\n adapter_image=depth_image,\n num_inference_steps=30,\n controlnet_conditioning_scale=strength,\n adapter_conditioning_scale=strength,\n).images\nimages[0].save(\"controlnet_and_adapter.png\")\n\n```\n\n### ControlNet + T2I Adapter + Inpainting Pipeline\n\n```py\nimport cv2\nimport numpy as np\nimport torch\nfrom controlnet_aux.midas import MidasDetector\nfrom PIL import Image\n\nfrom diffusers import AutoencoderKL, ControlNetModel, MultiAdapter, T2IAdapter\nfrom diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel\nfrom diffusers.utils import load_image\nfrom examples.community.pipeline_stable_diffusion_xl_controlnet_adapter_inpaint import (\n StableDiffusionXLControlNetAdapterInpaintPipeline,\n)\n\ncontrolnet_depth = ControlNetModel.from_pretrained(\n \"diffusers/controlnet-depth-sdxl-1.0\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n use_safetensors=True\n)\nadapter_depth = T2IAdapter.from_pretrained(\n \"TencentARC/t2i-adapter-depth-midas-sdxl-1.0\", torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n)\nvae = AutoencoderKL.from_pretrained(\"madebyollin/sdxl-vae-fp16-fix\", torch_dtype=torch.float16, use_safetensors=True)\n\npipe = StableDiffusionXLControlNetAdapterInpaintPipeline.from_pretrained(\n \"diffusers/stable-diffusion-xl-1.0-inpainting-0.1\",\n controlnet=controlnet_depth,\n adapter=adapter_depth,\n vae=vae,\n variant=\"fp16\",\n use_safetensors=True,\n torch_dtype=torch.float16,\n)\npipe = pipe.to(\"cuda\")\npipe.enable_xformers_memory_efficient_attention()\n# pipe.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)\nmidas_depth = MidasDetector.from_pretrained(\n \"valhalla/t2iadapter-aux-models\", filename=\"dpt_large_384.pt\", model_type=\"dpt_large\"\n).to(\"cuda\")\n\nprompt = \"a tiger sitting on a park bench\"\nimg_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\nmask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\n\nimage = load_image(img_url).resize((1024, 1024))\nmask_image = load_image(mask_url).resize((1024, 1024))\n\ndepth_image = midas_depth(\n image, detect_resolution=512, image_resolution=1024\n)\n\nstrength = 0.4\n\nimages = pipe(\n prompt,\n image=image,\n mask_image=mask_image,\n control_image=depth_image,\n adapter_image=depth_image,\n num_inference_steps=30,\n controlnet_conditioning_scale=strength,\n adapter_conditioning_scale=strength,\n strength=0.7,\n).images\nimages[0].save(\"controlnet_and_adapter_inpaint.png\")\n\n```\n\n### Regional Prompting Pipeline\n\nThis pipeline is a port of the [Regional Prompter extension](https://github.com/hako-mikan/sd-webui-regional-prompter) for [Stable Diffusion web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) to diffusers.\nThis code implements a pipeline for the Stable Diffusion model, enabling the division of the canvas into multiple regions, with different prompts applicable to each region. Users can specify regions in two ways: using `Cols` and `Rows` modes for grid-like divisions, or the `Prompt` mode for regions calculated based on prompts.\n\n![sample](https://github.com/hako-mikan/sd-webui-regional-prompter/blob/imgs/rp_pipeline1.png)\n\n### Usage\n\n### Sample Code\n\n```py\nfrom examples.community.regional_prompting_stable_diffusion import RegionalPromptingStableDiffusionPipeline\npipe = RegionalPromptingStableDiffusionPipeline.from_single_file(model_path, vae=vae)\n\nrp_args = {\n \"mode\":\"rows\",\n \"div\": \"1;1;1\"\n}\n\nprompt =\"\"\"\ngreen hair twintail BREAK\nred blouse BREAK\nblue skirt\n\"\"\"\n\nimages = pipe(\n prompt=prompt,\n negative_prompt=negative_prompt,\n guidance_scale=7.5,\n height = 768,\n width = 512,\n num_inference_steps =20,\n num_images_per_prompt = 1,\n rp_args = rp_args\n ).images\n\ntime = time.strftime(r\"%Y%m%d%H%M%S\")\ni = 1\nfor image in images:\n i += 1\n fileName = f'img-{time}-{i+1}.png'\n image.save(fileName)\n```\n\n### Cols, Rows mode\n\nIn the Cols, Rows mode, you can split the screen vertically and horizontally and assign prompts to each region. The split ratio can be specified by 'div', and you can set the division ratio like '3;3;2' or '0.1;0.5'. Furthermore, as will be described later, you can also subdivide the split Cols, Rows to specify more complex regions.\n\nIn this image, the image is divided into three parts, and a separate prompt is applied to each. The prompts are divided by 'BREAK', and each is applied to the respective region.\n![sample](https://github.com/hako-mikan/sd-webui-regional-prompter/blob/imgs/rp_pipeline2.png)\n\n```\ngreen hair twintail BREAK\nred blouse BREAK\nblue skirt\n```\n\n### 2-Dimentional division\n\nThe prompt consists of instructions separated by the term `BREAK` and is assigned to different regions of a two-dimensional space. The image is initially split in the main splitting direction, which in this case is rows, due to the presence of a single semicolon`;`, dividing the space into an upper and a lower section. Additional sub-splitting is then applied, indicated by commas. The upper row is split into ratios of `2:1:1`, while the lower row is split into a ratio of `4:6`. Rows themselves are split in a `1:2` ratio. According to the reference image, the blue sky is designated as the first region, green hair as the second, the bookshelf as the third, and so on, in a sequence based on their position from the top left. The terrarium is placed on the desk in the fourth region, and the orange dress and sofa are in the fifth region, conforming to their respective splits.\n\n```\nrp_args = {\n \"mode\":\"rows\",\n \"div\": \"1,2,1,1;2,4,6\"\n}\n\nprompt =\"\"\"\nblue sky BREAK\ngreen hair BREAK\nbook shelf BREAK\nterrarium on desk BREAK\norange dress and sofa\n\"\"\"\n```\n\n![sample](https://github.com/hako-mikan/sd-webui-regional-prompter/blob/imgs/rp_pipeline4.png)\n\n### Prompt Mode\n\nThere are limitations to methods of specifying regions in advance. This is because specifying regions can be a hindrance when designating complex shapes or dynamic compositions. In the region specified by the prompt, the regions is determined after the image generation has begun. This allows us to accommodate compositions and complex regions.\nFor further infomagen, see [here](https://github.com/hako-mikan/sd-webui-regional-prompter/blob/main/prompt_en.md).\n\n### syntax\n\n```\nbaseprompt target1 target2 BREAK\neffect1, target1 BREAK\neffect2 ,target2\n```\n\nFirst, write the base prompt. In the base prompt, write the words (target1, target2) for which you want to create a mask. Next, separate them with BREAK. Next, write the prompt corresponding to target1. Then enter a comma and write target1. The order of the targets in the base prompt and the order of the BREAK-separated targets can be back to back.\n\n```\ntarget2 baseprompt target1 BREAK\neffect1, target1 BREAK\neffect2 ,target2\n```\n\nis also effective.\n\n### Sample\n\nIn this example, masks are calculated for shirt, tie, skirt, and color prompts are specified only for those regions.\n\n```\nrp_args = {\n \"mode\":\"prompt-ex\",\n \"save_mask\":True,\n \"th\": \"0.4,0.6,0.6\",\n}\n\nprompt =\"\"\"\na girl in street with shirt, tie, skirt BREAK\nred, shirt BREAK\ngreen, tie BREAK\nblue , skirt\n\"\"\"\n```\n\n![sample](https://github.com/hako-mikan/sd-webui-regional-prompter/blob/imgs/rp_pipeline3.png)\n\n### threshold\n\nThe threshold used to determine the mask created by the prompt. This can be set as many times as there are masks, as the range varies widely depending on the target prompt. If multiple regions are used, enter them separated by commas. For example, hair tends to be ambiguous and requires a small value, while face tends to be large and requires a small value. These should be ordered by BREAK.\n\n```\na lady ,hair, face BREAK\nred, hair BREAK\ntanned ,face\n```\n\n`threshold : 0.4,0.6`\nIf only one input is given for multiple regions, they are all assumed to be the same value.\n\n### Prompt and Prompt-EX\n\nThe difference is that in Prompt, duplicate regions are added, whereas in Prompt-EX, duplicate regions are overwritten sequentially. Since they are processed in order, setting a TARGET with a large regions first makes it easier for the effect of small regions to remain unmuffled.\n\n### Accuracy\n\nIn the case of a 512 x 512 image, Attention mode reduces the size of the region to about 8 x 8 pixels deep in the U-Net, so that small regions get mixed up; Latent mode calculates 64*64, so that the region is exact.\n\n```\ngirl hair twintail frills,ribbons, dress, face BREAK\ngirl, ,face\n```\n\n### Mask\n\nWhen an image is generated, the generated mask is displayed. It is generated at the same size as the image, but is actually used at a much smaller size.\n\n### Use common prompt\n\nYou can attach the prompt up to ADDCOMM to all prompts by separating it first with ADDCOMM. This is useful when you want to include elements common to all regions. For example, when generating pictures of three people with different appearances, it's necessary to include the instruction of 'three people' in all regions. It's also useful when inserting quality tags and other things.\"For example, if you write as follows:\n\n```\nbest quality, 3persons in garden, ADDCOMM\na girl white dress BREAK\na boy blue shirt BREAK\nan old man red suit\n```\n\nIf common is enabled, this prompt is converted to the following:\n\n```\nbest quality, 3persons in garden, a girl white dress BREAK\nbest quality, 3persons in garden, a boy blue shirt BREAK\nbest quality, 3persons in garden, an old man red suit\n```\n\n### Negative prompt\n\nNegative prompts are equally effective across all regions, but it is possible to set region-specific prompts for negative prompts as well. The number of BREAKs must be the same as the number of prompts. If the number of prompts does not match, the negative prompts will be used without being divided into regions.\n\n### Parameters\n\nTo activate Regional Prompter, it is necessary to enter settings in rp_args. The items that can be set are as follows. rp_args is a dictionary type.\n\n### Input Parameters\n\nParameters are specified through the `rp_arg`(dictionary type).\n\n```\nrp_args = {\n \"mode\":\"rows\",\n \"div\": \"1;1;1\"\n}\n\npipe(prompt =prompt, rp_args = rp_args)\n```\n\n### Required Parameters\n\n- `mode`: Specifies the method for defining regions. Choose from `Cols`, `Rows`, `Prompt` or `Prompt-Ex`. This parameter is case-insensitive.\n- `divide`: Used in `Cols` and `Rows` modes. Details on how to specify this are provided under the respective `Cols` and `Rows` sections.\n- `th`: Used in `Prompt` mode. The method of specification is detailed under the `Prompt` section.\n\n### Optional Parameters\n\n- `save_mask`: In `Prompt` mode, choose whether to output the generated mask along with the image. The default is `False`.\n\nThe Pipeline supports `compel` syntax. Input prompts using the `compel` structure will be automatically applied and processed.\n\n### Diffusion Posterior Sampling Pipeline\n\n- Reference paper\n\n ```\n @article{chung2022diffusion,\n title={Diffusion posterior sampling for general noisy inverse problems},\n author={Chung, Hyungjin and Kim, Jeongsol and Mccann, Michael T and Klasky, Marc L and Ye, Jong Chul},\n journal={arXiv preprint arXiv:2209.14687},\n year={2022}\n }\n ```\n\n- This pipeline allows zero-shot conditional sampling from the posterior distribution $p(x|y)$, given observation on $y$, unconditional generative model $p(x)$ and differentiable operator $y=f(x)$.\n\n- For example, $f(.)$ can be downsample operator, then $y$ is a downsampled image, and the pipeline becomes a super-resolution pipeline.\n- To use this pipeline, you need to know your operator $f(.)$ and corrupted image $y$, and pass them during the call. For example, as in the main function of dps_pipeline.py, you need to first define the Gaussian blurring operator $f(.)$. The operator should be a callable nn.Module, with all the parameter gradient disabled:\n\n ```python\n import torch.nn.functional as F\n import scipy\n from torch import nn\n\n # define the Gaussian blurring operator first\n class GaussialBlurOperator(nn.Module):\n def __init__(self, kernel_size, intensity):\n super().__init__()\n\n class Blurkernel(nn.Module):\n def __init__(self, blur_type='gaussian', kernel_size=31, std=3.0):\n super().__init__()\n self.blur_type = blur_type\n self.kernel_size = kernel_size\n self.std = std\n self.seq = nn.Sequential(\n nn.ReflectionPad2d(self.kernel_size//2),\n nn.Conv2d(3, 3, self.kernel_size, stride=1, padding=0, bias=False, groups=3)\n )\n self.weights_init()\n\n def forward(self, x):\n return self.seq(x)\n\n def weights_init(self):\n if self.blur_type == \"gaussian\":\n n = np.zeros((self.kernel_size, self.kernel_size))\n n[self.kernel_size // 2,self.kernel_size // 2] = 1\n k = scipy.ndimage.gaussian_filter(n, sigma=self.std)\n k = torch.from_numpy(k)\n self.k = k\n for name, f in self.named_parameters():\n f.data.copy_(k)\n elif self.blur_type == \"motion\":\n k = Kernel(size=(self.kernel_size, self.kernel_size), intensity=self.std).kernelMatrix\n k = torch.from_numpy(k)\n self.k = k\n for name, f in self.named_parameters():\n f.data.copy_(k)\n\n def update_weights(self, k):\n if not torch.is_tensor(k):\n k = torch.from_numpy(k)\n for name, f in self.named_parameters():\n f.data.copy_(k)\n\n def get_kernel(self):\n return self.k\n\n self.kernel_size = kernel_size\n self.conv = Blurkernel(blur_type='gaussian',\n kernel_size=kernel_size,\n std=intensity)\n self.kernel = self.conv.get_kernel()\n self.conv.update_weights(self.kernel.type(torch.float32))\n\n for param in self.parameters():\n param.requires_grad=False\n\n def forward(self, data, **kwargs):\n return self.conv(data)\n\n def transpose(self, data, **kwargs):\n return data\n\n def get_kernel(self):\n return self.kernel.view(1, 1, self.kernel_size, self.kernel_size)\n ```\n\n- Next, you should obtain the corrupted image $y$ by the operator. In this example, we generate $y$ from the source image $x$. However in practice, having the operator $f(.)$ and corrupted image $y$ is enough:\n\n ```python\n # set up source image\n src = Image.open('sample.png')\n # read image into [1,3,H,W]\n src = torch.from_numpy(np.array(src, dtype=np.float32)).permute(2,0,1)[None]\n # normalize image to [-1,1]\n src = (src / 127.5) - 1.0\n src = src.to(\"cuda\")\n\n # set up operator and measurement\n operator = GaussialBlurOperator(kernel_size=61, intensity=3.0).to(\"cuda\")\n measurement = operator(src)\n\n # save the source and corrupted images\n save_image((src+1.0)/2.0, \"dps_src.png\")\n save_image((measurement+1.0)/2.0, \"dps_mea.png\")\n ```\n\n- We provide an example pair of saved source and corrupted images, using the Gaussian blur operator above\n - Source image:\n - ![sample](https://github.com/tongdaxu/Images/assets/22267548/4d2a1216-08d1-4aeb-9ce3-7a2d87561d65)\n - Gaussian blurred image:\n - ![ddpm_generated_image](https://github.com/tongdaxu/Images/assets/22267548/65076258-344b-4ed8-b704-a04edaade8ae)\n - You can download those image to run the example on your own.\n\n- Next, we need to define a loss function used for diffusion posterior sample. For most of the cases, the RMSE is fine:\n\n ```python\n def RMSELoss(yhat, y):\n return torch.sqrt(torch.sum((yhat-y)**2))\n ```\n\n- And next, as any other diffusion models, we need the score estimator and scheduler. As we are working with $256x256$ face images, we use ddmp-celebahq-256:\n\n ```python\n # set up scheduler\n scheduler = DDPMScheduler.from_pretrained(\"google/ddpm-celebahq-256\")\n scheduler.set_timesteps(1000)\n\n # set up model\n model = UNet2DModel.from_pretrained(\"google/ddpm-celebahq-256\").to(\"cuda\")\n ```\n\n- And finally, run the pipeline:\n\n ```python\n # finally, the pipeline\n dpspipe = DPSPipeline(model, scheduler)\n image = dpspipe(\n measurement = measurement,\n operator = operator,\n loss_fn = RMSELoss,\n zeta = 1.0,\n ).images[0]\n image.save(\"dps_generated_image.png\")\n ```\n\n- The zeta is a hyperparameter that is in range of $[0,1]$. It need to be tuned for best effect. By setting zeta=1, you should be able to have the reconstructed result:\n - Reconstructed image:\n - ![sample](https://github.com/tongdaxu/Images/assets/22267548/0ceb5575-d42e-4f0b-99c0-50e69c982209)\n\n- The reconstruction is perceptually similar to the source image, but different in details.\n- In dps_pipeline.py, we also provide a super-resolution example, which should produce:\n - Downsampled image:\n - ![dps_mea](https://github.com/tongdaxu/Images/assets/22267548/ff6a33d6-26f0-42aa-88ce-f8a76ba45a13)\n - Reconstructed image:\n - ![dps_generated_image](https://github.com/tongdaxu/Images/assets/22267548/b74f084d-93f4-4845-83d8-44c0fa758a5f)\n\n### AnimateDiff ControlNet Pipeline\n\nThis pipeline combines AnimateDiff and ControlNet. Enjoy precise motion control for your videos! Refer to [this](https://github.com/huggingface/diffusers/issues/5866) issue for more details.\n\n```py\nimport torch\nfrom diffusers import AutoencoderKL, ControlNetModel, MotionAdapter\nfrom diffusers.pipelines import DiffusionPipeline\nfrom diffusers.schedulers import DPMSolverMultistepScheduler\nfrom PIL import Image\n\nmotion_id = \"guoyww/animatediff-motion-adapter-v1-5-2\"\nadapter = MotionAdapter.from_pretrained(motion_id)\ncontrolnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11p_sd15_openpose\", torch_dtype=torch.float16)\nvae = AutoencoderKL.from_pretrained(\"stabilityai/sd-vae-ft-mse\", torch_dtype=torch.float16)\n\nmodel_id = \"SG161222/Realistic_Vision_V5.1_noVAE\"\npipe = DiffusionPipeline.from_pretrained(\n model_id,\n motion_adapter=adapter,\n controlnet=controlnet,\n vae=vae,\n custom_pipeline=\"pipeline_animatediff_controlnet\",\n).to(device=\"cuda\", dtype=torch.float16)\npipe.scheduler = DPMSolverMultistepScheduler.from_pretrained(\n model_id, subfolder=\"scheduler\", beta_schedule=\"linear\", clip_sample=False, timestep_spacing=\"linspace\", steps_offset=1\n)\npipe.enable_vae_slicing()\n\nconditioning_frames = []\nfor i in range(1, 16 + 1):\n conditioning_frames.append(Image.open(f\"frame_{i}.png\"))\n\nprompt = \"astronaut in space, dancing\"\nnegative_prompt = \"bad quality, worst quality, jpeg artifacts, ugly\"\nresult = pipe(\n prompt=prompt,\n negative_prompt=negative_prompt,\n width=512,\n height=768,\n conditioning_frames=conditioning_frames,\n num_inference_steps=20,\n).frames[0]\n\nfrom diffusers.utils import export_to_gif\nexport_to_gif(result.frames[0], \"result.gif\")\n```\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Conditioning Frames
\"input-frames\"
AnimateDiff model: SG161222/Realistic_Vision_V5.1_noVAE
\"gif-1\"\"gif-2\"
AnimateDiff model: CardosAnime
\"gif-1\"\"gif-2\"
\n\nYou can also use multiple controlnets at once!\n\n```python\nimport torch\nfrom diffusers import AutoencoderKL, ControlNetModel, MotionAdapter\nfrom diffusers.pipelines import DiffusionPipeline\nfrom diffusers.schedulers import DPMSolverMultistepScheduler\nfrom PIL import Image\n\nmotion_id = \"guoyww/animatediff-motion-adapter-v1-5-2\"\nadapter = MotionAdapter.from_pretrained(motion_id)\ncontrolnet1 = ControlNetModel.from_pretrained(\"lllyasviel/control_v11p_sd15_openpose\", torch_dtype=torch.float16)\ncontrolnet2 = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\", torch_dtype=torch.float16)\nvae = AutoencoderKL.from_pretrained(\"stabilityai/sd-vae-ft-mse\", torch_dtype=torch.float16)\n\nmodel_id = \"SG161222/Realistic_Vision_V5.1_noVAE\"\npipe = DiffusionPipeline.from_pretrained(\n model_id,\n motion_adapter=adapter,\n controlnet=[controlnet1, controlnet2],\n vae=vae,\n custom_pipeline=\"pipeline_animatediff_controlnet\",\n).to(device=\"cuda\", dtype=torch.float16)\npipe.scheduler = DPMSolverMultistepScheduler.from_pretrained(\n model_id, subfolder=\"scheduler\", clip_sample=False, timestep_spacing=\"linspace\", steps_offset=1, beta_schedule=\"linear\",\n)\npipe.enable_vae_slicing()\n\ndef load_video(file_path: str):\n images = []\n\n if file_path.startswith(('http://', 'https://')):\n # If the file_path is a URL\n response = requests.get(file_path)\n response.raise_for_status()\n content = BytesIO(response.content)\n vid = imageio.get_reader(content)\n else:\n # Assuming it's a local file path\n vid = imageio.get_reader(file_path)\n\n for frame in vid:\n pil_image = Image.fromarray(frame)\n images.append(pil_image)\n\n return images\n\nvideo = load_video(\"dance.gif\")\n\n# You need to install it using `pip install controlnet_aux`\nfrom controlnet_aux.processor import Processor\n\np1 = Processor(\"openpose_full\")\ncn1 = [p1(frame) for frame in video]\n\np2 = Processor(\"canny\")\ncn2 = [p2(frame) for frame in video]\n\nprompt = \"astronaut in space, dancing\"\nnegative_prompt = \"bad quality, worst quality, jpeg artifacts, ugly\"\nresult = pipe(\n prompt=prompt,\n negative_prompt=negative_prompt,\n width=512,\n height=768,\n conditioning_frames=[cn1, cn2],\n num_inference_steps=20,\n)\n\nfrom diffusers.utils import export_to_gif\nexport_to_gif(result.frames[0], \"result.gif\")\n```\n\n### DemoFusion\n\nThis pipeline is the official implementation of [DemoFusion: Democratising High-Resolution Image Generation With No $$$](https://arxiv.org/abs/2311.16973).\nThe original repo can be found at [repo](https://github.com/PRIS-CV/DemoFusion).\n\n- `view_batch_size` (`int`, defaults to 16):\n The batch size for multiple denoising paths. Typically, a larger batch size can result in higher efficiency but comes with increased GPU memory requirements.\n\n- `stride` (`int`, defaults to 64):\n The stride of moving local patches. A smaller stride is better for alleviating seam issues, but it also introduces additional computational overhead and inference time.\n\n- `cosine_scale_1` (`float`, defaults to 3):\n Control the strength of skip-residual. For specific impacts, please refer to Appendix C in the DemoFusion paper.\n\n- `cosine_scale_2` (`float`, defaults to 1):\n Control the strength of dilated sampling. For specific impacts, please refer to Appendix C in the DemoFusion paper.\n\n- `cosine_scale_3` (`float`, defaults to 1):\n Control the strength of the Gaussian filter. For specific impacts, please refer to Appendix C in the DemoFusion paper.\n\n- `sigma` (`float`, defaults to 1):\n The standard value of the Gaussian filter. Larger sigma promotes the global guidance of dilated sampling, but has the potential of over-smoothing.\n\n- `multi_decoder` (`bool`, defaults to True):\n Determine whether to use a tiled decoder. Generally, when the resolution exceeds 3072x3072, a tiled decoder becomes necessary.\n\n- `show_image` (`bool`, defaults to False):\n Determine whether to show intermediate results during generation.\n\n```py\nfrom diffusers import DiffusionPipeline\n\npipe = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n custom_pipeline=\"pipeline_demofusion_sdxl\",\n custom_revision=\"main\",\n torch_dtype=torch.float16,\n)\npipe = pipe.to(\"cuda\")\n\nprompt = \"Envision a portrait of an elderly woman, her face a canvas of time, framed by a headscarf with muted tones of rust and cream. Her eyes, blue like faded denim. Her attire, simple yet dignified.\"\nnegative_prompt = \"blurry, ugly, duplicate, poorly drawn, deformed, mosaic\"\n\nimages = pipe(\n prompt,\n negative_prompt=negative_prompt,\n height=3072,\n width=3072,\n view_batch_size=16,\n stride=64,\n num_inference_steps=50,\n guidance_scale=7.5,\n cosine_scale_1=3,\n cosine_scale_2=1,\n cosine_scale_3=1,\n sigma=0.8,\n multi_decoder=True,\n show_image=True\n)\n```\n\nYou can display and save the generated images as:\n\n```py\ndef image_grid(imgs, save_path=None):\n\n w = 0\n for i, img in enumerate(imgs):\n h_, w_ = imgs[i].size\n w += w_\n h = h_\n grid = Image.new('RGB', size=(w, h))\n grid_w, grid_h = grid.size\n\n w = 0\n for i, img in enumerate(imgs):\n h_, w_ = imgs[i].size\n grid.paste(img, box=(w, h - h_))\n if save_path != None:\n img.save(save_path + \"/img_{}.jpg\".format((i + 1) * 1024))\n w += w_\n\n return grid\n\nimage_grid(images, save_path=\"./outputs/\")\n```\n\n ![output_example](https://github.com/PRIS-CV/DemoFusion/blob/main/output_example.png)\n\n### SDE Drag pipeline\n\nThis pipeline provides drag-and-drop image editing using stochastic differential equations. It enables image editing by inputting prompt, image, mask_image, source_points, and target_points.\n\n![SDE Drag Image](https://github.com/huggingface/diffusers/assets/75928535/bd54f52f-f002-4951-9934-b2a4592771a5)\n\nSee [paper](https://arxiv.org/abs/2311.01410), [paper page](https://ml-gsai.github.io/SDE-Drag-demo/), [original repo](https://github.com/ML-GSAI/SDE-Drag) for more infomation.\n\n```py\nimport PIL\nimport torch\nfrom diffusers import DDIMScheduler, DiffusionPipeline\n\n# Load the pipeline\nmodel_path = \"runwayml/stable-diffusion-v1-5\"\nscheduler = DDIMScheduler.from_pretrained(model_path, subfolder=\"scheduler\")\npipe = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler, custom_pipeline=\"sde_drag\")\npipe.to('cuda')\n\n# To save GPU memory, torch.float16 can be used, but it may compromise image quality.\n# If not training LoRA, please avoid using torch.float16\n# pipe.to(torch.float16)\n\n# Provide prompt, image, mask image, and the starting and target points for drag editing.\nprompt = \"prompt of the image\"\nimage = PIL.Image.open('/path/to/image')\nmask_image = PIL.Image.open('/path/to/mask_image')\nsource_points = [[123, 456]]\ntarget_points = [[234, 567]]\n\n# train_lora is optional, and in most cases, using train_lora can better preserve consistency with the original image.\npipe.train_lora(prompt, image)\n\noutput = pipe(prompt, image, mask_image, source_points, target_points)\noutput_image = PIL.Image.fromarray(output)\noutput_image.save(\"./output.png\")\n\n```\n\n### Instaflow Pipeline\n\nInstaFlow is an ultra-fast, one-step image generator that achieves image quality close to Stable Diffusion, significantly reducing the demand of computational resources. This efficiency is made possible through a recent [Rectified Flow](https://github.com/gnobitab/RectifiedFlow) technique, which trains probability flows with straight trajectories, hence inherently requiring only a single step for fast inference.\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\n\npipe = DiffusionPipeline.from_pretrained(\"XCLIU/instaflow_0_9B_from_sd_1_5\", torch_dtype=torch.float16, custom_pipeline=\"instaflow_one_step\")\npipe.to(\"cuda\") ### if GPU is not available, comment this line\nprompt = \"A hyper-realistic photo of a cute cat.\"\n\nimages = pipe(prompt=prompt,\n num_inference_steps=1,\n guidance_scale=0.0).images\nimages[0].save(\"./image.png\")\n```\n\n![image1](https://huggingface.co/datasets/ayushtues/instaflow_images/resolve/main/instaflow_cat.png)\n\nYou can also combine it with LORA out of the box, like , to unlock cool use cases in single step!\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\n\npipe = DiffusionPipeline.from_pretrained(\"XCLIU/instaflow_0_9B_from_sd_1_5\", torch_dtype=torch.float16, custom_pipeline=\"instaflow_one_step\")\npipe.to(\"cuda\") ### if GPU is not available, comment this line\npipe.load_lora_weights(\"artificialguybr/logo-redmond-1-5v-logo-lora-for-liberteredmond-sd-1-5\")\nprompt = \"logo, A logo for a fitness app, dynamic running figure, energetic colors (red, orange) ),LogoRedAF ,\"\nimages = pipe(prompt=prompt,\n num_inference_steps=1,\n guidance_scale=0.0).images\nimages[0].save(\"./image.png\")\n```\n\n![image0](https://huggingface.co/datasets/ayushtues/instaflow_images/resolve/main/instaflow_logo.png)\n\n### Null-Text Inversion pipeline\n\nThis pipeline provides null-text inversion for editing real images. It enables null-text optimization, and DDIM reconstruction via w, w/o null-text optimization. No prompt-to-prompt code is implemented as there is a Prompt2PromptPipeline.\n\n- Reference paper\n\n ```@article{hertz2022prompt,\n title={Prompt-to-prompt image editing with cross attention control},\n author={Hertz, Amir and Mokady, Ron and Tenenbaum, Jay and Aberman, Kfir and Pritch, Yael and Cohen-Or, Daniel},\n booktitle={arXiv preprint arXiv:2208.01626},\n year={2022}\n ```}\n\n```py\nfrom diffusers.schedulers import DDIMScheduler\nfrom examples.community.pipeline_null_text_inversion import NullTextPipeline\nimport torch\n\n# Load the pipeline\ndevice = \"cuda\"\n# Provide invert_prompt and the image for null-text optimization.\ninvert_prompt = \"A lying cat\"\ninput_image = \"siamese.jpg\"\nsteps = 50\n\n# Provide prompt used for generation. Same if reconstruction\nprompt = \"A lying cat\"\n# or different if editing.\nprompt = \"A lying dog\"\n\n#Float32 is essential to a well optimization\nmodel_path = \"runwayml/stable-diffusion-v1-5\"\nscheduler = DDIMScheduler(num_train_timesteps=1000, beta_start=0.00085, beta_end=0.0120, beta_schedule=\"scaled_linear\")\npipeline = NullTextPipeline.from_pretrained(model_path, scheduler = scheduler, torch_dtype=torch.float32).to(device)\n\n#Saves the inverted_latent to save time\ninverted_latent, uncond = pipeline.invert(input_image, invert_prompt, num_inner_steps=10, early_stop_epsilon= 1e-5, num_inference_steps = steps)\npipeline(prompt, uncond, inverted_latent, guidance_scale=7.5, num_inference_steps=steps).images[0].save(input_image+\".output.jpg\")\n```\n\n### Rerender A Video\n\nThis is the Diffusers implementation of zero-shot video-to-video translation pipeline [Rerender A Video](https://github.com/williamyang1991/Rerender_A_Video) (without Ebsynth postprocessing). To run the code, please install gmflow. Then modify the path in `gmflow_dir`. After that, you can run the pipeline with:\n\n```py\nimport sys\ngmflow_dir = \"/path/to/gmflow\"\nsys.path.insert(0, gmflow_dir)\n\nfrom diffusers import ControlNetModel, AutoencoderKL, DDIMScheduler\nfrom diffusers.utils import export_to_video\nimport numpy as np\nimport torch\n\nimport cv2\nfrom PIL import Image\n\ndef video_to_frame(video_path: str, interval: int):\n vidcap = cv2.VideoCapture(video_path)\n success = True\n\n count = 0\n res = []\n while success:\n count += 1\n success, image = vidcap.read()\n if count % interval != 1:\n continue\n if image is not None:\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n res.append(image)\n\n vidcap.release()\n return res\n\ninput_video_path = 'path/to/video'\ninput_interval = 10\nframes = video_to_frame(\n input_video_path, input_interval)\n\ncontrol_frames = []\n# get canny image\nfor frame in frames:\n np_image = cv2.Canny(frame, 50, 100)\n np_image = np_image[:, :, None]\n np_image = np.concatenate([np_image, np_image, np_image], axis=2)\n canny_image = Image.fromarray(np_image)\n control_frames.append(canny_image)\n\n# You can use any ControlNet here\ncontrolnet = ControlNetModel.from_pretrained(\n \"lllyasviel/sd-controlnet-canny\").to('cuda')\n\n# You can use any fintuned SD here\npipe = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, custom_pipeline='rerender_a_video').to('cuda')\n\n# Optional: you can download vae-ft-mse-840000-ema-pruned.ckpt to enhance the results\n# pipe.vae = AutoencoderKL.from_single_file(\n# \"path/to/vae-ft-mse-840000-ema-pruned.ckpt\").to('cuda')\n\npipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\n\ngenerator = torch.manual_seed(0)\nframes = [Image.fromarray(frame) for frame in frames]\noutput_frames = pipe(\n \"a beautiful woman in CG style, best quality, extremely detailed\",\n frames,\n control_frames,\n num_inference_steps=20,\n strength=0.75,\n controlnet_conditioning_scale=0.7,\n generator=generator,\n warp_start=0.0,\n warp_end=0.1,\n mask_start=0.5,\n mask_end=0.8,\n mask_strength=0.5,\n negative_prompt='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'\n).frames[0]\n\nexport_to_video(\n output_frames, \"/path/to/video.mp4\", 5)\n```\n\n### StyleAligned Pipeline\n\nThis pipeline is the implementation of [Style Aligned Image Generation via Shared Attention](https://arxiv.org/abs/2312.02133). You can find more results [here](https://github.com/huggingface/diffusers/pull/6489#issuecomment-1881209354).\n\n> Large-scale Text-to-Image (T2I) models have rapidly gained prominence across creative fields, generating visually compelling outputs from textual prompts. However, controlling these models to ensure consistent style remains challenging, with existing methods necessitating fine-tuning and manual intervention to disentangle content and style. In this paper, we introduce StyleAligned, a novel technique designed to establish style alignment among a series of generated images. By employing minimal `attention sharing' during the diffusion process, our method maintains style consistency across images within T2I models. This approach allows for the creation of style-consistent images using a reference style through a straightforward inversion operation. Our method's evaluation across diverse styles and text prompts demonstrates high-quality synthesis and fidelity, underscoring its efficacy in achieving consistent style across various inputs.\n\n```python\nfrom typing import List\n\nimport torch\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline\nfrom PIL import Image\n\nmodel_id = \"a-r-r-o-w/dreamshaper-xl-turbo\"\npipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, variant=\"fp16\", custom_pipeline=\"pipeline_sdxl_style_aligned\")\npipe = pipe.to(\"cuda\")\n\n# Enable memory saving techniques\npipe.enable_vae_slicing()\npipe.enable_vae_tiling()\n\nprompt = [\n \"a toy train. macro photo. 3d game asset\",\n \"a toy airplane. macro photo. 3d game asset\",\n \"a toy bicycle. macro photo. 3d game asset\",\n \"a toy car. macro photo. 3d game asset\",\n]\nnegative_prompt = \"low quality, worst quality, \"\n\n# Enable StyleAligned\npipe.enable_style_aligned(\n share_group_norm=False,\n share_layer_norm=False,\n share_attention=True,\n adain_queries=True,\n adain_keys=True,\n adain_values=False,\n full_attention_share=False,\n shared_score_scale=1.0,\n shared_score_shift=0.0,\n only_self_level=0.0,\n)\n\n# Run inference\nimages = pipe(\n prompt=prompt,\n negative_prompt=negative_prompt,\n guidance_scale=2,\n height=1024,\n width=1024,\n num_inference_steps=10,\n generator=torch.Generator().manual_seed(42),\n).images\n\n# Disable StyleAligned if you do not wish to use it anymore\npipe.disable_style_aligned()\n```\n\n### AnimateDiff Image-To-Video Pipeline\n\nThis pipeline adds experimental support for the image-to-video task using AnimateDiff. Refer to [this](https://github.com/huggingface/diffusers/pull/6328) PR for more examples and results.\n\nThis pipeline relies on a \"hack\" discovered by the community that allows the generation of videos given an input image with AnimateDiff. It works by creating a copy of the image `num_frames` times and progressively adding more noise to the image based on the strength and latent interpolation method.\n\n```py\nimport torch\nfrom diffusers import MotionAdapter, DiffusionPipeline, DDIMScheduler\nfrom diffusers.utils import export_to_gif, load_image\n\nmodel_id = \"SG161222/Realistic_Vision_V5.1_noVAE\"\nadapter = MotionAdapter.from_pretrained(\"guoyww/animatediff-motion-adapter-v1-5-2\")\npipe = DiffusionPipeline.from_pretrained(model_id, motion_adapter=adapter, custom_pipeline=\"pipeline_animatediff_img2video\").to(\"cuda\")\npipe.scheduler = DDIMScheduler.from_pretrained(model_id, subfolder=\"scheduler\", clip_sample=False, timestep_spacing=\"linspace\", beta_schedule=\"linear\", steps_offset=1)\n\nimage = load_image(\"snail.png\")\noutput = pipe(\n image=image,\n prompt=\"A snail moving on the ground\",\n strength=0.8,\n latent_interpolation_method=\"slerp\", # can be lerp, slerp, or your own callback\n)\nframes = output.frames[0]\nexport_to_gif(frames, \"animation.gif\")\n```\n\n### IP Adapter Face ID\n\nIP Adapter FaceID is an experimental IP Adapter model that uses image embeddings generated by `insightface`, so no image encoder needs to be loaded.\nYou need to install `insightface` and all its requirements to use this model.\nYou must pass the image embedding tensor as `image_embeds` to the StableDiffusionPipeline instead of `ip_adapter_image`.\nYou can find more results [here](https://github.com/huggingface/diffusers/pull/6276).\n\n```py\nimport diffusers\nimport torch\nfrom diffusers.utils import load_image\nimport cv2\nimport numpy as np\nfrom diffusers import DiffusionPipeline, AutoencoderKL, DDIMScheduler\nfrom insightface.app import FaceAnalysis\n\n\nnoise_scheduler = DDIMScheduler(\n num_train_timesteps=1000,\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n steps_offset=1,\n)\nvae = AutoencoderKL.from_pretrained(\"stabilityai/sd-vae-ft-mse\").to(dtype=torch.float16)\npipeline = DiffusionPipeline.from_pretrained(\n \"SG161222/Realistic_Vision_V4.0_noVAE\",\n torch_dtype=torch.float16,\n scheduler=noise_scheduler,\n vae=vae,\n custom_pipeline=\"ip_adapter_face_id\"\n)\npipeline.load_ip_adapter_face_id(\"h94/IP-Adapter-FaceID\", \"ip-adapter-faceid_sd15.bin\")\npipeline.to(\"cuda\")\n\ngenerator = torch.Generator(device=\"cpu\").manual_seed(42)\nnum_images=2\n\nimage = load_image(\"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ai_face2.png\")\n\napp = FaceAnalysis(name=\"buffalo_l\", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])\napp.prepare(ctx_id=0, det_size=(640, 640))\nimage = cv2.cvtColor(np.asarray(image), cv2.COLOR_BGR2RGB)\nfaces = app.get(image)\nimage = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0)\nimages = pipeline(\n prompt=\"A photo of a girl wearing a black dress, holding red roses in hand, upper body, behind is the Eiffel Tower\",\n image_embeds=image,\n negative_prompt=\"monochrome, lowres, bad anatomy, worst quality, low quality\",\n num_inference_steps=20, num_images_per_prompt=num_images, width=512, height=704,\n generator=generator\n).images\n\nfor i in range(num_images):\n images[i].save(f\"c{i}.png\")\n```\n\n### InstantID Pipeline\n\nInstantID is a new state-of-the-art tuning-free method to achieve ID-Preserving generation with only single image, supporting various downstream tasks. For any usgae question, please refer to the [official implementation](https://github.com/InstantID/InstantID).\n\n```py\n# !pip install opencv-python transformers accelerate insightface\nimport diffusers\nfrom diffusers.utils import load_image\nfrom diffusers.models import ControlNetModel\n\nimport cv2\nimport torch\nimport numpy as np\nfrom PIL import Image\n\nfrom insightface.app import FaceAnalysis\nfrom pipeline_stable_diffusion_xl_instantid import StableDiffusionXLInstantIDPipeline, draw_kps\n\n# prepare 'antelopev2' under ./models\n# https://github.com/deepinsight/insightface/issues/1896#issuecomment-1023867304\napp = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])\napp.prepare(ctx_id=0, det_size=(640, 640))\n\n# prepare models under ./checkpoints\n# https://huggingface.co/InstantX/InstantID\nfrom huggingface_hub import hf_hub_download\nhf_hub_download(repo_id=\"InstantX/InstantID\", filename=\"ControlNetModel/config.json\", local_dir=\"./checkpoints\")\nhf_hub_download(repo_id=\"InstantX/InstantID\", filename=\"ControlNetModel/diffusion_pytorch_model.safetensors\", local_dir=\"./checkpoints\")\nhf_hub_download(repo_id=\"InstantX/InstantID\", filename=\"ip-adapter.bin\", local_dir=\"./checkpoints\")\n\nface_adapter = f'./checkpoints/ip-adapter.bin'\ncontrolnet_path = f'./checkpoints/ControlNetModel'\n\n# load IdentityNet\ncontrolnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)\n\nbase_model = 'wangqixun/YamerMIX_v8'\npipe = StableDiffusionXLInstantIDPipeline.from_pretrained(\n base_model,\n controlnet=controlnet,\n torch_dtype=torch.float16\n)\npipe.cuda()\n\n# load adapter\npipe.load_ip_adapter_instantid(face_adapter)\n\n# load an image\nface_image = load_image(\"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ai_face2.png\")\n\n# prepare face emb\nface_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))\nface_info = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])[-1] # only use the maximum face\nface_emb = face_info['embedding']\nface_kps = draw_kps(face_image, face_info['kps'])\n\n# prompt\nprompt = \"film noir style, ink sketch|vector, male man, highly detailed, sharp focus, ultra sharpness, monochrome, high contrast, dramatic shadows, 1940s style, mysterious, cinematic\"\nnegative_prompt = \"ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, vibrant, colorful\"\n\n# generate image\npipe.set_ip_adapter_scale(0.8)\nimage = pipe(\n prompt,\n image_embeds=face_emb,\n image=face_kps,\n controlnet_conditioning_scale=0.8,\n).images[0]\n```\n\n### UFOGen Scheduler\n\n[UFOGen](https://arxiv.org/abs/2311.09257) is a generative model designed for fast one-step text-to-image generation, trained via adversarial training starting from an initial pretrained diffusion model such as Stable Diffusion. `scheduling_ufogen.py` implements a onestep and multistep sampling algorithm for UFOGen models compatible with pipelines like `StableDiffusionPipeline`. A usage example is as follows:\n\n```py\nimport torch\nfrom diffusers import StableDiffusionPipeline\n\nfrom scheduling_ufogen import UFOGenScheduler\n\n# NOTE: currently, I am not aware of any publicly available UFOGen model checkpoints trained from SD v1.5.\nufogen_model_id_or_path = \"/path/to/ufogen/model\"\npipe = StableDiffusionPipeline(\n ufogen_model_id_or_path,\n torch_dtype=torch.float16,\n)\n\n# You can initialize a UFOGenScheduler as follows:\npipe.scheduler = UFOGenScheduler.from_config(pipe.scheduler.config)\n\nprompt = \"Three cats having dinner at a table at new years eve, cinematic shot, 8k.\"\n\n# Onestep sampling\nonestep_image = pipe(prompt, num_inference_steps=1).images[0]\n\n# Multistep sampling\nmultistep_image = pipe(prompt, num_inference_steps=4).images[0]\n```\n\n### FRESCO\n\nThis is the Diffusers implementation of zero-shot video-to-video translation pipeline [FRESCO](https://github.com/williamyang1991/FRESCO) (without Ebsynth postprocessing and background smooth). To run the code, please install gmflow. Then modify the path in `gmflow_dir`. After that, you can run the pipeline with:\n\n```py\nfrom PIL import Image\nimport cv2\nimport torch\nimport numpy as np\n\nfrom diffusers import ControlNetModel,DDIMScheduler, DiffusionPipeline\nimport sys\ngmflow_dir = \"/path/to/gmflow\"\nsys.path.insert(0, gmflow_dir)\n\ndef video_to_frame(video_path: str, interval: int):\n vidcap = cv2.VideoCapture(video_path)\n success = True\n\n count = 0\n res = []\n while success:\n count += 1\n success, image = vidcap.read()\n if count % interval != 1:\n continue\n if image is not None:\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n res.append(image)\n if len(res) >= 8:\n break\n\n vidcap.release()\n return res\n\n\ninput_video_path = 'https://github.com/williamyang1991/FRESCO/raw/main/data/car-turn.mp4'\noutput_video_path = 'car.gif'\n\n# You can use any fintuned SD here\nmodel_path = 'SG161222/Realistic_Vision_V2.0'\n\nprompt = 'a red car turns in the winter'\na_prompt = ', RAW photo, subject, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3, '\nn_prompt = '(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.4), (deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation'\n\ninput_interval = 5\nframes = video_to_frame(\n input_video_path, input_interval)\n\ncontrol_frames = []\n# get canny image\nfor frame in frames:\n image = cv2.Canny(frame, 50, 100)\n np_image = np.array(image)\n np_image = np_image[:, :, None]\n np_image = np.concatenate([np_image, np_image, np_image], axis=2)\n canny_image = Image.fromarray(np_image)\n control_frames.append(canny_image)\n\n# You can use any ControlNet here\ncontrolnet = ControlNetModel.from_pretrained(\n \"lllyasviel/sd-controlnet-canny\").to('cuda')\n\npipe = DiffusionPipeline.from_pretrained(\n model_path, controlnet=controlnet, custom_pipeline='fresco_v2v').to('cuda')\npipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\n\ngenerator = torch.manual_seed(0)\nframes = [Image.fromarray(frame) for frame in frames]\n\noutput_frames = pipe(\n prompt + a_prompt,\n frames,\n control_frames,\n num_inference_steps=20,\n strength=0.75,\n controlnet_conditioning_scale=0.7,\n generator=generator,\n negative_prompt=n_prompt\n).images\n\noutput_frames[0].save(output_video_path, save_all=True,\n append_images=output_frames[1:], duration=100, loop=0)\n\n```\n\n# Perturbed-Attention Guidance\n\n[Project](https://ku-cvlab.github.io/Perturbed-Attention-Guidance/) / [arXiv](https://arxiv.org/abs/2403.17377) / [GitHub](https://github.com/KU-CVLAB/Perturbed-Attention-Guidance)\n\nThis implementation is based on [Diffusers](https://huggingface.co/docs/diffusers/index). StableDiffusionPAGPipeline is a modification of StableDiffusionPipeline to support Perturbed-Attention Guidance (PAG).\n\n## Example Usage\n\n```py\nimport os\nimport torch\n\nfrom accelerate.utils import set_seed\n\nfrom diffusers import StableDiffusionPipeline\nfrom diffusers.utils import load_image, make_image_grid\nfrom diffusers.utils.torch_utils import randn_tensor\n\npipe = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n custom_pipeline=\"hyoungwoncho/sd_perturbed_attention_guidance\",\n torch_dtype=torch.float16\n)\n\ndevice=\"cuda\"\npipe = pipe.to(device)\n\npag_scale = 5.0\npag_applied_layers_index = ['m0']\n\nbatch_size = 4\nseed=10\n\nbase_dir = \"./results/\"\ngrid_dir = base_dir + \"/pag\" + str(pag_scale) + \"/\"\n\nif not os.path.exists(grid_dir):\n os.makedirs(grid_dir)\n\nset_seed(seed)\n\nlatent_input = randn_tensor(shape=(batch_size,4,64,64),generator=None, device=device, dtype=torch.float16)\n\noutput_baseline = pipe(\n \"\",\n width=512,\n height=512,\n num_inference_steps=50,\n guidance_scale=0.0,\n pag_scale=0.0,\n pag_applied_layers_index=pag_applied_layers_index,\n num_images_per_prompt=batch_size,\n latents=latent_input\n).images\n\noutput_pag = pipe(\n \"\",\n width=512,\n height=512,\n num_inference_steps=50,\n guidance_scale=0.0,\n pag_scale=5.0,\n pag_applied_layers_index=pag_applied_layers_index,\n num_images_per_prompt=batch_size,\n latents=latent_input\n).images\n\ngrid_image = make_image_grid(output_baseline + output_pag, rows=2, cols=batch_size)\ngrid_image.save(grid_dir + \"sample.png\")\n```\n\n## PAG Parameters\n\npag_scale : gudiance scale of PAG (ex: 5.0)\n\npag_applied_layers_index : index of the layer to apply perturbation (ex: ['m0'])\n" }, { "path": "diffusers/examples/community/README_community_scripts.md", "content": "# Community Scripts\n\n**Community scripts** consist of inference examples using Diffusers pipelines that have been added by the community. \nPlease have a look at the following table to get an overview of all community examples. Click on the **Code Example** to get a copy-and-paste code example that you can try out.\nIf a community script doesn't work as expected, please open an issue and ping the author on it.\n\n| Example | Description | Code Example | Colab | Author |\n|:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------:|\n| Using IP-Adapter with negative noise | Using negative noise with IP-adapter to better control the generation (see the [original post](https://github.com/huggingface/diffusers/discussions/7167) on the forum for more details) | [IP-Adapter Negative Noise](#ip-adapter-negative-noise) | | [Álvaro Somoza](https://github.com/asomoza)|\n| asymmetric tiling |configure seamless image tiling independently for the X and Y axes | [Asymmetric Tiling](#asymmetric-tiling ) | | [alexisrolland](https://github.com/alexisrolland)|\n\n\n## Example usages\n\n### IP Adapter Negative Noise\n\nDiffusers pipelines are fully integrated with IP-Adapter, which allows you to prompt the diffusion model with an image. However, it does not support negative image prompts (there is no `negative_ip_adapter_image` argument) the same way it supports negative text prompts. When you pass an `ip_adapter_image,` it will create a zero-filled tensor as a negative image. This script shows you how to create a negative noise from `ip_adapter_image` and use it to significantly improve the generation quality while preserving the composition of images.\n\n[cubiq](https://github.com/cubiq) initially developed this feature in his [repository](https://github.com/cubiq/ComfyUI_IPAdapter_plus). The community script was contributed by [asomoza](https://github.com/Somoza). You can find more details about this experimentation [this discussion](https://github.com/huggingface/diffusers/discussions/7167)\n\nIP-Adapter without negative noise\n|source|result|\n|---|---|\n|![20240229150812](https://github.com/huggingface/diffusers/assets/5442875/901d8bd8-7a59-4fe7-bda1-a0e0d6c7dffd)|![20240229163923_normal](https://github.com/huggingface/diffusers/assets/5442875/3432e25a-ece6-45f4-a3f4-fca354f40b5b)|\n\nIP-Adapter with negative noise\n|source|result|\n|---|---|\n|![20240229150812](https://github.com/huggingface/diffusers/assets/5442875/901d8bd8-7a59-4fe7-bda1-a0e0d6c7dffd)|![20240229163923](https://github.com/huggingface/diffusers/assets/5442875/736fd15a-36ba-40c0-a7d8-6ec1ac26f788)|\n\n```python\nimport torch\n\nfrom diffusers import AutoencoderKL, DPMSolverMultistepScheduler, StableDiffusionXLPipeline\nfrom diffusers.models import ImageProjection\nfrom diffusers.utils import load_image\n\n\ndef encode_image(\n image_encoder,\n feature_extractor,\n image,\n device,\n num_images_per_prompt,\n output_hidden_states=None,\n negative_image=None,\n):\n dtype = next(image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n\n if negative_image is None:\n uncond_image_enc_hidden_states = image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n else:\n if not isinstance(negative_image, torch.Tensor):\n negative_image = feature_extractor(negative_image, return_tensors=\"pt\").pixel_values\n negative_image = negative_image.to(device=device, dtype=dtype)\n uncond_image_enc_hidden_states = image_encoder(negative_image, output_hidden_states=True).hidden_states[-2]\n\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n\n@torch.no_grad()\ndef prepare_ip_adapter_image_embeds(\n unet,\n image_encoder,\n feature_extractor,\n ip_adapter_image,\n do_classifier_free_guidance,\n device,\n num_images_per_prompt,\n ip_adapter_negative_image=None,\n):\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = encode_image(\n image_encoder,\n feature_extractor,\n single_ip_adapter_image,\n device,\n 1,\n output_hidden_state,\n negative_image=ip_adapter_negative_image,\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack([single_negative_image_embeds] * num_images_per_prompt, dim=0)\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n\nvae = AutoencoderKL.from_pretrained(\n \"madebyollin/sdxl-vae-fp16-fix\",\n torch_dtype=torch.float16,\n).to(\"cuda\")\n\npipeline = StableDiffusionXLPipeline.from_pretrained(\n \"RunDiffusion/Juggernaut-XL-v9\",\n torch_dtype=torch.float16,\n vae=vae,\n variant=\"fp16\",\n).to(\"cuda\")\n\npipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)\npipeline.scheduler.config.use_karras_sigmas = True\n\npipeline.load_ip_adapter(\n \"h94/IP-Adapter\",\n subfolder=\"sdxl_models\",\n weight_name=\"ip-adapter-plus_sdxl_vit-h.safetensors\",\n image_encoder_folder=\"models/image_encoder\",\n)\npipeline.set_ip_adapter_scale(0.7)\n\nip_image = load_image(\"source.png\")\nnegative_ip_image = load_image(\"noise.png\")\n\nimage_embeds = prepare_ip_adapter_image_embeds(\n unet=pipeline.unet,\n image_encoder=pipeline.image_encoder,\n feature_extractor=pipeline.feature_extractor,\n ip_adapter_image=[[ip_image]],\n do_classifier_free_guidance=True,\n device=\"cuda\",\n num_images_per_prompt=1,\n ip_adapter_negative_image=negative_ip_image,\n)\n\n\nprompt = \"cinematic photo of a cyborg in the city, 4k, high quality, intricate, highly detailed\"\nnegative_prompt = \"blurry, smooth, plastic\"\n\nimage = pipeline(\n prompt=prompt,\n negative_prompt=negative_prompt,\n ip_adapter_image_embeds=image_embeds,\n guidance_scale=6.0,\n num_inference_steps=25,\n generator=torch.Generator(device=\"cpu\").manual_seed(1556265306),\n).images[0]\n\nimage.save(\"result.png\")\n```\n\n### Asymmetric Tiling\nStable Diffusion is not trained to generate seamless textures. However, you can use this simple script to add tiling to your generation. This script is contributed by [alexisrolland](https://github.com/alexisrolland). See more details in the [this issue](https://github.com/huggingface/diffusers/issues/556)\n\n\n|Generated|Tiled|\n|---|---|\n|![20240313003235_573631814](https://github.com/huggingface/diffusers/assets/5442875/eca174fb-06a4-464e-a3a7-00dbb024543e)|![wall](https://github.com/huggingface/diffusers/assets/5442875/b4aa774b-2a6a-4316-a8eb-8f30b5f4d024)|\n\n\n```py\nimport torch\nfrom typing import Optional\nfrom diffusers import StableDiffusionPipeline\nfrom diffusers.models.lora import LoRACompatibleConv\n\ndef seamless_tiling(pipeline, x_axis, y_axis):\n def asymmetric_conv2d_convforward(self, input: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None):\n self.paddingX = (self._reversed_padding_repeated_twice[0], self._reversed_padding_repeated_twice[1], 0, 0)\n self.paddingY = (0, 0, self._reversed_padding_repeated_twice[2], self._reversed_padding_repeated_twice[3])\n working = torch.nn.functional.pad(input, self.paddingX, mode=x_mode)\n working = torch.nn.functional.pad(working, self.paddingY, mode=y_mode)\n return torch.nn.functional.conv2d(working, weight, bias, self.stride, torch.nn.modules.utils._pair(0), self.dilation, self.groups)\n x_mode = 'circular' if x_axis else 'constant'\n y_mode = 'circular' if y_axis else 'constant'\n targets = [pipeline.vae, pipeline.text_encoder, pipeline.unet]\n convolution_layers = []\n for target in targets:\n for module in target.modules():\n if isinstance(module, torch.nn.Conv2d):\n convolution_layers.append(module)\n for layer in convolution_layers:\n if isinstance(layer, LoRACompatibleConv) and layer.lora_layer is None:\n layer.lora_layer = lambda * x: 0\n layer._conv_forward = asymmetric_conv2d_convforward.__get__(layer, torch.nn.Conv2d)\n return pipeline\n\npipeline = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, use_safetensors=True)\npipeline.enable_model_cpu_offload()\nprompt = [\"texture of a red brick wall\"]\nseed = 123456\ngenerator = torch.Generator(device='cuda').manual_seed(seed)\n\npipeline = seamless_tiling(pipeline=pipeline, x_axis=True, y_axis=True)\nimage = pipeline(\n prompt=prompt,\n width=512,\n height=512,\n num_inference_steps=20,\n guidance_scale=7,\n num_images_per_prompt=1,\n generator=generator\n).images[0]\nseamless_tiling(pipeline=pipeline, x_axis=False, y_axis=False)\n\ntorch.cuda.empty_cache()\nimage.save('image.png')\n```" }, { "path": "diffusers/examples/community/bit_diffusion.py", "content": "from typing import Optional, Tuple, Union\n\nimport torch\nfrom einops import rearrange, reduce\n\nfrom diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNet2DConditionModel\nfrom diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput\nfrom diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput\n\n\nBITS = 8\n\n\n# convert to bit representations and back taken from https://github.com/lucidrains/bit-diffusion/blob/main/bit_diffusion/bit_diffusion.py\ndef decimal_to_bits(x, bits=BITS):\n \"\"\"expects image tensor ranging from 0 to 1, outputs bit tensor ranging from -1 to 1\"\"\"\n device = x.device\n\n x = (x * 255).int().clamp(0, 255)\n\n mask = 2 ** torch.arange(bits - 1, -1, -1, device=device)\n mask = rearrange(mask, \"d -> d 1 1\")\n x = rearrange(x, \"b c h w -> b c 1 h w\")\n\n bits = ((x & mask) != 0).float()\n bits = rearrange(bits, \"b c d h w -> b (c d) h w\")\n bits = bits * 2 - 1\n return bits\n\n\ndef bits_to_decimal(x, bits=BITS):\n \"\"\"expects bits from -1 to 1, outputs image tensor from 0 to 1\"\"\"\n device = x.device\n\n x = (x > 0).int()\n mask = 2 ** torch.arange(bits - 1, -1, -1, device=device, dtype=torch.int32)\n\n mask = rearrange(mask, \"d -> d 1 1\")\n x = rearrange(x, \"b (c d) h w -> b c d h w\", d=8)\n dec = reduce(x * mask, \"b c d h w -> b c h w\", \"sum\")\n return (dec / 255).clamp(0.0, 1.0)\n\n\n# modified scheduler step functions for clamping the predicted x_0 between -bit_scale and +bit_scale\ndef ddim_bit_scheduler_step(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n eta: float = 0.0,\n use_clipped_model_output: bool = True,\n generator=None,\n return_dict: bool = True,\n) -> Union[DDIMSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion\n process from the learned model outputs (most often the predicted noise).\n Args:\n model_output (`torch.Tensor`): direct output from learned diffusion model.\n timestep (`int`): current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n current instance of sample being created by diffusion process.\n eta (`float`): weight of noise for added noise in diffusion step.\n use_clipped_model_output (`bool`): TODO\n generator: random number generator.\n return_dict (`bool`): option for returning tuple rather than DDIMSchedulerOutput class\n Returns:\n [`~schedulers.scheduling_utils.DDIMSchedulerOutput`] or `tuple`:\n [`~schedulers.scheduling_utils.DDIMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When\n returning a tuple, the first element is the sample tensor.\n \"\"\"\n if self.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf\n # Ideally, read DDIM paper in-detail understanding\n\n # Notation ( -> \n # - pred_noise_t -> e_theta(x_t, t)\n # - pred_original_sample -> f_theta(x_t, t) or x_0\n # - std_dev_t -> sigma_t\n # - eta -> η\n # - pred_sample_direction -> \"direction pointing to x_t\"\n # - pred_prev_sample -> \"x_t-1\"\n\n # 1. get previous step value (=t-1)\n prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps\n\n # 2. compute alphas, betas\n alpha_prod_t = self.alphas_cumprod[timestep]\n alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod\n\n beta_prod_t = 1 - alpha_prod_t\n\n # 3. compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)\n\n # 4. Clip \"predicted x_0\"\n scale = self.bit_scale\n if self.config.clip_sample:\n pred_original_sample = torch.clamp(pred_original_sample, -scale, scale)\n\n # 5. compute variance: \"sigma_t(η)\" -> see formula (16)\n # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)\n variance = self._get_variance(timestep, prev_timestep)\n std_dev_t = eta * variance ** (0.5)\n\n if use_clipped_model_output:\n # the model_output is always re-derived from the clipped x_0 in Glide\n model_output = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)\n\n # 6. compute \"direction pointing to x_t\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * model_output\n\n # 7. compute x_t without \"random noise\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction\n\n if eta > 0:\n # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072\n device = model_output.device if torch.is_tensor(model_output) else \"cpu\"\n noise = torch.randn(model_output.shape, dtype=model_output.dtype, generator=generator).to(device)\n variance = self._get_variance(timestep, prev_timestep) ** (0.5) * eta * noise\n\n prev_sample = prev_sample + variance\n\n if not return_dict:\n return (prev_sample,)\n\n return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)\n\n\ndef ddpm_bit_scheduler_step(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n prediction_type=\"epsilon\",\n generator=None,\n return_dict: bool = True,\n) -> Union[DDPMSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion\n process from the learned model outputs (most often the predicted noise).\n Args:\n model_output (`torch.Tensor`): direct output from learned diffusion model.\n timestep (`int`): current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n current instance of sample being created by diffusion process.\n prediction_type (`str`, default `epsilon`):\n indicates whether the model predicts the noise (epsilon), or the samples (`sample`).\n generator: random number generator.\n return_dict (`bool`): option for returning tuple rather than DDPMSchedulerOutput class\n Returns:\n [`~schedulers.scheduling_utils.DDPMSchedulerOutput`] or `tuple`:\n [`~schedulers.scheduling_utils.DDPMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When\n returning a tuple, the first element is the sample tensor.\n \"\"\"\n t = timestep\n\n if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in [\"learned\", \"learned_range\"]:\n model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1)\n else:\n predicted_variance = None\n\n # 1. compute alphas, betas\n alpha_prod_t = self.alphas_cumprod[t]\n alpha_prod_t_prev = self.alphas_cumprod[t - 1] if t > 0 else self.one\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n\n # 2. compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf\n if prediction_type == \"epsilon\":\n pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)\n elif prediction_type == \"sample\":\n pred_original_sample = model_output\n else:\n raise ValueError(f\"Unsupported prediction_type {prediction_type}.\")\n\n # 3. Clip \"predicted x_0\"\n scale = self.bit_scale\n if self.config.clip_sample:\n pred_original_sample = torch.clamp(pred_original_sample, -scale, scale)\n\n # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t\n # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf\n pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * self.betas[t]) / beta_prod_t\n current_sample_coeff = self.alphas[t] ** (0.5) * beta_prod_t_prev / beta_prod_t\n\n # 5. Compute predicted previous sample µ_t\n # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf\n pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample\n\n # 6. Add noise\n variance = 0\n if t > 0:\n noise = torch.randn(\n model_output.size(), dtype=model_output.dtype, layout=model_output.layout, generator=generator\n ).to(model_output.device)\n variance = (self._get_variance(t, predicted_variance=predicted_variance) ** 0.5) * noise\n\n pred_prev_sample = pred_prev_sample + variance\n\n if not return_dict:\n return (pred_prev_sample,)\n\n return DDPMSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample)\n\n\nclass BitDiffusion(DiffusionPipeline):\n def __init__(\n self,\n unet: UNet2DConditionModel,\n scheduler: Union[DDIMScheduler, DDPMScheduler],\n bit_scale: Optional[float] = 1.0,\n ):\n super().__init__()\n self.bit_scale = bit_scale\n self.scheduler.step = (\n ddim_bit_scheduler_step if isinstance(scheduler, DDIMScheduler) else ddpm_bit_scheduler_step\n )\n\n self.register_modules(unet=unet, scheduler=scheduler)\n\n @torch.no_grad()\n def __call__(\n self,\n height: Optional[int] = 256,\n width: Optional[int] = 256,\n num_inference_steps: Optional[int] = 50,\n generator: Optional[torch.Generator] = None,\n batch_size: Optional[int] = 1,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n **kwargs,\n ) -> Union[Tuple, ImagePipelineOutput]:\n latents = torch.randn(\n (batch_size, self.unet.config.in_channels, height, width),\n generator=generator,\n )\n latents = decimal_to_bits(latents) * self.bit_scale\n latents = latents.to(self.device)\n\n self.scheduler.set_timesteps(num_inference_steps)\n\n for t in self.progress_bar(self.scheduler.timesteps):\n # predict the noise residual\n noise_pred = self.unet(latents, t).sample\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents).prev_sample\n\n image = bits_to_decimal(latents)\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/examples/community/checkpoint_merger.py", "content": "import glob\nimport os\nfrom typing import Dict, List, Union\n\nimport safetensors.torch\nimport torch\nfrom huggingface_hub import snapshot_download\nfrom huggingface_hub.utils import validate_hf_hub_args\n\nfrom diffusers import DiffusionPipeline, __version__\nfrom diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME\nfrom diffusers.utils import CONFIG_NAME, ONNX_WEIGHTS_NAME, WEIGHTS_NAME\n\n\nclass CheckpointMergerPipeline(DiffusionPipeline):\n \"\"\"\n A class that supports merging diffusion models based on the discussion here:\n https://github.com/huggingface/diffusers/issues/877\n\n Example usage:-\n\n pipe = DiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", custom_pipeline=\"checkpoint_merger.py\")\n\n merged_pipe = pipe.merge([\"CompVis/stable-diffusion-v1-4\",\"prompthero/openjourney\"], interp = 'inv_sigmoid', alpha = 0.8, force = True)\n\n merged_pipe.to('cuda')\n\n prompt = \"An astronaut riding a unicycle on Mars\"\n\n results = merged_pipe(prompt)\n\n ## For more details, see the docstring for the merge method.\n\n \"\"\"\n\n def __init__(self):\n self.register_to_config()\n super().__init__()\n\n def _compare_model_configs(self, dict0, dict1):\n if dict0 == dict1:\n return True\n else:\n config0, meta_keys0 = self._remove_meta_keys(dict0)\n config1, meta_keys1 = self._remove_meta_keys(dict1)\n if config0 == config1:\n print(f\"Warning !: Mismatch in keys {meta_keys0} and {meta_keys1}.\")\n return True\n return False\n\n def _remove_meta_keys(self, config_dict: Dict):\n meta_keys = []\n temp_dict = config_dict.copy()\n for key in config_dict.keys():\n if key.startswith(\"_\"):\n temp_dict.pop(key)\n meta_keys.append(key)\n return (temp_dict, meta_keys)\n\n @torch.no_grad()\n @validate_hf_hub_args\n def merge(self, pretrained_model_name_or_path_list: List[Union[str, os.PathLike]], **kwargs):\n \"\"\"\n Returns a new pipeline object of the class 'DiffusionPipeline' with the merged checkpoints(weights) of the models passed\n in the argument 'pretrained_model_name_or_path_list' as a list.\n\n Parameters:\n -----------\n pretrained_model_name_or_path_list : A list of valid pretrained model names in the HuggingFace hub or paths to locally stored models in the HuggingFace format.\n\n **kwargs:\n Supports all the default DiffusionPipeline.get_config_dict kwargs viz..\n\n cache_dir, resume_download, force_download, proxies, local_files_only, token, revision, torch_dtype, device_map.\n\n alpha - The interpolation parameter. Ranges from 0 to 1. It affects the ratio in which the checkpoints are merged. A 0.8 alpha\n would mean that the first model checkpoints would affect the final result far less than an alpha of 0.2\n\n interp - The interpolation method to use for the merging. Supports \"sigmoid\", \"inv_sigmoid\", \"add_diff\" and None.\n Passing None uses the default interpolation which is weighted sum interpolation. For merging three checkpoints, only \"add_diff\" is supported.\n\n force - Whether to ignore mismatch in model_config.json for the current models. Defaults to False.\n\n variant - which variant of a pretrained model to load, e.g. \"fp16\" (None)\n\n \"\"\"\n # Default kwargs from DiffusionPipeline\n cache_dir = kwargs.pop(\"cache_dir\", None)\n resume_download = kwargs.pop(\"resume_download\", False)\n force_download = kwargs.pop(\"force_download\", False)\n proxies = kwargs.pop(\"proxies\", None)\n local_files_only = kwargs.pop(\"local_files_only\", False)\n token = kwargs.pop(\"token\", None)\n variant = kwargs.pop(\"variant\", None)\n revision = kwargs.pop(\"revision\", None)\n torch_dtype = kwargs.pop(\"torch_dtype\", None)\n device_map = kwargs.pop(\"device_map\", None)\n\n alpha = kwargs.pop(\"alpha\", 0.5)\n interp = kwargs.pop(\"interp\", None)\n\n print(\"Received list\", pretrained_model_name_or_path_list)\n print(f\"Combining with alpha={alpha}, interpolation mode={interp}\")\n\n checkpoint_count = len(pretrained_model_name_or_path_list)\n # Ignore result from model_index_json comparison of the two checkpoints\n force = kwargs.pop(\"force\", False)\n\n # If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.\n if checkpoint_count > 3 or checkpoint_count < 2:\n raise ValueError(\n \"Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being\"\n \" passed.\"\n )\n\n print(\"Received the right number of checkpoints\")\n # chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]\n # chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None\n\n # Validate that the checkpoints can be merged\n # Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'\n config_dicts = []\n for pretrained_model_name_or_path in pretrained_model_name_or_path_list:\n config_dict = DiffusionPipeline.load_config(\n pretrained_model_name_or_path,\n cache_dir=cache_dir,\n resume_download=resume_download,\n force_download=force_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n )\n config_dicts.append(config_dict)\n\n comparison_result = True\n for idx in range(1, len(config_dicts)):\n comparison_result &= self._compare_model_configs(config_dicts[idx - 1], config_dicts[idx])\n if not force and comparison_result is False:\n raise ValueError(\"Incompatible checkpoints. Please check model_index.json for the models.\")\n print(\"Compatible model_index.json files found\")\n # Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.\n cached_folders = []\n for pretrained_model_name_or_path, config_dict in zip(pretrained_model_name_or_path_list, config_dicts):\n folder_names = [k for k in config_dict.keys() if not k.startswith(\"_\")]\n allow_patterns = [os.path.join(k, \"*\") for k in folder_names]\n allow_patterns += [\n WEIGHTS_NAME,\n SCHEDULER_CONFIG_NAME,\n CONFIG_NAME,\n ONNX_WEIGHTS_NAME,\n DiffusionPipeline.config_name,\n ]\n requested_pipeline_class = config_dict.get(\"_class_name\")\n user_agent = {\"diffusers\": __version__, \"pipeline_class\": requested_pipeline_class}\n\n cached_folder = (\n pretrained_model_name_or_path\n if os.path.isdir(pretrained_model_name_or_path)\n else snapshot_download(\n pretrained_model_name_or_path,\n cache_dir=cache_dir,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n allow_patterns=allow_patterns,\n user_agent=user_agent,\n )\n )\n print(\"Cached Folder\", cached_folder)\n cached_folders.append(cached_folder)\n\n # Step 3:-\n # Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place\n final_pipe = DiffusionPipeline.from_pretrained(\n cached_folders[0],\n torch_dtype=torch_dtype,\n device_map=device_map,\n variant=variant,\n )\n final_pipe.to(self.device)\n\n checkpoint_path_2 = None\n if len(cached_folders) > 2:\n checkpoint_path_2 = os.path.join(cached_folders[2])\n\n if interp == \"sigmoid\":\n theta_func = CheckpointMergerPipeline.sigmoid\n elif interp == \"inv_sigmoid\":\n theta_func = CheckpointMergerPipeline.inv_sigmoid\n elif interp == \"add_diff\":\n theta_func = CheckpointMergerPipeline.add_difference\n else:\n theta_func = CheckpointMergerPipeline.weighted_sum\n\n # Find each module's state dict.\n for attr in final_pipe.config.keys():\n if not attr.startswith(\"_\"):\n checkpoint_path_1 = os.path.join(cached_folders[1], attr)\n if os.path.exists(checkpoint_path_1):\n files = [\n *glob.glob(os.path.join(checkpoint_path_1, \"*.safetensors\")),\n *glob.glob(os.path.join(checkpoint_path_1, \"*.bin\")),\n ]\n checkpoint_path_1 = files[0] if len(files) > 0 else None\n if len(cached_folders) < 3:\n checkpoint_path_2 = None\n else:\n checkpoint_path_2 = os.path.join(cached_folders[2], attr)\n if os.path.exists(checkpoint_path_2):\n files = [\n *glob.glob(os.path.join(checkpoint_path_2, \"*.safetensors\")),\n *glob.glob(os.path.join(checkpoint_path_2, \"*.bin\")),\n ]\n checkpoint_path_2 = files[0] if len(files) > 0 else None\n # For an attr if both checkpoint_path_1 and 2 are None, ignore.\n # If at least one is present, deal with it according to interp method, of course only if the state_dict keys match.\n if checkpoint_path_1 is None and checkpoint_path_2 is None:\n print(f\"Skipping {attr}: not present in 2nd or 3d model\")\n continue\n try:\n module = getattr(final_pipe, attr)\n if isinstance(module, bool): # ignore requires_safety_checker boolean\n continue\n theta_0 = getattr(module, \"state_dict\")\n theta_0 = theta_0()\n\n update_theta_0 = getattr(module, \"load_state_dict\")\n theta_1 = (\n safetensors.torch.load_file(checkpoint_path_1)\n if (checkpoint_path_1.endswith(\".safetensors\"))\n else torch.load(checkpoint_path_1, map_location=\"cpu\")\n )\n theta_2 = None\n if checkpoint_path_2:\n theta_2 = (\n safetensors.torch.load_file(checkpoint_path_2)\n if (checkpoint_path_2.endswith(\".safetensors\"))\n else torch.load(checkpoint_path_2, map_location=\"cpu\")\n )\n\n if not theta_0.keys() == theta_1.keys():\n print(f\"Skipping {attr}: key mismatch\")\n continue\n if theta_2 and not theta_1.keys() == theta_2.keys():\n print(f\"Skipping {attr}:y mismatch\")\n except Exception as e:\n print(f\"Skipping {attr} do to an unexpected error: {str(e)}\")\n continue\n print(f\"MERGING {attr}\")\n\n for key in theta_0.keys():\n if theta_2:\n theta_0[key] = theta_func(theta_0[key], theta_1[key], theta_2[key], alpha)\n else:\n theta_0[key] = theta_func(theta_0[key], theta_1[key], None, alpha)\n\n del theta_1\n del theta_2\n update_theta_0(theta_0)\n\n del theta_0\n return final_pipe\n\n @staticmethod\n def weighted_sum(theta0, theta1, theta2, alpha):\n return ((1 - alpha) * theta0) + (alpha * theta1)\n\n # Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)\n @staticmethod\n def sigmoid(theta0, theta1, theta2, alpha):\n alpha = alpha * alpha * (3 - (2 * alpha))\n return theta0 + ((theta1 - theta0) * alpha)\n\n # Inverse Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)\n @staticmethod\n def inv_sigmoid(theta0, theta1, theta2, alpha):\n import math\n\n alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)\n return theta0 + ((theta1 - theta0) * alpha)\n\n @staticmethod\n def add_difference(theta0, theta1, theta2, alpha):\n return theta0 + (theta1 - theta2) * (1.0 - alpha)\n" }, { "path": "diffusers/examples/community/clip_guided_images_mixing_stable_diffusion.py", "content": "# -*- coding: utf-8 -*-\nimport inspect\nfrom typing import Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom torch.nn import functional as F\nfrom torchvision import transforms\nfrom transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n UNet2DConditionModel,\n)\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.utils import PIL_INTERPOLATION\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\ndef preprocess(image, w, h):\n if isinstance(image, torch.Tensor):\n return image\n elif isinstance(image, PIL.Image.Image):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION[\"lanczos\"]))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n image = np.array(image).astype(np.float32) / 255.0\n image = image.transpose(0, 3, 1, 2)\n image = 2.0 * image - 1.0\n image = torch.from_numpy(image)\n elif isinstance(image[0], torch.Tensor):\n image = torch.cat(image, dim=0)\n return image\n\n\ndef slerp(t, v0, v1, DOT_THRESHOLD=0.9995):\n if not isinstance(v0, np.ndarray):\n inputs_are_torch = True\n input_device = v0.device\n v0 = v0.cpu().numpy()\n v1 = v1.cpu().numpy()\n\n dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))\n if np.abs(dot) > DOT_THRESHOLD:\n v2 = (1 - t) * v0 + t * v1\n else:\n theta_0 = np.arccos(dot)\n sin_theta_0 = np.sin(theta_0)\n theta_t = theta_0 * t\n sin_theta_t = np.sin(theta_t)\n s0 = np.sin(theta_0 - theta_t) / sin_theta_0\n s1 = sin_theta_t / sin_theta_0\n v2 = s0 * v0 + s1 * v1\n\n if inputs_are_torch:\n v2 = torch.from_numpy(v2).to(input_device)\n\n return v2\n\n\ndef spherical_dist_loss(x, y):\n x = F.normalize(x, dim=-1)\n y = F.normalize(y, dim=-1)\n return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)\n\n\ndef set_requires_grad(model, value):\n for param in model.parameters():\n param.requires_grad = value\n\n\nclass CLIPGuidedImagesMixingStableDiffusion(DiffusionPipeline, StableDiffusionMixin):\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n clip_model: CLIPModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler],\n feature_extractor: CLIPFeatureExtractor,\n coca_model=None,\n coca_tokenizer=None,\n coca_transform=None,\n ):\n super().__init__()\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n clip_model=clip_model,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n feature_extractor=feature_extractor,\n coca_model=coca_model,\n coca_tokenizer=coca_tokenizer,\n coca_transform=coca_transform,\n )\n self.feature_extractor_size = (\n feature_extractor.size\n if isinstance(feature_extractor.size, int)\n else feature_extractor.size[\"shortest_edge\"]\n )\n self.normalize = transforms.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)\n set_requires_grad(self.text_encoder, False)\n set_requires_grad(self.clip_model, False)\n\n def freeze_vae(self):\n set_requires_grad(self.vae, False)\n\n def unfreeze_vae(self):\n set_requires_grad(self.vae, True)\n\n def freeze_unet(self):\n set_requires_grad(self.unet, False)\n\n def unfreeze_unet(self):\n set_requires_grad(self.unet, True)\n\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start:]\n\n return timesteps, num_inference_steps - t_start\n\n def prepare_latents(self, image, timestep, batch_size, dtype, device, generator=None):\n if not isinstance(image, torch.Tensor):\n raise ValueError(f\"`image` has to be of type `torch.Tensor` but is {type(image)}\")\n\n image = image.to(device=device, dtype=dtype)\n\n if isinstance(generator, list):\n init_latents = [\n self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = self.vae.encode(image).latent_dist.sample(generator)\n\n # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor\n init_latents = 0.18215 * init_latents\n init_latents = init_latents.repeat_interleave(batch_size, dim=0)\n\n noise = randn_tensor(init_latents.shape, generator=generator, device=device, dtype=dtype)\n\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n latents = init_latents\n\n return latents\n\n def get_image_description(self, image):\n transformed_image = self.coca_transform(image).unsqueeze(0)\n with torch.no_grad(), torch.cuda.amp.autocast():\n generated = self.coca_model.generate(transformed_image.to(device=self.device, dtype=self.coca_model.dtype))\n generated = self.coca_tokenizer.decode(generated[0].cpu().numpy())\n return generated.split(\"\")[0].replace(\"\", \"\").rstrip(\" .,\")\n\n def get_clip_image_embeddings(self, image, batch_size):\n clip_image_input = self.feature_extractor.preprocess(image)\n clip_image_features = torch.from_numpy(clip_image_input[\"pixel_values\"][0]).unsqueeze(0).to(self.device).half()\n image_embeddings_clip = self.clip_model.get_image_features(clip_image_features)\n image_embeddings_clip = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=True)\n image_embeddings_clip = image_embeddings_clip.repeat_interleave(batch_size, dim=0)\n return image_embeddings_clip\n\n @torch.enable_grad()\n def cond_fn(\n self,\n latents,\n timestep,\n index,\n text_embeddings,\n noise_pred_original,\n original_image_embeddings_clip,\n clip_guidance_scale,\n ):\n latents = latents.detach().requires_grad_()\n\n latent_model_input = self.scheduler.scale_model_input(latents, timestep)\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, timestep, encoder_hidden_states=text_embeddings).sample\n\n if isinstance(self.scheduler, (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):\n alpha_prod_t = self.scheduler.alphas_cumprod[timestep]\n beta_prod_t = 1 - alpha_prod_t\n # compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)\n\n fac = torch.sqrt(beta_prod_t)\n sample = pred_original_sample * (fac) + latents * (1 - fac)\n elif isinstance(self.scheduler, LMSDiscreteScheduler):\n sigma = self.scheduler.sigmas[index]\n sample = latents - sigma * noise_pred\n else:\n raise ValueError(f\"scheduler type {type(self.scheduler)} not supported\")\n\n # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor\n sample = 1 / 0.18215 * sample\n image = self.vae.decode(sample).sample\n image = (image / 2 + 0.5).clamp(0, 1)\n\n image = transforms.Resize(self.feature_extractor_size)(image)\n image = self.normalize(image).to(latents.dtype)\n\n image_embeddings_clip = self.clip_model.get_image_features(image)\n image_embeddings_clip = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=True)\n\n loss = spherical_dist_loss(image_embeddings_clip, original_image_embeddings_clip).mean() * clip_guidance_scale\n\n grads = -torch.autograd.grad(loss, latents)[0]\n\n if isinstance(self.scheduler, LMSDiscreteScheduler):\n latents = latents.detach() + grads * (sigma**2)\n noise_pred = noise_pred_original\n else:\n noise_pred = noise_pred_original - torch.sqrt(beta_prod_t) * grads\n return noise_pred, latents\n\n @torch.no_grad()\n def __call__(\n self,\n style_image: Union[torch.Tensor, PIL.Image.Image],\n content_image: Union[torch.Tensor, PIL.Image.Image],\n style_prompt: Optional[str] = None,\n content_prompt: Optional[str] = None,\n height: Optional[int] = 512,\n width: Optional[int] = 512,\n noise_strength: float = 0.6,\n num_inference_steps: Optional[int] = 50,\n guidance_scale: Optional[float] = 7.5,\n batch_size: Optional[int] = 1,\n eta: float = 0.0,\n clip_guidance_scale: Optional[float] = 100,\n generator: Optional[torch.Generator] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n slerp_latent_style_strength: float = 0.8,\n slerp_prompt_style_strength: float = 0.1,\n slerp_clip_image_style_strength: float = 0.1,\n ):\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(f\"You have passed {batch_size} batch_size, but only {len(generator)} generators.\")\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if isinstance(generator, torch.Generator) and batch_size > 1:\n generator = [generator] + [None] * (batch_size - 1)\n\n coca_is_none = [\n (\"model\", self.coca_model is None),\n (\"tokenizer\", self.coca_tokenizer is None),\n (\"transform\", self.coca_transform is None),\n ]\n coca_is_none = [x[0] for x in coca_is_none if x[1]]\n coca_is_none_str = \", \".join(coca_is_none)\n # generate prompts with coca model if prompt is None\n if content_prompt is None:\n if len(coca_is_none):\n raise ValueError(\n f\"Content prompt is None and CoCa [{coca_is_none_str}] is None.\"\n f\"Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.\"\n )\n content_prompt = self.get_image_description(content_image)\n if style_prompt is None:\n if len(coca_is_none):\n raise ValueError(\n f\"Style prompt is None and CoCa [{coca_is_none_str}] is None.\"\n f\" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.\"\n )\n style_prompt = self.get_image_description(style_image)\n\n # get prompt text embeddings for content and style\n content_text_input = self.tokenizer(\n content_prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n content_text_embeddings = self.text_encoder(content_text_input.input_ids.to(self.device))[0]\n\n style_text_input = self.tokenizer(\n style_prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n style_text_embeddings = self.text_encoder(style_text_input.input_ids.to(self.device))[0]\n\n text_embeddings = slerp(slerp_prompt_style_strength, content_text_embeddings, style_text_embeddings)\n\n # duplicate text embeddings for each generation per prompt\n text_embeddings = text_embeddings.repeat_interleave(batch_size, dim=0)\n\n # set timesteps\n accepts_offset = \"offset\" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())\n extra_set_kwargs = {}\n if accepts_offset:\n extra_set_kwargs[\"offset\"] = 1\n\n self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)\n # Some schedulers like PNDM have timesteps as arrays\n # It's more optimized to move all timesteps to correct device beforehand\n self.scheduler.timesteps.to(self.device)\n\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, noise_strength, self.device)\n latent_timestep = timesteps[:1].repeat(batch_size)\n\n # Preprocess image\n preprocessed_content_image = preprocess(content_image, width, height)\n content_latents = self.prepare_latents(\n preprocessed_content_image, latent_timestep, batch_size, text_embeddings.dtype, self.device, generator\n )\n\n preprocessed_style_image = preprocess(style_image, width, height)\n style_latents = self.prepare_latents(\n preprocessed_style_image, latent_timestep, batch_size, text_embeddings.dtype, self.device, generator\n )\n\n latents = slerp(slerp_latent_style_strength, content_latents, style_latents)\n\n if clip_guidance_scale > 0:\n content_clip_image_embedding = self.get_clip_image_embeddings(content_image, batch_size)\n style_clip_image_embedding = self.get_clip_image_embeddings(style_image, batch_size)\n clip_image_embeddings = slerp(\n slerp_clip_image_style_strength, content_clip_image_embedding, style_clip_image_embedding\n )\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n max_length = content_text_input.input_ids.shape[-1]\n uncond_input = self.tokenizer([\"\"], padding=\"max_length\", max_length=max_length, return_tensors=\"pt\")\n uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]\n # duplicate unconditional embeddings for each generation per prompt\n uncond_embeddings = uncond_embeddings.repeat_interleave(batch_size, dim=0)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n\n # get the initial random noise unless the user supplied it\n\n # Unlike in other pipelines, latents need to be generated in the target device\n # for 1-to-1 results reproducibility with the CompVis implementation.\n # However this currently doesn't work in `mps`.\n latents_shape = (batch_size, self.unet.config.in_channels, height // 8, width // 8)\n latents_dtype = text_embeddings.dtype\n if latents is None:\n if self.device.type == \"mps\":\n # randn does not work reproducibly on mps\n latents = torch.randn(latents_shape, generator=generator, device=\"cpu\", dtype=latents_dtype).to(\n self.device\n )\n else:\n latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)\n else:\n if latents.shape != latents_shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {latents_shape}\")\n latents = latents.to(self.device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample\n\n # perform classifier free guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # perform clip guidance\n if clip_guidance_scale > 0:\n text_embeddings_for_guidance = (\n text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings\n )\n noise_pred, latents = self.cond_fn(\n latents,\n t,\n i,\n text_embeddings_for_guidance,\n noise_pred,\n clip_image_embeddings,\n clip_guidance_scale,\n )\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n progress_bar.update()\n # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor\n latents = 1 / 0.18215 * latents\n image = self.vae.decode(latents).sample\n\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).numpy()\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image, None)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)\n" }, { "path": "diffusers/examples/community/clip_guided_stable_diffusion.py", "content": "import inspect\nfrom typing import List, Optional, Union\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom torchvision import transforms\nfrom transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n UNet2DConditionModel,\n)\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput\n\n\nclass MakeCutouts(nn.Module):\n def __init__(self, cut_size, cut_power=1.0):\n super().__init__()\n\n self.cut_size = cut_size\n self.cut_power = cut_power\n\n def forward(self, pixel_values, num_cutouts):\n sideY, sideX = pixel_values.shape[2:4]\n max_size = min(sideX, sideY)\n min_size = min(sideX, sideY, self.cut_size)\n cutouts = []\n for _ in range(num_cutouts):\n size = int(torch.rand([]) ** self.cut_power * (max_size - min_size) + min_size)\n offsetx = torch.randint(0, sideX - size + 1, ())\n offsety = torch.randint(0, sideY - size + 1, ())\n cutout = pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]\n cutouts.append(F.adaptive_avg_pool2d(cutout, self.cut_size))\n return torch.cat(cutouts)\n\n\ndef spherical_dist_loss(x, y):\n x = F.normalize(x, dim=-1)\n y = F.normalize(y, dim=-1)\n return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)\n\n\ndef set_requires_grad(model, value):\n for param in model.parameters():\n param.requires_grad = value\n\n\nclass CLIPGuidedStableDiffusion(DiffusionPipeline, StableDiffusionMixin):\n \"\"\"CLIP guided stable diffusion based on the amazing repo by @crowsonkb and @Jack000\n - https://github.com/Jack000/glid-3-xl\n - https://github.dev/crowsonkb/k-diffusion\n \"\"\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n clip_model: CLIPModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler],\n feature_extractor: CLIPImageProcessor,\n ):\n super().__init__()\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n clip_model=clip_model,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n feature_extractor=feature_extractor,\n )\n\n self.normalize = transforms.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)\n self.cut_out_size = (\n feature_extractor.size\n if isinstance(feature_extractor.size, int)\n else feature_extractor.size[\"shortest_edge\"]\n )\n self.make_cutouts = MakeCutouts(self.cut_out_size)\n\n set_requires_grad(self.text_encoder, False)\n set_requires_grad(self.clip_model, False)\n\n def freeze_vae(self):\n set_requires_grad(self.vae, False)\n\n def unfreeze_vae(self):\n set_requires_grad(self.vae, True)\n\n def freeze_unet(self):\n set_requires_grad(self.unet, False)\n\n def unfreeze_unet(self):\n set_requires_grad(self.unet, True)\n\n @torch.enable_grad()\n def cond_fn(\n self,\n latents,\n timestep,\n index,\n text_embeddings,\n noise_pred_original,\n text_embeddings_clip,\n clip_guidance_scale,\n num_cutouts,\n use_cutouts=True,\n ):\n latents = latents.detach().requires_grad_()\n\n latent_model_input = self.scheduler.scale_model_input(latents, timestep)\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, timestep, encoder_hidden_states=text_embeddings).sample\n\n if isinstance(self.scheduler, (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):\n alpha_prod_t = self.scheduler.alphas_cumprod[timestep]\n beta_prod_t = 1 - alpha_prod_t\n # compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)\n\n fac = torch.sqrt(beta_prod_t)\n sample = pred_original_sample * (fac) + latents * (1 - fac)\n elif isinstance(self.scheduler, LMSDiscreteScheduler):\n sigma = self.scheduler.sigmas[index]\n sample = latents - sigma * noise_pred\n else:\n raise ValueError(f\"scheduler type {type(self.scheduler)} not supported\")\n\n sample = 1 / self.vae.config.scaling_factor * sample\n image = self.vae.decode(sample).sample\n image = (image / 2 + 0.5).clamp(0, 1)\n\n if use_cutouts:\n image = self.make_cutouts(image, num_cutouts)\n else:\n image = transforms.Resize(self.cut_out_size)(image)\n image = self.normalize(image).to(latents.dtype)\n\n image_embeddings_clip = self.clip_model.get_image_features(image)\n image_embeddings_clip = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=True)\n\n if use_cutouts:\n dists = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip)\n dists = dists.view([num_cutouts, sample.shape[0], -1])\n loss = dists.sum(2).mean(0).sum() * clip_guidance_scale\n else:\n loss = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip).mean() * clip_guidance_scale\n\n grads = -torch.autograd.grad(loss, latents)[0]\n\n if isinstance(self.scheduler, LMSDiscreteScheduler):\n latents = latents.detach() + grads * (sigma**2)\n noise_pred = noise_pred_original\n else:\n noise_pred = noise_pred_original - torch.sqrt(beta_prod_t) * grads\n return noise_pred, latents\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n height: Optional[int] = 512,\n width: Optional[int] = 512,\n num_inference_steps: Optional[int] = 50,\n guidance_scale: Optional[float] = 7.5,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n clip_guidance_scale: Optional[float] = 100,\n clip_prompt: Optional[Union[str, List[str]]] = None,\n num_cutouts: Optional[int] = 4,\n use_cutouts: Optional[bool] = True,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n ):\n if isinstance(prompt, str):\n batch_size = 1\n elif isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n # get prompt text embeddings\n text_input = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]\n # duplicate text embeddings for each generation per prompt\n text_embeddings = text_embeddings.repeat_interleave(num_images_per_prompt, dim=0)\n\n if clip_guidance_scale > 0:\n if clip_prompt is not None:\n clip_text_input = self.tokenizer(\n clip_prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n ).input_ids.to(self.device)\n else:\n clip_text_input = text_input.input_ids.to(self.device)\n text_embeddings_clip = self.clip_model.get_text_features(clip_text_input)\n text_embeddings_clip = text_embeddings_clip / text_embeddings_clip.norm(p=2, dim=-1, keepdim=True)\n # duplicate text embeddings clip for each generation per prompt\n text_embeddings_clip = text_embeddings_clip.repeat_interleave(num_images_per_prompt, dim=0)\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n max_length = text_input.input_ids.shape[-1]\n uncond_input = self.tokenizer([\"\"], padding=\"max_length\", max_length=max_length, return_tensors=\"pt\")\n uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]\n # duplicate unconditional embeddings for each generation per prompt\n uncond_embeddings = uncond_embeddings.repeat_interleave(num_images_per_prompt, dim=0)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n\n # get the initial random noise unless the user supplied it\n\n # Unlike in other pipelines, latents need to be generated in the target device\n # for 1-to-1 results reproducibility with the CompVis implementation.\n # However this currently doesn't work in `mps`.\n latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)\n latents_dtype = text_embeddings.dtype\n if latents is None:\n if self.device.type == \"mps\":\n # randn does not work reproducibly on mps\n latents = torch.randn(latents_shape, generator=generator, device=\"cpu\", dtype=latents_dtype).to(\n self.device\n )\n else:\n latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)\n else:\n if latents.shape != latents_shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {latents_shape}\")\n latents = latents.to(self.device)\n\n # set timesteps\n accepts_offset = \"offset\" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())\n extra_set_kwargs = {}\n if accepts_offset:\n extra_set_kwargs[\"offset\"] = 1\n\n self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)\n\n # Some schedulers like PNDM have timesteps as arrays\n # It's more optimized to move all timesteps to correct device beforehand\n timesteps_tensor = self.scheduler.timesteps.to(self.device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n\n for i, t in enumerate(self.progress_bar(timesteps_tensor)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample\n\n # perform classifier free guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # perform clip guidance\n if clip_guidance_scale > 0:\n text_embeddings_for_guidance = (\n text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings\n )\n noise_pred, latents = self.cond_fn(\n latents,\n t,\n i,\n text_embeddings_for_guidance,\n noise_pred,\n text_embeddings_clip,\n clip_guidance_scale,\n num_cutouts,\n use_cutouts,\n )\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # scale and decode the image latents with vae\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents).sample\n\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).numpy()\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image, None)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)\n" }, { "path": "diffusers/examples/community/clip_guided_stable_diffusion_img2img.py", "content": "import inspect\nfrom typing import List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom torchvision import transforms\nfrom transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n UNet2DConditionModel,\n)\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.utils import PIL_INTERPOLATION, deprecate\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n from io import BytesIO\n\n import requests\n import torch\n from diffusers import DiffusionPipeline\n from PIL import Image\n from transformers import CLIPFeatureExtractor, CLIPModel\n\n feature_extractor = CLIPFeatureExtractor.from_pretrained(\n \"laion/CLIP-ViT-B-32-laion2B-s34B-b79K\"\n )\n clip_model = CLIPModel.from_pretrained(\n \"laion/CLIP-ViT-B-32-laion2B-s34B-b79K\", torch_dtype=torch.float16\n )\n\n\n guided_pipeline = DiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n # custom_pipeline=\"clip_guided_stable_diffusion\",\n custom_pipeline=\"/home/njindal/diffusers/examples/community/clip_guided_stable_diffusion.py\",\n clip_model=clip_model,\n feature_extractor=feature_extractor,\n torch_dtype=torch.float16,\n )\n guided_pipeline.enable_attention_slicing()\n guided_pipeline = guided_pipeline.to(\"cuda\")\n\n prompt = \"fantasy book cover, full moon, fantasy forest landscape, golden vector elements, fantasy magic, dark light night, intricate, elegant, sharp focus, illustration, highly detailed, digital painting, concept art, matte, art by WLOP and Artgerm and Albert Bierstadt, masterpiece\"\n\n url = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n\n response = requests.get(url)\n init_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n\n image = guided_pipeline(\n prompt=prompt,\n num_inference_steps=30,\n image=init_image,\n strength=0.75,\n guidance_scale=7.5,\n clip_guidance_scale=100,\n num_cutouts=4,\n use_cutouts=False,\n ).images[0]\n display(image)\n ```\n\"\"\"\n\n\ndef preprocess(image, w, h):\n if isinstance(image, torch.Tensor):\n return image\n elif isinstance(image, PIL.Image.Image):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION[\"lanczos\"]))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n image = np.array(image).astype(np.float32) / 255.0\n image = image.transpose(0, 3, 1, 2)\n image = 2.0 * image - 1.0\n image = torch.from_numpy(image)\n elif isinstance(image[0], torch.Tensor):\n image = torch.cat(image, dim=0)\n return image\n\n\nclass MakeCutouts(nn.Module):\n def __init__(self, cut_size, cut_power=1.0):\n super().__init__()\n\n self.cut_size = cut_size\n self.cut_power = cut_power\n\n def forward(self, pixel_values, num_cutouts):\n sideY, sideX = pixel_values.shape[2:4]\n max_size = min(sideX, sideY)\n min_size = min(sideX, sideY, self.cut_size)\n cutouts = []\n for _ in range(num_cutouts):\n size = int(torch.rand([]) ** self.cut_power * (max_size - min_size) + min_size)\n offsetx = torch.randint(0, sideX - size + 1, ())\n offsety = torch.randint(0, sideY - size + 1, ())\n cutout = pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]\n cutouts.append(F.adaptive_avg_pool2d(cutout, self.cut_size))\n return torch.cat(cutouts)\n\n\ndef spherical_dist_loss(x, y):\n x = F.normalize(x, dim=-1)\n y = F.normalize(y, dim=-1)\n return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)\n\n\ndef set_requires_grad(model, value):\n for param in model.parameters():\n param.requires_grad = value\n\n\nclass CLIPGuidedStableDiffusion(DiffusionPipeline, StableDiffusionMixin):\n \"\"\"CLIP guided stable diffusion based on the amazing repo by @crowsonkb and @Jack000\n - https://github.com/Jack000/glid-3-xl\n - https://github.dev/crowsonkb/k-diffusion\n \"\"\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n clip_model: CLIPModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler],\n feature_extractor: CLIPFeatureExtractor,\n ):\n super().__init__()\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n clip_model=clip_model,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n feature_extractor=feature_extractor,\n )\n\n self.normalize = transforms.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)\n self.cut_out_size = (\n feature_extractor.size\n if isinstance(feature_extractor.size, int)\n else feature_extractor.size[\"shortest_edge\"]\n )\n self.make_cutouts = MakeCutouts(self.cut_out_size)\n\n set_requires_grad(self.text_encoder, False)\n set_requires_grad(self.clip_model, False)\n\n def freeze_vae(self):\n set_requires_grad(self.vae, False)\n\n def unfreeze_vae(self):\n set_requires_grad(self.vae, True)\n\n def freeze_unet(self):\n set_requires_grad(self.unet, False)\n\n def unfreeze_unet(self):\n set_requires_grad(self.unet, True)\n\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start:]\n\n return timesteps, num_inference_steps - t_start\n\n def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if isinstance(generator, list):\n init_latents = [\n self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = self.vae.encode(image).latent_dist.sample(generator)\n\n init_latents = self.vae.config.scaling_factor * init_latents\n\n if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:\n # expand init_latents for batch_size\n deprecation_message = (\n f\"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial\"\n \" images (`image`). Initial images are now duplicating to match the number of text prompts. Note\"\n \" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update\"\n \" your script to pass as many initial images as text prompts to suppress this warning.\"\n )\n deprecate(\"len(prompt) != len(image)\", \"1.0.0\", deprecation_message, standard_warn=False)\n additional_image_per_prompt = batch_size // init_latents.shape[0]\n init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)\n elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n init_latents = torch.cat([init_latents], dim=0)\n\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n latents = init_latents\n\n return latents\n\n @torch.enable_grad()\n def cond_fn(\n self,\n latents,\n timestep,\n index,\n text_embeddings,\n noise_pred_original,\n text_embeddings_clip,\n clip_guidance_scale,\n num_cutouts,\n use_cutouts=True,\n ):\n latents = latents.detach().requires_grad_()\n\n latent_model_input = self.scheduler.scale_model_input(latents, timestep)\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, timestep, encoder_hidden_states=text_embeddings).sample\n\n if isinstance(self.scheduler, (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):\n alpha_prod_t = self.scheduler.alphas_cumprod[timestep]\n beta_prod_t = 1 - alpha_prod_t\n # compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)\n\n fac = torch.sqrt(beta_prod_t)\n sample = pred_original_sample * (fac) + latents * (1 - fac)\n elif isinstance(self.scheduler, LMSDiscreteScheduler):\n sigma = self.scheduler.sigmas[index]\n sample = latents - sigma * noise_pred\n else:\n raise ValueError(f\"scheduler type {type(self.scheduler)} not supported\")\n\n sample = 1 / self.vae.config.scaling_factor * sample\n image = self.vae.decode(sample).sample\n image = (image / 2 + 0.5).clamp(0, 1)\n\n if use_cutouts:\n image = self.make_cutouts(image, num_cutouts)\n else:\n image = transforms.Resize(self.cut_out_size)(image)\n image = self.normalize(image).to(latents.dtype)\n\n image_embeddings_clip = self.clip_model.get_image_features(image)\n image_embeddings_clip = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=True)\n\n if use_cutouts:\n dists = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip)\n dists = dists.view([num_cutouts, sample.shape[0], -1])\n loss = dists.sum(2).mean(0).sum() * clip_guidance_scale\n else:\n loss = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip).mean() * clip_guidance_scale\n\n grads = -torch.autograd.grad(loss, latents)[0]\n\n if isinstance(self.scheduler, LMSDiscreteScheduler):\n latents = latents.detach() + grads * (sigma**2)\n noise_pred = noise_pred_original\n else:\n noise_pred = noise_pred_original - torch.sqrt(beta_prod_t) * grads\n return noise_pred, latents\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n height: Optional[int] = 512,\n width: Optional[int] = 512,\n image: Union[torch.Tensor, PIL.Image.Image] = None,\n strength: float = 0.8,\n num_inference_steps: Optional[int] = 50,\n guidance_scale: Optional[float] = 7.5,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n clip_guidance_scale: Optional[float] = 100,\n clip_prompt: Optional[Union[str, List[str]]] = None,\n num_cutouts: Optional[int] = 4,\n use_cutouts: Optional[bool] = True,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n ):\n if isinstance(prompt, str):\n batch_size = 1\n elif isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n # get prompt text embeddings\n text_input = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]\n # duplicate text embeddings for each generation per prompt\n text_embeddings = text_embeddings.repeat_interleave(num_images_per_prompt, dim=0)\n\n # set timesteps\n accepts_offset = \"offset\" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())\n extra_set_kwargs = {}\n if accepts_offset:\n extra_set_kwargs[\"offset\"] = 1\n\n self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)\n # Some schedulers like PNDM have timesteps as arrays\n # It's more optimized to move all timesteps to correct device beforehand\n self.scheduler.timesteps.to(self.device)\n\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, self.device)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n\n # Preprocess image\n image = preprocess(image, width, height)\n if latents is None:\n latents = self.prepare_latents(\n image,\n latent_timestep,\n batch_size,\n num_images_per_prompt,\n text_embeddings.dtype,\n self.device,\n generator,\n )\n\n if clip_guidance_scale > 0:\n if clip_prompt is not None:\n clip_text_input = self.tokenizer(\n clip_prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n ).input_ids.to(self.device)\n else:\n clip_text_input = text_input.input_ids.to(self.device)\n text_embeddings_clip = self.clip_model.get_text_features(clip_text_input)\n text_embeddings_clip = text_embeddings_clip / text_embeddings_clip.norm(p=2, dim=-1, keepdim=True)\n # duplicate text embeddings clip for each generation per prompt\n text_embeddings_clip = text_embeddings_clip.repeat_interleave(num_images_per_prompt, dim=0)\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n max_length = text_input.input_ids.shape[-1]\n uncond_input = self.tokenizer([\"\"], padding=\"max_length\", max_length=max_length, return_tensors=\"pt\")\n uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]\n # duplicate unconditional embeddings for each generation per prompt\n uncond_embeddings = uncond_embeddings.repeat_interleave(num_images_per_prompt, dim=0)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n\n # get the initial random noise unless the user supplied it\n\n # Unlike in other pipelines, latents need to be generated in the target device\n # for 1-to-1 results reproducibility with the CompVis implementation.\n # However this currently doesn't work in `mps`.\n latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)\n latents_dtype = text_embeddings.dtype\n if latents is None:\n if self.device.type == \"mps\":\n # randn does not work reproducibly on mps\n latents = torch.randn(latents_shape, generator=generator, device=\"cpu\", dtype=latents_dtype).to(\n self.device\n )\n else:\n latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)\n else:\n if latents.shape != latents_shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {latents_shape}\")\n latents = latents.to(self.device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n\n with self.progress_bar(total=num_inference_steps):\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample\n\n # perform classifier free guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # perform clip guidance\n if clip_guidance_scale > 0:\n text_embeddings_for_guidance = (\n text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings\n )\n noise_pred, latents = self.cond_fn(\n latents,\n t,\n i,\n text_embeddings_for_guidance,\n noise_pred,\n text_embeddings_clip,\n clip_guidance_scale,\n num_cutouts,\n use_cutouts,\n )\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # scale and decode the image latents with vae\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents).sample\n\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).numpy()\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image, None)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)\n" }, { "path": "diffusers/examples/community/composable_stable_diffusion.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Callable, List, Optional, Union\n\nimport torch\nfrom packaging import version\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import DiffusionPipeline\nfrom diffusers.configuration_utils import FrozenDict\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.pipelines.pipeline_utils import StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.schedulers import (\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n)\nfrom diffusers.utils import deprecate, logging\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass ComposableStableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[\n DDIMScheduler,\n PNDMScheduler,\n LMSDiscreteScheduler,\n EulerDiscreteScheduler,\n EulerAncestralDiscreteScheduler,\n DPMSolverMultistepScheduler,\n ],\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"clip_sample\"] = False\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `list(int)`):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n \"\"\"\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n text_embeddings = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n text_embeddings = text_embeddings[0]\n\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = text_embeddings.shape\n text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)\n text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = text_input_ids.shape[-1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n uncond_embeddings = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n uncond_embeddings = uncond_embeddings[0]\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = uncond_embeddings.shape[1]\n uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)\n uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n\n return text_embeddings\n\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n else:\n has_nsfw_concept = None\n return image, has_nsfw_concept\n\n def decode_latents(self, latents):\n latents = 1 / 0.18215 * latents\n image = self.vae.decode(latents).sample\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(self, prompt, height, width, callback_steps):\n if not isinstance(prompt, str) and not isinstance(prompt, list):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if latents is None:\n if device.type == \"mps\":\n # randn does not work reproducibly on mps\n latents = torch.randn(shape, generator=generator, device=\"cpu\", dtype=dtype).to(device)\n else:\n latents = torch.randn(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n weights: Optional[str] = \"\",\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 5.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation\n deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(prompt, height, width, callback_steps)\n\n # 2. Define call parameters\n batch_size = 1 if isinstance(prompt, str) else len(prompt)\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n if \"|\" in prompt:\n prompt = [x.strip() for x in prompt.split(\"|\")]\n print(f\"composing {prompt}...\")\n\n if not weights:\n # specify weights for prompts (excluding the unconditional score)\n print(\"using equal positive weights (conjunction) for all prompts...\")\n weights = torch.tensor([guidance_scale] * len(prompt), device=self.device).reshape(-1, 1, 1, 1)\n else:\n # set prompt weight for each\n num_prompts = len(prompt) if isinstance(prompt, list) else 1\n weights = [float(w.strip()) for w in weights.split(\"|\")]\n # guidance scale as the default\n if len(weights) < num_prompts:\n weights.append(guidance_scale)\n else:\n weights = weights[:num_prompts]\n assert len(weights) == len(prompt), \"weights specified are not equal to the number of prompts\"\n weights = torch.tensor(weights, device=self.device).reshape(-1, 1, 1, 1)\n else:\n weights = guidance_scale\n\n # 3. Encode input prompt\n text_embeddings = self._encode_prompt(\n prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt\n )\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n text_embeddings.dtype,\n device,\n generator,\n latents,\n )\n\n # composable diffusion\n if isinstance(prompt, list) and batch_size == 1:\n # remove extra unconditional embedding\n # N = one unconditional embed + conditional embeds\n text_embeddings = text_embeddings[len(prompt) - 1 :]\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = []\n for j in range(text_embeddings.shape[0]):\n noise_pred.append(\n self.unet(latent_model_input[:1], t, encoder_hidden_states=text_embeddings[j : j + 1]).sample\n )\n noise_pred = torch.cat(noise_pred, dim=0)\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred[:1], noise_pred[1:]\n noise_pred = noise_pred_uncond + (weights * (noise_pred_text - noise_pred_uncond)).sum(\n dim=0, keepdims=True\n )\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # 8. Post-processing\n image = self.decode_latents(latents)\n\n # 9. Run safety checker\n image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)\n\n # 10. Convert to PIL\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/examples/community/ddim_noise_comparative_analysis.py", "content": "# Copyright 2022 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import List, Optional, Tuple, Union\n\nimport PIL.Image\nimport torch\nfrom torchvision import transforms\n\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput\nfrom diffusers.schedulers import DDIMScheduler\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\ntrans = transforms.Compose(\n [\n transforms.Resize((256, 256)),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n)\n\n\ndef preprocess(image):\n if isinstance(image, torch.Tensor):\n return image\n elif isinstance(image, PIL.Image.Image):\n image = [image]\n\n image = [trans(img.convert(\"RGB\")) for img in image]\n image = torch.stack(image)\n return image\n\n\nclass DDIMNoiseComparativeAnalysisPipeline(DiffusionPipeline):\n r\"\"\"\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Parameters:\n unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of\n [`DDPMScheduler`], or [`DDIMScheduler`].\n \"\"\"\n\n def __init__(self, unet, scheduler):\n super().__init__()\n\n # make sure scheduler can always be converted to DDIM\n scheduler = DDIMScheduler.from_config(scheduler.config)\n\n self.register_modules(unet=unet, scheduler=scheduler)\n\n def check_inputs(self, strength):\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start:]\n\n return timesteps, num_inference_steps - t_start\n\n def prepare_latents(self, image, timestep, batch_size, dtype, device, generator=None):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n init_latents = image.to(device=device, dtype=dtype)\n\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n # get latents\n print(\"add noise to latents at timestep\", timestep)\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n latents = init_latents\n\n return latents\n\n @torch.no_grad()\n def __call__(\n self,\n image: Union[torch.Tensor, PIL.Image.Image] = None,\n strength: float = 0.8,\n batch_size: int = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n eta: float = 0.0,\n num_inference_steps: int = 50,\n use_clipped_model_output: Optional[bool] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n ) -> Union[ImagePipelineOutput, Tuple]:\n r\"\"\"\n Args:\n image (`torch.Tensor` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process.\n strength (`float`, *optional*, defaults to 0.8):\n Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`\n will be used as a starting point, adding more noise to it the larger the `strength`. The number of\n denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will\n be maximum and the denoising process will run for the full number of iterations specified in\n `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.\n batch_size (`int`, *optional*, defaults to 1):\n The number of images to generate.\n generator (`torch.Generator`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n eta (`float`, *optional*, defaults to 0.0):\n The eta parameter which controls the scale of the variance (0 is DDIM and 1 is one type of DDPM).\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n use_clipped_model_output (`bool`, *optional*, defaults to `None`):\n if `True` or `False`, see documentation for `DDIMScheduler.step`. If `None`, nothing is passed\n downstream to the scheduler. So use `None` for schedulers which don't support this argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if `return_dict` is\n True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images.\n \"\"\"\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(strength)\n\n # 2. Preprocess image\n image = preprocess(image)\n\n # 3. set timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=self.device)\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, self.device)\n latent_timestep = timesteps[:1].repeat(batch_size)\n\n # 4. Prepare latent variables\n latents = self.prepare_latents(image, latent_timestep, batch_size, self.unet.dtype, self.device, generator)\n image = latents\n\n # 5. Denoising loop\n for t in self.progress_bar(timesteps):\n # 1. predict noise model_output\n model_output = self.unet(image, t).sample\n\n # 2. predict previous mean of image x_t-1 and add variance depending on eta\n # eta corresponds to η in paper and should be between [0, 1]\n # do x_t -> x_t-1\n image = self.scheduler.step(\n model_output,\n t,\n image,\n eta=eta,\n use_clipped_model_output=use_clipped_model_output,\n generator=generator,\n ).prev_sample\n\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).numpy()\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image, latent_timestep.item())\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/examples/community/dps_pipeline.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nfrom math import pi\nfrom typing import Callable, List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nfrom PIL import Image\n\nfrom diffusers import DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNet2DModel\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nclass DPSPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for Diffusion Posterior Sampling.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Parameters:\n unet ([`UNet2DModel`]):\n A `UNet2DModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of\n [`DDPMScheduler`], or [`DDIMScheduler`].\n \"\"\"\n\n model_cpu_offload_seq = \"unet\"\n\n def __init__(self, unet, scheduler):\n super().__init__()\n self.register_modules(unet=unet, scheduler=scheduler)\n\n @torch.no_grad()\n def __call__(\n self,\n measurement: torch.Tensor,\n operator: torch.nn.Module,\n loss_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor],\n batch_size: int = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n num_inference_steps: int = 1000,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n zeta: float = 0.3,\n ) -> Union[ImagePipelineOutput, Tuple]:\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n measurement (`torch.Tensor`, *required*):\n A 'torch.Tensor', the corrupted image\n operator (`torch.nn.Module`, *required*):\n A 'torch.nn.Module', the operator generating the corrupted image\n loss_fn (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *required*):\n A 'Callable[[torch.Tensor, torch.Tensor], torch.Tensor]', the loss function used\n between the measurements, for most of the cases using RMSE is fine.\n batch_size (`int`, *optional*, defaults to 1):\n The number of images to generate.\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n num_inference_steps (`int`, *optional*, defaults to 1000):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n\n Example:\n\n ```py\n >>> from diffusers import DDPMPipeline\n\n >>> # load model and scheduler\n >>> pipe = DDPMPipeline.from_pretrained(\"google/ddpm-cat-256\")\n\n >>> # run pipeline in inference (sample random noise and denoise)\n >>> image = pipe().images[0]\n\n >>> # save image\n >>> image.save(\"ddpm_generated_image.png\")\n ```\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is\n returned where the first element is a list with the generated images\n \"\"\"\n # Sample gaussian noise to begin loop\n if isinstance(self.unet.config.sample_size, int):\n image_shape = (\n batch_size,\n self.unet.config.in_channels,\n self.unet.config.sample_size,\n self.unet.config.sample_size,\n )\n else:\n image_shape = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)\n\n if self.device.type == \"mps\":\n # randn does not work reproducibly on mps\n image = randn_tensor(image_shape, generator=generator)\n image = image.to(self.device)\n else:\n image = randn_tensor(image_shape, generator=generator, device=self.device)\n\n # set step values\n self.scheduler.set_timesteps(num_inference_steps)\n\n for t in self.progress_bar(self.scheduler.timesteps):\n with torch.enable_grad():\n # 1. predict noise model_output\n image = image.requires_grad_()\n model_output = self.unet(image, t).sample\n\n # 2. compute previous image x'_{t-1} and original prediction x0_{t}\n scheduler_out = self.scheduler.step(model_output, t, image, generator=generator)\n image_pred, origi_pred = scheduler_out.prev_sample, scheduler_out.pred_original_sample\n\n # 3. compute y'_t = f(x0_{t})\n measurement_pred = operator(origi_pred)\n\n # 4. compute loss = d(y, y'_t-1)\n loss = loss_fn(measurement, measurement_pred)\n loss.backward()\n\n print(\"distance: {0:.4f}\".format(loss.item()))\n\n with torch.no_grad():\n image_pred = image_pred - zeta * image.grad\n image = image_pred.detach()\n\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).numpy()\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n\n\nif __name__ == \"__main__\":\n import scipy\n from torch import nn\n from torchvision.utils import save_image\n\n # defining the operators f(.) of y = f(x)\n # super-resolution operator\n class SuperResolutionOperator(nn.Module):\n def __init__(self, in_shape, scale_factor):\n super().__init__()\n\n # Resizer local class, do not use outiside the SR operator class\n class Resizer(nn.Module):\n def __init__(self, in_shape, scale_factor=None, output_shape=None, kernel=None, antialiasing=True):\n super(Resizer, self).__init__()\n\n # First standardize values and fill missing arguments (if needed) by deriving scale from output shape or vice versa\n scale_factor, output_shape = self.fix_scale_and_size(in_shape, output_shape, scale_factor)\n\n # Choose interpolation method, each method has the matching kernel size\n def cubic(x):\n absx = np.abs(x)\n absx2 = absx**2\n absx3 = absx**3\n return (1.5 * absx3 - 2.5 * absx2 + 1) * (absx <= 1) + (\n -0.5 * absx3 + 2.5 * absx2 - 4 * absx + 2\n ) * ((1 < absx) & (absx <= 2))\n\n def lanczos2(x):\n return (\n (np.sin(pi * x) * np.sin(pi * x / 2) + np.finfo(np.float32).eps)\n / ((pi**2 * x**2 / 2) + np.finfo(np.float32).eps)\n ) * (abs(x) < 2)\n\n def box(x):\n return ((-0.5 <= x) & (x < 0.5)) * 1.0\n\n def lanczos3(x):\n return (\n (np.sin(pi * x) * np.sin(pi * x / 3) + np.finfo(np.float32).eps)\n / ((pi**2 * x**2 / 3) + np.finfo(np.float32).eps)\n ) * (abs(x) < 3)\n\n def linear(x):\n return (x + 1) * ((-1 <= x) & (x < 0)) + (1 - x) * ((0 <= x) & (x <= 1))\n\n method, kernel_width = {\n \"cubic\": (cubic, 4.0),\n \"lanczos2\": (lanczos2, 4.0),\n \"lanczos3\": (lanczos3, 6.0),\n \"box\": (box, 1.0),\n \"linear\": (linear, 2.0),\n None: (cubic, 4.0), # set default interpolation method as cubic\n }.get(kernel)\n\n # Antialiasing is only used when downscaling\n antialiasing *= np.any(np.array(scale_factor) < 1)\n\n # Sort indices of dimensions according to scale of each dimension. since we are going dim by dim this is efficient\n sorted_dims = np.argsort(np.array(scale_factor))\n self.sorted_dims = [int(dim) for dim in sorted_dims if scale_factor[dim] != 1]\n\n # Iterate over dimensions to calculate local weights for resizing and resize each time in one direction\n field_of_view_list = []\n weights_list = []\n for dim in self.sorted_dims:\n # for each coordinate (along 1 dim), calculate which coordinates in the input image affect its result and the\n # weights that multiply the values there to get its result.\n weights, field_of_view = self.contributions(\n in_shape[dim], output_shape[dim], scale_factor[dim], method, kernel_width, antialiasing\n )\n\n # convert to torch tensor\n weights = torch.tensor(weights.T, dtype=torch.float32)\n\n # We add singleton dimensions to the weight matrix so we can multiply it with the big tensor we get for\n # tmp_im[field_of_view.T], (bsxfun style)\n weights_list.append(\n nn.Parameter(\n torch.reshape(weights, list(weights.shape) + (len(scale_factor) - 1) * [1]),\n requires_grad=False,\n )\n )\n field_of_view_list.append(\n nn.Parameter(\n torch.tensor(field_of_view.T.astype(np.int32), dtype=torch.long), requires_grad=False\n )\n )\n\n self.field_of_view = nn.ParameterList(field_of_view_list)\n self.weights = nn.ParameterList(weights_list)\n\n def forward(self, in_tensor):\n x = in_tensor\n\n # Use the affecting position values and the set of weights to calculate the result of resizing along this 1 dim\n for dim, fov, w in zip(self.sorted_dims, self.field_of_view, self.weights):\n # To be able to act on each dim, we swap so that dim 0 is the wanted dim to resize\n x = torch.transpose(x, dim, 0)\n\n # This is a bit of a complicated multiplication: x[field_of_view.T] is a tensor of order image_dims+1.\n # for each pixel in the output-image it matches the positions the influence it from the input image (along 1 dim\n # only, this is why it only adds 1 dim to 5the shape). We then multiply, for each pixel, its set of positions with\n # the matching set of weights. we do this by this big tensor element-wise multiplication (MATLAB bsxfun style:\n # matching dims are multiplied element-wise while singletons mean that the matching dim is all multiplied by the\n # same number\n x = torch.sum(x[fov] * w, dim=0)\n\n # Finally we swap back the axes to the original order\n x = torch.transpose(x, dim, 0)\n\n return x\n\n def fix_scale_and_size(self, input_shape, output_shape, scale_factor):\n # First fixing the scale-factor (if given) to be standardized the function expects (a list of scale factors in the\n # same size as the number of input dimensions)\n if scale_factor is not None:\n # By default, if scale-factor is a scalar we assume 2d resizing and duplicate it.\n if np.isscalar(scale_factor) and len(input_shape) > 1:\n scale_factor = [scale_factor, scale_factor]\n\n # We extend the size of scale-factor list to the size of the input by assigning 1 to all the unspecified scales\n scale_factor = list(scale_factor)\n scale_factor = [1] * (len(input_shape) - len(scale_factor)) + scale_factor\n\n # Fixing output-shape (if given): extending it to the size of the input-shape, by assigning the original input-size\n # to all the unspecified dimensions\n if output_shape is not None:\n output_shape = list(input_shape[len(output_shape) :]) + list(np.uint(np.array(output_shape)))\n\n # Dealing with the case of non-give scale-factor, calculating according to output-shape. note that this is\n # sub-optimal, because there can be different scales to the same output-shape.\n if scale_factor is None:\n scale_factor = 1.0 * np.array(output_shape) / np.array(input_shape)\n\n # Dealing with missing output-shape. calculating according to scale-factor\n if output_shape is None:\n output_shape = np.uint(np.ceil(np.array(input_shape) * np.array(scale_factor)))\n\n return scale_factor, output_shape\n\n def contributions(self, in_length, out_length, scale, kernel, kernel_width, antialiasing):\n # This function calculates a set of 'filters' and a set of field_of_view that will later on be applied\n # such that each position from the field_of_view will be multiplied with a matching filter from the\n # 'weights' based on the interpolation method and the distance of the sub-pixel location from the pixel centers\n # around it. This is only done for one dimension of the image.\n\n # When anti-aliasing is activated (default and only for downscaling) the receptive field is stretched to size of\n # 1/sf. this means filtering is more 'low-pass filter'.\n fixed_kernel = (lambda arg: scale * kernel(scale * arg)) if antialiasing else kernel\n kernel_width *= 1.0 / scale if antialiasing else 1.0\n\n # These are the coordinates of the output image\n out_coordinates = np.arange(1, out_length + 1)\n\n # since both scale-factor and output size can be provided simulatneously, perserving the center of the image requires shifting\n # the output coordinates. the deviation is because out_length doesn't necesary equal in_length*scale.\n # to keep the center we need to subtract half of this deivation so that we get equal margins for boths sides and center is preserved.\n shifted_out_coordinates = out_coordinates - (out_length - in_length * scale) / 2\n\n # These are the matching positions of the output-coordinates on the input image coordinates.\n # Best explained by example: say we have 4 horizontal pixels for HR and we downscale by SF=2 and get 2 pixels:\n # [1,2,3,4] -> [1,2]. Remember each pixel number is the middle of the pixel.\n # The scaling is done between the distances and not pixel numbers (the right boundary of pixel 4 is transformed to\n # the right boundary of pixel 2. pixel 1 in the small image matches the boundary between pixels 1 and 2 in the big\n # one and not to pixel 2. This means the position is not just multiplication of the old pos by scale-factor).\n # So if we measure distance from the left border, middle of pixel 1 is at distance d=0.5, border between 1 and 2 is\n # at d=1, and so on (d = p - 0.5). we calculate (d_new = d_old / sf) which means:\n # (p_new-0.5 = (p_old-0.5) / sf) -> p_new = p_old/sf + 0.5 * (1-1/sf)\n match_coordinates = shifted_out_coordinates / scale + 0.5 * (1 - 1 / scale)\n\n # This is the left boundary to start multiplying the filter from, it depends on the size of the filter\n left_boundary = np.floor(match_coordinates - kernel_width / 2)\n\n # Kernel width needs to be enlarged because when covering has sub-pixel borders, it must 'see' the pixel centers\n # of the pixels it only covered a part from. So we add one pixel at each side to consider (weights can zeroize them)\n expanded_kernel_width = np.ceil(kernel_width) + 2\n\n # Determine a set of field_of_view for each each output position, these are the pixels in the input image\n # that the pixel in the output image 'sees'. We get a matrix whos horizontal dim is the output pixels (big) and the\n # vertical dim is the pixels it 'sees' (kernel_size + 2)\n field_of_view = np.squeeze(\n np.int16(np.expand_dims(left_boundary, axis=1) + np.arange(expanded_kernel_width) - 1)\n )\n\n # Assign weight to each pixel in the field of view. A matrix whos horizontal dim is the output pixels and the\n # vertical dim is a list of weights matching to the pixel in the field of view (that are specified in\n # 'field_of_view')\n weights = fixed_kernel(1.0 * np.expand_dims(match_coordinates, axis=1) - field_of_view - 1)\n\n # Normalize weights to sum up to 1. be careful from dividing by 0\n sum_weights = np.sum(weights, axis=1)\n sum_weights[sum_weights == 0] = 1.0\n weights = 1.0 * weights / np.expand_dims(sum_weights, axis=1)\n\n # We use this mirror structure as a trick for reflection padding at the boundaries\n mirror = np.uint(np.concatenate((np.arange(in_length), np.arange(in_length - 1, -1, step=-1))))\n field_of_view = mirror[np.mod(field_of_view, mirror.shape[0])]\n\n # Get rid of weights and pixel positions that are of zero weight\n non_zero_out_pixels = np.nonzero(np.any(weights, axis=0))\n weights = np.squeeze(weights[:, non_zero_out_pixels])\n field_of_view = np.squeeze(field_of_view[:, non_zero_out_pixels])\n\n # Final products are the relative positions and the matching weights, both are output_size X fixed_kernel_size\n return weights, field_of_view\n\n self.down_sample = Resizer(in_shape, 1 / scale_factor)\n for param in self.parameters():\n param.requires_grad = False\n\n def forward(self, data, **kwargs):\n return self.down_sample(data)\n\n # Gaussian blurring operator\n class GaussialBlurOperator(nn.Module):\n def __init__(self, kernel_size, intensity):\n super().__init__()\n\n class Blurkernel(nn.Module):\n def __init__(self, blur_type=\"gaussian\", kernel_size=31, std=3.0):\n super().__init__()\n self.blur_type = blur_type\n self.kernel_size = kernel_size\n self.std = std\n self.seq = nn.Sequential(\n nn.ReflectionPad2d(self.kernel_size // 2),\n nn.Conv2d(3, 3, self.kernel_size, stride=1, padding=0, bias=False, groups=3),\n )\n self.weights_init()\n\n def forward(self, x):\n return self.seq(x)\n\n def weights_init(self):\n if self.blur_type == \"gaussian\":\n n = np.zeros((self.kernel_size, self.kernel_size))\n n[self.kernel_size // 2, self.kernel_size // 2] = 1\n k = scipy.ndimage.gaussian_filter(n, sigma=self.std)\n k = torch.from_numpy(k)\n self.k = k\n for name, f in self.named_parameters():\n f.data.copy_(k)\n\n def update_weights(self, k):\n if not torch.is_tensor(k):\n k = torch.from_numpy(k)\n for name, f in self.named_parameters():\n f.data.copy_(k)\n\n def get_kernel(self):\n return self.k\n\n self.kernel_size = kernel_size\n self.conv = Blurkernel(blur_type=\"gaussian\", kernel_size=kernel_size, std=intensity)\n self.kernel = self.conv.get_kernel()\n self.conv.update_weights(self.kernel.type(torch.float32))\n\n for param in self.parameters():\n param.requires_grad = False\n\n def forward(self, data, **kwargs):\n return self.conv(data)\n\n def transpose(self, data, **kwargs):\n return data\n\n def get_kernel(self):\n return self.kernel.view(1, 1, self.kernel_size, self.kernel_size)\n\n # assuming the forward process y = f(x) is polluted by Gaussian noise, use l2 norm\n def RMSELoss(yhat, y):\n return torch.sqrt(torch.sum((yhat - y) ** 2))\n\n # set up source image\n src = Image.open(\"sample.png\")\n # read image into [1,3,H,W]\n src = torch.from_numpy(np.array(src, dtype=np.float32)).permute(2, 0, 1)[None]\n # normalize image to [-1,1]\n src = (src / 127.5) - 1.0\n src = src.to(\"cuda\")\n\n # set up operator and measurement\n # operator = SuperResolutionOperator(in_shape=src.shape, scale_factor=4).to(\"cuda\")\n operator = GaussialBlurOperator(kernel_size=61, intensity=3.0).to(\"cuda\")\n measurement = operator(src)\n\n # set up scheduler\n scheduler = DDPMScheduler.from_pretrained(\"google/ddpm-celebahq-256\")\n scheduler.set_timesteps(1000)\n\n # set up model\n model = UNet2DModel.from_pretrained(\"google/ddpm-celebahq-256\").to(\"cuda\")\n\n save_image((src + 1.0) / 2.0, \"dps_src.png\")\n save_image((measurement + 1.0) / 2.0, \"dps_mea.png\")\n\n # finally, the pipeline\n dpspipe = DPSPipeline(model, scheduler)\n image = dpspipe(\n measurement=measurement,\n operator=operator,\n loss_fn=RMSELoss,\n zeta=1.0,\n ).images[0]\n\n image.save(\"dps_generated_image.png\")\n" }, { "path": "diffusers/examples/community/edict_pipeline.py", "content": "from typing import Optional\n\nimport torch\nfrom PIL import Image\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNet2DConditionModel\nfrom diffusers.image_processor import VaeImageProcessor\nfrom diffusers.utils import (\n deprecate,\n)\n\n\nclass EDICTPipeline(DiffusionPipeline):\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: DDIMScheduler,\n mixing_coeff: float = 0.93,\n leapfrog_steps: bool = True,\n ):\n self.mixing_coeff = mixing_coeff\n self.leapfrog_steps = leapfrog_steps\n\n super().__init__()\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n )\n\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n def _encode_prompt(\n self, prompt: str, negative_prompt: Optional[str] = None, do_classifier_free_guidance: bool = False\n ):\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n prompt_embeds = self.text_encoder(text_inputs.input_ids.to(self.device)).last_hidden_state\n\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=self.device)\n\n if do_classifier_free_guidance:\n uncond_tokens = \"\" if negative_prompt is None else negative_prompt\n\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n negative_prompt_embeds = self.text_encoder(uncond_input.input_ids.to(self.device)).last_hidden_state\n\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n def denoise_mixing_layer(self, x: torch.Tensor, y: torch.Tensor):\n x = self.mixing_coeff * x + (1 - self.mixing_coeff) * y\n y = self.mixing_coeff * y + (1 - self.mixing_coeff) * x\n\n return [x, y]\n\n def noise_mixing_layer(self, x: torch.Tensor, y: torch.Tensor):\n y = (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff\n x = (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff\n\n return [x, y]\n\n def _get_alpha_and_beta(self, t: torch.Tensor):\n # as self.alphas_cumprod is always in cpu\n t = int(t)\n\n alpha_prod = self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod\n\n return alpha_prod, 1 - alpha_prod\n\n def noise_step(\n self,\n base: torch.Tensor,\n model_input: torch.Tensor,\n model_output: torch.Tensor,\n timestep: torch.Tensor,\n ):\n prev_timestep = timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps\n\n alpha_prod_t, beta_prod_t = self._get_alpha_and_beta(timestep)\n alpha_prod_t_prev, beta_prod_t_prev = self._get_alpha_and_beta(prev_timestep)\n\n a_t = (alpha_prod_t_prev / alpha_prod_t) ** 0.5\n b_t = -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5\n\n next_model_input = (base - b_t * model_output) / a_t\n\n return model_input, next_model_input.to(base.dtype)\n\n def denoise_step(\n self,\n base: torch.Tensor,\n model_input: torch.Tensor,\n model_output: torch.Tensor,\n timestep: torch.Tensor,\n ):\n prev_timestep = timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps\n\n alpha_prod_t, beta_prod_t = self._get_alpha_and_beta(timestep)\n alpha_prod_t_prev, beta_prod_t_prev = self._get_alpha_and_beta(prev_timestep)\n\n a_t = (alpha_prod_t_prev / alpha_prod_t) ** 0.5\n b_t = -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5\n next_model_input = a_t * base + b_t * model_output\n\n return model_input, next_model_input.to(base.dtype)\n\n @torch.no_grad()\n def decode_latents(self, latents: torch.Tensor):\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents).sample\n image = (image / 2 + 0.5).clamp(0, 1)\n return image\n\n @torch.no_grad()\n def prepare_latents(\n self,\n image: Image.Image,\n text_embeds: torch.Tensor,\n timesteps: torch.Tensor,\n guidance_scale: float,\n generator: Optional[torch.Generator] = None,\n ):\n do_classifier_free_guidance = guidance_scale > 1.0\n\n image = image.to(device=self.device, dtype=text_embeds.dtype)\n latent = self.vae.encode(image).latent_dist.sample(generator)\n\n latent = self.vae.config.scaling_factor * latent\n\n coupled_latents = [latent.clone(), latent.clone()]\n\n for i, t in tqdm(enumerate(timesteps), total=len(timesteps)):\n coupled_latents = self.noise_mixing_layer(x=coupled_latents[0], y=coupled_latents[1])\n\n # j - model_input index, k - base index\n for j in range(2):\n k = j ^ 1\n\n if self.leapfrog_steps:\n if i % 2 == 0:\n k, j = j, k\n\n model_input = coupled_latents[j]\n base = coupled_latents[k]\n\n latent_model_input = torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input\n\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeds).sample\n\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n base, model_input = self.noise_step(\n base=base,\n model_input=model_input,\n model_output=noise_pred,\n timestep=t,\n )\n\n coupled_latents[k] = model_input\n\n return coupled_latents\n\n @torch.no_grad()\n def __call__(\n self,\n base_prompt: str,\n target_prompt: str,\n image: Image.Image,\n guidance_scale: float = 3.0,\n num_inference_steps: int = 50,\n strength: float = 0.8,\n negative_prompt: Optional[str] = None,\n generator: Optional[torch.Generator] = None,\n output_type: Optional[str] = \"pil\",\n ):\n do_classifier_free_guidance = guidance_scale > 1.0\n\n image = self.image_processor.preprocess(image)\n\n base_embeds = self._encode_prompt(base_prompt, negative_prompt, do_classifier_free_guidance)\n target_embeds = self._encode_prompt(target_prompt, negative_prompt, do_classifier_free_guidance)\n\n self.scheduler.set_timesteps(num_inference_steps, self.device)\n\n t_limit = num_inference_steps - int(num_inference_steps * strength)\n fwd_timesteps = self.scheduler.timesteps[t_limit:]\n bwd_timesteps = fwd_timesteps.flip(0)\n\n coupled_latents = self.prepare_latents(image, base_embeds, bwd_timesteps, guidance_scale, generator)\n\n for i, t in tqdm(enumerate(fwd_timesteps), total=len(fwd_timesteps)):\n # j - model_input index, k - base index\n for k in range(2):\n j = k ^ 1\n\n if self.leapfrog_steps:\n if i % 2 == 1:\n k, j = j, k\n\n model_input = coupled_latents[j]\n base = coupled_latents[k]\n\n latent_model_input = torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input\n\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=target_embeds).sample\n\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n base, model_input = self.denoise_step(\n base=base,\n model_input=model_input,\n model_output=noise_pred,\n timestep=t,\n )\n\n coupled_latents[k] = model_input\n\n coupled_latents = self.denoise_mixing_layer(x=coupled_latents[0], y=coupled_latents[1])\n\n # either one is fine\n final_latent = coupled_latents[0]\n\n if output_type not in [\"latent\", \"pt\", \"np\", \"pil\"]:\n deprecation_message = (\n f\"the output_type {output_type} is outdated. Please make sure to set it to one of these instead: \"\n \"`pil`, `np`, `pt`, `latent`\"\n )\n deprecate(\"Unsupported output_type\", \"1.0.0\", deprecation_message, standard_warn=False)\n output_type = \"np\"\n\n if output_type == \"latent\":\n image = final_latent\n else:\n image = self.decode_latents(final_latent)\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n return image\n" }, { "path": "diffusers/examples/community/fresco_v2v.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.model_zoo\nfrom einops import rearrange, repeat\nfrom gmflow.gmflow import GMFlow\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom diffusers.image_processor import PipelineImageInput, VaeImageProcessor\nfrom diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin\nfrom diffusers.models import AutoencoderKL, ControlNetModel, ImageProjection, UNet2DConditionModel\nfrom diffusers.models.attention_processor import AttnProcessor2_0\nfrom diffusers.models.lora import adjust_lora_scale_text_encoder\nfrom diffusers.models.unets.unet_2d_condition import UNet2DConditionOutput\nfrom diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel\nfrom diffusers.pipelines.controlnet.pipeline_controlnet_img2img import StableDiffusionControlNetImg2ImgPipeline\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom diffusers.utils.torch_utils import is_compiled_module, randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef clear_cache():\n gc.collect()\n torch.cuda.empty_cache()\n\n\ndef coords_grid(b, h, w, homogeneous=False, device=None):\n y, x = torch.meshgrid(torch.arange(h), torch.arange(w)) # [H, W]\n\n stacks = [x, y]\n\n if homogeneous:\n ones = torch.ones_like(x) # [H, W]\n stacks.append(ones)\n\n grid = torch.stack(stacks, dim=0).float() # [2, H, W] or [3, H, W]\n\n grid = grid[None].repeat(b, 1, 1, 1) # [B, 2, H, W] or [B, 3, H, W]\n\n if device is not None:\n grid = grid.to(device)\n\n return grid\n\n\ndef bilinear_sample(img, sample_coords, mode=\"bilinear\", padding_mode=\"zeros\", return_mask=False):\n # img: [B, C, H, W]\n # sample_coords: [B, 2, H, W] in image scale\n if sample_coords.size(1) != 2: # [B, H, W, 2]\n sample_coords = sample_coords.permute(0, 3, 1, 2)\n\n b, _, h, w = sample_coords.shape\n\n # Normalize to [-1, 1]\n x_grid = 2 * sample_coords[:, 0] / (w - 1) - 1\n y_grid = 2 * sample_coords[:, 1] / (h - 1) - 1\n\n grid = torch.stack([x_grid, y_grid], dim=-1) # [B, H, W, 2]\n\n img = F.grid_sample(img, grid, mode=mode, padding_mode=padding_mode, align_corners=True)\n\n if return_mask:\n mask = (x_grid >= -1) & (y_grid >= -1) & (x_grid <= 1) & (y_grid <= 1) # [B, H, W]\n\n return img, mask\n\n return img\n\n\nclass Dilate:\n def __init__(self, kernel_size=7, channels=1, device=\"cpu\"):\n self.kernel_size = kernel_size\n self.channels = channels\n gaussian_kernel = torch.ones(1, 1, self.kernel_size, self.kernel_size)\n gaussian_kernel = gaussian_kernel.repeat(self.channels, 1, 1, 1)\n self.mean = (self.kernel_size - 1) // 2\n gaussian_kernel = gaussian_kernel.to(device)\n self.gaussian_filter = gaussian_kernel\n\n def __call__(self, x):\n x = F.pad(x, (self.mean, self.mean, self.mean, self.mean), \"replicate\")\n return torch.clamp(F.conv2d(x, self.gaussian_filter, bias=None), 0, 1)\n\n\ndef flow_warp(feature, flow, mask=False, mode=\"bilinear\", padding_mode=\"zeros\"):\n b, c, h, w = feature.size()\n assert flow.size(1) == 2\n\n grid = coords_grid(b, h, w).to(flow.device) + flow # [B, 2, H, W]\n grid = grid.to(feature.dtype)\n return bilinear_sample(feature, grid, mode=mode, padding_mode=padding_mode, return_mask=mask)\n\n\ndef forward_backward_consistency_check(fwd_flow, bwd_flow, alpha=0.01, beta=0.5):\n # fwd_flow, bwd_flow: [B, 2, H, W]\n # alpha and beta values are following UnFlow\n # (https://arxiv.org/abs/1711.07837)\n assert fwd_flow.dim() == 4 and bwd_flow.dim() == 4\n assert fwd_flow.size(1) == 2 and bwd_flow.size(1) == 2\n flow_mag = torch.norm(fwd_flow, dim=1) + torch.norm(bwd_flow, dim=1) # [B, H, W]\n\n warped_bwd_flow = flow_warp(bwd_flow, fwd_flow) # [B, 2, H, W]\n warped_fwd_flow = flow_warp(fwd_flow, bwd_flow) # [B, 2, H, W]\n\n diff_fwd = torch.norm(fwd_flow + warped_bwd_flow, dim=1) # [B, H, W]\n diff_bwd = torch.norm(bwd_flow + warped_fwd_flow, dim=1)\n\n threshold = alpha * flow_mag + beta\n\n fwd_occ = (diff_fwd > threshold).float() # [B, H, W]\n bwd_occ = (diff_bwd > threshold).float()\n\n return fwd_occ, bwd_occ\n\n\ndef numpy2tensor(img):\n x0 = torch.from_numpy(img.copy()).float().cuda() / 255.0 * 2.0 - 1.0\n x0 = torch.stack([x0], dim=0)\n # einops.rearrange(x0, 'b h w c -> b c h w').clone()\n return x0.permute(0, 3, 1, 2)\n\n\ndef calc_mean_std(feat, eps=1e-5, chunk=1):\n size = feat.size()\n assert len(size) == 4\n if chunk == 2:\n feat = torch.cat(feat.chunk(2), dim=3)\n N, C = size[:2]\n feat_var = feat.view(N // chunk, C, -1).var(dim=2) + eps\n feat_std = feat_var.sqrt().view(N, C, 1, 1)\n feat_mean = feat.view(N // chunk, C, -1).mean(dim=2).view(N // chunk, C, 1, 1)\n return feat_mean.repeat(chunk, 1, 1, 1), feat_std.repeat(chunk, 1, 1, 1)\n\n\ndef adaptive_instance_normalization(content_feat, style_feat, chunk=1):\n assert content_feat.size()[:2] == style_feat.size()[:2]\n size = content_feat.size()\n style_mean, style_std = calc_mean_std(style_feat, chunk)\n content_mean, content_std = calc_mean_std(content_feat)\n\n normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)\n return normalized_feat * style_std.expand(size) + style_mean.expand(size)\n\n\ndef optimize_feature(\n sample, flows, occs, correlation_matrix=[], intra_weight=1e2, iters=20, unet_chunk_size=2, optimize_temporal=True\n):\n \"\"\"\n FRESO-guided latent feature optimization\n * optimize spatial correspondence (match correlation_matrix)\n * optimize temporal correspondence (match warped_image)\n \"\"\"\n if (flows is None or occs is None or (not optimize_temporal)) and (\n intra_weight == 0 or len(correlation_matrix) == 0\n ):\n return sample\n # flows=[fwd_flows, bwd_flows]: (N-1)*2*H1*W1\n # occs=[fwd_occs, bwd_occs]: (N-1)*H1*W1\n # sample: 2N*C*H*W\n torch.cuda.empty_cache()\n video_length = sample.shape[0] // unet_chunk_size\n latent = rearrange(sample.to(torch.float32), \"(b f) c h w -> b f c h w\", f=video_length)\n\n cs = torch.nn.Parameter((latent.detach().clone()))\n optimizer = torch.optim.Adam([cs], lr=0.2)\n\n # unify resolution\n if flows is not None and occs is not None:\n scale = sample.shape[2] * 1.0 / flows[0].shape[2]\n kernel = int(1 / scale)\n bwd_flow_ = F.interpolate(flows[1] * scale, scale_factor=scale, mode=\"bilinear\").repeat(\n unet_chunk_size, 1, 1, 1\n )\n bwd_occ_ = F.max_pool2d(occs[1].unsqueeze(1), kernel_size=kernel).repeat(\n unet_chunk_size, 1, 1, 1\n ) # 2(N-1)*1*H1*W1\n fwd_flow_ = F.interpolate(flows[0] * scale, scale_factor=scale, mode=\"bilinear\").repeat(\n unet_chunk_size, 1, 1, 1\n )\n fwd_occ_ = F.max_pool2d(occs[0].unsqueeze(1), kernel_size=kernel).repeat(\n unet_chunk_size, 1, 1, 1\n ) # 2(N-1)*1*H1*W1\n # match frame 0,1,2,3 and frame 1,2,3,0\n reshuffle_list = list(range(1, video_length)) + [0]\n\n # attention_probs is the GRAM matrix of the normalized feature\n attention_probs = None\n for tmp in correlation_matrix:\n if sample.shape[2] * sample.shape[3] == tmp.shape[1]:\n attention_probs = tmp # 2N*HW*HW\n break\n\n n_iter = [0]\n while n_iter[0] < iters:\n\n def closure():\n optimizer.zero_grad()\n\n loss = 0\n\n # temporal consistency loss\n if optimize_temporal and flows is not None and occs is not None:\n c1 = rearrange(cs[:, :], \"b f c h w -> (b f) c h w\")\n c2 = rearrange(cs[:, reshuffle_list], \"b f c h w -> (b f) c h w\")\n warped_image1 = flow_warp(c1, bwd_flow_)\n warped_image2 = flow_warp(c2, fwd_flow_)\n loss = (\n abs((c2 - warped_image1) * (1 - bwd_occ_)) + abs((c1 - warped_image2) * (1 - fwd_occ_))\n ).mean() * 2\n\n # spatial consistency loss\n if attention_probs is not None and intra_weight > 0:\n cs_vector = rearrange(cs, \"b f c h w -> (b f) (h w) c\")\n # attention_scores = torch.bmm(cs_vector, cs_vector.transpose(-1, -2))\n # cs_attention_probs = attention_scores.softmax(dim=-1)\n cs_vector = cs_vector / ((cs_vector**2).sum(dim=2, keepdims=True) ** 0.5)\n cs_attention_probs = torch.bmm(cs_vector, cs_vector.transpose(-1, -2))\n tmp = F.l1_loss(cs_attention_probs, attention_probs) * intra_weight\n loss = tmp + loss\n\n loss.backward()\n n_iter[0] += 1\n\n return loss\n\n optimizer.step(closure)\n\n torch.cuda.empty_cache()\n return adaptive_instance_normalization(rearrange(cs.data.to(sample.dtype), \"b f c h w -> (b f) c h w\"), sample)\n\n\n@torch.no_grad()\ndef warp_tensor(sample, flows, occs, saliency, unet_chunk_size):\n \"\"\"\n Warp images or features based on optical flow\n Fuse the warped imges or features based on occusion masks and saliency map\n \"\"\"\n scale = sample.shape[2] * 1.0 / flows[0].shape[2]\n kernel = int(1 / scale)\n bwd_flow_ = F.interpolate(flows[1] * scale, scale_factor=scale, mode=\"bilinear\")\n bwd_occ_ = F.max_pool2d(occs[1].unsqueeze(1), kernel_size=kernel) # (N-1)*1*H1*W1\n if scale == 1:\n bwd_occ_ = Dilate(kernel_size=13, device=sample.device)(bwd_occ_)\n fwd_flow_ = F.interpolate(flows[0] * scale, scale_factor=scale, mode=\"bilinear\")\n fwd_occ_ = F.max_pool2d(occs[0].unsqueeze(1), kernel_size=kernel) # (N-1)*1*H1*W1\n if scale == 1:\n fwd_occ_ = Dilate(kernel_size=13, device=sample.device)(fwd_occ_)\n scale2 = sample.shape[2] * 1.0 / saliency.shape[2]\n saliency = F.interpolate(saliency, scale_factor=scale2, mode=\"bilinear\")\n latent = sample.to(torch.float32)\n video_length = sample.shape[0] // unet_chunk_size\n warp_saliency = flow_warp(saliency, bwd_flow_)\n warp_saliency_ = flow_warp(saliency[0:1], fwd_flow_[video_length - 1 : video_length])\n\n for j in range(unet_chunk_size):\n for ii in range(video_length - 1):\n i = video_length * j + ii\n warped_image = flow_warp(latent[i : i + 1], bwd_flow_[ii : ii + 1])\n mask = (1 - bwd_occ_[ii : ii + 1]) * saliency[ii + 1 : ii + 2] * warp_saliency[ii : ii + 1]\n latent[i + 1 : i + 2] = latent[i + 1 : i + 2] * (1 - mask) + warped_image * mask\n i = video_length * j\n ii = video_length - 1\n warped_image = flow_warp(latent[i : i + 1], fwd_flow_[ii : ii + 1])\n mask = (1 - fwd_occ_[ii : ii + 1]) * saliency[ii : ii + 1] * warp_saliency_\n latent[ii + i : ii + i + 1] = latent[ii + i : ii + i + 1] * (1 - mask) + warped_image * mask\n\n return latent.to(sample.dtype)\n\n\ndef my_forward(\n self,\n steps=[],\n layers=[0, 1, 2, 3],\n flows=None,\n occs=None,\n correlation_matrix=[],\n intra_weight=1e2,\n iters=20,\n optimize_temporal=True,\n saliency=None,\n):\n \"\"\"\n Hacked pipe.unet.forward()\n copied from https://github.com/huggingface/diffusers/blob/v0.19.3/src/diffusers/models/unet_2d_condition.py#L700\n if you are using a new version of diffusers, please copy the source code and modify it accordingly (find [HACK] in the code)\n * restore and return the decoder features\n * optimize the decoder features\n * perform background smoothing\n \"\"\"\n\n def forward(\n sample: torch.FloatTensor,\n timestep: Union[torch.Tensor, float, int],\n encoder_hidden_states: torch.Tensor,\n class_labels: Optional[torch.Tensor] = None,\n timestep_cond: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,\n down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\n mid_block_additional_residual: Optional[torch.Tensor] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ) -> Union[UNet2DConditionOutput, Tuple]:\n r\"\"\"\n The [`UNet2DConditionModel`] forward method.\n\n Args:\n sample (`torch.FloatTensor`):\n The noisy input tensor with the following shape `(batch, channel, height, width)`.\n timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.\n encoder_hidden_states (`torch.FloatTensor`):\n The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.\n encoder_attention_mask (`torch.Tensor`):\n A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If\n `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,\n which adds large negative values to the attention scores corresponding to \"discard\" tokens.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain\n tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].\n added_cond_kwargs: (`dict`, *optional*):\n A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that\n are passed along to the UNet blocks.\n\n Returns:\n [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:\n If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise\n a `tuple` is returned where the first element is the sample tensor.\n \"\"\"\n # By default samples have to be AT least a multiple of the overall upsampling factor.\n # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).\n # However, the upsampling interpolation output size can be forced to fit any upsampling size\n # on the fly if necessary.\n default_overall_up_factor = 2**self.num_upsamplers\n\n # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`\n forward_upsample_size = False\n upsample_size = None\n\n if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):\n logger.info(\"Forward upsample size to force interpolation output size.\")\n forward_upsample_size = True\n\n # ensure attention_mask is a bias, and give it a singleton query_tokens dimension\n # expects mask of shape:\n # [batch, key_tokens]\n # adds singleton query_tokens dimension:\n # [batch, 1, key_tokens]\n # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:\n # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)\n # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)\n if attention_mask is not None:\n # assume that mask is expressed as:\n # (1 = keep, 0 = discard)\n # convert mask into a bias that can be added to attention scores:\n # (keep = +0, discard = -10000.0)\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n # convert encoder_attention_mask to a bias the same way we do for attention_mask\n if encoder_attention_mask is not None:\n encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0\n encoder_attention_mask = encoder_attention_mask.unsqueeze(1)\n\n # 0. center input if necessary\n if self.config.center_input_sample:\n sample = 2 * sample - 1.0\n\n # 1. time\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can\n # This would be a good case for the `match` statement (Python 3.10+)\n is_mps = sample.device.type == \"mps\"\n if isinstance(timestep, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\n elif len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(sample.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timesteps = timesteps.expand(sample.shape[0])\n\n t_emb = self.time_proj(timesteps)\n\n # `Timesteps` does not contain any weights and will always return f32 tensors\n # but time_embedding might actually be running in fp16. so we need to cast here.\n # there might be better ways to encapsulate this.\n t_emb = t_emb.to(dtype=sample.dtype)\n\n emb = self.time_embedding(t_emb, timestep_cond)\n aug_emb = None\n\n if self.class_embedding is not None:\n if class_labels is None:\n raise ValueError(\"class_labels should be provided when num_class_embeds > 0\")\n\n if self.config.class_embed_type == \"timestep\":\n class_labels = self.time_proj(class_labels)\n\n # `Timesteps` does not contain any weights and will always return f32 tensors\n # there might be better ways to encapsulate this.\n class_labels = class_labels.to(dtype=sample.dtype)\n\n class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)\n\n if self.config.class_embeddings_concat:\n emb = torch.cat([emb, class_emb], dim=-1)\n else:\n emb = emb + class_emb\n\n if self.config.addition_embed_type == \"text\":\n aug_emb = self.add_embedding(encoder_hidden_states)\n elif self.config.addition_embed_type == \"text_image\":\n # Kandinsky 2.1 - style\n if \"image_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`\"\n )\n\n image_embs = added_cond_kwargs.get(\"image_embeds\")\n text_embs = added_cond_kwargs.get(\"text_embeds\", encoder_hidden_states)\n aug_emb = self.add_embedding(text_embs, image_embs)\n elif self.config.addition_embed_type == \"text_time\":\n # SDXL - style\n if \"text_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`\"\n )\n text_embeds = added_cond_kwargs.get(\"text_embeds\")\n if \"time_ids\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`\"\n )\n time_ids = added_cond_kwargs.get(\"time_ids\")\n time_embeds = self.add_time_proj(time_ids.flatten())\n time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))\n\n add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)\n add_embeds = add_embeds.to(emb.dtype)\n aug_emb = self.add_embedding(add_embeds)\n elif self.config.addition_embed_type == \"image\":\n # Kandinsky 2.2 - style\n if \"image_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`\"\n )\n image_embs = added_cond_kwargs.get(\"image_embeds\")\n aug_emb = self.add_embedding(image_embs)\n elif self.config.addition_embed_type == \"image_hint\":\n # Kandinsky 2.2 - style\n if \"image_embeds\" not in added_cond_kwargs or \"hint\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`\"\n )\n image_embs = added_cond_kwargs.get(\"image_embeds\")\n hint = added_cond_kwargs.get(\"hint\")\n aug_emb, hint = self.add_embedding(image_embs, hint)\n sample = torch.cat([sample, hint], dim=1)\n\n emb = emb + aug_emb if aug_emb is not None else emb\n\n if self.time_embed_act is not None:\n emb = self.time_embed_act(emb)\n\n if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == \"text_proj\":\n encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)\n elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == \"text_image_proj\":\n # Kadinsky 2.1 - style\n if \"image_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`\"\n )\n\n image_embeds = added_cond_kwargs.get(\"image_embeds\")\n encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)\n elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == \"image_proj\":\n # Kandinsky 2.2 - style\n if \"image_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`\"\n )\n image_embeds = added_cond_kwargs.get(\"image_embeds\")\n encoder_hidden_states = self.encoder_hid_proj(image_embeds)\n # 2. pre-process\n sample = self.conv_in(sample)\n\n # 3. down\n\n is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None\n is_adapter = mid_block_additional_residual is None and down_block_additional_residuals is not None\n\n down_block_res_samples = (sample,)\n for downsample_block in self.down_blocks:\n if hasattr(downsample_block, \"has_cross_attention\") and downsample_block.has_cross_attention:\n # For t2i-adapter CrossAttnDownBlock2D\n additional_residuals = {}\n if is_adapter and len(down_block_additional_residuals) > 0:\n additional_residuals[\"additional_residuals\"] = down_block_additional_residuals.pop(0)\n\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n encoder_attention_mask=encoder_attention_mask,\n **additional_residuals,\n )\n else:\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb)\n\n if is_adapter and len(down_block_additional_residuals) > 0:\n sample += down_block_additional_residuals.pop(0)\n down_block_res_samples += res_samples\n\n if is_controlnet:\n new_down_block_res_samples = ()\n\n for down_block_res_sample, down_block_additional_residual in zip(\n down_block_res_samples, down_block_additional_residuals\n ):\n down_block_res_sample = down_block_res_sample + down_block_additional_residual\n new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)\n\n down_block_res_samples = new_down_block_res_samples\n\n # 4. mid\n if self.mid_block is not None:\n sample = self.mid_block(\n sample,\n emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n encoder_attention_mask=encoder_attention_mask,\n )\n\n if is_controlnet:\n sample = sample + mid_block_additional_residual\n\n # 5. up\n \"\"\"\n [HACK] restore the decoder features in up_samples\n \"\"\"\n up_samples = ()\n # down_samples = ()\n for i, upsample_block in enumerate(self.up_blocks):\n is_final_block = i == len(self.up_blocks) - 1\n\n res_samples = down_block_res_samples[-len(upsample_block.resnets) :]\n down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]\n\n \"\"\"\n [HACK] restore the decoder features in up_samples\n [HACK] optimize the decoder features\n [HACK] perform background smoothing\n \"\"\"\n if i in layers:\n up_samples += (sample,)\n if timestep in steps and i in layers:\n sample = optimize_feature(\n sample, flows, occs, correlation_matrix, intra_weight, iters, optimize_temporal=optimize_temporal\n )\n if saliency is not None:\n sample = warp_tensor(sample, flows, occs, saliency, 2)\n\n # if we have not reached the final block and need to forward the\n # upsample size, we do it here\n if not is_final_block and forward_upsample_size:\n upsample_size = down_block_res_samples[-1].shape[2:]\n\n if hasattr(upsample_block, \"has_cross_attention\") and upsample_block.has_cross_attention:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n upsample_size=upsample_size,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n )\n else:\n sample = upsample_block(\n hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size\n )\n\n # 6. post-process\n if self.conv_norm_out:\n sample = self.conv_norm_out(sample)\n sample = self.conv_act(sample)\n sample = self.conv_out(sample)\n\n \"\"\"\n [HACK] return the output feature as well as the decoder features\n \"\"\"\n if not return_dict:\n return (sample,) + up_samples\n\n return UNet2DConditionOutput(sample=sample)\n\n return forward\n\n\n@torch.no_grad()\ndef get_single_mapping_ind(bwd_flow, bwd_occ, imgs, scale=1.0):\n \"\"\"\n FLATTEN: Optical fLow-guided attention (Temoporal-guided attention)\n Find the correspondence between every pixels in a pair of frames\n\n [input]\n bwd_flow: 1*2*H*W\n bwd_occ: 1*H*W i.e., f2 = warp(f1, bwd_flow) * bwd_occ\n imgs: 2*3*H*W i.e., [f1,f2]\n\n [output]\n mapping_ind: pixel index correspondence\n unlinkedmask: indicate whether a pixel has no correspondence\n i.e., f2 = f1[mapping_ind] * unlinkedmask\n \"\"\"\n flows = F.interpolate(bwd_flow, scale_factor=1.0 / scale, mode=\"bilinear\")[0][[1, 0]] / scale # 2*H*W\n _, H, W = flows.shape\n masks = torch.logical_not(F.interpolate(bwd_occ[None], scale_factor=1.0 / scale, mode=\"bilinear\") > 0.5)[\n 0\n ] # 1*H*W\n frames = F.interpolate(imgs, scale_factor=1.0 / scale, mode=\"bilinear\").view(2, 3, -1) # 2*3*HW\n grid = torch.stack(torch.meshgrid([torch.arange(H), torch.arange(W)]), dim=0).to(flows.device) # 2*H*W\n warp_grid = torch.round(grid + flows)\n mask = torch.logical_and(\n torch.logical_and(\n torch.logical_and(torch.logical_and(warp_grid[0] >= 0, warp_grid[0] < H), warp_grid[1] >= 0),\n warp_grid[1] < W,\n ),\n masks[0],\n ).view(-1) # HW\n warp_grid = warp_grid.view(2, -1) # 2*HW\n warp_ind = (warp_grid[0] * W + warp_grid[1]).to(torch.long) # HW\n mapping_ind = torch.zeros_like(warp_ind) - 1 # HW\n\n for f0ind, f1ind in enumerate(warp_ind):\n if mask[f0ind]:\n if mapping_ind[f1ind] == -1:\n mapping_ind[f1ind] = f0ind\n else:\n targetv = frames[0, :, f1ind]\n pref0ind = mapping_ind[f1ind]\n prev = frames[1, :, pref0ind]\n v = frames[1, :, f0ind]\n if ((prev - targetv) ** 2).mean() > ((v - targetv) ** 2).mean():\n mask[pref0ind] = False\n mapping_ind[f1ind] = f0ind\n else:\n mask[f0ind] = False\n\n unusedind = torch.arange(len(mask)).to(mask.device)[~mask]\n unlinkedmask = mapping_ind == -1\n mapping_ind[unlinkedmask] = unusedind\n return mapping_ind, unlinkedmask\n\n\n@torch.no_grad()\ndef get_mapping_ind(bwd_flows, bwd_occs, imgs, scale=1.0):\n \"\"\"\n FLATTEN: Optical fLow-guided attention (Temoporal-guided attention)\n Find pixel correspondence between every consecutive frames in a batch\n\n [input]\n bwd_flow: (N-1)*2*H*W\n bwd_occ: (N-1)*H*W\n imgs: N*3*H*W\n\n [output]\n fwd_mappings: N*1*HW\n bwd_mappings: N*1*HW\n flattn_mask: HW*1*N*N\n i.e., imgs[i,:,fwd_mappings[i]] corresponds to imgs[0]\n i.e., imgs[i,:,fwd_mappings[i]][:,bwd_mappings[i]] restore the original imgs[i]\n \"\"\"\n N, H, W = imgs.shape[0], int(imgs.shape[2] // scale), int(imgs.shape[3] // scale)\n iterattn_mask = torch.ones(H * W, N, N, dtype=torch.bool).to(imgs.device)\n for i in range(len(imgs) - 1):\n one_mask = torch.ones(N, N, dtype=torch.bool).to(imgs.device)\n one_mask[: i + 1, i + 1 :] = False\n one_mask[i + 1 :, : i + 1] = False\n mapping_ind, unlinkedmask = get_single_mapping_ind(\n bwd_flows[i : i + 1], bwd_occs[i : i + 1], imgs[i : i + 2], scale\n )\n if i == 0:\n fwd_mapping = [torch.arange(len(mapping_ind)).to(mapping_ind.device)]\n bwd_mapping = [torch.arange(len(mapping_ind)).to(mapping_ind.device)]\n iterattn_mask[unlinkedmask[fwd_mapping[-1]]] = torch.logical_and(\n iterattn_mask[unlinkedmask[fwd_mapping[-1]]], one_mask\n )\n fwd_mapping += [mapping_ind[fwd_mapping[-1]]]\n bwd_mapping += [torch.sort(fwd_mapping[-1])[1]]\n fwd_mappings = torch.stack(fwd_mapping, dim=0).unsqueeze(1)\n bwd_mappings = torch.stack(bwd_mapping, dim=0).unsqueeze(1)\n return fwd_mappings, bwd_mappings, iterattn_mask.unsqueeze(1)\n\n\ndef apply_FRESCO_opt(\n pipe,\n steps=[],\n layers=[0, 1, 2, 3],\n flows=None,\n occs=None,\n correlation_matrix=[],\n intra_weight=1e2,\n iters=20,\n optimize_temporal=True,\n saliency=None,\n):\n \"\"\"\n Apply FRESCO-based optimization to a StableDiffusionPipeline\n \"\"\"\n pipe.unet.forward = my_forward(\n pipe.unet, steps, layers, flows, occs, correlation_matrix, intra_weight, iters, optimize_temporal, saliency\n )\n\n\n@torch.no_grad()\ndef get_intraframe_paras(pipe, imgs, frescoProc, prompt_embeds, do_classifier_free_guidance=True, generator=None):\n \"\"\"\n Get parameters for spatial-guided attention and optimization\n * perform one step denoising\n * collect attention feature, stored in frescoProc.controller.stored_attn['decoder_attn']\n * compute the gram matrix of the normalized feature for spatial consistency loss\n \"\"\"\n\n noise_scheduler = pipe.scheduler\n timestep = noise_scheduler.timesteps[-1]\n device = pipe._execution_device\n B, C, H, W = imgs.shape\n\n frescoProc.controller.disable_controller()\n apply_FRESCO_opt(pipe)\n frescoProc.controller.clear_store()\n frescoProc.controller.enable_store()\n\n latents = pipe.prepare_latents(\n imgs.to(pipe.unet.dtype), timestep, B, 1, prompt_embeds.dtype, device, generator=generator, repeat_noise=False\n )\n\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n model_output = pipe.unet(\n latent_model_input,\n timestep,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=None,\n return_dict=False,\n )\n\n frescoProc.controller.disable_store()\n\n # gram matrix of the normalized feature for spatial consistency loss\n correlation_matrix = []\n for tmp in model_output[1:]:\n latent_vector = rearrange(tmp, \"b c h w -> b (h w) c\")\n latent_vector = latent_vector / ((latent_vector**2).sum(dim=2, keepdims=True) ** 0.5)\n attention_probs = torch.bmm(latent_vector, latent_vector.transpose(-1, -2))\n correlation_matrix += [attention_probs.detach().clone().to(torch.float32)]\n del attention_probs, latent_vector, tmp\n del model_output\n\n clear_cache()\n\n return correlation_matrix\n\n\n@torch.no_grad()\ndef get_flow_and_interframe_paras(flow_model, imgs):\n \"\"\"\n Get parameters for temporal-guided attention and optimization\n * predict optical flow and occlusion mask\n * compute pixel index correspondence for FLATTEN\n \"\"\"\n images = torch.stack([torch.from_numpy(img).permute(2, 0, 1).float() for img in imgs], dim=0).cuda()\n imgs_torch = torch.cat([numpy2tensor(img) for img in imgs], dim=0)\n\n reshuffle_list = list(range(1, len(images))) + [0]\n\n results_dict = flow_model(\n images,\n images[reshuffle_list],\n attn_splits_list=[2],\n corr_radius_list=[-1],\n prop_radius_list=[-1],\n pred_bidir_flow=True,\n )\n flow_pr = results_dict[\"flow_preds\"][-1] # [2*B, 2, H, W]\n fwd_flows, bwd_flows = flow_pr.chunk(2) # [B, 2, H, W]\n fwd_occs, bwd_occs = forward_backward_consistency_check(fwd_flows, bwd_flows) # [B, H, W]\n\n warped_image1 = flow_warp(images, bwd_flows)\n bwd_occs = torch.clamp(\n bwd_occs + (abs(images[reshuffle_list] - warped_image1).mean(dim=1) > 255 * 0.25).float(), 0, 1\n )\n\n warped_image2 = flow_warp(images[reshuffle_list], fwd_flows)\n fwd_occs = torch.clamp(fwd_occs + (abs(images - warped_image2).mean(dim=1) > 255 * 0.25).float(), 0, 1)\n\n attn_mask = []\n for scale in [8.0, 16.0, 32.0]:\n bwd_occs_ = F.interpolate(bwd_occs[:-1].unsqueeze(1), scale_factor=1.0 / scale, mode=\"bilinear\")\n attn_mask += [\n torch.cat((bwd_occs_[0:1].reshape(1, -1) > -1, bwd_occs_.reshape(bwd_occs_.shape[0], -1) > 0.5), dim=0)\n ]\n\n fwd_mappings = []\n bwd_mappings = []\n interattn_masks = []\n for scale in [8.0, 16.0]:\n fwd_mapping, bwd_mapping, interattn_mask = get_mapping_ind(bwd_flows, bwd_occs, imgs_torch, scale=scale)\n fwd_mappings += [fwd_mapping]\n bwd_mappings += [bwd_mapping]\n interattn_masks += [interattn_mask]\n\n interattn_paras = {}\n interattn_paras[\"fwd_mappings\"] = fwd_mappings\n interattn_paras[\"bwd_mappings\"] = bwd_mappings\n interattn_paras[\"interattn_masks\"] = interattn_masks\n\n clear_cache()\n\n return [fwd_flows, bwd_flows], [fwd_occs, bwd_occs], attn_mask, interattn_paras\n\n\nclass AttentionControl:\n \"\"\"\n Control FRESCO-based attention\n * enable/diable spatial-guided attention\n * enable/diable temporal-guided attention\n * enable/diable cross-frame attention\n * collect intermediate attention feature (for spatial-guided attention)\n \"\"\"\n\n def __init__(self):\n self.stored_attn = self.get_empty_store()\n self.store = False\n self.index = 0\n self.attn_mask = None\n self.interattn_paras = None\n self.use_interattn = False\n self.use_cfattn = False\n self.use_intraattn = False\n self.intraattn_bias = 0\n self.intraattn_scale_factor = 0.2\n self.interattn_scale_factor = 0.2\n\n @staticmethod\n def get_empty_store():\n return {\n \"decoder_attn\": [],\n }\n\n def clear_store(self):\n del self.stored_attn\n torch.cuda.empty_cache()\n gc.collect()\n self.stored_attn = self.get_empty_store()\n self.disable_intraattn()\n\n # store attention feature of the input frame for spatial-guided attention\n def enable_store(self):\n self.store = True\n\n def disable_store(self):\n self.store = False\n\n # spatial-guided attention\n def enable_intraattn(self):\n self.index = 0\n self.use_intraattn = True\n self.disable_store()\n if len(self.stored_attn[\"decoder_attn\"]) == 0:\n self.use_intraattn = False\n\n def disable_intraattn(self):\n self.index = 0\n self.use_intraattn = False\n self.disable_store()\n\n def disable_cfattn(self):\n self.use_cfattn = False\n\n # cross frame attention\n def enable_cfattn(self, attn_mask=None):\n if attn_mask:\n if self.attn_mask:\n del self.attn_mask\n torch.cuda.empty_cache()\n self.attn_mask = attn_mask\n self.use_cfattn = True\n else:\n if self.attn_mask:\n self.use_cfattn = True\n else:\n print(\"Warning: no valid cross-frame attention parameters available!\")\n self.disable_cfattn()\n\n def disable_interattn(self):\n self.use_interattn = False\n\n # temporal-guided attention\n def enable_interattn(self, interattn_paras=None):\n if interattn_paras:\n if self.interattn_paras:\n del self.interattn_paras\n torch.cuda.empty_cache()\n self.interattn_paras = interattn_paras\n self.use_interattn = True\n else:\n if self.interattn_paras:\n self.use_interattn = True\n else:\n print(\"Warning: no valid temporal-guided attention parameters available!\")\n self.disable_interattn()\n\n def disable_controller(self):\n self.disable_intraattn()\n self.disable_interattn()\n self.disable_cfattn()\n\n def enable_controller(self, interattn_paras=None, attn_mask=None):\n self.enable_intraattn()\n self.enable_interattn(interattn_paras)\n self.enable_cfattn(attn_mask)\n\n def forward(self, context):\n if self.store:\n self.stored_attn[\"decoder_attn\"].append(context.detach())\n if self.use_intraattn and len(self.stored_attn[\"decoder_attn\"]) > 0:\n tmp = self.stored_attn[\"decoder_attn\"][self.index]\n self.index = self.index + 1\n if self.index >= len(self.stored_attn[\"decoder_attn\"]):\n self.index = 0\n self.disable_store()\n return tmp\n return context\n\n def __call__(self, context):\n context = self.forward(context)\n return context\n\n\nclass FRESCOAttnProcessor2_0:\n \"\"\"\n Hack self attention to FRESCO-based attention\n * adding spatial-guided attention\n * adding temporal-guided attention\n * adding cross-frame attention\n\n Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).\n Usage\n frescoProc = FRESCOAttnProcessor2_0(2, attn_mask)\n attnProc = AttnProcessor2_0()\n\n attn_processor_dict = {}\n for k in pipe.unet.attn_processors.keys():\n if k.startswith(\"up_blocks.2\") or k.startswith(\"up_blocks.3\"):\n attn_processor_dict[k] = frescoProc\n else:\n attn_processor_dict[k] = attnProc\n pipe.unet.set_attn_processor(attn_processor_dict)\n \"\"\"\n\n def __init__(self, unet_chunk_size=2, controller=None):\n if not hasattr(F, \"scaled_dot_product_attention\"):\n raise ImportError(\"AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.\")\n self.unet_chunk_size = unet_chunk_size\n self.controller = controller\n\n def __call__(\n self,\n attn,\n hidden_states,\n encoder_hidden_states=None,\n attention_mask=None,\n temb=None,\n ):\n residual = hidden_states\n\n if attn.spatial_norm is not None:\n hidden_states = attn.spatial_norm(hidden_states, temb)\n\n input_ndim = hidden_states.ndim\n\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n\n if attention_mask is not None:\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n # scaled_dot_product_attention expects attention_mask shape to be\n # (batch, heads, source_length, target_length)\n attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])\n\n if attn.group_norm is not None:\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n\n query = attn.to_q(hidden_states)\n\n crossattn = False\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n if self.controller and self.controller.store:\n self.controller(hidden_states.detach().clone())\n else:\n crossattn = True\n if attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n # BC * HW * 8D\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n query_raw, key_raw = None, None\n if self.controller and self.controller.use_interattn and (not crossattn):\n query_raw, key_raw = query.clone(), key.clone()\n\n inner_dim = key.shape[-1] # 8D\n head_dim = inner_dim // attn.heads # D\n\n \"\"\"for efficient cross-frame attention\"\"\"\n if self.controller and self.controller.use_cfattn and (not crossattn):\n video_length = key.size()[0] // self.unet_chunk_size\n former_frame_index = [0] * video_length\n attn_mask = None\n if self.controller.attn_mask is not None:\n for m in self.controller.attn_mask:\n if m.shape[1] == key.shape[1]:\n attn_mask = m\n # BC * HW * 8D --> B * C * HW * 8D\n key = rearrange(key, \"(b f) d c -> b f d c\", f=video_length)\n # B * C * HW * 8D --> B * C * HW * 8D\n if attn_mask is None:\n key = key[:, former_frame_index]\n else:\n key = repeat(key[:, attn_mask], \"b d c -> b f d c\", f=video_length)\n # B * C * HW * 8D --> BC * HW * 8D\n key = rearrange(key, \"b f d c -> (b f) d c\").detach()\n value = rearrange(value, \"(b f) d c -> b f d c\", f=video_length)\n if attn_mask is None:\n value = value[:, former_frame_index]\n else:\n value = repeat(value[:, attn_mask], \"b d c -> b f d c\", f=video_length)\n value = rearrange(value, \"b f d c -> (b f) d c\").detach()\n\n # BC * HW * 8D --> BC * HW * 8 * D --> BC * 8 * HW * D\n query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n # BC * 8 * HW2 * D\n key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n # BC * 8 * HW2 * D2\n value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n \"\"\"for spatial-guided intra-frame attention\"\"\"\n if self.controller and self.controller.use_intraattn and (not crossattn):\n ref_hidden_states = self.controller(None)\n assert ref_hidden_states.shape == encoder_hidden_states.shape\n query_ = attn.to_q(ref_hidden_states)\n key_ = attn.to_k(ref_hidden_states)\n\n # BC * 8 * HW * D\n query_ = query_.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n key_ = key_.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n query = F.scaled_dot_product_attention(\n query_,\n key_ * self.controller.intraattn_scale_factor,\n query,\n attn_mask=torch.eye(query_.size(-2), key_.size(-2), dtype=query.dtype, device=query.device)\n * self.controller.intraattn_bias,\n ).detach()\n\n del query_, key_\n torch.cuda.empty_cache()\n\n # the output of sdp = (batch, num_heads, seq_len, head_dim)\n # TODO: add support for attn.scale when we move to Torch 2.1\n # output: BC * 8 * HW * D2\n hidden_states = F.scaled_dot_product_attention(\n query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False\n )\n\n \"\"\"for temporal-guided inter-frame attention (FLATTEN)\"\"\"\n if self.controller and self.controller.use_interattn and (not crossattn):\n del query, key, value\n torch.cuda.empty_cache()\n bwd_mapping = None\n fwd_mapping = None\n for i, f in enumerate(self.controller.interattn_paras[\"fwd_mappings\"]):\n if f.shape[2] == hidden_states.shape[2]:\n fwd_mapping = f\n bwd_mapping = self.controller.interattn_paras[\"bwd_mappings\"][i]\n interattn_mask = self.controller.interattn_paras[\"interattn_masks\"][i]\n video_length = key_raw.size()[0] // self.unet_chunk_size\n # BC * HW * 8D --> C * 8BD * HW\n key = rearrange(key_raw, \"(b f) d c -> f (b c) d\", f=video_length)\n query = rearrange(query_raw, \"(b f) d c -> f (b c) d\", f=video_length)\n # BC * 8 * HW * D --> C * 8BD * HW\n # key = rearrange(hidden_states, \"(b f) h d c -> f (b h c) d\", f=video_length) ########\n # query = rearrange(hidden_states, \"(b f) h d c -> f (b h c) d\", f=video_length) #######\n\n value = rearrange(hidden_states, \"(b f) h d c -> f (b h c) d\", f=video_length)\n key = torch.gather(key, 2, fwd_mapping.expand(-1, key.shape[1], -1))\n query = torch.gather(query, 2, fwd_mapping.expand(-1, query.shape[1], -1))\n value = torch.gather(value, 2, fwd_mapping.expand(-1, value.shape[1], -1))\n # C * 8BD * HW --> BHW, C, 8D\n key = rearrange(key, \"f (b c) d -> (b d) f c\", b=self.unet_chunk_size)\n query = rearrange(query, \"f (b c) d -> (b d) f c\", b=self.unet_chunk_size)\n value = rearrange(value, \"f (b c) d -> (b d) f c\", b=self.unet_chunk_size)\n # BHW * C * 8D --> BHW * C * 8 * D--> BHW * 8 * C * D\n query = query.view(-1, video_length, attn.heads, head_dim).transpose(1, 2).detach()\n key = key.view(-1, video_length, attn.heads, head_dim).transpose(1, 2).detach()\n value = value.view(-1, video_length, attn.heads, head_dim).transpose(1, 2).detach()\n hidden_states_ = F.scaled_dot_product_attention(\n query,\n key * self.controller.interattn_scale_factor,\n value,\n # .to(query.dtype)-1.0) * 1e6 -\n attn_mask=(interattn_mask.repeat(self.unet_chunk_size, 1, 1, 1)),\n # torch.eye(interattn_mask.shape[2]).to(query.device).to(query.dtype) * 1e4,\n )\n\n # BHW * 8 * C * D --> C * 8BD * HW\n hidden_states_ = rearrange(hidden_states_, \"(b d) h f c -> f (b h c) d\", b=self.unet_chunk_size)\n hidden_states_ = torch.gather(\n hidden_states_, 2, bwd_mapping.expand(-1, hidden_states_.shape[1], -1)\n ).detach()\n # C * 8BD * HW --> BC * 8 * HW * D\n hidden_states = rearrange(\n hidden_states_, \"f (b h c) d -> (b f) h d c\", b=self.unet_chunk_size, h=attn.heads\n )\n\n # BC * 8 * HW * D --> BC * HW * 8D\n hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)\n hidden_states = hidden_states.to(query.dtype)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n if input_ndim == 4:\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n if attn.residual_connection:\n hidden_states = hidden_states + residual\n\n hidden_states = hidden_states / attn.rescale_output_factor\n\n return hidden_states\n\n\ndef apply_FRESCO_attn(pipe):\n \"\"\"\n Apply FRESCO-guided attention to a StableDiffusionPipeline\n \"\"\"\n frescoProc = FRESCOAttnProcessor2_0(2, AttentionControl())\n attnProc = AttnProcessor2_0()\n attn_processor_dict = {}\n for k in pipe.unet.attn_processors.keys():\n if k.startswith(\"up_blocks.2\") or k.startswith(\"up_blocks.3\"):\n attn_processor_dict[k] = frescoProc\n else:\n attn_processor_dict[k] = attnProc\n pipe.unet.set_attn_processor(attn_processor_dict)\n return frescoProc\n\n\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n\ndef prepare_image(image):\n if isinstance(image, torch.Tensor):\n # Batch single image\n if image.ndim == 3:\n image = image.unsqueeze(0)\n\n image = image.to(dtype=torch.float32)\n else:\n # preprocess image\n if isinstance(image, (PIL.Image.Image, np.ndarray)):\n image = [image]\n\n if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):\n image = [np.array(i.convert(\"RGB\"))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n elif isinstance(image, list) and isinstance(image[0], np.ndarray):\n image = np.concatenate([i[None, :] for i in image], axis=0)\n\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0\n\n return image\n\n\nclass FrescoV2VPipeline(StableDiffusionControlNetImg2ImgPipeline):\n r\"\"\"\n Pipeline for video-to-video translation using Stable Diffusion with FRESCO Algorithm.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):\n Provides additional conditioning to the `unet` during the denoising process. If you set multiple\n ControlNets as a list, the outputs from each ControlNet are added together to create one combined\n additional conditioning.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"image_encoder\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder: CLIPVisionModelWithProjection = None,\n requires_safety_checker: bool = True,\n ):\n super().__init__(\n vae,\n text_encoder,\n tokenizer,\n unet,\n controlnet,\n scheduler,\n safety_checker,\n feature_extractor,\n image_encoder,\n requires_safety_checker,\n )\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n if isinstance(controlnet, (list, tuple)):\n controlnet = MultiControlNetModel(controlnet)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n controlnet=controlnet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)\n self.control_image_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False\n )\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n frescoProc = FRESCOAttnProcessor2_0(2, AttentionControl())\n attnProc = AttnProcessor2_0()\n attn_processor_dict = {}\n for k in self.unet.attn_processors.keys():\n if k.startswith(\"up_blocks.2\") or k.startswith(\"up_blocks.3\"):\n attn_processor_dict[k] = frescoProc\n else:\n attn_processor_dict[k] = attnProc\n self.unet.set_attn_processor(attn_processor_dict)\n self.frescoProc = frescoProc\n\n flow_model = GMFlow(\n feature_channels=128,\n num_scales=1,\n upsample_factor=8,\n num_head=1,\n attention_type=\"swin\",\n ffn_dim_expansion=4,\n num_transformer_layers=6,\n ).to(self.device)\n\n checkpoint = torch.utils.model_zoo.load_url(\n \"https://huggingface.co/Anonymous-sub/Rerender/resolve/main/models/gmflow_sintel-0c07dcb3.pth\",\n map_location=lambda storage, loc: storage,\n )\n weights = checkpoint[\"model\"] if \"model\" in checkpoint else checkpoint\n flow_model.load_state_dict(weights, strict=False)\n flow_model.eval()\n self.flow_model = flow_model\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n image,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n controlnet_conditioning_scale=1.0,\n control_guidance_start=0.0,\n control_guidance_end=1.0,\n callback_on_step_end_tensor_inputs=None,\n ):\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # `prompt` needs more sophisticated handling when there are multiple\n # conditionings.\n if isinstance(self.controlnet, MultiControlNetModel):\n if isinstance(prompt, list):\n logger.warning(\n f\"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}\"\n \" prompts. The conditionings will be fixed across the prompts.\"\n )\n\n # Check `image`\n is_compiled = hasattr(F, \"scaled_dot_product_attention\") and isinstance(\n self.controlnet, torch._dynamo.eval_frame.OptimizedModule\n )\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n self.check_image(image, prompt, prompt_embeds)\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if not isinstance(image, list):\n raise TypeError(\"For multiple controlnets: `image` must be type `list`\")\n\n # When `image` is a nested list:\n # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])\n elif any(isinstance(i, list) for i in image):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif len(image) != len(self.controlnet.nets):\n raise ValueError(\n f\"For multiple controlnets: `image` must have the same length as the number of controlnets, but got {len(image)} images and {len(self.controlnet.nets)} ControlNets.\"\n )\n\n for image_ in image:\n self.check_image(image_, prompt, prompt_embeds)\n else:\n assert False\n\n # Check `controlnet_conditioning_scale`\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n if not isinstance(controlnet_conditioning_scale, float):\n raise TypeError(\"For single controlnet: `controlnet_conditioning_scale` must be type `float`.\")\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if isinstance(controlnet_conditioning_scale, list):\n if any(isinstance(i, list) for i in controlnet_conditioning_scale):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(\n self.controlnet.nets\n ):\n raise ValueError(\n \"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have\"\n \" the same length as the number of controlnets\"\n )\n else:\n assert False\n\n if len(control_guidance_start) != len(control_guidance_end):\n raise ValueError(\n f\"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list.\"\n )\n\n if isinstance(self.controlnet, MultiControlNetModel):\n if len(control_guidance_start) != len(self.controlnet.nets):\n raise ValueError(\n f\"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}.\"\n )\n\n for start, end in zip(control_guidance_start, control_guidance_end):\n if start >= end:\n raise ValueError(\n f\"control guidance start: {start} cannot be larger or equal to control guidance end: {end}.\"\n )\n if start < 0.0:\n raise ValueError(f\"control guidance start: {start} can't be smaller than 0.\")\n if end > 1.0:\n raise ValueError(f\"control guidance end: {end} can't be larger than 1.0.\")\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n if ip_adapter_image_embeds is not None:\n if not isinstance(ip_adapter_image_embeds, list):\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}\"\n )\n elif ip_adapter_image_embeds[0].ndim not in [3, 4]:\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D\"\n )\n\n # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image\n def check_image(self, image, prompt, prompt_embeds):\n image_is_pil = isinstance(image, PIL.Image.Image)\n image_is_tensor = isinstance(image, torch.Tensor)\n image_is_np = isinstance(image, np.ndarray)\n image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)\n image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)\n image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)\n\n if (\n not image_is_pil\n and not image_is_tensor\n and not image_is_np\n and not image_is_pil_list\n and not image_is_tensor_list\n and not image_is_np_list\n ):\n raise TypeError(\n f\"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}\"\n )\n\n if image_is_pil:\n image_batch_size = 1\n else:\n image_batch_size = len(image)\n\n if prompt is not None and isinstance(prompt, str):\n prompt_batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n prompt_batch_size = len(prompt)\n elif prompt_embeds is not None:\n prompt_batch_size = prompt_embeds.shape[0]\n\n if image_batch_size != 1 and image_batch_size != prompt_batch_size:\n raise ValueError(\n f\"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}\"\n )\n\n # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image\n def prepare_control_image(\n self,\n image,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype,\n do_classifier_free_guidance=False,\n guess_mode=False,\n ):\n image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)\n image_batch_size = image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n image = image.repeat_interleave(repeat_by, dim=0)\n\n image = image.to(device=device, dtype=dtype)\n\n if do_classifier_free_guidance and not guess_mode:\n image = torch.cat([image] * 2)\n\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n if hasattr(self.scheduler, \"set_begin_index\"):\n self.scheduler.set_begin_index(t_start * self.scheduler.order)\n\n return timesteps, num_inference_steps - t_start\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.prepare_latents\n def prepare_latents(\n self, image, timestep, batch_size, num_images_per_prompt, dtype, device, repeat_noise, generator=None\n ):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n\n if image.shape[1] == 4:\n init_latents = image\n\n else:\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n elif isinstance(generator, list):\n init_latents = [\n retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])\n for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = retrieve_latents(self.vae.encode(image), generator=generator)\n\n init_latents = self.vae.config.scaling_factor * init_latents\n\n if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:\n # expand init_latents for batch_size\n deprecation_message = (\n f\"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial\"\n \" images (`image`). Initial images are now duplicating to match the number of text prompts. Note\"\n \" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update\"\n \" your script to pass as many initial images as text prompts to suppress this warning.\"\n )\n deprecate(\"len(prompt) != len(image)\", \"1.0.0\", deprecation_message, standard_warn=False)\n additional_image_per_prompt = batch_size // init_latents.shape[0]\n init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)\n elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n init_latents = torch.cat([init_latents], dim=0)\n\n shape = init_latents.shape\n if repeat_noise:\n noise = randn_tensor((1, *shape[1:]), generator=generator, device=device, dtype=dtype)\n one_tuple = (1,) * (len(shape) - 1)\n noise = noise.repeat(batch_size, *one_tuple)\n else:\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n latents = init_latents\n\n return latents\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n frames: Union[List[np.ndarray], torch.FloatTensor] = None,\n control_frames: Union[List[np.ndarray], torch.FloatTensor] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n strength: float = 0.8,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: Union[float, List[float]] = 0.8,\n guess_mode: bool = False,\n control_guidance_start: Union[float, List[float]] = 0.0,\n control_guidance_end: Union[float, List[float]] = 1.0,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n end_opt_step=15,\n num_intraattn_steps=1,\n step_interattn_end=350,\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n frames (`List[np.ndarray]` or `torch.FloatTensor`): The input images to be used as the starting point for the image generation process.\n control_frames (`List[np.ndarray]` or `torch.FloatTensor`): The ControlNet input images condition to provide guidance to the `unet` for generation.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n strength (`float`, *optional*, defaults to 0.8):\n Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a\n starting point and more noise is added the higher the `strength`. The number of denoising steps depends\n on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising\n process runs for the full number of iterations specified in `num_inference_steps`. A value of 1\n essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.FloatTensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of\n IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should\n contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not\n provided, embeddings are computed from the `ip_adapter_image` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):\n The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added\n to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set\n the corresponding scale as a list.\n guess_mode (`bool`, *optional*, defaults to `False`):\n The ControlNet encoder tries to recognize the content of the input image even if you remove all\n prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.\n control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):\n The percentage of total steps at which the ControlNet starts applying.\n control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):\n The percentage of total steps at which the ControlNet stops applying.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n end_opt_step:\n The feature optimization is activated from strength * num_inference_step to end_opt_step.\n num_intraattn_steps:\n Apply num_interattn_steps steps of spatial-guided attention.\n step_interattn_end:\n Apply temporal-guided attention in [step_interattn_end, 1000] steps\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n\n controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet\n\n # align format for control guidance\n if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):\n control_guidance_start = len(control_guidance_end) * [control_guidance_start]\n elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):\n control_guidance_end = len(control_guidance_start) * [control_guidance_end]\n elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):\n mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1\n control_guidance_start, control_guidance_end = (\n mult * [control_guidance_start],\n mult * [control_guidance_end],\n )\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n control_frames[0],\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n ip_adapter_image,\n ip_adapter_image_embeds,\n controlnet_conditioning_scale,\n control_guidance_start,\n control_guidance_end,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n\n # 2. Define call parameters\n batch_size = len(frames)\n\n device = self._execution_device\n\n if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):\n controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)\n\n global_pool_conditions = (\n controlnet.config.global_pool_conditions\n if isinstance(controlnet, ControlNetModel)\n else controlnet.nets[0].config.global_pool_conditions\n )\n guess_mode = guess_mode or global_pool_conditions\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=self.clip_skip,\n )\n prompt_embeds = prompt_embeds.repeat(batch_size, 1, 1)\n negative_prompt_embeds = negative_prompt_embeds.repeat(batch_size, 1, 1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_images_per_prompt,\n self.do_classifier_free_guidance,\n )\n\n # 4. Prepare image\n imgs_np = []\n for frame in frames:\n if isinstance(frame, PIL.Image.Image):\n imgs_np.append(np.asarray(frame))\n else:\n # np.ndarray\n imgs_np.append(frame)\n images_pt = self.image_processor.preprocess(frames).to(dtype=torch.float32)\n\n # 5. Prepare controlnet_conditioning_image\n if isinstance(controlnet, ControlNetModel):\n control_image = self.prepare_control_image(\n image=control_frames,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n elif isinstance(controlnet, MultiControlNetModel):\n control_images = []\n\n for control_image_ in control_frames:\n control_image_ = self.prepare_control_image(\n image=control_image_,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n\n control_images.append(control_image_)\n\n control_image = control_images\n else:\n assert False\n\n self.flow_model.to(device)\n\n flows, occs, attn_mask, interattn_paras = get_flow_and_interframe_paras(self.flow_model, imgs_np)\n correlation_matrix = get_intraframe_paras(self, images_pt, self.frescoProc, prompt_embeds, generator)\n\n \"\"\"\n Flexible settings for attention:\n * Turn off FRESCO-guided attention: frescoProc.controller.disable_controller()\n Then you can turn on one specific attention submodule\n * Turn on Cross-frame attention: frescoProc.controller.enable_cfattn(attn_mask)\n * Turn on Spatial-guided attention: frescoProc.controller.enable_intraattn()\n * Turn on Temporal-guided attention: frescoProc.controller.enable_interattn(interattn_paras)\n\n Flexible settings for optimization:\n * Turn off Spatial-guided optimization: set optimize_temporal = False in apply_FRESCO_opt()\n * Turn off Temporal-guided optimization: set correlation_matrix = [] in apply_FRESCO_opt()\n * Turn off FRESCO-guided optimization: disable_FRESCO_opt(pipe)\n\n Flexible settings for background smoothing:\n * Turn off background smoothing: set saliency = None in apply_FRESCO_opt()\n \"\"\"\n\n self.frescoProc.controller.enable_controller(interattn_paras=interattn_paras, attn_mask=attn_mask)\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n apply_FRESCO_opt(\n self,\n steps=timesteps[:end_opt_step],\n flows=flows,\n occs=occs,\n correlation_matrix=correlation_matrix,\n saliency=None,\n optimize_temporal=True,\n )\n\n clear_cache()\n\n # 5. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n self._num_timesteps = len(timesteps)\n\n # 6. Prepare latent variables\n latents = self.prepare_latents(\n images_pt,\n latent_timestep,\n batch_size,\n num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n generator=generator,\n repeat_noise=True,\n )\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7.1 Add image embeds for IP-Adapter\n added_cond_kwargs = (\n {\"image_embeds\": image_embeds}\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None\n else None\n )\n\n # 7.2 Create tensor stating which controlnets to keep\n controlnet_keep = []\n for i in range(len(timesteps)):\n keeps = [\n 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)\n for s, e in zip(control_guidance_start, control_guidance_end)\n ]\n controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n if i >= num_intraattn_steps:\n self.frescoProc.controller.disable_intraattn()\n if t < step_interattn_end:\n self.frescoProc.controller.disable_interattn()\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # controlnet(s) inference\n if guess_mode and self.do_classifier_free_guidance:\n # Infer ControlNet only for the conditional batch.\n control_model_input = latents\n control_model_input = self.scheduler.scale_model_input(control_model_input, t)\n controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]\n else:\n control_model_input = latent_model_input\n controlnet_prompt_embeds = prompt_embeds\n\n if isinstance(controlnet_keep[i], list):\n cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]\n else:\n controlnet_cond_scale = controlnet_conditioning_scale\n if isinstance(controlnet_cond_scale, list):\n controlnet_cond_scale = controlnet_cond_scale[0]\n cond_scale = controlnet_cond_scale * controlnet_keep[i]\n\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n control_model_input,\n t,\n encoder_hidden_states=controlnet_prompt_embeds,\n controlnet_cond=control_image,\n conditioning_scale=cond_scale,\n guess_mode=guess_mode,\n return_dict=False,\n )\n\n if guess_mode and self.do_classifier_free_guidance:\n # Infered ControlNet only for the conditional batch.\n # To apply the output of ControlNet to both the unconditional and conditional batches,\n # add 0 to the unconditional batch to keep it unchanged.\n down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]\n mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=self.cross_attention_kwargs,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # If we do sequential model offloading, let's offload unet and controlnet\n # manually for max memory savings\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.unet.to(\"cpu\")\n self.controlnet.to(\"cpu\")\n torch.cuda.empty_cache()\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[\n 0\n ]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/examples/community/gluegen.py", "content": "import inspect\nfrom typing import Any, Dict, List, Optional, Union\n\nimport torch\nimport torch.nn as nn\nfrom transformers import AutoModel, AutoTokenizer, CLIPImageProcessor\n\nfrom diffusers import DiffusionPipeline\nfrom diffusers.image_processor import VaeImageProcessor\nfrom diffusers.loaders import LoraLoaderMixin\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.models.lora import adjust_lora_scale_text_encoder\nfrom diffusers.pipelines.pipeline_utils import StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n USE_PEFT_BACKEND,\n logging,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass TranslatorBase(nn.Module):\n def __init__(self, num_tok, dim, dim_out, mult=2):\n super().__init__()\n\n self.dim_in = dim\n self.dim_out = dim_out\n\n self.net_tok = nn.Sequential(\n nn.Linear(num_tok, int(num_tok * mult)),\n nn.LayerNorm(int(num_tok * mult)),\n nn.GELU(),\n nn.Linear(int(num_tok * mult), int(num_tok * mult)),\n nn.LayerNorm(int(num_tok * mult)),\n nn.GELU(),\n nn.Linear(int(num_tok * mult), num_tok),\n nn.LayerNorm(num_tok),\n )\n\n self.net_sen = nn.Sequential(\n nn.Linear(dim, int(dim * mult)),\n nn.LayerNorm(int(dim * mult)),\n nn.GELU(),\n nn.Linear(int(dim * mult), int(dim * mult)),\n nn.LayerNorm(int(dim * mult)),\n nn.GELU(),\n nn.Linear(int(dim * mult), dim_out),\n nn.LayerNorm(dim_out),\n )\n\n def forward(self, x):\n if self.dim_in == self.dim_out:\n indentity_0 = x\n x = self.net_sen(x)\n x += indentity_0\n x = x.transpose(1, 2)\n\n indentity_1 = x\n x = self.net_tok(x)\n x += indentity_1\n x = x.transpose(1, 2)\n else:\n x = self.net_sen(x)\n x = x.transpose(1, 2)\n\n x = self.net_tok(x)\n x = x.transpose(1, 2)\n return x\n\n\nclass TranslatorBaseNoLN(nn.Module):\n def __init__(self, num_tok, dim, dim_out, mult=2):\n super().__init__()\n\n self.dim_in = dim\n self.dim_out = dim_out\n\n self.net_tok = nn.Sequential(\n nn.Linear(num_tok, int(num_tok * mult)),\n nn.GELU(),\n nn.Linear(int(num_tok * mult), int(num_tok * mult)),\n nn.GELU(),\n nn.Linear(int(num_tok * mult), num_tok),\n )\n\n self.net_sen = nn.Sequential(\n nn.Linear(dim, int(dim * mult)),\n nn.GELU(),\n nn.Linear(int(dim * mult), int(dim * mult)),\n nn.GELU(),\n nn.Linear(int(dim * mult), dim_out),\n )\n\n def forward(self, x):\n if self.dim_in == self.dim_out:\n indentity_0 = x\n x = self.net_sen(x)\n x += indentity_0\n x = x.transpose(1, 2)\n\n indentity_1 = x\n x = self.net_tok(x)\n x += indentity_1\n x = x.transpose(1, 2)\n else:\n x = self.net_sen(x)\n x = x.transpose(1, 2)\n\n x = self.net_tok(x)\n x = x.transpose(1, 2)\n return x\n\n\nclass TranslatorNoLN(nn.Module):\n def __init__(self, num_tok, dim, dim_out, mult=2, depth=5):\n super().__init__()\n\n self.blocks = nn.ModuleList([TranslatorBase(num_tok, dim, dim, mult=2) for d in range(depth)])\n self.gelu = nn.GELU()\n\n self.tail = TranslatorBaseNoLN(num_tok, dim, dim_out, mult=2)\n\n def forward(self, x):\n for block in self.blocks:\n x = block(x) + x\n x = self.gelu(x)\n\n x = self.tail(x)\n return x\n\n\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used,\n `timesteps` must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default\n timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`\n must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass GlueGenStableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin, LoraLoaderMixin):\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: AutoModel,\n tokenizer: AutoTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n language_adapter: TranslatorNoLN = None,\n tensor_norm: torch.Tensor = None,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n language_adapter=language_adapter,\n tensor_norm=tensor_norm,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n def load_language_adapter(\n self,\n model_path: str,\n num_token: int,\n dim: int,\n dim_out: int,\n tensor_norm: torch.Tensor,\n mult: int = 2,\n depth: int = 5,\n ):\n device = self._execution_device\n self.tensor_norm = tensor_norm.to(device)\n self.language_adapter = TranslatorNoLN(num_tok=num_token, dim=dim, dim_out=dim_out, mult=mult, depth=depth).to(\n device\n )\n self.language_adapter.load_state_dict(torch.load(model_path))\n\n def _adapt_language(self, prompt_embeds: torch.Tensor):\n prompt_embeds = prompt_embeds / 3\n prompt_embeds = self.language_adapter(prompt_embeds) * (self.tensor_norm / 2)\n return prompt_embeds\n\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n elif self.language_adapter is not None:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n # Run prompt language adapter\n if self.language_adapter is not None:\n prompt_embeds = self._adapt_language(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n # Run negative prompt language adapter\n if self.language_adapter is not None:\n negative_prompt_embeds = self._adapt_language(negative_prompt_embeds)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n height,\n width,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n timesteps (`torch.Tensor`):\n generate embedding vectors at these timesteps\n embedding_dim (`int`, *optional*, defaults to 512):\n dimension of the embeddings to generate\n dtype:\n data type of the generated embeddings\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def guidance_rescale(self):\n return self._guidance_rescale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @property\n def interrupt(self):\n return self._interrupt\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n clip_skip: Optional[int] = None,\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when\n using zero terminal SNR.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n # to deal with lora scaling and other possible forward hooks\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n height,\n width,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n )\n\n self._guidance_scale = guidance_scale\n self._guidance_rescale = guidance_rescale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._interrupt = False\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 6.2 Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n if self.interrupt:\n continue\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=self.cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[\n 0\n ]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/examples/community/hd_painter.py", "content": "import math\nimport numbers\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom diffusers.image_processor import PipelineImageInput\nfrom diffusers.models import AsymmetricAutoencoderKL, ImageProjection\nfrom diffusers.models.attention_processor import Attention, AttnProcessor\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint import (\n StableDiffusionInpaintPipeline,\n retrieve_timesteps,\n)\nfrom diffusers.utils import deprecate\n\n\nclass RASGAttnProcessor:\n def __init__(self, mask, token_idx, scale_factor):\n self.attention_scores = None # Stores the last output of the similarity matrix here. Each layer will get its own RASGAttnProcessor assigned\n self.mask = mask\n self.token_idx = token_idx\n self.scale_factor = scale_factor\n self.mask_resoltuion = mask.shape[-1] * mask.shape[-2] # 64 x 64 if the image is 512x512\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n temb: Optional[torch.Tensor] = None,\n scale: float = 1.0,\n ) -> torch.Tensor:\n # Same as the default AttnProcessor up untill the part where similarity matrix gets saved\n downscale_factor = self.mask_resoltuion // hidden_states.shape[1]\n residual = hidden_states\n\n if attn.spatial_norm is not None:\n hidden_states = attn.spatial_norm(hidden_states, temb)\n\n input_ndim = hidden_states.ndim\n\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n\n if attn.group_norm is not None:\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n\n query = attn.to_q(hidden_states)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n # Automatically recognize the resolution and save the attention similarity values\n # We need to use the values before the softmax function, hence the rewritten get_attention_scores function.\n if downscale_factor == self.scale_factor**2:\n self.attention_scores = get_attention_scores(attn, query, key, attention_mask)\n attention_probs = self.attention_scores.softmax(dim=-1)\n attention_probs = attention_probs.to(query.dtype)\n else:\n attention_probs = attn.get_attention_scores(query, key, attention_mask) # Original code\n\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n if input_ndim == 4:\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n if attn.residual_connection:\n hidden_states = hidden_states + residual\n\n hidden_states = hidden_states / attn.rescale_output_factor\n\n return hidden_states\n\n\nclass PAIntAAttnProcessor:\n def __init__(self, transformer_block, mask, token_idx, do_classifier_free_guidance, scale_factors):\n self.transformer_block = transformer_block # Stores the parent transformer block.\n self.mask = mask\n self.scale_factors = scale_factors\n self.do_classifier_free_guidance = do_classifier_free_guidance\n self.token_idx = token_idx\n self.shape = mask.shape[2:]\n self.mask_resoltuion = mask.shape[-1] * mask.shape[-2] # 64 x 64\n self.default_processor = AttnProcessor()\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n temb: Optional[torch.Tensor] = None,\n scale: float = 1.0,\n ) -> torch.Tensor:\n # Automatically recognize the resolution of the current attention layer and resize the masks accordingly\n downscale_factor = self.mask_resoltuion // hidden_states.shape[1]\n\n mask = None\n for factor in self.scale_factors:\n if downscale_factor == factor**2:\n shape = (self.shape[0] // factor, self.shape[1] // factor)\n mask = F.interpolate(self.mask, shape, mode=\"bicubic\") # B, 1, H, W\n break\n if mask is None:\n return self.default_processor(attn, hidden_states, encoder_hidden_states, attention_mask, temb, scale)\n\n # STARTS HERE\n residual = hidden_states\n # Save the input hidden_states for later use\n input_hidden_states = hidden_states\n\n # ================================================== #\n # =============== SELF ATTENTION 1 ================= #\n # ================================================== #\n\n if attn.spatial_norm is not None:\n hidden_states = attn.spatial_norm(hidden_states, temb)\n\n input_ndim = hidden_states.ndim\n\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n\n if attn.group_norm is not None:\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n\n query = attn.to_q(hidden_states)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n # self_attention_probs = attn.get_attention_scores(query, key, attention_mask) # We can't use post-softmax attention scores in this case\n self_attention_scores = get_attention_scores(\n attn, query, key, attention_mask\n ) # The custom function returns pre-softmax probabilities\n self_attention_probs = self_attention_scores.softmax(\n dim=-1\n ) # Manually compute the probabilities here, the scores will be reused in the second part of PAIntA\n self_attention_probs = self_attention_probs.to(query.dtype)\n\n hidden_states = torch.bmm(self_attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n # x = x + self.attn1(self.norm1(x))\n\n if input_ndim == 4:\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n if attn.residual_connection: # So many residuals everywhere\n hidden_states = hidden_states + residual\n\n self_attention_output_hidden_states = hidden_states / attn.rescale_output_factor\n\n # ================================================== #\n # ============ BasicTransformerBlock =============== #\n # ================================================== #\n # We use a hack by running the code from the BasicTransformerBlock that is between Self and Cross attentions here\n # The other option would've been modifying the BasicTransformerBlock and adding this functionality here.\n # I assumed that changing the BasicTransformerBlock would have been a bigger deal and decided to use this hack isntead.\n\n # The SelfAttention block recieves the normalized latents from the BasicTransformerBlock,\n # But the residual of the output is the non-normalized version.\n # Therefore we unnormalize the input hidden state here\n unnormalized_input_hidden_states = (\n input_hidden_states + self.transformer_block.norm1.bias\n ) * self.transformer_block.norm1.weight\n\n # TODO: return if neccessary\n # if self.use_ada_layer_norm_zero:\n # attn_output = gate_msa.unsqueeze(1) * attn_output\n # elif self.use_ada_layer_norm_single:\n # attn_output = gate_msa * attn_output\n\n transformer_hidden_states = self_attention_output_hidden_states + unnormalized_input_hidden_states\n if transformer_hidden_states.ndim == 4:\n transformer_hidden_states = transformer_hidden_states.squeeze(1)\n\n # TODO: return if neccessary\n # 2.5 GLIGEN Control\n # if gligen_kwargs is not None:\n # transformer_hidden_states = self.fuser(transformer_hidden_states, gligen_kwargs[\"objs\"])\n # NOTE: we experimented with using GLIGEN and HDPainter together, the results were not that great\n\n # 3. Cross-Attention\n if self.transformer_block.use_ada_layer_norm:\n # transformer_norm_hidden_states = self.transformer_block.norm2(transformer_hidden_states, timestep)\n raise NotImplementedError()\n elif self.transformer_block.use_ada_layer_norm_zero or self.transformer_block.use_layer_norm:\n transformer_norm_hidden_states = self.transformer_block.norm2(transformer_hidden_states)\n elif self.transformer_block.use_ada_layer_norm_single:\n # For PixArt norm2 isn't applied here:\n # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103\n transformer_norm_hidden_states = transformer_hidden_states\n elif self.transformer_block.use_ada_layer_norm_continuous:\n # transformer_norm_hidden_states = self.transformer_block.norm2(transformer_hidden_states, added_cond_kwargs[\"pooled_text_emb\"])\n raise NotImplementedError()\n else:\n raise ValueError(\"Incorrect norm\")\n\n if self.transformer_block.pos_embed is not None and self.transformer_block.use_ada_layer_norm_single is False:\n transformer_norm_hidden_states = self.transformer_block.pos_embed(transformer_norm_hidden_states)\n\n # ================================================== #\n # ================= CROSS ATTENTION ================ #\n # ================================================== #\n\n # We do an initial pass of the CrossAttention up to obtaining the similarity matrix here.\n # The similarity matrix is used to obtain scaling coefficients for the attention matrix of the self attention\n # We reuse the previously computed self-attention matrix, and only repeat the steps after the softmax\n\n cross_attention_input_hidden_states = (\n transformer_norm_hidden_states # Renaming the variable for the sake of readability\n )\n\n # TODO: check if classifier_free_guidance is being used before splitting here\n if self.do_classifier_free_guidance:\n # Our scaling coefficients depend only on the conditional part, so we split the inputs\n (\n _cross_attention_input_hidden_states_unconditional,\n cross_attention_input_hidden_states_conditional,\n ) = cross_attention_input_hidden_states.chunk(2)\n\n # Same split for the encoder_hidden_states i.e. the tokens\n # Since the SelfAttention processors don't get the encoder states as input, we inject them into the processor in the begining.\n _encoder_hidden_states_unconditional, encoder_hidden_states_conditional = self.encoder_hidden_states.chunk(\n 2\n )\n else:\n cross_attention_input_hidden_states_conditional = cross_attention_input_hidden_states\n encoder_hidden_states_conditional = self.encoder_hidden_states.chunk(2)\n\n # Rename the variables for the sake of readability\n # The part below is the beginning of the __call__ function of the following CrossAttention layer\n cross_attention_hidden_states = cross_attention_input_hidden_states_conditional\n cross_attention_encoder_hidden_states = encoder_hidden_states_conditional\n\n attn2 = self.transformer_block.attn2\n\n if attn2.spatial_norm is not None:\n cross_attention_hidden_states = attn2.spatial_norm(cross_attention_hidden_states, temb)\n\n input_ndim = cross_attention_hidden_states.ndim\n\n if input_ndim == 4:\n batch_size, channel, height, width = cross_attention_hidden_states.shape\n cross_attention_hidden_states = cross_attention_hidden_states.view(\n batch_size, channel, height * width\n ).transpose(1, 2)\n\n (\n batch_size,\n sequence_length,\n _,\n ) = cross_attention_hidden_states.shape # It is definitely a cross attention, so no need for an if block\n # TODO: change the attention_mask here\n attention_mask = attn2.prepare_attention_mask(\n None, sequence_length, batch_size\n ) # I assume the attention mask is the same...\n\n if attn2.group_norm is not None:\n cross_attention_hidden_states = attn2.group_norm(cross_attention_hidden_states.transpose(1, 2)).transpose(\n 1, 2\n )\n\n query2 = attn2.to_q(cross_attention_hidden_states)\n\n if attn2.norm_cross:\n cross_attention_encoder_hidden_states = attn2.norm_encoder_hidden_states(\n cross_attention_encoder_hidden_states\n )\n\n key2 = attn2.to_k(cross_attention_encoder_hidden_states)\n query2 = attn2.head_to_batch_dim(query2)\n key2 = attn2.head_to_batch_dim(key2)\n\n cross_attention_probs = attn2.get_attention_scores(query2, key2, attention_mask)\n\n # CrossAttention ends here, the remaining part is not used\n\n # ================================================== #\n # ================ SELF ATTENTION 2 ================ #\n # ================================================== #\n # DEJA VU!\n\n mask = (mask > 0.5).to(self_attention_output_hidden_states.dtype)\n m = mask.to(self_attention_output_hidden_states.device)\n # m = rearrange(m, 'b c h w -> b (h w) c').contiguous()\n m = m.permute(0, 2, 3, 1).reshape((m.shape[0], -1, m.shape[1])).contiguous() # B HW 1\n m = torch.matmul(m, m.permute(0, 2, 1)) + (1 - m)\n\n # # Compute scaling coefficients for the similarity matrix\n # # Select the cross attention values for the correct tokens only!\n # cross_attention_probs = cross_attention_probs.mean(dim = 0)\n # cross_attention_probs = cross_attention_probs[:, self.token_idx].sum(dim=1)\n\n # cross_attention_probs = cross_attention_probs.reshape(shape)\n # gaussian_smoothing = GaussianSmoothing(channels=1, kernel_size=3, sigma=0.5, dim=2).to(self_attention_output_hidden_states.device)\n # cross_attention_probs = gaussian_smoothing(cross_attention_probs.unsqueeze(0))[0] # optional smoothing\n # cross_attention_probs = cross_attention_probs.reshape(-1)\n # cross_attention_probs = ((cross_attention_probs - torch.median(cross_attention_probs.ravel())) / torch.max(cross_attention_probs.ravel())).clip(0, 1)\n\n # c = (1 - m) * cross_attention_probs.reshape(1, 1, -1) + m # PAIntA scaling coefficients\n\n # Compute scaling coefficients for the similarity matrix\n # Select the cross attention values for the correct tokens only!\n\n batch_size, dims, channels = cross_attention_probs.shape\n batch_size = batch_size // attn.heads\n cross_attention_probs = cross_attention_probs.reshape((batch_size, attn.heads, dims, channels)) # B, D, HW, T\n\n cross_attention_probs = cross_attention_probs.mean(dim=1) # B, HW, T\n cross_attention_probs = cross_attention_probs[..., self.token_idx].sum(dim=-1) # B, HW\n cross_attention_probs = cross_attention_probs.reshape((batch_size,) + shape) # , B, H, W\n\n gaussian_smoothing = GaussianSmoothing(channels=1, kernel_size=3, sigma=0.5, dim=2).to(\n self_attention_output_hidden_states.device\n )\n cross_attention_probs = gaussian_smoothing(cross_attention_probs[:, None])[:, 0] # optional smoothing B, H, W\n\n # Median normalization\n cross_attention_probs = cross_attention_probs.reshape(batch_size, -1) # B, HW\n cross_attention_probs = (\n cross_attention_probs - cross_attention_probs.median(dim=-1, keepdim=True).values\n ) / cross_attention_probs.max(dim=-1, keepdim=True).values\n cross_attention_probs = cross_attention_probs.clip(0, 1)\n\n c = (1 - m) * cross_attention_probs.reshape(batch_size, 1, -1) + m\n c = c.repeat_interleave(attn.heads, 0) # BD, HW\n if self.do_classifier_free_guidance:\n c = torch.cat([c, c]) # 2BD, HW\n\n # Rescaling the original self-attention matrix\n self_attention_scores_rescaled = self_attention_scores * c\n self_attention_probs_rescaled = self_attention_scores_rescaled.softmax(dim=-1)\n\n # Continuing the self attention normally using the new matrix\n hidden_states = torch.bmm(self_attention_probs_rescaled, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n if input_ndim == 4:\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n if attn.residual_connection:\n hidden_states = hidden_states + input_hidden_states\n\n hidden_states = hidden_states / attn.rescale_output_factor\n\n return hidden_states\n\n\nclass StableDiffusionHDPainterPipeline(StableDiffusionInpaintPipeline):\n def get_tokenized_prompt(self, prompt):\n out = self.tokenizer(prompt)\n return [self.tokenizer.decode(x) for x in out[\"input_ids\"]]\n\n def init_attn_processors(\n self,\n mask,\n token_idx,\n use_painta=True,\n use_rasg=True,\n painta_scale_factors=[2, 4], # 64x64 -> [16x16, 32x32]\n rasg_scale_factor=4, # 64x64 -> 16x16\n self_attention_layer_name=\"attn1\",\n cross_attention_layer_name=\"attn2\",\n list_of_painta_layer_names=None,\n list_of_rasg_layer_names=None,\n ):\n default_processor = AttnProcessor()\n width, height = mask.shape[-2:]\n width, height = width // self.vae_scale_factor, height // self.vae_scale_factor\n\n painta_scale_factors = [x * self.vae_scale_factor for x in painta_scale_factors]\n rasg_scale_factor = self.vae_scale_factor * rasg_scale_factor\n\n attn_processors = {}\n for x in self.unet.attn_processors:\n if (list_of_painta_layer_names is None and self_attention_layer_name in x) or (\n list_of_painta_layer_names is not None and x in list_of_painta_layer_names\n ):\n if use_painta:\n transformer_block = self.unet.get_submodule(x.replace(\".attn1.processor\", \"\"))\n attn_processors[x] = PAIntAAttnProcessor(\n transformer_block, mask, token_idx, self.do_classifier_free_guidance, painta_scale_factors\n )\n else:\n attn_processors[x] = default_processor\n elif (list_of_rasg_layer_names is None and cross_attention_layer_name in x) or (\n list_of_rasg_layer_names is not None and x in list_of_rasg_layer_names\n ):\n if use_rasg:\n attn_processors[x] = RASGAttnProcessor(mask, token_idx, rasg_scale_factor)\n else:\n attn_processors[x] = default_processor\n\n self.unet.set_attn_processor(attn_processors)\n # import json\n # with open('/home/hayk.manukyan/repos/diffusers/debug.txt', 'a') as f:\n # json.dump({x:str(y) for x,y in self.unet.attn_processors.items()}, f, indent=4)\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: PipelineImageInput = None,\n mask_image: PipelineImageInput = None,\n masked_image_latents: torch.Tensor = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n padding_mask_crop: Optional[int] = None,\n strength: float = 1.0,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n guidance_scale: float = 7.5,\n positive_prompt: Optional[str] = \"\",\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.01,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: int = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n use_painta=True,\n use_rasg=True,\n self_attention_layer_name=\".attn1\",\n cross_attention_layer_name=\".attn2\",\n painta_scale_factors=[2, 4], # 16 x 16 and 32 x 32\n rasg_scale_factor=4, # 16x16 by default\n list_of_painta_layer_names=None,\n list_of_rasg_layer_names=None,\n **kwargs,\n ):\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n #\n prompt_no_positives = prompt\n if isinstance(prompt, list):\n prompt = [x + positive_prompt for x in prompt]\n else:\n prompt = prompt + positive_prompt\n\n # 1. Check inputs\n self.check_inputs(\n prompt,\n image,\n mask_image,\n height,\n width,\n strength,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n callback_on_step_end_tensor_inputs,\n padding_mask_crop,\n )\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._interrupt = False\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # assert batch_size == 1, \"Does not work with batch size > 1 currently\"\n\n device = self._execution_device\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=self.clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n if ip_adapter_image is not None:\n output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True\n image_embeds, negative_image_embeds = self.encode_image(\n ip_adapter_image, device, num_images_per_prompt, output_hidden_state\n )\n if self.do_classifier_free_guidance:\n image_embeds = torch.cat([negative_image_embeds, image_embeds])\n\n # 4. set timesteps\n timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)\n timesteps, num_inference_steps = self.get_timesteps(\n num_inference_steps=num_inference_steps, strength=strength, device=device\n )\n # check that number of inference steps is not < 1 - as this doesn't make sense\n if num_inference_steps < 1:\n raise ValueError(\n f\"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline\"\n f\"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline.\"\n )\n # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise\n is_strength_max = strength == 1.0\n\n # 5. Preprocess mask and image\n\n if padding_mask_crop is not None:\n crops_coords = self.mask_processor.get_crop_region(mask_image, width, height, pad=padding_mask_crop)\n resize_mode = \"fill\"\n else:\n crops_coords = None\n resize_mode = \"default\"\n\n original_image = image\n init_image = self.image_processor.preprocess(\n image, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode\n )\n init_image = init_image.to(dtype=torch.float32)\n\n # 6. Prepare latent variables\n num_channels_latents = self.vae.config.latent_channels\n num_channels_unet = self.unet.config.in_channels\n return_image_latents = num_channels_unet == 4\n\n latents_outputs = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n image=init_image,\n timestep=latent_timestep,\n is_strength_max=is_strength_max,\n return_noise=True,\n return_image_latents=return_image_latents,\n )\n\n if return_image_latents:\n latents, noise, image_latents = latents_outputs\n else:\n latents, noise = latents_outputs\n\n # 7. Prepare mask latent variables\n mask_condition = self.mask_processor.preprocess(\n mask_image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords\n )\n\n if masked_image_latents is None:\n masked_image = init_image * (mask_condition < 0.5)\n else:\n masked_image = masked_image_latents\n\n mask, masked_image_latents = self.prepare_mask_latents(\n mask_condition,\n masked_image,\n batch_size * num_images_per_prompt,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n self.do_classifier_free_guidance,\n )\n\n # 7.5 Setting up HD-Painter\n\n # Get the indices of the tokens to be modified by both RASG and PAIntA\n token_idx = list(range(1, self.get_tokenized_prompt(prompt_no_positives).index(\"<|endoftext|>\"))) + [\n self.get_tokenized_prompt(prompt).index(\"<|endoftext|>\")\n ]\n\n # Setting up the attention processors\n self.init_attn_processors(\n mask_condition,\n token_idx,\n use_painta,\n use_rasg,\n painta_scale_factors=painta_scale_factors,\n rasg_scale_factor=rasg_scale_factor,\n self_attention_layer_name=self_attention_layer_name,\n cross_attention_layer_name=cross_attention_layer_name,\n list_of_painta_layer_names=list_of_painta_layer_names,\n list_of_rasg_layer_names=list_of_rasg_layer_names,\n )\n\n # 8. Check that sizes of mask, masked image and latents match\n if num_channels_unet == 9:\n # default case for runwayml/stable-diffusion-inpainting\n num_channels_mask = mask.shape[1]\n num_channels_masked_image = masked_image_latents.shape[1]\n if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:\n raise ValueError(\n f\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\"\n f\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\"\n f\" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}\"\n f\" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of\"\n \" `pipeline.unet` or your `mask_image` or `image` input.\"\n )\n elif num_channels_unet != 4:\n raise ValueError(\n f\"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}.\"\n )\n\n # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n if use_rasg:\n extra_step_kwargs[\"generator\"] = None\n\n # 9.1 Add image embeds for IP-Adapter\n added_cond_kwargs = {\"image_embeds\": image_embeds} if ip_adapter_image is not None else None\n\n # 9.2 Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n # 10. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n self._num_timesteps = len(timesteps)\n painta_active = True\n\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n if self.interrupt:\n continue\n\n if t < 500 and painta_active:\n self.init_attn_processors(\n mask_condition,\n token_idx,\n False,\n use_rasg,\n painta_scale_factors=painta_scale_factors,\n rasg_scale_factor=rasg_scale_factor,\n self_attention_layer_name=self_attention_layer_name,\n cross_attention_layer_name=cross_attention_layer_name,\n list_of_painta_layer_names=list_of_painta_layer_names,\n list_of_rasg_layer_names=list_of_rasg_layer_names,\n )\n painta_active = False\n\n with torch.enable_grad():\n self.unet.zero_grad()\n latents = latents.detach()\n latents.requires_grad = True\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n\n # concat latents, mask, masked_image_latents in the channel dimension\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n if num_channels_unet == 9:\n latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)\n\n self.scheduler.latents = latents\n self.encoder_hidden_states = prompt_embeds\n for attn_processor in self.unet.attn_processors.values():\n attn_processor.encoder_hidden_states = prompt_embeds\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=self.cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if use_rasg:\n # Perform RASG\n _, _, height, width = mask_condition.shape # 512 x 512\n scale_factor = self.vae_scale_factor * rasg_scale_factor # 8 * 4 = 32\n\n # TODO: Fix for > 1 batch_size\n rasg_mask = F.interpolate(\n mask_condition, (height // scale_factor, width // scale_factor), mode=\"bicubic\"\n )[0, 0] # mode is nearest by default, B, H, W\n\n # Aggregate the saved attention maps\n attn_map = []\n for processor in self.unet.attn_processors.values():\n if hasattr(processor, \"attention_scores\") and processor.attention_scores is not None:\n if self.do_classifier_free_guidance:\n attn_map.append(processor.attention_scores.chunk(2)[1]) # (B/2) x H, 256, 77\n else:\n attn_map.append(processor.attention_scores) # B x H, 256, 77 ?\n\n attn_map = (\n torch.cat(attn_map)\n .mean(0)\n .permute(1, 0)\n .reshape((-1, height // scale_factor, width // scale_factor))\n ) # 77, 16, 16\n\n # Compute the attention score\n attn_score = -sum(\n [\n F.binary_cross_entropy_with_logits(x - 1.0, rasg_mask.to(device))\n for x in attn_map[token_idx]\n ]\n )\n\n # Backward the score and compute the gradients\n attn_score.backward()\n\n # Normalzie the gradients and compute the noise component\n variance_noise = latents.grad.detach()\n # print(\"VARIANCE SHAPE\", variance_noise.shape)\n variance_noise -= torch.mean(variance_noise, [1, 2, 3], keepdim=True)\n variance_noise /= torch.std(variance_noise, [1, 2, 3], keepdim=True)\n else:\n variance_noise = None\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(\n noise_pred, t, latents, **extra_step_kwargs, return_dict=False, variance_noise=variance_noise\n )[0]\n\n if num_channels_unet == 4:\n init_latents_proper = image_latents\n if self.do_classifier_free_guidance:\n init_mask, _ = mask.chunk(2)\n else:\n init_mask = mask\n\n if i < len(timesteps) - 1:\n noise_timestep = timesteps[i + 1]\n init_latents_proper = self.scheduler.add_noise(\n init_latents_proper, noise, torch.tensor([noise_timestep])\n )\n\n latents = (1 - init_mask) * init_latents_proper + init_mask * latents\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n mask = callback_outputs.pop(\"mask\", mask)\n masked_image_latents = callback_outputs.pop(\"masked_image_latents\", masked_image_latents)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n condition_kwargs = {}\n if isinstance(self.vae, AsymmetricAutoencoderKL):\n init_image = init_image.to(device=device, dtype=masked_image_latents.dtype)\n init_image_condition = init_image.clone()\n init_image = self._encode_vae_image(init_image, generator=generator)\n mask_condition = mask_condition.to(device=device, dtype=masked_image_latents.dtype)\n condition_kwargs = {\"image\": init_image_condition, \"mask\": mask_condition}\n image = self.vae.decode(\n latents / self.vae.config.scaling_factor, return_dict=False, generator=generator, **condition_kwargs\n )[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n if padding_mask_crop is not None:\n image = [self.image_processor.apply_overlay(mask_image, original_image, i, crops_coords) for i in image]\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n\n\n# ============= Utility Functions ============== #\n\n\nclass GaussianSmoothing(nn.Module):\n \"\"\"\n Apply gaussian smoothing on a\n 1d, 2d or 3d tensor. Filtering is performed seperately for each channel\n in the input using a depthwise convolution.\n Arguments:\n channels (int, sequence): Number of channels of the input tensors. Output will\n have this number of channels as well.\n kernel_size (int, sequence): Size of the gaussian kernel.\n sigma (float, sequence): Standard deviation of the gaussian kernel.\n dim (int, optional): The number of dimensions of the data.\n Default value is 2 (spatial).\n \"\"\"\n\n def __init__(self, channels, kernel_size, sigma, dim=2):\n super(GaussianSmoothing, self).__init__()\n if isinstance(kernel_size, numbers.Number):\n kernel_size = [kernel_size] * dim\n if isinstance(sigma, numbers.Number):\n sigma = [sigma] * dim\n\n # The gaussian kernel is the product of the\n # gaussian function of each dimension.\n kernel = 1\n meshgrids = torch.meshgrid([torch.arange(size, dtype=torch.float32) for size in kernel_size])\n for size, std, mgrid in zip(kernel_size, sigma, meshgrids):\n mean = (size - 1) / 2\n kernel *= 1 / (std * math.sqrt(2 * math.pi)) * torch.exp(-(((mgrid - mean) / (2 * std)) ** 2))\n\n # Make sure sum of values in gaussian kernel equals 1.\n kernel = kernel / torch.sum(kernel)\n\n # Reshape to depthwise convolutional weight\n kernel = kernel.view(1, 1, *kernel.size())\n kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1))\n\n self.register_buffer(\"weight\", kernel)\n self.groups = channels\n\n if dim == 1:\n self.conv = F.conv1d\n elif dim == 2:\n self.conv = F.conv2d\n elif dim == 3:\n self.conv = F.conv3d\n else:\n raise RuntimeError(\"Only 1, 2 and 3 dimensions are supported. Received {}.\".format(dim))\n\n def forward(self, input):\n \"\"\"\n Apply gaussian filter to input.\n Arguments:\n input (torch.Tensor): Input to apply gaussian filter on.\n Returns:\n filtered (torch.Tensor): Filtered output.\n \"\"\"\n return self.conv(input, weight=self.weight.to(input.dtype), groups=self.groups, padding=\"same\")\n\n\ndef get_attention_scores(\n self, query: torch.Tensor, key: torch.Tensor, attention_mask: torch.Tensor = None\n) -> torch.Tensor:\n r\"\"\"\n Compute the attention scores.\n\n Args:\n query (`torch.Tensor`): The query tensor.\n key (`torch.Tensor`): The key tensor.\n attention_mask (`torch.Tensor`, *optional*): The attention mask to use. If `None`, no mask is applied.\n\n Returns:\n `torch.Tensor`: The attention probabilities/scores.\n \"\"\"\n if self.upcast_attention:\n query = query.float()\n key = key.float()\n\n if attention_mask is None:\n baddbmm_input = torch.empty(\n query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device\n )\n beta = 0\n else:\n baddbmm_input = attention_mask\n beta = 1\n\n attention_scores = torch.baddbmm(\n baddbmm_input,\n query,\n key.transpose(-1, -2),\n beta=beta,\n alpha=self.scale,\n )\n del baddbmm_input\n\n if self.upcast_softmax:\n attention_scores = attention_scores.float()\n\n return attention_scores\n" }, { "path": "diffusers/examples/community/iadb.py", "content": "from typing import List, Optional, Tuple, Union\n\nimport torch\n\nfrom diffusers import DiffusionPipeline\nfrom diffusers.configuration_utils import ConfigMixin\nfrom diffusers.pipelines.pipeline_utils import ImagePipelineOutput\nfrom diffusers.schedulers.scheduling_utils import SchedulerMixin\n\n\nclass IADBScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n IADBScheduler is a scheduler for the Iterative α-(de)Blending denoising method. It is simple and minimalist.\n\n For more details, see the original paper: https://arxiv.org/abs/2305.03486 and the blog post: https://ggx-research.github.io/publication/2023/05/10/publication-iadb.html\n \"\"\"\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: int,\n x_alpha: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"\n Predict the sample at the previous timestep by reversing the ODE. Core function to propagate the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`): direct output from learned diffusion model. It is the direction from x0 to x1.\n timestep (`float`): current timestep in the diffusion chain.\n x_alpha (`torch.Tensor`): x_alpha sample for the current timestep\n\n Returns:\n `torch.Tensor`: the sample at the previous timestep\n\n \"\"\"\n if self.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n alpha = timestep / self.num_inference_steps\n alpha_next = (timestep + 1) / self.num_inference_steps\n\n d = model_output\n\n x_alpha = x_alpha + (alpha_next - alpha) * d\n\n return x_alpha\n\n def set_timesteps(self, num_inference_steps: int):\n self.num_inference_steps = num_inference_steps\n\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n alpha: torch.Tensor,\n ) -> torch.Tensor:\n return original_samples * alpha + noise * (1 - alpha)\n\n def __len__(self):\n return self.config.num_train_timesteps\n\n\nclass IADBPipeline(DiffusionPipeline):\n r\"\"\"\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Parameters:\n unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of\n [`DDPMScheduler`], or [`DDIMScheduler`].\n \"\"\"\n\n def __init__(self, unet, scheduler):\n super().__init__()\n\n self.register_modules(unet=unet, scheduler=scheduler)\n\n @torch.no_grad()\n def __call__(\n self,\n batch_size: int = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n num_inference_steps: int = 50,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n ) -> Union[ImagePipelineOutput, Tuple]:\n r\"\"\"\n Args:\n batch_size (`int`, *optional*, defaults to 1):\n The number of images to generate.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if `return_dict` is\n True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images.\n \"\"\"\n\n # Sample gaussian noise to begin loop\n if isinstance(self.unet.config.sample_size, int):\n image_shape = (\n batch_size,\n self.unet.config.in_channels,\n self.unet.config.sample_size,\n self.unet.config.sample_size,\n )\n else:\n image_shape = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)\n\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n image = torch.randn(image_shape, generator=generator, device=self.device, dtype=self.unet.dtype)\n\n # set step values\n self.scheduler.set_timesteps(num_inference_steps)\n x_alpha = image.clone()\n for t in self.progress_bar(range(num_inference_steps)):\n alpha = t / num_inference_steps\n\n # 1. predict noise model_output\n model_output = self.unet(x_alpha, torch.tensor(alpha, device=x_alpha.device)).sample\n\n # 2. step\n x_alpha = self.scheduler.step(model_output, t, x_alpha)\n\n image = (x_alpha * 0.5 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).numpy()\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/examples/community/imagic_stable_diffusion.py", "content": "\"\"\"\nmodeled after the textual_inversion.py / train_dreambooth.py and the work\nof justinpinkney here: https://github.com/justinpinkney/stable-diffusion/blob/main/notebooks/imagic.ipynb\n\"\"\"\n\nimport inspect\nimport warnings\nfrom typing import List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom accelerate import Accelerator\n\n# TODO: remove and import from diffusers.utils when the new version of diffusers is released\nfrom packaging import version\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import DiffusionPipeline\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.pipelines.pipeline_utils import StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler\nfrom diffusers.utils import logging\n\n\nif version.parse(version.parse(PIL.__version__).base_version) >= version.parse(\"9.1.0\"):\n PIL_INTERPOLATION = {\n \"linear\": PIL.Image.Resampling.BILINEAR,\n \"bilinear\": PIL.Image.Resampling.BILINEAR,\n \"bicubic\": PIL.Image.Resampling.BICUBIC,\n \"lanczos\": PIL.Image.Resampling.LANCZOS,\n \"nearest\": PIL.Image.Resampling.NEAREST,\n }\nelse:\n PIL_INTERPOLATION = {\n \"linear\": PIL.Image.LINEAR,\n \"bilinear\": PIL.Image.BILINEAR,\n \"bicubic\": PIL.Image.BICUBIC,\n \"lanczos\": PIL.Image.LANCZOS,\n \"nearest\": PIL.Image.NEAREST,\n }\n# ------------------------------------------------------------------------------\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef preprocess(image):\n w, h = image.size\n w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32\n image = image.resize((w, h), resample=PIL_INTERPOLATION[\"lanczos\"])\n image = np.array(image).astype(np.float32) / 255.0\n image = image[None].transpose(0, 3, 1, 2)\n image = torch.from_numpy(image)\n return 2.0 * image - 1.0\n\n\nclass ImagicStableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin):\n r\"\"\"\n Pipeline for imagic image editing.\n See paper here: https://arxiv.org/pdf/2210.09276.pdf\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offsensive or harmful.\n Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n ):\n super().__init__()\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n\n def train(\n self,\n prompt: Union[str, List[str]],\n image: Union[torch.Tensor, PIL.Image.Image],\n height: Optional[int] = 512,\n width: Optional[int] = 512,\n generator: Optional[torch.Generator] = None,\n embedding_learning_rate: float = 0.001,\n diffusion_model_learning_rate: float = 2e-6,\n text_embedding_optimization_steps: int = 500,\n model_fine_tuning_optimization_steps: int = 1000,\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation\n deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n accelerator = Accelerator(\n gradient_accumulation_steps=1,\n mixed_precision=\"fp16\",\n )\n\n if \"torch_device\" in kwargs:\n device = kwargs.pop(\"torch_device\")\n warnings.warn(\n \"`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0.\"\n \" Consider using `pipe.to(torch_device)` instead.\"\n )\n\n if device is None:\n device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n self.to(device)\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n # Freeze vae and unet\n self.vae.requires_grad_(False)\n self.unet.requires_grad_(False)\n self.text_encoder.requires_grad_(False)\n self.unet.eval()\n self.vae.eval()\n self.text_encoder.eval()\n\n if accelerator.is_main_process:\n accelerator.init_trackers(\n \"imagic\",\n config={\n \"embedding_learning_rate\": embedding_learning_rate,\n \"text_embedding_optimization_steps\": text_embedding_optimization_steps,\n },\n )\n\n # get text embeddings for prompt\n text_input = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_embeddings = torch.nn.Parameter(\n self.text_encoder(text_input.input_ids.to(self.device))[0], requires_grad=True\n )\n text_embeddings = text_embeddings.detach()\n text_embeddings.requires_grad_()\n text_embeddings_orig = text_embeddings.clone()\n\n # Initialize the optimizer\n optimizer = torch.optim.Adam(\n [text_embeddings], # only optimize the embeddings\n lr=embedding_learning_rate,\n )\n\n if isinstance(image, PIL.Image.Image):\n image = preprocess(image)\n\n latents_dtype = text_embeddings.dtype\n image = image.to(device=self.device, dtype=latents_dtype)\n init_latent_image_dist = self.vae.encode(image).latent_dist\n image_latents = init_latent_image_dist.sample(generator=generator)\n image_latents = 0.18215 * image_latents\n\n progress_bar = tqdm(range(text_embedding_optimization_steps), disable=not accelerator.is_local_main_process)\n progress_bar.set_description(\"Steps\")\n\n global_step = 0\n\n logger.info(\"First optimizing the text embedding to better reconstruct the init image\")\n for _ in range(text_embedding_optimization_steps):\n with accelerator.accumulate(text_embeddings):\n # Sample noise that we'll add to the latents\n noise = torch.randn(image_latents.shape).to(image_latents.device)\n timesteps = torch.randint(1000, (1,), device=image_latents.device)\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = self.scheduler.add_noise(image_latents, noise, timesteps)\n\n # Predict the noise residual\n noise_pred = self.unet(noisy_latents, timesteps, text_embeddings).sample\n\n loss = F.mse_loss(noise_pred, noise, reduction=\"none\").mean([1, 2, 3]).mean()\n accelerator.backward(loss)\n\n optimizer.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n logs = {\"loss\": loss.detach().item()} # , \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n accelerator.wait_for_everyone()\n\n text_embeddings.requires_grad_(False)\n\n # Now we fine tune the unet to better reconstruct the image\n self.unet.requires_grad_(True)\n self.unet.train()\n optimizer = torch.optim.Adam(\n self.unet.parameters(), # only optimize unet\n lr=diffusion_model_learning_rate,\n )\n progress_bar = tqdm(range(model_fine_tuning_optimization_steps), disable=not accelerator.is_local_main_process)\n\n logger.info(\"Next fine tuning the entire model to better reconstruct the init image\")\n for _ in range(model_fine_tuning_optimization_steps):\n with accelerator.accumulate(self.unet.parameters()):\n # Sample noise that we'll add to the latents\n noise = torch.randn(image_latents.shape).to(image_latents.device)\n timesteps = torch.randint(1000, (1,), device=image_latents.device)\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = self.scheduler.add_noise(image_latents, noise, timesteps)\n\n # Predict the noise residual\n noise_pred = self.unet(noisy_latents, timesteps, text_embeddings).sample\n\n loss = F.mse_loss(noise_pred, noise, reduction=\"none\").mean([1, 2, 3]).mean()\n accelerator.backward(loss)\n\n optimizer.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n logs = {\"loss\": loss.detach().item()} # , \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n accelerator.wait_for_everyone()\n self.text_embeddings_orig = text_embeddings_orig\n self.text_embeddings = text_embeddings\n\n @torch.no_grad()\n def __call__(\n self,\n alpha: float = 1.2,\n height: Optional[int] = 512,\n width: Optional[int] = 512,\n num_inference_steps: Optional[int] = 50,\n generator: Optional[torch.Generator] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n guidance_scale: float = 7.5,\n eta: float = 0.0,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n Args:\n alpha (`float`, *optional*, defaults to 1.2):\n The interpolation factor between the original and optimized text embeddings. A value closer to 0\n will resemble the original input image.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n generator (`torch.Generator`, *optional*):\n A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation\n deterministic.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n if self.text_embeddings is None:\n raise ValueError(\"Please run the pipe.train() before trying to generate an image.\")\n if self.text_embeddings_orig is None:\n raise ValueError(\"Please run the pipe.train() before trying to generate an image.\")\n\n text_embeddings = alpha * self.text_embeddings_orig + (1 - alpha) * self.text_embeddings\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n uncond_tokens = [\"\"]\n max_length = self.tokenizer.model_max_length\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = uncond_embeddings.shape[1]\n uncond_embeddings = uncond_embeddings.view(1, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n\n # get the initial random noise unless the user supplied it\n\n # Unlike in other pipelines, latents need to be generated in the target device\n # for 1-to-1 results reproducibility with the CompVis implementation.\n # However this currently doesn't work in `mps`.\n latents_shape = (1, self.unet.config.in_channels, height // 8, width // 8)\n latents_dtype = text_embeddings.dtype\n if self.device.type == \"mps\":\n # randn does not exist on mps\n latents = torch.randn(latents_shape, generator=generator, device=\"cpu\", dtype=latents_dtype).to(\n self.device\n )\n else:\n latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)\n\n # set timesteps\n self.scheduler.set_timesteps(num_inference_steps)\n\n # Some schedulers like PNDM have timesteps as arrays\n # It's more optimized to move all timesteps to correct device beforehand\n timesteps_tensor = self.scheduler.timesteps.to(self.device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n for i, t in enumerate(self.progress_bar(timesteps_tensor)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n latents = 1 / 0.18215 * latents\n image = self.vae.decode(latents).sample\n\n image = (image / 2 + 0.5).clamp(0, 1)\n\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors=\"pt\").to(\n self.device\n )\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)\n )\n else:\n has_nsfw_concept = None\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/examples/community/img2img_inpainting.py", "content": "import inspect\nfrom typing import Callable, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import DiffusionPipeline\nfrom diffusers.configuration_utils import FrozenDict\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler\nfrom diffusers.utils import deprecate, logging\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef prepare_mask_and_masked_image(image, mask):\n image = np.array(image.convert(\"RGB\"))\n image = image[None].transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0\n\n mask = np.array(mask.convert(\"L\"))\n mask = mask.astype(np.float32) / 255.0\n mask = mask[None, None]\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n mask = torch.from_numpy(mask)\n\n masked_image = image * (mask < 0.5)\n\n return mask, masked_image\n\n\ndef check_size(image, height, width):\n if isinstance(image, PIL.Image.Image):\n w, h = image.size\n elif isinstance(image, torch.Tensor):\n *_, h, w = image.shape\n\n if h != height or w != width:\n raise ValueError(f\"Image size should be {height}x{width}, but got {h}x{w}\")\n\n\ndef overlay_inner_image(image, inner_image, paste_offset: Tuple[int] = (0, 0)):\n inner_image = inner_image.convert(\"RGBA\")\n image = image.convert(\"RGB\")\n\n image.paste(inner_image, paste_offset, inner_image)\n image = image.convert(\"RGB\")\n\n return image\n\n\nclass ImageToImageInpaintingPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for text-guided image-to-image inpainting using Stable Diffusion. *This is an experimental feature*.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n image: Union[torch.Tensor, PIL.Image.Image],\n inner_image: Union[torch.Tensor, PIL.Image.Image],\n mask_image: Union[torch.Tensor, PIL.Image.Image],\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n image (`torch.Tensor` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will\n be masked out with `mask_image` and repainted according to `prompt`.\n inner_image (`torch.Tensor` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch which will be overlayed onto `image`. Non-transparent\n regions of `inner_image` must fit inside white pixels in `mask_image`. Expects four channels, with\n the last channel representing the alpha channel, which will be used to blend `inner_image` with\n `image`. If not provided, it will be forcibly cast to RGBA.\n mask_image (`PIL.Image.Image`):\n `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be\n repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted\n to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)\n instead of 3, so the expected shape would be `(B, H, W, 1)`.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation\n deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n\n if isinstance(prompt, str):\n batch_size = 1\n elif isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n # check if input sizes are correct\n check_size(image, height, width)\n check_size(inner_image, height, width)\n check_size(mask_image, height, width)\n\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n\n if text_input_ids.shape[-1] > self.tokenizer.model_max_length:\n removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]\n text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]\n\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = text_embeddings.shape\n text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)\n text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"]\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = text_input_ids.shape[-1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = uncond_embeddings.shape[1]\n uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)\n uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n\n # get the initial random noise unless the user supplied it\n # Unlike in other pipelines, latents need to be generated in the target device\n # for 1-to-1 results reproducibility with the CompVis implementation.\n # However this currently doesn't work in `mps`.\n num_channels_latents = self.vae.config.latent_channels\n latents_shape = (batch_size * num_images_per_prompt, num_channels_latents, height // 8, width // 8)\n latents_dtype = text_embeddings.dtype\n if latents is None:\n if self.device.type == \"mps\":\n # randn does not exist on mps\n latents = torch.randn(latents_shape, generator=generator, device=\"cpu\", dtype=latents_dtype).to(\n self.device\n )\n else:\n latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)\n else:\n if latents.shape != latents_shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {latents_shape}\")\n latents = latents.to(self.device)\n\n # overlay the inner image\n image = overlay_inner_image(image, inner_image)\n\n # prepare mask and masked_image\n mask, masked_image = prepare_mask_and_masked_image(image, mask_image)\n mask = mask.to(device=self.device, dtype=text_embeddings.dtype)\n masked_image = masked_image.to(device=self.device, dtype=text_embeddings.dtype)\n\n # resize the mask to latents shape as we concatenate the mask to the latents\n mask = torch.nn.functional.interpolate(mask, size=(height // 8, width // 8))\n\n # encode the mask image into latents space so we can concatenate it to the latents\n masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)\n masked_image_latents = 0.18215 * masked_image_latents\n\n # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method\n mask = mask.repeat(batch_size * num_images_per_prompt, 1, 1, 1)\n masked_image_latents = masked_image_latents.repeat(batch_size * num_images_per_prompt, 1, 1, 1)\n\n mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask\n masked_image_latents = (\n torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents\n )\n\n num_channels_mask = mask.shape[1]\n num_channels_masked_image = masked_image_latents.shape[1]\n\n if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:\n raise ValueError(\n f\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\"\n f\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\"\n f\" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}\"\n f\" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of\"\n \" `pipeline.unet` or your `mask_image` or `image` input.\"\n )\n\n # set timesteps\n self.scheduler.set_timesteps(num_inference_steps)\n\n # Some schedulers like PNDM have timesteps as arrays\n # It's more optimized to move all timesteps to correct device beforehand\n timesteps_tensor = self.scheduler.timesteps.to(self.device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n for i, t in enumerate(self.progress_bar(timesteps_tensor)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n\n # concat latents, mask, masked_image_latents in the channel dimension\n latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)\n\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n latents = 1 / 0.18215 * latents\n image = self.vae.decode(latents).sample\n\n image = (image / 2 + 0.5).clamp(0, 1)\n\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors=\"pt\").to(\n self.device\n )\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)\n )\n else:\n has_nsfw_concept = None\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/examples/community/instaflow_one_step.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n# http://www.apache.org/licenses/LICENSE-2.0\r\n#\r\n# Unless required by applicable law or agreed to in writing, software\r\n# distributed under the License is distributed on an \"AS IS\" BASIS,\r\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n# See the License for the specific language governing permissions and\r\n# limitations under the License.\r\n\r\nimport inspect\r\nfrom typing import Any, Callable, Dict, List, Optional, Union\r\n\r\nimport torch\r\nfrom packaging import version\r\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\r\n\r\nfrom diffusers.configuration_utils import FrozenDict\r\nfrom diffusers.image_processor import VaeImageProcessor\r\nfrom diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin\r\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\r\nfrom diffusers.models.lora import adjust_lora_scale_text_encoder\r\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin\r\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\r\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\r\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\r\nfrom diffusers.utils import (\r\n deprecate,\r\n logging,\r\n)\r\nfrom diffusers.utils.torch_utils import randn_tensor\r\n\r\n\r\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\r\n\r\n\r\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\r\n \"\"\"\r\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\r\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\r\n \"\"\"\r\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\r\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\r\n # rescale the results from guidance (fixes overexposure)\r\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\r\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\r\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\r\n return noise_cfg\r\n\r\n\r\nclass InstaFlowPipeline(\r\n DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin\r\n):\r\n r\"\"\"\r\n Pipeline for text-to-image generation using Rectified Flow and Euler discretization.\r\n This customized pipeline is based on StableDiffusionPipeline from the official Diffusers library (0.21.4)\r\n\r\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\r\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\r\n\r\n The pipeline also inherits the following loading methods:\r\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\r\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\r\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\r\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\r\n\r\n Args:\r\n vae ([`AutoencoderKL`]):\r\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\r\n text_encoder ([`~transformers.CLIPTextModel`]):\r\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\r\n tokenizer ([`~transformers.CLIPTokenizer`]):\r\n A `CLIPTokenizer` to tokenize text.\r\n unet ([`UNet2DConditionModel`]):\r\n A `UNet2DConditionModel` to denoise the encoded image latents.\r\n scheduler ([`SchedulerMixin`]):\r\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\r\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\r\n safety_checker ([`StableDiffusionSafetyChecker`]):\r\n Classification module that estimates whether generated images could be considered offensive or harmful.\r\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\r\n about a model's potential harms.\r\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\r\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\r\n \"\"\"\r\n\r\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\r\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\r\n _exclude_from_cpu_offload = [\"safety_checker\"]\r\n\r\n def __init__(\r\n self,\r\n vae: AutoencoderKL,\r\n text_encoder: CLIPTextModel,\r\n tokenizer: CLIPTokenizer,\r\n unet: UNet2DConditionModel,\r\n scheduler: KarrasDiffusionSchedulers,\r\n safety_checker: StableDiffusionSafetyChecker,\r\n feature_extractor: CLIPImageProcessor,\r\n requires_safety_checker: bool = True,\r\n ):\r\n super().__init__()\r\n\r\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\r\n deprecation_message = (\r\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\r\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\r\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\r\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\r\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\r\n \" file\"\r\n )\r\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\r\n new_config = dict(scheduler.config)\r\n new_config[\"steps_offset\"] = 1\r\n scheduler._internal_dict = FrozenDict(new_config)\r\n\r\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\r\n deprecation_message = (\r\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\r\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\r\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\r\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\r\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\r\n )\r\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\r\n new_config = dict(scheduler.config)\r\n new_config[\"clip_sample\"] = False\r\n scheduler._internal_dict = FrozenDict(new_config)\r\n\r\n if safety_checker is None and requires_safety_checker:\r\n logger.warning(\r\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\r\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\r\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\r\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\r\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\r\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\r\n )\r\n\r\n if safety_checker is not None and feature_extractor is None:\r\n raise ValueError(\r\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\r\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\r\n )\r\n\r\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\r\n version.parse(unet.config._diffusers_version).base_version\r\n ) < version.parse(\"0.9.0.dev0\")\r\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\r\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\r\n deprecation_message = (\r\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\r\n \" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the\"\r\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\r\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\r\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\r\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\r\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\r\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\r\n \" the `unet/config.json` file\"\r\n )\r\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\r\n new_config = dict(unet.config)\r\n new_config[\"sample_size\"] = 64\r\n unet._internal_dict = FrozenDict(new_config)\r\n\r\n self.register_modules(\r\n vae=vae,\r\n text_encoder=text_encoder,\r\n tokenizer=tokenizer,\r\n unet=unet,\r\n scheduler=scheduler,\r\n safety_checker=safety_checker,\r\n feature_extractor=feature_extractor,\r\n )\r\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\r\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\r\n self.register_to_config(requires_safety_checker=requires_safety_checker)\r\n\r\n def _encode_prompt(\r\n self,\r\n prompt,\r\n device,\r\n num_images_per_prompt,\r\n do_classifier_free_guidance,\r\n negative_prompt=None,\r\n prompt_embeds: Optional[torch.Tensor] = None,\r\n negative_prompt_embeds: Optional[torch.Tensor] = None,\r\n lora_scale: Optional[float] = None,\r\n ):\r\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\r\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\r\n\r\n prompt_embeds_tuple = self.encode_prompt(\r\n prompt=prompt,\r\n device=device,\r\n num_images_per_prompt=num_images_per_prompt,\r\n do_classifier_free_guidance=do_classifier_free_guidance,\r\n negative_prompt=negative_prompt,\r\n prompt_embeds=prompt_embeds,\r\n negative_prompt_embeds=negative_prompt_embeds,\r\n lora_scale=lora_scale,\r\n )\r\n\r\n # concatenate for backwards comp\r\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\r\n\r\n return prompt_embeds\r\n\r\n def encode_prompt(\r\n self,\r\n prompt,\r\n device,\r\n num_images_per_prompt,\r\n do_classifier_free_guidance,\r\n negative_prompt=None,\r\n prompt_embeds: Optional[torch.Tensor] = None,\r\n negative_prompt_embeds: Optional[torch.Tensor] = None,\r\n lora_scale: Optional[float] = None,\r\n ):\r\n r\"\"\"\r\n Encodes the prompt into text encoder hidden states.\r\n\r\n Args:\r\n prompt (`str` or `List[str]`, *optional*):\r\n prompt to be encoded\r\n device: (`torch.device`):\r\n torch device\r\n num_images_per_prompt (`int`):\r\n number of images that should be generated per prompt\r\n do_classifier_free_guidance (`bool`):\r\n whether to use classifier free guidance or not\r\n negative_prompt (`str` or `List[str]`, *optional*):\r\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\r\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\r\n less than `1`).\r\n prompt_embeds (`torch.Tensor`, *optional*):\r\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\r\n provided, text embeddings will be generated from `prompt` input argument.\r\n negative_prompt_embeds (`torch.Tensor`, *optional*):\r\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\r\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\r\n argument.\r\n lora_scale (`float`, *optional*):\r\n A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\r\n \"\"\"\r\n # set lora scale so that monkey patched LoRA\r\n # function of text encoder can correctly access it\r\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\r\n self._lora_scale = lora_scale\r\n\r\n # dynamically adjust the LoRA scale\r\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\r\n\r\n if prompt is not None and isinstance(prompt, str):\r\n batch_size = 1\r\n elif prompt is not None and isinstance(prompt, list):\r\n batch_size = len(prompt)\r\n else:\r\n batch_size = prompt_embeds.shape[0]\r\n\r\n if prompt_embeds is None:\r\n # textual inversion: procecss multi-vector tokens if necessary\r\n if isinstance(self, TextualInversionLoaderMixin):\r\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\r\n\r\n text_inputs = self.tokenizer(\r\n prompt,\r\n padding=\"max_length\",\r\n max_length=self.tokenizer.model_max_length,\r\n truncation=True,\r\n return_tensors=\"pt\",\r\n )\r\n text_input_ids = text_inputs.input_ids\r\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\r\n\r\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\r\n text_input_ids, untruncated_ids\r\n ):\r\n removed_text = self.tokenizer.batch_decode(\r\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\r\n )\r\n logger.warning(\r\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\r\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\r\n )\r\n\r\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\r\n attention_mask = text_inputs.attention_mask.to(device)\r\n else:\r\n attention_mask = None\r\n\r\n prompt_embeds = self.text_encoder(\r\n text_input_ids.to(device),\r\n attention_mask=attention_mask,\r\n )\r\n prompt_embeds = prompt_embeds[0]\r\n\r\n if self.text_encoder is not None:\r\n prompt_embeds_dtype = self.text_encoder.dtype\r\n elif self.unet is not None:\r\n prompt_embeds_dtype = self.unet.dtype\r\n else:\r\n prompt_embeds_dtype = prompt_embeds.dtype\r\n\r\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\r\n\r\n bs_embed, seq_len, _ = prompt_embeds.shape\r\n # duplicate text embeddings for each generation per prompt, using mps friendly method\r\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\r\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\r\n\r\n # get unconditional embeddings for classifier free guidance\r\n if do_classifier_free_guidance and negative_prompt_embeds is None:\r\n uncond_tokens: List[str]\r\n if negative_prompt is None:\r\n uncond_tokens = [\"\"] * batch_size\r\n elif prompt is not None and type(prompt) is not type(negative_prompt):\r\n raise TypeError(\r\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\r\n f\" {type(prompt)}.\"\r\n )\r\n elif isinstance(negative_prompt, str):\r\n uncond_tokens = [negative_prompt]\r\n elif batch_size != len(negative_prompt):\r\n raise ValueError(\r\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\r\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\r\n \" the batch size of `prompt`.\"\r\n )\r\n else:\r\n uncond_tokens = negative_prompt\r\n\r\n # textual inversion: procecss multi-vector tokens if necessary\r\n if isinstance(self, TextualInversionLoaderMixin):\r\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\r\n\r\n max_length = prompt_embeds.shape[1]\r\n uncond_input = self.tokenizer(\r\n uncond_tokens,\r\n padding=\"max_length\",\r\n max_length=max_length,\r\n truncation=True,\r\n return_tensors=\"pt\",\r\n )\r\n\r\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\r\n attention_mask = uncond_input.attention_mask.to(device)\r\n else:\r\n attention_mask = None\r\n\r\n negative_prompt_embeds = self.text_encoder(\r\n uncond_input.input_ids.to(device),\r\n attention_mask=attention_mask,\r\n )\r\n negative_prompt_embeds = negative_prompt_embeds[0]\r\n\r\n if do_classifier_free_guidance:\r\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\r\n seq_len = negative_prompt_embeds.shape[1]\r\n\r\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\r\n\r\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\r\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\r\n\r\n return prompt_embeds, negative_prompt_embeds\r\n\r\n def run_safety_checker(self, image, device, dtype):\r\n if self.safety_checker is None:\r\n has_nsfw_concept = None\r\n else:\r\n if torch.is_tensor(image):\r\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\r\n else:\r\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\r\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\r\n image, has_nsfw_concept = self.safety_checker(\r\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\r\n )\r\n return image, has_nsfw_concept\r\n\r\n def decode_latents(self, latents):\r\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\r\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\r\n\r\n latents = 1 / self.vae.config.scaling_factor * latents\r\n image = self.vae.decode(latents, return_dict=False)[0]\r\n image = (image / 2 + 0.5).clamp(0, 1)\r\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\r\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\r\n return image\r\n\r\n def merge_dW_to_unet(pipe, dW_dict, alpha=1.0):\r\n _tmp_sd = pipe.unet.state_dict()\r\n for key in dW_dict.keys():\r\n _tmp_sd[key] += dW_dict[key] * alpha\r\n pipe.unet.load_state_dict(_tmp_sd, strict=False)\r\n return pipe\r\n\r\n def prepare_extra_step_kwargs(self, generator, eta):\r\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\r\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\r\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\r\n # and should be between [0, 1]\r\n\r\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\r\n extra_step_kwargs = {}\r\n if accepts_eta:\r\n extra_step_kwargs[\"eta\"] = eta\r\n\r\n # check if the scheduler accepts generator\r\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\r\n if accepts_generator:\r\n extra_step_kwargs[\"generator\"] = generator\r\n return extra_step_kwargs\r\n\r\n def check_inputs(\r\n self,\r\n prompt,\r\n height,\r\n width,\r\n callback_steps,\r\n negative_prompt=None,\r\n prompt_embeds=None,\r\n negative_prompt_embeds=None,\r\n ):\r\n if height % 8 != 0 or width % 8 != 0:\r\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\r\n\r\n if (callback_steps is None) or (\r\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\r\n ):\r\n raise ValueError(\r\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\r\n f\" {type(callback_steps)}.\"\r\n )\r\n\r\n if prompt is not None and prompt_embeds is not None:\r\n raise ValueError(\r\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\r\n \" only forward one of the two.\"\r\n )\r\n elif prompt is None and prompt_embeds is None:\r\n raise ValueError(\r\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\r\n )\r\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\r\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\r\n\r\n if negative_prompt is not None and negative_prompt_embeds is not None:\r\n raise ValueError(\r\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\r\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\r\n )\r\n\r\n if prompt_embeds is not None and negative_prompt_embeds is not None:\r\n if prompt_embeds.shape != negative_prompt_embeds.shape:\r\n raise ValueError(\r\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\r\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\r\n f\" {negative_prompt_embeds.shape}.\"\r\n )\r\n\r\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\r\n shape = (\r\n batch_size,\r\n num_channels_latents,\r\n int(height) // self.vae_scale_factor,\r\n int(width) // self.vae_scale_factor,\r\n )\r\n if isinstance(generator, list) and len(generator) != batch_size:\r\n raise ValueError(\r\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\r\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\r\n )\r\n\r\n if latents is None:\r\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\r\n else:\r\n latents = latents.to(device)\r\n\r\n # scale the initial noise by the standard deviation required by the scheduler\r\n latents = latents * self.scheduler.init_noise_sigma\r\n return latents\r\n\r\n @torch.no_grad()\r\n def __call__(\r\n self,\r\n prompt: Union[str, List[str]] = None,\r\n height: Optional[int] = None,\r\n width: Optional[int] = None,\r\n num_inference_steps: int = 50,\r\n guidance_scale: float = 7.5,\r\n negative_prompt: Optional[Union[str, List[str]]] = None,\r\n num_images_per_prompt: Optional[int] = 1,\r\n eta: float = 0.0,\r\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\r\n latents: Optional[torch.Tensor] = None,\r\n prompt_embeds: Optional[torch.Tensor] = None,\r\n negative_prompt_embeds: Optional[torch.Tensor] = None,\r\n output_type: Optional[str] = \"pil\",\r\n return_dict: bool = True,\r\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\r\n callback_steps: int = 1,\r\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\r\n guidance_rescale: float = 0.0,\r\n ):\r\n r\"\"\"\r\n The call function to the pipeline for generation.\r\n\r\n Args:\r\n prompt (`str` or `List[str]`, *optional*):\r\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\r\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\r\n The height in pixels of the generated image.\r\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\r\n The width in pixels of the generated image.\r\n num_inference_steps (`int`, *optional*, defaults to 50):\r\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\r\n expense of slower inference.\r\n guidance_scale (`float`, *optional*, defaults to 7.5):\r\n A higher guidance scale value encourages the model to generate images closely linked to the text\r\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\r\n negative_prompt (`str` or `List[str]`, *optional*):\r\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\r\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\r\n num_images_per_prompt (`int`, *optional*, defaults to 1):\r\n The number of images to generate per prompt.\r\n eta (`float`, *optional*, defaults to 0.0):\r\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\r\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\r\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\r\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\r\n generation deterministic.\r\n latents (`torch.Tensor`, *optional*):\r\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\r\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\r\n tensor is generated by sampling using the supplied random `generator`.\r\n prompt_embeds (`torch.Tensor`, *optional*):\r\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\r\n provided, text embeddings are generated from the `prompt` input argument.\r\n negative_prompt_embeds (`torch.Tensor`, *optional*):\r\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\r\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\r\n output_type (`str`, *optional*, defaults to `\"pil\"`):\r\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\r\n return_dict (`bool`, *optional*, defaults to `True`):\r\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\r\n plain tuple.\r\n callback (`Callable`, *optional*):\r\n A function that calls every `callback_steps` steps during inference. The function is called with the\r\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\r\n callback_steps (`int`, *optional*, defaults to 1):\r\n The frequency at which the `callback` function is called. If not specified, the callback is called at\r\n every step.\r\n cross_attention_kwargs (`dict`, *optional*):\r\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\r\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\r\n guidance_rescale (`float`, *optional*, defaults to 0.7):\r\n Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are\r\n Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when\r\n using zero terminal SNR.\r\n\r\n Examples:\r\n\r\n Returns:\r\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\r\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\r\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\r\n second element is a list of `bool`s indicating whether the corresponding generated image contains\r\n \"not-safe-for-work\" (nsfw) content.\r\n \"\"\"\r\n # 0. Default height and width to unet\r\n height = height or self.unet.config.sample_size * self.vae_scale_factor\r\n width = width or self.unet.config.sample_size * self.vae_scale_factor\r\n\r\n # 1. Check inputs. Raise error if not correct\r\n self.check_inputs(\r\n prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds\r\n )\r\n\r\n # 2. Define call parameters\r\n if prompt is not None and isinstance(prompt, str):\r\n batch_size = 1\r\n elif prompt is not None and isinstance(prompt, list):\r\n batch_size = len(prompt)\r\n else:\r\n batch_size = prompt_embeds.shape[0]\r\n\r\n device = self._execution_device\r\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\r\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\r\n # corresponds to doing no classifier free guidance.\r\n do_classifier_free_guidance = guidance_scale > 1.0\r\n\r\n # 3. Encode input prompt\r\n text_encoder_lora_scale = (\r\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\r\n )\r\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\r\n prompt,\r\n device,\r\n num_images_per_prompt,\r\n do_classifier_free_guidance,\r\n negative_prompt,\r\n prompt_embeds=prompt_embeds,\r\n negative_prompt_embeds=negative_prompt_embeds,\r\n lora_scale=text_encoder_lora_scale,\r\n )\r\n # For classifier free guidance, we need to do two forward passes.\r\n # Here we concatenate the unconditional and text embeddings into a single batch\r\n # to avoid doing two forward passes\r\n if do_classifier_free_guidance:\r\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\r\n\r\n # 4. Prepare timesteps\r\n timesteps = [(1.0 - i / num_inference_steps) * 1000.0 for i in range(num_inference_steps)]\r\n\r\n # 5. Prepare latent variables\r\n num_channels_latents = self.unet.config.in_channels\r\n latents = self.prepare_latents(\r\n batch_size * num_images_per_prompt,\r\n num_channels_latents,\r\n height,\r\n width,\r\n prompt_embeds.dtype,\r\n device,\r\n generator,\r\n latents,\r\n )\r\n\r\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\r\n dt = 1.0 / num_inference_steps\r\n\r\n # 7. Denoising loop of Euler discretization from t = 0 to t = 1\r\n with self.progress_bar(total=num_inference_steps) as progress_bar:\r\n for i, t in enumerate(timesteps):\r\n # expand the latents if we are doing classifier free guidance\r\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\r\n\r\n vec_t = torch.ones((latent_model_input.shape[0],), device=latents.device) * t\r\n\r\n v_pred = self.unet(latent_model_input, vec_t, encoder_hidden_states=prompt_embeds).sample\r\n\r\n # perform guidance\r\n if do_classifier_free_guidance:\r\n v_pred_neg, v_pred_text = v_pred.chunk(2)\r\n v_pred = v_pred_neg + guidance_scale * (v_pred_text - v_pred_neg)\r\n\r\n latents = latents + dt * v_pred\r\n\r\n # call the callback, if provided\r\n if i == len(timesteps) - 1 or ((i + 1) % self.scheduler.order == 0):\r\n progress_bar.update()\r\n if callback is not None and i % callback_steps == 0:\r\n step_idx = i // getattr(self.scheduler, \"order\", 1)\r\n callback(step_idx, t, latents)\r\n\r\n if not output_type == \"latent\":\r\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\r\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\r\n else:\r\n image = latents\r\n has_nsfw_concept = None\r\n\r\n if has_nsfw_concept is None:\r\n do_denormalize = [True] * image.shape[0]\r\n else:\r\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\r\n\r\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\r\n\r\n # Offload all models\r\n self.maybe_free_model_hooks()\r\n\r\n if not return_dict:\r\n return (image, has_nsfw_concept)\r\n\r\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\r\n" }, { "path": "diffusers/examples/community/interpolate_stable_diffusion.py", "content": "import inspect\nimport time\nfrom pathlib import Path\nfrom typing import Callable, List, Optional, Union\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers.configuration_utils import FrozenDict\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler\nfrom diffusers.utils import deprecate, logging\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef slerp(t, v0, v1, DOT_THRESHOLD=0.9995):\n \"\"\"helper function to spherically interpolate two arrays v1 v2\"\"\"\n\n if not isinstance(v0, np.ndarray):\n inputs_are_torch = True\n input_device = v0.device\n v0 = v0.cpu().numpy()\n v1 = v1.cpu().numpy()\n\n dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))\n if np.abs(dot) > DOT_THRESHOLD:\n v2 = (1 - t) * v0 + t * v1\n else:\n theta_0 = np.arccos(dot)\n sin_theta_0 = np.sin(theta_0)\n theta_t = theta_0 * t\n sin_theta_t = np.sin(theta_t)\n s0 = np.sin(theta_0 - theta_t) / sin_theta_0\n s1 = sin_theta_t / sin_theta_0\n v2 = s0 * v0 + s1 * v1\n\n if inputs_are_torch:\n v2 = torch.from_numpy(v2).to(input_device)\n\n return v2\n\n\nclass StableDiffusionWalkPipeline(DiffusionPipeline, StableDiffusionMixin):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Optional[Union[str, List[str]]] = None,\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n text_embeddings: Optional[torch.Tensor] = None,\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*, defaults to `None`):\n The prompt or prompts to guide the image generation. If not provided, `text_embeddings` is required.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation\n deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n text_embeddings (`torch.Tensor`, *optional*, defaults to `None`):\n Pre-generated text embeddings to be used as inputs for image generation. Can be used in place of\n `prompt` to avoid re-computing the embeddings. If not provided, the embeddings will be generated from\n the supplied `prompt`.\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if text_embeddings is None:\n if isinstance(prompt, str):\n batch_size = 1\n elif isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n\n if text_input_ids.shape[-1] > self.tokenizer.model_max_length:\n removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])\n print(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]\n text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]\n else:\n batch_size = text_embeddings.shape[0]\n\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = text_embeddings.shape\n text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)\n text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = self.tokenizer.model_max_length\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = uncond_embeddings.shape[1]\n uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)\n uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n\n # get the initial random noise unless the user supplied it\n\n # Unlike in other pipelines, latents need to be generated in the target device\n # for 1-to-1 results reproducibility with the CompVis implementation.\n # However this currently doesn't work in `mps`.\n latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)\n latents_dtype = text_embeddings.dtype\n if latents is None:\n if self.device.type == \"mps\":\n # randn does not work reproducibly on mps\n latents = torch.randn(latents_shape, generator=generator, device=\"cpu\", dtype=latents_dtype).to(\n self.device\n )\n else:\n latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)\n else:\n if latents.shape != latents_shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {latents_shape}\")\n latents = latents.to(self.device)\n\n # set timesteps\n self.scheduler.set_timesteps(num_inference_steps)\n\n # Some schedulers like PNDM have timesteps as arrays\n # It's more optimized to move all timesteps to correct device beforehand\n timesteps_tensor = self.scheduler.timesteps.to(self.device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n for i, t in enumerate(self.progress_bar(timesteps_tensor)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n latents = 1 / 0.18215 * latents\n image = self.vae.decode(latents).sample\n\n image = (image / 2 + 0.5).clamp(0, 1)\n\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors=\"pt\").to(\n self.device\n )\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)\n )\n else:\n has_nsfw_concept = None\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n\n def embed_text(self, text):\n \"\"\"takes in text and turns it into text embeddings\"\"\"\n text_input = self.tokenizer(\n text,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n with torch.no_grad():\n embed = self.text_encoder(text_input.input_ids.to(self.device))[0]\n return embed\n\n def get_noise(self, seed, dtype=torch.float32, height=512, width=512):\n \"\"\"Takes in random seed and returns corresponding noise vector\"\"\"\n return torch.randn(\n (1, self.unet.config.in_channels, height // 8, width // 8),\n generator=torch.Generator(device=self.device).manual_seed(seed),\n device=self.device,\n dtype=dtype,\n )\n\n def walk(\n self,\n prompts: List[str],\n seeds: List[int],\n num_interpolation_steps: Optional[int] = 6,\n output_dir: Optional[str] = \"./dreams\",\n name: Optional[str] = None,\n batch_size: Optional[int] = 1,\n height: Optional[int] = 512,\n width: Optional[int] = 512,\n guidance_scale: Optional[float] = 7.5,\n num_inference_steps: Optional[int] = 50,\n eta: Optional[float] = 0.0,\n ) -> List[str]:\n \"\"\"\n Walks through a series of prompts and seeds, interpolating between them and saving the results to disk.\n\n Args:\n prompts (`List[str]`):\n List of prompts to generate images for.\n seeds (`List[int]`):\n List of seeds corresponding to provided prompts. Must be the same length as prompts.\n num_interpolation_steps (`int`, *optional*, defaults to 6):\n Number of interpolation steps to take between prompts.\n output_dir (`str`, *optional*, defaults to `./dreams`):\n Directory to save the generated images to.\n name (`str`, *optional*, defaults to `None`):\n Subdirectory of `output_dir` to save the generated images to. If `None`, the name will\n be the current time.\n batch_size (`int`, *optional*, defaults to 1):\n Number of images to generate at once.\n height (`int`, *optional*, defaults to 512):\n Height of the generated images.\n width (`int`, *optional*, defaults to 512):\n Width of the generated images.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n\n Returns:\n `List[str]`: List of paths to the generated images.\n \"\"\"\n if not len(prompts) == len(seeds):\n raise ValueError(\n f\"Number of prompts and seeds must be equalGot {len(prompts)} prompts and {len(seeds)} seeds\"\n )\n\n name = name or time.strftime(\"%Y%m%d-%H%M%S\")\n save_path = Path(output_dir) / name\n save_path.mkdir(exist_ok=True, parents=True)\n\n frame_idx = 0\n frame_filepaths = []\n for prompt_a, prompt_b, seed_a, seed_b in zip(prompts, prompts[1:], seeds, seeds[1:]):\n # Embed Text\n embed_a = self.embed_text(prompt_a)\n embed_b = self.embed_text(prompt_b)\n\n # Get Noise\n noise_dtype = embed_a.dtype\n noise_a = self.get_noise(seed_a, noise_dtype, height, width)\n noise_b = self.get_noise(seed_b, noise_dtype, height, width)\n\n noise_batch, embeds_batch = None, None\n T = np.linspace(0.0, 1.0, num_interpolation_steps)\n for i, t in enumerate(T):\n noise = slerp(float(t), noise_a, noise_b)\n embed = torch.lerp(embed_a, embed_b, t)\n\n noise_batch = noise if noise_batch is None else torch.cat([noise_batch, noise], dim=0)\n embeds_batch = embed if embeds_batch is None else torch.cat([embeds_batch, embed], dim=0)\n\n batch_is_ready = embeds_batch.shape[0] == batch_size or i + 1 == T.shape[0]\n if batch_is_ready:\n outputs = self(\n latents=noise_batch,\n text_embeddings=embeds_batch,\n height=height,\n width=width,\n guidance_scale=guidance_scale,\n eta=eta,\n num_inference_steps=num_inference_steps,\n )\n noise_batch, embeds_batch = None, None\n\n for image in outputs[\"images\"]:\n frame_filepath = str(save_path / f\"frame_{frame_idx:06d}.png\")\n image.save(frame_filepath)\n frame_filepaths.append(frame_filepath)\n frame_idx += 1\n return frame_filepaths\n" }, { "path": "diffusers/examples/community/ip_adapter_face_id.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom packaging import version\nfrom safetensors import safe_open\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom diffusers.configuration_utils import FrozenDict\nfrom diffusers.image_processor import VaeImageProcessor\nfrom diffusers.loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.models.attention_processor import (\n AttnProcessor,\n AttnProcessor2_0,\n IPAdapterAttnProcessor,\n IPAdapterAttnProcessor2_0,\n)\nfrom diffusers.models.embeddings import MultiIPAdapterImageProjection\nfrom diffusers.models.lora import adjust_lora_scale_text_encoder\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n USE_PEFT_BACKEND,\n _get_model_file,\n deprecate,\n logging,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass IPAdapterFullImageProjection(nn.Module):\n def __init__(self, image_embed_dim=1024, cross_attention_dim=1024, mult=1, num_tokens=1):\n super().__init__()\n from diffusers.models.attention import FeedForward\n\n self.num_tokens = num_tokens\n self.cross_attention_dim = cross_attention_dim\n self.ff = FeedForward(image_embed_dim, cross_attention_dim * num_tokens, mult=mult, activation_fn=\"gelu\")\n self.norm = nn.LayerNorm(cross_attention_dim)\n\n def forward(self, image_embeds: torch.Tensor):\n x = self.ff(image_embeds)\n x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)\n return self.norm(x)\n\n\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used,\n `timesteps` must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default\n timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`\n must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass IPAdapterFaceIDStableDiffusionPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n TextualInversionLoaderMixin,\n LoraLoaderMixin,\n IPAdapterMixin,\n FromSingleFileMixin,\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->image_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"image_encoder\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder: CLIPVisionModelWithProjection = None,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"clip_sample\"] = False\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n def load_ip_adapter_face_id(self, pretrained_model_name_or_path_or_dict, weight_name, **kwargs):\n cache_dir = kwargs.pop(\"cache_dir\", None)\n force_download = kwargs.pop(\"force_download\", False)\n resume_download = kwargs.pop(\"resume_download\", False)\n proxies = kwargs.pop(\"proxies\", None)\n local_files_only = kwargs.pop(\"local_files_only\", None)\n token = kwargs.pop(\"token\", None)\n revision = kwargs.pop(\"revision\", None)\n subfolder = kwargs.pop(\"subfolder\", None)\n\n user_agent = {\n \"file_type\": \"attn_procs_weights\",\n \"framework\": \"pytorch\",\n }\n model_file = _get_model_file(\n pretrained_model_name_or_path_or_dict,\n weights_name=weight_name,\n cache_dir=cache_dir,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n subfolder=subfolder,\n user_agent=user_agent,\n )\n if weight_name.endswith(\".safetensors\"):\n state_dict = {\"image_proj\": {}, \"ip_adapter\": {}}\n with safe_open(model_file, framework=\"pt\", device=\"cpu\") as f:\n for key in f.keys():\n if key.startswith(\"image_proj.\"):\n state_dict[\"image_proj\"][key.replace(\"image_proj.\", \"\")] = f.get_tensor(key)\n elif key.startswith(\"ip_adapter.\"):\n state_dict[\"ip_adapter\"][key.replace(\"ip_adapter.\", \"\")] = f.get_tensor(key)\n else:\n state_dict = torch.load(model_file, map_location=\"cpu\")\n self._load_ip_adapter_weights(state_dict)\n\n def convert_ip_adapter_image_proj_to_diffusers(self, state_dict):\n updated_state_dict = {}\n clip_embeddings_dim_in = state_dict[\"proj.0.weight\"].shape[1]\n clip_embeddings_dim_out = state_dict[\"proj.0.weight\"].shape[0]\n multiplier = clip_embeddings_dim_out // clip_embeddings_dim_in\n norm_layer = \"norm.weight\"\n cross_attention_dim = state_dict[norm_layer].shape[0]\n num_tokens = state_dict[\"proj.2.weight\"].shape[0] // cross_attention_dim\n\n image_projection = IPAdapterFullImageProjection(\n cross_attention_dim=cross_attention_dim,\n image_embed_dim=clip_embeddings_dim_in,\n mult=multiplier,\n num_tokens=num_tokens,\n )\n\n for key, value in state_dict.items():\n diffusers_name = key.replace(\"proj.0\", \"ff.net.0.proj\")\n diffusers_name = diffusers_name.replace(\"proj.2\", \"ff.net.2\")\n updated_state_dict[diffusers_name] = value\n\n image_projection.load_state_dict(updated_state_dict)\n return image_projection\n\n def _load_ip_adapter_weights(self, state_dict):\n num_image_text_embeds = 4\n\n self.unet.encoder_hid_proj = None\n\n # set ip-adapter cross-attention processors & load state_dict\n attn_procs = {}\n lora_dict = {}\n key_id = 0\n for name in self.unet.attn_processors.keys():\n cross_attention_dim = None if name.endswith(\"attn1.processor\") else self.unet.config.cross_attention_dim\n if name.startswith(\"mid_block\"):\n hidden_size = self.unet.config.block_out_channels[-1]\n elif name.startswith(\"up_blocks\"):\n block_id = int(name[len(\"up_blocks.\")])\n hidden_size = list(reversed(self.unet.config.block_out_channels))[block_id]\n elif name.startswith(\"down_blocks\"):\n block_id = int(name[len(\"down_blocks.\")])\n hidden_size = self.unet.config.block_out_channels[block_id]\n if cross_attention_dim is None or \"motion_modules\" in name:\n attn_processor_class = (\n AttnProcessor2_0 if hasattr(F, \"scaled_dot_product_attention\") else AttnProcessor\n )\n attn_procs[name] = attn_processor_class()\n\n lora_dict.update(\n {f\"unet.{name}.to_k_lora.down.weight\": state_dict[\"ip_adapter\"][f\"{key_id}.to_k_lora.down.weight\"]}\n )\n lora_dict.update(\n {f\"unet.{name}.to_q_lora.down.weight\": state_dict[\"ip_adapter\"][f\"{key_id}.to_q_lora.down.weight\"]}\n )\n lora_dict.update(\n {f\"unet.{name}.to_v_lora.down.weight\": state_dict[\"ip_adapter\"][f\"{key_id}.to_v_lora.down.weight\"]}\n )\n lora_dict.update(\n {\n f\"unet.{name}.to_out_lora.down.weight\": state_dict[\"ip_adapter\"][\n f\"{key_id}.to_out_lora.down.weight\"\n ]\n }\n )\n lora_dict.update(\n {f\"unet.{name}.to_k_lora.up.weight\": state_dict[\"ip_adapter\"][f\"{key_id}.to_k_lora.up.weight\"]}\n )\n lora_dict.update(\n {f\"unet.{name}.to_q_lora.up.weight\": state_dict[\"ip_adapter\"][f\"{key_id}.to_q_lora.up.weight\"]}\n )\n lora_dict.update(\n {f\"unet.{name}.to_v_lora.up.weight\": state_dict[\"ip_adapter\"][f\"{key_id}.to_v_lora.up.weight\"]}\n )\n lora_dict.update(\n {f\"unet.{name}.to_out_lora.up.weight\": state_dict[\"ip_adapter\"][f\"{key_id}.to_out_lora.up.weight\"]}\n )\n key_id += 1\n else:\n attn_processor_class = (\n IPAdapterAttnProcessor2_0 if hasattr(F, \"scaled_dot_product_attention\") else IPAdapterAttnProcessor\n )\n attn_procs[name] = attn_processor_class(\n hidden_size=hidden_size,\n cross_attention_dim=cross_attention_dim,\n scale=1.0,\n num_tokens=num_image_text_embeds,\n ).to(dtype=self.dtype, device=self.device)\n\n lora_dict.update(\n {f\"unet.{name}.to_k_lora.down.weight\": state_dict[\"ip_adapter\"][f\"{key_id}.to_k_lora.down.weight\"]}\n )\n lora_dict.update(\n {f\"unet.{name}.to_q_lora.down.weight\": state_dict[\"ip_adapter\"][f\"{key_id}.to_q_lora.down.weight\"]}\n )\n lora_dict.update(\n {f\"unet.{name}.to_v_lora.down.weight\": state_dict[\"ip_adapter\"][f\"{key_id}.to_v_lora.down.weight\"]}\n )\n lora_dict.update(\n {\n f\"unet.{name}.to_out_lora.down.weight\": state_dict[\"ip_adapter\"][\n f\"{key_id}.to_out_lora.down.weight\"\n ]\n }\n )\n lora_dict.update(\n {f\"unet.{name}.to_k_lora.up.weight\": state_dict[\"ip_adapter\"][f\"{key_id}.to_k_lora.up.weight\"]}\n )\n lora_dict.update(\n {f\"unet.{name}.to_q_lora.up.weight\": state_dict[\"ip_adapter\"][f\"{key_id}.to_q_lora.up.weight\"]}\n )\n lora_dict.update(\n {f\"unet.{name}.to_v_lora.up.weight\": state_dict[\"ip_adapter\"][f\"{key_id}.to_v_lora.up.weight\"]}\n )\n lora_dict.update(\n {f\"unet.{name}.to_out_lora.up.weight\": state_dict[\"ip_adapter\"][f\"{key_id}.to_out_lora.up.weight\"]}\n )\n\n value_dict = {}\n value_dict.update({\"to_k_ip.0.weight\": state_dict[\"ip_adapter\"][f\"{key_id}.to_k_ip.weight\"]})\n value_dict.update({\"to_v_ip.0.weight\": state_dict[\"ip_adapter\"][f\"{key_id}.to_v_ip.weight\"]})\n attn_procs[name].load_state_dict(value_dict)\n key_id += 1\n\n self.unet.set_attn_processor(attn_procs)\n\n self.load_lora_weights(lora_dict, adapter_name=\"faceid\")\n self.set_adapters([\"faceid\"], adapter_weights=[1.0])\n\n # convert IP-Adapter Image Projection layers to diffusers\n image_projection = self.convert_ip_adapter_image_proj_to_diffusers(state_dict[\"image_proj\"])\n image_projection_layers = [image_projection.to(device=self.device, dtype=self.dtype)]\n\n self.unet.encoder_hid_proj = MultiIPAdapterImageProjection(image_projection_layers)\n self.unet.config.encoder_hid_dim_type = \"ip_image_proj\"\n\n def set_ip_adapter_scale(self, scale):\n unet = getattr(self, self.unet_name) if not hasattr(self, \"unet\") else self.unet\n for attn_processor in unet.attn_processors.values():\n if isinstance(attn_processor, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0)):\n attn_processor.scale = [scale]\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n timesteps (`torch.Tensor`):\n generate embedding vectors at these timesteps\n embedding_dim (`int`, *optional*, defaults to 512):\n dimension of the embeddings to generate\n dtype:\n data type of the generated embeddings\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def guidance_rescale(self):\n return self._guidance_rescale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @property\n def interrupt(self):\n return self._interrupt\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n image_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n image_embeds (`torch.Tensor`, *optional*):\n Pre-generated image embeddings.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when\n using zero terminal SNR.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n # to deal with lora scaling and other possible forward hooks\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._guidance_rescale = guidance_rescale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._interrupt = False\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n if image_embeds is not None:\n image_embeds = torch.stack([image_embeds] * num_images_per_prompt, dim=0).to(\n device=device, dtype=prompt_embeds.dtype\n )\n negative_image_embeds = torch.zeros_like(image_embeds)\n if self.do_classifier_free_guidance:\n image_embeds = torch.cat([negative_image_embeds, image_embeds])\n image_embeds = [image_embeds]\n # 4. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 6.1 Add image embeds for IP-Adapter\n added_cond_kwargs = {\"image_embeds\": image_embeds} if image_embeds is not None else {}\n\n # 6.2 Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n if self.interrupt:\n continue\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=self.cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[\n 0\n ]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/examples/community/kohya_hires_fix.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Any, Dict, List, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.utils.checkpoint\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom diffusers.configuration_utils import register_to_config\nfrom diffusers.image_processor import VaeImageProcessor\nfrom diffusers.models.autoencoders import AutoencoderKL\nfrom diffusers.models.unets.unet_2d_condition import UNet2DConditionModel, UNet2DConditionOutput\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipeline\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass UNet2DConditionModelHighResFix(UNet2DConditionModel):\n r\"\"\"\n A conditional 2D UNet model that applies Kohya fix proposed for high resolution image generation.\n\n This model inherits from [`UNet2DConditionModel`]. Check the superclass documentation for learning about all the parameters.\n\n Parameters:\n high_res_fix (`List[Dict]`, *optional*, defaults to `[{'timestep': 600, 'scale_factor': 0.5, 'block_num': 1}]`):\n Enables Kohya fix for high resolution generation. The activation maps are scaled based on the scale_factor up to the timestep at specified block_num.\n \"\"\"\n\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(self, high_res_fix: List[Dict] = [{\"timestep\": 600, \"scale_factor\": 0.5, \"block_num\": 1}], **kwargs):\n super().__init__(**kwargs)\n if high_res_fix:\n self.config.high_res_fix = sorted(high_res_fix, key=lambda x: x[\"timestep\"], reverse=True)\n\n @classmethod\n def _resize(cls, sample, target=None, scale_factor=1, mode=\"bicubic\"):\n dtype = sample.dtype\n if dtype == torch.bfloat16:\n sample = sample.to(torch.float32)\n\n if target is not None:\n if sample.shape[-2:] != target.shape[-2:]:\n sample = nn.functional.interpolate(sample, size=target.shape[-2:], mode=mode, align_corners=False)\n elif scale_factor != 1:\n sample = nn.functional.interpolate(sample, scale_factor=scale_factor, mode=mode, align_corners=False)\n\n return sample.to(dtype)\n\n def forward(\n self,\n sample: torch.FloatTensor,\n timestep: Union[torch.Tensor, float, int],\n encoder_hidden_states: torch.Tensor,\n class_labels: Optional[torch.Tensor] = None,\n timestep_cond: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,\n down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\n mid_block_additional_residual: Optional[torch.Tensor] = None,\n down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ) -> Union[UNet2DConditionOutput, Tuple]:\n r\"\"\"\n The [`UNet2DConditionModel`] forward method.\n\n Args:\n sample (`torch.FloatTensor`):\n The noisy input tensor with the following shape `(batch, channel, height, width)`.\n timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.\n encoder_hidden_states (`torch.FloatTensor`):\n The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.\n class_labels (`torch.Tensor`, *optional*, defaults to `None`):\n Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.\n timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`):\n Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed\n through the `self.time_embedding` layer to obtain the timestep embeddings.\n attention_mask (`torch.Tensor`, *optional*, defaults to `None`):\n An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask\n is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large\n negative values to the attention scores corresponding to \"discard\" tokens.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n added_cond_kwargs: (`dict`, *optional*):\n A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that\n are passed along to the UNet blocks.\n down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):\n A tuple of tensors that if specified are added to the residuals of down unet blocks.\n mid_block_additional_residual: (`torch.Tensor`, *optional*):\n A tensor that if specified is added to the residual of the middle unet block.\n down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*):\n additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s)\n encoder_attention_mask (`torch.Tensor`):\n A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If\n `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,\n which adds large negative values to the attention scores corresponding to \"discard\" tokens.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain\n tuple.\n\n Returns:\n [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:\n If `return_dict` is True, an [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] is returned,\n otherwise a `tuple` is returned where the first element is the sample tensor.\n \"\"\"\n # By default samples have to be AT least a multiple of the overall upsampling factor.\n # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).\n # However, the upsampling interpolation output size can be forced to fit any upsampling size\n # on the fly if necessary.\n default_overall_up_factor = 2**self.num_upsamplers\n\n # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`\n forward_upsample_size = False\n upsample_size = None\n\n for dim in sample.shape[-2:]:\n if dim % default_overall_up_factor != 0:\n # Forward upsample size to force interpolation output size.\n forward_upsample_size = True\n break\n\n # ensure attention_mask is a bias, and give it a singleton query_tokens dimension\n # expects mask of shape:\n # [batch, key_tokens]\n # adds singleton query_tokens dimension:\n # [batch, 1, key_tokens]\n # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:\n # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)\n # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)\n if attention_mask is not None:\n # assume that mask is expressed as:\n # (1 = keep, 0 = discard)\n # convert mask into a bias that can be added to attention scores:\n # (keep = +0, discard = -10000.0)\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n # convert encoder_attention_mask to a bias the same way we do for attention_mask\n if encoder_attention_mask is not None:\n encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0\n encoder_attention_mask = encoder_attention_mask.unsqueeze(1)\n\n # 0. center input if necessary\n if self.config.center_input_sample:\n sample = 2 * sample - 1.0\n\n # 1. time\n t_emb = self.get_time_embed(sample=sample, timestep=timestep)\n emb = self.time_embedding(t_emb, timestep_cond)\n aug_emb = None\n\n class_emb = self.get_class_embed(sample=sample, class_labels=class_labels)\n if class_emb is not None:\n if self.config.class_embeddings_concat:\n emb = torch.cat([emb, class_emb], dim=-1)\n else:\n emb = emb + class_emb\n\n aug_emb = self.get_aug_embed(\n emb=emb, encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs\n )\n if self.config.addition_embed_type == \"image_hint\":\n aug_emb, hint = aug_emb\n sample = torch.cat([sample, hint], dim=1)\n\n emb = emb + aug_emb if aug_emb is not None else emb\n\n if self.time_embed_act is not None:\n emb = self.time_embed_act(emb)\n\n encoder_hidden_states = self.process_encoder_hidden_states(\n encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs\n )\n\n # 2. pre-process\n sample = self.conv_in(sample)\n\n # 2.5 GLIGEN position net\n if cross_attention_kwargs is not None and cross_attention_kwargs.get(\"gligen\", None) is not None:\n cross_attention_kwargs = cross_attention_kwargs.copy()\n gligen_args = cross_attention_kwargs.pop(\"gligen\")\n cross_attention_kwargs[\"gligen\"] = {\"objs\": self.position_net(**gligen_args)}\n\n # 3. down\n # we're popping the `scale` instead of getting it because otherwise `scale` will be propagated\n # to the internal blocks and will raise deprecation warnings. this will be confusing for our users.\n if cross_attention_kwargs is not None:\n cross_attention_kwargs = cross_attention_kwargs.copy()\n lora_scale = cross_attention_kwargs.pop(\"scale\", 1.0)\n else:\n lora_scale = 1.0\n\n if USE_PEFT_BACKEND:\n # weight the lora layers by setting `lora_scale` for each PEFT layer\n scale_lora_layers(self, lora_scale)\n\n is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None\n # using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets\n is_adapter = down_intrablock_additional_residuals is not None\n # maintain backward compatibility for legacy usage, where\n # T2I-Adapter and ControlNet both use down_block_additional_residuals arg\n # but can only use one or the other\n if not is_adapter and mid_block_additional_residual is None and down_block_additional_residuals is not None:\n deprecate(\n \"T2I should not use down_block_additional_residuals\",\n \"1.3.0\",\n \"Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \\\n and will be removed in diffusers 1.3.0. `down_block_additional_residuals` should only be used \\\n for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. \",\n standard_warn=False,\n )\n down_intrablock_additional_residuals = down_block_additional_residuals\n is_adapter = True\n\n down_block_res_samples = (sample,)\n for down_i, downsample_block in enumerate(self.down_blocks):\n if hasattr(downsample_block, \"has_cross_attention\") and downsample_block.has_cross_attention:\n # For t2i-adapter CrossAttnDownBlock2D\n additional_residuals = {}\n if is_adapter and len(down_intrablock_additional_residuals) > 0:\n additional_residuals[\"additional_residuals\"] = down_intrablock_additional_residuals.pop(0)\n\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n encoder_attention_mask=encoder_attention_mask,\n **additional_residuals,\n )\n\n else:\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb)\n if is_adapter and len(down_intrablock_additional_residuals) > 0:\n sample += down_intrablock_additional_residuals.pop(0)\n\n down_block_res_samples += res_samples\n\n # kohya high res fix\n if self.config.high_res_fix:\n for high_res_fix in self.config.high_res_fix:\n if timestep > high_res_fix[\"timestep\"] and down_i == high_res_fix[\"block_num\"]:\n sample = self.__class__._resize(sample, scale_factor=high_res_fix[\"scale_factor\"])\n break\n\n if is_controlnet:\n new_down_block_res_samples = ()\n\n for down_block_res_sample, down_block_additional_residual in zip(\n down_block_res_samples, down_block_additional_residuals\n ):\n down_block_res_sample = down_block_res_sample + down_block_additional_residual\n new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)\n\n down_block_res_samples = new_down_block_res_samples\n\n # 4. mid\n if self.mid_block is not None:\n if hasattr(self.mid_block, \"has_cross_attention\") and self.mid_block.has_cross_attention:\n sample = self.mid_block(\n sample,\n emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n encoder_attention_mask=encoder_attention_mask,\n )\n else:\n sample = self.mid_block(sample, emb)\n\n # To support T2I-Adapter-XL\n if (\n is_adapter\n and len(down_intrablock_additional_residuals) > 0\n and sample.shape == down_intrablock_additional_residuals[0].shape\n ):\n sample += down_intrablock_additional_residuals.pop(0)\n\n if is_controlnet:\n sample = sample + mid_block_additional_residual\n\n # 5. up\n for i, upsample_block in enumerate(self.up_blocks):\n is_final_block = i == len(self.up_blocks) - 1\n\n res_samples = down_block_res_samples[-len(upsample_block.resnets) :]\n down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]\n\n # up scaling of kohya high res fix\n if self.config.high_res_fix is not None:\n if res_samples[0].shape[-2:] != sample.shape[-2:]:\n sample = self.__class__._resize(sample, target=res_samples[0])\n res_samples_up_sampled = (res_samples[0],)\n for res_sample in res_samples[1:]:\n res_samples_up_sampled += (self.__class__._resize(res_sample, target=res_samples[0]),)\n res_samples = res_samples_up_sampled\n\n # if we have not reached the final block and need to forward the\n # upsample size, we do it here\n if not is_final_block and forward_upsample_size:\n upsample_size = down_block_res_samples[-1].shape[2:]\n\n if hasattr(upsample_block, \"has_cross_attention\") and upsample_block.has_cross_attention:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n upsample_size=upsample_size,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n )\n else:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n upsample_size=upsample_size,\n )\n\n # 6. post-process\n if self.conv_norm_out:\n sample = self.conv_norm_out(sample)\n sample = self.conv_act(sample)\n sample = self.conv_out(sample)\n\n if USE_PEFT_BACKEND:\n # remove `lora_scale` from each PEFT layer\n unscale_lora_layers(self, lora_scale)\n\n if not return_dict:\n return (sample,)\n\n return UNet2DConditionOutput(sample=sample)\n\n @classmethod\n def from_unet(cls, unet: UNet2DConditionModel, high_res_fix: list):\n config = dict((unet.config))\n config[\"high_res_fix\"] = high_res_fix\n unet_high_res = cls(**config)\n unet_high_res.load_state_dict(unet.state_dict())\n unet_high_res.to(unet.dtype)\n return unet_high_res\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import DiffusionPipeline\n\n >>> pipe = DiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\",\n custom_pipeline=\"kohya_hires_fix\",\n torch_dtype=torch.float16,\n high_res_fix=[{'timestep': 600,\n 'scale_factor': 0.5,\n 'block_num': 1}])\n >>> pipe = pipe.to(\"cuda\")\n\n >>> prompt = \"a photo of an astronaut riding a horse on mars\"\n >>> image = pipe(prompt, height=1000, width=1600).images[0]\n ```\n\"\"\"\n\n\nclass StableDiffusionHighResFixPipeline(StableDiffusionPipeline):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion with Kohya fix for high resolution generation.\n\n This model inherits from [`StableDiffusionPipeline`]. Check the superclass documentation for the generic methods.\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n high_res_fix (`List[Dict]`, *optional*, defaults to `[{'timestep': 600, 'scale_factor': 0.5, 'block_num': 1}]`):\n Enables Kohya fix for high resolution generation. The activation maps are scaled based on the scale_factor up to the timestep at specified block_num.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->image_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"image_encoder\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder: CLIPVisionModelWithProjection = None,\n requires_safety_checker: bool = True,\n high_res_fix: List[Dict] = [{\"timestep\": 600, \"scale_factor\": 0.5, \"block_num\": 1}],\n ):\n super().__init__(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n requires_safety_checker=requires_safety_checker,\n )\n\n unet = UNet2DConditionModelHighResFix.from_unet(unet=unet, high_res_fix=high_res_fix)\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n" }, { "path": "diffusers/examples/community/latent_consistency_img2img.py", "content": "# Copyright 2024 Stanford University Team and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion\n# and https://github.com/hojonathanho/diffusion\n\nimport math\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import AutoencoderKL, ConfigMixin, DiffusionPipeline, SchedulerMixin, UNet2DConditionModel, logging\nfrom diffusers.configuration_utils import register_to_config\nfrom diffusers.image_processor import PipelineImageInput, VaeImageProcessor\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.utils import BaseOutput\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass LatentConsistencyModelImg2ImgPipeline(DiffusionPipeline):\n _optional_components = [\"scheduler\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: \"LCMSchedulerWithTimestamp\",\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n scheduler = (\n scheduler\n if scheduler is not None\n else LCMSchedulerWithTimestamp(\n beta_start=0.00085, beta_end=0.0120, beta_schedule=\"scaled_linear\", prediction_type=\"epsilon\"\n )\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n prompt_embeds: None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n \"\"\"\n\n if prompt is not None and isinstance(prompt, str):\n pass\n elif prompt is not None and isinstance(prompt, list):\n len(prompt)\n else:\n prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n prompt_embeds = prompt_embeds[0]\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # Don't need to get uncond prompt embedding because of LCM Guided Distillation\n return prompt_embeds\n\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n def prepare_latents(\n self,\n image,\n timestep,\n batch_size,\n num_channels_latents,\n height,\n width,\n dtype,\n device,\n latents=None,\n generator=None,\n ):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n image = image.to(device=device, dtype=dtype)\n\n # batch_size = batch_size * num_images_per_prompt\n\n if image.shape[1] == 4:\n init_latents = image\n\n else:\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n elif isinstance(generator, list):\n init_latents = [\n self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = self.vae.encode(image).latent_dist.sample(generator)\n\n init_latents = self.vae.config.scaling_factor * init_latents\n\n if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:\n # expand init_latents for batch_size\n (\n f\"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial\"\n \" images (`image`). Initial images are now duplicating to match the number of text prompts. Note\"\n \" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update\"\n \" your script to pass as many initial images as text prompts to suppress this warning.\"\n )\n # deprecate(\"len(prompt) != len(image)\", \"1.0.0\", deprecation_message, standard_warn=False)\n additional_image_per_prompt = batch_size // init_latents.shape[0]\n init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)\n elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n init_latents = torch.cat([init_latents], dim=0)\n\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n latents = init_latents\n\n return latents\n\n def get_w_embedding(self, w, embedding_dim=512, dtype=torch.float32):\n \"\"\"\n see https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n Args:\n timesteps: torch.Tensor: generate embedding vectors at these timesteps\n embedding_dim: int: dimension of the embeddings to generate\n dtype: data type of the generated embeddings\n Returns:\n embedding vectors with shape `(len(timesteps), embedding_dim)`\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n\n return timesteps, num_inference_steps - t_start\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: PipelineImageInput = None,\n strength: float = 0.8,\n height: Optional[int] = 768,\n width: Optional[int] = 768,\n guidance_scale: float = 7.5,\n num_images_per_prompt: Optional[int] = 1,\n latents: Optional[torch.Tensor] = None,\n num_inference_steps: int = 4,\n lcm_origin_steps: int = 50,\n prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n ):\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # do_classifier_free_guidance = guidance_scale > 0.0 # In LCM Implementation: cfg_noise = noise_cond + cfg_scale * (noise_cond - noise_uncond) , (cfg_scale > 0.0 using CFG)\n\n # 3. Encode input prompt\n prompt_embeds = self._encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n prompt_embeds=prompt_embeds,\n )\n\n # 3.5 encode image\n image = self.image_processor.preprocess(image)\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(strength, num_inference_steps, lcm_origin_steps)\n # timesteps = self.scheduler.timesteps\n # timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, 1.0, device)\n timesteps = self.scheduler.timesteps\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n\n print(\"timesteps: \", timesteps)\n\n # 5. Prepare latent variable\n num_channels_latents = self.unet.config.in_channels\n if latents is None:\n latents = self.prepare_latents(\n image,\n latent_timestep,\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n latents,\n )\n bs = batch_size * num_images_per_prompt\n\n # 6. Get Guidance Scale Embedding\n w = torch.tensor(guidance_scale).repeat(bs)\n w_embedding = self.get_w_embedding(w, embedding_dim=256).to(device=device, dtype=latents.dtype)\n\n # 7. LCM MultiStep Sampling Loop:\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n ts = torch.full((bs,), t, device=device, dtype=torch.long)\n latents = latents.to(prompt_embeds.dtype)\n\n # model prediction (v-prediction, eps, x)\n model_pred = self.unet(\n latents,\n ts,\n timestep_cond=w_embedding,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # compute the previous noisy sample x_t -> x_t-1\n latents, denoised = self.scheduler.step(model_pred, i, t, latents, return_dict=False)\n\n # # call the callback, if provided\n # if i == len(timesteps) - 1:\n progress_bar.update()\n\n denoised = denoised.to(prompt_embeds.dtype)\n if not output_type == \"latent\":\n image = self.vae.decode(denoised / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = denoised\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n\n\n@dataclass\n# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM\nclass LCMSchedulerOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's `step` function output.\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample `(x_{0})` based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.Tensor\n denoised: Optional[torch.Tensor] = None\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\ndef rescale_zero_terminal_snr(betas):\n \"\"\"\n Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)\n Args:\n betas (`torch.Tensor`):\n the betas that the scheduler is being initialized with.\n Returns:\n `torch.Tensor`: rescaled betas with zero terminal SNR\n \"\"\"\n # Convert betas to alphas_bar_sqrt\n alphas = 1.0 - betas\n alphas_cumprod = torch.cumprod(alphas, dim=0)\n alphas_bar_sqrt = alphas_cumprod.sqrt()\n\n # Store old values.\n alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()\n alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()\n\n # Shift so the last timestep is zero.\n alphas_bar_sqrt -= alphas_bar_sqrt_T\n\n # Scale so the first timestep is back to the old value.\n alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)\n\n # Convert alphas_bar_sqrt to betas\n alphas_bar = alphas_bar_sqrt**2 # Revert sqrt\n alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod\n alphas = torch.cat([alphas_bar[0:1], alphas])\n betas = 1 - alphas\n\n return betas\n\n\nclass LCMSchedulerWithTimestamp(SchedulerMixin, ConfigMixin):\n \"\"\"\n This class modifies LCMScheduler to add a timestamp argument to set_timesteps\n\n\n `LCMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with\n non-Markovian guidance.\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n beta_start (`float`, defaults to 0.0001):\n The starting `beta` value of inference.\n beta_end (`float`, defaults to 0.02):\n The final `beta` value.\n beta_schedule (`str`, defaults to `\"linear\"`):\n The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear`, `scaled_linear`, or `squaredcos_cap_v2`.\n trained_betas (`np.ndarray`, *optional*):\n Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.\n clip_sample (`bool`, defaults to `True`):\n Clip the predicted sample for numerical stability.\n clip_sample_range (`float`, defaults to 1.0):\n The maximum magnitude for sample clipping. Valid only when `clip_sample=True`.\n set_alpha_to_one (`bool`, defaults to `True`):\n Each diffusion step uses the alphas product value at that step and at the previous one. For the final step\n there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,\n otherwise it uses the alpha value at step 0.\n steps_offset (`int`, defaults to 0):\n An offset added to the inference steps, as required by some model families.\n prediction_type (`str`, defaults to `epsilon`, *optional*):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),\n `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen\n Video](https://imagen.research.google/video/paper.pdf) paper).\n thresholding (`bool`, defaults to `False`):\n Whether to use the \"dynamic thresholding\" method. This is unsuitable for latent-space diffusion models such\n as Stable Diffusion.\n dynamic_thresholding_ratio (`float`, defaults to 0.995):\n The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.\n sample_max_value (`float`, defaults to 1.0):\n The threshold value for dynamic thresholding. Valid only when `thresholding=True`.\n timestep_spacing (`str`, defaults to `\"leading\"`):\n The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.\n rescale_betas_zero_snr (`bool`, defaults to `False`):\n Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and\n dark samples instead of limiting it to samples with medium brightness. Loosely related to\n [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).\n \"\"\"\n\n # _compatibles = [e.name for e in KarrasDiffusionSchedulers]\n order = 1\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[Union[np.ndarray, List[float]]] = None,\n clip_sample: bool = True,\n set_alpha_to_one: bool = True,\n steps_offset: int = 0,\n prediction_type: str = \"epsilon\",\n thresholding: bool = False,\n dynamic_thresholding_ratio: float = 0.995,\n clip_sample_range: float = 1.0,\n sample_max_value: float = 1.0,\n timestep_spacing: str = \"leading\",\n rescale_betas_zero_snr: bool = False,\n ):\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n # Rescale for zero SNR\n if rescale_betas_zero_snr:\n self.betas = rescale_zero_terminal_snr(self.betas)\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n\n # At every step in ddim, we are looking into the previous alphas_cumprod\n # For the final step, there is no previous alphas_cumprod because we are already at 0\n # `set_alpha_to_one` decides whether we set this parameter simply to one or\n # whether we use the final alpha of the \"non-previous\" one.\n self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]\n\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = 1.0\n\n # setable values\n self.num_inference_steps = None\n self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64))\n\n def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`int`, *optional*):\n The current timestep in the diffusion chain.\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n return sample\n\n def _get_variance(self, timestep, prev_timestep):\n alpha_prod_t = self.alphas_cumprod[timestep]\n alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n\n variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)\n\n return variance\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample\n def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:\n \"\"\"\n \"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the\n prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by\n s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing\n pixels from saturation at each step. We find that dynamic thresholding results in significantly better\n photorealism as well as better image-text alignment, especially when using very large guidance weights.\"\n https://arxiv.org/abs/2205.11487\n \"\"\"\n dtype = sample.dtype\n batch_size, channels, height, width = sample.shape\n\n if dtype not in (torch.float32, torch.float64):\n sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half\n\n # Flatten sample for doing quantile calculation along each image\n sample = sample.reshape(batch_size, channels * height * width)\n\n abs_sample = sample.abs() # \"a certain percentile absolute pixel value\"\n\n s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)\n s = torch.clamp(\n s, min=1, max=self.config.sample_max_value\n ) # When clamped to min=1, equivalent to standard clipping to [-1, 1]\n\n s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0\n sample = torch.clamp(sample, -s, s) / s # \"we threshold xt0 to the range [-s, s] and then divide by s\"\n\n sample = sample.reshape(batch_size, channels, height, width)\n sample = sample.to(dtype)\n\n return sample\n\n def set_timesteps(\n self, stength, num_inference_steps: int, lcm_origin_steps: int, device: Union[str, torch.device] = None\n ):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n \"\"\"\n\n if num_inference_steps > self.config.num_train_timesteps:\n raise ValueError(\n f\"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:\"\n f\" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle\"\n f\" maximal {self.config.num_train_timesteps} timesteps.\"\n )\n\n self.num_inference_steps = num_inference_steps\n\n # LCM Timesteps Setting: # Linear Spacing\n c = self.config.num_train_timesteps // lcm_origin_steps\n lcm_origin_timesteps = (\n np.asarray(list(range(1, int(lcm_origin_steps * stength) + 1))) * c - 1\n ) # LCM Training Steps Schedule\n skipping_step = len(lcm_origin_timesteps) // num_inference_steps\n timesteps = lcm_origin_timesteps[::-skipping_step][:num_inference_steps] # LCM Inference Steps Schedule\n\n self.timesteps = torch.from_numpy(timesteps.copy()).to(device)\n\n def get_scalings_for_boundary_condition_discrete(self, t):\n self.sigma_data = 0.5 # Default: 0.5\n\n # By dividing 0.1: This is almost a delta function at t=0.\n c_skip = self.sigma_data**2 / ((t / 0.1) ** 2 + self.sigma_data**2)\n c_out = (t / 0.1) / ((t / 0.1) ** 2 + self.sigma_data**2) ** 0.5\n return c_skip, c_out\n\n def step(\n self,\n model_output: torch.Tensor,\n timeindex: int,\n timestep: int,\n sample: torch.Tensor,\n eta: float = 0.0,\n use_clipped_model_output: bool = False,\n generator=None,\n variance_noise: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ) -> Union[LCMSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise).\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`float`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n eta (`float`):\n The weight of noise for added noise in diffusion step.\n use_clipped_model_output (`bool`, defaults to `False`):\n If `True`, computes \"corrected\" `model_output` from the clipped predicted original sample. Necessary\n because predicted original sample is clipped to [-1, 1] when `self.config.clip_sample` is `True`. If no\n clipping has happened, \"corrected\" `model_output` would coincide with the one provided as input and\n `use_clipped_model_output` has no effect.\n generator (`torch.Generator`, *optional*):\n A random number generator.\n variance_noise (`torch.Tensor`):\n Alternative to generating noise with `generator` by directly providing the noise for the variance\n itself. Useful for methods such as [`CycleDiffusion`].\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] or `tuple`.\n Returns:\n [`~schedulers.scheduling_utils.LCMSchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n \"\"\"\n if self.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n # 1. get previous step value\n prev_timeindex = timeindex + 1\n if prev_timeindex < len(self.timesteps):\n prev_timestep = self.timesteps[prev_timeindex]\n else:\n prev_timestep = timestep\n\n # 2. compute alphas, betas\n alpha_prod_t = self.alphas_cumprod[timestep]\n alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod\n\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n\n # 3. Get scalings for boundary conditions\n c_skip, c_out = self.get_scalings_for_boundary_condition_discrete(timestep)\n\n # 4. Different Parameterization:\n parameterization = self.config.prediction_type\n\n if parameterization == \"epsilon\": # noise-prediction\n pred_x0 = (sample - beta_prod_t.sqrt() * model_output) / alpha_prod_t.sqrt()\n\n elif parameterization == \"sample\": # x-prediction\n pred_x0 = model_output\n\n elif parameterization == \"v_prediction\": # v-prediction\n pred_x0 = alpha_prod_t.sqrt() * sample - beta_prod_t.sqrt() * model_output\n\n # 4. Denoise model output using boundary conditions\n denoised = c_out * pred_x0 + c_skip * sample\n\n # 5. Sample z ~ N(0, I), For MultiStep Inference\n # Noise is not used for one-step sampling.\n if len(self.timesteps) > 1:\n noise = torch.randn(model_output.shape).to(model_output.device)\n prev_sample = alpha_prod_t_prev.sqrt() * denoised + beta_prod_t_prev.sqrt() * noise\n else:\n prev_sample = denoised\n\n if not return_dict:\n return (prev_sample, denoised)\n\n return LCMSchedulerOutput(prev_sample=prev_sample, denoised=denoised)\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.IntTensor,\n ) -> torch.Tensor:\n # Make sure alphas_cumprod and timestep have same device and dtype as original_samples\n alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)\n timesteps = timesteps.to(original_samples.device)\n\n sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n while len(sqrt_alpha_prod.shape) < len(original_samples.shape):\n sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n\n noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise\n return noisy_samples\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity\n def get_velocity(self, sample: torch.Tensor, noise: torch.Tensor, timesteps: torch.IntTensor) -> torch.Tensor:\n # Make sure alphas_cumprod and timestep have same device and dtype as sample\n alphas_cumprod = self.alphas_cumprod.to(device=sample.device, dtype=sample.dtype)\n timesteps = timesteps.to(sample.device)\n\n sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n while len(sqrt_alpha_prod.shape) < len(sample.shape):\n sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n\n velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample\n return velocity\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/examples/community/latent_consistency_interpolate.py", "content": "import inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers.image_processor import VaeImageProcessor\nfrom diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.models.lora import adjust_lora_scale_text_encoder\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker\nfrom diffusers.schedulers import LCMScheduler\nfrom diffusers.utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> import numpy as np\n\n >>> from diffusers import DiffusionPipeline\n\n >>> pipe = DiffusionPipeline.from_pretrained(\"SimianLuo/LCM_Dreamshaper_v7\", custom_pipeline=\"latent_consistency_interpolate\")\n >>> # To save GPU memory, torch.float16 can be used, but it may compromise image quality.\n >>> pipe.to(torch_device=\"cuda\", torch_dtype=torch.float32)\n\n >>> prompts = [\"A cat\", \"A dog\", \"A horse\"]\n >>> num_inference_steps = 4\n >>> num_interpolation_steps = 24\n >>> seed = 1337\n\n >>> torch.manual_seed(seed)\n >>> np.random.seed(seed)\n\n >>> images = pipe(\n prompt=prompts,\n height=512,\n width=512,\n num_inference_steps=num_inference_steps,\n num_interpolation_steps=num_interpolation_steps,\n guidance_scale=8.0,\n embedding_interpolation_type=\"lerp\",\n latent_interpolation_type=\"slerp\",\n process_batch_size=4, # Make it higher or lower based on your GPU memory\n generator=torch.Generator(seed),\n )\n\n >>> # Save the images as a video\n >>> import imageio\n >>> from PIL import Image\n\n >>> def pil_to_video(images: List[Image.Image], filename: str, fps: int = 60) -> None:\n frames = [np.array(image) for image in images]\n with imageio.get_writer(filename, fps=fps) as video_writer:\n for frame in frames:\n video_writer.append_data(frame)\n\n >>> pil_to_video(images, \"lcm_interpolate.mp4\", fps=24)\n ```\n\"\"\"\n\n\ndef lerp(\n v0: Union[torch.Tensor, np.ndarray],\n v1: Union[torch.Tensor, np.ndarray],\n t: Union[float, torch.Tensor, np.ndarray],\n) -> Union[torch.Tensor, np.ndarray]:\n \"\"\"\n Linearly interpolate between two vectors/tensors.\n\n Args:\n v0 (`torch.Tensor` or `np.ndarray`): First vector/tensor.\n v1 (`torch.Tensor` or `np.ndarray`): Second vector/tensor.\n t: (`float`, `torch.Tensor`, or `np.ndarray`):\n Interpolation factor. If float, must be between 0 and 1. If np.ndarray or\n torch.Tensor, must be one dimensional with values between 0 and 1.\n\n Returns:\n Union[torch.Tensor, np.ndarray]\n Interpolated vector/tensor between v0 and v1.\n \"\"\"\n inputs_are_torch = False\n t_is_float = False\n\n if isinstance(v0, torch.Tensor):\n inputs_are_torch = True\n input_device = v0.device\n v0 = v0.cpu().numpy()\n v1 = v1.cpu().numpy()\n\n if isinstance(t, torch.Tensor):\n inputs_are_torch = True\n input_device = t.device\n t = t.cpu().numpy()\n elif isinstance(t, float):\n t_is_float = True\n t = np.array([t])\n\n t = t[..., None]\n v0 = v0[None, ...]\n v1 = v1[None, ...]\n v2 = (1 - t) * v0 + t * v1\n\n if t_is_float and v0.ndim > 1:\n assert v2.shape[0] == 1\n v2 = np.squeeze(v2, axis=0)\n if inputs_are_torch:\n v2 = torch.from_numpy(v2).to(input_device)\n\n return v2\n\n\ndef slerp(\n v0: Union[torch.Tensor, np.ndarray],\n v1: Union[torch.Tensor, np.ndarray],\n t: Union[float, torch.Tensor, np.ndarray],\n DOT_THRESHOLD=0.9995,\n) -> Union[torch.Tensor, np.ndarray]:\n \"\"\"\n Spherical linear interpolation between two vectors/tensors.\n\n Args:\n v0 (`torch.Tensor` or `np.ndarray`): First vector/tensor.\n v1 (`torch.Tensor` or `np.ndarray`): Second vector/tensor.\n t: (`float`, `torch.Tensor`, or `np.ndarray`):\n Interpolation factor. If float, must be between 0 and 1. If np.ndarray or\n torch.Tensor, must be one dimensional with values between 0 and 1.\n DOT_THRESHOLD (`float`, *optional*, default=0.9995):\n Threshold for when to use linear interpolation instead of spherical interpolation.\n\n Returns:\n `torch.Tensor` or `np.ndarray`:\n Interpolated vector/tensor between v0 and v1.\n \"\"\"\n inputs_are_torch = False\n t_is_float = False\n\n if isinstance(v0, torch.Tensor):\n inputs_are_torch = True\n input_device = v0.device\n v0 = v0.cpu().numpy()\n v1 = v1.cpu().numpy()\n\n if isinstance(t, torch.Tensor):\n inputs_are_torch = True\n input_device = t.device\n t = t.cpu().numpy()\n elif isinstance(t, float):\n t_is_float = True\n t = np.array([t], dtype=v0.dtype)\n\n dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))\n if np.abs(dot) > DOT_THRESHOLD:\n # v1 and v2 are close to parallel\n # Use linear interpolation instead\n v2 = lerp(v0, v1, t)\n else:\n theta_0 = np.arccos(dot)\n sin_theta_0 = np.sin(theta_0)\n theta_t = theta_0 * t\n sin_theta_t = np.sin(theta_t)\n s0 = np.sin(theta_0 - theta_t) / sin_theta_0\n s1 = sin_theta_t / sin_theta_0\n s0 = s0[..., None]\n s1 = s1[..., None]\n v0 = v0[None, ...]\n v1 = v1[None, ...]\n v2 = s0 * v0 + s1 * v1\n\n if t_is_float and v0.ndim > 1:\n assert v2.shape[0] == 1\n v2 = np.squeeze(v2, axis=0)\n if inputs_are_torch:\n v2 = torch.from_numpy(v2).to(input_device)\n\n return v2\n\n\nclass LatentConsistencyModelWalkPipeline(\n DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin\n):\n r\"\"\"\n Pipeline for text-to-image generation using a latent consistency model.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Currently only\n supports [`LCMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n requires_safety_checker (`bool`, *optional*, defaults to `True`):\n Whether the pipeline requires a safety checker component.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"denoised\", \"prompt_embeds\", \"w_embedding\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: LCMScheduler,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n timesteps (`torch.Tensor`):\n generate embedding vectors at these timesteps\n embedding_dim (`int`, *optional*, defaults to 512):\n dimension of the embeddings to generate\n dtype:\n data type of the generated embeddings\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Currently StableDiffusionPipeline.check_inputs with negative prompt stuff removed\n def check_inputs(\n self,\n prompt: Union[str, List[str]],\n height: int,\n width: int,\n callback_steps: int,\n prompt_embeds: Optional[torch.Tensor] = None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n @torch.no_grad()\n def interpolate_embedding(\n self,\n start_embedding: torch.Tensor,\n end_embedding: torch.Tensor,\n num_interpolation_steps: Union[int, List[int]],\n interpolation_type: str,\n ) -> torch.Tensor:\n if interpolation_type == \"lerp\":\n interpolation_fn = lerp\n elif interpolation_type == \"slerp\":\n interpolation_fn = slerp\n else:\n raise ValueError(\n f\"embedding_interpolation_type must be one of ['lerp', 'slerp'], got {interpolation_type}.\"\n )\n\n embedding = torch.cat([start_embedding, end_embedding])\n steps = torch.linspace(0, 1, num_interpolation_steps, dtype=embedding.dtype).cpu().numpy()\n steps = np.expand_dims(steps, axis=tuple(range(1, embedding.ndim)))\n interpolations = []\n\n # Interpolate between text embeddings\n # TODO(aryan): Think of a better way of doing this\n # See if it can be done parallelly instead\n for i in range(embedding.shape[0] - 1):\n interpolations.append(interpolation_fn(embedding[i], embedding[i + 1], steps).squeeze(dim=1))\n\n interpolations = torch.cat(interpolations)\n return interpolations\n\n @torch.no_grad()\n def interpolate_latent(\n self,\n start_latent: torch.Tensor,\n end_latent: torch.Tensor,\n num_interpolation_steps: Union[int, List[int]],\n interpolation_type: str,\n ) -> torch.Tensor:\n if interpolation_type == \"lerp\":\n interpolation_fn = lerp\n elif interpolation_type == \"slerp\":\n interpolation_fn = slerp\n\n latent = torch.cat([start_latent, end_latent])\n steps = torch.linspace(0, 1, num_interpolation_steps, dtype=latent.dtype).cpu().numpy()\n steps = np.expand_dims(steps, axis=tuple(range(1, latent.ndim)))\n interpolations = []\n\n # Interpolate between latents\n # TODO: Think of a better way of doing this\n # See if it can be done parallelly instead\n for i in range(latent.shape[0] - 1):\n interpolations.append(interpolation_fn(latent[i], latent[i + 1], steps).squeeze(dim=1))\n\n return torch.cat(interpolations)\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 4,\n num_interpolation_steps: int = 8,\n original_inference_steps: int = None,\n guidance_scale: float = 8.5,\n num_images_per_prompt: Optional[int] = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n embedding_interpolation_type: str = \"lerp\",\n latent_interpolation_type: str = \"slerp\",\n process_batch_size: int = 4,\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n original_inference_steps (`int`, *optional*):\n The original number of inference steps use to generate a linearly-spaced timestep schedule, from which\n we will draw `num_inference_steps` evenly spaced timesteps from as our final timestep schedule,\n following the Skipping-Step method in the paper (see Section 4.3). If not set this will default to the\n scheduler's `original_inference_steps` attribute.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n Note that the original latent consistency models paper uses a different CFG formulation where the\n guidance scales are decreased by 1 (so in the paper formulation CFG is enabled when `guidance_scale >\n 0`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n embedding_interpolation_type (`str`, *optional*, defaults to `\"lerp\"`):\n The type of interpolation to use for interpolating between text embeddings. Choose between `\"lerp\"` and `\"slerp\"`.\n latent_interpolation_type (`str`, *optional*, defaults to `\"slerp\"`):\n The type of interpolation to use for interpolating between latents. Choose between `\"lerp\"` and `\"slerp\"`.\n process_batch_size (`int`, *optional*, defaults to 4):\n The batch size to use for processing the images. This is useful when generating a large number of images\n and you want to avoid running out of memory.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(prompt, height, width, callback_steps, prompt_embeds, callback_on_step_end_tensor_inputs)\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n if batch_size < 2:\n raise ValueError(f\"`prompt` must have length of at least 2 but found {batch_size}\")\n if num_images_per_prompt != 1:\n raise ValueError(\"`num_images_per_prompt` must be `1` as no other value is supported yet\")\n if prompt_embeds is not None:\n raise ValueError(\"`prompt_embeds` must be None since it is not supported yet\")\n if latents is not None:\n raise ValueError(\"`latents` must be None since it is not supported yet\")\n\n device = self._execution_device\n # do_classifier_free_guidance = guidance_scale > 1.0\n\n lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n\n self.scheduler.set_timesteps(num_inference_steps, device, original_inference_steps=original_inference_steps)\n timesteps = self.scheduler.timesteps\n num_channels_latents = self.unet.config.in_channels\n # bs = batch_size * num_images_per_prompt\n\n # 3. Encode initial input prompt\n prompt_embeds_1, _ = self.encode_prompt(\n prompt[:1],\n device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=False,\n negative_prompt=None,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=None,\n lora_scale=lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # 4. Prepare initial latent variables\n latents_1 = self.prepare_latents(\n 1,\n num_channels_latents,\n height,\n width,\n prompt_embeds_1.dtype,\n device,\n generator,\n latents,\n )\n\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, None)\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n self._num_timesteps = len(timesteps)\n images = []\n\n # 5. Iterate over prompts and perform latent walk. Note that we do this two prompts at a time\n # otherwise the memory usage ends up being too high.\n with self.progress_bar(total=batch_size - 1) as prompt_progress_bar:\n for i in range(1, batch_size):\n # 6. Encode current prompt\n prompt_embeds_2, _ = self.encode_prompt(\n prompt[i : i + 1],\n device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=False,\n negative_prompt=None,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=None,\n lora_scale=lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # 7. Prepare current latent variables\n latents_2 = self.prepare_latents(\n 1,\n num_channels_latents,\n height,\n width,\n prompt_embeds_2.dtype,\n device,\n generator,\n latents,\n )\n\n # 8. Interpolate between previous and current prompt embeddings and latents\n inference_embeddings = self.interpolate_embedding(\n start_embedding=prompt_embeds_1,\n end_embedding=prompt_embeds_2,\n num_interpolation_steps=num_interpolation_steps,\n interpolation_type=embedding_interpolation_type,\n )\n inference_latents = self.interpolate_latent(\n start_latent=latents_1,\n end_latent=latents_2,\n num_interpolation_steps=num_interpolation_steps,\n interpolation_type=latent_interpolation_type,\n )\n next_prompt_embeds = inference_embeddings[-1:].detach().clone()\n next_latents = inference_latents[-1:].detach().clone()\n bs = num_interpolation_steps\n\n # 9. Perform inference in batches. Note the use of `process_batch_size` to control the batch size\n # of the inference. This is useful for reducing memory usage and can be configured based on the\n # available GPU memory.\n with self.progress_bar(\n total=(bs + process_batch_size - 1) // process_batch_size\n ) as batch_progress_bar:\n for batch_index in range(0, bs, process_batch_size):\n batch_inference_latents = inference_latents[batch_index : batch_index + process_batch_size]\n batch_inference_embeddings = inference_embeddings[\n batch_index : batch_index + process_batch_size\n ]\n\n self.scheduler.set_timesteps(\n num_inference_steps, device, original_inference_steps=original_inference_steps\n )\n timesteps = self.scheduler.timesteps\n\n current_bs = batch_inference_embeddings.shape[0]\n w = torch.tensor(self.guidance_scale - 1).repeat(current_bs)\n w_embedding = self.get_guidance_scale_embedding(\n w, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents_1.dtype)\n\n # 10. Perform inference for current batch\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for index, t in enumerate(timesteps):\n batch_inference_latents = batch_inference_latents.to(batch_inference_embeddings.dtype)\n\n # model prediction (v-prediction, eps, x)\n model_pred = self.unet(\n batch_inference_latents,\n t,\n timestep_cond=w_embedding,\n encoder_hidden_states=batch_inference_embeddings,\n cross_attention_kwargs=self.cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # compute the previous noisy sample x_t -> x_t-1\n batch_inference_latents, denoised = self.scheduler.step(\n model_pred, t, batch_inference_latents, **extra_step_kwargs, return_dict=False\n )\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, index, t, callback_kwargs)\n\n batch_inference_latents = callback_outputs.pop(\"latents\", batch_inference_latents)\n batch_inference_embeddings = callback_outputs.pop(\n \"prompt_embeds\", batch_inference_embeddings\n )\n w_embedding = callback_outputs.pop(\"w_embedding\", w_embedding)\n denoised = callback_outputs.pop(\"denoised\", denoised)\n\n # call the callback, if provided\n if index == len(timesteps) - 1 or (\n (index + 1) > num_warmup_steps and (index + 1) % self.scheduler.order == 0\n ):\n progress_bar.update()\n if callback is not None and index % callback_steps == 0:\n step_idx = index // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, batch_inference_latents)\n\n denoised = denoised.to(batch_inference_embeddings.dtype)\n\n # Note: This is not supported because you would get black images in your latent walk if\n # NSFW concept is detected\n # if not output_type == \"latent\":\n # image = self.vae.decode(denoised / self.vae.config.scaling_factor, return_dict=False)[0]\n # image, has_nsfw_concept = self.run_safety_checker(image, device, inference_embeddings.dtype)\n # else:\n # image = denoised\n # has_nsfw_concept = None\n\n # if has_nsfw_concept is None:\n # do_denormalize = [True] * image.shape[0]\n # else:\n # do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.vae.decode(denoised / self.vae.config.scaling_factor, return_dict=False)[0]\n do_denormalize = [True] * image.shape[0]\n has_nsfw_concept = None\n\n image = self.image_processor.postprocess(\n image, output_type=output_type, do_denormalize=do_denormalize\n )\n images.append(image)\n\n batch_progress_bar.update()\n\n prompt_embeds_1 = next_prompt_embeds\n latents_1 = next_latents\n\n prompt_progress_bar.update()\n\n # 11. Determine what should be returned\n if output_type == \"pil\":\n images = [image for image_list in images for image in image_list]\n elif output_type == \"np\":\n images = np.concatenate(images)\n elif output_type == \"pt\":\n images = torch.cat(images)\n else:\n raise ValueError(\"`output_type` must be one of 'pil', 'np' or 'pt'.\")\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (images, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/examples/community/latent_consistency_txt2img.py", "content": "# Copyright 2024 Stanford University Team and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion\n# and https://github.com/hojonathanho/diffusion\n\nimport math\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import AutoencoderKL, ConfigMixin, DiffusionPipeline, SchedulerMixin, UNet2DConditionModel, logging\nfrom diffusers.configuration_utils import register_to_config\nfrom diffusers.image_processor import VaeImageProcessor\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.utils import BaseOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass LatentConsistencyModelPipeline(DiffusionPipeline):\n _optional_components = [\"scheduler\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: \"LCMScheduler\",\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n scheduler = (\n scheduler\n if scheduler is not None\n else LCMScheduler(\n beta_start=0.00085, beta_end=0.0120, beta_schedule=\"scaled_linear\", prediction_type=\"epsilon\"\n )\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n prompt_embeds: None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n \"\"\"\n\n if prompt is not None and isinstance(prompt, str):\n pass\n elif prompt is not None and isinstance(prompt, list):\n len(prompt)\n else:\n prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n prompt_embeds = prompt_embeds[0]\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # Don't need to get uncond prompt embedding because of LCM Guided Distillation\n return prompt_embeds\n\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if latents is None:\n latents = torch.randn(shape, dtype=dtype).to(device)\n else:\n latents = latents.to(device)\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def get_w_embedding(self, w, embedding_dim=512, dtype=torch.float32):\n \"\"\"\n see https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n Args:\n timesteps: torch.Tensor: generate embedding vectors at these timesteps\n embedding_dim: int: dimension of the embeddings to generate\n dtype: data type of the generated embeddings\n Returns:\n embedding vectors with shape `(len(timesteps), embedding_dim)`\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = 768,\n width: Optional[int] = 768,\n guidance_scale: float = 7.5,\n num_images_per_prompt: Optional[int] = 1,\n latents: Optional[torch.Tensor] = None,\n num_inference_steps: int = 4,\n lcm_origin_steps: int = 50,\n prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n ):\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # do_classifier_free_guidance = guidance_scale > 0.0 # In LCM Implementation: cfg_noise = noise_cond + cfg_scale * (noise_cond - noise_uncond) , (cfg_scale > 0.0 using CFG)\n\n # 3. Encode input prompt\n prompt_embeds = self._encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n prompt_embeds=prompt_embeds,\n )\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, lcm_origin_steps)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variable\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n latents,\n )\n bs = batch_size * num_images_per_prompt\n\n # 6. Get Guidance Scale Embedding\n w = torch.tensor(guidance_scale).repeat(bs)\n w_embedding = self.get_w_embedding(w, embedding_dim=256).to(device=device, dtype=latents.dtype)\n\n # 7. LCM MultiStep Sampling Loop:\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n ts = torch.full((bs,), t, device=device, dtype=torch.long)\n latents = latents.to(prompt_embeds.dtype)\n\n # model prediction (v-prediction, eps, x)\n model_pred = self.unet(\n latents,\n ts,\n timestep_cond=w_embedding,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # compute the previous noisy sample x_t -> x_t-1\n latents, denoised = self.scheduler.step(model_pred, i, t, latents, return_dict=False)\n\n # # call the callback, if provided\n # if i == len(timesteps) - 1:\n progress_bar.update()\n\n denoised = denoised.to(prompt_embeds.dtype)\n if not output_type == \"latent\":\n image = self.vae.decode(denoised / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = denoised\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n\n\n@dataclass\n# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM\nclass LCMSchedulerOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's `step` function output.\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample `(x_{0})` based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.Tensor\n denoised: Optional[torch.Tensor] = None\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\ndef rescale_zero_terminal_snr(betas):\n \"\"\"\n Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)\n Args:\n betas (`torch.Tensor`):\n the betas that the scheduler is being initialized with.\n Returns:\n `torch.Tensor`: rescaled betas with zero terminal SNR\n \"\"\"\n # Convert betas to alphas_bar_sqrt\n alphas = 1.0 - betas\n alphas_cumprod = torch.cumprod(alphas, dim=0)\n alphas_bar_sqrt = alphas_cumprod.sqrt()\n\n # Store old values.\n alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()\n alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()\n\n # Shift so the last timestep is zero.\n alphas_bar_sqrt -= alphas_bar_sqrt_T\n\n # Scale so the first timestep is back to the old value.\n alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)\n\n # Convert alphas_bar_sqrt to betas\n alphas_bar = alphas_bar_sqrt**2 # Revert sqrt\n alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod\n alphas = torch.cat([alphas_bar[0:1], alphas])\n betas = 1 - alphas\n\n return betas\n\n\nclass LCMScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n `LCMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with\n non-Markovian guidance.\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n beta_start (`float`, defaults to 0.0001):\n The starting `beta` value of inference.\n beta_end (`float`, defaults to 0.02):\n The final `beta` value.\n beta_schedule (`str`, defaults to `\"linear\"`):\n The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear`, `scaled_linear`, or `squaredcos_cap_v2`.\n trained_betas (`np.ndarray`, *optional*):\n Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.\n clip_sample (`bool`, defaults to `True`):\n Clip the predicted sample for numerical stability.\n clip_sample_range (`float`, defaults to 1.0):\n The maximum magnitude for sample clipping. Valid only when `clip_sample=True`.\n set_alpha_to_one (`bool`, defaults to `True`):\n Each diffusion step uses the alphas product value at that step and at the previous one. For the final step\n there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,\n otherwise it uses the alpha value at step 0.\n steps_offset (`int`, defaults to 0):\n An offset added to the inference steps, as required by some model families.\n prediction_type (`str`, defaults to `epsilon`, *optional*):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),\n `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen\n Video](https://imagen.research.google/video/paper.pdf) paper).\n thresholding (`bool`, defaults to `False`):\n Whether to use the \"dynamic thresholding\" method. This is unsuitable for latent-space diffusion models such\n as Stable Diffusion.\n dynamic_thresholding_ratio (`float`, defaults to 0.995):\n The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.\n sample_max_value (`float`, defaults to 1.0):\n The threshold value for dynamic thresholding. Valid only when `thresholding=True`.\n timestep_spacing (`str`, defaults to `\"leading\"`):\n The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.\n rescale_betas_zero_snr (`bool`, defaults to `False`):\n Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and\n dark samples instead of limiting it to samples with medium brightness. Loosely related to\n [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).\n \"\"\"\n\n # _compatibles = [e.name for e in KarrasDiffusionSchedulers]\n order = 1\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[Union[np.ndarray, List[float]]] = None,\n clip_sample: bool = True,\n set_alpha_to_one: bool = True,\n steps_offset: int = 0,\n prediction_type: str = \"epsilon\",\n thresholding: bool = False,\n dynamic_thresholding_ratio: float = 0.995,\n clip_sample_range: float = 1.0,\n sample_max_value: float = 1.0,\n timestep_spacing: str = \"leading\",\n rescale_betas_zero_snr: bool = False,\n ):\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n # Rescale for zero SNR\n if rescale_betas_zero_snr:\n self.betas = rescale_zero_terminal_snr(self.betas)\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n\n # At every step in ddim, we are looking into the previous alphas_cumprod\n # For the final step, there is no previous alphas_cumprod because we are already at 0\n # `set_alpha_to_one` decides whether we set this parameter simply to one or\n # whether we use the final alpha of the \"non-previous\" one.\n self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]\n\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = 1.0\n\n # setable values\n self.num_inference_steps = None\n self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64))\n\n def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`int`, *optional*):\n The current timestep in the diffusion chain.\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n return sample\n\n def _get_variance(self, timestep, prev_timestep):\n alpha_prod_t = self.alphas_cumprod[timestep]\n alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n\n variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)\n\n return variance\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample\n def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:\n \"\"\"\n \"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the\n prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by\n s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing\n pixels from saturation at each step. We find that dynamic thresholding results in significantly better\n photorealism as well as better image-text alignment, especially when using very large guidance weights.\"\n https://arxiv.org/abs/2205.11487\n \"\"\"\n dtype = sample.dtype\n batch_size, channels, height, width = sample.shape\n\n if dtype not in (torch.float32, torch.float64):\n sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half\n\n # Flatten sample for doing quantile calculation along each image\n sample = sample.reshape(batch_size, channels * height * width)\n\n abs_sample = sample.abs() # \"a certain percentile absolute pixel value\"\n\n s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)\n s = torch.clamp(\n s, min=1, max=self.config.sample_max_value\n ) # When clamped to min=1, equivalent to standard clipping to [-1, 1]\n\n s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0\n sample = torch.clamp(sample, -s, s) / s # \"we threshold xt0 to the range [-s, s] and then divide by s\"\n\n sample = sample.reshape(batch_size, channels, height, width)\n sample = sample.to(dtype)\n\n return sample\n\n def set_timesteps(self, num_inference_steps: int, lcm_origin_steps: int, device: Union[str, torch.device] = None):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n \"\"\"\n\n if num_inference_steps > self.config.num_train_timesteps:\n raise ValueError(\n f\"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:\"\n f\" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle\"\n f\" maximal {self.config.num_train_timesteps} timesteps.\"\n )\n\n self.num_inference_steps = num_inference_steps\n\n # LCM Timesteps Setting: # Linear Spacing\n c = self.config.num_train_timesteps // lcm_origin_steps\n lcm_origin_timesteps = np.asarray(list(range(1, lcm_origin_steps + 1))) * c - 1 # LCM Training Steps Schedule\n skipping_step = len(lcm_origin_timesteps) // num_inference_steps\n timesteps = lcm_origin_timesteps[::-skipping_step][:num_inference_steps] # LCM Inference Steps Schedule\n\n self.timesteps = torch.from_numpy(timesteps.copy()).to(device)\n\n def get_scalings_for_boundary_condition_discrete(self, t):\n self.sigma_data = 0.5 # Default: 0.5\n\n # By dividing 0.1: This is almost a delta function at t=0.\n c_skip = self.sigma_data**2 / ((t / 0.1) ** 2 + self.sigma_data**2)\n c_out = (t / 0.1) / ((t / 0.1) ** 2 + self.sigma_data**2) ** 0.5\n return c_skip, c_out\n\n def step(\n self,\n model_output: torch.Tensor,\n timeindex: int,\n timestep: int,\n sample: torch.Tensor,\n eta: float = 0.0,\n use_clipped_model_output: bool = False,\n generator=None,\n variance_noise: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ) -> Union[LCMSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise).\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`float`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n eta (`float`):\n The weight of noise for added noise in diffusion step.\n use_clipped_model_output (`bool`, defaults to `False`):\n If `True`, computes \"corrected\" `model_output` from the clipped predicted original sample. Necessary\n because predicted original sample is clipped to [-1, 1] when `self.config.clip_sample` is `True`. If no\n clipping has happened, \"corrected\" `model_output` would coincide with the one provided as input and\n `use_clipped_model_output` has no effect.\n generator (`torch.Generator`, *optional*):\n A random number generator.\n variance_noise (`torch.Tensor`):\n Alternative to generating noise with `generator` by directly providing the noise for the variance\n itself. Useful for methods such as [`CycleDiffusion`].\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] or `tuple`.\n Returns:\n [`~schedulers.scheduling_utils.LCMSchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n \"\"\"\n if self.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n # 1. get previous step value\n prev_timeindex = timeindex + 1\n if prev_timeindex < len(self.timesteps):\n prev_timestep = self.timesteps[prev_timeindex]\n else:\n prev_timestep = timestep\n\n # 2. compute alphas, betas\n alpha_prod_t = self.alphas_cumprod[timestep]\n alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod\n\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n\n # 3. Get scalings for boundary conditions\n c_skip, c_out = self.get_scalings_for_boundary_condition_discrete(timestep)\n\n # 4. Different Parameterization:\n parameterization = self.config.prediction_type\n\n if parameterization == \"epsilon\": # noise-prediction\n pred_x0 = (sample - beta_prod_t.sqrt() * model_output) / alpha_prod_t.sqrt()\n\n elif parameterization == \"sample\": # x-prediction\n pred_x0 = model_output\n\n elif parameterization == \"v_prediction\": # v-prediction\n pred_x0 = alpha_prod_t.sqrt() * sample - beta_prod_t.sqrt() * model_output\n\n # 4. Denoise model output using boundary conditions\n denoised = c_out * pred_x0 + c_skip * sample\n\n # 5. Sample z ~ N(0, I), For MultiStep Inference\n # Noise is not used for one-step sampling.\n if len(self.timesteps) > 1:\n noise = torch.randn(model_output.shape).to(model_output.device)\n prev_sample = alpha_prod_t_prev.sqrt() * denoised + beta_prod_t_prev.sqrt() * noise\n else:\n prev_sample = denoised\n\n if not return_dict:\n return (prev_sample, denoised)\n\n return LCMSchedulerOutput(prev_sample=prev_sample, denoised=denoised)\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.IntTensor,\n ) -> torch.Tensor:\n # Make sure alphas_cumprod and timestep have same device and dtype as original_samples\n alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)\n timesteps = timesteps.to(original_samples.device)\n\n sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n while len(sqrt_alpha_prod.shape) < len(original_samples.shape):\n sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n\n noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise\n return noisy_samples\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity\n def get_velocity(self, sample: torch.Tensor, noise: torch.Tensor, timesteps: torch.IntTensor) -> torch.Tensor:\n # Make sure alphas_cumprod and timestep have same device and dtype as sample\n alphas_cumprod = self.alphas_cumprod.to(device=sample.device, dtype=sample.dtype)\n timesteps = timesteps.to(sample.device)\n\n sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n while len(sqrt_alpha_prod.shape) < len(sample.shape):\n sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n\n velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample\n return velocity\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/examples/community/llm_grounded_diffusion.py", "content": "# Copyright 2024 Long Lian, the GLIGEN Authors, and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# This is a single file implementation of LMD+. See README.md for examples.\n\nimport ast\nimport gc\nimport inspect\nimport math\nimport warnings\nfrom collections.abc import Iterable\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport torch\nimport torch.nn.functional as F\nfrom packaging import version\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom diffusers.configuration_utils import FrozenDict\nfrom diffusers.image_processor import PipelineImageInput, VaeImageProcessor\nfrom diffusers.loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.models.attention import Attention, GatedSelfAttentionDense\nfrom diffusers.models.attention_processor import AttnProcessor2_0\nfrom diffusers.models.lora import adjust_lora_scale_text_encoder\nfrom diffusers.pipelines import DiffusionPipeline\nfrom diffusers.pipelines.pipeline_utils import StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import DiffusionPipeline\n\n >>> pipe = DiffusionPipeline.from_pretrained(\n ... \"longlian/lmd_plus\",\n ... custom_pipeline=\"llm_grounded_diffusion\",\n ... custom_revision=\"main\",\n ... variant=\"fp16\", torch_dtype=torch.float16\n ... )\n >>> pipe.enable_model_cpu_offload()\n\n >>> # Generate an image described by the prompt and\n >>> # insert objects described by text at the region defined by bounding boxes\n >>> prompt = \"a waterfall and a modern high speed train in a beautiful forest with fall foliage\"\n >>> boxes = [[0.1387, 0.2051, 0.4277, 0.7090], [0.4980, 0.4355, 0.8516, 0.7266]]\n >>> phrases = [\"a waterfall\", \"a modern high speed train\"]\n\n >>> images = pipe(\n ... prompt=prompt,\n ... phrases=phrases,\n ... boxes=boxes,\n ... gligen_scheduled_sampling_beta=0.4,\n ... output_type=\"pil\",\n ... num_inference_steps=50,\n ... lmd_guidance_kwargs={}\n ... ).images\n\n >>> images[0].save(\"./lmd_plus_generation.jpg\")\n\n >>> # Generate directly from a text prompt and an LLM response\n >>> prompt = \"a waterfall and a modern high speed train in a beautiful forest with fall foliage\"\n >>> phrases, boxes, bg_prompt, neg_prompt = pipe.parse_llm_response(\\\"\"\"\n [('a waterfall', [71, 105, 148, 258]), ('a modern high speed train', [255, 223, 181, 149])]\n Background prompt: A beautiful forest with fall foliage\n Negative prompt:\n \\\"\"\")\n\n >> images = pipe(\n ... prompt=prompt,\n ... negative_prompt=neg_prompt,\n ... phrases=phrases,\n ... boxes=boxes,\n ... gligen_scheduled_sampling_beta=0.4,\n ... output_type=\"pil\",\n ... num_inference_steps=50,\n ... lmd_guidance_kwargs={}\n ... ).images\n\n >>> images[0].save(\"./lmd_plus_generation.jpg\")\n\nimages[0]\n\n ```\n\"\"\"\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n# All keys in Stable Diffusion models: [('down', 0, 0, 0), ('down', 0, 1, 0), ('down', 1, 0, 0), ('down', 1, 1, 0), ('down', 2, 0, 0), ('down', 2, 1, 0), ('mid', 0, 0, 0), ('up', 1, 0, 0), ('up', 1, 1, 0), ('up', 1, 2, 0), ('up', 2, 0, 0), ('up', 2, 1, 0), ('up', 2, 2, 0), ('up', 3, 0, 0), ('up', 3, 1, 0), ('up', 3, 2, 0)]\n# Note that the first up block is `UpBlock2D` rather than `CrossAttnUpBlock2D` and does not have attention. The last index is always 0 in our case since we have one `BasicTransformerBlock` in each `Transformer2DModel`.\nDEFAULT_GUIDANCE_ATTN_KEYS = [\n (\"mid\", 0, 0, 0),\n (\"up\", 1, 0, 0),\n (\"up\", 1, 1, 0),\n (\"up\", 1, 2, 0),\n]\n\n\ndef convert_attn_keys(key):\n \"\"\"Convert the attention key from tuple format to the torch state format\"\"\"\n\n if key[0] == \"mid\":\n assert key[1] == 0, f\"mid block only has one block but the index is {key[1]}\"\n return f\"{key[0]}_block.attentions.{key[2]}.transformer_blocks.{key[3]}.attn2.processor\"\n\n return f\"{key[0]}_blocks.{key[1]}.attentions.{key[2]}.transformer_blocks.{key[3]}.attn2.processor\"\n\n\nDEFAULT_GUIDANCE_ATTN_KEYS = [convert_attn_keys(key) for key in DEFAULT_GUIDANCE_ATTN_KEYS]\n\n\ndef scale_proportion(obj_box, H, W):\n # Separately rounding box_w and box_h to allow shift invariant box sizes. Otherwise box sizes may change when both coordinates being rounded end with \".5\".\n x_min, y_min = round(obj_box[0] * W), round(obj_box[1] * H)\n box_w, box_h = round((obj_box[2] - obj_box[0]) * W), round((obj_box[3] - obj_box[1]) * H)\n x_max, y_max = x_min + box_w, y_min + box_h\n\n x_min, y_min = max(x_min, 0), max(y_min, 0)\n x_max, y_max = min(x_max, W), min(y_max, H)\n\n return x_min, y_min, x_max, y_max\n\n\n# Adapted from the parent class `AttnProcessor2_0`\nclass AttnProcessorWithHook(AttnProcessor2_0):\n def __init__(\n self,\n attn_processor_key,\n hidden_size,\n cross_attention_dim,\n hook=None,\n fast_attn=True,\n enabled=True,\n ):\n super().__init__()\n self.attn_processor_key = attn_processor_key\n self.hidden_size = hidden_size\n self.cross_attention_dim = cross_attention_dim\n self.hook = hook\n self.fast_attn = fast_attn\n self.enabled = enabled\n\n def __call__(\n self,\n attn: Attention,\n hidden_states,\n encoder_hidden_states=None,\n attention_mask=None,\n temb=None,\n scale: float = 1.0,\n ):\n residual = hidden_states\n\n if attn.spatial_norm is not None:\n hidden_states = attn.spatial_norm(hidden_states, temb)\n\n input_ndim = hidden_states.ndim\n\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n\n if attention_mask is not None:\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n\n if attn.group_norm is not None:\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n\n args = () if USE_PEFT_BACKEND else (scale,)\n query = attn.to_q(hidden_states, *args)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states, *args)\n value = attn.to_v(encoder_hidden_states, *args)\n\n inner_dim = key.shape[-1]\n head_dim = inner_dim // attn.heads\n\n if (self.hook is not None and self.enabled) or not self.fast_attn:\n query_batch_dim = attn.head_to_batch_dim(query)\n key_batch_dim = attn.head_to_batch_dim(key)\n value_batch_dim = attn.head_to_batch_dim(value)\n attention_probs = attn.get_attention_scores(query_batch_dim, key_batch_dim, attention_mask)\n\n if self.hook is not None and self.enabled:\n # Call the hook with query, key, value, and attention maps\n self.hook(\n self.attn_processor_key,\n query_batch_dim,\n key_batch_dim,\n value_batch_dim,\n attention_probs,\n )\n\n if self.fast_attn:\n query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n if attention_mask is not None:\n # scaled_dot_product_attention expects attention_mask shape to be\n # (batch, heads, source_length, target_length)\n attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])\n\n # the output of sdp = (batch, num_heads, seq_len, head_dim)\n # TODO: add support for attn.scale when we move to Torch 2.1\n hidden_states = F.scaled_dot_product_attention(\n query,\n key,\n value,\n attn_mask=attention_mask,\n dropout_p=0.0,\n is_causal=False,\n )\n hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)\n hidden_states = hidden_states.to(query.dtype)\n else:\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states, *args)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n if input_ndim == 4:\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n if attn.residual_connection:\n hidden_states = hidden_states + residual\n\n hidden_states = hidden_states / attn.rescale_output_factor\n\n return hidden_states\n\n\nclass LLMGroundedDiffusionPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n TextualInversionLoaderMixin,\n LoraLoaderMixin,\n IPAdapterMixin,\n FromSingleFileMixin,\n):\n r\"\"\"\n Pipeline for layout-grounded text-to-image generation using LLM-grounded Diffusion (LMD+): https://arxiv.org/pdf/2305.13655.pdf.\n\n This model inherits from [`StableDiffusionPipeline`] and aims at implementing the pipeline with minimal modifications. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n This is a simplified implementation that does not perform latent or attention transfer from single object generation to overall generation. The final image is generated directly with attention and adapters control.\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n requires_safety_checker (bool):\n Whether a safety checker is needed for this pipeline.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"image_encoder\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\"]\n\n objects_text = \"Objects: \"\n bg_prompt_text = \"Background prompt: \"\n bg_prompt_text_no_trailing_space = bg_prompt_text.rstrip()\n neg_prompt_text = \"Negative prompt: \"\n neg_prompt_text_no_trailing_space = neg_prompt_text.rstrip()\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder: CLIPVisionModelWithProjection = None,\n requires_safety_checker: bool = True,\n ):\n # This is copied from StableDiffusionPipeline, with hook initizations for LMD+.\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"clip_sample\"] = False\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Initialize the attention hooks for LLM-grounded Diffusion\n self.register_attn_hooks(unet)\n self._saved_attn = None\n\n def attn_hook(self, name, query, key, value, attention_probs):\n if name in DEFAULT_GUIDANCE_ATTN_KEYS:\n self._saved_attn[name] = attention_probs\n\n @classmethod\n def convert_box(cls, box, height, width):\n # box: x, y, w, h (in 512 format) -> x_min, y_min, x_max, y_max\n x_min, y_min = box[0] / width, box[1] / height\n w_box, h_box = box[2] / width, box[3] / height\n\n x_max, y_max = x_min + w_box, y_min + h_box\n\n return x_min, y_min, x_max, y_max\n\n @classmethod\n def _parse_response_with_negative(cls, text):\n if not text:\n raise ValueError(\"LLM response is empty\")\n\n if cls.objects_text in text:\n text = text.split(cls.objects_text)[1]\n\n text_split = text.split(cls.bg_prompt_text_no_trailing_space)\n if len(text_split) == 2:\n gen_boxes, text_rem = text_split\n else:\n raise ValueError(f\"LLM response is incomplete: {text}\")\n\n text_split = text_rem.split(cls.neg_prompt_text_no_trailing_space)\n\n if len(text_split) == 2:\n bg_prompt, neg_prompt = text_split\n else:\n raise ValueError(f\"LLM response is incomplete: {text}\")\n\n try:\n gen_boxes = ast.literal_eval(gen_boxes)\n except SyntaxError as e:\n # Sometimes the response is in plain text\n if \"No objects\" in gen_boxes or gen_boxes.strip() == \"\":\n gen_boxes = []\n else:\n raise e\n bg_prompt = bg_prompt.strip()\n neg_prompt = neg_prompt.strip()\n\n # LLM may return \"None\" to mean no negative prompt provided.\n if neg_prompt == \"None\":\n neg_prompt = \"\"\n\n return gen_boxes, bg_prompt, neg_prompt\n\n @classmethod\n def parse_llm_response(cls, response, canvas_height=512, canvas_width=512):\n # Infer from spec\n gen_boxes, bg_prompt, neg_prompt = cls._parse_response_with_negative(text=response)\n\n gen_boxes = sorted(gen_boxes, key=lambda gen_box: gen_box[0])\n\n phrases = [name for name, _ in gen_boxes]\n boxes = [cls.convert_box(box, height=canvas_height, width=canvas_width) for _, box in gen_boxes]\n\n return phrases, boxes, bg_prompt, neg_prompt\n\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n phrases,\n boxes,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n phrase_indices=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt is None and phrase_indices is None:\n raise ValueError(\"If the prompt is None, the phrase_indices cannot be None\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if len(phrases) != len(boxes):\n raise ValueError(\n \"length of `phrases` and `boxes` has to be same, but\"\n f\" got: `phrases` {len(phrases)} != `boxes` {len(boxes)}\"\n )\n\n def register_attn_hooks(self, unet):\n \"\"\"Registering hooks to obtain the attention maps for guidance\"\"\"\n\n attn_procs = {}\n\n for name in unet.attn_processors.keys():\n # Only obtain the queries and keys from cross-attention\n if name.endswith(\"attn1.processor\") or name.endswith(\"fuser.attn.processor\"):\n # Keep the same attn_processors for self-attention (no hooks for self-attention)\n attn_procs[name] = unet.attn_processors[name]\n continue\n\n cross_attention_dim = None if name.endswith(\"attn1.processor\") else unet.config.cross_attention_dim\n\n if name.startswith(\"mid_block\"):\n hidden_size = unet.config.block_out_channels[-1]\n elif name.startswith(\"up_blocks\"):\n block_id = int(name[len(\"up_blocks.\")])\n hidden_size = list(reversed(unet.config.block_out_channels))[block_id]\n elif name.startswith(\"down_blocks\"):\n block_id = int(name[len(\"down_blocks.\")])\n hidden_size = unet.config.block_out_channels[block_id]\n\n attn_procs[name] = AttnProcessorWithHook(\n attn_processor_key=name,\n hidden_size=hidden_size,\n cross_attention_dim=cross_attention_dim,\n hook=self.attn_hook,\n fast_attn=True,\n # Not enabled by default\n enabled=False,\n )\n\n unet.set_attn_processor(attn_procs)\n\n def enable_fuser(self, enabled=True):\n for module in self.unet.modules():\n if isinstance(module, GatedSelfAttentionDense):\n module.enabled = enabled\n\n def enable_attn_hook(self, enabled=True):\n for module in self.unet.attn_processors.values():\n if isinstance(module, AttnProcessorWithHook):\n module.enabled = enabled\n\n def get_token_map(self, prompt, padding=\"do_not_pad\", verbose=False):\n \"\"\"Get a list of mapping: prompt index to str (prompt in a list of token str)\"\"\"\n fg_prompt_tokens = self.tokenizer([prompt], padding=padding, max_length=77, return_tensors=\"np\")\n input_ids = fg_prompt_tokens[\"input_ids\"][0]\n\n token_map = []\n for ind, item in enumerate(input_ids.tolist()):\n token = self.tokenizer._convert_id_to_token(item)\n\n if verbose:\n logger.info(f\"{ind}, {token} ({item})\")\n\n token_map.append(token)\n\n return token_map\n\n def get_phrase_indices(\n self,\n prompt,\n phrases,\n token_map=None,\n add_suffix_if_not_found=False,\n verbose=False,\n ):\n for obj in phrases:\n # Suffix the prompt with object name for attention guidance if object is not in the prompt, using \"|\" to separate the prompt and the suffix\n if obj not in prompt:\n prompt += \"| \" + obj\n\n if token_map is None:\n # We allow using a pre-computed token map.\n token_map = self.get_token_map(prompt=prompt, padding=\"do_not_pad\", verbose=verbose)\n token_map_str = \" \".join(token_map)\n\n phrase_indices = []\n\n for obj in phrases:\n phrase_token_map = self.get_token_map(prompt=obj, padding=\"do_not_pad\", verbose=verbose)\n # Remove and in substr\n phrase_token_map = phrase_token_map[1:-1]\n phrase_token_map_len = len(phrase_token_map)\n phrase_token_map_str = \" \".join(phrase_token_map)\n\n if verbose:\n logger.info(\n \"Full str:\",\n token_map_str,\n \"Substr:\",\n phrase_token_map_str,\n \"Phrase:\",\n phrases,\n )\n\n # Count the number of token before substr\n # The substring comes with a trailing space that needs to be removed by minus one in the index.\n obj_first_index = len(token_map_str[: token_map_str.index(phrase_token_map_str) - 1].split(\" \"))\n\n obj_position = list(range(obj_first_index, obj_first_index + phrase_token_map_len))\n phrase_indices.append(obj_position)\n\n if add_suffix_if_not_found:\n return phrase_indices, prompt\n\n return phrase_indices\n\n def add_ca_loss_per_attn_map_to_loss(\n self,\n loss,\n attn_map,\n object_number,\n bboxes,\n phrase_indices,\n fg_top_p=0.2,\n bg_top_p=0.2,\n fg_weight=1.0,\n bg_weight=1.0,\n ):\n # b is the number of heads, not batch\n b, i, j = attn_map.shape\n H = W = int(math.sqrt(i))\n for obj_idx in range(object_number):\n obj_loss = 0\n mask = torch.zeros(size=(H, W), device=\"cuda\")\n obj_boxes = bboxes[obj_idx]\n\n # We support two level (one box per phrase) and three level (multiple boxes per phrase)\n if not isinstance(obj_boxes[0], Iterable):\n obj_boxes = [obj_boxes]\n\n for obj_box in obj_boxes:\n # x_min, y_min, x_max, y_max = int(obj_box[0] * W), int(obj_box[1] * H), int(obj_box[2] * W), int(obj_box[3] * H)\n x_min, y_min, x_max, y_max = scale_proportion(obj_box, H=H, W=W)\n mask[y_min:y_max, x_min:x_max] = 1\n\n for obj_position in phrase_indices[obj_idx]:\n # Could potentially optimize to compute this for loop in batch.\n # Could crop the ref cross attention before saving to save memory.\n\n ca_map_obj = attn_map[:, :, obj_position].reshape(b, H, W)\n\n # shape: (b, H * W)\n ca_map_obj = attn_map[:, :, obj_position] # .reshape(b, H, W)\n k_fg = (mask.sum() * fg_top_p).long().clamp_(min=1)\n k_bg = ((1 - mask).sum() * bg_top_p).long().clamp_(min=1)\n\n mask_1d = mask.view(1, -1)\n\n # Max-based loss function\n\n # Take the topk over spatial dimension, and then take the sum over heads dim\n # The mean is over k_fg and k_bg dimension, so we don't need to sum and divide on our own.\n obj_loss += (1 - (ca_map_obj * mask_1d).topk(k=k_fg).values.mean(dim=1)).sum(dim=0) * fg_weight\n obj_loss += ((ca_map_obj * (1 - mask_1d)).topk(k=k_bg).values.mean(dim=1)).sum(dim=0) * bg_weight\n\n loss += obj_loss / len(phrase_indices[obj_idx])\n\n return loss\n\n def compute_ca_loss(\n self,\n saved_attn,\n bboxes,\n phrase_indices,\n guidance_attn_keys,\n verbose=False,\n **kwargs,\n ):\n \"\"\"\n The `saved_attn` is supposed to be passed to `save_attn_to_dict` in `cross_attention_kwargs` prior to computing ths loss.\n `AttnProcessor` will put attention maps into the `save_attn_to_dict`.\n\n `index` is the timestep.\n `ref_ca_word_token_only`: This has precedence over `ref_ca_last_token_only` (i.e., if both are enabled, we take the token from word rather than the last token).\n `ref_ca_last_token_only`: `ref_ca_saved_attn` comes from the attention map of the last token of the phrase in single object generation, so we apply it only to the last token of the phrase in overall generation if this is set to True. If set to False, `ref_ca_saved_attn` will be applied to all the text tokens.\n \"\"\"\n loss = torch.tensor(0).float().cuda()\n object_number = len(bboxes)\n if object_number == 0:\n return loss\n\n for attn_key in guidance_attn_keys:\n # We only have 1 cross attention for mid.\n\n attn_map_integrated = saved_attn[attn_key]\n if not attn_map_integrated.is_cuda:\n attn_map_integrated = attn_map_integrated.cuda()\n # Example dimension: [20, 64, 77]\n attn_map = attn_map_integrated.squeeze(dim=0)\n\n loss = self.add_ca_loss_per_attn_map_to_loss(\n loss, attn_map, object_number, bboxes, phrase_indices, **kwargs\n )\n\n num_attn = len(guidance_attn_keys)\n\n if num_attn > 0:\n loss = loss / (object_number * num_attn)\n\n return loss\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n gligen_scheduled_sampling_beta: float = 0.3,\n phrases: List[str] = None,\n boxes: List[List[float]] = None,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: Optional[int] = None,\n lmd_guidance_kwargs: Optional[Dict[str, Any]] = {},\n phrase_indices: Optional[List[int]] = None,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n phrases (`List[str]`):\n The phrases to guide what to include in each of the regions defined by the corresponding\n `boxes`. There should only be one phrase per bounding box.\n boxes (`List[List[float]]`):\n The bounding boxes that identify rectangular regions of the image that are going to be filled with the\n content described by the corresponding `phrases`. Each rectangular box is defined as a\n `List[float]` of 4 elements `[xmin, ymin, xmax, ymax]` where each value is between [0,1].\n gligen_scheduled_sampling_beta (`float`, defaults to 0.3):\n Scheduled Sampling factor from [GLIGEN: Open-Set Grounded Text-to-Image\n Generation](https://arxiv.org/pdf/2301.07093.pdf). Scheduled Sampling factor is only varied for\n scheduled sampling during inference for improved quality and controllability.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when\n using zero terminal SNR.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n lmd_guidance_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to `latent_lmd_guidance` function. Useful keys include `loss_scale` (the guidance strength), `loss_threshold` (when loss is lower than this value, the guidance is not applied anymore), `max_iter` (the number of iterations of guidance for each step), and `guidance_timesteps` (the number of diffusion timesteps to apply guidance on). See `latent_lmd_guidance` for implementation details.\n phrase_indices (`list` of `list`, *optional*): The indices of the tokens of each phrase in the overall prompt. If omitted, the pipeline will match the first token subsequence. The pipeline will append the missing phrases to the end of the prompt by default.\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n height,\n width,\n callback_steps,\n phrases,\n boxes,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n phrase_indices,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n if phrase_indices is None:\n phrase_indices, prompt = self.get_phrase_indices(prompt, phrases, add_suffix_if_not_found=True)\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n if phrase_indices is None:\n phrase_indices = []\n prompt_parsed = []\n for prompt_item in prompt:\n (\n phrase_indices_parsed_item,\n prompt_parsed_item,\n ) = self.get_phrase_indices(prompt_item, add_suffix_if_not_found=True)\n phrase_indices.append(phrase_indices_parsed_item)\n prompt_parsed.append(prompt_parsed_item)\n prompt = prompt_parsed\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n clip_skip=clip_skip,\n )\n\n cond_prompt_embeds = prompt_embeds\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n if ip_adapter_image is not None:\n image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt)\n if self.do_classifier_free_guidance:\n image_embeds = torch.cat([negative_image_embeds, image_embeds])\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 5.1 Prepare GLIGEN variables\n max_objs = 30\n if len(boxes) > max_objs:\n warnings.warn(\n f\"More that {max_objs} objects found. Only first {max_objs} objects will be processed.\",\n FutureWarning,\n )\n phrases = phrases[:max_objs]\n boxes = boxes[:max_objs]\n\n n_objs = len(boxes)\n if n_objs:\n # prepare batched input to the PositionNet (boxes, phrases, mask)\n # Get tokens for phrases from pre-trained CLIPTokenizer\n tokenizer_inputs = self.tokenizer(phrases, padding=True, return_tensors=\"pt\").to(device)\n # For the token, we use the same pre-trained text encoder\n # to obtain its text feature\n _text_embeddings = self.text_encoder(**tokenizer_inputs).pooler_output\n\n # For each entity, described in phrases, is denoted with a bounding box,\n # we represent the location information as (xmin,ymin,xmax,ymax)\n cond_boxes = torch.zeros(max_objs, 4, device=device, dtype=self.text_encoder.dtype)\n if n_objs:\n cond_boxes[:n_objs] = torch.tensor(boxes)\n text_embeddings = torch.zeros(\n max_objs,\n self.unet.config.cross_attention_dim,\n device=device,\n dtype=self.text_encoder.dtype,\n )\n if n_objs:\n text_embeddings[:n_objs] = _text_embeddings\n # Generate a mask for each object that is entity described by phrases\n masks = torch.zeros(max_objs, device=device, dtype=self.text_encoder.dtype)\n masks[:n_objs] = 1\n\n repeat_batch = batch_size * num_images_per_prompt\n cond_boxes = cond_boxes.unsqueeze(0).expand(repeat_batch, -1, -1).clone()\n text_embeddings = text_embeddings.unsqueeze(0).expand(repeat_batch, -1, -1).clone()\n masks = masks.unsqueeze(0).expand(repeat_batch, -1).clone()\n if do_classifier_free_guidance:\n repeat_batch = repeat_batch * 2\n cond_boxes = torch.cat([cond_boxes] * 2)\n text_embeddings = torch.cat([text_embeddings] * 2)\n masks = torch.cat([masks] * 2)\n masks[: repeat_batch // 2] = 0\n if cross_attention_kwargs is None:\n cross_attention_kwargs = {}\n cross_attention_kwargs[\"gligen\"] = {\n \"boxes\": cond_boxes,\n \"positive_embeddings\": text_embeddings,\n \"masks\": masks,\n }\n\n num_grounding_steps = int(gligen_scheduled_sampling_beta * len(timesteps))\n self.enable_fuser(True)\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 6.1 Add image embeds for IP-Adapter\n added_cond_kwargs = {\"image_embeds\": image_embeds} if ip_adapter_image is not None else None\n\n loss_attn = torch.tensor(10000.0)\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # Scheduled sampling\n if i == num_grounding_steps:\n self.enable_fuser(False)\n\n if latents.shape[1] != 4:\n latents = torch.randn_like(latents[:, :4])\n\n # 7.1 Perform LMD guidance\n if boxes:\n latents, loss_attn = self.latent_lmd_guidance(\n cond_prompt_embeds,\n index=i,\n boxes=boxes,\n phrase_indices=phrase_indices,\n t=t,\n latents=latents,\n loss=loss_attn,\n **lmd_guidance_kwargs,\n )\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload last model to CPU\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n\n @torch.set_grad_enabled(True)\n def latent_lmd_guidance(\n self,\n cond_embeddings,\n index,\n boxes,\n phrase_indices,\n t,\n latents,\n loss,\n *,\n loss_scale=20,\n loss_threshold=5.0,\n max_iter=[3] * 5 + [2] * 5 + [1] * 5,\n guidance_timesteps=15,\n cross_attention_kwargs=None,\n guidance_attn_keys=DEFAULT_GUIDANCE_ATTN_KEYS,\n verbose=False,\n clear_cache=False,\n unet_additional_kwargs={},\n guidance_callback=None,\n **kwargs,\n ):\n scheduler, unet = self.scheduler, self.unet\n\n iteration = 0\n\n if index < guidance_timesteps:\n if isinstance(max_iter, list):\n max_iter = max_iter[index]\n\n if verbose:\n logger.info(\n f\"time index {index}, loss: {loss.item()/loss_scale:.3f} (de-scaled with scale {loss_scale:.1f}), loss threshold: {loss_threshold:.3f}\"\n )\n\n try:\n self.enable_attn_hook(enabled=True)\n\n while (\n loss.item() / loss_scale > loss_threshold and iteration < max_iter and index < guidance_timesteps\n ):\n self._saved_attn = {}\n\n latents.requires_grad_(True)\n latent_model_input = latents\n latent_model_input = scheduler.scale_model_input(latent_model_input, t)\n\n unet(\n latent_model_input,\n t,\n encoder_hidden_states=cond_embeddings,\n cross_attention_kwargs=cross_attention_kwargs,\n **unet_additional_kwargs,\n )\n\n # update latents with guidance\n loss = (\n self.compute_ca_loss(\n saved_attn=self._saved_attn,\n bboxes=boxes,\n phrase_indices=phrase_indices,\n guidance_attn_keys=guidance_attn_keys,\n verbose=verbose,\n **kwargs,\n )\n * loss_scale\n )\n\n if torch.isnan(loss):\n raise RuntimeError(\"**Loss is NaN**\")\n\n # This callback allows visualizations.\n if guidance_callback is not None:\n guidance_callback(self, latents, loss, iteration, index)\n\n self._saved_attn = None\n\n grad_cond = torch.autograd.grad(loss.requires_grad_(True), [latents])[0]\n\n latents.requires_grad_(False)\n\n # Scaling with classifier guidance\n alpha_prod_t = scheduler.alphas_cumprod[t]\n # Classifier guidance: https://arxiv.org/pdf/2105.05233.pdf\n # DDIM: https://arxiv.org/pdf/2010.02502.pdf\n scale = (1 - alpha_prod_t) ** (0.5)\n latents = latents - scale * grad_cond\n\n iteration += 1\n\n if clear_cache:\n gc.collect()\n torch.cuda.empty_cache()\n\n if verbose:\n logger.info(\n f\"time index {index}, loss: {loss.item()/loss_scale:.3f}, loss threshold: {loss_threshold:.3f}, iteration: {iteration}\"\n )\n\n finally:\n self.enable_attn_hook(enabled=False)\n\n return latents, loss\n\n # Below are methods copied from StableDiffusionPipeline\n # The design choice of not inheriting from StableDiffusionPipeline is discussed here: https://github.com/huggingface/diffusers/pull/5993#issuecomment-1834258517\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n output_hidden_states=True,\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n\n uncond_image_embeds = torch.zeros_like(image_embeds)\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(\n self,\n batch_size,\n num_channels_latents,\n height,\n width,\n dtype,\n device,\n generator,\n latents=None,\n ):\n shape = (\n batch_size,\n num_channels_latents,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n timesteps (`torch.Tensor`):\n generate embedding vectors at these timesteps\n embedding_dim (`int`, *optional*, defaults to 512):\n dimension of the embeddings to generate\n dtype:\n data type of the generated embeddings\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.guidance_scale\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.guidance_rescale\n def guidance_rescale(self):\n return self._guidance_rescale\n\n @property\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.clip_skip\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.do_classifier_free_guidance\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.cross_attention_kwargs\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.num_timesteps\n def num_timesteps(self):\n return self._num_timesteps\n" }, { "path": "diffusers/examples/community/lpw_stable_diffusion.py", "content": "import inspect\nimport re\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom packaging import version\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import DiffusionPipeline\nfrom diffusers.configuration_utils import FrozenDict\nfrom diffusers.image_processor import VaeImageProcessor\nfrom diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.pipelines.pipeline_utils import StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n PIL_INTERPOLATION,\n deprecate,\n logging,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\n# ------------------------------------------------------------------------------\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nre_attention = re.compile(\n r\"\"\"\n\\\\\\(|\n\\\\\\)|\n\\\\\\[|\n\\\\]|\n\\\\\\\\|\n\\\\|\n\\(|\n\\[|\n:([+-]?[.\\d]+)\\)|\n\\)|\n]|\n[^\\\\()\\[\\]:]+|\n:\n\"\"\",\n re.X,\n)\n\n\ndef parse_prompt_attention(text):\n \"\"\"\n Parses a string with attention tokens and returns a list of pairs: text and its associated weight.\n Accepted tokens are:\n (abc) - increases attention to abc by a multiplier of 1.1\n (abc:3.12) - increases attention to abc by a multiplier of 3.12\n [abc] - decreases attention to abc by a multiplier of 1.1\n \\\\( - literal character '('\n \\\\[ - literal character '['\n \\\\) - literal character ')'\n \\\\] - literal character ']'\n \\\\ - literal character '\\'\n anything else - just text\n >>> parse_prompt_attention('normal text')\n [['normal text', 1.0]]\n >>> parse_prompt_attention('an (important) word')\n [['an ', 1.0], ['important', 1.1], [' word', 1.0]]\n >>> parse_prompt_attention('(unbalanced')\n [['unbalanced', 1.1]]\n >>> parse_prompt_attention('\\\\(literal\\\\]')\n [['(literal]', 1.0]]\n >>> parse_prompt_attention('(unnecessary)(parens)')\n [['unnecessaryparens', 1.1]]\n >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')\n [['a ', 1.0],\n ['house', 1.5730000000000004],\n [' ', 1.1],\n ['on', 1.0],\n [' a ', 1.1],\n ['hill', 0.55],\n [', sun, ', 1.1],\n ['sky', 1.4641000000000006],\n ['.', 1.1]]\n \"\"\"\n\n res = []\n round_brackets = []\n square_brackets = []\n\n round_bracket_multiplier = 1.1\n square_bracket_multiplier = 1 / 1.1\n\n def multiply_range(start_position, multiplier):\n for p in range(start_position, len(res)):\n res[p][1] *= multiplier\n\n for m in re_attention.finditer(text):\n text = m.group(0)\n weight = m.group(1)\n\n if text.startswith(\"\\\\\"):\n res.append([text[1:], 1.0])\n elif text == \"(\":\n round_brackets.append(len(res))\n elif text == \"[\":\n square_brackets.append(len(res))\n elif weight is not None and len(round_brackets) > 0:\n multiply_range(round_brackets.pop(), float(weight))\n elif text == \")\" and len(round_brackets) > 0:\n multiply_range(round_brackets.pop(), round_bracket_multiplier)\n elif text == \"]\" and len(square_brackets) > 0:\n multiply_range(square_brackets.pop(), square_bracket_multiplier)\n else:\n res.append([text, 1.0])\n\n for pos in round_brackets:\n multiply_range(pos, round_bracket_multiplier)\n\n for pos in square_brackets:\n multiply_range(pos, square_bracket_multiplier)\n\n if len(res) == 0:\n res = [[\"\", 1.0]]\n\n # merge runs of identical weights\n i = 0\n while i + 1 < len(res):\n if res[i][1] == res[i + 1][1]:\n res[i][0] += res[i + 1][0]\n res.pop(i + 1)\n else:\n i += 1\n\n return res\n\n\ndef get_prompts_with_weights(pipe: DiffusionPipeline, prompt: List[str], max_length: int):\n r\"\"\"\n Tokenize a list of prompts and return its tokens with weights of each token.\n\n No padding, starting or ending token is included.\n \"\"\"\n tokens = []\n weights = []\n truncated = False\n for text in prompt:\n texts_and_weights = parse_prompt_attention(text)\n text_token = []\n text_weight = []\n for word, weight in texts_and_weights:\n # tokenize and discard the starting and the ending token\n token = pipe.tokenizer(word).input_ids[1:-1]\n text_token += token\n # copy the weight by length of token\n text_weight += [weight] * len(token)\n # stop if the text is too long (longer than truncation limit)\n if len(text_token) > max_length:\n truncated = True\n break\n # truncate\n if len(text_token) > max_length:\n truncated = True\n text_token = text_token[:max_length]\n text_weight = text_weight[:max_length]\n tokens.append(text_token)\n weights.append(text_weight)\n if truncated:\n logger.warning(\"Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples\")\n return tokens, weights\n\n\ndef pad_tokens_and_weights(tokens, weights, max_length, bos, eos, pad, no_boseos_middle=True, chunk_length=77):\n r\"\"\"\n Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length.\n \"\"\"\n max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)\n weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length\n for i in range(len(tokens)):\n tokens[i] = [bos] + tokens[i] + [pad] * (max_length - 1 - len(tokens[i]) - 1) + [eos]\n if no_boseos_middle:\n weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i]))\n else:\n w = []\n if len(weights[i]) == 0:\n w = [1.0] * weights_length\n else:\n for j in range(max_embeddings_multiples):\n w.append(1.0) # weight for starting token in this chunk\n w += weights[i][j * (chunk_length - 2) : min(len(weights[i]), (j + 1) * (chunk_length - 2))]\n w.append(1.0) # weight for ending token in this chunk\n w += [1.0] * (weights_length - len(w))\n weights[i] = w[:]\n\n return tokens, weights\n\n\ndef get_unweighted_text_embeddings(\n pipe: DiffusionPipeline,\n text_input: torch.Tensor,\n chunk_length: int,\n no_boseos_middle: Optional[bool] = True,\n):\n \"\"\"\n When the length of tokens is a multiple of the capacity of the text encoder,\n it should be split into chunks and sent to the text encoder individually.\n \"\"\"\n max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)\n if max_embeddings_multiples > 1:\n text_embeddings = []\n for i in range(max_embeddings_multiples):\n # extract the i-th chunk\n text_input_chunk = text_input[:, i * (chunk_length - 2) : (i + 1) * (chunk_length - 2) + 2].clone()\n\n # cover the head and the tail by the starting and the ending tokens\n text_input_chunk[:, 0] = text_input[0, 0]\n text_input_chunk[:, -1] = text_input[0, -1]\n text_embedding = pipe.text_encoder(text_input_chunk)[0]\n\n if no_boseos_middle:\n if i == 0:\n # discard the ending token\n text_embedding = text_embedding[:, :-1]\n elif i == max_embeddings_multiples - 1:\n # discard the starting token\n text_embedding = text_embedding[:, 1:]\n else:\n # discard both starting and ending tokens\n text_embedding = text_embedding[:, 1:-1]\n\n text_embeddings.append(text_embedding)\n text_embeddings = torch.concat(text_embeddings, axis=1)\n else:\n text_embeddings = pipe.text_encoder(text_input)[0]\n return text_embeddings\n\n\ndef get_weighted_text_embeddings(\n pipe: DiffusionPipeline,\n prompt: Union[str, List[str]],\n uncond_prompt: Optional[Union[str, List[str]]] = None,\n max_embeddings_multiples: Optional[int] = 3,\n no_boseos_middle: Optional[bool] = False,\n skip_parsing: Optional[bool] = False,\n skip_weighting: Optional[bool] = False,\n):\n r\"\"\"\n Prompts can be assigned with local weights using brackets. For example,\n prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful',\n and the embedding tokens corresponding to the words get multiplied by a constant, 1.1.\n\n Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.\n\n Args:\n pipe (`DiffusionPipeline`):\n Pipe to provide access to the tokenizer and the text encoder.\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n uncond_prompt (`str` or `List[str]`):\n The unconditional prompt or prompts for guide the image generation. If unconditional prompt\n is provided, the embeddings of prompt and uncond_prompt are concatenated.\n max_embeddings_multiples (`int`, *optional*, defaults to `3`):\n The max multiple length of prompt embeddings compared to the max output length of text encoder.\n no_boseos_middle (`bool`, *optional*, defaults to `False`):\n If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and\n ending token in each of the chunk in the middle.\n skip_parsing (`bool`, *optional*, defaults to `False`):\n Skip the parsing of brackets.\n skip_weighting (`bool`, *optional*, defaults to `False`):\n Skip the weighting. When the parsing is skipped, it is forced True.\n \"\"\"\n max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2\n if isinstance(prompt, str):\n prompt = [prompt]\n\n if not skip_parsing:\n prompt_tokens, prompt_weights = get_prompts_with_weights(pipe, prompt, max_length - 2)\n if uncond_prompt is not None:\n if isinstance(uncond_prompt, str):\n uncond_prompt = [uncond_prompt]\n uncond_tokens, uncond_weights = get_prompts_with_weights(pipe, uncond_prompt, max_length - 2)\n else:\n prompt_tokens = [\n token[1:-1] for token in pipe.tokenizer(prompt, max_length=max_length, truncation=True).input_ids\n ]\n prompt_weights = [[1.0] * len(token) for token in prompt_tokens]\n if uncond_prompt is not None:\n if isinstance(uncond_prompt, str):\n uncond_prompt = [uncond_prompt]\n uncond_tokens = [\n token[1:-1]\n for token in pipe.tokenizer(uncond_prompt, max_length=max_length, truncation=True).input_ids\n ]\n uncond_weights = [[1.0] * len(token) for token in uncond_tokens]\n\n # round up the longest length of tokens to a multiple of (model_max_length - 2)\n max_length = max([len(token) for token in prompt_tokens])\n if uncond_prompt is not None:\n max_length = max(max_length, max([len(token) for token in uncond_tokens]))\n\n max_embeddings_multiples = min(\n max_embeddings_multiples,\n (max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1,\n )\n max_embeddings_multiples = max(1, max_embeddings_multiples)\n max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2\n\n # pad the length of tokens and weights\n bos = pipe.tokenizer.bos_token_id\n eos = pipe.tokenizer.eos_token_id\n pad = getattr(pipe.tokenizer, \"pad_token_id\", eos)\n prompt_tokens, prompt_weights = pad_tokens_and_weights(\n prompt_tokens,\n prompt_weights,\n max_length,\n bos,\n eos,\n pad,\n no_boseos_middle=no_boseos_middle,\n chunk_length=pipe.tokenizer.model_max_length,\n )\n prompt_tokens = torch.tensor(prompt_tokens, dtype=torch.long, device=pipe.device)\n if uncond_prompt is not None:\n uncond_tokens, uncond_weights = pad_tokens_and_weights(\n uncond_tokens,\n uncond_weights,\n max_length,\n bos,\n eos,\n pad,\n no_boseos_middle=no_boseos_middle,\n chunk_length=pipe.tokenizer.model_max_length,\n )\n uncond_tokens = torch.tensor(uncond_tokens, dtype=torch.long, device=pipe.device)\n\n # get the embeddings\n text_embeddings = get_unweighted_text_embeddings(\n pipe,\n prompt_tokens,\n pipe.tokenizer.model_max_length,\n no_boseos_middle=no_boseos_middle,\n )\n prompt_weights = torch.tensor(prompt_weights, dtype=text_embeddings.dtype, device=text_embeddings.device)\n if uncond_prompt is not None:\n uncond_embeddings = get_unweighted_text_embeddings(\n pipe,\n uncond_tokens,\n pipe.tokenizer.model_max_length,\n no_boseos_middle=no_boseos_middle,\n )\n uncond_weights = torch.tensor(uncond_weights, dtype=uncond_embeddings.dtype, device=uncond_embeddings.device)\n\n # assign weights to the prompts and normalize in the sense of mean\n # TODO: should we normalize by chunk or in a whole (current implementation)?\n if (not skip_parsing) and (not skip_weighting):\n previous_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)\n text_embeddings *= prompt_weights.unsqueeze(-1)\n current_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)\n text_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)\n if uncond_prompt is not None:\n previous_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)\n uncond_embeddings *= uncond_weights.unsqueeze(-1)\n current_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)\n uncond_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)\n\n if uncond_prompt is not None:\n return text_embeddings, uncond_embeddings\n return text_embeddings, None\n\n\ndef preprocess_image(image, batch_size):\n w, h = image.size\n w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8\n image = image.resize((w, h), resample=PIL_INTERPOLATION[\"lanczos\"])\n image = np.array(image).astype(np.float32) / 255.0\n image = np.vstack([image[None].transpose(0, 3, 1, 2)] * batch_size)\n image = torch.from_numpy(image)\n return 2.0 * image - 1.0\n\n\ndef preprocess_mask(mask, batch_size, scale_factor=8):\n if not isinstance(mask, torch.Tensor):\n mask = mask.convert(\"L\")\n w, h = mask.size\n w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8\n mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION[\"nearest\"])\n mask = np.array(mask).astype(np.float32) / 255.0\n mask = np.tile(mask, (4, 1, 1))\n mask = np.vstack([mask[None]] * batch_size)\n mask = 1 - mask # repaint white, keep black\n mask = torch.from_numpy(mask)\n return mask\n\n else:\n valid_mask_channel_sizes = [1, 3]\n # if mask channel is fourth tensor dimension, permute dimensions to pytorch standard (B, C, H, W)\n if mask.shape[3] in valid_mask_channel_sizes:\n mask = mask.permute(0, 3, 1, 2)\n elif mask.shape[1] not in valid_mask_channel_sizes:\n raise ValueError(\n f\"Mask channel dimension of size in {valid_mask_channel_sizes} should be second or fourth dimension,\"\n f\" but received mask of shape {tuple(mask.shape)}\"\n )\n # (potentially) reduce mask channel dimension from 3 to 1 for broadcasting to latent shape\n mask = mask.mean(dim=1, keepdim=True)\n h, w = mask.shape[-2:]\n h, w = (x - x % 8 for x in (h, w)) # resize to integer multiple of 8\n mask = torch.nn.functional.interpolate(mask, (h // scale_factor, w // scale_factor))\n return mask\n\n\nclass StableDiffusionLongPromptWeightingPipeline(\n DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion without tokens length limit, and support parsing\n weighting in prompt.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder-->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"clip_sample\"] = False\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(\n requires_safety_checker=requires_safety_checker,\n )\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n max_embeddings_multiples=3,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `list(int)`):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n max_embeddings_multiples (`int`, *optional*, defaults to `3`):\n The max multiple length of prompt embeddings compared to the max output length of text encoder.\n \"\"\"\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if negative_prompt_embeds is None:\n if negative_prompt is None:\n negative_prompt = [\"\"] * batch_size\n elif isinstance(negative_prompt, str):\n negative_prompt = [negative_prompt] * batch_size\n if batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n if prompt_embeds is None or negative_prompt_embeds is None:\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = self.maybe_convert_prompt(negative_prompt, self.tokenizer)\n\n prompt_embeds1, negative_prompt_embeds1 = get_weighted_text_embeddings(\n pipe=self,\n prompt=prompt,\n uncond_prompt=negative_prompt if do_classifier_free_guidance else None,\n max_embeddings_multiples=max_embeddings_multiples,\n )\n if prompt_embeds is None:\n prompt_embeds = prompt_embeds1\n if negative_prompt_embeds is None:\n negative_prompt_embeds = negative_prompt_embeds1\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n bs_embed, seq_len, _ = negative_prompt_embeds.shape\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n def check_inputs(\n self,\n prompt,\n height,\n width,\n strength,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n def get_timesteps(self, num_inference_steps, strength, device, is_text2img):\n if is_text2img:\n return self.scheduler.timesteps.to(device), num_inference_steps\n else:\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n\n return timesteps, num_inference_steps - t_start\n\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n else:\n has_nsfw_concept = None\n return image, has_nsfw_concept\n\n def decode_latents(self, latents):\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents).sample\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def prepare_latents(\n self,\n image,\n timestep,\n num_images_per_prompt,\n batch_size,\n num_channels_latents,\n height,\n width,\n dtype,\n device,\n generator,\n latents=None,\n ):\n if image is None:\n batch_size = batch_size * num_images_per_prompt\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents, None, None\n else:\n image = image.to(device=self.device, dtype=dtype)\n init_latent_dist = self.vae.encode(image).latent_dist\n init_latents = init_latent_dist.sample(generator=generator)\n init_latents = self.vae.config.scaling_factor * init_latents\n\n # Expand init_latents for batch_size and num_images_per_prompt\n init_latents = torch.cat([init_latents] * num_images_per_prompt, dim=0)\n init_latents_orig = init_latents\n\n # add noise to latents using the timesteps\n noise = randn_tensor(init_latents.shape, generator=generator, device=self.device, dtype=dtype)\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n latents = init_latents\n return latents, init_latents_orig, noise\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n negative_prompt: Optional[Union[str, List[str]]] = None,\n image: Union[torch.Tensor, PIL.Image.Image] = None,\n mask_image: Union[torch.Tensor, PIL.Image.Image] = None,\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n strength: float = 0.8,\n num_images_per_prompt: Optional[int] = 1,\n add_predicted_noise: Optional[bool] = False,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n max_embeddings_multiples: Optional[int] = 3,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n is_cancelled_callback: Optional[Callable[[], bool]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n image (`torch.Tensor` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process.\n mask_image (`torch.Tensor` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be\n replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a\n PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should\n contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n strength (`float`, *optional*, defaults to 0.8):\n Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.\n `image` will be used as a starting point, adding more noise to it the larger the `strength`. The\n number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added\n noise will be maximum and the denoising process will run for the full number of iterations specified in\n `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n add_predicted_noise (`bool`, *optional*, defaults to True):\n Use predicted noise instead of random noise when constructing noisy versions of the original image in\n the reverse diffusion process\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n max_embeddings_multiples (`int`, *optional*, defaults to `3`):\n The max multiple length of prompt embeddings compared to the max output length of text encoder.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n is_cancelled_callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. If the function returns\n `True`, the inference will be cancelled.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n\n Returns:\n `None` if cancelled by `is_cancelled_callback`,\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt, height, width, strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n prompt_embeds = self._encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n max_embeddings_multiples,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n dtype = prompt_embeds.dtype\n\n # 4. Preprocess image and mask\n if isinstance(image, PIL.Image.Image):\n image = preprocess_image(image, batch_size)\n if image is not None:\n image = image.to(device=self.device, dtype=dtype)\n if isinstance(mask_image, PIL.Image.Image):\n mask_image = preprocess_mask(mask_image, batch_size, self.vae_scale_factor)\n if mask_image is not None:\n mask = mask_image.to(device=self.device, dtype=dtype)\n mask = torch.cat([mask] * num_images_per_prompt)\n else:\n mask = None\n\n # 5. set timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device, image is None)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n\n # 6. Prepare latent variables\n latents, init_latents_orig, noise = self.prepare_latents(\n image,\n latent_timestep,\n num_images_per_prompt,\n batch_size,\n self.unet.config.in_channels,\n height,\n width,\n dtype,\n device,\n generator,\n latents,\n )\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n if mask is not None:\n # masking\n if add_predicted_noise:\n init_latents_proper = self.scheduler.add_noise(\n init_latents_orig, noise_pred_uncond, torch.tensor([t])\n )\n else:\n init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t]))\n latents = (init_latents_proper * mask) + (latents * (1 - mask))\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if i % callback_steps == 0:\n if callback is not None:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n if is_cancelled_callback is not None and is_cancelled_callback():\n return None\n\n if output_type == \"latent\":\n image = latents\n has_nsfw_concept = None\n elif output_type == \"pil\":\n # 9. Post-processing\n image = self.decode_latents(latents)\n\n # 10. Run safety checker\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # 11. Convert to PIL\n image = self.numpy_to_pil(image)\n else:\n # 9. Post-processing\n image = self.decode_latents(latents)\n\n # 10. Run safety checker\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # Offload last model to CPU\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return image, has_nsfw_concept\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n\n def text2img(\n self,\n prompt: Union[str, List[str]],\n negative_prompt: Optional[Union[str, List[str]]] = None,\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n max_embeddings_multiples: Optional[int] = 3,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n is_cancelled_callback: Optional[Callable[[], bool]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n ):\n r\"\"\"\n Function for text-to-image generation.\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n max_embeddings_multiples (`int`, *optional*, defaults to `3`):\n The max multiple length of prompt embeddings compared to the max output length of text encoder.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n is_cancelled_callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. If the function returns\n `True`, the inference will be cancelled.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n\n Returns:\n `None` if cancelled by `is_cancelled_callback`,\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n return self.__call__(\n prompt=prompt,\n negative_prompt=negative_prompt,\n height=height,\n width=width,\n num_inference_steps=num_inference_steps,\n guidance_scale=guidance_scale,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n latents=latents,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n max_embeddings_multiples=max_embeddings_multiples,\n output_type=output_type,\n return_dict=return_dict,\n callback=callback,\n is_cancelled_callback=is_cancelled_callback,\n callback_steps=callback_steps,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n\n def img2img(\n self,\n image: Union[torch.Tensor, PIL.Image.Image],\n prompt: Union[str, List[str]],\n negative_prompt: Optional[Union[str, List[str]]] = None,\n strength: float = 0.8,\n num_inference_steps: Optional[int] = 50,\n guidance_scale: Optional[float] = 7.5,\n num_images_per_prompt: Optional[int] = 1,\n eta: Optional[float] = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n max_embeddings_multiples: Optional[int] = 3,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n is_cancelled_callback: Optional[Callable[[], bool]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n ):\n r\"\"\"\n Function for image-to-image generation.\n Args:\n image (`torch.Tensor` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process.\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n strength (`float`, *optional*, defaults to 0.8):\n Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.\n `image` will be used as a starting point, adding more noise to it the larger the `strength`. The\n number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added\n noise will be maximum and the denoising process will run for the full number of iterations specified in\n `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference. This parameter will be modulated by `strength`.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n max_embeddings_multiples (`int`, *optional*, defaults to `3`):\n The max multiple length of prompt embeddings compared to the max output length of text encoder.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n is_cancelled_callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. If the function returns\n `True`, the inference will be cancelled.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n\n Returns:\n `None` if cancelled by `is_cancelled_callback`,\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n return self.__call__(\n prompt=prompt,\n negative_prompt=negative_prompt,\n image=image,\n num_inference_steps=num_inference_steps,\n guidance_scale=guidance_scale,\n strength=strength,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n max_embeddings_multiples=max_embeddings_multiples,\n output_type=output_type,\n return_dict=return_dict,\n callback=callback,\n is_cancelled_callback=is_cancelled_callback,\n callback_steps=callback_steps,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n\n def inpaint(\n self,\n image: Union[torch.Tensor, PIL.Image.Image],\n mask_image: Union[torch.Tensor, PIL.Image.Image],\n prompt: Union[str, List[str]],\n negative_prompt: Optional[Union[str, List[str]]] = None,\n strength: float = 0.8,\n num_inference_steps: Optional[int] = 50,\n guidance_scale: Optional[float] = 7.5,\n num_images_per_prompt: Optional[int] = 1,\n add_predicted_noise: Optional[bool] = False,\n eta: Optional[float] = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n max_embeddings_multiples: Optional[int] = 3,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n is_cancelled_callback: Optional[Callable[[], bool]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n ):\n r\"\"\"\n Function for inpaint.\n Args:\n image (`torch.Tensor` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process. This is the image whose masked region will be inpainted.\n mask_image (`torch.Tensor` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be\n replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a\n PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should\n contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n strength (`float`, *optional*, defaults to 0.8):\n Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`\n is 1, the denoising process will be run on the masked area for the full number of iterations specified\n in `num_inference_steps`. `image` will be used as a reference for the masked area, adding more\n noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The reference number of denoising steps. More denoising steps usually lead to a higher quality image at\n the expense of slower inference. This parameter will be modulated by `strength`, as explained above.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n add_predicted_noise (`bool`, *optional*, defaults to True):\n Use predicted noise instead of random noise when constructing noisy versions of the original image in\n the reverse diffusion process\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n max_embeddings_multiples (`int`, *optional*, defaults to `3`):\n The max multiple length of prompt embeddings compared to the max output length of text encoder.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n is_cancelled_callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. If the function returns\n `True`, the inference will be cancelled.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n\n Returns:\n `None` if cancelled by `is_cancelled_callback`,\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n return self.__call__(\n prompt=prompt,\n negative_prompt=negative_prompt,\n image=image,\n mask_image=mask_image,\n num_inference_steps=num_inference_steps,\n guidance_scale=guidance_scale,\n strength=strength,\n num_images_per_prompt=num_images_per_prompt,\n add_predicted_noise=add_predicted_noise,\n eta=eta,\n generator=generator,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n max_embeddings_multiples=max_embeddings_multiples,\n output_type=output_type,\n return_dict=return_dict,\n callback=callback,\n is_cancelled_callback=is_cancelled_callback,\n callback_steps=callback_steps,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n" }, { "path": "diffusers/examples/community/lpw_stable_diffusion_onnx.py", "content": "import inspect\nimport re\nfrom typing import Callable, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom packaging import version\nfrom transformers import CLIPImageProcessor, CLIPTokenizer\n\nimport diffusers\nfrom diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, SchedulerMixin\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.utils import logging\n\n\ntry:\n from diffusers.pipelines.onnx_utils import ORT_TO_NP_TYPE\nexcept ImportError:\n ORT_TO_NP_TYPE = {\n \"tensor(bool)\": np.bool_,\n \"tensor(int8)\": np.int8,\n \"tensor(uint8)\": np.uint8,\n \"tensor(int16)\": np.int16,\n \"tensor(uint16)\": np.uint16,\n \"tensor(int32)\": np.int32,\n \"tensor(uint32)\": np.uint32,\n \"tensor(int64)\": np.int64,\n \"tensor(uint64)\": np.uint64,\n \"tensor(float16)\": np.float16,\n \"tensor(float)\": np.float32,\n \"tensor(double)\": np.float64,\n }\n\ntry:\n from diffusers.utils import PIL_INTERPOLATION\nexcept ImportError:\n if version.parse(version.parse(PIL.__version__).base_version) >= version.parse(\"9.1.0\"):\n PIL_INTERPOLATION = {\n \"linear\": PIL.Image.Resampling.BILINEAR,\n \"bilinear\": PIL.Image.Resampling.BILINEAR,\n \"bicubic\": PIL.Image.Resampling.BICUBIC,\n \"lanczos\": PIL.Image.Resampling.LANCZOS,\n \"nearest\": PIL.Image.Resampling.NEAREST,\n }\n else:\n PIL_INTERPOLATION = {\n \"linear\": PIL.Image.LINEAR,\n \"bilinear\": PIL.Image.BILINEAR,\n \"bicubic\": PIL.Image.BICUBIC,\n \"lanczos\": PIL.Image.LANCZOS,\n \"nearest\": PIL.Image.NEAREST,\n }\n# ------------------------------------------------------------------------------\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nre_attention = re.compile(\n r\"\"\"\n\\\\\\(|\n\\\\\\)|\n\\\\\\[|\n\\\\]|\n\\\\\\\\|\n\\\\|\n\\(|\n\\[|\n:([+-]?[.\\d]+)\\)|\n\\)|\n]|\n[^\\\\()\\[\\]:]+|\n:\n\"\"\",\n re.X,\n)\n\n\ndef parse_prompt_attention(text):\n \"\"\"\n Parses a string with attention tokens and returns a list of pairs: text and its associated weight.\n Accepted tokens are:\n (abc) - increases attention to abc by a multiplier of 1.1\n (abc:3.12) - increases attention to abc by a multiplier of 3.12\n [abc] - decreases attention to abc by a multiplier of 1.1\n \\\\( - literal character '('\n \\\\[ - literal character '['\n \\\\) - literal character ')'\n \\\\] - literal character ']'\n \\\\ - literal character '\\'\n anything else - just text\n >>> parse_prompt_attention('normal text')\n [['normal text', 1.0]]\n >>> parse_prompt_attention('an (important) word')\n [['an ', 1.0], ['important', 1.1], [' word', 1.0]]\n >>> parse_prompt_attention('(unbalanced')\n [['unbalanced', 1.1]]\n >>> parse_prompt_attention('\\\\(literal\\\\]')\n [['(literal]', 1.0]]\n >>> parse_prompt_attention('(unnecessary)(parens)')\n [['unnecessaryparens', 1.1]]\n >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')\n [['a ', 1.0],\n ['house', 1.5730000000000004],\n [' ', 1.1],\n ['on', 1.0],\n [' a ', 1.1],\n ['hill', 0.55],\n [', sun, ', 1.1],\n ['sky', 1.4641000000000006],\n ['.', 1.1]]\n \"\"\"\n\n res = []\n round_brackets = []\n square_brackets = []\n\n round_bracket_multiplier = 1.1\n square_bracket_multiplier = 1 / 1.1\n\n def multiply_range(start_position, multiplier):\n for p in range(start_position, len(res)):\n res[p][1] *= multiplier\n\n for m in re_attention.finditer(text):\n text = m.group(0)\n weight = m.group(1)\n\n if text.startswith(\"\\\\\"):\n res.append([text[1:], 1.0])\n elif text == \"(\":\n round_brackets.append(len(res))\n elif text == \"[\":\n square_brackets.append(len(res))\n elif weight is not None and len(round_brackets) > 0:\n multiply_range(round_brackets.pop(), float(weight))\n elif text == \")\" and len(round_brackets) > 0:\n multiply_range(round_brackets.pop(), round_bracket_multiplier)\n elif text == \"]\" and len(square_brackets) > 0:\n multiply_range(square_brackets.pop(), square_bracket_multiplier)\n else:\n res.append([text, 1.0])\n\n for pos in round_brackets:\n multiply_range(pos, round_bracket_multiplier)\n\n for pos in square_brackets:\n multiply_range(pos, square_bracket_multiplier)\n\n if len(res) == 0:\n res = [[\"\", 1.0]]\n\n # merge runs of identical weights\n i = 0\n while i + 1 < len(res):\n if res[i][1] == res[i + 1][1]:\n res[i][0] += res[i + 1][0]\n res.pop(i + 1)\n else:\n i += 1\n\n return res\n\n\ndef get_prompts_with_weights(pipe, prompt: List[str], max_length: int):\n r\"\"\"\n Tokenize a list of prompts and return its tokens with weights of each token.\n\n No padding, starting or ending token is included.\n \"\"\"\n tokens = []\n weights = []\n truncated = False\n for text in prompt:\n texts_and_weights = parse_prompt_attention(text)\n text_token = []\n text_weight = []\n for word, weight in texts_and_weights:\n # tokenize and discard the starting and the ending token\n token = pipe.tokenizer(word, return_tensors=\"np\").input_ids[0, 1:-1]\n text_token += list(token)\n # copy the weight by length of token\n text_weight += [weight] * len(token)\n # stop if the text is too long (longer than truncation limit)\n if len(text_token) > max_length:\n truncated = True\n break\n # truncate\n if len(text_token) > max_length:\n truncated = True\n text_token = text_token[:max_length]\n text_weight = text_weight[:max_length]\n tokens.append(text_token)\n weights.append(text_weight)\n if truncated:\n logger.warning(\"Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples\")\n return tokens, weights\n\n\ndef pad_tokens_and_weights(tokens, weights, max_length, bos, eos, pad, no_boseos_middle=True, chunk_length=77):\n r\"\"\"\n Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length.\n \"\"\"\n max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)\n weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length\n for i in range(len(tokens)):\n tokens[i] = [bos] + tokens[i] + [pad] * (max_length - 1 - len(tokens[i]) - 1) + [eos]\n if no_boseos_middle:\n weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i]))\n else:\n w = []\n if len(weights[i]) == 0:\n w = [1.0] * weights_length\n else:\n for j in range(max_embeddings_multiples):\n w.append(1.0) # weight for starting token in this chunk\n w += weights[i][j * (chunk_length - 2) : min(len(weights[i]), (j + 1) * (chunk_length - 2))]\n w.append(1.0) # weight for ending token in this chunk\n w += [1.0] * (weights_length - len(w))\n weights[i] = w[:]\n\n return tokens, weights\n\n\ndef get_unweighted_text_embeddings(\n pipe,\n text_input: np.array,\n chunk_length: int,\n no_boseos_middle: Optional[bool] = True,\n):\n \"\"\"\n When the length of tokens is a multiple of the capacity of the text encoder,\n it should be split into chunks and sent to the text encoder individually.\n \"\"\"\n max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)\n if max_embeddings_multiples > 1:\n text_embeddings = []\n for i in range(max_embeddings_multiples):\n # extract the i-th chunk\n text_input_chunk = text_input[:, i * (chunk_length - 2) : (i + 1) * (chunk_length - 2) + 2].copy()\n\n # cover the head and the tail by the starting and the ending tokens\n text_input_chunk[:, 0] = text_input[0, 0]\n text_input_chunk[:, -1] = text_input[0, -1]\n\n text_embedding = pipe.text_encoder(input_ids=text_input_chunk)[0]\n\n if no_boseos_middle:\n if i == 0:\n # discard the ending token\n text_embedding = text_embedding[:, :-1]\n elif i == max_embeddings_multiples - 1:\n # discard the starting token\n text_embedding = text_embedding[:, 1:]\n else:\n # discard both starting and ending tokens\n text_embedding = text_embedding[:, 1:-1]\n\n text_embeddings.append(text_embedding)\n text_embeddings = np.concatenate(text_embeddings, axis=1)\n else:\n text_embeddings = pipe.text_encoder(input_ids=text_input)[0]\n return text_embeddings\n\n\ndef get_weighted_text_embeddings(\n pipe,\n prompt: Union[str, List[str]],\n uncond_prompt: Optional[Union[str, List[str]]] = None,\n max_embeddings_multiples: Optional[int] = 4,\n no_boseos_middle: Optional[bool] = False,\n skip_parsing: Optional[bool] = False,\n skip_weighting: Optional[bool] = False,\n **kwargs,\n):\n r\"\"\"\n Prompts can be assigned with local weights using brackets. For example,\n prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful',\n and the embedding tokens corresponding to the words get multiplied by a constant, 1.1.\n\n Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.\n\n Args:\n pipe (`OnnxStableDiffusionPipeline`):\n Pipe to provide access to the tokenizer and the text encoder.\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n uncond_prompt (`str` or `List[str]`):\n The unconditional prompt or prompts for guide the image generation. If unconditional prompt\n is provided, the embeddings of prompt and uncond_prompt are concatenated.\n max_embeddings_multiples (`int`, *optional*, defaults to `1`):\n The max multiple length of prompt embeddings compared to the max output length of text encoder.\n no_boseos_middle (`bool`, *optional*, defaults to `False`):\n If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and\n ending token in each of the chunk in the middle.\n skip_parsing (`bool`, *optional*, defaults to `False`):\n Skip the parsing of brackets.\n skip_weighting (`bool`, *optional*, defaults to `False`):\n Skip the weighting. When the parsing is skipped, it is forced True.\n \"\"\"\n max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2\n if isinstance(prompt, str):\n prompt = [prompt]\n\n if not skip_parsing:\n prompt_tokens, prompt_weights = get_prompts_with_weights(pipe, prompt, max_length - 2)\n if uncond_prompt is not None:\n if isinstance(uncond_prompt, str):\n uncond_prompt = [uncond_prompt]\n uncond_tokens, uncond_weights = get_prompts_with_weights(pipe, uncond_prompt, max_length - 2)\n else:\n prompt_tokens = [\n token[1:-1]\n for token in pipe.tokenizer(prompt, max_length=max_length, truncation=True, return_tensors=\"np\").input_ids\n ]\n prompt_weights = [[1.0] * len(token) for token in prompt_tokens]\n if uncond_prompt is not None:\n if isinstance(uncond_prompt, str):\n uncond_prompt = [uncond_prompt]\n uncond_tokens = [\n token[1:-1]\n for token in pipe.tokenizer(\n uncond_prompt,\n max_length=max_length,\n truncation=True,\n return_tensors=\"np\",\n ).input_ids\n ]\n uncond_weights = [[1.0] * len(token) for token in uncond_tokens]\n\n # round up the longest length of tokens to a multiple of (model_max_length - 2)\n max_length = max([len(token) for token in prompt_tokens])\n if uncond_prompt is not None:\n max_length = max(max_length, max([len(token) for token in uncond_tokens]))\n\n max_embeddings_multiples = min(\n max_embeddings_multiples,\n (max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1,\n )\n max_embeddings_multiples = max(1, max_embeddings_multiples)\n max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2\n\n # pad the length of tokens and weights\n bos = pipe.tokenizer.bos_token_id\n eos = pipe.tokenizer.eos_token_id\n pad = getattr(pipe.tokenizer, \"pad_token_id\", eos)\n prompt_tokens, prompt_weights = pad_tokens_and_weights(\n prompt_tokens,\n prompt_weights,\n max_length,\n bos,\n eos,\n pad,\n no_boseos_middle=no_boseos_middle,\n chunk_length=pipe.tokenizer.model_max_length,\n )\n prompt_tokens = np.array(prompt_tokens, dtype=np.int32)\n if uncond_prompt is not None:\n uncond_tokens, uncond_weights = pad_tokens_and_weights(\n uncond_tokens,\n uncond_weights,\n max_length,\n bos,\n eos,\n pad,\n no_boseos_middle=no_boseos_middle,\n chunk_length=pipe.tokenizer.model_max_length,\n )\n uncond_tokens = np.array(uncond_tokens, dtype=np.int32)\n\n # get the embeddings\n text_embeddings = get_unweighted_text_embeddings(\n pipe,\n prompt_tokens,\n pipe.tokenizer.model_max_length,\n no_boseos_middle=no_boseos_middle,\n )\n prompt_weights = np.array(prompt_weights, dtype=text_embeddings.dtype)\n if uncond_prompt is not None:\n uncond_embeddings = get_unweighted_text_embeddings(\n pipe,\n uncond_tokens,\n pipe.tokenizer.model_max_length,\n no_boseos_middle=no_boseos_middle,\n )\n uncond_weights = np.array(uncond_weights, dtype=uncond_embeddings.dtype)\n\n # assign weights to the prompts and normalize in the sense of mean\n # TODO: should we normalize by chunk or in a whole (current implementation)?\n if (not skip_parsing) and (not skip_weighting):\n previous_mean = text_embeddings.mean(axis=(-2, -1))\n text_embeddings *= prompt_weights[:, :, None]\n text_embeddings *= (previous_mean / text_embeddings.mean(axis=(-2, -1)))[:, None, None]\n if uncond_prompt is not None:\n previous_mean = uncond_embeddings.mean(axis=(-2, -1))\n uncond_embeddings *= uncond_weights[:, :, None]\n uncond_embeddings *= (previous_mean / uncond_embeddings.mean(axis=(-2, -1)))[:, None, None]\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if uncond_prompt is not None:\n return text_embeddings, uncond_embeddings\n\n return text_embeddings\n\n\ndef preprocess_image(image):\n w, h = image.size\n w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32\n image = image.resize((w, h), resample=PIL_INTERPOLATION[\"lanczos\"])\n image = np.array(image).astype(np.float32) / 255.0\n image = image[None].transpose(0, 3, 1, 2)\n return 2.0 * image - 1.0\n\n\ndef preprocess_mask(mask, scale_factor=8):\n mask = mask.convert(\"L\")\n w, h = mask.size\n w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32\n mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION[\"nearest\"])\n mask = np.array(mask).astype(np.float32) / 255.0\n mask = np.tile(mask, (4, 1, 1))\n mask = mask[None].transpose(0, 1, 2, 3) # what does this step do?\n mask = 1 - mask # repaint white, keep black\n return mask\n\n\nclass OnnxStableDiffusionLongPromptWeightingPipeline(OnnxStableDiffusionPipeline):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion without tokens length limit, and support parsing\n weighting in prompt.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n \"\"\"\n\n if version.parse(version.parse(diffusers.__version__).base_version) >= version.parse(\"0.9.0\"):\n\n def __init__(\n self,\n vae_encoder: OnnxRuntimeModel,\n vae_decoder: OnnxRuntimeModel,\n text_encoder: OnnxRuntimeModel,\n tokenizer: CLIPTokenizer,\n unet: OnnxRuntimeModel,\n scheduler: SchedulerMixin,\n safety_checker: OnnxRuntimeModel,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__(\n vae_encoder=vae_encoder,\n vae_decoder=vae_decoder,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n requires_safety_checker=requires_safety_checker,\n )\n self.__init__additional__()\n\n else:\n\n def __init__(\n self,\n vae_encoder: OnnxRuntimeModel,\n vae_decoder: OnnxRuntimeModel,\n text_encoder: OnnxRuntimeModel,\n tokenizer: CLIPTokenizer,\n unet: OnnxRuntimeModel,\n scheduler: SchedulerMixin,\n safety_checker: OnnxRuntimeModel,\n feature_extractor: CLIPImageProcessor,\n ):\n super().__init__(\n vae_encoder=vae_encoder,\n vae_decoder=vae_decoder,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.__init__additional__()\n\n def __init__additional__(self):\n self.unet.config.in_channels = 4\n self.vae_scale_factor = 8\n\n def _encode_prompt(\n self,\n prompt,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n max_embeddings_multiples,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `list(int)`):\n prompt to be encoded\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n max_embeddings_multiples (`int`, *optional*, defaults to `3`):\n The max multiple length of prompt embeddings compared to the max output length of text encoder.\n \"\"\"\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n\n if negative_prompt is None:\n negative_prompt = [\"\"] * batch_size\n elif isinstance(negative_prompt, str):\n negative_prompt = [negative_prompt] * batch_size\n if batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n\n text_embeddings, uncond_embeddings = get_weighted_text_embeddings(\n pipe=self,\n prompt=prompt,\n uncond_prompt=negative_prompt if do_classifier_free_guidance else None,\n max_embeddings_multiples=max_embeddings_multiples,\n )\n\n text_embeddings = text_embeddings.repeat(num_images_per_prompt, 0)\n if do_classifier_free_guidance:\n uncond_embeddings = uncond_embeddings.repeat(num_images_per_prompt, 0)\n text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])\n\n return text_embeddings\n\n def check_inputs(self, prompt, height, width, strength, callback_steps):\n if not isinstance(prompt, str) and not isinstance(prompt, list):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n def get_timesteps(self, num_inference_steps, strength, is_text2img):\n if is_text2img:\n return self.scheduler.timesteps, num_inference_steps\n else:\n # get the original timestep using init_timestep\n offset = self.scheduler.config.get(\"steps_offset\", 0)\n init_timestep = int(num_inference_steps * strength) + offset\n init_timestep = min(init_timestep, num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep + offset, 0)\n timesteps = self.scheduler.timesteps[t_start:]\n return timesteps, num_inference_steps - t_start\n\n def run_safety_checker(self, image):\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(\n self.numpy_to_pil(image), return_tensors=\"np\"\n ).pixel_values.astype(image.dtype)\n # There will throw an error if use safety_checker directly and batchsize>1\n images, has_nsfw_concept = [], []\n for i in range(image.shape[0]):\n image_i, has_nsfw_concept_i = self.safety_checker(\n clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]\n )\n images.append(image_i)\n has_nsfw_concept.append(has_nsfw_concept_i[0])\n image = np.concatenate(images)\n else:\n has_nsfw_concept = None\n return image, has_nsfw_concept\n\n def decode_latents(self, latents):\n latents = 1 / 0.18215 * latents\n # image = self.vae_decoder(latent_sample=latents)[0]\n # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1\n image = np.concatenate(\n [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]\n )\n image = np.clip(image / 2 + 0.5, 0, 1)\n image = image.transpose((0, 2, 3, 1))\n return image\n\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def prepare_latents(self, image, timestep, batch_size, height, width, dtype, generator, latents=None):\n if image is None:\n shape = (\n batch_size,\n self.unet.config.in_channels,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n )\n\n if latents is None:\n latents = torch.randn(shape, generator=generator, device=\"cpu\").numpy().astype(dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = (torch.from_numpy(latents) * self.scheduler.init_noise_sigma).numpy()\n return latents, None, None\n else:\n init_latents = self.vae_encoder(sample=image)[0]\n init_latents = 0.18215 * init_latents\n init_latents = np.concatenate([init_latents] * batch_size, axis=0)\n init_latents_orig = init_latents\n shape = init_latents.shape\n\n # add noise to latents using the timesteps\n noise = torch.randn(shape, generator=generator, device=\"cpu\").numpy().astype(dtype)\n latents = self.scheduler.add_noise(\n torch.from_numpy(init_latents), torch.from_numpy(noise), timestep\n ).numpy()\n return latents, init_latents_orig, noise\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n negative_prompt: Optional[Union[str, List[str]]] = None,\n image: Union[np.ndarray, PIL.Image.Image] = None,\n mask_image: Union[np.ndarray, PIL.Image.Image] = None,\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n strength: float = 0.8,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[np.ndarray] = None,\n max_embeddings_multiples: Optional[int] = 3,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, np.ndarray], None]] = None,\n is_cancelled_callback: Optional[Callable[[], bool]] = None,\n callback_steps: int = 1,\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n image (`np.ndarray` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process.\n mask_image (`np.ndarray` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be\n replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a\n PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should\n contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n strength (`float`, *optional*, defaults to 0.8):\n Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.\n `image` will be used as a starting point, adding more noise to it the larger the `strength`. The\n number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added\n noise will be maximum and the denoising process will run for the full number of iterations specified in\n `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation\n deterministic.\n latents (`np.ndarray`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n max_embeddings_multiples (`int`, *optional*, defaults to `3`):\n The max multiple length of prompt embeddings compared to the max output length of text encoder.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.\n is_cancelled_callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. If the function returns\n `True`, the inference will be cancelled.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n\n Returns:\n `None` if cancelled by `is_cancelled_callback`,\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(prompt, height, width, strength, callback_steps)\n\n # 2. Define call parameters\n batch_size = 1 if isinstance(prompt, str) else len(prompt)\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_embeddings = self._encode_prompt(\n prompt,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n max_embeddings_multiples,\n )\n dtype = text_embeddings.dtype\n\n # 4. Preprocess image and mask\n if isinstance(image, PIL.Image.Image):\n image = preprocess_image(image)\n if image is not None:\n image = image.astype(dtype)\n if isinstance(mask_image, PIL.Image.Image):\n mask_image = preprocess_mask(mask_image, self.vae_scale_factor)\n if mask_image is not None:\n mask = mask_image.astype(dtype)\n mask = np.concatenate([mask] * batch_size * num_images_per_prompt)\n else:\n mask = None\n\n # 5. set timesteps\n self.scheduler.set_timesteps(num_inference_steps)\n timestep_dtype = next(\n (input.type for input in self.unet.model.get_inputs() if input.name == \"timestep\"), \"tensor(float)\"\n )\n timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, image is None)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n\n # 6. Prepare latent variables\n latents, init_latents_orig, noise = self.prepare_latents(\n image,\n latent_timestep,\n batch_size * num_images_per_prompt,\n height,\n width,\n dtype,\n generator,\n latents,\n )\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 8. Denoising loop\n for i, t in enumerate(self.progress_bar(timesteps)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)\n latent_model_input = latent_model_input.numpy()\n\n # predict the noise residual\n noise_pred = self.unet(\n sample=latent_model_input,\n timestep=np.array([t], dtype=timestep_dtype),\n encoder_hidden_states=text_embeddings,\n )\n noise_pred = noise_pred[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n scheduler_output = self.scheduler.step(\n torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs\n )\n latents = scheduler_output.prev_sample.numpy()\n\n if mask is not None:\n # masking\n init_latents_proper = self.scheduler.add_noise(\n torch.from_numpy(init_latents_orig),\n torch.from_numpy(noise),\n t,\n ).numpy()\n latents = (init_latents_proper * mask) + (latents * (1 - mask))\n\n # call the callback, if provided\n if i % callback_steps == 0:\n if callback is not None:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n if is_cancelled_callback is not None and is_cancelled_callback():\n return None\n\n # 9. Post-processing\n image = self.decode_latents(latents)\n\n # 10. Run safety checker\n image, has_nsfw_concept = self.run_safety_checker(image)\n\n # 11. Convert to PIL\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return image, has_nsfw_concept\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n\n def text2img(\n self,\n prompt: Union[str, List[str]],\n negative_prompt: Optional[Union[str, List[str]]] = None,\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[np.ndarray] = None,\n max_embeddings_multiples: Optional[int] = 3,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, np.ndarray], None]] = None,\n callback_steps: int = 1,\n **kwargs,\n ):\n r\"\"\"\n Function for text-to-image generation.\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation\n deterministic.\n latents (`np.ndarray`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n max_embeddings_multiples (`int`, *optional*, defaults to `3`):\n The max multiple length of prompt embeddings compared to the max output length of text encoder.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n return self.__call__(\n prompt=prompt,\n negative_prompt=negative_prompt,\n height=height,\n width=width,\n num_inference_steps=num_inference_steps,\n guidance_scale=guidance_scale,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n latents=latents,\n max_embeddings_multiples=max_embeddings_multiples,\n output_type=output_type,\n return_dict=return_dict,\n callback=callback,\n callback_steps=callback_steps,\n **kwargs,\n )\n\n def img2img(\n self,\n image: Union[np.ndarray, PIL.Image.Image],\n prompt: Union[str, List[str]],\n negative_prompt: Optional[Union[str, List[str]]] = None,\n strength: float = 0.8,\n num_inference_steps: Optional[int] = 50,\n guidance_scale: Optional[float] = 7.5,\n num_images_per_prompt: Optional[int] = 1,\n eta: Optional[float] = 0.0,\n generator: Optional[torch.Generator] = None,\n max_embeddings_multiples: Optional[int] = 3,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, np.ndarray], None]] = None,\n callback_steps: int = 1,\n **kwargs,\n ):\n r\"\"\"\n Function for image-to-image generation.\n Args:\n image (`np.ndarray` or `PIL.Image.Image`):\n `Image`, or ndarray representing an image batch, that will be used as the starting point for the\n process.\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n strength (`float`, *optional*, defaults to 0.8):\n Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.\n `image` will be used as a starting point, adding more noise to it the larger the `strength`. The\n number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added\n noise will be maximum and the denoising process will run for the full number of iterations specified in\n `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference. This parameter will be modulated by `strength`.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation\n deterministic.\n max_embeddings_multiples (`int`, *optional*, defaults to `3`):\n The max multiple length of prompt embeddings compared to the max output length of text encoder.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n return self.__call__(\n prompt=prompt,\n negative_prompt=negative_prompt,\n image=image,\n num_inference_steps=num_inference_steps,\n guidance_scale=guidance_scale,\n strength=strength,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n max_embeddings_multiples=max_embeddings_multiples,\n output_type=output_type,\n return_dict=return_dict,\n callback=callback,\n callback_steps=callback_steps,\n **kwargs,\n )\n\n def inpaint(\n self,\n image: Union[np.ndarray, PIL.Image.Image],\n mask_image: Union[np.ndarray, PIL.Image.Image],\n prompt: Union[str, List[str]],\n negative_prompt: Optional[Union[str, List[str]]] = None,\n strength: float = 0.8,\n num_inference_steps: Optional[int] = 50,\n guidance_scale: Optional[float] = 7.5,\n num_images_per_prompt: Optional[int] = 1,\n eta: Optional[float] = 0.0,\n generator: Optional[torch.Generator] = None,\n max_embeddings_multiples: Optional[int] = 3,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, np.ndarray], None]] = None,\n callback_steps: int = 1,\n **kwargs,\n ):\n r\"\"\"\n Function for inpaint.\n Args:\n image (`np.ndarray` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process. This is the image whose masked region will be inpainted.\n mask_image (`np.ndarray` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be\n replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a\n PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should\n contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n strength (`float`, *optional*, defaults to 0.8):\n Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`\n is 1, the denoising process will be run on the masked area for the full number of iterations specified\n in `num_inference_steps`. `image` will be used as a reference for the masked area, adding more\n noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The reference number of denoising steps. More denoising steps usually lead to a higher quality image at\n the expense of slower inference. This parameter will be modulated by `strength`, as explained above.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation\n deterministic.\n max_embeddings_multiples (`int`, *optional*, defaults to `3`):\n The max multiple length of prompt embeddings compared to the max output length of text encoder.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n return self.__call__(\n prompt=prompt,\n negative_prompt=negative_prompt,\n image=image,\n mask_image=mask_image,\n num_inference_steps=num_inference_steps,\n guidance_scale=guidance_scale,\n strength=strength,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n max_embeddings_multiples=max_embeddings_multiples,\n output_type=output_type,\n return_dict=return_dict,\n callback=callback,\n callback_steps=callback_steps,\n **kwargs,\n )\n" }, { "path": "diffusers/examples/community/lpw_stable_diffusion_xl.py", "content": "## ----------------------------------------------------------\n# A SDXL pipeline can take unlimited weighted prompt\n#\n# Author: Andrew Zhu\n# Github: https://github.com/xhinker\n# Medium: https://medium.com/@xhinker\n## -----------------------------------------------------------\n\nimport inspect\nimport os\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport torch\nfrom PIL import Image\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModel,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers import DiffusionPipeline, StableDiffusionXLPipeline\nfrom diffusers.image_processor import PipelineImageInput, VaeImageProcessor\nfrom diffusers.loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel\nfrom diffusers.models.attention_processor import (\n AttnProcessor2_0,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n XFormersAttnProcessor,\n)\nfrom diffusers.pipelines.pipeline_utils import StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n deprecate,\n is_accelerate_available,\n is_accelerate_version,\n is_invisible_watermark_available,\n logging,\n replace_example_docstring,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nif is_invisible_watermark_available():\n from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker\n\n\ndef parse_prompt_attention(text):\n \"\"\"\n Parses a string with attention tokens and returns a list of pairs: text and its associated weight.\n Accepted tokens are:\n (abc) - increases attention to abc by a multiplier of 1.1\n (abc:3.12) - increases attention to abc by a multiplier of 3.12\n [abc] - decreases attention to abc by a multiplier of 1.1\n \\\\( - literal character '('\n \\\\[ - literal character '['\n \\\\) - literal character ')'\n \\\\] - literal character ']'\n \\\\ - literal character '\\'\n anything else - just text\n\n >>> parse_prompt_attention('normal text')\n [['normal text', 1.0]]\n >>> parse_prompt_attention('an (important) word')\n [['an ', 1.0], ['important', 1.1], [' word', 1.0]]\n >>> parse_prompt_attention('(unbalanced')\n [['unbalanced', 1.1]]\n >>> parse_prompt_attention('\\\\(literal\\\\]')\n [['(literal]', 1.0]]\n >>> parse_prompt_attention('(unnecessary)(parens)')\n [['unnecessaryparens', 1.1]]\n >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')\n [['a ', 1.0],\n ['house', 1.5730000000000004],\n [' ', 1.1],\n ['on', 1.0],\n [' a ', 1.1],\n ['hill', 0.55],\n [', sun, ', 1.1],\n ['sky', 1.4641000000000006],\n ['.', 1.1]]\n \"\"\"\n import re\n\n re_attention = re.compile(\n r\"\"\"\n \\\\\\(|\\\\\\)|\\\\\\[|\\\\]|\\\\\\\\|\\\\|\\(|\\[|:([+-]?[.\\d]+)\\)|\n \\)|]|[^\\\\()\\[\\]:]+|:\n \"\"\",\n re.X,\n )\n\n re_break = re.compile(r\"\\s*\\bBREAK\\b\\s*\", re.S)\n\n res = []\n round_brackets = []\n square_brackets = []\n\n round_bracket_multiplier = 1.1\n square_bracket_multiplier = 1 / 1.1\n\n def multiply_range(start_position, multiplier):\n for p in range(start_position, len(res)):\n res[p][1] *= multiplier\n\n for m in re_attention.finditer(text):\n text = m.group(0)\n weight = m.group(1)\n\n if text.startswith(\"\\\\\"):\n res.append([text[1:], 1.0])\n elif text == \"(\":\n round_brackets.append(len(res))\n elif text == \"[\":\n square_brackets.append(len(res))\n elif weight is not None and len(round_brackets) > 0:\n multiply_range(round_brackets.pop(), float(weight))\n elif text == \")\" and len(round_brackets) > 0:\n multiply_range(round_brackets.pop(), round_bracket_multiplier)\n elif text == \"]\" and len(square_brackets) > 0:\n multiply_range(square_brackets.pop(), square_bracket_multiplier)\n else:\n parts = re.split(re_break, text)\n for i, part in enumerate(parts):\n if i > 0:\n res.append([\"BREAK\", -1])\n res.append([part, 1.0])\n\n for pos in round_brackets:\n multiply_range(pos, round_bracket_multiplier)\n\n for pos in square_brackets:\n multiply_range(pos, square_bracket_multiplier)\n\n if len(res) == 0:\n res = [[\"\", 1.0]]\n\n # merge runs of identical weights\n i = 0\n while i + 1 < len(res):\n if res[i][1] == res[i + 1][1]:\n res[i][0] += res[i + 1][0]\n res.pop(i + 1)\n else:\n i += 1\n\n return res\n\n\ndef get_prompts_tokens_with_weights(clip_tokenizer: CLIPTokenizer, prompt: str):\n \"\"\"\n Get prompt token ids and weights, this function works for both prompt and negative prompt\n\n Args:\n pipe (CLIPTokenizer)\n A CLIPTokenizer\n prompt (str)\n A prompt string with weights\n\n Returns:\n text_tokens (list)\n A list contains token ids\n text_weight (list)\n A list contains the correspondent weight of token ids\n\n Example:\n import torch\n from transformers import CLIPTokenizer\n\n clip_tokenizer = CLIPTokenizer.from_pretrained(\n \"stablediffusionapi/deliberate-v2\"\n , subfolder = \"tokenizer\"\n , dtype = torch.float16\n )\n\n token_id_list, token_weight_list = get_prompts_tokens_with_weights(\n clip_tokenizer = clip_tokenizer\n ,prompt = \"a (red:1.5) cat\"*70\n )\n \"\"\"\n texts_and_weights = parse_prompt_attention(prompt)\n text_tokens, text_weights = [], []\n for word, weight in texts_and_weights:\n # tokenize and discard the starting and the ending token\n token = clip_tokenizer(word, truncation=False).input_ids[1:-1] # so that tokenize whatever length prompt\n # the returned token is a 1d list: [320, 1125, 539, 320]\n\n # merge the new tokens to the all tokens holder: text_tokens\n text_tokens = [*text_tokens, *token]\n\n # each token chunk will come with one weight, like ['red cat', 2.0]\n # need to expand weight for each token.\n chunk_weights = [weight] * len(token)\n\n # append the weight back to the weight holder: text_weights\n text_weights = [*text_weights, *chunk_weights]\n return text_tokens, text_weights\n\n\ndef group_tokens_and_weights(token_ids: list, weights: list, pad_last_block=False):\n \"\"\"\n Produce tokens and weights in groups and pad the missing tokens\n\n Args:\n token_ids (list)\n The token ids from tokenizer\n weights (list)\n The weights list from function get_prompts_tokens_with_weights\n pad_last_block (bool)\n Control if fill the last token list to 75 tokens with eos\n Returns:\n new_token_ids (2d list)\n new_weights (2d list)\n\n Example:\n token_groups,weight_groups = group_tokens_and_weights(\n token_ids = token_id_list\n , weights = token_weight_list\n )\n \"\"\"\n bos, eos = 49406, 49407\n\n # this will be a 2d list\n new_token_ids = []\n new_weights = []\n while len(token_ids) >= 75:\n # get the first 75 tokens\n head_75_tokens = [token_ids.pop(0) for _ in range(75)]\n head_75_weights = [weights.pop(0) for _ in range(75)]\n\n # extract token ids and weights\n temp_77_token_ids = [bos] + head_75_tokens + [eos]\n temp_77_weights = [1.0] + head_75_weights + [1.0]\n\n # add 77 token and weights chunk to the holder list\n new_token_ids.append(temp_77_token_ids)\n new_weights.append(temp_77_weights)\n\n # padding the left\n if len(token_ids) > 0:\n padding_len = 75 - len(token_ids) if pad_last_block else 0\n\n temp_77_token_ids = [bos] + token_ids + [eos] * padding_len + [eos]\n new_token_ids.append(temp_77_token_ids)\n\n temp_77_weights = [1.0] + weights + [1.0] * padding_len + [1.0]\n new_weights.append(temp_77_weights)\n\n return new_token_ids, new_weights\n\n\ndef get_weighted_text_embeddings_sdxl(\n pipe: StableDiffusionXLPipeline,\n prompt: str = \"\",\n prompt_2: str = None,\n neg_prompt: str = \"\",\n neg_prompt_2: str = None,\n num_images_per_prompt: int = 1,\n device: Optional[torch.device] = None,\n clip_skip: Optional[int] = None,\n):\n \"\"\"\n This function can process long prompt with weights, no length limitation\n for Stable Diffusion XL\n\n Args:\n pipe (StableDiffusionPipeline)\n prompt (str)\n prompt_2 (str)\n neg_prompt (str)\n neg_prompt_2 (str)\n num_images_per_prompt (int)\n device (torch.device)\n clip_skip (int)\n Returns:\n prompt_embeds (torch.Tensor)\n neg_prompt_embeds (torch.Tensor)\n \"\"\"\n device = device or pipe._execution_device\n\n if prompt_2:\n prompt = f\"{prompt} {prompt_2}\"\n\n if neg_prompt_2:\n neg_prompt = f\"{neg_prompt} {neg_prompt_2}\"\n\n prompt_t1 = prompt_t2 = prompt\n neg_prompt_t1 = neg_prompt_t2 = neg_prompt\n\n if isinstance(pipe, TextualInversionLoaderMixin):\n prompt_t1 = pipe.maybe_convert_prompt(prompt_t1, pipe.tokenizer)\n neg_prompt_t1 = pipe.maybe_convert_prompt(neg_prompt_t1, pipe.tokenizer)\n prompt_t2 = pipe.maybe_convert_prompt(prompt_t2, pipe.tokenizer_2)\n neg_prompt_t2 = pipe.maybe_convert_prompt(neg_prompt_t2, pipe.tokenizer_2)\n\n eos = pipe.tokenizer.eos_token_id\n\n # tokenizer 1\n prompt_tokens, prompt_weights = get_prompts_tokens_with_weights(pipe.tokenizer, prompt_t1)\n neg_prompt_tokens, neg_prompt_weights = get_prompts_tokens_with_weights(pipe.tokenizer, neg_prompt_t1)\n\n # tokenizer 2\n prompt_tokens_2, prompt_weights_2 = get_prompts_tokens_with_weights(pipe.tokenizer_2, prompt_t2)\n neg_prompt_tokens_2, neg_prompt_weights_2 = get_prompts_tokens_with_weights(pipe.tokenizer_2, neg_prompt_t2)\n\n # padding the shorter one for prompt set 1\n prompt_token_len = len(prompt_tokens)\n neg_prompt_token_len = len(neg_prompt_tokens)\n\n if prompt_token_len > neg_prompt_token_len:\n # padding the neg_prompt with eos token\n neg_prompt_tokens = neg_prompt_tokens + [eos] * abs(prompt_token_len - neg_prompt_token_len)\n neg_prompt_weights = neg_prompt_weights + [1.0] * abs(prompt_token_len - neg_prompt_token_len)\n else:\n # padding the prompt\n prompt_tokens = prompt_tokens + [eos] * abs(prompt_token_len - neg_prompt_token_len)\n prompt_weights = prompt_weights + [1.0] * abs(prompt_token_len - neg_prompt_token_len)\n\n # padding the shorter one for token set 2\n prompt_token_len_2 = len(prompt_tokens_2)\n neg_prompt_token_len_2 = len(neg_prompt_tokens_2)\n\n if prompt_token_len_2 > neg_prompt_token_len_2:\n # padding the neg_prompt with eos token\n neg_prompt_tokens_2 = neg_prompt_tokens_2 + [eos] * abs(prompt_token_len_2 - neg_prompt_token_len_2)\n neg_prompt_weights_2 = neg_prompt_weights_2 + [1.0] * abs(prompt_token_len_2 - neg_prompt_token_len_2)\n else:\n # padding the prompt\n prompt_tokens_2 = prompt_tokens_2 + [eos] * abs(prompt_token_len_2 - neg_prompt_token_len_2)\n prompt_weights_2 = prompt_weights + [1.0] * abs(prompt_token_len_2 - neg_prompt_token_len_2)\n\n embeds = []\n neg_embeds = []\n\n prompt_token_groups, prompt_weight_groups = group_tokens_and_weights(prompt_tokens.copy(), prompt_weights.copy())\n\n neg_prompt_token_groups, neg_prompt_weight_groups = group_tokens_and_weights(\n neg_prompt_tokens.copy(), neg_prompt_weights.copy()\n )\n\n prompt_token_groups_2, prompt_weight_groups_2 = group_tokens_and_weights(\n prompt_tokens_2.copy(), prompt_weights_2.copy()\n )\n\n neg_prompt_token_groups_2, neg_prompt_weight_groups_2 = group_tokens_and_weights(\n neg_prompt_tokens_2.copy(), neg_prompt_weights_2.copy()\n )\n\n # get prompt embeddings one by one is not working.\n for i in range(len(prompt_token_groups)):\n # get positive prompt embeddings with weights\n token_tensor = torch.tensor([prompt_token_groups[i]], dtype=torch.long, device=device)\n weight_tensor = torch.tensor(prompt_weight_groups[i], dtype=torch.float16, device=device)\n\n token_tensor_2 = torch.tensor([prompt_token_groups_2[i]], dtype=torch.long, device=device)\n\n # use first text encoder\n prompt_embeds_1 = pipe.text_encoder(token_tensor.to(device), output_hidden_states=True)\n\n # use second text encoder\n prompt_embeds_2 = pipe.text_encoder_2(token_tensor_2.to(device), output_hidden_states=True)\n pooled_prompt_embeds = prompt_embeds_2[0]\n\n if clip_skip is None:\n prompt_embeds_1_hidden_states = prompt_embeds_1.hidden_states[-2]\n prompt_embeds_2_hidden_states = prompt_embeds_2.hidden_states[-2]\n else:\n # \"2\" because SDXL always indexes from the penultimate layer.\n prompt_embeds_1_hidden_states = prompt_embeds_1.hidden_states[-(clip_skip + 2)]\n prompt_embeds_2_hidden_states = prompt_embeds_2.hidden_states[-(clip_skip + 2)]\n\n prompt_embeds_list = [prompt_embeds_1_hidden_states, prompt_embeds_2_hidden_states]\n token_embedding = torch.concat(prompt_embeds_list, dim=-1).squeeze(0)\n\n for j in range(len(weight_tensor)):\n if weight_tensor[j] != 1.0:\n token_embedding[j] = (\n token_embedding[-1] + (token_embedding[j] - token_embedding[-1]) * weight_tensor[j]\n )\n\n token_embedding = token_embedding.unsqueeze(0)\n embeds.append(token_embedding)\n\n # get negative prompt embeddings with weights\n neg_token_tensor = torch.tensor([neg_prompt_token_groups[i]], dtype=torch.long, device=device)\n neg_token_tensor_2 = torch.tensor([neg_prompt_token_groups_2[i]], dtype=torch.long, device=device)\n neg_weight_tensor = torch.tensor(neg_prompt_weight_groups[i], dtype=torch.float16, device=device)\n\n # use first text encoder\n neg_prompt_embeds_1 = pipe.text_encoder(neg_token_tensor.to(device), output_hidden_states=True)\n neg_prompt_embeds_1_hidden_states = neg_prompt_embeds_1.hidden_states[-2]\n\n # use second text encoder\n neg_prompt_embeds_2 = pipe.text_encoder_2(neg_token_tensor_2.to(device), output_hidden_states=True)\n neg_prompt_embeds_2_hidden_states = neg_prompt_embeds_2.hidden_states[-2]\n negative_pooled_prompt_embeds = neg_prompt_embeds_2[0]\n\n neg_prompt_embeds_list = [neg_prompt_embeds_1_hidden_states, neg_prompt_embeds_2_hidden_states]\n neg_token_embedding = torch.concat(neg_prompt_embeds_list, dim=-1).squeeze(0)\n\n for z in range(len(neg_weight_tensor)):\n if neg_weight_tensor[z] != 1.0:\n neg_token_embedding[z] = (\n neg_token_embedding[-1] + (neg_token_embedding[z] - neg_token_embedding[-1]) * neg_weight_tensor[z]\n )\n\n neg_token_embedding = neg_token_embedding.unsqueeze(0)\n neg_embeds.append(neg_token_embedding)\n\n prompt_embeds = torch.cat(embeds, dim=1)\n negative_prompt_embeds = torch.cat(neg_embeds, dim=1)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt, 1).view(\n bs_embed * num_images_per_prompt, -1\n )\n negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt, 1).view(\n bs_embed * num_images_per_prompt, -1\n )\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n\n# -------------------------------------------------------------------------------------------------------------------------------\n# reuse the backbone code from StableDiffusionXLPipeline\n# -------------------------------------------------------------------------------------------------------------------------------\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n from diffusers import DiffusionPipeline\n import torch\n\n pipe = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\"\n , torch_dtype = torch.float16\n , use_safetensors = True\n , variant = \"fp16\"\n , custom_pipeline = \"lpw_stable_diffusion_xl\",\n )\n\n prompt = \"a white cat running on the grass\"*20\n prompt2 = \"play a football\"*20\n prompt = f\"{prompt},{prompt2}\"\n neg_prompt = \"blur, low quality\"\n\n pipe.to(\"cuda\")\n images = pipe(\n prompt = prompt\n , negative_prompt = neg_prompt\n ).images[0]\n\n pipe.to(\"cpu\")\n torch.cuda.empty_cache()\n images\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used,\n `timesteps` must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default\n timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`\n must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass SDXLLongPromptWeightingPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n FromSingleFileMixin,\n IPAdapterMixin,\n LoraLoaderMixin,\n TextualInversionLoaderMixin,\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion XL.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion XL uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n text_encoder_2 ([` CLIPTextModelWithProjection`]):\n Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),\n specifically the\n [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)\n variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n tokenizer_2 (`CLIPTokenizer`):\n Second Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]):\n Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->text_encoder_2->image_encoder->unet->vae\"\n _optional_components = [\n \"tokenizer\",\n \"tokenizer_2\",\n \"text_encoder\",\n \"text_encoder_2\",\n \"image_encoder\",\n \"feature_extractor\",\n ]\n _callback_tensor_inputs = [\n \"latents\",\n \"prompt_embeds\",\n \"negative_prompt_embeds\",\n \"add_text_embeds\",\n \"add_time_ids\",\n \"negative_pooled_prompt_embeds\",\n \"negative_add_time_ids\",\n ]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n text_encoder_2: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n tokenizer_2: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n feature_extractor: Optional[CLIPImageProcessor] = None,\n image_encoder: Optional[CLIPVisionModelWithProjection] = None,\n force_zeros_for_empty_prompt: bool = True,\n add_watermarker: Optional[bool] = None,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n unet=unet,\n scheduler=scheduler,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.mask_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True\n )\n self.default_sample_size = self.unet.config.sample_size\n\n add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()\n\n if add_watermarker:\n self.watermark = StableDiffusionXLWatermarker()\n else:\n self.watermark = None\n\n def enable_model_cpu_offload(self, gpu_id=0):\n r\"\"\"\n Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared\n to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`\n method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with\n `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.\n \"\"\"\n if is_accelerate_available() and is_accelerate_version(\">=\", \"0.17.0.dev0\"):\n from accelerate import cpu_offload_with_hook\n else:\n raise ImportError(\"`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.\")\n\n device = torch.device(f\"cuda:{gpu_id}\")\n\n if self.device.type != \"cpu\":\n self.to(\"cpu\", silence_dtype_warnings=True)\n torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)\n\n model_sequence = (\n [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]\n )\n model_sequence.extend([self.unet, self.vae])\n\n hook = None\n for cpu_offloaded_model in model_sequence:\n _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)\n\n # We'll offload the last model manually.\n self.final_offload_hook = hook\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt: str,\n prompt_2: Optional[str] = None,\n device: Optional[torch.device] = None,\n num_images_per_prompt: int = 1,\n do_classifier_free_guidance: bool = True,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n lora_scale (`float`, *optional*):\n A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n \"\"\"\n device = device or self._execution_device\n\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # Define tokenizers and text encoders\n tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]\n text_encoders = (\n [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]\n )\n\n if prompt_embeds is None:\n prompt_2 = prompt_2 or prompt\n # textual inversion: process multi-vector tokens if necessary\n prompt_embeds_list = []\n prompts = [prompt, prompt_2]\n for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, tokenizer)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = text_encoder(\n text_input_ids.to(device),\n output_hidden_states=True,\n )\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds.hidden_states[-2]\n\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n\n # get unconditional embeddings for classifier free guidance\n zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt\n if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:\n negative_prompt_embeds = torch.zeros_like(prompt_embeds)\n negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)\n elif do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n\n uncond_tokens: List[str]\n if prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt, negative_prompt_2]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = [negative_prompt, negative_prompt_2]\n\n negative_prompt_embeds_list = []\n for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n negative_prompt_embeds = text_encoder(\n uncond_input.input_ids.to(device),\n output_hidden_states=True,\n )\n # We are only ALWAYS interested in the pooled output of the final text encoder\n negative_pooled_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]\n\n negative_prompt_embeds_list.append(negative_prompt_embeds)\n\n negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)\n\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n if do_classifier_free_guidance:\n negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n prompt_2,\n height,\n width,\n strength,\n callback_steps,\n negative_prompt=None,\n negative_prompt_2=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n pooled_prompt_embeds=None,\n negative_pooled_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if prompt_embeds is not None and pooled_prompt_embeds is None:\n raise ValueError(\n \"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.\"\n )\n\n if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:\n raise ValueError(\n \"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.\"\n )\n\n def get_timesteps(self, num_inference_steps, strength, device, denoising_start=None):\n # get the original timestep using init_timestep\n if denoising_start is None:\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n t_start = max(num_inference_steps - init_timestep, 0)\n else:\n t_start = 0\n\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n\n # Strength is irrelevant if we directly request a timestep to start at;\n # that is, strength is determined by the denoising_start instead.\n if denoising_start is not None:\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (denoising_start * self.scheduler.config.num_train_timesteps)\n )\n )\n\n num_inference_steps = (timesteps < discrete_timestep_cutoff).sum().item()\n if self.scheduler.order == 2 and num_inference_steps % 2 == 0:\n # if the scheduler is a 2nd order scheduler we might have to do +1\n # because `num_inference_steps` might be even given that every timestep\n # (except the highest one) is duplicated. If `num_inference_steps` is even it would\n # mean that we cut the timesteps in the middle of the denoising step\n # (between 1st and 2nd derivative) which leads to incorrect results. By adding 1\n # we ensure that the denoising process always ends after the 2nd derivate step of the scheduler\n num_inference_steps = num_inference_steps + 1\n\n # because t_n+1 >= t_n, we slice the timesteps starting from the end\n timesteps = timesteps[-num_inference_steps:]\n return timesteps, num_inference_steps\n\n return timesteps, num_inference_steps - t_start\n\n def prepare_latents(\n self,\n image,\n mask,\n width,\n height,\n num_channels_latents,\n timestep,\n batch_size,\n num_images_per_prompt,\n dtype,\n device,\n generator=None,\n add_noise=True,\n latents=None,\n is_strength_max=True,\n return_noise=False,\n return_image_latents=False,\n ):\n batch_size *= num_images_per_prompt\n\n if image is None:\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n elif mask is None:\n if not isinstance(image, (torch.Tensor, Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n # Offload text encoder if `enable_model_cpu_offload` was enabled\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.text_encoder_2.to(\"cpu\")\n torch.cuda.empty_cache()\n\n image = image.to(device=device, dtype=dtype)\n\n if image.shape[1] == 4:\n init_latents = image\n\n else:\n # make sure the VAE is in float32 mode, as it overflows in float16\n if self.vae.config.force_upcast:\n image = image.float()\n self.vae.to(dtype=torch.float32)\n\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n elif isinstance(generator, list):\n init_latents = [\n retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])\n for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = retrieve_latents(self.vae.encode(image), generator=generator)\n\n if self.vae.config.force_upcast:\n self.vae.to(dtype)\n\n init_latents = init_latents.to(dtype)\n init_latents = self.vae.config.scaling_factor * init_latents\n\n if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:\n # expand init_latents for batch_size\n additional_image_per_prompt = batch_size // init_latents.shape[0]\n init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)\n elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n init_latents = torch.cat([init_latents], dim=0)\n\n if add_noise:\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n\n latents = init_latents\n return latents\n\n else:\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if (image is None or timestep is None) and not is_strength_max:\n raise ValueError(\n \"Since strength < 1. initial latents are to be initialised as a combination of Image + Noise.\"\n \"However, either the image or the noise timestep has not been provided.\"\n )\n\n if image.shape[1] == 4:\n image_latents = image.to(device=device, dtype=dtype)\n image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)\n elif return_image_latents or (latents is None and not is_strength_max):\n image = image.to(device=device, dtype=dtype)\n image_latents = self._encode_vae_image(image=image, generator=generator)\n image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)\n\n if latents is None and add_noise:\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n # if strength is 1. then initialise the latents to noise, else initial to image + noise\n latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)\n # if pure noise then scale the initial latents by the Scheduler's init sigma\n latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents\n elif add_noise:\n noise = latents.to(device)\n latents = noise * self.scheduler.init_noise_sigma\n else:\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n latents = image_latents.to(device)\n\n outputs = (latents,)\n\n if return_noise:\n outputs += (noise,)\n\n if return_image_latents:\n outputs += (image_latents,)\n\n return outputs\n\n def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):\n dtype = image.dtype\n if self.vae.config.force_upcast:\n image = image.float()\n self.vae.to(dtype=torch.float32)\n\n if isinstance(generator, list):\n image_latents = [\n retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])\n for i in range(image.shape[0])\n ]\n image_latents = torch.cat(image_latents, dim=0)\n else:\n image_latents = retrieve_latents(self.vae.encode(image), generator=generator)\n\n if self.vae.config.force_upcast:\n self.vae.to(dtype)\n\n image_latents = image_latents.to(dtype)\n image_latents = self.vae.config.scaling_factor * image_latents\n\n return image_latents\n\n def prepare_mask_latents(\n self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance\n ):\n # resize the mask to latents shape as we concatenate the mask to the latents\n # we do that before converting to dtype to avoid breaking in case we're using cpu_offload\n # and half precision\n mask = torch.nn.functional.interpolate(\n mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)\n )\n mask = mask.to(device=device, dtype=dtype)\n\n # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method\n if mask.shape[0] < batch_size:\n if not batch_size % mask.shape[0] == 0:\n raise ValueError(\n \"The passed mask and the required batch size don't match. Masks are supposed to be duplicated to\"\n f\" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number\"\n \" of masks that you pass is divisible by the total requested batch size.\"\n )\n mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)\n\n mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask\n\n if masked_image is not None and masked_image.shape[1] == 4:\n masked_image_latents = masked_image\n else:\n masked_image_latents = None\n\n if masked_image is not None:\n if masked_image_latents is None:\n masked_image = masked_image.to(device=device, dtype=dtype)\n masked_image_latents = self._encode_vae_image(masked_image, generator=generator)\n\n if masked_image_latents.shape[0] < batch_size:\n if not batch_size % masked_image_latents.shape[0] == 0:\n raise ValueError(\n \"The passed images and the required batch size don't match. Images are supposed to be duplicated\"\n f\" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed.\"\n \" Make sure the number of images that you pass is divisible by the total requested batch size.\"\n )\n masked_image_latents = masked_image_latents.repeat(\n batch_size // masked_image_latents.shape[0], 1, 1, 1\n )\n\n masked_image_latents = (\n torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents\n )\n\n # aligning device to prevent device errors when concating it with the latent model input\n masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)\n\n return mask, masked_image_latents\n\n def _get_add_time_ids(self, original_size, crops_coords_top_left, target_size, dtype):\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n\n passed_add_embed_dim = (\n self.unet.config.addition_time_embed_dim * len(add_time_ids) + self.text_encoder_2.config.projection_dim\n )\n expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features\n\n if expected_add_embed_dim != passed_add_embed_dim:\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.\"\n )\n\n add_time_ids = torch.tensor([add_time_ids], dtype=dtype)\n return add_time_ids\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae\n def upcast_vae(self):\n dtype = self.vae.dtype\n self.vae.to(dtype=torch.float32)\n use_torch_2_0_or_xformers = isinstance(\n self.vae.decoder.mid_block.attentions[0].processor,\n (\n AttnProcessor2_0,\n XFormersAttnProcessor,\n LoRAXFormersAttnProcessor,\n LoRAAttnProcessor2_0,\n ),\n )\n # if xformers or torch_2_0 is used attention block does not need\n # to be in float32 which can save lots of memory\n if use_torch_2_0_or_xformers:\n self.vae.post_quant_conv.to(dtype)\n self.vae.decoder.conv_in.to(dtype)\n self.vae.decoder.mid_block.to(dtype)\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n timesteps (`torch.Tensor`):\n generate embedding vectors at these timesteps\n embedding_dim (`int`, *optional*, defaults to 512):\n dimension of the embeddings to generate\n dtype:\n data type of the generated embeddings\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def guidance_rescale(self):\n return self._guidance_rescale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def denoising_end(self):\n return self._denoising_end\n\n @property\n def denoising_start(self):\n return self._denoising_start\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: str = None,\n prompt_2: Optional[str] = None,\n image: Optional[PipelineImageInput] = None,\n mask_image: Optional[PipelineImageInput] = None,\n masked_image_latents: Optional[torch.Tensor] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n strength: float = 0.8,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n denoising_start: Optional[float] = None,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Optional[Tuple[int, int]] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Optional[Tuple[int, int]] = None,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str`):\n The prompt to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n prompt_2 (`str`):\n The prompt to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n image (`PipelineImageInput`, *optional*):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process.\n mask_image (`PipelineImageInput`, *optional*):\n `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be\n replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a\n PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should\n contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n strength (`float`, *optional*, defaults to 0.8):\n Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.\n `image` will be used as a starting point, adding more noise to it the larger the `strength`. The\n number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added\n noise will be maximum and the denoising process will run for the full number of iterations specified in\n `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n denoising_start (`float`, *optional*):\n When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be\n bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and\n it is assumed that the passed `image` is a partly denoised image. Note that when this is specified,\n strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline\n is integrated into a \"Mixture of Denoisers\" multi-pipeline setup, as detailed in [**Refine Image\n Quality**](https://huggingface.co/docs/diffusers/using-diffusers/sdxl#refine-image-quality).\n denoising_end (`float`, *optional*):\n When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be\n completed before it is intentionally prematurely terminated. As a result, the returned sample will\n still retain a substantial amount of noise (ca. final 20% of timesteps still needed) and should be\n denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the\n final 20% of the scheduler. The denoising_end parameter should ideally be utilized when this pipeline\n forms a part of a \"Mixture of Denoisers\" multi-pipeline setup, as elaborated in [**Refine Image\n Quality**](https://huggingface.co/docs/diffusers/using-diffusers/sdxl#refine-image-quality).\n guidance_scale (`float`, *optional*, defaults to 5.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str`):\n The prompt not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str`):\n The prompt not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n ip_adapter_image: (`PipelineImageInput`, *optional*):\n Optional image input to work with IP Adapters.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead\n of a plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of\n [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).\n Guidance rescale factor should fix overexposure when using zero terminal SNR.\n original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.\n `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as\n explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n `crops_coords_top_left` can be used to generate an image that appears to be \"cropped\" from the position\n `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n For most cases, `target_size` should be set to the desired height and width of the generated image. If\n not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a\n `tuple`. When returning a tuple, the first element is a list with the generated images.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n\n # 0. Default height and width to unet\n height = height or self.default_sample_size * self.vae_scale_factor\n width = width or self.default_sample_size * self.vae_scale_factor\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n height,\n width,\n strength,\n callback_steps,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._guidance_rescale = guidance_rescale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._denoising_end = denoising_end\n self._denoising_start = denoising_start\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n if ip_adapter_image is not None:\n output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True\n image_embeds, negative_image_embeds = self.encode_image(\n ip_adapter_image, device, num_images_per_prompt, output_hidden_state\n )\n if self.do_classifier_free_guidance:\n image_embeds = torch.cat([negative_image_embeds, image_embeds])\n\n # 3. Encode input prompt\n (self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None)\n\n negative_prompt = negative_prompt if negative_prompt is not None else \"\"\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = get_weighted_text_embeddings_sdxl(\n pipe=self,\n prompt=prompt,\n neg_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n clip_skip=clip_skip,\n )\n dtype = prompt_embeds.dtype\n\n if isinstance(image, Image.Image):\n image = self.image_processor.preprocess(image, height=height, width=width)\n if image is not None:\n image = image.to(device=self.device, dtype=dtype)\n\n if isinstance(mask_image, Image.Image):\n mask = self.mask_processor.preprocess(mask_image, height=height, width=width)\n else:\n mask = mask_image\n if mask_image is not None:\n mask = mask.to(device=self.device, dtype=dtype)\n\n if masked_image_latents is not None:\n masked_image = masked_image_latents\n elif image.shape[1] == 4:\n # if image is in latent space, we can't mask it\n masked_image = None\n else:\n masked_image = image * (mask < 0.5)\n else:\n mask = None\n\n # 4. Prepare timesteps\n def denoising_value_valid(dnv):\n return isinstance(dnv, float) and 0 < dnv < 1\n\n timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)\n if image is not None:\n timesteps, num_inference_steps = self.get_timesteps(\n num_inference_steps,\n strength,\n device,\n denoising_start=self.denoising_start if denoising_value_valid(self.denoising_start) else None,\n )\n\n # check that number of inference steps is not < 1 - as this doesn't make sense\n if num_inference_steps < 1:\n raise ValueError(\n f\"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline\"\n f\"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline.\"\n )\n\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n is_strength_max = strength == 1.0\n add_noise = True if self.denoising_start is None else False\n\n # 5. Prepare latent variables\n num_channels_latents = self.vae.config.latent_channels\n num_channels_unet = self.unet.config.in_channels\n return_image_latents = num_channels_unet == 4\n\n latents = self.prepare_latents(\n image=image,\n mask=mask,\n width=width,\n height=height,\n num_channels_latents=num_channels_unet,\n timestep=latent_timestep,\n batch_size=batch_size,\n num_images_per_prompt=num_images_per_prompt,\n dtype=prompt_embeds.dtype,\n device=device,\n generator=generator,\n add_noise=add_noise,\n latents=latents,\n is_strength_max=is_strength_max,\n return_noise=True,\n return_image_latents=return_image_latents,\n )\n\n if mask is not None:\n if return_image_latents:\n latents, noise, image_latents = latents\n else:\n latents, noise = latents\n\n # 5.1 Prepare mask latent variables\n if mask is not None:\n mask, masked_image_latents = self.prepare_mask_latents(\n mask=mask,\n masked_image=masked_image,\n batch_size=batch_size * num_images_per_prompt,\n height=height,\n width=width,\n dtype=prompt_embeds.dtype,\n device=device,\n generator=generator,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n )\n\n # Check that sizes of mask, masked image and latents match\n if num_channels_unet == 9:\n # default case for runwayml/stable-diffusion-inpainting\n num_channels_mask = mask.shape[1]\n num_channels_masked_image = masked_image_latents.shape[1]\n if num_channels_latents + num_channels_mask + num_channels_masked_image != num_channels_unet:\n raise ValueError(\n f\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\"\n f\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\"\n f\" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}\"\n f\" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of\"\n \" `pipeline.unet` or your `mask_image` or `image` input.\"\n )\n elif num_channels_unet != 4:\n raise ValueError(\n f\"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}.\"\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 6.1 Add image embeds for IP-Adapter\n added_cond_kwargs = {\"image_embeds\": image_embeds} if ip_adapter_image is not None else {}\n\n height, width = latents.shape[-2:]\n height = height * self.vae_scale_factor\n width = width * self.vae_scale_factor\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n # 7. Prepare added time ids & embeddings\n add_text_embeds = pooled_prompt_embeds\n add_time_ids = self._get_add_time_ids(\n original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype\n )\n\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)\n\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n\n # 7.1 Apply denoising_end\n if (\n self.denoising_end is not None\n and self.denoising_start is not None\n and denoising_value_valid(self.denoising_end)\n and denoising_value_valid(self.denoising_start)\n and self.denoising_start >= self.denoising_end\n ):\n raise ValueError(\n f\"`denoising_start`: {self.denoising_start} cannot be larger than or equal to `denoising_end`: \"\n + f\" {self.denoising_end} when using type float.\"\n )\n elif self.denoising_end is not None and denoising_value_valid(self.denoising_end):\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (self.denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n # 8. Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n self._num_timesteps = len(timesteps)\n\n # 9. Denoising loop\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n if mask is not None and num_channels_unet == 9:\n latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)\n\n # predict the noise residual\n added_cond_kwargs.update({\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids})\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=self.cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if self.do_classifier_free_guidance and guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if mask is not None and num_channels_unet == 4:\n init_latents_proper = image_latents\n\n if self.do_classifier_free_guidance:\n init_mask, _ = mask.chunk(2)\n else:\n init_mask = mask\n\n if i < len(timesteps) - 1:\n noise_timestep = timesteps[i + 1]\n init_latents_proper = self.scheduler.add_noise(\n init_latents_proper, noise, torch.tensor([noise_timestep])\n )\n\n latents = (1 - init_mask) * init_latents_proper + init_mask * latents\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n add_text_embeds = callback_outputs.pop(\"add_text_embeds\", add_text_embeds)\n negative_pooled_prompt_embeds = callback_outputs.pop(\n \"negative_pooled_prompt_embeds\", negative_pooled_prompt_embeds\n )\n add_time_ids = callback_outputs.pop(\"add_time_ids\", add_time_ids)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n image = latents\n return StableDiffusionXLPipelineOutput(images=image)\n\n # apply watermark if available\n if self.watermark is not None:\n image = self.watermark.apply_watermark(image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload last model to CPU\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)\n\n def text2img(\n self,\n prompt: str = None,\n prompt_2: Optional[str] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n denoising_start: Optional[float] = None,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Optional[Tuple[int, int]] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Optional[Tuple[int, int]] = None,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling pipeline for text-to-image.\n\n Refer to the documentation of the `__call__` method for parameter descriptions.\n \"\"\"\n return self.__call__(\n prompt=prompt,\n prompt_2=prompt_2,\n height=height,\n width=width,\n num_inference_steps=num_inference_steps,\n timesteps=timesteps,\n denoising_start=denoising_start,\n denoising_end=denoising_end,\n guidance_scale=guidance_scale,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n latents=latents,\n ip_adapter_image=ip_adapter_image,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n output_type=output_type,\n return_dict=return_dict,\n cross_attention_kwargs=cross_attention_kwargs,\n guidance_rescale=guidance_rescale,\n original_size=original_size,\n crops_coords_top_left=crops_coords_top_left,\n target_size=target_size,\n clip_skip=clip_skip,\n callback_on_step_end=callback_on_step_end,\n callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,\n **kwargs,\n )\n\n def img2img(\n self,\n prompt: str = None,\n prompt_2: Optional[str] = None,\n image: Optional[PipelineImageInput] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n strength: float = 0.8,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n denoising_start: Optional[float] = None,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Optional[Tuple[int, int]] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Optional[Tuple[int, int]] = None,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling pipeline for image-to-image.\n\n Refer to the documentation of the `__call__` method for parameter descriptions.\n \"\"\"\n return self.__call__(\n prompt=prompt,\n prompt_2=prompt_2,\n image=image,\n height=height,\n width=width,\n strength=strength,\n num_inference_steps=num_inference_steps,\n timesteps=timesteps,\n denoising_start=denoising_start,\n denoising_end=denoising_end,\n guidance_scale=guidance_scale,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n latents=latents,\n ip_adapter_image=ip_adapter_image,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n output_type=output_type,\n return_dict=return_dict,\n cross_attention_kwargs=cross_attention_kwargs,\n guidance_rescale=guidance_rescale,\n original_size=original_size,\n crops_coords_top_left=crops_coords_top_left,\n target_size=target_size,\n clip_skip=clip_skip,\n callback_on_step_end=callback_on_step_end,\n callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,\n **kwargs,\n )\n\n def inpaint(\n self,\n prompt: str = None,\n prompt_2: Optional[str] = None,\n image: Optional[PipelineImageInput] = None,\n mask_image: Optional[PipelineImageInput] = None,\n masked_image_latents: Optional[torch.Tensor] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n strength: float = 0.8,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n denoising_start: Optional[float] = None,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Optional[Tuple[int, int]] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Optional[Tuple[int, int]] = None,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling pipeline for inpainting.\n\n Refer to the documentation of the `__call__` method for parameter descriptions.\n \"\"\"\n return self.__call__(\n prompt=prompt,\n prompt_2=prompt_2,\n image=image,\n mask_image=mask_image,\n masked_image_latents=masked_image_latents,\n height=height,\n width=width,\n strength=strength,\n num_inference_steps=num_inference_steps,\n timesteps=timesteps,\n denoising_start=denoising_start,\n denoising_end=denoising_end,\n guidance_scale=guidance_scale,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n latents=latents,\n ip_adapter_image=ip_adapter_image,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n output_type=output_type,\n return_dict=return_dict,\n cross_attention_kwargs=cross_attention_kwargs,\n guidance_rescale=guidance_rescale,\n original_size=original_size,\n crops_coords_top_left=crops_coords_top_left,\n target_size=target_size,\n clip_skip=clip_skip,\n callback_on_step_end=callback_on_step_end,\n callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,\n **kwargs,\n )\n\n # Override to properly handle the loading and unloading of the additional text encoder.\n def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):\n # We could have accessed the unet config from `lora_state_dict()` too. We pass\n # it here explicitly to be able to tell that it's coming from an SDXL\n # pipeline.\n state_dict, network_alphas = self.lora_state_dict(\n pretrained_model_name_or_path_or_dict,\n unet_config=self.unet.config,\n **kwargs,\n )\n self.load_lora_into_unet(state_dict, network_alphas=network_alphas, unet=self.unet)\n\n text_encoder_state_dict = {k: v for k, v in state_dict.items() if \"text_encoder.\" in k}\n if len(text_encoder_state_dict) > 0:\n self.load_lora_into_text_encoder(\n text_encoder_state_dict,\n network_alphas=network_alphas,\n text_encoder=self.text_encoder,\n prefix=\"text_encoder\",\n lora_scale=self.lora_scale,\n )\n\n text_encoder_2_state_dict = {k: v for k, v in state_dict.items() if \"text_encoder_2.\" in k}\n if len(text_encoder_2_state_dict) > 0:\n self.load_lora_into_text_encoder(\n text_encoder_2_state_dict,\n network_alphas=network_alphas,\n text_encoder=self.text_encoder_2,\n prefix=\"text_encoder_2\",\n lora_scale=self.lora_scale,\n )\n\n @classmethod\n def save_lora_weights(\n self,\n save_directory: Union[str, os.PathLike],\n unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,\n text_encoder_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,\n text_encoder_2_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,\n is_main_process: bool = True,\n weight_name: str = None,\n save_function: Callable = None,\n safe_serialization: bool = False,\n ):\n state_dict = {}\n\n def pack_weights(layers, prefix):\n layers_weights = layers.state_dict() if isinstance(layers, torch.nn.Module) else layers\n layers_state_dict = {f\"{prefix}.{module_name}\": param for module_name, param in layers_weights.items()}\n return layers_state_dict\n\n state_dict.update(pack_weights(unet_lora_layers, \"unet\"))\n\n if text_encoder_lora_layers and text_encoder_2_lora_layers:\n state_dict.update(pack_weights(text_encoder_lora_layers, \"text_encoder\"))\n state_dict.update(pack_weights(text_encoder_2_lora_layers, \"text_encoder_2\"))\n\n self.write_lora_layers(\n state_dict=state_dict,\n save_directory=save_directory,\n is_main_process=is_main_process,\n weight_name=weight_name,\n save_function=save_function,\n safe_serialization=safe_serialization,\n )\n\n def _remove_text_encoder_monkey_patch(self):\n self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder)\n self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder_2)\n" }, { "path": "diffusers/examples/community/magic_mix.py", "content": "from typing import Union\n\nimport torch\nfrom PIL import Image\nfrom torchvision import transforms as tfms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n DiffusionPipeline,\n LMSDiscreteScheduler,\n PNDMScheduler,\n UNet2DConditionModel,\n)\n\n\nclass MagicMixPipeline(DiffusionPipeline):\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler],\n ):\n super().__init__()\n\n self.register_modules(vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler)\n\n # convert PIL image to latents\n def encode(self, img):\n with torch.no_grad():\n latent = self.vae.encode(tfms.ToTensor()(img).unsqueeze(0).to(self.device) * 2 - 1)\n latent = 0.18215 * latent.latent_dist.sample()\n return latent\n\n # convert latents to PIL image\n def decode(self, latent):\n latent = (1 / 0.18215) * latent\n with torch.no_grad():\n img = self.vae.decode(latent).sample\n img = (img / 2 + 0.5).clamp(0, 1)\n img = img.detach().cpu().permute(0, 2, 3, 1).numpy()\n img = (img * 255).round().astype(\"uint8\")\n return Image.fromarray(img[0])\n\n # convert prompt into text embeddings, also unconditional embeddings\n def prep_text(self, prompt):\n text_input = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_embedding = self.text_encoder(text_input.input_ids.to(self.device))[0]\n\n uncond_input = self.tokenizer(\n \"\",\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n uncond_embedding = self.text_encoder(uncond_input.input_ids.to(self.device))[0]\n\n return torch.cat([uncond_embedding, text_embedding])\n\n def __call__(\n self,\n img: Image.Image,\n prompt: str,\n kmin: float = 0.3,\n kmax: float = 0.6,\n mix_factor: float = 0.5,\n seed: int = 42,\n steps: int = 50,\n guidance_scale: float = 7.5,\n ) -> Image.Image:\n tmin = steps - int(kmin * steps)\n tmax = steps - int(kmax * steps)\n\n text_embeddings = self.prep_text(prompt)\n\n self.scheduler.set_timesteps(steps)\n\n width, height = img.size\n encoded = self.encode(img)\n\n torch.manual_seed(seed)\n noise = torch.randn(\n (1, self.unet.config.in_channels, height // 8, width // 8),\n ).to(self.device)\n\n latents = self.scheduler.add_noise(\n encoded,\n noise,\n timesteps=self.scheduler.timesteps[tmax],\n )\n\n input = torch.cat([latents] * 2)\n\n input = self.scheduler.scale_model_input(input, self.scheduler.timesteps[tmax])\n\n with torch.no_grad():\n pred = self.unet(\n input,\n self.scheduler.timesteps[tmax],\n encoder_hidden_states=text_embeddings,\n ).sample\n\n pred_uncond, pred_text = pred.chunk(2)\n pred = pred_uncond + guidance_scale * (pred_text - pred_uncond)\n\n latents = self.scheduler.step(pred, self.scheduler.timesteps[tmax], latents).prev_sample\n\n for i, t in enumerate(tqdm(self.scheduler.timesteps)):\n if i > tmax:\n if i < tmin: # layout generation phase\n orig_latents = self.scheduler.add_noise(\n encoded,\n noise,\n timesteps=t,\n )\n\n input = (\n (mix_factor * latents) + (1 - mix_factor) * orig_latents\n ) # interpolating between layout noise and conditionally generated noise to preserve layout sematics\n input = torch.cat([input] * 2)\n\n else: # content generation phase\n input = torch.cat([latents] * 2)\n\n input = self.scheduler.scale_model_input(input, t)\n\n with torch.no_grad():\n pred = self.unet(\n input,\n t,\n encoder_hidden_states=text_embeddings,\n ).sample\n\n pred_uncond, pred_text = pred.chunk(2)\n pred = pred_uncond + guidance_scale * (pred_text - pred_uncond)\n\n latents = self.scheduler.step(pred, t, latents).prev_sample\n\n return self.decode(latents)\n" }, { "path": "diffusers/examples/community/marigold_depth_estimation.py", "content": "# Copyright 2024 Bingxin Ke, ETH Zurich and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# --------------------------------------------------------------------------\n# If you find this code useful, we kindly ask you to cite our paper in your work.\n# Please find bibtex at: https://github.com/prs-eth/Marigold#-citation\n# More information about the method can be found at https://marigoldmonodepth.github.io\n# --------------------------------------------------------------------------\n\n\nimport logging\nimport math\nfrom typing import Dict, Union\n\nimport matplotlib\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom PIL.Image import Resampling\nfrom scipy.optimize import minimize\nfrom torch.utils.data import DataLoader, TensorDataset\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n DiffusionPipeline,\n LCMScheduler,\n UNet2DConditionModel,\n)\nfrom diffusers.utils import BaseOutput, check_min_version\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\n\nclass MarigoldDepthOutput(BaseOutput):\n \"\"\"\n Output class for Marigold monocular depth prediction pipeline.\n\n Args:\n depth_np (`np.ndarray`):\n Predicted depth map, with depth values in the range of [0, 1].\n depth_colored (`None` or `PIL.Image.Image`):\n Colorized depth map, with the shape of [3, H, W] and values in [0, 1].\n uncertainty (`None` or `np.ndarray`):\n Uncalibrated uncertainty(MAD, median absolute deviation) coming from ensembling.\n \"\"\"\n\n depth_np: np.ndarray\n depth_colored: Union[None, Image.Image]\n uncertainty: Union[None, np.ndarray]\n\n\ndef get_pil_resample_method(method_str: str) -> Resampling:\n resample_method_dic = {\n \"bilinear\": Resampling.BILINEAR,\n \"bicubic\": Resampling.BICUBIC,\n \"nearest\": Resampling.NEAREST,\n }\n resample_method = resample_method_dic.get(method_str, None)\n if resample_method is None:\n raise ValueError(f\"Unknown resampling method: {resample_method}\")\n else:\n return resample_method\n\n\nclass MarigoldPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for monocular depth estimation using Marigold: https://marigoldmonodepth.github.io.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n unet (`UNet2DConditionModel`):\n Conditional U-Net to denoise the depth latent, conditioned on image latent.\n vae (`AutoencoderKL`):\n Variational Auto-Encoder (VAE) Model to encode and decode images and depth maps\n to and from latent representations.\n scheduler (`DDIMScheduler`):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents.\n text_encoder (`CLIPTextModel`):\n Text-encoder, for empty text embedding.\n tokenizer (`CLIPTokenizer`):\n CLIP tokenizer.\n \"\"\"\n\n rgb_latent_scale_factor = 0.18215\n depth_latent_scale_factor = 0.18215\n\n def __init__(\n self,\n unet: UNet2DConditionModel,\n vae: AutoencoderKL,\n scheduler: DDIMScheduler,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n ):\n super().__init__()\n\n self.register_modules(\n unet=unet,\n vae=vae,\n scheduler=scheduler,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n )\n\n self.empty_text_embed = None\n\n @torch.no_grad()\n def __call__(\n self,\n input_image: Image,\n denoising_steps: int = 10,\n ensemble_size: int = 10,\n processing_res: int = 768,\n match_input_res: bool = True,\n resample_method: str = \"bilinear\",\n batch_size: int = 0,\n seed: Union[int, None] = None,\n color_map: str = \"Spectral\",\n show_progress_bar: bool = True,\n ensemble_kwargs: Dict = None,\n ) -> MarigoldDepthOutput:\n \"\"\"\n Function invoked when calling the pipeline.\n\n Args:\n input_image (`Image`):\n Input RGB (or gray-scale) image.\n processing_res (`int`, *optional*, defaults to `768`):\n Maximum resolution of processing.\n If set to 0: will not resize at all.\n match_input_res (`bool`, *optional*, defaults to `True`):\n Resize depth prediction to match input resolution.\n Only valid if `processing_res` > 0.\n resample_method: (`str`, *optional*, defaults to `bilinear`):\n Resampling method used to resize images and depth predictions. This can be one of `bilinear`, `bicubic` or `nearest`, defaults to: `bilinear`.\n denoising_steps (`int`, *optional*, defaults to `10`):\n Number of diffusion denoising steps (DDIM) during inference.\n ensemble_size (`int`, *optional*, defaults to `10`):\n Number of predictions to be ensembled.\n batch_size (`int`, *optional*, defaults to `0`):\n Inference batch size, no bigger than `num_ensemble`.\n If set to 0, the script will automatically decide the proper batch size.\n seed (`int`, *optional*, defaults to `None`)\n Reproducibility seed.\n show_progress_bar (`bool`, *optional*, defaults to `True`):\n Display a progress bar of diffusion denoising.\n color_map (`str`, *optional*, defaults to `\"Spectral\"`, pass `None` to skip colorized depth map generation):\n Colormap used to colorize the depth map.\n ensemble_kwargs (`dict`, *optional*, defaults to `None`):\n Arguments for detailed ensembling settings.\n Returns:\n `MarigoldDepthOutput`: Output class for Marigold monocular depth prediction pipeline, including:\n - **depth_np** (`np.ndarray`) Predicted depth map, with depth values in the range of [0, 1]\n - **depth_colored** (`PIL.Image.Image`) Colorized depth map, with the shape of [3, H, W] and values in [0, 1], None if `color_map` is `None`\n - **uncertainty** (`None` or `np.ndarray`) Uncalibrated uncertainty(MAD, median absolute deviation)\n coming from ensembling. None if `ensemble_size = 1`\n \"\"\"\n\n device = self.device\n input_size = input_image.size\n\n if not match_input_res:\n assert processing_res is not None, \"Value error: `resize_output_back` is only valid with \"\n assert processing_res >= 0\n assert ensemble_size >= 1\n\n # Check if denoising step is reasonable\n self._check_inference_step(denoising_steps)\n\n resample_method: Resampling = get_pil_resample_method(resample_method)\n\n # ----------------- Image Preprocess -----------------\n # Resize image\n if processing_res > 0:\n input_image = self.resize_max_res(\n input_image,\n max_edge_resolution=processing_res,\n resample_method=resample_method,\n )\n # Convert the image to RGB, to 1.remove the alpha channel 2.convert B&W to 3-channel\n input_image = input_image.convert(\"RGB\")\n image = np.asarray(input_image)\n\n # Normalize rgb values\n rgb = np.transpose(image, (2, 0, 1)) # [H, W, rgb] -> [rgb, H, W]\n rgb_norm = rgb / 255.0 * 2.0 - 1.0 # [0, 255] -> [-1, 1]\n rgb_norm = torch.from_numpy(rgb_norm).to(self.dtype)\n rgb_norm = rgb_norm.to(device)\n assert rgb_norm.min() >= -1.0 and rgb_norm.max() <= 1.0\n\n # ----------------- Predicting depth -----------------\n # Batch repeated input image\n duplicated_rgb = torch.stack([rgb_norm] * ensemble_size)\n single_rgb_dataset = TensorDataset(duplicated_rgb)\n if batch_size > 0:\n _bs = batch_size\n else:\n _bs = self._find_batch_size(\n ensemble_size=ensemble_size,\n input_res=max(rgb_norm.shape[1:]),\n dtype=self.dtype,\n )\n\n single_rgb_loader = DataLoader(single_rgb_dataset, batch_size=_bs, shuffle=False)\n\n # Predict depth maps (batched)\n depth_pred_ls = []\n if show_progress_bar:\n iterable = tqdm(single_rgb_loader, desc=\" \" * 2 + \"Inference batches\", leave=False)\n else:\n iterable = single_rgb_loader\n for batch in iterable:\n (batched_img,) = batch\n depth_pred_raw = self.single_infer(\n rgb_in=batched_img,\n num_inference_steps=denoising_steps,\n show_pbar=show_progress_bar,\n seed=seed,\n )\n depth_pred_ls.append(depth_pred_raw.detach())\n depth_preds = torch.concat(depth_pred_ls, dim=0).squeeze()\n torch.cuda.empty_cache() # clear vram cache for ensembling\n\n # ----------------- Test-time ensembling -----------------\n if ensemble_size > 1:\n depth_pred, pred_uncert = self.ensemble_depths(depth_preds, **(ensemble_kwargs or {}))\n else:\n depth_pred = depth_preds\n pred_uncert = None\n\n # ----------------- Post processing -----------------\n # Scale prediction to [0, 1]\n min_d = torch.min(depth_pred)\n max_d = torch.max(depth_pred)\n depth_pred = (depth_pred - min_d) / (max_d - min_d)\n\n # Convert to numpy\n depth_pred = depth_pred.cpu().numpy().astype(np.float32)\n\n # Resize back to original resolution\n if match_input_res:\n pred_img = Image.fromarray(depth_pred)\n pred_img = pred_img.resize(input_size, resample=resample_method)\n depth_pred = np.asarray(pred_img)\n\n # Clip output range\n depth_pred = depth_pred.clip(0, 1)\n\n # Colorize\n if color_map is not None:\n depth_colored = self.colorize_depth_maps(\n depth_pred, 0, 1, cmap=color_map\n ).squeeze() # [3, H, W], value in (0, 1)\n depth_colored = (depth_colored * 255).astype(np.uint8)\n depth_colored_hwc = self.chw2hwc(depth_colored)\n depth_colored_img = Image.fromarray(depth_colored_hwc)\n else:\n depth_colored_img = None\n\n return MarigoldDepthOutput(\n depth_np=depth_pred,\n depth_colored=depth_colored_img,\n uncertainty=pred_uncert,\n )\n\n def _check_inference_step(self, n_step: int):\n \"\"\"\n Check if denoising step is reasonable\n Args:\n n_step (`int`): denoising steps\n \"\"\"\n assert n_step >= 1\n\n if isinstance(self.scheduler, DDIMScheduler):\n if n_step < 10:\n logging.warning(\n f\"Too few denoising steps: {n_step}. Recommended to use the LCM checkpoint for few-step inference.\"\n )\n elif isinstance(self.scheduler, LCMScheduler):\n if not 1 <= n_step <= 4:\n logging.warning(f\"Non-optimal setting of denoising steps: {n_step}. Recommended setting is 1-4 steps.\")\n else:\n raise RuntimeError(f\"Unsupported scheduler type: {type(self.scheduler)}\")\n\n def _encode_empty_text(self):\n \"\"\"\n Encode text embedding for empty prompt.\n \"\"\"\n prompt = \"\"\n text_inputs = self.tokenizer(\n prompt,\n padding=\"do_not_pad\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids.to(self.text_encoder.device)\n self.empty_text_embed = self.text_encoder(text_input_ids)[0].to(self.dtype)\n\n @torch.no_grad()\n def single_infer(\n self,\n rgb_in: torch.Tensor,\n num_inference_steps: int,\n seed: Union[int, None],\n show_pbar: bool,\n ) -> torch.Tensor:\n \"\"\"\n Perform an individual depth prediction without ensembling.\n\n Args:\n rgb_in (`torch.Tensor`):\n Input RGB image.\n num_inference_steps (`int`):\n Number of diffusion denoisign steps (DDIM) during inference.\n show_pbar (`bool`):\n Display a progress bar of diffusion denoising.\n Returns:\n `torch.Tensor`: Predicted depth map.\n \"\"\"\n device = rgb_in.device\n\n # Set timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps # [T]\n\n # Encode image\n rgb_latent = self.encode_rgb(rgb_in)\n\n # Initial depth map (noise)\n if seed is None:\n rand_num_generator = None\n else:\n rand_num_generator = torch.Generator(device=device)\n rand_num_generator.manual_seed(seed)\n depth_latent = torch.randn(\n rgb_latent.shape,\n device=device,\n dtype=self.dtype,\n generator=rand_num_generator,\n ) # [B, 4, h, w]\n\n # Batched empty text embedding\n if self.empty_text_embed is None:\n self._encode_empty_text()\n batch_empty_text_embed = self.empty_text_embed.repeat((rgb_latent.shape[0], 1, 1)) # [B, 2, 1024]\n\n # Denoising loop\n if show_pbar:\n iterable = tqdm(\n enumerate(timesteps),\n total=len(timesteps),\n leave=False,\n desc=\" \" * 4 + \"Diffusion denoising\",\n )\n else:\n iterable = enumerate(timesteps)\n\n for i, t in iterable:\n unet_input = torch.cat([rgb_latent, depth_latent], dim=1) # this order is important\n\n # predict the noise residual\n noise_pred = self.unet(unet_input, t, encoder_hidden_states=batch_empty_text_embed).sample # [B, 4, h, w]\n\n # compute the previous noisy sample x_t -> x_t-1\n depth_latent = self.scheduler.step(noise_pred, t, depth_latent, generator=rand_num_generator).prev_sample\n\n depth = self.decode_depth(depth_latent)\n\n # clip prediction\n depth = torch.clip(depth, -1.0, 1.0)\n # shift to [0, 1]\n depth = (depth + 1.0) / 2.0\n\n return depth\n\n def encode_rgb(self, rgb_in: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Encode RGB image into latent.\n\n Args:\n rgb_in (`torch.Tensor`):\n Input RGB image to be encoded.\n\n Returns:\n `torch.Tensor`: Image latent.\n \"\"\"\n # encode\n h = self.vae.encoder(rgb_in)\n moments = self.vae.quant_conv(h)\n mean, logvar = torch.chunk(moments, 2, dim=1)\n # scale latent\n rgb_latent = mean * self.rgb_latent_scale_factor\n return rgb_latent\n\n def decode_depth(self, depth_latent: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Decode depth latent into depth map.\n\n Args:\n depth_latent (`torch.Tensor`):\n Depth latent to be decoded.\n\n Returns:\n `torch.Tensor`: Decoded depth map.\n \"\"\"\n # scale latent\n depth_latent = depth_latent / self.depth_latent_scale_factor\n # decode\n z = self.vae.post_quant_conv(depth_latent)\n stacked = self.vae.decoder(z)\n # mean of output channels\n depth_mean = stacked.mean(dim=1, keepdim=True)\n return depth_mean\n\n @staticmethod\n def resize_max_res(img: Image.Image, max_edge_resolution: int, resample_method=Resampling.BILINEAR) -> Image.Image:\n \"\"\"\n Resize image to limit maximum edge length while keeping aspect ratio.\n\n Args:\n img (`Image.Image`):\n Image to be resized.\n max_edge_resolution (`int`):\n Maximum edge length (pixel).\n resample_method (`PIL.Image.Resampling`):\n Resampling method used to resize images.\n\n Returns:\n `Image.Image`: Resized image.\n \"\"\"\n original_width, original_height = img.size\n downscale_factor = min(max_edge_resolution / original_width, max_edge_resolution / original_height)\n\n new_width = int(original_width * downscale_factor)\n new_height = int(original_height * downscale_factor)\n\n resized_img = img.resize((new_width, new_height), resample=resample_method)\n return resized_img\n\n @staticmethod\n def colorize_depth_maps(depth_map, min_depth, max_depth, cmap=\"Spectral\", valid_mask=None):\n \"\"\"\n Colorize depth maps.\n \"\"\"\n assert len(depth_map.shape) >= 2, \"Invalid dimension\"\n\n if isinstance(depth_map, torch.Tensor):\n depth = depth_map.detach().clone().squeeze().numpy()\n elif isinstance(depth_map, np.ndarray):\n depth = depth_map.copy().squeeze()\n # reshape to [ (B,) H, W ]\n if depth.ndim < 3:\n depth = depth[np.newaxis, :, :]\n\n # colorize\n cm = matplotlib.colormaps[cmap]\n depth = ((depth - min_depth) / (max_depth - min_depth)).clip(0, 1)\n img_colored_np = cm(depth, bytes=False)[:, :, :, 0:3] # value from 0 to 1\n img_colored_np = np.rollaxis(img_colored_np, 3, 1)\n\n if valid_mask is not None:\n if isinstance(depth_map, torch.Tensor):\n valid_mask = valid_mask.detach().numpy()\n valid_mask = valid_mask.squeeze() # [H, W] or [B, H, W]\n if valid_mask.ndim < 3:\n valid_mask = valid_mask[np.newaxis, np.newaxis, :, :]\n else:\n valid_mask = valid_mask[:, np.newaxis, :, :]\n valid_mask = np.repeat(valid_mask, 3, axis=1)\n img_colored_np[~valid_mask] = 0\n\n if isinstance(depth_map, torch.Tensor):\n img_colored = torch.from_numpy(img_colored_np).float()\n elif isinstance(depth_map, np.ndarray):\n img_colored = img_colored_np\n\n return img_colored\n\n @staticmethod\n def chw2hwc(chw):\n assert 3 == len(chw.shape)\n if isinstance(chw, torch.Tensor):\n hwc = torch.permute(chw, (1, 2, 0))\n elif isinstance(chw, np.ndarray):\n hwc = np.moveaxis(chw, 0, -1)\n return hwc\n\n @staticmethod\n def _find_batch_size(ensemble_size: int, input_res: int, dtype: torch.dtype) -> int:\n \"\"\"\n Automatically search for suitable operating batch size.\n\n Args:\n ensemble_size (`int`):\n Number of predictions to be ensembled.\n input_res (`int`):\n Operating resolution of the input image.\n\n Returns:\n `int`: Operating batch size.\n \"\"\"\n # Search table for suggested max. inference batch size\n bs_search_table = [\n # tested on A100-PCIE-80GB\n {\"res\": 768, \"total_vram\": 79, \"bs\": 35, \"dtype\": torch.float32},\n {\"res\": 1024, \"total_vram\": 79, \"bs\": 20, \"dtype\": torch.float32},\n # tested on A100-PCIE-40GB\n {\"res\": 768, \"total_vram\": 39, \"bs\": 15, \"dtype\": torch.float32},\n {\"res\": 1024, \"total_vram\": 39, \"bs\": 8, \"dtype\": torch.float32},\n {\"res\": 768, \"total_vram\": 39, \"bs\": 30, \"dtype\": torch.float16},\n {\"res\": 1024, \"total_vram\": 39, \"bs\": 15, \"dtype\": torch.float16},\n # tested on RTX3090, RTX4090\n {\"res\": 512, \"total_vram\": 23, \"bs\": 20, \"dtype\": torch.float32},\n {\"res\": 768, \"total_vram\": 23, \"bs\": 7, \"dtype\": torch.float32},\n {\"res\": 1024, \"total_vram\": 23, \"bs\": 3, \"dtype\": torch.float32},\n {\"res\": 512, \"total_vram\": 23, \"bs\": 40, \"dtype\": torch.float16},\n {\"res\": 768, \"total_vram\": 23, \"bs\": 18, \"dtype\": torch.float16},\n {\"res\": 1024, \"total_vram\": 23, \"bs\": 10, \"dtype\": torch.float16},\n # tested on GTX1080Ti\n {\"res\": 512, \"total_vram\": 10, \"bs\": 5, \"dtype\": torch.float32},\n {\"res\": 768, \"total_vram\": 10, \"bs\": 2, \"dtype\": torch.float32},\n {\"res\": 512, \"total_vram\": 10, \"bs\": 10, \"dtype\": torch.float16},\n {\"res\": 768, \"total_vram\": 10, \"bs\": 5, \"dtype\": torch.float16},\n {\"res\": 1024, \"total_vram\": 10, \"bs\": 3, \"dtype\": torch.float16},\n ]\n\n if not torch.cuda.is_available():\n return 1\n\n total_vram = torch.cuda.mem_get_info()[1] / 1024.0**3\n filtered_bs_search_table = [s for s in bs_search_table if s[\"dtype\"] == dtype]\n for settings in sorted(\n filtered_bs_search_table,\n key=lambda k: (k[\"res\"], -k[\"total_vram\"]),\n ):\n if input_res <= settings[\"res\"] and total_vram >= settings[\"total_vram\"]:\n bs = settings[\"bs\"]\n if bs > ensemble_size:\n bs = ensemble_size\n elif bs > math.ceil(ensemble_size / 2) and bs < ensemble_size:\n bs = math.ceil(ensemble_size / 2)\n return bs\n\n return 1\n\n @staticmethod\n def ensemble_depths(\n input_images: torch.Tensor,\n regularizer_strength: float = 0.02,\n max_iter: int = 2,\n tol: float = 1e-3,\n reduction: str = \"median\",\n max_res: int = None,\n ):\n \"\"\"\n To ensemble multiple affine-invariant depth images (up to scale and shift),\n by aligning estimating the scale and shift\n \"\"\"\n\n def inter_distances(tensors: torch.Tensor):\n \"\"\"\n To calculate the distance between each two depth maps.\n \"\"\"\n distances = []\n for i, j in torch.combinations(torch.arange(tensors.shape[0])):\n arr1 = tensors[i : i + 1]\n arr2 = tensors[j : j + 1]\n distances.append(arr1 - arr2)\n dist = torch.concatenate(distances, dim=0)\n return dist\n\n device = input_images.device\n dtype = input_images.dtype\n np_dtype = np.float32\n\n original_input = input_images.clone()\n n_img = input_images.shape[0]\n ori_shape = input_images.shape\n\n if max_res is not None:\n scale_factor = torch.min(max_res / torch.tensor(ori_shape[-2:]))\n if scale_factor < 1:\n downscaler = torch.nn.Upsample(scale_factor=scale_factor, mode=\"nearest\")\n input_images = downscaler(torch.from_numpy(input_images)).numpy()\n\n # init guess\n _min = np.min(input_images.reshape((n_img, -1)).cpu().numpy(), axis=1)\n _max = np.max(input_images.reshape((n_img, -1)).cpu().numpy(), axis=1)\n s_init = 1.0 / (_max - _min).reshape((-1, 1, 1))\n t_init = (-1 * s_init.flatten() * _min.flatten()).reshape((-1, 1, 1))\n x = np.concatenate([s_init, t_init]).reshape(-1).astype(np_dtype)\n\n input_images = input_images.to(device)\n\n # objective function\n def closure(x):\n l = len(x)\n s = x[: int(l / 2)]\n t = x[int(l / 2) :]\n s = torch.from_numpy(s).to(dtype=dtype).to(device)\n t = torch.from_numpy(t).to(dtype=dtype).to(device)\n\n transformed_arrays = input_images * s.view((-1, 1, 1)) + t.view((-1, 1, 1))\n dists = inter_distances(transformed_arrays)\n sqrt_dist = torch.sqrt(torch.mean(dists**2))\n\n if \"mean\" == reduction:\n pred = torch.mean(transformed_arrays, dim=0)\n elif \"median\" == reduction:\n pred = torch.median(transformed_arrays, dim=0).values\n else:\n raise ValueError\n\n near_err = torch.sqrt((0 - torch.min(pred)) ** 2)\n far_err = torch.sqrt((1 - torch.max(pred)) ** 2)\n\n err = sqrt_dist + (near_err + far_err) * regularizer_strength\n err = err.detach().cpu().numpy().astype(np_dtype)\n return err\n\n res = minimize(\n closure,\n x,\n method=\"BFGS\",\n tol=tol,\n options={\"maxiter\": max_iter, \"disp\": False},\n )\n x = res.x\n l = len(x)\n s = x[: int(l / 2)]\n t = x[int(l / 2) :]\n\n # Prediction\n s = torch.from_numpy(s).to(dtype=dtype).to(device)\n t = torch.from_numpy(t).to(dtype=dtype).to(device)\n transformed_arrays = original_input * s.view(-1, 1, 1) + t.view(-1, 1, 1)\n if \"mean\" == reduction:\n aligned_images = torch.mean(transformed_arrays, dim=0)\n std = torch.std(transformed_arrays, dim=0)\n uncertainty = std\n elif \"median\" == reduction:\n aligned_images = torch.median(transformed_arrays, dim=0).values\n # MAD (median absolute deviation) as uncertainty indicator\n abs_dev = torch.abs(transformed_arrays - aligned_images)\n mad = torch.median(abs_dev, dim=0).values\n uncertainty = mad\n else:\n raise ValueError(f\"Unknown reduction method: {reduction}\")\n\n # Scale and shift to [0, 1]\n _min = torch.min(aligned_images)\n _max = torch.max(aligned_images)\n aligned_images = (aligned_images - _min) / (_max - _min)\n uncertainty /= _max - _min\n\n return aligned_images, uncertainty\n" }, { "path": "diffusers/examples/community/masked_stable_diffusion_img2img.py", "content": "from typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\n\nfrom diffusers import StableDiffusionImg2ImgPipeline\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\n\n\nclass MaskedStableDiffusionImg2ImgPipeline(StableDiffusionImg2ImgPipeline):\n debug_save = False\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: Union[\n torch.Tensor,\n PIL.Image.Image,\n np.ndarray,\n List[torch.Tensor],\n List[PIL.Image.Image],\n List[np.ndarray],\n ] = None,\n strength: float = 0.8,\n num_inference_steps: Optional[int] = 50,\n guidance_scale: Optional[float] = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: Optional[float] = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n mask: Union[\n torch.Tensor,\n PIL.Image.Image,\n np.ndarray,\n List[torch.Tensor],\n List[PIL.Image.Image],\n List[np.ndarray],\n ] = None,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image` or tensor representing an image batch to be used as the starting point. Can also accept image\n latents as `image`, but if passing latents directly it is not encoded again.\n strength (`float`, *optional*, defaults to 0.8):\n Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a\n starting point and more noise is added the higher the `strength`. The number of denoising steps depends\n on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising\n process runs for the full number of iterations specified in `num_inference_steps`. A value of 1\n essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference. This parameter is modulated by `strength`.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n mask (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`, *optional*):\n A mask with non-zero elements for the area to be inpainted. If not specified, no mask is applied.\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n # code adapted from parent class StableDiffusionImg2ImgPipeline\n\n # 0. Check inputs. Raise error if not correct\n self.check_inputs(prompt, strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)\n\n # 1. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 2. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n prompt_embeds = self._encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n\n # 3. Preprocess image\n image = self.image_processor.preprocess(image)\n\n # 4. set timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n\n # 5. Prepare latent variables\n # it is sampled from the latent distribution of the VAE\n latents = self.prepare_latents(\n image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator\n )\n\n # mean of the latent distribution\n init_latents = [\n self.vae.encode(image.to(device=device, dtype=prompt_embeds.dtype)[i : i + 1]).latent_dist.mean\n for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n\n # 6. create latent mask\n latent_mask = self._make_latent_mask(latents, mask)\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if latent_mask is not None:\n latents = torch.lerp(init_latents * self.vae.config.scaling_factor, latents, latent_mask)\n noise_pred = torch.lerp(torch.zeros_like(noise_pred), noise_pred, latent_mask)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n scaled = latents / self.vae.config.scaling_factor\n if latent_mask is not None:\n # scaled = latents / self.vae.config.scaling_factor * latent_mask + init_latents * (1 - latent_mask)\n scaled = torch.lerp(init_latents, scaled, latent_mask)\n image = self.vae.decode(scaled, return_dict=False)[0]\n if self.debug_save:\n image_gen = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image_gen = self.image_processor.postprocess(image_gen, output_type=output_type, do_denormalize=[True])\n image_gen[0].save(\"from_latent.png\")\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload last model to CPU\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n\n def _make_latent_mask(self, latents, mask):\n if mask is not None:\n latent_mask = []\n if not isinstance(mask, list):\n tmp_mask = [mask]\n else:\n tmp_mask = mask\n _, l_channels, l_height, l_width = latents.shape\n for m in tmp_mask:\n if not isinstance(m, PIL.Image.Image):\n if len(m.shape) == 2:\n m = m[..., np.newaxis]\n if m.max() > 1:\n m = m / 255.0\n m = self.image_processor.numpy_to_pil(m)[0]\n if m.mode != \"L\":\n m = m.convert(\"L\")\n resized = self.image_processor.resize(m, l_height, l_width)\n if self.debug_save:\n resized.save(\"latent_mask.png\")\n latent_mask.append(np.repeat(np.array(resized)[np.newaxis, :, :], l_channels, axis=0))\n latent_mask = torch.as_tensor(np.stack(latent_mask)).to(latents)\n latent_mask = latent_mask / latent_mask.max()\n return latent_mask\n" }, { "path": "diffusers/examples/community/mixture_canvas.py", "content": "import re\nfrom copy import deepcopy\nfrom dataclasses import asdict, dataclass\nfrom enum import Enum\nfrom typing import List, Optional, Union\n\nimport numpy as np\nimport torch\nfrom numpy import exp, pi, sqrt\nfrom torchvision.transforms.functional import resize\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker\nfrom diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler\n\n\ndef preprocess_image(image):\n from PIL import Image\n\n \"\"\"Preprocess an input image\n\n Same as\n https://github.com/huggingface/diffusers/blob/1138d63b519e37f0ce04e027b9f4a3261d27c628/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py#L44\n \"\"\"\n w, h = image.size\n w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32\n image = image.resize((w, h), resample=Image.LANCZOS)\n image = np.array(image).astype(np.float32) / 255.0\n image = image[None].transpose(0, 3, 1, 2)\n image = torch.from_numpy(image)\n return 2.0 * image - 1.0\n\n\n@dataclass\nclass CanvasRegion:\n \"\"\"Class defining a rectangular region in the canvas\"\"\"\n\n row_init: int # Region starting row in pixel space (included)\n row_end: int # Region end row in pixel space (not included)\n col_init: int # Region starting column in pixel space (included)\n col_end: int # Region end column in pixel space (not included)\n region_seed: int = None # Seed for random operations in this region\n noise_eps: float = 0.0 # Deviation of a zero-mean gaussian noise to be applied over the latents in this region. Useful for slightly \"rerolling\" latents\n\n def __post_init__(self):\n # Initialize arguments if not specified\n if self.region_seed is None:\n self.region_seed = np.random.randint(9999999999)\n # Check coordinates are non-negative\n for coord in [self.row_init, self.row_end, self.col_init, self.col_end]:\n if coord < 0:\n raise ValueError(\n f\"A CanvasRegion must be defined with non-negative indices, found ({self.row_init}, {self.row_end}, {self.col_init}, {self.col_end})\"\n )\n # Check coordinates are divisible by 8, else we end up with nasty rounding error when mapping to latent space\n for coord in [self.row_init, self.row_end, self.col_init, self.col_end]:\n if coord // 8 != coord / 8:\n raise ValueError(\n f\"A CanvasRegion must be defined with locations divisible by 8, found ({self.row_init}-{self.row_end}, {self.col_init}-{self.col_end})\"\n )\n # Check noise eps is non-negative\n if self.noise_eps < 0:\n raise ValueError(f\"A CanvasRegion must be defined noises eps non-negative, found {self.noise_eps}\")\n # Compute coordinates for this region in latent space\n self.latent_row_init = self.row_init // 8\n self.latent_row_end = self.row_end // 8\n self.latent_col_init = self.col_init // 8\n self.latent_col_end = self.col_end // 8\n\n @property\n def width(self):\n return self.col_end - self.col_init\n\n @property\n def height(self):\n return self.row_end - self.row_init\n\n def get_region_generator(self, device=\"cpu\"):\n \"\"\"Creates a torch.Generator based on the random seed of this region\"\"\"\n # Initialize region generator\n return torch.Generator(device).manual_seed(self.region_seed)\n\n @property\n def __dict__(self):\n return asdict(self)\n\n\nclass MaskModes(Enum):\n \"\"\"Modes in which the influence of diffuser is masked\"\"\"\n\n CONSTANT = \"constant\"\n GAUSSIAN = \"gaussian\"\n QUARTIC = \"quartic\" # See https://en.wikipedia.org/wiki/Kernel_(statistics)\n\n\n@dataclass\nclass DiffusionRegion(CanvasRegion):\n \"\"\"Abstract class defining a region where some class of diffusion process is acting\"\"\"\n\n pass\n\n\n@dataclass\nclass Text2ImageRegion(DiffusionRegion):\n \"\"\"Class defining a region where a text guided diffusion process is acting\"\"\"\n\n prompt: str = \"\" # Text prompt guiding the diffuser in this region\n guidance_scale: float = 7.5 # Guidance scale of the diffuser in this region. If None, randomize\n mask_type: MaskModes = MaskModes.GAUSSIAN.value # Kind of weight mask applied to this region\n mask_weight: float = 1.0 # Global weights multiplier of the mask\n tokenized_prompt = None # Tokenized prompt\n encoded_prompt = None # Encoded prompt\n\n def __post_init__(self):\n super().__post_init__()\n # Mask weight cannot be negative\n if self.mask_weight < 0:\n raise ValueError(\n f\"A Text2ImageRegion must be defined with non-negative mask weight, found {self.mask_weight}\"\n )\n # Mask type must be an actual known mask\n if self.mask_type not in [e.value for e in MaskModes]:\n raise ValueError(\n f\"A Text2ImageRegion was defined with mask {self.mask_type}, which is not an accepted mask ({[e.value for e in MaskModes]})\"\n )\n # Randomize arguments if given as None\n if self.guidance_scale is None:\n self.guidance_scale = np.random.randint(5, 30)\n # Clean prompt\n self.prompt = re.sub(\" +\", \" \", self.prompt).replace(\"\\n\", \" \")\n\n def tokenize_prompt(self, tokenizer):\n \"\"\"Tokenizes the prompt for this diffusion region using a given tokenizer\"\"\"\n self.tokenized_prompt = tokenizer(\n self.prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n def encode_prompt(self, text_encoder, device):\n \"\"\"Encodes the previously tokenized prompt for this diffusion region using a given encoder\"\"\"\n assert self.tokenized_prompt is not None, ValueError(\n \"Prompt in diffusion region must be tokenized before encoding\"\n )\n self.encoded_prompt = text_encoder(self.tokenized_prompt.input_ids.to(device))[0]\n\n\n@dataclass\nclass Image2ImageRegion(DiffusionRegion):\n \"\"\"Class defining a region where an image guided diffusion process is acting\"\"\"\n\n reference_image: torch.Tensor = None\n strength: float = 0.8 # Strength of the image\n\n def __post_init__(self):\n super().__post_init__()\n if self.reference_image is None:\n raise ValueError(\"Must provide a reference image when creating an Image2ImageRegion\")\n if self.strength < 0 or self.strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {self.strength}\")\n # Rescale image to region shape\n self.reference_image = resize(self.reference_image, size=[self.height, self.width])\n\n def encode_reference_image(self, encoder, device, generator, cpu_vae=False):\n \"\"\"Encodes the reference image for this Image2Image region into the latent space\"\"\"\n # Place encoder in CPU or not following the parameter cpu_vae\n if cpu_vae:\n # Note here we use mean instead of sample, to avoid moving also generator to CPU, which is troublesome\n self.reference_latents = encoder.cpu().encode(self.reference_image).latent_dist.mean.to(device)\n else:\n self.reference_latents = encoder.encode(self.reference_image.to(device)).latent_dist.sample(\n generator=generator\n )\n self.reference_latents = 0.18215 * self.reference_latents\n\n @property\n def __dict__(self):\n # This class requires special casting to dict because of the reference_image tensor. Otherwise it cannot be casted to JSON\n\n # Get all basic fields from parent class\n super_fields = {key: getattr(self, key) for key in DiffusionRegion.__dataclass_fields__.keys()}\n # Pack other fields\n return {**super_fields, \"reference_image\": self.reference_image.cpu().tolist(), \"strength\": self.strength}\n\n\nclass RerollModes(Enum):\n \"\"\"Modes in which the reroll regions operate\"\"\"\n\n RESET = \"reset\" # Completely reset the random noise in the region\n EPSILON = \"epsilon\" # Alter slightly the latents in the region\n\n\n@dataclass\nclass RerollRegion(CanvasRegion):\n \"\"\"Class defining a rectangular canvas region in which initial latent noise will be rerolled\"\"\"\n\n reroll_mode: RerollModes = RerollModes.RESET.value\n\n\n@dataclass\nclass MaskWeightsBuilder:\n \"\"\"Auxiliary class to compute a tensor of weights for a given diffusion region\"\"\"\n\n latent_space_dim: int # Size of the U-net latent space\n nbatch: int = 1 # Batch size in the U-net\n\n def compute_mask_weights(self, region: DiffusionRegion) -> torch.tensor:\n \"\"\"Computes a tensor of weights for a given diffusion region\"\"\"\n MASK_BUILDERS = {\n MaskModes.CONSTANT.value: self._constant_weights,\n MaskModes.GAUSSIAN.value: self._gaussian_weights,\n MaskModes.QUARTIC.value: self._quartic_weights,\n }\n return MASK_BUILDERS[region.mask_type](region)\n\n def _constant_weights(self, region: DiffusionRegion) -> torch.tensor:\n \"\"\"Computes a tensor of constant for a given diffusion region\"\"\"\n latent_width = region.latent_col_end - region.latent_col_init\n latent_height = region.latent_row_end - region.latent_row_init\n return torch.ones(self.nbatch, self.latent_space_dim, latent_height, latent_width) * region.mask_weight\n\n def _gaussian_weights(self, region: DiffusionRegion) -> torch.tensor:\n \"\"\"Generates a gaussian mask of weights for tile contributions\"\"\"\n latent_width = region.latent_col_end - region.latent_col_init\n latent_height = region.latent_row_end - region.latent_row_init\n\n var = 0.01\n midpoint = (latent_width - 1) / 2 # -1 because index goes from 0 to latent_width - 1\n x_probs = [\n exp(-(x - midpoint) * (x - midpoint) / (latent_width * latent_width) / (2 * var)) / sqrt(2 * pi * var)\n for x in range(latent_width)\n ]\n midpoint = (latent_height - 1) / 2\n y_probs = [\n exp(-(y - midpoint) * (y - midpoint) / (latent_height * latent_height) / (2 * var)) / sqrt(2 * pi * var)\n for y in range(latent_height)\n ]\n\n weights = np.outer(y_probs, x_probs) * region.mask_weight\n return torch.tile(torch.tensor(weights), (self.nbatch, self.latent_space_dim, 1, 1))\n\n def _quartic_weights(self, region: DiffusionRegion) -> torch.tensor:\n \"\"\"Generates a quartic mask of weights for tile contributions\n\n The quartic kernel has bounded support over the diffusion region, and a smooth decay to the region limits.\n \"\"\"\n quartic_constant = 15.0 / 16.0\n\n support = (np.array(range(region.latent_col_init, region.latent_col_end)) - region.latent_col_init) / (\n region.latent_col_end - region.latent_col_init - 1\n ) * 1.99 - (1.99 / 2.0)\n x_probs = quartic_constant * np.square(1 - np.square(support))\n support = (np.array(range(region.latent_row_init, region.latent_row_end)) - region.latent_row_init) / (\n region.latent_row_end - region.latent_row_init - 1\n ) * 1.99 - (1.99 / 2.0)\n y_probs = quartic_constant * np.square(1 - np.square(support))\n\n weights = np.outer(y_probs, x_probs) * region.mask_weight\n return torch.tile(torch.tensor(weights), (self.nbatch, self.latent_space_dim, 1, 1))\n\n\nclass StableDiffusionCanvasPipeline(DiffusionPipeline, StableDiffusionMixin):\n \"\"\"Stable Diffusion pipeline that mixes several diffusers in the same canvas\"\"\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler],\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPFeatureExtractor,\n ):\n super().__init__()\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n\n def decode_latents(self, latents, cpu_vae=False):\n \"\"\"Decodes a given array of latents into pixel space\"\"\"\n # scale and decode the image latents with vae\n if cpu_vae:\n lat = deepcopy(latents).cpu()\n vae = deepcopy(self.vae).cpu()\n else:\n lat = latents\n vae = self.vae\n\n lat = 1 / 0.18215 * lat\n image = vae.decode(lat).sample\n\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).numpy()\n\n return self.numpy_to_pil(image)\n\n def get_latest_timestep_img2img(self, num_inference_steps, strength):\n \"\"\"Finds the latest timesteps where an img2img strength does not impose latents anymore\"\"\"\n # get the original timestep using init_timestep\n offset = self.scheduler.config.get(\"steps_offset\", 0)\n init_timestep = int(num_inference_steps * (1 - strength)) + offset\n init_timestep = min(init_timestep, num_inference_steps)\n\n t_start = min(max(num_inference_steps - init_timestep + offset, 0), num_inference_steps - 1)\n latest_timestep = self.scheduler.timesteps[t_start]\n\n return latest_timestep\n\n @torch.no_grad()\n def __call__(\n self,\n canvas_height: int,\n canvas_width: int,\n regions: List[DiffusionRegion],\n num_inference_steps: Optional[int] = 50,\n seed: Optional[int] = 12345,\n reroll_regions: Optional[List[RerollRegion]] = None,\n cpu_vae: Optional[bool] = False,\n decode_steps: Optional[bool] = False,\n ):\n if reroll_regions is None:\n reroll_regions = []\n batch_size = 1\n\n if decode_steps:\n steps_images = []\n\n # Prepare scheduler\n self.scheduler.set_timesteps(num_inference_steps, device=self.device)\n\n # Split diffusion regions by their kind\n text2image_regions = [region for region in regions if isinstance(region, Text2ImageRegion)]\n image2image_regions = [region for region in regions if isinstance(region, Image2ImageRegion)]\n\n # Prepare text embeddings\n for region in text2image_regions:\n region.tokenize_prompt(self.tokenizer)\n region.encode_prompt(self.text_encoder, self.device)\n\n # Create original noisy latents using the timesteps\n latents_shape = (batch_size, self.unet.config.in_channels, canvas_height // 8, canvas_width // 8)\n generator = torch.Generator(self.device).manual_seed(seed)\n init_noise = torch.randn(latents_shape, generator=generator, device=self.device)\n\n # Reset latents in seed reroll regions, if requested\n for region in reroll_regions:\n if region.reroll_mode == RerollModes.RESET.value:\n region_shape = (\n latents_shape[0],\n latents_shape[1],\n region.latent_row_end - region.latent_row_init,\n region.latent_col_end - region.latent_col_init,\n )\n init_noise[\n :,\n :,\n region.latent_row_init : region.latent_row_end,\n region.latent_col_init : region.latent_col_end,\n ] = torch.randn(region_shape, generator=region.get_region_generator(self.device), device=self.device)\n\n # Apply epsilon noise to regions: first diffusion regions, then reroll regions\n all_eps_rerolls = regions + [r for r in reroll_regions if r.reroll_mode == RerollModes.EPSILON.value]\n for region in all_eps_rerolls:\n if region.noise_eps > 0:\n region_noise = init_noise[\n :,\n :,\n region.latent_row_init : region.latent_row_end,\n region.latent_col_init : region.latent_col_end,\n ]\n eps_noise = (\n torch.randn(\n region_noise.shape, generator=region.get_region_generator(self.device), device=self.device\n )\n * region.noise_eps\n )\n init_noise[\n :,\n :,\n region.latent_row_init : region.latent_row_end,\n region.latent_col_init : region.latent_col_end,\n ] += eps_noise\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = init_noise * self.scheduler.init_noise_sigma\n\n # Get unconditional embeddings for classifier free guidance in text2image regions\n for region in text2image_regions:\n max_length = region.tokenized_prompt.input_ids.shape[-1]\n uncond_input = self.tokenizer(\n [\"\"] * batch_size, padding=\"max_length\", max_length=max_length, return_tensors=\"pt\"\n )\n uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n region.encoded_prompt = torch.cat([uncond_embeddings, region.encoded_prompt])\n\n # Prepare image latents\n for region in image2image_regions:\n region.encode_reference_image(self.vae, device=self.device, generator=generator)\n\n # Prepare mask of weights for each region\n mask_builder = MaskWeightsBuilder(latent_space_dim=self.unet.config.in_channels, nbatch=batch_size)\n mask_weights = [mask_builder.compute_mask_weights(region).to(self.device) for region in text2image_regions]\n\n # Diffusion timesteps\n for i, t in tqdm(enumerate(self.scheduler.timesteps)):\n # Diffuse each region\n noise_preds_regions = []\n\n # text2image regions\n for region in text2image_regions:\n region_latents = latents[\n :,\n :,\n region.latent_row_init : region.latent_row_end,\n region.latent_col_init : region.latent_col_end,\n ]\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([region_latents] * 2)\n # scale model input following scheduler rules\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=region.encoded_prompt)[\"sample\"]\n # perform guidance\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred_region = noise_pred_uncond + region.guidance_scale * (noise_pred_text - noise_pred_uncond)\n noise_preds_regions.append(noise_pred_region)\n\n # Merge noise predictions for all tiles\n noise_pred = torch.zeros(latents.shape, device=self.device)\n contributors = torch.zeros(latents.shape, device=self.device)\n # Add each tile contribution to overall latents\n for region, noise_pred_region, mask_weights_region in zip(\n text2image_regions, noise_preds_regions, mask_weights\n ):\n noise_pred[\n :,\n :,\n region.latent_row_init : region.latent_row_end,\n region.latent_col_init : region.latent_col_end,\n ] += noise_pred_region * mask_weights_region\n contributors[\n :,\n :,\n region.latent_row_init : region.latent_row_end,\n region.latent_col_init : region.latent_col_end,\n ] += mask_weights_region\n # Average overlapping areas with more than 1 contributor\n noise_pred /= contributors\n noise_pred = torch.nan_to_num(\n noise_pred\n ) # Replace NaNs by zeros: NaN can appear if a position is not covered by any DiffusionRegion\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents).prev_sample\n\n # Image2Image regions: override latents generated by the scheduler\n for region in image2image_regions:\n influence_step = self.get_latest_timestep_img2img(num_inference_steps, region.strength)\n # Only override in the timesteps before the last influence step of the image (given by its strength)\n if t > influence_step:\n timestep = t.repeat(batch_size)\n region_init_noise = init_noise[\n :,\n :,\n region.latent_row_init : region.latent_row_end,\n region.latent_col_init : region.latent_col_end,\n ]\n region_latents = self.scheduler.add_noise(region.reference_latents, region_init_noise, timestep)\n latents[\n :,\n :,\n region.latent_row_init : region.latent_row_end,\n region.latent_col_init : region.latent_col_end,\n ] = region_latents\n\n if decode_steps:\n steps_images.append(self.decode_latents(latents, cpu_vae))\n\n # scale and decode the image latents with vae\n image = self.decode_latents(latents, cpu_vae)\n\n output = {\"images\": image}\n if decode_steps:\n output = {**output, \"steps_images\": steps_images}\n return output\n" }, { "path": "diffusers/examples/community/mixture_tiling.py", "content": "import inspect\nfrom copy import deepcopy\nfrom enum import Enum\nfrom typing import List, Optional, Tuple, Union\n\nimport torch\nfrom tqdm.auto import tqdm\n\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker\nfrom diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler\nfrom diffusers.utils import logging\n\n\ntry:\n from ligo.segments import segment\n from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer\nexcept ImportError:\n raise ImportError(\"Please install transformers and ligo-segments to use the mixture pipeline\")\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import LMSDiscreteScheduler, DiffusionPipeline\n\n >>> scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule=\"scaled_linear\", num_train_timesteps=1000)\n >>> pipeline = DiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", scheduler=scheduler, custom_pipeline=\"mixture_tiling\")\n >>> pipeline.to(\"cuda\")\n\n >>> image = pipeline(\n >>> prompt=[[\n >>> \"A charming house in the countryside, by jakub rozalski, sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece\",\n >>> \"A dirt road in the countryside crossing pastures, by jakub rozalski, sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece\",\n >>> \"An old and rusty giant robot lying on a dirt road, by jakub rozalski, dark sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece\"\n >>> ]],\n >>> tile_height=640,\n >>> tile_width=640,\n >>> tile_row_overlap=0,\n >>> tile_col_overlap=256,\n >>> guidance_scale=8,\n >>> seed=7178915308,\n >>> num_inference_steps=50,\n >>> )[\"images\"][0]\n ```\n\"\"\"\n\n\ndef _tile2pixel_indices(tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap):\n \"\"\"Given a tile row and column numbers returns the range of pixels affected by that tiles in the overall image\n\n Returns a tuple with:\n - Starting coordinates of rows in pixel space\n - Ending coordinates of rows in pixel space\n - Starting coordinates of columns in pixel space\n - Ending coordinates of columns in pixel space\n \"\"\"\n px_row_init = 0 if tile_row == 0 else tile_row * (tile_height - tile_row_overlap)\n px_row_end = px_row_init + tile_height\n px_col_init = 0 if tile_col == 0 else tile_col * (tile_width - tile_col_overlap)\n px_col_end = px_col_init + tile_width\n return px_row_init, px_row_end, px_col_init, px_col_end\n\n\ndef _pixel2latent_indices(px_row_init, px_row_end, px_col_init, px_col_end):\n \"\"\"Translates coordinates in pixel space to coordinates in latent space\"\"\"\n return px_row_init // 8, px_row_end // 8, px_col_init // 8, px_col_end // 8\n\n\ndef _tile2latent_indices(tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap):\n \"\"\"Given a tile row and column numbers returns the range of latents affected by that tiles in the overall image\n\n Returns a tuple with:\n - Starting coordinates of rows in latent space\n - Ending coordinates of rows in latent space\n - Starting coordinates of columns in latent space\n - Ending coordinates of columns in latent space\n \"\"\"\n px_row_init, px_row_end, px_col_init, px_col_end = _tile2pixel_indices(\n tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap\n )\n return _pixel2latent_indices(px_row_init, px_row_end, px_col_init, px_col_end)\n\n\ndef _tile2latent_exclusive_indices(\n tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap, rows, columns\n):\n \"\"\"Given a tile row and column numbers returns the range of latents affected only by that tile in the overall image\n\n Returns a tuple with:\n - Starting coordinates of rows in latent space\n - Ending coordinates of rows in latent space\n - Starting coordinates of columns in latent space\n - Ending coordinates of columns in latent space\n \"\"\"\n row_init, row_end, col_init, col_end = _tile2latent_indices(\n tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap\n )\n row_segment = segment(row_init, row_end)\n col_segment = segment(col_init, col_end)\n # Iterate over the rest of tiles, clipping the region for the current tile\n for row in range(rows):\n for column in range(columns):\n if row != tile_row and column != tile_col:\n clip_row_init, clip_row_end, clip_col_init, clip_col_end = _tile2latent_indices(\n row, column, tile_width, tile_height, tile_row_overlap, tile_col_overlap\n )\n row_segment = row_segment - segment(clip_row_init, clip_row_end)\n col_segment = col_segment - segment(clip_col_init, clip_col_end)\n # return row_init, row_end, col_init, col_end\n return row_segment[0], row_segment[1], col_segment[0], col_segment[1]\n\n\nclass StableDiffusionExtrasMixin:\n \"\"\"Mixin providing additional convenience method to Stable Diffusion pipelines\"\"\"\n\n def decode_latents(self, latents, cpu_vae=False):\n \"\"\"Decodes a given array of latents into pixel space\"\"\"\n # scale and decode the image latents with vae\n if cpu_vae:\n lat = deepcopy(latents).cpu()\n vae = deepcopy(self.vae).cpu()\n else:\n lat = latents\n vae = self.vae\n\n lat = 1 / 0.18215 * lat\n image = vae.decode(lat).sample\n\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).numpy()\n\n return self.numpy_to_pil(image)\n\n\nclass StableDiffusionTilingPipeline(DiffusionPipeline, StableDiffusionExtrasMixin):\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[DDIMScheduler, PNDMScheduler],\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPFeatureExtractor,\n ):\n super().__init__()\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n\n class SeedTilesMode(Enum):\n \"\"\"Modes in which the latents of a particular tile can be re-seeded\"\"\"\n\n FULL = \"full\"\n EXCLUSIVE = \"exclusive\"\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[List[str]]],\n num_inference_steps: Optional[int] = 50,\n guidance_scale: Optional[float] = 7.5,\n eta: Optional[float] = 0.0,\n seed: Optional[int] = None,\n tile_height: Optional[int] = 512,\n tile_width: Optional[int] = 512,\n tile_row_overlap: Optional[int] = 256,\n tile_col_overlap: Optional[int] = 256,\n guidance_scale_tiles: Optional[List[List[float]]] = None,\n seed_tiles: Optional[List[List[int]]] = None,\n seed_tiles_mode: Optional[Union[str, List[List[str]]]] = \"full\",\n seed_reroll_regions: Optional[List[Tuple[int, int, int, int, int]]] = None,\n cpu_vae: Optional[bool] = False,\n ):\n r\"\"\"\n Function to run the diffusion pipeline with tiling support.\n\n Args:\n prompt: either a single string (no tiling) or a list of lists with all the prompts to use (one list for each row of tiles). This will also define the tiling structure.\n num_inference_steps: number of diffusions steps.\n guidance_scale: classifier-free guidance.\n seed: general random seed to initialize latents.\n tile_height: height in pixels of each grid tile.\n tile_width: width in pixels of each grid tile.\n tile_row_overlap: number of overlap pixels between tiles in consecutive rows.\n tile_col_overlap: number of overlap pixels between tiles in consecutive columns.\n guidance_scale_tiles: specific weights for classifier-free guidance in each tile.\n guidance_scale_tiles: specific weights for classifier-free guidance in each tile. If None, the value provided in guidance_scale will be used.\n seed_tiles: specific seeds for the initialization latents in each tile. These will override the latents generated for the whole canvas using the standard seed parameter.\n seed_tiles_mode: either \"full\" \"exclusive\". If \"full\", all the latents affected by the tile be overriden. If \"exclusive\", only the latents that are affected exclusively by this tile (and no other tiles) will be overriden.\n seed_reroll_regions: a list of tuples in the form (start row, end row, start column, end column, seed) defining regions in pixel space for which the latents will be overriden using the given seed. Takes priority over seed_tiles.\n cpu_vae: the decoder from latent space to pixel space can require too mucho GPU RAM for large images. If you find out of memory errors at the end of the generation process, try setting this parameter to True to run the decoder in CPU. Slower, but should run without memory issues.\n\n Examples:\n\n Returns:\n A PIL image with the generated image.\n\n \"\"\"\n if not isinstance(prompt, list) or not all(isinstance(row, list) for row in prompt):\n raise ValueError(f\"`prompt` has to be a list of lists but is {type(prompt)}\")\n grid_rows = len(prompt)\n grid_cols = len(prompt[0])\n if not all(len(row) == grid_cols for row in prompt):\n raise ValueError(\"All prompt rows must have the same number of prompt columns\")\n if not isinstance(seed_tiles_mode, str) and (\n not isinstance(seed_tiles_mode, list) or not all(isinstance(row, list) for row in seed_tiles_mode)\n ):\n raise ValueError(f\"`seed_tiles_mode` has to be a string or list of lists but is {type(prompt)}\")\n if isinstance(seed_tiles_mode, str):\n seed_tiles_mode = [[seed_tiles_mode for _ in range(len(row))] for row in prompt]\n\n modes = [mode.value for mode in self.SeedTilesMode]\n if any(mode not in modes for row in seed_tiles_mode for mode in row):\n raise ValueError(f\"Seed tiles mode must be one of {modes}\")\n if seed_reroll_regions is None:\n seed_reroll_regions = []\n batch_size = 1\n\n # create original noisy latents using the timesteps\n height = tile_height + (grid_rows - 1) * (tile_height - tile_row_overlap)\n width = tile_width + (grid_cols - 1) * (tile_width - tile_col_overlap)\n latents_shape = (batch_size, self.unet.config.in_channels, height // 8, width // 8)\n generator = torch.Generator(\"cuda\").manual_seed(seed)\n latents = torch.randn(latents_shape, generator=generator, device=self.device)\n\n # overwrite latents for specific tiles if provided\n if seed_tiles is not None:\n for row in range(grid_rows):\n for col in range(grid_cols):\n if (seed_tile := seed_tiles[row][col]) is not None:\n mode = seed_tiles_mode[row][col]\n if mode == self.SeedTilesMode.FULL.value:\n row_init, row_end, col_init, col_end = _tile2latent_indices(\n row, col, tile_width, tile_height, tile_row_overlap, tile_col_overlap\n )\n else:\n row_init, row_end, col_init, col_end = _tile2latent_exclusive_indices(\n row,\n col,\n tile_width,\n tile_height,\n tile_row_overlap,\n tile_col_overlap,\n grid_rows,\n grid_cols,\n )\n tile_generator = torch.Generator(\"cuda\").manual_seed(seed_tile)\n tile_shape = (latents_shape[0], latents_shape[1], row_end - row_init, col_end - col_init)\n latents[:, :, row_init:row_end, col_init:col_end] = torch.randn(\n tile_shape, generator=tile_generator, device=self.device\n )\n\n # overwrite again for seed reroll regions\n for row_init, row_end, col_init, col_end, seed_reroll in seed_reroll_regions:\n row_init, row_end, col_init, col_end = _pixel2latent_indices(\n row_init, row_end, col_init, col_end\n ) # to latent space coordinates\n reroll_generator = torch.Generator(\"cuda\").manual_seed(seed_reroll)\n region_shape = (latents_shape[0], latents_shape[1], row_end - row_init, col_end - col_init)\n latents[:, :, row_init:row_end, col_init:col_end] = torch.randn(\n region_shape, generator=reroll_generator, device=self.device\n )\n\n # Prepare scheduler\n accepts_offset = \"offset\" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())\n extra_set_kwargs = {}\n if accepts_offset:\n extra_set_kwargs[\"offset\"] = 1\n self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)\n # if we use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas\n if isinstance(self.scheduler, LMSDiscreteScheduler):\n latents = latents * self.scheduler.sigmas[0]\n\n # get prompts text embeddings\n text_input = [\n [\n self.tokenizer(\n col,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n for col in row\n ]\n for row in prompt\n ]\n text_embeddings = [[self.text_encoder(col.input_ids.to(self.device))[0] for col in row] for row in text_input]\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0 # TODO: also active if any tile has guidance scale\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n for i in range(grid_rows):\n for j in range(grid_cols):\n max_length = text_input[i][j].input_ids.shape[-1]\n uncond_input = self.tokenizer(\n [\"\"] * batch_size, padding=\"max_length\", max_length=max_length, return_tensors=\"pt\"\n )\n uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n text_embeddings[i][j] = torch.cat([uncond_embeddings, text_embeddings[i][j]])\n\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # Mask for tile weights strength\n tile_weights = self._gaussian_weights(tile_width, tile_height, batch_size)\n\n # Diffusion timesteps\n for i, t in tqdm(enumerate(self.scheduler.timesteps)):\n # Diffuse each tile\n noise_preds = []\n for row in range(grid_rows):\n noise_preds_row = []\n for col in range(grid_cols):\n px_row_init, px_row_end, px_col_init, px_col_end = _tile2latent_indices(\n row, col, tile_width, tile_height, tile_row_overlap, tile_col_overlap\n )\n tile_latents = latents[:, :, px_row_init:px_row_end, px_col_init:px_col_end]\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([tile_latents] * 2) if do_classifier_free_guidance else tile_latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings[row][col])[\n \"sample\"\n ]\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n guidance = (\n guidance_scale\n if guidance_scale_tiles is None or guidance_scale_tiles[row][col] is None\n else guidance_scale_tiles[row][col]\n )\n noise_pred_tile = noise_pred_uncond + guidance * (noise_pred_text - noise_pred_uncond)\n noise_preds_row.append(noise_pred_tile)\n noise_preds.append(noise_preds_row)\n # Stitch noise predictions for all tiles\n noise_pred = torch.zeros(latents.shape, device=self.device)\n contributors = torch.zeros(latents.shape, device=self.device)\n # Add each tile contribution to overall latents\n for row in range(grid_rows):\n for col in range(grid_cols):\n px_row_init, px_row_end, px_col_init, px_col_end = _tile2latent_indices(\n row, col, tile_width, tile_height, tile_row_overlap, tile_col_overlap\n )\n noise_pred[:, :, px_row_init:px_row_end, px_col_init:px_col_end] += (\n noise_preds[row][col] * tile_weights\n )\n contributors[:, :, px_row_init:px_row_end, px_col_init:px_col_end] += tile_weights\n # Average overlapping areas with more than 1 contributor\n noise_pred /= contributors\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents).prev_sample\n\n # scale and decode the image latents with vae\n image = self.decode_latents(latents, cpu_vae)\n\n return {\"images\": image}\n\n def _gaussian_weights(self, tile_width, tile_height, nbatches):\n \"\"\"Generates a gaussian mask of weights for tile contributions\"\"\"\n import numpy as np\n from numpy import exp, pi, sqrt\n\n latent_width = tile_width // 8\n latent_height = tile_height // 8\n\n var = 0.01\n midpoint = (latent_width - 1) / 2 # -1 because index goes from 0 to latent_width - 1\n x_probs = [\n exp(-(x - midpoint) * (x - midpoint) / (latent_width * latent_width) / (2 * var)) / sqrt(2 * pi * var)\n for x in range(latent_width)\n ]\n midpoint = latent_height / 2\n y_probs = [\n exp(-(y - midpoint) * (y - midpoint) / (latent_height * latent_height) / (2 * var)) / sqrt(2 * pi * var)\n for y in range(latent_height)\n ]\n\n weights = np.outer(y_probs, x_probs)\n return torch.tile(torch.tensor(weights, device=self.device), (nbatches, self.unet.config.in_channels, 1, 1))\n" }, { "path": "diffusers/examples/community/multilingual_stable_diffusion.py", "content": "import inspect\nfrom typing import Callable, List, Optional, Union\n\nimport torch\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModel,\n CLIPTokenizer,\n MBart50TokenizerFast,\n MBartForConditionalGeneration,\n pipeline,\n)\n\nfrom diffusers.configuration_utils import FrozenDict\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler\nfrom diffusers.utils import deprecate, logging\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef detect_language(pipe, prompt, batch_size):\n \"\"\"helper function to detect language(s) of prompt\"\"\"\n\n if batch_size == 1:\n preds = pipe(prompt, top_k=1, truncation=True, max_length=128)\n return preds[0][\"label\"]\n else:\n detected_languages = []\n for p in prompt:\n preds = pipe(p, top_k=1, truncation=True, max_length=128)\n detected_languages.append(preds[0][\"label\"])\n\n return detected_languages\n\n\ndef translate_prompt(prompt, translation_tokenizer, translation_model, device):\n \"\"\"helper function to translate prompt to English\"\"\"\n\n encoded_prompt = translation_tokenizer(prompt, return_tensors=\"pt\").to(device)\n generated_tokens = translation_model.generate(**encoded_prompt, max_new_tokens=1000)\n en_trans = translation_tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)\n\n return en_trans[0]\n\n\nclass MultilingualStableDiffusion(DiffusionPipeline, StableDiffusionMixin):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion in different languages.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n detection_pipeline ([`pipeline`]):\n Transformers pipeline to detect prompt's language.\n translation_model ([`MBartForConditionalGeneration`]):\n Model to translate prompt to English, if necessary. Please refer to the\n [model card](https://huggingface.co/docs/transformers/model_doc/mbart) for details.\n translation_tokenizer ([`MBart50TokenizerFast`]):\n Tokenizer of the translation model.\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n def __init__(\n self,\n detection_pipeline: pipeline,\n translation_model: MBartForConditionalGeneration,\n translation_tokenizer: MBart50TokenizerFast,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n self.register_modules(\n detection_pipeline=detection_pipeline,\n translation_model=translation_model,\n translation_tokenizer=translation_tokenizer,\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation. Can be in different languages.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation\n deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n if isinstance(prompt, str):\n batch_size = 1\n elif isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n # detect language and translate if necessary\n prompt_language = detect_language(self.detection_pipeline, prompt, batch_size)\n if batch_size == 1 and prompt_language != \"en\":\n prompt = translate_prompt(prompt, self.translation_tokenizer, self.translation_model, self.device)\n\n if isinstance(prompt, list):\n for index in range(batch_size):\n if prompt_language[index] != \"en\":\n p = translate_prompt(\n prompt[index], self.translation_tokenizer, self.translation_model, self.device\n )\n prompt[index] = p\n\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n\n if text_input_ids.shape[-1] > self.tokenizer.model_max_length:\n removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]\n text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]\n\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = text_embeddings.shape\n text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)\n text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n # detect language and translate it if necessary\n negative_prompt_language = detect_language(self.detection_pipeline, negative_prompt, batch_size)\n if negative_prompt_language != \"en\":\n negative_prompt = translate_prompt(\n negative_prompt, self.translation_tokenizer, self.translation_model, self.device\n )\n if isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n # detect language and translate it if necessary\n if isinstance(negative_prompt, list):\n negative_prompt_languages = detect_language(self.detection_pipeline, negative_prompt, batch_size)\n for index in range(batch_size):\n if negative_prompt_languages[index] != \"en\":\n p = translate_prompt(\n negative_prompt[index], self.translation_tokenizer, self.translation_model, self.device\n )\n negative_prompt[index] = p\n uncond_tokens = negative_prompt\n\n max_length = text_input_ids.shape[-1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = uncond_embeddings.shape[1]\n uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)\n uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n\n # get the initial random noise unless the user supplied it\n\n # Unlike in other pipelines, latents need to be generated in the target device\n # for 1-to-1 results reproducibility with the CompVis implementation.\n # However this currently doesn't work in `mps`.\n latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)\n latents_dtype = text_embeddings.dtype\n if latents is None:\n if self.device.type == \"mps\":\n # randn does not work reproducibly on mps\n latents = torch.randn(latents_shape, generator=generator, device=\"cpu\", dtype=latents_dtype).to(\n self.device\n )\n else:\n latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)\n else:\n if latents.shape != latents_shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {latents_shape}\")\n latents = latents.to(self.device)\n\n # set timesteps\n self.scheduler.set_timesteps(num_inference_steps)\n\n # Some schedulers like PNDM have timesteps as arrays\n # It's more optimized to move all timesteps to correct device beforehand\n timesteps_tensor = self.scheduler.timesteps.to(self.device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n for i, t in enumerate(self.progress_bar(timesteps_tensor)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n latents = 1 / 0.18215 * latents\n image = self.vae.decode(latents).sample\n\n image = (image / 2 + 0.5).clamp(0, 1)\n\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors=\"pt\").to(\n self.device\n )\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)\n )\n else:\n has_nsfw_concept = None\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/examples/community/one_step_unet.py", "content": "#!/usr/bin/env python3\nimport torch\n\nfrom diffusers import DiffusionPipeline\n\n\nclass UnetSchedulerOneForwardPipeline(DiffusionPipeline):\n def __init__(self, unet, scheduler):\n super().__init__()\n\n self.register_modules(unet=unet, scheduler=scheduler)\n\n def __call__(self):\n image = torch.randn(\n (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),\n )\n timestep = 1\n\n model_output = self.unet(image, timestep).sample\n scheduler_output = self.scheduler.step(model_output, timestep, image).prev_sample\n\n result = scheduler_output - scheduler_output + torch.ones_like(scheduler_output)\n\n return result\n" }, { "path": "diffusers/examples/community/pipeline_animatediff_controlnet.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom PIL import Image\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom diffusers.image_processor import PipelineImageInput, VaeImageProcessor\nfrom diffusers.loaders import IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom diffusers.models import AutoencoderKL, ControlNetModel, ImageProjection, UNet2DConditionModel, UNetMotionModel\nfrom diffusers.models.lora import adjust_lora_scale_text_encoder\nfrom diffusers.models.unets.unet_motion_model import MotionAdapter\nfrom diffusers.pipelines.animatediff.pipeline_output import AnimateDiffPipelineOutput\nfrom diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom diffusers.schedulers import (\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n)\nfrom diffusers.utils import USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers\nfrom diffusers.utils.torch_utils import is_compiled_module, randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import AutoencoderKL, ControlNetModel, MotionAdapter\n >>> from diffusers.pipelines import DiffusionPipeline\n >>> from diffusers.schedulers import DPMSolverMultistepScheduler\n >>> from PIL import Image\n\n >>> motion_id = \"guoyww/animatediff-motion-adapter-v1-5-2\"\n >>> adapter = MotionAdapter.from_pretrained(motion_id)\n >>> controlnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11p_sd15_openpose\", torch_dtype=torch.float16)\n >>> vae = AutoencoderKL.from_pretrained(\"stabilityai/sd-vae-ft-mse\", torch_dtype=torch.float16)\n\n >>> model_id = \"SG161222/Realistic_Vision_V5.1_noVAE\"\n >>> pipe = DiffusionPipeline.from_pretrained(\n ... model_id,\n ... motion_adapter=adapter,\n ... controlnet=controlnet,\n ... vae=vae,\n ... custom_pipeline=\"pipeline_animatediff_controlnet\",\n ... ).to(device=\"cuda\", dtype=torch.float16)\n >>> pipe.scheduler = DPMSolverMultistepScheduler.from_pretrained(\n ... model_id, subfolder=\"scheduler\", clip_sample=False, timestep_spacing=\"linspace\", steps_offset=1, beta_schedule=\"linear\",\n ... )\n >>> pipe.enable_vae_slicing()\n\n >>> conditioning_frames = []\n >>> for i in range(1, 16 + 1):\n ... conditioning_frames.append(Image.open(f\"frame_{i}.png\"))\n\n >>> prompt = \"astronaut in space, dancing\"\n >>> negative_prompt = \"bad quality, worst quality, jpeg artifacts, ugly\"\n >>> result = pipe(\n ... prompt=prompt,\n ... negative_prompt=negative_prompt,\n ... width=512,\n ... height=768,\n ... conditioning_frames=conditioning_frames,\n ... num_inference_steps=12,\n ... )\n\n >>> from diffusers.utils import export_to_gif\n >>> export_to_gif(result.frames[0], \"result.gif\")\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.animatediff.pipeline_animatediff.tensor2vid\ndef tensor2vid(video: torch.Tensor, processor, output_type=\"np\"):\n batch_size, channels, num_frames, height, width = video.shape\n outputs = []\n for batch_idx in range(batch_size):\n batch_vid = video[batch_idx].permute(1, 0, 2, 3)\n batch_output = processor.postprocess(batch_vid, output_type)\n\n outputs.append(batch_output)\n\n if output_type == \"np\":\n outputs = np.stack(outputs)\n\n elif output_type == \"pt\":\n outputs = torch.stack(outputs)\n\n elif not output_type == \"pil\":\n raise ValueError(f\"{output_type} does not exist. Please choose one of ['np', 'pt', 'pil']\")\n\n return outputs\n\n\nclass AnimateDiffControlNetPipeline(\n DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, IPAdapterMixin, LoraLoaderMixin\n):\n r\"\"\"\n Pipeline for text-to-video generation.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer (`CLIPTokenizer`):\n A [`~transformers.CLIPTokenizer`] to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A [`UNet2DConditionModel`] used to create a UNetMotionModel to denoise the encoded video latents.\n motion_adapter ([`MotionAdapter`]):\n A [`MotionAdapter`] to be used in combination with `unet` to denoise the encoded video latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\"feature_extractor\", \"image_encoder\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n motion_adapter: MotionAdapter,\n controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],\n scheduler: Union[\n DDIMScheduler,\n PNDMScheduler,\n LMSDiscreteScheduler,\n EulerDiscreteScheduler,\n EulerAncestralDiscreteScheduler,\n DPMSolverMultistepScheduler,\n ],\n feature_extractor: Optional[CLIPImageProcessor] = None,\n image_encoder: Optional[CLIPVisionModelWithProjection] = None,\n ):\n super().__init__()\n unet = UNetMotionModel.from_unet2d(unet, motion_adapter)\n\n if isinstance(controlnet, (list, tuple)):\n controlnet = MultiControlNetModel(controlnet)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n motion_adapter=motion_adapter,\n controlnet=controlnet,\n scheduler=scheduler,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.control_image_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with num_images_per_prompt -> num_videos_per_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if self.do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n image_embeds = ip_adapter_image_embeds\n return image_embeds\n\n # Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents\n def decode_latents(self, latents):\n latents = 1 / self.vae.config.scaling_factor * latents\n\n batch_size, channels, num_frames, height, width = latents.shape\n latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)\n\n image = self.vae.decode(latents).sample\n video = (\n image[None, :]\n .reshape(\n (\n batch_size,\n num_frames,\n -1,\n )\n + image.shape[2:]\n )\n .permute(0, 2, 1, 3, 4)\n )\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n video = video.float()\n return video\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs\n def check_inputs(\n self,\n prompt,\n height,\n width,\n num_frames,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n image=None,\n controlnet_conditioning_scale=1.0,\n control_guidance_start=0.0,\n control_guidance_end=1.0,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # `prompt` needs more sophisticated handling when there are multiple\n # conditionings.\n if isinstance(self.controlnet, MultiControlNetModel):\n if isinstance(prompt, list):\n logger.warning(\n f\"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}\"\n \" prompts. The conditionings will be fixed across the prompts.\"\n )\n\n # Check `image`\n is_compiled = hasattr(F, \"scaled_dot_product_attention\") and isinstance(\n self.controlnet, torch._dynamo.eval_frame.OptimizedModule\n )\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n if not isinstance(image, list):\n raise TypeError(f\"For single controlnet, `image` must be of type `list` but got {type(image)}\")\n if len(image) != num_frames:\n raise ValueError(f\"Excepted image to have length {num_frames} but got {len(image)=}\")\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if not isinstance(image, list) or not isinstance(image[0], list):\n raise TypeError(f\"For multiple controlnets: `image` must be type list of lists but got {type(image)=}\")\n if len(image[0]) != num_frames:\n raise ValueError(f\"Expected length of image sublist as {num_frames} but got {len(image[0])=}\")\n if any(len(img) != len(image[0]) for img in image):\n raise ValueError(\"All conditioning frame batches for multicontrolnet must be same size\")\n else:\n assert False\n\n # Check `controlnet_conditioning_scale`\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n if not isinstance(controlnet_conditioning_scale, float):\n raise TypeError(\"For single controlnet: `controlnet_conditioning_scale` must be type `float`.\")\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if isinstance(controlnet_conditioning_scale, list):\n if any(isinstance(i, list) for i in controlnet_conditioning_scale):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(\n self.controlnet.nets\n ):\n raise ValueError(\n \"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have\"\n \" the same length as the number of controlnets\"\n )\n else:\n assert False\n\n if not isinstance(control_guidance_start, (tuple, list)):\n control_guidance_start = [control_guidance_start]\n\n if not isinstance(control_guidance_end, (tuple, list)):\n control_guidance_end = [control_guidance_end]\n\n if len(control_guidance_start) != len(control_guidance_end):\n raise ValueError(\n f\"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list.\"\n )\n\n if isinstance(self.controlnet, MultiControlNetModel):\n if len(control_guidance_start) != len(self.controlnet.nets):\n raise ValueError(\n f\"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}.\"\n )\n\n for start, end in zip(control_guidance_start, control_guidance_end):\n if start >= end:\n raise ValueError(\n f\"control guidance start: {start} cannot be larger or equal to control guidance end: {end}.\"\n )\n if start < 0.0:\n raise ValueError(f\"control guidance start: {start} can't be smaller than 0.\")\n if end > 1.0:\n raise ValueError(f\"control guidance end: {end} can't be larger than 1.0.\")\n\n # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image\n def check_image(self, image, prompt, prompt_embeds):\n image_is_pil = isinstance(image, Image.Image)\n image_is_tensor = isinstance(image, torch.Tensor)\n image_is_np = isinstance(image, np.ndarray)\n image_is_pil_list = isinstance(image, list) and isinstance(image[0], Image.Image)\n image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)\n image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)\n\n if (\n not image_is_pil\n and not image_is_tensor\n and not image_is_np\n and not image_is_pil_list\n and not image_is_tensor_list\n and not image_is_np_list\n ):\n raise TypeError(\n f\"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}\"\n )\n\n if image_is_pil:\n image_batch_size = 1\n else:\n image_batch_size = len(image)\n\n if prompt is not None and isinstance(prompt, str):\n prompt_batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n prompt_batch_size = len(prompt)\n elif prompt_embeds is not None:\n prompt_batch_size = prompt_embeds.shape[0]\n\n if image_batch_size != 1 and image_batch_size != prompt_batch_size:\n raise ValueError(\n f\"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}\"\n )\n\n # Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_synth.TextToVideoSDPipeline.prepare_latents\n def prepare_latents(\n self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None\n ):\n shape = (\n batch_size,\n num_channels_latents,\n num_frames,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image\n def prepare_image(\n self,\n image,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype,\n do_classifier_free_guidance=False,\n guess_mode=False,\n ):\n image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)\n image_batch_size = image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n image = image.repeat_interleave(repeat_by, dim=0)\n\n image = image.to(device=device, dtype=dtype)\n\n if do_classifier_free_guidance and not guess_mode:\n image = torch.cat([image] * 2)\n\n return image\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n num_frames: Optional[int] = 16,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_videos_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[PipelineImageInput] = None,\n conditioning_frames: Optional[List[PipelineImageInput]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: Union[float, List[float]] = 1.0,\n guess_mode: bool = False,\n control_guidance_start: Union[float, List[float]] = 0.0,\n control_guidance_end: Union[float, List[float]] = 1.0,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated video.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated video.\n num_frames (`int`, *optional*, defaults to 16):\n The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds\n amounts to 2 seconds of video.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality videos at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`. Latents should be of shape\n `(batch_size, num_channel, num_frames, height, width)`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n ip_adapter_image (`PipelineImageInput`, *optional*):\n Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters.\n Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should contain the negative image embedding\n if `do_classifier_free_guidance` is set to `True`.\n If not provided, embeddings are computed from the `ip_adapter_image` input argument.\n conditioning_frames (`List[PipelineImageInput]`, *optional*):\n The ControlNet input condition to provide guidance to the `unet` for generation. If multiple ControlNets\n are specified, images must be passed as a list such that each element of the list can be correctly\n batched for input to a single ControlNet.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated video. Choose between `torch.Tensor`, `PIL.Image` or\n `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] instead\n of a plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):\n The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added\n to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set\n the corresponding scale as a list.\n guess_mode (`bool`, *optional*, defaults to `False`):\n The ControlNet encoder tries to recognize the content of the input image even if you remove all\n prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.\n control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):\n The percentage of total steps at which the ControlNet starts applying.\n control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):\n The percentage of total steps at which the ControlNet stops applying.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.animatediff.pipeline_output.AnimateDiffPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.animatediff.pipeline_output.AnimateDiffPipelineOutput`] is\n returned, otherwise a `tuple` is returned where the first element is a list with the generated frames.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n\n controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet\n\n # align format for control guidance\n if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):\n control_guidance_start = len(control_guidance_end) * [control_guidance_start]\n elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):\n control_guidance_end = len(control_guidance_start) * [control_guidance_end]\n elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):\n mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1\n control_guidance_start, control_guidance_end = (\n mult * [control_guidance_start],\n mult * [control_guidance_end],\n )\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n num_videos_per_prompt = 1\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt=prompt,\n height=height,\n width=width,\n num_frames=num_frames,\n callback_steps=callback_steps,\n negative_prompt=negative_prompt,\n callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n image=conditioning_frames,\n controlnet_conditioning_scale=controlnet_conditioning_scale,\n control_guidance_start=control_guidance_start,\n control_guidance_end=control_guidance_end,\n )\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):\n controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)\n\n global_pool_conditions = (\n controlnet.config.global_pool_conditions\n if isinstance(controlnet, ControlNetModel)\n else controlnet.nets[0].config.global_pool_conditions\n )\n guess_mode = guess_mode or global_pool_conditions\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_videos_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=self.clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n if ip_adapter_image is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image, ip_adapter_image_embeds, device, batch_size * num_videos_per_prompt\n )\n\n if isinstance(controlnet, ControlNetModel):\n conditioning_frames = self.prepare_image(\n image=conditioning_frames,\n width=width,\n height=height,\n batch_size=batch_size * num_videos_per_prompt * num_frames,\n num_images_per_prompt=num_videos_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n elif isinstance(controlnet, MultiControlNetModel):\n cond_prepared_frames = []\n for frame_ in conditioning_frames:\n prepared_frame = self.prepare_image(\n image=frame_,\n width=width,\n height=height,\n batch_size=batch_size * num_videos_per_prompt * num_frames,\n num_images_per_prompt=num_videos_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n cond_prepared_frames.append(prepared_frame)\n conditioning_frames = cond_prepared_frames\n else:\n assert False\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n self._num_timesteps = len(timesteps)\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_videos_per_prompt,\n num_channels_latents,\n num_frames,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Add image embeds for IP-Adapter\n added_cond_kwargs = (\n {\"image_embeds\": image_embeds}\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None\n else None\n )\n\n # 7.1 Create tensor stating which controlnets to keep\n controlnet_keep = []\n for i in range(len(timesteps)):\n keeps = [\n 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)\n for s, e in zip(control_guidance_start, control_guidance_end)\n ]\n controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n if guess_mode and self.do_classifier_free_guidance:\n # Infer ControlNet only for the conditional batch.\n control_model_input = latents\n control_model_input = self.scheduler.scale_model_input(control_model_input, t)\n controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]\n else:\n control_model_input = latent_model_input\n controlnet_prompt_embeds = prompt_embeds\n controlnet_prompt_embeds = controlnet_prompt_embeds.repeat_interleave(num_frames, dim=0)\n\n if isinstance(controlnet_keep[i], list):\n cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]\n else:\n controlnet_cond_scale = controlnet_conditioning_scale\n if isinstance(controlnet_cond_scale, list):\n controlnet_cond_scale = controlnet_cond_scale[0]\n cond_scale = controlnet_cond_scale * controlnet_keep[i]\n\n control_model_input = torch.transpose(control_model_input, 1, 2)\n control_model_input = control_model_input.reshape(\n (-1, control_model_input.shape[2], control_model_input.shape[3], control_model_input.shape[4])\n )\n\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n control_model_input,\n t,\n encoder_hidden_states=controlnet_prompt_embeds,\n controlnet_cond=conditioning_frames,\n conditioning_scale=cond_scale,\n guess_mode=guess_mode,\n return_dict=False,\n )\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=self.cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n ).sample\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, latents)\n\n # 9. Post processing\n if output_type == \"latent\":\n video = latents\n else:\n video_tensor = self.decode_latents(latents)\n video = tensor2vid(video_tensor, self.image_processor, output_type=output_type)\n\n # 10. Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (video,)\n\n return AnimateDiffPipelineOutput(frames=video)\n" }, { "path": "diffusers/examples/community/pipeline_animatediff_img2video.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# Note:\n# This pipeline relies on a \"hack\" discovered by the community that allows\n# the generation of videos given an input image with AnimateDiff. It works\n# by creating a copy of the image `num_frames` times and progressively adding\n# more noise to the image based on the strength and latent interpolation method.\n\nimport inspect\nfrom types import FunctionType\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom diffusers.image_processor import PipelineImageInput, VaeImageProcessor\nfrom diffusers.loaders import IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel, UNetMotionModel\nfrom diffusers.models.lora import adjust_lora_scale_text_encoder\nfrom diffusers.models.unet_motion_model import MotionAdapter\nfrom diffusers.pipelines.animatediff.pipeline_output import AnimateDiffPipelineOutput\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom diffusers.schedulers import (\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n)\nfrom diffusers.utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import MotionAdapter, DiffusionPipeline, DDIMScheduler\n >>> from diffusers.utils import export_to_gif, load_image\n\n >>> model_id = \"SG161222/Realistic_Vision_V5.1_noVAE\"\n >>> adapter = MotionAdapter.from_pretrained(\"guoyww/animatediff-motion-adapter-v1-5-2\")\n >>> pipe = DiffusionPipeline.from_pretrained(\"SG161222/Realistic_Vision_V5.1_noVAE\", motion_adapter=adapter, custom_pipeline=\"pipeline_animatediff_img2video\").to(\"cuda\")\n >>> pipe.scheduler = pipe.scheduler = DDIMScheduler.from_pretrained(model_id, subfolder=\"scheduler\", clip_sample=False, timestep_spacing=\"linspace\", beta_schedule=\"linear\", steps_offset=1)\n\n >>> image = load_image(\"snail.png\")\n >>> output = pipe(image=image, prompt=\"A snail moving on the ground\", strength=0.8, latent_interpolation_method=\"slerp\")\n >>> frames = output.frames[0]\n >>> export_to_gif(frames, \"animation.gif\")\n ```\n\"\"\"\n\n\ndef lerp(\n v0: torch.Tensor,\n v1: torch.Tensor,\n t: Union[float, torch.Tensor],\n) -> torch.Tensor:\n r\"\"\"\n Linear Interpolation between two tensors.\n\n Args:\n v0 (`torch.Tensor`): First tensor.\n v1 (`torch.Tensor`): Second tensor.\n t: (`float` or `torch.Tensor`): Interpolation factor.\n \"\"\"\n t_is_float = False\n input_device = v0.device\n v0 = v0.cpu().numpy()\n v1 = v1.cpu().numpy()\n\n if isinstance(t, torch.Tensor):\n t = t.cpu().numpy()\n else:\n t_is_float = True\n t = np.array([t], dtype=v0.dtype)\n\n t = t[..., None]\n v0 = v0[None, ...]\n v1 = v1[None, ...]\n v2 = (1 - t) * v0 + t * v1\n\n if t_is_float and v0.ndim > 1:\n assert v2.shape[0] == 1\n v2 = np.squeeze(v2, axis=0)\n\n v2 = torch.from_numpy(v2).to(input_device)\n return v2\n\n\ndef slerp(\n v0: torch.Tensor,\n v1: torch.Tensor,\n t: Union[float, torch.Tensor],\n DOT_THRESHOLD: float = 0.9995,\n) -> torch.Tensor:\n r\"\"\"\n Spherical Linear Interpolation between two tensors.\n\n Args:\n v0 (`torch.Tensor`): First tensor.\n v1 (`torch.Tensor`): Second tensor.\n t: (`float` or `torch.Tensor`): Interpolation factor.\n DOT_THRESHOLD (`float`):\n Dot product threshold exceeding which linear interpolation will be used\n because input tensors are close to parallel.\n \"\"\"\n t_is_float = False\n input_device = v0.device\n v0 = v0.cpu().numpy()\n v1 = v1.cpu().numpy()\n\n if isinstance(t, torch.Tensor):\n t = t.cpu().numpy()\n else:\n t_is_float = True\n t = np.array([t], dtype=v0.dtype)\n\n dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))\n\n if np.abs(dot) > DOT_THRESHOLD:\n # v0 and v1 are close to parallel, so use linear interpolation instead\n v2 = lerp(v0, v1, t)\n else:\n theta_0 = np.arccos(dot)\n sin_theta_0 = np.sin(theta_0)\n theta_t = theta_0 * t\n sin_theta_t = np.sin(theta_t)\n s0 = np.sin(theta_0 - theta_t) / sin_theta_0\n s1 = sin_theta_t / sin_theta_0\n s0 = s0[..., None]\n s1 = s1[..., None]\n v0 = v0[None, ...]\n v1 = v1[None, ...]\n v2 = s0 * v0 + s1 * v1\n\n if t_is_float and v0.ndim > 1:\n assert v2.shape[0] == 1\n v2 = np.squeeze(v2, axis=0)\n\n v2 = torch.from_numpy(v2).to(input_device)\n return v2\n\n\n# Copied from diffusers.pipelines.animatediff.pipeline_animatediff.tensor2vid\ndef tensor2vid(video: torch.Tensor, processor, output_type=\"np\"):\n batch_size, channels, num_frames, height, width = video.shape\n outputs = []\n for batch_idx in range(batch_size):\n batch_vid = video[batch_idx].permute(1, 0, 2, 3)\n batch_output = processor.postprocess(batch_vid, output_type)\n\n outputs.append(batch_output)\n\n if output_type == \"np\":\n outputs = np.stack(outputs)\n\n elif output_type == \"pt\":\n outputs = torch.stack(outputs)\n\n elif not output_type == \"pil\":\n raise ValueError(f\"{output_type} does not exist. Please choose one of ['np', 'pt', 'pil']\")\n\n return outputs\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used,\n `timesteps` must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default\n timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`\n must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass AnimateDiffImgToVideoPipeline(\n DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, IPAdapterMixin, LoraLoaderMixin\n):\n r\"\"\"\n Pipeline for image-to-video generation.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer (`CLIPTokenizer`):\n A [`~transformers.CLIPTokenizer`] to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A [`UNet2DConditionModel`] used to create a UNetMotionModel to denoise the encoded video latents.\n motion_adapter ([`MotionAdapter`]):\n A [`MotionAdapter`] to be used in combination with `unet` to denoise the encoded video latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->image_encoder->unet->vae\"\n _optional_components = [\"feature_extractor\", \"image_encoder\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n motion_adapter: MotionAdapter,\n scheduler: Union[\n DDIMScheduler,\n PNDMScheduler,\n LMSDiscreteScheduler,\n EulerDiscreteScheduler,\n EulerAncestralDiscreteScheduler,\n DPMSolverMultistepScheduler,\n ],\n feature_extractor: CLIPImageProcessor = None,\n image_encoder: CLIPVisionModelWithProjection = None,\n ):\n super().__init__()\n unet = UNetMotionModel.from_unet2d(unet, motion_adapter)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n motion_adapter=motion_adapter,\n scheduler=scheduler,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with num_images_per_prompt -> num_videos_per_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: procecss multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: procecss multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if self.do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n image_embeds = ip_adapter_image_embeds\n return image_embeds\n\n # Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents\n def decode_latents(self, latents):\n latents = 1 / self.vae.config.scaling_factor * latents\n\n batch_size, channels, num_frames, height, width = latents.shape\n latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)\n\n image = self.vae.decode(latents).sample\n video = (\n image[None, :]\n .reshape(\n (\n batch_size,\n num_frames,\n -1,\n )\n + image.shape[2:]\n )\n .permute(0, 2, 1, 3, 4)\n )\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n video = video.float()\n return video\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n latent_interpolation_method=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if latent_interpolation_method is not None:\n if latent_interpolation_method not in [\"lerp\", \"slerp\"] and not isinstance(\n latent_interpolation_method, FunctionType\n ):\n raise ValueError(\n \"`latent_interpolation_method` must be one of `lerp`, `slerp` or a Callable[[torch.Tensor, torch.Tensor, int], torch.Tensor]\"\n )\n\n def prepare_latents(\n self,\n image,\n strength,\n batch_size,\n num_channels_latents,\n num_frames,\n height,\n width,\n dtype,\n device,\n generator,\n latents=None,\n latent_interpolation_method=\"slerp\",\n ):\n shape = (\n batch_size,\n num_channels_latents,\n num_frames,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n )\n\n if latents is None:\n image = image.to(device=device, dtype=dtype)\n\n if image.shape[1] == 4:\n latents = image\n else:\n # make sure the VAE is in float32 mode, as it overflows in float16\n if self.vae.config.force_upcast:\n image = image.float()\n self.vae.to(dtype=torch.float32)\n\n if isinstance(generator, list):\n if len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n init_latents = [\n retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])\n for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = retrieve_latents(self.vae.encode(image), generator=generator)\n\n if self.vae.config.force_upcast:\n self.vae.to(dtype)\n\n init_latents = init_latents.to(dtype)\n init_latents = self.vae.config.scaling_factor * init_latents\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n latents = latents * self.scheduler.init_noise_sigma\n\n if latent_interpolation_method == \"lerp\":\n\n def latent_cls(v0, v1, index):\n return lerp(v0, v1, index / num_frames * (1 - strength))\n elif latent_interpolation_method == \"slerp\":\n\n def latent_cls(v0, v1, index):\n return slerp(v0, v1, index / num_frames * (1 - strength))\n else:\n latent_cls = latent_interpolation_method\n\n for i in range(num_frames):\n latents[:, :, i, :, :] = latent_cls(latents[:, :, i, :, :], init_latents, i)\n else:\n if shape != latents.shape:\n # [B, C, F, H, W]\n raise ValueError(f\"`latents` expected to have {shape=}, but found {latents.shape=}\")\n latents = latents.to(device, dtype=dtype)\n\n return latents\n\n @torch.no_grad()\n def __call__(\n self,\n image: PipelineImageInput,\n prompt: Optional[Union[str, List[str]]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_frames: int = 16,\n num_inference_steps: int = 50,\n timesteps: Optional[List[int]] = None,\n guidance_scale: float = 7.5,\n strength: float = 0.8,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_videos_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[PipelineImageInput] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: Optional[int] = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: Optional[int] = None,\n latent_interpolation_method: Union[str, Callable[[torch.Tensor, torch.Tensor, int], torch.Tensor]] = \"slerp\",\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n image (`PipelineImageInput`):\n The input image to condition the generation on.\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated video.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated video.\n num_frames (`int`, *optional*, defaults to 16):\n The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds\n amounts to 2 seconds of video.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality videos at the\n expense of slower inference.\n strength (`float`, *optional*, defaults to 0.8):\n Higher strength leads to more differences between original image and generated video.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`. Latents should be of shape\n `(batch_size, num_channel, num_frames, height, width)`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*):\n Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters.\n Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should contain the negative image embedding\n if `do_classifier_free_guidance` is set to `True`.\n If not provided, embeddings are computed from the `ip_adapter_image` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated video. Choose between `torch.Tensor`, `PIL.Image` or\n `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`AnimateDiffImgToVideoPipelineOutput`] instead\n of a plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n latent_interpolation_method (`str` or `Callable[[torch.Tensor, torch.Tensor, int], torch.Tensor]]`, *optional*):\n Must be one of \"lerp\", \"slerp\" or a callable that takes in a random noisy latent, image latent and a frame index\n as input and returns an initial latent for sampling.\n Examples:\n\n Returns:\n [`~pipelines.animatediff.pipeline_output.AnimateDiffPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.animatediff.pipeline_output.AnimateDiffPipelineOutput`] is\n returned, otherwise a `tuple` is returned where the first element is a list with the generated frames.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n num_videos_per_prompt = 1\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt=prompt,\n height=height,\n width=width,\n callback_steps=callback_steps,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n latent_interpolation_method=latent_interpolation_method,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_videos_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=clip_skip,\n )\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n if ip_adapter_image is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image, ip_adapter_image_embeds, device, batch_size * num_videos_per_prompt\n )\n\n # 4. Preprocess image\n image = self.image_processor.preprocess(image, height=height, width=width)\n\n # 5. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)\n\n # 6. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n image=image,\n strength=strength,\n batch_size=batch_size * num_videos_per_prompt,\n num_channels_latents=num_channels_latents,\n num_frames=num_frames,\n height=height,\n width=width,\n dtype=prompt_embeds.dtype,\n device=device,\n generator=generator,\n latents=latents,\n latent_interpolation_method=latent_interpolation_method,\n )\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 8. Add image embeds for IP-Adapter\n added_cond_kwargs = (\n {\"image_embeds\": image_embeds}\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None\n else None\n )\n\n # 9. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, latents)\n\n if output_type == \"latent\":\n return AnimateDiffPipelineOutput(frames=latents)\n\n # 10. Post-processing\n if output_type == \"latent\":\n video = latents\n else:\n video_tensor = self.decode_latents(latents)\n video = tensor2vid(video_tensor, self.image_processor, output_type=output_type)\n\n # 11. Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (video,)\n\n return AnimateDiffPipelineOutput(frames=video)\n" }, { "path": "diffusers/examples/community/pipeline_demofusion_sdxl.py", "content": "import inspect\nimport os\nimport random\nimport warnings\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport matplotlib.pyplot as plt\nimport torch\nimport torch.nn.functional as F\nfrom transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom diffusers.image_processor import VaeImageProcessor\nfrom diffusers.loaders import (\n FromSingleFileMixin,\n LoraLoaderMixin,\n TextualInversionLoaderMixin,\n)\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.models.attention_processor import (\n AttnProcessor2_0,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n XFormersAttnProcessor,\n)\nfrom diffusers.models.lora import adjust_lora_scale_text_encoder\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n is_accelerate_available,\n is_accelerate_version,\n is_invisible_watermark_available,\n logging,\n replace_example_docstring,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nif is_invisible_watermark_available():\n from diffusers.pipelines.stable_diffusion_xl.watermark import (\n StableDiffusionXLWatermarker,\n )\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusionXLPipeline\n\n >>> pipe = StableDiffusionXLPipeline.from_pretrained(\n ... \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> prompt = \"a photo of an astronaut riding a horse on mars\"\n >>> image = pipe(prompt).images[0]\n ```\n\"\"\"\n\n\ndef gaussian_kernel(kernel_size=3, sigma=1.0, channels=3):\n x_coord = torch.arange(kernel_size)\n gaussian_1d = torch.exp(-((x_coord - (kernel_size - 1) / 2) ** 2) / (2 * sigma**2))\n gaussian_1d = gaussian_1d / gaussian_1d.sum()\n gaussian_2d = gaussian_1d[:, None] * gaussian_1d[None, :]\n kernel = gaussian_2d[None, None, :, :].repeat(channels, 1, 1, 1)\n\n return kernel\n\n\ndef gaussian_filter(latents, kernel_size=3, sigma=1.0):\n channels = latents.shape[1]\n kernel = gaussian_kernel(kernel_size, sigma, channels).to(latents.device, latents.dtype)\n blurred_latents = F.conv2d(latents, kernel, padding=kernel_size // 2, groups=channels)\n\n return blurred_latents\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\nclass DemoFusionSDXLPipeline(\n DiffusionPipeline, StableDiffusionMixin, FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion XL.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n In addition the pipeline inherits the following loading methods:\n - *LoRA*: [`StableDiffusionXLPipeline.load_lora_weights`]\n - *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`]\n\n as well as the following saving methods:\n - *LoRA*: [`loaders.StableDiffusionXLPipeline.save_lora_weights`]\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion XL uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n text_encoder_2 ([` CLIPTextModelWithProjection`]):\n Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),\n specifically the\n [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)\n variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n tokenizer_2 (`CLIPTokenizer`):\n Second Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `\"True\"`):\n Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of\n `stabilityai/stable-diffusion-xl-base-1-0`.\n add_watermarker (`bool`, *optional*):\n Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to\n watermark output images. If not defined, it will default to True if the package is installed, otherwise no\n watermarker will be used.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->text_encoder_2->unet->vae\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n text_encoder_2: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n tokenizer_2: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n force_zeros_for_empty_prompt: bool = True,\n add_watermarker: Optional[bool] = None,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n unet=unet,\n scheduler=scheduler,\n )\n self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.default_sample_size = self.unet.config.sample_size\n\n add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()\n\n if add_watermarker:\n self.watermark = StableDiffusionXLWatermarker()\n else:\n self.watermark = None\n\n def encode_prompt(\n self,\n prompt: str,\n prompt_2: Optional[str] = None,\n device: Optional[torch.device] = None,\n num_images_per_prompt: int = 1,\n do_classifier_free_guidance: bool = True,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n lora_scale (`float`, *optional*):\n A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n \"\"\"\n device = device or self._execution_device\n\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # Define tokenizers and text encoders\n tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]\n text_encoders = (\n [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]\n )\n\n if prompt_embeds is None:\n prompt_2 = prompt_2 or prompt\n # textual inversion: process multi-vector tokens if necessary\n prompt_embeds_list = []\n prompts = [prompt, prompt_2]\n for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, tokenizer)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = text_encoder(\n text_input_ids.to(device),\n output_hidden_states=True,\n )\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds.hidden_states[-2]\n\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n\n # get unconditional embeddings for classifier free guidance\n zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt\n if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:\n negative_prompt_embeds = torch.zeros_like(prompt_embeds)\n negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)\n elif do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n\n uncond_tokens: List[str]\n if prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt, negative_prompt_2]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = [negative_prompt, negative_prompt_2]\n\n negative_prompt_embeds_list = []\n for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n negative_prompt_embeds = text_encoder(\n uncond_input.input_ids.to(device),\n output_hidden_states=True,\n )\n # We are only ALWAYS interested in the pooled output of the final text encoder\n negative_pooled_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]\n\n negative_prompt_embeds_list.append(negative_prompt_embeds)\n\n negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)\n\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n if do_classifier_free_guidance:\n negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n prompt_2,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n negative_prompt_2=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n pooled_prompt_embeds=None,\n negative_pooled_prompt_embeds=None,\n num_images_per_prompt=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if prompt_embeds is not None and pooled_prompt_embeds is None:\n raise ValueError(\n \"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.\"\n )\n\n if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:\n raise ValueError(\n \"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.\"\n )\n\n # DemoFusion specific checks\n if max(height, width) % 1024 != 0:\n raise ValueError(\n f\"the larger one of `height` and `width` has to be divisible by 1024 but are {height} and {width}.\"\n )\n\n if num_images_per_prompt != 1:\n warnings.warn(\"num_images_per_prompt != 1 is not supported by DemoFusion and will be ignored.\")\n num_images_per_prompt = 1\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def _get_add_time_ids(self, original_size, crops_coords_top_left, target_size, dtype):\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n\n passed_add_embed_dim = (\n self.unet.config.addition_time_embed_dim * len(add_time_ids) + self.text_encoder_2.config.projection_dim\n )\n expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features\n\n if expected_add_embed_dim != passed_add_embed_dim:\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.\"\n )\n\n add_time_ids = torch.tensor([add_time_ids], dtype=dtype)\n return add_time_ids\n\n def get_views(self, height, width, window_size=128, stride=64, random_jitter=False):\n height //= self.vae_scale_factor\n width //= self.vae_scale_factor\n num_blocks_height = int((height - window_size) / stride - 1e-6) + 2 if height > window_size else 1\n num_blocks_width = int((width - window_size) / stride - 1e-6) + 2 if width > window_size else 1\n total_num_blocks = int(num_blocks_height * num_blocks_width)\n views = []\n for i in range(total_num_blocks):\n h_start = int((i // num_blocks_width) * stride)\n h_end = h_start + window_size\n w_start = int((i % num_blocks_width) * stride)\n w_end = w_start + window_size\n\n if h_end > height:\n h_start = int(h_start + height - h_end)\n h_end = int(height)\n if w_end > width:\n w_start = int(w_start + width - w_end)\n w_end = int(width)\n if h_start < 0:\n h_end = int(h_end - h_start)\n h_start = 0\n if w_start < 0:\n w_end = int(w_end - w_start)\n w_start = 0\n\n if random_jitter:\n jitter_range = (window_size - stride) // 4\n w_jitter = 0\n h_jitter = 0\n if (w_start != 0) and (w_end != width):\n w_jitter = random.randint(-jitter_range, jitter_range)\n elif (w_start == 0) and (w_end != width):\n w_jitter = random.randint(-jitter_range, 0)\n elif (w_start != 0) and (w_end == width):\n w_jitter = random.randint(0, jitter_range)\n if (h_start != 0) and (h_end != height):\n h_jitter = random.randint(-jitter_range, jitter_range)\n elif (h_start == 0) and (h_end != height):\n h_jitter = random.randint(-jitter_range, 0)\n elif (h_start != 0) and (h_end == height):\n h_jitter = random.randint(0, jitter_range)\n h_start += h_jitter + jitter_range\n h_end += h_jitter + jitter_range\n w_start += w_jitter + jitter_range\n w_end += w_jitter + jitter_range\n\n views.append((h_start, h_end, w_start, w_end))\n return views\n\n def tiled_decode(self, latents, current_height, current_width):\n core_size = self.unet.config.sample_size // 4\n core_stride = core_size\n pad_size = self.unet.config.sample_size // 4 * 3\n decoder_view_batch_size = 1\n\n views = self.get_views(current_height, current_width, stride=core_stride, window_size=core_size)\n views_batch = [views[i : i + decoder_view_batch_size] for i in range(0, len(views), decoder_view_batch_size)]\n latents_ = F.pad(latents, (pad_size, pad_size, pad_size, pad_size), \"constant\", 0)\n image = torch.zeros(latents.size(0), 3, current_height, current_width).to(latents.device)\n count = torch.zeros_like(image).to(latents.device)\n # get the latents corresponding to the current view coordinates\n with self.progress_bar(total=len(views_batch)) as progress_bar:\n for j, batch_view in enumerate(views_batch):\n len(batch_view)\n latents_for_view = torch.cat(\n [\n latents_[:, :, h_start : h_end + pad_size * 2, w_start : w_end + pad_size * 2]\n for h_start, h_end, w_start, w_end in batch_view\n ]\n )\n image_patch = self.vae.decode(latents_for_view / self.vae.config.scaling_factor, return_dict=False)[0]\n h_start, h_end, w_start, w_end = views[j]\n h_start, h_end, w_start, w_end = (\n h_start * self.vae_scale_factor,\n h_end * self.vae_scale_factor,\n w_start * self.vae_scale_factor,\n w_end * self.vae_scale_factor,\n )\n p_h_start, p_h_end, p_w_start, p_w_end = (\n pad_size * self.vae_scale_factor,\n image_patch.size(2) - pad_size * self.vae_scale_factor,\n pad_size * self.vae_scale_factor,\n image_patch.size(3) - pad_size * self.vae_scale_factor,\n )\n image[:, :, h_start:h_end, w_start:w_end] += image_patch[:, :, p_h_start:p_h_end, p_w_start:p_w_end]\n count[:, :, h_start:h_end, w_start:w_end] += 1\n progress_bar.update()\n image = image / count\n\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae\n def upcast_vae(self):\n dtype = self.vae.dtype\n self.vae.to(dtype=torch.float32)\n use_torch_2_0_or_xformers = isinstance(\n self.vae.decoder.mid_block.attentions[0].processor,\n (\n AttnProcessor2_0,\n XFormersAttnProcessor,\n LoRAXFormersAttnProcessor,\n LoRAAttnProcessor2_0,\n ),\n )\n # if xformers or torch_2_0 is used attention block does not need\n # to be in float32 which can save lots of memory\n if use_torch_2_0_or_xformers:\n self.vae.post_quant_conv.to(dtype)\n self.vae.decoder.conv_in.to(dtype)\n self.vae.decoder.mid_block.to(dtype)\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = False,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Optional[Tuple[int, int]] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Optional[Tuple[int, int]] = None,\n negative_original_size: Optional[Tuple[int, int]] = None,\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\n negative_target_size: Optional[Tuple[int, int]] = None,\n ################### DemoFusion specific parameters ####################\n view_batch_size: int = 16,\n multi_decoder: bool = True,\n stride: Optional[int] = 64,\n cosine_scale_1: Optional[float] = 3.0,\n cosine_scale_2: Optional[float] = 1.0,\n cosine_scale_3: Optional[float] = 1.0,\n sigma: Optional[float] = 0.8,\n show_image: bool = False,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image. This is set to 1024 by default for the best results.\n Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image. This is set to 1024 by default for the best results.\n Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n denoising_end (`float`, *optional*):\n When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be\n completed before it is intentionally prematurely terminated. As a result, the returned sample will\n still retain a substantial amount of noise as determined by the discrete timesteps selected by the\n scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a\n \"Mixture of Denoisers\" multi-pipeline setup, as elaborated in [**Refining the Image\n Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)\n guidance_scale (`float`, *optional*, defaults to 5.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead\n of a plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n guidance_rescale (`float`, *optional*, defaults to 0.7):\n Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of\n [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).\n Guidance rescale factor should fix overexposure when using zero terminal SNR.\n original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.\n `original_size` defaults to `(width, height)` if not specified. Part of SDXL's micro-conditioning as\n explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n `crops_coords_top_left` can be used to generate an image that appears to be \"cropped\" from the position\n `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n For most cases, `target_size` should be set to the desired height and width of the generated image. If\n not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a specific image resolution. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a target image resolution. It should be as same\n as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n ################### DemoFusion specific parameters ####################\n view_batch_size (`int`, defaults to 16):\n The batch size for multiple denoising paths. Typically, a larger batch size can result in higher\n efficiency but comes with increased GPU memory requirements.\n multi_decoder (`bool`, defaults to True):\n Determine whether to use a tiled decoder. Generally, when the resolution exceeds 3072x3072,\n a tiled decoder becomes necessary.\n stride (`int`, defaults to 64):\n The stride of moving local patches. A smaller stride is better for alleviating seam issues,\n but it also introduces additional computational overhead and inference time.\n cosine_scale_1 (`float`, defaults to 3):\n Control the strength of skip-residual. For specific impacts, please refer to Appendix C\n in the DemoFusion paper.\n cosine_scale_2 (`float`, defaults to 1):\n Control the strength of dilated sampling. For specific impacts, please refer to Appendix C\n in the DemoFusion paper.\n cosine_scale_3 (`float`, defaults to 1):\n Control the strength of the gaussion filter. For specific impacts, please refer to Appendix C\n in the DemoFusion paper.\n sigma (`float`, defaults to 1):\n The standerd value of the gaussian filter.\n show_image (`bool`, defaults to False):\n Determine whether to show intermediate results during generation.\n\n Examples:\n\n Returns:\n a `list` with the generated images at each phase.\n \"\"\"\n\n # 0. Default height and width to unet\n height = height or self.default_sample_size * self.vae_scale_factor\n width = width or self.default_sample_size * self.vae_scale_factor\n\n x1_size = self.default_sample_size * self.vae_scale_factor\n\n height_scale = height / x1_size\n width_scale = width / x1_size\n scale_num = int(max(height_scale, width_scale))\n aspect_ratio = min(height_scale, width_scale) / max(height_scale, width_scale)\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n height,\n width,\n callback_steps,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n num_images_per_prompt,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height // scale_num,\n width // scale_num,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Prepare added time ids & embeddings\n add_text_embeds = pooled_prompt_embeds\n add_time_ids = self._get_add_time_ids(\n original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype\n )\n if negative_original_size is not None and negative_target_size is not None:\n negative_add_time_ids = self._get_add_time_ids(\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype=prompt_embeds.dtype,\n )\n else:\n negative_add_time_ids = add_time_ids\n\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)\n\n # 8. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n\n # 7.1 Apply denoising_end\n if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1:\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n output_images = []\n\n ############################################################### Phase 1 #################################################################\n\n print(\"### Phase 1 Denoising ###\")\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n latents_for_view = latents\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = latents.repeat_interleave(2, dim=0) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred[::2], noise_pred[1::2]\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if do_classifier_free_guidance and guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n anchor_mean = latents.mean()\n anchor_std = latents.std()\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n print(\"### Phase 1 Decoding ###\")\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n if show_image:\n plt.figure(figsize=(10, 10))\n plt.imshow(image[0])\n plt.axis(\"off\") # Turn off axis numbers and ticks\n plt.show()\n output_images.append(image[0])\n\n ####################################################### Phase 2+ #####################################################\n\n for current_scale_num in range(2, scale_num + 1):\n print(\"### Phase {} Denoising ###\".format(current_scale_num))\n current_height = self.unet.config.sample_size * self.vae_scale_factor * current_scale_num\n current_width = self.unet.config.sample_size * self.vae_scale_factor * current_scale_num\n if height > width:\n current_width = int(current_width * aspect_ratio)\n else:\n current_height = int(current_height * aspect_ratio)\n\n latents = F.interpolate(\n latents,\n size=(int(current_height / self.vae_scale_factor), int(current_width / self.vae_scale_factor)),\n mode=\"bicubic\",\n )\n\n noise_latents = []\n noise = torch.randn_like(latents)\n for timestep in timesteps:\n noise_latent = self.scheduler.add_noise(latents, noise, timestep.unsqueeze(0))\n noise_latents.append(noise_latent)\n latents = noise_latents[0]\n\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n count = torch.zeros_like(latents)\n value = torch.zeros_like(latents)\n cosine_factor = (\n 0.5\n * (\n 1\n + torch.cos(\n torch.pi\n * (self.scheduler.config.num_train_timesteps - t)\n / self.scheduler.config.num_train_timesteps\n )\n ).cpu()\n )\n\n c1 = cosine_factor**cosine_scale_1\n latents = latents * (1 - c1) + noise_latents[i] * c1\n\n ############################################# MultiDiffusion #############################################\n\n views = self.get_views(\n current_height,\n current_width,\n stride=stride,\n window_size=self.unet.config.sample_size,\n random_jitter=True,\n )\n views_batch = [views[i : i + view_batch_size] for i in range(0, len(views), view_batch_size)]\n\n jitter_range = (self.unet.config.sample_size - stride) // 4\n latents_ = F.pad(latents, (jitter_range, jitter_range, jitter_range, jitter_range), \"constant\", 0)\n\n count_local = torch.zeros_like(latents_)\n value_local = torch.zeros_like(latents_)\n\n for j, batch_view in enumerate(views_batch):\n vb_size = len(batch_view)\n\n # get the latents corresponding to the current view coordinates\n latents_for_view = torch.cat(\n [\n latents_[:, :, h_start:h_end, w_start:w_end]\n for h_start, h_end, w_start, w_end in batch_view\n ]\n )\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = latents_for_view\n latent_model_input = (\n latent_model_input.repeat_interleave(2, dim=0)\n if do_classifier_free_guidance\n else latent_model_input\n )\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n prompt_embeds_input = torch.cat([prompt_embeds] * vb_size)\n add_text_embeds_input = torch.cat([add_text_embeds] * vb_size)\n add_time_ids_input = []\n for h_start, h_end, w_start, w_end in batch_view:\n add_time_ids_ = add_time_ids.clone()\n add_time_ids_[:, 2] = h_start * self.vae_scale_factor\n add_time_ids_[:, 3] = w_start * self.vae_scale_factor\n add_time_ids_input.append(add_time_ids_)\n add_time_ids_input = torch.cat(add_time_ids_input)\n\n # predict the noise residual\n added_cond_kwargs = {\"text_embeds\": add_text_embeds_input, \"time_ids\": add_time_ids_input}\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds_input,\n cross_attention_kwargs=cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred[::2], noise_pred[1::2]\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if do_classifier_free_guidance and guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(\n noise_pred, noise_pred_text, guidance_rescale=guidance_rescale\n )\n\n # compute the previous noisy sample x_t -> x_t-1\n self.scheduler._init_step_index(t)\n latents_denoised_batch = self.scheduler.step(\n noise_pred, t, latents_for_view, **extra_step_kwargs, return_dict=False\n )[0]\n\n # extract value from batch\n for latents_view_denoised, (h_start, h_end, w_start, w_end) in zip(\n latents_denoised_batch.chunk(vb_size), batch_view\n ):\n value_local[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised\n count_local[:, :, h_start:h_end, w_start:w_end] += 1\n\n value_local = value_local[\n :,\n :,\n jitter_range : jitter_range + current_height // self.vae_scale_factor,\n jitter_range : jitter_range + current_width // self.vae_scale_factor,\n ]\n count_local = count_local[\n :,\n :,\n jitter_range : jitter_range + current_height // self.vae_scale_factor,\n jitter_range : jitter_range + current_width // self.vae_scale_factor,\n ]\n\n c2 = cosine_factor**cosine_scale_2\n\n value += value_local / count_local * (1 - c2)\n count += torch.ones_like(value_local) * (1 - c2)\n\n ############################################# Dilated Sampling #############################################\n\n views = [[h, w] for h in range(current_scale_num) for w in range(current_scale_num)]\n views_batch = [views[i : i + view_batch_size] for i in range(0, len(views), view_batch_size)]\n\n h_pad = (current_scale_num - (latents.size(2) % current_scale_num)) % current_scale_num\n w_pad = (current_scale_num - (latents.size(3) % current_scale_num)) % current_scale_num\n latents_ = F.pad(latents, (w_pad, 0, h_pad, 0), \"constant\", 0)\n\n count_global = torch.zeros_like(latents_)\n value_global = torch.zeros_like(latents_)\n\n c3 = 0.99 * cosine_factor**cosine_scale_3 + 1e-2\n std_, mean_ = latents_.std(), latents_.mean()\n latents_gaussian = gaussian_filter(\n latents_, kernel_size=(2 * current_scale_num - 1), sigma=sigma * c3\n )\n latents_gaussian = (\n latents_gaussian - latents_gaussian.mean()\n ) / latents_gaussian.std() * std_ + mean_\n\n for j, batch_view in enumerate(views_batch):\n latents_for_view = torch.cat(\n [latents_[:, :, h::current_scale_num, w::current_scale_num] for h, w in batch_view]\n )\n latents_for_view_gaussian = torch.cat(\n [latents_gaussian[:, :, h::current_scale_num, w::current_scale_num] for h, w in batch_view]\n )\n\n vb_size = latents_for_view.size(0)\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = latents_for_view_gaussian\n latent_model_input = (\n latent_model_input.repeat_interleave(2, dim=0)\n if do_classifier_free_guidance\n else latent_model_input\n )\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n prompt_embeds_input = torch.cat([prompt_embeds] * vb_size)\n add_text_embeds_input = torch.cat([add_text_embeds] * vb_size)\n add_time_ids_input = torch.cat([add_time_ids] * vb_size)\n\n # predict the noise residual\n added_cond_kwargs = {\"text_embeds\": add_text_embeds_input, \"time_ids\": add_time_ids_input}\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds_input,\n cross_attention_kwargs=cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred[::2], noise_pred[1::2]\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if do_classifier_free_guidance and guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(\n noise_pred, noise_pred_text, guidance_rescale=guidance_rescale\n )\n\n # compute the previous noisy sample x_t -> x_t-1\n self.scheduler._init_step_index(t)\n latents_denoised_batch = self.scheduler.step(\n noise_pred, t, latents_for_view, **extra_step_kwargs, return_dict=False\n )[0]\n\n # extract value from batch\n for latents_view_denoised, (h, w) in zip(latents_denoised_batch.chunk(vb_size), batch_view):\n value_global[:, :, h::current_scale_num, w::current_scale_num] += latents_view_denoised\n count_global[:, :, h::current_scale_num, w::current_scale_num] += 1\n\n c2 = cosine_factor**cosine_scale_2\n\n value_global = value_global[:, :, h_pad:, w_pad:]\n\n value += value_global * c2\n count += torch.ones_like(value_global) * c2\n\n ###########################################################\n\n latents = torch.where(count > 0, value / count, value)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n #########################################################################################################################################\n\n latents = (latents - latents.mean()) / latents.std() * anchor_std + anchor_mean\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n print(\"### Phase {} Decoding ###\".format(current_scale_num))\n if multi_decoder:\n image = self.tiled_decode(latents, current_height, current_width)\n else:\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n image = latents\n\n if not output_type == \"latent\":\n image = self.image_processor.postprocess(image, output_type=output_type)\n if show_image:\n plt.figure(figsize=(10, 10))\n plt.imshow(image[0])\n plt.axis(\"off\") # Turn off axis numbers and ticks\n plt.show()\n output_images.append(image[0])\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n return output_images\n\n # Override to properly handle the loading and unloading of the additional text encoder.\n def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):\n # We could have accessed the unet config from `lora_state_dict()` too. We pass\n # it here explicitly to be able to tell that it's coming from an SDXL\n # pipeline.\n\n # Remove any existing hooks.\n if is_accelerate_available() and is_accelerate_version(\">=\", \"0.17.0.dev0\"):\n from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module\n else:\n raise ImportError(\"Offloading requires `accelerate v0.17.0` or higher.\")\n\n is_model_cpu_offload = False\n is_sequential_cpu_offload = False\n recursive = False\n for _, component in self.components.items():\n if isinstance(component, torch.nn.Module):\n if hasattr(component, \"_hf_hook\"):\n is_model_cpu_offload = isinstance(getattr(component, \"_hf_hook\"), CpuOffload)\n is_sequential_cpu_offload = (\n isinstance(getattr(component, \"_hf_hook\"), AlignDevicesHook)\n or hasattr(component._hf_hook, \"hooks\")\n and isinstance(component._hf_hook.hooks[0], AlignDevicesHook)\n )\n logger.info(\n \"Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again.\"\n )\n recursive = is_sequential_cpu_offload\n remove_hook_from_module(component, recurse=recursive)\n state_dict, network_alphas = self.lora_state_dict(\n pretrained_model_name_or_path_or_dict,\n unet_config=self.unet.config,\n **kwargs,\n )\n self.load_lora_into_unet(state_dict, network_alphas=network_alphas, unet=self.unet)\n\n text_encoder_state_dict = {k: v for k, v in state_dict.items() if \"text_encoder.\" in k}\n if len(text_encoder_state_dict) > 0:\n self.load_lora_into_text_encoder(\n text_encoder_state_dict,\n network_alphas=network_alphas,\n text_encoder=self.text_encoder,\n prefix=\"text_encoder\",\n lora_scale=self.lora_scale,\n )\n\n text_encoder_2_state_dict = {k: v for k, v in state_dict.items() if \"text_encoder_2.\" in k}\n if len(text_encoder_2_state_dict) > 0:\n self.load_lora_into_text_encoder(\n text_encoder_2_state_dict,\n network_alphas=network_alphas,\n text_encoder=self.text_encoder_2,\n prefix=\"text_encoder_2\",\n lora_scale=self.lora_scale,\n )\n\n # Offload back.\n if is_model_cpu_offload:\n self.enable_model_cpu_offload()\n elif is_sequential_cpu_offload:\n self.enable_sequential_cpu_offload()\n\n @classmethod\n def save_lora_weights(\n self,\n save_directory: Union[str, os.PathLike],\n unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,\n text_encoder_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,\n text_encoder_2_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,\n is_main_process: bool = True,\n weight_name: str = None,\n save_function: Callable = None,\n safe_serialization: bool = True,\n ):\n state_dict = {}\n\n def pack_weights(layers, prefix):\n layers_weights = layers.state_dict() if isinstance(layers, torch.nn.Module) else layers\n layers_state_dict = {f\"{prefix}.{module_name}\": param for module_name, param in layers_weights.items()}\n return layers_state_dict\n\n if not (unet_lora_layers or text_encoder_lora_layers or text_encoder_2_lora_layers):\n raise ValueError(\n \"You must pass at least one of `unet_lora_layers`, `text_encoder_lora_layers` or `text_encoder_2_lora_layers`.\"\n )\n\n if unet_lora_layers:\n state_dict.update(pack_weights(unet_lora_layers, \"unet\"))\n\n if text_encoder_lora_layers and text_encoder_2_lora_layers:\n state_dict.update(pack_weights(text_encoder_lora_layers, \"text_encoder\"))\n state_dict.update(pack_weights(text_encoder_2_lora_layers, \"text_encoder_2\"))\n\n self.write_lora_layers(\n state_dict=state_dict,\n save_directory=save_directory,\n is_main_process=is_main_process,\n weight_name=weight_name,\n save_function=save_function,\n safe_serialization=safe_serialization,\n )\n\n def _remove_text_encoder_monkey_patch(self):\n self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder)\n self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder_2)\n" }, { "path": "diffusers/examples/community/pipeline_fabric.py", "content": "# Copyright 2024 FABRIC authors and the HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import List, Optional, Union\n\nimport torch\nfrom packaging import version\nfrom PIL import Image\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.configuration_utils import FrozenDict\nfrom diffusers.image_processor import VaeImageProcessor\nfrom diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin\nfrom diffusers.models.attention import BasicTransformerBlock\nfrom diffusers.models.attention_processor import LoRAAttnProcessor\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.schedulers import EulerAncestralDiscreteScheduler, KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n deprecate,\n logging,\n replace_example_docstring,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import DiffusionPipeline\n >>> import torch\n\n >>> model_id = \"dreamlike-art/dreamlike-photoreal-2.0\"\n >>> pipe = DiffusionPipeline(model_id, torch_dtype=torch.float16, custom_pipeline=\"pipeline_fabric\")\n >>> pipe = pipe.to(\"cuda\")\n >>> prompt = \"a giant standing in a fantasy landscape best quality\"\n >>> liked = [] # list of images for positive feedback\n >>> disliked = [] # list of images for negative feedback\n >>> image = pipe(prompt, num_images=4, liked=liked, disliked=disliked).images[0]\n ```\n\"\"\"\n\n\nclass FabricCrossAttnProcessor:\n def __init__(self):\n self.attntion_probs = None\n\n def __call__(\n self,\n attn,\n hidden_states,\n encoder_hidden_states=None,\n attention_mask=None,\n weights=None,\n lora_scale=1.0,\n ):\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n\n if isinstance(attn.processor, LoRAAttnProcessor):\n query = attn.to_q(hidden_states) + lora_scale * attn.processor.to_q_lora(hidden_states)\n else:\n query = attn.to_q(hidden_states)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n if isinstance(attn.processor, LoRAAttnProcessor):\n key = attn.to_k(encoder_hidden_states) + lora_scale * attn.processor.to_k_lora(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states) + lora_scale * attn.processor.to_v_lora(encoder_hidden_states)\n else:\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n attention_probs = attn.get_attention_scores(query, key, attention_mask)\n\n if weights is not None:\n if weights.shape[0] != 1:\n weights = weights.repeat_interleave(attn.heads, dim=0)\n attention_probs = attention_probs * weights[:, None]\n attention_probs = attention_probs / attention_probs.sum(dim=-1, keepdim=True)\n\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n if isinstance(attn.processor, LoRAAttnProcessor):\n hidden_states = attn.to_out[0](hidden_states) + lora_scale * attn.processor.to_out_lora(hidden_states)\n else:\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n return hidden_states\n\n\nclass FabricPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion and conditioning the results using feedback images.\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`EulerAncestralDiscreteScheduler`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n \"\"\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n unet=unet,\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n scheduler=scheduler,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n prompt_embeds = prompt_embeds[0]\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n def get_unet_hidden_states(self, z_all, t, prompt_embd):\n cached_hidden_states = []\n for module in self.unet.modules():\n if isinstance(module, BasicTransformerBlock):\n\n def new_forward(self, hidden_states, *args, **kwargs):\n cached_hidden_states.append(hidden_states.clone().detach().cpu())\n return self.old_forward(hidden_states, *args, **kwargs)\n\n module.attn1.old_forward = module.attn1.forward\n module.attn1.forward = new_forward.__get__(module.attn1)\n\n # run forward pass to cache hidden states, output can be discarded\n _ = self.unet(z_all, t, encoder_hidden_states=prompt_embd)\n\n # restore original forward pass\n for module in self.unet.modules():\n if isinstance(module, BasicTransformerBlock):\n module.attn1.forward = module.attn1.old_forward\n del module.attn1.old_forward\n\n return cached_hidden_states\n\n def unet_forward_with_cached_hidden_states(\n self,\n z_all,\n t,\n prompt_embd,\n cached_pos_hiddens: Optional[List[torch.Tensor]] = None,\n cached_neg_hiddens: Optional[List[torch.Tensor]] = None,\n pos_weights=(0.8, 0.8),\n neg_weights=(0.5, 0.5),\n ):\n if cached_pos_hiddens is None and cached_neg_hiddens is None:\n return self.unet(z_all, t, encoder_hidden_states=prompt_embd)\n\n local_pos_weights = torch.linspace(*pos_weights, steps=len(self.unet.down_blocks) + 1)[:-1].tolist()\n local_neg_weights = torch.linspace(*neg_weights, steps=len(self.unet.down_blocks) + 1)[:-1].tolist()\n for block, pos_weight, neg_weight in zip(\n self.unet.down_blocks + [self.unet.mid_block] + self.unet.up_blocks,\n local_pos_weights + [pos_weights[1]] + local_pos_weights[::-1],\n local_neg_weights + [neg_weights[1]] + local_neg_weights[::-1],\n ):\n for module in block.modules():\n if isinstance(module, BasicTransformerBlock):\n\n def new_forward(\n self,\n hidden_states,\n pos_weight=pos_weight,\n neg_weight=neg_weight,\n **kwargs,\n ):\n cond_hiddens, uncond_hiddens = hidden_states.chunk(2, dim=0)\n batch_size, d_model = cond_hiddens.shape[:2]\n device, dtype = hidden_states.device, hidden_states.dtype\n\n weights = torch.ones(batch_size, d_model, device=device, dtype=dtype)\n out_pos = self.old_forward(hidden_states)\n out_neg = self.old_forward(hidden_states)\n\n if cached_pos_hiddens is not None:\n cached_pos_hs = cached_pos_hiddens.pop(0).to(hidden_states.device)\n cond_pos_hs = torch.cat([cond_hiddens, cached_pos_hs], dim=1)\n pos_weights = weights.clone().repeat(1, 1 + cached_pos_hs.shape[1] // d_model)\n pos_weights[:, d_model:] = pos_weight\n attn_with_weights = FabricCrossAttnProcessor()\n out_pos = attn_with_weights(\n self,\n cond_hiddens,\n encoder_hidden_states=cond_pos_hs,\n weights=pos_weights,\n )\n else:\n out_pos = self.old_forward(cond_hiddens)\n\n if cached_neg_hiddens is not None:\n cached_neg_hs = cached_neg_hiddens.pop(0).to(hidden_states.device)\n uncond_neg_hs = torch.cat([uncond_hiddens, cached_neg_hs], dim=1)\n neg_weights = weights.clone().repeat(1, 1 + cached_neg_hs.shape[1] // d_model)\n neg_weights[:, d_model:] = neg_weight\n attn_with_weights = FabricCrossAttnProcessor()\n out_neg = attn_with_weights(\n self,\n uncond_hiddens,\n encoder_hidden_states=uncond_neg_hs,\n weights=neg_weights,\n )\n else:\n out_neg = self.old_forward(uncond_hiddens)\n\n out = torch.cat([out_pos, out_neg], dim=0)\n return out\n\n module.attn1.old_forward = module.attn1.forward\n module.attn1.forward = new_forward.__get__(module.attn1)\n\n out = self.unet(z_all, t, encoder_hidden_states=prompt_embd)\n\n # restore original forward pass\n for module in self.unet.modules():\n if isinstance(module, BasicTransformerBlock):\n module.attn1.forward = module.attn1.old_forward\n del module.attn1.old_forward\n\n return out\n\n def preprocess_feedback_images(self, images, vae, dim, device, dtype, generator) -> torch.tensor:\n images_t = [self.image_to_tensor(img, dim, dtype) for img in images]\n images_t = torch.stack(images_t).to(device)\n latents = vae.config.scaling_factor * vae.encode(images_t).latent_dist.sample(generator)\n\n return torch.cat([latents], dim=0)\n\n def check_inputs(\n self,\n prompt,\n negative_prompt=None,\n liked=None,\n disliked=None,\n height=None,\n width=None,\n ):\n if prompt is None:\n raise ValueError(\"Provide `prompt`. Cannot leave both `prompt` undefined.\")\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and (\n not isinstance(negative_prompt, str) and not isinstance(negative_prompt, list)\n ):\n raise ValueError(f\"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}\")\n\n if liked is not None and not isinstance(liked, list):\n raise ValueError(f\"`liked` has to be of type `list` but is {type(liked)}\")\n\n if disliked is not None and not isinstance(disliked, list):\n raise ValueError(f\"`disliked` has to be of type `list` but is {type(disliked)}\")\n\n if height is not None and not isinstance(height, int):\n raise ValueError(f\"`height` has to be of type `int` but is {type(height)}\")\n\n if width is not None and not isinstance(width, int):\n raise ValueError(f\"`width` has to be of type `int` but is {type(width)}\")\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Optional[Union[str, List[str]]] = \"\",\n negative_prompt: Optional[Union[str, List[str]]] = \"lowres, bad anatomy, bad hands, cropped, worst quality\",\n liked: Optional[Union[List[str], List[Image.Image]]] = [],\n disliked: Optional[Union[List[str], List[Image.Image]]] = [],\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n height: int = 512,\n width: int = 512,\n return_dict: bool = True,\n num_images: int = 4,\n guidance_scale: float = 7.0,\n num_inference_steps: int = 20,\n output_type: Optional[str] = \"pil\",\n feedback_start_ratio: float = 0.33,\n feedback_end_ratio: float = 0.66,\n min_weight: float = 0.05,\n max_weight: float = 0.8,\n neg_scale: float = 0.5,\n pos_bottleneck_scale: float = 1.0,\n neg_bottleneck_scale: float = 1.0,\n latents: Optional[torch.Tensor] = None,\n ):\n r\"\"\"\n The call function to the pipeline for generation. Generate a trajectory of images with binary feedback. The\n feedback can be given as a list of liked and disliked images.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`\n instead.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n liked (`List[Image.Image]` or `List[str]`, *optional*):\n Encourages images with liked features.\n disliked (`List[Image.Image]` or `List[str]`, *optional*):\n Discourages images with disliked features.\n generator (`torch.Generator` or `List[torch.Generator]` or `int`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) or an `int` to\n make generation deterministic.\n height (`int`, *optional*, defaults to 512):\n Height of the generated image.\n width (`int`, *optional*, defaults to 512):\n Width of the generated image.\n num_images (`int`, *optional*, defaults to 4):\n The number of images to generate per prompt.\n guidance_scale (`float`, *optional*, defaults to 7.0):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n num_inference_steps (`int`, *optional*, defaults to 20):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n feedback_start_ratio (`float`, *optional*, defaults to `.33`):\n Start point for providing feedback (between 0 and 1).\n feedback_end_ratio (`float`, *optional*, defaults to `.66`):\n End point for providing feedback (between 0 and 1).\n min_weight (`float`, *optional*, defaults to `.05`):\n Minimum weight for feedback.\n max_weight (`float`, *optional*, defults tp `1.0`):\n Maximum weight for feedback.\n neg_scale (`float`, *optional*, defaults to `.5`):\n Scale factor for negative feedback.\n\n Examples:\n\n Returns:\n [`~pipelines.fabric.FabricPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n\n \"\"\"\n\n self.check_inputs(prompt, negative_prompt, liked, disliked)\n\n device = self._execution_device\n dtype = self.unet.dtype\n\n if isinstance(prompt, str) and prompt is not None:\n batch_size = 1\n elif isinstance(prompt, list) and prompt is not None:\n batch_size = len(prompt)\n else:\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if isinstance(negative_prompt, str):\n negative_prompt = negative_prompt\n elif isinstance(negative_prompt, list):\n negative_prompt = negative_prompt\n else:\n assert len(negative_prompt) == batch_size\n\n shape = (\n batch_size * num_images,\n self.unet.config.in_channels,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n )\n latent_noise = randn_tensor(\n shape,\n device=device,\n dtype=dtype,\n generator=generator,\n )\n\n positive_latents = (\n self.preprocess_feedback_images(liked, self.vae, (height, width), device, dtype, generator)\n if liked and len(liked) > 0\n else torch.tensor(\n [],\n device=device,\n dtype=dtype,\n )\n )\n negative_latents = (\n self.preprocess_feedback_images(disliked, self.vae, (height, width), device, dtype, generator)\n if disliked and len(disliked) > 0\n else torch.tensor(\n [],\n device=device,\n dtype=dtype,\n )\n )\n\n do_classifier_free_guidance = guidance_scale > 0.1\n\n (prompt_neg_embs, prompt_pos_embs) = self._encode_prompt(\n prompt,\n device,\n num_images,\n do_classifier_free_guidance,\n negative_prompt,\n ).split([num_images * batch_size, num_images * batch_size])\n\n batched_prompt_embd = torch.cat([prompt_pos_embs, prompt_neg_embs], dim=0)\n\n null_tokens = self.tokenizer(\n [\"\"],\n return_tensors=\"pt\",\n max_length=self.tokenizer.model_max_length,\n padding=\"max_length\",\n truncation=True,\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = null_tokens.attention_mask.to(device)\n else:\n attention_mask = None\n\n null_prompt_emb = self.text_encoder(\n input_ids=null_tokens.input_ids.to(device),\n attention_mask=attention_mask,\n ).last_hidden_state\n\n null_prompt_emb = null_prompt_emb.to(device=device, dtype=dtype)\n\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n latent_noise = latent_noise * self.scheduler.init_noise_sigma\n\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n\n ref_start_idx = round(len(timesteps) * feedback_start_ratio)\n ref_end_idx = round(len(timesteps) * feedback_end_ratio)\n\n with self.progress_bar(total=num_inference_steps) as pbar:\n for i, t in enumerate(timesteps):\n sigma = self.scheduler.sigma_t[t] if hasattr(self.scheduler, \"sigma_t\") else 0\n if hasattr(self.scheduler, \"sigmas\"):\n sigma = self.scheduler.sigmas[i]\n\n alpha_hat = 1 / (sigma**2 + 1)\n\n z_single = self.scheduler.scale_model_input(latent_noise, t)\n z_all = torch.cat([z_single] * 2, dim=0)\n z_ref = torch.cat([positive_latents, negative_latents], dim=0)\n\n if i >= ref_start_idx and i <= ref_end_idx:\n weight_factor = max_weight\n else:\n weight_factor = min_weight\n\n pos_ws = (weight_factor, weight_factor * pos_bottleneck_scale)\n neg_ws = (weight_factor * neg_scale, weight_factor * neg_scale * neg_bottleneck_scale)\n\n if z_ref.size(0) > 0 and weight_factor > 0:\n noise = torch.randn_like(z_ref)\n if isinstance(self.scheduler, EulerAncestralDiscreteScheduler):\n z_ref_noised = (alpha_hat**0.5 * z_ref + (1 - alpha_hat) ** 0.5 * noise).type(dtype)\n else:\n z_ref_noised = self.scheduler.add_noise(z_ref, noise, t)\n\n ref_prompt_embd = torch.cat(\n [null_prompt_emb] * (len(positive_latents) + len(negative_latents)), dim=0\n )\n cached_hidden_states = self.get_unet_hidden_states(z_ref_noised, t, ref_prompt_embd)\n\n n_pos, n_neg = positive_latents.shape[0], negative_latents.shape[0]\n cached_pos_hs, cached_neg_hs = [], []\n for hs in cached_hidden_states:\n cached_pos, cached_neg = hs.split([n_pos, n_neg], dim=0)\n cached_pos = cached_pos.view(1, -1, *cached_pos.shape[2:]).expand(num_images, -1, -1)\n cached_neg = cached_neg.view(1, -1, *cached_neg.shape[2:]).expand(num_images, -1, -1)\n cached_pos_hs.append(cached_pos)\n cached_neg_hs.append(cached_neg)\n\n if n_pos == 0:\n cached_pos_hs = None\n if n_neg == 0:\n cached_neg_hs = None\n else:\n cached_pos_hs, cached_neg_hs = None, None\n unet_out = self.unet_forward_with_cached_hidden_states(\n z_all,\n t,\n prompt_embd=batched_prompt_embd,\n cached_pos_hiddens=cached_pos_hs,\n cached_neg_hiddens=cached_neg_hs,\n pos_weights=pos_ws,\n neg_weights=neg_ws,\n )[0]\n\n noise_cond, noise_uncond = unet_out.chunk(2)\n guidance = noise_cond - noise_uncond\n noise_pred = noise_uncond + guidance_scale * guidance\n latent_noise = self.scheduler.step(noise_pred, t, latent_noise)[0]\n\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n pbar.update()\n\n y = self.vae.decode(latent_noise / self.vae.config.scaling_factor, return_dict=False)[0]\n imgs = self.image_processor.postprocess(\n y,\n output_type=output_type,\n )\n\n if not return_dict:\n return imgs\n\n return StableDiffusionPipelineOutput(imgs, False)\n\n def image_to_tensor(self, image: Union[str, Image.Image], dim: tuple, dtype):\n \"\"\"\n Convert latent PIL image to a torch tensor for further processing.\n \"\"\"\n if isinstance(image, str):\n image = Image.open(image)\n if not image.mode == \"RGB\":\n image = image.convert(\"RGB\")\n image = self.image_processor.preprocess(image, height=dim[0], width=dim[1])[0]\n return image.type(dtype)\n" }, { "path": "diffusers/examples/community/pipeline_null_text_inversion.py", "content": "import inspect\nimport os\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as nnf\nfrom PIL import Image\nfrom torch.optim.adam import Adam\nfrom tqdm import tqdm\n\nfrom diffusers import StableDiffusionPipeline\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\n\n\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps=None,\n device=None,\n timesteps=None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used,\n `timesteps` must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default\n timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`\n must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass NullTextPipeline(StableDiffusionPipeline):\n def get_noise_pred(self, latents, t, context):\n latents_input = torch.cat([latents] * 2)\n guidance_scale = 7.5\n noise_pred = self.unet(latents_input, t, encoder_hidden_states=context)[\"sample\"]\n noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)\n latents = self.prev_step(noise_pred, t, latents)\n return latents\n\n def get_noise_pred_single(self, latents, t, context):\n noise_pred = self.unet(latents, t, encoder_hidden_states=context)[\"sample\"]\n return noise_pred\n\n @torch.no_grad()\n def image2latent(self, image_path):\n image = Image.open(image_path).convert(\"RGB\")\n image = np.array(image)\n image = torch.from_numpy(image).float() / 127.5 - 1\n image = image.permute(2, 0, 1).unsqueeze(0).to(self.device)\n latents = self.vae.encode(image)[\"latent_dist\"].mean\n latents = latents * 0.18215\n return latents\n\n @torch.no_grad()\n def latent2image(self, latents):\n latents = 1 / 0.18215 * latents.detach()\n image = self.vae.decode(latents)[\"sample\"].detach()\n image = self.processor.postprocess(image, output_type=\"pil\")[0]\n return image\n\n def prev_step(self, model_output, timestep, sample):\n prev_timestep = timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps\n alpha_prod_t = self.scheduler.alphas_cumprod[timestep]\n alpha_prod_t_prev = (\n self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod\n )\n beta_prod_t = 1 - alpha_prod_t\n pred_original_sample = (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5\n pred_sample_direction = (1 - alpha_prod_t_prev) ** 0.5 * model_output\n prev_sample = alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction\n return prev_sample\n\n def next_step(self, model_output, timestep, sample):\n timestep, next_timestep = (\n min(timestep - self.scheduler.config.num_train_timesteps // self.num_inference_steps, 999),\n timestep,\n )\n alpha_prod_t = self.scheduler.alphas_cumprod[timestep] if timestep >= 0 else self.scheduler.final_alpha_cumprod\n alpha_prod_t_next = self.scheduler.alphas_cumprod[next_timestep]\n beta_prod_t = 1 - alpha_prod_t\n next_original_sample = (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5\n next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output\n next_sample = alpha_prod_t_next**0.5 * next_original_sample + next_sample_direction\n return next_sample\n\n def null_optimization(self, latents, context, num_inner_steps, epsilon):\n uncond_embeddings, cond_embeddings = context.chunk(2)\n uncond_embeddings_list = []\n latent_cur = latents[-1]\n bar = tqdm(total=num_inner_steps * self.num_inference_steps)\n for i in range(self.num_inference_steps):\n uncond_embeddings = uncond_embeddings.clone().detach()\n uncond_embeddings.requires_grad = True\n optimizer = Adam([uncond_embeddings], lr=1e-2 * (1.0 - i / 100.0))\n latent_prev = latents[len(latents) - i - 2]\n t = self.scheduler.timesteps[i]\n with torch.no_grad():\n noise_pred_cond = self.get_noise_pred_single(latent_cur, t, cond_embeddings)\n for j in range(num_inner_steps):\n noise_pred_uncond = self.get_noise_pred_single(latent_cur, t, uncond_embeddings)\n noise_pred = noise_pred_uncond + 7.5 * (noise_pred_cond - noise_pred_uncond)\n latents_prev_rec = self.prev_step(noise_pred, t, latent_cur)\n loss = nnf.mse_loss(latents_prev_rec, latent_prev)\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n loss_item = loss.item()\n bar.update()\n if loss_item < epsilon + i * 2e-5:\n break\n for j in range(j + 1, num_inner_steps):\n bar.update()\n uncond_embeddings_list.append(uncond_embeddings[:1].detach())\n with torch.no_grad():\n context = torch.cat([uncond_embeddings, cond_embeddings])\n latent_cur = self.get_noise_pred(latent_cur, t, context)\n bar.close()\n return uncond_embeddings_list\n\n @torch.no_grad()\n def ddim_inversion_loop(self, latent, context):\n self.scheduler.set_timesteps(self.num_inference_steps)\n _, cond_embeddings = context.chunk(2)\n all_latent = [latent]\n latent = latent.clone().detach()\n with torch.no_grad():\n for i in range(0, self.num_inference_steps):\n t = self.scheduler.timesteps[len(self.scheduler.timesteps) - i - 1]\n noise_pred = self.unet(latent, t, encoder_hidden_states=cond_embeddings)[\"sample\"]\n latent = self.next_step(noise_pred, t, latent)\n all_latent.append(latent)\n return all_latent\n\n def get_context(self, prompt):\n uncond_input = self.tokenizer(\n [\"\"], padding=\"max_length\", max_length=self.tokenizer.model_max_length, return_tensors=\"pt\"\n )\n uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]\n text_input = self.tokenizer(\n [prompt],\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]\n context = torch.cat([uncond_embeddings, text_embeddings])\n return context\n\n def invert(\n self, image_path: str, prompt: str, num_inner_steps=10, early_stop_epsilon=1e-6, num_inference_steps=50\n ):\n self.num_inference_steps = num_inference_steps\n context = self.get_context(prompt)\n latent = self.image2latent(image_path)\n ddim_latents = self.ddim_inversion_loop(latent, context)\n if os.path.exists(image_path + \".pt\"):\n uncond_embeddings = torch.load(image_path + \".pt\")\n else:\n uncond_embeddings = self.null_optimization(ddim_latents, context, num_inner_steps, early_stop_epsilon)\n uncond_embeddings = torch.stack(uncond_embeddings, 0)\n torch.save(uncond_embeddings, image_path + \".pt\")\n return ddim_latents[-1], uncond_embeddings\n\n @torch.no_grad()\n def __call__(\n self,\n prompt,\n uncond_embeddings,\n inverted_latent,\n num_inference_steps: int = 50,\n timesteps=None,\n guidance_scale=7.5,\n negative_prompt=None,\n num_images_per_prompt=1,\n generator=None,\n latents=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n output_type=\"pil\",\n ):\n self._guidance_scale = guidance_scale\n # 0. Default height and width to unet\n height = self.unet.config.sample_size * self.vae_scale_factor\n width = self.unet.config.sample_size * self.vae_scale_factor\n # to deal with lora scaling and other possible forward hook\n callback_steps = None\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n )\n # 2. Define call parameter\n device = self._execution_device\n # 3. Encode input prompt\n prompt_embeds, _ = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n # 4. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)\n latents = inverted_latent\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n noise_pred_uncond = self.unet(latents, t, encoder_hidden_states=uncond_embeddings[i])[\"sample\"]\n noise_pred = self.unet(latents, t, encoder_hidden_states=prompt_embeds)[\"sample\"]\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]\n progress_bar.update()\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[\n 0\n ]\n else:\n image = latents\n image = self.image_processor.postprocess(\n image, output_type=output_type, do_denormalize=[True] * image.shape[0]\n )\n # Offload all models\n self.maybe_free_model_hooks()\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=False)\n" }, { "path": "diffusers/examples/community/pipeline_prompt2prompt.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom __future__ import annotations\n\nimport abc\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom packaging import version\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModel,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers import AutoencoderKL, DiffusionPipeline, UNet2DConditionModel\nfrom diffusers.configuration_utils import FrozenDict, deprecate\nfrom diffusers.image_processor import VaeImageProcessor\nfrom diffusers.loaders import (\n FromSingleFileMixin,\n IPAdapterMixin,\n LoraLoaderMixin,\n TextualInversionLoaderMixin,\n)\nfrom diffusers.models.attention import Attention\nfrom diffusers.models.lora import adjust_lora_scale_text_encoder\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import (\n StableDiffusionSafetyChecker,\n)\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n USE_PEFT_BACKEND,\n logging,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__)\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\nclass Prompt2PromptPipeline(\n DiffusionPipeline,\n TextualInversionLoaderMixin,\n LoraLoaderMixin,\n IPAdapterMixin,\n FromSingleFileMixin,\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->image_encoder->unet->vae\"\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\"]\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder: CLIPVisionModelWithProjection = None,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"clip_sample\"] = False\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n output_hidden_states=True,\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(\n self,\n batch_size,\n num_channels_latents,\n height,\n width,\n dtype,\n device,\n generator,\n latents=None,\n ):\n shape = (\n batch_size,\n num_channels_latents,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: Optional[int] = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n\n The keyword arguments to configure the edit are:\n - edit_type (`str`). The edit type to apply. Can be either of `replace`, `refine`, `reweight`.\n - n_cross_replace (`int`): Number of diffusion steps in which cross attention should be replaced\n - n_self_replace (`int`): Number of diffusion steps in which self attention should be replaced\n - local_blend_words(`List[str]`, *optional*, default to `None`): Determines which area should be\n changed. If None, then the whole image can be changed.\n - equalizer_words(`List[str]`, *optional*, default to `None`): Required for edit type `reweight`.\n Determines which words should be enhanced.\n - equalizer_strengths (`List[float]`, *optional*, default to `None`) Required for edit type `reweight`.\n Determines which how much the words in `equalizer_words` should be enhanced.\n\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when\n using zero terminal SNR.\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n\n self.controller = create_controller(\n prompt,\n cross_attention_kwargs,\n num_inference_steps,\n tokenizer=self.tokenizer,\n device=self.device,\n )\n self.register_attention_control(self.controller) # add attention controller\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(prompt, height, width, callback_steps)\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n prompt_embeds = self._encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if do_classifier_free_guidance and guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # step callback\n latents = self.controller.step_callback(latents)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # 8. Post-processing\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n # 9. Run safety checker\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload last model to CPU\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n\n def register_attention_control(self, controller):\n attn_procs = {}\n cross_att_count = 0\n for name in self.unet.attn_processors.keys():\n (None if name.endswith(\"attn1.processor\") else self.unet.config.cross_attention_dim)\n if name.startswith(\"mid_block\"):\n self.unet.config.block_out_channels[-1]\n place_in_unet = \"mid\"\n elif name.startswith(\"up_blocks\"):\n block_id = int(name[len(\"up_blocks.\")])\n list(reversed(self.unet.config.block_out_channels))[block_id]\n place_in_unet = \"up\"\n elif name.startswith(\"down_blocks\"):\n block_id = int(name[len(\"down_blocks.\")])\n self.unet.config.block_out_channels[block_id]\n place_in_unet = \"down\"\n else:\n continue\n cross_att_count += 1\n attn_procs[name] = P2PCrossAttnProcessor(controller=controller, place_in_unet=place_in_unet)\n\n self.unet.set_attn_processor(attn_procs)\n controller.num_att_layers = cross_att_count\n\n\nclass P2PCrossAttnProcessor:\n def __init__(self, controller, place_in_unet):\n super().__init__()\n self.controller = controller\n self.place_in_unet = place_in_unet\n\n def __call__(\n self,\n attn: Attention,\n hidden_states,\n encoder_hidden_states=None,\n attention_mask=None,\n ):\n batch_size, sequence_length, _ = hidden_states.shape\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n\n query = attn.to_q(hidden_states)\n\n is_cross = encoder_hidden_states is not None\n encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n attention_probs = attn.get_attention_scores(query, key, attention_mask)\n\n # one line change\n self.controller(attention_probs, is_cross, self.place_in_unet)\n\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n return hidden_states\n\n\ndef create_controller(\n prompts: List[str],\n cross_attention_kwargs: Dict,\n num_inference_steps: int,\n tokenizer,\n device,\n) -> AttentionControl:\n edit_type = cross_attention_kwargs.get(\"edit_type\", None)\n local_blend_words = cross_attention_kwargs.get(\"local_blend_words\", None)\n equalizer_words = cross_attention_kwargs.get(\"equalizer_words\", None)\n equalizer_strengths = cross_attention_kwargs.get(\"equalizer_strengths\", None)\n n_cross_replace = cross_attention_kwargs.get(\"n_cross_replace\", 0.4)\n n_self_replace = cross_attention_kwargs.get(\"n_self_replace\", 0.4)\n\n # only replace\n if edit_type == \"replace\" and local_blend_words is None:\n return AttentionReplace(\n prompts,\n num_inference_steps,\n n_cross_replace,\n n_self_replace,\n tokenizer=tokenizer,\n device=device,\n )\n\n # replace + localblend\n if edit_type == \"replace\" and local_blend_words is not None:\n lb = LocalBlend(prompts, local_blend_words, tokenizer=tokenizer, device=device)\n return AttentionReplace(\n prompts,\n num_inference_steps,\n n_cross_replace,\n n_self_replace,\n lb,\n tokenizer=tokenizer,\n device=device,\n )\n\n # only refine\n if edit_type == \"refine\" and local_blend_words is None:\n return AttentionRefine(\n prompts,\n num_inference_steps,\n n_cross_replace,\n n_self_replace,\n tokenizer=tokenizer,\n device=device,\n )\n\n # refine + localblend\n if edit_type == \"refine\" and local_blend_words is not None:\n lb = LocalBlend(prompts, local_blend_words, tokenizer=tokenizer, device=device)\n return AttentionRefine(\n prompts,\n num_inference_steps,\n n_cross_replace,\n n_self_replace,\n lb,\n tokenizer=tokenizer,\n device=device,\n )\n\n # reweight\n if edit_type == \"reweight\":\n assert (\n equalizer_words is not None and equalizer_strengths is not None\n ), \"To use reweight edit, please specify equalizer_words and equalizer_strengths.\"\n assert len(equalizer_words) == len(\n equalizer_strengths\n ), \"equalizer_words and equalizer_strengths must be of same length.\"\n equalizer = get_equalizer(prompts[1], equalizer_words, equalizer_strengths, tokenizer=tokenizer)\n return AttentionReweight(\n prompts,\n num_inference_steps,\n n_cross_replace,\n n_self_replace,\n tokenizer=tokenizer,\n device=device,\n equalizer=equalizer,\n )\n\n raise ValueError(f\"Edit type {edit_type} not recognized. Use one of: replace, refine, reweight.\")\n\n\nclass AttentionControl(abc.ABC):\n def step_callback(self, x_t):\n return x_t\n\n def between_steps(self):\n return\n\n @property\n def num_uncond_att_layers(self):\n return 0\n\n @abc.abstractmethod\n def forward(self, attn, is_cross: bool, place_in_unet: str):\n raise NotImplementedError\n\n def __call__(self, attn, is_cross: bool, place_in_unet: str):\n if self.cur_att_layer >= self.num_uncond_att_layers:\n h = attn.shape[0]\n attn[h // 2 :] = self.forward(attn[h // 2 :], is_cross, place_in_unet)\n self.cur_att_layer += 1\n if self.cur_att_layer == self.num_att_layers + self.num_uncond_att_layers:\n self.cur_att_layer = 0\n self.cur_step += 1\n self.between_steps()\n return attn\n\n def reset(self):\n self.cur_step = 0\n self.cur_att_layer = 0\n\n def __init__(self):\n self.cur_step = 0\n self.num_att_layers = -1\n self.cur_att_layer = 0\n\n\nclass EmptyControl(AttentionControl):\n def forward(self, attn, is_cross: bool, place_in_unet: str):\n return attn\n\n\nclass AttentionStore(AttentionControl):\n @staticmethod\n def get_empty_store():\n return {\n \"down_cross\": [],\n \"mid_cross\": [],\n \"up_cross\": [],\n \"down_self\": [],\n \"mid_self\": [],\n \"up_self\": [],\n }\n\n def forward(self, attn, is_cross: bool, place_in_unet: str):\n key = f\"{place_in_unet}_{'cross' if is_cross else 'self'}\"\n if attn.shape[1] <= 32**2: # avoid memory overhead\n self.step_store[key].append(attn)\n return attn\n\n def between_steps(self):\n if len(self.attention_store) == 0:\n self.attention_store = self.step_store\n else:\n for key in self.attention_store:\n for i in range(len(self.attention_store[key])):\n self.attention_store[key][i] += self.step_store[key][i]\n self.step_store = self.get_empty_store()\n\n def get_average_attention(self):\n average_attention = {\n key: [item / self.cur_step for item in self.attention_store[key]] for key in self.attention_store\n }\n return average_attention\n\n def reset(self):\n super(AttentionStore, self).reset()\n self.step_store = self.get_empty_store()\n self.attention_store = {}\n\n def __init__(self):\n super(AttentionStore, self).__init__()\n self.step_store = self.get_empty_store()\n self.attention_store = {}\n\n\nclass LocalBlend:\n def __call__(self, x_t, attention_store):\n k = 1\n maps = attention_store[\"down_cross\"][2:4] + attention_store[\"up_cross\"][:3]\n maps = [item.reshape(self.alpha_layers.shape[0], -1, 1, 16, 16, self.max_num_words) for item in maps]\n maps = torch.cat(maps, dim=1)\n maps = (maps * self.alpha_layers).sum(-1).mean(1)\n mask = F.max_pool2d(maps, (k * 2 + 1, k * 2 + 1), (1, 1), padding=(k, k))\n mask = F.interpolate(mask, size=(x_t.shape[2:]))\n mask = mask / mask.max(2, keepdims=True)[0].max(3, keepdims=True)[0]\n mask = mask.gt(self.threshold)\n mask = (mask[:1] + mask[1:]).float()\n x_t = x_t[:1] + mask * (x_t - x_t[:1])\n return x_t\n\n def __init__(\n self,\n prompts: List[str],\n words: [List[List[str]]],\n tokenizer,\n device,\n threshold=0.3,\n max_num_words=77,\n ):\n self.max_num_words = 77\n\n alpha_layers = torch.zeros(len(prompts), 1, 1, 1, 1, self.max_num_words)\n for i, (prompt, words_) in enumerate(zip(prompts, words)):\n if isinstance(words_, str):\n words_ = [words_]\n for word in words_:\n ind = get_word_inds(prompt, word, tokenizer)\n alpha_layers[i, :, :, :, :, ind] = 1\n self.alpha_layers = alpha_layers.to(device)\n self.threshold = threshold\n\n\nclass AttentionControlEdit(AttentionStore, abc.ABC):\n def step_callback(self, x_t):\n if self.local_blend is not None:\n x_t = self.local_blend(x_t, self.attention_store)\n return x_t\n\n def replace_self_attention(self, attn_base, att_replace):\n if att_replace.shape[2] <= 16**2:\n return attn_base.unsqueeze(0).expand(att_replace.shape[0], *attn_base.shape)\n else:\n return att_replace\n\n @abc.abstractmethod\n def replace_cross_attention(self, attn_base, att_replace):\n raise NotImplementedError\n\n def forward(self, attn, is_cross: bool, place_in_unet: str):\n super(AttentionControlEdit, self).forward(attn, is_cross, place_in_unet)\n # FIXME not replace correctly\n if is_cross or (self.num_self_replace[0] <= self.cur_step < self.num_self_replace[1]):\n h = attn.shape[0] // (self.batch_size)\n attn = attn.reshape(self.batch_size, h, *attn.shape[1:])\n attn_base, attn_repalce = attn[0], attn[1:]\n if is_cross:\n alpha_words = self.cross_replace_alpha[self.cur_step]\n attn_repalce_new = (\n self.replace_cross_attention(attn_base, attn_repalce) * alpha_words\n + (1 - alpha_words) * attn_repalce\n )\n attn[1:] = attn_repalce_new\n else:\n attn[1:] = self.replace_self_attention(attn_base, attn_repalce)\n attn = attn.reshape(self.batch_size * h, *attn.shape[2:])\n return attn\n\n def __init__(\n self,\n prompts,\n num_steps: int,\n cross_replace_steps: Union[float, Tuple[float, float], Dict[str, Tuple[float, float]]],\n self_replace_steps: Union[float, Tuple[float, float]],\n local_blend: Optional[LocalBlend],\n tokenizer,\n device,\n ):\n super(AttentionControlEdit, self).__init__()\n # add tokenizer and device here\n\n self.tokenizer = tokenizer\n self.device = device\n\n self.batch_size = len(prompts)\n self.cross_replace_alpha = get_time_words_attention_alpha(\n prompts, num_steps, cross_replace_steps, self.tokenizer\n ).to(self.device)\n if isinstance(self_replace_steps, float):\n self_replace_steps = 0, self_replace_steps\n self.num_self_replace = int(num_steps * self_replace_steps[0]), int(num_steps * self_replace_steps[1])\n self.local_blend = local_blend # 在外面定义后传进来\n\n\nclass AttentionReplace(AttentionControlEdit):\n def replace_cross_attention(self, attn_base, att_replace):\n return torch.einsum(\"hpw,bwn->bhpn\", attn_base, self.mapper)\n\n def __init__(\n self,\n prompts,\n num_steps: int,\n cross_replace_steps: float,\n self_replace_steps: float,\n local_blend: Optional[LocalBlend] = None,\n tokenizer=None,\n device=None,\n ):\n super(AttentionReplace, self).__init__(\n prompts,\n num_steps,\n cross_replace_steps,\n self_replace_steps,\n local_blend,\n tokenizer,\n device,\n )\n self.mapper = get_replacement_mapper(prompts, self.tokenizer).to(self.device)\n\n\nclass AttentionRefine(AttentionControlEdit):\n def replace_cross_attention(self, attn_base, att_replace):\n attn_base_replace = attn_base[:, :, self.mapper].permute(2, 0, 1, 3)\n attn_replace = attn_base_replace * self.alphas + att_replace * (1 - self.alphas)\n return attn_replace\n\n def __init__(\n self,\n prompts,\n num_steps: int,\n cross_replace_steps: float,\n self_replace_steps: float,\n local_blend: Optional[LocalBlend] = None,\n tokenizer=None,\n device=None,\n ):\n super(AttentionRefine, self).__init__(\n prompts,\n num_steps,\n cross_replace_steps,\n self_replace_steps,\n local_blend,\n tokenizer,\n device,\n )\n self.mapper, alphas = get_refinement_mapper(prompts, self.tokenizer)\n self.mapper, alphas = self.mapper.to(self.device), alphas.to(self.device)\n self.alphas = alphas.reshape(alphas.shape[0], 1, 1, alphas.shape[1])\n\n\nclass AttentionReweight(AttentionControlEdit):\n def replace_cross_attention(self, attn_base, att_replace):\n if self.prev_controller is not None:\n attn_base = self.prev_controller.replace_cross_attention(attn_base, att_replace)\n attn_replace = attn_base[None, :, :, :] * self.equalizer[:, None, None, :]\n return attn_replace\n\n def __init__(\n self,\n prompts,\n num_steps: int,\n cross_replace_steps: float,\n self_replace_steps: float,\n equalizer,\n local_blend: Optional[LocalBlend] = None,\n controller: Optional[AttentionControlEdit] = None,\n tokenizer=None,\n device=None,\n ):\n super(AttentionReweight, self).__init__(\n prompts,\n num_steps,\n cross_replace_steps,\n self_replace_steps,\n local_blend,\n tokenizer,\n device,\n )\n self.equalizer = equalizer.to(self.device)\n self.prev_controller = controller\n\n\n### util functions for all Edits\ndef update_alpha_time_word(\n alpha,\n bounds: Union[float, Tuple[float, float]],\n prompt_ind: int,\n word_inds: Optional[torch.Tensor] = None,\n):\n if isinstance(bounds, float):\n bounds = 0, bounds\n start, end = int(bounds[0] * alpha.shape[0]), int(bounds[1] * alpha.shape[0])\n if word_inds is None:\n word_inds = torch.arange(alpha.shape[2])\n alpha[:start, prompt_ind, word_inds] = 0\n alpha[start:end, prompt_ind, word_inds] = 1\n alpha[end:, prompt_ind, word_inds] = 0\n return alpha\n\n\ndef get_time_words_attention_alpha(\n prompts,\n num_steps,\n cross_replace_steps: Union[float, Dict[str, Tuple[float, float]]],\n tokenizer,\n max_num_words=77,\n):\n if not isinstance(cross_replace_steps, dict):\n cross_replace_steps = {\"default_\": cross_replace_steps}\n if \"default_\" not in cross_replace_steps:\n cross_replace_steps[\"default_\"] = (0.0, 1.0)\n alpha_time_words = torch.zeros(num_steps + 1, len(prompts) - 1, max_num_words)\n for i in range(len(prompts) - 1):\n alpha_time_words = update_alpha_time_word(alpha_time_words, cross_replace_steps[\"default_\"], i)\n for key, item in cross_replace_steps.items():\n if key != \"default_\":\n inds = [get_word_inds(prompts[i], key, tokenizer) for i in range(1, len(prompts))]\n for i, ind in enumerate(inds):\n if len(ind) > 0:\n alpha_time_words = update_alpha_time_word(alpha_time_words, item, i, ind)\n alpha_time_words = alpha_time_words.reshape(num_steps + 1, len(prompts) - 1, 1, 1, max_num_words)\n return alpha_time_words\n\n\n### util functions for LocalBlend and ReplacementEdit\ndef get_word_inds(text: str, word_place: int, tokenizer):\n split_text = text.split(\" \")\n if isinstance(word_place, str):\n word_place = [i for i, word in enumerate(split_text) if word_place == word]\n elif isinstance(word_place, int):\n word_place = [word_place]\n out = []\n if len(word_place) > 0:\n words_encode = [tokenizer.decode([item]).strip(\"#\") for item in tokenizer.encode(text)][1:-1]\n cur_len, ptr = 0, 0\n\n for i in range(len(words_encode)):\n cur_len += len(words_encode[i])\n if ptr in word_place:\n out.append(i + 1)\n if cur_len >= len(split_text[ptr]):\n ptr += 1\n cur_len = 0\n return np.array(out)\n\n\n### util functions for ReplacementEdit\ndef get_replacement_mapper_(x: str, y: str, tokenizer, max_len=77):\n words_x = x.split(\" \")\n words_y = y.split(\" \")\n if len(words_x) != len(words_y):\n raise ValueError(\n f\"attention replacement edit can only be applied on prompts with the same length\"\n f\" but prompt A has {len(words_x)} words and prompt B has {len(words_y)} words.\"\n )\n inds_replace = [i for i in range(len(words_y)) if words_y[i] != words_x[i]]\n inds_source = [get_word_inds(x, i, tokenizer) for i in inds_replace]\n inds_target = [get_word_inds(y, i, tokenizer) for i in inds_replace]\n mapper = np.zeros((max_len, max_len))\n i = j = 0\n cur_inds = 0\n while i < max_len and j < max_len:\n if cur_inds < len(inds_source) and inds_source[cur_inds][0] == i:\n inds_source_, inds_target_ = inds_source[cur_inds], inds_target[cur_inds]\n if len(inds_source_) == len(inds_target_):\n mapper[inds_source_, inds_target_] = 1\n else:\n ratio = 1 / len(inds_target_)\n for i_t in inds_target_:\n mapper[inds_source_, i_t] = ratio\n cur_inds += 1\n i += len(inds_source_)\n j += len(inds_target_)\n elif cur_inds < len(inds_source):\n mapper[i, j] = 1\n i += 1\n j += 1\n else:\n mapper[j, j] = 1\n i += 1\n j += 1\n\n return torch.from_numpy(mapper).float()\n\n\ndef get_replacement_mapper(prompts, tokenizer, max_len=77):\n x_seq = prompts[0]\n mappers = []\n for i in range(1, len(prompts)):\n mapper = get_replacement_mapper_(x_seq, prompts[i], tokenizer, max_len)\n mappers.append(mapper)\n return torch.stack(mappers)\n\n\n### util functions for ReweightEdit\ndef get_equalizer(\n text: str,\n word_select: Union[int, Tuple[int, ...]],\n values: Union[List[float], Tuple[float, ...]],\n tokenizer,\n):\n if isinstance(word_select, (int, str)):\n word_select = (word_select,)\n equalizer = torch.ones(len(values), 77)\n values = torch.tensor(values, dtype=torch.float32)\n for word in word_select:\n inds = get_word_inds(text, word, tokenizer)\n equalizer[:, inds] = values\n return equalizer\n\n\n### util functions for RefinementEdit\nclass ScoreParams:\n def __init__(self, gap, match, mismatch):\n self.gap = gap\n self.match = match\n self.mismatch = mismatch\n\n def mis_match_char(self, x, y):\n if x != y:\n return self.mismatch\n else:\n return self.match\n\n\ndef get_matrix(size_x, size_y, gap):\n matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32)\n matrix[0, 1:] = (np.arange(size_y) + 1) * gap\n matrix[1:, 0] = (np.arange(size_x) + 1) * gap\n return matrix\n\n\ndef get_traceback_matrix(size_x, size_y):\n matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32)\n matrix[0, 1:] = 1\n matrix[1:, 0] = 2\n matrix[0, 0] = 4\n return matrix\n\n\ndef global_align(x, y, score):\n matrix = get_matrix(len(x), len(y), score.gap)\n trace_back = get_traceback_matrix(len(x), len(y))\n for i in range(1, len(x) + 1):\n for j in range(1, len(y) + 1):\n left = matrix[i, j - 1] + score.gap\n up = matrix[i - 1, j] + score.gap\n diag = matrix[i - 1, j - 1] + score.mis_match_char(x[i - 1], y[j - 1])\n matrix[i, j] = max(left, up, diag)\n if matrix[i, j] == left:\n trace_back[i, j] = 1\n elif matrix[i, j] == up:\n trace_back[i, j] = 2\n else:\n trace_back[i, j] = 3\n return matrix, trace_back\n\n\ndef get_aligned_sequences(x, y, trace_back):\n x_seq = []\n y_seq = []\n i = len(x)\n j = len(y)\n mapper_y_to_x = []\n while i > 0 or j > 0:\n if trace_back[i, j] == 3:\n x_seq.append(x[i - 1])\n y_seq.append(y[j - 1])\n i = i - 1\n j = j - 1\n mapper_y_to_x.append((j, i))\n elif trace_back[i][j] == 1:\n x_seq.append(\"-\")\n y_seq.append(y[j - 1])\n j = j - 1\n mapper_y_to_x.append((j, -1))\n elif trace_back[i][j] == 2:\n x_seq.append(x[i - 1])\n y_seq.append(\"-\")\n i = i - 1\n elif trace_back[i][j] == 4:\n break\n mapper_y_to_x.reverse()\n return x_seq, y_seq, torch.tensor(mapper_y_to_x, dtype=torch.int64)\n\n\ndef get_mapper(x: str, y: str, tokenizer, max_len=77):\n x_seq = tokenizer.encode(x)\n y_seq = tokenizer.encode(y)\n score = ScoreParams(0, 1, -1)\n matrix, trace_back = global_align(x_seq, y_seq, score)\n mapper_base = get_aligned_sequences(x_seq, y_seq, trace_back)[-1]\n alphas = torch.ones(max_len)\n alphas[: mapper_base.shape[0]] = mapper_base[:, 1].ne(-1).float()\n mapper = torch.zeros(max_len, dtype=torch.int64)\n mapper[: mapper_base.shape[0]] = mapper_base[:, 1]\n mapper[mapper_base.shape[0] :] = len(y_seq) + torch.arange(max_len - len(y_seq))\n return mapper, alphas\n\n\ndef get_refinement_mapper(prompts, tokenizer, max_len=77):\n x_seq = prompts[0]\n mappers, alphas = [], []\n for i in range(1, len(prompts)):\n mapper, alpha = get_mapper(x_seq, prompts[i], tokenizer, max_len)\n mappers.append(mapper)\n alphas.append(alpha)\n return torch.stack(mappers), torch.stack(alphas)\n" }, { "path": "diffusers/examples/community/pipeline_sdxl_style_aligned.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# Based on [Style Aligned Image Generation via Shared Attention](https://arxiv.org/abs/2312.02133).\n# Authors: Amir Hertz, Andrey Voynov, Shlomi Fruchter, Daniel Cohen-Or\n# Project Page: https://style-aligned-gen.github.io/\n# Code: https://github.com/google/style-aligned\n#\n# Adapted to Diffusers by [Aryan V S](https://github.com/a-r-r-o-w/).\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom PIL import Image\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModel,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers.image_processor import PipelineImageInput, VaeImageProcessor\nfrom diffusers.loaders import (\n FromSingleFileMixin,\n IPAdapterMixin,\n StableDiffusionXLLoraLoaderMixin,\n TextualInversionLoaderMixin,\n)\nfrom diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel\nfrom diffusers.models.attention_processor import (\n Attention,\n AttnProcessor2_0,\n FusedAttnProcessor2_0,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n XFormersAttnProcessor,\n)\nfrom diffusers.models.lora import adjust_lora_scale_text_encoder\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n USE_PEFT_BACKEND,\n deprecate,\n is_invisible_watermark_available,\n is_torch_xla_available,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nif is_invisible_watermark_available():\n from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker\n\nif is_torch_xla_available():\n import torch_xla.core.xla_model as xm\n\n XLA_AVAILABLE = True\nelse:\n XLA_AVAILABLE = False\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from typing import List\n\n >>> import torch\n >>> from diffusers.pipelines.pipeline_utils import DiffusionPipeline\n >>> from PIL import Image\n\n >>> model_id = \"a-r-r-o-w/dreamshaper-xl-turbo\"\n >>> pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, variant=\"fp16\", custom_pipeline=\"pipeline_sdxl_style_aligned\")\n >>> pipe = pipe.to(\"cuda\")\n\n # Enable memory saving techniques\n >>> pipe.enable_vae_slicing()\n >>> pipe.enable_vae_tiling()\n\n >>> prompt = [\n ... \"a toy train. macro photo. 3d game asset\",\n ... \"a toy airplane. macro photo. 3d game asset\",\n ... \"a toy bicycle. macro photo. 3d game asset\",\n ... \"a toy car. macro photo. 3d game asset\",\n ... ]\n >>> negative_prompt = \"low quality, worst quality, \"\n\n >>> # Enable StyleAligned\n >>> pipe.enable_style_aligned(\n ... share_group_norm=False,\n ... share_layer_norm=False,\n ... share_attention=True,\n ... adain_queries=True,\n ... adain_keys=True,\n ... adain_values=False,\n ... full_attention_share=False,\n ... shared_score_scale=1.0,\n ... shared_score_shift=0.0,\n ... only_self_level=0.0,\n >>> )\n\n >>> # Run inference\n >>> images = pipe(\n ... prompt=prompt,\n ... negative_prompt=negative_prompt,\n ... guidance_scale=2,\n ... height=1024,\n ... width=1024,\n ... num_inference_steps=10,\n ... generator=torch.Generator().manual_seed(42),\n >>> ).images\n\n >>> # Disable StyleAligned if you do not wish to use it anymore\n >>> pipe.disable_style_aligned()\n ```\n\"\"\"\n\n\ndef expand_first(feat: torch.Tensor, scale: float = 1.0) -> torch.Tensor:\n b = feat.shape[0]\n feat_style = torch.stack((feat[0], feat[b // 2])).unsqueeze(1)\n if scale == 1:\n feat_style = feat_style.expand(2, b // 2, *feat.shape[1:])\n else:\n feat_style = feat_style.repeat(1, b // 2, 1, 1, 1)\n feat_style = torch.cat([feat_style[:, :1], scale * feat_style[:, 1:]], dim=1)\n return feat_style.reshape(*feat.shape)\n\n\ndef concat_first(feat: torch.Tensor, dim: int = 2, scale: float = 1.0) -> torch.Tensor:\n feat_style = expand_first(feat, scale=scale)\n return torch.cat((feat, feat_style), dim=dim)\n\n\ndef calc_mean_std(feat: torch.Tensor, eps: float = 1e-5) -> Tuple[torch.Tensor, torch.Tensor]:\n feat_std = (feat.var(dim=-2, keepdims=True) + eps).sqrt()\n feat_mean = feat.mean(dim=-2, keepdims=True)\n return feat_mean, feat_std\n\n\ndef adain(feat: torch.Tensor) -> torch.Tensor:\n feat_mean, feat_std = calc_mean_std(feat)\n feat_style_mean = expand_first(feat_mean)\n feat_style_std = expand_first(feat_std)\n feat = (feat - feat_mean) / feat_std\n feat = feat * feat_style_std + feat_style_mean\n return feat\n\n\ndef get_switch_vec(total_num_layers, level):\n if level == 0:\n return torch.zeros(total_num_layers, dtype=torch.bool)\n if level == 1:\n return torch.ones(total_num_layers, dtype=torch.bool)\n to_flip = level > 0.5\n if to_flip:\n level = 1 - level\n num_switch = int(level * total_num_layers)\n vec = torch.arange(total_num_layers)\n vec = vec % (total_num_layers // num_switch)\n vec = vec == 0\n if to_flip:\n vec = ~vec\n return vec\n\n\nclass SharedAttentionProcessor(AttnProcessor2_0):\n def __init__(\n self,\n share_attention: bool = True,\n adain_queries: bool = True,\n adain_keys: bool = True,\n adain_values: bool = False,\n full_attention_share: bool = False,\n shared_score_scale: float = 1.0,\n shared_score_shift: float = 0.0,\n ):\n r\"\"\"Shared Attention Processor as proposed in the StyleAligned paper.\"\"\"\n super().__init__()\n self.share_attention = share_attention\n self.adain_queries = adain_queries\n self.adain_keys = adain_keys\n self.adain_values = adain_values\n self.full_attention_share = full_attention_share\n self.shared_score_scale = shared_score_scale\n self.shared_score_shift = shared_score_shift\n\n def shifted_scaled_dot_product_attention(\n self, attn: Attention, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor\n ) -> torch.Tensor:\n logits = torch.einsum(\"bhqd,bhkd->bhqk\", query, key) * attn.scale\n logits[:, :, :, query.shape[2] :] += self.shared_score_shift\n probs = logits.softmax(-1)\n return torch.einsum(\"bhqk,bhkd->bhqd\", probs, value)\n\n def shared_call(\n self,\n attn: Attention,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n **kwargs,\n ):\n residual = hidden_states\n input_ndim = hidden_states.ndim\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n\n if attention_mask is not None:\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n # scaled_dot_product_attention expects attention_mask shape to be\n # (batch, heads, source_length, target_length)\n attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])\n\n if attn.group_norm is not None:\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n\n query = attn.to_q(hidden_states)\n key = attn.to_k(hidden_states)\n value = attn.to_v(hidden_states)\n inner_dim = key.shape[-1]\n head_dim = inner_dim // attn.heads\n\n query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n if self.adain_queries:\n query = adain(query)\n if self.adain_keys:\n key = adain(key)\n if self.adain_values:\n value = adain(value)\n if self.share_attention:\n key = concat_first(key, -2, scale=self.shared_score_scale)\n value = concat_first(value, -2)\n if self.shared_score_shift != 0:\n hidden_states = self.shifted_scaled_dot_product_attention(attn, query, key, value)\n else:\n hidden_states = F.scaled_dot_product_attention(\n query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False\n )\n else:\n hidden_states = F.scaled_dot_product_attention(\n query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False\n )\n\n hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)\n hidden_states = hidden_states.to(query.dtype)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n if input_ndim == 4:\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n if attn.residual_connection:\n hidden_states = hidden_states + residual\n\n hidden_states = hidden_states / attn.rescale_output_factor\n return hidden_states\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n **kwargs,\n ):\n if self.full_attention_share:\n b, n, d = hidden_states.shape\n k = 2\n hidden_states = hidden_states.view(k, b, n, d).permute(0, 1, 3, 2).contiguous().view(-1, n, d)\n # hidden_states = einops.rearrange(hidden_states, \"(k b) n d -> k (b n) d\", k=2)\n hidden_states = super().__call__(\n attn,\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n **kwargs,\n )\n hidden_states = hidden_states.view(k, b, n, d).permute(0, 1, 3, 2).contiguous().view(-1, n, d)\n # hidden_states = einops.rearrange(hidden_states, \"k (b n) d -> (k b) n d\", n=n)\n else:\n hidden_states = self.shared_call(attn, hidden_states, hidden_states, attention_mask, **kwargs)\n\n return hidden_states\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used,\n `timesteps` must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default\n timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`\n must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n\nclass StyleAlignedSDXLPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n FromSingleFileMixin,\n StableDiffusionXLLoraLoaderMixin,\n TextualInversionLoaderMixin,\n IPAdapterMixin,\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion XL.\n\n This pipeline also adds experimental support for [StyleAligned](https://arxiv.org/abs/2312.02133). It can\n be enabled/disabled using `.enable_style_aligned()` or `.disable_style_aligned()` respectively.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion XL uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n text_encoder_2 ([` CLIPTextModelWithProjection`]):\n Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),\n specifically the\n [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)\n variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n tokenizer_2 (`CLIPTokenizer`):\n Second Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `\"True\"`):\n Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of\n `stabilityai/stable-diffusion-xl-base-1-0`.\n add_watermarker (`bool`, *optional*):\n Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to\n watermark output images. If not defined, it will default to True if the package is installed, otherwise no\n watermarker will be used.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->text_encoder_2->image_encoder->unet->vae\"\n _optional_components = [\n \"tokenizer\",\n \"tokenizer_2\",\n \"text_encoder\",\n \"text_encoder_2\",\n \"image_encoder\",\n \"feature_extractor\",\n ]\n _callback_tensor_inputs = [\n \"latents\",\n \"prompt_embeds\",\n \"negative_prompt_embeds\",\n \"add_text_embeds\",\n \"add_time_ids\",\n \"negative_pooled_prompt_embeds\",\n \"negative_add_time_ids\",\n ]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n text_encoder_2: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n tokenizer_2: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n image_encoder: CLIPVisionModelWithProjection = None,\n feature_extractor: CLIPImageProcessor = None,\n force_zeros_for_empty_prompt: bool = True,\n add_watermarker: Optional[bool] = None,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n unet=unet,\n scheduler=scheduler,\n image_encoder=image_encoder,\n feature_extractor=feature_extractor,\n )\n self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.mask_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True\n )\n\n self.default_sample_size = self.unet.config.sample_size\n\n add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()\n\n if add_watermarker:\n self.watermark = StableDiffusionXLWatermarker()\n else:\n self.watermark = None\n\n def encode_prompt(\n self,\n prompt: str,\n prompt_2: Optional[str] = None,\n device: Optional[torch.device] = None,\n num_images_per_prompt: int = 1,\n do_classifier_free_guidance: bool = True,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n lora_scale (`float`, *optional*):\n A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n device = device or self._execution_device\n\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if self.text_encoder is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)\n else:\n scale_lora_layers(self.text_encoder_2, lora_scale)\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n\n if prompt is not None:\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # Define tokenizers and text encoders\n tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]\n text_encoders = (\n [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]\n )\n\n if prompt_embeds is None:\n prompt_2 = prompt_2 or prompt\n prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2\n\n # textual inversion: process multi-vector tokens if necessary\n prompt_embeds_list = []\n prompts = [prompt, prompt_2]\n for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, tokenizer)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n if clip_skip is None:\n prompt_embeds = prompt_embeds.hidden_states[-2]\n else:\n # \"2\" because SDXL always indexes from the penultimate layer.\n prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]\n\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n\n # get unconditional embeddings for classifier free guidance\n zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt\n if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:\n negative_prompt_embeds = torch.zeros_like(prompt_embeds)\n negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)\n elif do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n\n # normalize str to list\n negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt\n negative_prompt_2 = (\n batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2\n )\n\n uncond_tokens: List[str]\n if prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = [negative_prompt, negative_prompt_2]\n\n negative_prompt_embeds_list = []\n for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n negative_prompt_embeds = text_encoder(\n uncond_input.input_ids.to(device),\n output_hidden_states=True,\n )\n # We are only ALWAYS interested in the pooled output of the final text encoder\n negative_pooled_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]\n\n negative_prompt_embeds_list.append(negative_prompt_embeds)\n\n negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)\n\n if self.text_encoder_2 is not None:\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n if self.text_encoder_2 is not None:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n if do_classifier_free_guidance:\n negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n\n if self.text_encoder is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder_2, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n prompt_2,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n negative_prompt_2=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n pooled_prompt_embeds=None,\n negative_pooled_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if prompt_embeds is not None and pooled_prompt_embeds is None:\n raise ValueError(\n \"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.\"\n )\n\n if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:\n raise ValueError(\n \"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.\"\n )\n\n def get_timesteps(self, num_inference_steps, strength, device, denoising_start=None):\n # get the original timestep using init_timestep\n if denoising_start is None:\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n t_start = max(num_inference_steps - init_timestep, 0)\n else:\n t_start = 0\n\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n\n # Strength is irrelevant if we directly request a timestep to start at;\n # that is, strength is determined by the denoising_start instead.\n if denoising_start is not None:\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (denoising_start * self.scheduler.config.num_train_timesteps)\n )\n )\n\n num_inference_steps = (timesteps < discrete_timestep_cutoff).sum().item()\n if self.scheduler.order == 2 and num_inference_steps % 2 == 0:\n # if the scheduler is a 2nd order scheduler we might have to do +1\n # because `num_inference_steps` might be even given that every timestep\n # (except the highest one) is duplicated. If `num_inference_steps` is even it would\n # mean that we cut the timesteps in the middle of the denoising step\n # (between 1st and 2nd derivative) which leads to incorrect results. By adding 1\n # we ensure that the denoising process always ends after the 2nd derivate step of the scheduler\n num_inference_steps = num_inference_steps + 1\n\n # because t_n+1 >= t_n, we slice the timesteps starting from the end\n timesteps = timesteps[-num_inference_steps:]\n return timesteps, num_inference_steps\n\n return timesteps, num_inference_steps - t_start\n\n def prepare_latents(\n self,\n image,\n mask,\n width,\n height,\n num_channels_latents,\n timestep,\n batch_size,\n num_images_per_prompt,\n dtype,\n device,\n generator=None,\n add_noise=True,\n latents=None,\n is_strength_max=True,\n return_noise=False,\n return_image_latents=False,\n ):\n batch_size *= num_images_per_prompt\n\n if image is None:\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n elif mask is None:\n if not isinstance(image, (torch.Tensor, Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n # Offload text encoder if `enable_model_cpu_offload` was enabled\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.text_encoder_2.to(\"cpu\")\n torch.cuda.empty_cache()\n\n image = image.to(device=device, dtype=dtype)\n\n if image.shape[1] == 4:\n init_latents = image\n\n else:\n # make sure the VAE is in float32 mode, as it overflows in float16\n if self.vae.config.force_upcast:\n image = image.float()\n self.vae.to(dtype=torch.float32)\n\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n elif isinstance(generator, list):\n init_latents = [\n retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])\n for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = retrieve_latents(self.vae.encode(image), generator=generator)\n\n if self.vae.config.force_upcast:\n self.vae.to(dtype)\n\n init_latents = init_latents.to(dtype)\n init_latents = self.vae.config.scaling_factor * init_latents\n\n if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:\n # expand init_latents for batch_size\n additional_image_per_prompt = batch_size // init_latents.shape[0]\n init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)\n elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n init_latents = torch.cat([init_latents], dim=0)\n\n if add_noise:\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n\n latents = init_latents\n return latents\n\n else:\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if (image is None or timestep is None) and not is_strength_max:\n raise ValueError(\n \"Since strength < 1. initial latents are to be initialised as a combination of Image + Noise.\"\n \"However, either the image or the noise timestep has not been provided.\"\n )\n\n if image.shape[1] == 4:\n image_latents = image.to(device=device, dtype=dtype)\n image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)\n elif return_image_latents or (latents is None and not is_strength_max):\n image = image.to(device=device, dtype=dtype)\n image_latents = self._encode_vae_image(image=image, generator=generator)\n image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)\n\n if latents is None and add_noise:\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n # if strength is 1. then initialise the latents to noise, else initial to image + noise\n latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)\n # if pure noise then scale the initial latents by the Scheduler's init sigma\n latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents\n elif add_noise:\n noise = latents.to(device)\n latents = noise * self.scheduler.init_noise_sigma\n else:\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n latents = image_latents.to(device)\n\n outputs = (latents,)\n\n if return_noise:\n outputs += (noise,)\n\n if return_image_latents:\n outputs += (image_latents,)\n\n return outputs\n\n def prepare_mask_latents(\n self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance\n ):\n # resize the mask to latents shape as we concatenate the mask to the latents\n # we do that before converting to dtype to avoid breaking in case we're using cpu_offload\n # and half precision\n mask = torch.nn.functional.interpolate(\n mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)\n )\n mask = mask.to(device=device, dtype=dtype)\n\n # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method\n if mask.shape[0] < batch_size:\n if not batch_size % mask.shape[0] == 0:\n raise ValueError(\n \"The passed mask and the required batch size don't match. Masks are supposed to be duplicated to\"\n f\" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number\"\n \" of masks that you pass is divisible by the total requested batch size.\"\n )\n mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)\n\n mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask\n\n if masked_image is not None and masked_image.shape[1] == 4:\n masked_image_latents = masked_image\n else:\n masked_image_latents = None\n\n if masked_image is not None:\n if masked_image_latents is None:\n masked_image = masked_image.to(device=device, dtype=dtype)\n masked_image_latents = self._encode_vae_image(masked_image, generator=generator)\n\n if masked_image_latents.shape[0] < batch_size:\n if not batch_size % masked_image_latents.shape[0] == 0:\n raise ValueError(\n \"The passed images and the required batch size don't match. Images are supposed to be duplicated\"\n f\" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed.\"\n \" Make sure the number of images that you pass is divisible by the total requested batch size.\"\n )\n masked_image_latents = masked_image_latents.repeat(\n batch_size // masked_image_latents.shape[0], 1, 1, 1\n )\n\n masked_image_latents = (\n torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents\n )\n\n # aligning device to prevent device errors when concating it with the latent model input\n masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)\n\n return mask, masked_image_latents\n\n def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):\n dtype = image.dtype\n if self.vae.config.force_upcast:\n image = image.float()\n self.vae.to(dtype=torch.float32)\n\n if isinstance(generator, list):\n image_latents = [\n retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])\n for i in range(image.shape[0])\n ]\n image_latents = torch.cat(image_latents, dim=0)\n else:\n image_latents = retrieve_latents(self.vae.encode(image), generator=generator)\n\n if self.vae.config.force_upcast:\n self.vae.to(dtype)\n\n image_latents = image_latents.to(dtype)\n image_latents = self.vae.config.scaling_factor * image_latents\n\n return image_latents\n\n def _get_add_time_ids(self, original_size, crops_coords_top_left, target_size, dtype):\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n\n passed_add_embed_dim = (\n self.unet.config.addition_time_embed_dim * len(add_time_ids) + self.text_encoder_2.config.projection_dim\n )\n expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features\n\n if expected_add_embed_dim != passed_add_embed_dim:\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.\"\n )\n\n add_time_ids = torch.tensor([add_time_ids], dtype=dtype)\n return add_time_ids\n\n def upcast_vae(self):\n dtype = self.vae.dtype\n self.vae.to(dtype=torch.float32)\n use_torch_2_0_or_xformers = isinstance(\n self.vae.decoder.mid_block.attentions[0].processor,\n (\n AttnProcessor2_0,\n XFormersAttnProcessor,\n LoRAXFormersAttnProcessor,\n LoRAAttnProcessor2_0,\n FusedAttnProcessor2_0,\n ),\n )\n # if xformers or torch_2_0 is used attention block does not need\n # to be in float32 which can save lots of memory\n if use_torch_2_0_or_xformers:\n self.vae.post_quant_conv.to(dtype)\n self.vae.decoder.conv_in.to(dtype)\n self.vae.decoder.mid_block.to(dtype)\n\n def _enable_shared_attention_processors(\n self,\n share_attention: bool,\n adain_queries: bool,\n adain_keys: bool,\n adain_values: bool,\n full_attention_share: bool,\n shared_score_scale: float,\n shared_score_shift: float,\n only_self_level: float,\n ):\n r\"\"\"Helper method to enable usage of Shared Attention Processor.\"\"\"\n attn_procs = {}\n num_self_layers = len([name for name in self.unet.attn_processors.keys() if \"attn1\" in name])\n\n only_self_vec = get_switch_vec(num_self_layers, only_self_level)\n\n for i, name in enumerate(self.unet.attn_processors.keys()):\n is_self_attention = \"attn1\" in name\n if is_self_attention:\n if only_self_vec[i // 2]:\n attn_procs[name] = AttnProcessor2_0()\n else:\n attn_procs[name] = SharedAttentionProcessor(\n share_attention=share_attention,\n adain_queries=adain_queries,\n adain_keys=adain_keys,\n adain_values=adain_values,\n full_attention_share=full_attention_share,\n shared_score_scale=shared_score_scale,\n shared_score_shift=shared_score_shift,\n )\n else:\n attn_procs[name] = AttnProcessor2_0()\n\n self.unet.set_attn_processor(attn_procs)\n\n def _disable_shared_attention_processors(self):\n r\"\"\"\n Helper method to disable usage of the Shared Attention Processor. All processors\n are reset to the default Attention Processor for pytorch versions above 2.0.\n \"\"\"\n attn_procs = {}\n\n for i, name in enumerate(self.unet.attn_processors.keys()):\n attn_procs[name] = AttnProcessor2_0()\n\n self.unet.set_attn_processor(attn_procs)\n\n def _register_shared_norm(self, share_group_norm: bool = True, share_layer_norm: bool = True):\n r\"\"\"Helper method to register shared group/layer normalization layers.\"\"\"\n\n def register_norm_forward(norm_layer: Union[nn.GroupNorm, nn.LayerNorm]) -> Union[nn.GroupNorm, nn.LayerNorm]:\n if not hasattr(norm_layer, \"orig_forward\"):\n setattr(norm_layer, \"orig_forward\", norm_layer.forward)\n orig_forward = norm_layer.orig_forward\n\n def forward_(hidden_states: torch.Tensor) -> torch.Tensor:\n n = hidden_states.shape[-2]\n hidden_states = concat_first(hidden_states, dim=-2)\n hidden_states = orig_forward(hidden_states)\n return hidden_states[..., :n, :]\n\n norm_layer.forward = forward_\n return norm_layer\n\n def get_norm_layers(pipeline_, norm_layers_: Dict[str, List[Union[nn.GroupNorm, nn.LayerNorm]]]):\n if isinstance(pipeline_, nn.LayerNorm) and share_layer_norm:\n norm_layers_[\"layer\"].append(pipeline_)\n if isinstance(pipeline_, nn.GroupNorm) and share_group_norm:\n norm_layers_[\"group\"].append(pipeline_)\n else:\n for layer in pipeline_.children():\n get_norm_layers(layer, norm_layers_)\n\n norm_layers = {\"group\": [], \"layer\": []}\n get_norm_layers(self.unet, norm_layers)\n\n norm_layers_list = []\n for key in [\"group\", \"layer\"]:\n for layer in norm_layers[key]:\n norm_layers_list.append(register_norm_forward(layer))\n\n return norm_layers_list\n\n @property\n def style_aligned_enabled(self):\n r\"\"\"Returns whether StyleAligned has been enabled in the pipeline or not.\"\"\"\n return hasattr(self, \"_style_aligned_norm_layers\") and self._style_aligned_norm_layers is not None\n\n def enable_style_aligned(\n self,\n share_group_norm: bool = True,\n share_layer_norm: bool = True,\n share_attention: bool = True,\n adain_queries: bool = True,\n adain_keys: bool = True,\n adain_values: bool = False,\n full_attention_share: bool = False,\n shared_score_scale: float = 1.0,\n shared_score_shift: float = 0.0,\n only_self_level: float = 0.0,\n ):\n r\"\"\"\n Enables the StyleAligned mechanism as in https://arxiv.org/abs/2312.02133.\n\n Args:\n share_group_norm (`bool`, defaults to `True`):\n Whether or not to use shared group normalization layers.\n share_layer_norm (`bool`, defaults to `True`):\n Whether or not to use shared layer normalization layers.\n share_attention (`bool`, defaults to `True`):\n Whether or not to use attention sharing between batch images.\n adain_queries (`bool`, defaults to `True`):\n Whether or not to apply the AdaIn operation on attention queries.\n adain_keys (`bool`, defaults to `True`):\n Whether or not to apply the AdaIn operation on attention keys.\n adain_values (`bool`, defaults to `False`):\n Whether or not to apply the AdaIn operation on attention values.\n full_attention_share (`bool`, defaults to `False`):\n Whether or not to use full attention sharing between all images in a batch. Can\n lead to content leakage within each batch and some loss in diversity.\n shared_score_scale (`float`, defaults to `1.0`):\n Scale for shared attention.\n \"\"\"\n self._style_aligned_norm_layers = self._register_shared_norm(share_group_norm, share_layer_norm)\n self._enable_shared_attention_processors(\n share_attention=share_attention,\n adain_queries=adain_queries,\n adain_keys=adain_keys,\n adain_values=adain_values,\n full_attention_share=full_attention_share,\n shared_score_scale=shared_score_scale,\n shared_score_shift=shared_score_shift,\n only_self_level=only_self_level,\n )\n\n def disable_style_aligned(self):\n r\"\"\"Disables the StyleAligned mechanism if it had been previously enabled.\"\"\"\n if self.style_aligned_enabled:\n for layer in self._style_aligned_norm_layers:\n layer.forward = layer.orig_forward\n\n self._style_aligned_norm_layers = None\n self._disable_shared_attention_processors()\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n timesteps (`torch.Tensor`):\n generate embedding vectors at these timesteps\n embedding_dim (`int`, *optional*, defaults to 512):\n dimension of the embeddings to generate\n dtype:\n data type of the generated embeddings\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def guidance_rescale(self):\n return self._guidance_rescale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def denoising_end(self):\n return self._denoising_end\n\n @property\n def denoising_start(self):\n return self._denoising_start\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @property\n def interrupt(self):\n return self._interrupt\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n image: Optional[PipelineImageInput] = None,\n mask_image: Optional[PipelineImageInput] = None,\n masked_image_latents: Optional[torch.Tensor] = None,\n strength: float = 0.3,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n denoising_start: Optional[float] = None,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Optional[Tuple[int, int]] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Optional[Tuple[int, int]] = None,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image. This is set to 1024 by default for the best results.\n Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image. This is set to 1024 by default for the best results.\n Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n denoising_end (`float`, *optional*):\n When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be\n completed before it is intentionally prematurely terminated. As a result, the returned sample will\n still retain a substantial amount of noise as determined by the discrete timesteps selected by the\n scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a\n \"Mixture of Denoisers\" multi-pipeline setup, as elaborated in [**Refining the Image\n Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)\n guidance_scale (`float`, *optional*, defaults to 5.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*):\n Optional image input to work with IP Adapters.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead\n of a plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of\n [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).\n Guidance rescale factor should fix overexposure when using zero terminal SNR.\n original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.\n `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as\n explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n `crops_coords_top_left` can be used to generate an image that appears to be \"cropped\" from the position\n `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n For most cases, `target_size` should be set to the desired height and width of the generated image. If\n not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a specific image resolution. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a target image resolution. It should be as same\n as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a\n `tuple`. When returning a tuple, the first element is a list with the generated images.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n\n # 0. Default height and width to unet\n height = height or self.default_sample_size * self.vae_scale_factor\n width = width or self.default_sample_size * self.vae_scale_factor\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt=prompt,\n prompt_2=prompt_2,\n height=height,\n width=width,\n callback_steps=callback_steps,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._guidance_rescale = guidance_rescale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._denoising_end = denoising_end\n self._denoising_start = denoising_start\n self._interrupt = False\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # 4. Preprocess image and mask_image\n if image is not None:\n image = self.image_processor.preprocess(image, height=height, width=width)\n image = image.to(device=self.device, dtype=prompt_embeds.dtype)\n\n if mask_image is not None:\n mask = self.mask_processor.preprocess(mask_image, height=height, width=width)\n mask = mask.to(device=self.device, dtype=prompt_embeds.dtype)\n\n if masked_image_latents is not None:\n masked_image = masked_image_latents\n elif image.shape[1] == 4:\n # if image is in latent space, we can't mask it\n masked_image = None\n else:\n masked_image = image * (mask < 0.5)\n else:\n mask = None\n\n # 4. Prepare timesteps\n def denoising_value_valid(dnv):\n return isinstance(dnv, float) and 0 < dnv < 1\n\n timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)\n\n if image is not None:\n timesteps, num_inference_steps = self.get_timesteps(\n num_inference_steps,\n strength,\n device,\n denoising_start=self.denoising_start if denoising_value_valid(self.denoising_start) else None,\n )\n\n # check that number of inference steps is not < 1 - as this doesn't make sense\n if num_inference_steps < 1:\n raise ValueError(\n f\"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline\"\n f\"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline.\"\n )\n\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n is_strength_max = strength == 1.0\n add_noise = True if self.denoising_start is None else False\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n num_channels_unet = self.unet.config.in_channels\n return_image_latents = num_channels_unet == 4\n\n latents = self.prepare_latents(\n image=image,\n mask=mask,\n width=width,\n height=height,\n num_channels_latents=num_channels_latents,\n timestep=latent_timestep,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n dtype=prompt_embeds.dtype,\n device=device,\n generator=generator,\n add_noise=add_noise,\n latents=latents,\n is_strength_max=is_strength_max,\n return_noise=True,\n return_image_latents=return_image_latents,\n )\n\n if mask is not None:\n if return_image_latents:\n latents, noise, image_latents = latents\n else:\n latents, noise = latents\n\n mask, masked_image_latents = self.prepare_mask_latents(\n mask=mask,\n masked_image=masked_image,\n batch_size=batch_size * num_images_per_prompt,\n height=height,\n width=width,\n dtype=prompt_embeds.dtype,\n device=device,\n generator=generator,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n )\n\n # Check that sizes of mask, masked image and latents match\n if num_channels_unet == 9:\n # default case for runwayml/stable-diffusion-inpainting\n num_channels_mask = mask.shape[1]\n num_channels_masked_image = masked_image_latents.shape[1]\n if num_channels_latents + num_channels_mask + num_channels_masked_image != num_channels_unet:\n raise ValueError(\n f\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\"\n f\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\"\n f\" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}\"\n f\" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of\"\n \" `pipeline.unet` or your `mask_image` or `image` input.\"\n )\n elif num_channels_unet != 4:\n raise ValueError(\n f\"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}.\"\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n height, width = latents.shape[-2:]\n height = height * self.vae_scale_factor\n width = width * self.vae_scale_factor\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n # 7. Prepare added time ids & embeddings\n add_text_embeds = pooled_prompt_embeds\n add_time_ids = self._get_add_time_ids(\n original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype\n )\n\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)\n\n if ip_adapter_image is not None:\n output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True\n image_embeds, negative_image_embeds = self.encode_image(\n ip_adapter_image, device, num_images_per_prompt, output_hidden_state\n )\n if self.do_classifier_free_guidance:\n image_embeds = torch.cat([negative_image_embeds, image_embeds])\n image_embeds = image_embeds.to(device)\n\n # 8. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n\n # 8.1 Apply denoising_end\n if (\n self.denoising_end is not None\n and isinstance(self.denoising_end, float)\n and self.denoising_end > 0\n and self.denoising_end < 1\n ):\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (self.denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n # 9. Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n self._num_timesteps = len(timesteps)\n\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n if self.interrupt:\n continue\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n if ip_adapter_image is not None:\n added_cond_kwargs[\"image_embeds\"] = image_embeds\n\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=self.cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if mask is not None and num_channels_unet == 4:\n init_latents_proper = image_latents\n\n if self.do_classifier_free_guidance:\n init_mask, _ = mask.chunk(2)\n else:\n init_mask = mask\n\n if i < len(timesteps) - 1:\n noise_timestep = timesteps[i + 1]\n init_latents_proper = self.scheduler.add_noise(\n init_latents_proper, noise, torch.tensor([noise_timestep])\n )\n\n latents = (1 - init_mask) * init_latents_proper + init_mask * latents\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n add_text_embeds = callback_outputs.pop(\"add_text_embeds\", add_text_embeds)\n negative_pooled_prompt_embeds = callback_outputs.pop(\n \"negative_pooled_prompt_embeds\", negative_pooled_prompt_embeds\n )\n add_time_ids = callback_outputs.pop(\"add_time_ids\", add_time_ids)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if XLA_AVAILABLE:\n xm.mark_step()\n\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n image = latents\n\n if not output_type == \"latent\":\n # apply watermark if available\n if self.watermark is not None:\n image = self.watermark.apply_watermark(image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)\n" }, { "path": "diffusers/examples/community/pipeline_stable_diffusion_boxdiff.py", "content": "# Copyright 2024 Jingyang Zhang and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport abc\nimport inspect\nimport math\nimport numbers\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom packaging import version\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom diffusers.configuration_utils import FrozenDict\nfrom diffusers.image_processor import PipelineImageInput, VaeImageProcessor\nfrom diffusers.loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel\nfrom diffusers.models.attention_processor import Attention, FusedAttnProcessor2_0\nfrom diffusers.models.lora import adjust_lora_scale_text_encoder\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline\nfrom diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusionPipeline\n\n >>> pipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16)\n >>> pipe = pipe.to(\"cuda\")\n\n >>> prompt = \"a photo of an astronaut riding a horse on mars\"\n >>> image = pipe(prompt).images[0]\n ```\n\"\"\"\n\n\nclass GaussianSmoothing(nn.Module):\n \"\"\"\n Copied from official repo: https://github.com/showlab/BoxDiff/blob/master/utils/gaussian_smoothing.py\n Apply gaussian smoothing on a\n 1d, 2d or 3d tensor. Filtering is performed seperately for each channel\n in the input using a depthwise convolution.\n Arguments:\n channels (int, sequence): Number of channels of the input tensors. Output will\n have this number of channels as well.\n kernel_size (int, sequence): Size of the gaussian kernel.\n sigma (float, sequence): Standard deviation of the gaussian kernel.\n dim (int, optional): The number of dimensions of the data.\n Default value is 2 (spatial).\n \"\"\"\n\n def __init__(self, channels, kernel_size, sigma, dim=2):\n super(GaussianSmoothing, self).__init__()\n if isinstance(kernel_size, numbers.Number):\n kernel_size = [kernel_size] * dim\n if isinstance(sigma, numbers.Number):\n sigma = [sigma] * dim\n\n # The gaussian kernel is the product of the\n # gaussian function of each dimension.\n kernel = 1\n meshgrids = torch.meshgrid([torch.arange(size, dtype=torch.float32) for size in kernel_size])\n for size, std, mgrid in zip(kernel_size, sigma, meshgrids):\n mean = (size - 1) / 2\n kernel *= 1 / (std * math.sqrt(2 * math.pi)) * torch.exp(-(((mgrid - mean) / (2 * std)) ** 2))\n\n # Make sure sum of values in gaussian kernel equals 1.\n kernel = kernel / torch.sum(kernel)\n\n # Reshape to depthwise convolutional weight\n kernel = kernel.view(1, 1, *kernel.size())\n kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1))\n\n self.register_buffer(\"weight\", kernel)\n self.groups = channels\n\n if dim == 1:\n self.conv = F.conv1d\n elif dim == 2:\n self.conv = F.conv2d\n elif dim == 3:\n self.conv = F.conv3d\n else:\n raise RuntimeError(\"Only 1, 2 and 3 dimensions are supported. Received {}.\".format(dim))\n\n def forward(self, input):\n \"\"\"\n Apply gaussian filter to input.\n Arguments:\n input (torch.Tensor): Input to apply gaussian filter on.\n Returns:\n filtered (torch.Tensor): Filtered output.\n \"\"\"\n return self.conv(input, weight=self.weight.to(input.dtype), groups=self.groups)\n\n\nclass AttendExciteCrossAttnProcessor:\n def __init__(self, attnstore, place_in_unet):\n super().__init__()\n self.attnstore = attnstore\n self.place_in_unet = place_in_unet\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.FloatTensor,\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\n attention_mask: Optional[torch.FloatTensor] = None,\n ) -> torch.Tensor:\n batch_size, sequence_length, _ = hidden_states.shape\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size=1)\n query = attn.to_q(hidden_states)\n\n is_cross = encoder_hidden_states is not None\n encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n attention_probs = attn.get_attention_scores(query, key, attention_mask)\n self.attnstore(attention_probs, is_cross, self.place_in_unet)\n\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n return hidden_states\n\n\nclass AttentionControl(abc.ABC):\n def step_callback(self, x_t):\n return x_t\n\n def between_steps(self):\n return\n\n # @property\n # def num_uncond_att_layers(self):\n # return 0\n\n @abc.abstractmethod\n def forward(self, attn, is_cross: bool, place_in_unet: str):\n raise NotImplementedError\n\n def __call__(self, attn, is_cross: bool, place_in_unet: str):\n if self.cur_att_layer >= self.num_uncond_att_layers:\n self.forward(attn, is_cross, place_in_unet)\n self.cur_att_layer += 1\n if self.cur_att_layer == self.num_att_layers + self.num_uncond_att_layers:\n self.cur_att_layer = 0\n self.cur_step += 1\n self.between_steps()\n\n def reset(self):\n self.cur_step = 0\n self.cur_att_layer = 0\n\n def __init__(self):\n self.cur_step = 0\n self.num_att_layers = -1\n self.cur_att_layer = 0\n\n\nclass AttentionStore(AttentionControl):\n @staticmethod\n def get_empty_store():\n return {\"down_cross\": [], \"mid_cross\": [], \"up_cross\": [], \"down_self\": [], \"mid_self\": [], \"up_self\": []}\n\n def forward(self, attn, is_cross: bool, place_in_unet: str):\n key = f\"{place_in_unet}_{'cross' if is_cross else 'self'}\"\n if attn.shape[1] <= 32**2: # avoid memory overhead\n self.step_store[key].append(attn)\n return attn\n\n def between_steps(self):\n self.attention_store = self.step_store\n if self.save_global_store:\n with torch.no_grad():\n if len(self.global_store) == 0:\n self.global_store = self.step_store\n else:\n for key in self.global_store:\n for i in range(len(self.global_store[key])):\n self.global_store[key][i] += self.step_store[key][i].detach()\n self.step_store = self.get_empty_store()\n self.step_store = self.get_empty_store()\n\n def get_average_attention(self):\n average_attention = self.attention_store\n return average_attention\n\n def get_average_global_attention(self):\n average_attention = {\n key: [item / self.cur_step for item in self.global_store[key]] for key in self.attention_store\n }\n return average_attention\n\n def reset(self):\n super(AttentionStore, self).reset()\n self.step_store = self.get_empty_store()\n self.attention_store = {}\n self.global_store = {}\n\n def __init__(self, save_global_store=False):\n \"\"\"\n Initialize an empty AttentionStore\n :param step_index: used to visualize only a specific step in the diffusion process\n \"\"\"\n super(AttentionStore, self).__init__()\n self.save_global_store = save_global_store\n self.step_store = self.get_empty_store()\n self.attention_store = {}\n self.global_store = {}\n self.curr_step_index = 0\n self.num_uncond_att_layers = 0\n\n\ndef aggregate_attention(\n attention_store: AttentionStore, res: int, from_where: List[str], is_cross: bool, select: int\n) -> torch.Tensor:\n \"\"\"Aggregates the attention across the different layers and heads at the specified resolution.\"\"\"\n out = []\n attention_maps = attention_store.get_average_attention()\n\n # for k, v in attention_maps.items():\n # for vv in v:\n # print(vv.shape)\n # exit()\n\n num_pixels = res**2\n for location in from_where:\n for item in attention_maps[f\"{location}_{'cross' if is_cross else 'self'}\"]:\n if item.shape[1] == num_pixels:\n cross_maps = item.reshape(1, -1, res, res, item.shape[-1])[select]\n out.append(cross_maps)\n out = torch.cat(out, dim=0)\n out = out.sum(0) / out.shape[0]\n return out\n\n\ndef register_attention_control(model, controller):\n attn_procs = {}\n cross_att_count = 0\n for name in model.unet.attn_processors.keys():\n # cross_attention_dim = None if name.endswith(\"attn1.processor\") else model.unet.config.cross_attention_dim\n if name.startswith(\"mid_block\"):\n # hidden_size = model.unet.config.block_out_channels[-1]\n place_in_unet = \"mid\"\n elif name.startswith(\"up_blocks\"):\n # block_id = int(name[len(\"up_blocks.\")])\n # hidden_size = list(reversed(model.unet.config.block_out_channels))[block_id]\n place_in_unet = \"up\"\n elif name.startswith(\"down_blocks\"):\n # block_id = int(name[len(\"down_blocks.\")])\n # hidden_size = model.unet.config.block_out_channels[block_id]\n place_in_unet = \"down\"\n else:\n continue\n\n cross_att_count += 1\n attn_procs[name] = AttendExciteCrossAttnProcessor(attnstore=controller, place_in_unet=place_in_unet)\n model.unet.set_attn_processor(attn_procs)\n controller.num_att_layers = cross_att_count\n\n\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used,\n `timesteps` must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default\n timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`\n must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass StableDiffusionBoxDiffPipeline(\n DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion with BoxDiff.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->image_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"image_encoder\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder: CLIPVisionModelWithProjection = None,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"clip_sample\"] = False\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n def enable_vae_slicing(self):\n r\"\"\"\n Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to\n compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.\n \"\"\"\n self.vae.enable_slicing()\n\n def disable_vae_slicing(self):\n r\"\"\"\n Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to\n computing decoding in one step.\n \"\"\"\n self.vae.disable_slicing()\n\n def enable_vae_tiling(self):\n r\"\"\"\n Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to\n compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow\n processing larger images.\n \"\"\"\n self.vae.enable_tiling()\n\n def disable_vae_tiling(self):\n r\"\"\"\n Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to\n computing decoding in one step.\n \"\"\"\n self.vae.disable_tiling()\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: procecss multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: procecss multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return text_inputs, prompt_embeds, negative_prompt_embeds\n\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n height,\n width,\n boxdiff_phrases,\n boxdiff_boxes,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if boxdiff_phrases is not None or boxdiff_boxes is not None:\n if not (boxdiff_phrases is not None and boxdiff_boxes is not None):\n raise ValueError(\"Either both `boxdiff_phrases` and `boxdiff_boxes` must be passed or none of them.\")\n\n if not isinstance(boxdiff_phrases, list) or not isinstance(boxdiff_boxes, list):\n raise ValueError(\"`boxdiff_phrases` and `boxdiff_boxes` must be lists.\")\n\n if len(boxdiff_phrases) != len(boxdiff_boxes):\n raise ValueError(\n \"`boxdiff_phrases` and `boxdiff_boxes` must have the same length,\"\n f\" got: `boxdiff_phrases` {len(boxdiff_phrases)} != `boxdiff_boxes`\"\n f\" {len(boxdiff_boxes)}.\"\n )\n\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):\n r\"\"\"Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.\n\n The suffixes after the scaling factors represent the stages where they are being applied.\n\n Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values\n that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.\n\n Args:\n s1 (`float`):\n Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to\n mitigate \"oversmoothing effect\" in the enhanced denoising process.\n s2 (`float`):\n Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to\n mitigate \"oversmoothing effect\" in the enhanced denoising process.\n b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.\n b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.\n \"\"\"\n if not hasattr(self, \"unet\"):\n raise ValueError(\"The pipeline must have `unet` for using FreeU.\")\n self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2)\n\n def disable_freeu(self):\n \"\"\"Disables the FreeU mechanism if enabled.\"\"\"\n self.unet.disable_freeu()\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.fuse_qkv_projections\n def fuse_qkv_projections(self, unet: bool = True, vae: bool = True):\n \"\"\"\n Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query,\n key, value) are fused. For cross-attention modules, key and value projection matrices are fused.\n\n \n\n This API is 🧪 experimental.\n\n \n\n Args:\n unet (`bool`, defaults to `True`): To apply fusion on the UNet.\n vae (`bool`, defaults to `True`): To apply fusion on the VAE.\n \"\"\"\n self.fusing_unet = False\n self.fusing_vae = False\n\n if unet:\n self.fusing_unet = True\n self.unet.fuse_qkv_projections()\n self.unet.set_attn_processor(FusedAttnProcessor2_0())\n\n if vae:\n if not isinstance(self.vae, AutoencoderKL):\n raise ValueError(\"`fuse_qkv_projections()` is only supported for the VAE of type `AutoencoderKL`.\")\n\n self.fusing_vae = True\n self.vae.fuse_qkv_projections()\n self.vae.set_attn_processor(FusedAttnProcessor2_0())\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.unfuse_qkv_projections\n def unfuse_qkv_projections(self, unet: bool = True, vae: bool = True):\n \"\"\"Disable QKV projection fusion if enabled.\n\n \n\n This API is 🧪 experimental.\n\n \n\n Args:\n unet (`bool`, defaults to `True`): To apply fusion on the UNet.\n vae (`bool`, defaults to `True`): To apply fusion on the VAE.\n\n \"\"\"\n if unet:\n if not self.fusing_unet:\n logger.warning(\"The UNet was not initially fused for QKV projections. Doing nothing.\")\n else:\n self.unet.unfuse_qkv_projections()\n self.fusing_unet = False\n\n if vae:\n if not self.fusing_vae:\n logger.warning(\"The VAE was not initially fused for QKV projections. Doing nothing.\")\n else:\n self.vae.unfuse_qkv_projections()\n self.fusing_vae = False\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n timesteps (`torch.Tensor`):\n generate embedding vectors at these timesteps\n embedding_dim (`int`, *optional*, defaults to 512):\n dimension of the embeddings to generate\n dtype:\n data type of the generated embeddings\n\n Returns:\n `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def guidance_rescale(self):\n return self._guidance_rescale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @property\n def interrupt(self):\n return self._interrupt\n\n def _compute_max_attention_per_index(\n self,\n attention_maps: torch.Tensor,\n indices_to_alter: List[int],\n smooth_attentions: bool = False,\n sigma: float = 0.5,\n kernel_size: int = 3,\n normalize_eot: bool = False,\n bboxes: List[int] = None,\n L: int = 1,\n P: float = 0.2,\n ) -> List[torch.Tensor]:\n \"\"\"Computes the maximum attention value for each of the tokens we wish to alter.\"\"\"\n last_idx = -1\n if normalize_eot:\n prompt = self.prompt\n if isinstance(self.prompt, list):\n prompt = self.prompt[0]\n last_idx = len(self.tokenizer(prompt)[\"input_ids\"]) - 1\n attention_for_text = attention_maps[:, :, 1:last_idx]\n attention_for_text *= 100\n attention_for_text = torch.nn.functional.softmax(attention_for_text, dim=-1)\n\n # Shift indices since we removed the first token \"1:last_idx\"\n indices_to_alter = [index - 1 for index in indices_to_alter]\n\n # Extract the maximum values\n max_indices_list_fg = []\n max_indices_list_bg = []\n dist_x = []\n dist_y = []\n\n cnt = 0\n for i in indices_to_alter:\n image = attention_for_text[:, :, i]\n\n # TODO\n # box = [max(round(b / (512 / image.shape[0])), 0) for b in bboxes[cnt]]\n # x1, y1, x2, y2 = box\n H, W = image.shape\n x1 = min(max(round(bboxes[cnt][0] * W), 0), W)\n y1 = min(max(round(bboxes[cnt][1] * H), 0), H)\n x2 = min(max(round(bboxes[cnt][2] * W), 0), W)\n y2 = min(max(round(bboxes[cnt][3] * H), 0), H)\n box = [x1, y1, x2, y2]\n cnt += 1\n\n # coordinates to masks\n obj_mask = torch.zeros_like(image)\n ones_mask = torch.ones([y2 - y1, x2 - x1], dtype=obj_mask.dtype).to(obj_mask.device)\n obj_mask[y1:y2, x1:x2] = ones_mask\n bg_mask = 1 - obj_mask\n\n if smooth_attentions:\n smoothing = GaussianSmoothing(channels=1, kernel_size=kernel_size, sigma=sigma, dim=2).to(image.device)\n input = F.pad(image.unsqueeze(0).unsqueeze(0), (1, 1, 1, 1), mode=\"reflect\")\n image = smoothing(input).squeeze(0).squeeze(0)\n\n # Inner-Box constraint\n k = (obj_mask.sum() * P).long()\n max_indices_list_fg.append((image * obj_mask).reshape(-1).topk(k)[0].mean())\n\n # Outer-Box constraint\n k = (bg_mask.sum() * P).long()\n max_indices_list_bg.append((image * bg_mask).reshape(-1).topk(k)[0].mean())\n\n # Corner Constraint\n gt_proj_x = torch.max(obj_mask, dim=0)[0]\n gt_proj_y = torch.max(obj_mask, dim=1)[0]\n corner_mask_x = torch.zeros_like(gt_proj_x)\n corner_mask_y = torch.zeros_like(gt_proj_y)\n\n # create gt according to the number config.L\n N = gt_proj_x.shape[0]\n corner_mask_x[max(box[0] - L, 0) : min(box[0] + L + 1, N)] = 1.0\n corner_mask_x[max(box[2] - L, 0) : min(box[2] + L + 1, N)] = 1.0\n corner_mask_y[max(box[1] - L, 0) : min(box[1] + L + 1, N)] = 1.0\n corner_mask_y[max(box[3] - L, 0) : min(box[3] + L + 1, N)] = 1.0\n dist_x.append((F.l1_loss(image.max(dim=0)[0], gt_proj_x, reduction=\"none\") * corner_mask_x).mean())\n dist_y.append((F.l1_loss(image.max(dim=1)[0], gt_proj_y, reduction=\"none\") * corner_mask_y).mean())\n\n return max_indices_list_fg, max_indices_list_bg, dist_x, dist_y\n\n def _aggregate_and_get_max_attention_per_token(\n self,\n attention_store: AttentionStore,\n indices_to_alter: List[int],\n attention_res: int = 16,\n smooth_attentions: bool = False,\n sigma: float = 0.5,\n kernel_size: int = 3,\n normalize_eot: bool = False,\n bboxes: List[int] = None,\n L: int = 1,\n P: float = 0.2,\n ):\n \"\"\"Aggregates the attention for each token and computes the max activation value for each token to alter.\"\"\"\n attention_maps = aggregate_attention(\n attention_store=attention_store,\n res=attention_res,\n from_where=(\"up\", \"down\", \"mid\"),\n is_cross=True,\n select=0,\n )\n max_attention_per_index_fg, max_attention_per_index_bg, dist_x, dist_y = self._compute_max_attention_per_index(\n attention_maps=attention_maps,\n indices_to_alter=indices_to_alter,\n smooth_attentions=smooth_attentions,\n sigma=sigma,\n kernel_size=kernel_size,\n normalize_eot=normalize_eot,\n bboxes=bboxes,\n L=L,\n P=P,\n )\n return max_attention_per_index_fg, max_attention_per_index_bg, dist_x, dist_y\n\n @staticmethod\n def _compute_loss(\n max_attention_per_index_fg: List[torch.Tensor],\n max_attention_per_index_bg: List[torch.Tensor],\n dist_x: List[torch.Tensor],\n dist_y: List[torch.Tensor],\n return_losses: bool = False,\n ) -> torch.Tensor:\n \"\"\"Computes the attend-and-excite loss using the maximum attention value for each token.\"\"\"\n losses_fg = [max(0, 1.0 - curr_max) for curr_max in max_attention_per_index_fg]\n losses_bg = [max(0, curr_max) for curr_max in max_attention_per_index_bg]\n loss = sum(losses_fg) + sum(losses_bg) + sum(dist_x) + sum(dist_y)\n if return_losses:\n return max(losses_fg), losses_fg\n else:\n return max(losses_fg), loss\n\n @staticmethod\n def _update_latent(latents: torch.Tensor, loss: torch.Tensor, step_size: float) -> torch.Tensor:\n \"\"\"Update the latent according to the computed loss.\"\"\"\n grad_cond = torch.autograd.grad(loss.requires_grad_(True), [latents], retain_graph=True)[0]\n latents = latents - step_size * grad_cond\n return latents\n\n def _perform_iterative_refinement_step(\n self,\n latents: torch.Tensor,\n indices_to_alter: List[int],\n loss_fg: torch.Tensor,\n threshold: float,\n text_embeddings: torch.Tensor,\n text_input,\n attention_store: AttentionStore,\n step_size: float,\n t: int,\n attention_res: int = 16,\n smooth_attentions: bool = True,\n sigma: float = 0.5,\n kernel_size: int = 3,\n max_refinement_steps: int = 20,\n normalize_eot: bool = False,\n bboxes: List[int] = None,\n L: int = 1,\n P: float = 0.2,\n ):\n \"\"\"\n Performs the iterative latent refinement introduced in the paper. Here, we continuously update the latent\n code according to our loss objective until the given threshold is reached for all tokens.\n \"\"\"\n iteration = 0\n target_loss = max(0, 1.0 - threshold)\n\n while loss_fg > target_loss:\n iteration += 1\n\n latents = latents.clone().detach().requires_grad_(True)\n # noise_pred_text = self.unet(latents, t, encoder_hidden_states=text_embeddings[1].unsqueeze(0)).sample\n self.unet.zero_grad()\n\n # Get max activation value for each subject token\n (\n max_attention_per_index_fg,\n max_attention_per_index_bg,\n dist_x,\n dist_y,\n ) = self._aggregate_and_get_max_attention_per_token(\n attention_store=attention_store,\n indices_to_alter=indices_to_alter,\n attention_res=attention_res,\n smooth_attentions=smooth_attentions,\n sigma=sigma,\n kernel_size=kernel_size,\n normalize_eot=normalize_eot,\n bboxes=bboxes,\n L=L,\n P=P,\n )\n\n loss_fg, losses_fg = self._compute_loss(\n max_attention_per_index_fg, max_attention_per_index_bg, dist_x, dist_y, return_losses=True\n )\n\n if loss_fg != 0:\n latents = self._update_latent(latents, loss_fg, step_size)\n\n # with torch.no_grad():\n # noise_pred_uncond = self.unet(latents, t, encoder_hidden_states=text_embeddings[0].unsqueeze(0)).sample\n # noise_pred_text = self.unet(latents, t, encoder_hidden_states=text_embeddings[1].unsqueeze(0)).sample\n\n # try:\n # low_token = np.argmax([l.item() if not isinstance(l, int) else l for l in losses_fg])\n # except Exception as e:\n # print(e) # catch edge case :)\n # low_token = np.argmax(losses_fg)\n\n # low_word = self.tokenizer.decode(text_input.input_ids[0][indices_to_alter[low_token]])\n # print(f'\\t Try {iteration}. {low_word} has a max attention of {max_attention_per_index_fg[low_token]}')\n\n if iteration >= max_refinement_steps:\n # print(f'\\t Exceeded max number of iterations ({max_refinement_steps})! '\n # f'Finished with a max attention of {max_attention_per_index_fg[low_token]}')\n break\n\n # Run one more time but don't compute gradients and update the latents.\n # We just need to compute the new loss - the grad update will occur below\n latents = latents.clone().detach().requires_grad_(True)\n # noise_pred_text = self.unet(latents, t, encoder_hidden_states=text_embeddings[1].unsqueeze(0)).sample\n self.unet.zero_grad()\n\n # Get max activation value for each subject token\n (\n max_attention_per_index_fg,\n max_attention_per_index_bg,\n dist_x,\n dist_y,\n ) = self._aggregate_and_get_max_attention_per_token(\n attention_store=attention_store,\n indices_to_alter=indices_to_alter,\n attention_res=attention_res,\n smooth_attentions=smooth_attentions,\n sigma=sigma,\n kernel_size=kernel_size,\n normalize_eot=normalize_eot,\n bboxes=bboxes,\n L=L,\n P=P,\n )\n loss_fg, losses_fg = self._compute_loss(\n max_attention_per_index_fg, max_attention_per_index_bg, dist_x, dist_y, return_losses=True\n )\n # print(f\"\\t Finished with loss of: {loss_fg}\")\n return loss_fg, latents, max_attention_per_index_fg\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n boxdiff_phrases: List[str] = None,\n boxdiff_boxes: List[List[float]] = None, # TODO\n boxdiff_kwargs: Optional[Dict[str, Any]] = {\n \"attention_res\": 16,\n \"P\": 0.2,\n \"L\": 1,\n \"max_iter_to_alter\": 25,\n \"loss_thresholds\": {0: 0.05, 10: 0.5, 20: 0.8},\n \"scale_factor\": 20,\n \"scale_range\": (1.0, 0.5),\n \"smooth_attentions\": True,\n \"sigma\": 0.5,\n \"kernel_size\": 3,\n \"refine\": False,\n \"normalize_eot\": True,\n },\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n\n boxdiff_attention_res (`int`, *optional*, defaults to 16):\n The resolution of the attention maps used for computing the BoxDiff loss.\n boxdiff_P (`float`, *optional*, defaults to 0.2):\n\n boxdiff_L (`int`, *optional*, defaults to 1):\n The number of pixels around the corner to be selected in BoxDiff loss.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.FloatTensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when\n using zero terminal SNR.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n\n # -1. Register attention control (for BoxDiff)\n attention_store = AttentionStore()\n register_attention_control(self, attention_store)\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n # to deal with lora scaling and other possible forward hooks\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n height,\n width,\n boxdiff_phrases,\n boxdiff_boxes,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n callback_on_step_end_tensor_inputs,\n )\n self.prompt = prompt\n\n self._guidance_scale = guidance_scale\n self._guidance_rescale = guidance_rescale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._interrupt = False\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n\n text_inputs, prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n if ip_adapter_image is not None:\n output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True\n image_embeds, negative_image_embeds = self.encode_image(\n ip_adapter_image, device, num_images_per_prompt, output_hidden_state\n )\n if self.do_classifier_free_guidance:\n image_embeds = torch.cat([negative_image_embeds, image_embeds])\n\n # 4. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 6.1 Add image embeds for IP-Adapter\n added_cond_kwargs = {\"image_embeds\": image_embeds} if ip_adapter_image is not None else None\n\n # 6.2 Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n # 6.3 Prepare BoxDiff inputs\n # a) Indices to alter\n input_ids = self.tokenizer(prompt)[\"input_ids\"]\n decoded = [self.tokenizer.decode([t]) for t in input_ids]\n indices_to_alter = []\n bboxes = []\n for phrase, box in zip(boxdiff_phrases, boxdiff_boxes):\n # it could happen that phrase does not correspond a single token?\n if phrase not in decoded:\n continue\n indices_to_alter.append(decoded.index(phrase))\n bboxes.append(box)\n\n # b) A bunch of hyperparameters\n attention_res = boxdiff_kwargs.get(\"attention_res\", 16)\n smooth_attentions = boxdiff_kwargs.get(\"smooth_attentions\", True)\n sigma = boxdiff_kwargs.get(\"sigma\", 0.5)\n kernel_size = boxdiff_kwargs.get(\"kernel_size\", 3)\n L = boxdiff_kwargs.get(\"L\", 1)\n P = boxdiff_kwargs.get(\"P\", 0.2)\n thresholds = boxdiff_kwargs.get(\"loss_thresholds\", {0: 0.05, 10: 0.5, 20: 0.8})\n max_iter_to_alter = boxdiff_kwargs.get(\"max_iter_to_alter\", len(self.scheduler.timesteps) + 1)\n scale_factor = boxdiff_kwargs.get(\"scale_factor\", 20)\n refine = boxdiff_kwargs.get(\"refine\", False)\n normalize_eot = boxdiff_kwargs.get(\"normalize_eot\", True)\n\n scale_range = boxdiff_kwargs.get(\"scale_range\", (1.0, 0.5))\n scale_range = np.linspace(scale_range[0], scale_range[1], len(self.scheduler.timesteps))\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n if self.interrupt:\n continue\n\n # BoxDiff optimization\n with torch.enable_grad():\n latents = latents.clone().detach().requires_grad_(True)\n\n # Forward pass of denoising with text conditioning\n noise_pred_text = self.unet(\n latents,\n t,\n encoder_hidden_states=prompt_embeds[1].unsqueeze(0),\n cross_attention_kwargs=cross_attention_kwargs,\n ).sample\n self.unet.zero_grad()\n\n # Get max activation value for each subject token\n (\n max_attention_per_index_fg,\n max_attention_per_index_bg,\n dist_x,\n dist_y,\n ) = self._aggregate_and_get_max_attention_per_token(\n attention_store=attention_store,\n indices_to_alter=indices_to_alter,\n attention_res=attention_res,\n smooth_attentions=smooth_attentions,\n sigma=sigma,\n kernel_size=kernel_size,\n normalize_eot=normalize_eot,\n bboxes=bboxes,\n L=L,\n P=P,\n )\n\n loss_fg, loss = self._compute_loss(\n max_attention_per_index_fg, max_attention_per_index_bg, dist_x, dist_y\n )\n\n # Refinement from attend-and-excite (not necessary)\n if refine and i in thresholds.keys() and loss_fg > 1.0 - thresholds[i]:\n del noise_pred_text\n torch.cuda.empty_cache()\n loss_fg, latents, max_attention_per_index_fg = self._perform_iterative_refinement_step(\n latents=latents,\n indices_to_alter=indices_to_alter,\n loss_fg=loss_fg,\n threshold=thresholds[i],\n text_embeddings=prompt_embeds,\n text_input=text_inputs,\n attention_store=attention_store,\n step_size=scale_factor * np.sqrt(scale_range[i]),\n t=t,\n attention_res=attention_res,\n smooth_attentions=smooth_attentions,\n sigma=sigma,\n kernel_size=kernel_size,\n normalize_eot=normalize_eot,\n bboxes=bboxes,\n L=L,\n P=P,\n )\n\n # Perform gradient update\n if i < max_iter_to_alter:\n _, loss = self._compute_loss(\n max_attention_per_index_fg, max_attention_per_index_bg, dist_x, dist_y\n )\n if loss != 0:\n latents = self._update_latent(\n latents=latents, loss=loss, step_size=scale_factor * np.sqrt(scale_range[i])\n )\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=self.cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[\n 0\n ]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/examples/community/pipeline_stable_diffusion_pag.py", "content": "# Implementation of StableDiffusionPipeline with PAG\n# https://ku-cvlab.github.io/Perturbed-Attention-Guidance\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport torch\nimport torch.nn.functional as F\nfrom packaging import version\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom diffusers.configuration_utils import FrozenDict\nfrom diffusers.image_processor import PipelineImageInput, VaeImageProcessor\nfrom diffusers.loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel\nfrom diffusers.models.attention_processor import Attention, AttnProcessor2_0, FusedAttnProcessor2_0\nfrom diffusers.models.lora import adjust_lora_scale_text_encoder\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline\nfrom diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusionPipeline\n >>> pipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16)\n >>> pipe = pipe.to(\"cuda\")\n >>> prompt = \"a photo of an astronaut riding a horse on mars\"\n >>> image = pipe(prompt).images[0]\n ```\n\"\"\"\n\n\nclass PAGIdentitySelfAttnProcessor:\n r\"\"\"\n Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).\n \"\"\"\n\n def __init__(self):\n if not hasattr(F, \"scaled_dot_product_attention\"):\n raise ImportError(\"AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.\")\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n temb: Optional[torch.Tensor] = None,\n *args,\n **kwargs,\n ) -> torch.Tensor:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n residual = hidden_states\n if attn.spatial_norm is not None:\n hidden_states = attn.spatial_norm(hidden_states, temb)\n\n input_ndim = hidden_states.ndim\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n\n # chunk\n hidden_states_org, hidden_states_ptb = hidden_states.chunk(2)\n\n # original path\n batch_size, sequence_length, _ = hidden_states_org.shape\n\n if attention_mask is not None:\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n # scaled_dot_product_attention expects attention_mask shape to be\n # (batch, heads, source_length, target_length)\n attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])\n\n if attn.group_norm is not None:\n hidden_states_org = attn.group_norm(hidden_states_org.transpose(1, 2)).transpose(1, 2)\n\n query = attn.to_q(hidden_states_org)\n key = attn.to_k(hidden_states_org)\n value = attn.to_v(hidden_states_org)\n\n inner_dim = key.shape[-1]\n head_dim = inner_dim // attn.heads\n\n query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n # the output of sdp = (batch, num_heads, seq_len, head_dim)\n # TODO: add support for attn.scale when we move to Torch 2.1\n hidden_states_org = F.scaled_dot_product_attention(\n query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False\n )\n\n hidden_states_org = hidden_states_org.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)\n hidden_states_org = hidden_states_org.to(query.dtype)\n\n # linear proj\n hidden_states_org = attn.to_out[0](hidden_states_org)\n # dropout\n hidden_states_org = attn.to_out[1](hidden_states_org)\n\n if input_ndim == 4:\n hidden_states_org = hidden_states_org.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n # perturbed path (identity attention)\n batch_size, sequence_length, _ = hidden_states_ptb.shape\n\n if attention_mask is not None:\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n # scaled_dot_product_attention expects attention_mask shape to be\n # (batch, heads, source_length, target_length)\n attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])\n\n if attn.group_norm is not None:\n hidden_states_ptb = attn.group_norm(hidden_states_ptb.transpose(1, 2)).transpose(1, 2)\n\n value = attn.to_v(hidden_states_ptb)\n\n # hidden_states_ptb = torch.zeros(value.shape).to(value.get_device())\n hidden_states_ptb = value\n\n hidden_states_ptb = hidden_states_ptb.to(query.dtype)\n\n # linear proj\n hidden_states_ptb = attn.to_out[0](hidden_states_ptb)\n # dropout\n hidden_states_ptb = attn.to_out[1](hidden_states_ptb)\n\n if input_ndim == 4:\n hidden_states_ptb = hidden_states_ptb.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n # cat\n hidden_states = torch.cat([hidden_states_org, hidden_states_ptb])\n\n if attn.residual_connection:\n hidden_states = hidden_states + residual\n\n hidden_states = hidden_states / attn.rescale_output_factor\n\n return hidden_states\n\n\nclass PAGCFGIdentitySelfAttnProcessor:\n r\"\"\"\n Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).\n \"\"\"\n\n def __init__(self):\n if not hasattr(F, \"scaled_dot_product_attention\"):\n raise ImportError(\"AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.\")\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n temb: Optional[torch.Tensor] = None,\n *args,\n **kwargs,\n ) -> torch.Tensor:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n residual = hidden_states\n if attn.spatial_norm is not None:\n hidden_states = attn.spatial_norm(hidden_states, temb)\n\n input_ndim = hidden_states.ndim\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n\n # chunk\n hidden_states_uncond, hidden_states_org, hidden_states_ptb = hidden_states.chunk(3)\n hidden_states_org = torch.cat([hidden_states_uncond, hidden_states_org])\n\n # original path\n batch_size, sequence_length, _ = hidden_states_org.shape\n\n if attention_mask is not None:\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n # scaled_dot_product_attention expects attention_mask shape to be\n # (batch, heads, source_length, target_length)\n attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])\n\n if attn.group_norm is not None:\n hidden_states_org = attn.group_norm(hidden_states_org.transpose(1, 2)).transpose(1, 2)\n\n query = attn.to_q(hidden_states_org)\n key = attn.to_k(hidden_states_org)\n value = attn.to_v(hidden_states_org)\n\n inner_dim = key.shape[-1]\n head_dim = inner_dim // attn.heads\n\n query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n # the output of sdp = (batch, num_heads, seq_len, head_dim)\n # TODO: add support for attn.scale when we move to Torch 2.1\n hidden_states_org = F.scaled_dot_product_attention(\n query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False\n )\n\n hidden_states_org = hidden_states_org.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)\n hidden_states_org = hidden_states_org.to(query.dtype)\n\n # linear proj\n hidden_states_org = attn.to_out[0](hidden_states_org)\n # dropout\n hidden_states_org = attn.to_out[1](hidden_states_org)\n\n if input_ndim == 4:\n hidden_states_org = hidden_states_org.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n # perturbed path (identity attention)\n batch_size, sequence_length, _ = hidden_states_ptb.shape\n\n if attention_mask is not None:\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n # scaled_dot_product_attention expects attention_mask shape to be\n # (batch, heads, source_length, target_length)\n attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])\n\n if attn.group_norm is not None:\n hidden_states_ptb = attn.group_norm(hidden_states_ptb.transpose(1, 2)).transpose(1, 2)\n\n value = attn.to_v(hidden_states_ptb)\n hidden_states_ptb = value\n hidden_states_ptb = hidden_states_ptb.to(query.dtype)\n\n # linear proj\n hidden_states_ptb = attn.to_out[0](hidden_states_ptb)\n # dropout\n hidden_states_ptb = attn.to_out[1](hidden_states_ptb)\n\n if input_ndim == 4:\n hidden_states_ptb = hidden_states_ptb.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n # cat\n hidden_states = torch.cat([hidden_states_org, hidden_states_ptb])\n\n if attn.residual_connection:\n hidden_states = hidden_states + residual\n\n hidden_states = hidden_states / attn.rescale_output_factor\n\n return hidden_states\n\n\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used,\n `timesteps` must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default\n timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`\n must be `None`.\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass StableDiffusionPAGPipeline(\n DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion.\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->image_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"image_encoder\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder: CLIPVisionModelWithProjection = None,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"clip_sample\"] = False\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n def enable_vae_slicing(self):\n r\"\"\"\n Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to\n compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.\n \"\"\"\n self.vae.enable_slicing()\n\n def disable_vae_slicing(self):\n r\"\"\"\n Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to\n computing decoding in one step.\n \"\"\"\n self.vae.disable_slicing()\n\n def enable_vae_tiling(self):\n r\"\"\"\n Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to\n compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow\n processing larger images.\n \"\"\"\n self.vae.enable_tiling()\n\n def disable_vae_tiling(self):\n r\"\"\"\n Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to\n computing decoding in one step.\n \"\"\"\n self.vae.disable_tiling()\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if self.do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n image_embeds = ip_adapter_image_embeds\n return image_embeds\n\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):\n r\"\"\"Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.\n The suffixes after the scaling factors represent the stages where they are being applied.\n Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values\n that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.\n Args:\n s1 (`float`):\n Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to\n mitigate \"oversmoothing effect\" in the enhanced denoising process.\n s2 (`float`):\n Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to\n mitigate \"oversmoothing effect\" in the enhanced denoising process.\n b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.\n b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.\n \"\"\"\n if not hasattr(self, \"unet\"):\n raise ValueError(\"The pipeline must have `unet` for using FreeU.\")\n self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2)\n\n def disable_freeu(self):\n \"\"\"Disables the FreeU mechanism if enabled.\"\"\"\n self.unet.disable_freeu()\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.fuse_qkv_projections\n def fuse_qkv_projections(self, unet: bool = True, vae: bool = True):\n \"\"\"\n Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query,\n key, value) are fused. For cross-attention modules, key and value projection matrices are fused.\n \n This API is 🧪 experimental.\n \n Args:\n unet (`bool`, defaults to `True`): To apply fusion on the UNet.\n vae (`bool`, defaults to `True`): To apply fusion on the VAE.\n \"\"\"\n self.fusing_unet = False\n self.fusing_vae = False\n\n if unet:\n self.fusing_unet = True\n self.unet.fuse_qkv_projections()\n self.unet.set_attn_processor(FusedAttnProcessor2_0())\n\n if vae:\n if not isinstance(self.vae, AutoencoderKL):\n raise ValueError(\"`fuse_qkv_projections()` is only supported for the VAE of type `AutoencoderKL`.\")\n\n self.fusing_vae = True\n self.vae.fuse_qkv_projections()\n self.vae.set_attn_processor(FusedAttnProcessor2_0())\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.unfuse_qkv_projections\n def unfuse_qkv_projections(self, unet: bool = True, vae: bool = True):\n \"\"\"Disable QKV projection fusion if enabled.\n \n This API is 🧪 experimental.\n \n Args:\n unet (`bool`, defaults to `True`): To apply fusion on the UNet.\n vae (`bool`, defaults to `True`): To apply fusion on the VAE.\n \"\"\"\n if unet:\n if not self.fusing_unet:\n logger.warning(\"The UNet was not initially fused for QKV projections. Doing nothing.\")\n else:\n self.unet.unfuse_qkv_projections()\n self.fusing_unet = False\n\n if vae:\n if not self.fusing_vae:\n logger.warning(\"The VAE was not initially fused for QKV projections. Doing nothing.\")\n else:\n self.vae.unfuse_qkv_projections()\n self.fusing_vae = False\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n Args:\n timesteps (`torch.Tensor`):\n generate embedding vectors at these timesteps\n embedding_dim (`int`, *optional*, defaults to 512):\n dimension of the embeddings to generate\n dtype:\n data type of the generated embeddings\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n def pred_z0(self, sample, model_output, timestep):\n alpha_prod_t = self.scheduler.alphas_cumprod[timestep].to(sample.device)\n\n beta_prod_t = 1 - alpha_prod_t\n if self.scheduler.config.prediction_type == \"epsilon\":\n pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)\n elif self.scheduler.config.prediction_type == \"sample\":\n pred_original_sample = model_output\n elif self.scheduler.config.prediction_type == \"v_prediction\":\n pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output\n # predict V\n model_output = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample\n else:\n raise ValueError(\n f\"prediction_type given as {self.scheduler.config.prediction_type} must be one of `epsilon`, `sample`,\"\n \" or `v_prediction`\"\n )\n\n return pred_original_sample\n\n def pred_x0(self, latents, noise_pred, t, generator, device, prompt_embeds, output_type):\n pred_z0 = self.pred_z0(latents, noise_pred, t)\n pred_x0 = self.vae.decode(pred_z0 / self.vae.config.scaling_factor, return_dict=False, generator=generator)[0]\n pred_x0, ____ = self.run_safety_checker(pred_x0, device, prompt_embeds.dtype)\n do_denormalize = [True] * pred_x0.shape[0]\n pred_x0 = self.image_processor.postprocess(pred_x0, output_type=output_type, do_denormalize=do_denormalize)\n\n return pred_x0\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def guidance_rescale(self):\n return self._guidance_rescale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @property\n def interrupt(self):\n return self._interrupt\n\n @property\n def pag_scale(self):\n return self._pag_scale\n\n @property\n def do_perturbed_attention_guidance(self):\n return self._pag_scale > 0\n\n @property\n def pag_adaptive_scaling(self):\n return self._pag_adaptive_scaling\n\n @property\n def do_pag_adaptive_scaling(self):\n return self._pag_adaptive_scaling > 0\n\n @property\n def pag_applied_layers_index(self):\n return self._pag_applied_layers_index\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n guidance_scale: float = 7.5,\n pag_scale: float = 0.0,\n pag_adaptive_scaling: float = 0.0,\n pag_applied_layers_index: List[str] = [\"d4\"], # ['d4', 'd5', 'm0']\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. If not\n provided, embeddings are computed from the `ip_adapter_image` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when\n using zero terminal SNR.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n Examples:\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n # to deal with lora scaling and other possible forward hooks\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n ip_adapter_image,\n ip_adapter_image_embeds,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._guidance_rescale = guidance_rescale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._interrupt = False\n\n self._pag_scale = pag_scale\n self._pag_adaptive_scaling = pag_adaptive_scaling\n self._pag_applied_layers_index = pag_applied_layers_index\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n\n # cfg\n if self.do_classifier_free_guidance and not self.do_perturbed_attention_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n # pag\n elif not self.do_classifier_free_guidance and self.do_perturbed_attention_guidance:\n prompt_embeds = torch.cat([prompt_embeds, prompt_embeds])\n # both\n elif self.do_classifier_free_guidance and self.do_perturbed_attention_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds, prompt_embeds])\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image, ip_adapter_image_embeds, device, batch_size * num_images_per_prompt\n )\n\n # 4. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 6.1 Add image embeds for IP-Adapter\n added_cond_kwargs = (\n {\"image_embeds\": image_embeds}\n if (ip_adapter_image is not None or ip_adapter_image_embeds is not None)\n else None\n )\n\n # 6.2 Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n # 7. Denoising loop\n if self.do_perturbed_attention_guidance:\n down_layers = []\n mid_layers = []\n up_layers = []\n for name, module in self.unet.named_modules():\n if \"attn1\" in name and \"to\" not in name:\n layer_type = name.split(\".\")[0].split(\"_\")[0]\n if layer_type == \"down\":\n down_layers.append(module)\n elif layer_type == \"mid\":\n mid_layers.append(module)\n elif layer_type == \"up\":\n up_layers.append(module)\n else:\n raise ValueError(f\"Invalid layer type: {layer_type}\")\n\n # change attention layer in UNet if use PAG\n if self.do_perturbed_attention_guidance:\n if self.do_classifier_free_guidance:\n replace_processor = PAGCFGIdentitySelfAttnProcessor()\n else:\n replace_processor = PAGIdentitySelfAttnProcessor()\n\n drop_layers = self.pag_applied_layers_index\n for drop_layer in drop_layers:\n try:\n if drop_layer[0] == \"d\":\n down_layers[int(drop_layer[1])].processor = replace_processor\n elif drop_layer[0] == \"m\":\n mid_layers[int(drop_layer[1])].processor = replace_processor\n elif drop_layer[0] == \"u\":\n up_layers[int(drop_layer[1])].processor = replace_processor\n else:\n raise ValueError(f\"Invalid layer type: {drop_layer[0]}\")\n except IndexError:\n raise ValueError(\n f\"Invalid layer index: {drop_layer}. Available layers: {len(down_layers)} down layers, {len(mid_layers)} mid layers, {len(up_layers)} up layers.\"\n )\n\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n if self.interrupt:\n continue\n\n # cfg\n if self.do_classifier_free_guidance and not self.do_perturbed_attention_guidance:\n latent_model_input = torch.cat([latents] * 2)\n # pag\n elif not self.do_classifier_free_guidance and self.do_perturbed_attention_guidance:\n latent_model_input = torch.cat([latents] * 2)\n # both\n elif self.do_classifier_free_guidance and self.do_perturbed_attention_guidance:\n latent_model_input = torch.cat([latents] * 3)\n # no\n else:\n latent_model_input = latents\n\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=self.cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n\n # cfg\n if self.do_classifier_free_guidance and not self.do_perturbed_attention_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n\n delta = noise_pred_text - noise_pred_uncond\n noise_pred = noise_pred_uncond + self.guidance_scale * delta\n\n # pag\n elif not self.do_classifier_free_guidance and self.do_perturbed_attention_guidance:\n noise_pred_original, noise_pred_perturb = noise_pred.chunk(2)\n\n signal_scale = self.pag_scale\n if self.do_pag_adaptive_scaling:\n signal_scale = self.pag_scale - self.pag_adaptive_scaling * (1000 - t)\n if signal_scale < 0:\n signal_scale = 0\n\n noise_pred = noise_pred_original + signal_scale * (noise_pred_original - noise_pred_perturb)\n\n # both\n elif self.do_classifier_free_guidance and self.do_perturbed_attention_guidance:\n noise_pred_uncond, noise_pred_text, noise_pred_text_perturb = noise_pred.chunk(3)\n\n signal_scale = self.pag_scale\n if self.do_pag_adaptive_scaling:\n signal_scale = self.pag_scale - self.pag_adaptive_scaling * (1000 - t)\n if signal_scale < 0:\n signal_scale = 0\n\n noise_pred = (\n noise_pred_text\n + (self.guidance_scale - 1.0) * (noise_pred_text - noise_pred_uncond)\n + signal_scale * (noise_pred_text - noise_pred_text_perturb)\n )\n\n if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[\n 0\n ]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n # change attention layer in UNet if use PAG\n if self.do_perturbed_attention_guidance:\n drop_layers = self.pag_applied_layers_index\n for drop_layer in drop_layers:\n try:\n if drop_layer[0] == \"d\":\n down_layers[int(drop_layer[1])].processor = AttnProcessor2_0()\n elif drop_layer[0] == \"m\":\n mid_layers[int(drop_layer[1])].processor = AttnProcessor2_0()\n elif drop_layer[0] == \"u\":\n up_layers[int(drop_layer[1])].processor = AttnProcessor2_0()\n else:\n raise ValueError(f\"Invalid layer type: {drop_layer[0]}\")\n except IndexError:\n raise ValueError(\n f\"Invalid layer index: {drop_layer}. Available layers: {len(down_layers)} down layers, {len(mid_layers)} mid layers, {len(up_layers)} up layers.\"\n )\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/examples/community/pipeline_stable_diffusion_upscale_ldm3d.py", "content": "# Copyright 2024 The Intel Labs Team Authors and the HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import DiffusionPipeline\nfrom diffusers.image_processor import PipelineDepthInput, PipelineImageInput, VaeImageProcessorLDM3D\nfrom diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.models.lora import adjust_lora_scale_text_encoder\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker\nfrom diffusers.pipelines.stable_diffusion_ldm3d.pipeline_stable_diffusion_ldm3d import LDM3DPipelineOutput\nfrom diffusers.schedulers import DDPMScheduler, KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```python\n >>> from diffusers import StableDiffusionUpscaleLDM3DPipeline\n >>> from PIL import Image\n >>> from io import BytesIO\n >>> import requests\n\n >>> pipe = StableDiffusionUpscaleLDM3DPipeline.from_pretrained(\"Intel/ldm3d-sr\")\n >>> pipe = pipe.to(\"cuda\")\n >>> rgb_path = \"https://huggingface.co/Intel/ldm3d-sr/resolve/main/lemons_ldm3d_rgb.jpg\"\n >>> depth_path = \"https://huggingface.co/Intel/ldm3d-sr/resolve/main/lemons_ldm3d_depth.png\"\n >>> low_res_rgb = Image.open(BytesIO(requests.get(rgb_path).content)).convert(\"RGB\")\n >>> low_res_depth = Image.open(BytesIO(requests.get(depth_path).content)).convert(\"L\")\n >>> output = pipe(\n ... prompt=\"high quality high resolution uhd 4k image\",\n ... rgb=low_res_rgb,\n ... depth=low_res_depth,\n ... num_inference_steps=50,\n ... target_res=[1024, 1024],\n ... )\n >>> rgb_image, depth_image = output.rgb, output.depth\n >>> rgb_image[0].save(\"hr_ldm3d_rgb.jpg\")\n >>> depth_image[0].save(\"hr_ldm3d_depth.png\")\n ```\n\"\"\"\n\n\nclass StableDiffusionUpscaleLDM3DPipeline(\n DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin\n):\n r\"\"\"\n Pipeline for text-to-image and 3D generation using LDM3D.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n low_res_scheduler ([`SchedulerMixin`]):\n A scheduler used to add initial noise to the low resolution conditioning image. It must be an instance of\n [`DDPMScheduler`].\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n low_res_scheduler: DDPMScheduler,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n watermarker: Optional[Any] = None,\n max_noise_level: int = 350,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n low_res_scheduler=low_res_scheduler,\n scheduler=scheduler,\n safety_checker=safety_checker,\n watermarker=watermarker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessorLDM3D(vae_scale_factor=self.vae_scale_factor, resample=\"bilinear\")\n # self.register_to_config(requires_safety_checker=requires_safety_checker)\n self.register_to_config(max_noise_level=max_noise_level)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_ldm3d.StableDiffusionLDM3DPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_ldm3d.StableDiffusionLDM3DPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n rgb_feature_extractor_input = feature_extractor_input[0]\n safety_checker_input = self.feature_extractor(rgb_feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n image,\n noise_level,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n target_res=None,\n ):\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if (\n not isinstance(image, torch.Tensor)\n and not isinstance(image, PIL.Image.Image)\n and not isinstance(image, np.ndarray)\n and not isinstance(image, list)\n ):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `np.ndarray`, `PIL.Image.Image` or `list` but is {type(image)}\"\n )\n\n # verify batch size of prompt and image are same if image is a list or tensor or numpy array\n if isinstance(image, (list, np.ndarray, torch.Tensor)):\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if isinstance(image, list):\n image_batch_size = len(image)\n else:\n image_batch_size = image.shape[0]\n if batch_size != image_batch_size:\n raise ValueError(\n f\"`prompt` has batch size {batch_size} and `image` has batch size {image_batch_size}.\"\n \" Please make sure that passed `prompt` matches the batch size of `image`.\"\n )\n\n # check noise level\n if noise_level > self.config.max_noise_level:\n raise ValueError(f\"`noise_level` has to be <= {self.config.max_noise_level} but is {noise_level}\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (batch_size, num_channels_latents, height, width)\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n # def upcast_vae(self):\n # dtype = self.vae.dtype\n # self.vae.to(dtype=torch.float32)\n # use_torch_2_0_or_xformers = isinstance(\n # self.vae.decoder.mid_block.attentions[0].processor,\n # (\n # AttnProcessor2_0,\n # XFormersAttnProcessor,\n # LoRAXFormersAttnProcessor,\n # LoRAAttnProcessor2_0,\n # ),\n # )\n # # if xformers or torch_2_0 is used attention block does not need\n # # to be in float32 which can save lots of memory\n # if use_torch_2_0_or_xformers:\n # self.vae.post_quant_conv.to(dtype)\n # self.vae.decoder.conv_in.to(dtype)\n # self.vae.decoder.mid_block.to(dtype)\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n rgb: PipelineImageInput = None,\n depth: PipelineDepthInput = None,\n num_inference_steps: int = 75,\n guidance_scale: float = 9.0,\n noise_level: int = 20,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n target_res: Optional[List[int]] = [1024, 1024],\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image` or tensor representing an image batch to be upscaled.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 5.0):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n rgb,\n noise_level,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n )\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Preprocess image\n rgb, depth = self.image_processor.preprocess(rgb, depth, target_res=target_res)\n rgb = rgb.to(dtype=prompt_embeds.dtype, device=device)\n depth = depth.to(dtype=prompt_embeds.dtype, device=device)\n\n # 5. set timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 6. Encode low resolutiom image to latent space\n image = torch.cat([rgb, depth], axis=1)\n latent_space_image = self.vae.encode(image).latent_dist.sample(generator)\n latent_space_image *= self.vae.scaling_factor\n noise_level = torch.tensor([noise_level], dtype=torch.long, device=device)\n # noise_rgb = randn_tensor(rgb.shape, generator=generator, device=device, dtype=prompt_embeds.dtype)\n # rgb = self.low_res_scheduler.add_noise(rgb, noise_rgb, noise_level)\n # noise_depth = randn_tensor(depth.shape, generator=generator, device=device, dtype=prompt_embeds.dtype)\n # depth = self.low_res_scheduler.add_noise(depth, noise_depth, noise_level)\n\n batch_multiplier = 2 if do_classifier_free_guidance else 1\n latent_space_image = torch.cat([latent_space_image] * batch_multiplier * num_images_per_prompt)\n noise_level = torch.cat([noise_level] * latent_space_image.shape[0])\n\n # 7. Prepare latent variables\n height, width = latent_space_image.shape[2:]\n num_channels_latents = self.vae.config.latent_channels\n\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 8. Check that sizes of image and latents match\n num_channels_image = latent_space_image.shape[1]\n if num_channels_latents + num_channels_image != self.unet.config.in_channels:\n raise ValueError(\n f\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\"\n f\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\"\n f\" `num_channels_image`: {num_channels_image} \"\n f\" = {num_channels_latents+num_channels_image}. Please verify the config of\"\n \" `pipeline.unet` or your `image` input.\"\n )\n\n # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 10. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n\n # concat latents, mask, masked_image_latents in the channel dimension\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n latent_model_input = torch.cat([latent_model_input, latent_space_image], dim=1)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n class_labels=noise_level,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, latents)\n\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n image = self.vae.decode(latents / self.vae.scaling_factor, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n rgb, depth = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # 11. Apply watermark\n if output_type == \"pil\" and self.watermarker is not None:\n rgb = self.watermarker.apply_watermark(rgb)\n\n # Offload last model to CPU\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return ((rgb, depth), has_nsfw_concept)\n\n return LDM3DPipelineOutput(rgb=rgb, depth=depth, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter.py", "content": "# Copyright 2024 TencentARC and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom diffusers.image_processor import PipelineImageInput, VaeImageProcessor\nfrom diffusers.loaders import FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin\nfrom diffusers.models import AutoencoderKL, ControlNetModel, MultiAdapter, T2IAdapter, UNet2DConditionModel\nfrom diffusers.models.attention_processor import (\n AttnProcessor2_0,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n XFormersAttnProcessor,\n)\nfrom diffusers.models.lora import adjust_lora_scale_text_encoder\nfrom diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n PIL_INTERPOLATION,\n USE_PEFT_BACKEND,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom diffusers.utils.torch_utils import is_compiled_module, randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import T2IAdapter, StableDiffusionXLAdapterPipeline, DDPMScheduler\n >>> from diffusers.utils import load_image\n >>> from controlnet_aux.midas import MidasDetector\n\n >>> img_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\n >>> mask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\n\n >>> image = load_image(img_url).resize((1024, 1024))\n >>> mask_image = load_image(mask_url).resize((1024, 1024))\n\n >>> midas_depth = MidasDetector.from_pretrained(\n ... \"valhalla/t2iadapter-aux-models\", filename=\"dpt_large_384.pt\", model_type=\"dpt_large\"\n ... ).to(\"cuda\")\n\n >>> depth_image = midas_depth(\n ... image, detect_resolution=512, image_resolution=1024\n ... )\n\n >>> model_id = \"stabilityai/stable-diffusion-xl-base-1.0\"\n\n >>> adapter = T2IAdapter.from_pretrained(\n ... \"Adapter/t2iadapter\",\n ... subfolder=\"sketch_sdxl_1.0\",\n ... torch_dtype=torch.float16,\n ... adapter_type=\"full_adapter_xl\",\n ... )\n\n >>> controlnet = ControlNetModel.from_pretrained(\n ... \"diffusers/controlnet-depth-sdxl-1.0\",\n ... torch_dtype=torch.float16,\n ... variant=\"fp16\",\n ... use_safetensors=True\n ... ).to(\"cuda\")\n\n >>> scheduler = DDPMScheduler.from_pretrained(model_id, subfolder=\"scheduler\")\n\n >>> pipe = StableDiffusionXLAdapterPipeline.from_pretrained(\n ... model_id,\n ... adapter=adapter,\n ... controlnet=controlnet,\n ... torch_dtype=torch.float16,\n ... variant=\"fp16\",\n ... scheduler=scheduler\n ... ).to(\"cuda\")\n\n >>> strength = 0.5\n\n >>> generator = torch.manual_seed(42)\n >>> sketch_image_out = pipe(\n ... prompt=\"a photo of a tiger sitting on a park bench\",\n ... negative_prompt=\"extra digit, fewer digits, cropped, worst quality, low quality\",\n ... adapter_image=depth_image,\n ... control_image=mask_image,\n ... adapter_conditioning_scale=strength,\n ... controlnet_conditioning_scale=strength,\n ... generator=generator,\n ... guidance_scale=7.5,\n ... ).images[0]\n ```\n\"\"\"\n\n\ndef _preprocess_adapter_image(image, height, width):\n if isinstance(image, torch.Tensor):\n return image\n elif isinstance(image, PIL.Image.Image):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n image = [np.array(i.resize((width, height), resample=PIL_INTERPOLATION[\"lanczos\"])) for i in image]\n image = [\n i[None, ..., None] if i.ndim == 2 else i[None, ...] for i in image\n ] # expand [h, w] or [h, w, c] to [b, h, w, c]\n image = np.concatenate(image, axis=0)\n image = np.array(image).astype(np.float32) / 255.0\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image)\n elif isinstance(image[0], torch.Tensor):\n if image[0].ndim == 3:\n image = torch.stack(image, dim=0)\n elif image[0].ndim == 4:\n image = torch.cat(image, dim=0)\n else:\n raise ValueError(\n f\"Invalid image tensor! Expecting image tensor with 3 or 4 dimension, but recive: {image[0].ndim}\"\n )\n return image\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\nclass StableDiffusionXLControlNetAdapterPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n FromSingleFileMixin,\n StableDiffusionXLLoraLoaderMixin,\n TextualInversionLoaderMixin,\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion augmented with T2I-Adapter\n https://arxiv.org/abs/2302.08453\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n adapter ([`T2IAdapter`] or [`MultiAdapter`] or `List[T2IAdapter]`):\n Provides additional conditioning to the unet during the denoising process. If you set multiple Adapter as a\n list, the outputs from each Adapter are added together to create one combined additional conditioning.\n adapter_weights (`List[float]`, *optional*, defaults to None):\n List of floats representing the weight which will be multiply to each adapter's output before adding them\n together.\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPFeatureExtractor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->text_encoder_2->unet->vae\"\n _optional_components = [\"tokenizer\", \"tokenizer_2\", \"text_encoder\", \"text_encoder_2\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n text_encoder_2: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n tokenizer_2: CLIPTokenizer,\n unet: UNet2DConditionModel,\n adapter: Union[T2IAdapter, MultiAdapter, List[T2IAdapter]],\n controlnet: Union[ControlNetModel, MultiControlNetModel],\n scheduler: KarrasDiffusionSchedulers,\n force_zeros_for_empty_prompt: bool = True,\n ):\n super().__init__()\n\n if isinstance(controlnet, (list, tuple)):\n controlnet = MultiControlNetModel(controlnet)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n unet=unet,\n adapter=adapter,\n controlnet=controlnet,\n scheduler=scheduler,\n )\n self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.control_image_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False\n )\n self.default_sample_size = self.unet.config.sample_size\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt: str,\n prompt_2: Optional[str] = None,\n device: Optional[torch.device] = None,\n num_images_per_prompt: int = 1,\n do_classifier_free_guidance: bool = True,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n lora_scale (`float`, *optional*):\n A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n device = device or self._execution_device\n\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if self.text_encoder is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)\n else:\n scale_lora_layers(self.text_encoder_2, lora_scale)\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n\n if prompt is not None:\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # Define tokenizers and text encoders\n tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]\n text_encoders = (\n [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]\n )\n\n if prompt_embeds is None:\n prompt_2 = prompt_2 or prompt\n prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2\n\n # textual inversion: process multi-vector tokens if necessary\n prompt_embeds_list = []\n prompts = [prompt, prompt_2]\n for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, tokenizer)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n if clip_skip is None:\n prompt_embeds = prompt_embeds.hidden_states[-2]\n else:\n # \"2\" because SDXL always indexes from the penultimate layer.\n prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]\n\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n\n # get unconditional embeddings for classifier free guidance\n zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt\n if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:\n negative_prompt_embeds = torch.zeros_like(prompt_embeds)\n negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)\n elif do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n\n # normalize str to list\n negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt\n negative_prompt_2 = (\n batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2\n )\n\n uncond_tokens: List[str]\n if prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = [negative_prompt, negative_prompt_2]\n\n negative_prompt_embeds_list = []\n for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n negative_prompt_embeds = text_encoder(\n uncond_input.input_ids.to(device),\n output_hidden_states=True,\n )\n # We are only ALWAYS interested in the pooled output of the final text encoder\n negative_pooled_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]\n\n negative_prompt_embeds_list.append(negative_prompt_embeds)\n\n negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)\n\n if self.text_encoder_2 is not None:\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n if self.text_encoder_2 is not None:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n if do_classifier_free_guidance:\n negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n\n if self.text_encoder is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder_2, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image\n def check_image(self, image, prompt, prompt_embeds):\n image_is_pil = isinstance(image, PIL.Image.Image)\n image_is_tensor = isinstance(image, torch.Tensor)\n image_is_np = isinstance(image, np.ndarray)\n image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)\n image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)\n image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)\n\n if (\n not image_is_pil\n and not image_is_tensor\n and not image_is_np\n and not image_is_pil_list\n and not image_is_tensor_list\n and not image_is_np_list\n ):\n raise TypeError(\n f\"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}\"\n )\n\n if image_is_pil:\n image_batch_size = 1\n else:\n image_batch_size = len(image)\n\n if prompt is not None and isinstance(prompt, str):\n prompt_batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n prompt_batch_size = len(prompt)\n elif prompt_embeds is not None:\n prompt_batch_size = prompt_embeds.shape[0]\n\n if image_batch_size != 1 and image_batch_size != prompt_batch_size:\n raise ValueError(\n f\"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.check_inputs\n def check_inputs(\n self,\n prompt,\n prompt_2,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n negative_prompt_2=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n pooled_prompt_embeds=None,\n negative_pooled_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if prompt_embeds is not None and pooled_prompt_embeds is None:\n raise ValueError(\n \"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.\"\n )\n\n if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:\n raise ValueError(\n \"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.\"\n )\n\n def check_conditions(\n self,\n prompt,\n prompt_embeds,\n adapter_image,\n control_image,\n adapter_conditioning_scale,\n controlnet_conditioning_scale,\n control_guidance_start,\n control_guidance_end,\n ):\n # controlnet checks\n if not isinstance(control_guidance_start, (tuple, list)):\n control_guidance_start = [control_guidance_start]\n\n if not isinstance(control_guidance_end, (tuple, list)):\n control_guidance_end = [control_guidance_end]\n\n if len(control_guidance_start) != len(control_guidance_end):\n raise ValueError(\n f\"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list.\"\n )\n\n if isinstance(self.controlnet, MultiControlNetModel):\n if len(control_guidance_start) != len(self.controlnet.nets):\n raise ValueError(\n f\"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}.\"\n )\n\n for start, end in zip(control_guidance_start, control_guidance_end):\n if start >= end:\n raise ValueError(\n f\"control guidance start: {start} cannot be larger or equal to control guidance end: {end}.\"\n )\n if start < 0.0:\n raise ValueError(f\"control guidance start: {start} can't be smaller than 0.\")\n if end > 1.0:\n raise ValueError(f\"control guidance end: {end} can't be larger than 1.0.\")\n\n # Check controlnet `image`\n is_compiled = hasattr(F, \"scaled_dot_product_attention\") and isinstance(\n self.controlnet, torch._dynamo.eval_frame.OptimizedModule\n )\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n self.check_image(control_image, prompt, prompt_embeds)\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if not isinstance(control_image, list):\n raise TypeError(\"For multiple controlnets: `control_image` must be type `list`\")\n\n # When `image` is a nested list:\n # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])\n elif any(isinstance(i, list) for i in control_image):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif len(control_image) != len(self.controlnet.nets):\n raise ValueError(\n f\"For multiple controlnets: `image` must have the same length as the number of controlnets, but got {len(control_image)} images and {len(self.controlnet.nets)} ControlNets.\"\n )\n\n for image_ in control_image:\n self.check_image(image_, prompt, prompt_embeds)\n else:\n assert False\n\n # Check `controlnet_conditioning_scale`\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n if not isinstance(controlnet_conditioning_scale, float):\n raise TypeError(\"For single controlnet: `controlnet_conditioning_scale` must be type `float`.\")\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if isinstance(controlnet_conditioning_scale, list):\n if any(isinstance(i, list) for i in controlnet_conditioning_scale):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(\n self.controlnet.nets\n ):\n raise ValueError(\n \"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have\"\n \" the same length as the number of controlnets\"\n )\n else:\n assert False\n\n # adapter checks\n if isinstance(self.adapter, T2IAdapter) or is_compiled and isinstance(self.adapter._orig_mod, T2IAdapter):\n self.check_image(adapter_image, prompt, prompt_embeds)\n elif (\n isinstance(self.adapter, MultiAdapter) or is_compiled and isinstance(self.adapter._orig_mod, MultiAdapter)\n ):\n if not isinstance(adapter_image, list):\n raise TypeError(\"For multiple adapters: `adapter_image` must be type `list`\")\n\n # When `image` is a nested list:\n # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])\n elif any(isinstance(i, list) for i in adapter_image):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif len(adapter_image) != len(self.adapter.adapters):\n raise ValueError(\n f\"For multiple adapters: `image` must have the same length as the number of adapters, but got {len(adapter_image)} images and {len(self.adapters.nets)} Adapters.\"\n )\n\n for image_ in adapter_image:\n self.check_image(image_, prompt, prompt_embeds)\n else:\n assert False\n\n # Check `adapter_conditioning_scale`\n if isinstance(self.adapter, T2IAdapter) or is_compiled and isinstance(self.adapter._orig_mod, T2IAdapter):\n if not isinstance(adapter_conditioning_scale, float):\n raise TypeError(\"For single adapter: `adapter_conditioning_scale` must be type `float`.\")\n elif (\n isinstance(self.adapter, MultiAdapter) or is_compiled and isinstance(self.adapter._orig_mod, MultiAdapter)\n ):\n if isinstance(adapter_conditioning_scale, list):\n if any(isinstance(i, list) for i in adapter_conditioning_scale):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif isinstance(adapter_conditioning_scale, list) and len(adapter_conditioning_scale) != len(\n self.adapter.adapters\n ):\n raise ValueError(\n \"For multiple adapters: When `adapter_conditioning_scale` is specified as `list`, it must have\"\n \" the same length as the number of adapters\"\n )\n else:\n assert False\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline._get_add_time_ids\n def _get_add_time_ids(\n self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None\n ):\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n\n passed_add_embed_dim = (\n self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim\n )\n expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features\n\n if expected_add_embed_dim != passed_add_embed_dim:\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.\"\n )\n\n add_time_ids = torch.tensor([add_time_ids], dtype=dtype)\n return add_time_ids\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae\n def upcast_vae(self):\n dtype = self.vae.dtype\n self.vae.to(dtype=torch.float32)\n use_torch_2_0_or_xformers = isinstance(\n self.vae.decoder.mid_block.attentions[0].processor,\n (\n AttnProcessor2_0,\n XFormersAttnProcessor,\n LoRAXFormersAttnProcessor,\n LoRAAttnProcessor2_0,\n ),\n )\n # if xformers or torch_2_0 is used attention block does not need\n # to be in float32 which can save lots of memory\n if use_torch_2_0_or_xformers:\n self.vae.post_quant_conv.to(dtype)\n self.vae.decoder.conv_in.to(dtype)\n self.vae.decoder.mid_block.to(dtype)\n\n # Copied from diffusers.pipelines.t2i_adapter.pipeline_stable_diffusion_adapter.StableDiffusionAdapterPipeline._default_height_width\n def _default_height_width(self, height, width, image):\n # NOTE: It is possible that a list of images have different\n # dimensions for each image, so just checking the first image\n # is not _exactly_ correct, but it is simple.\n while isinstance(image, list):\n image = image[0]\n\n if height is None:\n if isinstance(image, PIL.Image.Image):\n height = image.height\n elif isinstance(image, torch.Tensor):\n height = image.shape[-2]\n\n # round down to nearest multiple of `self.adapter.downscale_factor`\n height = (height // self.adapter.downscale_factor) * self.adapter.downscale_factor\n\n if width is None:\n if isinstance(image, PIL.Image.Image):\n width = image.width\n elif isinstance(image, torch.Tensor):\n width = image.shape[-1]\n\n # round down to nearest multiple of `self.adapter.downscale_factor`\n width = (width // self.adapter.downscale_factor) * self.adapter.downscale_factor\n\n return height, width\n\n def prepare_control_image(\n self,\n image,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype,\n do_classifier_free_guidance=False,\n guess_mode=False,\n ):\n image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)\n image_batch_size = image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n image = image.repeat_interleave(repeat_by, dim=0)\n\n image = image.to(device=device, dtype=dtype)\n\n if do_classifier_free_guidance and not guess_mode:\n image = torch.cat([image] * 2)\n\n return image\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n adapter_image: PipelineImageInput = None,\n control_image: PipelineImageInput = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Optional[Tuple[int, int]] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Optional[Tuple[int, int]] = None,\n negative_original_size: Optional[Tuple[int, int]] = None,\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\n negative_target_size: Optional[Tuple[int, int]] = None,\n adapter_conditioning_scale: Union[float, List[float]] = 1.0,\n adapter_conditioning_factor: float = 1.0,\n clip_skip: Optional[int] = None,\n controlnet_conditioning_scale=1.0,\n guess_mode: bool = False,\n control_guidance_start: float = 0.0,\n control_guidance_end: float = 1.0,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n adapter_image (`torch.Tensor`, `PIL.Image.Image`, `List[torch.Tensor]` or `List[PIL.Image.Image]` or `List[List[PIL.Image.Image]]`):\n The Adapter input condition. Adapter uses this input condition to generate guidance to Unet. If the\n type is specified as `torch.Tensor`, it is passed to Adapter as is. PIL.Image.Image` can also be\n accepted as an image. The control image is automatically resized to fit the output image.\n control_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:\n `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):\n The ControlNet input condition to provide guidance to the `unet` for generation. If the type is\n specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be\n accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height\n and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in\n `init`, images must be passed as a list such that each element of the list can be correctly batched for\n input to a single ControlNet.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image. Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image. Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n denoising_end (`float`, *optional*):\n When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be\n completed before it is intentionally prematurely terminated. As a result, the returned sample will\n still retain a substantial amount of noise as determined by the discrete timesteps selected by the\n scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a\n \"Mixture of Denoisers\" multi-pipeline setup, as elaborated in [**Refining the Image\n Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)\n guidance_scale (`float`, *optional*, defaults to 5.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionAdapterPipelineOutput`]\n instead of a plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of\n [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).\n Guidance rescale factor should fix overexposure when using zero terminal SNR.\n original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.\n `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as\n explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n `crops_coords_top_left` can be used to generate an image that appears to be \"cropped\" from the position\n `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n For most cases, `target_size` should be set to the desired height and width of the generated image. If\n not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a specific image resolution. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a target image resolution. It should be as same\n as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):\n The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added to the\n residual in the original unet. If multiple adapters are specified in init, you can set the\n corresponding scale as a list.\n adapter_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):\n The outputs of the adapter are multiplied by `adapter_conditioning_scale` before they are added to the\n residual in the original unet. If multiple adapters are specified in init, you can set the\n corresponding scale as a list.\n adapter_conditioning_factor (`float`, *optional*, defaults to 1.0):\n The fraction of timesteps for which adapter should be applied. If `adapter_conditioning_factor` is\n `0.0`, adapter is not applied at all. If `adapter_conditioning_factor` is `1.0`, adapter is applied for\n all timesteps. If `adapter_conditioning_factor` is `0.5`, adapter is applied for half of the timesteps.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionAdapterPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionAdapterPipelineOutput`] if `return_dict` is True, otherwise a\n `tuple`. When returning a tuple, the first element is a list with the generated images.\n \"\"\"\n controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet\n adapter = self.adapter._orig_mod if is_compiled_module(self.adapter) else self.adapter\n\n # 0. Default height and width to unet\n\n height, width = self._default_height_width(height, width, adapter_image)\n device = self._execution_device\n\n if isinstance(adapter, MultiAdapter):\n adapter_input = []\n\n for one_image in adapter_image:\n one_image = _preprocess_adapter_image(one_image, height, width)\n one_image = one_image.to(device=device, dtype=adapter.dtype)\n adapter_input.append(one_image)\n else:\n adapter_input = _preprocess_adapter_image(adapter_image, height, width)\n adapter_input = adapter_input.to(device=device, dtype=adapter.dtype)\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n # 0.1 align format for control guidance\n if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):\n control_guidance_start = len(control_guidance_end) * [control_guidance_start]\n elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):\n control_guidance_end = len(control_guidance_start) * [control_guidance_end]\n elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):\n mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1\n control_guidance_start, control_guidance_end = (\n mult * [control_guidance_start],\n mult * [control_guidance_end],\n )\n\n if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):\n controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)\n if isinstance(adapter, MultiAdapter) and isinstance(adapter_conditioning_scale, float):\n adapter_conditioning_scale = [adapter_conditioning_scale] * len(adapter.adapters)\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n height,\n width,\n callback_steps,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n )\n\n self.check_conditions(\n prompt,\n prompt_embeds,\n adapter_image,\n control_image,\n adapter_conditioning_scale,\n controlnet_conditioning_scale,\n control_guidance_start,\n control_guidance_end,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n clip_skip=clip_skip,\n )\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Prepare added time ids & embeddings & adapter features\n if isinstance(adapter, MultiAdapter):\n adapter_state = adapter(adapter_input, adapter_conditioning_scale)\n for k, v in enumerate(adapter_state):\n adapter_state[k] = v\n else:\n adapter_state = adapter(adapter_input)\n for k, v in enumerate(adapter_state):\n adapter_state[k] = v * adapter_conditioning_scale\n if num_images_per_prompt > 1:\n for k, v in enumerate(adapter_state):\n adapter_state[k] = v.repeat(num_images_per_prompt, 1, 1, 1)\n if do_classifier_free_guidance:\n for k, v in enumerate(adapter_state):\n adapter_state[k] = torch.cat([v] * 2, dim=0)\n\n # 7.2 Prepare control images\n if isinstance(controlnet, ControlNetModel):\n control_image = self.prepare_control_image(\n image=control_image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n elif isinstance(controlnet, MultiControlNetModel):\n control_images = []\n\n for control_image_ in control_image:\n control_image_ = self.prepare_control_image(\n image=control_image_,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n\n control_images.append(control_image_)\n\n control_image = control_images\n else:\n raise ValueError(f\"{controlnet.__class__} is not supported.\")\n\n # 8.2 Create tensor stating which controlnets to keep\n controlnet_keep = []\n for i in range(len(timesteps)):\n keeps = [\n 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)\n for s, e in zip(control_guidance_start, control_guidance_end)\n ]\n if isinstance(self.controlnet, MultiControlNetModel):\n controlnet_keep.append(keeps)\n else:\n controlnet_keep.append(keeps[0])\n\n add_text_embeds = pooled_prompt_embeds\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n add_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n if negative_original_size is not None and negative_target_size is not None:\n negative_add_time_ids = self._get_add_time_ids(\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n else:\n negative_add_time_ids = add_time_ids\n\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)\n\n # 8. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n\n # 7.1 Apply denoising_end\n if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1:\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n\n if i < int(num_inference_steps * adapter_conditioning_factor):\n down_intrablock_additional_residuals = [state.clone() for state in adapter_state]\n else:\n down_intrablock_additional_residuals = None\n\n # ----------- ControlNet\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input_controlnet = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n\n # concat latents, mask, masked_image_latents in the channel dimension\n latent_model_input_controlnet = self.scheduler.scale_model_input(latent_model_input_controlnet, t)\n\n # controlnet(s) inference\n if guess_mode and do_classifier_free_guidance:\n # Infer ControlNet only for the conditional batch.\n control_model_input = latents\n control_model_input = self.scheduler.scale_model_input(control_model_input, t)\n controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]\n controlnet_added_cond_kwargs = {\n \"text_embeds\": add_text_embeds.chunk(2)[1],\n \"time_ids\": add_time_ids.chunk(2)[1],\n }\n else:\n control_model_input = latent_model_input_controlnet\n controlnet_prompt_embeds = prompt_embeds\n controlnet_added_cond_kwargs = added_cond_kwargs\n\n if isinstance(controlnet_keep[i], list):\n cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]\n else:\n controlnet_cond_scale = controlnet_conditioning_scale\n if isinstance(controlnet_cond_scale, list):\n controlnet_cond_scale = controlnet_cond_scale[0]\n cond_scale = controlnet_cond_scale * controlnet_keep[i]\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n control_model_input,\n t,\n encoder_hidden_states=controlnet_prompt_embeds,\n controlnet_cond=control_image,\n conditioning_scale=cond_scale,\n guess_mode=guess_mode,\n added_cond_kwargs=controlnet_added_cond_kwargs,\n return_dict=False,\n )\n\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n down_intrablock_additional_residuals=down_intrablock_additional_residuals, # t2iadapter\n down_block_additional_residuals=down_block_res_samples, # controlnet\n mid_block_additional_residual=mid_block_res_sample, # controlnet\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if do_classifier_free_guidance and guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n image = latents\n return StableDiffusionXLPipelineOutput(images=image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)\n" }, { "path": "diffusers/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter_inpaint.py", "content": "# Copyright 2024 Jake Babbidge, TencentARC and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# ignore the entire file for precommit\n# type: ignore\n\nimport inspect\nfrom collections.abc import Callable\nfrom typing import Any, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL\nimport torch\nimport torch.nn.functional as F\nfrom transformers import (\n CLIPTextModel,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n)\n\nfrom diffusers import DiffusionPipeline\nfrom diffusers.image_processor import PipelineImageInput, VaeImageProcessor\nfrom diffusers.loaders import (\n FromSingleFileMixin,\n LoraLoaderMixin,\n StableDiffusionXLLoraLoaderMixin,\n TextualInversionLoaderMixin,\n)\nfrom diffusers.models import (\n AutoencoderKL,\n ControlNetModel,\n MultiAdapter,\n T2IAdapter,\n UNet2DConditionModel,\n)\nfrom diffusers.models.attention_processor import (\n AttnProcessor2_0,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n XFormersAttnProcessor,\n)\nfrom diffusers.models.lora import adjust_lora_scale_text_encoder\nfrom diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel\nfrom diffusers.pipelines.pipeline_utils import StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n PIL_INTERPOLATION,\n USE_PEFT_BACKEND,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom diffusers.utils.torch_utils import is_compiled_module, randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import DiffusionPipeline, T2IAdapter\n >>> from diffusers.utils import load_image\n >>> from PIL import Image\n >>> from controlnet_aux.midas import MidasDetector\n\n >>> adapter = T2IAdapter.from_pretrained(\n ... \"TencentARC/t2i-adapter-sketch-sdxl-1.0\", torch_dtype=torch.float16, variant=\"fp16\"\n ... ).to(\"cuda\")\n\n >>> controlnet = ControlNetModel.from_pretrained(\n ... \"diffusers/controlnet-depth-sdxl-1.0\",\n ... torch_dtype=torch.float16,\n ... variant=\"fp16\",\n ... use_safetensors=True\n ... ).to(\"cuda\")\n\n >>> pipe = DiffusionPipeline.from_pretrained(\n ... \"diffusers/stable-diffusion-xl-1.0-inpainting-0.1\",\n ... torch_dtype=torch.float16,\n ... variant=\"fp16\",\n ... use_safetensors=True,\n ... custom_pipeline=\"stable_diffusion_xl_adapter_controlnet_inpaint\",\n ... adapter=adapter,\n ... controlnet=controlnet,\n ... ).to(\"cuda\")\n\n >>> prompt = \"a tiger sitting on a park bench\"\n >>> img_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\n >>> mask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\n\n >>> image = load_image(img_url).resize((1024, 1024))\n >>> mask_image = load_image(mask_url).resize((1024, 1024))\n\n >>> midas_depth = MidasDetector.from_pretrained(\n ... \"valhalla/t2iadapter-aux-models\", filename=\"dpt_large_384.pt\", model_type=\"dpt_large\"\n ... ).to(\"cuda\")\n\n >>> depth_image = midas_depth(\n ... image, detect_resolution=512, image_resolution=1024\n ... )\n\n >>> strength = 0.4\n\n >>> generator = torch.manual_seed(42)\n\n >>> result_image = pipe(\n ... image=image,\n ... mask_image=mask,\n ... adapter_image=depth_image,\n ... control_image=depth_image,\n ... controlnet_conditioning_scale=strength,\n ... adapter_conditioning_scale=strength,\n ... strength=0.7,\n ... generator=generator,\n ... prompt=prompt,\n ... negative_prompt=\"extra digit, fewer digits, cropped, worst quality, low quality\",\n ... num_inference_steps=50\n ... ).images[0]\n ```\n\"\"\"\n\n\ndef _preprocess_adapter_image(image, height, width):\n if isinstance(image, torch.Tensor):\n return image\n elif isinstance(image, PIL.Image.Image):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n image = [np.array(i.resize((width, height), resample=PIL_INTERPOLATION[\"lanczos\"])) for i in image]\n image = [\n i[None, ..., None] if i.ndim == 2 else i[None, ...] for i in image\n ] # expand [h, w] or [h, w, c] to [b, h, w, c]\n image = np.concatenate(image, axis=0)\n image = np.array(image).astype(np.float32) / 255.0\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image)\n elif isinstance(image[0], torch.Tensor):\n if image[0].ndim == 3:\n image = torch.stack(image, dim=0)\n elif image[0].ndim == 4:\n image = torch.cat(image, dim=0)\n else:\n raise ValueError(\n f\"Invalid image tensor! Expecting image tensor with 3 or 4 dimension, but recive: {image[0].ndim}\"\n )\n return image\n\n\ndef mask_pil_to_torch(mask, height, width):\n # preprocess mask\n if isinstance(mask, Union[PIL.Image.Image, np.ndarray]):\n mask = [mask]\n\n if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):\n mask = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in mask]\n mask = np.concatenate([np.array(m.convert(\"L\"))[None, None, :] for m in mask], axis=0)\n mask = mask.astype(np.float32) / 255.0\n elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):\n mask = np.concatenate([m[None, None, :] for m in mask], axis=0)\n\n mask = torch.from_numpy(mask)\n return mask\n\n\ndef prepare_mask_and_masked_image(image, mask, height, width, return_image: bool = False):\n \"\"\"\n Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be\n converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the\n ``image`` and ``1`` for the ``mask``.\n\n The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be\n binarized (``mask > 0.5``) and cast to ``torch.float32`` too.\n\n Args:\n image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.\n It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``\n ``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.\n mask (_type_): The mask to apply to the image, i.e. regions to inpaint.\n It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``\n ``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.\n\n\n Raises:\n ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask\n should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.\n TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not\n (ot the other way around).\n\n Returns:\n tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4\n dimensions: ``batch x channels x height x width``.\n \"\"\"\n\n # checkpoint. #TODO(Yiyi) - need to clean this up later\n if image is None:\n raise ValueError(\"`image` input cannot be undefined.\")\n\n if mask is None:\n raise ValueError(\"`mask_image` input cannot be undefined.\")\n\n if isinstance(image, torch.Tensor):\n if not isinstance(mask, torch.Tensor):\n mask = mask_pil_to_torch(mask, height, width)\n\n if image.ndim == 3:\n image = image.unsqueeze(0)\n\n # Batch and add channel dim for single mask\n if mask.ndim == 2:\n mask = mask.unsqueeze(0).unsqueeze(0)\n\n # Batch single mask or add channel dim\n if mask.ndim == 3:\n # Single batched mask, no channel dim or single mask not batched but channel dim\n if mask.shape[0] == 1:\n mask = mask.unsqueeze(0)\n\n # Batched masks no channel dim\n else:\n mask = mask.unsqueeze(1)\n\n assert image.ndim == 4 and mask.ndim == 4, \"Image and Mask must have 4 dimensions\"\n # assert image.shape[-2:] == mask.shape[-2:], \"Image and Mask must have the same spatial dimensions\"\n assert image.shape[0] == mask.shape[0], \"Image and Mask must have the same batch size\"\n\n # Check image is in [-1, 1]\n # if image.min() < -1 or image.max() > 1:\n # raise ValueError(\"Image should be in [-1, 1] range\")\n\n # Check mask is in [0, 1]\n if mask.min() < 0 or mask.max() > 1:\n raise ValueError(\"Mask should be in [0, 1] range\")\n\n # Binarize mask\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n\n # Image as float32\n image = image.to(dtype=torch.float32)\n elif isinstance(mask, torch.Tensor):\n raise TypeError(f\"`mask` is a torch.Tensor but `image` (type: {type(image)} is not\")\n else:\n # preprocess image\n if isinstance(image, Union[PIL.Image.Image, np.ndarray]):\n image = [image]\n if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):\n # resize all images w.r.t passed height an width\n image = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in image]\n image = [np.array(i.convert(\"RGB\"))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n elif isinstance(image, list) and isinstance(image[0], np.ndarray):\n image = np.concatenate([i[None, :] for i in image], axis=0)\n\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0\n\n mask = mask_pil_to_torch(mask, height, width)\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n\n if image.shape[1] == 4:\n # images are in latent space and thus can't\n # be masked set masked_image to None\n # we assume that the checkpoint is not an inpainting\n # checkpoint. #TODO(Yiyi) - need to clean this up later\n masked_image = None\n else:\n masked_image = image * (mask < 0.5)\n\n # n.b. ensure backwards compatibility as old function does not return image\n if return_image:\n return mask, masked_image, image\n\n return mask, masked_image\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\nclass StableDiffusionXLControlNetAdapterInpaintPipeline(\n DiffusionPipeline, StableDiffusionMixin, FromSingleFileMixin, LoraLoaderMixin\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion augmented with T2I-Adapter\n https://arxiv.org/abs/2302.08453\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n adapter ([`T2IAdapter`] or [`MultiAdapter`] or `List[T2IAdapter]`):\n Provides additional conditioning to the unet during the denoising process. If you set multiple Adapter as a\n list, the outputs from each Adapter are added together to create one combined additional conditioning.\n adapter_weights (`List[float]`, *optional*, defaults to None):\n List of floats representing the weight which will be multiply to each adapter's output before adding them\n together.\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPFeatureExtractor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n requires_aesthetics_score (`bool`, *optional*, defaults to `\"False\"`):\n Whether the `unet` requires a aesthetic_score condition to be passed during inference. Also see the config\n of `stabilityai/stable-diffusion-xl-refiner-1-0`.\n force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `\"True\"`):\n Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of\n `stabilityai/stable-diffusion-xl-base-1-0`.\n \"\"\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n text_encoder_2: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n tokenizer_2: CLIPTokenizer,\n unet: UNet2DConditionModel,\n adapter: Union[T2IAdapter, MultiAdapter],\n controlnet: Union[ControlNetModel, MultiControlNetModel],\n scheduler: KarrasDiffusionSchedulers,\n requires_aesthetics_score: bool = False,\n force_zeros_for_empty_prompt: bool = True,\n ):\n super().__init__()\n\n if isinstance(controlnet, (list, tuple)):\n controlnet = MultiControlNetModel(controlnet)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n unet=unet,\n adapter=adapter,\n controlnet=controlnet,\n scheduler=scheduler,\n )\n self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)\n self.register_to_config(requires_aesthetics_score=requires_aesthetics_score)\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.control_image_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False\n )\n self.default_sample_size = self.unet.config.sample_size\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt: str,\n prompt_2: Optional[str] = None,\n device: Optional[torch.device] = None,\n num_images_per_prompt: int = 1,\n do_classifier_free_guidance: bool = True,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n lora_scale (`float`, *optional*):\n A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n device = device or self._execution_device\n\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if self.text_encoder is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)\n else:\n scale_lora_layers(self.text_encoder_2, lora_scale)\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n\n if prompt is not None:\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # Define tokenizers and text encoders\n tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]\n text_encoders = (\n [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]\n )\n\n if prompt_embeds is None:\n prompt_2 = prompt_2 or prompt\n prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2\n\n # textual inversion: process multi-vector tokens if necessary\n prompt_embeds_list = []\n prompts = [prompt, prompt_2]\n for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, tokenizer)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n if clip_skip is None:\n prompt_embeds = prompt_embeds.hidden_states[-2]\n else:\n # \"2\" because SDXL always indexes from the penultimate layer.\n prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]\n\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n\n # get unconditional embeddings for classifier free guidance\n zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt\n if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:\n negative_prompt_embeds = torch.zeros_like(prompt_embeds)\n negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)\n elif do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n\n # normalize str to list\n negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt\n negative_prompt_2 = (\n batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2\n )\n\n uncond_tokens: List[str]\n if prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = [negative_prompt, negative_prompt_2]\n\n negative_prompt_embeds_list = []\n for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n negative_prompt_embeds = text_encoder(\n uncond_input.input_ids.to(device),\n output_hidden_states=True,\n )\n # We are only ALWAYS interested in the pooled output of the final text encoder\n negative_pooled_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]\n\n negative_prompt_embeds_list.append(negative_prompt_embeds)\n\n negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)\n\n if self.text_encoder_2 is not None:\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n if self.text_encoder_2 is not None:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n if do_classifier_free_guidance:\n negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n\n if self.text_encoder is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder_2, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image\n def check_image(self, image, prompt, prompt_embeds):\n image_is_pil = isinstance(image, PIL.Image.Image)\n image_is_tensor = isinstance(image, torch.Tensor)\n image_is_np = isinstance(image, np.ndarray)\n image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)\n image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)\n image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)\n\n if (\n not image_is_pil\n and not image_is_tensor\n and not image_is_np\n and not image_is_pil_list\n and not image_is_tensor_list\n and not image_is_np_list\n ):\n raise TypeError(\n f\"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}\"\n )\n\n if image_is_pil:\n image_batch_size = 1\n else:\n image_batch_size = len(image)\n\n if prompt is not None and isinstance(prompt, str):\n prompt_batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n prompt_batch_size = len(prompt)\n elif prompt_embeds is not None:\n prompt_batch_size = prompt_embeds.shape[0]\n\n if image_batch_size != 1 and image_batch_size != prompt_batch_size:\n raise ValueError(\n f\"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.check_inputs\n def check_inputs(\n self,\n prompt,\n prompt_2,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n negative_prompt_2=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n pooled_prompt_embeds=None,\n negative_pooled_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if prompt_embeds is not None and pooled_prompt_embeds is None:\n raise ValueError(\n \"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.\"\n )\n\n if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:\n raise ValueError(\n \"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.\"\n )\n\n def check_conditions(\n self,\n prompt,\n prompt_embeds,\n adapter_image,\n control_image,\n adapter_conditioning_scale,\n controlnet_conditioning_scale,\n control_guidance_start,\n control_guidance_end,\n ):\n # controlnet checks\n if not isinstance(control_guidance_start, (tuple, list)):\n control_guidance_start = [control_guidance_start]\n\n if not isinstance(control_guidance_end, (tuple, list)):\n control_guidance_end = [control_guidance_end]\n\n if len(control_guidance_start) != len(control_guidance_end):\n raise ValueError(\n f\"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list.\"\n )\n\n if isinstance(self.controlnet, MultiControlNetModel):\n if len(control_guidance_start) != len(self.controlnet.nets):\n raise ValueError(\n f\"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}.\"\n )\n\n for start, end in zip(control_guidance_start, control_guidance_end):\n if start >= end:\n raise ValueError(\n f\"control guidance start: {start} cannot be larger or equal to control guidance end: {end}.\"\n )\n if start < 0.0:\n raise ValueError(f\"control guidance start: {start} can't be smaller than 0.\")\n if end > 1.0:\n raise ValueError(f\"control guidance end: {end} can't be larger than 1.0.\")\n\n # Check controlnet `image`\n is_compiled = hasattr(F, \"scaled_dot_product_attention\") and isinstance(\n self.controlnet, torch._dynamo.eval_frame.OptimizedModule\n )\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n self.check_image(control_image, prompt, prompt_embeds)\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if not isinstance(control_image, list):\n raise TypeError(\"For multiple controlnets: `control_image` must be type `list`\")\n\n # When `image` is a nested list:\n # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])\n elif any(isinstance(i, list) for i in control_image):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif len(control_image) != len(self.controlnet.nets):\n raise ValueError(\n f\"For multiple controlnets: `image` must have the same length as the number of controlnets, but got {len(control_image)} images and {len(self.controlnet.nets)} ControlNets.\"\n )\n\n for image_ in control_image:\n self.check_image(image_, prompt, prompt_embeds)\n else:\n assert False\n\n # Check `controlnet_conditioning_scale`\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n if not isinstance(controlnet_conditioning_scale, float):\n raise TypeError(\"For single controlnet: `controlnet_conditioning_scale` must be type `float`.\")\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if isinstance(controlnet_conditioning_scale, list):\n if any(isinstance(i, list) for i in controlnet_conditioning_scale):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(\n self.controlnet.nets\n ):\n raise ValueError(\n \"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have\"\n \" the same length as the number of controlnets\"\n )\n else:\n assert False\n\n # adapter checks\n if isinstance(self.adapter, T2IAdapter) or is_compiled and isinstance(self.adapter._orig_mod, T2IAdapter):\n self.check_image(adapter_image, prompt, prompt_embeds)\n elif (\n isinstance(self.adapter, MultiAdapter) or is_compiled and isinstance(self.adapter._orig_mod, MultiAdapter)\n ):\n if not isinstance(adapter_image, list):\n raise TypeError(\"For multiple adapters: `adapter_image` must be type `list`\")\n\n # When `image` is a nested list:\n # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])\n elif any(isinstance(i, list) for i in adapter_image):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif len(adapter_image) != len(self.adapter.adapters):\n raise ValueError(\n f\"For multiple adapters: `image` must have the same length as the number of adapters, but got {len(adapter_image)} images and {len(self.adapters.nets)} Adapters.\"\n )\n\n for image_ in adapter_image:\n self.check_image(image_, prompt, prompt_embeds)\n else:\n assert False\n\n # Check `adapter_conditioning_scale`\n if isinstance(self.adapter, T2IAdapter) or is_compiled and isinstance(self.adapter._orig_mod, T2IAdapter):\n if not isinstance(adapter_conditioning_scale, float):\n raise TypeError(\"For single adapter: `adapter_conditioning_scale` must be type `float`.\")\n elif (\n isinstance(self.adapter, MultiAdapter) or is_compiled and isinstance(self.adapter._orig_mod, MultiAdapter)\n ):\n if isinstance(adapter_conditioning_scale, list):\n if any(isinstance(i, list) for i in adapter_conditioning_scale):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif isinstance(adapter_conditioning_scale, list) and len(adapter_conditioning_scale) != len(\n self.adapter.adapters\n ):\n raise ValueError(\n \"For multiple adapters: When `adapter_conditioning_scale` is specified as `list`, it must have\"\n \" the same length as the number of adapters\"\n )\n else:\n assert False\n\n def prepare_latents(\n self,\n batch_size,\n num_channels_latents,\n height,\n width,\n dtype,\n device,\n generator,\n latents=None,\n image=None,\n timestep=None,\n is_strength_max=True,\n add_noise=True,\n return_noise=False,\n return_image_latents=False,\n ):\n shape = (\n batch_size,\n num_channels_latents,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if (image is None or timestep is None) and not is_strength_max:\n raise ValueError(\n \"Since strength < 1. initial latents are to be initialised as a combination of Image + Noise.\"\n \"However, either the image or the noise timestep has not been provided.\"\n )\n\n if image.shape[1] == 4:\n image_latents = image.to(device=device, dtype=dtype)\n elif return_image_latents or (latents is None and not is_strength_max):\n image = image.to(device=device, dtype=dtype)\n image_latents = self._encode_vae_image(image=image, generator=generator)\n\n image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)\n\n if latents is None and add_noise:\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n # if strength is 1. then initialise the latents to noise, else initial to image + noise\n latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)\n # if pure noise then scale the initial latents by the Scheduler's init sigma\n latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents\n elif add_noise:\n noise = latents.to(device)\n latents = noise * self.scheduler.init_noise_sigma\n else:\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n latents = image_latents.to(device)\n\n outputs = (latents,)\n\n if return_noise:\n outputs += (noise,)\n\n if return_image_latents:\n outputs += (image_latents,)\n\n return outputs\n\n def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):\n dtype = image.dtype\n if self.vae.config.force_upcast:\n image = image.float()\n self.vae.to(dtype=torch.float32)\n\n if isinstance(generator, list):\n image_latents = [\n self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i])\n for i in range(image.shape[0])\n ]\n image_latents = torch.cat(image_latents, dim=0)\n else:\n image_latents = self.vae.encode(image).latent_dist.sample(generator=generator)\n\n if self.vae.config.force_upcast:\n self.vae.to(dtype)\n\n image_latents = image_latents.to(dtype)\n image_latents = self.vae.config.scaling_factor * image_latents\n\n return image_latents\n\n def prepare_mask_latents(\n self,\n mask,\n masked_image,\n batch_size,\n height,\n width,\n dtype,\n device,\n generator,\n do_classifier_free_guidance,\n ):\n # resize the mask to latents shape as we concatenate the mask to the latents\n # we do that before converting to dtype to avoid breaking in case we're using cpu_offload\n # and half precision\n mask = torch.nn.functional.interpolate(\n mask,\n size=(\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n ),\n )\n mask = mask.to(device=device, dtype=dtype)\n\n # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method\n if mask.shape[0] < batch_size:\n if not batch_size % mask.shape[0] == 0:\n raise ValueError(\n \"The passed mask and the required batch size don't match. Masks are supposed to be duplicated to\"\n f\" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number\"\n \" of masks that you pass is divisible by the total requested batch size.\"\n )\n mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)\n\n mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask\n\n masked_image_latents = None\n if masked_image is not None:\n masked_image = masked_image.to(device=device, dtype=dtype)\n masked_image_latents = self._encode_vae_image(masked_image, generator=generator)\n if masked_image_latents.shape[0] < batch_size:\n if not batch_size % masked_image_latents.shape[0] == 0:\n raise ValueError(\n \"The passed images and the required batch size don't match. Images are supposed to be duplicated\"\n f\" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed.\"\n \" Make sure the number of images that you pass is divisible by the total requested batch size.\"\n )\n masked_image_latents = masked_image_latents.repeat(\n batch_size // masked_image_latents.shape[0], 1, 1, 1\n )\n\n masked_image_latents = (\n torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents\n )\n\n # aligning device to prevent device errors when concating it with the latent model input\n masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)\n\n return mask, masked_image_latents\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength, device, denoising_start=None):\n # get the original timestep using init_timestep\n if denoising_start is None:\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n t_start = max(num_inference_steps - init_timestep, 0)\n else:\n t_start = 0\n\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n\n # Strength is irrelevant if we directly request a timestep to start at;\n # that is, strength is determined by the denoising_start instead.\n if denoising_start is not None:\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (denoising_start * self.scheduler.config.num_train_timesteps)\n )\n )\n\n num_inference_steps = (timesteps < discrete_timestep_cutoff).sum().item()\n if self.scheduler.order == 2 and num_inference_steps % 2 == 0:\n # if the scheduler is a 2nd order scheduler we might have to do +1\n # because `num_inference_steps` might be even given that every timestep\n # (except the highest one) is duplicated. If `num_inference_steps` is even it would\n # mean that we cut the timesteps in the middle of the denoising step\n # (between 1st and 2nd derivative) which leads to incorrect results. By adding 1\n # we ensure that the denoising process always ends after the 2nd derivate step of the scheduler\n num_inference_steps = num_inference_steps + 1\n\n # because t_n+1 >= t_n, we slice the timesteps starting from the end\n timesteps = timesteps[-num_inference_steps:]\n return timesteps, num_inference_steps\n\n return timesteps, num_inference_steps - t_start\n\n def _get_add_time_ids(\n self,\n original_size,\n crops_coords_top_left,\n target_size,\n aesthetic_score,\n negative_aesthetic_score,\n dtype,\n text_encoder_projection_dim=None,\n ):\n if self.config.requires_aesthetics_score:\n add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,))\n add_neg_time_ids = list(original_size + crops_coords_top_left + (negative_aesthetic_score,))\n else:\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n add_neg_time_ids = list(original_size + crops_coords_top_left + target_size)\n\n passed_add_embed_dim = (\n self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim\n )\n expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features\n\n if (\n expected_add_embed_dim > passed_add_embed_dim\n and (expected_add_embed_dim - passed_add_embed_dim) == self.unet.config.addition_time_embed_dim\n ):\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to enable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=True)` to make sure `aesthetic_score` {aesthetic_score} and `negative_aesthetic_score` {negative_aesthetic_score} is correctly used by the model.\"\n )\n elif (\n expected_add_embed_dim < passed_add_embed_dim\n and (passed_add_embed_dim - expected_add_embed_dim) == self.unet.config.addition_time_embed_dim\n ):\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the model.\"\n )\n elif expected_add_embed_dim != passed_add_embed_dim:\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.\"\n )\n\n add_time_ids = torch.tensor([add_time_ids], dtype=dtype)\n add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype)\n\n return add_time_ids, add_neg_time_ids\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae\n def upcast_vae(self):\n dtype = self.vae.dtype\n self.vae.to(dtype=torch.float32)\n use_torch_2_0_or_xformers = isinstance(\n self.vae.decoder.mid_block.attentions[0].processor,\n (\n AttnProcessor2_0,\n XFormersAttnProcessor,\n LoRAXFormersAttnProcessor,\n LoRAAttnProcessor2_0,\n ),\n )\n # if xformers or torch_2_0 is used attention block does not need\n # to be in float32 which can save lots of memory\n if use_torch_2_0_or_xformers:\n self.vae.post_quant_conv.to(dtype)\n self.vae.decoder.conv_in.to(dtype)\n self.vae.decoder.mid_block.to(dtype)\n\n # Copied from diffusers.pipelines.t2i_adapter.pipeline_stable_diffusion_adapter.StableDiffusionAdapterPipeline._default_height_width\n def _default_height_width(self, height, width, image):\n # NOTE: It is possible that a list of images have different\n # dimensions for each image, so just checking the first image\n # is not _exactly_ correct, but it is simple.\n while isinstance(image, list):\n image = image[0]\n\n if height is None:\n if isinstance(image, PIL.Image.Image):\n height = image.height\n elif isinstance(image, torch.Tensor):\n height = image.shape[-2]\n\n # round down to nearest multiple of `self.adapter.downscale_factor`\n height = (height // self.adapter.downscale_factor) * self.adapter.downscale_factor\n\n if width is None:\n if isinstance(image, PIL.Image.Image):\n width = image.width\n elif isinstance(image, torch.Tensor):\n width = image.shape[-1]\n\n # round down to nearest multiple of `self.adapter.downscale_factor`\n width = (width // self.adapter.downscale_factor) * self.adapter.downscale_factor\n\n return height, width\n\n def prepare_control_image(\n self,\n image,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype,\n do_classifier_free_guidance=False,\n guess_mode=False,\n ):\n image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)\n image_batch_size = image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n image = image.repeat_interleave(repeat_by, dim=0)\n\n image = image.to(device=device, dtype=dtype)\n\n if do_classifier_free_guidance and not guess_mode:\n image = torch.cat([image] * 2)\n\n return image\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Optional[Union[str, List[str]]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n image: Optional[Union[torch.Tensor, PIL.Image.Image]] = None,\n mask_image: Optional[Union[torch.Tensor, PIL.Image.Image]] = None,\n adapter_image: PipelineImageInput = None,\n control_image: PipelineImageInput = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n strength: float = 0.9999,\n num_inference_steps: int = 50,\n denoising_start: Optional[float] = None,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[Union[torch.Tensor]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Optional[Tuple[int, int]] = None,\n crops_coords_top_left: Optional[Tuple[int, int]] = (0, 0),\n target_size: Optional[Tuple[int, int]] = None,\n adapter_conditioning_scale: Optional[Union[float, List[float]]] = 1.0,\n cond_tau: float = 1.0,\n aesthetic_score: float = 6.0,\n negative_aesthetic_score: float = 2.5,\n controlnet_conditioning_scale=1.0,\n guess_mode: bool = False,\n control_guidance_start=0.0,\n control_guidance_end=1.0,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n image (`PIL.Image.Image`):\n `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will\n be masked out with `mask_image` and repainted according to `prompt`.\n mask_image (`PIL.Image.Image`):\n `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be\n repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted\n to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)\n instead of 3, so the expected shape would be `(B, H, W, 1)`.\n adapter_image (`torch.Tensor`, `PIL.Image.Image`, `List[torch.Tensor]` or `List[PIL.Image.Image]` or `List[List[PIL.Image.Image]]`):\n The Adapter input condition. Adapter uses this input condition to generate guidance to Unet. If the\n type is specified as `torch.Tensor`, it is passed to Adapter as is. PIL.Image.Image` can also be\n accepted as an image. The control image is automatically resized to fit the output image.\n control_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:\n `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):\n The ControlNet input condition to provide guidance to the `unet` for generation. If the type is\n specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be\n accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height\n and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in\n `init`, images must be passed as a list such that each element of the list can be correctly batched for\n input to a single ControlNet.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n strength (`float`, *optional*, defaults to 1.0):\n Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a\n starting point and more noise is added the higher the `strength`. The number of denoising steps depends\n on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising\n process runs for the full number of iterations specified in `num_inference_steps`. A value of 1\n essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n denoising_start (`float`, *optional*):\n When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be\n bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and\n it is assumed that the passed `image` is a partly denoised image. Note that when this is specified,\n strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline\n is integrated into a \"Mixture of Denoisers\" multi-pipeline setup, as detailed in [**Refining the Image\n Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).\n denoising_end (`float`, *optional*):\n When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be\n completed before it is intentionally prematurely terminated. As a result, the returned sample will\n still retain a substantial amount of noise as determined by the discrete timesteps selected by the\n scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a\n \"Mixture of Denoisers\" multi-pipeline setup, as elaborated in [**Refining the Image\n Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)\n guidance_scale (`float`, *optional*, defaults to 5.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionAdapterPipelineOutput`]\n instead of a plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n guidance_rescale (`float`, *optional*, defaults to 0.7):\n Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of\n [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).\n Guidance rescale factor should fix overexposure when using zero terminal SNR.\n original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.\n `original_size` defaults to `(width, height)` if not specified. Part of SDXL's micro-conditioning as\n explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n `crops_coords_top_left` can be used to generate an image that appears to be \"cropped\" from the position\n `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n For most cases, `target_size` should be set to the desired height and width of the generated image. If\n not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):\n The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added to the\n residual in the original unet. If multiple adapters are specified in init, you can set the\n corresponding scale as a list.\n adapter_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):\n The outputs of the adapter are multiplied by `adapter_conditioning_scale` before they are added to the\n residual in the original unet. If multiple adapters are specified in init, you can set the\n corresponding scale as a list.\n aesthetic_score (`float`, *optional*, defaults to 6.0):\n Used to simulate an aesthetic score of the generated image by influencing the positive text condition.\n Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n negative_aesthetic_score (`float`, *optional*, defaults to 2.5):\n Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). Can be used to\n simulate an aesthetic score of the generated image by influencing the negative text condition.\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionAdapterPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionAdapterPipelineOutput`] if `return_dict` is True, otherwise a\n `tuple`. When returning a tuple, the first element is a list with the generated images.\n \"\"\"\n # 0. Default height and width to unet\n controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet\n adapter = self.adapter._orig_mod if is_compiled_module(self.adapter) else self.adapter\n height, width = self._default_height_width(height, width, adapter_image)\n device = self._execution_device\n\n if isinstance(adapter, MultiAdapter):\n adapter_input = []\n for one_image in adapter_image:\n one_image = _preprocess_adapter_image(one_image, height, width)\n one_image = one_image.to(device=device, dtype=adapter.dtype)\n adapter_input.append(one_image)\n else:\n adapter_input = _preprocess_adapter_image(adapter_image, height, width)\n adapter_input = adapter_input.to(device=device, dtype=adapter.dtype)\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n # 0.1 align format for control guidance\n if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):\n control_guidance_start = len(control_guidance_end) * [control_guidance_start]\n elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):\n control_guidance_end = len(control_guidance_start) * [control_guidance_end]\n elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):\n mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1\n control_guidance_start, control_guidance_end = (\n mult * [control_guidance_start],\n mult * [control_guidance_end],\n )\n\n if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):\n controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)\n if isinstance(adapter, MultiAdapter) and isinstance(adapter_conditioning_scale, float):\n adapter_conditioning_scale = [adapter_conditioning_scale] * len(adapter.nets)\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n height,\n width,\n callback_steps,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n )\n\n self.check_conditions(\n prompt,\n prompt_embeds,\n adapter_image,\n control_image,\n adapter_conditioning_scale,\n controlnet_conditioning_scale,\n control_guidance_start,\n control_guidance_end,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n )\n\n # 4. set timesteps\n def denoising_value_valid(dnv):\n return isinstance(dnv, float) and 0 < dnv < 1\n\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(\n num_inference_steps,\n strength,\n device,\n denoising_start=denoising_start if denoising_value_valid(denoising_start) else None,\n )\n # check that number of inference steps is not < 1 - as this doesn't make sense\n if num_inference_steps < 1:\n raise ValueError(\n f\"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline\"\n f\"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline.\"\n )\n # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise\n is_strength_max = strength == 1.0\n\n # 5. Preprocess mask and image - resizes image and mask w.r.t height and width\n mask, masked_image, init_image = prepare_mask_and_masked_image(\n image, mask_image, height, width, return_image=True\n )\n\n # 6. Prepare latent variables\n num_channels_latents = self.vae.config.latent_channels\n num_channels_unet = self.unet.config.in_channels\n return_image_latents = num_channels_unet == 4\n\n add_noise = denoising_start is None\n latents_outputs = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n image=init_image,\n timestep=latent_timestep,\n is_strength_max=is_strength_max,\n add_noise=add_noise,\n return_noise=True,\n return_image_latents=return_image_latents,\n )\n\n if return_image_latents:\n latents, noise, image_latents = latents_outputs\n else:\n latents, noise = latents_outputs\n\n # 7. Prepare mask latent variables\n mask, masked_image_latents = self.prepare_mask_latents(\n mask,\n masked_image,\n batch_size * num_images_per_prompt,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n do_classifier_free_guidance,\n )\n\n # 8. Check that sizes of mask, masked image and latents match\n if num_channels_unet == 9:\n # default case for runwayml/stable-diffusion-inpainting\n num_channels_mask = mask.shape[1]\n num_channels_masked_image = masked_image_latents.shape[1]\n if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:\n raise ValueError(\n f\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\"\n f\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\"\n f\" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}\"\n f\" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of\"\n \" `pipeline.unet` or your `mask_image` or `image` input.\"\n )\n elif num_channels_unet != 4:\n raise ValueError(\n f\"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}.\"\n )\n\n # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 10. Prepare added time ids & embeddings & adapter features\n if isinstance(adapter, MultiAdapter):\n adapter_state = adapter(adapter_input, adapter_conditioning_scale)\n for k, v in enumerate(adapter_state):\n adapter_state[k] = v\n else:\n adapter_state = adapter(adapter_input)\n for k, v in enumerate(adapter_state):\n adapter_state[k] = v * adapter_conditioning_scale\n if num_images_per_prompt > 1:\n for k, v in enumerate(adapter_state):\n adapter_state[k] = v.repeat(num_images_per_prompt, 1, 1, 1)\n if do_classifier_free_guidance:\n for k, v in enumerate(adapter_state):\n adapter_state[k] = torch.cat([v] * 2, dim=0)\n\n # 10.2 Prepare control images\n if isinstance(controlnet, ControlNetModel):\n control_image = self.prepare_control_image(\n image=control_image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n elif isinstance(controlnet, MultiControlNetModel):\n control_images = []\n\n for control_image_ in control_image:\n control_image_ = self.prepare_control_image(\n image=control_image_,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n\n control_images.append(control_image_)\n\n control_image = control_images\n else:\n raise ValueError(f\"{controlnet.__class__} is not supported.\")\n\n # 8.2 Create tensor stating which controlnets to keep\n controlnet_keep = []\n for i in range(len(timesteps)):\n keeps = [\n 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)\n for s, e in zip(control_guidance_start, control_guidance_end)\n ]\n if isinstance(self.controlnet, MultiControlNetModel):\n controlnet_keep.append(keeps)\n else:\n controlnet_keep.append(keeps[0])\n # ----------------------------------------------------------------\n\n add_text_embeds = pooled_prompt_embeds\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n add_time_ids, add_neg_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n aesthetic_score,\n negative_aesthetic_score,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)\n\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1)\n add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device)\n\n # 11. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n\n # 11.1 Apply denoising_end\n if (\n denoising_end is not None\n and denoising_start is not None\n and denoising_value_valid(denoising_end)\n and denoising_value_valid(denoising_start)\n and denoising_start >= denoising_end\n ):\n raise ValueError(\n f\"`denoising_start`: {denoising_start} cannot be larger than or equal to `denoising_end`: \"\n + f\" {denoising_end} when using type float.\"\n )\n elif denoising_end is not None and denoising_value_valid(denoising_end):\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n if num_channels_unet == 9:\n latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)\n\n # predict the noise residual\n added_cond_kwargs = {\n \"text_embeds\": add_text_embeds,\n \"time_ids\": add_time_ids,\n }\n\n if i < int(num_inference_steps * cond_tau):\n down_block_additional_residuals = [state.clone() for state in adapter_state]\n else:\n down_block_additional_residuals = None\n\n # ----------- ControlNet\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input_controlnet = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n\n # concat latents, mask, masked_image_latents in the channel dimension\n latent_model_input_controlnet = self.scheduler.scale_model_input(latent_model_input_controlnet, t)\n\n # controlnet(s) inference\n if guess_mode and do_classifier_free_guidance:\n # Infer ControlNet only for the conditional batch.\n control_model_input = latents\n control_model_input = self.scheduler.scale_model_input(control_model_input, t)\n controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]\n controlnet_added_cond_kwargs = {\n \"text_embeds\": add_text_embeds.chunk(2)[1],\n \"time_ids\": add_time_ids.chunk(2)[1],\n }\n else:\n control_model_input = latent_model_input_controlnet\n controlnet_prompt_embeds = prompt_embeds\n controlnet_added_cond_kwargs = added_cond_kwargs\n\n if isinstance(controlnet_keep[i], list):\n cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]\n else:\n controlnet_cond_scale = controlnet_conditioning_scale\n if isinstance(controlnet_cond_scale, list):\n controlnet_cond_scale = controlnet_cond_scale[0]\n cond_scale = controlnet_cond_scale * controlnet_keep[i]\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n control_model_input,\n t,\n encoder_hidden_states=controlnet_prompt_embeds,\n controlnet_cond=control_image,\n conditioning_scale=cond_scale,\n guess_mode=guess_mode,\n added_cond_kwargs=controlnet_added_cond_kwargs,\n return_dict=False,\n )\n\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n down_intrablock_additional_residuals=down_block_additional_residuals, # t2iadapter\n down_block_additional_residuals=down_block_res_samples, # controlnet\n mid_block_additional_residual=mid_block_res_sample, # controlnet\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if do_classifier_free_guidance and guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(\n noise_pred,\n noise_pred_text,\n guidance_rescale=guidance_rescale,\n )\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(\n noise_pred,\n t,\n latents,\n **extra_step_kwargs,\n return_dict=False,\n )[0]\n\n if num_channels_unet == 4:\n init_latents_proper = image_latents\n if do_classifier_free_guidance:\n init_mask, _ = mask.chunk(2)\n else:\n init_mask = mask\n\n if i < len(timesteps) - 1:\n noise_timestep = timesteps[i + 1]\n init_latents_proper = self.scheduler.add_noise(\n init_latents_proper,\n noise,\n torch.tensor([noise_timestep]),\n )\n\n latents = (1 - init_mask) * init_latents_proper + init_mask * latents\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, latents)\n\n # make sure the VAE is in float32 mode, as it overflows in float16\n if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n if output_type != \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n else:\n image = latents\n return StableDiffusionXLPipelineOutput(images=image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload last model to CPU\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)\n" }, { "path": "diffusers/examples/community/pipeline_stable_diffusion_xl_differential_img2img.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torchvision\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModel,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers.image_processor import PipelineImageInput, VaeImageProcessor\nfrom diffusers.loaders import (\n FromSingleFileMixin,\n IPAdapterMixin,\n StableDiffusionXLLoraLoaderMixin,\n TextualInversionLoaderMixin,\n)\nfrom diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel\nfrom diffusers.models.attention_processor import (\n AttnProcessor2_0,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n XFormersAttnProcessor,\n)\nfrom diffusers.models.lora import adjust_lora_scale_text_encoder\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n USE_PEFT_BACKEND,\n deprecate,\n is_invisible_watermark_available,\n is_torch_xla_available,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nif is_invisible_watermark_available():\n from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker\n\nif is_torch_xla_available():\n import torch_xla.core.xla_model as xm\n\n XLA_AVAILABLE = True\nelse:\n XLA_AVAILABLE = False\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusionXLImg2ImgPipeline\n >>> from diffusers.utils import load_image\n\n >>> pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(\n ... \"stabilityai/stable-diffusion-xl-refiner-1.0\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n >>> url = \"https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/aa_xl/000000009.png\"\n\n >>> init_image = load_image(url).convert(\"RGB\")\n >>> prompt = \"a photo of an astronaut riding a horse on mars\"\n >>> image = pipe(prompt, image=init_image).images[0]\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used,\n `timesteps` must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default\n timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`\n must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass StableDiffusionXLDifferentialImg2ImgPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n TextualInversionLoaderMixin,\n FromSingleFileMixin,\n StableDiffusionXLLoraLoaderMixin,\n IPAdapterMixin,\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion XL.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n In addition the pipeline inherits the following loading methods:\n - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]\n - *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`]\n - *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`]\n\n as well as the following saving methods:\n - *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`]\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion XL uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n text_encoder_2 ([` CLIPTextModelWithProjection`]):\n Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),\n specifically the\n [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)\n variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n tokenizer_2 (`CLIPTokenizer`):\n Second Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->text_encoder_2->image_encoder->unet->vae\"\n _optional_components = [\n \"tokenizer\",\n \"tokenizer_2\",\n \"text_encoder\",\n \"text_encoder_2\",\n \"image_encoder\",\n \"feature_extractor\",\n ]\n _callback_tensor_inputs = [\n \"latents\",\n \"prompt_embeds\",\n \"negative_prompt_embeds\",\n \"add_text_embeds\",\n \"add_time_ids\",\n \"negative_pooled_prompt_embeds\",\n \"add_neg_time_ids\",\n ]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n text_encoder_2: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n tokenizer_2: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n image_encoder: CLIPVisionModelWithProjection = None,\n feature_extractor: CLIPImageProcessor = None,\n requires_aesthetics_score: bool = False,\n force_zeros_for_empty_prompt: bool = True,\n add_watermarker: Optional[bool] = None,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n unet=unet,\n image_encoder=image_encoder,\n feature_extractor=feature_extractor,\n scheduler=scheduler,\n )\n self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)\n self.register_to_config(requires_aesthetics_score=requires_aesthetics_score)\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()\n\n if add_watermarker:\n self.watermark = StableDiffusionXLWatermarker()\n else:\n self.watermark = None\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt: str,\n prompt_2: Optional[str] = None,\n device: Optional[torch.device] = None,\n num_images_per_prompt: int = 1,\n do_classifier_free_guidance: bool = True,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n lora_scale (`float`, *optional*):\n A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n device = device or self._execution_device\n\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if self.text_encoder is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)\n else:\n scale_lora_layers(self.text_encoder_2, lora_scale)\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n\n if prompt is not None:\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # Define tokenizers and text encoders\n tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]\n text_encoders = (\n [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]\n )\n\n if prompt_embeds is None:\n prompt_2 = prompt_2 or prompt\n prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2\n\n # textual inversion: process multi-vector tokens if necessary\n prompt_embeds_list = []\n prompts = [prompt, prompt_2]\n for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, tokenizer)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n if clip_skip is None:\n prompt_embeds = prompt_embeds.hidden_states[-2]\n else:\n # \"2\" because SDXL always indexes from the penultimate layer.\n prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]\n\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n\n # get unconditional embeddings for classifier free guidance\n zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt\n if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:\n negative_prompt_embeds = torch.zeros_like(prompt_embeds)\n negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)\n elif do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n\n # normalize str to list\n negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt\n negative_prompt_2 = (\n batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2\n )\n\n uncond_tokens: List[str]\n if prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = [negative_prompt, negative_prompt_2]\n\n negative_prompt_embeds_list = []\n for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n negative_prompt_embeds = text_encoder(\n uncond_input.input_ids.to(device),\n output_hidden_states=True,\n )\n # We are only ALWAYS interested in the pooled output of the final text encoder\n negative_pooled_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]\n\n negative_prompt_embeds_list.append(negative_prompt_embeds)\n\n negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)\n\n if self.text_encoder_2 is not None:\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n if self.text_encoder_2 is not None:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n if do_classifier_free_guidance:\n negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n\n if self.text_encoder is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder_2, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n prompt_2,\n strength,\n num_inference_steps,\n callback_steps,\n negative_prompt=None,\n negative_prompt_2=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n if num_inference_steps is None:\n raise ValueError(\"`num_inference_steps` cannot be None.\")\n elif not isinstance(num_inference_steps, int) or num_inference_steps <= 0:\n raise ValueError(\n f\"`num_inference_steps` has to be a positive integer but is {num_inference_steps} of type\"\n f\" {type(num_inference_steps)}.\"\n )\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n if ip_adapter_image_embeds is not None:\n if not isinstance(ip_adapter_image_embeds, list):\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}\"\n )\n elif ip_adapter_image_embeds[0].ndim not in [3, 4]:\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D\"\n )\n\n def get_timesteps(self, num_inference_steps, strength, device, denoising_start=None):\n # get the original timestep using init_timestep\n if denoising_start is None:\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n t_start = max(num_inference_steps - init_timestep, 0)\n else:\n t_start = 0\n\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n\n # Strength is irrelevant if we directly request a timestep to start at;\n # that is, strength is determined by the denoising_start instead.\n if denoising_start is not None:\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (denoising_start * self.scheduler.config.num_train_timesteps)\n )\n )\n\n num_inference_steps = (timesteps < discrete_timestep_cutoff).sum().item()\n if self.scheduler.order == 2 and num_inference_steps % 2 == 0:\n # if the scheduler is a 2nd order scheduler we might have to do +1\n # because `num_inference_steps` might be even given that every timestep\n # (except the highest one) is duplicated. If `num_inference_steps` is even it would\n # mean that we cut the timesteps in the middle of the denoising step\n # (between 1st and 2nd derivative) which leads to incorrect results. By adding 1\n # we ensure that the denoising process always ends after the 2nd derivate step of the scheduler\n num_inference_steps = num_inference_steps + 1\n\n # because t_n+1 >= t_n, we slice the timesteps starting from the end\n timesteps = timesteps[-num_inference_steps:]\n return timesteps, num_inference_steps\n\n return timesteps, num_inference_steps - t_start\n\n def prepare_latents(\n self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None, add_noise=True\n ):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n # Offload text encoder if `enable_model_cpu_offload` was enabled\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.text_encoder_2.to(\"cpu\")\n torch.cuda.empty_cache()\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n\n if image.shape[1] == 4:\n init_latents = image\n\n else:\n # make sure the VAE is in float32 mode, as it overflows in float16\n if self.vae.config.force_upcast:\n image = image.float()\n self.vae.to(dtype=torch.float32)\n\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n elif isinstance(generator, list):\n init_latents = [\n retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])\n for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = retrieve_latents(self.vae.encode(image), generator=generator)\n\n if self.vae.config.force_upcast:\n self.vae.to(dtype)\n\n init_latents = init_latents.to(dtype)\n init_latents = self.vae.config.scaling_factor * init_latents\n\n if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:\n # expand init_latents for batch_size\n additional_image_per_prompt = batch_size // init_latents.shape[0]\n init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)\n elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n init_latents = torch.cat([init_latents], dim=0)\n\n if add_noise:\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n\n latents = init_latents\n\n return latents\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n def _get_add_time_ids(\n self,\n original_size,\n crops_coords_top_left,\n target_size,\n aesthetic_score,\n negative_aesthetic_score,\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype,\n text_encoder_projection_dim=None,\n ):\n if self.config.requires_aesthetics_score:\n add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,))\n add_neg_time_ids = list(\n negative_original_size + negative_crops_coords_top_left + (negative_aesthetic_score,)\n )\n else:\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n add_neg_time_ids = list(negative_original_size + crops_coords_top_left + negative_target_size)\n\n passed_add_embed_dim = (\n self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim\n )\n expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features\n\n if (\n expected_add_embed_dim > passed_add_embed_dim\n and (expected_add_embed_dim - passed_add_embed_dim) == self.unet.config.addition_time_embed_dim\n ):\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to enable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=True)` to make sure `aesthetic_score` {aesthetic_score} and `negative_aesthetic_score` {negative_aesthetic_score} is correctly used by the model.\"\n )\n elif (\n expected_add_embed_dim < passed_add_embed_dim\n and (passed_add_embed_dim - expected_add_embed_dim) == self.unet.config.addition_time_embed_dim\n ):\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the model.\"\n )\n elif expected_add_embed_dim != passed_add_embed_dim:\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.\"\n )\n\n add_time_ids = torch.tensor([add_time_ids], dtype=dtype)\n add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype)\n\n return add_time_ids, add_neg_time_ids\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae\n def upcast_vae(self):\n dtype = self.vae.dtype\n self.vae.to(dtype=torch.float32)\n use_torch_2_0_or_xformers = isinstance(\n self.vae.decoder.mid_block.attentions[0].processor,\n (\n AttnProcessor2_0,\n XFormersAttnProcessor,\n LoRAXFormersAttnProcessor,\n LoRAAttnProcessor2_0,\n ),\n )\n # if xformers or torch_2_0 is used attention block does not need\n # to be in float32 which can save lots of memory\n if use_torch_2_0_or_xformers:\n self.vae.post_quant_conv.to(dtype)\n self.vae.decoder.conv_in.to(dtype)\n self.vae.decoder.mid_block.to(dtype)\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(\n self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32\n ) -> torch.Tensor:\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n w (`torch.Tensor`):\n Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.\n embedding_dim (`int`, *optional*, defaults to 512):\n Dimension of the embeddings to generate.\n dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):\n Data type of the generated embeddings.\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def guidance_rescale(self):\n return self._guidance_rescale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def denoising_end(self):\n return self._denoising_end\n\n @property\n def denoising_start(self):\n return self._denoising_start\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @property\n def interrupt(self):\n return self._interrupt\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n image: Union[\n torch.Tensor,\n PIL.Image.Image,\n np.ndarray,\n List[torch.Tensor],\n List[PIL.Image.Image],\n List[np.ndarray],\n ] = None,\n strength: float = 0.3,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n denoising_start: Optional[float] = None,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Tuple[int, int] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Tuple[int, int] = None,\n negative_original_size: Optional[Tuple[int, int]] = None,\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\n negative_target_size: Optional[Tuple[int, int]] = None,\n aesthetic_score: float = 6.0,\n negative_aesthetic_score: float = 2.5,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n map: torch.Tensor = None,\n original_image: Union[\n torch.Tensor,\n PIL.Image.Image,\n np.ndarray,\n List[torch.Tensor],\n List[PIL.Image.Image],\n List[np.ndarray],\n ] = None,\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n image (`torch.Tensor` or `PIL.Image.Image` or `np.ndarray` or `List[torch.Tensor]` or `List[PIL.Image.Image]` or `List[np.ndarray]`):\n The image(s) to modify with the pipeline.\n strength (`float`, *optional*, defaults to 0.3):\n Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`\n will be used as a starting point, adding more noise to it the larger the `strength`. The number of\n denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will\n be maximum and the denoising process will run for the full number of iterations specified in\n `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. Note that in the case of\n `denoising_start` being declared as an integer, the value of `strength` will be ignored.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n denoising_start (`float`, *optional*):\n When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be\n bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and\n it is assumed that the passed `image` is a partly denoised image. Note that when this is specified,\n strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline\n is integrated into a \"Mixture of Denoisers\" multi-pipeline setup, as detailed in [**Refining the Image\n Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).\n denoising_end (`float`, *optional*):\n When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be\n completed before it is intentionally prematurely terminated. As a result, the returned sample will\n still retain a substantial amount of noise (ca. final 20% of timesteps still needed) and should be\n denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the\n final 20% of the scheduler. The denoising_end parameter should ideally be utilized when this pipeline\n forms a part of a \"Mixture of Denoisers\" multi-pipeline setup, as elaborated in [**Refining the Image\n Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters.\n Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should contain the negative image embedding\n if `do_classifier_free_guidance` is set to `True`.\n If not provided, embeddings are computed from the `ip_adapter_image` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).\n guidance_rescale (`float`, *optional*, defaults to 0.7):\n Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of\n [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).\n Guidance rescale factor should fix overexposure when using zero terminal SNR.\n original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.\n `original_size` defaults to `(width, height)` if not specified. Part of SDXL's micro-conditioning as\n explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n `crops_coords_top_left` can be used to generate an image that appears to be \"cropped\" from the position\n `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n For most cases, `target_size` should be set to the desired height and width of the generated image. If\n not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n aesthetic_score (`float`, *optional*, defaults to 6.0):\n Used to simulate an aesthetic score of the generated image by influencing the positive text condition.\n Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n negative_aesthetic_score (`float`, *optional*, defaults to 2.5):\n Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). Can be used to\n simulate an aesthetic score of the generated image by influencing the negative text condition.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a\n `tuple. When returning a tuple, the first element is a list with the generated images.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n strength,\n num_inference_steps,\n callback_steps,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n ip_adapter_image,\n ip_adapter_image_embeds,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._guidance_rescale = guidance_rescale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._denoising_end = denoising_end\n self._denoising_start = denoising_start\n self._interrupt = False\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n\n # 4. Preprocess image\n # image = self.image_processor.preprocess(image) #ideally we would have preprocess the image with diffusers, but for this POC we won't --- it throws a deprecated warning\n map = torchvision.transforms.Resize(\n tuple(s // self.vae_scale_factor for s in original_image.shape[2:]), antialias=None\n )(map)\n\n # 5. Prepare timesteps\n def denoising_value_valid(dnv):\n return isinstance(dnv, float) and 0 < dnv < 1\n\n timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)\n\n # begin diff diff change\n total_time_steps = num_inference_steps\n # end diff diff change\n\n timesteps, num_inference_steps = self.get_timesteps(\n num_inference_steps,\n strength,\n device,\n denoising_start=self.denoising_start if denoising_value_valid(self.denoising_start) else None,\n )\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n\n add_noise = True if denoising_start is None else False\n # 6. Prepare latent variables\n latents = self.prepare_latents(\n image,\n latent_timestep,\n batch_size,\n num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n generator,\n add_noise,\n )\n # 7. Prepare extra step kwargs.\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n height, width = latents.shape[-2:]\n height = height * self.vae_scale_factor\n width = width * self.vae_scale_factor\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n # 8. Prepare added time ids & embeddings\n if negative_original_size is None:\n negative_original_size = original_size\n if negative_target_size is None:\n negative_target_size = target_size\n\n add_text_embeds = pooled_prompt_embeds\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n add_time_ids, add_neg_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n aesthetic_score,\n negative_aesthetic_score,\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)\n\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1)\n add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device)\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_images_per_prompt,\n self.do_classifier_free_guidance,\n )\n\n # 9. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n\n # 9.1 Apply denoising_end\n if (\n denoising_end is not None\n and denoising_start is not None\n and denoising_value_valid(denoising_end)\n and denoising_value_valid(denoising_start)\n and denoising_start >= denoising_end\n ):\n raise ValueError(\n f\"`denoising_start`: {denoising_start} cannot be larger than or equal to `denoising_end`: \"\n + f\" {denoising_end} when using type float.\"\n )\n elif denoising_end is not None and denoising_value_valid(denoising_end):\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n # preparations for diff diff\n original_with_noise = self.prepare_latents(\n original_image, timesteps, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator\n )\n thresholds = torch.arange(total_time_steps, dtype=map.dtype) / total_time_steps\n thresholds = thresholds.unsqueeze(1).unsqueeze(1).to(device)\n masks = map > (thresholds + (denoising_start or 0))\n # end diff diff preparations\n\n # 9.2 Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n if self.interrupt:\n continue\n\n # diff diff\n if i == 0 and denoising_start is None:\n latents = original_with_noise[:1]\n else:\n mask = masks[i].unsqueeze(0)\n # cast mask to the same type as latents etc\n mask = mask.to(latents.dtype)\n mask = mask.unsqueeze(1) # fit shape\n latents = original_with_noise[i] * mask + latents * (1 - mask)\n # end diff diff\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n added_cond_kwargs[\"image_embeds\"] = image_embeds\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if self.do_classifier_free_guidance and guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents_dtype = latents.dtype\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n if latents.dtype != latents_dtype:\n if torch.backends.mps.is_available():\n # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272\n latents = latents.to(latents_dtype)\n else:\n raise ValueError(\n \"For the given accelerator, there seems to be an unexpected problem in type-casting. Please file an issue on the PyTorch GitHub repository. See also: https://github.com/huggingface/diffusers/pull/7446/.\"\n )\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n add_text_embeds = callback_outputs.pop(\"add_text_embeds\", add_text_embeds)\n negative_pooled_prompt_embeds = callback_outputs.pop(\n \"negative_pooled_prompt_embeds\", negative_pooled_prompt_embeds\n )\n add_time_ids = callback_outputs.pop(\"add_time_ids\", add_time_ids)\n add_neg_time_ids = callback_outputs.pop(\"add_neg_time_ids\", add_neg_time_ids)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if XLA_AVAILABLE:\n xm.mark_step()\n\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n elif latents.dtype != self.vae.dtype:\n if torch.backends.mps.is_available():\n # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272\n self.vae = self.vae.to(latents.dtype)\n else:\n raise ValueError(\n \"For the given accelerator, there seems to be an unexpected problem in type-casting. Please file an issue on the PyTorch GitHub repository. See also: https://github.com/huggingface/diffusers/pull/7446/.\"\n )\n # unscale/denormalize the latents\n # denormalize with the mean and std if available and not None\n has_latents_mean = hasattr(self.vae.config, \"latents_mean\") and self.vae.config.latents_mean is not None\n has_latents_std = hasattr(self.vae.config, \"latents_std\") and self.vae.config.latents_std is not None\n if has_latents_mean and has_latents_std:\n latents_mean = (\n torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)\n )\n latents_std = (\n torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)\n )\n latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean\n else:\n latents = latents / self.vae.config.scaling_factor\n\n image = self.vae.decode(latents, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n image = latents\n\n # apply watermark if available\n if self.watermark is not None:\n image = self.watermark.apply_watermark(image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)\n" }, { "path": "diffusers/examples/community/pipeline_stable_diffusion_xl_instandid_img2img.py", "content": "# Copyright 2024 The InstantX Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport math\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport cv2\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn as nn\n\nfrom diffusers import StableDiffusionXLControlNetImg2ImgPipeline\nfrom diffusers.image_processor import PipelineImageInput\nfrom diffusers.models import ControlNetModel\nfrom diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel\nfrom diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput\nfrom diffusers.utils import (\n deprecate,\n logging,\n replace_example_docstring,\n)\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module, is_torch_version\n\n\ntry:\n import xformers\n import xformers.ops\n\n xformers_available = True\nexcept Exception:\n xformers_available = False\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nlogger.warning(\n \"To use instant id pipelines, please make sure you have the `insightface` library installed: `pip install insightface`.\"\n \"Please refer to: https://huggingface.co/InstantX/InstantID for further instructions regarding inference\"\n)\n\n\ndef FeedForward(dim, mult=4):\n inner_dim = int(dim * mult)\n return nn.Sequential(\n nn.LayerNorm(dim),\n nn.Linear(dim, inner_dim, bias=False),\n nn.GELU(),\n nn.Linear(inner_dim, dim, bias=False),\n )\n\n\ndef reshape_tensor(x, heads):\n bs, length, width = x.shape\n # (bs, length, width) --> (bs, length, n_heads, dim_per_head)\n x = x.view(bs, length, heads, -1)\n # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)\n x = x.transpose(1, 2)\n # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)\n x = x.reshape(bs, heads, length, -1)\n return x\n\n\nclass PerceiverAttention(nn.Module):\n def __init__(self, *, dim, dim_head=64, heads=8):\n super().__init__()\n self.scale = dim_head**-0.5\n self.dim_head = dim_head\n self.heads = heads\n inner_dim = dim_head * heads\n\n self.norm1 = nn.LayerNorm(dim)\n self.norm2 = nn.LayerNorm(dim)\n\n self.to_q = nn.Linear(dim, inner_dim, bias=False)\n self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)\n self.to_out = nn.Linear(inner_dim, dim, bias=False)\n\n def forward(self, x, latents):\n \"\"\"\n Args:\n x (torch.Tensor): image features\n shape (b, n1, D)\n latent (torch.Tensor): latent features\n shape (b, n2, D)\n \"\"\"\n x = self.norm1(x)\n latents = self.norm2(latents)\n\n b, l, _ = latents.shape\n\n q = self.to_q(latents)\n kv_input = torch.cat((x, latents), dim=-2)\n k, v = self.to_kv(kv_input).chunk(2, dim=-1)\n\n q = reshape_tensor(q, self.heads)\n k = reshape_tensor(k, self.heads)\n v = reshape_tensor(v, self.heads)\n\n # attention\n scale = 1 / math.sqrt(math.sqrt(self.dim_head))\n weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards\n weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)\n out = weight @ v\n\n out = out.permute(0, 2, 1, 3).reshape(b, l, -1)\n\n return self.to_out(out)\n\n\nclass Resampler(nn.Module):\n def __init__(\n self,\n dim=1024,\n depth=8,\n dim_head=64,\n heads=16,\n num_queries=8,\n embedding_dim=768,\n output_dim=1024,\n ff_mult=4,\n ):\n super().__init__()\n\n self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)\n\n self.proj_in = nn.Linear(embedding_dim, dim)\n\n self.proj_out = nn.Linear(dim, output_dim)\n self.norm_out = nn.LayerNorm(output_dim)\n\n self.layers = nn.ModuleList([])\n for _ in range(depth):\n self.layers.append(\n nn.ModuleList(\n [\n PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),\n FeedForward(dim=dim, mult=ff_mult),\n ]\n )\n )\n\n def forward(self, x):\n latents = self.latents.repeat(x.size(0), 1, 1)\n x = self.proj_in(x)\n\n for attn, ff in self.layers:\n latents = attn(x, latents) + latents\n latents = ff(latents) + latents\n\n latents = self.proj_out(latents)\n return self.norm_out(latents)\n\n\nclass AttnProcessor(nn.Module):\n r\"\"\"\n Default processor for performing attention-related computations.\n \"\"\"\n\n def __init__(\n self,\n hidden_size=None,\n cross_attention_dim=None,\n ):\n super().__init__()\n\n def __call__(\n self,\n attn,\n hidden_states,\n encoder_hidden_states=None,\n attention_mask=None,\n temb=None,\n ):\n residual = hidden_states\n\n if attn.spatial_norm is not None:\n hidden_states = attn.spatial_norm(hidden_states, temb)\n\n input_ndim = hidden_states.ndim\n\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n\n if attn.group_norm is not None:\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n\n query = attn.to_q(hidden_states)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n attention_probs = attn.get_attention_scores(query, key, attention_mask)\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n if input_ndim == 4:\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n if attn.residual_connection:\n hidden_states = hidden_states + residual\n\n hidden_states = hidden_states / attn.rescale_output_factor\n\n return hidden_states\n\n\nclass IPAttnProcessor(nn.Module):\n r\"\"\"\n Attention processor for IP-Adapater.\n Args:\n hidden_size (`int`):\n The hidden size of the attention layer.\n cross_attention_dim (`int`):\n The number of channels in the `encoder_hidden_states`.\n scale (`float`, defaults to 1.0):\n the weight scale of image prompt.\n num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):\n The context length of the image features.\n \"\"\"\n\n def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4):\n super().__init__()\n\n self.hidden_size = hidden_size\n self.cross_attention_dim = cross_attention_dim\n self.scale = scale\n self.num_tokens = num_tokens\n\n self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)\n self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)\n\n def __call__(\n self,\n attn,\n hidden_states,\n encoder_hidden_states=None,\n attention_mask=None,\n temb=None,\n ):\n residual = hidden_states\n\n if attn.spatial_norm is not None:\n hidden_states = attn.spatial_norm(hidden_states, temb)\n\n input_ndim = hidden_states.ndim\n\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n\n if attn.group_norm is not None:\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n\n query = attn.to_q(hidden_states)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n else:\n # get encoder_hidden_states, ip_hidden_states\n end_pos = encoder_hidden_states.shape[1] - self.num_tokens\n encoder_hidden_states, ip_hidden_states = (\n encoder_hidden_states[:, :end_pos, :],\n encoder_hidden_states[:, end_pos:, :],\n )\n if attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n if xformers_available:\n hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask)\n else:\n attention_probs = attn.get_attention_scores(query, key, attention_mask)\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # for ip-adapter\n ip_key = self.to_k_ip(ip_hidden_states)\n ip_value = self.to_v_ip(ip_hidden_states)\n\n ip_key = attn.head_to_batch_dim(ip_key)\n ip_value = attn.head_to_batch_dim(ip_value)\n\n if xformers_available:\n ip_hidden_states = self._memory_efficient_attention_xformers(query, ip_key, ip_value, None)\n else:\n ip_attention_probs = attn.get_attention_scores(query, ip_key, None)\n ip_hidden_states = torch.bmm(ip_attention_probs, ip_value)\n ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states)\n\n hidden_states = hidden_states + self.scale * ip_hidden_states\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n if input_ndim == 4:\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n if attn.residual_connection:\n hidden_states = hidden_states + residual\n\n hidden_states = hidden_states / attn.rescale_output_factor\n\n return hidden_states\n\n def _memory_efficient_attention_xformers(self, query, key, value, attention_mask):\n # TODO attention_mask\n query = query.contiguous()\n key = key.contiguous()\n value = value.contiguous()\n hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask)\n return hidden_states\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> # !pip install opencv-python transformers accelerate insightface\n >>> import diffusers\n >>> from diffusers.utils import load_image\n >>> from diffusers.models import ControlNetModel\n\n >>> import cv2\n >>> import torch\n >>> import numpy as np\n >>> from PIL import Image\n\n >>> from insightface.app import FaceAnalysis\n >>> from pipeline_stable_diffusion_xl_instantid import StableDiffusionXLInstantIDPipeline, draw_kps\n\n >>> # download 'antelopev2' under ./models\n >>> app = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])\n >>> app.prepare(ctx_id=0, det_size=(640, 640))\n\n >>> # download models under ./checkpoints\n >>> face_adapter = f'./checkpoints/ip-adapter.bin'\n >>> controlnet_path = f'./checkpoints/ControlNetModel'\n\n >>> # load IdentityNet\n >>> controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)\n\n >>> pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(\n ... \"stabilityai/stable-diffusion-xl-base-1.0\", controlnet=controlnet, torch_dtype=torch.float16\n ... )\n >>> pipe.cuda()\n\n >>> # load adapter\n >>> pipe.load_ip_adapter_instantid(face_adapter)\n\n >>> prompt = \"analog film photo of a man. faded film, desaturated, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage, masterpiece, best quality\"\n >>> negative_prompt = \"(lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured (lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch,deformed, mutated, cross-eyed, ugly, disfigured\"\n\n >>> # load an image\n >>> image = load_image(\"your-example.jpg\")\n\n >>> face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))[-1]\n >>> face_emb = face_info['embedding']\n >>> face_kps = draw_kps(face_image, face_info['kps'])\n\n >>> pipe.set_ip_adapter_scale(0.8)\n\n >>> # generate image\n >>> image = pipe(\n ... prompt, image_embeds=face_emb, image=face_kps, controlnet_conditioning_scale=0.8\n ... ).images[0]\n ```\n\"\"\"\n\n\ndef draw_kps(image_pil, kps, color_list=[(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (255, 0, 255)]):\n stickwidth = 4\n limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]])\n kps = np.array(kps)\n\n w, h = image_pil.size\n out_img = np.zeros([h, w, 3])\n\n for i in range(len(limbSeq)):\n index = limbSeq[i]\n color = color_list[index[0]]\n\n x = kps[index][:, 0]\n y = kps[index][:, 1]\n length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5\n angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1]))\n polygon = cv2.ellipse2Poly(\n (int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1\n )\n out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color)\n out_img = (out_img * 0.6).astype(np.uint8)\n\n for idx_kp, kp in enumerate(kps):\n color = color_list[idx_kp]\n x, y = kp\n out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1)\n\n out_img_pil = PIL.Image.fromarray(out_img.astype(np.uint8))\n return out_img_pil\n\n\nclass StableDiffusionXLInstantIDImg2ImgPipeline(StableDiffusionXLControlNetImg2ImgPipeline):\n def cuda(self, dtype=torch.float16, use_xformers=False):\n self.to(\"cuda\", dtype)\n\n if hasattr(self, \"image_proj_model\"):\n self.image_proj_model.to(self.unet.device).to(self.unet.dtype)\n\n if use_xformers:\n if is_xformers_available():\n import xformers\n from packaging import version\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n self.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n def load_ip_adapter_instantid(self, model_ckpt, image_emb_dim=512, num_tokens=16, scale=0.5):\n self.set_image_proj_model(model_ckpt, image_emb_dim, num_tokens)\n self.set_ip_adapter(model_ckpt, num_tokens, scale)\n\n def set_image_proj_model(self, model_ckpt, image_emb_dim=512, num_tokens=16):\n image_proj_model = Resampler(\n dim=1280,\n depth=4,\n dim_head=64,\n heads=20,\n num_queries=num_tokens,\n embedding_dim=image_emb_dim,\n output_dim=self.unet.config.cross_attention_dim,\n ff_mult=4,\n )\n\n image_proj_model.eval()\n\n self.image_proj_model = image_proj_model.to(self.device, dtype=self.dtype)\n state_dict = torch.load(model_ckpt, map_location=\"cpu\")\n if \"image_proj\" in state_dict:\n state_dict = state_dict[\"image_proj\"]\n self.image_proj_model.load_state_dict(state_dict)\n\n self.image_proj_model_in_features = image_emb_dim\n\n def set_ip_adapter(self, model_ckpt, num_tokens, scale):\n unet = self.unet\n attn_procs = {}\n for name in unet.attn_processors.keys():\n cross_attention_dim = None if name.endswith(\"attn1.processor\") else unet.config.cross_attention_dim\n if name.startswith(\"mid_block\"):\n hidden_size = unet.config.block_out_channels[-1]\n elif name.startswith(\"up_blocks\"):\n block_id = int(name[len(\"up_blocks.\")])\n hidden_size = list(reversed(unet.config.block_out_channels))[block_id]\n elif name.startswith(\"down_blocks\"):\n block_id = int(name[len(\"down_blocks.\")])\n hidden_size = unet.config.block_out_channels[block_id]\n if cross_attention_dim is None:\n attn_procs[name] = AttnProcessor().to(unet.device, dtype=unet.dtype)\n else:\n attn_procs[name] = IPAttnProcessor(\n hidden_size=hidden_size,\n cross_attention_dim=cross_attention_dim,\n scale=scale,\n num_tokens=num_tokens,\n ).to(unet.device, dtype=unet.dtype)\n unet.set_attn_processor(attn_procs)\n\n state_dict = torch.load(model_ckpt, map_location=\"cpu\")\n ip_layers = torch.nn.ModuleList(self.unet.attn_processors.values())\n if \"ip_adapter\" in state_dict:\n state_dict = state_dict[\"ip_adapter\"]\n ip_layers.load_state_dict(state_dict)\n\n def set_ip_adapter_scale(self, scale):\n unet = getattr(self, self.unet_name) if not hasattr(self, \"unet\") else self.unet\n for attn_processor in unet.attn_processors.values():\n if isinstance(attn_processor, IPAttnProcessor):\n attn_processor.scale = scale\n\n def _encode_prompt_image_emb(self, prompt_image_emb, device, dtype, do_classifier_free_guidance):\n if isinstance(prompt_image_emb, torch.Tensor):\n prompt_image_emb = prompt_image_emb.clone().detach()\n else:\n prompt_image_emb = torch.tensor(prompt_image_emb)\n\n prompt_image_emb = prompt_image_emb.to(device=device, dtype=dtype)\n prompt_image_emb = prompt_image_emb.reshape([1, -1, self.image_proj_model_in_features])\n\n if do_classifier_free_guidance:\n prompt_image_emb = torch.cat([torch.zeros_like(prompt_image_emb), prompt_image_emb], dim=0)\n else:\n prompt_image_emb = torch.cat([prompt_image_emb], dim=0)\n\n prompt_image_emb = self.image_proj_model(prompt_image_emb)\n return prompt_image_emb\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n image: PipelineImageInput = None,\n control_image: PipelineImageInput = None,\n strength: float = 0.8,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n image_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: Union[float, List[float]] = 1.0,\n guess_mode: bool = False,\n control_guidance_start: Union[float, List[float]] = 0.0,\n control_guidance_end: Union[float, List[float]] = 1.0,\n original_size: Tuple[int, int] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Tuple[int, int] = None,\n negative_original_size: Optional[Tuple[int, int]] = None,\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\n negative_target_size: Optional[Tuple[int, int]] = None,\n aesthetic_score: float = 6.0,\n negative_aesthetic_score: float = 2.5,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:\n `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):\n The ControlNet input condition to provide guidance to the `unet` for generation. If the type is\n specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be\n accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height\n and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in\n `init`, images must be passed as a list such that each element of the list can be correctly batched for\n input to a single ControlNet.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image. Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image. Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 5.0):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. This is sent to `tokenizer_2`\n and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, pooled text embeddings are generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs (prompt\n weighting). If not provided, pooled `negative_prompt_embeds` are generated from `negative_prompt` input\n argument.\n image_embeds (`torch.Tensor`, *optional*):\n Pre-generated image embeddings.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):\n The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added\n to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set\n the corresponding scale as a list.\n guess_mode (`bool`, *optional*, defaults to `False`):\n The ControlNet encoder tries to recognize the content of the input image even if you remove all\n prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.\n control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):\n The percentage of total steps at which the ControlNet starts applying.\n control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):\n The percentage of total steps at which the ControlNet stops applying.\n original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.\n `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as\n explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n `crops_coords_top_left` can be used to generate an image that appears to be \"cropped\" from the position\n `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n For most cases, `target_size` should be set to the desired height and width of the generated image. If\n not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a specific image resolution. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a target image resolution. It should be as same\n as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned containing the output images.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n\n controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet\n\n # align format for control guidance\n if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):\n control_guidance_start = len(control_guidance_end) * [control_guidance_start]\n elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):\n control_guidance_end = len(control_guidance_start) * [control_guidance_end]\n elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):\n mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1\n control_guidance_start, control_guidance_end = (\n mult * [control_guidance_start],\n mult * [control_guidance_end],\n )\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n control_image,\n strength,\n num_inference_steps,\n callback_steps,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n None,\n None,\n controlnet_conditioning_scale,\n control_guidance_start,\n control_guidance_end,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):\n controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)\n\n global_pool_conditions = (\n controlnet.config.global_pool_conditions\n if isinstance(controlnet, ControlNetModel)\n else controlnet.nets[0].config.global_pool_conditions\n )\n guess_mode = guess_mode or global_pool_conditions\n\n # 3.1 Encode input prompt\n text_encoder_lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt,\n prompt_2,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # 3.2 Encode image prompt\n prompt_image_emb = self._encode_prompt_image_emb(\n image_embeds, device, self.unet.dtype, self.do_classifier_free_guidance\n )\n bs_embed, seq_len, _ = prompt_image_emb.shape\n prompt_image_emb = prompt_image_emb.repeat(1, num_images_per_prompt, 1)\n prompt_image_emb = prompt_image_emb.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # 4. Prepare image and controlnet_conditioning_image\n image = self.image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)\n\n if isinstance(controlnet, ControlNetModel):\n control_image = self.prepare_control_image(\n image=control_image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n height, width = control_image.shape[-2:]\n elif isinstance(controlnet, MultiControlNetModel):\n control_images = []\n\n for control_image_ in control_image:\n control_image_ = self.prepare_control_image(\n image=control_image_,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n\n control_images.append(control_image_)\n\n control_image = control_images\n height, width = control_image[0].shape[-2:]\n else:\n assert False\n\n # 5. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n self._num_timesteps = len(timesteps)\n\n # 6. Prepare latent variables\n latents = self.prepare_latents(\n image,\n latent_timestep,\n batch_size,\n num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n generator,\n True,\n )\n\n # # 6.5 Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7.1 Create tensor stating which controlnets to keep\n controlnet_keep = []\n for i in range(len(timesteps)):\n keeps = [\n 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)\n for s, e in zip(control_guidance_start, control_guidance_end)\n ]\n controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)\n\n # 7.2 Prepare added time ids & embeddings\n if isinstance(control_image, list):\n original_size = original_size or control_image[0].shape[-2:]\n else:\n original_size = original_size or control_image.shape[-2:]\n target_size = target_size or (height, width)\n\n if negative_original_size is None:\n negative_original_size = original_size\n if negative_target_size is None:\n negative_target_size = target_size\n add_text_embeds = pooled_prompt_embeds\n\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n add_time_ids, add_neg_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n aesthetic_score,\n negative_aesthetic_score,\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)\n\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1)\n add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)\n encoder_hidden_states = torch.cat([prompt_embeds, prompt_image_emb], dim=1)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n is_unet_compiled = is_compiled_module(self.unet)\n is_controlnet_compiled = is_compiled_module(self.controlnet)\n is_torch_higher_equal_2_1 = is_torch_version(\">=\", \"2.1\")\n\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # Relevant thread:\n # https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428\n if (is_unet_compiled and is_controlnet_compiled) and is_torch_higher_equal_2_1:\n torch._inductor.cudagraph_mark_step_begin()\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n\n # controlnet(s) inference\n if guess_mode and self.do_classifier_free_guidance:\n # Infer ControlNet only for the conditional batch.\n control_model_input = latents\n control_model_input = self.scheduler.scale_model_input(control_model_input, t)\n controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]\n controlnet_added_cond_kwargs = {\n \"text_embeds\": add_text_embeds.chunk(2)[1],\n \"time_ids\": add_time_ids.chunk(2)[1],\n }\n else:\n control_model_input = latent_model_input\n controlnet_prompt_embeds = prompt_embeds\n controlnet_added_cond_kwargs = added_cond_kwargs\n\n if isinstance(controlnet_keep[i], list):\n cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]\n else:\n controlnet_cond_scale = controlnet_conditioning_scale\n if isinstance(controlnet_cond_scale, list):\n controlnet_cond_scale = controlnet_cond_scale[0]\n cond_scale = controlnet_cond_scale * controlnet_keep[i]\n\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n control_model_input,\n t,\n encoder_hidden_states=prompt_image_emb,\n controlnet_cond=control_image,\n conditioning_scale=cond_scale,\n guess_mode=guess_mode,\n added_cond_kwargs=controlnet_added_cond_kwargs,\n return_dict=False,\n )\n\n if guess_mode and self.do_classifier_free_guidance:\n # Infered ControlNet only for the conditional batch.\n # To apply the output of ControlNet to both the unconditional and conditional batches,\n # add 0 to the unconditional batch to keep it unchanged.\n down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]\n mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=encoder_hidden_states,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=self.cross_attention_kwargs,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n image = latents\n\n if not output_type == \"latent\":\n # apply watermark if available\n if self.watermark is not None:\n image = self.watermark.apply_watermark(image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)\n" }, { "path": "diffusers/examples/community/pipeline_stable_diffusion_xl_instantid.py", "content": "# Copyright 2024 The InstantX Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport math\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport cv2\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn as nn\n\nfrom diffusers import StableDiffusionXLControlNetPipeline\nfrom diffusers.image_processor import PipelineImageInput\nfrom diffusers.models import ControlNetModel\nfrom diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel\nfrom diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput\nfrom diffusers.utils import (\n deprecate,\n logging,\n replace_example_docstring,\n)\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module, is_torch_version\n\n\ntry:\n import xformers\n import xformers.ops\n\n xformers_available = True\nexcept Exception:\n xformers_available = False\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nlogger.warning(\n \"To use instant id pipelines, please make sure you have the `insightface` library installed: `pip install insightface`.\"\n \"Please refer to: https://huggingface.co/InstantX/InstantID for further instructions regarding inference\"\n)\n\n\ndef FeedForward(dim, mult=4):\n inner_dim = int(dim * mult)\n return nn.Sequential(\n nn.LayerNorm(dim),\n nn.Linear(dim, inner_dim, bias=False),\n nn.GELU(),\n nn.Linear(inner_dim, dim, bias=False),\n )\n\n\ndef reshape_tensor(x, heads):\n bs, length, width = x.shape\n # (bs, length, width) --> (bs, length, n_heads, dim_per_head)\n x = x.view(bs, length, heads, -1)\n # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)\n x = x.transpose(1, 2)\n # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)\n x = x.reshape(bs, heads, length, -1)\n return x\n\n\nclass PerceiverAttention(nn.Module):\n def __init__(self, *, dim, dim_head=64, heads=8):\n super().__init__()\n self.scale = dim_head**-0.5\n self.dim_head = dim_head\n self.heads = heads\n inner_dim = dim_head * heads\n\n self.norm1 = nn.LayerNorm(dim)\n self.norm2 = nn.LayerNorm(dim)\n\n self.to_q = nn.Linear(dim, inner_dim, bias=False)\n self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)\n self.to_out = nn.Linear(inner_dim, dim, bias=False)\n\n def forward(self, x, latents):\n \"\"\"\n Args:\n x (torch.Tensor): image features\n shape (b, n1, D)\n latent (torch.Tensor): latent features\n shape (b, n2, D)\n \"\"\"\n x = self.norm1(x)\n latents = self.norm2(latents)\n\n b, l, _ = latents.shape\n\n q = self.to_q(latents)\n kv_input = torch.cat((x, latents), dim=-2)\n k, v = self.to_kv(kv_input).chunk(2, dim=-1)\n\n q = reshape_tensor(q, self.heads)\n k = reshape_tensor(k, self.heads)\n v = reshape_tensor(v, self.heads)\n\n # attention\n scale = 1 / math.sqrt(math.sqrt(self.dim_head))\n weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards\n weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)\n out = weight @ v\n\n out = out.permute(0, 2, 1, 3).reshape(b, l, -1)\n\n return self.to_out(out)\n\n\nclass Resampler(nn.Module):\n def __init__(\n self,\n dim=1024,\n depth=8,\n dim_head=64,\n heads=16,\n num_queries=8,\n embedding_dim=768,\n output_dim=1024,\n ff_mult=4,\n ):\n super().__init__()\n\n self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)\n\n self.proj_in = nn.Linear(embedding_dim, dim)\n\n self.proj_out = nn.Linear(dim, output_dim)\n self.norm_out = nn.LayerNorm(output_dim)\n\n self.layers = nn.ModuleList([])\n for _ in range(depth):\n self.layers.append(\n nn.ModuleList(\n [\n PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),\n FeedForward(dim=dim, mult=ff_mult),\n ]\n )\n )\n\n def forward(self, x):\n latents = self.latents.repeat(x.size(0), 1, 1)\n x = self.proj_in(x)\n\n for attn, ff in self.layers:\n latents = attn(x, latents) + latents\n latents = ff(latents) + latents\n\n latents = self.proj_out(latents)\n return self.norm_out(latents)\n\n\nclass AttnProcessor(nn.Module):\n r\"\"\"\n Default processor for performing attention-related computations.\n \"\"\"\n\n def __init__(\n self,\n hidden_size=None,\n cross_attention_dim=None,\n ):\n super().__init__()\n\n def __call__(\n self,\n attn,\n hidden_states,\n encoder_hidden_states=None,\n attention_mask=None,\n temb=None,\n ):\n residual = hidden_states\n\n if attn.spatial_norm is not None:\n hidden_states = attn.spatial_norm(hidden_states, temb)\n\n input_ndim = hidden_states.ndim\n\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n\n if attn.group_norm is not None:\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n\n query = attn.to_q(hidden_states)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n attention_probs = attn.get_attention_scores(query, key, attention_mask)\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n if input_ndim == 4:\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n if attn.residual_connection:\n hidden_states = hidden_states + residual\n\n hidden_states = hidden_states / attn.rescale_output_factor\n\n return hidden_states\n\n\nclass IPAttnProcessor(nn.Module):\n r\"\"\"\n Attention processor for IP-Adapater.\n Args:\n hidden_size (`int`):\n The hidden size of the attention layer.\n cross_attention_dim (`int`):\n The number of channels in the `encoder_hidden_states`.\n scale (`float`, defaults to 1.0):\n the weight scale of image prompt.\n num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):\n The context length of the image features.\n \"\"\"\n\n def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4):\n super().__init__()\n\n self.hidden_size = hidden_size\n self.cross_attention_dim = cross_attention_dim\n self.scale = scale\n self.num_tokens = num_tokens\n\n self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)\n self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)\n\n def __call__(\n self,\n attn,\n hidden_states,\n encoder_hidden_states=None,\n attention_mask=None,\n temb=None,\n ):\n residual = hidden_states\n\n if attn.spatial_norm is not None:\n hidden_states = attn.spatial_norm(hidden_states, temb)\n\n input_ndim = hidden_states.ndim\n\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n\n if attn.group_norm is not None:\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n\n query = attn.to_q(hidden_states)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n else:\n # get encoder_hidden_states, ip_hidden_states\n end_pos = encoder_hidden_states.shape[1] - self.num_tokens\n encoder_hidden_states, ip_hidden_states = (\n encoder_hidden_states[:, :end_pos, :],\n encoder_hidden_states[:, end_pos:, :],\n )\n if attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n if xformers_available:\n hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask)\n else:\n attention_probs = attn.get_attention_scores(query, key, attention_mask)\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # for ip-adapter\n ip_key = self.to_k_ip(ip_hidden_states)\n ip_value = self.to_v_ip(ip_hidden_states)\n\n ip_key = attn.head_to_batch_dim(ip_key)\n ip_value = attn.head_to_batch_dim(ip_value)\n\n if xformers_available:\n ip_hidden_states = self._memory_efficient_attention_xformers(query, ip_key, ip_value, None)\n else:\n ip_attention_probs = attn.get_attention_scores(query, ip_key, None)\n ip_hidden_states = torch.bmm(ip_attention_probs, ip_value)\n ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states)\n\n hidden_states = hidden_states + self.scale * ip_hidden_states\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n if input_ndim == 4:\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n if attn.residual_connection:\n hidden_states = hidden_states + residual\n\n hidden_states = hidden_states / attn.rescale_output_factor\n\n return hidden_states\n\n def _memory_efficient_attention_xformers(self, query, key, value, attention_mask):\n # TODO attention_mask\n query = query.contiguous()\n key = key.contiguous()\n value = value.contiguous()\n hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask)\n return hidden_states\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> # !pip install opencv-python transformers accelerate insightface\n >>> import diffusers\n >>> from diffusers.utils import load_image\n >>> from diffusers.models import ControlNetModel\n\n >>> import cv2\n >>> import torch\n >>> import numpy as np\n >>> from PIL import Image\n\n >>> from insightface.app import FaceAnalysis\n >>> from pipeline_stable_diffusion_xl_instantid import StableDiffusionXLInstantIDPipeline, draw_kps\n\n >>> # download 'antelopev2' under ./models\n >>> app = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])\n >>> app.prepare(ctx_id=0, det_size=(640, 640))\n\n >>> # download models under ./checkpoints\n >>> face_adapter = f'./checkpoints/ip-adapter.bin'\n >>> controlnet_path = f'./checkpoints/ControlNetModel'\n\n >>> # load IdentityNet\n >>> controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)\n\n >>> pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(\n ... \"stabilityai/stable-diffusion-xl-base-1.0\", controlnet=controlnet, torch_dtype=torch.float16\n ... )\n >>> pipe.cuda()\n\n >>> # load adapter\n >>> pipe.load_ip_adapter_instantid(face_adapter)\n\n >>> prompt = \"analog film photo of a man. faded film, desaturated, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage, masterpiece, best quality\"\n >>> negative_prompt = \"(lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured (lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch,deformed, mutated, cross-eyed, ugly, disfigured\"\n\n >>> # load an image\n >>> image = load_image(\"your-example.jpg\")\n\n >>> face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))[-1]\n >>> face_emb = face_info['embedding']\n >>> face_kps = draw_kps(face_image, face_info['kps'])\n\n >>> pipe.set_ip_adapter_scale(0.8)\n\n >>> # generate image\n >>> image = pipe(\n ... prompt, image_embeds=face_emb, image=face_kps, controlnet_conditioning_scale=0.8\n ... ).images[0]\n ```\n\"\"\"\n\n\ndef draw_kps(image_pil, kps, color_list=[(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (255, 0, 255)]):\n stickwidth = 4\n limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]])\n kps = np.array(kps)\n\n w, h = image_pil.size\n out_img = np.zeros([h, w, 3])\n\n for i in range(len(limbSeq)):\n index = limbSeq[i]\n color = color_list[index[0]]\n\n x = kps[index][:, 0]\n y = kps[index][:, 1]\n length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5\n angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1]))\n polygon = cv2.ellipse2Poly(\n (int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1\n )\n out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color)\n out_img = (out_img * 0.6).astype(np.uint8)\n\n for idx_kp, kp in enumerate(kps):\n color = color_list[idx_kp]\n x, y = kp\n out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1)\n\n out_img_pil = PIL.Image.fromarray(out_img.astype(np.uint8))\n return out_img_pil\n\n\nclass StableDiffusionXLInstantIDPipeline(StableDiffusionXLControlNetPipeline):\n def cuda(self, dtype=torch.float16, use_xformers=False):\n self.to(\"cuda\", dtype)\n\n if hasattr(self, \"image_proj_model\"):\n self.image_proj_model.to(self.unet.device).to(self.unet.dtype)\n\n if use_xformers:\n if is_xformers_available():\n import xformers\n from packaging import version\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n self.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n def load_ip_adapter_instantid(self, model_ckpt, image_emb_dim=512, num_tokens=16, scale=0.5):\n self.set_image_proj_model(model_ckpt, image_emb_dim, num_tokens)\n self.set_ip_adapter(model_ckpt, num_tokens, scale)\n\n def set_image_proj_model(self, model_ckpt, image_emb_dim=512, num_tokens=16):\n image_proj_model = Resampler(\n dim=1280,\n depth=4,\n dim_head=64,\n heads=20,\n num_queries=num_tokens,\n embedding_dim=image_emb_dim,\n output_dim=self.unet.config.cross_attention_dim,\n ff_mult=4,\n )\n\n image_proj_model.eval()\n\n self.image_proj_model = image_proj_model.to(self.device, dtype=self.dtype)\n state_dict = torch.load(model_ckpt, map_location=\"cpu\")\n if \"image_proj\" in state_dict:\n state_dict = state_dict[\"image_proj\"]\n self.image_proj_model.load_state_dict(state_dict)\n\n self.image_proj_model_in_features = image_emb_dim\n\n def set_ip_adapter(self, model_ckpt, num_tokens, scale):\n unet = self.unet\n attn_procs = {}\n for name in unet.attn_processors.keys():\n cross_attention_dim = None if name.endswith(\"attn1.processor\") else unet.config.cross_attention_dim\n if name.startswith(\"mid_block\"):\n hidden_size = unet.config.block_out_channels[-1]\n elif name.startswith(\"up_blocks\"):\n block_id = int(name[len(\"up_blocks.\")])\n hidden_size = list(reversed(unet.config.block_out_channels))[block_id]\n elif name.startswith(\"down_blocks\"):\n block_id = int(name[len(\"down_blocks.\")])\n hidden_size = unet.config.block_out_channels[block_id]\n if cross_attention_dim is None:\n attn_procs[name] = AttnProcessor().to(unet.device, dtype=unet.dtype)\n else:\n attn_procs[name] = IPAttnProcessor(\n hidden_size=hidden_size,\n cross_attention_dim=cross_attention_dim,\n scale=scale,\n num_tokens=num_tokens,\n ).to(unet.device, dtype=unet.dtype)\n unet.set_attn_processor(attn_procs)\n\n state_dict = torch.load(model_ckpt, map_location=\"cpu\")\n ip_layers = torch.nn.ModuleList(self.unet.attn_processors.values())\n if \"ip_adapter\" in state_dict:\n state_dict = state_dict[\"ip_adapter\"]\n ip_layers.load_state_dict(state_dict)\n\n def set_ip_adapter_scale(self, scale):\n unet = getattr(self, self.unet_name) if not hasattr(self, \"unet\") else self.unet\n for attn_processor in unet.attn_processors.values():\n if isinstance(attn_processor, IPAttnProcessor):\n attn_processor.scale = scale\n\n def _encode_prompt_image_emb(self, prompt_image_emb, device, dtype, do_classifier_free_guidance):\n if isinstance(prompt_image_emb, torch.Tensor):\n prompt_image_emb = prompt_image_emb.clone().detach()\n else:\n prompt_image_emb = torch.tensor(prompt_image_emb)\n\n prompt_image_emb = prompt_image_emb.to(device=device, dtype=dtype)\n prompt_image_emb = prompt_image_emb.reshape([1, -1, self.image_proj_model_in_features])\n\n if do_classifier_free_guidance:\n prompt_image_emb = torch.cat([torch.zeros_like(prompt_image_emb), prompt_image_emb], dim=0)\n else:\n prompt_image_emb = torch.cat([prompt_image_emb], dim=0)\n\n prompt_image_emb = self.image_proj_model(prompt_image_emb)\n return prompt_image_emb\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n image: PipelineImageInput = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n image_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: Union[float, List[float]] = 1.0,\n guess_mode: bool = False,\n control_guidance_start: Union[float, List[float]] = 0.0,\n control_guidance_end: Union[float, List[float]] = 1.0,\n original_size: Tuple[int, int] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Tuple[int, int] = None,\n negative_original_size: Optional[Tuple[int, int]] = None,\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\n negative_target_size: Optional[Tuple[int, int]] = None,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:\n `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):\n The ControlNet input condition to provide guidance to the `unet` for generation. If the type is\n specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be\n accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height\n and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in\n `init`, images must be passed as a list such that each element of the list can be correctly batched for\n input to a single ControlNet.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image. Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image. Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 5.0):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. This is sent to `tokenizer_2`\n and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, pooled text embeddings are generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs (prompt\n weighting). If not provided, pooled `negative_prompt_embeds` are generated from `negative_prompt` input\n argument.\n image_embeds (`torch.Tensor`, *optional*):\n Pre-generated image embeddings.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):\n The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added\n to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set\n the corresponding scale as a list.\n guess_mode (`bool`, *optional*, defaults to `False`):\n The ControlNet encoder tries to recognize the content of the input image even if you remove all\n prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.\n control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):\n The percentage of total steps at which the ControlNet starts applying.\n control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):\n The percentage of total steps at which the ControlNet stops applying.\n original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.\n `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as\n explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n `crops_coords_top_left` can be used to generate an image that appears to be \"cropped\" from the position\n `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n For most cases, `target_size` should be set to the desired height and width of the generated image. If\n not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a specific image resolution. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a target image resolution. It should be as same\n as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned containing the output images.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n\n controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet\n\n # align format for control guidance\n if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):\n control_guidance_start = len(control_guidance_end) * [control_guidance_start]\n elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):\n control_guidance_end = len(control_guidance_start) * [control_guidance_end]\n elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):\n mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1\n control_guidance_start, control_guidance_end = (\n mult * [control_guidance_start],\n mult * [control_guidance_end],\n )\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n image,\n callback_steps,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n controlnet_conditioning_scale,\n control_guidance_start,\n control_guidance_end,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):\n controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)\n\n global_pool_conditions = (\n controlnet.config.global_pool_conditions\n if isinstance(controlnet, ControlNetModel)\n else controlnet.nets[0].config.global_pool_conditions\n )\n guess_mode = guess_mode or global_pool_conditions\n\n # 3.1 Encode input prompt\n text_encoder_lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt,\n prompt_2,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # 3.2 Encode image prompt\n prompt_image_emb = self._encode_prompt_image_emb(\n image_embeds, device, self.unet.dtype, self.do_classifier_free_guidance\n )\n bs_embed, seq_len, _ = prompt_image_emb.shape\n prompt_image_emb = prompt_image_emb.repeat(1, num_images_per_prompt, 1)\n prompt_image_emb = prompt_image_emb.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # 4. Prepare image\n if isinstance(controlnet, ControlNetModel):\n image = self.prepare_image(\n image=image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n height, width = image.shape[-2:]\n elif isinstance(controlnet, MultiControlNetModel):\n images = []\n\n for image_ in image:\n image_ = self.prepare_image(\n image=image_,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n\n images.append(image_)\n\n image = images\n height, width = image[0].shape[-2:]\n else:\n assert False\n\n # 5. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n self._num_timesteps = len(timesteps)\n\n # 6. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6.5 Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7.1 Create tensor stating which controlnets to keep\n controlnet_keep = []\n for i in range(len(timesteps)):\n keeps = [\n 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)\n for s, e in zip(control_guidance_start, control_guidance_end)\n ]\n controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)\n\n # 7.2 Prepare added time ids & embeddings\n if isinstance(image, list):\n original_size = original_size or image[0].shape[-2:]\n else:\n original_size = original_size or image.shape[-2:]\n target_size = target_size or (height, width)\n\n add_text_embeds = pooled_prompt_embeds\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n add_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n\n if negative_original_size is not None and negative_target_size is not None:\n negative_add_time_ids = self._get_add_time_ids(\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n else:\n negative_add_time_ids = add_time_ids\n\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)\n encoder_hidden_states = torch.cat([prompt_embeds, prompt_image_emb], dim=1)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n is_unet_compiled = is_compiled_module(self.unet)\n is_controlnet_compiled = is_compiled_module(self.controlnet)\n is_torch_higher_equal_2_1 = is_torch_version(\">=\", \"2.1\")\n\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # Relevant thread:\n # https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428\n if (is_unet_compiled and is_controlnet_compiled) and is_torch_higher_equal_2_1:\n torch._inductor.cudagraph_mark_step_begin()\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n\n # controlnet(s) inference\n if guess_mode and self.do_classifier_free_guidance:\n # Infer ControlNet only for the conditional batch.\n control_model_input = latents\n control_model_input = self.scheduler.scale_model_input(control_model_input, t)\n controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]\n controlnet_added_cond_kwargs = {\n \"text_embeds\": add_text_embeds.chunk(2)[1],\n \"time_ids\": add_time_ids.chunk(2)[1],\n }\n else:\n control_model_input = latent_model_input\n controlnet_prompt_embeds = prompt_embeds\n controlnet_added_cond_kwargs = added_cond_kwargs\n\n if isinstance(controlnet_keep[i], list):\n cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]\n else:\n controlnet_cond_scale = controlnet_conditioning_scale\n if isinstance(controlnet_cond_scale, list):\n controlnet_cond_scale = controlnet_cond_scale[0]\n cond_scale = controlnet_cond_scale * controlnet_keep[i]\n\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n control_model_input,\n t,\n encoder_hidden_states=prompt_image_emb,\n controlnet_cond=image,\n conditioning_scale=cond_scale,\n guess_mode=guess_mode,\n added_cond_kwargs=controlnet_added_cond_kwargs,\n return_dict=False,\n )\n\n if guess_mode and self.do_classifier_free_guidance:\n # Infered ControlNet only for the conditional batch.\n # To apply the output of ControlNet to both the unconditional and conditional batches,\n # add 0 to the unconditional batch to keep it unchanged.\n down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]\n mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=encoder_hidden_states,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=self.cross_attention_kwargs,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n image = latents\n\n if not output_type == \"latent\":\n # apply watermark if available\n if self.watermark is not None:\n image = self.watermark.apply_watermark(image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)\n" }, { "path": "diffusers/examples/community/pipeline_stable_diffusion_xl_ipex.py", "content": "# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport intel_extension_for_pytorch as ipex\nimport torch\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModel,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers import StableDiffusionXLPipeline\nfrom diffusers.image_processor import PipelineImageInput, VaeImageProcessor\nfrom diffusers.loaders import (\n StableDiffusionXLLoraLoaderMixin,\n TextualInversionLoaderMixin,\n)\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.models.attention_processor import (\n AttnProcessor2_0,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n XFormersAttnProcessor,\n)\nfrom diffusers.models.lora import adjust_lora_scale_text_encoder\nfrom diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n USE_PEFT_BACKEND,\n deprecate,\n is_invisible_watermark_available,\n is_torch_xla_available,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nif is_invisible_watermark_available():\n from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker\n\nif is_torch_xla_available():\n import torch_xla.core.xla_model as xm\n\n XLA_AVAILABLE = True\nelse:\n XLA_AVAILABLE = False\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusionXLPipelineIpex\n\n >>> # SDXL-Turbo, a distilled version of SDXL 1.0, trained for real-time synthesis\n >>> pipe = StableDiffusionXLPipelineIpex.from_pretrained(\n ... \"stabilityai/sdxl-turbo\", low_cpu_mem_usage=True, use_safetensors=True\n ... )\n\n >>> num_inference_steps = 1\n >>> guidance_scale = 0.0\n >>> use_bf16 = True\n >>> data_type = torch.bfloat16 if use_bf16 else torch.float32\n >>> prompt = \"a photo of an astronaut riding a horse on mars\"\n\n >>> # value of image height/width should be consistent with the pipeline inference\n >>> # For Float32\n >>> pipe.prepare_for_ipex(torch.float32, prompt, height=512, width=512)\n >>> # For BFloat16\n >>> pipe.prepare_for_ipex(torch.bfloat16, prompt, height=512, width=512)\n\n >>> # value of image height/width should be consistent with 'prepare_for_ipex()'\n >>> # For Float32\n >>> image = pipe(prompt, num_inference_steps=num_inference_steps, height=512, width=512, guidance_scale=guidance_scale).images[0]\n >>> # For BFloat16\n >>> with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16):\n >>> image = pipe(prompt, num_inference_steps=num_inference_steps, height=512, width=512, guidance_scale=guidance_scale).images[0]\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used,\n `timesteps` must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default\n timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`\n must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass StableDiffusionXLPipelineIpex(\n StableDiffusionXLPipeline,\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion XL on IPEX.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n In addition the pipeline inherits the following loading methods:\n - *LoRA*: [`loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`]\n - *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`]\n\n as well as the following saving methods:\n - *LoRA*: [`loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`]\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion XL uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n text_encoder_2 ([` CLIPTextModelWithProjection`]):\n Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),\n specifically the\n [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)\n variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n tokenizer_2 (`CLIPTokenizer`):\n Second Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `\"True\"`):\n Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of\n `stabilityai/stable-diffusion-xl-base-1-0`.\n add_watermarker (`bool`, *optional*):\n Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to\n watermark output images. If not defined, it will default to True if the package is installed, otherwise no\n watermarker will be used.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->text_encoder_2->unet->vae\"\n _optional_components = [\n \"tokenizer\",\n \"tokenizer_2\",\n \"text_encoder\",\n \"text_encoder_2\",\n \"image_encoder\",\n \"feature_extractor\",\n ]\n _callback_tensor_inputs = [\n \"latents\",\n \"prompt_embeds\",\n \"negative_prompt_embeds\",\n \"add_text_embeds\",\n \"add_time_ids\",\n \"negative_pooled_prompt_embeds\",\n \"negative_add_time_ids\",\n ]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n text_encoder_2: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n tokenizer_2: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n image_encoder: CLIPVisionModelWithProjection = None,\n feature_extractor: CLIPImageProcessor = None,\n force_zeros_for_empty_prompt: bool = True,\n add_watermarker: Optional[bool] = None,\n ):\n # super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n unet=unet,\n scheduler=scheduler,\n image_encoder=image_encoder,\n feature_extractor=feature_extractor,\n )\n self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n self.default_sample_size = self.unet.config.sample_size\n\n add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()\n\n if add_watermarker:\n self.watermark = StableDiffusionXLWatermarker()\n else:\n self.watermark = None\n\n def encode_prompt(\n self,\n prompt: str,\n prompt_2: Optional[str] = None,\n device: Optional[torch.device] = None,\n num_images_per_prompt: int = 1,\n do_classifier_free_guidance: bool = True,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n lora_scale (`float`, *optional*):\n A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n device = device or self._execution_device\n\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if self.text_encoder is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)\n else:\n scale_lora_layers(self.text_encoder_2, lora_scale)\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n\n if prompt is not None:\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # Define tokenizers and text encoders\n tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]\n text_encoders = (\n [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]\n )\n\n if prompt_embeds is None:\n prompt_2 = prompt_2 or prompt\n prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2\n\n # textual inversion: procecss multi-vector tokens if necessary\n prompt_embeds_list = []\n prompts = [prompt, prompt_2]\n for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, tokenizer)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n if clip_skip is None:\n prompt_embeds = prompt_embeds.hidden_states[-2]\n else:\n # \"2\" because SDXL always indexes from the penultimate layer.\n prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]\n\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n\n # get unconditional embeddings for classifier free guidance\n zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt\n if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:\n negative_prompt_embeds = torch.zeros_like(prompt_embeds)\n negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)\n elif do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n\n # normalize str to list\n negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt\n negative_prompt_2 = (\n batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2\n )\n\n uncond_tokens: List[str]\n if prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = [negative_prompt, negative_prompt_2]\n\n negative_prompt_embeds_list = []\n for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n negative_prompt_embeds = text_encoder(\n uncond_input.input_ids.to(device),\n output_hidden_states=True,\n )\n # We are only ALWAYS interested in the pooled output of the final text encoder\n negative_pooled_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]\n\n negative_prompt_embeds_list.append(negative_prompt_embeds)\n\n negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)\n\n if self.text_encoder_2 is not None:\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n if self.text_encoder_2 is not None:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n if do_classifier_free_guidance:\n negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n\n if self.text_encoder is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder_2, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n\n uncond_image_embeds = torch.zeros_like(image_embeds)\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n prompt_2,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n negative_prompt_2=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n pooled_prompt_embeds=None,\n negative_pooled_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if prompt_embeds is not None and pooled_prompt_embeds is None:\n raise ValueError(\n \"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.\"\n )\n\n if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:\n raise ValueError(\n \"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=torch.float32)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def _get_add_time_ids(\n self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None\n ):\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n\n passed_add_embed_dim = (\n self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim\n )\n expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features\n\n if expected_add_embed_dim != passed_add_embed_dim:\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.\"\n )\n\n add_time_ids = torch.tensor([add_time_ids], dtype=dtype)\n return add_time_ids\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae\n def upcast_vae(self):\n dtype = self.vae.dtype\n self.vae.to(dtype=torch.float32)\n use_torch_2_0_or_xformers = isinstance(\n self.vae.decoder.mid_block.attentions[0].processor,\n (\n AttnProcessor2_0,\n XFormersAttnProcessor,\n LoRAXFormersAttnProcessor,\n LoRAAttnProcessor2_0,\n ),\n )\n # if xformers or torch_2_0 is used attention block does not need\n # to be in float32 which can save lots of memory\n if use_torch_2_0_or_xformers:\n self.vae.post_quant_conv.to(dtype)\n self.vae.decoder.conv_in.to(dtype)\n self.vae.decoder.mid_block.to(dtype)\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n timesteps (`torch.Tensor`):\n generate embedding vectors at these timesteps\n embedding_dim (`int`, *optional*, defaults to 512):\n dimension of the embeddings to generate\n dtype:\n data type of the generated embeddings\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def guidance_rescale(self):\n return self._guidance_rescale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def denoising_end(self):\n return self._denoising_end\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Optional[Tuple[int, int]] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Optional[Tuple[int, int]] = None,\n negative_original_size: Optional[Tuple[int, int]] = None,\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\n negative_target_size: Optional[Tuple[int, int]] = None,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image. This is set to 1024 by default for the best results.\n Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image. This is set to 1024 by default for the best results.\n Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n denoising_end (`float`, *optional*):\n When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be\n completed before it is intentionally prematurely terminated. As a result, the returned sample will\n still retain a substantial amount of noise as determined by the discrete timesteps selected by the\n scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a\n \"Mixture of Denoisers\" multi-pipeline setup, as elaborated in [**Refining the Image\n Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)\n guidance_scale (`float`, *optional*, defaults to 5.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead\n of a plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of\n [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).\n Guidance rescale factor should fix overexposure when using zero terminal SNR.\n original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.\n `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as\n explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n `crops_coords_top_left` can be used to generate an image that appears to be \"cropped\" from the position\n `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n For most cases, `target_size` should be set to the desired height and width of the generated image. If\n not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a specific image resolution. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a target image resolution. It should be as same\n as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a\n `tuple`. When returning a tuple, the first element is a list with the generated images.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n\n # 0. Default height and width to unet\n height = height or self.default_sample_size * self.vae_scale_factor\n width = width or self.default_sample_size * self.vae_scale_factor\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n height,\n width,\n callback_steps,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._guidance_rescale = guidance_rescale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._denoising_end = denoising_end\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # 4. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Prepare added time ids & embeddings\n add_text_embeds = pooled_prompt_embeds\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n add_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n if negative_original_size is not None and negative_target_size is not None:\n negative_add_time_ids = self._get_add_time_ids(\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n else:\n negative_add_time_ids = add_time_ids\n\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)\n\n if ip_adapter_image is not None:\n image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt)\n if self.do_classifier_free_guidance:\n image_embeds = torch.cat([negative_image_embeds, image_embeds])\n image_embeds = image_embeds.to(device)\n\n # 8. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n\n # 8.1 Apply denoising_end\n if (\n self.denoising_end is not None\n and isinstance(self.denoising_end, float)\n and self.denoising_end > 0\n and self.denoising_end < 1\n ):\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (self.denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n # 9. Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n if ip_adapter_image is not None:\n added_cond_kwargs[\"image_embeds\"] = image_embeds\n\n # noise_pred = self.unet(\n # latent_model_input,\n # t,\n # encoder_hidden_states=prompt_embeds,\n # timestep_cond=timestep_cond,\n # cross_attention_kwargs=self.cross_attention_kwargs,\n # added_cond_kwargs=added_cond_kwargs,\n # return_dict=False,\n # )[0]\n\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n added_cond_kwargs=added_cond_kwargs,\n )[\"sample\"]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n add_text_embeds = callback_outputs.pop(\"add_text_embeds\", add_text_embeds)\n negative_pooled_prompt_embeds = callback_outputs.pop(\n \"negative_pooled_prompt_embeds\", negative_pooled_prompt_embeds\n )\n add_time_ids = callback_outputs.pop(\"add_time_ids\", add_time_ids)\n negative_add_time_ids = callback_outputs.pop(\"negative_add_time_ids\", negative_add_time_ids)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if XLA_AVAILABLE:\n xm.mark_step()\n\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n image = latents\n\n if not output_type == \"latent\":\n # apply watermark if available\n if self.watermark is not None:\n image = self.watermark.apply_watermark(image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)\n\n @torch.no_grad()\n def prepare_for_ipex(\n self,\n dtype=torch.float32,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Optional[Tuple[int, int]] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Optional[Tuple[int, int]] = None,\n negative_original_size: Optional[Tuple[int, int]] = None,\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\n negative_target_size: Optional[Tuple[int, int]] = None,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n\n # 0. Default height and width to unet\n height = height or self.default_sample_size * self.vae_scale_factor\n width = width or self.default_sample_size * self.vae_scale_factor\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n height,\n width,\n callback_steps,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._guidance_rescale = guidance_rescale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._denoising_end = denoising_end\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = \"cpu\"\n do_classifier_free_guidance = self.do_classifier_free_guidance\n\n # 3. Encode input prompt\n lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 7. Prepare added time ids & embeddings\n add_text_embeds = pooled_prompt_embeds\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n add_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n if negative_original_size is not None and negative_target_size is not None:\n negative_add_time_ids = self._get_add_time_ids(\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n else:\n negative_add_time_ids = add_time_ids\n\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)\n\n if ip_adapter_image is not None:\n image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt)\n if self.do_classifier_free_guidance:\n image_embeds = torch.cat([negative_image_embeds, image_embeds])\n image_embeds = image_embeds.to(device)\n\n dummy = torch.ones(1, dtype=torch.int32)\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, dummy)\n\n # predict the noise residual\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n if ip_adapter_image is not None:\n added_cond_kwargs[\"image_embeds\"] = image_embeds\n\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n\n self.unet = self.unet.to(memory_format=torch.channels_last)\n self.vae.decoder = self.vae.decoder.to(memory_format=torch.channels_last)\n self.text_encoder = self.text_encoder.to(memory_format=torch.channels_last)\n\n unet_input_example = {\n \"sample\": latent_model_input,\n \"timestep\": dummy,\n \"encoder_hidden_states\": prompt_embeds,\n \"added_cond_kwargs\": added_cond_kwargs,\n }\n\n vae_decoder_input_example = latents\n\n # optimize with ipex\n if dtype == torch.bfloat16:\n self.unet = ipex.optimize(\n self.unet.eval(),\n dtype=torch.bfloat16,\n inplace=True,\n )\n self.vae.decoder = ipex.optimize(self.vae.decoder.eval(), dtype=torch.bfloat16, inplace=True)\n self.text_encoder = ipex.optimize(self.text_encoder.eval(), dtype=torch.bfloat16, inplace=True)\n elif dtype == torch.float32:\n self.unet = ipex.optimize(\n self.unet.eval(),\n dtype=torch.float32,\n inplace=True,\n level=\"O1\",\n weights_prepack=True,\n auto_kernel_selection=False,\n )\n self.vae.decoder = ipex.optimize(\n self.vae.decoder.eval(),\n dtype=torch.float32,\n inplace=True,\n level=\"O1\",\n weights_prepack=True,\n auto_kernel_selection=False,\n )\n self.text_encoder = ipex.optimize(\n self.text_encoder.eval(),\n dtype=torch.float32,\n inplace=True,\n level=\"O1\",\n weights_prepack=True,\n auto_kernel_selection=False,\n )\n else:\n raise ValueError(\" The value of 'dtype' should be 'torch.bfloat16' or 'torch.float32' !\")\n\n # trace unet model to get better performance on IPEX\n with torch.cpu.amp.autocast(enabled=dtype == torch.bfloat16), torch.no_grad():\n unet_trace_model = torch.jit.trace(\n self.unet, example_kwarg_inputs=unet_input_example, check_trace=False, strict=False\n )\n unet_trace_model = torch.jit.freeze(unet_trace_model)\n self.unet.forward = unet_trace_model.forward\n\n # trace vae.decoder model to get better performance on IPEX\n with torch.cpu.amp.autocast(enabled=dtype == torch.bfloat16), torch.no_grad():\n vae_decoder_trace_model = torch.jit.trace(\n self.vae.decoder, vae_decoder_input_example, check_trace=False, strict=False\n )\n vae_decoder_trace_model = torch.jit.freeze(vae_decoder_trace_model)\n self.vae.decoder.forward = vae_decoder_trace_model.forward\n" }, { "path": "diffusers/examples/community/pipeline_zero1to3.py", "content": "# A diffuser version implementation of Zero1to3 (https://github.com/cvlab-columbia/zero123), ICCV 2023\n# by Xin Kong\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport kornia\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom packaging import version\nfrom transformers import CLIPFeatureExtractor, CLIPVisionModelWithProjection\n\n# from ...configuration_utils import FrozenDict\n# from ...models import AutoencoderKL, UNet2DConditionModel\n# from ...schedulers import KarrasDiffusionSchedulers\n# from ...utils import (\n# deprecate,\n# is_accelerate_available,\n# is_accelerate_version,\n# logging,\n# randn_tensor,\n# replace_example_docstring,\n# )\n# from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\n# from . import StableDiffusionPipelineOutput\n# from .safety_checker import StableDiffusionSafetyChecker\nfrom diffusers import AutoencoderKL, DiffusionPipeline, StableDiffusionMixin, UNet2DConditionModel\nfrom diffusers.configuration_utils import ConfigMixin, FrozenDict\nfrom diffusers.models.modeling_utils import ModelMixin\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n deprecate,\n logging,\n replace_example_docstring,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n# todo\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusionPipeline\n\n >>> pipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16)\n >>> pipe = pipe.to(\"cuda\")\n\n >>> prompt = \"a photo of an astronaut riding a horse on mars\"\n >>> image = pipe(prompt).images[0]\n ```\n\"\"\"\n\n\nclass CCProjection(ModelMixin, ConfigMixin):\n def __init__(self, in_channel=772, out_channel=768):\n super().__init__()\n self.in_channel = in_channel\n self.out_channel = out_channel\n self.projection = torch.nn.Linear(in_channel, out_channel)\n\n def forward(self, x):\n return self.projection(x)\n\n\nclass Zero1to3StableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin):\n r\"\"\"\n Pipeline for single view conditioned novel view generation using Zero1to3.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n image_encoder ([`CLIPVisionModelWithProjection`]):\n Frozen CLIP image-encoder. Stable Diffusion Image Variation uses the vision portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPVisionModelWithProjection),\n specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPFeatureExtractor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n cc_projection ([`CCProjection`]):\n Projection layer to project the concated CLIP features and pose embeddings to the original CLIP feature size.\n \"\"\"\n\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n image_encoder: CLIPVisionModelWithProjection,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPFeatureExtractor,\n cc_projection: CCProjection,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"clip_sample\"] = False\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n image_encoder=image_encoder,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n cc_projection=cc_projection,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n # self.model_mode = None\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.\n Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n \"\"\"\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n prompt_embeds = prompt_embeds[0]\n\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n def CLIP_preprocess(self, x):\n dtype = x.dtype\n # following openai's implementation\n # TODO HF OpenAI CLIP preprocessing issue https://github.com/huggingface/transformers/issues/22505#issuecomment-1650170741\n # follow openai preprocessing to keep exact same, input tensor [-1, 1], otherwise the preprocessing will be different, https://github.com/huggingface/transformers/pull/22608\n if isinstance(x, torch.Tensor):\n if x.min() < -1.0 or x.max() > 1.0:\n raise ValueError(\"Expected input tensor to have values in the range [-1, 1]\")\n x = kornia.geometry.resize(\n x.to(torch.float32), (224, 224), interpolation=\"bicubic\", align_corners=True, antialias=False\n ).to(dtype=dtype)\n x = (x + 1.0) / 2.0\n # renormalize according to clip\n x = kornia.enhance.normalize(\n x, torch.Tensor([0.48145466, 0.4578275, 0.40821073]), torch.Tensor([0.26862954, 0.26130258, 0.27577711])\n )\n return x\n\n # from image_variation\n def _encode_image(self, image, device, num_images_per_prompt, do_classifier_free_guidance):\n dtype = next(self.image_encoder.parameters()).dtype\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n if isinstance(image, torch.Tensor):\n # Batch single image\n if image.ndim == 3:\n assert image.shape[0] == 3, \"Image outside a batch should be of shape (3, H, W)\"\n image = image.unsqueeze(0)\n\n assert image.ndim == 4, \"Image must have 4 dimensions\"\n\n # Check image is in [-1, 1]\n if image.min() < -1 or image.max() > 1:\n raise ValueError(\"Image should be in [-1, 1] range\")\n else:\n # preprocess image\n if isinstance(image, (PIL.Image.Image, np.ndarray)):\n image = [image]\n\n if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):\n image = [np.array(i.convert(\"RGB\"))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n elif isinstance(image, list) and isinstance(image[0], np.ndarray):\n image = np.concatenate([i[None, :] for i in image], axis=0)\n\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0\n\n image = image.to(device=device, dtype=dtype)\n\n image = self.CLIP_preprocess(image)\n # if not isinstance(image, torch.Tensor):\n # # 0-255\n # print(\"Warning: image is processed by hf's preprocess, which is different from openai original's.\")\n # image = self.feature_extractor(images=image, return_tensors=\"pt\").pixel_values\n image_embeddings = self.image_encoder(image).image_embeds.to(dtype=dtype)\n image_embeddings = image_embeddings.unsqueeze(1)\n\n # duplicate image embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = image_embeddings.shape\n image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)\n image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n negative_prompt_embeds = torch.zeros_like(image_embeddings)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings])\n\n return image_embeddings\n\n def _encode_pose(self, pose, device, num_images_per_prompt, do_classifier_free_guidance):\n dtype = next(self.cc_projection.parameters()).dtype\n if isinstance(pose, torch.Tensor):\n pose_embeddings = pose.unsqueeze(1).to(device=device, dtype=dtype)\n else:\n if isinstance(pose[0], list):\n pose = torch.Tensor(pose)\n else:\n pose = torch.Tensor([pose])\n x, y, z = pose[:, 0].unsqueeze(1), pose[:, 1].unsqueeze(1), pose[:, 2].unsqueeze(1)\n pose_embeddings = (\n torch.cat([torch.deg2rad(x), torch.sin(torch.deg2rad(y)), torch.cos(torch.deg2rad(y)), z], dim=-1)\n .unsqueeze(1)\n .to(device=device, dtype=dtype)\n ) # B, 1, 4\n # duplicate pose embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = pose_embeddings.shape\n pose_embeddings = pose_embeddings.repeat(1, num_images_per_prompt, 1)\n pose_embeddings = pose_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)\n if do_classifier_free_guidance:\n negative_prompt_embeds = torch.zeros_like(pose_embeddings)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n pose_embeddings = torch.cat([negative_prompt_embeds, pose_embeddings])\n return pose_embeddings\n\n def _encode_image_with_pose(self, image, pose, device, num_images_per_prompt, do_classifier_free_guidance):\n img_prompt_embeds = self._encode_image(image, device, num_images_per_prompt, False)\n pose_prompt_embeds = self._encode_pose(pose, device, num_images_per_prompt, False)\n prompt_embeds = torch.cat([img_prompt_embeds, pose_prompt_embeds], dim=-1)\n prompt_embeds = self.cc_projection(prompt_embeds)\n # prompt_embeds = img_prompt_embeds\n # follow 0123, add negative prompt, after projection\n if do_classifier_free_guidance:\n negative_prompt = torch.zeros_like(prompt_embeds)\n prompt_embeds = torch.cat([negative_prompt, prompt_embeds])\n return prompt_embeds\n\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n else:\n has_nsfw_concept = None\n return image, has_nsfw_concept\n\n def decode_latents(self, latents):\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents).sample\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(self, image, height, width, callback_steps):\n if (\n not isinstance(image, torch.Tensor)\n and not isinstance(image, PIL.Image.Image)\n and not isinstance(image, list)\n ):\n raise ValueError(\n \"`image` has to be of type `torch.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is\"\n f\" {type(image)}\"\n )\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def prepare_img_latents(self, image, batch_size, dtype, device, generator=None, do_classifier_free_guidance=False):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n if isinstance(image, torch.Tensor):\n # Batch single image\n if image.ndim == 3:\n assert image.shape[0] == 3, \"Image outside a batch should be of shape (3, H, W)\"\n image = image.unsqueeze(0)\n\n assert image.ndim == 4, \"Image must have 4 dimensions\"\n\n # Check image is in [-1, 1]\n if image.min() < -1 or image.max() > 1:\n raise ValueError(\"Image should be in [-1, 1] range\")\n else:\n # preprocess image\n if isinstance(image, (PIL.Image.Image, np.ndarray)):\n image = [image]\n\n if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):\n image = [np.array(i.convert(\"RGB\"))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n elif isinstance(image, list) and isinstance(image[0], np.ndarray):\n image = np.concatenate([i[None, :] for i in image], axis=0)\n\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0\n\n image = image.to(device=device, dtype=dtype)\n\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if isinstance(generator, list):\n init_latents = [\n self.vae.encode(image[i : i + 1]).latent_dist.mode(generator[i])\n for i in range(batch_size) # sample\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = self.vae.encode(image).latent_dist.mode()\n\n # init_latents = self.vae.config.scaling_factor * init_latents # todo in original zero123's inference gradio_new.py, model.encode_first_stage() is not scaled by scaling_factor\n if batch_size > init_latents.shape[0]:\n # init_latents = init_latents.repeat(batch_size // init_latents.shape[0], 1, 1, 1)\n num_images_per_prompt = batch_size // init_latents.shape[0]\n # duplicate image latents for each generation per prompt, using mps friendly method\n bs_embed, emb_c, emb_h, emb_w = init_latents.shape\n init_latents = init_latents.unsqueeze(1)\n init_latents = init_latents.repeat(1, num_images_per_prompt, 1, 1, 1)\n init_latents = init_latents.view(bs_embed * num_images_per_prompt, emb_c, emb_h, emb_w)\n\n # init_latents = torch.cat([init_latents]*2) if do_classifier_free_guidance else init_latents # follow zero123\n init_latents = (\n torch.cat([torch.zeros_like(init_latents), init_latents]) if do_classifier_free_guidance else init_latents\n )\n\n init_latents = init_latents.to(device=device, dtype=dtype)\n return init_latents\n\n # def load_cc_projection(self, pretrained_weights=None):\n # self.cc_projection = torch.nn.Linear(772, 768)\n # torch.nn.init.eye_(list(self.cc_projection.parameters())[0][:768, :768])\n # torch.nn.init.zeros_(list(self.cc_projection.parameters())[1])\n # if pretrained_weights is not None:\n # self.cc_projection.load_state_dict(pretrained_weights)\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n input_imgs: Union[torch.Tensor, PIL.Image.Image] = None,\n prompt_imgs: Union[torch.Tensor, PIL.Image.Image] = None,\n poses: Union[List[float], List[List[float]]] = None,\n torch_dtype=torch.float32,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 3.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: float = 1.0,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n input_imgs (`PIL` or `List[PIL]`, *optional*):\n The single input image for each 3D object\n prompt_imgs (`PIL` or `List[PIL]`, *optional*):\n Same as input_imgs, but will be used later as an image prompt condition, encoded by CLIP feature\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.\n Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttnProcessor` as defined under\n `self.processor` in\n [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n # input_image = hint_imgs\n self.check_inputs(input_imgs, height, width, callback_steps)\n\n # 2. Define call parameters\n if isinstance(input_imgs, PIL.Image.Image):\n batch_size = 1\n elif isinstance(input_imgs, list):\n batch_size = len(input_imgs)\n else:\n batch_size = input_imgs.shape[0]\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input image with pose as prompt\n prompt_embeds = self._encode_image_with_pose(\n prompt_imgs, poses, device, num_images_per_prompt, do_classifier_free_guidance\n )\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n 4,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare image latents\n img_latents = self.prepare_img_latents(\n input_imgs,\n batch_size * num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n generator,\n do_classifier_free_guidance,\n )\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n latent_model_input = torch.cat([latent_model_input, img_latents], dim=1)\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n # latents = self.scheduler.step(noise_pred.to(dtype=torch.float32), t, latents.to(dtype=torch.float32)).prev_sample.to(prompt_embeds.dtype)\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # 8. Post-processing\n has_nsfw_concept = None\n if output_type == \"latent\":\n image = latents\n elif output_type == \"pil\":\n # 8. Post-processing\n image = self.decode_latents(latents)\n # 10. Convert to PIL\n image = self.numpy_to_pil(image)\n else:\n # 8. Post-processing\n image = self.decode_latents(latents)\n\n # Offload last model to CPU\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/examples/community/regional_prompting_stable_diffusion.py", "content": "import math\r\nfrom typing import Dict, Optional\r\n\r\nimport torch\r\nimport torchvision.transforms.functional as FF\r\nfrom transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer\r\n\r\nfrom diffusers import StableDiffusionPipeline\r\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\r\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\r\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\r\nfrom diffusers.utils import USE_PEFT_BACKEND\r\n\r\n\r\ntry:\r\n from compel import Compel\r\nexcept ImportError:\r\n Compel = None\r\n\r\nKCOMM = \"ADDCOMM\"\r\nKBRK = \"BREAK\"\r\n\r\n\r\nclass RegionalPromptingStableDiffusionPipeline(StableDiffusionPipeline):\r\n r\"\"\"\r\n Args for Regional Prompting Pipeline:\r\n rp_args:dict\r\n Required\r\n rp_args[\"mode\"]: cols, rows, prompt, prompt-ex\r\n for cols, rows mode\r\n rp_args[\"div\"]: ex) 1;1;1(Divide into 3 regions)\r\n for prompt, prompt-ex mode\r\n rp_args[\"th\"]: ex) 0.5,0.5,0.6 (threshold for prompt mode)\r\n\r\n Optional\r\n rp_args[\"save_mask\"]: True/False (save masks in prompt mode)\r\n\r\n Pipeline for text-to-image generation using Stable Diffusion.\r\n\r\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\r\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\r\n\r\n Args:\r\n vae ([`AutoencoderKL`]):\r\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\r\n text_encoder ([`CLIPTextModel`]):\r\n Frozen text-encoder. Stable Diffusion uses the text portion of\r\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\r\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\r\n tokenizer (`CLIPTokenizer`):\r\n Tokenizer of class\r\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\r\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\r\n scheduler ([`SchedulerMixin`]):\r\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\r\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\r\n safety_checker ([`StableDiffusionSafetyChecker`]):\r\n Classification module that estimates whether generated images could be considered offensive or harmful.\r\n Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.\r\n feature_extractor ([`CLIPImageProcessor`]):\r\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\r\n \"\"\"\r\n\r\n def __init__(\r\n self,\r\n vae: AutoencoderKL,\r\n text_encoder: CLIPTextModel,\r\n tokenizer: CLIPTokenizer,\r\n unet: UNet2DConditionModel,\r\n scheduler: KarrasDiffusionSchedulers,\r\n safety_checker: StableDiffusionSafetyChecker,\r\n feature_extractor: CLIPFeatureExtractor,\r\n requires_safety_checker: bool = True,\r\n ):\r\n super().__init__(\r\n vae,\r\n text_encoder,\r\n tokenizer,\r\n unet,\r\n scheduler,\r\n safety_checker,\r\n feature_extractor,\r\n requires_safety_checker,\r\n )\r\n self.register_modules(\r\n vae=vae,\r\n text_encoder=text_encoder,\r\n tokenizer=tokenizer,\r\n unet=unet,\r\n scheduler=scheduler,\r\n safety_checker=safety_checker,\r\n feature_extractor=feature_extractor,\r\n )\r\n\r\n @torch.no_grad()\r\n def __call__(\r\n self,\r\n prompt: str,\r\n height: int = 512,\r\n width: int = 512,\r\n num_inference_steps: int = 50,\r\n guidance_scale: float = 7.5,\r\n negative_prompt: str = None,\r\n num_images_per_prompt: Optional[int] = 1,\r\n eta: float = 0.0,\r\n generator: Optional[torch.Generator] = None,\r\n latents: Optional[torch.Tensor] = None,\r\n output_type: Optional[str] = \"pil\",\r\n return_dict: bool = True,\r\n rp_args: Dict[str, str] = None,\r\n ):\r\n active = KBRK in prompt[0] if isinstance(prompt, list) else KBRK in prompt\r\n if negative_prompt is None:\r\n negative_prompt = \"\" if isinstance(prompt, str) else [\"\"] * len(prompt)\r\n\r\n device = self._execution_device\r\n regions = 0\r\n\r\n self.power = int(rp_args[\"power\"]) if \"power\" in rp_args else 1\r\n\r\n prompts = prompt if isinstance(prompt, list) else [prompt]\r\n n_prompts = negative_prompt if isinstance(prompt, str) else [negative_prompt]\r\n self.batch = batch = num_images_per_prompt * len(prompts)\r\n all_prompts_cn, all_prompts_p = promptsmaker(prompts, num_images_per_prompt)\r\n all_n_prompts_cn, _ = promptsmaker(n_prompts, num_images_per_prompt)\r\n\r\n equal = len(all_prompts_cn) == len(all_n_prompts_cn)\r\n\r\n if Compel:\r\n compel = Compel(tokenizer=self.tokenizer, text_encoder=self.text_encoder)\r\n\r\n def getcompelembs(prps):\r\n embl = []\r\n for prp in prps:\r\n embl.append(compel.build_conditioning_tensor(prp))\r\n return torch.cat(embl)\r\n\r\n conds = getcompelembs(all_prompts_cn)\r\n unconds = getcompelembs(all_n_prompts_cn)\r\n embs = getcompelembs(prompts)\r\n n_embs = getcompelembs(n_prompts)\r\n prompt = negative_prompt = None\r\n else:\r\n conds = self.encode_prompt(prompts, device, 1, True)[0]\r\n unconds = (\r\n self.encode_prompt(n_prompts, device, 1, True)[0]\r\n if equal\r\n else self.encode_prompt(all_n_prompts_cn, device, 1, True)[0]\r\n )\r\n embs = n_embs = None\r\n\r\n if not active:\r\n pcallback = None\r\n mode = None\r\n else:\r\n if any(x in rp_args[\"mode\"].upper() for x in [\"COL\", \"ROW\"]):\r\n mode = \"COL\" if \"COL\" in rp_args[\"mode\"].upper() else \"ROW\"\r\n ocells, icells, regions = make_cells(rp_args[\"div\"])\r\n\r\n elif \"PRO\" in rp_args[\"mode\"].upper():\r\n regions = len(all_prompts_p[0])\r\n mode = \"PROMPT\"\r\n reset_attnmaps(self)\r\n self.ex = \"EX\" in rp_args[\"mode\"].upper()\r\n self.target_tokens = target_tokens = tokendealer(self, all_prompts_p)\r\n thresholds = [float(x) for x in rp_args[\"th\"].split(\",\")]\r\n\r\n orig_hw = (height, width)\r\n revers = True\r\n\r\n def pcallback(s_self, step: int, timestep: int, latents: torch.Tensor, selfs=None):\r\n if \"PRO\" in mode: # in Prompt mode, make masks from sum of attension maps\r\n self.step = step\r\n\r\n if len(self.attnmaps_sizes) > 3:\r\n self.history[step] = self.attnmaps.copy()\r\n for hw in self.attnmaps_sizes:\r\n allmasks = []\r\n basemasks = [None] * batch\r\n for tt, th in zip(target_tokens, thresholds):\r\n for b in range(batch):\r\n key = f\"{tt}-{b}\"\r\n _, mask, _ = makepmask(self, self.attnmaps[key], hw[0], hw[1], th, step)\r\n mask = mask.unsqueeze(0).unsqueeze(-1)\r\n if self.ex:\r\n allmasks[b::batch] = [x - mask for x in allmasks[b::batch]]\r\n allmasks[b::batch] = [torch.where(x > 0, 1, 0) for x in allmasks[b::batch]]\r\n allmasks.append(mask)\r\n basemasks[b] = mask if basemasks[b] is None else basemasks[b] + mask\r\n basemasks = [1 - mask for mask in basemasks]\r\n basemasks = [torch.where(x > 0, 1, 0) for x in basemasks]\r\n allmasks = basemasks + allmasks\r\n\r\n self.attnmasks[hw] = torch.cat(allmasks)\r\n self.maskready = True\r\n return latents\r\n\r\n def hook_forward(module):\r\n # diffusers==0.23.2\r\n def forward(\r\n hidden_states: torch.Tensor,\r\n encoder_hidden_states: Optional[torch.Tensor] = None,\r\n attention_mask: Optional[torch.Tensor] = None,\r\n temb: Optional[torch.Tensor] = None,\r\n scale: float = 1.0,\r\n ) -> torch.Tensor:\r\n attn = module\r\n xshape = hidden_states.shape\r\n self.hw = (h, w) = split_dims(xshape[1], *orig_hw)\r\n\r\n if revers:\r\n nx, px = hidden_states.chunk(2)\r\n else:\r\n px, nx = hidden_states.chunk(2)\r\n\r\n if equal:\r\n hidden_states = torch.cat(\r\n [px for i in range(regions)] + [nx for i in range(regions)],\r\n 0,\r\n )\r\n encoder_hidden_states = torch.cat([conds] + [unconds])\r\n else:\r\n hidden_states = torch.cat([px for i in range(regions)] + [nx], 0)\r\n encoder_hidden_states = torch.cat([conds] + [unconds])\r\n\r\n residual = hidden_states\r\n\r\n args = () if USE_PEFT_BACKEND else (scale,)\r\n\r\n if attn.spatial_norm is not None:\r\n hidden_states = attn.spatial_norm(hidden_states, temb)\r\n\r\n input_ndim = hidden_states.ndim\r\n\r\n if input_ndim == 4:\r\n batch_size, channel, height, width = hidden_states.shape\r\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\r\n\r\n batch_size, sequence_length, _ = (\r\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\r\n )\r\n\r\n if attention_mask is not None:\r\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\r\n attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])\r\n\r\n if attn.group_norm is not None:\r\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\r\n\r\n args = () if USE_PEFT_BACKEND else (scale,)\r\n query = attn.to_q(hidden_states, *args)\r\n\r\n if encoder_hidden_states is None:\r\n encoder_hidden_states = hidden_states\r\n elif attn.norm_cross:\r\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\r\n\r\n key = attn.to_k(encoder_hidden_states, *args)\r\n value = attn.to_v(encoder_hidden_states, *args)\r\n\r\n inner_dim = key.shape[-1]\r\n head_dim = inner_dim // attn.heads\r\n\r\n query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\r\n\r\n key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\r\n value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\r\n\r\n # the output of sdp = (batch, num_heads, seq_len, head_dim)\r\n # TODO: add support for attn.scale when we move to Torch 2.1\r\n hidden_states = scaled_dot_product_attention(\r\n self,\r\n query,\r\n key,\r\n value,\r\n attn_mask=attention_mask,\r\n dropout_p=0.0,\r\n is_causal=False,\r\n getattn=\"PRO\" in mode,\r\n )\r\n\r\n hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)\r\n hidden_states = hidden_states.to(query.dtype)\r\n\r\n # linear proj\r\n hidden_states = attn.to_out[0](hidden_states, *args)\r\n # dropout\r\n hidden_states = attn.to_out[1](hidden_states)\r\n\r\n if input_ndim == 4:\r\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\r\n\r\n if attn.residual_connection:\r\n hidden_states = hidden_states + residual\r\n\r\n hidden_states = hidden_states / attn.rescale_output_factor\r\n\r\n #### Regional Prompting Col/Row mode\r\n if any(x in mode for x in [\"COL\", \"ROW\"]):\r\n reshaped = hidden_states.reshape(hidden_states.size()[0], h, w, hidden_states.size()[2])\r\n center = reshaped.shape[0] // 2\r\n px = reshaped[0:center] if equal else reshaped[0:-batch]\r\n nx = reshaped[center:] if equal else reshaped[-batch:]\r\n outs = [px, nx] if equal else [px]\r\n for out in outs:\r\n c = 0\r\n for i, ocell in enumerate(ocells):\r\n for icell in icells[i]:\r\n if \"ROW\" in mode:\r\n out[\r\n 0:batch,\r\n int(h * ocell[0]) : int(h * ocell[1]),\r\n int(w * icell[0]) : int(w * icell[1]),\r\n :,\r\n ] = out[\r\n c * batch : (c + 1) * batch,\r\n int(h * ocell[0]) : int(h * ocell[1]),\r\n int(w * icell[0]) : int(w * icell[1]),\r\n :,\r\n ]\r\n else:\r\n out[\r\n 0:batch,\r\n int(h * icell[0]) : int(h * icell[1]),\r\n int(w * ocell[0]) : int(w * ocell[1]),\r\n :,\r\n ] = out[\r\n c * batch : (c + 1) * batch,\r\n int(h * icell[0]) : int(h * icell[1]),\r\n int(w * ocell[0]) : int(w * ocell[1]),\r\n :,\r\n ]\r\n c += 1\r\n px, nx = (px[0:batch], nx[0:batch]) if equal else (px[0:batch], nx)\r\n hidden_states = torch.cat([nx, px], 0) if revers else torch.cat([px, nx], 0)\r\n hidden_states = hidden_states.reshape(xshape)\r\n\r\n #### Regional Prompting Prompt mode\r\n elif \"PRO\" in mode:\r\n px, nx = (\r\n torch.chunk(hidden_states) if equal else hidden_states[0:-batch],\r\n hidden_states[-batch:],\r\n )\r\n\r\n if (h, w) in self.attnmasks and self.maskready:\r\n\r\n def mask(input):\r\n out = torch.multiply(input, self.attnmasks[(h, w)])\r\n for b in range(batch):\r\n for r in range(1, regions):\r\n out[b] = out[b] + out[r * batch + b]\r\n return out\r\n\r\n px, nx = (mask(px), mask(nx)) if equal else (mask(px), nx)\r\n px, nx = (px[0:batch], nx[0:batch]) if equal else (px[0:batch], nx)\r\n hidden_states = torch.cat([nx, px], 0) if revers else torch.cat([px, nx], 0)\r\n return hidden_states\r\n\r\n return forward\r\n\r\n def hook_forwards(root_module: torch.nn.Module):\r\n for name, module in root_module.named_modules():\r\n if \"attn2\" in name and module.__class__.__name__ == \"Attention\":\r\n module.forward = hook_forward(module)\r\n\r\n hook_forwards(self.unet)\r\n\r\n output = StableDiffusionPipeline(**self.components)(\r\n prompt=prompt,\r\n prompt_embeds=embs,\r\n negative_prompt=negative_prompt,\r\n negative_prompt_embeds=n_embs,\r\n height=height,\r\n width=width,\r\n num_inference_steps=num_inference_steps,\r\n guidance_scale=guidance_scale,\r\n num_images_per_prompt=num_images_per_prompt,\r\n eta=eta,\r\n generator=generator,\r\n latents=latents,\r\n output_type=output_type,\r\n return_dict=return_dict,\r\n callback_on_step_end=pcallback,\r\n )\r\n\r\n if \"save_mask\" in rp_args:\r\n save_mask = rp_args[\"save_mask\"]\r\n else:\r\n save_mask = False\r\n\r\n if mode == \"PROMPT\" and save_mask:\r\n saveattnmaps(\r\n self,\r\n output,\r\n height,\r\n width,\r\n thresholds,\r\n num_inference_steps // 2,\r\n regions,\r\n )\r\n\r\n return output\r\n\r\n\r\n### Make prompt list for each regions\r\ndef promptsmaker(prompts, batch):\r\n out_p = []\r\n plen = len(prompts)\r\n for prompt in prompts:\r\n add = \"\"\r\n if KCOMM in prompt:\r\n add, prompt = prompt.split(KCOMM)\r\n add = add + \" \"\r\n prompts = prompt.split(KBRK)\r\n out_p.append([add + p for p in prompts])\r\n out = [None] * batch * len(out_p[0]) * len(out_p)\r\n for p, prs in enumerate(out_p): # inputs prompts\r\n for r, pr in enumerate(prs): # prompts for regions\r\n start = (p + r * plen) * batch\r\n out[start : start + batch] = [pr] * batch # P1R1B1,P1R1B2...,P1R2B1,P1R2B2...,P2R1B1...\r\n return out, out_p\r\n\r\n\r\n### make regions from ratios\r\n### \";\" makes outercells, \",\" makes inner cells\r\ndef make_cells(ratios):\r\n if \";\" not in ratios and \",\" in ratios:\r\n ratios = ratios.replace(\",\", \";\")\r\n ratios = ratios.split(\";\")\r\n ratios = [inratios.split(\",\") for inratios in ratios]\r\n\r\n icells = []\r\n ocells = []\r\n\r\n def startend(cells, array):\r\n current_start = 0\r\n array = [float(x) for x in array]\r\n for value in array:\r\n end = current_start + (value / sum(array))\r\n cells.append([current_start, end])\r\n current_start = end\r\n\r\n startend(ocells, [r[0] for r in ratios])\r\n\r\n for inratios in ratios:\r\n if 2 > len(inratios):\r\n icells.append([[0, 1]])\r\n else:\r\n add = []\r\n startend(add, inratios[1:])\r\n icells.append(add)\r\n\r\n return ocells, icells, sum(len(cell) for cell in icells)\r\n\r\n\r\ndef make_emblist(self, prompts):\r\n with torch.no_grad():\r\n tokens = self.tokenizer(\r\n prompts,\r\n max_length=self.tokenizer.model_max_length,\r\n padding=True,\r\n truncation=True,\r\n return_tensors=\"pt\",\r\n ).input_ids.to(self.device)\r\n embs = self.text_encoder(tokens, output_hidden_states=True).last_hidden_state.to(self.device, dtype=self.dtype)\r\n return embs\r\n\r\n\r\ndef split_dims(xs, height, width):\r\n xs = xs\r\n\r\n def repeat_div(x, y):\r\n while y > 0:\r\n x = math.ceil(x / 2)\r\n y = y - 1\r\n return x\r\n\r\n scale = math.ceil(math.log2(math.sqrt(height * width / xs)))\r\n dsh = repeat_div(height, scale)\r\n dsw = repeat_div(width, scale)\r\n return dsh, dsw\r\n\r\n\r\n##### for prompt mode\r\ndef get_attn_maps(self, attn):\r\n height, width = self.hw\r\n target_tokens = self.target_tokens\r\n if (height, width) not in self.attnmaps_sizes:\r\n self.attnmaps_sizes.append((height, width))\r\n\r\n for b in range(self.batch):\r\n for t in target_tokens:\r\n power = self.power\r\n add = attn[b, :, :, t[0] : t[0] + len(t)] ** (power) * (self.attnmaps_sizes.index((height, width)) + 1)\r\n add = torch.sum(add, dim=2)\r\n key = f\"{t}-{b}\"\r\n if key not in self.attnmaps:\r\n self.attnmaps[key] = add\r\n else:\r\n if self.attnmaps[key].shape[1] != add.shape[1]:\r\n add = add.view(8, height, width)\r\n add = FF.resize(add, self.attnmaps_sizes[0], antialias=None)\r\n add = add.reshape_as(self.attnmaps[key])\r\n\r\n self.attnmaps[key] = self.attnmaps[key] + add\r\n\r\n\r\ndef reset_attnmaps(self): # init parameters in every batch\r\n self.step = 0\r\n self.attnmaps = {} # maked from attention maps\r\n self.attnmaps_sizes = [] # height,width set of u-net blocks\r\n self.attnmasks = {} # maked from attnmaps for regions\r\n self.maskready = False\r\n self.history = {}\r\n\r\n\r\ndef saveattnmaps(self, output, h, w, th, step, regions):\r\n masks = []\r\n for i, mask in enumerate(self.history[step].values()):\r\n img, _, mask = makepmask(self, mask, h, w, th[i % len(th)], step)\r\n if self.ex:\r\n masks = [x - mask for x in masks]\r\n masks.append(mask)\r\n if len(masks) == regions - 1:\r\n output.images.extend([FF.to_pil_image(mask) for mask in masks])\r\n masks = []\r\n else:\r\n output.images.append(img)\r\n\r\n\r\ndef makepmask(\r\n self, mask, h, w, th, step\r\n): # make masks from attention cache return [for preview, for attention, for Latent]\r\n th = th - step * 0.005\r\n if 0.05 >= th:\r\n th = 0.05\r\n mask = torch.mean(mask, dim=0)\r\n mask = mask / mask.max().item()\r\n mask = torch.where(mask > th, 1, 0)\r\n mask = mask.float()\r\n mask = mask.view(1, *self.attnmaps_sizes[0])\r\n img = FF.to_pil_image(mask)\r\n img = img.resize((w, h))\r\n mask = FF.resize(mask, (h, w), interpolation=FF.InterpolationMode.NEAREST, antialias=None)\r\n lmask = mask\r\n mask = mask.reshape(h * w)\r\n mask = torch.where(mask > 0.1, 1, 0)\r\n return img, mask, lmask\r\n\r\n\r\ndef tokendealer(self, all_prompts):\r\n for prompts in all_prompts:\r\n targets = [p.split(\",\")[-1] for p in prompts[1:]]\r\n tt = []\r\n\r\n for target in targets:\r\n ptokens = (\r\n self.tokenizer(\r\n prompts,\r\n max_length=self.tokenizer.model_max_length,\r\n padding=True,\r\n truncation=True,\r\n return_tensors=\"pt\",\r\n ).input_ids\r\n )[0]\r\n ttokens = (\r\n self.tokenizer(\r\n target,\r\n max_length=self.tokenizer.model_max_length,\r\n padding=True,\r\n truncation=True,\r\n return_tensors=\"pt\",\r\n ).input_ids\r\n )[0]\r\n\r\n tlist = []\r\n\r\n for t in range(ttokens.shape[0] - 2):\r\n for p in range(ptokens.shape[0]):\r\n if ttokens[t + 1] == ptokens[p]:\r\n tlist.append(p)\r\n if tlist != []:\r\n tt.append(tlist)\r\n\r\n return tt\r\n\r\n\r\ndef scaled_dot_product_attention(\r\n self,\r\n query,\r\n key,\r\n value,\r\n attn_mask=None,\r\n dropout_p=0.0,\r\n is_causal=False,\r\n scale=None,\r\n getattn=False,\r\n) -> torch.Tensor:\r\n # Efficient implementation equivalent to the following:\r\n L, S = query.size(-2), key.size(-2)\r\n scale_factor = 1 / math.sqrt(query.size(-1)) if scale is None else scale\r\n attn_bias = torch.zeros(L, S, dtype=query.dtype, device=self.device)\r\n if is_causal:\r\n assert attn_mask is None\r\n temp_mask = torch.ones(L, S, dtype=torch.bool).tril(diagonal=0)\r\n attn_bias.masked_fill_(temp_mask.logical_not(), float(\"-inf\"))\r\n attn_bias.to(query.dtype)\r\n\r\n if attn_mask is not None:\r\n if attn_mask.dtype == torch.bool:\r\n attn_mask.masked_fill_(attn_mask.logical_not(), float(\"-inf\"))\r\n else:\r\n attn_bias += attn_mask\r\n attn_weight = query @ key.transpose(-2, -1) * scale_factor\r\n attn_weight += attn_bias\r\n attn_weight = torch.softmax(attn_weight, dim=-1)\r\n if getattn:\r\n get_attn_maps(self, attn_weight)\r\n attn_weight = torch.dropout(attn_weight, dropout_p, train=True)\r\n return attn_weight @ value\r\n" }, { "path": "diffusers/examples/community/rerender_a_video.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom dataclasses import dataclass\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nimport torchvision.transforms as T\nfrom gmflow.gmflow import GMFlow\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers.image_processor import VaeImageProcessor\nfrom diffusers.models import AutoencoderKL, ControlNetModel, UNet2DConditionModel\nfrom diffusers.models.attention_processor import Attention, AttnProcessor\nfrom diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel\nfrom diffusers.pipelines.controlnet.pipeline_controlnet_img2img import StableDiffusionControlNetImg2ImgPipeline\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import BaseOutput, deprecate, logging\nfrom diffusers.utils.torch_utils import is_compiled_module, randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef coords_grid(b, h, w, homogeneous=False, device=None):\n y, x = torch.meshgrid(torch.arange(h), torch.arange(w)) # [H, W]\n\n stacks = [x, y]\n\n if homogeneous:\n ones = torch.ones_like(x) # [H, W]\n stacks.append(ones)\n\n grid = torch.stack(stacks, dim=0).float() # [2, H, W] or [3, H, W]\n\n grid = grid[None].repeat(b, 1, 1, 1) # [B, 2, H, W] or [B, 3, H, W]\n\n if device is not None:\n grid = grid.to(device)\n\n return grid\n\n\ndef bilinear_sample(img, sample_coords, mode=\"bilinear\", padding_mode=\"zeros\", return_mask=False):\n # img: [B, C, H, W]\n # sample_coords: [B, 2, H, W] in image scale\n if sample_coords.size(1) != 2: # [B, H, W, 2]\n sample_coords = sample_coords.permute(0, 3, 1, 2)\n\n b, _, h, w = sample_coords.shape\n\n # Normalize to [-1, 1]\n x_grid = 2 * sample_coords[:, 0] / (w - 1) - 1\n y_grid = 2 * sample_coords[:, 1] / (h - 1) - 1\n\n grid = torch.stack([x_grid, y_grid], dim=-1) # [B, H, W, 2]\n\n img = F.grid_sample(img, grid, mode=mode, padding_mode=padding_mode, align_corners=True)\n\n if return_mask:\n mask = (x_grid >= -1) & (y_grid >= -1) & (x_grid <= 1) & (y_grid <= 1) # [B, H, W]\n\n return img, mask\n\n return img\n\n\ndef flow_warp(feature, flow, mask=False, mode=\"bilinear\", padding_mode=\"zeros\"):\n b, c, h, w = feature.size()\n assert flow.size(1) == 2\n\n grid = coords_grid(b, h, w).to(flow.device) + flow # [B, 2, H, W]\n grid = grid.to(feature.dtype)\n return bilinear_sample(feature, grid, mode=mode, padding_mode=padding_mode, return_mask=mask)\n\n\ndef forward_backward_consistency_check(fwd_flow, bwd_flow, alpha=0.01, beta=0.5):\n # fwd_flow, bwd_flow: [B, 2, H, W]\n # alpha and beta values are following UnFlow\n # (https://arxiv.org/abs/1711.07837)\n assert fwd_flow.dim() == 4 and bwd_flow.dim() == 4\n assert fwd_flow.size(1) == 2 and bwd_flow.size(1) == 2\n flow_mag = torch.norm(fwd_flow, dim=1) + torch.norm(bwd_flow, dim=1) # [B, H, W]\n\n warped_bwd_flow = flow_warp(bwd_flow, fwd_flow) # [B, 2, H, W]\n warped_fwd_flow = flow_warp(fwd_flow, bwd_flow) # [B, 2, H, W]\n\n diff_fwd = torch.norm(fwd_flow + warped_bwd_flow, dim=1) # [B, H, W]\n diff_bwd = torch.norm(bwd_flow + warped_fwd_flow, dim=1)\n\n threshold = alpha * flow_mag + beta\n\n fwd_occ = (diff_fwd > threshold).float() # [B, H, W]\n bwd_occ = (diff_bwd > threshold).float()\n\n return fwd_occ, bwd_occ\n\n\n@torch.no_grad()\ndef get_warped_and_mask(flow_model, image1, image2, image3=None, pixel_consistency=False, device=None):\n if image3 is None:\n image3 = image1\n padder = InputPadder(image1.shape, padding_factor=8)\n image1, image2 = padder.pad(image1[None].to(device), image2[None].to(device))\n results_dict = flow_model(\n image1, image2, attn_splits_list=[2], corr_radius_list=[-1], prop_radius_list=[-1], pred_bidir_flow=True\n )\n flow_pr = results_dict[\"flow_preds\"][-1] # [B, 2, H, W]\n fwd_flow = padder.unpad(flow_pr[0]).unsqueeze(0) # [1, 2, H, W]\n bwd_flow = padder.unpad(flow_pr[1]).unsqueeze(0) # [1, 2, H, W]\n fwd_occ, bwd_occ = forward_backward_consistency_check(fwd_flow, bwd_flow) # [1, H, W] float\n if pixel_consistency:\n warped_image1 = flow_warp(image1, bwd_flow)\n bwd_occ = torch.clamp(\n bwd_occ + (abs(image2 - warped_image1).mean(dim=1) > 255 * 0.25).float(), 0, 1\n ).unsqueeze(0)\n warped_results = flow_warp(image3, bwd_flow)\n return warped_results, bwd_occ, bwd_flow\n\n\nblur = T.GaussianBlur(kernel_size=(9, 9), sigma=(18, 18))\n\n\n@dataclass\nclass TextToVideoSDPipelineOutput(BaseOutput):\n \"\"\"\n Output class for text-to-video pipelines.\n\n Args:\n frames (`List[np.ndarray]` or `torch.Tensor`)\n List of denoised frames (essentially images) as NumPy arrays of shape `(height, width, num_channels)` or as\n a `torch` tensor. The length of the list denotes the video length (the number of frames).\n \"\"\"\n\n frames: Union[List[np.ndarray], torch.Tensor]\n\n\n@torch.no_grad()\ndef find_flat_region(mask):\n device = mask.device\n kernel_x = torch.Tensor([[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]]).unsqueeze(0).unsqueeze(0).to(device)\n kernel_y = torch.Tensor([[-1, -1, -1], [0, 0, 0], [1, 1, 1]]).unsqueeze(0).unsqueeze(0).to(device)\n mask_ = F.pad(mask.unsqueeze(0), (1, 1, 1, 1), mode=\"replicate\")\n\n grad_x = torch.nn.functional.conv2d(mask_, kernel_x)\n grad_y = torch.nn.functional.conv2d(mask_, kernel_y)\n return ((abs(grad_x) + abs(grad_y)) == 0).float()[0]\n\n\nclass AttnState:\n STORE = 0\n LOAD = 1\n LOAD_AND_STORE_PREV = 2\n\n def __init__(self):\n self.reset()\n\n @property\n def state(self):\n return self.__state\n\n @property\n def timestep(self):\n return self.__timestep\n\n def set_timestep(self, t):\n self.__timestep = t\n\n def reset(self):\n self.__state = AttnState.STORE\n self.__timestep = 0\n\n def to_load(self):\n self.__state = AttnState.LOAD\n\n def to_load_and_store_prev(self):\n self.__state = AttnState.LOAD_AND_STORE_PREV\n\n\nclass CrossFrameAttnProcessor(AttnProcessor):\n \"\"\"\n Cross frame attention processor. Each frame attends the first frame and previous frame.\n\n Args:\n attn_state: Whether the model is processing the first frame or an intermediate frame\n \"\"\"\n\n def __init__(self, attn_state: AttnState):\n super().__init__()\n self.attn_state = attn_state\n self.first_maps = {}\n self.prev_maps = {}\n\n def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, temb=None):\n # Is self attention\n if encoder_hidden_states is None:\n t = self.attn_state.timestep\n if self.attn_state.state == AttnState.STORE:\n self.first_maps[t] = hidden_states.detach()\n self.prev_maps[t] = hidden_states.detach()\n res = super().__call__(attn, hidden_states, encoder_hidden_states, attention_mask, temb)\n else:\n if self.attn_state.state == AttnState.LOAD_AND_STORE_PREV:\n tmp = hidden_states.detach()\n cross_map = torch.cat((self.first_maps[t], self.prev_maps[t]), dim=1)\n res = super().__call__(attn, hidden_states, cross_map, attention_mask, temb)\n if self.attn_state.state == AttnState.LOAD_AND_STORE_PREV:\n self.prev_maps[t] = tmp\n else:\n res = super().__call__(attn, hidden_states, encoder_hidden_states, attention_mask, temb)\n\n return res\n\n\ndef prepare_image(image):\n if isinstance(image, torch.Tensor):\n # Batch single image\n if image.ndim == 3:\n image = image.unsqueeze(0)\n\n image = image.to(dtype=torch.float32)\n else:\n # preprocess image\n if isinstance(image, (PIL.Image.Image, np.ndarray)):\n image = [image]\n\n if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):\n image = [np.array(i.convert(\"RGB\"))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n elif isinstance(image, list) and isinstance(image[0], np.ndarray):\n image = np.concatenate([i[None, :] for i in image], axis=0)\n\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0\n\n return image\n\n\nclass RerenderAVideoPipeline(StableDiffusionControlNetImg2ImgPipeline):\n r\"\"\"\n Pipeline for video-to-video translation using Stable Diffusion with Rerender Algorithm.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n In addition the pipeline inherits the following loading methods:\n - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):\n Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets\n as a list, the outputs from each ControlNet are added together to create one combined additional\n conditioning.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder=None,\n requires_safety_checker: bool = True,\n device=None,\n ):\n super().__init__(\n vae,\n text_encoder,\n tokenizer,\n unet,\n controlnet,\n scheduler,\n safety_checker,\n feature_extractor,\n image_encoder,\n requires_safety_checker,\n )\n self.to(device)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n if isinstance(controlnet, (list, tuple)):\n controlnet = MultiControlNetModel(controlnet)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n controlnet=controlnet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)\n self.control_image_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False\n )\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n self.attn_state = AttnState()\n attn_processor_dict = {}\n for k in unet.attn_processors.keys():\n if k.startswith(\"up\"):\n attn_processor_dict[k] = CrossFrameAttnProcessor(self.attn_state)\n else:\n attn_processor_dict[k] = AttnProcessor()\n\n self.unet.set_attn_processor(attn_processor_dict)\n\n flow_model = GMFlow(\n feature_channels=128,\n num_scales=1,\n upsample_factor=8,\n num_head=1,\n attention_type=\"swin\",\n ffn_dim_expansion=4,\n num_transformer_layers=6,\n ).to(self.device)\n\n checkpoint = torch.utils.model_zoo.load_url(\n \"https://huggingface.co/Anonymous-sub/Rerender/resolve/main/models/gmflow_sintel-0c07dcb3.pth\",\n map_location=lambda storage, loc: storage,\n )\n weights = checkpoint[\"model\"] if \"model\" in checkpoint else checkpoint\n flow_model.load_state_dict(weights, strict=False)\n flow_model.eval()\n self.flow_model = flow_model\n\n # Modified from src/diffusers/pipelines/controlnet/pipeline_controlnet.StableDiffusionControlNetImg2ImgPipeline.check_inputs\n def check_inputs(\n self,\n prompt,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n controlnet_conditioning_scale=1.0,\n control_guidance_start=0.0,\n control_guidance_end=1.0,\n ):\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # `prompt` needs more sophisticated handling when there are multiple\n # conditionings.\n if isinstance(self.controlnet, MultiControlNetModel):\n if isinstance(prompt, list):\n logger.warning(\n f\"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}\"\n \" prompts. The conditionings will be fixed across the prompts.\"\n )\n\n is_compiled = hasattr(F, \"scaled_dot_product_attention\") and isinstance(\n self.controlnet, torch._dynamo.eval_frame.OptimizedModule\n )\n\n # Check `controlnet_conditioning_scale`\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n if not isinstance(controlnet_conditioning_scale, float):\n raise TypeError(\"For single controlnet: `controlnet_conditioning_scale` must be type `float`.\")\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if isinstance(controlnet_conditioning_scale, list):\n if any(isinstance(i, list) for i in controlnet_conditioning_scale):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(\n self.controlnet.nets\n ):\n raise ValueError(\n \"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have\"\n \" the same length as the number of controlnets\"\n )\n else:\n assert False\n\n if len(control_guidance_start) != len(control_guidance_end):\n raise ValueError(\n f\"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list.\"\n )\n\n if isinstance(self.controlnet, MultiControlNetModel):\n if len(control_guidance_start) != len(self.controlnet.nets):\n raise ValueError(\n f\"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}.\"\n )\n\n for start, end in zip(control_guidance_start, control_guidance_end):\n if start >= end:\n raise ValueError(\n f\"control guidance start: {start} cannot be larger or equal to control guidance end: {end}.\"\n )\n if start < 0.0:\n raise ValueError(f\"control guidance start: {start} can't be smaller than 0.\")\n if end > 1.0:\n raise ValueError(f\"control guidance end: {end} can't be larger than 1.0.\")\n\n # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image\n def prepare_control_image(\n self,\n image,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype,\n do_classifier_free_guidance=False,\n guess_mode=False,\n ):\n image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)\n image_batch_size = image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n image = image.repeat_interleave(repeat_by, dim=0)\n\n image = image.to(device=device, dtype=dtype)\n\n if do_classifier_free_guidance and not guess_mode:\n image = torch.cat([image] * 2)\n\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n\n return timesteps, num_inference_steps - t_start\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.prepare_latents\n def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n\n if image.shape[1] == 4:\n init_latents = image\n\n else:\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n elif isinstance(generator, list):\n init_latents = [\n self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = self.vae.encode(image).latent_dist.sample(generator)\n\n init_latents = self.vae.config.scaling_factor * init_latents\n\n if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:\n # expand init_latents for batch_size\n deprecation_message = (\n f\"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial\"\n \" images (`image`). Initial images are now duplicating to match the number of text prompts. Note\"\n \" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update\"\n \" your script to pass as many initial images as text prompts to suppress this warning.\"\n )\n deprecate(\"len(prompt) != len(image)\", \"1.0.0\", deprecation_message, standard_warn=False)\n additional_image_per_prompt = batch_size // init_latents.shape[0]\n init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)\n elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n init_latents = torch.cat([init_latents], dim=0)\n\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n latents = init_latents\n\n return latents\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n frames: Union[List[np.ndarray], torch.Tensor] = None,\n control_frames: Union[List[np.ndarray], torch.Tensor] = None,\n strength: float = 0.8,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: Union[float, List[float]] = 0.8,\n guess_mode: bool = False,\n control_guidance_start: Union[float, List[float]] = 0.0,\n control_guidance_end: Union[float, List[float]] = 1.0,\n warp_start: Union[float, List[float]] = 0.0,\n warp_end: Union[float, List[float]] = 0.3,\n mask_start: Union[float, List[float]] = 0.5,\n mask_end: Union[float, List[float]] = 0.8,\n smooth_boundary: bool = True,\n mask_strength: Union[float, List[float]] = 0.5,\n inner_strength: Union[float, List[float]] = 0.9,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n frames (`List[np.ndarray]` or `torch.Tensor`): The input images to be used as the starting point for the image generation process.\n control_frames (`List[np.ndarray]` or `torch.Tensor`): The ControlNet input images condition to provide guidance to the `unet` for generation.\n strength ('float'): SDEdit strength.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):\n The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added\n to the residual in the original unet. If multiple ControlNets are specified in init, you can set the\n corresponding scale as a list. Note that by default, we use a smaller conditioning scale for inpainting\n than for [`~StableDiffusionControlNetPipeline.__call__`].\n guess_mode (`bool`, *optional*, defaults to `False`):\n In this mode, the ControlNet encoder will try best to recognize the content of the input image even if\n you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.\n control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):\n The percentage of total steps at which the controlnet starts applying.\n control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):\n The percentage of total steps at which the controlnet stops applying.\n warp_start (`float`): Shape-aware fusion start timestep.\n warp_end (`float`): Shape-aware fusion end timestep.\n mask_start (`float`): Pixel-aware fusion start timestep.\n mask_end (`float`):Pixel-aware fusion end timestep.\n smooth_boundary (`bool`): Smooth fusion boundary. Set `True` to prevent artifacts at boundary.\n mask_strength (`float`): Pixel-aware fusion strength.\n inner_strength (`float`): Pixel-aware fusion detail level.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet\n\n # align format for control guidance\n if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):\n control_guidance_start = len(control_guidance_end) * [control_guidance_start]\n elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):\n control_guidance_end = len(control_guidance_start) * [control_guidance_end]\n elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):\n mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1\n control_guidance_start, control_guidance_end = (\n mult * [control_guidance_start],\n mult * [control_guidance_end],\n )\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n controlnet_conditioning_scale,\n control_guidance_start,\n control_guidance_end,\n )\n\n # 2. Define call parameters\n # Currently we only support 1 prompt\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n assert False\n else:\n assert False\n num_images_per_prompt = 1\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):\n controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)\n\n global_pool_conditions = (\n controlnet.config.global_pool_conditions\n if isinstance(controlnet, ControlNetModel)\n else controlnet.nets[0].config.global_pool_conditions\n )\n guess_mode = guess_mode or global_pool_conditions\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n prompt_embeds = self._encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n\n # 4. Process the first frame\n height, width = None, None\n output_frames = []\n self.attn_state.reset()\n\n # 4.1 prepare frames\n image = self.image_processor.preprocess(frames[0]).to(dtype=torch.float32)\n first_image = image[0] # C, H, W\n\n # 4.2 Prepare controlnet_conditioning_image\n # Currently we only support single control\n if isinstance(controlnet, ControlNetModel):\n control_image = self.prepare_control_image(\n image=control_frames[0],\n width=width,\n height=height,\n batch_size=batch_size,\n num_images_per_prompt=1,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n else:\n assert False\n\n # 4.3 Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, cur_num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n latent_timestep = timesteps[:1].repeat(batch_size)\n\n # 4.4 Prepare latent variables\n latents = self.prepare_latents(\n image,\n latent_timestep,\n batch_size,\n num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n generator,\n )\n\n # 4.5 Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 4.6 Create tensor stating which controlnets to keep\n controlnet_keep = []\n for i in range(len(timesteps)):\n keeps = [\n 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)\n for s, e in zip(control_guidance_start, control_guidance_end)\n ]\n controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)\n\n first_x0_list = []\n\n # 4.7 Denoising loop\n num_warmup_steps = len(timesteps) - cur_num_inference_steps * self.scheduler.order\n with self.progress_bar(total=cur_num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n self.attn_state.set_timestep(t.item())\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # controlnet(s) inference\n if guess_mode and do_classifier_free_guidance:\n # Infer ControlNet only for the conditional batch.\n control_model_input = latents\n control_model_input = self.scheduler.scale_model_input(control_model_input, t)\n controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]\n else:\n control_model_input = latent_model_input\n controlnet_prompt_embeds = prompt_embeds\n\n if isinstance(controlnet_keep[i], list):\n cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]\n else:\n controlnet_cond_scale = controlnet_conditioning_scale\n if isinstance(controlnet_cond_scale, list):\n controlnet_cond_scale = controlnet_cond_scale[0]\n cond_scale = controlnet_cond_scale * controlnet_keep[i]\n\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n control_model_input,\n t,\n encoder_hidden_states=controlnet_prompt_embeds,\n controlnet_cond=control_image,\n conditioning_scale=cond_scale,\n guess_mode=guess_mode,\n return_dict=False,\n )\n\n if guess_mode and do_classifier_free_guidance:\n # Infered ControlNet only for the conditional batch.\n # To apply the output of ControlNet to both the unconditional and conditional batches,\n # add 0 to the unconditional batch to keep it unchanged.\n down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]\n mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n alpha_prod_t = self.scheduler.alphas_cumprod[t]\n beta_prod_t = 1 - alpha_prod_t\n pred_x0 = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)\n first_x0 = pred_x0.detach()\n first_x0_list.append(first_x0)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n else:\n image = latents\n\n first_result = image\n prev_result = image\n do_denormalize = [True] * image.shape[0]\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n output_frames.append(image[0])\n\n # 5. Process each frame\n for idx in range(1, len(frames)):\n image = frames[idx]\n prev_image = frames[idx - 1]\n control_image = control_frames[idx]\n # 5.1 prepare frames\n image = self.image_processor.preprocess(image).to(dtype=torch.float32)\n prev_image = self.image_processor.preprocess(prev_image).to(dtype=torch.float32)\n\n warped_0, bwd_occ_0, bwd_flow_0 = get_warped_and_mask(\n self.flow_model, first_image, image[0], first_result, False, self.device\n )\n blend_mask_0 = blur(F.max_pool2d(bwd_occ_0, kernel_size=9, stride=1, padding=4))\n blend_mask_0 = torch.clamp(blend_mask_0 + bwd_occ_0, 0, 1)\n\n warped_pre, bwd_occ_pre, bwd_flow_pre = get_warped_and_mask(\n self.flow_model, prev_image[0], image[0], prev_result, False, self.device\n )\n blend_mask_pre = blur(F.max_pool2d(bwd_occ_pre, kernel_size=9, stride=1, padding=4))\n blend_mask_pre = torch.clamp(blend_mask_pre + bwd_occ_pre, 0, 1)\n\n warp_mask = 1 - F.max_pool2d(blend_mask_0, kernel_size=8)\n warp_flow = F.interpolate(bwd_flow_0 / 8.0, scale_factor=1.0 / 8, mode=\"bilinear\")\n\n # 5.2 Prepare controlnet_conditioning_image\n # Currently we only support single control\n if isinstance(controlnet, ControlNetModel):\n control_image = self.prepare_control_image(\n image=control_image,\n width=width,\n height=height,\n batch_size=batch_size,\n num_images_per_prompt=1,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n else:\n assert False\n\n # 5.3 Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, cur_num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n latent_timestep = timesteps[:1].repeat(batch_size)\n\n skip_t = int(num_inference_steps * (1 - strength))\n warp_start_t = int(warp_start * num_inference_steps)\n warp_end_t = int(warp_end * num_inference_steps)\n mask_start_t = int(mask_start * num_inference_steps)\n mask_end_t = int(mask_end * num_inference_steps)\n\n # 5.4 Prepare latent variables\n init_latents = self.prepare_latents(\n image,\n latent_timestep,\n batch_size,\n num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n generator,\n )\n\n # 5.5 Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 5.6 Create tensor stating which controlnets to keep\n controlnet_keep = []\n for i in range(len(timesteps)):\n keeps = [\n 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)\n for s, e in zip(control_guidance_start, control_guidance_end)\n ]\n controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)\n\n # 5.7 Denoising loop\n num_warmup_steps = len(timesteps) - cur_num_inference_steps * self.scheduler.order\n\n def denoising_loop(latents, mask=None, xtrg=None, noise_rescale=None):\n dir_xt = 0\n latents_dtype = latents.dtype\n with self.progress_bar(total=cur_num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n self.attn_state.set_timestep(t.item())\n if i + skip_t >= mask_start_t and i + skip_t <= mask_end_t and xtrg is not None:\n rescale = torch.maximum(1.0 - mask, (1 - mask**2) ** 0.5 * inner_strength)\n if noise_rescale is not None:\n rescale = (1.0 - mask) * (1 - noise_rescale) + rescale * noise_rescale\n noise = randn_tensor(xtrg.shape, generator=generator, device=device, dtype=xtrg.dtype)\n latents_ref = self.scheduler.add_noise(xtrg, noise, t)\n latents = latents_ref * mask + (1.0 - mask) * (latents - dir_xt) + rescale * dir_xt\n latents = latents.to(latents_dtype)\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # controlnet(s) inference\n if guess_mode and do_classifier_free_guidance:\n # Infer ControlNet only for the conditional batch.\n control_model_input = latents\n control_model_input = self.scheduler.scale_model_input(control_model_input, t)\n controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]\n else:\n control_model_input = latent_model_input\n controlnet_prompt_embeds = prompt_embeds\n\n if isinstance(controlnet_keep[i], list):\n cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]\n else:\n controlnet_cond_scale = controlnet_conditioning_scale\n if isinstance(controlnet_cond_scale, list):\n controlnet_cond_scale = controlnet_cond_scale[0]\n cond_scale = controlnet_cond_scale * controlnet_keep[i]\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n control_model_input,\n t,\n encoder_hidden_states=controlnet_prompt_embeds,\n controlnet_cond=control_image,\n conditioning_scale=cond_scale,\n guess_mode=guess_mode,\n return_dict=False,\n )\n\n if guess_mode and do_classifier_free_guidance:\n # Infered ControlNet only for the conditional batch.\n # To apply the output of ControlNet to both the unconditional and conditional batches,\n # add 0 to the unconditional batch to keep it unchanged.\n down_block_res_samples = [\n torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples\n ]\n mid_block_res_sample = torch.cat(\n [torch.zeros_like(mid_block_res_sample), mid_block_res_sample]\n )\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # Get pred_x0 from scheduler\n alpha_prod_t = self.scheduler.alphas_cumprod[t]\n beta_prod_t = 1 - alpha_prod_t\n pred_x0 = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)\n\n if i + skip_t >= warp_start_t and i + skip_t <= warp_end_t:\n # warp x_0\n pred_x0 = (\n flow_warp(first_x0_list[i], warp_flow, mode=\"nearest\") * warp_mask\n + (1 - warp_mask) * pred_x0\n )\n\n # get x_t from x_0\n latents = self.scheduler.add_noise(pred_x0, noise_pred, t).to(latents_dtype)\n\n prev_t = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps\n if i == len(timesteps) - 1:\n alpha_t_prev = 1.0\n else:\n alpha_t_prev = self.scheduler.alphas_cumprod[prev_t]\n\n dir_xt = (1.0 - alpha_t_prev) ** 0.5 * noise_pred\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[\n 0\n ]\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or (\n (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0\n ):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, latents)\n\n return latents\n\n if mask_start_t <= mask_end_t:\n self.attn_state.to_load()\n else:\n self.attn_state.to_load_and_store_prev()\n latents = denoising_loop(init_latents)\n\n if mask_start_t <= mask_end_t:\n direct_result = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n\n blend_results = (1 - blend_mask_pre) * warped_pre + blend_mask_pre * direct_result\n blend_results = (1 - blend_mask_0) * warped_0 + blend_mask_0 * blend_results\n\n bwd_occ = 1 - torch.clamp(1 - bwd_occ_pre + 1 - bwd_occ_0, 0, 1)\n blend_mask = blur(F.max_pool2d(bwd_occ, kernel_size=9, stride=1, padding=4))\n blend_mask = 1 - torch.clamp(blend_mask + bwd_occ, 0, 1)\n\n blend_results = blend_results.to(latents.dtype)\n xtrg = self.vae.encode(blend_results).latent_dist.sample(generator)\n xtrg = self.vae.config.scaling_factor * xtrg\n blend_results_rec = self.vae.decode(xtrg / self.vae.config.scaling_factor, return_dict=False)[0]\n xtrg_rec = self.vae.encode(blend_results_rec).latent_dist.sample(generator)\n xtrg_rec = self.vae.config.scaling_factor * xtrg_rec\n xtrg_ = xtrg + (xtrg - xtrg_rec)\n blend_results_rec_new = self.vae.decode(xtrg_ / self.vae.config.scaling_factor, return_dict=False)[0]\n tmp = (abs(blend_results_rec_new - blend_results).mean(dim=1, keepdims=True) > 0.25).float()\n\n mask_x = F.max_pool2d(\n (F.interpolate(tmp, scale_factor=1 / 8.0, mode=\"bilinear\") > 0).float(),\n kernel_size=3,\n stride=1,\n padding=1,\n )\n\n mask = 1 - F.max_pool2d(1 - blend_mask, kernel_size=8) # * (1-mask_x)\n\n if smooth_boundary:\n noise_rescale = find_flat_region(mask)\n else:\n noise_rescale = torch.ones_like(mask)\n\n xtrg = (xtrg + (1 - mask_x) * (xtrg - xtrg_rec)) * mask\n xtrg = xtrg.to(latents.dtype)\n\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, cur_num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n\n self.attn_state.to_load_and_store_prev()\n latents = denoising_loop(init_latents, mask * mask_strength, xtrg, noise_rescale)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n else:\n image = latents\n\n prev_result = image\n\n do_denormalize = [True] * image.shape[0]\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n output_frames.append(image[0])\n\n # Offload last model to CPU\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return output_frames\n\n return TextToVideoSDPipelineOutput(frames=output_frames)\n\n\nclass InputPadder:\n \"\"\"Pads images such that dimensions are divisible by 8\"\"\"\n\n def __init__(self, dims, mode=\"sintel\", padding_factor=8):\n self.ht, self.wd = dims[-2:]\n pad_ht = (((self.ht // padding_factor) + 1) * padding_factor - self.ht) % padding_factor\n pad_wd = (((self.wd // padding_factor) + 1) * padding_factor - self.wd) % padding_factor\n if mode == \"sintel\":\n self._pad = [pad_wd // 2, pad_wd - pad_wd // 2, pad_ht // 2, pad_ht - pad_ht // 2]\n else:\n self._pad = [pad_wd // 2, pad_wd - pad_wd // 2, 0, pad_ht]\n\n def pad(self, *inputs):\n return [F.pad(x, self._pad, mode=\"replicate\") for x in inputs]\n\n def unpad(self, x):\n ht, wd = x.shape[-2:]\n c = [self._pad[2], ht - self._pad[3], self._pad[0], wd - self._pad[1]]\n return x[..., c[0] : c[1], c[2] : c[3]]\n" }, { "path": "diffusers/examples/community/run_onnx_controlnet.py", "content": "import argparse\nimport inspect\nimport os\nimport time\nimport warnings\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom PIL import Image\nfrom transformers import CLIPTokenizer\n\nfrom diffusers import OnnxRuntimeModel, StableDiffusionImg2ImgPipeline, UniPCMultistepScheduler\nfrom diffusers.image_processor import VaeImageProcessor\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n deprecate,\n logging,\n replace_example_docstring,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> # !pip install opencv-python transformers accelerate\n >>> from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, UniPCMultistepScheduler\n >>> from diffusers.utils import load_image\n >>> import numpy as np\n >>> import torch\n\n >>> import cv2\n >>> from PIL import Image\n\n >>> # download an image\n >>> image = load_image(\n ... \"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png\"\n ... )\n >>> np_image = np.array(image)\n\n >>> # get canny image\n >>> np_image = cv2.Canny(np_image, 100, 200)\n >>> np_image = np_image[:, :, None]\n >>> np_image = np.concatenate([np_image, np_image, np_image], axis=2)\n >>> canny_image = Image.fromarray(np_image)\n\n >>> # load control net and stable diffusion v1-5\n >>> controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\", torch_dtype=torch.float16)\n >>> pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(\n ... \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, torch_dtype=torch.float16\n ... )\n\n >>> # speed up diffusion process with faster scheduler and memory optimization\n >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n >>> pipe.enable_model_cpu_offload()\n\n >>> # generate image\n >>> generator = torch.manual_seed(0)\n >>> image = pipe(\n ... \"futuristic-looking woman\",\n ... num_inference_steps=20,\n ... generator=generator,\n ... image=image,\n ... control_image=canny_image,\n ... ).images[0]\n ```\n\"\"\"\n\n\ndef prepare_image(image):\n if isinstance(image, torch.Tensor):\n # Batch single image\n if image.ndim == 3:\n image = image.unsqueeze(0)\n\n image = image.to(dtype=torch.float32)\n else:\n # preprocess image\n if isinstance(image, (PIL.Image.Image, np.ndarray)):\n image = [image]\n\n if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):\n image = [np.array(i.convert(\"RGB\"))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n elif isinstance(image, list) and isinstance(image[0], np.ndarray):\n image = np.concatenate([i[None, :] for i in image], axis=0)\n\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0\n\n return image\n\n\nclass OnnxStableDiffusionControlNetImg2ImgPipeline(DiffusionPipeline):\n vae_encoder: OnnxRuntimeModel\n vae_decoder: OnnxRuntimeModel\n text_encoder: OnnxRuntimeModel\n tokenizer: CLIPTokenizer\n unet: OnnxRuntimeModel\n scheduler: KarrasDiffusionSchedulers\n\n def __init__(\n self,\n vae_encoder: OnnxRuntimeModel,\n vae_decoder: OnnxRuntimeModel,\n text_encoder: OnnxRuntimeModel,\n tokenizer: CLIPTokenizer,\n unet: OnnxRuntimeModel,\n scheduler: KarrasDiffusionSchedulers,\n ):\n super().__init__()\n\n self.register_modules(\n vae_encoder=vae_encoder,\n vae_decoder=vae_decoder,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n )\n self.vae_scale_factor = 2 ** (4 - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)\n self.control_image_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False\n )\n\n def _encode_prompt(\n self,\n prompt: Union[str, List[str]],\n num_images_per_prompt: Optional[int],\n do_classifier_free_guidance: bool,\n negative_prompt: Optional[str],\n prompt_embeds: Optional[np.ndarray] = None,\n negative_prompt_embeds: Optional[np.ndarray] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`):\n prompt to be encoded\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n \"\"\"\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"np\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"max_length\", return_tensors=\"np\").input_ids\n\n if not np.array_equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]\n\n prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt] * batch_size\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"np\",\n )\n negative_prompt_embeds = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0]\n\n if do_classifier_free_guidance:\n negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n warnings.warn(\n \"The decode_latents method is deprecated and will be removed in a future version. Please\"\n \" use VaeImageProcessor instead\",\n FutureWarning,\n )\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n num_controlnet,\n prompt,\n image,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n controlnet_conditioning_scale=1.0,\n control_guidance_start=0.0,\n control_guidance_end=1.0,\n ):\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # Check `image`\n if num_controlnet == 1:\n self.check_image(image, prompt, prompt_embeds)\n elif num_controlnet > 1:\n if not isinstance(image, list):\n raise TypeError(\"For multiple controlnets: `image` must be type `list`\")\n\n # When `image` is a nested list:\n # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])\n elif any(isinstance(i, list) for i in image):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif len(image) != num_controlnet:\n raise ValueError(\n f\"For multiple controlnets: `image` must have the same length as the number of controlnets, but got {len(image)} images and {num_controlnet} ControlNets.\"\n )\n\n for image_ in image:\n self.check_image(image_, prompt, prompt_embeds)\n else:\n assert False\n\n # Check `controlnet_conditioning_scale`\n if num_controlnet == 1:\n if not isinstance(controlnet_conditioning_scale, float):\n raise TypeError(\"For single controlnet: `controlnet_conditioning_scale` must be type `float`.\")\n elif num_controlnet > 1:\n if isinstance(controlnet_conditioning_scale, list):\n if any(isinstance(i, list) for i in controlnet_conditioning_scale):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif (\n isinstance(controlnet_conditioning_scale, list)\n and len(controlnet_conditioning_scale) != num_controlnet\n ):\n raise ValueError(\n \"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have\"\n \" the same length as the number of controlnets\"\n )\n else:\n assert False\n\n if len(control_guidance_start) != len(control_guidance_end):\n raise ValueError(\n f\"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list.\"\n )\n\n if num_controlnet > 1:\n if len(control_guidance_start) != num_controlnet:\n raise ValueError(\n f\"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {num_controlnet} controlnets available. Make sure to provide {num_controlnet}.\"\n )\n\n for start, end in zip(control_guidance_start, control_guidance_end):\n if start >= end:\n raise ValueError(\n f\"control guidance start: {start} cannot be larger or equal to control guidance end: {end}.\"\n )\n if start < 0.0:\n raise ValueError(f\"control guidance start: {start} can't be smaller than 0.\")\n if end > 1.0:\n raise ValueError(f\"control guidance end: {end} can't be larger than 1.0.\")\n\n # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image\n def check_image(self, image, prompt, prompt_embeds):\n image_is_pil = isinstance(image, PIL.Image.Image)\n image_is_tensor = isinstance(image, torch.Tensor)\n image_is_np = isinstance(image, np.ndarray)\n image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)\n image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)\n image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)\n\n if (\n not image_is_pil\n and not image_is_tensor\n and not image_is_np\n and not image_is_pil_list\n and not image_is_tensor_list\n and not image_is_np_list\n ):\n raise TypeError(\n f\"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}\"\n )\n\n if image_is_pil:\n image_batch_size = 1\n else:\n image_batch_size = len(image)\n\n if prompt is not None and isinstance(prompt, str):\n prompt_batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n prompt_batch_size = len(prompt)\n elif prompt_embeds is not None:\n prompt_batch_size = prompt_embeds.shape[0]\n\n if image_batch_size != 1 and image_batch_size != prompt_batch_size:\n raise ValueError(\n f\"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}\"\n )\n\n # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image\n def prepare_control_image(\n self,\n image,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype,\n do_classifier_free_guidance=False,\n guess_mode=False,\n ):\n image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)\n image_batch_size = image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n image = image.repeat_interleave(repeat_by, dim=0)\n\n image = image.to(device=device, dtype=dtype)\n\n if do_classifier_free_guidance and not guess_mode:\n image = torch.cat([image] * 2)\n\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n\n return timesteps, num_inference_steps - t_start\n\n def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n\n if image.shape[1] == 4:\n init_latents = image\n\n else:\n _image = image.cpu().detach().numpy()\n init_latents = self.vae_encoder(sample=_image)[0]\n init_latents = torch.from_numpy(init_latents).to(device=device, dtype=dtype)\n init_latents = 0.18215 * init_latents\n\n if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:\n # expand init_latents for batch_size\n deprecation_message = (\n f\"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial\"\n \" images (`image`). Initial images are now duplicating to match the number of text prompts. Note\"\n \" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update\"\n \" your script to pass as many initial images as text prompts to suppress this warning.\"\n )\n deprecate(\"len(prompt) != len(image)\", \"1.0.0\", deprecation_message, standard_warn=False)\n additional_image_per_prompt = batch_size // init_latents.shape[0]\n init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)\n elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n init_latents = torch.cat([init_latents], dim=0)\n\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n latents = init_latents\n\n return latents\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n num_controlnet: int,\n fp16: bool = True,\n prompt: Union[str, List[str]] = None,\n image: Union[\n torch.Tensor,\n PIL.Image.Image,\n np.ndarray,\n List[torch.Tensor],\n List[PIL.Image.Image],\n List[np.ndarray],\n ] = None,\n control_image: Union[\n torch.Tensor,\n PIL.Image.Image,\n np.ndarray,\n List[torch.Tensor],\n List[PIL.Image.Image],\n List[np.ndarray],\n ] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n strength: float = 0.8,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: Union[float, List[float]] = 0.8,\n guess_mode: bool = False,\n control_guidance_start: Union[float, List[float]] = 0.0,\n control_guidance_end: Union[float, List[float]] = 1.0,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:\n `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):\n The initial image will be used as the starting point for the image generation process. Can also accept\n image latents as `image`, if passing latents directly, it will not be encoded again.\n control_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:\n `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):\n The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If\n the type is specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can\n also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If\n height and/or width are passed, `image` is resized according to them. If multiple ControlNets are\n specified in init, images must be passed as a list such that each element of the list can be correctly\n batched for input to a single controlnet.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):\n The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added\n to the residual in the original unet. If multiple ControlNets are specified in init, you can set the\n corresponding scale as a list. Note that by default, we use a smaller conditioning scale for inpainting\n than for [`~StableDiffusionControlNetPipeline.__call__`].\n guess_mode (`bool`, *optional*, defaults to `False`):\n In this mode, the ControlNet encoder will try best to recognize the content of the input image even if\n you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.\n control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):\n The percentage of total steps at which the controlnet starts applying.\n control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):\n The percentage of total steps at which the controlnet stops applying.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n if fp16:\n torch_dtype = torch.float16\n np_dtype = np.float16\n else:\n torch_dtype = torch.float32\n np_dtype = np.float32\n\n # align format for control guidance\n if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):\n control_guidance_start = len(control_guidance_end) * [control_guidance_start]\n elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):\n control_guidance_end = len(control_guidance_start) * [control_guidance_end]\n elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):\n mult = num_controlnet\n control_guidance_start, control_guidance_end = (\n mult * [control_guidance_start],\n mult * [control_guidance_end],\n )\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n num_controlnet,\n prompt,\n control_image,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n controlnet_conditioning_scale,\n control_guidance_start,\n control_guidance_end,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n if num_controlnet > 1 and isinstance(controlnet_conditioning_scale, float):\n controlnet_conditioning_scale = [controlnet_conditioning_scale] * num_controlnet\n\n # 3. Encode input prompt\n prompt_embeds = self._encode_prompt(\n prompt,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n # 4. Prepare image\n image = self.image_processor.preprocess(image).to(dtype=torch.float32)\n\n # 5. Prepare controlnet_conditioning_image\n if num_controlnet == 1:\n control_image = self.prepare_control_image(\n image=control_image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=torch_dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n elif num_controlnet > 1:\n control_images = []\n\n for control_image_ in control_image:\n control_image_ = self.prepare_control_image(\n image=control_image_,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=torch_dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n\n control_images.append(control_image_)\n\n control_image = control_images\n else:\n assert False\n\n # 5. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n\n # 6. Prepare latent variables\n latents = self.prepare_latents(\n image,\n latent_timestep,\n batch_size,\n num_images_per_prompt,\n torch_dtype,\n device,\n generator,\n )\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7.1 Create tensor stating which controlnets to keep\n controlnet_keep = []\n for i in range(len(timesteps)):\n keeps = [\n 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)\n for s, e in zip(control_guidance_start, control_guidance_end)\n ]\n controlnet_keep.append(keeps[0] if num_controlnet == 1 else keeps)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n if isinstance(controlnet_keep[i], list):\n cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]\n else:\n controlnet_cond_scale = controlnet_conditioning_scale\n if isinstance(controlnet_cond_scale, list):\n controlnet_cond_scale = controlnet_cond_scale[0]\n cond_scale = controlnet_cond_scale * controlnet_keep[i]\n\n # predict the noise residual\n _latent_model_input = latent_model_input.cpu().detach().numpy()\n _prompt_embeds = np.array(prompt_embeds, dtype=np_dtype)\n _t = np.array([t.cpu().detach().numpy()], dtype=np_dtype)\n\n if num_controlnet == 1:\n control_images = np.array([control_image], dtype=np_dtype)\n else:\n control_images = []\n for _control_img in control_image:\n _control_img = _control_img.cpu().detach().numpy()\n control_images.append(_control_img)\n control_images = np.array(control_images, dtype=np_dtype)\n\n control_scales = np.array(cond_scale, dtype=np_dtype)\n control_scales = np.resize(control_scales, (num_controlnet, 1))\n\n noise_pred = self.unet(\n sample=_latent_model_input,\n timestep=_t,\n encoder_hidden_states=_prompt_embeds,\n controlnet_conds=control_images,\n conditioning_scales=control_scales,\n )[0]\n noise_pred = torch.from_numpy(noise_pred).to(device)\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n _latents = latents.cpu().detach().numpy() / 0.18215\n _latents = np.array(_latents, dtype=np_dtype)\n image = self.vae_decoder(latent_sample=_latents)[0]\n image = torch.from_numpy(image).to(device, dtype=torch.float32)\n has_nsfw_concept = None\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--sd_model\",\n type=str,\n required=True,\n help=\"Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).\",\n )\n\n parser.add_argument(\n \"--onnx_model_dir\",\n type=str,\n required=True,\n help=\"Path to the ONNX directory\",\n )\n\n parser.add_argument(\"--qr_img_path\", type=str, required=True, help=\"Path to the qr code image\")\n\n args = parser.parse_args()\n\n qr_image = Image.open(args.qr_img_path)\n qr_image = qr_image.resize((512, 512))\n\n # init stable diffusion pipeline\n pipeline = StableDiffusionImg2ImgPipeline.from_pretrained(args.sd_model)\n pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)\n\n provider = [\"CUDAExecutionProvider\", \"CPUExecutionProvider\"]\n onnx_pipeline = OnnxStableDiffusionControlNetImg2ImgPipeline(\n vae_encoder=OnnxRuntimeModel.from_pretrained(\n os.path.join(args.onnx_model_dir, \"vae_encoder\"), provider=provider\n ),\n vae_decoder=OnnxRuntimeModel.from_pretrained(\n os.path.join(args.onnx_model_dir, \"vae_decoder\"), provider=provider\n ),\n text_encoder=OnnxRuntimeModel.from_pretrained(\n os.path.join(args.onnx_model_dir, \"text_encoder\"), provider=provider\n ),\n tokenizer=pipeline.tokenizer,\n unet=OnnxRuntimeModel.from_pretrained(os.path.join(args.onnx_model_dir, \"unet\"), provider=provider),\n scheduler=pipeline.scheduler,\n )\n onnx_pipeline = onnx_pipeline.to(\"cuda\")\n\n prompt = \"a cute cat fly to the moon\"\n negative_prompt = \"paintings, sketches, worst quality, low quality, normal quality, lowres, normal quality, monochrome, grayscale, skin spots, acnes, skin blemishes, age spot, glans, nsfw, nipples, necklace, worst quality, low quality, watermark, username, signature, multiple breasts, lowres, bad anatomy, bad hands, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, bad feet, single color, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, disfigured, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck, bad body perspect\"\n\n for i in range(10):\n start_time = time.time()\n image = onnx_pipeline(\n num_controlnet=2,\n prompt=prompt,\n negative_prompt=negative_prompt,\n image=qr_image,\n control_image=[qr_image, qr_image],\n width=512,\n height=512,\n strength=0.75,\n num_inference_steps=20,\n num_images_per_prompt=1,\n controlnet_conditioning_scale=[0.8, 0.8],\n control_guidance_start=[0.3, 0.3],\n control_guidance_end=[0.9, 0.9],\n ).images[0]\n print(time.time() - start_time)\n image.save(\"output_qr_code.png\")\n" }, { "path": "diffusers/examples/community/run_tensorrt_controlnet.py", "content": "import argparse\nimport atexit\nimport inspect\nimport os\nimport time\nimport warnings\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport pycuda.driver as cuda\nimport tensorrt as trt\nimport torch\nfrom PIL import Image\nfrom pycuda.tools import make_default_context\nfrom transformers import CLIPTokenizer\n\nfrom diffusers import OnnxRuntimeModel, StableDiffusionImg2ImgPipeline, UniPCMultistepScheduler\nfrom diffusers.image_processor import VaeImageProcessor\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n deprecate,\n logging,\n replace_example_docstring,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\n# Initialize CUDA\ncuda.init()\ncontext = make_default_context()\ndevice = context.get_device()\natexit.register(context.pop)\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef load_engine(trt_runtime, engine_path):\n with open(engine_path, \"rb\") as f:\n engine_data = f.read()\n engine = trt_runtime.deserialize_cuda_engine(engine_data)\n return engine\n\n\nclass TensorRTModel:\n def __init__(\n self,\n trt_engine_path,\n **kwargs,\n ):\n cuda.init()\n stream = cuda.Stream()\n TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE)\n trt.init_libnvinfer_plugins(TRT_LOGGER, \"\")\n trt_runtime = trt.Runtime(TRT_LOGGER)\n engine = load_engine(trt_runtime, trt_engine_path)\n context = engine.create_execution_context()\n\n # allocates memory for network inputs/outputs on both CPU and GPU\n host_inputs = []\n cuda_inputs = []\n host_outputs = []\n cuda_outputs = []\n bindings = []\n input_names = []\n output_names = []\n\n for binding in engine:\n datatype = engine.get_binding_dtype(binding)\n if datatype == trt.DataType.HALF:\n dtype = np.float16\n else:\n dtype = np.float32\n\n shape = tuple(engine.get_binding_shape(binding))\n host_mem = cuda.pagelocked_empty(shape, dtype)\n cuda_mem = cuda.mem_alloc(host_mem.nbytes)\n bindings.append(int(cuda_mem))\n\n if engine.binding_is_input(binding):\n host_inputs.append(host_mem)\n cuda_inputs.append(cuda_mem)\n input_names.append(binding)\n else:\n host_outputs.append(host_mem)\n cuda_outputs.append(cuda_mem)\n output_names.append(binding)\n\n self.stream = stream\n self.context = context\n self.engine = engine\n\n self.host_inputs = host_inputs\n self.cuda_inputs = cuda_inputs\n self.host_outputs = host_outputs\n self.cuda_outputs = cuda_outputs\n self.bindings = bindings\n self.batch_size = engine.max_batch_size\n\n self.input_names = input_names\n self.output_names = output_names\n\n def __call__(self, **kwargs):\n context = self.context\n stream = self.stream\n bindings = self.bindings\n\n host_inputs = self.host_inputs\n cuda_inputs = self.cuda_inputs\n host_outputs = self.host_outputs\n cuda_outputs = self.cuda_outputs\n\n for idx, input_name in enumerate(self.input_names):\n _input = kwargs[input_name]\n np.copyto(host_inputs[idx], _input)\n # transfer input data to the GPU\n cuda.memcpy_htod_async(cuda_inputs[idx], host_inputs[idx], stream)\n\n context.execute_async_v2(bindings=bindings, stream_handle=stream.handle)\n\n result = {}\n for idx, output_name in enumerate(self.output_names):\n # transfer predictions back from the GPU\n cuda.memcpy_dtoh_async(host_outputs[idx], cuda_outputs[idx], stream)\n result[output_name] = host_outputs[idx]\n\n stream.synchronize()\n\n return result\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> # !pip install opencv-python transformers accelerate\n >>> from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, UniPCMultistepScheduler\n >>> from diffusers.utils import load_image\n >>> import numpy as np\n >>> import torch\n\n >>> import cv2\n >>> from PIL import Image\n\n >>> # download an image\n >>> image = load_image(\n ... \"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png\"\n ... )\n >>> np_image = np.array(image)\n\n >>> # get canny image\n >>> np_image = cv2.Canny(np_image, 100, 200)\n >>> np_image = np_image[:, :, None]\n >>> np_image = np.concatenate([np_image, np_image, np_image], axis=2)\n >>> canny_image = Image.fromarray(np_image)\n\n >>> # load control net and stable diffusion v1-5\n >>> controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\", torch_dtype=torch.float16)\n >>> pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(\n ... \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, torch_dtype=torch.float16\n ... )\n\n >>> # speed up diffusion process with faster scheduler and memory optimization\n >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n >>> pipe.enable_model_cpu_offload()\n\n >>> # generate image\n >>> generator = torch.manual_seed(0)\n >>> image = pipe(\n ... \"futuristic-looking woman\",\n ... num_inference_steps=20,\n ... generator=generator,\n ... image=image,\n ... control_image=canny_image,\n ... ).images[0]\n ```\n\"\"\"\n\n\ndef prepare_image(image):\n if isinstance(image, torch.Tensor):\n # Batch single image\n if image.ndim == 3:\n image = image.unsqueeze(0)\n\n image = image.to(dtype=torch.float32)\n else:\n # preprocess image\n if isinstance(image, (PIL.Image.Image, np.ndarray)):\n image = [image]\n\n if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):\n image = [np.array(i.convert(\"RGB\"))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n elif isinstance(image, list) and isinstance(image[0], np.ndarray):\n image = np.concatenate([i[None, :] for i in image], axis=0)\n\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0\n\n return image\n\n\nclass TensorRTStableDiffusionControlNetImg2ImgPipeline(DiffusionPipeline):\n vae_encoder: OnnxRuntimeModel\n vae_decoder: OnnxRuntimeModel\n text_encoder: OnnxRuntimeModel\n tokenizer: CLIPTokenizer\n unet: TensorRTModel\n scheduler: KarrasDiffusionSchedulers\n\n def __init__(\n self,\n vae_encoder: OnnxRuntimeModel,\n vae_decoder: OnnxRuntimeModel,\n text_encoder: OnnxRuntimeModel,\n tokenizer: CLIPTokenizer,\n unet: TensorRTModel,\n scheduler: KarrasDiffusionSchedulers,\n ):\n super().__init__()\n\n self.register_modules(\n vae_encoder=vae_encoder,\n vae_decoder=vae_decoder,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n )\n self.vae_scale_factor = 2 ** (4 - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)\n self.control_image_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False\n )\n\n def _encode_prompt(\n self,\n prompt: Union[str, List[str]],\n num_images_per_prompt: Optional[int],\n do_classifier_free_guidance: bool,\n negative_prompt: Optional[str],\n prompt_embeds: Optional[np.ndarray] = None,\n negative_prompt_embeds: Optional[np.ndarray] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`):\n prompt to be encoded\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n \"\"\"\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"np\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"max_length\", return_tensors=\"np\").input_ids\n\n if not np.array_equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]\n\n prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt] * batch_size\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"np\",\n )\n negative_prompt_embeds = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0]\n\n if do_classifier_free_guidance:\n negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n warnings.warn(\n \"The decode_latents method is deprecated and will be removed in a future version. Please\"\n \" use VaeImageProcessor instead\",\n FutureWarning,\n )\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n num_controlnet,\n prompt,\n image,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n controlnet_conditioning_scale=1.0,\n control_guidance_start=0.0,\n control_guidance_end=1.0,\n ):\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # Check `image`\n if num_controlnet == 1:\n self.check_image(image, prompt, prompt_embeds)\n elif num_controlnet > 1:\n if not isinstance(image, list):\n raise TypeError(\"For multiple controlnets: `image` must be type `list`\")\n\n # When `image` is a nested list:\n # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])\n elif any(isinstance(i, list) for i in image):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif len(image) != num_controlnet:\n raise ValueError(\n f\"For multiple controlnets: `image` must have the same length as the number of controlnets, but got {len(image)} images and {num_controlnet} ControlNets.\"\n )\n\n for image_ in image:\n self.check_image(image_, prompt, prompt_embeds)\n else:\n assert False\n\n # Check `controlnet_conditioning_scale`\n if num_controlnet == 1:\n if not isinstance(controlnet_conditioning_scale, float):\n raise TypeError(\"For single controlnet: `controlnet_conditioning_scale` must be type `float`.\")\n elif num_controlnet > 1:\n if isinstance(controlnet_conditioning_scale, list):\n if any(isinstance(i, list) for i in controlnet_conditioning_scale):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif (\n isinstance(controlnet_conditioning_scale, list)\n and len(controlnet_conditioning_scale) != num_controlnet\n ):\n raise ValueError(\n \"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have\"\n \" the same length as the number of controlnets\"\n )\n else:\n assert False\n\n if len(control_guidance_start) != len(control_guidance_end):\n raise ValueError(\n f\"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list.\"\n )\n\n if num_controlnet > 1:\n if len(control_guidance_start) != num_controlnet:\n raise ValueError(\n f\"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {num_controlnet} controlnets available. Make sure to provide {num_controlnet}.\"\n )\n\n for start, end in zip(control_guidance_start, control_guidance_end):\n if start >= end:\n raise ValueError(\n f\"control guidance start: {start} cannot be larger or equal to control guidance end: {end}.\"\n )\n if start < 0.0:\n raise ValueError(f\"control guidance start: {start} can't be smaller than 0.\")\n if end > 1.0:\n raise ValueError(f\"control guidance end: {end} can't be larger than 1.0.\")\n\n # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image\n def check_image(self, image, prompt, prompt_embeds):\n image_is_pil = isinstance(image, PIL.Image.Image)\n image_is_tensor = isinstance(image, torch.Tensor)\n image_is_np = isinstance(image, np.ndarray)\n image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)\n image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)\n image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)\n\n if (\n not image_is_pil\n and not image_is_tensor\n and not image_is_np\n and not image_is_pil_list\n and not image_is_tensor_list\n and not image_is_np_list\n ):\n raise TypeError(\n f\"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}\"\n )\n\n if image_is_pil:\n image_batch_size = 1\n else:\n image_batch_size = len(image)\n\n if prompt is not None and isinstance(prompt, str):\n prompt_batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n prompt_batch_size = len(prompt)\n elif prompt_embeds is not None:\n prompt_batch_size = prompt_embeds.shape[0]\n\n if image_batch_size != 1 and image_batch_size != prompt_batch_size:\n raise ValueError(\n f\"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}\"\n )\n\n # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image\n def prepare_control_image(\n self,\n image,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype,\n do_classifier_free_guidance=False,\n guess_mode=False,\n ):\n image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)\n image_batch_size = image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n image = image.repeat_interleave(repeat_by, dim=0)\n\n image = image.to(device=device, dtype=dtype)\n\n if do_classifier_free_guidance and not guess_mode:\n image = torch.cat([image] * 2)\n\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n\n return timesteps, num_inference_steps - t_start\n\n def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n\n if image.shape[1] == 4:\n init_latents = image\n\n else:\n _image = image.cpu().detach().numpy()\n init_latents = self.vae_encoder(sample=_image)[0]\n init_latents = torch.from_numpy(init_latents).to(device=device, dtype=dtype)\n init_latents = 0.18215 * init_latents\n\n if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:\n # expand init_latents for batch_size\n deprecation_message = (\n f\"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial\"\n \" images (`image`). Initial images are now duplicating to match the number of text prompts. Note\"\n \" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update\"\n \" your script to pass as many initial images as text prompts to suppress this warning.\"\n )\n deprecate(\"len(prompt) != len(image)\", \"1.0.0\", deprecation_message, standard_warn=False)\n additional_image_per_prompt = batch_size // init_latents.shape[0]\n init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)\n elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n init_latents = torch.cat([init_latents], dim=0)\n\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n latents = init_latents\n\n return latents\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n num_controlnet: int,\n fp16: bool = True,\n prompt: Union[str, List[str]] = None,\n image: Union[\n torch.Tensor,\n PIL.Image.Image,\n np.ndarray,\n List[torch.Tensor],\n List[PIL.Image.Image],\n List[np.ndarray],\n ] = None,\n control_image: Union[\n torch.Tensor,\n PIL.Image.Image,\n np.ndarray,\n List[torch.Tensor],\n List[PIL.Image.Image],\n List[np.ndarray],\n ] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n strength: float = 0.8,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: Union[float, List[float]] = 0.8,\n guess_mode: bool = False,\n control_guidance_start: Union[float, List[float]] = 0.0,\n control_guidance_end: Union[float, List[float]] = 1.0,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:\n `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):\n The initial image will be used as the starting point for the image generation process. Can also accept\n image latents as `image`, if passing latents directly, it will not be encoded again.\n control_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:\n `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):\n The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If\n the type is specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can\n also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If\n height and/or width are passed, `image` is resized according to them. If multiple ControlNets are\n specified in init, images must be passed as a list such that each element of the list can be correctly\n batched for input to a single controlnet.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):\n The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added\n to the residual in the original unet. If multiple ControlNets are specified in init, you can set the\n corresponding scale as a list. Note that by default, we use a smaller conditioning scale for inpainting\n than for [`~StableDiffusionControlNetPipeline.__call__`].\n guess_mode (`bool`, *optional*, defaults to `False`):\n In this mode, the ControlNet encoder will try best to recognize the content of the input image even if\n you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.\n control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):\n The percentage of total steps at which the controlnet starts applying.\n control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):\n The percentage of total steps at which the controlnet stops applying.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n if fp16:\n torch_dtype = torch.float16\n np_dtype = np.float16\n else:\n torch_dtype = torch.float32\n np_dtype = np.float32\n\n # align format for control guidance\n if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):\n control_guidance_start = len(control_guidance_end) * [control_guidance_start]\n elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):\n control_guidance_end = len(control_guidance_start) * [control_guidance_end]\n elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):\n mult = num_controlnet\n control_guidance_start, control_guidance_end = (\n mult * [control_guidance_start],\n mult * [control_guidance_end],\n )\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n num_controlnet,\n prompt,\n control_image,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n controlnet_conditioning_scale,\n control_guidance_start,\n control_guidance_end,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n if num_controlnet > 1 and isinstance(controlnet_conditioning_scale, float):\n controlnet_conditioning_scale = [controlnet_conditioning_scale] * num_controlnet\n\n # 3. Encode input prompt\n prompt_embeds = self._encode_prompt(\n prompt,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n # 4. Prepare image\n image = self.image_processor.preprocess(image).to(dtype=torch.float32)\n\n # 5. Prepare controlnet_conditioning_image\n if num_controlnet == 1:\n control_image = self.prepare_control_image(\n image=control_image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=torch_dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n elif num_controlnet > 1:\n control_images = []\n\n for control_image_ in control_image:\n control_image_ = self.prepare_control_image(\n image=control_image_,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=torch_dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n\n control_images.append(control_image_)\n\n control_image = control_images\n else:\n assert False\n\n # 5. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n\n # 6. Prepare latent variables\n latents = self.prepare_latents(\n image,\n latent_timestep,\n batch_size,\n num_images_per_prompt,\n torch_dtype,\n device,\n generator,\n )\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7.1 Create tensor stating which controlnets to keep\n controlnet_keep = []\n for i in range(len(timesteps)):\n keeps = [\n 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)\n for s, e in zip(control_guidance_start, control_guidance_end)\n ]\n controlnet_keep.append(keeps[0] if num_controlnet == 1 else keeps)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n if isinstance(controlnet_keep[i], list):\n cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]\n else:\n controlnet_cond_scale = controlnet_conditioning_scale\n if isinstance(controlnet_cond_scale, list):\n controlnet_cond_scale = controlnet_cond_scale[0]\n cond_scale = controlnet_cond_scale * controlnet_keep[i]\n\n # predict the noise residual\n _latent_model_input = latent_model_input.cpu().detach().numpy()\n _prompt_embeds = np.array(prompt_embeds, dtype=np_dtype)\n _t = np.array([t.cpu().detach().numpy()], dtype=np_dtype)\n\n if num_controlnet == 1:\n control_images = np.array([control_image], dtype=np_dtype)\n else:\n control_images = []\n for _control_img in control_image:\n _control_img = _control_img.cpu().detach().numpy()\n control_images.append(_control_img)\n control_images = np.array(control_images, dtype=np_dtype)\n\n control_scales = np.array(cond_scale, dtype=np_dtype)\n control_scales = np.resize(control_scales, (num_controlnet, 1))\n\n noise_pred = self.unet(\n sample=_latent_model_input,\n timestep=_t,\n encoder_hidden_states=_prompt_embeds,\n controlnet_conds=control_images,\n conditioning_scales=control_scales,\n )[\"noise_pred\"]\n noise_pred = torch.from_numpy(noise_pred).to(device)\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n _latents = latents.cpu().detach().numpy() / 0.18215\n _latents = np.array(_latents, dtype=np_dtype)\n image = self.vae_decoder(latent_sample=_latents)[0]\n image = torch.from_numpy(image).to(device, dtype=torch.float32)\n has_nsfw_concept = None\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--sd_model\",\n type=str,\n required=True,\n help=\"Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).\",\n )\n\n parser.add_argument(\n \"--onnx_model_dir\",\n type=str,\n required=True,\n help=\"Path to the ONNX directory\",\n )\n\n parser.add_argument(\n \"--unet_engine_path\",\n type=str,\n required=True,\n help=\"Path to the unet + controlnet tensorrt model\",\n )\n\n parser.add_argument(\"--qr_img_path\", type=str, required=True, help=\"Path to the qr code image\")\n\n args = parser.parse_args()\n\n qr_image = Image.open(args.qr_img_path)\n qr_image = qr_image.resize((512, 512))\n\n # init stable diffusion pipeline\n pipeline = StableDiffusionImg2ImgPipeline.from_pretrained(args.sd_model)\n pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)\n\n provider = [\"CUDAExecutionProvider\", \"CPUExecutionProvider\"]\n onnx_pipeline = TensorRTStableDiffusionControlNetImg2ImgPipeline(\n vae_encoder=OnnxRuntimeModel.from_pretrained(\n os.path.join(args.onnx_model_dir, \"vae_encoder\"), provider=provider\n ),\n vae_decoder=OnnxRuntimeModel.from_pretrained(\n os.path.join(args.onnx_model_dir, \"vae_decoder\"), provider=provider\n ),\n text_encoder=OnnxRuntimeModel.from_pretrained(\n os.path.join(args.onnx_model_dir, \"text_encoder\"), provider=provider\n ),\n tokenizer=pipeline.tokenizer,\n unet=TensorRTModel(args.unet_engine_path),\n scheduler=pipeline.scheduler,\n )\n onnx_pipeline = onnx_pipeline.to(\"cuda\")\n\n prompt = \"a cute cat fly to the moon\"\n negative_prompt = \"paintings, sketches, worst quality, low quality, normal quality, lowres, normal quality, monochrome, grayscale, skin spots, acnes, skin blemishes, age spot, glans, nsfw, nipples, necklace, worst quality, low quality, watermark, username, signature, multiple breasts, lowres, bad anatomy, bad hands, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, bad feet, single color, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, disfigured, bad anatomy, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, mutated hands, fused fingers, too many fingers, long neck, bad body perspect\"\n\n for i in range(10):\n start_time = time.time()\n image = onnx_pipeline(\n num_controlnet=2,\n prompt=prompt,\n negative_prompt=negative_prompt,\n image=qr_image,\n control_image=[qr_image, qr_image],\n width=512,\n height=512,\n strength=0.75,\n num_inference_steps=20,\n num_images_per_prompt=1,\n controlnet_conditioning_scale=[0.8, 0.8],\n control_guidance_start=[0.3, 0.3],\n control_guidance_end=[0.9, 0.9],\n ).images[0]\n print(time.time() - start_time)\n image.save(\"output_qr_code.png\")\n" }, { "path": "diffusers/examples/community/scheduling_ufogen.py", "content": "# Copyright 2024 UC Berkeley Team and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim\n\nimport math\nfrom dataclasses import dataclass\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom diffusers.configuration_utils import ConfigMixin, register_to_config\nfrom diffusers.schedulers.scheduling_utils import SchedulerMixin\nfrom diffusers.utils import BaseOutput\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\n@dataclass\n# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UFOGen\nclass UFOGenSchedulerOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's `step` function output.\n\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample `(x_{0})` based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.Tensor\n pred_original_sample: Optional[torch.Tensor] = None\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\n# Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr\ndef rescale_zero_terminal_snr(betas):\n \"\"\"\n Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)\n\n\n Args:\n betas (`torch.Tensor`):\n the betas that the scheduler is being initialized with.\n\n Returns:\n `torch.Tensor`: rescaled betas with zero terminal SNR\n \"\"\"\n # Convert betas to alphas_bar_sqrt\n alphas = 1.0 - betas\n alphas_cumprod = torch.cumprod(alphas, dim=0)\n alphas_bar_sqrt = alphas_cumprod.sqrt()\n\n # Store old values.\n alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()\n alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()\n\n # Shift so the last timestep is zero.\n alphas_bar_sqrt -= alphas_bar_sqrt_T\n\n # Scale so the first timestep is back to the old value.\n alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)\n\n # Convert alphas_bar_sqrt to betas\n alphas_bar = alphas_bar_sqrt**2 # Revert sqrt\n alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod\n alphas = torch.cat([alphas_bar[0:1], alphas])\n betas = 1 - alphas\n\n return betas\n\n\nclass UFOGenScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n `UFOGenScheduler` implements multistep and onestep sampling for a UFOGen model, introduced in\n [UFOGen: You Forward Once Large Scale Text-to-Image Generation via Diffusion GANs](https://arxiv.org/abs/2311.09257)\n by Yanwu Xu, Yang Zhao, Zhisheng Xiao, and Tingbo Hou. UFOGen is a varianet of the denoising diffusion GAN (DDGAN)\n model designed for one-step sampling.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n beta_start (`float`, defaults to 0.0001):\n The starting `beta` value of inference.\n beta_end (`float`, defaults to 0.02):\n The final `beta` value.\n beta_schedule (`str`, defaults to `\"linear\"`):\n The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear`, `scaled_linear`, or `squaredcos_cap_v2`.\n clip_sample (`bool`, defaults to `True`):\n Clip the predicted sample for numerical stability.\n clip_sample_range (`float`, defaults to 1.0):\n The maximum magnitude for sample clipping. Valid only when `clip_sample=True`.\n set_alpha_to_one (`bool`, defaults to `True`):\n Each diffusion step uses the alphas product value at that step and at the previous one. For the final step\n there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,\n otherwise it uses the alpha value at step 0.\n prediction_type (`str`, defaults to `epsilon`, *optional*):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),\n `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen\n Video](https://imagen.research.google/video/paper.pdf) paper).\n thresholding (`bool`, defaults to `False`):\n Whether to use the \"dynamic thresholding\" method. This is unsuitable for latent-space diffusion models such\n as Stable Diffusion.\n dynamic_thresholding_ratio (`float`, defaults to 0.995):\n The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.\n sample_max_value (`float`, defaults to 1.0):\n The threshold value for dynamic thresholding. Valid only when `thresholding=True`.\n timestep_spacing (`str`, defaults to `\"leading\"`):\n The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.\n steps_offset (`int`, defaults to 0):\n An offset added to the inference steps, as required by some model families.\n rescale_betas_zero_snr (`bool`, defaults to `False`):\n Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and\n dark samples instead of limiting it to samples with medium brightness. Loosely related to\n [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).\n denoising_step_size (`int`, defaults to 250):\n The denoising step size parameter from the UFOGen paper. The number of steps used for training is roughly\n `math.ceil(num_train_timesteps / denoising_step_size)`.\n \"\"\"\n\n order = 1\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[Union[np.ndarray, List[float]]] = None,\n clip_sample: bool = True,\n set_alpha_to_one: bool = True,\n prediction_type: str = \"epsilon\",\n thresholding: bool = False,\n dynamic_thresholding_ratio: float = 0.995,\n clip_sample_range: float = 1.0,\n sample_max_value: float = 1.0,\n timestep_spacing: str = \"leading\",\n steps_offset: int = 0,\n rescale_betas_zero_snr: bool = False,\n denoising_step_size: int = 250,\n ):\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n elif beta_schedule == \"sigmoid\":\n # GeoDiff sigmoid schedule\n betas = torch.linspace(-6, 6, num_train_timesteps)\n self.betas = torch.sigmoid(betas) * (beta_end - beta_start) + beta_start\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n # Rescale for zero SNR\n if rescale_betas_zero_snr:\n self.betas = rescale_zero_terminal_snr(self.betas)\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n\n # For the final step, there is no previous alphas_cumprod because we are already at 0\n # `set_alpha_to_one` decides whether we set this parameter simply to one or\n # whether we use the final alpha of the \"non-previous\" one.\n self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]\n\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = 1.0\n\n # setable values\n self.custom_timesteps = False\n self.num_inference_steps = None\n self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())\n\n def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`int`, *optional*):\n The current timestep in the diffusion chain.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n return sample\n\n def set_timesteps(\n self,\n num_inference_steps: Optional[int] = None,\n device: Union[str, torch.device] = None,\n timesteps: Optional[List[int]] = None,\n ):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used,\n `timesteps` must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default\n timestep spacing strategy of equal spacing between timesteps is used. If `timesteps` is passed,\n `num_inference_steps` must be `None`.\n\n \"\"\"\n if num_inference_steps is not None and timesteps is not None:\n raise ValueError(\"Can only pass one of `num_inference_steps` or `custom_timesteps`.\")\n\n if timesteps is not None:\n for i in range(1, len(timesteps)):\n if timesteps[i] >= timesteps[i - 1]:\n raise ValueError(\"`custom_timesteps` must be in descending order.\")\n\n if timesteps[0] >= self.config.num_train_timesteps:\n raise ValueError(\n f\"`timesteps` must start before `self.config.train_timesteps`:\"\n f\" {self.config.num_train_timesteps}.\"\n )\n\n timesteps = np.array(timesteps, dtype=np.int64)\n self.custom_timesteps = True\n else:\n if num_inference_steps > self.config.num_train_timesteps:\n raise ValueError(\n f\"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:\"\n f\" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle\"\n f\" maximal {self.config.num_train_timesteps} timesteps.\"\n )\n\n self.num_inference_steps = num_inference_steps\n self.custom_timesteps = False\n\n # TODO: For now, handle special case when num_inference_steps == 1 separately\n if num_inference_steps == 1:\n # Set the timestep schedule to num_train_timesteps - 1 rather than 0\n # (that is, the one-step timestep schedule is always trailing rather than leading or linspace)\n timesteps = np.array([self.config.num_train_timesteps - 1], dtype=np.int64)\n else:\n # TODO: For now, retain the DDPM timestep spacing logic\n # \"linspace\", \"leading\", \"trailing\" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891\n if self.config.timestep_spacing == \"linspace\":\n timesteps = (\n np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps)\n .round()[::-1]\n .copy()\n .astype(np.int64)\n )\n elif self.config.timestep_spacing == \"leading\":\n step_ratio = self.config.num_train_timesteps // self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64)\n timesteps += self.config.steps_offset\n elif self.config.timestep_spacing == \"trailing\":\n step_ratio = self.config.num_train_timesteps / self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = np.round(np.arange(self.config.num_train_timesteps, 0, -step_ratio)).astype(np.int64)\n timesteps -= 1\n else:\n raise ValueError(\n f\"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.\"\n )\n\n self.timesteps = torch.from_numpy(timesteps).to(device)\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample\n def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:\n \"\"\"\n \"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the\n prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by\n s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing\n pixels from saturation at each step. We find that dynamic thresholding results in significantly better\n photorealism as well as better image-text alignment, especially when using very large guidance weights.\"\n\n https://arxiv.org/abs/2205.11487\n \"\"\"\n dtype = sample.dtype\n batch_size, channels, *remaining_dims = sample.shape\n\n if dtype not in (torch.float32, torch.float64):\n sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half\n\n # Flatten sample for doing quantile calculation along each image\n sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))\n\n abs_sample = sample.abs() # \"a certain percentile absolute pixel value\"\n\n s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)\n s = torch.clamp(\n s, min=1, max=self.config.sample_max_value\n ) # When clamped to min=1, equivalent to standard clipping to [-1, 1]\n s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0\n sample = torch.clamp(sample, -s, s) / s # \"we threshold xt0 to the range [-s, s] and then divide by s\"\n\n sample = sample.reshape(batch_size, channels, *remaining_dims)\n sample = sample.to(dtype)\n\n return sample\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n generator: Optional[torch.Generator] = None,\n return_dict: bool = True,\n ) -> Union[UFOGenSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`float`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n generator (`torch.Generator`, *optional*):\n A random number generator.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~schedulers.scheduling_ufogen.UFOGenSchedulerOutput`] or `tuple`.\n\n Returns:\n [`~schedulers.scheduling_ddpm.UFOGenSchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_ufogen.UFOGenSchedulerOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n\n \"\"\"\n # 0. Resolve timesteps\n t = timestep\n prev_t = self.previous_timestep(t)\n\n # 1. compute alphas, betas\n alpha_prod_t = self.alphas_cumprod[t]\n alpha_prod_t_prev = self.alphas_cumprod[prev_t] if prev_t >= 0 else self.final_alpha_cumprod\n beta_prod_t = 1 - alpha_prod_t\n # beta_prod_t_prev = 1 - alpha_prod_t_prev\n # current_alpha_t = alpha_prod_t / alpha_prod_t_prev\n # current_beta_t = 1 - current_alpha_t\n\n # 2. compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf\n if self.config.prediction_type == \"epsilon\":\n pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)\n elif self.config.prediction_type == \"sample\":\n pred_original_sample = model_output\n elif self.config.prediction_type == \"v_prediction\":\n pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` or\"\n \" `v_prediction` for UFOGenScheduler.\"\n )\n\n # 3. Clip or threshold \"predicted x_0\"\n if self.config.thresholding:\n pred_original_sample = self._threshold_sample(pred_original_sample)\n elif self.config.clip_sample:\n pred_original_sample = pred_original_sample.clamp(\n -self.config.clip_sample_range, self.config.clip_sample_range\n )\n\n # 4. Single-step or multi-step sampling\n # Noise is not used on the final timestep of the timestep schedule.\n # This also means that noise is not used for one-step sampling.\n if t != self.timesteps[-1]:\n # TODO: is this correct?\n # Sample prev sample x_{t - 1} ~ q(x_{t - 1} | x_0 = G(x_t, t))\n device = model_output.device\n noise = randn_tensor(model_output.shape, generator=generator, device=device, dtype=model_output.dtype)\n sqrt_alpha_prod_t_prev = alpha_prod_t_prev**0.5\n sqrt_one_minus_alpha_prod_t_prev = (1 - alpha_prod_t_prev) ** 0.5\n pred_prev_sample = sqrt_alpha_prod_t_prev * pred_original_sample + sqrt_one_minus_alpha_prod_t_prev * noise\n else:\n # Simply return the pred_original_sample. If `prediction_type == \"sample\"`, this is equivalent to returning\n # the output of the GAN generator U-Net on the initial noisy latents x_T ~ N(0, I).\n pred_prev_sample = pred_original_sample\n\n if not return_dict:\n return (pred_prev_sample,)\n\n return UFOGenSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample)\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.IntTensor,\n ) -> torch.Tensor:\n # Make sure alphas_cumprod and timestep have same device and dtype as original_samples\n alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)\n timesteps = timesteps.to(original_samples.device)\n\n sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n while len(sqrt_alpha_prod.shape) < len(original_samples.shape):\n sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n\n noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise\n return noisy_samples\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity\n def get_velocity(self, sample: torch.Tensor, noise: torch.Tensor, timesteps: torch.IntTensor) -> torch.Tensor:\n # Make sure alphas_cumprod and timestep have same device and dtype as sample\n alphas_cumprod = self.alphas_cumprod.to(device=sample.device, dtype=sample.dtype)\n timesteps = timesteps.to(sample.device)\n\n sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n while len(sqrt_alpha_prod.shape) < len(sample.shape):\n sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n\n velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample\n return velocity\n\n def __len__(self):\n return self.config.num_train_timesteps\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.previous_timestep\n def previous_timestep(self, timestep):\n if self.custom_timesteps:\n index = (self.timesteps == timestep).nonzero(as_tuple=True)[0][0]\n if index == self.timesteps.shape[0] - 1:\n prev_t = torch.tensor(-1)\n else:\n prev_t = self.timesteps[index + 1]\n else:\n num_inference_steps = (\n self.num_inference_steps if self.num_inference_steps else self.config.num_train_timesteps\n )\n prev_t = timestep - self.config.num_train_timesteps // num_inference_steps\n\n return prev_t\n" }, { "path": "diffusers/examples/community/sd_text2img_k_diffusion.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport importlib\nimport warnings\nfrom typing import Callable, List, Optional, Union\n\nimport torch\nfrom k_diffusion.external import CompVisDenoiser, CompVisVDenoiser\n\nfrom diffusers import DiffusionPipeline, LMSDiscreteScheduler, StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.utils import logging\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass ModelWrapper:\n def __init__(self, model, alphas_cumprod):\n self.model = model\n self.alphas_cumprod = alphas_cumprod\n\n def apply_model(self, *args, **kwargs):\n if len(args) == 3:\n encoder_hidden_states = args[-1]\n args = args[:2]\n if kwargs.get(\"cond\", None) is not None:\n encoder_hidden_states = kwargs.pop(\"cond\")\n return self.model(*args, encoder_hidden_states=encoder_hidden_states, **kwargs).sample\n\n\nclass StableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n\n def __init__(\n self,\n vae,\n text_encoder,\n tokenizer,\n unet,\n scheduler,\n safety_checker,\n feature_extractor,\n ):\n super().__init__()\n\n if safety_checker is None:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n # get correct sigmas from LMS\n scheduler = LMSDiscreteScheduler.from_config(scheduler.config)\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n\n model = ModelWrapper(unet, scheduler.alphas_cumprod)\n if scheduler.config.prediction_type == \"v_prediction\":\n self.k_diffusion_model = CompVisVDenoiser(model)\n else:\n self.k_diffusion_model = CompVisDenoiser(model)\n\n def set_sampler(self, scheduler_type: str):\n warnings.warn(\"The `set_sampler` method is deprecated, please use `set_scheduler` instead.\")\n return self.set_scheduler(scheduler_type)\n\n def set_scheduler(self, scheduler_type: str):\n library = importlib.import_module(\"k_diffusion\")\n sampling = getattr(library, \"sampling\")\n self.sampler = getattr(sampling, scheduler_type)\n\n def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `list(int)`):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n \"\"\"\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"max_length\", return_tensors=\"pt\").input_ids\n\n if not torch.equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n text_embeddings = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n text_embeddings = text_embeddings[0]\n\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = text_embeddings.shape\n text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)\n text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = text_input_ids.shape[-1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n uncond_embeddings = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n uncond_embeddings = uncond_embeddings[0]\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = uncond_embeddings.shape[1]\n uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)\n uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n\n return text_embeddings\n\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n else:\n has_nsfw_concept = None\n return image, has_nsfw_concept\n\n def decode_latents(self, latents):\n latents = 1 / 0.18215 * latents\n image = self.vae.decode(latents).sample\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n def check_inputs(self, prompt, height, width, callback_steps):\n if not isinstance(prompt, str) and not isinstance(prompt, list):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (batch_size, num_channels_latents, height // 8, width // 8)\n if latents is None:\n if device.type == \"mps\":\n # randn does not work reproducibly on mps\n latents = torch.randn(shape, generator=generator, device=\"cpu\", dtype=dtype).to(device)\n else:\n latents = torch.randn(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n return latents\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation\n deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(prompt, height, width, callback_steps)\n\n # 2. Define call parameters\n batch_size = 1 if isinstance(prompt, str) else len(prompt)\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = True\n if guidance_scale <= 1.0:\n raise ValueError(\"has to use guidance_scale\")\n\n # 3. Encode input prompt\n text_embeddings = self._encode_prompt(\n prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt\n )\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=text_embeddings.device)\n sigmas = self.scheduler.sigmas\n sigmas = sigmas.to(text_embeddings.dtype)\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n text_embeddings.dtype,\n device,\n generator,\n latents,\n )\n latents = latents * sigmas[0]\n self.k_diffusion_model.sigmas = self.k_diffusion_model.sigmas.to(latents.device)\n self.k_diffusion_model.log_sigmas = self.k_diffusion_model.log_sigmas.to(latents.device)\n\n def model_fn(x, t):\n latent_model_input = torch.cat([x] * 2)\n\n noise_pred = self.k_diffusion_model(latent_model_input, t, cond=text_embeddings)\n\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n return noise_pred\n\n latents = self.sampler(model_fn, latents, sigmas)\n\n # 8. Post-processing\n image = self.decode_latents(latents)\n\n # 9. Run safety checker\n image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)\n\n # 10. Convert to PIL\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/examples/community/sde_drag.py", "content": "import math\nimport tempfile\nfrom typing import List, Optional\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom accelerate import Accelerator\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import AutoencoderKL, DiffusionPipeline, DPMSolverMultistepScheduler, UNet2DConditionModel\nfrom diffusers.loaders import AttnProcsLayers, LoraLoaderMixin\nfrom diffusers.models.attention_processor import (\n AttnAddedKVProcessor,\n AttnAddedKVProcessor2_0,\n LoRAAttnAddedKVProcessor,\n LoRAAttnProcessor,\n LoRAAttnProcessor2_0,\n SlicedAttnAddedKVProcessor,\n)\nfrom diffusers.optimization import get_scheduler\n\n\nclass SdeDragPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for image drag-and-drop editing using stochastic differential equations: https://arxiv.org/abs/2311.01410.\n Please refer to the [official repository](https://github.com/ML-GSAI/SDE-Drag) for more information.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Please use\n [`DDIMScheduler`].\n \"\"\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: DPMSolverMultistepScheduler,\n ):\n super().__init__()\n\n self.register_modules(vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler)\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: str,\n image: PIL.Image.Image,\n mask_image: PIL.Image.Image,\n source_points: List[List[int]],\n target_points: List[List[int]],\n t0: Optional[float] = 0.6,\n steps: Optional[int] = 200,\n step_size: Optional[int] = 2,\n image_scale: Optional[float] = 0.3,\n adapt_radius: Optional[int] = 5,\n min_lora_scale: Optional[float] = 0.5,\n generator: Optional[torch.Generator] = None,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for image editing.\n Args:\n prompt (`str`, *required*):\n The prompt to guide the image editing.\n image (`PIL.Image.Image`, *required*):\n Which will be edited, parts of the image will be masked out with `mask_image` and edited\n according to `prompt`.\n mask_image (`PIL.Image.Image`, *required*):\n To mask `image`. White pixels in the mask will be edited, while black pixels will be preserved.\n source_points (`List[List[int]]`, *required*):\n Used to mark the starting positions of drag editing in the image, with each pixel represented as a\n `List[int]` of length 2.\n target_points (`List[List[int]]`, *required*):\n Used to mark the target positions of drag editing in the image, with each pixel represented as a\n `List[int]` of length 2.\n t0 (`float`, *optional*, defaults to 0.6):\n The time parameter. Higher t0 improves the fidelity while lowering the faithfulness of the edited images\n and vice versa.\n steps (`int`, *optional*, defaults to 200):\n The number of sampling iterations.\n step_size (`int`, *optional*, defaults to 2):\n The drag diatance of each drag step.\n image_scale (`float`, *optional*, defaults to 0.3):\n To avoid duplicating the content, use image_scale to perturbs the source.\n adapt_radius (`int`, *optional*, defaults to 5):\n The size of the region for copy and paste operations during each step of the drag process.\n min_lora_scale (`float`, *optional*, defaults to 0.5):\n A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n min_lora_scale specifies the minimum LoRA scale during the image drag-editing process.\n generator ('torch.Generator', *optional*, defaults to None):\n To make generation deterministic(https://pytorch.org/docs/stable/generated/torch.Generator.html).\n Examples:\n ```py\n >>> import PIL\n >>> import torch\n >>> from diffusers import DDIMScheduler, DiffusionPipeline\n\n >>> # Load the pipeline\n >>> model_path = \"runwayml/stable-diffusion-v1-5\"\n >>> scheduler = DDIMScheduler.from_pretrained(model_path, subfolder=\"scheduler\")\n >>> pipe = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler, custom_pipeline=\"sde_drag\")\n >>> pipe.to('cuda')\n\n >>> # To save GPU memory, torch.float16 can be used, but it may compromise image quality.\n >>> # If not training LoRA, please avoid using torch.float16\n >>> # pipe.to(torch.float16)\n\n >>> # Provide prompt, image, mask image, and the starting and target points for drag editing.\n >>> prompt = \"prompt of the image\"\n >>> image = PIL.Image.open('/path/to/image')\n >>> mask_image = PIL.Image.open('/path/to/mask_image')\n >>> source_points = [[123, 456]]\n >>> target_points = [[234, 567]]\n\n >>> # train_lora is optional, and in most cases, using train_lora can better preserve consistency with the original image.\n >>> pipe.train_lora(prompt, image)\n\n >>> output = pipe(prompt, image, mask_image, source_points, target_points)\n >>> output_image = PIL.Image.fromarray(output)\n >>> output_image.save(\"./output.png\")\n ```\n \"\"\"\n\n self.scheduler.set_timesteps(steps)\n\n noise_scale = (1 - image_scale**2) ** (0.5)\n\n text_embeddings = self._get_text_embed(prompt)\n uncond_embeddings = self._get_text_embed([\"\"])\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n\n latent = self._get_img_latent(image)\n\n mask = mask_image.resize((latent.shape[3], latent.shape[2]))\n mask = torch.tensor(np.array(mask))\n mask = mask.unsqueeze(0).expand_as(latent).to(self.device)\n\n source_points = torch.tensor(source_points).div(torch.tensor([8]), rounding_mode=\"trunc\")\n target_points = torch.tensor(target_points).div(torch.tensor([8]), rounding_mode=\"trunc\")\n\n distance = target_points - source_points\n distance_norm_max = torch.norm(distance.float(), dim=1, keepdim=True).max()\n\n if distance_norm_max <= step_size:\n drag_num = 1\n else:\n drag_num = distance_norm_max.div(torch.tensor([step_size]), rounding_mode=\"trunc\")\n if (distance_norm_max / drag_num - step_size).abs() > (\n distance_norm_max / (drag_num + 1) - step_size\n ).abs():\n drag_num += 1\n\n latents = []\n for i in tqdm(range(int(drag_num)), desc=\"SDE Drag\"):\n source_new = source_points + (i / drag_num * distance).to(torch.int)\n target_new = source_points + ((i + 1) / drag_num * distance).to(torch.int)\n\n latent, noises, hook_latents, lora_scales, cfg_scales = self._forward(\n latent, steps, t0, min_lora_scale, text_embeddings, generator\n )\n latent = self._copy_and_paste(\n latent,\n source_new,\n target_new,\n adapt_radius,\n latent.shape[2] - 1,\n latent.shape[3] - 1,\n image_scale,\n noise_scale,\n generator,\n )\n latent = self._backward(\n latent, mask, steps, t0, noises, hook_latents, lora_scales, cfg_scales, text_embeddings, generator\n )\n\n latents.append(latent)\n\n result_image = 1 / 0.18215 * latents[-1]\n\n with torch.no_grad():\n result_image = self.vae.decode(result_image).sample\n\n result_image = (result_image / 2 + 0.5).clamp(0, 1)\n result_image = result_image.cpu().permute(0, 2, 3, 1).numpy()[0]\n result_image = (result_image * 255).astype(np.uint8)\n\n return result_image\n\n def train_lora(self, prompt, image, lora_step=100, lora_rank=16, generator=None):\n accelerator = Accelerator(gradient_accumulation_steps=1, mixed_precision=\"fp16\")\n\n self.vae.requires_grad_(False)\n self.text_encoder.requires_grad_(False)\n self.unet.requires_grad_(False)\n\n unet_lora_attn_procs = {}\n for name, attn_processor in self.unet.attn_processors.items():\n cross_attention_dim = None if name.endswith(\"attn1.processor\") else self.unet.config.cross_attention_dim\n if name.startswith(\"mid_block\"):\n hidden_size = self.unet.config.block_out_channels[-1]\n elif name.startswith(\"up_blocks\"):\n block_id = int(name[len(\"up_blocks.\")])\n hidden_size = list(reversed(self.unet.config.block_out_channels))[block_id]\n elif name.startswith(\"down_blocks\"):\n block_id = int(name[len(\"down_blocks.\")])\n hidden_size = self.unet.config.block_out_channels[block_id]\n else:\n raise NotImplementedError(\"name must start with up_blocks, mid_blocks, or down_blocks\")\n\n if isinstance(attn_processor, (AttnAddedKVProcessor, SlicedAttnAddedKVProcessor, AttnAddedKVProcessor2_0)):\n lora_attn_processor_class = LoRAAttnAddedKVProcessor\n else:\n lora_attn_processor_class = (\n LoRAAttnProcessor2_0\n if hasattr(torch.nn.functional, \"scaled_dot_product_attention\")\n else LoRAAttnProcessor\n )\n unet_lora_attn_procs[name] = lora_attn_processor_class(\n hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=lora_rank\n )\n\n self.unet.set_attn_processor(unet_lora_attn_procs)\n unet_lora_layers = AttnProcsLayers(self.unet.attn_processors)\n params_to_optimize = unet_lora_layers.parameters()\n\n optimizer = torch.optim.AdamW(\n params_to_optimize,\n lr=2e-4,\n betas=(0.9, 0.999),\n weight_decay=1e-2,\n eps=1e-08,\n )\n\n lr_scheduler = get_scheduler(\n \"constant\",\n optimizer=optimizer,\n num_warmup_steps=0,\n num_training_steps=lora_step,\n num_cycles=1,\n power=1.0,\n )\n\n unet_lora_layers = accelerator.prepare_model(unet_lora_layers)\n optimizer = accelerator.prepare_optimizer(optimizer)\n lr_scheduler = accelerator.prepare_scheduler(lr_scheduler)\n\n with torch.no_grad():\n text_inputs = self._tokenize_prompt(prompt, tokenizer_max_length=None)\n text_embedding = self._encode_prompt(\n text_inputs.input_ids, text_inputs.attention_mask, text_encoder_use_attention_mask=False\n )\n\n image_transforms = transforms.Compose(\n [\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n image = image_transforms(image).to(self.device, dtype=self.vae.dtype)\n image = image.unsqueeze(dim=0)\n latents_dist = self.vae.encode(image).latent_dist\n\n for _ in tqdm(range(lora_step), desc=\"Train LoRA\"):\n self.unet.train()\n model_input = latents_dist.sample() * self.vae.config.scaling_factor\n\n # Sample noise that we'll add to the latents\n noise = torch.randn(\n model_input.size(),\n dtype=model_input.dtype,\n layout=model_input.layout,\n device=model_input.device,\n generator=generator,\n )\n bsz, channels, height, width = model_input.shape\n\n # Sample a random timestep for each image\n timesteps = torch.randint(\n 0, self.scheduler.config.num_train_timesteps, (bsz,), device=model_input.device, generator=generator\n )\n timesteps = timesteps.long()\n\n # Add noise to the model input according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_model_input = self.scheduler.add_noise(model_input, noise, timesteps)\n\n # Predict the noise residual\n model_pred = self.unet(noisy_model_input, timesteps, text_embedding).sample\n\n # Get the target for loss depending on the prediction type\n if self.scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif self.scheduler.config.prediction_type == \"v_prediction\":\n target = self.scheduler.get_velocity(model_input, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {self.scheduler.config.prediction_type}\")\n\n loss = torch.nn.functional.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n accelerator.backward(loss)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n with tempfile.TemporaryDirectory() as save_lora_dir:\n LoraLoaderMixin.save_lora_weights(\n save_directory=save_lora_dir,\n unet_lora_layers=unet_lora_layers,\n text_encoder_lora_layers=None,\n )\n\n self.unet.load_attn_procs(save_lora_dir)\n\n def _tokenize_prompt(self, prompt, tokenizer_max_length=None):\n if tokenizer_max_length is not None:\n max_length = tokenizer_max_length\n else:\n max_length = self.tokenizer.model_max_length\n\n text_inputs = self.tokenizer(\n prompt,\n truncation=True,\n padding=\"max_length\",\n max_length=max_length,\n return_tensors=\"pt\",\n )\n\n return text_inputs\n\n def _encode_prompt(self, input_ids, attention_mask, text_encoder_use_attention_mask=False):\n text_input_ids = input_ids.to(self.device)\n\n if text_encoder_use_attention_mask:\n attention_mask = attention_mask.to(self.device)\n else:\n attention_mask = None\n\n prompt_embeds = self.text_encoder(\n text_input_ids,\n attention_mask=attention_mask,\n )\n prompt_embeds = prompt_embeds[0]\n\n return prompt_embeds\n\n @torch.no_grad()\n def _get_text_embed(self, prompt):\n text_input = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]\n return text_embeddings\n\n def _copy_and_paste(\n self, latent, source_new, target_new, adapt_radius, max_height, max_width, image_scale, noise_scale, generator\n ):\n def adaption_r(source, target, adapt_radius, max_height, max_width):\n r_x_lower = min(adapt_radius, source[0], target[0])\n r_x_upper = min(adapt_radius, max_width - source[0], max_width - target[0])\n r_y_lower = min(adapt_radius, source[1], target[1])\n r_y_upper = min(adapt_radius, max_height - source[1], max_height - target[1])\n return r_x_lower, r_x_upper, r_y_lower, r_y_upper\n\n for source_, target_ in zip(source_new, target_new):\n r_x_lower, r_x_upper, r_y_lower, r_y_upper = adaption_r(\n source_, target_, adapt_radius, max_height, max_width\n )\n\n source_feature = latent[\n :, :, source_[1] - r_y_lower : source_[1] + r_y_upper, source_[0] - r_x_lower : source_[0] + r_x_upper\n ].clone()\n\n latent[\n :, :, source_[1] - r_y_lower : source_[1] + r_y_upper, source_[0] - r_x_lower : source_[0] + r_x_upper\n ] = image_scale * source_feature + noise_scale * torch.randn(\n latent.shape[0],\n 4,\n r_y_lower + r_y_upper,\n r_x_lower + r_x_upper,\n device=self.device,\n generator=generator,\n )\n\n latent[\n :, :, target_[1] - r_y_lower : target_[1] + r_y_upper, target_[0] - r_x_lower : target_[0] + r_x_upper\n ] = source_feature * 1.1\n return latent\n\n @torch.no_grad()\n def _get_img_latent(self, image, height=None, weight=None):\n data = image.convert(\"RGB\")\n if height is not None:\n data = data.resize((weight, height))\n transform = transforms.ToTensor()\n data = transform(data).unsqueeze(0)\n data = (data * 2.0) - 1.0\n data = data.to(self.device, dtype=self.vae.dtype)\n latent = self.vae.encode(data).latent_dist.sample()\n latent = 0.18215 * latent\n return latent\n\n @torch.no_grad()\n def _get_eps(self, latent, timestep, guidance_scale, text_embeddings, lora_scale=None):\n latent_model_input = torch.cat([latent] * 2) if guidance_scale > 1.0 else latent\n text_embeddings = text_embeddings if guidance_scale > 1.0 else text_embeddings.chunk(2)[1]\n\n cross_attention_kwargs = None if lora_scale is None else {\"scale\": lora_scale}\n\n with torch.no_grad():\n noise_pred = self.unet(\n latent_model_input,\n timestep,\n encoder_hidden_states=text_embeddings,\n cross_attention_kwargs=cross_attention_kwargs,\n ).sample\n\n if guidance_scale > 1.0:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n elif guidance_scale == 1.0:\n noise_pred_text = noise_pred\n noise_pred_uncond = 0.0\n else:\n raise NotImplementedError(guidance_scale)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n return noise_pred\n\n def _forward_sde(\n self, timestep, sample, guidance_scale, text_embeddings, steps, eta=1.0, lora_scale=None, generator=None\n ):\n num_train_timesteps = len(self.scheduler)\n alphas_cumprod = self.scheduler.alphas_cumprod\n initial_alpha_cumprod = torch.tensor(1.0)\n\n prev_timestep = timestep + num_train_timesteps // steps\n\n alpha_prod_t = alphas_cumprod[timestep] if timestep >= 0 else initial_alpha_cumprod\n alpha_prod_t_prev = alphas_cumprod[prev_timestep]\n\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n\n x_prev = (alpha_prod_t_prev / alpha_prod_t) ** (0.5) * sample + (1 - alpha_prod_t_prev / alpha_prod_t) ** (\n 0.5\n ) * torch.randn(\n sample.size(), dtype=sample.dtype, layout=sample.layout, device=self.device, generator=generator\n )\n eps = self._get_eps(x_prev, prev_timestep, guidance_scale, text_embeddings, lora_scale)\n\n sigma_t_prev = (\n eta\n * (1 - alpha_prod_t) ** (0.5)\n * (1 - alpha_prod_t_prev / (1 - alpha_prod_t_prev) * (1 - alpha_prod_t) / alpha_prod_t) ** (0.5)\n )\n\n pred_original_sample = (x_prev - beta_prod_t_prev ** (0.5) * eps) / alpha_prod_t_prev ** (0.5)\n pred_sample_direction_coeff = (1 - alpha_prod_t - sigma_t_prev**2) ** (0.5)\n\n noise = (\n sample - alpha_prod_t ** (0.5) * pred_original_sample - pred_sample_direction_coeff * eps\n ) / sigma_t_prev\n\n return x_prev, noise\n\n def _sample(\n self,\n timestep,\n sample,\n guidance_scale,\n text_embeddings,\n steps,\n sde=False,\n noise=None,\n eta=1.0,\n lora_scale=None,\n generator=None,\n ):\n num_train_timesteps = len(self.scheduler)\n alphas_cumprod = self.scheduler.alphas_cumprod\n final_alpha_cumprod = torch.tensor(1.0)\n\n eps = self._get_eps(sample, timestep, guidance_scale, text_embeddings, lora_scale)\n\n prev_timestep = timestep - num_train_timesteps // steps\n\n alpha_prod_t = alphas_cumprod[timestep]\n alpha_prod_t_prev = alphas_cumprod[prev_timestep] if prev_timestep >= 0 else final_alpha_cumprod\n\n beta_prod_t = 1 - alpha_prod_t\n\n sigma_t = (\n eta\n * ((1 - alpha_prod_t_prev) / (1 - alpha_prod_t)) ** (0.5)\n * (1 - alpha_prod_t / alpha_prod_t_prev) ** (0.5)\n if sde\n else 0\n )\n\n pred_original_sample = (sample - beta_prod_t ** (0.5) * eps) / alpha_prod_t ** (0.5)\n pred_sample_direction_coeff = (1 - alpha_prod_t_prev - sigma_t**2) ** (0.5)\n\n noise = (\n torch.randn(\n sample.size(), dtype=sample.dtype, layout=sample.layout, device=self.device, generator=generator\n )\n if noise is None\n else noise\n )\n latent = (\n alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction_coeff * eps + sigma_t * noise\n )\n\n return latent\n\n def _forward(self, latent, steps, t0, lora_scale_min, text_embeddings, generator):\n def scale_schedule(begin, end, n, length, type=\"linear\"):\n if type == \"constant\":\n return end\n elif type == \"linear\":\n return begin + (end - begin) * n / length\n elif type == \"cos\":\n factor = (1 - math.cos(n * math.pi / length)) / 2\n return (1 - factor) * begin + factor * end\n else:\n raise NotImplementedError(type)\n\n noises = []\n latents = []\n lora_scales = []\n cfg_scales = []\n latents.append(latent)\n t0 = int(t0 * steps)\n t_begin = steps - t0\n\n length = len(self.scheduler.timesteps[t_begin - 1 : -1]) - 1\n index = 1\n for t in self.scheduler.timesteps[t_begin:].flip(dims=[0]):\n lora_scale = scale_schedule(1, lora_scale_min, index, length, type=\"cos\")\n cfg_scale = scale_schedule(1, 3.0, index, length, type=\"linear\")\n latent, noise = self._forward_sde(\n t, latent, cfg_scale, text_embeddings, steps, lora_scale=lora_scale, generator=generator\n )\n\n noises.append(noise)\n latents.append(latent)\n lora_scales.append(lora_scale)\n cfg_scales.append(cfg_scale)\n index += 1\n return latent, noises, latents, lora_scales, cfg_scales\n\n def _backward(\n self, latent, mask, steps, t0, noises, hook_latents, lora_scales, cfg_scales, text_embeddings, generator\n ):\n t0 = int(t0 * steps)\n t_begin = steps - t0\n\n hook_latent = hook_latents.pop()\n latent = torch.where(mask > 128, latent, hook_latent)\n for t in self.scheduler.timesteps[t_begin - 1 : -1]:\n latent = self._sample(\n t,\n latent,\n cfg_scales.pop(),\n text_embeddings,\n steps,\n sde=True,\n noise=noises.pop(),\n lora_scale=lora_scales.pop(),\n generator=generator,\n )\n hook_latent = hook_latents.pop()\n latent = torch.where(mask > 128, latent, hook_latent)\n return latent\n" }, { "path": "diffusers/examples/community/seed_resize_stable_diffusion.py", "content": "\"\"\"\nmodified based on diffusion library from Huggingface: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py\n\"\"\"\n\nimport inspect\nfrom typing import Callable, List, Optional, Union\n\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import DiffusionPipeline\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.pipelines.pipeline_utils import StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler\nfrom diffusers.utils import logging\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass SeedResizeStableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n ):\n super().__init__()\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n text_embeddings: Optional[torch.Tensor] = None,\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation\n deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n\n if isinstance(prompt, str):\n batch_size = 1\n elif isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n\n if text_input_ids.shape[-1] > self.tokenizer.model_max_length:\n removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]\n\n if text_embeddings is None:\n text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]\n\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = text_embeddings.shape\n text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)\n text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"]\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = text_input_ids.shape[-1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = uncond_embeddings.shape[1]\n uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)\n uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n\n # get the initial random noise unless the user supplied it\n\n # Unlike in other pipelines, latents need to be generated in the target device\n # for 1-to-1 results reproducibility with the CompVis implementation.\n # However this currently doesn't work in `mps`.\n latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)\n latents_shape_reference = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)\n latents_dtype = text_embeddings.dtype\n if latents is None:\n if self.device.type == \"mps\":\n # randn does not exist on mps\n latents_reference = torch.randn(\n latents_shape_reference, generator=generator, device=\"cpu\", dtype=latents_dtype\n ).to(self.device)\n latents = torch.randn(latents_shape, generator=generator, device=\"cpu\", dtype=latents_dtype).to(\n self.device\n )\n else:\n latents_reference = torch.randn(\n latents_shape_reference, generator=generator, device=self.device, dtype=latents_dtype\n )\n latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)\n else:\n if latents_reference.shape != latents_shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {latents_shape}\")\n latents_reference = latents_reference.to(self.device)\n latents = latents.to(self.device)\n\n # This is the key part of the pipeline where we\n # try to ensure that the generated images w/ the same seed\n # but different sizes actually result in similar images\n dx = (latents_shape[3] - latents_shape_reference[3]) // 2\n dy = (latents_shape[2] - latents_shape_reference[2]) // 2\n w = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx\n h = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy\n tx = 0 if dx < 0 else dx\n ty = 0 if dy < 0 else dy\n dx = max(-dx, 0)\n dy = max(-dy, 0)\n # import pdb\n # pdb.set_trace()\n latents[:, :, ty : ty + h, tx : tx + w] = latents_reference[:, :, dy : dy + h, dx : dx + w]\n\n # set timesteps\n self.scheduler.set_timesteps(num_inference_steps)\n\n # Some schedulers like PNDM have timesteps as arrays\n # It's more optimized to move all timesteps to correct device beforehand\n timesteps_tensor = self.scheduler.timesteps.to(self.device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n for i, t in enumerate(self.progress_bar(timesteps_tensor)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n latents = 1 / 0.18215 * latents\n image = self.vae.decode(latents).sample\n\n image = (image / 2 + 0.5).clamp(0, 1)\n\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors=\"pt\").to(\n self.device\n )\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)\n )\n else:\n has_nsfw_concept = None\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/examples/community/speech_to_image_diffusion.py", "content": "import inspect\nfrom typing import Callable, List, Optional, Union\n\nimport torch\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModel,\n CLIPTokenizer,\n WhisperForConditionalGeneration,\n WhisperProcessor,\n)\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n DiffusionPipeline,\n LMSDiscreteScheduler,\n PNDMScheduler,\n UNet2DConditionModel,\n)\nfrom diffusers.pipelines.pipeline_utils import StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.utils import logging\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass SpeechToImagePipeline(DiffusionPipeline, StableDiffusionMixin):\n def __init__(\n self,\n speech_model: WhisperForConditionalGeneration,\n speech_processor: WhisperProcessor,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n ):\n super().__init__()\n\n if safety_checker is None:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n self.register_modules(\n speech_model=speech_model,\n speech_processor=speech_processor,\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n feature_extractor=feature_extractor,\n )\n\n @torch.no_grad()\n def __call__(\n self,\n audio,\n sampling_rate=16_000,\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n **kwargs,\n ):\n inputs = self.speech_processor.feature_extractor(\n audio, return_tensors=\"pt\", sampling_rate=sampling_rate\n ).input_features.to(self.device)\n predicted_ids = self.speech_model.generate(inputs, max_length=480_000)\n\n prompt = self.speech_processor.tokenizer.batch_decode(predicted_ids, skip_special_tokens=True, normalize=True)[\n 0\n ]\n\n if isinstance(prompt, str):\n batch_size = 1\n elif isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n\n if text_input_ids.shape[-1] > self.tokenizer.model_max_length:\n removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]\n text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]\n\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = text_embeddings.shape\n text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)\n text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = text_input_ids.shape[-1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = uncond_embeddings.shape[1]\n uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)\n uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n\n # get the initial random noise unless the user supplied it\n\n # Unlike in other pipelines, latents need to be generated in the target device\n # for 1-to-1 results reproducibility with the CompVis implementation.\n # However this currently doesn't work in `mps`.\n latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)\n latents_dtype = text_embeddings.dtype\n if latents is None:\n if self.device.type == \"mps\":\n # randn does not exist on mps\n latents = torch.randn(latents_shape, generator=generator, device=\"cpu\", dtype=latents_dtype).to(\n self.device\n )\n else:\n latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)\n else:\n if latents.shape != latents_shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {latents_shape}\")\n latents = latents.to(self.device)\n\n # set timesteps\n self.scheduler.set_timesteps(num_inference_steps)\n\n # Some schedulers like PNDM have timesteps as arrays\n # It's more optimized to move all timesteps to correct device beforehand\n timesteps_tensor = self.scheduler.timesteps.to(self.device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n for i, t in enumerate(self.progress_bar(timesteps_tensor)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n latents = 1 / 0.18215 * latents\n image = self.vae.decode(latents).sample\n\n image = (image / 2 + 0.5).clamp(0, 1)\n\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return image\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)\n" }, { "path": "diffusers/examples/community/stable_diffusion_comparison.py", "content": "from typing import Any, Callable, Dict, List, Optional, Union\n\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n DiffusionPipeline,\n LMSDiscreteScheduler,\n PNDMScheduler,\n StableDiffusionPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.pipelines.pipeline_utils import StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\n\n\npipe1_model_id = \"CompVis/stable-diffusion-v1-1\"\npipe2_model_id = \"CompVis/stable-diffusion-v1-2\"\npipe3_model_id = \"CompVis/stable-diffusion-v1-3\"\npipe4_model_id = \"CompVis/stable-diffusion-v1-4\"\n\n\nclass StableDiffusionComparisonPipeline(DiffusionPipeline, StableDiffusionMixin):\n r\"\"\"\n Pipeline for parallel comparison of Stable Diffusion v1-v4\n This pipeline inherits from DiffusionPipeline and depends on the use of an Auth Token for\n downloading pre-trained checkpoints from Hugging Face Hub.\n If using Hugging Face Hub, pass the Model ID for Stable Diffusion v1.4 as the previous 3 checkpoints will be loaded\n automatically.\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionMegaSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super()._init_()\n\n self.pipe1 = StableDiffusionPipeline.from_pretrained(pipe1_model_id)\n self.pipe2 = StableDiffusionPipeline.from_pretrained(pipe2_model_id)\n self.pipe3 = StableDiffusionPipeline.from_pretrained(pipe3_model_id)\n self.pipe4 = StableDiffusionPipeline(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n requires_safety_checker=requires_safety_checker,\n )\n\n self.register_modules(pipeline1=self.pipe1, pipeline2=self.pipe2, pipeline3=self.pipe3, pipeline4=self.pipe4)\n\n @property\n def layers(self) -> Dict[str, Any]:\n return {k: getattr(self, k) for k in self.config.keys() if not k.startswith(\"_\")}\n\n @torch.no_grad()\n def text2img_sd1_1(\n self,\n prompt: Union[str, List[str]],\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n **kwargs,\n ):\n return self.pipe1(\n prompt=prompt,\n height=height,\n width=width,\n num_inference_steps=num_inference_steps,\n guidance_scale=guidance_scale,\n negative_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n latents=latents,\n output_type=output_type,\n return_dict=return_dict,\n callback=callback,\n callback_steps=callback_steps,\n **kwargs,\n )\n\n @torch.no_grad()\n def text2img_sd1_2(\n self,\n prompt: Union[str, List[str]],\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n **kwargs,\n ):\n return self.pipe2(\n prompt=prompt,\n height=height,\n width=width,\n num_inference_steps=num_inference_steps,\n guidance_scale=guidance_scale,\n negative_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n latents=latents,\n output_type=output_type,\n return_dict=return_dict,\n callback=callback,\n callback_steps=callback_steps,\n **kwargs,\n )\n\n @torch.no_grad()\n def text2img_sd1_3(\n self,\n prompt: Union[str, List[str]],\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n **kwargs,\n ):\n return self.pipe3(\n prompt=prompt,\n height=height,\n width=width,\n num_inference_steps=num_inference_steps,\n guidance_scale=guidance_scale,\n negative_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n latents=latents,\n output_type=output_type,\n return_dict=return_dict,\n callback=callback,\n callback_steps=callback_steps,\n **kwargs,\n )\n\n @torch.no_grad()\n def text2img_sd1_4(\n self,\n prompt: Union[str, List[str]],\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n **kwargs,\n ):\n return self.pipe4(\n prompt=prompt,\n height=height,\n width=width,\n num_inference_steps=num_inference_steps,\n guidance_scale=guidance_scale,\n negative_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n latents=latents,\n output_type=output_type,\n return_dict=return_dict,\n callback=callback,\n callback_steps=callback_steps,\n **kwargs,\n )\n\n @torch.no_grad()\n def _call_(\n self,\n prompt: Union[str, List[str]],\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation. This function will generate 4 results as part\n of running all the 4 pipelines for SD1.1-1.4 together in a serial-processing, parallel-invocation fashion.\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n height (`int`, optional, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, optional, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, optional, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, optional, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n eta (`float`, optional, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, optional):\n A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation\n deterministic.\n latents (`torch.Tensor`, optional):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, optional, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, optional, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n\n device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n self.to(device)\n\n # Checks if the height and width are divisible by 8 or not\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` must be divisible by 8 but are {height} and {width}.\")\n\n # Get first result from Stable Diffusion Checkpoint v1.1\n res1 = self.text2img_sd1_1(\n prompt=prompt,\n height=height,\n width=width,\n num_inference_steps=num_inference_steps,\n guidance_scale=guidance_scale,\n negative_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n latents=latents,\n output_type=output_type,\n return_dict=return_dict,\n callback=callback,\n callback_steps=callback_steps,\n **kwargs,\n )\n\n # Get first result from Stable Diffusion Checkpoint v1.2\n res2 = self.text2img_sd1_2(\n prompt=prompt,\n height=height,\n width=width,\n num_inference_steps=num_inference_steps,\n guidance_scale=guidance_scale,\n negative_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n latents=latents,\n output_type=output_type,\n return_dict=return_dict,\n callback=callback,\n callback_steps=callback_steps,\n **kwargs,\n )\n\n # Get first result from Stable Diffusion Checkpoint v1.3\n res3 = self.text2img_sd1_3(\n prompt=prompt,\n height=height,\n width=width,\n num_inference_steps=num_inference_steps,\n guidance_scale=guidance_scale,\n negative_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n latents=latents,\n output_type=output_type,\n return_dict=return_dict,\n callback=callback,\n callback_steps=callback_steps,\n **kwargs,\n )\n\n # Get first result from Stable Diffusion Checkpoint v1.4\n res4 = self.text2img_sd1_4(\n prompt=prompt,\n height=height,\n width=width,\n num_inference_steps=num_inference_steps,\n guidance_scale=guidance_scale,\n negative_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n latents=latents,\n output_type=output_type,\n return_dict=return_dict,\n callback=callback,\n callback_steps=callback_steps,\n **kwargs,\n )\n\n # Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result\n return StableDiffusionPipelineOutput([res1[0], res2[0], res3[0], res4[0]])\n" }, { "path": "diffusers/examples/community/stable_diffusion_controlnet_img2img.py", "content": "# Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import AutoencoderKL, ControlNetModel, UNet2DConditionModel, logging\nfrom diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n PIL_INTERPOLATION,\n replace_example_docstring,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import numpy as np\n >>> import torch\n >>> from PIL import Image\n >>> from diffusers import ControlNetModel, UniPCMultistepScheduler\n >>> from diffusers.utils import load_image\n\n >>> input_image = load_image(\"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png\")\n\n >>> controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\", torch_dtype=torch.float16)\n\n >>> pipe_controlnet = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n controlnet=controlnet,\n safety_checker=None,\n torch_dtype=torch.float16\n )\n\n >>> pipe_controlnet.scheduler = UniPCMultistepScheduler.from_config(pipe_controlnet.scheduler.config)\n >>> pipe_controlnet.enable_xformers_memory_efficient_attention()\n >>> pipe_controlnet.enable_model_cpu_offload()\n\n # using image with edges for our canny controlnet\n >>> control_image = load_image(\n \"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/vermeer_canny_edged.png\")\n\n\n >>> result_img = pipe_controlnet(controlnet_conditioning_image=control_image,\n image=input_image,\n prompt=\"an android robot, cyberpank, digitl art masterpiece\",\n num_inference_steps=20).images[0]\n\n >>> result_img.show()\n ```\n\"\"\"\n\n\ndef prepare_image(image):\n if isinstance(image, torch.Tensor):\n # Batch single image\n if image.ndim == 3:\n image = image.unsqueeze(0)\n\n image = image.to(dtype=torch.float32)\n else:\n # preprocess image\n if isinstance(image, (PIL.Image.Image, np.ndarray)):\n image = [image]\n\n if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):\n image = [np.array(i.convert(\"RGB\"))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n elif isinstance(image, list) and isinstance(image[0], np.ndarray):\n image = np.concatenate([i[None, :] for i in image], axis=0)\n\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0\n\n return image\n\n\ndef prepare_controlnet_conditioning_image(\n controlnet_conditioning_image,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype,\n do_classifier_free_guidance,\n):\n if not isinstance(controlnet_conditioning_image, torch.Tensor):\n if isinstance(controlnet_conditioning_image, PIL.Image.Image):\n controlnet_conditioning_image = [controlnet_conditioning_image]\n\n if isinstance(controlnet_conditioning_image[0], PIL.Image.Image):\n controlnet_conditioning_image = [\n np.array(i.resize((width, height), resample=PIL_INTERPOLATION[\"lanczos\"]))[None, :]\n for i in controlnet_conditioning_image\n ]\n controlnet_conditioning_image = np.concatenate(controlnet_conditioning_image, axis=0)\n controlnet_conditioning_image = np.array(controlnet_conditioning_image).astype(np.float32) / 255.0\n controlnet_conditioning_image = controlnet_conditioning_image.transpose(0, 3, 1, 2)\n controlnet_conditioning_image = torch.from_numpy(controlnet_conditioning_image)\n elif isinstance(controlnet_conditioning_image[0], torch.Tensor):\n controlnet_conditioning_image = torch.cat(controlnet_conditioning_image, dim=0)\n\n image_batch_size = controlnet_conditioning_image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n controlnet_conditioning_image = controlnet_conditioning_image.repeat_interleave(repeat_by, dim=0)\n\n controlnet_conditioning_image = controlnet_conditioning_image.to(device=device, dtype=dtype)\n\n if do_classifier_free_guidance:\n controlnet_conditioning_image = torch.cat([controlnet_conditioning_image] * 2)\n\n return controlnet_conditioning_image\n\n\nclass StableDiffusionControlNetImg2ImgPipeline(DiffusionPipeline, StableDiffusionMixin):\n \"\"\"\n Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/\n \"\"\"\n\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n if isinstance(controlnet, (list, tuple)):\n controlnet = MultiControlNetModel(controlnet)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n controlnet=controlnet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n \"\"\"\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n prompt_embeds = prompt_embeds[0]\n\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n else:\n has_nsfw_concept = None\n return image, has_nsfw_concept\n\n def decode_latents(self, latents):\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents).sample\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_controlnet_conditioning_image(self, image, prompt, prompt_embeds):\n image_is_pil = isinstance(image, PIL.Image.Image)\n image_is_tensor = isinstance(image, torch.Tensor)\n image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)\n image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)\n\n if not image_is_pil and not image_is_tensor and not image_is_pil_list and not image_is_tensor_list:\n raise TypeError(\n \"image must be passed and be one of PIL image, torch tensor, list of PIL images, or list of torch tensors\"\n )\n\n if image_is_pil:\n image_batch_size = 1\n elif image_is_tensor:\n image_batch_size = image.shape[0]\n elif image_is_pil_list:\n image_batch_size = len(image)\n elif image_is_tensor_list:\n image_batch_size = len(image)\n else:\n raise ValueError(\"controlnet condition image is not valid\")\n\n if prompt is not None and isinstance(prompt, str):\n prompt_batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n prompt_batch_size = len(prompt)\n elif prompt_embeds is not None:\n prompt_batch_size = prompt_embeds.shape[0]\n else:\n raise ValueError(\"prompt or prompt_embeds are not valid\")\n\n if image_batch_size != 1 and image_batch_size != prompt_batch_size:\n raise ValueError(\n f\"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}\"\n )\n\n def check_inputs(\n self,\n prompt,\n image,\n controlnet_conditioning_image,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n strength=None,\n controlnet_guidance_start=None,\n controlnet_guidance_end=None,\n controlnet_conditioning_scale=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # check controlnet condition image\n\n if isinstance(self.controlnet, ControlNetModel):\n self.check_controlnet_conditioning_image(controlnet_conditioning_image, prompt, prompt_embeds)\n elif isinstance(self.controlnet, MultiControlNetModel):\n if not isinstance(controlnet_conditioning_image, list):\n raise TypeError(\"For multiple controlnets: `image` must be type `list`\")\n\n if len(controlnet_conditioning_image) != len(self.controlnet.nets):\n raise ValueError(\n \"For multiple controlnets: `image` must have the same length as the number of controlnets.\"\n )\n\n for image_ in controlnet_conditioning_image:\n self.check_controlnet_conditioning_image(image_, prompt, prompt_embeds)\n else:\n assert False\n\n # Check `controlnet_conditioning_scale`\n\n if isinstance(self.controlnet, ControlNetModel):\n if not isinstance(controlnet_conditioning_scale, float):\n raise TypeError(\"For single controlnet: `controlnet_conditioning_scale` must be type `float`.\")\n elif isinstance(self.controlnet, MultiControlNetModel):\n if isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(\n self.controlnet.nets\n ):\n raise ValueError(\n \"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have\"\n \" the same length as the number of controlnets\"\n )\n else:\n assert False\n\n if isinstance(image, torch.Tensor):\n if image.ndim != 3 and image.ndim != 4:\n raise ValueError(\"`image` must have 3 or 4 dimensions\")\n\n if image.ndim == 3:\n image_batch_size = 1\n image_channels, image_height, image_width = image.shape\n elif image.ndim == 4:\n image_batch_size, image_channels, image_height, image_width = image.shape\n else:\n assert False\n\n if image_channels != 3:\n raise ValueError(\"`image` must have 3 channels\")\n\n if image.min() < -1 or image.max() > 1:\n raise ValueError(\"`image` should be in range [-1, 1]\")\n\n if self.vae.config.latent_channels != self.unet.config.in_channels:\n raise ValueError(\n f\"The config of `pipeline.unet` expects {self.unet.config.in_channels} but received\"\n f\" latent channels: {self.vae.config.latent_channels},\"\n f\" Please verify the config of `pipeline.unet` and the `pipeline.vae`\"\n )\n\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of `strength` should in [0.0, 1.0] but is {strength}\")\n\n if controlnet_guidance_start < 0 or controlnet_guidance_start > 1:\n raise ValueError(\n f\"The value of `controlnet_guidance_start` should in [0.0, 1.0] but is {controlnet_guidance_start}\"\n )\n\n if controlnet_guidance_end < 0 or controlnet_guidance_end > 1:\n raise ValueError(\n f\"The value of `controlnet_guidance_end` should in [0.0, 1.0] but is {controlnet_guidance_end}\"\n )\n\n if controlnet_guidance_start > controlnet_guidance_end:\n raise ValueError(\n \"The value of `controlnet_guidance_start` should be less than `controlnet_guidance_end`, but got\"\n f\" `controlnet_guidance_start` {controlnet_guidance_start} >= `controlnet_guidance_end` {controlnet_guidance_end}\"\n )\n\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start:]\n\n return timesteps, num_inference_steps - t_start\n\n def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if isinstance(generator, list):\n init_latents = [\n self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = self.vae.encode(image).latent_dist.sample(generator)\n\n init_latents = self.vae.config.scaling_factor * init_latents\n\n if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n init_latents = torch.cat([init_latents], dim=0)\n\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n latents = init_latents\n\n return latents\n\n def _default_height_width(self, height, width, image):\n if isinstance(image, list):\n image = image[0]\n\n if height is None:\n if isinstance(image, PIL.Image.Image):\n height = image.height\n elif isinstance(image, torch.Tensor):\n height = image.shape[3]\n\n height = (height // 8) * 8 # round down to nearest multiple of 8\n\n if width is None:\n if isinstance(image, PIL.Image.Image):\n width = image.width\n elif isinstance(image, torch.Tensor):\n width = image.shape[2]\n\n width = (width // 8) * 8 # round down to nearest multiple of 8\n\n return height, width\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: Union[torch.Tensor, PIL.Image.Image] = None,\n controlnet_conditioning_image: Union[\n torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]\n ] = None,\n strength: float = 0.8,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: Union[float, List[float]] = 1.0,\n controlnet_guidance_start: float = 0.0,\n controlnet_guidance_end: float = 1.0,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n image (`torch.Tensor` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will\n be masked out with `mask_image` and repainted according to `prompt`.\n controlnet_conditioning_image (`torch.Tensor`, `PIL.Image.Image`, `List[torch.Tensor]` or `List[PIL.Image.Image]`):\n The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If\n the type is specified as `torch.Tensor`, it is passed to ControlNet as is. PIL.Image.Image` can\n also be accepted as an image. The control image is automatically resized to fit the output image.\n strength (`float`, *optional*):\n Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`\n will be used as a starting point, adding more noise to it the larger the `strength`. The number of\n denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will\n be maximum and the denoising process will run for the full number of iterations specified in\n `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0):\n The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added\n to the residual in the original unet.\n controlnet_guidance_start ('float', *optional*, defaults to 0.0):\n The percentage of total steps the controlnet starts applying. Must be between 0 and 1.\n controlnet_guidance_end ('float', *optional*, defaults to 1.0):\n The percentage of total steps the controlnet ends applying. Must be between 0 and 1. Must be greater\n than `controlnet_guidance_start`.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n # 0. Default height and width to unet\n height, width = self._default_height_width(height, width, controlnet_conditioning_image)\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n image,\n controlnet_conditioning_image,\n height,\n width,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n strength,\n controlnet_guidance_start,\n controlnet_guidance_end,\n controlnet_conditioning_scale,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n if isinstance(self.controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):\n controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(self.controlnet.nets)\n\n # 3. Encode input prompt\n prompt_embeds = self._encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n\n # 4. Prepare image, and controlnet_conditioning_image\n image = prepare_image(image)\n\n # condition image(s)\n if isinstance(self.controlnet, ControlNetModel):\n controlnet_conditioning_image = prepare_controlnet_conditioning_image(\n controlnet_conditioning_image=controlnet_conditioning_image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=self.controlnet.dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n )\n elif isinstance(self.controlnet, MultiControlNetModel):\n controlnet_conditioning_images = []\n\n for image_ in controlnet_conditioning_image:\n image_ = prepare_controlnet_conditioning_image(\n controlnet_conditioning_image=image_,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=self.controlnet.dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n )\n\n controlnet_conditioning_images.append(image_)\n\n controlnet_conditioning_image = controlnet_conditioning_images\n else:\n assert False\n\n # 5. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n\n # 6. Prepare latent variables\n if latents is None:\n latents = self.prepare_latents(\n image,\n latent_timestep,\n batch_size,\n num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n generator,\n )\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # compute the percentage of total steps we are at\n current_sampling_percent = i / len(timesteps)\n\n if (\n current_sampling_percent < controlnet_guidance_start\n or current_sampling_percent > controlnet_guidance_end\n ):\n # do not apply the controlnet\n down_block_res_samples = None\n mid_block_res_sample = None\n else:\n # apply the controlnet\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n controlnet_cond=controlnet_conditioning_image,\n conditioning_scale=controlnet_conditioning_scale,\n return_dict=False,\n )\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # If we do sequential model offloading, let's offload unet and controlnet\n # manually for max memory savings\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.unet.to(\"cpu\")\n self.controlnet.to(\"cpu\")\n torch.cuda.empty_cache()\n\n if output_type == \"latent\":\n image = latents\n has_nsfw_concept = None\n elif output_type == \"pil\":\n # 8. Post-processing\n image = self.decode_latents(latents)\n\n # 9. Run safety checker\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # 10. Convert to PIL\n image = self.numpy_to_pil(image)\n else:\n # 8. Post-processing\n image = self.decode_latents(latents)\n\n # 9. Run safety checker\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # Offload last model to CPU\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/examples/community/stable_diffusion_controlnet_inpaint.py", "content": "# Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import AutoencoderKL, ControlNetModel, UNet2DConditionModel, logging\nfrom diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n PIL_INTERPOLATION,\n replace_example_docstring,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import numpy as np\n >>> import torch\n >>> from PIL import Image\n >>> from stable_diffusion_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline\n\n >>> from transformers import AutoImageProcessor, UperNetForSemanticSegmentation\n >>> from diffusers import ControlNetModel, UniPCMultistepScheduler\n >>> from diffusers.utils import load_image\n\n >>> def ade_palette():\n return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],\n [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],\n [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],\n [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],\n [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],\n [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],\n [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],\n [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],\n [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],\n [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],\n [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],\n [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],\n [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],\n [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],\n [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],\n [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],\n [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],\n [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],\n [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],\n [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],\n [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],\n [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],\n [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],\n [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],\n [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],\n [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],\n [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],\n [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],\n [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],\n [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],\n [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],\n [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],\n [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],\n [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],\n [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],\n [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],\n [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],\n [102, 255, 0], [92, 0, 255]]\n\n >>> image_processor = AutoImageProcessor.from_pretrained(\"openmmlab/upernet-convnext-small\")\n >>> image_segmentor = UperNetForSemanticSegmentation.from_pretrained(\"openmmlab/upernet-convnext-small\")\n\n >>> controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-seg\", torch_dtype=torch.float16)\n\n >>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16\n )\n\n >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n >>> pipe.enable_xformers_memory_efficient_attention()\n >>> pipe.enable_model_cpu_offload()\n\n >>> def image_to_seg(image):\n pixel_values = image_processor(image, return_tensors=\"pt\").pixel_values\n with torch.no_grad():\n outputs = image_segmentor(pixel_values)\n seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]\n color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3\n palette = np.array(ade_palette())\n for label, color in enumerate(palette):\n color_seg[seg == label, :] = color\n color_seg = color_seg.astype(np.uint8)\n seg_image = Image.fromarray(color_seg)\n return seg_image\n\n >>> image = load_image(\n \"https://github.com/CompVis/latent-diffusion/raw/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\n )\n\n >>> mask_image = load_image(\n \"https://github.com/CompVis/latent-diffusion/raw/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\n )\n\n >>> controlnet_conditioning_image = image_to_seg(image)\n\n >>> image = pipe(\n \"Face of a yellow cat, high resolution, sitting on a park bench\",\n image,\n mask_image,\n controlnet_conditioning_image,\n num_inference_steps=20,\n ).images[0]\n\n >>> image.save(\"out.png\")\n ```\n\"\"\"\n\n\ndef prepare_image(image):\n if isinstance(image, torch.Tensor):\n # Batch single image\n if image.ndim == 3:\n image = image.unsqueeze(0)\n\n image = image.to(dtype=torch.float32)\n else:\n # preprocess image\n if isinstance(image, (PIL.Image.Image, np.ndarray)):\n image = [image]\n\n if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):\n image = [np.array(i.convert(\"RGB\"))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n elif isinstance(image, list) and isinstance(image[0], np.ndarray):\n image = np.concatenate([i[None, :] for i in image], axis=0)\n\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0\n\n return image\n\n\ndef prepare_mask_image(mask_image):\n if isinstance(mask_image, torch.Tensor):\n if mask_image.ndim == 2:\n # Batch and add channel dim for single mask\n mask_image = mask_image.unsqueeze(0).unsqueeze(0)\n elif mask_image.ndim == 3 and mask_image.shape[0] == 1:\n # Single mask, the 0'th dimension is considered to be\n # the existing batch size of 1\n mask_image = mask_image.unsqueeze(0)\n elif mask_image.ndim == 3 and mask_image.shape[0] != 1:\n # Batch of mask, the 0'th dimension is considered to be\n # the batching dimension\n mask_image = mask_image.unsqueeze(1)\n\n # Binarize mask\n mask_image[mask_image < 0.5] = 0\n mask_image[mask_image >= 0.5] = 1\n else:\n # preprocess mask\n if isinstance(mask_image, (PIL.Image.Image, np.ndarray)):\n mask_image = [mask_image]\n\n if isinstance(mask_image, list) and isinstance(mask_image[0], PIL.Image.Image):\n mask_image = np.concatenate([np.array(m.convert(\"L\"))[None, None, :] for m in mask_image], axis=0)\n mask_image = mask_image.astype(np.float32) / 255.0\n elif isinstance(mask_image, list) and isinstance(mask_image[0], np.ndarray):\n mask_image = np.concatenate([m[None, None, :] for m in mask_image], axis=0)\n\n mask_image[mask_image < 0.5] = 0\n mask_image[mask_image >= 0.5] = 1\n mask_image = torch.from_numpy(mask_image)\n\n return mask_image\n\n\ndef prepare_controlnet_conditioning_image(\n controlnet_conditioning_image,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype,\n do_classifier_free_guidance,\n):\n if not isinstance(controlnet_conditioning_image, torch.Tensor):\n if isinstance(controlnet_conditioning_image, PIL.Image.Image):\n controlnet_conditioning_image = [controlnet_conditioning_image]\n\n if isinstance(controlnet_conditioning_image[0], PIL.Image.Image):\n controlnet_conditioning_image = [\n np.array(i.resize((width, height), resample=PIL_INTERPOLATION[\"lanczos\"]))[None, :]\n for i in controlnet_conditioning_image\n ]\n controlnet_conditioning_image = np.concatenate(controlnet_conditioning_image, axis=0)\n controlnet_conditioning_image = np.array(controlnet_conditioning_image).astype(np.float32) / 255.0\n controlnet_conditioning_image = controlnet_conditioning_image.transpose(0, 3, 1, 2)\n controlnet_conditioning_image = torch.from_numpy(controlnet_conditioning_image)\n elif isinstance(controlnet_conditioning_image[0], torch.Tensor):\n controlnet_conditioning_image = torch.cat(controlnet_conditioning_image, dim=0)\n\n image_batch_size = controlnet_conditioning_image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n controlnet_conditioning_image = controlnet_conditioning_image.repeat_interleave(repeat_by, dim=0)\n\n controlnet_conditioning_image = controlnet_conditioning_image.to(device=device, dtype=dtype)\n\n if do_classifier_free_guidance:\n controlnet_conditioning_image = torch.cat([controlnet_conditioning_image] * 2)\n\n return controlnet_conditioning_image\n\n\nclass StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, StableDiffusionMixin):\n \"\"\"\n Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/\n \"\"\"\n\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n if isinstance(controlnet, (list, tuple)):\n controlnet = MultiControlNetModel(controlnet)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n controlnet=controlnet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead.\n Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n \"\"\"\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n prompt_embeds = prompt_embeds[0]\n\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n else:\n has_nsfw_concept = None\n return image, has_nsfw_concept\n\n def decode_latents(self, latents):\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents).sample\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_controlnet_conditioning_image(self, image, prompt, prompt_embeds):\n image_is_pil = isinstance(image, PIL.Image.Image)\n image_is_tensor = isinstance(image, torch.Tensor)\n image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)\n image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)\n\n if not image_is_pil and not image_is_tensor and not image_is_pil_list and not image_is_tensor_list:\n raise TypeError(\n \"image must be passed and be one of PIL image, torch tensor, list of PIL images, or list of torch tensors\"\n )\n\n if image_is_pil:\n image_batch_size = 1\n elif image_is_tensor:\n image_batch_size = image.shape[0]\n elif image_is_pil_list:\n image_batch_size = len(image)\n elif image_is_tensor_list:\n image_batch_size = len(image)\n else:\n raise ValueError(\"controlnet condition image is not valid\")\n\n if prompt is not None and isinstance(prompt, str):\n prompt_batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n prompt_batch_size = len(prompt)\n elif prompt_embeds is not None:\n prompt_batch_size = prompt_embeds.shape[0]\n else:\n raise ValueError(\"prompt or prompt_embeds are not valid\")\n\n if image_batch_size != 1 and image_batch_size != prompt_batch_size:\n raise ValueError(\n f\"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}\"\n )\n\n def check_inputs(\n self,\n prompt,\n image,\n mask_image,\n controlnet_conditioning_image,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n controlnet_conditioning_scale=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # check controlnet condition image\n if isinstance(self.controlnet, ControlNetModel):\n self.check_controlnet_conditioning_image(controlnet_conditioning_image, prompt, prompt_embeds)\n elif isinstance(self.controlnet, MultiControlNetModel):\n if not isinstance(controlnet_conditioning_image, list):\n raise TypeError(\"For multiple controlnets: `image` must be type `list`\")\n if len(controlnet_conditioning_image) != len(self.controlnet.nets):\n raise ValueError(\n \"For multiple controlnets: `image` must have the same length as the number of controlnets.\"\n )\n for image_ in controlnet_conditioning_image:\n self.check_controlnet_conditioning_image(image_, prompt, prompt_embeds)\n else:\n assert False\n\n # Check `controlnet_conditioning_scale`\n if isinstance(self.controlnet, ControlNetModel):\n if not isinstance(controlnet_conditioning_scale, float):\n raise TypeError(\"For single controlnet: `controlnet_conditioning_scale` must be type `float`.\")\n elif isinstance(self.controlnet, MultiControlNetModel):\n if isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(\n self.controlnet.nets\n ):\n raise ValueError(\n \"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have\"\n \" the same length as the number of controlnets\"\n )\n else:\n assert False\n\n if isinstance(image, torch.Tensor) and not isinstance(mask_image, torch.Tensor):\n raise TypeError(\"if `image` is a tensor, `mask_image` must also be a tensor\")\n\n if isinstance(image, PIL.Image.Image) and not isinstance(mask_image, PIL.Image.Image):\n raise TypeError(\"if `image` is a PIL image, `mask_image` must also be a PIL image\")\n\n if isinstance(image, torch.Tensor):\n if image.ndim != 3 and image.ndim != 4:\n raise ValueError(\"`image` must have 3 or 4 dimensions\")\n\n if mask_image.ndim != 2 and mask_image.ndim != 3 and mask_image.ndim != 4:\n raise ValueError(\"`mask_image` must have 2, 3, or 4 dimensions\")\n\n if image.ndim == 3:\n image_batch_size = 1\n image_channels, image_height, image_width = image.shape\n elif image.ndim == 4:\n image_batch_size, image_channels, image_height, image_width = image.shape\n else:\n assert False\n\n if mask_image.ndim == 2:\n mask_image_batch_size = 1\n mask_image_channels = 1\n mask_image_height, mask_image_width = mask_image.shape\n elif mask_image.ndim == 3:\n mask_image_channels = 1\n mask_image_batch_size, mask_image_height, mask_image_width = mask_image.shape\n elif mask_image.ndim == 4:\n mask_image_batch_size, mask_image_channels, mask_image_height, mask_image_width = mask_image.shape\n\n if image_channels != 3:\n raise ValueError(\"`image` must have 3 channels\")\n\n if mask_image_channels != 1:\n raise ValueError(\"`mask_image` must have 1 channel\")\n\n if image_batch_size != mask_image_batch_size:\n raise ValueError(\"`image` and `mask_image` mush have the same batch sizes\")\n\n if image_height != mask_image_height or image_width != mask_image_width:\n raise ValueError(\"`image` and `mask_image` must have the same height and width dimensions\")\n\n if image.min() < -1 or image.max() > 1:\n raise ValueError(\"`image` should be in range [-1, 1]\")\n\n if mask_image.min() < 0 or mask_image.max() > 1:\n raise ValueError(\"`mask_image` should be in range [0, 1]\")\n else:\n mask_image_channels = 1\n image_channels = 3\n\n single_image_latent_channels = self.vae.config.latent_channels\n\n total_latent_channels = single_image_latent_channels * 2 + mask_image_channels\n\n if total_latent_channels != self.unet.config.in_channels:\n raise ValueError(\n f\"The config of `pipeline.unet` expects {self.unet.config.in_channels} but received\"\n f\" non inpainting latent channels: {single_image_latent_channels},\"\n f\" mask channels: {mask_image_channels}, and masked image channels: {single_image_latent_channels}.\"\n f\" Please verify the config of `pipeline.unet` and the `mask_image` and `image` inputs.\"\n )\n\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n\n return latents\n\n def prepare_mask_latents(self, mask_image, batch_size, height, width, dtype, device, do_classifier_free_guidance):\n # resize the mask to latents shape as we concatenate the mask to the latents\n # we do that before converting to dtype to avoid breaking in case we're using cpu_offload\n # and half precision\n mask_image = F.interpolate(mask_image, size=(height // self.vae_scale_factor, width // self.vae_scale_factor))\n mask_image = mask_image.to(device=device, dtype=dtype)\n\n # duplicate mask for each generation per prompt, using mps friendly method\n if mask_image.shape[0] < batch_size:\n if not batch_size % mask_image.shape[0] == 0:\n raise ValueError(\n \"The passed mask and the required batch size don't match. Masks are supposed to be duplicated to\"\n f\" a total batch size of {batch_size}, but {mask_image.shape[0]} masks were passed. Make sure the number\"\n \" of masks that you pass is divisible by the total requested batch size.\"\n )\n mask_image = mask_image.repeat(batch_size // mask_image.shape[0], 1, 1, 1)\n\n mask_image = torch.cat([mask_image] * 2) if do_classifier_free_guidance else mask_image\n\n mask_image_latents = mask_image\n\n return mask_image_latents\n\n def prepare_masked_image_latents(\n self, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance\n ):\n masked_image = masked_image.to(device=device, dtype=dtype)\n\n # encode the mask image into latents space so we can concatenate it to the latents\n if isinstance(generator, list):\n masked_image_latents = [\n self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i])\n for i in range(batch_size)\n ]\n masked_image_latents = torch.cat(masked_image_latents, dim=0)\n else:\n masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)\n masked_image_latents = self.vae.config.scaling_factor * masked_image_latents\n\n # duplicate masked_image_latents for each generation per prompt, using mps friendly method\n if masked_image_latents.shape[0] < batch_size:\n if not batch_size % masked_image_latents.shape[0] == 0:\n raise ValueError(\n \"The passed images and the required batch size don't match. Images are supposed to be duplicated\"\n f\" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed.\"\n \" Make sure the number of images that you pass is divisible by the total requested batch size.\"\n )\n masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)\n\n masked_image_latents = (\n torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents\n )\n\n # aligning device to prevent device errors when concating it with the latent model input\n masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)\n return masked_image_latents\n\n def _default_height_width(self, height, width, image):\n if isinstance(image, list):\n image = image[0]\n\n if height is None:\n if isinstance(image, PIL.Image.Image):\n height = image.height\n elif isinstance(image, torch.Tensor):\n height = image.shape[3]\n\n height = (height // 8) * 8 # round down to nearest multiple of 8\n\n if width is None:\n if isinstance(image, PIL.Image.Image):\n width = image.width\n elif isinstance(image, torch.Tensor):\n width = image.shape[2]\n\n width = (width // 8) * 8 # round down to nearest multiple of 8\n\n return height, width\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: Union[torch.Tensor, PIL.Image.Image] = None,\n mask_image: Union[torch.Tensor, PIL.Image.Image] = None,\n controlnet_conditioning_image: Union[\n torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]\n ] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: Union[float, List[float]] = 1.0,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n image (`torch.Tensor` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will\n be masked out with `mask_image` and repainted according to `prompt`.\n mask_image (`torch.Tensor` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be\n repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted\n to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)\n instead of 3, so the expected shape would be `(B, H, W, 1)`.\n controlnet_conditioning_image (`torch.Tensor`, `PIL.Image.Image`, `List[torch.Tensor]` or `List[PIL.Image.Image]`):\n The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If\n the type is specified as `torch.Tensor`, it is passed to ControlNet as is. PIL.Image.Image` can\n also be accepted as an image. The control image is automatically resized to fit the output image.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead.\n Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0):\n The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added\n to the residual in the original unet.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n # 0. Default height and width to unet\n height, width = self._default_height_width(height, width, controlnet_conditioning_image)\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n image,\n mask_image,\n controlnet_conditioning_image,\n height,\n width,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n controlnet_conditioning_scale,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n if isinstance(self.controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):\n controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(self.controlnet.nets)\n\n # 3. Encode input prompt\n prompt_embeds = self._encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n\n # 4. Prepare mask, image, and controlnet_conditioning_image\n image = prepare_image(image)\n\n mask_image = prepare_mask_image(mask_image)\n\n # condition image(s)\n if isinstance(self.controlnet, ControlNetModel):\n controlnet_conditioning_image = prepare_controlnet_conditioning_image(\n controlnet_conditioning_image=controlnet_conditioning_image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=self.controlnet.dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n )\n elif isinstance(self.controlnet, MultiControlNetModel):\n controlnet_conditioning_images = []\n\n for image_ in controlnet_conditioning_image:\n image_ = prepare_controlnet_conditioning_image(\n controlnet_conditioning_image=image_,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=self.controlnet.dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n )\n controlnet_conditioning_images.append(image_)\n\n controlnet_conditioning_image = controlnet_conditioning_images\n else:\n assert False\n\n masked_image = image * (mask_image < 0.5)\n\n # 5. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 6. Prepare latent variables\n num_channels_latents = self.vae.config.latent_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n mask_image_latents = self.prepare_mask_latents(\n mask_image,\n batch_size * num_images_per_prompt,\n height,\n width,\n prompt_embeds.dtype,\n device,\n do_classifier_free_guidance,\n )\n\n masked_image_latents = self.prepare_masked_image_latents(\n masked_image,\n batch_size * num_images_per_prompt,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n do_classifier_free_guidance,\n )\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n non_inpainting_latent_model_input = (\n torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n )\n\n non_inpainting_latent_model_input = self.scheduler.scale_model_input(\n non_inpainting_latent_model_input, t\n )\n\n inpainting_latent_model_input = torch.cat(\n [non_inpainting_latent_model_input, mask_image_latents, masked_image_latents], dim=1\n )\n\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n non_inpainting_latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n controlnet_cond=controlnet_conditioning_image,\n conditioning_scale=controlnet_conditioning_scale,\n return_dict=False,\n )\n\n # predict the noise residual\n noise_pred = self.unet(\n inpainting_latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # If we do sequential model offloading, let's offload unet and controlnet\n # manually for max memory savings\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.unet.to(\"cpu\")\n self.controlnet.to(\"cpu\")\n torch.cuda.empty_cache()\n\n if output_type == \"latent\":\n image = latents\n has_nsfw_concept = None\n elif output_type == \"pil\":\n # 8. Post-processing\n image = self.decode_latents(latents)\n\n # 9. Run safety checker\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # 10. Convert to PIL\n image = self.numpy_to_pil(image)\n else:\n # 8. Post-processing\n image = self.decode_latents(latents)\n\n # 9. Run safety checker\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # Offload last model to CPU\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/examples/community/stable_diffusion_controlnet_inpaint_img2img.py", "content": "# Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import AutoencoderKL, ControlNetModel, UNet2DConditionModel, logging\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n PIL_INTERPOLATION,\n replace_example_docstring,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import numpy as np\n >>> import torch\n >>> from PIL import Image\n >>> from stable_diffusion_controlnet_inpaint_img2img import StableDiffusionControlNetInpaintImg2ImgPipeline\n\n >>> from transformers import AutoImageProcessor, UperNetForSemanticSegmentation\n >>> from diffusers import ControlNetModel, UniPCMultistepScheduler\n >>> from diffusers.utils import load_image\n\n >>> def ade_palette():\n return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],\n [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],\n [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],\n [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],\n [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],\n [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],\n [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],\n [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],\n [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],\n [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],\n [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],\n [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],\n [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],\n [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],\n [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],\n [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],\n [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],\n [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],\n [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],\n [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],\n [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],\n [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],\n [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],\n [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],\n [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],\n [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],\n [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],\n [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],\n [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],\n [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],\n [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],\n [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],\n [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],\n [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],\n [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],\n [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],\n [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],\n [102, 255, 0], [92, 0, 255]]\n\n >>> image_processor = AutoImageProcessor.from_pretrained(\"openmmlab/upernet-convnext-small\")\n >>> image_segmentor = UperNetForSemanticSegmentation.from_pretrained(\"openmmlab/upernet-convnext-small\")\n\n >>> controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-seg\", torch_dtype=torch.float16)\n\n >>> pipe = StableDiffusionControlNetInpaintImg2ImgPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16\n )\n\n >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n >>> pipe.enable_xformers_memory_efficient_attention()\n >>> pipe.enable_model_cpu_offload()\n\n >>> def image_to_seg(image):\n pixel_values = image_processor(image, return_tensors=\"pt\").pixel_values\n with torch.no_grad():\n outputs = image_segmentor(pixel_values)\n seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]\n color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3\n palette = np.array(ade_palette())\n for label, color in enumerate(palette):\n color_seg[seg == label, :] = color\n color_seg = color_seg.astype(np.uint8)\n seg_image = Image.fromarray(color_seg)\n return seg_image\n\n >>> image = load_image(\n \"https://github.com/CompVis/latent-diffusion/raw/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\n )\n\n >>> mask_image = load_image(\n \"https://github.com/CompVis/latent-diffusion/raw/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\n )\n\n >>> controlnet_conditioning_image = image_to_seg(image)\n\n >>> image = pipe(\n \"Face of a yellow cat, high resolution, sitting on a park bench\",\n image,\n mask_image,\n controlnet_conditioning_image,\n num_inference_steps=20,\n ).images[0]\n\n >>> image.save(\"out.png\")\n ```\n\"\"\"\n\n\ndef prepare_image(image):\n if isinstance(image, torch.Tensor):\n # Batch single image\n if image.ndim == 3:\n image = image.unsqueeze(0)\n\n image = image.to(dtype=torch.float32)\n else:\n # preprocess image\n if isinstance(image, (PIL.Image.Image, np.ndarray)):\n image = [image]\n\n if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):\n image = [np.array(i.convert(\"RGB\"))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n elif isinstance(image, list) and isinstance(image[0], np.ndarray):\n image = np.concatenate([i[None, :] for i in image], axis=0)\n\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0\n\n return image\n\n\ndef prepare_mask_image(mask_image):\n if isinstance(mask_image, torch.Tensor):\n if mask_image.ndim == 2:\n # Batch and add channel dim for single mask\n mask_image = mask_image.unsqueeze(0).unsqueeze(0)\n elif mask_image.ndim == 3 and mask_image.shape[0] == 1:\n # Single mask, the 0'th dimension is considered to be\n # the existing batch size of 1\n mask_image = mask_image.unsqueeze(0)\n elif mask_image.ndim == 3 and mask_image.shape[0] != 1:\n # Batch of mask, the 0'th dimension is considered to be\n # the batching dimension\n mask_image = mask_image.unsqueeze(1)\n\n # Binarize mask\n mask_image[mask_image < 0.5] = 0\n mask_image[mask_image >= 0.5] = 1\n else:\n # preprocess mask\n if isinstance(mask_image, (PIL.Image.Image, np.ndarray)):\n mask_image = [mask_image]\n\n if isinstance(mask_image, list) and isinstance(mask_image[0], PIL.Image.Image):\n mask_image = np.concatenate([np.array(m.convert(\"L\"))[None, None, :] for m in mask_image], axis=0)\n mask_image = mask_image.astype(np.float32) / 255.0\n elif isinstance(mask_image, list) and isinstance(mask_image[0], np.ndarray):\n mask_image = np.concatenate([m[None, None, :] for m in mask_image], axis=0)\n\n mask_image[mask_image < 0.5] = 0\n mask_image[mask_image >= 0.5] = 1\n mask_image = torch.from_numpy(mask_image)\n\n return mask_image\n\n\ndef prepare_controlnet_conditioning_image(\n controlnet_conditioning_image, width, height, batch_size, num_images_per_prompt, device, dtype\n):\n if not isinstance(controlnet_conditioning_image, torch.Tensor):\n if isinstance(controlnet_conditioning_image, PIL.Image.Image):\n controlnet_conditioning_image = [controlnet_conditioning_image]\n\n if isinstance(controlnet_conditioning_image[0], PIL.Image.Image):\n controlnet_conditioning_image = [\n np.array(i.resize((width, height), resample=PIL_INTERPOLATION[\"lanczos\"]))[None, :]\n for i in controlnet_conditioning_image\n ]\n controlnet_conditioning_image = np.concatenate(controlnet_conditioning_image, axis=0)\n controlnet_conditioning_image = np.array(controlnet_conditioning_image).astype(np.float32) / 255.0\n controlnet_conditioning_image = controlnet_conditioning_image.transpose(0, 3, 1, 2)\n controlnet_conditioning_image = torch.from_numpy(controlnet_conditioning_image)\n elif isinstance(controlnet_conditioning_image[0], torch.Tensor):\n controlnet_conditioning_image = torch.cat(controlnet_conditioning_image, dim=0)\n\n image_batch_size = controlnet_conditioning_image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n controlnet_conditioning_image = controlnet_conditioning_image.repeat_interleave(repeat_by, dim=0)\n\n controlnet_conditioning_image = controlnet_conditioning_image.to(device=device, dtype=dtype)\n\n return controlnet_conditioning_image\n\n\nclass StableDiffusionControlNetInpaintImg2ImgPipeline(DiffusionPipeline, StableDiffusionMixin):\n \"\"\"\n Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/\n \"\"\"\n\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n controlnet: ControlNetModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n controlnet=controlnet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead.\n Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n \"\"\"\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n prompt_embeds = prompt_embeds[0]\n\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n else:\n has_nsfw_concept = None\n return image, has_nsfw_concept\n\n def decode_latents(self, latents):\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents).sample\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n image,\n mask_image,\n controlnet_conditioning_image,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n strength=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n controlnet_cond_image_is_pil = isinstance(controlnet_conditioning_image, PIL.Image.Image)\n controlnet_cond_image_is_tensor = isinstance(controlnet_conditioning_image, torch.Tensor)\n controlnet_cond_image_is_pil_list = isinstance(controlnet_conditioning_image, list) and isinstance(\n controlnet_conditioning_image[0], PIL.Image.Image\n )\n controlnet_cond_image_is_tensor_list = isinstance(controlnet_conditioning_image, list) and isinstance(\n controlnet_conditioning_image[0], torch.Tensor\n )\n\n if (\n not controlnet_cond_image_is_pil\n and not controlnet_cond_image_is_tensor\n and not controlnet_cond_image_is_pil_list\n and not controlnet_cond_image_is_tensor_list\n ):\n raise TypeError(\n \"image must be passed and be one of PIL image, torch tensor, list of PIL images, or list of torch tensors\"\n )\n\n if controlnet_cond_image_is_pil:\n controlnet_cond_image_batch_size = 1\n elif controlnet_cond_image_is_tensor:\n controlnet_cond_image_batch_size = controlnet_conditioning_image.shape[0]\n elif controlnet_cond_image_is_pil_list:\n controlnet_cond_image_batch_size = len(controlnet_conditioning_image)\n elif controlnet_cond_image_is_tensor_list:\n controlnet_cond_image_batch_size = len(controlnet_conditioning_image)\n\n if prompt is not None and isinstance(prompt, str):\n prompt_batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n prompt_batch_size = len(prompt)\n elif prompt_embeds is not None:\n prompt_batch_size = prompt_embeds.shape[0]\n\n if controlnet_cond_image_batch_size != 1 and controlnet_cond_image_batch_size != prompt_batch_size:\n raise ValueError(\n f\"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {controlnet_cond_image_batch_size}, prompt batch size: {prompt_batch_size}\"\n )\n\n if isinstance(image, torch.Tensor) and not isinstance(mask_image, torch.Tensor):\n raise TypeError(\"if `image` is a tensor, `mask_image` must also be a tensor\")\n\n if isinstance(image, PIL.Image.Image) and not isinstance(mask_image, PIL.Image.Image):\n raise TypeError(\"if `image` is a PIL image, `mask_image` must also be a PIL image\")\n\n if isinstance(image, torch.Tensor):\n if image.ndim != 3 and image.ndim != 4:\n raise ValueError(\"`image` must have 3 or 4 dimensions\")\n\n if mask_image.ndim != 2 and mask_image.ndim != 3 and mask_image.ndim != 4:\n raise ValueError(\"`mask_image` must have 2, 3, or 4 dimensions\")\n\n if image.ndim == 3:\n image_batch_size = 1\n image_channels, image_height, image_width = image.shape\n elif image.ndim == 4:\n image_batch_size, image_channels, image_height, image_width = image.shape\n\n if mask_image.ndim == 2:\n mask_image_batch_size = 1\n mask_image_channels = 1\n mask_image_height, mask_image_width = mask_image.shape\n elif mask_image.ndim == 3:\n mask_image_channels = 1\n mask_image_batch_size, mask_image_height, mask_image_width = mask_image.shape\n elif mask_image.ndim == 4:\n mask_image_batch_size, mask_image_channels, mask_image_height, mask_image_width = mask_image.shape\n\n if image_channels != 3:\n raise ValueError(\"`image` must have 3 channels\")\n\n if mask_image_channels != 1:\n raise ValueError(\"`mask_image` must have 1 channel\")\n\n if image_batch_size != mask_image_batch_size:\n raise ValueError(\"`image` and `mask_image` mush have the same batch sizes\")\n\n if image_height != mask_image_height or image_width != mask_image_width:\n raise ValueError(\"`image` and `mask_image` must have the same height and width dimensions\")\n\n if image.min() < -1 or image.max() > 1:\n raise ValueError(\"`image` should be in range [-1, 1]\")\n\n if mask_image.min() < 0 or mask_image.max() > 1:\n raise ValueError(\"`mask_image` should be in range [0, 1]\")\n else:\n mask_image_channels = 1\n image_channels = 3\n\n single_image_latent_channels = self.vae.config.latent_channels\n\n total_latent_channels = single_image_latent_channels * 2 + mask_image_channels\n\n if total_latent_channels != self.unet.config.in_channels:\n raise ValueError(\n f\"The config of `pipeline.unet` expects {self.unet.config.in_channels} but received\"\n f\" non inpainting latent channels: {single_image_latent_channels},\"\n f\" mask channels: {mask_image_channels}, and masked image channels: {single_image_latent_channels}.\"\n f\" Please verify the config of `pipeline.unet` and the `mask_image` and `image` inputs.\"\n )\n\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start:]\n\n return timesteps, num_inference_steps - t_start\n\n def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if isinstance(generator, list):\n init_latents = [\n self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = self.vae.encode(image).latent_dist.sample(generator)\n\n init_latents = self.vae.config.scaling_factor * init_latents\n\n if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n init_latents = torch.cat([init_latents], dim=0)\n\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n latents = init_latents\n\n return latents\n\n def prepare_mask_latents(self, mask_image, batch_size, height, width, dtype, device, do_classifier_free_guidance):\n # resize the mask to latents shape as we concatenate the mask to the latents\n # we do that before converting to dtype to avoid breaking in case we're using cpu_offload\n # and half precision\n mask_image = F.interpolate(mask_image, size=(height // self.vae_scale_factor, width // self.vae_scale_factor))\n mask_image = mask_image.to(device=device, dtype=dtype)\n\n # duplicate mask for each generation per prompt, using mps friendly method\n if mask_image.shape[0] < batch_size:\n if not batch_size % mask_image.shape[0] == 0:\n raise ValueError(\n \"The passed mask and the required batch size don't match. Masks are supposed to be duplicated to\"\n f\" a total batch size of {batch_size}, but {mask_image.shape[0]} masks were passed. Make sure the number\"\n \" of masks that you pass is divisible by the total requested batch size.\"\n )\n mask_image = mask_image.repeat(batch_size // mask_image.shape[0], 1, 1, 1)\n\n mask_image = torch.cat([mask_image] * 2) if do_classifier_free_guidance else mask_image\n\n mask_image_latents = mask_image\n\n return mask_image_latents\n\n def prepare_masked_image_latents(\n self, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance\n ):\n masked_image = masked_image.to(device=device, dtype=dtype)\n\n # encode the mask image into latents space so we can concatenate it to the latents\n if isinstance(generator, list):\n masked_image_latents = [\n self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i])\n for i in range(batch_size)\n ]\n masked_image_latents = torch.cat(masked_image_latents, dim=0)\n else:\n masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)\n masked_image_latents = self.vae.config.scaling_factor * masked_image_latents\n\n # duplicate masked_image_latents for each generation per prompt, using mps friendly method\n if masked_image_latents.shape[0] < batch_size:\n if not batch_size % masked_image_latents.shape[0] == 0:\n raise ValueError(\n \"The passed images and the required batch size don't match. Images are supposed to be duplicated\"\n f\" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed.\"\n \" Make sure the number of images that you pass is divisible by the total requested batch size.\"\n )\n masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)\n\n masked_image_latents = (\n torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents\n )\n\n # aligning device to prevent device errors when concating it with the latent model input\n masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)\n return masked_image_latents\n\n def _default_height_width(self, height, width, image):\n if isinstance(image, list):\n image = image[0]\n\n if height is None:\n if isinstance(image, PIL.Image.Image):\n height = image.height\n elif isinstance(image, torch.Tensor):\n height = image.shape[3]\n\n height = (height // 8) * 8 # round down to nearest multiple of 8\n\n if width is None:\n if isinstance(image, PIL.Image.Image):\n width = image.width\n elif isinstance(image, torch.Tensor):\n width = image.shape[2]\n\n width = (width // 8) * 8 # round down to nearest multiple of 8\n\n return height, width\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: Union[torch.Tensor, PIL.Image.Image] = None,\n mask_image: Union[torch.Tensor, PIL.Image.Image] = None,\n controlnet_conditioning_image: Union[\n torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]\n ] = None,\n strength: float = 0.8,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: float = 1.0,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n image (`torch.Tensor` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will\n be masked out with `mask_image` and repainted according to `prompt`.\n mask_image (`torch.Tensor` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be\n repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted\n to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)\n instead of 3, so the expected shape would be `(B, H, W, 1)`.\n controlnet_conditioning_image (`torch.Tensor`, `PIL.Image.Image`, `List[torch.Tensor]` or `List[PIL.Image.Image]`):\n The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If\n the type is specified as `torch.Tensor`, it is passed to ControlNet as is. PIL.Image.Image` can\n also be accepted as an image. The control image is automatically resized to fit the output image.\n strength (`float`, *optional*):\n Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`\n will be used as a starting point, adding more noise to it the larger the `strength`. The number of\n denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will\n be maximum and the denoising process will run for the full number of iterations specified in\n `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead.\n Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0):\n The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added\n to the residual in the original unet.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n # 0. Default height and width to unet\n height, width = self._default_height_width(height, width, controlnet_conditioning_image)\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n image,\n mask_image,\n controlnet_conditioning_image,\n height,\n width,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n strength,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n prompt_embeds = self._encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n\n # 4. Prepare mask, image, and controlnet_conditioning_image\n image = prepare_image(image)\n\n mask_image = prepare_mask_image(mask_image)\n\n controlnet_conditioning_image = prepare_controlnet_conditioning_image(\n controlnet_conditioning_image,\n width,\n height,\n batch_size * num_images_per_prompt,\n num_images_per_prompt,\n device,\n self.controlnet.dtype,\n )\n\n masked_image = image * (mask_image < 0.5)\n\n # 5. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n\n # 6. Prepare latent variables\n if latents is None:\n latents = self.prepare_latents(\n image,\n latent_timestep,\n batch_size,\n num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n generator,\n )\n\n mask_image_latents = self.prepare_mask_latents(\n mask_image,\n batch_size * num_images_per_prompt,\n height,\n width,\n prompt_embeds.dtype,\n device,\n do_classifier_free_guidance,\n )\n\n masked_image_latents = self.prepare_masked_image_latents(\n masked_image,\n batch_size * num_images_per_prompt,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n do_classifier_free_guidance,\n )\n\n if do_classifier_free_guidance:\n controlnet_conditioning_image = torch.cat([controlnet_conditioning_image] * 2)\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n non_inpainting_latent_model_input = (\n torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n )\n\n non_inpainting_latent_model_input = self.scheduler.scale_model_input(\n non_inpainting_latent_model_input, t\n )\n\n inpainting_latent_model_input = torch.cat(\n [non_inpainting_latent_model_input, mask_image_latents, masked_image_latents], dim=1\n )\n\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n non_inpainting_latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n controlnet_cond=controlnet_conditioning_image,\n return_dict=False,\n )\n\n down_block_res_samples = [\n down_block_res_sample * controlnet_conditioning_scale\n for down_block_res_sample in down_block_res_samples\n ]\n mid_block_res_sample *= controlnet_conditioning_scale\n\n # predict the noise residual\n noise_pred = self.unet(\n inpainting_latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # If we do sequential model offloading, let's offload unet and controlnet\n # manually for max memory savings\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.unet.to(\"cpu\")\n self.controlnet.to(\"cpu\")\n torch.cuda.empty_cache()\n\n if output_type == \"latent\":\n image = latents\n has_nsfw_concept = None\n elif output_type == \"pil\":\n # 8. Post-processing\n image = self.decode_latents(latents)\n\n # 9. Run safety checker\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # 10. Convert to PIL\n image = self.numpy_to_pil(image)\n else:\n # 8. Post-processing\n image = self.decode_latents(latents)\n\n # 9. Run safety checker\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # Offload last model to CPU\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/examples/community/stable_diffusion_controlnet_reference.py", "content": "# Inspired by: https://github.com/Mikubill/sd-webui-controlnet/discussions/1236 and https://github.com/Mikubill/sd-webui-controlnet/discussions/1280\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\n\nfrom diffusers import StableDiffusionControlNetPipeline\nfrom diffusers.models import ControlNetModel\nfrom diffusers.models.attention import BasicTransformerBlock\nfrom diffusers.models.unets.unet_2d_blocks import CrossAttnDownBlock2D, CrossAttnUpBlock2D, DownBlock2D, UpBlock2D\nfrom diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.utils import logging\nfrom diffusers.utils.torch_utils import is_compiled_module, randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import cv2\n >>> import torch\n >>> import numpy as np\n >>> from PIL import Image\n >>> from diffusers import UniPCMultistepScheduler\n >>> from diffusers.utils import load_image\n\n >>> input_image = load_image(\"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png\")\n\n >>> # get canny image\n >>> image = cv2.Canny(np.array(input_image), 100, 200)\n >>> image = image[:, :, None]\n >>> image = np.concatenate([image, image, image], axis=2)\n >>> canny_image = Image.fromarray(image)\n\n >>> controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\", torch_dtype=torch.float16)\n >>> pipe = StableDiffusionControlNetReferencePipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n controlnet=controlnet,\n safety_checker=None,\n torch_dtype=torch.float16\n ).to('cuda:0')\n\n >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe_controlnet.scheduler.config)\n\n >>> result_img = pipe(ref_image=input_image,\n prompt=\"1girl\",\n image=canny_image,\n num_inference_steps=20,\n reference_attn=True,\n reference_adain=True).images[0]\n\n >>> result_img.show()\n ```\n\"\"\"\n\n\ndef torch_dfs(model: torch.nn.Module):\n result = [model]\n for child in model.children():\n result += torch_dfs(child)\n return result\n\n\nclass StableDiffusionControlNetReferencePipeline(StableDiffusionControlNetPipeline):\n def prepare_ref_latents(self, refimage, batch_size, dtype, device, generator, do_classifier_free_guidance):\n refimage = refimage.to(device=device, dtype=dtype)\n\n # encode the mask image into latents space so we can concatenate it to the latents\n if isinstance(generator, list):\n ref_image_latents = [\n self.vae.encode(refimage[i : i + 1]).latent_dist.sample(generator=generator[i])\n for i in range(batch_size)\n ]\n ref_image_latents = torch.cat(ref_image_latents, dim=0)\n else:\n ref_image_latents = self.vae.encode(refimage).latent_dist.sample(generator=generator)\n ref_image_latents = self.vae.config.scaling_factor * ref_image_latents\n\n # duplicate mask and ref_image_latents for each generation per prompt, using mps friendly method\n if ref_image_latents.shape[0] < batch_size:\n if not batch_size % ref_image_latents.shape[0] == 0:\n raise ValueError(\n \"The passed images and the required batch size don't match. Images are supposed to be duplicated\"\n f\" to a total batch size of {batch_size}, but {ref_image_latents.shape[0]} images were passed.\"\n \" Make sure the number of images that you pass is divisible by the total requested batch size.\"\n )\n ref_image_latents = ref_image_latents.repeat(batch_size // ref_image_latents.shape[0], 1, 1, 1)\n\n ref_image_latents = torch.cat([ref_image_latents] * 2) if do_classifier_free_guidance else ref_image_latents\n\n # aligning device to prevent device errors when concating it with the latent model input\n ref_image_latents = ref_image_latents.to(device=device, dtype=dtype)\n return ref_image_latents\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: Union[\n torch.Tensor,\n PIL.Image.Image,\n np.ndarray,\n List[torch.Tensor],\n List[PIL.Image.Image],\n List[np.ndarray],\n ] = None,\n ref_image: Union[torch.Tensor, PIL.Image.Image] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: Union[float, List[float]] = 1.0,\n guess_mode: bool = False,\n attention_auto_machine_weight: float = 1.0,\n gn_auto_machine_weight: float = 1.0,\n style_fidelity: float = 0.5,\n reference_attn: bool = True,\n reference_adain: bool = True,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:\n `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):\n The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If\n the type is specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can\n also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If\n height and/or width are passed, `image` is resized according to them. If multiple ControlNets are\n specified in init, images must be passed as a list such that each element of the list can be correctly\n batched for input to a single controlnet.\n ref_image (`torch.Tensor`, `PIL.Image.Image`):\n The Reference Control input condition. Reference Control uses this input condition to generate guidance to Unet. If\n the type is specified as `torch.Tensor`, it is passed to Reference Control as is. `PIL.Image.Image` can\n also be accepted as an image.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):\n The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added\n to the residual in the original unet. If multiple ControlNets are specified in init, you can set the\n corresponding scale as a list.\n guess_mode (`bool`, *optional*, defaults to `False`):\n In this mode, the ControlNet encoder will try best to recognize the content of the input image even if\n you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.\n attention_auto_machine_weight (`float`):\n Weight of using reference query for self attention's context.\n If attention_auto_machine_weight=1.0, use reference query for all self attention's context.\n gn_auto_machine_weight (`float`):\n Weight of using reference adain. If gn_auto_machine_weight=2.0, use all reference adain plugins.\n style_fidelity (`float`):\n style fidelity of ref_uncond_xt. If style_fidelity=1.0, control more important,\n elif style_fidelity=0.0, prompt more important, else balanced.\n reference_attn (`bool`):\n Whether to use reference query for self attention's context.\n reference_adain (`bool`):\n Whether to use reference adain.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n assert reference_attn or reference_adain, \"`reference_attn` or `reference_adain` must be True.\"\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n image,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n controlnet_conditioning_scale,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet\n\n if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):\n controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)\n\n global_pool_conditions = (\n controlnet.config.global_pool_conditions\n if isinstance(controlnet, ControlNetModel)\n else controlnet.nets[0].config.global_pool_conditions\n )\n guess_mode = guess_mode or global_pool_conditions\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n prompt_embeds = self._encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n\n # 4. Prepare image\n if isinstance(controlnet, ControlNetModel):\n image = self.prepare_image(\n image=image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n height, width = image.shape[-2:]\n elif isinstance(controlnet, MultiControlNetModel):\n images = []\n\n for image_ in image:\n image_ = self.prepare_image(\n image=image_,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n\n images.append(image_)\n\n image = images\n height, width = image[0].shape[-2:]\n else:\n assert False\n\n # 5. Preprocess reference image\n ref_image = self.prepare_image(\n image=ref_image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=prompt_embeds.dtype,\n )\n\n # 6. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 7. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 8. Prepare reference latent variables\n ref_image_latents = self.prepare_ref_latents(\n ref_image,\n batch_size * num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n generator,\n do_classifier_free_guidance,\n )\n\n # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 9. Modify self attention and group norm\n MODE = \"write\"\n uc_mask = (\n torch.Tensor([1] * batch_size * num_images_per_prompt + [0] * batch_size * num_images_per_prompt)\n .type_as(ref_image_latents)\n .bool()\n )\n\n def hacked_basic_transformer_inner_forward(\n self,\n hidden_states: torch.Tensor,\n attention_mask: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n timestep: Optional[torch.LongTensor] = None,\n cross_attention_kwargs: Dict[str, Any] = None,\n class_labels: Optional[torch.LongTensor] = None,\n ):\n if self.use_ada_layer_norm:\n norm_hidden_states = self.norm1(hidden_states, timestep)\n elif self.use_ada_layer_norm_zero:\n norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(\n hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype\n )\n else:\n norm_hidden_states = self.norm1(hidden_states)\n\n # 1. Self-Attention\n cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}\n if self.only_cross_attention:\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n else:\n if MODE == \"write\":\n self.bank.append(norm_hidden_states.detach().clone())\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n if MODE == \"read\":\n if attention_auto_machine_weight > self.attn_weight:\n attn_output_uc = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=torch.cat([norm_hidden_states] + self.bank, dim=1),\n # attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n attn_output_c = attn_output_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n attn_output_c[uc_mask] = self.attn1(\n norm_hidden_states[uc_mask],\n encoder_hidden_states=norm_hidden_states[uc_mask],\n **cross_attention_kwargs,\n )\n attn_output = style_fidelity * attn_output_c + (1.0 - style_fidelity) * attn_output_uc\n self.bank.clear()\n else:\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n if self.use_ada_layer_norm_zero:\n attn_output = gate_msa.unsqueeze(1) * attn_output\n hidden_states = attn_output + hidden_states\n\n if self.attn2 is not None:\n norm_hidden_states = (\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\n )\n\n # 2. Cross-Attention\n attn_output = self.attn2(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=encoder_attention_mask,\n **cross_attention_kwargs,\n )\n hidden_states = attn_output + hidden_states\n\n # 3. Feed-forward\n norm_hidden_states = self.norm3(hidden_states)\n\n if self.use_ada_layer_norm_zero:\n norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]\n\n ff_output = self.ff(norm_hidden_states)\n\n if self.use_ada_layer_norm_zero:\n ff_output = gate_mlp.unsqueeze(1) * ff_output\n\n hidden_states = ff_output + hidden_states\n\n return hidden_states\n\n def hacked_mid_forward(self, *args, **kwargs):\n eps = 1e-6\n x = self.original_forward(*args, **kwargs)\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append(mean)\n self.var_bank.append(var)\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank) / float(len(self.mean_bank))\n var_acc = sum(self.var_bank) / float(len(self.var_bank))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n x_uc = (((x - mean) / std) * std_acc) + mean_acc\n x_c = x_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n x_c[uc_mask] = x[uc_mask]\n x = style_fidelity * x_c + (1.0 - style_fidelity) * x_uc\n self.mean_bank = []\n self.var_bank = []\n return x\n\n def hack_CrossAttnDownBlock2D_forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n ):\n eps = 1e-6\n\n # TODO(Patrick, William) - attention mask is not used\n output_states = ()\n\n for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask]\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n output_states = output_states + (hidden_states,)\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n def hacked_DownBlock2D_forward(self, hidden_states, temb=None, *args, **kwargs):\n eps = 1e-6\n\n output_states = ()\n\n for i, resnet in enumerate(self.resnets):\n hidden_states = resnet(hidden_states, temb)\n\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask]\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n output_states = output_states + (hidden_states,)\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n def hacked_CrossAttnUpBlock2D_forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n upsample_size: Optional[int] = None,\n attention_mask: Optional[torch.Tensor] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n ):\n eps = 1e-6\n # TODO(Patrick, William) - attention mask is not used\n for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask]\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n def hacked_UpBlock2D_forward(\n self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None, *args, **kwargs\n ):\n eps = 1e-6\n for i, resnet in enumerate(self.resnets):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n hidden_states = resnet(hidden_states, temb)\n\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask]\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n if reference_attn:\n attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock)]\n attn_modules = sorted(attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\n\n for i, module in enumerate(attn_modules):\n module._original_inner_forward = module.forward\n module.forward = hacked_basic_transformer_inner_forward.__get__(module, BasicTransformerBlock)\n module.bank = []\n module.attn_weight = float(i) / float(len(attn_modules))\n\n if reference_adain:\n gn_modules = [self.unet.mid_block]\n self.unet.mid_block.gn_weight = 0\n\n down_blocks = self.unet.down_blocks\n for w, module in enumerate(down_blocks):\n module.gn_weight = 1.0 - float(w) / float(len(down_blocks))\n gn_modules.append(module)\n\n up_blocks = self.unet.up_blocks\n for w, module in enumerate(up_blocks):\n module.gn_weight = float(w) / float(len(up_blocks))\n gn_modules.append(module)\n\n for i, module in enumerate(gn_modules):\n if getattr(module, \"original_forward\", None) is None:\n module.original_forward = module.forward\n if i == 0:\n # mid_block\n module.forward = hacked_mid_forward.__get__(module, torch.nn.Module)\n elif isinstance(module, CrossAttnDownBlock2D):\n module.forward = hack_CrossAttnDownBlock2D_forward.__get__(module, CrossAttnDownBlock2D)\n elif isinstance(module, DownBlock2D):\n module.forward = hacked_DownBlock2D_forward.__get__(module, DownBlock2D)\n elif isinstance(module, CrossAttnUpBlock2D):\n module.forward = hacked_CrossAttnUpBlock2D_forward.__get__(module, CrossAttnUpBlock2D)\n elif isinstance(module, UpBlock2D):\n module.forward = hacked_UpBlock2D_forward.__get__(module, UpBlock2D)\n module.mean_bank = []\n module.var_bank = []\n module.gn_weight *= 2\n\n # 11. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # controlnet(s) inference\n if guess_mode and do_classifier_free_guidance:\n # Infer ControlNet only for the conditional batch.\n control_model_input = latents\n control_model_input = self.scheduler.scale_model_input(control_model_input, t)\n controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]\n else:\n control_model_input = latent_model_input\n controlnet_prompt_embeds = prompt_embeds\n\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n control_model_input,\n t,\n encoder_hidden_states=controlnet_prompt_embeds,\n controlnet_cond=image,\n conditioning_scale=controlnet_conditioning_scale,\n guess_mode=guess_mode,\n return_dict=False,\n )\n\n if guess_mode and do_classifier_free_guidance:\n # Infered ControlNet only for the conditional batch.\n # To apply the output of ControlNet to both the unconditional and conditional batches,\n # add 0 to the unconditional batch to keep it unchanged.\n down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]\n mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])\n\n # ref only part\n noise = randn_tensor(\n ref_image_latents.shape, generator=generator, device=device, dtype=ref_image_latents.dtype\n )\n ref_xt = self.scheduler.add_noise(\n ref_image_latents,\n noise,\n t.reshape(\n 1,\n ),\n )\n ref_xt = self.scheduler.scale_model_input(ref_xt, t)\n\n MODE = \"write\"\n self.unet(\n ref_xt,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )\n\n # predict the noise residual\n MODE = \"read\"\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # If we do sequential model offloading, let's offload unet and controlnet\n # manually for max memory savings\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.unet.to(\"cpu\")\n self.controlnet.to(\"cpu\")\n torch.cuda.empty_cache()\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload last model to CPU\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/examples/community/stable_diffusion_ipex.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport intel_extension_for_pytorch as ipex\nimport torch\nfrom packaging import version\nfrom transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers.configuration_utils import FrozenDict\nfrom diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n deprecate,\n logging,\n replace_example_docstring,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusionPipeline\n\n >>> pipe = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", custom_pipeline=\"stable_diffusion_ipex\")\n\n >>> # For Float32\n >>> pipe.prepare_for_ipex(prompt, dtype=torch.float32, height=512, width=512) #value of image height/width should be consistent with the pipeline inference\n >>> # For BFloat16\n >>> pipe.prepare_for_ipex(prompt, dtype=torch.bfloat16, height=512, width=512) #value of image height/width should be consistent with the pipeline inference\n\n >>> prompt = \"a photo of an astronaut riding a horse on mars\"\n >>> # For Float32\n >>> image = pipe(prompt, num_inference_steps=num_inference_steps, height=512, width=512).images[0] #value of image height/width should be consistent with 'prepare_for_ipex()'\n >>> # For BFloat16\n >>> with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16):\n >>> image = pipe(prompt, num_inference_steps=num_inference_steps, height=512, width=512).images[0] #value of image height/width should be consistent with 'prepare_for_ipex()'\n ```\n\"\"\"\n\n\nclass StableDiffusionIPEXPipeline(\n DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, LoraLoaderMixin\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion on IPEX.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPFeatureExtractor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPFeatureExtractor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"clip_sample\"] = False\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n def get_input_example(self, prompt, height=None, width=None, guidance_scale=7.5, num_images_per_prompt=1):\n prompt_embeds = None\n negative_prompt_embeds = None\n negative_prompt = None\n callback_steps = 1\n generator = None\n latents = None\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n\n device = \"cpu\"\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n prompt_embeds = self._encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n\n # 5. Prepare latent variables\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n self.unet.config.in_channels,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n dummy = torch.ones(1, dtype=torch.int32)\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, dummy)\n\n unet_input_example = (latent_model_input, dummy, prompt_embeds)\n vae_decoder_input_example = latents\n\n return unet_input_example, vae_decoder_input_example\n\n def prepare_for_ipex(self, promt, dtype=torch.float32, height=None, width=None, guidance_scale=7.5):\n self.unet = self.unet.to(memory_format=torch.channels_last)\n self.vae.decoder = self.vae.decoder.to(memory_format=torch.channels_last)\n self.text_encoder = self.text_encoder.to(memory_format=torch.channels_last)\n if self.safety_checker is not None:\n self.safety_checker = self.safety_checker.to(memory_format=torch.channels_last)\n\n unet_input_example, vae_decoder_input_example = self.get_input_example(promt, height, width, guidance_scale)\n\n # optimize with ipex\n if dtype == torch.bfloat16:\n self.unet = ipex.optimize(self.unet.eval(), dtype=torch.bfloat16, inplace=True)\n self.vae.decoder = ipex.optimize(self.vae.decoder.eval(), dtype=torch.bfloat16, inplace=True)\n self.text_encoder = ipex.optimize(self.text_encoder.eval(), dtype=torch.bfloat16, inplace=True)\n if self.safety_checker is not None:\n self.safety_checker = ipex.optimize(self.safety_checker.eval(), dtype=torch.bfloat16, inplace=True)\n elif dtype == torch.float32:\n self.unet = ipex.optimize(\n self.unet.eval(),\n dtype=torch.float32,\n inplace=True,\n weights_prepack=True,\n auto_kernel_selection=False,\n )\n self.vae.decoder = ipex.optimize(\n self.vae.decoder.eval(),\n dtype=torch.float32,\n inplace=True,\n weights_prepack=True,\n auto_kernel_selection=False,\n )\n self.text_encoder = ipex.optimize(\n self.text_encoder.eval(),\n dtype=torch.float32,\n inplace=True,\n weights_prepack=True,\n auto_kernel_selection=False,\n )\n if self.safety_checker is not None:\n self.safety_checker = ipex.optimize(\n self.safety_checker.eval(),\n dtype=torch.float32,\n inplace=True,\n weights_prepack=True,\n auto_kernel_selection=False,\n )\n else:\n raise ValueError(\" The value of 'dtype' should be 'torch.bfloat16' or 'torch.float32' !\")\n\n # trace unet model to get better performance on IPEX\n with torch.cpu.amp.autocast(enabled=dtype == torch.bfloat16), torch.no_grad():\n unet_trace_model = torch.jit.trace(self.unet, unet_input_example, check_trace=False, strict=False)\n unet_trace_model = torch.jit.freeze(unet_trace_model)\n self.unet.forward = unet_trace_model.forward\n\n # trace vae.decoder model to get better performance on IPEX\n with torch.cpu.amp.autocast(enabled=dtype == torch.bfloat16), torch.no_grad():\n ave_decoder_trace_model = torch.jit.trace(\n self.vae.decoder, vae_decoder_input_example, check_trace=False, strict=False\n )\n ave_decoder_trace_model = torch.jit.freeze(ave_decoder_trace_model)\n self.vae.decoder.forward = ave_decoder_trace_model.forward\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.\n Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n \"\"\"\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n prompt_embeds = prompt_embeds[0]\n\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n else:\n has_nsfw_concept = None\n return image, has_nsfw_concept\n\n def decode_latents(self, latents):\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents).sample\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.\n Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttnProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n prompt_embeds = self._encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds)[\"sample\"]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if output_type == \"latent\":\n image = latents\n has_nsfw_concept = None\n elif output_type == \"pil\":\n # 8. Post-processing\n image = self.decode_latents(latents)\n\n # 9. Run safety checker\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # 10. Convert to PIL\n image = self.numpy_to_pil(image)\n else:\n # 8. Post-processing\n image = self.decode_latents(latents)\n\n # 9. Run safety checker\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # Offload last model to CPU\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/examples/community/stable_diffusion_mega.py", "content": "from typing import Any, Callable, Dict, List, Optional, Union\n\nimport PIL.Image\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n DiffusionPipeline,\n LMSDiscreteScheduler,\n PNDMScheduler,\n StableDiffusionImg2ImgPipeline,\n StableDiffusionInpaintPipelineLegacy,\n StableDiffusionPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.configuration_utils import FrozenDict\nfrom diffusers.pipelines.pipeline_utils import StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.utils import deprecate, logging\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass StableDiffusionMegaPipeline(DiffusionPipeline, StableDiffusionMixin):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionMegaSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n @property\n def components(self) -> Dict[str, Any]:\n return {k: getattr(self, k) for k in self.config.keys() if not k.startswith(\"_\")}\n\n @torch.no_grad()\n def inpaint(\n self,\n prompt: Union[str, List[str]],\n image: Union[torch.Tensor, PIL.Image.Image],\n mask_image: Union[torch.Tensor, PIL.Image.Image],\n strength: float = 0.8,\n num_inference_steps: Optional[int] = 50,\n guidance_scale: Optional[float] = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: Optional[float] = 0.0,\n generator: Optional[torch.Generator] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n ):\n # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline\n return StableDiffusionInpaintPipelineLegacy(**self.components)(\n prompt=prompt,\n image=image,\n mask_image=mask_image,\n strength=strength,\n num_inference_steps=num_inference_steps,\n guidance_scale=guidance_scale,\n negative_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n output_type=output_type,\n return_dict=return_dict,\n callback=callback,\n )\n\n @torch.no_grad()\n def img2img(\n self,\n prompt: Union[str, List[str]],\n image: Union[torch.Tensor, PIL.Image.Image],\n strength: float = 0.8,\n num_inference_steps: Optional[int] = 50,\n guidance_scale: Optional[float] = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: Optional[float] = 0.0,\n generator: Optional[torch.Generator] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n **kwargs,\n ):\n # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline\n return StableDiffusionImg2ImgPipeline(**self.components)(\n prompt=prompt,\n image=image,\n strength=strength,\n num_inference_steps=num_inference_steps,\n guidance_scale=guidance_scale,\n negative_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n output_type=output_type,\n return_dict=return_dict,\n callback=callback,\n callback_steps=callback_steps,\n )\n\n @torch.no_grad()\n def text2img(\n self,\n prompt: Union[str, List[str]],\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n ):\n # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline\n return StableDiffusionPipeline(**self.components)(\n prompt=prompt,\n height=height,\n width=width,\n num_inference_steps=num_inference_steps,\n guidance_scale=guidance_scale,\n negative_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n latents=latents,\n output_type=output_type,\n return_dict=return_dict,\n callback=callback,\n callback_steps=callback_steps,\n )\n" }, { "path": "diffusers/examples/community/stable_diffusion_reference.py", "content": "# Inspired by: https://github.com/Mikubill/sd-webui-controlnet/discussions/1236 and https://github.com/Mikubill/sd-webui-controlnet/discussions/1280\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom packaging import version\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import AutoencoderKL, DiffusionPipeline, UNet2DConditionModel\nfrom diffusers.configuration_utils import FrozenDict, deprecate\nfrom diffusers.image_processor import VaeImageProcessor\nfrom diffusers.loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom diffusers.models.attention import BasicTransformerBlock\nfrom diffusers.models.lora import adjust_lora_scale_text_encoder\nfrom diffusers.models.unets.unet_2d_blocks import CrossAttnDownBlock2D, CrossAttnUpBlock2D, DownBlock2D, UpBlock2D\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import rescale_noise_cfg\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n PIL_INTERPOLATION,\n USE_PEFT_BACKEND,\n logging,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import UniPCMultistepScheduler\n >>> from diffusers.utils import load_image\n\n >>> input_image = load_image(\"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png\")\n\n >>> pipe = StableDiffusionReferencePipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n safety_checker=None,\n torch_dtype=torch.float16\n ).to('cuda:0')\n\n >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n\n >>> result_img = pipe(ref_image=input_image,\n prompt=\"1girl\",\n num_inference_steps=20,\n reference_attn=True,\n reference_adain=True).images[0]\n\n >>> result_img.show()\n ```\n\"\"\"\n\n\ndef torch_dfs(model: torch.nn.Module):\n r\"\"\"\n Performs a depth-first search on the given PyTorch model and returns a list of all its child modules.\n\n Args:\n model (torch.nn.Module): The PyTorch model to perform the depth-first search on.\n\n Returns:\n list: A list of all child modules of the given model.\n \"\"\"\n result = [model]\n for child in model.children():\n result += torch_dfs(child)\n return result\n\n\nclass StableDiffusionReferencePipeline(\n DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin\n):\n r\"\"\"\n Pipeline for Stable Diffusion Reference.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"skip_prk_steps\") and scheduler.config.skip_prk_steps is False:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration\"\n \" `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make\"\n \" sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to\"\n \" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face\"\n \" Hub, it would be very nice if you could open a Pull request for the\"\n \" `scheduler/scheduler_config.json` file\"\n )\n deprecate(\n \"skip_prk_steps not set\",\n \"1.0.0\",\n deprecation_message,\n standard_warn=False,\n )\n new_config = dict(scheduler.config)\n new_config[\"skip_prk_steps\"] = True\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n # Check shapes, assume num_channels_latents == 4, num_channels_mask == 1, num_channels_masked == 4\n if unet.config.in_channels != 4:\n logger.warning(\n f\"You have loaded a UNet with {unet.config.in_channels} input channels, whereas by default,\"\n f\" {self.__class__} assumes that `pipeline.unet` has 4 input channels: 4 for `num_channels_latents`,\"\n \". If you did not intend to modify\"\n \" this behavior, please check whether you have loaded the right checkpoint.\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n def _default_height_width(\n self,\n height: Optional[int],\n width: Optional[int],\n image: Union[PIL.Image.Image, torch.Tensor, List[PIL.Image.Image]],\n ) -> Tuple[int, int]:\n r\"\"\"\n Calculate the default height and width for the given image.\n\n Args:\n height (int or None): The desired height of the image. If None, the height will be determined based on the input image.\n width (int or None): The desired width of the image. If None, the width will be determined based on the input image.\n image (PIL.Image.Image or torch.Tensor or list[PIL.Image.Image]): The input image or a list of images.\n\n Returns:\n Tuple[int, int]: A tuple containing the calculated height and width.\n\n \"\"\"\n # NOTE: It is possible that a list of images have different\n # dimensions for each image, so just checking the first image\n # is not _exactly_ correct, but it is simple.\n while isinstance(image, list):\n image = image[0]\n\n if height is None:\n if isinstance(image, PIL.Image.Image):\n height = image.height\n elif isinstance(image, torch.Tensor):\n height = image.shape[2]\n\n height = (height // 8) * 8 # round down to nearest multiple of 8\n\n if width is None:\n if isinstance(image, PIL.Image.Image):\n width = image.width\n elif isinstance(image, torch.Tensor):\n width = image.shape[3]\n\n width = (width // 8) * 8 # round down to nearest multiple of 8\n\n return height, width\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs\n def check_inputs(\n self,\n prompt: Optional[Union[str, List[str]]],\n height: int,\n width: int,\n callback_steps: Optional[int],\n negative_prompt: Optional[str] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[torch.Tensor] = None,\n ip_adapter_image_embeds: Optional[torch.Tensor] = None,\n callback_on_step_end_tensor_inputs: Optional[List[str]] = None,\n ) -> None:\n \"\"\"\n Check the validity of the input arguments for the diffusion model.\n\n Args:\n prompt (Optional[Union[str, List[str]]]): The prompt text or list of prompt texts.\n height (int): The height of the input image.\n width (int): The width of the input image.\n callback_steps (Optional[int]): The number of steps to perform the callback on.\n negative_prompt (Optional[str]): The negative prompt text.\n prompt_embeds (Optional[torch.Tensor]): The prompt embeddings.\n negative_prompt_embeds (Optional[torch.Tensor]): The negative prompt embeddings.\n ip_adapter_image (Optional[torch.Tensor]): The input adapter image.\n ip_adapter_image_embeds (Optional[torch.Tensor]): The input adapter image embeddings.\n callback_on_step_end_tensor_inputs (Optional[List[str]]): The list of tensor inputs to perform the callback on.\n\n Raises:\n ValueError: If `height` or `width` is not divisible by 8.\n ValueError: If `callback_steps` is not a positive integer.\n ValueError: If `callback_on_step_end_tensor_inputs` contains invalid tensor inputs.\n ValueError: If both `prompt` and `prompt_embeds` are provided.\n ValueError: If neither `prompt` nor `prompt_embeds` are provided.\n ValueError: If `prompt` is not of type `str` or `list`.\n ValueError: If both `negative_prompt` and `negative_prompt_embeds` are provided.\n ValueError: If both `prompt_embeds` and `negative_prompt_embeds` are provided and have different shapes.\n ValueError: If both `ip_adapter_image` and `ip_adapter_image_embeds` are provided.\n\n Returns:\n None\n \"\"\"\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt: Union[str, List[str]],\n device: torch.device,\n num_images_per_prompt: int,\n do_classifier_free_guidance: bool,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ) -> torch.Tensor:\n r\"\"\"\n Encodes the prompt into embeddings.\n\n Args:\n prompt (Union[str, List[str]]): The prompt text or a list of prompt texts.\n device (torch.device): The device to use for encoding.\n num_images_per_prompt (int): The number of images per prompt.\n do_classifier_free_guidance (bool): Whether to use classifier-free guidance.\n negative_prompt (Optional[Union[str, List[str]]], optional): The negative prompt text or a list of negative prompt texts. Defaults to None.\n prompt_embeds (Optional[torch.Tensor], optional): The prompt embeddings. Defaults to None.\n negative_prompt_embeds (Optional[torch.Tensor], optional): The negative prompt embeddings. Defaults to None.\n lora_scale (Optional[float], optional): The LoRA scale. Defaults to None.\n **kwargs: Additional keyword arguments.\n\n Returns:\n torch.Tensor: The encoded prompt embeddings.\n \"\"\"\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt: Optional[str],\n device: torch.device,\n num_images_per_prompt: int,\n do_classifier_free_guidance: bool,\n negative_prompt: Optional[str] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ) -> torch.Tensor:\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(\n self,\n batch_size: int,\n num_channels_latents: int,\n height: int,\n width: int,\n dtype: torch.dtype,\n device: torch.device,\n generator: Union[torch.Generator, List[torch.Generator]],\n latents: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n r\"\"\"\n Prepare the latent vectors for diffusion.\n\n Args:\n batch_size (int): The number of samples in the batch.\n num_channels_latents (int): The number of channels in the latent vectors.\n height (int): The height of the latent vectors.\n width (int): The width of the latent vectors.\n dtype (torch.dtype): The data type of the latent vectors.\n device (torch.device): The device to place the latent vectors on.\n generator (Union[torch.Generator, List[torch.Generator]]): The generator(s) to use for random number generation.\n latents (Optional[torch.Tensor]): The pre-existing latent vectors. If None, new latent vectors will be generated.\n\n Returns:\n torch.Tensor: The prepared latent vectors.\n \"\"\"\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(\n self, generator: Union[torch.Generator, List[torch.Generator]], eta: float\n ) -> Dict[str, Any]:\n r\"\"\"\n Prepare extra keyword arguments for the scheduler step.\n\n Args:\n generator (Union[torch.Generator, List[torch.Generator]]): The generator used for sampling.\n eta (float): The value of eta (η) used with the DDIMScheduler. Should be between 0 and 1.\n\n Returns:\n Dict[str, Any]: A dictionary containing the extra keyword arguments for the scheduler step.\n \"\"\"\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def prepare_image(\n self,\n image: Union[torch.Tensor, PIL.Image.Image, List[Union[torch.Tensor, PIL.Image.Image]]],\n width: int,\n height: int,\n batch_size: int,\n num_images_per_prompt: int,\n device: torch.device,\n dtype: torch.dtype,\n do_classifier_free_guidance: bool = False,\n guess_mode: bool = False,\n ) -> torch.Tensor:\n r\"\"\"\n Prepares the input image for processing.\n\n Args:\n image (torch.Tensor or PIL.Image.Image or list): The input image(s).\n width (int): The desired width of the image.\n height (int): The desired height of the image.\n batch_size (int): The batch size for processing.\n num_images_per_prompt (int): The number of images per prompt.\n device (torch.device): The device to use for processing.\n dtype (torch.dtype): The data type of the image.\n do_classifier_free_guidance (bool, optional): Whether to perform classifier-free guidance. Defaults to False.\n guess_mode (bool, optional): Whether to use guess mode. Defaults to False.\n\n Returns:\n torch.Tensor: The prepared image for processing.\n \"\"\"\n if not isinstance(image, torch.Tensor):\n if isinstance(image, PIL.Image.Image):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n images = []\n\n for image_ in image:\n image_ = image_.convert(\"RGB\")\n image_ = image_.resize((width, height), resample=PIL_INTERPOLATION[\"lanczos\"])\n image_ = np.array(image_)\n image_ = image_[None, :]\n images.append(image_)\n\n image = images\n\n image = np.concatenate(image, axis=0)\n image = np.array(image).astype(np.float32) / 255.0\n image = (image - 0.5) / 0.5\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image)\n elif isinstance(image[0], torch.Tensor):\n image = torch.cat(image, dim=0)\n\n image_batch_size = image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n image = image.repeat_interleave(repeat_by, dim=0)\n\n image = image.to(device=device, dtype=dtype)\n\n if do_classifier_free_guidance and not guess_mode:\n image = torch.cat([image] * 2)\n\n return image\n\n def prepare_ref_latents(\n self,\n refimage: torch.Tensor,\n batch_size: int,\n dtype: torch.dtype,\n device: torch.device,\n generator: Union[int, List[int]],\n do_classifier_free_guidance: bool,\n ) -> torch.Tensor:\n r\"\"\"\n Prepares reference latents for generating images.\n\n Args:\n refimage (torch.Tensor): The reference image.\n batch_size (int): The desired batch size.\n dtype (torch.dtype): The data type of the tensors.\n device (torch.device): The device to perform computations on.\n generator (int or list): The generator index or a list of generator indices.\n do_classifier_free_guidance (bool): Whether to use classifier-free guidance.\n\n Returns:\n torch.Tensor: The prepared reference latents.\n \"\"\"\n refimage = refimage.to(device=device, dtype=dtype)\n\n # encode the mask image into latents space so we can concatenate it to the latents\n if isinstance(generator, list):\n ref_image_latents = [\n self.vae.encode(refimage[i : i + 1]).latent_dist.sample(generator=generator[i])\n for i in range(batch_size)\n ]\n ref_image_latents = torch.cat(ref_image_latents, dim=0)\n else:\n ref_image_latents = self.vae.encode(refimage).latent_dist.sample(generator=generator)\n ref_image_latents = self.vae.config.scaling_factor * ref_image_latents\n\n # duplicate mask and ref_image_latents for each generation per prompt, using mps friendly method\n if ref_image_latents.shape[0] < batch_size:\n if not batch_size % ref_image_latents.shape[0] == 0:\n raise ValueError(\n \"The passed images and the required batch size don't match. Images are supposed to be duplicated\"\n f\" to a total batch size of {batch_size}, but {ref_image_latents.shape[0]} images were passed.\"\n \" Make sure the number of images that you pass is divisible by the total requested batch size.\"\n )\n ref_image_latents = ref_image_latents.repeat(batch_size // ref_image_latents.shape[0], 1, 1, 1)\n\n # aligning device to prevent device errors when concating it with the latent model input\n ref_image_latents = ref_image_latents.to(device=device, dtype=dtype)\n return ref_image_latents\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(\n self, image: Union[torch.Tensor, PIL.Image.Image], device: torch.device, dtype: torch.dtype\n ) -> Tuple[Union[torch.Tensor, PIL.Image.Image], Optional[bool]]:\n r\"\"\"\n Runs the safety checker on the given image.\n\n Args:\n image (Union[torch.Tensor, PIL.Image.Image]): The input image to be checked.\n device (torch.device): The device to run the safety checker on.\n dtype (torch.dtype): The data type of the input image.\n\n Returns:\n (image, has_nsfw_concept) Tuple[Union[torch.Tensor, PIL.Image.Image], Optional[bool]]: A tuple containing the processed image and\n a boolean indicating whether the image has a NSFW (Not Safe for Work) concept.\n \"\"\"\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n ref_image: Union[torch.Tensor, PIL.Image.Image] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n attention_auto_machine_weight: float = 1.0,\n gn_auto_machine_weight: float = 1.0,\n style_fidelity: float = 0.5,\n reference_attn: bool = True,\n reference_adain: bool = True,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n ref_image (`torch.Tensor`, `PIL.Image.Image`):\n The Reference Control input condition. Reference Control uses this input condition to generate guidance to Unet. If\n the type is specified as `torch.Tensor`, it is passed to Reference Control as is. `PIL.Image.Image` can\n also be accepted as an image.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of\n [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).\n Guidance rescale factor should fix overexposure when using zero terminal SNR.\n attention_auto_machine_weight (`float`):\n Weight of using reference query for self attention's context.\n If attention_auto_machine_weight=1.0, use reference query for all self attention's context.\n gn_auto_machine_weight (`float`):\n Weight of using reference adain. If gn_auto_machine_weight=2.0, use all reference adain plugins.\n style_fidelity (`float`):\n style fidelity of ref_uncond_xt. If style_fidelity=1.0, control more important,\n elif style_fidelity=0.0, prompt more important, else balanced.\n reference_attn (`bool`):\n Whether to use reference query for self attention's context.\n reference_adain (`bool`):\n Whether to use reference adain.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n assert reference_attn or reference_adain, \"`reference_attn` or `reference_adain` must be True.\"\n\n # 0. Default height and width to unet\n height, width = self._default_height_width(height, width, ref_image)\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n prompt_embeds = self._encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n\n # 4. Preprocess reference image\n ref_image = self.prepare_image(\n image=ref_image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=prompt_embeds.dtype,\n )\n\n # 5. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 6. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 7. Prepare reference latent variables\n ref_image_latents = self.prepare_ref_latents(\n ref_image,\n batch_size * num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n generator,\n do_classifier_free_guidance,\n )\n\n # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 9. Modify self attention and group norm\n MODE = \"write\"\n uc_mask = (\n torch.Tensor([1] * batch_size * num_images_per_prompt + [0] * batch_size * num_images_per_prompt)\n .type_as(ref_image_latents)\n .bool()\n )\n\n def hacked_basic_transformer_inner_forward(\n self,\n hidden_states: torch.Tensor,\n attention_mask: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n timestep: Optional[torch.LongTensor] = None,\n cross_attention_kwargs: Dict[str, Any] = None,\n class_labels: Optional[torch.LongTensor] = None,\n ):\n if self.use_ada_layer_norm:\n norm_hidden_states = self.norm1(hidden_states, timestep)\n elif self.use_ada_layer_norm_zero:\n norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(\n hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype\n )\n else:\n norm_hidden_states = self.norm1(hidden_states)\n\n # 1. Self-Attention\n cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}\n if self.only_cross_attention:\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n else:\n if MODE == \"write\":\n self.bank.append(norm_hidden_states.detach().clone())\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n if MODE == \"read\":\n if attention_auto_machine_weight > self.attn_weight:\n attn_output_uc = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=torch.cat([norm_hidden_states] + self.bank, dim=1),\n # attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n attn_output_c = attn_output_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n attn_output_c[uc_mask] = self.attn1(\n norm_hidden_states[uc_mask],\n encoder_hidden_states=norm_hidden_states[uc_mask],\n **cross_attention_kwargs,\n )\n attn_output = style_fidelity * attn_output_c + (1.0 - style_fidelity) * attn_output_uc\n self.bank.clear()\n else:\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n if self.use_ada_layer_norm_zero:\n attn_output = gate_msa.unsqueeze(1) * attn_output\n hidden_states = attn_output + hidden_states\n\n if self.attn2 is not None:\n norm_hidden_states = (\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\n )\n\n # 2. Cross-Attention\n attn_output = self.attn2(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=encoder_attention_mask,\n **cross_attention_kwargs,\n )\n hidden_states = attn_output + hidden_states\n\n # 3. Feed-forward\n norm_hidden_states = self.norm3(hidden_states)\n\n if self.use_ada_layer_norm_zero:\n norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]\n\n ff_output = self.ff(norm_hidden_states)\n\n if self.use_ada_layer_norm_zero:\n ff_output = gate_mlp.unsqueeze(1) * ff_output\n\n hidden_states = ff_output + hidden_states\n\n return hidden_states\n\n def hacked_mid_forward(self, *args, **kwargs):\n eps = 1e-6\n x = self.original_forward(*args, **kwargs)\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append(mean)\n self.var_bank.append(var)\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank) / float(len(self.mean_bank))\n var_acc = sum(self.var_bank) / float(len(self.var_bank))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n x_uc = (((x - mean) / std) * std_acc) + mean_acc\n x_c = x_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n x_c[uc_mask] = x[uc_mask]\n x = style_fidelity * x_c + (1.0 - style_fidelity) * x_uc\n self.mean_bank = []\n self.var_bank = []\n return x\n\n def hack_CrossAttnDownBlock2D_forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n ):\n eps = 1e-6\n\n # TODO(Patrick, William) - attention mask is not used\n output_states = ()\n\n for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask]\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n output_states = output_states + (hidden_states,)\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n def hacked_DownBlock2D_forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n **kwargs: Any,\n ) -> Tuple[torch.Tensor, ...]:\n eps = 1e-6\n\n output_states = ()\n\n for i, resnet in enumerate(self.resnets):\n hidden_states = resnet(hidden_states, temb)\n\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask]\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n output_states = output_states + (hidden_states,)\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n def hacked_CrossAttnUpBlock2D_forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n upsample_size: Optional[int] = None,\n attention_mask: Optional[torch.Tensor] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n eps = 1e-6\n # TODO(Patrick, William) - attention mask is not used\n for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask]\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n def hacked_UpBlock2D_forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n upsample_size: Optional[int] = None,\n **kwargs: Any,\n ) -> torch.Tensor:\n eps = 1e-6\n for i, resnet in enumerate(self.resnets):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n hidden_states = resnet(hidden_states, temb)\n\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask]\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n if reference_attn:\n attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock)]\n attn_modules = sorted(attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\n\n for i, module in enumerate(attn_modules):\n module._original_inner_forward = module.forward\n module.forward = hacked_basic_transformer_inner_forward.__get__(module, BasicTransformerBlock)\n module.bank = []\n module.attn_weight = float(i) / float(len(attn_modules))\n\n if reference_adain:\n gn_modules = [self.unet.mid_block]\n self.unet.mid_block.gn_weight = 0\n\n down_blocks = self.unet.down_blocks\n for w, module in enumerate(down_blocks):\n module.gn_weight = 1.0 - float(w) / float(len(down_blocks))\n gn_modules.append(module)\n\n up_blocks = self.unet.up_blocks\n for w, module in enumerate(up_blocks):\n module.gn_weight = float(w) / float(len(up_blocks))\n gn_modules.append(module)\n\n for i, module in enumerate(gn_modules):\n if getattr(module, \"original_forward\", None) is None:\n module.original_forward = module.forward\n if i == 0:\n # mid_block\n module.forward = hacked_mid_forward.__get__(module, torch.nn.Module)\n elif isinstance(module, CrossAttnDownBlock2D):\n module.forward = hack_CrossAttnDownBlock2D_forward.__get__(module, CrossAttnDownBlock2D)\n elif isinstance(module, DownBlock2D):\n module.forward = hacked_DownBlock2D_forward.__get__(module, DownBlock2D)\n elif isinstance(module, CrossAttnUpBlock2D):\n module.forward = hacked_CrossAttnUpBlock2D_forward.__get__(module, CrossAttnUpBlock2D)\n elif isinstance(module, UpBlock2D):\n module.forward = hacked_UpBlock2D_forward.__get__(module, UpBlock2D)\n module.mean_bank = []\n module.var_bank = []\n module.gn_weight *= 2\n\n # 10. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # ref only part\n noise = randn_tensor(\n ref_image_latents.shape, generator=generator, device=device, dtype=ref_image_latents.dtype\n )\n ref_xt = self.scheduler.add_noise(\n ref_image_latents,\n noise,\n t.reshape(\n 1,\n ),\n )\n ref_xt = torch.cat([ref_xt] * 2) if do_classifier_free_guidance else ref_xt\n ref_xt = self.scheduler.scale_model_input(ref_xt, t)\n\n MODE = \"write\"\n self.unet(\n ref_xt,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )\n\n # predict the noise residual\n MODE = \"read\"\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if do_classifier_free_guidance and guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload last model to CPU\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/examples/community/stable_diffusion_repaint.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Callable, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom packaging import version\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import AutoencoderKL, DiffusionPipeline, UNet2DConditionModel\nfrom diffusers.configuration_utils import FrozenDict, deprecate\nfrom diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin\nfrom diffusers.pipelines.pipeline_utils import StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import (\n StableDiffusionSafetyChecker,\n)\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n logging,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef prepare_mask_and_masked_image(image, mask):\n \"\"\"\n Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be\n converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the\n ``image`` and ``1`` for the ``mask``.\n The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be\n binarized (``mask > 0.5``) and cast to ``torch.float32`` too.\n Args:\n image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.\n It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``\n ``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.\n mask (_type_): The mask to apply to the image, i.e. regions to inpaint.\n It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``\n ``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.\n Raises:\n ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask\n should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.\n TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not\n (ot the other way around).\n Returns:\n tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4\n dimensions: ``batch x channels x height x width``.\n \"\"\"\n if isinstance(image, torch.Tensor):\n if not isinstance(mask, torch.Tensor):\n raise TypeError(f\"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not\")\n\n # Batch single image\n if image.ndim == 3:\n assert image.shape[0] == 3, \"Image outside a batch should be of shape (3, H, W)\"\n image = image.unsqueeze(0)\n\n # Batch and add channel dim for single mask\n if mask.ndim == 2:\n mask = mask.unsqueeze(0).unsqueeze(0)\n\n # Batch single mask or add channel dim\n if mask.ndim == 3:\n # Single batched mask, no channel dim or single mask not batched but channel dim\n if mask.shape[0] == 1:\n mask = mask.unsqueeze(0)\n\n # Batched masks no channel dim\n else:\n mask = mask.unsqueeze(1)\n\n assert image.ndim == 4 and mask.ndim == 4, \"Image and Mask must have 4 dimensions\"\n assert image.shape[-2:] == mask.shape[-2:], \"Image and Mask must have the same spatial dimensions\"\n assert image.shape[0] == mask.shape[0], \"Image and Mask must have the same batch size\"\n\n # Check image is in [-1, 1]\n if image.min() < -1 or image.max() > 1:\n raise ValueError(\"Image should be in [-1, 1] range\")\n\n # Check mask is in [0, 1]\n if mask.min() < 0 or mask.max() > 1:\n raise ValueError(\"Mask should be in [0, 1] range\")\n\n # Binarize mask\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n\n # Image as float32\n image = image.to(dtype=torch.float32)\n elif isinstance(mask, torch.Tensor):\n raise TypeError(f\"`mask` is a torch.Tensor but `image` (type: {type(image)} is not\")\n else:\n # preprocess image\n if isinstance(image, (PIL.Image.Image, np.ndarray)):\n image = [image]\n\n if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):\n image = [np.array(i.convert(\"RGB\"))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n elif isinstance(image, list) and isinstance(image[0], np.ndarray):\n image = np.concatenate([i[None, :] for i in image], axis=0)\n\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0\n\n # preprocess mask\n if isinstance(mask, (PIL.Image.Image, np.ndarray)):\n mask = [mask]\n\n if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):\n mask = np.concatenate([np.array(m.convert(\"L\"))[None, None, :] for m in mask], axis=0)\n mask = mask.astype(np.float32) / 255.0\n elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):\n mask = np.concatenate([m[None, None, :] for m in mask], axis=0)\n\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n mask = torch.from_numpy(mask)\n\n # masked_image = image * (mask >= 0.5)\n masked_image = image\n\n return mask, masked_image\n\n\nclass StableDiffusionRepaintPipeline(\n DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, LoraLoaderMixin\n):\n r\"\"\"\n Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*.\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n In addition the pipeline inherits the following loading methods:\n - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]\n - *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`]\n as well as the following saving methods:\n - *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`]\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"skip_prk_steps\") and scheduler.config.skip_prk_steps is False:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration\"\n \" `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make\"\n \" sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to\"\n \" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face\"\n \" Hub, it would be very nice if you could open a Pull request for the\"\n \" `scheduler/scheduler_config.json` file\"\n )\n deprecate(\n \"skip_prk_steps not set\",\n \"1.0.0\",\n deprecation_message,\n standard_warn=False,\n )\n new_config = dict(scheduler.config)\n new_config[\"skip_prk_steps\"] = True\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n # Check shapes, assume num_channels_latents == 4, num_channels_mask == 1, num_channels_masked == 4\n if unet.config.in_channels != 4:\n logger.warning(\n f\"You have loaded a UNet with {unet.config.in_channels} input channels, whereas by default,\"\n f\" {self.__class__} assumes that `pipeline.unet` has 4 input channels: 4 for `num_channels_latents`,\"\n \". If you did not intend to modify\"\n \" this behavior, please check whether you have loaded the right checkpoint.\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n \"\"\"\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n prompt_embeds = prompt_embeds[0]\n\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n else:\n has_nsfw_concept = None\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents).sample\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(\n self,\n batch_size,\n num_channels_latents,\n height,\n width,\n dtype,\n device,\n generator,\n latents=None,\n ):\n shape = (\n batch_size,\n num_channels_latents,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def prepare_mask_latents(\n self,\n mask,\n masked_image,\n batch_size,\n height,\n width,\n dtype,\n device,\n generator,\n do_classifier_free_guidance,\n ):\n # resize the mask to latents shape as we concatenate the mask to the latents\n # we do that before converting to dtype to avoid breaking in case we're using cpu_offload\n # and half precision\n mask = torch.nn.functional.interpolate(\n mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)\n )\n mask = mask.to(device=device, dtype=dtype)\n\n masked_image = masked_image.to(device=device, dtype=dtype)\n\n # encode the mask image into latents space so we can concatenate it to the latents\n if isinstance(generator, list):\n masked_image_latents = [\n self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i])\n for i in range(batch_size)\n ]\n masked_image_latents = torch.cat(masked_image_latents, dim=0)\n else:\n masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)\n masked_image_latents = self.vae.config.scaling_factor * masked_image_latents\n\n # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method\n if mask.shape[0] < batch_size:\n if not batch_size % mask.shape[0] == 0:\n raise ValueError(\n \"The passed mask and the required batch size don't match. Masks are supposed to be duplicated to\"\n f\" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number\"\n \" of masks that you pass is divisible by the total requested batch size.\"\n )\n mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)\n if masked_image_latents.shape[0] < batch_size:\n if not batch_size % masked_image_latents.shape[0] == 0:\n raise ValueError(\n \"The passed images and the required batch size don't match. Images are supposed to be duplicated\"\n f\" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed.\"\n \" Make sure the number of images that you pass is divisible by the total requested batch size.\"\n )\n masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)\n\n mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask\n masked_image_latents = (\n torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents\n )\n\n # aligning device to prevent device errors when concating it with the latent model input\n masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)\n return mask, masked_image_latents\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: Union[torch.Tensor, PIL.Image.Image] = None,\n mask_image: Union[torch.Tensor, PIL.Image.Image] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n jump_length: Optional[int] = 10,\n jump_n_sample: Optional[int] = 10,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n image (`PIL.Image.Image`):\n `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will\n be masked out with `mask_image` and repainted according to `prompt`.\n mask_image (`PIL.Image.Image`):\n `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be\n repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted\n to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)\n instead of 3, so the expected shape would be `(B, H, W, 1)`.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n jump_length (`int`, *optional*, defaults to 10):\n The number of steps taken forward in time before going backward in time for a single jump (\"j\" in\n RePaint paper). Take a look at Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf.\n jump_n_sample (`int`, *optional*, defaults to 10):\n The number of times we will make forward time jump for a given chosen time sample. Take a look at\n Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale`\n is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n Examples:\n ```py\n >>> import PIL\n >>> import requests\n >>> import torch\n >>> from io import BytesIO\n >>> from diffusers import StableDiffusionPipeline, RePaintScheduler\n >>> def download_image(url):\n ... response = requests.get(url)\n ... return PIL.Image.open(BytesIO(response.content)).convert(\"RGB\")\n >>> base_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/\"\n >>> img_url = base_url + \"overture-creations-5sI6fQgYIuo.png\"\n >>> mask_url = base_url + \"overture-creations-5sI6fQgYIuo_mask.png \"\n >>> init_image = download_image(img_url).resize((512, 512))\n >>> mask_image = download_image(mask_url).resize((512, 512))\n >>> pipe = DiffusionPipeline.from_pretrained(\n ... \"CompVis/stable-diffusion-v1-4\", torch_dtype=torch.float16, custom_pipeline=\"stable_diffusion_repaint\",\n ... )\n >>> pipe.scheduler = RePaintScheduler.from_config(pipe.scheduler.config)\n >>> pipe = pipe.to(\"cuda\")\n >>> prompt = \"Face of a yellow cat, high resolution, sitting on a park bench\"\n >>> image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]\n ```\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs\n self.check_inputs(\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n )\n\n if image is None:\n raise ValueError(\"`image` input cannot be undefined.\")\n\n if mask_image is None:\n raise ValueError(\"`mask_image` input cannot be undefined.\")\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n prompt_embeds = self._encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n\n # 4. Preprocess mask and image\n mask, masked_image = prepare_mask_and_masked_image(image, mask_image)\n\n # 5. set timesteps\n self.scheduler.set_timesteps(num_inference_steps, jump_length, jump_n_sample, device)\n self.scheduler.eta = eta\n\n timesteps = self.scheduler.timesteps\n # latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n\n # 6. Prepare latent variables\n num_channels_latents = self.vae.config.latent_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 7. Prepare mask latent variables\n mask, masked_image_latents = self.prepare_mask_latents(\n mask,\n masked_image,\n batch_size * num_images_per_prompt,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n do_classifier_free_guidance=False, # We do not need duplicate mask and image\n )\n\n # 8. Check that sizes of mask, masked image and latents match\n # num_channels_mask = mask.shape[1]\n # num_channels_masked_image = masked_image_latents.shape[1]\n if num_channels_latents != self.unet.config.in_channels:\n raise ValueError(\n f\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\"\n f\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} \"\n f\" = Please verify the config of\"\n \" `pipeline.unet` or your `mask_image` or `image` input.\"\n )\n\n # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n t_last = timesteps[0] + 1\n\n # 10. Denoising loop\n with self.progress_bar(total=len(timesteps)) as progress_bar:\n for i, t in enumerate(timesteps):\n if t >= t_last:\n # compute the reverse: x_t-1 -> x_t\n latents = self.scheduler.undo_step(latents, t_last, generator)\n progress_bar.update()\n t_last = t\n continue\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n\n # concat latents, mask, masked_image_latents in the channel dimension\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n # latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(\n noise_pred,\n t,\n latents,\n masked_image_latents,\n mask,\n **extra_step_kwargs,\n ).prev_sample\n\n # call the callback, if provided\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n t_last = t\n\n # 11. Post-processing\n image = self.decode_latents(latents)\n\n # 12. Run safety checker\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # 13. Convert to PIL\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n # Offload last model to CPU\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/examples/community/stable_diffusion_tensorrt_img2img.py", "content": "#\n# Copyright 2024 The HuggingFace Inc. team.\n# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n# SPDX-License-Identifier: Apache-2.0\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport os\nfrom collections import OrderedDict\nfrom copy import copy\nfrom typing import List, Optional, Union\n\nimport numpy as np\nimport onnx\nimport onnx_graphsurgeon as gs\nimport PIL.Image\nimport tensorrt as trt\nimport torch\nfrom huggingface_hub import snapshot_download\nfrom huggingface_hub.utils import validate_hf_hub_args\nfrom onnx import shape_inference\nfrom polygraphy import cuda\nfrom polygraphy.backend.common import bytes_from_path\nfrom polygraphy.backend.onnx.loader import fold_constants\nfrom polygraphy.backend.trt import (\n CreateConfig,\n Profile,\n engine_from_bytes,\n engine_from_network,\n network_from_onnx_path,\n save_engine,\n)\nfrom polygraphy.backend.trt import util as trt_util\nfrom transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.pipelines.stable_diffusion import (\n StableDiffusionImg2ImgPipeline,\n StableDiffusionPipelineOutput,\n StableDiffusionSafetyChecker,\n)\nfrom diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import retrieve_latents\nfrom diffusers.schedulers import DDIMScheduler\nfrom diffusers.utils import logging\n\n\n\"\"\"\nInstallation instructions\npython3 -m pip install --upgrade transformers diffusers>=0.16.0\npython3 -m pip install --upgrade tensorrt>=8.6.1\npython3 -m pip install --upgrade polygraphy>=0.47.0 onnx-graphsurgeon --extra-index-url https://pypi.ngc.nvidia.com\npython3 -m pip install onnxruntime\n\"\"\"\n\nTRT_LOGGER = trt.Logger(trt.Logger.ERROR)\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n# Map of numpy dtype -> torch dtype\nnumpy_to_torch_dtype_dict = {\n np.uint8: torch.uint8,\n np.int8: torch.int8,\n np.int16: torch.int16,\n np.int32: torch.int32,\n np.int64: torch.int64,\n np.float16: torch.float16,\n np.float32: torch.float32,\n np.float64: torch.float64,\n np.complex64: torch.complex64,\n np.complex128: torch.complex128,\n}\nif np.version.full_version >= \"1.24.0\":\n numpy_to_torch_dtype_dict[np.bool_] = torch.bool\nelse:\n numpy_to_torch_dtype_dict[np.bool] = torch.bool\n\n# Map of torch dtype -> numpy dtype\ntorch_to_numpy_dtype_dict = {value: key for (key, value) in numpy_to_torch_dtype_dict.items()}\n\n\ndef device_view(t):\n return cuda.DeviceView(ptr=t.data_ptr(), shape=t.shape, dtype=torch_to_numpy_dtype_dict[t.dtype])\n\n\ndef preprocess_image(image):\n \"\"\"\n image: torch.Tensor\n \"\"\"\n w, h = image.size\n w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32\n image = image.resize((w, h))\n image = np.array(image).astype(np.float32) / 255.0\n image = image[None].transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).contiguous()\n return 2.0 * image - 1.0\n\n\nclass Engine:\n def __init__(self, engine_path):\n self.engine_path = engine_path\n self.engine = None\n self.context = None\n self.buffers = OrderedDict()\n self.tensors = OrderedDict()\n\n def __del__(self):\n [buf.free() for buf in self.buffers.values() if isinstance(buf, cuda.DeviceArray)]\n del self.engine\n del self.context\n del self.buffers\n del self.tensors\n\n def build(\n self,\n onnx_path,\n fp16,\n input_profile=None,\n enable_preview=False,\n enable_all_tactics=False,\n timing_cache=None,\n workspace_size=0,\n ):\n logger.warning(f\"Building TensorRT engine for {onnx_path}: {self.engine_path}\")\n p = Profile()\n if input_profile:\n for name, dims in input_profile.items():\n assert len(dims) == 3\n p.add(name, min=dims[0], opt=dims[1], max=dims[2])\n\n config_kwargs = {}\n\n config_kwargs[\"preview_features\"] = [trt.PreviewFeature.DISABLE_EXTERNAL_TACTIC_SOURCES_FOR_CORE_0805]\n if enable_preview:\n # Faster dynamic shapes made optional since it increases engine build time.\n config_kwargs[\"preview_features\"].append(trt.PreviewFeature.FASTER_DYNAMIC_SHAPES_0805)\n if workspace_size > 0:\n config_kwargs[\"memory_pool_limits\"] = {trt.MemoryPoolType.WORKSPACE: workspace_size}\n if not enable_all_tactics:\n config_kwargs[\"tactic_sources\"] = []\n\n engine = engine_from_network(\n network_from_onnx_path(onnx_path, flags=[trt.OnnxParserFlag.NATIVE_INSTANCENORM]),\n config=CreateConfig(fp16=fp16, profiles=[p], load_timing_cache=timing_cache, **config_kwargs),\n save_timing_cache=timing_cache,\n )\n save_engine(engine, path=self.engine_path)\n\n def load(self):\n logger.warning(f\"Loading TensorRT engine: {self.engine_path}\")\n self.engine = engine_from_bytes(bytes_from_path(self.engine_path))\n\n def activate(self):\n self.context = self.engine.create_execution_context()\n\n def allocate_buffers(self, shape_dict=None, device=\"cuda\"):\n for idx in range(trt_util.get_bindings_per_profile(self.engine)):\n binding = self.engine[idx]\n if shape_dict and binding in shape_dict:\n shape = shape_dict[binding]\n else:\n shape = self.engine.get_binding_shape(binding)\n dtype = trt.nptype(self.engine.get_binding_dtype(binding))\n if self.engine.binding_is_input(binding):\n self.context.set_binding_shape(idx, shape)\n tensor = torch.empty(tuple(shape), dtype=numpy_to_torch_dtype_dict[dtype]).to(device=device)\n self.tensors[binding] = tensor\n self.buffers[binding] = cuda.DeviceView(ptr=tensor.data_ptr(), shape=shape, dtype=dtype)\n\n def infer(self, feed_dict, stream):\n start_binding, end_binding = trt_util.get_active_profile_bindings(self.context)\n # shallow copy of ordered dict\n device_buffers = copy(self.buffers)\n for name, buf in feed_dict.items():\n assert isinstance(buf, cuda.DeviceView)\n device_buffers[name] = buf\n bindings = [0] * start_binding + [buf.ptr for buf in device_buffers.values()]\n noerror = self.context.execute_async_v2(bindings=bindings, stream_handle=stream.ptr)\n if not noerror:\n raise ValueError(\"ERROR: inference failed.\")\n\n return self.tensors\n\n\nclass Optimizer:\n def __init__(self, onnx_graph):\n self.graph = gs.import_onnx(onnx_graph)\n\n def cleanup(self, return_onnx=False):\n self.graph.cleanup().toposort()\n if return_onnx:\n return gs.export_onnx(self.graph)\n\n def select_outputs(self, keep, names=None):\n self.graph.outputs = [self.graph.outputs[o] for o in keep]\n if names:\n for i, name in enumerate(names):\n self.graph.outputs[i].name = name\n\n def fold_constants(self, return_onnx=False):\n onnx_graph = fold_constants(gs.export_onnx(self.graph), allow_onnxruntime_shape_inference=True)\n self.graph = gs.import_onnx(onnx_graph)\n if return_onnx:\n return onnx_graph\n\n def infer_shapes(self, return_onnx=False):\n onnx_graph = gs.export_onnx(self.graph)\n if onnx_graph.ByteSize() > 2147483648:\n raise TypeError(\"ERROR: model size exceeds supported 2GB limit\")\n else:\n onnx_graph = shape_inference.infer_shapes(onnx_graph)\n\n self.graph = gs.import_onnx(onnx_graph)\n if return_onnx:\n return onnx_graph\n\n\nclass BaseModel:\n def __init__(self, model, fp16=False, device=\"cuda\", max_batch_size=16, embedding_dim=768, text_maxlen=77):\n self.model = model\n self.name = \"SD Model\"\n self.fp16 = fp16\n self.device = device\n\n self.min_batch = 1\n self.max_batch = max_batch_size\n self.min_image_shape = 256 # min image resolution: 256x256\n self.max_image_shape = 1024 # max image resolution: 1024x1024\n self.min_latent_shape = self.min_image_shape // 8\n self.max_latent_shape = self.max_image_shape // 8\n\n self.embedding_dim = embedding_dim\n self.text_maxlen = text_maxlen\n\n def get_model(self):\n return self.model\n\n def get_input_names(self):\n pass\n\n def get_output_names(self):\n pass\n\n def get_dynamic_axes(self):\n return None\n\n def get_sample_input(self, batch_size, image_height, image_width):\n pass\n\n def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):\n return None\n\n def get_shape_dict(self, batch_size, image_height, image_width):\n return None\n\n def optimize(self, onnx_graph):\n opt = Optimizer(onnx_graph)\n opt.cleanup()\n opt.fold_constants()\n opt.infer_shapes()\n onnx_opt_graph = opt.cleanup(return_onnx=True)\n return onnx_opt_graph\n\n def check_dims(self, batch_size, image_height, image_width):\n assert batch_size >= self.min_batch and batch_size <= self.max_batch\n assert image_height % 8 == 0 or image_width % 8 == 0\n latent_height = image_height // 8\n latent_width = image_width // 8\n assert latent_height >= self.min_latent_shape and latent_height <= self.max_latent_shape\n assert latent_width >= self.min_latent_shape and latent_width <= self.max_latent_shape\n return (latent_height, latent_width)\n\n def get_minmax_dims(self, batch_size, image_height, image_width, static_batch, static_shape):\n min_batch = batch_size if static_batch else self.min_batch\n max_batch = batch_size if static_batch else self.max_batch\n latent_height = image_height // 8\n latent_width = image_width // 8\n min_image_height = image_height if static_shape else self.min_image_shape\n max_image_height = image_height if static_shape else self.max_image_shape\n min_image_width = image_width if static_shape else self.min_image_shape\n max_image_width = image_width if static_shape else self.max_image_shape\n min_latent_height = latent_height if static_shape else self.min_latent_shape\n max_latent_height = latent_height if static_shape else self.max_latent_shape\n min_latent_width = latent_width if static_shape else self.min_latent_shape\n max_latent_width = latent_width if static_shape else self.max_latent_shape\n return (\n min_batch,\n max_batch,\n min_image_height,\n max_image_height,\n min_image_width,\n max_image_width,\n min_latent_height,\n max_latent_height,\n min_latent_width,\n max_latent_width,\n )\n\n\ndef getOnnxPath(model_name, onnx_dir, opt=True):\n return os.path.join(onnx_dir, model_name + (\".opt\" if opt else \"\") + \".onnx\")\n\n\ndef getEnginePath(model_name, engine_dir):\n return os.path.join(engine_dir, model_name + \".plan\")\n\n\ndef build_engines(\n models: dict,\n engine_dir,\n onnx_dir,\n onnx_opset,\n opt_image_height,\n opt_image_width,\n opt_batch_size=1,\n force_engine_rebuild=False,\n static_batch=False,\n static_shape=True,\n enable_preview=False,\n enable_all_tactics=False,\n timing_cache=None,\n max_workspace_size=0,\n):\n built_engines = {}\n if not os.path.isdir(onnx_dir):\n os.makedirs(onnx_dir)\n if not os.path.isdir(engine_dir):\n os.makedirs(engine_dir)\n\n # Export models to ONNX\n for model_name, model_obj in models.items():\n engine_path = getEnginePath(model_name, engine_dir)\n if force_engine_rebuild or not os.path.exists(engine_path):\n logger.warning(\"Building Engines...\")\n logger.warning(\"Engine build can take a while to complete\")\n onnx_path = getOnnxPath(model_name, onnx_dir, opt=False)\n onnx_opt_path = getOnnxPath(model_name, onnx_dir)\n if force_engine_rebuild or not os.path.exists(onnx_opt_path):\n if force_engine_rebuild or not os.path.exists(onnx_path):\n logger.warning(f\"Exporting model: {onnx_path}\")\n model = model_obj.get_model()\n with torch.inference_mode(), torch.autocast(\"cuda\"):\n inputs = model_obj.get_sample_input(opt_batch_size, opt_image_height, opt_image_width)\n torch.onnx.export(\n model,\n inputs,\n onnx_path,\n export_params=True,\n opset_version=onnx_opset,\n do_constant_folding=True,\n input_names=model_obj.get_input_names(),\n output_names=model_obj.get_output_names(),\n dynamic_axes=model_obj.get_dynamic_axes(),\n )\n del model\n torch.cuda.empty_cache()\n gc.collect()\n else:\n logger.warning(f\"Found cached model: {onnx_path}\")\n\n # Optimize onnx\n if force_engine_rebuild or not os.path.exists(onnx_opt_path):\n logger.warning(f\"Generating optimizing model: {onnx_opt_path}\")\n onnx_opt_graph = model_obj.optimize(onnx.load(onnx_path))\n onnx.save(onnx_opt_graph, onnx_opt_path)\n else:\n logger.warning(f\"Found cached optimized model: {onnx_opt_path} \")\n\n # Build TensorRT engines\n for model_name, model_obj in models.items():\n engine_path = getEnginePath(model_name, engine_dir)\n engine = Engine(engine_path)\n onnx_path = getOnnxPath(model_name, onnx_dir, opt=False)\n onnx_opt_path = getOnnxPath(model_name, onnx_dir)\n\n if force_engine_rebuild or not os.path.exists(engine.engine_path):\n engine.build(\n onnx_opt_path,\n fp16=True,\n input_profile=model_obj.get_input_profile(\n opt_batch_size,\n opt_image_height,\n opt_image_width,\n static_batch=static_batch,\n static_shape=static_shape,\n ),\n enable_preview=enable_preview,\n timing_cache=timing_cache,\n workspace_size=max_workspace_size,\n )\n built_engines[model_name] = engine\n\n # Load and activate TensorRT engines\n for model_name, model_obj in models.items():\n engine = built_engines[model_name]\n engine.load()\n engine.activate()\n\n return built_engines\n\n\ndef runEngine(engine, feed_dict, stream):\n return engine.infer(feed_dict, stream)\n\n\nclass CLIP(BaseModel):\n def __init__(self, model, device, max_batch_size, embedding_dim):\n super(CLIP, self).__init__(\n model=model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim\n )\n self.name = \"CLIP\"\n\n def get_input_names(self):\n return [\"input_ids\"]\n\n def get_output_names(self):\n return [\"text_embeddings\", \"pooler_output\"]\n\n def get_dynamic_axes(self):\n return {\"input_ids\": {0: \"B\"}, \"text_embeddings\": {0: \"B\"}}\n\n def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):\n self.check_dims(batch_size, image_height, image_width)\n min_batch, max_batch, _, _, _, _, _, _, _, _ = self.get_minmax_dims(\n batch_size, image_height, image_width, static_batch, static_shape\n )\n return {\n \"input_ids\": [(min_batch, self.text_maxlen), (batch_size, self.text_maxlen), (max_batch, self.text_maxlen)]\n }\n\n def get_shape_dict(self, batch_size, image_height, image_width):\n self.check_dims(batch_size, image_height, image_width)\n return {\n \"input_ids\": (batch_size, self.text_maxlen),\n \"text_embeddings\": (batch_size, self.text_maxlen, self.embedding_dim),\n }\n\n def get_sample_input(self, batch_size, image_height, image_width):\n self.check_dims(batch_size, image_height, image_width)\n return torch.zeros(batch_size, self.text_maxlen, dtype=torch.int32, device=self.device)\n\n def optimize(self, onnx_graph):\n opt = Optimizer(onnx_graph)\n opt.select_outputs([0]) # delete graph output#1\n opt.cleanup()\n opt.fold_constants()\n opt.infer_shapes()\n opt.select_outputs([0], names=[\"text_embeddings\"]) # rename network output\n opt_onnx_graph = opt.cleanup(return_onnx=True)\n return opt_onnx_graph\n\n\ndef make_CLIP(model, device, max_batch_size, embedding_dim, inpaint=False):\n return CLIP(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim)\n\n\nclass UNet(BaseModel):\n def __init__(\n self, model, fp16=False, device=\"cuda\", max_batch_size=16, embedding_dim=768, text_maxlen=77, unet_dim=4\n ):\n super(UNet, self).__init__(\n model=model,\n fp16=fp16,\n device=device,\n max_batch_size=max_batch_size,\n embedding_dim=embedding_dim,\n text_maxlen=text_maxlen,\n )\n self.unet_dim = unet_dim\n self.name = \"UNet\"\n\n def get_input_names(self):\n return [\"sample\", \"timestep\", \"encoder_hidden_states\"]\n\n def get_output_names(self):\n return [\"latent\"]\n\n def get_dynamic_axes(self):\n return {\n \"sample\": {0: \"2B\", 2: \"H\", 3: \"W\"},\n \"encoder_hidden_states\": {0: \"2B\"},\n \"latent\": {0: \"2B\", 2: \"H\", 3: \"W\"},\n }\n\n def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):\n latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)\n (\n min_batch,\n max_batch,\n _,\n _,\n _,\n _,\n min_latent_height,\n max_latent_height,\n min_latent_width,\n max_latent_width,\n ) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)\n return {\n \"sample\": [\n (2 * min_batch, self.unet_dim, min_latent_height, min_latent_width),\n (2 * batch_size, self.unet_dim, latent_height, latent_width),\n (2 * max_batch, self.unet_dim, max_latent_height, max_latent_width),\n ],\n \"encoder_hidden_states\": [\n (2 * min_batch, self.text_maxlen, self.embedding_dim),\n (2 * batch_size, self.text_maxlen, self.embedding_dim),\n (2 * max_batch, self.text_maxlen, self.embedding_dim),\n ],\n }\n\n def get_shape_dict(self, batch_size, image_height, image_width):\n latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)\n return {\n \"sample\": (2 * batch_size, self.unet_dim, latent_height, latent_width),\n \"encoder_hidden_states\": (2 * batch_size, self.text_maxlen, self.embedding_dim),\n \"latent\": (2 * batch_size, 4, latent_height, latent_width),\n }\n\n def get_sample_input(self, batch_size, image_height, image_width):\n latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)\n dtype = torch.float16 if self.fp16 else torch.float32\n return (\n torch.randn(\n 2 * batch_size, self.unet_dim, latent_height, latent_width, dtype=torch.float32, device=self.device\n ),\n torch.tensor([1.0], dtype=torch.float32, device=self.device),\n torch.randn(2 * batch_size, self.text_maxlen, self.embedding_dim, dtype=dtype, device=self.device),\n )\n\n\ndef make_UNet(model, device, max_batch_size, embedding_dim, inpaint=False):\n return UNet(\n model,\n fp16=True,\n device=device,\n max_batch_size=max_batch_size,\n embedding_dim=embedding_dim,\n unet_dim=(9 if inpaint else 4),\n )\n\n\nclass VAE(BaseModel):\n def __init__(self, model, device, max_batch_size, embedding_dim):\n super(VAE, self).__init__(\n model=model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim\n )\n self.name = \"VAE decoder\"\n\n def get_input_names(self):\n return [\"latent\"]\n\n def get_output_names(self):\n return [\"images\"]\n\n def get_dynamic_axes(self):\n return {\"latent\": {0: \"B\", 2: \"H\", 3: \"W\"}, \"images\": {0: \"B\", 2: \"8H\", 3: \"8W\"}}\n\n def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):\n latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)\n (\n min_batch,\n max_batch,\n _,\n _,\n _,\n _,\n min_latent_height,\n max_latent_height,\n min_latent_width,\n max_latent_width,\n ) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)\n return {\n \"latent\": [\n (min_batch, 4, min_latent_height, min_latent_width),\n (batch_size, 4, latent_height, latent_width),\n (max_batch, 4, max_latent_height, max_latent_width),\n ]\n }\n\n def get_shape_dict(self, batch_size, image_height, image_width):\n latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)\n return {\n \"latent\": (batch_size, 4, latent_height, latent_width),\n \"images\": (batch_size, 3, image_height, image_width),\n }\n\n def get_sample_input(self, batch_size, image_height, image_width):\n latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)\n return torch.randn(batch_size, 4, latent_height, latent_width, dtype=torch.float32, device=self.device)\n\n\ndef make_VAE(model, device, max_batch_size, embedding_dim, inpaint=False):\n return VAE(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim)\n\n\nclass TorchVAEEncoder(torch.nn.Module):\n def __init__(self, model):\n super().__init__()\n self.vae_encoder = model\n\n def forward(self, x):\n return retrieve_latents(self.vae_encoder.encode(x))\n\n\nclass VAEEncoder(BaseModel):\n def __init__(self, model, device, max_batch_size, embedding_dim):\n super(VAEEncoder, self).__init__(\n model=model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim\n )\n self.name = \"VAE encoder\"\n\n def get_model(self):\n vae_encoder = TorchVAEEncoder(self.model)\n return vae_encoder\n\n def get_input_names(self):\n return [\"images\"]\n\n def get_output_names(self):\n return [\"latent\"]\n\n def get_dynamic_axes(self):\n return {\"images\": {0: \"B\", 2: \"8H\", 3: \"8W\"}, \"latent\": {0: \"B\", 2: \"H\", 3: \"W\"}}\n\n def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):\n assert batch_size >= self.min_batch and batch_size <= self.max_batch\n min_batch = batch_size if static_batch else self.min_batch\n max_batch = batch_size if static_batch else self.max_batch\n self.check_dims(batch_size, image_height, image_width)\n (\n min_batch,\n max_batch,\n min_image_height,\n max_image_height,\n min_image_width,\n max_image_width,\n _,\n _,\n _,\n _,\n ) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)\n\n return {\n \"images\": [\n (min_batch, 3, min_image_height, min_image_width),\n (batch_size, 3, image_height, image_width),\n (max_batch, 3, max_image_height, max_image_width),\n ]\n }\n\n def get_shape_dict(self, batch_size, image_height, image_width):\n latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)\n return {\n \"images\": (batch_size, 3, image_height, image_width),\n \"latent\": (batch_size, 4, latent_height, latent_width),\n }\n\n def get_sample_input(self, batch_size, image_height, image_width):\n self.check_dims(batch_size, image_height, image_width)\n return torch.randn(batch_size, 3, image_height, image_width, dtype=torch.float32, device=self.device)\n\n\ndef make_VAEEncoder(model, device, max_batch_size, embedding_dim, inpaint=False):\n return VAEEncoder(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim)\n\n\nclass TensorRTStableDiffusionImg2ImgPipeline(StableDiffusionImg2ImgPipeline):\n r\"\"\"\n Pipeline for image-to-image generation using TensorRT accelerated Stable Diffusion.\n\n This model inherits from [`StableDiffusionImg2ImgPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPFeatureExtractor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: DDIMScheduler,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPFeatureExtractor,\n image_encoder: CLIPVisionModelWithProjection = None,\n requires_safety_checker: bool = True,\n stages=[\"clip\", \"unet\", \"vae\", \"vae_encoder\"],\n image_height: int = 512,\n image_width: int = 512,\n max_batch_size: int = 16,\n # ONNX export parameters\n onnx_opset: int = 17,\n onnx_dir: str = \"onnx\",\n # TensorRT engine build parameters\n engine_dir: str = \"engine\",\n build_preview_features: bool = True,\n force_engine_rebuild: bool = False,\n timing_cache: str = \"timing_cache\",\n ):\n super().__init__(\n vae,\n text_encoder,\n tokenizer,\n unet,\n scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n requires_safety_checker=requires_safety_checker,\n )\n\n self.vae.forward = self.vae.decode\n\n self.stages = stages\n self.image_height, self.image_width = image_height, image_width\n self.inpaint = False\n self.onnx_opset = onnx_opset\n self.onnx_dir = onnx_dir\n self.engine_dir = engine_dir\n self.force_engine_rebuild = force_engine_rebuild\n self.timing_cache = timing_cache\n self.build_static_batch = False\n self.build_dynamic_shape = False\n self.build_preview_features = build_preview_features\n\n self.max_batch_size = max_batch_size\n # TODO: Restrict batch size to 4 for larger image dimensions as a WAR for TensorRT limitation.\n if self.build_dynamic_shape or self.image_height > 512 or self.image_width > 512:\n self.max_batch_size = 4\n\n self.stream = None # loaded in loadResources()\n self.models = {} # loaded in __loadModels()\n self.engine = {} # loaded in build_engines()\n\n def __loadModels(self):\n # Load pipeline models\n self.embedding_dim = self.text_encoder.config.hidden_size\n models_args = {\n \"device\": self.torch_device,\n \"max_batch_size\": self.max_batch_size,\n \"embedding_dim\": self.embedding_dim,\n \"inpaint\": self.inpaint,\n }\n if \"clip\" in self.stages:\n self.models[\"clip\"] = make_CLIP(self.text_encoder, **models_args)\n if \"unet\" in self.stages:\n self.models[\"unet\"] = make_UNet(self.unet, **models_args)\n if \"vae\" in self.stages:\n self.models[\"vae\"] = make_VAE(self.vae, **models_args)\n if \"vae_encoder\" in self.stages:\n self.models[\"vae_encoder\"] = make_VAEEncoder(self.vae, **models_args)\n\n @classmethod\n @validate_hf_hub_args\n def set_cached_folder(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):\n cache_dir = kwargs.pop(\"cache_dir\", None)\n resume_download = kwargs.pop(\"resume_download\", False)\n proxies = kwargs.pop(\"proxies\", None)\n local_files_only = kwargs.pop(\"local_files_only\", False)\n token = kwargs.pop(\"token\", None)\n revision = kwargs.pop(\"revision\", None)\n\n cls.cached_folder = (\n pretrained_model_name_or_path\n if os.path.isdir(pretrained_model_name_or_path)\n else snapshot_download(\n pretrained_model_name_or_path,\n cache_dir=cache_dir,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n )\n )\n\n def to(self, torch_device: Optional[Union[str, torch.device]] = None, silence_dtype_warnings: bool = False):\n super().to(torch_device, silence_dtype_warnings=silence_dtype_warnings)\n\n self.onnx_dir = os.path.join(self.cached_folder, self.onnx_dir)\n self.engine_dir = os.path.join(self.cached_folder, self.engine_dir)\n self.timing_cache = os.path.join(self.cached_folder, self.timing_cache)\n\n # set device\n self.torch_device = self._execution_device\n logger.warning(f\"Running inference on device: {self.torch_device}\")\n\n # load models\n self.__loadModels()\n\n # build engines\n self.engine = build_engines(\n self.models,\n self.engine_dir,\n self.onnx_dir,\n self.onnx_opset,\n opt_image_height=self.image_height,\n opt_image_width=self.image_width,\n force_engine_rebuild=self.force_engine_rebuild,\n static_batch=self.build_static_batch,\n static_shape=not self.build_dynamic_shape,\n enable_preview=self.build_preview_features,\n timing_cache=self.timing_cache,\n )\n\n return self\n\n def __initialize_timesteps(self, timesteps, strength):\n self.scheduler.set_timesteps(timesteps)\n offset = self.scheduler.steps_offset if hasattr(self.scheduler, \"steps_offset\") else 0\n init_timestep = int(timesteps * strength) + offset\n init_timestep = min(init_timestep, timesteps)\n t_start = max(timesteps - init_timestep + offset, 0)\n timesteps = self.scheduler.timesteps[t_start:].to(self.torch_device)\n return timesteps, t_start\n\n def __preprocess_images(self, batch_size, images=()):\n init_images = []\n for image in images:\n image = image.to(self.torch_device).float()\n image = image.repeat(batch_size, 1, 1, 1)\n init_images.append(image)\n return tuple(init_images)\n\n def __encode_image(self, init_image):\n init_latents = runEngine(self.engine[\"vae_encoder\"], {\"images\": device_view(init_image)}, self.stream)[\n \"latent\"\n ]\n init_latents = 0.18215 * init_latents\n return init_latents\n\n def __encode_prompt(self, prompt, negative_prompt):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.\n Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n \"\"\"\n # Tokenize prompt\n text_input_ids = (\n self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n .input_ids.type(torch.int32)\n .to(self.torch_device)\n )\n\n text_input_ids_inp = device_view(text_input_ids)\n # NOTE: output tensor for CLIP must be cloned because it will be overwritten when called again for negative prompt\n text_embeddings = runEngine(self.engine[\"clip\"], {\"input_ids\": text_input_ids_inp}, self.stream)[\n \"text_embeddings\"\n ].clone()\n\n # Tokenize negative prompt\n uncond_input_ids = (\n self.tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n .input_ids.type(torch.int32)\n .to(self.torch_device)\n )\n uncond_input_ids_inp = device_view(uncond_input_ids)\n uncond_embeddings = runEngine(self.engine[\"clip\"], {\"input_ids\": uncond_input_ids_inp}, self.stream)[\n \"text_embeddings\"\n ]\n\n # Concatenate the unconditional and text embeddings into a single batch to avoid doing two forward passes for classifier free guidance\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings]).to(dtype=torch.float16)\n\n return text_embeddings\n\n def __denoise_latent(\n self, latents, text_embeddings, timesteps=None, step_offset=0, mask=None, masked_image_latents=None\n ):\n if not isinstance(timesteps, torch.Tensor):\n timesteps = self.scheduler.timesteps\n for step_index, timestep in enumerate(timesteps):\n # Expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2)\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, timestep)\n if isinstance(mask, torch.Tensor):\n latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)\n\n # Predict the noise residual\n timestep_float = timestep.float() if timestep.dtype != torch.float32 else timestep\n\n sample_inp = device_view(latent_model_input)\n timestep_inp = device_view(timestep_float)\n embeddings_inp = device_view(text_embeddings)\n noise_pred = runEngine(\n self.engine[\"unet\"],\n {\"sample\": sample_inp, \"timestep\": timestep_inp, \"encoder_hidden_states\": embeddings_inp},\n self.stream,\n )[\"latent\"]\n\n # Perform guidance\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n latents = self.scheduler.step(noise_pred, timestep, latents).prev_sample\n\n latents = 1.0 / 0.18215 * latents\n return latents\n\n def __decode_latent(self, latents):\n images = runEngine(self.engine[\"vae\"], {\"latent\": device_view(latents)}, self.stream)[\"images\"]\n images = (images / 2 + 0.5).clamp(0, 1)\n return images.cpu().permute(0, 2, 3, 1).float().numpy()\n\n def __loadResources(self, image_height, image_width, batch_size):\n self.stream = cuda.Stream()\n\n # Allocate buffers for TensorRT engine bindings\n for model_name, obj in self.models.items():\n self.engine[model_name].allocate_buffers(\n shape_dict=obj.get_shape_dict(batch_size, image_height, image_width), device=self.torch_device\n )\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: Union[torch.Tensor, PIL.Image.Image] = None,\n strength: float = 0.8,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n image (`PIL.Image.Image`):\n `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will\n be masked out with `mask_image` and repainted according to `prompt`.\n strength (`float`, *optional*, defaults to 0.8):\n Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`\n will be used as a starting point, adding more noise to it the larger the `strength`. The number of\n denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will\n be maximum and the denoising process will run for the full number of iterations specified in\n `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.\n Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n\n \"\"\"\n self.generator = generator\n self.denoising_steps = num_inference_steps\n self._guidance_scale = guidance_scale\n\n # Pre-compute latent input scales and linear multistep coefficients\n self.scheduler.set_timesteps(self.denoising_steps, device=self.torch_device)\n\n # Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n prompt = [prompt]\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n raise ValueError(f\"Expected prompt to be of type list or str but got {type(prompt)}\")\n\n if negative_prompt is None:\n negative_prompt = [\"\"] * batch_size\n\n if negative_prompt is not None and isinstance(negative_prompt, str):\n negative_prompt = [negative_prompt]\n\n assert len(prompt) == len(negative_prompt)\n\n if batch_size > self.max_batch_size:\n raise ValueError(\n f\"Batch size {len(prompt)} is larger than allowed {self.max_batch_size}. If dynamic shape is used, then maximum batch size is 4\"\n )\n\n # load resources\n self.__loadResources(self.image_height, self.image_width, batch_size)\n\n with torch.inference_mode(), torch.autocast(\"cuda\"), trt.Runtime(TRT_LOGGER):\n # Initialize timesteps\n timesteps, t_start = self.__initialize_timesteps(self.denoising_steps, strength)\n latent_timestep = timesteps[:1].repeat(batch_size)\n\n # Pre-process input image\n if isinstance(image, PIL.Image.Image):\n image = preprocess_image(image)\n init_image = self.__preprocess_images(batch_size, (image,))[0]\n\n # VAE encode init image\n init_latents = self.__encode_image(init_image)\n\n # Add noise to latents using timesteps\n noise = torch.randn(\n init_latents.shape, generator=self.generator, device=self.torch_device, dtype=torch.float32\n )\n latents = self.scheduler.add_noise(init_latents, noise, latent_timestep)\n\n # CLIP text encoder\n text_embeddings = self.__encode_prompt(prompt, negative_prompt)\n\n # UNet denoiser\n latents = self.__denoise_latent(latents, text_embeddings, timesteps=timesteps, step_offset=t_start)\n\n # VAE decode latent\n images = self.__decode_latent(latents)\n\n images = self.numpy_to_pil(images)\n return StableDiffusionPipelineOutput(images=images, nsfw_content_detected=None)\n" }, { "path": "diffusers/examples/community/stable_diffusion_tensorrt_inpaint.py", "content": "#\n# Copyright 2024 The HuggingFace Inc. team.\n# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n# SPDX-License-Identifier: Apache-2.0\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport os\nfrom collections import OrderedDict\nfrom copy import copy\nfrom typing import List, Optional, Union\n\nimport numpy as np\nimport onnx\nimport onnx_graphsurgeon as gs\nimport PIL.Image\nimport tensorrt as trt\nimport torch\nfrom huggingface_hub import snapshot_download\nfrom huggingface_hub.utils import validate_hf_hub_args\nfrom onnx import shape_inference\nfrom polygraphy import cuda\nfrom polygraphy.backend.common import bytes_from_path\nfrom polygraphy.backend.onnx.loader import fold_constants\nfrom polygraphy.backend.trt import (\n CreateConfig,\n Profile,\n engine_from_bytes,\n engine_from_network,\n network_from_onnx_path,\n save_engine,\n)\nfrom polygraphy.backend.trt import util as trt_util\nfrom transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.pipelines.stable_diffusion import (\n StableDiffusionInpaintPipeline,\n StableDiffusionPipelineOutput,\n StableDiffusionSafetyChecker,\n)\nfrom diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint import prepare_mask_and_masked_image\nfrom diffusers.schedulers import DDIMScheduler\nfrom diffusers.utils import logging\n\n\n\"\"\"\nInstallation instructions\npython3 -m pip install --upgrade transformers diffusers>=0.16.0\npython3 -m pip install --upgrade tensorrt>=8.6.1\npython3 -m pip install --upgrade polygraphy>=0.47.0 onnx-graphsurgeon --extra-index-url https://pypi.ngc.nvidia.com\npython3 -m pip install onnxruntime\n\"\"\"\n\nTRT_LOGGER = trt.Logger(trt.Logger.ERROR)\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n# Map of numpy dtype -> torch dtype\nnumpy_to_torch_dtype_dict = {\n np.uint8: torch.uint8,\n np.int8: torch.int8,\n np.int16: torch.int16,\n np.int32: torch.int32,\n np.int64: torch.int64,\n np.float16: torch.float16,\n np.float32: torch.float32,\n np.float64: torch.float64,\n np.complex64: torch.complex64,\n np.complex128: torch.complex128,\n}\nif np.version.full_version >= \"1.24.0\":\n numpy_to_torch_dtype_dict[np.bool_] = torch.bool\nelse:\n numpy_to_torch_dtype_dict[np.bool] = torch.bool\n\n# Map of torch dtype -> numpy dtype\ntorch_to_numpy_dtype_dict = {value: key for (key, value) in numpy_to_torch_dtype_dict.items()}\n\n\ndef device_view(t):\n return cuda.DeviceView(ptr=t.data_ptr(), shape=t.shape, dtype=torch_to_numpy_dtype_dict[t.dtype])\n\n\ndef preprocess_image(image):\n \"\"\"\n image: torch.Tensor\n \"\"\"\n w, h = image.size\n w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32\n image = image.resize((w, h))\n image = np.array(image).astype(np.float32) / 255.0\n image = image[None].transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).contiguous()\n return 2.0 * image - 1.0\n\n\nclass Engine:\n def __init__(self, engine_path):\n self.engine_path = engine_path\n self.engine = None\n self.context = None\n self.buffers = OrderedDict()\n self.tensors = OrderedDict()\n\n def __del__(self):\n [buf.free() for buf in self.buffers.values() if isinstance(buf, cuda.DeviceArray)]\n del self.engine\n del self.context\n del self.buffers\n del self.tensors\n\n def build(\n self,\n onnx_path,\n fp16,\n input_profile=None,\n enable_preview=False,\n enable_all_tactics=False,\n timing_cache=None,\n workspace_size=0,\n ):\n logger.warning(f\"Building TensorRT engine for {onnx_path}: {self.engine_path}\")\n p = Profile()\n if input_profile:\n for name, dims in input_profile.items():\n assert len(dims) == 3\n p.add(name, min=dims[0], opt=dims[1], max=dims[2])\n\n config_kwargs = {}\n\n config_kwargs[\"preview_features\"] = [trt.PreviewFeature.DISABLE_EXTERNAL_TACTIC_SOURCES_FOR_CORE_0805]\n if enable_preview:\n # Faster dynamic shapes made optional since it increases engine build time.\n config_kwargs[\"preview_features\"].append(trt.PreviewFeature.FASTER_DYNAMIC_SHAPES_0805)\n if workspace_size > 0:\n config_kwargs[\"memory_pool_limits\"] = {trt.MemoryPoolType.WORKSPACE: workspace_size}\n if not enable_all_tactics:\n config_kwargs[\"tactic_sources\"] = []\n\n engine = engine_from_network(\n network_from_onnx_path(onnx_path, flags=[trt.OnnxParserFlag.NATIVE_INSTANCENORM]),\n config=CreateConfig(fp16=fp16, profiles=[p], load_timing_cache=timing_cache, **config_kwargs),\n save_timing_cache=timing_cache,\n )\n save_engine(engine, path=self.engine_path)\n\n def load(self):\n logger.warning(f\"Loading TensorRT engine: {self.engine_path}\")\n self.engine = engine_from_bytes(bytes_from_path(self.engine_path))\n\n def activate(self):\n self.context = self.engine.create_execution_context()\n\n def allocate_buffers(self, shape_dict=None, device=\"cuda\"):\n for idx in range(trt_util.get_bindings_per_profile(self.engine)):\n binding = self.engine[idx]\n if shape_dict and binding in shape_dict:\n shape = shape_dict[binding]\n else:\n shape = self.engine.get_binding_shape(binding)\n dtype = trt.nptype(self.engine.get_binding_dtype(binding))\n if self.engine.binding_is_input(binding):\n self.context.set_binding_shape(idx, shape)\n tensor = torch.empty(tuple(shape), dtype=numpy_to_torch_dtype_dict[dtype]).to(device=device)\n self.tensors[binding] = tensor\n self.buffers[binding] = cuda.DeviceView(ptr=tensor.data_ptr(), shape=shape, dtype=dtype)\n\n def infer(self, feed_dict, stream):\n start_binding, end_binding = trt_util.get_active_profile_bindings(self.context)\n # shallow copy of ordered dict\n device_buffers = copy(self.buffers)\n for name, buf in feed_dict.items():\n assert isinstance(buf, cuda.DeviceView)\n device_buffers[name] = buf\n bindings = [0] * start_binding + [buf.ptr for buf in device_buffers.values()]\n noerror = self.context.execute_async_v2(bindings=bindings, stream_handle=stream.ptr)\n if not noerror:\n raise ValueError(\"ERROR: inference failed.\")\n\n return self.tensors\n\n\nclass Optimizer:\n def __init__(self, onnx_graph):\n self.graph = gs.import_onnx(onnx_graph)\n\n def cleanup(self, return_onnx=False):\n self.graph.cleanup().toposort()\n if return_onnx:\n return gs.export_onnx(self.graph)\n\n def select_outputs(self, keep, names=None):\n self.graph.outputs = [self.graph.outputs[o] for o in keep]\n if names:\n for i, name in enumerate(names):\n self.graph.outputs[i].name = name\n\n def fold_constants(self, return_onnx=False):\n onnx_graph = fold_constants(gs.export_onnx(self.graph), allow_onnxruntime_shape_inference=True)\n self.graph = gs.import_onnx(onnx_graph)\n if return_onnx:\n return onnx_graph\n\n def infer_shapes(self, return_onnx=False):\n onnx_graph = gs.export_onnx(self.graph)\n if onnx_graph.ByteSize() > 2147483648:\n raise TypeError(\"ERROR: model size exceeds supported 2GB limit\")\n else:\n onnx_graph = shape_inference.infer_shapes(onnx_graph)\n\n self.graph = gs.import_onnx(onnx_graph)\n if return_onnx:\n return onnx_graph\n\n\nclass BaseModel:\n def __init__(self, model, fp16=False, device=\"cuda\", max_batch_size=16, embedding_dim=768, text_maxlen=77):\n self.model = model\n self.name = \"SD Model\"\n self.fp16 = fp16\n self.device = device\n\n self.min_batch = 1\n self.max_batch = max_batch_size\n self.min_image_shape = 256 # min image resolution: 256x256\n self.max_image_shape = 1024 # max image resolution: 1024x1024\n self.min_latent_shape = self.min_image_shape // 8\n self.max_latent_shape = self.max_image_shape // 8\n\n self.embedding_dim = embedding_dim\n self.text_maxlen = text_maxlen\n\n def get_model(self):\n return self.model\n\n def get_input_names(self):\n pass\n\n def get_output_names(self):\n pass\n\n def get_dynamic_axes(self):\n return None\n\n def get_sample_input(self, batch_size, image_height, image_width):\n pass\n\n def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):\n return None\n\n def get_shape_dict(self, batch_size, image_height, image_width):\n return None\n\n def optimize(self, onnx_graph):\n opt = Optimizer(onnx_graph)\n opt.cleanup()\n opt.fold_constants()\n opt.infer_shapes()\n onnx_opt_graph = opt.cleanup(return_onnx=True)\n return onnx_opt_graph\n\n def check_dims(self, batch_size, image_height, image_width):\n assert batch_size >= self.min_batch and batch_size <= self.max_batch\n assert image_height % 8 == 0 or image_width % 8 == 0\n latent_height = image_height // 8\n latent_width = image_width // 8\n assert latent_height >= self.min_latent_shape and latent_height <= self.max_latent_shape\n assert latent_width >= self.min_latent_shape and latent_width <= self.max_latent_shape\n return (latent_height, latent_width)\n\n def get_minmax_dims(self, batch_size, image_height, image_width, static_batch, static_shape):\n min_batch = batch_size if static_batch else self.min_batch\n max_batch = batch_size if static_batch else self.max_batch\n latent_height = image_height // 8\n latent_width = image_width // 8\n min_image_height = image_height if static_shape else self.min_image_shape\n max_image_height = image_height if static_shape else self.max_image_shape\n min_image_width = image_width if static_shape else self.min_image_shape\n max_image_width = image_width if static_shape else self.max_image_shape\n min_latent_height = latent_height if static_shape else self.min_latent_shape\n max_latent_height = latent_height if static_shape else self.max_latent_shape\n min_latent_width = latent_width if static_shape else self.min_latent_shape\n max_latent_width = latent_width if static_shape else self.max_latent_shape\n return (\n min_batch,\n max_batch,\n min_image_height,\n max_image_height,\n min_image_width,\n max_image_width,\n min_latent_height,\n max_latent_height,\n min_latent_width,\n max_latent_width,\n )\n\n\ndef getOnnxPath(model_name, onnx_dir, opt=True):\n return os.path.join(onnx_dir, model_name + (\".opt\" if opt else \"\") + \".onnx\")\n\n\ndef getEnginePath(model_name, engine_dir):\n return os.path.join(engine_dir, model_name + \".plan\")\n\n\ndef build_engines(\n models: dict,\n engine_dir,\n onnx_dir,\n onnx_opset,\n opt_image_height,\n opt_image_width,\n opt_batch_size=1,\n force_engine_rebuild=False,\n static_batch=False,\n static_shape=True,\n enable_preview=False,\n enable_all_tactics=False,\n timing_cache=None,\n max_workspace_size=0,\n):\n built_engines = {}\n if not os.path.isdir(onnx_dir):\n os.makedirs(onnx_dir)\n if not os.path.isdir(engine_dir):\n os.makedirs(engine_dir)\n\n # Export models to ONNX\n for model_name, model_obj in models.items():\n engine_path = getEnginePath(model_name, engine_dir)\n if force_engine_rebuild or not os.path.exists(engine_path):\n logger.warning(\"Building Engines...\")\n logger.warning(\"Engine build can take a while to complete\")\n onnx_path = getOnnxPath(model_name, onnx_dir, opt=False)\n onnx_opt_path = getOnnxPath(model_name, onnx_dir)\n if force_engine_rebuild or not os.path.exists(onnx_opt_path):\n if force_engine_rebuild or not os.path.exists(onnx_path):\n logger.warning(f\"Exporting model: {onnx_path}\")\n model = model_obj.get_model()\n with torch.inference_mode(), torch.autocast(\"cuda\"):\n inputs = model_obj.get_sample_input(opt_batch_size, opt_image_height, opt_image_width)\n torch.onnx.export(\n model,\n inputs,\n onnx_path,\n export_params=True,\n opset_version=onnx_opset,\n do_constant_folding=True,\n input_names=model_obj.get_input_names(),\n output_names=model_obj.get_output_names(),\n dynamic_axes=model_obj.get_dynamic_axes(),\n )\n del model\n torch.cuda.empty_cache()\n gc.collect()\n else:\n logger.warning(f\"Found cached model: {onnx_path}\")\n\n # Optimize onnx\n if force_engine_rebuild or not os.path.exists(onnx_opt_path):\n logger.warning(f\"Generating optimizing model: {onnx_opt_path}\")\n onnx_opt_graph = model_obj.optimize(onnx.load(onnx_path))\n onnx.save(onnx_opt_graph, onnx_opt_path)\n else:\n logger.warning(f\"Found cached optimized model: {onnx_opt_path} \")\n\n # Build TensorRT engines\n for model_name, model_obj in models.items():\n engine_path = getEnginePath(model_name, engine_dir)\n engine = Engine(engine_path)\n onnx_path = getOnnxPath(model_name, onnx_dir, opt=False)\n onnx_opt_path = getOnnxPath(model_name, onnx_dir)\n\n if force_engine_rebuild or not os.path.exists(engine.engine_path):\n engine.build(\n onnx_opt_path,\n fp16=True,\n input_profile=model_obj.get_input_profile(\n opt_batch_size,\n opt_image_height,\n opt_image_width,\n static_batch=static_batch,\n static_shape=static_shape,\n ),\n enable_preview=enable_preview,\n timing_cache=timing_cache,\n workspace_size=max_workspace_size,\n )\n built_engines[model_name] = engine\n\n # Load and activate TensorRT engines\n for model_name, model_obj in models.items():\n engine = built_engines[model_name]\n engine.load()\n engine.activate()\n\n return built_engines\n\n\ndef runEngine(engine, feed_dict, stream):\n return engine.infer(feed_dict, stream)\n\n\nclass CLIP(BaseModel):\n def __init__(self, model, device, max_batch_size, embedding_dim):\n super(CLIP, self).__init__(\n model=model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim\n )\n self.name = \"CLIP\"\n\n def get_input_names(self):\n return [\"input_ids\"]\n\n def get_output_names(self):\n return [\"text_embeddings\", \"pooler_output\"]\n\n def get_dynamic_axes(self):\n return {\"input_ids\": {0: \"B\"}, \"text_embeddings\": {0: \"B\"}}\n\n def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):\n self.check_dims(batch_size, image_height, image_width)\n min_batch, max_batch, _, _, _, _, _, _, _, _ = self.get_minmax_dims(\n batch_size, image_height, image_width, static_batch, static_shape\n )\n return {\n \"input_ids\": [(min_batch, self.text_maxlen), (batch_size, self.text_maxlen), (max_batch, self.text_maxlen)]\n }\n\n def get_shape_dict(self, batch_size, image_height, image_width):\n self.check_dims(batch_size, image_height, image_width)\n return {\n \"input_ids\": (batch_size, self.text_maxlen),\n \"text_embeddings\": (batch_size, self.text_maxlen, self.embedding_dim),\n }\n\n def get_sample_input(self, batch_size, image_height, image_width):\n self.check_dims(batch_size, image_height, image_width)\n return torch.zeros(batch_size, self.text_maxlen, dtype=torch.int32, device=self.device)\n\n def optimize(self, onnx_graph):\n opt = Optimizer(onnx_graph)\n opt.select_outputs([0]) # delete graph output#1\n opt.cleanup()\n opt.fold_constants()\n opt.infer_shapes()\n opt.select_outputs([0], names=[\"text_embeddings\"]) # rename network output\n opt_onnx_graph = opt.cleanup(return_onnx=True)\n return opt_onnx_graph\n\n\ndef make_CLIP(model, device, max_batch_size, embedding_dim, inpaint=False):\n return CLIP(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim)\n\n\nclass UNet(BaseModel):\n def __init__(\n self, model, fp16=False, device=\"cuda\", max_batch_size=16, embedding_dim=768, text_maxlen=77, unet_dim=4\n ):\n super(UNet, self).__init__(\n model=model,\n fp16=fp16,\n device=device,\n max_batch_size=max_batch_size,\n embedding_dim=embedding_dim,\n text_maxlen=text_maxlen,\n )\n self.unet_dim = unet_dim\n self.name = \"UNet\"\n\n def get_input_names(self):\n return [\"sample\", \"timestep\", \"encoder_hidden_states\"]\n\n def get_output_names(self):\n return [\"latent\"]\n\n def get_dynamic_axes(self):\n return {\n \"sample\": {0: \"2B\", 2: \"H\", 3: \"W\"},\n \"encoder_hidden_states\": {0: \"2B\"},\n \"latent\": {0: \"2B\", 2: \"H\", 3: \"W\"},\n }\n\n def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):\n latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)\n (\n min_batch,\n max_batch,\n _,\n _,\n _,\n _,\n min_latent_height,\n max_latent_height,\n min_latent_width,\n max_latent_width,\n ) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)\n return {\n \"sample\": [\n (2 * min_batch, self.unet_dim, min_latent_height, min_latent_width),\n (2 * batch_size, self.unet_dim, latent_height, latent_width),\n (2 * max_batch, self.unet_dim, max_latent_height, max_latent_width),\n ],\n \"encoder_hidden_states\": [\n (2 * min_batch, self.text_maxlen, self.embedding_dim),\n (2 * batch_size, self.text_maxlen, self.embedding_dim),\n (2 * max_batch, self.text_maxlen, self.embedding_dim),\n ],\n }\n\n def get_shape_dict(self, batch_size, image_height, image_width):\n latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)\n return {\n \"sample\": (2 * batch_size, self.unet_dim, latent_height, latent_width),\n \"encoder_hidden_states\": (2 * batch_size, self.text_maxlen, self.embedding_dim),\n \"latent\": (2 * batch_size, 4, latent_height, latent_width),\n }\n\n def get_sample_input(self, batch_size, image_height, image_width):\n latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)\n dtype = torch.float16 if self.fp16 else torch.float32\n return (\n torch.randn(\n 2 * batch_size, self.unet_dim, latent_height, latent_width, dtype=torch.float32, device=self.device\n ),\n torch.tensor([1.0], dtype=torch.float32, device=self.device),\n torch.randn(2 * batch_size, self.text_maxlen, self.embedding_dim, dtype=dtype, device=self.device),\n )\n\n\ndef make_UNet(model, device, max_batch_size, embedding_dim, inpaint=False, unet_dim=4):\n return UNet(\n model,\n fp16=True,\n device=device,\n max_batch_size=max_batch_size,\n embedding_dim=embedding_dim,\n unet_dim=unet_dim,\n )\n\n\nclass VAE(BaseModel):\n def __init__(self, model, device, max_batch_size, embedding_dim):\n super(VAE, self).__init__(\n model=model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim\n )\n self.name = \"VAE decoder\"\n\n def get_input_names(self):\n return [\"latent\"]\n\n def get_output_names(self):\n return [\"images\"]\n\n def get_dynamic_axes(self):\n return {\"latent\": {0: \"B\", 2: \"H\", 3: \"W\"}, \"images\": {0: \"B\", 2: \"8H\", 3: \"8W\"}}\n\n def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):\n latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)\n (\n min_batch,\n max_batch,\n _,\n _,\n _,\n _,\n min_latent_height,\n max_latent_height,\n min_latent_width,\n max_latent_width,\n ) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)\n return {\n \"latent\": [\n (min_batch, 4, min_latent_height, min_latent_width),\n (batch_size, 4, latent_height, latent_width),\n (max_batch, 4, max_latent_height, max_latent_width),\n ]\n }\n\n def get_shape_dict(self, batch_size, image_height, image_width):\n latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)\n return {\n \"latent\": (batch_size, 4, latent_height, latent_width),\n \"images\": (batch_size, 3, image_height, image_width),\n }\n\n def get_sample_input(self, batch_size, image_height, image_width):\n latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)\n return torch.randn(batch_size, 4, latent_height, latent_width, dtype=torch.float32, device=self.device)\n\n\ndef make_VAE(model, device, max_batch_size, embedding_dim, inpaint=False):\n return VAE(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim)\n\n\nclass TorchVAEEncoder(torch.nn.Module):\n def __init__(self, model):\n super().__init__()\n self.vae_encoder = model\n\n def forward(self, x):\n return self.vae_encoder.encode(x).latent_dist.sample()\n\n\nclass VAEEncoder(BaseModel):\n def __init__(self, model, device, max_batch_size, embedding_dim):\n super(VAEEncoder, self).__init__(\n model=model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim\n )\n self.name = \"VAE encoder\"\n\n def get_model(self):\n vae_encoder = TorchVAEEncoder(self.model)\n return vae_encoder\n\n def get_input_names(self):\n return [\"images\"]\n\n def get_output_names(self):\n return [\"latent\"]\n\n def get_dynamic_axes(self):\n return {\"images\": {0: \"B\", 2: \"8H\", 3: \"8W\"}, \"latent\": {0: \"B\", 2: \"H\", 3: \"W\"}}\n\n def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):\n assert batch_size >= self.min_batch and batch_size <= self.max_batch\n min_batch = batch_size if static_batch else self.min_batch\n max_batch = batch_size if static_batch else self.max_batch\n self.check_dims(batch_size, image_height, image_width)\n (\n min_batch,\n max_batch,\n min_image_height,\n max_image_height,\n min_image_width,\n max_image_width,\n _,\n _,\n _,\n _,\n ) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)\n\n return {\n \"images\": [\n (min_batch, 3, min_image_height, min_image_width),\n (batch_size, 3, image_height, image_width),\n (max_batch, 3, max_image_height, max_image_width),\n ]\n }\n\n def get_shape_dict(self, batch_size, image_height, image_width):\n latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)\n return {\n \"images\": (batch_size, 3, image_height, image_width),\n \"latent\": (batch_size, 4, latent_height, latent_width),\n }\n\n def get_sample_input(self, batch_size, image_height, image_width):\n self.check_dims(batch_size, image_height, image_width)\n return torch.randn(batch_size, 3, image_height, image_width, dtype=torch.float32, device=self.device)\n\n\ndef make_VAEEncoder(model, device, max_batch_size, embedding_dim, inpaint=False):\n return VAEEncoder(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim)\n\n\nclass TensorRTStableDiffusionInpaintPipeline(StableDiffusionInpaintPipeline):\n r\"\"\"\n Pipeline for inpainting using TensorRT accelerated Stable Diffusion.\n\n This model inherits from [`StableDiffusionInpaintPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPFeatureExtractor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: DDIMScheduler,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPFeatureExtractor,\n image_encoder: CLIPVisionModelWithProjection = None,\n requires_safety_checker: bool = True,\n stages=[\"clip\", \"unet\", \"vae\", \"vae_encoder\"],\n image_height: int = 512,\n image_width: int = 512,\n max_batch_size: int = 16,\n # ONNX export parameters\n onnx_opset: int = 17,\n onnx_dir: str = \"onnx\",\n # TensorRT engine build parameters\n engine_dir: str = \"engine\",\n build_preview_features: bool = True,\n force_engine_rebuild: bool = False,\n timing_cache: str = \"timing_cache\",\n ):\n super().__init__(\n vae,\n text_encoder,\n tokenizer,\n unet,\n scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n requires_safety_checker=requires_safety_checker,\n )\n\n self.vae.forward = self.vae.decode\n\n self.stages = stages\n self.image_height, self.image_width = image_height, image_width\n self.inpaint = True\n self.onnx_opset = onnx_opset\n self.onnx_dir = onnx_dir\n self.engine_dir = engine_dir\n self.force_engine_rebuild = force_engine_rebuild\n self.timing_cache = timing_cache\n self.build_static_batch = False\n self.build_dynamic_shape = False\n self.build_preview_features = build_preview_features\n\n self.max_batch_size = max_batch_size\n # TODO: Restrict batch size to 4 for larger image dimensions as a WAR for TensorRT limitation.\n if self.build_dynamic_shape or self.image_height > 512 or self.image_width > 512:\n self.max_batch_size = 4\n\n self.stream = None # loaded in loadResources()\n self.models = {} # loaded in __loadModels()\n self.engine = {} # loaded in build_engines()\n\n def __loadModels(self):\n # Load pipeline models\n self.embedding_dim = self.text_encoder.config.hidden_size\n models_args = {\n \"device\": self.torch_device,\n \"max_batch_size\": self.max_batch_size,\n \"embedding_dim\": self.embedding_dim,\n \"inpaint\": self.inpaint,\n }\n if \"clip\" in self.stages:\n self.models[\"clip\"] = make_CLIP(self.text_encoder, **models_args)\n if \"unet\" in self.stages:\n self.models[\"unet\"] = make_UNet(self.unet, **models_args, unet_dim=self.unet.config.in_channels)\n if \"vae\" in self.stages:\n self.models[\"vae\"] = make_VAE(self.vae, **models_args)\n if \"vae_encoder\" in self.stages:\n self.models[\"vae_encoder\"] = make_VAEEncoder(self.vae, **models_args)\n\n @classmethod\n @validate_hf_hub_args\n def set_cached_folder(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):\n cache_dir = kwargs.pop(\"cache_dir\", None)\n resume_download = kwargs.pop(\"resume_download\", False)\n proxies = kwargs.pop(\"proxies\", None)\n local_files_only = kwargs.pop(\"local_files_only\", False)\n token = kwargs.pop(\"token\", None)\n revision = kwargs.pop(\"revision\", None)\n\n cls.cached_folder = (\n pretrained_model_name_or_path\n if os.path.isdir(pretrained_model_name_or_path)\n else snapshot_download(\n pretrained_model_name_or_path,\n cache_dir=cache_dir,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n )\n )\n\n def to(self, torch_device: Optional[Union[str, torch.device]] = None, silence_dtype_warnings: bool = False):\n super().to(torch_device, silence_dtype_warnings=silence_dtype_warnings)\n\n self.onnx_dir = os.path.join(self.cached_folder, self.onnx_dir)\n self.engine_dir = os.path.join(self.cached_folder, self.engine_dir)\n self.timing_cache = os.path.join(self.cached_folder, self.timing_cache)\n\n # set device\n self.torch_device = self._execution_device\n logger.warning(f\"Running inference on device: {self.torch_device}\")\n\n # load models\n self.__loadModels()\n\n # build engines\n self.engine = build_engines(\n self.models,\n self.engine_dir,\n self.onnx_dir,\n self.onnx_opset,\n opt_image_height=self.image_height,\n opt_image_width=self.image_width,\n force_engine_rebuild=self.force_engine_rebuild,\n static_batch=self.build_static_batch,\n static_shape=not self.build_dynamic_shape,\n enable_preview=self.build_preview_features,\n timing_cache=self.timing_cache,\n )\n\n return self\n\n def __initialize_timesteps(self, num_inference_steps, strength):\n self.scheduler.set_timesteps(num_inference_steps)\n offset = self.scheduler.config.steps_offset if hasattr(self.scheduler, \"steps_offset\") else 0\n init_timestep = int(num_inference_steps * strength) + offset\n init_timestep = min(init_timestep, num_inference_steps)\n t_start = max(num_inference_steps - init_timestep + offset, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :].to(self.torch_device)\n return timesteps, num_inference_steps - t_start\n\n def __preprocess_images(self, batch_size, images=()):\n init_images = []\n for image in images:\n image = image.to(self.torch_device).float()\n image = image.repeat(batch_size, 1, 1, 1)\n init_images.append(image)\n return tuple(init_images)\n\n def __encode_image(self, init_image):\n init_latents = runEngine(self.engine[\"vae_encoder\"], {\"images\": device_view(init_image)}, self.stream)[\n \"latent\"\n ]\n init_latents = 0.18215 * init_latents\n return init_latents\n\n def __encode_prompt(self, prompt, negative_prompt):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.\n Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n \"\"\"\n # Tokenize prompt\n text_input_ids = (\n self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n .input_ids.type(torch.int32)\n .to(self.torch_device)\n )\n\n text_input_ids_inp = device_view(text_input_ids)\n # NOTE: output tensor for CLIP must be cloned because it will be overwritten when called again for negative prompt\n text_embeddings = runEngine(self.engine[\"clip\"], {\"input_ids\": text_input_ids_inp}, self.stream)[\n \"text_embeddings\"\n ].clone()\n\n # Tokenize negative prompt\n uncond_input_ids = (\n self.tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n .input_ids.type(torch.int32)\n .to(self.torch_device)\n )\n uncond_input_ids_inp = device_view(uncond_input_ids)\n uncond_embeddings = runEngine(self.engine[\"clip\"], {\"input_ids\": uncond_input_ids_inp}, self.stream)[\n \"text_embeddings\"\n ]\n\n # Concatenate the unconditional and text embeddings into a single batch to avoid doing two forward passes for classifier free guidance\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings]).to(dtype=torch.float16)\n\n return text_embeddings\n\n def __denoise_latent(\n self, latents, text_embeddings, timesteps=None, step_offset=0, mask=None, masked_image_latents=None\n ):\n if not isinstance(timesteps, torch.Tensor):\n timesteps = self.scheduler.timesteps\n for step_index, timestep in enumerate(timesteps):\n # Expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2)\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, timestep)\n if isinstance(mask, torch.Tensor):\n latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)\n\n # Predict the noise residual\n timestep_float = timestep.float() if timestep.dtype != torch.float32 else timestep\n\n sample_inp = device_view(latent_model_input)\n timestep_inp = device_view(timestep_float)\n embeddings_inp = device_view(text_embeddings)\n noise_pred = runEngine(\n self.engine[\"unet\"],\n {\"sample\": sample_inp, \"timestep\": timestep_inp, \"encoder_hidden_states\": embeddings_inp},\n self.stream,\n )[\"latent\"]\n\n # Perform guidance\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n latents = self.scheduler.step(noise_pred, timestep, latents).prev_sample\n\n latents = 1.0 / 0.18215 * latents\n return latents\n\n def __decode_latent(self, latents):\n images = runEngine(self.engine[\"vae\"], {\"latent\": device_view(latents)}, self.stream)[\"images\"]\n images = (images / 2 + 0.5).clamp(0, 1)\n return images.cpu().permute(0, 2, 3, 1).float().numpy()\n\n def __loadResources(self, image_height, image_width, batch_size):\n self.stream = cuda.Stream()\n\n # Allocate buffers for TensorRT engine bindings\n for model_name, obj in self.models.items():\n self.engine[model_name].allocate_buffers(\n shape_dict=obj.get_shape_dict(batch_size, image_height, image_width), device=self.torch_device\n )\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: Union[torch.Tensor, PIL.Image.Image] = None,\n mask_image: Union[torch.Tensor, PIL.Image.Image] = None,\n strength: float = 1.0,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n image (`PIL.Image.Image`):\n `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will\n be masked out with `mask_image` and repainted according to `prompt`.\n mask_image (`PIL.Image.Image`):\n `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be\n repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted\n to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)\n instead of 3, so the expected shape would be `(B, H, W, 1)`.\n strength (`float`, *optional*, defaults to 0.8):\n Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`\n will be used as a starting point, adding more noise to it the larger the `strength`. The number of\n denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will\n be maximum and the denoising process will run for the full number of iterations specified in\n `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.\n Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n\n \"\"\"\n self.generator = generator\n self.denoising_steps = num_inference_steps\n self._guidance_scale = guidance_scale\n\n # Pre-compute latent input scales and linear multistep coefficients\n self.scheduler.set_timesteps(self.denoising_steps, device=self.torch_device)\n\n # Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n prompt = [prompt]\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n raise ValueError(f\"Expected prompt to be of type list or str but got {type(prompt)}\")\n\n if negative_prompt is None:\n negative_prompt = [\"\"] * batch_size\n\n if negative_prompt is not None and isinstance(negative_prompt, str):\n negative_prompt = [negative_prompt]\n\n assert len(prompt) == len(negative_prompt)\n\n if batch_size > self.max_batch_size:\n raise ValueError(\n f\"Batch size {len(prompt)} is larger than allowed {self.max_batch_size}. If dynamic shape is used, then maximum batch size is 4\"\n )\n\n # Validate image dimensions\n mask_width, mask_height = mask_image.size\n if mask_height != self.image_height or mask_width != self.image_width:\n raise ValueError(\n f\"Input image height and width {self.image_height} and {self.image_width} are not equal to \"\n f\"the respective dimensions of the mask image {mask_height} and {mask_width}\"\n )\n\n # load resources\n self.__loadResources(self.image_height, self.image_width, batch_size)\n\n with torch.inference_mode(), torch.autocast(\"cuda\"), trt.Runtime(TRT_LOGGER):\n # Spatial dimensions of latent tensor\n latent_height = self.image_height // 8\n latent_width = self.image_width // 8\n\n # Pre-process input images\n mask, masked_image, init_image = self.__preprocess_images(\n batch_size,\n prepare_mask_and_masked_image(\n image,\n mask_image,\n self.image_height,\n self.image_width,\n return_image=True,\n ),\n )\n\n mask = torch.nn.functional.interpolate(mask, size=(latent_height, latent_width))\n mask = torch.cat([mask] * 2)\n\n # Initialize timesteps\n timesteps, t_start = self.__initialize_timesteps(self.denoising_steps, strength)\n\n # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)\n latent_timestep = timesteps[:1].repeat(batch_size)\n # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise\n is_strength_max = strength == 1.0\n\n # Pre-initialize latents\n num_channels_latents = self.vae.config.latent_channels\n latents_outputs = self.prepare_latents(\n batch_size,\n num_channels_latents,\n self.image_height,\n self.image_width,\n torch.float32,\n self.torch_device,\n generator,\n image=init_image,\n timestep=latent_timestep,\n is_strength_max=is_strength_max,\n )\n\n latents = latents_outputs[0]\n\n # VAE encode masked image\n masked_latents = self.__encode_image(masked_image)\n masked_latents = torch.cat([masked_latents] * 2)\n\n # CLIP text encoder\n text_embeddings = self.__encode_prompt(prompt, negative_prompt)\n\n # UNet denoiser\n latents = self.__denoise_latent(\n latents,\n text_embeddings,\n timesteps=timesteps,\n step_offset=t_start,\n mask=mask,\n masked_image_latents=masked_latents,\n )\n\n # VAE decode latent\n images = self.__decode_latent(latents)\n\n images = self.numpy_to_pil(images)\n return StableDiffusionPipelineOutput(images=images, nsfw_content_detected=None)\n" }, { "path": "diffusers/examples/community/stable_diffusion_tensorrt_txt2img.py", "content": "#\n# Copyright 2024 The HuggingFace Inc. team.\n# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n# SPDX-License-Identifier: Apache-2.0\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport os\nfrom collections import OrderedDict\nfrom copy import copy\nfrom typing import List, Optional, Union\n\nimport numpy as np\nimport onnx\nimport onnx_graphsurgeon as gs\nimport tensorrt as trt\nimport torch\nfrom huggingface_hub import snapshot_download\nfrom huggingface_hub.utils import validate_hf_hub_args\nfrom onnx import shape_inference\nfrom polygraphy import cuda\nfrom polygraphy.backend.common import bytes_from_path\nfrom polygraphy.backend.onnx.loader import fold_constants\nfrom polygraphy.backend.trt import (\n CreateConfig,\n Profile,\n engine_from_bytes,\n engine_from_network,\n network_from_onnx_path,\n save_engine,\n)\nfrom polygraphy.backend.trt import util as trt_util\nfrom transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.pipelines.stable_diffusion import (\n StableDiffusionPipeline,\n StableDiffusionPipelineOutput,\n StableDiffusionSafetyChecker,\n)\nfrom diffusers.schedulers import DDIMScheduler\nfrom diffusers.utils import logging\n\n\n\"\"\"\nInstallation instructions\npython3 -m pip install --upgrade transformers diffusers>=0.16.0\npython3 -m pip install --upgrade tensorrt>=8.6.1\npython3 -m pip install --upgrade polygraphy>=0.47.0 onnx-graphsurgeon --extra-index-url https://pypi.ngc.nvidia.com\npython3 -m pip install onnxruntime\n\"\"\"\n\nTRT_LOGGER = trt.Logger(trt.Logger.ERROR)\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n# Map of numpy dtype -> torch dtype\nnumpy_to_torch_dtype_dict = {\n np.uint8: torch.uint8,\n np.int8: torch.int8,\n np.int16: torch.int16,\n np.int32: torch.int32,\n np.int64: torch.int64,\n np.float16: torch.float16,\n np.float32: torch.float32,\n np.float64: torch.float64,\n np.complex64: torch.complex64,\n np.complex128: torch.complex128,\n}\nif np.version.full_version >= \"1.24.0\":\n numpy_to_torch_dtype_dict[np.bool_] = torch.bool\nelse:\n numpy_to_torch_dtype_dict[np.bool] = torch.bool\n\n# Map of torch dtype -> numpy dtype\ntorch_to_numpy_dtype_dict = {value: key for (key, value) in numpy_to_torch_dtype_dict.items()}\n\n\ndef device_view(t):\n return cuda.DeviceView(ptr=t.data_ptr(), shape=t.shape, dtype=torch_to_numpy_dtype_dict[t.dtype])\n\n\nclass Engine:\n def __init__(self, engine_path):\n self.engine_path = engine_path\n self.engine = None\n self.context = None\n self.buffers = OrderedDict()\n self.tensors = OrderedDict()\n\n def __del__(self):\n [buf.free() for buf in self.buffers.values() if isinstance(buf, cuda.DeviceArray)]\n del self.engine\n del self.context\n del self.buffers\n del self.tensors\n\n def build(\n self,\n onnx_path,\n fp16,\n input_profile=None,\n enable_preview=False,\n enable_all_tactics=False,\n timing_cache=None,\n workspace_size=0,\n ):\n logger.warning(f\"Building TensorRT engine for {onnx_path}: {self.engine_path}\")\n p = Profile()\n if input_profile:\n for name, dims in input_profile.items():\n assert len(dims) == 3\n p.add(name, min=dims[0], opt=dims[1], max=dims[2])\n\n config_kwargs = {}\n\n config_kwargs[\"preview_features\"] = [trt.PreviewFeature.DISABLE_EXTERNAL_TACTIC_SOURCES_FOR_CORE_0805]\n if enable_preview:\n # Faster dynamic shapes made optional since it increases engine build time.\n config_kwargs[\"preview_features\"].append(trt.PreviewFeature.FASTER_DYNAMIC_SHAPES_0805)\n if workspace_size > 0:\n config_kwargs[\"memory_pool_limits\"] = {trt.MemoryPoolType.WORKSPACE: workspace_size}\n if not enable_all_tactics:\n config_kwargs[\"tactic_sources\"] = []\n\n engine = engine_from_network(\n network_from_onnx_path(onnx_path, flags=[trt.OnnxParserFlag.NATIVE_INSTANCENORM]),\n config=CreateConfig(fp16=fp16, profiles=[p], load_timing_cache=timing_cache, **config_kwargs),\n save_timing_cache=timing_cache,\n )\n save_engine(engine, path=self.engine_path)\n\n def load(self):\n logger.warning(f\"Loading TensorRT engine: {self.engine_path}\")\n self.engine = engine_from_bytes(bytes_from_path(self.engine_path))\n\n def activate(self):\n self.context = self.engine.create_execution_context()\n\n def allocate_buffers(self, shape_dict=None, device=\"cuda\"):\n for idx in range(trt_util.get_bindings_per_profile(self.engine)):\n binding = self.engine[idx]\n if shape_dict and binding in shape_dict:\n shape = shape_dict[binding]\n else:\n shape = self.engine.get_binding_shape(binding)\n dtype = trt.nptype(self.engine.get_binding_dtype(binding))\n if self.engine.binding_is_input(binding):\n self.context.set_binding_shape(idx, shape)\n tensor = torch.empty(tuple(shape), dtype=numpy_to_torch_dtype_dict[dtype]).to(device=device)\n self.tensors[binding] = tensor\n self.buffers[binding] = cuda.DeviceView(ptr=tensor.data_ptr(), shape=shape, dtype=dtype)\n\n def infer(self, feed_dict, stream):\n start_binding, end_binding = trt_util.get_active_profile_bindings(self.context)\n # shallow copy of ordered dict\n device_buffers = copy(self.buffers)\n for name, buf in feed_dict.items():\n assert isinstance(buf, cuda.DeviceView)\n device_buffers[name] = buf\n bindings = [0] * start_binding + [buf.ptr for buf in device_buffers.values()]\n noerror = self.context.execute_async_v2(bindings=bindings, stream_handle=stream.ptr)\n if not noerror:\n raise ValueError(\"ERROR: inference failed.\")\n\n return self.tensors\n\n\nclass Optimizer:\n def __init__(self, onnx_graph):\n self.graph = gs.import_onnx(onnx_graph)\n\n def cleanup(self, return_onnx=False):\n self.graph.cleanup().toposort()\n if return_onnx:\n return gs.export_onnx(self.graph)\n\n def select_outputs(self, keep, names=None):\n self.graph.outputs = [self.graph.outputs[o] for o in keep]\n if names:\n for i, name in enumerate(names):\n self.graph.outputs[i].name = name\n\n def fold_constants(self, return_onnx=False):\n onnx_graph = fold_constants(gs.export_onnx(self.graph), allow_onnxruntime_shape_inference=True)\n self.graph = gs.import_onnx(onnx_graph)\n if return_onnx:\n return onnx_graph\n\n def infer_shapes(self, return_onnx=False):\n onnx_graph = gs.export_onnx(self.graph)\n if onnx_graph.ByteSize() > 2147483648:\n raise TypeError(\"ERROR: model size exceeds supported 2GB limit\")\n else:\n onnx_graph = shape_inference.infer_shapes(onnx_graph)\n\n self.graph = gs.import_onnx(onnx_graph)\n if return_onnx:\n return onnx_graph\n\n\nclass BaseModel:\n def __init__(self, model, fp16=False, device=\"cuda\", max_batch_size=16, embedding_dim=768, text_maxlen=77):\n self.model = model\n self.name = \"SD Model\"\n self.fp16 = fp16\n self.device = device\n\n self.min_batch = 1\n self.max_batch = max_batch_size\n self.min_image_shape = 256 # min image resolution: 256x256\n self.max_image_shape = 1024 # max image resolution: 1024x1024\n self.min_latent_shape = self.min_image_shape // 8\n self.max_latent_shape = self.max_image_shape // 8\n\n self.embedding_dim = embedding_dim\n self.text_maxlen = text_maxlen\n\n def get_model(self):\n return self.model\n\n def get_input_names(self):\n pass\n\n def get_output_names(self):\n pass\n\n def get_dynamic_axes(self):\n return None\n\n def get_sample_input(self, batch_size, image_height, image_width):\n pass\n\n def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):\n return None\n\n def get_shape_dict(self, batch_size, image_height, image_width):\n return None\n\n def optimize(self, onnx_graph):\n opt = Optimizer(onnx_graph)\n opt.cleanup()\n opt.fold_constants()\n opt.infer_shapes()\n onnx_opt_graph = opt.cleanup(return_onnx=True)\n return onnx_opt_graph\n\n def check_dims(self, batch_size, image_height, image_width):\n assert batch_size >= self.min_batch and batch_size <= self.max_batch\n assert image_height % 8 == 0 or image_width % 8 == 0\n latent_height = image_height // 8\n latent_width = image_width // 8\n assert latent_height >= self.min_latent_shape and latent_height <= self.max_latent_shape\n assert latent_width >= self.min_latent_shape and latent_width <= self.max_latent_shape\n return (latent_height, latent_width)\n\n def get_minmax_dims(self, batch_size, image_height, image_width, static_batch, static_shape):\n min_batch = batch_size if static_batch else self.min_batch\n max_batch = batch_size if static_batch else self.max_batch\n latent_height = image_height // 8\n latent_width = image_width // 8\n min_image_height = image_height if static_shape else self.min_image_shape\n max_image_height = image_height if static_shape else self.max_image_shape\n min_image_width = image_width if static_shape else self.min_image_shape\n max_image_width = image_width if static_shape else self.max_image_shape\n min_latent_height = latent_height if static_shape else self.min_latent_shape\n max_latent_height = latent_height if static_shape else self.max_latent_shape\n min_latent_width = latent_width if static_shape else self.min_latent_shape\n max_latent_width = latent_width if static_shape else self.max_latent_shape\n return (\n min_batch,\n max_batch,\n min_image_height,\n max_image_height,\n min_image_width,\n max_image_width,\n min_latent_height,\n max_latent_height,\n min_latent_width,\n max_latent_width,\n )\n\n\ndef getOnnxPath(model_name, onnx_dir, opt=True):\n return os.path.join(onnx_dir, model_name + (\".opt\" if opt else \"\") + \".onnx\")\n\n\ndef getEnginePath(model_name, engine_dir):\n return os.path.join(engine_dir, model_name + \".plan\")\n\n\ndef build_engines(\n models: dict,\n engine_dir,\n onnx_dir,\n onnx_opset,\n opt_image_height,\n opt_image_width,\n opt_batch_size=1,\n force_engine_rebuild=False,\n static_batch=False,\n static_shape=True,\n enable_preview=False,\n enable_all_tactics=False,\n timing_cache=None,\n max_workspace_size=0,\n):\n built_engines = {}\n if not os.path.isdir(onnx_dir):\n os.makedirs(onnx_dir)\n if not os.path.isdir(engine_dir):\n os.makedirs(engine_dir)\n\n # Export models to ONNX\n for model_name, model_obj in models.items():\n engine_path = getEnginePath(model_name, engine_dir)\n if force_engine_rebuild or not os.path.exists(engine_path):\n logger.warning(\"Building Engines...\")\n logger.warning(\"Engine build can take a while to complete\")\n onnx_path = getOnnxPath(model_name, onnx_dir, opt=False)\n onnx_opt_path = getOnnxPath(model_name, onnx_dir)\n if force_engine_rebuild or not os.path.exists(onnx_opt_path):\n if force_engine_rebuild or not os.path.exists(onnx_path):\n logger.warning(f\"Exporting model: {onnx_path}\")\n model = model_obj.get_model()\n with torch.inference_mode(), torch.autocast(\"cuda\"):\n inputs = model_obj.get_sample_input(opt_batch_size, opt_image_height, opt_image_width)\n torch.onnx.export(\n model,\n inputs,\n onnx_path,\n export_params=True,\n opset_version=onnx_opset,\n do_constant_folding=True,\n input_names=model_obj.get_input_names(),\n output_names=model_obj.get_output_names(),\n dynamic_axes=model_obj.get_dynamic_axes(),\n )\n del model\n torch.cuda.empty_cache()\n gc.collect()\n else:\n logger.warning(f\"Found cached model: {onnx_path}\")\n\n # Optimize onnx\n if force_engine_rebuild or not os.path.exists(onnx_opt_path):\n logger.warning(f\"Generating optimizing model: {onnx_opt_path}\")\n onnx_opt_graph = model_obj.optimize(onnx.load(onnx_path))\n onnx.save(onnx_opt_graph, onnx_opt_path)\n else:\n logger.warning(f\"Found cached optimized model: {onnx_opt_path} \")\n\n # Build TensorRT engines\n for model_name, model_obj in models.items():\n engine_path = getEnginePath(model_name, engine_dir)\n engine = Engine(engine_path)\n onnx_path = getOnnxPath(model_name, onnx_dir, opt=False)\n onnx_opt_path = getOnnxPath(model_name, onnx_dir)\n\n if force_engine_rebuild or not os.path.exists(engine.engine_path):\n engine.build(\n onnx_opt_path,\n fp16=True,\n input_profile=model_obj.get_input_profile(\n opt_batch_size,\n opt_image_height,\n opt_image_width,\n static_batch=static_batch,\n static_shape=static_shape,\n ),\n enable_preview=enable_preview,\n timing_cache=timing_cache,\n workspace_size=max_workspace_size,\n )\n built_engines[model_name] = engine\n\n # Load and activate TensorRT engines\n for model_name, model_obj in models.items():\n engine = built_engines[model_name]\n engine.load()\n engine.activate()\n\n return built_engines\n\n\ndef runEngine(engine, feed_dict, stream):\n return engine.infer(feed_dict, stream)\n\n\nclass CLIP(BaseModel):\n def __init__(self, model, device, max_batch_size, embedding_dim):\n super(CLIP, self).__init__(\n model=model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim\n )\n self.name = \"CLIP\"\n\n def get_input_names(self):\n return [\"input_ids\"]\n\n def get_output_names(self):\n return [\"text_embeddings\", \"pooler_output\"]\n\n def get_dynamic_axes(self):\n return {\"input_ids\": {0: \"B\"}, \"text_embeddings\": {0: \"B\"}}\n\n def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):\n self.check_dims(batch_size, image_height, image_width)\n min_batch, max_batch, _, _, _, _, _, _, _, _ = self.get_minmax_dims(\n batch_size, image_height, image_width, static_batch, static_shape\n )\n return {\n \"input_ids\": [(min_batch, self.text_maxlen), (batch_size, self.text_maxlen), (max_batch, self.text_maxlen)]\n }\n\n def get_shape_dict(self, batch_size, image_height, image_width):\n self.check_dims(batch_size, image_height, image_width)\n return {\n \"input_ids\": (batch_size, self.text_maxlen),\n \"text_embeddings\": (batch_size, self.text_maxlen, self.embedding_dim),\n }\n\n def get_sample_input(self, batch_size, image_height, image_width):\n self.check_dims(batch_size, image_height, image_width)\n return torch.zeros(batch_size, self.text_maxlen, dtype=torch.int32, device=self.device)\n\n def optimize(self, onnx_graph):\n opt = Optimizer(onnx_graph)\n opt.select_outputs([0]) # delete graph output#1\n opt.cleanup()\n opt.fold_constants()\n opt.infer_shapes()\n opt.select_outputs([0], names=[\"text_embeddings\"]) # rename network output\n opt_onnx_graph = opt.cleanup(return_onnx=True)\n return opt_onnx_graph\n\n\ndef make_CLIP(model, device, max_batch_size, embedding_dim, inpaint=False):\n return CLIP(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim)\n\n\nclass UNet(BaseModel):\n def __init__(\n self, model, fp16=False, device=\"cuda\", max_batch_size=16, embedding_dim=768, text_maxlen=77, unet_dim=4\n ):\n super(UNet, self).__init__(\n model=model,\n fp16=fp16,\n device=device,\n max_batch_size=max_batch_size,\n embedding_dim=embedding_dim,\n text_maxlen=text_maxlen,\n )\n self.unet_dim = unet_dim\n self.name = \"UNet\"\n\n def get_input_names(self):\n return [\"sample\", \"timestep\", \"encoder_hidden_states\"]\n\n def get_output_names(self):\n return [\"latent\"]\n\n def get_dynamic_axes(self):\n return {\n \"sample\": {0: \"2B\", 2: \"H\", 3: \"W\"},\n \"encoder_hidden_states\": {0: \"2B\"},\n \"latent\": {0: \"2B\", 2: \"H\", 3: \"W\"},\n }\n\n def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):\n latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)\n (\n min_batch,\n max_batch,\n _,\n _,\n _,\n _,\n min_latent_height,\n max_latent_height,\n min_latent_width,\n max_latent_width,\n ) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)\n return {\n \"sample\": [\n (2 * min_batch, self.unet_dim, min_latent_height, min_latent_width),\n (2 * batch_size, self.unet_dim, latent_height, latent_width),\n (2 * max_batch, self.unet_dim, max_latent_height, max_latent_width),\n ],\n \"encoder_hidden_states\": [\n (2 * min_batch, self.text_maxlen, self.embedding_dim),\n (2 * batch_size, self.text_maxlen, self.embedding_dim),\n (2 * max_batch, self.text_maxlen, self.embedding_dim),\n ],\n }\n\n def get_shape_dict(self, batch_size, image_height, image_width):\n latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)\n return {\n \"sample\": (2 * batch_size, self.unet_dim, latent_height, latent_width),\n \"encoder_hidden_states\": (2 * batch_size, self.text_maxlen, self.embedding_dim),\n \"latent\": (2 * batch_size, 4, latent_height, latent_width),\n }\n\n def get_sample_input(self, batch_size, image_height, image_width):\n latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)\n dtype = torch.float16 if self.fp16 else torch.float32\n return (\n torch.randn(\n 2 * batch_size, self.unet_dim, latent_height, latent_width, dtype=torch.float32, device=self.device\n ),\n torch.tensor([1.0], dtype=torch.float32, device=self.device),\n torch.randn(2 * batch_size, self.text_maxlen, self.embedding_dim, dtype=dtype, device=self.device),\n )\n\n\ndef make_UNet(model, device, max_batch_size, embedding_dim, inpaint=False):\n return UNet(\n model,\n fp16=True,\n device=device,\n max_batch_size=max_batch_size,\n embedding_dim=embedding_dim,\n unet_dim=(9 if inpaint else 4),\n )\n\n\nclass VAE(BaseModel):\n def __init__(self, model, device, max_batch_size, embedding_dim):\n super(VAE, self).__init__(\n model=model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim\n )\n self.name = \"VAE decoder\"\n\n def get_input_names(self):\n return [\"latent\"]\n\n def get_output_names(self):\n return [\"images\"]\n\n def get_dynamic_axes(self):\n return {\"latent\": {0: \"B\", 2: \"H\", 3: \"W\"}, \"images\": {0: \"B\", 2: \"8H\", 3: \"8W\"}}\n\n def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):\n latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)\n (\n min_batch,\n max_batch,\n _,\n _,\n _,\n _,\n min_latent_height,\n max_latent_height,\n min_latent_width,\n max_latent_width,\n ) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)\n return {\n \"latent\": [\n (min_batch, 4, min_latent_height, min_latent_width),\n (batch_size, 4, latent_height, latent_width),\n (max_batch, 4, max_latent_height, max_latent_width),\n ]\n }\n\n def get_shape_dict(self, batch_size, image_height, image_width):\n latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)\n return {\n \"latent\": (batch_size, 4, latent_height, latent_width),\n \"images\": (batch_size, 3, image_height, image_width),\n }\n\n def get_sample_input(self, batch_size, image_height, image_width):\n latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)\n return torch.randn(batch_size, 4, latent_height, latent_width, dtype=torch.float32, device=self.device)\n\n\ndef make_VAE(model, device, max_batch_size, embedding_dim, inpaint=False):\n return VAE(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim)\n\n\nclass TensorRTStableDiffusionPipeline(StableDiffusionPipeline):\n r\"\"\"\n Pipeline for text-to-image generation using TensorRT accelerated Stable Diffusion.\n\n This model inherits from [`StableDiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPFeatureExtractor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: DDIMScheduler,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPFeatureExtractor,\n image_encoder: CLIPVisionModelWithProjection = None,\n requires_safety_checker: bool = True,\n stages=[\"clip\", \"unet\", \"vae\"],\n image_height: int = 768,\n image_width: int = 768,\n max_batch_size: int = 16,\n # ONNX export parameters\n onnx_opset: int = 17,\n onnx_dir: str = \"onnx\",\n # TensorRT engine build parameters\n engine_dir: str = \"engine\",\n build_preview_features: bool = True,\n force_engine_rebuild: bool = False,\n timing_cache: str = \"timing_cache\",\n ):\n super().__init__(\n vae,\n text_encoder,\n tokenizer,\n unet,\n scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n requires_safety_checker=requires_safety_checker,\n )\n\n self.vae.forward = self.vae.decode\n\n self.stages = stages\n self.image_height, self.image_width = image_height, image_width\n self.inpaint = False\n self.onnx_opset = onnx_opset\n self.onnx_dir = onnx_dir\n self.engine_dir = engine_dir\n self.force_engine_rebuild = force_engine_rebuild\n self.timing_cache = timing_cache\n self.build_static_batch = False\n self.build_dynamic_shape = False\n self.build_preview_features = build_preview_features\n\n self.max_batch_size = max_batch_size\n # TODO: Restrict batch size to 4 for larger image dimensions as a WAR for TensorRT limitation.\n if self.build_dynamic_shape or self.image_height > 512 or self.image_width > 512:\n self.max_batch_size = 4\n\n self.stream = None # loaded in loadResources()\n self.models = {} # loaded in __loadModels()\n self.engine = {} # loaded in build_engines()\n\n def __loadModels(self):\n # Load pipeline models\n self.embedding_dim = self.text_encoder.config.hidden_size\n models_args = {\n \"device\": self.torch_device,\n \"max_batch_size\": self.max_batch_size,\n \"embedding_dim\": self.embedding_dim,\n \"inpaint\": self.inpaint,\n }\n if \"clip\" in self.stages:\n self.models[\"clip\"] = make_CLIP(self.text_encoder, **models_args)\n if \"unet\" in self.stages:\n self.models[\"unet\"] = make_UNet(self.unet, **models_args)\n if \"vae\" in self.stages:\n self.models[\"vae\"] = make_VAE(self.vae, **models_args)\n\n @classmethod\n @validate_hf_hub_args\n def set_cached_folder(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):\n cache_dir = kwargs.pop(\"cache_dir\", None)\n resume_download = kwargs.pop(\"resume_download\", False)\n proxies = kwargs.pop(\"proxies\", None)\n local_files_only = kwargs.pop(\"local_files_only\", False)\n token = kwargs.pop(\"token\", None)\n revision = kwargs.pop(\"revision\", None)\n\n cls.cached_folder = (\n pretrained_model_name_or_path\n if os.path.isdir(pretrained_model_name_or_path)\n else snapshot_download(\n pretrained_model_name_or_path,\n cache_dir=cache_dir,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n )\n )\n\n def to(self, torch_device: Optional[Union[str, torch.device]] = None, silence_dtype_warnings: bool = False):\n super().to(torch_device, silence_dtype_warnings=silence_dtype_warnings)\n\n self.onnx_dir = os.path.join(self.cached_folder, self.onnx_dir)\n self.engine_dir = os.path.join(self.cached_folder, self.engine_dir)\n self.timing_cache = os.path.join(self.cached_folder, self.timing_cache)\n\n # set device\n self.torch_device = self._execution_device\n logger.warning(f\"Running inference on device: {self.torch_device}\")\n\n # load models\n self.__loadModels()\n\n # build engines\n self.engine = build_engines(\n self.models,\n self.engine_dir,\n self.onnx_dir,\n self.onnx_opset,\n opt_image_height=self.image_height,\n opt_image_width=self.image_width,\n force_engine_rebuild=self.force_engine_rebuild,\n static_batch=self.build_static_batch,\n static_shape=not self.build_dynamic_shape,\n enable_preview=self.build_preview_features,\n timing_cache=self.timing_cache,\n )\n\n return self\n\n def __encode_prompt(self, prompt, negative_prompt):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.\n Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n \"\"\"\n # Tokenize prompt\n text_input_ids = (\n self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n .input_ids.type(torch.int32)\n .to(self.torch_device)\n )\n\n text_input_ids_inp = device_view(text_input_ids)\n # NOTE: output tensor for CLIP must be cloned because it will be overwritten when called again for negative prompt\n text_embeddings = runEngine(self.engine[\"clip\"], {\"input_ids\": text_input_ids_inp}, self.stream)[\n \"text_embeddings\"\n ].clone()\n\n # Tokenize negative prompt\n uncond_input_ids = (\n self.tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n .input_ids.type(torch.int32)\n .to(self.torch_device)\n )\n uncond_input_ids_inp = device_view(uncond_input_ids)\n uncond_embeddings = runEngine(self.engine[\"clip\"], {\"input_ids\": uncond_input_ids_inp}, self.stream)[\n \"text_embeddings\"\n ]\n\n # Concatenate the unconditional and text embeddings into a single batch to avoid doing two forward passes for classifier free guidance\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings]).to(dtype=torch.float16)\n\n return text_embeddings\n\n def __denoise_latent(\n self, latents, text_embeddings, timesteps=None, step_offset=0, mask=None, masked_image_latents=None\n ):\n if not isinstance(timesteps, torch.Tensor):\n timesteps = self.scheduler.timesteps\n for step_index, timestep in enumerate(timesteps):\n # Expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2)\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, timestep)\n if isinstance(mask, torch.Tensor):\n latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)\n\n # Predict the noise residual\n timestep_float = timestep.float() if timestep.dtype != torch.float32 else timestep\n\n sample_inp = device_view(latent_model_input)\n timestep_inp = device_view(timestep_float)\n embeddings_inp = device_view(text_embeddings)\n noise_pred = runEngine(\n self.engine[\"unet\"],\n {\"sample\": sample_inp, \"timestep\": timestep_inp, \"encoder_hidden_states\": embeddings_inp},\n self.stream,\n )[\"latent\"]\n\n # Perform guidance\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n latents = self.scheduler.step(noise_pred, timestep, latents).prev_sample\n\n latents = 1.0 / 0.18215 * latents\n return latents\n\n def __decode_latent(self, latents):\n images = runEngine(self.engine[\"vae\"], {\"latent\": device_view(latents)}, self.stream)[\"images\"]\n images = (images / 2 + 0.5).clamp(0, 1)\n return images.cpu().permute(0, 2, 3, 1).float().numpy()\n\n def __loadResources(self, image_height, image_width, batch_size):\n self.stream = cuda.Stream()\n\n # Allocate buffers for TensorRT engine bindings\n for model_name, obj in self.models.items():\n self.engine[model_name].allocate_buffers(\n shape_dict=obj.get_shape_dict(batch_size, image_height, image_width), device=self.torch_device\n )\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.\n Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n\n \"\"\"\n self.generator = generator\n self.denoising_steps = num_inference_steps\n self._guidance_scale = guidance_scale\n\n # Pre-compute latent input scales and linear multistep coefficients\n self.scheduler.set_timesteps(self.denoising_steps, device=self.torch_device)\n\n # Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n prompt = [prompt]\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n raise ValueError(f\"Expected prompt to be of type list or str but got {type(prompt)}\")\n\n if negative_prompt is None:\n negative_prompt = [\"\"] * batch_size\n\n if negative_prompt is not None and isinstance(negative_prompt, str):\n negative_prompt = [negative_prompt]\n\n assert len(prompt) == len(negative_prompt)\n\n if batch_size > self.max_batch_size:\n raise ValueError(\n f\"Batch size {len(prompt)} is larger than allowed {self.max_batch_size}. If dynamic shape is used, then maximum batch size is 4\"\n )\n\n # load resources\n self.__loadResources(self.image_height, self.image_width, batch_size)\n\n with torch.inference_mode(), torch.autocast(\"cuda\"), trt.Runtime(TRT_LOGGER):\n # CLIP text encoder\n text_embeddings = self.__encode_prompt(prompt, negative_prompt)\n\n # Pre-initialize latents\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size,\n num_channels_latents,\n self.image_height,\n self.image_width,\n torch.float32,\n self.torch_device,\n generator,\n )\n\n # UNet denoiser\n latents = self.__denoise_latent(latents, text_embeddings)\n\n # VAE decode latent\n images = self.__decode_latent(latents)\n\n images, has_nsfw_concept = self.run_safety_checker(images, self.torch_device, text_embeddings.dtype)\n images = self.numpy_to_pil(images)\n return StableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/examples/community/stable_diffusion_xl_reference.py", "content": "# Based on stable_diffusion_reference.py\n\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\n\nfrom diffusers import StableDiffusionXLPipeline\nfrom diffusers.models.attention import BasicTransformerBlock\nfrom diffusers.models.unets.unet_2d_blocks import (\n CrossAttnDownBlock2D,\n CrossAttnUpBlock2D,\n DownBlock2D,\n UpBlock2D,\n)\nfrom diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput\nfrom diffusers.utils import PIL_INTERPOLATION, logging\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import UniPCMultistepScheduler\n >>> from diffusers.utils import load_image\n\n >>> input_image = load_image(\"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png\")\n\n >>> pipe = StableDiffusionXLReferencePipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n torch_dtype=torch.float16,\n use_safetensors=True,\n variant=\"fp16\").to('cuda:0')\n\n >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n >>> result_img = pipe(ref_image=input_image,\n prompt=\"1girl\",\n num_inference_steps=20,\n reference_attn=True,\n reference_adain=True).images[0]\n\n >>> result_img.show()\n ```\n\"\"\"\n\n\ndef torch_dfs(model: torch.nn.Module):\n result = [model]\n for child in model.children():\n result += torch_dfs(child)\n return result\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg\n\n\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\nclass StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):\n def _default_height_width(self, height, width, image):\n # NOTE: It is possible that a list of images have different\n # dimensions for each image, so just checking the first image\n # is not _exactly_ correct, but it is simple.\n while isinstance(image, list):\n image = image[0]\n\n if height is None:\n if isinstance(image, PIL.Image.Image):\n height = image.height\n elif isinstance(image, torch.Tensor):\n height = image.shape[2]\n\n height = (height // 8) * 8 # round down to nearest multiple of 8\n\n if width is None:\n if isinstance(image, PIL.Image.Image):\n width = image.width\n elif isinstance(image, torch.Tensor):\n width = image.shape[3]\n\n width = (width // 8) * 8\n\n return height, width\n\n def prepare_image(\n self,\n image,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype,\n do_classifier_free_guidance=False,\n guess_mode=False,\n ):\n if not isinstance(image, torch.Tensor):\n if isinstance(image, PIL.Image.Image):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n images = []\n\n for image_ in image:\n image_ = image_.convert(\"RGB\")\n image_ = image_.resize((width, height), resample=PIL_INTERPOLATION[\"lanczos\"])\n image_ = np.array(image_)\n image_ = image_[None, :]\n images.append(image_)\n\n image = images\n\n image = np.concatenate(image, axis=0)\n image = np.array(image).astype(np.float32) / 255.0\n image = (image - 0.5) / 0.5\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image)\n\n elif isinstance(image[0], torch.Tensor):\n image = torch.stack(image, dim=0)\n\n image_batch_size = image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n repeat_by = num_images_per_prompt\n\n image = image.repeat_interleave(repeat_by, dim=0)\n\n image = image.to(device=device, dtype=dtype)\n\n if do_classifier_free_guidance and not guess_mode:\n image = torch.cat([image] * 2)\n\n return image\n\n def prepare_ref_latents(self, refimage, batch_size, dtype, device, generator, do_classifier_free_guidance):\n refimage = refimage.to(device=device)\n if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:\n self.upcast_vae()\n refimage = refimage.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n if refimage.dtype != self.vae.dtype:\n refimage = refimage.to(dtype=self.vae.dtype)\n # encode the mask image into latents space so we can concatenate it to the latents\n if isinstance(generator, list):\n ref_image_latents = [\n self.vae.encode(refimage[i : i + 1]).latent_dist.sample(generator=generator[i])\n for i in range(batch_size)\n ]\n ref_image_latents = torch.cat(ref_image_latents, dim=0)\n else:\n ref_image_latents = self.vae.encode(refimage).latent_dist.sample(generator=generator)\n ref_image_latents = self.vae.config.scaling_factor * ref_image_latents\n\n # duplicate mask and ref_image_latents for each generation per prompt, using mps friendly method\n if ref_image_latents.shape[0] < batch_size:\n if not batch_size % ref_image_latents.shape[0] == 0:\n raise ValueError(\n \"The passed images and the required batch size don't match. Images are supposed to be duplicated\"\n f\" to a total batch size of {batch_size}, but {ref_image_latents.shape[0]} images were passed.\"\n \" Make sure the number of images that you pass is divisible by the total requested batch size.\"\n )\n ref_image_latents = ref_image_latents.repeat(batch_size // ref_image_latents.shape[0], 1, 1, 1)\n\n ref_image_latents = torch.cat([ref_image_latents] * 2) if do_classifier_free_guidance else ref_image_latents\n\n # aligning device to prevent device errors when concating it with the latent model input\n ref_image_latents = ref_image_latents.to(device=device, dtype=dtype)\n return ref_image_latents\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n ref_image: Union[torch.Tensor, PIL.Image.Image] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Optional[Tuple[int, int]] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Optional[Tuple[int, int]] = None,\n attention_auto_machine_weight: float = 1.0,\n gn_auto_machine_weight: float = 1.0,\n style_fidelity: float = 0.5,\n reference_attn: bool = True,\n reference_adain: bool = True,\n ):\n assert reference_attn or reference_adain, \"`reference_attn` or `reference_adain` must be True.\"\n\n # 0. Default height and width to unet\n # height, width = self._default_height_width(height, width, ref_image)\n\n height = height or self.default_sample_size * self.vae_scale_factor\n width = width or self.default_sample_size * self.vae_scale_factor\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n height,\n width,\n callback_steps,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n # 4. Preprocess reference image\n ref_image = self.prepare_image(\n image=ref_image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=prompt_embeds.dtype,\n )\n\n # 5. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n\n timesteps = self.scheduler.timesteps\n\n # 6. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n # 7. Prepare reference latent variables\n ref_image_latents = self.prepare_ref_latents(\n ref_image,\n batch_size * num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n generator,\n do_classifier_free_guidance,\n )\n\n # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 9. Modify self attebtion and group norm\n MODE = \"write\"\n uc_mask = (\n torch.Tensor([1] * batch_size * num_images_per_prompt + [0] * batch_size * num_images_per_prompt)\n .type_as(ref_image_latents)\n .bool()\n )\n\n def hacked_basic_transformer_inner_forward(\n self,\n hidden_states: torch.Tensor,\n attention_mask: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n timestep: Optional[torch.LongTensor] = None,\n cross_attention_kwargs: Dict[str, Any] = None,\n class_labels: Optional[torch.LongTensor] = None,\n ):\n if self.use_ada_layer_norm:\n norm_hidden_states = self.norm1(hidden_states, timestep)\n elif self.use_ada_layer_norm_zero:\n norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(\n hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype\n )\n else:\n norm_hidden_states = self.norm1(hidden_states)\n\n # 1. Self-Attention\n cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}\n if self.only_cross_attention:\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n else:\n if MODE == \"write\":\n self.bank.append(norm_hidden_states.detach().clone())\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n if MODE == \"read\":\n if attention_auto_machine_weight > self.attn_weight:\n attn_output_uc = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=torch.cat([norm_hidden_states] + self.bank, dim=1),\n # attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n attn_output_c = attn_output_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n attn_output_c[uc_mask] = self.attn1(\n norm_hidden_states[uc_mask],\n encoder_hidden_states=norm_hidden_states[uc_mask],\n **cross_attention_kwargs,\n )\n attn_output = style_fidelity * attn_output_c + (1.0 - style_fidelity) * attn_output_uc\n self.bank.clear()\n else:\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n if self.use_ada_layer_norm_zero:\n attn_output = gate_msa.unsqueeze(1) * attn_output\n hidden_states = attn_output + hidden_states\n\n if self.attn2 is not None:\n norm_hidden_states = (\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\n )\n\n # 2. Cross-Attention\n attn_output = self.attn2(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=encoder_attention_mask,\n **cross_attention_kwargs,\n )\n hidden_states = attn_output + hidden_states\n\n # 3. Feed-forward\n norm_hidden_states = self.norm3(hidden_states)\n\n if self.use_ada_layer_norm_zero:\n norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]\n\n ff_output = self.ff(norm_hidden_states)\n\n if self.use_ada_layer_norm_zero:\n ff_output = gate_mlp.unsqueeze(1) * ff_output\n\n hidden_states = ff_output + hidden_states\n\n return hidden_states\n\n def hacked_mid_forward(self, *args, **kwargs):\n eps = 1e-6\n x = self.original_forward(*args, **kwargs)\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append(mean)\n self.var_bank.append(var)\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank) / float(len(self.mean_bank))\n var_acc = sum(self.var_bank) / float(len(self.var_bank))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n x_uc = (((x - mean) / std) * std_acc) + mean_acc\n x_c = x_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n x_c[uc_mask] = x[uc_mask]\n x = style_fidelity * x_c + (1.0 - style_fidelity) * x_uc\n self.mean_bank = []\n self.var_bank = []\n return x\n\n def hack_CrossAttnDownBlock2D_forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n ):\n eps = 1e-6\n\n # TODO(Patrick, William) - attention mask is not used\n output_states = ()\n\n for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask]\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n output_states = output_states + (hidden_states,)\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n def hacked_DownBlock2D_forward(self, hidden_states, temb=None, *args, **kwargs):\n eps = 1e-6\n\n output_states = ()\n\n for i, resnet in enumerate(self.resnets):\n hidden_states = resnet(hidden_states, temb)\n\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask]\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n output_states = output_states + (hidden_states,)\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n def hacked_CrossAttnUpBlock2D_forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n upsample_size: Optional[int] = None,\n attention_mask: Optional[torch.Tensor] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n ):\n eps = 1e-6\n # TODO(Patrick, William) - attention mask is not used\n for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask]\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n def hacked_UpBlock2D_forward(\n self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None, **kwargs\n ):\n eps = 1e-6\n for i, resnet in enumerate(self.resnets):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n hidden_states = resnet(hidden_states, temb)\n\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask]\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n if reference_attn:\n attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock)]\n attn_modules = sorted(attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\n\n for i, module in enumerate(attn_modules):\n module._original_inner_forward = module.forward\n module.forward = hacked_basic_transformer_inner_forward.__get__(module, BasicTransformerBlock)\n module.bank = []\n module.attn_weight = float(i) / float(len(attn_modules))\n\n if reference_adain:\n gn_modules = [self.unet.mid_block]\n self.unet.mid_block.gn_weight = 0\n\n down_blocks = self.unet.down_blocks\n for w, module in enumerate(down_blocks):\n module.gn_weight = 1.0 - float(w) / float(len(down_blocks))\n gn_modules.append(module)\n\n up_blocks = self.unet.up_blocks\n for w, module in enumerate(up_blocks):\n module.gn_weight = float(w) / float(len(up_blocks))\n gn_modules.append(module)\n\n for i, module in enumerate(gn_modules):\n if getattr(module, \"original_forward\", None) is None:\n module.original_forward = module.forward\n if i == 0:\n # mid_block\n module.forward = hacked_mid_forward.__get__(module, torch.nn.Module)\n elif isinstance(module, CrossAttnDownBlock2D):\n module.forward = hack_CrossAttnDownBlock2D_forward.__get__(module, CrossAttnDownBlock2D)\n elif isinstance(module, DownBlock2D):\n module.forward = hacked_DownBlock2D_forward.__get__(module, DownBlock2D)\n elif isinstance(module, CrossAttnUpBlock2D):\n module.forward = hacked_CrossAttnUpBlock2D_forward.__get__(module, CrossAttnUpBlock2D)\n elif isinstance(module, UpBlock2D):\n module.forward = hacked_UpBlock2D_forward.__get__(module, UpBlock2D)\n module.mean_bank = []\n module.var_bank = []\n module.gn_weight *= 2\n\n # 10. Prepare added time ids & embeddings\n add_text_embeds = pooled_prompt_embeds\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n add_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)\n\n # 11. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n\n # 10.1 Apply denoising_end\n if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1:\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n\n # ref only part\n noise = randn_tensor(\n ref_image_latents.shape, generator=generator, device=device, dtype=ref_image_latents.dtype\n )\n ref_xt = self.scheduler.add_noise(\n ref_image_latents,\n noise,\n t.reshape(\n 1,\n ),\n )\n ref_xt = self.scheduler.scale_model_input(ref_xt, t)\n\n MODE = \"write\"\n\n self.unet(\n ref_xt,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )\n\n # predict the noise residual\n MODE = \"read\"\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if do_classifier_free_guidance and guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n image = latents\n return StableDiffusionXLPipelineOutput(images=image)\n\n # apply watermark if available\n if self.watermark is not None:\n image = self.watermark.apply_watermark(image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload last model to CPU\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)\n" }, { "path": "diffusers/examples/community/stable_unclip.py", "content": "import types\nfrom typing import List, Optional, Tuple, Union\n\nimport torch\nfrom transformers import CLIPTextModelWithProjection, CLIPTokenizer\nfrom transformers.models.clip.modeling_clip import CLIPTextModelOutput\n\nfrom diffusers.models import PriorTransformer\nfrom diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline\nfrom diffusers.schedulers import UnCLIPScheduler\nfrom diffusers.utils import logging\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef _encode_image(self, image, device, num_images_per_prompt, do_classifier_free_guidance):\n image = image.to(device=device)\n image_embeddings = image # take image as image_embeddings\n image_embeddings = image_embeddings.unsqueeze(1)\n\n # duplicate image embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = image_embeddings.shape\n image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)\n image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n uncond_embeddings = torch.zeros_like(image_embeddings)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n image_embeddings = torch.cat([uncond_embeddings, image_embeddings])\n\n return image_embeddings\n\n\nclass StableUnCLIPPipeline(DiffusionPipeline):\n def __init__(\n self,\n prior: PriorTransformer,\n tokenizer: CLIPTokenizer,\n text_encoder: CLIPTextModelWithProjection,\n prior_scheduler: UnCLIPScheduler,\n decoder_pipe_kwargs: Optional[dict] = None,\n ):\n super().__init__()\n\n decoder_pipe_kwargs = {\"image_encoder\": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs\n\n decoder_pipe_kwargs[\"torch_dtype\"] = decoder_pipe_kwargs.get(\"torch_dtype\", None) or prior.dtype\n\n self.decoder_pipe = StableDiffusionImageVariationPipeline.from_pretrained(\n \"lambdalabs/sd-image-variations-diffusers\", **decoder_pipe_kwargs\n )\n\n # replace `_encode_image` method\n self.decoder_pipe._encode_image = types.MethodType(_encode_image, self.decoder_pipe)\n\n self.register_modules(\n prior=prior,\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n prior_scheduler=prior_scheduler,\n )\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,\n text_attention_mask: Optional[torch.Tensor] = None,\n ):\n if text_model_output is None:\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n text_mask = text_inputs.attention_mask.bool().to(device)\n\n if text_input_ids.shape[-1] > self.tokenizer.model_max_length:\n removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]\n\n text_encoder_output = self.text_encoder(text_input_ids.to(device))\n\n text_embeddings = text_encoder_output.text_embeds\n text_encoder_hidden_states = text_encoder_output.last_hidden_state\n\n else:\n batch_size = text_model_output[0].shape[0]\n text_embeddings, text_encoder_hidden_states = text_model_output[0], text_model_output[1]\n text_mask = text_attention_mask\n\n text_embeddings = text_embeddings.repeat_interleave(num_images_per_prompt, dim=0)\n text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n if do_classifier_free_guidance:\n uncond_tokens = [\"\"] * batch_size\n\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n uncond_text_mask = uncond_input.attention_mask.bool().to(device)\n uncond_embeddings_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))\n\n uncond_embeddings = uncond_embeddings_text_encoder_output.text_embeds\n uncond_text_encoder_hidden_states = uncond_embeddings_text_encoder_output.last_hidden_state\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n\n seq_len = uncond_embeddings.shape[1]\n uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt)\n uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len)\n\n seq_len = uncond_text_encoder_hidden_states.shape[1]\n uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)\n uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(\n batch_size * num_images_per_prompt, seq_len, -1\n )\n uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n # done duplicates\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])\n\n text_mask = torch.cat([uncond_text_mask, text_mask])\n\n return text_embeddings, text_encoder_hidden_states, text_mask\n\n @property\n def _execution_device(self):\n r\"\"\"\n Returns the device on which the pipeline's models will be executed. After calling\n `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module\n hooks.\n \"\"\"\n if self.device != torch.device(\"meta\") or not hasattr(self.prior, \"_hf_hook\"):\n return self.device\n for module in self.prior.modules():\n if (\n hasattr(module, \"_hf_hook\")\n and hasattr(module._hf_hook, \"execution_device\")\n and module._hf_hook.execution_device is not None\n ):\n return torch.device(module._hf_hook.execution_device)\n return self.device\n\n def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n latents = latents * scheduler.init_noise_sigma\n return latents\n\n def to(self, torch_device: Optional[Union[str, torch.device]] = None):\n self.decoder_pipe.to(torch_device)\n super().to(torch_device)\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Optional[Union[str, List[str]]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_images_per_prompt: int = 1,\n prior_num_inference_steps: int = 25,\n generator: Optional[torch.Generator] = None,\n prior_latents: Optional[torch.Tensor] = None,\n text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,\n text_attention_mask: Optional[torch.Tensor] = None,\n prior_guidance_scale: float = 4.0,\n decoder_guidance_scale: float = 8.0,\n decoder_num_inference_steps: int = 50,\n decoder_num_images_per_prompt: Optional[int] = 1,\n decoder_eta: float = 0.0,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n ):\n if prompt is not None:\n if isinstance(prompt, str):\n batch_size = 1\n elif isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n else:\n batch_size = text_model_output[0].shape[0]\n\n device = self._execution_device\n\n batch_size = batch_size * num_images_per_prompt\n\n do_classifier_free_guidance = prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0\n\n text_embeddings, text_encoder_hidden_states, text_mask = self._encode_prompt(\n prompt, device, num_images_per_prompt, do_classifier_free_guidance, text_model_output, text_attention_mask\n )\n\n # prior\n\n self.prior_scheduler.set_timesteps(prior_num_inference_steps, device=device)\n prior_timesteps_tensor = self.prior_scheduler.timesteps\n\n embedding_dim = self.prior.config.embedding_dim\n\n prior_latents = self.prepare_latents(\n (batch_size, embedding_dim),\n text_embeddings.dtype,\n device,\n generator,\n prior_latents,\n self.prior_scheduler,\n )\n\n for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([prior_latents] * 2) if do_classifier_free_guidance else prior_latents\n\n predicted_image_embedding = self.prior(\n latent_model_input,\n timestep=t,\n proj_embedding=text_embeddings,\n encoder_hidden_states=text_encoder_hidden_states,\n attention_mask=text_mask,\n ).predicted_image_embedding\n\n if do_classifier_free_guidance:\n predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)\n predicted_image_embedding = predicted_image_embedding_uncond + prior_guidance_scale * (\n predicted_image_embedding_text - predicted_image_embedding_uncond\n )\n\n if i + 1 == prior_timesteps_tensor.shape[0]:\n prev_timestep = None\n else:\n prev_timestep = prior_timesteps_tensor[i + 1]\n\n prior_latents = self.prior_scheduler.step(\n predicted_image_embedding,\n timestep=t,\n sample=prior_latents,\n generator=generator,\n prev_timestep=prev_timestep,\n ).prev_sample\n\n prior_latents = self.prior.post_process_latents(prior_latents)\n\n image_embeddings = prior_latents\n\n output = self.decoder_pipe(\n image=image_embeddings,\n height=height,\n width=width,\n num_inference_steps=decoder_num_inference_steps,\n guidance_scale=decoder_guidance_scale,\n generator=generator,\n output_type=output_type,\n return_dict=return_dict,\n num_images_per_prompt=decoder_num_images_per_prompt,\n eta=decoder_eta,\n )\n return output\n" }, { "path": "diffusers/examples/community/text_inpainting.py", "content": "from typing import Callable, List, Optional, Union\n\nimport PIL.Image\nimport torch\nfrom transformers import (\n CLIPImageProcessor,\n CLIPSegForImageSegmentation,\n CLIPSegProcessor,\n CLIPTextModel,\n CLIPTokenizer,\n)\n\nfrom diffusers import DiffusionPipeline\nfrom diffusers.configuration_utils import FrozenDict\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.pipelines.pipeline_utils import StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler\nfrom diffusers.utils import deprecate, logging\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass TextInpainting(DiffusionPipeline, StableDiffusionMixin):\n r\"\"\"\n Pipeline for text based inpainting using Stable Diffusion.\n Uses CLIPSeg to get a mask from the given text, then calls the Inpainting pipeline with the generated mask\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n segmentation_model ([`CLIPSegForImageSegmentation`]):\n CLIPSeg Model to generate mask from the given text. Please refer to the [model card]() for details.\n segmentation_processor ([`CLIPSegProcessor`]):\n CLIPSeg processor to get image, text features to translate prompt to English, if necessary. Please refer to the\n [model card](https://huggingface.co/docs/transformers/model_doc/clipseg) for details.\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n def __init__(\n self,\n segmentation_model: CLIPSegForImageSegmentation,\n segmentation_processor: CLIPSegProcessor,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"skip_prk_steps\") and scheduler.config.skip_prk_steps is False:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration\"\n \" `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make\"\n \" sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to\"\n \" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face\"\n \" Hub, it would be very nice if you could open a Pull request for the\"\n \" `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"skip_prk_steps not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"skip_prk_steps\"] = True\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n self.register_modules(\n segmentation_model=segmentation_model,\n segmentation_processor=segmentation_processor,\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n image: Union[torch.Tensor, PIL.Image.Image],\n text: str,\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n image (`PIL.Image.Image`):\n `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will\n be masked out with `mask_image` and repainted according to `prompt`.\n text (`str``):\n The text to use to generate the mask.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation\n deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n\n # We use the input text to generate the mask\n inputs = self.segmentation_processor(\n text=[text], images=[image], padding=\"max_length\", return_tensors=\"pt\"\n ).to(self.device)\n outputs = self.segmentation_model(**inputs)\n mask = torch.sigmoid(outputs.logits).cpu().detach().unsqueeze(-1).numpy()\n mask_pil = self.numpy_to_pil(mask)[0].resize(image.size)\n\n # Run inpainting pipeline with the generated mask\n inpainting_pipeline = StableDiffusionInpaintPipeline(\n vae=self.vae,\n text_encoder=self.text_encoder,\n tokenizer=self.tokenizer,\n unet=self.unet,\n scheduler=self.scheduler,\n safety_checker=self.safety_checker,\n feature_extractor=self.feature_extractor,\n )\n return inpainting_pipeline(\n prompt=prompt,\n image=image,\n mask_image=mask_pil,\n height=height,\n width=width,\n num_inference_steps=num_inference_steps,\n guidance_scale=guidance_scale,\n negative_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n latents=latents,\n output_type=output_type,\n return_dict=return_dict,\n callback=callback,\n callback_steps=callback_steps,\n )\n" }, { "path": "diffusers/examples/community/tiled_upscaling.py", "content": "# Copyright 2024 Peter Willemsen . All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport math\nfrom typing import Callable, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom PIL import Image\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline\nfrom diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler\n\n\ndef make_transparency_mask(size, overlap_pixels, remove_borders=[]):\n size_x = size[0] - overlap_pixels * 2\n size_y = size[1] - overlap_pixels * 2\n for letter in [\"l\", \"r\"]:\n if letter in remove_borders:\n size_x += overlap_pixels\n for letter in [\"t\", \"b\"]:\n if letter in remove_borders:\n size_y += overlap_pixels\n mask = np.ones((size_y, size_x), dtype=np.uint8) * 255\n mask = np.pad(mask, mode=\"linear_ramp\", pad_width=overlap_pixels, end_values=0)\n\n if \"l\" in remove_borders:\n mask = mask[:, overlap_pixels : mask.shape[1]]\n if \"r\" in remove_borders:\n mask = mask[:, 0 : mask.shape[1] - overlap_pixels]\n if \"t\" in remove_borders:\n mask = mask[overlap_pixels : mask.shape[0], :]\n if \"b\" in remove_borders:\n mask = mask[0 : mask.shape[0] - overlap_pixels, :]\n return mask\n\n\ndef clamp(n, smallest, largest):\n return max(smallest, min(n, largest))\n\n\ndef clamp_rect(rect: [int], min: [int], max: [int]):\n return (\n clamp(rect[0], min[0], max[0]),\n clamp(rect[1], min[1], max[1]),\n clamp(rect[2], min[0], max[0]),\n clamp(rect[3], min[1], max[1]),\n )\n\n\ndef add_overlap_rect(rect: [int], overlap: int, image_size: [int]):\n rect = list(rect)\n rect[0] -= overlap\n rect[1] -= overlap\n rect[2] += overlap\n rect[3] += overlap\n rect = clamp_rect(rect, [0, 0], [image_size[0], image_size[1]])\n return rect\n\n\ndef squeeze_tile(tile, original_image, original_slice, slice_x):\n result = Image.new(\"RGB\", (tile.size[0] + original_slice, tile.size[1]))\n result.paste(\n original_image.resize((tile.size[0], tile.size[1]), Image.BICUBIC).crop(\n (slice_x, 0, slice_x + original_slice, tile.size[1])\n ),\n (0, 0),\n )\n result.paste(tile, (original_slice, 0))\n return result\n\n\ndef unsqueeze_tile(tile, original_image_slice):\n crop_rect = (original_image_slice * 4, 0, tile.size[0], tile.size[1])\n tile = tile.crop(crop_rect)\n return tile\n\n\ndef next_divisible(n, d):\n divisor = n % d\n return n - divisor\n\n\nclass StableDiffusionTiledUpscalePipeline(StableDiffusionUpscalePipeline):\n r\"\"\"\n Pipeline for tile-based text-guided image super-resolution using Stable Diffusion 2, trading memory for compute\n to create gigantic images.\n\n This model inherits from [`StableDiffusionUpscalePipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n low_res_scheduler ([`SchedulerMixin`]):\n A scheduler used to add initial noise to the low res conditioning image. It must be an instance of\n [`DDPMScheduler`].\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n \"\"\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n low_res_scheduler: DDPMScheduler,\n scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],\n max_noise_level: int = 350,\n ):\n super().__init__(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n low_res_scheduler=low_res_scheduler,\n scheduler=scheduler,\n max_noise_level=max_noise_level,\n )\n\n def _process_tile(self, original_image_slice, x, y, tile_size, tile_border, image, final_image, **kwargs):\n torch.manual_seed(0)\n crop_rect = (\n min(image.size[0] - (tile_size + original_image_slice), x * tile_size),\n min(image.size[1] - (tile_size + original_image_slice), y * tile_size),\n min(image.size[0], (x + 1) * tile_size),\n min(image.size[1], (y + 1) * tile_size),\n )\n crop_rect_with_overlap = add_overlap_rect(crop_rect, tile_border, image.size)\n tile = image.crop(crop_rect_with_overlap)\n translated_slice_x = ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]\n translated_slice_x = translated_slice_x - (original_image_slice / 2)\n translated_slice_x = max(0, translated_slice_x)\n to_input = squeeze_tile(tile, image, original_image_slice, translated_slice_x)\n orig_input_size = to_input.size\n to_input = to_input.resize((tile_size, tile_size), Image.BICUBIC)\n upscaled_tile = super(StableDiffusionTiledUpscalePipeline, self).__call__(image=to_input, **kwargs).images[0]\n upscaled_tile = upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4), Image.BICUBIC)\n upscaled_tile = unsqueeze_tile(upscaled_tile, original_image_slice)\n upscaled_tile = upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4), Image.BICUBIC)\n remove_borders = []\n if x == 0:\n remove_borders.append(\"l\")\n elif crop_rect[2] == image.size[0]:\n remove_borders.append(\"r\")\n if y == 0:\n remove_borders.append(\"t\")\n elif crop_rect[3] == image.size[1]:\n remove_borders.append(\"b\")\n transparency_mask = Image.fromarray(\n make_transparency_mask(\n (upscaled_tile.size[0], upscaled_tile.size[1]), tile_border * 4, remove_borders=remove_borders\n ),\n mode=\"L\",\n )\n final_image.paste(\n upscaled_tile, (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4), transparency_mask\n )\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n image: Union[PIL.Image.Image, List[PIL.Image.Image]],\n num_inference_steps: int = 75,\n guidance_scale: float = 9.0,\n noise_level: int = 50,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.Tensor] = None,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n tile_size: int = 128,\n tile_border: int = 32,\n original_image_slice: int = 32,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n image (`PIL.Image.Image` or List[`PIL.Image.Image`] or `torch.Tensor`):\n `Image`, or tensor representing an image batch which will be upscaled. *\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation\n deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n tile_size (`int`, *optional*):\n The size of the tiles. Too big can result in an OOM-error.\n tile_border (`int`, *optional*):\n The number of pixels around a tile to consider (bigger means less seams, too big can lead to an OOM-error).\n original_image_slice (`int`, *optional*):\n The amount of pixels of the original image to calculate with the current tile (bigger means more depth\n is preserved, less blur occurs in the final image, too big can lead to an OOM-error or loss in detail).\n callback (`Callable`, *optional*):\n A function that take a callback function with a single argument, a dict,\n that contains the (partially) processed image under \"image\",\n as well as the progress (0 to 1, where 1 is completed) under \"progress\".\n\n Returns: A PIL.Image that is 4 times larger than the original input image.\n\n \"\"\"\n\n final_image = Image.new(\"RGB\", (image.size[0] * 4, image.size[1] * 4))\n tcx = math.ceil(image.size[0] / tile_size)\n tcy = math.ceil(image.size[1] / tile_size)\n total_tile_count = tcx * tcy\n current_count = 0\n for y in range(tcy):\n for x in range(tcx):\n self._process_tile(\n original_image_slice,\n x,\n y,\n tile_size,\n tile_border,\n image,\n final_image,\n prompt=prompt,\n num_inference_steps=num_inference_steps,\n guidance_scale=guidance_scale,\n noise_level=noise_level,\n negative_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n latents=latents,\n )\n current_count += 1\n if callback is not None:\n callback({\"progress\": current_count / total_tile_count, \"image\": final_image})\n return final_image\n\n\ndef main():\n # Run a demo\n model_id = \"stabilityai/stable-diffusion-x4-upscaler\"\n pipe = StableDiffusionTiledUpscalePipeline.from_pretrained(model_id, revision=\"fp16\", torch_dtype=torch.float16)\n pipe = pipe.to(\"cuda\")\n image = Image.open(\"../../docs/source/imgs/diffusers_library.jpg\")\n\n def callback(obj):\n print(f\"progress: {obj['progress']:.4f}\")\n obj[\"image\"].save(\"diffusers_library_progress.jpg\")\n\n final_image = pipe(image=image, prompt=\"Black font, white background, vector\", noise_level=40, callback=callback)\n final_image.save(\"diffusers_library.jpg\")\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/community/unclip_image_interpolation.py", "content": "import inspect\nfrom typing import List, Optional, Union\n\nimport PIL.Image\nimport torch\nfrom torch.nn import functional as F\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers import (\n DiffusionPipeline,\n ImagePipelineOutput,\n UnCLIPScheduler,\n UNet2DConditionModel,\n UNet2DModel,\n)\nfrom diffusers.pipelines.unclip import UnCLIPTextProjModel\nfrom diffusers.utils import logging\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef slerp(val, low, high):\n \"\"\"\n Find the interpolation point between the 'low' and 'high' values for the given 'val'. See https://en.wikipedia.org/wiki/Slerp for more details on the topic.\n \"\"\"\n low_norm = low / torch.norm(low)\n high_norm = high / torch.norm(high)\n omega = torch.acos((low_norm * high_norm))\n so = torch.sin(omega)\n res = (torch.sin((1.0 - val) * omega) / so) * low + (torch.sin(val * omega) / so) * high\n return res\n\n\nclass UnCLIPImageInterpolationPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline to generate variations from an input image using unCLIP\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n text_encoder ([`CLIPTextModelWithProjection`]):\n Frozen text-encoder.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `image_encoder`.\n image_encoder ([`CLIPVisionModelWithProjection`]):\n Frozen CLIP image-encoder. unCLIP Image Variation uses the vision portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPVisionModelWithProjection),\n specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n text_proj ([`UnCLIPTextProjModel`]):\n Utility class to prepare and combine the embeddings before they are passed to the decoder.\n decoder ([`UNet2DConditionModel`]):\n The decoder to invert the image embedding into an image.\n super_res_first ([`UNet2DModel`]):\n Super resolution unet. Used in all but the last step of the super resolution diffusion process.\n super_res_last ([`UNet2DModel`]):\n Super resolution unet. Used in the last step of the super resolution diffusion process.\n decoder_scheduler ([`UnCLIPScheduler`]):\n Scheduler used in the decoder denoising process. Just a modified DDPMScheduler.\n super_res_scheduler ([`UnCLIPScheduler`]):\n Scheduler used in the super resolution denoising process. Just a modified DDPMScheduler.\n\n \"\"\"\n\n decoder: UNet2DConditionModel\n text_proj: UnCLIPTextProjModel\n text_encoder: CLIPTextModelWithProjection\n tokenizer: CLIPTokenizer\n feature_extractor: CLIPImageProcessor\n image_encoder: CLIPVisionModelWithProjection\n super_res_first: UNet2DModel\n super_res_last: UNet2DModel\n\n decoder_scheduler: UnCLIPScheduler\n super_res_scheduler: UnCLIPScheduler\n\n # Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline.__init__\n def __init__(\n self,\n decoder: UNet2DConditionModel,\n text_encoder: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n text_proj: UnCLIPTextProjModel,\n feature_extractor: CLIPImageProcessor,\n image_encoder: CLIPVisionModelWithProjection,\n super_res_first: UNet2DModel,\n super_res_last: UNet2DModel,\n decoder_scheduler: UnCLIPScheduler,\n super_res_scheduler: UnCLIPScheduler,\n ):\n super().__init__()\n\n self.register_modules(\n decoder=decoder,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n text_proj=text_proj,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n super_res_first=super_res_first,\n super_res_last=super_res_last,\n decoder_scheduler=decoder_scheduler,\n super_res_scheduler=super_res_scheduler,\n )\n\n # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents\n def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n latents = latents * scheduler.init_noise_sigma\n return latents\n\n # Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline._encode_prompt\n def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance):\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n text_mask = text_inputs.attention_mask.bool().to(device)\n text_encoder_output = self.text_encoder(text_input_ids.to(device))\n\n prompt_embeds = text_encoder_output.text_embeds\n text_encoder_hidden_states = text_encoder_output.last_hidden_state\n\n prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n if do_classifier_free_guidance:\n uncond_tokens = [\"\"] * batch_size\n\n max_length = text_input_ids.shape[-1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n uncond_text_mask = uncond_input.attention_mask.bool().to(device)\n negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))\n\n negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds\n uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)\n\n seq_len = uncond_text_encoder_hidden_states.shape[1]\n uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)\n uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(\n batch_size * num_images_per_prompt, seq_len, -1\n )\n uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n # done duplicates\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])\n\n text_mask = torch.cat([uncond_text_mask, text_mask])\n\n return prompt_embeds, text_encoder_hidden_states, text_mask\n\n # Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline._encode_image\n def _encode_image(self, image, device, num_images_per_prompt, image_embeddings: Optional[torch.Tensor] = None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if image_embeddings is None:\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(images=image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n image_embeddings = self.image_encoder(image).image_embeds\n\n image_embeddings = image_embeddings.repeat_interleave(num_images_per_prompt, dim=0)\n\n return image_embeddings\n\n @torch.no_grad()\n def __call__(\n self,\n image: Optional[Union[List[PIL.Image.Image], torch.Tensor]] = None,\n steps: int = 5,\n decoder_num_inference_steps: int = 25,\n super_res_num_inference_steps: int = 7,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n image_embeddings: Optional[torch.Tensor] = None,\n decoder_latents: Optional[torch.Tensor] = None,\n super_res_latents: Optional[torch.Tensor] = None,\n decoder_guidance_scale: float = 8.0,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n image (`List[PIL.Image.Image]` or `torch.Tensor`):\n The images to use for the image interpolation. Only accepts a list of two PIL Images or If you provide a tensor, it needs to comply with the\n configuration of\n [this](https://huggingface.co/fusing/karlo-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json)\n `CLIPImageProcessor` while still having a shape of two in the 0th dimension. Can be left to `None` only when `image_embeddings` are passed.\n steps (`int`, *optional*, defaults to 5):\n The number of interpolation images to generate.\n decoder_num_inference_steps (`int`, *optional*, defaults to 25):\n The number of denoising steps for the decoder. More denoising steps usually lead to a higher quality\n image at the expense of slower inference.\n super_res_num_inference_steps (`int`, *optional*, defaults to 7):\n The number of denoising steps for super resolution. More denoising steps usually lead to a higher\n quality image at the expense of slower inference.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n image_embeddings (`torch.Tensor`, *optional*):\n Pre-defined image embeddings that can be derived from the image encoder. Pre-defined image embeddings\n can be passed for tasks like image interpolations. `image` can the be left to `None`.\n decoder_latents (`torch.Tensor` of shape (batch size, channels, height, width), *optional*):\n Pre-generated noisy latents to be used as inputs for the decoder.\n super_res_latents (`torch.Tensor` of shape (batch size, channels, super res height, super res width), *optional*):\n Pre-generated noisy latents to be used as inputs for the decoder.\n decoder_guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n \"\"\"\n\n batch_size = steps\n\n device = self._execution_device\n\n if isinstance(image, List):\n if len(image) != 2:\n raise AssertionError(\n f\"Expected 'image' List to be of size 2, but passed 'image' length is {len(image)}\"\n )\n elif not (isinstance(image[0], PIL.Image.Image) and isinstance(image[0], PIL.Image.Image)):\n raise AssertionError(\n f\"Expected 'image' List to contain PIL.Image.Image, but passed 'image' contents are {type(image[0])} and {type(image[1])}\"\n )\n elif isinstance(image, torch.Tensor):\n if image.shape[0] != 2:\n raise AssertionError(\n f\"Expected 'image' to be torch.Tensor of shape 2 in 0th dimension, but passed 'image' size is {image.shape[0]}\"\n )\n elif isinstance(image_embeddings, torch.Tensor):\n if image_embeddings.shape[0] != 2:\n raise AssertionError(\n f\"Expected 'image_embeddings' to be torch.Tensor of shape 2 in 0th dimension, but passed 'image_embeddings' shape is {image_embeddings.shape[0]}\"\n )\n else:\n raise AssertionError(\n f\"Expected 'image' or 'image_embeddings' to be not None with types List[PIL.Image] or torch.Tensor respectively. Received {type(image)} and {type(image_embeddings)} repsectively\"\n )\n\n original_image_embeddings = self._encode_image(\n image=image, device=device, num_images_per_prompt=1, image_embeddings=image_embeddings\n )\n\n image_embeddings = []\n\n for interp_step in torch.linspace(0, 1, steps):\n temp_image_embeddings = slerp(\n interp_step, original_image_embeddings[0], original_image_embeddings[1]\n ).unsqueeze(0)\n image_embeddings.append(temp_image_embeddings)\n\n image_embeddings = torch.cat(image_embeddings).to(device)\n\n do_classifier_free_guidance = decoder_guidance_scale > 1.0\n\n prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt(\n prompt=[\"\" for i in range(steps)],\n device=device,\n num_images_per_prompt=1,\n do_classifier_free_guidance=do_classifier_free_guidance,\n )\n\n text_encoder_hidden_states, additive_clip_time_embeddings = self.text_proj(\n image_embeddings=image_embeddings,\n prompt_embeds=prompt_embeds,\n text_encoder_hidden_states=text_encoder_hidden_states,\n do_classifier_free_guidance=do_classifier_free_guidance,\n )\n\n if device.type == \"mps\":\n # HACK: MPS: There is a panic when padding bool tensors,\n # so cast to int tensor for the pad and back to bool afterwards\n text_mask = text_mask.type(torch.int)\n decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=1)\n decoder_text_mask = decoder_text_mask.type(torch.bool)\n else:\n decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=True)\n\n self.decoder_scheduler.set_timesteps(decoder_num_inference_steps, device=device)\n decoder_timesteps_tensor = self.decoder_scheduler.timesteps\n\n num_channels_latents = self.decoder.config.in_channels\n height = self.decoder.config.sample_size\n width = self.decoder.config.sample_size\n\n # Get the decoder latents for 1 step and then repeat the same tensor for the entire batch to keep same noise across all interpolation steps.\n decoder_latents = self.prepare_latents(\n (1, num_channels_latents, height, width),\n text_encoder_hidden_states.dtype,\n device,\n generator,\n decoder_latents,\n self.decoder_scheduler,\n )\n decoder_latents = decoder_latents.repeat((batch_size, 1, 1, 1))\n\n for i, t in enumerate(self.progress_bar(decoder_timesteps_tensor)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([decoder_latents] * 2) if do_classifier_free_guidance else decoder_latents\n\n noise_pred = self.decoder(\n sample=latent_model_input,\n timestep=t,\n encoder_hidden_states=text_encoder_hidden_states,\n class_labels=additive_clip_time_embeddings,\n attention_mask=decoder_text_mask,\n ).sample\n\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred_uncond, _ = noise_pred_uncond.split(latent_model_input.shape[1], dim=1)\n noise_pred_text, predicted_variance = noise_pred_text.split(latent_model_input.shape[1], dim=1)\n noise_pred = noise_pred_uncond + decoder_guidance_scale * (noise_pred_text - noise_pred_uncond)\n noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)\n\n if i + 1 == decoder_timesteps_tensor.shape[0]:\n prev_timestep = None\n else:\n prev_timestep = decoder_timesteps_tensor[i + 1]\n\n # compute the previous noisy sample x_t -> x_t-1\n decoder_latents = self.decoder_scheduler.step(\n noise_pred, t, decoder_latents, prev_timestep=prev_timestep, generator=generator\n ).prev_sample\n\n decoder_latents = decoder_latents.clamp(-1, 1)\n\n image_small = decoder_latents\n\n # done decoder\n\n # super res\n\n self.super_res_scheduler.set_timesteps(super_res_num_inference_steps, device=device)\n super_res_timesteps_tensor = self.super_res_scheduler.timesteps\n\n channels = self.super_res_first.config.in_channels // 2\n height = self.super_res_first.config.sample_size\n width = self.super_res_first.config.sample_size\n\n super_res_latents = self.prepare_latents(\n (batch_size, channels, height, width),\n image_small.dtype,\n device,\n generator,\n super_res_latents,\n self.super_res_scheduler,\n )\n\n if device.type == \"mps\":\n # MPS does not support many interpolations\n image_upscaled = F.interpolate(image_small, size=[height, width])\n else:\n interpolate_antialias = {}\n if \"antialias\" in inspect.signature(F.interpolate).parameters:\n interpolate_antialias[\"antialias\"] = True\n\n image_upscaled = F.interpolate(\n image_small, size=[height, width], mode=\"bicubic\", align_corners=False, **interpolate_antialias\n )\n\n for i, t in enumerate(self.progress_bar(super_res_timesteps_tensor)):\n # no classifier free guidance\n\n if i == super_res_timesteps_tensor.shape[0] - 1:\n unet = self.super_res_last\n else:\n unet = self.super_res_first\n\n latent_model_input = torch.cat([super_res_latents, image_upscaled], dim=1)\n\n noise_pred = unet(\n sample=latent_model_input,\n timestep=t,\n ).sample\n\n if i + 1 == super_res_timesteps_tensor.shape[0]:\n prev_timestep = None\n else:\n prev_timestep = super_res_timesteps_tensor[i + 1]\n\n # compute the previous noisy sample x_t -> x_t-1\n super_res_latents = self.super_res_scheduler.step(\n noise_pred, t, super_res_latents, prev_timestep=prev_timestep, generator=generator\n ).prev_sample\n\n image = super_res_latents\n # done super res\n\n # post processing\n\n image = image * 0.5 + 0.5\n image = image.clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/examples/community/unclip_text_interpolation.py", "content": "import inspect\nfrom typing import List, Optional, Tuple, Union\n\nimport torch\nfrom torch.nn import functional as F\nfrom transformers import CLIPTextModelWithProjection, CLIPTokenizer\nfrom transformers.models.clip.modeling_clip import CLIPTextModelOutput\n\nfrom diffusers import (\n DiffusionPipeline,\n ImagePipelineOutput,\n PriorTransformer,\n UnCLIPScheduler,\n UNet2DConditionModel,\n UNet2DModel,\n)\nfrom diffusers.pipelines.unclip import UnCLIPTextProjModel\nfrom diffusers.utils import logging\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef slerp(val, low, high):\n \"\"\"\n Find the interpolation point between the 'low' and 'high' values for the given 'val'. See https://en.wikipedia.org/wiki/Slerp for more details on the topic.\n \"\"\"\n low_norm = low / torch.norm(low)\n high_norm = high / torch.norm(high)\n omega = torch.acos((low_norm * high_norm))\n so = torch.sin(omega)\n res = (torch.sin((1.0 - val) * omega) / so) * low + (torch.sin(val * omega) / so) * high\n return res\n\n\nclass UnCLIPTextInterpolationPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for prompt-to-prompt interpolation on CLIP text embeddings and using the UnCLIP / Dall-E to decode them to images.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n text_encoder ([`CLIPTextModelWithProjection`]):\n Frozen text-encoder.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n prior ([`PriorTransformer`]):\n The canonical unCLIP prior to approximate the image embedding from the text embedding.\n text_proj ([`UnCLIPTextProjModel`]):\n Utility class to prepare and combine the embeddings before they are passed to the decoder.\n decoder ([`UNet2DConditionModel`]):\n The decoder to invert the image embedding into an image.\n super_res_first ([`UNet2DModel`]):\n Super resolution unet. Used in all but the last step of the super resolution diffusion process.\n super_res_last ([`UNet2DModel`]):\n Super resolution unet. Used in the last step of the super resolution diffusion process.\n prior_scheduler ([`UnCLIPScheduler`]):\n Scheduler used in the prior denoising process. Just a modified DDPMScheduler.\n decoder_scheduler ([`UnCLIPScheduler`]):\n Scheduler used in the decoder denoising process. Just a modified DDPMScheduler.\n super_res_scheduler ([`UnCLIPScheduler`]):\n Scheduler used in the super resolution denoising process. Just a modified DDPMScheduler.\n\n \"\"\"\n\n prior: PriorTransformer\n decoder: UNet2DConditionModel\n text_proj: UnCLIPTextProjModel\n text_encoder: CLIPTextModelWithProjection\n tokenizer: CLIPTokenizer\n super_res_first: UNet2DModel\n super_res_last: UNet2DModel\n\n prior_scheduler: UnCLIPScheduler\n decoder_scheduler: UnCLIPScheduler\n super_res_scheduler: UnCLIPScheduler\n\n # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.__init__\n def __init__(\n self,\n prior: PriorTransformer,\n decoder: UNet2DConditionModel,\n text_encoder: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n text_proj: UnCLIPTextProjModel,\n super_res_first: UNet2DModel,\n super_res_last: UNet2DModel,\n prior_scheduler: UnCLIPScheduler,\n decoder_scheduler: UnCLIPScheduler,\n super_res_scheduler: UnCLIPScheduler,\n ):\n super().__init__()\n\n self.register_modules(\n prior=prior,\n decoder=decoder,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n text_proj=text_proj,\n super_res_first=super_res_first,\n super_res_last=super_res_last,\n prior_scheduler=prior_scheduler,\n decoder_scheduler=decoder_scheduler,\n super_res_scheduler=super_res_scheduler,\n )\n\n # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents\n def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n latents = latents * scheduler.init_noise_sigma\n return latents\n\n # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,\n text_attention_mask: Optional[torch.Tensor] = None,\n ):\n if text_model_output is None:\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n text_mask = text_inputs.attention_mask.bool().to(device)\n\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]\n\n text_encoder_output = self.text_encoder(text_input_ids.to(device))\n\n prompt_embeds = text_encoder_output.text_embeds\n text_encoder_hidden_states = text_encoder_output.last_hidden_state\n\n else:\n batch_size = text_model_output[0].shape[0]\n prompt_embeds, text_encoder_hidden_states = text_model_output[0], text_model_output[1]\n text_mask = text_attention_mask\n\n prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n if do_classifier_free_guidance:\n uncond_tokens = [\"\"] * batch_size\n\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n uncond_text_mask = uncond_input.attention_mask.bool().to(device)\n negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))\n\n negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds\n uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)\n\n seq_len = uncond_text_encoder_hidden_states.shape[1]\n uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)\n uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(\n batch_size * num_images_per_prompt, seq_len, -1\n )\n uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n # done duplicates\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])\n\n text_mask = torch.cat([uncond_text_mask, text_mask])\n\n return prompt_embeds, text_encoder_hidden_states, text_mask\n\n @torch.no_grad()\n def __call__(\n self,\n start_prompt: str,\n end_prompt: str,\n steps: int = 5,\n prior_num_inference_steps: int = 25,\n decoder_num_inference_steps: int = 25,\n super_res_num_inference_steps: int = 7,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n prior_guidance_scale: float = 4.0,\n decoder_guidance_scale: float = 8.0,\n enable_sequential_cpu_offload=True,\n gpu_id=0,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n start_prompt (`str`):\n The prompt to start the image generation interpolation from.\n end_prompt (`str`):\n The prompt to end the image generation interpolation at.\n steps (`int`, *optional*, defaults to 5):\n The number of steps over which to interpolate from start_prompt to end_prompt. The pipeline returns\n the same number of images as this value.\n prior_num_inference_steps (`int`, *optional*, defaults to 25):\n The number of denoising steps for the prior. More denoising steps usually lead to a higher quality\n image at the expense of slower inference.\n decoder_num_inference_steps (`int`, *optional*, defaults to 25):\n The number of denoising steps for the decoder. More denoising steps usually lead to a higher quality\n image at the expense of slower inference.\n super_res_num_inference_steps (`int`, *optional*, defaults to 7):\n The number of denoising steps for super resolution. More denoising steps usually lead to a higher\n quality image at the expense of slower inference.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n prior_guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n decoder_guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n enable_sequential_cpu_offload (`bool`, *optional*, defaults to `True`):\n If True, offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's\n models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only\n when their specific submodule has its `forward` method called.\n gpu_id (`int`, *optional*, defaults to `0`):\n The gpu_id to be passed to enable_sequential_cpu_offload. Only works when enable_sequential_cpu_offload is set to True.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n \"\"\"\n\n if not isinstance(start_prompt, str) or not isinstance(end_prompt, str):\n raise ValueError(\n f\"`start_prompt` and `end_prompt` should be of type `str` but got {type(start_prompt)} and\"\n f\" {type(end_prompt)} instead\"\n )\n\n if enable_sequential_cpu_offload:\n self.enable_sequential_cpu_offload(gpu_id=gpu_id)\n\n device = self._execution_device\n\n # Turn the prompts into embeddings.\n inputs = self.tokenizer(\n [start_prompt, end_prompt],\n padding=\"max_length\",\n truncation=True,\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n )\n inputs.to(device)\n text_model_output = self.text_encoder(**inputs)\n\n text_attention_mask = torch.max(inputs.attention_mask[0], inputs.attention_mask[1])\n text_attention_mask = torch.cat([text_attention_mask.unsqueeze(0)] * steps).to(device)\n\n # Interpolate from the start to end prompt using slerp and add the generated images to an image output pipeline\n batch_text_embeds = []\n batch_last_hidden_state = []\n\n for interp_val in torch.linspace(0, 1, steps):\n text_embeds = slerp(interp_val, text_model_output.text_embeds[0], text_model_output.text_embeds[1])\n last_hidden_state = slerp(\n interp_val, text_model_output.last_hidden_state[0], text_model_output.last_hidden_state[1]\n )\n batch_text_embeds.append(text_embeds.unsqueeze(0))\n batch_last_hidden_state.append(last_hidden_state.unsqueeze(0))\n\n batch_text_embeds = torch.cat(batch_text_embeds)\n batch_last_hidden_state = torch.cat(batch_last_hidden_state)\n\n text_model_output = CLIPTextModelOutput(\n text_embeds=batch_text_embeds, last_hidden_state=batch_last_hidden_state\n )\n\n batch_size = text_model_output[0].shape[0]\n\n do_classifier_free_guidance = prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0\n\n prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt(\n prompt=None,\n device=device,\n num_images_per_prompt=1,\n do_classifier_free_guidance=do_classifier_free_guidance,\n text_model_output=text_model_output,\n text_attention_mask=text_attention_mask,\n )\n\n # prior\n\n self.prior_scheduler.set_timesteps(prior_num_inference_steps, device=device)\n prior_timesteps_tensor = self.prior_scheduler.timesteps\n\n embedding_dim = self.prior.config.embedding_dim\n\n prior_latents = self.prepare_latents(\n (batch_size, embedding_dim),\n prompt_embeds.dtype,\n device,\n generator,\n None,\n self.prior_scheduler,\n )\n\n for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([prior_latents] * 2) if do_classifier_free_guidance else prior_latents\n\n predicted_image_embedding = self.prior(\n latent_model_input,\n timestep=t,\n proj_embedding=prompt_embeds,\n encoder_hidden_states=text_encoder_hidden_states,\n attention_mask=text_mask,\n ).predicted_image_embedding\n\n if do_classifier_free_guidance:\n predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)\n predicted_image_embedding = predicted_image_embedding_uncond + prior_guidance_scale * (\n predicted_image_embedding_text - predicted_image_embedding_uncond\n )\n\n if i + 1 == prior_timesteps_tensor.shape[0]:\n prev_timestep = None\n else:\n prev_timestep = prior_timesteps_tensor[i + 1]\n\n prior_latents = self.prior_scheduler.step(\n predicted_image_embedding,\n timestep=t,\n sample=prior_latents,\n generator=generator,\n prev_timestep=prev_timestep,\n ).prev_sample\n\n prior_latents = self.prior.post_process_latents(prior_latents)\n\n image_embeddings = prior_latents\n\n # done prior\n\n # decoder\n\n text_encoder_hidden_states, additive_clip_time_embeddings = self.text_proj(\n image_embeddings=image_embeddings,\n prompt_embeds=prompt_embeds,\n text_encoder_hidden_states=text_encoder_hidden_states,\n do_classifier_free_guidance=do_classifier_free_guidance,\n )\n\n if device.type == \"mps\":\n # HACK: MPS: There is a panic when padding bool tensors,\n # so cast to int tensor for the pad and back to bool afterwards\n text_mask = text_mask.type(torch.int)\n decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=1)\n decoder_text_mask = decoder_text_mask.type(torch.bool)\n else:\n decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=True)\n\n self.decoder_scheduler.set_timesteps(decoder_num_inference_steps, device=device)\n decoder_timesteps_tensor = self.decoder_scheduler.timesteps\n\n num_channels_latents = self.decoder.config.in_channels\n height = self.decoder.config.sample_size\n width = self.decoder.config.sample_size\n\n decoder_latents = self.prepare_latents(\n (batch_size, num_channels_latents, height, width),\n text_encoder_hidden_states.dtype,\n device,\n generator,\n None,\n self.decoder_scheduler,\n )\n\n for i, t in enumerate(self.progress_bar(decoder_timesteps_tensor)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([decoder_latents] * 2) if do_classifier_free_guidance else decoder_latents\n\n noise_pred = self.decoder(\n sample=latent_model_input,\n timestep=t,\n encoder_hidden_states=text_encoder_hidden_states,\n class_labels=additive_clip_time_embeddings,\n attention_mask=decoder_text_mask,\n ).sample\n\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred_uncond, _ = noise_pred_uncond.split(latent_model_input.shape[1], dim=1)\n noise_pred_text, predicted_variance = noise_pred_text.split(latent_model_input.shape[1], dim=1)\n noise_pred = noise_pred_uncond + decoder_guidance_scale * (noise_pred_text - noise_pred_uncond)\n noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)\n\n if i + 1 == decoder_timesteps_tensor.shape[0]:\n prev_timestep = None\n else:\n prev_timestep = decoder_timesteps_tensor[i + 1]\n\n # compute the previous noisy sample x_t -> x_t-1\n decoder_latents = self.decoder_scheduler.step(\n noise_pred, t, decoder_latents, prev_timestep=prev_timestep, generator=generator\n ).prev_sample\n\n decoder_latents = decoder_latents.clamp(-1, 1)\n\n image_small = decoder_latents\n\n # done decoder\n\n # super res\n\n self.super_res_scheduler.set_timesteps(super_res_num_inference_steps, device=device)\n super_res_timesteps_tensor = self.super_res_scheduler.timesteps\n\n channels = self.super_res_first.config.in_channels // 2\n height = self.super_res_first.config.sample_size\n width = self.super_res_first.config.sample_size\n\n super_res_latents = self.prepare_latents(\n (batch_size, channels, height, width),\n image_small.dtype,\n device,\n generator,\n None,\n self.super_res_scheduler,\n )\n\n if device.type == \"mps\":\n # MPS does not support many interpolations\n image_upscaled = F.interpolate(image_small, size=[height, width])\n else:\n interpolate_antialias = {}\n if \"antialias\" in inspect.signature(F.interpolate).parameters:\n interpolate_antialias[\"antialias\"] = True\n\n image_upscaled = F.interpolate(\n image_small, size=[height, width], mode=\"bicubic\", align_corners=False, **interpolate_antialias\n )\n\n for i, t in enumerate(self.progress_bar(super_res_timesteps_tensor)):\n # no classifier free guidance\n\n if i == super_res_timesteps_tensor.shape[0] - 1:\n unet = self.super_res_last\n else:\n unet = self.super_res_first\n\n latent_model_input = torch.cat([super_res_latents, image_upscaled], dim=1)\n\n noise_pred = unet(\n sample=latent_model_input,\n timestep=t,\n ).sample\n\n if i + 1 == super_res_timesteps_tensor.shape[0]:\n prev_timestep = None\n else:\n prev_timestep = super_res_timesteps_tensor[i + 1]\n\n # compute the previous noisy sample x_t -> x_t-1\n super_res_latents = self.super_res_scheduler.step(\n noise_pred, t, super_res_latents, prev_timestep=prev_timestep, generator=generator\n ).prev_sample\n\n image = super_res_latents\n # done super res\n\n # post processing\n\n image = image * 0.5 + 0.5\n image = image.clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/examples/community/wildcard_stable_diffusion.py", "content": "import inspect\nimport os\nimport random\nimport re\nfrom dataclasses import dataclass\nfrom typing import Callable, Dict, List, Optional, Union\n\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers.configuration_utils import FrozenDict\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler\nfrom diffusers.utils import deprecate, logging\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nglobal_re_wildcard = re.compile(r\"__([^_]*)__\")\n\n\ndef get_filename(path: str):\n # this doesn't work on Windows\n return os.path.basename(path).split(\".txt\")[0]\n\n\ndef read_wildcard_values(path: str):\n with open(path, encoding=\"utf8\") as f:\n return f.read().splitlines()\n\n\ndef grab_wildcard_values(wildcard_option_dict: Dict[str, List[str]] = {}, wildcard_files: List[str] = []):\n for wildcard_file in wildcard_files:\n filename = get_filename(wildcard_file)\n read_values = read_wildcard_values(wildcard_file)\n if filename not in wildcard_option_dict:\n wildcard_option_dict[filename] = []\n wildcard_option_dict[filename].extend(read_values)\n return wildcard_option_dict\n\n\ndef replace_prompt_with_wildcards(\n prompt: str, wildcard_option_dict: Dict[str, List[str]] = {}, wildcard_files: List[str] = []\n):\n new_prompt = prompt\n\n # get wildcard options\n wildcard_option_dict = grab_wildcard_values(wildcard_option_dict, wildcard_files)\n\n for m in global_re_wildcard.finditer(new_prompt):\n wildcard_value = m.group()\n replace_value = random.choice(wildcard_option_dict[wildcard_value.strip(\"__\")])\n new_prompt = new_prompt.replace(wildcard_value, replace_value, 1)\n\n return new_prompt\n\n\n@dataclass\nclass WildcardStableDiffusionOutput(StableDiffusionPipelineOutput):\n prompts: List[str]\n\n\nclass WildcardStableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin):\n r\"\"\"\n Example Usage:\n pipe = WildcardStableDiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n\n torch_dtype=torch.float16,\n )\n prompt = \"__animal__ sitting on a __object__ wearing a __clothing__\"\n out = pipe(\n prompt,\n wildcard_option_dict={\n \"clothing\":[\"hat\", \"shirt\", \"scarf\", \"beret\"]\n },\n wildcard_files=[\"object.txt\", \"animal.txt\"],\n num_prompt_samples=1\n )\n\n\n Pipeline for text-to-image generation with wild cards using Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n wildcard_option_dict: Dict[str, List[str]] = {},\n wildcard_files: List[str] = [],\n num_prompt_samples: Optional[int] = 1,\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation\n deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n wildcard_option_dict (Dict[str, List[str]]):\n dict with key as `wildcard` and values as a list of possible replacements. For example if a prompt, \"A __animal__ sitting on a chair\". A wildcard_option_dict can provide possible values for \"animal\" like this: {\"animal\":[\"dog\", \"cat\", \"fox\"]}\n wildcard_files: (List[str])\n List of filenames of txt files for wildcard replacements. For example if a prompt, \"A __animal__ sitting on a chair\". A file can be provided [\"animal.txt\"]\n num_prompt_samples: int\n Number of times to sample wildcards for each prompt provided\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n\n if isinstance(prompt, str):\n prompt = [\n replace_prompt_with_wildcards(prompt, wildcard_option_dict, wildcard_files)\n for i in range(num_prompt_samples)\n ]\n batch_size = len(prompt)\n elif isinstance(prompt, list):\n prompt_list = []\n for p in prompt:\n for i in range(num_prompt_samples):\n prompt_list.append(replace_prompt_with_wildcards(p, wildcard_option_dict, wildcard_files))\n prompt = prompt_list\n batch_size = len(prompt)\n else:\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n\n if text_input_ids.shape[-1] > self.tokenizer.model_max_length:\n removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]\n text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]\n\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = text_embeddings.shape\n text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)\n text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = text_input_ids.shape[-1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = uncond_embeddings.shape[1]\n uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)\n uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n\n # get the initial random noise unless the user supplied it\n\n # Unlike in other pipelines, latents need to be generated in the target device\n # for 1-to-1 results reproducibility with the CompVis implementation.\n # However this currently doesn't work in `mps`.\n latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)\n latents_dtype = text_embeddings.dtype\n if latents is None:\n if self.device.type == \"mps\":\n # randn does not exist on mps\n latents = torch.randn(latents_shape, generator=generator, device=\"cpu\", dtype=latents_dtype).to(\n self.device\n )\n else:\n latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)\n else:\n if latents.shape != latents_shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {latents_shape}\")\n latents = latents.to(self.device)\n\n # set timesteps\n self.scheduler.set_timesteps(num_inference_steps)\n\n # Some schedulers like PNDM have timesteps as arrays\n # It's more optimized to move all timesteps to correct device beforehand\n timesteps_tensor = self.scheduler.timesteps.to(self.device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n for i, t in enumerate(self.progress_bar(timesteps_tensor)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n latents = 1 / 0.18215 * latents\n image = self.vae.decode(latents).sample\n\n image = (image / 2 + 0.5).clamp(0, 1)\n\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors=\"pt\").to(\n self.device\n )\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)\n )\n else:\n has_nsfw_concept = None\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return WildcardStableDiffusionOutput(images=image, nsfw_content_detected=has_nsfw_concept, prompts=prompt)\n" }, { "path": "diffusers/examples/conftest.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# tests directory-specific settings - this file is run automatically\n# by pytest before any tests are run\n\nimport sys\nimport warnings\nfrom os.path import abspath, dirname, join\n\n\n# allow having multiple repository checkouts and not needing to remember to rerun\n# 'pip install -e .[dev]' when switching between checkouts and running tests.\ngit_repo_path = abspath(join(dirname(dirname(dirname(__file__))), \"src\"))\nsys.path.insert(1, git_repo_path)\n\n\n# silence FutureWarning warnings in tests since often we can't act on them until\n# they become normal warnings - i.e. the tests still need to test the current functionality\nwarnings.simplefilter(action=\"ignore\", category=FutureWarning)\n\n\ndef pytest_addoption(parser):\n from diffusers.utils.testing_utils import pytest_addoption_shared\n\n pytest_addoption_shared(parser)\n\n\ndef pytest_terminal_summary(terminalreporter):\n from diffusers.utils.testing_utils import pytest_terminal_summary_main\n\n make_reports = terminalreporter.config.getoption(\"--make-reports\")\n if make_reports:\n pytest_terminal_summary_main(terminalreporter, id=make_reports)\n" }, { "path": "diffusers/examples/consistency_distillation/README.md", "content": "# Latent Consistency Distillation Example:\n\n[Latent Consistency Models (LCMs)](https://arxiv.org/abs/2310.04378) is a method to distill a latent diffusion model to enable swift inference with minimal steps. This example demonstrates how to use latent consistency distillation to distill stable-diffusion-v1.5 for inference with few timesteps.\n\n## Full model distillation\n\n### Running locally with PyTorch\n\n#### Installing the dependencies\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n**Important**\n\nTo make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install -e .\n```\n\nThen cd in the example folder and run\n```bash\npip install -r requirements.txt\n```\n\nAnd initialize an [🤗 Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\n\nOr for a default accelerate configuration without answering questions about your environment\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell e.g. a notebook\n\n```python\nfrom accelerate.utils import write_basic_config\nwrite_basic_config()\n```\n\nWhen running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups.\n\n\n#### Example\n\nThe following uses the [Conceptual Captions 12M (CC12M) dataset](https://github.com/google-research-datasets/conceptual-12m) as an example, and for illustrative purposes only. For best results you may consider large and high-quality text-image datasets such as [LAION](https://laion.ai/blog/laion-400-open-dataset/). You may also need to search the hyperparameter space according to the dataset you use.\n\n```bash\nexport MODEL_NAME=\"runwayml/stable-diffusion-v1-5\"\nexport OUTPUT_DIR=\"path/to/saved/model\"\n\naccelerate launch train_lcm_distill_sd_wds.py \\\n --pretrained_teacher_model=$MODEL_NAME \\\n --output_dir=$OUTPUT_DIR \\\n --mixed_precision=fp16 \\\n --resolution=512 \\\n --learning_rate=1e-6 --loss_type=\"huber\" --ema_decay=0.95 --adam_weight_decay=0.0 \\\n --max_train_steps=1000 \\\n --max_train_samples=4000000 \\\n --dataloader_num_workers=8 \\\n --train_shards_path_or_url=\"pipe:curl -L -s https://huggingface.co/datasets/laion/conceptual-captions-12m-webdataset/resolve/main/data/{00000..01099}.tar?download=true\" \\\n --validation_steps=200 \\\n --checkpointing_steps=200 --checkpoints_total_limit=10 \\\n --train_batch_size=12 \\\n --gradient_checkpointing --enable_xformers_memory_efficient_attention \\\n --gradient_accumulation_steps=1 \\\n --use_8bit_adam \\\n --resume_from_checkpoint=latest \\\n --report_to=wandb \\\n --seed=453645634 \\\n --push_to_hub\n```\n\n## LCM-LoRA\n\nInstead of fine-tuning the full model, we can also just train a LoRA that can be injected into any SDXL model.\n\n### Example\n\nThe following uses the [Conceptual Captions 12M (CC12M) dataset](https://github.com/google-research-datasets/conceptual-12m) as an example. For best results you may consider large and high-quality text-image datasets such as [LAION](https://laion.ai/blog/laion-400-open-dataset/).\n\n```bash\nexport MODEL_NAME=\"runwayml/stable-diffusion-v1-5\"\nexport OUTPUT_DIR=\"path/to/saved/model\"\n\naccelerate launch train_lcm_distill_lora_sd_wds.py \\\n --pretrained_teacher_model=$MODEL_NAME \\\n --output_dir=$OUTPUT_DIR \\\n --mixed_precision=fp16 \\\n --resolution=512 \\\n --lora_rank=64 \\\n --learning_rate=1e-4 --loss_type=\"huber\" --adam_weight_decay=0.0 \\\n --max_train_steps=1000 \\\n --max_train_samples=4000000 \\\n --dataloader_num_workers=8 \\\n --train_shards_path_or_url=\"pipe:curl -L -s https://huggingface.co/datasets/laion/conceptual-captions-12m-webdataset/resolve/main/data/{00000..01099}.tar?download=true\" \\\n --validation_steps=200 \\\n --checkpointing_steps=200 --checkpoints_total_limit=10 \\\n --train_batch_size=12 \\\n --gradient_checkpointing --enable_xformers_memory_efficient_attention \\\n --gradient_accumulation_steps=1 \\\n --use_8bit_adam \\\n --resume_from_checkpoint=latest \\\n --report_to=wandb \\\n --seed=453645634 \\\n --push_to_hub \\\n```" }, { "path": "diffusers/examples/consistency_distillation/README_sdxl.md", "content": "# Latent Consistency Distillation Example:\n\n[Latent Consistency Models (LCMs)](https://arxiv.org/abs/2310.04378) is a method to distill a latent diffusion model to enable swift inference with minimal steps. This example demonstrates how to use latent consistency distillation to distill SDXL for inference with few timesteps.\n\n## Full model distillation\n\n### Running locally with PyTorch\n\n#### Installing the dependencies\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n**Important**\n\nTo make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install -e .\n```\n\nThen cd in the example folder and run\n```bash\npip install -r requirements.txt\n```\n\nAnd initialize an [🤗 Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\n\nOr for a default accelerate configuration without answering questions about your environment\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell e.g. a notebook\n\n```python\nfrom accelerate.utils import write_basic_config\nwrite_basic_config()\n```\n\nWhen running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups.\n\n\n#### Example\n\nThe following uses the [Conceptual Captions 12M (CC12M) dataset](https://github.com/google-research-datasets/conceptual-12m) as an example, and for illustrative purposes only. For best results you may consider large and high-quality text-image datasets such as [LAION](https://laion.ai/blog/laion-400-open-dataset/). You may also need to search the hyperparameter space according to the dataset you use.\n\n```bash\nexport MODEL_NAME=\"stabilityai/stable-diffusion-xl-base-1.0\"\nexport OUTPUT_DIR=\"path/to/saved/model\"\n\naccelerate launch train_lcm_distill_sdxl_wds.py \\\n --pretrained_teacher_model=$MODEL_NAME \\\n --pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix \\\n --output_dir=$OUTPUT_DIR \\\n --mixed_precision=fp16 \\\n --resolution=1024 \\\n --learning_rate=1e-6 --loss_type=\"huber\" --use_fix_crop_and_size --ema_decay=0.95 --adam_weight_decay=0.0 \\\n --max_train_steps=1000 \\\n --max_train_samples=4000000 \\\n --dataloader_num_workers=8 \\\n --train_shards_path_or_url=\"pipe:curl -L -s https://huggingface.co/datasets/laion/conceptual-captions-12m-webdataset/resolve/main/data/{00000..01099}.tar?download=true\" \\\n --validation_steps=200 \\\n --checkpointing_steps=200 --checkpoints_total_limit=10 \\\n --train_batch_size=12 \\\n --gradient_checkpointing --enable_xformers_memory_efficient_attention \\\n --gradient_accumulation_steps=1 \\\n --use_8bit_adam \\\n --resume_from_checkpoint=latest \\\n --report_to=wandb \\\n --seed=453645634 \\\n --push_to_hub \\\n```\n\n## LCM-LoRA\n\nInstead of fine-tuning the full model, we can also just train a LoRA that can be injected into any SDXL model.\n\n### Example\n\nThe following uses the [Conceptual Captions 12M (CC12M) dataset](https://github.com/google-research-datasets/conceptual-12m) as an example. For best results you may consider large and high-quality text-image datasets such as [LAION](https://laion.ai/blog/laion-400-open-dataset/).\n\n```bash\nexport MODEL_NAME=\"stabilityai/stable-diffusion-xl-base-1.0\"\nexport OUTPUT_DIR=\"path/to/saved/model\"\n\naccelerate launch train_lcm_distill_lora_sdxl_wds.py \\\n --pretrained_teacher_model=$MODEL_DIR \\\n --pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix \\\n --output_dir=$OUTPUT_DIR \\\n --mixed_precision=fp16 \\\n --resolution=1024 \\\n --lora_rank=64 \\\n --learning_rate=1e-4 --loss_type=\"huber\" --use_fix_crop_and_size --adam_weight_decay=0.0 \\\n --max_train_steps=1000 \\\n --max_train_samples=4000000 \\\n --dataloader_num_workers=8 \\\n --train_shards_path_or_url=\"pipe:curl -L -s https://huggingface.co/datasets/laion/conceptual-captions-12m-webdataset/resolve/main/data/{00000..01099}.tar?download=true\" \\\n --validation_steps=200 \\\n --checkpointing_steps=200 --checkpoints_total_limit=10 \\\n --train_batch_size=12 \\\n --gradient_checkpointing --enable_xformers_memory_efficient_attention \\\n --gradient_accumulation_steps=1 \\\n --use_8bit_adam \\\n --resume_from_checkpoint=latest \\\n --report_to=wandb \\\n --seed=453645634 \\\n --push_to_hub \\\n```\n\nWe provide another version for LCM LoRA SDXL that follows best practices of `peft` and leverages the `datasets` library for quick experimentation. The script doesn't load two UNets unlike `train_lcm_distill_lora_sdxl_wds.py` which reduces the memory requirements quite a bit.\n\nBelow is an example training command that trains an LCM LoRA on the [Narutos dataset](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions):\n\n```bash\nexport MODEL_NAME=\"stabilityai/stable-diffusion-xl-base-1.0\"\nexport DATASET_NAME=\"lambdalabs/naruto-blip-captions\"\nexport VAE_PATH=\"madebyollin/sdxl-vae-fp16-fix\"\n\naccelerate launch train_lcm_distill_lora_sdxl.py \\\n --pretrained_teacher_model=${MODEL_NAME} \\\n --pretrained_vae_model_name_or_path=${VAE_PATH} \\\n --output_dir=\"narutos-lora-lcm-sdxl\" \\\n --mixed_precision=\"fp16\" \\\n --dataset_name=$DATASET_NAME \\\n --resolution=1024 \\\n --train_batch_size=24 \\\n --gradient_accumulation_steps=1 \\\n --gradient_checkpointing \\\n --use_8bit_adam \\\n --lora_rank=64 \\\n --learning_rate=1e-4 \\\n --report_to=\"wandb\" \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --max_train_steps=3000 \\\n --checkpointing_steps=500 \\\n --validation_steps=50 \\\n --seed=\"0\" \\\n --report_to=\"wandb\" \\\n --push_to_hub\n```\n\n" }, { "path": "diffusers/examples/consistency_distillation/requirements.txt", "content": "accelerate>=0.16.0\ntorchvision\ntransformers>=4.25.1\nftfy\ntensorboard\nJinja2\nwebdataset" }, { "path": "diffusers/examples/consistency_distillation/test_lcm_lora.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport logging\nimport os\nimport sys\nimport tempfile\n\nimport safetensors\n\n\nsys.path.append(\"..\")\nfrom test_examples_utils import ExamplesTestsAccelerate, run_command # noqa: E402\n\n\nlogging.basicConfig(level=logging.DEBUG)\n\nlogger = logging.getLogger()\nstream_handler = logging.StreamHandler(sys.stdout)\nlogger.addHandler(stream_handler)\n\n\nclass TextToImageLCM(ExamplesTestsAccelerate):\n def test_text_to_image_lcm_lora_sdxl(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/consistency_distillation/train_lcm_distill_lora_sdxl.py\n --pretrained_teacher_model hf-internal-testing/tiny-stable-diffusion-xl-pipe\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 64\n --lora_rank 4\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n # save_pretrained smoke test\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"pytorch_lora_weights.safetensors\")))\n\n # make sure the state_dict has the correct naming in the parameters.\n lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, \"pytorch_lora_weights.safetensors\"))\n is_lora = all(\"lora\" in k for k in lora_state_dict.keys())\n self.assertTrue(is_lora)\n\n def test_text_to_image_lcm_lora_sdxl_checkpointing(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/consistency_distillation/train_lcm_distill_lora_sdxl.py\n --pretrained_teacher_model hf-internal-testing/tiny-stable-diffusion-xl-pipe\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 64\n --lora_rank 4\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 7\n --checkpointing_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-2\", \"checkpoint-4\", \"checkpoint-6\"},\n )\n\n test_args = f\"\"\"\n examples/consistency_distillation/train_lcm_distill_lora_sdxl.py\n --pretrained_teacher_model hf-internal-testing/tiny-stable-diffusion-xl-pipe\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 64\n --lora_rank 4\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 9\n --checkpointing_steps 2\n --resume_from_checkpoint latest\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-2\", \"checkpoint-4\", \"checkpoint-6\", \"checkpoint-8\"},\n )\n" }, { "path": "diffusers/examples/consistency_distillation/train_lcm_distill_lora_sd_wds.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport functools\nimport gc\nimport itertools\nimport json\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom contextlib import nullcontext\nfrom pathlib import Path\nfrom typing import List, Union\n\nimport accelerate\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport torchvision.transforms.functional as TF\nimport transformers\nimport webdataset as wds\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom braceexpand import braceexpand\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom peft import LoraConfig, get_peft_model, get_peft_model_state_dict\nfrom torch.utils.data import default_collate\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, CLIPTextModel, PretrainedConfig\nfrom webdataset.tariterators import (\n base_plus_ext,\n tar_file_expander,\n url_opener,\n valid_sample,\n)\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n LCMScheduler,\n StableDiffusionPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import resolve_interpolation_mode\nfrom diffusers.utils import check_min_version, is_wandb_available\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\nMAX_SEQ_LENGTH = 77\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef get_module_kohya_state_dict(module, prefix: str, dtype: torch.dtype, adapter_name: str = \"default\"):\n kohya_ss_state_dict = {}\n for peft_key, weight in get_peft_model_state_dict(module, adapter_name=adapter_name).items():\n kohya_key = peft_key.replace(\"base_model.model\", prefix)\n kohya_key = kohya_key.replace(\"lora_A\", \"lora_down\")\n kohya_key = kohya_key.replace(\"lora_B\", \"lora_up\")\n kohya_key = kohya_key.replace(\".\", \"_\", kohya_key.count(\".\") - 2)\n kohya_ss_state_dict[kohya_key] = weight.to(dtype)\n\n # Set alpha parameter\n if \"lora_down\" in kohya_key:\n alpha_key = f'{kohya_key.split(\".\")[0]}.alpha'\n kohya_ss_state_dict[alpha_key] = torch.tensor(module.peft_config[adapter_name].lora_alpha).to(dtype)\n\n return kohya_ss_state_dict\n\n\ndef filter_keys(key_set):\n def _f(dictionary):\n return {k: v for k, v in dictionary.items() if k in key_set}\n\n return _f\n\n\ndef group_by_keys_nothrow(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None):\n \"\"\"Return function over iterator that groups key, value pairs into samples.\n\n :param keys: function that splits the key into key and extension (base_plus_ext) :param lcase: convert suffixes to\n lower case (Default value = True)\n \"\"\"\n current_sample = None\n for filesample in data:\n assert isinstance(filesample, dict)\n fname, value = filesample[\"fname\"], filesample[\"data\"]\n prefix, suffix = keys(fname)\n if prefix is None:\n continue\n if lcase:\n suffix = suffix.lower()\n # FIXME webdataset version throws if suffix in current_sample, but we have a potential for\n # this happening in the current LAION400m dataset if a tar ends with same prefix as the next\n # begins, rare, but can happen since prefix aren't unique across tar files in that dataset\n if current_sample is None or prefix != current_sample[\"__key__\"] or suffix in current_sample:\n if valid_sample(current_sample):\n yield current_sample\n current_sample = {\"__key__\": prefix, \"__url__\": filesample[\"__url__\"]}\n if suffixes is None or suffix in suffixes:\n current_sample[suffix] = value\n if valid_sample(current_sample):\n yield current_sample\n\n\ndef tarfile_to_samples_nothrow(src, handler=wds.warn_and_continue):\n # NOTE this is a re-impl of the webdataset impl with group_by_keys that doesn't throw\n streams = url_opener(src, handler=handler)\n files = tar_file_expander(streams, handler=handler)\n samples = group_by_keys_nothrow(files, handler=handler)\n return samples\n\n\nclass WebdatasetFilter:\n def __init__(self, min_size=1024, max_pwatermark=0.5):\n self.min_size = min_size\n self.max_pwatermark = max_pwatermark\n\n def __call__(self, x):\n try:\n if \"json\" in x:\n x_json = json.loads(x[\"json\"])\n filter_size = (x_json.get(\"original_width\", 0.0) or 0.0) >= self.min_size and x_json.get(\n \"original_height\", 0\n ) >= self.min_size\n filter_watermark = (x_json.get(\"pwatermark\", 1.0) or 1.0) <= self.max_pwatermark\n return filter_size and filter_watermark\n else:\n return False\n except Exception:\n return False\n\n\nclass SDText2ImageDataset:\n def __init__(\n self,\n train_shards_path_or_url: Union[str, List[str]],\n num_train_examples: int,\n per_gpu_batch_size: int,\n global_batch_size: int,\n num_workers: int,\n resolution: int = 512,\n interpolation_type: str = \"bilinear\",\n shuffle_buffer_size: int = 1000,\n pin_memory: bool = False,\n persistent_workers: bool = False,\n ):\n if not isinstance(train_shards_path_or_url, str):\n train_shards_path_or_url = [list(braceexpand(urls)) for urls in train_shards_path_or_url]\n # flatten list using itertools\n train_shards_path_or_url = list(itertools.chain.from_iterable(train_shards_path_or_url))\n\n interpolation_mode = resolve_interpolation_mode(interpolation_type)\n\n def transform(example):\n # resize image\n image = example[\"image\"]\n image = TF.resize(image, resolution, interpolation=interpolation_mode)\n\n # get crop coordinates and crop image\n c_top, c_left, _, _ = transforms.RandomCrop.get_params(image, output_size=(resolution, resolution))\n image = TF.crop(image, c_top, c_left, resolution, resolution)\n image = TF.to_tensor(image)\n image = TF.normalize(image, [0.5], [0.5])\n\n example[\"image\"] = image\n return example\n\n processing_pipeline = [\n wds.decode(\"pil\", handler=wds.ignore_and_continue),\n wds.rename(image=\"jpg;png;jpeg;webp\", text=\"text;txt;caption\", handler=wds.warn_and_continue),\n wds.map(filter_keys({\"image\", \"text\"})),\n wds.map(transform),\n wds.to_tuple(\"image\", \"text\"),\n ]\n\n # Create train dataset and loader\n pipeline = [\n wds.ResampledShards(train_shards_path_or_url),\n tarfile_to_samples_nothrow,\n wds.shuffle(shuffle_buffer_size),\n *processing_pipeline,\n wds.batched(per_gpu_batch_size, partial=False, collation_fn=default_collate),\n ]\n\n num_worker_batches = math.ceil(num_train_examples / (global_batch_size * num_workers)) # per dataloader worker\n num_batches = num_worker_batches * num_workers\n num_samples = num_batches * global_batch_size\n\n # each worker is iterating over this\n self._train_dataset = wds.DataPipeline(*pipeline).with_epoch(num_worker_batches)\n self._train_dataloader = wds.WebLoader(\n self._train_dataset,\n batch_size=None,\n shuffle=False,\n num_workers=num_workers,\n pin_memory=pin_memory,\n persistent_workers=persistent_workers,\n )\n # add meta-data to dataloader instance for convenience\n self._train_dataloader.num_batches = num_batches\n self._train_dataloader.num_samples = num_samples\n\n @property\n def train_dataset(self):\n return self._train_dataset\n\n @property\n def train_dataloader(self):\n return self._train_dataloader\n\n\ndef log_validation(vae, unet, args, accelerator, weight_dtype, step):\n logger.info(\"Running validation... \")\n if torch.backends.mps.is_available():\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type, dtype=weight_dtype)\n\n unet = accelerator.unwrap_model(unet)\n pipeline = StableDiffusionPipeline.from_pretrained(\n args.pretrained_teacher_model,\n vae=vae,\n scheduler=LCMScheduler.from_pretrained(args.pretrained_teacher_model, subfolder=\"scheduler\"),\n revision=args.revision,\n torch_dtype=weight_dtype,\n safety_checker=None,\n )\n pipeline.set_progress_bar_config(disable=True)\n\n lora_state_dict = get_module_kohya_state_dict(unet, \"lora_unet\", weight_dtype)\n pipeline.load_lora_weights(lora_state_dict)\n pipeline.fuse_lora()\n\n pipeline = pipeline.to(accelerator.device, dtype=weight_dtype)\n if args.enable_xformers_memory_efficient_attention:\n pipeline.enable_xformers_memory_efficient_attention()\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n validation_prompts = [\n \"portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography\",\n \"Self-portrait oil painting, a beautiful cyborg with golden hair, 8k\",\n \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\",\n \"A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece\",\n ]\n\n image_logs = []\n\n for _, prompt in enumerate(validation_prompts):\n images = []\n with autocast_ctx:\n images = pipeline(\n prompt=prompt,\n num_inference_steps=4,\n num_images_per_prompt=4,\n generator=generator,\n guidance_scale=1.0,\n ).images\n image_logs.append({\"validation_prompt\": prompt, \"images\": images})\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n for log in image_logs:\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n formatted_images = []\n for image in images:\n formatted_images.append(np.asarray(image))\n\n formatted_images = np.stack(formatted_images)\n\n tracker.writer.add_images(validation_prompt, formatted_images, step, dataformats=\"NHWC\")\n elif tracker.name == \"wandb\":\n formatted_images = []\n\n for log in image_logs:\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n for image in images:\n image = wandb.Image(image, caption=validation_prompt)\n formatted_images.append(image)\n\n tracker.log({\"validation\": formatted_images})\n else:\n logger.warning(f\"image logging not implemented for {tracker.name}\")\n\n del pipeline\n gc.collect()\n torch.cuda.empty_cache()\n\n return image_logs\n\n\n# From LatentConsistencyModel.get_guidance_scale_embedding\ndef guidance_scale_embedding(w, embedding_dim=512, dtype=torch.float32):\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n timesteps (`torch.Tensor`):\n generate embedding vectors at these timesteps\n embedding_dim (`int`, *optional*, defaults to 512):\n dimension of the embeddings to generate\n dtype:\n data type of the generated embeddings\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n\ndef append_dims(x, target_dims):\n \"\"\"Appends dimensions to the end of a tensor until it has target_dims dimensions.\"\"\"\n dims_to_append = target_dims - x.ndim\n if dims_to_append < 0:\n raise ValueError(f\"input has {x.ndim} dims but target_dims is {target_dims}, which is less\")\n return x[(...,) + (None,) * dims_to_append]\n\n\n# From LCMScheduler.get_scalings_for_boundary_condition_discrete\ndef scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0):\n scaled_timestep = timestep_scaling * timestep\n c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2)\n c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5\n return c_skip, c_out\n\n\n# Compare LCMScheduler.step, Step 4\ndef get_predicted_original_sample(model_output, timesteps, sample, prediction_type, alphas, sigmas):\n alphas = extract_into_tensor(alphas, timesteps, sample.shape)\n sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)\n if prediction_type == \"epsilon\":\n pred_x_0 = (sample - sigmas * model_output) / alphas\n elif prediction_type == \"sample\":\n pred_x_0 = model_output\n elif prediction_type == \"v_prediction\":\n pred_x_0 = alphas * sample - sigmas * model_output\n else:\n raise ValueError(\n f\"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`\"\n f\" are supported.\"\n )\n\n return pred_x_0\n\n\n# Based on step 4 in DDIMScheduler.step\ndef get_predicted_noise(model_output, timesteps, sample, prediction_type, alphas, sigmas):\n alphas = extract_into_tensor(alphas, timesteps, sample.shape)\n sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)\n if prediction_type == \"epsilon\":\n pred_epsilon = model_output\n elif prediction_type == \"sample\":\n pred_epsilon = (sample - alphas * model_output) / sigmas\n elif prediction_type == \"v_prediction\":\n pred_epsilon = alphas * model_output + sigmas * sample\n else:\n raise ValueError(\n f\"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`\"\n f\" are supported.\"\n )\n\n return pred_epsilon\n\n\ndef extract_into_tensor(a, t, x_shape):\n b, *_ = t.shape\n out = a.gather(-1, t)\n return out.reshape(b, *((1,) * (len(x_shape) - 1)))\n\n\nclass DDIMSolver:\n def __init__(self, alpha_cumprods, timesteps=1000, ddim_timesteps=50):\n # DDIM sampling parameters\n step_ratio = timesteps // ddim_timesteps\n self.ddim_timesteps = (np.arange(1, ddim_timesteps + 1) * step_ratio).round().astype(np.int64) - 1\n self.ddim_alpha_cumprods = alpha_cumprods[self.ddim_timesteps]\n self.ddim_alpha_cumprods_prev = np.asarray(\n [alpha_cumprods[0]] + alpha_cumprods[self.ddim_timesteps[:-1]].tolist()\n )\n # convert to torch tensors\n self.ddim_timesteps = torch.from_numpy(self.ddim_timesteps).long()\n self.ddim_alpha_cumprods = torch.from_numpy(self.ddim_alpha_cumprods)\n self.ddim_alpha_cumprods_prev = torch.from_numpy(self.ddim_alpha_cumprods_prev)\n\n def to(self, device):\n self.ddim_timesteps = self.ddim_timesteps.to(device)\n self.ddim_alpha_cumprods = self.ddim_alpha_cumprods.to(device)\n self.ddim_alpha_cumprods_prev = self.ddim_alpha_cumprods_prev.to(device)\n return self\n\n def ddim_step(self, pred_x0, pred_noise, timestep_index):\n alpha_cumprod_prev = extract_into_tensor(self.ddim_alpha_cumprods_prev, timestep_index, pred_x0.shape)\n dir_xt = (1.0 - alpha_cumprod_prev).sqrt() * pred_noise\n x_prev = alpha_cumprod_prev.sqrt() * pred_x0 + dir_xt\n return x_prev\n\n\n@torch.no_grad()\ndef update_ema(target_params, source_params, rate=0.99):\n \"\"\"\n Update target parameters to be closer to those of source parameters using\n an exponential moving average.\n\n :param target_params: the target parameter sequence.\n :param source_params: the source parameter sequence.\n :param rate: the EMA rate (closer to 1 means slower).\n \"\"\"\n for targ, src in zip(target_params, source_params):\n targ.detach().mul_(rate).add_(src, alpha=1 - rate)\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n # ----------Model Checkpoint Loading Arguments----------\n parser.add_argument(\n \"--pretrained_teacher_model\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained LDM teacher model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_vae_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.\",\n )\n parser.add_argument(\n \"--teacher_revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained LDM teacher model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained LDM model identifier from huggingface.co/models.\",\n )\n # ----------Training Arguments----------\n # ----General Training Arguments----\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"lcm-xl-distilled\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n # ----Logging----\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n # ----Checkpointing----\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n # ----Image Processing----\n parser.add_argument(\n \"--train_shards_path_or_url\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--interpolation_type\",\n type=str,\n default=\"bilinear\",\n help=(\n \"The interpolation function used when resizing images to the desired resolution. Choose between `bilinear`,\"\n \" `bicubic`, `box`, `nearest`, `nearest_exact`, `hamming`, and `lanczos`.\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n # ----Dataloader----\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n # ----Batch Size and Training Steps----\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n # ----Learning Rate----\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n # ----Optimizer (Adam)----\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n # ----Diffusion Training Arguments----\n parser.add_argument(\n \"--proportion_empty_prompts\",\n type=float,\n default=0,\n help=\"Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).\",\n )\n # ----Latent Consistency Distillation (LCD) Specific Arguments----\n parser.add_argument(\n \"--w_min\",\n type=float,\n default=5.0,\n required=False,\n help=(\n \"The minimum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG\"\n \" formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as\"\n \" compared to the original paper.\"\n ),\n )\n parser.add_argument(\n \"--w_max\",\n type=float,\n default=15.0,\n required=False,\n help=(\n \"The maximum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG\"\n \" formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as\"\n \" compared to the original paper.\"\n ),\n )\n parser.add_argument(\n \"--num_ddim_timesteps\",\n type=int,\n default=50,\n help=\"The number of timesteps to use for DDIM sampling.\",\n )\n parser.add_argument(\n \"--loss_type\",\n type=str,\n default=\"l2\",\n choices=[\"l2\", \"huber\"],\n help=\"The type of loss to use for the LCD loss.\",\n )\n parser.add_argument(\n \"--huber_c\",\n type=float,\n default=0.001,\n help=\"The huber loss parameter. Only used if `--loss_type=huber`.\",\n )\n parser.add_argument(\n \"--lora_rank\",\n type=int,\n default=64,\n help=\"The rank of the LoRA projection matrix.\",\n )\n parser.add_argument(\n \"--lora_alpha\",\n type=int,\n default=64,\n help=(\n \"The value of the LoRA alpha parameter, which controls the scaling factor in front of the LoRA weight\"\n \" update delta_W. No scaling will be performed if this value is equal to `lora_rank`.\"\n ),\n )\n parser.add_argument(\n \"--lora_dropout\",\n type=float,\n default=0.0,\n help=\"The dropout probability for the dropout layer added before applying the LoRA to each layer input.\",\n )\n parser.add_argument(\n \"--lora_target_modules\",\n type=str,\n default=None,\n help=(\n \"A comma-separated string of target module keys to add LoRA to. If not set, a default list of modules will\"\n \" be used. By default, LoRA will be applied to all conv and linear layers.\"\n ),\n )\n parser.add_argument(\n \"--vae_encode_batch_size\",\n type=int,\n default=32,\n required=False,\n help=(\n \"The batch size used when encoding (and decoding) images to latents (and vice versa) using the VAE.\"\n \" Encoding or decoding the whole batch at once may run into OOM issues.\"\n ),\n )\n parser.add_argument(\n \"--timestep_scaling_factor\",\n type=float,\n default=10.0,\n help=(\n \"The multiplicative timestep scaling factor used when calculating the boundary scalings for LCM. The\"\n \" higher the scaling is, the lower the approximation error, but the default value of 10.0 should typically\"\n \" suffice.\"\n ),\n )\n # ----Mixed Precision----\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--cast_teacher_unet\",\n action=\"store_true\",\n help=\"Whether to cast the teacher U-Net to the precision specified by `--mixed_precision`.\",\n )\n # ----Training Optimizations----\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n # ----Distributed Training----\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n # ----------Validation Arguments----------\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=200,\n help=\"Run validation every X steps.\",\n )\n # ----------Huggingface Hub Arguments-----------\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n # ----------Accelerate Arguments----------\n parser.add_argument(\n \"--tracker_project_name\",\n type=str,\n default=\"text2image-fine-tune\",\n help=(\n \"The `project_name` argument passed to Accelerator.init_trackers for\"\n \" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator\"\n ),\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:\n raise ValueError(\"`--proportion_empty_prompts` must be in the range [0, 1].\")\n\n return args\n\n\n# Adapted from pipelines.StableDiffusionPipeline.encode_prompt\ndef encode_prompt(prompt_batch, text_encoder, tokenizer, proportion_empty_prompts, is_train=True):\n captions = []\n for caption in prompt_batch:\n if random.random() < proportion_empty_prompts:\n captions.append(\"\")\n elif isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n\n with torch.no_grad():\n text_inputs = tokenizer(\n captions,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n prompt_embeds = text_encoder(text_input_ids.to(text_encoder.device))[0]\n\n return prompt_embeds\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n split_batches=True, # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be devide by the number of processes assuming batches are multiplied by the number of processes\n )\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name,\n exist_ok=True,\n token=args.hub_token,\n private=True,\n ).repo_id\n\n # 1. Create the noise scheduler and the desired noise schedule.\n noise_scheduler = DDPMScheduler.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"scheduler\", revision=args.teacher_revision\n )\n\n # DDPMScheduler calculates the alpha and sigma noise schedules (based on the alpha bars) for us\n alpha_schedule = torch.sqrt(noise_scheduler.alphas_cumprod)\n sigma_schedule = torch.sqrt(1 - noise_scheduler.alphas_cumprod)\n # Initialize the DDIM ODE solver for distillation.\n solver = DDIMSolver(\n noise_scheduler.alphas_cumprod.numpy(),\n timesteps=noise_scheduler.config.num_train_timesteps,\n ddim_timesteps=args.num_ddim_timesteps,\n )\n\n # 2. Load tokenizers from SD 1.X/2.X checkpoint.\n tokenizer = AutoTokenizer.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"tokenizer\", revision=args.teacher_revision, use_fast=False\n )\n\n # 3. Load text encoders from SD 1.X/2.X checkpoint.\n # import correct text encoder classes\n text_encoder = CLIPTextModel.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"text_encoder\", revision=args.teacher_revision\n )\n\n # 4. Load VAE from SD 1.X/2.X checkpoint\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_teacher_model,\n subfolder=\"vae\",\n revision=args.teacher_revision,\n )\n\n # 5. Load teacher U-Net from SD 1.X/2.X checkpoint\n teacher_unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"unet\", revision=args.teacher_revision\n )\n\n # 6. Freeze teacher vae, text_encoder, and teacher_unet\n vae.requires_grad_(False)\n text_encoder.requires_grad_(False)\n teacher_unet.requires_grad_(False)\n\n # 7. Create online student U-Net.\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"unet\", revision=args.teacher_revision\n )\n unet.train()\n\n # Check that all trainable models are in full precision\n low_precision_error_string = (\n \" Please make sure to always have all model weights in full float32 precision when starting training - even if\"\n \" doing mixed precision training, copy of the weights should still be float32.\"\n )\n\n if accelerator.unwrap_model(unet).dtype != torch.float32:\n raise ValueError(\n f\"Controlnet loaded as datatype {accelerator.unwrap_model(unet).dtype}. {low_precision_error_string}\"\n )\n\n # 8. Add LoRA to the student U-Net, only the LoRA projection matrix will be updated by the optimizer.\n if args.lora_target_modules is not None:\n lora_target_modules = [module_key.strip() for module_key in args.lora_target_modules.split(\",\")]\n else:\n lora_target_modules = [\n \"to_q\",\n \"to_k\",\n \"to_v\",\n \"to_out.0\",\n \"proj_in\",\n \"proj_out\",\n \"ff.net.0.proj\",\n \"ff.net.2\",\n \"conv1\",\n \"conv2\",\n \"conv_shortcut\",\n \"downsamplers.0.conv\",\n \"upsamplers.0.conv\",\n \"time_emb_proj\",\n ]\n lora_config = LoraConfig(\n r=args.lora_rank,\n target_modules=lora_target_modules,\n lora_alpha=args.lora_alpha,\n lora_dropout=args.lora_dropout,\n )\n unet = get_peft_model(unet, lora_config)\n\n # 9. Handle mixed precision and device placement\n # For mixed precision training we cast all non-trainable weigths to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move unet, vae and text_encoder to device and cast to weight_dtype\n # The VAE is in float32 to avoid NaN losses.\n vae.to(accelerator.device)\n if args.pretrained_vae_model_name_or_path is not None:\n vae.to(dtype=weight_dtype)\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n\n # Move teacher_unet to device, optionally cast to weight_dtype\n teacher_unet.to(accelerator.device)\n if args.cast_teacher_unet:\n teacher_unet.to(dtype=weight_dtype)\n\n # Also move the alpha and sigma noise schedules to accelerator.device.\n alpha_schedule = alpha_schedule.to(accelerator.device)\n sigma_schedule = sigma_schedule.to(accelerator.device)\n # Move the ODE solver to accelerator.device.\n solver = solver.to(accelerator.device)\n\n # 10. Handle saving and loading of checkpoints\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n unet_ = accelerator.unwrap_model(unet)\n lora_state_dict = get_peft_model_state_dict(unet_, adapter_name=\"default\")\n StableDiffusionPipeline.save_lora_weights(os.path.join(output_dir, \"unet_lora\"), lora_state_dict)\n # save weights in peft format to be able to load them back\n unet_.save_pretrained(output_dir)\n\n for _, model in enumerate(models):\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n def load_model_hook(models, input_dir):\n # load the LoRA into the model\n unet_ = accelerator.unwrap_model(unet)\n unet_.load_adapter(input_dir, \"default\", is_trainable=True)\n\n for _ in range(len(models)):\n # pop models so that they are not loaded again\n models.pop()\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # 11. Enable optimizations\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n teacher_unet.enable_xformers_memory_efficient_attention()\n # target_unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # 12. Optimizer creation\n optimizer = optimizer_class(\n unet.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # 13. Dataset creation and data processing\n # Here, we compute not just the text embeddings but also the additional embeddings\n # needed for the SD XL UNet to operate.\n def compute_embeddings(prompt_batch, proportion_empty_prompts, text_encoder, tokenizer, is_train=True):\n prompt_embeds = encode_prompt(prompt_batch, text_encoder, tokenizer, proportion_empty_prompts, is_train)\n return {\"prompt_embeds\": prompt_embeds}\n\n dataset = SDText2ImageDataset(\n train_shards_path_or_url=args.train_shards_path_or_url,\n num_train_examples=args.max_train_samples,\n per_gpu_batch_size=args.train_batch_size,\n global_batch_size=args.train_batch_size * accelerator.num_processes,\n num_workers=args.dataloader_num_workers,\n resolution=args.resolution,\n interpolation_type=args.interpolation_type,\n shuffle_buffer_size=1000,\n pin_memory=True,\n persistent_workers=True,\n )\n train_dataloader = dataset.train_dataloader\n\n compute_embeddings_fn = functools.partial(\n compute_embeddings,\n proportion_empty_prompts=0,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n )\n\n # 14. LR Scheduler creation\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps,\n num_training_steps=args.max_train_steps,\n )\n\n # 15. Prepare for training\n # Prepare everything with our `accelerator`.\n unet, optimizer, lr_scheduler = accelerator.prepare(unet, optimizer, lr_scheduler)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = dict(vars(args))\n accelerator.init_trackers(args.tracker_project_name, config=tracker_config)\n\n uncond_input_ids = tokenizer(\n [\"\"] * args.train_batch_size, return_tensors=\"pt\", padding=\"max_length\", max_length=77\n ).input_ids.to(accelerator.device)\n uncond_prompt_embeds = text_encoder(uncond_input_ids)[0]\n\n if torch.backends.mps.is_available():\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type)\n\n # 16. Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num batches each epoch = {train_dataloader.num_batches}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n for epoch in range(first_epoch, args.num_train_epochs):\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n # 1. Load and process the image and text conditioning\n image, text = batch\n\n image = image.to(accelerator.device, non_blocking=True)\n encoded_text = compute_embeddings_fn(text)\n\n pixel_values = image.to(dtype=weight_dtype)\n if vae.dtype != weight_dtype:\n vae.to(dtype=weight_dtype)\n\n # encode pixel values with batch size of at most args.vae_encode_batch_size\n latents = []\n for i in range(0, pixel_values.shape[0], args.vae_encode_batch_size):\n latents.append(vae.encode(pixel_values[i : i + args.vae_encode_batch_size]).latent_dist.sample())\n latents = torch.cat(latents, dim=0)\n\n latents = latents * vae.config.scaling_factor\n latents = latents.to(weight_dtype)\n bsz = latents.shape[0]\n\n # 2. Sample a random timestep for each image t_n from the ODE solver timesteps without bias.\n # For the DDIM solver, the timestep schedule is [T - 1, T - k - 1, T - 2 * k - 1, ...]\n topk = noise_scheduler.config.num_train_timesteps // args.num_ddim_timesteps\n index = torch.randint(0, args.num_ddim_timesteps, (bsz,), device=latents.device).long()\n start_timesteps = solver.ddim_timesteps[index]\n timesteps = start_timesteps - topk\n timesteps = torch.where(timesteps < 0, torch.zeros_like(timesteps), timesteps)\n\n # 3. Get boundary scalings for start_timesteps and (end) timesteps.\n c_skip_start, c_out_start = scalings_for_boundary_conditions(\n start_timesteps, timestep_scaling=args.timestep_scaling_factor\n )\n c_skip_start, c_out_start = [append_dims(x, latents.ndim) for x in [c_skip_start, c_out_start]]\n c_skip, c_out = scalings_for_boundary_conditions(\n timesteps, timestep_scaling=args.timestep_scaling_factor\n )\n c_skip, c_out = [append_dims(x, latents.ndim) for x in [c_skip, c_out]]\n\n # 4. Sample noise from the prior and add it to the latents according to the noise magnitude at each\n # timestep (this is the forward diffusion process) [z_{t_{n + k}} in Algorithm 1]\n noise = torch.randn_like(latents)\n noisy_model_input = noise_scheduler.add_noise(latents, noise, start_timesteps)\n\n # 5. Sample a random guidance scale w from U[w_min, w_max]\n # Note that for LCM-LoRA distillation it is not necessary to use a guidance scale embedding\n w = (args.w_max - args.w_min) * torch.rand((bsz,)) + args.w_min\n w = w.reshape(bsz, 1, 1, 1)\n w = w.to(device=latents.device, dtype=latents.dtype)\n\n # 6. Prepare prompt embeds and unet_added_conditions\n prompt_embeds = encoded_text.pop(\"prompt_embeds\")\n\n # 7. Get online LCM prediction on z_{t_{n + k}} (noisy_model_input), w, c, t_{n + k} (start_timesteps)\n noise_pred = unet(\n noisy_model_input,\n start_timesteps,\n timestep_cond=None,\n encoder_hidden_states=prompt_embeds.float(),\n added_cond_kwargs=encoded_text,\n ).sample\n\n pred_x_0 = get_predicted_original_sample(\n noise_pred,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n\n model_pred = c_skip_start * noisy_model_input + c_out_start * pred_x_0\n\n # 8. Compute the conditional and unconditional teacher model predictions to get CFG estimates of the\n # predicted noise eps_0 and predicted original sample x_0, then run the ODE solver using these\n # estimates to predict the data point in the augmented PF-ODE trajectory corresponding to the next ODE\n # solver timestep.\n with torch.no_grad():\n with autocast_ctx:\n # 1. Get teacher model prediction on noisy_model_input z_{t_{n + k}} and conditional embedding c\n cond_teacher_output = teacher_unet(\n noisy_model_input.to(weight_dtype),\n start_timesteps,\n encoder_hidden_states=prompt_embeds.to(weight_dtype),\n ).sample\n cond_pred_x0 = get_predicted_original_sample(\n cond_teacher_output,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n cond_pred_noise = get_predicted_noise(\n cond_teacher_output,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n\n # 2. Get teacher model prediction on noisy_model_input z_{t_{n + k}} and unconditional embedding 0\n uncond_teacher_output = teacher_unet(\n noisy_model_input.to(weight_dtype),\n start_timesteps,\n encoder_hidden_states=uncond_prompt_embeds.to(weight_dtype),\n ).sample\n uncond_pred_x0 = get_predicted_original_sample(\n uncond_teacher_output,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n uncond_pred_noise = get_predicted_noise(\n uncond_teacher_output,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n\n # 3. Calculate the CFG estimate of x_0 (pred_x0) and eps_0 (pred_noise)\n # Note that this uses the LCM paper's CFG formulation rather than the Imagen CFG formulation\n pred_x0 = cond_pred_x0 + w * (cond_pred_x0 - uncond_pred_x0)\n pred_noise = cond_pred_noise + w * (cond_pred_noise - uncond_pred_noise)\n # 4. Run one step of the ODE solver to estimate the next point x_prev on the\n # augmented PF-ODE trajectory (solving backward in time)\n # Note that the DDIM step depends on both the predicted x_0 and source noise eps_0.\n x_prev = solver.ddim_step(pred_x0, pred_noise, index)\n\n # 9. Get target LCM prediction on x_prev, w, c, t_n (timesteps)\n # Note that we do not use a separate target network for LCM-LoRA distillation.\n with torch.no_grad():\n with autocast_ctx:\n target_noise_pred = unet(\n x_prev.float(),\n timesteps,\n timestep_cond=None,\n encoder_hidden_states=prompt_embeds.float(),\n ).sample\n pred_x_0 = get_predicted_original_sample(\n target_noise_pred,\n timesteps,\n x_prev,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n target = c_skip * x_prev + c_out * pred_x_0\n\n # 10. Calculate loss\n if args.loss_type == \"l2\":\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n elif args.loss_type == \"huber\":\n loss = torch.mean(\n torch.sqrt((model_pred.float() - target.float()) ** 2 + args.huber_c**2) - args.huber_c\n )\n\n # 11. Backpropagate on the online student model (`unet`)\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad(set_to_none=True)\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n if global_step % args.validation_steps == 0:\n log_validation(vae, unet, args, accelerator, weight_dtype, global_step)\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n # Create the pipeline using using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = accelerator.unwrap_model(unet)\n unet.save_pretrained(args.output_dir)\n lora_state_dict = get_peft_model_state_dict(unet, adapter_name=\"default\")\n StableDiffusionPipeline.save_lora_weights(os.path.join(args.output_dir, \"unet_lora\"), lora_state_dict)\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/consistency_distillation/train_lcm_distill_lora_sdxl.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The LCM team and the HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport copy\nimport functools\nimport gc\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom contextlib import nullcontext\nfrom pathlib import Path\n\nimport accelerate\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom peft import LoraConfig, get_peft_model_state_dict, set_peft_model_state_dict\nfrom torchvision import transforms\nfrom torchvision.transforms.functional import crop\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n LCMScheduler,\n StableDiffusionXLPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import cast_training_params, resolve_interpolation_mode\nfrom diffusers.utils import (\n check_min_version,\n convert_state_dict_to_diffusers,\n convert_unet_state_dict_to_peft,\n is_wandb_available,\n)\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__)\n\nDATASET_NAME_MAPPING = {\n \"lambdalabs/naruto-blip-captions\": (\"image\", \"text\"),\n}\n\n\nclass DDIMSolver:\n def __init__(self, alpha_cumprods, timesteps=1000, ddim_timesteps=50):\n # DDIM sampling parameters\n step_ratio = timesteps // ddim_timesteps\n\n self.ddim_timesteps = (np.arange(1, ddim_timesteps + 1) * step_ratio).round().astype(np.int64) - 1\n self.ddim_alpha_cumprods = alpha_cumprods[self.ddim_timesteps]\n self.ddim_alpha_cumprods_prev = np.asarray(\n [alpha_cumprods[0]] + alpha_cumprods[self.ddim_timesteps[:-1]].tolist()\n )\n # convert to torch tensors\n self.ddim_timesteps = torch.from_numpy(self.ddim_timesteps).long()\n self.ddim_alpha_cumprods = torch.from_numpy(self.ddim_alpha_cumprods)\n self.ddim_alpha_cumprods_prev = torch.from_numpy(self.ddim_alpha_cumprods_prev)\n\n def to(self, device):\n self.ddim_timesteps = self.ddim_timesteps.to(device)\n self.ddim_alpha_cumprods = self.ddim_alpha_cumprods.to(device)\n self.ddim_alpha_cumprods_prev = self.ddim_alpha_cumprods_prev.to(device)\n return self\n\n def ddim_step(self, pred_x0, pred_noise, timestep_index):\n alpha_cumprod_prev = extract_into_tensor(self.ddim_alpha_cumprods_prev, timestep_index, pred_x0.shape)\n dir_xt = (1.0 - alpha_cumprod_prev).sqrt() * pred_noise\n x_prev = alpha_cumprod_prev.sqrt() * pred_x0 + dir_xt\n return x_prev\n\n\ndef log_validation(vae, args, accelerator, weight_dtype, step, unet=None, is_final_validation=False):\n logger.info(\"Running validation... \")\n\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n args.pretrained_teacher_model,\n vae=vae,\n scheduler=LCMScheduler.from_pretrained(args.pretrained_teacher_model, subfolder=\"scheduler\"),\n revision=args.revision,\n torch_dtype=weight_dtype,\n ).to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n to_load = None\n if not is_final_validation:\n if unet is None:\n raise ValueError(\"Must provide a `unet` when doing intermediate validation.\")\n unet = accelerator.unwrap_model(unet)\n state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(unet))\n to_load = state_dict\n else:\n to_load = args.output_dir\n\n pipeline.load_lora_weights(to_load)\n pipeline.fuse_lora()\n\n if args.enable_xformers_memory_efficient_attention:\n pipeline.enable_xformers_memory_efficient_attention()\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n validation_prompts = [\n \"cute sundar pichai character\",\n \"robotic cat with wings\",\n \"a photo of yoda\",\n \"a cute creature with blue eyes\",\n ]\n\n image_logs = []\n\n for _, prompt in enumerate(validation_prompts):\n images = []\n if torch.backends.mps.is_available():\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type, dtype=weight_dtype)\n\n with autocast_ctx:\n images = pipeline(\n prompt=prompt,\n num_inference_steps=4,\n num_images_per_prompt=4,\n generator=generator,\n guidance_scale=0.0,\n ).images\n image_logs.append({\"validation_prompt\": prompt, \"images\": images})\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n for log in image_logs:\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n formatted_images = []\n for image in images:\n formatted_images.append(np.asarray(image))\n\n formatted_images = np.stack(formatted_images)\n\n tracker.writer.add_images(validation_prompt, formatted_images, step, dataformats=\"NHWC\")\n elif tracker.name == \"wandb\":\n formatted_images = []\n\n for log in image_logs:\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n for image in images:\n image = wandb.Image(image, caption=validation_prompt)\n formatted_images.append(image)\n logger_name = \"test\" if is_final_validation else \"validation\"\n tracker.log({logger_name: formatted_images})\n else:\n logger.warning(f\"image logging not implemented for {tracker.name}\")\n\n del pipeline\n gc.collect()\n torch.cuda.empty_cache()\n\n return image_logs\n\n\ndef append_dims(x, target_dims):\n \"\"\"Appends dimensions to the end of a tensor until it has target_dims dimensions.\"\"\"\n dims_to_append = target_dims - x.ndim\n if dims_to_append < 0:\n raise ValueError(f\"input has {x.ndim} dims but target_dims is {target_dims}, which is less\")\n return x[(...,) + (None,) * dims_to_append]\n\n\n# From LCMScheduler.get_scalings_for_boundary_condition_discrete\ndef scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0):\n scaled_timestep = timestep_scaling * timestep\n c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2)\n c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5\n return c_skip, c_out\n\n\n# Compare LCMScheduler.step, Step 4\ndef get_predicted_original_sample(model_output, timesteps, sample, prediction_type, alphas, sigmas):\n alphas = extract_into_tensor(alphas, timesteps, sample.shape)\n sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)\n if prediction_type == \"epsilon\":\n pred_x_0 = (sample - sigmas * model_output) / alphas\n elif prediction_type == \"sample\":\n pred_x_0 = model_output\n elif prediction_type == \"v_prediction\":\n pred_x_0 = alphas * sample - sigmas * model_output\n else:\n raise ValueError(\n f\"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`\"\n f\" are supported.\"\n )\n\n return pred_x_0\n\n\n# Based on step 4 in DDIMScheduler.step\ndef get_predicted_noise(model_output, timesteps, sample, prediction_type, alphas, sigmas):\n alphas = extract_into_tensor(alphas, timesteps, sample.shape)\n sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)\n if prediction_type == \"epsilon\":\n pred_epsilon = model_output\n elif prediction_type == \"sample\":\n pred_epsilon = (sample - alphas * model_output) / sigmas\n elif prediction_type == \"v_prediction\":\n pred_epsilon = alphas * model_output + sigmas * sample\n else:\n raise ValueError(\n f\"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`\"\n f\" are supported.\"\n )\n\n return pred_epsilon\n\n\ndef extract_into_tensor(a, t, x_shape):\n b, *_ = t.shape\n out = a.gather(-1, t)\n return out.reshape(b, *((1,) * (len(x_shape) - 1)))\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n # ----------Model Checkpoint Loading Arguments----------\n parser.add_argument(\n \"--pretrained_teacher_model\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained LDM teacher model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_vae_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.\",\n )\n parser.add_argument(\n \"--teacher_revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained LDM teacher model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained LDM model identifier from huggingface.co/models.\",\n )\n # ----------Training Arguments----------\n # ----General Training Arguments----\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"lcm-xl-distilled\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n # ----Logging----\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n # ----Checkpointing----\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n # ----Image Processing----\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing an image.\"\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=\"text\",\n help=\"The column of the dataset containing a caption or a list of captions.\",\n )\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=1024,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--interpolation_type\",\n type=str,\n default=\"bilinear\",\n help=(\n \"The interpolation function used when resizing images to the desired resolution. Choose between `bilinear`,\"\n \" `bicubic`, `box`, `nearest`, `nearest_exact`, `hamming`, and `lanczos`.\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--encode_batch_size\",\n type=int,\n default=8,\n help=\"Batch size to use for VAE encoding of the images for efficient processing.\",\n )\n # ----Dataloader----\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n # ----Batch Size and Training Steps----\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n # ----Learning Rate----\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-6,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n # ----Optimizer (Adam)----\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n # ----Diffusion Training Arguments----\n # ----Latent Consistency Distillation (LCD) Specific Arguments----\n parser.add_argument(\n \"--w_min\",\n type=float,\n default=3.0,\n required=False,\n help=(\n \"The minimum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG\"\n \" formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as\"\n \" compared to the original paper.\"\n ),\n )\n parser.add_argument(\n \"--w_max\",\n type=float,\n default=15.0,\n required=False,\n help=(\n \"The maximum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG\"\n \" formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as\"\n \" compared to the original paper.\"\n ),\n )\n parser.add_argument(\n \"--num_ddim_timesteps\",\n type=int,\n default=50,\n help=\"The number of timesteps to use for DDIM sampling.\",\n )\n parser.add_argument(\n \"--loss_type\",\n type=str,\n default=\"l2\",\n choices=[\"l2\", \"huber\"],\n help=\"The type of loss to use for the LCD loss.\",\n )\n parser.add_argument(\n \"--huber_c\",\n type=float,\n default=0.001,\n help=\"The huber loss parameter. Only used if `--loss_type=huber`.\",\n )\n parser.add_argument(\n \"--lora_rank\",\n type=int,\n default=64,\n help=\"The rank of the LoRA projection matrix.\",\n )\n parser.add_argument(\n \"--lora_alpha\",\n type=int,\n default=64,\n help=(\n \"The value of the LoRA alpha parameter, which controls the scaling factor in front of the LoRA weight\"\n \" update delta_W. No scaling will be performed if this value is equal to `lora_rank`.\"\n ),\n )\n parser.add_argument(\n \"--lora_dropout\",\n type=float,\n default=0.0,\n help=\"The dropout probability for the dropout layer added before applying the LoRA to each layer input.\",\n )\n parser.add_argument(\n \"--lora_target_modules\",\n type=str,\n default=None,\n help=(\n \"A comma-separated string of target module keys to add LoRA to. If not set, a default list of modules will\"\n \" be used. By default, LoRA will be applied to all conv and linear layers.\"\n ),\n )\n parser.add_argument(\n \"--vae_encode_batch_size\",\n type=int,\n default=8,\n required=False,\n help=(\n \"The batch size used when encoding (and decoding) images to latents (and vice versa) using the VAE.\"\n \" Encoding or decoding the whole batch at once may run into OOM issues.\"\n ),\n )\n parser.add_argument(\n \"--timestep_scaling_factor\",\n type=float,\n default=10.0,\n help=(\n \"The multiplicative timestep scaling factor used when calculating the boundary scalings for LCM. The\"\n \" higher the scaling is, the lower the approximation error, but the default value of 10.0 should typically\"\n \" suffice.\"\n ),\n )\n # ----Mixed Precision----\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n # ----Training Optimizations----\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n # ----Distributed Training----\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n # ----------Validation Arguments----------\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=200,\n help=\"Run validation every X steps.\",\n )\n # ----------Huggingface Hub Arguments-----------\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n # ----------Accelerate Arguments----------\n parser.add_argument(\n \"--tracker_project_name\",\n type=str,\n default=\"text2image-fine-tune\",\n help=(\n \"The `project_name` argument passed to Accelerator.init_trackers for\"\n \" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator\"\n ),\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n return args\n\n\n# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt\ndef encode_prompt(prompt_batch, text_encoders, tokenizers, is_train=True):\n prompt_embeds_list = []\n\n captions = []\n for caption in prompt_batch:\n if isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n\n with torch.no_grad():\n for tokenizer, text_encoder in zip(tokenizers, text_encoders):\n text_inputs = tokenizer(\n captions,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n prompt_embeds = text_encoder(\n text_input_ids.to(text_encoder.device),\n output_hidden_states=True,\n )\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds.hidden_states[-2]\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)\n return prompt_embeds, pooled_prompt_embeds\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n split_batches=True, # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be devide by the number of processes assuming batches are multiplied by the number of processes\n )\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name,\n exist_ok=True,\n token=args.hub_token,\n private=True,\n ).repo_id\n\n # 1. Create the noise scheduler and the desired noise schedule.\n noise_scheduler = DDPMScheduler.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"scheduler\", revision=args.teacher_revision\n )\n\n # DDPMScheduler calculates the alpha and sigma noise schedules (based on the alpha bars) for us\n alpha_schedule = torch.sqrt(noise_scheduler.alphas_cumprod)\n sigma_schedule = torch.sqrt(1 - noise_scheduler.alphas_cumprod)\n # Initialize the DDIM ODE solver for distillation.\n solver = DDIMSolver(\n noise_scheduler.alphas_cumprod.numpy(),\n timesteps=noise_scheduler.config.num_train_timesteps,\n ddim_timesteps=args.num_ddim_timesteps,\n )\n\n # 2. Load tokenizers from SDXL checkpoint.\n tokenizer_one = AutoTokenizer.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"tokenizer\", revision=args.teacher_revision, use_fast=False\n )\n tokenizer_two = AutoTokenizer.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"tokenizer_2\", revision=args.teacher_revision, use_fast=False\n )\n\n # 3. Load text encoders from SDXL checkpoint.\n # import correct text encoder classes\n text_encoder_cls_one = import_model_class_from_model_name_or_path(\n args.pretrained_teacher_model, args.teacher_revision\n )\n text_encoder_cls_two = import_model_class_from_model_name_or_path(\n args.pretrained_teacher_model, args.teacher_revision, subfolder=\"text_encoder_2\"\n )\n\n text_encoder_one = text_encoder_cls_one.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"text_encoder\", revision=args.teacher_revision\n )\n text_encoder_two = text_encoder_cls_two.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"text_encoder_2\", revision=args.teacher_revision\n )\n\n # 4. Load VAE from SDXL checkpoint (or more stable VAE)\n vae_path = (\n args.pretrained_teacher_model\n if args.pretrained_vae_model_name_or_path is None\n else args.pretrained_vae_model_name_or_path\n )\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.teacher_revision,\n )\n\n # 6. Freeze teacher vae, text_encoders.\n vae.requires_grad_(False)\n text_encoder_one.requires_grad_(False)\n text_encoder_two.requires_grad_(False)\n\n # 7. Create online student U-Net.\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"unet\", revision=args.teacher_revision\n )\n unet.requires_grad_(False)\n\n # Check that all trainable models are in full precision\n low_precision_error_string = (\n \" Please make sure to always have all model weights in full float32 precision when starting training - even if\"\n \" doing mixed precision training, copy of the weights should still be float32.\"\n )\n\n if accelerator.unwrap_model(unet).dtype != torch.float32:\n raise ValueError(\n f\"Controlnet loaded as datatype {accelerator.unwrap_model(unet).dtype}. {low_precision_error_string}\"\n )\n\n # 8. Handle mixed precision and device placement\n # For mixed precision training we cast all non-trainable weigths to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move unet, vae and text_encoder to device and cast to weight_dtype\n # The VAE is in float32 to avoid NaN losses.\n unet.to(accelerator.device, dtype=weight_dtype)\n if args.pretrained_vae_model_name_or_path is None:\n vae.to(accelerator.device, dtype=torch.float32)\n else:\n vae.to(accelerator.device, dtype=weight_dtype)\n text_encoder_one.to(accelerator.device, dtype=weight_dtype)\n text_encoder_two.to(accelerator.device, dtype=weight_dtype)\n\n # 9. Add LoRA to the student U-Net, only the LoRA projection matrix will be updated by the optimizer.\n if args.lora_target_modules is not None:\n lora_target_modules = [module_key.strip() for module_key in args.lora_target_modules.split(\",\")]\n else:\n lora_target_modules = [\n \"to_q\",\n \"to_k\",\n \"to_v\",\n \"to_out.0\",\n \"proj_in\",\n \"proj_out\",\n \"ff.net.0.proj\",\n \"ff.net.2\",\n \"conv1\",\n \"conv2\",\n \"conv_shortcut\",\n \"downsamplers.0.conv\",\n \"upsamplers.0.conv\",\n \"time_emb_proj\",\n ]\n lora_config = LoraConfig(\n r=args.lora_rank,\n target_modules=lora_target_modules,\n lora_alpha=args.lora_alpha,\n lora_dropout=args.lora_dropout,\n )\n unet.add_adapter(lora_config)\n\n # Also move the alpha and sigma noise schedules to accelerator.device.\n alpha_schedule = alpha_schedule.to(accelerator.device)\n sigma_schedule = sigma_schedule.to(accelerator.device)\n solver = solver.to(accelerator.device)\n\n # 10. Handle saving and loading of checkpoints\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n unet_ = accelerator.unwrap_model(unet)\n # also save the checkpoints in native `diffusers` format so that it can be easily\n # be independently loaded via `load_lora_weights()`.\n state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(unet_))\n StableDiffusionXLPipeline.save_lora_weights(output_dir, unet_lora_layers=state_dict)\n\n for _, model in enumerate(models):\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n def load_model_hook(models, input_dir):\n # load the LoRA into the model\n unet_ = accelerator.unwrap_model(unet)\n lora_state_dict, _ = StableDiffusionXLPipeline.lora_state_dict(input_dir)\n unet_state_dict = {\n f'{k.replace(\"unet.\", \"\")}': v for k, v in lora_state_dict.items() if k.startswith(\"unet.\")\n }\n unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict)\n incompatible_keys = set_peft_model_state_dict(unet_, unet_state_dict, adapter_name=\"default\")\n if incompatible_keys is not None:\n # check only for unexpected keys\n unexpected_keys = getattr(incompatible_keys, \"unexpected_keys\", None)\n if unexpected_keys:\n logger.warning(\n f\"Loading adapter weights from state_dict led to unexpected keys not found in the model: \"\n f\" {unexpected_keys}. \"\n )\n\n for _ in range(len(models)):\n # pop models so that they are not loaded again\n models.pop()\n\n # Make sure the trainable params are in float32. This is again needed since the base models\n # are in `weight_dtype`. More details:\n # https://github.com/huggingface/diffusers/pull/6514#discussion_r1449796804\n if args.mixed_precision == \"fp16\":\n cast_training_params(unet_, dtype=torch.float32)\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # 11. Enable optimizations\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # 12. Optimizer creation\n params_to_optimize = filter(lambda p: p.requires_grad, unet.parameters())\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # 13. Dataset creation and data processing\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n )\n else:\n data_files = {}\n if args.train_data_dir is not None:\n data_files[\"train\"] = os.path.join(args.train_data_dir, \"**\")\n dataset = load_dataset(\n \"imagefolder\",\n data_files=data_files,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n column_names = dataset[\"train\"].column_names\n\n # Get the column names for input/target.\n dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)\n if args.image_column is None:\n image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.caption_column is None:\n caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n else:\n caption_column = args.caption_column\n if caption_column not in column_names:\n raise ValueError(\n f\"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}\"\n )\n\n # Preprocessing the datasets.\n interpolation_mode = resolve_interpolation_mode(args.interpolation_type)\n train_resize = transforms.Resize(args.resolution, interpolation=interpolation_mode)\n train_crop = transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution)\n train_flip = transforms.RandomHorizontalFlip(p=1.0)\n train_transforms = transforms.Compose([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])\n\n def preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n # image aug\n original_sizes = []\n all_images = []\n crop_top_lefts = []\n for image in images:\n original_sizes.append((image.height, image.width))\n image = train_resize(image)\n if args.center_crop:\n y1 = max(0, int(round((image.height - args.resolution) / 2.0)))\n x1 = max(0, int(round((image.width - args.resolution) / 2.0)))\n image = train_crop(image)\n else:\n y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))\n image = crop(image, y1, x1, h, w)\n if args.random_flip and random.random() < 0.5:\n # flip\n x1 = image.width - x1\n image = train_flip(image)\n crop_top_left = (y1, x1)\n crop_top_lefts.append(crop_top_left)\n image = train_transforms(image)\n all_images.append(image)\n\n examples[\"original_sizes\"] = original_sizes\n examples[\"crop_top_lefts\"] = crop_top_lefts\n examples[\"pixel_values\"] = all_images\n examples[\"captions\"] = list(examples[caption_column])\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n def collate_fn(examples):\n pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n original_sizes = [example[\"original_sizes\"] for example in examples]\n crop_top_lefts = [example[\"crop_top_lefts\"] for example in examples]\n captions = [example[\"captions\"] for example in examples]\n\n return {\n \"pixel_values\": pixel_values,\n \"captions\": captions,\n \"original_sizes\": original_sizes,\n \"crop_top_lefts\": crop_top_lefts,\n }\n\n # DataLoaders creation:\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # 14. Embeddings for the UNet.\n # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids\n def compute_embeddings(prompt_batch, original_sizes, crop_coords, text_encoders, tokenizers, is_train=True):\n def compute_time_ids(original_size, crops_coords_top_left):\n target_size = (args.resolution, args.resolution)\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n add_time_ids = torch.tensor([add_time_ids])\n add_time_ids = add_time_ids.to(accelerator.device, dtype=weight_dtype)\n return add_time_ids\n\n prompt_embeds, pooled_prompt_embeds = encode_prompt(prompt_batch, text_encoders, tokenizers, is_train)\n add_text_embeds = pooled_prompt_embeds\n\n add_time_ids = torch.cat([compute_time_ids(s, c) for s, c in zip(original_sizes, crop_coords)])\n\n prompt_embeds = prompt_embeds.to(accelerator.device)\n add_text_embeds = add_text_embeds.to(accelerator.device)\n unet_added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n\n return {\"prompt_embeds\": prompt_embeds, **unet_added_cond_kwargs}\n\n text_encoders = [text_encoder_one, text_encoder_two]\n tokenizers = [tokenizer_one, tokenizer_two]\n\n compute_embeddings_fn = functools.partial(compute_embeddings, text_encoders=text_encoders, tokenizers=tokenizers)\n\n # 15. LR Scheduler creation\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Make sure the trainable params are in float32.\n if args.mixed_precision == \"fp16\":\n # only upcast trainable parameters (LoRA) into fp32\n cast_training_params(unet, dtype=torch.float32)\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n )\n\n # 16. Prepare for training\n # Prepare everything with our `accelerator`.\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = dict(vars(args))\n accelerator.init_trackers(args.tracker_project_name, config=tracker_config)\n\n # 17. Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n unet.train()\n for epoch in range(first_epoch, args.num_train_epochs):\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n # 1. Load and process the image and text conditioning\n pixel_values, text, orig_size, crop_coords = (\n batch[\"pixel_values\"],\n batch[\"captions\"],\n batch[\"original_sizes\"],\n batch[\"crop_top_lefts\"],\n )\n\n encoded_text = compute_embeddings_fn(text, orig_size, crop_coords)\n\n # encode pixel values with batch size of at most args.vae_encode_batch_size\n pixel_values = pixel_values.to(dtype=vae.dtype)\n latents = []\n for i in range(0, pixel_values.shape[0], args.vae_encode_batch_size):\n latents.append(vae.encode(pixel_values[i : i + args.vae_encode_batch_size]).latent_dist.sample())\n latents = torch.cat(latents, dim=0)\n\n latents = latents * vae.config.scaling_factor\n if args.pretrained_vae_model_name_or_path is None:\n latents = latents.to(weight_dtype)\n\n # 2. Sample a random timestep for each image t_n from the ODE solver timesteps without bias.\n # For the DDIM solver, the timestep schedule is [T - 1, T - k - 1, T - 2 * k - 1, ...]\n bsz = latents.shape[0]\n topk = noise_scheduler.config.num_train_timesteps // args.num_ddim_timesteps\n index = torch.randint(0, args.num_ddim_timesteps, (bsz,), device=latents.device).long()\n start_timesteps = solver.ddim_timesteps[index]\n timesteps = start_timesteps - topk\n timesteps = torch.where(timesteps < 0, torch.zeros_like(timesteps), timesteps)\n\n # 3. Get boundary scalings for start_timesteps and (end) timesteps.\n c_skip_start, c_out_start = scalings_for_boundary_conditions(\n start_timesteps, timestep_scaling=args.timestep_scaling_factor\n )\n c_skip_start, c_out_start = [append_dims(x, latents.ndim) for x in [c_skip_start, c_out_start]]\n c_skip, c_out = scalings_for_boundary_conditions(\n timesteps, timestep_scaling=args.timestep_scaling_factor\n )\n c_skip, c_out = [append_dims(x, latents.ndim) for x in [c_skip, c_out]]\n\n # 4. Sample noise from the prior and add it to the latents according to the noise magnitude at each\n # timestep (this is the forward diffusion process) [z_{t_{n + k}} in Algorithm 1]\n noise = torch.randn_like(latents)\n noisy_model_input = noise_scheduler.add_noise(latents, noise, start_timesteps)\n\n # 5. Sample a random guidance scale w from U[w_min, w_max]\n # Note that for LCM-LoRA distillation it is not necessary to use a guidance scale embedding\n w = (args.w_max - args.w_min) * torch.rand((bsz,)) + args.w_min\n w = w.reshape(bsz, 1, 1, 1)\n w = w.to(device=latents.device, dtype=latents.dtype)\n\n # 6. Prepare prompt embeds and unet_added_conditions\n prompt_embeds = encoded_text.pop(\"prompt_embeds\")\n\n # 7. Get online LCM prediction on z_{t_{n + k}} (noisy_model_input), w, c, t_{n + k} (start_timesteps)\n noise_pred = unet(\n noisy_model_input,\n start_timesteps,\n encoder_hidden_states=prompt_embeds,\n added_cond_kwargs=encoded_text,\n ).sample\n pred_x_0 = get_predicted_original_sample(\n noise_pred,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n model_pred = c_skip_start * noisy_model_input + c_out_start * pred_x_0\n\n # 8. Compute the conditional and unconditional teacher model predictions to get CFG estimates of the\n # predicted noise eps_0 and predicted original sample x_0, then run the ODE solver using these\n # estimates to predict the data point in the augmented PF-ODE trajectory corresponding to the next ODE\n # solver timestep.\n\n # With the adapters disabled, the `unet` is the regular teacher model.\n accelerator.unwrap_model(unet).disable_adapters()\n with torch.no_grad():\n # 1. Get teacher model prediction on noisy_model_input z_{t_{n + k}} and conditional embedding c\n cond_teacher_output = unet(\n noisy_model_input,\n start_timesteps,\n encoder_hidden_states=prompt_embeds,\n added_cond_kwargs={k: v.to(weight_dtype) for k, v in encoded_text.items()},\n ).sample\n cond_pred_x0 = get_predicted_original_sample(\n cond_teacher_output,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n cond_pred_noise = get_predicted_noise(\n cond_teacher_output,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n\n # 2. Get teacher model prediction on noisy_model_input z_{t_{n + k}} and unconditional embedding 0\n uncond_prompt_embeds = torch.zeros_like(prompt_embeds)\n uncond_pooled_prompt_embeds = torch.zeros_like(encoded_text[\"text_embeds\"])\n uncond_added_conditions = copy.deepcopy(encoded_text)\n uncond_added_conditions[\"text_embeds\"] = uncond_pooled_prompt_embeds\n uncond_teacher_output = unet(\n noisy_model_input,\n start_timesteps,\n encoder_hidden_states=uncond_prompt_embeds.to(weight_dtype),\n added_cond_kwargs={k: v.to(weight_dtype) for k, v in uncond_added_conditions.items()},\n ).sample\n uncond_pred_x0 = get_predicted_original_sample(\n uncond_teacher_output,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n uncond_pred_noise = get_predicted_noise(\n uncond_teacher_output,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n\n # 3. Calculate the CFG estimate of x_0 (pred_x0) and eps_0 (pred_noise)\n # Note that this uses the LCM paper's CFG formulation rather than the Imagen CFG formulation\n pred_x0 = cond_pred_x0 + w * (cond_pred_x0 - uncond_pred_x0)\n pred_noise = cond_pred_noise + w * (cond_pred_noise - uncond_pred_noise)\n # 4. Run one step of the ODE solver to estimate the next point x_prev on the\n # augmented PF-ODE trajectory (solving backward in time)\n # Note that the DDIM step depends on both the predicted x_0 and source noise eps_0.\n x_prev = solver.ddim_step(pred_x0, pred_noise, index).to(unet.dtype)\n\n # re-enable unet adapters to turn the `unet` into a student unet.\n accelerator.unwrap_model(unet).enable_adapters()\n\n # 9. Get target LCM prediction on x_prev, w, c, t_n (timesteps)\n # Note that we do not use a separate target network for LCM-LoRA distillation.\n with torch.no_grad():\n target_noise_pred = unet(\n x_prev,\n timesteps,\n encoder_hidden_states=prompt_embeds,\n added_cond_kwargs={k: v.to(weight_dtype) for k, v in encoded_text.items()},\n ).sample\n pred_x_0 = get_predicted_original_sample(\n target_noise_pred,\n timesteps,\n x_prev,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n target = c_skip * x_prev + c_out * pred_x_0\n\n # 10. Calculate loss\n if args.loss_type == \"l2\":\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n elif args.loss_type == \"huber\":\n loss = torch.mean(\n torch.sqrt((model_pred.float() - target.float()) ** 2 + args.huber_c**2) - args.huber_c\n )\n\n # 11. Backpropagate on the online student model (`unet`) (only LoRA)\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(params_to_optimize, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad(set_to_none=True)\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n if global_step % args.validation_steps == 0:\n log_validation(\n vae, args, accelerator, weight_dtype, global_step, unet=unet, is_final_validation=False\n )\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n # Create the pipeline using using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = accelerator.unwrap_model(unet)\n unet_lora_state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(unet))\n StableDiffusionXLPipeline.save_lora_weights(args.output_dir, unet_lora_layers=unet_lora_state_dict)\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n del unet\n torch.cuda.empty_cache()\n\n # Final inference.\n if args.validation_steps is not None:\n log_validation(vae, args, accelerator, weight_dtype, step=global_step, unet=None, is_final_validation=True)\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/consistency_distillation/train_lcm_distill_lora_sdxl_wds.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport copy\nimport functools\nimport gc\nimport itertools\nimport json\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom contextlib import nullcontext\nfrom pathlib import Path\nfrom typing import List, Union\n\nimport accelerate\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport torchvision.transforms.functional as TF\nimport transformers\nimport webdataset as wds\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom braceexpand import braceexpand\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom peft import LoraConfig, get_peft_model, get_peft_model_state_dict\nfrom torch.utils.data import default_collate\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\nfrom webdataset.tariterators import (\n base_plus_ext,\n tar_file_expander,\n url_opener,\n valid_sample,\n)\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n LCMScheduler,\n StableDiffusionXLPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import resolve_interpolation_mode\nfrom diffusers.utils import check_min_version, is_wandb_available\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\nMAX_SEQ_LENGTH = 77\n\n# Adjust for your dataset\nWDS_JSON_WIDTH = \"width\" # original_width for LAION\nWDS_JSON_HEIGHT = \"height\" # original_height for LAION\nMIN_SIZE = 700 # ~960 for LAION, ideal: 1024 if the dataset contains large images\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef get_module_kohya_state_dict(module, prefix: str, dtype: torch.dtype, adapter_name: str = \"default\"):\n kohya_ss_state_dict = {}\n for peft_key, weight in get_peft_model_state_dict(module, adapter_name=adapter_name).items():\n kohya_key = peft_key.replace(\"base_model.model\", prefix)\n kohya_key = kohya_key.replace(\"lora_A\", \"lora_down\")\n kohya_key = kohya_key.replace(\"lora_B\", \"lora_up\")\n kohya_key = kohya_key.replace(\".\", \"_\", kohya_key.count(\".\") - 2)\n kohya_ss_state_dict[kohya_key] = weight.to(dtype)\n\n # Set alpha parameter\n if \"lora_down\" in kohya_key:\n alpha_key = f'{kohya_key.split(\".\")[0]}.alpha'\n kohya_ss_state_dict[alpha_key] = torch.tensor(module.peft_config[adapter_name].lora_alpha).to(dtype)\n\n return kohya_ss_state_dict\n\n\ndef filter_keys(key_set):\n def _f(dictionary):\n return {k: v for k, v in dictionary.items() if k in key_set}\n\n return _f\n\n\ndef group_by_keys_nothrow(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None):\n \"\"\"Return function over iterator that groups key, value pairs into samples.\n\n :param keys: function that splits the key into key and extension (base_plus_ext) :param lcase: convert suffixes to\n lower case (Default value = True)\n \"\"\"\n current_sample = None\n for filesample in data:\n assert isinstance(filesample, dict)\n fname, value = filesample[\"fname\"], filesample[\"data\"]\n prefix, suffix = keys(fname)\n if prefix is None:\n continue\n if lcase:\n suffix = suffix.lower()\n # FIXME webdataset version throws if suffix in current_sample, but we have a potential for\n # this happening in the current LAION400m dataset if a tar ends with same prefix as the next\n # begins, rare, but can happen since prefix aren't unique across tar files in that dataset\n if current_sample is None or prefix != current_sample[\"__key__\"] or suffix in current_sample:\n if valid_sample(current_sample):\n yield current_sample\n current_sample = {\"__key__\": prefix, \"__url__\": filesample[\"__url__\"]}\n if suffixes is None or suffix in suffixes:\n current_sample[suffix] = value\n if valid_sample(current_sample):\n yield current_sample\n\n\ndef tarfile_to_samples_nothrow(src, handler=wds.warn_and_continue):\n # NOTE this is a re-impl of the webdataset impl with group_by_keys that doesn't throw\n streams = url_opener(src, handler=handler)\n files = tar_file_expander(streams, handler=handler)\n samples = group_by_keys_nothrow(files, handler=handler)\n return samples\n\n\nclass WebdatasetFilter:\n def __init__(self, min_size=1024, max_pwatermark=0.5):\n self.min_size = min_size\n self.max_pwatermark = max_pwatermark\n\n def __call__(self, x):\n try:\n if \"json\" in x:\n x_json = json.loads(x[\"json\"])\n filter_size = (x_json.get(WDS_JSON_WIDTH, 0.0) or 0.0) >= self.min_size and x_json.get(\n WDS_JSON_HEIGHT, 0\n ) >= self.min_size\n filter_watermark = (x_json.get(\"pwatermark\", 0.0) or 0.0) <= self.max_pwatermark\n return filter_size and filter_watermark\n else:\n return False\n except Exception:\n return False\n\n\nclass SDXLText2ImageDataset:\n def __init__(\n self,\n train_shards_path_or_url: Union[str, List[str]],\n num_train_examples: int,\n per_gpu_batch_size: int,\n global_batch_size: int,\n num_workers: int,\n resolution: int = 1024,\n interpolation_type: str = \"bilinear\",\n shuffle_buffer_size: int = 1000,\n pin_memory: bool = False,\n persistent_workers: bool = False,\n use_fix_crop_and_size: bool = False,\n ):\n if not isinstance(train_shards_path_or_url, str):\n train_shards_path_or_url = [list(braceexpand(urls)) for urls in train_shards_path_or_url]\n # flatten list using itertools\n train_shards_path_or_url = list(itertools.chain.from_iterable(train_shards_path_or_url))\n\n def get_orig_size(json):\n if use_fix_crop_and_size:\n return (resolution, resolution)\n else:\n return (int(json.get(WDS_JSON_WIDTH, 0.0)), int(json.get(WDS_JSON_HEIGHT, 0.0)))\n\n interpolation_mode = resolve_interpolation_mode(interpolation_type)\n\n def transform(example):\n # resize image\n image = example[\"image\"]\n image = TF.resize(image, resolution, interpolation=interpolation_mode)\n\n # get crop coordinates and crop image\n c_top, c_left, _, _ = transforms.RandomCrop.get_params(image, output_size=(resolution, resolution))\n image = TF.crop(image, c_top, c_left, resolution, resolution)\n image = TF.to_tensor(image)\n image = TF.normalize(image, [0.5], [0.5])\n\n example[\"image\"] = image\n example[\"crop_coords\"] = (c_top, c_left) if not use_fix_crop_and_size else (0, 0)\n return example\n\n processing_pipeline = [\n wds.decode(\"pil\", handler=wds.ignore_and_continue),\n wds.rename(\n image=\"jpg;png;jpeg;webp\", text=\"text;txt;caption\", orig_size=\"json\", handler=wds.warn_and_continue\n ),\n wds.map(filter_keys({\"image\", \"text\", \"orig_size\"})),\n wds.map_dict(orig_size=get_orig_size),\n wds.map(transform),\n wds.to_tuple(\"image\", \"text\", \"orig_size\", \"crop_coords\"),\n ]\n\n # Create train dataset and loader\n pipeline = [\n wds.ResampledShards(train_shards_path_or_url),\n tarfile_to_samples_nothrow,\n wds.select(WebdatasetFilter(min_size=MIN_SIZE)),\n wds.shuffle(shuffle_buffer_size),\n *processing_pipeline,\n wds.batched(per_gpu_batch_size, partial=False, collation_fn=default_collate),\n ]\n\n num_worker_batches = math.ceil(num_train_examples / (global_batch_size * num_workers)) # per dataloader worker\n num_batches = num_worker_batches * num_workers\n num_samples = num_batches * global_batch_size\n\n # each worker is iterating over this\n self._train_dataset = wds.DataPipeline(*pipeline).with_epoch(num_worker_batches)\n self._train_dataloader = wds.WebLoader(\n self._train_dataset,\n batch_size=None,\n shuffle=False,\n num_workers=num_workers,\n pin_memory=pin_memory,\n persistent_workers=persistent_workers,\n )\n # add meta-data to dataloader instance for convenience\n self._train_dataloader.num_batches = num_batches\n self._train_dataloader.num_samples = num_samples\n\n @property\n def train_dataset(self):\n return self._train_dataset\n\n @property\n def train_dataloader(self):\n return self._train_dataloader\n\n\ndef log_validation(vae, unet, args, accelerator, weight_dtype, step):\n logger.info(\"Running validation... \")\n if torch.backends.mps.is_available():\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type, dtype=weight_dtype)\n\n unet = accelerator.unwrap_model(unet)\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n args.pretrained_teacher_model,\n vae=vae,\n scheduler=LCMScheduler.from_pretrained(args.pretrained_teacher_model, subfolder=\"scheduler\"),\n revision=args.revision,\n torch_dtype=weight_dtype,\n )\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n lora_state_dict = get_module_kohya_state_dict(unet, \"lora_unet\", weight_dtype)\n pipeline.load_lora_weights(lora_state_dict)\n pipeline.fuse_lora()\n\n if args.enable_xformers_memory_efficient_attention:\n pipeline.enable_xformers_memory_efficient_attention()\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n validation_prompts = [\n \"portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography\",\n \"Self-portrait oil painting, a beautiful cyborg with golden hair, 8k\",\n \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\",\n \"A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece\",\n ]\n\n image_logs = []\n\n for _, prompt in enumerate(validation_prompts):\n images = []\n with autocast_ctx:\n images = pipeline(\n prompt=prompt,\n num_inference_steps=4,\n num_images_per_prompt=4,\n generator=generator,\n guidance_scale=0.0,\n ).images\n image_logs.append({\"validation_prompt\": prompt, \"images\": images})\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n for log in image_logs:\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n formatted_images = []\n for image in images:\n formatted_images.append(np.asarray(image))\n\n formatted_images = np.stack(formatted_images)\n\n tracker.writer.add_images(validation_prompt, formatted_images, step, dataformats=\"NHWC\")\n elif tracker.name == \"wandb\":\n formatted_images = []\n\n for log in image_logs:\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n for image in images:\n image = wandb.Image(image, caption=validation_prompt)\n formatted_images.append(image)\n\n tracker.log({\"validation\": formatted_images})\n else:\n logger.warning(f\"image logging not implemented for {tracker.name}\")\n\n del pipeline\n gc.collect()\n torch.cuda.empty_cache()\n\n return image_logs\n\n\ndef append_dims(x, target_dims):\n \"\"\"Appends dimensions to the end of a tensor until it has target_dims dimensions.\"\"\"\n dims_to_append = target_dims - x.ndim\n if dims_to_append < 0:\n raise ValueError(f\"input has {x.ndim} dims but target_dims is {target_dims}, which is less\")\n return x[(...,) + (None,) * dims_to_append]\n\n\n# From LCMScheduler.get_scalings_for_boundary_condition_discrete\ndef scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0):\n scaled_timestep = timestep_scaling * timestep\n c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2)\n c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5\n return c_skip, c_out\n\n\n# Compare LCMScheduler.step, Step 4\ndef get_predicted_original_sample(model_output, timesteps, sample, prediction_type, alphas, sigmas):\n alphas = extract_into_tensor(alphas, timesteps, sample.shape)\n sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)\n if prediction_type == \"epsilon\":\n pred_x_0 = (sample - sigmas * model_output) / alphas\n elif prediction_type == \"sample\":\n pred_x_0 = model_output\n elif prediction_type == \"v_prediction\":\n pred_x_0 = alphas * sample - sigmas * model_output\n else:\n raise ValueError(\n f\"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`\"\n f\" are supported.\"\n )\n\n return pred_x_0\n\n\n# Based on step 4 in DDIMScheduler.step\ndef get_predicted_noise(model_output, timesteps, sample, prediction_type, alphas, sigmas):\n alphas = extract_into_tensor(alphas, timesteps, sample.shape)\n sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)\n if prediction_type == \"epsilon\":\n pred_epsilon = model_output\n elif prediction_type == \"sample\":\n pred_epsilon = (sample - alphas * model_output) / sigmas\n elif prediction_type == \"v_prediction\":\n pred_epsilon = alphas * model_output + sigmas * sample\n else:\n raise ValueError(\n f\"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`\"\n f\" are supported.\"\n )\n\n return pred_epsilon\n\n\ndef extract_into_tensor(a, t, x_shape):\n b, *_ = t.shape\n out = a.gather(-1, t)\n return out.reshape(b, *((1,) * (len(x_shape) - 1)))\n\n\nclass DDIMSolver:\n def __init__(self, alpha_cumprods, timesteps=1000, ddim_timesteps=50):\n # DDIM sampling parameters\n step_ratio = timesteps // ddim_timesteps\n\n self.ddim_timesteps = (np.arange(1, ddim_timesteps + 1) * step_ratio).round().astype(np.int64) - 1\n self.ddim_alpha_cumprods = alpha_cumprods[self.ddim_timesteps]\n self.ddim_alpha_cumprods_prev = np.asarray(\n [alpha_cumprods[0]] + alpha_cumprods[self.ddim_timesteps[:-1]].tolist()\n )\n # convert to torch tensors\n self.ddim_timesteps = torch.from_numpy(self.ddim_timesteps).long()\n self.ddim_alpha_cumprods = torch.from_numpy(self.ddim_alpha_cumprods)\n self.ddim_alpha_cumprods_prev = torch.from_numpy(self.ddim_alpha_cumprods_prev)\n\n def to(self, device):\n self.ddim_timesteps = self.ddim_timesteps.to(device)\n self.ddim_alpha_cumprods = self.ddim_alpha_cumprods.to(device)\n self.ddim_alpha_cumprods_prev = self.ddim_alpha_cumprods_prev.to(device)\n return self\n\n def ddim_step(self, pred_x0, pred_noise, timestep_index):\n alpha_cumprod_prev = extract_into_tensor(self.ddim_alpha_cumprods_prev, timestep_index, pred_x0.shape)\n dir_xt = (1.0 - alpha_cumprod_prev).sqrt() * pred_noise\n x_prev = alpha_cumprod_prev.sqrt() * pred_x0 + dir_xt\n return x_prev\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n # ----------Model Checkpoint Loading Arguments----------\n parser.add_argument(\n \"--pretrained_teacher_model\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained LDM teacher model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_vae_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.\",\n )\n parser.add_argument(\n \"--teacher_revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained LDM teacher model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained LDM model identifier from huggingface.co/models.\",\n )\n # ----------Training Arguments----------\n # ----General Training Arguments----\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"lcm-xl-distilled\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n # ----Logging----\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n # ----Checkpointing----\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n # ----Image Processing----\n parser.add_argument(\n \"--train_shards_path_or_url\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=1024,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--interpolation_type\",\n type=str,\n default=\"bilinear\",\n help=(\n \"The interpolation function used when resizing images to the desired resolution. Choose between `bilinear`,\"\n \" `bicubic`, `box`, `nearest`, `nearest_exact`, `hamming`, and `lanczos`.\"\n ),\n )\n parser.add_argument(\n \"--use_fix_crop_and_size\",\n action=\"store_true\",\n help=\"Whether or not to use the fixed crop and size for the teacher model.\",\n default=False,\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n # ----Dataloader----\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n # ----Batch Size and Training Steps----\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n # ----Learning Rate----\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n # ----Optimizer (Adam)----\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n # ----Diffusion Training Arguments----\n parser.add_argument(\n \"--proportion_empty_prompts\",\n type=float,\n default=0,\n help=\"Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).\",\n )\n # ----Latent Consistency Distillation (LCD) Specific Arguments----\n parser.add_argument(\n \"--w_min\",\n type=float,\n default=3.0,\n required=False,\n help=(\n \"The minimum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG\"\n \" formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as\"\n \" compared to the original paper.\"\n ),\n )\n parser.add_argument(\n \"--w_max\",\n type=float,\n default=15.0,\n required=False,\n help=(\n \"The maximum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG\"\n \" formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as\"\n \" compared to the original paper.\"\n ),\n )\n parser.add_argument(\n \"--num_ddim_timesteps\",\n type=int,\n default=50,\n help=\"The number of timesteps to use for DDIM sampling.\",\n )\n parser.add_argument(\n \"--loss_type\",\n type=str,\n default=\"l2\",\n choices=[\"l2\", \"huber\"],\n help=\"The type of loss to use for the LCD loss.\",\n )\n parser.add_argument(\n \"--huber_c\",\n type=float,\n default=0.001,\n help=\"The huber loss parameter. Only used if `--loss_type=huber`.\",\n )\n parser.add_argument(\n \"--lora_rank\",\n type=int,\n default=64,\n help=\"The rank of the LoRA projection matrix.\",\n )\n parser.add_argument(\n \"--lora_alpha\",\n type=int,\n default=64,\n help=(\n \"The value of the LoRA alpha parameter, which controls the scaling factor in front of the LoRA weight\"\n \" update delta_W. No scaling will be performed if this value is equal to `lora_rank`.\"\n ),\n )\n parser.add_argument(\n \"--lora_dropout\",\n type=float,\n default=0.0,\n help=\"The dropout probability for the dropout layer added before applying the LoRA to each layer input.\",\n )\n parser.add_argument(\n \"--lora_target_modules\",\n type=str,\n default=None,\n help=(\n \"A comma-separated string of target module keys to add LoRA to. If not set, a default list of modules will\"\n \" be used. By default, LoRA will be applied to all conv and linear layers.\"\n ),\n )\n parser.add_argument(\n \"--vae_encode_batch_size\",\n type=int,\n default=8,\n required=False,\n help=(\n \"The batch size used when encoding (and decoding) images to latents (and vice versa) using the VAE.\"\n \" Encoding or decoding the whole batch at once may run into OOM issues.\"\n ),\n )\n parser.add_argument(\n \"--timestep_scaling_factor\",\n type=float,\n default=10.0,\n help=(\n \"The multiplicative timestep scaling factor used when calculating the boundary scalings for LCM. The\"\n \" higher the scaling is, the lower the approximation error, but the default value of 10.0 should typically\"\n \" suffice.\"\n ),\n )\n # ----Mixed Precision----\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--cast_teacher_unet\",\n action=\"store_true\",\n help=\"Whether to cast the teacher U-Net to the precision specified by `--mixed_precision`.\",\n )\n # ----Training Optimizations----\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n # ----Distributed Training----\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n # ----------Validation Arguments----------\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=200,\n help=\"Run validation every X steps.\",\n )\n # ----------Huggingface Hub Arguments-----------\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n # ----------Accelerate Arguments----------\n parser.add_argument(\n \"--tracker_project_name\",\n type=str,\n default=\"text2image-fine-tune\",\n help=(\n \"The `project_name` argument passed to Accelerator.init_trackers for\"\n \" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator\"\n ),\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:\n raise ValueError(\"`--proportion_empty_prompts` must be in the range [0, 1].\")\n\n return args\n\n\n# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt\ndef encode_prompt(prompt_batch, text_encoders, tokenizers, proportion_empty_prompts, is_train=True):\n prompt_embeds_list = []\n\n captions = []\n for caption in prompt_batch:\n if random.random() < proportion_empty_prompts:\n captions.append(\"\")\n elif isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n\n with torch.no_grad():\n for tokenizer, text_encoder in zip(tokenizers, text_encoders):\n text_inputs = tokenizer(\n captions,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n prompt_embeds = text_encoder(\n text_input_ids.to(text_encoder.device),\n output_hidden_states=True,\n )\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds.hidden_states[-2]\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)\n return prompt_embeds, pooled_prompt_embeds\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n split_batches=True, # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be devide by the number of processes assuming batches are multiplied by the number of processes\n )\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name,\n exist_ok=True,\n token=args.hub_token,\n private=True,\n ).repo_id\n\n # 1. Create the noise scheduler and the desired noise schedule.\n noise_scheduler = DDPMScheduler.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"scheduler\", revision=args.teacher_revision\n )\n\n # DDPMScheduler calculates the alpha and sigma noise schedules (based on the alpha bars) for us\n alpha_schedule = torch.sqrt(noise_scheduler.alphas_cumprod)\n sigma_schedule = torch.sqrt(1 - noise_scheduler.alphas_cumprod)\n # Initialize the DDIM ODE solver for distillation.\n solver = DDIMSolver(\n noise_scheduler.alphas_cumprod.numpy(),\n timesteps=noise_scheduler.config.num_train_timesteps,\n ddim_timesteps=args.num_ddim_timesteps,\n )\n\n # 2. Load tokenizers from SD-XL checkpoint.\n tokenizer_one = AutoTokenizer.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"tokenizer\", revision=args.teacher_revision, use_fast=False\n )\n tokenizer_two = AutoTokenizer.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"tokenizer_2\", revision=args.teacher_revision, use_fast=False\n )\n\n # 3. Load text encoders from SD-XL checkpoint.\n # import correct text encoder classes\n text_encoder_cls_one = import_model_class_from_model_name_or_path(\n args.pretrained_teacher_model, args.teacher_revision\n )\n text_encoder_cls_two = import_model_class_from_model_name_or_path(\n args.pretrained_teacher_model, args.teacher_revision, subfolder=\"text_encoder_2\"\n )\n\n text_encoder_one = text_encoder_cls_one.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"text_encoder\", revision=args.teacher_revision\n )\n text_encoder_two = text_encoder_cls_two.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"text_encoder_2\", revision=args.teacher_revision\n )\n\n # 4. Load VAE from SD-XL checkpoint (or more stable VAE)\n vae_path = (\n args.pretrained_teacher_model\n if args.pretrained_vae_model_name_or_path is None\n else args.pretrained_vae_model_name_or_path\n )\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.teacher_revision,\n )\n\n # 5. Load teacher U-Net from SD-XL checkpoint\n teacher_unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"unet\", revision=args.teacher_revision\n )\n\n # 6. Freeze teacher vae, text_encoders, and teacher_unet\n vae.requires_grad_(False)\n text_encoder_one.requires_grad_(False)\n text_encoder_two.requires_grad_(False)\n teacher_unet.requires_grad_(False)\n\n # 7. Create online student U-Net.\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"unet\", revision=args.teacher_revision\n )\n unet.train()\n\n # Check that all trainable models are in full precision\n low_precision_error_string = (\n \" Please make sure to always have all model weights in full float32 precision when starting training - even if\"\n \" doing mixed precision training, copy of the weights should still be float32.\"\n )\n\n if accelerator.unwrap_model(unet).dtype != torch.float32:\n raise ValueError(\n f\"Controlnet loaded as datatype {accelerator.unwrap_model(unet).dtype}. {low_precision_error_string}\"\n )\n\n # 8. Add LoRA to the student U-Net, only the LoRA projection matrix will be updated by the optimizer.\n if args.lora_target_modules is not None:\n lora_target_modules = [module_key.strip() for module_key in args.lora_target_modules.split(\",\")]\n else:\n lora_target_modules = [\n \"to_q\",\n \"to_k\",\n \"to_v\",\n \"to_out.0\",\n \"proj_in\",\n \"proj_out\",\n \"ff.net.0.proj\",\n \"ff.net.2\",\n \"conv1\",\n \"conv2\",\n \"conv_shortcut\",\n \"downsamplers.0.conv\",\n \"upsamplers.0.conv\",\n \"time_emb_proj\",\n ]\n lora_config = LoraConfig(\n r=args.lora_rank,\n target_modules=lora_target_modules,\n lora_alpha=args.lora_alpha,\n lora_dropout=args.lora_dropout,\n )\n unet = get_peft_model(unet, lora_config)\n\n # 9. Handle mixed precision and device placement\n # For mixed precision training we cast all non-trainable weigths to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move unet, vae and text_encoder to device and cast to weight_dtype\n # The VAE is in float32 to avoid NaN losses.\n vae.to(accelerator.device)\n if args.pretrained_vae_model_name_or_path is not None:\n vae.to(dtype=weight_dtype)\n text_encoder_one.to(accelerator.device, dtype=weight_dtype)\n text_encoder_two.to(accelerator.device, dtype=weight_dtype)\n\n # Move teacher_unet to device, optionally cast to weight_dtype\n teacher_unet.to(accelerator.device)\n if args.cast_teacher_unet:\n teacher_unet.to(dtype=weight_dtype)\n\n # Also move the alpha and sigma noise schedules to accelerator.device.\n alpha_schedule = alpha_schedule.to(accelerator.device)\n sigma_schedule = sigma_schedule.to(accelerator.device)\n # Move the ODE solver to accelerator.device.\n solver = solver.to(accelerator.device)\n\n # 10. Handle saving and loading of checkpoints\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n unet_ = accelerator.unwrap_model(unet)\n lora_state_dict = get_peft_model_state_dict(unet_, adapter_name=\"default\")\n StableDiffusionXLPipeline.save_lora_weights(os.path.join(output_dir, \"unet_lora\"), lora_state_dict)\n # save weights in peft format to be able to load them back\n unet_.save_pretrained(output_dir)\n\n for _, model in enumerate(models):\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n def load_model_hook(models, input_dir):\n # load the LoRA into the model\n unet_ = accelerator.unwrap_model(unet)\n unet_.load_adapter(input_dir, \"default\", is_trainable=True)\n\n for _ in range(len(models)):\n # pop models so that they are not loaded again\n models.pop()\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # 11. Enable optimizations\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n teacher_unet.enable_xformers_memory_efficient_attention()\n # target_unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # 12. Optimizer creation\n optimizer = optimizer_class(\n unet.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # 13. Dataset creation and data processing\n # Here, we compute not just the text embeddings but also the additional embeddings\n # needed for the SD XL UNet to operate.\n def compute_embeddings(\n prompt_batch, original_sizes, crop_coords, proportion_empty_prompts, text_encoders, tokenizers, is_train=True\n ):\n target_size = (args.resolution, args.resolution)\n original_sizes = list(map(list, zip(*original_sizes)))\n crops_coords_top_left = list(map(list, zip(*crop_coords)))\n\n original_sizes = torch.tensor(original_sizes, dtype=torch.long)\n crops_coords_top_left = torch.tensor(crops_coords_top_left, dtype=torch.long)\n\n prompt_embeds, pooled_prompt_embeds = encode_prompt(\n prompt_batch, text_encoders, tokenizers, proportion_empty_prompts, is_train\n )\n add_text_embeds = pooled_prompt_embeds\n\n # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids\n add_time_ids = list(target_size)\n add_time_ids = torch.tensor([add_time_ids])\n add_time_ids = add_time_ids.repeat(len(prompt_batch), 1)\n add_time_ids = torch.cat([original_sizes, crops_coords_top_left, add_time_ids], dim=-1)\n add_time_ids = add_time_ids.to(accelerator.device, dtype=prompt_embeds.dtype)\n\n prompt_embeds = prompt_embeds.to(accelerator.device)\n add_text_embeds = add_text_embeds.to(accelerator.device)\n unet_added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n\n return {\"prompt_embeds\": prompt_embeds, **unet_added_cond_kwargs}\n\n dataset = SDXLText2ImageDataset(\n train_shards_path_or_url=args.train_shards_path_or_url,\n num_train_examples=args.max_train_samples,\n per_gpu_batch_size=args.train_batch_size,\n global_batch_size=args.train_batch_size * accelerator.num_processes,\n num_workers=args.dataloader_num_workers,\n resolution=args.resolution,\n interpolation_type=args.interpolation_type,\n shuffle_buffer_size=1000,\n pin_memory=True,\n persistent_workers=True,\n use_fix_crop_and_size=args.use_fix_crop_and_size,\n )\n train_dataloader = dataset.train_dataloader\n\n # Let's first compute all the embeddings so that we can free up the text encoders\n # from memory.\n text_encoders = [text_encoder_one, text_encoder_two]\n tokenizers = [tokenizer_one, tokenizer_two]\n\n compute_embeddings_fn = functools.partial(\n compute_embeddings,\n proportion_empty_prompts=0,\n text_encoders=text_encoders,\n tokenizers=tokenizers,\n )\n\n # 14. LR Scheduler creation\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps,\n num_training_steps=args.max_train_steps,\n )\n\n # 15. Prepare for training\n # Prepare everything with our `accelerator`.\n unet, optimizer, lr_scheduler = accelerator.prepare(unet, optimizer, lr_scheduler)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = dict(vars(args))\n accelerator.init_trackers(args.tracker_project_name, config=tracker_config)\n\n # Create uncond embeds for classifier free guidance\n uncond_prompt_embeds = torch.zeros(args.train_batch_size, 77, 2048).to(accelerator.device)\n uncond_pooled_prompt_embeds = torch.zeros(args.train_batch_size, 1280).to(accelerator.device)\n\n # 16. Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num batches each epoch = {train_dataloader.num_batches}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n for epoch in range(first_epoch, args.num_train_epochs):\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n # 1. Load and process the image, text, and micro-conditioning (original image size, crop coordinates)\n image, text, orig_size, crop_coords = batch\n\n image = image.to(accelerator.device, non_blocking=True)\n encoded_text = compute_embeddings_fn(text, orig_size, crop_coords)\n\n if args.pretrained_vae_model_name_or_path is not None:\n pixel_values = image.to(dtype=weight_dtype)\n if vae.dtype != weight_dtype:\n vae.to(dtype=weight_dtype)\n else:\n pixel_values = image\n\n # encode pixel values with batch size of at most args.vae_encode_batch_size\n latents = []\n for i in range(0, pixel_values.shape[0], args.vae_encode_batch_size):\n latents.append(vae.encode(pixel_values[i : i + args.vae_encode_batch_size]).latent_dist.sample())\n latents = torch.cat(latents, dim=0)\n\n latents = latents * vae.config.scaling_factor\n if args.pretrained_vae_model_name_or_path is None:\n latents = latents.to(weight_dtype)\n bsz = latents.shape[0]\n\n # 2. Sample a random timestep for each image t_n from the ODE solver timesteps without bias.\n # For the DDIM solver, the timestep schedule is [T - 1, T - k - 1, T - 2 * k - 1, ...]\n topk = noise_scheduler.config.num_train_timesteps // args.num_ddim_timesteps\n index = torch.randint(0, args.num_ddim_timesteps, (bsz,), device=latents.device).long()\n start_timesteps = solver.ddim_timesteps[index]\n timesteps = start_timesteps - topk\n timesteps = torch.where(timesteps < 0, torch.zeros_like(timesteps), timesteps)\n\n # 3. Get boundary scalings for start_timesteps and (end) timesteps.\n c_skip_start, c_out_start = scalings_for_boundary_conditions(\n start_timesteps, timestep_scaling=args.timestep_scaling_factor\n )\n c_skip_start, c_out_start = [append_dims(x, latents.ndim) for x in [c_skip_start, c_out_start]]\n c_skip, c_out = scalings_for_boundary_conditions(\n timesteps, timestep_scaling=args.timestep_scaling_factor\n )\n c_skip, c_out = [append_dims(x, latents.ndim) for x in [c_skip, c_out]]\n\n # 4. Sample noise from the prior and add it to the latents according to the noise magnitude at each\n # timestep (this is the forward diffusion process) [z_{t_{n + k}} in Algorithm 1]\n noise = torch.randn_like(latents)\n noisy_model_input = noise_scheduler.add_noise(latents, noise, start_timesteps)\n\n # 5. Sample a random guidance scale w from U[w_min, w_max]\n # Note that for LCM-LoRA distillation it is not necessary to use a guidance scale embedding\n w = (args.w_max - args.w_min) * torch.rand((bsz,)) + args.w_min\n w = w.reshape(bsz, 1, 1, 1)\n w = w.to(device=latents.device, dtype=latents.dtype)\n\n # 6. Prepare prompt embeds and unet_added_conditions\n prompt_embeds = encoded_text.pop(\"prompt_embeds\")\n\n # 7. Get online LCM prediction on z_{t_{n + k}} (noisy_model_input), w, c, t_{n + k} (start_timesteps)\n noise_pred = unet(\n noisy_model_input,\n start_timesteps,\n timestep_cond=None,\n encoder_hidden_states=prompt_embeds.float(),\n added_cond_kwargs=encoded_text,\n ).sample\n\n pred_x_0 = get_predicted_original_sample(\n noise_pred,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n\n model_pred = c_skip_start * noisy_model_input + c_out_start * pred_x_0\n\n # 8. Compute the conditional and unconditional teacher model predictions to get CFG estimates of the\n # predicted noise eps_0 and predicted original sample x_0, then run the ODE solver using these\n # estimates to predict the data point in the augmented PF-ODE trajectory corresponding to the next ODE\n # solver timestep.\n with torch.no_grad():\n if torch.backends.mps.is_available() or \"playground\" in args.pretrained_teacher_model:\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type)\n\n with autocast_ctx:\n # 1. Get teacher model prediction on noisy_model_input z_{t_{n + k}} and conditional embedding c\n cond_teacher_output = teacher_unet(\n noisy_model_input.to(weight_dtype),\n start_timesteps,\n encoder_hidden_states=prompt_embeds.to(weight_dtype),\n added_cond_kwargs={k: v.to(weight_dtype) for k, v in encoded_text.items()},\n ).sample\n cond_pred_x0 = get_predicted_original_sample(\n cond_teacher_output,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n cond_pred_noise = get_predicted_noise(\n cond_teacher_output,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n\n # 2. Get teacher model prediction on noisy_model_input z_{t_{n + k}} and unconditional embedding 0\n uncond_added_conditions = copy.deepcopy(encoded_text)\n uncond_added_conditions[\"text_embeds\"] = uncond_pooled_prompt_embeds\n uncond_teacher_output = teacher_unet(\n noisy_model_input.to(weight_dtype),\n start_timesteps,\n encoder_hidden_states=uncond_prompt_embeds.to(weight_dtype),\n added_cond_kwargs={k: v.to(weight_dtype) for k, v in uncond_added_conditions.items()},\n ).sample\n uncond_pred_x0 = get_predicted_original_sample(\n uncond_teacher_output,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n uncond_pred_noise = get_predicted_noise(\n uncond_teacher_output,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n\n # 3. Calculate the CFG estimate of x_0 (pred_x0) and eps_0 (pred_noise)\n # Note that this uses the LCM paper's CFG formulation rather than the Imagen CFG formulation\n pred_x0 = cond_pred_x0 + w * (cond_pred_x0 - uncond_pred_x0)\n pred_noise = cond_pred_noise + w * (cond_pred_noise - uncond_pred_noise)\n # 4. Run one step of the ODE solver to estimate the next point x_prev on the\n # augmented PF-ODE trajectory (solving backward in time)\n # Note that the DDIM step depends on both the predicted x_0 and source noise eps_0.\n x_prev = solver.ddim_step(pred_x0, pred_noise, index)\n\n # 9. Get target LCM prediction on x_prev, w, c, t_n (timesteps)\n # Note that we do not use a separate target network for LCM-LoRA distillation.\n with torch.no_grad():\n if torch.backends.mps.is_available():\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type, dtype=weight_dtype)\n\n with autocast_ctx:\n target_noise_pred = unet(\n x_prev.float(),\n timesteps,\n timestep_cond=None,\n encoder_hidden_states=prompt_embeds.float(),\n added_cond_kwargs=encoded_text,\n ).sample\n pred_x_0 = get_predicted_original_sample(\n target_noise_pred,\n timesteps,\n x_prev,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n target = c_skip * x_prev + c_out * pred_x_0\n\n # 10. Calculate loss\n if args.loss_type == \"l2\":\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n elif args.loss_type == \"huber\":\n loss = torch.mean(\n torch.sqrt((model_pred.float() - target.float()) ** 2 + args.huber_c**2) - args.huber_c\n )\n\n # 11. Backpropagate on the online student model (`unet`)\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad(set_to_none=True)\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n if global_step % args.validation_steps == 0:\n log_validation(vae, unet, args, accelerator, weight_dtype, global_step)\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n # Create the pipeline using using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = accelerator.unwrap_model(unet)\n unet.save_pretrained(args.output_dir)\n lora_state_dict = get_peft_model_state_dict(unet, adapter_name=\"default\")\n StableDiffusionXLPipeline.save_lora_weights(os.path.join(args.output_dir, \"unet_lora\"), lora_state_dict)\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/consistency_distillation/train_lcm_distill_sd_wds.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport functools\nimport gc\nimport itertools\nimport json\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom contextlib import nullcontext\nfrom pathlib import Path\nfrom typing import List, Union\n\nimport accelerate\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport torchvision.transforms.functional as TF\nimport transformers\nimport webdataset as wds\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom braceexpand import braceexpand\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom torch.utils.data import default_collate\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, CLIPTextModel, PretrainedConfig\nfrom webdataset.tariterators import (\n base_plus_ext,\n tar_file_expander,\n url_opener,\n valid_sample,\n)\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n LCMScheduler,\n StableDiffusionPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import resolve_interpolation_mode\nfrom diffusers.utils import check_min_version, is_wandb_available\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\nMAX_SEQ_LENGTH = 77\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef filter_keys(key_set):\n def _f(dictionary):\n return {k: v for k, v in dictionary.items() if k in key_set}\n\n return _f\n\n\ndef group_by_keys_nothrow(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None):\n \"\"\"Return function over iterator that groups key, value pairs into samples.\n\n :param keys: function that splits the key into key and extension (base_plus_ext) :param lcase: convert suffixes to\n lower case (Default value = True)\n \"\"\"\n current_sample = None\n for filesample in data:\n assert isinstance(filesample, dict)\n fname, value = filesample[\"fname\"], filesample[\"data\"]\n prefix, suffix = keys(fname)\n if prefix is None:\n continue\n if lcase:\n suffix = suffix.lower()\n # FIXME webdataset version throws if suffix in current_sample, but we have a potential for\n # this happening in the current LAION400m dataset if a tar ends with same prefix as the next\n # begins, rare, but can happen since prefix aren't unique across tar files in that dataset\n if current_sample is None or prefix != current_sample[\"__key__\"] or suffix in current_sample:\n if valid_sample(current_sample):\n yield current_sample\n current_sample = {\"__key__\": prefix, \"__url__\": filesample[\"__url__\"]}\n if suffixes is None or suffix in suffixes:\n current_sample[suffix] = value\n if valid_sample(current_sample):\n yield current_sample\n\n\ndef tarfile_to_samples_nothrow(src, handler=wds.warn_and_continue):\n # NOTE this is a re-impl of the webdataset impl with group_by_keys that doesn't throw\n streams = url_opener(src, handler=handler)\n files = tar_file_expander(streams, handler=handler)\n samples = group_by_keys_nothrow(files, handler=handler)\n return samples\n\n\nclass WebdatasetFilter:\n def __init__(self, min_size=1024, max_pwatermark=0.5):\n self.min_size = min_size\n self.max_pwatermark = max_pwatermark\n\n def __call__(self, x):\n try:\n if \"json\" in x:\n x_json = json.loads(x[\"json\"])\n filter_size = (x_json.get(\"original_width\", 0.0) or 0.0) >= self.min_size and x_json.get(\n \"original_height\", 0\n ) >= self.min_size\n filter_watermark = (x_json.get(\"pwatermark\", 1.0) or 1.0) <= self.max_pwatermark\n return filter_size and filter_watermark\n else:\n return False\n except Exception:\n return False\n\n\nclass SDText2ImageDataset:\n def __init__(\n self,\n train_shards_path_or_url: Union[str, List[str]],\n num_train_examples: int,\n per_gpu_batch_size: int,\n global_batch_size: int,\n num_workers: int,\n resolution: int = 512,\n interpolation_type: str = \"bilinear\",\n shuffle_buffer_size: int = 1000,\n pin_memory: bool = False,\n persistent_workers: bool = False,\n ):\n if not isinstance(train_shards_path_or_url, str):\n train_shards_path_or_url = [list(braceexpand(urls)) for urls in train_shards_path_or_url]\n # flatten list using itertools\n train_shards_path_or_url = list(itertools.chain.from_iterable(train_shards_path_or_url))\n\n interpolation_mode = resolve_interpolation_mode(interpolation_type)\n\n def transform(example):\n # resize image\n image = example[\"image\"]\n image = TF.resize(image, resolution, interpolation=interpolation_mode)\n\n # get crop coordinates and crop image\n c_top, c_left, _, _ = transforms.RandomCrop.get_params(image, output_size=(resolution, resolution))\n image = TF.crop(image, c_top, c_left, resolution, resolution)\n image = TF.to_tensor(image)\n image = TF.normalize(image, [0.5], [0.5])\n\n example[\"image\"] = image\n return example\n\n processing_pipeline = [\n wds.decode(\"pil\", handler=wds.ignore_and_continue),\n wds.rename(image=\"jpg;png;jpeg;webp\", text=\"text;txt;caption\", handler=wds.warn_and_continue),\n wds.map(filter_keys({\"image\", \"text\"})),\n wds.map(transform),\n wds.to_tuple(\"image\", \"text\"),\n ]\n\n # Create train dataset and loader\n pipeline = [\n wds.ResampledShards(train_shards_path_or_url),\n tarfile_to_samples_nothrow,\n wds.shuffle(shuffle_buffer_size),\n *processing_pipeline,\n wds.batched(per_gpu_batch_size, partial=False, collation_fn=default_collate),\n ]\n\n num_worker_batches = math.ceil(num_train_examples / (global_batch_size * num_workers)) # per dataloader worker\n num_batches = num_worker_batches * num_workers\n num_samples = num_batches * global_batch_size\n\n # each worker is iterating over this\n self._train_dataset = wds.DataPipeline(*pipeline).with_epoch(num_worker_batches)\n self._train_dataloader = wds.WebLoader(\n self._train_dataset,\n batch_size=None,\n shuffle=False,\n num_workers=num_workers,\n pin_memory=pin_memory,\n persistent_workers=persistent_workers,\n )\n # add meta-data to dataloader instance for convenience\n self._train_dataloader.num_batches = num_batches\n self._train_dataloader.num_samples = num_samples\n\n @property\n def train_dataset(self):\n return self._train_dataset\n\n @property\n def train_dataloader(self):\n return self._train_dataloader\n\n\ndef log_validation(vae, unet, args, accelerator, weight_dtype, step, name=\"target\"):\n logger.info(\"Running validation... \")\n\n unet = accelerator.unwrap_model(unet)\n pipeline = StableDiffusionPipeline.from_pretrained(\n args.pretrained_teacher_model,\n vae=vae,\n unet=unet,\n scheduler=LCMScheduler.from_pretrained(args.pretrained_teacher_model, subfolder=\"scheduler\"),\n revision=args.revision,\n torch_dtype=weight_dtype,\n )\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n if args.enable_xformers_memory_efficient_attention:\n pipeline.enable_xformers_memory_efficient_attention()\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n validation_prompts = [\n \"portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography\",\n \"Self-portrait oil painting, a beautiful cyborg with golden hair, 8k\",\n \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\",\n \"A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece\",\n ]\n\n image_logs = []\n\n for _, prompt in enumerate(validation_prompts):\n images = []\n if torch.backends.mps.is_available():\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type)\n\n with autocast_ctx:\n images = pipeline(\n prompt=prompt,\n num_inference_steps=4,\n num_images_per_prompt=4,\n generator=generator,\n ).images\n image_logs.append({\"validation_prompt\": prompt, \"images\": images})\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n for log in image_logs:\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n formatted_images = []\n for image in images:\n formatted_images.append(np.asarray(image))\n\n formatted_images = np.stack(formatted_images)\n\n tracker.writer.add_images(validation_prompt, formatted_images, step, dataformats=\"NHWC\")\n elif tracker.name == \"wandb\":\n formatted_images = []\n\n for log in image_logs:\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n for image in images:\n image = wandb.Image(image, caption=validation_prompt)\n formatted_images.append(image)\n\n tracker.log({f\"validation/{name}\": formatted_images})\n else:\n logger.warning(f\"image logging not implemented for {tracker.name}\")\n\n del pipeline\n gc.collect()\n torch.cuda.empty_cache()\n\n return image_logs\n\n\n# From LatentConsistencyModel.get_guidance_scale_embedding\ndef guidance_scale_embedding(w, embedding_dim=512, dtype=torch.float32):\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n timesteps (`torch.Tensor`):\n generate embedding vectors at these timesteps\n embedding_dim (`int`, *optional*, defaults to 512):\n dimension of the embeddings to generate\n dtype:\n data type of the generated embeddings\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n\ndef append_dims(x, target_dims):\n \"\"\"Appends dimensions to the end of a tensor until it has target_dims dimensions.\"\"\"\n dims_to_append = target_dims - x.ndim\n if dims_to_append < 0:\n raise ValueError(f\"input has {x.ndim} dims but target_dims is {target_dims}, which is less\")\n return x[(...,) + (None,) * dims_to_append]\n\n\n# From LCMScheduler.get_scalings_for_boundary_condition_discrete\ndef scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0):\n scaled_timestep = timestep_scaling * timestep\n c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2)\n c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5\n return c_skip, c_out\n\n\n# Compare LCMScheduler.step, Step 4\ndef get_predicted_original_sample(model_output, timesteps, sample, prediction_type, alphas, sigmas):\n alphas = extract_into_tensor(alphas, timesteps, sample.shape)\n sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)\n if prediction_type == \"epsilon\":\n pred_x_0 = (sample - sigmas * model_output) / alphas\n elif prediction_type == \"sample\":\n pred_x_0 = model_output\n elif prediction_type == \"v_prediction\":\n pred_x_0 = alphas * sample - sigmas * model_output\n else:\n raise ValueError(\n f\"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`\"\n f\" are supported.\"\n )\n\n return pred_x_0\n\n\n# Based on step 4 in DDIMScheduler.step\ndef get_predicted_noise(model_output, timesteps, sample, prediction_type, alphas, sigmas):\n alphas = extract_into_tensor(alphas, timesteps, sample.shape)\n sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)\n if prediction_type == \"epsilon\":\n pred_epsilon = model_output\n elif prediction_type == \"sample\":\n pred_epsilon = (sample - alphas * model_output) / sigmas\n elif prediction_type == \"v_prediction\":\n pred_epsilon = alphas * model_output + sigmas * sample\n else:\n raise ValueError(\n f\"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`\"\n f\" are supported.\"\n )\n\n return pred_epsilon\n\n\ndef extract_into_tensor(a, t, x_shape):\n b, *_ = t.shape\n out = a.gather(-1, t)\n return out.reshape(b, *((1,) * (len(x_shape) - 1)))\n\n\nclass DDIMSolver:\n def __init__(self, alpha_cumprods, timesteps=1000, ddim_timesteps=50):\n # DDIM sampling parameters\n step_ratio = timesteps // ddim_timesteps\n self.ddim_timesteps = (np.arange(1, ddim_timesteps + 1) * step_ratio).round().astype(np.int64) - 1\n self.ddim_alpha_cumprods = alpha_cumprods[self.ddim_timesteps]\n self.ddim_alpha_cumprods_prev = np.asarray(\n [alpha_cumprods[0]] + alpha_cumprods[self.ddim_timesteps[:-1]].tolist()\n )\n # convert to torch tensors\n self.ddim_timesteps = torch.from_numpy(self.ddim_timesteps).long()\n self.ddim_alpha_cumprods = torch.from_numpy(self.ddim_alpha_cumprods)\n self.ddim_alpha_cumprods_prev = torch.from_numpy(self.ddim_alpha_cumprods_prev)\n\n def to(self, device):\n self.ddim_timesteps = self.ddim_timesteps.to(device)\n self.ddim_alpha_cumprods = self.ddim_alpha_cumprods.to(device)\n self.ddim_alpha_cumprods_prev = self.ddim_alpha_cumprods_prev.to(device)\n return self\n\n def ddim_step(self, pred_x0, pred_noise, timestep_index):\n alpha_cumprod_prev = extract_into_tensor(self.ddim_alpha_cumprods_prev, timestep_index, pred_x0.shape)\n dir_xt = (1.0 - alpha_cumprod_prev).sqrt() * pred_noise\n x_prev = alpha_cumprod_prev.sqrt() * pred_x0 + dir_xt\n return x_prev\n\n\n@torch.no_grad()\ndef update_ema(target_params, source_params, rate=0.99):\n \"\"\"\n Update target parameters to be closer to those of source parameters using\n an exponential moving average.\n\n :param target_params: the target parameter sequence.\n :param source_params: the source parameter sequence.\n :param rate: the EMA rate (closer to 1 means slower).\n \"\"\"\n for targ, src in zip(target_params, source_params):\n targ.detach().mul_(rate).add_(src, alpha=1 - rate)\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n # ----------Model Checkpoint Loading Arguments----------\n parser.add_argument(\n \"--pretrained_teacher_model\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained LDM teacher model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_vae_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.\",\n )\n parser.add_argument(\n \"--teacher_revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained LDM teacher model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained LDM model identifier from huggingface.co/models.\",\n )\n # ----------Training Arguments----------\n # ----General Training Arguments----\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"lcm-xl-distilled\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n # ----Logging----\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n # ----Checkpointing----\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n # ----Image Processing----\n parser.add_argument(\n \"--train_shards_path_or_url\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--interpolation_type\",\n type=str,\n default=\"bilinear\",\n help=(\n \"The interpolation function used when resizing images to the desired resolution. Choose between `bilinear`,\"\n \" `bicubic`, `box`, `nearest`, `nearest_exact`, `hamming`, and `lanczos`.\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n # ----Dataloader----\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n # ----Batch Size and Training Steps----\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n # ----Learning Rate----\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n # ----Optimizer (Adam)----\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n # ----Diffusion Training Arguments----\n parser.add_argument(\n \"--proportion_empty_prompts\",\n type=float,\n default=0,\n help=\"Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).\",\n )\n # ----Latent Consistency Distillation (LCD) Specific Arguments----\n parser.add_argument(\n \"--w_min\",\n type=float,\n default=5.0,\n required=False,\n help=(\n \"The minimum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG\"\n \" formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as\"\n \" compared to the original paper.\"\n ),\n )\n parser.add_argument(\n \"--w_max\",\n type=float,\n default=15.0,\n required=False,\n help=(\n \"The maximum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG\"\n \" formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as\"\n \" compared to the original paper.\"\n ),\n )\n parser.add_argument(\n \"--num_ddim_timesteps\",\n type=int,\n default=50,\n help=\"The number of timesteps to use for DDIM sampling.\",\n )\n parser.add_argument(\n \"--loss_type\",\n type=str,\n default=\"l2\",\n choices=[\"l2\", \"huber\"],\n help=\"The type of loss to use for the LCD loss.\",\n )\n parser.add_argument(\n \"--huber_c\",\n type=float,\n default=0.001,\n help=\"The huber loss parameter. Only used if `--loss_type=huber`.\",\n )\n parser.add_argument(\n \"--unet_time_cond_proj_dim\",\n type=int,\n default=256,\n help=(\n \"The dimension of the guidance scale embedding in the U-Net, which will be used if the teacher U-Net\"\n \" does not have `time_cond_proj_dim` set.\"\n ),\n )\n parser.add_argument(\n \"--vae_encode_batch_size\",\n type=int,\n default=32,\n required=False,\n help=(\n \"The batch size used when encoding (and decoding) images to latents (and vice versa) using the VAE.\"\n \" Encoding or decoding the whole batch at once may run into OOM issues.\"\n ),\n )\n parser.add_argument(\n \"--timestep_scaling_factor\",\n type=float,\n default=10.0,\n help=(\n \"The multiplicative timestep scaling factor used when calculating the boundary scalings for LCM. The\"\n \" higher the scaling is, the lower the approximation error, but the default value of 10.0 should typically\"\n \" suffice.\"\n ),\n )\n # ----Exponential Moving Average (EMA)----\n parser.add_argument(\n \"--ema_decay\",\n type=float,\n default=0.95,\n required=False,\n help=\"The exponential moving average (EMA) rate or decay factor.\",\n )\n # ----Mixed Precision----\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--cast_teacher_unet\",\n action=\"store_true\",\n help=\"Whether to cast the teacher U-Net to the precision specified by `--mixed_precision`.\",\n )\n # ----Training Optimizations----\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n # ----Distributed Training----\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n # ----------Validation Arguments----------\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=200,\n help=\"Run validation every X steps.\",\n )\n # ----------Huggingface Hub Arguments-----------\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n # ----------Accelerate Arguments----------\n parser.add_argument(\n \"--tracker_project_name\",\n type=str,\n default=\"text2image-fine-tune\",\n help=(\n \"The `project_name` argument passed to Accelerator.init_trackers for\"\n \" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator\"\n ),\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:\n raise ValueError(\"`--proportion_empty_prompts` must be in the range [0, 1].\")\n\n return args\n\n\n# Adapted from pipelines.StableDiffusionPipeline.encode_prompt\ndef encode_prompt(prompt_batch, text_encoder, tokenizer, proportion_empty_prompts, is_train=True):\n captions = []\n for caption in prompt_batch:\n if random.random() < proportion_empty_prompts:\n captions.append(\"\")\n elif isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n\n with torch.no_grad():\n text_inputs = tokenizer(\n captions,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n prompt_embeds = text_encoder(text_input_ids.to(text_encoder.device))[0]\n\n return prompt_embeds\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n split_batches=True, # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be devide by the number of processes assuming batches are multiplied by the number of processes\n )\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name,\n exist_ok=True,\n token=args.hub_token,\n private=True,\n ).repo_id\n\n # 1. Create the noise scheduler and the desired noise schedule.\n noise_scheduler = DDPMScheduler.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"scheduler\", revision=args.teacher_revision\n )\n\n # DDPMScheduler calculates the alpha and sigma noise schedules (based on the alpha bars) for us\n alpha_schedule = torch.sqrt(noise_scheduler.alphas_cumprod)\n sigma_schedule = torch.sqrt(1 - noise_scheduler.alphas_cumprod)\n # Initialize the DDIM ODE solver for distillation.\n solver = DDIMSolver(\n noise_scheduler.alphas_cumprod.numpy(),\n timesteps=noise_scheduler.config.num_train_timesteps,\n ddim_timesteps=args.num_ddim_timesteps,\n )\n\n # 2. Load tokenizers from SD 1.X/2.X checkpoint.\n tokenizer = AutoTokenizer.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"tokenizer\", revision=args.teacher_revision, use_fast=False\n )\n\n # 3. Load text encoders from SD 1.X/2.X checkpoint.\n # import correct text encoder classes\n text_encoder = CLIPTextModel.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"text_encoder\", revision=args.teacher_revision\n )\n\n # 4. Load VAE from SD 1.X/2.X checkpoint\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_teacher_model,\n subfolder=\"vae\",\n revision=args.teacher_revision,\n )\n\n # 5. Load teacher U-Net from SD 1.X/2.X checkpoint\n teacher_unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"unet\", revision=args.teacher_revision\n )\n\n # 6. Freeze teacher vae, text_encoder, and teacher_unet\n vae.requires_grad_(False)\n text_encoder.requires_grad_(False)\n teacher_unet.requires_grad_(False)\n\n # 7. Create online student U-Net. This will be updated by the optimizer (e.g. via backpropagation.)\n # Add `time_cond_proj_dim` to the student U-Net if `teacher_unet.config.time_cond_proj_dim` is None\n time_cond_proj_dim = (\n teacher_unet.config.time_cond_proj_dim\n if teacher_unet.config.time_cond_proj_dim is not None\n else args.unet_time_cond_proj_dim\n )\n unet = UNet2DConditionModel.from_config(teacher_unet.config, time_cond_proj_dim=time_cond_proj_dim)\n # load teacher_unet weights into unet\n unet.load_state_dict(teacher_unet.state_dict(), strict=False)\n unet.train()\n\n # 8. Create target student U-Net. This will be updated via EMA updates (polyak averaging).\n # Initialize from (online) unet\n target_unet = UNet2DConditionModel.from_config(unet.config)\n target_unet.load_state_dict(unet.state_dict())\n target_unet.train()\n target_unet.requires_grad_(False)\n\n # Check that all trainable models are in full precision\n low_precision_error_string = (\n \" Please make sure to always have all model weights in full float32 precision when starting training - even if\"\n \" doing mixed precision training, copy of the weights should still be float32.\"\n )\n\n if accelerator.unwrap_model(unet).dtype != torch.float32:\n raise ValueError(\n f\"Controlnet loaded as datatype {accelerator.unwrap_model(unet).dtype}. {low_precision_error_string}\"\n )\n\n # 9. Handle mixed precision and device placement\n # For mixed precision training we cast all non-trainable weigths to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move unet, vae and text_encoder to device and cast to weight_dtype\n # The VAE is in float32 to avoid NaN losses.\n vae.to(accelerator.device)\n if args.pretrained_vae_model_name_or_path is not None:\n vae.to(dtype=weight_dtype)\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n\n # Move teacher_unet to device, optionally cast to weight_dtype\n target_unet.to(accelerator.device)\n teacher_unet.to(accelerator.device)\n if args.cast_teacher_unet:\n teacher_unet.to(dtype=weight_dtype)\n\n # Also move the alpha and sigma noise schedules to accelerator.device.\n alpha_schedule = alpha_schedule.to(accelerator.device)\n sigma_schedule = sigma_schedule.to(accelerator.device)\n solver = solver.to(accelerator.device)\n\n # 10. Handle saving and loading of checkpoints\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n target_unet.save_pretrained(os.path.join(output_dir, \"unet_target\"))\n\n for i, model in enumerate(models):\n model.save_pretrained(os.path.join(output_dir, \"unet\"))\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n def load_model_hook(models, input_dir):\n load_model = UNet2DConditionModel.from_pretrained(os.path.join(input_dir, \"unet_target\"))\n target_unet.load_state_dict(load_model.state_dict())\n target_unet.to(accelerator.device)\n del load_model\n\n for i in range(len(models)):\n # pop models so that they are not loaded again\n model = models.pop()\n\n # load diffusers style into model\n load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder=\"unet\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # 11. Enable optimizations\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n teacher_unet.enable_xformers_memory_efficient_attention()\n target_unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # 12. Optimizer creation\n optimizer = optimizer_class(\n unet.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # 13. Dataset creation and data processing\n # Here, we compute not just the text embeddings but also the additional embeddings\n # needed for the SD XL UNet to operate.\n def compute_embeddings(prompt_batch, proportion_empty_prompts, text_encoder, tokenizer, is_train=True):\n prompt_embeds = encode_prompt(prompt_batch, text_encoder, tokenizer, proportion_empty_prompts, is_train)\n return {\"prompt_embeds\": prompt_embeds}\n\n dataset = SDText2ImageDataset(\n train_shards_path_or_url=args.train_shards_path_or_url,\n num_train_examples=args.max_train_samples,\n per_gpu_batch_size=args.train_batch_size,\n global_batch_size=args.train_batch_size * accelerator.num_processes,\n num_workers=args.dataloader_num_workers,\n resolution=args.resolution,\n interpolation_type=args.interpolation_type,\n shuffle_buffer_size=1000,\n pin_memory=True,\n persistent_workers=True,\n )\n train_dataloader = dataset.train_dataloader\n\n compute_embeddings_fn = functools.partial(\n compute_embeddings,\n proportion_empty_prompts=0,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n )\n\n # 14. LR Scheduler creation\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps,\n num_training_steps=args.max_train_steps,\n )\n\n # 15. Prepare for training\n # Prepare everything with our `accelerator`.\n unet, optimizer, lr_scheduler = accelerator.prepare(unet, optimizer, lr_scheduler)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = dict(vars(args))\n accelerator.init_trackers(args.tracker_project_name, config=tracker_config)\n\n uncond_input_ids = tokenizer(\n [\"\"] * args.train_batch_size, return_tensors=\"pt\", padding=\"max_length\", max_length=77\n ).input_ids.to(accelerator.device)\n uncond_prompt_embeds = text_encoder(uncond_input_ids)[0]\n\n # 16. Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num batches each epoch = {train_dataloader.num_batches}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n for epoch in range(first_epoch, args.num_train_epochs):\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n # 1. Load and process the image and text conditioning\n image, text = batch\n\n image = image.to(accelerator.device, non_blocking=True)\n encoded_text = compute_embeddings_fn(text)\n\n pixel_values = image.to(dtype=weight_dtype)\n if vae.dtype != weight_dtype:\n vae.to(dtype=weight_dtype)\n\n # encode pixel values with batch size of at most args.vae_encode_batch_size\n latents = []\n for i in range(0, pixel_values.shape[0], args.vae_encode_batch_size):\n latents.append(vae.encode(pixel_values[i : i + args.vae_encode_batch_size]).latent_dist.sample())\n latents = torch.cat(latents, dim=0)\n\n latents = latents * vae.config.scaling_factor\n latents = latents.to(weight_dtype)\n bsz = latents.shape[0]\n\n # 2. Sample a random timestep for each image t_n from the ODE solver timesteps without bias.\n # For the DDIM solver, the timestep schedule is [T - 1, T - k - 1, T - 2 * k - 1, ...]\n topk = noise_scheduler.config.num_train_timesteps // args.num_ddim_timesteps\n index = torch.randint(0, args.num_ddim_timesteps, (bsz,), device=latents.device).long()\n start_timesteps = solver.ddim_timesteps[index]\n timesteps = start_timesteps - topk\n timesteps = torch.where(timesteps < 0, torch.zeros_like(timesteps), timesteps)\n\n # 3. Get boundary scalings for start_timesteps and (end) timesteps.\n c_skip_start, c_out_start = scalings_for_boundary_conditions(\n start_timesteps, timestep_scaling=args.timestep_scaling_factor\n )\n c_skip_start, c_out_start = [append_dims(x, latents.ndim) for x in [c_skip_start, c_out_start]]\n c_skip, c_out = scalings_for_boundary_conditions(\n timesteps, timestep_scaling=args.timestep_scaling_factor\n )\n c_skip, c_out = [append_dims(x, latents.ndim) for x in [c_skip, c_out]]\n\n # 4. Sample noise from the prior and add it to the latents according to the noise magnitude at each\n # timestep (this is the forward diffusion process) [z_{t_{n + k}} in Algorithm 1]\n noise = torch.randn_like(latents)\n noisy_model_input = noise_scheduler.add_noise(latents, noise, start_timesteps)\n\n # 5. Sample a random guidance scale w from U[w_min, w_max] and embed it\n w = (args.w_max - args.w_min) * torch.rand((bsz,)) + args.w_min\n w_embedding = guidance_scale_embedding(w, embedding_dim=time_cond_proj_dim)\n w = w.reshape(bsz, 1, 1, 1)\n # Move to U-Net device and dtype\n w = w.to(device=latents.device, dtype=latents.dtype)\n w_embedding = w_embedding.to(device=latents.device, dtype=latents.dtype)\n\n # 6. Prepare prompt embeds and unet_added_conditions\n prompt_embeds = encoded_text.pop(\"prompt_embeds\")\n\n # 7. Get online LCM prediction on z_{t_{n + k}} (noisy_model_input), w, c, t_{n + k} (start_timesteps)\n noise_pred = unet(\n noisy_model_input,\n start_timesteps,\n timestep_cond=w_embedding,\n encoder_hidden_states=prompt_embeds.float(),\n added_cond_kwargs=encoded_text,\n ).sample\n\n pred_x_0 = get_predicted_original_sample(\n noise_pred,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n\n model_pred = c_skip_start * noisy_model_input + c_out_start * pred_x_0\n\n # 8. Compute the conditional and unconditional teacher model predictions to get CFG estimates of the\n # predicted noise eps_0 and predicted original sample x_0, then run the ODE solver using these\n # estimates to predict the data point in the augmented PF-ODE trajectory corresponding to the next ODE\n # solver timestep.\n with torch.no_grad():\n if torch.backends.mps.is_available():\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type)\n\n with autocast_ctx:\n # 1. Get teacher model prediction on noisy_model_input z_{t_{n + k}} and conditional embedding c\n cond_teacher_output = teacher_unet(\n noisy_model_input.to(weight_dtype),\n start_timesteps,\n encoder_hidden_states=prompt_embeds.to(weight_dtype),\n ).sample\n cond_pred_x0 = get_predicted_original_sample(\n cond_teacher_output,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n cond_pred_noise = get_predicted_noise(\n cond_teacher_output,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n\n # 2. Get teacher model prediction on noisy_model_input z_{t_{n + k}} and unconditional embedding 0\n uncond_teacher_output = teacher_unet(\n noisy_model_input.to(weight_dtype),\n start_timesteps,\n encoder_hidden_states=uncond_prompt_embeds.to(weight_dtype),\n ).sample\n uncond_pred_x0 = get_predicted_original_sample(\n uncond_teacher_output,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n uncond_pred_noise = get_predicted_noise(\n uncond_teacher_output,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n\n # 3. Calculate the CFG estimate of x_0 (pred_x0) and eps_0 (pred_noise)\n # Note that this uses the LCM paper's CFG formulation rather than the Imagen CFG formulation\n pred_x0 = cond_pred_x0 + w * (cond_pred_x0 - uncond_pred_x0)\n pred_noise = cond_pred_noise + w * (cond_pred_noise - uncond_pred_noise)\n # 4. Run one step of the ODE solver to estimate the next point x_prev on the\n # augmented PF-ODE trajectory (solving backward in time)\n # Note that the DDIM step depends on both the predicted x_0 and source noise eps_0.\n x_prev = solver.ddim_step(pred_x0, pred_noise, index)\n\n # 9. Get target LCM prediction on x_prev, w, c, t_n (timesteps)\n with torch.no_grad():\n if torch.backends.mps.is_available():\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type, dtype=weight_dtype)\n\n with autocast_ctx:\n target_noise_pred = target_unet(\n x_prev.float(),\n timesteps,\n timestep_cond=w_embedding,\n encoder_hidden_states=prompt_embeds.float(),\n ).sample\n pred_x_0 = get_predicted_original_sample(\n target_noise_pred,\n timesteps,\n x_prev,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n target = c_skip * x_prev + c_out * pred_x_0\n\n # 10. Calculate loss\n if args.loss_type == \"l2\":\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n elif args.loss_type == \"huber\":\n loss = torch.mean(\n torch.sqrt((model_pred.float() - target.float()) ** 2 + args.huber_c**2) - args.huber_c\n )\n\n # 11. Backpropagate on the online student model (`unet`)\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad(set_to_none=True)\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n # 12. Make EMA update to target student model parameters (`target_unet`)\n update_ema(target_unet.parameters(), unet.parameters(), args.ema_decay)\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n if global_step % args.validation_steps == 0:\n log_validation(vae, target_unet, args, accelerator, weight_dtype, global_step, \"target\")\n log_validation(vae, unet, args, accelerator, weight_dtype, global_step, \"online\")\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n # Create the pipeline using using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = accelerator.unwrap_model(unet)\n unet.save_pretrained(os.path.join(args.output_dir, \"unet\"))\n\n target_unet = accelerator.unwrap_model(target_unet)\n target_unet.save_pretrained(os.path.join(args.output_dir, \"unet_target\"))\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/consistency_distillation/train_lcm_distill_sdxl_wds.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport copy\nimport functools\nimport gc\nimport itertools\nimport json\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom contextlib import nullcontext\nfrom pathlib import Path\nfrom typing import List, Union\n\nimport accelerate\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport torchvision.transforms.functional as TF\nimport transformers\nimport webdataset as wds\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom braceexpand import braceexpand\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom torch.utils.data import default_collate\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\nfrom webdataset.tariterators import (\n base_plus_ext,\n tar_file_expander,\n url_opener,\n valid_sample,\n)\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n LCMScheduler,\n StableDiffusionXLPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import resolve_interpolation_mode\nfrom diffusers.utils import check_min_version, is_wandb_available\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\nMAX_SEQ_LENGTH = 77\n\n# Adjust for your dataset\nWDS_JSON_WIDTH = \"width\" # original_width for LAION\nWDS_JSON_HEIGHT = \"height\" # original_height for LAION\nMIN_SIZE = 700 # ~960 for LAION, ideal: 1024 if the dataset contains large images\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef filter_keys(key_set):\n def _f(dictionary):\n return {k: v for k, v in dictionary.items() if k in key_set}\n\n return _f\n\n\ndef group_by_keys_nothrow(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None):\n \"\"\"Return function over iterator that groups key, value pairs into samples.\n\n :param keys: function that splits the key into key and extension (base_plus_ext) :param lcase: convert suffixes to\n lower case (Default value = True)\n \"\"\"\n current_sample = None\n for filesample in data:\n assert isinstance(filesample, dict)\n fname, value = filesample[\"fname\"], filesample[\"data\"]\n prefix, suffix = keys(fname)\n if prefix is None:\n continue\n if lcase:\n suffix = suffix.lower()\n # FIXME webdataset version throws if suffix in current_sample, but we have a potential for\n # this happening in the current LAION400m dataset if a tar ends with same prefix as the next\n # begins, rare, but can happen since prefix aren't unique across tar files in that dataset\n if current_sample is None or prefix != current_sample[\"__key__\"] or suffix in current_sample:\n if valid_sample(current_sample):\n yield current_sample\n current_sample = {\"__key__\": prefix, \"__url__\": filesample[\"__url__\"]}\n if suffixes is None or suffix in suffixes:\n current_sample[suffix] = value\n if valid_sample(current_sample):\n yield current_sample\n\n\ndef tarfile_to_samples_nothrow(src, handler=wds.warn_and_continue):\n # NOTE this is a re-impl of the webdataset impl with group_by_keys that doesn't throw\n streams = url_opener(src, handler=handler)\n files = tar_file_expander(streams, handler=handler)\n samples = group_by_keys_nothrow(files, handler=handler)\n return samples\n\n\nclass WebdatasetFilter:\n def __init__(self, min_size=1024, max_pwatermark=0.5):\n self.min_size = min_size\n self.max_pwatermark = max_pwatermark\n\n def __call__(self, x):\n try:\n if \"json\" in x:\n x_json = json.loads(x[\"json\"])\n filter_size = (x_json.get(WDS_JSON_WIDTH, 0.0) or 0.0) >= self.min_size and x_json.get(\n WDS_JSON_HEIGHT, 0\n ) >= self.min_size\n filter_watermark = (x_json.get(\"pwatermark\", 0.0) or 0.0) <= self.max_pwatermark\n return filter_size and filter_watermark\n else:\n return False\n except Exception:\n return False\n\n\nclass SDXLText2ImageDataset:\n def __init__(\n self,\n train_shards_path_or_url: Union[str, List[str]],\n num_train_examples: int,\n per_gpu_batch_size: int,\n global_batch_size: int,\n num_workers: int,\n resolution: int = 1024,\n interpolation_type: str = \"bilinear\",\n shuffle_buffer_size: int = 1000,\n pin_memory: bool = False,\n persistent_workers: bool = False,\n use_fix_crop_and_size: bool = False,\n ):\n if not isinstance(train_shards_path_or_url, str):\n train_shards_path_or_url = [list(braceexpand(urls)) for urls in train_shards_path_or_url]\n # flatten list using itertools\n train_shards_path_or_url = list(itertools.chain.from_iterable(train_shards_path_or_url))\n\n def get_orig_size(json):\n if use_fix_crop_and_size:\n return (resolution, resolution)\n else:\n return (int(json.get(WDS_JSON_WIDTH, 0.0)), int(json.get(WDS_JSON_HEIGHT, 0.0)))\n\n interpolation_mode = resolve_interpolation_mode(interpolation_type)\n\n def transform(example):\n # resize image\n image = example[\"image\"]\n image = TF.resize(image, resolution, interpolation=interpolation_mode)\n\n # get crop coordinates and crop image\n c_top, c_left, _, _ = transforms.RandomCrop.get_params(image, output_size=(resolution, resolution))\n image = TF.crop(image, c_top, c_left, resolution, resolution)\n image = TF.to_tensor(image)\n image = TF.normalize(image, [0.5], [0.5])\n\n example[\"image\"] = image\n example[\"crop_coords\"] = (c_top, c_left) if not use_fix_crop_and_size else (0, 0)\n return example\n\n processing_pipeline = [\n wds.decode(\"pil\", handler=wds.ignore_and_continue),\n wds.rename(\n image=\"jpg;png;jpeg;webp\", text=\"text;txt;caption\", orig_size=\"json\", handler=wds.warn_and_continue\n ),\n wds.map(filter_keys({\"image\", \"text\", \"orig_size\"})),\n wds.map_dict(orig_size=get_orig_size),\n wds.map(transform),\n wds.to_tuple(\"image\", \"text\", \"orig_size\", \"crop_coords\"),\n ]\n\n # Create train dataset and loader\n pipeline = [\n wds.ResampledShards(train_shards_path_or_url),\n tarfile_to_samples_nothrow,\n wds.select(WebdatasetFilter(min_size=MIN_SIZE)),\n wds.shuffle(shuffle_buffer_size),\n *processing_pipeline,\n wds.batched(per_gpu_batch_size, partial=False, collation_fn=default_collate),\n ]\n\n num_worker_batches = math.ceil(num_train_examples / (global_batch_size * num_workers)) # per dataloader worker\n num_batches = num_worker_batches * num_workers\n num_samples = num_batches * global_batch_size\n\n # each worker is iterating over this\n self._train_dataset = wds.DataPipeline(*pipeline).with_epoch(num_worker_batches)\n self._train_dataloader = wds.WebLoader(\n self._train_dataset,\n batch_size=None,\n shuffle=False,\n num_workers=num_workers,\n pin_memory=pin_memory,\n persistent_workers=persistent_workers,\n )\n # add meta-data to dataloader instance for convenience\n self._train_dataloader.num_batches = num_batches\n self._train_dataloader.num_samples = num_samples\n\n @property\n def train_dataset(self):\n return self._train_dataset\n\n @property\n def train_dataloader(self):\n return self._train_dataloader\n\n\ndef log_validation(vae, unet, args, accelerator, weight_dtype, step, name=\"target\"):\n logger.info(\"Running validation... \")\n\n unet = accelerator.unwrap_model(unet)\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n args.pretrained_teacher_model,\n vae=vae,\n unet=unet,\n scheduler=LCMScheduler.from_pretrained(args.pretrained_teacher_model, subfolder=\"scheduler\"),\n revision=args.revision,\n torch_dtype=weight_dtype,\n )\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n if args.enable_xformers_memory_efficient_attention:\n pipeline.enable_xformers_memory_efficient_attention()\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n validation_prompts = [\n \"portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography\",\n \"Self-portrait oil painting, a beautiful cyborg with golden hair, 8k\",\n \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\",\n \"A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece\",\n ]\n\n image_logs = []\n\n for _, prompt in enumerate(validation_prompts):\n images = []\n if torch.backends.mps.is_available():\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type)\n\n with autocast_ctx:\n images = pipeline(\n prompt=prompt,\n num_inference_steps=4,\n num_images_per_prompt=4,\n generator=generator,\n ).images\n image_logs.append({\"validation_prompt\": prompt, \"images\": images})\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n for log in image_logs:\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n formatted_images = []\n for image in images:\n formatted_images.append(np.asarray(image))\n\n formatted_images = np.stack(formatted_images)\n\n tracker.writer.add_images(validation_prompt, formatted_images, step, dataformats=\"NHWC\")\n elif tracker.name == \"wandb\":\n formatted_images = []\n\n for log in image_logs:\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n for image in images:\n image = wandb.Image(image, caption=validation_prompt)\n formatted_images.append(image)\n\n tracker.log({f\"validation/{name}\": formatted_images})\n else:\n logger.warning(f\"image logging not implemented for {tracker.name}\")\n\n del pipeline\n gc.collect()\n torch.cuda.empty_cache()\n\n return image_logs\n\n\ndef append_dims(x, target_dims):\n \"\"\"Appends dimensions to the end of a tensor until it has target_dims dimensions.\"\"\"\n dims_to_append = target_dims - x.ndim\n if dims_to_append < 0:\n raise ValueError(f\"input has {x.ndim} dims but target_dims is {target_dims}, which is less\")\n return x[(...,) + (None,) * dims_to_append]\n\n\n# From LCMScheduler.get_scalings_for_boundary_condition_discrete\ndef scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0):\n scaled_timestep = timestep_scaling * timestep\n c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2)\n c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5\n return c_skip, c_out\n\n\n# Compare LCMScheduler.step, Step 4\ndef get_predicted_original_sample(model_output, timesteps, sample, prediction_type, alphas, sigmas):\n alphas = extract_into_tensor(alphas, timesteps, sample.shape)\n sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)\n if prediction_type == \"epsilon\":\n pred_x_0 = (sample - sigmas * model_output) / alphas\n elif prediction_type == \"sample\":\n pred_x_0 = model_output\n elif prediction_type == \"v_prediction\":\n pred_x_0 = alphas * sample - sigmas * model_output\n else:\n raise ValueError(\n f\"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`\"\n f\" are supported.\"\n )\n\n return pred_x_0\n\n\n# Based on step 4 in DDIMScheduler.step\ndef get_predicted_noise(model_output, timesteps, sample, prediction_type, alphas, sigmas):\n alphas = extract_into_tensor(alphas, timesteps, sample.shape)\n sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)\n if prediction_type == \"epsilon\":\n pred_epsilon = model_output\n elif prediction_type == \"sample\":\n pred_epsilon = (sample - alphas * model_output) / sigmas\n elif prediction_type == \"v_prediction\":\n pred_epsilon = alphas * model_output + sigmas * sample\n else:\n raise ValueError(\n f\"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`\"\n f\" are supported.\"\n )\n\n return pred_epsilon\n\n\ndef extract_into_tensor(a, t, x_shape):\n b, *_ = t.shape\n out = a.gather(-1, t)\n return out.reshape(b, *((1,) * (len(x_shape) - 1)))\n\n\n@torch.no_grad()\ndef update_ema(target_params, source_params, rate=0.99):\n \"\"\"\n Update target parameters to be closer to those of source parameters using\n an exponential moving average.\n\n :param target_params: the target parameter sequence.\n :param source_params: the source parameter sequence.\n :param rate: the EMA rate (closer to 1 means slower).\n \"\"\"\n for targ, src in zip(target_params, source_params):\n targ.detach().mul_(rate).add_(src, alpha=1 - rate)\n\n\n# From LatentConsistencyModel.get_guidance_scale_embedding\ndef guidance_scale_embedding(w, embedding_dim=512, dtype=torch.float32):\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n timesteps (`torch.Tensor`):\n generate embedding vectors at these timesteps\n embedding_dim (`int`, *optional*, defaults to 512):\n dimension of the embeddings to generate\n dtype:\n data type of the generated embeddings\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n\nclass DDIMSolver:\n def __init__(self, alpha_cumprods, timesteps=1000, ddim_timesteps=50):\n # DDIM sampling parameters\n step_ratio = timesteps // ddim_timesteps\n\n self.ddim_timesteps = (np.arange(1, ddim_timesteps + 1) * step_ratio).round().astype(np.int64) - 1\n self.ddim_alpha_cumprods = alpha_cumprods[self.ddim_timesteps]\n self.ddim_alpha_cumprods_prev = np.asarray(\n [alpha_cumprods[0]] + alpha_cumprods[self.ddim_timesteps[:-1]].tolist()\n )\n # convert to torch tensors\n self.ddim_timesteps = torch.from_numpy(self.ddim_timesteps).long()\n self.ddim_alpha_cumprods = torch.from_numpy(self.ddim_alpha_cumprods)\n self.ddim_alpha_cumprods_prev = torch.from_numpy(self.ddim_alpha_cumprods_prev)\n\n def to(self, device):\n self.ddim_timesteps = self.ddim_timesteps.to(device)\n self.ddim_alpha_cumprods = self.ddim_alpha_cumprods.to(device)\n self.ddim_alpha_cumprods_prev = self.ddim_alpha_cumprods_prev.to(device)\n return self\n\n def ddim_step(self, pred_x0, pred_noise, timestep_index):\n alpha_cumprod_prev = extract_into_tensor(self.ddim_alpha_cumprods_prev, timestep_index, pred_x0.shape)\n dir_xt = (1.0 - alpha_cumprod_prev).sqrt() * pred_noise\n x_prev = alpha_cumprod_prev.sqrt() * pred_x0 + dir_xt\n return x_prev\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n # ----------Model Checkpoint Loading Arguments----------\n parser.add_argument(\n \"--pretrained_teacher_model\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained LDM teacher model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_vae_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.\",\n )\n parser.add_argument(\n \"--teacher_revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained LDM teacher model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained LDM model identifier from huggingface.co/models.\",\n )\n # ----------Training Arguments----------\n # ----General Training Arguments----\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"lcm-xl-distilled\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n # ----Logging----\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n # ----Checkpointing----\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n # ----Image Processing----\n parser.add_argument(\n \"--train_shards_path_or_url\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=1024,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--interpolation_type\",\n type=str,\n default=\"bilinear\",\n help=(\n \"The interpolation function used when resizing images to the desired resolution. Choose between `bilinear`,\"\n \" `bicubic`, `box`, `nearest`, `nearest_exact`, `hamming`, and `lanczos`.\"\n ),\n )\n parser.add_argument(\n \"--use_fix_crop_and_size\",\n action=\"store_true\",\n help=\"Whether or not to use the fixed crop and size for the teacher model.\",\n default=False,\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n # ----Dataloader----\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n # ----Batch Size and Training Steps----\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n # ----Learning Rate----\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n # ----Optimizer (Adam)----\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n # ----Diffusion Training Arguments----\n parser.add_argument(\n \"--proportion_empty_prompts\",\n type=float,\n default=0,\n help=\"Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).\",\n )\n # ----Latent Consistency Distillation (LCD) Specific Arguments----\n parser.add_argument(\n \"--w_min\",\n type=float,\n default=3.0,\n required=False,\n help=(\n \"The minimum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG\"\n \" formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as\"\n \" compared to the original paper.\"\n ),\n )\n parser.add_argument(\n \"--w_max\",\n type=float,\n default=15.0,\n required=False,\n help=(\n \"The maximum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG\"\n \" formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as\"\n \" compared to the original paper.\"\n ),\n )\n parser.add_argument(\n \"--num_ddim_timesteps\",\n type=int,\n default=50,\n help=\"The number of timesteps to use for DDIM sampling.\",\n )\n parser.add_argument(\n \"--loss_type\",\n type=str,\n default=\"l2\",\n choices=[\"l2\", \"huber\"],\n help=\"The type of loss to use for the LCD loss.\",\n )\n parser.add_argument(\n \"--huber_c\",\n type=float,\n default=0.001,\n help=\"The huber loss parameter. Only used if `--loss_type=huber`.\",\n )\n parser.add_argument(\n \"--unet_time_cond_proj_dim\",\n type=int,\n default=256,\n help=(\n \"The dimension of the guidance scale embedding in the U-Net, which will be used if the teacher U-Net\"\n \" does not have `time_cond_proj_dim` set.\"\n ),\n )\n parser.add_argument(\n \"--vae_encode_batch_size\",\n type=int,\n default=8,\n required=False,\n help=(\n \"The batch size used when encoding (and decoding) images to latents (and vice versa) using the VAE.\"\n \" Encoding or decoding the whole batch at once may run into OOM issues.\"\n ),\n )\n parser.add_argument(\n \"--timestep_scaling_factor\",\n type=float,\n default=10.0,\n help=(\n \"The multiplicative timestep scaling factor used when calculating the boundary scalings for LCM. The\"\n \" higher the scaling is, the lower the approximation error, but the default value of 10.0 should typically\"\n \" suffice.\"\n ),\n )\n # ----Exponential Moving Average (EMA)----\n parser.add_argument(\n \"--ema_decay\",\n type=float,\n default=0.95,\n required=False,\n help=\"The exponential moving average (EMA) rate or decay factor.\",\n )\n # ----Mixed Precision----\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--cast_teacher_unet\",\n action=\"store_true\",\n help=\"Whether to cast the teacher U-Net to the precision specified by `--mixed_precision`.\",\n )\n # ----Training Optimizations----\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n # ----Distributed Training----\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n # ----------Validation Arguments----------\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=200,\n help=\"Run validation every X steps.\",\n )\n # ----------Huggingface Hub Arguments-----------\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n # ----------Accelerate Arguments----------\n parser.add_argument(\n \"--tracker_project_name\",\n type=str,\n default=\"text2image-fine-tune\",\n help=(\n \"The `project_name` argument passed to Accelerator.init_trackers for\"\n \" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator\"\n ),\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:\n raise ValueError(\"`--proportion_empty_prompts` must be in the range [0, 1].\")\n\n return args\n\n\n# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt\ndef encode_prompt(prompt_batch, text_encoders, tokenizers, proportion_empty_prompts, is_train=True):\n prompt_embeds_list = []\n\n captions = []\n for caption in prompt_batch:\n if random.random() < proportion_empty_prompts:\n captions.append(\"\")\n elif isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n\n with torch.no_grad():\n for tokenizer, text_encoder in zip(tokenizers, text_encoders):\n text_inputs = tokenizer(\n captions,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n prompt_embeds = text_encoder(\n text_input_ids.to(text_encoder.device),\n output_hidden_states=True,\n )\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds.hidden_states[-2]\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)\n return prompt_embeds, pooled_prompt_embeds\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n split_batches=True, # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be devide by the number of processes assuming batches are multiplied by the number of processes\n )\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name,\n exist_ok=True,\n token=args.hub_token,\n private=True,\n ).repo_id\n\n # 1. Create the noise scheduler and the desired noise schedule.\n noise_scheduler = DDPMScheduler.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"scheduler\", revision=args.teacher_revision\n )\n\n # DDPMScheduler calculates the alpha and sigma noise schedules (based on the alpha bars) for us\n alpha_schedule = torch.sqrt(noise_scheduler.alphas_cumprod)\n sigma_schedule = torch.sqrt(1 - noise_scheduler.alphas_cumprod)\n # Initialize the DDIM ODE solver for distillation.\n solver = DDIMSolver(\n noise_scheduler.alphas_cumprod.numpy(),\n timesteps=noise_scheduler.config.num_train_timesteps,\n ddim_timesteps=args.num_ddim_timesteps,\n )\n\n # 2. Load tokenizers from SD-XL checkpoint.\n tokenizer_one = AutoTokenizer.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"tokenizer\", revision=args.teacher_revision, use_fast=False\n )\n tokenizer_two = AutoTokenizer.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"tokenizer_2\", revision=args.teacher_revision, use_fast=False\n )\n\n # 3. Load text encoders from SD-XL checkpoint.\n # import correct text encoder classes\n text_encoder_cls_one = import_model_class_from_model_name_or_path(\n args.pretrained_teacher_model, args.teacher_revision\n )\n text_encoder_cls_two = import_model_class_from_model_name_or_path(\n args.pretrained_teacher_model, args.teacher_revision, subfolder=\"text_encoder_2\"\n )\n\n text_encoder_one = text_encoder_cls_one.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"text_encoder\", revision=args.teacher_revision\n )\n text_encoder_two = text_encoder_cls_two.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"text_encoder_2\", revision=args.teacher_revision\n )\n\n # 4. Load VAE from SD-XL checkpoint (or more stable VAE)\n vae_path = (\n args.pretrained_teacher_model\n if args.pretrained_vae_model_name_or_path is None\n else args.pretrained_vae_model_name_or_path\n )\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.teacher_revision,\n )\n\n # 5. Load teacher U-Net from SD-XL checkpoint\n teacher_unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_teacher_model, subfolder=\"unet\", revision=args.teacher_revision\n )\n\n # 6. Freeze teacher vae, text_encoders, and teacher_unet\n vae.requires_grad_(False)\n text_encoder_one.requires_grad_(False)\n text_encoder_two.requires_grad_(False)\n teacher_unet.requires_grad_(False)\n\n # 7. Create online student U-Net. This will be updated by the optimizer (e.g. via backpropagation.)\n # Add `time_cond_proj_dim` to the student U-Net if `teacher_unet.config.time_cond_proj_dim` is None\n time_cond_proj_dim = (\n teacher_unet.config.time_cond_proj_dim\n if teacher_unet.config.time_cond_proj_dim is not None\n else args.unet_time_cond_proj_dim\n )\n unet = UNet2DConditionModel.from_config(teacher_unet.config, time_cond_proj_dim=time_cond_proj_dim)\n # load teacher_unet weights into unet\n unet.load_state_dict(teacher_unet.state_dict(), strict=False)\n unet.train()\n\n # 8. Create target student U-Net. This will be updated via EMA updates (polyak averaging).\n # Initialize from (online) unet\n target_unet = UNet2DConditionModel.from_config(unet.config)\n target_unet.load_state_dict(unet.state_dict())\n target_unet.train()\n target_unet.requires_grad_(False)\n\n # Check that all trainable models are in full precision\n low_precision_error_string = (\n \" Please make sure to always have all model weights in full float32 precision when starting training - even if\"\n \" doing mixed precision training, copy of the weights should still be float32.\"\n )\n\n if accelerator.unwrap_model(unet).dtype != torch.float32:\n raise ValueError(\n f\"Controlnet loaded as datatype {accelerator.unwrap_model(unet).dtype}. {low_precision_error_string}\"\n )\n\n # 9. Handle mixed precision and device placement\n # For mixed precision training we cast all non-trainable weigths to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move unet, vae and text_encoder to device and cast to weight_dtype\n # The VAE is in float32 to avoid NaN losses.\n vae.to(accelerator.device)\n if args.pretrained_vae_model_name_or_path is not None:\n vae.to(dtype=weight_dtype)\n text_encoder_one.to(accelerator.device, dtype=weight_dtype)\n text_encoder_two.to(accelerator.device, dtype=weight_dtype)\n target_unet.to(accelerator.device)\n # Move teacher_unet to device, optionally cast to weight_dtype\n teacher_unet.to(accelerator.device)\n if args.cast_teacher_unet:\n teacher_unet.to(dtype=weight_dtype)\n\n # Also move the alpha and sigma noise schedules to accelerator.device.\n alpha_schedule = alpha_schedule.to(accelerator.device)\n sigma_schedule = sigma_schedule.to(accelerator.device)\n # Move the ODE solver to accelerator.device.\n solver = solver.to(accelerator.device)\n\n # 10. Handle saving and loading of checkpoints\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n target_unet.save_pretrained(os.path.join(output_dir, \"unet_target\"))\n\n for i, model in enumerate(models):\n model.save_pretrained(os.path.join(output_dir, \"unet\"))\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n def load_model_hook(models, input_dir):\n load_model = UNet2DConditionModel.from_pretrained(os.path.join(input_dir, \"unet_target\"))\n target_unet.load_state_dict(load_model.state_dict())\n target_unet.to(accelerator.device)\n del load_model\n\n for i in range(len(models)):\n # pop models so that they are not loaded again\n model = models.pop()\n\n # load diffusers style into model\n load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder=\"unet\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # 11. Enable optimizations\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n teacher_unet.enable_xformers_memory_efficient_attention()\n target_unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # 12. Optimizer creation\n optimizer = optimizer_class(\n unet.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # 13. Dataset creation and data processing\n # Here, we compute not just the text embeddings but also the additional embeddings\n # needed for the SD XL UNet to operate.\n def compute_embeddings(\n prompt_batch, original_sizes, crop_coords, proportion_empty_prompts, text_encoders, tokenizers, is_train=True\n ):\n target_size = (args.resolution, args.resolution)\n original_sizes = list(map(list, zip(*original_sizes)))\n crops_coords_top_left = list(map(list, zip(*crop_coords)))\n\n original_sizes = torch.tensor(original_sizes, dtype=torch.long)\n crops_coords_top_left = torch.tensor(crops_coords_top_left, dtype=torch.long)\n\n prompt_embeds, pooled_prompt_embeds = encode_prompt(\n prompt_batch, text_encoders, tokenizers, proportion_empty_prompts, is_train\n )\n add_text_embeds = pooled_prompt_embeds\n\n # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids\n add_time_ids = list(target_size)\n add_time_ids = torch.tensor([add_time_ids])\n add_time_ids = add_time_ids.repeat(len(prompt_batch), 1)\n add_time_ids = torch.cat([original_sizes, crops_coords_top_left, add_time_ids], dim=-1)\n add_time_ids = add_time_ids.to(accelerator.device, dtype=prompt_embeds.dtype)\n\n prompt_embeds = prompt_embeds.to(accelerator.device)\n add_text_embeds = add_text_embeds.to(accelerator.device)\n unet_added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n\n return {\"prompt_embeds\": prompt_embeds, **unet_added_cond_kwargs}\n\n dataset = SDXLText2ImageDataset(\n train_shards_path_or_url=args.train_shards_path_or_url,\n num_train_examples=args.max_train_samples,\n per_gpu_batch_size=args.train_batch_size,\n global_batch_size=args.train_batch_size * accelerator.num_processes,\n num_workers=args.dataloader_num_workers,\n resolution=args.resolution,\n interpolation_type=args.interpolation_type,\n shuffle_buffer_size=1000,\n pin_memory=True,\n persistent_workers=True,\n use_fix_crop_and_size=args.use_fix_crop_and_size,\n )\n train_dataloader = dataset.train_dataloader\n\n # Let's first compute all the embeddings so that we can free up the text encoders\n # from memory.\n text_encoders = [text_encoder_one, text_encoder_two]\n tokenizers = [tokenizer_one, tokenizer_two]\n\n compute_embeddings_fn = functools.partial(\n compute_embeddings,\n proportion_empty_prompts=0,\n text_encoders=text_encoders,\n tokenizers=tokenizers,\n )\n\n # 14. LR Scheduler creation\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps,\n num_training_steps=args.max_train_steps,\n )\n\n # 15. Prepare for training\n # Prepare everything with our `accelerator`.\n unet, optimizer, lr_scheduler = accelerator.prepare(unet, optimizer, lr_scheduler)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = dict(vars(args))\n accelerator.init_trackers(args.tracker_project_name, config=tracker_config)\n\n # Create uncond embeds for classifier free guidance\n uncond_prompt_embeds = torch.zeros(args.train_batch_size, 77, 2048).to(accelerator.device)\n uncond_pooled_prompt_embeds = torch.zeros(args.train_batch_size, 1280).to(accelerator.device)\n\n # 16. Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num batches each epoch = {train_dataloader.num_batches}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n for epoch in range(first_epoch, args.num_train_epochs):\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n # 1. Load and process the image, text, and micro-conditioning (original image size, crop coordinates)\n image, text, orig_size, crop_coords = batch\n\n image = image.to(accelerator.device, non_blocking=True)\n encoded_text = compute_embeddings_fn(text, orig_size, crop_coords)\n\n if args.pretrained_vae_model_name_or_path is not None:\n pixel_values = image.to(dtype=weight_dtype)\n if vae.dtype != weight_dtype:\n vae.to(dtype=weight_dtype)\n else:\n pixel_values = image\n\n # encode pixel values with batch size of at most args.vae_encode_batch_size\n latents = []\n for i in range(0, pixel_values.shape[0], args.vae_encode_batch_size):\n latents.append(vae.encode(pixel_values[i : i + args.vae_encode_batch_size]).latent_dist.sample())\n latents = torch.cat(latents, dim=0)\n\n latents = latents * vae.config.scaling_factor\n if args.pretrained_vae_model_name_or_path is None:\n latents = latents.to(weight_dtype)\n bsz = latents.shape[0]\n\n # 2. Sample a random timestep for each image t_n from the ODE solver timesteps without bias.\n # For the DDIM solver, the timestep schedule is [T - 1, T - k - 1, T - 2 * k - 1, ...]\n topk = noise_scheduler.config.num_train_timesteps // args.num_ddim_timesteps\n index = torch.randint(0, args.num_ddim_timesteps, (bsz,), device=latents.device).long()\n start_timesteps = solver.ddim_timesteps[index]\n timesteps = start_timesteps - topk\n timesteps = torch.where(timesteps < 0, torch.zeros_like(timesteps), timesteps)\n\n # 3. Get boundary scalings for start_timesteps and (end) timesteps.\n c_skip_start, c_out_start = scalings_for_boundary_conditions(\n start_timesteps, timestep_scaling=args.timestep_scaling_factor\n )\n c_skip_start, c_out_start = [append_dims(x, latents.ndim) for x in [c_skip_start, c_out_start]]\n c_skip, c_out = scalings_for_boundary_conditions(\n timesteps, timestep_scaling=args.timestep_scaling_factor\n )\n c_skip, c_out = [append_dims(x, latents.ndim) for x in [c_skip, c_out]]\n\n # 4. Sample noise from the prior and add it to the latents according to the noise magnitude at each\n # timestep (this is the forward diffusion process) [z_{t_{n + k}} in Algorithm 1]\n noise = torch.randn_like(latents)\n noisy_model_input = noise_scheduler.add_noise(latents, noise, start_timesteps)\n\n # 5. Sample a random guidance scale w from U[w_min, w_max] and embed it\n w = (args.w_max - args.w_min) * torch.rand((bsz,)) + args.w_min\n w_embedding = guidance_scale_embedding(w, embedding_dim=time_cond_proj_dim)\n w = w.reshape(bsz, 1, 1, 1)\n # Move to U-Net device and dtype\n w = w.to(device=latents.device, dtype=latents.dtype)\n w_embedding = w_embedding.to(device=latents.device, dtype=latents.dtype)\n\n # 6. Prepare prompt embeds and unet_added_conditions\n prompt_embeds = encoded_text.pop(\"prompt_embeds\")\n\n # 7. Get online LCM prediction on z_{t_{n + k}} (noisy_model_input), w, c, t_{n + k} (start_timesteps)\n noise_pred = unet(\n noisy_model_input,\n start_timesteps,\n timestep_cond=w_embedding,\n encoder_hidden_states=prompt_embeds.float(),\n added_cond_kwargs=encoded_text,\n ).sample\n\n pred_x_0 = get_predicted_original_sample(\n noise_pred,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n\n model_pred = c_skip_start * noisy_model_input + c_out_start * pred_x_0\n\n # 8. Compute the conditional and unconditional teacher model predictions to get CFG estimates of the\n # predicted noise eps_0 and predicted original sample x_0, then run the ODE solver using these\n # estimates to predict the data point in the augmented PF-ODE trajectory corresponding to the next ODE\n # solver timestep.\n with torch.no_grad():\n if torch.backends.mps.is_available():\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type)\n\n with autocast_ctx:\n # 1. Get teacher model prediction on noisy_model_input z_{t_{n + k}} and conditional embedding c\n cond_teacher_output = teacher_unet(\n noisy_model_input.to(weight_dtype),\n start_timesteps,\n encoder_hidden_states=prompt_embeds.to(weight_dtype),\n added_cond_kwargs={k: v.to(weight_dtype) for k, v in encoded_text.items()},\n ).sample\n cond_pred_x0 = get_predicted_original_sample(\n cond_teacher_output,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n cond_pred_noise = get_predicted_noise(\n cond_teacher_output,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n\n # 2. Get teacher model prediction on noisy_model_input z_{t_{n + k}} and unconditional embedding 0\n uncond_added_conditions = copy.deepcopy(encoded_text)\n uncond_added_conditions[\"text_embeds\"] = uncond_pooled_prompt_embeds\n uncond_teacher_output = teacher_unet(\n noisy_model_input.to(weight_dtype),\n start_timesteps,\n encoder_hidden_states=uncond_prompt_embeds.to(weight_dtype),\n added_cond_kwargs={k: v.to(weight_dtype) for k, v in uncond_added_conditions.items()},\n ).sample\n uncond_pred_x0 = get_predicted_original_sample(\n uncond_teacher_output,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n uncond_pred_noise = get_predicted_noise(\n uncond_teacher_output,\n start_timesteps,\n noisy_model_input,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n\n # 3. Calculate the CFG estimate of x_0 (pred_x0) and eps_0 (pred_noise)\n # Note that this uses the LCM paper's CFG formulation rather than the Imagen CFG formulation\n pred_x0 = cond_pred_x0 + w * (cond_pred_x0 - uncond_pred_x0)\n pred_noise = cond_pred_noise + w * (cond_pred_noise - uncond_pred_noise)\n # 4. Run one step of the ODE solver to estimate the next point x_prev on the\n # augmented PF-ODE trajectory (solving backward in time)\n # Note that the DDIM step depends on both the predicted x_0 and source noise eps_0.\n x_prev = solver.ddim_step(pred_x0, pred_noise, index)\n\n # 9. Get target LCM prediction on x_prev, w, c, t_n (timesteps)\n with torch.no_grad():\n if torch.backends.mps.is_available():\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type, dtype=weight_dtype)\n\n with autocast_ctx:\n target_noise_pred = target_unet(\n x_prev.float(),\n timesteps,\n timestep_cond=w_embedding,\n encoder_hidden_states=prompt_embeds.float(),\n added_cond_kwargs=encoded_text,\n ).sample\n pred_x_0 = get_predicted_original_sample(\n target_noise_pred,\n timesteps,\n x_prev,\n noise_scheduler.config.prediction_type,\n alpha_schedule,\n sigma_schedule,\n )\n target = c_skip * x_prev + c_out * pred_x_0\n\n # 10. Calculate loss\n if args.loss_type == \"l2\":\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n elif args.loss_type == \"huber\":\n loss = torch.mean(\n torch.sqrt((model_pred.float() - target.float()) ** 2 + args.huber_c**2) - args.huber_c\n )\n\n # 11. Backpropagate on the online student model (`unet`)\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad(set_to_none=True)\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n # 12. Make EMA update to target student model parameters (`target_unet`)\n update_ema(target_unet.parameters(), unet.parameters(), args.ema_decay)\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n if global_step % args.validation_steps == 0:\n log_validation(vae, target_unet, args, accelerator, weight_dtype, global_step, \"target\")\n log_validation(vae, unet, args, accelerator, weight_dtype, global_step, \"online\")\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n # Create the pipeline using using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = accelerator.unwrap_model(unet)\n unet.save_pretrained(os.path.join(args.output_dir, \"unet\"))\n\n target_unet = accelerator.unwrap_model(target_unet)\n target_unet.save_pretrained(os.path.join(args.output_dir, \"unet_target\"))\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/controlnet/README.md", "content": "# ControlNet training example\n\n[Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543) by Lvmin Zhang and Maneesh Agrawala.\n\nThis example is based on the [training example in the original ControlNet repository](https://github.com/lllyasviel/ControlNet/blob/main/docs/train.md). It trains a ControlNet to fill circles using a [small synthetic dataset](https://huggingface.co/datasets/fusing/fill50k).\n\n## Installing the dependencies\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n**Important**\n\nTo make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install -e .\n```\n\nThen cd in the example folder and run\n```bash\npip install -r requirements.txt\n```\n\nAnd initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\n\nOr for a default accelerate configuration without answering questions about your environment\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell e.g. a notebook\n\n```python\nfrom accelerate.utils import write_basic_config\nwrite_basic_config()\n```\n\n## Circle filling dataset\n\nThe original dataset is hosted in the [ControlNet repo](https://huggingface.co/lllyasviel/ControlNet/blob/main/training/fill50k.zip). We re-uploaded it to be compatible with `datasets` [here](https://huggingface.co/datasets/fusing/fill50k). Note that `datasets` handles dataloading within the training script.\n\nOur training examples use [Stable Diffusion 1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) as the original set of ControlNet models were trained from it. However, ControlNet can be trained to augment any Stable Diffusion compatible model (such as [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4)) or [stabilityai/stable-diffusion-2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1).\n\n## Training\n\nOur training examples use two test conditioning images. They can be downloaded by running\n\n```sh\nwget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png\n\nwget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png\n```\n\n\n```bash\nexport MODEL_DIR=\"runwayml/stable-diffusion-v1-5\"\nexport OUTPUT_DIR=\"path to save model\"\n\naccelerate launch train_controlnet.py \\\n --pretrained_model_name_or_path=$MODEL_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --dataset_name=fusing/fill50k \\\n --resolution=512 \\\n --learning_rate=1e-5 \\\n --validation_image \"./conditioning_image_1.png\" \"./conditioning_image_2.png\" \\\n --validation_prompt \"red circle with blue background\" \"cyan circle with brown floral background\" \\\n --train_batch_size=4\n```\n\nThis default configuration requires ~38GB VRAM.\n\nBy default, the training script logs outputs to tensorboard. Pass `--report_to wandb` to use weights and\nbiases.\n\nGradient accumulation with a smaller batch size can be used to reduce training requirements to ~20 GB VRAM.\n\n```bash\nexport MODEL_DIR=\"runwayml/stable-diffusion-v1-5\"\nexport OUTPUT_DIR=\"path to save model\"\n\naccelerate launch train_controlnet.py \\\n --pretrained_model_name_or_path=$MODEL_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --dataset_name=fusing/fill50k \\\n --resolution=512 \\\n --learning_rate=1e-5 \\\n --validation_image \"./conditioning_image_1.png\" \"./conditioning_image_2.png\" \\\n --validation_prompt \"red circle with blue background\" \"cyan circle with brown floral background\" \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4\n```\n\n## Training with multiple GPUs\n\n`accelerate` allows for seamless multi-GPU training. Follow the instructions [here](https://huggingface.co/docs/accelerate/basic_tutorials/launch)\nfor running distributed training with `accelerate`. Here is an example command:\n\n```bash\nexport MODEL_DIR=\"runwayml/stable-diffusion-v1-5\"\nexport OUTPUT_DIR=\"path to save model\"\n\naccelerate launch --mixed_precision=\"fp16\" --multi_gpu train_controlnet.py \\\n --pretrained_model_name_or_path=$MODEL_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --dataset_name=fusing/fill50k \\\n --resolution=512 \\\n --learning_rate=1e-5 \\\n --validation_image \"./conditioning_image_1.png\" \"./conditioning_image_2.png\" \\\n --validation_prompt \"red circle with blue background\" \"cyan circle with brown floral background\" \\\n --train_batch_size=4 \\\n --mixed_precision=\"fp16\" \\\n --tracker_project_name=\"controlnet-demo\" \\\n --report_to=wandb\n```\n\n## Example results\n\n#### After 300 steps with batch size 8\n\n| | |\n|-------------------|:-------------------------:|\n| | red circle with blue background |\n![conditioning image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png) | ![red circle with blue background](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/red_circle_with_blue_background_300_steps.png) |\n| | cyan circle with brown floral background |\n![conditioning image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png) | ![cyan circle with brown floral background](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/cyan_circle_with_brown_floral_background_300_steps.png) |\n\n\n#### After 6000 steps with batch size 8:\n\n| | |\n|-------------------|:-------------------------:|\n| | red circle with blue background |\n![conditioning image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png) | ![red circle with blue background](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/red_circle_with_blue_background_6000_steps.png) |\n| | cyan circle with brown floral background |\n![conditioning image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png) | ![cyan circle with brown floral background](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/cyan_circle_with_brown_floral_background_6000_steps.png) |\n\n## Training on a 16 GB GPU\n\nOptimizations:\n- Gradient checkpointing\n- bitsandbyte's 8-bit optimizer\n\n[bitandbytes install instructions](https://github.com/TimDettmers/bitsandbytes#requirements--installation).\n\n```bash\nexport MODEL_DIR=\"runwayml/stable-diffusion-v1-5\"\nexport OUTPUT_DIR=\"path to save model\"\n\naccelerate launch train_controlnet.py \\\n --pretrained_model_name_or_path=$MODEL_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --dataset_name=fusing/fill50k \\\n --resolution=512 \\\n --learning_rate=1e-5 \\\n --validation_image \"./conditioning_image_1.png\" \"./conditioning_image_2.png\" \\\n --validation_prompt \"red circle with blue background\" \"cyan circle with brown floral background\" \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --use_8bit_adam\n```\n\n## Training on a 12 GB GPU\n\nOptimizations:\n- Gradient checkpointing\n- bitsandbyte's 8-bit optimizer\n- xformers\n- set grads to none\n\n```bash\nexport MODEL_DIR=\"runwayml/stable-diffusion-v1-5\"\nexport OUTPUT_DIR=\"path to save model\"\n\naccelerate launch train_controlnet.py \\\n --pretrained_model_name_or_path=$MODEL_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --dataset_name=fusing/fill50k \\\n --resolution=512 \\\n --learning_rate=1e-5 \\\n --validation_image \"./conditioning_image_1.png\" \"./conditioning_image_2.png\" \\\n --validation_prompt \"red circle with blue background\" \"cyan circle with brown floral background\" \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --use_8bit_adam \\\n --enable_xformers_memory_efficient_attention \\\n --set_grads_to_none\n```\n\nWhen using `enable_xformers_memory_efficient_attention`, please make sure to install `xformers` by `pip install xformers`.\n\n## Training on an 8 GB GPU\n\nWe have not exhaustively tested DeepSpeed support for ControlNet. While the configuration does\nsave memory, we have not confirmed the configuration to train successfully. You will very likely\nhave to make changes to the config to have a successful training run.\n\nOptimizations:\n- Gradient checkpointing\n- xformers\n- set grads to none\n- DeepSpeed stage 2 with parameter and optimizer offloading\n- fp16 mixed precision\n\n[DeepSpeed](https://www.deepspeed.ai/) can offload tensors from VRAM to either\nCPU or NVME. This requires significantly more RAM (about 25 GB).\n\nUse `accelerate config` to enable DeepSpeed stage 2.\n\nThe relevant parts of the resulting accelerate config file are\n\n```yaml\ncompute_environment: LOCAL_MACHINE\ndeepspeed_config:\n gradient_accumulation_steps: 4\n offload_optimizer_device: cpu\n offload_param_device: cpu\n zero3_init_flag: false\n zero_stage: 2\ndistributed_type: DEEPSPEED\n```\n\nSee [documentation](https://huggingface.co/docs/accelerate/usage_guides/deepspeed) for more DeepSpeed configuration options.\n\nChanging the default Adam optimizer to DeepSpeed's Adam\n`deepspeed.ops.adam.DeepSpeedCPUAdam` gives a substantial speedup but\nit requires CUDA toolchain with the same version as pytorch. 8-bit optimizer\ndoes not seem to be compatible with DeepSpeed at the moment.\n\n```bash\nexport MODEL_DIR=\"runwayml/stable-diffusion-v1-5\"\nexport OUTPUT_DIR=\"path to save model\"\n\naccelerate launch train_controlnet.py \\\n --pretrained_model_name_or_path=$MODEL_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --dataset_name=fusing/fill50k \\\n --resolution=512 \\\n --validation_image \"./conditioning_image_1.png\" \"./conditioning_image_2.png\" \\\n --validation_prompt \"red circle with blue background\" \"cyan circle with brown floral background\" \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --enable_xformers_memory_efficient_attention \\\n --set_grads_to_none \\\n --mixed_precision fp16\n```\n\n## Performing inference with the trained ControlNet\n\nThe trained model can be run the same as the original ControlNet pipeline with the newly trained ControlNet.\nSet `base_model_path` and `controlnet_path` to the values `--pretrained_model_name_or_path` and\n`--output_dir` were respectively set to in the training script.\n\n```py\nfrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\nfrom diffusers.utils import load_image\nimport torch\n\nbase_model_path = \"path to model\"\ncontrolnet_path = \"path to controlnet\"\n\ncontrolnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)\npipe = StableDiffusionControlNetPipeline.from_pretrained(\n base_model_path, controlnet=controlnet, torch_dtype=torch.float16\n)\n\n# speed up diffusion process with faster scheduler and memory optimization\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n# remove following line if xformers is not installed or when using Torch 2.0.\npipe.enable_xformers_memory_efficient_attention()\n# memory optimization.\npipe.enable_model_cpu_offload()\n\ncontrol_image = load_image(\"./conditioning_image_1.png\")\nprompt = \"pale golden rod circle with old lace background\"\n\n# generate image\ngenerator = torch.manual_seed(0)\nimage = pipe(\n prompt, num_inference_steps=20, generator=generator, image=control_image\n).images[0]\nimage.save(\"./output.png\")\n```\n\n## Training with Flax/JAX\n\nFor faster training on TPUs and GPUs you can leverage the flax training example. Follow the instructions above to get the model and dataset before running the script.\n\n### Running on Google Cloud TPU\n\nSee below for commands to set up a TPU VM(`--accelerator-type v4-8`). For more details about how to set up and use TPUs, refer to [Cloud docs for single VM setup](https://cloud.google.com/tpu/docs/run-calculation-jax).\n\nFirst create a single TPUv4-8 VM and connect to it:\n\n```\nZONE=us-central2-b\nTPU_TYPE=v4-8\nVM_NAME=hg_flax\n\ngcloud alpha compute tpus tpu-vm create $VM_NAME \\\n --zone $ZONE \\\n --accelerator-type $TPU_TYPE \\\n --version tpu-vm-v4-base\n\ngcloud alpha compute tpus tpu-vm ssh $VM_NAME --zone $ZONE -- \\\n```\n\nWhen connected install JAX `0.4.5`:\n\n```sh\npip install \"jax[tpu]==0.4.5\" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html\n```\n\nTo verify that JAX was correctly installed, you can run the following command:\n\n```py\nimport jax\njax.device_count()\n```\n\nThis should display the number of TPU cores, which should be 4 on a TPUv4-8 VM.\n\nThen install Diffusers and the library's training dependencies:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nThen cd in the example folder and run\n\n```bash\npip install -U -r requirements_flax.txt\n```\n\nIf you want to use Weights and Biases logging, you should also install `wandb` now\n\n```bash\npip install wandb\n```\n\n\nNow let's downloading two conditioning images that we will use to run validation during the training in order to track our progress\n\n```sh\nwget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png\nwget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png\n```\n\nWe encourage you to store or share your model with the community. To use huggingface hub, please login to your Hugging Face account, or ([create one](https://huggingface.co/docs/diffusers/main/en/training/hf.co/join) if you don’t have one already):\n\n```sh\nhuggingface-cli login\n```\n\nMake sure you have the `MODEL_DIR`,`OUTPUT_DIR` and `HUB_MODEL_ID` environment variables set. The `OUTPUT_DIR` and `HUB_MODEL_ID` variables specify where to save the model to on the Hub:\n\n```bash\nexport MODEL_DIR=\"runwayml/stable-diffusion-v1-5\"\nexport OUTPUT_DIR=\"runs/fill-circle-{timestamp}\"\nexport HUB_MODEL_ID=\"controlnet-fill-circle\"\n```\n\nAnd finally start the training\n\n```bash\npython3 train_controlnet_flax.py \\\n --pretrained_model_name_or_path=$MODEL_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --dataset_name=fusing/fill50k \\\n --resolution=512 \\\n --learning_rate=1e-5 \\\n --validation_image \"./conditioning_image_1.png\" \"./conditioning_image_2.png\" \\\n --validation_prompt \"red circle with blue background\" \"cyan circle with brown floral background\" \\\n --validation_steps=1000 \\\n --train_batch_size=2 \\\n --revision=\"non-ema\" \\\n --from_pt \\\n --report_to=\"wandb\" \\\n --tracker_project_name=$HUB_MODEL_ID \\\n --num_train_epochs=11 \\\n --push_to_hub \\\n --hub_model_id=$HUB_MODEL_ID\n ```\n\nSince we passed the `--push_to_hub` flag, it will automatically create a model repo under your huggingface account based on `$HUB_MODEL_ID`. By the end of training, the final checkpoint will be automatically stored on the hub. You can find an example model repo [here](https://huggingface.co/YiYiXu/fill-circle-controlnet).\n\nOur training script also provides limited support for streaming large datasets from the Hugging Face Hub. In order to enable streaming, one must also set `--max_train_samples`. Here is an example command (from [this blog article](https://huggingface.co/blog/train-your-controlnet)):\n\n```bash\nexport MODEL_DIR=\"runwayml/stable-diffusion-v1-5\"\nexport OUTPUT_DIR=\"runs/uncanny-faces-{timestamp}\"\nexport HUB_MODEL_ID=\"controlnet-uncanny-faces\"\n\npython3 train_controlnet_flax.py \\\n --pretrained_model_name_or_path=$MODEL_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --dataset_name=multimodalart/facesyntheticsspigacaptioned \\\n --streaming \\\n --conditioning_image_column=spiga_seg \\\n --image_column=image \\\n --caption_column=image_caption \\\n --resolution=512 \\\n --max_train_samples 100000 \\\n --learning_rate=1e-5 \\\n --train_batch_size=1 \\\n --revision=\"flax\" \\\n --report_to=\"wandb\" \\\n --tracker_project_name=$HUB_MODEL_ID\n```\n\nNote, however, that the performance of the TPUs might get bottlenecked as streaming with `datasets` is not optimized for images. For ensuring maximum throughput, we encourage you to explore the following options:\n\n* [Webdataset](https://webdataset.github.io/webdataset/)\n* [TorchData](https://github.com/pytorch/data)\n* [TensorFlow Datasets](https://www.tensorflow.org/datasets/tfless_tfds)\n\nWhen work with a larger dataset, you may need to run training process for a long time and it’s useful to save regular checkpoints during the process. You can use the following argument to enable intermediate checkpointing:\n\n```bash\n --checkpointing_steps=500\n```\nThis will save the trained model in subfolders of your output_dir. Subfolder names is the number of steps performed so far; for example: a checkpoint saved after 500 training steps would be saved in a subfolder named 500\n\nYou can then start your training from this saved checkpoint with\n\n```bash\n --controlnet_model_name_or_path=\"./control_out/500\"\n```\n\nWe support training with the Min-SNR weighting strategy proposed in [Efficient Diffusion Training via Min-SNR Weighting Strategy](https://arxiv.org/abs/2303.09556) which helps to achieve faster convergence by rebalancing the loss. To use it, one needs to set the `--snr_gamma` argument. The recommended value when using it is `5.0`.\n\nWe also support gradient accumulation - it is a technique that lets you use a bigger batch size than your machine would normally be able to fit into memory. You can use `gradient_accumulation_steps` argument to set gradient accumulation steps. The ControlNet author recommends using gradient accumulation to achieve better convergence. Read more [here](https://github.com/lllyasviel/ControlNet/blob/main/docs/train.md#more-consideration-sudden-converge-phenomenon-and-gradient-accumulation).\n\nYou can **profile your code** with:\n\n```bash\n --profile_steps==5\n```\n\nRefer to the [JAX documentation on profiling](https://jax.readthedocs.io/en/latest/profiling.html). To inspect the profile trace, you'll have to install and start Tensorboard with the profile plugin:\n\n```bash\npip install tensorflow tensorboard-plugin-profile\ntensorboard --logdir runs/fill-circle-100steps-20230411_165612/\n```\n\nThe profile can then be inspected at http://localhost:6006/#profile\n\nSometimes you'll get version conflicts (error messages like `Duplicate plugins for name projector`), which means that you have to uninstall and reinstall all versions of Tensorflow/Tensorboard (e.g. with `pip uninstall tensorflow tf-nightly tensorboard tb-nightly tensorboard-plugin-profile && pip install tf-nightly tbp-nightly tensorboard-plugin-profile`).\n\nNote that the debugging functionality of the Tensorboard `profile` plugin is still under active development. Not all views are fully functional, and for example the `trace_viewer` cuts off events after 1M (which can result in all your device traces getting lost if you for example profile the compilation step by accident).\n\n## Support for Stable Diffusion XL\n\nWe provide a training script for training a ControlNet with [Stable Diffusion XL](https://huggingface.co/papers/2307.01952). Please refer to [README_sdxl.md](./README_sdxl.md) for more details.\n" }, { "path": "diffusers/examples/controlnet/README_sdxl.md", "content": "# ControlNet training example for Stable Diffusion XL (SDXL)\n\nThe `train_controlnet_sdxl.py` script shows how to implement the ControlNet training procedure and adapt it for [Stable Diffusion XL](https://huggingface.co/papers/2307.01952).\n\n## Running locally with PyTorch\n\n### Installing the dependencies\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n**Important**\n\nTo make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install -e .\n```\n\nThen cd in the `examples/controlnet` folder and run\n```bash\npip install -r requirements_sdxl.txt\n```\n\nAnd initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\n\nOr for a default accelerate configuration without answering questions about your environment\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell (e.g., a notebook)\n\n```python\nfrom accelerate.utils import write_basic_config\nwrite_basic_config()\n```\n\nWhen running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups. \n\n## Circle filling dataset\n\nThe original dataset is hosted in the [ControlNet repo](https://huggingface.co/lllyasviel/ControlNet/blob/main/training/fill50k.zip). We re-uploaded it to be compatible with `datasets` [here](https://huggingface.co/datasets/fusing/fill50k). Note that `datasets` handles dataloading within the training script.\n\n## Training\n\nOur training examples use two test conditioning images. They can be downloaded by running\n\n```sh\nwget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png\n\nwget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png\n```\n\nThen run `huggingface-cli login` to log into your Hugging Face account. This is needed to be able to push the trained ControlNet parameters to Hugging Face Hub.\n\n```bash\nexport MODEL_DIR=\"stabilityai/stable-diffusion-xl-base-1.0\"\nexport OUTPUT_DIR=\"path to save model\"\n\naccelerate launch train_controlnet_sdxl.py \\\n --pretrained_model_name_or_path=$MODEL_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --dataset_name=fusing/fill50k \\\n --mixed_precision=\"fp16\" \\\n --resolution=1024 \\\n --learning_rate=1e-5 \\\n --max_train_steps=15000 \\\n --validation_image \"./conditioning_image_1.png\" \"./conditioning_image_2.png\" \\\n --validation_prompt \"red circle with blue background\" \"cyan circle with brown floral background\" \\\n --validation_steps=100 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --report_to=\"wandb\" \\\n --seed=42 \\\n --push_to_hub\n```\n\nTo better track our training experiments, we're using the following flags in the command above:\n\n* `report_to=\"wandb` will ensure the training runs are tracked on Weights and Biases. To use it, be sure to install `wandb` with `pip install wandb`.\n* `validation_image`, `validation_prompt`, and `validation_steps` to allow the script to do a few validation inference runs. This allows us to qualitatively check if the training is progressing as expected. \n\nOur experiments were conducted on a single 40GB A100 GPU.\n\n### Inference\n\nOnce training is done, we can perform inference like so:\n\n```python\nfrom diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\nfrom diffusers.utils import load_image\nimport torch\n\nbase_model_path = \"stabilityai/stable-diffusion-xl-base-1.0\"\ncontrolnet_path = \"path to controlnet\"\n\ncontrolnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)\npipe = StableDiffusionXLControlNetPipeline.from_pretrained(\n base_model_path, controlnet=controlnet, torch_dtype=torch.float16\n)\n\n# speed up diffusion process with faster scheduler and memory optimization\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n# remove following line if xformers is not installed or when using Torch 2.0.\npipe.enable_xformers_memory_efficient_attention()\n# memory optimization.\npipe.enable_model_cpu_offload()\n\ncontrol_image = load_image(\"./conditioning_image_1.png\").resize((1024, 1024))\nprompt = \"pale golden rod circle with old lace background\"\n\n# generate image\ngenerator = torch.manual_seed(0)\nimage = pipe(\n prompt, num_inference_steps=20, generator=generator, image=control_image\n).images[0]\nimage.save(\"./output.png\")\n```\n\n## Notes\n\n### Specifying a better VAE\n\nSDXL's VAE is known to suffer from numerical instability issues. This is why we also expose a CLI argument namely `--pretrained_vae_model_name_or_path` that lets you specify the location of an alternative VAE (such as [`madebyollin/sdxl-vae-fp16-fix`](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix)).\n\nIf you're using this VAE during training, you need to ensure you're using it during inference too. You do so by:\n\n```diff\n+ vae = AutoencoderKL.from_pretrained(vae_path_or_repo_id, torch_dtype=torch.float16)\ncontrolnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)\npipe = StableDiffusionXLControlNetPipeline.from_pretrained(\n base_model_path, controlnet=controlnet, torch_dtype=torch.float16,\n+ vae=vae,\n)\n" }, { "path": "diffusers/examples/controlnet/requirements.txt", "content": "accelerate>=0.16.0\ntorchvision\ntransformers>=4.25.1\nftfy\ntensorboard\ndatasets\n" }, { "path": "diffusers/examples/controlnet/requirements_flax.txt", "content": "transformers>=4.25.1\ndatasets\nflax\noptax\ntorch\ntorchvision\nftfy\ntensorboard\nJinja2\n" }, { "path": "diffusers/examples/controlnet/requirements_sdxl.txt", "content": "accelerate>=0.16.0\ntorchvision\ntransformers>=4.25.1\nftfy\ntensorboard\nJinja2\ndatasets\nwandb\n" }, { "path": "diffusers/examples/controlnet/test_controlnet.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport logging\nimport os\nimport sys\nimport tempfile\n\n\nsys.path.append(\"..\")\nfrom test_examples_utils import ExamplesTestsAccelerate, run_command # noqa: E402\n\n\nlogging.basicConfig(level=logging.DEBUG)\n\nlogger = logging.getLogger()\nstream_handler = logging.StreamHandler(sys.stdout)\nlogger.addHandler(stream_handler)\n\n\nclass ControlNet(ExamplesTestsAccelerate):\n def test_controlnet_checkpointing_checkpoints_total_limit(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/controlnet/train_controlnet.py\n --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe\n --dataset_name=hf-internal-testing/fill10\n --output_dir={tmpdir}\n --resolution=64\n --train_batch_size=1\n --gradient_accumulation_steps=1\n --max_train_steps=6\n --checkpoints_total_limit=2\n --checkpointing_steps=2\n --controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-4\", \"checkpoint-6\"},\n )\n\n def test_controlnet_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/controlnet/train_controlnet.py\n --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe\n --dataset_name=hf-internal-testing/fill10\n --output_dir={tmpdir}\n --resolution=64\n --train_batch_size=1\n --gradient_accumulation_steps=1\n --controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet\n --max_train_steps=6\n --checkpointing_steps=2\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-2\", \"checkpoint-4\", \"checkpoint-6\"},\n )\n\n resume_run_args = f\"\"\"\n examples/controlnet/train_controlnet.py\n --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe\n --dataset_name=hf-internal-testing/fill10\n --output_dir={tmpdir}\n --resolution=64\n --train_batch_size=1\n --gradient_accumulation_steps=1\n --controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet\n --max_train_steps=8\n --checkpointing_steps=2\n --resume_from_checkpoint=checkpoint-6\n --checkpoints_total_limit=2\n \"\"\".split()\n\n run_command(self._launch_args + resume_run_args)\n\n self.assertEqual({x for x in os.listdir(tmpdir) if \"checkpoint\" in x}, {\"checkpoint-6\", \"checkpoint-8\"})\n\n\nclass ControlNetSDXL(ExamplesTestsAccelerate):\n def test_controlnet_sdxl(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/controlnet/train_controlnet_sdxl.py\n --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-xl-pipe\n --dataset_name=hf-internal-testing/fill10\n --output_dir={tmpdir}\n --resolution=64\n --train_batch_size=1\n --gradient_accumulation_steps=1\n --controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet-sdxl\n --max_train_steps=4\n --checkpointing_steps=2\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"diffusion_pytorch_model.safetensors\")))\n" }, { "path": "diffusers/examples/controlnet/train_controlnet.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport contextlib\nimport gc\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom pathlib import Path\n\nimport accelerate\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom PIL import Image\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n ControlNetModel,\n DDPMScheduler,\n StableDiffusionControlNetPipeline,\n UNet2DConditionModel,\n UniPCMultistepScheduler,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import check_min_version, is_wandb_available\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef image_grid(imgs, rows, cols):\n assert len(imgs) == rows * cols\n\n w, h = imgs[0].size\n grid = Image.new(\"RGB\", size=(cols * w, rows * h))\n\n for i, img in enumerate(imgs):\n grid.paste(img, box=(i % cols * w, i // cols * h))\n return grid\n\n\ndef log_validation(\n vae, text_encoder, tokenizer, unet, controlnet, args, accelerator, weight_dtype, step, is_final_validation=False\n):\n logger.info(\"Running validation... \")\n\n if not is_final_validation:\n controlnet = accelerator.unwrap_model(controlnet)\n else:\n controlnet = ControlNetModel.from_pretrained(args.output_dir, torch_dtype=weight_dtype)\n\n pipeline = StableDiffusionControlNetPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n controlnet=controlnet,\n safety_checker=None,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n if args.enable_xformers_memory_efficient_attention:\n pipeline.enable_xformers_memory_efficient_attention()\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n if len(args.validation_image) == len(args.validation_prompt):\n validation_images = args.validation_image\n validation_prompts = args.validation_prompt\n elif len(args.validation_image) == 1:\n validation_images = args.validation_image * len(args.validation_prompt)\n validation_prompts = args.validation_prompt\n elif len(args.validation_prompt) == 1:\n validation_images = args.validation_image\n validation_prompts = args.validation_prompt * len(args.validation_image)\n else:\n raise ValueError(\n \"number of `args.validation_image` and `args.validation_prompt` should be checked in `parse_args`\"\n )\n\n image_logs = []\n inference_ctx = contextlib.nullcontext() if is_final_validation else torch.autocast(\"cuda\")\n\n for validation_prompt, validation_image in zip(validation_prompts, validation_images):\n validation_image = Image.open(validation_image).convert(\"RGB\")\n\n images = []\n\n for _ in range(args.num_validation_images):\n with inference_ctx:\n image = pipeline(\n validation_prompt, validation_image, num_inference_steps=20, generator=generator\n ).images[0]\n\n images.append(image)\n\n image_logs.append(\n {\"validation_image\": validation_image, \"images\": images, \"validation_prompt\": validation_prompt}\n )\n\n tracker_key = \"test\" if is_final_validation else \"validation\"\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n for log in image_logs:\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n validation_image = log[\"validation_image\"]\n\n formatted_images = []\n\n formatted_images.append(np.asarray(validation_image))\n\n for image in images:\n formatted_images.append(np.asarray(image))\n\n formatted_images = np.stack(formatted_images)\n\n tracker.writer.add_images(validation_prompt, formatted_images, step, dataformats=\"NHWC\")\n elif tracker.name == \"wandb\":\n formatted_images = []\n\n for log in image_logs:\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n validation_image = log[\"validation_image\"]\n\n formatted_images.append(wandb.Image(validation_image, caption=\"Controlnet conditioning\"))\n\n for image in images:\n image = wandb.Image(image, caption=validation_prompt)\n formatted_images.append(image)\n\n tracker.log({tracker_key: formatted_images})\n else:\n logger.warning(f\"image logging not implemented for {tracker.name}\")\n\n del pipeline\n gc.collect()\n torch.cuda.empty_cache()\n\n return image_logs\n\n\ndef import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path,\n subfolder=\"text_encoder\",\n revision=revision,\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"RobertaSeriesModelWithTransformation\":\n from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation\n\n return RobertaSeriesModelWithTransformation\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef save_model_card(repo_id: str, image_logs=None, base_model=str, repo_folder=None):\n img_str = \"\"\n if image_logs is not None:\n img_str = \"You can find some example images below.\\n\\n\"\n for i, log in enumerate(image_logs):\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n validation_image = log[\"validation_image\"]\n validation_image.save(os.path.join(repo_folder, \"image_control.png\"))\n img_str += f\"prompt: {validation_prompt}\\n\"\n images = [validation_image] + images\n image_grid(images, 1, len(images)).save(os.path.join(repo_folder, f\"images_{i}.png\"))\n img_str += f\"![images_{i})](./images_{i}.png)\\n\"\n\n model_description = f\"\"\"\n# controlnet-{repo_id}\n\nThese are controlnet weights trained on {base_model} with new type of conditioning.\n{img_str}\n\"\"\"\n model_card = load_or_create_model_card(\n repo_id_or_path=repo_id,\n from_training=True,\n license=\"creativeml-openrail-m\",\n base_model=base_model,\n model_description=model_description,\n inference=True,\n )\n\n tags = [\n \"stable-diffusion\",\n \"stable-diffusion-diffusers\",\n \"text-to-image\",\n \"diffusers\",\n \"controlnet\",\n \"diffusers-training\",\n ]\n model_card = populate_model_card(model_card, tags=tags)\n\n model_card.save(os.path.join(repo_folder, \"README.md\"))\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a ControlNet training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--controlnet_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to pretrained controlnet model or model identifier from huggingface.co/models.\"\n \" If not specified controlnet weights are initialized from unet.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--tokenizer_name\",\n type=str,\n default=None,\n help=\"Pretrained tokenizer name or path if not the same as model_name\",\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"controlnet-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. \"\n \"In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference.\"\n \"Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components.\"\n \"See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step\"\n \"instructions.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=5e-6,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\"--lr_power\", type=float, default=1.0, help=\"Power factor of the polynomial scheduler.\")\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--set_grads_to_none\",\n action=\"store_true\",\n help=(\n \"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain\"\n \" behaviors, so disable this argument if it causes any problems. More info:\"\n \" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html\"\n ),\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing the target image.\"\n )\n parser.add_argument(\n \"--conditioning_image_column\",\n type=str,\n default=\"conditioning_image\",\n help=\"The column of the dataset containing the controlnet conditioning image.\",\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=\"text\",\n help=\"The column of the dataset containing a caption or a list of captions.\",\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--proportion_empty_prompts\",\n type=float,\n default=0,\n help=\"Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).\",\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n nargs=\"+\",\n help=(\n \"A set of prompts evaluated every `--validation_steps` and logged to `--report_to`.\"\n \" Provide either a matching number of `--validation_image`s, a single `--validation_image`\"\n \" to be used with all prompts, or a single prompt that will be used with all `--validation_image`s.\"\n ),\n )\n parser.add_argument(\n \"--validation_image\",\n type=str,\n default=None,\n nargs=\"+\",\n help=(\n \"A set of paths to the controlnet conditioning image be evaluated every `--validation_steps`\"\n \" and logged to `--report_to`. Provide either a matching number of `--validation_prompt`s, a\"\n \" a single `--validation_prompt` to be used with all `--validation_image`s, or a single\"\n \" `--validation_image` that will be used with all `--validation_prompt`s.\"\n ),\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images to be generated for each `--validation_image`, `--validation_prompt` pair\",\n )\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=100,\n help=(\n \"Run validation every X steps. Validation consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`\"\n \" and logging the images.\"\n ),\n )\n parser.add_argument(\n \"--tracker_project_name\",\n type=str,\n default=\"train_controlnet\",\n help=(\n \"The `project_name` argument passed to Accelerator.init_trackers for\"\n \" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator\"\n ),\n )\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Specify either `--dataset_name` or `--train_data_dir`\")\n\n if args.dataset_name is not None and args.train_data_dir is not None:\n raise ValueError(\"Specify only one of `--dataset_name` or `--train_data_dir`\")\n\n if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:\n raise ValueError(\"`--proportion_empty_prompts` must be in the range [0, 1].\")\n\n if args.validation_prompt is not None and args.validation_image is None:\n raise ValueError(\"`--validation_image` must be set if `--validation_prompt` is set\")\n\n if args.validation_prompt is None and args.validation_image is not None:\n raise ValueError(\"`--validation_prompt` must be set if `--validation_image` is set\")\n\n if (\n args.validation_image is not None\n and args.validation_prompt is not None\n and len(args.validation_image) != 1\n and len(args.validation_prompt) != 1\n and len(args.validation_image) != len(args.validation_prompt)\n ):\n raise ValueError(\n \"Must provide either 1 `--validation_image`, 1 `--validation_prompt`,\"\n \" or the same number of `--validation_prompt`s and `--validation_image`s\"\n )\n\n if args.resolution % 8 != 0:\n raise ValueError(\n \"`--resolution` must be divisible by 8 for consistently sized encoded images between the VAE and the controlnet encoder.\"\n )\n\n return args\n\n\ndef make_train_dataset(args, tokenizer, accelerator):\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n )\n else:\n if args.train_data_dir is not None:\n dataset = load_dataset(\n args.train_data_dir,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n if args.image_column is None:\n image_column = column_names[0]\n logger.info(f\"image column defaulting to {image_column}\")\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n\n if args.caption_column is None:\n caption_column = column_names[1]\n logger.info(f\"caption column defaulting to {caption_column}\")\n else:\n caption_column = args.caption_column\n if caption_column not in column_names:\n raise ValueError(\n f\"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n\n if args.conditioning_image_column is None:\n conditioning_image_column = column_names[2]\n logger.info(f\"conditioning image column defaulting to {conditioning_image_column}\")\n else:\n conditioning_image_column = args.conditioning_image_column\n if conditioning_image_column not in column_names:\n raise ValueError(\n f\"`--conditioning_image_column` value '{args.conditioning_image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n\n def tokenize_captions(examples, is_train=True):\n captions = []\n for caption in examples[caption_column]:\n if random.random() < args.proportion_empty_prompts:\n captions.append(\"\")\n elif isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n else:\n raise ValueError(\n f\"Caption column `{caption_column}` should contain either strings or lists of strings.\"\n )\n inputs = tokenizer(\n captions, max_length=tokenizer.model_max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\"\n )\n return inputs.input_ids\n\n image_transforms = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(args.resolution),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n conditioning_image_transforms = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(args.resolution),\n transforms.ToTensor(),\n ]\n )\n\n def preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n images = [image_transforms(image) for image in images]\n\n conditioning_images = [image.convert(\"RGB\") for image in examples[conditioning_image_column]]\n conditioning_images = [conditioning_image_transforms(image) for image in conditioning_images]\n\n examples[\"pixel_values\"] = images\n examples[\"conditioning_pixel_values\"] = conditioning_images\n examples[\"input_ids\"] = tokenize_captions(examples)\n\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n return train_dataset\n\n\ndef collate_fn(examples):\n pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n\n conditioning_pixel_values = torch.stack([example[\"conditioning_pixel_values\"] for example in examples])\n conditioning_pixel_values = conditioning_pixel_values.to(memory_format=torch.contiguous_format).float()\n\n input_ids = torch.stack([example[\"input_ids\"] for example in examples])\n\n return {\n \"pixel_values\": pixel_values,\n \"conditioning_pixel_values\": conditioning_pixel_values,\n \"input_ids\": input_ids,\n }\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizer\n if args.tokenizer_name:\n tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)\n elif args.pretrained_model_name_or_path:\n tokenizer = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n\n # import correct text encoder class\n text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n text_encoder = text_encoder_cls.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision, variant=args.variant\n )\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n if args.controlnet_model_name_or_path:\n logger.info(\"Loading existing controlnet weights\")\n controlnet = ControlNetModel.from_pretrained(args.controlnet_model_name_or_path)\n else:\n logger.info(\"Initializing controlnet weights from unet\")\n controlnet = ControlNetModel.from_unet(unet)\n\n # Taken from [Sayak Paul's Diffusers PR #6511](https://github.com/huggingface/diffusers/pull/6511/files)\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n i = len(weights) - 1\n\n while len(weights) > 0:\n weights.pop()\n model = models[i]\n\n sub_dir = \"controlnet\"\n model.save_pretrained(os.path.join(output_dir, sub_dir))\n\n i -= 1\n\n def load_model_hook(models, input_dir):\n while len(models) > 0:\n # pop models so that they are not loaded again\n model = models.pop()\n\n # load diffusers style into model\n load_model = ControlNetModel.from_pretrained(input_dir, subfolder=\"controlnet\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n vae.requires_grad_(False)\n unet.requires_grad_(False)\n text_encoder.requires_grad_(False)\n controlnet.train()\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n controlnet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n if args.gradient_checkpointing:\n controlnet.enable_gradient_checkpointing()\n\n # Check that all trainable models are in full precision\n low_precision_error_string = (\n \" Please make sure to always have all model weights in full float32 precision when starting training - even if\"\n \" doing mixed precision training, copy of the weights should still be float32.\"\n )\n\n if unwrap_model(controlnet).dtype != torch.float32:\n raise ValueError(\n f\"Controlnet loaded as datatype {unwrap_model(controlnet).dtype}. {low_precision_error_string}\"\n )\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # Optimizer creation\n params_to_optimize = controlnet.parameters()\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n train_dataset = make_train_dataset(args, tokenizer, accelerator)\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_cycles=args.lr_num_cycles,\n power=args.lr_power,\n )\n\n # Prepare everything with our `accelerator`.\n controlnet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n controlnet, optimizer, train_dataloader, lr_scheduler\n )\n\n # For mixed precision training we cast the text_encoder and vae weights to half-precision\n # as these models are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move vae, unet and text_encoder to device and cast to weight_dtype\n vae.to(accelerator.device, dtype=weight_dtype)\n unet.to(accelerator.device, dtype=weight_dtype)\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = dict(vars(args))\n\n # tensorboard cannot handle list types for config\n tracker_config.pop(\"validation_prompt\")\n tracker_config.pop(\"validation_image\")\n\n accelerator.init_trackers(args.tracker_project_name, config=tracker_config)\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num batches each epoch = {len(train_dataloader)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n image_logs = None\n for epoch in range(first_epoch, args.num_train_epochs):\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(controlnet):\n # Convert images to latent space\n latents = vae.encode(batch[\"pixel_values\"].to(dtype=weight_dtype)).latent_dist.sample()\n latents = latents * vae.config.scaling_factor\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # Get the text embedding for conditioning\n encoder_hidden_states = text_encoder(batch[\"input_ids\"], return_dict=False)[0]\n\n controlnet_image = batch[\"conditioning_pixel_values\"].to(dtype=weight_dtype)\n\n down_block_res_samples, mid_block_res_sample = controlnet(\n noisy_latents,\n timesteps,\n encoder_hidden_states=encoder_hidden_states,\n controlnet_cond=controlnet_image,\n return_dict=False,\n )\n\n # Predict the noise residual\n model_pred = unet(\n noisy_latents,\n timesteps,\n encoder_hidden_states=encoder_hidden_states,\n down_block_additional_residuals=[\n sample.to(dtype=weight_dtype) for sample in down_block_res_samples\n ],\n mid_block_additional_residual=mid_block_res_sample.to(dtype=weight_dtype),\n return_dict=False,\n )[0]\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n params_to_clip = controlnet.parameters()\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad(set_to_none=args.set_grads_to_none)\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n if args.validation_prompt is not None and global_step % args.validation_steps == 0:\n image_logs = log_validation(\n vae,\n text_encoder,\n tokenizer,\n unet,\n controlnet,\n args,\n accelerator,\n weight_dtype,\n global_step,\n )\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n # Create the pipeline using using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n controlnet = unwrap_model(controlnet)\n controlnet.save_pretrained(args.output_dir)\n\n # Run a final round of validation.\n image_logs = None\n if args.validation_prompt is not None:\n image_logs = log_validation(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n controlnet=None,\n args=args,\n accelerator=accelerator,\n weight_dtype=weight_dtype,\n step=global_step,\n is_final_validation=True,\n )\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n image_logs=image_logs,\n base_model=args.pretrained_model_name_or_path,\n repo_folder=args.output_dir,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/controlnet/train_controlnet_flax.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport logging\nimport math\nimport os\nimport random\nimport time\nfrom pathlib import Path\n\nimport jax\nimport jax.numpy as jnp\nimport numpy as np\nimport optax\nimport torch\nimport torch.utils.checkpoint\nimport transformers\nfrom datasets import load_dataset, load_from_disk\nfrom flax import jax_utils\nfrom flax.core.frozen_dict import unfreeze\nfrom flax.training import train_state\nfrom flax.training.common_utils import shard\nfrom huggingface_hub import create_repo, upload_folder\nfrom PIL import Image, PngImagePlugin\nfrom torch.utils.data import IterableDataset\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTokenizer, FlaxCLIPTextModel, set_seed\n\nfrom diffusers import (\n FlaxAutoencoderKL,\n FlaxControlNetModel,\n FlaxDDPMScheduler,\n FlaxStableDiffusionControlNetPipeline,\n FlaxUNet2DConditionModel,\n)\nfrom diffusers.utils import check_min_version, is_wandb_available, make_image_grid\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\n\n\n# To prevent an error that occurs when there are abnormally large compressed data chunk in the png image\n# see more https://github.com/python-pillow/Pillow/issues/5610\nLARGE_ENOUGH_NUMBER = 100\nPngImagePlugin.MAX_TEXT_CHUNK = LARGE_ENOUGH_NUMBER * (1024**2)\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = logging.getLogger(__name__)\n\n\ndef log_validation(pipeline, pipeline_params, controlnet_params, tokenizer, args, rng, weight_dtype):\n logger.info(\"Running validation...\")\n\n pipeline_params = pipeline_params.copy()\n pipeline_params[\"controlnet\"] = controlnet_params\n\n num_samples = jax.device_count()\n prng_seed = jax.random.split(rng, jax.device_count())\n\n if len(args.validation_image) == len(args.validation_prompt):\n validation_images = args.validation_image\n validation_prompts = args.validation_prompt\n elif len(args.validation_image) == 1:\n validation_images = args.validation_image * len(args.validation_prompt)\n validation_prompts = args.validation_prompt\n elif len(args.validation_prompt) == 1:\n validation_images = args.validation_image\n validation_prompts = args.validation_prompt * len(args.validation_image)\n else:\n raise ValueError(\n \"number of `args.validation_image` and `args.validation_prompt` should be checked in `parse_args`\"\n )\n\n image_logs = []\n\n for validation_prompt, validation_image in zip(validation_prompts, validation_images):\n prompts = num_samples * [validation_prompt]\n prompt_ids = pipeline.prepare_text_inputs(prompts)\n prompt_ids = shard(prompt_ids)\n\n validation_image = Image.open(validation_image).convert(\"RGB\")\n processed_image = pipeline.prepare_image_inputs(num_samples * [validation_image])\n processed_image = shard(processed_image)\n images = pipeline(\n prompt_ids=prompt_ids,\n image=processed_image,\n params=pipeline_params,\n prng_seed=prng_seed,\n num_inference_steps=50,\n jit=True,\n ).images\n\n images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])\n images = pipeline.numpy_to_pil(images)\n\n image_logs.append(\n {\"validation_image\": validation_image, \"images\": images, \"validation_prompt\": validation_prompt}\n )\n\n if args.report_to == \"wandb\":\n formatted_images = []\n for log in image_logs:\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n validation_image = log[\"validation_image\"]\n\n formatted_images.append(wandb.Image(validation_image, caption=\"Controlnet conditioning\"))\n for image in images:\n image = wandb.Image(image, caption=validation_prompt)\n formatted_images.append(image)\n\n wandb.log({\"validation\": formatted_images})\n else:\n logger.warning(f\"image logging not implemented for {args.report_to}\")\n\n return image_logs\n\n\ndef save_model_card(repo_id: str, image_logs=None, base_model=str, repo_folder=None):\n img_str = \"\"\n if image_logs is not None:\n for i, log in enumerate(image_logs):\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n validation_image = log[\"validation_image\"]\n validation_image.save(os.path.join(repo_folder, \"image_control.png\"))\n img_str += f\"prompt: {validation_prompt}\\n\"\n images = [validation_image] + images\n make_image_grid(images, 1, len(images)).save(os.path.join(repo_folder, f\"images_{i}.png\"))\n img_str += f\"![images_{i})](./images_{i}.png)\\n\"\n\n model_description = f\"\"\"\n# controlnet- {repo_id}\n\nThese are controlnet weights trained on {base_model} with new type of conditioning. You can find some example images in the following. \\n\n{img_str}\n\"\"\"\n\n model_card = load_or_create_model_card(\n repo_id_or_path=repo_id,\n from_training=True,\n license=\"creativeml-openrail-m\",\n base_model=base_model,\n model_description=model_description,\n inference=True,\n )\n\n tags = [\n \"stable-diffusion\",\n \"stable-diffusion-diffusers\",\n \"text-to-image\",\n \"diffusers\",\n \"controlnet\",\n \"jax-diffusers-event\",\n \"diffusers-training\",\n ]\n model_card = populate_model_card(model_card, tags=tags)\n\n model_card.save(os.path.join(repo_folder, \"README.md\"))\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--controlnet_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to pretrained controlnet model or model identifier from huggingface.co/models.\"\n \" If not specified controlnet weights are initialized from unet.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--from_pt\",\n action=\"store_true\",\n help=\"Load the pretrained model from a PyTorch checkpoint.\",\n )\n parser.add_argument(\n \"--controlnet_revision\",\n type=str,\n default=None,\n help=\"Revision of controlnet model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--profile_steps\",\n type=int,\n default=0,\n help=\"How many training steps to profile in the beginning.\",\n )\n parser.add_argument(\n \"--profile_validation\",\n action=\"store_true\",\n help=\"Whether to profile the (last) validation.\",\n )\n parser.add_argument(\n \"--profile_memory\",\n action=\"store_true\",\n help=\"Whether to dump an initial (before training loop) and a final (at program end) memory profile.\",\n )\n parser.add_argument(\n \"--ccache\",\n type=str,\n default=None,\n help=\"Enables compilation cache.\",\n )\n parser.add_argument(\n \"--controlnet_from_pt\",\n action=\"store_true\",\n help=\"Load the controlnet model from a PyTorch checkpoint.\",\n )\n parser.add_argument(\n \"--tokenizer_name\",\n type=str,\n default=None,\n help=\"Pretrained tokenizer name or path if not the same as model_name\",\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"runs/{timestamp}\",\n help=\"The output directory where the model predictions and checkpoints will be written. \"\n \"Can contain placeholders: {timestamp}.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=0, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=1, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=5000,\n help=(\"Save a checkpoint of the training state every X updates.\"),\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--snr_gamma\",\n type=float,\n default=None,\n help=\"SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. \"\n \"More details here: https://arxiv.org/abs/2303.09556.\",\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_steps\",\n type=int,\n default=100,\n help=(\"log training metric every X steps to `--report_t`\"),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"wandb\",\n help=('The integration to report the results and logs to. Currently only supported platforms are `\"wandb\"`'),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=\"no\",\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose\"\n \"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.\"\n \"and an Nvidia Ampere GPU.\"\n ),\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\"--streaming\", action=\"store_true\", help=\"To stream a large dataset from Hub.\")\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training dataset. By default it will use `load_dataset` method to load a custom dataset from the folder.\"\n \"Folder must contain a dataset script as described here https://huggingface.co/docs/datasets/dataset_script) .\"\n \"If `--load_from_disk` flag is passed, it will use `load_from_disk` method instead. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--load_from_disk\",\n action=\"store_true\",\n help=(\n \"If True, will load a dataset that was previously saved using `save_to_disk` from `--train_data_dir`\"\n \"See more https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.load_from_disk\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing the target image.\"\n )\n parser.add_argument(\n \"--conditioning_image_column\",\n type=str,\n default=\"conditioning_image\",\n help=\"The column of the dataset containing the controlnet conditioning image.\",\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=\"text\",\n help=\"The column of the dataset containing a caption or a list of captions.\",\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set. Needed if `streaming` is set to True.\"\n ),\n )\n parser.add_argument(\n \"--proportion_empty_prompts\",\n type=float,\n default=0,\n help=\"Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).\",\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n nargs=\"+\",\n help=(\n \"A set of prompts evaluated every `--validation_steps` and logged to `--report_to`.\"\n \" Provide either a matching number of `--validation_image`s, a single `--validation_image`\"\n \" to be used with all prompts, or a single prompt that will be used with all `--validation_image`s.\"\n ),\n )\n parser.add_argument(\n \"--validation_image\",\n type=str,\n default=None,\n nargs=\"+\",\n help=(\n \"A set of paths to the controlnet conditioning image be evaluated every `--validation_steps`\"\n \" and logged to `--report_to`. Provide either a matching number of `--validation_prompt`s, a\"\n \" a single `--validation_prompt` to be used with all `--validation_image`s, or a single\"\n \" `--validation_image` that will be used with all `--validation_prompt`s.\"\n ),\n )\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=100,\n help=(\n \"Run validation every X steps. Validation consists of running the prompt\"\n \" `args.validation_prompt` and logging the images.\"\n ),\n )\n parser.add_argument(\"--wandb_entity\", type=str, default=None, help=(\"The wandb entity to use (for teams).\"))\n parser.add_argument(\n \"--tracker_project_name\",\n type=str,\n default=\"train_controlnet_flax\",\n help=(\"The `project` argument passed to wandb\"),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\", type=int, default=1, help=\"Number of steps to accumulate gradients over\"\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n\n args = parser.parse_args()\n args.output_dir = args.output_dir.replace(\"{timestamp}\", time.strftime(\"%Y%m%d_%H%M%S\"))\n\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n if args.dataset_name is not None and args.train_data_dir is not None:\n raise ValueError(\"Specify only one of `--dataset_name` or `--train_data_dir`\")\n\n if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:\n raise ValueError(\"`--proportion_empty_prompts` must be in the range [0, 1].\")\n\n if args.validation_prompt is not None and args.validation_image is None:\n raise ValueError(\"`--validation_image` must be set if `--validation_prompt` is set\")\n\n if args.validation_prompt is None and args.validation_image is not None:\n raise ValueError(\"`--validation_prompt` must be set if `--validation_image` is set\")\n\n if (\n args.validation_image is not None\n and args.validation_prompt is not None\n and len(args.validation_image) != 1\n and len(args.validation_prompt) != 1\n and len(args.validation_image) != len(args.validation_prompt)\n ):\n raise ValueError(\n \"Must provide either 1 `--validation_image`, 1 `--validation_prompt`,\"\n \" or the same number of `--validation_prompt`s and `--validation_image`s\"\n )\n\n # This idea comes from\n # https://github.com/borisdayma/dalle-mini/blob/d2be512d4a6a9cda2d63ba04afc33038f98f705f/src/dalle_mini/data.py#L370\n if args.streaming and args.max_train_samples is None:\n raise ValueError(\"You must specify `max_train_samples` when using dataset streaming.\")\n\n return args\n\n\ndef make_train_dataset(args, tokenizer, batch_size=None):\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n streaming=args.streaming,\n )\n else:\n if args.train_data_dir is not None:\n if args.load_from_disk:\n dataset = load_from_disk(\n args.train_data_dir,\n )\n else:\n dataset = load_dataset(\n args.train_data_dir,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n if isinstance(dataset[\"train\"], IterableDataset):\n column_names = next(iter(dataset[\"train\"])).keys()\n else:\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n if args.image_column is None:\n image_column = column_names[0]\n logger.info(f\"image column defaulting to {image_column}\")\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n\n if args.caption_column is None:\n caption_column = column_names[1]\n logger.info(f\"caption column defaulting to {caption_column}\")\n else:\n caption_column = args.caption_column\n if caption_column not in column_names:\n raise ValueError(\n f\"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n\n if args.conditioning_image_column is None:\n conditioning_image_column = column_names[2]\n logger.info(f\"conditioning image column defaulting to {caption_column}\")\n else:\n conditioning_image_column = args.conditioning_image_column\n if conditioning_image_column not in column_names:\n raise ValueError(\n f\"`--conditioning_image_column` value '{args.conditioning_image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n\n def tokenize_captions(examples, is_train=True):\n captions = []\n for caption in examples[caption_column]:\n if random.random() < args.proportion_empty_prompts:\n captions.append(\"\")\n elif isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n else:\n raise ValueError(\n f\"Caption column `{caption_column}` should contain either strings or lists of strings.\"\n )\n inputs = tokenizer(\n captions, max_length=tokenizer.model_max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\"\n )\n return inputs.input_ids\n\n image_transforms = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(args.resolution),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n conditioning_image_transforms = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(args.resolution),\n transforms.ToTensor(),\n ]\n )\n\n def preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n images = [image_transforms(image) for image in images]\n\n conditioning_images = [image.convert(\"RGB\") for image in examples[conditioning_image_column]]\n conditioning_images = [conditioning_image_transforms(image) for image in conditioning_images]\n\n examples[\"pixel_values\"] = images\n examples[\"conditioning_pixel_values\"] = conditioning_images\n examples[\"input_ids\"] = tokenize_captions(examples)\n\n return examples\n\n if jax.process_index() == 0:\n if args.max_train_samples is not None:\n if args.streaming:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).take(args.max_train_samples)\n else:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n if args.streaming:\n train_dataset = dataset[\"train\"].map(\n preprocess_train,\n batched=True,\n batch_size=batch_size,\n remove_columns=list(dataset[\"train\"].features.keys()),\n )\n else:\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n return train_dataset\n\n\ndef collate_fn(examples):\n pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n\n conditioning_pixel_values = torch.stack([example[\"conditioning_pixel_values\"] for example in examples])\n conditioning_pixel_values = conditioning_pixel_values.to(memory_format=torch.contiguous_format).float()\n\n input_ids = torch.stack([example[\"input_ids\"] for example in examples])\n\n batch = {\n \"pixel_values\": pixel_values,\n \"conditioning_pixel_values\": conditioning_pixel_values,\n \"input_ids\": input_ids,\n }\n batch = {k: v.numpy() for k, v in batch.items()}\n return batch\n\n\ndef get_params_to_save(params):\n return jax.device_get(jax.tree_util.tree_map(lambda x: x[0], params))\n\n\ndef main():\n args = parse_args()\n\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n # Setup logging, we only want one process per machine to log things on the screen.\n logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)\n if jax.process_index() == 0:\n transformers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n\n # wandb init\n if jax.process_index() == 0 and args.report_to == \"wandb\":\n wandb.init(\n entity=args.wandb_entity,\n project=args.tracker_project_name,\n job_type=\"train\",\n config=args,\n )\n\n if args.seed is not None:\n set_seed(args.seed)\n\n rng = jax.random.PRNGKey(0)\n\n # Handle the repository creation\n if jax.process_index() == 0:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizer and add the placeholder token as a additional special token\n if args.tokenizer_name:\n tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)\n elif args.pretrained_model_name_or_path:\n tokenizer = CLIPTokenizer.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"tokenizer\", revision=args.revision\n )\n else:\n raise NotImplementedError(\"No tokenizer specified!\")\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n total_train_batch_size = args.train_batch_size * jax.local_device_count() * args.gradient_accumulation_steps\n train_dataset = make_train_dataset(args, tokenizer, batch_size=total_train_batch_size)\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=not args.streaming,\n collate_fn=collate_fn,\n batch_size=total_train_batch_size,\n num_workers=args.dataloader_num_workers,\n drop_last=True,\n )\n\n weight_dtype = jnp.float32\n if args.mixed_precision == \"fp16\":\n weight_dtype = jnp.float16\n elif args.mixed_precision == \"bf16\":\n weight_dtype = jnp.bfloat16\n\n # Load models and create wrapper for stable diffusion\n text_encoder = FlaxCLIPTextModel.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"text_encoder\",\n dtype=weight_dtype,\n revision=args.revision,\n from_pt=args.from_pt,\n )\n vae, vae_params = FlaxAutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path,\n revision=args.revision,\n subfolder=\"vae\",\n dtype=weight_dtype,\n from_pt=args.from_pt,\n )\n unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"unet\",\n dtype=weight_dtype,\n revision=args.revision,\n from_pt=args.from_pt,\n )\n\n if args.controlnet_model_name_or_path:\n logger.info(\"Loading existing controlnet weights\")\n controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(\n args.controlnet_model_name_or_path,\n revision=args.controlnet_revision,\n from_pt=args.controlnet_from_pt,\n dtype=jnp.float32,\n )\n else:\n logger.info(\"Initializing controlnet weights from unet\")\n rng, rng_params = jax.random.split(rng)\n\n controlnet = FlaxControlNetModel(\n in_channels=unet.config.in_channels,\n down_block_types=unet.config.down_block_types,\n only_cross_attention=unet.config.only_cross_attention,\n block_out_channels=unet.config.block_out_channels,\n layers_per_block=unet.config.layers_per_block,\n attention_head_dim=unet.config.attention_head_dim,\n cross_attention_dim=unet.config.cross_attention_dim,\n use_linear_projection=unet.config.use_linear_projection,\n flip_sin_to_cos=unet.config.flip_sin_to_cos,\n freq_shift=unet.config.freq_shift,\n )\n controlnet_params = controlnet.init_weights(rng=rng_params)\n controlnet_params = unfreeze(controlnet_params)\n for key in [\n \"conv_in\",\n \"time_embedding\",\n \"down_blocks_0\",\n \"down_blocks_1\",\n \"down_blocks_2\",\n \"down_blocks_3\",\n \"mid_block\",\n ]:\n controlnet_params[key] = unet_params[key]\n\n pipeline, pipeline_params = FlaxStableDiffusionControlNetPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n tokenizer=tokenizer,\n controlnet=controlnet,\n safety_checker=None,\n dtype=weight_dtype,\n revision=args.revision,\n from_pt=args.from_pt,\n )\n pipeline_params = jax_utils.replicate(pipeline_params)\n\n # Optimization\n if args.scale_lr:\n args.learning_rate = args.learning_rate * total_train_batch_size\n\n constant_scheduler = optax.constant_schedule(args.learning_rate)\n\n adamw = optax.adamw(\n learning_rate=constant_scheduler,\n b1=args.adam_beta1,\n b2=args.adam_beta2,\n eps=args.adam_epsilon,\n weight_decay=args.adam_weight_decay,\n )\n\n optimizer = optax.chain(\n optax.clip_by_global_norm(args.max_grad_norm),\n adamw,\n )\n\n state = train_state.TrainState.create(apply_fn=controlnet.__call__, params=controlnet_params, tx=optimizer)\n\n noise_scheduler, noise_scheduler_state = FlaxDDPMScheduler.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"scheduler\"\n )\n\n # Initialize our training\n validation_rng, train_rngs = jax.random.split(rng)\n train_rngs = jax.random.split(train_rngs, jax.local_device_count())\n\n def compute_snr(timesteps):\n \"\"\"\n Computes SNR as per https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849\n \"\"\"\n alphas_cumprod = noise_scheduler_state.common.alphas_cumprod\n sqrt_alphas_cumprod = alphas_cumprod**0.5\n sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5\n\n alpha = sqrt_alphas_cumprod[timesteps]\n sigma = sqrt_one_minus_alphas_cumprod[timesteps]\n # Compute SNR.\n snr = (alpha / sigma) ** 2\n return snr\n\n def train_step(state, unet_params, text_encoder_params, vae_params, batch, train_rng):\n # reshape batch, add grad_step_dim if gradient_accumulation_steps > 1\n if args.gradient_accumulation_steps > 1:\n grad_steps = args.gradient_accumulation_steps\n batch = jax.tree_map(lambda x: x.reshape((grad_steps, x.shape[0] // grad_steps) + x.shape[1:]), batch)\n\n def compute_loss(params, minibatch, sample_rng):\n # Convert images to latent space\n vae_outputs = vae.apply(\n {\"params\": vae_params}, minibatch[\"pixel_values\"], deterministic=True, method=vae.encode\n )\n latents = vae_outputs.latent_dist.sample(sample_rng)\n # (NHWC) -> (NCHW)\n latents = jnp.transpose(latents, (0, 3, 1, 2))\n latents = latents * vae.config.scaling_factor\n\n # Sample noise that we'll add to the latents\n noise_rng, timestep_rng = jax.random.split(sample_rng)\n noise = jax.random.normal(noise_rng, latents.shape)\n # Sample a random timestep for each image\n bsz = latents.shape[0]\n timesteps = jax.random.randint(\n timestep_rng,\n (bsz,),\n 0,\n noise_scheduler.config.num_train_timesteps,\n )\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(noise_scheduler_state, latents, noise, timesteps)\n\n # Get the text embedding for conditioning\n encoder_hidden_states = text_encoder(\n minibatch[\"input_ids\"],\n params=text_encoder_params,\n train=False,\n )[0]\n\n controlnet_cond = minibatch[\"conditioning_pixel_values\"]\n\n # Predict the noise residual and compute loss\n down_block_res_samples, mid_block_res_sample = controlnet.apply(\n {\"params\": params},\n noisy_latents,\n timesteps,\n encoder_hidden_states,\n controlnet_cond,\n train=True,\n return_dict=False,\n )\n\n model_pred = unet.apply(\n {\"params\": unet_params},\n noisy_latents,\n timesteps,\n encoder_hidden_states,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n ).sample\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(noise_scheduler_state, latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n loss = (target - model_pred) ** 2\n\n if args.snr_gamma is not None:\n snr = jnp.array(compute_snr(timesteps))\n snr_loss_weights = jnp.where(snr < args.snr_gamma, snr, jnp.ones_like(snr) * args.snr_gamma)\n if noise_scheduler.config.prediction_type == \"epsilon\":\n snr_loss_weights = snr_loss_weights / snr\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n snr_loss_weights = snr_loss_weights / (snr + 1)\n\n loss = loss * snr_loss_weights\n\n loss = loss.mean()\n\n return loss\n\n grad_fn = jax.value_and_grad(compute_loss)\n\n # get a minibatch (one gradient accumulation slice)\n def get_minibatch(batch, grad_idx):\n return jax.tree_util.tree_map(\n lambda x: jax.lax.dynamic_index_in_dim(x, grad_idx, keepdims=False),\n batch,\n )\n\n def loss_and_grad(grad_idx, train_rng):\n # create minibatch for the grad step\n minibatch = get_minibatch(batch, grad_idx) if grad_idx is not None else batch\n sample_rng, train_rng = jax.random.split(train_rng, 2)\n loss, grad = grad_fn(state.params, minibatch, sample_rng)\n return loss, grad, train_rng\n\n if args.gradient_accumulation_steps == 1:\n loss, grad, new_train_rng = loss_and_grad(None, train_rng)\n else:\n init_loss_grad_rng = (\n 0.0, # initial value for cumul_loss\n jax.tree_map(jnp.zeros_like, state.params), # initial value for cumul_grad\n train_rng, # initial value for train_rng\n )\n\n def cumul_grad_step(grad_idx, loss_grad_rng):\n cumul_loss, cumul_grad, train_rng = loss_grad_rng\n loss, grad, new_train_rng = loss_and_grad(grad_idx, train_rng)\n cumul_loss, cumul_grad = jax.tree_map(jnp.add, (cumul_loss, cumul_grad), (loss, grad))\n return cumul_loss, cumul_grad, new_train_rng\n\n loss, grad, new_train_rng = jax.lax.fori_loop(\n 0,\n args.gradient_accumulation_steps,\n cumul_grad_step,\n init_loss_grad_rng,\n )\n loss, grad = jax.tree_map(lambda x: x / args.gradient_accumulation_steps, (loss, grad))\n\n grad = jax.lax.pmean(grad, \"batch\")\n\n new_state = state.apply_gradients(grads=grad)\n\n metrics = {\"loss\": loss}\n metrics = jax.lax.pmean(metrics, axis_name=\"batch\")\n\n def l2(xs):\n return jnp.sqrt(sum([jnp.vdot(x, x) for x in jax.tree_util.tree_leaves(xs)]))\n\n metrics[\"l2_grads\"] = l2(jax.tree_util.tree_leaves(grad))\n\n return new_state, metrics, new_train_rng\n\n # Create parallel version of the train step\n p_train_step = jax.pmap(train_step, \"batch\", donate_argnums=(0,))\n\n # Replicate the train state on each device\n state = jax_utils.replicate(state)\n unet_params = jax_utils.replicate(unet_params)\n text_encoder_params = jax_utils.replicate(text_encoder.params)\n vae_params = jax_utils.replicate(vae_params)\n\n # Train!\n if args.streaming:\n dataset_length = args.max_train_samples\n else:\n dataset_length = len(train_dataloader)\n num_update_steps_per_epoch = math.ceil(dataset_length / args.gradient_accumulation_steps)\n\n # Scheduler and math around the number of training steps.\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {args.max_train_samples if args.streaming else len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel & distributed) = {total_train_batch_size}\")\n logger.info(f\" Total optimization steps = {args.num_train_epochs * num_update_steps_per_epoch}\")\n\n if jax.process_index() == 0 and args.report_to == \"wandb\":\n wandb.define_metric(\"*\", step_metric=\"train/step\")\n wandb.define_metric(\"train/step\", step_metric=\"walltime\")\n wandb.config.update(\n {\n \"num_train_examples\": args.max_train_samples if args.streaming else len(train_dataset),\n \"total_train_batch_size\": total_train_batch_size,\n \"total_optimization_step\": args.num_train_epochs * num_update_steps_per_epoch,\n \"num_devices\": jax.device_count(),\n \"controlnet_params\": sum(np.prod(x.shape) for x in jax.tree_util.tree_leaves(state.params)),\n }\n )\n\n global_step = step0 = 0\n epochs = tqdm(\n range(args.num_train_epochs),\n desc=\"Epoch ... \",\n position=0,\n disable=jax.process_index() > 0,\n )\n if args.profile_memory:\n jax.profiler.save_device_memory_profile(os.path.join(args.output_dir, \"memory_initial.prof\"))\n t00 = t0 = time.monotonic()\n for epoch in epochs:\n # ======================== Training ================================\n\n train_metrics = []\n train_metric = None\n\n steps_per_epoch = (\n args.max_train_samples // total_train_batch_size\n if args.streaming or args.max_train_samples\n else len(train_dataset) // total_train_batch_size\n )\n train_step_progress_bar = tqdm(\n total=steps_per_epoch,\n desc=\"Training...\",\n position=1,\n leave=False,\n disable=jax.process_index() > 0,\n )\n # train\n for batch in train_dataloader:\n if args.profile_steps and global_step == 1:\n train_metric[\"loss\"].block_until_ready()\n jax.profiler.start_trace(args.output_dir)\n if args.profile_steps and global_step == 1 + args.profile_steps:\n train_metric[\"loss\"].block_until_ready()\n jax.profiler.stop_trace()\n\n batch = shard(batch)\n with jax.profiler.StepTraceAnnotation(\"train\", step_num=global_step):\n state, train_metric, train_rngs = p_train_step(\n state, unet_params, text_encoder_params, vae_params, batch, train_rngs\n )\n train_metrics.append(train_metric)\n\n train_step_progress_bar.update(1)\n\n global_step += 1\n if global_step >= args.max_train_steps:\n break\n\n if (\n args.validation_prompt is not None\n and global_step % args.validation_steps == 0\n and jax.process_index() == 0\n ):\n _ = log_validation(\n pipeline, pipeline_params, state.params, tokenizer, args, validation_rng, weight_dtype\n )\n\n if global_step % args.logging_steps == 0 and jax.process_index() == 0:\n if args.report_to == \"wandb\":\n train_metrics = jax_utils.unreplicate(train_metrics)\n train_metrics = jax.tree_util.tree_map(lambda *m: jnp.array(m).mean(), *train_metrics)\n wandb.log(\n {\n \"walltime\": time.monotonic() - t00,\n \"train/step\": global_step,\n \"train/epoch\": global_step / dataset_length,\n \"train/steps_per_sec\": (global_step - step0) / (time.monotonic() - t0),\n **{f\"train/{k}\": v for k, v in train_metrics.items()},\n }\n )\n t0, step0 = time.monotonic(), global_step\n train_metrics = []\n if global_step % args.checkpointing_steps == 0 and jax.process_index() == 0:\n controlnet.save_pretrained(\n f\"{args.output_dir}/{global_step}\",\n params=get_params_to_save(state.params),\n )\n\n train_metric = jax_utils.unreplicate(train_metric)\n train_step_progress_bar.close()\n epochs.write(f\"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})\")\n\n # Final validation & store model.\n if jax.process_index() == 0:\n if args.validation_prompt is not None:\n if args.profile_validation:\n jax.profiler.start_trace(args.output_dir)\n image_logs = log_validation(\n pipeline, pipeline_params, state.params, tokenizer, args, validation_rng, weight_dtype\n )\n if args.profile_validation:\n jax.profiler.stop_trace()\n else:\n image_logs = None\n\n controlnet.save_pretrained(\n args.output_dir,\n params=get_params_to_save(state.params),\n )\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n image_logs=image_logs,\n base_model=args.pretrained_model_name_or_path,\n repo_folder=args.output_dir,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n if args.profile_memory:\n jax.profiler.save_device_memory_profile(os.path.join(args.output_dir, \"memory_final.prof\"))\n logger.info(\"Finished training.\")\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/controlnet/train_controlnet_sdxl.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport functools\nimport gc\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom contextlib import nullcontext\nfrom pathlib import Path\n\nimport accelerate\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import DistributedType, ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom PIL import Image\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n ControlNetModel,\n DDPMScheduler,\n StableDiffusionXLControlNetPipeline,\n UNet2DConditionModel,\n UniPCMultistepScheduler,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import check_min_version, is_wandb_available, make_image_grid\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom diffusers.utils.import_utils import is_torch_npu_available, is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__)\nif is_torch_npu_available():\n torch.npu.config.allow_internal_format = False\n\n\ndef log_validation(vae, unet, controlnet, args, accelerator, weight_dtype, step, is_final_validation=False):\n logger.info(\"Running validation... \")\n\n if not is_final_validation:\n controlnet = accelerator.unwrap_model(controlnet)\n pipeline = StableDiffusionXLControlNetPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n unet=unet,\n controlnet=controlnet,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n else:\n controlnet = ControlNetModel.from_pretrained(args.output_dir, torch_dtype=weight_dtype)\n if args.pretrained_vae_model_name_or_path is not None:\n vae = AutoencoderKL.from_pretrained(args.pretrained_vae_model_name_or_path, torch_dtype=weight_dtype)\n else:\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"vae\", torch_dtype=weight_dtype\n )\n\n pipeline = StableDiffusionXLControlNetPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n controlnet=controlnet,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n\n pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n if args.enable_xformers_memory_efficient_attention:\n pipeline.enable_xformers_memory_efficient_attention()\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n if len(args.validation_image) == len(args.validation_prompt):\n validation_images = args.validation_image\n validation_prompts = args.validation_prompt\n elif len(args.validation_image) == 1:\n validation_images = args.validation_image * len(args.validation_prompt)\n validation_prompts = args.validation_prompt\n elif len(args.validation_prompt) == 1:\n validation_images = args.validation_image\n validation_prompts = args.validation_prompt * len(args.validation_image)\n else:\n raise ValueError(\n \"number of `args.validation_image` and `args.validation_prompt` should be checked in `parse_args`\"\n )\n\n image_logs = []\n if is_final_validation or torch.backends.mps.is_available():\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type)\n\n for validation_prompt, validation_image in zip(validation_prompts, validation_images):\n validation_image = Image.open(validation_image).convert(\"RGB\")\n validation_image = validation_image.resize((args.resolution, args.resolution))\n\n images = []\n\n for _ in range(args.num_validation_images):\n with autocast_ctx:\n image = pipeline(\n prompt=validation_prompt, image=validation_image, num_inference_steps=20, generator=generator\n ).images[0]\n images.append(image)\n\n image_logs.append(\n {\"validation_image\": validation_image, \"images\": images, \"validation_prompt\": validation_prompt}\n )\n\n tracker_key = \"test\" if is_final_validation else \"validation\"\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n for log in image_logs:\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n validation_image = log[\"validation_image\"]\n\n formatted_images = []\n\n formatted_images.append(np.asarray(validation_image))\n\n for image in images:\n formatted_images.append(np.asarray(image))\n\n formatted_images = np.stack(formatted_images)\n\n tracker.writer.add_images(validation_prompt, formatted_images, step, dataformats=\"NHWC\")\n elif tracker.name == \"wandb\":\n formatted_images = []\n\n for log in image_logs:\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n validation_image = log[\"validation_image\"]\n\n formatted_images.append(wandb.Image(validation_image, caption=\"Controlnet conditioning\"))\n\n for image in images:\n image = wandb.Image(image, caption=validation_prompt)\n formatted_images.append(image)\n\n tracker.log({tracker_key: formatted_images})\n else:\n logger.warning(f\"image logging not implemented for {tracker.name}\")\n\n del pipeline\n gc.collect()\n torch.cuda.empty_cache()\n\n return image_logs\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef save_model_card(repo_id: str, image_logs=None, base_model=str, repo_folder=None):\n img_str = \"\"\n if image_logs is not None:\n img_str = \"You can find some example images below.\\n\\n\"\n for i, log in enumerate(image_logs):\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n validation_image = log[\"validation_image\"]\n validation_image.save(os.path.join(repo_folder, \"image_control.png\"))\n img_str += f\"prompt: {validation_prompt}\\n\"\n images = [validation_image] + images\n make_image_grid(images, 1, len(images)).save(os.path.join(repo_folder, f\"images_{i}.png\"))\n img_str += f\"![images_{i})](./images_{i}.png)\\n\"\n\n model_description = f\"\"\"\n# controlnet-{repo_id}\n\nThese are controlnet weights trained on {base_model} with new type of conditioning.\n{img_str}\n\"\"\"\n\n model_card = load_or_create_model_card(\n repo_id_or_path=repo_id,\n from_training=True,\n license=\"openrail++\",\n base_model=base_model,\n model_description=model_description,\n inference=True,\n )\n\n tags = [\n \"stable-diffusion-xl\",\n \"stable-diffusion-xl-diffusers\",\n \"text-to-image\",\n \"diffusers\",\n \"controlnet\",\n \"diffusers-training\",\n ]\n model_card = populate_model_card(model_card, tags=tags)\n\n model_card.save(os.path.join(repo_folder, \"README.md\"))\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a ControlNet training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_vae_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to an improved VAE to stabilize training. For more details check out: https://github.com/huggingface/diffusers/pull/4038.\",\n )\n parser.add_argument(\n \"--controlnet_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to pretrained controlnet model or model identifier from huggingface.co/models.\"\n \" If not specified controlnet weights are initialized from unet.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--tokenizer_name\",\n type=str,\n default=None,\n help=\"Pretrained tokenizer name or path if not the same as model_name\",\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"controlnet-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--crops_coords_top_left_h\",\n type=int,\n default=0,\n help=(\"Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet.\"),\n )\n parser.add_argument(\n \"--crops_coords_top_left_w\",\n type=int,\n default=0,\n help=(\"Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet.\"),\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. \"\n \"In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference.\"\n \"Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components.\"\n \"See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step\"\n \"instructions.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=5e-6,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\"--lr_power\", type=float, default=1.0, help=\"Power factor of the polynomial scheduler.\")\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--enable_npu_flash_attention\", action=\"store_true\", help=\"Whether or not to use npu flash attention.\"\n )\n parser.add_argument(\n \"--set_grads_to_none\",\n action=\"store_true\",\n help=(\n \"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain\"\n \" behaviors, so disable this argument if it causes any problems. More info:\"\n \" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html\"\n ),\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing the target image.\"\n )\n parser.add_argument(\n \"--conditioning_image_column\",\n type=str,\n default=\"conditioning_image\",\n help=\"The column of the dataset containing the controlnet conditioning image.\",\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=\"text\",\n help=\"The column of the dataset containing a caption or a list of captions.\",\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--proportion_empty_prompts\",\n type=float,\n default=0,\n help=\"Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).\",\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n nargs=\"+\",\n help=(\n \"A set of prompts evaluated every `--validation_steps` and logged to `--report_to`.\"\n \" Provide either a matching number of `--validation_image`s, a single `--validation_image`\"\n \" to be used with all prompts, or a single prompt that will be used with all `--validation_image`s.\"\n ),\n )\n parser.add_argument(\n \"--validation_image\",\n type=str,\n default=None,\n nargs=\"+\",\n help=(\n \"A set of paths to the controlnet conditioning image be evaluated every `--validation_steps`\"\n \" and logged to `--report_to`. Provide either a matching number of `--validation_prompt`s, a\"\n \" a single `--validation_prompt` to be used with all `--validation_image`s, or a single\"\n \" `--validation_image` that will be used with all `--validation_prompt`s.\"\n ),\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images to be generated for each `--validation_image`, `--validation_prompt` pair\",\n )\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=100,\n help=(\n \"Run validation every X steps. Validation consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`\"\n \" and logging the images.\"\n ),\n )\n parser.add_argument(\n \"--tracker_project_name\",\n type=str,\n default=\"sd_xl_train_controlnet\",\n help=(\n \"The `project_name` argument passed to Accelerator.init_trackers for\"\n \" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator\"\n ),\n )\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Specify either `--dataset_name` or `--train_data_dir`\")\n\n if args.dataset_name is not None and args.train_data_dir is not None:\n raise ValueError(\"Specify only one of `--dataset_name` or `--train_data_dir`\")\n\n if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:\n raise ValueError(\"`--proportion_empty_prompts` must be in the range [0, 1].\")\n\n if args.validation_prompt is not None and args.validation_image is None:\n raise ValueError(\"`--validation_image` must be set if `--validation_prompt` is set\")\n\n if args.validation_prompt is None and args.validation_image is not None:\n raise ValueError(\"`--validation_prompt` must be set if `--validation_image` is set\")\n\n if (\n args.validation_image is not None\n and args.validation_prompt is not None\n and len(args.validation_image) != 1\n and len(args.validation_prompt) != 1\n and len(args.validation_image) != len(args.validation_prompt)\n ):\n raise ValueError(\n \"Must provide either 1 `--validation_image`, 1 `--validation_prompt`,\"\n \" or the same number of `--validation_prompt`s and `--validation_image`s\"\n )\n\n if args.resolution % 8 != 0:\n raise ValueError(\n \"`--resolution` must be divisible by 8 for consistently sized encoded images between the VAE and the controlnet encoder.\"\n )\n\n return args\n\n\ndef get_train_dataset(args, accelerator):\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n )\n else:\n if args.train_data_dir is not None:\n dataset = load_dataset(\n args.train_data_dir,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n if args.image_column is None:\n image_column = column_names[0]\n logger.info(f\"image column defaulting to {image_column}\")\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n\n if args.caption_column is None:\n caption_column = column_names[1]\n logger.info(f\"caption column defaulting to {caption_column}\")\n else:\n caption_column = args.caption_column\n if caption_column not in column_names:\n raise ValueError(\n f\"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n\n if args.conditioning_image_column is None:\n conditioning_image_column = column_names[2]\n logger.info(f\"conditioning image column defaulting to {conditioning_image_column}\")\n else:\n conditioning_image_column = args.conditioning_image_column\n if conditioning_image_column not in column_names:\n raise ValueError(\n f\"`--conditioning_image_column` value '{args.conditioning_image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n\n with accelerator.main_process_first():\n train_dataset = dataset[\"train\"].shuffle(seed=args.seed)\n if args.max_train_samples is not None:\n train_dataset = train_dataset.select(range(args.max_train_samples))\n return train_dataset\n\n\n# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt\ndef encode_prompt(prompt_batch, text_encoders, tokenizers, proportion_empty_prompts, is_train=True):\n prompt_embeds_list = []\n\n captions = []\n for caption in prompt_batch:\n if random.random() < proportion_empty_prompts:\n captions.append(\"\")\n elif isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n\n with torch.no_grad():\n for tokenizer, text_encoder in zip(tokenizers, text_encoders):\n text_inputs = tokenizer(\n captions,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n prompt_embeds = text_encoder(\n text_input_ids.to(text_encoder.device),\n output_hidden_states=True,\n )\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds.hidden_states[-2]\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)\n return prompt_embeds, pooled_prompt_embeds\n\n\ndef prepare_train_dataset(dataset, accelerator):\n image_transforms = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(args.resolution),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n conditioning_image_transforms = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(args.resolution),\n transforms.ToTensor(),\n ]\n )\n\n def preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[args.image_column]]\n images = [image_transforms(image) for image in images]\n\n conditioning_images = [image.convert(\"RGB\") for image in examples[args.conditioning_image_column]]\n conditioning_images = [conditioning_image_transforms(image) for image in conditioning_images]\n\n examples[\"pixel_values\"] = images\n examples[\"conditioning_pixel_values\"] = conditioning_images\n\n return examples\n\n with accelerator.main_process_first():\n dataset = dataset.with_transform(preprocess_train)\n\n return dataset\n\n\ndef collate_fn(examples):\n pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n\n conditioning_pixel_values = torch.stack([example[\"conditioning_pixel_values\"] for example in examples])\n conditioning_pixel_values = conditioning_pixel_values.to(memory_format=torch.contiguous_format).float()\n\n prompt_ids = torch.stack([torch.tensor(example[\"prompt_embeds\"]) for example in examples])\n\n add_text_embeds = torch.stack([torch.tensor(example[\"text_embeds\"]) for example in examples])\n add_time_ids = torch.stack([torch.tensor(example[\"time_ids\"]) for example in examples])\n\n return {\n \"pixel_values\": pixel_values,\n \"conditioning_pixel_values\": conditioning_pixel_values,\n \"prompt_ids\": prompt_ids,\n \"unet_added_conditions\": {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids},\n }\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n if torch.backends.mps.is_available() and args.mixed_precision == \"bf16\":\n # due to pytorch#99272, MPS does not yet support bfloat16.\n raise ValueError(\n \"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead.\"\n )\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizers\n tokenizer_one = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n tokenizer_two = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer_2\",\n revision=args.revision,\n use_fast=False,\n )\n\n # import correct text encoder classes\n text_encoder_cls_one = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision\n )\n text_encoder_cls_two = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision, subfolder=\"text_encoder_2\"\n )\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n text_encoder_one = text_encoder_cls_one.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n text_encoder_two = text_encoder_cls_two.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder_2\", revision=args.revision, variant=args.variant\n )\n vae_path = (\n args.pretrained_model_name_or_path\n if args.pretrained_vae_model_name_or_path is None\n else args.pretrained_vae_model_name_or_path\n )\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n variant=args.variant,\n )\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n if args.controlnet_model_name_or_path:\n logger.info(\"Loading existing controlnet weights\")\n controlnet = ControlNetModel.from_pretrained(args.controlnet_model_name_or_path)\n else:\n logger.info(\"Initializing controlnet weights from unet\")\n controlnet = ControlNetModel.from_unet(unet)\n\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n i = len(weights) - 1\n\n while len(weights) > 0:\n weights.pop()\n model = models[i]\n\n sub_dir = \"controlnet\"\n model.save_pretrained(os.path.join(output_dir, sub_dir))\n\n i -= 1\n\n def load_model_hook(models, input_dir):\n while len(models) > 0:\n # pop models so that they are not loaded again\n model = models.pop()\n\n # load diffusers style into model\n load_model = ControlNetModel.from_pretrained(input_dir, subfolder=\"controlnet\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n vae.requires_grad_(False)\n unet.requires_grad_(False)\n text_encoder_one.requires_grad_(False)\n text_encoder_two.requires_grad_(False)\n controlnet.train()\n\n if args.enable_npu_flash_attention:\n if is_torch_npu_available():\n logger.info(\"npu flash attention enabled.\")\n unet.enable_npu_flash_attention()\n else:\n raise ValueError(\"npu flash attention requires torch_npu extensions and is supported only on npu devices.\")\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n controlnet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n if args.gradient_checkpointing:\n controlnet.enable_gradient_checkpointing()\n unet.enable_gradient_checkpointing()\n\n # Check that all trainable models are in full precision\n low_precision_error_string = (\n \" Please make sure to always have all model weights in full float32 precision when starting training - even if\"\n \" doing mixed precision training, copy of the weights should still be float32.\"\n )\n\n if unwrap_model(controlnet).dtype != torch.float32:\n raise ValueError(\n f\"Controlnet loaded as datatype {unwrap_model(controlnet).dtype}. {low_precision_error_string}\"\n )\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # Optimizer creation\n params_to_optimize = controlnet.parameters()\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # For mixed precision training we cast the text_encoder and vae weights to half-precision\n # as these models are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move vae, unet and text_encoder to device and cast to weight_dtype\n # The VAE is in float32 to avoid NaN losses.\n if args.pretrained_vae_model_name_or_path is not None:\n vae.to(accelerator.device, dtype=weight_dtype)\n else:\n vae.to(accelerator.device, dtype=torch.float32)\n unet.to(accelerator.device, dtype=weight_dtype)\n text_encoder_one.to(accelerator.device, dtype=weight_dtype)\n text_encoder_two.to(accelerator.device, dtype=weight_dtype)\n\n # Here, we compute not just the text embeddings but also the additional embeddings\n # needed for the SD XL UNet to operate.\n def compute_embeddings(batch, proportion_empty_prompts, text_encoders, tokenizers, is_train=True):\n original_size = (args.resolution, args.resolution)\n target_size = (args.resolution, args.resolution)\n crops_coords_top_left = (args.crops_coords_top_left_h, args.crops_coords_top_left_w)\n prompt_batch = batch[args.caption_column]\n\n prompt_embeds, pooled_prompt_embeds = encode_prompt(\n prompt_batch, text_encoders, tokenizers, proportion_empty_prompts, is_train\n )\n add_text_embeds = pooled_prompt_embeds\n\n # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n add_time_ids = torch.tensor([add_time_ids])\n\n prompt_embeds = prompt_embeds.to(accelerator.device)\n add_text_embeds = add_text_embeds.to(accelerator.device)\n add_time_ids = add_time_ids.repeat(len(prompt_batch), 1)\n add_time_ids = add_time_ids.to(accelerator.device, dtype=prompt_embeds.dtype)\n unet_added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n\n return {\"prompt_embeds\": prompt_embeds, **unet_added_cond_kwargs}\n\n # Let's first compute all the embeddings so that we can free up the text encoders\n # from memory.\n text_encoders = [text_encoder_one, text_encoder_two]\n tokenizers = [tokenizer_one, tokenizer_two]\n train_dataset = get_train_dataset(args, accelerator)\n compute_embeddings_fn = functools.partial(\n compute_embeddings,\n text_encoders=text_encoders,\n tokenizers=tokenizers,\n proportion_empty_prompts=args.proportion_empty_prompts,\n )\n with accelerator.main_process_first():\n from datasets.fingerprint import Hasher\n\n # fingerprint used by the cache for the other processes to load the result\n # details: https://github.com/huggingface/diffusers/pull/4038#discussion_r1266078401\n new_fingerprint = Hasher.hash(args)\n train_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint)\n\n del text_encoders, tokenizers\n gc.collect()\n torch.cuda.empty_cache()\n\n # Then get the training dataset ready to be passed to the dataloader.\n train_dataset = prepare_train_dataset(train_dataset, accelerator)\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_cycles=args.lr_num_cycles,\n power=args.lr_power,\n )\n\n # Prepare everything with our `accelerator`.\n controlnet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n controlnet, optimizer, train_dataloader, lr_scheduler\n )\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = dict(vars(args))\n\n # tensorboard cannot handle list types for config\n tracker_config.pop(\"validation_prompt\")\n tracker_config.pop(\"validation_image\")\n\n accelerator.init_trackers(args.tracker_project_name, config=tracker_config)\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num batches each epoch = {len(train_dataloader)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n image_logs = None\n for epoch in range(first_epoch, args.num_train_epochs):\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(controlnet):\n # Convert images to latent space\n if args.pretrained_vae_model_name_or_path is not None:\n pixel_values = batch[\"pixel_values\"].to(dtype=weight_dtype)\n else:\n pixel_values = batch[\"pixel_values\"]\n latents = vae.encode(pixel_values).latent_dist.sample()\n latents = latents * vae.config.scaling_factor\n if args.pretrained_vae_model_name_or_path is None:\n latents = latents.to(weight_dtype)\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # ControlNet conditioning.\n controlnet_image = batch[\"conditioning_pixel_values\"].to(dtype=weight_dtype)\n down_block_res_samples, mid_block_res_sample = controlnet(\n noisy_latents,\n timesteps,\n encoder_hidden_states=batch[\"prompt_ids\"],\n added_cond_kwargs=batch[\"unet_added_conditions\"],\n controlnet_cond=controlnet_image,\n return_dict=False,\n )\n\n # Predict the noise residual\n model_pred = unet(\n noisy_latents,\n timesteps,\n encoder_hidden_states=batch[\"prompt_ids\"],\n added_cond_kwargs=batch[\"unet_added_conditions\"],\n down_block_additional_residuals=[\n sample.to(dtype=weight_dtype) for sample in down_block_res_samples\n ],\n mid_block_additional_residual=mid_block_res_sample.to(dtype=weight_dtype),\n return_dict=False,\n )[0]\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n params_to_clip = controlnet.parameters()\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad(set_to_none=args.set_grads_to_none)\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n # DeepSpeed requires saving weights on every device; saving weights only on the main process would cause issues.\n if accelerator.distributed_type == DistributedType.DEEPSPEED or accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n if args.validation_prompt is not None and global_step % args.validation_steps == 0:\n image_logs = log_validation(\n vae=vae,\n unet=unet,\n controlnet=controlnet,\n args=args,\n accelerator=accelerator,\n weight_dtype=weight_dtype,\n step=global_step,\n )\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n # Create the pipeline using using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n controlnet = unwrap_model(controlnet)\n controlnet.save_pretrained(args.output_dir)\n\n # Run a final round of validation.\n # Setting `vae`, `unet`, and `controlnet` to None to load automatically from `args.output_dir`.\n image_logs = None\n if args.validation_prompt is not None:\n image_logs = log_validation(\n vae=None,\n unet=None,\n controlnet=None,\n args=args,\n accelerator=accelerator,\n weight_dtype=weight_dtype,\n step=global_step,\n is_final_validation=True,\n )\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n image_logs=image_logs,\n base_model=args.pretrained_model_name_or_path,\n repo_folder=args.output_dir,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/custom_diffusion/README.md", "content": "# Custom Diffusion training example \n\n[Custom Diffusion](https://arxiv.org/abs/2212.04488) is a method to customize text-to-image models like Stable Diffusion given just a few (4~5) images of a subject.\nThe `train_custom_diffusion.py` script shows how to implement the training procedure and adapt it for stable diffusion.\n\n## Running locally with PyTorch\n\n### Installing the dependencies\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n**Important**\n\nTo make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install -e .\n```\n\nThen cd in the example folder and run\n\n```bash\npip install -r requirements.txt\npip install clip-retrieval \n```\n\nAnd initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\n\nOr for a default accelerate configuration without answering questions about your environment\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell e.g. a notebook\n\n```python\nfrom accelerate.utils import write_basic_config\nwrite_basic_config()\n```\n### Cat example 😺\n\nNow let's get our dataset. Download dataset from [here](https://www.cs.cmu.edu/~custom-diffusion/assets/data.zip) and unzip it. \n\nWe also collect 200 real images using `clip-retrieval` which are combined with the target images in the training dataset as a regularization. This prevents overfitting to the given target image. The following flags enable the regularization `with_prior_preservation`, `real_prior` with `prior_loss_weight=1.`. \nThe `class_prompt` should be the category name same as target image. The collected real images are with text captions similar to the `class_prompt`. The retrieved image are saved in `class_data_dir`. You can disable `real_prior` to use generated images as regularization. To collect the real images use this command first before training. \n\n```bash\npip install clip-retrieval\npython retrieve.py --class_prompt cat --class_data_dir real_reg/samples_cat --num_class_images 200\n```\n\n**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport OUTPUT_DIR=\"path-to-save-model\"\nexport INSTANCE_DIR=\"./data/cat\"\n\naccelerate launch train_custom_diffusion.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --class_data_dir=./real_reg/samples_cat/ \\\n --with_prior_preservation --real_prior --prior_loss_weight=1.0 \\\n --class_prompt=\"cat\" --num_class_images=200 \\\n --instance_prompt=\"photo of a cat\" \\\n --resolution=512 \\\n --train_batch_size=2 \\\n --learning_rate=1e-5 \\\n --lr_warmup_steps=0 \\\n --max_train_steps=250 \\\n --scale_lr --hflip \\\n --modifier_token \"\" \n```\n\n**Use `--enable_xformers_memory_efficient_attention` for faster training with lower VRAM requirement (16GB per GPU). Follow [this guide](https://github.com/facebookresearch/xformers) for installation instructions.**\n\nTo track your experiments using Weights and Biases (`wandb`) and to save intermediate results (which we HIGHLY recommend), follow these steps:\n\n* Install `wandb`: `pip install wandb`.\n* Authorize: `wandb login`. \n* Then specify a `validation_prompt` and set `report_to` to `wandb` while launching training. You can also configure the following related arguments:\n * `num_validation_images`\n * `validation_steps`\n\nHere is an example command:\n\n```bash\naccelerate launch train_custom_diffusion.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --class_data_dir=./real_reg/samples_cat/ \\\n --with_prior_preservation --real_prior --prior_loss_weight=1.0 \\\n --class_prompt=\"cat\" --num_class_images=200 \\\n --instance_prompt=\"photo of a cat\" \\\n --resolution=512 \\\n --train_batch_size=2 \\\n --learning_rate=1e-5 \\\n --lr_warmup_steps=0 \\\n --max_train_steps=250 \\\n --scale_lr --hflip \\\n --modifier_token \"\" \\\n --validation_prompt=\" cat sitting in a bucket\" \\\n --report_to=\"wandb\"\n```\n\nHere is an example [Weights and Biases page](https://wandb.ai/sayakpaul/custom-diffusion/runs/26ghrcau) where you can check out the intermediate results along with other training details. \n\nIf you specify `--push_to_hub`, the learned parameters will be pushed to a repository on the Hugging Face Hub. Here is an [example repository](https://huggingface.co/sayakpaul/custom-diffusion-cat).\n\n### Training on multiple concepts 🐱🪵\n\nProvide a [json](https://github.com/adobe-research/custom-diffusion/blob/main/assets/concept_list.json) file with the info about each concept, similar to [this](https://github.com/ShivamShrirao/diffusers/blob/main/examples/dreambooth/train_dreambooth.py).\n\nTo collect the real images run this command for each concept in the json file. \n\n```bash\npip install clip-retrieval\npython retrieve.py --class_prompt {} --class_data_dir {} --num_class_images 200\n```\n\nAnd then we're ready to start training!\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\naccelerate launch train_custom_diffusion.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --output_dir=$OUTPUT_DIR \\\n --concepts_list=./concept_list.json \\\n --with_prior_preservation --real_prior --prior_loss_weight=1.0 \\\n --resolution=512 \\\n --train_batch_size=2 \\\n --learning_rate=1e-5 \\\n --lr_warmup_steps=0 \\\n --max_train_steps=500 \\\n --num_class_images=200 \\\n --scale_lr --hflip \\\n --modifier_token \"+\" \n```\n\nHere is an example [Weights and Biases page](https://wandb.ai/sayakpaul/custom-diffusion/runs/3990tzkg) where you can check out the intermediate results along with other training details. \n\n### Training on human faces\n\nFor fine-tuning on human faces we found the following configuration to work better: `learning_rate=5e-6`, `max_train_steps=1000 to 2000`, and `freeze_model=crossattn` with at least 15-20 images. \n\nTo collect the real images use this command first before training. \n\n```bash\npip install clip-retrieval\npython retrieve.py --class_prompt person --class_data_dir real_reg/samples_person --num_class_images 200\n```\n\nThen start training!\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport OUTPUT_DIR=\"path-to-save-model\"\nexport INSTANCE_DIR=\"path-to-images\"\n\naccelerate launch train_custom_diffusion.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --class_data_dir=./real_reg/samples_person/ \\\n --with_prior_preservation --real_prior --prior_loss_weight=1.0 \\\n --class_prompt=\"person\" --num_class_images=200 \\\n --instance_prompt=\"photo of a person\" \\\n --resolution=512 \\\n --train_batch_size=2 \\\n --learning_rate=5e-6 \\\n --lr_warmup_steps=0 \\\n --max_train_steps=1000 \\\n --scale_lr --hflip --noaug \\\n --freeze_model crossattn \\\n --modifier_token \"\" \\\n --enable_xformers_memory_efficient_attention \n```\n\n## Inference\n\nOnce you have trained a model using the above command, you can run inference using the below command. Make sure to include the `modifier token` (e.g. \\ in above example) in your prompt.\n\n```python\nimport torch\nfrom diffusers import DiffusionPipeline\n\npipe = DiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\", torch_dtype=torch.float16\n).to(\"cuda\")\npipe.unet.load_attn_procs(\n \"path-to-save-model\", weight_name=\"pytorch_custom_diffusion_weights.bin\"\n)\npipe.load_textual_inversion(\"path-to-save-model\", weight_name=\".bin\")\n\nimage = pipe(\n \" cat sitting in a bucket\",\n num_inference_steps=100,\n guidance_scale=6.0,\n eta=1.0,\n).images[0]\nimage.save(\"cat.png\")\n```\n\nIt's possible to directly load these parameters from a Hub repository:\n\n```python\nimport torch\nfrom huggingface_hub.repocard import RepoCard\nfrom diffusers import DiffusionPipeline\n\nmodel_id = \"sayakpaul/custom-diffusion-cat\"\ncard = RepoCard.load(model_id)\nbase_model_id = card.data.to_dict()[\"base_model\"]\n\npipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16).to(\n\"cuda\")\npipe.unet.load_attn_procs(model_id, weight_name=\"pytorch_custom_diffusion_weights.bin\")\npipe.load_textual_inversion(model_id, weight_name=\".bin\")\n\nimage = pipe(\n \" cat sitting in a bucket\",\n num_inference_steps=100,\n guidance_scale=6.0,\n eta=1.0,\n).images[0]\nimage.save(\"cat.png\")\n```\n\nHere is an example of performing inference with multiple concepts:\n\n```python\nimport torch\nfrom huggingface_hub.repocard import RepoCard\nfrom diffusers import DiffusionPipeline\n\nmodel_id = \"sayakpaul/custom-diffusion-cat-wooden-pot\"\ncard = RepoCard.load(model_id)\nbase_model_id = card.data.to_dict()[\"base_model\"]\n\npipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16).to(\n\"cuda\")\npipe.unet.load_attn_procs(model_id, weight_name=\"pytorch_custom_diffusion_weights.bin\")\npipe.load_textual_inversion(model_id, weight_name=\".bin\")\npipe.load_textual_inversion(model_id, weight_name=\".bin\")\n\nimage = pipe(\n \"the cat sculpture in the style of a wooden pot\",\n num_inference_steps=100,\n guidance_scale=6.0,\n eta=1.0,\n).images[0]\nimage.save(\"multi-subject.png\")\n```\n\nHere, `cat` and `wooden pot` refer to the multiple concepts.\n\n### Inference from a training checkpoint\n\nYou can also perform inference from one of the complete checkpoint saved during the training process, if you used the `--checkpointing_steps` argument. \n\nTODO.\n\n## Set grads to none\nTo save even more memory, pass the `--set_grads_to_none` argument to the script. This will set grads to None instead of zero. However, be aware that it changes certain behaviors, so if you start experiencing any problems, remove this argument.\n\nMore info: https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html\n\n## Experimental results\nYou can refer to [our webpage](https://www.cs.cmu.edu/~custom-diffusion/) that discusses our experiments in detail. We also released a more extensive dataset of 101 concepts for evaluating model customization methods. For more details please refer to our [dataset webpage](https://www.cs.cmu.edu/~custom-diffusion/dataset.html)." }, { "path": "diffusers/examples/custom_diffusion/requirements.txt", "content": "accelerate\ntorchvision\ntransformers>=4.25.1\nftfy\ntensorboard\nJinja2\n" }, { "path": "diffusers/examples/custom_diffusion/retrieve.py", "content": "# Copyright 2024 Custom Diffusion authors. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport argparse\nimport os\nfrom io import BytesIO\nfrom pathlib import Path\n\nimport requests\nfrom clip_retrieval.clip_client import ClipClient\nfrom PIL import Image\nfrom tqdm import tqdm\n\n\ndef retrieve(class_prompt, class_data_dir, num_class_images):\n factor = 1.5\n num_images = int(factor * num_class_images)\n client = ClipClient(\n url=\"https://knn.laion.ai/knn-service\", indice_name=\"laion_400m\", num_images=num_images, aesthetic_weight=0.1\n )\n\n os.makedirs(f\"{class_data_dir}/images\", exist_ok=True)\n if len(list(Path(f\"{class_data_dir}/images\").iterdir())) >= num_class_images:\n return\n\n while True:\n class_images = client.query(text=class_prompt)\n if len(class_images) >= factor * num_class_images or num_images > 1e4:\n break\n else:\n num_images = int(factor * num_images)\n client = ClipClient(\n url=\"https://knn.laion.ai/knn-service\",\n indice_name=\"laion_400m\",\n num_images=num_images,\n aesthetic_weight=0.1,\n )\n\n count = 0\n total = 0\n pbar = tqdm(desc=\"downloading real regularization images\", total=num_class_images)\n\n with open(f\"{class_data_dir}/caption.txt\", \"w\") as f1, open(f\"{class_data_dir}/urls.txt\", \"w\") as f2, open(\n f\"{class_data_dir}/images.txt\", \"w\"\n ) as f3:\n while total < num_class_images:\n images = class_images[count]\n count += 1\n try:\n img = requests.get(images[\"url\"], timeout=30)\n if img.status_code == 200:\n _ = Image.open(BytesIO(img.content))\n with open(f\"{class_data_dir}/images/{total}.jpg\", \"wb\") as f:\n f.write(img.content)\n f1.write(images[\"caption\"] + \"\\n\")\n f2.write(images[\"url\"] + \"\\n\")\n f3.write(f\"{class_data_dir}/images/{total}.jpg\" + \"\\n\")\n total += 1\n pbar.update(1)\n else:\n continue\n except Exception:\n continue\n return\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(\"\", add_help=False)\n parser.add_argument(\"--class_prompt\", help=\"text prompt to retrieve images\", required=True, type=str)\n parser.add_argument(\"--class_data_dir\", help=\"path to save images\", required=True, type=str)\n parser.add_argument(\"--num_class_images\", help=\"number of images to download\", default=200, type=int)\n return parser.parse_args()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)\n" }, { "path": "diffusers/examples/custom_diffusion/test_custom_diffusion.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport logging\nimport os\nimport sys\nimport tempfile\n\n\nsys.path.append(\"..\")\nfrom test_examples_utils import ExamplesTestsAccelerate, run_command # noqa: E402\n\n\nlogging.basicConfig(level=logging.DEBUG)\n\nlogger = logging.getLogger()\nstream_handler = logging.StreamHandler(sys.stdout)\nlogger.addHandler(stream_handler)\n\n\nclass CustomDiffusion(ExamplesTestsAccelerate):\n def test_custom_diffusion(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/custom_diffusion/train_custom_diffusion.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe\n --instance_data_dir docs/source/en/imgs\n --instance_prompt \n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 1.0e-05\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --modifier_token \n --no_safe_serialization\n --output_dir {tmpdir}\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n # save_pretrained smoke test\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"pytorch_custom_diffusion_weights.bin\")))\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \".bin\")))\n\n def test_custom_diffusion_checkpointing_checkpoints_total_limit(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/custom_diffusion/train_custom_diffusion.py\n --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe\n --instance_data_dir=docs/source/en/imgs\n --output_dir={tmpdir}\n --instance_prompt=\n --resolution=64\n --train_batch_size=1\n --modifier_token=\n --dataloader_num_workers=0\n --max_train_steps=6\n --checkpoints_total_limit=2\n --checkpointing_steps=2\n --no_safe_serialization\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n\n self.assertEqual({x for x in os.listdir(tmpdir) if \"checkpoint\" in x}, {\"checkpoint-4\", \"checkpoint-6\"})\n\n def test_custom_diffusion_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/custom_diffusion/train_custom_diffusion.py\n --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe\n --instance_data_dir=docs/source/en/imgs\n --output_dir={tmpdir}\n --instance_prompt=\n --resolution=64\n --train_batch_size=1\n --modifier_token=\n --dataloader_num_workers=0\n --max_train_steps=4\n --checkpointing_steps=2\n --no_safe_serialization\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-2\", \"checkpoint-4\"},\n )\n\n resume_run_args = f\"\"\"\n examples/custom_diffusion/train_custom_diffusion.py\n --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe\n --instance_data_dir=docs/source/en/imgs\n --output_dir={tmpdir}\n --instance_prompt=\n --resolution=64\n --train_batch_size=1\n --modifier_token=\n --dataloader_num_workers=0\n --max_train_steps=8\n --checkpointing_steps=2\n --resume_from_checkpoint=checkpoint-4\n --checkpoints_total_limit=2\n --no_safe_serialization\n \"\"\".split()\n\n run_command(self._launch_args + resume_run_args)\n\n self.assertEqual({x for x in os.listdir(tmpdir) if \"checkpoint\" in x}, {\"checkpoint-6\", \"checkpoint-8\"})\n" }, { "path": "diffusers/examples/custom_diffusion/train_custom_diffusion.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 Custom Diffusion authors and the HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport itertools\nimport json\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nimport warnings\nfrom pathlib import Path\n\nimport numpy as np\nimport safetensors\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom huggingface_hub import HfApi, create_repo\nfrom huggingface_hub.utils import insecure_hashlib\nfrom packaging import version\nfrom PIL import Image\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n DiffusionPipeline,\n DPMSolverMultistepScheduler,\n UNet2DConditionModel,\n)\nfrom diffusers.loaders import AttnProcsLayers\nfrom diffusers.models.attention_processor import (\n CustomDiffusionAttnProcessor,\n CustomDiffusionAttnProcessor2_0,\n CustomDiffusionXFormersAttnProcessor,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import check_min_version, is_wandb_available\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef freeze_params(params):\n for param in params:\n param.requires_grad = False\n\n\ndef save_model_card(repo_id: str, images=None, base_model=str, prompt=str, repo_folder=None):\n img_str = \"\"\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n img_str += f\"![img_{i}](./image_{i}.png)\\n\"\n\n model_description = f\"\"\"\n# Custom Diffusion - {repo_id}\n\nThese are Custom Diffusion adaption weights for {base_model}. The weights were trained on {prompt} using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. \\n\n{img_str}\n\n\\nFor more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).\n\"\"\"\n model_card = load_or_create_model_card(\n repo_id_or_path=repo_id,\n from_training=True,\n license=\"creativeml-openrail-m\",\n base_model=base_model,\n prompt=prompt,\n model_description=model_description,\n inference=True,\n )\n\n tags = [\n \"text-to-image\",\n \"diffusers\",\n \"stable-diffusion\",\n \"stable-diffusion-diffusers\",\n \"custom-diffusion\",\n \"diffusers-training\",\n ]\n model_card = populate_model_card(model_card, tags=tags)\n\n model_card.save(os.path.join(repo_folder, \"README.md\"))\n\n\ndef import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path,\n subfolder=\"text_encoder\",\n revision=revision,\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"RobertaSeriesModelWithTransformation\":\n from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation\n\n return RobertaSeriesModelWithTransformation\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef collate_fn(examples, with_prior_preservation):\n input_ids = [example[\"instance_prompt_ids\"] for example in examples]\n pixel_values = [example[\"instance_images\"] for example in examples]\n mask = [example[\"mask\"] for example in examples]\n # Concat class and instance examples for prior preservation.\n # We do this to avoid doing two forward passes.\n if with_prior_preservation:\n input_ids += [example[\"class_prompt_ids\"] for example in examples]\n pixel_values += [example[\"class_images\"] for example in examples]\n mask += [example[\"class_mask\"] for example in examples]\n\n input_ids = torch.cat(input_ids, dim=0)\n pixel_values = torch.stack(pixel_values)\n mask = torch.stack(mask)\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n mask = mask.to(memory_format=torch.contiguous_format).float()\n\n batch = {\"input_ids\": input_ids, \"pixel_values\": pixel_values, \"mask\": mask.unsqueeze(1)}\n return batch\n\n\nclass PromptDataset(Dataset):\n \"\"\"A simple dataset to prepare the prompts to generate class images on multiple GPUs.\"\"\"\n\n def __init__(self, prompt, num_samples):\n self.prompt = prompt\n self.num_samples = num_samples\n\n def __len__(self):\n return self.num_samples\n\n def __getitem__(self, index):\n example = {}\n example[\"prompt\"] = self.prompt\n example[\"index\"] = index\n return example\n\n\nclass CustomDiffusionDataset(Dataset):\n \"\"\"\n A dataset to prepare the instance and class images with the prompts for fine-tuning the model.\n It pre-processes the images and the tokenizes prompts.\n \"\"\"\n\n def __init__(\n self,\n concepts_list,\n tokenizer,\n size=512,\n mask_size=64,\n center_crop=False,\n with_prior_preservation=False,\n num_class_images=200,\n hflip=False,\n aug=True,\n ):\n self.size = size\n self.mask_size = mask_size\n self.center_crop = center_crop\n self.tokenizer = tokenizer\n self.interpolation = Image.BILINEAR\n self.aug = aug\n\n self.instance_images_path = []\n self.class_images_path = []\n self.with_prior_preservation = with_prior_preservation\n for concept in concepts_list:\n inst_img_path = [\n (x, concept[\"instance_prompt\"]) for x in Path(concept[\"instance_data_dir\"]).iterdir() if x.is_file()\n ]\n self.instance_images_path.extend(inst_img_path)\n\n if with_prior_preservation:\n class_data_root = Path(concept[\"class_data_dir\"])\n if os.path.isdir(class_data_root):\n class_images_path = list(class_data_root.iterdir())\n class_prompt = [concept[\"class_prompt\"] for _ in range(len(class_images_path))]\n else:\n with open(class_data_root, \"r\") as f:\n class_images_path = f.read().splitlines()\n with open(concept[\"class_prompt\"], \"r\") as f:\n class_prompt = f.read().splitlines()\n\n class_img_path = list(zip(class_images_path, class_prompt))\n self.class_images_path.extend(class_img_path[:num_class_images])\n\n random.shuffle(self.instance_images_path)\n self.num_instance_images = len(self.instance_images_path)\n self.num_class_images = len(self.class_images_path)\n self._length = max(self.num_class_images, self.num_instance_images)\n self.flip = transforms.RandomHorizontalFlip(0.5 * hflip)\n\n self.image_transforms = transforms.Compose(\n [\n self.flip,\n transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def __len__(self):\n return self._length\n\n def preprocess(self, image, scale, resample):\n outer, inner = self.size, scale\n factor = self.size // self.mask_size\n if scale > self.size:\n outer, inner = scale, self.size\n top, left = np.random.randint(0, outer - inner + 1), np.random.randint(0, outer - inner + 1)\n image = image.resize((scale, scale), resample=resample)\n image = np.array(image).astype(np.uint8)\n image = (image / 127.5 - 1.0).astype(np.float32)\n instance_image = np.zeros((self.size, self.size, 3), dtype=np.float32)\n mask = np.zeros((self.size // factor, self.size // factor))\n if scale > self.size:\n instance_image = image[top : top + inner, left : left + inner, :]\n mask = np.ones((self.size // factor, self.size // factor))\n else:\n instance_image[top : top + inner, left : left + inner, :] = image\n mask[\n top // factor + 1 : (top + scale) // factor - 1, left // factor + 1 : (left + scale) // factor - 1\n ] = 1.0\n return instance_image, mask\n\n def __getitem__(self, index):\n example = {}\n instance_image, instance_prompt = self.instance_images_path[index % self.num_instance_images]\n instance_image = Image.open(instance_image)\n if not instance_image.mode == \"RGB\":\n instance_image = instance_image.convert(\"RGB\")\n instance_image = self.flip(instance_image)\n\n # apply resize augmentation and create a valid image region mask\n random_scale = self.size\n if self.aug:\n random_scale = (\n np.random.randint(self.size // 3, self.size + 1)\n if np.random.uniform() < 0.66\n else np.random.randint(int(1.2 * self.size), int(1.4 * self.size))\n )\n instance_image, mask = self.preprocess(instance_image, random_scale, self.interpolation)\n\n if random_scale < 0.6 * self.size:\n instance_prompt = np.random.choice([\"a far away \", \"very small \"]) + instance_prompt\n elif random_scale > self.size:\n instance_prompt = np.random.choice([\"zoomed in \", \"close up \"]) + instance_prompt\n\n example[\"instance_images\"] = torch.from_numpy(instance_image).permute(2, 0, 1)\n example[\"mask\"] = torch.from_numpy(mask)\n example[\"instance_prompt_ids\"] = self.tokenizer(\n instance_prompt,\n truncation=True,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n ).input_ids\n\n if self.with_prior_preservation:\n class_image, class_prompt = self.class_images_path[index % self.num_class_images]\n class_image = Image.open(class_image)\n if not class_image.mode == \"RGB\":\n class_image = class_image.convert(\"RGB\")\n example[\"class_images\"] = self.image_transforms(class_image)\n example[\"class_mask\"] = torch.ones_like(example[\"mask\"])\n example[\"class_prompt_ids\"] = self.tokenizer(\n class_prompt,\n truncation=True,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n ).input_ids\n\n return example\n\n\ndef save_new_embed(text_encoder, modifier_token_id, accelerator, args, output_dir, safe_serialization=True):\n \"\"\"Saves the new token embeddings from the text encoder.\"\"\"\n logger.info(\"Saving embeddings\")\n learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight\n for x, y in zip(modifier_token_id, args.modifier_token):\n learned_embeds_dict = {}\n learned_embeds_dict[y] = learned_embeds[x]\n filename = f\"{output_dir}/{y}.bin\"\n\n if safe_serialization:\n safetensors.torch.save_file(learned_embeds_dict, filename, metadata={\"format\": \"pt\"})\n else:\n torch.save(learned_embeds_dict, filename)\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Custom Diffusion training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--tokenizer_name\",\n type=str,\n default=None,\n help=\"Pretrained tokenizer name or path if not the same as model_name\",\n )\n parser.add_argument(\n \"--instance_data_dir\",\n type=str,\n default=None,\n help=\"A folder containing the training data of instance images.\",\n )\n parser.add_argument(\n \"--class_data_dir\",\n type=str,\n default=None,\n help=\"A folder containing the training data of class images.\",\n )\n parser.add_argument(\n \"--instance_prompt\",\n type=str,\n default=None,\n help=\"The prompt with identifier specifying the instance\",\n )\n parser.add_argument(\n \"--class_prompt\",\n type=str,\n default=None,\n help=\"The prompt to specify images in the same class as provided instance images.\",\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n help=\"A prompt that is used during validation to verify that the model is learning.\",\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=2,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=50,\n help=(\n \"Run dreambooth validation every X epochs. Dreambooth validation consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--with_prior_preservation\",\n default=False,\n action=\"store_true\",\n help=\"Flag to add prior preservation loss.\",\n )\n parser.add_argument(\n \"--real_prior\",\n default=False,\n action=\"store_true\",\n help=\"real images as prior.\",\n )\n parser.add_argument(\"--prior_loss_weight\", type=float, default=1.0, help=\"The weight of prior preservation loss.\")\n parser.add_argument(\n \"--num_class_images\",\n type=int,\n default=200,\n help=(\n \"Minimal class images for prior preservation loss. If there are not enough images already present in\"\n \" class_data_dir, additional images will be sampled with class_prompt.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"custom-diffusion-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=42, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\n \"--sample_batch_size\", type=int, default=4, help=\"Batch size (per device) for sampling images.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=250,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final\"\n \" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-5,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=2,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\n \"--freeze_model\",\n type=str,\n default=\"crossattn_kv\",\n choices=[\"crossattn_kv\", \"crossattn\"],\n help=\"crossattn to enable fine-tuning of all params in the cross attention\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--prior_generation_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp32\", \"fp16\", \"bf16\"],\n help=(\n \"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32.\"\n ),\n )\n parser.add_argument(\n \"--concepts_list\",\n type=str,\n default=None,\n help=\"Path to json containing multiple concepts, will overwrite parameters like instance_prompt, class_prompt, etc.\",\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--set_grads_to_none\",\n action=\"store_true\",\n help=(\n \"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain\"\n \" behaviors, so disable this argument if it causes any problems. More info:\"\n \" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html\"\n ),\n )\n parser.add_argument(\n \"--modifier_token\",\n type=str,\n default=None,\n help=\"A token to use as a modifier for the concept.\",\n )\n parser.add_argument(\n \"--initializer_token\", type=str, default=\"ktn+pll+ucd\", help=\"A token to use as initializer word.\"\n )\n parser.add_argument(\"--hflip\", action=\"store_true\", help=\"Apply horizontal flip data augmentation.\")\n parser.add_argument(\n \"--noaug\",\n action=\"store_true\",\n help=\"Dont apply augmentation during data augmentation when this flag is enabled.\",\n )\n parser.add_argument(\n \"--no_safe_serialization\",\n action=\"store_true\",\n help=\"If specified save the checkpoint not in `safetensors` format, but in original PyTorch format instead.\",\n )\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.with_prior_preservation:\n if args.concepts_list is None:\n if args.class_data_dir is None:\n raise ValueError(\"You must specify a data directory for class images.\")\n if args.class_prompt is None:\n raise ValueError(\"You must specify prompt for class images.\")\n else:\n # logger is not available yet\n if args.class_data_dir is not None:\n warnings.warn(\"You need not use --class_data_dir without --with_prior_preservation.\")\n if args.class_prompt is not None:\n warnings.warn(\"You need not use --class_prompt without --with_prior_preservation.\")\n\n return args\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n import wandb\n\n # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate\n # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.\n # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"custom-diffusion\", config=vars(args))\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n if args.concepts_list is None:\n args.concepts_list = [\n {\n \"instance_prompt\": args.instance_prompt,\n \"class_prompt\": args.class_prompt,\n \"instance_data_dir\": args.instance_data_dir,\n \"class_data_dir\": args.class_data_dir,\n }\n ]\n else:\n with open(args.concepts_list, \"r\") as f:\n args.concepts_list = json.load(f)\n\n # Generate class images if prior preservation is enabled.\n if args.with_prior_preservation:\n for i, concept in enumerate(args.concepts_list):\n class_images_dir = Path(concept[\"class_data_dir\"])\n if not class_images_dir.exists():\n class_images_dir.mkdir(parents=True, exist_ok=True)\n if args.real_prior:\n assert (\n class_images_dir / \"images\"\n ).exists(), f\"Please run: python retrieve.py --class_prompt \\\"{concept['class_prompt']}\\\" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}\"\n assert (\n len(list((class_images_dir / \"images\").iterdir())) == args.num_class_images\n ), f\"Please run: python retrieve.py --class_prompt \\\"{concept['class_prompt']}\\\" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}\"\n assert (\n class_images_dir / \"caption.txt\"\n ).exists(), f\"Please run: python retrieve.py --class_prompt \\\"{concept['class_prompt']}\\\" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}\"\n assert (\n class_images_dir / \"images.txt\"\n ).exists(), f\"Please run: python retrieve.py --class_prompt \\\"{concept['class_prompt']}\\\" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}\"\n concept[\"class_prompt\"] = os.path.join(class_images_dir, \"caption.txt\")\n concept[\"class_data_dir\"] = os.path.join(class_images_dir, \"images.txt\")\n args.concepts_list[i] = concept\n accelerator.wait_for_everyone()\n else:\n cur_class_images = len(list(class_images_dir.iterdir()))\n\n if cur_class_images < args.num_class_images:\n torch_dtype = torch.float16 if accelerator.device.type == \"cuda\" else torch.float32\n if args.prior_generation_precision == \"fp32\":\n torch_dtype = torch.float32\n elif args.prior_generation_precision == \"fp16\":\n torch_dtype = torch.float16\n elif args.prior_generation_precision == \"bf16\":\n torch_dtype = torch.bfloat16\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n torch_dtype=torch_dtype,\n safety_checker=None,\n revision=args.revision,\n variant=args.variant,\n )\n pipeline.set_progress_bar_config(disable=True)\n\n num_new_images = args.num_class_images - cur_class_images\n logger.info(f\"Number of class images to sample: {num_new_images}.\")\n\n sample_dataset = PromptDataset(concept[\"class_prompt\"], num_new_images)\n sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)\n\n sample_dataloader = accelerator.prepare(sample_dataloader)\n pipeline.to(accelerator.device)\n\n for example in tqdm(\n sample_dataloader,\n desc=\"Generating class images\",\n disable=not accelerator.is_local_main_process,\n ):\n images = pipeline(example[\"prompt\"]).images\n\n for i, image in enumerate(images):\n hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()\n image_filename = (\n class_images_dir / f\"{example['index'][i] + cur_class_images}-{hash_image}.jpg\"\n )\n image.save(image_filename)\n\n del pipeline\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizer\n if args.tokenizer_name:\n tokenizer = AutoTokenizer.from_pretrained(\n args.tokenizer_name,\n revision=args.revision,\n use_fast=False,\n )\n elif args.pretrained_model_name_or_path:\n tokenizer = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n\n # import correct text encoder class\n text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n text_encoder = text_encoder_cls.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision, variant=args.variant\n )\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n # Adding a modifier token which is optimized ####\n # Code taken from https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py\n modifier_token_id = []\n initializer_token_id = []\n if args.modifier_token is not None:\n args.modifier_token = args.modifier_token.split(\"+\")\n args.initializer_token = args.initializer_token.split(\"+\")\n if len(args.modifier_token) > len(args.initializer_token):\n raise ValueError(\"You must specify + separated initializer token for each modifier token.\")\n for modifier_token, initializer_token in zip(\n args.modifier_token, args.initializer_token[: len(args.modifier_token)]\n ):\n # Add the placeholder token in tokenizer\n num_added_tokens = tokenizer.add_tokens(modifier_token)\n if num_added_tokens == 0:\n raise ValueError(\n f\"The tokenizer already contains the token {modifier_token}. Please pass a different\"\n \" `modifier_token` that is not already in the tokenizer.\"\n )\n\n # Convert the initializer_token, placeholder_token to ids\n token_ids = tokenizer.encode([initializer_token], add_special_tokens=False)\n print(token_ids)\n # Check if initializer_token is a single token or a sequence of tokens\n if len(token_ids) > 1:\n raise ValueError(\"The initializer token must be a single token.\")\n\n initializer_token_id.append(token_ids[0])\n modifier_token_id.append(tokenizer.convert_tokens_to_ids(modifier_token))\n\n # Resize the token embeddings as we are adding new special tokens to the tokenizer\n text_encoder.resize_token_embeddings(len(tokenizer))\n\n # Initialise the newly added placeholder token with the embeddings of the initializer token\n token_embeds = text_encoder.get_input_embeddings().weight.data\n for x, y in zip(modifier_token_id, initializer_token_id):\n token_embeds[x] = token_embeds[y]\n\n # Freeze all parameters except for the token embeddings in text encoder\n params_to_freeze = itertools.chain(\n text_encoder.text_model.encoder.parameters(),\n text_encoder.text_model.final_layer_norm.parameters(),\n text_encoder.text_model.embeddings.position_embedding.parameters(),\n )\n freeze_params(params_to_freeze)\n ########################################################\n ########################################################\n\n vae.requires_grad_(False)\n if args.modifier_token is None:\n text_encoder.requires_grad_(False)\n unet.requires_grad_(False)\n # For mixed precision training we cast the text_encoder and vae weights to half-precision\n # as these models are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move unet, vae and text_encoder to device and cast to weight_dtype\n if accelerator.mixed_precision != \"fp16\" and args.modifier_token is not None:\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n unet.to(accelerator.device, dtype=weight_dtype)\n vae.to(accelerator.device, dtype=weight_dtype)\n\n attention_class = (\n CustomDiffusionAttnProcessor2_0 if hasattr(F, \"scaled_dot_product_attention\") else CustomDiffusionAttnProcessor\n )\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n attention_class = CustomDiffusionXFormersAttnProcessor\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # now we will add new Custom Diffusion weights to the attention layers\n # It's important to realize here how many attention weights will be added and of which sizes\n # The sizes of the attention layers consist only of two different variables:\n # 1) - the \"hidden_size\", which is increased according to `unet.config.block_out_channels`.\n # 2) - the \"cross attention size\", which is set to `unet.config.cross_attention_dim`.\n\n # Let's first see how many attention processors we will have to set.\n # For Stable Diffusion, it should be equal to:\n # - down blocks (2x attention layers) * (2x transformer layers) * (3x down blocks) = 12\n # - mid blocks (2x attention layers) * (1x transformer layers) * (1x mid blocks) = 2\n # - up blocks (2x attention layers) * (3x transformer layers) * (3x down blocks) = 18\n # => 32 layers\n\n # Only train key, value projection layers if freeze_model = 'crossattn_kv' else train all params in the cross attention layer\n train_kv = True\n train_q_out = False if args.freeze_model == \"crossattn_kv\" else True\n custom_diffusion_attn_procs = {}\n\n st = unet.state_dict()\n for name, _ in unet.attn_processors.items():\n cross_attention_dim = None if name.endswith(\"attn1.processor\") else unet.config.cross_attention_dim\n if name.startswith(\"mid_block\"):\n hidden_size = unet.config.block_out_channels[-1]\n elif name.startswith(\"up_blocks\"):\n block_id = int(name[len(\"up_blocks.\")])\n hidden_size = list(reversed(unet.config.block_out_channels))[block_id]\n elif name.startswith(\"down_blocks\"):\n block_id = int(name[len(\"down_blocks.\")])\n hidden_size = unet.config.block_out_channels[block_id]\n layer_name = name.split(\".processor\")[0]\n weights = {\n \"to_k_custom_diffusion.weight\": st[layer_name + \".to_k.weight\"],\n \"to_v_custom_diffusion.weight\": st[layer_name + \".to_v.weight\"],\n }\n if train_q_out:\n weights[\"to_q_custom_diffusion.weight\"] = st[layer_name + \".to_q.weight\"]\n weights[\"to_out_custom_diffusion.0.weight\"] = st[layer_name + \".to_out.0.weight\"]\n weights[\"to_out_custom_diffusion.0.bias\"] = st[layer_name + \".to_out.0.bias\"]\n if cross_attention_dim is not None:\n custom_diffusion_attn_procs[name] = attention_class(\n train_kv=train_kv,\n train_q_out=train_q_out,\n hidden_size=hidden_size,\n cross_attention_dim=cross_attention_dim,\n ).to(unet.device)\n custom_diffusion_attn_procs[name].load_state_dict(weights)\n else:\n custom_diffusion_attn_procs[name] = attention_class(\n train_kv=False,\n train_q_out=False,\n hidden_size=hidden_size,\n cross_attention_dim=cross_attention_dim,\n )\n del st\n unet.set_attn_processor(custom_diffusion_attn_procs)\n custom_diffusion_layers = AttnProcsLayers(unet.attn_processors)\n\n accelerator.register_for_checkpointing(custom_diffusion_layers)\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n if args.modifier_token is not None:\n text_encoder.gradient_checkpointing_enable()\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n if args.with_prior_preservation:\n args.learning_rate = args.learning_rate * 2.0\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # Optimizer creation\n optimizer = optimizer_class(\n itertools.chain(text_encoder.get_input_embeddings().parameters(), custom_diffusion_layers.parameters())\n if args.modifier_token is not None\n else custom_diffusion_layers.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Dataset and DataLoaders creation:\n train_dataset = CustomDiffusionDataset(\n concepts_list=args.concepts_list,\n tokenizer=tokenizer,\n with_prior_preservation=args.with_prior_preservation,\n size=args.resolution,\n mask_size=vae.encode(\n torch.randn(1, 3, args.resolution, args.resolution).to(dtype=weight_dtype).to(accelerator.device)\n )\n .latent_dist.sample()\n .size()[-1],\n center_crop=args.center_crop,\n num_class_images=args.num_class_images,\n hflip=args.hflip,\n aug=not args.noaug,\n )\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n batch_size=args.train_batch_size,\n shuffle=True,\n collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n )\n\n # Prepare everything with our `accelerator`.\n if args.modifier_token is not None:\n custom_diffusion_layers, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n custom_diffusion_layers, text_encoder, optimizer, train_dataloader, lr_scheduler\n )\n else:\n custom_diffusion_layers, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n custom_diffusion_layers, optimizer, train_dataloader, lr_scheduler\n )\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num batches each epoch = {len(train_dataloader)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n for epoch in range(first_epoch, args.num_train_epochs):\n unet.train()\n if args.modifier_token is not None:\n text_encoder.train()\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet), accelerator.accumulate(text_encoder):\n # Convert images to latent space\n latents = vae.encode(batch[\"pixel_values\"].to(dtype=weight_dtype)).latent_dist.sample()\n latents = latents * vae.config.scaling_factor\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # Get the text embedding for conditioning\n encoder_hidden_states = text_encoder(batch[\"input_ids\"])[0]\n\n # Predict the noise residual\n model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n if args.with_prior_preservation:\n # Chunk the noise and model_pred into two parts and compute the loss on each part separately.\n model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)\n target, target_prior = torch.chunk(target, 2, dim=0)\n mask = torch.chunk(batch[\"mask\"], 2, dim=0)[0]\n # Compute instance loss\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"none\")\n loss = ((loss * mask).sum([1, 2, 3]) / mask.sum([1, 2, 3])).mean()\n\n # Compute prior loss\n prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction=\"mean\")\n\n # Add the prior loss to the instance loss.\n loss = loss + args.prior_loss_weight * prior_loss\n else:\n mask = batch[\"mask\"]\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"none\")\n loss = ((loss * mask).sum([1, 2, 3]) / mask.sum([1, 2, 3])).mean()\n accelerator.backward(loss)\n # Zero out the gradients for all token embeddings except the newly added\n # embeddings for the concept, as we only want to optimize the concept embeddings\n if args.modifier_token is not None:\n if accelerator.num_processes > 1:\n grads_text_encoder = text_encoder.module.get_input_embeddings().weight.grad\n else:\n grads_text_encoder = text_encoder.get_input_embeddings().weight.grad\n # Get the index for tokens that we want to zero the grads for\n index_grads_to_zero = torch.arange(len(tokenizer)) != modifier_token_id[0]\n for i in range(1, len(modifier_token_id)):\n index_grads_to_zero = index_grads_to_zero & (\n torch.arange(len(tokenizer)) != modifier_token_id[i]\n )\n grads_text_encoder.data[index_grads_to_zero, :] = grads_text_encoder.data[\n index_grads_to_zero, :\n ].fill_(0)\n\n if accelerator.sync_gradients:\n params_to_clip = (\n itertools.chain(text_encoder.parameters(), custom_diffusion_layers.parameters())\n if args.modifier_token is not None\n else custom_diffusion_layers.parameters()\n )\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad(set_to_none=args.set_grads_to_none)\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if global_step % args.checkpointing_steps == 0:\n if accelerator.is_main_process:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n images = []\n\n if args.validation_prompt is not None and global_step % args.validation_steps == 0:\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n # create pipeline\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n unet=accelerator.unwrap_model(unet),\n text_encoder=accelerator.unwrap_model(text_encoder),\n tokenizer=tokenizer,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n images = [\n pipeline(args.validation_prompt, num_inference_steps=25, generator=generator, eta=1.0).images[\n 0\n ]\n for _ in range(args.num_validation_images)\n ]\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\")\n for i, image in enumerate(images)\n ]\n }\n )\n\n del pipeline\n torch.cuda.empty_cache()\n\n # Save the custom diffusion layers\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = unet.to(torch.float32)\n unet.save_attn_procs(args.output_dir, safe_serialization=not args.no_safe_serialization)\n save_new_embed(\n text_encoder,\n modifier_token_id,\n accelerator,\n args,\n args.output_dir,\n safe_serialization=not args.no_safe_serialization,\n )\n\n # Final inference\n # Load previous pipeline\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path, revision=args.revision, variant=args.variant, torch_dtype=weight_dtype\n )\n pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)\n pipeline = pipeline.to(accelerator.device)\n\n # load attention processors\n weight_name = (\n \"pytorch_custom_diffusion_weights.safetensors\"\n if not args.no_safe_serialization\n else \"pytorch_custom_diffusion_weights.bin\"\n )\n pipeline.unet.load_attn_procs(args.output_dir, weight_name=weight_name)\n for token in args.modifier_token:\n token_weight_name = f\"{token}.safetensors\" if not args.no_safe_serialization else f\"{token}.bin\"\n pipeline.load_textual_inversion(args.output_dir, weight_name=token_weight_name)\n\n # run inference\n if args.validation_prompt and args.num_validation_images > 0:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n images = [\n pipeline(args.validation_prompt, num_inference_steps=25, generator=generator, eta=1.0).images[0]\n for _ in range(args.num_validation_images)\n ]\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"test\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"test\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\")\n for i, image in enumerate(images)\n ]\n }\n )\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n images=images,\n base_model=args.pretrained_model_name_or_path,\n prompt=args.instance_prompt,\n repo_folder=args.output_dir,\n )\n api = HfApi(token=args.hub_token)\n api.upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/dreambooth/README.md", "content": "# DreamBooth training example\n\n[DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text2image models like stable diffusion given just a few(3~5) images of a subject.\nThe `train_dreambooth.py` script shows how to implement the training procedure and adapt it for stable diffusion.\n\n\n## Running locally with PyTorch\n\n### Installing the dependencies\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n**Important**\n\nTo make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install -e .\n```\n\nThen cd in the example folder and run\n```bash\npip install -r requirements.txt\n```\n\nAnd initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\n\nOr for a default accelerate configuration without answering questions about your environment\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell e.g. a notebook\n\n```python\nfrom accelerate.utils import write_basic_config\nwrite_basic_config()\n```\n\nWhen running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups.\nNote also that we use PEFT library as backend for LoRA training, make sure to have `peft>=0.6.0` installed in your environment.\n\n### Dog toy example\n\nNow let's get our dataset. For this example we will use some dog images: https://huggingface.co/datasets/diffusers/dog-example.\n\nLet's first download it locally:\n\n```python\nfrom huggingface_hub import snapshot_download\n\nlocal_dir = \"./dog\"\nsnapshot_download(\n \"diffusers/dog-example\",\n local_dir=local_dir, repo_type=\"dataset\",\n ignore_patterns=\".gitattributes\",\n)\n```\n\nAnd launch the training using:\n\n**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport INSTANCE_DIR=\"dog\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\naccelerate launch train_dreambooth.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --instance_prompt=\"a photo of sks dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=1 \\\n --learning_rate=5e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --max_train_steps=400 \\\n --push_to_hub\n```\n\n### Training with prior-preservation loss\n\nPrior-preservation is used to avoid overfitting and language-drift. Refer to the paper to learn more about it. For prior-preservation we first generate images using the model with a class prompt and then use those during training along with our data.\nAccording to the paper, it's recommended to generate `num_epochs * num_samples` images for prior-preservation. 200-300 works well for most cases. The `num_class_images` flag sets the number of images to generate with the class prompt. You can place existing images in `class_data_dir`, and the training script will generate any additional images so that `num_class_images` are present in `class_data_dir` during training time.\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport INSTANCE_DIR=\"dog\"\nexport CLASS_DIR=\"path-to-class-images\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\naccelerate launch train_dreambooth.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --class_data_dir=$CLASS_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --with_prior_preservation --prior_loss_weight=1.0 \\\n --instance_prompt=\"a photo of sks dog\" \\\n --class_prompt=\"a photo of dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=1 \\\n --learning_rate=5e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --num_class_images=200 \\\n --max_train_steps=800 \\\n --push_to_hub\n```\n\n\n### Training on a 16GB GPU:\n\nWith the help of gradient checkpointing and the 8-bit optimizer from bitsandbytes it's possible to run train dreambooth on a 16GB GPU.\n\nTo install `bitsandbytes` please refer to this [readme](https://github.com/TimDettmers/bitsandbytes#requirements--installation).\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport INSTANCE_DIR=\"dog\"\nexport CLASS_DIR=\"path-to-class-images\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\naccelerate launch train_dreambooth.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --class_data_dir=$CLASS_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --with_prior_preservation --prior_loss_weight=1.0 \\\n --instance_prompt=\"a photo of sks dog\" \\\n --class_prompt=\"a photo of dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=2 --gradient_checkpointing \\\n --use_8bit_adam \\\n --learning_rate=5e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --num_class_images=200 \\\n --max_train_steps=800 \\\n --push_to_hub\n```\n\n\n### Training on a 12GB GPU:\n\nIt is possible to run dreambooth on a 12GB GPU by using the following optimizations:\n- [gradient checkpointing and the 8-bit optimizer](#training-on-a-16gb-gpu)\n- [xformers](#training-with-xformers)\n- [setting grads to none](#set-grads-to-none)\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport INSTANCE_DIR=\"dog\"\nexport CLASS_DIR=\"path-to-class-images\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\naccelerate launch train_dreambooth.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --class_data_dir=$CLASS_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --with_prior_preservation --prior_loss_weight=1.0 \\\n --instance_prompt=\"a photo of sks dog\" \\\n --class_prompt=\"a photo of dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=1 --gradient_checkpointing \\\n --use_8bit_adam \\\n --enable_xformers_memory_efficient_attention \\\n --set_grads_to_none \\\n --learning_rate=2e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --num_class_images=200 \\\n --max_train_steps=800 \\\n --push_to_hub\n```\n\n\n### Training on a 8 GB GPU:\n\nBy using [DeepSpeed](https://www.deepspeed.ai/) it's possible to offload some\ntensors from VRAM to either CPU or NVME allowing to train with less VRAM.\n\nDeepSpeed needs to be enabled with `accelerate config`. During configuration\nanswer yes to \"Do you want to use DeepSpeed?\". With DeepSpeed stage 2, fp16\nmixed precision and offloading both parameters and optimizer state to cpu it's\npossible to train on under 8 GB VRAM with a drawback of requiring significantly\nmore RAM (about 25 GB). See [documentation](https://huggingface.co/docs/accelerate/usage_guides/deepspeed) for more DeepSpeed configuration options.\n\nChanging the default Adam optimizer to DeepSpeed's special version of Adam\n`deepspeed.ops.adam.DeepSpeedCPUAdam` gives a substantial speedup but enabling\nit requires CUDA toolchain with the same version as pytorch. 8-bit optimizer\ndoes not seem to be compatible with DeepSpeed at the moment.\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport INSTANCE_DIR=\"dog\"\nexport CLASS_DIR=\"path-to-class-images\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\naccelerate launch --mixed_precision=\"fp16\" train_dreambooth.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --class_data_dir=$CLASS_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --with_prior_preservation --prior_loss_weight=1.0 \\\n --instance_prompt=\"a photo of sks dog\" \\\n --class_prompt=\"a photo of dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --sample_batch_size=1 \\\n --gradient_accumulation_steps=1 --gradient_checkpointing \\\n --learning_rate=5e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --num_class_images=200 \\\n --max_train_steps=800 \\\n --push_to_hub\n```\n\n### Fine-tune text encoder with the UNet.\n\nThe script also allows to fine-tune the `text_encoder` along with the `unet`. It's been observed experimentally that fine-tuning `text_encoder` gives much better results especially on faces.\nPass the `--train_text_encoder` argument to the script to enable training `text_encoder`.\n\n___Note: Training text encoder requires more memory, with this option the training won't fit on 16GB GPU. It needs at least 24GB VRAM.___\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport INSTANCE_DIR=\"dog\"\nexport CLASS_DIR=\"path-to-class-images\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\naccelerate launch train_dreambooth.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_text_encoder \\\n --instance_data_dir=$INSTANCE_DIR \\\n --class_data_dir=$CLASS_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --with_prior_preservation --prior_loss_weight=1.0 \\\n --instance_prompt=\"a photo of sks dog\" \\\n --class_prompt=\"a photo of dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --use_8bit_adam \\\n --gradient_checkpointing \\\n --learning_rate=2e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --num_class_images=200 \\\n --max_train_steps=800 \\\n --push_to_hub\n```\n\n### Using DreamBooth for pipelines other than Stable Diffusion\n\nThe [AltDiffusion pipeline](https://huggingface.co/docs/diffusers/api/pipelines/alt_diffusion) also supports dreambooth fine-tuning. The process is the same as above, all you need to do is replace the `MODEL_NAME` like this:\n\n```\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\" --> export MODEL_NAME=\"BAAI/AltDiffusion-m9\"\nor\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\" --> export MODEL_NAME=\"BAAI/AltDiffusion\"\n```\n\n### Inference\n\nOnce you have trained a model using the above command, you can run inference simply using the `StableDiffusionPipeline`. Make sure to include the `identifier` (e.g. sks in above example) in your prompt.\n\n```python\nfrom diffusers import StableDiffusionPipeline\nimport torch\n\nmodel_id = \"path-to-your-trained-model\"\npipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(\"cuda\")\n\nprompt = \"A photo of sks dog in a bucket\"\nimage = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]\n\nimage.save(\"dog-bucket.png\")\n```\n\n### Inference from a training checkpoint\n\nYou can also perform inference from one of the checkpoints saved during the training process, if you used the `--checkpointing_steps` argument. Please, refer to [the documentation](https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint) to see how to do it.\n\n## Training with Low-Rank Adaptation of Large Language Models (LoRA)\n\nLow-Rank Adaption of Large Language Models was first introduced by Microsoft in [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) by *Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen*\n\nIn a nutshell, LoRA allows to adapt pretrained models by adding pairs of rank-decomposition matrices to existing weights and **only** training those newly added weights. This has a couple of advantages:\n- Previous pretrained weights are kept frozen so that the model is not prone to [catastrophic forgetting](https://www.pnas.org/doi/10.1073/pnas.1611835114)\n- Rank-decomposition matrices have significantly fewer parameters than the original model, which means that trained LoRA weights are easily portable.\n- LoRA attention layers allow to control to which extent the model is adapted towards new training images via a `scale` parameter.\n\n[cloneofsimo](https://github.com/cloneofsimo) was the first to try out LoRA training for Stable Diffusion in\nthe popular [lora](https://github.com/cloneofsimo/lora) GitHub repository.\n\n### Training\n\nLet's get started with a simple example. We will re-use the dog example of the [previous section](#dog-toy-example).\n\nFirst, you need to set-up your dreambooth training example as is explained in the [installation section](#Installing-the-dependencies).\nNext, let's download the dog dataset. Download images from [here](https://drive.google.com/drive/folders/1BO_dyz-p65qhBRRMRA4TbZ8qW4rB99JZ) and save them in a directory. Make sure to set `INSTANCE_DIR` to the name of your directory further below. This will be our training data.\n\nNow, you can launch the training. Here we will use [Stable Diffusion 1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5).\n\n**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**\n\n**___Note: It is quite useful to monitor the training progress by regularly generating sample images during training. [wandb](https://docs.wandb.ai/quickstart) is a nice solution to easily see generating images during training. All you need to do is to run `pip install wandb` before training and pass `--report_to=\"wandb\"` to automatically log images.___**\n\n\n```bash\nexport MODEL_NAME=\"runwayml/stable-diffusion-v1-5\"\nexport INSTANCE_DIR=\"dog\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n```\n\nFor this example we want to directly store the trained LoRA embeddings on the Hub, so\nwe need to be logged in and add the `--push_to_hub` flag.\n\n```bash\nhuggingface-cli login\n```\n\nNow we can start training!\n\n```bash\naccelerate launch train_dreambooth_lora.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --instance_prompt=\"a photo of sks dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=1 \\\n --checkpointing_steps=100 \\\n --learning_rate=1e-4 \\\n --report_to=\"wandb\" \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --max_train_steps=500 \\\n --validation_prompt=\"A photo of sks dog in a bucket\" \\\n --validation_epochs=50 \\\n --seed=\"0\" \\\n --push_to_hub\n```\n\n**___Note: When using LoRA we can use a much higher learning rate compared to vanilla dreambooth. Here we\nuse *1e-4* instead of the usual *2e-6*.___**\n\nThe final LoRA embedding weights have been uploaded to [patrickvonplaten/lora_dreambooth_dog_example](https://huggingface.co/patrickvonplaten/lora_dreambooth_dog_example). **___Note: [The final weights](https://huggingface.co/patrickvonplaten/lora/blob/main/pytorch_attn_procs.bin) are only 3 MB in size which is orders of magnitudes smaller than the original model.**\n\nThe training results are summarized [here](https://api.wandb.ai/report/patrickvonplaten/xm6cd5q5).\nYou can use the `Step` slider to see how the model learned the features of our subject while the model trained.\n\nOptionally, we can also train additional LoRA layers for the text encoder. Specify the `--train_text_encoder` argument above for that. If you're interested to know more about how we\nenable this support, check out this [PR](https://github.com/huggingface/diffusers/pull/2918).\n\nWith the default hyperparameters from the above, the training seems to go in a positive direction. Check out [this panel](https://wandb.ai/sayakpaul/dreambooth-lora/reports/test-23-04-17-17-00-13---Vmlldzo0MDkwNjMy). The trained LoRA layers are available [here](https://huggingface.co/sayakpaul/dreambooth).\n\n\n### Inference\n\nAfter training, LoRA weights can be loaded very easily into the original pipeline. First, you need to\nload the original pipeline:\n\n```python\nfrom diffusers import DiffusionPipeline\npipe = DiffusionPipeline.from_pretrained(\"base-model-name\").to(\"cuda\")\n```\n\nNext, we can load the adapter layers into the pipeline with the [`load_lora_weights` function](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters#lora).\n\n```python\npipe.load_lora_weights(\"path-to-the-lora-checkpoint\")\n```\n\nFinally, we can run the model in inference.\n\n```python\nimage = pipe(\"A picture of a sks dog in a bucket\", num_inference_steps=25).images[0]\n```\n\nIf you are loading the LoRA parameters from the Hub and if the Hub repository has\na `base_model` tag (such as [this](https://huggingface.co/patrickvonplaten/lora_dreambooth_dog_example/blob/main/README.md?code=true#L4)), then\nyou can do:\n\n```py\nfrom huggingface_hub.repocard import RepoCard\n\nlora_model_id = \"patrickvonplaten/lora_dreambooth_dog_example\"\ncard = RepoCard.load(lora_model_id)\nbase_model_id = card.data.to_dict()[\"base_model\"]\n\npipe = StableDiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16)\n...\n```\n\nIf you used `--train_text_encoder` during training, then use `pipe.load_lora_weights()` to load the LoRA\nweights. For example:\n\n```python\nfrom huggingface_hub.repocard import RepoCard\nfrom diffusers import StableDiffusionPipeline\nimport torch\n\nlora_model_id = \"sayakpaul/dreambooth-text-encoder-test\"\ncard = RepoCard.load(lora_model_id)\nbase_model_id = card.data.to_dict()[\"base_model\"]\n\npipe = StableDiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16)\npipe = pipe.to(\"cuda\")\npipe.load_lora_weights(lora_model_id)\nimage = pipe(\"A picture of a sks dog in a bucket\", num_inference_steps=25).images[0]\n```\n\nNote that the use of [`LoraLoaderMixin.load_lora_weights`](https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraLoaderMixin.load_lora_weights) is preferred to [`UNet2DConditionLoadersMixin.load_attn_procs`](https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.UNet2DConditionLoadersMixin.load_attn_procs) for loading LoRA parameters. This is because\n`LoraLoaderMixin.load_lora_weights` can handle the following situations:\n\n* LoRA parameters that don't have separate identifiers for the UNet and the text encoder (such as [`\"patrickvonplaten/lora_dreambooth_dog_example\"`](https://huggingface.co/patrickvonplaten/lora_dreambooth_dog_example)). So, you can just do:\n\n ```py\n pipe.load_lora_weights(lora_model_path)\n ```\n\n* LoRA parameters that have separate identifiers for the UNet and the text encoder such as: [`\"sayakpaul/dreambooth\"`](https://huggingface.co/sayakpaul/dreambooth).\n\n## Training with Flax/JAX\n\nFor faster training on TPUs and GPUs you can leverage the flax training example. Follow the instructions above to get the model and dataset before running the script.\n\n____Note: The flax example don't yet support features like gradient checkpoint, gradient accumulation etc, so to use flax for faster training we will need >30GB cards.___\n\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n```bash\npip install -U -r requirements_flax.txt\n```\n\n\n### Training without prior preservation loss\n\n```bash\nexport MODEL_NAME=\"duongna/stable-diffusion-v1-4-flax\"\nexport INSTANCE_DIR=\"dog\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\npython train_dreambooth_flax.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --instance_prompt=\"a photo of sks dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --learning_rate=5e-6 \\\n --max_train_steps=400\n```\n\n\n### Training with prior preservation loss\n\n```bash\nexport MODEL_NAME=\"duongna/stable-diffusion-v1-4-flax\"\nexport INSTANCE_DIR=\"dog\"\nexport CLASS_DIR=\"path-to-class-images\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\npython train_dreambooth_flax.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --class_data_dir=$CLASS_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --with_prior_preservation --prior_loss_weight=1.0 \\\n --instance_prompt=\"a photo of sks dog\" \\\n --class_prompt=\"a photo of dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --learning_rate=5e-6 \\\n --num_class_images=200 \\\n --max_train_steps=800\n```\n\n\n### Fine-tune text encoder with the UNet.\n\n```bash\nexport MODEL_NAME=\"duongna/stable-diffusion-v1-4-flax\"\nexport INSTANCE_DIR=\"dog\"\nexport CLASS_DIR=\"path-to-class-images\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\npython train_dreambooth_flax.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_text_encoder \\\n --instance_data_dir=$INSTANCE_DIR \\\n --class_data_dir=$CLASS_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --with_prior_preservation --prior_loss_weight=1.0 \\\n --instance_prompt=\"a photo of sks dog\" \\\n --class_prompt=\"a photo of dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --learning_rate=2e-6 \\\n --num_class_images=200 \\\n --max_train_steps=800\n```\n\n### Training with xformers:\nYou can enable memory efficient attention by [installing xFormers](https://github.com/facebookresearch/xformers#installing-xformers) and padding the `--enable_xformers_memory_efficient_attention` argument to the script. This is not available with the Flax/JAX implementation.\n\nYou can also use Dreambooth to train the specialized in-painting model. See [the script in the research folder for details](https://github.com/huggingface/diffusers/tree/main/examples/research_projects/dreambooth_inpaint).\n\n### Set grads to none\n\nTo save even more memory, pass the `--set_grads_to_none` argument to the script. This will set grads to None instead of zero. However, be aware that it changes certain behaviors, so if you start experiencing any problems, remove this argument.\n\nMore info: https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html\n\n### Experimental results\nYou can refer to [this blog post](https://huggingface.co/blog/dreambooth) that discusses some of DreamBooth experiments in detail. Specifically, it recommends a set of DreamBooth-specific tips and tricks that we have found to work well for a variety of subjects.\n\n## IF\n\nYou can use the lora and full dreambooth scripts to train the text to image [IF model](https://huggingface.co/DeepFloyd/IF-I-XL-v1.0) and the stage II upscaler\n[IF model](https://huggingface.co/DeepFloyd/IF-II-L-v1.0).\n\nNote that IF has a predicted variance, and our finetuning scripts only train the models predicted error, so for finetuned IF models we switch to a fixed\nvariance schedule. The full finetuning scripts will update the scheduler config for the full saved model. However, when loading saved LoRA weights, you\nmust also update the pipeline's scheduler config.\n\n```py\nfrom diffusers import DiffusionPipeline\n\npipe = DiffusionPipeline.from_pretrained(\"DeepFloyd/IF-I-XL-v1.0\")\n\npipe.load_lora_weights(\"\")\n\n# Update scheduler config to fixed variance schedule\npipe.scheduler = pipe.scheduler.__class__.from_config(pipe.scheduler.config, variance_type=\"fixed_small\")\n```\n\nAdditionally, a few alternative cli flags are needed for IF.\n\n`--resolution=64`: IF is a pixel space diffusion model. In order to operate on un-compressed pixels, the input images are of a much smaller resolution.\n\n`--pre_compute_text_embeddings`: IF uses [T5](https://huggingface.co/docs/transformers/model_doc/t5) for its text encoder. In order to save GPU memory, we pre compute all text embeddings and then de-allocate\nT5.\n\n`--tokenizer_max_length=77`: T5 has a longer default text length, but the default IF encoding procedure uses a smaller number.\n\n`--text_encoder_use_attention_mask`: T5 passes the attention mask to the text encoder.\n\n### Tips and Tricks\nWe find LoRA to be sufficient for finetuning the stage I model as the low resolution of the model makes representing finegrained detail hard regardless.\n\nFor common and/or not-visually complex object concepts, you can get away with not-finetuning the upscaler. Just be sure to adjust the prompt passed to the\nupscaler to remove the new token from the instance prompt. I.e. if your stage I prompt is \"a sks dog\", use \"a dog\" for your stage II prompt.\n\nFor finegrained detail like faces that aren't present in the original training set, we find that full finetuning of the stage II upscaler is better than\nLoRA finetuning stage II.\n\nFor finegrained detail like faces, we find that lower learning rates along with larger batch sizes work best.\n\nFor stage II, we find that lower learning rates are also needed.\n\nWe found experimentally that the DDPM scheduler with the default larger number of denoising steps to sometimes work better than the DPM Solver scheduler\nused in the training scripts.\n\n### Stage II additional validation images\n\nThe stage II validation requires images to upscale, we can download a downsized version of the training set:\n\n```py\nfrom huggingface_hub import snapshot_download\n\nlocal_dir = \"./dog_downsized\"\nsnapshot_download(\n \"diffusers/dog-example-downsized\",\n local_dir=local_dir,\n repo_type=\"dataset\",\n ignore_patterns=\".gitattributes\",\n)\n```\n\n### IF stage I LoRA Dreambooth\nThis training configuration requires ~28 GB VRAM.\n\n```sh\nexport MODEL_NAME=\"DeepFloyd/IF-I-XL-v1.0\"\nexport INSTANCE_DIR=\"dog\"\nexport OUTPUT_DIR=\"dreambooth_dog_lora\"\n\naccelerate launch train_dreambooth_lora.py \\\n --report_to wandb \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --instance_prompt=\"a sks dog\" \\\n --resolution=64 \\\n --train_batch_size=4 \\\n --gradient_accumulation_steps=1 \\\n --learning_rate=5e-6 \\\n --scale_lr \\\n --max_train_steps=1200 \\\n --validation_prompt=\"a sks dog\" \\\n --validation_epochs=25 \\\n --checkpointing_steps=100 \\\n --pre_compute_text_embeddings \\\n --tokenizer_max_length=77 \\\n --text_encoder_use_attention_mask\n```\n\n### IF stage II LoRA Dreambooth\n\n`--validation_images`: These images are upscaled during validation steps.\n\n`--class_labels_conditioning=timesteps`: Pass additional conditioning to the UNet needed for stage II.\n\n`--learning_rate=1e-6`: Lower learning rate than stage I.\n\n`--resolution=256`: The upscaler expects higher resolution inputs\n\n```sh\nexport MODEL_NAME=\"DeepFloyd/IF-II-L-v1.0\"\nexport INSTANCE_DIR=\"dog\"\nexport OUTPUT_DIR=\"dreambooth_dog_upscale\"\nexport VALIDATION_IMAGES=\"dog_downsized/image_1.png dog_downsized/image_2.png dog_downsized/image_3.png dog_downsized/image_4.png\"\n\npython train_dreambooth_lora.py \\\n --report_to wandb \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --instance_prompt=\"a sks dog\" \\\n --resolution=256 \\\n --train_batch_size=4 \\\n --gradient_accumulation_steps=1 \\\n --learning_rate=1e-6 \\\n --max_train_steps=2000 \\\n --validation_prompt=\"a sks dog\" \\\n --validation_epochs=100 \\\n --checkpointing_steps=500 \\\n --pre_compute_text_embeddings \\\n --tokenizer_max_length=77 \\\n --text_encoder_use_attention_mask \\\n --validation_images $VALIDATION_IMAGES \\\n --class_labels_conditioning=timesteps\n```\n\n### IF Stage I Full Dreambooth\n`--skip_save_text_encoder`: When training the full model, this will skip saving the entire T5 with the finetuned model. You can still load the pipeline\nwith a T5 loaded from the original model.\n\n`use_8bit_adam`: Due to the size of the optimizer states, we recommend training the full XL IF model with 8bit adam.\n\n`--learning_rate=1e-7`: For full dreambooth, IF requires very low learning rates. With higher learning rates model quality will degrade. Note that it is\nlikely the learning rate can be increased with larger batch sizes.\n\nUsing 8bit adam and a batch size of 4, the model can be trained in ~48 GB VRAM.\n\n`--validation_scheduler`: Set a particular scheduler via a string. We found that it is better to use the DDPMScheduler for validation when training DeepFloyd IF.\n\n```sh\nexport MODEL_NAME=\"DeepFloyd/IF-I-XL-v1.0\"\n\nexport INSTANCE_DIR=\"dog\"\nexport OUTPUT_DIR=\"dreambooth_if\"\n\naccelerate launch train_dreambooth.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --instance_prompt=\"a photo of sks dog\" \\\n --resolution=64 \\\n --train_batch_size=4 \\\n --gradient_accumulation_steps=1 \\\n --learning_rate=1e-7 \\\n --max_train_steps=150 \\\n --validation_prompt \"a photo of sks dog\" \\\n --validation_steps 25 \\\n --text_encoder_use_attention_mask \\\n --tokenizer_max_length 77 \\\n --pre_compute_text_embeddings \\\n --use_8bit_adam \\\n --set_grads_to_none \\\n --skip_save_text_encoder \\\n --validation_scheduler DDPMScheduler \\\n --push_to_hub\n```\n\n### IF Stage II Full Dreambooth\n\n`--learning_rate=5e-6`: With a smaller effective batch size of 4, we found that we required learning rates as low as\n1e-8.\n\n`--resolution=256`: The upscaler expects higher resolution inputs\n\n`--train_batch_size=2` and `--gradient_accumulation_steps=6`: We found that full training of stage II particularly with\nfaces required large effective batch sizes.\n\n```sh\nexport MODEL_NAME=\"DeepFloyd/IF-II-L-v1.0\"\nexport INSTANCE_DIR=\"dog\"\nexport OUTPUT_DIR=\"dreambooth_dog_upscale\"\nexport VALIDATION_IMAGES=\"dog_downsized/image_1.png dog_downsized/image_2.png dog_downsized/image_3.png dog_downsized/image_4.png\"\n\naccelerate launch train_dreambooth.py \\\n --report_to wandb \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --instance_prompt=\"a sks dog\" \\\n --resolution=256 \\\n --train_batch_size=2 \\\n --gradient_accumulation_steps=6 \\\n --learning_rate=5e-6 \\\n --max_train_steps=2000 \\\n --validation_prompt=\"a sks dog\" \\\n --validation_steps=150 \\\n --checkpointing_steps=500 \\\n --pre_compute_text_embeddings \\\n --tokenizer_max_length=77 \\\n --text_encoder_use_attention_mask \\\n --validation_images $VALIDATION_IMAGES \\\n --class_labels_conditioning timesteps \\\n --validation_scheduler DDPMScheduler\\\n --push_to_hub\n```\n\n## Stable Diffusion XL\n\nWe support fine-tuning of the UNet shipped in [Stable Diffusion XL](https://huggingface.co/papers/2307.01952) with DreamBooth and LoRA via the `train_dreambooth_lora_sdxl.py` script. Please refer to the docs [here](./README_sdxl.md).\n" }, { "path": "diffusers/examples/dreambooth/README_sd3.md", "content": "# DreamBooth training example for Stable Diffusion 3 (SD3)\n\n[DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text2image models like stable diffusion given just a few (3~5) images of a subject.\n\nThe `train_dreambooth_sd3.py` script shows how to implement the training procedure and adapt it for [Stable Diffusion 3](https://huggingface.co/papers/2403.03206). We also provide a LoRA implementation in the `train_dreambooth_lora_sd3.py` script. \n\n> [!NOTE] \n> As the model is gated, before using it with diffusers you first need to go to the [Stable Diffusion 3 Medium Hugging Face page](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers), fill in the form and accept the gate. Once you are in, you need to log in so that your system knows you’ve accepted the gate. Use the command below to log in: \n\n```bash\nhuggingface-cli login\n```\n\n## Running locally with PyTorch\n\n### Installing the dependencies\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n**Important**\n\nTo make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install -e .\n```\n\nThen cd in the `examples/dreambooth` folder and run\n```bash\npip install -r requirements_sd3.txt\n```\n\nAnd initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\n\nOr for a default accelerate configuration without answering questions about your environment\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell (e.g., a notebook)\n\n```python\nfrom accelerate.utils import write_basic_config\nwrite_basic_config()\n```\n\nWhen running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups. \nNote also that we use PEFT library as backend for LoRA training, make sure to have `peft>=0.6.0` installed in your environment.\n\n\n### Dog toy example\n\nNow let's get our dataset. For this example we will use some dog images: https://huggingface.co/datasets/diffusers/dog-example.\n\nLet's first download it locally:\n\n```python\nfrom huggingface_hub import snapshot_download\n\nlocal_dir = \"./dog\"\nsnapshot_download(\n \"diffusers/dog-example\",\n local_dir=local_dir, repo_type=\"dataset\",\n ignore_patterns=\".gitattributes\",\n)\n```\n\nThis will also allow us to push the trained LoRA parameters to the Hugging Face Hub platform. \n\nNow, we can launch training using:\n\n```bash\nexport MODEL_NAME=\"stabilityai/stable-diffusion-3-medium-diffusers\"\nexport INSTANCE_DIR=\"dog\"\nexport OUTPUT_DIR=\"trained-sd3\"\n\naccelerate launch train_dreambooth_sd3.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --mixed_precision=\"fp16\" \\\n --instance_prompt=\"a photo of sks dog\" \\\n --resolution=1024 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --learning_rate=1e-4 \\\n --report_to=\"wandb\" \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --max_train_steps=500 \\\n --validation_prompt=\"A photo of sks dog in a bucket\" \\\n --validation_epochs=25 \\\n --seed=\"0\" \\\n --push_to_hub\n```\n\nTo better track our training experiments, we're using the following flags in the command above:\n\n* `report_to=\"wandb` will ensure the training runs are tracked on Weights and Biases. To use it, be sure to install `wandb` with `pip install wandb`.\n* `validation_prompt` and `validation_epochs` to allow the script to do a few validation inference runs. This allows us to qualitatively check if the training is progressing as expected. \n\n> [!NOTE] \n> If you want to train using long prompts with the T5 text encoder, you can use `--max_sequence_length` to set the token limit. The default is 77, but it can be increased to as high as 512. Note that this will use more resources and may slow down the training in some cases.\n\n> [!TIP]\n> You can pass `--use_8bit_adam` to reduce the memory requirements of training. Make sure to install `bitsandbytes` if you want to do so.\n\n## LoRA + DreamBooth\n\n[LoRA](https://huggingface.co/docs/peft/conceptual_guides/adapter#low-rank-adaptation-lora) is a popular parameter-efficient fine-tuning technique that allows you to achieve full-finetuning like performance but with a fraction of learnable parameters.\n\nTo perform DreamBooth with LoRA, run:\n\n```bash\nexport MODEL_NAME=\"stabilityai/stable-diffusion-3-medium-diffusers\"\nexport INSTANCE_DIR=\"dog\"\nexport OUTPUT_DIR=\"trained-sd3-lora\"\n\naccelerate launch train_dreambooth_lora_sd3.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --mixed_precision=\"fp16\" \\\n --instance_prompt=\"a photo of sks dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --learning_rate=1e-5 \\\n --report_to=\"wandb\" \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --max_train_steps=500 \\\n --validation_prompt=\"A photo of sks dog in a bucket\" \\\n --validation_epochs=25 \\\n --seed=\"0\" \\\n --push_to_hub\n```\n" }, { "path": "diffusers/examples/dreambooth/README_sdxl.md", "content": "# DreamBooth training example for Stable Diffusion XL (SDXL)\n\n[DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text2image models like stable diffusion given just a few (3~5) images of a subject.\n\nThe `train_dreambooth_lora_sdxl.py` script shows how to implement the training procedure and adapt it for [Stable Diffusion XL](https://huggingface.co/papers/2307.01952).\n\n> 💡 **Note**: For now, we only allow DreamBooth fine-tuning of the SDXL UNet via LoRA. LoRA is a parameter-efficient fine-tuning technique introduced in [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) by *Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen*. \n\n## Running locally with PyTorch\n\n### Installing the dependencies\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n**Important**\n\nTo make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install -e .\n```\n\nThen cd in the `examples/dreambooth` folder and run\n```bash\npip install -r requirements_sdxl.txt\n```\n\nAnd initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\n\nOr for a default accelerate configuration without answering questions about your environment\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell (e.g., a notebook)\n\n```python\nfrom accelerate.utils import write_basic_config\nwrite_basic_config()\n```\n\nWhen running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups. \nNote also that we use PEFT library as backend for LoRA training, make sure to have `peft>=0.6.0` installed in your environment.\n\n### Dog toy example\n\nNow let's get our dataset. For this example we will use some dog images: https://huggingface.co/datasets/diffusers/dog-example.\n\nLet's first download it locally:\n\n```python\nfrom huggingface_hub import snapshot_download\n\nlocal_dir = \"./dog\"\nsnapshot_download(\n \"diffusers/dog-example\",\n local_dir=local_dir, repo_type=\"dataset\",\n ignore_patterns=\".gitattributes\",\n)\n```\n\nThis will also allow us to push the trained LoRA parameters to the Hugging Face Hub platform. \n\nNow, we can launch training using:\n\n```bash\nexport MODEL_NAME=\"stabilityai/stable-diffusion-xl-base-1.0\"\nexport INSTANCE_DIR=\"dog\"\nexport OUTPUT_DIR=\"lora-trained-xl\"\nexport VAE_PATH=\"madebyollin/sdxl-vae-fp16-fix\"\n\naccelerate launch train_dreambooth_lora_sdxl.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --pretrained_vae_model_name_or_path=$VAE_PATH \\\n --output_dir=$OUTPUT_DIR \\\n --mixed_precision=\"fp16\" \\\n --instance_prompt=\"a photo of sks dog\" \\\n --resolution=1024 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --learning_rate=1e-4 \\\n --report_to=\"wandb\" \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --max_train_steps=500 \\\n --validation_prompt=\"A photo of sks dog in a bucket\" \\\n --validation_epochs=25 \\\n --seed=\"0\" \\\n --push_to_hub\n```\n\nTo better track our training experiments, we're using the following flags in the command above:\n\n* `report_to=\"wandb` will ensure the training runs are tracked on Weights and Biases. To use it, be sure to install `wandb` with `pip install wandb`.\n* `validation_prompt` and `validation_epochs` to allow the script to do a few validation inference runs. This allows us to qualitatively check if the training is progressing as expected. \n\nOur experiments were conducted on a single 40GB A100 GPU.\n\n### Dog toy example with < 16GB VRAM\n\nBy making use of [`gradient_checkpointing`](https://pytorch.org/docs/stable/checkpoint.html) (which is natively supported in Diffusers), [`xformers`](https://github.com/facebookresearch/xformers), and [`bitsandbytes`](https://github.com/TimDettmers/bitsandbytes) libraries, you can train SDXL LoRAs with less than 16GB of VRAM by adding the following flags to your accelerate launch command:\n\n```diff\n+ --enable_xformers_memory_efficient_attention \\\n+ --gradient_checkpointing \\\n+ --use_8bit_adam \\\n+ --mixed_precision=\"fp16\" \\\n```\n\nand making sure that you have the following libraries installed:\n\n```\nbitsandbytes>=0.40.0\nxformers>=0.0.20\n```\n\n### Inference\n\nOnce training is done, we can perform inference like so:\n\n```python\nfrom huggingface_hub.repocard import RepoCard\nfrom diffusers import DiffusionPipeline\nimport torch\n\nlora_model_id = <\"lora-sdxl-dreambooth-id\">\ncard = RepoCard.load(lora_model_id)\nbase_model_id = card.data.to_dict()[\"base_model\"]\n\npipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16)\npipe = pipe.to(\"cuda\")\npipe.load_lora_weights(lora_model_id)\nimage = pipe(\"A picture of a sks dog in a bucket\", num_inference_steps=25).images[0]\nimage.save(\"sks_dog.png\")\n```\n\nWe can further refine the outputs with the [Refiner](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0):\n\n```python\nfrom huggingface_hub.repocard import RepoCard\nfrom diffusers import DiffusionPipeline, StableDiffusionXLImg2ImgPipeline\nimport torch\n\nlora_model_id = <\"lora-sdxl-dreambooth-id\">\ncard = RepoCard.load(lora_model_id)\nbase_model_id = card.data.to_dict()[\"base_model\"]\n\n# Load the base pipeline and load the LoRA parameters into it. \npipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16)\npipe = pipe.to(\"cuda\")\npipe.load_lora_weights(lora_model_id)\n\n# Load the refiner.\nrefiner = StableDiffusionXLImg2ImgPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-refiner-1.0\", torch_dtype=torch.float16, use_safetensors=True, variant=\"fp16\"\n)\nrefiner.to(\"cuda\")\n\nprompt = \"A picture of a sks dog in a bucket\"\ngenerator = torch.Generator(\"cuda\").manual_seed(0)\n\n# Run inference.\nimage = pipe(prompt=prompt, output_type=\"latent\", generator=generator).images[0]\nimage = refiner(prompt=prompt, image=image[None, :], generator=generator).images[0]\nimage.save(\"refined_sks_dog.png\")\n```\n\nHere's a side-by-side comparison of the with and without Refiner pipeline outputs:\n\n| Without Refiner | With Refiner |\n|---|---|\n| ![](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/sd_xl/sks_dog.png) | ![](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/sd_xl/refined_sks_dog.png) |\n\n### Training with text encoder(s)\n\nAlongside the UNet, LoRA fine-tuning of the text encoders is also supported. To do so, just specify `--train_text_encoder` while launching training. Please keep the following points in mind:\n\n* SDXL has two text encoders. So, we fine-tune both using LoRA.\n* When not fine-tuning the text encoders, we ALWAYS precompute the text embeddings to save memory.\n\n### Specifying a better VAE\n\nSDXL's VAE is known to suffer from numerical instability issues. This is why we also expose a CLI argument namely `--pretrained_vae_model_name_or_path` that lets you specify the location of a better VAE (such as [this one](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix)).\n\n## Notes\n\nIn our experiments, we found that SDXL yields good initial results without extensive hyperparameter tuning. For example, without fine-tuning the text encoders and without using prior-preservation, we observed decent results. We didn't explore further hyper-parameter tuning experiments, but we do encourage the community to explore this avenue further and share their results with us 🤗\n\n## Results\n\nYou can explore the results from a couple of our internal experiments by checking out this link: [https://wandb.ai/sayakpaul/dreambooth-lora-sd-xl](https://wandb.ai/sayakpaul/dreambooth-lora-sd-xl). Specifically, we used the same script with the exact same hyperparameters on the following datasets:\n\n* [Dogs](https://huggingface.co/datasets/diffusers/dog-example)\n* [Starbucks logo](https://huggingface.co/datasets/diffusers/starbucks-example)\n* [Mr. Potato Head](https://huggingface.co/datasets/diffusers/potato-head-example)\n* [Keramer face](https://huggingface.co/datasets/diffusers/keramer-face-example)\n\n## Running on a free-tier Colab Notebook\n\nCheck out [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/SDXL_DreamBooth_LoRA_.ipynb). \n\n## Conducting EDM-style training\n\nIt's now possible to perform EDM-style training as proposed in [Elucidating the Design Space of Diffusion-Based Generative Models](https://arxiv.org/abs/2206.00364). \n\nFor the SDXL model, simple set:\n\n```diff\n+ --do_edm_style_training \\\n```\n\nOther SDXL-like models that use the EDM formulation, such as [playgroundai/playground-v2.5-1024px-aesthetic](https://huggingface.co/playgroundai/playground-v2.5-1024px-aesthetic), can also be DreamBooth'd with the script. Below is an example command:\n\n```bash\naccelerate launch train_dreambooth_lora_sdxl.py \\\n --pretrained_model_name_or_path=\"playgroundai/playground-v2.5-1024px-aesthetic\" \\\n --instance_data_dir=\"dog\" \\\n --output_dir=\"dog-playground-lora\" \\\n --mixed_precision=\"fp16\" \\\n --instance_prompt=\"a photo of sks dog\" \\\n --resolution=1024 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --learning_rate=1e-4 \\\n --use_8bit_adam \\\n --report_to=\"wandb\" \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --max_train_steps=500 \\\n --validation_prompt=\"A photo of sks dog in a bucket\" \\\n --validation_epochs=25 \\\n --seed=\"0\" \\\n --push_to_hub\n```\n\n> [!CAUTION]\n> Min-SNR gamma is not supported with the EDM-style training yet. When training with the PlaygroundAI model, it's recommended to not pass any \"variant\".\n\n### DoRA training \nThe script now supports DoRA training too!\n> Proposed in [DoRA: Weight-Decomposed Low-Rank Adaptation](https://arxiv.org/abs/2402.09353), \n**DoRA** is very similar to LoRA, except it decomposes the pre-trained weight into two components, **magnitude** and **direction** and employs LoRA for _directional_ updates to efficiently minimize the number of trainable parameters. \nThe authors found that by using DoRA, both the learning capacity and training stability of LoRA are enhanced without any additional overhead during inference. \n\n> [!NOTE]\n> 💡DoRA training is still _experimental_ \n> and is likely to require different hyperparameter values to perform best compared to a LoRA.\n> Specifically, we've noticed 2 differences to take into account your training: \n> 1. **LoRA seem to converge faster than DoRA** (so a set of parameters that may lead to overfitting when training a LoRA may be working well for a DoRA)\n> 2. **DoRA quality superior to LoRA especially in lower ranks** the difference in quality of DoRA of rank 8 and LoRA of rank 8 appears to be more significant than when training ranks of 32 or 64 for example. \n> This is also aligned with some of the quantitative analysis shown in the paper. \n\n**Usage**\n1. To use DoRA you need to upgrade the installation of `peft`: \n```bash\npip install-U peft\n```\n2. Enable DoRA training by adding this flag\n```bash\n--use_dora\n```\n**Inference** \nThe inference is the same as if you train a regular LoRA 🤗\n\n## Format compatibility\n\nYou can pass `--output_kohya_format` to additionally generate a state dictionary which should be compatible with other platforms and tools such as Automatic 1111, Comfy, Kohya, etc. The `output_dir` will contain a file named \"pytorch_lora_weights_kohya.safetensors\"." }, { "path": "diffusers/examples/dreambooth/requirements.txt", "content": "accelerate>=0.16.0\ntorchvision\ntransformers>=4.25.1\nftfy\ntensorboard\nJinja2\npeft==0.7.0" }, { "path": "diffusers/examples/dreambooth/requirements_flax.txt", "content": "transformers>=4.25.1\nflax\noptax\ntorch\ntorchvision\nftfy\ntensorboard\nJinja2\n" }, { "path": "diffusers/examples/dreambooth/requirements_sd3.txt", "content": "accelerate>=0.31.0\ntorchvision\ntransformers>=4.41.2\nftfy\ntensorboard\nJinja2\npeft==0.11.1\nsentencepiece" }, { "path": "diffusers/examples/dreambooth/requirements_sdxl.txt", "content": "accelerate>=0.16.0\ntorchvision\ntransformers>=4.25.1\nftfy\ntensorboard\nJinja2\npeft==0.7.0" }, { "path": "diffusers/examples/dreambooth/test_dreambooth.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport logging\nimport os\nimport shutil\nimport sys\nimport tempfile\n\nfrom diffusers import DiffusionPipeline, UNet2DConditionModel\n\n\nsys.path.append(\"..\")\nfrom test_examples_utils import ExamplesTestsAccelerate, run_command # noqa: E402\n\n\nlogging.basicConfig(level=logging.DEBUG)\n\nlogger = logging.getLogger()\nstream_handler = logging.StreamHandler(sys.stdout)\nlogger.addHandler(stream_handler)\n\n\nclass DreamBooth(ExamplesTestsAccelerate):\n def test_dreambooth(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/dreambooth/train_dreambooth.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe\n --instance_data_dir docs/source/en/imgs\n --instance_prompt photo\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n # save_pretrained smoke test\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"unet\", \"diffusion_pytorch_model.safetensors\")))\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"scheduler\", \"scheduler_config.json\")))\n\n def test_dreambooth_if(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/dreambooth/train_dreambooth.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-if-pipe\n --instance_data_dir docs/source/en/imgs\n --instance_prompt photo\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --pre_compute_text_embeddings\n --tokenizer_max_length=77\n --text_encoder_use_attention_mask\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n # save_pretrained smoke test\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"unet\", \"diffusion_pytorch_model.safetensors\")))\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"scheduler\", \"scheduler_config.json\")))\n\n def test_dreambooth_checkpointing(self):\n instance_prompt = \"photo\"\n pretrained_model_name_or_path = \"hf-internal-testing/tiny-stable-diffusion-pipe\"\n\n with tempfile.TemporaryDirectory() as tmpdir:\n # Run training script with checkpointing\n # max_train_steps == 4, checkpointing_steps == 2\n # Should create checkpoints at steps 2, 4\n\n initial_run_args = f\"\"\"\n examples/dreambooth/train_dreambooth.py\n --pretrained_model_name_or_path {pretrained_model_name_or_path}\n --instance_data_dir docs/source/en/imgs\n --instance_prompt {instance_prompt}\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 4\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --checkpointing_steps=2\n --seed=0\n \"\"\".split()\n\n run_command(self._launch_args + initial_run_args)\n\n # check can run the original fully trained output pipeline\n pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)\n pipe(instance_prompt, num_inference_steps=1)\n\n # check checkpoint directories exist\n self.assertTrue(os.path.isdir(os.path.join(tmpdir, \"checkpoint-2\")))\n self.assertTrue(os.path.isdir(os.path.join(tmpdir, \"checkpoint-4\")))\n\n # check can run an intermediate checkpoint\n unet = UNet2DConditionModel.from_pretrained(tmpdir, subfolder=\"checkpoint-2/unet\")\n pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, unet=unet, safety_checker=None)\n pipe(instance_prompt, num_inference_steps=1)\n\n # Remove checkpoint 2 so that we can check only later checkpoints exist after resuming\n shutil.rmtree(os.path.join(tmpdir, \"checkpoint-2\"))\n\n # Run training script for 7 total steps resuming from checkpoint 4\n\n resume_run_args = f\"\"\"\n examples/dreambooth/train_dreambooth.py\n --pretrained_model_name_or_path {pretrained_model_name_or_path}\n --instance_data_dir docs/source/en/imgs\n --instance_prompt {instance_prompt}\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 6\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --checkpointing_steps=2\n --resume_from_checkpoint=checkpoint-4\n --seed=0\n \"\"\".split()\n\n run_command(self._launch_args + resume_run_args)\n\n # check can run new fully trained pipeline\n pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)\n pipe(instance_prompt, num_inference_steps=1)\n\n # check old checkpoints do not exist\n self.assertFalse(os.path.isdir(os.path.join(tmpdir, \"checkpoint-2\")))\n\n # check new checkpoints exist\n self.assertTrue(os.path.isdir(os.path.join(tmpdir, \"checkpoint-4\")))\n self.assertTrue(os.path.isdir(os.path.join(tmpdir, \"checkpoint-6\")))\n\n def test_dreambooth_checkpointing_checkpoints_total_limit(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/dreambooth/train_dreambooth.py\n --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe\n --instance_data_dir=docs/source/en/imgs\n --output_dir={tmpdir}\n --instance_prompt=prompt\n --resolution=64\n --train_batch_size=1\n --gradient_accumulation_steps=1\n --max_train_steps=6\n --checkpoints_total_limit=2\n --checkpointing_steps=2\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-4\", \"checkpoint-6\"},\n )\n\n def test_dreambooth_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/dreambooth/train_dreambooth.py\n --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe\n --instance_data_dir=docs/source/en/imgs\n --output_dir={tmpdir}\n --instance_prompt=prompt\n --resolution=64\n --train_batch_size=1\n --gradient_accumulation_steps=1\n --max_train_steps=4\n --checkpointing_steps=2\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-2\", \"checkpoint-4\"},\n )\n\n resume_run_args = f\"\"\"\n examples/dreambooth/train_dreambooth.py\n --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe\n --instance_data_dir=docs/source/en/imgs\n --output_dir={tmpdir}\n --instance_prompt=prompt\n --resolution=64\n --train_batch_size=1\n --gradient_accumulation_steps=1\n --max_train_steps=8\n --checkpointing_steps=2\n --resume_from_checkpoint=checkpoint-4\n --checkpoints_total_limit=2\n \"\"\".split()\n\n run_command(self._launch_args + resume_run_args)\n\n self.assertEqual({x for x in os.listdir(tmpdir) if \"checkpoint\" in x}, {\"checkpoint-6\", \"checkpoint-8\"})\n" }, { "path": "diffusers/examples/dreambooth/test_dreambooth_lora.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport logging\nimport os\nimport sys\nimport tempfile\n\nimport safetensors\n\n\nsys.path.append(\"..\")\nfrom test_examples_utils import ExamplesTestsAccelerate, run_command # noqa: E402\n\nfrom diffusers import DiffusionPipeline # noqa: E402\n\n\nlogging.basicConfig(level=logging.DEBUG)\n\nlogger = logging.getLogger()\nstream_handler = logging.StreamHandler(sys.stdout)\nlogger.addHandler(stream_handler)\n\n\nclass DreamBoothLoRA(ExamplesTestsAccelerate):\n def test_dreambooth_lora(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/dreambooth/train_dreambooth_lora.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe\n --instance_data_dir docs/source/en/imgs\n --instance_prompt photo\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n # save_pretrained smoke test\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"pytorch_lora_weights.safetensors\")))\n\n # make sure the state_dict has the correct naming in the parameters.\n lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, \"pytorch_lora_weights.safetensors\"))\n is_lora = all(\"lora\" in k for k in lora_state_dict.keys())\n self.assertTrue(is_lora)\n\n # when not training the text encoder, all the parameters in the state dict should start\n # with `\"unet\"` in their names.\n starts_with_unet = all(key.startswith(\"unet\") for key in lora_state_dict.keys())\n self.assertTrue(starts_with_unet)\n\n def test_dreambooth_lora_with_text_encoder(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/dreambooth/train_dreambooth_lora.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe\n --instance_data_dir docs/source/en/imgs\n --instance_prompt photo\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --train_text_encoder\n --output_dir {tmpdir}\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n # save_pretrained smoke test\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"pytorch_lora_weights.safetensors\")))\n\n # check `text_encoder` is present at all.\n lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, \"pytorch_lora_weights.safetensors\"))\n keys = lora_state_dict.keys()\n is_text_encoder_present = any(k.startswith(\"text_encoder\") for k in keys)\n self.assertTrue(is_text_encoder_present)\n\n # the names of the keys of the state dict should either start with `unet`\n # or `text_encoder`.\n is_correct_naming = all(k.startswith(\"unet\") or k.startswith(\"text_encoder\") for k in keys)\n self.assertTrue(is_correct_naming)\n\n def test_dreambooth_lora_checkpointing_checkpoints_total_limit(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/dreambooth/train_dreambooth_lora.py\n --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe\n --instance_data_dir=docs/source/en/imgs\n --output_dir={tmpdir}\n --instance_prompt=prompt\n --resolution=64\n --train_batch_size=1\n --gradient_accumulation_steps=1\n --max_train_steps=6\n --checkpoints_total_limit=2\n --checkpointing_steps=2\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-4\", \"checkpoint-6\"},\n )\n\n def test_dreambooth_lora_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/dreambooth/train_dreambooth_lora.py\n --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe\n --instance_data_dir=docs/source/en/imgs\n --output_dir={tmpdir}\n --instance_prompt=prompt\n --resolution=64\n --train_batch_size=1\n --gradient_accumulation_steps=1\n --max_train_steps=4\n --checkpointing_steps=2\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n\n self.assertEqual({x for x in os.listdir(tmpdir) if \"checkpoint\" in x}, {\"checkpoint-2\", \"checkpoint-4\"})\n\n resume_run_args = f\"\"\"\n examples/dreambooth/train_dreambooth_lora.py\n --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe\n --instance_data_dir=docs/source/en/imgs\n --output_dir={tmpdir}\n --instance_prompt=prompt\n --resolution=64\n --train_batch_size=1\n --gradient_accumulation_steps=1\n --max_train_steps=8\n --checkpointing_steps=2\n --resume_from_checkpoint=checkpoint-4\n --checkpoints_total_limit=2\n \"\"\".split()\n\n run_command(self._launch_args + resume_run_args)\n\n self.assertEqual({x for x in os.listdir(tmpdir) if \"checkpoint\" in x}, {\"checkpoint-6\", \"checkpoint-8\"})\n\n def test_dreambooth_lora_if_model(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/dreambooth/train_dreambooth_lora.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-if-pipe\n --instance_data_dir docs/source/en/imgs\n --instance_prompt photo\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --pre_compute_text_embeddings\n --tokenizer_max_length=77\n --text_encoder_use_attention_mask\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n # save_pretrained smoke test\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"pytorch_lora_weights.safetensors\")))\n\n # make sure the state_dict has the correct naming in the parameters.\n lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, \"pytorch_lora_weights.safetensors\"))\n is_lora = all(\"lora\" in k for k in lora_state_dict.keys())\n self.assertTrue(is_lora)\n\n # when not training the text encoder, all the parameters in the state dict should start\n # with `\"unet\"` in their names.\n starts_with_unet = all(key.startswith(\"unet\") for key in lora_state_dict.keys())\n self.assertTrue(starts_with_unet)\n\n\nclass DreamBoothLoRASDXL(ExamplesTestsAccelerate):\n def test_dreambooth_lora_sdxl(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/dreambooth/train_dreambooth_lora_sdxl.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe\n --instance_data_dir docs/source/en/imgs\n --instance_prompt photo\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n # save_pretrained smoke test\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"pytorch_lora_weights.safetensors\")))\n\n # make sure the state_dict has the correct naming in the parameters.\n lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, \"pytorch_lora_weights.safetensors\"))\n is_lora = all(\"lora\" in k for k in lora_state_dict.keys())\n self.assertTrue(is_lora)\n\n # when not training the text encoder, all the parameters in the state dict should start\n # with `\"unet\"` in their names.\n starts_with_unet = all(key.startswith(\"unet\") for key in lora_state_dict.keys())\n self.assertTrue(starts_with_unet)\n\n def test_dreambooth_lora_sdxl_with_text_encoder(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/dreambooth/train_dreambooth_lora_sdxl.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe\n --instance_data_dir docs/source/en/imgs\n --instance_prompt photo\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --train_text_encoder\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n # save_pretrained smoke test\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"pytorch_lora_weights.safetensors\")))\n\n # make sure the state_dict has the correct naming in the parameters.\n lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, \"pytorch_lora_weights.safetensors\"))\n is_lora = all(\"lora\" in k for k in lora_state_dict.keys())\n self.assertTrue(is_lora)\n\n # when not training the text encoder, all the parameters in the state dict should start\n # with `\"unet\"` or `\"text_encoder\"` or `\"text_encoder_2\"` in their names.\n keys = lora_state_dict.keys()\n starts_with_unet = all(\n k.startswith(\"unet\") or k.startswith(\"text_encoder\") or k.startswith(\"text_encoder_2\") for k in keys\n )\n self.assertTrue(starts_with_unet)\n\n def test_dreambooth_lora_sdxl_custom_captions(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/dreambooth/train_dreambooth_lora_sdxl.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe\n --dataset_name hf-internal-testing/dummy_image_text_data\n --caption_column text\n --instance_prompt photo\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n\n def test_dreambooth_lora_sdxl_text_encoder_custom_captions(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/dreambooth/train_dreambooth_lora_sdxl.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe\n --dataset_name hf-internal-testing/dummy_image_text_data\n --caption_column text\n --instance_prompt photo\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --train_text_encoder\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n\n def test_dreambooth_lora_sdxl_checkpointing_checkpoints_total_limit(self):\n pipeline_path = \"hf-internal-testing/tiny-stable-diffusion-xl-pipe\"\n\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/dreambooth/train_dreambooth_lora_sdxl.py\n --pretrained_model_name_or_path {pipeline_path}\n --instance_data_dir docs/source/en/imgs\n --instance_prompt photo\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 6\n --checkpointing_steps=2\n --checkpoints_total_limit=2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n\n pipe = DiffusionPipeline.from_pretrained(pipeline_path)\n pipe.load_lora_weights(tmpdir)\n pipe(\"a prompt\", num_inference_steps=1)\n\n # check checkpoint directories exist\n # checkpoint-2 should have been deleted\n self.assertEqual({x for x in os.listdir(tmpdir) if \"checkpoint\" in x}, {\"checkpoint-4\", \"checkpoint-6\"})\n\n def test_dreambooth_lora_sdxl_text_encoder_checkpointing_checkpoints_total_limit(self):\n pipeline_path = \"hf-internal-testing/tiny-stable-diffusion-xl-pipe\"\n\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/dreambooth/train_dreambooth_lora_sdxl.py\n --pretrained_model_name_or_path {pipeline_path}\n --instance_data_dir docs/source/en/imgs\n --instance_prompt photo\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 7\n --checkpointing_steps=2\n --checkpoints_total_limit=2\n --train_text_encoder\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n\n pipe = DiffusionPipeline.from_pretrained(pipeline_path)\n pipe.load_lora_weights(tmpdir)\n pipe(\"a prompt\", num_inference_steps=2)\n\n # check checkpoint directories exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n # checkpoint-2 should have been deleted\n {\"checkpoint-4\", \"checkpoint-6\"},\n )\n" }, { "path": "diffusers/examples/dreambooth/test_dreambooth_lora_edm.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport logging\nimport os\nimport sys\nimport tempfile\n\nimport safetensors\n\n\nsys.path.append(\"..\")\nfrom test_examples_utils import ExamplesTestsAccelerate, run_command # noqa: E402\n\n\nlogging.basicConfig(level=logging.DEBUG)\n\nlogger = logging.getLogger()\nstream_handler = logging.StreamHandler(sys.stdout)\nlogger.addHandler(stream_handler)\n\n\nclass DreamBoothLoRASDXLWithEDM(ExamplesTestsAccelerate):\n def test_dreambooth_lora_sdxl_with_edm(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/dreambooth/train_dreambooth_lora_sdxl.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe\n --do_edm_style_training\n --instance_data_dir docs/source/en/imgs\n --instance_prompt photo\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n # save_pretrained smoke test\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"pytorch_lora_weights.safetensors\")))\n\n # make sure the state_dict has the correct naming in the parameters.\n lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, \"pytorch_lora_weights.safetensors\"))\n is_lora = all(\"lora\" in k for k in lora_state_dict.keys())\n self.assertTrue(is_lora)\n\n # when not training the text encoder, all the parameters in the state dict should start\n # with `\"unet\"` in their names.\n starts_with_unet = all(key.startswith(\"unet\") for key in lora_state_dict.keys())\n self.assertTrue(starts_with_unet)\n\n def test_dreambooth_lora_playground(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/dreambooth/train_dreambooth_lora_sdxl.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-playground-v2-5-pipe\n --instance_data_dir docs/source/en/imgs\n --instance_prompt photo\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n # save_pretrained smoke test\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"pytorch_lora_weights.safetensors\")))\n\n # make sure the state_dict has the correct naming in the parameters.\n lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, \"pytorch_lora_weights.safetensors\"))\n is_lora = all(\"lora\" in k for k in lora_state_dict.keys())\n self.assertTrue(is_lora)\n\n # when not training the text encoder, all the parameters in the state dict should start\n # with `\"unet\"` in their names.\n starts_with_unet = all(key.startswith(\"unet\") for key in lora_state_dict.keys())\n self.assertTrue(starts_with_unet)\n" }, { "path": "diffusers/examples/dreambooth/train_dreambooth.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport copy\nimport gc\nimport importlib\nimport itertools\nimport logging\nimport math\nimport os\nimport shutil\nimport warnings\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom huggingface_hub import create_repo, model_info, upload_folder\nfrom huggingface_hub.utils import insecure_hashlib\nfrom packaging import version\nfrom PIL import Image\nfrom PIL.ImageOps import exif_transpose\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n DiffusionPipeline,\n StableDiffusionPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import compute_snr\nfrom diffusers.utils import check_min_version, is_wandb_available\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef save_model_card(\n repo_id: str,\n images: list = None,\n base_model: str = None,\n train_text_encoder=False,\n prompt: str = None,\n repo_folder: str = None,\n pipeline: DiffusionPipeline = None,\n):\n img_str = \"\"\n if images is not None:\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n img_str += f\"![img_{i}](./image_{i}.png)\\n\"\n\n model_description = f\"\"\"\n# DreamBooth - {repo_id}\n\nThis is a dreambooth model derived from {base_model}. The weights were trained on {prompt} using [DreamBooth](https://dreambooth.github.io/).\nYou can find some example images in the following. \\n\n{img_str}\n\nDreamBooth for the text encoder was enabled: {train_text_encoder}.\n\"\"\"\n model_card = load_or_create_model_card(\n repo_id_or_path=repo_id,\n from_training=True,\n license=\"creativeml-openrail-m\",\n base_model=base_model,\n prompt=prompt,\n model_description=model_description,\n inference=True,\n )\n\n tags = [\"text-to-image\", \"dreambooth\", \"diffusers-training\"]\n if isinstance(pipeline, StableDiffusionPipeline):\n tags.extend([\"stable-diffusion\", \"stable-diffusion-diffusers\"])\n else:\n tags.extend([\"if\", \"if-diffusers\"])\n model_card = populate_model_card(model_card, tags=tags)\n\n model_card.save(os.path.join(repo_folder, \"README.md\"))\n\n\ndef log_validation(\n text_encoder,\n tokenizer,\n unet,\n vae,\n args,\n accelerator,\n weight_dtype,\n global_step,\n prompt_embeds,\n negative_prompt_embeds,\n):\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n\n pipeline_args = {}\n\n if vae is not None:\n pipeline_args[\"vae\"] = vae\n\n # create pipeline (note: unet and vae are loaded again in float32)\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n unet=unet,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n **pipeline_args,\n )\n\n # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it\n scheduler_args = {}\n\n if \"variance_type\" in pipeline.scheduler.config:\n variance_type = pipeline.scheduler.config.variance_type\n\n if variance_type in [\"learned\", \"learned_range\"]:\n variance_type = \"fixed_small\"\n\n scheduler_args[\"variance_type\"] = variance_type\n\n module = importlib.import_module(\"diffusers\")\n scheduler_class = getattr(module, args.validation_scheduler)\n pipeline.scheduler = scheduler_class.from_config(pipeline.scheduler.config, **scheduler_args)\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n if args.pre_compute_text_embeddings:\n pipeline_args = {\n \"prompt_embeds\": prompt_embeds,\n \"negative_prompt_embeds\": negative_prompt_embeds,\n }\n else:\n pipeline_args = {\"prompt\": args.validation_prompt}\n\n # run inference\n generator = None if args.seed is None else torch.Generator(device=accelerator.device).manual_seed(args.seed)\n images = []\n if args.validation_images is None:\n for _ in range(args.num_validation_images):\n with torch.autocast(\"cuda\"):\n image = pipeline(**pipeline_args, num_inference_steps=25, generator=generator).images[0]\n images.append(image)\n else:\n for image in args.validation_images:\n image = Image.open(image)\n image = pipeline(**pipeline_args, image=image, generator=generator).images[0]\n images.append(image)\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, global_step, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\") for i, image in enumerate(images)\n ]\n }\n )\n\n del pipeline\n torch.cuda.empty_cache()\n\n return images\n\n\ndef import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path,\n subfolder=\"text_encoder\",\n revision=revision,\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"RobertaSeriesModelWithTransformation\":\n from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation\n\n return RobertaSeriesModelWithTransformation\n elif model_class == \"T5EncoderModel\":\n from transformers import T5EncoderModel\n\n return T5EncoderModel\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--tokenizer_name\",\n type=str,\n default=None,\n help=\"Pretrained tokenizer name or path if not the same as model_name\",\n )\n parser.add_argument(\n \"--instance_data_dir\",\n type=str,\n default=None,\n required=True,\n help=\"A folder containing the training data of instance images.\",\n )\n parser.add_argument(\n \"--class_data_dir\",\n type=str,\n default=None,\n required=False,\n help=\"A folder containing the training data of class images.\",\n )\n parser.add_argument(\n \"--instance_prompt\",\n type=str,\n default=None,\n required=True,\n help=\"The prompt with identifier specifying the instance\",\n )\n parser.add_argument(\n \"--class_prompt\",\n type=str,\n default=None,\n help=\"The prompt to specify images in the same class as provided instance images.\",\n )\n parser.add_argument(\n \"--with_prior_preservation\",\n default=False,\n action=\"store_true\",\n help=\"Flag to add prior preservation loss.\",\n )\n parser.add_argument(\"--prior_loss_weight\", type=float, default=1.0, help=\"The weight of prior preservation loss.\")\n parser.add_argument(\n \"--num_class_images\",\n type=int,\n default=100,\n help=(\n \"Minimal class images for prior preservation loss. If there are not enough images already present in\"\n \" class_data_dir, additional images will be sampled with class_prompt.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"dreambooth-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--train_text_encoder\",\n action=\"store_true\",\n help=\"Whether to train the text encoder. If set, the text encoder should be float32 precision.\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\n \"--sample_batch_size\", type=int, default=4, help=\"Batch size (per device) for sampling images.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. \"\n \"In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference.\"\n \"Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components.\"\n \"See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step\"\n \"instructions.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\n \"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`.\"\n \" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state\"\n \" for more details\"\n ),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=5e-6,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\"--lr_power\", type=float, default=1.0, help=\"Power factor of the polynomial scheduler.\")\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n help=\"A prompt that is used during validation to verify that the model is learning.\",\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=100,\n help=(\n \"Run validation every X steps. Validation consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`\"\n \" and logging the images.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--prior_generation_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp32\", \"fp16\", \"bf16\"],\n help=(\n \"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--set_grads_to_none\",\n action=\"store_true\",\n help=(\n \"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain\"\n \" behaviors, so disable this argument if it causes any problems. More info:\"\n \" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html\"\n ),\n )\n\n parser.add_argument(\n \"--offset_noise\",\n action=\"store_true\",\n default=False,\n help=(\n \"Fine-tuning against a modified noise\"\n \" See: https://www.crosslabs.org//blog/diffusion-with-offset-noise for more information.\"\n ),\n )\n parser.add_argument(\n \"--snr_gamma\",\n type=float,\n default=None,\n help=\"SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. \"\n \"More details here: https://arxiv.org/abs/2303.09556.\",\n )\n parser.add_argument(\n \"--pre_compute_text_embeddings\",\n action=\"store_true\",\n help=\"Whether or not to pre-compute text embeddings. If text embeddings are pre-computed, the text encoder will not be kept in memory during training and will leave more GPU memory available for training the rest of the model. This is not compatible with `--train_text_encoder`.\",\n )\n parser.add_argument(\n \"--tokenizer_max_length\",\n type=int,\n default=None,\n required=False,\n help=\"The maximum length of the tokenizer. If not set, will default to the tokenizer's max length.\",\n )\n parser.add_argument(\n \"--text_encoder_use_attention_mask\",\n action=\"store_true\",\n required=False,\n help=\"Whether to use attention mask for the text encoder\",\n )\n parser.add_argument(\n \"--skip_save_text_encoder\", action=\"store_true\", required=False, help=\"Set to not save text encoder\"\n )\n parser.add_argument(\n \"--validation_images\",\n required=False,\n default=None,\n nargs=\"+\",\n help=\"Optional set of images to use for validation. Used when the target pipeline takes an initial image as input such as when training image variation or superresolution.\",\n )\n parser.add_argument(\n \"--class_labels_conditioning\",\n required=False,\n default=None,\n help=\"The optional `class_label` conditioning to pass to the unet, available values are `timesteps`.\",\n )\n parser.add_argument(\n \"--validation_scheduler\",\n type=str,\n default=\"DPMSolverMultistepScheduler\",\n choices=[\"DPMSolverMultistepScheduler\", \"DDPMScheduler\"],\n help=\"Select which scheduler to use for validation. DDPMScheduler is recommended for DeepFloyd IF.\",\n )\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.with_prior_preservation:\n if args.class_data_dir is None:\n raise ValueError(\"You must specify a data directory for class images.\")\n if args.class_prompt is None:\n raise ValueError(\"You must specify prompt for class images.\")\n else:\n # logger is not available yet\n if args.class_data_dir is not None:\n warnings.warn(\"You need not use --class_data_dir without --with_prior_preservation.\")\n if args.class_prompt is not None:\n warnings.warn(\"You need not use --class_prompt without --with_prior_preservation.\")\n\n if args.train_text_encoder and args.pre_compute_text_embeddings:\n raise ValueError(\"`--train_text_encoder` cannot be used with `--pre_compute_text_embeddings`\")\n\n return args\n\n\nclass DreamBoothDataset(Dataset):\n \"\"\"\n A dataset to prepare the instance and class images with the prompts for fine-tuning the model.\n It pre-processes the images and the tokenizes prompts.\n \"\"\"\n\n def __init__(\n self,\n instance_data_root,\n instance_prompt,\n tokenizer,\n class_data_root=None,\n class_prompt=None,\n class_num=None,\n size=512,\n center_crop=False,\n encoder_hidden_states=None,\n class_prompt_encoder_hidden_states=None,\n tokenizer_max_length=None,\n ):\n self.size = size\n self.center_crop = center_crop\n self.tokenizer = tokenizer\n self.encoder_hidden_states = encoder_hidden_states\n self.class_prompt_encoder_hidden_states = class_prompt_encoder_hidden_states\n self.tokenizer_max_length = tokenizer_max_length\n\n self.instance_data_root = Path(instance_data_root)\n if not self.instance_data_root.exists():\n raise ValueError(f\"Instance {self.instance_data_root} images root doesn't exists.\")\n\n self.instance_images_path = list(Path(instance_data_root).iterdir())\n self.num_instance_images = len(self.instance_images_path)\n self.instance_prompt = instance_prompt\n self._length = self.num_instance_images\n\n if class_data_root is not None:\n self.class_data_root = Path(class_data_root)\n self.class_data_root.mkdir(parents=True, exist_ok=True)\n self.class_images_path = list(self.class_data_root.iterdir())\n if class_num is not None:\n self.num_class_images = min(len(self.class_images_path), class_num)\n else:\n self.num_class_images = len(self.class_images_path)\n self._length = max(self.num_class_images, self.num_instance_images)\n self.class_prompt = class_prompt\n else:\n self.class_data_root = None\n\n self.image_transforms = transforms.Compose(\n [\n transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, index):\n example = {}\n instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])\n instance_image = exif_transpose(instance_image)\n\n if not instance_image.mode == \"RGB\":\n instance_image = instance_image.convert(\"RGB\")\n example[\"instance_images\"] = self.image_transforms(instance_image)\n\n if self.encoder_hidden_states is not None:\n example[\"instance_prompt_ids\"] = self.encoder_hidden_states\n else:\n text_inputs = tokenize_prompt(\n self.tokenizer, self.instance_prompt, tokenizer_max_length=self.tokenizer_max_length\n )\n example[\"instance_prompt_ids\"] = text_inputs.input_ids\n example[\"instance_attention_mask\"] = text_inputs.attention_mask\n\n if self.class_data_root:\n class_image = Image.open(self.class_images_path[index % self.num_class_images])\n class_image = exif_transpose(class_image)\n\n if not class_image.mode == \"RGB\":\n class_image = class_image.convert(\"RGB\")\n example[\"class_images\"] = self.image_transforms(class_image)\n\n if self.class_prompt_encoder_hidden_states is not None:\n example[\"class_prompt_ids\"] = self.class_prompt_encoder_hidden_states\n else:\n class_text_inputs = tokenize_prompt(\n self.tokenizer, self.class_prompt, tokenizer_max_length=self.tokenizer_max_length\n )\n example[\"class_prompt_ids\"] = class_text_inputs.input_ids\n example[\"class_attention_mask\"] = class_text_inputs.attention_mask\n\n return example\n\n\ndef collate_fn(examples, with_prior_preservation=False):\n has_attention_mask = \"instance_attention_mask\" in examples[0]\n\n input_ids = [example[\"instance_prompt_ids\"] for example in examples]\n pixel_values = [example[\"instance_images\"] for example in examples]\n\n if has_attention_mask:\n attention_mask = [example[\"instance_attention_mask\"] for example in examples]\n\n # Concat class and instance examples for prior preservation.\n # We do this to avoid doing two forward passes.\n if with_prior_preservation:\n input_ids += [example[\"class_prompt_ids\"] for example in examples]\n pixel_values += [example[\"class_images\"] for example in examples]\n\n if has_attention_mask:\n attention_mask += [example[\"class_attention_mask\"] for example in examples]\n\n pixel_values = torch.stack(pixel_values)\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n\n input_ids = torch.cat(input_ids, dim=0)\n\n batch = {\n \"input_ids\": input_ids,\n \"pixel_values\": pixel_values,\n }\n\n if has_attention_mask:\n attention_mask = torch.cat(attention_mask, dim=0)\n batch[\"attention_mask\"] = attention_mask\n\n return batch\n\n\nclass PromptDataset(Dataset):\n \"\"\"A simple dataset to prepare the prompts to generate class images on multiple GPUs.\"\"\"\n\n def __init__(self, prompt, num_samples):\n self.prompt = prompt\n self.num_samples = num_samples\n\n def __len__(self):\n return self.num_samples\n\n def __getitem__(self, index):\n example = {}\n example[\"prompt\"] = self.prompt\n example[\"index\"] = index\n return example\n\n\ndef model_has_vae(args):\n config_file_name = Path(\"vae\", AutoencoderKL.config_name).as_posix()\n if os.path.isdir(args.pretrained_model_name_or_path):\n config_file_name = os.path.join(args.pretrained_model_name_or_path, config_file_name)\n return os.path.isfile(config_file_name)\n else:\n files_in_repo = model_info(args.pretrained_model_name_or_path, revision=args.revision).siblings\n return any(file.rfilename == config_file_name for file in files_in_repo)\n\n\ndef tokenize_prompt(tokenizer, prompt, tokenizer_max_length=None):\n if tokenizer_max_length is not None:\n max_length = tokenizer_max_length\n else:\n max_length = tokenizer.model_max_length\n\n text_inputs = tokenizer(\n prompt,\n truncation=True,\n padding=\"max_length\",\n max_length=max_length,\n return_tensors=\"pt\",\n )\n\n return text_inputs\n\n\ndef encode_prompt(text_encoder, input_ids, attention_mask, text_encoder_use_attention_mask=None):\n text_input_ids = input_ids.to(text_encoder.device)\n\n if text_encoder_use_attention_mask:\n attention_mask = attention_mask.to(text_encoder.device)\n else:\n attention_mask = None\n\n prompt_embeds = text_encoder(\n text_input_ids,\n attention_mask=attention_mask,\n return_dict=False,\n )\n prompt_embeds = prompt_embeds[0]\n\n return prompt_embeds\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n\n # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate\n # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.\n # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.\n if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:\n raise ValueError(\n \"Gradient accumulation is not supported when training the text encoder in distributed training. \"\n \"Please set gradient_accumulation_steps to 1. This feature will be supported in the future.\"\n )\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Generate class images if prior preservation is enabled.\n if args.with_prior_preservation:\n class_images_dir = Path(args.class_data_dir)\n if not class_images_dir.exists():\n class_images_dir.mkdir(parents=True)\n cur_class_images = len(list(class_images_dir.iterdir()))\n\n if cur_class_images < args.num_class_images:\n torch_dtype = torch.float16 if accelerator.device.type == \"cuda\" else torch.float32\n if args.prior_generation_precision == \"fp32\":\n torch_dtype = torch.float32\n elif args.prior_generation_precision == \"fp16\":\n torch_dtype = torch.float16\n elif args.prior_generation_precision == \"bf16\":\n torch_dtype = torch.bfloat16\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n torch_dtype=torch_dtype,\n safety_checker=None,\n revision=args.revision,\n variant=args.variant,\n )\n pipeline.set_progress_bar_config(disable=True)\n\n num_new_images = args.num_class_images - cur_class_images\n logger.info(f\"Number of class images to sample: {num_new_images}.\")\n\n sample_dataset = PromptDataset(args.class_prompt, num_new_images)\n sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)\n\n sample_dataloader = accelerator.prepare(sample_dataloader)\n pipeline.to(accelerator.device)\n\n for example in tqdm(\n sample_dataloader, desc=\"Generating class images\", disable=not accelerator.is_local_main_process\n ):\n images = pipeline(example[\"prompt\"]).images\n\n for i, image in enumerate(images):\n hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()\n image_filename = class_images_dir / f\"{example['index'][i] + cur_class_images}-{hash_image}.jpg\"\n image.save(image_filename)\n\n del pipeline\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizer\n if args.tokenizer_name:\n tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)\n elif args.pretrained_model_name_or_path:\n tokenizer = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n\n # import correct text encoder class\n text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n text_encoder = text_encoder_cls.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n\n if model_has_vae(args):\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision, variant=args.variant\n )\n else:\n vae = None\n\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n for model in models:\n sub_dir = \"unet\" if isinstance(model, type(unwrap_model(unet))) else \"text_encoder\"\n model.save_pretrained(os.path.join(output_dir, sub_dir))\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n def load_model_hook(models, input_dir):\n while len(models) > 0:\n # pop models so that they are not loaded again\n model = models.pop()\n\n if isinstance(model, type(unwrap_model(text_encoder))):\n # load transformers style into model\n load_model = text_encoder_cls.from_pretrained(input_dir, subfolder=\"text_encoder\")\n model.config = load_model.config\n else:\n # load diffusers style into model\n load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder=\"unet\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n if vae is not None:\n vae.requires_grad_(False)\n\n if not args.train_text_encoder:\n text_encoder.requires_grad_(False)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n if args.train_text_encoder:\n text_encoder.gradient_checkpointing_enable()\n\n # Check that all trainable models are in full precision\n low_precision_error_string = (\n \"Please make sure to always have all model weights in full float32 precision when starting training - even if\"\n \" doing mixed precision training. copy of the weights should still be float32.\"\n )\n\n if unwrap_model(unet).dtype != torch.float32:\n raise ValueError(f\"Unet loaded as datatype {unwrap_model(unet).dtype}. {low_precision_error_string}\")\n\n if args.train_text_encoder and unwrap_model(text_encoder).dtype != torch.float32:\n raise ValueError(\n f\"Text encoder loaded as datatype {unwrap_model(text_encoder).dtype}.\" f\" {low_precision_error_string}\"\n )\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # Optimizer creation\n params_to_optimize = (\n itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters()\n )\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n if args.pre_compute_text_embeddings:\n\n def compute_text_embeddings(prompt):\n with torch.no_grad():\n text_inputs = tokenize_prompt(tokenizer, prompt, tokenizer_max_length=args.tokenizer_max_length)\n prompt_embeds = encode_prompt(\n text_encoder,\n text_inputs.input_ids,\n text_inputs.attention_mask,\n text_encoder_use_attention_mask=args.text_encoder_use_attention_mask,\n )\n\n return prompt_embeds\n\n pre_computed_encoder_hidden_states = compute_text_embeddings(args.instance_prompt)\n validation_prompt_negative_prompt_embeds = compute_text_embeddings(\"\")\n\n if args.validation_prompt is not None:\n validation_prompt_encoder_hidden_states = compute_text_embeddings(args.validation_prompt)\n else:\n validation_prompt_encoder_hidden_states = None\n\n if args.class_prompt is not None:\n pre_computed_class_prompt_encoder_hidden_states = compute_text_embeddings(args.class_prompt)\n else:\n pre_computed_class_prompt_encoder_hidden_states = None\n\n text_encoder = None\n tokenizer = None\n\n gc.collect()\n torch.cuda.empty_cache()\n else:\n pre_computed_encoder_hidden_states = None\n validation_prompt_encoder_hidden_states = None\n validation_prompt_negative_prompt_embeds = None\n pre_computed_class_prompt_encoder_hidden_states = None\n\n # Dataset and DataLoaders creation:\n train_dataset = DreamBoothDataset(\n instance_data_root=args.instance_data_dir,\n instance_prompt=args.instance_prompt,\n class_data_root=args.class_data_dir if args.with_prior_preservation else None,\n class_prompt=args.class_prompt,\n class_num=args.num_class_images,\n tokenizer=tokenizer,\n size=args.resolution,\n center_crop=args.center_crop,\n encoder_hidden_states=pre_computed_encoder_hidden_states,\n class_prompt_encoder_hidden_states=pre_computed_class_prompt_encoder_hidden_states,\n tokenizer_max_length=args.tokenizer_max_length,\n )\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n batch_size=args.train_batch_size,\n shuffle=True,\n collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_cycles=args.lr_num_cycles,\n power=args.lr_power,\n )\n\n # Prepare everything with our `accelerator`.\n if args.train_text_encoder:\n unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, text_encoder, optimizer, train_dataloader, lr_scheduler\n )\n else:\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move vae and text_encoder to device and cast to weight_dtype\n if vae is not None:\n vae.to(accelerator.device, dtype=weight_dtype)\n\n if not args.train_text_encoder and text_encoder is not None:\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = vars(copy.deepcopy(args))\n tracker_config.pop(\"validation_images\")\n accelerator.init_trackers(\"dreambooth\", config=tracker_config)\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num batches each epoch = {len(train_dataloader)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n for epoch in range(first_epoch, args.num_train_epochs):\n unet.train()\n if args.train_text_encoder:\n text_encoder.train()\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n pixel_values = batch[\"pixel_values\"].to(dtype=weight_dtype)\n\n if vae is not None:\n # Convert images to latent space\n model_input = vae.encode(batch[\"pixel_values\"].to(dtype=weight_dtype)).latent_dist.sample()\n model_input = model_input * vae.config.scaling_factor\n else:\n model_input = pixel_values\n\n # Sample noise that we'll add to the model input\n if args.offset_noise:\n noise = torch.randn_like(model_input) + 0.1 * torch.randn(\n model_input.shape[0], model_input.shape[1], 1, 1, device=model_input.device\n )\n else:\n noise = torch.randn_like(model_input)\n bsz, channels, height, width = model_input.shape\n # Sample a random timestep for each image\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device\n )\n timesteps = timesteps.long()\n\n # Add noise to the model input according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)\n\n # Get the text embedding for conditioning\n if args.pre_compute_text_embeddings:\n encoder_hidden_states = batch[\"input_ids\"]\n else:\n encoder_hidden_states = encode_prompt(\n text_encoder,\n batch[\"input_ids\"],\n batch[\"attention_mask\"],\n text_encoder_use_attention_mask=args.text_encoder_use_attention_mask,\n )\n\n if unwrap_model(unet).config.in_channels == channels * 2:\n noisy_model_input = torch.cat([noisy_model_input, noisy_model_input], dim=1)\n\n if args.class_labels_conditioning == \"timesteps\":\n class_labels = timesteps\n else:\n class_labels = None\n\n # Predict the noise residual\n model_pred = unet(\n noisy_model_input, timesteps, encoder_hidden_states, class_labels=class_labels, return_dict=False\n )[0]\n\n if model_pred.shape[1] == 6:\n model_pred, _ = torch.chunk(model_pred, 2, dim=1)\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(model_input, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n if args.with_prior_preservation:\n # Chunk the noise and model_pred into two parts and compute the loss on each part separately.\n model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)\n target, target_prior = torch.chunk(target, 2, dim=0)\n # Compute prior loss\n prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction=\"mean\")\n\n # Compute instance loss\n if args.snr_gamma is None:\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n else:\n # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.\n # Since we predict the noise instead of x_0, the original formulation is slightly changed.\n # This is discussed in Section 4.2 of the same paper.\n snr = compute_snr(noise_scheduler, timesteps)\n base_weight = (\n torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr\n )\n\n if noise_scheduler.config.prediction_type == \"v_prediction\":\n # Velocity objective needs to be floored to an SNR weight of one.\n mse_loss_weights = base_weight + 1\n else:\n # Epsilon and sample both use the same loss weights.\n mse_loss_weights = base_weight\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"none\")\n loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights\n loss = loss.mean()\n\n if args.with_prior_preservation:\n # Add the prior loss to the instance loss.\n loss = loss + args.prior_loss_weight * prior_loss\n\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n params_to_clip = (\n itertools.chain(unet.parameters(), text_encoder.parameters())\n if args.train_text_encoder\n else unet.parameters()\n )\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad(set_to_none=args.set_grads_to_none)\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n images = []\n\n if args.validation_prompt is not None and global_step % args.validation_steps == 0:\n images = log_validation(\n unwrap_model(text_encoder) if text_encoder is not None else text_encoder,\n tokenizer,\n unwrap_model(unet),\n vae,\n args,\n accelerator,\n weight_dtype,\n global_step,\n validation_prompt_encoder_hidden_states,\n validation_prompt_negative_prompt_embeds,\n )\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n # Create the pipeline using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n pipeline_args = {}\n\n if text_encoder is not None:\n pipeline_args[\"text_encoder\"] = unwrap_model(text_encoder)\n\n if args.skip_save_text_encoder:\n pipeline_args[\"text_encoder\"] = None\n\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n unet=unwrap_model(unet),\n revision=args.revision,\n variant=args.variant,\n **pipeline_args,\n )\n\n # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it\n scheduler_args = {}\n\n if \"variance_type\" in pipeline.scheduler.config:\n variance_type = pipeline.scheduler.config.variance_type\n\n if variance_type in [\"learned\", \"learned_range\"]:\n variance_type = \"fixed_small\"\n\n scheduler_args[\"variance_type\"] = variance_type\n\n pipeline.scheduler = pipeline.scheduler.from_config(pipeline.scheduler.config, **scheduler_args)\n\n pipeline.save_pretrained(args.output_dir)\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n images=images,\n base_model=args.pretrained_model_name_or_path,\n train_text_encoder=args.train_text_encoder,\n prompt=args.instance_prompt,\n repo_folder=args.output_dir,\n pipeline=pipeline,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/dreambooth/train_dreambooth_flax.py", "content": "import argparse\nimport logging\nimport math\nimport os\nfrom pathlib import Path\n\nimport jax\nimport jax.numpy as jnp\nimport numpy as np\nimport optax\nimport torch\nimport torch.utils.checkpoint\nimport transformers\nfrom flax import jax_utils\nfrom flax.training import train_state\nfrom flax.training.common_utils import shard\nfrom huggingface_hub import create_repo, upload_folder\nfrom huggingface_hub.utils import insecure_hashlib\nfrom jax.experimental.compilation_cache import compilation_cache as cc\nfrom PIL import Image\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel, set_seed\n\nfrom diffusers import (\n FlaxAutoencoderKL,\n FlaxDDPMScheduler,\n FlaxPNDMScheduler,\n FlaxStableDiffusionPipeline,\n FlaxUNet2DConditionModel,\n)\nfrom diffusers.pipelines.stable_diffusion import FlaxStableDiffusionSafetyChecker\nfrom diffusers.utils import check_min_version\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\n# Cache compiled models across invocations of this script.\ncc.initialize_cache(os.path.expanduser(\"~/.cache/jax/compilation_cache\"))\n\nlogger = logging.getLogger(__name__)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_vae_name_or_path\",\n type=str,\n default=None,\n help=\"Path to pretrained vae or vae identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--tokenizer_name\",\n type=str,\n default=None,\n help=\"Pretrained tokenizer name or path if not the same as model_name\",\n )\n parser.add_argument(\n \"--instance_data_dir\",\n type=str,\n default=None,\n required=True,\n help=\"A folder containing the training data of instance images.\",\n )\n parser.add_argument(\n \"--class_data_dir\",\n type=str,\n default=None,\n required=False,\n help=\"A folder containing the training data of class images.\",\n )\n parser.add_argument(\n \"--instance_prompt\",\n type=str,\n default=None,\n help=\"The prompt with identifier specifying the instance\",\n )\n parser.add_argument(\n \"--class_prompt\",\n type=str,\n default=None,\n help=\"The prompt to specify images in the same class as provided instance images.\",\n )\n parser.add_argument(\n \"--with_prior_preservation\",\n default=False,\n action=\"store_true\",\n help=\"Flag to add prior preservation loss.\",\n )\n parser.add_argument(\"--prior_loss_weight\", type=float, default=1.0, help=\"The weight of prior preservation loss.\")\n parser.add_argument(\n \"--num_class_images\",\n type=int,\n default=100,\n help=(\n \"Minimal class images for prior preservation loss. If there are not enough images already present in\"\n \" class_data_dir, additional images will be sampled with class_prompt.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"text-inversion-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--save_steps\", type=int, default=None, help=\"Save a checkpoint every X steps.\")\n parser.add_argument(\"--seed\", type=int, default=0, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\"--train_text_encoder\", action=\"store_true\", help=\"Whether to train the text encoder\")\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\n \"--sample_batch_size\", type=int, default=4, help=\"Batch size (per device) for sampling images.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=5e-6,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=\"no\",\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose\"\n \"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.\"\n \"and an Nvidia Ampere GPU.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.instance_data_dir is None:\n raise ValueError(\"You must specify a train data directory.\")\n\n if args.with_prior_preservation:\n if args.class_data_dir is None:\n raise ValueError(\"You must specify a data directory for class images.\")\n if args.class_prompt is None:\n raise ValueError(\"You must specify prompt for class images.\")\n\n return args\n\n\nclass DreamBoothDataset(Dataset):\n \"\"\"\n A dataset to prepare the instance and class images with the prompts for fine-tuning the model.\n It pre-processes the images and the tokenizes prompts.\n \"\"\"\n\n def __init__(\n self,\n instance_data_root,\n instance_prompt,\n tokenizer,\n class_data_root=None,\n class_prompt=None,\n class_num=None,\n size=512,\n center_crop=False,\n ):\n self.size = size\n self.center_crop = center_crop\n self.tokenizer = tokenizer\n\n self.instance_data_root = Path(instance_data_root)\n if not self.instance_data_root.exists():\n raise ValueError(\"Instance images root doesn't exists.\")\n\n self.instance_images_path = list(Path(instance_data_root).iterdir())\n self.num_instance_images = len(self.instance_images_path)\n self.instance_prompt = instance_prompt\n self._length = self.num_instance_images\n\n if class_data_root is not None:\n self.class_data_root = Path(class_data_root)\n self.class_data_root.mkdir(parents=True, exist_ok=True)\n self.class_images_path = list(self.class_data_root.iterdir())\n if class_num is not None:\n self.num_class_images = min(len(self.class_images_path), class_num)\n else:\n self.num_class_images = len(self.class_images_path)\n self._length = max(self.num_class_images, self.num_instance_images)\n self.class_prompt = class_prompt\n else:\n self.class_data_root = None\n\n self.image_transforms = transforms.Compose(\n [\n transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, index):\n example = {}\n instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])\n if not instance_image.mode == \"RGB\":\n instance_image = instance_image.convert(\"RGB\")\n example[\"instance_images\"] = self.image_transforms(instance_image)\n example[\"instance_prompt_ids\"] = self.tokenizer(\n self.instance_prompt,\n padding=\"do_not_pad\",\n truncation=True,\n max_length=self.tokenizer.model_max_length,\n ).input_ids\n\n if self.class_data_root:\n class_image = Image.open(self.class_images_path[index % self.num_class_images])\n if not class_image.mode == \"RGB\":\n class_image = class_image.convert(\"RGB\")\n example[\"class_images\"] = self.image_transforms(class_image)\n example[\"class_prompt_ids\"] = self.tokenizer(\n self.class_prompt,\n padding=\"do_not_pad\",\n truncation=True,\n max_length=self.tokenizer.model_max_length,\n ).input_ids\n\n return example\n\n\nclass PromptDataset(Dataset):\n \"\"\"A simple dataset to prepare the prompts to generate class images on multiple GPUs.\"\"\"\n\n def __init__(self, prompt, num_samples):\n self.prompt = prompt\n self.num_samples = num_samples\n\n def __len__(self):\n return self.num_samples\n\n def __getitem__(self, index):\n example = {}\n example[\"prompt\"] = self.prompt\n example[\"index\"] = index\n return example\n\n\ndef get_params_to_save(params):\n return jax.device_get(jax.tree_util.tree_map(lambda x: x[0], params))\n\n\ndef main():\n args = parse_args()\n\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n # Setup logging, we only want one process per machine to log things on the screen.\n logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)\n if jax.process_index() == 0:\n transformers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n\n if args.seed is not None:\n set_seed(args.seed)\n\n rng = jax.random.PRNGKey(args.seed)\n\n if args.with_prior_preservation:\n class_images_dir = Path(args.class_data_dir)\n if not class_images_dir.exists():\n class_images_dir.mkdir(parents=True)\n cur_class_images = len(list(class_images_dir.iterdir()))\n\n if cur_class_images < args.num_class_images:\n pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path, safety_checker=None, revision=args.revision\n )\n pipeline.set_progress_bar_config(disable=True)\n\n num_new_images = args.num_class_images - cur_class_images\n logger.info(f\"Number of class images to sample: {num_new_images}.\")\n\n sample_dataset = PromptDataset(args.class_prompt, num_new_images)\n total_sample_batch_size = args.sample_batch_size * jax.local_device_count()\n sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=total_sample_batch_size)\n\n for example in tqdm(\n sample_dataloader, desc=\"Generating class images\", disable=not jax.process_index() == 0\n ):\n prompt_ids = pipeline.prepare_inputs(example[\"prompt\"])\n prompt_ids = shard(prompt_ids)\n p_params = jax_utils.replicate(params)\n rng = jax.random.split(rng)[0]\n sample_rng = jax.random.split(rng, jax.device_count())\n images = pipeline(prompt_ids, p_params, sample_rng, jit=True).images\n images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])\n images = pipeline.numpy_to_pil(np.array(images))\n\n for i, image in enumerate(images):\n hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()\n image_filename = class_images_dir / f\"{example['index'][i] + cur_class_images}-{hash_image}.jpg\"\n image.save(image_filename)\n\n del pipeline\n\n # Handle the repository creation\n if jax.process_index() == 0:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizer and add the placeholder token as a additional special token\n if args.tokenizer_name:\n tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)\n elif args.pretrained_model_name_or_path:\n tokenizer = CLIPTokenizer.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"tokenizer\", revision=args.revision\n )\n else:\n raise NotImplementedError(\"No tokenizer specified!\")\n\n train_dataset = DreamBoothDataset(\n instance_data_root=args.instance_data_dir,\n instance_prompt=args.instance_prompt,\n class_data_root=args.class_data_dir if args.with_prior_preservation else None,\n class_prompt=args.class_prompt,\n class_num=args.num_class_images,\n tokenizer=tokenizer,\n size=args.resolution,\n center_crop=args.center_crop,\n )\n\n def collate_fn(examples):\n input_ids = [example[\"instance_prompt_ids\"] for example in examples]\n pixel_values = [example[\"instance_images\"] for example in examples]\n\n # Concat class and instance examples for prior preservation.\n # We do this to avoid doing two forward passes.\n if args.with_prior_preservation:\n input_ids += [example[\"class_prompt_ids\"] for example in examples]\n pixel_values += [example[\"class_images\"] for example in examples]\n\n pixel_values = torch.stack(pixel_values)\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n\n input_ids = tokenizer.pad(\n {\"input_ids\": input_ids}, padding=\"max_length\", max_length=tokenizer.model_max_length, return_tensors=\"pt\"\n ).input_ids\n\n batch = {\n \"input_ids\": input_ids,\n \"pixel_values\": pixel_values,\n }\n batch = {k: v.numpy() for k, v in batch.items()}\n return batch\n\n total_train_batch_size = args.train_batch_size * jax.local_device_count()\n if len(train_dataset) < total_train_batch_size:\n raise ValueError(\n f\"Training batch size is {total_train_batch_size}, but your dataset only contains\"\n f\" {len(train_dataset)} images. Please, use a larger dataset or reduce the effective batch size. Note that\"\n f\" there are {jax.local_device_count()} parallel devices, so your batch size can't be smaller than that.\"\n )\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset, batch_size=total_train_batch_size, shuffle=True, collate_fn=collate_fn, drop_last=True\n )\n\n weight_dtype = jnp.float32\n if args.mixed_precision == \"fp16\":\n weight_dtype = jnp.float16\n elif args.mixed_precision == \"bf16\":\n weight_dtype = jnp.bfloat16\n\n if args.pretrained_vae_name_or_path:\n # TODO(patil-suraj): Upload flax weights for the VAE\n vae_arg, vae_kwargs = (args.pretrained_vae_name_or_path, {\"from_pt\": True})\n else:\n vae_arg, vae_kwargs = (args.pretrained_model_name_or_path, {\"subfolder\": \"vae\", \"revision\": args.revision})\n\n # Load models and create wrapper for stable diffusion\n text_encoder = FlaxCLIPTextModel.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"text_encoder\",\n dtype=weight_dtype,\n revision=args.revision,\n )\n vae, vae_params = FlaxAutoencoderKL.from_pretrained(\n vae_arg,\n dtype=weight_dtype,\n **vae_kwargs,\n )\n unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"unet\",\n dtype=weight_dtype,\n revision=args.revision,\n )\n\n # Optimization\n if args.scale_lr:\n args.learning_rate = args.learning_rate * total_train_batch_size\n\n constant_scheduler = optax.constant_schedule(args.learning_rate)\n\n adamw = optax.adamw(\n learning_rate=constant_scheduler,\n b1=args.adam_beta1,\n b2=args.adam_beta2,\n eps=args.adam_epsilon,\n weight_decay=args.adam_weight_decay,\n )\n\n optimizer = optax.chain(\n optax.clip_by_global_norm(args.max_grad_norm),\n adamw,\n )\n\n unet_state = train_state.TrainState.create(apply_fn=unet.__call__, params=unet_params, tx=optimizer)\n text_encoder_state = train_state.TrainState.create(\n apply_fn=text_encoder.__call__, params=text_encoder.params, tx=optimizer\n )\n\n noise_scheduler = FlaxDDPMScheduler(\n beta_start=0.00085, beta_end=0.012, beta_schedule=\"scaled_linear\", num_train_timesteps=1000\n )\n noise_scheduler_state = noise_scheduler.create_state()\n\n # Initialize our training\n train_rngs = jax.random.split(rng, jax.local_device_count())\n\n def train_step(unet_state, text_encoder_state, vae_params, batch, train_rng):\n dropout_rng, sample_rng, new_train_rng = jax.random.split(train_rng, 3)\n\n if args.train_text_encoder:\n params = {\"text_encoder\": text_encoder_state.params, \"unet\": unet_state.params}\n else:\n params = {\"unet\": unet_state.params}\n\n def compute_loss(params):\n # Convert images to latent space\n vae_outputs = vae.apply(\n {\"params\": vae_params}, batch[\"pixel_values\"], deterministic=True, method=vae.encode\n )\n latents = vae_outputs.latent_dist.sample(sample_rng)\n # (NHWC) -> (NCHW)\n latents = jnp.transpose(latents, (0, 3, 1, 2))\n latents = latents * vae.config.scaling_factor\n\n # Sample noise that we'll add to the latents\n noise_rng, timestep_rng = jax.random.split(sample_rng)\n noise = jax.random.normal(noise_rng, latents.shape)\n # Sample a random timestep for each image\n bsz = latents.shape[0]\n timesteps = jax.random.randint(\n timestep_rng,\n (bsz,),\n 0,\n noise_scheduler.config.num_train_timesteps,\n )\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(noise_scheduler_state, latents, noise, timesteps)\n\n # Get the text embedding for conditioning\n if args.train_text_encoder:\n encoder_hidden_states = text_encoder_state.apply_fn(\n batch[\"input_ids\"], params=params[\"text_encoder\"], dropout_rng=dropout_rng, train=True\n )[0]\n else:\n encoder_hidden_states = text_encoder(\n batch[\"input_ids\"], params=text_encoder_state.params, train=False\n )[0]\n\n # Predict the noise residual\n model_pred = unet.apply(\n {\"params\": params[\"unet\"]}, noisy_latents, timesteps, encoder_hidden_states, train=True\n ).sample\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(noise_scheduler_state, latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n if args.with_prior_preservation:\n # Chunk the noise and noise_pred into two parts and compute the loss on each part separately.\n model_pred, model_pred_prior = jnp.split(model_pred, 2, axis=0)\n target, target_prior = jnp.split(target, 2, axis=0)\n\n # Compute instance loss\n loss = (target - model_pred) ** 2\n loss = loss.mean()\n\n # Compute prior loss\n prior_loss = (target_prior - model_pred_prior) ** 2\n prior_loss = prior_loss.mean()\n\n # Add the prior loss to the instance loss.\n loss = loss + args.prior_loss_weight * prior_loss\n else:\n loss = (target - model_pred) ** 2\n loss = loss.mean()\n\n return loss\n\n grad_fn = jax.value_and_grad(compute_loss)\n loss, grad = grad_fn(params)\n grad = jax.lax.pmean(grad, \"batch\")\n\n new_unet_state = unet_state.apply_gradients(grads=grad[\"unet\"])\n if args.train_text_encoder:\n new_text_encoder_state = text_encoder_state.apply_gradients(grads=grad[\"text_encoder\"])\n else:\n new_text_encoder_state = text_encoder_state\n\n metrics = {\"loss\": loss}\n metrics = jax.lax.pmean(metrics, axis_name=\"batch\")\n\n return new_unet_state, new_text_encoder_state, metrics, new_train_rng\n\n # Create parallel version of the train step\n p_train_step = jax.pmap(train_step, \"batch\", donate_argnums=(0, 1))\n\n # Replicate the train state on each device\n unet_state = jax_utils.replicate(unet_state)\n text_encoder_state = jax_utils.replicate(text_encoder_state)\n vae_params = jax_utils.replicate(vae_params)\n\n # Train!\n num_update_steps_per_epoch = math.ceil(len(train_dataloader))\n\n # Scheduler and math around the number of training steps.\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel & distributed) = {total_train_batch_size}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n\n def checkpoint(step=None):\n # Create the pipeline using the trained modules and save it.\n scheduler, _ = FlaxPNDMScheduler.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"scheduler\")\n safety_checker = FlaxStableDiffusionSafetyChecker.from_pretrained(\n \"CompVis/stable-diffusion-safety-checker\", from_pt=True\n )\n pipeline = FlaxStableDiffusionPipeline(\n text_encoder=text_encoder,\n vae=vae,\n unet=unet,\n tokenizer=tokenizer,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=CLIPImageProcessor.from_pretrained(\"openai/clip-vit-base-patch32\"),\n )\n\n outdir = os.path.join(args.output_dir, str(step)) if step else args.output_dir\n pipeline.save_pretrained(\n outdir,\n params={\n \"text_encoder\": get_params_to_save(text_encoder_state.params),\n \"vae\": get_params_to_save(vae_params),\n \"unet\": get_params_to_save(unet_state.params),\n \"safety_checker\": safety_checker.params,\n },\n )\n\n if args.push_to_hub:\n message = f\"checkpoint-{step}\" if step is not None else \"End of training\"\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=message,\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n global_step = 0\n\n epochs = tqdm(range(args.num_train_epochs), desc=\"Epoch ... \", position=0)\n for epoch in epochs:\n # ======================== Training ================================\n\n train_metrics = []\n\n steps_per_epoch = len(train_dataset) // total_train_batch_size\n train_step_progress_bar = tqdm(total=steps_per_epoch, desc=\"Training...\", position=1, leave=False)\n # train\n for batch in train_dataloader:\n batch = shard(batch)\n unet_state, text_encoder_state, train_metric, train_rngs = p_train_step(\n unet_state, text_encoder_state, vae_params, batch, train_rngs\n )\n train_metrics.append(train_metric)\n\n train_step_progress_bar.update(jax.local_device_count())\n\n global_step += 1\n if jax.process_index() == 0 and args.save_steps and global_step % args.save_steps == 0:\n checkpoint(global_step)\n if global_step >= args.max_train_steps:\n break\n\n train_metric = jax_utils.unreplicate(train_metric)\n\n train_step_progress_bar.close()\n epochs.write(f\"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})\")\n\n if jax.process_index() == 0:\n checkpoint()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/dreambooth/train_dreambooth_lora.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport copy\nimport gc\nimport logging\nimport math\nimport os\nimport shutil\nimport warnings\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom huggingface_hub import create_repo, upload_folder\nfrom huggingface_hub.utils import insecure_hashlib\nfrom packaging import version\nfrom peft import LoraConfig\nfrom peft.utils import get_peft_model_state_dict, set_peft_model_state_dict\nfrom PIL import Image\nfrom PIL.ImageOps import exif_transpose\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n DiffusionPipeline,\n DPMSolverMultistepScheduler,\n StableDiffusionPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.loaders import LoraLoaderMixin\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import _set_state_dict_into_text_encoder, cast_training_params\nfrom diffusers.utils import (\n check_min_version,\n convert_state_dict_to_diffusers,\n convert_unet_state_dict_to_peft,\n is_wandb_available,\n)\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef save_model_card(\n repo_id: str,\n images=None,\n base_model=str,\n train_text_encoder=False,\n prompt=str,\n repo_folder=None,\n pipeline: DiffusionPipeline = None,\n):\n img_str = \"\"\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n img_str += f\"![img_{i}](./image_{i}.png)\\n\"\n\n model_description = f\"\"\"\n# LoRA DreamBooth - {repo_id}\n\nThese are LoRA adaption weights for {base_model}. The weights were trained on {prompt} using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. \\n\n{img_str}\n\nLoRA for the text encoder was enabled: {train_text_encoder}.\n\"\"\"\n model_card = load_or_create_model_card(\n repo_id_or_path=repo_id,\n from_training=True,\n license=\"creativeml-openrail-m\",\n base_model=base_model,\n prompt=prompt,\n model_description=model_description,\n inference=True,\n )\n tags = [\"text-to-image\", \"diffusers\", \"lora\", \"diffusers-training\"]\n if isinstance(pipeline, StableDiffusionPipeline):\n tags.extend([\"stable-diffusion\", \"stable-diffusion-diffusers\"])\n else:\n tags.extend([\"if\", \"if-diffusers\"])\n model_card = populate_model_card(model_card, tags=tags)\n\n model_card.save(os.path.join(repo_folder, \"README.md\"))\n\n\ndef log_validation(\n pipeline,\n args,\n accelerator,\n pipeline_args,\n epoch,\n is_final_validation=False,\n):\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it\n scheduler_args = {}\n\n if \"variance_type\" in pipeline.scheduler.config:\n variance_type = pipeline.scheduler.config.variance_type\n\n if variance_type in [\"learned\", \"learned_range\"]:\n variance_type = \"fixed_small\"\n\n scheduler_args[\"variance_type\"] = variance_type\n\n pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, **scheduler_args)\n\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n\n if args.validation_images is None:\n images = []\n for _ in range(args.num_validation_images):\n with torch.cuda.amp.autocast():\n image = pipeline(**pipeline_args, generator=generator).images[0]\n images.append(image)\n else:\n images = []\n for image in args.validation_images:\n image = Image.open(image)\n with torch.cuda.amp.autocast():\n image = pipeline(**pipeline_args, image=image, generator=generator).images[0]\n images.append(image)\n\n for tracker in accelerator.trackers:\n phase_name = \"test\" if is_final_validation else \"validation\"\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(phase_name, np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n phase_name: [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\") for i, image in enumerate(images)\n ]\n }\n )\n\n del pipeline\n torch.cuda.empty_cache()\n\n return images\n\n\ndef import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path,\n subfolder=\"text_encoder\",\n revision=revision,\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"RobertaSeriesModelWithTransformation\":\n from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation\n\n return RobertaSeriesModelWithTransformation\n elif model_class == \"T5EncoderModel\":\n from transformers import T5EncoderModel\n\n return T5EncoderModel\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--tokenizer_name\",\n type=str,\n default=None,\n help=\"Pretrained tokenizer name or path if not the same as model_name\",\n )\n parser.add_argument(\n \"--instance_data_dir\",\n type=str,\n default=None,\n required=True,\n help=\"A folder containing the training data of instance images.\",\n )\n parser.add_argument(\n \"--class_data_dir\",\n type=str,\n default=None,\n required=False,\n help=\"A folder containing the training data of class images.\",\n )\n parser.add_argument(\n \"--instance_prompt\",\n type=str,\n default=None,\n required=True,\n help=\"The prompt with identifier specifying the instance\",\n )\n parser.add_argument(\n \"--class_prompt\",\n type=str,\n default=None,\n help=\"The prompt to specify images in the same class as provided instance images.\",\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n help=\"A prompt that is used during validation to verify that the model is learning.\",\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=50,\n help=(\n \"Run dreambooth validation every X epochs. Dreambooth validation consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--with_prior_preservation\",\n default=False,\n action=\"store_true\",\n help=\"Flag to add prior preservation loss.\",\n )\n parser.add_argument(\"--prior_loss_weight\", type=float, default=1.0, help=\"The weight of prior preservation loss.\")\n parser.add_argument(\n \"--num_class_images\",\n type=int,\n default=100,\n help=(\n \"Minimal class images for prior preservation loss. If there are not enough images already present in\"\n \" class_data_dir, additional images will be sampled with class_prompt.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"lora-dreambooth-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--train_text_encoder\",\n action=\"store_true\",\n help=\"Whether to train the text encoder. If set, the text encoder should be float32 precision.\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\n \"--sample_batch_size\", type=int, default=4, help=\"Batch size (per device) for sampling images.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final\"\n \" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=5e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\"--lr_power\", type=float, default=1.0, help=\"Power factor of the polynomial scheduler.\")\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--prior_generation_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp32\", \"fp16\", \"bf16\"],\n help=(\n \"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--pre_compute_text_embeddings\",\n action=\"store_true\",\n help=\"Whether or not to pre-compute text embeddings. If text embeddings are pre-computed, the text encoder will not be kept in memory during training and will leave more GPU memory available for training the rest of the model. This is not compatible with `--train_text_encoder`.\",\n )\n parser.add_argument(\n \"--tokenizer_max_length\",\n type=int,\n default=None,\n required=False,\n help=\"The maximum length of the tokenizer. If not set, will default to the tokenizer's max length.\",\n )\n parser.add_argument(\n \"--text_encoder_use_attention_mask\",\n action=\"store_true\",\n required=False,\n help=\"Whether to use attention mask for the text encoder\",\n )\n parser.add_argument(\n \"--validation_images\",\n required=False,\n default=None,\n nargs=\"+\",\n help=\"Optional set of images to use for validation. Used when the target pipeline takes an initial image as input such as when training image variation or superresolution.\",\n )\n parser.add_argument(\n \"--class_labels_conditioning\",\n required=False,\n default=None,\n help=\"The optional `class_label` conditioning to pass to the unet, available values are `timesteps`.\",\n )\n parser.add_argument(\n \"--rank\",\n type=int,\n default=4,\n help=(\"The dimension of the LoRA update matrices.\"),\n )\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.with_prior_preservation:\n if args.class_data_dir is None:\n raise ValueError(\"You must specify a data directory for class images.\")\n if args.class_prompt is None:\n raise ValueError(\"You must specify prompt for class images.\")\n else:\n # logger is not available yet\n if args.class_data_dir is not None:\n warnings.warn(\"You need not use --class_data_dir without --with_prior_preservation.\")\n if args.class_prompt is not None:\n warnings.warn(\"You need not use --class_prompt without --with_prior_preservation.\")\n\n if args.train_text_encoder and args.pre_compute_text_embeddings:\n raise ValueError(\"`--train_text_encoder` cannot be used with `--pre_compute_text_embeddings`\")\n\n return args\n\n\nclass DreamBoothDataset(Dataset):\n \"\"\"\n A dataset to prepare the instance and class images with the prompts for fine-tuning the model.\n It pre-processes the images and the tokenizes prompts.\n \"\"\"\n\n def __init__(\n self,\n instance_data_root,\n instance_prompt,\n tokenizer,\n class_data_root=None,\n class_prompt=None,\n class_num=None,\n size=512,\n center_crop=False,\n encoder_hidden_states=None,\n class_prompt_encoder_hidden_states=None,\n tokenizer_max_length=None,\n ):\n self.size = size\n self.center_crop = center_crop\n self.tokenizer = tokenizer\n self.encoder_hidden_states = encoder_hidden_states\n self.class_prompt_encoder_hidden_states = class_prompt_encoder_hidden_states\n self.tokenizer_max_length = tokenizer_max_length\n\n self.instance_data_root = Path(instance_data_root)\n if not self.instance_data_root.exists():\n raise ValueError(\"Instance images root doesn't exists.\")\n\n self.instance_images_path = list(Path(instance_data_root).iterdir())\n self.num_instance_images = len(self.instance_images_path)\n self.instance_prompt = instance_prompt\n self._length = self.num_instance_images\n\n if class_data_root is not None:\n self.class_data_root = Path(class_data_root)\n self.class_data_root.mkdir(parents=True, exist_ok=True)\n self.class_images_path = list(self.class_data_root.iterdir())\n if class_num is not None:\n self.num_class_images = min(len(self.class_images_path), class_num)\n else:\n self.num_class_images = len(self.class_images_path)\n self._length = max(self.num_class_images, self.num_instance_images)\n self.class_prompt = class_prompt\n else:\n self.class_data_root = None\n\n self.image_transforms = transforms.Compose(\n [\n transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, index):\n example = {}\n instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])\n instance_image = exif_transpose(instance_image)\n\n if not instance_image.mode == \"RGB\":\n instance_image = instance_image.convert(\"RGB\")\n example[\"instance_images\"] = self.image_transforms(instance_image)\n\n if self.encoder_hidden_states is not None:\n example[\"instance_prompt_ids\"] = self.encoder_hidden_states\n else:\n text_inputs = tokenize_prompt(\n self.tokenizer, self.instance_prompt, tokenizer_max_length=self.tokenizer_max_length\n )\n example[\"instance_prompt_ids\"] = text_inputs.input_ids\n example[\"instance_attention_mask\"] = text_inputs.attention_mask\n\n if self.class_data_root:\n class_image = Image.open(self.class_images_path[index % self.num_class_images])\n class_image = exif_transpose(class_image)\n\n if not class_image.mode == \"RGB\":\n class_image = class_image.convert(\"RGB\")\n example[\"class_images\"] = self.image_transforms(class_image)\n\n if self.class_prompt_encoder_hidden_states is not None:\n example[\"class_prompt_ids\"] = self.class_prompt_encoder_hidden_states\n else:\n class_text_inputs = tokenize_prompt(\n self.tokenizer, self.class_prompt, tokenizer_max_length=self.tokenizer_max_length\n )\n example[\"class_prompt_ids\"] = class_text_inputs.input_ids\n example[\"class_attention_mask\"] = class_text_inputs.attention_mask\n\n return example\n\n\ndef collate_fn(examples, with_prior_preservation=False):\n has_attention_mask = \"instance_attention_mask\" in examples[0]\n\n input_ids = [example[\"instance_prompt_ids\"] for example in examples]\n pixel_values = [example[\"instance_images\"] for example in examples]\n\n if has_attention_mask:\n attention_mask = [example[\"instance_attention_mask\"] for example in examples]\n\n # Concat class and instance examples for prior preservation.\n # We do this to avoid doing two forward passes.\n if with_prior_preservation:\n input_ids += [example[\"class_prompt_ids\"] for example in examples]\n pixel_values += [example[\"class_images\"] for example in examples]\n if has_attention_mask:\n attention_mask += [example[\"class_attention_mask\"] for example in examples]\n\n pixel_values = torch.stack(pixel_values)\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n\n input_ids = torch.cat(input_ids, dim=0)\n\n batch = {\n \"input_ids\": input_ids,\n \"pixel_values\": pixel_values,\n }\n\n if has_attention_mask:\n batch[\"attention_mask\"] = attention_mask\n\n return batch\n\n\nclass PromptDataset(Dataset):\n \"\"\"A simple dataset to prepare the prompts to generate class images on multiple GPUs.\"\"\"\n\n def __init__(self, prompt, num_samples):\n self.prompt = prompt\n self.num_samples = num_samples\n\n def __len__(self):\n return self.num_samples\n\n def __getitem__(self, index):\n example = {}\n example[\"prompt\"] = self.prompt\n example[\"index\"] = index\n return example\n\n\ndef tokenize_prompt(tokenizer, prompt, tokenizer_max_length=None):\n if tokenizer_max_length is not None:\n max_length = tokenizer_max_length\n else:\n max_length = tokenizer.model_max_length\n\n text_inputs = tokenizer(\n prompt,\n truncation=True,\n padding=\"max_length\",\n max_length=max_length,\n return_tensors=\"pt\",\n )\n\n return text_inputs\n\n\ndef encode_prompt(text_encoder, input_ids, attention_mask, text_encoder_use_attention_mask=None):\n text_input_ids = input_ids.to(text_encoder.device)\n\n if text_encoder_use_attention_mask:\n attention_mask = attention_mask.to(text_encoder.device)\n else:\n attention_mask = None\n\n prompt_embeds = text_encoder(\n text_input_ids,\n attention_mask=attention_mask,\n return_dict=False,\n )\n prompt_embeds = prompt_embeds[0]\n\n return prompt_embeds\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n\n # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate\n # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.\n # TODO (sayakpaul): Remove this check when gradient accumulation with two models is enabled in accelerate.\n if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:\n raise ValueError(\n \"Gradient accumulation is not supported when training the text encoder in distributed training. \"\n \"Please set gradient_accumulation_steps to 1. This feature will be supported in the future.\"\n )\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Generate class images if prior preservation is enabled.\n if args.with_prior_preservation:\n class_images_dir = Path(args.class_data_dir)\n if not class_images_dir.exists():\n class_images_dir.mkdir(parents=True)\n cur_class_images = len(list(class_images_dir.iterdir()))\n\n if cur_class_images < args.num_class_images:\n torch_dtype = torch.float16 if accelerator.device.type == \"cuda\" else torch.float32\n if args.prior_generation_precision == \"fp32\":\n torch_dtype = torch.float32\n elif args.prior_generation_precision == \"fp16\":\n torch_dtype = torch.float16\n elif args.prior_generation_precision == \"bf16\":\n torch_dtype = torch.bfloat16\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n torch_dtype=torch_dtype,\n safety_checker=None,\n revision=args.revision,\n variant=args.variant,\n )\n pipeline.set_progress_bar_config(disable=True)\n\n num_new_images = args.num_class_images - cur_class_images\n logger.info(f\"Number of class images to sample: {num_new_images}.\")\n\n sample_dataset = PromptDataset(args.class_prompt, num_new_images)\n sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)\n\n sample_dataloader = accelerator.prepare(sample_dataloader)\n pipeline.to(accelerator.device)\n\n for example in tqdm(\n sample_dataloader, desc=\"Generating class images\", disable=not accelerator.is_local_main_process\n ):\n images = pipeline(example[\"prompt\"]).images\n\n for i, image in enumerate(images):\n hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()\n image_filename = class_images_dir / f\"{example['index'][i] + cur_class_images}-{hash_image}.jpg\"\n image.save(image_filename)\n\n del pipeline\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizer\n if args.tokenizer_name:\n tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)\n elif args.pretrained_model_name_or_path:\n tokenizer = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n\n # import correct text encoder class\n text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n text_encoder = text_encoder_cls.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n try:\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision, variant=args.variant\n )\n except OSError:\n # IF does not have a VAE so let's just set it to None\n # We don't have to error out here\n vae = None\n\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n # We only train the additional adapter LoRA layers\n if vae is not None:\n vae.requires_grad_(False)\n text_encoder.requires_grad_(False)\n unet.requires_grad_(False)\n\n # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move unet, vae and text_encoder to device and cast to weight_dtype\n unet.to(accelerator.device, dtype=weight_dtype)\n if vae is not None:\n vae.to(accelerator.device, dtype=weight_dtype)\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n if args.train_text_encoder:\n text_encoder.gradient_checkpointing_enable()\n\n # now we will add new LoRA weights to the attention layers\n unet_lora_config = LoraConfig(\n r=args.rank,\n lora_alpha=args.rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"to_k\", \"to_q\", \"to_v\", \"to_out.0\", \"add_k_proj\", \"add_v_proj\"],\n )\n unet.add_adapter(unet_lora_config)\n\n # The text encoder comes from 🤗 transformers, we will also attach adapters to it.\n if args.train_text_encoder:\n text_lora_config = LoraConfig(\n r=args.rank,\n lora_alpha=args.rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"out_proj\"],\n )\n text_encoder.add_adapter(text_lora_config)\n\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n # there are only two options here. Either are just the unet attn processor layers\n # or there are the unet and text encoder atten layers\n unet_lora_layers_to_save = None\n text_encoder_lora_layers_to_save = None\n\n for model in models:\n if isinstance(model, type(unwrap_model(unet))):\n unet_lora_layers_to_save = convert_state_dict_to_diffusers(get_peft_model_state_dict(model))\n elif isinstance(model, type(unwrap_model(text_encoder))):\n text_encoder_lora_layers_to_save = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(model)\n )\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n LoraLoaderMixin.save_lora_weights(\n output_dir,\n unet_lora_layers=unet_lora_layers_to_save,\n text_encoder_lora_layers=text_encoder_lora_layers_to_save,\n )\n\n def load_model_hook(models, input_dir):\n unet_ = None\n text_encoder_ = None\n\n while len(models) > 0:\n model = models.pop()\n\n if isinstance(model, type(unwrap_model(unet))):\n unet_ = model\n elif isinstance(model, type(unwrap_model(text_encoder))):\n text_encoder_ = model\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n lora_state_dict, network_alphas = LoraLoaderMixin.lora_state_dict(input_dir)\n\n unet_state_dict = {f'{k.replace(\"unet.\", \"\")}': v for k, v in lora_state_dict.items() if k.startswith(\"unet.\")}\n unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict)\n incompatible_keys = set_peft_model_state_dict(unet_, unet_state_dict, adapter_name=\"default\")\n\n if incompatible_keys is not None:\n # check only for unexpected keys\n unexpected_keys = getattr(incompatible_keys, \"unexpected_keys\", None)\n if unexpected_keys:\n logger.warning(\n f\"Loading adapter weights from state_dict led to unexpected keys not found in the model: \"\n f\" {unexpected_keys}. \"\n )\n\n if args.train_text_encoder:\n _set_state_dict_into_text_encoder(lora_state_dict, prefix=\"text_encoder.\", text_encoder=text_encoder_)\n\n # Make sure the trainable params are in float32. This is again needed since the base models\n # are in `weight_dtype`. More details:\n # https://github.com/huggingface/diffusers/pull/6514#discussion_r1449796804\n if args.mixed_precision == \"fp16\":\n models = [unet_]\n if args.train_text_encoder:\n models.append(text_encoder_)\n\n # only upcast trainable parameters (LoRA) into fp32\n cast_training_params(models, dtype=torch.float32)\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Make sure the trainable params are in float32.\n if args.mixed_precision == \"fp16\":\n models = [unet]\n if args.train_text_encoder:\n models.append(text_encoder)\n\n # only upcast trainable parameters (LoRA) into fp32\n cast_training_params(models, dtype=torch.float32)\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # Optimizer creation\n params_to_optimize = list(filter(lambda p: p.requires_grad, unet.parameters()))\n if args.train_text_encoder:\n params_to_optimize = params_to_optimize + list(filter(lambda p: p.requires_grad, text_encoder.parameters()))\n\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n if args.pre_compute_text_embeddings:\n\n def compute_text_embeddings(prompt):\n with torch.no_grad():\n text_inputs = tokenize_prompt(tokenizer, prompt, tokenizer_max_length=args.tokenizer_max_length)\n prompt_embeds = encode_prompt(\n text_encoder,\n text_inputs.input_ids,\n text_inputs.attention_mask,\n text_encoder_use_attention_mask=args.text_encoder_use_attention_mask,\n )\n\n return prompt_embeds\n\n pre_computed_encoder_hidden_states = compute_text_embeddings(args.instance_prompt)\n validation_prompt_negative_prompt_embeds = compute_text_embeddings(\"\")\n\n if args.validation_prompt is not None:\n validation_prompt_encoder_hidden_states = compute_text_embeddings(args.validation_prompt)\n else:\n validation_prompt_encoder_hidden_states = None\n\n if args.class_prompt is not None:\n pre_computed_class_prompt_encoder_hidden_states = compute_text_embeddings(args.class_prompt)\n else:\n pre_computed_class_prompt_encoder_hidden_states = None\n\n text_encoder = None\n tokenizer = None\n\n gc.collect()\n torch.cuda.empty_cache()\n else:\n pre_computed_encoder_hidden_states = None\n validation_prompt_encoder_hidden_states = None\n validation_prompt_negative_prompt_embeds = None\n pre_computed_class_prompt_encoder_hidden_states = None\n\n # Dataset and DataLoaders creation:\n train_dataset = DreamBoothDataset(\n instance_data_root=args.instance_data_dir,\n instance_prompt=args.instance_prompt,\n class_data_root=args.class_data_dir if args.with_prior_preservation else None,\n class_prompt=args.class_prompt,\n class_num=args.num_class_images,\n tokenizer=tokenizer,\n size=args.resolution,\n center_crop=args.center_crop,\n encoder_hidden_states=pre_computed_encoder_hidden_states,\n class_prompt_encoder_hidden_states=pre_computed_class_prompt_encoder_hidden_states,\n tokenizer_max_length=args.tokenizer_max_length,\n )\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n batch_size=args.train_batch_size,\n shuffle=True,\n collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_cycles=args.lr_num_cycles,\n power=args.lr_power,\n )\n\n # Prepare everything with our `accelerator`.\n if args.train_text_encoder:\n unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, text_encoder, optimizer, train_dataloader, lr_scheduler\n )\n else:\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = vars(copy.deepcopy(args))\n tracker_config.pop(\"validation_images\")\n accelerator.init_trackers(\"dreambooth-lora\", config=tracker_config)\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num batches each epoch = {len(train_dataloader)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the mos recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n for epoch in range(first_epoch, args.num_train_epochs):\n unet.train()\n if args.train_text_encoder:\n text_encoder.train()\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n pixel_values = batch[\"pixel_values\"].to(dtype=weight_dtype)\n\n if vae is not None:\n # Convert images to latent space\n model_input = vae.encode(pixel_values).latent_dist.sample()\n model_input = model_input * vae.config.scaling_factor\n else:\n model_input = pixel_values\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(model_input)\n bsz, channels, height, width = model_input.shape\n # Sample a random timestep for each image\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device\n )\n timesteps = timesteps.long()\n\n # Add noise to the model input according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)\n\n # Get the text embedding for conditioning\n if args.pre_compute_text_embeddings:\n encoder_hidden_states = batch[\"input_ids\"]\n else:\n encoder_hidden_states = encode_prompt(\n text_encoder,\n batch[\"input_ids\"],\n batch[\"attention_mask\"],\n text_encoder_use_attention_mask=args.text_encoder_use_attention_mask,\n )\n\n if unwrap_model(unet).config.in_channels == channels * 2:\n noisy_model_input = torch.cat([noisy_model_input, noisy_model_input], dim=1)\n\n if args.class_labels_conditioning == \"timesteps\":\n class_labels = timesteps\n else:\n class_labels = None\n\n # Predict the noise residual\n model_pred = unet(\n noisy_model_input,\n timesteps,\n encoder_hidden_states,\n class_labels=class_labels,\n return_dict=False,\n )[0]\n\n # if model predicts variance, throw away the prediction. we will only train on the\n # simplified training objective. This means that all schedulers using the fine tuned\n # model must be configured to use one of the fixed variance variance types.\n if model_pred.shape[1] == 6:\n model_pred, _ = torch.chunk(model_pred, 2, dim=1)\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(model_input, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n if args.with_prior_preservation:\n # Chunk the noise and model_pred into two parts and compute the loss on each part separately.\n model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)\n target, target_prior = torch.chunk(target, 2, dim=0)\n\n # Compute instance loss\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n\n # Compute prior loss\n prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction=\"mean\")\n\n # Add the prior loss to the instance loss.\n loss = loss + args.prior_loss_weight * prior_loss\n else:\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(params_to_optimize, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompt is not None and epoch % args.validation_epochs == 0:\n # create pipeline\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n unet=unwrap_model(unet),\n text_encoder=None if args.pre_compute_text_embeddings else unwrap_model(text_encoder),\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n\n if args.pre_compute_text_embeddings:\n pipeline_args = {\n \"prompt_embeds\": validation_prompt_encoder_hidden_states,\n \"negative_prompt_embeds\": validation_prompt_negative_prompt_embeds,\n }\n else:\n pipeline_args = {\"prompt\": args.validation_prompt}\n\n images = log_validation(\n pipeline,\n args,\n accelerator,\n pipeline_args,\n epoch,\n )\n\n # Save the lora layers\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = unwrap_model(unet)\n unet = unet.to(torch.float32)\n\n unet_lora_state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(unet))\n\n if args.train_text_encoder:\n text_encoder = unwrap_model(text_encoder)\n text_encoder_state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(text_encoder))\n else:\n text_encoder_state_dict = None\n\n LoraLoaderMixin.save_lora_weights(\n save_directory=args.output_dir,\n unet_lora_layers=unet_lora_state_dict,\n text_encoder_lora_layers=text_encoder_state_dict,\n )\n\n # Final inference\n # Load previous pipeline\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path, revision=args.revision, variant=args.variant, torch_dtype=weight_dtype\n )\n\n # load attention processors\n pipeline.load_lora_weights(args.output_dir, weight_name=\"pytorch_lora_weights.safetensors\")\n\n # run inference\n images = []\n if args.validation_prompt and args.num_validation_images > 0:\n pipeline_args = {\"prompt\": args.validation_prompt, \"num_inference_steps\": 25}\n images = log_validation(\n pipeline,\n args,\n accelerator,\n pipeline_args,\n epoch,\n is_final_validation=True,\n )\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n images=images,\n base_model=args.pretrained_model_name_or_path,\n train_text_encoder=args.train_text_encoder,\n prompt=args.instance_prompt,\n repo_folder=args.output_dir,\n pipeline=pipeline,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/dreambooth/train_dreambooth_lora_sd3.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport copy\nimport gc\nimport itertools\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nimport warnings\nfrom contextlib import nullcontext\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import DistributedDataParallelKwargs, ProjectConfiguration, set_seed\nfrom huggingface_hub import create_repo, upload_folder\nfrom huggingface_hub.utils import insecure_hashlib\nfrom peft import LoraConfig, set_peft_model_state_dict\nfrom peft.utils import get_peft_model_state_dict\nfrom PIL import Image\nfrom PIL.ImageOps import exif_transpose\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom torchvision.transforms.functional import crop\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTokenizer, PretrainedConfig, T5TokenizerFast\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n FlowMatchEulerDiscreteScheduler,\n SD3Transformer2DModel,\n StableDiffusion3Pipeline,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import cast_training_params\nfrom diffusers.utils import (\n check_min_version,\n convert_unet_state_dict_to_peft,\n is_wandb_available,\n)\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef save_model_card(\n repo_id: str,\n images=None,\n base_model: str = None,\n instance_prompt=None,\n validation_prompt=None,\n repo_folder=None,\n):\n widget_dict = []\n if images is not None:\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n widget_dict.append(\n {\"text\": validation_prompt if validation_prompt else \" \", \"output\": {\"url\": f\"image_{i}.png\"}}\n )\n\n model_description = f\"\"\"\n# SD3 DreamBooth LoRA - {repo_id}\n\n\n\n## Model description\n\nThese are {repo_id} DreamBooth weights for {base_model}.\n\nThe weights were trained using [DreamBooth](https://dreambooth.github.io/).\n\n## Trigger words\n\nYou should use {instance_prompt} to trigger the image generation.\n\n## Download model\n\n[Download]({repo_id}/tree/main) them in the Files & versions tab.\n\n## License\n\nPlease adhere to the licensing terms as described `[here](https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/LICENSE)`.\n\"\"\"\n model_card = load_or_create_model_card(\n repo_id_or_path=repo_id,\n from_training=True,\n license=\"openrail++\",\n base_model=base_model,\n prompt=instance_prompt,\n model_description=model_description,\n widget=widget_dict,\n )\n tags = [\n \"text-to-image\",\n \"diffusers-training\",\n \"diffusers\",\n \"sd3\",\n \"sd3-diffusers\",\n \"template:sd-lora\",\n ]\n\n model_card = populate_model_card(model_card, tags=tags)\n model_card.save(os.path.join(repo_folder, \"README.md\"))\n\n\ndef load_text_encoders(class_one, class_two, class_three):\n text_encoder_one = class_one.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n text_encoder_two = class_two.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder_2\", revision=args.revision, variant=args.variant\n )\n text_encoder_three = class_three.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder_3\", revision=args.revision, variant=args.variant\n )\n return text_encoder_one, text_encoder_two, text_encoder_three\n\n\ndef log_validation(\n pipeline,\n args,\n accelerator,\n pipeline_args,\n epoch,\n is_final_validation=False,\n):\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n # autocast_ctx = torch.autocast(accelerator.device.type) if not is_final_validation else nullcontext()\n autocast_ctx = nullcontext()\n\n with autocast_ctx:\n images = [pipeline(**pipeline_args, generator=generator).images[0] for _ in range(args.num_validation_images)]\n\n for tracker in accelerator.trackers:\n phase_name = \"test\" if is_final_validation else \"validation\"\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(phase_name, np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n phase_name: [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\") for i, image in enumerate(images)\n ]\n }\n )\n\n del pipeline\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\n return images\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n if model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n elif model_class == \"T5EncoderModel\":\n from transformers import T5EncoderModel\n\n return T5EncoderModel\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) containing the training data of instance images (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--instance_data_dir\",\n type=str,\n default=None,\n help=(\"A folder containing the training data. \"),\n )\n\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n\n parser.add_argument(\n \"--image_column\",\n type=str,\n default=\"image\",\n help=\"The column of the dataset containing the target image. By \"\n \"default, the standard Image Dataset maps out 'file_name' \"\n \"to 'image'.\",\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=None,\n help=\"The column of the dataset containing the instance prompt for each image\",\n )\n\n parser.add_argument(\"--repeats\", type=int, default=1, help=\"How many times to repeat the training data.\")\n\n parser.add_argument(\n \"--class_data_dir\",\n type=str,\n default=None,\n required=False,\n help=\"A folder containing the training data of class images.\",\n )\n parser.add_argument(\n \"--instance_prompt\",\n type=str,\n default=None,\n required=True,\n help=\"The prompt with identifier specifying the instance, e.g. 'photo of a TOK dog', 'in the style of TOK'\",\n )\n parser.add_argument(\n \"--class_prompt\",\n type=str,\n default=None,\n help=\"The prompt to specify images in the same class as provided instance images.\",\n )\n parser.add_argument(\n \"--max_sequence_length\",\n type=int,\n default=77,\n help=\"Maximum sequence length to use with with the T5 text encoder\",\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n help=\"A prompt that is used during validation to verify that the model is learning.\",\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=50,\n help=(\n \"Run dreambooth validation every X epochs. Dreambooth validation consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--rank\",\n type=int,\n default=4,\n help=(\"The dimension of the LoRA update matrices.\"),\n )\n parser.add_argument(\n \"--with_prior_preservation\",\n default=False,\n action=\"store_true\",\n help=\"Flag to add prior preservation loss.\",\n )\n parser.add_argument(\"--prior_loss_weight\", type=float, default=1.0, help=\"The weight of prior preservation loss.\")\n parser.add_argument(\n \"--num_class_images\",\n type=int,\n default=100,\n help=(\n \"Minimal class images for prior preservation loss. If there are not enough images already present in\"\n \" class_data_dir, additional images will be sampled with class_prompt.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"sd3-dreambooth\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\n \"--sample_batch_size\", type=int, default=4, help=\"Batch size (per device) for sampling images.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final\"\n \" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n\n parser.add_argument(\n \"--text_encoder_lr\",\n type=float,\n default=5e-6,\n help=\"Text encoder learning rate to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\"--lr_power\", type=float, default=1.0, help=\"Power factor of the polynomial scheduler.\")\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\n \"--weighting_scheme\", type=str, default=\"sigma_sqrt\", choices=[\"sigma_sqrt\", \"logit_normal\", \"mode\"]\n )\n parser.add_argument(\"--logit_mean\", type=float, default=0.0)\n parser.add_argument(\"--logit_std\", type=float, default=1.0)\n parser.add_argument(\"--mode_scale\", type=float, default=1.29)\n parser.add_argument(\n \"--optimizer\",\n type=str,\n default=\"AdamW\",\n help=('The optimizer type to use. Choose between [\"AdamW\", \"prodigy\"]'),\n )\n\n parser.add_argument(\n \"--use_8bit_adam\",\n action=\"store_true\",\n help=\"Whether or not to use 8-bit Adam from bitsandbytes. Ignored if optimizer is not set to AdamW\",\n )\n\n parser.add_argument(\n \"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam and Prodigy optimizers.\"\n )\n parser.add_argument(\n \"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam and Prodigy optimizers.\"\n )\n parser.add_argument(\n \"--prodigy_beta3\",\n type=float,\n default=None,\n help=\"coefficients for computing the Prodidy stepsize using running averages. If set to None, \"\n \"uses the value of square root of beta2. Ignored if optimizer is adamW\",\n )\n parser.add_argument(\"--prodigy_decouple\", type=bool, default=True, help=\"Use AdamW style decoupled weight decay\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-04, help=\"Weight decay to use for unet params\")\n parser.add_argument(\n \"--adam_weight_decay_text_encoder\", type=float, default=1e-03, help=\"Weight decay to use for text_encoder\"\n )\n\n parser.add_argument(\n \"--adam_epsilon\",\n type=float,\n default=1e-08,\n help=\"Epsilon value for the Adam optimizer and Prodigy optimizers.\",\n )\n\n parser.add_argument(\n \"--prodigy_use_bias_correction\",\n type=bool,\n default=True,\n help=\"Turn on Adam's bias correction. True by default. Ignored if optimizer is adamW\",\n )\n parser.add_argument(\n \"--prodigy_safeguard_warmup\",\n type=bool,\n default=True,\n help=\"Remove lr from the denominator of D estimate to avoid issues during warm-up stage. True by default. \"\n \"Ignored if optimizer is adamW\",\n )\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--prior_generation_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp32\", \"fp16\", \"bf16\"],\n help=(\n \"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n if args.dataset_name is None and args.instance_data_dir is None:\n raise ValueError(\"Specify either `--dataset_name` or `--instance_data_dir`\")\n\n if args.dataset_name is not None and args.instance_data_dir is not None:\n raise ValueError(\"Specify only one of `--dataset_name` or `--instance_data_dir`\")\n\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.with_prior_preservation:\n if args.class_data_dir is None:\n raise ValueError(\"You must specify a data directory for class images.\")\n if args.class_prompt is None:\n raise ValueError(\"You must specify prompt for class images.\")\n else:\n # logger is not available yet\n if args.class_data_dir is not None:\n warnings.warn(\"You need not use --class_data_dir without --with_prior_preservation.\")\n if args.class_prompt is not None:\n warnings.warn(\"You need not use --class_prompt without --with_prior_preservation.\")\n\n return args\n\n\nclass DreamBoothDataset(Dataset):\n \"\"\"\n A dataset to prepare the instance and class images with the prompts for fine-tuning the model.\n It pre-processes the images.\n \"\"\"\n\n def __init__(\n self,\n instance_data_root,\n instance_prompt,\n class_prompt,\n class_data_root=None,\n class_num=None,\n size=1024,\n repeats=1,\n center_crop=False,\n ):\n self.size = size\n self.center_crop = center_crop\n\n self.instance_prompt = instance_prompt\n self.custom_instance_prompts = None\n self.class_prompt = class_prompt\n\n # if --dataset_name is provided or a metadata jsonl file is provided in the local --instance_data directory,\n # we load the training data using load_dataset\n if args.dataset_name is not None:\n try:\n from datasets import load_dataset\n except ImportError:\n raise ImportError(\n \"You are trying to load your data using the datasets library. If you wish to train using custom \"\n \"captions please install the datasets library: `pip install datasets`. If you wish to load a \"\n \"local folder containing images only, specify --instance_data_dir instead.\"\n )\n # Downloading and loading a dataset from the hub.\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n )\n # Preprocessing the datasets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n if args.image_column is None:\n image_column = column_names[0]\n logger.info(f\"image column defaulting to {image_column}\")\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n instance_images = dataset[\"train\"][image_column]\n\n if args.caption_column is None:\n logger.info(\n \"No caption column provided, defaulting to instance_prompt for all images. If your dataset \"\n \"contains captions/prompts for the images, make sure to specify the \"\n \"column as --caption_column\"\n )\n self.custom_instance_prompts = None\n else:\n if args.caption_column not in column_names:\n raise ValueError(\n f\"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n custom_instance_prompts = dataset[\"train\"][args.caption_column]\n # create final list of captions according to --repeats\n self.custom_instance_prompts = []\n for caption in custom_instance_prompts:\n self.custom_instance_prompts.extend(itertools.repeat(caption, repeats))\n else:\n self.instance_data_root = Path(instance_data_root)\n if not self.instance_data_root.exists():\n raise ValueError(\"Instance images root doesn't exists.\")\n\n instance_images = [Image.open(path) for path in list(Path(instance_data_root).iterdir())]\n self.custom_instance_prompts = None\n\n self.instance_images = []\n for img in instance_images:\n self.instance_images.extend(itertools.repeat(img, repeats))\n\n self.pixel_values = []\n train_resize = transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR)\n train_crop = transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size)\n train_flip = transforms.RandomHorizontalFlip(p=1.0)\n train_transforms = transforms.Compose(\n [\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n for image in self.instance_images:\n image = exif_transpose(image)\n if not image.mode == \"RGB\":\n image = image.convert(\"RGB\")\n image = train_resize(image)\n if args.random_flip and random.random() < 0.5:\n # flip\n image = train_flip(image)\n if args.center_crop:\n y1 = max(0, int(round((image.height - args.resolution) / 2.0)))\n x1 = max(0, int(round((image.width - args.resolution) / 2.0)))\n image = train_crop(image)\n else:\n y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))\n image = crop(image, y1, x1, h, w)\n image = train_transforms(image)\n self.pixel_values.append(image)\n\n self.num_instance_images = len(self.instance_images)\n self._length = self.num_instance_images\n\n if class_data_root is not None:\n self.class_data_root = Path(class_data_root)\n self.class_data_root.mkdir(parents=True, exist_ok=True)\n self.class_images_path = list(self.class_data_root.iterdir())\n if class_num is not None:\n self.num_class_images = min(len(self.class_images_path), class_num)\n else:\n self.num_class_images = len(self.class_images_path)\n self._length = max(self.num_class_images, self.num_instance_images)\n else:\n self.class_data_root = None\n\n self.image_transforms = transforms.Compose(\n [\n transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, index):\n example = {}\n instance_image = self.pixel_values[index % self.num_instance_images]\n example[\"instance_images\"] = instance_image\n\n if self.custom_instance_prompts:\n caption = self.custom_instance_prompts[index % self.num_instance_images]\n if caption:\n example[\"instance_prompt\"] = caption\n else:\n example[\"instance_prompt\"] = self.instance_prompt\n\n else: # costum prompts were provided, but length does not match size of image dataset\n example[\"instance_prompt\"] = self.instance_prompt\n\n if self.class_data_root:\n class_image = Image.open(self.class_images_path[index % self.num_class_images])\n class_image = exif_transpose(class_image)\n\n if not class_image.mode == \"RGB\":\n class_image = class_image.convert(\"RGB\")\n example[\"class_images\"] = self.image_transforms(class_image)\n example[\"class_prompt\"] = self.class_prompt\n\n return example\n\n\ndef collate_fn(examples, with_prior_preservation=False):\n pixel_values = [example[\"instance_images\"] for example in examples]\n prompts = [example[\"instance_prompt\"] for example in examples]\n\n # Concat class and instance examples for prior preservation.\n # We do this to avoid doing two forward passes.\n if with_prior_preservation:\n pixel_values += [example[\"class_images\"] for example in examples]\n prompts += [example[\"class_prompt\"] for example in examples]\n\n pixel_values = torch.stack(pixel_values)\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n\n batch = {\"pixel_values\": pixel_values, \"prompts\": prompts}\n return batch\n\n\nclass PromptDataset(Dataset):\n \"A simple dataset to prepare the prompts to generate class images on multiple GPUs.\"\n\n def __init__(self, prompt, num_samples):\n self.prompt = prompt\n self.num_samples = num_samples\n\n def __len__(self):\n return self.num_samples\n\n def __getitem__(self, index):\n example = {}\n example[\"prompt\"] = self.prompt\n example[\"index\"] = index\n return example\n\n\ndef tokenize_prompt(tokenizer, prompt):\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=77,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n return text_input_ids\n\n\ndef _encode_prompt_with_t5(\n text_encoder,\n tokenizer,\n max_sequence_length,\n prompt=None,\n num_images_per_prompt=1,\n device=None,\n):\n prompt = [prompt] if isinstance(prompt, str) else prompt\n batch_size = len(prompt)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=max_sequence_length,\n truncation=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n prompt_embeds = text_encoder(text_input_ids.to(device))[0]\n\n dtype = text_encoder.dtype\n prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)\n\n _, seq_len, _ = prompt_embeds.shape\n\n # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n return prompt_embeds\n\n\ndef _encode_prompt_with_clip(\n text_encoder,\n tokenizer,\n prompt: str,\n device=None,\n num_images_per_prompt: int = 1,\n):\n prompt = [prompt] if isinstance(prompt, str) else prompt\n batch_size = len(prompt)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=77,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)\n\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds.hidden_states[-2]\n prompt_embeds = prompt_embeds.to(dtype=text_encoder.dtype, device=device)\n\n _, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n return prompt_embeds, pooled_prompt_embeds\n\n\ndef encode_prompt(\n text_encoders,\n tokenizers,\n prompt: str,\n max_sequence_length,\n device=None,\n num_images_per_prompt: int = 1,\n):\n prompt = [prompt] if isinstance(prompt, str) else prompt\n\n clip_tokenizers = tokenizers[:2]\n clip_text_encoders = text_encoders[:2]\n\n clip_prompt_embeds_list = []\n clip_pooled_prompt_embeds_list = []\n for tokenizer, text_encoder in zip(clip_tokenizers, clip_text_encoders):\n prompt_embeds, pooled_prompt_embeds = _encode_prompt_with_clip(\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n prompt=prompt,\n device=device if device is not None else text_encoder.device,\n num_images_per_prompt=num_images_per_prompt,\n )\n clip_prompt_embeds_list.append(prompt_embeds)\n clip_pooled_prompt_embeds_list.append(pooled_prompt_embeds)\n\n clip_prompt_embeds = torch.cat(clip_prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = torch.cat(clip_pooled_prompt_embeds_list, dim=-1)\n\n t5_prompt_embed = _encode_prompt_with_t5(\n text_encoders[-1],\n tokenizers[-1],\n max_sequence_length,\n prompt=prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device if device is not None else text_encoders[-1].device,\n )\n\n clip_prompt_embeds = torch.nn.functional.pad(\n clip_prompt_embeds, (0, t5_prompt_embed.shape[-1] - clip_prompt_embeds.shape[-1])\n )\n prompt_embeds = torch.cat([clip_prompt_embeds, t5_prompt_embed], dim=-2)\n\n return prompt_embeds, pooled_prompt_embeds\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n if torch.backends.mps.is_available() and args.mixed_precision == \"bf16\":\n # due to pytorch#99272, MPS does not yet support bfloat16.\n raise ValueError(\n \"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n kwargs_handlers=[kwargs],\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Generate class images if prior preservation is enabled.\n if args.with_prior_preservation:\n class_images_dir = Path(args.class_data_dir)\n if not class_images_dir.exists():\n class_images_dir.mkdir(parents=True)\n cur_class_images = len(list(class_images_dir.iterdir()))\n\n if cur_class_images < args.num_class_images:\n has_supported_fp16_accelerator = torch.cuda.is_available() or torch.backends.mps.is_available()\n torch_dtype = torch.float16 if has_supported_fp16_accelerator else torch.float32\n if args.prior_generation_precision == \"fp32\":\n torch_dtype = torch.float32\n elif args.prior_generation_precision == \"fp16\":\n torch_dtype = torch.float16\n elif args.prior_generation_precision == \"bf16\":\n torch_dtype = torch.bfloat16\n pipeline = StableDiffusion3Pipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n torch_dtype=torch_dtype,\n revision=args.revision,\n variant=args.variant,\n )\n pipeline.set_progress_bar_config(disable=True)\n\n num_new_images = args.num_class_images - cur_class_images\n logger.info(f\"Number of class images to sample: {num_new_images}.\")\n\n sample_dataset = PromptDataset(args.class_prompt, num_new_images)\n sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)\n\n sample_dataloader = accelerator.prepare(sample_dataloader)\n pipeline.to(accelerator.device)\n\n for example in tqdm(\n sample_dataloader, desc=\"Generating class images\", disable=not accelerator.is_local_main_process\n ):\n images = pipeline(example[\"prompt\"]).images\n\n for i, image in enumerate(images):\n hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()\n image_filename = class_images_dir / f\"{example['index'][i] + cur_class_images}-{hash_image}.jpg\"\n image.save(image_filename)\n\n del pipeline\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name,\n exist_ok=True,\n ).repo_id\n\n # Load the tokenizers\n tokenizer_one = CLIPTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n )\n tokenizer_two = CLIPTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer_2\",\n revision=args.revision,\n )\n tokenizer_three = T5TokenizerFast.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer_3\",\n revision=args.revision,\n )\n\n # import correct text encoder classes\n text_encoder_cls_one = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision\n )\n text_encoder_cls_two = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision, subfolder=\"text_encoder_2\"\n )\n text_encoder_cls_three = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision, subfolder=\"text_encoder_3\"\n )\n\n # Load scheduler and models\n noise_scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"scheduler\"\n )\n noise_scheduler_copy = copy.deepcopy(noise_scheduler)\n text_encoder_one, text_encoder_two, text_encoder_three = load_text_encoders(\n text_encoder_cls_one, text_encoder_cls_two, text_encoder_cls_three\n )\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"vae\",\n revision=args.revision,\n variant=args.variant,\n )\n transformer = SD3Transformer2DModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"transformer\", revision=args.revision, variant=args.variant\n )\n\n transformer.requires_grad_(False)\n vae.requires_grad_(False)\n text_encoder_one.requires_grad_(False)\n text_encoder_two.requires_grad_(False)\n text_encoder_three.requires_grad_(False)\n\n # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora transformer) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n if torch.backends.mps.is_available() and weight_dtype == torch.bfloat16:\n # due to pytorch#99272, MPS does not yet support bfloat16.\n raise ValueError(\n \"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead.\"\n )\n\n vae.to(accelerator.device, dtype=torch.float32)\n transformer.to(accelerator.device, dtype=weight_dtype)\n text_encoder_one.to(accelerator.device, dtype=weight_dtype)\n text_encoder_two.to(accelerator.device, dtype=weight_dtype)\n text_encoder_three.to(accelerator.device, dtype=weight_dtype)\n\n if args.gradient_checkpointing:\n transformer.enable_gradient_checkpointing()\n\n # now we will add new LoRA weights to the attention layers\n transformer_lora_config = LoraConfig(\n r=args.rank,\n lora_alpha=args.rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"to_k\", \"to_q\", \"to_v\", \"to_out.0\"],\n )\n transformer.add_adapter(transformer_lora_config)\n\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n transformer_lora_layers_to_save = None\n\n for model in models:\n if isinstance(model, type(unwrap_model(transformer))):\n transformer_lora_layers_to_save = get_peft_model_state_dict(model)\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n StableDiffusion3Pipeline.save_lora_weights(\n output_dir, transformer_lora_layers=transformer_lora_layers_to_save\n )\n\n def load_model_hook(models, input_dir):\n transformer_ = None\n\n while len(models) > 0:\n model = models.pop()\n\n if isinstance(model, type(unwrap_model(transformer))):\n transformer_ = model\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n lora_state_dict = StableDiffusion3Pipeline.lora_state_dict(input_dir)\n\n transformer_state_dict = {\n f'{k.replace(\"transformer.\", \"\")}': v for k, v in lora_state_dict.items() if k.startswith(\"unet.\")\n }\n transformer_state_dict = convert_unet_state_dict_to_peft(transformer_state_dict)\n incompatible_keys = set_peft_model_state_dict(transformer_, transformer_state_dict, adapter_name=\"default\")\n if incompatible_keys is not None:\n # check only for unexpected keys\n unexpected_keys = getattr(incompatible_keys, \"unexpected_keys\", None)\n if unexpected_keys:\n logger.warning(\n f\"Loading adapter weights from state_dict led to unexpected keys not found in the model: \"\n f\" {unexpected_keys}. \"\n )\n\n # Make sure the trainable params are in float32. This is again needed since the base models\n # are in `weight_dtype`. More details:\n # https://github.com/huggingface/diffusers/pull/6514#discussion_r1449796804\n if args.mixed_precision == \"fp16\":\n models = [transformer_]\n # only upcast trainable parameters (LoRA) into fp32\n cast_training_params(models)\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32 and torch.cuda.is_available():\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Make sure the trainable params are in float32.\n if args.mixed_precision == \"fp16\":\n models = [transformer]\n # only upcast trainable parameters (LoRA) into fp32\n cast_training_params(models, dtype=torch.float32)\n\n transformer_lora_parameters = list(filter(lambda p: p.requires_grad, transformer.parameters()))\n\n # Optimization parameters\n transformer_parameters_with_lr = {\"params\": transformer_lora_parameters, \"lr\": args.learning_rate}\n params_to_optimize = [transformer_parameters_with_lr]\n\n # Optimizer creation\n if not (args.optimizer.lower() == \"prodigy\" or args.optimizer.lower() == \"adamw\"):\n logger.warning(\n f\"Unsupported choice of optimizer: {args.optimizer}.Supported optimizers include [adamW, prodigy].\"\n \"Defaulting to adamW\"\n )\n args.optimizer = \"adamw\"\n\n if args.use_8bit_adam and not args.optimizer.lower() == \"adamw\":\n logger.warning(\n f\"use_8bit_adam is ignored when optimizer is not set to 'AdamW'. Optimizer was \"\n f\"set to {args.optimizer.lower()}\"\n )\n\n if args.optimizer.lower() == \"adamw\":\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n optimizer = optimizer_class(\n params_to_optimize,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n if args.optimizer.lower() == \"prodigy\":\n try:\n import prodigyopt\n except ImportError:\n raise ImportError(\"To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`\")\n\n optimizer_class = prodigyopt.Prodigy\n\n if args.learning_rate <= 0.1:\n logger.warning(\n \"Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0\"\n )\n\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n beta3=args.prodigy_beta3,\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n decouple=args.prodigy_decouple,\n use_bias_correction=args.prodigy_use_bias_correction,\n safeguard_warmup=args.prodigy_safeguard_warmup,\n )\n\n # Dataset and DataLoaders creation:\n train_dataset = DreamBoothDataset(\n instance_data_root=args.instance_data_dir,\n instance_prompt=args.instance_prompt,\n class_prompt=args.class_prompt,\n class_data_root=args.class_data_dir if args.with_prior_preservation else None,\n class_num=args.num_class_images,\n size=args.resolution,\n repeats=args.repeats,\n center_crop=args.center_crop,\n )\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n batch_size=args.train_batch_size,\n shuffle=True,\n collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),\n num_workers=args.dataloader_num_workers,\n )\n\n tokenizers = [tokenizer_one, tokenizer_two, tokenizer_three]\n text_encoders = [text_encoder_one, text_encoder_two, text_encoder_three]\n\n def compute_text_embeddings(prompt, text_encoders, tokenizers):\n with torch.no_grad():\n prompt_embeds, pooled_prompt_embeds = encode_prompt(\n text_encoders, tokenizers, prompt, args.max_sequence_length\n )\n prompt_embeds = prompt_embeds.to(accelerator.device)\n pooled_prompt_embeds = pooled_prompt_embeds.to(accelerator.device)\n return prompt_embeds, pooled_prompt_embeds\n\n if not train_dataset.custom_instance_prompts:\n instance_prompt_hidden_states, instance_pooled_prompt_embeds = compute_text_embeddings(\n args.instance_prompt, text_encoders, tokenizers\n )\n\n # Handle class prompt for prior-preservation.\n if args.with_prior_preservation:\n class_prompt_hidden_states, class_pooled_prompt_embeds = compute_text_embeddings(\n args.class_prompt, text_encoders, tokenizers\n )\n\n # Clear the memory here\n if not train_dataset.custom_instance_prompts:\n del tokenizers, text_encoders\n # Explicitly delete the objects as well, otherwise only the lists are deleted and the original references remain, preventing garbage collection\n del tokenizer_one, tokenizer_two, tokenizer_three\n del text_encoder_one, text_encoder_two, text_encoder_three\n gc.collect()\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\n # If custom instance prompts are NOT provided (i.e. the instance prompt is used for all images),\n # pack the statically computed variables appropriately here. This is so that we don't\n # have to pass them to the dataloader.\n\n if not train_dataset.custom_instance_prompts:\n prompt_embeds = instance_prompt_hidden_states\n pooled_prompt_embeds = instance_pooled_prompt_embeds\n if args.with_prior_preservation:\n prompt_embeds = torch.cat([prompt_embeds, class_prompt_hidden_states], dim=0)\n pooled_prompt_embeds = torch.cat([pooled_prompt_embeds, class_pooled_prompt_embeds], dim=0)\n # if we're optmizing the text encoder (both if instance prompt is used for all images or custom prompts) we need to tokenize and encode the\n # batch prompts on all training steps\n else:\n tokens_one = tokenize_prompt(tokenizer_one, args.instance_prompt)\n tokens_two = tokenize_prompt(tokenizer_two, args.instance_prompt)\n tokens_three = tokenize_prompt(tokenizer_three, args.instance_prompt)\n if args.with_prior_preservation:\n class_tokens_one = tokenize_prompt(tokenizer_one, args.class_prompt)\n class_tokens_two = tokenize_prompt(tokenizer_two, args.class_prompt)\n class_tokens_three = tokenize_prompt(tokenizer_three, args.class_prompt)\n tokens_one = torch.cat([tokens_one, class_tokens_one], dim=0)\n tokens_two = torch.cat([tokens_two, class_tokens_two], dim=0)\n tokens_three = torch.cat([tokens_three, class_tokens_three], dim=0)\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_cycles=args.lr_num_cycles,\n power=args.lr_power,\n )\n\n # Prepare everything with our `accelerator`.\n transformer, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n transformer, optimizer, train_dataloader, lr_scheduler\n )\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_name = \"dreambooth-sd3-lora\"\n accelerator.init_trackers(tracker_name, config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num batches each epoch = {len(train_dataloader)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the mos recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):\n sigmas = noise_scheduler_copy.sigmas.to(device=accelerator.device, dtype=dtype)\n schedule_timesteps = noise_scheduler_copy.timesteps.to(accelerator.device)\n timesteps = timesteps.to(accelerator.device)\n step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]\n\n sigma = sigmas[step_indices].flatten()\n while len(sigma.shape) < n_dim:\n sigma = sigma.unsqueeze(-1)\n return sigma\n\n for epoch in range(first_epoch, args.num_train_epochs):\n transformer.train()\n\n for step, batch in enumerate(train_dataloader):\n models_to_accumulate = [transformer]\n with accelerator.accumulate(models_to_accumulate):\n pixel_values = batch[\"pixel_values\"].to(dtype=vae.dtype)\n prompts = batch[\"prompts\"]\n\n # encode batch prompts when custom prompts are provided for each image -\n if train_dataset.custom_instance_prompts:\n prompt_embeds, pooled_prompt_embeds = compute_text_embeddings(prompts, text_encoders, tokenizers)\n\n # Convert images to latent space\n model_input = vae.encode(pixel_values).latent_dist.sample()\n model_input = model_input * vae.config.scaling_factor\n model_input = model_input.to(dtype=weight_dtype)\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(model_input)\n bsz = model_input.shape[0]\n\n # Sample a random timestep for each image\n # for weighting schemes where we sample timesteps non-uniformly\n if args.weighting_scheme == \"logit_normal\":\n # See 3.1 in the SD3 paper ($rf/lognorm(0.00,1.00)$).\n u = torch.normal(mean=args.logit_mean, std=args.logit_std, size=(bsz,), device=\"cpu\")\n u = torch.nn.functional.sigmoid(u)\n elif args.weighting_scheme == \"mode\":\n u = torch.rand(size=(bsz,), device=\"cpu\")\n u = 1 - u - args.mode_scale * (torch.cos(math.pi * u / 2) ** 2 - 1 + u)\n else:\n u = torch.rand(size=(bsz,), device=\"cpu\")\n\n indices = (u * noise_scheduler_copy.config.num_train_timesteps).long()\n timesteps = noise_scheduler_copy.timesteps[indices].to(device=model_input.device)\n\n # Add noise according to flow matching.\n sigmas = get_sigmas(timesteps, n_dim=model_input.ndim, dtype=model_input.dtype)\n noisy_model_input = sigmas * noise + (1.0 - sigmas) * model_input\n\n # Predict the noise residual\n model_pred = transformer(\n hidden_states=noisy_model_input,\n timestep=timesteps,\n encoder_hidden_states=prompt_embeds,\n pooled_projections=pooled_prompt_embeds,\n return_dict=False,\n )[0]\n\n # Follow: Section 5 of https://arxiv.org/abs/2206.00364.\n # Preconditioning of the model outputs.\n model_pred = model_pred * (-sigmas) + noisy_model_input\n\n # TODO (kashif, sayakpaul): weighting sceme needs to be experimented with :)\n # these weighting schemes use a uniform timestep sampling\n # and instead post-weight the loss\n if args.weighting_scheme == \"sigma_sqrt\":\n weighting = (sigmas**-2.0).float()\n elif args.weighting_scheme == \"cosmap\":\n bot = 1 - 2 * sigmas + 2 * sigmas**2\n weighting = 2 / (math.pi * bot)\n else:\n weighting = torch.ones_like(sigmas)\n\n # simplified flow matching aka 0-rectified flow matching loss\n # target = model_input - noise\n target = model_input\n\n if args.with_prior_preservation:\n # Chunk the noise and model_pred into two parts and compute the loss on each part separately.\n model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)\n target, target_prior = torch.chunk(target, 2, dim=0)\n\n # Compute prior loss\n prior_loss = torch.mean(\n (weighting.float() * (model_pred_prior.float() - target_prior.float()) ** 2).reshape(\n target_prior.shape[0], -1\n ),\n 1,\n )\n prior_loss = prior_loss.mean()\n\n # Compute regular loss.\n loss = torch.mean(\n (weighting.float() * (model_pred.float() - target.float()) ** 2).reshape(target.shape[0], -1),\n 1,\n )\n loss = loss.mean()\n\n if args.with_prior_preservation:\n # Add the prior loss to the instance loss.\n loss = loss + args.prior_loss_weight * prior_loss\n\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n params_to_clip = transformer_lora_parameters\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompt is not None and epoch % args.validation_epochs == 0:\n # create pipeline\n text_encoder_one, text_encoder_two, text_encoder_three = load_text_encoders(\n text_encoder_cls_one, text_encoder_cls_two, text_encoder_cls_three\n )\n pipeline = StableDiffusion3Pipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n text_encoder=accelerator.unwrap_model(text_encoder_one),\n text_encoder_2=accelerator.unwrap_model(text_encoder_two),\n text_encoder_3=accelerator.unwrap_model(text_encoder_three),\n transformer=accelerator.unwrap_model(transformer),\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n pipeline_args = {\"prompt\": args.validation_prompt}\n images = log_validation(\n pipeline=pipeline,\n args=args,\n accelerator=accelerator,\n pipeline_args=pipeline_args,\n epoch=epoch,\n )\n del text_encoder_one, text_encoder_two, text_encoder_three\n torch.cuda.empty_cache()\n gc.collect()\n\n # Save the lora layers\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n transformer = unwrap_model(transformer)\n transformer = transformer.to(torch.float32)\n transformer_lora_layers = get_peft_model_state_dict(transformer)\n\n StableDiffusion3Pipeline.save_lora_weights(\n save_directory=args.output_dir, transformer_lora_layers=transformer_lora_layers\n )\n\n pipeline = StableDiffusion3Pipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n\n # Final inference\n # Load previous pipeline\n pipeline = StableDiffusion3Pipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n # load attention processors\n pipeline.load_lora_weights(args.output_dir)\n\n # run inference\n images = []\n if args.validation_prompt and args.num_validation_images > 0:\n pipeline_args = {\"prompt\": args.validation_prompt}\n images = log_validation(\n pipeline=pipeline,\n args=args,\n accelerator=accelerator,\n pipeline_args=pipeline_args,\n epoch=epoch,\n is_final_validation=True,\n )\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n images=images,\n base_model=args.pretrained_model_name_or_path,\n instance_prompt=args.instance_prompt,\n validation_prompt=args.validation_prompt,\n repo_folder=args.output_dir,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/dreambooth/train_dreambooth_lora_sdxl.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport gc\nimport itertools\nimport json\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nimport warnings\nfrom contextlib import nullcontext\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import DistributedDataParallelKwargs, ProjectConfiguration, set_seed\nfrom huggingface_hub import create_repo, hf_hub_download, upload_folder\nfrom huggingface_hub.utils import insecure_hashlib\nfrom packaging import version\nfrom peft import LoraConfig, set_peft_model_state_dict\nfrom peft.utils import get_peft_model_state_dict\nfrom PIL import Image\nfrom PIL.ImageOps import exif_transpose\nfrom safetensors.torch import load_file, save_file\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom torchvision.transforms.functional import crop\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n DPMSolverMultistepScheduler,\n EDMEulerScheduler,\n EulerDiscreteScheduler,\n StableDiffusionXLPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.loaders import LoraLoaderMixin\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import _set_state_dict_into_text_encoder, cast_training_params, compute_snr\nfrom diffusers.utils import (\n check_min_version,\n convert_all_state_dict_to_peft,\n convert_state_dict_to_diffusers,\n convert_state_dict_to_kohya,\n convert_unet_state_dict_to_peft,\n is_wandb_available,\n)\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef determine_scheduler_type(pretrained_model_name_or_path, revision):\n model_index_filename = \"model_index.json\"\n if os.path.isdir(pretrained_model_name_or_path):\n model_index = os.path.join(pretrained_model_name_or_path, model_index_filename)\n else:\n model_index = hf_hub_download(\n repo_id=pretrained_model_name_or_path, filename=model_index_filename, revision=revision\n )\n\n with open(model_index, \"r\") as f:\n scheduler_type = json.load(f)[\"scheduler\"][1]\n return scheduler_type\n\n\ndef save_model_card(\n repo_id: str,\n use_dora: bool,\n images=None,\n base_model: str = None,\n train_text_encoder=False,\n instance_prompt=None,\n validation_prompt=None,\n repo_folder=None,\n vae_path=None,\n):\n widget_dict = []\n if images is not None:\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n widget_dict.append(\n {\"text\": validation_prompt if validation_prompt else \" \", \"output\": {\"url\": f\"image_{i}.png\"}}\n )\n\n model_description = f\"\"\"\n# {'SDXL' if 'playground' not in base_model else 'Playground'} LoRA DreamBooth - {repo_id}\n\n\n\n## Model description\n\nThese are {repo_id} LoRA adaption weights for {base_model}.\n\nThe weights were trained using [DreamBooth](https://dreambooth.github.io/).\n\nLoRA for the text encoder was enabled: {train_text_encoder}.\n\nSpecial VAE used for training: {vae_path}.\n\n## Trigger words\n\nYou should use {instance_prompt} to trigger the image generation.\n\n## Download model\n\nWeights for this model are available in Safetensors format.\n\n[Download]({repo_id}/tree/main) them in the Files & versions tab.\n\n\"\"\"\n if \"playground\" in base_model:\n model_description += \"\"\"\\n\n## License\n\nPlease adhere to the licensing terms as described [here](https://huggingface.co/playgroundai/playground-v2.5-1024px-aesthetic/blob/main/LICENSE.md).\n\"\"\"\n model_card = load_or_create_model_card(\n repo_id_or_path=repo_id,\n from_training=True,\n license=\"openrail++\" if \"playground\" not in base_model else \"playground-v2dot5-community\",\n base_model=base_model,\n prompt=instance_prompt,\n model_description=model_description,\n widget=widget_dict,\n )\n tags = [\n \"text-to-image\",\n \"text-to-image\",\n \"diffusers-training\",\n \"diffusers\",\n \"lora\" if not use_dora else \"dora\",\n \"template:sd-lora\",\n ]\n if \"playground\" in base_model:\n tags.extend([\"playground\", \"playground-diffusers\"])\n else:\n tags.extend([\"stable-diffusion-xl\", \"stable-diffusion-xl-diffusers\"])\n\n model_card = populate_model_card(model_card, tags=tags)\n model_card.save(os.path.join(repo_folder, \"README.md\"))\n\n\ndef log_validation(\n pipeline,\n args,\n accelerator,\n pipeline_args,\n epoch,\n is_final_validation=False,\n):\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n\n # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it\n scheduler_args = {}\n\n if not args.do_edm_style_training:\n if \"variance_type\" in pipeline.scheduler.config:\n variance_type = pipeline.scheduler.config.variance_type\n\n if variance_type in [\"learned\", \"learned_range\"]:\n variance_type = \"fixed_small\"\n\n scheduler_args[\"variance_type\"] = variance_type\n\n pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, **scheduler_args)\n\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n # Currently the context determination is a bit hand-wavy. We can improve it in the future if there's a better\n # way to condition it. Reference: https://github.com/huggingface/diffusers/pull/7126#issuecomment-1968523051\n if torch.backends.mps.is_available() or \"playground\" in args.pretrained_model_name_or_path:\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type)\n\n with autocast_ctx:\n images = [pipeline(**pipeline_args, generator=generator).images[0] for _ in range(args.num_validation_images)]\n\n for tracker in accelerator.trackers:\n phase_name = \"test\" if is_final_validation else \"validation\"\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(phase_name, np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n phase_name: [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\") for i, image in enumerate(images)\n ]\n }\n )\n\n del pipeline\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\n return images\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_vae_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) containing the training data of instance images (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--instance_data_dir\",\n type=str,\n default=None,\n help=(\"A folder containing the training data. \"),\n )\n\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n\n parser.add_argument(\n \"--image_column\",\n type=str,\n default=\"image\",\n help=\"The column of the dataset containing the target image. By \"\n \"default, the standard Image Dataset maps out 'file_name' \"\n \"to 'image'.\",\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=None,\n help=\"The column of the dataset containing the instance prompt for each image\",\n )\n\n parser.add_argument(\"--repeats\", type=int, default=1, help=\"How many times to repeat the training data.\")\n\n parser.add_argument(\n \"--class_data_dir\",\n type=str,\n default=None,\n required=False,\n help=\"A folder containing the training data of class images.\",\n )\n parser.add_argument(\n \"--instance_prompt\",\n type=str,\n default=None,\n required=True,\n help=\"The prompt with identifier specifying the instance, e.g. 'photo of a TOK dog', 'in the style of TOK'\",\n )\n parser.add_argument(\n \"--class_prompt\",\n type=str,\n default=None,\n help=\"The prompt to specify images in the same class as provided instance images.\",\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n help=\"A prompt that is used during validation to verify that the model is learning.\",\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=50,\n help=(\n \"Run dreambooth validation every X epochs. Dreambooth validation consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--do_edm_style_training\",\n default=False,\n action=\"store_true\",\n help=\"Flag to conduct training using the EDM formulation as introduced in https://arxiv.org/abs/2206.00364.\",\n )\n parser.add_argument(\n \"--with_prior_preservation\",\n default=False,\n action=\"store_true\",\n help=\"Flag to add prior preservation loss.\",\n )\n parser.add_argument(\"--prior_loss_weight\", type=float, default=1.0, help=\"The weight of prior preservation loss.\")\n parser.add_argument(\n \"--num_class_images\",\n type=int,\n default=100,\n help=(\n \"Minimal class images for prior preservation loss. If there are not enough images already present in\"\n \" class_data_dir, additional images will be sampled with class_prompt.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"lora-dreambooth-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--output_kohya_format\",\n action=\"store_true\",\n help=\"Flag to additionally generate final state dict in the Kohya format so that it becomes compatible with A111, Comfy, Kohya, etc.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=1024,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--train_text_encoder\",\n action=\"store_true\",\n help=\"Whether to train the text encoder. If set, the text encoder should be float32 precision.\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\n \"--sample_batch_size\", type=int, default=4, help=\"Batch size (per device) for sampling images.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final\"\n \" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n\n parser.add_argument(\n \"--text_encoder_lr\",\n type=float,\n default=5e-6,\n help=\"Text encoder learning rate to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n\n parser.add_argument(\n \"--snr_gamma\",\n type=float,\n default=None,\n help=\"SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. \"\n \"More details here: https://arxiv.org/abs/2303.09556.\",\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\"--lr_power\", type=float, default=1.0, help=\"Power factor of the polynomial scheduler.\")\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n\n parser.add_argument(\n \"--optimizer\",\n type=str,\n default=\"AdamW\",\n help=('The optimizer type to use. Choose between [\"AdamW\", \"prodigy\"]'),\n )\n\n parser.add_argument(\n \"--use_8bit_adam\",\n action=\"store_true\",\n help=\"Whether or not to use 8-bit Adam from bitsandbytes. Ignored if optimizer is not set to AdamW\",\n )\n\n parser.add_argument(\n \"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam and Prodigy optimizers.\"\n )\n parser.add_argument(\n \"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam and Prodigy optimizers.\"\n )\n parser.add_argument(\n \"--prodigy_beta3\",\n type=float,\n default=None,\n help=\"coefficients for computing the Prodidy stepsize using running averages. If set to None, \"\n \"uses the value of square root of beta2. Ignored if optimizer is adamW\",\n )\n parser.add_argument(\"--prodigy_decouple\", type=bool, default=True, help=\"Use AdamW style decoupled weight decay\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-04, help=\"Weight decay to use for unet params\")\n parser.add_argument(\n \"--adam_weight_decay_text_encoder\", type=float, default=1e-03, help=\"Weight decay to use for text_encoder\"\n )\n\n parser.add_argument(\n \"--adam_epsilon\",\n type=float,\n default=1e-08,\n help=\"Epsilon value for the Adam optimizer and Prodigy optimizers.\",\n )\n\n parser.add_argument(\n \"--prodigy_use_bias_correction\",\n type=bool,\n default=True,\n help=\"Turn on Adam's bias correction. True by default. Ignored if optimizer is adamW\",\n )\n parser.add_argument(\n \"--prodigy_safeguard_warmup\",\n type=bool,\n default=True,\n help=\"Remove lr from the denominator of D estimate to avoid issues during warm-up stage. True by default. \"\n \"Ignored if optimizer is adamW\",\n )\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--prior_generation_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp32\", \"fp16\", \"bf16\"],\n help=(\n \"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--rank\",\n type=int,\n default=4,\n help=(\"The dimension of the LoRA update matrices.\"),\n )\n parser.add_argument(\n \"--use_dora\",\n action=\"store_true\",\n default=False,\n help=(\n \"Wether to train a DoRA as proposed in- DoRA: Weight-Decomposed Low-Rank Adaptation https://arxiv.org/abs/2402.09353. \"\n \"Note: to use DoRA you need to install peft from main, `pip install git+https://github.com/huggingface/peft.git`\"\n ),\n )\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n if args.dataset_name is None and args.instance_data_dir is None:\n raise ValueError(\"Specify either `--dataset_name` or `--instance_data_dir`\")\n\n if args.dataset_name is not None and args.instance_data_dir is not None:\n raise ValueError(\"Specify only one of `--dataset_name` or `--instance_data_dir`\")\n\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.with_prior_preservation:\n if args.class_data_dir is None:\n raise ValueError(\"You must specify a data directory for class images.\")\n if args.class_prompt is None:\n raise ValueError(\"You must specify prompt for class images.\")\n else:\n # logger is not available yet\n if args.class_data_dir is not None:\n warnings.warn(\"You need not use --class_data_dir without --with_prior_preservation.\")\n if args.class_prompt is not None:\n warnings.warn(\"You need not use --class_prompt without --with_prior_preservation.\")\n\n return args\n\n\nclass DreamBoothDataset(Dataset):\n \"\"\"\n A dataset to prepare the instance and class images with the prompts for fine-tuning the model.\n It pre-processes the images.\n \"\"\"\n\n def __init__(\n self,\n instance_data_root,\n instance_prompt,\n class_prompt,\n class_data_root=None,\n class_num=None,\n size=1024,\n repeats=1,\n center_crop=False,\n ):\n self.size = size\n self.center_crop = center_crop\n\n self.instance_prompt = instance_prompt\n self.custom_instance_prompts = None\n self.class_prompt = class_prompt\n\n # if --dataset_name is provided or a metadata jsonl file is provided in the local --instance_data directory,\n # we load the training data using load_dataset\n if args.dataset_name is not None:\n try:\n from datasets import load_dataset\n except ImportError:\n raise ImportError(\n \"You are trying to load your data using the datasets library. If you wish to train using custom \"\n \"captions please install the datasets library: `pip install datasets`. If you wish to load a \"\n \"local folder containing images only, specify --instance_data_dir instead.\"\n )\n # Downloading and loading a dataset from the hub.\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n )\n # Preprocessing the datasets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n if args.image_column is None:\n image_column = column_names[0]\n logger.info(f\"image column defaulting to {image_column}\")\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n instance_images = dataset[\"train\"][image_column]\n\n if args.caption_column is None:\n logger.info(\n \"No caption column provided, defaulting to instance_prompt for all images. If your dataset \"\n \"contains captions/prompts for the images, make sure to specify the \"\n \"column as --caption_column\"\n )\n self.custom_instance_prompts = None\n else:\n if args.caption_column not in column_names:\n raise ValueError(\n f\"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n custom_instance_prompts = dataset[\"train\"][args.caption_column]\n # create final list of captions according to --repeats\n self.custom_instance_prompts = []\n for caption in custom_instance_prompts:\n self.custom_instance_prompts.extend(itertools.repeat(caption, repeats))\n else:\n self.instance_data_root = Path(instance_data_root)\n if not self.instance_data_root.exists():\n raise ValueError(\"Instance images root doesn't exists.\")\n\n instance_images = [Image.open(path) for path in list(Path(instance_data_root).iterdir())]\n self.custom_instance_prompts = None\n\n self.instance_images = []\n for img in instance_images:\n self.instance_images.extend(itertools.repeat(img, repeats))\n\n # image processing to prepare for using SD-XL micro-conditioning\n self.original_sizes = []\n self.crop_top_lefts = []\n self.pixel_values = []\n train_resize = transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR)\n train_crop = transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size)\n train_flip = transforms.RandomHorizontalFlip(p=1.0)\n train_transforms = transforms.Compose(\n [\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n for image in self.instance_images:\n image = exif_transpose(image)\n if not image.mode == \"RGB\":\n image = image.convert(\"RGB\")\n self.original_sizes.append((image.height, image.width))\n image = train_resize(image)\n if args.random_flip and random.random() < 0.5:\n # flip\n image = train_flip(image)\n if args.center_crop:\n y1 = max(0, int(round((image.height - args.resolution) / 2.0)))\n x1 = max(0, int(round((image.width - args.resolution) / 2.0)))\n image = train_crop(image)\n else:\n y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))\n image = crop(image, y1, x1, h, w)\n crop_top_left = (y1, x1)\n self.crop_top_lefts.append(crop_top_left)\n image = train_transforms(image)\n self.pixel_values.append(image)\n\n self.num_instance_images = len(self.instance_images)\n self._length = self.num_instance_images\n\n if class_data_root is not None:\n self.class_data_root = Path(class_data_root)\n self.class_data_root.mkdir(parents=True, exist_ok=True)\n self.class_images_path = list(self.class_data_root.iterdir())\n if class_num is not None:\n self.num_class_images = min(len(self.class_images_path), class_num)\n else:\n self.num_class_images = len(self.class_images_path)\n self._length = max(self.num_class_images, self.num_instance_images)\n else:\n self.class_data_root = None\n\n self.image_transforms = transforms.Compose(\n [\n transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, index):\n example = {}\n instance_image = self.pixel_values[index % self.num_instance_images]\n original_size = self.original_sizes[index % self.num_instance_images]\n crop_top_left = self.crop_top_lefts[index % self.num_instance_images]\n example[\"instance_images\"] = instance_image\n example[\"original_size\"] = original_size\n example[\"crop_top_left\"] = crop_top_left\n\n if self.custom_instance_prompts:\n caption = self.custom_instance_prompts[index % self.num_instance_images]\n if caption:\n example[\"instance_prompt\"] = caption\n else:\n example[\"instance_prompt\"] = self.instance_prompt\n\n else: # costum prompts were provided, but length does not match size of image dataset\n example[\"instance_prompt\"] = self.instance_prompt\n\n if self.class_data_root:\n class_image = Image.open(self.class_images_path[index % self.num_class_images])\n class_image = exif_transpose(class_image)\n\n if not class_image.mode == \"RGB\":\n class_image = class_image.convert(\"RGB\")\n example[\"class_images\"] = self.image_transforms(class_image)\n example[\"class_prompt\"] = self.class_prompt\n\n return example\n\n\ndef collate_fn(examples, with_prior_preservation=False):\n pixel_values = [example[\"instance_images\"] for example in examples]\n prompts = [example[\"instance_prompt\"] for example in examples]\n original_sizes = [example[\"original_size\"] for example in examples]\n crop_top_lefts = [example[\"crop_top_left\"] for example in examples]\n\n # Concat class and instance examples for prior preservation.\n # We do this to avoid doing two forward passes.\n if with_prior_preservation:\n pixel_values += [example[\"class_images\"] for example in examples]\n prompts += [example[\"class_prompt\"] for example in examples]\n original_sizes += [example[\"original_size\"] for example in examples]\n crop_top_lefts += [example[\"crop_top_left\"] for example in examples]\n\n pixel_values = torch.stack(pixel_values)\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n\n batch = {\n \"pixel_values\": pixel_values,\n \"prompts\": prompts,\n \"original_sizes\": original_sizes,\n \"crop_top_lefts\": crop_top_lefts,\n }\n return batch\n\n\nclass PromptDataset(Dataset):\n \"\"\"A simple dataset to prepare the prompts to generate class images on multiple GPUs.\"\"\"\n\n def __init__(self, prompt, num_samples):\n self.prompt = prompt\n self.num_samples = num_samples\n\n def __len__(self):\n return self.num_samples\n\n def __getitem__(self, index):\n example = {}\n example[\"prompt\"] = self.prompt\n example[\"index\"] = index\n return example\n\n\ndef tokenize_prompt(tokenizer, prompt):\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n return text_input_ids\n\n\n# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt\ndef encode_prompt(text_encoders, tokenizers, prompt, text_input_ids_list=None):\n prompt_embeds_list = []\n\n for i, text_encoder in enumerate(text_encoders):\n if tokenizers is not None:\n tokenizer = tokenizers[i]\n text_input_ids = tokenize_prompt(tokenizer, prompt)\n else:\n assert text_input_ids_list is not None\n text_input_ids = text_input_ids_list[i]\n\n prompt_embeds = text_encoder(\n text_input_ids.to(text_encoder.device), output_hidden_states=True, return_dict=False\n )\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds[-1][-2]\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)\n return prompt_embeds, pooled_prompt_embeds\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n if args.do_edm_style_training and args.snr_gamma is not None:\n raise ValueError(\"Min-SNR formulation is not supported when conducting EDM-style training.\")\n\n if torch.backends.mps.is_available() and args.mixed_precision == \"bf16\":\n # due to pytorch#99272, MPS does not yet support bfloat16.\n raise ValueError(\n \"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n kwargs_handlers=[kwargs],\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Generate class images if prior preservation is enabled.\n if args.with_prior_preservation:\n class_images_dir = Path(args.class_data_dir)\n if not class_images_dir.exists():\n class_images_dir.mkdir(parents=True)\n cur_class_images = len(list(class_images_dir.iterdir()))\n\n if cur_class_images < args.num_class_images:\n has_supported_fp16_accelerator = torch.cuda.is_available() or torch.backends.mps.is_available()\n torch_dtype = torch.float16 if has_supported_fp16_accelerator else torch.float32\n if args.prior_generation_precision == \"fp32\":\n torch_dtype = torch.float32\n elif args.prior_generation_precision == \"fp16\":\n torch_dtype = torch.float16\n elif args.prior_generation_precision == \"bf16\":\n torch_dtype = torch.bfloat16\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n torch_dtype=torch_dtype,\n revision=args.revision,\n variant=args.variant,\n )\n pipeline.set_progress_bar_config(disable=True)\n\n num_new_images = args.num_class_images - cur_class_images\n logger.info(f\"Number of class images to sample: {num_new_images}.\")\n\n sample_dataset = PromptDataset(args.class_prompt, num_new_images)\n sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)\n\n sample_dataloader = accelerator.prepare(sample_dataloader)\n pipeline.to(accelerator.device)\n\n for example in tqdm(\n sample_dataloader, desc=\"Generating class images\", disable=not accelerator.is_local_main_process\n ):\n images = pipeline(example[\"prompt\"]).images\n\n for i, image in enumerate(images):\n hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()\n image_filename = class_images_dir / f\"{example['index'][i] + cur_class_images}-{hash_image}.jpg\"\n image.save(image_filename)\n\n del pipeline\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizers\n tokenizer_one = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n tokenizer_two = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer_2\",\n revision=args.revision,\n use_fast=False,\n )\n\n # import correct text encoder classes\n text_encoder_cls_one = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision\n )\n text_encoder_cls_two = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision, subfolder=\"text_encoder_2\"\n )\n\n # Load scheduler and models\n scheduler_type = determine_scheduler_type(args.pretrained_model_name_or_path, args.revision)\n if \"EDM\" in scheduler_type:\n args.do_edm_style_training = True\n noise_scheduler = EDMEulerScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n logger.info(\"Performing EDM-style training!\")\n elif args.do_edm_style_training:\n noise_scheduler = EulerDiscreteScheduler.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"scheduler\"\n )\n logger.info(\"Performing EDM-style training!\")\n else:\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n\n text_encoder_one = text_encoder_cls_one.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n text_encoder_two = text_encoder_cls_two.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder_2\", revision=args.revision, variant=args.variant\n )\n vae_path = (\n args.pretrained_model_name_or_path\n if args.pretrained_vae_model_name_or_path is None\n else args.pretrained_vae_model_name_or_path\n )\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n variant=args.variant,\n )\n latents_mean = latents_std = None\n if hasattr(vae.config, \"latents_mean\") and vae.config.latents_mean is not None:\n latents_mean = torch.tensor(vae.config.latents_mean).view(1, 4, 1, 1)\n if hasattr(vae.config, \"latents_std\") and vae.config.latents_std is not None:\n latents_std = torch.tensor(vae.config.latents_std).view(1, 4, 1, 1)\n\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n # We only train the additional adapter LoRA layers\n vae.requires_grad_(False)\n text_encoder_one.requires_grad_(False)\n text_encoder_two.requires_grad_(False)\n unet.requires_grad_(False)\n\n # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n if torch.backends.mps.is_available() and weight_dtype == torch.bfloat16:\n # due to pytorch#99272, MPS does not yet support bfloat16.\n raise ValueError(\n \"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead.\"\n )\n\n # Move unet, vae and text_encoder to device and cast to weight_dtype\n unet.to(accelerator.device, dtype=weight_dtype)\n\n # The VAE is always in float32 to avoid NaN losses.\n vae.to(accelerator.device, dtype=torch.float32)\n\n text_encoder_one.to(accelerator.device, dtype=weight_dtype)\n text_encoder_two.to(accelerator.device, dtype=weight_dtype)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, \"\n \"please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n if args.train_text_encoder:\n text_encoder_one.gradient_checkpointing_enable()\n text_encoder_two.gradient_checkpointing_enable()\n\n # now we will add new LoRA weights to the attention layers\n unet_lora_config = LoraConfig(\n r=args.rank,\n use_dora=args.use_dora,\n lora_alpha=args.rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"to_k\", \"to_q\", \"to_v\", \"to_out.0\"],\n )\n unet.add_adapter(unet_lora_config)\n\n # The text encoder comes from 🤗 transformers, so we cannot directly modify it.\n # So, instead, we monkey-patch the forward calls of its attention-blocks.\n if args.train_text_encoder:\n text_lora_config = LoraConfig(\n r=args.rank,\n use_dora=args.use_dora,\n lora_alpha=args.rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"out_proj\"],\n )\n text_encoder_one.add_adapter(text_lora_config)\n text_encoder_two.add_adapter(text_lora_config)\n\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n # there are only two options here. Either are just the unet attn processor layers\n # or there are the unet and text encoder atten layers\n unet_lora_layers_to_save = None\n text_encoder_one_lora_layers_to_save = None\n text_encoder_two_lora_layers_to_save = None\n\n for model in models:\n if isinstance(model, type(unwrap_model(unet))):\n unet_lora_layers_to_save = convert_state_dict_to_diffusers(get_peft_model_state_dict(model))\n elif isinstance(model, type(unwrap_model(text_encoder_one))):\n text_encoder_one_lora_layers_to_save = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(model)\n )\n elif isinstance(model, type(unwrap_model(text_encoder_two))):\n text_encoder_two_lora_layers_to_save = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(model)\n )\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n StableDiffusionXLPipeline.save_lora_weights(\n output_dir,\n unet_lora_layers=unet_lora_layers_to_save,\n text_encoder_lora_layers=text_encoder_one_lora_layers_to_save,\n text_encoder_2_lora_layers=text_encoder_two_lora_layers_to_save,\n )\n\n def load_model_hook(models, input_dir):\n unet_ = None\n text_encoder_one_ = None\n text_encoder_two_ = None\n\n while len(models) > 0:\n model = models.pop()\n\n if isinstance(model, type(unwrap_model(unet))):\n unet_ = model\n elif isinstance(model, type(unwrap_model(text_encoder_one))):\n text_encoder_one_ = model\n elif isinstance(model, type(unwrap_model(text_encoder_two))):\n text_encoder_two_ = model\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n lora_state_dict, network_alphas = LoraLoaderMixin.lora_state_dict(input_dir)\n\n unet_state_dict = {f'{k.replace(\"unet.\", \"\")}': v for k, v in lora_state_dict.items() if k.startswith(\"unet.\")}\n unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict)\n incompatible_keys = set_peft_model_state_dict(unet_, unet_state_dict, adapter_name=\"default\")\n if incompatible_keys is not None:\n # check only for unexpected keys\n unexpected_keys = getattr(incompatible_keys, \"unexpected_keys\", None)\n if unexpected_keys:\n logger.warning(\n f\"Loading adapter weights from state_dict led to unexpected keys not found in the model: \"\n f\" {unexpected_keys}. \"\n )\n\n if args.train_text_encoder:\n # Do we need to call `scale_lora_layers()` here?\n _set_state_dict_into_text_encoder(lora_state_dict, prefix=\"text_encoder.\", text_encoder=text_encoder_one_)\n\n _set_state_dict_into_text_encoder(\n lora_state_dict, prefix=\"text_encoder_2.\", text_encoder=text_encoder_two_\n )\n\n # Make sure the trainable params are in float32. This is again needed since the base models\n # are in `weight_dtype`. More details:\n # https://github.com/huggingface/diffusers/pull/6514#discussion_r1449796804\n if args.mixed_precision == \"fp16\":\n models = [unet_]\n if args.train_text_encoder:\n models.extend([text_encoder_one_, text_encoder_two_])\n # only upcast trainable parameters (LoRA) into fp32\n cast_training_params(models)\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32 and torch.cuda.is_available():\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Make sure the trainable params are in float32.\n if args.mixed_precision == \"fp16\":\n models = [unet]\n if args.train_text_encoder:\n models.extend([text_encoder_one, text_encoder_two])\n\n # only upcast trainable parameters (LoRA) into fp32\n cast_training_params(models, dtype=torch.float32)\n\n unet_lora_parameters = list(filter(lambda p: p.requires_grad, unet.parameters()))\n\n if args.train_text_encoder:\n text_lora_parameters_one = list(filter(lambda p: p.requires_grad, text_encoder_one.parameters()))\n text_lora_parameters_two = list(filter(lambda p: p.requires_grad, text_encoder_two.parameters()))\n\n # Optimization parameters\n unet_lora_parameters_with_lr = {\"params\": unet_lora_parameters, \"lr\": args.learning_rate}\n if args.train_text_encoder:\n # different learning rate for text encoder and unet\n text_lora_parameters_one_with_lr = {\n \"params\": text_lora_parameters_one,\n \"weight_decay\": args.adam_weight_decay_text_encoder,\n \"lr\": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate,\n }\n text_lora_parameters_two_with_lr = {\n \"params\": text_lora_parameters_two,\n \"weight_decay\": args.adam_weight_decay_text_encoder,\n \"lr\": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate,\n }\n params_to_optimize = [\n unet_lora_parameters_with_lr,\n text_lora_parameters_one_with_lr,\n text_lora_parameters_two_with_lr,\n ]\n else:\n params_to_optimize = [unet_lora_parameters_with_lr]\n\n # Optimizer creation\n if not (args.optimizer.lower() == \"prodigy\" or args.optimizer.lower() == \"adamw\"):\n logger.warning(\n f\"Unsupported choice of optimizer: {args.optimizer}.Supported optimizers include [adamW, prodigy].\"\n \"Defaulting to adamW\"\n )\n args.optimizer = \"adamw\"\n\n if args.use_8bit_adam and not args.optimizer.lower() == \"adamw\":\n logger.warning(\n f\"use_8bit_adam is ignored when optimizer is not set to 'AdamW'. Optimizer was \"\n f\"set to {args.optimizer.lower()}\"\n )\n\n if args.optimizer.lower() == \"adamw\":\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n optimizer = optimizer_class(\n params_to_optimize,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n if args.optimizer.lower() == \"prodigy\":\n try:\n import prodigyopt\n except ImportError:\n raise ImportError(\"To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`\")\n\n optimizer_class = prodigyopt.Prodigy\n\n if args.learning_rate <= 0.1:\n logger.warning(\n \"Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0\"\n )\n if args.train_text_encoder and args.text_encoder_lr:\n logger.warning(\n f\"Learning rates were provided both for the unet and the text encoder- e.g. text_encoder_lr:\"\n f\" {args.text_encoder_lr} and learning_rate: {args.learning_rate}. \"\n f\"When using prodigy only learning_rate is used as the initial learning rate.\"\n )\n # changes the learning rate of text_encoder_parameters_one and text_encoder_parameters_two to be\n # --learning_rate\n params_to_optimize[1][\"lr\"] = args.learning_rate\n params_to_optimize[2][\"lr\"] = args.learning_rate\n\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n beta3=args.prodigy_beta3,\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n decouple=args.prodigy_decouple,\n use_bias_correction=args.prodigy_use_bias_correction,\n safeguard_warmup=args.prodigy_safeguard_warmup,\n )\n\n # Dataset and DataLoaders creation:\n train_dataset = DreamBoothDataset(\n instance_data_root=args.instance_data_dir,\n instance_prompt=args.instance_prompt,\n class_prompt=args.class_prompt,\n class_data_root=args.class_data_dir if args.with_prior_preservation else None,\n class_num=args.num_class_images,\n size=args.resolution,\n repeats=args.repeats,\n center_crop=args.center_crop,\n )\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n batch_size=args.train_batch_size,\n shuffle=True,\n collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),\n num_workers=args.dataloader_num_workers,\n )\n\n # Computes additional embeddings/ids required by the SDXL UNet.\n # regular text embeddings (when `train_text_encoder` is not True)\n # pooled text embeddings\n # time ids\n\n def compute_time_ids(original_size, crops_coords_top_left):\n # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids\n target_size = (args.resolution, args.resolution)\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n add_time_ids = torch.tensor([add_time_ids])\n add_time_ids = add_time_ids.to(accelerator.device, dtype=weight_dtype)\n return add_time_ids\n\n if not args.train_text_encoder:\n tokenizers = [tokenizer_one, tokenizer_two]\n text_encoders = [text_encoder_one, text_encoder_two]\n\n def compute_text_embeddings(prompt, text_encoders, tokenizers):\n with torch.no_grad():\n prompt_embeds, pooled_prompt_embeds = encode_prompt(text_encoders, tokenizers, prompt)\n prompt_embeds = prompt_embeds.to(accelerator.device)\n pooled_prompt_embeds = pooled_prompt_embeds.to(accelerator.device)\n return prompt_embeds, pooled_prompt_embeds\n\n # If no type of tuning is done on the text_encoder and custom instance prompts are NOT\n # provided (i.e. the --instance_prompt is used for all images), we encode the instance prompt once to avoid\n # the redundant encoding.\n if not args.train_text_encoder and not train_dataset.custom_instance_prompts:\n instance_prompt_hidden_states, instance_pooled_prompt_embeds = compute_text_embeddings(\n args.instance_prompt, text_encoders, tokenizers\n )\n\n # Handle class prompt for prior-preservation.\n if args.with_prior_preservation:\n if not args.train_text_encoder:\n class_prompt_hidden_states, class_pooled_prompt_embeds = compute_text_embeddings(\n args.class_prompt, text_encoders, tokenizers\n )\n\n # Clear the memory here\n if not args.train_text_encoder and not train_dataset.custom_instance_prompts:\n del tokenizers, text_encoders\n gc.collect()\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\n # If custom instance prompts are NOT provided (i.e. the instance prompt is used for all images),\n # pack the statically computed variables appropriately here. This is so that we don't\n # have to pass them to the dataloader.\n\n if not train_dataset.custom_instance_prompts:\n if not args.train_text_encoder:\n prompt_embeds = instance_prompt_hidden_states\n unet_add_text_embeds = instance_pooled_prompt_embeds\n if args.with_prior_preservation:\n prompt_embeds = torch.cat([prompt_embeds, class_prompt_hidden_states], dim=0)\n unet_add_text_embeds = torch.cat([unet_add_text_embeds, class_pooled_prompt_embeds], dim=0)\n # if we're optmizing the text encoder (both if instance prompt is used for all images or custom prompts) we need to tokenize and encode the\n # batch prompts on all training steps\n else:\n tokens_one = tokenize_prompt(tokenizer_one, args.instance_prompt)\n tokens_two = tokenize_prompt(tokenizer_two, args.instance_prompt)\n if args.with_prior_preservation:\n class_tokens_one = tokenize_prompt(tokenizer_one, args.class_prompt)\n class_tokens_two = tokenize_prompt(tokenizer_two, args.class_prompt)\n tokens_one = torch.cat([tokens_one, class_tokens_one], dim=0)\n tokens_two = torch.cat([tokens_two, class_tokens_two], dim=0)\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_cycles=args.lr_num_cycles,\n power=args.lr_power,\n )\n\n # Prepare everything with our `accelerator`.\n if args.train_text_encoder:\n unet, text_encoder_one, text_encoder_two, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, text_encoder_one, text_encoder_two, optimizer, train_dataloader, lr_scheduler\n )\n else:\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_name = (\n \"dreambooth-lora-sd-xl\"\n if \"playground\" not in args.pretrained_model_name_or_path\n else \"dreambooth-lora-playground\"\n )\n accelerator.init_trackers(tracker_name, config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num batches each epoch = {len(train_dataloader)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the mos recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):\n sigmas = noise_scheduler.sigmas.to(device=accelerator.device, dtype=dtype)\n schedule_timesteps = noise_scheduler.timesteps.to(accelerator.device)\n timesteps = timesteps.to(accelerator.device)\n\n step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]\n\n sigma = sigmas[step_indices].flatten()\n while len(sigma.shape) < n_dim:\n sigma = sigma.unsqueeze(-1)\n return sigma\n\n for epoch in range(first_epoch, args.num_train_epochs):\n unet.train()\n if args.train_text_encoder:\n text_encoder_one.train()\n text_encoder_two.train()\n\n # set top parameter requires_grad = True for gradient checkpointing works\n accelerator.unwrap_model(text_encoder_one).text_model.embeddings.requires_grad_(True)\n accelerator.unwrap_model(text_encoder_two).text_model.embeddings.requires_grad_(True)\n\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n pixel_values = batch[\"pixel_values\"].to(dtype=vae.dtype)\n prompts = batch[\"prompts\"]\n\n # encode batch prompts when custom prompts are provided for each image -\n if train_dataset.custom_instance_prompts:\n if not args.train_text_encoder:\n prompt_embeds, unet_add_text_embeds = compute_text_embeddings(\n prompts, text_encoders, tokenizers\n )\n else:\n tokens_one = tokenize_prompt(tokenizer_one, prompts)\n tokens_two = tokenize_prompt(tokenizer_two, prompts)\n\n # Convert images to latent space\n model_input = vae.encode(pixel_values).latent_dist.sample()\n\n if latents_mean is None and latents_std is None:\n model_input = model_input * vae.config.scaling_factor\n if args.pretrained_vae_model_name_or_path is None:\n model_input = model_input.to(weight_dtype)\n else:\n latents_mean = latents_mean.to(device=model_input.device, dtype=model_input.dtype)\n latents_std = latents_std.to(device=model_input.device, dtype=model_input.dtype)\n model_input = (model_input - latents_mean) * vae.config.scaling_factor / latents_std\n model_input = model_input.to(dtype=weight_dtype)\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(model_input)\n bsz = model_input.shape[0]\n\n # Sample a random timestep for each image\n if not args.do_edm_style_training:\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device\n )\n timesteps = timesteps.long()\n else:\n # in EDM formulation, the model is conditioned on the pre-conditioned noise levels\n # instead of discrete timesteps, so here we sample indices to get the noise levels\n # from `scheduler.timesteps`\n indices = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,))\n timesteps = noise_scheduler.timesteps[indices].to(device=model_input.device)\n\n # Add noise to the model input according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)\n # For EDM-style training, we first obtain the sigmas based on the continuous timesteps.\n # We then precondition the final model inputs based on these sigmas instead of the timesteps.\n # Follow: Section 5 of https://arxiv.org/abs/2206.00364.\n if args.do_edm_style_training:\n sigmas = get_sigmas(timesteps, len(noisy_model_input.shape), noisy_model_input.dtype)\n if \"EDM\" in scheduler_type:\n inp_noisy_latents = noise_scheduler.precondition_inputs(noisy_model_input, sigmas)\n else:\n inp_noisy_latents = noisy_model_input / ((sigmas**2 + 1) ** 0.5)\n\n # time ids\n add_time_ids = torch.cat(\n [\n compute_time_ids(original_size=s, crops_coords_top_left=c)\n for s, c in zip(batch[\"original_sizes\"], batch[\"crop_top_lefts\"])\n ]\n )\n\n # Calculate the elements to repeat depending on the use of prior-preservation and custom captions.\n if not train_dataset.custom_instance_prompts:\n elems_to_repeat_text_embeds = bsz // 2 if args.with_prior_preservation else bsz\n else:\n elems_to_repeat_text_embeds = 1\n\n # Predict the noise residual\n if not args.train_text_encoder:\n unet_added_conditions = {\n \"time_ids\": add_time_ids,\n \"text_embeds\": unet_add_text_embeds.repeat(elems_to_repeat_text_embeds, 1),\n }\n prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat_text_embeds, 1, 1)\n model_pred = unet(\n inp_noisy_latents if args.do_edm_style_training else noisy_model_input,\n timesteps,\n prompt_embeds_input,\n added_cond_kwargs=unet_added_conditions,\n return_dict=False,\n )[0]\n else:\n unet_added_conditions = {\"time_ids\": add_time_ids}\n prompt_embeds, pooled_prompt_embeds = encode_prompt(\n text_encoders=[text_encoder_one, text_encoder_two],\n tokenizers=None,\n prompt=None,\n text_input_ids_list=[tokens_one, tokens_two],\n )\n unet_added_conditions.update(\n {\"text_embeds\": pooled_prompt_embeds.repeat(elems_to_repeat_text_embeds, 1)}\n )\n prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat_text_embeds, 1, 1)\n model_pred = unet(\n inp_noisy_latents if args.do_edm_style_training else noisy_model_input,\n timesteps,\n prompt_embeds_input,\n added_cond_kwargs=unet_added_conditions,\n return_dict=False,\n )[0]\n\n weighting = None\n if args.do_edm_style_training:\n # Similar to the input preconditioning, the model predictions are also preconditioned\n # on noised model inputs (before preconditioning) and the sigmas.\n # Follow: Section 5 of https://arxiv.org/abs/2206.00364.\n if \"EDM\" in scheduler_type:\n model_pred = noise_scheduler.precondition_outputs(noisy_model_input, model_pred, sigmas)\n else:\n if noise_scheduler.config.prediction_type == \"epsilon\":\n model_pred = model_pred * (-sigmas) + noisy_model_input\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n model_pred = model_pred * (-sigmas / (sigmas**2 + 1) ** 0.5) + (\n noisy_model_input / (sigmas**2 + 1)\n )\n # We are not doing weighting here because it tends result in numerical problems.\n # See: https://github.com/huggingface/diffusers/pull/7126#issuecomment-1968523051\n # There might be other alternatives for weighting as well:\n # https://github.com/huggingface/diffusers/pull/7126#discussion_r1505404686\n if \"EDM\" not in scheduler_type:\n weighting = (sigmas**-2.0).float()\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = model_input if args.do_edm_style_training else noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = (\n model_input\n if args.do_edm_style_training\n else noise_scheduler.get_velocity(model_input, noise, timesteps)\n )\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n if args.with_prior_preservation:\n # Chunk the noise and model_pred into two parts and compute the loss on each part separately.\n model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)\n target, target_prior = torch.chunk(target, 2, dim=0)\n\n # Compute prior loss\n if weighting is not None:\n prior_loss = torch.mean(\n (weighting.float() * (model_pred_prior.float() - target_prior.float()) ** 2).reshape(\n target_prior.shape[0], -1\n ),\n 1,\n )\n prior_loss = prior_loss.mean()\n else:\n prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction=\"mean\")\n\n if args.snr_gamma is None:\n if weighting is not None:\n loss = torch.mean(\n (weighting.float() * (model_pred.float() - target.float()) ** 2).reshape(\n target.shape[0], -1\n ),\n 1,\n )\n loss = loss.mean()\n else:\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n else:\n # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.\n # Since we predict the noise instead of x_0, the original formulation is slightly changed.\n # This is discussed in Section 4.2 of the same paper.\n snr = compute_snr(noise_scheduler, timesteps)\n base_weight = (\n torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr\n )\n\n if noise_scheduler.config.prediction_type == \"v_prediction\":\n # Velocity objective needs to be floored to an SNR weight of one.\n mse_loss_weights = base_weight + 1\n else:\n # Epsilon and sample both use the same loss weights.\n mse_loss_weights = base_weight\n\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"none\")\n loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights\n loss = loss.mean()\n\n if args.with_prior_preservation:\n # Add the prior loss to the instance loss.\n loss = loss + args.prior_loss_weight * prior_loss\n\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n params_to_clip = (\n itertools.chain(unet_lora_parameters, text_lora_parameters_one, text_lora_parameters_two)\n if args.train_text_encoder\n else unet_lora_parameters\n )\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompt is not None and epoch % args.validation_epochs == 0:\n # create pipeline\n if not args.train_text_encoder:\n text_encoder_one = text_encoder_cls_one.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"text_encoder\",\n revision=args.revision,\n variant=args.variant,\n )\n text_encoder_two = text_encoder_cls_two.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"text_encoder_2\",\n revision=args.revision,\n variant=args.variant,\n )\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n text_encoder=accelerator.unwrap_model(text_encoder_one),\n text_encoder_2=accelerator.unwrap_model(text_encoder_two),\n unet=accelerator.unwrap_model(unet),\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n pipeline_args = {\"prompt\": args.validation_prompt}\n\n images = log_validation(\n pipeline,\n args,\n accelerator,\n pipeline_args,\n epoch,\n )\n\n # Save the lora layers\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = unwrap_model(unet)\n unet = unet.to(torch.float32)\n unet_lora_layers = convert_state_dict_to_diffusers(get_peft_model_state_dict(unet))\n\n if args.train_text_encoder:\n text_encoder_one = unwrap_model(text_encoder_one)\n text_encoder_lora_layers = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(text_encoder_one.to(torch.float32))\n )\n text_encoder_two = unwrap_model(text_encoder_two)\n text_encoder_2_lora_layers = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(text_encoder_two.to(torch.float32))\n )\n else:\n text_encoder_lora_layers = None\n text_encoder_2_lora_layers = None\n\n StableDiffusionXLPipeline.save_lora_weights(\n save_directory=args.output_dir,\n unet_lora_layers=unet_lora_layers,\n text_encoder_lora_layers=text_encoder_lora_layers,\n text_encoder_2_lora_layers=text_encoder_2_lora_layers,\n )\n if args.output_kohya_format:\n lora_state_dict = load_file(f\"{args.output_dir}/pytorch_lora_weights.safetensors\")\n peft_state_dict = convert_all_state_dict_to_peft(lora_state_dict)\n kohya_state_dict = convert_state_dict_to_kohya(peft_state_dict)\n save_file(kohya_state_dict, f\"{args.output_dir}/pytorch_lora_weights_kohya.safetensors\")\n\n # Final inference\n # Load previous pipeline\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n\n # load attention processors\n pipeline.load_lora_weights(args.output_dir)\n\n # run inference\n images = []\n if args.validation_prompt and args.num_validation_images > 0:\n pipeline_args = {\"prompt\": args.validation_prompt, \"num_inference_steps\": 25}\n images = log_validation(\n pipeline,\n args,\n accelerator,\n pipeline_args,\n epoch,\n is_final_validation=True,\n )\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n use_dora=args.use_dora,\n images=images,\n base_model=args.pretrained_model_name_or_path,\n train_text_encoder=args.train_text_encoder,\n instance_prompt=args.instance_prompt,\n validation_prompt=args.validation_prompt,\n repo_folder=args.output_dir,\n vae_path=args.pretrained_vae_model_name_or_path,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/dreambooth/train_dreambooth_sd3.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport copy\nimport gc\nimport itertools\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nimport warnings\nfrom contextlib import nullcontext\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import DistributedDataParallelKwargs, ProjectConfiguration, set_seed\nfrom huggingface_hub import create_repo, upload_folder\nfrom huggingface_hub.utils import insecure_hashlib\nfrom PIL import Image\nfrom PIL.ImageOps import exif_transpose\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom torchvision.transforms.functional import crop\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModelWithProjection, CLIPTokenizer, PretrainedConfig, T5EncoderModel, T5TokenizerFast\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n FlowMatchEulerDiscreteScheduler,\n SD3Transformer2DModel,\n StableDiffusion3Pipeline,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import (\n check_min_version,\n is_wandb_available,\n)\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef save_model_card(\n repo_id: str,\n images=None,\n base_model: str = None,\n train_text_encoder=False,\n instance_prompt=None,\n validation_prompt=None,\n repo_folder=None,\n):\n widget_dict = []\n if images is not None:\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n widget_dict.append(\n {\"text\": validation_prompt if validation_prompt else \" \", \"output\": {\"url\": f\"image_{i}.png\"}}\n )\n\n model_description = f\"\"\"\n# SD3 DreamBooth - {repo_id}\n\n\n\n## Model description\n\nThese are {repo_id} DreamBooth weights for {base_model}.\n\nThe weights were trained using [DreamBooth](https://dreambooth.github.io/).\n\nText encoder was fine-tuned: {train_text_encoder}.\n\n## Trigger words\n\nYou should use {instance_prompt} to trigger the image generation.\n\n## Download model\n\n[Download]({repo_id}/tree/main) them in the Files & versions tab.\n\n## License\n\nPlease adhere to the licensing terms as described `[here](https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/LICENSE)`.\n\"\"\"\n model_card = load_or_create_model_card(\n repo_id_or_path=repo_id,\n from_training=True,\n license=\"openrail++\",\n base_model=base_model,\n prompt=instance_prompt,\n model_description=model_description,\n widget=widget_dict,\n )\n tags = [\n \"text-to-image\",\n \"diffusers-training\",\n \"diffusers\",\n \"sd3\",\n \"sd3-diffusers\",\n \"template:sd-lora\",\n ]\n\n model_card = populate_model_card(model_card, tags=tags)\n model_card.save(os.path.join(repo_folder, \"README.md\"))\n\n\ndef load_text_encoders(class_one, class_two, class_three):\n text_encoder_one = class_one.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n text_encoder_two = class_two.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder_2\", revision=args.revision, variant=args.variant\n )\n text_encoder_three = class_three.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder_3\", revision=args.revision, variant=args.variant\n )\n return text_encoder_one, text_encoder_two, text_encoder_three\n\n\ndef log_validation(\n pipeline,\n args,\n accelerator,\n pipeline_args,\n epoch,\n is_final_validation=False,\n):\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n # autocast_ctx = torch.autocast(accelerator.device.type) if not is_final_validation else nullcontext()\n autocast_ctx = nullcontext()\n\n with autocast_ctx:\n images = [pipeline(**pipeline_args, generator=generator).images[0] for _ in range(args.num_validation_images)]\n\n for tracker in accelerator.trackers:\n phase_name = \"test\" if is_final_validation else \"validation\"\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(phase_name, np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n phase_name: [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\") for i, image in enumerate(images)\n ]\n }\n )\n\n del pipeline\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\n return images\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n if model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n elif model_class == \"T5EncoderModel\":\n from transformers import T5EncoderModel\n\n return T5EncoderModel\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) containing the training data of instance images (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--instance_data_dir\",\n type=str,\n default=None,\n help=(\"A folder containing the training data. \"),\n )\n\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n\n parser.add_argument(\n \"--image_column\",\n type=str,\n default=\"image\",\n help=\"The column of the dataset containing the target image. By \"\n \"default, the standard Image Dataset maps out 'file_name' \"\n \"to 'image'.\",\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=None,\n help=\"The column of the dataset containing the instance prompt for each image\",\n )\n\n parser.add_argument(\"--repeats\", type=int, default=1, help=\"How many times to repeat the training data.\")\n\n parser.add_argument(\n \"--class_data_dir\",\n type=str,\n default=None,\n required=False,\n help=\"A folder containing the training data of class images.\",\n )\n parser.add_argument(\n \"--instance_prompt\",\n type=str,\n default=None,\n required=True,\n help=\"The prompt with identifier specifying the instance, e.g. 'photo of a TOK dog', 'in the style of TOK'\",\n )\n parser.add_argument(\n \"--class_prompt\",\n type=str,\n default=None,\n help=\"The prompt to specify images in the same class as provided instance images.\",\n )\n parser.add_argument(\n \"--max_sequence_length\",\n type=int,\n default=77,\n help=\"Maximum sequence length to use with with the T5 text encoder\",\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n help=\"A prompt that is used during validation to verify that the model is learning.\",\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=50,\n help=(\n \"Run dreambooth validation every X epochs. Dreambooth validation consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--with_prior_preservation\",\n default=False,\n action=\"store_true\",\n help=\"Flag to add prior preservation loss.\",\n )\n parser.add_argument(\"--prior_loss_weight\", type=float, default=1.0, help=\"The weight of prior preservation loss.\")\n parser.add_argument(\n \"--num_class_images\",\n type=int,\n default=100,\n help=(\n \"Minimal class images for prior preservation loss. If there are not enough images already present in\"\n \" class_data_dir, additional images will be sampled with class_prompt.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"sd3-dreambooth\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--train_text_encoder\",\n action=\"store_true\",\n help=\"Whether to train the text encoder. If set, the text encoder should be float32 precision.\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\n \"--sample_batch_size\", type=int, default=4, help=\"Batch size (per device) for sampling images.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final\"\n \" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n\n parser.add_argument(\n \"--text_encoder_lr\",\n type=float,\n default=5e-6,\n help=\"Text encoder learning rate to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\"--lr_power\", type=float, default=1.0, help=\"Power factor of the polynomial scheduler.\")\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\n \"--weighting_scheme\", type=str, default=\"sigma_sqrt\", choices=[\"sigma_sqrt\", \"logit_normal\", \"mode\"]\n )\n parser.add_argument(\"--logit_mean\", type=float, default=0.0)\n parser.add_argument(\"--logit_std\", type=float, default=1.0)\n parser.add_argument(\"--mode_scale\", type=float, default=1.29)\n parser.add_argument(\n \"--optimizer\",\n type=str,\n default=\"AdamW\",\n help=('The optimizer type to use. Choose between [\"AdamW\", \"prodigy\"]'),\n )\n\n parser.add_argument(\n \"--use_8bit_adam\",\n action=\"store_true\",\n help=\"Whether or not to use 8-bit Adam from bitsandbytes. Ignored if optimizer is not set to AdamW\",\n )\n\n parser.add_argument(\n \"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam and Prodigy optimizers.\"\n )\n parser.add_argument(\n \"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam and Prodigy optimizers.\"\n )\n parser.add_argument(\n \"--prodigy_beta3\",\n type=float,\n default=None,\n help=\"coefficients for computing the Prodidy stepsize using running averages. If set to None, \"\n \"uses the value of square root of beta2. Ignored if optimizer is adamW\",\n )\n parser.add_argument(\"--prodigy_decouple\", type=bool, default=True, help=\"Use AdamW style decoupled weight decay\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-04, help=\"Weight decay to use for unet params\")\n parser.add_argument(\n \"--adam_weight_decay_text_encoder\", type=float, default=1e-03, help=\"Weight decay to use for text_encoder\"\n )\n\n parser.add_argument(\n \"--adam_epsilon\",\n type=float,\n default=1e-08,\n help=\"Epsilon value for the Adam optimizer and Prodigy optimizers.\",\n )\n\n parser.add_argument(\n \"--prodigy_use_bias_correction\",\n type=bool,\n default=True,\n help=\"Turn on Adam's bias correction. True by default. Ignored if optimizer is adamW\",\n )\n parser.add_argument(\n \"--prodigy_safeguard_warmup\",\n type=bool,\n default=True,\n help=\"Remove lr from the denominator of D estimate to avoid issues during warm-up stage. True by default. \"\n \"Ignored if optimizer is adamW\",\n )\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--prior_generation_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp32\", \"fp16\", \"bf16\"],\n help=(\n \"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n if args.dataset_name is None and args.instance_data_dir is None:\n raise ValueError(\"Specify either `--dataset_name` or `--instance_data_dir`\")\n\n if args.dataset_name is not None and args.instance_data_dir is not None:\n raise ValueError(\"Specify only one of `--dataset_name` or `--instance_data_dir`\")\n\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.with_prior_preservation:\n if args.class_data_dir is None:\n raise ValueError(\"You must specify a data directory for class images.\")\n if args.class_prompt is None:\n raise ValueError(\"You must specify prompt for class images.\")\n else:\n # logger is not available yet\n if args.class_data_dir is not None:\n warnings.warn(\"You need not use --class_data_dir without --with_prior_preservation.\")\n if args.class_prompt is not None:\n warnings.warn(\"You need not use --class_prompt without --with_prior_preservation.\")\n\n return args\n\n\nclass DreamBoothDataset(Dataset):\n \"\"\"\n A dataset to prepare the instance and class images with the prompts for fine-tuning the model.\n It pre-processes the images.\n \"\"\"\n\n def __init__(\n self,\n instance_data_root,\n instance_prompt,\n class_prompt,\n class_data_root=None,\n class_num=None,\n size=1024,\n repeats=1,\n center_crop=False,\n ):\n self.size = size\n self.center_crop = center_crop\n\n self.instance_prompt = instance_prompt\n self.custom_instance_prompts = None\n self.class_prompt = class_prompt\n\n # if --dataset_name is provided or a metadata jsonl file is provided in the local --instance_data directory,\n # we load the training data using load_dataset\n if args.dataset_name is not None:\n try:\n from datasets import load_dataset\n except ImportError:\n raise ImportError(\n \"You are trying to load your data using the datasets library. If you wish to train using custom \"\n \"captions please install the datasets library: `pip install datasets`. If you wish to load a \"\n \"local folder containing images only, specify --instance_data_dir instead.\"\n )\n # Downloading and loading a dataset from the hub.\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n )\n # Preprocessing the datasets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n if args.image_column is None:\n image_column = column_names[0]\n logger.info(f\"image column defaulting to {image_column}\")\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n instance_images = dataset[\"train\"][image_column]\n\n if args.caption_column is None:\n logger.info(\n \"No caption column provided, defaulting to instance_prompt for all images. If your dataset \"\n \"contains captions/prompts for the images, make sure to specify the \"\n \"column as --caption_column\"\n )\n self.custom_instance_prompts = None\n else:\n if args.caption_column not in column_names:\n raise ValueError(\n f\"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n custom_instance_prompts = dataset[\"train\"][args.caption_column]\n # create final list of captions according to --repeats\n self.custom_instance_prompts = []\n for caption in custom_instance_prompts:\n self.custom_instance_prompts.extend(itertools.repeat(caption, repeats))\n else:\n self.instance_data_root = Path(instance_data_root)\n if not self.instance_data_root.exists():\n raise ValueError(\"Instance images root doesn't exists.\")\n\n instance_images = [Image.open(path) for path in list(Path(instance_data_root).iterdir())]\n self.custom_instance_prompts = None\n\n self.instance_images = []\n for img in instance_images:\n self.instance_images.extend(itertools.repeat(img, repeats))\n\n self.pixel_values = []\n train_resize = transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR)\n train_crop = transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size)\n train_flip = transforms.RandomHorizontalFlip(p=1.0)\n train_transforms = transforms.Compose(\n [\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n for image in self.instance_images:\n image = exif_transpose(image)\n if not image.mode == \"RGB\":\n image = image.convert(\"RGB\")\n image = train_resize(image)\n if args.random_flip and random.random() < 0.5:\n # flip\n image = train_flip(image)\n if args.center_crop:\n y1 = max(0, int(round((image.height - args.resolution) / 2.0)))\n x1 = max(0, int(round((image.width - args.resolution) / 2.0)))\n image = train_crop(image)\n else:\n y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))\n image = crop(image, y1, x1, h, w)\n image = train_transforms(image)\n self.pixel_values.append(image)\n\n self.num_instance_images = len(self.instance_images)\n self._length = self.num_instance_images\n\n if class_data_root is not None:\n self.class_data_root = Path(class_data_root)\n self.class_data_root.mkdir(parents=True, exist_ok=True)\n self.class_images_path = list(self.class_data_root.iterdir())\n if class_num is not None:\n self.num_class_images = min(len(self.class_images_path), class_num)\n else:\n self.num_class_images = len(self.class_images_path)\n self._length = max(self.num_class_images, self.num_instance_images)\n else:\n self.class_data_root = None\n\n self.image_transforms = transforms.Compose(\n [\n transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, index):\n example = {}\n instance_image = self.pixel_values[index % self.num_instance_images]\n example[\"instance_images\"] = instance_image\n\n if self.custom_instance_prompts:\n caption = self.custom_instance_prompts[index % self.num_instance_images]\n if caption:\n example[\"instance_prompt\"] = caption\n else:\n example[\"instance_prompt\"] = self.instance_prompt\n\n else: # costum prompts were provided, but length does not match size of image dataset\n example[\"instance_prompt\"] = self.instance_prompt\n\n if self.class_data_root:\n class_image = Image.open(self.class_images_path[index % self.num_class_images])\n class_image = exif_transpose(class_image)\n\n if not class_image.mode == \"RGB\":\n class_image = class_image.convert(\"RGB\")\n example[\"class_images\"] = self.image_transforms(class_image)\n example[\"class_prompt\"] = self.class_prompt\n\n return example\n\n\ndef collate_fn(examples, with_prior_preservation=False):\n pixel_values = [example[\"instance_images\"] for example in examples]\n prompts = [example[\"instance_prompt\"] for example in examples]\n\n # Concat class and instance examples for prior preservation.\n # We do this to avoid doing two forward passes.\n if with_prior_preservation:\n pixel_values += [example[\"class_images\"] for example in examples]\n prompts += [example[\"class_prompt\"] for example in examples]\n\n pixel_values = torch.stack(pixel_values)\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n\n batch = {\"pixel_values\": pixel_values, \"prompts\": prompts}\n return batch\n\n\nclass PromptDataset(Dataset):\n \"A simple dataset to prepare the prompts to generate class images on multiple GPUs.\"\n\n def __init__(self, prompt, num_samples):\n self.prompt = prompt\n self.num_samples = num_samples\n\n def __len__(self):\n return self.num_samples\n\n def __getitem__(self, index):\n example = {}\n example[\"prompt\"] = self.prompt\n example[\"index\"] = index\n return example\n\n\ndef tokenize_prompt(tokenizer, prompt):\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=77,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n return text_input_ids\n\n\ndef _encode_prompt_with_t5(\n text_encoder,\n tokenizer,\n max_sequence_length,\n prompt=None,\n num_images_per_prompt=1,\n device=None,\n):\n prompt = [prompt] if isinstance(prompt, str) else prompt\n batch_size = len(prompt)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=max_sequence_length,\n truncation=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n prompt_embeds = text_encoder(text_input_ids.to(device))[0]\n\n dtype = text_encoder.dtype\n prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)\n\n _, seq_len, _ = prompt_embeds.shape\n\n # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n return prompt_embeds\n\n\ndef _encode_prompt_with_clip(\n text_encoder,\n tokenizer,\n prompt: str,\n device=None,\n num_images_per_prompt: int = 1,\n):\n prompt = [prompt] if isinstance(prompt, str) else prompt\n batch_size = len(prompt)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=77,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)\n\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds.hidden_states[-2]\n prompt_embeds = prompt_embeds.to(dtype=text_encoder.dtype, device=device)\n\n _, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n return prompt_embeds, pooled_prompt_embeds\n\n\ndef encode_prompt(\n text_encoders,\n tokenizers,\n prompt: str,\n max_sequence_length,\n device=None,\n num_images_per_prompt: int = 1,\n):\n prompt = [prompt] if isinstance(prompt, str) else prompt\n\n clip_tokenizers = tokenizers[:2]\n clip_text_encoders = text_encoders[:2]\n\n clip_prompt_embeds_list = []\n clip_pooled_prompt_embeds_list = []\n for tokenizer, text_encoder in zip(clip_tokenizers, clip_text_encoders):\n prompt_embeds, pooled_prompt_embeds = _encode_prompt_with_clip(\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n prompt=prompt,\n device=device if device is not None else text_encoder.device,\n num_images_per_prompt=num_images_per_prompt,\n )\n clip_prompt_embeds_list.append(prompt_embeds)\n clip_pooled_prompt_embeds_list.append(pooled_prompt_embeds)\n\n clip_prompt_embeds = torch.cat(clip_prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = torch.cat(clip_pooled_prompt_embeds_list, dim=-1)\n\n t5_prompt_embed = _encode_prompt_with_t5(\n text_encoders[-1],\n tokenizers[-1],\n max_sequence_length,\n prompt=prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device if device is not None else text_encoders[-1].device,\n )\n\n clip_prompt_embeds = torch.nn.functional.pad(\n clip_prompt_embeds, (0, t5_prompt_embed.shape[-1] - clip_prompt_embeds.shape[-1])\n )\n prompt_embeds = torch.cat([clip_prompt_embeds, t5_prompt_embed], dim=-2)\n\n return prompt_embeds, pooled_prompt_embeds\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n if torch.backends.mps.is_available() and args.mixed_precision == \"bf16\":\n # due to pytorch#99272, MPS does not yet support bfloat16.\n raise ValueError(\n \"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n kwargs_handlers=[kwargs],\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Generate class images if prior preservation is enabled.\n if args.with_prior_preservation:\n class_images_dir = Path(args.class_data_dir)\n if not class_images_dir.exists():\n class_images_dir.mkdir(parents=True)\n cur_class_images = len(list(class_images_dir.iterdir()))\n\n if cur_class_images < args.num_class_images:\n has_supported_fp16_accelerator = torch.cuda.is_available() or torch.backends.mps.is_available()\n torch_dtype = torch.float16 if has_supported_fp16_accelerator else torch.float32\n if args.prior_generation_precision == \"fp32\":\n torch_dtype = torch.float32\n elif args.prior_generation_precision == \"fp16\":\n torch_dtype = torch.float16\n elif args.prior_generation_precision == \"bf16\":\n torch_dtype = torch.bfloat16\n pipeline = StableDiffusion3Pipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n torch_dtype=torch_dtype,\n revision=args.revision,\n variant=args.variant,\n )\n pipeline.set_progress_bar_config(disable=True)\n\n num_new_images = args.num_class_images - cur_class_images\n logger.info(f\"Number of class images to sample: {num_new_images}.\")\n\n sample_dataset = PromptDataset(args.class_prompt, num_new_images)\n sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)\n\n sample_dataloader = accelerator.prepare(sample_dataloader)\n pipeline.to(accelerator.device)\n\n for example in tqdm(\n sample_dataloader, desc=\"Generating class images\", disable=not accelerator.is_local_main_process\n ):\n images = pipeline(example[\"prompt\"]).images\n\n for i, image in enumerate(images):\n hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()\n image_filename = class_images_dir / f\"{example['index'][i] + cur_class_images}-{hash_image}.jpg\"\n image.save(image_filename)\n\n del pipeline\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name,\n exist_ok=True,\n ).repo_id\n\n # Load the tokenizers\n tokenizer_one = CLIPTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n )\n tokenizer_two = CLIPTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer_2\",\n revision=args.revision,\n )\n tokenizer_three = T5TokenizerFast.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer_3\",\n revision=args.revision,\n )\n\n # import correct text encoder classes\n text_encoder_cls_one = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision\n )\n text_encoder_cls_two = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision, subfolder=\"text_encoder_2\"\n )\n text_encoder_cls_three = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision, subfolder=\"text_encoder_3\"\n )\n\n # Load scheduler and models\n noise_scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"scheduler\"\n )\n noise_scheduler_copy = copy.deepcopy(noise_scheduler)\n text_encoder_one, text_encoder_two, text_encoder_three = load_text_encoders(\n text_encoder_cls_one, text_encoder_cls_two, text_encoder_cls_three\n )\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"vae\",\n revision=args.revision,\n variant=args.variant,\n )\n transformer = SD3Transformer2DModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"transformer\", revision=args.revision, variant=args.variant\n )\n\n transformer.requires_grad_(True)\n vae.requires_grad_(False)\n if args.train_text_encoder:\n text_encoder_one.requires_grad_(True)\n text_encoder_two.requires_grad_(True)\n text_encoder_three.requires_grad_(True)\n else:\n text_encoder_one.requires_grad_(False)\n text_encoder_two.requires_grad_(False)\n text_encoder_three.requires_grad_(False)\n\n # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora transformer) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n if torch.backends.mps.is_available() and weight_dtype == torch.bfloat16:\n # due to pytorch#99272, MPS does not yet support bfloat16.\n raise ValueError(\n \"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead.\"\n )\n\n vae.to(accelerator.device, dtype=torch.float32)\n if not args.train_text_encoder:\n text_encoder_one.to(accelerator.device, dtype=weight_dtype)\n text_encoder_two.to(accelerator.device, dtype=weight_dtype)\n text_encoder_three.to(accelerator.device, dtype=weight_dtype)\n\n if args.gradient_checkpointing:\n transformer.enable_gradient_checkpointing()\n if args.train_text_encoder:\n text_encoder_one.gradient_checkpointing_enable()\n text_encoder_two.gradient_checkpointing_enable()\n text_encoder_three.gradient_checkpointing_enable()\n\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n for i, model in enumerate(models):\n if isinstance(unwrap_model(model), SD3Transformer2DModel):\n unwrap_model(model).save_pretrained(os.path.join(output_dir, \"transformer\"))\n elif isinstance(unwrap_model(model), (CLIPTextModelWithProjection, T5EncoderModel)):\n if isinstance(unwrap_model(model), CLIPTextModelWithProjection):\n hidden_size = unwrap_model(model).config.hidden_size\n if hidden_size == 768:\n unwrap_model(model).save_pretrained(os.path.join(output_dir, \"text_encoder\"))\n elif hidden_size == 1280:\n unwrap_model(model).save_pretrained(os.path.join(output_dir, \"text_encoder_2\"))\n else:\n unwrap_model(model).save_pretrained(os.path.join(output_dir, \"text_encoder_3\"))\n else:\n raise ValueError(f\"Wrong model supplied: {type(model)=}.\")\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n def load_model_hook(models, input_dir):\n for _ in range(len(models)):\n # pop models so that they are not loaded again\n model = models.pop()\n\n # load diffusers style into model\n if isinstance(unwrap_model(model), SD3Transformer2DModel):\n load_model = SD3Transformer2DModel.from_pretrained(input_dir, subfolder=\"transformer\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n elif isinstance(unwrap_model(model), (CLIPTextModelWithProjection, T5EncoderModel)):\n try:\n load_model = CLIPTextModelWithProjection.from_pretrained(input_dir, subfolder=\"text_encoder\")\n model(**load_model.config)\n model.load_state_dict(load_model.state_dict())\n except Exception:\n try:\n load_model = CLIPTextModelWithProjection.from_pretrained(input_dir, subfolder=\"text_encoder_2\")\n model(**load_model.config)\n model.load_state_dict(load_model.state_dict())\n except Exception:\n try:\n load_model = T5EncoderModel.from_pretrained(input_dir, subfolder=\"text_encoder_3\")\n model(**load_model.config)\n model.load_state_dict(load_model.state_dict())\n except Exception:\n raise ValueError(f\"Couldn't load the model of type: ({type(model)}).\")\n else:\n raise ValueError(f\"Unsupported model found: {type(model)=}\")\n\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32 and torch.cuda.is_available():\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Optimization parameters\n transformer_parameters_with_lr = {\"params\": transformer.parameters(), \"lr\": args.learning_rate}\n if args.train_text_encoder:\n # different learning rate for text encoder and unet\n text_parameters_one_with_lr = {\n \"params\": text_encoder_one.parameters(),\n \"weight_decay\": args.adam_weight_decay_text_encoder,\n \"lr\": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate,\n }\n text_parameters_two_with_lr = {\n \"params\": text_encoder_two.parameters(),\n \"weight_decay\": args.adam_weight_decay_text_encoder,\n \"lr\": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate,\n }\n text_parameters_three_with_lr = {\n \"params\": text_encoder_three.parameters(),\n \"weight_decay\": args.adam_weight_decay_text_encoder,\n \"lr\": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate,\n }\n params_to_optimize = [\n transformer_parameters_with_lr,\n text_parameters_one_with_lr,\n text_parameters_two_with_lr,\n text_parameters_three_with_lr,\n ]\n else:\n params_to_optimize = [transformer_parameters_with_lr]\n\n # Optimizer creation\n if not (args.optimizer.lower() == \"prodigy\" or args.optimizer.lower() == \"adamw\"):\n logger.warning(\n f\"Unsupported choice of optimizer: {args.optimizer}.Supported optimizers include [adamW, prodigy].\"\n \"Defaulting to adamW\"\n )\n args.optimizer = \"adamw\"\n\n if args.use_8bit_adam and not args.optimizer.lower() == \"adamw\":\n logger.warning(\n f\"use_8bit_adam is ignored when optimizer is not set to 'AdamW'. Optimizer was \"\n f\"set to {args.optimizer.lower()}\"\n )\n\n if args.optimizer.lower() == \"adamw\":\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n optimizer = optimizer_class(\n params_to_optimize,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n if args.optimizer.lower() == \"prodigy\":\n try:\n import prodigyopt\n except ImportError:\n raise ImportError(\"To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`\")\n\n optimizer_class = prodigyopt.Prodigy\n\n if args.learning_rate <= 0.1:\n logger.warning(\n \"Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0\"\n )\n if args.train_text_encoder and args.text_encoder_lr:\n logger.warning(\n f\"Learning rates were provided both for the transformer and the text encoder- e.g. text_encoder_lr:\"\n f\" {args.text_encoder_lr} and learning_rate: {args.learning_rate}. \"\n f\"When using prodigy only learning_rate is used as the initial learning rate.\"\n )\n # changes the learning rate of text_encoder_parameters_one and text_encoder_parameters_two to be\n # --learning_rate\n params_to_optimize[1][\"lr\"] = args.learning_rate\n params_to_optimize[2][\"lr\"] = args.learning_rate\n params_to_optimize[3][\"lr\"] = args.learning_rate\n\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n beta3=args.prodigy_beta3,\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n decouple=args.prodigy_decouple,\n use_bias_correction=args.prodigy_use_bias_correction,\n safeguard_warmup=args.prodigy_safeguard_warmup,\n )\n\n # Dataset and DataLoaders creation:\n train_dataset = DreamBoothDataset(\n instance_data_root=args.instance_data_dir,\n instance_prompt=args.instance_prompt,\n class_prompt=args.class_prompt,\n class_data_root=args.class_data_dir if args.with_prior_preservation else None,\n class_num=args.num_class_images,\n size=args.resolution,\n repeats=args.repeats,\n center_crop=args.center_crop,\n )\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n batch_size=args.train_batch_size,\n shuffle=True,\n collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),\n num_workers=args.dataloader_num_workers,\n )\n\n if not args.train_text_encoder:\n tokenizers = [tokenizer_one, tokenizer_two, tokenizer_three]\n text_encoders = [text_encoder_one, text_encoder_two, text_encoder_three]\n\n def compute_text_embeddings(prompt, text_encoders, tokenizers):\n with torch.no_grad():\n prompt_embeds, pooled_prompt_embeds = encode_prompt(\n text_encoders, tokenizers, prompt, args.max_sequence_length\n )\n prompt_embeds = prompt_embeds.to(accelerator.device)\n pooled_prompt_embeds = pooled_prompt_embeds.to(accelerator.device)\n return prompt_embeds, pooled_prompt_embeds\n\n # If no type of tuning is done on the text_encoder and custom instance prompts are NOT\n # provided (i.e. the --instance_prompt is used for all images), we encode the instance prompt once to avoid\n # the redundant encoding.\n if not args.train_text_encoder and not train_dataset.custom_instance_prompts:\n instance_prompt_hidden_states, instance_pooled_prompt_embeds = compute_text_embeddings(\n args.instance_prompt, text_encoders, tokenizers\n )\n\n # Handle class prompt for prior-preservation.\n if args.with_prior_preservation:\n if not args.train_text_encoder:\n class_prompt_hidden_states, class_pooled_prompt_embeds = compute_text_embeddings(\n args.class_prompt, text_encoders, tokenizers\n )\n\n # Clear the memory here\n if not args.train_text_encoder and not train_dataset.custom_instance_prompts:\n del tokenizers, text_encoders\n # Explicitly delete the objects as well, otherwise only the lists are deleted and the original references remain, preventing garbage collection\n del tokenizer_one, tokenizer_two, tokenizer_three\n del text_encoder_one, text_encoder_two, text_encoder_three\n gc.collect()\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\n # If custom instance prompts are NOT provided (i.e. the instance prompt is used for all images),\n # pack the statically computed variables appropriately here. This is so that we don't\n # have to pass them to the dataloader.\n\n if not train_dataset.custom_instance_prompts:\n if not args.train_text_encoder:\n prompt_embeds = instance_prompt_hidden_states\n pooled_prompt_embeds = instance_pooled_prompt_embeds\n if args.with_prior_preservation:\n prompt_embeds = torch.cat([prompt_embeds, class_prompt_hidden_states], dim=0)\n pooled_prompt_embeds = torch.cat([pooled_prompt_embeds, class_pooled_prompt_embeds], dim=0)\n # if we're optmizing the text encoder (both if instance prompt is used for all images or custom prompts) we need to tokenize and encode the\n # batch prompts on all training steps\n else:\n tokens_one = tokenize_prompt(tokenizer_one, args.instance_prompt)\n tokens_two = tokenize_prompt(tokenizer_two, args.instance_prompt)\n tokens_three = tokenize_prompt(tokenizer_three, args.instance_prompt)\n if args.with_prior_preservation:\n class_tokens_one = tokenize_prompt(tokenizer_one, args.class_prompt)\n class_tokens_two = tokenize_prompt(tokenizer_two, args.class_prompt)\n class_tokens_three = tokenize_prompt(tokenizer_three, args.class_prompt)\n tokens_one = torch.cat([tokens_one, class_tokens_one], dim=0)\n tokens_two = torch.cat([tokens_two, class_tokens_two], dim=0)\n tokens_three = torch.cat([tokens_three, class_tokens_three], dim=0)\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_cycles=args.lr_num_cycles,\n power=args.lr_power,\n )\n\n # Prepare everything with our `accelerator`.\n if args.train_text_encoder:\n (\n transformer,\n text_encoder_one,\n text_encoder_two,\n text_encoder_three,\n optimizer,\n train_dataloader,\n lr_scheduler,\n ) = accelerator.prepare(\n transformer,\n text_encoder_one,\n text_encoder_two,\n text_encoder_three,\n optimizer,\n train_dataloader,\n lr_scheduler,\n )\n else:\n transformer, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n transformer, optimizer, train_dataloader, lr_scheduler\n )\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_name = \"dreambooth-sd3\"\n accelerator.init_trackers(tracker_name, config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num batches each epoch = {len(train_dataloader)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the mos recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):\n sigmas = noise_scheduler_copy.sigmas.to(device=accelerator.device, dtype=dtype)\n schedule_timesteps = noise_scheduler_copy.timesteps.to(accelerator.device)\n timesteps = timesteps.to(accelerator.device)\n step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]\n\n sigma = sigmas[step_indices].flatten()\n while len(sigma.shape) < n_dim:\n sigma = sigma.unsqueeze(-1)\n return sigma\n\n for epoch in range(first_epoch, args.num_train_epochs):\n transformer.train()\n if args.train_text_encoder:\n text_encoder_one.train()\n text_encoder_two.train()\n text_encoder_three.train()\n\n for step, batch in enumerate(train_dataloader):\n models_to_accumulate = [transformer]\n if args.train_text_encoder:\n models_to_accumulate.extend([text_encoder_one, text_encoder_two, text_encoder_three])\n with accelerator.accumulate(models_to_accumulate):\n pixel_values = batch[\"pixel_values\"].to(dtype=vae.dtype)\n prompts = batch[\"prompts\"]\n\n # encode batch prompts when custom prompts are provided for each image -\n if train_dataset.custom_instance_prompts:\n if not args.train_text_encoder:\n prompt_embeds, pooled_prompt_embeds = compute_text_embeddings(\n prompts, text_encoders, tokenizers\n )\n else:\n tokens_one = tokenize_prompt(tokenizer_one, prompts)\n tokens_two = tokenize_prompt(tokenizer_two, prompts)\n tokens_three = tokenize_prompt(tokenizer_three, prompts)\n\n # Convert images to latent space\n model_input = vae.encode(pixel_values).latent_dist.sample()\n model_input = model_input * vae.config.scaling_factor\n model_input = model_input.to(dtype=weight_dtype)\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(model_input)\n bsz = model_input.shape[0]\n\n # Sample a random timestep for each image\n # for weighting schemes where we sample timesteps non-uniformly\n if args.weighting_scheme == \"logit_normal\":\n # See 3.1 in the SD3 paper ($rf/lognorm(0.00,1.00)$).\n u = torch.normal(mean=args.logit_mean, std=args.logit_std, size=(bsz,), device=\"cpu\")\n u = torch.nn.functional.sigmoid(u)\n elif args.weighting_scheme == \"mode\":\n u = torch.rand(size=(bsz,), device=\"cpu\")\n u = 1 - u - args.mode_scale * (torch.cos(math.pi * u / 2) ** 2 - 1 + u)\n else:\n u = torch.rand(size=(bsz,), device=\"cpu\")\n\n indices = (u * noise_scheduler_copy.config.num_train_timesteps).long()\n timesteps = noise_scheduler_copy.timesteps[indices].to(device=model_input.device)\n\n # Add noise according to flow matching.\n sigmas = get_sigmas(timesteps, n_dim=model_input.ndim, dtype=model_input.dtype)\n noisy_model_input = sigmas * noise + (1.0 - sigmas) * model_input\n\n # Predict the noise residual\n if not args.train_text_encoder:\n model_pred = transformer(\n hidden_states=noisy_model_input,\n timestep=timesteps,\n encoder_hidden_states=prompt_embeds,\n pooled_projections=pooled_prompt_embeds,\n return_dict=False,\n )[0]\n else:\n prompt_embeds, pooled_prompt_embeds = encode_prompt(\n text_encoders=[text_encoder_one, text_encoder_two, text_encoder_three],\n tokenizers=None,\n prompt=None,\n text_input_ids_list=[tokens_one, tokens_two, tokens_three],\n )\n model_pred = transformer(\n hidden_states=noisy_model_input,\n timestep=timesteps,\n encoder_hidden_states=prompt_embeds,\n pooled_projections=pooled_prompt_embeds,\n return_dict=False,\n )[0]\n\n # Follow: Section 5 of https://arxiv.org/abs/2206.00364.\n # Preconditioning of the model outputs.\n model_pred = model_pred * (-sigmas) + noisy_model_input\n # these weighting schemes use a uniform timestep sampling\n # and instead post-weight the loss\n if args.weighting_scheme == \"sigma_sqrt\":\n weighting = (sigmas**-2.0).float()\n elif args.weighting_scheme == \"cosmap\":\n bot = 1 - 2 * sigmas + 2 * sigmas**2\n weighting = 2 / (math.pi * bot)\n else:\n weighting = torch.ones_like(sigmas)\n\n # simplified flow matching aka 0-rectified flow matching loss\n # target = model_input - noise\n target = model_input\n\n if args.with_prior_preservation:\n # Chunk the noise and model_pred into two parts and compute the loss on each part separately.\n model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)\n target, target_prior = torch.chunk(target, 2, dim=0)\n\n # Compute prior loss\n prior_loss = torch.mean(\n (weighting.float() * (model_pred_prior.float() - target_prior.float()) ** 2).reshape(\n target_prior.shape[0], -1\n ),\n 1,\n )\n prior_loss = prior_loss.mean()\n\n # Compute regular loss.\n loss = torch.mean(\n (weighting.float() * (model_pred.float() - target.float()) ** 2).reshape(target.shape[0], -1),\n 1,\n )\n loss = loss.mean()\n\n if args.with_prior_preservation:\n # Add the prior loss to the instance loss.\n loss = loss + args.prior_loss_weight * prior_loss\n\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n params_to_clip = (\n itertools.chain(\n transformer.parameters(),\n text_encoder_one.parameters(),\n text_encoder_two.parameters(),\n text_encoder_three.parameters(),\n )\n if args.train_text_encoder\n else transformer.parameters()\n )\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompt is not None and epoch % args.validation_epochs == 0:\n # create pipeline\n if not args.train_text_encoder:\n text_encoder_one, text_encoder_two, text_encoder_three = load_text_encoders(\n text_encoder_cls_one, text_encoder_cls_two, text_encoder_cls_three\n )\n pipeline = StableDiffusion3Pipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n text_encoder=accelerator.unwrap_model(text_encoder_one),\n text_encoder_2=accelerator.unwrap_model(text_encoder_two),\n text_encoder_3=accelerator.unwrap_model(text_encoder_three),\n transformer=accelerator.unwrap_model(transformer),\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n pipeline_args = {\"prompt\": args.validation_prompt}\n images = log_validation(\n pipeline=pipeline,\n args=args,\n accelerator=accelerator,\n pipeline_args=pipeline_args,\n epoch=epoch,\n )\n if not args.train_text_encoder:\n del text_encoder_one, text_encoder_two, text_encoder_three\n torch.cuda.empty_cache()\n gc.collect()\n\n # Save the lora layers\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n transformer = unwrap_model(transformer)\n\n if args.train_text_encoder:\n text_encoder_one = unwrap_model(text_encoder_one)\n text_encoder_two = unwrap_model(text_encoder_two)\n text_encoder_three = unwrap_model(text_encoder_three)\n pipeline = StableDiffusion3Pipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n transformer=transformer,\n text_encoder=text_encoder_one,\n text_encoder_2=text_encoder_two,\n text_encoder_3=text_encoder_three,\n )\n else:\n pipeline = StableDiffusion3Pipeline.from_pretrained(\n args.pretrained_model_name_or_path, transformer=transformer\n )\n\n # save the pipeline\n pipeline.save_pretrained(args.output_dir)\n\n # Final inference\n # Load previous pipeline\n pipeline = StableDiffusion3Pipeline.from_pretrained(\n args.output_dir,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n\n # run inference\n images = []\n if args.validation_prompt and args.num_validation_images > 0:\n pipeline_args = {\"prompt\": args.validation_prompt}\n images = log_validation(\n pipeline=pipeline,\n args=args,\n accelerator=accelerator,\n pipeline_args=pipeline_args,\n epoch=epoch,\n is_final_validation=True,\n )\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n images=images,\n base_model=args.pretrained_model_name_or_path,\n train_text_encoder=args.train_text_encoder,\n instance_prompt=args.instance_prompt,\n validation_prompt=args.validation_prompt,\n repo_folder=args.output_dir,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/inference/README.md", "content": "# Inference Examples\n\n**The inference examples folder is deprecated and will be removed in a future version**.\n**Officially supported inference examples can be found in the [Pipelines folder](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines)**.\n\n- For `Image-to-Image text-guided generation with Stable Diffusion`, please have a look at the official [Pipeline examples](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines#examples)\n- For `In-painting using Stable Diffusion`, please have a look at the official [Pipeline examples](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines#examples)\n- For `Tweak prompts reusing seeds and latents`, please have a look at the official [Pipeline examples](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines#examples)\n" }, { "path": "diffusers/examples/inference/image_to_image.py", "content": "import warnings\n\nfrom diffusers import StableDiffusionImg2ImgPipeline # noqa F401\n\n\nwarnings.warn(\n \"The `image_to_image.py` script is outdated. Please use directly `from diffusers import\"\n \" StableDiffusionImg2ImgPipeline` instead.\"\n)\n" }, { "path": "diffusers/examples/inference/inpainting.py", "content": "import warnings\n\nfrom diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401\n\n\nwarnings.warn(\n \"The `inpainting.py` script is outdated. Please use directly `from diffusers import\"\n \" StableDiffusionInpaintPipeline` instead.\"\n)\n" }, { "path": "diffusers/examples/instruct_pix2pix/README.md", "content": "# InstructPix2Pix training example\n\n[InstructPix2Pix](https://arxiv.org/abs/2211.09800) is a method to fine-tune text-conditioned diffusion models such that they can follow an edit instruction for an input image. Models fine-tuned using this method take the following as inputs:\n\n

\n \"instructpix2pix-inputs\"\n

\n\nThe output is an \"edited\" image that reflects the edit instruction applied on the input image:\n\n

\n \"instructpix2pix-output\"\n

\n\nThe `train_instruct_pix2pix.py` script shows how to implement the training procedure and adapt it for Stable Diffusion.\n\n***Disclaimer: Even though `train_instruct_pix2pix.py` implements the InstructPix2Pix\ntraining procedure while being faithful to the [original implementation](https://github.com/timothybrooks/instruct-pix2pix) we have only tested it on a [small-scale dataset](https://huggingface.co/datasets/fusing/instructpix2pix-1000-samples). This can impact the end results. For better results, we recommend longer training runs with a larger dataset. [Here](https://huggingface.co/datasets/timbrooks/instructpix2pix-clip-filtered) you can find a large dataset for InstructPix2Pix training.***\n\n## Running locally with PyTorch\n\n### Installing the dependencies\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n**Important**\n\nTo make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install -e .\n```\n\nThen cd in the example folder and run\n```bash\npip install -r requirements.txt\n```\n\nAnd initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\n\nOr for a default accelerate configuration without answering questions about your environment\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell e.g. a notebook\n\n```python\nfrom accelerate.utils import write_basic_config\nwrite_basic_config()\n```\n\n### Toy example\n\nAs mentioned before, we'll use a [small toy dataset](https://huggingface.co/datasets/fusing/instructpix2pix-1000-samples) for training. The dataset \nis a smaller version of the [original dataset](https://huggingface.co/datasets/timbrooks/instructpix2pix-clip-filtered) used in the InstructPix2Pix paper.\n\nConfigure environment variables such as the dataset identifier and the Stable Diffusion\ncheckpoint:\n\n```bash\nexport MODEL_NAME=\"runwayml/stable-diffusion-v1-5\"\nexport DATASET_ID=\"fusing/instructpix2pix-1000-samples\"\n```\n\nNow, we can launch training:\n\n```bash\naccelerate launch --mixed_precision=\"fp16\" train_instruct_pix2pix.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --dataset_name=$DATASET_ID \\\n --enable_xformers_memory_efficient_attention \\\n --resolution=256 --random_flip \\\n --train_batch_size=4 --gradient_accumulation_steps=4 --gradient_checkpointing \\\n --max_train_steps=15000 \\\n --checkpointing_steps=5000 --checkpoints_total_limit=1 \\\n --learning_rate=5e-05 --max_grad_norm=1 --lr_warmup_steps=0 \\\n --conditioning_dropout_prob=0.05 \\\n --mixed_precision=fp16 \\\n --seed=42 \\\n --push_to_hub\n```\n\nAdditionally, we support performing validation inference to monitor training progress\nwith Weights and Biases. You can enable this feature with `report_to=\"wandb\"`:\n\n```bash\naccelerate launch --mixed_precision=\"fp16\" train_instruct_pix2pix.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --dataset_name=$DATASET_ID \\\n --enable_xformers_memory_efficient_attention \\\n --resolution=256 --random_flip \\\n --train_batch_size=4 --gradient_accumulation_steps=4 --gradient_checkpointing \\\n --max_train_steps=15000 \\\n --checkpointing_steps=5000 --checkpoints_total_limit=1 \\\n --learning_rate=5e-05 --max_grad_norm=1 --lr_warmup_steps=0 \\\n --conditioning_dropout_prob=0.05 \\\n --mixed_precision=fp16 \\\n --val_image_url=\"https://hf.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png\" \\\n --validation_prompt=\"make the mountains snowy\" \\\n --seed=42 \\\n --report_to=wandb \\\n --push_to_hub\n ```\n\n We recommend this type of validation as it can be useful for model debugging. Note that you need `wandb` installed to use this. You can install `wandb` by running `pip install wandb`. \n\n [Here](https://wandb.ai/sayakpaul/instruct-pix2pix/runs/ctr3kovq), you can find an example training run that includes some validation samples and the training hyperparameters.\n\n ***Note: In the original paper, the authors observed that even when the model is trained with an image resolution of 256x256, it generalizes well to bigger resolutions such as 512x512. This is likely because of the larger dataset they used during training.***\n\n ## Training with multiple GPUs\n\n`accelerate` allows for seamless multi-GPU training. Follow the instructions [here](https://huggingface.co/docs/accelerate/basic_tutorials/launch)\nfor running distributed training with `accelerate`. Here is an example command:\n\n```bash \naccelerate launch --mixed_precision=\"fp16\" --multi_gpu train_instruct_pix2pix.py \\\n --pretrained_model_name_or_path=runwayml/stable-diffusion-v1-5 \\\n --dataset_name=sayakpaul/instructpix2pix-1000-samples \\\n --use_ema \\\n --enable_xformers_memory_efficient_attention \\\n --resolution=512 --random_flip \\\n --train_batch_size=4 --gradient_accumulation_steps=4 --gradient_checkpointing \\\n --max_train_steps=15000 \\\n --checkpointing_steps=5000 --checkpoints_total_limit=1 \\\n --learning_rate=5e-05 --lr_warmup_steps=0 \\\n --conditioning_dropout_prob=0.05 \\\n --mixed_precision=fp16 \\\n --seed=42 \\\n --push_to_hub\n```\n\n ## Inference\n\n Once training is complete, we can perform inference:\n\n ```python\nimport PIL\nimport requests\nimport torch\nfrom diffusers import StableDiffusionInstructPix2PixPipeline\n\nmodel_id = \"your_model_id\" # <- replace this \npipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(\"cuda\")\ngenerator = torch.Generator(\"cuda\").manual_seed(0)\n\nurl = \"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/test_pix2pix_4.png\"\n\n\ndef download_image(url):\n image = PIL.Image.open(requests.get(url, stream=True).raw)\n image = PIL.ImageOps.exif_transpose(image)\n image = image.convert(\"RGB\")\n return image\n\nimage = download_image(url)\nprompt = \"wipe out the lake\"\nnum_inference_steps = 20\nimage_guidance_scale = 1.5\nguidance_scale = 10\n\nedited_image = pipe(prompt, \n image=image, \n num_inference_steps=num_inference_steps, \n image_guidance_scale=image_guidance_scale, \n guidance_scale=guidance_scale,\n generator=generator,\n).images[0]\nedited_image.save(\"edited_image.png\")\n```\n\nAn example model repo obtained using this training script can be found\nhere - [sayakpaul/instruct-pix2pix](https://huggingface.co/sayakpaul/instruct-pix2pix).\n\nWe encourage you to play with the following three parameters to control\nspeed and quality during performance:\n\n* `num_inference_steps`\n* `image_guidance_scale`\n* `guidance_scale`\n\nParticularly, `image_guidance_scale` and `guidance_scale` can have a profound impact\non the generated (\"edited\") image (see [here](https://twitter.com/RisingSayak/status/1628392199196151808?s=20) for an example).\n\nIf you're looking for some interesting ways to use the InstructPix2Pix training methodology, we welcome you to check out this blog post: [Instruction-tuning Stable Diffusion with InstructPix2Pix](https://huggingface.co/blog/instruction-tuning-sd). \n\n## Stable Diffusion XL\n\nThere's an equivalent `train_instruct_pix2pix_sdxl.py` script for [Stable Diffusion XL](https://huggingface.co/papers/2307.01952). Please refer to the docs [here](./README_sdxl.md) to learn more.\n" }, { "path": "diffusers/examples/instruct_pix2pix/README_sdxl.md", "content": "# InstructPix2Pix SDXL training example\n\n***This is based on the original InstructPix2Pix training example.***\n\n[Stable Diffusion XL](https://huggingface.co/papers/2307.01952) (or SDXL) is the latest image generation model that is tailored towards more photorealistic outputs with more detailed imagery and composition compared to previous SD models. It leverages a three times larger UNet backbone. The increase of model parameters is mainly due to more attention blocks and a larger cross-attention context as SDXL uses a second text encoder. \n\nThe `train_instruct_pix2pix_sdxl.py` script shows how to implement the training procedure and adapt it for Stable Diffusion XL.\n\n***Disclaimer: Even though `train_instruct_pix2pix_sdxl.py` implements the InstructPix2Pix\ntraining procedure while being faithful to the [original implementation](https://github.com/timothybrooks/instruct-pix2pix) we have only tested it on a [small-scale dataset](https://huggingface.co/datasets/fusing/instructpix2pix-1000-samples). This can impact the end results. For better results, we recommend longer training runs with a larger dataset. [Here](https://huggingface.co/datasets/timbrooks/instructpix2pix-clip-filtered) you can find a large dataset for InstructPix2Pix training.***\n\n## Running locally with PyTorch\n\n### Installing the dependencies\n\nRefer to the original InstructPix2Pix training example for installing the dependencies.\n\nYou will also need to get access of SDXL by filling the [form](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0). \n\n### Toy example\n\nAs mentioned before, we'll use a [small toy dataset](https://huggingface.co/datasets/fusing/instructpix2pix-1000-samples) for training. The dataset \nis a smaller version of the [original dataset](https://huggingface.co/datasets/timbrooks/instructpix2pix-clip-filtered) used in the InstructPix2Pix paper.\n\nConfigure environment variables such as the dataset identifier and the Stable Diffusion\ncheckpoint:\n\n```bash\nexport MODEL_NAME=\"stabilityai/stable-diffusion-xl-base-1.0\"\nexport DATASET_ID=\"fusing/instructpix2pix-1000-samples\"\n```\n\nNow, we can launch training:\n\n```bash\naccelerate launch train_instruct_pix2pix_sdxl.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --dataset_name=$DATASET_ID \\\n --enable_xformers_memory_efficient_attention \\\n --resolution=256 --random_flip \\\n --train_batch_size=4 --gradient_accumulation_steps=4 --gradient_checkpointing \\\n --max_train_steps=15000 \\\n --checkpointing_steps=5000 --checkpoints_total_limit=1 \\\n --learning_rate=5e-05 --max_grad_norm=1 --lr_warmup_steps=0 \\\n --conditioning_dropout_prob=0.05 \\\n --seed=42 \\\n --push_to_hub\n```\n\nAdditionally, we support performing validation inference to monitor training progress\nwith Weights and Biases. You can enable this feature with `report_to=\"wandb\"`:\n\n```bash\naccelerate launch train_instruct_pix2pix_sdxl.py \\\n --pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0 \\\n --dataset_name=$DATASET_ID \\\n --use_ema \\\n --enable_xformers_memory_efficient_attention \\\n --resolution=512 --random_flip \\\n --train_batch_size=4 --gradient_accumulation_steps=4 --gradient_checkpointing \\\n --max_train_steps=15000 \\\n --checkpointing_steps=5000 --checkpoints_total_limit=1 \\\n --learning_rate=5e-05 --lr_warmup_steps=0 \\\n --conditioning_dropout_prob=0.05 \\\n --seed=42 \\\n --val_image_url_or_path=\"https://datasets-server.huggingface.co/assets/fusing/instructpix2pix-1000-samples/--/fusing--instructpix2pix-1000-samples/train/23/input_image/image.jpg\" \\\n --validation_prompt=\"make it in japan\" \\\n --report_to=wandb \\\n --push_to_hub\n ```\n\n We recommend this type of validation as it can be useful for model debugging. Note that you need `wandb` installed to use this. You can install `wandb` by running `pip install wandb`. \n\n [Here](https://wandb.ai/sayakpaul/instruct-pix2pix-sdxl-new/runs/sw53gxmc), you can find an example training run that includes some validation samples and the training hyperparameters.\n\n ***Note: In the original paper, the authors observed that even when the model is trained with an image resolution of 256x256, it generalizes well to bigger resolutions such as 512x512. This is likely because of the larger dataset they used during training.***\n\n ## Training with multiple GPUs\n\n`accelerate` allows for seamless multi-GPU training. Follow the instructions [here](https://huggingface.co/docs/accelerate/basic_tutorials/launch)\nfor running distributed training with `accelerate`. Here is an example command:\n\n```bash \naccelerate launch --mixed_precision=\"fp16\" --multi_gpu train_instruct_pix2pix_sdxl.py \\\n --pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0 \\\n --dataset_name=$DATASET_ID \\\n --use_ema \\\n --enable_xformers_memory_efficient_attention \\\n --resolution=512 --random_flip \\\n --train_batch_size=4 --gradient_accumulation_steps=4 --gradient_checkpointing \\\n --max_train_steps=15000 \\\n --checkpointing_steps=5000 --checkpoints_total_limit=1 \\\n --learning_rate=5e-05 --lr_warmup_steps=0 \\\n --conditioning_dropout_prob=0.05 \\\n --seed=42 \\\n --val_image_url_or_path=\"https://datasets-server.huggingface.co/assets/fusing/instructpix2pix-1000-samples/--/fusing--instructpix2pix-1000-samples/train/23/input_image/image.jpg\" \\\n --validation_prompt=\"make it in japan\" \\\n --report_to=wandb \\\n --push_to_hub\n```\n\n ## Inference\n\n Once training is complete, we can perform inference:\n\n ```python\nimport PIL\nimport requests\nimport torch\nfrom diffusers import StableDiffusionXLInstructPix2PixPipeline\n\nmodel_id = \"your_model_id\" # <- replace this \npipe = StableDiffusionXLInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(\"cuda\")\ngenerator = torch.Generator(\"cuda\").manual_seed(0)\n\nurl = \"https://datasets-server.huggingface.co/assets/fusing/instructpix2pix-1000-samples/--/fusing--instructpix2pix-1000-samples/train/23/input_image/image.jpg\"\n\n\ndef download_image(url):\n image = PIL.Image.open(requests.get(url, stream=True).raw)\n image = PIL.ImageOps.exif_transpose(image)\n image = image.convert(\"RGB\")\n return image\n\nimage = download_image(url)\nprompt = \"make it Japan\"\nnum_inference_steps = 20\nimage_guidance_scale = 1.5\nguidance_scale = 10\n\nedited_image = pipe(prompt, \n image=image, \n num_inference_steps=num_inference_steps, \n image_guidance_scale=image_guidance_scale, \n guidance_scale=guidance_scale,\n generator=generator,\n).images[0]\nedited_image.save(\"edited_image.png\")\n```\n\nWe encourage you to play with the following three parameters to control\nspeed and quality during performance:\n\n* `num_inference_steps`\n* `image_guidance_scale`\n* `guidance_scale`\n\nParticularly, `image_guidance_scale` and `guidance_scale` can have a profound impact\non the generated (\"edited\") image (see [here](https://twitter.com/RisingSayak/status/1628392199196151808?s=20) for an example).\n\nIf you're looking for some interesting ways to use the InstructPix2Pix training methodology, we welcome you to check out this blog post: [Instruction-tuning Stable Diffusion with InstructPix2Pix](https://huggingface.co/blog/instruction-tuning-sd). \n\n## Compare between SD and SDXL\n\nWe aim to understand the differences resulting from the use of SD-1.5 and SDXL-0.9 as pretrained models. To achieve this, we trained on the [small toy dataset](https://huggingface.co/datasets/fusing/instructpix2pix-1000-samples) using both of these pretrained models. The training script is as follows:\n\n```bash\nexport MODEL_NAME=\"runwayml/stable-diffusion-v1-5\" or \"stabilityai/stable-diffusion-xl-base-0.9\"\nexport DATASET_ID=\"fusing/instructpix2pix-1000-samples\"\n\naccelerate launch train_instruct_pix2pix.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --dataset_name=$DATASET_ID \\\n --use_ema \\\n --enable_xformers_memory_efficient_attention \\\n --resolution=512 --random_flip \\\n --train_batch_size=4 --gradient_accumulation_steps=4 --gradient_checkpointing \\\n --max_train_steps=15000 \\\n --checkpointing_steps=5000 --checkpoints_total_limit=1 \\\n --learning_rate=5e-05 --lr_warmup_steps=0 \\\n --conditioning_dropout_prob=0.05 \\\n --seed=42 \\\n --val_image_url=\"https://datasets-server.huggingface.co/assets/fusing/instructpix2pix-1000-samples/--/fusing--instructpix2pix-1000-samples/train/23/input_image/image.jpg\" \\\n --validation_prompt=\"make it in Japan\" \\\n --report_to=wandb \\\n --push_to_hub\n```\n\nWe discovered that compared to training with SD-1.5 as the pretrained model, SDXL-0.9 results in a lower training loss value (SD-1.5 yields 0.0599, SDXL scores 0.0254). Moreover, from a visual perspective, the results obtained using SDXL demonstrated fewer artifacts and a richer detail. Notably, SDXL starts to preserve the structure of the original image earlier on.\n\nThe following two GIFs provide intuitive visual results. We observed, for each step, what kind of results could be achieved using the image \n

\n \"input\n

\nwith \"make it in Japan” as the prompt. It can be seen that SDXL starts preserving the details of the original image earlier, resulting in higher fidelity outcomes sooner.\n\n* SD-1.5: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sd_ip2p_training_val_img_progress.gif\n\n

\n \"input\n

\n\n* SDXL: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_ip2p_training_val_img_progress.gif\n\n

\n \"input\n

\n" }, { "path": "diffusers/examples/instruct_pix2pix/requirements.txt", "content": "accelerate>=0.16.0\ntorchvision\ntransformers>=4.25.1\ndatasets\nftfy\ntensorboard" }, { "path": "diffusers/examples/instruct_pix2pix/test_instruct_pix2pix.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport logging\nimport os\nimport sys\nimport tempfile\n\n\nsys.path.append(\"..\")\nfrom test_examples_utils import ExamplesTestsAccelerate, run_command # noqa: E402\n\n\nlogging.basicConfig(level=logging.DEBUG)\n\nlogger = logging.getLogger()\nstream_handler = logging.StreamHandler(sys.stdout)\nlogger.addHandler(stream_handler)\n\n\nclass InstructPix2Pix(ExamplesTestsAccelerate):\n def test_instruct_pix2pix_checkpointing_checkpoints_total_limit(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/instruct_pix2pix/train_instruct_pix2pix.py\n --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe\n --dataset_name=hf-internal-testing/instructpix2pix-10-samples\n --resolution=64\n --random_flip\n --train_batch_size=1\n --max_train_steps=6\n --checkpointing_steps=2\n --checkpoints_total_limit=2\n --output_dir {tmpdir}\n --seed=0\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-4\", \"checkpoint-6\"},\n )\n\n def test_instruct_pix2pix_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/instruct_pix2pix/train_instruct_pix2pix.py\n --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe\n --dataset_name=hf-internal-testing/instructpix2pix-10-samples\n --resolution=64\n --random_flip\n --train_batch_size=1\n --max_train_steps=4\n --checkpointing_steps=2\n --output_dir {tmpdir}\n --seed=0\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n\n # check checkpoint directories exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-2\", \"checkpoint-4\"},\n )\n\n resume_run_args = f\"\"\"\n examples/instruct_pix2pix/train_instruct_pix2pix.py\n --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe\n --dataset_name=hf-internal-testing/instructpix2pix-10-samples\n --resolution=64\n --random_flip\n --train_batch_size=1\n --max_train_steps=8\n --checkpointing_steps=2\n --output_dir {tmpdir}\n --seed=0\n --resume_from_checkpoint=checkpoint-4\n --checkpoints_total_limit=2\n \"\"\".split()\n\n run_command(self._launch_args + resume_run_args)\n\n # check checkpoint directories exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-6\", \"checkpoint-8\"},\n )\n" }, { "path": "diffusers/examples/instruct_pix2pix/train_instruct_pix2pix.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Script to fine-tune Stable Diffusion for InstructPix2Pix.\"\"\"\n\nimport argparse\nimport logging\nimport math\nimport os\nimport shutil\nfrom contextlib import nullcontext\nfrom pathlib import Path\n\nimport accelerate\nimport datasets\nimport numpy as np\nimport PIL\nimport requests\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nimport diffusers\nfrom diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionInstructPix2PixPipeline, UNet2DConditionModel\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import EMAModel\nfrom diffusers.utils import check_min_version, deprecate, is_wandb_available\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\nDATASET_NAME_MAPPING = {\n \"fusing/instructpix2pix-1000-samples\": (\"input_image\", \"edit_prompt\", \"edited_image\"),\n}\nWANDB_TABLE_COL_NAMES = [\"original_image\", \"edited_image\", \"edit_prompt\"]\n\n\ndef log_validation(\n pipeline,\n args,\n accelerator,\n generator,\n):\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n original_image = download_image(args.val_image_url)\n edited_images = []\n if torch.backends.mps.is_available():\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type)\n\n with autocast_ctx:\n for _ in range(args.num_validation_images):\n edited_images.append(\n pipeline(\n args.validation_prompt,\n image=original_image,\n num_inference_steps=20,\n image_guidance_scale=1.5,\n guidance_scale=7,\n generator=generator,\n ).images[0]\n )\n\n for tracker in accelerator.trackers:\n if tracker.name == \"wandb\":\n wandb_table = wandb.Table(columns=WANDB_TABLE_COL_NAMES)\n for edited_image in edited_images:\n wandb_table.add_data(wandb.Image(original_image), wandb.Image(edited_image), args.validation_prompt)\n tracker.log({\"validation\": wandb_table})\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script for InstructPix2Pix.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--original_image_column\",\n type=str,\n default=\"input_image\",\n help=\"The column of the dataset containing the original image on which edits where made.\",\n )\n parser.add_argument(\n \"--edited_image_column\",\n type=str,\n default=\"edited_image\",\n help=\"The column of the dataset containing the edited image.\",\n )\n parser.add_argument(\n \"--edit_prompt_column\",\n type=str,\n default=\"edit_prompt\",\n help=\"The column of the dataset containing the edit instruction.\",\n )\n parser.add_argument(\n \"--val_image_url\",\n type=str,\n default=None,\n help=\"URL to the original image that you would like to edit (used during inference for debugging purposes).\",\n )\n parser.add_argument(\n \"--validation_prompt\", type=str, default=None, help=\"A prompt that is sampled during training for inference.\"\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=1,\n help=(\n \"Run fine-tuning validation every X epochs. The validation process consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"instruct-pix2pix-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=256,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--conditioning_dropout_prob\",\n type=float,\n default=None,\n help=\"Conditioning dropout probability. Drops out the conditionings (image and edit prompt) used in training InstructPix2Pix. See section 3.2.1 in the paper: https://arxiv.org/abs/2211.09800.\",\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\"--use_ema\", action=\"store_true\", help=\"Whether to use EMA model.\")\n parser.add_argument(\n \"--non_ema_revision\",\n type=str,\n default=None,\n required=False,\n help=(\n \"Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or\"\n \" remote repository specified with --pretrained_model_name_or_path.\"\n ),\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n\n # default to using the same revision for the non-ema model if not specified\n if args.non_ema_revision is None:\n args.non_ema_revision = args.revision\n\n return args\n\n\ndef convert_to_np(image, resolution):\n image = image.convert(\"RGB\").resize((resolution, resolution))\n return np.array(image).transpose(2, 0, 1)\n\n\ndef download_image(url):\n image = PIL.Image.open(requests.get(url, stream=True).raw)\n image = PIL.ImageOps.exif_transpose(image)\n image = image.convert(\"RGB\")\n return image\n\n\ndef main():\n args = parse_args()\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n if args.non_ema_revision is not None:\n deprecate(\n \"non_ema_revision!=None\",\n \"0.15.0\",\n message=(\n \"Downloading 'non_ema' weights from revision branches of the Hub is deprecated. Please make sure to\"\n \" use `--variant=non_ema` instead.\"\n ),\n )\n logging_dir = os.path.join(args.output_dir, args.logging_dir)\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load scheduler, tokenizer and models.\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n tokenizer = CLIPTokenizer.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"tokenizer\", revision=args.revision\n )\n text_encoder = CLIPTextModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision, variant=args.variant\n )\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.non_ema_revision\n )\n\n # InstructPix2Pix uses an additional image for conditioning. To accommodate that,\n # it uses 8 channels (instead of 4) in the first (conv) layer of the UNet. This UNet is\n # then fine-tuned on the custom InstructPix2Pix dataset. This modified UNet is initialized\n # from the pre-trained checkpoints. For the extra channels added to the first layer, they are\n # initialized to zero.\n logger.info(\"Initializing the InstructPix2Pix UNet from the pretrained UNet.\")\n in_channels = 8\n out_channels = unet.conv_in.out_channels\n unet.register_to_config(in_channels=in_channels)\n\n with torch.no_grad():\n new_conv_in = nn.Conv2d(\n in_channels, out_channels, unet.conv_in.kernel_size, unet.conv_in.stride, unet.conv_in.padding\n )\n new_conv_in.weight.zero_()\n new_conv_in.weight[:, :4, :, :].copy_(unet.conv_in.weight)\n unet.conv_in = new_conv_in\n\n # Freeze vae and text_encoder\n vae.requires_grad_(False)\n text_encoder.requires_grad_(False)\n\n # Create EMA for the unet.\n if args.use_ema:\n ema_unet = EMAModel(unet.parameters(), model_cls=UNet2DConditionModel, model_config=unet.config)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n if args.use_ema:\n ema_unet.save_pretrained(os.path.join(output_dir, \"unet_ema\"))\n\n for i, model in enumerate(models):\n model.save_pretrained(os.path.join(output_dir, \"unet\"))\n\n # make sure to pop weight so that corresponding model is not saved again\n if weights:\n weights.pop()\n\n def load_model_hook(models, input_dir):\n if args.use_ema:\n load_model = EMAModel.from_pretrained(os.path.join(input_dir, \"unet_ema\"), UNet2DConditionModel)\n ema_unet.load_state_dict(load_model.state_dict())\n ema_unet.to(accelerator.device)\n del load_model\n\n for i in range(len(models)):\n # pop models so that they are not loaded again\n model = models.pop()\n\n # load diffusers style into model\n load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder=\"unet\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Initialize the optimizer\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`\"\n )\n\n optimizer_cls = bnb.optim.AdamW8bit\n else:\n optimizer_cls = torch.optim.AdamW\n\n optimizer = optimizer_cls(\n unet.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n )\n else:\n data_files = {}\n if args.train_data_dir is not None:\n data_files[\"train\"] = os.path.join(args.train_data_dir, \"**\")\n dataset = load_dataset(\n \"imagefolder\",\n data_files=data_files,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/main/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)\n if args.original_image_column is None:\n original_image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n else:\n original_image_column = args.original_image_column\n if original_image_column not in column_names:\n raise ValueError(\n f\"--original_image_column' value '{args.original_image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.edit_prompt_column is None:\n edit_prompt_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n else:\n edit_prompt_column = args.edit_prompt_column\n if edit_prompt_column not in column_names:\n raise ValueError(\n f\"--edit_prompt_column' value '{args.edit_prompt_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.edited_image_column is None:\n edited_image_column = dataset_columns[2] if dataset_columns is not None else column_names[2]\n else:\n edited_image_column = args.edited_image_column\n if edited_image_column not in column_names:\n raise ValueError(\n f\"--edited_image_column' value '{args.edited_image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n\n # Preprocessing the datasets.\n # We need to tokenize input captions and transform the images.\n def tokenize_captions(captions):\n inputs = tokenizer(\n captions, max_length=tokenizer.model_max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\"\n )\n return inputs.input_ids\n\n # Preprocessing the datasets.\n train_transforms = transforms.Compose(\n [\n transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),\n transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),\n ]\n )\n\n def preprocess_images(examples):\n original_images = np.concatenate(\n [convert_to_np(image, args.resolution) for image in examples[original_image_column]]\n )\n edited_images = np.concatenate(\n [convert_to_np(image, args.resolution) for image in examples[edited_image_column]]\n )\n # We need to ensure that the original and the edited images undergo the same\n # augmentation transforms.\n images = np.concatenate([original_images, edited_images])\n images = torch.tensor(images)\n images = 2 * (images / 255) - 1\n return train_transforms(images)\n\n def preprocess_train(examples):\n # Preprocess images.\n preprocessed_images = preprocess_images(examples)\n # Since the original and edited images were concatenated before\n # applying the transformations, we need to separate them and reshape\n # them accordingly.\n original_images, edited_images = preprocessed_images.chunk(2)\n original_images = original_images.reshape(-1, 3, args.resolution, args.resolution)\n edited_images = edited_images.reshape(-1, 3, args.resolution, args.resolution)\n\n # Collate the preprocessed images into the `examples`.\n examples[\"original_pixel_values\"] = original_images\n examples[\"edited_pixel_values\"] = edited_images\n\n # Preprocess the captions.\n captions = list(examples[edit_prompt_column])\n examples[\"input_ids\"] = tokenize_captions(captions)\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n def collate_fn(examples):\n original_pixel_values = torch.stack([example[\"original_pixel_values\"] for example in examples])\n original_pixel_values = original_pixel_values.to(memory_format=torch.contiguous_format).float()\n edited_pixel_values = torch.stack([example[\"edited_pixel_values\"] for example in examples])\n edited_pixel_values = edited_pixel_values.to(memory_format=torch.contiguous_format).float()\n input_ids = torch.stack([example[\"input_ids\"] for example in examples])\n return {\n \"original_pixel_values\": original_pixel_values,\n \"edited_pixel_values\": edited_pixel_values,\n \"input_ids\": input_ids,\n }\n\n # DataLoaders creation:\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n )\n\n # Prepare everything with our `accelerator`.\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n if args.use_ema:\n ema_unet.to(accelerator.device)\n\n # For mixed precision training we cast the text_encoder and vae weights to half-precision\n # as these models are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move text_encode and vae to gpu and cast to weight_dtype\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n vae.to(accelerator.device, dtype=weight_dtype)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"instruct-pix2pix\", config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n resume_global_step = global_step * args.gradient_accumulation_steps\n first_epoch = global_step // num_update_steps_per_epoch\n resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)\n\n # Only show the progress bar once on each machine.\n progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)\n progress_bar.set_description(\"Steps\")\n\n for epoch in range(first_epoch, args.num_train_epochs):\n unet.train()\n train_loss = 0.0\n for step, batch in enumerate(train_dataloader):\n # Skip steps until we reach the resumed step\n if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:\n if step % args.gradient_accumulation_steps == 0:\n progress_bar.update(1)\n continue\n\n with accelerator.accumulate(unet):\n # We want to learn the denoising process w.r.t the edited images which\n # are conditioned on the original image (which was edited) and the edit instruction.\n # So, first, convert images to latent space.\n latents = vae.encode(batch[\"edited_pixel_values\"].to(weight_dtype)).latent_dist.sample()\n latents = latents * vae.config.scaling_factor\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # Get the text embedding for conditioning.\n encoder_hidden_states = text_encoder(batch[\"input_ids\"])[0]\n\n # Get the additional image embedding for conditioning.\n # Instead of getting a diagonal Gaussian here, we simply take the mode.\n original_image_embeds = vae.encode(batch[\"original_pixel_values\"].to(weight_dtype)).latent_dist.mode()\n\n # Conditioning dropout to support classifier-free guidance during inference. For more details\n # check out the section 3.2.1 of the original paper https://arxiv.org/abs/2211.09800.\n if args.conditioning_dropout_prob is not None:\n random_p = torch.rand(bsz, device=latents.device, generator=generator)\n # Sample masks for the edit prompts.\n prompt_mask = random_p < 2 * args.conditioning_dropout_prob\n prompt_mask = prompt_mask.reshape(bsz, 1, 1)\n # Final text conditioning.\n null_conditioning = text_encoder(tokenize_captions([\"\"]).to(accelerator.device))[0]\n encoder_hidden_states = torch.where(prompt_mask, null_conditioning, encoder_hidden_states)\n\n # Sample masks for the original images.\n image_mask_dtype = original_image_embeds.dtype\n image_mask = 1 - (\n (random_p >= args.conditioning_dropout_prob).to(image_mask_dtype)\n * (random_p < 3 * args.conditioning_dropout_prob).to(image_mask_dtype)\n )\n image_mask = image_mask.reshape(bsz, 1, 1, 1)\n # Final image conditioning.\n original_image_embeds = image_mask * original_image_embeds\n\n # Concatenate the `original_image_embeds` with the `noisy_latents`.\n concatenated_noisy_latents = torch.cat([noisy_latents, original_image_embeds], dim=1)\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n # Predict the noise residual and compute loss\n model_pred = unet(concatenated_noisy_latents, timesteps, encoder_hidden_states, return_dict=False)[0]\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n\n # Gather the losses across all processes for logging (if we use distributed training).\n avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n train_loss += avg_loss.item() / args.gradient_accumulation_steps\n\n # Backpropagate\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n if args.use_ema:\n ema_unet.step(unet.parameters())\n progress_bar.update(1)\n global_step += 1\n accelerator.log({\"train_loss\": train_loss}, step=global_step)\n train_loss = 0.0\n\n if global_step % args.checkpointing_steps == 0:\n if accelerator.is_main_process:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"step_loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if (\n (args.val_image_url is not None)\n and (args.validation_prompt is not None)\n and (epoch % args.validation_epochs == 0)\n ):\n if args.use_ema:\n # Store the UNet parameters temporarily and load the EMA parameters to perform inference.\n ema_unet.store(unet.parameters())\n ema_unet.copy_to(unet.parameters())\n # The models need unwrapping because for compatibility in distributed training mode.\n pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n unet=unwrap_model(unet),\n text_encoder=unwrap_model(text_encoder),\n vae=unwrap_model(vae),\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n\n log_validation(\n pipeline,\n args,\n accelerator,\n generator,\n )\n\n if args.use_ema:\n # Switch back to the original UNet parameters.\n ema_unet.restore(unet.parameters())\n\n del pipeline\n torch.cuda.empty_cache()\n\n # Create the pipeline using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n if args.use_ema:\n ema_unet.copy_to(unet.parameters())\n\n pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n text_encoder=unwrap_model(text_encoder),\n vae=unwrap_model(vae),\n unet=unwrap_model(unet),\n revision=args.revision,\n variant=args.variant,\n )\n pipeline.save_pretrained(args.output_dir)\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n if (args.val_image_url is not None) and (args.validation_prompt is not None):\n log_validation(\n pipeline,\n args,\n accelerator,\n generator,\n )\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/instruct_pix2pix/train_instruct_pix2pix_sdxl.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 Harutatsu Akiyama and The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport argparse\nimport logging\nimport math\nimport os\nimport shutil\nimport warnings\nfrom contextlib import nullcontext\nfrom pathlib import Path\nfrom urllib.parse import urlparse\n\nimport accelerate\nimport datasets\nimport numpy as np\nimport PIL\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom PIL import Image\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_instruct_pix2pix import (\n StableDiffusionXLInstructPix2PixPipeline,\n)\nfrom diffusers.training_utils import EMAModel\nfrom diffusers.utils import check_min_version, deprecate, is_wandb_available, load_image\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\nDATASET_NAME_MAPPING = {\n \"fusing/instructpix2pix-1000-samples\": (\"file_name\", \"edited_image\", \"edit_prompt\"),\n}\nWANDB_TABLE_COL_NAMES = [\"file_name\", \"edited_image\", \"edit_prompt\"]\nTORCH_DTYPE_MAPPING = {\"fp32\": torch.float32, \"fp16\": torch.float16, \"bf16\": torch.bfloat16}\n\n\ndef log_validation(pipeline, args, accelerator, generator, global_step, is_final_validation=False):\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n val_save_dir = os.path.join(args.output_dir, \"validation_images\")\n if not os.path.exists(val_save_dir):\n os.makedirs(val_save_dir)\n\n original_image = (\n lambda image_url_or_path: load_image(image_url_or_path)\n if urlparse(image_url_or_path).scheme\n else Image.open(image_url_or_path).convert(\"RGB\")\n )(args.val_image_url_or_path)\n\n if torch.backends.mps.is_available():\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type)\n\n with autocast_ctx:\n edited_images = []\n # Run inference\n for val_img_idx in range(args.num_validation_images):\n a_val_img = pipeline(\n args.validation_prompt,\n image=original_image,\n num_inference_steps=20,\n image_guidance_scale=1.5,\n guidance_scale=7,\n generator=generator,\n ).images[0]\n edited_images.append(a_val_img)\n # Save validation images\n a_val_img.save(os.path.join(val_save_dir, f\"step_{global_step}_val_img_{val_img_idx}.png\"))\n\n for tracker in accelerator.trackers:\n if tracker.name == \"wandb\":\n wandb_table = wandb.Table(columns=WANDB_TABLE_COL_NAMES)\n for edited_image in edited_images:\n wandb_table.add_data(wandb.Image(original_image), wandb.Image(edited_image), args.validation_prompt)\n logger_name = \"test\" if is_final_validation else \"validation\"\n tracker.log({logger_name: wandb_table})\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Script to train Stable Diffusion XL for InstructPix2Pix.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_vae_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to an improved VAE to stabilize training. For more details check out: https://github.com/huggingface/diffusers/pull/4038.\",\n )\n parser.add_argument(\n \"--vae_precision\",\n type=str,\n choices=[\"fp32\", \"fp16\", \"bf16\"],\n default=\"fp32\",\n help=(\n \"The vanilla SDXL 1.0 VAE can cause NaNs due to large activation values. Some custom models might already have a solution\"\n \" to this problem, and this flag allows you to use mixed precision to stabilize training.\"\n ),\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--original_image_column\",\n type=str,\n default=\"input_image\",\n help=\"The column of the dataset containing the original image on which edits where made.\",\n )\n parser.add_argument(\n \"--edited_image_column\",\n type=str,\n default=\"edited_image\",\n help=\"The column of the dataset containing the edited image.\",\n )\n parser.add_argument(\n \"--edit_prompt_column\",\n type=str,\n default=\"edit_prompt\",\n help=\"The column of the dataset containing the edit instruction.\",\n )\n parser.add_argument(\n \"--val_image_url_or_path\",\n type=str,\n default=None,\n help=\"URL to the original image that you would like to edit (used during inference for debugging purposes).\",\n )\n parser.add_argument(\n \"--validation_prompt\", type=str, default=None, help=\"A prompt that is sampled during training for inference.\"\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=100,\n help=(\n \"Run fine-tuning validation every X steps. The validation process consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"instruct-pix2pix-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=256,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this resolution.\"\n ),\n )\n parser.add_argument(\n \"--crops_coords_top_left_h\",\n type=int,\n default=0,\n help=(\"Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet.\"),\n )\n parser.add_argument(\n \"--crops_coords_top_left_w\",\n type=int,\n default=0,\n help=(\"Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet.\"),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--conditioning_dropout_prob\",\n type=float,\n default=None,\n help=\"Conditioning dropout probability. Drops out the conditionings (image and edit prompt) used in training InstructPix2Pix. See section 3.2.1 in the paper: https://arxiv.org/abs/2211.09800.\",\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\"--use_ema\", action=\"store_true\", help=\"Whether to use EMA model.\")\n parser.add_argument(\n \"--non_ema_revision\",\n type=str,\n default=None,\n required=False,\n help=(\n \"Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or\"\n \" remote repository specified with --pretrained_model_name_or_path.\"\n ),\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n\n # default to using the same revision for the non-ema model if not specified\n if args.non_ema_revision is None:\n args.non_ema_revision = args.revision\n\n return args\n\n\ndef convert_to_np(image, resolution):\n if isinstance(image, str):\n image = PIL.Image.open(image)\n image = image.convert(\"RGB\").resize((resolution, resolution))\n return np.array(image).transpose(2, 0, 1)\n\n\ndef main():\n args = parse_args()\n\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n if args.non_ema_revision is not None:\n deprecate(\n \"non_ema_revision!=None\",\n \"0.15.0\",\n message=(\n \"Downloading 'non_ema' weights from revision branches of the Hub is deprecated. Please make sure to\"\n \" use `--variant=non_ema` instead.\"\n ),\n )\n logging_dir = os.path.join(args.output_dir, args.logging_dir)\n\n if torch.backends.mps.is_available() and args.mixed_precision == \"bf16\":\n # due to pytorch#99272, MPS does not yet support bfloat16.\n raise ValueError(\n \"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead.\"\n )\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n vae_path = (\n args.pretrained_model_name_or_path\n if args.pretrained_vae_model_name_or_path is None\n else args.pretrained_vae_model_name_or_path\n )\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n variant=args.variant,\n )\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n # InstructPix2Pix uses an additional image for conditioning. To accommodate that,\n # it uses 8 channels (instead of 4) in the first (conv) layer of the UNet. This UNet is\n # then fine-tuned on the custom InstructPix2Pix dataset. This modified UNet is initialized\n # from the pre-trained checkpoints. For the extra channels added to the first layer, they are\n # initialized to zero.\n logger.info(\"Initializing the XL InstructPix2Pix UNet from the pretrained UNet.\")\n in_channels = 8\n out_channels = unet.conv_in.out_channels\n unet.register_to_config(in_channels=in_channels)\n\n with torch.no_grad():\n new_conv_in = nn.Conv2d(\n in_channels, out_channels, unet.conv_in.kernel_size, unet.conv_in.stride, unet.conv_in.padding\n )\n new_conv_in.weight.zero_()\n new_conv_in.weight[:, :4, :, :].copy_(unet.conv_in.weight)\n unet.conv_in = new_conv_in\n\n # Create EMA for the unet.\n if args.use_ema:\n ema_unet = EMAModel(unet.parameters(), model_cls=UNet2DConditionModel, model_config=unet.config)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n if args.use_ema:\n ema_unet.save_pretrained(os.path.join(output_dir, \"unet_ema\"))\n\n for i, model in enumerate(models):\n model.save_pretrained(os.path.join(output_dir, \"unet\"))\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n def load_model_hook(models, input_dir):\n if args.use_ema:\n load_model = EMAModel.from_pretrained(os.path.join(input_dir, \"unet_ema\"), UNet2DConditionModel)\n ema_unet.load_state_dict(load_model.state_dict())\n ema_unet.to(accelerator.device)\n del load_model\n\n for i in range(len(models)):\n # pop models so that they are not loaded again\n model = models.pop()\n\n # load diffusers style into model\n load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder=\"unet\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Initialize the optimizer\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`\"\n )\n\n optimizer_cls = bnb.optim.AdamW8bit\n else:\n optimizer_cls = torch.optim.AdamW\n\n optimizer = optimizer_cls(\n unet.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n )\n else:\n data_files = {}\n if args.train_data_dir is not None:\n data_files[\"train\"] = os.path.join(args.train_data_dir, \"**\")\n dataset = load_dataset(\n \"imagefolder\",\n data_files=data_files,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/main/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)\n if args.original_image_column is None:\n original_image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n else:\n original_image_column = args.original_image_column\n if original_image_column not in column_names:\n raise ValueError(\n f\"--original_image_column' value '{args.original_image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.edit_prompt_column is None:\n edit_prompt_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n else:\n edit_prompt_column = args.edit_prompt_column\n if edit_prompt_column not in column_names:\n raise ValueError(\n f\"--edit_prompt_column' value '{args.edit_prompt_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.edited_image_column is None:\n edited_image_column = dataset_columns[2] if dataset_columns is not None else column_names[2]\n else:\n edited_image_column = args.edited_image_column\n if edited_image_column not in column_names:\n raise ValueError(\n f\"--edited_image_column' value '{args.edited_image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n\n # For mixed precision training we cast the text_encoder and vae weights to half-precision\n # as these models are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n warnings.warn(f\"weight_dtype {weight_dtype} may cause nan during vae encoding\", UserWarning)\n\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n warnings.warn(f\"weight_dtype {weight_dtype} may cause nan during vae encoding\", UserWarning)\n\n # Preprocessing the datasets.\n # We need to tokenize input captions and transform the images.\n def tokenize_captions(captions, tokenizer):\n inputs = tokenizer(\n captions,\n max_length=tokenizer.model_max_length,\n padding=\"max_length\",\n truncation=True,\n return_tensors=\"pt\",\n )\n return inputs.input_ids\n\n # Preprocessing the datasets.\n train_transforms = transforms.Compose(\n [\n transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),\n transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),\n ]\n )\n\n def preprocess_images(examples):\n original_images = np.concatenate(\n [convert_to_np(image, args.resolution) for image in examples[original_image_column]]\n )\n edited_images = np.concatenate(\n [convert_to_np(image, args.resolution) for image in examples[edited_image_column]]\n )\n # We need to ensure that the original and the edited images undergo the same\n # augmentation transforms.\n images = np.concatenate([original_images, edited_images])\n images = torch.tensor(images)\n images = 2 * (images / 255) - 1\n return train_transforms(images)\n\n # Load scheduler, tokenizer and models.\n tokenizer_1 = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n tokenizer_2 = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer_2\",\n revision=args.revision,\n use_fast=False,\n )\n text_encoder_cls_1 = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)\n text_encoder_cls_2 = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision, subfolder=\"text_encoder_2\"\n )\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n text_encoder_1 = text_encoder_cls_1.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n text_encoder_2 = text_encoder_cls_2.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder_2\", revision=args.revision, variant=args.variant\n )\n\n # We ALWAYS pre-compute the additional condition embeddings needed for SDXL\n # UNet as the model is already big and it uses two text encoders.\n text_encoder_1.to(accelerator.device, dtype=weight_dtype)\n text_encoder_2.to(accelerator.device, dtype=weight_dtype)\n tokenizers = [tokenizer_1, tokenizer_2]\n text_encoders = [text_encoder_1, text_encoder_2]\n\n # Freeze vae and text_encoders\n vae.requires_grad_(False)\n text_encoder_1.requires_grad_(False)\n text_encoder_2.requires_grad_(False)\n\n # Set UNet to trainable.\n unet.train()\n\n # Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt\n def encode_prompt(text_encoders, tokenizers, prompt):\n prompt_embeds_list = []\n\n for tokenizer, text_encoder in zip(tokenizers, text_encoders):\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = text_encoder(\n text_input_ids.to(text_encoder.device),\n output_hidden_states=True,\n )\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds.hidden_states[-2]\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)\n return prompt_embeds, pooled_prompt_embeds\n\n # Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt\n def encode_prompts(text_encoders, tokenizers, prompts):\n prompt_embeds_all = []\n pooled_prompt_embeds_all = []\n\n for prompt in prompts:\n prompt_embeds, pooled_prompt_embeds = encode_prompt(text_encoders, tokenizers, prompt)\n prompt_embeds_all.append(prompt_embeds)\n pooled_prompt_embeds_all.append(pooled_prompt_embeds)\n\n return torch.stack(prompt_embeds_all), torch.stack(pooled_prompt_embeds_all)\n\n # Adapted from examples.dreambooth.train_dreambooth_lora_sdxl\n # Here, we compute not just the text embeddings but also the additional embeddings\n # needed for the SD XL UNet to operate.\n def compute_embeddings_for_prompts(prompts, text_encoders, tokenizers):\n with torch.no_grad():\n prompt_embeds_all, pooled_prompt_embeds_all = encode_prompts(text_encoders, tokenizers, prompts)\n add_text_embeds_all = pooled_prompt_embeds_all\n\n prompt_embeds_all = prompt_embeds_all.to(accelerator.device)\n add_text_embeds_all = add_text_embeds_all.to(accelerator.device)\n return prompt_embeds_all, add_text_embeds_all\n\n # Get null conditioning\n def compute_null_conditioning():\n null_conditioning_list = []\n for a_tokenizer, a_text_encoder in zip(tokenizers, text_encoders):\n null_conditioning_list.append(\n a_text_encoder(\n tokenize_captions([\"\"], tokenizer=a_tokenizer).to(accelerator.device),\n output_hidden_states=True,\n ).hidden_states[-2]\n )\n return torch.concat(null_conditioning_list, dim=-1)\n\n null_conditioning = compute_null_conditioning()\n\n def compute_time_ids():\n crops_coords_top_left = (args.crops_coords_top_left_h, args.crops_coords_top_left_w)\n original_size = target_size = (args.resolution, args.resolution)\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n add_time_ids = torch.tensor([add_time_ids], dtype=weight_dtype)\n return add_time_ids.to(accelerator.device).repeat(args.train_batch_size, 1)\n\n add_time_ids = compute_time_ids()\n\n def preprocess_train(examples):\n # Preprocess images.\n preprocessed_images = preprocess_images(examples)\n # Since the original and edited images were concatenated before\n # applying the transformations, we need to separate them and reshape\n # them accordingly.\n original_images, edited_images = preprocessed_images.chunk(2)\n original_images = original_images.reshape(-1, 3, args.resolution, args.resolution)\n edited_images = edited_images.reshape(-1, 3, args.resolution, args.resolution)\n\n # Collate the preprocessed images into the `examples`.\n examples[\"original_pixel_values\"] = original_images\n examples[\"edited_pixel_values\"] = edited_images\n\n # Preprocess the captions.\n captions = list(examples[edit_prompt_column])\n prompt_embeds_all, add_text_embeds_all = compute_embeddings_for_prompts(captions, text_encoders, tokenizers)\n examples[\"prompt_embeds\"] = prompt_embeds_all\n examples[\"add_text_embeds\"] = add_text_embeds_all\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n def collate_fn(examples):\n original_pixel_values = torch.stack([example[\"original_pixel_values\"] for example in examples])\n original_pixel_values = original_pixel_values.to(memory_format=torch.contiguous_format).float()\n edited_pixel_values = torch.stack([example[\"edited_pixel_values\"] for example in examples])\n edited_pixel_values = edited_pixel_values.to(memory_format=torch.contiguous_format).float()\n prompt_embeds = torch.concat([example[\"prompt_embeds\"] for example in examples], dim=0)\n add_text_embeds = torch.concat([example[\"add_text_embeds\"] for example in examples], dim=0)\n return {\n \"original_pixel_values\": original_pixel_values,\n \"edited_pixel_values\": edited_pixel_values,\n \"prompt_embeds\": prompt_embeds,\n \"add_text_embeds\": add_text_embeds,\n }\n\n # DataLoaders creation:\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,\n num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,\n )\n\n # Prepare everything with our `accelerator`.\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n if args.use_ema:\n ema_unet.to(accelerator.device)\n\n # Move vae, unet and text_encoder to device and cast to weight_dtype\n # The VAE is in float32 to avoid NaN losses.\n if args.pretrained_vae_model_name_or_path is not None:\n vae.to(accelerator.device, dtype=weight_dtype)\n else:\n vae.to(accelerator.device, dtype=TORCH_DTYPE_MAPPING[args.vae_precision])\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"instruct-pix2pix-xl\", config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n for epoch in range(first_epoch, args.num_train_epochs):\n train_loss = 0.0\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n # We want to learn the denoising process w.r.t the edited images which\n # are conditioned on the original image (which was edited) and the edit instruction.\n # So, first, convert images to latent space.\n if args.pretrained_vae_model_name_or_path is not None:\n edited_pixel_values = batch[\"edited_pixel_values\"].to(dtype=weight_dtype)\n else:\n edited_pixel_values = batch[\"edited_pixel_values\"]\n latents = vae.encode(edited_pixel_values).latent_dist.sample()\n latents = latents * vae.config.scaling_factor\n if args.pretrained_vae_model_name_or_path is None:\n latents = latents.to(weight_dtype)\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # SDXL additional inputs\n encoder_hidden_states = batch[\"prompt_embeds\"]\n add_text_embeds = batch[\"add_text_embeds\"]\n\n # Get the additional image embedding for conditioning.\n # Instead of getting a diagonal Gaussian here, we simply take the mode.\n if args.pretrained_vae_model_name_or_path is not None:\n original_pixel_values = batch[\"original_pixel_values\"].to(dtype=weight_dtype)\n else:\n original_pixel_values = batch[\"original_pixel_values\"]\n original_image_embeds = vae.encode(original_pixel_values).latent_dist.sample()\n if args.pretrained_vae_model_name_or_path is None:\n original_image_embeds = original_image_embeds.to(weight_dtype)\n\n # Conditioning dropout to support classifier-free guidance during inference. For more details\n # check out the section 3.2.1 of the original paper https://arxiv.org/abs/2211.09800.\n if args.conditioning_dropout_prob is not None:\n random_p = torch.rand(bsz, device=latents.device, generator=generator)\n # Sample masks for the edit prompts.\n prompt_mask = random_p < 2 * args.conditioning_dropout_prob\n prompt_mask = prompt_mask.reshape(bsz, 1, 1)\n # Final text conditioning.\n encoder_hidden_states = torch.where(prompt_mask, null_conditioning, encoder_hidden_states)\n\n # Sample masks for the original images.\n image_mask_dtype = original_image_embeds.dtype\n image_mask = 1 - (\n (random_p >= args.conditioning_dropout_prob).to(image_mask_dtype)\n * (random_p < 3 * args.conditioning_dropout_prob).to(image_mask_dtype)\n )\n image_mask = image_mask.reshape(bsz, 1, 1, 1)\n # Final image conditioning.\n original_image_embeds = image_mask * original_image_embeds\n\n # Concatenate the `original_image_embeds` with the `noisy_latents`.\n concatenated_noisy_latents = torch.cat([noisy_latents, original_image_embeds], dim=1)\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n # Predict the noise residual and compute loss\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n\n model_pred = unet(\n concatenated_noisy_latents,\n timesteps,\n encoder_hidden_states,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n\n # Gather the losses across all processes for logging (if we use distributed training).\n avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n train_loss += avg_loss.item() / args.gradient_accumulation_steps\n\n # Backpropagate\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n if args.use_ema:\n ema_unet.step(unet.parameters())\n progress_bar.update(1)\n global_step += 1\n accelerator.log({\"train_loss\": train_loss}, step=global_step)\n train_loss = 0.0\n\n if global_step % args.checkpointing_steps == 0:\n if accelerator.is_main_process:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"step_loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n\n ### BEGIN: Perform validation every `validation_epochs` steps\n if global_step % args.validation_steps == 0:\n if (args.val_image_url_or_path is not None) and (args.validation_prompt is not None):\n # create pipeline\n if args.use_ema:\n # Store the UNet parameters temporarily and load the EMA parameters to perform inference.\n ema_unet.store(unet.parameters())\n ema_unet.copy_to(unet.parameters())\n\n # The models need unwrapping because for compatibility in distributed training mode.\n pipeline = StableDiffusionXLInstructPix2PixPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n unet=unwrap_model(unet),\n text_encoder=text_encoder_1,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer_1,\n tokenizer_2=tokenizer_2,\n vae=vae,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n\n log_validation(\n pipeline,\n args,\n accelerator,\n generator,\n global_step,\n is_final_validation=False,\n )\n\n if args.use_ema:\n # Switch back to the original UNet parameters.\n ema_unet.restore(unet.parameters())\n\n del pipeline\n torch.cuda.empty_cache()\n ### END: Perform validation every `validation_epochs` steps\n\n if global_step >= args.max_train_steps:\n break\n\n # Create the pipeline using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n if args.use_ema:\n ema_unet.copy_to(unet.parameters())\n\n pipeline = StableDiffusionXLInstructPix2PixPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n text_encoder=text_encoder_1,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer_1,\n tokenizer_2=tokenizer_2,\n vae=vae,\n unet=unwrap_model(unet),\n revision=args.revision,\n variant=args.variant,\n )\n\n pipeline.save_pretrained(args.output_dir)\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n if (args.val_image_url_or_path is not None) and (args.validation_prompt is not None):\n log_validation(\n pipeline,\n args,\n accelerator,\n generator,\n global_step,\n is_final_validation=True,\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/kandinsky2_2/text_to_image/README.md", "content": "# Kandinsky2.2 text-to-image fine-tuning\n\nKandinsky 2.2 includes a prior pipeline that generates image embeddings from text prompts, and a decoder pipeline that generates the output image based on the image embeddings. We provide `train_text_to_image_prior.py` and `train_text_to_image_decoder.py` scripts to show you how to fine-tune the Kandinsky prior and decoder models separately based on your own dataset. To achieve the best results, you should fine-tune **_both_** your prior and decoder models.\n\n___Note___:\n\n___This script is experimental. The script fine-tunes the whole model and often times the model overfits and runs into issues like catastrophic forgetting. It's recommended to try different hyperparameters to get the best result on your dataset.___\n\n\n## Running locally with PyTorch\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n**Important**\n\nTo make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nThen cd in the example folder and run\n```bash\npip install -r requirements.txt\n```\n\nAnd initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\nFor this example we want to directly store the trained LoRA embeddings on the Hub, so we need to be logged in and add the --push_to_hub flag.\n\n___\n\n### Naruto example\n\nFor all our examples, we will directly store the trained weights on the Hub, so we need to be logged in and add the `--push_to_hub` flag. In order to do that, you have to be a registered user on the 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to the [User Access Tokens](https://huggingface.co/docs/hub/security-tokens) guide.\n\nRun the following command to authenticate your token\n\n```bash\nhuggingface-cli login\n```\n\nWe also use [Weights and Biases](https://docs.wandb.ai/quickstart) logging by default, because it is really useful to monitor the training progress by regularly generating sample images during training. To install wandb, run\n\n```bash\npip install wandb\n```\n\nTo disable wandb logging, remove the `--report_to==\"wandb\"` and `--validation_prompts=\"A robot naruto, 4k photo\"` flags from below examples\n\n#### Fine-tune decoder\n
\n\n\n```bash\nexport DATASET_NAME=\"lambdalabs/naruto-blip-captions\"\n\naccelerate launch --mixed_precision=\"fp16\" train_text_to_image_decoder.py \\\n --dataset_name=$DATASET_NAME \\\n --resolution=768 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --max_train_steps=15000 \\\n --learning_rate=1e-05 \\\n --max_grad_norm=1 \\\n --checkpoints_total_limit=3 \\\n --lr_scheduler=\"constant\" --lr_warmup_steps=0 \\\n --validation_prompts=\"A robot naruto, 4k photo\" \\\n --report_to=\"wandb\" \\\n --push_to_hub \\\n --output_dir=\"kandi2-decoder-naruto-model\"\n```\n\n\n\nTo train on your own training files, prepare the dataset according to the format required by `datasets`. You can find the instructions for how to do that in the [ImageFolder with metadata](https://huggingface.co/docs/datasets/en/image_load#imagefolder-with-metadata) guide.\nIf you wish to use custom loading logic, you should modify the script and we have left pointers for that in the training script.\n\n```bash\nexport TRAIN_DIR=\"path_to_your_dataset\"\n\naccelerate launch --mixed_precision=\"fp16\" train_text_to_image_decoder.py \\\n --train_data_dir=$TRAIN_DIR \\\n --resolution=768 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --max_train_steps=15000 \\\n --learning_rate=1e-05 \\\n --max_grad_norm=1 \\\n --checkpoints_total_limit=3 \\\n --lr_scheduler=\"constant\" --lr_warmup_steps=0 \\\n --validation_prompts=\"A robot naruto, 4k photo\" \\\n --report_to=\"wandb\" \\\n --push_to_hub \\\n --output_dir=\"kandi22-decoder-naruto-model\"\n```\n\n\nOnce the training is finished the model will be saved in the `output_dir` specified in the command. In this example it's `kandi22-decoder-naruto-model`. To load the fine-tuned model for inference just pass that path to `AutoPipelineForText2Image`\n\n```python\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipe = AutoPipelineForText2Image.from_pretrained(output_dir, torch_dtype=torch.float16)\npipe.enable_model_cpu_offload()\n\nprompt='A robot naruto, 4k photo'\nimages = pipe(prompt=prompt).images\nimages[0].save(\"robot-naruto.png\")\n```\n\nCheckpoints only save the unet, so to run inference from a checkpoint, just load the unet\n```python\nfrom diffusers import AutoPipelineForText2Image, UNet2DConditionModel\n\nmodel_path = \"path_to_saved_model\"\n\nunet = UNet2DConditionModel.from_pretrained(model_path + \"/checkpoint-/unet\")\n\npipe = AutoPipelineForText2Image.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\", unet=unet, torch_dtype=torch.float16)\npipe.enable_model_cpu_offload()\n\nimage = pipe(prompt=\"A robot naruto, 4k photo\").images[0]\nimage.save(\"robot-naruto.png\")\n```\n\n#### Fine-tune prior\n\nYou can fine-tune the Kandinsky prior model with `train_text_to_image_prior.py` script. Note that we currently do not support `--gradient_checkpointing` for prior model fine-tuning.\n\n
\n\n\n```bash\nexport DATASET_NAME=\"lambdalabs/naruto-blip-captions\"\n\naccelerate launch --mixed_precision=\"fp16\" train_text_to_image_prior.py \\\n --dataset_name=$DATASET_NAME \\\n --resolution=768 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --max_train_steps=15000 \\\n --learning_rate=1e-05 \\\n --max_grad_norm=1 \\\n --checkpoints_total_limit=3 \\\n --lr_scheduler=\"constant\" --lr_warmup_steps=0 \\\n --validation_prompts=\"A robot naruto, 4k photo\" \\\n --report_to=\"wandb\" \\\n --push_to_hub \\\n --output_dir=\"kandi2-prior-naruto-model\"\n```\n\n\n\nTo perform inference with the fine-tuned prior model, you will need to first create a prior pipeline by passing the `output_dir` to `DiffusionPipeline`. Then create a `KandinskyV22CombinedPipeline` from a pretrained or fine-tuned decoder checkpoint along with all the modules of the prior pipeline you just created.\n\n```python\nfrom diffusers import AutoPipelineForText2Image, DiffusionPipeline\nimport torch\n\npipe_prior = DiffusionPipeline.from_pretrained(output_dir, torch_dtype=torch.float16)\nprior_components = {\"prior_\" + k: v for k,v in pipe_prior.components.items()}\npipe = AutoPipelineForText2Image.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\", **prior_components, torch_dtype=torch.float16)\n\npipe.enable_model_cpu_offload()\nprompt='A robot naruto, 4k photo'\nimages = pipe(prompt=prompt, negative_prompt=negative_prompt).images\nimages[0]\n```\n\nIf you want to use a fine-tuned decoder checkpoint along with your fine-tuned prior checkpoint, you can simply replace the \"kandinsky-community/kandinsky-2-2-decoder\" in above code with your custom model repo name. Note that in order to be able to create a `KandinskyV22CombinedPipeline`, your model repository need to have a prior tag. If you have created your model repo using our training script, the prior tag is automatically included.\n\n#### Training with multiple GPUs\n\n`accelerate` allows for seamless multi-GPU training. Follow the instructions [here](https://huggingface.co/docs/accelerate/basic_tutorials/launch)\nfor running distributed training with `accelerate`. Here is an example command:\n\n```bash\nexport DATASET_NAME=\"lambdalabs/naruto-blip-captions\"\n\naccelerate launch --mixed_precision=\"fp16\" --multi_gpu train_text_to_image_decoder.py \\\n --dataset_name=$DATASET_NAME \\\n --resolution=768 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --max_train_steps=15000 \\\n --learning_rate=1e-05 \\\n --max_grad_norm=1 \\\n --checkpoints_total_limit=3 \\\n --lr_scheduler=\"constant\" --lr_warmup_steps=0 \\\n --validation_prompts=\"A robot naruto, 4k photo\" \\\n --report_to=\"wandb\" \\\n --push_to_hub \\\n --output_dir=\"kandi2-decoder-naruto-model\"\n```\n\n\n#### Training with Min-SNR weighting\n\nWe support training with the Min-SNR weighting strategy proposed in [Efficient Diffusion Training via Min-SNR Weighting Strategy](https://arxiv.org/abs/2303.09556) which helps achieve faster convergence\nby rebalancing the loss. Enable the `--snr_gamma` argument and set it to the recommended\nvalue of 5.0.\n\n\n## Training with LoRA\n\nLow-Rank Adaption of Large Language Models was first introduced by Microsoft in [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) by *Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen*.\n\nIn a nutshell, LoRA allows adapting pretrained models by adding pairs of rank-decomposition matrices to existing weights and **only** training those newly added weights. This has a couple of advantages:\n\n- Previous pretrained weights are kept frozen so that model is not prone to [catastrophic forgetting](https://www.pnas.org/doi/10.1073/pnas.1611835114).\n- Rank-decomposition matrices have significantly fewer parameters than original model, which means that trained LoRA weights are easily portable.\n- LoRA attention layers allow to control to which extent the model is adapted toward new training images via a `scale` parameter.\n\n[cloneofsimo](https://github.com/cloneofsimo) was the first to try out LoRA training for Stable Diffusion in the popular [lora](https://github.com/cloneofsimo/lora) GitHub repository.\n\nWith LoRA, it's possible to fine-tune Kandinsky 2.2 on a custom image-caption pair dataset\non consumer GPUs like Tesla T4, Tesla V100.\n\n### Training\n\nFirst, you need to set up your development environment as explained in the [installation](#installing-the-dependencies). Make sure to set the `MODEL_NAME` and `DATASET_NAME` environment variables. Here, we will use [Kandinsky 2.2](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder) and the [Narutos dataset](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions).\n\n\n#### Train decoder\n\n```bash\nexport DATASET_NAME=\"lambdalabs/naruto-blip-captions\"\n\naccelerate launch --mixed_precision=\"fp16\" train_text_to_image_decoder_lora.py \\\n --dataset_name=$DATASET_NAME --caption_column=\"text\" \\\n --resolution=768 \\\n --train_batch_size=1 \\\n --num_train_epochs=100 --checkpointing_steps=5000 \\\n --learning_rate=1e-04 --lr_scheduler=\"constant\" --lr_warmup_steps=0 \\\n --seed=42 \\\n --rank=4 \\\n --gradient_checkpointing \\\n --output_dir=\"kandi22-decoder-naruto-lora\" \\\n --validation_prompt=\"cute dragon creature\" --report_to=\"wandb\" \\\n --push_to_hub \\\n```\n\n#### Train prior\n\n```bash\nexport DATASET_NAME=\"lambdalabs/naruto-blip-captions\"\n\naccelerate launch --mixed_precision=\"fp16\" train_text_to_image_prior_lora.py \\\n --dataset_name=$DATASET_NAME --caption_column=\"text\" \\\n --resolution=768 \\\n --train_batch_size=1 \\\n --num_train_epochs=100 --checkpointing_steps=5000 \\\n --learning_rate=1e-04 --lr_scheduler=\"constant\" --lr_warmup_steps=0 \\\n --seed=42 \\\n --rank=4 \\\n --output_dir=\"kandi22-prior-naruto-lora\" \\\n --validation_prompt=\"cute dragon creature\" --report_to=\"wandb\" \\\n --push_to_hub \\\n```\n\n**___Note: When using LoRA we can use a much higher learning rate compared to non-LoRA fine-tuning. Here we use *1e-4* instead of the usual *1e-5*. Also, by using LoRA, it's possible to run above scripts in consumer GPUs like T4 or V100.___**\n\n\n### Inference\n\n#### Inference using fine-tuned LoRA checkpoint for decoder\n\nOnce you have trained a Kandinsky decoder model using the above command, inference can be done with the `AutoPipelineForText2Image` after loading the trained LoRA weights. You need to pass the `output_dir` for loading the LoRA weights, which in this case is `kandi22-decoder-naruto-lora`.\n\n\n```python\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipe = AutoPipelineForText2Image.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16)\npipe.unet.load_attn_procs(output_dir)\npipe.enable_model_cpu_offload()\n\nprompt='A robot naruto, 4k photo'\nimage = pipe(prompt=prompt).images[0]\nimage.save(\"robot_naruto.png\")\n```\n\n#### Inference using fine-tuned LoRA checkpoint for prior\n\n```python\nfrom diffusers import AutoPipelineForText2Image\nimport torch\n\npipe = AutoPipelineForText2Image.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16)\npipe.prior_prior.load_attn_procs(output_dir)\npipe.enable_model_cpu_offload()\n\nprompt='A robot naruto, 4k photo'\nimage = pipe(prompt=prompt).images[0]\nimage.save(\"robot_naruto.png\")\nimage\n```\n\n### Training with xFormers:\n\nYou can enable memory efficient attention by [installing xFormers](https://huggingface.co/docs/diffusers/main/en/optimization/xformers) and passing the `--enable_xformers_memory_efficient_attention` argument to the script.\n\nxFormers training is not available for fine-tuning the prior model.\n\n**Note**:\n\nAccording to [this issue](https://github.com/huggingface/diffusers/issues/2234#issuecomment-1416931212), xFormers `v0.0.16` cannot be used for training in some GPUs. If you observe that problem, please install a development version as indicated in that comment." }, { "path": "diffusers/examples/kandinsky2_2/text_to_image/requirements.txt", "content": "accelerate>=0.16.0\ntorchvision\ntransformers>=4.25.1\ndatasets\nftfy\ntensorboard\nJinja2\n" }, { "path": "diffusers/examples/kandinsky2_2/text_to_image/train_text_to_image_decoder.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport logging\nimport math\nimport os\nimport shutil\nfrom pathlib import Path\n\nimport accelerate\nimport datasets\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.state import AcceleratorState\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom PIL import Image\nfrom tqdm import tqdm\nfrom transformers import CLIPImageProcessor, CLIPVisionModelWithProjection\nfrom transformers.utils import ContextManagers\n\nimport diffusers\nfrom diffusers import AutoPipelineForText2Image, DDPMScheduler, UNet2DConditionModel, VQModel\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import EMAModel, compute_snr\nfrom diffusers.utils import check_min_version, is_wandb_available, make_image_grid\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\nif is_wandb_available():\n import wandb\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\n\ndef save_model_card(\n args,\n repo_id: str,\n images=None,\n repo_folder=None,\n):\n img_str = \"\"\n if len(images) > 0:\n image_grid = make_image_grid(images, 1, len(args.validation_prompts))\n image_grid.save(os.path.join(repo_folder, \"val_imgs_grid.png\"))\n img_str += \"![val_imgs_grid](./val_imgs_grid.png)\\n\"\n\n yaml = f\"\"\"\n---\nlicense: creativeml-openrail-m\nbase_model: {args.pretrained_decoder_model_name_or_path}\ndatasets:\n- {args.dataset_name}\nprior:\n- {args.pretrained_prior_model_name_or_path}\ntags:\n- kandinsky\n- text-to-image\n- diffusers\n- diffusers-training\ninference: true\n---\n \"\"\"\n model_card = f\"\"\"\n# Finetuning - {repo_id}\n\nThis pipeline was finetuned from **{args.pretrained_decoder_model_name_or_path}** on the **{args.dataset_name}** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: {args.validation_prompts}: \\n\n{img_str}\n\n## Pipeline usage\n\nYou can use the pipeline like so:\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\"{repo_id}\", torch_dtype=torch.float16)\nprompt = \"{args.validation_prompts[0]}\"\nimage = pipeline(prompt).images[0]\nimage.save(\"my_image.png\")\n```\n\n## Training info\n\nThese are the key hyperparameters used during training:\n\n* Epochs: {args.num_train_epochs}\n* Learning rate: {args.learning_rate}\n* Batch size: {args.train_batch_size}\n* Gradient accumulation steps: {args.gradient_accumulation_steps}\n* Image resolution: {args.resolution}\n* Mixed-precision: {args.mixed_precision}\n\n\"\"\"\n wandb_info = \"\"\n if is_wandb_available():\n wandb_run_url = None\n if wandb.run is not None:\n wandb_run_url = wandb.run.url\n\n if wandb_run_url is not None:\n wandb_info = f\"\"\"\nMore information on all the CLI arguments and the environment are available on your [`wandb` run page]({wandb_run_url}).\n\"\"\"\n\n model_card += wandb_info\n\n with open(os.path.join(repo_folder, \"README.md\"), \"w\") as f:\n f.write(yaml + model_card)\n\n\ndef log_validation(vae, image_encoder, image_processor, unet, args, accelerator, weight_dtype, epoch):\n logger.info(\"Running validation... \")\n\n pipeline = AutoPipelineForText2Image.from_pretrained(\n args.pretrained_decoder_model_name_or_path,\n vae=accelerator.unwrap_model(vae),\n prior_image_encoder=accelerator.unwrap_model(image_encoder),\n prior_image_processor=image_processor,\n unet=accelerator.unwrap_model(unet),\n torch_dtype=weight_dtype,\n )\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n if args.enable_xformers_memory_efficient_attention:\n pipeline.enable_xformers_memory_efficient_attention()\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n images = []\n for i in range(len(args.validation_prompts)):\n with torch.autocast(\"cuda\"):\n image = pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]\n\n images.append(image)\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, epoch, dataformats=\"NHWC\")\n elif tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompts[i]}\")\n for i, image in enumerate(images)\n ]\n }\n )\n else:\n logger.warning(f\"image logging not implemented for {tracker.name}\")\n\n del pipeline\n torch.cuda.empty_cache()\n\n return images\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of finetuning Kandinsky 2.2.\")\n parser.add_argument(\n \"--pretrained_decoder_model_name_or_path\",\n type=str,\n default=\"kandinsky-community/kandinsky-2-2-decoder\",\n required=False,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_prior_model_name_or_path\",\n type=str,\n default=\"kandinsky-community/kandinsky-2-2-prior\",\n required=False,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing an image.\"\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--validation_prompts\",\n type=str,\n default=None,\n nargs=\"+\",\n help=(\"A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`.\"),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"kandi_2_2-model-finetuned\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=1, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"learning rate\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--snr_gamma\",\n type=float,\n default=None,\n help=\"SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. \"\n \"More details here: https://arxiv.org/abs/2303.09556.\",\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\"--use_ema\", action=\"store_true\", help=\"Whether to use EMA model.\")\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\n \"--adam_weight_decay\",\n type=float,\n default=0.0,\n required=False,\n help=\"weight decay_to_use\",\n )\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=5,\n help=\"Run validation every X epochs.\",\n )\n parser.add_argument(\n \"--tracker_project_name\",\n type=str,\n default=\"text2image-fine-tune\",\n help=(\n \"The `project_name` argument passed to Accelerator.init_trackers for\"\n \" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator\"\n ),\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n\n return args\n\n\ndef main():\n args = parse_args()\n\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = os.path.join(args.output_dir, args.logging_dir)\n accelerator_project_config = ProjectConfiguration(\n total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir\n )\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_decoder_model_name_or_path, subfolder=\"scheduler\")\n image_processor = CLIPImageProcessor.from_pretrained(\n args.pretrained_prior_model_name_or_path, subfolder=\"image_processor\"\n )\n\n def deepspeed_zero_init_disabled_context_manager():\n \"\"\"\n returns either a context list that includes one that will disable zero.Init or an empty context list\n \"\"\"\n deepspeed_plugin = AcceleratorState().deepspeed_plugin if accelerate.state.is_initialized() else None\n if deepspeed_plugin is None:\n return []\n\n return [deepspeed_plugin.zero3_init_context_manager(enable=False)]\n\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n with ContextManagers(deepspeed_zero_init_disabled_context_manager()):\n vae = VQModel.from_pretrained(\n args.pretrained_decoder_model_name_or_path, subfolder=\"movq\", torch_dtype=weight_dtype\n ).eval()\n image_encoder = CLIPVisionModelWithProjection.from_pretrained(\n args.pretrained_prior_model_name_or_path, subfolder=\"image_encoder\", torch_dtype=weight_dtype\n ).eval()\n unet = UNet2DConditionModel.from_pretrained(args.pretrained_decoder_model_name_or_path, subfolder=\"unet\")\n\n # Freeze vae and image_encoder\n vae.requires_grad_(False)\n image_encoder.requires_grad_(False)\n\n # Set unet to trainable.\n unet.train()\n\n # Create EMA for the unet.\n if args.use_ema:\n ema_unet = UNet2DConditionModel.from_pretrained(args.pretrained_decoder_model_name_or_path, subfolder=\"unet\")\n ema_unet = EMAModel(ema_unet.parameters(), model_cls=UNet2DConditionModel, model_config=ema_unet.config)\n ema_unet.to(accelerator.device)\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if args.use_ema:\n ema_unet.save_pretrained(os.path.join(output_dir, \"unet_ema\"))\n\n for i, model in enumerate(models):\n model.save_pretrained(os.path.join(output_dir, \"unet\"))\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n def load_model_hook(models, input_dir):\n if args.use_ema:\n load_model = EMAModel.from_pretrained(os.path.join(input_dir, \"unet_ema\"), UNet2DConditionModel)\n ema_unet.load_state_dict(load_model.state_dict())\n ema_unet.to(accelerator.device)\n del load_model\n\n for i in range(len(models)):\n # pop models so that they are not loaded again\n model = models.pop()\n\n # load diffusers style into model\n load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder=\"unet\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`\"\n )\n\n optimizer_cls = bnb.optim.AdamW8bit\n else:\n optimizer_cls = torch.optim.AdamW\n\n optimizer = optimizer_cls(\n unet.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n )\n else:\n data_files = {}\n if args.train_data_dir is not None:\n data_files[\"train\"] = os.path.join(args.train_data_dir, \"**\")\n dataset = load_dataset(\n \"imagefolder\",\n data_files=data_files,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(f\"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}\")\n\n def center_crop(image):\n width, height = image.size\n new_size = min(width, height)\n left = (width - new_size) / 2\n top = (height - new_size) / 2\n right = (width + new_size) / 2\n bottom = (height + new_size) / 2\n return image.crop((left, top, right, bottom))\n\n def train_transforms(img):\n img = center_crop(img)\n img = img.resize((args.resolution, args.resolution), resample=Image.BICUBIC, reducing_gap=1)\n img = np.array(img).astype(np.float32) / 127.5 - 1\n img = torch.from_numpy(np.transpose(img, [2, 0, 1]))\n return img\n\n def preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n examples[\"pixel_values\"] = [train_transforms(image) for image in images]\n examples[\"clip_pixel_values\"] = image_processor(images, return_tensors=\"pt\").pixel_values\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n def collate_fn(examples):\n pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n clip_pixel_values = torch.stack([example[\"clip_pixel_values\"] for example in examples])\n clip_pixel_values = clip_pixel_values.to(memory_format=torch.contiguous_format).float()\n return {\"pixel_values\": pixel_values, \"clip_pixel_values\": clip_pixel_values}\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,\n num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,\n )\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n # Move image_encode and vae to gpu and cast to weight_dtype\n image_encoder.to(accelerator.device, dtype=weight_dtype)\n vae.to(accelerator.device, dtype=weight_dtype)\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = dict(vars(args))\n tracker_config.pop(\"validation_prompts\")\n accelerator.init_trackers(args.tracker_project_name, tracker_config)\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n for epoch in range(first_epoch, args.num_train_epochs):\n train_loss = 0.0\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n # Convert images to latent space\n images = batch[\"pixel_values\"].to(weight_dtype)\n clip_images = batch[\"clip_pixel_values\"].to(weight_dtype)\n latents = vae.encode(images).latents\n image_embeds = image_encoder(clip_images).image_embeds\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n target = noise\n\n # Predict the noise residual and compute loss\n added_cond_kwargs = {\"image_embeds\": image_embeds}\n\n model_pred = unet(noisy_latents, timesteps, None, added_cond_kwargs=added_cond_kwargs).sample[:, :4]\n\n if args.snr_gamma is None:\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n else:\n # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.\n # Since we predict the noise instead of x_0, the original formulation is slightly changed.\n # This is discussed in Section 4.2 of the same paper.\n snr = compute_snr(noise_scheduler, timesteps)\n mse_loss_weights = torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(\n dim=1\n )[0]\n if noise_scheduler.config.prediction_type == \"epsilon\":\n mse_loss_weights = mse_loss_weights / snr\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n mse_loss_weights = mse_loss_weights / (snr + 1)\n\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"none\")\n loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights\n loss = loss.mean()\n\n # Gather the losses across all processes for logging (if we use distributed training).\n avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n train_loss += avg_loss.item() / args.gradient_accumulation_steps\n\n # Backpropagate\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n if args.use_ema:\n ema_unet.step(unet.parameters())\n progress_bar.update(1)\n global_step += 1\n accelerator.log({\"train_loss\": train_loss}, step=global_step)\n train_loss = 0.0\n\n if global_step % args.checkpointing_steps == 0:\n if accelerator.is_main_process:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"step_loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompts is not None and epoch % args.validation_epochs == 0:\n if args.use_ema:\n # Store the UNet parameters temporarily and load the EMA parameters to perform inference.\n ema_unet.store(unet.parameters())\n ema_unet.copy_to(unet.parameters())\n log_validation(\n vae,\n image_encoder,\n image_processor,\n unet,\n args,\n accelerator,\n weight_dtype,\n global_step,\n )\n if args.use_ema:\n # Switch back to the original UNet parameters.\n ema_unet.restore(unet.parameters())\n\n # Create the pipeline using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = accelerator.unwrap_model(unet)\n if args.use_ema:\n ema_unet.copy_to(unet.parameters())\n\n pipeline = AutoPipelineForText2Image.from_pretrained(\n args.pretrained_decoder_model_name_or_path,\n vae=vae,\n unet=unet,\n )\n pipeline.decoder_pipe.save_pretrained(args.output_dir)\n\n # Run a final round of inference.\n images = []\n if args.validation_prompts is not None:\n logger.info(\"Running inference for collecting generated images...\")\n pipeline.torch_dtype = weight_dtype\n pipeline.set_progress_bar_config(disable=True)\n pipeline.enable_model_cpu_offload()\n\n if args.enable_xformers_memory_efficient_attention:\n pipeline.enable_xformers_memory_efficient_attention()\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n for i in range(len(args.validation_prompts)):\n with torch.autocast(\"cuda\"):\n image = pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]\n images.append(image)\n\n if args.push_to_hub:\n save_model_card(args, repo_id, images, repo_folder=args.output_dir)\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/kandinsky2_2/text_to_image/train_text_to_image_lora_decoder.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Fine-tuning script for Kandinsky with support for LoRA.\"\"\"\n\nimport argparse\nimport logging\nimport math\nimport os\nimport shutil\nfrom pathlib import Path\n\nimport datasets\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom PIL import Image\nfrom tqdm import tqdm\nfrom transformers import CLIPImageProcessor, CLIPVisionModelWithProjection\n\nimport diffusers\nfrom diffusers import AutoPipelineForText2Image, DDPMScheduler, UNet2DConditionModel, VQModel\nfrom diffusers.loaders import AttnProcsLayers\nfrom diffusers.models.attention_processor import LoRAAttnAddedKVProcessor\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import compute_snr\nfrom diffusers.utils import check_min_version, is_wandb_available\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\n\ndef save_model_card(repo_id: str, images=None, base_model=str, dataset_name=str, repo_folder=None):\n img_str = \"\"\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n img_str += f\"![img_{i}](./image_{i}.png)\\n\"\n\n yaml = f\"\"\"\n---\nlicense: creativeml-openrail-m\nbase_model: {base_model}\ntags:\n- kandinsky\n- text-to-image\n- diffusers\n- diffusers-training\n- lora\ninference: true\n---\n \"\"\"\n model_card = f\"\"\"\n# LoRA text2image fine-tuning - {repo_id}\nThese are LoRA adaption weights for {base_model}. The weights were fine-tuned on the {dataset_name} dataset. You can find some example images in the following. \\n\n{img_str}\n\"\"\"\n with open(os.path.join(repo_folder, \"README.md\"), \"w\") as f:\n f.write(yaml + model_card)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of finetuning Kandinsky 2.2 with LoRA.\")\n parser.add_argument(\n \"--pretrained_decoder_model_name_or_path\",\n type=str,\n default=\"kandinsky-community/kandinsky-2-2-decoder\",\n required=False,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_prior_model_name_or_path\",\n type=str,\n default=\"kandinsky-community/kandinsky-2-2-prior\",\n required=False,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing an image.\"\n )\n parser.add_argument(\n \"--validation_prompt\", type=str, default=None, help=\"A prompt that is sampled during training for inference.\"\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=1,\n help=(\n \"Run fine-tuning validation every X epochs. The validation process consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"kandi_2_2-model-finetuned-lora\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=1, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--snr_gamma\",\n type=float,\n default=None,\n help=\"SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. \"\n \"More details here: https://arxiv.org/abs/2303.09556.\",\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=0.0, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--rank\",\n type=int,\n default=4,\n help=(\"The dimension of the LoRA update matrices.\"),\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n\n return args\n\n\ndef main():\n args = parse_args()\n\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n accelerator_project_config = ProjectConfiguration(\n total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir\n )\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n import wandb\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n # Load scheduler, tokenizer and models.\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_decoder_model_name_or_path, subfolder=\"scheduler\")\n image_processor = CLIPImageProcessor.from_pretrained(\n args.pretrained_prior_model_name_or_path, subfolder=\"image_processor\"\n )\n image_encoder = CLIPVisionModelWithProjection.from_pretrained(\n args.pretrained_prior_model_name_or_path, subfolder=\"image_encoder\"\n )\n\n vae = VQModel.from_pretrained(args.pretrained_decoder_model_name_or_path, subfolder=\"movq\")\n\n unet = UNet2DConditionModel.from_pretrained(args.pretrained_decoder_model_name_or_path, subfolder=\"unet\")\n # freeze parameters of models to save more memory\n unet.requires_grad_(False)\n vae.requires_grad_(False)\n\n image_encoder.requires_grad_(False)\n\n # For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move unet, vae and text_encoder to device and cast to weight_dtype\n unet.to(accelerator.device, dtype=weight_dtype)\n vae.to(accelerator.device, dtype=weight_dtype)\n image_encoder.to(accelerator.device, dtype=weight_dtype)\n\n lora_attn_procs = {}\n for name in unet.attn_processors.keys():\n cross_attention_dim = None if name.endswith(\"attn1.processor\") else unet.config.cross_attention_dim\n if name.startswith(\"mid_block\"):\n hidden_size = unet.config.block_out_channels[-1]\n elif name.startswith(\"up_blocks\"):\n block_id = int(name[len(\"up_blocks.\")])\n hidden_size = list(reversed(unet.config.block_out_channels))[block_id]\n elif name.startswith(\"down_blocks\"):\n block_id = int(name[len(\"down_blocks.\")])\n hidden_size = unet.config.block_out_channels[block_id]\n\n lora_attn_procs[name] = LoRAAttnAddedKVProcessor(\n hidden_size=hidden_size,\n cross_attention_dim=cross_attention_dim,\n rank=args.rank,\n )\n\n unet.set_attn_processor(lora_attn_procs)\n\n lora_layers = AttnProcsLayers(unet.attn_processors)\n\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`\"\n )\n\n optimizer_cls = bnb.optim.AdamW8bit\n else:\n optimizer_cls = torch.optim.AdamW\n\n optimizer = optimizer_cls(\n lora_layers.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n )\n else:\n data_files = {}\n if args.train_data_dir is not None:\n data_files[\"train\"] = os.path.join(args.train_data_dir, \"**\")\n dataset = load_dataset(\n \"imagefolder\",\n data_files=data_files,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(f\"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}\")\n\n def center_crop(image):\n width, height = image.size\n new_size = min(width, height)\n left = (width - new_size) / 2\n top = (height - new_size) / 2\n right = (width + new_size) / 2\n bottom = (height + new_size) / 2\n return image.crop((left, top, right, bottom))\n\n def train_transforms(img):\n img = center_crop(img)\n img = img.resize((args.resolution, args.resolution), resample=Image.BICUBIC, reducing_gap=1)\n img = np.array(img).astype(np.float32) / 127.5 - 1\n img = torch.from_numpy(np.transpose(img, [2, 0, 1]))\n return img\n\n def preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n examples[\"pixel_values\"] = [train_transforms(image) for image in images]\n examples[\"clip_pixel_values\"] = image_processor(images, return_tensors=\"pt\").pixel_values\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n def collate_fn(examples):\n pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n clip_pixel_values = torch.stack([example[\"clip_pixel_values\"] for example in examples])\n clip_pixel_values = clip_pixel_values.to(memory_format=torch.contiguous_format).float()\n return {\"pixel_values\": pixel_values, \"clip_pixel_values\": clip_pixel_values}\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,\n num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,\n )\n # Prepare everything with our `accelerator`.\n lora_layers, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n lora_layers, optimizer, train_dataloader, lr_scheduler\n )\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"text2image-fine-tune\", config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n for epoch in range(first_epoch, args.num_train_epochs):\n unet.train()\n train_loss = 0.0\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n # Convert images to latent space\n images = batch[\"pixel_values\"].to(weight_dtype)\n clip_images = batch[\"clip_pixel_values\"].to(weight_dtype)\n latents = vae.encode(images).latents\n image_embeds = image_encoder(clip_images).image_embeds\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n target = noise\n\n # Predict the noise residual and compute loss\n added_cond_kwargs = {\"image_embeds\": image_embeds}\n\n model_pred = unet(noisy_latents, timesteps, None, added_cond_kwargs=added_cond_kwargs).sample[:, :4]\n\n if args.snr_gamma is None:\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n else:\n # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.\n # Since we predict the noise instead of x_0, the original formulation is slightly changed.\n # This is discussed in Section 4.2 of the same paper.\n snr = compute_snr(noise_scheduler, timesteps)\n mse_loss_weights = torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(\n dim=1\n )[0]\n if noise_scheduler.config.prediction_type == \"epsilon\":\n mse_loss_weights = mse_loss_weights / snr\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n mse_loss_weights = mse_loss_weights / (snr + 1)\n\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"none\")\n loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights\n loss = loss.mean()\n\n # Gather the losses across all processes for logging (if we use distributed training).\n avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n train_loss += avg_loss.item() / args.gradient_accumulation_steps\n\n # Backpropagate\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n params_to_clip = lora_layers.parameters()\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n accelerator.log({\"train_loss\": train_loss}, step=global_step)\n train_loss = 0.0\n\n if global_step % args.checkpointing_steps == 0:\n if accelerator.is_main_process:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"step_loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompt is not None and epoch % args.validation_epochs == 0:\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n # create pipeline\n pipeline = AutoPipelineForText2Image.from_pretrained(\n args.pretrained_decoder_model_name_or_path,\n unet=accelerator.unwrap_model(unet),\n torch_dtype=weight_dtype,\n )\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = torch.Generator(device=accelerator.device)\n if args.seed is not None:\n generator = generator.manual_seed(args.seed)\n images = []\n for _ in range(args.num_validation_images):\n images.append(\n pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0]\n )\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\")\n for i, image in enumerate(images)\n ]\n }\n )\n\n del pipeline\n torch.cuda.empty_cache()\n\n # Save the lora layers\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = unet.to(torch.float32)\n unet.save_attn_procs(args.output_dir)\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n images=images,\n base_model=args.pretrained_decoder_model_name_or_path,\n dataset_name=args.dataset_name,\n repo_folder=args.output_dir,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n # Final inference\n # Load previous pipeline\n pipeline = AutoPipelineForText2Image.from_pretrained(\n args.pretrained_decoder_model_name_or_path, torch_dtype=weight_dtype\n )\n pipeline = pipeline.to(accelerator.device)\n\n # load attention processors\n pipeline.unet.load_attn_procs(args.output_dir)\n\n # run inference\n generator = torch.Generator(device=accelerator.device)\n if args.seed is not None:\n generator = generator.manual_seed(args.seed)\n images = []\n for _ in range(args.num_validation_images):\n images.append(pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0])\n\n if accelerator.is_main_process:\n for tracker in accelerator.trackers:\n if len(images) != 0:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"test\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"test\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\")\n for i, image in enumerate(images)\n ]\n }\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/kandinsky2_2/text_to_image/train_text_to_image_lora_prior.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Fine-tuning script for Stable Diffusion for text2image with support for LoRA.\"\"\"\n\nimport argparse\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom pathlib import Path\n\nimport datasets\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom tqdm import tqdm\nfrom transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection\n\nimport diffusers\nfrom diffusers import AutoPipelineForText2Image, DDPMScheduler, PriorTransformer\nfrom diffusers.loaders import AttnProcsLayers\nfrom diffusers.models.attention_processor import LoRAAttnProcessor\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import compute_snr\nfrom diffusers.utils import check_min_version, is_wandb_available\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\n\ndef save_model_card(repo_id: str, images=None, base_model=str, dataset_name=str, repo_folder=None):\n img_str = \"\"\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n img_str += f\"![img_{i}](./image_{i}.png)\\n\"\n\n yaml = f\"\"\"\n---\nlicense: creativeml-openrail-m\nbase_model: {base_model}\ntags:\n- kandinsky\n- text-to-image\n- diffusers\n- diffusers-training\n- lora\ninference: true\n---\n \"\"\"\n model_card = f\"\"\"\n# LoRA text2image fine-tuning - {repo_id}\nThese are LoRA adaption weights for {base_model}. The weights were fine-tuned on the {dataset_name} dataset. You can find some example images in the following. \\n\n{img_str}\n\"\"\"\n with open(os.path.join(repo_folder, \"README.md\"), \"w\") as f:\n f.write(yaml + model_card)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of finetuning Kandinsky 2.2.\")\n parser.add_argument(\n \"--pretrained_decoder_model_name_or_path\",\n type=str,\n default=\"kandinsky-community/kandinsky-2-2-decoder\",\n required=False,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_prior_model_name_or_path\",\n type=str,\n default=\"kandinsky-community/kandinsky-2-2-prior\",\n required=False,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing an image.\"\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=\"text\",\n help=\"The column of the dataset containing a caption or a list of captions.\",\n )\n parser.add_argument(\n \"--validation_prompt\", type=str, default=None, help=\"A prompt that is sampled during training for inference.\"\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=1,\n help=(\n \"Run fine-tuning validation every X epochs. The validation process consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"kandi_2_2-model-finetuned-lora\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=1, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"learning rate\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--snr_gamma\",\n type=float,\n default=None,\n help=\"SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. \"\n \"More details here: https://arxiv.org/abs/2303.09556.\",\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\n \"--adam_weight_decay\",\n type=float,\n default=0.0,\n required=False,\n help=\"weight decay_to_use\",\n )\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--rank\",\n type=int,\n default=4,\n help=(\"The dimension of the LoRA update matrices.\"),\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n\n return args\n\n\nDATASET_NAME_MAPPING = {\n \"lambdalabs/naruto-blip-captions\": (\"image\", \"text\"),\n}\n\n\ndef main():\n args = parse_args()\n\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(\n total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir\n )\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n import wandb\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n # Load scheduler, image_processor, tokenizer and models.\n noise_scheduler = DDPMScheduler(beta_schedule=\"squaredcos_cap_v2\", prediction_type=\"sample\")\n image_processor = CLIPImageProcessor.from_pretrained(\n args.pretrained_prior_model_name_or_path, subfolder=\"image_processor\"\n )\n tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder=\"tokenizer\")\n image_encoder = CLIPVisionModelWithProjection.from_pretrained(\n args.pretrained_prior_model_name_or_path, subfolder=\"image_encoder\"\n )\n text_encoder = CLIPTextModelWithProjection.from_pretrained(\n args.pretrained_prior_model_name_or_path, subfolder=\"text_encoder\"\n )\n prior = PriorTransformer.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder=\"prior\")\n # freeze parameters of models to save more memory\n image_encoder.requires_grad_(False)\n prior.requires_grad_(False)\n text_encoder.requires_grad_(False)\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move image_encoder, text_encoder and prior to device and cast to weight_dtype\n prior.to(accelerator.device, dtype=weight_dtype)\n image_encoder.to(accelerator.device, dtype=weight_dtype)\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n lora_attn_procs = {}\n for name in prior.attn_processors.keys():\n lora_attn_procs[name] = LoRAAttnProcessor(hidden_size=2048, rank=args.rank)\n\n prior.set_attn_processor(lora_attn_procs)\n lora_layers = AttnProcsLayers(prior.attn_processors)\n\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`\"\n )\n\n optimizer_cls = bnb.optim.AdamW8bit\n else:\n optimizer_cls = torch.optim.AdamW\n\n optimizer = optimizer_cls(\n lora_layers.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n )\n else:\n data_files = {}\n if args.train_data_dir is not None:\n data_files[\"train\"] = os.path.join(args.train_data_dir, \"**\")\n dataset = load_dataset(\n \"imagefolder\",\n data_files=data_files,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)\n if args.image_column is None:\n image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.caption_column is None:\n caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n else:\n caption_column = args.caption_column\n if caption_column not in column_names:\n raise ValueError(\n f\"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}\"\n )\n\n # Preprocessing the datasets.\n # We need to tokenize input captions and transform the images.\n def tokenize_captions(examples, is_train=True):\n captions = []\n for caption in examples[caption_column]:\n if isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n else:\n raise ValueError(\n f\"Caption column `{caption_column}` should contain either strings or lists of strings.\"\n )\n inputs = tokenizer(\n captions, max_length=tokenizer.model_max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\"\n )\n text_input_ids = inputs.input_ids\n text_mask = inputs.attention_mask.bool()\n return text_input_ids, text_mask\n\n def preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n examples[\"clip_pixel_values\"] = image_processor(images, return_tensors=\"pt\").pixel_values\n examples[\"text_input_ids\"], examples[\"text_mask\"] = tokenize_captions(examples)\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n def collate_fn(examples):\n clip_pixel_values = torch.stack([example[\"clip_pixel_values\"] for example in examples])\n clip_pixel_values = clip_pixel_values.to(memory_format=torch.contiguous_format).float()\n text_input_ids = torch.stack([example[\"text_input_ids\"] for example in examples])\n text_mask = torch.stack([example[\"text_mask\"] for example in examples])\n return {\"clip_pixel_values\": clip_pixel_values, \"text_input_ids\": text_input_ids, \"text_mask\": text_mask}\n\n # DataLoaders creation:\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,\n num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,\n )\n clip_mean = prior.clip_mean.clone()\n clip_std = prior.clip_std.clone()\n lora_layers, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n lora_layers, optimizer, train_dataloader, lr_scheduler\n )\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"text2image-fine-tune\", config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n clip_mean = clip_mean.to(weight_dtype).to(accelerator.device)\n clip_std = clip_std.to(weight_dtype).to(accelerator.device)\n\n for epoch in range(first_epoch, args.num_train_epochs):\n prior.train()\n train_loss = 0.0\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(prior):\n # Convert images to latent space\n text_input_ids, text_mask, clip_images = (\n batch[\"text_input_ids\"],\n batch[\"text_mask\"],\n batch[\"clip_pixel_values\"].to(weight_dtype),\n )\n with torch.no_grad():\n text_encoder_output = text_encoder(text_input_ids)\n prompt_embeds = text_encoder_output.text_embeds\n text_encoder_hidden_states = text_encoder_output.last_hidden_state\n\n image_embeds = image_encoder(clip_images).image_embeds\n # Sample noise that we'll add to the image_embeds\n noise = torch.randn_like(image_embeds)\n bsz = image_embeds.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=image_embeds.device\n )\n timesteps = timesteps.long()\n image_embeds = (image_embeds - clip_mean) / clip_std\n noisy_latents = noise_scheduler.add_noise(image_embeds, noise, timesteps)\n\n target = image_embeds\n\n # Predict the noise residual and compute loss\n model_pred = prior(\n noisy_latents,\n timestep=timesteps,\n proj_embedding=prompt_embeds,\n encoder_hidden_states=text_encoder_hidden_states,\n attention_mask=text_mask,\n ).predicted_image_embedding\n\n if args.snr_gamma is None:\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n else:\n # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.\n # Since we predict the noise instead of x_0, the original formulation is slightly changed.\n # This is discussed in Section 4.2 of the same paper.\n snr = compute_snr(noise_scheduler, timesteps)\n mse_loss_weights = torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(\n dim=1\n )[0]\n if noise_scheduler.config.prediction_type == \"epsilon\":\n mse_loss_weights = mse_loss_weights / snr\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n mse_loss_weights = mse_loss_weights / (snr + 1)\n\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"none\")\n loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights\n loss = loss.mean()\n\n # Gather the losses across all processes for logging (if we use distributed training).\n avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n train_loss += avg_loss.item() / args.gradient_accumulation_steps\n\n # Backpropagate\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(lora_layers.parameters(), args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n accelerator.log({\"train_loss\": train_loss}, step=global_step)\n train_loss = 0.0\n\n if global_step % args.checkpointing_steps == 0:\n if accelerator.is_main_process:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"step_loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompt is not None and epoch % args.validation_epochs == 0:\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n # create pipeline\n pipeline = AutoPipelineForText2Image.from_pretrained(\n args.pretrained_decoder_model_name_or_path,\n prior_prior=accelerator.unwrap_model(prior),\n torch_dtype=weight_dtype,\n )\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = torch.Generator(device=accelerator.device)\n if args.seed is not None:\n generator = generator.manual_seed(args.seed)\n images = []\n for _ in range(args.num_validation_images):\n images.append(\n pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0]\n )\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\")\n for i, image in enumerate(images)\n ]\n }\n )\n\n del pipeline\n torch.cuda.empty_cache()\n\n # Save the lora layers\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n prior = prior.to(torch.float32)\n prior.save_attn_procs(args.output_dir)\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n images=images,\n base_model=args.pretrained_prior_model_name_or_path,\n dataset_name=args.dataset_name,\n repo_folder=args.output_dir,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n # Final inference\n # Load previous pipeline\n pipeline = AutoPipelineForText2Image.from_pretrained(\n args.pretrained_decoder_model_name_or_path, torch_dtype=weight_dtype\n )\n pipeline = pipeline.to(accelerator.device)\n\n # load attention processors\n pipeline.prior_prior.load_attn_procs(args.output_dir)\n\n # run inference\n generator = torch.Generator(device=accelerator.device)\n if args.seed is not None:\n generator = generator.manual_seed(args.seed)\n images = []\n for _ in range(args.num_validation_images):\n images.append(pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0])\n\n if accelerator.is_main_process:\n for tracker in accelerator.trackers:\n if len(images) != 0:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"test\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"test\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\")\n for i, image in enumerate(images)\n ]\n }\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/kandinsky2_2/text_to_image/train_text_to_image_prior.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom pathlib import Path\n\nimport accelerate\nimport datasets\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.state import AcceleratorState\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom tqdm import tqdm\nfrom transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection\nfrom transformers.utils import ContextManagers\n\nimport diffusers\nfrom diffusers import AutoPipelineForText2Image, DDPMScheduler, PriorTransformer\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import EMAModel, compute_snr\nfrom diffusers.utils import check_min_version, is_wandb_available, make_image_grid\n\n\nif is_wandb_available():\n import wandb\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\nDATASET_NAME_MAPPING = {\n \"lambdalabs/naruto-blip-captions\": (\"image\", \"text\"),\n}\n\n\ndef save_model_card(\n args,\n repo_id: str,\n images=None,\n repo_folder=None,\n):\n img_str = \"\"\n if len(images) > 0:\n image_grid = make_image_grid(images, 1, len(args.validation_prompts))\n image_grid.save(os.path.join(repo_folder, \"val_imgs_grid.png\"))\n img_str += \"![val_imgs_grid](./val_imgs_grid.png)\\n\"\n\n yaml = f\"\"\"\n---\nlicense: creativeml-openrail-m\nbase_model: {args.pretrained_prior_model_name_or_path}\ndatasets:\n- {args.dataset_name}\ntags:\n- kandinsky\n- text-to-image\n- diffusers\n- diffusers-training\ninference: true\n---\n \"\"\"\n model_card = f\"\"\"\n# Finetuning - {repo_id}\n\nThis pipeline was finetuned from **{args.pretrained_prior_model_name_or_path}** on the **{args.dataset_name}** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: {args.validation_prompts}: \\n\n{img_str}\n\n## Pipeline usage\n\nYou can use the pipeline like so:\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipe_prior = DiffusionPipeline.from_pretrained(\"{repo_id}\", torch_dtype=torch.float16)\npipe_t2i = DiffusionPipeline.from_pretrained(\"{args.pretrained_decoder_model_name_or_path}\", torch_dtype=torch.float16)\nprompt = \"{args.validation_prompts[0]}\"\nimage_embeds, negative_image_embeds = pipe_prior(prompt, guidance_scale=1.0).to_tuple()\nimage = pipe_t2i(image_embeds=image_embeds, negative_image_embeds=negative_image_embeds).images[0]\nimage.save(\"my_image.png\")\n```\n\n## Training info\n\nThese are the key hyperparameters used during training:\n\n* Epochs: {args.num_train_epochs}\n* Learning rate: {args.learning_rate}\n* Batch size: {args.train_batch_size}\n* Gradient accumulation steps: {args.gradient_accumulation_steps}\n* Image resolution: {args.resolution}\n* Mixed-precision: {args.mixed_precision}\n\n\"\"\"\n wandb_info = \"\"\n if is_wandb_available():\n wandb_run_url = None\n if wandb.run is not None:\n wandb_run_url = wandb.run.url\n\n if wandb_run_url is not None:\n wandb_info = f\"\"\"\nMore information on all the CLI arguments and the environment are available on your [`wandb` run page]({wandb_run_url}).\n\"\"\"\n\n model_card += wandb_info\n\n with open(os.path.join(repo_folder, \"README.md\"), \"w\") as f:\n f.write(yaml + model_card)\n\n\ndef log_validation(\n image_encoder, image_processor, text_encoder, tokenizer, prior, args, accelerator, weight_dtype, epoch\n):\n logger.info(\"Running validation... \")\n\n pipeline = AutoPipelineForText2Image.from_pretrained(\n args.pretrained_decoder_model_name_or_path,\n prior_image_encoder=accelerator.unwrap_model(image_encoder),\n prior_image_processor=image_processor,\n prior_text_encoder=accelerator.unwrap_model(text_encoder),\n prior_tokenizer=tokenizer,\n prior_prior=accelerator.unwrap_model(prior),\n torch_dtype=weight_dtype,\n )\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n images = []\n for i in range(len(args.validation_prompts)):\n with torch.autocast(\"cuda\"):\n image = pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]\n\n images.append(image)\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, epoch, dataformats=\"NHWC\")\n elif tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompts[i]}\")\n for i, image in enumerate(images)\n ]\n }\n )\n else:\n logger.warning(f\"image logging not implemented for {tracker.name}\")\n\n del pipeline\n torch.cuda.empty_cache()\n\n return images\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of finetuning Kandinsky 2.2.\")\n parser.add_argument(\n \"--pretrained_decoder_model_name_or_path\",\n type=str,\n default=\"kandinsky-community/kandinsky-2-2-decoder\",\n required=False,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_prior_model_name_or_path\",\n type=str,\n default=\"kandinsky-community/kandinsky-2-2-prior\",\n required=False,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing an image.\"\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=\"text\",\n help=\"The column of the dataset containing a caption or a list of captions.\",\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--validation_prompts\",\n type=str,\n default=None,\n nargs=\"+\",\n help=(\"A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`.\"),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"kandi_2_2-model-finetuned\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=1, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"learning rate\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--snr_gamma\",\n type=float,\n default=None,\n help=\"SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. \"\n \"More details here: https://arxiv.org/abs/2303.09556.\",\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\"--use_ema\", action=\"store_true\", help=\"Whether to use EMA model.\")\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\n \"--adam_weight_decay\",\n type=float,\n default=0.0,\n required=False,\n help=\"weight decay_to_use\",\n )\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=5,\n help=\"Run validation every X epochs.\",\n )\n parser.add_argument(\n \"--tracker_project_name\",\n type=str,\n default=\"text2image-fine-tune\",\n help=(\n \"The `project_name` argument passed to Accelerator.init_trackers for\"\n \" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator\"\n ),\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n\n return args\n\n\ndef main():\n args = parse_args()\n\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = os.path.join(args.output_dir, args.logging_dir)\n accelerator_project_config = ProjectConfiguration(\n total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir\n )\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load scheduler, image_processor, tokenizer and models.\n noise_scheduler = DDPMScheduler(beta_schedule=\"squaredcos_cap_v2\", prediction_type=\"sample\")\n image_processor = CLIPImageProcessor.from_pretrained(\n args.pretrained_prior_model_name_or_path, subfolder=\"image_processor\"\n )\n tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder=\"tokenizer\")\n\n def deepspeed_zero_init_disabled_context_manager():\n \"\"\"\n returns either a context list that includes one that will disable zero.Init or an empty context list\n \"\"\"\n deepspeed_plugin = AcceleratorState().deepspeed_plugin if accelerate.state.is_initialized() else None\n if deepspeed_plugin is None:\n return []\n\n return [deepspeed_plugin.zero3_init_context_manager(enable=False)]\n\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n with ContextManagers(deepspeed_zero_init_disabled_context_manager()):\n image_encoder = CLIPVisionModelWithProjection.from_pretrained(\n args.pretrained_prior_model_name_or_path, subfolder=\"image_encoder\", torch_dtype=weight_dtype\n ).eval()\n text_encoder = CLIPTextModelWithProjection.from_pretrained(\n args.pretrained_prior_model_name_or_path, subfolder=\"text_encoder\", torch_dtype=weight_dtype\n ).eval()\n\n prior = PriorTransformer.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder=\"prior\")\n\n # Freeze text_encoder and image_encoder\n text_encoder.requires_grad_(False)\n image_encoder.requires_grad_(False)\n\n # Set prior to trainable.\n prior.train()\n\n # Create EMA for the prior.\n if args.use_ema:\n ema_prior = PriorTransformer.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder=\"prior\")\n ema_prior = EMAModel(ema_prior.parameters(), model_cls=PriorTransformer, model_config=ema_prior.config)\n ema_prior.to(accelerator.device)\n\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if args.use_ema:\n ema_prior.save_pretrained(os.path.join(output_dir, \"prior_ema\"))\n\n for i, model in enumerate(models):\n model.save_pretrained(os.path.join(output_dir, \"prior\"))\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n def load_model_hook(models, input_dir):\n if args.use_ema:\n load_model = EMAModel.from_pretrained(os.path.join(input_dir, \"prior_ema\"), PriorTransformer)\n ema_prior.load_state_dict(load_model.state_dict())\n ema_prior.to(accelerator.device)\n del load_model\n\n for i in range(len(models)):\n # pop models so that they are not loaded again\n model = models.pop()\n\n # load diffusers style into model\n load_model = PriorTransformer.from_pretrained(input_dir, subfolder=\"prior\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`\"\n )\n\n optimizer_cls = bnb.optim.AdamW8bit\n else:\n optimizer_cls = torch.optim.AdamW\n optimizer = optimizer_cls(\n prior.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n )\n else:\n data_files = {}\n if args.train_data_dir is not None:\n data_files[\"train\"] = os.path.join(args.train_data_dir, \"**\")\n dataset = load_dataset(\n \"imagefolder\",\n data_files=data_files,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)\n if args.image_column is None:\n image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.caption_column is None:\n caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n else:\n caption_column = args.caption_column\n if caption_column not in column_names:\n raise ValueError(\n f\"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}\"\n )\n\n # Preprocessing the datasets.\n # We need to tokenize input captions and transform the images.\n def tokenize_captions(examples, is_train=True):\n captions = []\n for caption in examples[caption_column]:\n if isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n else:\n raise ValueError(\n f\"Caption column `{caption_column}` should contain either strings or lists of strings.\"\n )\n inputs = tokenizer(\n captions, max_length=tokenizer.model_max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\"\n )\n text_input_ids = inputs.input_ids\n text_mask = inputs.attention_mask.bool()\n return text_input_ids, text_mask\n\n def preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n examples[\"clip_pixel_values\"] = image_processor(images, return_tensors=\"pt\").pixel_values\n examples[\"text_input_ids\"], examples[\"text_mask\"] = tokenize_captions(examples)\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n def collate_fn(examples):\n clip_pixel_values = torch.stack([example[\"clip_pixel_values\"] for example in examples])\n clip_pixel_values = clip_pixel_values.to(memory_format=torch.contiguous_format).float()\n text_input_ids = torch.stack([example[\"text_input_ids\"] for example in examples])\n text_mask = torch.stack([example[\"text_mask\"] for example in examples])\n return {\"clip_pixel_values\": clip_pixel_values, \"text_input_ids\": text_input_ids, \"text_mask\": text_mask}\n\n # DataLoaders creation:\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,\n num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,\n )\n\n clip_mean = prior.clip_mean.clone()\n clip_std = prior.clip_std.clone()\n\n prior, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n prior, optimizer, train_dataloader, lr_scheduler\n )\n\n image_encoder.to(accelerator.device, dtype=weight_dtype)\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = dict(vars(args))\n tracker_config.pop(\"validation_prompts\")\n accelerator.init_trackers(args.tracker_project_name, tracker_config)\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n clip_mean = clip_mean.to(weight_dtype).to(accelerator.device)\n clip_std = clip_std.to(weight_dtype).to(accelerator.device)\n\n for epoch in range(first_epoch, args.num_train_epochs):\n train_loss = 0.0\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(prior):\n # Convert images to latent space\n text_input_ids, text_mask, clip_images = (\n batch[\"text_input_ids\"],\n batch[\"text_mask\"],\n batch[\"clip_pixel_values\"].to(weight_dtype),\n )\n with torch.no_grad():\n text_encoder_output = text_encoder(text_input_ids)\n prompt_embeds = text_encoder_output.text_embeds\n text_encoder_hidden_states = text_encoder_output.last_hidden_state\n\n image_embeds = image_encoder(clip_images).image_embeds\n # Sample noise that we'll add to the image_embeds\n noise = torch.randn_like(image_embeds)\n bsz = image_embeds.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=image_embeds.device\n )\n timesteps = timesteps.long()\n image_embeds = (image_embeds - clip_mean) / clip_std\n noisy_latents = noise_scheduler.add_noise(image_embeds, noise, timesteps)\n\n target = image_embeds\n\n # Predict the noise residual and compute loss\n model_pred = prior(\n noisy_latents,\n timestep=timesteps,\n proj_embedding=prompt_embeds,\n encoder_hidden_states=text_encoder_hidden_states,\n attention_mask=text_mask,\n ).predicted_image_embedding\n\n if args.snr_gamma is None:\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n else:\n # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.\n # Since we predict the noise instead of x_0, the original formulation is slightly changed.\n # This is discussed in Section 4.2 of the same paper.\n snr = compute_snr(noise_scheduler, timesteps)\n mse_loss_weights = torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(\n dim=1\n )[0]\n if noise_scheduler.config.prediction_type == \"epsilon\":\n mse_loss_weights = mse_loss_weights / snr\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n mse_loss_weights = mse_loss_weights / (snr + 1)\n\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"none\")\n loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights\n loss = loss.mean()\n\n # Gather the losses across all processes for logging (if we use distributed training).\n avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n train_loss += avg_loss.item() / args.gradient_accumulation_steps\n\n # Backpropagate\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(prior.parameters(), args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n if args.use_ema:\n ema_prior.step(prior.parameters())\n progress_bar.update(1)\n global_step += 1\n accelerator.log({\"train_loss\": train_loss}, step=global_step)\n train_loss = 0.0\n\n if global_step % args.checkpointing_steps == 0:\n if accelerator.is_main_process:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"step_loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompts is not None and epoch % args.validation_epochs == 0:\n if args.use_ema:\n # Store the UNet parameters temporarily and load the EMA parameters to perform inference.\n ema_prior.store(prior.parameters())\n ema_prior.copy_to(prior.parameters())\n log_validation(\n image_encoder,\n image_processor,\n text_encoder,\n tokenizer,\n prior,\n args,\n accelerator,\n weight_dtype,\n global_step,\n )\n if args.use_ema:\n # Switch back to the original UNet parameters.\n ema_prior.restore(prior.parameters())\n\n # Create the pipeline using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n prior = accelerator.unwrap_model(prior)\n if args.use_ema:\n ema_prior.copy_to(prior.parameters())\n\n pipeline = AutoPipelineForText2Image.from_pretrained(\n args.pretrained_decoder_model_name_or_path,\n prior_image_encoder=image_encoder,\n prior_text_encoder=text_encoder,\n prior_prior=prior,\n )\n pipeline.prior_pipe.save_pretrained(args.output_dir)\n\n # Run a final round of inference.\n images = []\n if args.validation_prompts is not None:\n logger.info(\"Running inference for collecting generated images...\")\n pipeline = pipeline.to(accelerator.device)\n pipeline.torch_dtype = weight_dtype\n pipeline.set_progress_bar_config(disable=True)\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n for i in range(len(args.validation_prompts)):\n with torch.autocast(\"cuda\"):\n image = pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]\n images.append(image)\n\n if args.push_to_hub:\n save_model_card(args, repo_id, images, repo_folder=args.output_dir)\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/reinforcement_learning/README.md", "content": "# Overview\n\nThese examples show how to run [Diffuser](https://arxiv.org/abs/2205.09991) in Diffusers.\nThere are two ways to use the script, `run_diffuser_locomotion.py`.\n\nThe key option is a change of the variable `n_guide_steps`.\nWhen `n_guide_steps=0`, the trajectories are sampled from the diffusion model, but not fine-tuned to maximize reward in the environment.\nBy default, `n_guide_steps=2` to match the original implementation.\n\n\nYou will need some RL specific requirements to run the examples:\n\n```sh\npip install -f https://download.pytorch.org/whl/torch_stable.html \\\n free-mujoco-py \\\n einops \\\n gym==0.24.1 \\\n protobuf==3.20.1 \\\n git+https://github.com/rail-berkeley/d4rl.git \\\n mediapy \\\n Pillow==9.0.0\n```\n" }, { "path": "diffusers/examples/reinforcement_learning/run_diffuser_locomotion.py", "content": "import d4rl # noqa\nimport gym\nimport tqdm\nfrom diffusers.experimental import ValueGuidedRLPipeline\n\n\nconfig = {\n \"n_samples\": 64,\n \"horizon\": 32,\n \"num_inference_steps\": 20,\n \"n_guide_steps\": 2, # can set to 0 for faster sampling, does not use value network\n \"scale_grad_by_std\": True,\n \"scale\": 0.1,\n \"eta\": 0.0,\n \"t_grad_cutoff\": 2,\n \"device\": \"cpu\",\n}\n\n\nif __name__ == \"__main__\":\n env_name = \"hopper-medium-v2\"\n env = gym.make(env_name)\n\n pipeline = ValueGuidedRLPipeline.from_pretrained(\n \"bglick13/hopper-medium-v2-value-function-hor32\",\n env=env,\n )\n\n env.seed(0)\n obs = env.reset()\n total_reward = 0\n total_score = 0\n T = 1000\n rollout = [obs.copy()]\n try:\n for t in tqdm.tqdm(range(T)):\n # call the policy\n denorm_actions = pipeline(obs, planning_horizon=32)\n\n # execute action in environment\n next_observation, reward, terminal, _ = env.step(denorm_actions)\n score = env.get_normalized_score(total_reward)\n\n # update return\n total_reward += reward\n total_score += score\n print(\n f\"Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:\"\n f\" {total_score}\"\n )\n\n # save observations for rendering\n rollout.append(next_observation.copy())\n\n obs = next_observation\n except KeyboardInterrupt:\n pass\n\n print(f\"Total reward: {total_reward}\")\n" }, { "path": "diffusers/examples/research_projects/README.md", "content": "# Research projects\n\nThis folder contains various research projects using 🧨 Diffusers.\nThey are not really maintained by the core maintainers of this library and often require a specific version of Diffusers that is indicated in the requirements file of each folder.\nUpdating them to the most recent version of the library will require some work.\n\nTo use any of them, just run the command\n\n```sh\npip install -r requirements.txt\n```\ninside the folder of your choice.\n\nIf you need help with any of those, please open an issue where you directly ping the author(s), as indicated at the top of the README of each folder.\n" }, { "path": "diffusers/examples/research_projects/colossalai/README.md", "content": "# [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) by [colossalai](https://github.com/hpcaitech/ColossalAI.git)\n\n[DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text2image models like stable diffusion given just a few(3~5) images of a subject.\nThe `train_dreambooth_colossalai.py` script shows how to implement the training procedure and adapt it for stable diffusion.\n\nBy accommodating model data in CPU and GPU and moving the data to the computing device when necessary, [Gemini](https://www.colossalai.org/docs/advanced_tutorials/meet_gemini), the Heterogeneous Memory Manager of [Colossal-AI](https://github.com/hpcaitech/ColossalAI) can breakthrough the GPU memory wall by using GPU and CPU memory (composed of CPU DRAM or nvme SSD memory) together at the same time. Moreover, the model scale can be further improved by combining heterogeneous training with the other parallel approaches, such as data parallel, tensor parallel and pipeline parallel.\n\n## Installing the dependencies\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n```bash\npip install -r requirements.txt\n```\n\n## Install [ColossalAI](https://github.com/hpcaitech/ColossalAI.git)\n\n**From PyPI**\n```bash\npip install colossalai\n```\n\n**From source**\n\n```bash\ngit clone https://github.com/hpcaitech/ColossalAI.git\ncd ColossalAI\n\n# install colossalai\npip install .\n```\n\n## Dataset for Teyvat BLIP captions\nDataset used to train [Teyvat characters text to image model](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion).\n\nBLIP generated captions for characters images from [genshin-impact fandom wiki](https://genshin-impact.fandom.com/wiki/Character#Playable_Characters)and [biligame wiki for genshin impact](https://wiki.biligame.com/ys/%E8%A7%92%E8%89%B2).\n\nFor each row the dataset contains `image` and `text` keys. `image` is a varying size PIL png, and `text` is the accompanying text caption. Only a train split is provided.\n\nThe `text` include the tag `Teyvat`, `Name`,`Element`, `Weapon`, `Region`, `Model type`, and `Description`, the `Description` is captioned with the [pre-trained BLIP model](https://github.com/salesforce/BLIP).\n\n## Training\n\nThe argument `placement` can be `cpu`, `auto`, `cuda`, with `cpu` the GPU RAM required can be minimized to 4GB but will deceleration, with `cuda` you can also reduce GPU memory by half but accelerated training， with `auto` a more balanced solution for speed and memory can be obtained。\n\n**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport INSTANCE_DIR=\"path-to-instance-images\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\ntorchrun --nproc_per_node 2 train_dreambooth_colossalai.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --instance_prompt=\"a photo of sks dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --learning_rate=5e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --max_train_steps=400 \\\n --placement=\"cuda\"\n```\n\n\n### Training with prior-preservation loss\n\nPrior-preservation is used to avoid overfitting and language-drift. Refer to the paper to learn more about it. For prior-preservation we first generate images using the model with a class prompt and then use those during training along with our data.\nAccording to the paper, it's recommended to generate `num_epochs * num_samples` images for prior-preservation. 200-300 works well for most cases. The `num_class_images` flag sets the number of images to generate with the class prompt. You can place existing images in `class_data_dir`, and the training script will generate any additional images so that `num_class_images` are present in `class_data_dir` during training time.\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport INSTANCE_DIR=\"path-to-instance-images\"\nexport CLASS_DIR=\"path-to-class-images\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\ntorchrun --nproc_per_node 2 train_dreambooth_colossalai.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --class_data_dir=$CLASS_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --with_prior_preservation --prior_loss_weight=1.0 \\\n --instance_prompt=\"a photo of sks dog\" \\\n --class_prompt=\"a photo of dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --learning_rate=5e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --max_train_steps=800 \\\n --placement=\"cuda\"\n```\n\n## Inference\n\nOnce you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline`. Make sure to include the `identifier`(e.g. sks in above example) in your prompt.\n\n```python\nfrom diffusers import StableDiffusionPipeline\nimport torch\n\nmodel_id = \"path-to-save-model\"\npipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(\"cuda\")\n\nprompt = \"A photo of sks dog in a bucket\"\nimage = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]\n\nimage.save(\"dog-bucket.png\")\n```\n" }, { "path": "diffusers/examples/research_projects/colossalai/inference.py", "content": "import torch\n\nfrom diffusers import StableDiffusionPipeline\n\n\nmodel_id = \"path-to-your-trained-model\"\npipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(\"cuda\")\n\nprompt = \"A photo of sks dog in a bucket\"\nimage = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]\n\nimage.save(\"dog-bucket.png\")\n" }, { "path": "diffusers/examples/research_projects/colossalai/requirement.txt", "content": "diffusers\ntorch\ntorchvision\nftfy\ntensorboard\nJinja2\ntransformers" }, { "path": "diffusers/examples/research_projects/colossalai/train_dreambooth_colossalai.py", "content": "import argparse\nimport math\nimport os\nfrom pathlib import Path\n\nimport colossalai\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nfrom colossalai.context.parallel_mode import ParallelMode\nfrom colossalai.core import global_context as gpc\nfrom colossalai.logging import disable_existing_loggers, get_dist_logger\nfrom colossalai.nn.optimizer.gemini_optimizer import GeminiAdamOptimizer\nfrom colossalai.nn.parallel.utils import get_static_torch_model\nfrom colossalai.utils import get_current_device\nfrom colossalai.utils.model.colo_init_context import ColoInitContext\nfrom huggingface_hub import create_repo, upload_folder\nfrom huggingface_hub.utils import insecure_hashlib\nfrom PIL import Image\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nfrom diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel\nfrom diffusers.optimization import get_scheduler\n\n\ndisable_existing_loggers()\nlogger = get_dist_logger()\n\n\ndef import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path,\n subfolder=\"text_encoder\",\n revision=args.revision,\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"RobertaSeriesModelWithTransformation\":\n from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation\n\n return RobertaSeriesModelWithTransformation\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--tokenizer_name\",\n type=str,\n default=None,\n help=\"Pretrained tokenizer name or path if not the same as model_name\",\n )\n parser.add_argument(\n \"--instance_data_dir\",\n type=str,\n default=None,\n required=True,\n help=\"A folder containing the training data of instance images.\",\n )\n parser.add_argument(\n \"--class_data_dir\",\n type=str,\n default=None,\n required=False,\n help=\"A folder containing the training data of class images.\",\n )\n parser.add_argument(\n \"--instance_prompt\",\n type=str,\n default=\"a photo of sks dog\",\n required=False,\n help=\"The prompt with identifier specifying the instance\",\n )\n parser.add_argument(\n \"--class_prompt\",\n type=str,\n default=None,\n help=\"The prompt to specify images in the same class as provided instance images.\",\n )\n parser.add_argument(\n \"--with_prior_preservation\",\n default=False,\n action=\"store_true\",\n help=\"Flag to add prior preservation loss.\",\n )\n parser.add_argument(\"--prior_loss_weight\", type=float, default=1.0, help=\"The weight of prior preservation loss.\")\n parser.add_argument(\n \"--num_class_images\",\n type=int,\n default=100,\n help=(\n \"Minimal class images for prior preservation loss. If there are not enough images already present in\"\n \" class_data_dir, additional images will be sampled with class_prompt.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"text-inversion-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--placement\",\n type=str,\n default=\"cpu\",\n help=\"Placement Policy for Gemini. Valid when using colossalai as dist plan.\",\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\n \"--sample_batch_size\", type=int, default=4, help=\"Batch size (per device) for sampling images.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\"--save_steps\", type=int, default=500, help=\"Save checkpoint every X updates steps.\")\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=5e-6,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.with_prior_preservation:\n if args.class_data_dir is None:\n raise ValueError(\"You must specify a data directory for class images.\")\n if args.class_prompt is None:\n raise ValueError(\"You must specify prompt for class images.\")\n else:\n if args.class_data_dir is not None:\n logger.warning(\"You need not use --class_data_dir without --with_prior_preservation.\")\n if args.class_prompt is not None:\n logger.warning(\"You need not use --class_prompt without --with_prior_preservation.\")\n\n return args\n\n\nclass DreamBoothDataset(Dataset):\n \"\"\"\n A dataset to prepare the instance and class images with the prompts for fine-tuning the model.\n It pre-processes the images and the tokenizes prompts.\n \"\"\"\n\n def __init__(\n self,\n instance_data_root,\n instance_prompt,\n tokenizer,\n class_data_root=None,\n class_prompt=None,\n size=512,\n center_crop=False,\n ):\n self.size = size\n self.center_crop = center_crop\n self.tokenizer = tokenizer\n\n self.instance_data_root = Path(instance_data_root)\n if not self.instance_data_root.exists():\n raise ValueError(\"Instance images root doesn't exists.\")\n\n self.instance_images_path = list(Path(instance_data_root).iterdir())\n self.num_instance_images = len(self.instance_images_path)\n self.instance_prompt = instance_prompt\n self._length = self.num_instance_images\n\n if class_data_root is not None:\n self.class_data_root = Path(class_data_root)\n self.class_data_root.mkdir(parents=True, exist_ok=True)\n self.class_images_path = list(self.class_data_root.iterdir())\n self.num_class_images = len(self.class_images_path)\n self._length = max(self.num_class_images, self.num_instance_images)\n self.class_prompt = class_prompt\n else:\n self.class_data_root = None\n\n self.image_transforms = transforms.Compose(\n [\n transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, index):\n example = {}\n instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])\n if not instance_image.mode == \"RGB\":\n instance_image = instance_image.convert(\"RGB\")\n example[\"instance_images\"] = self.image_transforms(instance_image)\n example[\"instance_prompt_ids\"] = self.tokenizer(\n self.instance_prompt,\n padding=\"do_not_pad\",\n truncation=True,\n max_length=self.tokenizer.model_max_length,\n ).input_ids\n\n if self.class_data_root:\n class_image = Image.open(self.class_images_path[index % self.num_class_images])\n if not class_image.mode == \"RGB\":\n class_image = class_image.convert(\"RGB\")\n example[\"class_images\"] = self.image_transforms(class_image)\n example[\"class_prompt_ids\"] = self.tokenizer(\n self.class_prompt,\n padding=\"do_not_pad\",\n truncation=True,\n max_length=self.tokenizer.model_max_length,\n ).input_ids\n\n return example\n\n\nclass PromptDataset(Dataset):\n \"\"\"A simple dataset to prepare the prompts to generate class images on multiple GPUs.\"\"\"\n\n def __init__(self, prompt, num_samples):\n self.prompt = prompt\n self.num_samples = num_samples\n\n def __len__(self):\n return self.num_samples\n\n def __getitem__(self, index):\n example = {}\n example[\"prompt\"] = self.prompt\n example[\"index\"] = index\n return example\n\n\n# Gemini + ZeRO DDP\ndef gemini_zero_dpp(model: torch.nn.Module, placememt_policy: str = \"auto\"):\n from colossalai.nn.parallel import GeminiDDP\n\n model = GeminiDDP(\n model, device=get_current_device(), placement_policy=placememt_policy, pin_memory=True, search_range_mb=64\n )\n return model\n\n\ndef main(args):\n if args.seed is None:\n colossalai.launch_from_torch(config={})\n else:\n colossalai.launch_from_torch(config={}, seed=args.seed)\n\n local_rank = gpc.get_local_rank(ParallelMode.DATA)\n world_size = gpc.get_world_size(ParallelMode.DATA)\n\n if args.with_prior_preservation:\n class_images_dir = Path(args.class_data_dir)\n if not class_images_dir.exists():\n class_images_dir.mkdir(parents=True)\n cur_class_images = len(list(class_images_dir.iterdir()))\n\n if cur_class_images < args.num_class_images:\n torch_dtype = torch.float16 if get_current_device() == \"cuda\" else torch.float32\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n torch_dtype=torch_dtype,\n safety_checker=None,\n revision=args.revision,\n )\n pipeline.set_progress_bar_config(disable=True)\n\n num_new_images = args.num_class_images - cur_class_images\n logger.info(f\"Number of class images to sample: {num_new_images}.\")\n\n sample_dataset = PromptDataset(args.class_prompt, num_new_images)\n sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)\n\n pipeline.to(get_current_device())\n\n for example in tqdm(\n sample_dataloader,\n desc=\"Generating class images\",\n disable=not local_rank == 0,\n ):\n images = pipeline(example[\"prompt\"]).images\n\n for i, image in enumerate(images):\n hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()\n image_filename = class_images_dir / f\"{example['index'][i] + cur_class_images}-{hash_image}.jpg\"\n image.save(image_filename)\n\n del pipeline\n\n # Handle the repository creation\n if local_rank == 0:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizer\n if args.tokenizer_name:\n logger.info(f\"Loading tokenizer from {args.tokenizer_name}\", ranks=[0])\n tokenizer = AutoTokenizer.from_pretrained(\n args.tokenizer_name,\n revision=args.revision,\n use_fast=False,\n )\n elif args.pretrained_model_name_or_path:\n logger.info(\"Loading tokenizer from pretrained model\", ranks=[0])\n tokenizer = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n # import correct text encoder class\n text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path)\n\n # Load models and create wrapper for stable diffusion\n\n logger.info(f\"Loading text_encoder from {args.pretrained_model_name_or_path}\", ranks=[0])\n\n text_encoder = text_encoder_cls.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"text_encoder\",\n revision=args.revision,\n )\n\n logger.info(f\"Loading AutoencoderKL from {args.pretrained_model_name_or_path}\", ranks=[0])\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"vae\",\n revision=args.revision,\n )\n\n logger.info(f\"Loading UNet2DConditionModel from {args.pretrained_model_name_or_path}\", ranks=[0])\n with ColoInitContext(device=get_current_device()):\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, low_cpu_mem_usage=False\n )\n\n vae.requires_grad_(False)\n text_encoder.requires_grad_(False)\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n if args.scale_lr:\n args.learning_rate = args.learning_rate * args.train_batch_size * world_size\n\n unet = gemini_zero_dpp(unet, args.placement)\n\n # config optimizer for colossalai zero\n optimizer = GeminiAdamOptimizer(unet, lr=args.learning_rate, initial_scale=2**5, clipping_norm=args.max_grad_norm)\n\n # load noise_scheduler\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n\n # prepare dataset\n logger.info(f\"Prepare dataset from {args.instance_data_dir}\", ranks=[0])\n train_dataset = DreamBoothDataset(\n instance_data_root=args.instance_data_dir,\n instance_prompt=args.instance_prompt,\n class_data_root=args.class_data_dir if args.with_prior_preservation else None,\n class_prompt=args.class_prompt,\n tokenizer=tokenizer,\n size=args.resolution,\n center_crop=args.center_crop,\n )\n\n def collate_fn(examples):\n input_ids = [example[\"instance_prompt_ids\"] for example in examples]\n pixel_values = [example[\"instance_images\"] for example in examples]\n\n # Concat class and instance examples for prior preservation.\n # We do this to avoid doing two forward passes.\n if args.with_prior_preservation:\n input_ids += [example[\"class_prompt_ids\"] for example in examples]\n pixel_values += [example[\"class_images\"] for example in examples]\n\n pixel_values = torch.stack(pixel_values)\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n\n input_ids = tokenizer.pad(\n {\"input_ids\": input_ids},\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n return_tensors=\"pt\",\n ).input_ids\n\n batch = {\n \"input_ids\": input_ids,\n \"pixel_values\": pixel_values,\n }\n return batch\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn, num_workers=1\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader))\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps,\n num_training_steps=args.max_train_steps,\n )\n weight_dtype = torch.float32\n if args.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif args.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move text_encode and vae to gpu.\n # For mixed precision training we cast the text_encoder and vae weights to half-precision\n # as these models are only used for inference, keeping weights in full precision is not required.\n vae.to(get_current_device(), dtype=weight_dtype)\n text_encoder.to(get_current_device(), dtype=weight_dtype)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader))\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # Train!\n total_batch_size = args.train_batch_size * world_size\n\n logger.info(\"***** Running training *****\", ranks=[0])\n logger.info(f\" Num examples = {len(train_dataset)}\", ranks=[0])\n logger.info(f\" Num batches each epoch = {len(train_dataloader)}\", ranks=[0])\n logger.info(f\" Num Epochs = {args.num_train_epochs}\", ranks=[0])\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\", ranks=[0])\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\", ranks=[0])\n logger.info(f\" Total optimization steps = {args.max_train_steps}\", ranks=[0])\n\n # Only show the progress bar once on each machine.\n progress_bar = tqdm(range(args.max_train_steps), disable=not local_rank == 0)\n progress_bar.set_description(\"Steps\")\n global_step = 0\n\n torch.cuda.synchronize()\n for epoch in range(args.num_train_epochs):\n unet.train()\n for step, batch in enumerate(train_dataloader):\n torch.cuda.reset_peak_memory_stats()\n # Move batch to gpu\n for key, value in batch.items():\n batch[key] = value.to(get_current_device(), non_blocking=True)\n\n # Convert images to latent space\n optimizer.zero_grad()\n\n latents = vae.encode(batch[\"pixel_values\"].to(dtype=weight_dtype)).latent_dist.sample()\n latents = latents * 0.18215\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # Get the text embedding for conditioning\n encoder_hidden_states = text_encoder(batch[\"input_ids\"])[0]\n\n # Predict the noise residual\n model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n if args.with_prior_preservation:\n # Chunk the noise and model_pred into two parts and compute the loss on each part separately.\n model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)\n target, target_prior = torch.chunk(target, 2, dim=0)\n\n # Compute instance loss\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"none\").mean([1, 2, 3]).mean()\n\n # Compute prior loss\n prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction=\"mean\")\n\n # Add the prior loss to the instance loss.\n loss = loss + args.prior_loss_weight * prior_loss\n else:\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n\n optimizer.backward(loss)\n\n optimizer.step()\n lr_scheduler.step()\n logger.info(f\"max GPU_mem cost is {torch.cuda.max_memory_allocated()/2**20} MB\", ranks=[0])\n # Checks if the accelerator has performed an optimization step behind the scenes\n progress_bar.update(1)\n global_step += 1\n logs = {\n \"loss\": loss.detach().item(),\n \"lr\": optimizer.param_groups[0][\"lr\"],\n } # lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n\n if global_step % args.save_steps == 0:\n torch.cuda.synchronize()\n torch_unet = get_static_torch_model(unet)\n if local_rank == 0:\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n unet=torch_unet,\n revision=args.revision,\n )\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n pipeline.save_pretrained(save_path)\n logger.info(f\"Saving model checkpoint to {save_path}\", ranks=[0])\n if global_step >= args.max_train_steps:\n break\n\n torch.cuda.synchronize()\n unet = get_static_torch_model(unet)\n\n if local_rank == 0:\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n unet=unet,\n revision=args.revision,\n )\n\n pipeline.save_pretrained(args.output_dir)\n logger.info(f\"Saving model checkpoint to {args.output_dir}\", ranks=[0])\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/research_projects/consistency_training/README.md", "content": "# Consistency Training\n\n`train_cm_ct_unconditional.py` trains a consistency model (CM) from scratch following the consistency training (CT) algorithm introduced in [Consistency Models](https://arxiv.org/abs/2303.01469) and refined in [Improved Techniques for Training Consistency Models](https://arxiv.org/abs/2310.14189). Both unconditional and class-conditional training are supported.\n\nA usage example is as follows:\n\n```bash\naccelerate launch examples/research_projects/consistency_training/train_cm_ct_unconditional.py \\\n --dataset_name=\"cifar10\" \\\n --dataset_image_column_name=\"img\" \\\n --output_dir=\"/path/to/output/dir\" \\\n --mixed_precision=fp16 \\\n --resolution=32 \\\n --max_train_steps=1000 --max_train_samples=10000 \\\n --dataloader_num_workers=8 \\\n --noise_precond_type=\"cm\" --input_precond_type=\"cm\" \\\n --train_batch_size=4 \\\n --learning_rate=1e-04 --lr_scheduler=\"constant\" --lr_warmup_steps=0 \\\n --use_8bit_adam \\\n --use_ema \\\n --validation_steps=100 --eval_batch_size=4 \\\n --checkpointing_steps=100 --checkpoints_total_limit=10 \\\n --class_conditional --num_classes=10 \\\n```" }, { "path": "diffusers/examples/research_projects/consistency_training/requirements.txt", "content": "accelerate>=0.16.0\ntorchvision\ntransformers>=4.25.1\nftfy\ntensorboard\nJinja2" }, { "path": "diffusers/examples/research_projects/consistency_training/train_cm_ct_unconditional.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\"\"\"Script to train a consistency model from scratch via (improved) consistency training.\"\"\"\n\nimport argparse\nimport gc\nimport logging\nimport math\nimport os\nimport shutil\nfrom datetime import timedelta\nfrom pathlib import Path\n\nimport accelerate\nimport datasets\nimport numpy as np\nimport torch\nfrom accelerate import Accelerator, InitProcessGroupKwargs\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\n\nimport diffusers\nfrom diffusers import (\n CMStochasticIterativeScheduler,\n ConsistencyModelPipeline,\n UNet2DModel,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import EMAModel, resolve_interpolation_mode\nfrom diffusers.utils import is_tensorboard_available, is_wandb_available\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\nif is_wandb_available():\n import wandb\n\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\n\ndef _extract_into_tensor(arr, timesteps, broadcast_shape):\n \"\"\"\n Extract values from a 1-D numpy array for a batch of indices.\n\n :param arr: the 1-D numpy array.\n :param timesteps: a tensor of indices into the array to extract.\n :param broadcast_shape: a larger shape of K dimensions with the batch\n dimension equal to the length of timesteps.\n :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.\n \"\"\"\n if not isinstance(arr, torch.Tensor):\n arr = torch.from_numpy(arr)\n res = arr[timesteps].float().to(timesteps.device)\n while len(res.shape) < len(broadcast_shape):\n res = res[..., None]\n return res.expand(broadcast_shape)\n\n\ndef append_dims(x, target_dims):\n \"\"\"Appends dimensions to the end of a tensor until it has target_dims dimensions.\"\"\"\n dims_to_append = target_dims - x.ndim\n if dims_to_append < 0:\n raise ValueError(f\"input has {x.ndim} dims but target_dims is {target_dims}, which is less\")\n return x[(...,) + (None,) * dims_to_append]\n\n\ndef extract_into_tensor(a, t, x_shape):\n b, *_ = t.shape\n out = a.gather(-1, t)\n return out.reshape(b, *((1,) * (len(x_shape) - 1)))\n\n\ndef get_discretization_steps(global_step: int, max_train_steps: int, s_0: int = 10, s_1: int = 1280, constant=False):\n \"\"\"\n Calculates the current discretization steps at global step k using the discretization curriculum N(k).\n \"\"\"\n if constant:\n return s_0 + 1\n\n k_prime = math.floor(max_train_steps / (math.log2(math.floor(s_1 / s_0)) + 1))\n num_discretization_steps = min(s_0 * 2 ** math.floor(global_step / k_prime), s_1) + 1\n\n return num_discretization_steps\n\n\ndef get_skip_steps(global_step, initial_skip: int = 1):\n # Currently only support constant skip curriculum.\n return initial_skip\n\n\ndef get_karras_sigmas(\n num_discretization_steps: int,\n sigma_min: float = 0.002,\n sigma_max: float = 80.0,\n rho: float = 7.0,\n dtype=torch.float32,\n):\n \"\"\"\n Calculates the Karras sigmas timestep discretization of [sigma_min, sigma_max].\n \"\"\"\n ramp = np.linspace(0, 1, num_discretization_steps)\n min_inv_rho = sigma_min ** (1 / rho)\n max_inv_rho = sigma_max ** (1 / rho)\n sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho\n # Make sure sigmas are in increasing rather than decreasing order (see section 2 of the iCT paper)\n sigmas = sigmas[::-1].copy()\n sigmas = torch.from_numpy(sigmas).to(dtype=dtype)\n return sigmas\n\n\ndef get_discretized_lognormal_weights(noise_levels: torch.Tensor, p_mean: float = -1.1, p_std: float = 2.0):\n \"\"\"\n Calculates the unnormalized weights for a 1D array of noise level sigma_i based on the discretized lognormal\"\n \" distribution used in the iCT paper (given in Equation 10).\n \"\"\"\n upper_prob = torch.special.erf((torch.log(noise_levels[1:]) - p_mean) / (math.sqrt(2) * p_std))\n lower_prob = torch.special.erf((torch.log(noise_levels[:-1]) - p_mean) / (math.sqrt(2) * p_std))\n weights = upper_prob - lower_prob\n return weights\n\n\ndef get_loss_weighting_schedule(noise_levels: torch.Tensor):\n \"\"\"\n Calculates the loss weighting schedule lambda given a set of noise levels.\n \"\"\"\n return 1.0 / (noise_levels[1:] - noise_levels[:-1])\n\n\ndef add_noise(original_samples: torch.Tensor, noise: torch.Tensor, timesteps: torch.Tensor):\n # Make sure timesteps (Karras sigmas) have the same device and dtype as original_samples\n sigmas = timesteps.to(device=original_samples.device, dtype=original_samples.dtype)\n while len(sigmas.shape) < len(original_samples.shape):\n sigmas = sigmas.unsqueeze(-1)\n\n noisy_samples = original_samples + noise * sigmas\n\n return noisy_samples\n\n\ndef get_noise_preconditioning(sigmas, noise_precond_type: str = \"cm\"):\n \"\"\"\n Calculates the noise preconditioning function c_noise, which is used to transform the raw Karras sigmas into the\n timestep input for the U-Net.\n \"\"\"\n if noise_precond_type == \"none\":\n return sigmas\n elif noise_precond_type == \"edm\":\n return 0.25 * torch.log(sigmas)\n elif noise_precond_type == \"cm\":\n return 1000 * 0.25 * torch.log(sigmas + 1e-44)\n else:\n raise ValueError(\n f\"Noise preconditioning type {noise_precond_type} is not current supported. Currently supported noise\"\n f\" preconditioning types are `none` (which uses the sigmas as is), `edm`, and `cm`.\"\n )\n\n\ndef get_input_preconditioning(sigmas, sigma_data=0.5, input_precond_type: str = \"cm\"):\n \"\"\"\n Calculates the input preconditioning factor c_in, which is used to scale the U-Net image input.\n \"\"\"\n if input_precond_type == \"none\":\n return 1\n elif input_precond_type == \"cm\":\n return 1.0 / (sigmas**2 + sigma_data**2)\n else:\n raise ValueError(\n f\"Input preconditioning type {input_precond_type} is not current supported. Currently supported input\"\n f\" preconditioning types are `none` (which uses a scaling factor of 1.0) and `cm`.\"\n )\n\n\ndef scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=1.0):\n scaled_timestep = timestep_scaling * timestep\n c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2)\n c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5\n return c_skip, c_out\n\n\ndef log_validation(unet, scheduler, args, accelerator, weight_dtype, step, name=\"teacher\"):\n logger.info(\"Running validation... \")\n\n unet = accelerator.unwrap_model(unet)\n pipeline = ConsistencyModelPipeline(\n unet=unet,\n scheduler=scheduler,\n )\n pipeline = pipeline.to(device=accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n if args.enable_xformers_memory_efficient_attention:\n pipeline.enable_xformers_memory_efficient_attention()\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n class_labels = [None]\n if args.class_conditional:\n if args.num_classes is not None:\n class_labels = list(range(args.num_classes))\n else:\n logger.warning(\n \"The model is class-conditional but the number of classes is not set. The generated images will be\"\n \" unconditional rather than class-conditional.\"\n )\n\n image_logs = []\n\n for class_label in class_labels:\n images = []\n with torch.autocast(\"cuda\"):\n images = pipeline(\n num_inference_steps=1,\n batch_size=args.eval_batch_size,\n class_labels=[class_label] * args.eval_batch_size,\n generator=generator,\n ).images\n log = {\"images\": images}\n if args.class_conditional and class_label is not None:\n log[\"class_label\"] = str(class_label)\n else:\n log[\"class_label\"] = \"images\"\n image_logs.append(log)\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n for log in image_logs:\n images = log[\"images\"]\n class_label = log[\"class_label\"]\n formatted_images = []\n for image in images:\n formatted_images.append(np.asarray(image))\n\n formatted_images = np.stack(formatted_images)\n\n tracker.writer.add_images(class_label, formatted_images, step, dataformats=\"NHWC\")\n elif tracker.name == \"wandb\":\n formatted_images = []\n\n for log in image_logs:\n images = log[\"images\"]\n class_label = log[\"class_label\"]\n for image in images:\n image = wandb.Image(image, caption=class_label)\n formatted_images.append(image)\n\n tracker.log({f\"validation/{name}\": formatted_images})\n else:\n logger.warning(f\"image logging not implemented for {tracker.name}\")\n\n del pipeline\n gc.collect()\n torch.cuda.empty_cache()\n\n return image_logs\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n # ------------Model Arguments-----------\n parser.add_argument(\n \"--model_config_name_or_path\",\n type=str,\n default=None,\n help=\"The config of the UNet model to train, leave as None to use standard DDPM configuration.\",\n )\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n help=(\n \"If initializing the weights from a pretrained model, the path to the pretrained model or model identifier\"\n \" from huggingface.co/models.\"\n ),\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=(\n \"Variant of the model files of the pretrained model identifier from huggingface.co/models, e.g. `fp16`,\"\n \" `non_ema`, etc.\",\n ),\n )\n # ------------Dataset Arguments-----------\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that HF Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--dataset_image_column_name\",\n type=str,\n default=\"image\",\n help=\"The name of the image column in the dataset to use for training.\",\n )\n parser.add_argument(\n \"--dataset_class_label_column_name\",\n type=str,\n default=\"label\",\n help=\"If doing class-conditional training, the name of the class label column in the dataset to use.\",\n )\n # ------------Image Processing Arguments-----------\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=64,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--interpolation_type\",\n type=str,\n default=\"bilinear\",\n help=(\n \"The interpolation function used when resizing images to the desired resolution. Choose between `bilinear`,\"\n \" `bicubic`, `box`, `nearest`, `nearest_exact`, `hamming`, and `lanczos`.\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n default=False,\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--class_conditional\",\n action=\"store_true\",\n help=(\n \"Whether to train a class-conditional model. If set, the class labels will be taken from the `label`\"\n \" column of the provided dataset.\"\n ),\n )\n parser.add_argument(\n \"--num_classes\",\n type=int,\n default=None,\n help=\"The number of classes in the training data, if training a class-conditional model.\",\n )\n parser.add_argument(\n \"--class_embed_type\",\n type=str,\n default=None,\n help=(\n \"The class embedding type to use. Choose from `None`, `identity`, and `timestep`. If `class_conditional`\"\n \" and `num_classes` and set, but `class_embed_type` is `None`, a embedding matrix will be used.\"\n ),\n )\n # ------------Dataloader Arguments-----------\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"The number of subprocesses to use for data loading. 0 means that the data will be loaded in the main\"\n \" process.\"\n ),\n )\n # ------------Training Arguments-----------\n # ----General Training Arguments----\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"ddpm-model-64\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--overwrite_output_dir\", action=\"store_true\")\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n # ----Batch Size and Training Length----\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n # ----Learning Rate----\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"cosine\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n # ----Optimizer (Adam) Arguments----\n parser.add_argument(\n \"--optimizer_type\",\n type=str,\n default=\"adamw\",\n help=(\n \"The optimizer algorithm to use for training. Choose between `radam` and `adamw`. The iCT paper uses\"\n \" RAdam.\"\n ),\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.95, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\n \"--adam_weight_decay\", type=float, default=1e-6, help=\"Weight decay magnitude for the Adam optimizer.\"\n )\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer.\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n # ----Consistency Training (CT) Specific Arguments----\n parser.add_argument(\n \"--prediction_type\",\n type=str,\n default=\"sample\",\n choices=[\"sample\"],\n help=\"Whether the model should predict the 'epsilon'/noise error or directly the reconstructed image 'x0'.\",\n )\n parser.add_argument(\"--ddpm_num_steps\", type=int, default=1000)\n parser.add_argument(\"--ddpm_num_inference_steps\", type=int, default=1000)\n parser.add_argument(\"--ddpm_beta_schedule\", type=str, default=\"linear\")\n parser.add_argument(\n \"--sigma_min\",\n type=float,\n default=0.002,\n help=(\n \"The lower boundary for the timestep discretization, which should be set to a small positive value close\"\n \" to zero to avoid numerical issues when solving the PF-ODE backwards in time.\"\n ),\n )\n parser.add_argument(\n \"--sigma_max\",\n type=float,\n default=80.0,\n help=(\n \"The upper boundary for the timestep discretization, which also determines the variance of the Gaussian\"\n \" prior.\"\n ),\n )\n parser.add_argument(\n \"--rho\",\n type=float,\n default=7.0,\n help=\"The rho parameter for the Karras sigmas timestep dicretization.\",\n )\n parser.add_argument(\n \"--huber_c\",\n type=float,\n default=None,\n help=(\n \"The Pseudo-Huber loss parameter c. If not set, this will default to the value recommended in the Improved\"\n \" Consistency Training (iCT) paper of 0.00054 * sqrt(d), where d is the data dimensionality.\"\n ),\n )\n parser.add_argument(\n \"--discretization_s_0\",\n type=int,\n default=10,\n help=(\n \"The s_0 parameter in the discretization curriculum N(k). This controls the number of training steps after\"\n \" which the number of discretization steps N will be doubled.\"\n ),\n )\n parser.add_argument(\n \"--discretization_s_1\",\n type=int,\n default=1280,\n help=(\n \"The s_1 parameter in the discretization curriculum N(k). This controls the upper limit to the number of\"\n \" discretization steps used. Increasing this value will reduce the bias at the cost of higher variance.\"\n ),\n )\n parser.add_argument(\n \"--constant_discretization_steps\",\n action=\"store_true\",\n help=(\n \"Whether to set the discretization curriculum N(k) to be the constant value `discretization_s_0 + 1`. This\"\n \" is useful for testing when `max_number_steps` is small, when `k_prime` would otherwise be 0, causing\"\n \" a divide-by-zero error.\"\n ),\n )\n parser.add_argument(\n \"--p_mean\",\n type=float,\n default=-1.1,\n help=(\n \"The mean parameter P_mean for the (discretized) lognormal noise schedule, which controls the probability\"\n \" of sampling a (discrete) noise level sigma_i.\"\n ),\n )\n parser.add_argument(\n \"--p_std\",\n type=float,\n default=2.0,\n help=(\n \"The standard deviation parameter P_std for the (discretized) noise schedule, which controls the\"\n \" probability of sampling a (discrete) noise level sigma_i.\"\n ),\n )\n parser.add_argument(\n \"--noise_precond_type\",\n type=str,\n default=\"cm\",\n help=(\n \"The noise preconditioning function to use for transforming the raw Karras sigmas into the timestep\"\n \" argument of the U-Net. Choose between `none` (the identity function), `edm`, and `cm`.\"\n ),\n )\n parser.add_argument(\n \"--input_precond_type\",\n type=str,\n default=\"cm\",\n help=(\n \"The input preconditioning function to use for scaling the image input of the U-Net. Choose between `none`\"\n \" (a scaling factor of 1) and `cm`.\"\n ),\n )\n parser.add_argument(\n \"--skip_steps\",\n type=int,\n default=1,\n help=(\n \"The gap in indices between the student and teacher noise levels. In the iCT paper this is always set to\"\n \" 1, but theoretically this could be greater than 1 and/or altered according to a curriculum throughout\"\n \" training, much like the number of discretization steps is.\"\n ),\n )\n parser.add_argument(\n \"--cast_teacher\",\n action=\"store_true\",\n help=\"Whether to cast the teacher U-Net model to `weight_dtype` or leave it in full precision.\",\n )\n # ----Exponential Moving Average (EMA) Arguments----\n parser.add_argument(\n \"--use_ema\",\n action=\"store_true\",\n help=\"Whether to use Exponential Moving Average for the final model weights.\",\n )\n parser.add_argument(\n \"--ema_min_decay\",\n type=float,\n default=None,\n help=(\n \"The minimum decay magnitude for EMA. If not set, this will default to the value of `ema_max_decay`,\"\n \" resulting in a constant EMA decay rate.\"\n ),\n )\n parser.add_argument(\n \"--ema_max_decay\",\n type=float,\n default=0.99993,\n help=(\n \"The maximum decay magnitude for EMA. Setting `ema_min_decay` equal to this value will result in a\"\n \" constant decay rate.\"\n ),\n )\n parser.add_argument(\n \"--use_ema_warmup\",\n action=\"store_true\",\n help=\"Whether to use EMA warmup.\",\n )\n parser.add_argument(\"--ema_inv_gamma\", type=float, default=1.0, help=\"The inverse gamma value for the EMA decay.\")\n parser.add_argument(\"--ema_power\", type=float, default=3 / 4, help=\"The power value for the EMA decay.\")\n # ----Training Optimization Arguments----\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=\"no\",\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose\"\n \"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.\"\n \"and an Nvidia Ampere GPU.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n # ----Distributed Training Arguments----\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n # ------------Validation Arguments-----------\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=200,\n help=\"Run validation every X steps.\",\n )\n parser.add_argument(\n \"--eval_batch_size\",\n type=int,\n default=16,\n help=(\n \"The number of images to generate for evaluation. Note that if `class_conditional` and `num_classes` is\"\n \" set the effective number of images generated per evaluation step is `eval_batch_size * num_classes`.\"\n ),\n )\n parser.add_argument(\"--save_images_epochs\", type=int, default=10, help=\"How often to save images during training.\")\n # ------------Validation Arguments-----------\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--save_model_epochs\", type=int, default=10, help=\"How often to save the model during training.\"\n )\n # ------------Logging Arguments-----------\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n # ------------HuggingFace Hub Arguments-----------\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--hub_private_repo\", action=\"store_true\", help=\"Whether or not to create a private repository.\"\n )\n # ------------Accelerate Arguments-----------\n parser.add_argument(\n \"--tracker_project_name\",\n type=str,\n default=\"consistency-training\",\n help=(\n \"The `project_name` argument passed to Accelerator.init_trackers for\"\n \" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator\"\n ),\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"You must specify either a dataset name from the hub or a train data directory.\")\n\n return args\n\n\ndef main(args):\n logging_dir = os.path.join(args.output_dir, args.logging_dir)\n\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n kwargs = InitProcessGroupKwargs(timeout=timedelta(seconds=7200)) # a big number for high resolution or big dataset\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n kwargs_handlers=[kwargs],\n )\n\n if args.report_to == \"tensorboard\":\n if not is_tensorboard_available():\n raise ImportError(\"Make sure to install tensorboard if you want to use it for logging during training.\")\n\n elif args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # 1. Initialize the noise scheduler.\n initial_discretization_steps = get_discretization_steps(\n 0,\n args.max_train_steps,\n s_0=args.discretization_s_0,\n s_1=args.discretization_s_1,\n constant=args.constant_discretization_steps,\n )\n noise_scheduler = CMStochasticIterativeScheduler(\n num_train_timesteps=initial_discretization_steps,\n sigma_min=args.sigma_min,\n sigma_max=args.sigma_max,\n rho=args.rho,\n )\n\n # 2. Initialize the student U-Net model.\n if args.pretrained_model_name_or_path is not None:\n logger.info(f\"Loading pretrained U-Net weights from {args.pretrained_model_name_or_path}... \")\n unet = UNet2DModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n elif args.model_config_name_or_path is None:\n # TODO: use default architectures from iCT paper\n if not args.class_conditional and (args.num_classes is not None or args.class_embed_type is not None):\n logger.warning(\n f\"`--class_conditional` is set to `False` but `--num_classes` is set to {args.num_classes} and\"\n f\" `--class_embed_type` is set to {args.class_embed_type}. These values will be overridden to `None`.\"\n )\n args.num_classes = None\n args.class_embed_type = None\n elif args.class_conditional and args.num_classes is None and args.class_embed_type is None:\n logger.warning(\n \"`--class_conditional` is set to `True` but neither `--num_classes` nor `--class_embed_type` is set.\"\n \"`class_conditional` will be overridden to `False`.\"\n )\n args.class_conditional = False\n unet = UNet2DModel(\n sample_size=args.resolution,\n in_channels=3,\n out_channels=3,\n layers_per_block=2,\n block_out_channels=(128, 128, 256, 256, 512, 512),\n down_block_types=(\n \"DownBlock2D\",\n \"DownBlock2D\",\n \"DownBlock2D\",\n \"DownBlock2D\",\n \"AttnDownBlock2D\",\n \"DownBlock2D\",\n ),\n up_block_types=(\n \"UpBlock2D\",\n \"AttnUpBlock2D\",\n \"UpBlock2D\",\n \"UpBlock2D\",\n \"UpBlock2D\",\n \"UpBlock2D\",\n ),\n class_embed_type=args.class_embed_type,\n num_class_embeds=args.num_classes,\n )\n else:\n config = UNet2DModel.load_config(args.model_config_name_or_path)\n unet = UNet2DModel.from_config(config)\n unet.train()\n\n # Create EMA for the student U-Net model.\n if args.use_ema:\n if args.ema_min_decay is None:\n args.ema_min_decay = args.ema_max_decay\n ema_unet = EMAModel(\n unet.parameters(),\n decay=args.ema_max_decay,\n min_decay=args.ema_min_decay,\n use_ema_warmup=args.use_ema_warmup,\n inv_gamma=args.ema_inv_gamma,\n power=args.ema_power,\n model_cls=UNet2DModel,\n model_config=unet.config,\n )\n\n # 3. Initialize the teacher U-Net model from the student U-Net model.\n # Note that following the improved Consistency Training paper, the teacher U-Net is not updated via EMA (e.g. the\n # EMA decay rate is 0.)\n teacher_unet = UNet2DModel.from_config(unet.config)\n teacher_unet.load_state_dict(unet.state_dict())\n teacher_unet.train()\n teacher_unet.requires_grad_(False)\n\n # 4. Handle mixed precision and device placement\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n args.mixed_precision = accelerator.mixed_precision\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n args.mixed_precision = accelerator.mixed_precision\n\n # Cast teacher_unet to weight_dtype if cast_teacher is set.\n if args.cast_teacher:\n teacher_dtype = weight_dtype\n else:\n teacher_dtype = torch.float32\n\n teacher_unet.to(accelerator.device)\n if args.use_ema:\n ema_unet.to(accelerator.device)\n\n # 5. Handle saving and loading of checkpoints.\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n teacher_unet.save_pretrained(os.path.join(output_dir, \"unet_teacher\"))\n if args.use_ema:\n ema_unet.save_pretrained(os.path.join(output_dir, \"unet_ema\"))\n\n for i, model in enumerate(models):\n model.save_pretrained(os.path.join(output_dir, \"unet\"))\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n def load_model_hook(models, input_dir):\n load_model = UNet2DModel.from_pretrained(os.path.join(input_dir, \"unet_teacher\"))\n teacher_unet.load_state_dict(load_model.state_dict())\n teacher_unet.to(accelerator.device)\n del load_model\n\n if args.use_ema:\n load_model = EMAModel.from_pretrained(os.path.join(input_dir, \"unet_ema\"), UNet2DModel)\n ema_unet.load_state_dict(load_model.state_dict())\n ema_unet.to(accelerator.device)\n del load_model\n\n for i in range(len(models)):\n # pop models so that they are not loaded again\n model = models.pop()\n\n # load diffusers style into model\n load_model = UNet2DModel.from_pretrained(input_dir, subfolder=\"unet\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # 6. Enable optimizations\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n teacher_unet.enable_xformers_memory_efficient_attention()\n if args.use_ema:\n ema_unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n if args.optimizer_type == \"radam\":\n optimizer_class = torch.optim.RAdam\n elif args.optimizer_type == \"adamw\":\n # Use 8-bit Adam for lower memory usage or to fine-tune the model for 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n else:\n raise ValueError(\n f\"Optimizer type {args.optimizer_type} is not supported. Currently supported optimizer types are `radam`\"\n f\" and `adamw`.\"\n )\n\n # 7. Initialize the optimizer\n optimizer = optimizer_class(\n unet.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # 8. Dataset creation and data preprocessing\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n split=\"train\",\n )\n else:\n dataset = load_dataset(\"imagefolder\", data_dir=args.train_data_dir, cache_dir=args.cache_dir, split=\"train\")\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder\n\n # Preprocessing the datasets and DataLoaders creation.\n interpolation_mode = resolve_interpolation_mode(args.interpolation_type)\n augmentations = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=interpolation_mode),\n transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),\n transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def transform_images(examples):\n images = [augmentations(image.convert(\"RGB\")) for image in examples[args.dataset_image_column_name]]\n batch_dict = {\"images\": images}\n if args.class_conditional:\n batch_dict[\"class_labels\"] = examples[args.dataset_class_label_column_name]\n return batch_dict\n\n logger.info(f\"Dataset size: {len(dataset)}\")\n\n dataset.set_transform(transform_images)\n train_dataloader = torch.utils.data.DataLoader(\n dataset, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers\n )\n\n # 9. Initialize the learning rate scheduler\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps,\n num_training_steps=args.max_train_steps,\n )\n\n # 10. Prepare for training\n # Prepare everything with our `accelerator`.\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n def recalculate_num_discretization_step_values(discretization_steps, skip_steps):\n \"\"\"\n Recalculates all quantities depending on the number of discretization steps N.\n \"\"\"\n noise_scheduler = CMStochasticIterativeScheduler(\n num_train_timesteps=discretization_steps,\n sigma_min=args.sigma_min,\n sigma_max=args.sigma_max,\n rho=args.rho,\n )\n current_timesteps = get_karras_sigmas(discretization_steps, args.sigma_min, args.sigma_max, args.rho)\n valid_teacher_timesteps_plus_one = current_timesteps[: len(current_timesteps) - skip_steps + 1]\n # timestep_weights are the unnormalized probabilities of sampling the timestep/noise level at each index\n timestep_weights = get_discretized_lognormal_weights(\n valid_teacher_timesteps_plus_one, p_mean=args.p_mean, p_std=args.p_std\n )\n # timestep_loss_weights is the timestep-dependent loss weighting schedule lambda(sigma_i)\n timestep_loss_weights = get_loss_weighting_schedule(valid_teacher_timesteps_plus_one)\n\n current_timesteps = current_timesteps.to(accelerator.device)\n timestep_weights = timestep_weights.to(accelerator.device)\n timestep_loss_weights = timestep_loss_weights.to(accelerator.device)\n\n return noise_scheduler, current_timesteps, timestep_weights, timestep_loss_weights\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = dict(vars(args))\n accelerator.init_trackers(args.tracker_project_name, config=tracker_config)\n\n # Function for unwraping if torch.compile() was used in accelerate.\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n # Resolve the c parameter for the Pseudo-Huber loss\n if args.huber_c is None:\n args.huber_c = 0.00054 * args.resolution * math.sqrt(unet.config.in_channels)\n\n # Get current number of discretization steps N according to our discretization curriculum\n current_discretization_steps = get_discretization_steps(\n initial_global_step,\n args.max_train_steps,\n s_0=args.discretization_s_0,\n s_1=args.discretization_s_1,\n constant=args.constant_discretization_steps,\n )\n current_skip_steps = get_skip_steps(initial_global_step, initial_skip=args.skip_steps)\n if current_skip_steps >= current_discretization_steps:\n raise ValueError(\n f\"The current skip steps is {current_skip_steps}, but should be smaller than the current number of\"\n f\" discretization steps {current_discretization_steps}\"\n )\n # Recalculate all quantities depending on the number of discretization steps N\n (\n noise_scheduler,\n current_timesteps,\n timestep_weights,\n timestep_loss_weights,\n ) = recalculate_num_discretization_step_values(current_discretization_steps, current_skip_steps)\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n # 11. Train!\n for epoch in range(first_epoch, args.num_train_epochs):\n unet.train()\n for step, batch in enumerate(train_dataloader):\n # 1. Get batch of images from dataloader (sample x ~ p_data(x))\n clean_images = batch[\"images\"].to(weight_dtype)\n if args.class_conditional:\n class_labels = batch[\"class_labels\"]\n else:\n class_labels = None\n bsz = clean_images.shape[0]\n\n # 2. Sample a random timestep for each image according to the noise schedule.\n # Sample random indices i ~ p(i), where p(i) is the dicretized lognormal distribution in the iCT paper\n # NOTE: timestep_indices should be in the range [0, len(current_timesteps) - k - 1] inclusive\n timestep_indices = torch.multinomial(timestep_weights, bsz, replacement=True).long()\n teacher_timesteps = current_timesteps[timestep_indices]\n student_timesteps = current_timesteps[timestep_indices + current_skip_steps]\n\n # 3. Sample noise and add it to the clean images for both teacher and student unets\n # Sample noise z ~ N(0, I) that we'll add to the images\n noise = torch.randn(clean_images.shape, dtype=weight_dtype, device=clean_images.device)\n # Add noise to the clean images according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n teacher_noisy_images = add_noise(clean_images, noise, teacher_timesteps)\n student_noisy_images = add_noise(clean_images, noise, student_timesteps)\n\n # 4. Calculate preconditioning and scalings for boundary conditions for the consistency model.\n teacher_rescaled_timesteps = get_noise_preconditioning(teacher_timesteps, args.noise_precond_type)\n student_rescaled_timesteps = get_noise_preconditioning(student_timesteps, args.noise_precond_type)\n\n c_in_teacher = get_input_preconditioning(teacher_timesteps, input_precond_type=args.input_precond_type)\n c_in_student = get_input_preconditioning(student_timesteps, input_precond_type=args.input_precond_type)\n\n c_skip_teacher, c_out_teacher = scalings_for_boundary_conditions(teacher_timesteps)\n c_skip_student, c_out_student = scalings_for_boundary_conditions(student_timesteps)\n\n c_skip_teacher, c_out_teacher, c_in_teacher = [\n append_dims(x, clean_images.ndim) for x in [c_skip_teacher, c_out_teacher, c_in_teacher]\n ]\n c_skip_student, c_out_student, c_in_student = [\n append_dims(x, clean_images.ndim) for x in [c_skip_student, c_out_student, c_in_student]\n ]\n\n with accelerator.accumulate(unet):\n # 5. Get the student unet denoising prediction on the student timesteps\n # Get rng state now to ensure that dropout is synced between the student and teacher models.\n dropout_state = torch.get_rng_state()\n student_model_output = unet(\n c_in_student * student_noisy_images, student_rescaled_timesteps, class_labels=class_labels\n ).sample\n # NOTE: currently only support prediction_type == sample, so no need to convert model_output\n student_denoise_output = c_skip_student * student_noisy_images + c_out_student * student_model_output\n\n # 6. Get the teacher unet denoising prediction on the teacher timesteps\n with torch.no_grad(), torch.autocast(\"cuda\", dtype=teacher_dtype):\n torch.set_rng_state(dropout_state)\n teacher_model_output = teacher_unet(\n c_in_teacher * teacher_noisy_images, teacher_rescaled_timesteps, class_labels=class_labels\n ).sample\n # NOTE: currently only support prediction_type == sample, so no need to convert model_output\n teacher_denoise_output = (\n c_skip_teacher * teacher_noisy_images + c_out_teacher * teacher_model_output\n )\n\n # 7. Calculate the weighted Pseudo-Huber loss\n if args.prediction_type == \"sample\":\n # Note that the loss weights should be those at the (teacher) timestep indices.\n lambda_t = _extract_into_tensor(\n timestep_loss_weights, timestep_indices, (bsz,) + (1,) * (clean_images.ndim - 1)\n )\n loss = lambda_t * (\n torch.sqrt(\n (student_denoise_output.float() - teacher_denoise_output.float()) ** 2 + args.huber_c**2\n )\n - args.huber_c\n )\n loss = loss.mean()\n else:\n raise ValueError(\n f\"Unsupported prediction type: {args.prediction_type}. Currently, only `sample` is supported.\"\n )\n\n # 8. Backpropagate on the consistency training loss\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n # 9. Update teacher_unet and ema_unet parameters using unet's parameters.\n teacher_unet.load_state_dict(unet.state_dict())\n if args.use_ema:\n ema_unet.step(unet.parameters())\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n # 10. Recalculate quantities depending on the global step, if necessary.\n new_discretization_steps = get_discretization_steps(\n global_step,\n args.max_train_steps,\n s_0=args.discretization_s_0,\n s_1=args.discretization_s_1,\n constant=args.constant_discretization_steps,\n )\n current_skip_steps = get_skip_steps(global_step, initial_skip=args.skip_steps)\n if current_skip_steps >= new_discretization_steps:\n raise ValueError(\n f\"The current skip steps is {current_skip_steps}, but should be smaller than the current\"\n f\" number of discretization steps {new_discretization_steps}.\"\n )\n if new_discretization_steps != current_discretization_steps:\n (\n noise_scheduler,\n current_timesteps,\n timestep_weights,\n timestep_loss_weights,\n ) = recalculate_num_discretization_step_values(new_discretization_steps, current_skip_steps)\n current_discretization_steps = new_discretization_steps\n\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n if global_step % args.validation_steps == 0:\n # NOTE: since we do not use EMA for the teacher model, the teacher parameters and student\n # parameters are the same at this point in time\n log_validation(unet, noise_scheduler, args, accelerator, weight_dtype, global_step, \"teacher\")\n # teacher_unet.to(dtype=teacher_dtype)\n\n if args.use_ema:\n # Store the student unet weights and load the EMA weights.\n ema_unet.store(unet.parameters())\n ema_unet.copy_to(unet.parameters())\n\n log_validation(\n unet,\n noise_scheduler,\n args,\n accelerator,\n weight_dtype,\n global_step,\n \"ema_student\",\n )\n\n # Restore student unet weights\n ema_unet.restore(unet.parameters())\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0], \"step\": global_step}\n if args.use_ema:\n logs[\"ema_decay\"] = ema_unet.cur_decay_value\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n # progress_bar.close()\n\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = unwrap_model(unet)\n pipeline = ConsistencyModelPipeline(unet=unet, scheduler=noise_scheduler)\n pipeline.save_pretrained(args.output_dir)\n\n # If using EMA, save EMA weights as well.\n if args.use_ema:\n ema_unet.copy_to(unet.parameters())\n\n unet.save_pretrained(os.path.join(args.output_dir, \"ema_unet\"))\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/research_projects/controlnet/train_controlnet_webdataset.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport functools\nimport gc\nimport itertools\nimport json\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom pathlib import Path\nfrom typing import List, Optional, Union\n\nimport accelerate\nimport cv2\nimport numpy as np\nimport torch\nimport torch.utils.checkpoint\nimport transformers\nimport webdataset as wds\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom braceexpand import braceexpand\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom PIL import Image\nfrom torch.utils.data import default_collate\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, DPTFeatureExtractor, DPTForDepthEstimation, PretrainedConfig\nfrom webdataset.tariterators import (\n base_plus_ext,\n tar_file_expander,\n url_opener,\n valid_sample,\n)\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n ControlNetModel,\n EulerDiscreteScheduler,\n StableDiffusionXLControlNetPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import check_min_version, is_wandb_available\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\nMAX_SEQ_LENGTH = 77\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.18.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef filter_keys(key_set):\n def _f(dictionary):\n return {k: v for k, v in dictionary.items() if k in key_set}\n\n return _f\n\n\ndef group_by_keys_nothrow(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None):\n \"\"\"Return function over iterator that groups key, value pairs into samples.\n\n :param keys: function that splits the key into key and extension (base_plus_ext)\n :param lcase: convert suffixes to lower case (Default value = True)\n \"\"\"\n current_sample = None\n for filesample in data:\n assert isinstance(filesample, dict)\n fname, value = filesample[\"fname\"], filesample[\"data\"]\n prefix, suffix = keys(fname)\n if prefix is None:\n continue\n if lcase:\n suffix = suffix.lower()\n # FIXME webdataset version throws if suffix in current_sample, but we have a potential for\n # this happening in the current LAION400m dataset if a tar ends with same prefix as the next\n # begins, rare, but can happen since prefix aren't unique across tar files in that dataset\n if current_sample is None or prefix != current_sample[\"__key__\"] or suffix in current_sample:\n if valid_sample(current_sample):\n yield current_sample\n current_sample = {\"__key__\": prefix, \"__url__\": filesample[\"__url__\"]}\n if suffixes is None or suffix in suffixes:\n current_sample[suffix] = value\n if valid_sample(current_sample):\n yield current_sample\n\n\ndef tarfile_to_samples_nothrow(src, handler=wds.warn_and_continue):\n # NOTE this is a re-impl of the webdataset impl with group_by_keys that doesn't throw\n streams = url_opener(src, handler=handler)\n files = tar_file_expander(streams, handler=handler)\n samples = group_by_keys_nothrow(files, handler=handler)\n return samples\n\n\ndef control_transform(image):\n image = np.array(image)\n\n low_threshold = 100\n high_threshold = 200\n\n image = cv2.Canny(image, low_threshold, high_threshold)\n image = image[:, :, None]\n image = np.concatenate([image, image, image], axis=2)\n control_image = Image.fromarray(image)\n return control_image\n\n\ndef canny_image_transform(example, resolution=1024):\n image = example[\"image\"]\n image = transforms.Resize(resolution, interpolation=transforms.InterpolationMode.BILINEAR)(image)\n # get crop coordinates\n c_top, c_left, _, _ = transforms.RandomCrop.get_params(image, output_size=(resolution, resolution))\n image = transforms.functional.crop(image, c_top, c_left, resolution, resolution)\n control_image = control_transform(image)\n\n image = transforms.ToTensor()(image)\n image = transforms.Normalize([0.5], [0.5])(image)\n control_image = transforms.ToTensor()(control_image)\n\n example[\"image\"] = image\n example[\"control_image\"] = control_image\n example[\"crop_coords\"] = (c_top, c_left)\n\n return example\n\n\ndef depth_image_transform(example, feature_extractor, resolution=1024):\n image = example[\"image\"]\n image = transforms.Resize(resolution, interpolation=transforms.InterpolationMode.BILINEAR)(image)\n # get crop coordinates\n c_top, c_left, _, _ = transforms.RandomCrop.get_params(image, output_size=(resolution, resolution))\n image = transforms.functional.crop(image, c_top, c_left, resolution, resolution)\n\n control_image = feature_extractor(images=image, return_tensors=\"pt\").pixel_values.squeeze(0)\n\n image = transforms.ToTensor()(image)\n image = transforms.Normalize([0.5], [0.5])(image)\n\n example[\"image\"] = image\n example[\"control_image\"] = control_image\n example[\"crop_coords\"] = (c_top, c_left)\n\n return example\n\n\nclass WebdatasetFilter:\n def __init__(self, min_size=1024, max_pwatermark=0.5):\n self.min_size = min_size\n self.max_pwatermark = max_pwatermark\n\n def __call__(self, x):\n try:\n if \"json\" in x:\n x_json = json.loads(x[\"json\"])\n filter_size = (x_json.get(\"original_width\", 0.0) or 0.0) >= self.min_size and x_json.get(\n \"original_height\", 0\n ) >= self.min_size\n filter_watermark = (x_json.get(\"pwatermark\", 1.0) or 1.0) <= self.max_pwatermark\n return filter_size and filter_watermark\n else:\n return False\n except Exception:\n return False\n\n\nclass Text2ImageDataset:\n def __init__(\n self,\n train_shards_path_or_url: Union[str, List[str]],\n eval_shards_path_or_url: Union[str, List[str]],\n num_train_examples: int,\n per_gpu_batch_size: int,\n global_batch_size: int,\n num_workers: int,\n resolution: int = 256,\n center_crop: bool = True,\n random_flip: bool = False,\n shuffle_buffer_size: int = 1000,\n pin_memory: bool = False,\n persistent_workers: bool = False,\n control_type: str = \"canny\",\n feature_extractor: Optional[DPTFeatureExtractor] = None,\n ):\n if not isinstance(train_shards_path_or_url, str):\n train_shards_path_or_url = [list(braceexpand(urls)) for urls in train_shards_path_or_url]\n # flatten list using itertools\n train_shards_path_or_url = list(itertools.chain.from_iterable(train_shards_path_or_url))\n\n if not isinstance(eval_shards_path_or_url, str):\n eval_shards_path_or_url = [list(braceexpand(urls)) for urls in eval_shards_path_or_url]\n # flatten list using itertools\n eval_shards_path_or_url = list(itertools.chain.from_iterable(eval_shards_path_or_url))\n\n def get_orig_size(json):\n return (int(json.get(\"original_width\", 0.0)), int(json.get(\"original_height\", 0.0)))\n\n if control_type == \"canny\":\n image_transform = functools.partial(canny_image_transform, resolution=resolution)\n elif control_type == \"depth\":\n image_transform = functools.partial(\n depth_image_transform, feature_extractor=feature_extractor, resolution=resolution\n )\n\n processing_pipeline = [\n wds.decode(\"pil\", handler=wds.ignore_and_continue),\n wds.rename(\n image=\"jpg;png;jpeg;webp\",\n control_image=\"jpg;png;jpeg;webp\",\n text=\"text;txt;caption\",\n orig_size=\"json\",\n handler=wds.warn_and_continue,\n ),\n wds.map(filter_keys({\"image\", \"control_image\", \"text\", \"orig_size\"})),\n wds.map_dict(orig_size=get_orig_size),\n wds.map(image_transform),\n wds.to_tuple(\"image\", \"control_image\", \"text\", \"orig_size\", \"crop_coords\"),\n ]\n\n # Create train dataset and loader\n pipeline = [\n wds.ResampledShards(train_shards_path_or_url),\n tarfile_to_samples_nothrow,\n wds.select(WebdatasetFilter(min_size=512)),\n wds.shuffle(shuffle_buffer_size),\n *processing_pipeline,\n wds.batched(per_gpu_batch_size, partial=False, collation_fn=default_collate),\n ]\n\n num_worker_batches = math.ceil(num_train_examples / (global_batch_size * num_workers)) # per dataloader worker\n num_batches = num_worker_batches * num_workers\n num_samples = num_batches * global_batch_size\n\n # each worker is iterating over this\n self._train_dataset = wds.DataPipeline(*pipeline).with_epoch(num_worker_batches)\n self._train_dataloader = wds.WebLoader(\n self._train_dataset,\n batch_size=None,\n shuffle=False,\n num_workers=num_workers,\n pin_memory=pin_memory,\n persistent_workers=persistent_workers,\n )\n # add meta-data to dataloader instance for convenience\n self._train_dataloader.num_batches = num_batches\n self._train_dataloader.num_samples = num_samples\n\n # Create eval dataset and loader\n pipeline = [\n wds.SimpleShardList(eval_shards_path_or_url),\n wds.split_by_worker,\n wds.tarfile_to_samples(handler=wds.ignore_and_continue),\n *processing_pipeline,\n wds.batched(per_gpu_batch_size, partial=False, collation_fn=default_collate),\n ]\n self._eval_dataset = wds.DataPipeline(*pipeline)\n self._eval_dataloader = wds.WebLoader(\n self._eval_dataset,\n batch_size=None,\n shuffle=False,\n num_workers=num_workers,\n pin_memory=pin_memory,\n persistent_workers=persistent_workers,\n )\n\n @property\n def train_dataset(self):\n return self._train_dataset\n\n @property\n def train_dataloader(self):\n return self._train_dataloader\n\n @property\n def eval_dataset(self):\n return self._eval_dataset\n\n @property\n def eval_dataloader(self):\n return self._eval_dataloader\n\n\ndef image_grid(imgs, rows, cols):\n assert len(imgs) == rows * cols\n\n w, h = imgs[0].size\n grid = Image.new(\"RGB\", size=(cols * w, rows * h))\n\n for i, img in enumerate(imgs):\n grid.paste(img, box=(i % cols * w, i // cols * h))\n return grid\n\n\ndef log_validation(vae, unet, controlnet, args, accelerator, weight_dtype, step):\n logger.info(\"Running validation... \")\n\n controlnet = accelerator.unwrap_model(controlnet)\n\n pipeline = StableDiffusionXLControlNetPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n unet=unet,\n controlnet=controlnet,\n revision=args.revision,\n torch_dtype=weight_dtype,\n )\n # pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n if args.enable_xformers_memory_efficient_attention:\n pipeline.enable_xformers_memory_efficient_attention()\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n if len(args.validation_image) == len(args.validation_prompt):\n validation_images = args.validation_image\n validation_prompts = args.validation_prompt\n elif len(args.validation_image) == 1:\n validation_images = args.validation_image * len(args.validation_prompt)\n validation_prompts = args.validation_prompt\n elif len(args.validation_prompt) == 1:\n validation_images = args.validation_image\n validation_prompts = args.validation_prompt * len(args.validation_image)\n else:\n raise ValueError(\n \"number of `args.validation_image` and `args.validation_prompt` should be checked in `parse_args`\"\n )\n\n image_logs = []\n\n for validation_prompt, validation_image in zip(validation_prompts, validation_images):\n validation_image = Image.open(validation_image).convert(\"RGB\")\n validation_image = validation_image.resize((args.resolution, args.resolution))\n\n images = []\n\n for _ in range(args.num_validation_images):\n with torch.autocast(\"cuda\"):\n image = pipeline(\n validation_prompt, image=validation_image, num_inference_steps=20, generator=generator\n ).images[0]\n images.append(image)\n\n image_logs.append(\n {\"validation_image\": validation_image, \"images\": images, \"validation_prompt\": validation_prompt}\n )\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n for log in image_logs:\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n validation_image = log[\"validation_image\"]\n\n formatted_images = []\n\n formatted_images.append(np.asarray(validation_image))\n\n for image in images:\n formatted_images.append(np.asarray(image))\n\n formatted_images = np.stack(formatted_images)\n\n tracker.writer.add_images(validation_prompt, formatted_images, step, dataformats=\"NHWC\")\n elif tracker.name == \"wandb\":\n formatted_images = []\n\n for log in image_logs:\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n validation_image = log[\"validation_image\"]\n\n formatted_images.append(wandb.Image(validation_image, caption=\"Controlnet conditioning\"))\n\n for image in images:\n image = wandb.Image(image, caption=validation_prompt)\n formatted_images.append(image)\n\n tracker.log({\"validation\": formatted_images})\n else:\n logger.warning(f\"image logging not implemented for {tracker.name}\")\n\n del pipeline\n gc.collect()\n torch.cuda.empty_cache()\n\n return image_logs\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef save_model_card(repo_id: str, image_logs=None, base_model=str, repo_folder=None):\n img_str = \"\"\n if image_logs is not None:\n img_str = \"You can find some example images below.\\n\"\n for i, log in enumerate(image_logs):\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n validation_image = log[\"validation_image\"]\n validation_image.save(os.path.join(repo_folder, \"image_control.png\"))\n img_str += f\"prompt: {validation_prompt}\\n\"\n images = [validation_image] + images\n image_grid(images, 1, len(images)).save(os.path.join(repo_folder, f\"images_{i}.png\"))\n img_str += f\"![images_{i})](./images_{i}.png)\\n\"\n\n yaml = f\"\"\"\n---\nlicense: creativeml-openrail-m\nbase_model: {base_model}\ntags:\n- stable-diffusion-xl\n- stable-diffusion-xl-diffusers\n- text-to-image\n- diffusers\n- controlnet\n- diffusers-training\n- webdataset\ninference: true\n---\n \"\"\"\n model_card = f\"\"\"\n# controlnet-{repo_id}\n\nThese are controlnet weights trained on {base_model} with new type of conditioning.\n{img_str}\n\"\"\"\n with open(os.path.join(repo_folder, \"README.md\"), \"w\") as f:\n f.write(yaml + model_card)\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a ControlNet training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_vae_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to an improved VAE to stabilize training. For more details check out: https://github.com/huggingface/diffusers/pull/4038.\",\n )\n parser.add_argument(\n \"--controlnet_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to pretrained controlnet model or model identifier from huggingface.co/models.\"\n \" If not specified controlnet weights are initialized from unet.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=(\n \"Revision of pretrained model identifier from huggingface.co/models. Trainable model components should be\"\n \" float32 precision.\"\n ),\n )\n parser.add_argument(\n \"--tokenizer_name\",\n type=str,\n default=None,\n help=\"Pretrained tokenizer name or path if not the same as model_name\",\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"controlnet-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--crops_coords_top_left_h\",\n type=int,\n default=0,\n help=(\"Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet.\"),\n )\n parser.add_argument(\n \"--crops_coords_top_left_w\",\n type=int,\n default=0,\n help=(\"Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet.\"),\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. \"\n \"In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference.\"\n \"Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components.\"\n \"See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step\"\n \"instructions.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=3,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=5e-6,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\"--lr_power\", type=float, default=1.0, help=\"Power factor of the polynomial scheduler.\")\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=1,\n help=(\"Number of subprocesses to use for data loading.\"),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--set_grads_to_none\",\n action=\"store_true\",\n help=(\n \"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain\"\n \" behaviors, so disable this argument if it causes any problems. More info:\"\n \" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html\"\n ),\n )\n parser.add_argument(\n \"--train_shards_path_or_url\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--eval_shards_path_or_url\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing the target image.\"\n )\n parser.add_argument(\n \"--conditioning_image_column\",\n type=str,\n default=\"conditioning_image\",\n help=\"The column of the dataset containing the controlnet conditioning image.\",\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=\"text\",\n help=\"The column of the dataset containing a caption or a list of captions.\",\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--proportion_empty_prompts\",\n type=float,\n default=0,\n help=\"Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).\",\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n nargs=\"+\",\n help=(\n \"A set of prompts evaluated every `--validation_steps` and logged to `--report_to`.\"\n \" Provide either a matching number of `--validation_image`s, a single `--validation_image`\"\n \" to be used with all prompts, or a single prompt that will be used with all `--validation_image`s.\"\n ),\n )\n parser.add_argument(\n \"--validation_image\",\n type=str,\n default=None,\n nargs=\"+\",\n help=(\n \"A set of paths to the controlnet conditioning image be evaluated every `--validation_steps`\"\n \" and logged to `--report_to`. Provide either a matching number of `--validation_prompt`s, a\"\n \" a single `--validation_prompt` to be used with all `--validation_image`s, or a single\"\n \" `--validation_image` that will be used with all `--validation_prompt`s.\"\n ),\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images to be generated for each `--validation_image`, `--validation_prompt` pair\",\n )\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=100,\n help=(\n \"Run validation every X steps. Validation consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`\"\n \" and logging the images.\"\n ),\n )\n parser.add_argument(\n \"--tracker_project_name\",\n type=str,\n default=\"sd_xl_train_controlnet\",\n help=(\n \"The `project_name` argument passed to Accelerator.init_trackers for\"\n \" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator\"\n ),\n )\n parser.add_argument(\n \"--control_type\",\n type=str,\n default=\"canny\",\n help=(\"The type of controlnet conditioning image to use. One of `canny`, `depth`\" \" Defaults to `canny`.\"),\n )\n parser.add_argument(\n \"--transformer_layers_per_block\",\n type=str,\n default=None,\n help=(\"The number of layers per block in the transformer. If None, defaults to\" \" `args.transformer_layers`.\"),\n )\n parser.add_argument(\n \"--old_style_controlnet\",\n action=\"store_true\",\n default=False,\n help=(\n \"Use the old style controlnet, which is a single transformer layer with\"\n \" a single head. Defaults to False.\"\n ),\n )\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:\n raise ValueError(\"`--proportion_empty_prompts` must be in the range [0, 1].\")\n\n if args.validation_prompt is not None and args.validation_image is None:\n raise ValueError(\"`--validation_image` must be set if `--validation_prompt` is set\")\n\n if args.validation_prompt is None and args.validation_image is not None:\n raise ValueError(\"`--validation_prompt` must be set if `--validation_image` is set\")\n\n if (\n args.validation_image is not None\n and args.validation_prompt is not None\n and len(args.validation_image) != 1\n and len(args.validation_prompt) != 1\n and len(args.validation_image) != len(args.validation_prompt)\n ):\n raise ValueError(\n \"Must provide either 1 `--validation_image`, 1 `--validation_prompt`,\"\n \" or the same number of `--validation_prompt`s and `--validation_image`s\"\n )\n\n if args.resolution % 8 != 0:\n raise ValueError(\n \"`--resolution` must be divisible by 8 for consistently sized encoded images between the VAE and the controlnet encoder.\"\n )\n\n return args\n\n\n# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt\ndef encode_prompt(prompt_batch, text_encoders, tokenizers, proportion_empty_prompts, is_train=True):\n prompt_embeds_list = []\n\n captions = []\n for caption in prompt_batch:\n if random.random() < proportion_empty_prompts:\n captions.append(\"\")\n elif isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n\n with torch.no_grad():\n for tokenizer, text_encoder in zip(tokenizers, text_encoders):\n text_inputs = tokenizer(\n captions,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n prompt_embeds = text_encoder(\n text_input_ids.to(text_encoder.device),\n output_hidden_states=True,\n )\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds.hidden_states[-2]\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)\n return prompt_embeds, pooled_prompt_embeds\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name,\n exist_ok=True,\n token=args.hub_token,\n private=True,\n ).repo_id\n\n # Load the tokenizers\n tokenizer_one = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"tokenizer\", revision=args.revision, use_fast=False\n )\n tokenizer_two = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"tokenizer_2\", revision=args.revision, use_fast=False\n )\n\n # import correct text encoder classes\n text_encoder_cls_one = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision\n )\n text_encoder_cls_two = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision, subfolder=\"text_encoder_2\"\n )\n\n # Load scheduler and models\n # noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n noise_scheduler = EulerDiscreteScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n text_encoder_one = text_encoder_cls_one.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision\n )\n text_encoder_two = text_encoder_cls_two.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder_2\", revision=args.revision\n )\n vae_path = (\n args.pretrained_model_name_or_path\n if args.pretrained_vae_model_name_or_path is None\n else args.pretrained_vae_model_name_or_path\n )\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n )\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision\n )\n\n if args.controlnet_model_name_or_path:\n logger.info(\"Loading existing controlnet weights\")\n pre_controlnet = ControlNetModel.from_pretrained(args.controlnet_model_name_or_path)\n else:\n logger.info(\"Initializing controlnet weights from unet\")\n pre_controlnet = ControlNetModel.from_unet(unet)\n\n if args.transformer_layers_per_block is not None:\n transformer_layers_per_block = [int(x) for x in args.transformer_layers_per_block.split(\",\")]\n down_block_types = [\"DownBlock2D\" if l == 0 else \"CrossAttnDownBlock2D\" for l in transformer_layers_per_block]\n controlnet = ControlNetModel.from_config(\n pre_controlnet.config,\n down_block_types=down_block_types,\n transformer_layers_per_block=transformer_layers_per_block,\n )\n controlnet.load_state_dict(pre_controlnet.state_dict(), strict=False)\n del pre_controlnet\n else:\n controlnet = pre_controlnet\n\n if args.control_type == \"depth\":\n feature_extractor = DPTFeatureExtractor.from_pretrained(\"Intel/dpt-hybrid-midas\")\n depth_model = DPTForDepthEstimation.from_pretrained(\"Intel/dpt-hybrid-midas\")\n depth_model.requires_grad_(False)\n else:\n feature_extractor = None\n depth_model = None\n\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n i = len(weights) - 1\n\n while len(weights) > 0:\n weights.pop()\n model = models[i]\n\n sub_dir = \"controlnet\"\n model.save_pretrained(os.path.join(output_dir, sub_dir))\n\n i -= 1\n\n def load_model_hook(models, input_dir):\n while len(models) > 0:\n # pop models so that they are not loaded again\n model = models.pop()\n\n # load diffusers style into model\n load_model = ControlNetModel.from_pretrained(input_dir, subfolder=\"controlnet\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n vae.requires_grad_(False)\n unet.requires_grad_(False)\n text_encoder_one.requires_grad_(False)\n text_encoder_two.requires_grad_(False)\n controlnet.train()\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n controlnet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n if args.gradient_checkpointing:\n controlnet.enable_gradient_checkpointing()\n\n # Check that all trainable models are in full precision\n low_precision_error_string = (\n \" Please make sure to always have all model weights in full float32 precision when starting training - even if\"\n \" doing mixed precision training, copy of the weights should still be float32.\"\n )\n\n if accelerator.unwrap_model(controlnet).dtype != torch.float32:\n raise ValueError(\n f\"Controlnet loaded as datatype {accelerator.unwrap_model(controlnet).dtype}. {low_precision_error_string}\"\n )\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # Optimizer creation\n params_to_optimize = controlnet.parameters()\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # For mixed precision training we cast the text_encoder and vae weights to half-precision\n # as these models are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move vae, unet and text_encoder to device and cast to weight_dtype\n # The VAE is in float32 to avoid NaN losses.\n if args.pretrained_vae_model_name_or_path is not None:\n vae.to(accelerator.device, dtype=weight_dtype)\n else:\n vae.to(accelerator.device, dtype=torch.float32)\n unet.to(accelerator.device, dtype=weight_dtype)\n text_encoder_one.to(accelerator.device, dtype=weight_dtype)\n text_encoder_two.to(accelerator.device, dtype=weight_dtype)\n if args.control_type == \"depth\":\n depth_model.to(accelerator.device, dtype=weight_dtype)\n\n # Here, we compute not just the text embeddings but also the additional embeddings\n # needed for the SD XL UNet to operate.\n def compute_embeddings(\n prompt_batch, original_sizes, crop_coords, proportion_empty_prompts, text_encoders, tokenizers, is_train=True\n ):\n target_size = (args.resolution, args.resolution)\n original_sizes = list(map(list, zip(*original_sizes)))\n crops_coords_top_left = list(map(list, zip(*crop_coords)))\n\n original_sizes = torch.tensor(original_sizes, dtype=torch.long)\n crops_coords_top_left = torch.tensor(crops_coords_top_left, dtype=torch.long)\n\n # crops_coords_top_left = (args.crops_coords_top_left_h, args.crops_coords_top_left_w)\n prompt_embeds, pooled_prompt_embeds = encode_prompt(\n prompt_batch, text_encoders, tokenizers, proportion_empty_prompts, is_train\n )\n add_text_embeds = pooled_prompt_embeds\n\n # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids\n # add_time_ids = list(crops_coords_top_left + target_size)\n add_time_ids = list(target_size)\n add_time_ids = torch.tensor([add_time_ids])\n add_time_ids = add_time_ids.repeat(len(prompt_batch), 1)\n # add_time_ids = torch.cat([torch.tensor(original_sizes, dtype=torch.long), add_time_ids], dim=-1)\n add_time_ids = torch.cat([original_sizes, crops_coords_top_left, add_time_ids], dim=-1)\n add_time_ids = add_time_ids.to(accelerator.device, dtype=prompt_embeds.dtype)\n\n prompt_embeds = prompt_embeds.to(accelerator.device)\n add_text_embeds = add_text_embeds.to(accelerator.device)\n unet_added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n\n return {\"prompt_embeds\": prompt_embeds, **unet_added_cond_kwargs}\n\n def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):\n sigmas = noise_scheduler.sigmas.to(device=accelerator.device, dtype=dtype)\n schedule_timesteps = noise_scheduler.timesteps.to(accelerator.device)\n timesteps = timesteps.to(accelerator.device)\n\n step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]\n\n sigma = sigmas[step_indices].flatten()\n while len(sigma.shape) < n_dim:\n sigma = sigma.unsqueeze(-1)\n return sigma\n\n dataset = Text2ImageDataset(\n train_shards_path_or_url=args.train_shards_path_or_url,\n eval_shards_path_or_url=args.eval_shards_path_or_url,\n num_train_examples=args.max_train_samples,\n per_gpu_batch_size=args.train_batch_size,\n global_batch_size=args.train_batch_size * accelerator.num_processes,\n num_workers=args.dataloader_num_workers,\n resolution=args.resolution,\n center_crop=False,\n random_flip=False,\n shuffle_buffer_size=1000,\n pin_memory=True,\n persistent_workers=True,\n control_type=args.control_type,\n feature_extractor=feature_extractor,\n )\n train_dataloader = dataset.train_dataloader\n\n # Let's first compute all the embeddings so that we can free up the text encoders\n # from memory.\n text_encoders = [text_encoder_one, text_encoder_two]\n tokenizers = [tokenizer_one, tokenizer_two]\n\n compute_embeddings_fn = functools.partial(\n compute_embeddings,\n proportion_empty_prompts=args.proportion_empty_prompts,\n text_encoders=text_encoders,\n tokenizers=tokenizers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_cycles=args.lr_num_cycles,\n power=args.lr_power,\n )\n\n # Prepare everything with our `accelerator`.\n controlnet, optimizer, lr_scheduler = accelerator.prepare(controlnet, optimizer, lr_scheduler)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = dict(vars(args))\n\n # tensorboard cannot handle list types for config\n tracker_config.pop(\"validation_prompt\")\n tracker_config.pop(\"validation_image\")\n\n accelerator.init_trackers(args.tracker_project_name, config=tracker_config)\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num batches each epoch = {train_dataloader.num_batches}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n image_logs = None\n for epoch in range(first_epoch, args.num_train_epochs):\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(controlnet):\n image, control_image, text, orig_size, crop_coords = batch\n\n encoded_text = compute_embeddings_fn(text, orig_size, crop_coords)\n image = image.to(accelerator.device, non_blocking=True)\n control_image = control_image.to(accelerator.device, non_blocking=True)\n\n if args.pretrained_vae_model_name_or_path is not None:\n pixel_values = image.to(dtype=weight_dtype)\n if vae.dtype != weight_dtype:\n vae.to(dtype=weight_dtype)\n else:\n pixel_values = image\n\n # latents = vae.encode(pixel_values).latent_dist.sample()\n # encode pixel values with batch size of at most 8\n latents = []\n for i in range(0, pixel_values.shape[0], 8):\n latents.append(vae.encode(pixel_values[i : i + 8]).latent_dist.sample())\n latents = torch.cat(latents, dim=0)\n\n latents = latents * vae.config.scaling_factor\n if args.pretrained_vae_model_name_or_path is None:\n latents = latents.to(weight_dtype)\n\n if args.control_type == \"depth\":\n control_image = control_image.to(weight_dtype)\n with torch.autocast(\"cuda\"):\n depth_map = depth_model(control_image).predicted_depth\n depth_map = torch.nn.functional.interpolate(\n depth_map.unsqueeze(1),\n size=image.shape[2:],\n mode=\"bicubic\",\n align_corners=False,\n )\n depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)\n depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)\n depth_map = (depth_map - depth_min) / (depth_max - depth_min)\n control_image = (depth_map * 255.0).to(torch.uint8).float() / 255.0 # hack to match inference\n control_image = torch.cat([control_image] * 3, dim=1)\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n sigmas = get_sigmas(timesteps, len(noisy_latents.shape), noisy_latents.dtype)\n inp_noisy_latents = noisy_latents / ((sigmas**2 + 1) ** 0.5)\n\n # ControlNet conditioning.\n controlnet_image = control_image.to(dtype=weight_dtype)\n prompt_embeds = encoded_text.pop(\"prompt_embeds\")\n down_block_res_samples, mid_block_res_sample = controlnet(\n inp_noisy_latents,\n timesteps,\n encoder_hidden_states=prompt_embeds,\n added_cond_kwargs=encoded_text,\n controlnet_cond=controlnet_image,\n return_dict=False,\n )\n\n # Predict the noise residual\n model_pred = unet(\n inp_noisy_latents,\n timesteps,\n encoder_hidden_states=prompt_embeds,\n added_cond_kwargs=encoded_text,\n down_block_additional_residuals=[\n sample.to(dtype=weight_dtype) for sample in down_block_res_samples\n ],\n mid_block_additional_residual=mid_block_res_sample.to(dtype=weight_dtype),\n ).sample\n\n model_pred = model_pred * (-sigmas) + noisy_latents\n weighing = sigmas**-2.0\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = latents # compute loss against the denoised latents\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n loss = torch.mean(\n (weighing.float() * (model_pred.float() - target.float()) ** 2).reshape(target.shape[0], -1), 1\n )\n loss = loss.mean()\n\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n params_to_clip = controlnet.parameters()\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad(set_to_none=args.set_grads_to_none)\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n if args.validation_prompt is not None and global_step % args.validation_steps == 0:\n image_logs = log_validation(\n vae, unet, controlnet, args, accelerator, weight_dtype, global_step\n )\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n # Create the pipeline using using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n controlnet = accelerator.unwrap_model(controlnet)\n controlnet.save_pretrained(args.output_dir)\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n image_logs=image_logs,\n base_model=args.pretrained_model_name_or_path,\n repo_folder=args.output_dir,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/research_projects/diffusion_dpo/README.md", "content": "# Diffusion Model Alignment Using Direct Preference Optimization\n\nThis directory provides LoRA implementations of Diffusion DPO proposed in [DiffusionModel Alignment Using Direct Preference Optimization](https://arxiv.org/abs/2311.12908) by Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, and Nikhil Naik.\n\nWe provide implementations for both Stable Diffusion (SD) and Stable Diffusion XL (SDXL). The original checkpoints are available at the URLs below:\n\n* [mhdang/dpo-sd1.5-text2image-v1](https://huggingface.co/mhdang/dpo-sd1.5-text2image-v1)\n* [mhdang/dpo-sdxl-text2image-v1](https://huggingface.co/mhdang/dpo-sdxl-text2image-v1)\n\n> 💡 Note: The scripts are highly experimental and were only tested on low-data regimes. Proceed with caution. Feel free to let us know about your findings via GitHub issues. \n\n## SD training command\n\n```bash\naccelerate launch train_diffusion_dpo.py \\\n --pretrained_model_name_or_path=runwayml/stable-diffusion-v1-5 \\\n --output_dir=\"diffusion-dpo\" \\\n --mixed_precision=\"fp16\" \\\n --dataset_name=kashif/pickascore \\\n --resolution=512 \\\n --train_batch_size=16 \\\n --gradient_accumulation_steps=2 \\\n --gradient_checkpointing \\\n --use_8bit_adam \\\n --rank=8 \\\n --learning_rate=1e-5 \\\n --report_to=\"wandb\" \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --max_train_steps=10000 \\\n --checkpointing_steps=2000 \\\n --run_validation --validation_steps=200 \\\n --seed=\"0\" \\\n --report_to=\"wandb\" \\\n --push_to_hub\n```\n\n## SDXL training command\n\n```bash\naccelerate launch train_diffusion_dpo_sdxl.py \\\n --pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0 \\\n --pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix \\\n --output_dir=\"diffusion-sdxl-dpo\" \\\n --mixed_precision=\"fp16\" \\\n --dataset_name=kashif/pickascore \\\n --train_batch_size=8 \\\n --gradient_accumulation_steps=2 \\\n --gradient_checkpointing \\\n --use_8bit_adam \\\n --rank=8 \\\n --learning_rate=1e-5 \\\n --report_to=\"wandb\" \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --max_train_steps=2000 \\\n --checkpointing_steps=500 \\\n --run_validation --validation_steps=50 \\\n --seed=\"0\" \\\n --report_to=\"wandb\" \\\n --push_to_hub\n```\n\n## SDXL Turbo training command\n\n```bash\naccelerate launch train_diffusion_dpo_sdxl.py \\\n --pretrained_model_name_or_path=stabilityai/sdxl-turbo \\\n --pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix \\\n --output_dir=\"diffusion-sdxl-turbo-dpo\" \\\n --mixed_precision=\"fp16\" \\\n --dataset_name=kashif/pickascore \\\n --train_batch_size=8 \\\n --gradient_accumulation_steps=2 \\\n --gradient_checkpointing \\\n --use_8bit_adam \\\n --rank=8 \\\n --learning_rate=1e-5 \\\n --report_to=\"wandb\" \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --max_train_steps=2000 \\\n --checkpointing_steps=500 \\\n --run_validation --validation_steps=50 \\\n --seed=\"0\" \\\n --report_to=\"wandb\" \\\n --is_turbo --resolution 512 \\\n --push_to_hub\n```\n\n\n## Acknowledgements\n\nThis is based on the amazing work done by [Bram](https://github.com/bram-w) here for Diffusion DPO: https://github.com/bram-w/trl/blob/dpo/. \n" }, { "path": "diffusers/examples/research_projects/diffusion_dpo/requirements.txt", "content": "accelerate>=0.16.0\ntorchvision\ntransformers>=4.25.1\nftfy\ntensorboard\nJinja2\npeft\nwandb" }, { "path": "diffusers/examples/research_projects/diffusion_dpo/train_diffusion_dpo.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 bram-w, The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport contextlib\nimport io\nimport logging\nimport math\nimport os\nimport shutil\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nimport wandb\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom peft import LoraConfig\nfrom peft.utils import get_peft_model_state_dict\nfrom PIL import Image\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n DiffusionPipeline,\n DPMSolverMultistepScheduler,\n UNet2DConditionModel,\n)\nfrom diffusers.loaders import LoraLoaderMixin\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import check_min_version, convert_state_dict_to_diffusers\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.25.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\nVALIDATION_PROMPTS = [\n \"portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography\",\n \"Self-portrait oil painting, a beautiful cyborg with golden hair, 8k\",\n \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\",\n \"A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece\",\n]\n\n\ndef import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path,\n subfolder=\"text_encoder\",\n revision=revision,\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef log_validation(args, unet, accelerator, weight_dtype, epoch, is_final_validation=False):\n logger.info(f\"Running validation... \\n Generating images with prompts:\\n\" f\" {VALIDATION_PROMPTS}.\")\n\n # create pipeline\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n if not is_final_validation:\n pipeline.unet = accelerator.unwrap_model(unet)\n else:\n pipeline.load_lora_weights(args.output_dir, weight_name=\"pytorch_lora_weights.safetensors\")\n\n pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n images = []\n context = contextlib.nullcontext() if is_final_validation else torch.cuda.amp.autocast()\n\n for prompt in VALIDATION_PROMPTS:\n with context:\n image = pipeline(prompt, num_inference_steps=25, generator=generator).images[0]\n images.append(image)\n\n tracker_key = \"test\" if is_final_validation else \"validation\"\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(tracker_key, np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n tracker_key: [\n wandb.Image(image, caption=f\"{i}: {VALIDATION_PROMPTS[i]}\") for i, image in enumerate(images)\n ]\n }\n )\n\n # Also log images without the LoRA params for comparison.\n if is_final_validation:\n pipeline.disable_lora()\n no_lora_images = [\n pipeline(prompt, num_inference_steps=25, generator=generator).images[0] for prompt in VALIDATION_PROMPTS\n ]\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in no_lora_images])\n tracker.writer.add_images(\"test_without_lora\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"test_without_lora\": [\n wandb.Image(image, caption=f\"{i}: {VALIDATION_PROMPTS[i]}\")\n for i, image in enumerate(no_lora_images)\n ]\n }\n )\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_split_name\",\n type=str,\n default=\"validation\",\n help=\"Dataset split to be used during training. Helpful to specify for conducting experimental runs.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--run_validation\",\n default=False,\n action=\"store_true\",\n help=\"Whether to run validation inference in between training and also after training. Helps to track progress.\",\n )\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=200,\n help=\"Run validation every X steps.\",\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"diffusion-dpo-lora\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--vae_encode_batch_size\",\n type=int,\n default=8,\n help=\"Batch size to use for VAE encoding of the images for efficient processing.\",\n )\n parser.add_argument(\n \"--no_hflip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--random_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to random crop the input images to the resolution. If not set, the images will be center-cropped.\"\n ),\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final\"\n \" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--beta_dpo\",\n type=int,\n default=2500,\n help=\"DPO KL Divergence penalty.\",\n )\n parser.add_argument(\n \"--loss_type\",\n type=str,\n default=\"sigmoid\",\n help=\"DPO loss type. Can be one of 'sigmoid' (default), 'ipo', or 'cpo'\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=5e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\"--lr_power\", type=float, default=1.0, help=\"Power factor of the polynomial scheduler.\")\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--prior_generation_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp32\", \"fp16\", \"bf16\"],\n help=(\n \"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--rank\",\n type=int,\n default=4,\n help=(\"The dimension of the LoRA update matrices.\"),\n )\n parser.add_argument(\n \"--tracker_name\",\n type=str,\n default=\"diffusion-dpo-lora\",\n help=(\"The name of the tracker to report results to.\"),\n )\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n if args.dataset_name is None:\n raise ValueError(\"Must provide a `dataset_name`.\")\n\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n return args\n\n\ndef tokenize_captions(tokenizer, examples):\n max_length = tokenizer.model_max_length\n captions = []\n for caption in examples[\"caption\"]:\n captions.append(caption)\n\n text_inputs = tokenizer(\n captions, truncation=True, padding=\"max_length\", max_length=max_length, return_tensors=\"pt\"\n )\n\n return text_inputs.input_ids\n\n\n@torch.no_grad()\ndef encode_prompt(text_encoder, input_ids):\n text_input_ids = input_ids.to(text_encoder.device)\n attention_mask = None\n\n prompt_embeds = text_encoder(text_input_ids, attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n\n return prompt_embeds\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizer\n tokenizer = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n\n # import correct text encoder class\n text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n text_encoder = text_encoder_cls.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision, variant=args.variant\n )\n\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n vae.requires_grad_(False)\n text_encoder.requires_grad_(False)\n unet.requires_grad_(False)\n\n # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move unet, vae and text_encoder to device and cast to weight_dtype\n unet.to(accelerator.device, dtype=weight_dtype)\n vae.to(accelerator.device, dtype=weight_dtype)\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n\n # Set up LoRA.\n unet_lora_config = LoraConfig(\n r=args.rank,\n lora_alpha=args.rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"to_k\", \"to_q\", \"to_v\", \"to_out.0\"],\n )\n # Add adapter and make sure the trainable params are in float32.\n unet.add_adapter(unet_lora_config)\n if args.mixed_precision == \"fp16\":\n for param in unet.parameters():\n # only upcast trainable parameters (LoRA) into fp32\n if param.requires_grad:\n param.data = param.to(torch.float32)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n # there are only two options here. Either are just the unet attn processor layers\n # or there are the unet and text encoder atten layers\n unet_lora_layers_to_save = None\n\n for model in models:\n if isinstance(model, type(accelerator.unwrap_model(unet))):\n unet_lora_layers_to_save = convert_state_dict_to_diffusers(get_peft_model_state_dict(model))\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n LoraLoaderMixin.save_lora_weights(\n output_dir,\n unet_lora_layers=unet_lora_layers_to_save,\n text_encoder_lora_layers=None,\n )\n\n def load_model_hook(models, input_dir):\n unet_ = None\n\n while len(models) > 0:\n model = models.pop()\n\n if isinstance(model, type(accelerator.unwrap_model(unet))):\n unet_ = model\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n lora_state_dict, network_alphas = LoraLoaderMixin.lora_state_dict(input_dir)\n LoraLoaderMixin.load_lora_into_unet(lora_state_dict, network_alphas=network_alphas, unet=unet_)\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # Optimizer creation\n params_to_optimize = list(filter(lambda p: p.requires_grad, unet.parameters()))\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Dataset and DataLoaders creation:\n train_dataset = load_dataset(\n args.dataset_name,\n cache_dir=args.cache_dir,\n split=args.dataset_split_name,\n )\n\n train_transforms = transforms.Compose(\n [\n transforms.Resize(int(args.resolution), interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.RandomCrop(args.resolution) if args.random_crop else transforms.CenterCrop(args.resolution),\n transforms.Lambda(lambda x: x) if args.no_hflip else transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def preprocess_train(examples):\n all_pixel_values = []\n for col_name in [\"jpg_0\", \"jpg_1\"]:\n images = [Image.open(io.BytesIO(im_bytes)).convert(\"RGB\") for im_bytes in examples[col_name]]\n pixel_values = [train_transforms(image) for image in images]\n all_pixel_values.append(pixel_values)\n\n # Double on channel dim, jpg_y then jpg_w\n im_tup_iterator = zip(*all_pixel_values)\n combined_pixel_values = []\n for im_tup, label_0 in zip(im_tup_iterator, examples[\"label_0\"]):\n if label_0 == 0:\n im_tup = im_tup[::-1]\n combined_im = torch.cat(im_tup, dim=0) # no batch dim\n combined_pixel_values.append(combined_im)\n examples[\"pixel_values\"] = combined_pixel_values\n\n examples[\"input_ids\"] = tokenize_captions(tokenizer, examples)\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n train_dataset = train_dataset.shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = train_dataset.with_transform(preprocess_train)\n\n def collate_fn(examples):\n pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n final_dict = {\"pixel_values\": pixel_values}\n final_dict[\"input_ids\"] = torch.stack([example[\"input_ids\"] for example in examples])\n return final_dict\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n batch_size=args.train_batch_size,\n shuffle=True,\n collate_fn=collate_fn,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_cycles=args.lr_num_cycles,\n power=args.lr_power,\n )\n\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(args.tracker_name, config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num batches each epoch = {len(train_dataloader)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the mos recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n unet.train()\n for epoch in range(first_epoch, args.num_train_epochs):\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n # (batch_size, 2*channels, h, w) -> (2*batch_size, channels, h, w)\n pixel_values = batch[\"pixel_values\"].to(dtype=weight_dtype)\n feed_pixel_values = torch.cat(pixel_values.chunk(2, dim=1))\n\n latents = []\n for i in range(0, feed_pixel_values.shape[0], args.vae_encode_batch_size):\n latents.append(\n vae.encode(feed_pixel_values[i : i + args.vae_encode_batch_size]).latent_dist.sample()\n )\n latents = torch.cat(latents, dim=0)\n latents = latents * vae.config.scaling_factor\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents).chunk(2)[0].repeat(2, 1, 1, 1)\n\n # Sample a random timestep for each image\n bsz = latents.shape[0] // 2\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device, dtype=torch.long\n ).repeat(2)\n\n # Add noise to the model input according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_model_input = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # Get the text embedding for conditioning\n encoder_hidden_states = encode_prompt(text_encoder, batch[\"input_ids\"]).repeat(2, 1, 1)\n\n # Predict the noise residual\n model_pred = unet(\n noisy_model_input,\n timesteps,\n encoder_hidden_states,\n ).sample\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n # Compute losses.\n model_losses = F.mse_loss(model_pred.float(), target.float(), reduction=\"none\")\n model_losses = model_losses.mean(dim=list(range(1, len(model_losses.shape))))\n model_losses_w, model_losses_l = model_losses.chunk(2)\n\n # For logging\n raw_model_loss = 0.5 * (model_losses_w.mean() + model_losses_l.mean())\n model_diff = model_losses_w - model_losses_l # These are both LBS (as is t)\n\n # Reference model predictions.\n accelerator.unwrap_model(unet).disable_adapters()\n with torch.no_grad():\n ref_preds = unet(\n noisy_model_input,\n timesteps,\n encoder_hidden_states,\n ).sample.detach()\n ref_loss = F.mse_loss(ref_preds.float(), target.float(), reduction=\"none\")\n ref_loss = ref_loss.mean(dim=list(range(1, len(ref_loss.shape))))\n\n ref_losses_w, ref_losses_l = ref_loss.chunk(2)\n ref_diff = ref_losses_w - ref_losses_l\n raw_ref_loss = ref_loss.mean()\n\n # Re-enable adapters.\n accelerator.unwrap_model(unet).enable_adapters()\n\n # Final loss.\n logits = ref_diff - model_diff\n if args.loss_type == \"sigmoid\":\n loss = -1 * F.logsigmoid(args.beta_dpo * logits).mean()\n elif args.loss_type == \"hinge\":\n loss = torch.relu(1 - args.beta_dpo * logits).mean()\n elif args.loss_type == \"ipo\":\n losses = (logits - 1 / (2 * args.beta)) ** 2\n loss = losses.mean()\n else:\n raise ValueError(f\"Unknown loss type {args.loss_type}\")\n\n implicit_acc = (logits > 0).sum().float() / logits.size(0)\n implicit_acc += 0.5 * (logits == 0).sum().float() / logits.size(0)\n\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(params_to_optimize, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n if args.run_validation and global_step % args.validation_steps == 0:\n log_validation(\n args, unet=unet, accelerator=accelerator, weight_dtype=weight_dtype, epoch=epoch\n )\n\n logs = {\n \"loss\": loss.detach().item(),\n \"raw_model_loss\": raw_model_loss.detach().item(),\n \"ref_loss\": raw_ref_loss.detach().item(),\n \"implicit_acc\": implicit_acc.detach().item(),\n \"lr\": lr_scheduler.get_last_lr()[0],\n }\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n # Save the lora layers\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = accelerator.unwrap_model(unet)\n unet = unet.to(torch.float32)\n unet_lora_state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(unet))\n\n LoraLoaderMixin.save_lora_weights(\n save_directory=args.output_dir, unet_lora_layers=unet_lora_state_dict, text_encoder_lora_layers=None\n )\n\n # Final validation?\n if args.run_validation:\n log_validation(\n args,\n unet=None,\n accelerator=accelerator,\n weight_dtype=weight_dtype,\n epoch=epoch,\n is_final_validation=True,\n )\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/research_projects/diffusion_dpo/train_diffusion_dpo_sdxl.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 bram-w, The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport contextlib\nimport io\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nimport wandb\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom peft import LoraConfig\nfrom peft.utils import get_peft_model_state_dict\nfrom PIL import Image\nfrom torchvision import transforms\nfrom torchvision.transforms.functional import crop\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n DiffusionPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.loaders import StableDiffusionXLLoraLoaderMixin\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import check_min_version, convert_state_dict_to_diffusers\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.25.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\nVALIDATION_PROMPTS = [\n \"portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography\",\n \"Self-portrait oil painting, a beautiful cyborg with golden hair, 8k\",\n \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\",\n \"A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece\",\n]\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef log_validation(args, unet, vae, accelerator, weight_dtype, epoch, is_final_validation=False):\n logger.info(f\"Running validation... \\n Generating images with prompts:\\n\" f\" {VALIDATION_PROMPTS}.\")\n\n if is_final_validation:\n if args.mixed_precision == \"fp16\":\n vae.to(weight_dtype)\n\n # create pipeline\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n if not is_final_validation:\n pipeline.unet = accelerator.unwrap_model(unet)\n else:\n pipeline.load_lora_weights(args.output_dir, weight_name=\"pytorch_lora_weights.safetensors\")\n\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n images = []\n context = contextlib.nullcontext() if is_final_validation else torch.cuda.amp.autocast()\n\n guidance_scale = 5.0\n num_inference_steps = 25\n if args.is_turbo:\n guidance_scale = 0.0\n num_inference_steps = 4\n for prompt in VALIDATION_PROMPTS:\n with context:\n image = pipeline(\n prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, generator=generator\n ).images[0]\n images.append(image)\n\n tracker_key = \"test\" if is_final_validation else \"validation\"\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(tracker_key, np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n tracker_key: [\n wandb.Image(image, caption=f\"{i}: {VALIDATION_PROMPTS[i]}\") for i, image in enumerate(images)\n ]\n }\n )\n\n # Also log images without the LoRA params for comparison.\n if is_final_validation:\n pipeline.disable_lora()\n no_lora_images = [\n pipeline(\n prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, generator=generator\n ).images[0]\n for prompt in VALIDATION_PROMPTS\n ]\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in no_lora_images])\n tracker.writer.add_images(\"test_without_lora\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"test_without_lora\": [\n wandb.Image(image, caption=f\"{i}: {VALIDATION_PROMPTS[i]}\")\n for i, image in enumerate(no_lora_images)\n ]\n }\n )\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_vae_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_split_name\",\n type=str,\n default=\"validation\",\n help=\"Dataset split to be used during training. Helpful to specify for conducting experimental runs.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--run_validation\",\n default=False,\n action=\"store_true\",\n help=\"Whether to run validation inference in between training and also after training. Helps to track progress.\",\n )\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=200,\n help=\"Run validation every X steps.\",\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"diffusion-dpo-lora\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=1024,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--vae_encode_batch_size\",\n type=int,\n default=8,\n help=\"Batch size to use for VAE encoding of the images for efficient processing.\",\n )\n parser.add_argument(\n \"--no_hflip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--random_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to random crop the input images to the resolution. If not set, the images will be center-cropped.\"\n ),\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final\"\n \" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--beta_dpo\",\n type=int,\n default=5000,\n help=\"DPO KL Divergence penalty.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=5e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\"--lr_power\", type=float, default=1.0, help=\"Power factor of the polynomial scheduler.\")\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--prior_generation_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp32\", \"fp16\", \"bf16\"],\n help=(\n \"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--is_turbo\",\n action=\"store_true\",\n help=(\"Use if tuning SDXL Turbo instead of SDXL\"),\n )\n parser.add_argument(\n \"--rank\",\n type=int,\n default=4,\n help=(\"The dimension of the LoRA update matrices.\"),\n )\n parser.add_argument(\n \"--tracker_name\",\n type=str,\n default=\"diffusion-dpo-lora-sdxl\",\n help=(\"The name of the tracker to report results to.\"),\n )\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n if args.dataset_name is None:\n raise ValueError(\"Must provide a `dataset_name`.\")\n\n if args.is_turbo:\n assert \"turbo\" in args.pretrained_model_name_or_path\n\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n return args\n\n\ndef tokenize_captions(tokenizers, examples):\n captions = []\n for caption in examples[\"caption\"]:\n captions.append(caption)\n\n tokens_one = tokenizers[0](\n captions, truncation=True, padding=\"max_length\", max_length=tokenizers[0].model_max_length, return_tensors=\"pt\"\n ).input_ids\n tokens_two = tokenizers[1](\n captions, truncation=True, padding=\"max_length\", max_length=tokenizers[1].model_max_length, return_tensors=\"pt\"\n ).input_ids\n\n return tokens_one, tokens_two\n\n\n@torch.no_grad()\ndef encode_prompt(text_encoders, text_input_ids_list):\n prompt_embeds_list = []\n\n for i, text_encoder in enumerate(text_encoders):\n text_input_ids = text_input_ids_list[i]\n\n prompt_embeds = text_encoder(\n text_input_ids.to(text_encoder.device),\n output_hidden_states=True,\n )\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds.hidden_states[-2]\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)\n return prompt_embeds, pooled_prompt_embeds\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizers\n tokenizer_one = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n tokenizer_two = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer_2\",\n revision=args.revision,\n use_fast=False,\n )\n\n # import correct text encoder classes\n text_encoder_cls_one = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision\n )\n text_encoder_cls_two = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision, subfolder=\"text_encoder_2\"\n )\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n\n def enforce_zero_terminal_snr(scheduler):\n # Modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py#L93\n # Original implementation https://arxiv.org/pdf/2305.08891.pdf\n # Turbo needs zero terminal SNR\n # Turbo: https://static1.squarespace.com/static/6213c340453c3f502425776e/t/65663480a92fba51d0e1023f/1701197769659/adversarial_diffusion_distillation.pdf\n # Convert betas to alphas_bar_sqrt\n alphas = 1 - scheduler.betas\n alphas_bar = alphas.cumprod(0)\n alphas_bar_sqrt = alphas_bar.sqrt()\n\n # Store old values.\n alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()\n alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()\n # Shift so last timestep is zero.\n alphas_bar_sqrt -= alphas_bar_sqrt_T\n # Scale so first timestep is back to old value.\n alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)\n\n alphas_bar = alphas_bar_sqrt**2\n alphas = alphas_bar[1:] / alphas_bar[:-1]\n alphas = torch.cat([alphas_bar[0:1], alphas])\n\n alphas_cumprod = torch.cumprod(alphas, dim=0)\n scheduler.alphas_cumprod = alphas_cumprod\n return\n\n if args.is_turbo:\n enforce_zero_terminal_snr(noise_scheduler)\n\n text_encoder_one = text_encoder_cls_one.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n text_encoder_two = text_encoder_cls_two.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder_2\", revision=args.revision, variant=args.variant\n )\n vae_path = (\n args.pretrained_model_name_or_path\n if args.pretrained_vae_model_name_or_path is None\n else args.pretrained_vae_model_name_or_path\n )\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n variant=args.variant,\n )\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n # We only train the additional adapter LoRA layers\n vae.requires_grad_(False)\n text_encoder_one.requires_grad_(False)\n text_encoder_two.requires_grad_(False)\n unet.requires_grad_(False)\n\n # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move unet and text_encoders to device and cast to weight_dtype\n unet.to(accelerator.device, dtype=weight_dtype)\n text_encoder_one.to(accelerator.device, dtype=weight_dtype)\n text_encoder_two.to(accelerator.device, dtype=weight_dtype)\n\n # The VAE is always in float32 to avoid NaN losses.\n vae.to(accelerator.device, dtype=torch.float32)\n\n # Set up LoRA.\n unet_lora_config = LoraConfig(\n r=args.rank,\n lora_alpha=args.rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"to_k\", \"to_q\", \"to_v\", \"to_out.0\"],\n )\n # Add adapter and make sure the trainable params are in float32.\n unet.add_adapter(unet_lora_config)\n if args.mixed_precision == \"fp16\":\n for param in unet.parameters():\n # only upcast trainable parameters (LoRA) into fp32\n if param.requires_grad:\n param.data = param.to(torch.float32)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n # there are only two options here. Either are just the unet attn processor layers\n # or there are the unet and text encoder atten layers\n unet_lora_layers_to_save = None\n\n for model in models:\n if isinstance(model, type(accelerator.unwrap_model(unet))):\n unet_lora_layers_to_save = convert_state_dict_to_diffusers(get_peft_model_state_dict(model))\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n StableDiffusionXLLoraLoaderMixin.save_lora_weights(output_dir, unet_lora_layers=unet_lora_layers_to_save)\n\n def load_model_hook(models, input_dir):\n unet_ = None\n\n while len(models) > 0:\n model = models.pop()\n\n if isinstance(model, type(accelerator.unwrap_model(unet))):\n unet_ = model\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n lora_state_dict, network_alphas = StableDiffusionXLLoraLoaderMixin.lora_state_dict(input_dir)\n StableDiffusionXLLoraLoaderMixin.load_lora_into_unet(\n lora_state_dict, network_alphas=network_alphas, unet=unet_\n )\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # Optimizer creation\n params_to_optimize = list(filter(lambda p: p.requires_grad, unet.parameters()))\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Dataset and DataLoaders creation:\n train_dataset = load_dataset(\n args.dataset_name,\n cache_dir=args.cache_dir,\n split=args.dataset_split_name,\n )\n\n # Preprocessing the datasets.\n train_resize = transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR)\n train_crop = transforms.RandomCrop(args.resolution) if args.random_crop else transforms.CenterCrop(args.resolution)\n train_flip = transforms.RandomHorizontalFlip(p=1.0)\n to_tensor = transforms.ToTensor()\n normalize = transforms.Normalize([0.5], [0.5])\n\n def preprocess_train(examples):\n all_pixel_values = []\n images = [Image.open(io.BytesIO(im_bytes)).convert(\"RGB\") for im_bytes in examples[\"jpg_0\"]]\n original_sizes = [(image.height, image.width) for image in images]\n crop_top_lefts = []\n\n for col_name in [\"jpg_0\", \"jpg_1\"]:\n images = [Image.open(io.BytesIO(im_bytes)).convert(\"RGB\") for im_bytes in examples[col_name]]\n if col_name == \"jpg_1\":\n # Need to bring down the image to the same resolution.\n # This seems like the simplest reasonable approach.\n # \"::-1\" because PIL resize takes (width, height).\n images = [image.resize(original_sizes[i][::-1]) for i, image in enumerate(images)]\n pixel_values = [to_tensor(image) for image in images]\n all_pixel_values.append(pixel_values)\n\n # Double on channel dim, jpg_y then jpg_w\n im_tup_iterator = zip(*all_pixel_values)\n combined_pixel_values = []\n for im_tup, label_0 in zip(im_tup_iterator, examples[\"label_0\"]):\n if label_0 == 0:\n im_tup = im_tup[::-1]\n\n combined_im = torch.cat(im_tup, dim=0) # no batch dim\n\n # Resize.\n combined_im = train_resize(combined_im)\n\n # Flipping.\n if not args.no_hflip and random.random() < 0.5:\n combined_im = train_flip(combined_im)\n\n # Cropping.\n if not args.random_crop:\n y1 = max(0, int(round((combined_im.shape[1] - args.resolution) / 2.0)))\n x1 = max(0, int(round((combined_im.shape[2] - args.resolution) / 2.0)))\n combined_im = train_crop(combined_im)\n else:\n y1, x1, h, w = train_crop.get_params(combined_im, (args.resolution, args.resolution))\n combined_im = crop(combined_im, y1, x1, h, w)\n\n crop_top_left = (y1, x1)\n crop_top_lefts.append(crop_top_left)\n combined_im = normalize(combined_im)\n combined_pixel_values.append(combined_im)\n\n examples[\"pixel_values\"] = combined_pixel_values\n examples[\"original_sizes\"] = original_sizes\n examples[\"crop_top_lefts\"] = crop_top_lefts\n tokens_one, tokens_two = tokenize_captions([tokenizer_one, tokenizer_two], examples)\n examples[\"input_ids_one\"] = tokens_one\n examples[\"input_ids_two\"] = tokens_two\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n train_dataset = train_dataset.shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = train_dataset.with_transform(preprocess_train)\n\n def collate_fn(examples):\n pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n original_sizes = [example[\"original_sizes\"] for example in examples]\n crop_top_lefts = [example[\"crop_top_lefts\"] for example in examples]\n input_ids_one = torch.stack([example[\"input_ids_one\"] for example in examples])\n input_ids_two = torch.stack([example[\"input_ids_two\"] for example in examples])\n\n return {\n \"pixel_values\": pixel_values,\n \"input_ids_one\": input_ids_one,\n \"input_ids_two\": input_ids_two,\n \"original_sizes\": original_sizes,\n \"crop_top_lefts\": crop_top_lefts,\n }\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n batch_size=args.train_batch_size,\n shuffle=True,\n collate_fn=collate_fn,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_cycles=args.lr_num_cycles,\n power=args.lr_power,\n )\n\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(args.tracker_name, config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num batches each epoch = {len(train_dataloader)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the mos recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n unet.train()\n for epoch in range(first_epoch, args.num_train_epochs):\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n # (batch_size, 2*channels, h, w) -> (2*batch_size, channels, h, w)\n pixel_values = batch[\"pixel_values\"].to(dtype=vae.dtype)\n feed_pixel_values = torch.cat(pixel_values.chunk(2, dim=1))\n\n latents = []\n for i in range(0, feed_pixel_values.shape[0], args.vae_encode_batch_size):\n latents.append(\n vae.encode(feed_pixel_values[i : i + args.vae_encode_batch_size]).latent_dist.sample()\n )\n latents = torch.cat(latents, dim=0)\n latents = latents * vae.config.scaling_factor\n if args.pretrained_vae_model_name_or_path is None:\n latents = latents.to(weight_dtype)\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents).chunk(2)[0].repeat(2, 1, 1, 1)\n\n # Sample a random timestep for each image\n bsz = latents.shape[0] // 2\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device, dtype=torch.long\n ).repeat(2)\n if args.is_turbo:\n # Learn a 4 timestep schedule\n timesteps_0_to_3 = timesteps % 4\n timesteps = 250 * timesteps_0_to_3 + 249\n\n # Add noise to the model input according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_model_input = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # time ids\n def compute_time_ids(original_size, crops_coords_top_left):\n # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids\n target_size = (args.resolution, args.resolution)\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n add_time_ids = torch.tensor([add_time_ids])\n add_time_ids = add_time_ids.to(accelerator.device, dtype=weight_dtype)\n return add_time_ids\n\n add_time_ids = torch.cat(\n [compute_time_ids(s, c) for s, c in zip(batch[\"original_sizes\"], batch[\"crop_top_lefts\"])]\n ).repeat(2, 1)\n\n # Get the text embedding for conditioning\n prompt_embeds, pooled_prompt_embeds = encode_prompt(\n [text_encoder_one, text_encoder_two], [batch[\"input_ids_one\"], batch[\"input_ids_two\"]]\n )\n prompt_embeds = prompt_embeds.repeat(2, 1, 1)\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(2, 1)\n\n # Predict the noise residual\n model_pred = unet(\n noisy_model_input,\n timesteps,\n prompt_embeds,\n added_cond_kwargs={\"time_ids\": add_time_ids, \"text_embeds\": pooled_prompt_embeds},\n ).sample\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n # Compute losses.\n model_losses = F.mse_loss(model_pred.float(), target.float(), reduction=\"none\")\n model_losses = model_losses.mean(dim=list(range(1, len(model_losses.shape))))\n model_losses_w, model_losses_l = model_losses.chunk(2)\n\n # For logging\n raw_model_loss = 0.5 * (model_losses_w.mean() + model_losses_l.mean())\n model_diff = model_losses_w - model_losses_l # These are both LBS (as is t)\n\n # Reference model predictions.\n accelerator.unwrap_model(unet).disable_adapters()\n with torch.no_grad():\n ref_preds = unet(\n noisy_model_input,\n timesteps,\n prompt_embeds,\n added_cond_kwargs={\"time_ids\": add_time_ids, \"text_embeds\": pooled_prompt_embeds},\n ).sample\n ref_loss = F.mse_loss(ref_preds.float(), target.float(), reduction=\"none\")\n ref_loss = ref_loss.mean(dim=list(range(1, len(ref_loss.shape))))\n\n ref_losses_w, ref_losses_l = ref_loss.chunk(2)\n ref_diff = ref_losses_w - ref_losses_l\n raw_ref_loss = ref_loss.mean()\n\n # Re-enable adapters.\n accelerator.unwrap_model(unet).enable_adapters()\n\n # Final loss.\n scale_term = -0.5 * args.beta_dpo\n inside_term = scale_term * (model_diff - ref_diff)\n loss = -1 * F.logsigmoid(inside_term).mean()\n\n implicit_acc = (inside_term > 0).sum().float() / inside_term.size(0)\n implicit_acc += 0.5 * (inside_term == 0).sum().float() / inside_term.size(0)\n\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(params_to_optimize, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n if args.run_validation and global_step % args.validation_steps == 0:\n log_validation(\n args, unet=unet, vae=vae, accelerator=accelerator, weight_dtype=weight_dtype, epoch=epoch\n )\n\n logs = {\n \"loss\": loss.detach().item(),\n \"raw_model_loss\": raw_model_loss.detach().item(),\n \"ref_loss\": raw_ref_loss.detach().item(),\n \"implicit_acc\": implicit_acc.detach().item(),\n \"lr\": lr_scheduler.get_last_lr()[0],\n }\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n # Save the lora layers\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = accelerator.unwrap_model(unet)\n unet = unet.to(torch.float32)\n unet_lora_state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(unet))\n\n StableDiffusionXLLoraLoaderMixin.save_lora_weights(\n save_directory=args.output_dir,\n unet_lora_layers=unet_lora_state_dict,\n text_encoder_lora_layers=None,\n text_encoder_2_lora_layers=None,\n )\n\n # Final validation?\n if args.run_validation:\n log_validation(\n args,\n unet=None,\n vae=vae,\n accelerator=accelerator,\n weight_dtype=weight_dtype,\n epoch=epoch,\n is_final_validation=True,\n )\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/research_projects/diffusion_orpo/README.md", "content": "This project has a new home now: [https://mapo-t2i.github.io/](https://mapo-t2i.github.io/). We formally studied the use of ORPO in the context of diffusion models and open-sourced our codebase, models, and datasets. We released our paper too!\n" }, { "path": "diffusers/examples/research_projects/diffusion_orpo/requirements.txt", "content": "datasets\naccelerate\ntransformers\ntorchvision\nwandb\npeft\nwebdataset" }, { "path": "diffusers/examples/research_projects/diffusion_orpo/train_diffusion_orpo_sdxl_lora.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport contextlib\nimport io\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nimport wandb\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom peft import LoraConfig, set_peft_model_state_dict\nfrom peft.utils import get_peft_model_state_dict\nfrom PIL import Image\nfrom torchvision import transforms\nfrom torchvision.transforms.functional import crop\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n DiffusionPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.loaders import StableDiffusionXLLoraLoaderMixin\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import check_min_version, convert_state_dict_to_diffusers, convert_unet_state_dict_to_peft\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.25.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\nVALIDATION_PROMPTS = [\n \"portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography\",\n \"Self-portrait oil painting, a beautiful cyborg with golden hair, 8k\",\n \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\",\n \"A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece\",\n]\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef log_validation(args, unet, vae, accelerator, weight_dtype, epoch, is_final_validation=False):\n logger.info(f\"Running validation... \\n Generating images with prompts:\\n\" f\" {VALIDATION_PROMPTS}.\")\n\n if is_final_validation:\n if args.mixed_precision == \"fp16\":\n vae.to(weight_dtype)\n\n # create pipeline\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n if not is_final_validation:\n pipeline.unet = accelerator.unwrap_model(unet)\n else:\n pipeline.load_lora_weights(args.output_dir, weight_name=\"pytorch_lora_weights.safetensors\")\n\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n images = []\n context = contextlib.nullcontext() if is_final_validation else torch.cuda.amp.autocast()\n\n guidance_scale = 5.0\n num_inference_steps = 25\n for prompt in VALIDATION_PROMPTS:\n with context:\n image = pipeline(\n prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, generator=generator\n ).images[0]\n images.append(image)\n\n tracker_key = \"test\" if is_final_validation else \"validation\"\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(tracker_key, np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n tracker_key: [\n wandb.Image(image, caption=f\"{i}: {VALIDATION_PROMPTS[i]}\") for i, image in enumerate(images)\n ]\n }\n )\n\n # Also log images without the LoRA params for comparison.\n if is_final_validation:\n pipeline.disable_lora()\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n no_lora_images = [\n pipeline(\n prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, generator=generator\n ).images[0]\n for prompt in VALIDATION_PROMPTS\n ]\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in no_lora_images])\n tracker.writer.add_images(\"test_without_lora\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"test_without_lora\": [\n wandb.Image(image, caption=f\"{i}: {VALIDATION_PROMPTS[i]}\")\n for i, image in enumerate(no_lora_images)\n ]\n }\n )\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_vae_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_split_name\",\n type=str,\n default=\"validation\",\n help=\"Dataset split to be used during training. Helpful to specify for conducting experimental runs.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--run_validation\",\n default=False,\n action=\"store_true\",\n help=\"Whether to run validation inference in between training and also after training. Helps to track progress.\",\n )\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=200,\n help=\"Run validation every X steps.\",\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"diffusion-orpo-lora-sdxl\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=1024,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--vae_encode_batch_size\",\n type=int,\n default=8,\n help=\"Batch size to use for VAE encoding of the images for efficient processing.\",\n )\n parser.add_argument(\n \"--no_hflip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--random_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to random crop the input images to the resolution. If not set, the images will be center-cropped.\"\n ),\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final\"\n \" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--beta_orpo\",\n type=float,\n default=0.1,\n help=\"ORPO contribution factor.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=5e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\"--lr_power\", type=float, default=1.0, help=\"Power factor of the polynomial scheduler.\")\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--prior_generation_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp32\", \"fp16\", \"bf16\"],\n help=(\n \"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--rank\",\n type=int,\n default=4,\n help=(\"The dimension of the LoRA update matrices.\"),\n )\n parser.add_argument(\n \"--tracker_name\",\n type=str,\n default=\"diffusion-orpo-lora-sdxl\",\n help=(\"The name of the tracker to report results to.\"),\n )\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n if args.dataset_name is None:\n raise ValueError(\"Must provide a `dataset_name`.\")\n\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n return args\n\n\ndef tokenize_captions(tokenizers, examples):\n captions = []\n for caption in examples[\"caption\"]:\n captions.append(caption)\n\n tokens_one = tokenizers[0](\n captions, truncation=True, padding=\"max_length\", max_length=tokenizers[0].model_max_length, return_tensors=\"pt\"\n ).input_ids\n tokens_two = tokenizers[1](\n captions, truncation=True, padding=\"max_length\", max_length=tokenizers[1].model_max_length, return_tensors=\"pt\"\n ).input_ids\n\n return tokens_one, tokens_two\n\n\n@torch.no_grad()\ndef encode_prompt(text_encoders, text_input_ids_list):\n prompt_embeds_list = []\n\n for i, text_encoder in enumerate(text_encoders):\n text_input_ids = text_input_ids_list[i]\n\n prompt_embeds = text_encoder(\n text_input_ids.to(text_encoder.device),\n output_hidden_states=True,\n )\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds.hidden_states[-2]\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)\n return prompt_embeds, pooled_prompt_embeds\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizers\n tokenizer_one = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n tokenizer_two = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer_2\",\n revision=args.revision,\n use_fast=False,\n )\n\n # import correct text encoder classes\n text_encoder_cls_one = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision\n )\n text_encoder_cls_two = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision, subfolder=\"text_encoder_2\"\n )\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n\n text_encoder_one = text_encoder_cls_one.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n text_encoder_two = text_encoder_cls_two.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder_2\", revision=args.revision, variant=args.variant\n )\n vae_path = (\n args.pretrained_model_name_or_path\n if args.pretrained_vae_model_name_or_path is None\n else args.pretrained_vae_model_name_or_path\n )\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n variant=args.variant,\n )\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n # We only train the additional adapter LoRA layers\n vae.requires_grad_(False)\n text_encoder_one.requires_grad_(False)\n text_encoder_two.requires_grad_(False)\n unet.requires_grad_(False)\n\n # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move unet and text_encoders to device and cast to weight_dtype\n unet.to(accelerator.device, dtype=weight_dtype)\n text_encoder_one.to(accelerator.device, dtype=weight_dtype)\n text_encoder_two.to(accelerator.device, dtype=weight_dtype)\n\n # The VAE is always in float32 to avoid NaN losses.\n vae.to(accelerator.device, dtype=torch.float32)\n\n # Set up LoRA.\n unet_lora_config = LoraConfig(\n r=args.rank,\n lora_alpha=args.rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"to_k\", \"to_q\", \"to_v\", \"to_out.0\"],\n )\n # Add adapter and make sure the trainable params are in float32.\n unet.add_adapter(unet_lora_config)\n if args.mixed_precision == \"fp16\":\n for param in unet.parameters():\n # only upcast trainable parameters (LoRA) into fp32\n if param.requires_grad:\n param.data = param.to(torch.float32)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n # there are only two options here. Either are just the unet attn processor layers\n # or there are the unet and text encoder atten layers\n unet_lora_layers_to_save = None\n\n for model in models:\n if isinstance(model, type(accelerator.unwrap_model(unet))):\n unet_lora_layers_to_save = convert_state_dict_to_diffusers(get_peft_model_state_dict(model))\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n StableDiffusionXLLoraLoaderMixin.save_lora_weights(\n output_dir,\n unet_lora_layers=unet_lora_layers_to_save,\n text_encoder_lora_layers=None,\n text_encoder_2_lora_layers=None,\n )\n\n def load_model_hook(models, input_dir):\n unet_ = None\n\n while len(models) > 0:\n model = models.pop()\n\n if isinstance(model, type(accelerator.unwrap_model(unet))):\n unet_ = model\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n lora_state_dict, network_alphas = StableDiffusionXLLoraLoaderMixin.lora_state_dict(input_dir)\n\n unet_state_dict = {f'{k.replace(\"unet.\", \"\")}': v for k, v in lora_state_dict.items() if k.startswith(\"unet.\")}\n unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict)\n incompatible_keys = set_peft_model_state_dict(unet_, unet_state_dict, adapter_name=\"default\")\n if incompatible_keys is not None:\n # check only for unexpected keys\n unexpected_keys = getattr(incompatible_keys, \"unexpected_keys\", None)\n if unexpected_keys:\n logger.warning(\n f\"Loading adapter weights from state_dict led to unexpected keys not found in the model: \"\n f\" {unexpected_keys}. \"\n )\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # Optimizer creation\n params_to_optimize = list(filter(lambda p: p.requires_grad, unet.parameters()))\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Dataset and DataLoaders creation:\n train_dataset = load_dataset(\n args.dataset_name,\n cache_dir=args.cache_dir,\n split=args.dataset_split_name,\n )\n\n # Preprocessing the datasets.\n train_resize = transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR)\n train_crop = transforms.RandomCrop(args.resolution) if args.random_crop else transforms.CenterCrop(args.resolution)\n train_flip = transforms.RandomHorizontalFlip(p=1.0)\n to_tensor = transforms.ToTensor()\n normalize = transforms.Normalize([0.5], [0.5])\n\n def preprocess_train(examples):\n all_pixel_values = []\n images = [Image.open(io.BytesIO(im_bytes)).convert(\"RGB\") for im_bytes in examples[\"jpg_0\"]]\n original_sizes = [(image.height, image.width) for image in images]\n crop_top_lefts = []\n\n for col_name in [\"jpg_0\", \"jpg_1\"]:\n images = [Image.open(io.BytesIO(im_bytes)).convert(\"RGB\") for im_bytes in examples[col_name]]\n if col_name == \"jpg_1\":\n # Need to bring down the image to the same resolution.\n # This seems like the simplest reasonable approach.\n # \"::-1\" because PIL resize takes (width, height).\n images = [image.resize(original_sizes[i][::-1]) for i, image in enumerate(images)]\n pixel_values = [to_tensor(image) for image in images]\n all_pixel_values.append(pixel_values)\n\n # Double on channel dim, jpg_y then jpg_w\n im_tup_iterator = zip(*all_pixel_values)\n combined_pixel_values = []\n for im_tup, label_0 in zip(im_tup_iterator, examples[\"label_0\"]):\n # We randomize selection and rejection.\n if label_0 == 0.5:\n if random.random() < 0.5:\n label_0 = 0\n else:\n label_0 = 1\n\n if label_0 == 0:\n im_tup = im_tup[::-1]\n\n combined_im = torch.cat(im_tup, dim=0) # no batch dim\n\n # Resize.\n combined_im = train_resize(combined_im)\n\n # Flipping.\n if not args.no_hflip and random.random() < 0.5:\n combined_im = train_flip(combined_im)\n\n # Cropping.\n if not args.random_crop:\n y1 = max(0, int(round((combined_im.shape[1] - args.resolution) / 2.0)))\n x1 = max(0, int(round((combined_im.shape[2] - args.resolution) / 2.0)))\n combined_im = train_crop(combined_im)\n else:\n y1, x1, h, w = train_crop.get_params(combined_im, (args.resolution, args.resolution))\n combined_im = crop(combined_im, y1, x1, h, w)\n\n crop_top_left = (y1, x1)\n crop_top_lefts.append(crop_top_left)\n combined_im = normalize(combined_im)\n combined_pixel_values.append(combined_im)\n\n examples[\"pixel_values\"] = combined_pixel_values\n examples[\"original_sizes\"] = original_sizes\n examples[\"crop_top_lefts\"] = crop_top_lefts\n tokens_one, tokens_two = tokenize_captions([tokenizer_one, tokenizer_two], examples)\n examples[\"input_ids_one\"] = tokens_one\n examples[\"input_ids_two\"] = tokens_two\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n train_dataset = train_dataset.shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = train_dataset.with_transform(preprocess_train)\n\n def collate_fn(examples):\n pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n original_sizes = [example[\"original_sizes\"] for example in examples]\n crop_top_lefts = [example[\"crop_top_lefts\"] for example in examples]\n input_ids_one = torch.stack([example[\"input_ids_one\"] for example in examples])\n input_ids_two = torch.stack([example[\"input_ids_two\"] for example in examples])\n\n return {\n \"pixel_values\": pixel_values,\n \"input_ids_one\": input_ids_one,\n \"input_ids_two\": input_ids_two,\n \"original_sizes\": original_sizes,\n \"crop_top_lefts\": crop_top_lefts,\n }\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n batch_size=args.train_batch_size,\n shuffle=True,\n collate_fn=collate_fn,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_cycles=args.lr_num_cycles,\n power=args.lr_power,\n )\n\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(args.tracker_name, config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num batches each epoch = {len(train_dataloader)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the mos recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n unet.train()\n for epoch in range(first_epoch, args.num_train_epochs):\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n # (batch_size, 2*channels, h, w) -> (2*batch_size, channels, h, w)\n pixel_values = batch[\"pixel_values\"].to(dtype=vae.dtype)\n feed_pixel_values = torch.cat(pixel_values.chunk(2, dim=1))\n\n latents = []\n for i in range(0, feed_pixel_values.shape[0], args.vae_encode_batch_size):\n latents.append(\n vae.encode(feed_pixel_values[i : i + args.vae_encode_batch_size]).latent_dist.sample()\n )\n latents = torch.cat(latents, dim=0)\n latents = latents * vae.config.scaling_factor\n if args.pretrained_vae_model_name_or_path is None:\n latents = latents.to(weight_dtype)\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents).chunk(2)[0].repeat(2, 1, 1, 1)\n\n # Sample a random timestep for each image\n bsz = latents.shape[0] // 2\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device, dtype=torch.long\n ).repeat(2)\n\n # Add noise to the model input according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_model_input = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # time ids\n def compute_time_ids(original_size, crops_coords_top_left):\n # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids\n target_size = (args.resolution, args.resolution)\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n add_time_ids = torch.tensor([add_time_ids])\n add_time_ids = add_time_ids.to(accelerator.device, dtype=weight_dtype)\n return add_time_ids\n\n add_time_ids = torch.cat(\n [compute_time_ids(s, c) for s, c in zip(batch[\"original_sizes\"], batch[\"crop_top_lefts\"])]\n ).repeat(2, 1)\n\n # Get the text embedding for conditioning\n prompt_embeds, pooled_prompt_embeds = encode_prompt(\n [text_encoder_one, text_encoder_two], [batch[\"input_ids_one\"], batch[\"input_ids_two\"]]\n )\n prompt_embeds = prompt_embeds.repeat(2, 1, 1)\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(2, 1)\n\n # Predict the noise residual\n model_pred = unet(\n noisy_model_input,\n timesteps,\n prompt_embeds,\n added_cond_kwargs={\"time_ids\": add_time_ids, \"text_embeds\": pooled_prompt_embeds},\n ).sample\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n # ODDS ratio loss.\n # In the diffusion formulation, we're assuming that the MSE loss\n # approximates the logp.\n model_losses = F.mse_loss(model_pred.float(), target.float(), reduction=\"none\")\n model_losses = model_losses.mean(dim=list(range(1, len(model_losses.shape))))\n model_losses_w, model_losses_l = model_losses.chunk(2)\n log_odds = model_losses_w - model_losses_l\n\n # Ratio loss.\n ratio = F.logsigmoid(log_odds)\n ratio_losses = args.beta_orpo * ratio\n\n # Full ORPO loss\n loss = model_losses_w.mean() - ratio_losses.mean()\n\n # Backprop.\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(params_to_optimize, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n if args.run_validation and global_step % args.validation_steps == 0:\n log_validation(\n args, unet=unet, vae=vae, accelerator=accelerator, weight_dtype=weight_dtype, epoch=epoch\n )\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n # Save the lora layers\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = accelerator.unwrap_model(unet)\n unet = unet.to(torch.float32)\n unet_lora_state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(unet))\n\n StableDiffusionXLLoraLoaderMixin.save_lora_weights(\n save_directory=args.output_dir,\n unet_lora_layers=unet_lora_state_dict,\n text_encoder_lora_layers=None,\n text_encoder_2_lora_layers=None,\n )\n\n # Final validation?\n if args.run_validation:\n log_validation(\n args,\n unet=None,\n vae=vae,\n accelerator=accelerator,\n weight_dtype=weight_dtype,\n epoch=epoch,\n is_final_validation=True,\n )\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/research_projects/diffusion_orpo/train_diffusion_orpo_sdxl_lora_wds.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport contextlib\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nimport wandb\nimport webdataset as wds\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom peft import LoraConfig, set_peft_model_state_dict\nfrom peft.utils import get_peft_model_state_dict\nfrom torchvision import transforms\nfrom torchvision.transforms.functional import crop\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n DiffusionPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.loaders import StableDiffusionXLLoraLoaderMixin\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import check_min_version, convert_state_dict_to_diffusers, convert_unet_state_dict_to_peft\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.25.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\nVALIDATION_PROMPTS = [\n \"portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography\",\n \"Self-portrait oil painting, a beautiful cyborg with golden hair, 8k\",\n \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\",\n \"A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece\",\n]\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef log_validation(args, unet, vae, accelerator, weight_dtype, epoch, is_final_validation=False):\n logger.info(f\"Running validation... \\n Generating images with prompts:\\n\" f\" {VALIDATION_PROMPTS}.\")\n\n if is_final_validation:\n if args.mixed_precision == \"fp16\":\n vae.to(weight_dtype)\n\n # create pipeline\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n if not is_final_validation:\n pipeline.unet = accelerator.unwrap_model(unet)\n else:\n pipeline.load_lora_weights(args.output_dir, weight_name=\"pytorch_lora_weights.safetensors\")\n\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n images = []\n context = contextlib.nullcontext() if is_final_validation else torch.cuda.amp.autocast()\n\n guidance_scale = 5.0\n num_inference_steps = 25\n for prompt in VALIDATION_PROMPTS:\n with context:\n image = pipeline(\n prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, generator=generator\n ).images[0]\n images.append(image)\n\n tracker_key = \"test\" if is_final_validation else \"validation\"\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(tracker_key, np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n tracker_key: [\n wandb.Image(image, caption=f\"{i}: {VALIDATION_PROMPTS[i]}\") for i, image in enumerate(images)\n ]\n }\n )\n\n # Also log images without the LoRA params for comparison.\n if is_final_validation:\n pipeline.disable_lora()\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n no_lora_images = [\n pipeline(\n prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, generator=generator\n ).images[0]\n for prompt in VALIDATION_PROMPTS\n ]\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in no_lora_images])\n tracker.writer.add_images(\"test_without_lora\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"test_without_lora\": [\n wandb.Image(image, caption=f\"{i}: {VALIDATION_PROMPTS[i]}\")\n for i, image in enumerate(no_lora_images)\n ]\n }\n )\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_vae_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--dataset_path\",\n type=str,\n default=\"pipe:aws s3 cp s3://diffusion-preference-opt/{00000..00644}.tar -\",\n )\n parser.add_argument(\n \"--num_train_examples\",\n type=int,\n default=1001352,\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--run_validation\",\n default=False,\n action=\"store_true\",\n help=\"Whether to run validation inference in between training and also after training. Helps to track progress.\",\n )\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=200,\n help=\"Run validation every X steps.\",\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"diffusion-orpo-lora-sdxl\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=1024,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--vae_encode_batch_size\",\n type=int,\n default=8,\n help=\"Batch size to use for VAE encoding of the images for efficient processing.\",\n )\n parser.add_argument(\n \"--no_hflip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--random_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to random crop the input images to the resolution. If not set, the images will be center-cropped.\"\n ),\n )\n parser.add_argument(\"--global_batch_size\", type=int, default=64, help=\"Total batch size.\")\n parser.add_argument(\n \"--per_gpu_batch_size\", type=int, default=8, help=\"Number of samples in a batch for a single GPU.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final\"\n \" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--beta_orpo\",\n type=float,\n default=0.1,\n help=\"ORPO contribution factor.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=5e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\"--lr_power\", type=float, default=1.0, help=\"Power factor of the polynomial scheduler.\")\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--prior_generation_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp32\", \"fp16\", \"bf16\"],\n help=(\n \"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--rank\",\n type=int,\n default=4,\n help=(\"The dimension of the LoRA update matrices.\"),\n )\n parser.add_argument(\n \"--tracker_name\",\n type=str,\n default=\"diffusion-orpo-lora-sdxl\",\n help=(\"The name of the tracker to report results to.\"),\n )\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n return args\n\n\ndef tokenize_captions(tokenizers, sample):\n tokens_one = tokenizers[0](\n sample[\"original_prompt\"],\n truncation=True,\n padding=\"max_length\",\n max_length=tokenizers[0].model_max_length,\n return_tensors=\"pt\",\n ).input_ids\n tokens_two = tokenizers[1](\n sample[\"original_prompt\"],\n truncation=True,\n padding=\"max_length\",\n max_length=tokenizers[1].model_max_length,\n return_tensors=\"pt\",\n ).input_ids\n\n return tokens_one, tokens_two\n\n\n@torch.no_grad()\ndef encode_prompt(text_encoders, text_input_ids_list):\n prompt_embeds_list = []\n\n for i, text_encoder in enumerate(text_encoders):\n text_input_ids = text_input_ids_list[i]\n\n prompt_embeds = text_encoder(\n text_input_ids.to(text_encoder.device),\n output_hidden_states=True,\n )\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds.hidden_states[-2]\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)\n return prompt_embeds, pooled_prompt_embeds\n\n\ndef get_dataset(args):\n dataset = (\n wds.WebDataset(args.dataset_path, resampled=True, handler=wds.warn_and_continue)\n .shuffle(690, handler=wds.warn_and_continue)\n .decode(\"pil\", handler=wds.warn_and_continue)\n .rename(\n original_prompt=\"original_prompt.txt\",\n jpg_0=\"jpg_0.jpg\",\n jpg_1=\"jpg_1.jpg\",\n label_0=\"label_0.txt\",\n label_1=\"label_1.txt\",\n handler=wds.warn_and_continue,\n )\n )\n return dataset\n\n\ndef get_loader(args, tokenizer_one, tokenizer_two):\n # 1,001,352\n num_batches = math.ceil(args.num_train_examples / args.global_batch_size)\n num_worker_batches = math.ceil(\n args.num_train_examples / (args.global_batch_size * args.dataloader_num_workers)\n ) # per dataloader worker\n num_batches = num_worker_batches * args.dataloader_num_workers\n num_samples = num_batches * args.global_batch_size\n\n dataset = get_dataset(args)\n\n train_resize = transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR)\n train_crop = transforms.RandomCrop(args.resolution) if args.random_crop else transforms.CenterCrop(args.resolution)\n train_flip = transforms.RandomHorizontalFlip(p=1.0)\n to_tensor = transforms.ToTensor()\n normalize = transforms.Normalize([0.5], [0.5])\n\n def preprocess_images(sample):\n jpg_0_image = sample[\"jpg_0\"]\n original_size = (jpg_0_image.height, jpg_0_image.width)\n crop_top_left = []\n\n jpg_1_image = sample[\"jpg_1\"]\n # Need to bring down the image to the same resolution.\n # This seems like the simplest reasonable approach.\n # \"::-1\" because PIL resize takes (width, height).\n jpg_1_image = jpg_1_image.resize(original_size[::-1])\n\n # We randomize selection and rejection.\n label_0 = sample[\"label_0\"]\n if sample[\"label_0\"] == 0.5:\n if random.random() < 0.5:\n label_0 = 0\n else:\n label_0 = 1\n\n # Double on channel dim, jpg_y then jpg_w\n if label_0 == 0:\n pixel_values = torch.cat([to_tensor(image) for image in [jpg_1_image, jpg_0_image]])\n else:\n pixel_values = torch.cat([to_tensor(image) for image in [jpg_0_image, jpg_1_image]])\n\n # Resize.\n combined_im = train_resize(pixel_values)\n\n # Flipping.\n if not args.no_hflip and random.random() < 0.5:\n combined_im = train_flip(combined_im)\n\n # Cropping.\n if not args.random_crop:\n y1 = max(0, int(round((combined_im.shape[1] - args.resolution) / 2.0)))\n x1 = max(0, int(round((combined_im.shape[2] - args.resolution) / 2.0)))\n combined_im = train_crop(combined_im)\n else:\n y1, x1, h, w = train_crop.get_params(combined_im, (args.resolution, args.resolution))\n combined_im = crop(combined_im, y1, x1, h, w)\n\n crop_top_left = (y1, x1)\n combined_im = normalize(combined_im)\n tokens_one, tokens_two = tokenize_captions([tokenizer_one, tokenizer_two], sample)\n\n return {\n \"pixel_values\": combined_im,\n \"original_size\": original_size,\n \"crop_top_left\": crop_top_left,\n \"tokens_one\": tokens_one,\n \"tokens_two\": tokens_two,\n }\n\n def collate_fn(samples):\n pixel_values = torch.stack([sample[\"pixel_values\"] for sample in samples])\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n\n original_sizes = [example[\"original_size\"] for example in samples]\n crop_top_lefts = [example[\"crop_top_left\"] for example in samples]\n input_ids_one = torch.stack([example[\"tokens_one\"] for example in samples])\n input_ids_two = torch.stack([example[\"tokens_two\"] for example in samples])\n\n return {\n \"pixel_values\": pixel_values,\n \"input_ids_one\": input_ids_one,\n \"input_ids_two\": input_ids_two,\n \"original_sizes\": original_sizes,\n \"crop_top_lefts\": crop_top_lefts,\n }\n\n dataset = dataset.map(preprocess_images, handler=wds.warn_and_continue)\n dataset = dataset.batched(args.per_gpu_batch_size, partial=False, collation_fn=collate_fn)\n dataset = dataset.with_epoch(num_worker_batches)\n\n dataloader = wds.WebLoader(\n dataset,\n batch_size=None,\n shuffle=False,\n num_workers=args.dataloader_num_workers,\n pin_memory=True,\n persistent_workers=True,\n )\n # add meta-data to dataloader instance for convenience\n dataloader.num_batches = num_batches\n dataloader.num_samples = num_samples\n return dataloader\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizers\n tokenizer_one = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n tokenizer_two = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer_2\",\n revision=args.revision,\n use_fast=False,\n )\n\n # import correct text encoder classes\n text_encoder_cls_one = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision\n )\n text_encoder_cls_two = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision, subfolder=\"text_encoder_2\"\n )\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n\n text_encoder_one = text_encoder_cls_one.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n text_encoder_two = text_encoder_cls_two.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder_2\", revision=args.revision, variant=args.variant\n )\n vae_path = (\n args.pretrained_model_name_or_path\n if args.pretrained_vae_model_name_or_path is None\n else args.pretrained_vae_model_name_or_path\n )\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n variant=args.variant,\n )\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n # We only train the additional adapter LoRA layers\n vae.requires_grad_(False)\n text_encoder_one.requires_grad_(False)\n text_encoder_two.requires_grad_(False)\n unet.requires_grad_(False)\n\n # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move unet and text_encoders to device and cast to weight_dtype\n unet.to(accelerator.device, dtype=weight_dtype)\n text_encoder_one.to(accelerator.device, dtype=weight_dtype)\n text_encoder_two.to(accelerator.device, dtype=weight_dtype)\n\n # The VAE is always in float32 to avoid NaN losses.\n vae.to(accelerator.device, dtype=torch.float32)\n\n # Set up LoRA.\n unet_lora_config = LoraConfig(\n r=args.rank,\n lora_alpha=args.rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"to_k\", \"to_q\", \"to_v\", \"to_out.0\"],\n )\n # Add adapter and make sure the trainable params are in float32.\n unet.add_adapter(unet_lora_config)\n if args.mixed_precision == \"fp16\":\n for param in unet.parameters():\n # only upcast trainable parameters (LoRA) into fp32\n if param.requires_grad:\n param.data = param.to(torch.float32)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n # there are only two options here. Either are just the unet attn processor layers\n # or there are the unet and text encoder atten layers\n unet_lora_layers_to_save = None\n\n for model in models:\n if isinstance(model, type(accelerator.unwrap_model(unet))):\n unet_lora_layers_to_save = convert_state_dict_to_diffusers(get_peft_model_state_dict(model))\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n StableDiffusionXLLoraLoaderMixin.save_lora_weights(\n output_dir,\n unet_lora_layers=unet_lora_layers_to_save,\n text_encoder_lora_layers=None,\n text_encoder_2_lora_layers=None,\n )\n\n def load_model_hook(models, input_dir):\n unet_ = None\n\n while len(models) > 0:\n model = models.pop()\n\n if isinstance(model, type(accelerator.unwrap_model(unet))):\n unet_ = model\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n lora_state_dict, network_alphas = StableDiffusionXLLoraLoaderMixin.lora_state_dict(input_dir)\n\n unet_state_dict = {f'{k.replace(\"unet.\", \"\")}': v for k, v in lora_state_dict.items() if k.startswith(\"unet.\")}\n unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict)\n incompatible_keys = set_peft_model_state_dict(unet_, unet_state_dict, adapter_name=\"default\")\n if incompatible_keys is not None:\n # check only for unexpected keys\n unexpected_keys = getattr(incompatible_keys, \"unexpected_keys\", None)\n if unexpected_keys:\n logger.warning(\n f\"Loading adapter weights from state_dict led to unexpected keys not found in the model: \"\n f\" {unexpected_keys}. \"\n )\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rat * args.gradient_accumulation_steps * args.per_gpu_batch_size * accelerator.num_processes\n )\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # Optimizer creation\n params_to_optimize = list(filter(lambda p: p.requires_grad, unet.parameters()))\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Dataset and DataLoaders creation:\n args.global_batch_size = args.per_gpu_batch_size * accelerator.num_processes\n train_dataloader = get_loader(args, tokenizer_one=tokenizer_one, tokenizer_two=tokenizer_two)\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_cycles=args.lr_num_cycles,\n power=args.lr_power,\n )\n\n unet, optimizer, lr_scheduler = accelerator.prepare(unet, optimizer, lr_scheduler)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(args.tracker_name, config=vars(args))\n\n # Train!\n total_batch_size = args.per_gpu_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {train_dataloader.num_samples}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.per_gpu_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the mos recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n unet.train()\n for epoch in range(first_epoch, args.num_train_epochs):\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n # (batch_size, 2*channels, h, w) -> (2*batch_size, channels, h, w)\n pixel_values = batch[\"pixel_values\"].to(dtype=vae.dtype, device=accelerator.device, non_blocking=True)\n feed_pixel_values = torch.cat(pixel_values.chunk(2, dim=1))\n\n latents = []\n for i in range(0, feed_pixel_values.shape[0], args.vae_encode_batch_size):\n latents.append(\n vae.encode(feed_pixel_values[i : i + args.vae_encode_batch_size]).latent_dist.sample()\n )\n latents = torch.cat(latents, dim=0)\n latents = latents * vae.config.scaling_factor\n if args.pretrained_vae_model_name_or_path is None:\n latents = latents.to(weight_dtype)\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents).chunk(2)[0].repeat(2, 1, 1, 1)\n\n # Sample a random timestep for each image\n bsz = latents.shape[0] // 2\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device, dtype=torch.long\n ).repeat(2)\n\n # Add noise to the model input according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_model_input = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # time ids\n def compute_time_ids(original_size, crops_coords_top_left):\n # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids\n target_size = (args.resolution, args.resolution)\n add_time_ids = list(tuple(original_size) + tuple(crops_coords_top_left) + target_size)\n add_time_ids = torch.tensor([add_time_ids])\n add_time_ids = add_time_ids.to(accelerator.device, dtype=weight_dtype)\n return add_time_ids\n\n add_time_ids = torch.cat(\n [compute_time_ids(s, c) for s, c in zip(batch[\"original_sizes\"], batch[\"crop_top_lefts\"])]\n ).repeat(2, 1)\n\n # Get the text embedding for conditioning\n prompt_embeds, pooled_prompt_embeds = encode_prompt(\n [text_encoder_one, text_encoder_two], [batch[\"input_ids_one\"], batch[\"input_ids_two\"]]\n )\n prompt_embeds = prompt_embeds.repeat(2, 1, 1)\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(2, 1)\n\n # Predict the noise residual\n model_pred = unet(\n noisy_model_input,\n timesteps,\n prompt_embeds,\n added_cond_kwargs={\"time_ids\": add_time_ids, \"text_embeds\": pooled_prompt_embeds},\n ).sample\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n # ODDS ratio loss.\n # In the diffusion formulation, we're assuming that the MSE loss\n # approximates the logp.\n model_losses = F.mse_loss(model_pred.float(), target.float(), reduction=\"none\")\n model_losses = model_losses.mean(dim=list(range(1, len(model_losses.shape))))\n model_losses_w, model_losses_l = model_losses.chunk(2)\n log_odds = model_losses_w - model_losses_l\n\n # Ratio loss.\n ratio = F.logsigmoid(log_odds)\n ratio_losses = args.beta_orpo * ratio\n\n # Full ORPO loss\n loss = model_losses_w.mean() - ratio_losses.mean()\n\n # Backprop.\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(params_to_optimize, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n if args.run_validation and global_step % args.validation_steps == 0:\n log_validation(\n args, unet=unet, vae=vae, accelerator=accelerator, weight_dtype=weight_dtype, epoch=epoch\n )\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n # Save the lora layers\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = accelerator.unwrap_model(unet)\n unet = unet.to(torch.float32)\n unet_lora_state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(unet))\n\n StableDiffusionXLLoraLoaderMixin.save_lora_weights(\n save_directory=args.output_dir,\n unet_lora_layers=unet_lora_state_dict,\n text_encoder_lora_layers=None,\n text_encoder_2_lora_layers=None,\n )\n\n # Final validation?\n if args.run_validation:\n log_validation(\n args,\n unet=None,\n vae=vae,\n accelerator=accelerator,\n weight_dtype=weight_dtype,\n epoch=epoch,\n is_final_validation=True,\n )\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/research_projects/dreambooth_inpaint/README.md", "content": "# Dreambooth for the inpainting model\n\nThis script was added by @thedarkzeno .\n\nPlease note that this script is not actively maintained, you can open an issue and tag @thedarkzeno or @patil-suraj though.\n\n```bash\nexport MODEL_NAME=\"runwayml/stable-diffusion-inpainting\"\nexport INSTANCE_DIR=\"path-to-instance-images\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\naccelerate launch train_dreambooth_inpaint.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --instance_prompt=\"a photo of sks dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=1 \\\n --learning_rate=5e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --max_train_steps=400\n```\n\n### Training with prior-preservation loss\n\nPrior-preservation is used to avoid overfitting and language-drift. Refer to the paper to learn more about it. For prior-preservation we first generate images using the model with a class prompt and then use those during training along with our data.\nAccording to the paper, it's recommended to generate `num_epochs * num_samples` images for prior-preservation. 200-300 works well for most cases.\n\n```bash\nexport MODEL_NAME=\"runwayml/stable-diffusion-inpainting\"\nexport INSTANCE_DIR=\"path-to-instance-images\"\nexport CLASS_DIR=\"path-to-class-images\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\naccelerate launch train_dreambooth_inpaint.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --class_data_dir=$CLASS_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --with_prior_preservation --prior_loss_weight=1.0 \\\n --instance_prompt=\"a photo of sks dog\" \\\n --class_prompt=\"a photo of dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=1 \\\n --learning_rate=5e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --num_class_images=200 \\\n --max_train_steps=800\n```\n\n\n### Training with gradient checkpointing and 8-bit optimizer:\n\nWith the help of gradient checkpointing and the 8-bit optimizer from bitsandbytes it's possible to run train dreambooth on a 16GB GPU.\n\nTo install `bitandbytes` please refer to this [readme](https://github.com/TimDettmers/bitsandbytes#requirements--installation).\n\n```bash\nexport MODEL_NAME=\"runwayml/stable-diffusion-inpainting\"\nexport INSTANCE_DIR=\"path-to-instance-images\"\nexport CLASS_DIR=\"path-to-class-images\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\naccelerate launch train_dreambooth_inpaint.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --class_data_dir=$CLASS_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --with_prior_preservation --prior_loss_weight=1.0 \\\n --instance_prompt=\"a photo of sks dog\" \\\n --class_prompt=\"a photo of dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=2 --gradient_checkpointing \\\n --use_8bit_adam \\\n --learning_rate=5e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --num_class_images=200 \\\n --max_train_steps=800\n```\n\n### Fine-tune text encoder with the UNet.\n\nThe script also allows to fine-tune the `text_encoder` along with the `unet`. It's been observed experimentally that fine-tuning `text_encoder` gives much better results especially on faces. \nPass the `--train_text_encoder` argument to the script to enable training `text_encoder`.\n\n___Note: Training text encoder requires more memory, with this option the training won't fit on 16GB GPU. It needs at least 24GB VRAM.___\n\n```bash\nexport MODEL_NAME=\"runwayml/stable-diffusion-inpainting\"\nexport INSTANCE_DIR=\"path-to-instance-images\"\nexport CLASS_DIR=\"path-to-class-images\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\naccelerate launch train_dreambooth_inpaint.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_text_encoder \\\n --instance_data_dir=$INSTANCE_DIR \\\n --class_data_dir=$CLASS_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --with_prior_preservation --prior_loss_weight=1.0 \\\n --instance_prompt=\"a photo of sks dog\" \\\n --class_prompt=\"a photo of dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --use_8bit_adam \\\n --gradient_checkpointing \\\n --learning_rate=2e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --num_class_images=200 \\\n --max_train_steps=800\n```\n" }, { "path": "diffusers/examples/research_projects/dreambooth_inpaint/requirements.txt", "content": "diffusers==0.9.0\naccelerate>=0.16.0\ntorchvision\ntransformers>=4.21.0\nftfy\ntensorboard\nJinja2\n" }, { "path": "diffusers/examples/research_projects/dreambooth_inpaint/train_dreambooth_inpaint.py", "content": "import argparse\nimport itertools\nimport math\nimport os\nimport random\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom huggingface_hub import create_repo, upload_folder\nfrom huggingface_hub.utils import insecure_hashlib\nfrom PIL import Image, ImageDraw\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n StableDiffusionInpaintPipeline,\n StableDiffusionPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import check_min_version\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.13.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef prepare_mask_and_masked_image(image, mask):\n image = np.array(image.convert(\"RGB\"))\n image = image[None].transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0\n\n mask = np.array(mask.convert(\"L\"))\n mask = mask.astype(np.float32) / 255.0\n mask = mask[None, None]\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n mask = torch.from_numpy(mask)\n\n masked_image = image * (mask < 0.5)\n\n return mask, masked_image\n\n\n# generate random masks\ndef random_mask(im_shape, ratio=1, mask_full_image=False):\n mask = Image.new(\"L\", im_shape, 0)\n draw = ImageDraw.Draw(mask)\n size = (random.randint(0, int(im_shape[0] * ratio)), random.randint(0, int(im_shape[1] * ratio)))\n # use this to always mask the whole image\n if mask_full_image:\n size = (int(im_shape[0] * ratio), int(im_shape[1] * ratio))\n limits = (im_shape[0] - size[0] // 2, im_shape[1] - size[1] // 2)\n center = (random.randint(size[0] // 2, limits[0]), random.randint(size[1] // 2, limits[1]))\n draw_type = random.randint(0, 1)\n if draw_type == 0 or mask_full_image:\n draw.rectangle(\n (center[0] - size[0] // 2, center[1] - size[1] // 2, center[0] + size[0] // 2, center[1] + size[1] // 2),\n fill=255,\n )\n else:\n draw.ellipse(\n (center[0] - size[0] // 2, center[1] - size[1] // 2, center[0] + size[0] // 2, center[1] + size[1] // 2),\n fill=255,\n )\n\n return mask\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--tokenizer_name\",\n type=str,\n default=None,\n help=\"Pretrained tokenizer name or path if not the same as model_name\",\n )\n parser.add_argument(\n \"--instance_data_dir\",\n type=str,\n default=None,\n required=True,\n help=\"A folder containing the training data of instance images.\",\n )\n parser.add_argument(\n \"--class_data_dir\",\n type=str,\n default=None,\n required=False,\n help=\"A folder containing the training data of class images.\",\n )\n parser.add_argument(\n \"--instance_prompt\",\n type=str,\n default=None,\n help=\"The prompt with identifier specifying the instance\",\n )\n parser.add_argument(\n \"--class_prompt\",\n type=str,\n default=None,\n help=\"The prompt to specify images in the same class as provided instance images.\",\n )\n parser.add_argument(\n \"--with_prior_preservation\",\n default=False,\n action=\"store_true\",\n help=\"Flag to add prior preservation loss.\",\n )\n parser.add_argument(\"--prior_loss_weight\", type=float, default=1.0, help=\"The weight of prior preservation loss.\")\n parser.add_argument(\n \"--num_class_images\",\n type=int,\n default=100,\n help=(\n \"Minimal class images for prior preservation loss. If not have enough images, additional images will be\"\n \" sampled with class_prompt.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"text-inversion-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\"--train_text_encoder\", action=\"store_true\", help=\"Whether to train the text encoder\")\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\n \"--sample_batch_size\", type=int, default=4, help=\"Batch size (per device) for sampling images.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=5e-6,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=\"no\",\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose\"\n \"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.\"\n \"and an Nvidia Ampere GPU.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final\"\n \" checkpoints in case they are better than the last checkpoint and are suitable for resuming training\"\n \" using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\n \"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`.\"\n \" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state\"\n \" for more docs\"\n ),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.instance_data_dir is None:\n raise ValueError(\"You must specify a train data directory.\")\n\n if args.with_prior_preservation:\n if args.class_data_dir is None:\n raise ValueError(\"You must specify a data directory for class images.\")\n if args.class_prompt is None:\n raise ValueError(\"You must specify prompt for class images.\")\n\n return args\n\n\nclass DreamBoothDataset(Dataset):\n \"\"\"\n A dataset to prepare the instance and class images with the prompts for fine-tuning the model.\n It pre-processes the images and the tokenizes prompts.\n \"\"\"\n\n def __init__(\n self,\n instance_data_root,\n instance_prompt,\n tokenizer,\n class_data_root=None,\n class_prompt=None,\n size=512,\n center_crop=False,\n ):\n self.size = size\n self.center_crop = center_crop\n self.tokenizer = tokenizer\n\n self.instance_data_root = Path(instance_data_root)\n if not self.instance_data_root.exists():\n raise ValueError(\"Instance images root doesn't exists.\")\n\n self.instance_images_path = list(Path(instance_data_root).iterdir())\n self.num_instance_images = len(self.instance_images_path)\n self.instance_prompt = instance_prompt\n self._length = self.num_instance_images\n\n if class_data_root is not None:\n self.class_data_root = Path(class_data_root)\n self.class_data_root.mkdir(parents=True, exist_ok=True)\n self.class_images_path = list(self.class_data_root.iterdir())\n self.num_class_images = len(self.class_images_path)\n self._length = max(self.num_class_images, self.num_instance_images)\n self.class_prompt = class_prompt\n else:\n self.class_data_root = None\n\n self.image_transforms_resize_and_crop = transforms.Compose(\n [\n transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),\n ]\n )\n\n self.image_transforms = transforms.Compose(\n [\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, index):\n example = {}\n instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])\n if not instance_image.mode == \"RGB\":\n instance_image = instance_image.convert(\"RGB\")\n instance_image = self.image_transforms_resize_and_crop(instance_image)\n\n example[\"PIL_images\"] = instance_image\n example[\"instance_images\"] = self.image_transforms(instance_image)\n\n example[\"instance_prompt_ids\"] = self.tokenizer(\n self.instance_prompt,\n padding=\"do_not_pad\",\n truncation=True,\n max_length=self.tokenizer.model_max_length,\n ).input_ids\n\n if self.class_data_root:\n class_image = Image.open(self.class_images_path[index % self.num_class_images])\n if not class_image.mode == \"RGB\":\n class_image = class_image.convert(\"RGB\")\n class_image = self.image_transforms_resize_and_crop(class_image)\n example[\"class_images\"] = self.image_transforms(class_image)\n example[\"class_PIL_images\"] = class_image\n example[\"class_prompt_ids\"] = self.tokenizer(\n self.class_prompt,\n padding=\"do_not_pad\",\n truncation=True,\n max_length=self.tokenizer.model_max_length,\n ).input_ids\n\n return example\n\n\nclass PromptDataset(Dataset):\n \"\"\"A simple dataset to prepare the prompts to generate class images on multiple GPUs.\"\"\"\n\n def __init__(self, prompt, num_samples):\n self.prompt = prompt\n self.num_samples = num_samples\n\n def __len__(self):\n return self.num_samples\n\n def __getitem__(self, index):\n example = {}\n example[\"prompt\"] = self.prompt\n example[\"index\"] = index\n return example\n\n\ndef main():\n args = parse_args()\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n project_config = ProjectConfiguration(\n total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir\n )\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=\"tensorboard\",\n project_config=project_config,\n )\n\n # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate\n # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.\n # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.\n if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:\n raise ValueError(\n \"Gradient accumulation is not supported when training the text encoder in distributed training. \"\n \"Please set gradient_accumulation_steps to 1. This feature will be supported in the future.\"\n )\n\n if args.seed is not None:\n set_seed(args.seed)\n\n if args.with_prior_preservation:\n class_images_dir = Path(args.class_data_dir)\n if not class_images_dir.exists():\n class_images_dir.mkdir(parents=True)\n cur_class_images = len(list(class_images_dir.iterdir()))\n\n if cur_class_images < args.num_class_images:\n torch_dtype = torch.float16 if accelerator.device.type == \"cuda\" else torch.float32\n pipeline = StableDiffusionInpaintPipeline.from_pretrained(\n args.pretrained_model_name_or_path, torch_dtype=torch_dtype, safety_checker=None\n )\n pipeline.set_progress_bar_config(disable=True)\n\n num_new_images = args.num_class_images - cur_class_images\n logger.info(f\"Number of class images to sample: {num_new_images}.\")\n\n sample_dataset = PromptDataset(args.class_prompt, num_new_images)\n sample_dataloader = torch.utils.data.DataLoader(\n sample_dataset, batch_size=args.sample_batch_size, num_workers=1\n )\n\n sample_dataloader = accelerator.prepare(sample_dataloader)\n pipeline.to(accelerator.device)\n transform_to_pil = transforms.ToPILImage()\n for example in tqdm(\n sample_dataloader, desc=\"Generating class images\", disable=not accelerator.is_local_main_process\n ):\n bsz = len(example[\"prompt\"])\n fake_images = torch.rand((3, args.resolution, args.resolution))\n transform_to_pil = transforms.ToPILImage()\n fake_pil_images = transform_to_pil(fake_images)\n\n fake_mask = random_mask((args.resolution, args.resolution), ratio=1, mask_full_image=True)\n\n images = pipeline(prompt=example[\"prompt\"], mask_image=fake_mask, image=fake_pil_images).images\n\n for i, image in enumerate(images):\n hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()\n image_filename = class_images_dir / f\"{example['index'][i] + cur_class_images}-{hash_image}.jpg\"\n image.save(image_filename)\n\n del pipeline\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizer\n if args.tokenizer_name:\n tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)\n elif args.pretrained_model_name_or_path:\n tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"tokenizer\")\n\n # Load models and create wrapper for stable diffusion\n text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"text_encoder\")\n vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"vae\")\n unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"unet\")\n\n vae.requires_grad_(False)\n if not args.train_text_encoder:\n text_encoder.requires_grad_(False)\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n if args.train_text_encoder:\n text_encoder.gradient_checkpointing_enable()\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n params_to_optimize = (\n itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters()\n )\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n\n train_dataset = DreamBoothDataset(\n instance_data_root=args.instance_data_dir,\n instance_prompt=args.instance_prompt,\n class_data_root=args.class_data_dir if args.with_prior_preservation else None,\n class_prompt=args.class_prompt,\n tokenizer=tokenizer,\n size=args.resolution,\n center_crop=args.center_crop,\n )\n\n def collate_fn(examples):\n input_ids = [example[\"instance_prompt_ids\"] for example in examples]\n pixel_values = [example[\"instance_images\"] for example in examples]\n\n # Concat class and instance examples for prior preservation.\n # We do this to avoid doing two forward passes.\n if args.with_prior_preservation:\n input_ids += [example[\"class_prompt_ids\"] for example in examples]\n pixel_values += [example[\"class_images\"] for example in examples]\n pior_pil = [example[\"class_PIL_images\"] for example in examples]\n\n masks = []\n masked_images = []\n for example in examples:\n pil_image = example[\"PIL_images\"]\n # generate a random mask\n mask = random_mask(pil_image.size, 1, False)\n # prepare mask and masked image\n mask, masked_image = prepare_mask_and_masked_image(pil_image, mask)\n\n masks.append(mask)\n masked_images.append(masked_image)\n\n if args.with_prior_preservation:\n for pil_image in pior_pil:\n # generate a random mask\n mask = random_mask(pil_image.size, 1, False)\n # prepare mask and masked image\n mask, masked_image = prepare_mask_and_masked_image(pil_image, mask)\n\n masks.append(mask)\n masked_images.append(masked_image)\n\n pixel_values = torch.stack(pixel_values)\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n\n input_ids = tokenizer.pad({\"input_ids\": input_ids}, padding=True, return_tensors=\"pt\").input_ids\n masks = torch.stack(masks)\n masked_images = torch.stack(masked_images)\n batch = {\"input_ids\": input_ids, \"pixel_values\": pixel_values, \"masks\": masks, \"masked_images\": masked_images}\n return batch\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n )\n\n if args.train_text_encoder:\n unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, text_encoder, optimizer, train_dataloader, lr_scheduler\n )\n else:\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n accelerator.register_for_checkpointing(lr_scheduler)\n\n weight_dtype = torch.float32\n if args.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif args.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move text_encode and vae to gpu.\n # For mixed precision training we cast the text_encoder and vae weights to half-precision\n # as these models are only used for inference, keeping weights in full precision is not required.\n vae.to(accelerator.device, dtype=weight_dtype)\n if not args.train_text_encoder:\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"dreambooth\", config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num batches each epoch = {len(train_dataloader)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n resume_global_step = global_step * args.gradient_accumulation_steps\n first_epoch = global_step // num_update_steps_per_epoch\n resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)\n\n # Only show the progress bar once on each machine.\n progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)\n progress_bar.set_description(\"Steps\")\n\n for epoch in range(first_epoch, args.num_train_epochs):\n unet.train()\n for step, batch in enumerate(train_dataloader):\n # Skip steps until we reach the resumed step\n if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:\n if step % args.gradient_accumulation_steps == 0:\n progress_bar.update(1)\n continue\n\n with accelerator.accumulate(unet):\n # Convert images to latent space\n\n latents = vae.encode(batch[\"pixel_values\"].to(dtype=weight_dtype)).latent_dist.sample()\n latents = latents * vae.config.scaling_factor\n\n # Convert masked images to latent space\n masked_latents = vae.encode(\n batch[\"masked_images\"].reshape(batch[\"pixel_values\"].shape).to(dtype=weight_dtype)\n ).latent_dist.sample()\n masked_latents = masked_latents * vae.config.scaling_factor\n\n masks = batch[\"masks\"]\n # resize the mask to latents shape as we concatenate the mask to the latents\n mask = torch.stack(\n [\n torch.nn.functional.interpolate(mask, size=(args.resolution // 8, args.resolution // 8))\n for mask in masks\n ]\n )\n mask = mask.reshape(-1, 1, args.resolution // 8, args.resolution // 8)\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # concatenate the noised latents with the mask and the masked latents\n latent_model_input = torch.cat([noisy_latents, mask, masked_latents], dim=1)\n\n # Get the text embedding for conditioning\n encoder_hidden_states = text_encoder(batch[\"input_ids\"])[0]\n\n # Predict the noise residual\n noise_pred = unet(latent_model_input, timesteps, encoder_hidden_states).sample\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n if args.with_prior_preservation:\n # Chunk the noise and noise_pred into two parts and compute the loss on each part separately.\n noise_pred, noise_pred_prior = torch.chunk(noise_pred, 2, dim=0)\n target, target_prior = torch.chunk(target, 2, dim=0)\n\n # Compute instance loss\n loss = F.mse_loss(noise_pred.float(), target.float(), reduction=\"none\").mean([1, 2, 3]).mean()\n\n # Compute prior loss\n prior_loss = F.mse_loss(noise_pred_prior.float(), target_prior.float(), reduction=\"mean\")\n\n # Add the prior loss to the instance loss.\n loss = loss + args.prior_loss_weight * prior_loss\n else:\n loss = F.mse_loss(noise_pred.float(), target.float(), reduction=\"mean\")\n\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n params_to_clip = (\n itertools.chain(unet.parameters(), text_encoder.parameters())\n if args.train_text_encoder\n else unet.parameters()\n )\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if global_step % args.checkpointing_steps == 0:\n if accelerator.is_main_process:\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n accelerator.wait_for_everyone()\n\n # Create the pipeline using using the trained modules and save it.\n if accelerator.is_main_process:\n pipeline = StableDiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n unet=accelerator.unwrap_model(unet),\n text_encoder=accelerator.unwrap_model(text_encoder),\n )\n pipeline.save_pretrained(args.output_dir)\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/research_projects/dreambooth_inpaint/train_dreambooth_inpaint_lora.py", "content": "import argparse\nimport math\nimport os\nimport random\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom huggingface_hub import create_repo, upload_folder\nfrom huggingface_hub.utils import insecure_hashlib\nfrom PIL import Image, ImageDraw\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionInpaintPipeline, UNet2DConditionModel\nfrom diffusers.loaders import AttnProcsLayers\nfrom diffusers.models.attention_processor import LoRAAttnProcessor\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import check_min_version\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.13.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef prepare_mask_and_masked_image(image, mask):\n image = np.array(image.convert(\"RGB\"))\n image = image[None].transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0\n\n mask = np.array(mask.convert(\"L\"))\n mask = mask.astype(np.float32) / 255.0\n mask = mask[None, None]\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n mask = torch.from_numpy(mask)\n\n masked_image = image * (mask < 0.5)\n\n return mask, masked_image\n\n\n# generate random masks\ndef random_mask(im_shape, ratio=1, mask_full_image=False):\n mask = Image.new(\"L\", im_shape, 0)\n draw = ImageDraw.Draw(mask)\n size = (random.randint(0, int(im_shape[0] * ratio)), random.randint(0, int(im_shape[1] * ratio)))\n # use this to always mask the whole image\n if mask_full_image:\n size = (int(im_shape[0] * ratio), int(im_shape[1] * ratio))\n limits = (im_shape[0] - size[0] // 2, im_shape[1] - size[1] // 2)\n center = (random.randint(size[0] // 2, limits[0]), random.randint(size[1] // 2, limits[1]))\n draw_type = random.randint(0, 1)\n if draw_type == 0 or mask_full_image:\n draw.rectangle(\n (center[0] - size[0] // 2, center[1] - size[1] // 2, center[0] + size[0] // 2, center[1] + size[1] // 2),\n fill=255,\n )\n else:\n draw.ellipse(\n (center[0] - size[0] // 2, center[1] - size[1] // 2, center[0] + size[0] // 2, center[1] + size[1] // 2),\n fill=255,\n )\n\n return mask\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--tokenizer_name\",\n type=str,\n default=None,\n help=\"Pretrained tokenizer name or path if not the same as model_name\",\n )\n parser.add_argument(\n \"--instance_data_dir\",\n type=str,\n default=None,\n required=True,\n help=\"A folder containing the training data of instance images.\",\n )\n parser.add_argument(\n \"--class_data_dir\",\n type=str,\n default=None,\n required=False,\n help=\"A folder containing the training data of class images.\",\n )\n parser.add_argument(\n \"--instance_prompt\",\n type=str,\n default=None,\n help=\"The prompt with identifier specifying the instance\",\n )\n parser.add_argument(\n \"--class_prompt\",\n type=str,\n default=None,\n help=\"The prompt to specify images in the same class as provided instance images.\",\n )\n parser.add_argument(\n \"--with_prior_preservation\",\n default=False,\n action=\"store_true\",\n help=\"Flag to add prior preservation loss.\",\n )\n parser.add_argument(\"--prior_loss_weight\", type=float, default=1.0, help=\"The weight of prior preservation loss.\")\n parser.add_argument(\n \"--num_class_images\",\n type=int,\n default=100,\n help=(\n \"Minimal class images for prior preservation loss. If not have enough images, additional images will be\"\n \" sampled with class_prompt.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"dreambooth-inpaint-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\"--train_text_encoder\", action=\"store_true\", help=\"Whether to train the text encoder\")\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\n \"--sample_batch_size\", type=int, default=4, help=\"Batch size (per device) for sampling images.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=5e-6,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=\"no\",\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose\"\n \"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.\"\n \"and an Nvidia Ampere GPU.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final\"\n \" checkpoints in case they are better than the last checkpoint and are suitable for resuming training\"\n \" using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\n \"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`.\"\n \" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state\"\n \" for more docs\"\n ),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.instance_data_dir is None:\n raise ValueError(\"You must specify a train data directory.\")\n\n if args.with_prior_preservation:\n if args.class_data_dir is None:\n raise ValueError(\"You must specify a data directory for class images.\")\n if args.class_prompt is None:\n raise ValueError(\"You must specify prompt for class images.\")\n\n return args\n\n\nclass DreamBoothDataset(Dataset):\n \"\"\"\n A dataset to prepare the instance and class images with the prompts for fine-tuning the model.\n It pre-processes the images and the tokenizes prompts.\n \"\"\"\n\n def __init__(\n self,\n instance_data_root,\n instance_prompt,\n tokenizer,\n class_data_root=None,\n class_prompt=None,\n size=512,\n center_crop=False,\n ):\n self.size = size\n self.center_crop = center_crop\n self.tokenizer = tokenizer\n\n self.instance_data_root = Path(instance_data_root)\n if not self.instance_data_root.exists():\n raise ValueError(\"Instance images root doesn't exists.\")\n\n self.instance_images_path = list(Path(instance_data_root).iterdir())\n self.num_instance_images = len(self.instance_images_path)\n self.instance_prompt = instance_prompt\n self._length = self.num_instance_images\n\n if class_data_root is not None:\n self.class_data_root = Path(class_data_root)\n self.class_data_root.mkdir(parents=True, exist_ok=True)\n self.class_images_path = list(self.class_data_root.iterdir())\n self.num_class_images = len(self.class_images_path)\n self._length = max(self.num_class_images, self.num_instance_images)\n self.class_prompt = class_prompt\n else:\n self.class_data_root = None\n\n self.image_transforms_resize_and_crop = transforms.Compose(\n [\n transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),\n ]\n )\n\n self.image_transforms = transforms.Compose(\n [\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, index):\n example = {}\n instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])\n if not instance_image.mode == \"RGB\":\n instance_image = instance_image.convert(\"RGB\")\n instance_image = self.image_transforms_resize_and_crop(instance_image)\n\n example[\"PIL_images\"] = instance_image\n example[\"instance_images\"] = self.image_transforms(instance_image)\n\n example[\"instance_prompt_ids\"] = self.tokenizer(\n self.instance_prompt,\n padding=\"do_not_pad\",\n truncation=True,\n max_length=self.tokenizer.model_max_length,\n ).input_ids\n\n if self.class_data_root:\n class_image = Image.open(self.class_images_path[index % self.num_class_images])\n if not class_image.mode == \"RGB\":\n class_image = class_image.convert(\"RGB\")\n class_image = self.image_transforms_resize_and_crop(class_image)\n example[\"class_images\"] = self.image_transforms(class_image)\n example[\"class_PIL_images\"] = class_image\n example[\"class_prompt_ids\"] = self.tokenizer(\n self.class_prompt,\n padding=\"do_not_pad\",\n truncation=True,\n max_length=self.tokenizer.model_max_length,\n ).input_ids\n\n return example\n\n\nclass PromptDataset(Dataset):\n \"\"\"A simple dataset to prepare the prompts to generate class images on multiple GPUs.\"\"\"\n\n def __init__(self, prompt, num_samples):\n self.prompt = prompt\n self.num_samples = num_samples\n\n def __len__(self):\n return self.num_samples\n\n def __getitem__(self, index):\n example = {}\n example[\"prompt\"] = self.prompt\n example[\"index\"] = index\n return example\n\n\ndef main():\n args = parse_args()\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(\n total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir\n )\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=\"tensorboard\",\n project_config=accelerator_project_config,\n )\n\n # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate\n # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.\n # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.\n if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:\n raise ValueError(\n \"Gradient accumulation is not supported when training the text encoder in distributed training. \"\n \"Please set gradient_accumulation_steps to 1. This feature will be supported in the future.\"\n )\n\n if args.seed is not None:\n set_seed(args.seed)\n\n if args.with_prior_preservation:\n class_images_dir = Path(args.class_data_dir)\n if not class_images_dir.exists():\n class_images_dir.mkdir(parents=True)\n cur_class_images = len(list(class_images_dir.iterdir()))\n\n if cur_class_images < args.num_class_images:\n torch_dtype = torch.float16 if accelerator.device.type == \"cuda\" else torch.float32\n pipeline = StableDiffusionInpaintPipeline.from_pretrained(\n args.pretrained_model_name_or_path, torch_dtype=torch_dtype, safety_checker=None\n )\n pipeline.set_progress_bar_config(disable=True)\n\n num_new_images = args.num_class_images - cur_class_images\n logger.info(f\"Number of class images to sample: {num_new_images}.\")\n\n sample_dataset = PromptDataset(args.class_prompt, num_new_images)\n sample_dataloader = torch.utils.data.DataLoader(\n sample_dataset, batch_size=args.sample_batch_size, num_workers=1\n )\n\n sample_dataloader = accelerator.prepare(sample_dataloader)\n pipeline.to(accelerator.device)\n transform_to_pil = transforms.ToPILImage()\n for example in tqdm(\n sample_dataloader, desc=\"Generating class images\", disable=not accelerator.is_local_main_process\n ):\n bsz = len(example[\"prompt\"])\n fake_images = torch.rand((3, args.resolution, args.resolution))\n transform_to_pil = transforms.ToPILImage()\n fake_pil_images = transform_to_pil(fake_images)\n\n fake_mask = random_mask((args.resolution, args.resolution), ratio=1, mask_full_image=True)\n\n images = pipeline(prompt=example[\"prompt\"], mask_image=fake_mask, image=fake_pil_images).images\n\n for i, image in enumerate(images):\n hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()\n image_filename = class_images_dir / f\"{example['index'][i] + cur_class_images}-{hash_image}.jpg\"\n image.save(image_filename)\n\n del pipeline\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizer\n if args.tokenizer_name:\n tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)\n elif args.pretrained_model_name_or_path:\n tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"tokenizer\")\n\n # Load models and create wrapper for stable diffusion\n text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"text_encoder\")\n vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"vae\")\n unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"unet\")\n\n # We only train the additional adapter LoRA layers\n vae.requires_grad_(False)\n text_encoder.requires_grad_(False)\n unet.requires_grad_(False)\n\n weight_dtype = torch.float32\n if args.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif args.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move text_encode and vae to gpu.\n # For mixed precision training we cast the text_encoder and vae weights to half-precision\n # as these models are only used for inference, keeping weights in full precision is not required.\n unet.to(accelerator.device, dtype=weight_dtype)\n vae.to(accelerator.device, dtype=weight_dtype)\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # now we will add new LoRA weights to the attention layers\n # It's important to realize here how many attention weights will be added and of which sizes\n # The sizes of the attention layers consist only of two different variables:\n # 1) - the \"hidden_size\", which is increased according to `unet.config.block_out_channels`.\n # 2) - the \"cross attention size\", which is set to `unet.config.cross_attention_dim`.\n\n # Let's first see how many attention processors we will have to set.\n # For Stable Diffusion, it should be equal to:\n # - down blocks (2x attention layers) * (2x transformer layers) * (3x down blocks) = 12\n # - mid blocks (2x attention layers) * (1x transformer layers) * (1x mid blocks) = 2\n # - up blocks (2x attention layers) * (3x transformer layers) * (3x down blocks) = 18\n # => 32 layers\n\n # Set correct lora layers\n lora_attn_procs = {}\n for name in unet.attn_processors.keys():\n cross_attention_dim = None if name.endswith(\"attn1.processor\") else unet.config.cross_attention_dim\n if name.startswith(\"mid_block\"):\n hidden_size = unet.config.block_out_channels[-1]\n elif name.startswith(\"up_blocks\"):\n block_id = int(name[len(\"up_blocks.\")])\n hidden_size = list(reversed(unet.config.block_out_channels))[block_id]\n elif name.startswith(\"down_blocks\"):\n block_id = int(name[len(\"down_blocks.\")])\n hidden_size = unet.config.block_out_channels[block_id]\n\n lora_attn_procs[name] = LoRAAttnProcessor(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim)\n\n unet.set_attn_processor(lora_attn_procs)\n lora_layers = AttnProcsLayers(unet.attn_processors)\n\n accelerator.register_for_checkpointing(lora_layers)\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n optimizer = optimizer_class(\n lora_layers.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n\n train_dataset = DreamBoothDataset(\n instance_data_root=args.instance_data_dir,\n instance_prompt=args.instance_prompt,\n class_data_root=args.class_data_dir if args.with_prior_preservation else None,\n class_prompt=args.class_prompt,\n tokenizer=tokenizer,\n size=args.resolution,\n center_crop=args.center_crop,\n )\n\n def collate_fn(examples):\n input_ids = [example[\"instance_prompt_ids\"] for example in examples]\n pixel_values = [example[\"instance_images\"] for example in examples]\n\n # Concat class and instance examples for prior preservation.\n # We do this to avoid doing two forward passes.\n if args.with_prior_preservation:\n input_ids += [example[\"class_prompt_ids\"] for example in examples]\n pixel_values += [example[\"class_images\"] for example in examples]\n pior_pil = [example[\"class_PIL_images\"] for example in examples]\n\n masks = []\n masked_images = []\n for example in examples:\n pil_image = example[\"PIL_images\"]\n # generate a random mask\n mask = random_mask(pil_image.size, 1, False)\n # prepare mask and masked image\n mask, masked_image = prepare_mask_and_masked_image(pil_image, mask)\n\n masks.append(mask)\n masked_images.append(masked_image)\n\n if args.with_prior_preservation:\n for pil_image in pior_pil:\n # generate a random mask\n mask = random_mask(pil_image.size, 1, False)\n # prepare mask and masked image\n mask, masked_image = prepare_mask_and_masked_image(pil_image, mask)\n\n masks.append(mask)\n masked_images.append(masked_image)\n\n pixel_values = torch.stack(pixel_values)\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n\n input_ids = tokenizer.pad({\"input_ids\": input_ids}, padding=True, return_tensors=\"pt\").input_ids\n masks = torch.stack(masks)\n masked_images = torch.stack(masked_images)\n batch = {\"input_ids\": input_ids, \"pixel_values\": pixel_values, \"masks\": masks, \"masked_images\": masked_images}\n return batch\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n )\n\n # Prepare everything with our `accelerator`.\n lora_layers, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n lora_layers, optimizer, train_dataloader, lr_scheduler\n )\n # accelerator.register_for_checkpointing(lr_scheduler)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"dreambooth-inpaint-lora\", config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num batches each epoch = {len(train_dataloader)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n resume_global_step = global_step * args.gradient_accumulation_steps\n first_epoch = global_step // num_update_steps_per_epoch\n resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)\n\n # Only show the progress bar once on each machine.\n progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)\n progress_bar.set_description(\"Steps\")\n\n for epoch in range(first_epoch, args.num_train_epochs):\n unet.train()\n for step, batch in enumerate(train_dataloader):\n # Skip steps until we reach the resumed step\n if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:\n if step % args.gradient_accumulation_steps == 0:\n progress_bar.update(1)\n continue\n\n with accelerator.accumulate(unet):\n # Convert images to latent space\n\n latents = vae.encode(batch[\"pixel_values\"].to(dtype=weight_dtype)).latent_dist.sample()\n latents = latents * vae.config.scaling_factor\n\n # Convert masked images to latent space\n masked_latents = vae.encode(\n batch[\"masked_images\"].reshape(batch[\"pixel_values\"].shape).to(dtype=weight_dtype)\n ).latent_dist.sample()\n masked_latents = masked_latents * vae.config.scaling_factor\n\n masks = batch[\"masks\"]\n # resize the mask to latents shape as we concatenate the mask to the latents\n mask = torch.stack(\n [\n torch.nn.functional.interpolate(mask, size=(args.resolution // 8, args.resolution // 8))\n for mask in masks\n ]\n ).to(dtype=weight_dtype)\n mask = mask.reshape(-1, 1, args.resolution // 8, args.resolution // 8)\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # concatenate the noised latents with the mask and the masked latents\n latent_model_input = torch.cat([noisy_latents, mask, masked_latents], dim=1)\n\n # Get the text embedding for conditioning\n encoder_hidden_states = text_encoder(batch[\"input_ids\"])[0]\n\n # Predict the noise residual\n noise_pred = unet(latent_model_input, timesteps, encoder_hidden_states).sample\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n if args.with_prior_preservation:\n # Chunk the noise and noise_pred into two parts and compute the loss on each part separately.\n noise_pred, noise_pred_prior = torch.chunk(noise_pred, 2, dim=0)\n target, target_prior = torch.chunk(target, 2, dim=0)\n\n # Compute instance loss\n loss = F.mse_loss(noise_pred.float(), target.float(), reduction=\"none\").mean([1, 2, 3]).mean()\n\n # Compute prior loss\n prior_loss = F.mse_loss(noise_pred_prior.float(), target_prior.float(), reduction=\"mean\")\n\n # Add the prior loss to the instance loss.\n loss = loss + args.prior_loss_weight * prior_loss\n else:\n loss = F.mse_loss(noise_pred.float(), target.float(), reduction=\"mean\")\n\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n params_to_clip = lora_layers.parameters()\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if global_step % args.checkpointing_steps == 0:\n if accelerator.is_main_process:\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n accelerator.wait_for_everyone()\n\n # Save the lora layers\n if accelerator.is_main_process:\n unet = unet.to(torch.float32)\n unet.save_attn_procs(args.output_dir)\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/research_projects/geodiff/README.md", "content": "# GeoDiff\n\n> [!TIP]\n> This notebook is not actively maintained by the Diffusers team. For any questions or comments, please contact [natolambert](https://twitter.com/natolambert).\n\nThis is an experimental research notebook demonstrating how to generate stable 3D structures of molecules with [GeoDiff](https://github.com/MinkaiXu/GeoDiff) and Diffusers.\n" }, { "path": "diffusers/examples/research_projects/geodiff/geodiff_molecule_conformation.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"F88mignPnalS\"\n },\n \"source\": [\n \"# Introduction\\n\",\n \"\\n\",\n \"This colab is design to run the pretrained models from [GeoDiff](https://github.com/MinkaiXu/GeoDiff).\\n\",\n \"The visualization code is inspired by this PyMol [colab](https://colab.research.google.com/gist/iwatobipen/2ec7faeafe5974501e69fcc98c122922/pymol.ipynb#scrollTo=Hm4kY7CaZSlw).\\n\",\n \"\\n\",\n \"The goal is to generate physically accurate molecules. Given the input of a molecule graph (atom and bond structures with their connectivity -- in the form of a 2d graph). What we want to generate is a stable 3d structure of the molecule.\\n\",\n \"\\n\",\n \"This colab uses GEOM datasets that have multiple 3d targets per configuration, which provide more compelling targets for generative methods.\\n\",\n \"\\n\",\n \"> Colab made by [natolambert](https://twitter.com/natolambert).\\n\",\n \"\\n\",\n \"![diffusers_library](https://github.com/huggingface/diffusers/raw/main/docs/source/imgs/diffusers_library.jpg)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"7cnwXMocnuzB\"\n },\n \"source\": [\n \"## Installations\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"### Install Conda\"\n ],\n \"metadata\": {\n \"id\": \"ff9SxWnaNId9\"\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"1g_6zOabItDk\"\n },\n \"source\": [\n \"Here we check the `cuda` version of colab. When this was built, the version was always 11.1, which impacts some installation decisions below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"K0ofXobG5Y-X\",\n \"outputId\": \"572c3d25-6f19-4c1e-83f5-a1d084a3207f\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"nvcc: NVIDIA (R) Cuda compiler driver\\n\",\n \"Copyright (c) 2005-2021 NVIDIA Corporation\\n\",\n \"Built on Sun_Feb_14_21:12:58_PST_2021\\n\",\n \"Cuda compilation tools, release 11.2, V11.2.152\\n\",\n \"Build cuda_11.2.r11.2/compiler.29618528_0\\n\"\n ]\n }\n ],\n \"source\": [\n \"!nvcc --version\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"VfthW90vI0nw\"\n },\n \"source\": [\n \"Install Conda for some more complex dependencies for geometric networks.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"2WNFzSnbiE0k\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"outputId\": \"690d0d4d-9d0a-4ead-c6dc-086f113f532f\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"\\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\\u001b[0m\\u001b[33m\\n\",\n \"\\u001b[0m\"\n ]\n }\n ],\n \"source\": [\n \"!pip install -q condacolab\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"NUsbWYCUI7Km\"\n },\n \"source\": [\n \"Setup Conda\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"FZelreINdmd0\",\n \"outputId\": \"635f0cb8-0af4-499f-e0a4-b3790cb12e9f\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"✨🍰✨ Everything looks OK!\\n\"\n ]\n }\n ],\n \"source\": [\n \"import condacolab\\n\",\n \"condacolab.install()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"JzDHaPU7I9Sn\"\n },\n \"source\": [\n \"Install pytorch requirements (this takes a few minutes, go grab yourself a coffee 🤗)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"JMxRjHhL7w8V\",\n \"outputId\": \"6ed511b3-9262-49e8-b340-08e76b05ebd8\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"Collecting package metadata (current_repodata.json): - \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\bdone\\n\",\n \"Solving environment: \\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\bdone\\n\",\n \"\\n\",\n \"## Package Plan ##\\n\",\n \"\\n\",\n \" environment location: /usr/local\\n\",\n \"\\n\",\n \" added / updated specs:\\n\",\n \" - cudatoolkit=11.1\\n\",\n \" - pytorch\\n\",\n \" - torchaudio\\n\",\n \" - torchvision\\n\",\n \"\\n\",\n \"\\n\",\n \"The following packages will be downloaded:\\n\",\n \"\\n\",\n \" package | build\\n\",\n \" ---------------------------|-----------------\\n\",\n \" conda-22.9.0 | py37h89c1867_1 960 KB conda-forge\\n\",\n \" ------------------------------------------------------------\\n\",\n \" Total: 960 KB\\n\",\n \"\\n\",\n \"The following packages will be UPDATED:\\n\",\n \"\\n\",\n \" conda 4.14.0-py37h89c1867_0 --> 22.9.0-py37h89c1867_1\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Downloading and Extracting Packages\\n\",\n \"conda-22.9.0 | 960 KB | : 100% 1.0/1 [00:00<00:00, 4.15it/s]\\n\",\n \"Preparing transaction: / \\b\\bdone\\n\",\n \"Verifying transaction: \\\\ \\b\\bdone\\n\",\n \"Executing transaction: / \\b\\bdone\\n\",\n \"Retrieving notices: ...working... done\\n\"\n ]\n }\n ],\n \"source\": [\n \"!conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch-lts -c nvidia\\n\",\n \"# !conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"Need to remove a pathspec for colab that specifies the incorrect cuda version.\"\n ],\n \"metadata\": {\n \"id\": \"QDS6FPZ0Tu5b\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"!rm /usr/local/conda-meta/pinned\"\n ],\n \"metadata\": {\n \"id\": \"dq1lxR10TtrR\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"outputId\": \"ed9c5a71-b449-418f-abb7-072b74e7f6c8\"\n },\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"rm: cannot remove '/usr/local/conda-meta/pinned': No such file or directory\\n\"\n ]\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Z1L3DdZOJB30\"\n },\n \"source\": [\n \"Install torch geometric (used in the model later)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"D5ukfCOWfjzK\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"outputId\": \"8437485a-5aa6-4d53-8f7f-23517ac1ace6\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"Collecting package metadata (current_repodata.json): - \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\bdone\\n\",\n \"Solving environment: | \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\bdone\\n\",\n \"\\n\",\n \"## Package Plan ##\\n\",\n \"\\n\",\n \" environment location: /usr/local\\n\",\n \"\\n\",\n \" added / updated specs:\\n\",\n \" - pytorch-geometric=1.7.2\\n\",\n \"\\n\",\n \"\\n\",\n \"The following packages will be downloaded:\\n\",\n \"\\n\",\n \" package | build\\n\",\n \" ---------------------------|-----------------\\n\",\n \" decorator-4.4.2 | py_0 11 KB conda-forge\\n\",\n \" googledrivedownloader-0.4 | pyhd3deb0d_1 7 KB conda-forge\\n\",\n \" jinja2-3.1.2 | pyhd8ed1ab_1 99 KB conda-forge\\n\",\n \" joblib-1.2.0 | pyhd8ed1ab_0 205 KB conda-forge\\n\",\n \" markupsafe-2.1.1 | py37h540881e_1 22 KB conda-forge\\n\",\n \" networkx-2.5.1 | pyhd8ed1ab_0 1.2 MB conda-forge\\n\",\n \" pandas-1.2.3 | py37hdc94413_0 11.8 MB conda-forge\\n\",\n \" pyparsing-3.0.9 | pyhd8ed1ab_0 79 KB conda-forge\\n\",\n \" python-dateutil-2.8.2 | pyhd8ed1ab_0 240 KB conda-forge\\n\",\n \" python-louvain-0.15 | pyhd8ed1ab_1 13 KB conda-forge\\n\",\n \" pytorch-cluster-1.5.9 |py37_torch_1.8.0_cu111 1.2 MB rusty1s\\n\",\n \" pytorch-geometric-1.7.2 |py37_torch_1.8.0_cu111 445 KB rusty1s\\n\",\n \" pytorch-scatter-2.0.8 |py37_torch_1.8.0_cu111 6.1 MB rusty1s\\n\",\n \" pytorch-sparse-0.6.12 |py37_torch_1.8.0_cu111 2.9 MB rusty1s\\n\",\n \" pytorch-spline-conv-1.2.1 |py37_torch_1.8.0_cu111 736 KB rusty1s\\n\",\n \" pytz-2022.4 | pyhd8ed1ab_0 232 KB conda-forge\\n\",\n \" scikit-learn-1.0.2 | py37hf9e9bfc_0 7.8 MB conda-forge\\n\",\n \" scipy-1.7.3 | py37hf2a6cf1_0 21.8 MB conda-forge\\n\",\n \" setuptools-59.8.0 | py37h89c1867_1 1.0 MB conda-forge\\n\",\n \" threadpoolctl-3.1.0 | pyh8a188c0_0 18 KB conda-forge\\n\",\n \" ------------------------------------------------------------\\n\",\n \" Total: 55.9 MB\\n\",\n \"\\n\",\n \"The following NEW packages will be INSTALLED:\\n\",\n \"\\n\",\n \" decorator conda-forge/noarch::decorator-4.4.2-py_0 None\\n\",\n \" googledrivedownlo~ conda-forge/noarch::googledrivedownloader-0.4-pyhd3deb0d_1 None\\n\",\n \" jinja2 conda-forge/noarch::jinja2-3.1.2-pyhd8ed1ab_1 None\\n\",\n \" joblib conda-forge/noarch::joblib-1.2.0-pyhd8ed1ab_0 None\\n\",\n \" markupsafe conda-forge/linux-64::markupsafe-2.1.1-py37h540881e_1 None\\n\",\n \" networkx conda-forge/noarch::networkx-2.5.1-pyhd8ed1ab_0 None\\n\",\n \" pandas conda-forge/linux-64::pandas-1.2.3-py37hdc94413_0 None\\n\",\n \" pyparsing conda-forge/noarch::pyparsing-3.0.9-pyhd8ed1ab_0 None\\n\",\n \" python-dateutil conda-forge/noarch::python-dateutil-2.8.2-pyhd8ed1ab_0 None\\n\",\n \" python-louvain conda-forge/noarch::python-louvain-0.15-pyhd8ed1ab_1 None\\n\",\n \" pytorch-cluster rusty1s/linux-64::pytorch-cluster-1.5.9-py37_torch_1.8.0_cu111 None\\n\",\n \" pytorch-geometric rusty1s/linux-64::pytorch-geometric-1.7.2-py37_torch_1.8.0_cu111 None\\n\",\n \" pytorch-scatter rusty1s/linux-64::pytorch-scatter-2.0.8-py37_torch_1.8.0_cu111 None\\n\",\n \" pytorch-sparse rusty1s/linux-64::pytorch-sparse-0.6.12-py37_torch_1.8.0_cu111 None\\n\",\n \" pytorch-spline-co~ rusty1s/linux-64::pytorch-spline-conv-1.2.1-py37_torch_1.8.0_cu111 None\\n\",\n \" pytz conda-forge/noarch::pytz-2022.4-pyhd8ed1ab_0 None\\n\",\n \" scikit-learn conda-forge/linux-64::scikit-learn-1.0.2-py37hf9e9bfc_0 None\\n\",\n \" scipy conda-forge/linux-64::scipy-1.7.3-py37hf2a6cf1_0 None\\n\",\n \" threadpoolctl conda-forge/noarch::threadpoolctl-3.1.0-pyh8a188c0_0 None\\n\",\n \"\\n\",\n \"The following packages will be DOWNGRADED:\\n\",\n \"\\n\",\n \" setuptools 65.3.0-py37h89c1867_0 --> 59.8.0-py37h89c1867_1 None\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Downloading and Extracting Packages\\n\",\n \"scikit-learn-1.0.2 | 7.8 MB | : 100% 1.0/1 [00:01<00:00, 1.37s/it] \\n\",\n \"pytorch-scatter-2.0. | 6.1 MB | : 100% 1.0/1 [00:06<00:00, 6.18s/it]\\n\",\n \"pytorch-geometric-1. | 445 KB | : 100% 1.0/1 [00:02<00:00, 2.53s/it]\\n\",\n \"scipy-1.7.3 | 21.8 MB | : 100% 1.0/1 [00:03<00:00, 3.06s/it]\\n\",\n \"python-dateutil-2.8. | 240 KB | : 100% 1.0/1 [00:00<00:00, 21.48it/s]\\n\",\n \"pytorch-spline-conv- | 736 KB | : 100% 1.0/1 [00:01<00:00, 1.00s/it]\\n\",\n \"pytorch-sparse-0.6.1 | 2.9 MB | : 100% 1.0/1 [00:07<00:00, 7.51s/it]\\n\",\n \"pyparsing-3.0.9 | 79 KB | : 100% 1.0/1 [00:00<00:00, 26.32it/s]\\n\",\n \"pytorch-cluster-1.5. | 1.2 MB | : 100% 1.0/1 [00:02<00:00, 2.78s/it]\\n\",\n \"jinja2-3.1.2 | 99 KB | : 100% 1.0/1 [00:00<00:00, 20.28it/s]\\n\",\n \"decorator-4.4.2 | 11 KB | : 100% 1.0/1 [00:00<00:00, 21.57it/s]\\n\",\n \"joblib-1.2.0 | 205 KB | : 100% 1.0/1 [00:00<00:00, 15.04it/s]\\n\",\n \"pytz-2022.4 | 232 KB | : 100% 1.0/1 [00:00<00:00, 10.21it/s]\\n\",\n \"python-louvain-0.15 | 13 KB | : 100% 1.0/1 [00:00<00:00, 3.34it/s]\\n\",\n \"googledrivedownloade | 7 KB | : 100% 1.0/1 [00:00<00:00, 3.33it/s]\\n\",\n \"threadpoolctl-3.1.0 | 18 KB | : 100% 1.0/1 [00:00<00:00, 29.40it/s]\\n\",\n \"markupsafe-2.1.1 | 22 KB | : 100% 1.0/1 [00:00<00:00, 28.62it/s]\\n\",\n \"pandas-1.2.3 | 11.8 MB | : 100% 1.0/1 [00:02<00:00, 2.08s/it] \\n\",\n \"networkx-2.5.1 | 1.2 MB | : 100% 1.0/1 [00:01<00:00, 1.39s/it]\\n\",\n \"setuptools-59.8.0 | 1.0 MB | : 100% 1.0/1 [00:00<00:00, 4.25it/s]\\n\",\n \"Preparing transaction: / \\b\\b- \\b\\b\\\\ \\b\\bdone\\n\",\n \"Verifying transaction: / \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\bdone\\n\",\n \"Executing transaction: / \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\b\\\\ \\b\\b| \\b\\b/ \\b\\b- \\b\\bdone\\n\",\n \"Retrieving notices: ...working... done\\n\"\n ]\n }\n ],\n \"source\": [\n \"!conda install -c rusty1s pytorch-geometric=1.7.2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"ppxv6Mdkalbc\"\n },\n \"source\": [\n \"### Install Diffusers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"mgQA_XN-XGY2\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"outputId\": \"85392615-b6a4-4052-9d2a-79604be62c94\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"/content\\n\",\n \"Cloning into 'diffusers'...\\n\",\n \"remote: Enumerating objects: 9298, done.\\u001b[K\\n\",\n \"remote: Counting objects: 100% (40/40), done.\\u001b[K\\n\",\n \"remote: Compressing objects: 100% (23/23), done.\\u001b[K\\n\",\n \"remote: Total 9298 (delta 17), reused 23 (delta 11), pack-reused 9258\\u001b[K\\n\",\n \"Receiving objects: 100% (9298/9298), 7.38 MiB | 5.28 MiB/s, done.\\n\",\n \"Resolving deltas: 100% (6168/6168), done.\\n\",\n \" Installing build dependencies ... \\u001b[?25l\\u001b[?25hdone\\n\",\n \" Getting requirements to build wheel ... \\u001b[?25l\\u001b[?25hdone\\n\",\n \" Preparing metadata (pyproject.toml) ... \\u001b[?25l\\u001b[?25hdone\\n\",\n \"\\u001b[2K \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\\u001b[0m\\u001b[33m\\n\",\n \"\\u001b[0m\"\n ]\n }\n ],\n \"source\": [\n \"%cd /content\\n\",\n \"\\n\",\n \"# install latest HF diffusers (will update to the release once added)\\n\",\n \"!git clone https://github.com/huggingface/diffusers.git\\n\",\n \"!pip install -q /content/diffusers\\n\",\n \"\\n\",\n \"# dependencies for diffusers\\n\",\n \"!pip install -q datasets transformers\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"LZO6AJKuJKO8\"\n },\n \"source\": [\n \"Check that torch is installed correctly and utilizing the GPU in the colab\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"gZt7BNi1e1PA\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 53\n },\n \"outputId\": \"a0e1832c-9c02-49aa-cff8-1339e6cdc889\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"True\\n\"\n ]\n },\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"'1.8.2'\"\n ],\n \"application/vnd.google.colaboratory.intrinsic+json\": {\n \"type\": \"string\"\n }\n },\n \"metadata\": {},\n \"execution_count\": 8\n }\n ],\n \"source\": [\n \"import torch\\n\",\n \"print(torch.cuda.is_available())\\n\",\n \"torch.__version__\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"KLE7CqlfJNUO\"\n },\n \"source\": [\n \"### Install Chemistry-specific Dependencies\\n\",\n \"\\n\",\n \"Install RDKit, a tool for working with and visualizing chemsitry in python (you use this to visualize the generate models later).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"0CPv_NvehRz3\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"outputId\": \"6ee0ae4e-4511-4816-de29-22b1c21d49bc\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\\n\",\n \"Collecting rdkit\\n\",\n \" Downloading rdkit-2022.3.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (36.8 MB)\\n\",\n \"\\u001b[2K \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m36.8/36.8 MB\\u001b[0m \\u001b[31m34.6 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n \"\\u001b[?25hRequirement already satisfied: Pillow in /usr/local/lib/python3.7/site-packages (from rdkit) (9.2.0)\\n\",\n \"Requirement already satisfied: numpy in /usr/local/lib/python3.7/site-packages (from rdkit) (1.21.6)\\n\",\n \"Installing collected packages: rdkit\\n\",\n \"Successfully installed rdkit-2022.3.5\\n\",\n \"\\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\\u001b[0m\\u001b[33m\\n\",\n \"\\u001b[0m\"\n ]\n }\n ],\n \"source\": [\n \"!pip install rdkit\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"88GaDbDPxJ5I\"\n },\n \"source\": [\n \"### Get viewer from nglview\\n\",\n \"\\n\",\n \"The model you will use outputs a position matrix tensor. This pytorch geometric data object will have many features (positions, known features, edge features -- all tensors).\\n\",\n \"The data we give to the model will also have a rdmol object (which can extract features to geometric if needed).\\n\",\n \"The rdmol in this object is a source of ground truth for the generated molecules.\\n\",\n \"\\n\",\n \"You will use one rendering function from nglviewer later!\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"jcl8GCS2mz6t\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 1000\n },\n \"outputId\": \"99b5cc40-67bb-4d8e-faa0-47d7cb33e98f\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\\n\",\n \"Collecting nglview\\n\",\n \" Downloading nglview-3.0.3.tar.gz (5.7 MB)\\n\",\n \"\\u001b[2K 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\" Stored in directory: /root/.cache/pip/wheels/01/0c/49/c6f79d8edba8fe89752bf20de2d99040bfa57db0548975c5d5\\n\",\n \"Successfully built nglview\\n\",\n \"Installing collected packages: wcwidth, ptyprocess, pickleshare, backcall, widgetsnbextension, traitlets, tornado, pyzmq, pygments, psutil, prompt-toolkit, pexpect, parso, nest-asyncio, jupyterlab-widgets, entrypoints, debugpy, matplotlib-inline, jupyter-core, jedi, jupyter-client, ipython, ipykernel, ipywidgets, nglview\\n\",\n \"Successfully installed backcall-0.2.0 debugpy-1.6.3 entrypoints-0.4 ipykernel-6.16.0 ipython-7.34.0 ipywidgets-8.0.2 jedi-0.18.1 jupyter-client-7.4.2 jupyter-core-4.11.1 jupyterlab-widgets-3.0.3 matplotlib-inline-0.1.6 nest-asyncio-1.5.6 nglview-3.0.3 parso-0.8.3 pexpect-4.8.0 pickleshare-0.7.5 prompt-toolkit-3.0.31 psutil-5.9.2 ptyprocess-0.7.0 pygments-2.13.0 pyzmq-24.0.1 tornado-6.2 traitlets-5.4.0 wcwidth-0.2.5 widgetsnbextension-4.0.3\\n\",\n \"\\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\\u001b[0m\\u001b[33m\\n\",\n \"\\u001b[0m\"\n ]\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"application/vnd.colab-display-data+json\": {\n \"pip_warning\": {\n \"packages\": [\n \"pexpect\",\n \"pickleshare\",\n \"wcwidth\"\n ]\n }\n }\n },\n \"metadata\": {}\n }\n ],\n \"source\": [\n \"!pip install nglview\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Create a diffusion model\"\n ],\n \"metadata\": {\n \"id\": \"8t8_e_uVLdKB\"\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"### Model class(es)\"\n ],\n \"metadata\": {\n \"id\": \"G0rMncVtNSqU\"\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"Imports\"\n ],\n \"metadata\": {\n \"id\": \"L5FEXz5oXkzt\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"# Model adapted from GeoDiff https://github.com/MinkaiXu/GeoDiff\\n\",\n \"# Model inspired by https://github.com/DeepGraphLearning/torchdrug/tree/master/torchdrug/models\\n\",\n \"from dataclasses import dataclass\\n\",\n \"from typing import Callable, Tuple, Union\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import torch\\n\",\n \"import torch.nn.functional as F\\n\",\n \"from torch import Tensor, nn\\n\",\n \"from torch.nn import Embedding, Linear, Module, ModuleList, Sequential\\n\",\n \"\\n\",\n \"from torch_geometric.nn import MessagePassing, radius, radius_graph\\n\",\n \"from torch_geometric.typing import Adj, OptPairTensor, OptTensor, Size\\n\",\n \"from torch_geometric.utils import dense_to_sparse, to_dense_adj\\n\",\n \"from torch_scatter import scatter_add\\n\",\n \"from torch_sparse import SparseTensor, coalesce\\n\",\n \"\\n\",\n \"from diffusers.configuration_utils import ConfigMixin, register_to_config\\n\",\n \"from diffusers.modeling_utils import ModelMixin\\n\",\n \"from diffusers.utils import BaseOutput\\n\"\n ],\n \"metadata\": {\n \"id\": \"-3-P4w5sXkRU\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"Helper classes\"\n ],\n \"metadata\": {\n \"id\": \"EzJQXPN_XrMX\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"@dataclass\\n\",\n \"class MoleculeGNNOutput(BaseOutput):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Args:\\n\",\n \" sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):\\n\",\n \" Hidden states output. Output of last layer of model.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" sample: torch.Tensor\\n\",\n \"\\n\",\n \"\\n\",\n \"class MultiLayerPerceptron(nn.Module):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Multi-layer Perceptron. Note there is no activation or dropout in the last layer.\\n\",\n \" Args:\\n\",\n \" input_dim (int): input dimension\\n\",\n \" hidden_dim (list of int): hidden dimensions\\n\",\n \" activation (str or function, optional): activation function\\n\",\n \" dropout (float, optional): dropout rate\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" def __init__(self, input_dim, hidden_dims, activation=\\\"relu\\\", dropout=0):\\n\",\n \" super(MultiLayerPerceptron, self).__init__()\\n\",\n \"\\n\",\n \" self.dims = [input_dim] + hidden_dims\\n\",\n \" if isinstance(activation, str):\\n\",\n \" self.activation = getattr(F, activation)\\n\",\n \" else:\\n\",\n \" print(f\\\"Warning, activation passed {activation} is not string and ignored\\\")\\n\",\n \" self.activation = None\\n\",\n \" if dropout > 0:\\n\",\n \" self.dropout = nn.Dropout(dropout)\\n\",\n \" else:\\n\",\n \" self.dropout = None\\n\",\n \"\\n\",\n \" self.layers = nn.ModuleList()\\n\",\n \" for i in range(len(self.dims) - 1):\\n\",\n \" self.layers.append(nn.Linear(self.dims[i], self.dims[i + 1]))\\n\",\n \"\\n\",\n \" def forward(self, x):\\n\",\n \" \\\"\\\"\\\"\\\"\\\"\\\"\\n\",\n \" for i, layer in enumerate(self.layers):\\n\",\n \" x = layer(x)\\n\",\n \" if i < len(self.layers) - 1:\\n\",\n \" if self.activation:\\n\",\n \" x = self.activation(x)\\n\",\n \" if self.dropout:\\n\",\n \" x = self.dropout(x)\\n\",\n \" return x\\n\",\n \"\\n\",\n \"\\n\",\n \"class ShiftedSoftplus(torch.nn.Module):\\n\",\n \" def __init__(self):\\n\",\n \" super(ShiftedSoftplus, self).__init__()\\n\",\n \" self.shift = torch.log(torch.tensor(2.0)).item()\\n\",\n \"\\n\",\n \" def forward(self, x):\\n\",\n \" return F.softplus(x) - self.shift\\n\",\n \"\\n\",\n \"\\n\",\n \"class CFConv(MessagePassing):\\n\",\n \" def __init__(self, in_channels, out_channels, num_filters, mlp, cutoff, smooth):\\n\",\n \" super(CFConv, self).__init__(aggr=\\\"add\\\")\\n\",\n \" self.lin1 = Linear(in_channels, num_filters, bias=False)\\n\",\n \" self.lin2 = Linear(num_filters, out_channels)\\n\",\n \" self.nn = mlp\\n\",\n \" self.cutoff = cutoff\\n\",\n \" self.smooth = smooth\\n\",\n \"\\n\",\n \" self.reset_parameters()\\n\",\n \"\\n\",\n \" def reset_parameters(self):\\n\",\n \" torch.nn.init.xavier_uniform_(self.lin1.weight)\\n\",\n \" torch.nn.init.xavier_uniform_(self.lin2.weight)\\n\",\n \" self.lin2.bias.data.fill_(0)\\n\",\n \"\\n\",\n \" def forward(self, x, edge_index, edge_length, edge_attr):\\n\",\n \" if self.smooth:\\n\",\n \" C = 0.5 * (torch.cos(edge_length * np.pi / self.cutoff) + 1.0)\\n\",\n \" C = C * (edge_length <= self.cutoff) * (edge_length >= 0.0) # Modification: cutoff\\n\",\n \" else:\\n\",\n \" C = (edge_length <= self.cutoff).float()\\n\",\n \" W = self.nn(edge_attr) * C.view(-1, 1)\\n\",\n \"\\n\",\n \" x = self.lin1(x)\\n\",\n \" x = self.propagate(edge_index, x=x, W=W)\\n\",\n \" x = self.lin2(x)\\n\",\n \" return x\\n\",\n \"\\n\",\n \" def message(self, x_j: torch.Tensor, W) -> torch.Tensor:\\n\",\n \" return x_j * W\\n\",\n \"\\n\",\n \"\\n\",\n \"class InteractionBlock(torch.nn.Module):\\n\",\n \" def __init__(self, hidden_channels, num_gaussians, num_filters, cutoff, smooth):\\n\",\n \" super(InteractionBlock, self).__init__()\\n\",\n \" mlp = Sequential(\\n\",\n \" Linear(num_gaussians, num_filters),\\n\",\n \" ShiftedSoftplus(),\\n\",\n \" Linear(num_filters, num_filters),\\n\",\n \" )\\n\",\n \" self.conv = CFConv(hidden_channels, hidden_channels, num_filters, mlp, cutoff, smooth)\\n\",\n \" self.act = ShiftedSoftplus()\\n\",\n \" self.lin = Linear(hidden_channels, hidden_channels)\\n\",\n \"\\n\",\n \" def forward(self, x, edge_index, edge_length, edge_attr):\\n\",\n \" x = self.conv(x, edge_index, edge_length, edge_attr)\\n\",\n \" x = self.act(x)\\n\",\n \" x = self.lin(x)\\n\",\n \" return x\\n\",\n \"\\n\",\n \"\\n\",\n \"class SchNetEncoder(Module):\\n\",\n \" def __init__(\\n\",\n \" self, hidden_channels=128, num_filters=128, num_interactions=6, edge_channels=100, cutoff=10.0, smooth=False\\n\",\n \" ):\\n\",\n \" super().__init__()\\n\",\n \"\\n\",\n \" self.hidden_channels = hidden_channels\\n\",\n \" self.num_filters = num_filters\\n\",\n \" self.num_interactions = num_interactions\\n\",\n \" self.cutoff = cutoff\\n\",\n \"\\n\",\n \" self.embedding = Embedding(100, hidden_channels, max_norm=10.0)\\n\",\n \"\\n\",\n \" self.interactions = ModuleList()\\n\",\n \" for _ in range(num_interactions):\\n\",\n \" block = InteractionBlock(hidden_channels, edge_channels, num_filters, cutoff, smooth)\\n\",\n \" self.interactions.append(block)\\n\",\n \"\\n\",\n \" def forward(self, z, edge_index, edge_length, edge_attr, embed_node=True):\\n\",\n \" if embed_node:\\n\",\n \" assert z.dim() == 1 and z.dtype == torch.long\\n\",\n \" h = self.embedding(z)\\n\",\n \" else:\\n\",\n \" h = z\\n\",\n \" for interaction in self.interactions:\\n\",\n \" h = h + interaction(h, edge_index, edge_length, edge_attr)\\n\",\n \"\\n\",\n \" return h\\n\",\n \"\\n\",\n \"\\n\",\n \"class GINEConv(MessagePassing):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Custom class of the graph isomorphism operator from the \\\"How Powerful are Graph Neural Networks?\\n\",\n \" https://arxiv.org/abs/1810.00826 paper. Note that this implementation has the added option of a custom activation.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" def __init__(self, mlp: Callable, eps: float = 0.0, train_eps: bool = False, activation=\\\"softplus\\\", **kwargs):\\n\",\n \" super(GINEConv, self).__init__(aggr=\\\"add\\\", **kwargs)\\n\",\n \" self.nn = mlp\\n\",\n \" self.initial_eps = eps\\n\",\n \"\\n\",\n \" if isinstance(activation, str):\\n\",\n \" self.activation = getattr(F, activation)\\n\",\n \" else:\\n\",\n \" self.activation = None\\n\",\n \"\\n\",\n \" if train_eps:\\n\",\n \" self.eps = torch.nn.Parameter(torch.Tensor([eps]))\\n\",\n \" else:\\n\",\n \" self.register_buffer(\\\"eps\\\", torch.Tensor([eps]))\\n\",\n \"\\n\",\n \" def forward(\\n\",\n \" self, x: Union[Tensor, OptPairTensor], edge_index: Adj, edge_attr: OptTensor = None, size: Size = None\\n\",\n \" ) -> torch.Tensor:\\n\",\n \" \\\"\\\"\\\"\\\"\\\"\\\"\\n\",\n \" if isinstance(x, torch.Tensor):\\n\",\n \" x: OptPairTensor = (x, x)\\n\",\n \"\\n\",\n \" # Node and edge feature dimensionalites need to match.\\n\",\n \" if isinstance(edge_index, torch.Tensor):\\n\",\n \" assert edge_attr is not None\\n\",\n \" assert x[0].size(-1) == edge_attr.size(-1)\\n\",\n \" elif isinstance(edge_index, SparseTensor):\\n\",\n \" assert x[0].size(-1) == edge_index.size(-1)\\n\",\n \"\\n\",\n \" # propagate_type: (x: OptPairTensor, edge_attr: OptTensor)\\n\",\n \" out = self.propagate(edge_index, x=x, edge_attr=edge_attr, size=size)\\n\",\n \"\\n\",\n \" x_r = x[1]\\n\",\n \" if x_r is not None:\\n\",\n \" out += (1 + self.eps) * x_r\\n\",\n \"\\n\",\n \" return self.nn(out)\\n\",\n \"\\n\",\n \" def message(self, x_j: torch.Tensor, edge_attr: torch.Tensor) -> torch.Tensor:\\n\",\n \" if self.activation:\\n\",\n \" return self.activation(x_j + edge_attr)\\n\",\n \" else:\\n\",\n \" return x_j + edge_attr\\n\",\n \"\\n\",\n \" def __repr__(self):\\n\",\n \" return \\\"{}(nn={})\\\".format(self.__class__.__name__, self.nn)\\n\",\n \"\\n\",\n \"\\n\",\n \"class GINEncoder(torch.nn.Module):\\n\",\n \" def __init__(self, hidden_dim, num_convs=3, activation=\\\"relu\\\", short_cut=True, concat_hidden=False):\\n\",\n \" super().__init__()\\n\",\n \"\\n\",\n \" self.hidden_dim = hidden_dim\\n\",\n \" self.num_convs = num_convs\\n\",\n \" self.short_cut = short_cut\\n\",\n \" self.concat_hidden = concat_hidden\\n\",\n \" self.node_emb = nn.Embedding(100, hidden_dim)\\n\",\n \"\\n\",\n \" if isinstance(activation, str):\\n\",\n \" self.activation = getattr(F, activation)\\n\",\n \" else:\\n\",\n \" self.activation = None\\n\",\n \"\\n\",\n \" self.convs = nn.ModuleList()\\n\",\n \" for i in range(self.num_convs):\\n\",\n \" self.convs.append(\\n\",\n \" GINEConv(\\n\",\n \" MultiLayerPerceptron(hidden_dim, [hidden_dim, hidden_dim], activation=activation),\\n\",\n \" activation=activation,\\n\",\n \" )\\n\",\n \" )\\n\",\n \"\\n\",\n \" def forward(self, z, edge_index, edge_attr):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Input:\\n\",\n \" data: (torch_geometric.data.Data): batched graph edge_index: bond indices of the original graph (num_node,\\n\",\n \" hidden) edge_attr: edge feature tensor with shape (num_edge, hidden)\\n\",\n \" Output:\\n\",\n \" node_feature: graph feature\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" node_attr = self.node_emb(z) # (num_node, hidden)\\n\",\n \"\\n\",\n \" hiddens = []\\n\",\n \" conv_input = node_attr # (num_node, hidden)\\n\",\n \"\\n\",\n \" for conv_idx, conv in enumerate(self.convs):\\n\",\n \" hidden = conv(conv_input, edge_index, edge_attr)\\n\",\n \" if conv_idx < len(self.convs) - 1 and self.activation is not None:\\n\",\n \" hidden = self.activation(hidden)\\n\",\n \" assert hidden.shape == conv_input.shape\\n\",\n \" if self.short_cut and hidden.shape == conv_input.shape:\\n\",\n \" hidden += conv_input\\n\",\n \"\\n\",\n \" hiddens.append(hidden)\\n\",\n \" conv_input = hidden\\n\",\n \"\\n\",\n \" if self.concat_hidden:\\n\",\n \" node_feature = torch.cat(hiddens, dim=-1)\\n\",\n \" else:\\n\",\n \" node_feature = hiddens[-1]\\n\",\n \"\\n\",\n \" return node_feature\\n\",\n \"\\n\",\n \"\\n\",\n \"class MLPEdgeEncoder(Module):\\n\",\n \" def __init__(self, hidden_dim=100, activation=\\\"relu\\\"):\\n\",\n \" super().__init__()\\n\",\n \" self.hidden_dim = hidden_dim\\n\",\n \" self.bond_emb = Embedding(100, embedding_dim=self.hidden_dim)\\n\",\n \" self.mlp = MultiLayerPerceptron(1, [self.hidden_dim, self.hidden_dim], activation=activation)\\n\",\n \"\\n\",\n \" @property\\n\",\n \" def out_channels(self):\\n\",\n \" return self.hidden_dim\\n\",\n \"\\n\",\n \" def forward(self, edge_length, edge_type):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Input:\\n\",\n \" edge_length: The length of edges, shape=(E, 1). edge_type: The type pf edges, shape=(E,)\\n\",\n \" Returns:\\n\",\n \" edge_attr: The representation of edges. (E, 2 * num_gaussians)\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" d_emb = self.mlp(edge_length) # (num_edge, hidden_dim)\\n\",\n \" edge_attr = self.bond_emb(edge_type) # (num_edge, hidden_dim)\\n\",\n \" return d_emb * edge_attr # (num_edge, hidden)\\n\",\n \"\\n\",\n \"\\n\",\n \"def assemble_atom_pair_feature(node_attr, edge_index, edge_attr):\\n\",\n \" h_row, h_col = node_attr[edge_index[0]], node_attr[edge_index[1]]\\n\",\n \" h_pair = torch.cat([h_row * h_col, edge_attr], dim=-1) # (E, 2H)\\n\",\n \" return h_pair\\n\",\n \"\\n\",\n \"\\n\",\n \"def _extend_graph_order(num_nodes, edge_index, edge_type, order=3):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Args:\\n\",\n \" num_nodes: Number of atoms.\\n\",\n \" edge_index: Bond indices of the original graph.\\n\",\n \" edge_type: Bond types of the original graph.\\n\",\n \" order: Extension order.\\n\",\n \" Returns:\\n\",\n \" new_edge_index: Extended edge indices. new_edge_type: Extended edge types.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" def binarize(x):\\n\",\n \" return torch.where(x > 0, torch.ones_like(x), torch.zeros_like(x))\\n\",\n \"\\n\",\n \" def get_higher_order_adj_matrix(adj, order):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Args:\\n\",\n \" adj: (N, N)\\n\",\n \" type_mat: (N, N)\\n\",\n \" Returns:\\n\",\n \" Following attributes will be updated:\\n\",\n \" - edge_index\\n\",\n \" - edge_type\\n\",\n \" Following attributes will be added to the data object:\\n\",\n \" - bond_edge_index: Original edge_index.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" adj_mats = [\\n\",\n \" torch.eye(adj.size(0), dtype=torch.long, device=adj.device),\\n\",\n \" binarize(adj + torch.eye(adj.size(0), dtype=torch.long, device=adj.device)),\\n\",\n \" ]\\n\",\n \"\\n\",\n \" for i in range(2, order + 1):\\n\",\n \" adj_mats.append(binarize(adj_mats[i - 1] @ adj_mats[1]))\\n\",\n \" order_mat = torch.zeros_like(adj)\\n\",\n \"\\n\",\n \" for i in range(1, order + 1):\\n\",\n \" order_mat += (adj_mats[i] - adj_mats[i - 1]) * i\\n\",\n \"\\n\",\n \" return order_mat\\n\",\n \"\\n\",\n \" num_types = 22\\n\",\n \" # given from len(BOND_TYPES), where BOND_TYPES = {t: i for i, t in enumerate(BT.names.values())}\\n\",\n \" # from rdkit.Chem.rdchem import BondType as BT\\n\",\n \" N = num_nodes\\n\",\n \" adj = to_dense_adj(edge_index).squeeze(0)\\n\",\n \" adj_order = get_higher_order_adj_matrix(adj, order) # (N, N)\\n\",\n \"\\n\",\n \" type_mat = to_dense_adj(edge_index, edge_attr=edge_type).squeeze(0) # (N, N)\\n\",\n \" type_highorder = torch.where(adj_order > 1, num_types + adj_order - 1, torch.zeros_like(adj_order))\\n\",\n \" assert (type_mat * type_highorder == 0).all()\\n\",\n \" type_new = type_mat + type_highorder\\n\",\n \"\\n\",\n \" new_edge_index, new_edge_type = dense_to_sparse(type_new)\\n\",\n \" _, edge_order = dense_to_sparse(adj_order)\\n\",\n \"\\n\",\n \" # data.bond_edge_index = data.edge_index # Save original edges\\n\",\n \" new_edge_index, new_edge_type = coalesce(new_edge_index, new_edge_type.long(), N, N) # modify data\\n\",\n \"\\n\",\n \" return new_edge_index, new_edge_type\\n\",\n \"\\n\",\n \"\\n\",\n \"def _extend_to_radius_graph(pos, edge_index, edge_type, cutoff, batch, unspecified_type_number=0, is_sidechain=None):\\n\",\n \" assert edge_type.dim() == 1\\n\",\n \" N = pos.size(0)\\n\",\n \"\\n\",\n \" bgraph_adj = torch.sparse.LongTensor(edge_index, edge_type, torch.Size([N, N]))\\n\",\n \"\\n\",\n \" if is_sidechain is None:\\n\",\n \" rgraph_edge_index = radius_graph(pos, r=cutoff, batch=batch) # (2, E_r)\\n\",\n \" else:\\n\",\n \" # fetch sidechain and its batch index\\n\",\n \" is_sidechain = is_sidechain.bool()\\n\",\n \" dummy_index = torch.arange(pos.size(0), device=pos.device)\\n\",\n \" sidechain_pos = pos[is_sidechain]\\n\",\n \" sidechain_index = dummy_index[is_sidechain]\\n\",\n \" sidechain_batch = batch[is_sidechain]\\n\",\n \"\\n\",\n \" assign_index = radius(x=pos, y=sidechain_pos, r=cutoff, batch_x=batch, batch_y=sidechain_batch)\\n\",\n \" r_edge_index_x = assign_index[1]\\n\",\n \" r_edge_index_y = assign_index[0]\\n\",\n \" r_edge_index_y = sidechain_index[r_edge_index_y]\\n\",\n \"\\n\",\n \" rgraph_edge_index1 = torch.stack((r_edge_index_x, r_edge_index_y)) # (2, E)\\n\",\n \" rgraph_edge_index2 = torch.stack((r_edge_index_y, r_edge_index_x)) # (2, E)\\n\",\n \" rgraph_edge_index = torch.cat((rgraph_edge_index1, rgraph_edge_index2), dim=-1) # (2, 2E)\\n\",\n \" # delete self loop\\n\",\n \" rgraph_edge_index = rgraph_edge_index[:, (rgraph_edge_index[0] != rgraph_edge_index[1])]\\n\",\n \"\\n\",\n \" rgraph_adj = torch.sparse.LongTensor(\\n\",\n \" rgraph_edge_index,\\n\",\n \" torch.ones(rgraph_edge_index.size(1)).long().to(pos.device) * unspecified_type_number,\\n\",\n \" torch.Size([N, N]),\\n\",\n \" )\\n\",\n \"\\n\",\n \" composed_adj = (bgraph_adj + rgraph_adj).coalesce() # Sparse (N, N, T)\\n\",\n \"\\n\",\n \" new_edge_index = composed_adj.indices()\\n\",\n \" new_edge_type = composed_adj.values().long()\\n\",\n \"\\n\",\n \" return new_edge_index, new_edge_type\\n\",\n \"\\n\",\n \"\\n\",\n \"def extend_graph_order_radius(\\n\",\n \" num_nodes,\\n\",\n \" pos,\\n\",\n \" edge_index,\\n\",\n \" edge_type,\\n\",\n \" batch,\\n\",\n \" order=3,\\n\",\n \" cutoff=10.0,\\n\",\n \" extend_order=True,\\n\",\n \" extend_radius=True,\\n\",\n \" is_sidechain=None,\\n\",\n \"):\\n\",\n \" if extend_order:\\n\",\n \" edge_index, edge_type = _extend_graph_order(\\n\",\n \" num_nodes=num_nodes, edge_index=edge_index, edge_type=edge_type, order=order\\n\",\n \" )\\n\",\n \"\\n\",\n \" if extend_radius:\\n\",\n \" edge_index, edge_type = _extend_to_radius_graph(\\n\",\n \" pos=pos, edge_index=edge_index, edge_type=edge_type, cutoff=cutoff, batch=batch, is_sidechain=is_sidechain\\n\",\n \" )\\n\",\n \"\\n\",\n \" return edge_index, edge_type\\n\",\n \"\\n\",\n \"\\n\",\n \"def get_distance(pos, edge_index):\\n\",\n \" return (pos[edge_index[0]] - pos[edge_index[1]]).norm(dim=-1)\\n\",\n \"\\n\",\n \"\\n\",\n \"def graph_field_network(score_d, pos, edge_index, edge_length):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Transformation to make the epsilon predicted from the diffusion model roto-translational equivariant. See equations\\n\",\n \" 5-7 of the GeoDiff Paper https://arxiv.org/pdf/2203.02923.pdf\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" N = pos.size(0)\\n\",\n \" dd_dr = (1.0 / edge_length) * (pos[edge_index[0]] - pos[edge_index[1]]) # (E, 3)\\n\",\n \" score_pos = scatter_add(dd_dr * score_d, edge_index[0], dim=0, dim_size=N) + scatter_add(\\n\",\n \" -dd_dr * score_d, edge_index[1], dim=0, dim_size=N\\n\",\n \" ) # (N, 3)\\n\",\n \" return score_pos\\n\",\n \"\\n\",\n \"\\n\",\n \"def clip_norm(vec, limit, p=2):\\n\",\n \" norm = torch.norm(vec, dim=-1, p=2, keepdim=True)\\n\",\n \" denom = torch.where(norm > limit, limit / norm, torch.ones_like(norm))\\n\",\n \" return vec * denom\\n\",\n \"\\n\",\n \"\\n\",\n \"def is_local_edge(edge_type):\\n\",\n \" return edge_type > 0\\n\"\n ],\n \"metadata\": {\n \"id\": \"oR1Y56QiLY90\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"Main model class!\"\n ],\n \"metadata\": {\n \"id\": \"QWrHJFcYXyUB\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"class MoleculeGNN(ModelMixin, ConfigMixin):\\n\",\n \" @register_to_config\\n\",\n \" def __init__(\\n\",\n \" self,\\n\",\n \" hidden_dim=128,\\n\",\n \" num_convs=6,\\n\",\n \" num_convs_local=4,\\n\",\n \" cutoff=10.0,\\n\",\n \" mlp_act=\\\"relu\\\",\\n\",\n \" edge_order=3,\\n\",\n \" edge_encoder=\\\"mlp\\\",\\n\",\n \" smooth_conv=True,\\n\",\n \" ):\\n\",\n \" super().__init__()\\n\",\n \" self.cutoff = cutoff\\n\",\n \" self.edge_encoder = edge_encoder\\n\",\n \" self.edge_order = edge_order\\n\",\n \"\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" edge_encoder: Takes both edge type and edge length as input and outputs a vector [Note]: node embedding is done\\n\",\n \" in SchNetEncoder\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" self.edge_encoder_global = MLPEdgeEncoder(hidden_dim, mlp_act) # get_edge_encoder(config)\\n\",\n \" self.edge_encoder_local = MLPEdgeEncoder(hidden_dim, mlp_act) # get_edge_encoder(config)\\n\",\n \"\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" The graph neural network that extracts node-wise features.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" self.encoder_global = SchNetEncoder(\\n\",\n \" hidden_channels=hidden_dim,\\n\",\n \" num_filters=hidden_dim,\\n\",\n \" num_interactions=num_convs,\\n\",\n \" edge_channels=self.edge_encoder_global.out_channels,\\n\",\n \" cutoff=cutoff,\\n\",\n \" smooth=smooth_conv,\\n\",\n \" )\\n\",\n \" self.encoder_local = GINEncoder(\\n\",\n \" hidden_dim=hidden_dim,\\n\",\n \" num_convs=num_convs_local,\\n\",\n \" )\\n\",\n \"\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" `output_mlp` takes a mixture of two nodewise features and edge features as input and outputs\\n\",\n \" gradients w.r.t. edge_length (out_dim = 1).\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" self.grad_global_dist_mlp = MultiLayerPerceptron(\\n\",\n \" 2 * hidden_dim, [hidden_dim, hidden_dim // 2, 1], activation=mlp_act\\n\",\n \" )\\n\",\n \"\\n\",\n \" self.grad_local_dist_mlp = MultiLayerPerceptron(\\n\",\n \" 2 * hidden_dim, [hidden_dim, hidden_dim // 2, 1], activation=mlp_act\\n\",\n \" )\\n\",\n \"\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Incorporate parameters together\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" self.model_global = nn.ModuleList([self.edge_encoder_global, self.encoder_global, self.grad_global_dist_mlp])\\n\",\n \" self.model_local = nn.ModuleList([self.edge_encoder_local, self.encoder_local, self.grad_local_dist_mlp])\\n\",\n \"\\n\",\n \" def _forward(\\n\",\n \" self,\\n\",\n \" atom_type,\\n\",\n \" pos,\\n\",\n \" bond_index,\\n\",\n \" bond_type,\\n\",\n \" batch,\\n\",\n \" time_step, # NOTE, model trained without timestep performed best\\n\",\n \" edge_index=None,\\n\",\n \" edge_type=None,\\n\",\n \" edge_length=None,\\n\",\n \" return_edges=False,\\n\",\n \" extend_order=True,\\n\",\n \" extend_radius=True,\\n\",\n \" is_sidechain=None,\\n\",\n \" ):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Args:\\n\",\n \" atom_type: Types of atoms, (N, ).\\n\",\n \" bond_index: Indices of bonds (not extended, not radius-graph), (2, E).\\n\",\n \" bond_type: Bond types, (E, ).\\n\",\n \" batch: Node index to graph index, (N, ).\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" N = atom_type.size(0)\\n\",\n \" if edge_index is None or edge_type is None or edge_length is None:\\n\",\n \" edge_index, edge_type = extend_graph_order_radius(\\n\",\n \" num_nodes=N,\\n\",\n \" pos=pos,\\n\",\n \" edge_index=bond_index,\\n\",\n \" edge_type=bond_type,\\n\",\n \" batch=batch,\\n\",\n \" order=self.edge_order,\\n\",\n \" cutoff=self.cutoff,\\n\",\n \" extend_order=extend_order,\\n\",\n \" extend_radius=extend_radius,\\n\",\n \" is_sidechain=is_sidechain,\\n\",\n \" )\\n\",\n \" edge_length = get_distance(pos, edge_index).unsqueeze(-1) # (E, 1)\\n\",\n \" local_edge_mask = is_local_edge(edge_type) # (E, )\\n\",\n \"\\n\",\n \" # with the parameterization of NCSNv2\\n\",\n \" # DDPM loss implicit handle the noise variance scale conditioning\\n\",\n \" sigma_edge = torch.ones(size=(edge_index.size(1), 1), device=pos.device) # (E, 1)\\n\",\n \"\\n\",\n \" # Encoding global\\n\",\n \" edge_attr_global = self.edge_encoder_global(edge_length=edge_length, edge_type=edge_type) # Embed edges\\n\",\n \"\\n\",\n \" # Global\\n\",\n \" node_attr_global = self.encoder_global(\\n\",\n \" z=atom_type,\\n\",\n \" edge_index=edge_index,\\n\",\n \" edge_length=edge_length,\\n\",\n \" edge_attr=edge_attr_global,\\n\",\n \" )\\n\",\n \" # Assemble pairwise features\\n\",\n \" h_pair_global = assemble_atom_pair_feature(\\n\",\n \" node_attr=node_attr_global,\\n\",\n \" edge_index=edge_index,\\n\",\n \" edge_attr=edge_attr_global,\\n\",\n \" ) # (E_global, 2H)\\n\",\n \" # Invariant features of edges (radius graph, global)\\n\",\n \" edge_inv_global = self.grad_global_dist_mlp(h_pair_global) * (1.0 / sigma_edge) # (E_global, 1)\\n\",\n \"\\n\",\n \" # Encoding local\\n\",\n \" edge_attr_local = self.edge_encoder_global(edge_length=edge_length, edge_type=edge_type) # Embed edges\\n\",\n \" # edge_attr += temb_edge\\n\",\n \"\\n\",\n \" # Local\\n\",\n \" node_attr_local = self.encoder_local(\\n\",\n \" z=atom_type,\\n\",\n \" edge_index=edge_index[:, local_edge_mask],\\n\",\n \" edge_attr=edge_attr_local[local_edge_mask],\\n\",\n \" )\\n\",\n \" # Assemble pairwise features\\n\",\n \" h_pair_local = assemble_atom_pair_feature(\\n\",\n \" node_attr=node_attr_local,\\n\",\n \" edge_index=edge_index[:, local_edge_mask],\\n\",\n \" edge_attr=edge_attr_local[local_edge_mask],\\n\",\n \" ) # (E_local, 2H)\\n\",\n \"\\n\",\n \" # Invariant features of edges (bond graph, local)\\n\",\n \" if isinstance(sigma_edge, torch.Tensor):\\n\",\n \" edge_inv_local = self.grad_local_dist_mlp(h_pair_local) * (\\n\",\n \" 1.0 / sigma_edge[local_edge_mask]\\n\",\n \" ) # (E_local, 1)\\n\",\n \" else:\\n\",\n \" edge_inv_local = self.grad_local_dist_mlp(h_pair_local) * (1.0 / sigma_edge) # (E_local, 1)\\n\",\n \"\\n\",\n \" if return_edges:\\n\",\n \" return edge_inv_global, edge_inv_local, edge_index, edge_type, edge_length, local_edge_mask\\n\",\n \" else:\\n\",\n \" return edge_inv_global, edge_inv_local\\n\",\n \"\\n\",\n \" def forward(\\n\",\n \" self,\\n\",\n \" sample,\\n\",\n \" timestep: Union[torch.Tensor, float, int],\\n\",\n \" return_dict: bool = True,\\n\",\n \" sigma=1.0,\\n\",\n \" global_start_sigma=0.5,\\n\",\n \" w_global=1.0,\\n\",\n \" extend_order=False,\\n\",\n \" extend_radius=True,\\n\",\n \" clip_local=None,\\n\",\n \" clip_global=1000.0,\\n\",\n \" ) -> Union[MoleculeGNNOutput, Tuple]:\\n\",\n \" r\\\"\\\"\\\"\\n\",\n \" Args:\\n\",\n \" sample: packed torch geometric object\\n\",\n \" timestep (`torch.Tensor` or `float` or `int): TODO verify type and shape (batch) timesteps\\n\",\n \" return_dict (`bool`, *optional*, defaults to `True`):\\n\",\n \" Whether or not to return a [`~models.molecule_gnn.MoleculeGNNOutput`] instead of a plain tuple.\\n\",\n \" Returns:\\n\",\n \" [`~models.molecule_gnn.MoleculeGNNOutput`] or `tuple`: [`~models.molecule_gnn.MoleculeGNNOutput`] if\\n\",\n \" `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" # unpack sample\\n\",\n \" atom_type = sample.atom_type\\n\",\n \" bond_index = sample.edge_index\\n\",\n \" bond_type = sample.edge_type\\n\",\n \" num_graphs = sample.num_graphs\\n\",\n \" pos = sample.pos\\n\",\n \"\\n\",\n \" timesteps = torch.full(size=(num_graphs,), fill_value=timestep, dtype=torch.long, device=pos.device)\\n\",\n \"\\n\",\n \" edge_inv_global, edge_inv_local, edge_index, edge_type, edge_length, local_edge_mask = self._forward(\\n\",\n \" atom_type=atom_type,\\n\",\n \" pos=sample.pos,\\n\",\n \" bond_index=bond_index,\\n\",\n \" bond_type=bond_type,\\n\",\n \" batch=sample.batch,\\n\",\n \" time_step=timesteps,\\n\",\n \" return_edges=True,\\n\",\n \" extend_order=extend_order,\\n\",\n \" extend_radius=extend_radius,\\n\",\n \" ) # (E_global, 1), (E_local, 1)\\n\",\n \"\\n\",\n \" # Important equation in the paper for equivariant features - eqns 5-7 of GeoDiff\\n\",\n \" node_eq_local = graph_field_network(\\n\",\n \" edge_inv_local, pos, edge_index[:, local_edge_mask], edge_length[local_edge_mask]\\n\",\n \" )\\n\",\n \" if clip_local is not None:\\n\",\n \" node_eq_local = clip_norm(node_eq_local, limit=clip_local)\\n\",\n \"\\n\",\n \" # Global\\n\",\n \" if sigma < global_start_sigma:\\n\",\n \" edge_inv_global = edge_inv_global * (1 - local_edge_mask.view(-1, 1).float())\\n\",\n \" node_eq_global = graph_field_network(edge_inv_global, pos, edge_index, edge_length)\\n\",\n \" node_eq_global = clip_norm(node_eq_global, limit=clip_global)\\n\",\n \" else:\\n\",\n \" node_eq_global = 0\\n\",\n \"\\n\",\n \" # Sum\\n\",\n \" eps_pos = node_eq_local + node_eq_global * w_global\\n\",\n \"\\n\",\n \" if not return_dict:\\n\",\n \" return (-eps_pos,)\\n\",\n \"\\n\",\n \" return MoleculeGNNOutput(sample=torch.Tensor(-eps_pos).to(pos.device))\"\n ],\n \"metadata\": {\n \"id\": \"MCeZA1qQXzoK\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"CCIrPYSJj9wd\"\n },\n \"source\": [\n \"### Load pretrained model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"YdrAr6Ch--Ab\"\n },\n \"source\": [\n \"#### Load a model\\n\",\n \"The model used is a design an\\n\",\n \"equivariant convolutional layer, named graph field network (GFN).\\n\",\n \"\\n\",\n \"The warning about `betas` and `alphas` can be ignored, those were moved to the scheduler.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"DyCo0nsqjbml\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 172,\n \"referenced_widgets\": [\n \"d90f304e9560472eacfbdd11e46765eb\",\n \"1c6246f15b654f4daa11c9bcf997b78c\",\n \"c2321b3bff6f490ca12040a20308f555\",\n \"b7feb522161f4cf4b7cc7c1a078ff12d\",\n \"e2d368556e494ae7ae4e2e992af2cd4f\",\n \"bbef741e76ec41b7ab7187b487a383df\",\n \"561f742d418d4721b0670cc8dd62e22c\",\n \"872915dd1bb84f538c44e26badabafdd\",\n \"d022575f1fa2446d891650897f187b4d\",\n \"fdc393f3468c432aa0ada05e238a5436\",\n \"2c9362906e4b40189f16d14aa9a348da\",\n \"6010fc8daa7a44d5aec4b830ec2ebaa1\",\n \"7e0bb1b8d65249d3974200686b193be2\",\n \"ba98aa6d6a884e4ab8bbb5dfb5e4cf7a\",\n \"6526646be5ed415c84d1245b040e629b\",\n \"24d31fc3576e43dd9f8301d2ef3a37ab\",\n \"2918bfaadc8d4b1a9832522c40dfefb8\",\n \"a4bfdca35cc54dae8812720f1b276a08\",\n \"e4901541199b45c6a18824627692fc39\",\n \"f915cf874246446595206221e900b2fe\",\n \"a9e388f22a9742aaaf538e22575c9433\",\n \"42f6c3db29d7484ba6b4f73590abd2f4\"\n ]\n },\n \"outputId\": \"d6bce9d5-c51e-43a4-e680-e1e81bdfaf45\"\n },\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"Downloading: 0%| | 0.00/3.27M [00:00] 124.78K 180KB/s in 0.7s \\n\",\n \"\\n\",\n \"2022-10-12 18:32:20 (180 KB/s) - ‘molecules.pkl’ saved [127774/127774]\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"import torch\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"!wget https://huggingface.co/datasets/fusing/geodiff-example-data/resolve/main/data/molecules.pkl\\n\",\n \"dataset = torch.load('/content/molecules.pkl')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"QZcmy1EvKQRk\"\n },\n \"source\": [\n \"Print out one entry of the dataset, it contains molecular formulas, atom types, positions, and more.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"JVjz6iH_H6Eh\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"outputId\": \"898cb0cf-a0b3-411b-fd4c-bea1fbfd17fe\"\n },\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"Data(atom_type=[51], bond_edge_index=[2, 108], edge_index=[2, 598], edge_order=[598], edge_type=[598], idx=[1], is_bond=[598], num_nodes_per_graph=[1], num_pos_ref=[1], nx=, pos=[51, 3], pos_ref=[255, 3], rdmol=, smiles=\\\"CC1CCCN(C(=O)C2CCN(S(=O)(=O)c3cccc4nonc34)CC2)C1\\\")\"\n ]\n },\n \"metadata\": {},\n \"execution_count\": 20\n }\n ],\n \"source\": [\n \"dataset[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Run the diffusion process\"\n ],\n \"metadata\": {\n \"id\": \"vHNiZAUxNgoy\"\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"jZ1KZrxKqENg\"\n },\n \"source\": [\n \"#### Helper Functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"s240tYueqKKf\"\n },\n \"outputs\": [],\n \"source\": [\n \"from torch_geometric.data import Data, Batch\\n\",\n \"from torch_scatter import scatter_add, scatter_mean\\n\",\n \"from tqdm import tqdm\\n\",\n \"import copy\\n\",\n \"import os\\n\",\n \"\\n\",\n \"def repeat_data(data: Data, num_repeat) -> Batch:\\n\",\n \" datas = [copy.deepcopy(data) for i in range(num_repeat)]\\n\",\n \" return Batch.from_data_list(datas)\\n\",\n \"\\n\",\n \"def repeat_batch(batch: Batch, num_repeat) -> Batch:\\n\",\n \" datas = batch.to_data_list()\\n\",\n \" new_data = []\\n\",\n \" for i in range(num_repeat):\\n\",\n \" new_data += copy.deepcopy(datas)\\n\",\n \" return Batch.from_data_list(new_data)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"AMnQTk0eqT7Z\"\n },\n \"source\": [\n \"#### Constants\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"WYGkzqgzrHmF\"\n },\n \"outputs\": [],\n \"source\": [\n \"num_samples = 1 # solutions per molecule\\n\",\n \"num_molecules = 3\\n\",\n \"\\n\",\n \"DEVICE = 'cuda'\\n\",\n \"sampling_type = 'ddpm_noisy' #'' # paper also uses \\\"generalize\\\" and \\\"ld\\\"\\n\",\n \"# constants for inference\\n\",\n \"w_global = 0.5 #0,.3 for qm9\\n\",\n \"global_start_sigma = 0.5\\n\",\n \"eta = 1.0\\n\",\n \"clip_local = None\\n\",\n \"clip_pos = None\\n\",\n \"\\n\",\n \"# constands for data handling\\n\",\n \"save_traj = False\\n\",\n \"save_data = False\\n\",\n \"output_dir = '/content/'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"-xD5bJ3SqM7t\"\n },\n \"source\": [\n \"#### Generate samples!\\n\",\n \"Note that the 3d representation of a molecule is referred to as the **conformation**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"x9xuLUNg26z1\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"outputId\": \"236d2a60-09ed-4c4d-97c1-6e3c0f2d26c4\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stderr\",\n \"text\": [\n \"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\\n\",\n \" after removing the cwd from sys.path.\\n\",\n \"100%|██████████| 5/5 [00:55<00:00, 11.06s/it]\\n\"\n ]\n }\n ],\n \"source\": [\n \"results = []\\n\",\n \"\\n\",\n \"# define sigmas\\n\",\n \"sigmas = torch.tensor(1.0 - scheduler.alphas_cumprod).sqrt() / torch.tensor(scheduler.alphas_cumprod).sqrt()\\n\",\n \"sigmas = sigmas.to(DEVICE)\\n\",\n \"\\n\",\n \"for count, data in enumerate(tqdm(dataset)):\\n\",\n \" num_samples = max(data.pos_ref.size(0) // data.num_nodes, 1)\\n\",\n \"\\n\",\n \" data_input = data.clone()\\n\",\n \" data_input['pos_ref'] = None\\n\",\n \" batch = repeat_data(data_input, num_samples).to(DEVICE)\\n\",\n \"\\n\",\n \" # initial configuration\\n\",\n \" pos_init = torch.randn(batch.num_nodes, 3).to(DEVICE)\\n\",\n \"\\n\",\n \" # for logging animation of denoising\\n\",\n \" pos_traj = []\\n\",\n \" with torch.no_grad():\\n\",\n \"\\n\",\n \" # scale initial sample\\n\",\n \" pos = pos_init * sigmas[-1]\\n\",\n \" for t in scheduler.timesteps:\\n\",\n \" batch.pos = pos\\n\",\n \"\\n\",\n \" # generate geometry with model, then filter it\\n\",\n \" epsilon = model.forward(batch, t, sigma=sigmas[t], return_dict=False)[0]\\n\",\n \"\\n\",\n \" # Update\\n\",\n \" reconstructed_pos = scheduler.step(epsilon, t, pos)[\\\"prev_sample\\\"].to(DEVICE)\\n\",\n \"\\n\",\n \" pos = reconstructed_pos\\n\",\n \"\\n\",\n \" if torch.isnan(pos).any():\\n\",\n \" print(\\\"NaN detected. Please restart.\\\")\\n\",\n \" raise FloatingPointError()\\n\",\n \"\\n\",\n \" # recenter graph of positions for next iteration\\n\",\n \" pos = pos - scatter_mean(pos, batch.batch, dim=0)[batch.batch]\\n\",\n \"\\n\",\n \" # optional clipping\\n\",\n \" if clip_pos is not None:\\n\",\n \" pos = torch.clamp(pos, min=-clip_pos, max=clip_pos)\\n\",\n \" pos_traj.append(pos.clone().cpu())\\n\",\n \"\\n\",\n \" pos_gen = pos.cpu()\\n\",\n \" if save_traj:\\n\",\n \" pos_gen_traj = pos_traj.cpu()\\n\",\n \" data.pos_gen = torch.stack(pos_gen_traj)\\n\",\n \" else:\\n\",\n \" data.pos_gen = pos_gen\\n\",\n \" results.append(data)\\n\",\n \"\\n\",\n \"\\n\",\n \"if save_data:\\n\",\n \" save_path = os.path.join(output_dir, 'samples_all.pkl')\\n\",\n \"\\n\",\n \" with open(save_path, 'wb') as f:\\n\",\n \" pickle.dump(results, f)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Render the results!\"\n ],\n \"metadata\": {\n \"id\": \"fSApwSaZNndW\"\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"d47Zxo2OKdgZ\"\n },\n \"source\": [\n \"This function allows us to render 3d in colab.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"e9Cd0kCAv9b8\"\n },\n \"outputs\": [],\n \"source\": [\n \"from google.colab import output\\n\",\n \"output.enable_custom_widget_manager()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"### Helper functions\"\n ],\n \"metadata\": {\n \"id\": \"RjaVuR15NqzF\"\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"28rBYa9NKhlz\"\n },\n \"source\": [\n \"Here is a helper function for copying the generated tensors into a format used by RDKit & NGLViewer.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"LKdKdwxcyTQ6\"\n },\n \"outputs\": [],\n \"source\": [\n \"from copy import deepcopy\\n\",\n \"def set_rdmol_positions(rdkit_mol, pos):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Args:\\n\",\n \" rdkit_mol: An `rdkit.Chem.rdchem.Mol` object.\\n\",\n \" pos: (N_atoms, 3)\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" mol = deepcopy(rdkit_mol)\\n\",\n \" set_rdmol_positions_(mol, pos)\\n\",\n \" return mol\\n\",\n \"\\n\",\n \"def set_rdmol_positions_(mol, pos):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Args:\\n\",\n \" rdkit_mol: An `rdkit.Chem.rdchem.Mol` object.\\n\",\n \" pos: (N_atoms, 3)\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" for i in range(pos.shape[0]):\\n\",\n \" mol.GetConformer(0).SetAtomPosition(i, pos[i].tolist())\\n\",\n \" return mol\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"NuE10hcpKmzK\"\n },\n \"source\": [\n \"Process the generated data to make it easy to view.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"KieVE1vc0_Vs\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"outputId\": \"6faa185d-b1bc-47e8-be18-30d1e557e7c8\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"collect 5 generated molecules in `mols`\\n\"\n ]\n }\n ],\n \"source\": [\n \"# the model can generate multiple conformations per 2d geometry\\n\",\n \"num_gen = results[0]['pos_gen'].shape[0]\\n\",\n \"\\n\",\n \"# init storage objects\\n\",\n \"mols_gen = []\\n\",\n \"mols_orig = []\\n\",\n \"for to_process in results:\\n\",\n \"\\n\",\n \" # store the reference 3d position\\n\",\n \" to_process['pos_ref'] = to_process['pos_ref'].reshape(-1, to_process['rdmol'].GetNumAtoms(), 3)\\n\",\n \"\\n\",\n \" # store the generated 3d position\\n\",\n \" to_process['pos_gen'] = to_process['pos_gen'].reshape(-1, to_process['rdmol'].GetNumAtoms(), 3)\\n\",\n \"\\n\",\n \" # copy data to new object\\n\",\n \" new_mol = set_rdmol_positions(to_process.rdmol, to_process['pos_gen'][0])\\n\",\n \"\\n\",\n \" # append results\\n\",\n \" mols_gen.append(new_mol)\\n\",\n \" mols_orig.append(to_process.rdmol)\\n\",\n \"\\n\",\n \"print(f\\\"collect {len(mols_gen)} generated molecules in `mols`\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"tin89JwMKp4v\"\n },\n \"source\": [\n \"Import tools to visualize the 2d chemical diagram of the molecule.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"yqV6gllSZn38\"\n },\n \"outputs\": [],\n \"source\": [\n \"from rdkit.Chem import AllChem\\n\",\n \"from rdkit import Chem\\n\",\n \"from rdkit.Chem.Draw import rdMolDraw2D as MD2\\n\",\n \"from IPython.display import SVG, display\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"TFNKmGddVoOk\"\n },\n \"source\": [\n \"Select molecule to visualize\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"KzuwLlrrVaGc\"\n },\n \"outputs\": [],\n \"source\": [\n \"idx = 0\\n\",\n \"assert idx < len(results), 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\"button_color\": null,\n \"font_weight\": \"\"\n }\n }\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}" }, { "path": "diffusers/examples/research_projects/gligen/README.md", "content": "# GLIGEN: Open-Set Grounded Text-to-Image Generation\n\nThese scripts contain the code to prepare the grounding data and train the GLIGEN model on COCO dataset.\n\n### Install the requirements\n\n```bash\nconda create -n diffusers python==3.10\nconda activate diffusers\npip install -r requirements.txt\n```\n\nAnd initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\n\nOr for a default accelerate configuration without answering questions about your environment\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell e.g. a notebook\n\n```python\nfrom accelerate.utils import write_basic_config\n\nwrite_basic_config()\n```\n\n### Prepare the training data\n\nIf you want to make your own grounding data, you need to install the requirements.\n\nI used [RAM](https://github.com/xinyu1205/recognize-anything) to tag\nimages, [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO/issues?q=refer) to detect objects,\nand [BLIP2](https://huggingface.co/docs/transformers/en/model_doc/blip-2) to caption instances.\n\nOnly RAM needs to be installed manually:\n\n```bash\npip install git+https://github.com/xinyu1205/recognize-anything.git --no-deps\n```\n\nDownload the pre-trained model:\n\n```bash\nhuggingface-cli download --resume-download xinyu1205/recognize_anything_model ram_swin_large_14m.pth\nhuggingface-cli download --resume-download IDEA-Research/grounding-dino-base\nhuggingface-cli download --resume-download Salesforce/blip2-flan-t5-xxl\nhuggingface-cli download --resume-download clip-vit-large-patch14\nhuggingface-cli download --resume-download masterful/gligen-1-4-generation-text-box\n```\n\nMake the training data on 8 GPUs:\n\n```bash\ntorchrun --master_port 17673 --nproc_per_node=8 make_datasets.py \\\n --data_root /mnt/workspace/workgroup/zhizhonghuang/dataset/COCO/train2017 \\\n --save_root /root/gligen_data \\\n --ram_checkpoint /root/.cache/huggingface/hub/models--xinyu1205--recognize_anything_model/snapshots/ebc52dc741e86466202a5ab8ab22eae6e7d48bf1/ram_swin_large_14m.pth\n```\n\nYou can download the COCO training data from\n\n```bash\nhuggingface-cli download --resume-download Hzzone/GLIGEN_COCO coco_train2017.pth\n```\n\nIt's in the format of\n\n```json\n[\n ...\n {\n 'file_path': Path,\n 'annos': [\n {\n 'caption': Instance\n Caption,\n 'bbox': bbox\n in\n xyxy,\n 'text_embeddings_before_projection': CLIP\n text\n embedding\n before\n linear\n projection\n }\n ]\n }\n ...\n]\n```\n\n### Training commands\n\nThe training script is heavily based\non https://github.com/huggingface/diffusers/blob/main/examples/controlnet/train_controlnet.py\n\n```bash\naccelerate launch train_gligen_text.py \\\n --data_path /root/data/zhizhonghuang/coco_train2017.pth \\\n --image_path /mnt/workspace/workgroup/zhizhonghuang/dataset/COCO/train2017 \\\n --train_batch_size 8 \\\n --max_train_steps 100000 \\\n --checkpointing_steps 1000 \\\n --checkpoints_total_limit 10 \\\n --learning_rate 5e-5 \\\n --dataloader_num_workers 16 \\\n --mixed_precision fp16 \\\n --report_to wandb \\\n --tracker_project_name gligen \\\n --output_dir /root/data/zhizhonghuang/ckpt/GLIGEN_Text_Retrain_COCO\n```\n\nI trained the model on 8 A100 GPUs for about 11 hours (at least 24GB GPU memory). The generated images will follow the\nlayout possibly at 50k iterations.\n\nNote that although the pre-trained GLIGEN model has been loaded, the parameters of `fuser` and `position_net` have been reset (see line 420 in `train_gligen_text.py`)\n\nThe trained model can be downloaded from\n\n```bash\nhuggingface-cli download --resume-download Hzzone/GLIGEN_COCO config.json diffusion_pytorch_model.safetensors\n```\n\nYou can run `demo.ipynb` to visualize the generated images.\n\nExample prompts:\n\n```python\nprompt = 'A realistic image of landscape scene depicting a green car parking on the left of a blue truck, with a red air balloon and a bird in the sky'\nboxes = [[0.041015625, 0.548828125, 0.453125, 0.859375],\n [0.525390625, 0.552734375, 0.93359375, 0.865234375],\n [0.12890625, 0.015625, 0.412109375, 0.279296875],\n [0.578125, 0.08203125, 0.857421875, 0.27734375]]\ngligen_phrases = ['a green car', 'a blue truck', 'a red air balloon', 'a bird']\n```\n\nExample images:\n![alt text](generated-images-100000-00.png)\n\n### Citation\n\n```\n@article{li2023gligen,\n title={GLIGEN: Open-Set Grounded Text-to-Image Generation},\n author={Li, Yuheng and Liu, Haotian and Wu, Qingyang and Mu, Fangzhou and Yang, Jianwei and Gao, Jianfeng and Li, Chunyuan and Lee, Yong Jae},\n journal={CVPR},\n year={2023}\n}\n```" }, { "path": "diffusers/examples/research_projects/gligen/dataset.py", "content": "import os\nimport random\n\nimport torch\nimport torchvision.transforms as transforms\nfrom PIL import Image\n\n\ndef recalculate_box_and_verify_if_valid(x, y, w, h, image_size, original_image_size, min_box_size):\n scale = image_size / min(original_image_size)\n crop_y = (original_image_size[1] * scale - image_size) // 2\n crop_x = (original_image_size[0] * scale - image_size) // 2\n x0 = max(x * scale - crop_x, 0)\n y0 = max(y * scale - crop_y, 0)\n x1 = min((x + w) * scale - crop_x, image_size)\n y1 = min((y + h) * scale - crop_y, image_size)\n if (x1 - x0) * (y1 - y0) / (image_size * image_size) < min_box_size:\n return False, (None, None, None, None)\n return True, (x0, y0, x1, y1)\n\n\nclass COCODataset(torch.utils.data.Dataset):\n def __init__(\n self,\n data_path,\n image_path,\n image_size=512,\n min_box_size=0.01,\n max_boxes_per_data=8,\n tokenizer=None,\n ):\n super().__init__()\n self.min_box_size = min_box_size\n self.max_boxes_per_data = max_boxes_per_data\n self.image_size = image_size\n self.image_path = image_path\n self.tokenizer = tokenizer\n self.transforms = transforms.Compose(\n [\n transforms.Resize(image_size, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(image_size),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n self.data_list = torch.load(data_path, map_location=\"cpu\")\n\n def __getitem__(self, index):\n if self.max_boxes_per_data > 99:\n assert False, \"Are you sure setting such large number of boxes per image?\"\n\n out = {}\n\n data = self.data_list[index]\n image = Image.open(os.path.join(self.image_path, data[\"file_path\"])).convert(\"RGB\")\n original_image_size = image.size\n out[\"pixel_values\"] = self.transforms(image)\n\n annos = data[\"annos\"]\n\n areas, valid_annos = [], []\n for anno in annos:\n # x, y, w, h = anno['bbox']\n x0, y0, x1, y1 = anno[\"bbox\"]\n x, y, w, h = x0, y0, x1 - x0, y1 - y0\n valid, (x0, y0, x1, y1) = recalculate_box_and_verify_if_valid(\n x, y, w, h, self.image_size, original_image_size, self.min_box_size\n )\n if valid:\n anno[\"bbox\"] = [x0, y0, x1, y1]\n areas.append((x1 - x0) * (y1 - y0))\n valid_annos.append(anno)\n\n # Sort according to area and choose the largest N objects\n wanted_idxs = torch.tensor(areas).sort(descending=True)[1]\n wanted_idxs = wanted_idxs[: self.max_boxes_per_data]\n valid_annos = [valid_annos[i] for i in wanted_idxs]\n\n out[\"boxes\"] = torch.zeros(self.max_boxes_per_data, 4)\n out[\"masks\"] = torch.zeros(self.max_boxes_per_data)\n out[\"text_embeddings_before_projection\"] = torch.zeros(self.max_boxes_per_data, 768)\n\n for i, anno in enumerate(valid_annos):\n out[\"boxes\"][i] = torch.tensor(anno[\"bbox\"]) / self.image_size\n out[\"masks\"][i] = 1\n out[\"text_embeddings_before_projection\"][i] = anno[\"text_embeddings_before_projection\"]\n\n prob_drop_boxes = 0.1\n if random.random() < prob_drop_boxes:\n out[\"masks\"][:] = 0\n\n caption = random.choice(data[\"captions\"])\n\n prob_drop_captions = 0.5\n if random.random() < prob_drop_captions:\n caption = \"\"\n caption = self.tokenizer(\n caption,\n max_length=self.tokenizer.model_max_length,\n padding=\"max_length\",\n truncation=True,\n return_tensors=\"pt\",\n )\n out[\"caption\"] = caption\n\n return out\n\n def __len__(self):\n return len(self.data_list)\n" }, { "path": "diffusers/examples/research_projects/gligen/demo.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The autoreload extension is already loaded. To reload it, use:\\n\",\n \" %reload_ext autoreload\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/root/miniconda/envs/densecaption/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\\n\",\n \" from .autonotebook import tqdm as notebook_tqdm\\n\"\n ]\n }\n ],\n \"source\": [\n \"%load_ext autoreload\\n\",\n \"%autoreload 2\\n\",\n \"\\n\",\n \"import torch\\n\",\n \"from diffusers import StableDiffusionGLIGENTextImagePipeline, StableDiffusionGLIGENPipeline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"import diffusers\\n\",\n \"from diffusers import (\\n\",\n \" AutoencoderKL,\\n\",\n \" DDPMScheduler,\\n\",\n \" UNet2DConditionModel,\\n\",\n \" UniPCMultistepScheduler,\\n\",\n \" EulerDiscreteScheduler,\\n\",\n \")\\n\",\n \"from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer\\n\",\n \"# pretrained_model_name_or_path = 'masterful/gligen-1-4-generation-text-box'\\n\",\n \"\\n\",\n \"pretrained_model_name_or_path = '/root/data/zhizhonghuang/checkpoints/models--masterful--gligen-1-4-generation-text-box/snapshots/d2820dc1e9ba6ca082051ce79cfd3eb468ae2c83'\\n\",\n \"\\n\",\n \"tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder=\\\"tokenizer\\\")\\n\",\n \"noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder=\\\"scheduler\\\")\\n\",\n \"text_encoder = CLIPTextModel.from_pretrained(\\n\",\n \" pretrained_model_name_or_path, subfolder=\\\"text_encoder\\\"\\n\",\n \")\\n\",\n \"vae = AutoencoderKL.from_pretrained(\\n\",\n \" pretrained_model_name_or_path, subfolder=\\\"vae\\\"\\n\",\n \")\\n\",\n \"# unet = UNet2DConditionModel.from_pretrained(\\n\",\n \"# pretrained_model_name_or_path, subfolder=\\\"unet\\\"\\n\",\n \"# )\\n\",\n \"\\n\",\n \"noise_scheduler = EulerDiscreteScheduler.from_config(noise_scheduler.config)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"unet = UNet2DConditionModel.from_pretrained(\\n\",\n \" '/root/data/zhizhonghuang/ckpt/GLIGEN_Text_Retrain_COCO'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"You have disabled the safety checker for by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\\n\"\n ]\n }\n ],\n \"source\": [\n \"pipe = StableDiffusionGLIGENPipeline(\\n\",\n \" vae,\\n\",\n \" text_encoder,\\n\",\n \" tokenizer,\\n\",\n \" unet,\\n\",\n \" noise_scheduler,\\n\",\n \" safety_checker=None,\\n\",\n \" feature_extractor=None,\\n\",\n \")\\n\",\n \"pipe = pipe.to(\\\"cuda\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# prompt = 'A realistic image of landscape scene depicting a green car parking on the left of a blue truck, with a red air balloon and a bird in the sky'\\n\",\n \"# gen_boxes = [('a green car', [21, 281, 211, 159]), ('a blue truck', [269, 283, 209, 160]), ('a red air balloon', [66, 8, 145, 135]), ('a bird', [296, 42, 143, 100])]\\n\",\n \"\\n\",\n \"# prompt = 'A realistic top-down view of a wooden table with two apples on it'\\n\",\n \"# gen_boxes = [('a wooden table', [20, 148, 472, 216]), ('an apple', [150, 226, 100, 100]), ('an apple', [280, 226, 100, 100])]\\n\",\n \"\\n\",\n \"# prompt = 'A realistic scene of three skiers standing in a line on the snow near a palm tree'\\n\",\n \"# gen_boxes = [('a skier', [5, 152, 139, 168]), ('a skier', [278, 192, 121, 158]), ('a skier', [148, 173, 124, 155]), ('a palm tree', [404, 105, 103, 251])]\\n\",\n \"\\n\",\n \"prompt = 'An oil painting of a pink dolphin jumping on the left of a steam boat on the sea'\\n\",\n \"gen_boxes = [('a steam boat', [232, 225, 257, 149]), ('a jumping pink dolphin', [21, 249, 189, 123])]\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"boxes = np.array([x[1] for x in gen_boxes])\\n\",\n \"boxes = boxes / 512\\n\",\n \"boxes[:, 2] = boxes[:, 0] + boxes[:, 2]\\n\",\n \"boxes[:, 3] = boxes[:, 1] + boxes[:, 3]\\n\",\n \"boxes = boxes.tolist()\\n\",\n \"gligen_phrases = [x[0] for x in gen_boxes]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/root/miniconda/envs/densecaption/lib/python3.11/site-packages/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py:683: FutureWarning: Accessing config attribute `in_channels` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.in_channels'.\\n\",\n \" num_channels_latents = self.unet.in_channels\\n\",\n \"/root/miniconda/envs/densecaption/lib/python3.11/site-packages/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py:716: FutureWarning: Accessing config attribute `cross_attention_dim` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'cross_attention_dim' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.cross_attention_dim'.\\n\",\n \" max_objs, self.unet.cross_attention_dim, device=device, dtype=self.text_encoder.dtype\\n\",\n \"100%|██████████| 50/50 [01:21<00:00, 1.64s/it]\\n\"\n ]\n }\n ],\n \"source\": [\n \"images = pipe(\\n\",\n \" prompt=prompt,\\n\",\n \" gligen_phrases=gligen_phrases,\\n\",\n \" gligen_boxes=boxes,\\n\",\n \" gligen_scheduled_sampling_beta=1.0,\\n\",\n \" output_type=\\\"pil\\\",\\n\",\n \" num_inference_steps=50,\\n\",\n \" negative_prompt=\\\"artifacts, blurry, smooth texture, bad quality, distortions, unrealistic, distorted image, bad proportions, duplicate\\\",\\n\",\n \" num_images_per_prompt=16,\\n\",\n \").images\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"diffusers.utils.make_image_grid(images, 4, len(images)//4)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"densecaption\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.9\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "diffusers/examples/research_projects/gligen/make_datasets.py", "content": "import argparse\nimport os\nimport random\n\nimport torch\nimport torchvision\nimport torchvision.transforms as TS\nfrom PIL import Image\nfrom ram import inference_ram\nfrom ram.models import ram\nfrom tqdm import tqdm\nfrom transformers import (\n AutoModelForZeroShotObjectDetection,\n AutoProcessor,\n Blip2ForConditionalGeneration,\n Blip2Processor,\n CLIPTextModel,\n CLIPTokenizer,\n)\n\n\ntorch.autograd.set_grad_enabled(False)\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(\"Caption Generation script\", add_help=False)\n parser.add_argument(\"--data_root\", type=str, required=True, help=\"path to COCO\")\n parser.add_argument(\"--save_root\", type=str, required=True, help=\"path to save\")\n parser.add_argument(\"--ram_checkpoint\", type=str, required=True, help=\"path to save\")\n args = parser.parse_args()\n\n # ram_checkpoint = '/root/.cache/huggingface/hub/models--xinyu1205--recognize_anything_model/snapshots/ebc52dc741e86466202a5ab8ab22eae6e7d48bf1/ram_swin_large_14m.pth'\n # data_root = '/mnt/workspace/workgroup/zhizhonghuang/dataset/COCO/train2017'\n # save_root = '/root/gligen_data'\n box_threshold = 0.25\n text_threshold = 0.2\n\n import torch.distributed as dist\n\n dist.init_process_group(backend=\"nccl\", init_method=\"env://\")\n local_rank = torch.distributed.get_rank() % torch.cuda.device_count()\n device = f\"cuda:{local_rank}\"\n torch.cuda.set_device(local_rank)\n\n ram_model = ram(pretrained=args.ram_checkpoint, image_size=384, vit=\"swin_l\").cuda().eval()\n ram_processor = TS.Compose(\n [TS.Resize((384, 384)), TS.ToTensor(), TS.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]\n )\n\n grounding_dino_processor = AutoProcessor.from_pretrained(\"IDEA-Research/grounding-dino-base\")\n grounding_dino_model = AutoModelForZeroShotObjectDetection.from_pretrained(\n \"IDEA-Research/grounding-dino-base\"\n ).cuda()\n\n blip2_processor = Blip2Processor.from_pretrained(\"Salesforce/blip2-flan-t5-xxl\")\n blip2_model = Blip2ForConditionalGeneration.from_pretrained(\n \"Salesforce/blip2-flan-t5-xxl\", torch_dtype=torch.float16\n ).cuda()\n\n clip_text_encoder = CLIPTextModel.from_pretrained(\"openai/clip-vit-large-patch14\").cuda()\n clip_tokenizer = CLIPTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\")\n\n image_paths = [os.path.join(args.data_root, x) for x in os.listdir(args.data_root)]\n random.shuffle(image_paths)\n\n for image_path in tqdm.tqdm(image_paths):\n pth_path = os.path.join(args.save_root, os.path.basename(image_path))\n if os.path.exists(pth_path):\n continue\n\n sample = {\"file_path\": os.path.basename(image_path), \"annos\": []}\n\n raw_image = Image.open(image_path).convert(\"RGB\")\n\n res = inference_ram(ram_processor(raw_image).unsqueeze(0).cuda(), ram_model)\n\n text = res[0].replace(\" |\", \".\")\n\n inputs = grounding_dino_processor(images=raw_image, text=text, return_tensors=\"pt\")\n inputs = {k: v.cuda() for k, v in inputs.items()}\n outputs = grounding_dino_model(**inputs)\n\n results = grounding_dino_processor.post_process_grounded_object_detection(\n outputs,\n inputs[\"input_ids\"],\n box_threshold=box_threshold,\n text_threshold=text_threshold,\n target_sizes=[raw_image.size[::-1]],\n )\n boxes = results[0][\"boxes\"]\n labels = results[0][\"labels\"]\n scores = results[0][\"scores\"]\n indices = torchvision.ops.nms(boxes, scores, 0.5)\n boxes = boxes[indices]\n category_names = [labels[i] for i in indices]\n\n for i, bbox in enumerate(boxes):\n bbox = bbox.tolist()\n inputs = blip2_processor(images=raw_image.crop(bbox), return_tensors=\"pt\")\n inputs = {k: v.cuda().to(torch.float16) for k, v in inputs.items()}\n outputs = blip2_model.generate(**inputs)\n caption = blip2_processor.decode(outputs[0], skip_special_tokens=True)\n inputs = clip_tokenizer(\n caption,\n padding=\"max_length\",\n max_length=clip_tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n inputs = {k: v.cuda() for k, v in inputs.items()}\n text_embeddings_before_projection = clip_text_encoder(**inputs).pooler_output.squeeze(0)\n\n sample[\"annos\"].append(\n {\n \"caption\": caption,\n \"bbox\": bbox,\n \"text_embeddings_before_projection\": text_embeddings_before_projection,\n }\n )\n torch.save(sample, pth_path)\n" }, { "path": "diffusers/examples/research_projects/gligen/requirements.txt", "content": "accelerate>=0.16.0\ntorchvision\ntransformers>=4.25.1\nftfy\ntensorboard\nJinja2\ndiffusers\nscipy\ntimm\nfairscale\nwandb" }, { "path": "diffusers/examples/research_projects/gligen/train_gligen_text.py", "content": "# from accelerate.utils import write_basic_config\n#\n# write_basic_config()\nimport argparse\nimport logging\nimport math\nimport os\nimport shutil\nfrom pathlib import Path\n\nimport accelerate\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom packaging import version\nfrom tqdm.auto import tqdm\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n EulerDiscreteScheduler,\n StableDiffusionGLIGENPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import is_wandb_available, make_image_grid\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\nif is_wandb_available():\n pass\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\n# check_min_version(\"0.28.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\n@torch.no_grad()\ndef log_validation(vae, text_encoder, tokenizer, unet, noise_scheduler, args, accelerator, step, weight_dtype):\n if accelerator.is_main_process:\n print(\"generate test images...\")\n unet = accelerator.unwrap_model(unet)\n vae.to(accelerator.device, dtype=torch.float32)\n\n pipeline = StableDiffusionGLIGENPipeline(\n vae,\n text_encoder,\n tokenizer,\n unet,\n EulerDiscreteScheduler.from_config(noise_scheduler.config),\n safety_checker=None,\n feature_extractor=None,\n )\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=not accelerator.is_main_process)\n if args.enable_xformers_memory_efficient_attention:\n pipeline.enable_xformers_memory_efficient_attention()\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n prompt = \"A realistic image of landscape scene depicting a green car parking on the left of a blue truck, with a red air balloon and a bird in the sky\"\n boxes = [\n [0.041015625, 0.548828125, 0.453125, 0.859375],\n [0.525390625, 0.552734375, 0.93359375, 0.865234375],\n [0.12890625, 0.015625, 0.412109375, 0.279296875],\n [0.578125, 0.08203125, 0.857421875, 0.27734375],\n ]\n gligen_phrases = [\"a green car\", \"a blue truck\", \"a red air balloon\", \"a bird\"]\n images = pipeline(\n prompt=prompt,\n gligen_phrases=gligen_phrases,\n gligen_boxes=boxes,\n gligen_scheduled_sampling_beta=1.0,\n output_type=\"pil\",\n num_inference_steps=50,\n negative_prompt=\"artifacts, blurry, smooth texture, bad quality, distortions, unrealistic, distorted image, bad proportions, duplicate\",\n num_images_per_prompt=4,\n generator=generator,\n ).images\n os.makedirs(os.path.join(args.output_dir, \"images\"), exist_ok=True)\n make_image_grid(images, 1, 4).save(\n os.path.join(args.output_dir, \"images\", f\"generated-images-{step:06d}-{accelerator.process_index:02d}.png\")\n )\n\n vae.to(accelerator.device, dtype=weight_dtype)\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a ControlNet training script.\")\n parser.add_argument(\n \"--data_path\",\n type=str,\n default=\"coco_train2017.pth\",\n help=\"Path to training dataset.\",\n )\n parser.add_argument(\n \"--image_path\",\n type=str,\n default=\"coco_train2017.pth\",\n help=\"Path to training images.\",\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"controlnet-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=0, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. \"\n \"In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference.\"\n \"Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components.\"\n \"See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step\"\n \"instructions.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=5e-6,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\"--lr_power\", type=float, default=1.0, help=\"Power factor of the polynomial scheduler.\")\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--set_grads_to_none\",\n action=\"store_true\",\n help=(\n \"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain\"\n \" behaviors, so disable this argument if it causes any problems. More info:\"\n \" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html\"\n ),\n )\n parser.add_argument(\n \"--tracker_project_name\",\n type=str,\n default=\"train_controlnet\",\n help=(\n \"The `project_name` argument passed to Accelerator.init_trackers for\"\n \" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator\"\n ),\n )\n args = parser.parse_args()\n return args\n\n\ndef main(args):\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n # import correct text encoder class\n # text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)\n # Load scheduler and models\n from transformers import CLIPTextModel, CLIPTokenizer\n\n pretrained_model_name_or_path = \"masterful/gligen-1-4-generation-text-box\"\n tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder=\"tokenizer\")\n noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder=\"scheduler\")\n text_encoder = CLIPTextModel.from_pretrained(pretrained_model_name_or_path, subfolder=\"text_encoder\")\n\n vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder=\"vae\")\n unet = UNet2DConditionModel.from_pretrained(pretrained_model_name_or_path, subfolder=\"unet\")\n\n # Taken from [Sayak Paul's Diffusers PR #6511](https://github.com/huggingface/diffusers/pull/6511/files)\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n i = len(weights) - 1\n\n while len(weights) > 0:\n weights.pop()\n model = models[i]\n\n sub_dir = \"unet\"\n model.save_pretrained(os.path.join(output_dir, sub_dir))\n\n i -= 1\n\n def load_model_hook(models, input_dir):\n while len(models) > 0:\n # pop models so that they are not loaded again\n model = models.pop()\n\n # load diffusers style into model\n load_model = unet.from_pretrained(input_dir, subfolder=\"unet\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n vae.requires_grad_(False)\n unet.requires_grad_(False)\n text_encoder.requires_grad_(False)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n # controlnet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # if args.gradient_checkpointing:\n # controlnet.enable_gradient_checkpointing()\n\n # Check that all trainable models are in full precision\n low_precision_error_string = (\n \" Please make sure to always have all model weights in full float32 precision when starting training - even if\"\n \" doing mixed precision training, copy of the weights should still be float32.\"\n )\n\n if unwrap_model(unet).dtype != torch.float32:\n raise ValueError(f\"Controlnet loaded as datatype {unwrap_model(unet).dtype}. {low_precision_error_string}\")\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n optimizer_class = torch.optim.AdamW\n # Optimizer creation\n for n, m in unet.named_modules():\n if (\"fuser\" in n) or (\"position_net\" in n):\n import torch.nn as nn\n\n if isinstance(m, (nn.Linear, nn.LayerNorm)):\n m.reset_parameters()\n params_to_optimize = []\n for n, p in unet.named_parameters():\n if (\"fuser\" in n) or (\"position_net\" in n):\n p.requires_grad = True\n params_to_optimize.append(p)\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n from dataset import COCODataset\n\n train_dataset = COCODataset(\n data_path=args.data_path,\n image_path=args.image_path,\n tokenizer=tokenizer,\n image_size=args.resolution,\n max_boxes_per_data=30,\n )\n\n print(\"num samples: \", len(train_dataset))\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n # collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_cycles=args.lr_num_cycles,\n power=args.lr_power,\n )\n\n # Prepare everything with our `accelerator`.\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n # For mixed precision training we cast the text_encoder and vae weights to half-precision\n # as these models are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move vae, unet and text_encoder to device and cast to weight_dtype\n vae.to(accelerator.device, dtype=weight_dtype)\n # unet.to(accelerator.device, dtype=weight_dtype)\n unet.to(accelerator.device, dtype=torch.float32)\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = dict(vars(args))\n\n # tensorboard cannot handle list types for config\n # tracker_config.pop(\"validation_prompt\")\n # tracker_config.pop(\"validation_image\")\n\n accelerator.init_trackers(args.tracker_project_name, config=tracker_config)\n\n # Train!\n # total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n # logger.info(\"***** Running training *****\")\n # logger.info(f\" Num examples = {len(train_dataset)}\")\n # logger.info(f\" Num batches each epoch = {len(train_dataloader)}\")\n # logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n # logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n # logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n # logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n # logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n log_validation(\n vae,\n text_encoder,\n tokenizer,\n unet,\n noise_scheduler,\n args,\n accelerator,\n global_step,\n weight_dtype,\n )\n\n # image_logs = None\n for epoch in range(first_epoch, args.num_train_epochs):\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n # Convert images to latent space\n latents = vae.encode(batch[\"pixel_values\"].to(dtype=weight_dtype)).latent_dist.sample()\n latents = latents * vae.config.scaling_factor\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n with torch.no_grad():\n # Get the text embedding for conditioning\n encoder_hidden_states = text_encoder(\n batch[\"caption\"][\"input_ids\"].squeeze(1),\n # batch['caption']['attention_mask'].squeeze(1),\n return_dict=False,\n )[0]\n\n cross_attention_kwargs = {}\n cross_attention_kwargs[\"gligen\"] = {\n \"boxes\": batch[\"boxes\"],\n \"positive_embeddings\": batch[\"text_embeddings_before_projection\"],\n \"masks\": batch[\"masks\"],\n }\n # Predict the noise residual\n model_pred = unet(\n noisy_latents,\n timesteps,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(params_to_optimize, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad(set_to_none=args.set_grads_to_none)\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if global_step % args.checkpointing_steps == 0:\n if accelerator.is_main_process:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step:06d}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n # if args.validation_prompt is not None and global_step % args.validation_steps == 0:\n log_validation(\n vae,\n text_encoder,\n tokenizer,\n unet,\n noise_scheduler,\n args,\n accelerator,\n global_step,\n weight_dtype,\n )\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n # Create the pipeline using using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = unwrap_model(unet)\n unet.save_pretrained(args.output_dir)\n #\n # # Run a final round of validation.\n # image_logs = None\n # if args.validation_prompt is not None:\n # image_logs = log_validation(\n # vae=vae,\n # text_encoder=text_encoder,\n # tokenizer=tokenizer,\n # unet=unet,\n # controlnet=None,\n # args=args,\n # accelerator=accelerator,\n # weight_dtype=weight_dtype,\n # step=global_step,\n # is_final_validation=True,\n # )\n #\n # if args.push_to_hub:\n # save_model_card(\n # repo_id,\n # image_logs=image_logs,\n # base_model=args.pretrained_model_name_or_path,\n # repo_folder=args.output_dir,\n # )\n # upload_folder(\n # repo_id=repo_id,\n # folder_path=args.output_dir,\n # commit_message=\"End of training\",\n # ignore_patterns=[\"step_*\", \"epoch_*\"],\n # )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/research_projects/instructpix2pix_lora/README.md", "content": "# InstructPix2Pix text-to-edit-image fine-tuning\nThis extended LoRA training script was authored by [Aiden-Frost](https://github.com/Aiden-Frost).\nThis is an experimental LoRA extension of [this example](https://github.com/huggingface/diffusers/blob/main/examples/instruct_pix2pix/train_instruct_pix2pix.py). This script provides further support add LoRA layers for unet model.\n\n## Training script example\n\n```bash\nexport MODEL_ID=\"timbrooks/instruct-pix2pix\"\nexport DATASET_ID=\"instruction-tuning-sd/cartoonization\"\nexport OUTPUT_DIR=\"instructPix2Pix-cartoonization\"\n\naccelerate launch finetune_instruct_pix2pix.py \\\n --pretrained_model_name_or_path=$MODEL_ID \\\n --dataset_name=$DATASET_ID \\\n --enable_xformers_memory_efficient_attention \\\n --resolution=256 --random_flip \\\n --train_batch_size=2 --gradient_accumulation_steps=4 --gradient_checkpointing \\\n --max_train_steps=15000 \\\n --checkpointing_steps=5000 --checkpoints_total_limit=1 \\\n --learning_rate=5e-05 --lr_warmup_steps=0 \\\n --val_image_url=\"https://hf.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png\" \\\n --validation_prompt=\"Generate a cartoonized version of the natural image\" \\\n --seed=42 \\\n --rank=4 \\\n --output_dir=$OUTPUT_DIR \\\n --report_to=wandb \\\n --push_to_hub\n```\n\n## Inference\nAfter training the model and the lora weight of the model is stored in the ```$OUTPUT_DIR```. \n\n```bash\n# load the base model pipeline\npipe_lora = StableDiffusionInstructPix2PixPipeline.from_pretrained(\"timbrooks/instruct-pix2pix\")\n\n# Load LoRA weights from the provided path\noutput_dir = \"path/to/lora_weight_directory\"\npipe_lora.unet.load_attn_procs(output_dir)\n\ninput_image_path = \"/path/to/input_image\"\ninput_image = Image.open(input_image_path)\nedited_images = pipe_lora(num_images_per_prompt=1, prompt=args.edit_prompt, image=input_image, num_inference_steps=1000).images\nedited_images[0].show()\n\n```\n\n## Results\n\nHere is an example of using the script to train a instructpix2pix model. \nTrained on google colab T4 GPU\n\n```bash\nMODEL_ID=\"timbrooks/instruct-pix2pix\"\nDATASET_ID=\"instruction-tuning-sd/cartoonization\"\nTRAIN_EPOCHS=100\n```\n\nBelow are few examples for given the input image, edit_prompt and the edited_image (output of the model)\n\n

\n \"instructpix2pix-inputs\"\n

\n\n\nHere are some rough statistics about the training model using this script\n\n

\n \"instructpix2pix-inputs\"\n

\n\n## References \n\n* InstructPix2Pix - https://github.com/timothybrooks/instruct-pix2pix\n* Dataset and example training script - https://huggingface.co/blog/instruction-tuning-sd\n* For more information about the project - https://github.com/Aiden-Frost/Efficiently-teaching-counting-and-cartoonization-to-InstructPix2Pix.-" }, { "path": "diffusers/examples/research_projects/instructpix2pix_lora/train_instruct_pix2pix_lora.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Script to fine-tune Stable Diffusion for InstructPix2Pix.\"\"\"\n\nimport argparse\nimport logging\nimport math\nimport os\nimport shutil\nfrom contextlib import nullcontext\nfrom pathlib import Path\n\nimport accelerate\nimport datasets\nimport numpy as np\nimport PIL\nimport requests\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nimport diffusers\nfrom diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionInstructPix2PixPipeline, UNet2DConditionModel\nfrom diffusers.models.lora import LoRALinearLayer\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import EMAModel\nfrom diffusers.utils import check_min_version, deprecate, is_wandb_available\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.26.0.dev0\")\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\nDATASET_NAME_MAPPING = {\n \"fusing/instructpix2pix-1000-samples\": (\"input_image\", \"edit_prompt\", \"edited_image\"),\n}\nWANDB_TABLE_COL_NAMES = [\"original_image\", \"edited_image\", \"edit_prompt\"]\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script for InstructPix2Pix.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--original_image_column\",\n type=str,\n default=\"input_image\",\n help=\"The column of the dataset containing the original image on which edits where made.\",\n )\n parser.add_argument(\n \"--edited_image_column\",\n type=str,\n default=\"edited_image\",\n help=\"The column of the dataset containing the edited image.\",\n )\n parser.add_argument(\n \"--edit_prompt_column\",\n type=str,\n default=\"edit_prompt\",\n help=\"The column of the dataset containing the edit instruction.\",\n )\n parser.add_argument(\n \"--val_image_url\",\n type=str,\n default=None,\n help=\"URL to the original image that you would like to edit (used during inference for debugging purposes).\",\n )\n parser.add_argument(\n \"--validation_prompt\", type=str, default=None, help=\"A prompt that is sampled during training for inference.\"\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=1,\n help=(\n \"Run fine-tuning validation every X epochs. The validation process consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"instruct-pix2pix-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=256,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--conditioning_dropout_prob\",\n type=float,\n default=None,\n help=\"Conditioning dropout probability. Drops out the conditionings (image and edit prompt) used in training InstructPix2Pix. See section 3.2.1 in the paper: https://arxiv.org/abs/2211.09800.\",\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\"--use_ema\", action=\"store_true\", help=\"Whether to use EMA model.\")\n parser.add_argument(\n \"--non_ema_revision\",\n type=str,\n default=None,\n required=False,\n help=(\n \"Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or\"\n \" remote repository specified with --pretrained_model_name_or_path.\"\n ),\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--rank\",\n type=int,\n default=4,\n help=(\"The dimension of the LoRA update matrices.\"),\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n\n # default to using the same revision for the non-ema model if not specified\n if args.non_ema_revision is None:\n args.non_ema_revision = args.revision\n\n return args\n\n\ndef convert_to_np(image, resolution):\n image = image.convert(\"RGB\").resize((resolution, resolution))\n return np.array(image).transpose(2, 0, 1)\n\n\ndef download_image(url):\n image = PIL.Image.open(requests.get(url, stream=True).raw)\n image = PIL.ImageOps.exif_transpose(image)\n image = image.convert(\"RGB\")\n return image\n\n\ndef main():\n args = parse_args()\n\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n if args.non_ema_revision is not None:\n deprecate(\n \"non_ema_revision!=None\",\n \"0.15.0\",\n message=(\n \"Downloading 'non_ema' weights from revision branches of the Hub is deprecated. Please make sure to\"\n \" use `--variant=non_ema` instead.\"\n ),\n )\n logging_dir = os.path.join(args.output_dir, args.logging_dir)\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n import wandb\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load scheduler, tokenizer and models.\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n tokenizer = CLIPTokenizer.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"tokenizer\", revision=args.revision\n )\n text_encoder = CLIPTextModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision, variant=args.variant\n )\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.non_ema_revision\n )\n\n # Freeze vae, text_encoder and unet\n vae.requires_grad_(False)\n text_encoder.requires_grad_(False)\n unet.requires_grad_(False)\n\n # referred to https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py\n unet_lora_parameters = []\n for attn_processor_name, attn_processor in unet.attn_processors.items():\n # Parse the attention module.\n attn_module = unet\n for n in attn_processor_name.split(\".\")[:-1]:\n attn_module = getattr(attn_module, n)\n\n # Set the `lora_layer` attribute of the attention-related matrices.\n attn_module.to_q.set_lora_layer(\n LoRALinearLayer(\n in_features=attn_module.to_q.in_features, out_features=attn_module.to_q.out_features, rank=args.rank\n )\n )\n attn_module.to_k.set_lora_layer(\n LoRALinearLayer(\n in_features=attn_module.to_k.in_features, out_features=attn_module.to_k.out_features, rank=args.rank\n )\n )\n\n attn_module.to_v.set_lora_layer(\n LoRALinearLayer(\n in_features=attn_module.to_v.in_features, out_features=attn_module.to_v.out_features, rank=args.rank\n )\n )\n attn_module.to_out[0].set_lora_layer(\n LoRALinearLayer(\n in_features=attn_module.to_out[0].in_features,\n out_features=attn_module.to_out[0].out_features,\n rank=args.rank,\n )\n )\n\n # Accumulate the LoRA params to optimize.\n unet_lora_parameters.extend(attn_module.to_q.lora_layer.parameters())\n unet_lora_parameters.extend(attn_module.to_k.lora_layer.parameters())\n unet_lora_parameters.extend(attn_module.to_v.lora_layer.parameters())\n unet_lora_parameters.extend(attn_module.to_out[0].lora_layer.parameters())\n\n # Create EMA for the unet.\n if args.use_ema:\n ema_unet = EMAModel(unet.parameters(), model_cls=UNet2DConditionModel, model_config=unet.config)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n if args.use_ema:\n ema_unet.save_pretrained(os.path.join(output_dir, \"unet_ema\"))\n\n for i, model in enumerate(models):\n model.save_pretrained(os.path.join(output_dir, \"unet\"))\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n def load_model_hook(models, input_dir):\n if args.use_ema:\n load_model = EMAModel.from_pretrained(os.path.join(input_dir, \"unet_ema\"), UNet2DConditionModel)\n ema_unet.load_state_dict(load_model.state_dict())\n ema_unet.to(accelerator.device)\n del load_model\n\n for i in range(len(models)):\n # pop models so that they are not loaded again\n model = models.pop()\n\n # load diffusers style into model\n load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder=\"unet\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Initialize the optimizer\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`\"\n )\n\n optimizer_cls = bnb.optim.AdamW8bit\n else:\n optimizer_cls = torch.optim.AdamW\n\n # train on only unet_lora_parameters\n optimizer = optimizer_cls(\n unet_lora_parameters,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n )\n else:\n data_files = {}\n if args.train_data_dir is not None:\n data_files[\"train\"] = os.path.join(args.train_data_dir, \"**\")\n dataset = load_dataset(\n \"imagefolder\",\n data_files=data_files,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/main/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)\n if args.original_image_column is None:\n original_image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n else:\n original_image_column = args.original_image_column\n if original_image_column not in column_names:\n raise ValueError(\n f\"--original_image_column' value '{args.original_image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.edit_prompt_column is None:\n edit_prompt_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n else:\n edit_prompt_column = args.edit_prompt_column\n if edit_prompt_column not in column_names:\n raise ValueError(\n f\"--edit_prompt_column' value '{args.edit_prompt_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.edited_image_column is None:\n edited_image_column = dataset_columns[2] if dataset_columns is not None else column_names[2]\n else:\n edited_image_column = args.edited_image_column\n if edited_image_column not in column_names:\n raise ValueError(\n f\"--edited_image_column' value '{args.edited_image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n\n # Preprocessing the datasets.\n # We need to tokenize input captions and transform the images.\n def tokenize_captions(captions):\n inputs = tokenizer(\n captions, max_length=tokenizer.model_max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\"\n )\n return inputs.input_ids\n\n # Preprocessing the datasets.\n train_transforms = transforms.Compose(\n [\n transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),\n transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),\n ]\n )\n\n def preprocess_images(examples):\n original_images = np.concatenate(\n [convert_to_np(image, args.resolution) for image in examples[original_image_column]]\n )\n edited_images = np.concatenate(\n [convert_to_np(image, args.resolution) for image in examples[edited_image_column]]\n )\n # We need to ensure that the original and the edited images undergo the same\n # augmentation transforms.\n images = np.concatenate([original_images, edited_images])\n images = torch.tensor(images)\n images = 2 * (images / 255) - 1\n return train_transforms(images)\n\n def preprocess_train(examples):\n # Preprocess images.\n preprocessed_images = preprocess_images(examples)\n # Since the original and edited images were concatenated before\n # applying the transformations, we need to separate them and reshape\n # them accordingly.\n original_images, edited_images = preprocessed_images.chunk(2)\n original_images = original_images.reshape(-1, 3, args.resolution, args.resolution)\n edited_images = edited_images.reshape(-1, 3, args.resolution, args.resolution)\n\n # Collate the preprocessed images into the `examples`.\n examples[\"original_pixel_values\"] = original_images\n examples[\"edited_pixel_values\"] = edited_images\n\n # Preprocess the captions.\n captions = list(examples[edit_prompt_column])\n examples[\"input_ids\"] = tokenize_captions(captions)\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n def collate_fn(examples):\n original_pixel_values = torch.stack([example[\"original_pixel_values\"] for example in examples])\n original_pixel_values = original_pixel_values.to(memory_format=torch.contiguous_format).float()\n edited_pixel_values = torch.stack([example[\"edited_pixel_values\"] for example in examples])\n edited_pixel_values = edited_pixel_values.to(memory_format=torch.contiguous_format).float()\n input_ids = torch.stack([example[\"input_ids\"] for example in examples])\n return {\n \"original_pixel_values\": original_pixel_values,\n \"edited_pixel_values\": edited_pixel_values,\n \"input_ids\": input_ids,\n }\n\n # DataLoaders creation:\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n )\n\n # Prepare everything with our `accelerator`.\n unet, unet_lora_parameters, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, unet_lora_parameters, optimizer, train_dataloader, lr_scheduler\n )\n\n if args.use_ema:\n ema_unet.to(accelerator.device)\n\n # For mixed precision training we cast the text_encoder and vae weights to half-precision\n # as these models are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move text_encode and vae to gpu and cast to weight_dtype\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n vae.to(accelerator.device, dtype=weight_dtype)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"instruct-pix2pix\", config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n resume_global_step = global_step * args.gradient_accumulation_steps\n first_epoch = global_step // num_update_steps_per_epoch\n resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)\n\n # Only show the progress bar once on each machine.\n progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)\n progress_bar.set_description(\"Steps\")\n\n for epoch in range(first_epoch, args.num_train_epochs):\n unet.train()\n train_loss = 0.0\n for step, batch in enumerate(train_dataloader):\n # Skip steps until we reach the resumed step\n if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:\n if step % args.gradient_accumulation_steps == 0:\n progress_bar.update(1)\n continue\n\n with accelerator.accumulate(unet):\n # We want to learn the denoising process w.r.t the edited images which\n # are conditioned on the original image (which was edited) and the edit instruction.\n # So, first, convert images to latent space.\n latents = vae.encode(batch[\"edited_pixel_values\"].to(weight_dtype)).latent_dist.sample()\n latents = latents * vae.config.scaling_factor\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # Get the text embedding for conditioning.\n encoder_hidden_states = text_encoder(batch[\"input_ids\"])[0]\n\n # Get the additional image embedding for conditioning.\n # Instead of getting a diagonal Gaussian here, we simply take the mode.\n original_image_embeds = vae.encode(batch[\"original_pixel_values\"].to(weight_dtype)).latent_dist.mode()\n\n # Conditioning dropout to support classifier-free guidance during inference. For more details\n # check out the section 3.2.1 of the original paper https://arxiv.org/abs/2211.09800.\n if args.conditioning_dropout_prob is not None:\n random_p = torch.rand(bsz, device=latents.device, generator=generator)\n # Sample masks for the edit prompts.\n prompt_mask = random_p < 2 * args.conditioning_dropout_prob\n prompt_mask = prompt_mask.reshape(bsz, 1, 1)\n # Final text conditioning.\n null_conditioning = text_encoder(tokenize_captions([\"\"]).to(accelerator.device))[0]\n encoder_hidden_states = torch.where(prompt_mask, null_conditioning, encoder_hidden_states)\n\n # Sample masks for the original images.\n image_mask_dtype = original_image_embeds.dtype\n image_mask = 1 - (\n (random_p >= args.conditioning_dropout_prob).to(image_mask_dtype)\n * (random_p < 3 * args.conditioning_dropout_prob).to(image_mask_dtype)\n )\n image_mask = image_mask.reshape(bsz, 1, 1, 1)\n # Final image conditioning.\n original_image_embeds = image_mask * original_image_embeds\n\n # Concatenate the `original_image_embeds` with the `noisy_latents`.\n concatenated_noisy_latents = torch.cat([noisy_latents, original_image_embeds], dim=1)\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n # Predict the noise residual and compute loss\n model_pred = unet(concatenated_noisy_latents, timesteps, encoder_hidden_states).sample\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n\n # Gather the losses across all processes for logging (if we use distributed training).\n avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n train_loss += avg_loss.item() / args.gradient_accumulation_steps\n\n # Backpropagate\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(unet_lora_parameters, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n if args.use_ema:\n ema_unet.step(unet_lora_parameters)\n progress_bar.update(1)\n global_step += 1\n accelerator.log({\"train_loss\": train_loss}, step=global_step)\n train_loss = 0.0\n\n if global_step % args.checkpointing_steps == 0:\n if accelerator.is_main_process:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"step_loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if (\n (args.val_image_url is not None)\n and (args.validation_prompt is not None)\n and (epoch % args.validation_epochs == 0)\n ):\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n # create pipeline\n if args.use_ema:\n # Store the UNet parameters temporarily and load the EMA parameters to perform inference.\n ema_unet.store(unet.parameters())\n ema_unet.copy_to(unet.parameters())\n # The models need unwrapping because for compatibility in distributed training mode.\n pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n unet=accelerator.unwrap_model(unet),\n text_encoder=accelerator.unwrap_model(text_encoder),\n vae=accelerator.unwrap_model(vae),\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n original_image = download_image(args.val_image_url)\n edited_images = []\n if torch.backends.mps.is_available():\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type)\n\n with autocast_ctx:\n for _ in range(args.num_validation_images):\n edited_images.append(\n pipeline(\n args.validation_prompt,\n image=original_image,\n num_inference_steps=20,\n image_guidance_scale=1.5,\n guidance_scale=7,\n generator=generator,\n ).images[0]\n )\n\n for tracker in accelerator.trackers:\n if tracker.name == \"wandb\":\n wandb_table = wandb.Table(columns=WANDB_TABLE_COL_NAMES)\n for edited_image in edited_images:\n wandb_table.add_data(\n wandb.Image(original_image), wandb.Image(edited_image), args.validation_prompt\n )\n tracker.log({\"validation\": wandb_table})\n if args.use_ema:\n # Switch back to the original UNet parameters.\n ema_unet.restore(unet.parameters())\n\n del pipeline\n torch.cuda.empty_cache()\n\n # Create the pipeline using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = accelerator.unwrap_model(unet)\n if args.use_ema:\n ema_unet.copy_to(unet.parameters())\n\n pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n text_encoder=accelerator.unwrap_model(text_encoder),\n vae=accelerator.unwrap_model(vae),\n unet=unet,\n revision=args.revision,\n variant=args.variant,\n )\n # store only LORA layers\n unet.save_attn_procs(args.output_dir)\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n if args.validation_prompt is not None:\n edited_images = []\n pipeline = pipeline.to(accelerator.device)\n with torch.autocast(str(accelerator.device).replace(\":0\", \"\")):\n for _ in range(args.num_validation_images):\n edited_images.append(\n pipeline(\n args.validation_prompt,\n image=original_image,\n num_inference_steps=20,\n image_guidance_scale=1.5,\n guidance_scale=7,\n generator=generator,\n ).images[0]\n )\n\n for tracker in accelerator.trackers:\n if tracker.name == \"wandb\":\n wandb_table = wandb.Table(columns=WANDB_TABLE_COL_NAMES)\n for edited_image in edited_images:\n wandb_table.add_data(\n wandb.Image(original_image), wandb.Image(edited_image), args.validation_prompt\n )\n tracker.log({\"test\": wandb_table})\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/research_projects/intel_opts/README.md", "content": "## Diffusers examples with Intel optimizations\n\n**This research project is not actively maintained by the diffusers team. For any questions or comments, please make sure to tag @hshen14 .**\n\nThis aims to provide diffusers examples with Intel optimizations such as Bfloat16 for training/fine-tuning acceleration and 8-bit integer (INT8) for inference acceleration on Intel platforms.\n\n## Accelerating the fine-tuning for textual inversion\n\nWe accelereate the fine-tuning for textual inversion with Intel Extension for PyTorch. The [examples](textual_inversion) enable both single node and multi-node distributed training with Bfloat16 support on Intel Xeon Scalable Processor.\n\n## Accelerating the inference for Stable Diffusion using Bfloat16\n\nWe start the inference acceleration with Bfloat16 using Intel Extension for PyTorch. The [script](inference_bf16.py) is generally designed to support standard Stable Diffusion models with Bfloat16 support.\n```bash\npip install diffusers transformers accelerate scipy safetensors\n\nexport KMP_BLOCKTIME=1\nexport KMP_SETTINGS=1\nexport KMP_AFFINITY=granularity=fine,compact,1,0\n\n# Intel OpenMP\nexport OMP_NUM_THREADS=< Cores to use >\nexport LD_PRELOAD=${LD_PRELOAD}:/path/to/lib/libiomp5.so\n# Jemalloc is a recommended malloc implementation that emphasizes fragmentation avoidance and scalable concurrency support.\nexport LD_PRELOAD=${LD_PRELOAD}:/path/to/lib/libjemalloc.so\nexport MALLOC_CONF=\"oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:-1,muzzy_decay_ms:9000000000\"\n\n# Launch with default DDIM\nnumactl --membind -C python python inference_bf16.py\n# Launch with DPMSolverMultistepScheduler\nnumactl --membind -C python python inference_bf16.py --dpm\n\n```\n\n## Accelerating the inference for Stable Diffusion using INT8\n\nComing soon ...\n" }, { "path": "diffusers/examples/research_projects/intel_opts/inference_bf16.py", "content": "import argparse\n\nimport intel_extension_for_pytorch as ipex\nimport torch\n\nfrom diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline\n\n\nparser = argparse.ArgumentParser(\"Stable Diffusion script with intel optimization\", add_help=False)\nparser.add_argument(\"--dpm\", action=\"store_true\", help=\"Enable DPMSolver or not\")\nparser.add_argument(\"--steps\", default=None, type=int, help=\"Num inference steps\")\nargs = parser.parse_args()\n\n\ndevice = \"cpu\"\nprompt = \"a lovely in red dress and hat, in the snowly and brightly night, with many brighly buildings\"\n\nmodel_id = \"path-to-your-trained-model\"\npipe = StableDiffusionPipeline.from_pretrained(model_id)\nif args.dpm:\n pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\npipe = pipe.to(device)\n\n# to channels last\npipe.unet = pipe.unet.to(memory_format=torch.channels_last)\npipe.vae = pipe.vae.to(memory_format=torch.channels_last)\npipe.text_encoder = pipe.text_encoder.to(memory_format=torch.channels_last)\nif pipe.requires_safety_checker:\n pipe.safety_checker = pipe.safety_checker.to(memory_format=torch.channels_last)\n\n# optimize with ipex\nsample = torch.randn(2, 4, 64, 64)\ntimestep = torch.rand(1) * 999\nencoder_hidden_status = torch.randn(2, 77, 768)\ninput_example = (sample, timestep, encoder_hidden_status)\ntry:\n pipe.unet = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloat16, inplace=True, sample_input=input_example)\nexcept Exception:\n pipe.unet = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloat16, inplace=True)\npipe.vae = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloat16, inplace=True)\npipe.text_encoder = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloat16, inplace=True)\nif pipe.requires_safety_checker:\n pipe.safety_checker = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloat16, inplace=True)\n\n# compute\nseed = 666\ngenerator = torch.Generator(device).manual_seed(seed)\ngenerate_kwargs = {\"generator\": generator}\nif args.steps is not None:\n generate_kwargs[\"num_inference_steps\"] = args.steps\n\nwith torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16):\n image = pipe(prompt, **generate_kwargs).images[0]\n\n# save image\nimage.save(\"generated.png\")\n" }, { "path": "diffusers/examples/research_projects/intel_opts/textual_inversion/README.md", "content": "## Textual Inversion fine-tuning example\n\n[Textual inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like stable diffusion on your own images using just 3-5 examples.\nThe `textual_inversion.py` script shows how to implement the training procedure and adapt it for stable diffusion.\n\n## Training with Intel Extension for PyTorch\n\nIntel Extension for PyTorch provides the optimizations for faster training and inference on CPUs. You can leverage the training example \"textual_inversion.py\". Follow the [instructions](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion) to get the model and [dataset](https://huggingface.co/sd-concepts-library/dicoo2) before running the script.\n\nThe example supports both single node and multi-node distributed training:\n\n### Single node training\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport DATA_DIR=\"path-to-dir-containing-dicoo-images\"\n\npython textual_inversion.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_data_dir=$DATA_DIR \\\n --learnable_property=\"object\" \\\n --placeholder_token=\"\" --initializer_token=\"toy\" \\\n --seed=7 \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=1 \\\n --max_train_steps=3000 \\\n --learning_rate=2.5e-03 --scale_lr \\\n --output_dir=\"textual_inversion_dicoo\"\n```\n\nNote: Bfloat16 is available on Intel Xeon Scalable Processors Cooper Lake or Sapphire Rapids. You may not get performance speedup without Bfloat16 support.\n\n### Multi-node distributed training\n\nBefore running the scripts, make sure to install the library's training dependencies successfully:\n\n```bash\npython -m pip install oneccl_bind_pt==1.13 -f https://developer.intel.com/ipex-whl-stable-cpu\n```\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport DATA_DIR=\"path-to-dir-containing-dicoo-images\"\n\noneccl_bindings_for_pytorch_path=$(python -c \"from oneccl_bindings_for_pytorch import cwd; print(cwd)\")\nsource $oneccl_bindings_for_pytorch_path/env/setvars.sh\n\npython -m intel_extension_for_pytorch.cpu.launch --distributed \\\n --hostfile hostfile --nnodes 2 --nproc_per_node 2 textual_inversion.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_data_dir=$DATA_DIR \\\n --learnable_property=\"object\" \\\n --placeholder_token=\"\" --initializer_token=\"toy\" \\\n --seed=7 \\ \n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=1 \\\n --max_train_steps=750 \\\n --learning_rate=2.5e-03 --scale_lr \\\n --output_dir=\"textual_inversion_dicoo\"\n```\nThe above is a simple distributed training usage on 2 nodes with 2 processes on each node. Add the right hostname or ip address in the \"hostfile\" and make sure these 2 nodes are reachable from each other. For more details, please refer to the [user guide](https://github.com/intel/torch-ccl).\n\n\n### Reference\n\nWe publish a [Medium blog](https://medium.com/intel-analytics-software/personalized-stable-diffusion-with-few-shot-fine-tuning-on-a-single-cpu-f01a3316b13) on how to create your own Stable Diffusion model on CPUs using textual inversion. Try it out now, if you have interests.\n" }, { "path": "diffusers/examples/research_projects/intel_opts/textual_inversion/requirements.txt", "content": "accelerate>=0.16.0\ntorchvision\ntransformers>=4.21.0\nftfy\ntensorboard\nJinja2\nintel_extension_for_pytorch>=1.13\n" }, { "path": "diffusers/examples/research_projects/intel_opts/textual_inversion/textual_inversion_bf16.py", "content": "import argparse\nimport itertools\nimport math\nimport os\nimport random\nfrom pathlib import Path\n\nimport intel_extension_for_pytorch as ipex\nimport numpy as np\nimport PIL\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom huggingface_hub import create_repo, upload_folder\n\n# TODO: remove and import from diffusers.utils when the new version of diffusers is released\nfrom packaging import version\nfrom PIL import Image\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import AutoencoderKL, DDPMScheduler, PNDMScheduler, StableDiffusionPipeline, UNet2DConditionModel\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker\nfrom diffusers.utils import check_min_version\n\n\nif version.parse(version.parse(PIL.__version__).base_version) >= version.parse(\"9.1.0\"):\n PIL_INTERPOLATION = {\n \"linear\": PIL.Image.Resampling.BILINEAR,\n \"bilinear\": PIL.Image.Resampling.BILINEAR,\n \"bicubic\": PIL.Image.Resampling.BICUBIC,\n \"lanczos\": PIL.Image.Resampling.LANCZOS,\n \"nearest\": PIL.Image.Resampling.NEAREST,\n }\nelse:\n PIL_INTERPOLATION = {\n \"linear\": PIL.Image.LINEAR,\n \"bilinear\": PIL.Image.BILINEAR,\n \"bicubic\": PIL.Image.BICUBIC,\n \"lanczos\": PIL.Image.LANCZOS,\n \"nearest\": PIL.Image.NEAREST,\n }\n# ------------------------------------------------------------------------------\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.13.0.dev0\")\n\n\nlogger = get_logger(__name__)\n\n\ndef save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path):\n logger.info(\"Saving embeddings\")\n learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_id]\n learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()}\n torch.save(learned_embeds_dict, save_path)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--save_steps\",\n type=int,\n default=500,\n help=\"Save learned_embeds.bin every X updates steps.\",\n )\n parser.add_argument(\n \"--only_save_embeds\",\n action=\"store_true\",\n default=False,\n help=\"Save only the embeddings for the new concept.\",\n )\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--tokenizer_name\",\n type=str,\n default=None,\n help=\"Pretrained tokenizer name or path if not the same as model_name\",\n )\n parser.add_argument(\n \"--train_data_dir\", type=str, default=None, required=True, help=\"A folder containing the training data.\"\n )\n parser.add_argument(\n \"--placeholder_token\",\n type=str,\n default=None,\n required=True,\n help=\"A token to use as a placeholder for the concept.\",\n )\n parser.add_argument(\n \"--initializer_token\", type=str, default=None, required=True, help=\"A token to use as initializer word.\"\n )\n parser.add_argument(\"--learnable_property\", type=str, default=\"object\", help=\"Choose between 'object' and 'style'\")\n parser.add_argument(\"--repeats\", type=int, default=100, help=\"How many times to repeat the training data.\")\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"text-inversion-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\", action=\"store_true\", help=\"Whether to center crop images before resizing to resolution.\"\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=5000,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=True,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=\"no\",\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose\"\n \"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.\"\n \"and an Nvidia Ampere GPU.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.train_data_dir is None:\n raise ValueError(\"You must specify a train data directory.\")\n\n return args\n\n\nimagenet_templates_small = [\n \"a photo of a {}\",\n \"a rendering of a {}\",\n \"a cropped photo of the {}\",\n \"the photo of a {}\",\n \"a photo of a clean {}\",\n \"a photo of a dirty {}\",\n \"a dark photo of the {}\",\n \"a photo of my {}\",\n \"a photo of the cool {}\",\n \"a close-up photo of a {}\",\n \"a bright photo of the {}\",\n \"a cropped photo of a {}\",\n \"a photo of the {}\",\n \"a good photo of the {}\",\n \"a photo of one {}\",\n \"a close-up photo of the {}\",\n \"a rendition of the {}\",\n \"a photo of the clean {}\",\n \"a rendition of a {}\",\n \"a photo of a nice {}\",\n \"a good photo of a {}\",\n \"a photo of the nice {}\",\n \"a photo of the small {}\",\n \"a photo of the weird {}\",\n \"a photo of the large {}\",\n \"a photo of a cool {}\",\n \"a photo of a small {}\",\n]\n\nimagenet_style_templates_small = [\n \"a painting in the style of {}\",\n \"a rendering in the style of {}\",\n \"a cropped painting in the style of {}\",\n \"the painting in the style of {}\",\n \"a clean painting in the style of {}\",\n \"a dirty painting in the style of {}\",\n \"a dark painting in the style of {}\",\n \"a picture in the style of {}\",\n \"a cool painting in the style of {}\",\n \"a close-up painting in the style of {}\",\n \"a bright painting in the style of {}\",\n \"a cropped painting in the style of {}\",\n \"a good painting in the style of {}\",\n \"a close-up painting in the style of {}\",\n \"a rendition in the style of {}\",\n \"a nice painting in the style of {}\",\n \"a small painting in the style of {}\",\n \"a weird painting in the style of {}\",\n \"a large painting in the style of {}\",\n]\n\n\nclass TextualInversionDataset(Dataset):\n def __init__(\n self,\n data_root,\n tokenizer,\n learnable_property=\"object\", # [object, style]\n size=512,\n repeats=100,\n interpolation=\"bicubic\",\n flip_p=0.5,\n set=\"train\",\n placeholder_token=\"*\",\n center_crop=False,\n ):\n self.data_root = data_root\n self.tokenizer = tokenizer\n self.learnable_property = learnable_property\n self.size = size\n self.placeholder_token = placeholder_token\n self.center_crop = center_crop\n self.flip_p = flip_p\n\n self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]\n\n self.num_images = len(self.image_paths)\n self._length = self.num_images\n\n if set == \"train\":\n self._length = self.num_images * repeats\n\n self.interpolation = {\n \"linear\": PIL_INTERPOLATION[\"linear\"],\n \"bilinear\": PIL_INTERPOLATION[\"bilinear\"],\n \"bicubic\": PIL_INTERPOLATION[\"bicubic\"],\n \"lanczos\": PIL_INTERPOLATION[\"lanczos\"],\n }[interpolation]\n\n self.templates = imagenet_style_templates_small if learnable_property == \"style\" else imagenet_templates_small\n self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, i):\n example = {}\n image = Image.open(self.image_paths[i % self.num_images])\n\n if not image.mode == \"RGB\":\n image = image.convert(\"RGB\")\n\n placeholder_string = self.placeholder_token\n text = random.choice(self.templates).format(placeholder_string)\n\n example[\"input_ids\"] = self.tokenizer(\n text,\n padding=\"max_length\",\n truncation=True,\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n ).input_ids[0]\n\n # default to score-sde preprocessing\n img = np.array(image).astype(np.uint8)\n\n if self.center_crop:\n crop = min(img.shape[0], img.shape[1])\n (\n h,\n w,\n ) = (\n img.shape[0],\n img.shape[1],\n )\n img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]\n\n image = Image.fromarray(img)\n image = image.resize((self.size, self.size), resample=self.interpolation)\n\n image = self.flip_transform(image)\n image = np.array(image).astype(np.uint8)\n image = (image / 127.5 - 1.0).astype(np.float32)\n\n example[\"pixel_values\"] = torch.from_numpy(image).permute(2, 0, 1)\n return example\n\n\ndef freeze_params(params):\n for param in params:\n param.requires_grad = False\n\n\ndef main():\n args = parse_args()\n\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = os.path.join(args.output_dir, args.logging_dir)\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizer and add the placeholder token as a additional special token\n if args.tokenizer_name:\n tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)\n elif args.pretrained_model_name_or_path:\n tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"tokenizer\")\n\n # Add the placeholder token in tokenizer\n num_added_tokens = tokenizer.add_tokens(args.placeholder_token)\n if num_added_tokens == 0:\n raise ValueError(\n f\"The tokenizer already contains the token {args.placeholder_token}. Please pass a different\"\n \" `placeholder_token` that is not already in the tokenizer.\"\n )\n\n # Convert the initializer_token, placeholder_token to ids\n token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)\n # Check if initializer_token is a single token or a sequence of tokens\n if len(token_ids) > 1:\n raise ValueError(\"The initializer token must be a single token.\")\n\n initializer_token_id = token_ids[0]\n placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)\n\n # Load models and create wrapper for stable diffusion\n text_encoder = CLIPTextModel.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"text_encoder\",\n revision=args.revision,\n )\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"vae\",\n revision=args.revision,\n )\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"unet\",\n revision=args.revision,\n )\n\n # Resize the token embeddings as we are adding new special tokens to the tokenizer\n text_encoder.resize_token_embeddings(len(tokenizer))\n\n # Initialise the newly added placeholder token with the embeddings of the initializer token\n token_embeds = text_encoder.get_input_embeddings().weight.data\n token_embeds[placeholder_token_id] = token_embeds[initializer_token_id]\n\n # Freeze vae and unet\n freeze_params(vae.parameters())\n freeze_params(unet.parameters())\n # Freeze all parameters except for the token embeddings in text encoder\n params_to_freeze = itertools.chain(\n text_encoder.text_model.encoder.parameters(),\n text_encoder.text_model.final_layer_norm.parameters(),\n text_encoder.text_model.embeddings.position_embedding.parameters(),\n )\n freeze_params(params_to_freeze)\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Initialize the optimizer\n optimizer = torch.optim.AdamW(\n text_encoder.get_input_embeddings().parameters(), # only optimize the embeddings\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n\n train_dataset = TextualInversionDataset(\n data_root=args.train_data_dir,\n tokenizer=tokenizer,\n size=args.resolution,\n placeholder_token=args.placeholder_token,\n repeats=args.repeats,\n learnable_property=args.learnable_property,\n center_crop=args.center_crop,\n set=\"train\",\n )\n train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.train_batch_size, shuffle=True)\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n )\n\n text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n text_encoder, optimizer, train_dataloader, lr_scheduler\n )\n\n # Move vae and unet to device\n vae.to(accelerator.device)\n unet.to(accelerator.device)\n\n # Keep vae and unet in eval model as we don't train these\n vae.eval()\n unet.eval()\n\n unet = ipex.optimize(unet, dtype=torch.bfloat16, inplace=True)\n vae = ipex.optimize(vae, dtype=torch.bfloat16, inplace=True)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"textual_inversion\", config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n # Only show the progress bar once on each machine.\n progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)\n progress_bar.set_description(\"Steps\")\n global_step = 0\n\n text_encoder.train()\n text_encoder, optimizer = ipex.optimize(text_encoder, optimizer=optimizer, dtype=torch.bfloat16)\n\n for epoch in range(args.num_train_epochs):\n for step, batch in enumerate(train_dataloader):\n with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16):\n with accelerator.accumulate(text_encoder):\n # Convert images to latent space\n latents = vae.encode(batch[\"pixel_values\"]).latent_dist.sample().detach()\n latents = latents * vae.config.scaling_factor\n\n # Sample noise that we'll add to the latents\n noise = torch.randn(latents.shape).to(latents.device)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device\n ).long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # Get the text embedding for conditioning\n encoder_hidden_states = text_encoder(batch[\"input_ids\"])[0]\n\n # Predict the noise residual\n model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n loss = F.mse_loss(model_pred, target, reduction=\"none\").mean([1, 2, 3]).mean()\n accelerator.backward(loss)\n\n # Zero out the gradients for all token embeddings except the newly added\n # embeddings for the concept, as we only want to optimize the concept embeddings\n if accelerator.num_processes > 1:\n grads = text_encoder.module.get_input_embeddings().weight.grad\n else:\n grads = text_encoder.get_input_embeddings().weight.grad\n # Get the index for tokens that we want to zero the grads for\n index_grads_to_zero = torch.arange(len(tokenizer)) != placeholder_token_id\n grads.data[index_grads_to_zero, :] = grads.data[index_grads_to_zero, :].fill_(0)\n\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n if global_step % args.save_steps == 0:\n save_path = os.path.join(args.output_dir, f\"learned_embeds-steps-{global_step}.bin\")\n save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n accelerator.wait_for_everyone()\n\n # Create the pipeline using using the trained modules and save it.\n if accelerator.is_main_process:\n if args.push_to_hub and args.only_save_embeds:\n logger.warning(\"Enabling full model saving because --push_to_hub=True was specified.\")\n save_full_model = True\n else:\n save_full_model = not args.only_save_embeds\n if save_full_model:\n pipeline = StableDiffusionPipeline(\n text_encoder=accelerator.unwrap_model(text_encoder),\n vae=vae,\n unet=unet,\n tokenizer=tokenizer,\n scheduler=PNDMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\"),\n safety_checker=StableDiffusionSafetyChecker.from_pretrained(\"CompVis/stable-diffusion-safety-checker\"),\n feature_extractor=CLIPImageProcessor.from_pretrained(\"openai/clip-vit-base-patch32\"),\n )\n pipeline.save_pretrained(args.output_dir)\n # Save the newly trained embeddings\n save_path = os.path.join(args.output_dir, \"learned_embeds.bin\")\n save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/research_projects/intel_opts/textual_inversion_dfq/README.md", "content": "# Distillation for quantization on Textual Inversion models to personalize text2image\n\n[Textual inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like stable diffusion on your own images._By using just 3-5 images new concepts can be taught to Stable Diffusion and the model personalized on your own images_\nThe `textual_inversion.py` script shows how to implement the training procedure and adapt it for stable diffusion.\nWe have enabled distillation for quantization in `textual_inversion.py` to do quantization aware training as well as distillation on the model generated by Textual Inversion method.\n\n## Installing the dependencies\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n```bash\npip install -r requirements.txt\n```\n\n## Prepare Datasets\n\nOne picture which is from the huggingface datasets [sd-concepts-library/dicoo2](https://huggingface.co/sd-concepts-library/dicoo2) is needed, and save it to the `./dicoo` directory. The picture is shown below:\n\n\n \n\n\n## Get a FP32 Textual Inversion model\n\nUse the following command to fine-tune the Stable Diffusion model on the above dataset to obtain the FP32 Textual Inversion model.\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport DATA_DIR=\"./dicoo\"\n\naccelerate launch textual_inversion.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_data_dir=$DATA_DIR \\\n --learnable_property=\"object\" \\\n --placeholder_token=\"\" --initializer_token=\"toy\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --max_train_steps=3000 \\\n --learning_rate=5.0e-04 --scale_lr \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --output_dir=\"dicoo_model\"\n```\n\n## Do distillation for quantization\n\nDistillation for quantization is a method that combines [intermediate layer knowledge distillation](https://github.com/intel/neural-compressor/blob/master/docs/source/distillation.md#intermediate-layer-knowledge-distillation) and [quantization aware training](https://github.com/intel/neural-compressor/blob/master/docs/source/quantization.md#quantization-aware-training) in the same training process to improve the performance of the quantized model. Provided a FP32 model, the distillation for quantization approach will take this model itself as the teacher model and transfer the knowledges of the specified layers to the student model, i.e. quantized version of the FP32 model, during the quantization aware training process.\n\nOnce you have the FP32 Textual Inversion model, the following command will take the FP32 Textual Inversion model as input to do distillation for quantization and generate the INT8 Textual Inversion model.\n\n```bash\nexport FP32_MODEL_NAME=\"./dicoo_model\"\nexport DATA_DIR=\"./dicoo\"\n\naccelerate launch textual_inversion.py \\\n --pretrained_model_name_or_path=$FP32_MODEL_NAME \\\n --train_data_dir=$DATA_DIR \\\n --use_ema --learnable_property=\"object\" \\\n --placeholder_token=\"\" --initializer_token=\"toy\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --max_train_steps=300 \\\n --learning_rate=5.0e-04 --max_grad_norm=3 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --output_dir=\"int8_model\" \\\n --do_quantization --do_distillation --verify_loading\n```\n\nAfter the distillation for quantization process, the quantized UNet would be 4 times smaller (3279MB -> 827MB).\n\n## Inference\n\nOnce you have trained a INT8 model with the above command, the inference can be done simply using the `text2images.py` script. Make sure to include the `placeholder_token` in your prompt.\n\n```bash\nexport INT8_MODEL_NAME=\"./int8_model\"\n\npython text2images.py \\\n --pretrained_model_name_or_path=$INT8_MODEL_NAME \\\n --caption \"a lovely in red dress and hat, in the snowly and brightly night, with many brighly buildings.\" \\\n --images_num 4\n```\n\nHere is the comparison of images generated by the FP32 model (left) and INT8 model (right) respectively:\n\n

\n \"FP32\"\n \"INT8\"\n

\n\n" }, { "path": "diffusers/examples/research_projects/intel_opts/textual_inversion_dfq/requirements.txt", "content": "accelerate\ntorchvision\ntransformers>=4.25.0\nftfy\ntensorboard\nmodelcards\nneural-compressor" }, { "path": "diffusers/examples/research_projects/intel_opts/textual_inversion_dfq/text2images.py", "content": "import argparse\nimport math\nimport os\n\nimport torch\nfrom neural_compressor.utils.pytorch import load\nfrom PIL import Image\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import AutoencoderKL, StableDiffusionPipeline, UNet2DConditionModel\n\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"-m\",\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"-c\",\n \"--caption\",\n type=str,\n default=\"robotic cat with wings\",\n help=\"Text used to generate images.\",\n )\n parser.add_argument(\n \"-n\",\n \"--images_num\",\n type=int,\n default=4,\n help=\"How much images to generate.\",\n )\n parser.add_argument(\n \"-s\",\n \"--seed\",\n type=int,\n default=42,\n help=\"Seed for random process.\",\n )\n parser.add_argument(\n \"-ci\",\n \"--cuda_id\",\n type=int,\n default=0,\n help=\"cuda_id.\",\n )\n args = parser.parse_args()\n return args\n\n\ndef image_grid(imgs, rows, cols):\n if not len(imgs) == rows * cols:\n raise ValueError(\"The specified number of rows and columns are not correct.\")\n\n w, h = imgs[0].size\n grid = Image.new(\"RGB\", size=(cols * w, rows * h))\n grid_w, grid_h = grid.size\n\n for i, img in enumerate(imgs):\n grid.paste(img, box=(i % cols * w, i // cols * h))\n return grid\n\n\ndef generate_images(\n pipeline,\n prompt=\"robotic cat with wings\",\n guidance_scale=7.5,\n num_inference_steps=50,\n num_images_per_prompt=1,\n seed=42,\n):\n generator = torch.Generator(pipeline.device).manual_seed(seed)\n images = pipeline(\n prompt,\n guidance_scale=guidance_scale,\n num_inference_steps=num_inference_steps,\n generator=generator,\n num_images_per_prompt=num_images_per_prompt,\n ).images\n _rows = int(math.sqrt(num_images_per_prompt))\n grid = image_grid(images, rows=_rows, cols=num_images_per_prompt // _rows)\n return grid, images\n\n\nargs = parse_args()\n# Load models and create wrapper for stable diffusion\ntokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"tokenizer\")\ntext_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"text_encoder\")\nvae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"vae\")\nunet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"unet\")\n\npipeline = StableDiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer\n)\npipeline.safety_checker = lambda images, clip_input: (images, False)\nif os.path.exists(os.path.join(args.pretrained_model_name_or_path, \"best_model.pt\")):\n unet = load(args.pretrained_model_name_or_path, model=unet)\n unet.eval()\n setattr(pipeline, \"unet\", unet)\nelse:\n unet = unet.to(torch.device(\"cuda\", args.cuda_id))\npipeline = pipeline.to(unet.device)\ngrid, images = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)\ngrid.save(os.path.join(args.pretrained_model_name_or_path, \"{}.png\".format(\"_\".join(args.caption.split()))))\ndirname = os.path.join(args.pretrained_model_name_or_path, \"_\".join(args.caption.split()))\nos.makedirs(dirname, exist_ok=True)\nfor idx, image in enumerate(images):\n image.save(os.path.join(dirname, \"{}.png\".format(idx + 1)))\n" }, { "path": "diffusers/examples/research_projects/intel_opts/textual_inversion_dfq/textual_inversion.py", "content": "import argparse\nimport itertools\nimport math\nimport os\nimport random\nfrom pathlib import Path\nfrom typing import Iterable\n\nimport numpy as np\nimport PIL\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nfrom accelerate import Accelerator\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom huggingface_hub import create_repo, upload_folder\nfrom neural_compressor.utils import logger\nfrom packaging import version\nfrom PIL import Image\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import make_image_grid\n\n\nif version.parse(version.parse(PIL.__version__).base_version) >= version.parse(\"9.1.0\"):\n PIL_INTERPOLATION = {\n \"linear\": PIL.Image.Resampling.BILINEAR,\n \"bilinear\": PIL.Image.Resampling.BILINEAR,\n \"bicubic\": PIL.Image.Resampling.BICUBIC,\n \"lanczos\": PIL.Image.Resampling.LANCZOS,\n \"nearest\": PIL.Image.Resampling.NEAREST,\n }\nelse:\n PIL_INTERPOLATION = {\n \"linear\": PIL.Image.LINEAR,\n \"bilinear\": PIL.Image.BILINEAR,\n \"bicubic\": PIL.Image.BICUBIC,\n \"lanczos\": PIL.Image.LANCZOS,\n \"nearest\": PIL.Image.NEAREST,\n }\n# ------------------------------------------------------------------------------\n\n\ndef save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path):\n logger.info(\"Saving embeddings\")\n learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_id]\n learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()}\n torch.save(learned_embeds_dict, save_path)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Example of distillation for quantization on Textual Inversion.\")\n parser.add_argument(\n \"--save_steps\",\n type=int,\n default=500,\n help=\"Save learned_embeds.bin every X updates steps.\",\n )\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--tokenizer_name\",\n type=str,\n default=None,\n help=\"Pretrained tokenizer name or path if not the same as model_name\",\n )\n parser.add_argument(\n \"--train_data_dir\", type=str, default=None, required=True, help=\"A folder containing the training data.\"\n )\n parser.add_argument(\n \"--placeholder_token\",\n type=str,\n default=None,\n required=True,\n help=\"A token to use as a placeholder for the concept.\",\n )\n parser.add_argument(\n \"--initializer_token\", type=str, default=None, required=True, help=\"A token to use as initializer word.\"\n )\n parser.add_argument(\"--learnable_property\", type=str, default=\"object\", help=\"Choose between 'object' and 'style'\")\n parser.add_argument(\"--repeats\", type=int, default=100, help=\"How many times to repeat the training data.\")\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"text-inversion-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=42, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\", action=\"store_true\", help=\"Whether to center crop images before resizing to resolution\"\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=5000,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=\"no\",\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose\"\n \"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.\"\n \"and an Nvidia Ampere GPU.\"\n ),\n )\n parser.add_argument(\"--use_ema\", action=\"store_true\", help=\"Whether to use EMA model.\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--do_quantization\", action=\"store_true\", help=\"Whether or not to do quantization.\")\n parser.add_argument(\"--do_distillation\", action=\"store_true\", help=\"Whether or not to do distillation.\")\n parser.add_argument(\n \"--verify_loading\", action=\"store_true\", help=\"Whether or not to verify the loading of the quantized model.\"\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.train_data_dir is None:\n raise ValueError(\"You must specify a train data directory.\")\n\n return args\n\n\nimagenet_templates_small = [\n \"a photo of a {}\",\n \"a rendering of a {}\",\n \"a cropped photo of the {}\",\n \"the photo of a {}\",\n \"a photo of a clean {}\",\n \"a photo of a dirty {}\",\n \"a dark photo of the {}\",\n \"a photo of my {}\",\n \"a photo of the cool {}\",\n \"a close-up photo of a {}\",\n \"a bright photo of the {}\",\n \"a cropped photo of a {}\",\n \"a photo of the {}\",\n \"a good photo of the {}\",\n \"a photo of one {}\",\n \"a close-up photo of the {}\",\n \"a rendition of the {}\",\n \"a photo of the clean {}\",\n \"a rendition of a {}\",\n \"a photo of a nice {}\",\n \"a good photo of a {}\",\n \"a photo of the nice {}\",\n \"a photo of the small {}\",\n \"a photo of the weird {}\",\n \"a photo of the large {}\",\n \"a photo of a cool {}\",\n \"a photo of a small {}\",\n]\n\nimagenet_style_templates_small = [\n \"a painting in the style of {}\",\n \"a rendering in the style of {}\",\n \"a cropped painting in the style of {}\",\n \"the painting in the style of {}\",\n \"a clean painting in the style of {}\",\n \"a dirty painting in the style of {}\",\n \"a dark painting in the style of {}\",\n \"a picture in the style of {}\",\n \"a cool painting in the style of {}\",\n \"a close-up painting in the style of {}\",\n \"a bright painting in the style of {}\",\n \"a cropped painting in the style of {}\",\n \"a good painting in the style of {}\",\n \"a close-up painting in the style of {}\",\n \"a rendition in the style of {}\",\n \"a nice painting in the style of {}\",\n \"a small painting in the style of {}\",\n \"a weird painting in the style of {}\",\n \"a large painting in the style of {}\",\n]\n\n\n# Adapted from torch-ema https://github.com/fadel/pytorch_ema/blob/master/torch_ema/ema.py#L14\nclass EMAModel:\n \"\"\"\n Exponential Moving Average of models weights\n \"\"\"\n\n def __init__(self, parameters: Iterable[torch.nn.Parameter], decay=0.9999):\n parameters = list(parameters)\n self.shadow_params = [p.clone().detach() for p in parameters]\n\n self.decay = decay\n self.optimization_step = 0\n\n def get_decay(self, optimization_step):\n \"\"\"\n Compute the decay factor for the exponential moving average.\n \"\"\"\n value = (1 + optimization_step) / (10 + optimization_step)\n return 1 - min(self.decay, value)\n\n @torch.no_grad()\n def step(self, parameters):\n parameters = list(parameters)\n\n self.optimization_step += 1\n self.decay = self.get_decay(self.optimization_step)\n\n for s_param, param in zip(self.shadow_params, parameters):\n if param.requires_grad:\n tmp = self.decay * (s_param - param)\n s_param.sub_(tmp)\n else:\n s_param.copy_(param)\n\n torch.cuda.empty_cache()\n\n def copy_to(self, parameters: Iterable[torch.nn.Parameter]) -> None:\n \"\"\"\n Copy current averaged parameters into given collection of parameters.\n Args:\n parameters: Iterable of `torch.nn.Parameter`; the parameters to be\n updated with the stored moving averages. If `None`, the\n parameters with which this `ExponentialMovingAverage` was\n initialized will be used.\n \"\"\"\n parameters = list(parameters)\n for s_param, param in zip(self.shadow_params, parameters):\n param.data.copy_(s_param.data)\n\n def to(self, device=None, dtype=None) -> None:\n r\"\"\"Move internal buffers of the ExponentialMovingAverage to `device`.\n Args:\n device: like `device` argument to `torch.Tensor.to`\n \"\"\"\n # .to() on the tensors handles None correctly\n self.shadow_params = [\n p.to(device=device, dtype=dtype) if p.is_floating_point() else p.to(device=device)\n for p in self.shadow_params\n ]\n\n\nclass TextualInversionDataset(Dataset):\n def __init__(\n self,\n data_root,\n tokenizer,\n learnable_property=\"object\", # [object, style]\n size=512,\n repeats=100,\n interpolation=\"bicubic\",\n flip_p=0.5,\n set=\"train\",\n placeholder_token=\"*\",\n center_crop=False,\n ):\n self.data_root = data_root\n self.tokenizer = tokenizer\n self.learnable_property = learnable_property\n self.size = size\n self.placeholder_token = placeholder_token\n self.center_crop = center_crop\n self.flip_p = flip_p\n\n self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]\n\n self.num_images = len(self.image_paths)\n self._length = self.num_images\n\n if set == \"train\":\n self._length = self.num_images * repeats\n\n self.interpolation = {\n \"linear\": PIL_INTERPOLATION[\"linear\"],\n \"bilinear\": PIL_INTERPOLATION[\"bilinear\"],\n \"bicubic\": PIL_INTERPOLATION[\"bicubic\"],\n \"lanczos\": PIL_INTERPOLATION[\"lanczos\"],\n }[interpolation]\n\n self.templates = imagenet_style_templates_small if learnable_property == \"style\" else imagenet_templates_small\n self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, i):\n example = {}\n image = Image.open(self.image_paths[i % self.num_images])\n\n if not image.mode == \"RGB\":\n image = image.convert(\"RGB\")\n\n placeholder_string = self.placeholder_token\n text = random.choice(self.templates).format(placeholder_string)\n\n example[\"input_ids\"] = self.tokenizer(\n text,\n padding=\"max_length\",\n truncation=True,\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n ).input_ids[0]\n\n # default to score-sde preprocessing\n img = np.array(image).astype(np.uint8)\n\n if self.center_crop:\n crop = min(img.shape[0], img.shape[1])\n (\n h,\n w,\n ) = (\n img.shape[0],\n img.shape[1],\n )\n img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]\n\n image = Image.fromarray(img)\n image = image.resize((self.size, self.size), resample=self.interpolation)\n\n image = self.flip_transform(image)\n image = np.array(image).astype(np.uint8)\n image = (image / 127.5 - 1.0).astype(np.float32)\n\n example[\"pixel_values\"] = torch.from_numpy(image).permute(2, 0, 1)\n return example\n\n\ndef freeze_params(params):\n for param in params:\n param.requires_grad = False\n\n\ndef generate_images(pipeline, prompt=\"\", guidance_scale=7.5, num_inference_steps=50, num_images_per_prompt=1, seed=42):\n generator = torch.Generator(pipeline.device).manual_seed(seed)\n images = pipeline(\n prompt,\n guidance_scale=guidance_scale,\n num_inference_steps=num_inference_steps,\n generator=generator,\n num_images_per_prompt=num_images_per_prompt,\n ).images\n _rows = int(math.sqrt(num_images_per_prompt))\n grid = make_image_grid(images, rows=_rows, cols=num_images_per_prompt // _rows)\n return grid\n\n\ndef main():\n args = parse_args()\n logging_dir = os.path.join(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=\"tensorboard\",\n project_config=accelerator_project_config,\n )\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizer and add the placeholder token as a additional special token\n if args.tokenizer_name:\n tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)\n elif args.pretrained_model_name_or_path:\n tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"tokenizer\")\n\n # Load models and create wrapper for stable diffusion\n noise_scheduler = DDPMScheduler.from_config(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n text_encoder = CLIPTextModel.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"text_encoder\",\n revision=args.revision,\n )\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"vae\",\n revision=args.revision,\n )\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"unet\",\n revision=args.revision,\n )\n\n train_unet = False\n # Freeze vae and unet\n freeze_params(vae.parameters())\n if not args.do_quantization and not args.do_distillation:\n # Add the placeholder token in tokenizer\n num_added_tokens = tokenizer.add_tokens(args.placeholder_token)\n if num_added_tokens == 0:\n raise ValueError(\n f\"The tokenizer already contains the token {args.placeholder_token}. Please pass a different\"\n \" `placeholder_token` that is not already in the tokenizer.\"\n )\n\n # Convert the initializer_token, placeholder_token to ids\n token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)\n # Check if initializer_token is a single token or a sequence of tokens\n if len(token_ids) > 1:\n raise ValueError(\"The initializer token must be a single token.\")\n\n initializer_token_id = token_ids[0]\n placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)\n # Resize the token embeddings as we are adding new special tokens to the tokenizer\n text_encoder.resize_token_embeddings(len(tokenizer))\n\n # Initialise the newly added placeholder token with the embeddings of the initializer token\n token_embeds = text_encoder.get_input_embeddings().weight.data\n token_embeds[placeholder_token_id] = token_embeds[initializer_token_id]\n\n freeze_params(unet.parameters())\n # Freeze all parameters except for the token embeddings in text encoder\n params_to_freeze = itertools.chain(\n text_encoder.text_model.encoder.parameters(),\n text_encoder.text_model.final_layer_norm.parameters(),\n text_encoder.text_model.embeddings.position_embedding.parameters(),\n )\n freeze_params(params_to_freeze)\n else:\n train_unet = True\n freeze_params(text_encoder.parameters())\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Initialize the optimizer\n optimizer = torch.optim.AdamW(\n # only optimize the unet or embeddings of text_encoder\n unet.parameters() if train_unet else text_encoder.get_input_embeddings().parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n train_dataset = TextualInversionDataset(\n data_root=args.train_data_dir,\n tokenizer=tokenizer,\n size=args.resolution,\n placeholder_token=args.placeholder_token,\n repeats=args.repeats,\n learnable_property=args.learnable_property,\n center_crop=args.center_crop,\n set=\"train\",\n )\n train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.train_batch_size, shuffle=True)\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n )\n\n if not train_unet:\n text_encoder = accelerator.prepare(text_encoder)\n unet.to(accelerator.device)\n unet.eval()\n else:\n unet = accelerator.prepare(unet)\n text_encoder.to(accelerator.device)\n text_encoder.eval()\n optimizer, train_dataloader, lr_scheduler = accelerator.prepare(optimizer, train_dataloader, lr_scheduler)\n\n # Move vae to device\n vae.to(accelerator.device)\n\n # Keep vae in eval model as we don't train these\n vae.eval()\n\n compression_manager = None\n\n def train_func(model):\n if train_unet:\n unet_ = model\n text_encoder_ = text_encoder\n else:\n unet_ = unet\n text_encoder_ = model\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"textual_inversion\", config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n # Only show the progress bar once on each machine.\n progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)\n progress_bar.set_description(\"Steps\")\n global_step = 0\n\n if train_unet and args.use_ema:\n ema_unet = EMAModel(unet_.parameters())\n\n for epoch in range(args.num_train_epochs):\n model.train()\n train_loss = 0.0\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(model):\n # Convert images to latent space\n latents = vae.encode(batch[\"pixel_values\"]).latent_dist.sample().detach()\n latents = latents * 0.18215\n\n # Sample noise that we'll add to the latents\n noise = torch.randn(latents.shape).to(latents.device)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device\n ).long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # Get the text embedding for conditioning\n encoder_hidden_states = text_encoder_(batch[\"input_ids\"])[0]\n\n # Predict the noise residual\n model_pred = unet_(noisy_latents, timesteps, encoder_hidden_states).sample\n\n loss = F.mse_loss(model_pred, noise, reduction=\"none\").mean([1, 2, 3]).mean()\n if train_unet and compression_manager:\n unet_inputs = {\n \"sample\": noisy_latents,\n \"timestep\": timesteps,\n \"encoder_hidden_states\": encoder_hidden_states,\n }\n loss = compression_manager.callbacks.on_after_compute_loss(unet_inputs, model_pred, loss)\n\n # Gather the losses across all processes for logging (if we use distributed training).\n avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n train_loss += avg_loss.item() / args.gradient_accumulation_steps\n\n # Backpropagate\n accelerator.backward(loss)\n\n if train_unet:\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(unet_.parameters(), args.max_grad_norm)\n else:\n # Zero out the gradients for all token embeddings except the newly added\n # embeddings for the concept, as we only want to optimize the concept embeddings\n if accelerator.num_processes > 1:\n grads = text_encoder_.module.get_input_embeddings().weight.grad\n else:\n grads = text_encoder_.get_input_embeddings().weight.grad\n # Get the index for tokens that we want to zero the grads for\n index_grads_to_zero = torch.arange(len(tokenizer)) != placeholder_token_id\n grads.data[index_grads_to_zero, :] = grads.data[index_grads_to_zero, :].fill_(0)\n\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n if train_unet and args.use_ema:\n ema_unet.step(unet_.parameters())\n progress_bar.update(1)\n global_step += 1\n accelerator.log({\"train_loss\": train_loss}, step=global_step)\n train_loss = 0.0\n if not train_unet and global_step % args.save_steps == 0:\n save_path = os.path.join(args.output_dir, f\"learned_embeds-steps-{global_step}.bin\")\n save_progress(text_encoder_, placeholder_token_id, accelerator, args, save_path)\n\n logs = {\"step_loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n accelerator.wait_for_everyone()\n\n if train_unet and args.use_ema:\n ema_unet.copy_to(unet_.parameters())\n\n if not train_unet:\n return text_encoder_\n\n if not train_unet:\n text_encoder = train_func(text_encoder)\n else:\n import copy\n\n model = copy.deepcopy(unet)\n confs = []\n if args.do_quantization:\n from neural_compressor import QuantizationAwareTrainingConfig\n\n q_conf = QuantizationAwareTrainingConfig()\n confs.append(q_conf)\n\n if args.do_distillation:\n teacher_model = copy.deepcopy(model)\n\n def attention_fetcher(x):\n return x.sample\n\n layer_mappings = [\n [\n [\n \"conv_in\",\n ]\n ],\n [\n [\n \"time_embedding\",\n ]\n ],\n [[\"down_blocks.0.attentions.0\", attention_fetcher]],\n [[\"down_blocks.0.attentions.1\", attention_fetcher]],\n [\n [\n \"down_blocks.0.resnets.0\",\n ]\n ],\n [\n [\n \"down_blocks.0.resnets.1\",\n ]\n ],\n [\n [\n \"down_blocks.0.downsamplers.0\",\n ]\n ],\n [[\"down_blocks.1.attentions.0\", attention_fetcher]],\n [[\"down_blocks.1.attentions.1\", attention_fetcher]],\n [\n [\n \"down_blocks.1.resnets.0\",\n ]\n ],\n [\n [\n \"down_blocks.1.resnets.1\",\n ]\n ],\n [\n [\n \"down_blocks.1.downsamplers.0\",\n ]\n ],\n [[\"down_blocks.2.attentions.0\", attention_fetcher]],\n [[\"down_blocks.2.attentions.1\", attention_fetcher]],\n [\n [\n \"down_blocks.2.resnets.0\",\n ]\n ],\n [\n [\n \"down_blocks.2.resnets.1\",\n ]\n ],\n [\n [\n \"down_blocks.2.downsamplers.0\",\n ]\n ],\n [\n [\n \"down_blocks.3.resnets.0\",\n ]\n ],\n [\n [\n \"down_blocks.3.resnets.1\",\n ]\n ],\n [\n [\n \"up_blocks.0.resnets.0\",\n ]\n ],\n [\n [\n \"up_blocks.0.resnets.1\",\n ]\n ],\n [\n [\n \"up_blocks.0.resnets.2\",\n ]\n ],\n [\n [\n \"up_blocks.0.upsamplers.0\",\n ]\n ],\n [[\"up_blocks.1.attentions.0\", attention_fetcher]],\n [[\"up_blocks.1.attentions.1\", attention_fetcher]],\n [[\"up_blocks.1.attentions.2\", attention_fetcher]],\n [\n [\n \"up_blocks.1.resnets.0\",\n ]\n ],\n [\n [\n \"up_blocks.1.resnets.1\",\n ]\n ],\n [\n [\n \"up_blocks.1.resnets.2\",\n ]\n ],\n [\n [\n \"up_blocks.1.upsamplers.0\",\n ]\n ],\n [[\"up_blocks.2.attentions.0\", attention_fetcher]],\n [[\"up_blocks.2.attentions.1\", attention_fetcher]],\n [[\"up_blocks.2.attentions.2\", attention_fetcher]],\n [\n [\n \"up_blocks.2.resnets.0\",\n ]\n ],\n [\n [\n \"up_blocks.2.resnets.1\",\n ]\n ],\n [\n [\n \"up_blocks.2.resnets.2\",\n ]\n ],\n [\n [\n \"up_blocks.2.upsamplers.0\",\n ]\n ],\n [[\"up_blocks.3.attentions.0\", attention_fetcher]],\n [[\"up_blocks.3.attentions.1\", attention_fetcher]],\n [[\"up_blocks.3.attentions.2\", attention_fetcher]],\n [\n [\n \"up_blocks.3.resnets.0\",\n ]\n ],\n [\n [\n \"up_blocks.3.resnets.1\",\n ]\n ],\n [\n [\n \"up_blocks.3.resnets.2\",\n ]\n ],\n [[\"mid_block.attentions.0\", attention_fetcher]],\n [\n [\n \"mid_block.resnets.0\",\n ]\n ],\n [\n [\n \"mid_block.resnets.1\",\n ]\n ],\n [\n [\n \"conv_out\",\n ]\n ],\n ]\n layer_names = [layer_mapping[0][0] for layer_mapping in layer_mappings]\n if not set(layer_names).issubset([n[0] for n in model.named_modules()]):\n raise ValueError(\n \"Provided model is not compatible with the default layer_mappings, \"\n 'please use the model fine-tuned from \"CompVis/stable-diffusion-v1-4\", '\n \"or modify the layer_mappings variable to fit your model.\"\n f\"\\nDefault layer_mappings are as such:\\n{layer_mappings}\"\n )\n from neural_compressor.config import DistillationConfig, IntermediateLayersKnowledgeDistillationLossConfig\n\n distillation_criterion = IntermediateLayersKnowledgeDistillationLossConfig(\n layer_mappings=layer_mappings,\n loss_types=[\"MSE\"] * len(layer_mappings),\n loss_weights=[1.0 / len(layer_mappings)] * len(layer_mappings),\n add_origin_loss=True,\n )\n d_conf = DistillationConfig(teacher_model=teacher_model, criterion=distillation_criterion)\n confs.append(d_conf)\n\n from neural_compressor.training import prepare_compression\n\n compression_manager = prepare_compression(model, confs)\n compression_manager.callbacks.on_train_begin()\n model = compression_manager.model\n train_func(model)\n compression_manager.callbacks.on_train_end()\n\n # Save the resulting model and its corresponding configuration in the given directory\n model.save(args.output_dir)\n\n logger.info(f\"Optimized model saved to: {args.output_dir}.\")\n\n # change to framework model for further use\n model = model.model\n\n # Create the pipeline using using the trained modules and save it.\n templates = imagenet_style_templates_small if args.learnable_property == \"style\" else imagenet_templates_small\n prompt = templates[0].format(args.placeholder_token)\n if accelerator.is_main_process:\n pipeline = StableDiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n text_encoder=accelerator.unwrap_model(text_encoder),\n vae=vae,\n unet=accelerator.unwrap_model(unet),\n tokenizer=tokenizer,\n )\n pipeline.save_pretrained(args.output_dir)\n pipeline = pipeline.to(unet.device)\n baseline_model_images = generate_images(pipeline, prompt=prompt, seed=args.seed)\n baseline_model_images.save(\n os.path.join(args.output_dir, \"{}_baseline_model.png\".format(\"_\".join(prompt.split())))\n )\n\n if not train_unet:\n # Also save the newly trained embeddings\n save_path = os.path.join(args.output_dir, \"learned_embeds.bin\")\n save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)\n else:\n setattr(pipeline, \"unet\", accelerator.unwrap_model(model))\n if args.do_quantization:\n pipeline = pipeline.to(torch.device(\"cpu\"))\n\n optimized_model_images = generate_images(pipeline, prompt=prompt, seed=args.seed)\n optimized_model_images.save(\n os.path.join(args.output_dir, \"{}_optimized_model.png\".format(\"_\".join(prompt.split())))\n )\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n if args.do_quantization and args.verify_loading:\n # Load the model obtained after Intel Neural Compressor quantization\n from neural_compressor.utils.pytorch import load\n\n loaded_model = load(args.output_dir, model=unet)\n loaded_model.eval()\n\n setattr(pipeline, \"unet\", loaded_model)\n if args.do_quantization:\n pipeline = pipeline.to(torch.device(\"cpu\"))\n\n loaded_model_images = generate_images(pipeline, prompt=prompt, seed=args.seed)\n if loaded_model_images != optimized_model_images:\n logger.info(\"The quantized model was not successfully loaded.\")\n else:\n logger.info(\"The quantized model was successfully loaded.\")\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/research_projects/lora/README.md", "content": "# Stable Diffusion text-to-image fine-tuning\nThis extended LoRA training script was authored by [haofanwang](https://github.com/haofanwang).\nThis is an experimental LoRA extension of [this example](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py). We further support add LoRA layers for text encoder.\n\n## Training with LoRA\n\nLow-Rank Adaption of Large Language Models was first introduced by Microsoft in [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) by *Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen*.\n\nIn a nutshell, LoRA allows adapting pretrained models by adding pairs of rank-decomposition matrices to existing weights and **only** training those newly added weights. This has a couple of advantages:\n\n- Previous pretrained weights are kept frozen so that model is not prone to [catastrophic forgetting](https://www.pnas.org/doi/10.1073/pnas.1611835114).\n- Rank-decomposition matrices have significantly fewer parameters than original model, which means that trained LoRA weights are easily portable.\n- LoRA attention layers allow to control to which extent the model is adapted toward new training images via a `scale` parameter.\n\n[cloneofsimo](https://github.com/cloneofsimo) was the first to try out LoRA training for Stable Diffusion in the popular [lora](https://github.com/cloneofsimo/lora) GitHub repository.\n\nWith LoRA, it's possible to fine-tune Stable Diffusion on a custom image-caption pair dataset\non consumer GPUs like Tesla T4, Tesla V100.\n\n### Training\n\nFirst, you need to set up your development environment as is explained in the [installation section](#installing-the-dependencies). Make sure to set the `MODEL_NAME` and `DATASET_NAME` environment variables. Here, we will use [Stable Diffusion v1-4](https://hf.co/CompVis/stable-diffusion-v1-4) and the [Narutos dataset](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions).\n\n**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**\n\n**___Note: It is quite useful to monitor the training progress by regularly generating sample images during training. [Weights and Biases](https://docs.wandb.ai/quickstart) is a nice solution to easily see generating images during training. All you need to do is to run `pip install wandb` before training to automatically log images.___**\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport DATASET_NAME=\"lambdalabs/naruto-blip-captions\"\n```\n\nFor this example we want to directly store the trained LoRA embeddings on the Hub, so\nwe need to be logged in and add the `--push_to_hub` flag.\n\n```bash\nhuggingface-cli login\n```\n\nNow we can start training!\n\n```bash\naccelerate launch --mixed_precision=\"fp16\" train_text_to_image_lora.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --dataset_name=$DATASET_NAME --caption_column=\"text\" \\\n --resolution=512 --random_flip \\\n --train_batch_size=1 \\\n --num_train_epochs=100 --checkpointing_steps=5000 \\\n --learning_rate=1e-04 --lr_scheduler=\"constant\" --lr_warmup_steps=0 \\\n --seed=42 \\\n --output_dir=\"sd-naruto-model-lora\" \\\n --validation_prompt=\"cute dragon creature\" --report_to=\"wandb\"\n --use_peft \\\n --lora_r=4 --lora_alpha=32 \\\n --lora_text_encoder_r=4 --lora_text_encoder_alpha=32\n```\n\nThe above command will also run inference as fine-tuning progresses and log the results to Weights and Biases.\n\n**___Note: When using LoRA we can use a much higher learning rate compared to non-LoRA fine-tuning. Here we use *1e-4* instead of the usual *1e-5*. Also, by using LoRA, it's possible to run `train_text_to_image_lora.py` in consumer GPUs like T4 or V100.___**\n\nThe final LoRA embedding weights have been uploaded to [sayakpaul/sd-model-finetuned-lora-t4](https://huggingface.co/sayakpaul/sd-model-finetuned-lora-t4). **___Note: [The final weights](https://huggingface.co/sayakpaul/sd-model-finetuned-lora-t4/blob/main/pytorch_lora_weights.bin) are only 3 MB in size, which is orders of magnitudes smaller than the original model.___**\n\nYou can check some inference samples that were logged during the course of the fine-tuning process [here](https://wandb.ai/sayakpaul/text2image-fine-tune/runs/q4lc0xsw).\n\n### Inference\n\nOnce you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline` after loading the trained LoRA weights. You\nneed to pass the `output_dir` for loading the LoRA weights which, in this case, is `sd-naruto-model-lora`.\n\n```python\nfrom diffusers import StableDiffusionPipeline\nimport torch\n\nmodel_path = \"sayakpaul/sd-model-finetuned-lora-t4\"\npipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", torch_dtype=torch.float16)\npipe.unet.load_attn_procs(model_path)\npipe.to(\"cuda\")\n\nprompt = \"A naruto with green eyes and red legs.\"\nimage = pipe(prompt, num_inference_steps=30, guidance_scale=7.5).images[0]\nimage.save(\"naruto.png\")\n```" }, { "path": "diffusers/examples/research_projects/lora/requirements.txt", "content": "accelerate>=0.16.0\ntorchvision\ntransformers>=4.25.1\ndatasets\nftfy\ntensorboard\nJinja2\ngit+https://github.com/huggingface/peft.git" }, { "path": "diffusers/examples/research_projects/lora/train_text_to_image_lora.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Fine-tuning script for Stable Diffusion for text2image with support for LoRA.\"\"\"\n\nimport argparse\nimport itertools\nimport json\nimport logging\nimport math\nimport os\nimport random\nfrom pathlib import Path\n\nimport datasets\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nimport diffusers\nfrom diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel\nfrom diffusers.loaders import AttnProcsLayers\nfrom diffusers.models.attention_processor import LoRAAttnProcessor\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import check_min_version, is_wandb_available\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.14.0.dev0\")\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\n\ndef save_model_card(repo_id: str, images=None, base_model=str, dataset_name=str, repo_folder=None):\n img_str = \"\"\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n img_str += f\"![img_{i}](./image_{i}.png)\\n\"\n\n yaml = f\"\"\"\n---\nlicense: creativeml-openrail-m\nbase_model: {base_model}\ntags:\n- stable-diffusion\n- stable-diffusion-diffusers\n- text-to-image\n- diffusers\n- diffusers-training\n- lora\ninference: true\n---\n \"\"\"\n model_card = f\"\"\"\n# LoRA text2image fine-tuning - {repo_id}\nThese are LoRA adaption weights for {base_model}. The weights were fine-tuned on the {dataset_name} dataset. You can find some example images in the following. \\n\n{img_str}\n\"\"\"\n with open(os.path.join(repo_folder, \"README.md\"), \"w\") as f:\n f.write(yaml + model_card)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing an image.\"\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=\"text\",\n help=\"The column of the dataset containing a caption or a list of captions.\",\n )\n parser.add_argument(\n \"--validation_prompt\", type=str, default=None, help=\"A prompt that is sampled during training for inference.\"\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=1,\n help=(\n \"Run fine-tuning validation every X epochs. The validation process consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"sd-model-finetuned-lora\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\"--train_text_encoder\", action=\"store_true\", help=\"Whether to train the text encoder\")\n\n # lora args\n parser.add_argument(\"--use_peft\", action=\"store_true\", help=\"Whether to use peft to support lora\")\n parser.add_argument(\"--lora_r\", type=int, default=4, help=\"Lora rank, only used if use_lora is True\")\n parser.add_argument(\"--lora_alpha\", type=int, default=32, help=\"Lora alpha, only used if lora is True\")\n parser.add_argument(\"--lora_dropout\", type=float, default=0.0, help=\"Lora dropout, only used if use_lora is True\")\n parser.add_argument(\n \"--lora_bias\",\n type=str,\n default=\"none\",\n help=\"Bias type for Lora. Can be 'none', 'all' or 'lora_only', only used if use_lora is True\",\n )\n parser.add_argument(\n \"--lora_text_encoder_r\",\n type=int,\n default=4,\n help=\"Lora rank for text encoder, only used if `use_lora` and `train_text_encoder` are True\",\n )\n parser.add_argument(\n \"--lora_text_encoder_alpha\",\n type=int,\n default=32,\n help=\"Lora alpha for text encoder, only used if `use_lora` and `train_text_encoder` are True\",\n )\n parser.add_argument(\n \"--lora_text_encoder_dropout\",\n type=float,\n default=0.0,\n help=\"Lora dropout for text encoder, only used if `use_lora` and `train_text_encoder` are True\",\n )\n parser.add_argument(\n \"--lora_text_encoder_bias\",\n type=str,\n default=\"none\",\n help=\"Bias type for Lora. Can be 'none', 'all' or 'lora_only', only used if use_lora and `train_text_encoder` are True\",\n )\n\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\n \"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`.\"\n \" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state\"\n \" for more docs\"\n ),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n\n return args\n\n\nDATASET_NAME_MAPPING = {\n \"lambdalabs/naruto-blip-captions\": (\"image\", \"text\"),\n}\n\n\ndef main():\n args = parse_args()\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = os.path.join(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(\n total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir\n )\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n import wandb\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load scheduler, tokenizer and models.\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n tokenizer = CLIPTokenizer.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"tokenizer\", revision=args.revision\n )\n text_encoder = CLIPTextModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision\n )\n vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision)\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision\n )\n\n # For mixed precision training we cast the text_encoder and vae weights to half-precision\n # as these models are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n if args.use_peft:\n from peft import LoraConfig, LoraModel, get_peft_model_state_dict, set_peft_model_state_dict\n\n UNET_TARGET_MODULES = [\"to_q\", \"to_v\", \"query\", \"value\"]\n TEXT_ENCODER_TARGET_MODULES = [\"q_proj\", \"v_proj\"]\n\n config = LoraConfig(\n r=args.lora_r,\n lora_alpha=args.lora_alpha,\n target_modules=UNET_TARGET_MODULES,\n lora_dropout=args.lora_dropout,\n bias=args.lora_bias,\n )\n unet = LoraModel(config, unet)\n\n vae.requires_grad_(False)\n if args.train_text_encoder:\n config = LoraConfig(\n r=args.lora_text_encoder_r,\n lora_alpha=args.lora_text_encoder_alpha,\n target_modules=TEXT_ENCODER_TARGET_MODULES,\n lora_dropout=args.lora_text_encoder_dropout,\n bias=args.lora_text_encoder_bias,\n )\n text_encoder = LoraModel(config, text_encoder)\n else:\n # freeze parameters of models to save more memory\n unet.requires_grad_(False)\n vae.requires_grad_(False)\n\n text_encoder.requires_grad_(False)\n\n # now we will add new LoRA weights to the attention layers\n # It's important to realize here how many attention weights will be added and of which sizes\n # The sizes of the attention layers consist only of two different variables:\n # 1) - the \"hidden_size\", which is increased according to `unet.config.block_out_channels`.\n # 2) - the \"cross attention size\", which is set to `unet.config.cross_attention_dim`.\n\n # Let's first see how many attention processors we will have to set.\n # For Stable Diffusion, it should be equal to:\n # - down blocks (2x attention layers) * (2x transformer layers) * (3x down blocks) = 12\n # - mid blocks (2x attention layers) * (1x transformer layers) * (1x mid blocks) = 2\n # - up blocks (2x attention layers) * (3x transformer layers) * (3x down blocks) = 18\n # => 32 layers\n\n # Set correct lora layers\n lora_attn_procs = {}\n for name in unet.attn_processors.keys():\n cross_attention_dim = None if name.endswith(\"attn1.processor\") else unet.config.cross_attention_dim\n if name.startswith(\"mid_block\"):\n hidden_size = unet.config.block_out_channels[-1]\n elif name.startswith(\"up_blocks\"):\n block_id = int(name[len(\"up_blocks.\")])\n hidden_size = list(reversed(unet.config.block_out_channels))[block_id]\n elif name.startswith(\"down_blocks\"):\n block_id = int(name[len(\"down_blocks.\")])\n hidden_size = unet.config.block_out_channels[block_id]\n\n lora_attn_procs[name] = LoRAAttnProcessor(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim)\n\n unet.set_attn_processor(lora_attn_procs)\n lora_layers = AttnProcsLayers(unet.attn_processors)\n\n # Move unet, vae and text_encoder to device and cast to weight_dtype\n vae.to(accelerator.device, dtype=weight_dtype)\n if not args.train_text_encoder:\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Initialize the optimizer\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`\"\n )\n\n optimizer_cls = bnb.optim.AdamW8bit\n else:\n optimizer_cls = torch.optim.AdamW\n\n if args.use_peft:\n # Optimizer creation\n params_to_optimize = (\n itertools.chain(unet.parameters(), text_encoder.parameters())\n if args.train_text_encoder\n else unet.parameters()\n )\n optimizer = optimizer_cls(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n else:\n optimizer = optimizer_cls(\n lora_layers.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n )\n else:\n data_files = {}\n if args.train_data_dir is not None:\n data_files[\"train\"] = os.path.join(args.train_data_dir, \"**\")\n dataset = load_dataset(\n \"imagefolder\",\n data_files=data_files,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)\n if args.image_column is None:\n image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.caption_column is None:\n caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n else:\n caption_column = args.caption_column\n if caption_column not in column_names:\n raise ValueError(\n f\"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}\"\n )\n\n # Preprocessing the datasets.\n # We need to tokenize input captions and transform the images.\n def tokenize_captions(examples, is_train=True):\n captions = []\n for caption in examples[caption_column]:\n if isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n else:\n raise ValueError(\n f\"Caption column `{caption_column}` should contain either strings or lists of strings.\"\n )\n inputs = tokenizer(\n captions, max_length=tokenizer.model_max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\"\n )\n return inputs.input_ids\n\n # Preprocessing the datasets.\n train_transforms = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),\n transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n examples[\"pixel_values\"] = [train_transforms(image) for image in images]\n examples[\"input_ids\"] = tokenize_captions(examples)\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n def collate_fn(examples):\n pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n input_ids = torch.stack([example[\"input_ids\"] for example in examples])\n return {\"pixel_values\": pixel_values, \"input_ids\": input_ids}\n\n # DataLoaders creation:\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n )\n\n # Prepare everything with our `accelerator`.\n if args.use_peft:\n if args.train_text_encoder:\n unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, text_encoder, optimizer, train_dataloader, lr_scheduler\n )\n else:\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n else:\n lora_layers, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n lora_layers, optimizer, train_dataloader, lr_scheduler\n )\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"text2image-fine-tune\", config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n resume_global_step = global_step * args.gradient_accumulation_steps\n first_epoch = global_step // num_update_steps_per_epoch\n resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)\n\n # Only show the progress bar once on each machine.\n progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)\n progress_bar.set_description(\"Steps\")\n\n for epoch in range(first_epoch, args.num_train_epochs):\n unet.train()\n if args.train_text_encoder:\n text_encoder.train()\n train_loss = 0.0\n for step, batch in enumerate(train_dataloader):\n # Skip steps until we reach the resumed step\n if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:\n if step % args.gradient_accumulation_steps == 0:\n progress_bar.update(1)\n continue\n\n with accelerator.accumulate(unet):\n # Convert images to latent space\n latents = vae.encode(batch[\"pixel_values\"].to(dtype=weight_dtype)).latent_dist.sample()\n latents = latents * vae.config.scaling_factor\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # Get the text embedding for conditioning\n encoder_hidden_states = text_encoder(batch[\"input_ids\"])[0]\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n # Predict the noise residual and compute loss\n model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n\n # Gather the losses across all processes for logging (if we use distributed training).\n avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n train_loss += avg_loss.item() / args.gradient_accumulation_steps\n\n # Backpropagate\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n if args.use_peft:\n params_to_clip = (\n itertools.chain(unet.parameters(), text_encoder.parameters())\n if args.train_text_encoder\n else unet.parameters()\n )\n else:\n params_to_clip = lora_layers.parameters()\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n accelerator.log({\"train_loss\": train_loss}, step=global_step)\n train_loss = 0.0\n\n if global_step % args.checkpointing_steps == 0:\n if accelerator.is_main_process:\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"step_loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompt is not None and epoch % args.validation_epochs == 0:\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n # create pipeline\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n unet=accelerator.unwrap_model(unet),\n text_encoder=accelerator.unwrap_model(text_encoder),\n revision=args.revision,\n torch_dtype=weight_dtype,\n )\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n images = []\n for _ in range(args.num_validation_images):\n images.append(\n pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0]\n )\n\n if accelerator.is_main_process:\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\")\n for i, image in enumerate(images)\n ]\n }\n )\n\n del pipeline\n torch.cuda.empty_cache()\n\n # Save the lora layers\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n if args.use_peft:\n lora_config = {}\n unwarpped_unet = accelerator.unwrap_model(unet)\n state_dict = get_peft_model_state_dict(unwarpped_unet, state_dict=accelerator.get_state_dict(unet))\n lora_config[\"peft_config\"] = unwarpped_unet.get_peft_config_as_dict(inference=True)\n if args.train_text_encoder:\n unwarpped_text_encoder = accelerator.unwrap_model(text_encoder)\n text_encoder_state_dict = get_peft_model_state_dict(\n unwarpped_text_encoder, state_dict=accelerator.get_state_dict(text_encoder)\n )\n text_encoder_state_dict = {f\"text_encoder_{k}\": v for k, v in text_encoder_state_dict.items()}\n state_dict.update(text_encoder_state_dict)\n lora_config[\"text_encoder_peft_config\"] = unwarpped_text_encoder.get_peft_config_as_dict(\n inference=True\n )\n\n accelerator.save(state_dict, os.path.join(args.output_dir, f\"{global_step}_lora.pt\"))\n with open(os.path.join(args.output_dir, f\"{global_step}_lora_config.json\"), \"w\") as f:\n json.dump(lora_config, f)\n else:\n unet = unet.to(torch.float32)\n unet.save_attn_procs(args.output_dir)\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n images=images,\n base_model=args.pretrained_model_name_or_path,\n dataset_name=args.dataset_name,\n repo_folder=args.output_dir,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n # Final inference\n # Load previous pipeline\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path, revision=args.revision, torch_dtype=weight_dtype\n )\n\n if args.use_peft:\n\n def load_and_set_lora_ckpt(pipe, ckpt_dir, global_step, device, dtype):\n with open(os.path.join(args.output_dir, f\"{global_step}_lora_config.json\"), \"r\") as f:\n lora_config = json.load(f)\n print(lora_config)\n\n checkpoint = os.path.join(args.output_dir, f\"{global_step}_lora.pt\")\n lora_checkpoint_sd = torch.load(checkpoint)\n unet_lora_ds = {k: v for k, v in lora_checkpoint_sd.items() if \"text_encoder_\" not in k}\n text_encoder_lora_ds = {\n k.replace(\"text_encoder_\", \"\"): v for k, v in lora_checkpoint_sd.items() if \"text_encoder_\" in k\n }\n\n unet_config = LoraConfig(**lora_config[\"peft_config\"])\n pipe.unet = LoraModel(unet_config, pipe.unet)\n set_peft_model_state_dict(pipe.unet, unet_lora_ds)\n\n if \"text_encoder_peft_config\" in lora_config:\n text_encoder_config = LoraConfig(**lora_config[\"text_encoder_peft_config\"])\n pipe.text_encoder = LoraModel(text_encoder_config, pipe.text_encoder)\n set_peft_model_state_dict(pipe.text_encoder, text_encoder_lora_ds)\n\n if dtype in (torch.float16, torch.bfloat16):\n pipe.unet.half()\n pipe.text_encoder.half()\n\n pipe.to(device)\n return pipe\n\n pipeline = load_and_set_lora_ckpt(pipeline, args.output_dir, global_step, accelerator.device, weight_dtype)\n\n else:\n pipeline = pipeline.to(accelerator.device)\n # load attention processors\n pipeline.unet.load_attn_procs(args.output_dir)\n\n # run inference\n if args.seed is not None:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n else:\n generator = None\n images = []\n for _ in range(args.num_validation_images):\n images.append(pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0])\n\n if accelerator.is_main_process:\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"test\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"test\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\")\n for i, image in enumerate(images)\n ]\n }\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/research_projects/multi_subject_dreambooth/README.md", "content": "# Multi Subject DreamBooth training\n\n[DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text2image models like stable diffusion given just a few(3~5) images of a subject.\nThis `train_multi_subject_dreambooth.py` script shows how to implement the training procedure for one or more subjects and adapt it for stable diffusion. Note that this code is based off of the `examples/dreambooth/train_dreambooth.py` script as of 01/06/2022.\n\nThis script was added by @kopsahlong, and is not actively maintained. However, if you come across anything that could use fixing, feel free to open an issue and tag @kopsahlong.\n\n## Running locally with PyTorch\n### Installing the dependencies\n\nBefore running the script, make sure to install the library's training dependencies:\n\nTo start, execute the following steps in a new virtual environment:\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install -e .\n```\n\nThen cd into the folder `diffusers/examples/research_projects/multi_subject_dreambooth` and run the following:\n```bash\npip install -r requirements.txt\n```\n\nAnd initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\n\nOr for a default accelerate configuration without answering questions about your environment\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell e.g. a notebook\n\n```python\nfrom accelerate.utils import write_basic_config\nwrite_basic_config()\n```\n\n### Multi Subject Training Example\nIn order to have your model learn multiple concepts at once, we simply add in the additional data directories and prompts to our `instance_data_dir` and `instance_prompt` (as well as `class_data_dir` and `class_prompt` if `--with_prior_preservation` is specified) as one comma separated string.\n\nSee an example with 2 subjects below, which learns a model for one dog subject and one human subject:\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\n# Subject 1\nexport INSTANCE_DIR_1=\"path-to-instance-images-concept-1\"\nexport INSTANCE_PROMPT_1=\"a photo of a sks dog\"\nexport CLASS_DIR_1=\"path-to-class-images-dog\"\nexport CLASS_PROMPT_1=\"a photo of a dog\"\n\n# Subject 2\nexport INSTANCE_DIR_2=\"path-to-instance-images-concept-2\"\nexport INSTANCE_PROMPT_2=\"a photo of a t@y person\"\nexport CLASS_DIR_2=\"path-to-class-images-person\"\nexport CLASS_PROMPT_2=\"a photo of a person\"\n\naccelerate launch train_multi_subject_dreambooth.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=\"$INSTANCE_DIR_1,$INSTANCE_DIR_2\" \\\n --output_dir=$OUTPUT_DIR \\\n --train_text_encoder \\\n --instance_prompt=\"$INSTANCE_PROMPT_1,$INSTANCE_PROMPT_2\" \\\n --with_prior_preservation \\\n --prior_loss_weight=1.0 \\\n --class_data_dir=\"$CLASS_DIR_1,$CLASS_DIR_2\" \\\n --class_prompt=\"$CLASS_PROMPT_1,$CLASS_PROMPT_2\"\\\n --num_class_images=50 \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=1 \\\n --learning_rate=1e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --max_train_steps=1500\n```\n\nThis example shows training for 2 subjects, but please note that the model can be trained on any number of new concepts. This can be done by continuing to add in the corresponding directories and prompts to the corresponding comma separated string.\n\nNote also that in this script, `sks` and `t@y` were used as tokens to learn the new subjects ([this thread](https://github.com/XavierXiao/Dreambooth-Stable-Diffusion/issues/71) inspired the use of `t@y` as our second identifier). However, there may be better rare tokens to experiment with, and results also seemed to be good when more intuitive words are used.\n\n**Important**: New parameters are added to the script, making possible to validate the progress of the training by \ngenerating images at specified steps. Taking also into account that a comma separated list in a text field for a prompt\nit's never a good idea (simply because it is very common in prompts to have them as part of a regular text) we \nintroduce the `concept_list` parameter: allowing to specify a json-like file where you can define the different \nconfiguration for each subject that you want to train.\n\nAn example of how to generate the file:\n```python\nimport json\n\n# here we are using parameters for prior-preservation and validation as well. \nconcepts_list = [\n {\n \"instance_prompt\": \"drawing of a t@y meme\",\n \"class_prompt\": \"drawing of a meme\",\n \"instance_data_dir\": \"/some_folder/meme_toy\",\n \"class_data_dir\": \"/data/meme\",\n \"validation_prompt\": \"drawing of a t@y meme about football in Uruguay\",\n \"validation_negative_prompt\": \"black and white\"\n },\n {\n \"instance_prompt\": \"drawing of a sks sir\",\n \"class_prompt\": \"drawing of a sir\",\n \"instance_data_dir\": \"/some_other_folder/sir_sks\",\n \"class_data_dir\": \"/data/sir\",\n \"validation_prompt\": \"drawing of a sks sir with the Uruguayan sun in his chest\",\n \"validation_negative_prompt\": \"an old man\",\n \"validation_guidance_scale\": 20,\n \"validation_number_images\": 3,\n \"validation_inference_steps\": 10\n }\n]\n\nwith open(\"concepts_list.json\", \"w\") as f:\n json.dump(concepts_list, f, indent=4)\n```\nAnd then just point to the file when executing the script:\n\n```bash\n# exports...\naccelerate launch train_multi_subject_dreambooth.py \\\n# more parameters...\n--concepts_list=\"concepts_list.json\"\n```\n\nYou can use the helper from the script to get a better sense of each parameter.\n\n### Inference\n\nOnce you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline`. Make sure to include the `identifier`(e.g. sks in above example) in your prompt.\n\n```python\nfrom diffusers import StableDiffusionPipeline\nimport torch\n\nmodel_id = \"path-to-your-trained-model\"\npipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(\"cuda\")\n\nprompt = \"A photo of a t@y person petting an sks dog\"\nimage = pipe(prompt, num_inference_steps=200, guidance_scale=7.5).images[0]\n\nimage.save(\"person-petting-dog.png\")\n```\n\n### Inference from a training checkpoint\n\nYou can also perform inference from one of the checkpoints saved during the training process, if you used the `--checkpointing_steps` argument. Please, refer to [the documentation](https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint) to see how to do it.\n\n## Additional Dreambooth documentation\nBecause the `train_multi_subject_dreambooth.py` script here was forked from an original version of `train_dreambooth.py` in the `examples/dreambooth` folder, I've included the original applicable training documentation for single subject examples below.\n\nThis should explain how to play with training variables such as prior preservation, fine tuning the text encoder, etc. which is still applicable to our multi subject training code. Note also that the examples below, which are single subject examples, also work with `train_multi_subject_dreambooth.py`, as this script supports 1 (or more) subjects.\n\n### Single subject dog toy example\n\nLet's get our dataset. Download images from [here](https://drive.google.com/drive/folders/1BO_dyz-p65qhBRRMRA4TbZ8qW4rB99JZ) and save them in a directory. This will be our training data.\n\nAnd launch the training using\n\n**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport INSTANCE_DIR=\"path-to-instance-images\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\naccelerate launch train_dreambooth.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --instance_prompt=\"a photo of sks dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=1 \\\n --learning_rate=5e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --max_train_steps=400\n```\n\n### Training with prior-preservation loss\n\nPrior-preservation is used to avoid overfitting and language-drift. Refer to the paper to learn more about it. For prior-preservation we first generate images using the model with a class prompt and then use those during training along with our data.\nAccording to the paper, it's recommended to generate `num_epochs * num_samples` images for prior-preservation. 200-300 works well for most cases. The `num_class_images` flag sets the number of images to generate with the class prompt. You can place existing images in `class_data_dir`, and the training script will generate any additional images so that `num_class_images` are present in `class_data_dir` during training time.\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport INSTANCE_DIR=\"path-to-instance-images\"\nexport CLASS_DIR=\"path-to-class-images\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\naccelerate launch train_dreambooth.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --class_data_dir=$CLASS_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --with_prior_preservation --prior_loss_weight=1.0 \\\n --instance_prompt=\"a photo of sks dog\" \\\n --class_prompt=\"a photo of dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=1 \\\n --learning_rate=5e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --num_class_images=200 \\\n --max_train_steps=800\n```\n\n\n### Training on a 16GB GPU:\n\nWith the help of gradient checkpointing and the 8-bit optimizer from bitsandbytes it's possible to run train dreambooth on a 16GB GPU.\n\nTo install `bitandbytes` please refer to this [readme](https://github.com/TimDettmers/bitsandbytes#requirements--installation).\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport INSTANCE_DIR=\"path-to-instance-images\"\nexport CLASS_DIR=\"path-to-class-images\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\naccelerate launch train_dreambooth.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --class_data_dir=$CLASS_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --with_prior_preservation --prior_loss_weight=1.0 \\\n --instance_prompt=\"a photo of sks dog\" \\\n --class_prompt=\"a photo of dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=2 --gradient_checkpointing \\\n --use_8bit_adam \\\n --learning_rate=5e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --num_class_images=200 \\\n --max_train_steps=800\n```\n\n### Training on a 8 GB GPU:\n\nBy using [DeepSpeed](https://www.deepspeed.ai/) it's possible to offload some\ntensors from VRAM to either CPU or NVME allowing to train with less VRAM.\n\nDeepSpeed needs to be enabled with `accelerate config`. During configuration\nanswer yes to \"Do you want to use DeepSpeed?\". With DeepSpeed stage 2, fp16\nmixed precision and offloading both parameters and optimizer state to cpu it's\npossible to train on under 8 GB VRAM with a drawback of requiring significantly\nmore RAM (about 25 GB). See [documentation](https://huggingface.co/docs/accelerate/usage_guides/deepspeed) for more DeepSpeed configuration options.\n\nChanging the default Adam optimizer to DeepSpeed's special version of Adam\n`deepspeed.ops.adam.DeepSpeedCPUAdam` gives a substantial speedup but enabling\nit requires CUDA toolchain with the same version as pytorch. 8-bit optimizer\ndoes not seem to be compatible with DeepSpeed at the moment.\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport INSTANCE_DIR=\"path-to-instance-images\"\nexport CLASS_DIR=\"path-to-class-images\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\naccelerate launch --mixed_precision=\"fp16\" train_dreambooth.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --class_data_dir=$CLASS_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --with_prior_preservation --prior_loss_weight=1.0 \\\n --instance_prompt=\"a photo of sks dog\" \\\n --class_prompt=\"a photo of dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --sample_batch_size=1 \\\n --gradient_accumulation_steps=1 --gradient_checkpointing \\\n --learning_rate=5e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --num_class_images=200 \\\n --max_train_steps=800\n```\n\n### Fine-tune text encoder with the UNet.\n\nThe script also allows to fine-tune the `text_encoder` along with the `unet`. It's been observed experimentally that fine-tuning `text_encoder` gives much better results especially on faces. \nPass the `--train_text_encoder` argument to the script to enable training `text_encoder`.\n\n___Note: Training text encoder requires more memory, with this option the training won't fit on 16GB GPU. It needs at least 24GB VRAM.___\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport INSTANCE_DIR=\"path-to-instance-images\"\nexport CLASS_DIR=\"path-to-class-images\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\naccelerate launch train_dreambooth.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_text_encoder \\\n --instance_data_dir=$INSTANCE_DIR \\\n --class_data_dir=$CLASS_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --with_prior_preservation --prior_loss_weight=1.0 \\\n --instance_prompt=\"a photo of sks dog\" \\\n --class_prompt=\"a photo of dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --use_8bit_adam \\\n --gradient_checkpointing \\\n --learning_rate=2e-6 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --num_class_images=200 \\\n --max_train_steps=800\n```\n\n### Using DreamBooth for other pipelines than Stable Diffusion\n\nAltdiffusion also support dreambooth now, the runing comman is basically the same as above, all you need to do is replace the `MODEL_NAME` like this:\nOne can now simply change the `pretrained_model_name_or_path` to another architecture such as [`AltDiffusion`](https://huggingface.co/docs/diffusers/api/pipelines/alt_diffusion).\n\n```\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\" --> export MODEL_NAME=\"BAAI/AltDiffusion-m9\"\nor\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\" --> export MODEL_NAME=\"BAAI/AltDiffusion\"\n```\n\n### Training with xformers:\nYou can enable memory efficient attention by [installing xFormers](https://github.com/facebookresearch/xformers#installing-xformers) and padding the `--enable_xformers_memory_efficient_attention` argument to the script. This is not available with the Flax/JAX implementation.\n\nYou can also use Dreambooth to train the specialized in-painting model. See [the script in the research folder for details](https://github.com/huggingface/diffusers/tree/main/examples/research_projects/dreambooth_inpaint)." }, { "path": "diffusers/examples/research_projects/multi_subject_dreambooth/requirements.txt", "content": "accelerate>=0.16.0\ntorchvision\ntransformers>=4.25.1\nftfy\ntensorboard\nJinja2" }, { "path": "diffusers/examples/research_projects/multi_subject_dreambooth/train_multi_subject_dreambooth.py", "content": "import argparse\nimport itertools\nimport json\nimport logging\nimport math\nimport uuid\nimport warnings\nfrom os import environ, listdir, makedirs\nfrom os.path import basename, join\nfrom pathlib import Path\nfrom typing import List\n\nimport datasets\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom huggingface_hub import create_repo, upload_folder\nfrom huggingface_hub.utils import insecure_hashlib\nfrom PIL import Image\nfrom torch import dtype\nfrom torch.nn import Module\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n DiffusionPipeline,\n DPMSolverMultistepScheduler,\n UNet2DConditionModel,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import check_min_version, is_wandb_available\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.13.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef log_validation_images_to_tracker(\n images: List[np.array], label: str, validation_prompt: str, accelerator: Accelerator, epoch: int\n):\n logger.info(f\"Logging images to tracker for validation prompt: {validation_prompt}.\")\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{label}_{epoch}_{i}: {validation_prompt}\")\n for i, image in enumerate(images)\n ]\n }\n )\n\n\n# TODO: Add `prompt_embeds` and `negative_prompt_embeds` parameters to the function when `pre_compute_text_embeddings`\n# argument is implemented.\ndef generate_validation_images(\n text_encoder: Module,\n tokenizer: Module,\n unet: Module,\n vae: Module,\n arguments: argparse.Namespace,\n accelerator: Accelerator,\n weight_dtype: dtype,\n):\n logger.info(\"Running validation images.\")\n\n pipeline_args = {}\n\n if text_encoder is not None:\n pipeline_args[\"text_encoder\"] = accelerator.unwrap_model(text_encoder)\n\n if vae is not None:\n pipeline_args[\"vae\"] = vae\n\n # create pipeline (note: unet and vae are loaded again in float32)\n pipeline = DiffusionPipeline.from_pretrained(\n arguments.pretrained_model_name_or_path,\n tokenizer=tokenizer,\n unet=accelerator.unwrap_model(unet),\n revision=arguments.revision,\n torch_dtype=weight_dtype,\n **pipeline_args,\n )\n\n # We train on the simplified learning objective. If we were previously predicting a variance, we need the\n # scheduler to ignore it\n scheduler_args = {}\n\n if \"variance_type\" in pipeline.scheduler.config:\n variance_type = pipeline.scheduler.config.variance_type\n\n if variance_type in [\"learned\", \"learned_range\"]:\n variance_type = \"fixed_small\"\n\n scheduler_args[\"variance_type\"] = variance_type\n\n pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, **scheduler_args)\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n generator = (\n None if arguments.seed is None else torch.Generator(device=accelerator.device).manual_seed(arguments.seed)\n )\n\n images_sets = []\n for vp, nvi, vnp, vis, vgs in zip(\n arguments.validation_prompt,\n arguments.validation_number_images,\n arguments.validation_negative_prompt,\n arguments.validation_inference_steps,\n arguments.validation_guidance_scale,\n ):\n images = []\n if vp is not None:\n logger.info(\n f\"Generating {nvi} images with prompt: '{vp}', negative prompt: '{vnp}', inference steps: {vis}, \"\n f\"guidance scale: {vgs}.\"\n )\n\n pipeline_args = {\"prompt\": vp, \"negative_prompt\": vnp, \"num_inference_steps\": vis, \"guidance_scale\": vgs}\n\n # run inference\n # TODO: it would be good to measure whether it's faster to run inference on all images at once, one at a\n # time or in small batches\n for _ in range(nvi):\n with torch.autocast(\"cuda\"):\n image = pipeline(**pipeline_args, num_images_per_prompt=1, generator=generator).images[0]\n images.append(image)\n\n images_sets.append(images)\n\n del pipeline\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\n return images_sets\n\n\ndef import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path,\n subfolder=\"text_encoder\",\n revision=revision,\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"RobertaSeriesModelWithTransformation\":\n from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation\n\n return RobertaSeriesModelWithTransformation\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--tokenizer_name\",\n type=str,\n default=None,\n help=\"Pretrained tokenizer name or path if not the same as model_name\",\n )\n parser.add_argument(\n \"--instance_data_dir\",\n type=str,\n default=None,\n required=False,\n help=\"A folder containing the training data of instance images.\",\n )\n parser.add_argument(\n \"--class_data_dir\",\n type=str,\n default=None,\n required=False,\n help=\"A folder containing the training data of class images.\",\n )\n parser.add_argument(\n \"--instance_prompt\",\n type=str,\n default=None,\n required=False,\n help=\"The prompt with identifier specifying the instance\",\n )\n parser.add_argument(\n \"--class_prompt\",\n type=str,\n default=None,\n help=\"The prompt to specify images in the same class as provided instance images.\",\n )\n parser.add_argument(\n \"--with_prior_preservation\",\n default=False,\n action=\"store_true\",\n help=\"Flag to add prior preservation loss.\",\n )\n parser.add_argument(\"--prior_loss_weight\", type=float, default=1.0, help=\"The weight of prior preservation loss.\")\n parser.add_argument(\n \"--num_class_images\",\n type=int,\n default=100,\n help=(\n \"Minimal class images for prior preservation loss. If there are not enough images already present in\"\n \" class_data_dir, additional images will be sampled with class_prompt.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"text-inversion-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\"--train_text_encoder\", action=\"store_true\", help=\"Whether to train the text encoder\")\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\n \"--sample_batch_size\", type=int, default=4, help=\"Batch size (per device) for sampling images.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final\"\n \" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\n \"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`.\"\n \" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state\"\n \" for more docs\"\n ),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=5e-6,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\"--lr_power\", type=float, default=1.0, help=\"Power factor of the polynomial scheduler.\")\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=None,\n help=(\n \"Run validation every X steps. Validation consists of running the prompt(s) `validation_prompt` \"\n \"multiple times (`validation_number_images`) and logging the images.\"\n ),\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n help=\"A prompt that is used during validation to verify that the model is learning. You can use commas to \"\n \"define multiple negative prompts. This parameter can be defined also within the file given by \"\n \"`concepts_list` parameter in the respective subject.\",\n )\n parser.add_argument(\n \"--validation_number_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with the validation parameters given. This \"\n \"can be defined within the file given by `concepts_list` parameter in the respective subject.\",\n )\n parser.add_argument(\n \"--validation_negative_prompt\",\n type=str,\n default=None,\n help=\"A negative prompt that is used during validation to verify that the model is learning. You can use commas\"\n \" to define multiple negative prompts, each one corresponding to a validation prompt. This parameter can \"\n \"be defined also within the file given by `concepts_list` parameter in the respective subject.\",\n )\n parser.add_argument(\n \"--validation_inference_steps\",\n type=int,\n default=25,\n help=\"Number of inference steps (denoising steps) to run during validation. This can be defined within the \"\n \"file given by `concepts_list` parameter in the respective subject.\",\n )\n parser.add_argument(\n \"--validation_guidance_scale\",\n type=float,\n default=7.5,\n help=\"To control how much the image generation process follows the text prompt. This can be defined within the \"\n \"file given by `concepts_list` parameter in the respective subject.\",\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--prior_generation_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp32\", \"fp16\", \"bf16\"],\n help=(\n \"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--set_grads_to_none\",\n action=\"store_true\",\n help=(\n \"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain\"\n \" behaviors, so disable this argument if it causes any problems. More info:\"\n \" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html\"\n ),\n )\n parser.add_argument(\n \"--concepts_list\",\n type=str,\n default=None,\n help=\"Path to json file containing a list of multiple concepts, will overwrite parameters like instance_prompt,\"\n \" class_prompt, etc.\",\n )\n\n if input_args:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n if not args.concepts_list and (not args.instance_data_dir or not args.instance_prompt):\n raise ValueError(\n \"You must specify either instance parameters (data directory, prompt, etc.) or use \"\n \"the `concept_list` parameter and specify them within the file.\"\n )\n\n if args.concepts_list:\n if args.instance_prompt:\n raise ValueError(\"If you are using `concepts_list` parameter, define the instance prompt within the file.\")\n if args.instance_data_dir:\n raise ValueError(\n \"If you are using `concepts_list` parameter, define the instance data directory within the file.\"\n )\n if args.validation_steps and (args.validation_prompt or args.validation_negative_prompt):\n raise ValueError(\n \"If you are using `concepts_list` parameter, define validation parameters for \"\n \"each subject within the file:\\n - `validation_prompt`.\"\n \"\\n - `validation_negative_prompt`.\\n - `validation_guidance_scale`.\"\n \"\\n - `validation_number_images`.\\n - `validation_prompt`.\"\n \"\\n - `validation_inference_steps`.\\nThe `validation_steps` parameter is the only one \"\n \"that needs to be defined outside the file.\"\n )\n\n env_local_rank = int(environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.with_prior_preservation:\n if not args.concepts_list:\n if not args.class_data_dir:\n raise ValueError(\"You must specify a data directory for class images.\")\n if not args.class_prompt:\n raise ValueError(\"You must specify prompt for class images.\")\n else:\n if args.class_data_dir:\n raise ValueError(\n \"If you are using `concepts_list` parameter, define the class data directory within the file.\"\n )\n if args.class_prompt:\n raise ValueError(\n \"If you are using `concepts_list` parameter, define the class prompt within the file.\"\n )\n else:\n # logger is not available yet\n if not args.class_data_dir:\n warnings.warn(\n \"Ignoring `class_data_dir` parameter, you need to use it together with `with_prior_preservation`.\"\n )\n if not args.class_prompt:\n warnings.warn(\n \"Ignoring `class_prompt` parameter, you need to use it together with `with_prior_preservation`.\"\n )\n\n return args\n\n\nclass DreamBoothDataset(Dataset):\n \"\"\"\n A dataset to prepare the instance and class images with the prompts for fine-tuning the model.\n It pre-processes the images and then tokenizes prompts.\n \"\"\"\n\n def __init__(\n self,\n instance_data_root,\n instance_prompt,\n tokenizer,\n class_data_root=None,\n class_prompt=None,\n size=512,\n center_crop=False,\n ):\n self.size = size\n self.center_crop = center_crop\n self.tokenizer = tokenizer\n\n self.instance_data_root = []\n self.instance_images_path = []\n self.num_instance_images = []\n self.instance_prompt = []\n self.class_data_root = [] if class_data_root is not None else None\n self.class_images_path = []\n self.num_class_images = []\n self.class_prompt = []\n self._length = 0\n\n for i in range(len(instance_data_root)):\n self.instance_data_root.append(Path(instance_data_root[i]))\n if not self.instance_data_root[i].exists():\n raise ValueError(\"Instance images root doesn't exists.\")\n\n self.instance_images_path.append(list(Path(instance_data_root[i]).iterdir()))\n self.num_instance_images.append(len(self.instance_images_path[i]))\n self.instance_prompt.append(instance_prompt[i])\n self._length += self.num_instance_images[i]\n\n if class_data_root is not None:\n self.class_data_root.append(Path(class_data_root[i]))\n self.class_data_root[i].mkdir(parents=True, exist_ok=True)\n self.class_images_path.append(list(self.class_data_root[i].iterdir()))\n self.num_class_images.append(len(self.class_images_path))\n if self.num_class_images[i] > self.num_instance_images[i]:\n self._length -= self.num_instance_images[i]\n self._length += self.num_class_images[i]\n self.class_prompt.append(class_prompt[i])\n\n self.image_transforms = transforms.Compose(\n [\n transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, index):\n example = {}\n for i in range(len(self.instance_images_path)):\n instance_image = Image.open(self.instance_images_path[i][index % self.num_instance_images[i]])\n if not instance_image.mode == \"RGB\":\n instance_image = instance_image.convert(\"RGB\")\n example[f\"instance_images_{i}\"] = self.image_transforms(instance_image)\n example[f\"instance_prompt_ids_{i}\"] = self.tokenizer(\n self.instance_prompt[i],\n truncation=True,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n ).input_ids\n\n if self.class_data_root:\n for i in range(len(self.class_data_root)):\n class_image = Image.open(self.class_images_path[i][index % self.num_class_images[i]])\n if not class_image.mode == \"RGB\":\n class_image = class_image.convert(\"RGB\")\n example[f\"class_images_{i}\"] = self.image_transforms(class_image)\n example[f\"class_prompt_ids_{i}\"] = self.tokenizer(\n self.class_prompt[i],\n truncation=True,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n ).input_ids\n\n return example\n\n\ndef collate_fn(num_instances, examples, with_prior_preservation=False):\n input_ids = []\n pixel_values = []\n\n for i in range(num_instances):\n input_ids += [example[f\"instance_prompt_ids_{i}\"] for example in examples]\n pixel_values += [example[f\"instance_images_{i}\"] for example in examples]\n\n # Concat class and instance examples for prior preservation.\n # We do this to avoid doing two forward passes.\n if with_prior_preservation:\n for i in range(num_instances):\n input_ids += [example[f\"class_prompt_ids_{i}\"] for example in examples]\n pixel_values += [example[f\"class_images_{i}\"] for example in examples]\n\n pixel_values = torch.stack(pixel_values)\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n\n input_ids = torch.cat(input_ids, dim=0)\n\n batch = {\n \"input_ids\": input_ids,\n \"pixel_values\": pixel_values,\n }\n return batch\n\n\nclass PromptDataset(Dataset):\n \"\"\"A simple dataset to prepare the prompts to generate class images on multiple GPUs.\"\"\"\n\n def __init__(self, prompt, num_samples):\n self.prompt = prompt\n self.num_samples = num_samples\n\n def __len__(self):\n return self.num_samples\n\n def __getitem__(self, index):\n example = {}\n example[\"prompt\"] = self.prompt\n example[\"index\"] = index\n return example\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n accelerator_project_config = ProjectConfiguration(\n total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir\n )\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n\n # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate\n # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.\n # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.\n if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:\n raise ValueError(\n \"Gradient accumulation is not supported when training the text encoder in distributed training. \"\n \"Please set gradient_accumulation_steps to 1. This feature will be supported in the future.\"\n )\n\n instance_data_dir = []\n instance_prompt = []\n class_data_dir = [] if args.with_prior_preservation else None\n class_prompt = [] if args.with_prior_preservation else None\n if args.concepts_list:\n with open(args.concepts_list, \"r\") as f:\n concepts_list = json.load(f)\n\n if args.validation_steps:\n args.validation_prompt = []\n args.validation_number_images = []\n args.validation_negative_prompt = []\n args.validation_inference_steps = []\n args.validation_guidance_scale = []\n\n for concept in concepts_list:\n instance_data_dir.append(concept[\"instance_data_dir\"])\n instance_prompt.append(concept[\"instance_prompt\"])\n\n if args.with_prior_preservation:\n try:\n class_data_dir.append(concept[\"class_data_dir\"])\n class_prompt.append(concept[\"class_prompt\"])\n except KeyError:\n raise KeyError(\n \"`class_data_dir` or `class_prompt` not found in concepts_list while using \"\n \"`with_prior_preservation`.\"\n )\n else:\n if \"class_data_dir\" in concept:\n warnings.warn(\n \"Ignoring `class_data_dir` key, to use it you need to enable `with_prior_preservation`.\"\n )\n if \"class_prompt\" in concept:\n warnings.warn(\n \"Ignoring `class_prompt` key, to use it you need to enable `with_prior_preservation`.\"\n )\n\n if args.validation_steps:\n args.validation_prompt.append(concept.get(\"validation_prompt\", None))\n args.validation_number_images.append(concept.get(\"validation_number_images\", 4))\n args.validation_negative_prompt.append(concept.get(\"validation_negative_prompt\", None))\n args.validation_inference_steps.append(concept.get(\"validation_inference_steps\", 25))\n args.validation_guidance_scale.append(concept.get(\"validation_guidance_scale\", 7.5))\n else:\n # Parse instance and class inputs, and double check that lengths match\n instance_data_dir = args.instance_data_dir.split(\",\")\n instance_prompt = args.instance_prompt.split(\",\")\n assert all(\n x == len(instance_data_dir) for x in [len(instance_data_dir), len(instance_prompt)]\n ), \"Instance data dir and prompt inputs are not of the same length.\"\n\n if args.with_prior_preservation:\n class_data_dir = args.class_data_dir.split(\",\")\n class_prompt = args.class_prompt.split(\",\")\n assert all(\n x == len(instance_data_dir)\n for x in [len(instance_data_dir), len(instance_prompt), len(class_data_dir), len(class_prompt)]\n ), \"Instance & class data dir or prompt inputs are not of the same length.\"\n\n if args.validation_steps:\n validation_prompts = args.validation_prompt.split(\",\")\n num_of_validation_prompts = len(validation_prompts)\n args.validation_prompt = validation_prompts\n args.validation_number_images = [args.validation_number_images] * num_of_validation_prompts\n\n negative_validation_prompts = [None] * num_of_validation_prompts\n if args.validation_negative_prompt:\n negative_validation_prompts = args.validation_negative_prompt.split(\",\")\n while len(negative_validation_prompts) < num_of_validation_prompts:\n negative_validation_prompts.append(None)\n args.validation_negative_prompt = negative_validation_prompts\n\n assert num_of_validation_prompts == len(\n negative_validation_prompts\n ), \"The length of negative prompts for validation is greater than the number of validation prompts.\"\n args.validation_inference_steps = [args.validation_inference_steps] * num_of_validation_prompts\n args.validation_guidance_scale = [args.validation_guidance_scale] * num_of_validation_prompts\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Generate class images if prior preservation is enabled.\n if args.with_prior_preservation:\n for i in range(len(class_data_dir)):\n class_images_dir = Path(class_data_dir[i])\n if not class_images_dir.exists():\n class_images_dir.mkdir(parents=True)\n cur_class_images = len(list(class_images_dir.iterdir()))\n\n if cur_class_images < args.num_class_images:\n torch_dtype = torch.float16 if accelerator.device.type == \"cuda\" else torch.float32\n if args.prior_generation_precision == \"fp32\":\n torch_dtype = torch.float32\n elif args.prior_generation_precision == \"fp16\":\n torch_dtype = torch.float16\n elif args.prior_generation_precision == \"bf16\":\n torch_dtype = torch.bfloat16\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n torch_dtype=torch_dtype,\n safety_checker=None,\n revision=args.revision,\n )\n pipeline.set_progress_bar_config(disable=True)\n\n num_new_images = args.num_class_images - cur_class_images\n logger.info(f\"Number of class images to sample: {num_new_images}.\")\n\n sample_dataset = PromptDataset(class_prompt[i], num_new_images)\n sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)\n\n sample_dataloader = accelerator.prepare(sample_dataloader)\n pipeline.to(accelerator.device)\n\n for example in tqdm(\n sample_dataloader, desc=\"Generating class images\", disable=not accelerator.is_local_main_process\n ):\n images = pipeline(example[\"prompt\"]).images\n\n for ii, image in enumerate(images):\n hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()\n image_filename = (\n class_images_dir / f\"{example['index'][ii] + cur_class_images}-{hash_image}.jpg\"\n )\n image.save(image_filename)\n\n # Clean up the memory deleting one-time-use variables.\n del pipeline\n del sample_dataloader\n del sample_dataset\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizer\n tokenizer = None\n if args.tokenizer_name:\n tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)\n elif args.pretrained_model_name_or_path:\n tokenizer = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n\n # import correct text encoder class\n text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n text_encoder = text_encoder_cls.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision\n )\n vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision)\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision\n )\n\n vae.requires_grad_(False)\n if not args.train_text_encoder:\n text_encoder.requires_grad_(False)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n if args.train_text_encoder:\n text_encoder.gradient_checkpointing_enable()\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # Optimizer creation\n params_to_optimize = (\n itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters()\n )\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Dataset and DataLoaders creation:\n train_dataset = DreamBoothDataset(\n instance_data_root=instance_data_dir,\n instance_prompt=instance_prompt,\n class_data_root=class_data_dir,\n class_prompt=class_prompt,\n tokenizer=tokenizer,\n size=args.resolution,\n center_crop=args.center_crop,\n )\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n batch_size=args.train_batch_size,\n shuffle=True,\n collate_fn=lambda examples: collate_fn(len(instance_data_dir), examples, args.with_prior_preservation),\n num_workers=1,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_cycles=args.lr_num_cycles,\n power=args.lr_power,\n )\n\n # Prepare everything with our `accelerator`.\n if args.train_text_encoder:\n unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, text_encoder, optimizer, train_dataloader, lr_scheduler\n )\n else:\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n # For mixed precision training we cast the text_encoder and vae weights to half-precision\n # as these models are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move vae and text_encoder to device and cast to weight_dtype\n vae.to(accelerator.device, dtype=weight_dtype)\n if not args.train_text_encoder:\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initialize automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"dreambooth\", config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num batches each epoch = {len(train_dataloader)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = basename(args.resume_from_checkpoint)\n else:\n # Get the mos recent checkpoint\n dirs = listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n resume_global_step = global_step * args.gradient_accumulation_steps\n first_epoch = global_step // num_update_steps_per_epoch\n resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)\n\n # Only show the progress bar once on each machine.\n progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)\n progress_bar.set_description(\"Steps\")\n\n for epoch in range(first_epoch, args.num_train_epochs):\n unet.train()\n if args.train_text_encoder:\n text_encoder.train()\n for step, batch in enumerate(train_dataloader):\n # Skip steps until we reach the resumed step\n if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:\n if step % args.gradient_accumulation_steps == 0:\n progress_bar.update(1)\n continue\n\n with accelerator.accumulate(unet):\n # Convert images to latent space\n latents = vae.encode(batch[\"pixel_values\"].to(dtype=weight_dtype)).latent_dist.sample()\n latents = latents * vae.config.scaling_factor\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n time_steps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device\n )\n time_steps = time_steps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, time_steps)\n\n # Get the text embedding for conditioning\n encoder_hidden_states = text_encoder(batch[\"input_ids\"])[0]\n\n # Predict the noise residual\n model_pred = unet(noisy_latents, time_steps, encoder_hidden_states).sample\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, time_steps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n if args.with_prior_preservation:\n # Chunk the noise and model_pred into two parts and compute the loss on each part separately.\n model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)\n target, target_prior = torch.chunk(target, 2, dim=0)\n\n # Compute instance loss\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n\n # Compute prior loss\n prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction=\"mean\")\n\n # Add the prior loss to the instance loss.\n loss = loss + args.prior_loss_weight * prior_loss\n else:\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n params_to_clip = (\n itertools.chain(unet.parameters(), text_encoder.parameters())\n if args.train_text_encoder\n else unet.parameters()\n )\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad(set_to_none=args.set_grads_to_none)\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n save_path = join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n if (\n args.validation_steps\n and any(args.validation_prompt)\n and global_step % args.validation_steps == 0\n ):\n images_set = generate_validation_images(\n text_encoder, tokenizer, unet, vae, args, accelerator, weight_dtype\n )\n for images, validation_prompt in zip(images_set, args.validation_prompt):\n if len(images) > 0:\n label = str(uuid.uuid1())[:8] # generate an id for different set of images\n log_validation_images_to_tracker(\n images, label, validation_prompt, accelerator, global_step\n )\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n # Create the pipeline using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n unet=accelerator.unwrap_model(unet),\n text_encoder=accelerator.unwrap_model(text_encoder),\n revision=args.revision,\n )\n pipeline.save_pretrained(args.output_dir)\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/research_projects/multi_subject_dreambooth_inpainting/README.md", "content": "# Multi Subject Dreambooth for Inpainting Models\n\nPlease note that this project is not actively maintained. However, you can open an issue and tag @gzguevara.\n\n[DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text2image models like stable diffusion given just a few(3~5) images of a subject. This project consists of **two parts**. Training Stable Diffusion for inpainting requieres prompt-image-mask pairs. The Unet of inpainiting models have 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself).\n\n**The first part**, the `multi_inpaint_dataset.ipynb` notebook, demonstrates how make a 🤗 dataset of prompt-image-mask pairs. You can, however, skip the first part and move straight to the second part with the example datasets in this project. ([cat toy dataset masked](https://huggingface.co/datasets/gzguevara/cat_toy_masked), [mr. potato head dataset masked](https://huggingface.co/datasets/gzguevara/mr_potato_head_masked))\n\n**The second part**, the `train_multi_subject_inpainting.py` training script, demonstrates how to implement a training procedure for one or more subjects and adapt it for stable diffusion for inpainting.\n\n## 1. Data Collection: Make Prompt-Image-Mask Pairs\n\n Earlier training scripts have provided approaches like random masking for the training images. This project provides a notebook for more precise mask setting.\n\nThe notebook can be found here: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1JNEASI_B7pLW1srxhgln6nM0HoGAQT32?usp=sharing)\n\nThe `multi_inpaint_dataset.ipynb` notebook, takes training & validation images, on which the user draws masks and provides prompts to make a prompt-image-mask pairs. This ensures that during training, the loss is computed on the area masking the object of interest, rather than on random areas. Moreover, the `multi_inpaint_dataset.ipynb` notebook allows you to build a validation dataset with corresponding masks for monitoring the training process. Example below:\n\n![train_val_pairs](https://drive.google.com/uc?id=1PzwH8E3icl_ubVmA19G0HZGLImFX3x5I)\n\nYou can build multiple datasets for every subject and upload them to the 🤗 hub. Later, when launching the training script you can indicate the paths of the datasets, on which you would like to finetune Stable Diffusion for inpaining.\n\n## 2. Train Multi Subject Dreambooth for Inpainting\n\n### 2.1. Setting The Training Configuration\n\nBefore launching the training script, make sure to select the inpainting the target model, the output directory and the 🤗 datasets.\n\n```bash\nexport MODEL_NAME=\"runwayml/stable-diffusion-inpainting\"\nexport OUTPUT_DIR=\"path-to-save-model\"\n\nexport DATASET_1=\"gzguevara/mr_potato_head_masked\"\nexport DATASET_2=\"gzguevara/cat_toy_masked\"\n... # Further paths to 🤗 datasets\n```\n\n### 2.2. Launching The Training Script\n\n```bash\naccelerate launch train_multi_subject_dreambooth_inpaint.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir $DATASET_1 $DATASET_2 \\\n --output_dir=$OUTPUT_DIR \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=2 \\\n --learning_rate=3e-6 \\\n --max_train_steps=500 \\\n --report_to_wandb\n```\n\n### 2.3. Fine-tune text encoder with the UNet.\n\nThe script also allows to fine-tune the `text_encoder` along with the `unet`. It's been observed experimentally that fine-tuning `text_encoder` gives much better results especially on faces.\nPass the `--train_text_encoder` argument to the script to enable training `text_encoder`.\n\n___Note: Training text encoder requires more memory, with this option the training won't fit on 16GB GPU. It needs at least 24GB VRAM.___\n\n```bash\naccelerate launch train_multi_subject_dreambooth_inpaint.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir $DATASET_1 $DATASET_2 \\\n --output_dir=$OUTPUT_DIR \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=2 \\\n --learning_rate=2e-6 \\\n --max_train_steps=500 \\\n --report_to_wandb \\\n --train_text_encoder\n```\n\n## 3. Results\n\nA [![Weights & Biases](https://img.shields.io/badge/Weights%20&%20Biases-Report-blue)](https://wandb.ai/gzguevara/uncategorized/reports/Multi-Subject-Dreambooth-for-Inpainting--Vmlldzo2MzY5NDQ4?accessToken=y0nya2d7baguhbryxaikbfr1203amvn1jsmyl07vk122mrs7tnph037u1nqgse8t) is provided showing the training progress by every 50 steps. Note, the reported weights & baises run was performed on a A100 GPU with the following stetting:\n\n```bash\naccelerate launch train_multi_subject_dreambooth_inpaint.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir $DATASET_1 $DATASET_2 \\\n --output_dir=$OUTPUT_DIR \\\n --resolution=512 \\\n --train_batch_size=10 \\\n --gradient_accumulation_steps=1 \\\n --learning_rate=1e-6 \\\n --max_train_steps=500 \\\n --report_to_wandb \\\n --train_text_encoder\n```\nHere you can see the target objects on my desk and next to my plant:\n\n![Results](https://drive.google.com/uc?id=1kQisOiiF5cj4rOYjdq8SCZenNsUP2aK0)\n" }, { "path": "diffusers/examples/research_projects/multi_subject_dreambooth_inpainting/requirements.txt", "content": "accelerate>=0.16.0\ntorchvision\ntransformers>=4.25.1\ndatasets>=2.16.0\nwandb>=0.16.1\nftfy\ntensorboard\nJinja2" }, { "path": "diffusers/examples/research_projects/multi_subject_dreambooth_inpainting/train_multi_subject_dreambooth_inpainting.py", "content": "import argparse\nimport copy\nimport itertools\nimport logging\nimport math\nimport os\nimport random\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import concatenate_datasets, load_dataset\nfrom PIL import Image\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n StableDiffusionInpaintPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import check_min_version, is_wandb_available\n\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.13.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\"--instance_data_dir\", nargs=\"+\", help=\"Instance data directories\")\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"text-inversion-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--train_text_encoder\", default=False, action=\"store_true\", help=\"Whether to train the text encoder\"\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\n \"--sample_batch_size\", type=int, default=4, help=\"Batch size (per device) for sampling images.\"\n )\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=5e-6,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=\"no\",\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose\"\n \"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.\"\n \"and an Nvidia Ampere GPU.\"\n ),\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=1000,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final\"\n \" checkpoints in case they are better than the last checkpoint and are suitable for resuming training\"\n \" using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpointing_from\",\n type=int,\n default=1000,\n help=(\"Start to checkpoint from step\"),\n )\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=50,\n help=(\n \"Run validation every X steps. Validation consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`\"\n \" and logging the images.\"\n ),\n )\n parser.add_argument(\n \"--validation_from\",\n type=int,\n default=0,\n help=(\"Start to validate from step\"),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\n \"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`.\"\n \" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state\"\n \" for more docs\"\n ),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--validation_project_name\",\n type=str,\n default=None,\n help=\"The w&b name.\",\n )\n parser.add_argument(\n \"--report_to_wandb\", default=False, action=\"store_true\", help=\"Whether to report to weights and biases\"\n )\n\n args = parser.parse_args()\n\n return args\n\n\ndef prepare_mask_and_masked_image(image, mask):\n image = np.array(image.convert(\"RGB\"))\n image = image[None].transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0\n\n mask = np.array(mask.convert(\"L\"))\n mask = mask.astype(np.float32) / 255.0\n mask = mask[None, None]\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n mask = torch.from_numpy(mask)\n\n masked_image = image * (mask < 0.5)\n\n return mask, masked_image\n\n\nclass DreamBoothDataset(Dataset):\n def __init__(\n self,\n tokenizer,\n datasets_paths,\n ):\n self.tokenizer = tokenizer\n self.datasets_paths = (datasets_paths,)\n self.datasets = [load_dataset(dataset_path) for dataset_path in self.datasets_paths[0]]\n self.train_data = concatenate_datasets([dataset[\"train\"] for dataset in self.datasets])\n self.test_data = concatenate_datasets([dataset[\"test\"] for dataset in self.datasets])\n\n self.image_normalize = transforms.Compose(\n [\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def set_image(self, img, switch):\n if img.mode not in [\"RGB\", \"L\"]:\n img = img.convert(\"RGB\")\n\n if switch:\n img = img.transpose(Image.FLIP_LEFT_RIGHT)\n\n img = img.resize((512, 512), Image.BILINEAR)\n\n return img\n\n def __len__(self):\n return len(self.train_data)\n\n def __getitem__(self, index):\n # Lettings\n example = {}\n img_idx = index % len(self.train_data)\n switch = random.choice([True, False])\n\n # Load image\n image = self.set_image(self.train_data[img_idx][\"image\"], switch)\n\n # Normalize image\n image_norm = self.image_normalize(image)\n\n # Tokenise prompt\n tokenized_prompt = self.tokenizer(\n self.train_data[img_idx][\"prompt\"],\n padding=\"do_not_pad\",\n truncation=True,\n max_length=self.tokenizer.model_max_length,\n ).input_ids\n\n # Load masks for image\n masks = [\n self.set_image(self.train_data[img_idx][key], switch) for key in self.train_data[img_idx] if \"mask\" in key\n ]\n\n # Build example\n example[\"PIL_image\"] = image\n example[\"instance_image\"] = image_norm\n example[\"instance_prompt_id\"] = tokenized_prompt\n example[\"instance_masks\"] = masks\n\n return example\n\n\ndef weighted_mask(masks):\n # Convert each mask to a NumPy array and ensure it's binary\n mask_arrays = [np.array(mask) / 255 for mask in masks] # Normalizing to 0-1 range\n\n # Generate random weights and apply them to each mask\n weights = [random.random() for _ in masks]\n weights = [weight / sum(weights) for weight in weights]\n weighted_masks = [mask * weight for mask, weight in zip(mask_arrays, weights)]\n\n # Sum the weighted masks\n summed_mask = np.sum(weighted_masks, axis=0)\n\n # Apply a threshold to create the final mask\n threshold = 0.5 # This threshold can be adjusted\n result_mask = summed_mask >= threshold\n\n # Convert the result back to a PIL image\n return Image.fromarray(result_mask.astype(np.uint8) * 255)\n\n\ndef collate_fn(examples, tokenizer):\n input_ids = [example[\"instance_prompt_id\"] for example in examples]\n pixel_values = [example[\"instance_image\"] for example in examples]\n\n masks, masked_images = [], []\n\n for example in examples:\n # generate a random mask\n mask = weighted_mask(example[\"instance_masks\"])\n\n # prepare mask and masked image\n mask, masked_image = prepare_mask_and_masked_image(example[\"PIL_image\"], mask)\n\n masks.append(mask)\n masked_images.append(masked_image)\n\n pixel_values = torch.stack(pixel_values).to(memory_format=torch.contiguous_format).float()\n masks = torch.stack(masks)\n masked_images = torch.stack(masked_images)\n input_ids = tokenizer.pad({\"input_ids\": input_ids}, padding=True, return_tensors=\"pt\").input_ids\n\n batch = {\"input_ids\": input_ids, \"pixel_values\": pixel_values, \"masks\": masks, \"masked_images\": masked_images}\n\n return batch\n\n\ndef log_validation(pipeline, text_encoder, unet, val_pairs, accelerator):\n # update pipeline (note: unet and vae are loaded again in float32)\n pipeline.text_encoder = accelerator.unwrap_model(text_encoder)\n pipeline.unet = accelerator.unwrap_model(unet)\n\n with torch.autocast(\"cuda\"):\n val_results = [{\"data_or_path\": pipeline(**pair).images[0], \"caption\": pair[\"prompt\"]} for pair in val_pairs]\n\n torch.cuda.empty_cache()\n\n wandb.log({\"validation\": [wandb.Image(**val_result) for val_result in val_results]})\n\n\ndef checkpoint(args, global_step, accelerator):\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n\ndef main():\n args = parse_args()\n\n project_config = ProjectConfiguration(\n total_limit=args.checkpoints_total_limit,\n project_dir=args.output_dir,\n logging_dir=Path(args.output_dir, args.logging_dir),\n )\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n project_config=project_config,\n log_with=\"wandb\" if args.report_to_wandb else None,\n )\n\n if args.report_to_wandb and not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n\n if args.seed is not None:\n set_seed(args.seed)\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n\n # Load the tokenizer & models and create wrapper for stable diffusion\n tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"tokenizer\")\n text_encoder = CLIPTextModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\"\n ).requires_grad_(args.train_text_encoder)\n vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"vae\").requires_grad_(False)\n unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"unet\")\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n optimizer = torch.optim.AdamW(\n params=itertools.chain(unet.parameters(), text_encoder.parameters())\n if args.train_text_encoder\n else unet.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n\n train_dataset = DreamBoothDataset(\n tokenizer=tokenizer,\n datasets_paths=args.instance_data_dir,\n )\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n batch_size=args.train_batch_size,\n shuffle=True,\n collate_fn=lambda examples: collate_fn(examples, tokenizer),\n )\n\n # Scheduler and math around the number of training steps.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n )\n\n if args.train_text_encoder:\n unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, text_encoder, optimizer, train_dataloader, lr_scheduler\n )\n else:\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n accelerator.register_for_checkpointing(lr_scheduler)\n\n if args.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif args.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n else:\n weight_dtype = torch.float32\n\n # Move text_encode and vae to gpu.\n # For mixed precision training we cast the text_encoder and vae weights to half-precision\n # as these models are only used for inference, keeping weights in full precision is not required.\n vae.to(accelerator.device, dtype=weight_dtype)\n if not args.train_text_encoder:\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n\n # Afterwards we calculate our number of training epochs\n num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = vars(copy.deepcopy(args))\n accelerator.init_trackers(args.validation_project_name, config=tracker_config)\n\n # create validation pipeline (note: unet and vae are loaded again in float32)\n val_pipeline = StableDiffusionInpaintPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n unet=unet,\n vae=vae,\n torch_dtype=weight_dtype,\n safety_checker=None,\n )\n val_pipeline.set_progress_bar_config(disable=True)\n\n # prepare validation dataset\n val_pairs = [\n {\n \"image\": example[\"image\"],\n \"mask_image\": mask,\n \"prompt\": example[\"prompt\"],\n }\n for example in train_dataset.test_data\n for mask in [example[key] for key in example if \"mask\" in key]\n ]\n\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n for model in models:\n sub_dir = \"unet\" if isinstance(model, type(accelerator.unwrap_model(unet))) else \"text_encoder\"\n model.save_pretrained(os.path.join(output_dir, sub_dir))\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n\n print()\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num batches each epoch = {len(train_dataloader)}\")\n logger.info(f\" Num Epochs = {num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n\n global_step = 0\n first_epoch = 0\n\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n resume_global_step = global_step * args.gradient_accumulation_steps\n first_epoch = global_step // num_update_steps_per_epoch\n resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)\n\n # Only show the progress bar once on each machine.\n progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)\n progress_bar.set_description(\"Steps\")\n\n for epoch in range(first_epoch, num_train_epochs):\n unet.train()\n for step, batch in enumerate(train_dataloader):\n # Skip steps until we reach the resumed step\n if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:\n if step % args.gradient_accumulation_steps == 0:\n progress_bar.update(1)\n continue\n\n with accelerator.accumulate(unet):\n # Convert images to latent space\n latents = vae.encode(batch[\"pixel_values\"].to(dtype=weight_dtype)).latent_dist.sample()\n latents = latents * vae.config.scaling_factor\n\n # Convert masked images to latent space\n masked_latents = vae.encode(\n batch[\"masked_images\"].reshape(batch[\"pixel_values\"].shape).to(dtype=weight_dtype)\n ).latent_dist.sample()\n masked_latents = masked_latents * vae.config.scaling_factor\n\n masks = batch[\"masks\"]\n # resize the mask to latents shape as we concatenate the mask to the latents\n mask = torch.stack(\n [\n torch.nn.functional.interpolate(mask, size=(args.resolution // 8, args.resolution // 8))\n for mask in masks\n ]\n )\n mask = mask.reshape(-1, 1, args.resolution // 8, args.resolution // 8)\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # concatenate the noised latents with the mask and the masked latents\n latent_model_input = torch.cat([noisy_latents, mask, masked_latents], dim=1)\n\n # Get the text embedding for conditioning\n encoder_hidden_states = text_encoder(batch[\"input_ids\"])[0]\n\n # Predict the noise residual\n noise_pred = unet(latent_model_input, timesteps, encoder_hidden_states).sample\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n loss = F.mse_loss(noise_pred.float(), target.float(), reduction=\"mean\")\n\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n params_to_clip = (\n itertools.chain(unet.parameters(), text_encoder.parameters())\n if args.train_text_encoder\n else unet.parameters()\n )\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if (\n global_step % args.validation_steps == 0\n and global_step >= args.validation_from\n and args.report_to_wandb\n ):\n log_validation(\n val_pipeline,\n text_encoder,\n unet,\n val_pairs,\n accelerator,\n )\n\n if global_step % args.checkpointing_steps == 0 and global_step >= args.checkpointing_from:\n checkpoint(\n args,\n global_step,\n accelerator,\n )\n\n # Step logging\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n accelerator.wait_for_everyone()\n\n # Terminate training\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/research_projects/multi_token_textual_inversion/README.md", "content": "## [Deprecated] Multi Token Textual Inversion\n\n**IMPORTART: This research project is deprecated. Multi Token Textual Inversion is now supported natively in [the official textual inversion example](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion#running-locally-with-pytorch).**\n\nThe author of this project is [Isamu Isozaki](https://github.com/isamu-isozaki) - please make sure to tag the author for issue and PRs as well as @patrickvonplaten.\n\nWe add multi token support to textual inversion. I added\n1. num_vec_per_token for the number of used to reference that token\n2. progressive_tokens for progressively training the token from 1 token to 2 token etc\n3. progressive_tokens_max_steps for the max number of steps until we start full training\n4. vector_shuffle to shuffle vectors\n\nFeel free to add these options to your training! In practice num_vec_per_token around 10+vector shuffle works great!\n\n## Textual Inversion fine-tuning example\n\n[Textual inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like stable diffusion on your own images using just 3-5 examples.\nThe `textual_inversion.py` script shows how to implement the training procedure and adapt it for stable diffusion.\n\n## Running on Colab\n\nColab for training\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb)\n\nColab for inference\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb)\n\n## Running locally with PyTorch\n### Installing the dependencies\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n**Important**\n\nTo make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nThen cd in the example folder and run\n```bash\npip install -r requirements.txt\n```\n\nAnd initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\n\n\n### Cat toy example\n\nYou need to accept the model license before downloading or using the weights. In this example we'll use model version `v1-5`, so you'll need to visit [its card](https://huggingface.co/runwayml/stable-diffusion-v1-5), read the license and tick the checkbox if you agree.\n\nYou have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).\n\nRun the following command to authenticate your token\n\n```bash\nhuggingface-cli login\n```\n\nIf you have already cloned the repo, then you won't need to go through these steps.\n\n
\n\nNow let's get our dataset.Download 3-4 images from [here](https://drive.google.com/drive/folders/1fmJMs25nxS_rSNqS5hTcRdLem_YQXbq5) and save them in a directory. This will be our training data.\n\nAnd launch the training using\n\n**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**\n\n```bash\nexport MODEL_NAME=\"runwayml/stable-diffusion-v1-5\"\nexport DATA_DIR=\"path-to-dir-containing-images\"\n\naccelerate launch textual_inversion.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_data_dir=$DATA_DIR \\\n --learnable_property=\"object\" \\\n --placeholder_token=\"\" --initializer_token=\"toy\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --max_train_steps=3000 \\\n --learning_rate=5.0e-04 --scale_lr \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --output_dir=\"textual_inversion_cat\"\n```\n\nA full training run takes ~1 hour on one V100 GPU.\n\n### Inference\n\nOnce you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline`. Make sure to include the `placeholder_token` in your prompt.\n\n```python\nfrom diffusers import StableDiffusionPipeline\n\nmodel_id = \"path-to-your-trained-model\"\npipe = StableDiffusionPipeline.from_pretrained(model_id,torch_dtype=torch.float16).to(\"cuda\")\n\nprompt = \"A backpack\"\n\nimage = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]\n\nimage.save(\"cat-backpack.png\")\n```\n\n\n## Training with Flax/JAX\n\nFor faster training on TPUs and GPUs you can leverage the flax training example. Follow the instructions above to get the model and dataset before running the script.\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n```bash\npip install -U -r requirements_flax.txt\n```\n\n```bash\nexport MODEL_NAME=\"duongna/stable-diffusion-v1-4-flax\"\nexport DATA_DIR=\"path-to-dir-containing-images\"\n\npython textual_inversion_flax.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_data_dir=$DATA_DIR \\\n --learnable_property=\"object\" \\\n --placeholder_token=\"\" --initializer_token=\"toy\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --max_train_steps=3000 \\\n --learning_rate=5.0e-04 --scale_lr \\\n --output_dir=\"textual_inversion_cat\"\n```\nIt should be at least 70% faster than the PyTorch script with the same configuration.\n\n### Training with xformers:\nYou can enable memory efficient attention by [installing xFormers](https://github.com/facebookresearch/xformers#installing-xformers) and padding the `--enable_xformers_memory_efficient_attention` argument to the script. This is not available with the Flax/JAX implementation.\n" }, { "path": "diffusers/examples/research_projects/multi_token_textual_inversion/multi_token_clip.py", "content": "\"\"\"\nThe main idea for this code is to provide a way for users to not need to bother with the hassle of multiple tokens for a concept by typing\na photo of _0 _1 ... and so on\nand instead just do\na photo of \nwhich gets translated to the above. This needs to work for both inference and training.\nFor inference,\nthe tokenizer encodes the text. So, we would want logic for our tokenizer to replace the placeholder token with\nit's underlying vectors\nFor training,\nwe would want to abstract away some logic like\n1. Adding tokens\n2. Updating gradient mask\n3. Saving embeddings\nto our Util class here.\nso\nTODO:\n1. have tokenizer keep track of concept, multiconcept pairs and replace during encode call x\n2. have mechanism for adding tokens x\n3. have mech for saving emebeddings x\n4. get mask to update x\n5. Loading tokens from embedding x\n6. Integrate to training x\n7. Test\n\"\"\"\n\nimport copy\nimport random\n\nfrom transformers import CLIPTokenizer\n\n\nclass MultiTokenCLIPTokenizer(CLIPTokenizer):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.token_map = {}\n\n def try_adding_tokens(self, placeholder_token, *args, **kwargs):\n num_added_tokens = super().add_tokens(placeholder_token, *args, **kwargs)\n if num_added_tokens == 0:\n raise ValueError(\n f\"The tokenizer already contains the token {placeholder_token}. Please pass a different\"\n \" `placeholder_token` that is not already in the tokenizer.\"\n )\n\n def add_placeholder_tokens(self, placeholder_token, *args, num_vec_per_token=1, **kwargs):\n output = []\n if num_vec_per_token == 1:\n self.try_adding_tokens(placeholder_token, *args, **kwargs)\n output.append(placeholder_token)\n else:\n output = []\n for i in range(num_vec_per_token):\n ith_token = placeholder_token + f\"_{i}\"\n self.try_adding_tokens(ith_token, *args, **kwargs)\n output.append(ith_token)\n # handle cases where there is a new placeholder token that contains the current placeholder token but is larger\n for token in self.token_map:\n if token in placeholder_token:\n raise ValueError(\n f\"The tokenizer already has placeholder token {token} that can get confused with\"\n f\" {placeholder_token}keep placeholder tokens independent\"\n )\n self.token_map[placeholder_token] = output\n\n def replace_placeholder_tokens_in_text(self, text, vector_shuffle=False, prop_tokens_to_load=1.0):\n \"\"\"\n Here, we replace the placeholder tokens in text recorded in token_map so that the text_encoder\n can encode them\n vector_shuffle was inspired by https://github.com/rinongal/textual_inversion/pull/119\n where shuffling tokens were found to force the model to learn the concepts more descriptively.\n \"\"\"\n if isinstance(text, list):\n output = []\n for i in range(len(text)):\n output.append(self.replace_placeholder_tokens_in_text(text[i], vector_shuffle=vector_shuffle))\n return output\n for placeholder_token in self.token_map:\n if placeholder_token in text:\n tokens = self.token_map[placeholder_token]\n tokens = tokens[: 1 + int(len(tokens) * prop_tokens_to_load)]\n if vector_shuffle:\n tokens = copy.copy(tokens)\n random.shuffle(tokens)\n text = text.replace(placeholder_token, \" \".join(tokens))\n return text\n\n def __call__(self, text, *args, vector_shuffle=False, prop_tokens_to_load=1.0, **kwargs):\n return super().__call__(\n self.replace_placeholder_tokens_in_text(\n text, vector_shuffle=vector_shuffle, prop_tokens_to_load=prop_tokens_to_load\n ),\n *args,\n **kwargs,\n )\n\n def encode(self, text, *args, vector_shuffle=False, prop_tokens_to_load=1.0, **kwargs):\n return super().encode(\n self.replace_placeholder_tokens_in_text(\n text, vector_shuffle=vector_shuffle, prop_tokens_to_load=prop_tokens_to_load\n ),\n *args,\n **kwargs,\n )\n" }, { "path": "diffusers/examples/research_projects/multi_token_textual_inversion/requirements.txt", "content": "accelerate>=0.16.0\ntorchvision\ntransformers>=4.25.1\nftfy\ntensorboard\nJinja2\n" }, { "path": "diffusers/examples/research_projects/multi_token_textual_inversion/requirements_flax.txt", "content": "transformers>=4.25.1\nflax\noptax\ntorch\ntorchvision\nftfy\ntensorboard\nJinja2\n" }, { "path": "diffusers/examples/research_projects/multi_token_textual_inversion/textual_inversion.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport logging\nimport math\nimport os\nimport random\nfrom pathlib import Path\n\nimport numpy as np\nimport PIL\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom huggingface_hub import create_repo, upload_folder\nfrom multi_token_clip import MultiTokenCLIPTokenizer\n\n# TODO: remove and import from diffusers.utils when the new version of diffusers is released\nfrom packaging import version\nfrom PIL import Image\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModel\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n DiffusionPipeline,\n DPMSolverMultistepScheduler,\n StableDiffusionPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import check_min_version, is_wandb_available\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\nif version.parse(version.parse(PIL.__version__).base_version) >= version.parse(\"9.1.0\"):\n PIL_INTERPOLATION = {\n \"linear\": PIL.Image.Resampling.BILINEAR,\n \"bilinear\": PIL.Image.Resampling.BILINEAR,\n \"bicubic\": PIL.Image.Resampling.BICUBIC,\n \"lanczos\": PIL.Image.Resampling.LANCZOS,\n \"nearest\": PIL.Image.Resampling.NEAREST,\n }\nelse:\n PIL_INTERPOLATION = {\n \"linear\": PIL.Image.LINEAR,\n \"bilinear\": PIL.Image.BILINEAR,\n \"bicubic\": PIL.Image.BICUBIC,\n \"lanczos\": PIL.Image.LANCZOS,\n \"nearest\": PIL.Image.NEAREST,\n }\n# ------------------------------------------------------------------------------\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.14.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef add_tokens(tokenizer, text_encoder, placeholder_token, num_vec_per_token=1, initializer_token=None):\n \"\"\"\n Add tokens to the tokenizer and set the initial value of token embeddings\n \"\"\"\n tokenizer.add_placeholder_tokens(placeholder_token, num_vec_per_token=num_vec_per_token)\n text_encoder.resize_token_embeddings(len(tokenizer))\n token_embeds = text_encoder.get_input_embeddings().weight.data\n placeholder_token_ids = tokenizer.encode(placeholder_token, add_special_tokens=False)\n if initializer_token:\n token_ids = tokenizer.encode(initializer_token, add_special_tokens=False)\n for i, placeholder_token_id in enumerate(placeholder_token_ids):\n token_embeds[placeholder_token_id] = token_embeds[token_ids[i * len(token_ids) // num_vec_per_token]]\n else:\n for i, placeholder_token_id in enumerate(placeholder_token_ids):\n token_embeds[placeholder_token_id] = torch.randn_like(token_embeds[placeholder_token_id])\n return placeholder_token\n\n\ndef save_progress(tokenizer, text_encoder, accelerator, save_path):\n for placeholder_token in tokenizer.token_map:\n placeholder_token_ids = tokenizer.encode(placeholder_token, add_special_tokens=False)\n learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_ids]\n if len(placeholder_token_ids) == 1:\n learned_embeds = learned_embeds[None]\n learned_embeds_dict = {placeholder_token: learned_embeds.detach().cpu()}\n torch.save(learned_embeds_dict, save_path)\n\n\ndef load_multitoken_tokenizer(tokenizer, text_encoder, learned_embeds_dict):\n for placeholder_token in learned_embeds_dict:\n placeholder_embeds = learned_embeds_dict[placeholder_token]\n num_vec_per_token = placeholder_embeds.shape[0]\n placeholder_embeds = placeholder_embeds.to(dtype=text_encoder.dtype)\n add_tokens(tokenizer, text_encoder, placeholder_token, num_vec_per_token=num_vec_per_token)\n placeholder_token_ids = tokenizer.encode(placeholder_token, add_special_tokens=False)\n token_embeds = text_encoder.get_input_embeddings().weight.data\n for i, placeholder_token_id in enumerate(placeholder_token_ids):\n token_embeds[placeholder_token_id] = placeholder_embeds[i]\n\n\ndef load_multitoken_tokenizer_from_automatic(tokenizer, text_encoder, automatic_dict, placeholder_token):\n \"\"\"\n Automatic1111's tokens have format\n {'string_to_token': {'*': 265}, 'string_to_param': {'*': tensor([[ 0.0833, 0.0030, 0.0057, ..., -0.0264, -0.0616, -0.0529],\n [ 0.0058, -0.0190, -0.0584, ..., -0.0025, -0.0945, -0.0490],\n [ 0.0916, 0.0025, 0.0365, ..., -0.0685, -0.0124, 0.0728],\n [ 0.0812, -0.0199, -0.0100, ..., -0.0581, -0.0780, 0.0254]],\n requires_grad=True)}, 'name': 'FloralMarble-400', 'step': 399, 'sd_checkpoint': '4bdfc29c', 'sd_checkpoint_name': 'SD2.1-768'}\n \"\"\"\n learned_embeds_dict = {}\n learned_embeds_dict[placeholder_token] = automatic_dict[\"string_to_param\"][\"*\"]\n load_multitoken_tokenizer(tokenizer, text_encoder, learned_embeds_dict)\n\n\ndef get_mask(tokenizer, accelerator):\n # Get the mask of the weights that won't change\n mask = torch.ones(len(tokenizer)).to(accelerator.device, dtype=torch.bool)\n for placeholder_token in tokenizer.token_map:\n placeholder_token_ids = tokenizer.encode(placeholder_token, add_special_tokens=False)\n for i in range(len(placeholder_token_ids)):\n mask = mask & (torch.arange(len(tokenizer)) != placeholder_token_ids[i]).to(accelerator.device)\n return mask\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--progressive_tokens_max_steps\",\n type=int,\n default=2000,\n help=\"The number of steps until all tokens will be used.\",\n )\n parser.add_argument(\n \"--progressive_tokens\",\n action=\"store_true\",\n help=\"Progressively train the tokens. For example, first train for 1 token, then 2 tokens and so on.\",\n )\n parser.add_argument(\"--vector_shuffle\", action=\"store_true\", help=\"Shuffling tokens durint training\")\n parser.add_argument(\n \"--num_vec_per_token\",\n type=int,\n default=1,\n help=(\n \"The number of vectors used to represent the placeholder token. The higher the number, the better the\"\n \" result at the cost of editability. This can be fixed by prompt editing.\"\n ),\n )\n parser.add_argument(\n \"--save_steps\",\n type=int,\n default=500,\n help=\"Save learned_embeds.bin every X updates steps.\",\n )\n parser.add_argument(\n \"--only_save_embeds\",\n action=\"store_true\",\n default=False,\n help=\"Save only the embeddings for the new concept.\",\n )\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--tokenizer_name\",\n type=str,\n default=None,\n help=\"Pretrained tokenizer name or path if not the same as model_name\",\n )\n parser.add_argument(\n \"--train_data_dir\", type=str, default=None, required=True, help=\"A folder containing the training data.\"\n )\n parser.add_argument(\n \"--placeholder_token\",\n type=str,\n default=None,\n required=True,\n help=\"A token to use as a placeholder for the concept.\",\n )\n parser.add_argument(\n \"--initializer_token\", type=str, default=None, required=True, help=\"A token to use as initializer word.\"\n )\n parser.add_argument(\"--learnable_property\", type=str, default=\"object\", help=\"Choose between 'object' and 'style'\")\n parser.add_argument(\"--repeats\", type=int, default=100, help=\"How many times to repeat the training data.\")\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"text-inversion-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\", action=\"store_true\", help=\"Whether to center crop images before resizing to resolution.\"\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=5000,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=\"no\",\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose\"\n \"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.\"\n \"and an Nvidia Ampere GPU.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n help=\"A prompt that is used during validation to verify that the model is learning.\",\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=50,\n help=(\n \"Run validation every X epochs. Validation consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`\"\n \" and logging the images.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\n \"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`.\"\n \" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state\"\n \" for more docs\"\n ),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.train_data_dir is None:\n raise ValueError(\"You must specify a train data directory.\")\n\n return args\n\n\nimagenet_templates_small = [\n \"a photo of a {}\",\n \"a rendering of a {}\",\n \"a cropped photo of the {}\",\n \"the photo of a {}\",\n \"a photo of a clean {}\",\n \"a photo of a dirty {}\",\n \"a dark photo of the {}\",\n \"a photo of my {}\",\n \"a photo of the cool {}\",\n \"a close-up photo of a {}\",\n \"a bright photo of the {}\",\n \"a cropped photo of a {}\",\n \"a photo of the {}\",\n \"a good photo of the {}\",\n \"a photo of one {}\",\n \"a close-up photo of the {}\",\n \"a rendition of the {}\",\n \"a photo of the clean {}\",\n \"a rendition of a {}\",\n \"a photo of a nice {}\",\n \"a good photo of a {}\",\n \"a photo of the nice {}\",\n \"a photo of the small {}\",\n \"a photo of the weird {}\",\n \"a photo of the large {}\",\n \"a photo of a cool {}\",\n \"a photo of a small {}\",\n]\n\nimagenet_style_templates_small = [\n \"a painting in the style of {}\",\n \"a rendering in the style of {}\",\n \"a cropped painting in the style of {}\",\n \"the painting in the style of {}\",\n \"a clean painting in the style of {}\",\n \"a dirty painting in the style of {}\",\n \"a dark painting in the style of {}\",\n \"a picture in the style of {}\",\n \"a cool painting in the style of {}\",\n \"a close-up painting in the style of {}\",\n \"a bright painting in the style of {}\",\n \"a cropped painting in the style of {}\",\n \"a good painting in the style of {}\",\n \"a close-up painting in the style of {}\",\n \"a rendition in the style of {}\",\n \"a nice painting in the style of {}\",\n \"a small painting in the style of {}\",\n \"a weird painting in the style of {}\",\n \"a large painting in the style of {}\",\n]\n\n\nclass TextualInversionDataset(Dataset):\n def __init__(\n self,\n data_root,\n tokenizer,\n learnable_property=\"object\", # [object, style]\n size=512,\n repeats=100,\n interpolation=\"bicubic\",\n flip_p=0.5,\n set=\"train\",\n placeholder_token=\"*\",\n center_crop=False,\n vector_shuffle=False,\n progressive_tokens=False,\n ):\n self.data_root = data_root\n self.tokenizer = tokenizer\n self.learnable_property = learnable_property\n self.size = size\n self.placeholder_token = placeholder_token\n self.center_crop = center_crop\n self.flip_p = flip_p\n self.vector_shuffle = vector_shuffle\n self.progressive_tokens = progressive_tokens\n self.prop_tokens_to_load = 0\n\n self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]\n\n self.num_images = len(self.image_paths)\n self._length = self.num_images\n\n if set == \"train\":\n self._length = self.num_images * repeats\n\n self.interpolation = {\n \"linear\": PIL_INTERPOLATION[\"linear\"],\n \"bilinear\": PIL_INTERPOLATION[\"bilinear\"],\n \"bicubic\": PIL_INTERPOLATION[\"bicubic\"],\n \"lanczos\": PIL_INTERPOLATION[\"lanczos\"],\n }[interpolation]\n\n self.templates = imagenet_style_templates_small if learnable_property == \"style\" else imagenet_templates_small\n self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, i):\n example = {}\n image = Image.open(self.image_paths[i % self.num_images])\n\n if not image.mode == \"RGB\":\n image = image.convert(\"RGB\")\n\n placeholder_string = self.placeholder_token\n text = random.choice(self.templates).format(placeholder_string)\n\n example[\"input_ids\"] = self.tokenizer.encode(\n text,\n padding=\"max_length\",\n truncation=True,\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n vector_shuffle=self.vector_shuffle,\n prop_tokens_to_load=self.prop_tokens_to_load if self.progressive_tokens else 1.0,\n )[0]\n\n # default to score-sde preprocessing\n img = np.array(image).astype(np.uint8)\n\n if self.center_crop:\n crop = min(img.shape[0], img.shape[1])\n (\n h,\n w,\n ) = (\n img.shape[0],\n img.shape[1],\n )\n img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]\n\n image = Image.fromarray(img)\n image = image.resize((self.size, self.size), resample=self.interpolation)\n\n image = self.flip_transform(image)\n image = np.array(image).astype(np.uint8)\n image = (image / 127.5 - 1.0).astype(np.float32)\n\n example[\"pixel_values\"] = torch.from_numpy(image).permute(2, 0, 1)\n return example\n\n\ndef main():\n args = parse_args()\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = os.path.join(args.output_dir, args.logging_dir)\n accelerator_project_config = ProjectConfiguration(\n total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir\n )\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n import wandb\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load tokenizer\n if args.tokenizer_name:\n tokenizer = MultiTokenCLIPTokenizer.from_pretrained(args.tokenizer_name)\n elif args.pretrained_model_name_or_path:\n tokenizer = MultiTokenCLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"tokenizer\")\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n text_encoder = CLIPTextModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision\n )\n vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision)\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision\n )\n if is_xformers_available():\n try:\n unet.enable_xformers_memory_efficient_attention()\n except Exception as e:\n logger.warning(\n \"Could not enable memory efficient attention. Make sure xformers is installed\"\n f\" correctly and a GPU is available: {e}\"\n )\n add_tokens(tokenizer, text_encoder, args.placeholder_token, args.num_vec_per_token, args.initializer_token)\n\n # Freeze vae and unet\n vae.requires_grad_(False)\n unet.requires_grad_(False)\n # Freeze all parameters except for the token embeddings in text encoder\n text_encoder.text_model.encoder.requires_grad_(False)\n text_encoder.text_model.final_layer_norm.requires_grad_(False)\n text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)\n\n if args.gradient_checkpointing:\n # Keep unet in train mode if we are using gradient checkpointing to save memory.\n # The dropout cannot be != 0 so it doesn't matter if we are in eval or train mode.\n unet.train()\n text_encoder.gradient_checkpointing_enable()\n unet.enable_gradient_checkpointing()\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Initialize the optimizer\n optimizer = torch.optim.AdamW(\n text_encoder.get_input_embeddings().parameters(), # only optimize the embeddings\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Dataset and DataLoaders creation:\n train_dataset = TextualInversionDataset(\n data_root=args.train_data_dir,\n tokenizer=tokenizer,\n size=args.resolution,\n placeholder_token=args.placeholder_token,\n repeats=args.repeats,\n learnable_property=args.learnable_property,\n center_crop=args.center_crop,\n set=\"train\",\n )\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n )\n\n # Prepare everything with our `accelerator`.\n text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n text_encoder, optimizer, train_dataloader, lr_scheduler\n )\n\n # For mixed precision training we cast the unet and vae weights to half-precision\n # as these models are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move vae and unet to device and cast to weight_dtype\n unet.to(accelerator.device, dtype=weight_dtype)\n vae.to(accelerator.device, dtype=weight_dtype)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"textual_inversion\", config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n resume_global_step = global_step * args.gradient_accumulation_steps\n first_epoch = global_step // num_update_steps_per_epoch\n resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)\n\n # Only show the progress bar once on each machine.\n progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)\n progress_bar.set_description(\"Steps\")\n\n # keep original embeddings as reference\n orig_embeds_params = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight.data.clone()\n\n for epoch in range(first_epoch, args.num_train_epochs):\n text_encoder.train()\n for step, batch in enumerate(train_dataloader):\n # Skip steps until we reach the resumed step\n if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:\n if step % args.gradient_accumulation_steps == 0:\n progress_bar.update(1)\n continue\n if args.progressive_tokens:\n train_dataset.prop_tokens_to_load = float(global_step) / args.progressive_tokens_max_steps\n\n with accelerator.accumulate(text_encoder):\n # Convert images to latent space\n latents = vae.encode(batch[\"pixel_values\"].to(dtype=weight_dtype)).latent_dist.sample().detach()\n latents = latents * vae.config.scaling_factor\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # Get the text embedding for conditioning\n encoder_hidden_states = text_encoder(batch[\"input_ids\"])[0].to(dtype=weight_dtype)\n\n # Predict the noise residual\n model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n\n accelerator.backward(loss)\n\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Let's make sure we don't update any embedding weights besides the newly added token\n index_no_updates = get_mask(tokenizer, accelerator)\n with torch.no_grad():\n accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[\n index_no_updates\n ] = orig_embeds_params[index_no_updates]\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n if global_step % args.save_steps == 0:\n save_path = os.path.join(args.output_dir, f\"learned_embeds-steps-{global_step}.bin\")\n save_progress(tokenizer, text_encoder, accelerator, save_path)\n\n if global_step % args.checkpointing_steps == 0:\n if accelerator.is_main_process:\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process and args.validation_prompt is not None and epoch % args.validation_epochs == 0:\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n # create pipeline (note: unet and vae are loaded again in float32)\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n text_encoder=accelerator.unwrap_model(text_encoder),\n tokenizer=tokenizer,\n unet=unet,\n vae=vae,\n revision=args.revision,\n torch_dtype=weight_dtype,\n )\n pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = (\n None if args.seed is None else torch.Generator(device=accelerator.device).manual_seed(args.seed)\n )\n images = []\n for _ in range(args.num_validation_images):\n with torch.autocast(\"cuda\"):\n image = pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]\n images.append(image)\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\")\n for i, image in enumerate(images)\n ]\n }\n )\n\n del pipeline\n torch.cuda.empty_cache()\n\n # Create the pipeline using using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n if args.push_to_hub and args.only_save_embeds:\n logger.warning(\"Enabling full model saving because --push_to_hub=True was specified.\")\n save_full_model = True\n else:\n save_full_model = not args.only_save_embeds\n if save_full_model:\n pipeline = StableDiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n text_encoder=accelerator.unwrap_model(text_encoder),\n vae=vae,\n unet=unet,\n tokenizer=tokenizer,\n )\n pipeline.save_pretrained(args.output_dir)\n # Save the newly trained embeddings\n save_path = os.path.join(args.output_dir, \"learned_embeds.bin\")\n save_progress(tokenizer, text_encoder, accelerator, save_path)\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/research_projects/multi_token_textual_inversion/textual_inversion_flax.py", "content": "import argparse\nimport logging\nimport math\nimport os\nimport random\nfrom pathlib import Path\n\nimport jax\nimport jax.numpy as jnp\nimport numpy as np\nimport optax\nimport PIL\nimport torch\nimport torch.utils.checkpoint\nimport transformers\nfrom flax import jax_utils\nfrom flax.training import train_state\nfrom flax.training.common_utils import shard\nfrom huggingface_hub import create_repo, upload_folder\n\n# TODO: remove and import from diffusers.utils when the new version of diffusers is released\nfrom packaging import version\nfrom PIL import Image\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel, set_seed\n\nfrom diffusers import (\n FlaxAutoencoderKL,\n FlaxDDPMScheduler,\n FlaxPNDMScheduler,\n FlaxStableDiffusionPipeline,\n FlaxUNet2DConditionModel,\n)\nfrom diffusers.pipelines.stable_diffusion import FlaxStableDiffusionSafetyChecker\nfrom diffusers.utils import check_min_version\n\n\nif version.parse(version.parse(PIL.__version__).base_version) >= version.parse(\"9.1.0\"):\n PIL_INTERPOLATION = {\n \"linear\": PIL.Image.Resampling.BILINEAR,\n \"bilinear\": PIL.Image.Resampling.BILINEAR,\n \"bicubic\": PIL.Image.Resampling.BICUBIC,\n \"lanczos\": PIL.Image.Resampling.LANCZOS,\n \"nearest\": PIL.Image.Resampling.NEAREST,\n }\nelse:\n PIL_INTERPOLATION = {\n \"linear\": PIL.Image.LINEAR,\n \"bilinear\": PIL.Image.BILINEAR,\n \"bicubic\": PIL.Image.BICUBIC,\n \"lanczos\": PIL.Image.LANCZOS,\n \"nearest\": PIL.Image.NEAREST,\n }\n# ------------------------------------------------------------------------------\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.14.0.dev0\")\n\nlogger = logging.getLogger(__name__)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--tokenizer_name\",\n type=str,\n default=None,\n help=\"Pretrained tokenizer name or path if not the same as model_name\",\n )\n parser.add_argument(\n \"--train_data_dir\", type=str, default=None, required=True, help=\"A folder containing the training data.\"\n )\n parser.add_argument(\n \"--placeholder_token\",\n type=str,\n default=None,\n required=True,\n help=\"A token to use as a placeholder for the concept.\",\n )\n parser.add_argument(\n \"--initializer_token\", type=str, default=None, required=True, help=\"A token to use as initializer word.\"\n )\n parser.add_argument(\"--learnable_property\", type=str, default=\"object\", help=\"Choose between 'object' and 'style'\")\n parser.add_argument(\"--repeats\", type=int, default=100, help=\"How many times to repeat the training data.\")\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"text-inversion-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=42, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\", action=\"store_true\", help=\"Whether to center crop images before resizing to resolution.\"\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=5000,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=True,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\n \"--use_auth_token\",\n action=\"store_true\",\n help=(\n \"Will use the token generated when running `huggingface-cli login` (necessary to use this script with\"\n \" private models).\"\n ),\n )\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.train_data_dir is None:\n raise ValueError(\"You must specify a train data directory.\")\n\n return args\n\n\nimagenet_templates_small = [\n \"a photo of a {}\",\n \"a rendering of a {}\",\n \"a cropped photo of the {}\",\n \"the photo of a {}\",\n \"a photo of a clean {}\",\n \"a photo of a dirty {}\",\n \"a dark photo of the {}\",\n \"a photo of my {}\",\n \"a photo of the cool {}\",\n \"a close-up photo of a {}\",\n \"a bright photo of the {}\",\n \"a cropped photo of a {}\",\n \"a photo of the {}\",\n \"a good photo of the {}\",\n \"a photo of one {}\",\n \"a close-up photo of the {}\",\n \"a rendition of the {}\",\n \"a photo of the clean {}\",\n \"a rendition of a {}\",\n \"a photo of a nice {}\",\n \"a good photo of a {}\",\n \"a photo of the nice {}\",\n \"a photo of the small {}\",\n \"a photo of the weird {}\",\n \"a photo of the large {}\",\n \"a photo of a cool {}\",\n \"a photo of a small {}\",\n]\n\nimagenet_style_templates_small = [\n \"a painting in the style of {}\",\n \"a rendering in the style of {}\",\n \"a cropped painting in the style of {}\",\n \"the painting in the style of {}\",\n \"a clean painting in the style of {}\",\n \"a dirty painting in the style of {}\",\n \"a dark painting in the style of {}\",\n \"a picture in the style of {}\",\n \"a cool painting in the style of {}\",\n \"a close-up painting in the style of {}\",\n \"a bright painting in the style of {}\",\n \"a cropped painting in the style of {}\",\n \"a good painting in the style of {}\",\n \"a close-up painting in the style of {}\",\n \"a rendition in the style of {}\",\n \"a nice painting in the style of {}\",\n \"a small painting in the style of {}\",\n \"a weird painting in the style of {}\",\n \"a large painting in the style of {}\",\n]\n\n\nclass TextualInversionDataset(Dataset):\n def __init__(\n self,\n data_root,\n tokenizer,\n learnable_property=\"object\", # [object, style]\n size=512,\n repeats=100,\n interpolation=\"bicubic\",\n flip_p=0.5,\n set=\"train\",\n placeholder_token=\"*\",\n center_crop=False,\n ):\n self.data_root = data_root\n self.tokenizer = tokenizer\n self.learnable_property = learnable_property\n self.size = size\n self.placeholder_token = placeholder_token\n self.center_crop = center_crop\n self.flip_p = flip_p\n\n self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]\n\n self.num_images = len(self.image_paths)\n self._length = self.num_images\n\n if set == \"train\":\n self._length = self.num_images * repeats\n\n self.interpolation = {\n \"linear\": PIL_INTERPOLATION[\"linear\"],\n \"bilinear\": PIL_INTERPOLATION[\"bilinear\"],\n \"bicubic\": PIL_INTERPOLATION[\"bicubic\"],\n \"lanczos\": PIL_INTERPOLATION[\"lanczos\"],\n }[interpolation]\n\n self.templates = imagenet_style_templates_small if learnable_property == \"style\" else imagenet_templates_small\n self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, i):\n example = {}\n image = Image.open(self.image_paths[i % self.num_images])\n\n if not image.mode == \"RGB\":\n image = image.convert(\"RGB\")\n\n placeholder_string = self.placeholder_token\n text = random.choice(self.templates).format(placeholder_string)\n\n example[\"input_ids\"] = self.tokenizer(\n text,\n padding=\"max_length\",\n truncation=True,\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n ).input_ids[0]\n\n # default to score-sde preprocessing\n img = np.array(image).astype(np.uint8)\n\n if self.center_crop:\n crop = min(img.shape[0], img.shape[1])\n (\n h,\n w,\n ) = (\n img.shape[0],\n img.shape[1],\n )\n img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]\n\n image = Image.fromarray(img)\n image = image.resize((self.size, self.size), resample=self.interpolation)\n\n image = self.flip_transform(image)\n image = np.array(image).astype(np.uint8)\n image = (image / 127.5 - 1.0).astype(np.float32)\n\n example[\"pixel_values\"] = torch.from_numpy(image).permute(2, 0, 1)\n return example\n\n\ndef resize_token_embeddings(model, new_num_tokens, initializer_token_id, placeholder_token_id, rng):\n if model.config.vocab_size == new_num_tokens or new_num_tokens is None:\n return\n model.config.vocab_size = new_num_tokens\n\n params = model.params\n old_embeddings = params[\"text_model\"][\"embeddings\"][\"token_embedding\"][\"embedding\"]\n old_num_tokens, emb_dim = old_embeddings.shape\n\n initializer = jax.nn.initializers.normal()\n\n new_embeddings = initializer(rng, (new_num_tokens, emb_dim))\n new_embeddings = new_embeddings.at[:old_num_tokens].set(old_embeddings)\n new_embeddings = new_embeddings.at[placeholder_token_id].set(new_embeddings[initializer_token_id])\n params[\"text_model\"][\"embeddings\"][\"token_embedding\"][\"embedding\"] = new_embeddings\n\n model.params = params\n return model\n\n\ndef get_params_to_save(params):\n return jax.device_get(jax.tree_util.tree_map(lambda x: x[0], params))\n\n\ndef main():\n args = parse_args()\n\n if args.seed is not None:\n set_seed(args.seed)\n\n if jax.process_index() == 0:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n # Setup logging, we only want one process per machine to log things on the screen.\n logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)\n if jax.process_index() == 0:\n transformers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n\n # Load the tokenizer and add the placeholder token as a additional special token\n if args.tokenizer_name:\n tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)\n elif args.pretrained_model_name_or_path:\n tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"tokenizer\")\n\n # Add the placeholder token in tokenizer\n num_added_tokens = tokenizer.add_tokens(args.placeholder_token)\n if num_added_tokens == 0:\n raise ValueError(\n f\"The tokenizer already contains the token {args.placeholder_token}. Please pass a different\"\n \" `placeholder_token` that is not already in the tokenizer.\"\n )\n\n # Convert the initializer_token, placeholder_token to ids\n token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)\n # Check if initializer_token is a single token or a sequence of tokens\n if len(token_ids) > 1:\n raise ValueError(\"The initializer token must be a single token.\")\n\n initializer_token_id = token_ids[0]\n placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)\n\n # Load models and create wrapper for stable diffusion\n text_encoder = FlaxCLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"text_encoder\")\n vae, vae_params = FlaxAutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"vae\")\n unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"unet\")\n\n # Create sampling rng\n rng = jax.random.PRNGKey(args.seed)\n rng, _ = jax.random.split(rng)\n # Resize the token embeddings as we are adding new special tokens to the tokenizer\n text_encoder = resize_token_embeddings(\n text_encoder, len(tokenizer), initializer_token_id, placeholder_token_id, rng\n )\n original_token_embeds = text_encoder.params[\"text_model\"][\"embeddings\"][\"token_embedding\"][\"embedding\"]\n\n train_dataset = TextualInversionDataset(\n data_root=args.train_data_dir,\n tokenizer=tokenizer,\n size=args.resolution,\n placeholder_token=args.placeholder_token,\n repeats=args.repeats,\n learnable_property=args.learnable_property,\n center_crop=args.center_crop,\n set=\"train\",\n )\n\n def collate_fn(examples):\n pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n input_ids = torch.stack([example[\"input_ids\"] for example in examples])\n\n batch = {\"pixel_values\": pixel_values, \"input_ids\": input_ids}\n batch = {k: v.numpy() for k, v in batch.items()}\n\n return batch\n\n total_train_batch_size = args.train_batch_size * jax.local_device_count()\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset, batch_size=total_train_batch_size, shuffle=True, drop_last=True, collate_fn=collate_fn\n )\n\n # Optimization\n if args.scale_lr:\n args.learning_rate = args.learning_rate * total_train_batch_size\n\n constant_scheduler = optax.constant_schedule(args.learning_rate)\n\n optimizer = optax.adamw(\n learning_rate=constant_scheduler,\n b1=args.adam_beta1,\n b2=args.adam_beta2,\n eps=args.adam_epsilon,\n weight_decay=args.adam_weight_decay,\n )\n\n def create_mask(params, label_fn):\n def _map(params, mask, label_fn):\n for k in params:\n if label_fn(k):\n mask[k] = \"token_embedding\"\n else:\n if isinstance(params[k], dict):\n mask[k] = {}\n _map(params[k], mask[k], label_fn)\n else:\n mask[k] = \"zero\"\n\n mask = {}\n _map(params, mask, label_fn)\n return mask\n\n def zero_grads():\n # from https://github.com/deepmind/optax/issues/159#issuecomment-896459491\n def init_fn(_):\n return ()\n\n def update_fn(updates, state, params=None):\n return jax.tree_util.tree_map(jnp.zeros_like, updates), ()\n\n return optax.GradientTransformation(init_fn, update_fn)\n\n # Zero out gradients of layers other than the token embedding layer\n tx = optax.multi_transform(\n {\"token_embedding\": optimizer, \"zero\": zero_grads()},\n create_mask(text_encoder.params, lambda s: s == \"token_embedding\"),\n )\n\n state = train_state.TrainState.create(apply_fn=text_encoder.__call__, params=text_encoder.params, tx=tx)\n\n noise_scheduler = FlaxDDPMScheduler(\n beta_start=0.00085, beta_end=0.012, beta_schedule=\"scaled_linear\", num_train_timesteps=1000\n )\n noise_scheduler_state = noise_scheduler.create_state()\n\n # Initialize our training\n train_rngs = jax.random.split(rng, jax.local_device_count())\n\n # Define gradient train step fn\n def train_step(state, vae_params, unet_params, batch, train_rng):\n dropout_rng, sample_rng, new_train_rng = jax.random.split(train_rng, 3)\n\n def compute_loss(params):\n vae_outputs = vae.apply(\n {\"params\": vae_params}, batch[\"pixel_values\"], deterministic=True, method=vae.encode\n )\n latents = vae_outputs.latent_dist.sample(sample_rng)\n # (NHWC) -> (NCHW)\n latents = jnp.transpose(latents, (0, 3, 1, 2))\n latents = latents * vae.config.scaling_factor\n\n noise_rng, timestep_rng = jax.random.split(sample_rng)\n noise = jax.random.normal(noise_rng, latents.shape)\n bsz = latents.shape[0]\n timesteps = jax.random.randint(\n timestep_rng,\n (bsz,),\n 0,\n noise_scheduler.config.num_train_timesteps,\n )\n noisy_latents = noise_scheduler.add_noise(noise_scheduler_state, latents, noise, timesteps)\n encoder_hidden_states = state.apply_fn(\n batch[\"input_ids\"], params=params, dropout_rng=dropout_rng, train=True\n )[0]\n # Predict the noise residual and compute loss\n model_pred = unet.apply(\n {\"params\": unet_params}, noisy_latents, timesteps, encoder_hidden_states, train=False\n ).sample\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(noise_scheduler_state, latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n loss = (target - model_pred) ** 2\n loss = loss.mean()\n\n return loss\n\n grad_fn = jax.value_and_grad(compute_loss)\n loss, grad = grad_fn(state.params)\n grad = jax.lax.pmean(grad, \"batch\")\n new_state = state.apply_gradients(grads=grad)\n\n # Keep the token embeddings fixed except the newly added embeddings for the concept,\n # as we only want to optimize the concept embeddings\n token_embeds = original_token_embeds.at[placeholder_token_id].set(\n new_state.params[\"text_model\"][\"embeddings\"][\"token_embedding\"][\"embedding\"][placeholder_token_id]\n )\n new_state.params[\"text_model\"][\"embeddings\"][\"token_embedding\"][\"embedding\"] = token_embeds\n\n metrics = {\"loss\": loss}\n metrics = jax.lax.pmean(metrics, axis_name=\"batch\")\n return new_state, metrics, new_train_rng\n\n # Create parallel version of the train and eval step\n p_train_step = jax.pmap(train_step, \"batch\", donate_argnums=(0,))\n\n # Replicate the train state on each device\n state = jax_utils.replicate(state)\n vae_params = jax_utils.replicate(vae_params)\n unet_params = jax_utils.replicate(unet_params)\n\n # Train!\n num_update_steps_per_epoch = math.ceil(len(train_dataloader))\n\n # Scheduler and math around the number of training steps.\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel & distributed) = {total_train_batch_size}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n\n global_step = 0\n\n epochs = tqdm(range(args.num_train_epochs), desc=f\"Epoch ... (1/{args.num_train_epochs})\", position=0)\n for epoch in epochs:\n # ======================== Training ================================\n\n train_metrics = []\n\n steps_per_epoch = len(train_dataset) // total_train_batch_size\n train_step_progress_bar = tqdm(total=steps_per_epoch, desc=\"Training...\", position=1, leave=False)\n # train\n for batch in train_dataloader:\n batch = shard(batch)\n state, train_metric, train_rngs = p_train_step(state, vae_params, unet_params, batch, train_rngs)\n train_metrics.append(train_metric)\n\n train_step_progress_bar.update(1)\n global_step += 1\n\n if global_step >= args.max_train_steps:\n break\n\n train_metric = jax_utils.unreplicate(train_metric)\n\n train_step_progress_bar.close()\n epochs.write(f\"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})\")\n\n # Create the pipeline using using the trained modules and save it.\n if jax.process_index() == 0:\n scheduler = FlaxPNDMScheduler(\n beta_start=0.00085, beta_end=0.012, beta_schedule=\"scaled_linear\", skip_prk_steps=True\n )\n safety_checker = FlaxStableDiffusionSafetyChecker.from_pretrained(\n \"CompVis/stable-diffusion-safety-checker\", from_pt=True\n )\n pipeline = FlaxStableDiffusionPipeline(\n text_encoder=text_encoder,\n vae=vae,\n unet=unet,\n tokenizer=tokenizer,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=CLIPImageProcessor.from_pretrained(\"openai/clip-vit-base-patch32\"),\n )\n\n pipeline.save_pretrained(\n args.output_dir,\n params={\n \"text_encoder\": get_params_to_save(state.params),\n \"vae\": get_params_to_save(vae_params),\n \"unet\": get_params_to_save(unet_params),\n \"safety_checker\": safety_checker.params,\n },\n )\n\n # Also save the newly trained embeddings\n learned_embeds = get_params_to_save(state.params)[\"text_model\"][\"embeddings\"][\"token_embedding\"][\"embedding\"][\n placeholder_token_id\n ]\n learned_embeds_dict = {args.placeholder_token: learned_embeds}\n jnp.save(os.path.join(args.output_dir, \"learned_embeds.npy\"), learned_embeds_dict)\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/research_projects/onnxruntime/README.md", "content": "## Diffusers examples with ONNXRuntime optimizations\n\n**This research project is not actively maintained by the diffusers team. For any questions or comments, please contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions.**\n\nThis aims to provide diffusers examples with ONNXRuntime optimizations for training/fine-tuning unconditional image generation, text to image, and textual inversion. Please see individual directories for more details on how to run each task using ONNXRuntime.\n" }, { "path": "diffusers/examples/research_projects/onnxruntime/text_to_image/README.md", "content": "# Stable Diffusion text-to-image fine-tuning\n\nThe `train_text_to_image.py` script shows how to fine-tune stable diffusion model on your own dataset.\n\n___Note___:\n\n___This script is experimental. The script fine-tunes the whole model and often times the model overfits and runs into issues like catastrophic forgetting. It's recommended to try different hyperparamters to get the best result on your dataset.___\n\n\n## Running locally with PyTorch\n### Installing the dependencies\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n**Important**\n\nTo make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nThen cd in the example folder and run\n```bash\npip install -r requirements.txt\n```\n\nAnd initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\n\n### Naruto example\n\nYou need to accept the model license before downloading or using the weights. In this example we'll use model version `v1-4`, so you'll need to visit [its card](https://huggingface.co/CompVis/stable-diffusion-v1-4), read the license and tick the checkbox if you agree.\n\nYou have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).\n\nRun the following command to authenticate your token\n\n```bash\nhuggingface-cli login\n```\n\nIf you have already cloned the repo, then you won't need to go through these steps.\n\n
\n\n## Use ONNXRuntime to accelerate training\nIn order to leverage onnxruntime to accelerate training, please use train_text_to_image.py\n\nThe command to train a DDPM UNetCondition model on the Naruto dataset with onnxruntime:\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport dataset_name=\"lambdalabs/naruto-blip-captions\"\naccelerate launch --mixed_precision=\"fp16\" train_text_to_image.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --dataset_name=$dataset_name \\\n --use_ema \\\n --resolution=512 --center_crop --random_flip \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --max_train_steps=15000 \\\n --learning_rate=1e-05 \\\n --max_grad_norm=1 \\\n --lr_scheduler=\"constant\" --lr_warmup_steps=0 \\\n --output_dir=\"sd-naruto-model\"\n```\n\nPlease contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions." }, { "path": "diffusers/examples/research_projects/onnxruntime/text_to_image/requirements.txt", "content": "accelerate>=0.16.0\ntorchvision\ntransformers>=4.25.1\ndatasets\nftfy\ntensorboard\nmodelcards\n" }, { "path": "diffusers/examples/research_projects/onnxruntime/text_to_image/train_text_to_image.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport logging\nimport math\nimport os\nimport random\nfrom pathlib import Path\n\nimport accelerate\nimport datasets\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.state import AcceleratorState\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom onnxruntime.training.optim.fp16_optimizer import FP16_Optimizer as ORT_FP16_Optimizer\nfrom onnxruntime.training.ortmodule import ORTModule\nfrom packaging import version\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModel, CLIPTokenizer\nfrom transformers.utils import ContextManagers\n\nimport diffusers\nfrom diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import EMAModel, compute_snr\nfrom diffusers.utils import check_min_version, deprecate, is_wandb_available\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\nif is_wandb_available():\n import wandb\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.17.0.dev0\")\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\nDATASET_NAME_MAPPING = {\n \"lambdalabs/naruto-blip-captions\": (\"image\", \"text\"),\n}\n\n\ndef log_validation(vae, text_encoder, tokenizer, unet, args, accelerator, weight_dtype, epoch):\n logger.info(\"Running validation... \")\n\n pipeline = StableDiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=accelerator.unwrap_model(vae),\n text_encoder=accelerator.unwrap_model(text_encoder),\n tokenizer=tokenizer,\n unet=accelerator.unwrap_model(unet),\n safety_checker=None,\n revision=args.revision,\n torch_dtype=weight_dtype,\n )\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n if args.enable_xformers_memory_efficient_attention:\n pipeline.enable_xformers_memory_efficient_attention()\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n images = []\n for i in range(len(args.validation_prompts)):\n with torch.autocast(\"cuda\"):\n image = pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]\n\n images.append(image)\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, epoch, dataformats=\"NHWC\")\n elif tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompts[i]}\")\n for i, image in enumerate(images)\n ]\n }\n )\n else:\n logger.warning(f\"image logging not implemented for {tracker.name}\")\n\n del pipeline\n torch.cuda.empty_cache()\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--input_pertubation\", type=float, default=0, help=\"The scale of input pretubation. Recommended 0.1.\"\n )\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing an image.\"\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=\"text\",\n help=\"The column of the dataset containing a caption or a list of captions.\",\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--validation_prompts\",\n type=str,\n default=None,\n nargs=\"+\",\n help=(\"A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`.\"),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"sd-model-finetuned\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--snr_gamma\",\n type=float,\n default=None,\n help=\"SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. \"\n \"More details here: https://arxiv.org/abs/2303.09556.\",\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\"--use_ema\", action=\"store_true\", help=\"Whether to use EMA model.\")\n parser.add_argument(\n \"--non_ema_revision\",\n type=str,\n default=None,\n required=False,\n help=(\n \"Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or\"\n \" remote repository specified with --pretrained_model_name_or_path.\"\n ),\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\n \"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`.\"\n \" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state\"\n \" for more docs\"\n ),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\"--noise_offset\", type=float, default=0, help=\"The scale of noise offset.\")\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=5,\n help=\"Run validation every X epochs.\",\n )\n parser.add_argument(\n \"--tracker_project_name\",\n type=str,\n default=\"text2image-fine-tune\",\n help=(\n \"The `project_name` argument passed to Accelerator.init_trackers for\"\n \" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator\"\n ),\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n\n # default to using the same revision for the non-ema model if not specified\n if args.non_ema_revision is None:\n args.non_ema_revision = args.revision\n\n return args\n\n\ndef main():\n args = parse_args()\n\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n if args.non_ema_revision is not None:\n deprecate(\n \"non_ema_revision!=None\",\n \"0.15.0\",\n message=(\n \"Downloading 'non_ema' weights from revision branches of the Hub is deprecated. Please make sure to\"\n \" use `--variant=non_ema` instead.\"\n ),\n )\n logging_dir = os.path.join(args.output_dir, args.logging_dir)\n accelerator_project_config = ProjectConfiguration(\n total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir\n )\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load scheduler, tokenizer and models.\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n tokenizer = CLIPTokenizer.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"tokenizer\", revision=args.revision\n )\n\n def deepspeed_zero_init_disabled_context_manager():\n \"\"\"\n returns either a context list that includes one that will disable zero.Init or an empty context list\n \"\"\"\n deepspeed_plugin = AcceleratorState().deepspeed_plugin if accelerate.state.is_initialized() else None\n if deepspeed_plugin is None:\n return []\n\n return [deepspeed_plugin.zero3_init_context_manager(enable=False)]\n\n # Currently Accelerate doesn't know how to handle multiple models under Deepspeed ZeRO stage 3.\n # For this to work properly all models must be run through `accelerate.prepare`. But accelerate\n # will try to assign the same optimizer with the same weights to all models during\n # `deepspeed.initialize`, which of course doesn't work.\n #\n # For now the following workaround will partially support Deepspeed ZeRO-3, by excluding the 2\n # frozen models from being partitioned during `zero.Init` which gets called during\n # `from_pretrained` So CLIPTextModel and AutoencoderKL will not enjoy the parameter sharding\n # across multiple gpus and only UNet2DConditionModel will get ZeRO sharded.\n with ContextManagers(deepspeed_zero_init_disabled_context_manager()):\n text_encoder = CLIPTextModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision\n )\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision\n )\n\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.non_ema_revision\n )\n\n # Freeze vae and text_encoder\n vae.requires_grad_(False)\n text_encoder.requires_grad_(False)\n\n # Create EMA for the unet.\n if args.use_ema:\n ema_unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision\n )\n ema_unet = EMAModel(ema_unet.parameters(), model_cls=UNet2DConditionModel, model_config=ema_unet.config)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n if args.use_ema:\n ema_unet.save_pretrained(os.path.join(output_dir, \"unet_ema\"))\n\n for i, model in enumerate(models):\n model.save_pretrained(os.path.join(output_dir, \"unet\"))\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n def load_model_hook(models, input_dir):\n if args.use_ema:\n load_model = EMAModel.from_pretrained(os.path.join(input_dir, \"unet_ema\"), UNet2DConditionModel)\n ema_unet.load_state_dict(load_model.state_dict())\n ema_unet.to(accelerator.device)\n del load_model\n\n for i in range(len(models)):\n # pop models so that they are not loaded again\n model = models.pop()\n\n # load diffusers style into model\n load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder=\"unet\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Initialize the optimizer\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`\"\n )\n\n optimizer_cls = bnb.optim.AdamW8bit\n else:\n optimizer_cls = torch.optim.AdamW\n\n optimizer = optimizer_cls(\n unet.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n optimizer = ORT_FP16_Optimizer(optimizer)\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n )\n else:\n data_files = {}\n if args.train_data_dir is not None:\n data_files[\"train\"] = os.path.join(args.train_data_dir, \"**\")\n dataset = load_dataset(\n \"imagefolder\",\n data_files=data_files,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)\n if args.image_column is None:\n image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.caption_column is None:\n caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n else:\n caption_column = args.caption_column\n if caption_column not in column_names:\n raise ValueError(\n f\"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}\"\n )\n\n # Preprocessing the datasets.\n # We need to tokenize input captions and transform the images.\n def tokenize_captions(examples, is_train=True):\n captions = []\n for caption in examples[caption_column]:\n if isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n else:\n raise ValueError(\n f\"Caption column `{caption_column}` should contain either strings or lists of strings.\"\n )\n inputs = tokenizer(\n captions, max_length=tokenizer.model_max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\"\n )\n return inputs.input_ids\n\n # Preprocessing the datasets.\n train_transforms = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),\n transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n examples[\"pixel_values\"] = [train_transforms(image) for image in images]\n examples[\"input_ids\"] = tokenize_captions(examples)\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n def collate_fn(examples):\n pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n input_ids = torch.stack([example[\"input_ids\"] for example in examples])\n return {\"pixel_values\": pixel_values, \"input_ids\": input_ids}\n\n # DataLoaders creation:\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n )\n\n # Prepare everything with our `accelerator`.\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n if args.use_ema:\n ema_unet.to(accelerator.device)\n\n unet = ORTModule(unet)\n\n # For mixed precision training we cast the text_encoder and vae weights to half-precision\n # as these models are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move text_encode and vae to gpu and cast to weight_dtype\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n vae.to(accelerator.device, dtype=weight_dtype)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = dict(vars(args))\n tracker_config.pop(\"validation_prompts\")\n accelerator.init_trackers(args.tracker_project_name, tracker_config)\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n resume_global_step = global_step * args.gradient_accumulation_steps\n first_epoch = global_step // num_update_steps_per_epoch\n resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)\n\n # Only show the progress bar once on each machine.\n progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)\n progress_bar.set_description(\"Steps\")\n\n for epoch in range(first_epoch, args.num_train_epochs):\n unet.train()\n train_loss = 0.0\n for step, batch in enumerate(train_dataloader):\n # Skip steps until we reach the resumed step\n if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:\n if step % args.gradient_accumulation_steps == 0:\n progress_bar.update(1)\n continue\n\n with accelerator.accumulate(unet):\n # Convert images to latent space\n latents = vae.encode(batch[\"pixel_values\"].to(weight_dtype)).latent_dist.sample()\n latents = latents * vae.config.scaling_factor\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n if args.noise_offset:\n # https://www.crosslabs.org//blog/diffusion-with-offset-noise\n noise += args.noise_offset * torch.randn(\n (latents.shape[0], latents.shape[1], 1, 1), device=latents.device\n )\n if args.input_pertubation:\n new_noise = noise + args.input_pertubation * torch.randn_like(noise)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n if args.input_pertubation:\n noisy_latents = noise_scheduler.add_noise(latents, new_noise, timesteps)\n else:\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # Get the text embedding for conditioning\n encoder_hidden_states = text_encoder(batch[\"input_ids\"])[0]\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n # Predict the noise residual and compute loss\n model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample\n\n if args.snr_gamma is None:\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n else:\n # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.\n # Since we predict the noise instead of x_0, the original formulation is slightly changed.\n # This is discussed in Section 4.2 of the same paper.\n snr = compute_snr(noise_scheduler, timesteps)\n mse_loss_weights = torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(\n dim=1\n )[0]\n if noise_scheduler.config.prediction_type == \"epsilon\":\n mse_loss_weights = mse_loss_weights / snr\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n mse_loss_weights = mse_loss_weights / (snr + 1)\n\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"none\")\n loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights\n loss = loss.mean()\n\n # Gather the losses across all processes for logging (if we use distributed training).\n avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n train_loss += avg_loss.item() / args.gradient_accumulation_steps\n\n # Backpropagate\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n if args.use_ema:\n ema_unet.step(unet.parameters())\n progress_bar.update(1)\n global_step += 1\n accelerator.log({\"train_loss\": train_loss}, step=global_step)\n train_loss = 0.0\n\n if global_step % args.checkpointing_steps == 0:\n if accelerator.is_main_process:\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"step_loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompts is not None and epoch % args.validation_epochs == 0:\n if args.use_ema:\n # Store the UNet parameters temporarily and load the EMA parameters to perform inference.\n ema_unet.store(unet.parameters())\n ema_unet.copy_to(unet.parameters())\n log_validation(\n vae,\n text_encoder,\n tokenizer,\n unet,\n args,\n accelerator,\n weight_dtype,\n global_step,\n )\n if args.use_ema:\n # Switch back to the original UNet parameters.\n ema_unet.restore(unet.parameters())\n\n # Create the pipeline using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = accelerator.unwrap_model(unet)\n if args.use_ema:\n ema_unet.copy_to(unet.parameters())\n\n pipeline = StableDiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n text_encoder=text_encoder,\n vae=vae,\n unet=unet,\n revision=args.revision,\n )\n pipeline.save_pretrained(args.output_dir)\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/research_projects/onnxruntime/textual_inversion/README.md", "content": "## Textual Inversion fine-tuning example\n\n[Textual inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like stable diffusion on your own images using just 3-5 examples.\nThe `textual_inversion.py` script shows how to implement the training procedure and adapt it for stable diffusion.\n\n## Running on Colab\n\nColab for training\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb)\n\nColab for inference\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb)\n\n## Running locally with PyTorch\n### Installing the dependencies\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n**Important**\n\nTo make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nThen cd in the example folder and run\n```bash\npip install -r requirements.txt\n```\n\nAnd initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\n\n\n### Cat toy example\n\nYou need to accept the model license before downloading or using the weights. In this example we'll use model version `v1-5`, so you'll need to visit [its card](https://huggingface.co/runwayml/stable-diffusion-v1-5), read the license and tick the checkbox if you agree.\n\nYou have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).\n\nRun the following command to authenticate your token\n\n```bash\nhuggingface-cli login\n```\n\nIf you have already cloned the repo, then you won't need to go through these steps.\n\n
\n\nNow let's get our dataset. For this example we will use some cat images: https://huggingface.co/datasets/diffusers/cat_toy_example .\n\nLet's first download it locally:\n\n```py\nfrom huggingface_hub import snapshot_download\n\nlocal_dir = \"./cat\"\nsnapshot_download(\"diffusers/cat_toy_example\", local_dir=local_dir, repo_type=\"dataset\", ignore_patterns=\".gitattributes\")\n```\n\nThis will be our training data.\nNow we can launch the training using\n\n## Use ONNXRuntime to accelerate training\nIn order to leverage onnxruntime to accelerate training, please use textual_inversion.py\n\nThe command to train on custom data with onnxruntime:\n\n```bash\nexport MODEL_NAME=\"runwayml/stable-diffusion-v1-5\"\nexport DATA_DIR=\"path-to-dir-containing-images\"\n\naccelerate launch textual_inversion.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_data_dir=$DATA_DIR \\\n --learnable_property=\"object\" \\\n --placeholder_token=\"\" --initializer_token=\"toy\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --max_train_steps=3000 \\\n --learning_rate=5.0e-04 --scale_lr \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --output_dir=\"textual_inversion_cat\"\n```\n\nPlease contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions." }, { "path": "diffusers/examples/research_projects/onnxruntime/textual_inversion/requirements.txt", "content": "accelerate>=0.16.0\ntorchvision\ntransformers>=4.25.1\nftfy\ntensorboard\nmodelcards\n" }, { "path": "diffusers/examples/research_projects/onnxruntime/textual_inversion/textual_inversion.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport logging\nimport math\nimport os\nimport random\nimport warnings\nfrom pathlib import Path\n\nimport numpy as np\nimport PIL\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom huggingface_hub import create_repo, upload_folder\nfrom onnxruntime.training.optim.fp16_optimizer import FP16_Optimizer as ORT_FP16_Optimizer\nfrom onnxruntime.training.ortmodule import ORTModule\n\n# TODO: remove and import from diffusers.utils when the new version of diffusers is released\nfrom packaging import version\nfrom PIL import Image\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n DiffusionPipeline,\n DPMSolverMultistepScheduler,\n StableDiffusionPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import check_min_version, is_wandb_available\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\nif is_wandb_available():\n import wandb\n\nif version.parse(version.parse(PIL.__version__).base_version) >= version.parse(\"9.1.0\"):\n PIL_INTERPOLATION = {\n \"linear\": PIL.Image.Resampling.BILINEAR,\n \"bilinear\": PIL.Image.Resampling.BILINEAR,\n \"bicubic\": PIL.Image.Resampling.BICUBIC,\n \"lanczos\": PIL.Image.Resampling.LANCZOS,\n \"nearest\": PIL.Image.Resampling.NEAREST,\n }\nelse:\n PIL_INTERPOLATION = {\n \"linear\": PIL.Image.LINEAR,\n \"bilinear\": PIL.Image.BILINEAR,\n \"bicubic\": PIL.Image.BICUBIC,\n \"lanczos\": PIL.Image.LANCZOS,\n \"nearest\": PIL.Image.NEAREST,\n }\n# ------------------------------------------------------------------------------\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.17.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef save_model_card(repo_id: str, images=None, base_model=str, repo_folder=None):\n img_str = \"\"\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n img_str += f\"![img_{i}](./image_{i}.png)\\n\"\n\n yaml = f\"\"\"\n---\nlicense: creativeml-openrail-m\nbase_model: {base_model}\ntags:\n- stable-diffusion\n- stable-diffusion-diffusers\n- text-to-image\n- diffusers\n- textual_inversion\n- diffusers-training\n- onxruntime\ninference: true\n---\n \"\"\"\n model_card = f\"\"\"\n# Textual inversion text2image fine-tuning - {repo_id}\nThese are textual inversion adaption weights for {base_model}. You can find some example images in the following. \\n\n{img_str}\n\"\"\"\n with open(os.path.join(repo_folder, \"README.md\"), \"w\") as f:\n f.write(yaml + model_card)\n\n\ndef log_validation(text_encoder, tokenizer, unet, vae, args, accelerator, weight_dtype, epoch):\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n # create pipeline (note: unet and vae are loaded again in float32)\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n text_encoder=accelerator.unwrap_model(text_encoder),\n tokenizer=tokenizer,\n unet=unet,\n vae=vae,\n safety_checker=None,\n revision=args.revision,\n torch_dtype=weight_dtype,\n )\n pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = None if args.seed is None else torch.Generator(device=accelerator.device).manual_seed(args.seed)\n images = []\n for _ in range(args.num_validation_images):\n with torch.autocast(\"cuda\"):\n image = pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]\n images.append(image)\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\") for i, image in enumerate(images)\n ]\n }\n )\n\n del pipeline\n torch.cuda.empty_cache()\n return images\n\n\ndef save_progress(text_encoder, placeholder_token_ids, accelerator, args, save_path):\n logger.info(\"Saving embeddings\")\n learned_embeds = (\n accelerator.unwrap_model(text_encoder)\n .get_input_embeddings()\n .weight[min(placeholder_token_ids) : max(placeholder_token_ids) + 1]\n )\n learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()}\n torch.save(learned_embeds_dict, save_path)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--save_steps\",\n type=int,\n default=500,\n help=\"Save learned_embeds.bin every X updates steps.\",\n )\n parser.add_argument(\n \"--save_as_full_pipeline\",\n action=\"store_true\",\n help=\"Save the complete stable diffusion pipeline.\",\n )\n parser.add_argument(\n \"--num_vectors\",\n type=int,\n default=1,\n help=\"How many textual inversion vectors shall be used to learn the concept.\",\n )\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--tokenizer_name\",\n type=str,\n default=None,\n help=\"Pretrained tokenizer name or path if not the same as model_name\",\n )\n parser.add_argument(\n \"--train_data_dir\", type=str, default=None, required=True, help=\"A folder containing the training data.\"\n )\n parser.add_argument(\n \"--placeholder_token\",\n type=str,\n default=None,\n required=True,\n help=\"A token to use as a placeholder for the concept.\",\n )\n parser.add_argument(\n \"--initializer_token\", type=str, default=None, required=True, help=\"A token to use as initializer word.\"\n )\n parser.add_argument(\"--learnable_property\", type=str, default=\"object\", help=\"Choose between 'object' and 'style'\")\n parser.add_argument(\"--repeats\", type=int, default=100, help=\"How many times to repeat the training data.\")\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"text-inversion-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\", action=\"store_true\", help=\"Whether to center crop images before resizing to resolution.\"\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=5000,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=\"no\",\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose\"\n \"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.\"\n \"and an Nvidia Ampere GPU.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n help=\"A prompt that is used during validation to verify that the model is learning.\",\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=100,\n help=(\n \"Run validation every X steps. Validation consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`\"\n \" and logging the images.\"\n ),\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=None,\n help=(\n \"Deprecated in favor of validation_steps. Run validation every X epochs. Validation consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`\"\n \" and logging the images.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\n \"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`.\"\n \" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state\"\n \" for more docs\"\n ),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.train_data_dir is None:\n raise ValueError(\"You must specify a train data directory.\")\n\n return args\n\n\nimagenet_templates_small = [\n \"a photo of a {}\",\n \"a rendering of a {}\",\n \"a cropped photo of the {}\",\n \"the photo of a {}\",\n \"a photo of a clean {}\",\n \"a photo of a dirty {}\",\n \"a dark photo of the {}\",\n \"a photo of my {}\",\n \"a photo of the cool {}\",\n \"a close-up photo of a {}\",\n \"a bright photo of the {}\",\n \"a cropped photo of a {}\",\n \"a photo of the {}\",\n \"a good photo of the {}\",\n \"a photo of one {}\",\n \"a close-up photo of the {}\",\n \"a rendition of the {}\",\n \"a photo of the clean {}\",\n \"a rendition of a {}\",\n \"a photo of a nice {}\",\n \"a good photo of a {}\",\n \"a photo of the nice {}\",\n \"a photo of the small {}\",\n \"a photo of the weird {}\",\n \"a photo of the large {}\",\n \"a photo of a cool {}\",\n \"a photo of a small {}\",\n]\n\nimagenet_style_templates_small = [\n \"a painting in the style of {}\",\n \"a rendering in the style of {}\",\n \"a cropped painting in the style of {}\",\n \"the painting in the style of {}\",\n \"a clean painting in the style of {}\",\n \"a dirty painting in the style of {}\",\n \"a dark painting in the style of {}\",\n \"a picture in the style of {}\",\n \"a cool painting in the style of {}\",\n \"a close-up painting in the style of {}\",\n \"a bright painting in the style of {}\",\n \"a cropped painting in the style of {}\",\n \"a good painting in the style of {}\",\n \"a close-up painting in the style of {}\",\n \"a rendition in the style of {}\",\n \"a nice painting in the style of {}\",\n \"a small painting in the style of {}\",\n \"a weird painting in the style of {}\",\n \"a large painting in the style of {}\",\n]\n\n\nclass TextualInversionDataset(Dataset):\n def __init__(\n self,\n data_root,\n tokenizer,\n learnable_property=\"object\", # [object, style]\n size=512,\n repeats=100,\n interpolation=\"bicubic\",\n flip_p=0.5,\n set=\"train\",\n placeholder_token=\"*\",\n center_crop=False,\n ):\n self.data_root = data_root\n self.tokenizer = tokenizer\n self.learnable_property = learnable_property\n self.size = size\n self.placeholder_token = placeholder_token\n self.center_crop = center_crop\n self.flip_p = flip_p\n\n self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]\n\n self.num_images = len(self.image_paths)\n self._length = self.num_images\n\n if set == \"train\":\n self._length = self.num_images * repeats\n\n self.interpolation = {\n \"linear\": PIL_INTERPOLATION[\"linear\"],\n \"bilinear\": PIL_INTERPOLATION[\"bilinear\"],\n \"bicubic\": PIL_INTERPOLATION[\"bicubic\"],\n \"lanczos\": PIL_INTERPOLATION[\"lanczos\"],\n }[interpolation]\n\n self.templates = imagenet_style_templates_small if learnable_property == \"style\" else imagenet_templates_small\n self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, i):\n example = {}\n image = Image.open(self.image_paths[i % self.num_images])\n\n if not image.mode == \"RGB\":\n image = image.convert(\"RGB\")\n\n placeholder_string = self.placeholder_token\n text = random.choice(self.templates).format(placeholder_string)\n\n example[\"input_ids\"] = self.tokenizer(\n text,\n padding=\"max_length\",\n truncation=True,\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n ).input_ids[0]\n\n # default to score-sde preprocessing\n img = np.array(image).astype(np.uint8)\n\n if self.center_crop:\n crop = min(img.shape[0], img.shape[1])\n (\n h,\n w,\n ) = (\n img.shape[0],\n img.shape[1],\n )\n img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]\n\n image = Image.fromarray(img)\n image = image.resize((self.size, self.size), resample=self.interpolation)\n\n image = self.flip_transform(image)\n image = np.array(image).astype(np.uint8)\n image = (image / 127.5 - 1.0).astype(np.float32)\n\n example[\"pixel_values\"] = torch.from_numpy(image).permute(2, 0, 1)\n return example\n\n\ndef main():\n args = parse_args()\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = os.path.join(args.output_dir, args.logging_dir)\n accelerator_project_config = ProjectConfiguration(\n total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir\n )\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load tokenizer\n if args.tokenizer_name:\n tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)\n elif args.pretrained_model_name_or_path:\n tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"tokenizer\")\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n text_encoder = CLIPTextModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision\n )\n vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision)\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision\n )\n\n # Add the placeholder token in tokenizer\n placeholder_tokens = [args.placeholder_token]\n\n if args.num_vectors < 1:\n raise ValueError(f\"--num_vectors has to be larger or equal to 1, but is {args.num_vectors}\")\n\n # add dummy tokens for multi-vector\n additional_tokens = []\n for i in range(1, args.num_vectors):\n additional_tokens.append(f\"{args.placeholder_token}_{i}\")\n placeholder_tokens += additional_tokens\n\n num_added_tokens = tokenizer.add_tokens(placeholder_tokens)\n if num_added_tokens != args.num_vectors:\n raise ValueError(\n f\"The tokenizer already contains the token {args.placeholder_token}. Please pass a different\"\n \" `placeholder_token` that is not already in the tokenizer.\"\n )\n\n # Convert the initializer_token, placeholder_token to ids\n token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)\n # Check if initializer_token is a single token or a sequence of tokens\n if len(token_ids) > 1:\n raise ValueError(\"The initializer token must be a single token.\")\n\n initializer_token_id = token_ids[0]\n placeholder_token_ids = tokenizer.convert_tokens_to_ids(placeholder_tokens)\n\n # Resize the token embeddings as we are adding new special tokens to the tokenizer\n text_encoder.resize_token_embeddings(len(tokenizer))\n\n # Initialise the newly added placeholder token with the embeddings of the initializer token\n token_embeds = text_encoder.get_input_embeddings().weight.data\n with torch.no_grad():\n for token_id in placeholder_token_ids:\n token_embeds[token_id] = token_embeds[initializer_token_id].clone()\n\n # Freeze vae and unet\n vae.requires_grad_(False)\n unet.requires_grad_(False)\n # Freeze all parameters except for the token embeddings in text encoder\n text_encoder.text_model.encoder.requires_grad_(False)\n text_encoder.text_model.final_layer_norm.requires_grad_(False)\n text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)\n\n if args.gradient_checkpointing:\n # Keep unet in train mode if we are using gradient checkpointing to save memory.\n # The dropout cannot be != 0 so it doesn't matter if we are in eval or train mode.\n unet.train()\n text_encoder.gradient_checkpointing_enable()\n unet.enable_gradient_checkpointing()\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Initialize the optimizer\n optimizer = torch.optim.AdamW(\n text_encoder.get_input_embeddings().parameters(), # only optimize the embeddings\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n optimizer = ORT_FP16_Optimizer(optimizer)\n\n # Dataset and DataLoaders creation:\n train_dataset = TextualInversionDataset(\n data_root=args.train_data_dir,\n tokenizer=tokenizer,\n size=args.resolution,\n placeholder_token=args.placeholder_token,\n repeats=args.repeats,\n learnable_property=args.learnable_property,\n center_crop=args.center_crop,\n set=\"train\",\n )\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers\n )\n if args.validation_epochs is not None:\n warnings.warn(\n f\"FutureWarning: You are doing logging with validation_epochs={args.validation_epochs}.\"\n \" Deprecated validation_epochs in favor of `validation_steps`\"\n f\"Setting `args.validation_steps` to {args.validation_epochs * len(train_dataset)}\",\n FutureWarning,\n stacklevel=2,\n )\n args.validation_steps = args.validation_epochs * len(train_dataset)\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n )\n\n # Prepare everything with our `accelerator`.\n text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n text_encoder, optimizer, train_dataloader, lr_scheduler\n )\n\n text_encoder = ORTModule(text_encoder)\n unet = ORTModule(unet)\n vae = ORTModule(vae)\n\n # For mixed precision training we cast the unet and vae weights to half-precision\n # as these models are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move vae and unet to device and cast to weight_dtype\n unet.to(accelerator.device, dtype=weight_dtype)\n vae.to(accelerator.device, dtype=weight_dtype)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"textual_inversion\", config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n resume_global_step = global_step * args.gradient_accumulation_steps\n first_epoch = global_step // num_update_steps_per_epoch\n resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)\n\n # Only show the progress bar once on each machine.\n progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)\n progress_bar.set_description(\"Steps\")\n\n # keep original embeddings as reference\n orig_embeds_params = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight.data.clone()\n\n for epoch in range(first_epoch, args.num_train_epochs):\n text_encoder.train()\n for step, batch in enumerate(train_dataloader):\n # Skip steps until we reach the resumed step\n if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:\n if step % args.gradient_accumulation_steps == 0:\n progress_bar.update(1)\n continue\n\n with accelerator.accumulate(text_encoder):\n # Convert images to latent space\n latents = vae.encode(batch[\"pixel_values\"].to(dtype=weight_dtype)).latent_dist.sample().detach()\n latents = latents * vae.config.scaling_factor\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # Get the text embedding for conditioning\n encoder_hidden_states = text_encoder(batch[\"input_ids\"])[0].to(dtype=weight_dtype)\n\n # Predict the noise residual\n model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n\n accelerator.backward(loss)\n\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Let's make sure we don't update any embedding weights besides the newly added token\n index_no_updates = torch.ones((len(tokenizer),), dtype=torch.bool)\n index_no_updates[min(placeholder_token_ids) : max(placeholder_token_ids) + 1] = False\n\n with torch.no_grad():\n accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[\n index_no_updates\n ] = orig_embeds_params[index_no_updates]\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n images = []\n progress_bar.update(1)\n global_step += 1\n if global_step % args.save_steps == 0:\n save_path = os.path.join(args.output_dir, f\"learned_embeds-steps-{global_step}.bin\")\n save_progress(text_encoder, placeholder_token_ids, accelerator, args, save_path)\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n if args.validation_prompt is not None and global_step % args.validation_steps == 0:\n images = log_validation(\n text_encoder, tokenizer, unet, vae, args, accelerator, weight_dtype, epoch\n )\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n # Create the pipeline using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n if args.push_to_hub and not args.save_as_full_pipeline:\n logger.warning(\"Enabling full model saving because --push_to_hub=True was specified.\")\n save_full_model = True\n else:\n save_full_model = args.save_as_full_pipeline\n if save_full_model:\n pipeline = StableDiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n text_encoder=accelerator.unwrap_model(text_encoder),\n vae=vae,\n unet=unet,\n tokenizer=tokenizer,\n )\n pipeline.save_pretrained(args.output_dir)\n # Save the newly trained embeddings\n save_path = os.path.join(args.output_dir, \"learned_embeds.bin\")\n save_progress(text_encoder, placeholder_token_ids, accelerator, args, save_path)\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n images=images,\n base_model=args.pretrained_model_name_or_path,\n repo_folder=args.output_dir,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/research_projects/onnxruntime/unconditional_image_generation/README.md", "content": "## Training examples\n\nCreating a training image set is [described in a different document](https://huggingface.co/docs/datasets/image_process#image-datasets).\n\n### Installing the dependencies\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n**Important**\n\nTo make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nThen cd in the example folder and run\n```bash\npip install -r requirements.txt\n```\n\n\nAnd initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\n\n#### Use ONNXRuntime to accelerate training\n\nIn order to leverage onnxruntime to accelerate training, please use train_unconditional_ort.py\n\nThe command to train a DDPM UNet model on the Oxford Flowers dataset with onnxruntime:\n\n```bash\naccelerate launch train_unconditional.py \\\n --dataset_name=\"huggan/flowers-102-categories\" \\\n --resolution=64 --center_crop --random_flip \\\n --output_dir=\"ddpm-ema-flowers-64\" \\\n --use_ema \\\n --train_batch_size=16 \\\n --num_epochs=1 \\\n --gradient_accumulation_steps=1 \\\n --learning_rate=1e-4 \\\n --lr_warmup_steps=500 \\\n --mixed_precision=fp16\n ```\n\nPlease contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions.\n" }, { "path": "diffusers/examples/research_projects/onnxruntime/unconditional_image_generation/requirements.txt", "content": "accelerate>=0.16.0\ntorchvision\ndatasets\ntensorboard" }, { "path": "diffusers/examples/research_projects/onnxruntime/unconditional_image_generation/train_unconditional.py", "content": "import argparse\nimport inspect\nimport logging\nimport math\nimport os\nfrom pathlib import Path\n\nimport accelerate\nimport datasets\nimport torch\nimport torch.nn.functional as F\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom onnxruntime.training.optim.fp16_optimizer import FP16_Optimizer as ORT_FP16_Optimizer\nfrom onnxruntime.training.ortmodule import ORTModule\nfrom packaging import version\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\n\nimport diffusers\nfrom diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import EMAModel\nfrom diffusers.utils import check_min_version, is_accelerate_version, is_tensorboard_available, is_wandb_available\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.17.0.dev0\")\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\n\ndef _extract_into_tensor(arr, timesteps, broadcast_shape):\n \"\"\"\n Extract values from a 1-D numpy array for a batch of indices.\n\n :param arr: the 1-D numpy array.\n :param timesteps: a tensor of indices into the array to extract.\n :param broadcast_shape: a larger shape of K dimensions with the batch\n dimension equal to the length of timesteps.\n :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.\n \"\"\"\n if not isinstance(arr, torch.Tensor):\n arr = torch.from_numpy(arr)\n res = arr[timesteps].float().to(timesteps.device)\n while len(res.shape) < len(broadcast_shape):\n res = res[..., None]\n return res.expand(broadcast_shape)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that HF Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--model_config_name_or_path\",\n type=str,\n default=None,\n help=\"The config of the UNet model to train, leave as None to use standard DDPM configuration.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"ddpm-model-64\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--overwrite_output_dir\", action=\"store_true\")\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=64,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n default=False,\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\n \"--eval_batch_size\", type=int, default=16, help=\"The number of images to generate for evaluation.\"\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"The number of subprocesses to use for data loading. 0 means that the data will be loaded in the main\"\n \" process.\"\n ),\n )\n parser.add_argument(\"--num_epochs\", type=int, default=100)\n parser.add_argument(\"--save_images_epochs\", type=int, default=10, help=\"How often to save images during training.\")\n parser.add_argument(\n \"--save_model_epochs\", type=int, default=10, help=\"How often to save the model during training.\"\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"cosine\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.95, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\n \"--adam_weight_decay\", type=float, default=1e-6, help=\"Weight decay magnitude for the Adam optimizer.\"\n )\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer.\")\n parser.add_argument(\n \"--use_ema\",\n action=\"store_true\",\n help=\"Whether to use Exponential Moving Average for the final model weights.\",\n )\n parser.add_argument(\"--ema_inv_gamma\", type=float, default=1.0, help=\"The inverse gamma value for the EMA decay.\")\n parser.add_argument(\"--ema_power\", type=float, default=3 / 4, help=\"The power value for the EMA decay.\")\n parser.add_argument(\"--ema_max_decay\", type=float, default=0.9999, help=\"The maximum decay magnitude for EMA.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--hub_private_repo\", action=\"store_true\", help=\"Whether or not to create a private repository.\"\n )\n parser.add_argument(\n \"--logger\",\n type=str,\n default=\"tensorboard\",\n choices=[\"tensorboard\", \"wandb\"],\n help=(\n \"Whether to use [tensorboard](https://www.tensorflow.org/tensorboard) or [wandb](https://www.wandb.ai)\"\n \" for experiment tracking and logging of model metrics and model checkpoints\"\n ),\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=\"no\",\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose\"\n \"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.\"\n \"and an Nvidia Ampere GPU.\"\n ),\n )\n parser.add_argument(\n \"--prediction_type\",\n type=str,\n default=\"epsilon\",\n choices=[\"epsilon\", \"sample\"],\n help=\"Whether the model should predict the 'epsilon'/noise error or directly the reconstructed image 'x0'.\",\n )\n parser.add_argument(\"--ddpm_num_steps\", type=int, default=1000)\n parser.add_argument(\"--ddpm_num_inference_steps\", type=int, default=1000)\n parser.add_argument(\"--ddpm_beta_schedule\", type=str, default=\"linear\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\n \"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`.\"\n \" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state\"\n \" for more docs\"\n ),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"You must specify either a dataset name from the hub or a train data directory.\")\n\n return args\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = os.path.join(args.output_dir, args.logging_dir)\n accelerator_project_config = ProjectConfiguration(\n total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir\n )\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n if args.logger == \"tensorboard\":\n if not is_tensorboard_available():\n raise ImportError(\"Make sure to install tensorboard if you want to use it for logging during training.\")\n\n elif args.logger == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n import wandb\n\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n if args.use_ema:\n ema_model.save_pretrained(os.path.join(output_dir, \"unet_ema\"))\n\n for i, model in enumerate(models):\n model.save_pretrained(os.path.join(output_dir, \"unet\"))\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n def load_model_hook(models, input_dir):\n if args.use_ema:\n load_model = EMAModel.from_pretrained(os.path.join(input_dir, \"unet_ema\"), UNet2DModel)\n ema_model.load_state_dict(load_model.state_dict())\n ema_model.to(accelerator.device)\n del load_model\n\n for i in range(len(models)):\n # pop models so that they are not loaded again\n model = models.pop()\n\n # load diffusers style into model\n load_model = UNet2DModel.from_pretrained(input_dir, subfolder=\"unet\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Initialize the model\n if args.model_config_name_or_path is None:\n model = UNet2DModel(\n sample_size=args.resolution,\n in_channels=3,\n out_channels=3,\n layers_per_block=2,\n block_out_channels=(128, 128, 256, 256, 512, 512),\n down_block_types=(\n \"DownBlock2D\",\n \"DownBlock2D\",\n \"DownBlock2D\",\n \"DownBlock2D\",\n \"AttnDownBlock2D\",\n \"DownBlock2D\",\n ),\n up_block_types=(\n \"UpBlock2D\",\n \"AttnUpBlock2D\",\n \"UpBlock2D\",\n \"UpBlock2D\",\n \"UpBlock2D\",\n \"UpBlock2D\",\n ),\n )\n else:\n config = UNet2DModel.load_config(args.model_config_name_or_path)\n model = UNet2DModel.from_config(config)\n\n # Create EMA for the model.\n if args.use_ema:\n ema_model = EMAModel(\n model.parameters(),\n decay=args.ema_max_decay,\n use_ema_warmup=True,\n inv_gamma=args.ema_inv_gamma,\n power=args.ema_power,\n model_cls=UNet2DModel,\n model_config=model.config,\n )\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n model.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # Initialize the scheduler\n accepts_prediction_type = \"prediction_type\" in set(inspect.signature(DDPMScheduler.__init__).parameters.keys())\n if accepts_prediction_type:\n noise_scheduler = DDPMScheduler(\n num_train_timesteps=args.ddpm_num_steps,\n beta_schedule=args.ddpm_beta_schedule,\n prediction_type=args.prediction_type,\n )\n else:\n noise_scheduler = DDPMScheduler(num_train_timesteps=args.ddpm_num_steps, beta_schedule=args.ddpm_beta_schedule)\n\n # Initialize the optimizer\n optimizer = torch.optim.AdamW(\n model.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n optimizer = ORT_FP16_Optimizer(optimizer)\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n split=\"train\",\n )\n else:\n dataset = load_dataset(\"imagefolder\", data_dir=args.train_data_dir, cache_dir=args.cache_dir, split=\"train\")\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder\n\n # Preprocessing the datasets and DataLoaders creation.\n augmentations = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),\n transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def transform_images(examples):\n images = [augmentations(image.convert(\"RGB\")) for image in examples[\"image\"]]\n return {\"input\": images}\n\n logger.info(f\"Dataset size: {len(dataset)}\")\n\n dataset.set_transform(transform_images)\n train_dataloader = torch.utils.data.DataLoader(\n dataset, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers\n )\n\n # Initialize the learning rate scheduler\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,\n num_training_steps=(len(train_dataloader) * args.num_epochs),\n )\n\n # Prepare everything with our `accelerator`.\n model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n model, optimizer, train_dataloader, lr_scheduler\n )\n\n if args.use_ema:\n ema_model.to(accelerator.device)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n run = os.path.split(__file__)[-1].split(\".\")[0]\n accelerator.init_trackers(run)\n\n model = ORTModule(model)\n\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n max_train_steps = args.num_epochs * num_update_steps_per_epoch\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(dataset)}\")\n logger.info(f\" Num Epochs = {args.num_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {max_train_steps}\")\n\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n resume_global_step = global_step * args.gradient_accumulation_steps\n first_epoch = global_step // num_update_steps_per_epoch\n resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)\n\n # Train!\n for epoch in range(first_epoch, args.num_epochs):\n model.train()\n progress_bar = tqdm(total=num_update_steps_per_epoch, disable=not accelerator.is_local_main_process)\n progress_bar.set_description(f\"Epoch {epoch}\")\n for step, batch in enumerate(train_dataloader):\n # Skip steps until we reach the resumed step\n if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:\n if step % args.gradient_accumulation_steps == 0:\n progress_bar.update(1)\n continue\n\n clean_images = batch[\"input\"]\n # Sample noise that we'll add to the images\n noise = torch.randn(\n clean_images.shape, dtype=(torch.float32 if args.mixed_precision == \"no\" else torch.float16)\n ).to(clean_images.device)\n bsz = clean_images.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=clean_images.device\n ).long()\n\n # Add noise to the clean images according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)\n\n with accelerator.accumulate(model):\n # Predict the noise residual\n model_output = model(noisy_images, timesteps, return_dict=False)[0]\n\n if args.prediction_type == \"epsilon\":\n loss = F.mse_loss(model_output, noise) # this could have different weights!\n elif args.prediction_type == \"sample\":\n alpha_t = _extract_into_tensor(\n noise_scheduler.alphas_cumprod, timesteps, (clean_images.shape[0], 1, 1, 1)\n )\n snr_weights = alpha_t / (1 - alpha_t)\n loss = snr_weights * F.mse_loss(\n model_output, clean_images, reduction=\"none\"\n ) # use SNR weighting from distillation paper\n loss = loss.mean()\n else:\n raise ValueError(f\"Unsupported prediction type: {args.prediction_type}\")\n\n accelerator.backward(loss)\n\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(model.parameters(), 1.0)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n if args.use_ema:\n ema_model.step(model.parameters())\n progress_bar.update(1)\n global_step += 1\n\n if global_step % args.checkpointing_steps == 0:\n if accelerator.is_main_process:\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0], \"step\": global_step}\n if args.use_ema:\n logs[\"ema_decay\"] = ema_model.cur_decay_value\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n progress_bar.close()\n\n accelerator.wait_for_everyone()\n\n # Generate sample images for visual inspection\n if accelerator.is_main_process:\n if epoch % args.save_images_epochs == 0 or epoch == args.num_epochs - 1:\n unet = accelerator.unwrap_model(model)\n\n if args.use_ema:\n ema_model.store(unet.parameters())\n ema_model.copy_to(unet.parameters())\n\n pipeline = DDPMPipeline(\n unet=unet,\n scheduler=noise_scheduler,\n )\n\n generator = torch.Generator(device=pipeline.device).manual_seed(0)\n # run pipeline in inference (sample random noise and denoise)\n images = pipeline(\n generator=generator,\n batch_size=args.eval_batch_size,\n num_inference_steps=args.ddpm_num_inference_steps,\n output_type=\"np\",\n ).images\n\n if args.use_ema:\n ema_model.restore(unet.parameters())\n\n # denormalize the images and save to tensorboard\n images_processed = (images * 255).round().astype(\"uint8\")\n\n if args.logger == \"tensorboard\":\n if is_accelerate_version(\">=\", \"0.17.0.dev0\"):\n tracker = accelerator.get_tracker(\"tensorboard\", unwrap=True)\n else:\n tracker = accelerator.get_tracker(\"tensorboard\")\n tracker.add_images(\"test_samples\", images_processed.transpose(0, 3, 1, 2), epoch)\n elif args.logger == \"wandb\":\n # Upcoming `log_images` helper coming in https://github.com/huggingface/accelerate/pull/962/files\n accelerator.get_tracker(\"wandb\").log(\n {\"test_samples\": [wandb.Image(img) for img in images_processed], \"epoch\": epoch},\n step=global_step,\n )\n\n if epoch % args.save_model_epochs == 0 or epoch == args.num_epochs - 1:\n # save the model\n unet = accelerator.unwrap_model(model)\n\n if args.use_ema:\n ema_model.store(unet.parameters())\n ema_model.copy_to(unet.parameters())\n\n pipeline = DDPMPipeline(\n unet=unet,\n scheduler=noise_scheduler,\n )\n\n pipeline.save_pretrained(args.output_dir)\n\n if args.use_ema:\n ema_model.restore(unet.parameters())\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=f\"Epoch {epoch}\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/research_projects/promptdiffusion/README.md", "content": "# PromptDiffusion Pipeline\n\nFrom the project [page](https://zhendong-wang.github.io/prompt-diffusion.github.io/)\n\n\"With a prompt consisting of a task-specific example pair of images and text guidance, and a new query image, Prompt Diffusion can comprehend the desired task and generate the corresponding output image on both seen (trained) and unseen (new) task types.\"\n\nFor any usage questions, please refer to the [paper](https://arxiv.org/abs/2305.01115).\n\nPrepare models by converting them from the [checkpoint](https://huggingface.co/zhendongw/prompt-diffusion)\n\nTo convert the controlnet, use cldm_v15.yaml from the [repository](https://github.com/Zhendong-Wang/Prompt-Diffusion/tree/main/models/):\n\n```bash\npython convert_original_promptdiffusion_to_diffusers.py --checkpoint_path path-to-network-step04999.ckpt --original_config_file path-to-cldm_v15.yaml --dump_path path-to-output-directory\n```\n\nTo learn about how to convert the fine-tuned stable diffusion model, see the [Load different Stable Diffusion formats guide](https://huggingface.co/docs/diffusers/main/en/using-diffusers/other-formats).\n\n\n```py\nimport torch\nfrom diffusers import UniPCMultistepScheduler\nfrom diffusers.utils import load_image\nfrom promptdiffusioncontrolnet import PromptDiffusionControlNetModel\nfrom pipeline_prompt_diffusion import PromptDiffusionPipeline\n\n\nfrom PIL import ImageOps\n\nimage_a = ImageOps.invert(load_image(\"https://github.com/Zhendong-Wang/Prompt-Diffusion/blob/main/images_to_try/house_line.png?raw=true\"))\n\nimage_b = load_image(\"https://github.com/Zhendong-Wang/Prompt-Diffusion/blob/main/images_to_try/house.png?raw=true\")\nquery = ImageOps.invert(load_image(\"https://github.com/Zhendong-Wang/Prompt-Diffusion/blob/main/images_to_try/new_01.png?raw=true\"))\n\n# load prompt diffusion controlnet and prompt diffusion\n\ncontrolnet = PromptDiffusionControlNetModel.from_pretrained(\"iczaw/prompt-diffusion-diffusers\", subfolder=\"controlnet\", torch_dtype=torch.float16)\nmodel_id = \"path-to-model\"\npipe = PromptDiffusionPipeline.from_pretrained(\"iczaw/prompt-diffusion-diffusers\", subfolder=\"base\", controlnet=controlnet, torch_dtype=torch.float16, variant=\"fp16\")\n\n# speed up diffusion process with faster scheduler and memory optimization\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n# remove following line if xformers is not installed\npipe.enable_xformers_memory_efficient_attention()\npipe.enable_model_cpu_offload()\n# generate image\ngenerator = torch.manual_seed(0)\nimage = pipe(\"a tortoise\", num_inference_steps=20, generator=generator, image_pair=[image_a,image_b], image=query).images[0]\n\n```\n" }, { "path": "diffusers/examples/research_projects/promptdiffusion/convert_original_promptdiffusion_to_diffusers.py", "content": "# coding=utf-8\n# Copyright 2023 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Conversion script for stable diffusion checkpoints which _only_ contain a controlnet.\"\"\"\n\nimport argparse\nimport re\nfrom contextlib import nullcontext\nfrom io import BytesIO\nfrom typing import Dict, Optional, Union\n\nimport requests\nimport torch\nimport yaml\nfrom promptdiffusioncontrolnet import PromptDiffusionControlNetModel\nfrom transformers import (\n AutoFeatureExtractor,\n BertTokenizerFast,\n CLIPImageProcessor,\n CLIPTextConfig,\n CLIPTextModel,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionConfig,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers.models import (\n AutoencoderKL,\n ControlNetModel,\n PriorTransformer,\n UNet2DConditionModel,\n)\nfrom diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel\nfrom diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer\nfrom diffusers.schedulers import (\n DDIMScheduler,\n DDPMScheduler,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n HeunDiscreteScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n UnCLIPScheduler,\n)\nfrom diffusers.utils import is_accelerate_available, logging\n\n\nif is_accelerate_available():\n from accelerate import init_empty_weights\n from accelerate.utils import set_module_tensor_to_device\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef shave_segments(path, n_shave_prefix_segments=1):\n \"\"\"\n Removes segments. Positive values shave the first segments, negative shave the last segments.\n \"\"\"\n if n_shave_prefix_segments >= 0:\n return \".\".join(path.split(\".\")[n_shave_prefix_segments:])\n else:\n return \".\".join(path.split(\".\")[:n_shave_prefix_segments])\n\n\ndef renew_resnet_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside resnets to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item.replace(\"in_layers.0\", \"norm1\")\n new_item = new_item.replace(\"in_layers.2\", \"conv1\")\n\n new_item = new_item.replace(\"out_layers.0\", \"norm2\")\n new_item = new_item.replace(\"out_layers.3\", \"conv2\")\n\n new_item = new_item.replace(\"emb_layers.1\", \"time_emb_proj\")\n new_item = new_item.replace(\"skip_connection\", \"conv_shortcut\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside resnets to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n new_item = new_item.replace(\"nin_shortcut\", \"conv_shortcut\")\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef renew_attention_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside attentions to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n # new_item = new_item.replace('norm.weight', 'group_norm.weight')\n # new_item = new_item.replace('norm.bias', 'group_norm.bias')\n\n # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')\n # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')\n\n # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside attentions to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n new_item = new_item.replace(\"norm.weight\", \"group_norm.weight\")\n new_item = new_item.replace(\"norm.bias\", \"group_norm.bias\")\n\n new_item = new_item.replace(\"q.weight\", \"to_q.weight\")\n new_item = new_item.replace(\"q.bias\", \"to_q.bias\")\n\n new_item = new_item.replace(\"k.weight\", \"to_k.weight\")\n new_item = new_item.replace(\"k.bias\", \"to_k.bias\")\n\n new_item = new_item.replace(\"v.weight\", \"to_v.weight\")\n new_item = new_item.replace(\"v.bias\", \"to_v.bias\")\n\n new_item = new_item.replace(\"proj_out.weight\", \"to_out.0.weight\")\n new_item = new_item.replace(\"proj_out.bias\", \"to_out.0.bias\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef assign_to_checkpoint(\n paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None\n):\n \"\"\"\n This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits\n attention layers, and takes into account additional replacements that may arise.\n\n Assigns the weights to the new checkpoint.\n \"\"\"\n assert isinstance(paths, list), \"Paths should be a list of dicts containing 'old' and 'new' keys.\"\n\n # Splits the attention layers into three variables.\n if attention_paths_to_split is not None:\n for path, path_map in attention_paths_to_split.items():\n old_tensor = old_checkpoint[path]\n channels = old_tensor.shape[0] // 3\n\n target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)\n\n num_heads = old_tensor.shape[0] // config[\"num_head_channels\"] // 3\n\n old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])\n query, key, value = old_tensor.split(channels // num_heads, dim=1)\n\n checkpoint[path_map[\"query\"]] = query.reshape(target_shape)\n checkpoint[path_map[\"key\"]] = key.reshape(target_shape)\n checkpoint[path_map[\"value\"]] = value.reshape(target_shape)\n\n for path in paths:\n new_path = path[\"new\"]\n\n # These have already been assigned\n if attention_paths_to_split is not None and new_path in attention_paths_to_split:\n continue\n\n # Global renaming happens here\n new_path = new_path.replace(\"middle_block.0\", \"mid_block.resnets.0\")\n new_path = new_path.replace(\"middle_block.1\", \"mid_block.attentions.0\")\n new_path = new_path.replace(\"middle_block.2\", \"mid_block.resnets.1\")\n\n if additional_replacements is not None:\n for replacement in additional_replacements:\n new_path = new_path.replace(replacement[\"old\"], replacement[\"new\"])\n\n # proj_attn.weight has to be converted from conv 1D to linear\n is_attn_weight = \"proj_attn.weight\" in new_path or (\"attentions\" in new_path and \"to_\" in new_path)\n shape = old_checkpoint[path[\"old\"]].shape\n if is_attn_weight and len(shape) == 3:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]][:, :, 0]\n elif is_attn_weight and len(shape) == 4:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]][:, :, 0, 0]\n else:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]]\n\n\ndef conv_attn_to_linear(checkpoint):\n keys = list(checkpoint.keys())\n attn_keys = [\"query.weight\", \"key.weight\", \"value.weight\"]\n for key in keys:\n if \".\".join(key.split(\".\")[-2:]) in attn_keys:\n if checkpoint[key].ndim > 2:\n checkpoint[key] = checkpoint[key][:, :, 0, 0]\n elif \"proj_attn.weight\" in key:\n if checkpoint[key].ndim > 2:\n checkpoint[key] = checkpoint[key][:, :, 0]\n\n\ndef create_unet_diffusers_config(original_config, image_size: int, controlnet=False):\n \"\"\"\n Creates a config for the diffusers based on the config of the LDM model.\n \"\"\"\n if controlnet:\n unet_params = original_config[\"model\"][\"params\"][\"control_stage_config\"][\"params\"]\n else:\n if (\n \"unet_config\" in original_config[\"model\"][\"params\"]\n and original_config[\"model\"][\"params\"][\"unet_config\"] is not None\n ):\n unet_params = original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"]\n else:\n unet_params = original_config[\"model\"][\"params\"][\"network_config\"][\"params\"]\n\n vae_params = original_config[\"model\"][\"params\"][\"first_stage_config\"][\"params\"][\"ddconfig\"]\n\n block_out_channels = [unet_params[\"model_channels\"] * mult for mult in unet_params[\"channel_mult\"]]\n\n down_block_types = []\n resolution = 1\n for i in range(len(block_out_channels)):\n block_type = \"CrossAttnDownBlock2D\" if resolution in unet_params[\"attention_resolutions\"] else \"DownBlock2D\"\n down_block_types.append(block_type)\n if i != len(block_out_channels) - 1:\n resolution *= 2\n\n up_block_types = []\n for i in range(len(block_out_channels)):\n block_type = \"CrossAttnUpBlock2D\" if resolution in unet_params[\"attention_resolutions\"] else \"UpBlock2D\"\n up_block_types.append(block_type)\n resolution //= 2\n\n if unet_params[\"transformer_depth\"] is not None:\n transformer_layers_per_block = (\n unet_params[\"transformer_depth\"]\n if isinstance(unet_params[\"transformer_depth\"], int)\n else list(unet_params[\"transformer_depth\"])\n )\n else:\n transformer_layers_per_block = 1\n\n vae_scale_factor = 2 ** (len(vae_params[\"ch_mult\"]) - 1)\n\n head_dim = unet_params[\"num_heads\"] if \"num_heads\" in unet_params else None\n use_linear_projection = (\n unet_params[\"use_linear_in_transformer\"] if \"use_linear_in_transformer\" in unet_params else False\n )\n if use_linear_projection:\n # stable diffusion 2-base-512 and 2-768\n if head_dim is None:\n head_dim_mult = unet_params[\"model_channels\"] // unet_params[\"num_head_channels\"]\n head_dim = [head_dim_mult * c for c in list(unet_params[\"channel_mult\"])]\n\n class_embed_type = None\n addition_embed_type = None\n addition_time_embed_dim = None\n projection_class_embeddings_input_dim = None\n context_dim = None\n\n if unet_params[\"context_dim\"] is not None:\n context_dim = (\n unet_params[\"context_dim\"]\n if isinstance(unet_params[\"context_dim\"], int)\n else unet_params[\"context_dim\"][0]\n )\n\n if \"num_classes\" in unet_params:\n if unet_params[\"num_classes\"] == \"sequential\":\n if context_dim in [2048, 1280]:\n # SDXL\n addition_embed_type = \"text_time\"\n addition_time_embed_dim = 256\n else:\n class_embed_type = \"projection\"\n assert \"adm_in_channels\" in unet_params\n projection_class_embeddings_input_dim = unet_params[\"adm_in_channels\"]\n\n config = {\n \"sample_size\": image_size // vae_scale_factor,\n \"in_channels\": unet_params[\"in_channels\"],\n \"down_block_types\": tuple(down_block_types),\n \"block_out_channels\": tuple(block_out_channels),\n \"layers_per_block\": unet_params[\"num_res_blocks\"],\n \"cross_attention_dim\": context_dim,\n \"attention_head_dim\": head_dim,\n \"use_linear_projection\": use_linear_projection,\n \"class_embed_type\": class_embed_type,\n \"addition_embed_type\": addition_embed_type,\n \"addition_time_embed_dim\": addition_time_embed_dim,\n \"projection_class_embeddings_input_dim\": projection_class_embeddings_input_dim,\n \"transformer_layers_per_block\": transformer_layers_per_block,\n }\n\n if \"disable_self_attentions\" in unet_params:\n config[\"only_cross_attention\"] = unet_params[\"disable_self_attentions\"]\n\n if \"num_classes\" in unet_params and isinstance(unet_params[\"num_classes\"], int):\n config[\"num_class_embeds\"] = unet_params[\"num_classes\"]\n\n if controlnet:\n config[\"conditioning_channels\"] = unet_params[\"hint_channels\"]\n else:\n config[\"out_channels\"] = unet_params[\"out_channels\"]\n config[\"up_block_types\"] = tuple(up_block_types)\n\n return config\n\n\ndef create_vae_diffusers_config(original_config, image_size: int):\n \"\"\"\n Creates a config for the diffusers based on the config of the LDM model.\n \"\"\"\n vae_params = original_config[\"model\"][\"params\"][\"first_stage_config\"][\"params\"][\"ddconfig\"]\n _ = original_config[\"model\"][\"params\"][\"first_stage_config\"][\"params\"][\"embed_dim\"]\n\n block_out_channels = [vae_params[\"ch\"] * mult for mult in vae_params[\"ch_mult\"]]\n down_block_types = [\"DownEncoderBlock2D\"] * len(block_out_channels)\n up_block_types = [\"UpDecoderBlock2D\"] * len(block_out_channels)\n\n config = {\n \"sample_size\": image_size,\n \"in_channels\": vae_params[\"in_channels\"],\n \"out_channels\": vae_params[\"out_ch\"],\n \"down_block_types\": tuple(down_block_types),\n \"up_block_types\": tuple(up_block_types),\n \"block_out_channels\": tuple(block_out_channels),\n \"latent_channels\": vae_params[\"z_channels\"],\n \"layers_per_block\": vae_params[\"num_res_blocks\"],\n }\n return config\n\n\ndef create_diffusers_schedular(original_config):\n schedular = DDIMScheduler(\n num_train_timesteps=original_config[\"model\"][\"params\"][\"timesteps\"],\n beta_start=original_config[\"model\"][\"params\"][\"linear_start\"],\n beta_end=original_config[\"model\"][\"params\"][\"linear_end\"],\n beta_schedule=\"scaled_linear\",\n )\n return schedular\n\n\ndef create_ldm_bert_config(original_config):\n bert_params = original_config[\"model\"][\"params\"][\"cond_stage_config\"][\"params\"]\n config = LDMBertConfig(\n d_model=bert_params.n_embed,\n encoder_layers=bert_params.n_layer,\n encoder_ffn_dim=bert_params.n_embed * 4,\n )\n return config\n\n\ndef convert_ldm_unet_checkpoint(\n checkpoint,\n config,\n path=None,\n extract_ema=False,\n controlnet=False,\n skip_extract_state_dict=False,\n promptdiffusion=False,\n):\n \"\"\"\n Takes a state dict and a config, and returns a converted checkpoint.\n \"\"\"\n\n if skip_extract_state_dict:\n unet_state_dict = checkpoint\n else:\n # extract state_dict for UNet\n unet_state_dict = {}\n keys = list(checkpoint.keys())\n\n if controlnet:\n unet_key = \"control_model.\"\n else:\n unet_key = \"model.diffusion_model.\"\n\n # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA\n if sum(k.startswith(\"model_ema\") for k in keys) > 100 and extract_ema:\n logger.warning(f\"Checkpoint {path} has both EMA and non-EMA weights.\")\n logger.warning(\n \"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA\"\n \" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.\"\n )\n for key in keys:\n if key.startswith(\"model.diffusion_model\"):\n flat_ema_key = \"model_ema.\" + \"\".join(key.split(\".\")[1:])\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint.pop(flat_ema_key)\n else:\n if sum(k.startswith(\"model_ema\") for k in keys) > 100:\n logger.warning(\n \"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA\"\n \" weights (usually better for inference), please make sure to add the `--extract_ema` flag.\"\n )\n\n for key in keys:\n if key.startswith(unet_key):\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint.pop(key)\n\n new_checkpoint = {}\n\n new_checkpoint[\"time_embedding.linear_1.weight\"] = unet_state_dict[\"time_embed.0.weight\"]\n new_checkpoint[\"time_embedding.linear_1.bias\"] = unet_state_dict[\"time_embed.0.bias\"]\n new_checkpoint[\"time_embedding.linear_2.weight\"] = unet_state_dict[\"time_embed.2.weight\"]\n new_checkpoint[\"time_embedding.linear_2.bias\"] = unet_state_dict[\"time_embed.2.bias\"]\n\n if config[\"class_embed_type\"] is None:\n # No parameters to port\n ...\n elif config[\"class_embed_type\"] == \"timestep\" or config[\"class_embed_type\"] == \"projection\":\n new_checkpoint[\"class_embedding.linear_1.weight\"] = unet_state_dict[\"label_emb.0.0.weight\"]\n new_checkpoint[\"class_embedding.linear_1.bias\"] = unet_state_dict[\"label_emb.0.0.bias\"]\n new_checkpoint[\"class_embedding.linear_2.weight\"] = unet_state_dict[\"label_emb.0.2.weight\"]\n new_checkpoint[\"class_embedding.linear_2.bias\"] = unet_state_dict[\"label_emb.0.2.bias\"]\n else:\n raise NotImplementedError(f\"Not implemented `class_embed_type`: {config['class_embed_type']}\")\n\n if config[\"addition_embed_type\"] == \"text_time\":\n new_checkpoint[\"add_embedding.linear_1.weight\"] = unet_state_dict[\"label_emb.0.0.weight\"]\n new_checkpoint[\"add_embedding.linear_1.bias\"] = unet_state_dict[\"label_emb.0.0.bias\"]\n new_checkpoint[\"add_embedding.linear_2.weight\"] = unet_state_dict[\"label_emb.0.2.weight\"]\n new_checkpoint[\"add_embedding.linear_2.bias\"] = unet_state_dict[\"label_emb.0.2.bias\"]\n\n # Relevant to StableDiffusionUpscalePipeline\n if \"num_class_embeds\" in config:\n if (config[\"num_class_embeds\"] is not None) and (\"label_emb.weight\" in unet_state_dict):\n new_checkpoint[\"class_embedding.weight\"] = unet_state_dict[\"label_emb.weight\"]\n\n new_checkpoint[\"conv_in.weight\"] = unet_state_dict[\"input_blocks.0.0.weight\"]\n new_checkpoint[\"conv_in.bias\"] = unet_state_dict[\"input_blocks.0.0.bias\"]\n\n if not controlnet:\n new_checkpoint[\"conv_norm_out.weight\"] = unet_state_dict[\"out.0.weight\"]\n new_checkpoint[\"conv_norm_out.bias\"] = unet_state_dict[\"out.0.bias\"]\n new_checkpoint[\"conv_out.weight\"] = unet_state_dict[\"out.2.weight\"]\n new_checkpoint[\"conv_out.bias\"] = unet_state_dict[\"out.2.bias\"]\n\n # Retrieves the keys for the input blocks only\n num_input_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"input_blocks\" in layer})\n input_blocks = {\n layer_id: [key for key in unet_state_dict if f\"input_blocks.{layer_id}\" in key]\n for layer_id in range(num_input_blocks)\n }\n\n # Retrieves the keys for the middle blocks only\n num_middle_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"middle_block\" in layer})\n middle_blocks = {\n layer_id: [key for key in unet_state_dict if f\"middle_block.{layer_id}\" in key]\n for layer_id in range(num_middle_blocks)\n }\n\n # Retrieves the keys for the output blocks only\n num_output_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"output_blocks\" in layer})\n output_blocks = {\n layer_id: [key for key in unet_state_dict if f\"output_blocks.{layer_id}\" in key]\n for layer_id in range(num_output_blocks)\n }\n\n for i in range(1, num_input_blocks):\n block_id = (i - 1) // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = (i - 1) % (config[\"layers_per_block\"] + 1)\n\n resnets = [\n key for key in input_blocks[i] if f\"input_blocks.{i}.0\" in key and f\"input_blocks.{i}.0.op\" not in key\n ]\n attentions = [key for key in input_blocks[i] if f\"input_blocks.{i}.1\" in key]\n\n if f\"input_blocks.{i}.0.op.weight\" in unet_state_dict:\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.weight\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.op.weight\"\n )\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.bias\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.op.bias\"\n )\n\n paths = renew_resnet_paths(resnets)\n meta_path = {\"old\": f\"input_blocks.{i}.0\", \"new\": f\"down_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n\n meta_path = {\"old\": f\"input_blocks.{i}.1\", \"new\": f\"down_blocks.{block_id}.attentions.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n resnet_0 = middle_blocks[0]\n attentions = middle_blocks[1]\n resnet_1 = middle_blocks[2]\n\n resnet_0_paths = renew_resnet_paths(resnet_0)\n assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)\n\n resnet_1_paths = renew_resnet_paths(resnet_1)\n assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)\n\n attentions_paths = renew_attention_paths(attentions)\n meta_path = {\"old\": \"middle_block.1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(\n attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n for i in range(num_output_blocks):\n block_id = i // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = i % (config[\"layers_per_block\"] + 1)\n output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]\n output_block_list = {}\n\n for layer in output_block_layers:\n layer_id, layer_name = layer.split(\".\")[0], shave_segments(layer, 1)\n if layer_id in output_block_list:\n output_block_list[layer_id].append(layer_name)\n else:\n output_block_list[layer_id] = [layer_name]\n\n if len(output_block_list) > 1:\n resnets = [key for key in output_blocks[i] if f\"output_blocks.{i}.0\" in key]\n attentions = [key for key in output_blocks[i] if f\"output_blocks.{i}.1\" in key]\n\n resnet_0_paths = renew_resnet_paths(resnets)\n paths = renew_resnet_paths(resnets)\n\n meta_path = {\"old\": f\"output_blocks.{i}.0\", \"new\": f\"up_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n output_block_list = {k: sorted(v) for k, v in output_block_list.items()}\n if [\"conv.bias\", \"conv.weight\"] in output_block_list.values():\n index = list(output_block_list.values()).index([\"conv.bias\", \"conv.weight\"])\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.weight\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.weight\"\n ]\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.bias\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.bias\"\n ]\n\n # Clear attentions as they have been attributed above.\n if len(attentions) == 2:\n attentions = []\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n meta_path = {\n \"old\": f\"output_blocks.{i}.1\",\n \"new\": f\"up_blocks.{block_id}.attentions.{layer_in_block_id}\",\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n else:\n resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)\n for path in resnet_0_paths:\n old_path = \".\".join([\"output_blocks\", str(i), path[\"old\"]])\n new_path = \".\".join([\"up_blocks\", str(block_id), \"resnets\", str(layer_in_block_id), path[\"new\"]])\n\n new_checkpoint[new_path] = unet_state_dict[old_path]\n\n if controlnet and not promptdiffusion:\n # conditioning embedding\n\n orig_index = 0\n\n new_checkpoint[\"controlnet_cond_embedding.conv_in.weight\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.weight\"\n )\n new_checkpoint[\"controlnet_cond_embedding.conv_in.bias\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.bias\"\n )\n\n orig_index += 2\n\n diffusers_index = 0\n\n while diffusers_index < 6:\n new_checkpoint[f\"controlnet_cond_embedding.blocks.{diffusers_index}.weight\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.weight\"\n )\n new_checkpoint[f\"controlnet_cond_embedding.blocks.{diffusers_index}.bias\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.bias\"\n )\n diffusers_index += 1\n orig_index += 2\n\n new_checkpoint[\"controlnet_cond_embedding.conv_out.weight\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.weight\"\n )\n new_checkpoint[\"controlnet_cond_embedding.conv_out.bias\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.bias\"\n )\n\n # down blocks\n for i in range(num_input_blocks):\n new_checkpoint[f\"controlnet_down_blocks.{i}.weight\"] = unet_state_dict.pop(f\"zero_convs.{i}.0.weight\")\n new_checkpoint[f\"controlnet_down_blocks.{i}.bias\"] = unet_state_dict.pop(f\"zero_convs.{i}.0.bias\")\n\n # mid block\n new_checkpoint[\"controlnet_mid_block.weight\"] = unet_state_dict.pop(\"middle_block_out.0.weight\")\n new_checkpoint[\"controlnet_mid_block.bias\"] = unet_state_dict.pop(\"middle_block_out.0.bias\")\n\n if promptdiffusion:\n # conditioning embedding\n\n orig_index = 0\n\n new_checkpoint[\"controlnet_cond_embedding.conv_in.weight\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.weight\"\n )\n new_checkpoint[\"controlnet_cond_embedding.conv_in.bias\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.bias\"\n )\n\n new_checkpoint[\"controlnet_query_cond_embedding.conv_in.weight\"] = unet_state_dict.pop(\n f\"input_cond_block.{orig_index}.weight\"\n )\n new_checkpoint[\"controlnet_query_cond_embedding.conv_in.bias\"] = unet_state_dict.pop(\n f\"input_cond_block.{orig_index}.bias\"\n )\n orig_index += 2\n\n diffusers_index = 0\n\n while diffusers_index < 6:\n new_checkpoint[f\"controlnet_cond_embedding.blocks.{diffusers_index}.weight\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.weight\"\n )\n new_checkpoint[f\"controlnet_cond_embedding.blocks.{diffusers_index}.bias\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.bias\"\n )\n new_checkpoint[f\"controlnet_query_cond_embedding.blocks.{diffusers_index}.weight\"] = unet_state_dict.pop(\n f\"input_cond_block.{orig_index}.weight\"\n )\n new_checkpoint[f\"controlnet_query_cond_embedding.blocks.{diffusers_index}.bias\"] = unet_state_dict.pop(\n f\"input_cond_block.{orig_index}.bias\"\n )\n diffusers_index += 1\n orig_index += 2\n\n new_checkpoint[\"controlnet_cond_embedding.conv_out.weight\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.weight\"\n )\n new_checkpoint[\"controlnet_cond_embedding.conv_out.bias\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.bias\"\n )\n\n new_checkpoint[\"controlnet_query_cond_embedding.conv_out.weight\"] = unet_state_dict.pop(\n f\"input_cond_block.{orig_index}.weight\"\n )\n new_checkpoint[\"controlnet_query_cond_embedding.conv_out.bias\"] = unet_state_dict.pop(\n f\"input_cond_block.{orig_index}.bias\"\n )\n # down blocks\n for i in range(num_input_blocks):\n new_checkpoint[f\"controlnet_down_blocks.{i}.weight\"] = unet_state_dict.pop(f\"zero_convs.{i}.0.weight\")\n new_checkpoint[f\"controlnet_down_blocks.{i}.bias\"] = unet_state_dict.pop(f\"zero_convs.{i}.0.bias\")\n\n # mid block\n new_checkpoint[\"controlnet_mid_block.weight\"] = unet_state_dict.pop(\"middle_block_out.0.weight\")\n new_checkpoint[\"controlnet_mid_block.bias\"] = unet_state_dict.pop(\"middle_block_out.0.bias\")\n\n return new_checkpoint\n\n\ndef convert_ldm_vae_checkpoint(checkpoint, config):\n # extract state dict for VAE\n vae_state_dict = {}\n keys = list(checkpoint.keys())\n vae_key = \"first_stage_model.\" if any(k.startswith(\"first_stage_model.\") for k in keys) else \"\"\n for key in keys:\n if key.startswith(vae_key):\n vae_state_dict[key.replace(vae_key, \"\")] = checkpoint.get(key)\n\n new_checkpoint = {}\n\n new_checkpoint[\"encoder.conv_in.weight\"] = vae_state_dict[\"encoder.conv_in.weight\"]\n new_checkpoint[\"encoder.conv_in.bias\"] = vae_state_dict[\"encoder.conv_in.bias\"]\n new_checkpoint[\"encoder.conv_out.weight\"] = vae_state_dict[\"encoder.conv_out.weight\"]\n new_checkpoint[\"encoder.conv_out.bias\"] = vae_state_dict[\"encoder.conv_out.bias\"]\n new_checkpoint[\"encoder.conv_norm_out.weight\"] = vae_state_dict[\"encoder.norm_out.weight\"]\n new_checkpoint[\"encoder.conv_norm_out.bias\"] = vae_state_dict[\"encoder.norm_out.bias\"]\n\n new_checkpoint[\"decoder.conv_in.weight\"] = vae_state_dict[\"decoder.conv_in.weight\"]\n new_checkpoint[\"decoder.conv_in.bias\"] = vae_state_dict[\"decoder.conv_in.bias\"]\n new_checkpoint[\"decoder.conv_out.weight\"] = vae_state_dict[\"decoder.conv_out.weight\"]\n new_checkpoint[\"decoder.conv_out.bias\"] = vae_state_dict[\"decoder.conv_out.bias\"]\n new_checkpoint[\"decoder.conv_norm_out.weight\"] = vae_state_dict[\"decoder.norm_out.weight\"]\n new_checkpoint[\"decoder.conv_norm_out.bias\"] = vae_state_dict[\"decoder.norm_out.bias\"]\n\n new_checkpoint[\"quant_conv.weight\"] = vae_state_dict[\"quant_conv.weight\"]\n new_checkpoint[\"quant_conv.bias\"] = vae_state_dict[\"quant_conv.bias\"]\n new_checkpoint[\"post_quant_conv.weight\"] = vae_state_dict[\"post_quant_conv.weight\"]\n new_checkpoint[\"post_quant_conv.bias\"] = vae_state_dict[\"post_quant_conv.bias\"]\n\n # Retrieves the keys for the encoder down blocks only\n num_down_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in vae_state_dict if \"encoder.down\" in layer})\n down_blocks = {\n layer_id: [key for key in vae_state_dict if f\"down.{layer_id}\" in key] for layer_id in range(num_down_blocks)\n }\n\n # Retrieves the keys for the decoder up blocks only\n num_up_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in vae_state_dict if \"decoder.up\" in layer})\n up_blocks = {\n layer_id: [key for key in vae_state_dict if f\"up.{layer_id}\" in key] for layer_id in range(num_up_blocks)\n }\n\n for i in range(num_down_blocks):\n resnets = [key for key in down_blocks[i] if f\"down.{i}\" in key and f\"down.{i}.downsample\" not in key]\n\n if f\"encoder.down.{i}.downsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.weight\"] = vae_state_dict.pop(\n f\"encoder.down.{i}.downsample.conv.weight\"\n )\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.bias\"] = vae_state_dict.pop(\n f\"encoder.down.{i}.downsample.conv.bias\"\n )\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"down.{i}.block\", \"new\": f\"down_blocks.{i}.resnets\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_resnets = [key for key in vae_state_dict if \"encoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"encoder.mid.block_{i}\" in key]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_attentions = [key for key in vae_state_dict if \"encoder.mid.attn\" in key]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n conv_attn_to_linear(new_checkpoint)\n\n for i in range(num_up_blocks):\n block_id = num_up_blocks - 1 - i\n resnets = [\n key for key in up_blocks[block_id] if f\"up.{block_id}\" in key and f\"up.{block_id}.upsample\" not in key\n ]\n\n if f\"decoder.up.{block_id}.upsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.weight\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.weight\"\n ]\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.bias\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.bias\"\n ]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"up.{block_id}.block\", \"new\": f\"up_blocks.{i}.resnets\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_resnets = [key for key in vae_state_dict if \"decoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"decoder.mid.block_{i}\" in key]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_attentions = [key for key in vae_state_dict if \"decoder.mid.attn\" in key]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n conv_attn_to_linear(new_checkpoint)\n return new_checkpoint\n\n\ndef convert_ldm_bert_checkpoint(checkpoint, config):\n def _copy_attn_layer(hf_attn_layer, pt_attn_layer):\n hf_attn_layer.q_proj.weight.data = pt_attn_layer.to_q.weight\n hf_attn_layer.k_proj.weight.data = pt_attn_layer.to_k.weight\n hf_attn_layer.v_proj.weight.data = pt_attn_layer.to_v.weight\n\n hf_attn_layer.out_proj.weight = pt_attn_layer.to_out.weight\n hf_attn_layer.out_proj.bias = pt_attn_layer.to_out.bias\n\n def _copy_linear(hf_linear, pt_linear):\n hf_linear.weight = pt_linear.weight\n hf_linear.bias = pt_linear.bias\n\n def _copy_layer(hf_layer, pt_layer):\n # copy layer norms\n _copy_linear(hf_layer.self_attn_layer_norm, pt_layer[0][0])\n _copy_linear(hf_layer.final_layer_norm, pt_layer[1][0])\n\n # copy attn\n _copy_attn_layer(hf_layer.self_attn, pt_layer[0][1])\n\n # copy MLP\n pt_mlp = pt_layer[1][1]\n _copy_linear(hf_layer.fc1, pt_mlp.net[0][0])\n _copy_linear(hf_layer.fc2, pt_mlp.net[2])\n\n def _copy_layers(hf_layers, pt_layers):\n for i, hf_layer in enumerate(hf_layers):\n if i != 0:\n i += i\n pt_layer = pt_layers[i : i + 2]\n _copy_layer(hf_layer, pt_layer)\n\n hf_model = LDMBertModel(config).eval()\n\n # copy embeds\n hf_model.model.embed_tokens.weight = checkpoint.transformer.token_emb.weight\n hf_model.model.embed_positions.weight.data = checkpoint.transformer.pos_emb.emb.weight\n\n # copy layer norm\n _copy_linear(hf_model.model.layer_norm, checkpoint.transformer.norm)\n\n # copy hidden layers\n _copy_layers(hf_model.model.layers, checkpoint.transformer.attn_layers.layers)\n\n _copy_linear(hf_model.to_logits, checkpoint.transformer.to_logits)\n\n return hf_model\n\n\ndef convert_ldm_clip_checkpoint(checkpoint, local_files_only=False, text_encoder=None):\n if text_encoder is None:\n config_name = \"openai/clip-vit-large-patch14\"\n try:\n config = CLIPTextConfig.from_pretrained(config_name, local_files_only=local_files_only)\n except Exception:\n raise ValueError(\n f\"With local_files_only set to {local_files_only}, you must first locally save the configuration in the following path: 'openai/clip-vit-large-patch14'.\"\n )\n\n ctx = init_empty_weights if is_accelerate_available() else nullcontext\n with ctx():\n text_model = CLIPTextModel(config)\n else:\n text_model = text_encoder\n\n keys = list(checkpoint.keys())\n\n text_model_dict = {}\n\n remove_prefixes = [\"cond_stage_model.transformer\", \"conditioner.embedders.0.transformer\"]\n\n for key in keys:\n for prefix in remove_prefixes:\n if key.startswith(prefix):\n text_model_dict[key[len(prefix + \".\") :]] = checkpoint[key]\n\n if is_accelerate_available():\n for param_name, param in text_model_dict.items():\n set_module_tensor_to_device(text_model, param_name, \"cpu\", value=param)\n else:\n if not (hasattr(text_model, \"embeddings\") and hasattr(text_model.embeddings.position_ids)):\n text_model_dict.pop(\"text_model.embeddings.position_ids\", None)\n\n text_model.load_state_dict(text_model_dict)\n\n return text_model\n\n\ntextenc_conversion_lst = [\n (\"positional_embedding\", \"text_model.embeddings.position_embedding.weight\"),\n (\"token_embedding.weight\", \"text_model.embeddings.token_embedding.weight\"),\n (\"ln_final.weight\", \"text_model.final_layer_norm.weight\"),\n (\"ln_final.bias\", \"text_model.final_layer_norm.bias\"),\n (\"text_projection\", \"text_projection.weight\"),\n]\ntextenc_conversion_map = {x[0]: x[1] for x in textenc_conversion_lst}\n\ntextenc_transformer_conversion_lst = [\n # (stable-diffusion, HF Diffusers)\n (\"resblocks.\", \"text_model.encoder.layers.\"),\n (\"ln_1\", \"layer_norm1\"),\n (\"ln_2\", \"layer_norm2\"),\n (\".c_fc.\", \".fc1.\"),\n (\".c_proj.\", \".fc2.\"),\n (\".attn\", \".self_attn\"),\n (\"ln_final.\", \"transformer.text_model.final_layer_norm.\"),\n (\"token_embedding.weight\", \"transformer.text_model.embeddings.token_embedding.weight\"),\n (\"positional_embedding\", \"transformer.text_model.embeddings.position_embedding.weight\"),\n]\nprotected = {re.escape(x[0]): x[1] for x in textenc_transformer_conversion_lst}\ntextenc_pattern = re.compile(\"|\".join(protected.keys()))\n\n\ndef convert_paint_by_example_checkpoint(checkpoint, local_files_only=False):\n config = CLIPVisionConfig.from_pretrained(\"openai/clip-vit-large-patch14\", local_files_only=local_files_only)\n model = PaintByExampleImageEncoder(config)\n\n keys = list(checkpoint.keys())\n\n text_model_dict = {}\n\n for key in keys:\n if key.startswith(\"cond_stage_model.transformer\"):\n text_model_dict[key[len(\"cond_stage_model.transformer.\") :]] = checkpoint[key]\n\n # load clip vision\n model.model.load_state_dict(text_model_dict)\n\n # load mapper\n keys_mapper = {\n k[len(\"cond_stage_model.mapper.res\") :]: v\n for k, v in checkpoint.items()\n if k.startswith(\"cond_stage_model.mapper\")\n }\n\n MAPPING = {\n \"attn.c_qkv\": [\"attn1.to_q\", \"attn1.to_k\", \"attn1.to_v\"],\n \"attn.c_proj\": [\"attn1.to_out.0\"],\n \"ln_1\": [\"norm1\"],\n \"ln_2\": [\"norm3\"],\n \"mlp.c_fc\": [\"ff.net.0.proj\"],\n \"mlp.c_proj\": [\"ff.net.2\"],\n }\n\n mapped_weights = {}\n for key, value in keys_mapper.items():\n prefix = key[: len(\"blocks.i\")]\n suffix = key.split(prefix)[-1].split(\".\")[-1]\n name = key.split(prefix)[-1].split(suffix)[0][1:-1]\n mapped_names = MAPPING[name]\n\n num_splits = len(mapped_names)\n for i, mapped_name in enumerate(mapped_names):\n new_name = \".\".join([prefix, mapped_name, suffix])\n shape = value.shape[0] // num_splits\n mapped_weights[new_name] = value[i * shape : (i + 1) * shape]\n\n model.mapper.load_state_dict(mapped_weights)\n\n # load final layer norm\n model.final_layer_norm.load_state_dict(\n {\n \"bias\": checkpoint[\"cond_stage_model.final_ln.bias\"],\n \"weight\": checkpoint[\"cond_stage_model.final_ln.weight\"],\n }\n )\n\n # load final proj\n model.proj_out.load_state_dict(\n {\n \"bias\": checkpoint[\"proj_out.bias\"],\n \"weight\": checkpoint[\"proj_out.weight\"],\n }\n )\n\n # load uncond vector\n model.uncond_vector.data = torch.nn.Parameter(checkpoint[\"learnable_vector\"])\n return model\n\n\ndef convert_open_clip_checkpoint(\n checkpoint,\n config_name,\n prefix=\"cond_stage_model.model.\",\n has_projection=False,\n local_files_only=False,\n **config_kwargs,\n):\n # text_model = CLIPTextModel.from_pretrained(\"stabilityai/stable-diffusion-2\", subfolder=\"text_encoder\")\n # text_model = CLIPTextModelWithProjection.from_pretrained(\n # \"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\", projection_dim=1280\n # )\n try:\n config = CLIPTextConfig.from_pretrained(config_name, **config_kwargs, local_files_only=local_files_only)\n except Exception:\n raise ValueError(\n f\"With local_files_only set to {local_files_only}, you must first locally save the configuration in the following path: '{config_name}'.\"\n )\n\n ctx = init_empty_weights if is_accelerate_available() else nullcontext\n with ctx():\n text_model = CLIPTextModelWithProjection(config) if has_projection else CLIPTextModel(config)\n\n keys = list(checkpoint.keys())\n\n keys_to_ignore = []\n if config_name == \"stabilityai/stable-diffusion-2\" and config.num_hidden_layers == 23:\n # make sure to remove all keys > 22\n keys_to_ignore += [k for k in keys if k.startswith(\"cond_stage_model.model.transformer.resblocks.23\")]\n keys_to_ignore += [\"cond_stage_model.model.text_projection\"]\n\n text_model_dict = {}\n\n if prefix + \"text_projection\" in checkpoint:\n d_model = int(checkpoint[prefix + \"text_projection\"].shape[0])\n else:\n d_model = 1024\n\n text_model_dict[\"text_model.embeddings.position_ids\"] = text_model.text_model.embeddings.get_buffer(\"position_ids\")\n\n for key in keys:\n if key in keys_to_ignore:\n continue\n if key[len(prefix) :] in textenc_conversion_map:\n if key.endswith(\"text_projection\"):\n value = checkpoint[key].T.contiguous()\n else:\n value = checkpoint[key]\n\n text_model_dict[textenc_conversion_map[key[len(prefix) :]]] = value\n\n if key.startswith(prefix + \"transformer.\"):\n new_key = key[len(prefix + \"transformer.\") :]\n if new_key.endswith(\".in_proj_weight\"):\n new_key = new_key[: -len(\".in_proj_weight\")]\n new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)\n text_model_dict[new_key + \".q_proj.weight\"] = checkpoint[key][:d_model, :]\n text_model_dict[new_key + \".k_proj.weight\"] = checkpoint[key][d_model : d_model * 2, :]\n text_model_dict[new_key + \".v_proj.weight\"] = checkpoint[key][d_model * 2 :, :]\n elif new_key.endswith(\".in_proj_bias\"):\n new_key = new_key[: -len(\".in_proj_bias\")]\n new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)\n text_model_dict[new_key + \".q_proj.bias\"] = checkpoint[key][:d_model]\n text_model_dict[new_key + \".k_proj.bias\"] = checkpoint[key][d_model : d_model * 2]\n text_model_dict[new_key + \".v_proj.bias\"] = checkpoint[key][d_model * 2 :]\n else:\n new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)\n\n text_model_dict[new_key] = checkpoint[key]\n\n if is_accelerate_available():\n for param_name, param in text_model_dict.items():\n set_module_tensor_to_device(text_model, param_name, \"cpu\", value=param)\n else:\n if not (hasattr(text_model, \"embeddings\") and hasattr(text_model.embeddings.position_ids)):\n text_model_dict.pop(\"text_model.embeddings.position_ids\", None)\n\n text_model.load_state_dict(text_model_dict)\n\n return text_model\n\n\ndef stable_unclip_image_encoder(original_config, local_files_only=False):\n \"\"\"\n Returns the image processor and clip image encoder for the img2img unclip pipeline.\n\n We currently know of two types of stable unclip models which separately use the clip and the openclip image\n encoders.\n \"\"\"\n\n image_embedder_config = original_config[\"model\"][\"params\"][\"embedder_config\"]\n\n sd_clip_image_embedder_class = image_embedder_config[\"target\"]\n sd_clip_image_embedder_class = sd_clip_image_embedder_class.split(\".\")[-1]\n\n if sd_clip_image_embedder_class == \"ClipImageEmbedder\":\n clip_model_name = image_embedder_config.params.model\n\n if clip_model_name == \"ViT-L/14\":\n feature_extractor = CLIPImageProcessor()\n image_encoder = CLIPVisionModelWithProjection.from_pretrained(\n \"openai/clip-vit-large-patch14\", local_files_only=local_files_only\n )\n else:\n raise NotImplementedError(f\"Unknown CLIP checkpoint name in stable diffusion checkpoint {clip_model_name}\")\n\n elif sd_clip_image_embedder_class == \"FrozenOpenCLIPImageEmbedder\":\n feature_extractor = CLIPImageProcessor()\n image_encoder = CLIPVisionModelWithProjection.from_pretrained(\n \"laion/CLIP-ViT-H-14-laion2B-s32B-b79K\", local_files_only=local_files_only\n )\n else:\n raise NotImplementedError(\n f\"Unknown CLIP image embedder class in stable diffusion checkpoint {sd_clip_image_embedder_class}\"\n )\n\n return feature_extractor, image_encoder\n\n\ndef stable_unclip_image_noising_components(\n original_config, clip_stats_path: Optional[str] = None, device: Optional[str] = None\n):\n \"\"\"\n Returns the noising components for the img2img and txt2img unclip pipelines.\n\n Converts the stability noise augmentor into\n 1. a `StableUnCLIPImageNormalizer` for holding the CLIP stats\n 2. a `DDPMScheduler` for holding the noise schedule\n\n If the noise augmentor config specifies a clip stats path, the `clip_stats_path` must be provided.\n \"\"\"\n noise_aug_config = original_config[\"model\"][\"params\"][\"noise_aug_config\"]\n noise_aug_class = noise_aug_config[\"target\"]\n noise_aug_class = noise_aug_class.split(\".\")[-1]\n\n if noise_aug_class == \"CLIPEmbeddingNoiseAugmentation\":\n noise_aug_config = noise_aug_config.params\n embedding_dim = noise_aug_config.timestep_dim\n max_noise_level = noise_aug_config.noise_schedule_config.timesteps\n beta_schedule = noise_aug_config.noise_schedule_config.beta_schedule\n\n image_normalizer = StableUnCLIPImageNormalizer(embedding_dim=embedding_dim)\n image_noising_scheduler = DDPMScheduler(num_train_timesteps=max_noise_level, beta_schedule=beta_schedule)\n\n if \"clip_stats_path\" in noise_aug_config:\n if clip_stats_path is None:\n raise ValueError(\"This stable unclip config requires a `clip_stats_path`\")\n\n clip_mean, clip_std = torch.load(clip_stats_path, map_location=device)\n clip_mean = clip_mean[None, :]\n clip_std = clip_std[None, :]\n\n clip_stats_state_dict = {\n \"mean\": clip_mean,\n \"std\": clip_std,\n }\n\n image_normalizer.load_state_dict(clip_stats_state_dict)\n else:\n raise NotImplementedError(f\"Unknown noise augmentor class: {noise_aug_class}\")\n\n return image_normalizer, image_noising_scheduler\n\n\ndef convert_controlnet_checkpoint(\n checkpoint,\n original_config,\n checkpoint_path,\n image_size,\n upcast_attention,\n extract_ema,\n use_linear_projection=None,\n cross_attention_dim=None,\n):\n ctrlnet_config = create_unet_diffusers_config(original_config, image_size=image_size, controlnet=True)\n ctrlnet_config[\"upcast_attention\"] = upcast_attention\n\n ctrlnet_config.pop(\"sample_size\")\n\n if use_linear_projection is not None:\n ctrlnet_config[\"use_linear_projection\"] = use_linear_projection\n\n if cross_attention_dim is not None:\n ctrlnet_config[\"cross_attention_dim\"] = cross_attention_dim\n\n ctx = init_empty_weights if is_accelerate_available() else nullcontext\n with ctx():\n controlnet = ControlNetModel(**ctrlnet_config)\n\n # Some controlnet ckpt files are distributed independently from the rest of the\n # model components i.e. https://huggingface.co/thibaud/controlnet-sd21/\n if \"time_embed.0.weight\" in checkpoint:\n skip_extract_state_dict = True\n else:\n skip_extract_state_dict = False\n\n converted_ctrl_checkpoint = convert_ldm_unet_checkpoint(\n checkpoint,\n ctrlnet_config,\n path=checkpoint_path,\n extract_ema=extract_ema,\n controlnet=True,\n skip_extract_state_dict=skip_extract_state_dict,\n )\n\n if is_accelerate_available():\n for param_name, param in converted_ctrl_checkpoint.items():\n set_module_tensor_to_device(controlnet, param_name, \"cpu\", value=param)\n else:\n controlnet.load_state_dict(converted_ctrl_checkpoint)\n\n return controlnet\n\n\ndef convert_promptdiffusion_checkpoint(\n checkpoint,\n original_config,\n checkpoint_path,\n image_size,\n upcast_attention,\n extract_ema,\n use_linear_projection=None,\n cross_attention_dim=None,\n):\n ctrlnet_config = create_unet_diffusers_config(original_config, image_size=image_size, controlnet=True)\n ctrlnet_config[\"upcast_attention\"] = upcast_attention\n\n ctrlnet_config.pop(\"sample_size\")\n\n if use_linear_projection is not None:\n ctrlnet_config[\"use_linear_projection\"] = use_linear_projection\n\n if cross_attention_dim is not None:\n ctrlnet_config[\"cross_attention_dim\"] = cross_attention_dim\n\n ctx = init_empty_weights if is_accelerate_available() else nullcontext\n with ctx():\n controlnet = PromptDiffusionControlNetModel(**ctrlnet_config)\n\n # Some controlnet ckpt files are distributed independently from the rest of the\n # model components i.e. https://huggingface.co/thibaud/controlnet-sd21/\n if \"time_embed.0.weight\" in checkpoint:\n skip_extract_state_dict = True\n else:\n skip_extract_state_dict = False\n\n converted_ctrl_checkpoint = convert_ldm_unet_checkpoint(\n checkpoint,\n ctrlnet_config,\n path=checkpoint_path,\n extract_ema=extract_ema,\n promptdiffusion=True,\n controlnet=True,\n skip_extract_state_dict=skip_extract_state_dict,\n )\n\n if is_accelerate_available():\n for param_name, param in converted_ctrl_checkpoint.items():\n set_module_tensor_to_device(controlnet, param_name, \"cpu\", value=param)\n else:\n controlnet.load_state_dict(converted_ctrl_checkpoint)\n\n return controlnet\n\n\ndef download_from_original_stable_diffusion_ckpt(\n checkpoint_path_or_dict: Union[str, Dict[str, torch.Tensor]],\n original_config_file: str = None,\n image_size: Optional[int] = None,\n prediction_type: str = None,\n model_type: str = None,\n extract_ema: bool = False,\n scheduler_type: str = \"pndm\",\n num_in_channels: Optional[int] = None,\n upcast_attention: Optional[bool] = None,\n device: str = None,\n from_safetensors: bool = False,\n stable_unclip: Optional[str] = None,\n stable_unclip_prior: Optional[str] = None,\n clip_stats_path: Optional[str] = None,\n controlnet: Optional[bool] = None,\n adapter: Optional[bool] = None,\n load_safety_checker: bool = True,\n pipeline_class: DiffusionPipeline = None,\n local_files_only=False,\n vae_path=None,\n vae=None,\n text_encoder=None,\n text_encoder_2=None,\n tokenizer=None,\n tokenizer_2=None,\n config_files=None,\n) -> DiffusionPipeline:\n \"\"\"\n Load a Stable Diffusion pipeline object from a CompVis-style `.ckpt`/`.safetensors` file and (ideally) a `.yaml`\n config file.\n\n Although many of the arguments can be automatically inferred, some of these rely on brittle checks against the\n global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is\n recommended that you override the default values and/or supply an `original_config_file` wherever possible.\n\n Args:\n checkpoint_path_or_dict (`str` or `dict`): Path to `.ckpt` file, or the state dict.\n original_config_file (`str`):\n Path to `.yaml` config file corresponding to the original architecture. If `None`, will be automatically\n inferred by looking for a key that only exists in SD2.0 models.\n image_size (`int`, *optional*, defaults to 512):\n The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Diffusion v2\n Base. Use 768 for Stable Diffusion v2.\n prediction_type (`str`, *optional*):\n The prediction type that the model was trained on. Use `'epsilon'` for Stable Diffusion v1.X and Stable\n Diffusion v2 Base. Use `'v_prediction'` for Stable Diffusion v2.\n num_in_channels (`int`, *optional*, defaults to None):\n The number of input channels. If `None`, it will be automatically inferred.\n scheduler_type (`str`, *optional*, defaults to 'pndm'):\n Type of scheduler to use. Should be one of `[\"pndm\", \"lms\", \"heun\", \"euler\", \"euler-ancestral\", \"dpm\",\n \"ddim\"]`.\n model_type (`str`, *optional*, defaults to `None`):\n The pipeline type. `None` to automatically infer, or one of `[\"FrozenOpenCLIPEmbedder\",\n \"FrozenCLIPEmbedder\", \"PaintByExample\"]`.\n is_img2img (`bool`, *optional*, defaults to `False`):\n Whether the model should be loaded as an img2img pipeline.\n extract_ema (`bool`, *optional*, defaults to `False`): Only relevant for\n checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults to\n `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher quality images for\n inference. Non-EMA weights are usually better to continue fine-tuning.\n upcast_attention (`bool`, *optional*, defaults to `None`):\n Whether the attention computation should always be upcasted. This is necessary when running stable\n diffusion 2.1.\n device (`str`, *optional*, defaults to `None`):\n The device to use. Pass `None` to determine automatically.\n from_safetensors (`str`, *optional*, defaults to `False`):\n If `checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.\n load_safety_checker (`bool`, *optional*, defaults to `True`):\n Whether to load the safety checker or not. Defaults to `True`.\n pipeline_class (`str`, *optional*, defaults to `None`):\n The pipeline class to use. Pass `None` to determine automatically.\n local_files_only (`bool`, *optional*, defaults to `False`):\n Whether or not to only look at local files (i.e., do not try to download the model).\n vae (`AutoencoderKL`, *optional*, defaults to `None`):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. If\n this parameter is `None`, the function will load a new instance of [CLIP] by itself, if needed.\n text_encoder (`CLIPTextModel`, *optional*, defaults to `None`):\n An instance of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel)\n to use, specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)\n variant. If this parameter is `None`, the function will load a new instance of [CLIP] by itself, if needed.\n tokenizer (`CLIPTokenizer`, *optional*, defaults to `None`):\n An instance of\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer)\n to use. If this parameter is `None`, the function will load a new instance of [CLIPTokenizer] by itself, if\n needed.\n config_files (`Dict[str, str]`, *optional*, defaults to `None`):\n A dictionary mapping from config file names to their contents. If this parameter is `None`, the function\n will load the config files by itself, if needed. Valid keys are:\n - `v1`: Config file for Stable Diffusion v1\n - `v2`: Config file for Stable Diffusion v2\n - `xl`: Config file for Stable Diffusion XL\n - `xl_refiner`: Config file for Stable Diffusion XL Refiner\n return: A StableDiffusionPipeline object representing the passed-in `.ckpt`/`.safetensors` file.\n \"\"\"\n\n # import pipelines here to avoid circular import error when using from_single_file method\n from diffusers import (\n LDMTextToImagePipeline,\n PaintByExamplePipeline,\n StableDiffusionControlNetPipeline,\n StableDiffusionInpaintPipeline,\n StableDiffusionPipeline,\n StableDiffusionUpscalePipeline,\n StableDiffusionXLControlNetInpaintPipeline,\n StableDiffusionXLImg2ImgPipeline,\n StableDiffusionXLInpaintPipeline,\n StableDiffusionXLPipeline,\n StableUnCLIPImg2ImgPipeline,\n StableUnCLIPPipeline,\n )\n\n if prediction_type == \"v-prediction\":\n prediction_type = \"v_prediction\"\n\n if isinstance(checkpoint_path_or_dict, str):\n if from_safetensors:\n from safetensors.torch import load_file as safe_load\n\n checkpoint = safe_load(checkpoint_path_or_dict, device=\"cpu\")\n else:\n if device is None:\n device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n checkpoint = torch.load(checkpoint_path_or_dict, map_location=device)\n else:\n checkpoint = torch.load(checkpoint_path_or_dict, map_location=device)\n elif isinstance(checkpoint_path_or_dict, dict):\n checkpoint = checkpoint_path_or_dict\n\n # Sometimes models don't have the global_step item\n if \"global_step\" in checkpoint:\n global_step = checkpoint[\"global_step\"]\n else:\n logger.debug(\"global_step key not found in model\")\n global_step = None\n\n # NOTE: this while loop isn't great but this controlnet checkpoint has one additional\n # \"state_dict\" key https://huggingface.co/thibaud/controlnet-canny-sd21\n while \"state_dict\" in checkpoint:\n checkpoint = checkpoint[\"state_dict\"]\n\n if original_config_file is None:\n key_name_v2_1 = \"model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight\"\n key_name_sd_xl_base = \"conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.bias\"\n key_name_sd_xl_refiner = \"conditioner.embedders.0.model.transformer.resblocks.9.mlp.c_proj.bias\"\n is_upscale = pipeline_class == StableDiffusionUpscalePipeline\n\n config_url = None\n\n # model_type = \"v1\"\n if config_files is not None and \"v1\" in config_files:\n original_config_file = config_files[\"v1\"]\n else:\n config_url = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml\"\n\n if key_name_v2_1 in checkpoint and checkpoint[key_name_v2_1].shape[-1] == 1024:\n # model_type = \"v2\"\n if config_files is not None and \"v2\" in config_files:\n original_config_file = config_files[\"v2\"]\n else:\n config_url = \"https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml\"\n if global_step == 110000:\n # v2.1 needs to upcast attention\n upcast_attention = True\n elif key_name_sd_xl_base in checkpoint:\n # only base xl has two text embedders\n if config_files is not None and \"xl\" in config_files:\n original_config_file = config_files[\"xl\"]\n else:\n config_url = \"https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml\"\n elif key_name_sd_xl_refiner in checkpoint:\n # only refiner xl has embedder and one text embedders\n if config_files is not None and \"xl_refiner\" in config_files:\n original_config_file = config_files[\"xl_refiner\"]\n else:\n config_url = \"https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_refiner.yaml\"\n\n if is_upscale:\n config_url = \"https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/x4-upscaling.yaml\"\n\n if config_url is not None:\n original_config_file = BytesIO(requests.get(config_url).content)\n else:\n with open(original_config_file, \"r\") as f:\n original_config_file = f.read()\n\n original_config = yaml.safe_load(original_config_file)\n\n # Convert the text model.\n if (\n model_type is None\n and \"cond_stage_config\" in original_config[\"model\"][\"params\"]\n and original_config[\"model\"][\"params\"][\"cond_stage_config\"] is not None\n ):\n model_type = original_config[\"model\"][\"params\"][\"cond_stage_config\"][\"target\"].split(\".\")[-1]\n logger.debug(f\"no `model_type` given, `model_type` inferred as: {model_type}\")\n elif model_type is None and original_config[\"model\"][\"params\"][\"network_config\"] is not None:\n if original_config[\"model\"][\"params\"][\"network_config\"][\"params\"][\"context_dim\"] == 2048:\n model_type = \"SDXL\"\n else:\n model_type = \"SDXL-Refiner\"\n if image_size is None:\n image_size = 1024\n\n if pipeline_class is None:\n # Check if we have a SDXL or SD model and initialize default pipeline\n if model_type not in [\"SDXL\", \"SDXL-Refiner\"]:\n pipeline_class = StableDiffusionPipeline if not controlnet else StableDiffusionControlNetPipeline\n else:\n pipeline_class = StableDiffusionXLPipeline if model_type == \"SDXL\" else StableDiffusionXLImg2ImgPipeline\n\n if num_in_channels is None and pipeline_class in [\n StableDiffusionInpaintPipeline,\n StableDiffusionXLInpaintPipeline,\n StableDiffusionXLControlNetInpaintPipeline,\n ]:\n num_in_channels = 9\n if num_in_channels is None and pipeline_class == StableDiffusionUpscalePipeline:\n num_in_channels = 7\n elif num_in_channels is None:\n num_in_channels = 4\n\n if \"unet_config\" in original_config[\"model\"][\"params\"]:\n original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"][\"in_channels\"] = num_in_channels\n\n if (\n \"parameterization\" in original_config[\"model\"][\"params\"]\n and original_config[\"model\"][\"params\"][\"parameterization\"] == \"v\"\n ):\n if prediction_type is None:\n # NOTE: For stable diffusion 2 base it is recommended to pass `prediction_type==\"epsilon\"`\n # as it relies on a brittle global step parameter here\n prediction_type = \"epsilon\" if global_step == 875000 else \"v_prediction\"\n if image_size is None:\n # NOTE: For stable diffusion 2 base one has to pass `image_size==512`\n # as it relies on a brittle global step parameter here\n image_size = 512 if global_step == 875000 else 768\n else:\n if prediction_type is None:\n prediction_type = \"epsilon\"\n if image_size is None:\n image_size = 512\n\n if controlnet is None and \"control_stage_config\" in original_config[\"model\"][\"params\"]:\n path = checkpoint_path_or_dict if isinstance(checkpoint_path_or_dict, str) else \"\"\n controlnet = convert_controlnet_checkpoint(\n checkpoint, original_config, path, image_size, upcast_attention, extract_ema\n )\n\n if \"timesteps\" in original_config[\"model\"][\"params\"]:\n num_train_timesteps = original_config[\"model\"][\"params\"][\"timesteps\"]\n else:\n num_train_timesteps = 1000\n\n if model_type in [\"SDXL\", \"SDXL-Refiner\"]:\n scheduler_dict = {\n \"beta_schedule\": \"scaled_linear\",\n \"beta_start\": 0.00085,\n \"beta_end\": 0.012,\n \"interpolation_type\": \"linear\",\n \"num_train_timesteps\": num_train_timesteps,\n \"prediction_type\": \"epsilon\",\n \"sample_max_value\": 1.0,\n \"set_alpha_to_one\": False,\n \"skip_prk_steps\": True,\n \"steps_offset\": 1,\n \"timestep_spacing\": \"leading\",\n }\n scheduler = EulerDiscreteScheduler.from_config(scheduler_dict)\n scheduler_type = \"euler\"\n else:\n if \"linear_start\" in original_config[\"model\"][\"params\"]:\n beta_start = original_config[\"model\"][\"params\"][\"linear_start\"]\n else:\n beta_start = 0.02\n\n if \"linear_end\" in original_config[\"model\"][\"params\"]:\n beta_end = original_config[\"model\"][\"params\"][\"linear_end\"]\n else:\n beta_end = 0.085\n scheduler = DDIMScheduler(\n beta_end=beta_end,\n beta_schedule=\"scaled_linear\",\n beta_start=beta_start,\n num_train_timesteps=num_train_timesteps,\n steps_offset=1,\n clip_sample=False,\n set_alpha_to_one=False,\n prediction_type=prediction_type,\n )\n # make sure scheduler works correctly with DDIM\n scheduler.register_to_config(clip_sample=False)\n\n if scheduler_type == \"pndm\":\n config = dict(scheduler.config)\n config[\"skip_prk_steps\"] = True\n scheduler = PNDMScheduler.from_config(config)\n elif scheduler_type == \"lms\":\n scheduler = LMSDiscreteScheduler.from_config(scheduler.config)\n elif scheduler_type == \"heun\":\n scheduler = HeunDiscreteScheduler.from_config(scheduler.config)\n elif scheduler_type == \"euler\":\n scheduler = EulerDiscreteScheduler.from_config(scheduler.config)\n elif scheduler_type == \"euler-ancestral\":\n scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config)\n elif scheduler_type == \"dpm\":\n scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)\n elif scheduler_type == \"ddim\":\n scheduler = scheduler\n else:\n raise ValueError(f\"Scheduler of type {scheduler_type} doesn't exist!\")\n\n if pipeline_class == StableDiffusionUpscalePipeline:\n image_size = original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"][\"image_size\"]\n\n # Convert the UNet2DConditionModel model.\n unet_config = create_unet_diffusers_config(original_config, image_size=image_size)\n unet_config[\"upcast_attention\"] = upcast_attention\n\n path = checkpoint_path_or_dict if isinstance(checkpoint_path_or_dict, str) else \"\"\n converted_unet_checkpoint = convert_ldm_unet_checkpoint(\n checkpoint, unet_config, path=path, extract_ema=extract_ema\n )\n\n ctx = init_empty_weights if is_accelerate_available() else nullcontext\n with ctx():\n unet = UNet2DConditionModel(**unet_config)\n\n if is_accelerate_available():\n if model_type not in [\"SDXL\", \"SDXL-Refiner\"]: # SBM Delay this.\n for param_name, param in converted_unet_checkpoint.items():\n set_module_tensor_to_device(unet, param_name, \"cpu\", value=param)\n else:\n unet.load_state_dict(converted_unet_checkpoint)\n\n # Convert the VAE model.\n if vae_path is None and vae is None:\n vae_config = create_vae_diffusers_config(original_config, image_size=image_size)\n converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)\n\n if (\n \"model\" in original_config\n and \"params\" in original_config[\"model\"]\n and \"scale_factor\" in original_config[\"model\"][\"params\"]\n ):\n vae_scaling_factor = original_config[\"model\"][\"params\"][\"scale_factor\"]\n else:\n vae_scaling_factor = 0.18215 # default SD scaling factor\n\n vae_config[\"scaling_factor\"] = vae_scaling_factor\n\n ctx = init_empty_weights if is_accelerate_available() else nullcontext\n with ctx():\n vae = AutoencoderKL(**vae_config)\n\n if is_accelerate_available():\n for param_name, param in converted_vae_checkpoint.items():\n set_module_tensor_to_device(vae, param_name, \"cpu\", value=param)\n else:\n vae.load_state_dict(converted_vae_checkpoint)\n elif vae is None:\n vae = AutoencoderKL.from_pretrained(vae_path, local_files_only=local_files_only)\n\n if model_type == \"FrozenOpenCLIPEmbedder\":\n config_name = \"stabilityai/stable-diffusion-2\"\n config_kwargs = {\"subfolder\": \"text_encoder\"}\n\n if text_encoder is None:\n text_model = convert_open_clip_checkpoint(\n checkpoint, config_name, local_files_only=local_files_only, **config_kwargs\n )\n else:\n text_model = text_encoder\n\n try:\n tokenizer = CLIPTokenizer.from_pretrained(\n \"stabilityai/stable-diffusion-2\", subfolder=\"tokenizer\", local_files_only=local_files_only\n )\n except Exception:\n raise ValueError(\n f\"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'stabilityai/stable-diffusion-2'.\"\n )\n\n if stable_unclip is None:\n if controlnet:\n pipe = pipeline_class(\n vae=vae,\n text_encoder=text_model,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n controlnet=controlnet,\n safety_checker=None,\n feature_extractor=None,\n )\n if hasattr(pipe, \"requires_safety_checker\"):\n pipe.requires_safety_checker = False\n\n elif pipeline_class == StableDiffusionUpscalePipeline:\n scheduler = DDIMScheduler.from_pretrained(\n \"stabilityai/stable-diffusion-x4-upscaler\", subfolder=\"scheduler\"\n )\n low_res_scheduler = DDPMScheduler.from_pretrained(\n \"stabilityai/stable-diffusion-x4-upscaler\", subfolder=\"low_res_scheduler\"\n )\n\n pipe = pipeline_class(\n vae=vae,\n text_encoder=text_model,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n low_res_scheduler=low_res_scheduler,\n safety_checker=None,\n feature_extractor=None,\n )\n\n else:\n pipe = pipeline_class(\n vae=vae,\n text_encoder=text_model,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=None,\n feature_extractor=None,\n )\n if hasattr(pipe, \"requires_safety_checker\"):\n pipe.requires_safety_checker = False\n\n else:\n image_normalizer, image_noising_scheduler = stable_unclip_image_noising_components(\n original_config, clip_stats_path=clip_stats_path, device=device\n )\n\n if stable_unclip == \"img2img\":\n feature_extractor, image_encoder = stable_unclip_image_encoder(original_config)\n\n pipe = StableUnCLIPImg2ImgPipeline(\n # image encoding components\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n # image noising components\n image_normalizer=image_normalizer,\n image_noising_scheduler=image_noising_scheduler,\n # regular denoising components\n tokenizer=tokenizer,\n text_encoder=text_model,\n unet=unet,\n scheduler=scheduler,\n # vae\n vae=vae,\n )\n elif stable_unclip == \"txt2img\":\n if stable_unclip_prior is None or stable_unclip_prior == \"karlo\":\n karlo_model = \"kakaobrain/karlo-v1-alpha\"\n prior = PriorTransformer.from_pretrained(\n karlo_model, subfolder=\"prior\", local_files_only=local_files_only\n )\n\n try:\n prior_tokenizer = CLIPTokenizer.from_pretrained(\n \"openai/clip-vit-large-patch14\", local_files_only=local_files_only\n )\n except Exception:\n raise ValueError(\n f\"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'openai/clip-vit-large-patch14'.\"\n )\n prior_text_model = CLIPTextModelWithProjection.from_pretrained(\n \"openai/clip-vit-large-patch14\", local_files_only=local_files_only\n )\n\n prior_scheduler = UnCLIPScheduler.from_pretrained(\n karlo_model, subfolder=\"prior_scheduler\", local_files_only=local_files_only\n )\n prior_scheduler = DDPMScheduler.from_config(prior_scheduler.config)\n else:\n raise NotImplementedError(f\"unknown prior for stable unclip model: {stable_unclip_prior}\")\n\n pipe = StableUnCLIPPipeline(\n # prior components\n prior_tokenizer=prior_tokenizer,\n prior_text_encoder=prior_text_model,\n prior=prior,\n prior_scheduler=prior_scheduler,\n # image noising components\n image_normalizer=image_normalizer,\n image_noising_scheduler=image_noising_scheduler,\n # regular denoising components\n tokenizer=tokenizer,\n text_encoder=text_model,\n unet=unet,\n scheduler=scheduler,\n # vae\n vae=vae,\n )\n else:\n raise NotImplementedError(f\"unknown `stable_unclip` type: {stable_unclip}\")\n elif model_type == \"PaintByExample\":\n vision_model = convert_paint_by_example_checkpoint(checkpoint)\n try:\n tokenizer = CLIPTokenizer.from_pretrained(\n \"openai/clip-vit-large-patch14\", local_files_only=local_files_only\n )\n except Exception:\n raise ValueError(\n f\"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'openai/clip-vit-large-patch14'.\"\n )\n try:\n feature_extractor = AutoFeatureExtractor.from_pretrained(\n \"CompVis/stable-diffusion-safety-checker\", local_files_only=local_files_only\n )\n except Exception:\n raise ValueError(\n f\"With local_files_only set to {local_files_only}, you must first locally save the feature_extractor in the following path: 'CompVis/stable-diffusion-safety-checker'.\"\n )\n pipe = PaintByExamplePipeline(\n vae=vae,\n image_encoder=vision_model,\n unet=unet,\n scheduler=scheduler,\n safety_checker=None,\n feature_extractor=feature_extractor,\n )\n elif model_type == \"FrozenCLIPEmbedder\":\n text_model = convert_ldm_clip_checkpoint(\n checkpoint, local_files_only=local_files_only, text_encoder=text_encoder\n )\n try:\n tokenizer = (\n CLIPTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", local_files_only=local_files_only)\n if tokenizer is None\n else tokenizer\n )\n except Exception:\n raise ValueError(\n f\"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'openai/clip-vit-large-patch14'.\"\n )\n\n if load_safety_checker:\n safety_checker = StableDiffusionSafetyChecker.from_pretrained(\n \"CompVis/stable-diffusion-safety-checker\", local_files_only=local_files_only\n )\n feature_extractor = AutoFeatureExtractor.from_pretrained(\n \"CompVis/stable-diffusion-safety-checker\", local_files_only=local_files_only\n )\n else:\n safety_checker = None\n feature_extractor = None\n\n if controlnet:\n pipe = pipeline_class(\n vae=vae,\n text_encoder=text_model,\n tokenizer=tokenizer,\n unet=unet,\n controlnet=controlnet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n else:\n pipe = pipeline_class(\n vae=vae,\n text_encoder=text_model,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n elif model_type in [\"SDXL\", \"SDXL-Refiner\"]:\n is_refiner = model_type == \"SDXL-Refiner\"\n\n if (is_refiner is False) and (tokenizer is None):\n try:\n tokenizer = CLIPTokenizer.from_pretrained(\n \"openai/clip-vit-large-patch14\", local_files_only=local_files_only\n )\n except Exception:\n raise ValueError(\n f\"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'openai/clip-vit-large-patch14'.\"\n )\n\n if (is_refiner is False) and (text_encoder is None):\n text_encoder = convert_ldm_clip_checkpoint(checkpoint, local_files_only=local_files_only)\n\n if tokenizer_2 is None:\n try:\n tokenizer_2 = CLIPTokenizer.from_pretrained(\n \"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\", pad_token=\"!\", local_files_only=local_files_only\n )\n except Exception:\n raise ValueError(\n f\"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'laion/CLIP-ViT-bigG-14-laion2B-39B-b160k' with `pad_token` set to '!'.\"\n )\n\n if text_encoder_2 is None:\n config_name = \"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\"\n config_kwargs = {\"projection_dim\": 1280}\n prefix = \"conditioner.embedders.0.model.\" if is_refiner else \"conditioner.embedders.1.model.\"\n\n text_encoder_2 = convert_open_clip_checkpoint(\n checkpoint,\n config_name,\n prefix=prefix,\n has_projection=True,\n local_files_only=local_files_only,\n **config_kwargs,\n )\n\n if is_accelerate_available(): # SBM Now move model to cpu.\n for param_name, param in converted_unet_checkpoint.items():\n set_module_tensor_to_device(unet, param_name, \"cpu\", value=param)\n\n if controlnet:\n pipe = pipeline_class(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n text_encoder_2=text_encoder_2,\n tokenizer_2=tokenizer_2,\n unet=unet,\n controlnet=controlnet,\n scheduler=scheduler,\n force_zeros_for_empty_prompt=True,\n )\n elif adapter:\n pipe = pipeline_class(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n text_encoder_2=text_encoder_2,\n tokenizer_2=tokenizer_2,\n unet=unet,\n adapter=adapter,\n scheduler=scheduler,\n force_zeros_for_empty_prompt=True,\n )\n\n else:\n pipeline_kwargs = {\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"text_encoder_2\": text_encoder_2,\n \"tokenizer_2\": tokenizer_2,\n \"unet\": unet,\n \"scheduler\": scheduler,\n }\n\n if (pipeline_class == StableDiffusionXLImg2ImgPipeline) or (\n pipeline_class == StableDiffusionXLInpaintPipeline\n ):\n pipeline_kwargs.update({\"requires_aesthetics_score\": is_refiner})\n\n if is_refiner:\n pipeline_kwargs.update({\"force_zeros_for_empty_prompt\": False})\n\n pipe = pipeline_class(**pipeline_kwargs)\n else:\n text_config = create_ldm_bert_config(original_config)\n text_model = convert_ldm_bert_checkpoint(checkpoint, text_config)\n tokenizer = BertTokenizerFast.from_pretrained(\"bert-base-uncased\", local_files_only=local_files_only)\n pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler)\n\n return pipe\n\n\ndef download_controlnet_from_original_ckpt(\n checkpoint_path: str,\n original_config_file: str,\n image_size: int = 512,\n extract_ema: bool = False,\n num_in_channels: Optional[int] = None,\n upcast_attention: Optional[bool] = None,\n device: str = None,\n from_safetensors: bool = False,\n use_linear_projection: Optional[bool] = None,\n cross_attention_dim: Optional[bool] = None,\n) -> DiffusionPipeline:\n if from_safetensors:\n from safetensors import safe_open\n\n checkpoint = {}\n with safe_open(checkpoint_path, framework=\"pt\", device=\"cpu\") as f:\n for key in f.keys():\n checkpoint[key] = f.get_tensor(key)\n else:\n if device is None:\n device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n checkpoint = torch.load(checkpoint_path, map_location=device)\n else:\n checkpoint = torch.load(checkpoint_path, map_location=device)\n\n # NOTE: this while loop isn't great but this controlnet checkpoint has one additional\n # \"state_dict\" key https://huggingface.co/thibaud/controlnet-canny-sd21\n while \"state_dict\" in checkpoint:\n checkpoint = checkpoint[\"state_dict\"]\n\n original_config = yaml.safe_load(original_config_file)\n\n if num_in_channels is not None:\n original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"][\"in_channels\"] = num_in_channels\n\n if \"control_stage_config\" not in original_config[\"model\"][\"params\"]:\n raise ValueError(\"`control_stage_config` not present in original config\")\n\n controlnet = convert_controlnet_checkpoint(\n checkpoint,\n original_config,\n checkpoint_path,\n image_size,\n upcast_attention,\n extract_ema,\n use_linear_projection=use_linear_projection,\n cross_attention_dim=cross_attention_dim,\n )\n\n return controlnet\n\n\ndef download_promptdiffusion_from_original_ckpt(\n checkpoint_path: str,\n original_config_file: str,\n image_size: int = 512,\n extract_ema: bool = False,\n num_in_channels: Optional[int] = None,\n upcast_attention: Optional[bool] = None,\n device: str = None,\n from_safetensors: bool = False,\n use_linear_projection: Optional[bool] = None,\n cross_attention_dim: Optional[bool] = None,\n) -> DiffusionPipeline:\n if from_safetensors:\n from safetensors import safe_open\n\n checkpoint = {}\n with safe_open(checkpoint_path, framework=\"pt\", device=\"cpu\") as f:\n for key in f.keys():\n checkpoint[key] = f.get_tensor(key)\n else:\n if device is None:\n device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n checkpoint = torch.load(checkpoint_path, map_location=device)\n else:\n checkpoint = torch.load(checkpoint_path, map_location=device)\n\n # NOTE: this while loop isn't great but this controlnet checkpoint has one additional\n # \"state_dict\" key https://huggingface.co/thibaud/controlnet-canny-sd21\n while \"state_dict\" in checkpoint:\n checkpoint = checkpoint[\"state_dict\"]\n\n original_config = yaml.safe_load(open(original_config_file))\n\n if num_in_channels is not None:\n original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"][\"in_channels\"] = num_in_channels\n if \"control_stage_config\" not in original_config[\"model\"][\"params\"]:\n raise ValueError(\"`control_stage_config` not present in original config\")\n\n controlnet = convert_promptdiffusion_checkpoint(\n checkpoint,\n original_config,\n checkpoint_path,\n image_size,\n upcast_attention,\n extract_ema,\n use_linear_projection=use_linear_projection,\n cross_attention_dim=cross_attention_dim,\n )\n\n return controlnet\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--checkpoint_path\", default=None, type=str, required=True, help=\"Path to the checkpoint to convert.\"\n )\n parser.add_argument(\n \"--original_config_file\",\n type=str,\n required=True,\n help=\"The YAML config file corresponding to the original architecture.\",\n )\n parser.add_argument(\n \"--num_in_channels\",\n default=None,\n type=int,\n help=\"The number of input channels. If `None` number of input channels will be automatically inferred.\",\n )\n parser.add_argument(\n \"--image_size\",\n default=512,\n type=int,\n help=(\n \"The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2\"\n \" Base. Use 768 for Stable Diffusion v2.\"\n ),\n )\n parser.add_argument(\n \"--extract_ema\",\n action=\"store_true\",\n help=(\n \"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights\"\n \" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield\"\n \" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.\"\n ),\n )\n parser.add_argument(\n \"--upcast_attention\",\n action=\"store_true\",\n help=(\n \"Whether the attention computation should always be upcasted. This is necessary when running stable\"\n \" diffusion 2.1.\"\n ),\n )\n parser.add_argument(\n \"--from_safetensors\",\n action=\"store_true\",\n help=\"If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.\",\n )\n parser.add_argument(\n \"--to_safetensors\",\n action=\"store_true\",\n help=\"Whether to store pipeline in safetensors format or not.\",\n )\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n parser.add_argument(\"--device\", type=str, help=\"Device to use (e.g. cpu, cuda:0, cuda:1, etc.)\")\n\n # small workaround to get argparser to parse a boolean input as either true _or_ false\n def parse_bool(string):\n if string == \"True\":\n return True\n elif string == \"False\":\n return False\n else:\n raise ValueError(f\"could not parse string as bool {string}\")\n\n parser.add_argument(\n \"--use_linear_projection\", help=\"Override for use linear projection\", required=False, type=parse_bool\n )\n\n parser.add_argument(\"--cross_attention_dim\", help=\"Override for cross attention_dim\", required=False, type=int)\n\n args = parser.parse_args()\n\n controlnet = download_promptdiffusion_from_original_ckpt(\n checkpoint_path=args.checkpoint_path,\n original_config_file=args.original_config_file,\n image_size=args.image_size,\n extract_ema=args.extract_ema,\n num_in_channels=args.num_in_channels,\n upcast_attention=args.upcast_attention,\n from_safetensors=args.from_safetensors,\n device=args.device,\n use_linear_projection=args.use_linear_projection,\n cross_attention_dim=args.cross_attention_dim,\n )\n\n controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)\n" }, { "path": "diffusers/examples/research_projects/promptdiffusion/pipeline_prompt_diffusion.py", "content": "# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# Based on [In-Context Learning Unlocked for Diffusion Models](https://arxiv.org/abs/2305.01115)\n# Authors: Zhendong Wang, Yifan Jiang, Yadong Lu, Yelong Shen, Pengcheng He, Weizhu Chen, Zhangyang Wang, Mingyuan Zhou\n# Project Page: https://zhendong-wang.github.io/prompt-diffusion.github.io/\n# Code: https://github.com/Zhendong-Wang/Prompt-Diffusion\n#\n# Adapted to Diffusers by [iczaw](https://github.com/iczaw).\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom diffusers.image_processor import PipelineImageInput, VaeImageProcessor\nfrom diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom diffusers.models import AutoencoderKL, ControlNetModel, UNet2DConditionModel\nfrom diffusers.models.lora import adjust_lora_scale_text_encoder\nfrom diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline\nfrom diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom diffusers.utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> # !pip install opencv-python transformers accelerate\n >>> from promptdiffusioncontrolnet import PromptDiffusionControlNetModel\n >>> from diffusers.utils import load_image\n >>> import torch\n\n >>> from diffusers.pipelines.pipeline_utils import DiffusionPipeline\n >>> from diffusers import UniPCMultistepScheduler\n >>> from PIL import ImageOps\n\n >>> # download an image\n >>> image_a = ImageOps.invert(load_image(\n ... \"https://github.com/Zhendong-Wang/Prompt-Diffusion/blob/main/images_to_try/house_line.png?raw=true\"\n ... ))\n\n >>> # download an image\n >>> image_b = load_image(\n ... \"https://github.com/Zhendong-Wang/Prompt-Diffusion/blob/main/images_to_try/house.png?raw=true\"\n ... )\n\n >>> # download an image\n >>> query = ImageOps.invert(load_image(\n ... \"https://github.com/Zhendong-Wang/Prompt-Diffusion/blob/main/images_to_try/new_01.png?raw=true\"\n ... ))\n\n >>> # load prompt diffusion control net and prompt diffusion\n >>> controlnet = PromptDiffusionControlNetModel.from_pretrained(\"path-to-converted-promptdiffusion-controlnet\", torch_dtype=torch.float16)\n >>> pipe = DiffusionPipeline.from_pretrained(model_id, controlnet=controlnet, torch_dtype=torch.float16, variant=\"fp16\", custom_pipeline=\"pipeline_prompt_diffusion\")\n\n >>> # speed up diffusion process with faster scheduler and memory optimization\n >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n >>> # remove following line if xformers is not installed\n >>> pipe.enable_xformers_memory_efficient_attention()\n\n >>> pipe.enable_model_cpu_offload()\n\n >>> # generate image\n >>> generator = torch.manual_seed(0)\n >>> image = pipe(\n ... \"a tortoise\", num_inference_steps=20, generator=generator, image_pair=[image_a,image_b], image=query\n ... ).images[0]\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used,\n `timesteps` must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default\n timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`\n must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass PromptDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.\n\n This pipeline also adds experimental support for [Prompt Diffusion](https://arxiv.org/abs/2305.01115).\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):\n Provides additional conditioning to the `unet` during the denoising process. If you set multiple\n ControlNets as a list, the outputs from each ControlNet are added together to create one combined\n additional conditioning.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->image_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"image_encoder\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder: CLIPVisionModelWithProjection = None,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n if isinstance(controlnet, (list, tuple)):\n controlnet = MultiControlNetModel(controlnet)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n controlnet=controlnet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)\n self.control_image_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False\n )\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing\n def enable_vae_slicing(self):\n r\"\"\"\n Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to\n compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.\n \"\"\"\n self.vae.enable_slicing()\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing\n def disable_vae_slicing(self):\n r\"\"\"\n Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to\n computing decoding in one step.\n \"\"\"\n self.vae.disable_slicing()\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling\n def enable_vae_tiling(self):\n r\"\"\"\n Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to\n compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow\n processing larger images.\n \"\"\"\n self.vae.enable_tiling()\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling\n def disable_vae_tiling(self):\n r\"\"\"\n Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to\n computing decoding in one step.\n \"\"\"\n self.vae.disable_tiling()\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: procecss multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: procecss multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n image,\n image_pair,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n controlnet_conditioning_scale=1.0,\n control_guidance_start=0.0,\n control_guidance_end=1.0,\n callback_on_step_end_tensor_inputs=None,\n ):\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # `prompt` needs more sophisticated handling when there are multiple\n # conditionings.\n if isinstance(self.controlnet, MultiControlNetModel):\n if isinstance(prompt, list):\n logger.warning(\n f\"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}\"\n \" prompts. The conditionings will be fixed across the prompts.\"\n )\n\n # Check `image`\n is_compiled = hasattr(F, \"scaled_dot_product_attention\") and isinstance(\n self.controlnet, torch._dynamo.eval_frame.OptimizedModule\n )\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n self.check_image(image, prompt, prompt_embeds)\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if not isinstance(image, list):\n raise TypeError(\"For multiple controlnets: `image` must be type `list`\")\n\n # When `image` is a nested list:\n # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])\n elif any(isinstance(i, list) for i in image):\n raise ValueError(\"A single batch of multiple conditionings is not supported at the moment.\")\n elif len(image) != len(self.controlnet.nets):\n raise ValueError(\n f\"For multiple controlnets: `image` must have the same length as the number of controlnets, but got {len(image)} images and {len(self.controlnet.nets)} ControlNets.\"\n )\n\n for image_ in image:\n self.check_image(image_, prompt, prompt_embeds)\n else:\n assert False\n\n # Check `image_pair`\n if len(image_pair) == 2:\n for image in image_pair:\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n self.check_image(image, prompt, prompt_embeds)\n else:\n raise ValueError(\n f\"You have passed a list of images of length {len(image_pair)}.\"\n f\"Make sure the list size equals to two.\"\n )\n\n # Check `controlnet_conditioning_scale`\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n if not isinstance(controlnet_conditioning_scale, float):\n raise TypeError(\"For single controlnet: `controlnet_conditioning_scale` must be type `float`.\")\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if isinstance(controlnet_conditioning_scale, list):\n if any(isinstance(i, list) for i in controlnet_conditioning_scale):\n raise ValueError(\"A single batch of multiple conditionings is not supported at the moment.\")\n elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(\n self.controlnet.nets\n ):\n raise ValueError(\n \"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have\"\n \" the same length as the number of controlnets\"\n )\n else:\n assert False\n\n if not isinstance(control_guidance_start, (tuple, list)):\n control_guidance_start = [control_guidance_start]\n\n if not isinstance(control_guidance_end, (tuple, list)):\n control_guidance_end = [control_guidance_end]\n\n if len(control_guidance_start) != len(control_guidance_end):\n raise ValueError(\n f\"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list.\"\n )\n\n if isinstance(self.controlnet, MultiControlNetModel):\n if len(control_guidance_start) != len(self.controlnet.nets):\n raise ValueError(\n f\"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}.\"\n )\n\n for start, end in zip(control_guidance_start, control_guidance_end):\n if start >= end:\n raise ValueError(\n f\"control guidance start: {start} cannot be larger or equal to control guidance end: {end}.\"\n )\n if start < 0.0:\n raise ValueError(f\"control guidance start: {start} can't be smaller than 0.\")\n if end > 1.0:\n raise ValueError(f\"control guidance end: {end} can't be larger than 1.0.\")\n\n # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image\n def check_image(self, image, prompt, prompt_embeds):\n image_is_pil = isinstance(image, PIL.Image.Image)\n image_is_tensor = isinstance(image, torch.Tensor)\n image_is_np = isinstance(image, np.ndarray)\n image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)\n image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)\n image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)\n\n if (\n not image_is_pil\n and not image_is_tensor\n and not image_is_np\n and not image_is_pil_list\n and not image_is_tensor_list\n and not image_is_np_list\n ):\n raise TypeError(\n f\"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}\"\n )\n\n if image_is_pil:\n image_batch_size = 1\n else:\n image_batch_size = len(image)\n\n if prompt is not None and isinstance(prompt, str):\n prompt_batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n prompt_batch_size = len(prompt)\n elif prompt_embeds is not None:\n prompt_batch_size = prompt_embeds.shape[0]\n\n if image_batch_size != 1 and image_batch_size != prompt_batch_size:\n raise ValueError(\n f\"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}\"\n )\n\n # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image\n def prepare_image(\n self,\n image,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype,\n do_classifier_free_guidance=False,\n guess_mode=False,\n ):\n image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)\n image_batch_size = image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n image = image.repeat_interleave(repeat_by, dim=0)\n\n image = image.to(device=device, dtype=dtype)\n\n if do_classifier_free_guidance and not guess_mode:\n image = torch.cat([image] * 2)\n\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_freeu\n def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):\n r\"\"\"Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.\n\n The suffixes after the scaling factors represent the stages where they are being applied.\n\n Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values\n that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.\n\n Args:\n s1 (`float`):\n Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to\n mitigate \"oversmoothing effect\" in the enhanced denoising process.\n s2 (`float`):\n Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to\n mitigate \"oversmoothing effect\" in the enhanced denoising process.\n b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.\n b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.\n \"\"\"\n if not hasattr(self, \"unet\"):\n raise ValueError(\"The pipeline must have `unet` for using FreeU.\")\n self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_freeu\n def disable_freeu(self):\n \"\"\"Disables the FreeU mechanism if enabled.\"\"\"\n self.unet.disable_freeu()\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n timesteps (`torch.Tensor`):\n generate embedding vectors at these timesteps\n embedding_dim (`int`, *optional*, defaults to 512):\n dimension of the embeddings to generate\n dtype:\n data type of the generated embeddings\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: PipelineImageInput = None,\n image_pair: List[PipelineImageInput] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: Union[float, List[float]] = 1.0,\n guess_mode: bool = False,\n control_guidance_start: Union[float, List[float]] = 0.0,\n control_guidance_end: Union[float, List[float]] = 1.0,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:\n `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):\n The ControlNet input condition to provide guidance to the `unet` for generation. If the type is\n specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be\n accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height\n and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in\n `init`, images must be passed as a list such that each element of the list can be correctly batched for\n input to a single ControlNet.\n image_pair `List[PIL.Image.Image]`:\n a pair of task-specific example images\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):\n The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added\n to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set\n the corresponding scale as a list.\n guess_mode (`bool`, *optional*, defaults to `False`):\n The ControlNet encoder tries to recognize the content of the input image even if you remove all\n prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.\n control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):\n The percentage of total steps at which the ControlNet starts applying.\n control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):\n The percentage of total steps at which the ControlNet stops applying.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n\n controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet\n\n # align format for control guidance\n if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):\n control_guidance_start = len(control_guidance_end) * [control_guidance_start]\n elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):\n control_guidance_end = len(control_guidance_start) * [control_guidance_end]\n elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):\n mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1\n control_guidance_start, control_guidance_end = (\n mult * [control_guidance_start],\n mult * [control_guidance_end],\n )\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n image,\n image_pair,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n controlnet_conditioning_scale,\n control_guidance_start,\n control_guidance_end,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):\n controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)\n\n global_pool_conditions = (\n controlnet.config.global_pool_conditions\n if isinstance(controlnet, ControlNetModel)\n else controlnet.nets[0].config.global_pool_conditions\n )\n guess_mode = guess_mode or global_pool_conditions\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=self.clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 3.1 Prepare image pair\n\n if isinstance(controlnet, ControlNetModel):\n image_pair = torch.cat(\n [\n self.prepare_image(\n image=im,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n for im in image_pair\n ],\n 1,\n )\n # 4. Prepare image\n if isinstance(controlnet, ControlNetModel):\n image = self.prepare_image(\n image=image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n height, width = image.shape[-2:]\n elif isinstance(controlnet, MultiControlNetModel):\n images = []\n\n for image_ in image:\n image_ = self.prepare_image(\n image=image_,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n\n images.append(image_)\n\n image = images\n height, width = image[0].shape[-2:]\n else:\n assert False\n\n # 5. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)\n self._num_timesteps = len(timesteps)\n\n # 6. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6.5 Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7.2 Create tensor stating which controlnets to keep\n controlnet_keep = []\n for i in range(len(timesteps)):\n keeps = [\n 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)\n for s, e in zip(control_guidance_start, control_guidance_end)\n ]\n controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n is_unet_compiled = is_compiled_module(self.unet)\n is_controlnet_compiled = is_compiled_module(self.controlnet)\n is_torch_higher_equal_2_1 = is_torch_version(\">=\", \"2.1\")\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # Relevant thread:\n # https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428\n if (is_unet_compiled and is_controlnet_compiled) and is_torch_higher_equal_2_1:\n torch._inductor.cudagraph_mark_step_begin()\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # controlnet(s) inference\n if guess_mode and self.do_classifier_free_guidance:\n # Infer ControlNet only for the conditional batch.\n control_model_input = latents\n control_model_input = self.scheduler.scale_model_input(control_model_input, t)\n controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]\n else:\n control_model_input = latent_model_input\n controlnet_prompt_embeds = prompt_embeds\n\n if isinstance(controlnet_keep[i], list):\n cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]\n else:\n controlnet_cond_scale = controlnet_conditioning_scale\n if isinstance(controlnet_cond_scale, list):\n controlnet_cond_scale = controlnet_cond_scale[0]\n cond_scale = controlnet_cond_scale * controlnet_keep[i]\n\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n control_model_input,\n t,\n encoder_hidden_states=controlnet_prompt_embeds,\n controlnet_query_cond=image,\n controlnet_cond=image_pair,\n conditioning_scale=cond_scale,\n guess_mode=guess_mode,\n return_dict=False,\n )\n\n if guess_mode and self.do_classifier_free_guidance:\n # Infered ControlNet only for the conditional batch.\n # To apply the output of ControlNet to both the unconditional and conditional batches,\n # add 0 to the unconditional batch to keep it unchanged.\n down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]\n mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=self.cross_attention_kwargs,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # If we do sequential model offloading, let's offload unet and controlnet\n # manually for max memory savings\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.unet.to(\"cpu\")\n self.controlnet.to(\"cpu\")\n torch.cuda.empty_cache()\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[\n 0\n ]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/examples/research_projects/promptdiffusion/promptdiffusioncontrolnet.py", "content": "# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Any, Dict, Optional, Tuple, Union\n\nimport torch\n\nfrom diffusers.configuration_utils import register_to_config\nfrom diffusers.models.controlnet import (\n ControlNetConditioningEmbedding,\n ControlNetModel,\n ControlNetOutput,\n)\nfrom diffusers.utils import logging\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass PromptDiffusionControlNetModel(ControlNetModel):\n \"\"\"\n A PromptDiffusionControlNet model.\n\n Args:\n in_channels (`int`, defaults to 4):\n The number of channels in the input sample.\n flip_sin_to_cos (`bool`, defaults to `True`):\n Whether to flip the sin to cos in the time embedding.\n freq_shift (`int`, defaults to 0):\n The frequency shift to apply to the time embedding.\n down_block_types (`tuple[str]`, defaults to `(\"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\", \"DownBlock2D\")`):\n The tuple of downsample blocks to use.\n only_cross_attention (`Union[bool, Tuple[bool]]`, defaults to `False`):\n block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`):\n The tuple of output channels for each block.\n layers_per_block (`int`, defaults to 2):\n The number of layers per block.\n downsample_padding (`int`, defaults to 1):\n The padding to use for the downsampling convolution.\n mid_block_scale_factor (`float`, defaults to 1):\n The scale factor to use for the mid block.\n act_fn (`str`, defaults to \"silu\"):\n The activation function to use.\n norm_num_groups (`int`, *optional*, defaults to 32):\n The number of groups to use for the normalization. If None, normalization and activation layers is skipped\n in post-processing.\n norm_eps (`float`, defaults to 1e-5):\n The epsilon to use for the normalization.\n cross_attention_dim (`int`, defaults to 1280):\n The dimension of the cross attention features.\n transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):\n The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for\n [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],\n [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].\n encoder_hid_dim (`int`, *optional*, defaults to None):\n If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`\n dimension to `cross_attention_dim`.\n encoder_hid_dim_type (`str`, *optional*, defaults to `None`):\n If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text\n embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.\n attention_head_dim (`Union[int, Tuple[int]]`, defaults to 8):\n The dimension of the attention heads.\n use_linear_projection (`bool`, defaults to `False`):\n class_embed_type (`str`, *optional*, defaults to `None`):\n The type of class embedding to use which is ultimately summed with the time embeddings. Choose from None,\n `\"timestep\"`, `\"identity\"`, `\"projection\"`, or `\"simple_projection\"`.\n addition_embed_type (`str`, *optional*, defaults to `None`):\n Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or\n \"text\". \"text\" will use the `TextTimeEmbedding` layer.\n num_class_embeds (`int`, *optional*, defaults to 0):\n Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing\n class conditioning with `class_embed_type` equal to `None`.\n upcast_attention (`bool`, defaults to `False`):\n resnet_time_scale_shift (`str`, defaults to `\"default\"`):\n Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`.\n projection_class_embeddings_input_dim (`int`, *optional*, defaults to `None`):\n The dimension of the `class_labels` input when `class_embed_type=\"projection\"`. Required when\n `class_embed_type=\"projection\"`.\n controlnet_conditioning_channel_order (`str`, defaults to `\"rgb\"`):\n The channel order of conditional image. Will convert to `rgb` if it's `bgr`.\n conditioning_embedding_out_channels (`tuple[int]`, *optional*, defaults to `(16, 32, 96, 256)`):\n The tuple of output channel for each block in the `conditioning_embedding` layer.\n global_pool_conditions (`bool`, defaults to `False`):\n TODO(Patrick) - unused parameter.\n addition_embed_type_num_heads (`int`, defaults to 64):\n The number of heads to use for the `TextTimeEmbedding` layer.\n \"\"\"\n\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n in_channels: int = 4,\n conditioning_channels: int = 3,\n flip_sin_to_cos: bool = True,\n freq_shift: int = 0,\n down_block_types: Tuple[str, ...] = (\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"DownBlock2D\",\n ),\n mid_block_type: Optional[str] = \"UNetMidBlock2DCrossAttn\",\n only_cross_attention: Union[bool, Tuple[bool]] = False,\n block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),\n layers_per_block: int = 2,\n downsample_padding: int = 1,\n mid_block_scale_factor: float = 1,\n act_fn: str = \"silu\",\n norm_num_groups: Optional[int] = 32,\n norm_eps: float = 1e-5,\n cross_attention_dim: int = 1280,\n transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1,\n encoder_hid_dim: Optional[int] = None,\n encoder_hid_dim_type: Optional[str] = None,\n attention_head_dim: Union[int, Tuple[int, ...]] = 8,\n num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None,\n use_linear_projection: bool = False,\n class_embed_type: Optional[str] = None,\n addition_embed_type: Optional[str] = None,\n addition_time_embed_dim: Optional[int] = None,\n num_class_embeds: Optional[int] = None,\n upcast_attention: bool = False,\n resnet_time_scale_shift: str = \"default\",\n projection_class_embeddings_input_dim: Optional[int] = None,\n controlnet_conditioning_channel_order: str = \"rgb\",\n conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),\n global_pool_conditions: bool = False,\n addition_embed_type_num_heads: int = 64,\n ):\n super().__init__(\n in_channels,\n conditioning_channels,\n flip_sin_to_cos,\n freq_shift,\n down_block_types,\n mid_block_type,\n only_cross_attention,\n block_out_channels,\n layers_per_block,\n downsample_padding,\n mid_block_scale_factor,\n act_fn,\n norm_num_groups,\n norm_eps,\n cross_attention_dim,\n transformer_layers_per_block,\n encoder_hid_dim,\n encoder_hid_dim_type,\n attention_head_dim,\n num_attention_heads,\n use_linear_projection,\n class_embed_type,\n addition_embed_type,\n addition_time_embed_dim,\n num_class_embeds,\n upcast_attention,\n resnet_time_scale_shift,\n projection_class_embeddings_input_dim,\n controlnet_conditioning_channel_order,\n conditioning_embedding_out_channels,\n global_pool_conditions,\n addition_embed_type_num_heads,\n )\n self.controlnet_query_cond_embedding = ControlNetConditioningEmbedding(\n conditioning_embedding_channels=block_out_channels[0],\n block_out_channels=conditioning_embedding_out_channels,\n conditioning_channels=3,\n )\n\n def forward(\n self,\n sample: torch.Tensor,\n timestep: Union[torch.Tensor, float, int],\n encoder_hidden_states: torch.Tensor,\n controlnet_cond: torch.Tensor,\n controlnet_query_cond: torch.Tensor,\n conditioning_scale: float = 1.0,\n class_labels: Optional[torch.Tensor] = None,\n timestep_cond: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guess_mode: bool = False,\n return_dict: bool = True,\n ) -> Union[ControlNetOutput, Tuple[Tuple[torch.Tensor, ...], torch.Tensor]]:\n \"\"\"\n The [`~PromptDiffusionControlNetModel`] forward method.\n\n Args:\n sample (`torch.Tensor`):\n The noisy input tensor.\n timestep (`Union[torch.Tensor, float, int]`):\n The number of timesteps to denoise an input.\n encoder_hidden_states (`torch.Tensor`):\n The encoder hidden states.\n controlnet_cond (`torch.Tensor`):\n The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.\n controlnet_query_cond (`torch.Tensor`):\n The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.\n conditioning_scale (`float`, defaults to `1.0`):\n The scale factor for ControlNet outputs.\n class_labels (`torch.Tensor`, *optional*, defaults to `None`):\n Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.\n timestep_cond (`torch.Tensor`, *optional*, defaults to `None`):\n Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the\n timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep\n embeddings.\n attention_mask (`torch.Tensor`, *optional*, defaults to `None`):\n An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask\n is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large\n negative values to the attention scores corresponding to \"discard\" tokens.\n added_cond_kwargs (`dict`):\n Additional conditions for the Stable Diffusion XL UNet.\n cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`):\n A kwargs dictionary that if specified is passed along to the `AttnProcessor`.\n guess_mode (`bool`, defaults to `False`):\n In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if\n you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.\n return_dict (`bool`, defaults to `True`):\n Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.\n\n Returns:\n [`~models.controlnet.ControlNetOutput`] **or** `tuple`:\n If `return_dict` is `True`, a [`~models.controlnet.ControlNetOutput`] is returned, otherwise a tuple is\n returned where the first element is the sample tensor.\n \"\"\"\n # check channel order\n channel_order = self.config.controlnet_conditioning_channel_order\n\n if channel_order == \"rgb\":\n # in rgb order by default\n ...\n elif channel_order == \"bgr\":\n controlnet_cond = torch.flip(controlnet_cond, dims=[1])\n else:\n raise ValueError(f\"unknown `controlnet_conditioning_channel_order`: {channel_order}\")\n\n # prepare attention_mask\n if attention_mask is not None:\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n # 1. time\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can\n # This would be a good case for the `match` statement (Python 3.10+)\n is_mps = sample.device.type == \"mps\"\n if isinstance(timestep, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\n elif len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(sample.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timesteps = timesteps.expand(sample.shape[0])\n\n t_emb = self.time_proj(timesteps)\n\n # timesteps does not contain any weights and will always return f32 tensors\n # but time_embedding might actually be running in fp16. so we need to cast here.\n # there might be better ways to encapsulate this.\n t_emb = t_emb.to(dtype=sample.dtype)\n\n emb = self.time_embedding(t_emb, timestep_cond)\n aug_emb = None\n\n if self.class_embedding is not None:\n if class_labels is None:\n raise ValueError(\"class_labels should be provided when num_class_embeds > 0\")\n\n if self.config.class_embed_type == \"timestep\":\n class_labels = self.time_proj(class_labels)\n\n class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)\n emb = emb + class_emb\n\n if self.config.addition_embed_type is not None:\n if self.config.addition_embed_type == \"text\":\n aug_emb = self.add_embedding(encoder_hidden_states)\n\n elif self.config.addition_embed_type == \"text_time\":\n if \"text_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`\"\n )\n text_embeds = added_cond_kwargs.get(\"text_embeds\")\n if \"time_ids\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`\"\n )\n time_ids = added_cond_kwargs.get(\"time_ids\")\n time_embeds = self.add_time_proj(time_ids.flatten())\n time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))\n\n add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)\n add_embeds = add_embeds.to(emb.dtype)\n aug_emb = self.add_embedding(add_embeds)\n\n emb = emb + aug_emb if aug_emb is not None else emb\n\n # 2. pre-process\n sample = self.conv_in(sample)\n\n controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)\n controlnet_query_cond = self.controlnet_query_cond_embedding(controlnet_query_cond)\n sample = sample + controlnet_cond + controlnet_query_cond\n\n # 3. down\n down_block_res_samples = (sample,)\n for downsample_block in self.down_blocks:\n if hasattr(downsample_block, \"has_cross_attention\") and downsample_block.has_cross_attention:\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n else:\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb)\n\n down_block_res_samples += res_samples\n\n # 4. mid\n if self.mid_block is not None:\n if hasattr(self.mid_block, \"has_cross_attention\") and self.mid_block.has_cross_attention:\n sample = self.mid_block(\n sample,\n emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n else:\n sample = self.mid_block(sample, emb)\n\n # 5. Control net blocks\n\n controlnet_down_block_res_samples = ()\n\n for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):\n down_block_res_sample = controlnet_block(down_block_res_sample)\n controlnet_down_block_res_samples = controlnet_down_block_res_samples + (down_block_res_sample,)\n\n down_block_res_samples = controlnet_down_block_res_samples\n\n mid_block_res_sample = self.controlnet_mid_block(sample)\n\n # 6. scaling\n if guess_mode and not self.config.global_pool_conditions:\n scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device) # 0.1 to 1.0\n scales = scales * conditioning_scale\n down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)]\n mid_block_res_sample = mid_block_res_sample * scales[-1] # last one\n else:\n down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]\n mid_block_res_sample = mid_block_res_sample * conditioning_scale\n\n if self.config.global_pool_conditions:\n down_block_res_samples = [\n torch.mean(sample, dim=(2, 3), keepdim=True) for sample in down_block_res_samples\n ]\n mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True)\n\n if not return_dict:\n return (down_block_res_samples, mid_block_res_sample)\n\n return ControlNetOutput(\n down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample\n )\n" }, { "path": "diffusers/examples/research_projects/rdm/README.md", "content": "## Diffusers examples with ONNXRuntime optimizations\n\n**This research project is not actively maintained by the diffusers team. For any questions or comments, please contact Isamu Isozaki(isamu-isozaki) on github with any questions.**\n\nThe aim of this project is to provide retrieval augmented diffusion models to diffusers!" }, { "path": "diffusers/examples/research_projects/rdm/pipeline_rdm.py", "content": "import inspect\nfrom typing import Callable, List, Optional, Union\n\nimport torch\nfrom PIL import Image\nfrom retriever import Retriever, normalize_images, preprocess_images\nfrom transformers import CLIPFeatureExtractor, CLIPModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n DiffusionPipeline,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n ImagePipelineOutput,\n LMSDiscreteScheduler,\n PNDMScheduler,\n UNet2DConditionModel,\n)\nfrom diffusers.image_processor import VaeImageProcessor\nfrom diffusers.pipelines.pipeline_utils import StableDiffusionMixin\nfrom diffusers.utils import logging\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass RDMPipeline(DiffusionPipeline, StableDiffusionMixin):\n r\"\"\"\n Pipeline for text-to-image generation using Retrieval Augmented Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n clip ([`CLIPModel`]):\n Frozen CLIP model. Retrieval Augmented Diffusion uses the CLIP model, specifically the\n [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n feature_extractor ([`CLIPFeatureExtractor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n clip: CLIPModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[\n DDIMScheduler,\n PNDMScheduler,\n LMSDiscreteScheduler,\n EulerDiscreteScheduler,\n EulerAncestralDiscreteScheduler,\n DPMSolverMultistepScheduler,\n ],\n feature_extractor: CLIPFeatureExtractor,\n retriever: Optional[Retriever] = None,\n ):\n super().__init__()\n self.register_modules(\n vae=vae,\n clip=clip,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n feature_extractor=feature_extractor,\n )\n # Copy from statement here and all the methods we take from stable_diffusion_pipeline\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.retriever = retriever\n\n def _encode_prompt(self, prompt):\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n\n if text_input_ids.shape[-1] > self.tokenizer.model_max_length:\n removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]\n prompt_embeds = self.clip.get_text_features(text_input_ids.to(self.device))\n prompt_embeds = prompt_embeds / torch.linalg.norm(prompt_embeds, dim=-1, keepdim=True)\n prompt_embeds = prompt_embeds[:, None, :]\n return prompt_embeds\n\n def _encode_image(self, retrieved_images, batch_size):\n if len(retrieved_images[0]) == 0:\n return None\n for i in range(len(retrieved_images)):\n retrieved_images[i] = normalize_images(retrieved_images[i])\n retrieved_images[i] = preprocess_images(retrieved_images[i], self.feature_extractor).to(\n self.clip.device, dtype=self.clip.dtype\n )\n _, c, h, w = retrieved_images[0].shape\n\n retrieved_images = torch.reshape(torch.cat(retrieved_images, dim=0), (-1, c, h, w))\n image_embeddings = self.clip.get_image_features(retrieved_images)\n image_embeddings = image_embeddings / torch.linalg.norm(image_embeddings, dim=-1, keepdim=True)\n _, d = image_embeddings.shape\n image_embeddings = torch.reshape(image_embeddings, (batch_size, -1, d))\n return image_embeddings\n\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def retrieve_images(self, retrieved_images, prompt_embeds, knn=10):\n if self.retriever is not None:\n additional_images = self.retriever.retrieve_imgs_batch(prompt_embeds[:, 0].cpu(), knn).total_examples\n for i in range(len(retrieved_images)):\n retrieved_images[i] += additional_images[i][self.retriever.config.image_column]\n return retrieved_images\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n retrieved_images: Optional[List[Image.Image]] = None,\n height: int = 768,\n width: int = 768,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: Optional[int] = 1,\n knn: Optional[int] = 10,\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation\n deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n\n Returns:\n [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if\n `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the\n generated images.\n \"\"\"\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n if isinstance(prompt, str):\n batch_size = 1\n elif isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n if retrieved_images is not None:\n retrieved_images = [retrieved_images for _ in range(batch_size)]\n else:\n retrieved_images = [[] for _ in range(batch_size)]\n device = self._execution_device\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if prompt_embeds is None:\n prompt_embeds = self._encode_prompt(prompt)\n retrieved_images = self.retrieve_images(retrieved_images, prompt_embeds, knn=knn)\n image_embeddings = self._encode_image(retrieved_images, batch_size)\n if image_embeddings is not None:\n prompt_embeds = torch.cat([prompt_embeds, image_embeddings], dim=1)\n\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n uncond_embeddings = torch.zeros_like(prompt_embeds).to(prompt_embeds.device)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([uncond_embeddings, prompt_embeds])\n # get the initial random noise unless the user supplied it\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # set timesteps\n self.scheduler.set_timesteps(num_inference_steps)\n\n # Some schedulers like PNDM have timesteps as arrays\n # It's more optimized to move all timesteps to correct device beforehand\n timesteps_tensor = self.scheduler.timesteps.to(self.device)\n\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n\n for i, t in enumerate(self.progress_bar(timesteps_tensor)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n else:\n image = latents\n\n image = self.image_processor.postprocess(\n image, output_type=output_type, do_denormalize=[True] * image.shape[0]\n )\n\n # Offload last model to CPU\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/examples/research_projects/rdm/retriever.py", "content": "import os\nfrom typing import List\n\nimport faiss\nimport numpy as np\nimport torch\nfrom datasets import Dataset, load_dataset\nfrom PIL import Image\nfrom transformers import CLIPFeatureExtractor, CLIPModel, PretrainedConfig\n\nfrom diffusers import logging\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef normalize_images(images: List[Image.Image]):\n images = [np.array(image) for image in images]\n images = [image / 127.5 - 1 for image in images]\n return images\n\n\ndef preprocess_images(images: List[np.array], feature_extractor: CLIPFeatureExtractor) -> torch.Tensor:\n \"\"\"\n Preprocesses a list of images into a batch of tensors.\n\n Args:\n images (:obj:`List[Image.Image]`):\n A list of images to preprocess.\n\n Returns:\n :obj:`torch.Tensor`: A batch of tensors.\n \"\"\"\n images = [np.array(image) for image in images]\n images = [(image + 1.0) / 2.0 for image in images]\n images = feature_extractor(images, return_tensors=\"pt\").pixel_values\n return images\n\n\nclass IndexConfig(PretrainedConfig):\n def __init__(\n self,\n clip_name_or_path=\"openai/clip-vit-large-patch14\",\n dataset_name=\"Isamu136/oxford_pets_with_l14_emb\",\n image_column=\"image\",\n index_name=\"embeddings\",\n index_path=None,\n dataset_set=\"train\",\n metric_type=faiss.METRIC_L2,\n faiss_device=-1,\n **kwargs,\n ):\n super().__init__(**kwargs)\n self.clip_name_or_path = clip_name_or_path\n self.dataset_name = dataset_name\n self.image_column = image_column\n self.index_name = index_name\n self.index_path = index_path\n self.dataset_set = dataset_set\n self.metric_type = metric_type\n self.faiss_device = faiss_device\n\n\nclass Index:\n \"\"\"\n Each index for a retrieval model is specific to the clip model used and the dataset used.\n \"\"\"\n\n def __init__(self, config: IndexConfig, dataset: Dataset):\n self.config = config\n self.dataset = dataset\n self.index_initialized = False\n self.index_name = config.index_name\n self.index_path = config.index_path\n self.init_index()\n\n def set_index_name(self, index_name: str):\n self.index_name = index_name\n\n def init_index(self):\n if not self.index_initialized:\n if self.index_path and self.index_name:\n try:\n self.dataset.add_faiss_index(\n column=self.index_name, metric_type=self.config.metric_type, device=self.config.faiss_device\n )\n self.index_initialized = True\n except Exception as e:\n print(e)\n logger.info(\"Index not initialized\")\n if self.index_name in self.dataset.features:\n self.dataset.add_faiss_index(column=self.index_name)\n self.index_initialized = True\n\n def build_index(\n self,\n model=None,\n feature_extractor: CLIPFeatureExtractor = None,\n torch_dtype=torch.float32,\n ):\n if not self.index_initialized:\n model = model or CLIPModel.from_pretrained(self.config.clip_name_or_path).to(dtype=torch_dtype)\n feature_extractor = feature_extractor or CLIPFeatureExtractor.from_pretrained(\n self.config.clip_name_or_path\n )\n self.dataset = get_dataset_with_emb_from_clip_model(\n self.dataset,\n model,\n feature_extractor,\n image_column=self.config.image_column,\n index_name=self.config.index_name,\n )\n self.init_index()\n\n def retrieve_imgs(self, vec, k: int = 20):\n vec = np.array(vec).astype(np.float32)\n return self.dataset.get_nearest_examples(self.index_name, vec, k=k)\n\n def retrieve_imgs_batch(self, vec, k: int = 20):\n vec = np.array(vec).astype(np.float32)\n return self.dataset.get_nearest_examples_batch(self.index_name, vec, k=k)\n\n def retrieve_indices(self, vec, k: int = 20):\n vec = np.array(vec).astype(np.float32)\n return self.dataset.search(self.index_name, vec, k=k)\n\n def retrieve_indices_batch(self, vec, k: int = 20):\n vec = np.array(vec).astype(np.float32)\n return self.dataset.search_batch(self.index_name, vec, k=k)\n\n\nclass Retriever:\n def __init__(\n self,\n config: IndexConfig,\n index: Index = None,\n dataset: Dataset = None,\n model=None,\n feature_extractor: CLIPFeatureExtractor = None,\n ):\n self.config = config\n self.index = index or self._build_index(config, dataset, model=model, feature_extractor=feature_extractor)\n\n @classmethod\n def from_pretrained(\n cls,\n retriever_name_or_path: str,\n index: Index = None,\n dataset: Dataset = None,\n model=None,\n feature_extractor: CLIPFeatureExtractor = None,\n **kwargs,\n ):\n config = kwargs.pop(\"config\", None) or IndexConfig.from_pretrained(retriever_name_or_path, **kwargs)\n return cls(config, index=index, dataset=dataset, model=model, feature_extractor=feature_extractor)\n\n @staticmethod\n def _build_index(\n config: IndexConfig, dataset: Dataset = None, model=None, feature_extractor: CLIPFeatureExtractor = None\n ):\n dataset = dataset or load_dataset(config.dataset_name)\n dataset = dataset[config.dataset_set]\n index = Index(config, dataset)\n index.build_index(model=model, feature_extractor=feature_extractor)\n return index\n\n def save_pretrained(self, save_directory):\n os.makedirs(save_directory, exist_ok=True)\n if self.config.index_path is None:\n index_path = os.path.join(save_directory, \"hf_dataset_index.faiss\")\n self.index.dataset.get_index(self.config.index_name).save(index_path)\n self.config.index_path = index_path\n self.config.save_pretrained(save_directory)\n\n def init_retrieval(self):\n logger.info(\"initializing retrieval\")\n self.index.init_index()\n\n def retrieve_imgs(self, embeddings: np.ndarray, k: int):\n return self.index.retrieve_imgs(embeddings, k)\n\n def retrieve_imgs_batch(self, embeddings: np.ndarray, k: int):\n return self.index.retrieve_imgs_batch(embeddings, k)\n\n def retrieve_indices(self, embeddings: np.ndarray, k: int):\n return self.index.retrieve_indices(embeddings, k)\n\n def retrieve_indices_batch(self, embeddings: np.ndarray, k: int):\n return self.index.retrieve_indices_batch(embeddings, k)\n\n def __call__(\n self,\n embeddings,\n k: int = 20,\n ):\n return self.index.retrieve_imgs(embeddings, k)\n\n\ndef map_txt_to_clip_feature(clip_model, tokenizer, prompt):\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n\n if text_input_ids.shape[-1] > tokenizer.model_max_length:\n removed_text = tokenizer.batch_decode(text_input_ids[:, tokenizer.model_max_length :])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n text_input_ids = text_input_ids[:, : tokenizer.model_max_length]\n text_embeddings = clip_model.get_text_features(text_input_ids.to(clip_model.device))\n text_embeddings = text_embeddings / torch.linalg.norm(text_embeddings, dim=-1, keepdim=True)\n text_embeddings = text_embeddings[:, None, :]\n return text_embeddings[0][0].cpu().detach().numpy()\n\n\ndef map_img_to_model_feature(model, feature_extractor, imgs, device):\n for i, image in enumerate(imgs):\n if not image.mode == \"RGB\":\n imgs[i] = image.convert(\"RGB\")\n imgs = normalize_images(imgs)\n retrieved_images = preprocess_images(imgs, feature_extractor).to(device)\n image_embeddings = model(retrieved_images)\n image_embeddings = image_embeddings / torch.linalg.norm(image_embeddings, dim=-1, keepdim=True)\n image_embeddings = image_embeddings[None, ...]\n return image_embeddings.cpu().detach().numpy()[0][0]\n\n\ndef get_dataset_with_emb_from_model(dataset, model, feature_extractor, image_column=\"image\", index_name=\"embeddings\"):\n return dataset.map(\n lambda example: {\n index_name: map_img_to_model_feature(model, feature_extractor, [example[image_column]], model.device)\n }\n )\n\n\ndef get_dataset_with_emb_from_clip_model(\n dataset, clip_model, feature_extractor, image_column=\"image\", index_name=\"embeddings\"\n):\n return dataset.map(\n lambda example: {\n index_name: map_img_to_model_feature(\n clip_model.get_image_features, feature_extractor, [example[image_column]], clip_model.device\n )\n }\n )\n" }, { "path": "diffusers/examples/research_projects/realfill/README.md", "content": "# RealFill\n\n[RealFill](https://arxiv.org/abs/2309.16668) is a method to personalize text2image inpainting models like stable diffusion inpainting given just a few(1~5) images of a scene.\nThe `train_realfill.py` script shows how to implement the training procedure for stable diffusion inpainting.\n\n\n## Running locally with PyTorch\n\n### Installing the dependencies\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\ncd to the realfill folder and run\n```bash\ncd realfill\npip install -r requirements.txt\n```\n\nAnd initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\n\nOr for a default accelerate configuration without answering questions about your environment\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell e.g. a notebook\n\n```python\nfrom accelerate.utils import write_basic_config\nwrite_basic_config()\n```\n\nWhen running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups.\n\n### Toy example\n\nNow let's fill the real. For this example, we will use some images of the flower girl example from the paper.\n\nWe already provide some images for testing in [this link](https://github.com/thuanz123/realfill/tree/main/data/flowerwoman)\n\nYou only have to launch the training using:\n\n```bash\nexport MODEL_NAME=\"stabilityai/stable-diffusion-2-inpainting\"\nexport TRAIN_DIR=\"data/flowerwoman\"\nexport OUTPUT_DIR=\"flowerwoman-model\"\n\naccelerate launch train_realfill.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_data_dir=$TRAIN_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --resolution=512 \\\n --train_batch_size=16 \\\n --gradient_accumulation_steps=1 \\\n --unet_learning_rate=2e-4 \\\n --text_encoder_learning_rate=4e-5 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=100 \\\n --max_train_steps=2000 \\\n --lora_rank=8 \\\n --lora_dropout=0.1 \\\n --lora_alpha=16 \\\n```\n\n### Training on a low-memory GPU:\n\nIt is possible to run realfill on a low-memory GPU by using the following optimizations:\n- [gradient checkpointing and the 8-bit optimizer](#training-with-gradient-checkpointing-and-8-bit-optimizers)\n- [xformers](#training-with-xformers)\n- [setting grads to none](#set-grads-to-none)\n\n```bash\nexport MODEL_NAME=\"stabilityai/stable-diffusion-2-inpainting\"\nexport TRAIN_DIR=\"data/flowerwoman\"\nexport OUTPUT_DIR=\"flowerwoman-model\"\n\naccelerate launch train_realfill.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_data_dir=$TRAIN_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --resolution=512 \\\n --train_batch_size=16 \\\n --gradient_accumulation_steps=1 --gradient_checkpointing \\\n --use_8bit_adam \\\n --enable_xformers_memory_efficient_attention \\\n --set_grads_to_none \\\n --unet_learning_rate=2e-4 \\\n --text_encoder_learning_rate=4e-5 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=100 \\\n --max_train_steps=2000 \\\n --lora_rank=8 \\\n --lora_dropout=0.1 \\\n --lora_alpha=16 \\\n```\n\n### Training with gradient checkpointing and 8-bit optimizers:\n\nWith the help of gradient checkpointing and the 8-bit optimizer from bitsandbytes it's possible to run train realfill on a 16GB GPU.\n\nTo install `bitsandbytes` please refer to this [readme](https://github.com/TimDettmers/bitsandbytes#requirements--installation).\n\n### Training with xformers:\nYou can enable memory efficient attention by [installing xFormers](https://github.com/facebookresearch/xformers#installing-xformers) and padding the `--enable_xformers_memory_efficient_attention` argument to the script.\n\n### Set grads to none\n\nTo save even more memory, pass the `--set_grads_to_none` argument to the script. This will set grads to None instead of zero. However, be aware that it changes certain behaviors, so if you start experiencing any problems, remove this argument.\n\nMore info: https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html\n\n## Acknowledge\nThis repo is built upon the code of DreamBooth from diffusers and we thank the developers for their great works and efforts to release source code. Furthermore, a special \"thank you\" to RealFill's authors for publishing such an amazing work.\n" }, { "path": "diffusers/examples/research_projects/realfill/infer.py", "content": "import argparse\nimport os\n\nimport torch\nfrom PIL import Image, ImageFilter\nfrom transformers import CLIPTextModel\n\nfrom diffusers import DPMSolverMultistepScheduler, StableDiffusionInpaintPipeline, UNet2DConditionModel\n\n\nparser = argparse.ArgumentParser(description=\"Inference\")\nparser.add_argument(\n \"--model_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n)\nparser.add_argument(\n \"--validation_image\",\n type=str,\n default=None,\n required=True,\n help=\"The directory of the validation image\",\n)\nparser.add_argument(\n \"--validation_mask\",\n type=str,\n default=None,\n required=True,\n help=\"The directory of the validation mask\",\n)\nparser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"./test-infer/\",\n help=\"The output directory where predictions are saved\",\n)\nparser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible inference.\")\n\nargs = parser.parse_args()\n\nif __name__ == \"__main__\":\n os.makedirs(args.output_dir, exist_ok=True)\n generator = None\n\n # create & load model\n pipe = StableDiffusionInpaintPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-inpainting\", torch_dtype=torch.float32, revision=None\n )\n\n pipe.unet = UNet2DConditionModel.from_pretrained(\n args.model_path,\n subfolder=\"unet\",\n revision=None,\n )\n pipe.text_encoder = CLIPTextModel.from_pretrained(\n args.model_path,\n subfolder=\"text_encoder\",\n revision=None,\n )\n pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n pipe = pipe.to(\"cuda\")\n\n if args.seed is not None:\n generator = torch.Generator(device=\"cuda\").manual_seed(args.seed)\n\n image = Image.open(args.validation_image)\n mask_image = Image.open(args.validation_mask)\n\n results = pipe(\n [\"a photo of sks\"] * 16,\n image=image,\n mask_image=mask_image,\n num_inference_steps=25,\n guidance_scale=5,\n generator=generator,\n ).images\n\n erode_kernel = ImageFilter.MaxFilter(3)\n mask_image = mask_image.filter(erode_kernel)\n\n blur_kernel = ImageFilter.BoxBlur(1)\n mask_image = mask_image.filter(blur_kernel)\n\n for idx, result in enumerate(results):\n result = Image.composite(result, image, mask_image)\n result.save(f\"{args.output_dir}/{idx}.png\")\n\n del pipe\n torch.cuda.empty_cache()\n" }, { "path": "diffusers/examples/research_projects/realfill/requirements.txt", "content": "diffusers==0.20.1\naccelerate==0.23.0\ntransformers==4.38.0\npeft==0.5.0\ntorch==2.0.1\ntorchvision>=0.16\nftfy==6.1.1\ntensorboard==2.14.0\nJinja2==3.1.3\n" }, { "path": "diffusers/examples/research_projects/realfill/train_realfill.py", "content": "import argparse\nimport copy\nimport itertools\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport torchvision.transforms.v2 as transforms_v2\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import set_seed\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom peft import LoraConfig, PeftModel, get_peft_model\nfrom PIL import Image\nfrom PIL.ImageOps import exif_transpose\nfrom torch.utils.data import Dataset\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, CLIPTextModel\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n DPMSolverMultistepScheduler,\n StableDiffusionInpaintPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import check_min_version, is_wandb_available\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.20.1\")\n\nlogger = get_logger(__name__)\n\n\ndef make_mask(images, resolution, times=30):\n mask, times = torch.ones_like(images[0:1, :, :]), np.random.randint(1, times)\n min_size, max_size, margin = np.array([0.03, 0.25, 0.01]) * resolution\n max_size = min(max_size, resolution - margin * 2)\n\n for _ in range(times):\n width = np.random.randint(int(min_size), int(max_size))\n height = np.random.randint(int(min_size), int(max_size))\n\n x_start = np.random.randint(int(margin), resolution - int(margin) - width + 1)\n y_start = np.random.randint(int(margin), resolution - int(margin) - height + 1)\n mask[:, y_start : y_start + height, x_start : x_start + width] = 0\n\n mask = 1 - mask if random.random() < 0.5 else mask\n return mask\n\n\ndef save_model_card(\n repo_id: str,\n images=None,\n base_model=str,\n repo_folder=None,\n):\n img_str = \"\"\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n img_str += f\"![img_{i}](./image_{i}.png)\\n\"\n\n yaml = f\"\"\"\n---\nlicense: creativeml-openrail-m\nbase_model: {base_model}\nprompt: \"a photo of sks\"\ntags:\n- stable-diffusion-inpainting\n- stable-diffusion-inpainting-diffusers\n- text-to-image\n- diffusers\n- realfill\n- diffusers-training\ninference: true\n---\n \"\"\"\n model_card = f\"\"\"\n# RealFill - {repo_id}\n\nThis is a realfill model derived from {base_model}. The weights were trained using [RealFill](https://realfill.github.io/).\nYou can find some example images in the following. \\n\n{img_str}\n\"\"\"\n with open(os.path.join(repo_folder, \"README.md\"), \"w\") as f:\n f.write(yaml + model_card)\n\n\ndef log_validation(\n text_encoder,\n tokenizer,\n unet,\n args,\n accelerator,\n weight_dtype,\n epoch,\n):\n logger.info(f\"Running validation... \\nGenerating {args.num_validation_images} images\")\n\n # create pipeline (note: unet and vae are loaded again in float32)\n pipeline = StableDiffusionInpaintPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n tokenizer=tokenizer,\n revision=args.revision,\n torch_dtype=weight_dtype,\n )\n\n # set `keep_fp32_wrapper` to True because we do not want to remove\n # mixed precision hooks while we are still training\n pipeline.unet = accelerator.unwrap_model(unet, keep_fp32_wrapper=True)\n pipeline.text_encoder = accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=True)\n pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)\n\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = None if args.seed is None else torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n target_dir = Path(args.train_data_dir) / \"target\"\n target_image, target_mask = target_dir / \"target.png\", target_dir / \"mask.png\"\n image, mask_image = Image.open(target_image), Image.open(target_mask)\n\n if image.mode != \"RGB\":\n image = image.convert(\"RGB\")\n\n images = []\n for _ in range(args.num_validation_images):\n image = pipeline(\n prompt=\"a photo of sks\",\n image=image,\n mask_image=mask_image,\n num_inference_steps=25,\n guidance_scale=5,\n generator=generator,\n ).images[0]\n images.append(image)\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log({\"validation\": [wandb.Image(image, caption=str(i)) for i, image in enumerate(images)]})\n\n del pipeline\n torch.cuda.empty_cache()\n\n return images\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--tokenizer_name\",\n type=str,\n default=None,\n help=\"Pretrained tokenizer name or path if not the same as model_name\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n required=True,\n help=\"A folder containing the training data of images.\",\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_conditioning`.\",\n )\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=100,\n help=(\n \"Run realfill validation every X steps. RealFill validation consists of running the conditioning\"\n \" `args.validation_conditioning` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"realfill-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final\"\n \" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--unet_learning_rate\",\n type=float,\n default=2e-4,\n help=\"Learning rate to use for unet.\",\n )\n parser.add_argument(\n \"--text_encoder_learning_rate\",\n type=float,\n default=4e-5,\n help=\"Learning rate to use for text encoder.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\"--lr_power\", type=float, default=1.0, help=\"Power factor of the polynomial scheduler.\")\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--wandb_key\",\n type=str,\n default=None,\n help=(\"If report to option is set to wandb, api-key for wandb used for login to wandb \"),\n )\n parser.add_argument(\n \"--wandb_project_name\",\n type=str,\n default=None,\n help=(\"If report to option is set to wandb, project name in wandb for log tracking \"),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--set_grads_to_none\",\n action=\"store_true\",\n help=(\n \"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain\"\n \" behaviors, so disable this argument if it causes any problems. More info:\"\n \" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html\"\n ),\n )\n parser.add_argument(\n \"--lora_rank\",\n type=int,\n default=16,\n help=(\"The dimension of the LoRA update matrices.\"),\n )\n parser.add_argument(\n \"--lora_alpha\",\n type=int,\n default=27,\n help=(\"The alpha constant of the LoRA update matrices.\"),\n )\n parser.add_argument(\n \"--lora_dropout\",\n type=float,\n default=0.0,\n help=\"The dropout rate of the LoRA update matrices.\",\n )\n parser.add_argument(\n \"--lora_bias\",\n type=str,\n default=\"none\",\n help=\"The bias type of the Lora update matrices. Must be 'none', 'all' or 'lora_only'.\",\n )\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n return args\n\n\nclass RealFillDataset(Dataset):\n \"\"\"\n A dataset to prepare the training and conditioning images and\n the masks with the dummy prompt for fine-tuning the model.\n It pre-processes the images, masks and tokenizes the prompts.\n \"\"\"\n\n def __init__(\n self,\n train_data_root,\n tokenizer,\n size=512,\n ):\n self.size = size\n self.tokenizer = tokenizer\n\n self.ref_data_root = Path(train_data_root) / \"ref\"\n self.target_image = Path(train_data_root) / \"target\" / \"target.png\"\n self.target_mask = Path(train_data_root) / \"target\" / \"mask.png\"\n if not (self.ref_data_root.exists() and self.target_image.exists() and self.target_mask.exists()):\n raise ValueError(\"Train images root doesn't exists.\")\n\n self.train_images_path = list(self.ref_data_root.iterdir()) + [self.target_image]\n self.num_train_images = len(self.train_images_path)\n self.train_prompt = \"a photo of sks\"\n\n self.transform = transforms_v2.Compose(\n [\n transforms_v2.ToImage(),\n transforms_v2.RandomResize(size, int(1.125 * size)),\n transforms_v2.RandomCrop(size),\n transforms_v2.ToDtype(torch.float32, scale=True),\n transforms_v2.Normalize([0.5], [0.5]),\n ]\n )\n\n def __len__(self):\n return self.num_train_images\n\n def __getitem__(self, index):\n example = {}\n\n image = Image.open(self.train_images_path[index])\n image = exif_transpose(image)\n\n if not image.mode == \"RGB\":\n image = image.convert(\"RGB\")\n\n if index < len(self) - 1:\n weighting = Image.new(\"L\", image.size)\n else:\n weighting = Image.open(self.target_mask)\n weighting = exif_transpose(weighting)\n\n image, weighting = self.transform(image, weighting)\n example[\"images\"], example[\"weightings\"] = image, weighting < 0\n\n if random.random() < 0.1:\n example[\"masks\"] = torch.ones_like(example[\"images\"][0:1, :, :])\n else:\n example[\"masks\"] = make_mask(example[\"images\"], self.size)\n\n example[\"conditioning_images\"] = example[\"images\"] * (example[\"masks\"] < 0.5)\n\n train_prompt = \"\" if random.random() < 0.1 else self.train_prompt\n example[\"prompt_ids\"] = self.tokenizer(\n train_prompt,\n truncation=True,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n ).input_ids\n\n return example\n\n\ndef collate_fn(examples):\n input_ids = [example[\"prompt_ids\"] for example in examples]\n images = [example[\"images\"] for example in examples]\n\n masks = [example[\"masks\"] for example in examples]\n weightings = [example[\"weightings\"] for example in examples]\n conditioning_images = [example[\"conditioning_images\"] for example in examples]\n\n images = torch.stack(images)\n images = images.to(memory_format=torch.contiguous_format).float()\n\n masks = torch.stack(masks)\n masks = masks.to(memory_format=torch.contiguous_format).float()\n\n weightings = torch.stack(weightings)\n weightings = weightings.to(memory_format=torch.contiguous_format).float()\n\n conditioning_images = torch.stack(conditioning_images)\n conditioning_images = conditioning_images.to(memory_format=torch.contiguous_format).float()\n\n input_ids = torch.cat(input_ids, dim=0)\n\n batch = {\n \"input_ids\": input_ids,\n \"images\": images,\n \"masks\": masks,\n \"weightings\": weightings,\n \"conditioning_images\": conditioning_images,\n }\n return batch\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_dir=logging_dir,\n )\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n\n wandb.login(key=args.wandb_key)\n wandb.init(project=args.wandb_project_name)\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizer\n if args.tokenizer_name:\n tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)\n elif args.pretrained_model_name_or_path:\n tokenizer = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n text_encoder = CLIPTextModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision\n )\n vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision)\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision\n )\n\n config = LoraConfig(\n r=args.lora_rank,\n lora_alpha=args.lora_alpha,\n target_modules=[\"to_k\", \"to_q\", \"to_v\", \"key\", \"query\", \"value\"],\n lora_dropout=args.lora_dropout,\n bias=args.lora_bias,\n )\n unet = get_peft_model(unet, config)\n\n config = LoraConfig(\n r=args.lora_rank,\n lora_alpha=args.lora_alpha,\n target_modules=[\"k_proj\", \"q_proj\", \"v_proj\"],\n lora_dropout=args.lora_dropout,\n bias=args.lora_bias,\n )\n text_encoder = get_peft_model(text_encoder, config)\n\n vae.requires_grad_(False)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n text_encoder.gradient_checkpointing_enable()\n\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n for model in models:\n sub_dir = (\n \"unet\"\n if isinstance(model.base_model.model, type(accelerator.unwrap_model(unet).base_model.model))\n else \"text_encoder\"\n )\n model.save_pretrained(os.path.join(output_dir, sub_dir))\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n def load_model_hook(models, input_dir):\n while len(models) > 0:\n # pop models so that they are not loaded again\n model = models.pop()\n\n sub_dir = (\n \"unet\"\n if isinstance(model.base_model.model, type(accelerator.unwrap_model(unet).base_model.model))\n else \"text_encoder\"\n )\n model_cls = (\n UNet2DConditionModel\n if isinstance(model.base_model.model, type(accelerator.unwrap_model(unet).base_model.model))\n else CLIPTextModel\n )\n\n load_model = model_cls.from_pretrained(args.pretrained_model_name_or_path, subfolder=sub_dir)\n load_model = PeftModel.from_pretrained(load_model, input_dir, subfolder=sub_dir)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.unet_learning_rate = (\n args.unet_learning_rate\n * args.gradient_accumulation_steps\n * args.train_batch_size\n * accelerator.num_processes\n )\n\n args.text_encoder_learning_rate = (\n args.text_encoder_learning_rate\n * args.gradient_accumulation_steps\n * args.train_batch_size\n * accelerator.num_processes\n )\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # Optimizer creation\n optimizer = optimizer_class(\n [\n {\"params\": unet.parameters(), \"lr\": args.unet_learning_rate},\n {\"params\": text_encoder.parameters(), \"lr\": args.text_encoder_learning_rate},\n ],\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Dataset and DataLoaders creation:\n train_dataset = RealFillDataset(\n train_data_root=args.train_data_dir,\n tokenizer=tokenizer,\n size=args.resolution,\n )\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n batch_size=args.train_batch_size,\n shuffle=True,\n collate_fn=collate_fn,\n num_workers=1,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,\n num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,\n num_cycles=args.lr_num_cycles,\n power=args.lr_power,\n )\n\n # Prepare everything with our `accelerator`.\n unet, text_encoder, optimizer, train_dataloader = accelerator.prepare(\n unet, text_encoder, optimizer, train_dataloader\n )\n\n # For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move vae to device and cast to weight_dtype\n vae.to(accelerator.device, dtype=weight_dtype)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = vars(copy.deepcopy(args))\n accelerator.init_trackers(\"realfill\", config=tracker_config)\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num batches each epoch = {len(train_dataloader)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the mos recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n for epoch in range(first_epoch, args.num_train_epochs):\n unet.train()\n text_encoder.train()\n\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet, text_encoder):\n # Convert images to latent space\n latents = vae.encode(batch[\"images\"].to(dtype=weight_dtype)).latent_dist.sample()\n latents = latents * 0.18215\n\n # Convert masked images to latent space\n conditionings = vae.encode(batch[\"conditioning_images\"].to(dtype=weight_dtype)).latent_dist.sample()\n conditionings = conditionings * 0.18215\n\n # Downsample mask and weighting so that they match with the latents\n masks, size = batch[\"masks\"].to(dtype=weight_dtype), latents.shape[2:]\n masks = F.interpolate(masks, size=size)\n\n weightings = batch[\"weightings\"].to(dtype=weight_dtype)\n weightings = F.interpolate(weightings, size=size)\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # Concatenate noisy latents, masks and conditionings to get inputs to unet\n inputs = torch.cat([noisy_latents, masks, conditionings], dim=1)\n\n # Get the text embedding for conditioning\n encoder_hidden_states = text_encoder(batch[\"input_ids\"])[0]\n\n # Predict the noise residual\n model_pred = unet(inputs, timesteps, encoder_hidden_states).sample\n\n # Compute the diffusion loss\n assert noise_scheduler.config.prediction_type == \"epsilon\"\n loss = (weightings * F.mse_loss(model_pred.float(), noise.float(), reduction=\"none\")).mean()\n\n # Backpropagate\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n params_to_clip = itertools.chain(unet.parameters(), text_encoder.parameters())\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad(set_to_none=args.set_grads_to_none)\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n if args.report_to == \"wandb\":\n accelerator.print(progress_bar)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n if global_step % args.validation_steps == 0:\n log_validation(\n text_encoder,\n tokenizer,\n unet,\n args,\n accelerator,\n weight_dtype,\n global_step,\n )\n\n logs = {\"loss\": loss.detach().item()}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n # Save the lora layers\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n pipeline = StableDiffusionInpaintPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n unet=accelerator.unwrap_model(unet, keep_fp32_wrapper=True).merge_and_unload(),\n text_encoder=accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=True).merge_and_unload(),\n revision=args.revision,\n )\n\n pipeline.save_pretrained(args.output_dir)\n\n # Final inference\n images = log_validation(\n text_encoder,\n tokenizer,\n unet,\n args,\n accelerator,\n weight_dtype,\n global_step,\n )\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n images=images,\n base_model=args.pretrained_model_name_or_path,\n repo_folder=args.output_dir,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/research_projects/scheduled_huber_loss_training/README.md", "content": "# Scheduled Pseudo-Huber Loss for Diffusers\n\nThese are the modifications of to include the possibility of training text2image models with Scheduled Pseudo Huber loss, introduced in https://arxiv.org/abs/2403.16728. (https://github.com/kabachuha/SPHL-for-stable-diffusion)\n\n## Why this might be useful?\n\n- If you suspect that the part of the training dataset might be corrupted, and you don't want these outliers to distort the model's supposed output\n\n- If you want to improve the aesthetic quality of pictures by helping the model disentangle concepts and be less influenced by another sorts of pictures.\n\nSee https://github.com/huggingface/diffusers/issues/7488 for the detailed description.\n\n## Instructions\n\nThe same usage as in the case of the corresponding vanilla Diffusers scripts https://github.com/huggingface/diffusers/tree/main/examples\n" }, { "path": "diffusers/examples/research_projects/scheduled_huber_loss_training/dreambooth/train_dreambooth.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport copy\nimport gc\nimport importlib\nimport itertools\nimport logging\nimport math\nimport os\nimport shutil\nimport warnings\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom huggingface_hub import create_repo, model_info, upload_folder\nfrom huggingface_hub.utils import insecure_hashlib\nfrom packaging import version\nfrom PIL import Image\nfrom PIL.ImageOps import exif_transpose\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n DiffusionPipeline,\n StableDiffusionPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import compute_snr\nfrom diffusers.utils import check_min_version, is_wandb_available\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.28.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef save_model_card(\n repo_id: str,\n images: list = None,\n base_model: str = None,\n train_text_encoder=False,\n prompt: str = None,\n repo_folder: str = None,\n pipeline: DiffusionPipeline = None,\n):\n img_str = \"\"\n if images is not None:\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n img_str += f\"![img_{i}](./image_{i}.png)\\n\"\n\n model_description = f\"\"\"\n# DreamBooth - {repo_id}\n\nThis is a dreambooth model derived from {base_model}. The weights were trained on {prompt} using [DreamBooth](https://dreambooth.github.io/).\nYou can find some example images in the following. \\n\n{img_str}\n\nDreamBooth for the text encoder was enabled: {train_text_encoder}.\n\"\"\"\n model_card = load_or_create_model_card(\n repo_id_or_path=repo_id,\n from_training=True,\n license=\"creativeml-openrail-m\",\n base_model=base_model,\n prompt=prompt,\n model_description=model_description,\n inference=True,\n )\n\n tags = [\"text-to-image\", \"dreambooth\", \"diffusers-training\"]\n if isinstance(pipeline, StableDiffusionPipeline):\n tags.extend([\"stable-diffusion\", \"stable-diffusion-diffusers\"])\n else:\n tags.extend([\"if\", \"if-diffusers\"])\n model_card = populate_model_card(model_card, tags=tags)\n\n model_card.save(os.path.join(repo_folder, \"README.md\"))\n\n\ndef log_validation(\n text_encoder,\n tokenizer,\n unet,\n vae,\n args,\n accelerator,\n weight_dtype,\n global_step,\n prompt_embeds,\n negative_prompt_embeds,\n):\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n\n pipeline_args = {}\n\n if vae is not None:\n pipeline_args[\"vae\"] = vae\n\n # create pipeline (note: unet and vae are loaded again in float32)\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n unet=unet,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n **pipeline_args,\n )\n\n # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it\n scheduler_args = {}\n\n if \"variance_type\" in pipeline.scheduler.config:\n variance_type = pipeline.scheduler.config.variance_type\n\n if variance_type in [\"learned\", \"learned_range\"]:\n variance_type = \"fixed_small\"\n\n scheduler_args[\"variance_type\"] = variance_type\n\n module = importlib.import_module(\"diffusers\")\n scheduler_class = getattr(module, args.validation_scheduler)\n pipeline.scheduler = scheduler_class.from_config(pipeline.scheduler.config, **scheduler_args)\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n if args.pre_compute_text_embeddings:\n pipeline_args = {\n \"prompt_embeds\": prompt_embeds,\n \"negative_prompt_embeds\": negative_prompt_embeds,\n }\n else:\n pipeline_args = {\"prompt\": args.validation_prompt}\n\n # run inference\n generator = None if args.seed is None else torch.Generator(device=accelerator.device).manual_seed(args.seed)\n images = []\n if args.validation_images is None:\n for _ in range(args.num_validation_images):\n with torch.autocast(\"cuda\"):\n image = pipeline(**pipeline_args, num_inference_steps=25, generator=generator).images[0]\n images.append(image)\n else:\n for image in args.validation_images:\n image = Image.open(image)\n image = pipeline(**pipeline_args, image=image, generator=generator).images[0]\n images.append(image)\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, global_step, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\") for i, image in enumerate(images)\n ]\n }\n )\n\n del pipeline\n torch.cuda.empty_cache()\n\n return images\n\n\ndef import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path,\n subfolder=\"text_encoder\",\n revision=revision,\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"RobertaSeriesModelWithTransformation\":\n from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation\n\n return RobertaSeriesModelWithTransformation\n elif model_class == \"T5EncoderModel\":\n from transformers import T5EncoderModel\n\n return T5EncoderModel\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--tokenizer_name\",\n type=str,\n default=None,\n help=\"Pretrained tokenizer name or path if not the same as model_name\",\n )\n parser.add_argument(\n \"--instance_data_dir\",\n type=str,\n default=None,\n required=True,\n help=\"A folder containing the training data of instance images.\",\n )\n parser.add_argument(\n \"--class_data_dir\",\n type=str,\n default=None,\n required=False,\n help=\"A folder containing the training data of class images.\",\n )\n parser.add_argument(\n \"--instance_prompt\",\n type=str,\n default=None,\n required=True,\n help=\"The prompt with identifier specifying the instance\",\n )\n parser.add_argument(\n \"--class_prompt\",\n type=str,\n default=None,\n help=\"The prompt to specify images in the same class as provided instance images.\",\n )\n parser.add_argument(\n \"--with_prior_preservation\",\n default=False,\n action=\"store_true\",\n help=\"Flag to add prior preservation loss.\",\n )\n parser.add_argument(\"--prior_loss_weight\", type=float, default=1.0, help=\"The weight of prior preservation loss.\")\n parser.add_argument(\n \"--num_class_images\",\n type=int,\n default=100,\n help=(\n \"Minimal class images for prior preservation loss. If there are not enough images already present in\"\n \" class_data_dir, additional images will be sampled with class_prompt.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"dreambooth-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--train_text_encoder\",\n action=\"store_true\",\n help=\"Whether to train the text encoder. If set, the text encoder should be float32 precision.\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\n \"--sample_batch_size\", type=int, default=4, help=\"Batch size (per device) for sampling images.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. \"\n \"In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference.\"\n \"Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components.\"\n \"See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step\"\n \"instructions.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\n \"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`.\"\n \" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state\"\n \" for more details\"\n ),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=5e-6,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\"--lr_power\", type=float, default=1.0, help=\"Power factor of the polynomial scheduler.\")\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n help=\"A prompt that is used during validation to verify that the model is learning.\",\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=100,\n help=(\n \"Run validation every X steps. Validation consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`\"\n \" and logging the images.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--prior_generation_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp32\", \"fp16\", \"bf16\"],\n help=(\n \"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--set_grads_to_none\",\n action=\"store_true\",\n help=(\n \"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain\"\n \" behaviors, so disable this argument if it causes any problems. More info:\"\n \" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html\"\n ),\n )\n\n parser.add_argument(\n \"--offset_noise\",\n action=\"store_true\",\n default=False,\n help=(\n \"Fine-tuning against a modified noise\"\n \" See: https://www.crosslabs.org//blog/diffusion-with-offset-noise for more information.\"\n ),\n )\n parser.add_argument(\n \"--snr_gamma\",\n type=float,\n default=None,\n help=\"SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. \"\n \"More details here: https://arxiv.org/abs/2303.09556.\",\n )\n parser.add_argument(\n \"--pre_compute_text_embeddings\",\n action=\"store_true\",\n help=\"Whether or not to pre-compute text embeddings. If text embeddings are pre-computed, the text encoder will not be kept in memory during training and will leave more GPU memory available for training the rest of the model. This is not compatible with `--train_text_encoder`.\",\n )\n parser.add_argument(\n \"--tokenizer_max_length\",\n type=int,\n default=None,\n required=False,\n help=\"The maximum length of the tokenizer. If not set, will default to the tokenizer's max length.\",\n )\n parser.add_argument(\n \"--text_encoder_use_attention_mask\",\n action=\"store_true\",\n required=False,\n help=\"Whether to use attention mask for the text encoder\",\n )\n parser.add_argument(\n \"--skip_save_text_encoder\", action=\"store_true\", required=False, help=\"Set to not save text encoder\"\n )\n parser.add_argument(\n \"--validation_images\",\n required=False,\n default=None,\n nargs=\"+\",\n help=\"Optional set of images to use for validation. Used when the target pipeline takes an initial image as input such as when training image variation or superresolution.\",\n )\n parser.add_argument(\n \"--class_labels_conditioning\",\n required=False,\n default=None,\n help=\"The optional `class_label` conditioning to pass to the unet, available values are `timesteps`.\",\n )\n parser.add_argument(\n \"--loss_type\",\n type=str,\n default=\"l2\",\n choices=[\"l2\", \"huber\", \"smooth_l1\"],\n help=\"The type of loss to use and whether it's timestep-scheduled. See Issue #7488 for more info.\",\n )\n parser.add_argument(\n \"--huber_schedule\",\n type=str,\n default=\"snr\",\n choices=[\"constant\", \"exponential\", \"snr\"],\n help=\"The schedule to use for the huber losses parameter\",\n )\n parser.add_argument(\n \"--huber_c\",\n type=float,\n default=0.1,\n help=\"The huber loss parameter. Only used if one of the huber loss modes (huber or smooth l1) is selected with loss_type.\",\n )\n parser.add_argument(\n \"--validation_scheduler\",\n type=str,\n default=\"DPMSolverMultistepScheduler\",\n choices=[\"DPMSolverMultistepScheduler\", \"DDPMScheduler\"],\n help=\"Select which scheduler to use for validation. DDPMScheduler is recommended for DeepFloyd IF.\",\n )\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.with_prior_preservation:\n if args.class_data_dir is None:\n raise ValueError(\"You must specify a data directory for class images.\")\n if args.class_prompt is None:\n raise ValueError(\"You must specify prompt for class images.\")\n else:\n # logger is not available yet\n if args.class_data_dir is not None:\n warnings.warn(\"You need not use --class_data_dir without --with_prior_preservation.\")\n if args.class_prompt is not None:\n warnings.warn(\"You need not use --class_prompt without --with_prior_preservation.\")\n\n if args.train_text_encoder and args.pre_compute_text_embeddings:\n raise ValueError(\"`--train_text_encoder` cannot be used with `--pre_compute_text_embeddings`\")\n\n return args\n\n\nclass DreamBoothDataset(Dataset):\n \"\"\"\n A dataset to prepare the instance and class images with the prompts for fine-tuning the model.\n It pre-processes the images and the tokenizes prompts.\n \"\"\"\n\n def __init__(\n self,\n instance_data_root,\n instance_prompt,\n tokenizer,\n class_data_root=None,\n class_prompt=None,\n class_num=None,\n size=512,\n center_crop=False,\n encoder_hidden_states=None,\n class_prompt_encoder_hidden_states=None,\n tokenizer_max_length=None,\n ):\n self.size = size\n self.center_crop = center_crop\n self.tokenizer = tokenizer\n self.encoder_hidden_states = encoder_hidden_states\n self.class_prompt_encoder_hidden_states = class_prompt_encoder_hidden_states\n self.tokenizer_max_length = tokenizer_max_length\n\n self.instance_data_root = Path(instance_data_root)\n if not self.instance_data_root.exists():\n raise ValueError(f\"Instance {self.instance_data_root} images root doesn't exists.\")\n\n self.instance_images_path = list(Path(instance_data_root).iterdir())\n self.num_instance_images = len(self.instance_images_path)\n self.instance_prompt = instance_prompt\n self._length = self.num_instance_images\n\n if class_data_root is not None:\n self.class_data_root = Path(class_data_root)\n self.class_data_root.mkdir(parents=True, exist_ok=True)\n self.class_images_path = list(self.class_data_root.iterdir())\n if class_num is not None:\n self.num_class_images = min(len(self.class_images_path), class_num)\n else:\n self.num_class_images = len(self.class_images_path)\n self._length = max(self.num_class_images, self.num_instance_images)\n self.class_prompt = class_prompt\n else:\n self.class_data_root = None\n\n self.image_transforms = transforms.Compose(\n [\n transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, index):\n example = {}\n instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])\n instance_image = exif_transpose(instance_image)\n\n if not instance_image.mode == \"RGB\":\n instance_image = instance_image.convert(\"RGB\")\n example[\"instance_images\"] = self.image_transforms(instance_image)\n\n if self.encoder_hidden_states is not None:\n example[\"instance_prompt_ids\"] = self.encoder_hidden_states\n else:\n text_inputs = tokenize_prompt(\n self.tokenizer, self.instance_prompt, tokenizer_max_length=self.tokenizer_max_length\n )\n example[\"instance_prompt_ids\"] = text_inputs.input_ids\n example[\"instance_attention_mask\"] = text_inputs.attention_mask\n\n if self.class_data_root:\n class_image = Image.open(self.class_images_path[index % self.num_class_images])\n class_image = exif_transpose(class_image)\n\n if not class_image.mode == \"RGB\":\n class_image = class_image.convert(\"RGB\")\n example[\"class_images\"] = self.image_transforms(class_image)\n\n if self.class_prompt_encoder_hidden_states is not None:\n example[\"class_prompt_ids\"] = self.class_prompt_encoder_hidden_states\n else:\n class_text_inputs = tokenize_prompt(\n self.tokenizer, self.class_prompt, tokenizer_max_length=self.tokenizer_max_length\n )\n example[\"class_prompt_ids\"] = class_text_inputs.input_ids\n example[\"class_attention_mask\"] = class_text_inputs.attention_mask\n\n return example\n\n\ndef collate_fn(examples, with_prior_preservation=False):\n has_attention_mask = \"instance_attention_mask\" in examples[0]\n\n input_ids = [example[\"instance_prompt_ids\"] for example in examples]\n pixel_values = [example[\"instance_images\"] for example in examples]\n\n if has_attention_mask:\n attention_mask = [example[\"instance_attention_mask\"] for example in examples]\n\n # Concat class and instance examples for prior preservation.\n # We do this to avoid doing two forward passes.\n if with_prior_preservation:\n input_ids += [example[\"class_prompt_ids\"] for example in examples]\n pixel_values += [example[\"class_images\"] for example in examples]\n\n if has_attention_mask:\n attention_mask += [example[\"class_attention_mask\"] for example in examples]\n\n pixel_values = torch.stack(pixel_values)\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n\n input_ids = torch.cat(input_ids, dim=0)\n\n batch = {\n \"input_ids\": input_ids,\n \"pixel_values\": pixel_values,\n }\n\n if has_attention_mask:\n attention_mask = torch.cat(attention_mask, dim=0)\n batch[\"attention_mask\"] = attention_mask\n\n return batch\n\n\nclass PromptDataset(Dataset):\n \"\"\"A simple dataset to prepare the prompts to generate class images on multiple GPUs.\"\"\"\n\n def __init__(self, prompt, num_samples):\n self.prompt = prompt\n self.num_samples = num_samples\n\n def __len__(self):\n return self.num_samples\n\n def __getitem__(self, index):\n example = {}\n example[\"prompt\"] = self.prompt\n example[\"index\"] = index\n return example\n\n\ndef model_has_vae(args):\n config_file_name = os.path.join(\"vae\", AutoencoderKL.config_name)\n if os.path.isdir(args.pretrained_model_name_or_path):\n config_file_name = os.path.join(args.pretrained_model_name_or_path, config_file_name)\n return os.path.isfile(config_file_name)\n else:\n files_in_repo = model_info(args.pretrained_model_name_or_path, revision=args.revision).siblings\n return any(file.rfilename == config_file_name for file in files_in_repo)\n\n\ndef tokenize_prompt(tokenizer, prompt, tokenizer_max_length=None):\n if tokenizer_max_length is not None:\n max_length = tokenizer_max_length\n else:\n max_length = tokenizer.model_max_length\n\n text_inputs = tokenizer(\n prompt,\n truncation=True,\n padding=\"max_length\",\n max_length=max_length,\n return_tensors=\"pt\",\n )\n\n return text_inputs\n\n\ndef encode_prompt(text_encoder, input_ids, attention_mask, text_encoder_use_attention_mask=None):\n text_input_ids = input_ids.to(text_encoder.device)\n\n if text_encoder_use_attention_mask:\n attention_mask = attention_mask.to(text_encoder.device)\n else:\n attention_mask = None\n\n prompt_embeds = text_encoder(\n text_input_ids,\n attention_mask=attention_mask,\n return_dict=False,\n )\n prompt_embeds = prompt_embeds[0]\n\n return prompt_embeds\n\n\n# NOTE: if you're using the scheduled version, huber_c has to depend on the timesteps already\ndef conditional_loss(\n model_pred: torch.Tensor,\n target: torch.Tensor,\n reduction: str = \"mean\",\n loss_type: str = \"l2\",\n huber_c: float = 0.1,\n):\n if loss_type == \"l2\":\n loss = F.mse_loss(model_pred, target, reduction=reduction)\n elif loss_type == \"huber\":\n loss = 2 * huber_c * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c)\n if reduction == \"mean\":\n loss = torch.mean(loss)\n elif reduction == \"sum\":\n loss = torch.sum(loss)\n elif loss_type == \"smooth_l1\":\n loss = 2 * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c)\n if reduction == \"mean\":\n loss = torch.mean(loss)\n elif reduction == \"sum\":\n loss = torch.sum(loss)\n else:\n raise NotImplementedError(f\"Unsupported Loss Type {loss_type}\")\n return loss\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n\n # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate\n # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.\n # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.\n if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:\n raise ValueError(\n \"Gradient accumulation is not supported when training the text encoder in distributed training. \"\n \"Please set gradient_accumulation_steps to 1. This feature will be supported in the future.\"\n )\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Generate class images if prior preservation is enabled.\n if args.with_prior_preservation:\n class_images_dir = Path(args.class_data_dir)\n if not class_images_dir.exists():\n class_images_dir.mkdir(parents=True)\n cur_class_images = len(list(class_images_dir.iterdir()))\n\n if cur_class_images < args.num_class_images:\n torch_dtype = torch.float16 if accelerator.device.type == \"cuda\" else torch.float32\n if args.prior_generation_precision == \"fp32\":\n torch_dtype = torch.float32\n elif args.prior_generation_precision == \"fp16\":\n torch_dtype = torch.float16\n elif args.prior_generation_precision == \"bf16\":\n torch_dtype = torch.bfloat16\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n torch_dtype=torch_dtype,\n safety_checker=None,\n revision=args.revision,\n variant=args.variant,\n )\n pipeline.set_progress_bar_config(disable=True)\n\n num_new_images = args.num_class_images - cur_class_images\n logger.info(f\"Number of class images to sample: {num_new_images}.\")\n\n sample_dataset = PromptDataset(args.class_prompt, num_new_images)\n sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)\n\n sample_dataloader = accelerator.prepare(sample_dataloader)\n pipeline.to(accelerator.device)\n\n for example in tqdm(\n sample_dataloader, desc=\"Generating class images\", disable=not accelerator.is_local_main_process\n ):\n images = pipeline(example[\"prompt\"]).images\n\n for i, image in enumerate(images):\n hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()\n image_filename = class_images_dir / f\"{example['index'][i] + cur_class_images}-{hash_image}.jpg\"\n image.save(image_filename)\n\n del pipeline\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizer\n if args.tokenizer_name:\n tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)\n elif args.pretrained_model_name_or_path:\n tokenizer = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n\n # import correct text encoder class\n text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n text_encoder = text_encoder_cls.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n\n if model_has_vae(args):\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision, variant=args.variant\n )\n else:\n vae = None\n\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n for model in models:\n sub_dir = \"unet\" if isinstance(model, type(unwrap_model(unet))) else \"text_encoder\"\n model.save_pretrained(os.path.join(output_dir, sub_dir))\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n def load_model_hook(models, input_dir):\n while len(models) > 0:\n # pop models so that they are not loaded again\n model = models.pop()\n\n if isinstance(model, type(unwrap_model(text_encoder))):\n # load transformers style into model\n load_model = text_encoder_cls.from_pretrained(input_dir, subfolder=\"text_encoder\")\n model.config = load_model.config\n else:\n # load diffusers style into model\n load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder=\"unet\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n if vae is not None:\n vae.requires_grad_(False)\n\n if not args.train_text_encoder:\n text_encoder.requires_grad_(False)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n if args.train_text_encoder:\n text_encoder.gradient_checkpointing_enable()\n\n # Check that all trainable models are in full precision\n low_precision_error_string = (\n \"Please make sure to always have all model weights in full float32 precision when starting training - even if\"\n \" doing mixed precision training. copy of the weights should still be float32.\"\n )\n\n if unwrap_model(unet).dtype != torch.float32:\n raise ValueError(f\"Unet loaded as datatype {unwrap_model(unet).dtype}. {low_precision_error_string}\")\n\n if args.train_text_encoder and unwrap_model(text_encoder).dtype != torch.float32:\n raise ValueError(\n f\"Text encoder loaded as datatype {unwrap_model(text_encoder).dtype}.\" f\" {low_precision_error_string}\"\n )\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # Optimizer creation\n params_to_optimize = (\n itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters()\n )\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n if args.pre_compute_text_embeddings:\n\n def compute_text_embeddings(prompt):\n with torch.no_grad():\n text_inputs = tokenize_prompt(tokenizer, prompt, tokenizer_max_length=args.tokenizer_max_length)\n prompt_embeds = encode_prompt(\n text_encoder,\n text_inputs.input_ids,\n text_inputs.attention_mask,\n text_encoder_use_attention_mask=args.text_encoder_use_attention_mask,\n )\n\n return prompt_embeds\n\n pre_computed_encoder_hidden_states = compute_text_embeddings(args.instance_prompt)\n validation_prompt_negative_prompt_embeds = compute_text_embeddings(\"\")\n\n if args.validation_prompt is not None:\n validation_prompt_encoder_hidden_states = compute_text_embeddings(args.validation_prompt)\n else:\n validation_prompt_encoder_hidden_states = None\n\n if args.class_prompt is not None:\n pre_computed_class_prompt_encoder_hidden_states = compute_text_embeddings(args.class_prompt)\n else:\n pre_computed_class_prompt_encoder_hidden_states = None\n\n text_encoder = None\n tokenizer = None\n\n gc.collect()\n torch.cuda.empty_cache()\n else:\n pre_computed_encoder_hidden_states = None\n validation_prompt_encoder_hidden_states = None\n validation_prompt_negative_prompt_embeds = None\n pre_computed_class_prompt_encoder_hidden_states = None\n\n # Dataset and DataLoaders creation:\n train_dataset = DreamBoothDataset(\n instance_data_root=args.instance_data_dir,\n instance_prompt=args.instance_prompt,\n class_data_root=args.class_data_dir if args.with_prior_preservation else None,\n class_prompt=args.class_prompt,\n class_num=args.num_class_images,\n tokenizer=tokenizer,\n size=args.resolution,\n center_crop=args.center_crop,\n encoder_hidden_states=pre_computed_encoder_hidden_states,\n class_prompt_encoder_hidden_states=pre_computed_class_prompt_encoder_hidden_states,\n tokenizer_max_length=args.tokenizer_max_length,\n )\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n batch_size=args.train_batch_size,\n shuffle=True,\n collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_cycles=args.lr_num_cycles,\n power=args.lr_power,\n )\n\n # Prepare everything with our `accelerator`.\n if args.train_text_encoder:\n unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, text_encoder, optimizer, train_dataloader, lr_scheduler\n )\n else:\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move vae and text_encoder to device and cast to weight_dtype\n if vae is not None:\n vae.to(accelerator.device, dtype=weight_dtype)\n\n if not args.train_text_encoder and text_encoder is not None:\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = vars(copy.deepcopy(args))\n tracker_config.pop(\"validation_images\")\n accelerator.init_trackers(\"dreambooth\", config=tracker_config)\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num batches each epoch = {len(train_dataloader)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n for epoch in range(first_epoch, args.num_train_epochs):\n unet.train()\n if args.train_text_encoder:\n text_encoder.train()\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n pixel_values = batch[\"pixel_values\"].to(dtype=weight_dtype)\n\n if vae is not None:\n # Convert images to latent space\n model_input = vae.encode(batch[\"pixel_values\"].to(dtype=weight_dtype)).latent_dist.sample()\n model_input = model_input * vae.config.scaling_factor\n else:\n model_input = pixel_values\n\n # Sample noise that we'll add to the model input\n if args.offset_noise:\n noise = torch.randn_like(model_input) + 0.1 * torch.randn(\n model_input.shape[0], model_input.shape[1], 1, 1, device=model_input.device\n )\n else:\n noise = torch.randn_like(model_input)\n bsz, channels, height, width = model_input.shape\n # Sample a random timestep for each image\n if args.loss_type == \"huber\" or args.loss_type == \"smooth_l1\":\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (1,), device=\"cpu\")\n timestep = timesteps.item()\n\n if args.huber_schedule == \"exponential\":\n alpha = -math.log(args.huber_c) / noise_scheduler.config.num_train_timesteps\n huber_c = math.exp(-alpha * timestep)\n elif args.huber_schedule == \"snr\":\n alphas_cumprod = noise_scheduler.alphas_cumprod[timestep]\n sigmas = ((1.0 - alphas_cumprod) / alphas_cumprod) ** 0.5\n huber_c = (1 - args.huber_c) / (1 + sigmas) ** 2 + args.huber_c\n elif args.huber_schedule == \"constant\":\n huber_c = args.huber_c\n else:\n raise NotImplementedError(f\"Unknown Huber loss schedule {args.huber_schedule}!\")\n\n timesteps = timesteps.repeat(bsz).to(model_input.device)\n elif args.loss_type == \"l2\":\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device\n )\n huber_c = 1 # may be anything, as it's not used\n else:\n raise NotImplementedError(f\"Unknown loss type {args.loss_type}\")\n\n timesteps = timesteps.long()\n\n # Add noise to the model input according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)\n\n # Get the text embedding for conditioning\n if args.pre_compute_text_embeddings:\n encoder_hidden_states = batch[\"input_ids\"]\n else:\n encoder_hidden_states = encode_prompt(\n text_encoder,\n batch[\"input_ids\"],\n batch[\"attention_mask\"],\n text_encoder_use_attention_mask=args.text_encoder_use_attention_mask,\n )\n\n if unwrap_model(unet).config.in_channels == channels * 2:\n noisy_model_input = torch.cat([noisy_model_input, noisy_model_input], dim=1)\n\n if args.class_labels_conditioning == \"timesteps\":\n class_labels = timesteps\n else:\n class_labels = None\n\n # Predict the noise residual\n model_pred = unet(\n noisy_model_input, timesteps, encoder_hidden_states, class_labels=class_labels, return_dict=False\n )[0]\n\n if model_pred.shape[1] == 6:\n model_pred, _ = torch.chunk(model_pred, 2, dim=1)\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(model_input, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n if args.with_prior_preservation:\n # Chunk the noise and model_pred into two parts and compute the loss on each part separately.\n model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)\n target, target_prior = torch.chunk(target, 2, dim=0)\n # Compute prior loss\n prior_loss = conditional_loss(\n model_pred_prior.float(),\n target_prior.float(),\n reduction=\"mean\",\n loss_type=args.loss_type,\n huber_c=huber_c,\n )\n\n # Compute instance loss\n if args.snr_gamma is None:\n loss = conditional_loss(\n model_pred.float(), target.float(), reduction=\"mean\", loss_type=args.loss_type, huber_c=huber_c\n )\n else:\n # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.\n # Since we predict the noise instead of x_0, the original formulation is slightly changed.\n # This is discussed in Section 4.2 of the same paper.\n snr = compute_snr(noise_scheduler, timesteps)\n base_weight = (\n torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr\n )\n\n if noise_scheduler.config.prediction_type == \"v_prediction\":\n # Velocity objective needs to be floored to an SNR weight of one.\n mse_loss_weights = base_weight + 1\n else:\n # Epsilon and sample both use the same loss weights.\n mse_loss_weights = base_weight\n loss = conditional_loss(\n model_pred.float(), target.float(), reduction=\"none\", loss_type=args.loss_type, huber_c=huber_c\n )\n loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights\n loss = loss.mean()\n\n if args.with_prior_preservation:\n # Add the prior loss to the instance loss.\n loss = loss + args.prior_loss_weight * prior_loss\n\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n params_to_clip = (\n itertools.chain(unet.parameters(), text_encoder.parameters())\n if args.train_text_encoder\n else unet.parameters()\n )\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad(set_to_none=args.set_grads_to_none)\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n images = []\n\n if args.validation_prompt is not None and global_step % args.validation_steps == 0:\n images = log_validation(\n unwrap_model(text_encoder) if text_encoder is not None else text_encoder,\n tokenizer,\n unwrap_model(unet),\n vae,\n args,\n accelerator,\n weight_dtype,\n global_step,\n validation_prompt_encoder_hidden_states,\n validation_prompt_negative_prompt_embeds,\n )\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n # Create the pipeline using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n pipeline_args = {}\n\n if text_encoder is not None:\n pipeline_args[\"text_encoder\"] = unwrap_model(text_encoder)\n\n if args.skip_save_text_encoder:\n pipeline_args[\"text_encoder\"] = None\n\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n unet=unwrap_model(unet),\n revision=args.revision,\n variant=args.variant,\n **pipeline_args,\n )\n\n # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it\n scheduler_args = {}\n\n if \"variance_type\" in pipeline.scheduler.config:\n variance_type = pipeline.scheduler.config.variance_type\n\n if variance_type in [\"learned\", \"learned_range\"]:\n variance_type = \"fixed_small\"\n\n scheduler_args[\"variance_type\"] = variance_type\n\n pipeline.scheduler = pipeline.scheduler.from_config(pipeline.scheduler.config, **scheduler_args)\n\n pipeline.save_pretrained(args.output_dir)\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n images=images,\n base_model=args.pretrained_model_name_or_path,\n train_text_encoder=args.train_text_encoder,\n prompt=args.instance_prompt,\n repo_folder=args.output_dir,\n pipeline=pipeline,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/research_projects/scheduled_huber_loss_training/dreambooth/train_dreambooth_lora.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport copy\nimport gc\nimport logging\nimport math\nimport os\nimport shutil\nimport warnings\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom huggingface_hub import create_repo, upload_folder\nfrom huggingface_hub.utils import insecure_hashlib\nfrom packaging import version\nfrom peft import LoraConfig\nfrom peft.utils import get_peft_model_state_dict, set_peft_model_state_dict\nfrom PIL import Image\nfrom PIL.ImageOps import exif_transpose\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n DiffusionPipeline,\n DPMSolverMultistepScheduler,\n StableDiffusionPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.loaders import LoraLoaderMixin\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import _set_state_dict_into_text_encoder, cast_training_params\nfrom diffusers.utils import (\n check_min_version,\n convert_state_dict_to_diffusers,\n convert_unet_state_dict_to_peft,\n is_wandb_available,\n)\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.28.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef save_model_card(\n repo_id: str,\n images=None,\n base_model=str,\n train_text_encoder=False,\n prompt=str,\n repo_folder=None,\n pipeline: DiffusionPipeline = None,\n):\n img_str = \"\"\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n img_str += f\"![img_{i}](./image_{i}.png)\\n\"\n\n model_description = f\"\"\"\n# LoRA DreamBooth - {repo_id}\n\nThese are LoRA adaption weights for {base_model}. The weights were trained on {prompt} using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. \\n\n{img_str}\n\nLoRA for the text encoder was enabled: {train_text_encoder}.\n\"\"\"\n model_card = load_or_create_model_card(\n repo_id_or_path=repo_id,\n from_training=True,\n license=\"creativeml-openrail-m\",\n base_model=base_model,\n prompt=prompt,\n model_description=model_description,\n inference=True,\n )\n tags = [\"text-to-image\", \"diffusers\", \"lora\", \"diffusers-training\"]\n if isinstance(pipeline, StableDiffusionPipeline):\n tags.extend([\"stable-diffusion\", \"stable-diffusion-diffusers\"])\n else:\n tags.extend([\"if\", \"if-diffusers\"])\n model_card = populate_model_card(model_card, tags=tags)\n\n model_card.save(os.path.join(repo_folder, \"README.md\"))\n\n\ndef log_validation(\n pipeline,\n args,\n accelerator,\n pipeline_args,\n epoch,\n is_final_validation=False,\n):\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it\n scheduler_args = {}\n\n if \"variance_type\" in pipeline.scheduler.config:\n variance_type = pipeline.scheduler.config.variance_type\n\n if variance_type in [\"learned\", \"learned_range\"]:\n variance_type = \"fixed_small\"\n\n scheduler_args[\"variance_type\"] = variance_type\n\n pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, **scheduler_args)\n\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n\n if args.validation_images is None:\n images = []\n for _ in range(args.num_validation_images):\n with torch.cuda.amp.autocast():\n image = pipeline(**pipeline_args, generator=generator).images[0]\n images.append(image)\n else:\n images = []\n for image in args.validation_images:\n image = Image.open(image)\n with torch.cuda.amp.autocast():\n image = pipeline(**pipeline_args, image=image, generator=generator).images[0]\n images.append(image)\n\n for tracker in accelerator.trackers:\n phase_name = \"test\" if is_final_validation else \"validation\"\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(phase_name, np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n phase_name: [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\") for i, image in enumerate(images)\n ]\n }\n )\n\n del pipeline\n torch.cuda.empty_cache()\n\n return images\n\n\ndef import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path,\n subfolder=\"text_encoder\",\n revision=revision,\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"RobertaSeriesModelWithTransformation\":\n from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation\n\n return RobertaSeriesModelWithTransformation\n elif model_class == \"T5EncoderModel\":\n from transformers import T5EncoderModel\n\n return T5EncoderModel\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--tokenizer_name\",\n type=str,\n default=None,\n help=\"Pretrained tokenizer name or path if not the same as model_name\",\n )\n parser.add_argument(\n \"--instance_data_dir\",\n type=str,\n default=None,\n required=True,\n help=\"A folder containing the training data of instance images.\",\n )\n parser.add_argument(\n \"--class_data_dir\",\n type=str,\n default=None,\n required=False,\n help=\"A folder containing the training data of class images.\",\n )\n parser.add_argument(\n \"--instance_prompt\",\n type=str,\n default=None,\n required=True,\n help=\"The prompt with identifier specifying the instance\",\n )\n parser.add_argument(\n \"--class_prompt\",\n type=str,\n default=None,\n help=\"The prompt to specify images in the same class as provided instance images.\",\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n help=\"A prompt that is used during validation to verify that the model is learning.\",\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=50,\n help=(\n \"Run dreambooth validation every X epochs. Dreambooth validation consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--with_prior_preservation\",\n default=False,\n action=\"store_true\",\n help=\"Flag to add prior preservation loss.\",\n )\n parser.add_argument(\"--prior_loss_weight\", type=float, default=1.0, help=\"The weight of prior preservation loss.\")\n parser.add_argument(\n \"--num_class_images\",\n type=int,\n default=100,\n help=(\n \"Minimal class images for prior preservation loss. If there are not enough images already present in\"\n \" class_data_dir, additional images will be sampled with class_prompt.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"lora-dreambooth-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--train_text_encoder\",\n action=\"store_true\",\n help=\"Whether to train the text encoder. If set, the text encoder should be float32 precision.\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\n \"--sample_batch_size\", type=int, default=4, help=\"Batch size (per device) for sampling images.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final\"\n \" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=5e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\"--lr_power\", type=float, default=1.0, help=\"Power factor of the polynomial scheduler.\")\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--prior_generation_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp32\", \"fp16\", \"bf16\"],\n help=(\n \"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--pre_compute_text_embeddings\",\n action=\"store_true\",\n help=\"Whether or not to pre-compute text embeddings. If text embeddings are pre-computed, the text encoder will not be kept in memory during training and will leave more GPU memory available for training the rest of the model. This is not compatible with `--train_text_encoder`.\",\n )\n parser.add_argument(\n \"--tokenizer_max_length\",\n type=int,\n default=None,\n required=False,\n help=\"The maximum length of the tokenizer. If not set, will default to the tokenizer's max length.\",\n )\n parser.add_argument(\n \"--text_encoder_use_attention_mask\",\n action=\"store_true\",\n required=False,\n help=\"Whether to use attention mask for the text encoder\",\n )\n parser.add_argument(\n \"--validation_images\",\n required=False,\n default=None,\n nargs=\"+\",\n help=\"Optional set of images to use for validation. Used when the target pipeline takes an initial image as input such as when training image variation or superresolution.\",\n )\n parser.add_argument(\n \"--class_labels_conditioning\",\n required=False,\n default=None,\n help=\"The optional `class_label` conditioning to pass to the unet, available values are `timesteps`.\",\n )\n parser.add_argument(\n \"--loss_type\",\n type=str,\n default=\"l2\",\n choices=[\"l2\", \"huber\", \"smooth_l1\"],\n help=\"The type of loss to use and whether it's timestep-scheduled. See Issue #7488 for more info.\",\n )\n parser.add_argument(\n \"--huber_schedule\",\n type=str,\n default=\"snr\",\n choices=[\"constant\", \"exponential\", \"snr\"],\n help=\"The schedule to use for the huber losses parameter\",\n )\n parser.add_argument(\n \"--huber_c\",\n type=float,\n default=0.1,\n help=\"The huber loss parameter. Only used if one of the huber loss modes (huber or smooth l1) is selected with loss_type.\",\n )\n parser.add_argument(\n \"--rank\",\n type=int,\n default=4,\n help=(\"The dimension of the LoRA update matrices.\"),\n )\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.with_prior_preservation:\n if args.class_data_dir is None:\n raise ValueError(\"You must specify a data directory for class images.\")\n if args.class_prompt is None:\n raise ValueError(\"You must specify prompt for class images.\")\n else:\n # logger is not available yet\n if args.class_data_dir is not None:\n warnings.warn(\"You need not use --class_data_dir without --with_prior_preservation.\")\n if args.class_prompt is not None:\n warnings.warn(\"You need not use --class_prompt without --with_prior_preservation.\")\n\n if args.train_text_encoder and args.pre_compute_text_embeddings:\n raise ValueError(\"`--train_text_encoder` cannot be used with `--pre_compute_text_embeddings`\")\n\n return args\n\n\nclass DreamBoothDataset(Dataset):\n \"\"\"\n A dataset to prepare the instance and class images with the prompts for fine-tuning the model.\n It pre-processes the images and the tokenizes prompts.\n \"\"\"\n\n def __init__(\n self,\n instance_data_root,\n instance_prompt,\n tokenizer,\n class_data_root=None,\n class_prompt=None,\n class_num=None,\n size=512,\n center_crop=False,\n encoder_hidden_states=None,\n class_prompt_encoder_hidden_states=None,\n tokenizer_max_length=None,\n ):\n self.size = size\n self.center_crop = center_crop\n self.tokenizer = tokenizer\n self.encoder_hidden_states = encoder_hidden_states\n self.class_prompt_encoder_hidden_states = class_prompt_encoder_hidden_states\n self.tokenizer_max_length = tokenizer_max_length\n\n self.instance_data_root = Path(instance_data_root)\n if not self.instance_data_root.exists():\n raise ValueError(\"Instance images root doesn't exists.\")\n\n self.instance_images_path = list(Path(instance_data_root).iterdir())\n self.num_instance_images = len(self.instance_images_path)\n self.instance_prompt = instance_prompt\n self._length = self.num_instance_images\n\n if class_data_root is not None:\n self.class_data_root = Path(class_data_root)\n self.class_data_root.mkdir(parents=True, exist_ok=True)\n self.class_images_path = list(self.class_data_root.iterdir())\n if class_num is not None:\n self.num_class_images = min(len(self.class_images_path), class_num)\n else:\n self.num_class_images = len(self.class_images_path)\n self._length = max(self.num_class_images, self.num_instance_images)\n self.class_prompt = class_prompt\n else:\n self.class_data_root = None\n\n self.image_transforms = transforms.Compose(\n [\n transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, index):\n example = {}\n instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])\n instance_image = exif_transpose(instance_image)\n\n if not instance_image.mode == \"RGB\":\n instance_image = instance_image.convert(\"RGB\")\n example[\"instance_images\"] = self.image_transforms(instance_image)\n\n if self.encoder_hidden_states is not None:\n example[\"instance_prompt_ids\"] = self.encoder_hidden_states\n else:\n text_inputs = tokenize_prompt(\n self.tokenizer, self.instance_prompt, tokenizer_max_length=self.tokenizer_max_length\n )\n example[\"instance_prompt_ids\"] = text_inputs.input_ids\n example[\"instance_attention_mask\"] = text_inputs.attention_mask\n\n if self.class_data_root:\n class_image = Image.open(self.class_images_path[index % self.num_class_images])\n class_image = exif_transpose(class_image)\n\n if not class_image.mode == \"RGB\":\n class_image = class_image.convert(\"RGB\")\n example[\"class_images\"] = self.image_transforms(class_image)\n\n if self.class_prompt_encoder_hidden_states is not None:\n example[\"class_prompt_ids\"] = self.class_prompt_encoder_hidden_states\n else:\n class_text_inputs = tokenize_prompt(\n self.tokenizer, self.class_prompt, tokenizer_max_length=self.tokenizer_max_length\n )\n example[\"class_prompt_ids\"] = class_text_inputs.input_ids\n example[\"class_attention_mask\"] = class_text_inputs.attention_mask\n\n return example\n\n\ndef collate_fn(examples, with_prior_preservation=False):\n has_attention_mask = \"instance_attention_mask\" in examples[0]\n\n input_ids = [example[\"instance_prompt_ids\"] for example in examples]\n pixel_values = [example[\"instance_images\"] for example in examples]\n\n if has_attention_mask:\n attention_mask = [example[\"instance_attention_mask\"] for example in examples]\n\n # Concat class and instance examples for prior preservation.\n # We do this to avoid doing two forward passes.\n if with_prior_preservation:\n input_ids += [example[\"class_prompt_ids\"] for example in examples]\n pixel_values += [example[\"class_images\"] for example in examples]\n if has_attention_mask:\n attention_mask += [example[\"class_attention_mask\"] for example in examples]\n\n pixel_values = torch.stack(pixel_values)\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n\n input_ids = torch.cat(input_ids, dim=0)\n\n batch = {\n \"input_ids\": input_ids,\n \"pixel_values\": pixel_values,\n }\n\n if has_attention_mask:\n batch[\"attention_mask\"] = attention_mask\n\n return batch\n\n\nclass PromptDataset(Dataset):\n \"\"\"A simple dataset to prepare the prompts to generate class images on multiple GPUs.\"\"\"\n\n def __init__(self, prompt, num_samples):\n self.prompt = prompt\n self.num_samples = num_samples\n\n def __len__(self):\n return self.num_samples\n\n def __getitem__(self, index):\n example = {}\n example[\"prompt\"] = self.prompt\n example[\"index\"] = index\n return example\n\n\ndef tokenize_prompt(tokenizer, prompt, tokenizer_max_length=None):\n if tokenizer_max_length is not None:\n max_length = tokenizer_max_length\n else:\n max_length = tokenizer.model_max_length\n\n text_inputs = tokenizer(\n prompt,\n truncation=True,\n padding=\"max_length\",\n max_length=max_length,\n return_tensors=\"pt\",\n )\n\n return text_inputs\n\n\ndef encode_prompt(text_encoder, input_ids, attention_mask, text_encoder_use_attention_mask=None):\n text_input_ids = input_ids.to(text_encoder.device)\n\n if text_encoder_use_attention_mask:\n attention_mask = attention_mask.to(text_encoder.device)\n else:\n attention_mask = None\n\n prompt_embeds = text_encoder(\n text_input_ids,\n attention_mask=attention_mask,\n return_dict=False,\n )\n prompt_embeds = prompt_embeds[0]\n\n return prompt_embeds\n\n\n# NOTE: if you're using the scheduled version, huber_c has to depend on the timesteps already\ndef conditional_loss(\n model_pred: torch.Tensor,\n target: torch.Tensor,\n reduction: str = \"mean\",\n loss_type: str = \"l2\",\n huber_c: float = 0.1,\n):\n if loss_type == \"l2\":\n loss = F.mse_loss(model_pred, target, reduction=reduction)\n elif loss_type == \"huber\":\n loss = 2 * huber_c * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c)\n if reduction == \"mean\":\n loss = torch.mean(loss)\n elif reduction == \"sum\":\n loss = torch.sum(loss)\n elif loss_type == \"smooth_l1\":\n loss = 2 * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c)\n if reduction == \"mean\":\n loss = torch.mean(loss)\n elif reduction == \"sum\":\n loss = torch.sum(loss)\n else:\n raise NotImplementedError(f\"Unsupported Loss Type {loss_type}\")\n return loss\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n\n # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate\n # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.\n # TODO (sayakpaul): Remove this check when gradient accumulation with two models is enabled in accelerate.\n if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:\n raise ValueError(\n \"Gradient accumulation is not supported when training the text encoder in distributed training. \"\n \"Please set gradient_accumulation_steps to 1. This feature will be supported in the future.\"\n )\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Generate class images if prior preservation is enabled.\n if args.with_prior_preservation:\n class_images_dir = Path(args.class_data_dir)\n if not class_images_dir.exists():\n class_images_dir.mkdir(parents=True)\n cur_class_images = len(list(class_images_dir.iterdir()))\n\n if cur_class_images < args.num_class_images:\n torch_dtype = torch.float16 if accelerator.device.type == \"cuda\" else torch.float32\n if args.prior_generation_precision == \"fp32\":\n torch_dtype = torch.float32\n elif args.prior_generation_precision == \"fp16\":\n torch_dtype = torch.float16\n elif args.prior_generation_precision == \"bf16\":\n torch_dtype = torch.bfloat16\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n torch_dtype=torch_dtype,\n safety_checker=None,\n revision=args.revision,\n variant=args.variant,\n )\n pipeline.set_progress_bar_config(disable=True)\n\n num_new_images = args.num_class_images - cur_class_images\n logger.info(f\"Number of class images to sample: {num_new_images}.\")\n\n sample_dataset = PromptDataset(args.class_prompt, num_new_images)\n sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)\n\n sample_dataloader = accelerator.prepare(sample_dataloader)\n pipeline.to(accelerator.device)\n\n for example in tqdm(\n sample_dataloader, desc=\"Generating class images\", disable=not accelerator.is_local_main_process\n ):\n images = pipeline(example[\"prompt\"]).images\n\n for i, image in enumerate(images):\n hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()\n image_filename = class_images_dir / f\"{example['index'][i] + cur_class_images}-{hash_image}.jpg\"\n image.save(image_filename)\n\n del pipeline\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizer\n if args.tokenizer_name:\n tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)\n elif args.pretrained_model_name_or_path:\n tokenizer = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n\n # import correct text encoder class\n text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n text_encoder = text_encoder_cls.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n try:\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision, variant=args.variant\n )\n except OSError:\n # IF does not have a VAE so let's just set it to None\n # We don't have to error out here\n vae = None\n\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n # We only train the additional adapter LoRA layers\n if vae is not None:\n vae.requires_grad_(False)\n text_encoder.requires_grad_(False)\n unet.requires_grad_(False)\n\n # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move unet, vae and text_encoder to device and cast to weight_dtype\n unet.to(accelerator.device, dtype=weight_dtype)\n if vae is not None:\n vae.to(accelerator.device, dtype=weight_dtype)\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n if args.train_text_encoder:\n text_encoder.gradient_checkpointing_enable()\n\n # now we will add new LoRA weights to the attention layers\n unet_lora_config = LoraConfig(\n r=args.rank,\n lora_alpha=args.rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"to_k\", \"to_q\", \"to_v\", \"to_out.0\", \"add_k_proj\", \"add_v_proj\"],\n )\n unet.add_adapter(unet_lora_config)\n\n # The text encoder comes from 🤗 transformers, we will also attach adapters to it.\n if args.train_text_encoder:\n text_lora_config = LoraConfig(\n r=args.rank,\n lora_alpha=args.rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"out_proj\"],\n )\n text_encoder.add_adapter(text_lora_config)\n\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n # there are only two options here. Either are just the unet attn processor layers\n # or there are the unet and text encoder atten layers\n unet_lora_layers_to_save = None\n text_encoder_lora_layers_to_save = None\n\n for model in models:\n if isinstance(model, type(unwrap_model(unet))):\n unet_lora_layers_to_save = convert_state_dict_to_diffusers(get_peft_model_state_dict(model))\n elif isinstance(model, type(unwrap_model(text_encoder))):\n text_encoder_lora_layers_to_save = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(model)\n )\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n LoraLoaderMixin.save_lora_weights(\n output_dir,\n unet_lora_layers=unet_lora_layers_to_save,\n text_encoder_lora_layers=text_encoder_lora_layers_to_save,\n )\n\n def load_model_hook(models, input_dir):\n unet_ = None\n text_encoder_ = None\n\n while len(models) > 0:\n model = models.pop()\n\n if isinstance(model, type(unwrap_model(unet))):\n unet_ = model\n elif isinstance(model, type(unwrap_model(text_encoder))):\n text_encoder_ = model\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n lora_state_dict, network_alphas = LoraLoaderMixin.lora_state_dict(input_dir)\n\n unet_state_dict = {f'{k.replace(\"unet.\", \"\")}': v for k, v in lora_state_dict.items() if k.startswith(\"unet.\")}\n unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict)\n incompatible_keys = set_peft_model_state_dict(unet_, unet_state_dict, adapter_name=\"default\")\n\n if incompatible_keys is not None:\n # check only for unexpected keys\n unexpected_keys = getattr(incompatible_keys, \"unexpected_keys\", None)\n if unexpected_keys:\n logger.warning(\n f\"Loading adapter weights from state_dict led to unexpected keys not found in the model: \"\n f\" {unexpected_keys}. \"\n )\n\n if args.train_text_encoder:\n _set_state_dict_into_text_encoder(lora_state_dict, prefix=\"text_encoder.\", text_encoder=text_encoder_)\n\n # Make sure the trainable params are in float32. This is again needed since the base models\n # are in `weight_dtype`. More details:\n # https://github.com/huggingface/diffusers/pull/6514#discussion_r1449796804\n if args.mixed_precision == \"fp16\":\n models = [unet_]\n if args.train_text_encoder:\n models.append(text_encoder_)\n\n # only upcast trainable parameters (LoRA) into fp32\n cast_training_params(models, dtype=torch.float32)\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Make sure the trainable params are in float32.\n if args.mixed_precision == \"fp16\":\n models = [unet]\n if args.train_text_encoder:\n models.append(text_encoder)\n\n # only upcast trainable parameters (LoRA) into fp32\n cast_training_params(models, dtype=torch.float32)\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # Optimizer creation\n params_to_optimize = list(filter(lambda p: p.requires_grad, unet.parameters()))\n if args.train_text_encoder:\n params_to_optimize = params_to_optimize + list(filter(lambda p: p.requires_grad, text_encoder.parameters()))\n\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n if args.pre_compute_text_embeddings:\n\n def compute_text_embeddings(prompt):\n with torch.no_grad():\n text_inputs = tokenize_prompt(tokenizer, prompt, tokenizer_max_length=args.tokenizer_max_length)\n prompt_embeds = encode_prompt(\n text_encoder,\n text_inputs.input_ids,\n text_inputs.attention_mask,\n text_encoder_use_attention_mask=args.text_encoder_use_attention_mask,\n )\n\n return prompt_embeds\n\n pre_computed_encoder_hidden_states = compute_text_embeddings(args.instance_prompt)\n validation_prompt_negative_prompt_embeds = compute_text_embeddings(\"\")\n\n if args.validation_prompt is not None:\n validation_prompt_encoder_hidden_states = compute_text_embeddings(args.validation_prompt)\n else:\n validation_prompt_encoder_hidden_states = None\n\n if args.class_prompt is not None:\n pre_computed_class_prompt_encoder_hidden_states = compute_text_embeddings(args.class_prompt)\n else:\n pre_computed_class_prompt_encoder_hidden_states = None\n\n text_encoder = None\n tokenizer = None\n\n gc.collect()\n torch.cuda.empty_cache()\n else:\n pre_computed_encoder_hidden_states = None\n validation_prompt_encoder_hidden_states = None\n validation_prompt_negative_prompt_embeds = None\n pre_computed_class_prompt_encoder_hidden_states = None\n\n # Dataset and DataLoaders creation:\n train_dataset = DreamBoothDataset(\n instance_data_root=args.instance_data_dir,\n instance_prompt=args.instance_prompt,\n class_data_root=args.class_data_dir if args.with_prior_preservation else None,\n class_prompt=args.class_prompt,\n class_num=args.num_class_images,\n tokenizer=tokenizer,\n size=args.resolution,\n center_crop=args.center_crop,\n encoder_hidden_states=pre_computed_encoder_hidden_states,\n class_prompt_encoder_hidden_states=pre_computed_class_prompt_encoder_hidden_states,\n tokenizer_max_length=args.tokenizer_max_length,\n )\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n batch_size=args.train_batch_size,\n shuffle=True,\n collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_cycles=args.lr_num_cycles,\n power=args.lr_power,\n )\n\n # Prepare everything with our `accelerator`.\n if args.train_text_encoder:\n unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, text_encoder, optimizer, train_dataloader, lr_scheduler\n )\n else:\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = vars(copy.deepcopy(args))\n tracker_config.pop(\"validation_images\")\n accelerator.init_trackers(\"dreambooth-lora\", config=tracker_config)\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num batches each epoch = {len(train_dataloader)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the mos recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n for epoch in range(first_epoch, args.num_train_epochs):\n unet.train()\n if args.train_text_encoder:\n text_encoder.train()\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n pixel_values = batch[\"pixel_values\"].to(dtype=weight_dtype)\n\n if vae is not None:\n # Convert images to latent space\n model_input = vae.encode(pixel_values).latent_dist.sample()\n model_input = model_input * vae.config.scaling_factor\n else:\n model_input = pixel_values\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(model_input)\n bsz, channels, height, width = model_input.shape\n # Sample a random timestep for each image\n if args.loss_type == \"huber\" or args.loss_type == \"smooth_l1\":\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (1,), device=\"cpu\")\n timestep = timesteps.item()\n\n if args.huber_schedule == \"exponential\":\n alpha = -math.log(args.huber_c) / noise_scheduler.config.num_train_timesteps\n huber_c = math.exp(-alpha * timestep)\n elif args.huber_schedule == \"snr\":\n alphas_cumprod = noise_scheduler.alphas_cumprod[timestep]\n sigmas = ((1.0 - alphas_cumprod) / alphas_cumprod) ** 0.5\n huber_c = (1 - args.huber_c) / (1 + sigmas) ** 2 + args.huber_c\n elif args.huber_schedule == \"constant\":\n huber_c = args.huber_c\n else:\n raise NotImplementedError(f\"Unknown Huber loss schedule {args.huber_schedule}!\")\n\n timesteps = timesteps.repeat(bsz).to(model_input.device)\n elif args.loss_type == \"l2\":\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device\n )\n huber_c = 1 # may be anything, as it's not used\n else:\n raise NotImplementedError(f\"Unknown loss type {args.loss_type}\")\n\n timesteps = timesteps.long()\n\n # Add noise to the model input according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)\n\n # Get the text embedding for conditioning\n if args.pre_compute_text_embeddings:\n encoder_hidden_states = batch[\"input_ids\"]\n else:\n encoder_hidden_states = encode_prompt(\n text_encoder,\n batch[\"input_ids\"],\n batch[\"attention_mask\"],\n text_encoder_use_attention_mask=args.text_encoder_use_attention_mask,\n )\n\n if unwrap_model(unet).config.in_channels == channels * 2:\n noisy_model_input = torch.cat([noisy_model_input, noisy_model_input], dim=1)\n\n if args.class_labels_conditioning == \"timesteps\":\n class_labels = timesteps\n else:\n class_labels = None\n\n # Predict the noise residual\n model_pred = unet(\n noisy_model_input,\n timesteps,\n encoder_hidden_states,\n class_labels=class_labels,\n return_dict=False,\n )[0]\n\n # if model predicts variance, throw away the prediction. we will only train on the\n # simplified training objective. This means that all schedulers using the fine tuned\n # model must be configured to use one of the fixed variance variance types.\n if model_pred.shape[1] == 6:\n model_pred, _ = torch.chunk(model_pred, 2, dim=1)\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(model_input, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n if args.with_prior_preservation:\n # Chunk the noise and model_pred into two parts and compute the loss on each part separately.\n model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)\n target, target_prior = torch.chunk(target, 2, dim=0)\n\n # Compute instance loss\n loss = conditional_loss(\n model_pred.float(), target.float(), reduction=\"mean\", loss_type=args.loss_type, huber_c=huber_c\n )\n\n # Compute prior loss\n prior_loss = conditional_loss(\n model_pred_prior.float(),\n target_prior.float(),\n reduction=\"mean\",\n loss_type=args.loss_type,\n huber_c=huber_c,\n )\n\n # Add the prior loss to the instance loss.\n loss = loss + args.prior_loss_weight * prior_loss\n else:\n loss = conditional_loss(\n model_pred.float(), target.float(), reduction=\"mean\", loss_type=args.loss_type, huber_c=huber_c\n )\n\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(params_to_optimize, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompt is not None and epoch % args.validation_epochs == 0:\n # create pipeline\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n unet=unwrap_model(unet),\n text_encoder=None if args.pre_compute_text_embeddings else unwrap_model(text_encoder),\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n\n if args.pre_compute_text_embeddings:\n pipeline_args = {\n \"prompt_embeds\": validation_prompt_encoder_hidden_states,\n \"negative_prompt_embeds\": validation_prompt_negative_prompt_embeds,\n }\n else:\n pipeline_args = {\"prompt\": args.validation_prompt}\n\n images = log_validation(\n pipeline,\n args,\n accelerator,\n pipeline_args,\n epoch,\n )\n\n # Save the lora layers\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = unwrap_model(unet)\n unet = unet.to(torch.float32)\n\n unet_lora_state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(unet))\n\n if args.train_text_encoder:\n text_encoder = unwrap_model(text_encoder)\n text_encoder_state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(text_encoder))\n else:\n text_encoder_state_dict = None\n\n LoraLoaderMixin.save_lora_weights(\n save_directory=args.output_dir,\n unet_lora_layers=unet_lora_state_dict,\n text_encoder_lora_layers=text_encoder_state_dict,\n )\n\n # Final inference\n # Load previous pipeline\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path, revision=args.revision, variant=args.variant, torch_dtype=weight_dtype\n )\n\n # load attention processors\n pipeline.load_lora_weights(args.output_dir, weight_name=\"pytorch_lora_weights.safetensors\")\n\n # run inference\n images = []\n if args.validation_prompt and args.num_validation_images > 0:\n pipeline_args = {\"prompt\": args.validation_prompt, \"num_inference_steps\": 25}\n images = log_validation(\n pipeline,\n args,\n accelerator,\n pipeline_args,\n epoch,\n is_final_validation=True,\n )\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n images=images,\n base_model=args.pretrained_model_name_or_path,\n train_text_encoder=args.train_text_encoder,\n prompt=args.instance_prompt,\n repo_folder=args.output_dir,\n pipeline=pipeline,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/research_projects/scheduled_huber_loss_training/dreambooth/train_dreambooth_lora_sdxl.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport contextlib\nimport gc\nimport itertools\nimport json\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nimport warnings\nfrom pathlib import Path\nfrom typing import Optional\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import DistributedDataParallelKwargs, ProjectConfiguration, set_seed\nfrom huggingface_hub import create_repo, hf_hub_download, upload_folder\nfrom huggingface_hub.utils import insecure_hashlib\nfrom packaging import version\nfrom peft import LoraConfig, set_peft_model_state_dict\nfrom peft.utils import get_peft_model_state_dict\nfrom PIL import Image\nfrom PIL.ImageOps import exif_transpose\nfrom safetensors.torch import load_file, save_file\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom torchvision.transforms.functional import crop\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n DPMSolverMultistepScheduler,\n EDMEulerScheduler,\n EulerDiscreteScheduler,\n StableDiffusionXLPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.loaders import LoraLoaderMixin\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import _set_state_dict_into_text_encoder, cast_training_params, compute_snr\nfrom diffusers.utils import (\n check_min_version,\n convert_all_state_dict_to_peft,\n convert_state_dict_to_diffusers,\n convert_state_dict_to_kohya,\n convert_unet_state_dict_to_peft,\n is_wandb_available,\n)\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.28.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef determine_scheduler_type(pretrained_model_name_or_path, revision):\n model_index_filename = \"model_index.json\"\n if os.path.isdir(pretrained_model_name_or_path):\n model_index = os.path.join(pretrained_model_name_or_path, model_index_filename)\n else:\n model_index = hf_hub_download(\n repo_id=pretrained_model_name_or_path, filename=model_index_filename, revision=revision\n )\n\n with open(model_index, \"r\") as f:\n scheduler_type = json.load(f)[\"scheduler\"][1]\n return scheduler_type\n\n\ndef save_model_card(\n repo_id: str,\n use_dora: bool,\n images=None,\n base_model: str = None,\n train_text_encoder=False,\n instance_prompt=None,\n validation_prompt=None,\n repo_folder=None,\n vae_path=None,\n):\n widget_dict = []\n if images is not None:\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n widget_dict.append(\n {\"text\": validation_prompt if validation_prompt else \" \", \"output\": {\"url\": f\"image_{i}.png\"}}\n )\n\n model_description = f\"\"\"\n# {'SDXL' if 'playground' not in base_model else 'Playground'} LoRA DreamBooth - {repo_id}\n\n\n\n## Model description\n\nThese are {repo_id} LoRA adaption weights for {base_model}.\n\nThe weights were trained using [DreamBooth](https://dreambooth.github.io/).\n\nLoRA for the text encoder was enabled: {train_text_encoder}.\n\nSpecial VAE used for training: {vae_path}.\n\n## Trigger words\n\nYou should use {instance_prompt} to trigger the image generation.\n\n## Download model\n\nWeights for this model are available in Safetensors format.\n\n[Download]({repo_id}/tree/main) them in the Files & versions tab.\n\n\"\"\"\n if \"playground\" in base_model:\n model_description += \"\"\"\\n\n## License\n\nPlease adhere to the licensing terms as described [here](https://huggingface.co/playgroundai/playground-v2.5-1024px-aesthetic/blob/main/LICENSE.md).\n\"\"\"\n model_card = load_or_create_model_card(\n repo_id_or_path=repo_id,\n from_training=True,\n license=\"openrail++\" if \"playground\" not in base_model else \"playground-v2dot5-community\",\n base_model=base_model,\n prompt=instance_prompt,\n model_description=model_description,\n widget=widget_dict,\n )\n tags = [\n \"text-to-image\",\n \"text-to-image\",\n \"diffusers-training\",\n \"diffusers\",\n \"lora\" if not use_dora else \"dora\",\n \"template:sd-lora\",\n ]\n if \"playground\" in base_model:\n tags.extend([\"playground\", \"playground-diffusers\"])\n else:\n tags.extend([\"stable-diffusion-xl\", \"stable-diffusion-xl-diffusers\"])\n\n model_card = populate_model_card(model_card, tags=tags)\n model_card.save(os.path.join(repo_folder, \"README.md\"))\n\n\ndef log_validation(\n pipeline,\n args,\n accelerator,\n pipeline_args,\n epoch,\n is_final_validation=False,\n):\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n\n # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it\n scheduler_args = {}\n\n if not args.do_edm_style_training:\n if \"variance_type\" in pipeline.scheduler.config:\n variance_type = pipeline.scheduler.config.variance_type\n\n if variance_type in [\"learned\", \"learned_range\"]:\n variance_type = \"fixed_small\"\n\n scheduler_args[\"variance_type\"] = variance_type\n\n pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, **scheduler_args)\n\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n # Currently the context determination is a bit hand-wavy. We can improve it in the future if there's a better\n # way to condition it. Reference: https://github.com/huggingface/diffusers/pull/7126#issuecomment-1968523051\n inference_ctx = (\n contextlib.nullcontext() if \"playground\" in args.pretrained_model_name_or_path else torch.cuda.amp.autocast()\n )\n\n with inference_ctx:\n images = [pipeline(**pipeline_args, generator=generator).images[0] for _ in range(args.num_validation_images)]\n\n for tracker in accelerator.trackers:\n phase_name = \"test\" if is_final_validation else \"validation\"\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(phase_name, np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n phase_name: [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\") for i, image in enumerate(images)\n ]\n }\n )\n\n del pipeline\n torch.cuda.empty_cache()\n\n return images\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_vae_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) containing the training data of instance images (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--instance_data_dir\",\n type=str,\n default=None,\n help=(\"A folder containing the training data. \"),\n )\n\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n\n parser.add_argument(\n \"--image_column\",\n type=str,\n default=\"image\",\n help=\"The column of the dataset containing the target image. By \"\n \"default, the standard Image Dataset maps out 'file_name' \"\n \"to 'image'.\",\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=None,\n help=\"The column of the dataset containing the instance prompt for each image\",\n )\n\n parser.add_argument(\"--repeats\", type=int, default=1, help=\"How many times to repeat the training data.\")\n\n parser.add_argument(\n \"--class_data_dir\",\n type=str,\n default=None,\n required=False,\n help=\"A folder containing the training data of class images.\",\n )\n parser.add_argument(\n \"--instance_prompt\",\n type=str,\n default=None,\n required=True,\n help=\"The prompt with identifier specifying the instance, e.g. 'photo of a TOK dog', 'in the style of TOK'\",\n )\n parser.add_argument(\n \"--class_prompt\",\n type=str,\n default=None,\n help=\"The prompt to specify images in the same class as provided instance images.\",\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n help=\"A prompt that is used during validation to verify that the model is learning.\",\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=50,\n help=(\n \"Run dreambooth validation every X epochs. Dreambooth validation consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--do_edm_style_training\",\n default=False,\n action=\"store_true\",\n help=\"Flag to conduct training using the EDM formulation as introduced in https://arxiv.org/abs/2206.00364.\",\n )\n parser.add_argument(\n \"--with_prior_preservation\",\n default=False,\n action=\"store_true\",\n help=\"Flag to add prior preservation loss.\",\n )\n parser.add_argument(\"--prior_loss_weight\", type=float, default=1.0, help=\"The weight of prior preservation loss.\")\n parser.add_argument(\n \"--num_class_images\",\n type=int,\n default=100,\n help=(\n \"Minimal class images for prior preservation loss. If there are not enough images already present in\"\n \" class_data_dir, additional images will be sampled with class_prompt.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"lora-dreambooth-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--output_kohya_format\",\n action=\"store_true\",\n help=\"Flag to additionally generate final state dict in the Kohya format so that it becomes compatible with A111, Comfy, Kohya, etc.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=1024,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--train_text_encoder\",\n action=\"store_true\",\n help=\"Whether to train the text encoder. If set, the text encoder should be float32 precision.\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\n \"--sample_batch_size\", type=int, default=4, help=\"Batch size (per device) for sampling images.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final\"\n \" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n\n parser.add_argument(\n \"--text_encoder_lr\",\n type=float,\n default=5e-6,\n help=\"Text encoder learning rate to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n\n parser.add_argument(\n \"--snr_gamma\",\n type=float,\n default=None,\n help=\"SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. \"\n \"More details here: https://arxiv.org/abs/2303.09556.\",\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\"--lr_power\", type=float, default=1.0, help=\"Power factor of the polynomial scheduler.\")\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n\n parser.add_argument(\n \"--optimizer\",\n type=str,\n default=\"AdamW\",\n help=('The optimizer type to use. Choose between [\"AdamW\", \"prodigy\"]'),\n )\n\n parser.add_argument(\n \"--use_8bit_adam\",\n action=\"store_true\",\n help=\"Whether or not to use 8-bit Adam from bitsandbytes. Ignored if optimizer is not set to AdamW\",\n )\n\n parser.add_argument(\n \"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam and Prodigy optimizers.\"\n )\n parser.add_argument(\n \"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam and Prodigy optimizers.\"\n )\n parser.add_argument(\n \"--prodigy_beta3\",\n type=float,\n default=None,\n help=\"coefficients for computing the Prodidy stepsize using running averages. If set to None, \"\n \"uses the value of square root of beta2. Ignored if optimizer is adamW\",\n )\n parser.add_argument(\"--prodigy_decouple\", type=bool, default=True, help=\"Use AdamW style decoupled weight decay\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-04, help=\"Weight decay to use for unet params\")\n parser.add_argument(\n \"--adam_weight_decay_text_encoder\", type=float, default=1e-03, help=\"Weight decay to use for text_encoder\"\n )\n\n parser.add_argument(\n \"--adam_epsilon\",\n type=float,\n default=1e-08,\n help=\"Epsilon value for the Adam optimizer and Prodigy optimizers.\",\n )\n\n parser.add_argument(\n \"--prodigy_use_bias_correction\",\n type=bool,\n default=True,\n help=\"Turn on Adam's bias correction. True by default. Ignored if optimizer is adamW\",\n )\n parser.add_argument(\n \"--prodigy_safeguard_warmup\",\n type=bool,\n default=True,\n help=\"Remove lr from the denominator of D estimate to avoid issues during warm-up stage. True by default. \"\n \"Ignored if optimizer is adamW\",\n )\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--prior_generation_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp32\", \"fp16\", \"bf16\"],\n help=(\n \"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--rank\",\n type=int,\n default=4,\n help=(\"The dimension of the LoRA update matrices.\"),\n )\n parser.add_argument(\n \"--use_dora\",\n action=\"store_true\",\n default=False,\n help=(\n \"Wether to train a DoRA as proposed in- DoRA: Weight-Decomposed Low-Rank Adaptation https://arxiv.org/abs/2402.09353. \"\n \"Note: to use DoRA you need to install peft from main, `pip install git+https://github.com/huggingface/peft.git`\"\n ),\n )\n parser.add_argument(\n \"--loss_type\",\n type=str,\n default=\"l2\",\n choices=[\"l2\", \"huber\", \"smooth_l1\"],\n help=\"The type of loss to use and whether it's timestep-scheduled. See Issue #7488 for more info.\",\n )\n parser.add_argument(\n \"--huber_schedule\",\n type=str,\n default=\"snr\",\n choices=[\"constant\", \"exponential\", \"snr\"],\n help=\"The schedule to use for the huber losses parameter\",\n )\n parser.add_argument(\n \"--huber_c\",\n type=float,\n default=0.1,\n help=\"The huber loss parameter. Only used if one of the huber loss modes (huber or smooth l1) is selected with loss_type.\",\n )\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n if args.dataset_name is None and args.instance_data_dir is None:\n raise ValueError(\"Specify either `--dataset_name` or `--instance_data_dir`\")\n\n if args.dataset_name is not None and args.instance_data_dir is not None:\n raise ValueError(\"Specify only one of `--dataset_name` or `--instance_data_dir`\")\n\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.with_prior_preservation:\n if args.class_data_dir is None:\n raise ValueError(\"You must specify a data directory for class images.\")\n if args.class_prompt is None:\n raise ValueError(\"You must specify prompt for class images.\")\n else:\n # logger is not available yet\n if args.class_data_dir is not None:\n warnings.warn(\"You need not use --class_data_dir without --with_prior_preservation.\")\n if args.class_prompt is not None:\n warnings.warn(\"You need not use --class_prompt without --with_prior_preservation.\")\n\n return args\n\n\nclass DreamBoothDataset(Dataset):\n \"\"\"\n A dataset to prepare the instance and class images with the prompts for fine-tuning the model.\n It pre-processes the images.\n \"\"\"\n\n def __init__(\n self,\n instance_data_root,\n instance_prompt,\n class_prompt,\n class_data_root=None,\n class_num=None,\n size=1024,\n repeats=1,\n center_crop=False,\n ):\n self.size = size\n self.center_crop = center_crop\n\n self.instance_prompt = instance_prompt\n self.custom_instance_prompts = None\n self.class_prompt = class_prompt\n\n # if --dataset_name is provided or a metadata jsonl file is provided in the local --instance_data directory,\n # we load the training data using load_dataset\n if args.dataset_name is not None:\n try:\n from datasets import load_dataset\n except ImportError:\n raise ImportError(\n \"You are trying to load your data using the datasets library. If you wish to train using custom \"\n \"captions please install the datasets library: `pip install datasets`. If you wish to load a \"\n \"local folder containing images only, specify --instance_data_dir instead.\"\n )\n # Downloading and loading a dataset from the hub.\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n )\n # Preprocessing the datasets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n if args.image_column is None:\n image_column = column_names[0]\n logger.info(f\"image column defaulting to {image_column}\")\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n instance_images = dataset[\"train\"][image_column]\n\n if args.caption_column is None:\n logger.info(\n \"No caption column provided, defaulting to instance_prompt for all images. If your dataset \"\n \"contains captions/prompts for the images, make sure to specify the \"\n \"column as --caption_column\"\n )\n self.custom_instance_prompts = None\n else:\n if args.caption_column not in column_names:\n raise ValueError(\n f\"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n custom_instance_prompts = dataset[\"train\"][args.caption_column]\n # create final list of captions according to --repeats\n self.custom_instance_prompts = []\n for caption in custom_instance_prompts:\n self.custom_instance_prompts.extend(itertools.repeat(caption, repeats))\n else:\n self.instance_data_root = Path(instance_data_root)\n if not self.instance_data_root.exists():\n raise ValueError(\"Instance images root doesn't exists.\")\n\n instance_images = [Image.open(path) for path in list(Path(instance_data_root).iterdir())]\n self.custom_instance_prompts = None\n\n self.instance_images = []\n for img in instance_images:\n self.instance_images.extend(itertools.repeat(img, repeats))\n\n # image processing to prepare for using SD-XL micro-conditioning\n self.original_sizes = []\n self.crop_top_lefts = []\n self.pixel_values = []\n train_resize = transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR)\n train_crop = transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size)\n train_flip = transforms.RandomHorizontalFlip(p=1.0)\n train_transforms = transforms.Compose(\n [\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n for image in self.instance_images:\n image = exif_transpose(image)\n if not image.mode == \"RGB\":\n image = image.convert(\"RGB\")\n self.original_sizes.append((image.height, image.width))\n image = train_resize(image)\n if args.random_flip and random.random() < 0.5:\n # flip\n image = train_flip(image)\n if args.center_crop:\n y1 = max(0, int(round((image.height - args.resolution) / 2.0)))\n x1 = max(0, int(round((image.width - args.resolution) / 2.0)))\n image = train_crop(image)\n else:\n y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))\n image = crop(image, y1, x1, h, w)\n crop_top_left = (y1, x1)\n self.crop_top_lefts.append(crop_top_left)\n image = train_transforms(image)\n self.pixel_values.append(image)\n\n self.num_instance_images = len(self.instance_images)\n self._length = self.num_instance_images\n\n if class_data_root is not None:\n self.class_data_root = Path(class_data_root)\n self.class_data_root.mkdir(parents=True, exist_ok=True)\n self.class_images_path = list(self.class_data_root.iterdir())\n if class_num is not None:\n self.num_class_images = min(len(self.class_images_path), class_num)\n else:\n self.num_class_images = len(self.class_images_path)\n self._length = max(self.num_class_images, self.num_instance_images)\n else:\n self.class_data_root = None\n\n self.image_transforms = transforms.Compose(\n [\n transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, index):\n example = {}\n instance_image = self.pixel_values[index % self.num_instance_images]\n original_size = self.original_sizes[index % self.num_instance_images]\n crop_top_left = self.crop_top_lefts[index % self.num_instance_images]\n example[\"instance_images\"] = instance_image\n example[\"original_size\"] = original_size\n example[\"crop_top_left\"] = crop_top_left\n\n if self.custom_instance_prompts:\n caption = self.custom_instance_prompts[index % self.num_instance_images]\n if caption:\n example[\"instance_prompt\"] = caption\n else:\n example[\"instance_prompt\"] = self.instance_prompt\n\n else: # costum prompts were provided, but length does not match size of image dataset\n example[\"instance_prompt\"] = self.instance_prompt\n\n if self.class_data_root:\n class_image = Image.open(self.class_images_path[index % self.num_class_images])\n class_image = exif_transpose(class_image)\n\n if not class_image.mode == \"RGB\":\n class_image = class_image.convert(\"RGB\")\n example[\"class_images\"] = self.image_transforms(class_image)\n example[\"class_prompt\"] = self.class_prompt\n\n return example\n\n\ndef collate_fn(examples, with_prior_preservation=False):\n pixel_values = [example[\"instance_images\"] for example in examples]\n prompts = [example[\"instance_prompt\"] for example in examples]\n original_sizes = [example[\"original_size\"] for example in examples]\n crop_top_lefts = [example[\"crop_top_left\"] for example in examples]\n\n # Concat class and instance examples for prior preservation.\n # We do this to avoid doing two forward passes.\n if with_prior_preservation:\n pixel_values += [example[\"class_images\"] for example in examples]\n prompts += [example[\"class_prompt\"] for example in examples]\n original_sizes += [example[\"original_size\"] for example in examples]\n crop_top_lefts += [example[\"crop_top_left\"] for example in examples]\n\n pixel_values = torch.stack(pixel_values)\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n\n batch = {\n \"pixel_values\": pixel_values,\n \"prompts\": prompts,\n \"original_sizes\": original_sizes,\n \"crop_top_lefts\": crop_top_lefts,\n }\n return batch\n\n\nclass PromptDataset(Dataset):\n \"\"\"A simple dataset to prepare the prompts to generate class images on multiple GPUs.\"\"\"\n\n def __init__(self, prompt, num_samples):\n self.prompt = prompt\n self.num_samples = num_samples\n\n def __len__(self):\n return self.num_samples\n\n def __getitem__(self, index):\n example = {}\n example[\"prompt\"] = self.prompt\n example[\"index\"] = index\n return example\n\n\ndef tokenize_prompt(tokenizer, prompt):\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n return text_input_ids\n\n\n# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt\ndef encode_prompt(text_encoders, tokenizers, prompt, text_input_ids_list=None):\n prompt_embeds_list = []\n\n for i, text_encoder in enumerate(text_encoders):\n if tokenizers is not None:\n tokenizer = tokenizers[i]\n text_input_ids = tokenize_prompt(tokenizer, prompt)\n else:\n assert text_input_ids_list is not None\n text_input_ids = text_input_ids_list[i]\n\n prompt_embeds = text_encoder(\n text_input_ids.to(text_encoder.device), output_hidden_states=True, return_dict=False\n )\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds[-1][-2]\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)\n return prompt_embeds, pooled_prompt_embeds\n\n\n# NOTE: if you're using the scheduled version, huber_c has to depend on the timesteps already\ndef conditional_loss(\n model_pred: torch.Tensor,\n target: torch.Tensor,\n reduction: str = \"mean\",\n loss_type: str = \"l2\",\n huber_c: float = 0.1,\n weighting: Optional[torch.Tensor] = None,\n):\n if loss_type == \"l2\":\n if weighting is not None:\n loss = torch.mean(\n (weighting * (model_pred.float() - target.float()) ** 2).reshape(target.shape[0], -1),\n 1,\n )\n if reduction == \"mean\":\n loss = torch.mean(loss)\n elif reduction == \"sum\":\n loss = torch.sum(loss)\n else:\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=reduction)\n\n elif loss_type == \"huber\":\n if weighting is not None:\n loss = torch.mean(\n (\n 2\n * huber_c\n * (\n torch.sqrt(weighting.float() * (model_pred.float() - target.float()) ** 2 + huber_c**2)\n - huber_c\n )\n ).reshape(target.shape[0], -1),\n 1,\n )\n if reduction == \"mean\":\n loss = torch.mean(loss)\n elif reduction == \"sum\":\n loss = torch.sum(loss)\n else:\n loss = 2 * huber_c * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c)\n if reduction == \"mean\":\n loss = torch.mean(loss)\n elif reduction == \"sum\":\n loss = torch.sum(loss)\n elif loss_type == \"smooth_l1\":\n if weighting is not None:\n loss = torch.mean(\n (\n 2\n * (\n torch.sqrt(weighting.float() * (model_pred.float() - target.float()) ** 2 + huber_c**2)\n - huber_c\n )\n ).reshape(target.shape[0], -1),\n 1,\n )\n if reduction == \"mean\":\n loss = torch.mean(loss)\n elif reduction == \"sum\":\n loss = torch.sum(loss)\n else:\n loss = 2 * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c)\n if reduction == \"mean\":\n loss = torch.mean(loss)\n elif reduction == \"sum\":\n loss = torch.sum(loss)\n else:\n raise NotImplementedError(f\"Unsupported Loss Type {loss_type}\")\n return loss\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n if args.do_edm_style_training and args.snr_gamma is not None:\n raise ValueError(\"Min-SNR formulation is not supported when conducting EDM-style training.\")\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n kwargs_handlers=[kwargs],\n )\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Generate class images if prior preservation is enabled.\n if args.with_prior_preservation:\n class_images_dir = Path(args.class_data_dir)\n if not class_images_dir.exists():\n class_images_dir.mkdir(parents=True)\n cur_class_images = len(list(class_images_dir.iterdir()))\n\n if cur_class_images < args.num_class_images:\n torch_dtype = torch.float16 if accelerator.device.type == \"cuda\" else torch.float32\n if args.prior_generation_precision == \"fp32\":\n torch_dtype = torch.float32\n elif args.prior_generation_precision == \"fp16\":\n torch_dtype = torch.float16\n elif args.prior_generation_precision == \"bf16\":\n torch_dtype = torch.bfloat16\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n torch_dtype=torch_dtype,\n revision=args.revision,\n variant=args.variant,\n )\n pipeline.set_progress_bar_config(disable=True)\n\n num_new_images = args.num_class_images - cur_class_images\n logger.info(f\"Number of class images to sample: {num_new_images}.\")\n\n sample_dataset = PromptDataset(args.class_prompt, num_new_images)\n sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)\n\n sample_dataloader = accelerator.prepare(sample_dataloader)\n pipeline.to(accelerator.device)\n\n for example in tqdm(\n sample_dataloader, desc=\"Generating class images\", disable=not accelerator.is_local_main_process\n ):\n images = pipeline(example[\"prompt\"]).images\n\n for i, image in enumerate(images):\n hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()\n image_filename = class_images_dir / f\"{example['index'][i] + cur_class_images}-{hash_image}.jpg\"\n image.save(image_filename)\n\n del pipeline\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizers\n tokenizer_one = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n tokenizer_two = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer_2\",\n revision=args.revision,\n use_fast=False,\n )\n\n # import correct text encoder classes\n text_encoder_cls_one = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision\n )\n text_encoder_cls_two = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision, subfolder=\"text_encoder_2\"\n )\n\n # Load scheduler and models\n scheduler_type = determine_scheduler_type(args.pretrained_model_name_or_path, args.revision)\n if \"EDM\" in scheduler_type:\n args.do_edm_style_training = True\n noise_scheduler = EDMEulerScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n logger.info(\"Performing EDM-style training!\")\n elif args.do_edm_style_training:\n noise_scheduler = EulerDiscreteScheduler.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"scheduler\"\n )\n logger.info(\"Performing EDM-style training!\")\n else:\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n\n text_encoder_one = text_encoder_cls_one.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n text_encoder_two = text_encoder_cls_two.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder_2\", revision=args.revision, variant=args.variant\n )\n vae_path = (\n args.pretrained_model_name_or_path\n if args.pretrained_vae_model_name_or_path is None\n else args.pretrained_vae_model_name_or_path\n )\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n variant=args.variant,\n )\n latents_mean = latents_std = None\n if hasattr(vae.config, \"latents_mean\") and vae.config.latents_mean is not None:\n latents_mean = torch.tensor(vae.config.latents_mean).view(1, 4, 1, 1)\n if hasattr(vae.config, \"latents_std\") and vae.config.latents_std is not None:\n latents_std = torch.tensor(vae.config.latents_std).view(1, 4, 1, 1)\n\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n # We only train the additional adapter LoRA layers\n vae.requires_grad_(False)\n text_encoder_one.requires_grad_(False)\n text_encoder_two.requires_grad_(False)\n unet.requires_grad_(False)\n\n # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move unet, vae and text_encoder to device and cast to weight_dtype\n unet.to(accelerator.device, dtype=weight_dtype)\n\n # The VAE is always in float32 to avoid NaN losses.\n vae.to(accelerator.device, dtype=torch.float32)\n\n text_encoder_one.to(accelerator.device, dtype=weight_dtype)\n text_encoder_two.to(accelerator.device, dtype=weight_dtype)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, \"\n \"please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n if args.train_text_encoder:\n text_encoder_one.gradient_checkpointing_enable()\n text_encoder_two.gradient_checkpointing_enable()\n\n # now we will add new LoRA weights to the attention layers\n unet_lora_config = LoraConfig(\n r=args.rank,\n use_dora=args.use_dora,\n lora_alpha=args.rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"to_k\", \"to_q\", \"to_v\", \"to_out.0\"],\n )\n unet.add_adapter(unet_lora_config)\n\n # The text encoder comes from 🤗 transformers, so we cannot directly modify it.\n # So, instead, we monkey-patch the forward calls of its attention-blocks.\n if args.train_text_encoder:\n text_lora_config = LoraConfig(\n r=args.rank,\n use_dora=args.use_dora,\n lora_alpha=args.rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"out_proj\"],\n )\n text_encoder_one.add_adapter(text_lora_config)\n text_encoder_two.add_adapter(text_lora_config)\n\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n # there are only two options here. Either are just the unet attn processor layers\n # or there are the unet and text encoder atten layers\n unet_lora_layers_to_save = None\n text_encoder_one_lora_layers_to_save = None\n text_encoder_two_lora_layers_to_save = None\n\n for model in models:\n if isinstance(model, type(unwrap_model(unet))):\n unet_lora_layers_to_save = convert_state_dict_to_diffusers(get_peft_model_state_dict(model))\n elif isinstance(model, type(unwrap_model(text_encoder_one))):\n text_encoder_one_lora_layers_to_save = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(model)\n )\n elif isinstance(model, type(unwrap_model(text_encoder_two))):\n text_encoder_two_lora_layers_to_save = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(model)\n )\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n StableDiffusionXLPipeline.save_lora_weights(\n output_dir,\n unet_lora_layers=unet_lora_layers_to_save,\n text_encoder_lora_layers=text_encoder_one_lora_layers_to_save,\n text_encoder_2_lora_layers=text_encoder_two_lora_layers_to_save,\n )\n\n def load_model_hook(models, input_dir):\n unet_ = None\n text_encoder_one_ = None\n text_encoder_two_ = None\n\n while len(models) > 0:\n model = models.pop()\n\n if isinstance(model, type(unwrap_model(unet))):\n unet_ = model\n elif isinstance(model, type(unwrap_model(text_encoder_one))):\n text_encoder_one_ = model\n elif isinstance(model, type(unwrap_model(text_encoder_two))):\n text_encoder_two_ = model\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n lora_state_dict, network_alphas = LoraLoaderMixin.lora_state_dict(input_dir)\n\n unet_state_dict = {f'{k.replace(\"unet.\", \"\")}': v for k, v in lora_state_dict.items() if k.startswith(\"unet.\")}\n unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict)\n incompatible_keys = set_peft_model_state_dict(unet_, unet_state_dict, adapter_name=\"default\")\n if incompatible_keys is not None:\n # check only for unexpected keys\n unexpected_keys = getattr(incompatible_keys, \"unexpected_keys\", None)\n if unexpected_keys:\n logger.warning(\n f\"Loading adapter weights from state_dict led to unexpected keys not found in the model: \"\n f\" {unexpected_keys}. \"\n )\n\n if args.train_text_encoder:\n # Do we need to call `scale_lora_layers()` here?\n _set_state_dict_into_text_encoder(lora_state_dict, prefix=\"text_encoder.\", text_encoder=text_encoder_one_)\n\n _set_state_dict_into_text_encoder(\n lora_state_dict, prefix=\"text_encoder_2.\", text_encoder=text_encoder_two_\n )\n\n # Make sure the trainable params are in float32. This is again needed since the base models\n # are in `weight_dtype`. More details:\n # https://github.com/huggingface/diffusers/pull/6514#discussion_r1449796804\n if args.mixed_precision == \"fp16\":\n models = [unet_]\n if args.train_text_encoder:\n models.extend([text_encoder_one_, text_encoder_two_])\n # only upcast trainable parameters (LoRA) into fp32\n cast_training_params(models)\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Make sure the trainable params are in float32.\n if args.mixed_precision == \"fp16\":\n models = [unet]\n if args.train_text_encoder:\n models.extend([text_encoder_one, text_encoder_two])\n\n # only upcast trainable parameters (LoRA) into fp32\n cast_training_params(models, dtype=torch.float32)\n\n unet_lora_parameters = list(filter(lambda p: p.requires_grad, unet.parameters()))\n\n if args.train_text_encoder:\n text_lora_parameters_one = list(filter(lambda p: p.requires_grad, text_encoder_one.parameters()))\n text_lora_parameters_two = list(filter(lambda p: p.requires_grad, text_encoder_two.parameters()))\n\n # Optimization parameters\n unet_lora_parameters_with_lr = {\"params\": unet_lora_parameters, \"lr\": args.learning_rate}\n if args.train_text_encoder:\n # different learning rate for text encoder and unet\n text_lora_parameters_one_with_lr = {\n \"params\": text_lora_parameters_one,\n \"weight_decay\": args.adam_weight_decay_text_encoder,\n \"lr\": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate,\n }\n text_lora_parameters_two_with_lr = {\n \"params\": text_lora_parameters_two,\n \"weight_decay\": args.adam_weight_decay_text_encoder,\n \"lr\": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate,\n }\n params_to_optimize = [\n unet_lora_parameters_with_lr,\n text_lora_parameters_one_with_lr,\n text_lora_parameters_two_with_lr,\n ]\n else:\n params_to_optimize = [unet_lora_parameters_with_lr]\n\n # Optimizer creation\n if not (args.optimizer.lower() == \"prodigy\" or args.optimizer.lower() == \"adamw\"):\n logger.warning(\n f\"Unsupported choice of optimizer: {args.optimizer}.Supported optimizers include [adamW, prodigy].\"\n \"Defaulting to adamW\"\n )\n args.optimizer = \"adamw\"\n\n if args.use_8bit_adam and not args.optimizer.lower() == \"adamw\":\n logger.warning(\n f\"use_8bit_adam is ignored when optimizer is not set to 'AdamW'. Optimizer was \"\n f\"set to {args.optimizer.lower()}\"\n )\n\n if args.optimizer.lower() == \"adamw\":\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n optimizer = optimizer_class(\n params_to_optimize,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n if args.optimizer.lower() == \"prodigy\":\n try:\n import prodigyopt\n except ImportError:\n raise ImportError(\"To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`\")\n\n optimizer_class = prodigyopt.Prodigy\n\n if args.learning_rate <= 0.1:\n logger.warning(\n \"Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0\"\n )\n if args.train_text_encoder and args.text_encoder_lr:\n logger.warning(\n f\"Learning rates were provided both for the unet and the text encoder- e.g. text_encoder_lr:\"\n f\" {args.text_encoder_lr} and learning_rate: {args.learning_rate}. \"\n f\"When using prodigy only learning_rate is used as the initial learning rate.\"\n )\n # changes the learning rate of text_encoder_parameters_one and text_encoder_parameters_two to be\n # --learning_rate\n params_to_optimize[1][\"lr\"] = args.learning_rate\n params_to_optimize[2][\"lr\"] = args.learning_rate\n\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n beta3=args.prodigy_beta3,\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n decouple=args.prodigy_decouple,\n use_bias_correction=args.prodigy_use_bias_correction,\n safeguard_warmup=args.prodigy_safeguard_warmup,\n )\n\n # Dataset and DataLoaders creation:\n train_dataset = DreamBoothDataset(\n instance_data_root=args.instance_data_dir,\n instance_prompt=args.instance_prompt,\n class_prompt=args.class_prompt,\n class_data_root=args.class_data_dir if args.with_prior_preservation else None,\n class_num=args.num_class_images,\n size=args.resolution,\n repeats=args.repeats,\n center_crop=args.center_crop,\n )\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n batch_size=args.train_batch_size,\n shuffle=True,\n collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),\n num_workers=args.dataloader_num_workers,\n )\n\n # Computes additional embeddings/ids required by the SDXL UNet.\n # regular text embeddings (when `train_text_encoder` is not True)\n # pooled text embeddings\n # time ids\n\n def compute_time_ids(original_size, crops_coords_top_left):\n # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids\n target_size = (args.resolution, args.resolution)\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n add_time_ids = torch.tensor([add_time_ids])\n add_time_ids = add_time_ids.to(accelerator.device, dtype=weight_dtype)\n return add_time_ids\n\n if not args.train_text_encoder:\n tokenizers = [tokenizer_one, tokenizer_two]\n text_encoders = [text_encoder_one, text_encoder_two]\n\n def compute_text_embeddings(prompt, text_encoders, tokenizers):\n with torch.no_grad():\n prompt_embeds, pooled_prompt_embeds = encode_prompt(text_encoders, tokenizers, prompt)\n prompt_embeds = prompt_embeds.to(accelerator.device)\n pooled_prompt_embeds = pooled_prompt_embeds.to(accelerator.device)\n return prompt_embeds, pooled_prompt_embeds\n\n # If no type of tuning is done on the text_encoder and custom instance prompts are NOT\n # provided (i.e. the --instance_prompt is used for all images), we encode the instance prompt once to avoid\n # the redundant encoding.\n if not args.train_text_encoder and not train_dataset.custom_instance_prompts:\n instance_prompt_hidden_states, instance_pooled_prompt_embeds = compute_text_embeddings(\n args.instance_prompt, text_encoders, tokenizers\n )\n\n # Handle class prompt for prior-preservation.\n if args.with_prior_preservation:\n if not args.train_text_encoder:\n class_prompt_hidden_states, class_pooled_prompt_embeds = compute_text_embeddings(\n args.class_prompt, text_encoders, tokenizers\n )\n\n # Clear the memory here\n if not args.train_text_encoder and not train_dataset.custom_instance_prompts:\n del tokenizers, text_encoders\n gc.collect()\n torch.cuda.empty_cache()\n\n # If custom instance prompts are NOT provided (i.e. the instance prompt is used for all images),\n # pack the statically computed variables appropriately here. This is so that we don't\n # have to pass them to the dataloader.\n\n if not train_dataset.custom_instance_prompts:\n if not args.train_text_encoder:\n prompt_embeds = instance_prompt_hidden_states\n unet_add_text_embeds = instance_pooled_prompt_embeds\n if args.with_prior_preservation:\n prompt_embeds = torch.cat([prompt_embeds, class_prompt_hidden_states], dim=0)\n unet_add_text_embeds = torch.cat([unet_add_text_embeds, class_pooled_prompt_embeds], dim=0)\n # if we're optmizing the text encoder (both if instance prompt is used for all images or custom prompts) we need to tokenize and encode the\n # batch prompts on all training steps\n else:\n tokens_one = tokenize_prompt(tokenizer_one, args.instance_prompt)\n tokens_two = tokenize_prompt(tokenizer_two, args.instance_prompt)\n if args.with_prior_preservation:\n class_tokens_one = tokenize_prompt(tokenizer_one, args.class_prompt)\n class_tokens_two = tokenize_prompt(tokenizer_two, args.class_prompt)\n tokens_one = torch.cat([tokens_one, class_tokens_one], dim=0)\n tokens_two = torch.cat([tokens_two, class_tokens_two], dim=0)\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_cycles=args.lr_num_cycles,\n power=args.lr_power,\n )\n\n # Prepare everything with our `accelerator`.\n if args.train_text_encoder:\n unet, text_encoder_one, text_encoder_two, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, text_encoder_one, text_encoder_two, optimizer, train_dataloader, lr_scheduler\n )\n else:\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_name = (\n \"dreambooth-lora-sd-xl\"\n if \"playground\" not in args.pretrained_model_name_or_path\n else \"dreambooth-lora-playground\"\n )\n accelerator.init_trackers(tracker_name, config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num batches each epoch = {len(train_dataloader)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the mos recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):\n sigmas = noise_scheduler.sigmas.to(device=accelerator.device, dtype=dtype)\n schedule_timesteps = noise_scheduler.timesteps.to(accelerator.device)\n timesteps = timesteps.to(accelerator.device)\n\n step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]\n\n sigma = sigmas[step_indices].flatten()\n while len(sigma.shape) < n_dim:\n sigma = sigma.unsqueeze(-1)\n return sigma\n\n for epoch in range(first_epoch, args.num_train_epochs):\n unet.train()\n if args.train_text_encoder:\n text_encoder_one.train()\n text_encoder_two.train()\n\n # set top parameter requires_grad = True for gradient checkpointing works\n accelerator.unwrap_model(text_encoder_one).text_model.embeddings.requires_grad_(True)\n accelerator.unwrap_model(text_encoder_two).text_model.embeddings.requires_grad_(True)\n\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n pixel_values = batch[\"pixel_values\"].to(dtype=vae.dtype)\n prompts = batch[\"prompts\"]\n\n # encode batch prompts when custom prompts are provided for each image -\n if train_dataset.custom_instance_prompts:\n if not args.train_text_encoder:\n prompt_embeds, unet_add_text_embeds = compute_text_embeddings(\n prompts, text_encoders, tokenizers\n )\n else:\n tokens_one = tokenize_prompt(tokenizer_one, prompts)\n tokens_two = tokenize_prompt(tokenizer_two, prompts)\n\n # Convert images to latent space\n model_input = vae.encode(pixel_values).latent_dist.sample()\n\n if latents_mean is None and latents_std is None:\n model_input = model_input * vae.config.scaling_factor\n if args.pretrained_vae_model_name_or_path is None:\n model_input = model_input.to(weight_dtype)\n else:\n latents_mean = latents_mean.to(device=model_input.device, dtype=model_input.dtype)\n latents_std = latents_std.to(device=model_input.device, dtype=model_input.dtype)\n model_input = (model_input - latents_mean) * vae.config.scaling_factor / latents_std\n model_input = model_input.to(dtype=weight_dtype)\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(model_input)\n bsz = model_input.shape[0]\n\n # Sample a random timestep for each image\n if not args.do_edm_style_training:\n if args.loss_type == \"huber\" or args.loss_type == \"smooth_l1\":\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (1,), device=\"cpu\")\n timestep = timesteps.item()\n\n if args.huber_schedule == \"exponential\":\n alpha = -math.log(args.huber_c) / noise_scheduler.config.num_train_timesteps\n huber_c = math.exp(-alpha * timestep)\n elif args.huber_schedule == \"snr\":\n alphas_cumprod = noise_scheduler.alphas_cumprod[timestep]\n sigmas = ((1.0 - alphas_cumprod) / alphas_cumprod) ** 0.5\n huber_c = (1 - args.huber_c) / (1 + sigmas) ** 2 + args.huber_c\n elif args.huber_schedule == \"constant\":\n huber_c = args.huber_c\n else:\n raise NotImplementedError(f\"Unknown Huber loss schedule {args.huber_schedule}!\")\n\n timesteps = timesteps.repeat(bsz).to(model_input.device)\n elif args.loss_type == \"l2\":\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device\n )\n huber_c = 1 # may be anything, as it's not used\n else:\n raise NotImplementedError(f\"Unknown loss type {args.loss_type}\")\n timesteps = timesteps.long()\n else:\n if \"huber\" in args.loss_type or \"l1\" in args.loss_type:\n raise NotImplementedError(\"Huber loss is not implemented for EDM training yet!\")\n # in EDM formulation, the model is conditioned on the pre-conditioned noise levels\n # instead of discrete timesteps, so here we sample indices to get the noise levels\n # from `scheduler.timesteps`\n indices = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,))\n timesteps = noise_scheduler.timesteps[indices].to(device=model_input.device)\n\n # Add noise to the model input according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)\n # For EDM-style training, we first obtain the sigmas based on the continuous timesteps.\n # We then precondition the final model inputs based on these sigmas instead of the timesteps.\n # Follow: Section 5 of https://arxiv.org/abs/2206.00364.\n if args.do_edm_style_training:\n sigmas = get_sigmas(timesteps, len(noisy_model_input.shape), noisy_model_input.dtype)\n if \"EDM\" in scheduler_type:\n inp_noisy_latents = noise_scheduler.precondition_inputs(noisy_model_input, sigmas)\n else:\n inp_noisy_latents = noisy_model_input / ((sigmas**2 + 1) ** 0.5)\n\n # time ids\n add_time_ids = torch.cat(\n [\n compute_time_ids(original_size=s, crops_coords_top_left=c)\n for s, c in zip(batch[\"original_sizes\"], batch[\"crop_top_lefts\"])\n ]\n )\n\n # Calculate the elements to repeat depending on the use of prior-preservation and custom captions.\n if not train_dataset.custom_instance_prompts:\n elems_to_repeat_text_embeds = bsz // 2 if args.with_prior_preservation else bsz\n else:\n elems_to_repeat_text_embeds = 1\n\n # Predict the noise residual\n if not args.train_text_encoder:\n unet_added_conditions = {\n \"time_ids\": add_time_ids,\n \"text_embeds\": unet_add_text_embeds.repeat(elems_to_repeat_text_embeds, 1),\n }\n prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat_text_embeds, 1, 1)\n model_pred = unet(\n inp_noisy_latents if args.do_edm_style_training else noisy_model_input,\n timesteps,\n prompt_embeds_input,\n added_cond_kwargs=unet_added_conditions,\n return_dict=False,\n )[0]\n else:\n unet_added_conditions = {\"time_ids\": add_time_ids}\n prompt_embeds, pooled_prompt_embeds = encode_prompt(\n text_encoders=[text_encoder_one, text_encoder_two],\n tokenizers=None,\n prompt=None,\n text_input_ids_list=[tokens_one, tokens_two],\n )\n unet_added_conditions.update(\n {\"text_embeds\": pooled_prompt_embeds.repeat(elems_to_repeat_text_embeds, 1)}\n )\n prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat_text_embeds, 1, 1)\n model_pred = unet(\n inp_noisy_latents if args.do_edm_style_training else noisy_model_input,\n timesteps,\n prompt_embeds_input,\n added_cond_kwargs=unet_added_conditions,\n return_dict=False,\n )[0]\n\n weighting = None\n if args.do_edm_style_training:\n # Similar to the input preconditioning, the model predictions are also preconditioned\n # on noised model inputs (before preconditioning) and the sigmas.\n # Follow: Section 5 of https://arxiv.org/abs/2206.00364.\n if \"EDM\" in scheduler_type:\n model_pred = noise_scheduler.precondition_outputs(noisy_model_input, model_pred, sigmas)\n else:\n if noise_scheduler.config.prediction_type == \"epsilon\":\n model_pred = model_pred * (-sigmas) + noisy_model_input\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n model_pred = model_pred * (-sigmas / (sigmas**2 + 1) ** 0.5) + (\n noisy_model_input / (sigmas**2 + 1)\n )\n # We are not doing weighting here because it tends result in numerical problems.\n # See: https://github.com/huggingface/diffusers/pull/7126#issuecomment-1968523051\n # There might be other alternatives for weighting as well:\n # https://github.com/huggingface/diffusers/pull/7126#discussion_r1505404686\n if \"EDM\" not in scheduler_type:\n weighting = (sigmas**-2.0).float()\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = model_input if args.do_edm_style_training else noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = (\n model_input\n if args.do_edm_style_training\n else noise_scheduler.get_velocity(model_input, noise, timesteps)\n )\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n if args.with_prior_preservation:\n # Chunk the noise and model_pred into two parts and compute the loss on each part separately.\n model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)\n target, target_prior = torch.chunk(target, 2, dim=0)\n\n # Compute prior loss\n prior_loss = conditional_loss(\n model_pred_prior,\n target_prior,\n reduction=\"mean\",\n loss_type=args.loss_type,\n huber_c=huber_c,\n weighting=weighting,\n )\n\n if args.snr_gamma is None:\n loss = conditional_loss(\n model_pred,\n target,\n reduction=\"mean\",\n loss_type=args.loss_type,\n huber_c=huber_c,\n weighting=weighting,\n )\n else:\n # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.\n # Since we predict the noise instead of x_0, the original formulation is slightly changed.\n # This is discussed in Section 4.2 of the same paper.\n snr = compute_snr(noise_scheduler, timesteps)\n base_weight = (\n torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr\n )\n\n if noise_scheduler.config.prediction_type == \"v_prediction\":\n # Velocity objective needs to be floored to an SNR weight of one.\n mse_loss_weights = base_weight + 1\n else:\n # Epsilon and sample both use the same loss weights.\n mse_loss_weights = base_weight\n\n loss = conditional_loss(\n model_pred, target, reduction=\"none\", loss_type=args.loss_type, huber_c=huber_c, weighting=None\n )\n loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights\n loss = loss.mean()\n\n if args.with_prior_preservation:\n # Add the prior loss to the instance loss.\n loss = loss + args.prior_loss_weight * prior_loss\n\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n params_to_clip = (\n itertools.chain(unet_lora_parameters, text_lora_parameters_one, text_lora_parameters_two)\n if args.train_text_encoder\n else unet_lora_parameters\n )\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompt is not None and epoch % args.validation_epochs == 0:\n # create pipeline\n if not args.train_text_encoder:\n text_encoder_one = text_encoder_cls_one.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"text_encoder\",\n revision=args.revision,\n variant=args.variant,\n )\n text_encoder_two = text_encoder_cls_two.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"text_encoder_2\",\n revision=args.revision,\n variant=args.variant,\n )\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n text_encoder=accelerator.unwrap_model(text_encoder_one),\n text_encoder_2=accelerator.unwrap_model(text_encoder_two),\n unet=accelerator.unwrap_model(unet),\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n pipeline_args = {\"prompt\": args.validation_prompt}\n\n images = log_validation(\n pipeline,\n args,\n accelerator,\n pipeline_args,\n epoch,\n )\n\n # Save the lora layers\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = unwrap_model(unet)\n unet = unet.to(torch.float32)\n unet_lora_layers = convert_state_dict_to_diffusers(get_peft_model_state_dict(unet))\n\n if args.train_text_encoder:\n text_encoder_one = unwrap_model(text_encoder_one)\n text_encoder_lora_layers = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(text_encoder_one.to(torch.float32))\n )\n text_encoder_two = unwrap_model(text_encoder_two)\n text_encoder_2_lora_layers = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(text_encoder_two.to(torch.float32))\n )\n else:\n text_encoder_lora_layers = None\n text_encoder_2_lora_layers = None\n\n StableDiffusionXLPipeline.save_lora_weights(\n save_directory=args.output_dir,\n unet_lora_layers=unet_lora_layers,\n text_encoder_lora_layers=text_encoder_lora_layers,\n text_encoder_2_lora_layers=text_encoder_2_lora_layers,\n )\n if args.output_kohya_format:\n lora_state_dict = load_file(f\"{args.output_dir}/pytorch_lora_weights.safetensors\")\n peft_state_dict = convert_all_state_dict_to_peft(lora_state_dict)\n kohya_state_dict = convert_state_dict_to_kohya(peft_state_dict)\n save_file(kohya_state_dict, f\"{args.output_dir}/pytorch_lora_weights_kohya.safetensors\")\n\n # Final inference\n # Load previous pipeline\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n\n # load attention processors\n pipeline.load_lora_weights(args.output_dir)\n\n # run inference\n images = []\n if args.validation_prompt and args.num_validation_images > 0:\n pipeline_args = {\"prompt\": args.validation_prompt, \"num_inference_steps\": 25}\n images = log_validation(\n pipeline,\n args,\n accelerator,\n pipeline_args,\n epoch,\n is_final_validation=True,\n )\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n use_dora=args.use_dora,\n images=images,\n base_model=args.pretrained_model_name_or_path,\n train_text_encoder=args.train_text_encoder,\n instance_prompt=args.instance_prompt,\n validation_prompt=args.validation_prompt,\n repo_folder=args.output_dir,\n vae_path=args.pretrained_vae_model_name_or_path,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/research_projects/scheduled_huber_loss_training/text_to_image/train_text_to_image.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport argparse\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom pathlib import Path\n\nimport accelerate\nimport datasets\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.state import AcceleratorState\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModel, CLIPTokenizer\nfrom transformers.utils import ContextManagers\n\nimport diffusers\nfrom diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import EMAModel, compute_snr\nfrom diffusers.utils import check_min_version, deprecate, is_wandb_available, make_image_grid\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\nif is_wandb_available():\n import wandb\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.28.0.dev0\")\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\nDATASET_NAME_MAPPING = {\n \"lambdalabs/naruto-blip-captions\": (\"image\", \"text\"),\n}\n\n\ndef save_model_card(\n args,\n repo_id: str,\n images: list = None,\n repo_folder: str = None,\n):\n img_str = \"\"\n if len(images) > 0:\n image_grid = make_image_grid(images, 1, len(args.validation_prompts))\n image_grid.save(os.path.join(repo_folder, \"val_imgs_grid.png\"))\n img_str += \"![val_imgs_grid](./val_imgs_grid.png)\\n\"\n\n model_description = f\"\"\"\n# Text-to-image finetuning - {repo_id}\n\nThis pipeline was finetuned from **{args.pretrained_model_name_or_path}** on the **{args.dataset_name}** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: {args.validation_prompts}: \\n\n{img_str}\n\n## Pipeline usage\n\nYou can use the pipeline like so:\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipeline = DiffusionPipeline.from_pretrained(\"{repo_id}\", torch_dtype=torch.float16)\nprompt = \"{args.validation_prompts[0]}\"\nimage = pipeline(prompt).images[0]\nimage.save(\"my_image.png\")\n```\n\n## Training info\n\nThese are the key hyperparameters used during training:\n\n* Epochs: {args.num_train_epochs}\n* Learning rate: {args.learning_rate}\n* Batch size: {args.train_batch_size}\n* Gradient accumulation steps: {args.gradient_accumulation_steps}\n* Image resolution: {args.resolution}\n* Mixed-precision: {args.mixed_precision}\n\n\"\"\"\n wandb_info = \"\"\n if is_wandb_available():\n wandb_run_url = None\n if wandb.run is not None:\n wandb_run_url = wandb.run.url\n\n if wandb_run_url is not None:\n wandb_info = f\"\"\"\nMore information on all the CLI arguments and the environment are available on your [`wandb` run page]({wandb_run_url}).\n\"\"\"\n\n model_description += wandb_info\n\n model_card = load_or_create_model_card(\n repo_id_or_path=repo_id,\n from_training=True,\n license=\"creativeml-openrail-m\",\n base_model=args.pretrained_model_name_or_path,\n model_description=model_description,\n inference=True,\n )\n\n tags = [\"stable-diffusion\", \"stable-diffusion-diffusers\", \"text-to-image\", \"diffusers\", \"diffusers-training\"]\n model_card = populate_model_card(model_card, tags=tags)\n\n model_card.save(os.path.join(repo_folder, \"README.md\"))\n\n\ndef log_validation(vae, text_encoder, tokenizer, unet, args, accelerator, weight_dtype, epoch):\n logger.info(\"Running validation... \")\n\n pipeline = StableDiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=accelerator.unwrap_model(vae),\n text_encoder=accelerator.unwrap_model(text_encoder),\n tokenizer=tokenizer,\n unet=accelerator.unwrap_model(unet),\n safety_checker=None,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n if args.enable_xformers_memory_efficient_attention:\n pipeline.enable_xformers_memory_efficient_attention()\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n images = []\n for i in range(len(args.validation_prompts)):\n with torch.autocast(\"cuda\"):\n image = pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]\n\n images.append(image)\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, epoch, dataformats=\"NHWC\")\n elif tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompts[i]}\")\n for i, image in enumerate(images)\n ]\n }\n )\n else:\n logger.warning(f\"image logging not implemented for {tracker.name}\")\n\n del pipeline\n torch.cuda.empty_cache()\n\n return images\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--input_perturbation\", type=float, default=0, help=\"The scale of input perturbation. Recommended 0.1.\"\n )\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing an image.\"\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=\"text\",\n help=\"The column of the dataset containing a caption or a list of captions.\",\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--validation_prompts\",\n type=str,\n default=None,\n nargs=\"+\",\n help=(\"A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`.\"),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"sd-model-finetuned\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--snr_gamma\",\n type=float,\n default=None,\n help=\"SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. \"\n \"More details here: https://arxiv.org/abs/2303.09556.\",\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\"--use_ema\", action=\"store_true\", help=\"Whether to use EMA model.\")\n parser.add_argument(\n \"--non_ema_revision\",\n type=str,\n default=None,\n required=False,\n help=(\n \"Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or\"\n \" remote repository specified with --pretrained_model_name_or_path.\"\n ),\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--prediction_type\",\n type=str,\n default=None,\n help=\"The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediction_type` is chosen.\",\n )\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\"--noise_offset\", type=float, default=0, help=\"The scale of noise offset.\")\n parser.add_argument(\n \"--loss_type\",\n type=str,\n default=\"l2\",\n choices=[\"l2\", \"huber\", \"smooth_l1\"],\n help=\"The type of loss to use and whether it's timestep-scheduled. See Issue #7488 for more info.\",\n )\n parser.add_argument(\n \"--huber_schedule\",\n type=str,\n default=\"snr\",\n choices=[\"constant\", \"exponential\", \"snr\"],\n help=\"The schedule to use for the huber losses parameter\",\n )\n parser.add_argument(\n \"--huber_c\",\n type=float,\n default=0.1,\n help=\"The huber loss parameter. Only used if one of the huber loss modes (huber or smooth l1) is selected with loss_type.\",\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=5,\n help=\"Run validation every X epochs.\",\n )\n parser.add_argument(\n \"--tracker_project_name\",\n type=str,\n default=\"text2image-fine-tune\",\n help=(\n \"The `project_name` argument passed to Accelerator.init_trackers for\"\n \" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator\"\n ),\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n\n # default to using the same revision for the non-ema model if not specified\n if args.non_ema_revision is None:\n args.non_ema_revision = args.revision\n\n return args\n\n\n# NOTE: if you're using the scheduled version, huber_c has to depend on the timesteps already\ndef conditional_loss(\n model_pred: torch.Tensor,\n target: torch.Tensor,\n reduction: str = \"mean\",\n loss_type: str = \"l2\",\n huber_c: float = 0.1,\n):\n if loss_type == \"l2\":\n loss = F.mse_loss(model_pred, target, reduction=reduction)\n elif loss_type == \"huber\":\n loss = 2 * huber_c * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c)\n if reduction == \"mean\":\n loss = torch.mean(loss)\n elif reduction == \"sum\":\n loss = torch.sum(loss)\n elif loss_type == \"smooth_l1\":\n loss = 2 * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c)\n if reduction == \"mean\":\n loss = torch.mean(loss)\n elif reduction == \"sum\":\n loss = torch.sum(loss)\n else:\n raise NotImplementedError(f\"Unsupported Loss Type {loss_type}\")\n return loss\n\n\ndef main():\n args = parse_args()\n\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n if args.non_ema_revision is not None:\n deprecate(\n \"non_ema_revision!=None\",\n \"0.15.0\",\n message=(\n \"Downloading 'non_ema' weights from revision branches of the Hub is deprecated. Please make sure to\"\n \" use `--variant=non_ema` instead.\"\n ),\n )\n logging_dir = os.path.join(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load scheduler, tokenizer and models.\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n tokenizer = CLIPTokenizer.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"tokenizer\", revision=args.revision\n )\n\n def deepspeed_zero_init_disabled_context_manager():\n \"\"\"\n returns either a context list that includes one that will disable zero.Init or an empty context list\n \"\"\"\n deepspeed_plugin = AcceleratorState().deepspeed_plugin if accelerate.state.is_initialized() else None\n if deepspeed_plugin is None:\n return []\n\n return [deepspeed_plugin.zero3_init_context_manager(enable=False)]\n\n # Currently Accelerate doesn't know how to handle multiple models under Deepspeed ZeRO stage 3.\n # For this to work properly all models must be run through `accelerate.prepare`. But accelerate\n # will try to assign the same optimizer with the same weights to all models during\n # `deepspeed.initialize`, which of course doesn't work.\n #\n # For now the following workaround will partially support Deepspeed ZeRO-3, by excluding the 2\n # frozen models from being partitioned during `zero.Init` which gets called during\n # `from_pretrained` So CLIPTextModel and AutoencoderKL will not enjoy the parameter sharding\n # across multiple gpus and only UNet2DConditionModel will get ZeRO sharded.\n with ContextManagers(deepspeed_zero_init_disabled_context_manager()):\n text_encoder = CLIPTextModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision, variant=args.variant\n )\n\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.non_ema_revision\n )\n\n # Freeze vae and text_encoder and set unet to trainable\n vae.requires_grad_(False)\n text_encoder.requires_grad_(False)\n unet.train()\n\n # Create EMA for the unet.\n if args.use_ema:\n ema_unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n ema_unet = EMAModel(ema_unet.parameters(), model_cls=UNet2DConditionModel, model_config=ema_unet.config)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n if args.use_ema:\n ema_unet.save_pretrained(os.path.join(output_dir, \"unet_ema\"))\n\n for i, model in enumerate(models):\n model.save_pretrained(os.path.join(output_dir, \"unet\"))\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n def load_model_hook(models, input_dir):\n if args.use_ema:\n load_model = EMAModel.from_pretrained(os.path.join(input_dir, \"unet_ema\"), UNet2DConditionModel)\n ema_unet.load_state_dict(load_model.state_dict())\n ema_unet.to(accelerator.device)\n del load_model\n\n for _ in range(len(models)):\n # pop models so that they are not loaded again\n model = models.pop()\n\n # load diffusers style into model\n load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder=\"unet\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Initialize the optimizer\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`\"\n )\n\n optimizer_cls = bnb.optim.AdamW8bit\n else:\n optimizer_cls = torch.optim.AdamW\n\n optimizer = optimizer_cls(\n unet.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n data_dir=args.train_data_dir,\n )\n else:\n data_files = {}\n if args.train_data_dir is not None:\n data_files[\"train\"] = os.path.join(args.train_data_dir, \"**\")\n dataset = load_dataset(\n \"imagefolder\",\n data_files=data_files,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)\n if args.image_column is None:\n image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.caption_column is None:\n caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n else:\n caption_column = args.caption_column\n if caption_column not in column_names:\n raise ValueError(\n f\"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}\"\n )\n\n # Preprocessing the datasets.\n # We need to tokenize input captions and transform the images.\n def tokenize_captions(examples, is_train=True):\n captions = []\n for caption in examples[caption_column]:\n if isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n else:\n raise ValueError(\n f\"Caption column `{caption_column}` should contain either strings or lists of strings.\"\n )\n inputs = tokenizer(\n captions, max_length=tokenizer.model_max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\"\n )\n return inputs.input_ids\n\n # Preprocessing the datasets.\n train_transforms = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),\n transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n examples[\"pixel_values\"] = [train_transforms(image) for image in images]\n examples[\"input_ids\"] = tokenize_captions(examples)\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n def collate_fn(examples):\n pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n input_ids = torch.stack([example[\"input_ids\"] for example in examples])\n return {\"pixel_values\": pixel_values, \"input_ids\": input_ids}\n\n # DataLoaders creation:\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n )\n\n # Prepare everything with our `accelerator`.\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n if args.use_ema:\n ema_unet.to(accelerator.device)\n\n # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n args.mixed_precision = accelerator.mixed_precision\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n args.mixed_precision = accelerator.mixed_precision\n\n # Move text_encode and vae to gpu and cast to weight_dtype\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n vae.to(accelerator.device, dtype=weight_dtype)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = dict(vars(args))\n tracker_config.pop(\"validation_prompts\")\n accelerator.init_trackers(args.tracker_project_name, tracker_config)\n\n # Function for unwrapping if model was compiled with `torch.compile`.\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n for epoch in range(first_epoch, args.num_train_epochs):\n train_loss = 0.0\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n # Convert images to latent space\n latents = vae.encode(batch[\"pixel_values\"].to(weight_dtype)).latent_dist.sample()\n latents = latents * vae.config.scaling_factor\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n if args.noise_offset:\n # https://www.crosslabs.org//blog/diffusion-with-offset-noise\n noise += args.noise_offset * torch.randn(\n (latents.shape[0], latents.shape[1], 1, 1), device=latents.device\n )\n if args.input_perturbation:\n new_noise = noise + args.input_perturbation * torch.randn_like(noise)\n bsz = latents.shape[0]\n\n # Sample a random timestep for each image\n if args.loss_type == \"huber\" or args.loss_type == \"smooth_l1\":\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (1,), device=\"cpu\")\n timestep = timesteps.item()\n\n if args.huber_schedule == \"exponential\":\n alpha = -math.log(args.huber_c) / noise_scheduler.config.num_train_timesteps\n huber_c = math.exp(-alpha * timestep)\n elif args.huber_schedule == \"snr\":\n alphas_cumprod = noise_scheduler.alphas_cumprod[timestep]\n sigmas = ((1.0 - alphas_cumprod) / alphas_cumprod) ** 0.5\n huber_c = (1 - args.huber_c) / (1 + sigmas) ** 2 + args.huber_c\n elif args.huber_schedule == \"constant\":\n huber_c = args.huber_c\n else:\n raise NotImplementedError(f\"Unknown Huber loss schedule {args.huber_schedule}!\")\n\n timesteps = timesteps.repeat(bsz).to(latents.device)\n elif args.loss_type == \"l2\":\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device\n )\n huber_c = 1 # may be anything, as it's not used\n else:\n raise NotImplementedError(f\"Unknown loss type {args.loss_type}\")\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n if args.input_perturbation:\n noisy_latents = noise_scheduler.add_noise(latents, new_noise, timesteps)\n else:\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # Get the text embedding for conditioning\n encoder_hidden_states = text_encoder(batch[\"input_ids\"], return_dict=False)[0]\n\n # Get the target for loss depending on the prediction type\n if args.prediction_type is not None:\n # set prediction_type of scheduler if defined\n noise_scheduler.register_to_config(prediction_type=args.prediction_type)\n\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n # Predict the noise residual and compute loss\n model_pred = unet(noisy_latents, timesteps, encoder_hidden_states, return_dict=False)[0]\n\n if args.snr_gamma is None:\n loss = conditional_loss(\n model_pred.float(), target.float(), reduction=\"mean\", loss_type=args.loss_type, huber_c=huber_c\n )\n else:\n # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.\n # Since we predict the noise instead of x_0, the original formulation is slightly changed.\n # This is discussed in Section 4.2 of the same paper.\n snr = compute_snr(noise_scheduler, timesteps)\n mse_loss_weights = torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(\n dim=1\n )[0]\n if noise_scheduler.config.prediction_type == \"epsilon\":\n mse_loss_weights = mse_loss_weights / snr\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n mse_loss_weights = mse_loss_weights / (snr + 1)\n\n loss = conditional_loss(\n model_pred.float(), target.float(), reduction=\"none\", loss_type=args.loss_type, huber_c=huber_c\n )\n loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights\n loss = loss.mean()\n\n # Gather the losses across all processes for logging (if we use distributed training).\n avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n train_loss += avg_loss.item() / args.gradient_accumulation_steps\n\n # Backpropagate\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n if args.use_ema:\n ema_unet.step(unet.parameters())\n progress_bar.update(1)\n global_step += 1\n accelerator.log({\"train_loss\": train_loss}, step=global_step)\n train_loss = 0.0\n\n if global_step % args.checkpointing_steps == 0:\n if accelerator.is_main_process:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"step_loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompts is not None and epoch % args.validation_epochs == 0:\n if args.use_ema:\n # Store the UNet parameters temporarily and load the EMA parameters to perform inference.\n ema_unet.store(unet.parameters())\n ema_unet.copy_to(unet.parameters())\n log_validation(\n vae,\n text_encoder,\n tokenizer,\n unet,\n args,\n accelerator,\n weight_dtype,\n global_step,\n )\n if args.use_ema:\n # Switch back to the original UNet parameters.\n ema_unet.restore(unet.parameters())\n\n # Create the pipeline using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = unwrap_model(unet)\n if args.use_ema:\n ema_unet.copy_to(unet.parameters())\n\n pipeline = StableDiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n text_encoder=text_encoder,\n vae=vae,\n unet=unet,\n revision=args.revision,\n variant=args.variant,\n )\n pipeline.save_pretrained(args.output_dir)\n\n # Run a final round of inference.\n images = []\n if args.validation_prompts is not None:\n logger.info(\"Running inference for collecting generated images...\")\n pipeline = pipeline.to(accelerator.device)\n pipeline.torch_dtype = weight_dtype\n pipeline.set_progress_bar_config(disable=True)\n\n if args.enable_xformers_memory_efficient_attention:\n pipeline.enable_xformers_memory_efficient_attention()\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n for i in range(len(args.validation_prompts)):\n with torch.autocast(\"cuda\"):\n image = pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]\n images.append(image)\n\n if args.push_to_hub:\n save_model_card(args, repo_id, images, repo_folder=args.output_dir)\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/research_projects/scheduled_huber_loss_training/text_to_image/train_text_to_image_lora.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Fine-tuning script for Stable Diffusion for text2image with support for LoRA.\"\"\"\n\nimport argparse\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom pathlib import Path\n\nimport datasets\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom peft import LoraConfig\nfrom peft.utils import get_peft_model_state_dict\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nimport diffusers\nfrom diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, StableDiffusionPipeline, UNet2DConditionModel\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import cast_training_params, compute_snr\nfrom diffusers.utils import check_min_version, convert_state_dict_to_diffusers, is_wandb_available\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.28.0.dev0\")\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\n\ndef save_model_card(\n repo_id: str,\n images: list = None,\n base_model: str = None,\n dataset_name: str = None,\n repo_folder: str = None,\n):\n img_str = \"\"\n if images is not None:\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n img_str += f\"![img_{i}](./image_{i}.png)\\n\"\n\n model_description = f\"\"\"\n# LoRA text2image fine-tuning - {repo_id}\nThese are LoRA adaption weights for {base_model}. The weights were fine-tuned on the {dataset_name} dataset. You can find some example images in the following. \\n\n{img_str}\n\"\"\"\n\n model_card = load_or_create_model_card(\n repo_id_or_path=repo_id,\n from_training=True,\n license=\"creativeml-openrail-m\",\n base_model=base_model,\n model_description=model_description,\n inference=True,\n )\n\n tags = [\n \"stable-diffusion\",\n \"stable-diffusion-diffusers\",\n \"text-to-image\",\n \"diffusers\",\n \"diffusers-training\",\n \"lora\",\n ]\n model_card = populate_model_card(model_card, tags=tags)\n\n model_card.save(os.path.join(repo_folder, \"README.md\"))\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing an image.\"\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=\"text\",\n help=\"The column of the dataset containing a caption or a list of captions.\",\n )\n parser.add_argument(\n \"--validation_prompt\", type=str, default=None, help=\"A prompt that is sampled during training for inference.\"\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=1,\n help=(\n \"Run fine-tuning validation every X epochs. The validation process consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"sd-model-finetuned-lora\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--snr_gamma\",\n type=float,\n default=None,\n help=\"SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. \"\n \"More details here: https://arxiv.org/abs/2303.09556.\",\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--prediction_type\",\n type=str,\n default=None,\n help=\"The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediction_type` is chosen.\",\n )\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\"--noise_offset\", type=float, default=0, help=\"The scale of noise offset.\")\n parser.add_argument(\n \"--loss_type\",\n type=str,\n default=\"l2\",\n choices=[\"l2\", \"huber\", \"smooth_l1\"],\n help=\"The type of loss to use and whether it's timestep-scheduled. See Issue #7488 for more info.\",\n )\n parser.add_argument(\n \"--huber_schedule\",\n type=str,\n default=\"snr\",\n choices=[\"constant\", \"exponential\", \"snr\"],\n help=\"The schedule to use for the huber losses parameter\",\n )\n parser.add_argument(\n \"--huber_c\",\n type=float,\n default=0.1,\n help=\"The huber loss parameter. Only used if one of the huber loss modes (huber or smooth l1) is selected with loss_type.\",\n )\n parser.add_argument(\n \"--rank\",\n type=int,\n default=4,\n help=(\"The dimension of the LoRA update matrices.\"),\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n\n return args\n\n\nDATASET_NAME_MAPPING = {\n \"lambdalabs/naruto-blip-captions\": (\"image\", \"text\"),\n}\n\n\n# NOTE: if you're using the scheduled version, huber_c has to depend on the timesteps already\ndef conditional_loss(\n model_pred: torch.Tensor,\n target: torch.Tensor,\n reduction: str = \"mean\",\n loss_type: str = \"l2\",\n huber_c: float = 0.1,\n):\n if loss_type == \"l2\":\n loss = F.mse_loss(model_pred, target, reduction=reduction)\n elif loss_type == \"huber\":\n loss = 2 * huber_c * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c)\n if reduction == \"mean\":\n loss = torch.mean(loss)\n elif reduction == \"sum\":\n loss = torch.sum(loss)\n elif loss_type == \"smooth_l1\":\n loss = 2 * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c)\n if reduction == \"mean\":\n loss = torch.mean(loss)\n elif reduction == \"sum\":\n loss = torch.sum(loss)\n else:\n raise NotImplementedError(f\"Unsupported Loss Type {loss_type}\")\n return loss\n\n\ndef main():\n args = parse_args()\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n import wandb\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n # Load scheduler, tokenizer and models.\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n tokenizer = CLIPTokenizer.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"tokenizer\", revision=args.revision\n )\n text_encoder = CLIPTextModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision\n )\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision, variant=args.variant\n )\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n # freeze parameters of models to save more memory\n unet.requires_grad_(False)\n vae.requires_grad_(False)\n text_encoder.requires_grad_(False)\n\n # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Freeze the unet parameters before adding adapters\n for param in unet.parameters():\n param.requires_grad_(False)\n\n unet_lora_config = LoraConfig(\n r=args.rank,\n lora_alpha=args.rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"to_k\", \"to_q\", \"to_v\", \"to_out.0\"],\n )\n\n # Move unet, vae and text_encoder to device and cast to weight_dtype\n unet.to(accelerator.device, dtype=weight_dtype)\n vae.to(accelerator.device, dtype=weight_dtype)\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n\n # Add adapter and make sure the trainable params are in float32.\n unet.add_adapter(unet_lora_config)\n if args.mixed_precision == \"fp16\":\n # only upcast trainable parameters (LoRA) into fp32\n cast_training_params(unet, dtype=torch.float32)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n lora_layers = filter(lambda p: p.requires_grad, unet.parameters())\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Initialize the optimizer\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`\"\n )\n\n optimizer_cls = bnb.optim.AdamW8bit\n else:\n optimizer_cls = torch.optim.AdamW\n\n optimizer = optimizer_cls(\n lora_layers,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n data_dir=args.train_data_dir,\n )\n else:\n data_files = {}\n if args.train_data_dir is not None:\n data_files[\"train\"] = os.path.join(args.train_data_dir, \"**\")\n dataset = load_dataset(\n \"imagefolder\",\n data_files=data_files,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)\n if args.image_column is None:\n image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.caption_column is None:\n caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n else:\n caption_column = args.caption_column\n if caption_column not in column_names:\n raise ValueError(\n f\"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}\"\n )\n\n # Preprocessing the datasets.\n # We need to tokenize input captions and transform the images.\n def tokenize_captions(examples, is_train=True):\n captions = []\n for caption in examples[caption_column]:\n if isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n else:\n raise ValueError(\n f\"Caption column `{caption_column}` should contain either strings or lists of strings.\"\n )\n inputs = tokenizer(\n captions, max_length=tokenizer.model_max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\"\n )\n return inputs.input_ids\n\n # Preprocessing the datasets.\n train_transforms = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),\n transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n def preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n examples[\"pixel_values\"] = [train_transforms(image) for image in images]\n examples[\"input_ids\"] = tokenize_captions(examples)\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n def collate_fn(examples):\n pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n input_ids = torch.stack([example[\"input_ids\"] for example in examples])\n return {\"pixel_values\": pixel_values, \"input_ids\": input_ids}\n\n # DataLoaders creation:\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n )\n\n # Prepare everything with our `accelerator`.\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"text2image-fine-tune\", config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n for epoch in range(first_epoch, args.num_train_epochs):\n unet.train()\n train_loss = 0.0\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n # Convert images to latent space\n latents = vae.encode(batch[\"pixel_values\"].to(dtype=weight_dtype)).latent_dist.sample()\n latents = latents * vae.config.scaling_factor\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n if args.noise_offset:\n # https://www.crosslabs.org//blog/diffusion-with-offset-noise\n noise += args.noise_offset * torch.randn(\n (latents.shape[0], latents.shape[1], 1, 1), device=latents.device\n )\n\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n if args.loss_type == \"huber\" or args.loss_type == \"smooth_l1\":\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (1,), device=\"cpu\")\n timestep = timesteps.item()\n\n if args.huber_schedule == \"exponential\":\n alpha = -math.log(args.huber_c) / noise_scheduler.config.num_train_timesteps\n huber_c = math.exp(-alpha * timestep)\n elif args.huber_schedule == \"snr\":\n alphas_cumprod = noise_scheduler.alphas_cumprod[timestep]\n sigmas = ((1.0 - alphas_cumprod) / alphas_cumprod) ** 0.5\n huber_c = (1 - args.huber_c) / (1 + sigmas) ** 2 + args.huber_c\n elif args.huber_schedule == \"constant\":\n huber_c = args.huber_c\n else:\n raise NotImplementedError(f\"Unknown Huber loss schedule {args.huber_schedule}!\")\n\n timesteps = timesteps.repeat(bsz).to(latents.device)\n elif args.loss_type == \"l2\":\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device\n )\n huber_c = 1 # may be anything, as it's not used\n else:\n raise NotImplementedError(f\"Unknown loss type {args.loss_type}\")\n\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # Get the text embedding for conditioning\n encoder_hidden_states = text_encoder(batch[\"input_ids\"], return_dict=False)[0]\n\n # Get the target for loss depending on the prediction type\n if args.prediction_type is not None:\n # set prediction_type of scheduler if defined\n noise_scheduler.register_to_config(prediction_type=args.prediction_type)\n\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n # Predict the noise residual and compute loss\n model_pred = unet(noisy_latents, timesteps, encoder_hidden_states, return_dict=False)[0]\n\n if args.snr_gamma is None:\n loss = conditional_loss(\n model_pred.float(), target.float(), reduction=\"mean\", loss_type=args.loss_type, huber_c=huber_c\n )\n else:\n # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.\n # Since we predict the noise instead of x_0, the original formulation is slightly changed.\n # This is discussed in Section 4.2 of the same paper.\n snr = compute_snr(noise_scheduler, timesteps)\n mse_loss_weights = torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(\n dim=1\n )[0]\n if noise_scheduler.config.prediction_type == \"epsilon\":\n mse_loss_weights = mse_loss_weights / snr\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n mse_loss_weights = mse_loss_weights / (snr + 1)\n\n loss = conditional_loss(\n model_pred.float(), target.float(), reduction=\"none\", loss_type=args.loss_type, huber_c=huber_c\n )\n loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights\n loss = loss.mean()\n\n # Gather the losses across all processes for logging (if we use distributed training).\n avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n train_loss += avg_loss.item() / args.gradient_accumulation_steps\n\n # Backpropagate\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n params_to_clip = lora_layers\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n accelerator.log({\"train_loss\": train_loss}, step=global_step)\n train_loss = 0.0\n\n if global_step % args.checkpointing_steps == 0:\n if accelerator.is_main_process:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n\n unwrapped_unet = unwrap_model(unet)\n unet_lora_state_dict = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(unwrapped_unet)\n )\n\n StableDiffusionPipeline.save_lora_weights(\n save_directory=save_path,\n unet_lora_layers=unet_lora_state_dict,\n safe_serialization=True,\n )\n\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"step_loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompt is not None and epoch % args.validation_epochs == 0:\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n # create pipeline\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n unet=unwrap_model(unet),\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = torch.Generator(device=accelerator.device)\n if args.seed is not None:\n generator = generator.manual_seed(args.seed)\n images = []\n with torch.cuda.amp.autocast():\n for _ in range(args.num_validation_images):\n images.append(\n pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0]\n )\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\")\n for i, image in enumerate(images)\n ]\n }\n )\n\n del pipeline\n torch.cuda.empty_cache()\n\n # Save the lora layers\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = unet.to(torch.float32)\n\n unwrapped_unet = unwrap_model(unet)\n unet_lora_state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(unwrapped_unet))\n StableDiffusionPipeline.save_lora_weights(\n save_directory=args.output_dir,\n unet_lora_layers=unet_lora_state_dict,\n safe_serialization=True,\n )\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n images=images,\n base_model=args.pretrained_model_name_or_path,\n dataset_name=args.dataset_name,\n repo_folder=args.output_dir,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n # Final inference\n # Load previous pipeline\n if args.validation_prompt is not None:\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n pipeline = pipeline.to(accelerator.device)\n\n # load attention processors\n pipeline.load_lora_weights(args.output_dir)\n\n # run inference\n generator = torch.Generator(device=accelerator.device)\n if args.seed is not None:\n generator = generator.manual_seed(args.seed)\n images = []\n with torch.cuda.amp.autocast():\n for _ in range(args.num_validation_images):\n images.append(\n pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0]\n )\n\n for tracker in accelerator.trackers:\n if len(images) != 0:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"test\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"test\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\")\n for i, image in enumerate(images)\n ]\n }\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/research_projects/scheduled_huber_loss_training/text_to_image/train_text_to_image_lora_sdxl.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Fine-tuning script for Stable Diffusion XL for text2image with support for LoRA.\"\"\"\n\nimport argparse\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom pathlib import Path\n\nimport datasets\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import DistributedDataParallelKwargs, ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom peft import LoraConfig, set_peft_model_state_dict\nfrom peft.utils import get_peft_model_state_dict\nfrom torchvision import transforms\nfrom torchvision.transforms.functional import crop\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n StableDiffusionXLPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.loaders import LoraLoaderMixin\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import _set_state_dict_into_text_encoder, cast_training_params, compute_snr\nfrom diffusers.utils import (\n check_min_version,\n convert_state_dict_to_diffusers,\n convert_unet_state_dict_to_peft,\n is_wandb_available,\n)\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.28.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef save_model_card(\n repo_id: str,\n images: list = None,\n base_model: str = None,\n dataset_name: str = None,\n train_text_encoder: bool = False,\n repo_folder: str = None,\n vae_path: str = None,\n):\n img_str = \"\"\n if images is not None:\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n img_str += f\"![img_{i}](./image_{i}.png)\\n\"\n\n model_description = f\"\"\"\n# LoRA text2image fine-tuning - {repo_id}\n\nThese are LoRA adaption weights for {base_model}. The weights were fine-tuned on the {dataset_name} dataset. You can find some example images in the following. \\n\n{img_str}\n\nLoRA for the text encoder was enabled: {train_text_encoder}.\n\nSpecial VAE used for training: {vae_path}.\n\"\"\"\n model_card = load_or_create_model_card(\n repo_id_or_path=repo_id,\n from_training=True,\n license=\"creativeml-openrail-m\",\n base_model=base_model,\n model_description=model_description,\n inference=True,\n )\n\n tags = [\n \"stable-diffusion-xl\",\n \"stable-diffusion-xl-diffusers\",\n \"text-to-image\",\n \"diffusers\",\n \"diffusers-training\",\n \"lora\",\n ]\n model_card = populate_model_card(model_card, tags=tags)\n\n model_card.save(os.path.join(repo_folder, \"README.md\"))\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_vae_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing an image.\"\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=\"text\",\n help=\"The column of the dataset containing a caption or a list of captions.\",\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n help=\"A prompt that is used during validation to verify that the model is learning.\",\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=1,\n help=(\n \"Run fine-tuning validation every X epochs. The validation process consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"sd-model-finetuned-lora\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=1024,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--train_text_encoder\",\n action=\"store_true\",\n help=\"Whether to train the text encoder. If set, the text encoder should be float32 precision.\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final\"\n \" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--snr_gamma\",\n type=float,\n default=None,\n help=\"SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. \"\n \"More details here: https://arxiv.org/abs/2303.09556.\",\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--prediction_type\",\n type=str,\n default=None,\n help=\"The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediction_type` is chosen.\",\n )\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\"--noise_offset\", type=float, default=0, help=\"The scale of noise offset.\")\n parser.add_argument(\n \"--loss_type\",\n type=str,\n default=\"l2\",\n choices=[\"l2\", \"huber\", \"smooth_l1\"],\n help=\"The type of loss to use and whether it's timestep-scheduled. See Issue #7488 for more info.\",\n )\n parser.add_argument(\n \"--huber_schedule\",\n type=str,\n default=\"snr\",\n choices=[\"constant\", \"exponential\", \"snr\"],\n help=\"The schedule to use for the huber losses parameter\",\n )\n parser.add_argument(\n \"--huber_c\",\n type=float,\n default=0.1,\n help=\"The huber loss parameter. Only used if one of the huber loss modes (huber or smooth l1) is selected with loss_type.\",\n )\n parser.add_argument(\n \"--rank\",\n type=int,\n default=4,\n help=(\"The dimension of the LoRA update matrices.\"),\n )\n parser.add_argument(\n \"--debug_loss\",\n action=\"store_true\",\n help=\"debug loss for each image, if filenames are awailable in the dataset\",\n )\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n\n return args\n\n\nDATASET_NAME_MAPPING = {\n \"lambdalabs/naruto-blip-captions\": (\"image\", \"text\"),\n}\n\n\ndef tokenize_prompt(tokenizer, prompt):\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n return text_input_ids\n\n\n# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt\ndef encode_prompt(text_encoders, tokenizers, prompt, text_input_ids_list=None):\n prompt_embeds_list = []\n\n for i, text_encoder in enumerate(text_encoders):\n if tokenizers is not None:\n tokenizer = tokenizers[i]\n text_input_ids = tokenize_prompt(tokenizer, prompt)\n else:\n assert text_input_ids_list is not None\n text_input_ids = text_input_ids_list[i]\n\n prompt_embeds = text_encoder(\n text_input_ids.to(text_encoder.device), output_hidden_states=True, return_dict=False\n )\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds[-1][-2]\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)\n return prompt_embeds, pooled_prompt_embeds\n\n\n# NOTE: if you're using the scheduled version, huber_c has to depend on the timesteps already\ndef conditional_loss(\n model_pred: torch.Tensor,\n target: torch.Tensor,\n reduction: str = \"mean\",\n loss_type: str = \"l2\",\n huber_c: float = 0.1,\n):\n if loss_type == \"l2\":\n loss = F.mse_loss(model_pred, target, reduction=reduction)\n elif loss_type == \"huber\" or loss_type == \"huber_scheduled\":\n loss = huber_c * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c)\n if reduction == \"mean\":\n loss = torch.mean(loss)\n elif reduction == \"sum\":\n loss = torch.sum(loss)\n else:\n raise NotImplementedError(f\"Unsupported Loss Type {loss_type}\")\n return loss\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n kwargs_handlers=[kwargs],\n )\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n import wandb\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizers\n tokenizer_one = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n tokenizer_two = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer_2\",\n revision=args.revision,\n use_fast=False,\n )\n\n # import correct text encoder classes\n text_encoder_cls_one = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision\n )\n text_encoder_cls_two = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision, subfolder=\"text_encoder_2\"\n )\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n text_encoder_one = text_encoder_cls_one.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n text_encoder_two = text_encoder_cls_two.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder_2\", revision=args.revision, variant=args.variant\n )\n vae_path = (\n args.pretrained_model_name_or_path\n if args.pretrained_vae_model_name_or_path is None\n else args.pretrained_vae_model_name_or_path\n )\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n variant=args.variant,\n )\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n # We only train the additional adapter LoRA layers\n vae.requires_grad_(False)\n text_encoder_one.requires_grad_(False)\n text_encoder_two.requires_grad_(False)\n unet.requires_grad_(False)\n\n # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move unet, vae and text_encoder to device and cast to weight_dtype\n # The VAE is in float32 to avoid NaN losses.\n unet.to(accelerator.device, dtype=weight_dtype)\n\n if args.pretrained_vae_model_name_or_path is None:\n vae.to(accelerator.device, dtype=torch.float32)\n else:\n vae.to(accelerator.device, dtype=weight_dtype)\n text_encoder_one.to(accelerator.device, dtype=weight_dtype)\n text_encoder_two.to(accelerator.device, dtype=weight_dtype)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # now we will add new LoRA weights to the attention layers\n # Set correct lora layers\n unet_lora_config = LoraConfig(\n r=args.rank,\n lora_alpha=args.rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"to_k\", \"to_q\", \"to_v\", \"to_out.0\"],\n )\n\n unet.add_adapter(unet_lora_config)\n\n # The text encoder comes from 🤗 transformers, we will also attach adapters to it.\n if args.train_text_encoder:\n # ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16\n text_lora_config = LoraConfig(\n r=args.rank,\n lora_alpha=args.rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"out_proj\"],\n )\n text_encoder_one.add_adapter(text_lora_config)\n text_encoder_two.add_adapter(text_lora_config)\n\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n # there are only two options here. Either are just the unet attn processor layers\n # or there are the unet and text encoder attn layers\n unet_lora_layers_to_save = None\n text_encoder_one_lora_layers_to_save = None\n text_encoder_two_lora_layers_to_save = None\n\n for model in models:\n if isinstance(unwrap_model(model), type(unwrap_model(unet))):\n unet_lora_layers_to_save = convert_state_dict_to_diffusers(get_peft_model_state_dict(model))\n elif isinstance(unwrap_model(model), type(unwrap_model(text_encoder_one))):\n text_encoder_one_lora_layers_to_save = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(model)\n )\n elif isinstance(unwrap_model(model), type(unwrap_model(text_encoder_two))):\n text_encoder_two_lora_layers_to_save = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(model)\n )\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n # make sure to pop weight so that corresponding model is not saved again\n if weights:\n weights.pop()\n\n StableDiffusionXLPipeline.save_lora_weights(\n output_dir,\n unet_lora_layers=unet_lora_layers_to_save,\n text_encoder_lora_layers=text_encoder_one_lora_layers_to_save,\n text_encoder_2_lora_layers=text_encoder_two_lora_layers_to_save,\n )\n\n def load_model_hook(models, input_dir):\n unet_ = None\n text_encoder_one_ = None\n text_encoder_two_ = None\n\n while len(models) > 0:\n model = models.pop()\n\n if isinstance(model, type(unwrap_model(unet))):\n unet_ = model\n elif isinstance(model, type(unwrap_model(text_encoder_one))):\n text_encoder_one_ = model\n elif isinstance(model, type(unwrap_model(text_encoder_two))):\n text_encoder_two_ = model\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n lora_state_dict, _ = LoraLoaderMixin.lora_state_dict(input_dir)\n unet_state_dict = {f'{k.replace(\"unet.\", \"\")}': v for k, v in lora_state_dict.items() if k.startswith(\"unet.\")}\n unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict)\n incompatible_keys = set_peft_model_state_dict(unet_, unet_state_dict, adapter_name=\"default\")\n if incompatible_keys is not None:\n # check only for unexpected keys\n unexpected_keys = getattr(incompatible_keys, \"unexpected_keys\", None)\n if unexpected_keys:\n logger.warning(\n f\"Loading adapter weights from state_dict led to unexpected keys not found in the model: \"\n f\" {unexpected_keys}. \"\n )\n\n if args.train_text_encoder:\n _set_state_dict_into_text_encoder(lora_state_dict, prefix=\"text_encoder.\", text_encoder=text_encoder_one_)\n\n _set_state_dict_into_text_encoder(\n lora_state_dict, prefix=\"text_encoder_2.\", text_encoder=text_encoder_two_\n )\n\n # Make sure the trainable params are in float32. This is again needed since the base models\n # are in `weight_dtype`. More details:\n # https://github.com/huggingface/diffusers/pull/6514#discussion_r1449796804\n if args.mixed_precision == \"fp16\":\n models = [unet_]\n if args.train_text_encoder:\n models.extend([text_encoder_one_, text_encoder_two_])\n cast_training_params(models, dtype=torch.float32)\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n if args.train_text_encoder:\n text_encoder_one.gradient_checkpointing_enable()\n text_encoder_two.gradient_checkpointing_enable()\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Make sure the trainable params are in float32.\n if args.mixed_precision == \"fp16\":\n models = [unet]\n if args.train_text_encoder:\n models.extend([text_encoder_one, text_encoder_two])\n cast_training_params(models, dtype=torch.float32)\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # Optimizer creation\n params_to_optimize = list(filter(lambda p: p.requires_grad, unet.parameters()))\n if args.train_text_encoder:\n params_to_optimize = (\n params_to_optimize\n + list(filter(lambda p: p.requires_grad, text_encoder_one.parameters()))\n + list(filter(lambda p: p.requires_grad, text_encoder_two.parameters()))\n )\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name, args.dataset_config_name, cache_dir=args.cache_dir, data_dir=args.train_data_dir\n )\n else:\n data_files = {}\n if args.train_data_dir is not None:\n data_files[\"train\"] = os.path.join(args.train_data_dir, \"**\")\n dataset = load_dataset(\n \"imagefolder\",\n data_files=data_files,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)\n if args.image_column is None:\n image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.caption_column is None:\n caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n else:\n caption_column = args.caption_column\n if caption_column not in column_names:\n raise ValueError(\n f\"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}\"\n )\n\n # Preprocessing the datasets.\n # We need to tokenize input captions and transform the images.\n def tokenize_captions(examples, is_train=True):\n captions = []\n for caption in examples[caption_column]:\n if isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n else:\n raise ValueError(\n f\"Caption column `{caption_column}` should contain either strings or lists of strings.\"\n )\n tokens_one = tokenize_prompt(tokenizer_one, captions)\n tokens_two = tokenize_prompt(tokenizer_two, captions)\n return tokens_one, tokens_two\n\n # Preprocessing the datasets.\n train_resize = transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR)\n train_crop = transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution)\n train_flip = transforms.RandomHorizontalFlip(p=1.0)\n train_transforms = transforms.Compose(\n [\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n # image aug\n original_sizes = []\n all_images = []\n crop_top_lefts = []\n for image in images:\n original_sizes.append((image.height, image.width))\n image = train_resize(image)\n if args.random_flip and random.random() < 0.5:\n # flip\n image = train_flip(image)\n if args.center_crop:\n y1 = max(0, int(round((image.height - args.resolution) / 2.0)))\n x1 = max(0, int(round((image.width - args.resolution) / 2.0)))\n image = train_crop(image)\n else:\n y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))\n image = crop(image, y1, x1, h, w)\n crop_top_left = (y1, x1)\n crop_top_lefts.append(crop_top_left)\n image = train_transforms(image)\n all_images.append(image)\n\n examples[\"original_sizes\"] = original_sizes\n examples[\"crop_top_lefts\"] = crop_top_lefts\n examples[\"pixel_values\"] = all_images\n tokens_one, tokens_two = tokenize_captions(examples)\n examples[\"input_ids_one\"] = tokens_one\n examples[\"input_ids_two\"] = tokens_two\n if args.debug_loss:\n fnames = [os.path.basename(image.filename) for image in examples[image_column] if image.filename]\n if fnames:\n examples[\"filenames\"] = fnames\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = dataset[\"train\"].with_transform(preprocess_train, output_all_columns=True)\n\n def collate_fn(examples):\n pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n original_sizes = [example[\"original_sizes\"] for example in examples]\n crop_top_lefts = [example[\"crop_top_lefts\"] for example in examples]\n input_ids_one = torch.stack([example[\"input_ids_one\"] for example in examples])\n input_ids_two = torch.stack([example[\"input_ids_two\"] for example in examples])\n result = {\n \"pixel_values\": pixel_values,\n \"input_ids_one\": input_ids_one,\n \"input_ids_two\": input_ids_two,\n \"original_sizes\": original_sizes,\n \"crop_top_lefts\": crop_top_lefts,\n }\n\n filenames = [example[\"filenames\"] for example in examples if \"filenames\" in example]\n if filenames:\n result[\"filenames\"] = filenames\n return result\n\n # DataLoaders creation:\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,\n num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,\n )\n\n # Prepare everything with our `accelerator`.\n if args.train_text_encoder:\n unet, text_encoder_one, text_encoder_two, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, text_encoder_one, text_encoder_two, optimizer, train_dataloader, lr_scheduler\n )\n else:\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"text2image-fine-tune\", config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n for epoch in range(first_epoch, args.num_train_epochs):\n unet.train()\n if args.train_text_encoder:\n text_encoder_one.train()\n text_encoder_two.train()\n train_loss = 0.0\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n # Convert images to latent space\n if args.pretrained_vae_model_name_or_path is not None:\n pixel_values = batch[\"pixel_values\"].to(dtype=weight_dtype)\n else:\n pixel_values = batch[\"pixel_values\"]\n\n model_input = vae.encode(pixel_values).latent_dist.sample()\n model_input = model_input * vae.config.scaling_factor\n if args.pretrained_vae_model_name_or_path is None:\n model_input = model_input.to(weight_dtype)\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(model_input)\n if args.noise_offset:\n # https://www.crosslabs.org//blog/diffusion-with-offset-noise\n noise += args.noise_offset * torch.randn(\n (model_input.shape[0], model_input.shape[1], 1, 1), device=model_input.device\n )\n\n bsz = model_input.shape[0]\n # Sample a random timestep for each image\n if args.loss_type == \"huber\" or args.loss_type == \"smooth_l1\":\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (1,), device=\"cpu\")\n timestep = timesteps.item()\n\n if args.huber_schedule == \"exponential\":\n alpha = -math.log(args.huber_c) / noise_scheduler.config.num_train_timesteps\n huber_c = math.exp(-alpha * timestep)\n elif args.huber_schedule == \"snr\":\n alphas_cumprod = noise_scheduler.alphas_cumprod[timestep]\n sigmas = ((1.0 - alphas_cumprod) / alphas_cumprod) ** 0.5\n huber_c = (1 - args.huber_c) / (1 + sigmas) ** 2 + args.huber_c\n elif args.huber_schedule == \"constant\":\n huber_c = args.huber_c\n else:\n raise NotImplementedError(f\"Unknown Huber loss schedule {args.huber_schedule}!\")\n\n timesteps = timesteps.repeat(bsz).to(model_input.device)\n elif args.loss_type == \"l2\":\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device\n )\n huber_c = 1 # may be anything, as it's not used\n else:\n raise NotImplementedError(f\"Unknown loss type {args.loss_type}\")\n\n timesteps = timesteps.long()\n\n # Add noise to the model input according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)\n\n # time ids\n def compute_time_ids(original_size, crops_coords_top_left):\n # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids\n target_size = (args.resolution, args.resolution)\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n add_time_ids = torch.tensor([add_time_ids])\n add_time_ids = add_time_ids.to(accelerator.device, dtype=weight_dtype)\n return add_time_ids\n\n add_time_ids = torch.cat(\n [compute_time_ids(s, c) for s, c in zip(batch[\"original_sizes\"], batch[\"crop_top_lefts\"])]\n )\n\n # Predict the noise residual\n unet_added_conditions = {\"time_ids\": add_time_ids}\n prompt_embeds, pooled_prompt_embeds = encode_prompt(\n text_encoders=[text_encoder_one, text_encoder_two],\n tokenizers=None,\n prompt=None,\n text_input_ids_list=[batch[\"input_ids_one\"], batch[\"input_ids_two\"]],\n )\n unet_added_conditions.update({\"text_embeds\": pooled_prompt_embeds})\n model_pred = unet(\n noisy_model_input,\n timesteps,\n prompt_embeds,\n added_cond_kwargs=unet_added_conditions,\n return_dict=False,\n )[0]\n\n # Get the target for loss depending on the prediction type\n if args.prediction_type is not None:\n # set prediction_type of scheduler if defined\n noise_scheduler.register_to_config(prediction_type=args.prediction_type)\n\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(model_input, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n if args.snr_gamma is None:\n loss = conditional_loss(\n model_pred.float(), target.float(), reduction=\"mean\", loss_type=args.loss_type, huber_c=huber_c\n )\n else:\n # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.\n # Since we predict the noise instead of x_0, the original formulation is slightly changed.\n # This is discussed in Section 4.2 of the same paper.\n snr = compute_snr(noise_scheduler, timesteps)\n mse_loss_weights = torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(\n dim=1\n )[0]\n if noise_scheduler.config.prediction_type == \"epsilon\":\n mse_loss_weights = mse_loss_weights / snr\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n mse_loss_weights = mse_loss_weights / (snr + 1)\n\n loss = conditional_loss(\n model_pred.float(), target.float(), reduction=\"none\", loss_type=args.loss_type, huber_c=huber_c\n )\n loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights\n loss = loss.mean()\n if args.debug_loss and \"filenames\" in batch:\n for fname in batch[\"filenames\"]:\n accelerator.log({\"loss_for_\" + fname: loss}, step=global_step)\n # Gather the losses across all processes for logging (if we use distributed training).\n avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n train_loss += avg_loss.item() / args.gradient_accumulation_steps\n\n # Backpropagate\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(params_to_optimize, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n accelerator.log({\"train_loss\": train_loss}, step=global_step)\n train_loss = 0.0\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"step_loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompt is not None and epoch % args.validation_epochs == 0:\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n # create pipeline\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n text_encoder=unwrap_model(text_encoder_one),\n text_encoder_2=unwrap_model(text_encoder_two),\n unet=unwrap_model(unet),\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n pipeline_args = {\"prompt\": args.validation_prompt}\n\n with torch.cuda.amp.autocast():\n images = [\n pipeline(**pipeline_args, generator=generator).images[0]\n for _ in range(args.num_validation_images)\n ]\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\")\n for i, image in enumerate(images)\n ]\n }\n )\n\n del pipeline\n torch.cuda.empty_cache()\n\n # Save the lora layers\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = unwrap_model(unet)\n unet_lora_state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(unet))\n\n if args.train_text_encoder:\n text_encoder_one = unwrap_model(text_encoder_one)\n text_encoder_two = unwrap_model(text_encoder_two)\n\n text_encoder_lora_layers = convert_state_dict_to_diffusers(get_peft_model_state_dict(text_encoder_one))\n text_encoder_2_lora_layers = convert_state_dict_to_diffusers(get_peft_model_state_dict(text_encoder_two))\n else:\n text_encoder_lora_layers = None\n text_encoder_2_lora_layers = None\n\n StableDiffusionXLPipeline.save_lora_weights(\n save_directory=args.output_dir,\n unet_lora_layers=unet_lora_state_dict,\n text_encoder_lora_layers=text_encoder_lora_layers,\n text_encoder_2_lora_layers=text_encoder_2_lora_layers,\n )\n\n del unet\n del text_encoder_one\n del text_encoder_two\n del text_encoder_lora_layers\n del text_encoder_2_lora_layers\n torch.cuda.empty_cache()\n\n # Final inference\n # Make sure vae.dtype is consistent with the unet.dtype\n if args.mixed_precision == \"fp16\":\n vae.to(weight_dtype)\n # Load previous pipeline\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n pipeline = pipeline.to(accelerator.device)\n\n # load attention processors\n pipeline.load_lora_weights(args.output_dir)\n\n # run inference\n images = []\n if args.validation_prompt and args.num_validation_images > 0:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n images = [\n pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]\n for _ in range(args.num_validation_images)\n ]\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"test\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"test\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\")\n for i, image in enumerate(images)\n ]\n }\n )\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n images=images,\n base_model=args.pretrained_model_name_or_path,\n dataset_name=args.dataset_name,\n train_text_encoder=args.train_text_encoder,\n repo_folder=args.output_dir,\n vae_path=args.pretrained_vae_model_name_or_path,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/research_projects/scheduled_huber_loss_training/text_to_image/train_text_to_image_sdxl.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Fine-tuning script for Stable Diffusion XL for text2image.\"\"\"\n\nimport argparse\nimport functools\nimport gc\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom pathlib import Path\n\nimport accelerate\nimport datasets\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import concatenate_datasets, load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom torchvision import transforms\nfrom torchvision.transforms.functional import crop\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionXLPipeline, UNet2DConditionModel\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import EMAModel, compute_snr\nfrom diffusers.utils import check_min_version, is_wandb_available\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.28.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\nDATASET_NAME_MAPPING = {\n \"lambdalabs/naruto-blip-captions\": (\"image\", \"text\"),\n}\n\n\ndef save_model_card(\n repo_id: str,\n images: list = None,\n validation_prompt: str = None,\n base_model: str = None,\n dataset_name: str = None,\n repo_folder: str = None,\n vae_path: str = None,\n):\n img_str = \"\"\n if images is not None:\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n img_str += f\"![img_{i}](./image_{i}.png)\\n\"\n\n model_description = f\"\"\"\n# Text-to-image finetuning - {repo_id}\n\nThis pipeline was finetuned from **{base_model}** on the **{dataset_name}** dataset. Below are some example images generated with the finetuned pipeline using the following prompt: {validation_prompt}: \\n\n{img_str}\n\nSpecial VAE used for training: {vae_path}.\n\"\"\"\n\n model_card = load_or_create_model_card(\n repo_id_or_path=repo_id,\n from_training=True,\n license=\"creativeml-openrail-m\",\n base_model=base_model,\n model_description=model_description,\n inference=True,\n )\n\n tags = [\n \"stable-diffusion-xl\",\n \"stable-diffusion-xl-diffusers\",\n \"text-to-image\",\n \"diffusers-training\",\n \"diffusers\",\n ]\n model_card = populate_model_card(model_card, tags=tags)\n\n model_card.save(os.path.join(repo_folder, \"README.md\"))\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_vae_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing an image.\"\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=\"text\",\n help=\"The column of the dataset containing a caption or a list of captions.\",\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n help=\"A prompt that is used during validation to verify that the model is learning.\",\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=1,\n help=(\n \"Run fine-tuning validation every X epochs. The validation process consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--proportion_empty_prompts\",\n type=float,\n default=0,\n help=\"Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).\",\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"sdxl-model-finetuned\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=1024,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final\"\n \" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--timestep_bias_strategy\",\n type=str,\n default=\"none\",\n choices=[\"earlier\", \"later\", \"range\", \"none\"],\n help=(\n \"The timestep bias strategy, which may help direct the model toward learning low or high frequency details.\"\n \" Choices: ['earlier', 'later', 'range', 'none'].\"\n \" The default is 'none', which means no bias is applied, and training proceeds normally.\"\n \" The value of 'later' will increase the frequency of the model's final training timesteps.\"\n ),\n )\n parser.add_argument(\n \"--timestep_bias_multiplier\",\n type=float,\n default=1.0,\n help=(\n \"The multiplier for the bias. Defaults to 1.0, which means no bias is applied.\"\n \" A value of 2.0 will double the weight of the bias, and a value of 0.5 will halve it.\"\n ),\n )\n parser.add_argument(\n \"--timestep_bias_begin\",\n type=int,\n default=0,\n help=(\n \"When using `--timestep_bias_strategy=range`, the beginning (inclusive) timestep to bias.\"\n \" Defaults to zero, which equates to having no specific bias.\"\n ),\n )\n parser.add_argument(\n \"--timestep_bias_end\",\n type=int,\n default=1000,\n help=(\n \"When using `--timestep_bias_strategy=range`, the final timestep (inclusive) to bias.\"\n \" Defaults to 1000, which is the number of timesteps that Stable Diffusion is trained on.\"\n ),\n )\n parser.add_argument(\n \"--timestep_bias_portion\",\n type=float,\n default=0.25,\n help=(\n \"The portion of timesteps to bias. Defaults to 0.25, which 25% of timesteps will be biased.\"\n \" A value of 0.5 will bias one half of the timesteps. The value provided for `--timestep_bias_strategy` determines\"\n \" whether the biased portions are in the earlier or later timesteps.\"\n ),\n )\n parser.add_argument(\n \"--snr_gamma\",\n type=float,\n default=None,\n help=\"SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. \"\n \"More details here: https://arxiv.org/abs/2303.09556.\",\n )\n parser.add_argument(\"--use_ema\", action=\"store_true\", help=\"Whether to use EMA model.\")\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--prediction_type\",\n type=str,\n default=None,\n help=\"The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediction_type` is chosen.\",\n )\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\"--noise_offset\", type=float, default=0, help=\"The scale of noise offset.\")\n parser.add_argument(\n \"--loss_type\",\n type=str,\n default=\"l2\",\n choices=[\"l2\", \"huber\", \"smooth_l1\"],\n help=\"The type of loss to use and whether it's timestep-scheduled. See Issue #7488 for more info.\",\n )\n parser.add_argument(\n \"--huber_schedule\",\n type=str,\n default=\"snr\",\n choices=[\"constant\", \"exponential\", \"snr\"],\n help=\"The schedule to use for the huber losses parameter\",\n )\n parser.add_argument(\n \"--huber_c\",\n type=float,\n default=0.1,\n help=\"The huber loss parameter. Only used if one of the huber loss modes (huber or smooth l1) is selected with loss_type.\",\n )\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n\n if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:\n raise ValueError(\"`--proportion_empty_prompts` must be in the range [0, 1].\")\n\n return args\n\n\n# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt\ndef encode_prompt(batch, text_encoders, tokenizers, proportion_empty_prompts, caption_column, is_train=True):\n prompt_embeds_list = []\n prompt_batch = batch[caption_column]\n\n captions = []\n for caption in prompt_batch:\n if random.random() < proportion_empty_prompts:\n captions.append(\"\")\n elif isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n\n with torch.no_grad():\n for tokenizer, text_encoder in zip(tokenizers, text_encoders):\n text_inputs = tokenizer(\n captions,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n prompt_embeds = text_encoder(\n text_input_ids.to(text_encoder.device),\n output_hidden_states=True,\n return_dict=False,\n )\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds[-1][-2]\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)\n return {\"prompt_embeds\": prompt_embeds.cpu(), \"pooled_prompt_embeds\": pooled_prompt_embeds.cpu()}\n\n\ndef compute_vae_encodings(batch, vae):\n images = batch.pop(\"pixel_values\")\n pixel_values = torch.stack(list(images))\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n pixel_values = pixel_values.to(vae.device, dtype=vae.dtype)\n\n with torch.no_grad():\n model_input = vae.encode(pixel_values).latent_dist.sample()\n model_input = model_input * vae.config.scaling_factor\n return {\"model_input\": model_input.cpu()}\n\n\ndef generate_timestep_weights(args, num_timesteps):\n weights = torch.ones(num_timesteps)\n\n # Determine the indices to bias\n num_to_bias = int(args.timestep_bias_portion * num_timesteps)\n\n if args.timestep_bias_strategy == \"later\":\n bias_indices = slice(-num_to_bias, None)\n elif args.timestep_bias_strategy == \"earlier\":\n bias_indices = slice(0, num_to_bias)\n elif args.timestep_bias_strategy == \"range\":\n # Out of the possible 1000 timesteps, we might want to focus on eg. 200-500.\n range_begin = args.timestep_bias_begin\n range_end = args.timestep_bias_end\n if range_begin < 0:\n raise ValueError(\n \"When using the range strategy for timestep bias, you must provide a beginning timestep greater or equal to zero.\"\n )\n if range_end > num_timesteps:\n raise ValueError(\n \"When using the range strategy for timestep bias, you must provide an ending timestep smaller than the number of timesteps.\"\n )\n bias_indices = slice(range_begin, range_end)\n else: # 'none' or any other string\n return weights\n if args.timestep_bias_multiplier <= 0:\n return ValueError(\n \"The parameter --timestep_bias_multiplier is not intended to be used to disable the training of specific timesteps.\"\n \" If it was intended to disable timestep bias, use `--timestep_bias_strategy none` instead.\"\n \" A timestep bias multiplier less than or equal to 0 is not allowed.\"\n )\n\n # Apply the bias\n weights[bias_indices] *= args.timestep_bias_multiplier\n\n # Normalize\n weights /= weights.sum()\n\n return weights\n\n\n# NOTE: if you're using the scheduled version, huber_c has to depend on the timesteps already\ndef conditional_loss(\n model_pred: torch.Tensor,\n target: torch.Tensor,\n reduction: str = \"mean\",\n loss_type: str = \"l2\",\n huber_c: float = 0.1,\n):\n if loss_type == \"l2\":\n loss = F.mse_loss(model_pred, target, reduction=reduction)\n elif loss_type == \"huber\":\n loss = 2 * huber_c * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c)\n if reduction == \"mean\":\n loss = torch.mean(loss)\n elif reduction == \"sum\":\n loss = torch.sum(loss)\n elif loss_type == \"smooth_l1\":\n loss = 2 * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c)\n if reduction == \"mean\":\n loss = torch.mean(loss)\n elif reduction == \"sum\":\n loss = torch.sum(loss)\n else:\n raise NotImplementedError(f\"Unsupported Loss Type {loss_type}\")\n return loss\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n import wandb\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizers\n tokenizer_one = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n tokenizer_two = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer_2\",\n revision=args.revision,\n use_fast=False,\n )\n\n # import correct text encoder classes\n text_encoder_cls_one = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision\n )\n text_encoder_cls_two = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision, subfolder=\"text_encoder_2\"\n )\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n # Check for terminal SNR in combination with SNR Gamma\n text_encoder_one = text_encoder_cls_one.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n text_encoder_two = text_encoder_cls_two.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder_2\", revision=args.revision, variant=args.variant\n )\n vae_path = (\n args.pretrained_model_name_or_path\n if args.pretrained_vae_model_name_or_path is None\n else args.pretrained_vae_model_name_or_path\n )\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n variant=args.variant,\n )\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n # Freeze vae and text encoders.\n vae.requires_grad_(False)\n text_encoder_one.requires_grad_(False)\n text_encoder_two.requires_grad_(False)\n # Set unet as trainable.\n unet.train()\n\n # For mixed precision training we cast all non-trainable weights to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move unet, vae and text_encoder to device and cast to weight_dtype\n # The VAE is in float32 to avoid NaN losses.\n vae.to(accelerator.device, dtype=torch.float32)\n text_encoder_one.to(accelerator.device, dtype=weight_dtype)\n text_encoder_two.to(accelerator.device, dtype=weight_dtype)\n\n # Create EMA for the unet.\n if args.use_ema:\n ema_unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n ema_unet = EMAModel(ema_unet.parameters(), model_cls=UNet2DConditionModel, model_config=ema_unet.config)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n if args.use_ema:\n ema_unet.save_pretrained(os.path.join(output_dir, \"unet_ema\"))\n\n for i, model in enumerate(models):\n model.save_pretrained(os.path.join(output_dir, \"unet\"))\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n def load_model_hook(models, input_dir):\n if args.use_ema:\n load_model = EMAModel.from_pretrained(os.path.join(input_dir, \"unet_ema\"), UNet2DConditionModel)\n ema_unet.load_state_dict(load_model.state_dict())\n ema_unet.to(accelerator.device)\n del load_model\n\n for _ in range(len(models)):\n # pop models so that they are not loaded again\n model = models.pop()\n\n # load diffusers style into model\n load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder=\"unet\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # Optimizer creation\n params_to_optimize = unet.parameters()\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n )\n else:\n data_files = {}\n if args.train_data_dir is not None:\n data_files[\"train\"] = os.path.join(args.train_data_dir, \"**\")\n dataset = load_dataset(\n \"imagefolder\",\n data_files=data_files,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)\n if args.image_column is None:\n image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.caption_column is None:\n caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n else:\n caption_column = args.caption_column\n if caption_column not in column_names:\n raise ValueError(\n f\"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}\"\n )\n\n # Preprocessing the datasets.\n train_resize = transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR)\n train_crop = transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution)\n train_flip = transforms.RandomHorizontalFlip(p=1.0)\n train_transforms = transforms.Compose([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])\n\n def preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n # image aug\n original_sizes = []\n all_images = []\n crop_top_lefts = []\n for image in images:\n original_sizes.append((image.height, image.width))\n image = train_resize(image)\n if args.random_flip and random.random() < 0.5:\n # flip\n image = train_flip(image)\n if args.center_crop:\n y1 = max(0, int(round((image.height - args.resolution) / 2.0)))\n x1 = max(0, int(round((image.width - args.resolution) / 2.0)))\n image = train_crop(image)\n else:\n y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))\n image = crop(image, y1, x1, h, w)\n crop_top_left = (y1, x1)\n crop_top_lefts.append(crop_top_left)\n image = train_transforms(image)\n all_images.append(image)\n\n examples[\"original_sizes\"] = original_sizes\n examples[\"crop_top_lefts\"] = crop_top_lefts\n examples[\"pixel_values\"] = all_images\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n # Let's first compute all the embeddings so that we can free up the text encoders\n # from memory. We will pre-compute the VAE encodings too.\n text_encoders = [text_encoder_one, text_encoder_two]\n tokenizers = [tokenizer_one, tokenizer_two]\n compute_embeddings_fn = functools.partial(\n encode_prompt,\n text_encoders=text_encoders,\n tokenizers=tokenizers,\n proportion_empty_prompts=args.proportion_empty_prompts,\n caption_column=args.caption_column,\n )\n compute_vae_encodings_fn = functools.partial(compute_vae_encodings, vae=vae)\n with accelerator.main_process_first():\n from datasets.fingerprint import Hasher\n\n # fingerprint used by the cache for the other processes to load the result\n # details: https://github.com/huggingface/diffusers/pull/4038#discussion_r1266078401\n new_fingerprint = Hasher.hash(args)\n new_fingerprint_for_vae = Hasher.hash(vae_path)\n train_dataset_with_embeddings = train_dataset.map(\n compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint\n )\n train_dataset_with_vae = train_dataset.map(\n compute_vae_encodings_fn,\n batched=True,\n batch_size=args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps,\n new_fingerprint=new_fingerprint_for_vae,\n )\n precomputed_dataset = concatenate_datasets(\n [train_dataset_with_embeddings, train_dataset_with_vae.remove_columns([\"image\", \"text\"])], axis=1\n )\n precomputed_dataset = precomputed_dataset.with_transform(preprocess_train)\n\n del compute_vae_encodings_fn, compute_embeddings_fn, text_encoder_one, text_encoder_two\n del text_encoders, tokenizers, vae\n gc.collect()\n torch.cuda.empty_cache()\n\n def collate_fn(examples):\n model_input = torch.stack([torch.tensor(example[\"model_input\"]) for example in examples])\n original_sizes = [example[\"original_sizes\"] for example in examples]\n crop_top_lefts = [example[\"crop_top_lefts\"] for example in examples]\n prompt_embeds = torch.stack([torch.tensor(example[\"prompt_embeds\"]) for example in examples])\n pooled_prompt_embeds = torch.stack([torch.tensor(example[\"pooled_prompt_embeds\"]) for example in examples])\n\n return {\n \"model_input\": model_input,\n \"prompt_embeds\": prompt_embeds,\n \"pooled_prompt_embeds\": pooled_prompt_embeds,\n \"original_sizes\": original_sizes,\n \"crop_top_lefts\": crop_top_lefts,\n }\n\n # DataLoaders creation:\n train_dataloader = torch.utils.data.DataLoader(\n precomputed_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,\n num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,\n )\n\n # Prepare everything with our `accelerator`.\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n if args.use_ema:\n ema_unet.to(accelerator.device)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"text2image-fine-tune-sdxl\", config=vars(args))\n\n # Function for unwrapping if torch.compile() was used in accelerate.\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(precomputed_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n for epoch in range(first_epoch, args.num_train_epochs):\n train_loss = 0.0\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n # Sample noise that we'll add to the latents\n model_input = batch[\"model_input\"].to(accelerator.device)\n noise = torch.randn_like(model_input)\n if args.noise_offset:\n # https://www.crosslabs.org//blog/diffusion-with-offset-noise\n noise += args.noise_offset * torch.randn(\n (model_input.shape[0], model_input.shape[1], 1, 1), device=model_input.device\n )\n\n bsz = model_input.shape[0]\n if args.timestep_bias_strategy == \"none\":\n # Sample a random timestep for each image\n if args.loss_type == \"huber\" or args.loss_type == \"smooth_l1\":\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (1,), device=\"cpu\")\n timestep = timesteps.item()\n\n if args.huber_schedule == \"exponential\":\n alpha = -math.log(args.huber_c) / noise_scheduler.config.num_train_timesteps\n huber_c = math.exp(-alpha * timestep)\n elif args.huber_schedule == \"snr\":\n alphas_cumprod = noise_scheduler.alphas_cumprod[timestep]\n sigmas = ((1.0 - alphas_cumprod) / alphas_cumprod) ** 0.5\n huber_c = (1 - args.huber_c) / (1 + sigmas) ** 2 + args.huber_c\n elif args.huber_schedule == \"constant\":\n huber_c = args.huber_c\n else:\n raise NotImplementedError(f\"Unknown Huber loss schedule {args.huber_schedule}!\")\n\n timesteps = timesteps.repeat(bsz).to(model_input.device)\n elif args.loss_type == \"l2\":\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device\n )\n huber_c = 1 # may be anything, as it's not used\n else:\n raise NotImplementedError(f\"Unknown loss type {args.loss_type}\")\n\n timesteps = timesteps.long()\n\n else:\n if \"huber_scheduled\" in args.loss_type:\n raise NotImplementedError(\n \"Randomly weighted timesteps not implemented yet for scheduled huber loss!\"\n )\n else:\n huber_c = args.huber_c\n # Sample a random timestep for each image, potentially biased by the timestep weights.\n # Biasing the timestep weights allows us to spend less time training irrelevant timesteps.\n weights = generate_timestep_weights(args, noise_scheduler.config.num_train_timesteps).to(\n model_input.device\n )\n timesteps = torch.multinomial(weights, bsz, replacement=True).long()\n\n # Add noise to the model input according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)\n\n # time ids\n def compute_time_ids(original_size, crops_coords_top_left):\n # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids\n target_size = (args.resolution, args.resolution)\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n add_time_ids = torch.tensor([add_time_ids])\n add_time_ids = add_time_ids.to(accelerator.device, dtype=weight_dtype)\n return add_time_ids\n\n add_time_ids = torch.cat(\n [compute_time_ids(s, c) for s, c in zip(batch[\"original_sizes\"], batch[\"crop_top_lefts\"])]\n )\n\n # Predict the noise residual\n unet_added_conditions = {\"time_ids\": add_time_ids}\n prompt_embeds = batch[\"prompt_embeds\"].to(accelerator.device)\n pooled_prompt_embeds = batch[\"pooled_prompt_embeds\"].to(accelerator.device)\n unet_added_conditions.update({\"text_embeds\": pooled_prompt_embeds})\n model_pred = unet(\n noisy_model_input,\n timesteps,\n prompt_embeds,\n added_cond_kwargs=unet_added_conditions,\n return_dict=False,\n )[0]\n\n # Get the target for loss depending on the prediction type\n if args.prediction_type is not None:\n # set prediction_type of scheduler if defined\n noise_scheduler.register_to_config(prediction_type=args.prediction_type)\n\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(model_input, noise, timesteps)\n elif noise_scheduler.config.prediction_type == \"sample\":\n # We set the target to latents here, but the model_pred will return the noise sample prediction.\n target = model_input\n # We will have to subtract the noise residual from the prediction to get the target sample.\n model_pred = model_pred - noise\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n if args.snr_gamma is None:\n loss = conditional_loss(\n model_pred.float(), target.float(), reduction=\"mean\", loss_type=args.loss_type, huber_c=huber_c\n )\n else:\n # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.\n # Since we predict the noise instead of x_0, the original formulation is slightly changed.\n # This is discussed in Section 4.2 of the same paper.\n snr = compute_snr(noise_scheduler, timesteps)\n mse_loss_weights = torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(\n dim=1\n )[0]\n if noise_scheduler.config.prediction_type == \"epsilon\":\n mse_loss_weights = mse_loss_weights / snr\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n mse_loss_weights = mse_loss_weights / (snr + 1)\n\n loss = conditional_loss(\n model_pred.float(), target.float(), reduction=\"none\", loss_type=args.loss_type, huber_c=huber_c\n )\n loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights\n loss = loss.mean()\n\n # Gather the losses across all processes for logging (if we use distributed training).\n avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n train_loss += avg_loss.item() / args.gradient_accumulation_steps\n\n # Backpropagate\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n params_to_clip = unet.parameters()\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n if args.use_ema:\n ema_unet.step(unet.parameters())\n progress_bar.update(1)\n global_step += 1\n accelerator.log({\"train_loss\": train_loss}, step=global_step)\n train_loss = 0.0\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"step_loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompt is not None and epoch % args.validation_epochs == 0:\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n if args.use_ema:\n # Store the UNet parameters temporarily and load the EMA parameters to perform inference.\n ema_unet.store(unet.parameters())\n ema_unet.copy_to(unet.parameters())\n\n # create pipeline\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n variant=args.variant,\n )\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n unet=accelerator.unwrap_model(unet),\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n if args.prediction_type is not None:\n scheduler_args = {\"prediction_type\": args.prediction_type}\n pipeline.scheduler = pipeline.scheduler.from_config(pipeline.scheduler.config, **scheduler_args)\n\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n pipeline_args = {\"prompt\": args.validation_prompt}\n\n with torch.cuda.amp.autocast():\n images = [\n pipeline(**pipeline_args, generator=generator, num_inference_steps=25).images[0]\n for _ in range(args.num_validation_images)\n ]\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\")\n for i, image in enumerate(images)\n ]\n }\n )\n\n del pipeline\n torch.cuda.empty_cache()\n\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = unwrap_model(unet)\n if args.use_ema:\n ema_unet.copy_to(unet.parameters())\n\n # Serialize pipeline.\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n unet=unet,\n vae=vae,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n if args.prediction_type is not None:\n scheduler_args = {\"prediction_type\": args.prediction_type}\n pipeline.scheduler = pipeline.scheduler.from_config(pipeline.scheduler.config, **scheduler_args)\n pipeline.save_pretrained(args.output_dir)\n\n # run inference\n images = []\n if args.validation_prompt and args.num_validation_images > 0:\n pipeline = pipeline.to(accelerator.device)\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n with torch.cuda.amp.autocast():\n images = [\n pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]\n for _ in range(args.num_validation_images)\n ]\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"test\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"test\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\")\n for i, image in enumerate(images)\n ]\n }\n )\n\n if args.push_to_hub:\n save_model_card(\n repo_id=repo_id,\n images=images,\n validation_prompt=args.validation_prompt,\n base_model=args.pretrained_model_name_or_path,\n dataset_name=args.dataset_name,\n repo_folder=args.output_dir,\n vae_path=args.pretrained_vae_model_name_or_path,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/research_projects/sdxl_flax/README.md", "content": "# Stable Diffusion XL for JAX + TPUv5e\n\n[TPU v5e](https://cloud.google.com/blog/products/compute/how-cloud-tpu-v5e-accelerates-large-scale-ai-inference) is a new generation of TPUs from Google Cloud. It is the most cost-effective, versatile, and scalable Cloud TPU to date. This makes them ideal for serving and scaling large diffusion models.\n\n[JAX](https://github.com/google/jax) is a high-performance numerical computation library that is well-suited to develop and deploy diffusion models:\n\n- **High performance**. All JAX operations are implemented in terms of operations in [XLA](https://www.tensorflow.org/xla/) - the Accelerated Linear Algebra compiler\n\n- **Compilation**. JAX uses just-in-time (jit) compilation of JAX Python functions so it can be executed efficiently in XLA. In order to get the best performance, we must use static shapes for jitted functions, this is because JAX transforms work by tracing a function and to determine its effect on inputs of a specific shape and type. When a new shape is introduced to an already compiled function, it retriggers compilation on the new shape, which can greatly reduce performance. **Note**: JIT compilation is particularly well-suited for text-to-image generation because all inputs and outputs (image input / output sizes) are static.\n\n- **Parallelization**. Workloads can be scaled across multiple devices using JAX's [pmap](https://jax.readthedocs.io/en/latest/_autosummary/jax.pmap.html), which expresses single-program multiple-data (SPMD) programs. Applying pmap to a function will compile a function with XLA, then execute in parallel on XLA devices. For text-to-image generation workloads this means that increasing the number of images rendered simultaneously is straightforward to implement and doesn't compromise performance.\n\n👉 Try it out for yourself:\n\n[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/google/sdxl)\n\n## Stable Diffusion XL pipeline in JAX\n\nUpon having access to a TPU VM (TPUs higher than version 3), you should first install\na TPU-compatible version of JAX:\n```sh\npip install jax[tpu] -f https://storage.googleapis.com/jax-releases/libtpu_releases.html\n```\n\nNext, we can install [flax](https://github.com/google/flax) and the diffusers library:\n\n```sh\npip install flax diffusers transformers\n```\n\nIn [sdxl_single.py](./sdxl_single.py) we give a simple example of how to write a text-to-image generation pipeline in JAX using [StabilityAI's Stable Diffusion XL](stabilityai/stable-diffusion-xl-base-1.0).\n\nLet's explain it step-by-step:\n\n**Imports and Setup**\n\n```python\nimport jax\nimport jax.numpy as jnp\nimport numpy as np\nfrom flax.jax_utils import replicate\nfrom diffusers import FlaxStableDiffusionXLPipeline\n\nfrom jax.experimental.compilation_cache import compilation_cache as cc\ncc.initialize_cache(\"/tmp/sdxl_cache\")\nimport time\n\nNUM_DEVICES = jax.device_count()\n```\n\nFirst, we import the necessary libraries:\n- `jax` is provides the primitives for TPU operations\n- `flax.jax_utils` contains some useful utility functions for `Flax`, a neural network library built on top of JAX\n- `diffusers` has all the code that is relevant for SDXL.\n- We also initialize a cache to speed up the JAX model compilation.\n- We automatically determine the number of available TPU devices.\n\n**1. Downloading Model and Loading Pipeline**\n\n```python\npipeline, params = FlaxStableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", revision=\"refs/pr/95\", split_head_dim=True\n)\n```\nHere, a pre-trained model `stable-diffusion-xl-base-1.0` from the namespace `stabilityai` is loaded. It returns a pipeline for inference and its parameters.\n\n**2. Casting Parameter Types**\n\n```python\nscheduler_state = params.pop(\"scheduler\")\nparams = jax.tree_util.tree_map(lambda x: x.astype(jnp.bfloat16), params)\nparams[\"scheduler\"] = scheduler_state\n```\nThis section adjusts the data types of the model parameters.\nWe convert all parameters to `bfloat16` to speed-up the computation with model weights.\n**Note** that the scheduler parameters are **not** converted to `blfoat16` as the loss\nin precision is degrading the pipeline's performance too significantly.\n\n**3. Define Inputs to Pipeline**\n\n```python\ndefault_prompt = ...\ndefault_neg_prompt = ...\ndefault_seed = 33\ndefault_guidance_scale = 5.0\ndefault_num_steps = 25\n```\nHere, various default inputs for the pipeline are set, including the prompt, negative prompt, random seed, guidance scale, and the number of inference steps.\n\n**4. Tokenizing Inputs**\n\n```python\ndef tokenize_prompt(prompt, neg_prompt):\n prompt_ids = pipeline.prepare_inputs(prompt)\n neg_prompt_ids = pipeline.prepare_inputs(neg_prompt)\n return prompt_ids, neg_prompt_ids\n```\nThis function tokenizes the given prompts. It's essential because the text encoders of SDXL don't understand raw text; they work with numbers. Tokenization converts text to numbers.\n\n**5. Parallelization and Replication**\n\n```python\np_params = replicate(params)\n\ndef replicate_all(prompt_ids, neg_prompt_ids, seed):\n ...\n```\nTo utilize JAX's parallel capabilities, the parameters and input tensors are duplicated across devices. The `replicate_all` function also ensures that every device produces a different image by creating a unique random seed for each device.\n\n**6. Putting Everything Together**\n\n```python\ndef generate(...):\n ...\n```\nThis function integrates all the steps to produce the desired outputs from the model. It takes in prompts, tokenizes them, replicates them across devices, runs them through the pipeline, and converts the images to a format that's more interpretable (PIL format).\n\n**7. Compilation Step**\n\n```python\nstart = time.time()\nprint(f\"Compiling ...\")\ngenerate(default_prompt, default_neg_prompt)\nprint(f\"Compiled in {time.time() - start}\")\n```\nThe initial run of the `generate` function will be slow because JAX compiles the function during this call. By running it once here, subsequent calls will be much faster. This section measures and prints the compilation time.\n\n**8. Fast Inference**\n\n```python\nstart = time.time()\nprompt = ...\nneg_prompt = ...\nimages = generate(prompt, neg_prompt)\nprint(f\"Inference in {time.time() - start}\")\n```\nNow that the function is compiled, this section shows how to use it for fast inference. It measures and prints the inference time.\n\nIn summary, the code demonstrates how to load a pre-trained model using Flax and JAX, prepare it for inference, and run it efficiently using JAX's capabilities.\n\n## Ahead of Time (AOT) Compilation\n\nFlaxStableDiffusionXLPipeline takes care of parallelization across multiple devices using jit. Now let's build parallelization ourselves.\n\nFor this we will be using a JAX feature called [Ahead of Time](https://jax.readthedocs.io/en/latest/aot.html) (AOT) lowering and compilation. AOT allows to fully compile prior to execution time and have control over different parts of the compilation process.\n\nIn [sdxl_single_aot.py](./sdxl_single_aot.py) we give a simple example of how to write our own parallelization logic for text-to-image generation pipeline in JAX using [StabilityAI's Stable Diffusion XL](stabilityai/stable-diffusion-xl-base-1.0)\n\nWe add a `aot_compile` function that compiles the `pipeline._generate` function\ntelling JAX which input arguments are static, that is, arguments that\nare known at compile time and won't change. In our case, it is num_inference_steps,\nheight, width and return_latents.\n\nOnce the function is compiled, these parameters are omitted from future calls and\ncannot be changed without modifying the code and recompiling.\n\n```python\ndef aot_compile(\n prompt=default_prompt,\n negative_prompt=default_neg_prompt,\n seed=default_seed,\n guidance_scale=default_guidance_scale,\n num_inference_steps=default_num_steps\n):\n prompt_ids, neg_prompt_ids = tokenize_prompt(prompt, negative_prompt)\n prompt_ids, neg_prompt_ids, rng = replicate_all(prompt_ids, neg_prompt_ids, seed)\n g = jnp.array([guidance_scale] * prompt_ids.shape[0], dtype=jnp.float32)\n g = g[:, None]\n\n return pmap(\n pipeline._generate,static_broadcasted_argnums=[3, 4, 5, 9]\n ).lower(\n prompt_ids,\n p_params,\n rng,\n num_inference_steps, # num_inference_steps\n height, # height\n width, # width\n g,\n None,\n neg_prompt_ids,\n False # return_latents\n ).compile()\n````\n\nNext we can compile the generate function by executing `aot_compile`.\n\n```python\nstart = time.time()\nprint(\"Compiling ...\")\np_generate = aot_compile()\nprint(f\"Compiled in {time.time() - start}\")\n```\nAnd again we put everything together in a `generate` function.\n\n```python\ndef generate(\n prompt,\n negative_prompt,\n seed=default_seed,\n guidance_scale=default_guidance_scale\n):\n prompt_ids, neg_prompt_ids = tokenize_prompt(prompt, negative_prompt)\n prompt_ids, neg_prompt_ids, rng = replicate_all(prompt_ids, neg_prompt_ids, seed)\n g = jnp.array([guidance_scale] * prompt_ids.shape[0], dtype=jnp.float32)\n g = g[:, None]\n images = p_generate(\n prompt_ids,\n p_params,\n rng,\n g,\n None,\n neg_prompt_ids)\n\n # convert the images to PIL\n images = images.reshape((images.shape[0] * images.shape[1], ) + images.shape[-3:])\n return pipeline.numpy_to_pil(np.array(images))\n```\n\nThe first forward pass after AOT compilation still takes a while longer than\nsubsequent passes, this is because on the first pass, JAX uses Python dispatch, which\nFills the C++ dispatch cache.\nWhen using jit, this extra step is done automatically, but when using AOT compilation,\nit doesn't happen until the function call is made.\n\n```python\nstart = time.time()\nprompt = \"photo of a rhino dressed suit and tie sitting at a table in a bar with a bar stools, award winning photography, Elke vogelsang\"\nneg_prompt = \"cartoon, illustration, animation. face. male, female\"\nimages = generate(prompt, neg_prompt)\nprint(f\"First inference in {time.time() - start}\")\n```\n\nFrom this point forward, any calls to generate should result in a faster inference\ntime and it won't change.\n\n```python\nstart = time.time()\nprompt = \"photo of a rhino dressed suit and tie sitting at a table in a bar with a bar stools, award winning photography, Elke vogelsang\"\nneg_prompt = \"cartoon, illustration, animation. face. male, female\"\nimages = generate(prompt, neg_prompt)\nprint(f\"Inference in {time.time() - start}\")\n```\n" }, { "path": "diffusers/examples/research_projects/sdxl_flax/sdxl_single.py", "content": "# Show best practices for SDXL JAX\nimport time\n\nimport jax\nimport jax.numpy as jnp\nimport numpy as np\nfrom flax.jax_utils import replicate\n\n# Let's cache the model compilation, so that it doesn't take as long the next time around.\nfrom jax.experimental.compilation_cache import compilation_cache as cc\n\nfrom diffusers import FlaxStableDiffusionXLPipeline\n\n\ncc.initialize_cache(\"/tmp/sdxl_cache\")\n\n\nNUM_DEVICES = jax.device_count()\n\n# 1. Let's start by downloading the model and loading it into our pipeline class\n# Adhering to JAX's functional approach, the model's parameters are returned seperatetely and\n# will have to be passed to the pipeline during inference\npipeline, params = FlaxStableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", revision=\"refs/pr/95\", split_head_dim=True\n)\n\n# 2. We cast all parameters to bfloat16 EXCEPT the scheduler which we leave in\n# float32 to keep maximal precision\nscheduler_state = params.pop(\"scheduler\")\nparams = jax.tree_util.tree_map(lambda x: x.astype(jnp.bfloat16), params)\nparams[\"scheduler\"] = scheduler_state\n\n# 3. Next, we define the different inputs to the pipeline\ndefault_prompt = \"a colorful photo of a castle in the middle of a forest with trees and bushes, by Ismail Inceoglu, shadows, high contrast, dynamic shading, hdr, detailed vegetation, digital painting, digital drawing, detailed painting, a detailed digital painting, gothic art, featured on deviantart\"\ndefault_neg_prompt = \"fog, grainy, purple\"\ndefault_seed = 33\ndefault_guidance_scale = 5.0\ndefault_num_steps = 25\n\n\n# 4. In order to be able to compile the pipeline\n# all inputs have to be tensors or strings\n# Let's tokenize the prompt and negative prompt\ndef tokenize_prompt(prompt, neg_prompt):\n prompt_ids = pipeline.prepare_inputs(prompt)\n neg_prompt_ids = pipeline.prepare_inputs(neg_prompt)\n return prompt_ids, neg_prompt_ids\n\n\n# 5. To make full use of JAX's parallelization capabilities\n# the parameters and input tensors are duplicated across devices\n# To make sure every device generates a different image, we create\n# different seeds for each image. The model parameters won't change\n# during inference so we do not wrap them into a function\np_params = replicate(params)\n\n\ndef replicate_all(prompt_ids, neg_prompt_ids, seed):\n p_prompt_ids = replicate(prompt_ids)\n p_neg_prompt_ids = replicate(neg_prompt_ids)\n rng = jax.random.PRNGKey(seed)\n rng = jax.random.split(rng, NUM_DEVICES)\n return p_prompt_ids, p_neg_prompt_ids, rng\n\n\n# 6. Let's now put it all together in a generate function\ndef generate(\n prompt,\n negative_prompt,\n seed=default_seed,\n guidance_scale=default_guidance_scale,\n num_inference_steps=default_num_steps,\n):\n prompt_ids, neg_prompt_ids = tokenize_prompt(prompt, negative_prompt)\n prompt_ids, neg_prompt_ids, rng = replicate_all(prompt_ids, neg_prompt_ids, seed)\n images = pipeline(\n prompt_ids,\n p_params,\n rng,\n num_inference_steps=num_inference_steps,\n neg_prompt_ids=neg_prompt_ids,\n guidance_scale=guidance_scale,\n jit=True,\n ).images\n\n # convert the images to PIL\n images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])\n return pipeline.numpy_to_pil(np.array(images))\n\n\n# 7. Remember that the first call will compile the function and hence be very slow. Let's run generate once\n# so that the pipeline call is compiled\nstart = time.time()\nprint(\"Compiling ...\")\ngenerate(default_prompt, default_neg_prompt)\nprint(f\"Compiled in {time.time() - start}\")\n\n# 8. Now the model forward pass will run very quickly, let's try it again\nstart = time.time()\nprompt = \"photo of a rhino dressed suit and tie sitting at a table in a bar with a bar stools, award winning photography, Elke vogelsang\"\nneg_prompt = \"cartoon, illustration, animation. face. male, female\"\nimages = generate(prompt, neg_prompt)\nprint(f\"Inference in {time.time() - start}\")\n\nfor i, image in enumerate(images):\n image.save(f\"castle_{i}.png\")\n" }, { "path": "diffusers/examples/research_projects/sdxl_flax/sdxl_single_aot.py", "content": "import time\n\nimport jax\nimport jax.numpy as jnp\nimport numpy as np\nfrom flax.jax_utils import replicate\nfrom jax import pmap\n\n# Let's cache the model compilation, so that it doesn't take as long the next time around.\nfrom jax.experimental.compilation_cache import compilation_cache as cc\n\nfrom diffusers import FlaxStableDiffusionXLPipeline\n\n\ncc.initialize_cache(\"/tmp/sdxl_cache\")\n\n\nNUM_DEVICES = jax.device_count()\n\n# 1. Let's start by downloading the model and loading it into our pipeline class\n# Adhering to JAX's functional approach, the model's parameters are returned seperatetely and\n# will have to be passed to the pipeline during inference\npipeline, params = FlaxStableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", revision=\"refs/pr/95\", split_head_dim=True\n)\n\n# 2. We cast all parameters to bfloat16 EXCEPT the scheduler which we leave in\n# float32 to keep maximal precision\nscheduler_state = params.pop(\"scheduler\")\nparams = jax.tree_util.tree_map(lambda x: x.astype(jnp.bfloat16), params)\nparams[\"scheduler\"] = scheduler_state\n\n# 3. Next, we define the different inputs to the pipeline\ndefault_prompt = \"a colorful photo of a castle in the middle of a forest with trees and bushes, by Ismail Inceoglu, shadows, high contrast, dynamic shading, hdr, detailed vegetation, digital painting, digital drawing, detailed painting, a detailed digital painting, gothic art, featured on deviantart\"\ndefault_neg_prompt = \"fog, grainy, purple\"\ndefault_seed = 33\ndefault_guidance_scale = 5.0\ndefault_num_steps = 25\nwidth = 1024\nheight = 1024\n\n\n# 4. In order to be able to compile the pipeline\n# all inputs have to be tensors or strings\n# Let's tokenize the prompt and negative prompt\ndef tokenize_prompt(prompt, neg_prompt):\n prompt_ids = pipeline.prepare_inputs(prompt)\n neg_prompt_ids = pipeline.prepare_inputs(neg_prompt)\n return prompt_ids, neg_prompt_ids\n\n\n# 5. To make full use of JAX's parallelization capabilities\n# the parameters and input tensors are duplicated across devices\n# To make sure every device generates a different image, we create\n# different seeds for each image. The model parameters won't change\n# during inference so we do not wrap them into a function\np_params = replicate(params)\n\n\ndef replicate_all(prompt_ids, neg_prompt_ids, seed):\n p_prompt_ids = replicate(prompt_ids)\n p_neg_prompt_ids = replicate(neg_prompt_ids)\n rng = jax.random.PRNGKey(seed)\n rng = jax.random.split(rng, NUM_DEVICES)\n return p_prompt_ids, p_neg_prompt_ids, rng\n\n\n# 6. To compile the pipeline._generate function, we must pass all parameters\n# to the function and tell JAX which are static arguments, that is, arguments that\n# are known at compile time and won't change. In our case, it is num_inference_steps,\n# height, width and return_latents.\n# Once the function is compiled, these parameters are ommited from future calls and\n# cannot be changed without modifying the code and recompiling.\ndef aot_compile(\n prompt=default_prompt,\n negative_prompt=default_neg_prompt,\n seed=default_seed,\n guidance_scale=default_guidance_scale,\n num_inference_steps=default_num_steps,\n):\n prompt_ids, neg_prompt_ids = tokenize_prompt(prompt, negative_prompt)\n prompt_ids, neg_prompt_ids, rng = replicate_all(prompt_ids, neg_prompt_ids, seed)\n g = jnp.array([guidance_scale] * prompt_ids.shape[0], dtype=jnp.float32)\n g = g[:, None]\n\n return (\n pmap(pipeline._generate, static_broadcasted_argnums=[3, 4, 5, 9])\n .lower(\n prompt_ids,\n p_params,\n rng,\n num_inference_steps, # num_inference_steps\n height, # height\n width, # width\n g,\n None,\n neg_prompt_ids,\n False, # return_latents\n )\n .compile()\n )\n\n\nstart = time.time()\nprint(\"Compiling ...\")\np_generate = aot_compile()\nprint(f\"Compiled in {time.time() - start}\")\n\n\n# 7. Let's now put it all together in a generate function.\ndef generate(prompt, negative_prompt, seed=default_seed, guidance_scale=default_guidance_scale):\n prompt_ids, neg_prompt_ids = tokenize_prompt(prompt, negative_prompt)\n prompt_ids, neg_prompt_ids, rng = replicate_all(prompt_ids, neg_prompt_ids, seed)\n g = jnp.array([guidance_scale] * prompt_ids.shape[0], dtype=jnp.float32)\n g = g[:, None]\n images = p_generate(prompt_ids, p_params, rng, g, None, neg_prompt_ids)\n\n # convert the images to PIL\n images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])\n return pipeline.numpy_to_pil(np.array(images))\n\n\n# 8. The first forward pass after AOT compilation still takes a while longer than\n# subsequent passes, this is because on the first pass, JAX uses Python dispatch, which\n# Fills the C++ dispatch cache.\n# When using jit, this extra step is done automatically, but when using AOT compilation,\n# it doesn't happen until the function call is made.\nstart = time.time()\nprompt = \"photo of a rhino dressed suit and tie sitting at a table in a bar with a bar stools, award winning photography, Elke vogelsang\"\nneg_prompt = \"cartoon, illustration, animation. face. male, female\"\nimages = generate(prompt, neg_prompt)\nprint(f\"First inference in {time.time() - start}\")\n\n# 9. From this point forward, any calls to generate should result in a faster inference\n# time and it won't change.\nstart = time.time()\nprompt = \"photo of a rhino dressed suit and tie sitting at a table in a bar with a bar stools, award winning photography, Elke vogelsang\"\nneg_prompt = \"cartoon, illustration, animation. face. male, female\"\nimages = generate(prompt, neg_prompt)\nprint(f\"Inference in {time.time() - start}\")\n\nfor i, image in enumerate(images):\n image.save(f\"castle_{i}.png\")\n" }, { "path": "diffusers/examples/t2i_adapter/README.md", "content": "We don't yet support training T2I-Adapters on Stable Diffusion yet. For training T2I-Adapters on Stable Diffusion XL, refer [here](./README_sdxl.md)." }, { "path": "diffusers/examples/t2i_adapter/README_sdxl.md", "content": "# T2I-Adapter training example for Stable Diffusion XL (SDXL)\n\nThe `train_t2i_adapter_sdxl.py` script shows how to implement the [T2I-Adapter training procedure](https://hf.co/papers/2302.08453) for [Stable Diffusion XL](https://huggingface.co/papers/2307.01952).\n\n## Running locally with PyTorch\n\n### Installing the dependencies\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n**Important**\n\nTo make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install -e .\n```\n\nThen cd in the `examples/t2i_adapter` folder and run\n```bash\npip install -r requirements.txt\n```\n\nAnd initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\n\nOr for a default accelerate configuration without answering questions about your environment\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell (e.g., a notebook)\n\n```python\nfrom accelerate.utils import write_basic_config\nwrite_basic_config()\n```\n\nWhen running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups.\n\n## Circle filling dataset\n\nThe original dataset is hosted in the [ControlNet repo](https://huggingface.co/lllyasviel/ControlNet/blob/main/training/fill50k.zip). We re-uploaded it to be compatible with `datasets` [here](https://huggingface.co/datasets/fusing/fill50k). Note that `datasets` handles dataloading within the training script.\n\n## Training\n\nOur training examples use two test conditioning images. They can be downloaded by running\n\n```sh\nwget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png\n\nwget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png\n```\n\nThen run `huggingface-cli login` to log into your Hugging Face account. This is needed to be able to push the trained T2IAdapter parameters to Hugging Face Hub.\n\n```bash\nexport MODEL_DIR=\"stabilityai/stable-diffusion-xl-base-1.0\"\nexport OUTPUT_DIR=\"path to save model\"\n\naccelerate launch train_t2i_adapter_sdxl.py \\\n --pretrained_model_name_or_path=$MODEL_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --dataset_name=fusing/fill50k \\\n --mixed_precision=\"fp16\" \\\n --resolution=1024 \\\n --learning_rate=1e-5 \\\n --max_train_steps=15000 \\\n --validation_image \"./conditioning_image_1.png\" \"./conditioning_image_2.png\" \\\n --validation_prompt \"red circle with blue background\" \"cyan circle with brown floral background\" \\\n --validation_steps=100 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --report_to=\"wandb\" \\\n --seed=42 \\\n --push_to_hub\n```\n\nTo better track our training experiments, we're using the following flags in the command above:\n\n* `report_to=\"wandb` will ensure the training runs are tracked on Weights and Biases. To use it, be sure to install `wandb` with `pip install wandb`.\n* `validation_image`, `validation_prompt`, and `validation_steps` to allow the script to do a few validation inference runs. This allows us to qualitatively check if the training is progressing as expected.\n\nOur experiments were conducted on a single 40GB A100 GPU.\n\n### Inference\n\nOnce training is done, we can perform inference like so:\n\n```python\nfrom diffusers import StableDiffusionXLAdapterPipeline, T2IAdapter, EulerAncestralDiscreteSchedulerTest\nfrom diffusers.utils import load_image\nimport torch\n\nbase_model_path = \"stabilityai/stable-diffusion-xl-base-1.0\"\nadapter_path = \"path to adapter\"\n\nadapter = T2IAdapter.from_pretrained(adapter_path, torch_dtype=torch.float16)\npipe = StableDiffusionXLAdapterPipeline.from_pretrained(\n base_model_path, adapter=adapter, torch_dtype=torch.float16\n)\n\n# speed up diffusion process with faster scheduler and memory optimization\npipe.scheduler = EulerAncestralDiscreteSchedulerTest.from_config(pipe.scheduler.config)\n# remove following line if xformers is not installed or when using Torch 2.0.\npipe.enable_xformers_memory_efficient_attention()\n# memory optimization.\npipe.enable_model_cpu_offload()\n\ncontrol_image = load_image(\"./conditioning_image_1.png\")\nprompt = \"pale golden rod circle with old lace background\"\n\n# generate image\ngenerator = torch.manual_seed(0)\nimage = pipe(\n prompt, num_inference_steps=20, generator=generator, image=control_image\n).images[0]\nimage.save(\"./output.png\")\n```\n\n## Notes\n\n### Specifying a better VAE\n\nSDXL's VAE is known to suffer from numerical instability issues. This is why we also expose a CLI argument namely `--pretrained_vae_model_name_or_path` that lets you specify the location of a better VAE (such as [this one](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix)).\n" }, { "path": "diffusers/examples/t2i_adapter/requirements.txt", "content": "transformers>=4.25.1\naccelerate>=0.16.0\nsafetensors\ndatasets\ntorchvision\nftfy\ntensorboard\nwandb" }, { "path": "diffusers/examples/t2i_adapter/test_t2i_adapter.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport logging\nimport os\nimport sys\nimport tempfile\n\n\nsys.path.append(\"..\")\nfrom test_examples_utils import ExamplesTestsAccelerate, run_command # noqa: E402\n\n\nlogging.basicConfig(level=logging.DEBUG)\n\nlogger = logging.getLogger()\nstream_handler = logging.StreamHandler(sys.stdout)\nlogger.addHandler(stream_handler)\n\n\nclass T2IAdapter(ExamplesTestsAccelerate):\n def test_t2i_adapter_sdxl(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/t2i_adapter/train_t2i_adapter_sdxl.py\n --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-xl-pipe\n --adapter_model_name_or_path=hf-internal-testing/tiny-adapter\n --dataset_name=hf-internal-testing/fill10\n --output_dir={tmpdir}\n --resolution=64\n --train_batch_size=1\n --gradient_accumulation_steps=1\n --max_train_steps=9\n --checkpointing_steps=2\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"diffusion_pytorch_model.safetensors\")))\n" }, { "path": "diffusers/examples/t2i_adapter/train_t2i_adapter_sdxl.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport functools\nimport gc\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom pathlib import Path\n\nimport accelerate\nimport numpy as np\nimport torch\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom PIL import Image\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n EulerDiscreteScheduler,\n StableDiffusionXLAdapterPipeline,\n T2IAdapter,\n UNet2DConditionModel,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import check_min_version, is_wandb_available\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\nMAX_SEQ_LENGTH = 77\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef image_grid(imgs, rows, cols):\n assert len(imgs) == rows * cols\n\n w, h = imgs[0].size\n grid = Image.new(\"RGB\", size=(cols * w, rows * h))\n\n for i, img in enumerate(imgs):\n grid.paste(img, box=(i % cols * w, i // cols * h))\n return grid\n\n\ndef log_validation(vae, unet, adapter, args, accelerator, weight_dtype, step):\n logger.info(\"Running validation... \")\n\n adapter = accelerator.unwrap_model(adapter)\n\n pipeline = StableDiffusionXLAdapterPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n unet=unet,\n adapter=adapter,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n if args.enable_xformers_memory_efficient_attention:\n pipeline.enable_xformers_memory_efficient_attention()\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n if len(args.validation_image) == len(args.validation_prompt):\n validation_images = args.validation_image\n validation_prompts = args.validation_prompt\n elif len(args.validation_image) == 1:\n validation_images = args.validation_image * len(args.validation_prompt)\n validation_prompts = args.validation_prompt\n elif len(args.validation_prompt) == 1:\n validation_images = args.validation_image\n validation_prompts = args.validation_prompt * len(args.validation_image)\n else:\n raise ValueError(\n \"number of `args.validation_image` and `args.validation_prompt` should be checked in `parse_args`\"\n )\n\n image_logs = []\n\n for validation_prompt, validation_image in zip(validation_prompts, validation_images):\n validation_image = Image.open(validation_image).convert(\"RGB\")\n validation_image = validation_image.resize((args.resolution, args.resolution))\n\n images = []\n\n for _ in range(args.num_validation_images):\n with torch.autocast(\"cuda\"):\n image = pipeline(\n prompt=validation_prompt, image=validation_image, num_inference_steps=20, generator=generator\n ).images[0]\n images.append(image)\n\n image_logs.append(\n {\"validation_image\": validation_image, \"images\": images, \"validation_prompt\": validation_prompt}\n )\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n for log in image_logs:\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n validation_image = log[\"validation_image\"]\n\n formatted_images = []\n\n formatted_images.append(np.asarray(validation_image))\n\n for image in images:\n formatted_images.append(np.asarray(image))\n\n formatted_images = np.stack(formatted_images)\n\n tracker.writer.add_images(validation_prompt, formatted_images, step, dataformats=\"NHWC\")\n elif tracker.name == \"wandb\":\n formatted_images = []\n\n for log in image_logs:\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n validation_image = log[\"validation_image\"]\n\n formatted_images.append(wandb.Image(validation_image, caption=\"adapter conditioning\"))\n\n for image in images:\n image = wandb.Image(image, caption=validation_prompt)\n formatted_images.append(image)\n\n tracker.log({\"validation\": formatted_images})\n else:\n logger.warning(f\"image logging not implemented for {tracker.name}\")\n\n del pipeline\n gc.collect()\n torch.cuda.empty_cache()\n\n return image_logs\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef save_model_card(repo_id: str, image_logs: dict = None, base_model: str = None, repo_folder: str = None):\n img_str = \"\"\n if image_logs is not None:\n img_str = \"You can find some example images below.\\n\"\n for i, log in enumerate(image_logs):\n images = log[\"images\"]\n validation_prompt = log[\"validation_prompt\"]\n validation_image = log[\"validation_image\"]\n validation_image.save(os.path.join(repo_folder, \"image_control.png\"))\n img_str += f\"prompt: {validation_prompt}\\n\"\n images = [validation_image] + images\n image_grid(images, 1, len(images)).save(os.path.join(repo_folder, f\"images_{i}.png\"))\n img_str += f\"![images_{i})](./images_{i}.png)\\n\"\n\n model_description = f\"\"\"\n# t2iadapter-{repo_id}\n\nThese are t2iadapter weights trained on {base_model} with new type of conditioning.\n{img_str}\n\"\"\"\n model_card = load_or_create_model_card(\n repo_id_or_path=repo_id,\n from_training=True,\n license=\"creativeml-openrail-m\",\n base_model=base_model,\n model_description=model_description,\n inference=True,\n )\n\n tags = [\n \"stable-diffusion-xl\",\n \"stable-diffusion-xl-diffusers\",\n \"text-to-image\",\n \"diffusers\",\n \"t2iadapter\",\n \"diffusers-training\",\n ]\n model_card = populate_model_card(model_card, tags=tags)\n\n model_card.save(os.path.join(repo_folder, \"README.md\"))\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a ControlNet training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_vae_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to an improved VAE to stabilize training. For more details check out: https://github.com/huggingface/diffusers/pull/4038.\",\n )\n parser.add_argument(\n \"--adapter_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to pretrained adapter model or model identifier from huggingface.co/models.\"\n \" If not specified adapter weights are initialized w.r.t the configurations of SDXL.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=(\n \"Revision of pretrained model identifier from huggingface.co/models. Trainable model components should be\"\n \" float32 precision.\"\n ),\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--tokenizer_name\",\n type=str,\n default=None,\n help=\"Pretrained tokenizer name or path if not the same as model_name\",\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"t2iadapter-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=1024,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--detection_resolution\",\n type=int,\n default=None,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--crops_coords_top_left_h\",\n type=int,\n default=0,\n help=(\"Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet.\"),\n )\n parser.add_argument(\n \"--crops_coords_top_left_w\",\n type=int,\n default=0,\n help=(\"Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet.\"),\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=4, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=1)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. \"\n \"In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference.\"\n \"Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components.\"\n \"See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step\"\n \"instructions.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=3,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=5e-6,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\"--lr_power\", type=float, default=1.0, help=\"Power factor of the polynomial scheduler.\")\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=1,\n help=(\"Number of subprocesses to use for data loading.\"),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--set_grads_to_none\",\n action=\"store_true\",\n help=(\n \"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain\"\n \" behaviors, so disable this argument if it causes any problems. More info:\"\n \" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html\"\n ),\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing the target image.\"\n )\n parser.add_argument(\n \"--conditioning_image_column\",\n type=str,\n default=\"conditioning_image\",\n help=\"The column of the dataset containing the adapter conditioning image.\",\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=\"text\",\n help=\"The column of the dataset containing a caption or a list of captions.\",\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--proportion_empty_prompts\",\n type=float,\n default=0,\n help=\"Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).\",\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n nargs=\"+\",\n help=(\n \"A set of prompts evaluated every `--validation_steps` and logged to `--report_to`.\"\n \" Provide either a matching number of `--validation_image`s, a single `--validation_image`\"\n \" to be used with all prompts, or a single prompt that will be used with all `--validation_image`s.\"\n ),\n )\n parser.add_argument(\n \"--validation_image\",\n type=str,\n default=None,\n nargs=\"+\",\n help=(\n \"A set of paths to the t2iadapter conditioning image be evaluated every `--validation_steps`\"\n \" and logged to `--report_to`. Provide either a matching number of `--validation_prompt`s, a\"\n \" a single `--validation_prompt` to be used with all `--validation_image`s, or a single\"\n \" `--validation_image` that will be used with all `--validation_prompt`s.\"\n ),\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images to be generated for each `--validation_image`, `--validation_prompt` pair\",\n )\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=100,\n help=(\n \"Run validation every X steps. Validation consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`\"\n \" and logging the images.\"\n ),\n )\n parser.add_argument(\n \"--tracker_project_name\",\n type=str,\n default=\"sd_xl_train_t2iadapter\",\n help=(\n \"The `project_name` argument passed to Accelerator.init_trackers for\"\n \" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator\"\n ),\n )\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Specify either `--dataset_name` or `--train_data_dir`\")\n\n if args.dataset_name is not None and args.train_data_dir is not None:\n raise ValueError(\"Specify only one of `--dataset_name` or `--train_data_dir`\")\n\n if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:\n raise ValueError(\"`--proportion_empty_prompts` must be in the range [0, 1].\")\n\n if args.validation_prompt is not None and args.validation_image is None:\n raise ValueError(\"`--validation_image` must be set if `--validation_prompt` is set\")\n\n if args.validation_prompt is None and args.validation_image is not None:\n raise ValueError(\"`--validation_prompt` must be set if `--validation_image` is set\")\n\n if (\n args.validation_image is not None\n and args.validation_prompt is not None\n and len(args.validation_image) != 1\n and len(args.validation_prompt) != 1\n and len(args.validation_image) != len(args.validation_prompt)\n ):\n raise ValueError(\n \"Must provide either 1 `--validation_image`, 1 `--validation_prompt`,\"\n \" or the same number of `--validation_prompt`s and `--validation_image`s\"\n )\n\n if args.resolution % 8 != 0:\n raise ValueError(\n \"`--resolution` must be divisible by 8 for consistently sized encoded images between the VAE and the t2iadapter encoder.\"\n )\n\n return args\n\n\ndef get_train_dataset(args, accelerator):\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n )\n else:\n if args.train_data_dir is not None:\n dataset = load_dataset(\n args.train_data_dir,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n if args.image_column is None:\n image_column = column_names[0]\n logger.info(f\"image column defaulting to {image_column}\")\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n\n if args.caption_column is None:\n caption_column = column_names[1]\n logger.info(f\"caption column defaulting to {caption_column}\")\n else:\n caption_column = args.caption_column\n if caption_column not in column_names:\n raise ValueError(\n f\"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n\n if args.conditioning_image_column is None:\n conditioning_image_column = column_names[2]\n logger.info(f\"conditioning image column defaulting to {conditioning_image_column}\")\n else:\n conditioning_image_column = args.conditioning_image_column\n if conditioning_image_column not in column_names:\n raise ValueError(\n f\"`--conditioning_image_column` value '{args.conditioning_image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}\"\n )\n\n with accelerator.main_process_first():\n train_dataset = dataset[\"train\"].shuffle(seed=args.seed)\n if args.max_train_samples is not None:\n train_dataset = train_dataset.select(range(args.max_train_samples))\n return train_dataset\n\n\n# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt\ndef encode_prompt(prompt_batch, text_encoders, tokenizers, proportion_empty_prompts, is_train=True):\n prompt_embeds_list = []\n\n captions = []\n for caption in prompt_batch:\n if random.random() < proportion_empty_prompts:\n captions.append(\"\")\n elif isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n\n with torch.no_grad():\n for tokenizer, text_encoder in zip(tokenizers, text_encoders):\n text_inputs = tokenizer(\n captions,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n prompt_embeds = text_encoder(\n text_input_ids.to(text_encoder.device),\n output_hidden_states=True,\n )\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds.hidden_states[-2]\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)\n return prompt_embeds, pooled_prompt_embeds\n\n\ndef prepare_train_dataset(dataset, accelerator):\n image_transforms = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(args.resolution),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n conditioning_image_transforms = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(args.resolution),\n transforms.ToTensor(),\n ]\n )\n\n def preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[args.image_column]]\n images = [image_transforms(image) for image in images]\n\n conditioning_images = [image.convert(\"RGB\") for image in examples[args.conditioning_image_column]]\n conditioning_images = [conditioning_image_transforms(image) for image in conditioning_images]\n\n examples[\"pixel_values\"] = images\n examples[\"conditioning_pixel_values\"] = conditioning_images\n\n return examples\n\n with accelerator.main_process_first():\n dataset = dataset.with_transform(preprocess_train)\n\n return dataset\n\n\ndef collate_fn(examples):\n pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n\n conditioning_pixel_values = torch.stack([example[\"conditioning_pixel_values\"] for example in examples])\n conditioning_pixel_values = conditioning_pixel_values.to(memory_format=torch.contiguous_format).float()\n\n prompt_ids = torch.stack([torch.tensor(example[\"prompt_embeds\"]) for example in examples])\n\n add_text_embeds = torch.stack([torch.tensor(example[\"text_embeds\"]) for example in examples])\n add_time_ids = torch.stack([torch.tensor(example[\"time_ids\"]) for example in examples])\n\n return {\n \"pixel_values\": pixel_values,\n \"conditioning_pixel_values\": conditioning_pixel_values,\n \"prompt_ids\": prompt_ids,\n \"unet_added_conditions\": {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids},\n }\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name,\n exist_ok=True,\n token=args.hub_token,\n private=True,\n ).repo_id\n\n # Load the tokenizers\n tokenizer_one = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n tokenizer_two = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer_2\",\n revision=args.revision,\n use_fast=False,\n )\n\n # import correct text encoder classes\n text_encoder_cls_one = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision\n )\n text_encoder_cls_two = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision, subfolder=\"text_encoder_2\"\n )\n\n # Load scheduler and models\n noise_scheduler = EulerDiscreteScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n text_encoder_one = text_encoder_cls_one.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n text_encoder_two = text_encoder_cls_two.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder_2\", revision=args.revision, variant=args.variant\n )\n vae_path = (\n args.pretrained_model_name_or_path\n if args.pretrained_vae_model_name_or_path is None\n else args.pretrained_vae_model_name_or_path\n )\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n variant=args.variant,\n )\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n if args.adapter_model_name_or_path:\n logger.info(\"Loading existing adapter weights.\")\n t2iadapter = T2IAdapter.from_pretrained(args.adapter_model_name_or_path)\n else:\n logger.info(\"Initializing t2iadapter weights.\")\n t2iadapter = T2IAdapter(\n in_channels=3,\n channels=(320, 640, 1280, 1280),\n num_res_blocks=2,\n downscale_factor=16,\n adapter_type=\"full_adapter_xl\",\n )\n\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n i = len(weights) - 1\n\n while len(weights) > 0:\n weights.pop()\n model = models[i]\n\n sub_dir = \"t2iadapter\"\n model.save_pretrained(os.path.join(output_dir, sub_dir))\n\n i -= 1\n\n def load_model_hook(models, input_dir):\n while len(models) > 0:\n # pop models so that they are not loaded again\n model = models.pop()\n\n # load diffusers style into model\n load_model = T2IAdapter.from_pretrained(os.path.join(input_dir, \"t2iadapter\"))\n\n if args.control_type != \"style\":\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n vae.requires_grad_(False)\n text_encoder_one.requires_grad_(False)\n text_encoder_two.requires_grad_(False)\n t2iadapter.train()\n unet.train()\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n # Check that all trainable models are in full precision\n low_precision_error_string = (\n \" Please make sure to always have all model weights in full float32 precision when starting training - even if\"\n \" doing mixed precision training, copy of the weights should still be float32.\"\n )\n\n if unwrap_model(t2iadapter).dtype != torch.float32:\n raise ValueError(\n f\"Controlnet loaded as datatype {unwrap_model(t2iadapter).dtype}. {low_precision_error_string}\"\n )\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # Optimizer creation\n params_to_optimize = t2iadapter.parameters()\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # For mixed precision training we cast the text_encoder and vae weights to half-precision\n # as these models are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move vae, unet and text_encoder to device and cast to weight_dtype\n # The VAE is in float32 to avoid NaN losses.\n if args.pretrained_vae_model_name_or_path is not None:\n vae.to(accelerator.device, dtype=weight_dtype)\n else:\n vae.to(accelerator.device, dtype=torch.float32)\n unet.to(accelerator.device, dtype=weight_dtype)\n text_encoder_one.to(accelerator.device, dtype=weight_dtype)\n text_encoder_two.to(accelerator.device, dtype=weight_dtype)\n\n # Here, we compute not just the text embeddings but also the additional embeddings\n # needed for the SD XL UNet to operate.\n def compute_embeddings(batch, proportion_empty_prompts, text_encoders, tokenizers, is_train=True):\n original_size = (args.resolution, args.resolution)\n target_size = (args.resolution, args.resolution)\n crops_coords_top_left = (args.crops_coords_top_left_h, args.crops_coords_top_left_w)\n prompt_batch = batch[args.caption_column]\n\n prompt_embeds, pooled_prompt_embeds = encode_prompt(\n prompt_batch, text_encoders, tokenizers, proportion_empty_prompts, is_train\n )\n add_text_embeds = pooled_prompt_embeds\n\n # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n add_time_ids = torch.tensor([add_time_ids])\n\n prompt_embeds = prompt_embeds.to(accelerator.device)\n add_text_embeds = add_text_embeds.to(accelerator.device)\n add_time_ids = add_time_ids.repeat(len(prompt_batch), 1)\n add_time_ids = add_time_ids.to(accelerator.device, dtype=prompt_embeds.dtype)\n unet_added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n\n return {\"prompt_embeds\": prompt_embeds, **unet_added_cond_kwargs}\n\n def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):\n sigmas = noise_scheduler.sigmas.to(device=accelerator.device, dtype=dtype)\n schedule_timesteps = noise_scheduler.timesteps.to(accelerator.device)\n timesteps = timesteps.to(accelerator.device)\n\n step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]\n\n sigma = sigmas[step_indices].flatten()\n while len(sigma.shape) < n_dim:\n sigma = sigma.unsqueeze(-1)\n return sigma\n\n # Let's first compute all the embeddings so that we can free up the text encoders\n # from memory.\n text_encoders = [text_encoder_one, text_encoder_two]\n tokenizers = [tokenizer_one, tokenizer_two]\n train_dataset = get_train_dataset(args, accelerator)\n compute_embeddings_fn = functools.partial(\n compute_embeddings,\n proportion_empty_prompts=args.proportion_empty_prompts,\n text_encoders=text_encoders,\n tokenizers=tokenizers,\n )\n with accelerator.main_process_first():\n from datasets.fingerprint import Hasher\n\n # fingerprint used by the cache for the other processes to load the result\n # details: https://github.com/huggingface/diffusers/pull/4038#discussion_r1266078401\n new_fingerprint = Hasher.hash(args)\n train_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint)\n\n # Then get the training dataset ready to be passed to the dataloader.\n train_dataset = prepare_train_dataset(train_dataset, accelerator)\n\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps,\n num_training_steps=args.max_train_steps,\n num_cycles=args.lr_num_cycles,\n power=args.lr_power,\n )\n\n # Prepare everything with our `accelerator`.\n t2iadapter, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n t2iadapter, optimizer, train_dataloader, lr_scheduler\n )\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = dict(vars(args))\n\n # tensorboard cannot handle list types for config\n tracker_config.pop(\"validation_prompt\")\n tracker_config.pop(\"validation_image\")\n\n accelerator.init_trackers(args.tracker_project_name, config=tracker_config)\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num batches each epoch = {len(train_dataloader)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n image_logs = None\n for epoch in range(first_epoch, args.num_train_epochs):\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(t2iadapter):\n if args.pretrained_vae_model_name_or_path is not None:\n pixel_values = batch[\"pixel_values\"].to(dtype=weight_dtype)\n else:\n pixel_values = batch[\"pixel_values\"]\n\n # encode pixel values with batch size of at most 8 to avoid OOM\n latents = []\n for i in range(0, pixel_values.shape[0], 8):\n latents.append(vae.encode(pixel_values[i : i + 8]).latent_dist.sample())\n latents = torch.cat(latents, dim=0)\n latents = latents * vae.config.scaling_factor\n if args.pretrained_vae_model_name_or_path is None:\n latents = latents.to(weight_dtype)\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n\n # Cubic sampling to sample a random timestep for each image.\n # For more details about why cubic sampling is used, refer to section 3.4 of https://arxiv.org/abs/2302.08453\n timesteps = torch.rand((bsz,), device=latents.device)\n timesteps = (1 - timesteps**3) * noise_scheduler.config.num_train_timesteps\n timesteps = timesteps.long().to(noise_scheduler.timesteps.dtype)\n timesteps = timesteps.clamp(0, noise_scheduler.config.num_train_timesteps - 1)\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # Scale the noisy latents for the UNet\n sigmas = get_sigmas(timesteps, len(noisy_latents.shape), noisy_latents.dtype)\n inp_noisy_latents = noisy_latents / ((sigmas**2 + 1) ** 0.5)\n\n # Adapter conditioning.\n t2iadapter_image = batch[\"conditioning_pixel_values\"].to(dtype=weight_dtype)\n down_block_additional_residuals = t2iadapter(t2iadapter_image)\n down_block_additional_residuals = [\n sample.to(dtype=weight_dtype) for sample in down_block_additional_residuals\n ]\n\n # Predict the noise residual\n model_pred = unet(\n inp_noisy_latents,\n timesteps,\n encoder_hidden_states=batch[\"prompt_ids\"],\n added_cond_kwargs=batch[\"unet_added_conditions\"],\n down_block_additional_residuals=down_block_additional_residuals,\n return_dict=False,\n )[0]\n\n # Denoise the latents\n denoised_latents = model_pred * (-sigmas) + noisy_latents\n weighing = sigmas**-2.0\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = latents # we are computing loss against denoise latents\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n # MSE loss\n loss = torch.mean(\n (weighing.float() * (denoised_latents.float() - target.float()) ** 2).reshape(target.shape[0], -1),\n dim=1,\n )\n loss = loss.mean()\n\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n params_to_clip = t2iadapter.parameters()\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad(set_to_none=args.set_grads_to_none)\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n if args.validation_prompt is not None and global_step % args.validation_steps == 0:\n image_logs = log_validation(\n vae,\n unet,\n t2iadapter,\n args,\n accelerator,\n weight_dtype,\n global_step,\n )\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n\n # Create the pipeline using using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n t2iadapter = unwrap_model(t2iadapter)\n t2iadapter.save_pretrained(args.output_dir)\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n image_logs=image_logs,\n base_model=args.pretrained_model_name_or_path,\n repo_folder=args.output_dir,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/test_examples_utils.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os\nimport shutil\nimport subprocess\nimport tempfile\nimport unittest\nfrom typing import List\n\nfrom accelerate.utils import write_basic_config\n\n\n# These utils relate to ensuring the right error message is received when running scripts\nclass SubprocessCallException(Exception):\n pass\n\n\ndef run_command(command: List[str], return_stdout=False):\n \"\"\"\n Runs `command` with `subprocess.check_output` and will potentially return the `stdout`. Will also properly capture\n if an error occurred while running `command`\n \"\"\"\n try:\n output = subprocess.check_output(command, stderr=subprocess.STDOUT)\n if return_stdout:\n if hasattr(output, \"decode\"):\n output = output.decode(\"utf-8\")\n return output\n except subprocess.CalledProcessError as e:\n raise SubprocessCallException(\n f\"Command `{' '.join(command)}` failed with the following error:\\n\\n{e.output.decode()}\"\n ) from e\n\n\nclass ExamplesTestsAccelerate(unittest.TestCase):\n @classmethod\n def setUpClass(cls):\n super().setUpClass()\n cls._tmpdir = tempfile.mkdtemp()\n cls.configPath = os.path.join(cls._tmpdir, \"default_config.yml\")\n\n write_basic_config(save_location=cls.configPath)\n cls._launch_args = [\"accelerate\", \"launch\", \"--config_file\", cls.configPath]\n\n @classmethod\n def tearDownClass(cls):\n super().tearDownClass()\n shutil.rmtree(cls._tmpdir)\n" }, { "path": "diffusers/examples/text_to_image/README.md", "content": "# Stable Diffusion text-to-image fine-tuning\n\nThe `train_text_to_image.py` script shows how to fine-tune stable diffusion model on your own dataset.\n\n___Note___:\n\n___This script is experimental. The script fine-tunes the whole model and often times the model overfits and runs into issues like catastrophic forgetting. It's recommended to try different hyperparameters to get the best result on your dataset.___\n\n\n## Running locally with PyTorch\n### Installing the dependencies\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n**Important**\n\nTo make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nThen cd in the example folder and run\n```bash\npip install -r requirements.txt\n```\n\nAnd initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\n\nNote also that we use PEFT library as backend for LoRA training, make sure to have `peft>=0.6.0` installed in your environment.\n\n### Naruto example\n\nYou need to accept the model license before downloading or using the weights. In this example we'll use model version `v1-4`, so you'll need to visit [its card](https://huggingface.co/CompVis/stable-diffusion-v1-4), read the license and tick the checkbox if you agree.\n\nYou have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).\n\nRun the following command to authenticate your token\n\n```bash\nhuggingface-cli login\n```\n\nIf you have already cloned the repo, then you won't need to go through these steps.\n\n
\n\n#### Hardware\nWith `gradient_checkpointing` and `mixed_precision` it should be possible to fine tune the model on a single 24GB GPU. For higher `batch_size` and faster training it's better to use GPUs with >30GB memory.\n\n**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport DATASET_NAME=\"lambdalabs/naruto-blip-captions\"\n\naccelerate launch --mixed_precision=\"fp16\" train_text_to_image.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --dataset_name=$DATASET_NAME \\\n --use_ema \\\n --resolution=512 --center_crop --random_flip \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --max_train_steps=15000 \\\n --learning_rate=1e-05 \\\n --max_grad_norm=1 \\\n --lr_scheduler=\"constant\" --lr_warmup_steps=0 \\\n --output_dir=\"sd-naruto-model\"\n```\n\n\n\nTo run on your own training files prepare the dataset according to the format required by `datasets`, you can find the instructions for how to do that in this [document](https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder-with-metadata).\nIf you wish to use custom loading logic, you should modify the script, we have left pointers for that in the training script.\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport TRAIN_DIR=\"path_to_your_dataset\"\n\naccelerate launch --mixed_precision=\"fp16\" train_text_to_image.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_data_dir=$TRAIN_DIR \\\n --use_ema \\\n --resolution=512 --center_crop --random_flip \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --max_train_steps=15000 \\\n --learning_rate=1e-05 \\\n --max_grad_norm=1 \\\n --lr_scheduler=\"constant\" --lr_warmup_steps=0 \\\n --output_dir=\"sd-naruto-model\"\n```\n\n\nOnce the training is finished the model will be saved in the `output_dir` specified in the command. In this example it's `sd-naruto-model`. To load the fine-tuned model for inference just pass that path to `StableDiffusionPipeline`\n\n```python\nimport torch\nfrom diffusers import StableDiffusionPipeline\n\nmodel_path = \"path_to_saved_model\"\npipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)\npipe.to(\"cuda\")\n\nimage = pipe(prompt=\"yoda\").images[0]\nimage.save(\"yoda-naruto.png\")\n```\n\nCheckpoints only save the unet, so to run inference from a checkpoint, just load the unet\n\n```python\nimport torch\nfrom diffusers import StableDiffusionPipeline, UNet2DConditionModel\n\nmodel_path = \"path_to_saved_model\"\nunet = UNet2DConditionModel.from_pretrained(model_path + \"/checkpoint-/unet\", torch_dtype=torch.float16)\n\npipe = StableDiffusionPipeline.from_pretrained(\"\", unet=unet, torch_dtype=torch.float16)\npipe.to(\"cuda\")\n\nimage = pipe(prompt=\"yoda\").images[0]\nimage.save(\"yoda-naruto.png\")\n```\n\n#### Training with multiple GPUs\n\n`accelerate` allows for seamless multi-GPU training. Follow the instructions [here](https://huggingface.co/docs/accelerate/basic_tutorials/launch)\nfor running distributed training with `accelerate`. Here is an example command:\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport DATASET_NAME=\"lambdalabs/naruto-blip-captions\"\n\naccelerate launch --mixed_precision=\"fp16\" --multi_gpu train_text_to_image.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --dataset_name=$DATASET_NAME \\\n --use_ema \\\n --resolution=512 --center_crop --random_flip \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --max_train_steps=15000 \\\n --learning_rate=1e-05 \\\n --max_grad_norm=1 \\\n --lr_scheduler=\"constant\" --lr_warmup_steps=0 \\\n --output_dir=\"sd-naruto-model\"\n```\n\n\n#### Training with Min-SNR weighting\n\nWe support training with the Min-SNR weighting strategy proposed in [Efficient Diffusion Training via Min-SNR Weighting Strategy](https://arxiv.org/abs/2303.09556) which helps to achieve faster convergence\nby rebalancing the loss. In order to use it, one needs to set the `--snr_gamma` argument. The recommended\nvalue when using it is 5.0.\n\nYou can find [this project on Weights and Biases](https://wandb.ai/sayakpaul/text2image-finetune-minsnr) that compares the loss surfaces of the following setups:\n\n* Training without the Min-SNR weighting strategy\n* Training with the Min-SNR weighting strategy (`snr_gamma` set to 5.0)\n* Training with the Min-SNR weighting strategy (`snr_gamma` set to 1.0)\n\nFor our small Narutos dataset, the effects of Min-SNR weighting strategy might not appear to be pronounced, but for larger datasets, we believe the effects will be more pronounced.\n\nAlso, note that in this example, we either predict `epsilon` (i.e., the noise) or the `v_prediction`. For both of these cases, the formulation of the Min-SNR weighting strategy that we have used holds.\n\n#### Training with DREAM\n\nWe support training epsilon (noise) prediction models using the [DREAM (Diffusion Rectification and Estimation-Adaptive Models) strategy](https://arxiv.org/abs/2312.00210). DREAM claims to increase model fidelity for the performance cost of an extra grad-less unet `forward` step in the training loop. You can turn on DREAM training by using the `--dream_training` argument. The `--dream_detail_preservation` argument controls the detail preservation variable p and is the default of 1 from the paper.\n\n\n## Training with LoRA\n\nLow-Rank Adaption of Large Language Models was first introduced by Microsoft in [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) by *Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen*.\n\nIn a nutshell, LoRA allows adapting pretrained models by adding pairs of rank-decomposition matrices to existing weights and **only** training those newly added weights. This has a couple of advantages:\n\n- Previous pretrained weights are kept frozen so that model is not prone to [catastrophic forgetting](https://www.pnas.org/doi/10.1073/pnas.1611835114).\n- Rank-decomposition matrices have significantly fewer parameters than original model, which means that trained LoRA weights are easily portable.\n- LoRA attention layers allow to control to which extent the model is adapted toward new training images via a `scale` parameter.\n\n[cloneofsimo](https://github.com/cloneofsimo) was the first to try out LoRA training for Stable Diffusion in the popular [lora](https://github.com/cloneofsimo/lora) GitHub repository.\n\nWith LoRA, it's possible to fine-tune Stable Diffusion on a custom image-caption pair dataset\non consumer GPUs like Tesla T4, Tesla V100.\n\n### Training\n\nFirst, you need to set up your development environment as is explained in the [installation section](#installing-the-dependencies). Make sure to set the `MODEL_NAME` and `DATASET_NAME` environment variables. Here, we will use [Stable Diffusion v1-4](https://hf.co/CompVis/stable-diffusion-v1-4) and the [Narutos dataset](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions).\n\n**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**\n\n**___Note: It is quite useful to monitor the training progress by regularly generating sample images during training. [Weights and Biases](https://docs.wandb.ai/quickstart) is a nice solution to easily see generating images during training. All you need to do is to run `pip install wandb` before training to automatically log images.___**\n\n```bash\nexport MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"\nexport DATASET_NAME=\"lambdalabs/naruto-blip-captions\"\n```\n\nFor this example we want to directly store the trained LoRA embeddings on the Hub, so\nwe need to be logged in and add the `--push_to_hub` flag.\n\n```bash\nhuggingface-cli login\n```\n\nNow we can start training!\n\n```bash\naccelerate launch --mixed_precision=\"fp16\" train_text_to_image_lora.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --dataset_name=$DATASET_NAME --caption_column=\"text\" \\\n --resolution=512 --random_flip \\\n --train_batch_size=1 \\\n --num_train_epochs=100 --checkpointing_steps=5000 \\\n --learning_rate=1e-04 --lr_scheduler=\"constant\" --lr_warmup_steps=0 \\\n --seed=42 \\\n --output_dir=\"sd-naruto-model-lora\" \\\n --validation_prompt=\"cute dragon creature\" --report_to=\"wandb\"\n```\n\nThe above command will also run inference as fine-tuning progresses and log the results to Weights and Biases.\n\n**___Note: When using LoRA we can use a much higher learning rate compared to non-LoRA fine-tuning. Here we use *1e-4* instead of the usual *1e-5*. Also, by using LoRA, it's possible to run `train_text_to_image_lora.py` in consumer GPUs like T4 or V100.___**\n\nThe final LoRA embedding weights have been uploaded to [sayakpaul/sd-model-finetuned-lora-t4](https://huggingface.co/sayakpaul/sd-model-finetuned-lora-t4). **___Note: [The final weights](https://huggingface.co/sayakpaul/sd-model-finetuned-lora-t4/blob/main/pytorch_lora_weights.bin) are only 3 MB in size, which is orders of magnitudes smaller than the original model.___**\n\nYou can check some inference samples that were logged during the course of the fine-tuning process [here](https://wandb.ai/sayakpaul/text2image-fine-tune/runs/q4lc0xsw).\n\n### Inference\n\nOnce you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline` after loading the trained LoRA weights. You\nneed to pass the `output_dir` for loading the LoRA weights which, in this case, is `sd-naruto-model-lora`.\n\n```python\nfrom diffusers import StableDiffusionPipeline\nimport torch\n\nmodel_path = \"sayakpaul/sd-model-finetuned-lora-t4\"\npipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", torch_dtype=torch.float16)\npipe.unet.load_attn_procs(model_path)\npipe.to(\"cuda\")\n\nprompt = \"A naruto with green eyes and red legs.\"\nimage = pipe(prompt, num_inference_steps=30, guidance_scale=7.5).images[0]\nimage.save(\"naruto.png\")\n```\n\nIf you are loading the LoRA parameters from the Hub and if the Hub repository has\na `base_model` tag (such as [this](https://huggingface.co/sayakpaul/sd-model-finetuned-lora-t4/blob/main/README.md?code=true#L4)), then\nyou can do:\n\n```py\nfrom huggingface_hub.repocard import RepoCard\n\nlora_model_id = \"sayakpaul/sd-model-finetuned-lora-t4\"\ncard = RepoCard.load(lora_model_id)\nbase_model_id = card.data.to_dict()[\"base_model\"]\n\npipe = StableDiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16)\n...\n```\n\n## Training with Flax/JAX\n\nFor faster training on TPUs and GPUs you can leverage the flax training example. Follow the instructions above to get the model and dataset before running the script.\n\n**___Note: The flax example doesn't yet support features like gradient checkpoint, gradient accumulation etc, so to use flax for faster training we will need >30GB cards or TPU v3.___**\n\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n```bash\npip install -U -r requirements_flax.txt\n```\n\n```bash\nexport MODEL_NAME=\"duongna/stable-diffusion-v1-4-flax\"\nexport DATASET_NAME=\"lambdalabs/naruto-blip-captions\"\n\npython train_text_to_image_flax.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --dataset_name=$DATASET_NAME \\\n --resolution=512 --center_crop --random_flip \\\n --train_batch_size=1 \\\n --mixed_precision=\"fp16\" \\\n --max_train_steps=15000 \\\n --learning_rate=1e-05 \\\n --max_grad_norm=1 \\\n --output_dir=\"sd-naruto-model\"\n```\n\nTo run on your own training files prepare the dataset according to the format required by `datasets`, you can find the instructions for how to do that in this [document](https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder-with-metadata).\nIf you wish to use custom loading logic, you should modify the script, we have left pointers for that in the training script.\n\n```bash\nexport MODEL_NAME=\"duongna/stable-diffusion-v1-4-flax\"\nexport TRAIN_DIR=\"path_to_your_dataset\"\n\npython train_text_to_image_flax.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_data_dir=$TRAIN_DIR \\\n --resolution=512 --center_crop --random_flip \\\n --train_batch_size=1 \\\n --mixed_precision=\"fp16\" \\\n --max_train_steps=15000 \\\n --learning_rate=1e-05 \\\n --max_grad_norm=1 \\\n --output_dir=\"sd-naruto-model\"\n```\n\n### Training with xFormers:\n\nYou can enable memory efficient attention by [installing xFormers](https://huggingface.co/docs/diffusers/main/en/optimization/xformers) and passing the `--enable_xformers_memory_efficient_attention` argument to the script.\n\nxFormers training is not available for Flax/JAX.\n\n**Note**:\n\nAccording to [this issue](https://github.com/huggingface/diffusers/issues/2234#issuecomment-1416931212), xFormers `v0.0.16` cannot be used for training in some GPUs. If you observe that problem, please install a development version as indicated in that comment.\n\n## Stable Diffusion XL\n\n* We support fine-tuning the UNet shipped in [Stable Diffusion XL](https://huggingface.co/papers/2307.01952) via the `train_text_to_image_sdxl.py` script. Please refer to the docs [here](./README_sdxl.md).\n* We also support fine-tuning of the UNet and Text Encoder shipped in [Stable Diffusion XL](https://huggingface.co/papers/2307.01952) with LoRA via the `train_text_to_image_lora_sdxl.py` script. Please refer to the docs [here](./README_sdxl.md).\n" }, { "path": "diffusers/examples/text_to_image/README_sdxl.md", "content": "# Stable Diffusion XL text-to-image fine-tuning\n\nThe `train_text_to_image_sdxl.py` script shows how to fine-tune Stable Diffusion XL (SDXL) on your own dataset.\n\n🚨 This script is experimental. The script fine-tunes the whole model and often times the model overfits and runs into issues like catastrophic forgetting. It's recommended to try different hyperparameters to get the best result on your dataset. 🚨\n\n## Running locally with PyTorch\n\n### Installing the dependencies\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n**Important**\n\nTo make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:\n\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install -e .\n```\n\nThen cd in the `examples/text_to_image` folder and run\n```bash\npip install -r requirements_sdxl.txt\n```\n\nAnd initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\n\nOr for a default accelerate configuration without answering questions about your environment\n\n```bash\naccelerate config default\n```\n\nOr if your environment doesn't support an interactive shell (e.g., a notebook)\n\n```python\nfrom accelerate.utils import write_basic_config\nwrite_basic_config()\n```\n\nWhen running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups.\nNote also that we use PEFT library as backend for LoRA training, make sure to have `peft>=0.6.0` installed in your environment.\n\n### Training\n\n```bash\nexport MODEL_NAME=\"stabilityai/stable-diffusion-xl-base-1.0\"\nexport VAE_NAME=\"madebyollin/sdxl-vae-fp16-fix\"\nexport DATASET_NAME=\"lambdalabs/naruto-blip-captions\"\n\naccelerate launch train_text_to_image_sdxl.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --pretrained_vae_model_name_or_path=$VAE_NAME \\\n --dataset_name=$DATASET_NAME \\\n --enable_xformers_memory_efficient_attention \\\n --resolution=512 --center_crop --random_flip \\\n --proportion_empty_prompts=0.2 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 --gradient_checkpointing \\\n --max_train_steps=10000 \\\n --use_8bit_adam \\\n --learning_rate=1e-06 --lr_scheduler=\"constant\" --lr_warmup_steps=0 \\\n --mixed_precision=\"fp16\" \\\n --report_to=\"wandb\" \\\n --validation_prompt=\"a cute Sundar Pichai creature\" --validation_epochs 5 \\\n --checkpointing_steps=5000 \\\n --output_dir=\"sdxl-naruto-model\" \\\n --push_to_hub\n```\n\n**Notes**:\n\n* The `train_text_to_image_sdxl.py` script pre-computes text embeddings and the VAE encodings and keeps them in memory. While for smaller datasets like [`lambdalabs/naruto-blip-captions`](https://hf.co/datasets/lambdalabs/naruto-blip-captions), it might not be a problem, it can definitely lead to memory problems when the script is used on a larger dataset. For those purposes, you would want to serialize these pre-computed representations to disk separately and load them during the fine-tuning process. Refer to [this PR](https://github.com/huggingface/diffusers/pull/4505) for a more in-depth discussion.\n* The training script is compute-intensive and may not run on a consumer GPU like Tesla T4.\n* The training command shown above performs intermediate quality validation in between the training epochs and logs the results to Weights and Biases. `--report_to`, `--validation_prompt`, and `--validation_epochs` are the relevant CLI arguments here.\n* SDXL's VAE is known to suffer from numerical instability issues. This is why we also expose a CLI argument namely `--pretrained_vae_model_name_or_path` that lets you specify the location of a better VAE (such as [this one](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix)).\n\n### Inference\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\nmodel_path = \"you-model-id-goes-here\" # <-- change this\npipe = DiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)\npipe.to(\"cuda\")\n\nprompt = \"A naruto with green eyes and red legs.\"\nimage = pipe(prompt, num_inference_steps=30, guidance_scale=7.5).images[0]\nimage.save(\"naruto.png\")\n```\n\n### Inference in Pytorch XLA\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\nimport torch_xla.core.xla_model as xm\n\nmodel_id = \"stabilityai/stable-diffusion-xl-base-1.0\"\npipe = DiffusionPipeline.from_pretrained(model_id)\n\ndevice = xm.xla_device()\npipe.to(device)\n\nprompt = \"A naruto with green eyes and red legs.\"\nstart = time()\nimage = pipe(prompt, num_inference_steps=inference_steps).images[0]\nprint(f'Compilation time is {time()-start} sec')\nimage.save(\"naruto.png\")\n\nstart = time()\nimage = pipe(prompt, num_inference_steps=inference_steps).images[0]\nprint(f'Inference time is {time()-start} sec after compilation')\n```\n\nNote: There is a warmup step in PyTorch XLA. This takes longer because of\ncompilation and optimization. To see the real benefits of Pytorch XLA and\nspeedup, we need to call the pipe again on the input with the same length\nas the original prompt to reuse the optimized graph and get the performance\nboost.\n\n## LoRA training example for Stable Diffusion XL (SDXL)\n\nLow-Rank Adaption of Large Language Models was first introduced by Microsoft in [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) by *Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen*.\n\nIn a nutshell, LoRA allows adapting pretrained models by adding pairs of rank-decomposition matrices to existing weights and **only** training those newly added weights. This has a couple of advantages:\n\n- Previous pretrained weights are kept frozen so that model is not prone to [catastrophic forgetting](https://www.pnas.org/doi/10.1073/pnas.1611835114).\n- Rank-decomposition matrices have significantly fewer parameters than original model, which means that trained LoRA weights are easily portable.\n- LoRA attention layers allow to control to which extent the model is adapted toward new training images via a `scale` parameter.\n\n[cloneofsimo](https://github.com/cloneofsimo) was the first to try out LoRA training for Stable Diffusion in the popular [lora](https://github.com/cloneofsimo/lora) GitHub repository.\n\nWith LoRA, it's possible to fine-tune Stable Diffusion on a custom image-caption pair dataset\non consumer GPUs like Tesla T4, Tesla V100.\n\n### Training\n\nFirst, you need to set up your development environment as is explained in the [installation section](#installing-the-dependencies). Make sure to set the `MODEL_NAME` and `DATASET_NAME` environment variables and, optionally, the `VAE_NAME` variable. Here, we will use [Stable Diffusion XL 1.0-base](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and the [Narutos dataset](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions).\n\n**___Note: It is quite useful to monitor the training progress by regularly generating sample images during training. [Weights and Biases](https://docs.wandb.ai/quickstart) is a nice solution to easily see generating images during training. All you need to do is to run `pip install wandb` before training to automatically log images.___**\n\n```bash\nexport MODEL_NAME=\"stabilityai/stable-diffusion-xl-base-1.0\"\nexport VAE_NAME=\"madebyollin/sdxl-vae-fp16-fix\"\nexport DATASET_NAME=\"lambdalabs/naruto-blip-captions\"\n```\n\nFor this example we want to directly store the trained LoRA embeddings on the Hub, so\nwe need to be logged in and add the `--push_to_hub` flag.\n\n```bash\nhuggingface-cli login\n```\n\nNow we can start training!\n\n```bash\naccelerate launch train_text_to_image_lora_sdxl.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --pretrained_vae_model_name_or_path=$VAE_NAME \\\n --dataset_name=$DATASET_NAME --caption_column=\"text\" \\\n --resolution=1024 --random_flip \\\n --train_batch_size=1 \\\n --num_train_epochs=2 --checkpointing_steps=500 \\\n --learning_rate=1e-04 --lr_scheduler=\"constant\" --lr_warmup_steps=0 \\\n --mixed_precision=\"fp16\" \\\n --seed=42 \\\n --output_dir=\"sd-naruto-model-lora-sdxl\" \\\n --validation_prompt=\"cute dragon creature\" --report_to=\"wandb\" \\\n --push_to_hub\n```\n\nThe above command will also run inference as fine-tuning progresses and log the results to Weights and Biases.\n\n**Notes**:\n\n* SDXL's VAE is known to suffer from numerical instability issues. This is why we also expose a CLI argument namely `--pretrained_vae_model_name_or_path` that lets you specify the location of a better VAE (such as [this one](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix)).\n\n\n### Using DeepSpeed\nUsing DeepSpeed one can reduce the consumption of GPU memory, enabling the training of models on GPUs with smaller memory sizes. DeepSpeed is capable of offloading model parameters to the machine's memory, or it can distribute parameters, gradients, and optimizer states across multiple GPUs. This allows for the training of larger models under the same hardware configuration.\n\nFirst, you need to use the `accelerate config` command to choose to use DeepSpeed, or manually use the accelerate config file to set up DeepSpeed.\n\nHere is an example of a config file for using DeepSpeed. For more detailed explanations of the configuration, you can refer to this [link](https://huggingface.co/docs/accelerate/usage_guides/deepspeed).\n```yaml\ncompute_environment: LOCAL_MACHINE\ndebug: true\ndeepspeed_config:\n gradient_accumulation_steps: 1\n gradient_clipping: 1.0\n offload_optimizer_device: none\n offload_param_device: none\n zero3_init_flag: false\n zero_stage: 2\ndistributed_type: DEEPSPEED\ndowncast_bf16: 'no'\nmachine_rank: 0\nmain_training_function: main\nmixed_precision: fp16\nnum_machines: 1\nnum_processes: 1\nrdzv_backend: static\nsame_network: true\ntpu_env: []\ntpu_use_cluster: false\ntpu_use_sudo: false\nuse_cpu: false\n```\nYou need to save the mentioned configuration as an `accelerate_config.yaml` file. Then, you need to input the path of your `accelerate_config.yaml` file into the `ACCELERATE_CONFIG_FILE` parameter. This way you can use DeepSpeed to train your SDXL model in LoRA. Additionally, you can use DeepSpeed to train other SD models in this way.\n\n```shell\nexport MODEL_NAME=\"stabilityai/stable-diffusion-xl-base-1.0\"\nexport VAE_NAME=\"madebyollin/sdxl-vae-fp16-fix\"\nexport DATASET_NAME=\"lambdalabs/naruto-blip-captions\"\nexport ACCELERATE_CONFIG_FILE=\"your accelerate_config.yaml\"\n\naccelerate launch --config_file $ACCELERATE_CONFIG_FILE train_text_to_image_lora_sdxl.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --pretrained_vae_model_name_or_path=$VAE_NAME \\\n --dataset_name=$DATASET_NAME --caption_column=\"text\" \\\n --resolution=1024 \\\n --train_batch_size=1 \\\n --num_train_epochs=2 \\\n --checkpointing_steps=2 \\\n --learning_rate=1e-04 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --mixed_precision=\"fp16\" \\\n --max_train_steps=20 \\\n --validation_epochs=20 \\\n --seed=1234 \\\n --output_dir=\"sd-naruto-model-lora-sdxl\" \\\n --validation_prompt=\"cute dragon creature\"\n\n```\n\n\n### Finetuning the text encoder and UNet\n\nThe script also allows you to finetune the `text_encoder` along with the `unet`.\n\n🚨 Training the text encoder requires additional memory.\n\nPass the `--train_text_encoder` argument to the training script to enable finetuning the `text_encoder` and `unet`:\n\n```bash\naccelerate launch train_text_to_image_lora_sdxl.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --dataset_name=$DATASET_NAME --caption_column=\"text\" \\\n --resolution=1024 --random_flip \\\n --train_batch_size=1 \\\n --num_train_epochs=2 --checkpointing_steps=500 \\\n --learning_rate=1e-04 --lr_scheduler=\"constant\" --lr_warmup_steps=0 \\\n --seed=42 \\\n --output_dir=\"sd-naruto-model-lora-sdxl-txt\" \\\n --train_text_encoder \\\n --validation_prompt=\"cute dragon creature\" --report_to=\"wandb\" \\\n --push_to_hub\n```\n\n### Inference\n\nOnce you have trained a model using above command, the inference can be done simply using the `DiffusionPipeline` after loading the trained LoRA weights. You\nneed to pass the `output_dir` for loading the LoRA weights which, in this case, is `sd-naruto-model-lora-sdxl`.\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\nmodel_path = \"takuoko/sd-naruto-model-lora-sdxl\"\npipe = DiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16)\npipe.to(\"cuda\")\npipe.load_lora_weights(model_path)\n\nprompt = \"A naruto with green eyes and red legs.\"\nimage = pipe(prompt, num_inference_steps=30, guidance_scale=7.5).images[0]\nimage.save(\"naruto.png\")\n```\n" }, { "path": "diffusers/examples/text_to_image/requirements.txt", "content": "accelerate>=0.16.0\ntorchvision\ntransformers>=4.25.1\ndatasets>=2.19.1\nftfy\ntensorboard\nJinja2\npeft==0.7.0\n" }, { "path": "diffusers/examples/text_to_image/requirements_flax.txt", "content": "transformers>=4.25.1\ndatasets\nflax\noptax\ntorch\ntorchvision\nftfy\ntensorboard\nJinja2\n" }, { "path": "diffusers/examples/text_to_image/requirements_sdxl.txt", "content": "accelerate>=0.22.0\ntorchvision\ntransformers>=4.25.1\nftfy\ntensorboard\nJinja2\ndatasets\npeft==0.7.0" }, { "path": "diffusers/examples/text_to_image/test_text_to_image.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport logging\nimport os\nimport shutil\nimport sys\nimport tempfile\n\nfrom diffusers import DiffusionPipeline, UNet2DConditionModel # noqa: E402\n\n\nsys.path.append(\"..\")\nfrom test_examples_utils import ExamplesTestsAccelerate, run_command # noqa: E402\n\n\nlogging.basicConfig(level=logging.DEBUG)\n\nlogger = logging.getLogger()\nstream_handler = logging.StreamHandler(sys.stdout)\nlogger.addHandler(stream_handler)\n\n\nclass TextToImage(ExamplesTestsAccelerate):\n def test_text_to_image(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/text_to_image/train_text_to_image.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 64\n --center_crop\n --random_flip\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n # save_pretrained smoke test\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"unet\", \"diffusion_pytorch_model.safetensors\")))\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"scheduler\", \"scheduler_config.json\")))\n\n def test_text_to_image_checkpointing(self):\n pretrained_model_name_or_path = \"hf-internal-testing/tiny-stable-diffusion-pipe\"\n prompt = \"a prompt\"\n\n with tempfile.TemporaryDirectory() as tmpdir:\n # Run training script with checkpointing\n # max_train_steps == 4, checkpointing_steps == 2\n # Should create checkpoints at steps 2, 4\n\n initial_run_args = f\"\"\"\n examples/text_to_image/train_text_to_image.py\n --pretrained_model_name_or_path {pretrained_model_name_or_path}\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 64\n --center_crop\n --random_flip\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 4\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --checkpointing_steps=2\n --seed=0\n \"\"\".split()\n\n run_command(self._launch_args + initial_run_args)\n\n pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)\n pipe(prompt, num_inference_steps=1)\n\n # check checkpoint directories exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-2\", \"checkpoint-4\"},\n )\n\n # check can run an intermediate checkpoint\n unet = UNet2DConditionModel.from_pretrained(tmpdir, subfolder=\"checkpoint-2/unet\")\n pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, unet=unet, safety_checker=None)\n pipe(prompt, num_inference_steps=1)\n\n # Remove checkpoint 2 so that we can check only later checkpoints exist after resuming\n shutil.rmtree(os.path.join(tmpdir, \"checkpoint-2\"))\n\n # Run training script for 2 total steps resuming from checkpoint 4\n\n resume_run_args = f\"\"\"\n examples/text_to_image/train_text_to_image.py\n --pretrained_model_name_or_path {pretrained_model_name_or_path}\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 64\n --center_crop\n --random_flip\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --checkpointing_steps=1\n --resume_from_checkpoint=checkpoint-4\n --seed=0\n \"\"\".split()\n\n run_command(self._launch_args + resume_run_args)\n\n # check can run new fully trained pipeline\n pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)\n pipe(prompt, num_inference_steps=1)\n\n # no checkpoint-2 -> check old checkpoints do not exist\n # check new checkpoints exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-4\", \"checkpoint-5\"},\n )\n\n def test_text_to_image_checkpointing_use_ema(self):\n pretrained_model_name_or_path = \"hf-internal-testing/tiny-stable-diffusion-pipe\"\n prompt = \"a prompt\"\n\n with tempfile.TemporaryDirectory() as tmpdir:\n # Run training script with checkpointing\n # max_train_steps == 4, checkpointing_steps == 2\n # Should create checkpoints at steps 2, 4\n\n initial_run_args = f\"\"\"\n examples/text_to_image/train_text_to_image.py\n --pretrained_model_name_or_path {pretrained_model_name_or_path}\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 64\n --center_crop\n --random_flip\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 4\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --checkpointing_steps=2\n --use_ema\n --seed=0\n \"\"\".split()\n\n run_command(self._launch_args + initial_run_args)\n\n pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)\n pipe(prompt, num_inference_steps=2)\n\n # check checkpoint directories exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-2\", \"checkpoint-4\"},\n )\n\n # check can run an intermediate checkpoint\n unet = UNet2DConditionModel.from_pretrained(tmpdir, subfolder=\"checkpoint-2/unet\")\n pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, unet=unet, safety_checker=None)\n pipe(prompt, num_inference_steps=1)\n\n # Remove checkpoint 2 so that we can check only later checkpoints exist after resuming\n shutil.rmtree(os.path.join(tmpdir, \"checkpoint-2\"))\n\n # Run training script for 2 total steps resuming from checkpoint 4\n\n resume_run_args = f\"\"\"\n examples/text_to_image/train_text_to_image.py\n --pretrained_model_name_or_path {pretrained_model_name_or_path}\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 64\n --center_crop\n --random_flip\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --checkpointing_steps=1\n --resume_from_checkpoint=checkpoint-4\n --use_ema\n --seed=0\n \"\"\".split()\n\n run_command(self._launch_args + resume_run_args)\n\n # check can run new fully trained pipeline\n pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)\n pipe(prompt, num_inference_steps=1)\n\n # no checkpoint-2 -> check old checkpoints do not exist\n # check new checkpoints exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-4\", \"checkpoint-5\"},\n )\n\n def test_text_to_image_checkpointing_checkpoints_total_limit(self):\n pretrained_model_name_or_path = \"hf-internal-testing/tiny-stable-diffusion-pipe\"\n prompt = \"a prompt\"\n\n with tempfile.TemporaryDirectory() as tmpdir:\n # Run training script with checkpointing\n # max_train_steps == 6, checkpointing_steps == 2, checkpoints_total_limit == 2\n # Should create checkpoints at steps 2, 4, 6\n # with checkpoint at step 2 deleted\n\n initial_run_args = f\"\"\"\n examples/text_to_image/train_text_to_image.py\n --pretrained_model_name_or_path {pretrained_model_name_or_path}\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 64\n --center_crop\n --random_flip\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 6\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --checkpointing_steps=2\n --checkpoints_total_limit=2\n --seed=0\n \"\"\".split()\n\n run_command(self._launch_args + initial_run_args)\n\n pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)\n pipe(prompt, num_inference_steps=1)\n\n # check checkpoint directories exist\n # checkpoint-2 should have been deleted\n self.assertEqual({x for x in os.listdir(tmpdir) if \"checkpoint\" in x}, {\"checkpoint-4\", \"checkpoint-6\"})\n\n def test_text_to_image_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):\n pretrained_model_name_or_path = \"hf-internal-testing/tiny-stable-diffusion-pipe\"\n prompt = \"a prompt\"\n\n with tempfile.TemporaryDirectory() as tmpdir:\n # Run training script with checkpointing\n # max_train_steps == 4, checkpointing_steps == 2\n # Should create checkpoints at steps 2, 4\n\n initial_run_args = f\"\"\"\n examples/text_to_image/train_text_to_image.py\n --pretrained_model_name_or_path {pretrained_model_name_or_path}\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 64\n --center_crop\n --random_flip\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 4\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --checkpointing_steps=2\n --seed=0\n \"\"\".split()\n\n run_command(self._launch_args + initial_run_args)\n\n pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)\n pipe(prompt, num_inference_steps=1)\n\n # check checkpoint directories exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-2\", \"checkpoint-4\"},\n )\n\n # resume and we should try to checkpoint at 6, where we'll have to remove\n # checkpoint-2 and checkpoint-4 instead of just a single previous checkpoint\n\n resume_run_args = f\"\"\"\n examples/text_to_image/train_text_to_image.py\n --pretrained_model_name_or_path {pretrained_model_name_or_path}\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 64\n --center_crop\n --random_flip\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 8\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --checkpointing_steps=2\n --resume_from_checkpoint=checkpoint-4\n --checkpoints_total_limit=2\n --seed=0\n \"\"\".split()\n\n run_command(self._launch_args + resume_run_args)\n\n pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)\n pipe(prompt, num_inference_steps=1)\n\n # check checkpoint directories exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-6\", \"checkpoint-8\"},\n )\n\n\nclass TextToImageSDXL(ExamplesTestsAccelerate):\n def test_text_to_image_sdxl(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/text_to_image/train_text_to_image_sdxl.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 64\n --center_crop\n --random_flip\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n # save_pretrained smoke test\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"unet\", \"diffusion_pytorch_model.safetensors\")))\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"scheduler\", \"scheduler_config.json\")))\n" }, { "path": "diffusers/examples/text_to_image/test_text_to_image_lora.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport logging\nimport os\nimport sys\nimport tempfile\n\nimport safetensors\n\nfrom diffusers import DiffusionPipeline # noqa: E402\n\n\nsys.path.append(\"..\")\nfrom test_examples_utils import ExamplesTestsAccelerate, run_command # noqa: E402\n\n\nlogging.basicConfig(level=logging.DEBUG)\n\nlogger = logging.getLogger()\nstream_handler = logging.StreamHandler(sys.stdout)\nlogger.addHandler(stream_handler)\n\n\nclass TextToImageLoRA(ExamplesTestsAccelerate):\n def test_text_to_image_lora_sdxl_checkpointing_checkpoints_total_limit(self):\n prompt = \"a prompt\"\n pipeline_path = \"hf-internal-testing/tiny-stable-diffusion-xl-pipe\"\n\n with tempfile.TemporaryDirectory() as tmpdir:\n # Run training script with checkpointing\n # max_train_steps == 6, checkpointing_steps == 2, checkpoints_total_limit == 2\n # Should create checkpoints at steps 2, 4, 6\n # with checkpoint at step 2 deleted\n\n initial_run_args = f\"\"\"\n examples/text_to_image/train_text_to_image_lora_sdxl.py\n --pretrained_model_name_or_path {pipeline_path}\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 6\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --checkpointing_steps=2\n --checkpoints_total_limit=2\n \"\"\".split()\n\n run_command(self._launch_args + initial_run_args)\n\n pipe = DiffusionPipeline.from_pretrained(pipeline_path)\n pipe.load_lora_weights(tmpdir)\n pipe(prompt, num_inference_steps=1)\n\n # check checkpoint directories exist\n # checkpoint-2 should have been deleted\n self.assertEqual({x for x in os.listdir(tmpdir) if \"checkpoint\" in x}, {\"checkpoint-4\", \"checkpoint-6\"})\n\n def test_text_to_image_lora_checkpointing_checkpoints_total_limit(self):\n pretrained_model_name_or_path = \"hf-internal-testing/tiny-stable-diffusion-pipe\"\n prompt = \"a prompt\"\n\n with tempfile.TemporaryDirectory() as tmpdir:\n # Run training script with checkpointing\n # max_train_steps == 6, checkpointing_steps == 2, checkpoints_total_limit == 2\n # Should create checkpoints at steps 2, 4, 6\n # with checkpoint at step 2 deleted\n\n initial_run_args = f\"\"\"\n examples/text_to_image/train_text_to_image_lora.py\n --pretrained_model_name_or_path {pretrained_model_name_or_path}\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 64\n --center_crop\n --random_flip\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 6\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --checkpointing_steps=2\n --checkpoints_total_limit=2\n --seed=0\n --num_validation_images=0\n \"\"\".split()\n\n run_command(self._launch_args + initial_run_args)\n\n pipe = DiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-pipe\", safety_checker=None\n )\n pipe.load_lora_weights(tmpdir)\n pipe(prompt, num_inference_steps=1)\n\n # check checkpoint directories exist\n # checkpoint-2 should have been deleted\n self.assertEqual({x for x in os.listdir(tmpdir) if \"checkpoint\" in x}, {\"checkpoint-4\", \"checkpoint-6\"})\n\n def test_text_to_image_lora_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):\n pretrained_model_name_or_path = \"hf-internal-testing/tiny-stable-diffusion-pipe\"\n prompt = \"a prompt\"\n\n with tempfile.TemporaryDirectory() as tmpdir:\n # Run training script with checkpointing\n # max_train_steps == 4, checkpointing_steps == 2\n # Should create checkpoints at steps 2, 4\n\n initial_run_args = f\"\"\"\n examples/text_to_image/train_text_to_image_lora.py\n --pretrained_model_name_or_path {pretrained_model_name_or_path}\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 64\n --center_crop\n --random_flip\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 4\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --checkpointing_steps=2\n --seed=0\n --num_validation_images=0\n \"\"\".split()\n\n run_command(self._launch_args + initial_run_args)\n\n pipe = DiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-pipe\", safety_checker=None\n )\n pipe.load_lora_weights(tmpdir)\n pipe(prompt, num_inference_steps=1)\n\n # check checkpoint directories exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-2\", \"checkpoint-4\"},\n )\n\n # resume and we should try to checkpoint at 6, where we'll have to remove\n # checkpoint-2 and checkpoint-4 instead of just a single previous checkpoint\n\n resume_run_args = f\"\"\"\n examples/text_to_image/train_text_to_image_lora.py\n --pretrained_model_name_or_path {pretrained_model_name_or_path}\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 64\n --center_crop\n --random_flip\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 8\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --checkpointing_steps=2\n --resume_from_checkpoint=checkpoint-4\n --checkpoints_total_limit=2\n --seed=0\n --num_validation_images=0\n \"\"\".split()\n\n run_command(self._launch_args + resume_run_args)\n\n pipe = DiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-pipe\", safety_checker=None\n )\n pipe.load_lora_weights(tmpdir)\n pipe(prompt, num_inference_steps=1)\n\n # check checkpoint directories exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-6\", \"checkpoint-8\"},\n )\n\n\nclass TextToImageLoRASDXL(ExamplesTestsAccelerate):\n def test_text_to_image_lora_sdxl(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/text_to_image/train_text_to_image_lora_sdxl.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n # save_pretrained smoke test\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"pytorch_lora_weights.safetensors\")))\n\n # make sure the state_dict has the correct naming in the parameters.\n lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, \"pytorch_lora_weights.safetensors\"))\n is_lora = all(\"lora\" in k for k in lora_state_dict.keys())\n self.assertTrue(is_lora)\n\n def test_text_to_image_lora_sdxl_with_text_encoder(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/text_to_image/train_text_to_image_lora_sdxl.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --train_text_encoder\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n # save_pretrained smoke test\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"pytorch_lora_weights.safetensors\")))\n\n # make sure the state_dict has the correct naming in the parameters.\n lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, \"pytorch_lora_weights.safetensors\"))\n is_lora = all(\"lora\" in k for k in lora_state_dict.keys())\n self.assertTrue(is_lora)\n\n # when not training the text encoder, all the parameters in the state dict should start\n # with `\"unet\"` or `\"text_encoder\"` or `\"text_encoder_2\"` in their names.\n keys = lora_state_dict.keys()\n starts_with_unet = all(\n k.startswith(\"unet\") or k.startswith(\"text_encoder\") or k.startswith(\"text_encoder_2\") for k in keys\n )\n self.assertTrue(starts_with_unet)\n\n def test_text_to_image_lora_sdxl_text_encoder_checkpointing_checkpoints_total_limit(self):\n prompt = \"a prompt\"\n pipeline_path = \"hf-internal-testing/tiny-stable-diffusion-xl-pipe\"\n\n with tempfile.TemporaryDirectory() as tmpdir:\n # Run training script with checkpointing\n # max_train_steps == 6, checkpointing_steps == 2, checkpoints_total_limit == 2\n # Should create checkpoints at steps 2, 4, 6\n # with checkpoint at step 2 deleted\n\n initial_run_args = f\"\"\"\n examples/text_to_image/train_text_to_image_lora_sdxl.py\n --pretrained_model_name_or_path {pipeline_path}\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 6\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --train_text_encoder\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --checkpointing_steps=2\n --checkpoints_total_limit=2\n \"\"\".split()\n\n run_command(self._launch_args + initial_run_args)\n\n pipe = DiffusionPipeline.from_pretrained(pipeline_path)\n pipe.load_lora_weights(tmpdir)\n pipe(prompt, num_inference_steps=1)\n\n # check checkpoint directories exist\n # checkpoint-2 should have been deleted\n self.assertEqual({x for x in os.listdir(tmpdir) if \"checkpoint\" in x}, {\"checkpoint-4\", \"checkpoint-6\"})\n" }, { "path": "diffusers/examples/text_to_image/train_text_to_image.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport argparse\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom contextlib import nullcontext\nfrom pathlib import Path\n\nimport accelerate\nimport datasets\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.state import AcceleratorState\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModel, CLIPTokenizer\nfrom transformers.utils import ContextManagers\n\nimport diffusers\nfrom diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import EMAModel, compute_dream_and_update_latents, compute_snr\nfrom diffusers.utils import check_min_version, deprecate, is_wandb_available, make_image_grid\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\nif is_wandb_available():\n import wandb\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\nDATASET_NAME_MAPPING = {\n \"lambdalabs/naruto-blip-captions\": (\"image\", \"text\"),\n}\n\n\ndef save_model_card(\n args,\n repo_id: str,\n images: list = None,\n repo_folder: str = None,\n):\n img_str = \"\"\n if len(images) > 0:\n image_grid = make_image_grid(images, 1, len(args.validation_prompts))\n image_grid.save(os.path.join(repo_folder, \"val_imgs_grid.png\"))\n img_str += \"![val_imgs_grid](./val_imgs_grid.png)\\n\"\n\n model_description = f\"\"\"\n# Text-to-image finetuning - {repo_id}\n\nThis pipeline was finetuned from **{args.pretrained_model_name_or_path}** on the **{args.dataset_name}** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: {args.validation_prompts}: \\n\n{img_str}\n\n## Pipeline usage\n\nYou can use the pipeline like so:\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipeline = DiffusionPipeline.from_pretrained(\"{repo_id}\", torch_dtype=torch.float16)\nprompt = \"{args.validation_prompts[0]}\"\nimage = pipeline(prompt).images[0]\nimage.save(\"my_image.png\")\n```\n\n## Training info\n\nThese are the key hyperparameters used during training:\n\n* Epochs: {args.num_train_epochs}\n* Learning rate: {args.learning_rate}\n* Batch size: {args.train_batch_size}\n* Gradient accumulation steps: {args.gradient_accumulation_steps}\n* Image resolution: {args.resolution}\n* Mixed-precision: {args.mixed_precision}\n\n\"\"\"\n wandb_info = \"\"\n if is_wandb_available():\n wandb_run_url = None\n if wandb.run is not None:\n wandb_run_url = wandb.run.url\n\n if wandb_run_url is not None:\n wandb_info = f\"\"\"\nMore information on all the CLI arguments and the environment are available on your [`wandb` run page]({wandb_run_url}).\n\"\"\"\n\n model_description += wandb_info\n\n model_card = load_or_create_model_card(\n repo_id_or_path=repo_id,\n from_training=True,\n license=\"creativeml-openrail-m\",\n base_model=args.pretrained_model_name_or_path,\n model_description=model_description,\n inference=True,\n )\n\n tags = [\"stable-diffusion\", \"stable-diffusion-diffusers\", \"text-to-image\", \"diffusers\", \"diffusers-training\"]\n model_card = populate_model_card(model_card, tags=tags)\n\n model_card.save(os.path.join(repo_folder, \"README.md\"))\n\n\ndef log_validation(vae, text_encoder, tokenizer, unet, args, accelerator, weight_dtype, epoch):\n logger.info(\"Running validation... \")\n\n pipeline = StableDiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=accelerator.unwrap_model(vae),\n text_encoder=accelerator.unwrap_model(text_encoder),\n tokenizer=tokenizer,\n unet=accelerator.unwrap_model(unet),\n safety_checker=None,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n if args.enable_xformers_memory_efficient_attention:\n pipeline.enable_xformers_memory_efficient_attention()\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n images = []\n for i in range(len(args.validation_prompts)):\n if torch.backends.mps.is_available():\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type)\n\n with autocast_ctx:\n image = pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]\n\n images.append(image)\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, epoch, dataformats=\"NHWC\")\n elif tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompts[i]}\")\n for i, image in enumerate(images)\n ]\n }\n )\n else:\n logger.warning(f\"image logging not implemented for {tracker.name}\")\n\n del pipeline\n torch.cuda.empty_cache()\n\n return images\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--input_perturbation\", type=float, default=0, help=\"The scale of input perturbation. Recommended 0.1.\"\n )\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing an image.\"\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=\"text\",\n help=\"The column of the dataset containing a caption or a list of captions.\",\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--validation_prompts\",\n type=str,\n default=None,\n nargs=\"+\",\n help=(\"A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`.\"),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"sd-model-finetuned\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--snr_gamma\",\n type=float,\n default=None,\n help=\"SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. \"\n \"More details here: https://arxiv.org/abs/2303.09556.\",\n )\n parser.add_argument(\n \"--dream_training\",\n action=\"store_true\",\n help=(\n \"Use the DREAM training method, which makes training more efficient and accurate at the \",\n \"expense of doing an extra forward pass. See: https://arxiv.org/abs/2312.00210\",\n ),\n )\n parser.add_argument(\n \"--dream_detail_preservation\",\n type=float,\n default=1.0,\n help=\"Dream detail preservation factor p (should be greater than 0; default=1.0, as suggested in the paper)\",\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\"--use_ema\", action=\"store_true\", help=\"Whether to use EMA model.\")\n parser.add_argument(\n \"--non_ema_revision\",\n type=str,\n default=None,\n required=False,\n help=(\n \"Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or\"\n \" remote repository specified with --pretrained_model_name_or_path.\"\n ),\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--prediction_type\",\n type=str,\n default=None,\n help=\"The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediction_type` is chosen.\",\n )\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\"--noise_offset\", type=float, default=0, help=\"The scale of noise offset.\")\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=5,\n help=\"Run validation every X epochs.\",\n )\n parser.add_argument(\n \"--tracker_project_name\",\n type=str,\n default=\"text2image-fine-tune\",\n help=(\n \"The `project_name` argument passed to Accelerator.init_trackers for\"\n \" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator\"\n ),\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n\n # default to using the same revision for the non-ema model if not specified\n if args.non_ema_revision is None:\n args.non_ema_revision = args.revision\n\n return args\n\n\ndef main():\n args = parse_args()\n\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n if args.non_ema_revision is not None:\n deprecate(\n \"non_ema_revision!=None\",\n \"0.15.0\",\n message=(\n \"Downloading 'non_ema' weights from revision branches of the Hub is deprecated. Please make sure to\"\n \" use `--variant=non_ema` instead.\"\n ),\n )\n logging_dir = os.path.join(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load scheduler, tokenizer and models.\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n tokenizer = CLIPTokenizer.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"tokenizer\", revision=args.revision\n )\n\n def deepspeed_zero_init_disabled_context_manager():\n \"\"\"\n returns either a context list that includes one that will disable zero.Init or an empty context list\n \"\"\"\n deepspeed_plugin = AcceleratorState().deepspeed_plugin if accelerate.state.is_initialized() else None\n if deepspeed_plugin is None:\n return []\n\n return [deepspeed_plugin.zero3_init_context_manager(enable=False)]\n\n # Currently Accelerate doesn't know how to handle multiple models under Deepspeed ZeRO stage 3.\n # For this to work properly all models must be run through `accelerate.prepare`. But accelerate\n # will try to assign the same optimizer with the same weights to all models during\n # `deepspeed.initialize`, which of course doesn't work.\n #\n # For now the following workaround will partially support Deepspeed ZeRO-3, by excluding the 2\n # frozen models from being partitioned during `zero.Init` which gets called during\n # `from_pretrained` So CLIPTextModel and AutoencoderKL will not enjoy the parameter sharding\n # across multiple gpus and only UNet2DConditionModel will get ZeRO sharded.\n with ContextManagers(deepspeed_zero_init_disabled_context_manager()):\n text_encoder = CLIPTextModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision, variant=args.variant\n )\n\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.non_ema_revision\n )\n\n # Freeze vae and text_encoder and set unet to trainable\n vae.requires_grad_(False)\n text_encoder.requires_grad_(False)\n unet.train()\n\n # Create EMA for the unet.\n if args.use_ema:\n ema_unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n ema_unet = EMAModel(ema_unet.parameters(), model_cls=UNet2DConditionModel, model_config=ema_unet.config)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n if args.use_ema:\n ema_unet.save_pretrained(os.path.join(output_dir, \"unet_ema\"))\n\n for i, model in enumerate(models):\n model.save_pretrained(os.path.join(output_dir, \"unet\"))\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n def load_model_hook(models, input_dir):\n if args.use_ema:\n load_model = EMAModel.from_pretrained(os.path.join(input_dir, \"unet_ema\"), UNet2DConditionModel)\n ema_unet.load_state_dict(load_model.state_dict())\n ema_unet.to(accelerator.device)\n del load_model\n\n for _ in range(len(models)):\n # pop models so that they are not loaded again\n model = models.pop()\n\n # load diffusers style into model\n load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder=\"unet\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Initialize the optimizer\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`\"\n )\n\n optimizer_cls = bnb.optim.AdamW8bit\n else:\n optimizer_cls = torch.optim.AdamW\n\n optimizer = optimizer_cls(\n unet.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n data_dir=args.train_data_dir,\n )\n else:\n data_files = {}\n if args.train_data_dir is not None:\n data_files[\"train\"] = os.path.join(args.train_data_dir, \"**\")\n dataset = load_dataset(\n \"imagefolder\",\n data_files=data_files,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)\n if args.image_column is None:\n image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.caption_column is None:\n caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n else:\n caption_column = args.caption_column\n if caption_column not in column_names:\n raise ValueError(\n f\"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}\"\n )\n\n # Preprocessing the datasets.\n # We need to tokenize input captions and transform the images.\n def tokenize_captions(examples, is_train=True):\n captions = []\n for caption in examples[caption_column]:\n if isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n else:\n raise ValueError(\n f\"Caption column `{caption_column}` should contain either strings or lists of strings.\"\n )\n inputs = tokenizer(\n captions, max_length=tokenizer.model_max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\"\n )\n return inputs.input_ids\n\n # Preprocessing the datasets.\n train_transforms = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),\n transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n examples[\"pixel_values\"] = [train_transforms(image) for image in images]\n examples[\"input_ids\"] = tokenize_captions(examples)\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n def collate_fn(examples):\n pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n input_ids = torch.stack([example[\"input_ids\"] for example in examples])\n return {\"pixel_values\": pixel_values, \"input_ids\": input_ids}\n\n # DataLoaders creation:\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n )\n\n # Prepare everything with our `accelerator`.\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n if args.use_ema:\n ema_unet.to(accelerator.device)\n\n # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n args.mixed_precision = accelerator.mixed_precision\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n args.mixed_precision = accelerator.mixed_precision\n\n # Move text_encode and vae to gpu and cast to weight_dtype\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n vae.to(accelerator.device, dtype=weight_dtype)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = dict(vars(args))\n tracker_config.pop(\"validation_prompts\")\n accelerator.init_trackers(args.tracker_project_name, tracker_config)\n\n # Function for unwrapping if model was compiled with `torch.compile`.\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n for epoch in range(first_epoch, args.num_train_epochs):\n train_loss = 0.0\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n # Convert images to latent space\n latents = vae.encode(batch[\"pixel_values\"].to(weight_dtype)).latent_dist.sample()\n latents = latents * vae.config.scaling_factor\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n if args.noise_offset:\n # https://www.crosslabs.org//blog/diffusion-with-offset-noise\n noise += args.noise_offset * torch.randn(\n (latents.shape[0], latents.shape[1], 1, 1), device=latents.device\n )\n if args.input_perturbation:\n new_noise = noise + args.input_perturbation * torch.randn_like(noise)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n if args.input_perturbation:\n noisy_latents = noise_scheduler.add_noise(latents, new_noise, timesteps)\n else:\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # Get the text embedding for conditioning\n encoder_hidden_states = text_encoder(batch[\"input_ids\"], return_dict=False)[0]\n\n # Get the target for loss depending on the prediction type\n if args.prediction_type is not None:\n # set prediction_type of scheduler if defined\n noise_scheduler.register_to_config(prediction_type=args.prediction_type)\n\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n if args.dream_training:\n noisy_latents, target = compute_dream_and_update_latents(\n unet,\n noise_scheduler,\n timesteps,\n noise,\n noisy_latents,\n target,\n encoder_hidden_states,\n args.dream_detail_preservation,\n )\n\n # Predict the noise residual and compute loss\n model_pred = unet(noisy_latents, timesteps, encoder_hidden_states, return_dict=False)[0]\n\n if args.snr_gamma is None:\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n else:\n # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.\n # Since we predict the noise instead of x_0, the original formulation is slightly changed.\n # This is discussed in Section 4.2 of the same paper.\n snr = compute_snr(noise_scheduler, timesteps)\n mse_loss_weights = torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(\n dim=1\n )[0]\n if noise_scheduler.config.prediction_type == \"epsilon\":\n mse_loss_weights = mse_loss_weights / snr\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n mse_loss_weights = mse_loss_weights / (snr + 1)\n\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"none\")\n loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights\n loss = loss.mean()\n\n # Gather the losses across all processes for logging (if we use distributed training).\n avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n train_loss += avg_loss.item() / args.gradient_accumulation_steps\n\n # Backpropagate\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n if args.use_ema:\n ema_unet.step(unet.parameters())\n progress_bar.update(1)\n global_step += 1\n accelerator.log({\"train_loss\": train_loss}, step=global_step)\n train_loss = 0.0\n\n if global_step % args.checkpointing_steps == 0:\n if accelerator.is_main_process:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"step_loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompts is not None and epoch % args.validation_epochs == 0:\n if args.use_ema:\n # Store the UNet parameters temporarily and load the EMA parameters to perform inference.\n ema_unet.store(unet.parameters())\n ema_unet.copy_to(unet.parameters())\n log_validation(\n vae,\n text_encoder,\n tokenizer,\n unet,\n args,\n accelerator,\n weight_dtype,\n global_step,\n )\n if args.use_ema:\n # Switch back to the original UNet parameters.\n ema_unet.restore(unet.parameters())\n\n # Create the pipeline using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = unwrap_model(unet)\n if args.use_ema:\n ema_unet.copy_to(unet.parameters())\n\n pipeline = StableDiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n text_encoder=text_encoder,\n vae=vae,\n unet=unet,\n revision=args.revision,\n variant=args.variant,\n )\n pipeline.save_pretrained(args.output_dir)\n\n # Run a final round of inference.\n images = []\n if args.validation_prompts is not None:\n logger.info(\"Running inference for collecting generated images...\")\n pipeline = pipeline.to(accelerator.device)\n pipeline.torch_dtype = weight_dtype\n pipeline.set_progress_bar_config(disable=True)\n\n if args.enable_xformers_memory_efficient_attention:\n pipeline.enable_xformers_memory_efficient_attention()\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n for i in range(len(args.validation_prompts)):\n with torch.autocast(\"cuda\"):\n image = pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]\n images.append(image)\n\n if args.push_to_hub:\n save_model_card(args, repo_id, images, repo_folder=args.output_dir)\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/text_to_image/train_text_to_image_flax.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport argparse\nimport logging\nimport math\nimport os\nimport random\nfrom pathlib import Path\n\nimport jax\nimport jax.numpy as jnp\nimport numpy as np\nimport optax\nimport torch\nimport torch.utils.checkpoint\nimport transformers\nfrom datasets import load_dataset\nfrom flax import jax_utils\nfrom flax.training import train_state\nfrom flax.training.common_utils import shard\nfrom huggingface_hub import create_repo, upload_folder\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel, set_seed\n\nfrom diffusers import (\n FlaxAutoencoderKL,\n FlaxDDPMScheduler,\n FlaxPNDMScheduler,\n FlaxStableDiffusionPipeline,\n FlaxUNet2DConditionModel,\n)\nfrom diffusers.pipelines.stable_diffusion import FlaxStableDiffusionSafetyChecker\nfrom diffusers.utils import check_min_version\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = logging.getLogger(__name__)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing an image.\"\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=\"text\",\n help=\"The column of the dataset containing a caption or a list of captions.\",\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"sd-model-finetuned\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=0, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=\"no\",\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose\"\n \"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.\"\n \"and an Nvidia Ampere GPU.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--from_pt\",\n action=\"store_true\",\n default=False,\n help=\"Flag to indicate whether to convert models from PyTorch.\",\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n\n return args\n\n\ndataset_name_mapping = {\n \"lambdalabs/naruto-blip-captions\": (\"image\", \"text\"),\n}\n\n\ndef get_params_to_save(params):\n return jax.device_get(jax.tree_util.tree_map(lambda x: x[0], params))\n\n\ndef main():\n args = parse_args()\n\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n # Setup logging, we only want one process per machine to log things on the screen.\n logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)\n if jax.process_index() == 0:\n transformers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if jax.process_index() == 0:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name, args.dataset_config_name, cache_dir=args.cache_dir, data_dir=args.train_data_dir\n )\n else:\n data_files = {}\n if args.train_data_dir is not None:\n data_files[\"train\"] = os.path.join(args.train_data_dir, \"**\")\n dataset = load_dataset(\n \"imagefolder\",\n data_files=data_files,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n dataset_columns = dataset_name_mapping.get(args.dataset_name, None)\n if args.image_column is None:\n image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.caption_column is None:\n caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n else:\n caption_column = args.caption_column\n if caption_column not in column_names:\n raise ValueError(\n f\"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}\"\n )\n\n # Preprocessing the datasets.\n # We need to tokenize input captions and transform the images.\n def tokenize_captions(examples, is_train=True):\n captions = []\n for caption in examples[caption_column]:\n if isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n else:\n raise ValueError(\n f\"Caption column `{caption_column}` should contain either strings or lists of strings.\"\n )\n inputs = tokenizer(captions, max_length=tokenizer.model_max_length, padding=\"do_not_pad\", truncation=True)\n input_ids = inputs.input_ids\n return input_ids\n\n train_transforms = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),\n transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n examples[\"pixel_values\"] = [train_transforms(image) for image in images]\n examples[\"input_ids\"] = tokenize_captions(examples)\n\n return examples\n\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n def collate_fn(examples):\n pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n input_ids = [example[\"input_ids\"] for example in examples]\n\n padded_tokens = tokenizer.pad(\n {\"input_ids\": input_ids}, padding=\"max_length\", max_length=tokenizer.model_max_length, return_tensors=\"pt\"\n )\n batch = {\n \"pixel_values\": pixel_values,\n \"input_ids\": padded_tokens.input_ids,\n }\n batch = {k: v.numpy() for k, v in batch.items()}\n\n return batch\n\n total_train_batch_size = args.train_batch_size * jax.local_device_count()\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=total_train_batch_size, drop_last=True\n )\n\n weight_dtype = jnp.float32\n if args.mixed_precision == \"fp16\":\n weight_dtype = jnp.float16\n elif args.mixed_precision == \"bf16\":\n weight_dtype = jnp.bfloat16\n\n # Load models and create wrapper for stable diffusion\n tokenizer = CLIPTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n from_pt=args.from_pt,\n revision=args.revision,\n subfolder=\"tokenizer\",\n )\n text_encoder = FlaxCLIPTextModel.from_pretrained(\n args.pretrained_model_name_or_path,\n from_pt=args.from_pt,\n revision=args.revision,\n subfolder=\"text_encoder\",\n dtype=weight_dtype,\n )\n vae, vae_params = FlaxAutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path,\n from_pt=args.from_pt,\n revision=args.revision,\n subfolder=\"vae\",\n dtype=weight_dtype,\n )\n unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path,\n from_pt=args.from_pt,\n revision=args.revision,\n subfolder=\"unet\",\n dtype=weight_dtype,\n )\n\n # Optimization\n if args.scale_lr:\n args.learning_rate = args.learning_rate * total_train_batch_size\n\n constant_scheduler = optax.constant_schedule(args.learning_rate)\n\n adamw = optax.adamw(\n learning_rate=constant_scheduler,\n b1=args.adam_beta1,\n b2=args.adam_beta2,\n eps=args.adam_epsilon,\n weight_decay=args.adam_weight_decay,\n )\n\n optimizer = optax.chain(\n optax.clip_by_global_norm(args.max_grad_norm),\n adamw,\n )\n\n state = train_state.TrainState.create(apply_fn=unet.__call__, params=unet_params, tx=optimizer)\n\n noise_scheduler = FlaxDDPMScheduler(\n beta_start=0.00085, beta_end=0.012, beta_schedule=\"scaled_linear\", num_train_timesteps=1000\n )\n noise_scheduler_state = noise_scheduler.create_state()\n\n # Initialize our training\n rng = jax.random.PRNGKey(args.seed)\n train_rngs = jax.random.split(rng, jax.local_device_count())\n\n def train_step(state, text_encoder_params, vae_params, batch, train_rng):\n dropout_rng, sample_rng, new_train_rng = jax.random.split(train_rng, 3)\n\n def compute_loss(params):\n # Convert images to latent space\n vae_outputs = vae.apply(\n {\"params\": vae_params}, batch[\"pixel_values\"], deterministic=True, method=vae.encode\n )\n latents = vae_outputs.latent_dist.sample(sample_rng)\n # (NHWC) -> (NCHW)\n latents = jnp.transpose(latents, (0, 3, 1, 2))\n latents = latents * vae.config.scaling_factor\n\n # Sample noise that we'll add to the latents\n noise_rng, timestep_rng = jax.random.split(sample_rng)\n noise = jax.random.normal(noise_rng, latents.shape)\n # Sample a random timestep for each image\n bsz = latents.shape[0]\n timesteps = jax.random.randint(\n timestep_rng,\n (bsz,),\n 0,\n noise_scheduler.config.num_train_timesteps,\n )\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(noise_scheduler_state, latents, noise, timesteps)\n\n # Get the text embedding for conditioning\n encoder_hidden_states = text_encoder(\n batch[\"input_ids\"],\n params=text_encoder_params,\n train=False,\n )[0]\n\n # Predict the noise residual and compute loss\n model_pred = unet.apply(\n {\"params\": params}, noisy_latents, timesteps, encoder_hidden_states, train=True\n ).sample\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(noise_scheduler_state, latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n loss = (target - model_pred) ** 2\n loss = loss.mean()\n\n return loss\n\n grad_fn = jax.value_and_grad(compute_loss)\n loss, grad = grad_fn(state.params)\n grad = jax.lax.pmean(grad, \"batch\")\n\n new_state = state.apply_gradients(grads=grad)\n\n metrics = {\"loss\": loss}\n metrics = jax.lax.pmean(metrics, axis_name=\"batch\")\n\n return new_state, metrics, new_train_rng\n\n # Create parallel version of the train step\n p_train_step = jax.pmap(train_step, \"batch\", donate_argnums=(0,))\n\n # Replicate the train state on each device\n state = jax_utils.replicate(state)\n text_encoder_params = jax_utils.replicate(text_encoder.params)\n vae_params = jax_utils.replicate(vae_params)\n\n # Train!\n num_update_steps_per_epoch = math.ceil(len(train_dataloader))\n\n # Scheduler and math around the number of training steps.\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel & distributed) = {total_train_batch_size}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n\n global_step = 0\n\n epochs = tqdm(range(args.num_train_epochs), desc=\"Epoch ... \", position=0)\n for epoch in epochs:\n # ======================== Training ================================\n\n train_metrics = []\n\n steps_per_epoch = len(train_dataset) // total_train_batch_size\n train_step_progress_bar = tqdm(total=steps_per_epoch, desc=\"Training...\", position=1, leave=False)\n # train\n for batch in train_dataloader:\n batch = shard(batch)\n state, train_metric, train_rngs = p_train_step(state, text_encoder_params, vae_params, batch, train_rngs)\n train_metrics.append(train_metric)\n\n train_step_progress_bar.update(1)\n\n global_step += 1\n if global_step >= args.max_train_steps:\n break\n\n train_metric = jax_utils.unreplicate(train_metric)\n\n train_step_progress_bar.close()\n epochs.write(f\"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})\")\n\n # Create the pipeline using using the trained modules and save it.\n if jax.process_index() == 0:\n scheduler = FlaxPNDMScheduler(\n beta_start=0.00085, beta_end=0.012, beta_schedule=\"scaled_linear\", skip_prk_steps=True\n )\n safety_checker = FlaxStableDiffusionSafetyChecker.from_pretrained(\n \"CompVis/stable-diffusion-safety-checker\", from_pt=True\n )\n pipeline = FlaxStableDiffusionPipeline(\n text_encoder=text_encoder,\n vae=vae,\n unet=unet,\n tokenizer=tokenizer,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=CLIPImageProcessor.from_pretrained(\"openai/clip-vit-base-patch32\"),\n )\n\n pipeline.save_pretrained(\n args.output_dir,\n params={\n \"text_encoder\": get_params_to_save(text_encoder_params),\n \"vae\": get_params_to_save(vae_params),\n \"unet\": get_params_to_save(state.params),\n \"safety_checker\": safety_checker.params,\n },\n )\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/text_to_image/train_text_to_image_lora.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Fine-tuning script for Stable Diffusion for text2image with support for LoRA.\"\"\"\n\nimport argparse\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom contextlib import nullcontext\nfrom pathlib import Path\n\nimport datasets\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom peft import LoraConfig\nfrom peft.utils import get_peft_model_state_dict\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nimport diffusers\nfrom diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, StableDiffusionPipeline, UNet2DConditionModel\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import cast_training_params, compute_snr\nfrom diffusers.utils import check_min_version, convert_state_dict_to_diffusers, is_wandb_available\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\n\ndef save_model_card(\n repo_id: str,\n images: list = None,\n base_model: str = None,\n dataset_name: str = None,\n repo_folder: str = None,\n):\n img_str = \"\"\n if images is not None:\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n img_str += f\"![img_{i}](./image_{i}.png)\\n\"\n\n model_description = f\"\"\"\n# LoRA text2image fine-tuning - {repo_id}\nThese are LoRA adaption weights for {base_model}. The weights were fine-tuned on the {dataset_name} dataset. You can find some example images in the following. \\n\n{img_str}\n\"\"\"\n\n model_card = load_or_create_model_card(\n repo_id_or_path=repo_id,\n from_training=True,\n license=\"creativeml-openrail-m\",\n base_model=base_model,\n model_description=model_description,\n inference=True,\n )\n\n tags = [\n \"stable-diffusion\",\n \"stable-diffusion-diffusers\",\n \"text-to-image\",\n \"diffusers\",\n \"diffusers-training\",\n \"lora\",\n ]\n model_card = populate_model_card(model_card, tags=tags)\n\n model_card.save(os.path.join(repo_folder, \"README.md\"))\n\n\ndef log_validation(\n pipeline,\n args,\n accelerator,\n epoch,\n is_final_validation=False,\n):\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n generator = torch.Generator(device=accelerator.device)\n if args.seed is not None:\n generator = generator.manual_seed(args.seed)\n images = []\n if torch.backends.mps.is_available():\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type)\n\n with autocast_ctx:\n for _ in range(args.num_validation_images):\n images.append(pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0])\n\n for tracker in accelerator.trackers:\n phase_name = \"test\" if is_final_validation else \"validation\"\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(phase_name, np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n phase_name: [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\") for i, image in enumerate(images)\n ]\n }\n )\n return images\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing an image.\"\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=\"text\",\n help=\"The column of the dataset containing a caption or a list of captions.\",\n )\n parser.add_argument(\n \"--validation_prompt\", type=str, default=None, help=\"A prompt that is sampled during training for inference.\"\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=1,\n help=(\n \"Run fine-tuning validation every X epochs. The validation process consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"sd-model-finetuned-lora\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--snr_gamma\",\n type=float,\n default=None,\n help=\"SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. \"\n \"More details here: https://arxiv.org/abs/2303.09556.\",\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--prediction_type\",\n type=str,\n default=None,\n help=\"The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediction_type` is chosen.\",\n )\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\"--noise_offset\", type=float, default=0, help=\"The scale of noise offset.\")\n parser.add_argument(\n \"--rank\",\n type=int,\n default=4,\n help=(\"The dimension of the LoRA update matrices.\"),\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n\n return args\n\n\nDATASET_NAME_MAPPING = {\n \"lambdalabs/naruto-blip-captions\": (\"image\", \"text\"),\n}\n\n\ndef main():\n args = parse_args()\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n # Load scheduler, tokenizer and models.\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n tokenizer = CLIPTokenizer.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"tokenizer\", revision=args.revision\n )\n text_encoder = CLIPTextModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision\n )\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision, variant=args.variant\n )\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n # freeze parameters of models to save more memory\n unet.requires_grad_(False)\n vae.requires_grad_(False)\n text_encoder.requires_grad_(False)\n\n # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Freeze the unet parameters before adding adapters\n for param in unet.parameters():\n param.requires_grad_(False)\n\n unet_lora_config = LoraConfig(\n r=args.rank,\n lora_alpha=args.rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"to_k\", \"to_q\", \"to_v\", \"to_out.0\"],\n )\n\n # Move unet, vae and text_encoder to device and cast to weight_dtype\n unet.to(accelerator.device, dtype=weight_dtype)\n vae.to(accelerator.device, dtype=weight_dtype)\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n\n # Add adapter and make sure the trainable params are in float32.\n unet.add_adapter(unet_lora_config)\n if args.mixed_precision == \"fp16\":\n # only upcast trainable parameters (LoRA) into fp32\n cast_training_params(unet, dtype=torch.float32)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n lora_layers = filter(lambda p: p.requires_grad, unet.parameters())\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Initialize the optimizer\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`\"\n )\n\n optimizer_cls = bnb.optim.AdamW8bit\n else:\n optimizer_cls = torch.optim.AdamW\n\n optimizer = optimizer_cls(\n lora_layers,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n data_dir=args.train_data_dir,\n )\n else:\n data_files = {}\n if args.train_data_dir is not None:\n data_files[\"train\"] = os.path.join(args.train_data_dir, \"**\")\n dataset = load_dataset(\n \"imagefolder\",\n data_files=data_files,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)\n if args.image_column is None:\n image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.caption_column is None:\n caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n else:\n caption_column = args.caption_column\n if caption_column not in column_names:\n raise ValueError(\n f\"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}\"\n )\n\n # Preprocessing the datasets.\n # We need to tokenize input captions and transform the images.\n def tokenize_captions(examples, is_train=True):\n captions = []\n for caption in examples[caption_column]:\n if isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n else:\n raise ValueError(\n f\"Caption column `{caption_column}` should contain either strings or lists of strings.\"\n )\n inputs = tokenizer(\n captions, max_length=tokenizer.model_max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\"\n )\n return inputs.input_ids\n\n # Preprocessing the datasets.\n train_transforms = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),\n transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n def preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n examples[\"pixel_values\"] = [train_transforms(image) for image in images]\n examples[\"input_ids\"] = tokenize_captions(examples)\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n def collate_fn(examples):\n pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n input_ids = torch.stack([example[\"input_ids\"] for example in examples])\n return {\"pixel_values\": pixel_values, \"input_ids\": input_ids}\n\n # DataLoaders creation:\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n # Check the PR https://github.com/huggingface/diffusers/pull/8312 for detailed explanation.\n num_warmup_steps_for_scheduler = args.lr_warmup_steps * accelerator.num_processes\n if args.max_train_steps is None:\n len_train_dataloader_after_sharding = math.ceil(len(train_dataloader) / accelerator.num_processes)\n num_update_steps_per_epoch = math.ceil(len_train_dataloader_after_sharding / args.gradient_accumulation_steps)\n num_training_steps_for_scheduler = (\n args.num_train_epochs * num_update_steps_per_epoch * accelerator.num_processes\n )\n else:\n num_training_steps_for_scheduler = args.max_train_steps * accelerator.num_processes\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=num_warmup_steps_for_scheduler,\n num_training_steps=num_training_steps_for_scheduler,\n )\n\n # Prepare everything with our `accelerator`.\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n if num_training_steps_for_scheduler != args.max_train_steps * accelerator.num_processes:\n logger.warning(\n f\"The length of the 'train_dataloader' after 'accelerator.prepare' ({len(train_dataloader)}) does not match \"\n f\"the expected length ({len_train_dataloader_after_sharding}) when the learning rate scheduler was created. \"\n f\"This inconsistency may result in the learning rate scheduler not functioning properly.\"\n )\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"text2image-fine-tune\", config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n for epoch in range(first_epoch, args.num_train_epochs):\n unet.train()\n train_loss = 0.0\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n # Convert images to latent space\n latents = vae.encode(batch[\"pixel_values\"].to(dtype=weight_dtype)).latent_dist.sample()\n latents = latents * vae.config.scaling_factor\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n if args.noise_offset:\n # https://www.crosslabs.org//blog/diffusion-with-offset-noise\n noise += args.noise_offset * torch.randn(\n (latents.shape[0], latents.shape[1], 1, 1), device=latents.device\n )\n\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # Get the text embedding for conditioning\n encoder_hidden_states = text_encoder(batch[\"input_ids\"], return_dict=False)[0]\n\n # Get the target for loss depending on the prediction type\n if args.prediction_type is not None:\n # set prediction_type of scheduler if defined\n noise_scheduler.register_to_config(prediction_type=args.prediction_type)\n\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n # Predict the noise residual and compute loss\n model_pred = unet(noisy_latents, timesteps, encoder_hidden_states, return_dict=False)[0]\n\n if args.snr_gamma is None:\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n else:\n # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.\n # Since we predict the noise instead of x_0, the original formulation is slightly changed.\n # This is discussed in Section 4.2 of the same paper.\n snr = compute_snr(noise_scheduler, timesteps)\n mse_loss_weights = torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(\n dim=1\n )[0]\n if noise_scheduler.config.prediction_type == \"epsilon\":\n mse_loss_weights = mse_loss_weights / snr\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n mse_loss_weights = mse_loss_weights / (snr + 1)\n\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"none\")\n loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights\n loss = loss.mean()\n\n # Gather the losses across all processes for logging (if we use distributed training).\n avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n train_loss += avg_loss.item() / args.gradient_accumulation_steps\n\n # Backpropagate\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n params_to_clip = lora_layers\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n accelerator.log({\"train_loss\": train_loss}, step=global_step)\n train_loss = 0.0\n\n if global_step % args.checkpointing_steps == 0:\n if accelerator.is_main_process:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n\n unwrapped_unet = unwrap_model(unet)\n unet_lora_state_dict = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(unwrapped_unet)\n )\n\n StableDiffusionPipeline.save_lora_weights(\n save_directory=save_path,\n unet_lora_layers=unet_lora_state_dict,\n safe_serialization=True,\n )\n\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"step_loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompt is not None and epoch % args.validation_epochs == 0:\n # create pipeline\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n unet=unwrap_model(unet),\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n images = log_validation(pipeline, args, accelerator, epoch)\n\n del pipeline\n torch.cuda.empty_cache()\n\n # Save the lora layers\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = unet.to(torch.float32)\n\n unwrapped_unet = unwrap_model(unet)\n unet_lora_state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(unwrapped_unet))\n StableDiffusionPipeline.save_lora_weights(\n save_directory=args.output_dir,\n unet_lora_layers=unet_lora_state_dict,\n safe_serialization=True,\n )\n\n # Final inference\n # Load previous pipeline\n if args.validation_prompt is not None:\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n\n # load attention processors\n pipeline.load_lora_weights(args.output_dir)\n\n # run inference\n images = log_validation(pipeline, args, accelerator, epoch, is_final_validation=True)\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n images=images,\n base_model=args.pretrained_model_name_or_path,\n dataset_name=args.dataset_name,\n repo_folder=args.output_dir,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/text_to_image/train_text_to_image_lora_sdxl.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Fine-tuning script for Stable Diffusion XL for text2image with support for LoRA.\"\"\"\n\nimport argparse\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom contextlib import nullcontext\nfrom pathlib import Path\n\nimport datasets\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import DistributedDataParallelKwargs, DistributedType, ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom peft import LoraConfig, set_peft_model_state_dict\nfrom peft.utils import get_peft_model_state_dict\nfrom torchvision import transforms\nfrom torchvision.transforms.functional import crop\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n StableDiffusionXLPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.loaders import LoraLoaderMixin\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import _set_state_dict_into_text_encoder, cast_training_params, compute_snr\nfrom diffusers.utils import (\n check_min_version,\n convert_state_dict_to_diffusers,\n convert_unet_state_dict_to_peft,\n is_wandb_available,\n)\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom diffusers.utils.import_utils import is_torch_npu_available, is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__)\nif is_torch_npu_available():\n torch.npu.config.allow_internal_format = False\n\n\ndef save_model_card(\n repo_id: str,\n images: list = None,\n base_model: str = None,\n dataset_name: str = None,\n train_text_encoder: bool = False,\n repo_folder: str = None,\n vae_path: str = None,\n):\n img_str = \"\"\n if images is not None:\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n img_str += f\"![img_{i}](./image_{i}.png)\\n\"\n\n model_description = f\"\"\"\n# LoRA text2image fine-tuning - {repo_id}\n\nThese are LoRA adaption weights for {base_model}. The weights were fine-tuned on the {dataset_name} dataset. You can find some example images in the following. \\n\n{img_str}\n\nLoRA for the text encoder was enabled: {train_text_encoder}.\n\nSpecial VAE used for training: {vae_path}.\n\"\"\"\n model_card = load_or_create_model_card(\n repo_id_or_path=repo_id,\n from_training=True,\n license=\"creativeml-openrail-m\",\n base_model=base_model,\n model_description=model_description,\n inference=True,\n )\n\n tags = [\n \"stable-diffusion-xl\",\n \"stable-diffusion-xl-diffusers\",\n \"text-to-image\",\n \"diffusers\",\n \"diffusers-training\",\n \"lora\",\n ]\n model_card = populate_model_card(model_card, tags=tags)\n\n model_card.save(os.path.join(repo_folder, \"README.md\"))\n\n\ndef log_validation(\n pipeline,\n args,\n accelerator,\n epoch,\n is_final_validation=False,\n):\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n pipeline_args = {\"prompt\": args.validation_prompt}\n if torch.backends.mps.is_available():\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type)\n\n with autocast_ctx:\n images = [pipeline(**pipeline_args, generator=generator).images[0] for _ in range(args.num_validation_images)]\n\n for tracker in accelerator.trackers:\n phase_name = \"test\" if is_final_validation else \"validation\"\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(phase_name, np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n phase_name: [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\") for i, image in enumerate(images)\n ]\n }\n )\n return images\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_vae_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing an image.\"\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=\"text\",\n help=\"The column of the dataset containing a caption or a list of captions.\",\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n help=\"A prompt that is used during validation to verify that the model is learning.\",\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=1,\n help=(\n \"Run fine-tuning validation every X epochs. The validation process consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"sd-model-finetuned-lora\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=1024,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--train_text_encoder\",\n action=\"store_true\",\n help=\"Whether to train the text encoder. If set, the text encoder should be float32 precision.\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final\"\n \" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--snr_gamma\",\n type=float,\n default=None,\n help=\"SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. \"\n \"More details here: https://arxiv.org/abs/2303.09556.\",\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--prediction_type\",\n type=str,\n default=None,\n help=\"The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediction_type` is chosen.\",\n )\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--enable_npu_flash_attention\", action=\"store_true\", help=\"Whether or not to use npu flash attention.\"\n )\n parser.add_argument(\"--noise_offset\", type=float, default=0, help=\"The scale of noise offset.\")\n parser.add_argument(\n \"--rank\",\n type=int,\n default=4,\n help=(\"The dimension of the LoRA update matrices.\"),\n )\n parser.add_argument(\n \"--debug_loss\",\n action=\"store_true\",\n help=\"debug loss for each image, if filenames are awailable in the dataset\",\n )\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n\n return args\n\n\nDATASET_NAME_MAPPING = {\n \"lambdalabs/naruto-blip-captions\": (\"image\", \"text\"),\n}\n\n\ndef tokenize_prompt(tokenizer, prompt):\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n return text_input_ids\n\n\n# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt\ndef encode_prompt(text_encoders, tokenizers, prompt, text_input_ids_list=None):\n prompt_embeds_list = []\n\n for i, text_encoder in enumerate(text_encoders):\n if tokenizers is not None:\n tokenizer = tokenizers[i]\n text_input_ids = tokenize_prompt(tokenizer, prompt)\n else:\n assert text_input_ids_list is not None\n text_input_ids = text_input_ids_list[i]\n\n prompt_embeds = text_encoder(\n text_input_ids.to(text_encoder.device), output_hidden_states=True, return_dict=False\n )\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds[-1][-2]\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)\n return prompt_embeds, pooled_prompt_embeds\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n if torch.backends.mps.is_available() and args.mixed_precision == \"bf16\":\n # due to pytorch#99272, MPS does not yet support bfloat16.\n raise ValueError(\n \"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead.\"\n )\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n kwargs_handlers=[kwargs],\n )\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizers\n tokenizer_one = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n tokenizer_two = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer_2\",\n revision=args.revision,\n use_fast=False,\n )\n\n # import correct text encoder classes\n text_encoder_cls_one = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision\n )\n text_encoder_cls_two = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision, subfolder=\"text_encoder_2\"\n )\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n text_encoder_one = text_encoder_cls_one.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n text_encoder_two = text_encoder_cls_two.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder_2\", revision=args.revision, variant=args.variant\n )\n vae_path = (\n args.pretrained_model_name_or_path\n if args.pretrained_vae_model_name_or_path is None\n else args.pretrained_vae_model_name_or_path\n )\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n variant=args.variant,\n )\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n # We only train the additional adapter LoRA layers\n vae.requires_grad_(False)\n text_encoder_one.requires_grad_(False)\n text_encoder_two.requires_grad_(False)\n unet.requires_grad_(False)\n\n # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move unet, vae and text_encoder to device and cast to weight_dtype\n # The VAE is in float32 to avoid NaN losses.\n unet.to(accelerator.device, dtype=weight_dtype)\n\n if args.pretrained_vae_model_name_or_path is None:\n vae.to(accelerator.device, dtype=torch.float32)\n else:\n vae.to(accelerator.device, dtype=weight_dtype)\n text_encoder_one.to(accelerator.device, dtype=weight_dtype)\n text_encoder_two.to(accelerator.device, dtype=weight_dtype)\n\n if args.enable_npu_flash_attention:\n if is_torch_npu_available():\n logger.info(\"npu flash attention enabled.\")\n unet.enable_npu_flash_attention()\n else:\n raise ValueError(\"npu flash attention requires torch_npu extensions and is supported only on npu devices.\")\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # now we will add new LoRA weights to the attention layers\n # Set correct lora layers\n unet_lora_config = LoraConfig(\n r=args.rank,\n lora_alpha=args.rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"to_k\", \"to_q\", \"to_v\", \"to_out.0\"],\n )\n\n unet.add_adapter(unet_lora_config)\n\n # The text encoder comes from 🤗 transformers, we will also attach adapters to it.\n if args.train_text_encoder:\n # ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16\n text_lora_config = LoraConfig(\n r=args.rank,\n lora_alpha=args.rank,\n init_lora_weights=\"gaussian\",\n target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"out_proj\"],\n )\n text_encoder_one.add_adapter(text_lora_config)\n text_encoder_two.add_adapter(text_lora_config)\n\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n # there are only two options here. Either are just the unet attn processor layers\n # or there are the unet and text encoder attn layers\n unet_lora_layers_to_save = None\n text_encoder_one_lora_layers_to_save = None\n text_encoder_two_lora_layers_to_save = None\n\n for model in models:\n if isinstance(unwrap_model(model), type(unwrap_model(unet))):\n unet_lora_layers_to_save = convert_state_dict_to_diffusers(get_peft_model_state_dict(model))\n elif isinstance(unwrap_model(model), type(unwrap_model(text_encoder_one))):\n text_encoder_one_lora_layers_to_save = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(model)\n )\n elif isinstance(unwrap_model(model), type(unwrap_model(text_encoder_two))):\n text_encoder_two_lora_layers_to_save = convert_state_dict_to_diffusers(\n get_peft_model_state_dict(model)\n )\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n # make sure to pop weight so that corresponding model is not saved again\n if weights:\n weights.pop()\n\n StableDiffusionXLPipeline.save_lora_weights(\n output_dir,\n unet_lora_layers=unet_lora_layers_to_save,\n text_encoder_lora_layers=text_encoder_one_lora_layers_to_save,\n text_encoder_2_lora_layers=text_encoder_two_lora_layers_to_save,\n )\n\n def load_model_hook(models, input_dir):\n unet_ = None\n text_encoder_one_ = None\n text_encoder_two_ = None\n\n while len(models) > 0:\n model = models.pop()\n\n if isinstance(model, type(unwrap_model(unet))):\n unet_ = model\n elif isinstance(model, type(unwrap_model(text_encoder_one))):\n text_encoder_one_ = model\n elif isinstance(model, type(unwrap_model(text_encoder_two))):\n text_encoder_two_ = model\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n lora_state_dict, _ = LoraLoaderMixin.lora_state_dict(input_dir)\n unet_state_dict = {f'{k.replace(\"unet.\", \"\")}': v for k, v in lora_state_dict.items() if k.startswith(\"unet.\")}\n unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict)\n incompatible_keys = set_peft_model_state_dict(unet_, unet_state_dict, adapter_name=\"default\")\n if incompatible_keys is not None:\n # check only for unexpected keys\n unexpected_keys = getattr(incompatible_keys, \"unexpected_keys\", None)\n if unexpected_keys:\n logger.warning(\n f\"Loading adapter weights from state_dict led to unexpected keys not found in the model: \"\n f\" {unexpected_keys}. \"\n )\n\n if args.train_text_encoder:\n _set_state_dict_into_text_encoder(lora_state_dict, prefix=\"text_encoder.\", text_encoder=text_encoder_one_)\n\n _set_state_dict_into_text_encoder(\n lora_state_dict, prefix=\"text_encoder_2.\", text_encoder=text_encoder_two_\n )\n\n # Make sure the trainable params are in float32. This is again needed since the base models\n # are in `weight_dtype`. More details:\n # https://github.com/huggingface/diffusers/pull/6514#discussion_r1449796804\n if args.mixed_precision == \"fp16\":\n models = [unet_]\n if args.train_text_encoder:\n models.extend([text_encoder_one_, text_encoder_two_])\n cast_training_params(models, dtype=torch.float32)\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n if args.train_text_encoder:\n text_encoder_one.gradient_checkpointing_enable()\n text_encoder_two.gradient_checkpointing_enable()\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Make sure the trainable params are in float32.\n if args.mixed_precision == \"fp16\":\n models = [unet]\n if args.train_text_encoder:\n models.extend([text_encoder_one, text_encoder_two])\n cast_training_params(models, dtype=torch.float32)\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # Optimizer creation\n params_to_optimize = list(filter(lambda p: p.requires_grad, unet.parameters()))\n if args.train_text_encoder:\n params_to_optimize = (\n params_to_optimize\n + list(filter(lambda p: p.requires_grad, text_encoder_one.parameters()))\n + list(filter(lambda p: p.requires_grad, text_encoder_two.parameters()))\n )\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name, args.dataset_config_name, cache_dir=args.cache_dir, data_dir=args.train_data_dir\n )\n else:\n data_files = {}\n if args.train_data_dir is not None:\n data_files[\"train\"] = os.path.join(args.train_data_dir, \"**\")\n dataset = load_dataset(\n \"imagefolder\",\n data_files=data_files,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)\n if args.image_column is None:\n image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.caption_column is None:\n caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n else:\n caption_column = args.caption_column\n if caption_column not in column_names:\n raise ValueError(\n f\"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}\"\n )\n\n # Preprocessing the datasets.\n # We need to tokenize input captions and transform the images.\n def tokenize_captions(examples, is_train=True):\n captions = []\n for caption in examples[caption_column]:\n if isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n else:\n raise ValueError(\n f\"Caption column `{caption_column}` should contain either strings or lists of strings.\"\n )\n tokens_one = tokenize_prompt(tokenizer_one, captions)\n tokens_two = tokenize_prompt(tokenizer_two, captions)\n return tokens_one, tokens_two\n\n # Preprocessing the datasets.\n train_resize = transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR)\n train_crop = transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution)\n train_flip = transforms.RandomHorizontalFlip(p=1.0)\n train_transforms = transforms.Compose(\n [\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n # image aug\n original_sizes = []\n all_images = []\n crop_top_lefts = []\n for image in images:\n original_sizes.append((image.height, image.width))\n image = train_resize(image)\n if args.random_flip and random.random() < 0.5:\n # flip\n image = train_flip(image)\n if args.center_crop:\n y1 = max(0, int(round((image.height - args.resolution) / 2.0)))\n x1 = max(0, int(round((image.width - args.resolution) / 2.0)))\n image = train_crop(image)\n else:\n y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))\n image = crop(image, y1, x1, h, w)\n crop_top_left = (y1, x1)\n crop_top_lefts.append(crop_top_left)\n image = train_transforms(image)\n all_images.append(image)\n\n examples[\"original_sizes\"] = original_sizes\n examples[\"crop_top_lefts\"] = crop_top_lefts\n examples[\"pixel_values\"] = all_images\n tokens_one, tokens_two = tokenize_captions(examples)\n examples[\"input_ids_one\"] = tokens_one\n examples[\"input_ids_two\"] = tokens_two\n if args.debug_loss:\n fnames = [os.path.basename(image.filename) for image in examples[image_column] if image.filename]\n if fnames:\n examples[\"filenames\"] = fnames\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = dataset[\"train\"].with_transform(preprocess_train, output_all_columns=True)\n\n def collate_fn(examples):\n pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n original_sizes = [example[\"original_sizes\"] for example in examples]\n crop_top_lefts = [example[\"crop_top_lefts\"] for example in examples]\n input_ids_one = torch.stack([example[\"input_ids_one\"] for example in examples])\n input_ids_two = torch.stack([example[\"input_ids_two\"] for example in examples])\n result = {\n \"pixel_values\": pixel_values,\n \"input_ids_one\": input_ids_one,\n \"input_ids_two\": input_ids_two,\n \"original_sizes\": original_sizes,\n \"crop_top_lefts\": crop_top_lefts,\n }\n\n filenames = [example[\"filenames\"] for example in examples if \"filenames\" in example]\n if filenames:\n result[\"filenames\"] = filenames\n return result\n\n # DataLoaders creation:\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,\n num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,\n )\n\n # Prepare everything with our `accelerator`.\n if args.train_text_encoder:\n unet, text_encoder_one, text_encoder_two, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, text_encoder_one, text_encoder_two, optimizer, train_dataloader, lr_scheduler\n )\n else:\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"text2image-fine-tune\", config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n for epoch in range(first_epoch, args.num_train_epochs):\n unet.train()\n if args.train_text_encoder:\n text_encoder_one.train()\n text_encoder_two.train()\n train_loss = 0.0\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n # Convert images to latent space\n if args.pretrained_vae_model_name_or_path is not None:\n pixel_values = batch[\"pixel_values\"].to(dtype=weight_dtype)\n else:\n pixel_values = batch[\"pixel_values\"]\n\n model_input = vae.encode(pixel_values).latent_dist.sample()\n model_input = model_input * vae.config.scaling_factor\n if args.pretrained_vae_model_name_or_path is None:\n model_input = model_input.to(weight_dtype)\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(model_input)\n if args.noise_offset:\n # https://www.crosslabs.org//blog/diffusion-with-offset-noise\n noise += args.noise_offset * torch.randn(\n (model_input.shape[0], model_input.shape[1], 1, 1), device=model_input.device\n )\n\n bsz = model_input.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device\n )\n timesteps = timesteps.long()\n\n # Add noise to the model input according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)\n\n # time ids\n def compute_time_ids(original_size, crops_coords_top_left):\n # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids\n target_size = (args.resolution, args.resolution)\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n add_time_ids = torch.tensor([add_time_ids])\n add_time_ids = add_time_ids.to(accelerator.device, dtype=weight_dtype)\n return add_time_ids\n\n add_time_ids = torch.cat(\n [compute_time_ids(s, c) for s, c in zip(batch[\"original_sizes\"], batch[\"crop_top_lefts\"])]\n )\n\n # Predict the noise residual\n unet_added_conditions = {\"time_ids\": add_time_ids}\n prompt_embeds, pooled_prompt_embeds = encode_prompt(\n text_encoders=[text_encoder_one, text_encoder_two],\n tokenizers=None,\n prompt=None,\n text_input_ids_list=[batch[\"input_ids_one\"], batch[\"input_ids_two\"]],\n )\n unet_added_conditions.update({\"text_embeds\": pooled_prompt_embeds})\n model_pred = unet(\n noisy_model_input,\n timesteps,\n prompt_embeds,\n added_cond_kwargs=unet_added_conditions,\n return_dict=False,\n )[0]\n\n # Get the target for loss depending on the prediction type\n if args.prediction_type is not None:\n # set prediction_type of scheduler if defined\n noise_scheduler.register_to_config(prediction_type=args.prediction_type)\n\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(model_input, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n if args.snr_gamma is None:\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n else:\n # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.\n # Since we predict the noise instead of x_0, the original formulation is slightly changed.\n # This is discussed in Section 4.2 of the same paper.\n snr = compute_snr(noise_scheduler, timesteps)\n mse_loss_weights = torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(\n dim=1\n )[0]\n if noise_scheduler.config.prediction_type == \"epsilon\":\n mse_loss_weights = mse_loss_weights / snr\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n mse_loss_weights = mse_loss_weights / (snr + 1)\n\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"none\")\n loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights\n loss = loss.mean()\n if args.debug_loss and \"filenames\" in batch:\n for fname in batch[\"filenames\"]:\n accelerator.log({\"loss_for_\" + fname: loss}, step=global_step)\n # Gather the losses across all processes for logging (if we use distributed training).\n avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n train_loss += avg_loss.item() / args.gradient_accumulation_steps\n\n # Backpropagate\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(params_to_optimize, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n accelerator.log({\"train_loss\": train_loss}, step=global_step)\n train_loss = 0.0\n\n # DeepSpeed requires saving weights on every device; saving weights only on the main process would cause issues.\n if accelerator.distributed_type == DistributedType.DEEPSPEED or accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"step_loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompt is not None and epoch % args.validation_epochs == 0:\n # create pipeline\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n text_encoder=unwrap_model(text_encoder_one),\n text_encoder_2=unwrap_model(text_encoder_two),\n unet=unwrap_model(unet),\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n\n images = log_validation(pipeline, args, accelerator, epoch)\n\n del pipeline\n torch.cuda.empty_cache()\n\n # Save the lora layers\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = unwrap_model(unet)\n unet_lora_state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(unet))\n\n if args.train_text_encoder:\n text_encoder_one = unwrap_model(text_encoder_one)\n text_encoder_two = unwrap_model(text_encoder_two)\n\n text_encoder_lora_layers = convert_state_dict_to_diffusers(get_peft_model_state_dict(text_encoder_one))\n text_encoder_2_lora_layers = convert_state_dict_to_diffusers(get_peft_model_state_dict(text_encoder_two))\n else:\n text_encoder_lora_layers = None\n text_encoder_2_lora_layers = None\n\n StableDiffusionXLPipeline.save_lora_weights(\n save_directory=args.output_dir,\n unet_lora_layers=unet_lora_state_dict,\n text_encoder_lora_layers=text_encoder_lora_layers,\n text_encoder_2_lora_layers=text_encoder_2_lora_layers,\n )\n\n del unet\n del text_encoder_one\n del text_encoder_two\n del text_encoder_lora_layers\n del text_encoder_2_lora_layers\n torch.cuda.empty_cache()\n\n # Final inference\n # Make sure vae.dtype is consistent with the unet.dtype\n if args.mixed_precision == \"fp16\":\n vae.to(weight_dtype)\n # Load previous pipeline\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n\n # load attention processors\n pipeline.load_lora_weights(args.output_dir)\n\n # run inference\n if args.validation_prompt and args.num_validation_images > 0:\n images = log_validation(pipeline, args, accelerator, epoch, is_final_validation=True)\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n images=images,\n base_model=args.pretrained_model_name_or_path,\n dataset_name=args.dataset_name,\n train_text_encoder=args.train_text_encoder,\n repo_folder=args.output_dir,\n vae_path=args.pretrained_vae_model_name_or_path,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/text_to_image/train_text_to_image_sdxl.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Fine-tuning script for Stable Diffusion XL for text2image.\"\"\"\n\nimport argparse\nimport functools\nimport gc\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom contextlib import nullcontext\nfrom pathlib import Path\n\nimport accelerate\nimport datasets\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import DistributedType, ProjectConfiguration, set_seed\nfrom datasets import concatenate_datasets, load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom torchvision import transforms\nfrom torchvision.transforms.functional import crop\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionXLPipeline, UNet2DConditionModel\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import EMAModel, compute_snr\nfrom diffusers.utils import check_min_version, is_wandb_available\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom diffusers.utils.import_utils import is_torch_npu_available, is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__)\nif is_torch_npu_available():\n torch.npu.config.allow_internal_format = False\n\nDATASET_NAME_MAPPING = {\n \"lambdalabs/naruto-blip-captions\": (\"image\", \"text\"),\n}\n\n\ndef save_model_card(\n repo_id: str,\n images: list = None,\n validation_prompt: str = None,\n base_model: str = None,\n dataset_name: str = None,\n repo_folder: str = None,\n vae_path: str = None,\n):\n img_str = \"\"\n if images is not None:\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n img_str += f\"![img_{i}](./image_{i}.png)\\n\"\n\n model_description = f\"\"\"\n# Text-to-image finetuning - {repo_id}\n\nThis pipeline was finetuned from **{base_model}** on the **{dataset_name}** dataset. Below are some example images generated with the finetuned pipeline using the following prompt: {validation_prompt}: \\n\n{img_str}\n\nSpecial VAE used for training: {vae_path}.\n\"\"\"\n\n model_card = load_or_create_model_card(\n repo_id_or_path=repo_id,\n from_training=True,\n license=\"creativeml-openrail-m\",\n base_model=base_model,\n model_description=model_description,\n inference=True,\n )\n\n tags = [\n \"stable-diffusion-xl\",\n \"stable-diffusion-xl-diffusers\",\n \"text-to-image\",\n \"diffusers-training\",\n \"diffusers\",\n ]\n model_card = populate_model_card(model_card, tags=tags)\n\n model_card.save(os.path.join(repo_folder, \"README.md\"))\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef parse_args(input_args=None):\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_vae_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing an image.\"\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=\"text\",\n help=\"The column of the dataset containing a caption or a list of captions.\",\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n help=\"A prompt that is used during validation to verify that the model is learning.\",\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=1,\n help=(\n \"Run fine-tuning validation every X epochs. The validation process consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--proportion_empty_prompts\",\n type=float,\n default=0,\n help=\"Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).\",\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"sdxl-model-finetuned\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=1024,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final\"\n \" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--timestep_bias_strategy\",\n type=str,\n default=\"none\",\n choices=[\"earlier\", \"later\", \"range\", \"none\"],\n help=(\n \"The timestep bias strategy, which may help direct the model toward learning low or high frequency details.\"\n \" Choices: ['earlier', 'later', 'range', 'none'].\"\n \" The default is 'none', which means no bias is applied, and training proceeds normally.\"\n \" The value of 'later' will increase the frequency of the model's final training timesteps.\"\n ),\n )\n parser.add_argument(\n \"--timestep_bias_multiplier\",\n type=float,\n default=1.0,\n help=(\n \"The multiplier for the bias. Defaults to 1.0, which means no bias is applied.\"\n \" A value of 2.0 will double the weight of the bias, and a value of 0.5 will halve it.\"\n ),\n )\n parser.add_argument(\n \"--timestep_bias_begin\",\n type=int,\n default=0,\n help=(\n \"When using `--timestep_bias_strategy=range`, the beginning (inclusive) timestep to bias.\"\n \" Defaults to zero, which equates to having no specific bias.\"\n ),\n )\n parser.add_argument(\n \"--timestep_bias_end\",\n type=int,\n default=1000,\n help=(\n \"When using `--timestep_bias_strategy=range`, the final timestep (inclusive) to bias.\"\n \" Defaults to 1000, which is the number of timesteps that Stable Diffusion is trained on.\"\n ),\n )\n parser.add_argument(\n \"--timestep_bias_portion\",\n type=float,\n default=0.25,\n help=(\n \"The portion of timesteps to bias. Defaults to 0.25, which 25% of timesteps will be biased.\"\n \" A value of 0.5 will bias one half of the timesteps. The value provided for `--timestep_bias_strategy` determines\"\n \" whether the biased portions are in the earlier or later timesteps.\"\n ),\n )\n parser.add_argument(\n \"--snr_gamma\",\n type=float,\n default=None,\n help=\"SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. \"\n \"More details here: https://arxiv.org/abs/2303.09556.\",\n )\n parser.add_argument(\"--use_ema\", action=\"store_true\", help=\"Whether to use EMA model.\")\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--prediction_type\",\n type=str,\n default=None,\n help=\"The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediction_type` is chosen.\",\n )\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--enable_npu_flash_attention\", action=\"store_true\", help=\"Whether or not to use npu flash attention.\"\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\"--noise_offset\", type=float, default=0, help=\"The scale of noise offset.\")\n\n if input_args is not None:\n args = parser.parse_args(input_args)\n else:\n args = parser.parse_args()\n\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n\n if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:\n raise ValueError(\"`--proportion_empty_prompts` must be in the range [0, 1].\")\n\n return args\n\n\n# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt\ndef encode_prompt(batch, text_encoders, tokenizers, proportion_empty_prompts, caption_column, is_train=True):\n prompt_embeds_list = []\n prompt_batch = batch[caption_column]\n\n captions = []\n for caption in prompt_batch:\n if random.random() < proportion_empty_prompts:\n captions.append(\"\")\n elif isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n\n with torch.no_grad():\n for tokenizer, text_encoder in zip(tokenizers, text_encoders):\n text_inputs = tokenizer(\n captions,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n prompt_embeds = text_encoder(\n text_input_ids.to(text_encoder.device),\n output_hidden_states=True,\n return_dict=False,\n )\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds[-1][-2]\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)\n return {\"prompt_embeds\": prompt_embeds.cpu(), \"pooled_prompt_embeds\": pooled_prompt_embeds.cpu()}\n\n\ndef compute_vae_encodings(batch, vae):\n images = batch.pop(\"pixel_values\")\n pixel_values = torch.stack(list(images))\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n pixel_values = pixel_values.to(vae.device, dtype=vae.dtype)\n\n with torch.no_grad():\n model_input = vae.encode(pixel_values).latent_dist.sample()\n model_input = model_input * vae.config.scaling_factor\n return {\"model_input\": model_input.cpu()}\n\n\ndef generate_timestep_weights(args, num_timesteps):\n weights = torch.ones(num_timesteps)\n\n # Determine the indices to bias\n num_to_bias = int(args.timestep_bias_portion * num_timesteps)\n\n if args.timestep_bias_strategy == \"later\":\n bias_indices = slice(-num_to_bias, None)\n elif args.timestep_bias_strategy == \"earlier\":\n bias_indices = slice(0, num_to_bias)\n elif args.timestep_bias_strategy == \"range\":\n # Out of the possible 1000 timesteps, we might want to focus on eg. 200-500.\n range_begin = args.timestep_bias_begin\n range_end = args.timestep_bias_end\n if range_begin < 0:\n raise ValueError(\n \"When using the range strategy for timestep bias, you must provide a beginning timestep greater or equal to zero.\"\n )\n if range_end > num_timesteps:\n raise ValueError(\n \"When using the range strategy for timestep bias, you must provide an ending timestep smaller than the number of timesteps.\"\n )\n bias_indices = slice(range_begin, range_end)\n else: # 'none' or any other string\n return weights\n if args.timestep_bias_multiplier <= 0:\n return ValueError(\n \"The parameter --timestep_bias_multiplier is not intended to be used to disable the training of specific timesteps.\"\n \" If it was intended to disable timestep bias, use `--timestep_bias_strategy none` instead.\"\n \" A timestep bias multiplier less than or equal to 0 is not allowed.\"\n )\n\n # Apply the bias\n weights[bias_indices] *= args.timestep_bias_multiplier\n\n # Normalize\n weights /= weights.sum()\n\n return weights\n\n\ndef main(args):\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = Path(args.output_dir, args.logging_dir)\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n if torch.backends.mps.is_available() and args.mixed_precision == \"bf16\":\n # due to pytorch#99272, MPS does not yet support bfloat16.\n raise ValueError(\n \"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead.\"\n )\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n import wandb\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load the tokenizers\n tokenizer_one = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n tokenizer_two = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer_2\",\n revision=args.revision,\n use_fast=False,\n )\n\n # import correct text encoder classes\n text_encoder_cls_one = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision\n )\n text_encoder_cls_two = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision, subfolder=\"text_encoder_2\"\n )\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n # Check for terminal SNR in combination with SNR Gamma\n text_encoder_one = text_encoder_cls_one.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n text_encoder_two = text_encoder_cls_two.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder_2\", revision=args.revision, variant=args.variant\n )\n vae_path = (\n args.pretrained_model_name_or_path\n if args.pretrained_vae_model_name_or_path is None\n else args.pretrained_vae_model_name_or_path\n )\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n variant=args.variant,\n )\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n # Freeze vae and text encoders.\n vae.requires_grad_(False)\n text_encoder_one.requires_grad_(False)\n text_encoder_two.requires_grad_(False)\n # Set unet as trainable.\n unet.train()\n\n # For mixed precision training we cast all non-trainable weights to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move unet, vae and text_encoder to device and cast to weight_dtype\n # The VAE is in float32 to avoid NaN losses.\n vae.to(accelerator.device, dtype=torch.float32)\n text_encoder_one.to(accelerator.device, dtype=weight_dtype)\n text_encoder_two.to(accelerator.device, dtype=weight_dtype)\n\n # Create EMA for the unet.\n if args.use_ema:\n ema_unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n ema_unet = EMAModel(ema_unet.parameters(), model_cls=UNet2DConditionModel, model_config=ema_unet.config)\n if args.enable_npu_flash_attention:\n if is_torch_npu_available():\n logger.info(\"npu flash attention enabled.\")\n unet.enable_npu_flash_attention()\n else:\n raise ValueError(\"npu flash attention requires torch_npu extensions and is supported only on npu devices.\")\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n if args.use_ema:\n ema_unet.save_pretrained(os.path.join(output_dir, \"unet_ema\"))\n\n for i, model in enumerate(models):\n model.save_pretrained(os.path.join(output_dir, \"unet\"))\n\n # make sure to pop weight so that corresponding model is not saved again\n if weights:\n weights.pop()\n\n def load_model_hook(models, input_dir):\n if args.use_ema:\n load_model = EMAModel.from_pretrained(os.path.join(input_dir, \"unet_ema\"), UNet2DConditionModel)\n ema_unet.load_state_dict(load_model.state_dict())\n ema_unet.to(accelerator.device)\n del load_model\n\n for _ in range(len(models)):\n # pop models so that they are not loaded again\n model = models.pop()\n\n # load diffusers style into model\n load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder=\"unet\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n # Optimizer creation\n params_to_optimize = unet.parameters()\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n )\n else:\n data_files = {}\n if args.train_data_dir is not None:\n data_files[\"train\"] = os.path.join(args.train_data_dir, \"**\")\n dataset = load_dataset(\n \"imagefolder\",\n data_files=data_files,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)\n if args.image_column is None:\n image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.caption_column is None:\n caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n else:\n caption_column = args.caption_column\n if caption_column not in column_names:\n raise ValueError(\n f\"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}\"\n )\n\n # Preprocessing the datasets.\n train_resize = transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR)\n train_crop = transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution)\n train_flip = transforms.RandomHorizontalFlip(p=1.0)\n train_transforms = transforms.Compose([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])\n\n def preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n # image aug\n original_sizes = []\n all_images = []\n crop_top_lefts = []\n for image in images:\n original_sizes.append((image.height, image.width))\n image = train_resize(image)\n if args.random_flip and random.random() < 0.5:\n # flip\n image = train_flip(image)\n if args.center_crop:\n y1 = max(0, int(round((image.height - args.resolution) / 2.0)))\n x1 = max(0, int(round((image.width - args.resolution) / 2.0)))\n image = train_crop(image)\n else:\n y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))\n image = crop(image, y1, x1, h, w)\n crop_top_left = (y1, x1)\n crop_top_lefts.append(crop_top_left)\n image = train_transforms(image)\n all_images.append(image)\n\n examples[\"original_sizes\"] = original_sizes\n examples[\"crop_top_lefts\"] = crop_top_lefts\n examples[\"pixel_values\"] = all_images\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n # Let's first compute all the embeddings so that we can free up the text encoders\n # from memory. We will pre-compute the VAE encodings too.\n text_encoders = [text_encoder_one, text_encoder_two]\n tokenizers = [tokenizer_one, tokenizer_two]\n compute_embeddings_fn = functools.partial(\n encode_prompt,\n text_encoders=text_encoders,\n tokenizers=tokenizers,\n proportion_empty_prompts=args.proportion_empty_prompts,\n caption_column=args.caption_column,\n )\n compute_vae_encodings_fn = functools.partial(compute_vae_encodings, vae=vae)\n with accelerator.main_process_first():\n from datasets.fingerprint import Hasher\n\n # fingerprint used by the cache for the other processes to load the result\n # details: https://github.com/huggingface/diffusers/pull/4038#discussion_r1266078401\n new_fingerprint = Hasher.hash(args)\n new_fingerprint_for_vae = Hasher.hash(vae_path)\n train_dataset_with_embeddings = train_dataset.map(\n compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint\n )\n train_dataset_with_vae = train_dataset.map(\n compute_vae_encodings_fn,\n batched=True,\n batch_size=args.train_batch_size,\n new_fingerprint=new_fingerprint_for_vae,\n )\n precomputed_dataset = concatenate_datasets(\n [train_dataset_with_embeddings, train_dataset_with_vae.remove_columns([\"image\", \"text\"])], axis=1\n )\n precomputed_dataset = precomputed_dataset.with_transform(preprocess_train)\n\n del compute_vae_encodings_fn, compute_embeddings_fn, text_encoder_one, text_encoder_two\n del text_encoders, tokenizers, vae\n gc.collect()\n torch.cuda.empty_cache()\n\n def collate_fn(examples):\n model_input = torch.stack([torch.tensor(example[\"model_input\"]) for example in examples])\n original_sizes = [example[\"original_sizes\"] for example in examples]\n crop_top_lefts = [example[\"crop_top_lefts\"] for example in examples]\n prompt_embeds = torch.stack([torch.tensor(example[\"prompt_embeds\"]) for example in examples])\n pooled_prompt_embeds = torch.stack([torch.tensor(example[\"pooled_prompt_embeds\"]) for example in examples])\n\n return {\n \"model_input\": model_input,\n \"prompt_embeds\": prompt_embeds,\n \"pooled_prompt_embeds\": pooled_prompt_embeds,\n \"original_sizes\": original_sizes,\n \"crop_top_lefts\": crop_top_lefts,\n }\n\n # DataLoaders creation:\n train_dataloader = torch.utils.data.DataLoader(\n precomputed_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,\n num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,\n )\n\n # Prepare everything with our `accelerator`.\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n if args.use_ema:\n ema_unet.to(accelerator.device)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"text2image-fine-tune-sdxl\", config=vars(args))\n\n # Function for unwrapping if torch.compile() was used in accelerate.\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n if torch.backends.mps.is_available() or \"playground\" in args.pretrained_model_name_or_path:\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type)\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(precomputed_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n for epoch in range(first_epoch, args.num_train_epochs):\n train_loss = 0.0\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(unet):\n # Sample noise that we'll add to the latents\n model_input = batch[\"model_input\"].to(accelerator.device)\n noise = torch.randn_like(model_input)\n if args.noise_offset:\n # https://www.crosslabs.org//blog/diffusion-with-offset-noise\n noise += args.noise_offset * torch.randn(\n (model_input.shape[0], model_input.shape[1], 1, 1), device=model_input.device\n )\n\n bsz = model_input.shape[0]\n if args.timestep_bias_strategy == \"none\":\n # Sample a random timestep for each image without bias.\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device\n )\n else:\n # Sample a random timestep for each image, potentially biased by the timestep weights.\n # Biasing the timestep weights allows us to spend less time training irrelevant timesteps.\n weights = generate_timestep_weights(args, noise_scheduler.config.num_train_timesteps).to(\n model_input.device\n )\n timesteps = torch.multinomial(weights, bsz, replacement=True).long()\n\n # Add noise to the model input according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)\n\n # time ids\n def compute_time_ids(original_size, crops_coords_top_left):\n # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids\n target_size = (args.resolution, args.resolution)\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n add_time_ids = torch.tensor([add_time_ids])\n add_time_ids = add_time_ids.to(accelerator.device, dtype=weight_dtype)\n return add_time_ids\n\n add_time_ids = torch.cat(\n [compute_time_ids(s, c) for s, c in zip(batch[\"original_sizes\"], batch[\"crop_top_lefts\"])]\n )\n\n # Predict the noise residual\n unet_added_conditions = {\"time_ids\": add_time_ids}\n prompt_embeds = batch[\"prompt_embeds\"].to(accelerator.device)\n pooled_prompt_embeds = batch[\"pooled_prompt_embeds\"].to(accelerator.device)\n unet_added_conditions.update({\"text_embeds\": pooled_prompt_embeds})\n model_pred = unet(\n noisy_model_input,\n timesteps,\n prompt_embeds,\n added_cond_kwargs=unet_added_conditions,\n return_dict=False,\n )[0]\n\n # Get the target for loss depending on the prediction type\n if args.prediction_type is not None:\n # set prediction_type of scheduler if defined\n noise_scheduler.register_to_config(prediction_type=args.prediction_type)\n\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(model_input, noise, timesteps)\n elif noise_scheduler.config.prediction_type == \"sample\":\n # We set the target to latents here, but the model_pred will return the noise sample prediction.\n target = model_input\n # We will have to subtract the noise residual from the prediction to get the target sample.\n model_pred = model_pred - noise\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n if args.snr_gamma is None:\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n else:\n # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.\n # Since we predict the noise instead of x_0, the original formulation is slightly changed.\n # This is discussed in Section 4.2 of the same paper.\n snr = compute_snr(noise_scheduler, timesteps)\n mse_loss_weights = torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(\n dim=1\n )[0]\n if noise_scheduler.config.prediction_type == \"epsilon\":\n mse_loss_weights = mse_loss_weights / snr\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n mse_loss_weights = mse_loss_weights / (snr + 1)\n\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"none\")\n loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights\n loss = loss.mean()\n\n # Gather the losses across all processes for logging (if we use distributed training).\n avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n train_loss += avg_loss.item() / args.gradient_accumulation_steps\n\n # Backpropagate\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n params_to_clip = unet.parameters()\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n if args.use_ema:\n ema_unet.step(unet.parameters())\n progress_bar.update(1)\n global_step += 1\n accelerator.log({\"train_loss\": train_loss}, step=global_step)\n train_loss = 0.0\n\n # DeepSpeed requires saving weights on every device; saving weights only on the main process would cause issues.\n if accelerator.distributed_type == DistributedType.DEEPSPEED or accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"step_loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompt is not None and epoch % args.validation_epochs == 0:\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n if args.use_ema:\n # Store the UNet parameters temporarily and load the EMA parameters to perform inference.\n ema_unet.store(unet.parameters())\n ema_unet.copy_to(unet.parameters())\n\n # create pipeline\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n variant=args.variant,\n )\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n vae=vae,\n unet=accelerator.unwrap_model(unet),\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n if args.prediction_type is not None:\n scheduler_args = {\"prediction_type\": args.prediction_type}\n pipeline.scheduler = pipeline.scheduler.from_config(pipeline.scheduler.config, **scheduler_args)\n\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n pipeline_args = {\"prompt\": args.validation_prompt}\n\n with autocast_ctx:\n images = [\n pipeline(**pipeline_args, generator=generator, num_inference_steps=25).images[0]\n for _ in range(args.num_validation_images)\n ]\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\")\n for i, image in enumerate(images)\n ]\n }\n )\n\n del pipeline\n torch.cuda.empty_cache()\n\n if args.use_ema:\n # Switch back to the original UNet parameters.\n ema_unet.restore(unet.parameters())\n\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n unet = unwrap_model(unet)\n if args.use_ema:\n ema_unet.copy_to(unet.parameters())\n\n # Serialize pipeline.\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n unet=unet,\n vae=vae,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n if args.prediction_type is not None:\n scheduler_args = {\"prediction_type\": args.prediction_type}\n pipeline.scheduler = pipeline.scheduler.from_config(pipeline.scheduler.config, **scheduler_args)\n pipeline.save_pretrained(args.output_dir)\n\n # run inference\n images = []\n if args.validation_prompt and args.num_validation_images > 0:\n pipeline = pipeline.to(accelerator.device)\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None\n\n with autocast_ctx:\n images = [\n pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]\n for _ in range(args.num_validation_images)\n ]\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"test\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"test\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\")\n for i, image in enumerate(images)\n ]\n }\n )\n\n if args.push_to_hub:\n save_model_card(\n repo_id=repo_id,\n images=images,\n validation_prompt=args.validation_prompt,\n base_model=args.pretrained_model_name_or_path,\n dataset_name=args.dataset_name,\n repo_folder=args.output_dir,\n vae_path=args.pretrained_vae_model_name_or_path,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/textual_inversion/README.md", "content": "## Textual Inversion fine-tuning example\n\n[Textual inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like stable diffusion on your own images using just 3-5 examples.\nThe `textual_inversion.py` script shows how to implement the training procedure and adapt it for stable diffusion.\n\n## Running on Colab\n\nColab for training\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb)\n\nColab for inference\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb)\n\n## Running locally with PyTorch\n### Installing the dependencies\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n**Important**\n\nTo make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nThen cd in the example folder and run:\n```bash\npip install -r requirements.txt\n```\n\nAnd initialize an [🤗 Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\n\n### Cat toy example\n\nFirst, let's login so that we can upload the checkpoint to the Hub during training:\n\n```bash\nhuggingface-cli login\n```\n\nNow let's get our dataset. For this example we will use some cat images: https://huggingface.co/datasets/diffusers/cat_toy_example .\n\nLet's first download it locally:\n\n```py\nfrom huggingface_hub import snapshot_download\n\nlocal_dir = \"./cat\"\nsnapshot_download(\"diffusers/cat_toy_example\", local_dir=local_dir, repo_type=\"dataset\", ignore_patterns=\".gitattributes\")\n```\n\nThis will be our training data.\nNow we can launch the training using:\n\n**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**\n\n**___Note: Please follow the [README_sdxl.md](./README_sdxl.md) if you are using the [stable-diffusion-xl](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0).___**\n\n```bash\nexport MODEL_NAME=\"runwayml/stable-diffusion-v1-5\"\nexport DATA_DIR=\"./cat\"\n\naccelerate launch textual_inversion.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_data_dir=$DATA_DIR \\\n --learnable_property=\"object\" \\\n --placeholder_token=\"\" \\\n --initializer_token=\"toy\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --max_train_steps=3000 \\\n --learning_rate=5.0e-04 \\\n --scale_lr \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --push_to_hub \\\n --output_dir=\"textual_inversion_cat\"\n```\n\nA full training run takes ~1 hour on one V100 GPU.\n\n**Note**: As described in [the official paper](https://arxiv.org/abs/2208.01618)\nonly one embedding vector is used for the placeholder token, *e.g.* `\"\"`.\nHowever, one can also add multiple embedding vectors for the placeholder token\nto increase the number of fine-tuneable parameters. This can help the model to learn\nmore complex details. To use multiple embedding vectors, you should define `--num_vectors`\nto a number larger than one, *e.g.*:\n```bash\n--num_vectors 5\n```\n\nThe saved textual inversion vectors will then be larger in size compared to the default case.\n\n### Inference\n\nOnce you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline`. Make sure to include the `placeholder_token` in your prompt.\n\n```python\nfrom diffusers import StableDiffusionPipeline\nimport torch\n\nmodel_id = \"path-to-your-trained-model\"\npipe = StableDiffusionPipeline.from_pretrained(model_id,torch_dtype=torch.float16).to(\"cuda\")\n\nprompt = \"A backpack\"\n\nimage = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]\n\nimage.save(\"cat-backpack.png\")\n```\n\n\n## Training with Flax/JAX\n\nFor faster training on TPUs and GPUs you can leverage the flax training example. Follow the instructions above to get the model and dataset before running the script.\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n```bash\npip install -U -r requirements_flax.txt\n```\n\n```bash\nexport MODEL_NAME=\"duongna/stable-diffusion-v1-4-flax\"\nexport DATA_DIR=\"path-to-dir-containing-images\"\n\npython textual_inversion_flax.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_data_dir=$DATA_DIR \\\n --learnable_property=\"object\" \\\n --placeholder_token=\"\" \\\n --initializer_token=\"toy\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --max_train_steps=3000 \\\n --learning_rate=5.0e-04 \\\n --scale_lr \\\n --output_dir=\"textual_inversion_cat\"\n```\nIt should be at least 70% faster than the PyTorch script with the same configuration.\n\n### Training with xformers:\nYou can enable memory efficient attention by [installing xFormers](https://github.com/facebookresearch/xformers#installing-xformers) and padding the `--enable_xformers_memory_efficient_attention` argument to the script. This is not available with the Flax/JAX implementation.\n" }, { "path": "diffusers/examples/textual_inversion/README_sdxl.md", "content": "## Textual Inversion fine-tuning example for SDXL\n\n```sh\nexport MODEL_NAME=\"stabilityai/stable-diffusion-xl-base-1.0\"\nexport DATA_DIR=\"./cat\"\n\naccelerate launch textual_inversion_sdxl.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --train_data_dir=$DATA_DIR \\\n --learnable_property=\"object\" \\\n --placeholder_token=\"\" \\\n --initializer_token=\"toy\" \\\n --mixed_precision=\"bf16\" \\\n --resolution=768 \\\n --train_batch_size=1 \\\n --gradient_accumulation_steps=4 \\\n --max_train_steps=500 \\\n --learning_rate=5.0e-04 \\\n --scale_lr \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --save_as_full_pipeline \\\n --output_dir=\"./textual_inversion_cat_sdxl\"\n```\n\nFor now, only training of the first text encoder is supported." }, { "path": "diffusers/examples/textual_inversion/requirements.txt", "content": "accelerate>=0.16.0\ntorchvision\ntransformers>=4.25.1\nftfy\ntensorboard\nJinja2\n" }, { "path": "diffusers/examples/textual_inversion/requirements_flax.txt", "content": "transformers>=4.25.1\nflax\noptax\ntorch\ntorchvision\nftfy\ntensorboard\nJinja2\n" }, { "path": "diffusers/examples/textual_inversion/test_textual_inversion.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport logging\nimport os\nimport sys\nimport tempfile\n\n\nsys.path.append(\"..\")\nfrom test_examples_utils import ExamplesTestsAccelerate, run_command # noqa: E402\n\n\nlogging.basicConfig(level=logging.DEBUG)\n\nlogger = logging.getLogger()\nstream_handler = logging.StreamHandler(sys.stdout)\nlogger.addHandler(stream_handler)\n\n\nclass TextualInversion(ExamplesTestsAccelerate):\n def test_textual_inversion(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/textual_inversion/textual_inversion.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe\n --train_data_dir docs/source/en/imgs\n --learnable_property object\n --placeholder_token \n --initializer_token a\n --save_steps 1\n --num_vectors 2\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n # save_pretrained smoke test\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"learned_embeds.safetensors\")))\n\n def test_textual_inversion_checkpointing(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/textual_inversion/textual_inversion.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe\n --train_data_dir docs/source/en/imgs\n --learnable_property object\n --placeholder_token \n --initializer_token a\n --save_steps 1\n --num_vectors 2\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 3\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --checkpointing_steps=1\n --checkpoints_total_limit=2\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n\n # check checkpoint directories exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-2\", \"checkpoint-3\"},\n )\n\n def test_textual_inversion_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/textual_inversion/textual_inversion.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe\n --train_data_dir docs/source/en/imgs\n --learnable_property object\n --placeholder_token \n --initializer_token a\n --save_steps 1\n --num_vectors 2\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --checkpointing_steps=1\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n\n # check checkpoint directories exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-1\", \"checkpoint-2\"},\n )\n\n resume_run_args = f\"\"\"\n examples/textual_inversion/textual_inversion.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe\n --train_data_dir docs/source/en/imgs\n --learnable_property object\n --placeholder_token \n --initializer_token a\n --save_steps 1\n --num_vectors 2\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --checkpointing_steps=1\n --resume_from_checkpoint=checkpoint-2\n --checkpoints_total_limit=2\n \"\"\".split()\n\n run_command(self._launch_args + resume_run_args)\n\n # check checkpoint directories exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-2\", \"checkpoint-3\"},\n )\n" }, { "path": "diffusers/examples/textual_inversion/test_textual_inversion_sdxl.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport logging\nimport os\nimport sys\nimport tempfile\n\n\nsys.path.append(\"..\")\nfrom test_examples_utils import ExamplesTestsAccelerate, run_command # noqa: E402\n\n\nlogging.basicConfig(level=logging.DEBUG)\n\nlogger = logging.getLogger()\nstream_handler = logging.StreamHandler(sys.stdout)\nlogger.addHandler(stream_handler)\n\n\nclass TextualInversionSdxl(ExamplesTestsAccelerate):\n def test_textual_inversion_sdxl(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/textual_inversion/textual_inversion_sdxl.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-sdxl-pipe\n --train_data_dir docs/source/en/imgs\n --learnable_property object\n --placeholder_token \n --initializer_token a\n --save_steps 1\n --num_vectors 2\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n # save_pretrained smoke test\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"learned_embeds.safetensors\")))\n\n def test_textual_inversion_sdxl_checkpointing(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/textual_inversion/textual_inversion_sdxl.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-sdxl-pipe\n --train_data_dir docs/source/en/imgs\n --learnable_property object\n --placeholder_token \n --initializer_token a\n --save_steps 1\n --num_vectors 2\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 3\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --checkpointing_steps=1\n --checkpoints_total_limit=2\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n\n # check checkpoint directories exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-2\", \"checkpoint-3\"},\n )\n\n def test_textual_inversion_sdxl_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/textual_inversion/textual_inversion_sdxl.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-sdxl-pipe\n --train_data_dir docs/source/en/imgs\n --learnable_property object\n --placeholder_token \n --initializer_token a\n --save_steps 1\n --num_vectors 2\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --checkpointing_steps=1\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n\n # check checkpoint directories exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-1\", \"checkpoint-2\"},\n )\n\n resume_run_args = f\"\"\"\n examples/textual_inversion/textual_inversion_sdxl.py\n --pretrained_model_name_or_path hf-internal-testing/tiny-sdxl-pipe\n --train_data_dir docs/source/en/imgs\n --learnable_property object\n --placeholder_token \n --initializer_token a\n --save_steps 1\n --num_vectors 2\n --resolution 64\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --output_dir {tmpdir}\n --checkpointing_steps=1\n --resume_from_checkpoint=checkpoint-2\n --checkpoints_total_limit=2\n \"\"\".split()\n\n run_command(self._launch_args + resume_run_args)\n\n # check checkpoint directories exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-2\", \"checkpoint-3\"},\n )\n" }, { "path": "diffusers/examples/textual_inversion/textual_inversion.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nimport warnings\nfrom contextlib import nullcontext\nfrom pathlib import Path\n\nimport numpy as np\nimport PIL\nimport safetensors\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom huggingface_hub import create_repo, upload_folder\n\n# TODO: remove and import from diffusers.utils when the new version of diffusers is released\nfrom packaging import version\nfrom PIL import Image\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n DiffusionPipeline,\n DPMSolverMultistepScheduler,\n StableDiffusionPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import check_min_version, is_wandb_available\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\nif is_wandb_available():\n import wandb\n\nif version.parse(version.parse(PIL.__version__).base_version) >= version.parse(\"9.1.0\"):\n PIL_INTERPOLATION = {\n \"linear\": PIL.Image.Resampling.BILINEAR,\n \"bilinear\": PIL.Image.Resampling.BILINEAR,\n \"bicubic\": PIL.Image.Resampling.BICUBIC,\n \"lanczos\": PIL.Image.Resampling.LANCZOS,\n \"nearest\": PIL.Image.Resampling.NEAREST,\n }\nelse:\n PIL_INTERPOLATION = {\n \"linear\": PIL.Image.LINEAR,\n \"bilinear\": PIL.Image.BILINEAR,\n \"bicubic\": PIL.Image.BICUBIC,\n \"lanczos\": PIL.Image.LANCZOS,\n \"nearest\": PIL.Image.NEAREST,\n }\n# ------------------------------------------------------------------------------\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef save_model_card(repo_id: str, images: list = None, base_model: str = None, repo_folder: str = None):\n img_str = \"\"\n if images is not None:\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n img_str += f\"![img_{i}](./image_{i}.png)\\n\"\n model_description = f\"\"\"\n# Textual inversion text2image fine-tuning - {repo_id}\nThese are textual inversion adaption weights for {base_model}. You can find some example images in the following. \\n\n{img_str}\n\"\"\"\n model_card = load_or_create_model_card(\n repo_id_or_path=repo_id,\n from_training=True,\n license=\"creativeml-openrail-m\",\n base_model=base_model,\n model_description=model_description,\n inference=True,\n )\n\n tags = [\n \"stable-diffusion\",\n \"stable-diffusion-diffusers\",\n \"text-to-image\",\n \"diffusers\",\n \"textual_inversion\",\n \"diffusers-training\",\n ]\n model_card = populate_model_card(model_card, tags=tags)\n\n model_card.save(os.path.join(repo_folder, \"README.md\"))\n\n\ndef log_validation(text_encoder, tokenizer, unet, vae, args, accelerator, weight_dtype, epoch):\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n # create pipeline (note: unet and vae are loaded again in float32)\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n text_encoder=accelerator.unwrap_model(text_encoder),\n tokenizer=tokenizer,\n unet=unet,\n vae=vae,\n safety_checker=None,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = None if args.seed is None else torch.Generator(device=accelerator.device).manual_seed(args.seed)\n images = []\n for _ in range(args.num_validation_images):\n if torch.backends.mps.is_available():\n autocast_ctx = nullcontext()\n else:\n autocast_ctx = torch.autocast(accelerator.device.type)\n\n with autocast_ctx:\n image = pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]\n images.append(image)\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\") for i, image in enumerate(images)\n ]\n }\n )\n\n del pipeline\n torch.cuda.empty_cache()\n return images\n\n\ndef save_progress(text_encoder, placeholder_token_ids, accelerator, args, save_path, safe_serialization=True):\n logger.info(\"Saving embeddings\")\n learned_embeds = (\n accelerator.unwrap_model(text_encoder)\n .get_input_embeddings()\n .weight[min(placeholder_token_ids) : max(placeholder_token_ids) + 1]\n )\n learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()}\n\n if safe_serialization:\n safetensors.torch.save_file(learned_embeds_dict, save_path, metadata={\"format\": \"pt\"})\n else:\n torch.save(learned_embeds_dict, save_path)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--save_steps\",\n type=int,\n default=500,\n help=\"Save learned_embeds.bin every X updates steps.\",\n )\n parser.add_argument(\n \"--save_as_full_pipeline\",\n action=\"store_true\",\n help=\"Save the complete stable diffusion pipeline.\",\n )\n parser.add_argument(\n \"--num_vectors\",\n type=int,\n default=1,\n help=\"How many textual inversion vectors shall be used to learn the concept.\",\n )\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--tokenizer_name\",\n type=str,\n default=None,\n help=\"Pretrained tokenizer name or path if not the same as model_name\",\n )\n parser.add_argument(\n \"--train_data_dir\", type=str, default=None, required=True, help=\"A folder containing the training data.\"\n )\n parser.add_argument(\n \"--placeholder_token\",\n type=str,\n default=None,\n required=True,\n help=\"A token to use as a placeholder for the concept.\",\n )\n parser.add_argument(\n \"--initializer_token\", type=str, default=None, required=True, help=\"A token to use as initializer word.\"\n )\n parser.add_argument(\"--learnable_property\", type=str, default=\"object\", help=\"Choose between 'object' and 'style'\")\n parser.add_argument(\"--repeats\", type=int, default=100, help=\"How many times to repeat the training data.\")\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"text-inversion-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\", action=\"store_true\", help=\"Whether to center crop images before resizing to resolution.\"\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=5000,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=\"no\",\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose\"\n \"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.\"\n \"and Nvidia Ampere GPU or Intel Gen 4 Xeon (and later) .\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n help=\"A prompt that is used during validation to verify that the model is learning.\",\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=100,\n help=(\n \"Run validation every X steps. Validation consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`\"\n \" and logging the images.\"\n ),\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=None,\n help=(\n \"Deprecated in favor of validation_steps. Run validation every X epochs. Validation consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`\"\n \" and logging the images.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--no_safe_serialization\",\n action=\"store_true\",\n help=\"If specified save the checkpoint not in `safetensors` format, but in original PyTorch format instead.\",\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.train_data_dir is None:\n raise ValueError(\"You must specify a train data directory.\")\n\n return args\n\n\nimagenet_templates_small = [\n \"a photo of a {}\",\n \"a rendering of a {}\",\n \"a cropped photo of the {}\",\n \"the photo of a {}\",\n \"a photo of a clean {}\",\n \"a photo of a dirty {}\",\n \"a dark photo of the {}\",\n \"a photo of my {}\",\n \"a photo of the cool {}\",\n \"a close-up photo of a {}\",\n \"a bright photo of the {}\",\n \"a cropped photo of a {}\",\n \"a photo of the {}\",\n \"a good photo of the {}\",\n \"a photo of one {}\",\n \"a close-up photo of the {}\",\n \"a rendition of the {}\",\n \"a photo of the clean {}\",\n \"a rendition of a {}\",\n \"a photo of a nice {}\",\n \"a good photo of a {}\",\n \"a photo of the nice {}\",\n \"a photo of the small {}\",\n \"a photo of the weird {}\",\n \"a photo of the large {}\",\n \"a photo of a cool {}\",\n \"a photo of a small {}\",\n]\n\nimagenet_style_templates_small = [\n \"a painting in the style of {}\",\n \"a rendering in the style of {}\",\n \"a cropped painting in the style of {}\",\n \"the painting in the style of {}\",\n \"a clean painting in the style of {}\",\n \"a dirty painting in the style of {}\",\n \"a dark painting in the style of {}\",\n \"a picture in the style of {}\",\n \"a cool painting in the style of {}\",\n \"a close-up painting in the style of {}\",\n \"a bright painting in the style of {}\",\n \"a cropped painting in the style of {}\",\n \"a good painting in the style of {}\",\n \"a close-up painting in the style of {}\",\n \"a rendition in the style of {}\",\n \"a nice painting in the style of {}\",\n \"a small painting in the style of {}\",\n \"a weird painting in the style of {}\",\n \"a large painting in the style of {}\",\n]\n\n\nclass TextualInversionDataset(Dataset):\n def __init__(\n self,\n data_root,\n tokenizer,\n learnable_property=\"object\", # [object, style]\n size=512,\n repeats=100,\n interpolation=\"bicubic\",\n flip_p=0.5,\n set=\"train\",\n placeholder_token=\"*\",\n center_crop=False,\n ):\n self.data_root = data_root\n self.tokenizer = tokenizer\n self.learnable_property = learnable_property\n self.size = size\n self.placeholder_token = placeholder_token\n self.center_crop = center_crop\n self.flip_p = flip_p\n\n self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]\n\n self.num_images = len(self.image_paths)\n self._length = self.num_images\n\n if set == \"train\":\n self._length = self.num_images * repeats\n\n self.interpolation = {\n \"linear\": PIL_INTERPOLATION[\"linear\"],\n \"bilinear\": PIL_INTERPOLATION[\"bilinear\"],\n \"bicubic\": PIL_INTERPOLATION[\"bicubic\"],\n \"lanczos\": PIL_INTERPOLATION[\"lanczos\"],\n }[interpolation]\n\n self.templates = imagenet_style_templates_small if learnable_property == \"style\" else imagenet_templates_small\n self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, i):\n example = {}\n image = Image.open(self.image_paths[i % self.num_images])\n\n if not image.mode == \"RGB\":\n image = image.convert(\"RGB\")\n\n placeholder_string = self.placeholder_token\n text = random.choice(self.templates).format(placeholder_string)\n\n example[\"input_ids\"] = self.tokenizer(\n text,\n padding=\"max_length\",\n truncation=True,\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n ).input_ids[0]\n\n # default to score-sde preprocessing\n img = np.array(image).astype(np.uint8)\n\n if self.center_crop:\n crop = min(img.shape[0], img.shape[1])\n (\n h,\n w,\n ) = (\n img.shape[0],\n img.shape[1],\n )\n img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]\n\n image = Image.fromarray(img)\n image = image.resize((self.size, self.size), resample=self.interpolation)\n\n image = self.flip_transform(image)\n image = np.array(image).astype(np.uint8)\n image = (image / 127.5 - 1.0).astype(np.float32)\n\n example[\"pixel_values\"] = torch.from_numpy(image).permute(2, 0, 1)\n return example\n\n\ndef main():\n args = parse_args()\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = os.path.join(args.output_dir, args.logging_dir)\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load tokenizer\n if args.tokenizer_name:\n tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)\n elif args.pretrained_model_name_or_path:\n tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"tokenizer\")\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n text_encoder = CLIPTextModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision\n )\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision, variant=args.variant\n )\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n # Add the placeholder token in tokenizer\n placeholder_tokens = [args.placeholder_token]\n\n if args.num_vectors < 1:\n raise ValueError(f\"--num_vectors has to be larger or equal to 1, but is {args.num_vectors}\")\n\n # add dummy tokens for multi-vector\n additional_tokens = []\n for i in range(1, args.num_vectors):\n additional_tokens.append(f\"{args.placeholder_token}_{i}\")\n placeholder_tokens += additional_tokens\n\n num_added_tokens = tokenizer.add_tokens(placeholder_tokens)\n if num_added_tokens != args.num_vectors:\n raise ValueError(\n f\"The tokenizer already contains the token {args.placeholder_token}. Please pass a different\"\n \" `placeholder_token` that is not already in the tokenizer.\"\n )\n\n # Convert the initializer_token, placeholder_token to ids\n token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)\n # Check if initializer_token is a single token or a sequence of tokens\n if len(token_ids) > 1:\n raise ValueError(\"The initializer token must be a single token.\")\n\n initializer_token_id = token_ids[0]\n placeholder_token_ids = tokenizer.convert_tokens_to_ids(placeholder_tokens)\n\n # Resize the token embeddings as we are adding new special tokens to the tokenizer\n text_encoder.resize_token_embeddings(len(tokenizer))\n\n # Initialise the newly added placeholder token with the embeddings of the initializer token\n token_embeds = text_encoder.get_input_embeddings().weight.data\n with torch.no_grad():\n for token_id in placeholder_token_ids:\n token_embeds[token_id] = token_embeds[initializer_token_id].clone()\n\n # Freeze vae and unet\n vae.requires_grad_(False)\n unet.requires_grad_(False)\n # Freeze all parameters except for the token embeddings in text encoder\n text_encoder.text_model.encoder.requires_grad_(False)\n text_encoder.text_model.final_layer_norm.requires_grad_(False)\n text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)\n\n if args.gradient_checkpointing:\n # Keep unet in train mode if we are using gradient checkpointing to save memory.\n # The dropout cannot be != 0 so it doesn't matter if we are in eval or train mode.\n unet.train()\n text_encoder.gradient_checkpointing_enable()\n unet.enable_gradient_checkpointing()\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Initialize the optimizer\n optimizer = torch.optim.AdamW(\n text_encoder.get_input_embeddings().parameters(), # only optimize the embeddings\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Dataset and DataLoaders creation:\n train_dataset = TextualInversionDataset(\n data_root=args.train_data_dir,\n tokenizer=tokenizer,\n size=args.resolution,\n placeholder_token=(\" \".join(tokenizer.convert_ids_to_tokens(placeholder_token_ids))),\n repeats=args.repeats,\n learnable_property=args.learnable_property,\n center_crop=args.center_crop,\n set=\"train\",\n )\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers\n )\n if args.validation_epochs is not None:\n warnings.warn(\n f\"FutureWarning: You are doing logging with validation_epochs={args.validation_epochs}.\"\n \" Deprecated validation_epochs in favor of `validation_steps`\"\n f\"Setting `args.validation_steps` to {args.validation_epochs * len(train_dataset)}\",\n FutureWarning,\n stacklevel=2,\n )\n args.validation_steps = args.validation_epochs * len(train_dataset)\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_cycles=args.lr_num_cycles,\n )\n\n text_encoder.train()\n # Prepare everything with our `accelerator`.\n text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n text_encoder, optimizer, train_dataloader, lr_scheduler\n )\n\n # For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move vae and unet to device and cast to weight_dtype\n unet.to(accelerator.device, dtype=weight_dtype)\n vae.to(accelerator.device, dtype=weight_dtype)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"textual_inversion\", config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n # keep original embeddings as reference\n orig_embeds_params = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight.data.clone()\n\n for epoch in range(first_epoch, args.num_train_epochs):\n text_encoder.train()\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(text_encoder):\n # Convert images to latent space\n latents = vae.encode(batch[\"pixel_values\"].to(dtype=weight_dtype)).latent_dist.sample().detach()\n latents = latents * vae.config.scaling_factor\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # Get the text embedding for conditioning\n encoder_hidden_states = text_encoder(batch[\"input_ids\"])[0].to(dtype=weight_dtype)\n\n # Predict the noise residual\n model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n\n accelerator.backward(loss)\n\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Let's make sure we don't update any embedding weights besides the newly added token\n index_no_updates = torch.ones((len(tokenizer),), dtype=torch.bool)\n index_no_updates[min(placeholder_token_ids) : max(placeholder_token_ids) + 1] = False\n\n with torch.no_grad():\n accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[\n index_no_updates\n ] = orig_embeds_params[index_no_updates]\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n images = []\n progress_bar.update(1)\n global_step += 1\n if global_step % args.save_steps == 0:\n weight_name = (\n f\"learned_embeds-steps-{global_step}.bin\"\n if args.no_safe_serialization\n else f\"learned_embeds-steps-{global_step}.safetensors\"\n )\n save_path = os.path.join(args.output_dir, weight_name)\n save_progress(\n text_encoder,\n placeholder_token_ids,\n accelerator,\n args,\n save_path,\n safe_serialization=not args.no_safe_serialization,\n )\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n if args.validation_prompt is not None and global_step % args.validation_steps == 0:\n images = log_validation(\n text_encoder, tokenizer, unet, vae, args, accelerator, weight_dtype, epoch\n )\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n # Create the pipeline using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n if args.push_to_hub and not args.save_as_full_pipeline:\n logger.warning(\"Enabling full model saving because --push_to_hub=True was specified.\")\n save_full_model = True\n else:\n save_full_model = args.save_as_full_pipeline\n if save_full_model:\n pipeline = StableDiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n text_encoder=accelerator.unwrap_model(text_encoder),\n vae=vae,\n unet=unet,\n tokenizer=tokenizer,\n )\n pipeline.save_pretrained(args.output_dir)\n # Save the newly trained embeddings\n weight_name = \"learned_embeds.bin\" if args.no_safe_serialization else \"learned_embeds.safetensors\"\n save_path = os.path.join(args.output_dir, weight_name)\n save_progress(\n text_encoder,\n placeholder_token_ids,\n accelerator,\n args,\n save_path,\n safe_serialization=not args.no_safe_serialization,\n )\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n images=images,\n base_model=args.pretrained_model_name_or_path,\n repo_folder=args.output_dir,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/textual_inversion/textual_inversion_flax.py", "content": "import argparse\nimport logging\nimport math\nimport os\nimport random\nfrom pathlib import Path\n\nimport jax\nimport jax.numpy as jnp\nimport numpy as np\nimport optax\nimport PIL\nimport torch\nimport torch.utils.checkpoint\nimport transformers\nfrom flax import jax_utils\nfrom flax.training import train_state\nfrom flax.training.common_utils import shard\nfrom huggingface_hub import create_repo, upload_folder\n\n# TODO: remove and import from diffusers.utils when the new version of diffusers is released\nfrom packaging import version\nfrom PIL import Image\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel, set_seed\n\nfrom diffusers import (\n FlaxAutoencoderKL,\n FlaxDDPMScheduler,\n FlaxPNDMScheduler,\n FlaxStableDiffusionPipeline,\n FlaxUNet2DConditionModel,\n)\nfrom diffusers.pipelines.stable_diffusion import FlaxStableDiffusionSafetyChecker\nfrom diffusers.utils import check_min_version\n\n\nif version.parse(version.parse(PIL.__version__).base_version) >= version.parse(\"9.1.0\"):\n PIL_INTERPOLATION = {\n \"linear\": PIL.Image.Resampling.BILINEAR,\n \"bilinear\": PIL.Image.Resampling.BILINEAR,\n \"bicubic\": PIL.Image.Resampling.BICUBIC,\n \"lanczos\": PIL.Image.Resampling.LANCZOS,\n \"nearest\": PIL.Image.Resampling.NEAREST,\n }\nelse:\n PIL_INTERPOLATION = {\n \"linear\": PIL.Image.LINEAR,\n \"bilinear\": PIL.Image.BILINEAR,\n \"bicubic\": PIL.Image.BICUBIC,\n \"lanczos\": PIL.Image.LANCZOS,\n \"nearest\": PIL.Image.NEAREST,\n }\n# ------------------------------------------------------------------------------\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = logging.getLogger(__name__)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--tokenizer_name\",\n type=str,\n default=None,\n help=\"Pretrained tokenizer name or path if not the same as model_name\",\n )\n parser.add_argument(\n \"--train_data_dir\", type=str, default=None, required=True, help=\"A folder containing the training data.\"\n )\n parser.add_argument(\n \"--placeholder_token\",\n type=str,\n default=None,\n required=True,\n help=\"A token to use as a placeholder for the concept.\",\n )\n parser.add_argument(\n \"--initializer_token\", type=str, default=None, required=True, help=\"A token to use as initializer word.\"\n )\n parser.add_argument(\"--learnable_property\", type=str, default=\"object\", help=\"Choose between 'object' and 'style'\")\n parser.add_argument(\"--repeats\", type=int, default=100, help=\"How many times to repeat the training data.\")\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"text-inversion-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=42, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\", action=\"store_true\", help=\"Whether to center crop images before resizing to resolution.\"\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=5000,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--save_steps\",\n type=int,\n default=500,\n help=\"Save learned_embeds.bin every X updates steps.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=True,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\n \"--use_auth_token\",\n action=\"store_true\",\n help=(\n \"Will use the token generated when running `huggingface-cli login` (necessary to use this script with\"\n \" private models).\"\n ),\n )\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.train_data_dir is None:\n raise ValueError(\"You must specify a train data directory.\")\n\n return args\n\n\nimagenet_templates_small = [\n \"a photo of a {}\",\n \"a rendering of a {}\",\n \"a cropped photo of the {}\",\n \"the photo of a {}\",\n \"a photo of a clean {}\",\n \"a photo of a dirty {}\",\n \"a dark photo of the {}\",\n \"a photo of my {}\",\n \"a photo of the cool {}\",\n \"a close-up photo of a {}\",\n \"a bright photo of the {}\",\n \"a cropped photo of a {}\",\n \"a photo of the {}\",\n \"a good photo of the {}\",\n \"a photo of one {}\",\n \"a close-up photo of the {}\",\n \"a rendition of the {}\",\n \"a photo of the clean {}\",\n \"a rendition of a {}\",\n \"a photo of a nice {}\",\n \"a good photo of a {}\",\n \"a photo of the nice {}\",\n \"a photo of the small {}\",\n \"a photo of the weird {}\",\n \"a photo of the large {}\",\n \"a photo of a cool {}\",\n \"a photo of a small {}\",\n]\n\nimagenet_style_templates_small = [\n \"a painting in the style of {}\",\n \"a rendering in the style of {}\",\n \"a cropped painting in the style of {}\",\n \"the painting in the style of {}\",\n \"a clean painting in the style of {}\",\n \"a dirty painting in the style of {}\",\n \"a dark painting in the style of {}\",\n \"a picture in the style of {}\",\n \"a cool painting in the style of {}\",\n \"a close-up painting in the style of {}\",\n \"a bright painting in the style of {}\",\n \"a cropped painting in the style of {}\",\n \"a good painting in the style of {}\",\n \"a close-up painting in the style of {}\",\n \"a rendition in the style of {}\",\n \"a nice painting in the style of {}\",\n \"a small painting in the style of {}\",\n \"a weird painting in the style of {}\",\n \"a large painting in the style of {}\",\n]\n\n\nclass TextualInversionDataset(Dataset):\n def __init__(\n self,\n data_root,\n tokenizer,\n learnable_property=\"object\", # [object, style]\n size=512,\n repeats=100,\n interpolation=\"bicubic\",\n flip_p=0.5,\n set=\"train\",\n placeholder_token=\"*\",\n center_crop=False,\n ):\n self.data_root = data_root\n self.tokenizer = tokenizer\n self.learnable_property = learnable_property\n self.size = size\n self.placeholder_token = placeholder_token\n self.center_crop = center_crop\n self.flip_p = flip_p\n\n self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]\n\n self.num_images = len(self.image_paths)\n self._length = self.num_images\n\n if set == \"train\":\n self._length = self.num_images * repeats\n\n self.interpolation = {\n \"linear\": PIL_INTERPOLATION[\"linear\"],\n \"bilinear\": PIL_INTERPOLATION[\"bilinear\"],\n \"bicubic\": PIL_INTERPOLATION[\"bicubic\"],\n \"lanczos\": PIL_INTERPOLATION[\"lanczos\"],\n }[interpolation]\n\n self.templates = imagenet_style_templates_small if learnable_property == \"style\" else imagenet_templates_small\n self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, i):\n example = {}\n image = Image.open(self.image_paths[i % self.num_images])\n\n if not image.mode == \"RGB\":\n image = image.convert(\"RGB\")\n\n placeholder_string = self.placeholder_token\n text = random.choice(self.templates).format(placeholder_string)\n\n example[\"input_ids\"] = self.tokenizer(\n text,\n padding=\"max_length\",\n truncation=True,\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n ).input_ids[0]\n\n # default to score-sde preprocessing\n img = np.array(image).astype(np.uint8)\n\n if self.center_crop:\n crop = min(img.shape[0], img.shape[1])\n (\n h,\n w,\n ) = (\n img.shape[0],\n img.shape[1],\n )\n img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]\n\n image = Image.fromarray(img)\n image = image.resize((self.size, self.size), resample=self.interpolation)\n\n image = self.flip_transform(image)\n image = np.array(image).astype(np.uint8)\n image = (image / 127.5 - 1.0).astype(np.float32)\n\n example[\"pixel_values\"] = torch.from_numpy(image).permute(2, 0, 1)\n return example\n\n\ndef resize_token_embeddings(model, new_num_tokens, initializer_token_id, placeholder_token_id, rng):\n if model.config.vocab_size == new_num_tokens or new_num_tokens is None:\n return\n model.config.vocab_size = new_num_tokens\n\n params = model.params\n old_embeddings = params[\"text_model\"][\"embeddings\"][\"token_embedding\"][\"embedding\"]\n old_num_tokens, emb_dim = old_embeddings.shape\n\n initializer = jax.nn.initializers.normal()\n\n new_embeddings = initializer(rng, (new_num_tokens, emb_dim))\n new_embeddings = new_embeddings.at[:old_num_tokens].set(old_embeddings)\n new_embeddings = new_embeddings.at[placeholder_token_id].set(new_embeddings[initializer_token_id])\n params[\"text_model\"][\"embeddings\"][\"token_embedding\"][\"embedding\"] = new_embeddings\n\n model.params = params\n return model\n\n\ndef get_params_to_save(params):\n return jax.device_get(jax.tree_util.tree_map(lambda x: x[0], params))\n\n\ndef main():\n args = parse_args()\n\n if args.seed is not None:\n set_seed(args.seed)\n\n if jax.process_index() == 0:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n # Setup logging, we only want one process per machine to log things on the screen.\n logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)\n if jax.process_index() == 0:\n transformers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n\n # Load the tokenizer and add the placeholder token as a additional special token\n if args.tokenizer_name:\n tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)\n elif args.pretrained_model_name_or_path:\n tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"tokenizer\")\n\n # Add the placeholder token in tokenizer\n num_added_tokens = tokenizer.add_tokens(args.placeholder_token)\n if num_added_tokens == 0:\n raise ValueError(\n f\"The tokenizer already contains the token {args.placeholder_token}. Please pass a different\"\n \" `placeholder_token` that is not already in the tokenizer.\"\n )\n\n # Convert the initializer_token, placeholder_token to ids\n token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)\n # Check if initializer_token is a single token or a sequence of tokens\n if len(token_ids) > 1:\n raise ValueError(\"The initializer token must be a single token.\")\n\n initializer_token_id = token_ids[0]\n placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)\n\n # Load models and create wrapper for stable diffusion\n text_encoder = FlaxCLIPTextModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision\n )\n vae, vae_params = FlaxAutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision\n )\n unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision\n )\n\n # Create sampling rng\n rng = jax.random.PRNGKey(args.seed)\n rng, _ = jax.random.split(rng)\n # Resize the token embeddings as we are adding new special tokens to the tokenizer\n text_encoder = resize_token_embeddings(\n text_encoder, len(tokenizer), initializer_token_id, placeholder_token_id, rng\n )\n original_token_embeds = text_encoder.params[\"text_model\"][\"embeddings\"][\"token_embedding\"][\"embedding\"]\n\n train_dataset = TextualInversionDataset(\n data_root=args.train_data_dir,\n tokenizer=tokenizer,\n size=args.resolution,\n placeholder_token=args.placeholder_token,\n repeats=args.repeats,\n learnable_property=args.learnable_property,\n center_crop=args.center_crop,\n set=\"train\",\n )\n\n def collate_fn(examples):\n pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n input_ids = torch.stack([example[\"input_ids\"] for example in examples])\n\n batch = {\"pixel_values\": pixel_values, \"input_ids\": input_ids}\n batch = {k: v.numpy() for k, v in batch.items()}\n\n return batch\n\n total_train_batch_size = args.train_batch_size * jax.local_device_count()\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset, batch_size=total_train_batch_size, shuffle=True, drop_last=True, collate_fn=collate_fn\n )\n\n # Optimization\n if args.scale_lr:\n args.learning_rate = args.learning_rate * total_train_batch_size\n\n constant_scheduler = optax.constant_schedule(args.learning_rate)\n\n optimizer = optax.adamw(\n learning_rate=constant_scheduler,\n b1=args.adam_beta1,\n b2=args.adam_beta2,\n eps=args.adam_epsilon,\n weight_decay=args.adam_weight_decay,\n )\n\n def create_mask(params, label_fn):\n def _map(params, mask, label_fn):\n for k in params:\n if label_fn(k):\n mask[k] = \"token_embedding\"\n else:\n if isinstance(params[k], dict):\n mask[k] = {}\n _map(params[k], mask[k], label_fn)\n else:\n mask[k] = \"zero\"\n\n mask = {}\n _map(params, mask, label_fn)\n return mask\n\n def zero_grads():\n # from https://github.com/deepmind/optax/issues/159#issuecomment-896459491\n def init_fn(_):\n return ()\n\n def update_fn(updates, state, params=None):\n return jax.tree_util.tree_map(jnp.zeros_like, updates), ()\n\n return optax.GradientTransformation(init_fn, update_fn)\n\n # Zero out gradients of layers other than the token embedding layer\n tx = optax.multi_transform(\n {\"token_embedding\": optimizer, \"zero\": zero_grads()},\n create_mask(text_encoder.params, lambda s: s == \"token_embedding\"),\n )\n\n state = train_state.TrainState.create(apply_fn=text_encoder.__call__, params=text_encoder.params, tx=tx)\n\n noise_scheduler = FlaxDDPMScheduler(\n beta_start=0.00085, beta_end=0.012, beta_schedule=\"scaled_linear\", num_train_timesteps=1000\n )\n noise_scheduler_state = noise_scheduler.create_state()\n\n # Initialize our training\n train_rngs = jax.random.split(rng, jax.local_device_count())\n\n # Define gradient train step fn\n def train_step(state, vae_params, unet_params, batch, train_rng):\n dropout_rng, sample_rng, new_train_rng = jax.random.split(train_rng, 3)\n\n def compute_loss(params):\n vae_outputs = vae.apply(\n {\"params\": vae_params}, batch[\"pixel_values\"], deterministic=True, method=vae.encode\n )\n latents = vae_outputs.latent_dist.sample(sample_rng)\n # (NHWC) -> (NCHW)\n latents = jnp.transpose(latents, (0, 3, 1, 2))\n latents = latents * vae.config.scaling_factor\n\n noise_rng, timestep_rng = jax.random.split(sample_rng)\n noise = jax.random.normal(noise_rng, latents.shape)\n bsz = latents.shape[0]\n timesteps = jax.random.randint(\n timestep_rng,\n (bsz,),\n 0,\n noise_scheduler.config.num_train_timesteps,\n )\n noisy_latents = noise_scheduler.add_noise(noise_scheduler_state, latents, noise, timesteps)\n encoder_hidden_states = state.apply_fn(\n batch[\"input_ids\"], params=params, dropout_rng=dropout_rng, train=True\n )[0]\n # Predict the noise residual and compute loss\n model_pred = unet.apply(\n {\"params\": unet_params}, noisy_latents, timesteps, encoder_hidden_states, train=False\n ).sample\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(noise_scheduler_state, latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n loss = (target - model_pred) ** 2\n loss = loss.mean()\n\n return loss\n\n grad_fn = jax.value_and_grad(compute_loss)\n loss, grad = grad_fn(state.params)\n grad = jax.lax.pmean(grad, \"batch\")\n new_state = state.apply_gradients(grads=grad)\n\n # Keep the token embeddings fixed except the newly added embeddings for the concept,\n # as we only want to optimize the concept embeddings\n token_embeds = original_token_embeds.at[placeholder_token_id].set(\n new_state.params[\"text_model\"][\"embeddings\"][\"token_embedding\"][\"embedding\"][placeholder_token_id]\n )\n new_state.params[\"text_model\"][\"embeddings\"][\"token_embedding\"][\"embedding\"] = token_embeds\n\n metrics = {\"loss\": loss}\n metrics = jax.lax.pmean(metrics, axis_name=\"batch\")\n return new_state, metrics, new_train_rng\n\n # Create parallel version of the train and eval step\n p_train_step = jax.pmap(train_step, \"batch\", donate_argnums=(0,))\n\n # Replicate the train state on each device\n state = jax_utils.replicate(state)\n vae_params = jax_utils.replicate(vae_params)\n unet_params = jax_utils.replicate(unet_params)\n\n # Train!\n num_update_steps_per_epoch = math.ceil(len(train_dataloader))\n\n # Scheduler and math around the number of training steps.\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel & distributed) = {total_train_batch_size}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n\n global_step = 0\n\n epochs = tqdm(range(args.num_train_epochs), desc=f\"Epoch ... (1/{args.num_train_epochs})\", position=0)\n for epoch in epochs:\n # ======================== Training ================================\n\n train_metrics = []\n\n steps_per_epoch = len(train_dataset) // total_train_batch_size\n train_step_progress_bar = tqdm(total=steps_per_epoch, desc=\"Training...\", position=1, leave=False)\n # train\n for batch in train_dataloader:\n batch = shard(batch)\n state, train_metric, train_rngs = p_train_step(state, vae_params, unet_params, batch, train_rngs)\n train_metrics.append(train_metric)\n\n train_step_progress_bar.update(1)\n global_step += 1\n\n if global_step >= args.max_train_steps:\n break\n if global_step % args.save_steps == 0:\n learned_embeds = get_params_to_save(state.params)[\"text_model\"][\"embeddings\"][\"token_embedding\"][\n \"embedding\"\n ][placeholder_token_id]\n learned_embeds_dict = {args.placeholder_token: learned_embeds}\n jnp.save(\n os.path.join(args.output_dir, \"learned_embeds-\" + str(global_step) + \".npy\"), learned_embeds_dict\n )\n\n train_metric = jax_utils.unreplicate(train_metric)\n\n train_step_progress_bar.close()\n epochs.write(f\"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})\")\n\n # Create the pipeline using using the trained modules and save it.\n if jax.process_index() == 0:\n scheduler = FlaxPNDMScheduler(\n beta_start=0.00085, beta_end=0.012, beta_schedule=\"scaled_linear\", skip_prk_steps=True\n )\n safety_checker = FlaxStableDiffusionSafetyChecker.from_pretrained(\n \"CompVis/stable-diffusion-safety-checker\", from_pt=True\n )\n pipeline = FlaxStableDiffusionPipeline(\n text_encoder=text_encoder,\n vae=vae,\n unet=unet,\n tokenizer=tokenizer,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=CLIPImageProcessor.from_pretrained(\"openai/clip-vit-base-patch32\"),\n )\n\n pipeline.save_pretrained(\n args.output_dir,\n params={\n \"text_encoder\": get_params_to_save(state.params),\n \"vae\": get_params_to_save(vae_params),\n \"unet\": get_params_to_save(unet_params),\n \"safety_checker\": safety_checker.params,\n },\n )\n\n # Also save the newly trained embeddings\n learned_embeds = get_params_to_save(state.params)[\"text_model\"][\"embeddings\"][\"token_embedding\"][\"embedding\"][\n placeholder_token_id\n ]\n learned_embeds_dict = {args.placeholder_token: learned_embeds}\n jnp.save(os.path.join(args.output_dir, \"learned_embeds.npy\"), learned_embeds_dict)\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/textual_inversion/textual_inversion_sdxl.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom pathlib import Path\n\nimport numpy as np\nimport PIL\nimport safetensors\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom PIL import Image\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n DiffusionPipeline,\n DPMSolverMultistepScheduler,\n UNet2DConditionModel,\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import check_min_version, is_wandb_available\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\nif is_wandb_available():\n import wandb\n\nif version.parse(version.parse(PIL.__version__).base_version) >= version.parse(\"9.1.0\"):\n PIL_INTERPOLATION = {\n \"linear\": PIL.Image.Resampling.BILINEAR,\n \"bilinear\": PIL.Image.Resampling.BILINEAR,\n \"bicubic\": PIL.Image.Resampling.BICUBIC,\n \"lanczos\": PIL.Image.Resampling.LANCZOS,\n \"nearest\": PIL.Image.Resampling.NEAREST,\n }\nelse:\n PIL_INTERPOLATION = {\n \"linear\": PIL.Image.LINEAR,\n \"bilinear\": PIL.Image.BILINEAR,\n \"bicubic\": PIL.Image.BICUBIC,\n \"lanczos\": PIL.Image.LANCZOS,\n \"nearest\": PIL.Image.NEAREST,\n }\n# ------------------------------------------------------------------------------\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__)\n\n\ndef save_model_card(repo_id: str, images=None, base_model=str, repo_folder=None):\n img_str = \"\"\n for i, image in enumerate(images):\n image.save(os.path.join(repo_folder, f\"image_{i}.png\"))\n img_str += f\"![img_{i}](./image_{i}.png)\\n\"\n\n model_description = f\"\"\"\n# Textual inversion text2image fine-tuning - {repo_id}\nThese are textual inversion adaption weights for {base_model}. You can find some example images in the following. \\n\n{img_str}\n\"\"\"\n model_card = load_or_create_model_card(\n repo_id_or_path=repo_id,\n from_training=True,\n license=\"creativeml-openrail-m\",\n base_model=base_model,\n model_description=model_description,\n inference=True,\n )\n\n tags = [\n \"stable-diffusion-xl\",\n \"stable-diffusion-xl-diffusers\",\n \"text-to-image\",\n \"diffusers\",\n \"diffusers-training\",\n \"textual_inversion\",\n ]\n\n model_card = populate_model_card(model_card, tags=tags)\n\n model_card.save(os.path.join(repo_folder, \"README.md\"))\n\n\ndef log_validation(\n text_encoder_1,\n text_encoder_2,\n tokenizer_1,\n tokenizer_2,\n unet,\n vae,\n args,\n accelerator,\n weight_dtype,\n epoch,\n is_final_validation=False,\n):\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n text_encoder=accelerator.unwrap_model(text_encoder_1),\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer_1,\n tokenizer_2=tokenizer_2,\n unet=unet,\n vae=vae,\n safety_checker=None,\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n # run inference\n generator = None if args.seed is None else torch.Generator(device=accelerator.device).manual_seed(args.seed)\n images = []\n for _ in range(args.num_validation_images):\n image = pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]\n images.append(image)\n\n tracker_key = \"test\" if is_final_validation else \"validation\"\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(tracker_key, np_images, epoch, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n tracker_key: [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompt}\") for i, image in enumerate(images)\n ]\n }\n )\n\n del pipeline\n torch.cuda.empty_cache()\n return images\n\n\ndef save_progress(text_encoder, placeholder_token_ids, accelerator, args, save_path, safe_serialization=True):\n logger.info(\"Saving embeddings\")\n learned_embeds = (\n accelerator.unwrap_model(text_encoder)\n .get_input_embeddings()\n .weight[min(placeholder_token_ids) : max(placeholder_token_ids) + 1]\n )\n learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()}\n\n if safe_serialization:\n safetensors.torch.save_file(learned_embeds_dict, save_path, metadata={\"format\": \"pt\"})\n else:\n torch.save(learned_embeds_dict, save_path)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--save_steps\",\n type=int,\n default=500,\n help=\"Save learned_embeds.bin every X updates steps.\",\n )\n parser.add_argument(\n \"--save_as_full_pipeline\",\n action=\"store_true\",\n help=\"Save the complete stable diffusion pipeline.\",\n )\n parser.add_argument(\n \"--num_vectors\",\n type=int,\n default=1,\n help=\"How many textual inversion vectors shall be used to learn the concept.\",\n )\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--train_data_dir\", type=str, default=None, required=True, help=\"A folder containing the training data.\"\n )\n parser.add_argument(\n \"--placeholder_token\",\n type=str,\n default=None,\n required=True,\n help=\"A token to use as a placeholder for the concept.\",\n )\n parser.add_argument(\n \"--initializer_token\", type=str, default=None, required=True, help=\"A token to use as initializer word.\"\n )\n parser.add_argument(\"--learnable_property\", type=str, default=\"object\", help=\"Choose between 'object' and 'style'\")\n parser.add_argument(\"--repeats\", type=int, default=100, help=\"How many times to repeat the training data.\")\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"text-inversion-model\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\", action=\"store_true\", help=\"Whether to center crop images before resizing to resolution.\"\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=5000,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=\"no\",\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose\"\n \"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.\"\n \"and an Nvidia Ampere GPU.\"\n ),\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=None,\n help=\"A prompt that is used during validation to verify that the model is learning.\",\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=100,\n help=(\n \"Run validation every X steps. Validation consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`\"\n \" and logging the images.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.train_data_dir is None:\n raise ValueError(\"You must specify a train data directory.\")\n\n return args\n\n\nimagenet_templates_small = [\n \"a photo of a {}\",\n \"a rendering of a {}\",\n \"a cropped photo of the {}\",\n \"the photo of a {}\",\n \"a photo of a clean {}\",\n \"a photo of a dirty {}\",\n \"a dark photo of the {}\",\n \"a photo of my {}\",\n \"a photo of the cool {}\",\n \"a close-up photo of a {}\",\n \"a bright photo of the {}\",\n \"a cropped photo of a {}\",\n \"a photo of the {}\",\n \"a good photo of the {}\",\n \"a photo of one {}\",\n \"a close-up photo of the {}\",\n \"a rendition of the {}\",\n \"a photo of the clean {}\",\n \"a rendition of a {}\",\n \"a photo of a nice {}\",\n \"a good photo of a {}\",\n \"a photo of the nice {}\",\n \"a photo of the small {}\",\n \"a photo of the weird {}\",\n \"a photo of the large {}\",\n \"a photo of a cool {}\",\n \"a photo of a small {}\",\n]\n\nimagenet_style_templates_small = [\n \"a painting in the style of {}\",\n \"a rendering in the style of {}\",\n \"a cropped painting in the style of {}\",\n \"the painting in the style of {}\",\n \"a clean painting in the style of {}\",\n \"a dirty painting in the style of {}\",\n \"a dark painting in the style of {}\",\n \"a picture in the style of {}\",\n \"a cool painting in the style of {}\",\n \"a close-up painting in the style of {}\",\n \"a bright painting in the style of {}\",\n \"a cropped painting in the style of {}\",\n \"a good painting in the style of {}\",\n \"a close-up painting in the style of {}\",\n \"a rendition in the style of {}\",\n \"a nice painting in the style of {}\",\n \"a small painting in the style of {}\",\n \"a weird painting in the style of {}\",\n \"a large painting in the style of {}\",\n]\n\n\nclass TextualInversionDataset(Dataset):\n def __init__(\n self,\n data_root,\n tokenizer_1,\n tokenizer_2,\n learnable_property=\"object\", # [object, style]\n size=512,\n repeats=100,\n interpolation=\"bicubic\",\n flip_p=0.5,\n set=\"train\",\n placeholder_token=\"*\",\n center_crop=False,\n ):\n self.data_root = data_root\n self.tokenizer_1 = tokenizer_1\n self.tokenizer_2 = tokenizer_2\n self.learnable_property = learnable_property\n self.size = size\n self.placeholder_token = placeholder_token\n self.center_crop = center_crop\n self.flip_p = flip_p\n\n self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]\n\n self.num_images = len(self.image_paths)\n self._length = self.num_images\n\n if set == \"train\":\n self._length = self.num_images * repeats\n\n self.interpolation = {\n \"linear\": PIL_INTERPOLATION[\"linear\"],\n \"bilinear\": PIL_INTERPOLATION[\"bilinear\"],\n \"bicubic\": PIL_INTERPOLATION[\"bicubic\"],\n \"lanczos\": PIL_INTERPOLATION[\"lanczos\"],\n }[interpolation]\n\n self.templates = imagenet_style_templates_small if learnable_property == \"style\" else imagenet_templates_small\n self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)\n self.crop = transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size)\n\n def __len__(self):\n return self._length\n\n def __getitem__(self, i):\n example = {}\n image = Image.open(self.image_paths[i % self.num_images])\n\n if not image.mode == \"RGB\":\n image = image.convert(\"RGB\")\n\n placeholder_string = self.placeholder_token\n text = random.choice(self.templates).format(placeholder_string)\n\n example[\"original_size\"] = (image.height, image.width)\n\n image = image.resize((self.size, self.size), resample=self.interpolation)\n\n if self.center_crop:\n y1 = max(0, int(round((image.height - self.size) / 2.0)))\n x1 = max(0, int(round((image.width - self.size) / 2.0)))\n image = self.crop(image)\n else:\n y1, x1, h, w = self.crop.get_params(image, (self.size, self.size))\n image = transforms.functional.crop(image, y1, x1, h, w)\n\n example[\"crop_top_left\"] = (y1, x1)\n\n example[\"input_ids_1\"] = self.tokenizer_1(\n text,\n padding=\"max_length\",\n truncation=True,\n max_length=self.tokenizer_1.model_max_length,\n return_tensors=\"pt\",\n ).input_ids[0]\n\n example[\"input_ids_2\"] = self.tokenizer_2(\n text,\n padding=\"max_length\",\n truncation=True,\n max_length=self.tokenizer_2.model_max_length,\n return_tensors=\"pt\",\n ).input_ids[0]\n\n # default to score-sde preprocessing\n img = np.array(image).astype(np.uint8)\n\n image = Image.fromarray(img)\n\n image = self.flip_transform(image)\n image = np.array(image).astype(np.uint8)\n image = (image / 127.5 - 1.0).astype(np.float32)\n\n example[\"pixel_values\"] = torch.from_numpy(image).permute(2, 0, 1)\n return example\n\n\ndef main():\n args = parse_args()\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = os.path.join(args.output_dir, args.logging_dir)\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load tokenizer\n tokenizer_1 = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"tokenizer\")\n tokenizer_2 = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"tokenizer_2\")\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n text_encoder_1 = CLIPTextModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision\n )\n text_encoder_2 = CLIPTextModelWithProjection.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder_2\", revision=args.revision\n )\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"vae\", revision=args.revision, variant=args.variant\n )\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n # Add the placeholder token in tokenizer_1\n placeholder_tokens = [args.placeholder_token]\n\n if args.num_vectors < 1:\n raise ValueError(f\"--num_vectors has to be larger or equal to 1, but is {args.num_vectors}\")\n\n # add dummy tokens for multi-vector\n additional_tokens = []\n for i in range(1, args.num_vectors):\n additional_tokens.append(f\"{args.placeholder_token}_{i}\")\n placeholder_tokens += additional_tokens\n\n num_added_tokens = tokenizer_1.add_tokens(placeholder_tokens)\n if num_added_tokens != args.num_vectors:\n raise ValueError(\n f\"The tokenizer already contains the token {args.placeholder_token}. Please pass a different\"\n \" `placeholder_token` that is not already in the tokenizer.\"\n )\n\n # Convert the initializer_token, placeholder_token to ids\n token_ids = tokenizer_1.encode(args.initializer_token, add_special_tokens=False)\n # Check if initializer_token is a single token or a sequence of tokens\n if len(token_ids) > 1:\n raise ValueError(\"The initializer token must be a single token.\")\n\n initializer_token_id = token_ids[0]\n placeholder_token_ids = tokenizer_1.convert_tokens_to_ids(placeholder_tokens)\n\n # Resize the token embeddings as we are adding new special tokens to the tokenizer\n text_encoder_1.resize_token_embeddings(len(tokenizer_1))\n\n # Initialise the newly added placeholder token with the embeddings of the initializer token\n token_embeds = text_encoder_1.get_input_embeddings().weight.data\n with torch.no_grad():\n for token_id in placeholder_token_ids:\n token_embeds[token_id] = token_embeds[initializer_token_id].clone()\n\n # Freeze vae and unet\n vae.requires_grad_(False)\n unet.requires_grad_(False)\n text_encoder_2.requires_grad_(False)\n # Freeze all parameters except for the token embeddings in text encoder\n text_encoder_1.text_model.encoder.requires_grad_(False)\n text_encoder_1.text_model.final_layer_norm.requires_grad_(False)\n text_encoder_1.text_model.embeddings.position_embedding.requires_grad_(False)\n\n if args.gradient_checkpointing:\n text_encoder_1.gradient_checkpointing_enable()\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Initialize the optimizer\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n optimizer = optimizer_class(\n text_encoder_1.get_input_embeddings().parameters(), # only optimize the embeddings\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n placeholder_token = \" \".join(tokenizer_1.convert_ids_to_tokens(placeholder_token_ids))\n # Dataset and DataLoaders creation:\n train_dataset = TextualInversionDataset(\n data_root=args.train_data_dir,\n tokenizer_1=tokenizer_1,\n tokenizer_2=tokenizer_2,\n size=args.resolution,\n placeholder_token=placeholder_token,\n repeats=args.repeats,\n learnable_property=args.learnable_property,\n center_crop=args.center_crop,\n set=\"train\",\n )\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_cycles=args.lr_num_cycles,\n )\n\n text_encoder_1.train()\n # Prepare everything with our `accelerator`.\n text_encoder_1, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n text_encoder_1, optimizer, train_dataloader, lr_scheduler\n )\n\n # For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n # Move vae and unet and text_encoder_2 to device and cast to weight_dtype\n unet.to(accelerator.device, dtype=weight_dtype)\n vae.to(accelerator.device, dtype=weight_dtype)\n text_encoder_2.to(accelerator.device, dtype=weight_dtype)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"textual_inversion\", config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n # keep original embeddings as reference\n orig_embeds_params = accelerator.unwrap_model(text_encoder_1).get_input_embeddings().weight.data.clone()\n\n for epoch in range(first_epoch, args.num_train_epochs):\n text_encoder_1.train()\n for step, batch in enumerate(train_dataloader):\n with accelerator.accumulate(text_encoder_1):\n # Convert images to latent space\n latents = vae.encode(batch[\"pixel_values\"].to(dtype=weight_dtype)).latent_dist.sample().detach()\n latents = latents * vae.config.scaling_factor\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # Get the text embedding for conditioning\n encoder_hidden_states_1 = (\n text_encoder_1(batch[\"input_ids_1\"], output_hidden_states=True)\n .hidden_states[-2]\n .to(dtype=weight_dtype)\n )\n encoder_output_2 = text_encoder_2(\n batch[\"input_ids_2\"].reshape(batch[\"input_ids_1\"].shape[0], -1), output_hidden_states=True\n )\n encoder_hidden_states_2 = encoder_output_2.hidden_states[-2].to(dtype=weight_dtype)\n original_size = [\n (batch[\"original_size\"][0][i].item(), batch[\"original_size\"][1][i].item())\n for i in range(args.train_batch_size)\n ]\n crop_top_left = [\n (batch[\"crop_top_left\"][0][i].item(), batch[\"crop_top_left\"][1][i].item())\n for i in range(args.train_batch_size)\n ]\n target_size = (args.resolution, args.resolution)\n add_time_ids = torch.cat(\n [\n torch.tensor(original_size[i] + crop_top_left[i] + target_size)\n for i in range(args.train_batch_size)\n ]\n ).to(accelerator.device, dtype=weight_dtype)\n added_cond_kwargs = {\"text_embeds\": encoder_output_2[0], \"time_ids\": add_time_ids}\n encoder_hidden_states = torch.cat([encoder_hidden_states_1, encoder_hidden_states_2], dim=-1)\n\n # Predict the noise residual\n model_pred = unet(\n noisy_latents, timesteps, encoder_hidden_states, added_cond_kwargs=added_cond_kwargs\n ).sample\n\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n\n accelerator.backward(loss)\n\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Let's make sure we don't update any embedding weights besides the newly added token\n index_no_updates = torch.ones((len(tokenizer_1),), dtype=torch.bool)\n index_no_updates[min(placeholder_token_ids) : max(placeholder_token_ids) + 1] = False\n\n with torch.no_grad():\n accelerator.unwrap_model(text_encoder_1).get_input_embeddings().weight[\n index_no_updates\n ] = orig_embeds_params[index_no_updates]\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n images = []\n progress_bar.update(1)\n global_step += 1\n if global_step % args.save_steps == 0:\n weight_name = f\"learned_embeds-steps-{global_step}.safetensors\"\n save_path = os.path.join(args.output_dir, weight_name)\n save_progress(\n text_encoder_1,\n placeholder_token_ids,\n accelerator,\n args,\n save_path,\n safe_serialization=True,\n )\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n if args.validation_prompt is not None and global_step % args.validation_steps == 0:\n images = log_validation(\n text_encoder_1,\n text_encoder_2,\n tokenizer_1,\n tokenizer_2,\n unet,\n vae,\n args,\n accelerator,\n weight_dtype,\n epoch,\n )\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n\n if global_step >= args.max_train_steps:\n break\n # Create the pipeline using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n if args.validation_prompt:\n images = log_validation(\n text_encoder_1,\n text_encoder_2,\n tokenizer_1,\n tokenizer_2,\n unet,\n vae,\n args,\n accelerator,\n weight_dtype,\n epoch,\n is_final_validation=True,\n )\n\n if args.push_to_hub and not args.save_as_full_pipeline:\n logger.warning(\"Enabling full model saving because --push_to_hub=True was specified.\")\n save_full_model = True\n else:\n save_full_model = args.save_as_full_pipeline\n if save_full_model:\n pipeline = DiffusionPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n text_encoder=accelerator.unwrap_model(text_encoder_1),\n text_encoder_2=text_encoder_2,\n vae=vae,\n unet=unet,\n tokenizer=tokenizer_1,\n tokenizer_2=tokenizer_2,\n )\n pipeline.save_pretrained(args.output_dir)\n # Save the newly trained embeddings\n weight_name = \"learned_embeds.safetensors\"\n save_path = os.path.join(args.output_dir, weight_name)\n save_progress(\n text_encoder_1,\n placeholder_token_ids,\n accelerator,\n args,\n save_path,\n safe_serialization=True,\n )\n\n if args.push_to_hub:\n save_model_card(\n repo_id,\n images=images,\n base_model=args.pretrained_model_name_or_path,\n repo_folder=args.output_dir,\n )\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/unconditional_image_generation/README.md", "content": "## Training an unconditional diffusion model\n\nCreating a training image set is [described in a different document](https://huggingface.co/docs/datasets/image_process#image-datasets).\n\n### Installing the dependencies\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n**Important**\n\nTo make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nThen cd in the example folder and run\n```bash\npip install -r requirements.txt\n```\n\n\nAnd initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\n\n### Unconditional Flowers\n\nThe command to train a DDPM UNet model on the Oxford Flowers dataset:\n\n```bash\naccelerate launch train_unconditional.py \\\n --dataset_name=\"huggan/flowers-102-categories\" \\\n --resolution=64 --center_crop --random_flip \\\n --output_dir=\"ddpm-ema-flowers-64\" \\\n --train_batch_size=16 \\\n --num_epochs=100 \\\n --gradient_accumulation_steps=1 \\\n --use_ema \\\n --learning_rate=1e-4 \\\n --lr_warmup_steps=500 \\\n --mixed_precision=no \\\n --push_to_hub\n```\nAn example trained model: https://huggingface.co/anton-l/ddpm-ema-flowers-64\n\nA full training run takes 2 hours on 4xV100 GPUs.\n\n\n\n\n### Unconditional Pokemon\n\nThe command to train a DDPM UNet model on the Pokemon dataset:\n\n```bash\naccelerate launch train_unconditional.py \\\n --dataset_name=\"huggan/pokemon\" \\\n --resolution=64 --center_crop --random_flip \\\n --output_dir=\"ddpm-ema-pokemon-64\" \\\n --train_batch_size=16 \\\n --num_epochs=100 \\\n --gradient_accumulation_steps=1 \\\n --use_ema \\\n --learning_rate=1e-4 \\\n --lr_warmup_steps=500 \\\n --mixed_precision=no \\\n --push_to_hub\n```\nAn example trained model: https://huggingface.co/anton-l/ddpm-ema-pokemon-64\n\nA full training run takes 2 hours on 4xV100 GPUs.\n\n\n\n### Training with multiple GPUs\n\n`accelerate` allows for seamless multi-GPU training. Follow the instructions [here](https://huggingface.co/docs/accelerate/basic_tutorials/launch)\nfor running distributed training with `accelerate`. Here is an example command:\n\n```bash\naccelerate launch --mixed_precision=\"fp16\" --multi_gpu train_unconditional.py \\\n --dataset_name=\"huggan/pokemon\" \\\n --resolution=64 --center_crop --random_flip \\\n --output_dir=\"ddpm-ema-pokemon-64\" \\\n --train_batch_size=16 \\\n --num_epochs=100 \\\n --gradient_accumulation_steps=1 \\\n --use_ema \\\n --learning_rate=1e-4 \\\n --lr_warmup_steps=500 \\\n --mixed_precision=\"fp16\" \\\n --logger=\"wandb\"\n```\n\nTo be able to use Weights and Biases (`wandb`) as a logger you need to install the library: `pip install wandb`.\n\n### Using your own data\n\nTo use your own dataset, there are 2 ways:\n- you can either provide your own folder as `--train_data_dir`\n- or you can upload your dataset to the hub (possibly as a private repo, if you prefer so), and simply pass the `--dataset_name` argument.\n\nBelow, we explain both in more detail.\n\n#### Provide the dataset as a folder\n\nIf you provide your own folders with images, the script expects the following directory structure:\n\n```bash\ndata_dir/xxx.png\ndata_dir/xxy.png\ndata_dir/[...]/xxz.png\n```\n\nIn other words, the script will take care of gathering all images inside the folder. You can then run the script like this:\n\n```bash\naccelerate launch train_unconditional.py \\\n --train_data_dir \\\n \n```\n\nInternally, the script will use the [`ImageFolder`](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder) feature which will automatically turn the folders into 🤗 Dataset objects.\n\n#### Upload your data to the hub, as a (possibly private) repo\n\nIt's very easy (and convenient) to upload your image dataset to the hub using the [`ImageFolder`](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder) feature available in 🤗 Datasets. Simply do the following:\n\n```python\nfrom datasets import load_dataset\n\n# example 1: local folder\ndataset = load_dataset(\"imagefolder\", data_dir=\"path_to_your_folder\")\n\n# example 2: local files (supported formats are tar, gzip, zip, xz, rar, zstd)\ndataset = load_dataset(\"imagefolder\", data_files=\"path_to_zip_file\")\n\n# example 3: remote files (supported formats are tar, gzip, zip, xz, rar, zstd)\ndataset = load_dataset(\"imagefolder\", data_files=\"https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip\")\n\n# example 4: providing several splits\ndataset = load_dataset(\"imagefolder\", data_files={\"train\": [\"path/to/file1\", \"path/to/file2\"], \"test\": [\"path/to/file3\", \"path/to/file4\"]})\n```\n\n`ImageFolder` will create an `image` column containing the PIL-encoded images.\n\nNext, push it to the hub!\n\n```python\n# assuming you have ran the huggingface-cli login command in a terminal\ndataset.push_to_hub(\"name_of_your_dataset\")\n\n# if you want to push to a private repo, simply pass private=True:\ndataset.push_to_hub(\"name_of_your_dataset\", private=True)\n```\n\nand that's it! You can now train your model by simply setting the `--dataset_name` argument to the name of your dataset on the hub.\n\nMore on this can also be found in [this blog post](https://huggingface.co/blog/image-search-datasets).\n" }, { "path": "diffusers/examples/unconditional_image_generation/requirements.txt", "content": "accelerate>=0.16.0\ntorchvision\ndatasets\n" }, { "path": "diffusers/examples/unconditional_image_generation/test_unconditional.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport logging\nimport os\nimport sys\nimport tempfile\n\n\nsys.path.append(\"..\")\nfrom test_examples_utils import ExamplesTestsAccelerate, run_command # noqa: E402\n\n\nlogging.basicConfig(level=logging.DEBUG)\n\nlogger = logging.getLogger()\nstream_handler = logging.StreamHandler(sys.stdout)\nlogger.addHandler(stream_handler)\n\n\nclass Unconditional(ExamplesTestsAccelerate):\n def test_train_unconditional(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n test_args = f\"\"\"\n examples/unconditional_image_generation/train_unconditional.py\n --dataset_name hf-internal-testing/dummy_image_class_data\n --model_config_name_or_path diffusers/ddpm_dummy\n --resolution 64\n --output_dir {tmpdir}\n --train_batch_size 2\n --num_epochs 1\n --gradient_accumulation_steps 1\n --ddpm_num_inference_steps 2\n --learning_rate 1e-3\n --lr_warmup_steps 5\n \"\"\".split()\n\n run_command(self._launch_args + test_args, return_stdout=True)\n # save_pretrained smoke test\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"unet\", \"diffusion_pytorch_model.safetensors\")))\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"scheduler\", \"scheduler_config.json\")))\n\n def test_unconditional_checkpointing_checkpoints_total_limit(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n initial_run_args = f\"\"\"\n examples/unconditional_image_generation/train_unconditional.py\n --dataset_name hf-internal-testing/dummy_image_class_data\n --model_config_name_or_path diffusers/ddpm_dummy\n --resolution 64\n --output_dir {tmpdir}\n --train_batch_size 1\n --num_epochs 1\n --gradient_accumulation_steps 1\n --ddpm_num_inference_steps 2\n --learning_rate 1e-3\n --lr_warmup_steps 5\n --checkpointing_steps=2\n --checkpoints_total_limit=2\n \"\"\".split()\n\n run_command(self._launch_args + initial_run_args)\n\n # check checkpoint directories exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n # checkpoint-2 should have been deleted\n {\"checkpoint-4\", \"checkpoint-6\"},\n )\n\n def test_unconditional_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n initial_run_args = f\"\"\"\n examples/unconditional_image_generation/train_unconditional.py\n --dataset_name hf-internal-testing/dummy_image_class_data\n --model_config_name_or_path diffusers/ddpm_dummy\n --resolution 64\n --output_dir {tmpdir}\n --train_batch_size 1\n --num_epochs 1\n --gradient_accumulation_steps 1\n --ddpm_num_inference_steps 1\n --learning_rate 1e-3\n --lr_warmup_steps 5\n --checkpointing_steps=2\n \"\"\".split()\n\n run_command(self._launch_args + initial_run_args)\n\n # check checkpoint directories exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-2\", \"checkpoint-4\", \"checkpoint-6\"},\n )\n\n resume_run_args = f\"\"\"\n examples/unconditional_image_generation/train_unconditional.py\n --dataset_name hf-internal-testing/dummy_image_class_data\n --model_config_name_or_path diffusers/ddpm_dummy\n --resolution 64\n --output_dir {tmpdir}\n --train_batch_size 1\n --num_epochs 2\n --gradient_accumulation_steps 1\n --ddpm_num_inference_steps 1\n --learning_rate 1e-3\n --lr_warmup_steps 5\n --resume_from_checkpoint=checkpoint-6\n --checkpointing_steps=2\n --checkpoints_total_limit=2\n \"\"\".split()\n\n run_command(self._launch_args + resume_run_args)\n\n # check checkpoint directories exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-10\", \"checkpoint-12\"},\n )\n" }, { "path": "diffusers/examples/unconditional_image_generation/train_unconditional.py", "content": "import argparse\nimport inspect\nimport logging\nimport math\nimport os\nimport shutil\nfrom datetime import timedelta\nfrom pathlib import Path\n\nimport accelerate\nimport datasets\nimport torch\nimport torch.nn.functional as F\nfrom accelerate import Accelerator, InitProcessGroupKwargs\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom packaging import version\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\n\nimport diffusers\nfrom diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import EMAModel\nfrom diffusers.utils import check_min_version, is_accelerate_version, is_tensorboard_available, is_wandb_available\nfrom diffusers.utils.import_utils import is_xformers_available\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\n\ndef _extract_into_tensor(arr, timesteps, broadcast_shape):\n \"\"\"\n Extract values from a 1-D numpy array for a batch of indices.\n\n :param arr: the 1-D numpy array.\n :param timesteps: a tensor of indices into the array to extract.\n :param broadcast_shape: a larger shape of K dimensions with the batch\n dimension equal to the length of timesteps.\n :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.\n \"\"\"\n if not isinstance(arr, torch.Tensor):\n arr = torch.from_numpy(arr)\n res = arr[timesteps].float().to(timesteps.device)\n while len(res.shape) < len(broadcast_shape):\n res = res[..., None]\n return res.expand(broadcast_shape)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that HF Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--model_config_name_or_path\",\n type=str,\n default=None,\n help=\"The config of the UNet model to train, leave as None to use standard DDPM configuration.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"ddpm-model-64\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\"--overwrite_output_dir\", action=\"store_true\")\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=64,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n default=False,\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\n \"--eval_batch_size\", type=int, default=16, help=\"The number of images to generate for evaluation.\"\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"The number of subprocesses to use for data loading. 0 means that the data will be loaded in the main\"\n \" process.\"\n ),\n )\n parser.add_argument(\"--num_epochs\", type=int, default=100)\n parser.add_argument(\"--save_images_epochs\", type=int, default=10, help=\"How often to save images during training.\")\n parser.add_argument(\n \"--save_model_epochs\", type=int, default=10, help=\"How often to save the model during training.\"\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"cosine\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.95, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\n \"--adam_weight_decay\", type=float, default=1e-6, help=\"Weight decay magnitude for the Adam optimizer.\"\n )\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer.\")\n parser.add_argument(\n \"--use_ema\",\n action=\"store_true\",\n help=\"Whether to use Exponential Moving Average for the final model weights.\",\n )\n parser.add_argument(\"--ema_inv_gamma\", type=float, default=1.0, help=\"The inverse gamma value for the EMA decay.\")\n parser.add_argument(\"--ema_power\", type=float, default=3 / 4, help=\"The power value for the EMA decay.\")\n parser.add_argument(\"--ema_max_decay\", type=float, default=0.9999, help=\"The maximum decay magnitude for EMA.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--hub_private_repo\", action=\"store_true\", help=\"Whether or not to create a private repository.\"\n )\n parser.add_argument(\n \"--logger\",\n type=str,\n default=\"tensorboard\",\n choices=[\"tensorboard\", \"wandb\"],\n help=(\n \"Whether to use [tensorboard](https://www.tensorflow.org/tensorboard) or [wandb](https://www.wandb.ai)\"\n \" for experiment tracking and logging of model metrics and model checkpoints\"\n ),\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=\"no\",\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose\"\n \"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.\"\n \"and an Nvidia Ampere GPU.\"\n ),\n )\n parser.add_argument(\n \"--prediction_type\",\n type=str,\n default=\"epsilon\",\n choices=[\"epsilon\", \"sample\"],\n help=\"Whether the model should predict the 'epsilon'/noise error or directly the reconstructed image 'x0'.\",\n )\n parser.add_argument(\"--ddpm_num_steps\", type=int, default=1000)\n parser.add_argument(\"--ddpm_num_inference_steps\", type=int, default=1000)\n parser.add_argument(\"--ddpm_beta_schedule\", type=str, default=\"linear\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"You must specify either a dataset name from the hub or a train data directory.\")\n\n return args\n\n\ndef main(args):\n logging_dir = os.path.join(args.output_dir, args.logging_dir)\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n kwargs = InitProcessGroupKwargs(timeout=timedelta(seconds=7200)) # a big number for high resolution or big dataset\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.logger,\n project_config=accelerator_project_config,\n kwargs_handlers=[kwargs],\n )\n\n if args.logger == \"tensorboard\":\n if not is_tensorboard_available():\n raise ImportError(\"Make sure to install tensorboard if you want to use it for logging during training.\")\n\n elif args.logger == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n import wandb\n\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n if args.use_ema:\n ema_model.save_pretrained(os.path.join(output_dir, \"unet_ema\"))\n\n for i, model in enumerate(models):\n model.save_pretrained(os.path.join(output_dir, \"unet\"))\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n def load_model_hook(models, input_dir):\n if args.use_ema:\n load_model = EMAModel.from_pretrained(os.path.join(input_dir, \"unet_ema\"), UNet2DModel)\n ema_model.load_state_dict(load_model.state_dict())\n ema_model.to(accelerator.device)\n del load_model\n\n for i in range(len(models)):\n # pop models so that they are not loaded again\n model = models.pop()\n\n # load diffusers style into model\n load_model = UNet2DModel.from_pretrained(input_dir, subfolder=\"unet\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Initialize the model\n if args.model_config_name_or_path is None:\n model = UNet2DModel(\n sample_size=args.resolution,\n in_channels=3,\n out_channels=3,\n layers_per_block=2,\n block_out_channels=(128, 128, 256, 256, 512, 512),\n down_block_types=(\n \"DownBlock2D\",\n \"DownBlock2D\",\n \"DownBlock2D\",\n \"DownBlock2D\",\n \"AttnDownBlock2D\",\n \"DownBlock2D\",\n ),\n up_block_types=(\n \"UpBlock2D\",\n \"AttnUpBlock2D\",\n \"UpBlock2D\",\n \"UpBlock2D\",\n \"UpBlock2D\",\n \"UpBlock2D\",\n ),\n )\n else:\n config = UNet2DModel.load_config(args.model_config_name_or_path)\n model = UNet2DModel.from_config(config)\n\n # Create EMA for the model.\n if args.use_ema:\n ema_model = EMAModel(\n model.parameters(),\n decay=args.ema_max_decay,\n use_ema_warmup=True,\n inv_gamma=args.ema_inv_gamma,\n power=args.ema_power,\n model_cls=UNet2DModel,\n model_config=model.config,\n )\n\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n args.mixed_precision = accelerator.mixed_precision\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n args.mixed_precision = accelerator.mixed_precision\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n model.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n # Initialize the scheduler\n accepts_prediction_type = \"prediction_type\" in set(inspect.signature(DDPMScheduler.__init__).parameters.keys())\n if accepts_prediction_type:\n noise_scheduler = DDPMScheduler(\n num_train_timesteps=args.ddpm_num_steps,\n beta_schedule=args.ddpm_beta_schedule,\n prediction_type=args.prediction_type,\n )\n else:\n noise_scheduler = DDPMScheduler(num_train_timesteps=args.ddpm_num_steps, beta_schedule=args.ddpm_beta_schedule)\n\n # Initialize the optimizer\n optimizer = torch.optim.AdamW(\n model.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n split=\"train\",\n )\n else:\n dataset = load_dataset(\"imagefolder\", data_dir=args.train_data_dir, cache_dir=args.cache_dir, split=\"train\")\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder\n\n # Preprocessing the datasets and DataLoaders creation.\n augmentations = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),\n transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5]),\n ]\n )\n\n def transform_images(examples):\n images = [augmentations(image.convert(\"RGB\")) for image in examples[\"image\"]]\n return {\"input\": images}\n\n logger.info(f\"Dataset size: {len(dataset)}\")\n\n dataset.set_transform(transform_images)\n train_dataloader = torch.utils.data.DataLoader(\n dataset, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers\n )\n\n # Initialize the learning rate scheduler\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,\n num_training_steps=(len(train_dataloader) * args.num_epochs),\n )\n\n # Prepare everything with our `accelerator`.\n model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n model, optimizer, train_dataloader, lr_scheduler\n )\n\n if args.use_ema:\n ema_model.to(accelerator.device)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n run = os.path.split(__file__)[-1].split(\".\")[0]\n accelerator.init_trackers(run)\n\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n max_train_steps = args.num_epochs * num_update_steps_per_epoch\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(dataset)}\")\n logger.info(f\" Num Epochs = {args.num_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {max_train_steps}\")\n\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n resume_global_step = global_step * args.gradient_accumulation_steps\n first_epoch = global_step // num_update_steps_per_epoch\n resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)\n\n # Train!\n for epoch in range(first_epoch, args.num_epochs):\n model.train()\n progress_bar = tqdm(total=num_update_steps_per_epoch, disable=not accelerator.is_local_main_process)\n progress_bar.set_description(f\"Epoch {epoch}\")\n for step, batch in enumerate(train_dataloader):\n # Skip steps until we reach the resumed step\n if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:\n if step % args.gradient_accumulation_steps == 0:\n progress_bar.update(1)\n continue\n\n clean_images = batch[\"input\"].to(weight_dtype)\n # Sample noise that we'll add to the images\n noise = torch.randn(clean_images.shape, dtype=weight_dtype, device=clean_images.device)\n bsz = clean_images.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(\n 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=clean_images.device\n ).long()\n\n # Add noise to the clean images according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)\n\n with accelerator.accumulate(model):\n # Predict the noise residual\n model_output = model(noisy_images, timesteps).sample\n\n if args.prediction_type == \"epsilon\":\n loss = F.mse_loss(model_output.float(), noise.float()) # this could have different weights!\n elif args.prediction_type == \"sample\":\n alpha_t = _extract_into_tensor(\n noise_scheduler.alphas_cumprod, timesteps, (clean_images.shape[0], 1, 1, 1)\n )\n snr_weights = alpha_t / (1 - alpha_t)\n # use SNR weighting from distillation paper\n loss = snr_weights * F.mse_loss(model_output.float(), clean_images.float(), reduction=\"none\")\n loss = loss.mean()\n else:\n raise ValueError(f\"Unsupported prediction type: {args.prediction_type}\")\n\n accelerator.backward(loss)\n\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(model.parameters(), 1.0)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n if args.use_ema:\n ema_model.step(model.parameters())\n progress_bar.update(1)\n global_step += 1\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0], \"step\": global_step}\n if args.use_ema:\n logs[\"ema_decay\"] = ema_model.cur_decay_value\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n progress_bar.close()\n\n accelerator.wait_for_everyone()\n\n # Generate sample images for visual inspection\n if accelerator.is_main_process:\n if epoch % args.save_images_epochs == 0 or epoch == args.num_epochs - 1:\n unet = accelerator.unwrap_model(model)\n\n if args.use_ema:\n ema_model.store(unet.parameters())\n ema_model.copy_to(unet.parameters())\n\n pipeline = DDPMPipeline(\n unet=unet,\n scheduler=noise_scheduler,\n )\n\n generator = torch.Generator(device=pipeline.device).manual_seed(0)\n # run pipeline in inference (sample random noise and denoise)\n images = pipeline(\n generator=generator,\n batch_size=args.eval_batch_size,\n num_inference_steps=args.ddpm_num_inference_steps,\n output_type=\"np\",\n ).images\n\n if args.use_ema:\n ema_model.restore(unet.parameters())\n\n # denormalize the images and save to tensorboard\n images_processed = (images * 255).round().astype(\"uint8\")\n\n if args.logger == \"tensorboard\":\n if is_accelerate_version(\">=\", \"0.17.0.dev0\"):\n tracker = accelerator.get_tracker(\"tensorboard\", unwrap=True)\n else:\n tracker = accelerator.get_tracker(\"tensorboard\")\n tracker.add_images(\"test_samples\", images_processed.transpose(0, 3, 1, 2), epoch)\n elif args.logger == \"wandb\":\n # Upcoming `log_images` helper coming in https://github.com/huggingface/accelerate/pull/962/files\n accelerator.get_tracker(\"wandb\").log(\n {\"test_samples\": [wandb.Image(img) for img in images_processed], \"epoch\": epoch},\n step=global_step,\n )\n\n if epoch % args.save_model_epochs == 0 or epoch == args.num_epochs - 1:\n # save the model\n unet = accelerator.unwrap_model(model)\n\n if args.use_ema:\n ema_model.store(unet.parameters())\n ema_model.copy_to(unet.parameters())\n\n pipeline = DDPMPipeline(\n unet=unet,\n scheduler=noise_scheduler,\n )\n\n pipeline.save_pretrained(args.output_dir)\n\n if args.use_ema:\n ema_model.restore(unet.parameters())\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=f\"Epoch {epoch}\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n" }, { "path": "diffusers/examples/vqgan/README.md", "content": "## Training an VQGAN VAE\nVQVAEs were first introduced in [Neural Discrete Representation Learning](https://arxiv.org/abs/1711.00937) and was combined with a GAN in the paper [Taming Transformers for High-Resolution Image Synthesis](https://arxiv.org/abs/2012.09841). The basic idea of a VQVAE is it's a type of a variational auto encoder with tokens as the latent space similar to tokens for LLMs. This script was adapted from a [pr to huggingface's open-muse project](https://github.com/huggingface/open-muse/pull/52) with general code following [lucidrian's implementation of the vqgan training script](https://github.com/lucidrains/muse-maskgit-pytorch/blob/main/muse_maskgit_pytorch/trainers.py) but both of these implementation follow from the [taming transformer repo](https://github.com/CompVis/taming-transformers?tab=readme-ov-file).\n\n\nCreating a training image set is [described in a different document](https://huggingface.co/docs/datasets/image_process#image-datasets).\n\n### Installing the dependencies\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n**Important**\n\nTo make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nThen cd in the example folder and run\n```bash\npip install -r requirements.txt\n```\n\n\nAnd initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\n\n### Training on CIFAR10\n\nThe command to train a VQGAN model on cifar10 dataset:\n\n```bash\naccelerate launch train_vqgan.py \\\n --dataset_name=cifar10 \\\n --image_column=img \\\n --validation_images images/bird.jpg images/car.jpg images/dog.jpg images/frog.jpg \\\n --resolution=128 \\\n --train_batch_size=2 \\\n --gradient_accumulation_steps=8 \\\n --report_to=wandb\n```\n\nAn example training run is [here](https://wandb.ai/sayakpaul/vqgan-training/runs/0m5kzdfp) by @sayakpaul and a lower scale one [here](https://wandb.ai/dsbuddy27/vqgan-training/runs/eqd6xi4n?nw=nwuserisamu). The validation images can be obtained from [here](https://huggingface.co/datasets/diffusers/docs-images/tree/main/vqgan_validation_images).\nThe simplest way to improve the quality of a VQGAN model is to maximize the amount of information present in the bottleneck. The easiest way to do this is increasing the image resolution. However, other ways include, but not limited to, lowering compression by downsampling fewer times or increasing the vocaburary size which at most can be around 16384. How to do this is shown below.\n\n# Modifying the architecture\n\nTo modify the architecture of the vqgan model you can save the config taken from [here](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder/blob/main/movq/config.json) and then provide that to the script with the option --model_config_name_or_path. This config is below\n```\n{\n \"_class_name\": \"VQModel\",\n \"_diffusers_version\": \"0.17.0.dev0\",\n \"act_fn\": \"silu\",\n \"block_out_channels\": [\n 128,\n 256,\n 256,\n 512\n ],\n \"down_block_types\": [\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n \"AttnDownEncoderBlock2D\"\n ],\n \"in_channels\": 3,\n \"latent_channels\": 4,\n \"layers_per_block\": 2,\n \"norm_num_groups\": 32,\n \"norm_type\": \"spatial\",\n \"num_vq_embeddings\": 16384,\n \"out_channels\": 3,\n \"sample_size\": 32,\n \"scaling_factor\": 0.18215,\n \"up_block_types\": [\n \"AttnUpDecoderBlock2D\",\n \"UpDecoderBlock2D\",\n \"UpDecoderBlock2D\",\n \"UpDecoderBlock2D\"\n ],\n \"vq_embed_dim\": 4\n}\n```\nTo lower the amount of layers in a VQGan, you can remove layers by modifying the block_out_channels, down_block_types, and up_block_types like below\n```\n{\n \"_class_name\": \"VQModel\",\n \"_diffusers_version\": \"0.17.0.dev0\",\n \"act_fn\": \"silu\",\n \"block_out_channels\": [\n 128,\n 256,\n 256,\n ],\n \"down_block_types\": [\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n ],\n \"in_channels\": 3,\n \"latent_channels\": 4,\n \"layers_per_block\": 2,\n \"norm_num_groups\": 32,\n \"norm_type\": \"spatial\",\n \"num_vq_embeddings\": 16384,\n \"out_channels\": 3,\n \"sample_size\": 32,\n \"scaling_factor\": 0.18215,\n \"up_block_types\": [\n \"UpDecoderBlock2D\",\n \"UpDecoderBlock2D\",\n \"UpDecoderBlock2D\"\n ],\n \"vq_embed_dim\": 4\n}\n```\nFor increasing the size of the vocaburaries you can increase num_vq_embeddings. However, [some research](https://magvit.cs.cmu.edu/v2/) shows that the representation of VQGANs start degrading after 2^14~16384 vq embeddings so it's not recommended to go past that.\n\n## Extra training tips/ideas\nDuring logging take care to make sure data_time is low. data_time is the amount spent loading the data and where the GPU is not active. So essentially, it's the time wasted. The easiest way to lower data time is to increase the --dataloader_num_workers to a higher number like 4. Due to a bug in Pytorch, this only works on linux based systems. For more details check [here](https://github.com/huggingface/diffusers/issues/7646)\nSecondly, training should seem to be done when both the discriminator and the generator loss converges.\nThirdly, another low hanging fruit is just using ema using the --use_ema parameter. This tends to make the output images smoother. This has a con where you have to lower your batch size by 1 but it may be worth it.\nAnother more experimental low hanging fruit is changing from the vgg19 to different models for the lpips loss using the --timm_model_backend. If you do this, I recommend also changing the timm_model_layers parameter to the layer in your model which you think is best for representation. However, becareful with the feature map norms since this can easily overdominate the loss." }, { "path": "diffusers/examples/vqgan/discriminator.py", "content": "\"\"\"\nPorted from Paella\n\"\"\"\n\nimport torch\nfrom torch import nn\n\nfrom diffusers.configuration_utils import ConfigMixin, register_to_config\nfrom diffusers.models.modeling_utils import ModelMixin\n\n\n# Discriminator model ported from Paella https://github.com/dome272/Paella/blob/main/src_distributed/vqgan.py\nclass Discriminator(ModelMixin, ConfigMixin):\n @register_to_config\n def __init__(self, in_channels=3, cond_channels=0, hidden_channels=512, depth=6):\n super().__init__()\n d = max(depth - 3, 3)\n layers = [\n nn.utils.spectral_norm(\n nn.Conv2d(in_channels, hidden_channels // (2**d), kernel_size=3, stride=2, padding=1)\n ),\n nn.LeakyReLU(0.2),\n ]\n for i in range(depth - 1):\n c_in = hidden_channels // (2 ** max((d - i), 0))\n c_out = hidden_channels // (2 ** max((d - 1 - i), 0))\n layers.append(nn.utils.spectral_norm(nn.Conv2d(c_in, c_out, kernel_size=3, stride=2, padding=1)))\n layers.append(nn.InstanceNorm2d(c_out))\n layers.append(nn.LeakyReLU(0.2))\n self.encoder = nn.Sequential(*layers)\n self.shuffle = nn.Conv2d(\n (hidden_channels + cond_channels) if cond_channels > 0 else hidden_channels, 1, kernel_size=1\n )\n self.logits = nn.Sigmoid()\n\n def forward(self, x, cond=None):\n x = self.encoder(x)\n if cond is not None:\n cond = cond.view(\n cond.size(0),\n cond.size(1),\n 1,\n 1,\n ).expand(-1, -1, x.size(-2), x.size(-1))\n x = torch.cat([x, cond], dim=1)\n x = self.shuffle(x)\n x = self.logits(x)\n return x\n" }, { "path": "diffusers/examples/vqgan/requirements.txt", "content": "accelerate>=0.16.0\ntorchvision\ntransformers>=4.25.1\ndatasets\ntimm\nnumpy\ntqdm\ntensorboard" }, { "path": "diffusers/examples/vqgan/test_vqgan.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport json\nimport logging\nimport os\nimport shutil\nimport sys\nimport tempfile\n\nimport torch\n\nfrom diffusers import VQModel\nfrom diffusers.utils.testing_utils import require_timm\n\n\nsys.path.append(\"..\")\nfrom test_examples_utils import ExamplesTestsAccelerate, run_command # noqa: E402\n\n\nlogging.basicConfig(level=logging.DEBUG)\n\nlogger = logging.getLogger()\nstream_handler = logging.StreamHandler(sys.stdout)\nlogger.addHandler(stream_handler)\n\n\n@require_timm\nclass TextToImage(ExamplesTestsAccelerate):\n @property\n def test_vqmodel_config(self):\n return {\n \"_class_name\": \"VQModel\",\n \"_diffusers_version\": \"0.17.0.dev0\",\n \"act_fn\": \"silu\",\n \"block_out_channels\": [\n 32,\n ],\n \"down_block_types\": [\n \"DownEncoderBlock2D\",\n ],\n \"in_channels\": 3,\n \"latent_channels\": 4,\n \"layers_per_block\": 2,\n \"norm_num_groups\": 32,\n \"norm_type\": \"spatial\",\n \"num_vq_embeddings\": 32,\n \"out_channels\": 3,\n \"sample_size\": 32,\n \"scaling_factor\": 0.18215,\n \"up_block_types\": [\n \"UpDecoderBlock2D\",\n ],\n \"vq_embed_dim\": 4,\n }\n\n @property\n def test_discriminator_config(self):\n return {\n \"_class_name\": \"Discriminator\",\n \"_diffusers_version\": \"0.27.0.dev0\",\n \"in_channels\": 3,\n \"cond_channels\": 0,\n \"hidden_channels\": 8,\n \"depth\": 4,\n }\n\n def get_vq_and_discriminator_configs(self, tmpdir):\n vqmodel_config_path = os.path.join(tmpdir, \"vqmodel.json\")\n discriminator_config_path = os.path.join(tmpdir, \"discriminator.json\")\n with open(vqmodel_config_path, \"w\") as fp:\n json.dump(self.test_vqmodel_config, fp)\n with open(discriminator_config_path, \"w\") as fp:\n json.dump(self.test_discriminator_config, fp)\n return vqmodel_config_path, discriminator_config_path\n\n def test_vqmodel(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n vqmodel_config_path, discriminator_config_path = self.get_vq_and_discriminator_configs(tmpdir)\n test_args = f\"\"\"\n examples/vqgan/train_vqgan.py\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 32\n --image_column image\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 2\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --model_config_name_or_path {vqmodel_config_path}\n --discriminator_config_name_or_path {discriminator_config_path}\n --output_dir {tmpdir}\n \"\"\".split()\n\n run_command(self._launch_args + test_args)\n # save_pretrained smoke test\n self.assertTrue(\n os.path.isfile(os.path.join(tmpdir, \"discriminator\", \"diffusion_pytorch_model.safetensors\"))\n )\n self.assertTrue(os.path.isfile(os.path.join(tmpdir, \"vqmodel\", \"diffusion_pytorch_model.safetensors\")))\n\n def test_vqmodel_checkpointing(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n vqmodel_config_path, discriminator_config_path = self.get_vq_and_discriminator_configs(tmpdir)\n # Run training script with checkpointing\n # max_train_steps == 4, checkpointing_steps == 2\n # Should create checkpoints at steps 2, 4\n\n initial_run_args = f\"\"\"\n examples/vqgan/train_vqgan.py\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 32\n --image_column image\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 4\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --model_config_name_or_path {vqmodel_config_path}\n --discriminator_config_name_or_path {discriminator_config_path}\n --checkpointing_steps=2\n --output_dir {tmpdir}\n --seed=0\n \"\"\".split()\n\n run_command(self._launch_args + initial_run_args)\n\n # check checkpoint directories exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-2\", \"checkpoint-4\"},\n )\n\n # check can run an intermediate checkpoint\n model = VQModel.from_pretrained(tmpdir, subfolder=\"checkpoint-2/vqmodel\")\n image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)\n _ = model(image)\n\n # Remove checkpoint 2 so that we can check only later checkpoints exist after resuming\n shutil.rmtree(os.path.join(tmpdir, \"checkpoint-2\"))\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-4\"},\n )\n\n # Run training script for 2 total steps resuming from checkpoint 4\n\n resume_run_args = f\"\"\"\n examples/vqgan/train_vqgan.py\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 32\n --image_column image\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 6\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --model_config_name_or_path {vqmodel_config_path}\n --discriminator_config_name_or_path {discriminator_config_path}\n --checkpointing_steps=1\n --resume_from_checkpoint={os.path.join(tmpdir, 'checkpoint-4')}\n --output_dir {tmpdir}\n --seed=0\n \"\"\".split()\n\n run_command(self._launch_args + resume_run_args)\n\n # check can run new fully trained pipeline\n model = VQModel.from_pretrained(tmpdir, subfolder=\"vqmodel\")\n image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)\n _ = model(image)\n\n # no checkpoint-2 -> check old checkpoints do not exist\n # check new checkpoints exist\n # In the current script, checkpointing_steps 1 is equivalent to checkpointing_steps 2 as after the generator gets trained for one step,\n # the discriminator gets trained and loss and saving happens after that. Thus we do not expect to get a checkpoint-5\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-4\", \"checkpoint-6\"},\n )\n\n def test_vqmodel_checkpointing_use_ema(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n vqmodel_config_path, discriminator_config_path = self.get_vq_and_discriminator_configs(tmpdir)\n # Run training script with checkpointing\n # max_train_steps == 4, checkpointing_steps == 2\n # Should create checkpoints at steps 2, 4\n\n initial_run_args = f\"\"\"\n examples/vqgan/train_vqgan.py\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 32\n --image_column image\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 4\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --model_config_name_or_path {vqmodel_config_path}\n --discriminator_config_name_or_path {discriminator_config_path}\n --checkpointing_steps=2\n --output_dir {tmpdir}\n --use_ema\n --seed=0\n \"\"\".split()\n\n run_command(self._launch_args + initial_run_args)\n\n model = VQModel.from_pretrained(tmpdir, subfolder=\"vqmodel\")\n image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)\n _ = model(image)\n\n # check checkpoint directories exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-2\", \"checkpoint-4\"},\n )\n\n # check can run an intermediate checkpoint\n model = VQModel.from_pretrained(tmpdir, subfolder=\"checkpoint-2/vqmodel\")\n image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)\n _ = model(image)\n\n # Remove checkpoint 2 so that we can check only later checkpoints exist after resuming\n shutil.rmtree(os.path.join(tmpdir, \"checkpoint-2\"))\n\n # Run training script for 2 total steps resuming from checkpoint 4\n\n resume_run_args = f\"\"\"\n examples/vqgan/train_vqgan.py\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 32\n --image_column image\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 6\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --model_config_name_or_path {vqmodel_config_path}\n --discriminator_config_name_or_path {discriminator_config_path}\n --checkpointing_steps=1\n --resume_from_checkpoint={os.path.join(tmpdir, 'checkpoint-4')}\n --output_dir {tmpdir}\n --use_ema\n --seed=0\n \"\"\".split()\n\n run_command(self._launch_args + resume_run_args)\n\n # check can run new fully trained pipeline\n model = VQModel.from_pretrained(tmpdir, subfolder=\"vqmodel\")\n image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)\n _ = model(image)\n\n # no checkpoint-2 -> check old checkpoints do not exist\n # check new checkpoints exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-4\", \"checkpoint-6\"},\n )\n\n def test_vqmodel_checkpointing_checkpoints_total_limit(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n vqmodel_config_path, discriminator_config_path = self.get_vq_and_discriminator_configs(tmpdir)\n # Run training script with checkpointing\n # max_train_steps == 6, checkpointing_steps == 2, checkpoints_total_limit == 2\n # Should create checkpoints at steps 2, 4, 6\n # with checkpoint at step 2 deleted\n\n initial_run_args = f\"\"\"\n examples/vqgan/train_vqgan.py\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 32\n --image_column image\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 6\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --model_config_name_or_path {vqmodel_config_path}\n --discriminator_config_name_or_path {discriminator_config_path}\n --output_dir {tmpdir}\n --checkpointing_steps=2\n --checkpoints_total_limit=2\n --seed=0\n \"\"\".split()\n\n run_command(self._launch_args + initial_run_args)\n\n model = VQModel.from_pretrained(tmpdir, subfolder=\"vqmodel\")\n image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)\n _ = model(image)\n\n # check checkpoint directories exist\n # checkpoint-2 should have been deleted\n self.assertEqual({x for x in os.listdir(tmpdir) if \"checkpoint\" in x}, {\"checkpoint-4\", \"checkpoint-6\"})\n\n def test_vqmodel_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n vqmodel_config_path, discriminator_config_path = self.get_vq_and_discriminator_configs(tmpdir)\n # Run training script with checkpointing\n # max_train_steps == 4, checkpointing_steps == 2\n # Should create checkpoints at steps 2, 4\n\n initial_run_args = f\"\"\"\n examples/vqgan/train_vqgan.py\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 32\n --image_column image\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 4\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --model_config_name_or_path {vqmodel_config_path}\n --discriminator_config_name_or_path {discriminator_config_path}\n --checkpointing_steps=2\n --output_dir {tmpdir}\n --seed=0\n \"\"\".split()\n\n run_command(self._launch_args + initial_run_args)\n\n model = VQModel.from_pretrained(tmpdir, subfolder=\"vqmodel\")\n image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)\n _ = model(image)\n\n # check checkpoint directories exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-2\", \"checkpoint-4\"},\n )\n\n # resume and we should try to checkpoint at 6, where we'll have to remove\n # checkpoint-2 and checkpoint-4 instead of just a single previous checkpoint\n\n resume_run_args = f\"\"\"\n examples/vqgan/train_vqgan.py\n --dataset_name hf-internal-testing/dummy_image_text_data\n --resolution 32\n --image_column image\n --train_batch_size 1\n --gradient_accumulation_steps 1\n --max_train_steps 8\n --learning_rate 5.0e-04\n --scale_lr\n --lr_scheduler constant\n --lr_warmup_steps 0\n --model_config_name_or_path {vqmodel_config_path}\n --discriminator_config_name_or_path {discriminator_config_path}\n --output_dir {tmpdir}\n --checkpointing_steps=2\n --resume_from_checkpoint={os.path.join(tmpdir, 'checkpoint-4')}\n --checkpoints_total_limit=2\n --seed=0\n \"\"\".split()\n\n run_command(self._launch_args + resume_run_args)\n\n model = VQModel.from_pretrained(tmpdir, subfolder=\"vqmodel\")\n image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)\n _ = model(image)\n\n # check checkpoint directories exist\n self.assertEqual(\n {x for x in os.listdir(tmpdir) if \"checkpoint\" in x},\n {\"checkpoint-6\", \"checkpoint-8\"},\n )\n" }, { "path": "diffusers/examples/vqgan/train_vqgan.py", "content": "# coding=utf-8\n# Copyright 2023 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport argparse\nimport math\nimport os\nimport shutil\nimport time\nfrom pathlib import Path\n\nimport accelerate\nimport numpy as np\nimport PIL\nimport PIL.Image\nimport timm\nimport torch\nimport torch.nn.functional as F\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import DistributedType, ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom discriminator import Discriminator\nfrom huggingface_hub import create_repo\nfrom packaging import version\nfrom PIL import Image\nfrom timm.data import resolve_data_config\nfrom timm.data.transforms_factory import create_transform\nfrom torchvision import transforms\nfrom tqdm import tqdm\n\nfrom diffusers import VQModel\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.training_utils import EMAModel\nfrom diffusers.utils import check_min_version, is_wandb_available\n\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\n\nclass AverageMeter(object):\n \"\"\"Computes and stores the average and current value\"\"\"\n\n def __init__(self):\n self.reset()\n\n def reset(self):\n self.val = 0\n self.avg = 0\n self.sum = 0\n self.count = 0\n\n def update(self, val, n=1):\n self.val = val\n self.sum += val * n\n self.count += n\n self.avg = self.sum / self.count\n\n\ndef _map_layer_to_idx(backbone, layers, offset=0):\n \"\"\"Maps set of layer names to indices of model. Ported from anomalib\n\n Returns:\n Feature map extracted from the CNN\n \"\"\"\n idx = []\n features = timm.create_model(\n backbone,\n pretrained=False,\n features_only=False,\n exportable=True,\n )\n for i in layers:\n try:\n idx.append(list(dict(features.named_children()).keys()).index(i) - offset)\n except ValueError:\n raise ValueError(\n f\"Layer {i} not found in model {backbone}. Select layer from {list(dict(features.named_children()).keys())}. The network architecture is {features}\"\n )\n return idx\n\n\ndef get_perceptual_loss(pixel_values, fmap, timm_model, timm_model_resolution, timm_model_normalization):\n img_timm_model_input = timm_model_normalization(F.interpolate(pixel_values, timm_model_resolution))\n fmap_timm_model_input = timm_model_normalization(F.interpolate(fmap, timm_model_resolution))\n\n if pixel_values.shape[1] == 1:\n # handle grayscale for timm_model\n img_timm_model_input, fmap_timm_model_input = (\n t.repeat(1, 3, 1, 1) for t in (img_timm_model_input, fmap_timm_model_input)\n )\n\n img_timm_model_feats = timm_model(img_timm_model_input)\n recon_timm_model_feats = timm_model(fmap_timm_model_input)\n perceptual_loss = F.mse_loss(img_timm_model_feats[0], recon_timm_model_feats[0])\n for i in range(1, len(img_timm_model_feats)):\n perceptual_loss += F.mse_loss(img_timm_model_feats[i], recon_timm_model_feats[i])\n perceptual_loss /= len(img_timm_model_feats)\n return perceptual_loss\n\n\ndef grad_layer_wrt_loss(loss, layer):\n return torch.autograd.grad(\n outputs=loss,\n inputs=layer,\n grad_outputs=torch.ones_like(loss),\n retain_graph=True,\n )[0].detach()\n\n\ndef gradient_penalty(images, output, weight=10):\n gradients = torch.autograd.grad(\n outputs=output,\n inputs=images,\n grad_outputs=torch.ones(output.size(), device=images.device),\n create_graph=True,\n retain_graph=True,\n only_inputs=True,\n )[0]\n bsz = gradients.shape[0]\n gradients = torch.reshape(gradients, (bsz, -1))\n return weight * ((gradients.norm(2, dim=1) - 1) ** 2).mean()\n\n\n@torch.no_grad()\ndef log_validation(model, args, validation_transform, accelerator, global_step):\n logger.info(\"Generating images...\")\n dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n dtype = torch.bfloat16\n original_images = []\n for image_path in args.validation_images:\n image = PIL.Image.open(image_path)\n if not image.mode == \"RGB\":\n image = image.convert(\"RGB\")\n image = validation_transform(image).to(accelerator.device, dtype=dtype)\n original_images.append(image[None])\n # Generate images\n model.eval()\n images = []\n for original_image in original_images:\n image = accelerator.unwrap_model(model)(original_image).sample\n images.append(image)\n model.train()\n original_images = torch.cat(original_images, dim=0)\n images = torch.cat(images, dim=0)\n\n # Convert to PIL images\n images = torch.clamp(images, 0.0, 1.0)\n original_images = torch.clamp(original_images, 0.0, 1.0)\n images *= 255.0\n original_images *= 255.0\n images = images.permute(0, 2, 3, 1).cpu().numpy().astype(np.uint8)\n original_images = original_images.permute(0, 2, 3, 1).cpu().numpy().astype(np.uint8)\n images = np.concatenate([original_images, images], axis=2)\n images = [Image.fromarray(image) for image in images]\n\n # Log images\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, global_step, dataformats=\"NHWC\")\n if tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{i}: Original, Generated\") for i, image in enumerate(images)\n ]\n },\n step=global_step,\n )\n torch.cuda.empty_cache()\n return images\n\n\ndef log_grad_norm(model, accelerator, global_step):\n for name, param in model.named_parameters():\n if param.grad is not None:\n grads = param.grad.detach().data\n grad_norm = (grads.norm(p=2) / grads.numel()).item()\n accelerator.log({\"grad_norm/\" + name: grad_norm}, step=global_step)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n parser.add_argument(\n \"--log_grad_norm_steps\",\n type=int,\n default=500,\n help=(\"Print logs of gradient norms every X steps.\"),\n )\n parser.add_argument(\n \"--log_steps\",\n type=int,\n default=50,\n help=(\"Print logs every X steps.\"),\n )\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=100,\n help=(\n \"Run validation every X steps. Validation consists of running reconstruction on images in\"\n \" `args.validation_images` and logging the reconstructed images.\"\n ),\n )\n parser.add_argument(\n \"--vae_loss\",\n type=str,\n default=\"l2\",\n help=\"The loss function for vae reconstruction loss.\",\n )\n parser.add_argument(\n \"--timm_model_offset\",\n type=int,\n default=0,\n help=\"Offset of timm layers to indices.\",\n )\n parser.add_argument(\n \"--timm_model_layers\",\n type=str,\n default=\"head\",\n help=\"The layers to get output from in the timm model.\",\n )\n parser.add_argument(\n \"--timm_model_backend\",\n type=str,\n default=\"vgg19\",\n help=\"Timm model used to get the lpips loss\",\n )\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--model_config_name_or_path\",\n type=str,\n default=None,\n help=\"The config of the Vq model to train, leave as None to use standard Vq model configuration.\",\n )\n parser.add_argument(\n \"--discriminator_config_name_or_path\",\n type=str,\n default=None,\n help=\"The config of the discriminator model to train, leave as None to use standard Vq model configuration.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing an image.\"\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--validation_images\",\n type=str,\n default=None,\n nargs=\"+\",\n help=(\"A set of validation images evaluated every `--validation_steps` and logged to `--report_to`.\"),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"vqgan-output\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--discr_learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--discr_lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\"--use_ema\", action=\"store_true\", help=\"Whether to use EMA model.\")\n parser.add_argument(\n \"--non_ema_revision\",\n type=str,\n default=None,\n required=False,\n help=(\n \"Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or\"\n \" remote repository specified with --pretrained_model_name_or_path.\"\n ),\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--prediction_type\",\n type=str,\n default=None,\n help=\"The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediciton_type` is chosen.\",\n )\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--tracker_project_name\",\n type=str,\n default=\"vqgan-training\",\n help=(\n \"The `project_name` argument passed to Accelerator.init_trackers for\"\n \" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator\"\n ),\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n\n # default to using the same revision for the non-ema model if not specified\n if args.non_ema_revision is None:\n args.non_ema_revision = args.revision\n\n return args\n\n\ndef main():\n #########################\n # SETUP Accelerator #\n #########################\n args = parse_args()\n\n # Enable TF32 on Ampere GPUs\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n torch.backends.cudnn.benchmark = True\n torch.backends.cudnn.deterministic = False\n\n logging_dir = os.path.join(args.output_dir, args.logging_dir)\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n if accelerator.distributed_type == DistributedType.DEEPSPEED:\n accelerator.state.deepspeed_plugin.deepspeed_config[\"train_micro_batch_size_per_gpu\"] = args.train_batch_size\n\n #####################################\n # SETUP LOGGING, SEED and CONFIG #\n #####################################\n\n if accelerator.is_main_process:\n tracker_config = dict(vars(args))\n tracker_config.pop(\"validation_images\")\n accelerator.init_trackers(args.tracker_project_name, tracker_config)\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n #########################\n # MODELS and OPTIMIZER #\n #########################\n logger.info(\"Loading models and optimizer\")\n\n if args.model_config_name_or_path is None and args.pretrained_model_name_or_path is None:\n # Taken from config of movq at kandinsky-community/kandinsky-2-2-decoder but without the attention layers\n model = VQModel(\n act_fn=\"silu\",\n block_out_channels=[\n 128,\n 256,\n 512,\n ],\n down_block_types=[\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n ],\n in_channels=3,\n latent_channels=4,\n layers_per_block=2,\n norm_num_groups=32,\n norm_type=\"spatial\",\n num_vq_embeddings=16384,\n out_channels=3,\n sample_size=32,\n scaling_factor=0.18215,\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n vq_embed_dim=4,\n )\n elif args.pretrained_model_name_or_path is not None:\n model = VQModel.from_pretrained(args.pretrained_model_name_or_path)\n else:\n config = VQModel.load_config(args.model_config_name_or_path)\n model = VQModel.from_config(config)\n if args.use_ema:\n ema_model = EMAModel(model.parameters(), model_cls=VQModel, model_config=model.config)\n if args.discriminator_config_name_or_path is None:\n discriminator = Discriminator()\n else:\n config = Discriminator.load_config(args.discriminator_config_name_or_path)\n discriminator = Discriminator.from_config(config)\n\n idx = _map_layer_to_idx(args.timm_model_backend, args.timm_model_layers.split(\"|\"), args.timm_model_offset)\n\n timm_model = timm.create_model(\n args.timm_model_backend,\n pretrained=True,\n features_only=True,\n exportable=True,\n out_indices=idx,\n )\n timm_model = timm_model.to(accelerator.device)\n timm_model.requires_grad = False\n timm_model.eval()\n timm_transform = create_transform(**resolve_data_config(timm_model.pretrained_cfg, model=timm_model))\n try:\n # Gets the resolution of the timm transformation after centercrop\n timm_centercrop_transform = timm_transform.transforms[1]\n assert isinstance(\n timm_centercrop_transform, transforms.CenterCrop\n ), f\"Timm model {timm_model} is currently incompatible with this script. Try vgg19.\"\n timm_model_resolution = timm_centercrop_transform.size[0]\n # Gets final normalization\n timm_model_normalization = timm_transform.transforms[-1]\n assert isinstance(\n timm_model_normalization, transforms.Normalize\n ), f\"Timm model {timm_model} is currently incompatible with this script. Try vgg19.\"\n except AssertionError as e:\n raise NotImplementedError(e)\n # Enable flash attention if asked\n if args.enable_xformers_memory_efficient_attention:\n model.enable_xformers_memory_efficient_attention()\n\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n if args.use_ema:\n ema_model.save_pretrained(os.path.join(output_dir, \"vqmodel_ema\"))\n vqmodel = models[0]\n discriminator = models[1]\n vqmodel.save_pretrained(os.path.join(output_dir, \"vqmodel\"))\n discriminator.save_pretrained(os.path.join(output_dir, \"discriminator\"))\n weights.pop()\n weights.pop()\n\n def load_model_hook(models, input_dir):\n if args.use_ema:\n load_model = EMAModel.from_pretrained(os.path.join(input_dir, \"vqmodel_ema\"), VQModel)\n ema_model.load_state_dict(load_model.state_dict())\n ema_model.to(accelerator.device)\n del load_model\n discriminator = models.pop()\n load_model = Discriminator.from_pretrained(input_dir, subfolder=\"discriminator\")\n discriminator.register_to_config(**load_model.config)\n discriminator.load_state_dict(load_model.state_dict())\n del load_model\n vqmodel = models.pop()\n load_model = VQModel.from_pretrained(input_dir, subfolder=\"vqmodel\")\n vqmodel.register_to_config(**load_model.config)\n vqmodel.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n learning_rate = args.learning_rate\n if args.scale_lr:\n learning_rate = (\n learning_rate * args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n )\n\n # Initialize the optimizer\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`\"\n )\n\n optimizer_cls = bnb.optim.AdamW8bit\n else:\n optimizer_cls = torch.optim.AdamW\n\n optimizer = optimizer_cls(\n list(model.parameters()),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n discr_optimizer = optimizer_cls(\n list(discriminator.parameters()),\n lr=args.discr_learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n ##################################\n # DATLOADER and LR-SCHEDULER #\n #################################\n logger.info(\"Creating dataloaders and lr_scheduler\")\n\n args.train_batch_size * accelerator.num_processes\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n # DataLoaders creation:\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n data_dir=args.train_data_dir,\n )\n else:\n data_files = {}\n if args.train_data_dir is not None:\n data_files[\"train\"] = os.path.join(args.train_data_dir, \"**\")\n dataset = load_dataset(\n \"imagefolder\",\n data_files=data_files,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n assert args.image_column is not None\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(f\"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}\")\n # Preprocessing the datasets.\n train_transforms = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),\n transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),\n transforms.ToTensor(),\n ]\n )\n validation_transform = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),\n transforms.ToTensor(),\n ]\n )\n\n def preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n examples[\"pixel_values\"] = [train_transforms(image) for image in images]\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n def collate_fn(examples):\n pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()\n return {\"pixel_values\": pixel_values}\n\n # DataLoaders creation:\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_training_steps=args.max_train_steps,\n num_warmup_steps=args.lr_warmup_steps,\n )\n discr_lr_scheduler = get_scheduler(\n args.discr_lr_scheduler,\n optimizer=discr_optimizer,\n num_training_steps=args.max_train_steps,\n num_warmup_steps=args.lr_warmup_steps,\n )\n\n # Prepare everything with accelerator\n logger.info(\"Preparing model, optimizer and dataloaders\")\n # The dataloader are already aware of distributed training, so we don't need to prepare them.\n model, discriminator, optimizer, discr_optimizer, lr_scheduler, discr_lr_scheduler = accelerator.prepare(\n model, discriminator, optimizer, discr_optimizer, lr_scheduler, discr_lr_scheduler\n )\n if args.use_ema:\n ema_model.to(accelerator.device)\n # Train!\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # Potentially load in the weights and states from a previous save\n resume_from_checkpoint = args.resume_from_checkpoint\n if resume_from_checkpoint:\n if resume_from_checkpoint != \"latest\":\n path = resume_from_checkpoint\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n path = os.path.join(args.output_dir, path)\n\n if path is None:\n accelerator.print(f\"Checkpoint '{resume_from_checkpoint}' does not exist. Starting a new training run.\")\n resume_from_checkpoint = None\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(path)\n accelerator.wait_for_everyone()\n global_step = int(os.path.basename(path).split(\"-\")[1])\n first_epoch = global_step // num_update_steps_per_epoch\n\n batch_time_m = AverageMeter()\n data_time_m = AverageMeter()\n end = time.time()\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n # As stated above, we are not doing epoch based training here, but just using this for book keeping and being able to\n # reuse the same training loop with other datasets/loaders.\n avg_gen_loss, avg_discr_loss = None, None\n for epoch in range(first_epoch, args.num_train_epochs):\n model.train()\n discriminator.train()\n for i, batch in enumerate(train_dataloader):\n pixel_values = batch[\"pixel_values\"]\n pixel_values = pixel_values.to(accelerator.device, non_blocking=True)\n data_time_m.update(time.time() - end)\n generator_step = ((i // args.gradient_accumulation_steps) % 2) == 0\n # Train Step\n # The behavior of accelerator.accumulate is to\n # 1. Check if gradients are synced(reached gradient-accumulation_steps)\n # 2. If so sync gradients by stopping the not syncing process\n if generator_step:\n optimizer.zero_grad(set_to_none=True)\n else:\n discr_optimizer.zero_grad(set_to_none=True)\n # encode images to the latent space and get the commit loss from vq tokenization\n # Return commit loss\n fmap, commit_loss = model(pixel_values, return_dict=False)\n\n if generator_step:\n with accelerator.accumulate(model):\n # reconstruction loss. Pixel level differences between input vs output\n if args.vae_loss == \"l2\":\n loss = F.mse_loss(pixel_values, fmap)\n else:\n loss = F.l1_loss(pixel_values, fmap)\n # perceptual loss. The high level feature mean squared error loss\n perceptual_loss = get_perceptual_loss(\n pixel_values,\n fmap,\n timm_model,\n timm_model_resolution=timm_model_resolution,\n timm_model_normalization=timm_model_normalization,\n )\n # generator loss\n gen_loss = -discriminator(fmap).mean()\n last_dec_layer = accelerator.unwrap_model(model).decoder.conv_out.weight\n norm_grad_wrt_perceptual_loss = grad_layer_wrt_loss(perceptual_loss, last_dec_layer).norm(p=2)\n norm_grad_wrt_gen_loss = grad_layer_wrt_loss(gen_loss, last_dec_layer).norm(p=2)\n\n adaptive_weight = norm_grad_wrt_perceptual_loss / norm_grad_wrt_gen_loss.clamp(min=1e-8)\n adaptive_weight = adaptive_weight.clamp(max=1e4)\n loss += commit_loss\n loss += perceptual_loss\n loss += adaptive_weight * gen_loss\n # Gather the losses across all processes for logging (if we use distributed training).\n avg_gen_loss = accelerator.gather(loss.repeat(args.train_batch_size)).float().mean()\n accelerator.backward(loss)\n\n if args.max_grad_norm is not None and accelerator.sync_gradients:\n accelerator.clip_grad_norm_(model.parameters(), args.max_grad_norm)\n\n optimizer.step()\n lr_scheduler.step()\n # log gradient norm before zeroing it\n if (\n accelerator.sync_gradients\n and global_step % args.log_grad_norm_steps == 0\n and accelerator.is_main_process\n ):\n log_grad_norm(model, accelerator, global_step)\n else:\n # Return discriminator loss\n with accelerator.accumulate(discriminator):\n fmap.detach_()\n pixel_values.requires_grad_()\n real = discriminator(pixel_values)\n fake = discriminator(fmap)\n loss = (F.relu(1 + fake) + F.relu(1 - real)).mean()\n gp = gradient_penalty(pixel_values, real)\n loss += gp\n avg_discr_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n accelerator.backward(loss)\n\n if args.max_grad_norm is not None and accelerator.sync_gradients:\n accelerator.clip_grad_norm_(discriminator.parameters(), args.max_grad_norm)\n\n discr_optimizer.step()\n discr_lr_scheduler.step()\n if (\n accelerator.sync_gradients\n and global_step % args.log_grad_norm_steps == 0\n and accelerator.is_main_process\n ):\n log_grad_norm(discriminator, accelerator, global_step)\n batch_time_m.update(time.time() - end)\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n global_step += 1\n progress_bar.update(1)\n if args.use_ema:\n ema_model.step(model.parameters())\n if accelerator.sync_gradients and not generator_step and accelerator.is_main_process:\n # wait for both generator and discriminator to settle\n # Log metrics\n if global_step % args.log_steps == 0:\n samples_per_second_per_gpu = (\n args.gradient_accumulation_steps * args.train_batch_size / batch_time_m.val\n )\n logs = {\n \"step_discr_loss\": avg_discr_loss.item(),\n \"lr\": lr_scheduler.get_last_lr()[0],\n \"samples/sec/gpu\": samples_per_second_per_gpu,\n \"data_time\": data_time_m.val,\n \"batch_time\": batch_time_m.val,\n }\n if avg_gen_loss is not None:\n logs[\"step_gen_loss\"] = avg_gen_loss.item()\n accelerator.log(logs, step=global_step)\n\n # resetting batch / data time meters per log window\n batch_time_m.reset()\n data_time_m.reset()\n # Save model checkpoint\n if global_step % args.checkpointing_steps == 0:\n if accelerator.is_main_process:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n # Generate images\n if global_step % args.validation_steps == 0:\n if args.use_ema:\n # Store the VQGAN parameters temporarily and load the EMA parameters to perform inference.\n ema_model.store(model.parameters())\n ema_model.copy_to(model.parameters())\n log_validation(model, args, validation_transform, accelerator, global_step)\n if args.use_ema:\n # Switch back to the original VQGAN parameters.\n ema_model.restore(model.parameters())\n end = time.time()\n # Stop training if max steps is reached\n if global_step >= args.max_train_steps:\n break\n # End for\n\n accelerator.wait_for_everyone()\n\n # Save the final trained checkpoint\n if accelerator.is_main_process:\n model = accelerator.unwrap_model(model)\n discriminator = accelerator.unwrap_model(discriminator)\n if args.use_ema:\n ema_model.copy_to(model.parameters())\n model.save_pretrained(os.path.join(args.output_dir, \"vqmodel\"))\n discriminator.save_pretrained(os.path.join(args.output_dir, \"discriminator\"))\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/wuerstchen/text_to_image/README.md", "content": "# Würstchen text-to-image fine-tuning\n\n## Running locally with PyTorch\n\nBefore running the scripts, make sure to install the library's training dependencies:\n\n**Important**\n\nTo make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date. To do this, execute the following steps in a new virtual environment:\n```bash\ngit clone https://github.com/huggingface/diffusers\ncd diffusers\npip install .\n```\n\nThen cd into the example folder and run\n```bash\ncd examples/wuerstchen/text_to_image\npip install -r requirements.txt\n```\n\nAnd initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:\n\n```bash\naccelerate config\n```\nFor this example we want to directly store the trained LoRA embeddings on the Hub, so we need to be logged in and add the `--push_to_hub` flag to the training script. To log in, run:\n```bash\nhuggingface-cli login\n```\n\n## Prior training\n\nYou can fine-tune the Würstchen prior model with the `train_text_to_image_prior.py` script. Note that we currently support `--gradient_checkpointing` for prior model fine-tuning so you can use it for more GPU memory constrained setups.\n\n
\n\n\n```bash\nexport DATASET_NAME=\"lambdalabs/naruto-blip-captions\"\n\naccelerate launch train_text_to_image_prior.py \\\n --mixed_precision=\"fp16\" \\\n --dataset_name=$DATASET_NAME \\\n --resolution=768 \\\n --train_batch_size=4 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --dataloader_num_workers=4 \\\n --max_train_steps=15000 \\\n --learning_rate=1e-05 \\\n --max_grad_norm=1 \\\n --checkpoints_total_limit=3 \\\n --lr_scheduler=\"constant\" --lr_warmup_steps=0 \\\n --validation_prompts=\"A robot naruto, 4k photo\" \\\n --report_to=\"wandb\" \\\n --push_to_hub \\\n --output_dir=\"wuerstchen-prior-naruto-model\"\n```\n\n\n## Training with LoRA\n\nLow-Rank Adaption of Large Language Models (or LoRA) was first introduced by Microsoft in [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) by *Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen*.\n\nIn a nutshell, LoRA allows adapting pretrained models by adding pairs of rank-decomposition matrices to existing weights and **only** training those newly added weights. This has a couple of advantages:\n\n- Previous pretrained weights are kept frozen so that the model is not prone to [catastrophic forgetting](https://www.pnas.org/doi/10.1073/pnas.1611835114).\n- Rank-decomposition matrices have significantly fewer parameters than original model, which means that trained LoRA weights are easily portable.\n- LoRA attention layers allow to control to which extent the model is adapted toward new training images via a `scale` parameter.\n\n\n### Prior Training\n\nFirst, you need to set up your development environment as explained in the [installation](#Running-locally-with-PyTorch) section. Make sure to set the `DATASET_NAME` environment variable. Here, we will use the [Naruto captions dataset](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions).\n\n```bash\nexport DATASET_NAME=\"lambdalabs/naruto-blip-captions\"\n\naccelerate launch train_text_to_image_lora_prior.py \\\n --mixed_precision=\"fp16\" \\\n --dataset_name=$DATASET_NAME --caption_column=\"text\" \\\n --resolution=768 \\\n --train_batch_size=8 \\\n --num_train_epochs=100 --checkpointing_steps=5000 \\\n --learning_rate=1e-04 --lr_scheduler=\"constant\" --lr_warmup_steps=0 \\\n --seed=42 \\\n --rank=4 \\\n --validation_prompt=\"cute dragon creature\" \\\n --report_to=\"wandb\" \\\n --push_to_hub \\\n --output_dir=\"wuerstchen-prior-naruto-lora\"\n```\n" }, { "path": "diffusers/examples/wuerstchen/text_to_image/__init__.py", "content": "" }, { "path": "diffusers/examples/wuerstchen/text_to_image/modeling_efficient_net_encoder.py", "content": "import torch.nn as nn\nfrom torchvision.models import efficientnet_v2_l, efficientnet_v2_s\n\nfrom diffusers.configuration_utils import ConfigMixin, register_to_config\nfrom diffusers.models.modeling_utils import ModelMixin\n\n\nclass EfficientNetEncoder(ModelMixin, ConfigMixin):\n @register_to_config\n def __init__(self, c_latent=16, c_cond=1280, effnet=\"efficientnet_v2_s\"):\n super().__init__()\n\n if effnet == \"efficientnet_v2_s\":\n self.backbone = efficientnet_v2_s(weights=\"DEFAULT\").features\n else:\n self.backbone = efficientnet_v2_l(weights=\"DEFAULT\").features\n self.mapper = nn.Sequential(\n nn.Conv2d(c_cond, c_latent, kernel_size=1, bias=False),\n nn.BatchNorm2d(c_latent), # then normalize them to have mean 0 and std 1\n )\n\n def forward(self, x):\n return self.mapper(self.backbone(x))\n" }, { "path": "diffusers/examples/wuerstchen/text_to_image/requirements.txt", "content": "accelerate>=0.16.0\ntorchvision\ntransformers>=4.25.1\nwandb\nbitsandbytes\ndeepspeed\npeft>=0.6.0\n" }, { "path": "diffusers/examples/wuerstchen/text_to_image/train_text_to_image_lora_prior.py", "content": "# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom pathlib import Path\n\nimport datasets\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.state import AcceleratorState, is_initialized\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, hf_hub_download, upload_folder\nfrom modeling_efficient_net_encoder import EfficientNetEncoder\nfrom peft import LoraConfig\nfrom peft.utils import get_peft_model_state_dict\nfrom torchvision import transforms\nfrom tqdm import tqdm\nfrom transformers import CLIPTextModel, PreTrainedTokenizerFast\nfrom transformers.utils import ContextManagers\n\nfrom diffusers import AutoPipelineForText2Image, DDPMWuerstchenScheduler, WuerstchenPriorPipeline\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS, WuerstchenPrior\nfrom diffusers.utils import check_min_version, is_wandb_available, make_image_grid\nfrom diffusers.utils.logging import set_verbosity_error, set_verbosity_info\n\n\nif is_wandb_available():\n import wandb\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\nDATASET_NAME_MAPPING = {\n \"lambdalabs/naruto-blip-captions\": (\"image\", \"text\"),\n}\n\n\ndef save_model_card(\n args,\n repo_id: str,\n images=None,\n repo_folder=None,\n):\n img_str = \"\"\n if len(images) > 0:\n image_grid = make_image_grid(images, 1, len(args.validation_prompts))\n image_grid.save(os.path.join(repo_folder, \"val_imgs_grid.png\"))\n img_str += \"![val_imgs_grid](./val_imgs_grid.png)\\n\"\n\n yaml = f\"\"\"\n---\nlicense: mit\nbase_model: {args.pretrained_prior_model_name_or_path}\ndatasets:\n- {args.dataset_name}\ntags:\n- wuerstchen\n- text-to-image\n- diffusers\n- diffusers-training\n- lora\ninference: true\n---\n \"\"\"\n model_card = f\"\"\"\n# LoRA Finetuning - {repo_id}\n\nThis pipeline was finetuned from **{args.pretrained_prior_model_name_or_path}** on the **{args.dataset_name}** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: {args.validation_prompts}: \\n\n{img_str}\n\n## Pipeline usage\n\nYou can use the pipeline like so:\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipeline = AutoPipelineForText2Image.from_pretrained(\n \"{args.pretrained_decoder_model_name_or_path}\", torch_dtype={args.weight_dtype}\n )\n# load lora weights from folder:\npipeline.prior_pipe.load_lora_weights(\"{repo_id}\", torch_dtype={args.weight_dtype})\n\nimage = pipeline(prompt=prompt).images[0]\nimage.save(\"my_image.png\")\n```\n\n## Training info\n\nThese are the key hyperparameters used during training:\n\n* LoRA rank: {args.rank}\n* Epochs: {args.num_train_epochs}\n* Learning rate: {args.learning_rate}\n* Batch size: {args.train_batch_size}\n* Gradient accumulation steps: {args.gradient_accumulation_steps}\n* Image resolution: {args.resolution}\n* Mixed-precision: {args.mixed_precision}\n\n\"\"\"\n wandb_info = \"\"\n if is_wandb_available():\n wandb_run_url = None\n if wandb.run is not None:\n wandb_run_url = wandb.run.url\n\n if wandb_run_url is not None:\n wandb_info = f\"\"\"\nMore information on all the CLI arguments and the environment are available on your [`wandb` run page]({wandb_run_url}).\n\"\"\"\n\n model_card += wandb_info\n\n with open(os.path.join(repo_folder, \"README.md\"), \"w\") as f:\n f.write(yaml + model_card)\n\n\ndef log_validation(text_encoder, tokenizer, prior, args, accelerator, weight_dtype, epoch):\n logger.info(\"Running validation... \")\n\n pipeline = AutoPipelineForText2Image.from_pretrained(\n args.pretrained_decoder_model_name_or_path,\n prior=accelerator.unwrap_model(prior),\n prior_text_encoder=accelerator.unwrap_model(text_encoder),\n prior_tokenizer=tokenizer,\n torch_dtype=weight_dtype,\n )\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n images = []\n for i in range(len(args.validation_prompts)):\n with torch.cuda.amp.autocast():\n image = pipeline(\n args.validation_prompts[i],\n prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS,\n generator=generator,\n height=args.resolution,\n width=args.resolution,\n ).images[0]\n images.append(image)\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, epoch, dataformats=\"NHWC\")\n elif tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompts[i]}\")\n for i, image in enumerate(images)\n ]\n }\n )\n else:\n logger.warning(f\"image logging not implemented for {tracker.name}\")\n\n del pipeline\n torch.cuda.empty_cache()\n\n return images\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of finetuning Würstchen Prior.\")\n parser.add_argument(\n \"--rank\",\n type=int,\n default=4,\n help=(\"The dimension of the LoRA update matrices.\"),\n )\n parser.add_argument(\n \"--pretrained_decoder_model_name_or_path\",\n type=str,\n default=\"warp-ai/wuerstchen\",\n required=False,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_prior_model_name_or_path\",\n type=str,\n default=\"warp-ai/wuerstchen-prior\",\n required=False,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing an image.\"\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=\"text\",\n help=\"The column of the dataset containing a caption or a list of captions.\",\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--validation_prompts\",\n type=str,\n default=None,\n nargs=\"+\",\n help=(\"A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`.\"),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"wuerstchen-model-finetuned-lora\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=1, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"learning rate\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\n \"--adam_weight_decay\",\n type=float,\n default=0.0,\n required=False,\n help=\"weight decay_to_use\",\n )\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=5,\n help=\"Run validation every X epochs.\",\n )\n parser.add_argument(\n \"--tracker_project_name\",\n type=str,\n default=\"text2image-fine-tune\",\n help=(\n \"The `project_name` argument passed to Accelerator.init_trackers for\"\n \" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator\"\n ),\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n\n return args\n\n\ndef main():\n args = parse_args()\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = os.path.join(args.output_dir, args.logging_dir)\n accelerator_project_config = ProjectConfiguration(\n total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir\n )\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n transformers.utils.logging.set_verbosity_warning()\n set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n transformers.utils.logging.set_verbosity_error()\n set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load scheduler, effnet, tokenizer, clip_model\n noise_scheduler = DDPMWuerstchenScheduler()\n tokenizer = PreTrainedTokenizerFast.from_pretrained(\n args.pretrained_prior_model_name_or_path, subfolder=\"tokenizer\"\n )\n\n def deepspeed_zero_init_disabled_context_manager():\n \"\"\"\n returns either a context list that includes one that will disable zero.Init or an empty context list\n \"\"\"\n deepspeed_plugin = AcceleratorState().deepspeed_plugin if is_initialized() else None\n if deepspeed_plugin is None:\n return []\n\n return [deepspeed_plugin.zero3_init_context_manager(enable=False)]\n\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n with ContextManagers(deepspeed_zero_init_disabled_context_manager()):\n pretrained_checkpoint_file = hf_hub_download(\"dome272/wuerstchen\", filename=\"model_v2_stage_b.pt\")\n state_dict = torch.load(pretrained_checkpoint_file, map_location=\"cpu\")\n image_encoder = EfficientNetEncoder()\n image_encoder.load_state_dict(state_dict[\"effnet_state_dict\"])\n image_encoder.eval()\n\n text_encoder = CLIPTextModel.from_pretrained(\n args.pretrained_prior_model_name_or_path, subfolder=\"text_encoder\", torch_dtype=weight_dtype\n ).eval()\n\n # Freeze text_encoder, cast to weight_dtype and image_encoder and move to device\n text_encoder.requires_grad_(False)\n image_encoder.requires_grad_(False)\n image_encoder.to(accelerator.device, dtype=weight_dtype)\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n\n # load prior model, cast to weight_dtype and move to device\n prior = WuerstchenPrior.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder=\"prior\")\n prior.to(accelerator.device, dtype=weight_dtype)\n\n # lora attn processor\n prior_lora_config = LoraConfig(\n r=args.rank,\n lora_alpha=args.rank,\n target_modules=[\"to_k\", \"to_q\", \"to_v\", \"to_out.0\", \"add_k_proj\", \"add_v_proj\"],\n )\n # Add adapter and make sure the trainable params are in float32.\n prior.add_adapter(prior_lora_config)\n if args.mixed_precision == \"fp16\":\n for param in prior.parameters():\n # only upcast trainable parameters (LoRA) into fp32\n if param.requires_grad:\n param.data = param.to(torch.float32)\n\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n prior_lora_layers_to_save = None\n\n for model in models:\n if isinstance(model, type(accelerator.unwrap_model(prior))):\n prior_lora_layers_to_save = get_peft_model_state_dict(model)\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n WuerstchenPriorPipeline.save_lora_weights(\n output_dir,\n unet_lora_layers=prior_lora_layers_to_save,\n )\n\n def load_model_hook(models, input_dir):\n prior_ = None\n\n while len(models) > 0:\n model = models.pop()\n\n if isinstance(model, type(accelerator.unwrap_model(prior))):\n prior_ = model\n else:\n raise ValueError(f\"unexpected save model: {model.__class__}\")\n\n lora_state_dict, network_alphas = WuerstchenPriorPipeline.lora_state_dict(input_dir)\n WuerstchenPriorPipeline.load_lora_into_unet(lora_state_dict, network_alphas=network_alphas, unet=prior_)\n WuerstchenPriorPipeline.load_lora_into_text_encoder(\n lora_state_dict,\n network_alphas=network_alphas,\n )\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`\"\n )\n\n optimizer_cls = bnb.optim.AdamW8bit\n else:\n optimizer_cls = torch.optim.AdamW\n params_to_optimize = list(filter(lambda p: p.requires_grad, prior.parameters()))\n optimizer = optimizer_cls(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n )\n else:\n data_files = {}\n if args.train_data_dir is not None:\n data_files[\"train\"] = os.path.join(args.train_data_dir, \"**\")\n dataset = load_dataset(\n \"imagefolder\",\n data_files=data_files,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # Get the column names for input/target.\n dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)\n if args.image_column is None:\n image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.caption_column is None:\n caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n else:\n caption_column = args.caption_column\n if caption_column not in column_names:\n raise ValueError(\n f\"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}\"\n )\n\n # Preprocessing the datasets.\n # We need to tokenize input captions and transform the images\n def tokenize_captions(examples, is_train=True):\n captions = []\n for caption in examples[caption_column]:\n if isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n else:\n raise ValueError(\n f\"Caption column `{caption_column}` should contain either strings or lists of strings.\"\n )\n inputs = tokenizer(\n captions, max_length=tokenizer.model_max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\"\n )\n text_input_ids = inputs.input_ids\n text_mask = inputs.attention_mask.bool()\n return text_input_ids, text_mask\n\n effnet_transforms = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR, antialias=True),\n transforms.CenterCrop(args.resolution),\n transforms.ToTensor(),\n transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),\n ]\n )\n\n def preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n examples[\"effnet_pixel_values\"] = [effnet_transforms(image) for image in images]\n examples[\"text_input_ids\"], examples[\"text_mask\"] = tokenize_captions(examples)\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n def collate_fn(examples):\n effnet_pixel_values = torch.stack([example[\"effnet_pixel_values\"] for example in examples])\n effnet_pixel_values = effnet_pixel_values.to(memory_format=torch.contiguous_format).float()\n text_input_ids = torch.stack([example[\"text_input_ids\"] for example in examples])\n text_mask = torch.stack([example[\"text_mask\"] for example in examples])\n return {\"effnet_pixel_values\": effnet_pixel_values, \"text_input_ids\": text_input_ids, \"text_mask\": text_mask}\n\n # DataLoaders creation:\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,\n num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,\n )\n\n prior, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n prior, optimizer, train_dataloader, lr_scheduler\n )\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = dict(vars(args))\n tracker_config.pop(\"validation_prompts\")\n accelerator.init_trackers(args.tracker_project_name, tracker_config)\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n resume_global_step = global_step * args.gradient_accumulation_steps\n first_epoch = global_step // num_update_steps_per_epoch\n resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)\n\n # Only show the progress bar once on each machine.\n progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)\n progress_bar.set_description(\"Steps\")\n\n for epoch in range(first_epoch, args.num_train_epochs):\n prior.train()\n train_loss = 0.0\n for step, batch in enumerate(train_dataloader):\n # Skip steps until we reach the resumed step\n if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:\n if step % args.gradient_accumulation_steps == 0:\n progress_bar.update(1)\n continue\n\n with accelerator.accumulate(prior):\n # Convert images to latent space\n text_input_ids, text_mask, effnet_images = (\n batch[\"text_input_ids\"],\n batch[\"text_mask\"],\n batch[\"effnet_pixel_values\"].to(weight_dtype),\n )\n\n with torch.no_grad():\n text_encoder_output = text_encoder(text_input_ids, attention_mask=text_mask)\n prompt_embeds = text_encoder_output.last_hidden_state\n image_embeds = image_encoder(effnet_images)\n # scale\n image_embeds = image_embeds.add(1.0).div(42.0)\n\n # Sample noise that we'll add to the image_embeds\n noise = torch.randn_like(image_embeds)\n bsz = image_embeds.shape[0]\n\n # Sample a random timestep for each image\n timesteps = torch.rand((bsz,), device=image_embeds.device, dtype=weight_dtype)\n\n # add noise to latent\n noisy_latents = noise_scheduler.add_noise(image_embeds, noise, timesteps)\n\n # Predict the noise residual and compute losscd\n pred_noise = prior(noisy_latents, timesteps, prompt_embeds)\n\n # vanilla loss\n loss = F.mse_loss(pred_noise.float(), noise.float(), reduction=\"mean\")\n\n # Gather the losses across all processes for logging (if we use distributed training).\n avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n train_loss += avg_loss.item() / args.gradient_accumulation_steps\n\n # Backpropagate\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(params_to_optimize, args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n accelerator.log({\"train_loss\": train_loss}, step=global_step)\n train_loss = 0.0\n\n if global_step % args.checkpointing_steps == 0:\n if accelerator.is_main_process:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"step_loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompts is not None and epoch % args.validation_epochs == 0:\n log_validation(text_encoder, tokenizer, prior, args, accelerator, weight_dtype, global_step)\n\n # Create the pipeline using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n prior = accelerator.unwrap_model(prior)\n prior = prior.to(torch.float32)\n\n prior_lora_state_dict = get_peft_model_state_dict(prior)\n\n WuerstchenPriorPipeline.save_lora_weights(\n save_directory=args.output_dir,\n unet_lora_layers=prior_lora_state_dict,\n )\n\n # Run a final round of inference.\n images = []\n if args.validation_prompts is not None:\n logger.info(\"Running inference for collecting generated images...\")\n pipeline = AutoPipelineForText2Image.from_pretrained(\n args.pretrained_decoder_model_name_or_path,\n prior_text_encoder=accelerator.unwrap_model(text_encoder),\n prior_tokenizer=tokenizer,\n torch_dtype=weight_dtype,\n )\n pipeline = pipeline.to(accelerator.device)\n\n # load lora weights\n pipeline.prior_pipe.load_lora_weights(args.output_dir, weight_name=\"pytorch_lora_weights.safetensors\")\n pipeline.set_progress_bar_config(disable=True)\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n for i in range(len(args.validation_prompts)):\n with torch.cuda.amp.autocast():\n image = pipeline(\n args.validation_prompts[i],\n prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS,\n generator=generator,\n width=args.resolution,\n height=args.resolution,\n ).images[0]\n images.append(image)\n\n if args.push_to_hub:\n save_model_card(args, repo_id, images, repo_folder=args.output_dir)\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/examples/wuerstchen/text_to_image/train_text_to_image_prior.py", "content": "# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\nimport argparse\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom pathlib import Path\n\nimport accelerate\nimport datasets\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.state import AcceleratorState, is_initialized\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, hf_hub_download, upload_folder\nfrom modeling_efficient_net_encoder import EfficientNetEncoder\nfrom packaging import version\nfrom torchvision import transforms\nfrom tqdm import tqdm\nfrom transformers import CLIPTextModel, PreTrainedTokenizerFast\nfrom transformers.utils import ContextManagers\n\nfrom diffusers import AutoPipelineForText2Image, DDPMWuerstchenScheduler\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS, WuerstchenPrior\nfrom diffusers.training_utils import EMAModel\nfrom diffusers.utils import check_min_version, is_wandb_available, make_image_grid\nfrom diffusers.utils.logging import set_verbosity_error, set_verbosity_info\n\n\nif is_wandb_available():\n import wandb\n\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.30.0.dev0\")\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\nDATASET_NAME_MAPPING = {\n \"lambdalabs/naruto-blip-captions\": (\"image\", \"text\"),\n}\n\n\ndef save_model_card(\n args,\n repo_id: str,\n images=None,\n repo_folder=None,\n):\n img_str = \"\"\n if len(images) > 0:\n image_grid = make_image_grid(images, 1, len(args.validation_prompts))\n image_grid.save(os.path.join(repo_folder, \"val_imgs_grid.png\"))\n img_str += \"![val_imgs_grid](./val_imgs_grid.png)\\n\"\n\n yaml = f\"\"\"\n---\nlicense: mit\nbase_model: {args.pretrained_prior_model_name_or_path}\ndatasets:\n- {args.dataset_name}\ntags:\n- wuerstchen\n- text-to-image\n- diffusers\n- diffusers-training\ninference: true\n---\n \"\"\"\n model_card = f\"\"\"\n# Finetuning - {repo_id}\n\nThis pipeline was finetuned from **{args.pretrained_prior_model_name_or_path}** on the **{args.dataset_name}** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: {args.validation_prompts}: \\n\n{img_str}\n\n## Pipeline usage\n\nYou can use the pipeline like so:\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipe_prior = DiffusionPipeline.from_pretrained(\"{repo_id}\", torch_dtype={args.weight_dtype})\npipe_t2i = DiffusionPipeline.from_pretrained(\"{args.pretrained_decoder_model_name_or_path}\", torch_dtype={args.weight_dtype})\nprompt = \"{args.validation_prompts[0]}\"\n(image_embeds,) = pipe_prior(prompt).to_tuple()\nimage = pipe_t2i(image_embeddings=image_embeds, prompt=prompt).images[0]\nimage.save(\"my_image.png\")\n```\n\n## Training info\n\nThese are the key hyperparameters used during training:\n\n* Epochs: {args.num_train_epochs}\n* Learning rate: {args.learning_rate}\n* Batch size: {args.train_batch_size}\n* Gradient accumulation steps: {args.gradient_accumulation_steps}\n* Image resolution: {args.resolution}\n* Mixed-precision: {args.mixed_precision}\n\n\"\"\"\n wandb_info = \"\"\n if is_wandb_available():\n wandb_run_url = None\n if wandb.run is not None:\n wandb_run_url = wandb.run.url\n\n if wandb_run_url is not None:\n wandb_info = f\"\"\"\nMore information on all the CLI arguments and the environment are available on your [`wandb` run page]({wandb_run_url}).\n\"\"\"\n\n model_card += wandb_info\n\n with open(os.path.join(repo_folder, \"README.md\"), \"w\") as f:\n f.write(yaml + model_card)\n\n\ndef log_validation(text_encoder, tokenizer, prior, args, accelerator, weight_dtype, epoch):\n logger.info(\"Running validation... \")\n\n pipeline = AutoPipelineForText2Image.from_pretrained(\n args.pretrained_decoder_model_name_or_path,\n prior_prior=accelerator.unwrap_model(prior),\n prior_text_encoder=accelerator.unwrap_model(text_encoder),\n prior_tokenizer=tokenizer,\n torch_dtype=weight_dtype,\n )\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n images = []\n for i in range(len(args.validation_prompts)):\n with torch.autocast(\"cuda\"):\n image = pipeline(\n args.validation_prompts[i],\n prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS,\n generator=generator,\n height=args.resolution,\n width=args.resolution,\n ).images[0]\n\n images.append(image)\n\n for tracker in accelerator.trackers:\n if tracker.name == \"tensorboard\":\n np_images = np.stack([np.asarray(img) for img in images])\n tracker.writer.add_images(\"validation\", np_images, epoch, dataformats=\"NHWC\")\n elif tracker.name == \"wandb\":\n tracker.log(\n {\n \"validation\": [\n wandb.Image(image, caption=f\"{i}: {args.validation_prompts[i]}\")\n for i, image in enumerate(images)\n ]\n }\n )\n else:\n logger.warning(f\"image logging not implemented for {tracker.name}\")\n\n del pipeline\n torch.cuda.empty_cache()\n\n return images\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of finetuning Würstchen Prior.\")\n parser.add_argument(\n \"--pretrained_decoder_model_name_or_path\",\n type=str,\n default=\"warp-ai/wuerstchen\",\n required=False,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_prior_model_name_or_path\",\n type=str,\n default=\"warp-ai/wuerstchen-prior\",\n required=False,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_dir\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--image_column\", type=str, default=\"image\", help=\"The column of the dataset containing an image.\"\n )\n parser.add_argument(\n \"--caption_column\",\n type=str,\n default=\"text\",\n help=\"The column of the dataset containing a caption or a list of captions.\",\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--validation_prompts\",\n type=str,\n default=None,\n nargs=\"+\",\n help=(\"A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`.\"),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"wuerstchen-model-finetuned\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=1, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"learning rate\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\"--use_ema\", action=\"store_true\", help=\"Whether to use EMA model.\")\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\n \"--adam_weight_decay\",\n type=float,\n default=0.0,\n required=False,\n help=\"weight decay_to_use\",\n )\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=5,\n help=\"Run validation every X epochs.\",\n )\n parser.add_argument(\n \"--tracker_project_name\",\n type=str,\n default=\"text2image-fine-tune\",\n help=(\n \"The `project_name` argument passed to Accelerator.init_trackers for\"\n \" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator\"\n ),\n )\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_dir is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n\n return args\n\n\ndef main():\n args = parse_args()\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = os.path.join(args.output_dir, args.logging_dir)\n accelerator_project_config = ProjectConfiguration(\n total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir\n )\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n # Disable AMP for MPS.\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n transformers.utils.logging.set_verbosity_warning()\n set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n transformers.utils.logging.set_verbosity_error()\n set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load scheduler, effnet, tokenizer, clip_model\n noise_scheduler = DDPMWuerstchenScheduler()\n tokenizer = PreTrainedTokenizerFast.from_pretrained(\n args.pretrained_prior_model_name_or_path, subfolder=\"tokenizer\"\n )\n\n def deepspeed_zero_init_disabled_context_manager():\n \"\"\"\n returns either a context list that includes one that will disable zero.Init or an empty context list\n \"\"\"\n deepspeed_plugin = AcceleratorState().deepspeed_plugin if is_initialized() else None\n if deepspeed_plugin is None:\n return []\n\n return [deepspeed_plugin.zero3_init_context_manager(enable=False)]\n\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n with ContextManagers(deepspeed_zero_init_disabled_context_manager()):\n pretrained_checkpoint_file = hf_hub_download(\"dome272/wuerstchen\", filename=\"model_v2_stage_b.pt\")\n state_dict = torch.load(pretrained_checkpoint_file, map_location=\"cpu\")\n image_encoder = EfficientNetEncoder()\n image_encoder.load_state_dict(state_dict[\"effnet_state_dict\"])\n image_encoder.eval()\n\n text_encoder = CLIPTextModel.from_pretrained(\n args.pretrained_prior_model_name_or_path, subfolder=\"text_encoder\", torch_dtype=weight_dtype\n ).eval()\n\n # Freeze text_encoder and image_encoder\n text_encoder.requires_grad_(False)\n image_encoder.requires_grad_(False)\n\n # load prior model\n prior = WuerstchenPrior.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder=\"prior\")\n\n # Create EMA for the prior\n if args.use_ema:\n ema_prior = WuerstchenPrior.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder=\"prior\")\n ema_prior = EMAModel(ema_prior.parameters(), model_cls=WuerstchenPrior, model_config=ema_prior.config)\n ema_prior.to(accelerator.device)\n\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if args.use_ema:\n ema_prior.save_pretrained(os.path.join(output_dir, \"prior_ema\"))\n\n for i, model in enumerate(models):\n model.save_pretrained(os.path.join(output_dir, \"prior\"))\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n def load_model_hook(models, input_dir):\n if args.use_ema:\n load_model = EMAModel.from_pretrained(os.path.join(input_dir, \"prior_ema\"), WuerstchenPrior)\n ema_prior.load_state_dict(load_model.state_dict())\n ema_prior.to(accelerator.device)\n del load_model\n\n for i in range(len(models)):\n # pop models so that they are not loaded again\n model = models.pop()\n\n # load diffusers style into model\n load_model = WuerstchenPrior.from_pretrained(input_dir, subfolder=\"prior\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n if args.gradient_checkpointing:\n prior.enable_gradient_checkpointing()\n\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`\"\n )\n\n optimizer_cls = bnb.optim.AdamW8bit\n else:\n optimizer_cls = torch.optim.AdamW\n optimizer = optimizer_cls(\n prior.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n )\n else:\n data_files = {}\n if args.train_data_dir is not None:\n data_files[\"train\"] = os.path.join(args.train_data_dir, \"**\")\n dataset = load_dataset(\n \"imagefolder\",\n data_files=data_files,\n cache_dir=args.cache_dir,\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # Get the column names for input/target.\n dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)\n if args.image_column is None:\n image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n else:\n image_column = args.image_column\n if image_column not in column_names:\n raise ValueError(\n f\"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.caption_column is None:\n caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n else:\n caption_column = args.caption_column\n if caption_column not in column_names:\n raise ValueError(\n f\"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}\"\n )\n\n # Preprocessing the datasets.\n # We need to tokenize input captions and transform the images\n def tokenize_captions(examples, is_train=True):\n captions = []\n for caption in examples[caption_column]:\n if isinstance(caption, str):\n captions.append(caption)\n elif isinstance(caption, (list, np.ndarray)):\n # take a random caption if there are multiple\n captions.append(random.choice(caption) if is_train else caption[0])\n else:\n raise ValueError(\n f\"Caption column `{caption_column}` should contain either strings or lists of strings.\"\n )\n inputs = tokenizer(\n captions, max_length=tokenizer.model_max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\"\n )\n text_input_ids = inputs.input_ids\n text_mask = inputs.attention_mask.bool()\n return text_input_ids, text_mask\n\n effnet_transforms = transforms.Compose(\n [\n transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR, antialias=True),\n transforms.CenterCrop(args.resolution),\n transforms.ToTensor(),\n transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),\n ]\n )\n\n def preprocess_train(examples):\n images = [image.convert(\"RGB\") for image in examples[image_column]]\n examples[\"effnet_pixel_values\"] = [effnet_transforms(image) for image in images]\n examples[\"text_input_ids\"], examples[\"text_mask\"] = tokenize_captions(examples)\n return examples\n\n with accelerator.main_process_first():\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n def collate_fn(examples):\n effnet_pixel_values = torch.stack([example[\"effnet_pixel_values\"] for example in examples])\n effnet_pixel_values = effnet_pixel_values.to(memory_format=torch.contiguous_format).float()\n text_input_ids = torch.stack([example[\"text_input_ids\"] for example in examples])\n text_mask = torch.stack([example[\"text_mask\"] for example in examples])\n return {\"effnet_pixel_values\": effnet_pixel_values, \"text_input_ids\": text_input_ids, \"text_mask\": text_mask}\n\n # DataLoaders creation:\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,\n num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,\n )\n\n prior, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n prior, optimizer, train_dataloader, lr_scheduler\n )\n image_encoder.to(accelerator.device, dtype=weight_dtype)\n text_encoder.to(accelerator.device, dtype=weight_dtype)\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n tracker_config = dict(vars(args))\n tracker_config.pop(\"validation_prompts\")\n accelerator.init_trackers(args.tracker_project_name, tracker_config)\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n resume_global_step = global_step * args.gradient_accumulation_steps\n first_epoch = global_step // num_update_steps_per_epoch\n resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)\n\n # Only show the progress bar once on each machine.\n progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)\n progress_bar.set_description(\"Steps\")\n\n for epoch in range(first_epoch, args.num_train_epochs):\n prior.train()\n train_loss = 0.0\n for step, batch in enumerate(train_dataloader):\n # Skip steps until we reach the resumed step\n if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:\n if step % args.gradient_accumulation_steps == 0:\n progress_bar.update(1)\n continue\n\n with accelerator.accumulate(prior):\n # Convert images to latent space\n text_input_ids, text_mask, effnet_images = (\n batch[\"text_input_ids\"],\n batch[\"text_mask\"],\n batch[\"effnet_pixel_values\"].to(weight_dtype),\n )\n\n with torch.no_grad():\n text_encoder_output = text_encoder(text_input_ids, attention_mask=text_mask)\n prompt_embeds = text_encoder_output.last_hidden_state\n image_embeds = image_encoder(effnet_images)\n # scale\n image_embeds = image_embeds.add(1.0).div(42.0)\n\n # Sample noise that we'll add to the image_embeds\n noise = torch.randn_like(image_embeds)\n bsz = image_embeds.shape[0]\n\n # Sample a random timestep for each image\n timesteps = torch.rand((bsz,), device=image_embeds.device, dtype=weight_dtype)\n\n # add noise to latent\n noisy_latents = noise_scheduler.add_noise(image_embeds, noise, timesteps)\n\n # Predict the noise residual and compute losscd\n pred_noise = prior(noisy_latents, timesteps, prompt_embeds)\n\n # vanilla loss\n loss = F.mse_loss(pred_noise.float(), noise.float(), reduction=\"mean\")\n\n # Gather the losses across all processes for logging (if we use distributed training).\n avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n train_loss += avg_loss.item() / args.gradient_accumulation_steps\n\n # Backpropagate\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(prior.parameters(), args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n if args.use_ema:\n ema_prior.step(prior.parameters())\n progress_bar.update(1)\n global_step += 1\n accelerator.log({\"train_loss\": train_loss}, step=global_step)\n train_loss = 0.0\n\n if global_step % args.checkpointing_steps == 0:\n if accelerator.is_main_process:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"step_loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n\n if global_step >= args.max_train_steps:\n break\n\n if accelerator.is_main_process:\n if args.validation_prompts is not None and epoch % args.validation_epochs == 0:\n if args.use_ema:\n # Store the UNet parameters temporarily and load the EMA parameters to perform inference.\n ema_prior.store(prior.parameters())\n ema_prior.copy_to(prior.parameters())\n log_validation(text_encoder, tokenizer, prior, args, accelerator, weight_dtype, global_step)\n if args.use_ema:\n # Switch back to the original UNet parameters.\n ema_prior.restore(prior.parameters())\n\n # Create the pipeline using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n prior = accelerator.unwrap_model(prior)\n if args.use_ema:\n ema_prior.copy_to(prior.parameters())\n\n pipeline = AutoPipelineForText2Image.from_pretrained(\n args.pretrained_decoder_model_name_or_path,\n prior_prior=prior,\n prior_text_encoder=accelerator.unwrap_model(text_encoder),\n prior_tokenizer=tokenizer,\n )\n pipeline.prior_pipe.save_pretrained(os.path.join(args.output_dir, \"prior_pipeline\"))\n\n # Run a final round of inference.\n images = []\n if args.validation_prompts is not None:\n logger.info(\"Running inference for collecting generated images...\")\n pipeline = pipeline.to(accelerator.device, torch_dtype=weight_dtype)\n pipeline.set_progress_bar_config(disable=True)\n\n if args.seed is None:\n generator = None\n else:\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n for i in range(len(args.validation_prompts)):\n with torch.autocast(\"cuda\"):\n image = pipeline(\n args.validation_prompts[i],\n prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS,\n generator=generator,\n width=args.resolution,\n height=args.resolution,\n ).images[0]\n images.append(image)\n\n if args.push_to_hub:\n save_model_card(args, repo_id, images, repo_folder=args.output_dir)\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/pyproject.toml", "content": "[tool.ruff]\nline-length = 119\n\n[tool.ruff.lint]\n# Never enforce `E501` (line length violations).\nignore = [\"C901\", \"E501\", \"E741\", \"F402\", \"F823\"]\nselect = [\"C\", \"E\", \"F\", \"I\", \"W\"]\n\n# Ignore import violations in all `__init__.py` files.\n[tool.ruff.lint.per-file-ignores]\n\"__init__.py\" = [\"E402\", \"F401\", \"F403\", \"F811\"]\n\"src/diffusers/utils/dummy_*.py\" = [\"F401\"]\n\n[tool.ruff.lint.isort]\nlines-after-imports = 2\nknown-first-party = [\"diffusers\"]\n\n[tool.ruff.format]\n# Like Black, use double quotes for strings.\nquote-style = \"double\"\n\n# Like Black, indent with spaces, rather than tabs.\nindent-style = \"space\"\n\n# Like Black, respect magic trailing commas.\nskip-magic-trailing-comma = false\n\n# Like Black, automatically detect the appropriate line ending.\nline-ending = \"auto\"\n" }, { "path": "diffusers/scripts/__init__.py", "content": "" }, { "path": "diffusers/scripts/change_naming_configs_and_checkpoints.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Conversion script for the LDM checkpoints.\"\"\"\n\nimport argparse\nimport json\nimport os\n\nimport torch\nfrom transformers.file_utils import has_file\n\nfrom diffusers import UNet2DConditionModel, UNet2DModel\n\n\ndo_only_config = False\ndo_only_weights = True\ndo_only_renaming = False\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--repo_path\",\n default=None,\n type=str,\n required=True,\n help=\"The config json file corresponding to the architecture.\",\n )\n\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n\n args = parser.parse_args()\n\n config_parameters_to_change = {\n \"image_size\": \"sample_size\",\n \"num_res_blocks\": \"layers_per_block\",\n \"block_channels\": \"block_out_channels\",\n \"down_blocks\": \"down_block_types\",\n \"up_blocks\": \"up_block_types\",\n \"downscale_freq_shift\": \"freq_shift\",\n \"resnet_num_groups\": \"norm_num_groups\",\n \"resnet_act_fn\": \"act_fn\",\n \"resnet_eps\": \"norm_eps\",\n \"num_head_channels\": \"attention_head_dim\",\n }\n\n key_parameters_to_change = {\n \"time_steps\": \"time_proj\",\n \"mid\": \"mid_block\",\n \"downsample_blocks\": \"down_blocks\",\n \"upsample_blocks\": \"up_blocks\",\n }\n\n subfolder = \"\" if has_file(args.repo_path, \"config.json\") else \"unet\"\n\n with open(os.path.join(args.repo_path, subfolder, \"config.json\"), \"r\", encoding=\"utf-8\") as reader:\n text = reader.read()\n config = json.loads(text)\n\n if do_only_config:\n for key in config_parameters_to_change.keys():\n config.pop(key, None)\n\n if has_file(args.repo_path, \"config.json\"):\n model = UNet2DModel(**config)\n else:\n class_name = UNet2DConditionModel if \"ldm-text2im-large-256\" in args.repo_path else UNet2DModel\n model = class_name(**config)\n\n if do_only_config:\n model.save_config(os.path.join(args.repo_path, subfolder))\n\n config = dict(model.config)\n\n if do_only_renaming:\n for key, value in config_parameters_to_change.items():\n if key in config:\n config[value] = config[key]\n del config[key]\n\n config[\"down_block_types\"] = [k.replace(\"UNetRes\", \"\") for k in config[\"down_block_types\"]]\n config[\"up_block_types\"] = [k.replace(\"UNetRes\", \"\") for k in config[\"up_block_types\"]]\n\n if do_only_weights:\n state_dict = torch.load(os.path.join(args.repo_path, subfolder, \"diffusion_pytorch_model.bin\"))\n\n new_state_dict = {}\n for param_key, param_value in state_dict.items():\n if param_key.endswith(\".op.bias\") or param_key.endswith(\".op.weight\"):\n continue\n has_changed = False\n for key, new_key in key_parameters_to_change.items():\n if not has_changed and param_key.split(\".\")[0] == key:\n new_state_dict[\".\".join([new_key] + param_key.split(\".\")[1:])] = param_value\n has_changed = True\n if not has_changed:\n new_state_dict[param_key] = param_value\n\n model.load_state_dict(new_state_dict)\n model.save_pretrained(os.path.join(args.repo_path, subfolder))\n" }, { "path": "diffusers/scripts/conversion_ldm_uncond.py", "content": "import argparse\n\nimport torch\nimport yaml\n\nfrom diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel\n\n\ndef convert_ldm_original(checkpoint_path, config_path, output_path):\n config = yaml.safe_load(config_path)\n state_dict = torch.load(checkpoint_path, map_location=\"cpu\")[\"model\"]\n keys = list(state_dict.keys())\n\n # extract state_dict for VQVAE\n first_stage_dict = {}\n first_stage_key = \"first_stage_model.\"\n for key in keys:\n if key.startswith(first_stage_key):\n first_stage_dict[key.replace(first_stage_key, \"\")] = state_dict[key]\n\n # extract state_dict for UNetLDM\n unet_state_dict = {}\n unet_key = \"model.diffusion_model.\"\n for key in keys:\n if key.startswith(unet_key):\n unet_state_dict[key.replace(unet_key, \"\")] = state_dict[key]\n\n vqvae_init_args = config[\"model\"][\"params\"][\"first_stage_config\"][\"params\"]\n unet_init_args = config[\"model\"][\"params\"][\"unet_config\"][\"params\"]\n\n vqvae = VQModel(**vqvae_init_args).eval()\n vqvae.load_state_dict(first_stage_dict)\n\n unet = UNetLDMModel(**unet_init_args).eval()\n unet.load_state_dict(unet_state_dict)\n\n noise_scheduler = DDIMScheduler(\n timesteps=config[\"model\"][\"params\"][\"timesteps\"],\n beta_schedule=\"scaled_linear\",\n beta_start=config[\"model\"][\"params\"][\"linear_start\"],\n beta_end=config[\"model\"][\"params\"][\"linear_end\"],\n clip_sample=False,\n )\n\n pipeline = LDMPipeline(vqvae, unet, noise_scheduler)\n pipeline.save_pretrained(output_path)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--checkpoint_path\", type=str, required=True)\n parser.add_argument(\"--config_path\", type=str, required=True)\n parser.add_argument(\"--output_path\", type=str, required=True)\n args = parser.parse_args()\n\n convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)\n" }, { "path": "diffusers/scripts/convert_amused.py", "content": "import inspect\nimport os\nfrom argparse import ArgumentParser\n\nimport numpy as np\nimport torch\nfrom muse import MaskGiTUViT, VQGANModel\nfrom muse import PipelineMuse as OldPipelineMuse\nfrom transformers import CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom diffusers import VQModel\nfrom diffusers.models.attention_processor import AttnProcessor\nfrom diffusers.models.unets.uvit_2d import UVit2DModel\nfrom diffusers.pipelines.amused.pipeline_amused import AmusedPipeline\nfrom diffusers.schedulers import AmusedScheduler\n\n\ntorch.backends.cuda.enable_flash_sdp(False)\ntorch.backends.cuda.enable_mem_efficient_sdp(False)\ntorch.backends.cuda.enable_math_sdp(True)\n\nos.environ[\"CUDA_LAUNCH_BLOCKING\"] = \"1\"\nos.environ[\"CUBLAS_WORKSPACE_CONFIG\"] = \":16:8\"\ntorch.use_deterministic_algorithms(True)\n\n# Enable CUDNN deterministic mode\ntorch.backends.cudnn.deterministic = True\ntorch.backends.cudnn.benchmark = False\ntorch.backends.cuda.matmul.allow_tf32 = False\n\ndevice = \"cuda\"\n\n\ndef main():\n args = ArgumentParser()\n args.add_argument(\"--model_256\", action=\"store_true\")\n args.add_argument(\"--write_to\", type=str, required=False, default=None)\n args.add_argument(\"--transformer_path\", type=str, required=False, default=None)\n args = args.parse_args()\n\n transformer_path = args.transformer_path\n subfolder = \"transformer\"\n\n if transformer_path is None:\n if args.model_256:\n transformer_path = \"openMUSE/muse-256\"\n else:\n transformer_path = (\n \"../research-run-512-checkpoints/research-run-512-with-downsample-checkpoint-554000/unwrapped_model/\"\n )\n subfolder = None\n\n old_transformer = MaskGiTUViT.from_pretrained(transformer_path, subfolder=subfolder)\n\n old_transformer.to(device)\n\n old_vae = VQGANModel.from_pretrained(\"openMUSE/muse-512\", subfolder=\"vae\")\n old_vae.to(device)\n\n vqvae = make_vqvae(old_vae)\n\n tokenizer = CLIPTokenizer.from_pretrained(\"openMUSE/muse-512\", subfolder=\"text_encoder\")\n\n text_encoder = CLIPTextModelWithProjection.from_pretrained(\"openMUSE/muse-512\", subfolder=\"text_encoder\")\n text_encoder.to(device)\n\n transformer = make_transformer(old_transformer, args.model_256)\n\n scheduler = AmusedScheduler(mask_token_id=old_transformer.config.mask_token_id)\n\n new_pipe = AmusedPipeline(\n vqvae=vqvae, tokenizer=tokenizer, text_encoder=text_encoder, transformer=transformer, scheduler=scheduler\n )\n\n old_pipe = OldPipelineMuse(\n vae=old_vae, transformer=old_transformer, text_encoder=text_encoder, tokenizer=tokenizer\n )\n old_pipe.to(device)\n\n if args.model_256:\n transformer_seq_len = 256\n orig_size = (256, 256)\n else:\n transformer_seq_len = 1024\n orig_size = (512, 512)\n\n old_out = old_pipe(\n \"dog\",\n generator=torch.Generator(device).manual_seed(0),\n transformer_seq_len=transformer_seq_len,\n orig_size=orig_size,\n timesteps=12,\n )[0]\n\n new_out = new_pipe(\"dog\", generator=torch.Generator(device).manual_seed(0)).images[0]\n\n old_out = np.array(old_out)\n new_out = np.array(new_out)\n\n diff = np.abs(old_out.astype(np.float64) - new_out.astype(np.float64))\n\n # assert diff diff.sum() == 0\n print(\"skipping pipeline full equivalence check\")\n\n print(f\"max diff: {diff.max()}, diff.sum() / diff.size {diff.sum() / diff.size}\")\n\n if args.model_256:\n assert diff.max() <= 3\n assert diff.sum() / diff.size < 0.7\n else:\n assert diff.max() <= 1\n assert diff.sum() / diff.size < 0.4\n\n if args.write_to is not None:\n new_pipe.save_pretrained(args.write_to)\n\n\ndef make_transformer(old_transformer, model_256):\n args = dict(old_transformer.config)\n force_down_up_sample = args[\"force_down_up_sample\"]\n\n signature = inspect.signature(UVit2DModel.__init__)\n\n args_ = {\n \"downsample\": force_down_up_sample,\n \"upsample\": force_down_up_sample,\n \"block_out_channels\": args[\"block_out_channels\"][0],\n \"sample_size\": 16 if model_256 else 32,\n }\n\n for s in list(signature.parameters.keys()):\n if s in [\"self\", \"downsample\", \"upsample\", \"sample_size\", \"block_out_channels\"]:\n continue\n\n args_[s] = args[s]\n\n new_transformer = UVit2DModel(**args_)\n new_transformer.to(device)\n\n new_transformer.set_attn_processor(AttnProcessor())\n\n state_dict = old_transformer.state_dict()\n\n state_dict[\"cond_embed.linear_1.weight\"] = state_dict.pop(\"cond_embed.0.weight\")\n state_dict[\"cond_embed.linear_2.weight\"] = state_dict.pop(\"cond_embed.2.weight\")\n\n for i in range(22):\n state_dict[f\"transformer_layers.{i}.norm1.norm.weight\"] = state_dict.pop(\n f\"transformer_layers.{i}.attn_layer_norm.weight\"\n )\n state_dict[f\"transformer_layers.{i}.norm1.linear.weight\"] = state_dict.pop(\n f\"transformer_layers.{i}.self_attn_adaLN_modulation.mapper.weight\"\n )\n\n state_dict[f\"transformer_layers.{i}.attn1.to_q.weight\"] = state_dict.pop(\n f\"transformer_layers.{i}.attention.query.weight\"\n )\n state_dict[f\"transformer_layers.{i}.attn1.to_k.weight\"] = state_dict.pop(\n f\"transformer_layers.{i}.attention.key.weight\"\n )\n state_dict[f\"transformer_layers.{i}.attn1.to_v.weight\"] = state_dict.pop(\n f\"transformer_layers.{i}.attention.value.weight\"\n )\n state_dict[f\"transformer_layers.{i}.attn1.to_out.0.weight\"] = state_dict.pop(\n f\"transformer_layers.{i}.attention.out.weight\"\n )\n\n state_dict[f\"transformer_layers.{i}.norm2.norm.weight\"] = state_dict.pop(\n f\"transformer_layers.{i}.crossattn_layer_norm.weight\"\n )\n state_dict[f\"transformer_layers.{i}.norm2.linear.weight\"] = state_dict.pop(\n f\"transformer_layers.{i}.cross_attn_adaLN_modulation.mapper.weight\"\n )\n\n state_dict[f\"transformer_layers.{i}.attn2.to_q.weight\"] = state_dict.pop(\n f\"transformer_layers.{i}.crossattention.query.weight\"\n )\n state_dict[f\"transformer_layers.{i}.attn2.to_k.weight\"] = state_dict.pop(\n f\"transformer_layers.{i}.crossattention.key.weight\"\n )\n state_dict[f\"transformer_layers.{i}.attn2.to_v.weight\"] = state_dict.pop(\n f\"transformer_layers.{i}.crossattention.value.weight\"\n )\n state_dict[f\"transformer_layers.{i}.attn2.to_out.0.weight\"] = state_dict.pop(\n f\"transformer_layers.{i}.crossattention.out.weight\"\n )\n\n state_dict[f\"transformer_layers.{i}.norm3.norm.weight\"] = state_dict.pop(\n f\"transformer_layers.{i}.ffn.pre_mlp_layer_norm.weight\"\n )\n state_dict[f\"transformer_layers.{i}.norm3.linear.weight\"] = state_dict.pop(\n f\"transformer_layers.{i}.ffn.adaLN_modulation.mapper.weight\"\n )\n\n wi_0_weight = state_dict.pop(f\"transformer_layers.{i}.ffn.wi_0.weight\")\n wi_1_weight = state_dict.pop(f\"transformer_layers.{i}.ffn.wi_1.weight\")\n proj_weight = torch.concat([wi_1_weight, wi_0_weight], dim=0)\n state_dict[f\"transformer_layers.{i}.ff.net.0.proj.weight\"] = proj_weight\n\n state_dict[f\"transformer_layers.{i}.ff.net.2.weight\"] = state_dict.pop(f\"transformer_layers.{i}.ffn.wo.weight\")\n\n if force_down_up_sample:\n state_dict[\"down_block.downsample.norm.weight\"] = state_dict.pop(\"down_blocks.0.downsample.0.norm.weight\")\n state_dict[\"down_block.downsample.conv.weight\"] = state_dict.pop(\"down_blocks.0.downsample.1.weight\")\n\n state_dict[\"up_block.upsample.norm.weight\"] = state_dict.pop(\"up_blocks.0.upsample.0.norm.weight\")\n state_dict[\"up_block.upsample.conv.weight\"] = state_dict.pop(\"up_blocks.0.upsample.1.weight\")\n\n state_dict[\"mlm_layer.layer_norm.weight\"] = state_dict.pop(\"mlm_layer.layer_norm.norm.weight\")\n\n for i in range(3):\n state_dict[f\"down_block.res_blocks.{i}.norm.weight\"] = state_dict.pop(\n f\"down_blocks.0.res_blocks.{i}.norm.norm.weight\"\n )\n state_dict[f\"down_block.res_blocks.{i}.channelwise_linear_1.weight\"] = state_dict.pop(\n f\"down_blocks.0.res_blocks.{i}.channelwise.0.weight\"\n )\n state_dict[f\"down_block.res_blocks.{i}.channelwise_norm.gamma\"] = state_dict.pop(\n f\"down_blocks.0.res_blocks.{i}.channelwise.2.gamma\"\n )\n state_dict[f\"down_block.res_blocks.{i}.channelwise_norm.beta\"] = state_dict.pop(\n f\"down_blocks.0.res_blocks.{i}.channelwise.2.beta\"\n )\n state_dict[f\"down_block.res_blocks.{i}.channelwise_linear_2.weight\"] = state_dict.pop(\n f\"down_blocks.0.res_blocks.{i}.channelwise.4.weight\"\n )\n state_dict[f\"down_block.res_blocks.{i}.cond_embeds_mapper.weight\"] = state_dict.pop(\n f\"down_blocks.0.res_blocks.{i}.adaLN_modulation.mapper.weight\"\n )\n\n state_dict[f\"down_block.attention_blocks.{i}.norm1.weight\"] = state_dict.pop(\n f\"down_blocks.0.attention_blocks.{i}.attn_layer_norm.weight\"\n )\n state_dict[f\"down_block.attention_blocks.{i}.attn1.to_q.weight\"] = state_dict.pop(\n f\"down_blocks.0.attention_blocks.{i}.attention.query.weight\"\n )\n state_dict[f\"down_block.attention_blocks.{i}.attn1.to_k.weight\"] = state_dict.pop(\n f\"down_blocks.0.attention_blocks.{i}.attention.key.weight\"\n )\n state_dict[f\"down_block.attention_blocks.{i}.attn1.to_v.weight\"] = state_dict.pop(\n f\"down_blocks.0.attention_blocks.{i}.attention.value.weight\"\n )\n state_dict[f\"down_block.attention_blocks.{i}.attn1.to_out.0.weight\"] = state_dict.pop(\n f\"down_blocks.0.attention_blocks.{i}.attention.out.weight\"\n )\n\n state_dict[f\"down_block.attention_blocks.{i}.norm2.weight\"] = state_dict.pop(\n f\"down_blocks.0.attention_blocks.{i}.crossattn_layer_norm.weight\"\n )\n state_dict[f\"down_block.attention_blocks.{i}.attn2.to_q.weight\"] = state_dict.pop(\n f\"down_blocks.0.attention_blocks.{i}.crossattention.query.weight\"\n )\n state_dict[f\"down_block.attention_blocks.{i}.attn2.to_k.weight\"] = state_dict.pop(\n f\"down_blocks.0.attention_blocks.{i}.crossattention.key.weight\"\n )\n state_dict[f\"down_block.attention_blocks.{i}.attn2.to_v.weight\"] = state_dict.pop(\n f\"down_blocks.0.attention_blocks.{i}.crossattention.value.weight\"\n )\n state_dict[f\"down_block.attention_blocks.{i}.attn2.to_out.0.weight\"] = state_dict.pop(\n f\"down_blocks.0.attention_blocks.{i}.crossattention.out.weight\"\n )\n\n state_dict[f\"up_block.res_blocks.{i}.norm.weight\"] = state_dict.pop(\n f\"up_blocks.0.res_blocks.{i}.norm.norm.weight\"\n )\n state_dict[f\"up_block.res_blocks.{i}.channelwise_linear_1.weight\"] = state_dict.pop(\n f\"up_blocks.0.res_blocks.{i}.channelwise.0.weight\"\n )\n state_dict[f\"up_block.res_blocks.{i}.channelwise_norm.gamma\"] = state_dict.pop(\n f\"up_blocks.0.res_blocks.{i}.channelwise.2.gamma\"\n )\n state_dict[f\"up_block.res_blocks.{i}.channelwise_norm.beta\"] = state_dict.pop(\n f\"up_blocks.0.res_blocks.{i}.channelwise.2.beta\"\n )\n state_dict[f\"up_block.res_blocks.{i}.channelwise_linear_2.weight\"] = state_dict.pop(\n f\"up_blocks.0.res_blocks.{i}.channelwise.4.weight\"\n )\n state_dict[f\"up_block.res_blocks.{i}.cond_embeds_mapper.weight\"] = state_dict.pop(\n f\"up_blocks.0.res_blocks.{i}.adaLN_modulation.mapper.weight\"\n )\n\n state_dict[f\"up_block.attention_blocks.{i}.norm1.weight\"] = state_dict.pop(\n f\"up_blocks.0.attention_blocks.{i}.attn_layer_norm.weight\"\n )\n state_dict[f\"up_block.attention_blocks.{i}.attn1.to_q.weight\"] = state_dict.pop(\n f\"up_blocks.0.attention_blocks.{i}.attention.query.weight\"\n )\n state_dict[f\"up_block.attention_blocks.{i}.attn1.to_k.weight\"] = state_dict.pop(\n f\"up_blocks.0.attention_blocks.{i}.attention.key.weight\"\n )\n state_dict[f\"up_block.attention_blocks.{i}.attn1.to_v.weight\"] = state_dict.pop(\n f\"up_blocks.0.attention_blocks.{i}.attention.value.weight\"\n )\n state_dict[f\"up_block.attention_blocks.{i}.attn1.to_out.0.weight\"] = state_dict.pop(\n f\"up_blocks.0.attention_blocks.{i}.attention.out.weight\"\n )\n\n state_dict[f\"up_block.attention_blocks.{i}.norm2.weight\"] = state_dict.pop(\n f\"up_blocks.0.attention_blocks.{i}.crossattn_layer_norm.weight\"\n )\n state_dict[f\"up_block.attention_blocks.{i}.attn2.to_q.weight\"] = state_dict.pop(\n f\"up_blocks.0.attention_blocks.{i}.crossattention.query.weight\"\n )\n state_dict[f\"up_block.attention_blocks.{i}.attn2.to_k.weight\"] = state_dict.pop(\n f\"up_blocks.0.attention_blocks.{i}.crossattention.key.weight\"\n )\n state_dict[f\"up_block.attention_blocks.{i}.attn2.to_v.weight\"] = state_dict.pop(\n f\"up_blocks.0.attention_blocks.{i}.crossattention.value.weight\"\n )\n state_dict[f\"up_block.attention_blocks.{i}.attn2.to_out.0.weight\"] = state_dict.pop(\n f\"up_blocks.0.attention_blocks.{i}.crossattention.out.weight\"\n )\n\n for key in list(state_dict.keys()):\n if key.startswith(\"up_blocks.0\"):\n key_ = \"up_block.\" + \".\".join(key.split(\".\")[2:])\n state_dict[key_] = state_dict.pop(key)\n\n if key.startswith(\"down_blocks.0\"):\n key_ = \"down_block.\" + \".\".join(key.split(\".\")[2:])\n state_dict[key_] = state_dict.pop(key)\n\n new_transformer.load_state_dict(state_dict)\n\n input_ids = torch.randint(0, 10, (1, 32, 32), device=old_transformer.device)\n encoder_hidden_states = torch.randn((1, 77, 768), device=old_transformer.device)\n cond_embeds = torch.randn((1, 768), device=old_transformer.device)\n micro_conds = torch.tensor([[512, 512, 0, 0, 6]], dtype=torch.float32, device=old_transformer.device)\n\n old_out = old_transformer(input_ids.reshape(1, -1), encoder_hidden_states, cond_embeds, micro_conds)\n old_out = old_out.reshape(1, 32, 32, 8192).permute(0, 3, 1, 2)\n\n new_out = new_transformer(input_ids, encoder_hidden_states, cond_embeds, micro_conds)\n\n # NOTE: these differences are solely due to using the geglu block that has a single linear layer of\n # double output dimension instead of two different linear layers\n max_diff = (old_out - new_out).abs().max()\n total_diff = (old_out - new_out).abs().sum()\n print(f\"Transformer max_diff: {max_diff} total_diff: {total_diff}\")\n assert max_diff < 0.01\n assert total_diff < 1500\n\n return new_transformer\n\n\ndef make_vqvae(old_vae):\n new_vae = VQModel(\n act_fn=\"silu\",\n block_out_channels=[128, 256, 256, 512, 768],\n down_block_types=[\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n ],\n in_channels=3,\n latent_channels=64,\n layers_per_block=2,\n norm_num_groups=32,\n num_vq_embeddings=8192,\n out_channels=3,\n sample_size=32,\n up_block_types=[\n \"UpDecoderBlock2D\",\n \"UpDecoderBlock2D\",\n \"UpDecoderBlock2D\",\n \"UpDecoderBlock2D\",\n \"UpDecoderBlock2D\",\n ],\n mid_block_add_attention=False,\n lookup_from_codebook=True,\n )\n new_vae.to(device)\n\n # fmt: off\n\n new_state_dict = {}\n\n old_state_dict = old_vae.state_dict()\n\n new_state_dict[\"encoder.conv_in.weight\"] = old_state_dict.pop(\"encoder.conv_in.weight\")\n new_state_dict[\"encoder.conv_in.bias\"] = old_state_dict.pop(\"encoder.conv_in.bias\")\n\n convert_vae_block_state_dict(old_state_dict, \"encoder.down.0\", new_state_dict, \"encoder.down_blocks.0\")\n convert_vae_block_state_dict(old_state_dict, \"encoder.down.1\", new_state_dict, \"encoder.down_blocks.1\")\n convert_vae_block_state_dict(old_state_dict, \"encoder.down.2\", new_state_dict, \"encoder.down_blocks.2\")\n convert_vae_block_state_dict(old_state_dict, \"encoder.down.3\", new_state_dict, \"encoder.down_blocks.3\")\n convert_vae_block_state_dict(old_state_dict, \"encoder.down.4\", new_state_dict, \"encoder.down_blocks.4\")\n\n new_state_dict[\"encoder.mid_block.resnets.0.norm1.weight\"] = old_state_dict.pop(\"encoder.mid.block_1.norm1.weight\")\n new_state_dict[\"encoder.mid_block.resnets.0.norm1.bias\"] = old_state_dict.pop(\"encoder.mid.block_1.norm1.bias\")\n new_state_dict[\"encoder.mid_block.resnets.0.conv1.weight\"] = old_state_dict.pop(\"encoder.mid.block_1.conv1.weight\")\n new_state_dict[\"encoder.mid_block.resnets.0.conv1.bias\"] = old_state_dict.pop(\"encoder.mid.block_1.conv1.bias\")\n new_state_dict[\"encoder.mid_block.resnets.0.norm2.weight\"] = old_state_dict.pop(\"encoder.mid.block_1.norm2.weight\")\n new_state_dict[\"encoder.mid_block.resnets.0.norm2.bias\"] = old_state_dict.pop(\"encoder.mid.block_1.norm2.bias\")\n new_state_dict[\"encoder.mid_block.resnets.0.conv2.weight\"] = old_state_dict.pop(\"encoder.mid.block_1.conv2.weight\")\n new_state_dict[\"encoder.mid_block.resnets.0.conv2.bias\"] = old_state_dict.pop(\"encoder.mid.block_1.conv2.bias\")\n new_state_dict[\"encoder.mid_block.resnets.1.norm1.weight\"] = old_state_dict.pop(\"encoder.mid.block_2.norm1.weight\")\n new_state_dict[\"encoder.mid_block.resnets.1.norm1.bias\"] = old_state_dict.pop(\"encoder.mid.block_2.norm1.bias\")\n new_state_dict[\"encoder.mid_block.resnets.1.conv1.weight\"] = old_state_dict.pop(\"encoder.mid.block_2.conv1.weight\")\n new_state_dict[\"encoder.mid_block.resnets.1.conv1.bias\"] = old_state_dict.pop(\"encoder.mid.block_2.conv1.bias\")\n new_state_dict[\"encoder.mid_block.resnets.1.norm2.weight\"] = old_state_dict.pop(\"encoder.mid.block_2.norm2.weight\")\n new_state_dict[\"encoder.mid_block.resnets.1.norm2.bias\"] = old_state_dict.pop(\"encoder.mid.block_2.norm2.bias\")\n new_state_dict[\"encoder.mid_block.resnets.1.conv2.weight\"] = old_state_dict.pop(\"encoder.mid.block_2.conv2.weight\")\n new_state_dict[\"encoder.mid_block.resnets.1.conv2.bias\"] = old_state_dict.pop(\"encoder.mid.block_2.conv2.bias\")\n new_state_dict[\"encoder.conv_norm_out.weight\"] = old_state_dict.pop(\"encoder.norm_out.weight\")\n new_state_dict[\"encoder.conv_norm_out.bias\"] = old_state_dict.pop(\"encoder.norm_out.bias\")\n new_state_dict[\"encoder.conv_out.weight\"] = old_state_dict.pop(\"encoder.conv_out.weight\")\n new_state_dict[\"encoder.conv_out.bias\"] = old_state_dict.pop(\"encoder.conv_out.bias\")\n new_state_dict[\"quant_conv.weight\"] = old_state_dict.pop(\"quant_conv.weight\")\n new_state_dict[\"quant_conv.bias\"] = old_state_dict.pop(\"quant_conv.bias\")\n new_state_dict[\"quantize.embedding.weight\"] = old_state_dict.pop(\"quantize.embedding.weight\")\n new_state_dict[\"post_quant_conv.weight\"] = old_state_dict.pop(\"post_quant_conv.weight\")\n new_state_dict[\"post_quant_conv.bias\"] = old_state_dict.pop(\"post_quant_conv.bias\")\n new_state_dict[\"decoder.conv_in.weight\"] = old_state_dict.pop(\"decoder.conv_in.weight\")\n new_state_dict[\"decoder.conv_in.bias\"] = old_state_dict.pop(\"decoder.conv_in.bias\")\n new_state_dict[\"decoder.mid_block.resnets.0.norm1.weight\"] = old_state_dict.pop(\"decoder.mid.block_1.norm1.weight\")\n new_state_dict[\"decoder.mid_block.resnets.0.norm1.bias\"] = old_state_dict.pop(\"decoder.mid.block_1.norm1.bias\")\n new_state_dict[\"decoder.mid_block.resnets.0.conv1.weight\"] = old_state_dict.pop(\"decoder.mid.block_1.conv1.weight\")\n new_state_dict[\"decoder.mid_block.resnets.0.conv1.bias\"] = old_state_dict.pop(\"decoder.mid.block_1.conv1.bias\")\n new_state_dict[\"decoder.mid_block.resnets.0.norm2.weight\"] = old_state_dict.pop(\"decoder.mid.block_1.norm2.weight\")\n new_state_dict[\"decoder.mid_block.resnets.0.norm2.bias\"] = old_state_dict.pop(\"decoder.mid.block_1.norm2.bias\")\n new_state_dict[\"decoder.mid_block.resnets.0.conv2.weight\"] = old_state_dict.pop(\"decoder.mid.block_1.conv2.weight\")\n new_state_dict[\"decoder.mid_block.resnets.0.conv2.bias\"] = old_state_dict.pop(\"decoder.mid.block_1.conv2.bias\")\n new_state_dict[\"decoder.mid_block.resnets.1.norm1.weight\"] = old_state_dict.pop(\"decoder.mid.block_2.norm1.weight\")\n new_state_dict[\"decoder.mid_block.resnets.1.norm1.bias\"] = old_state_dict.pop(\"decoder.mid.block_2.norm1.bias\")\n new_state_dict[\"decoder.mid_block.resnets.1.conv1.weight\"] = old_state_dict.pop(\"decoder.mid.block_2.conv1.weight\")\n new_state_dict[\"decoder.mid_block.resnets.1.conv1.bias\"] = old_state_dict.pop(\"decoder.mid.block_2.conv1.bias\")\n new_state_dict[\"decoder.mid_block.resnets.1.norm2.weight\"] = old_state_dict.pop(\"decoder.mid.block_2.norm2.weight\")\n new_state_dict[\"decoder.mid_block.resnets.1.norm2.bias\"] = old_state_dict.pop(\"decoder.mid.block_2.norm2.bias\")\n new_state_dict[\"decoder.mid_block.resnets.1.conv2.weight\"] = old_state_dict.pop(\"decoder.mid.block_2.conv2.weight\")\n new_state_dict[\"decoder.mid_block.resnets.1.conv2.bias\"] = old_state_dict.pop(\"decoder.mid.block_2.conv2.bias\")\n\n convert_vae_block_state_dict(old_state_dict, \"decoder.up.0\", new_state_dict, \"decoder.up_blocks.4\")\n convert_vae_block_state_dict(old_state_dict, \"decoder.up.1\", new_state_dict, \"decoder.up_blocks.3\")\n convert_vae_block_state_dict(old_state_dict, \"decoder.up.2\", new_state_dict, \"decoder.up_blocks.2\")\n convert_vae_block_state_dict(old_state_dict, \"decoder.up.3\", new_state_dict, \"decoder.up_blocks.1\")\n convert_vae_block_state_dict(old_state_dict, \"decoder.up.4\", new_state_dict, \"decoder.up_blocks.0\")\n\n new_state_dict[\"decoder.conv_norm_out.weight\"] = old_state_dict.pop(\"decoder.norm_out.weight\")\n new_state_dict[\"decoder.conv_norm_out.bias\"] = old_state_dict.pop(\"decoder.norm_out.bias\")\n new_state_dict[\"decoder.conv_out.weight\"] = old_state_dict.pop(\"decoder.conv_out.weight\")\n new_state_dict[\"decoder.conv_out.bias\"] = old_state_dict.pop(\"decoder.conv_out.bias\")\n\n # fmt: on\n\n assert len(old_state_dict.keys()) == 0\n\n new_vae.load_state_dict(new_state_dict)\n\n input = torch.randn((1, 3, 512, 512), device=device)\n input = input.clamp(-1, 1)\n\n old_encoder_output = old_vae.quant_conv(old_vae.encoder(input))\n new_encoder_output = new_vae.quant_conv(new_vae.encoder(input))\n assert (old_encoder_output == new_encoder_output).all()\n\n old_decoder_output = old_vae.decoder(old_vae.post_quant_conv(old_encoder_output))\n new_decoder_output = new_vae.decoder(new_vae.post_quant_conv(new_encoder_output))\n\n # assert (old_decoder_output == new_decoder_output).all()\n print(\"kipping vae decoder equivalence check\")\n print(f\"vae decoder diff {(old_decoder_output - new_decoder_output).float().abs().sum()}\")\n\n old_output = old_vae(input)[0]\n new_output = new_vae(input)[0]\n\n # assert (old_output == new_output).all()\n print(\"skipping full vae equivalence check\")\n print(f\"vae full diff { (old_output - new_output).float().abs().sum()}\")\n\n return new_vae\n\n\ndef convert_vae_block_state_dict(old_state_dict, prefix_from, new_state_dict, prefix_to):\n # fmt: off\n\n new_state_dict[f\"{prefix_to}.resnets.0.norm1.weight\"] = old_state_dict.pop(f\"{prefix_from}.block.0.norm1.weight\")\n new_state_dict[f\"{prefix_to}.resnets.0.norm1.bias\"] = old_state_dict.pop(f\"{prefix_from}.block.0.norm1.bias\")\n new_state_dict[f\"{prefix_to}.resnets.0.conv1.weight\"] = old_state_dict.pop(f\"{prefix_from}.block.0.conv1.weight\")\n new_state_dict[f\"{prefix_to}.resnets.0.conv1.bias\"] = old_state_dict.pop(f\"{prefix_from}.block.0.conv1.bias\")\n new_state_dict[f\"{prefix_to}.resnets.0.norm2.weight\"] = old_state_dict.pop(f\"{prefix_from}.block.0.norm2.weight\")\n new_state_dict[f\"{prefix_to}.resnets.0.norm2.bias\"] = old_state_dict.pop(f\"{prefix_from}.block.0.norm2.bias\")\n new_state_dict[f\"{prefix_to}.resnets.0.conv2.weight\"] = old_state_dict.pop(f\"{prefix_from}.block.0.conv2.weight\")\n new_state_dict[f\"{prefix_to}.resnets.0.conv2.bias\"] = old_state_dict.pop(f\"{prefix_from}.block.0.conv2.bias\")\n\n if f\"{prefix_from}.block.0.nin_shortcut.weight\" in old_state_dict:\n new_state_dict[f\"{prefix_to}.resnets.0.conv_shortcut.weight\"] = old_state_dict.pop(f\"{prefix_from}.block.0.nin_shortcut.weight\")\n new_state_dict[f\"{prefix_to}.resnets.0.conv_shortcut.bias\"] = old_state_dict.pop(f\"{prefix_from}.block.0.nin_shortcut.bias\")\n\n new_state_dict[f\"{prefix_to}.resnets.1.norm1.weight\"] = old_state_dict.pop(f\"{prefix_from}.block.1.norm1.weight\")\n new_state_dict[f\"{prefix_to}.resnets.1.norm1.bias\"] = old_state_dict.pop(f\"{prefix_from}.block.1.norm1.bias\")\n new_state_dict[f\"{prefix_to}.resnets.1.conv1.weight\"] = old_state_dict.pop(f\"{prefix_from}.block.1.conv1.weight\")\n new_state_dict[f\"{prefix_to}.resnets.1.conv1.bias\"] = old_state_dict.pop(f\"{prefix_from}.block.1.conv1.bias\")\n new_state_dict[f\"{prefix_to}.resnets.1.norm2.weight\"] = old_state_dict.pop(f\"{prefix_from}.block.1.norm2.weight\")\n new_state_dict[f\"{prefix_to}.resnets.1.norm2.bias\"] = old_state_dict.pop(f\"{prefix_from}.block.1.norm2.bias\")\n new_state_dict[f\"{prefix_to}.resnets.1.conv2.weight\"] = old_state_dict.pop(f\"{prefix_from}.block.1.conv2.weight\")\n new_state_dict[f\"{prefix_to}.resnets.1.conv2.bias\"] = old_state_dict.pop(f\"{prefix_from}.block.1.conv2.bias\")\n\n if f\"{prefix_from}.downsample.conv.weight\" in old_state_dict:\n new_state_dict[f\"{prefix_to}.downsamplers.0.conv.weight\"] = old_state_dict.pop(f\"{prefix_from}.downsample.conv.weight\")\n new_state_dict[f\"{prefix_to}.downsamplers.0.conv.bias\"] = old_state_dict.pop(f\"{prefix_from}.downsample.conv.bias\")\n\n if f\"{prefix_from}.upsample.conv.weight\" in old_state_dict:\n new_state_dict[f\"{prefix_to}.upsamplers.0.conv.weight\"] = old_state_dict.pop(f\"{prefix_from}.upsample.conv.weight\")\n new_state_dict[f\"{prefix_to}.upsamplers.0.conv.bias\"] = old_state_dict.pop(f\"{prefix_from}.upsample.conv.bias\")\n\n if f\"{prefix_from}.block.2.norm1.weight\" in old_state_dict:\n new_state_dict[f\"{prefix_to}.resnets.2.norm1.weight\"] = old_state_dict.pop(f\"{prefix_from}.block.2.norm1.weight\")\n new_state_dict[f\"{prefix_to}.resnets.2.norm1.bias\"] = old_state_dict.pop(f\"{prefix_from}.block.2.norm1.bias\")\n new_state_dict[f\"{prefix_to}.resnets.2.conv1.weight\"] = old_state_dict.pop(f\"{prefix_from}.block.2.conv1.weight\")\n new_state_dict[f\"{prefix_to}.resnets.2.conv1.bias\"] = old_state_dict.pop(f\"{prefix_from}.block.2.conv1.bias\")\n new_state_dict[f\"{prefix_to}.resnets.2.norm2.weight\"] = old_state_dict.pop(f\"{prefix_from}.block.2.norm2.weight\")\n new_state_dict[f\"{prefix_to}.resnets.2.norm2.bias\"] = old_state_dict.pop(f\"{prefix_from}.block.2.norm2.bias\")\n new_state_dict[f\"{prefix_to}.resnets.2.conv2.weight\"] = old_state_dict.pop(f\"{prefix_from}.block.2.conv2.weight\")\n new_state_dict[f\"{prefix_to}.resnets.2.conv2.bias\"] = old_state_dict.pop(f\"{prefix_from}.block.2.conv2.bias\")\n\n # fmt: on\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/scripts/convert_animatediff_motion_lora_to_diffusers.py", "content": "import argparse\n\nimport torch\nfrom safetensors.torch import load_file, save_file\n\n\ndef convert_motion_module(original_state_dict):\n converted_state_dict = {}\n for k, v in original_state_dict.items():\n if \"pos_encoder\" in k:\n continue\n\n else:\n converted_state_dict[\n k.replace(\".norms.0\", \".norm1\")\n .replace(\".norms.1\", \".norm2\")\n .replace(\".ff_norm\", \".norm3\")\n .replace(\".attention_blocks.0\", \".attn1\")\n .replace(\".attention_blocks.1\", \".attn2\")\n .replace(\".temporal_transformer\", \"\")\n ] = v\n\n return converted_state_dict\n\n\ndef get_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--ckpt_path\", type=str, required=True)\n parser.add_argument(\"--output_path\", type=str, required=True)\n\n return parser.parse_args()\n\n\nif __name__ == \"__main__\":\n args = get_args()\n\n if args.ckpt_path.endswith(\".safetensors\"):\n state_dict = load_file(args.ckpt_path)\n else:\n state_dict = torch.load(args.ckpt_path, map_location=\"cpu\")\n\n if \"state_dict\" in state_dict.keys():\n state_dict = state_dict[\"state_dict\"]\n\n conv_state_dict = convert_motion_module(state_dict)\n\n # convert to new format\n output_dict = {}\n for module_name, params in conv_state_dict.items():\n if type(params) is not torch.Tensor:\n continue\n output_dict.update({f\"unet.{module_name}\": params})\n\n save_file(output_dict, f\"{args.output_path}/diffusion_pytorch_model.safetensors\")\n" }, { "path": "diffusers/scripts/convert_animatediff_motion_module_to_diffusers.py", "content": "import argparse\n\nimport torch\nfrom safetensors.torch import load_file\n\nfrom diffusers import MotionAdapter\n\n\ndef convert_motion_module(original_state_dict):\n converted_state_dict = {}\n for k, v in original_state_dict.items():\n if \"pos_encoder\" in k:\n continue\n\n else:\n converted_state_dict[\n k.replace(\".norms.0\", \".norm1\")\n .replace(\".norms.1\", \".norm2\")\n .replace(\".ff_norm\", \".norm3\")\n .replace(\".attention_blocks.0\", \".attn1\")\n .replace(\".attention_blocks.1\", \".attn2\")\n .replace(\".temporal_transformer\", \"\")\n ] = v\n\n return converted_state_dict\n\n\ndef get_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--ckpt_path\", type=str, required=True)\n parser.add_argument(\"--output_path\", type=str, required=True)\n parser.add_argument(\"--use_motion_mid_block\", action=\"store_true\")\n parser.add_argument(\"--motion_max_seq_length\", type=int, default=32)\n parser.add_argument(\"--block_out_channels\", nargs=\"+\", default=[320, 640, 1280, 1280], type=int)\n parser.add_argument(\"--save_fp16\", action=\"store_true\")\n\n return parser.parse_args()\n\n\nif __name__ == \"__main__\":\n args = get_args()\n\n if args.ckpt_path.endswith(\".safetensors\"):\n state_dict = load_file(args.ckpt_path)\n else:\n state_dict = torch.load(args.ckpt_path, map_location=\"cpu\")\n\n if \"state_dict\" in state_dict.keys():\n state_dict = state_dict[\"state_dict\"]\n\n conv_state_dict = convert_motion_module(state_dict)\n adapter = MotionAdapter(\n block_out_channels=args.block_out_channels,\n use_motion_mid_block=args.use_motion_mid_block,\n motion_max_seq_length=args.motion_max_seq_length,\n )\n # skip loading position embeddings\n adapter.load_state_dict(conv_state_dict, strict=False)\n adapter.save_pretrained(args.output_path)\n\n if args.save_fp16:\n adapter.to(dtype=torch.float16).save_pretrained(args.output_path, variant=\"fp16\")\n" }, { "path": "diffusers/scripts/convert_asymmetric_vqgan_to_diffusers.py", "content": "import argparse\nimport time\nfrom pathlib import Path\nfrom typing import Any, Dict, Literal\n\nimport torch\n\nfrom diffusers import AsymmetricAutoencoderKL\n\n\nASYMMETRIC_AUTOENCODER_KL_x_1_5_CONFIG = {\n \"in_channels\": 3,\n \"out_channels\": 3,\n \"down_block_types\": [\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n ],\n \"down_block_out_channels\": [128, 256, 512, 512],\n \"layers_per_down_block\": 2,\n \"up_block_types\": [\n \"UpDecoderBlock2D\",\n \"UpDecoderBlock2D\",\n \"UpDecoderBlock2D\",\n \"UpDecoderBlock2D\",\n ],\n \"up_block_out_channels\": [192, 384, 768, 768],\n \"layers_per_up_block\": 3,\n \"act_fn\": \"silu\",\n \"latent_channels\": 4,\n \"norm_num_groups\": 32,\n \"sample_size\": 256,\n \"scaling_factor\": 0.18215,\n}\n\nASYMMETRIC_AUTOENCODER_KL_x_2_CONFIG = {\n \"in_channels\": 3,\n \"out_channels\": 3,\n \"down_block_types\": [\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n ],\n \"down_block_out_channels\": [128, 256, 512, 512],\n \"layers_per_down_block\": 2,\n \"up_block_types\": [\n \"UpDecoderBlock2D\",\n \"UpDecoderBlock2D\",\n \"UpDecoderBlock2D\",\n \"UpDecoderBlock2D\",\n ],\n \"up_block_out_channels\": [256, 512, 1024, 1024],\n \"layers_per_up_block\": 5,\n \"act_fn\": \"silu\",\n \"latent_channels\": 4,\n \"norm_num_groups\": 32,\n \"sample_size\": 256,\n \"scaling_factor\": 0.18215,\n}\n\n\ndef convert_asymmetric_autoencoder_kl_state_dict(original_state_dict: Dict[str, Any]) -> Dict[str, Any]:\n converted_state_dict = {}\n for k, v in original_state_dict.items():\n if k.startswith(\"encoder.\"):\n converted_state_dict[\n k.replace(\"encoder.down.\", \"encoder.down_blocks.\")\n .replace(\"encoder.mid.\", \"encoder.mid_block.\")\n .replace(\"encoder.norm_out.\", \"encoder.conv_norm_out.\")\n .replace(\".downsample.\", \".downsamplers.0.\")\n .replace(\".nin_shortcut.\", \".conv_shortcut.\")\n .replace(\".block.\", \".resnets.\")\n .replace(\".block_1.\", \".resnets.0.\")\n .replace(\".block_2.\", \".resnets.1.\")\n .replace(\".attn_1.k.\", \".attentions.0.to_k.\")\n .replace(\".attn_1.q.\", \".attentions.0.to_q.\")\n .replace(\".attn_1.v.\", \".attentions.0.to_v.\")\n .replace(\".attn_1.proj_out.\", \".attentions.0.to_out.0.\")\n .replace(\".attn_1.norm.\", \".attentions.0.group_norm.\")\n ] = v\n elif k.startswith(\"decoder.\") and \"up_layers\" not in k:\n converted_state_dict[\n k.replace(\"decoder.encoder.\", \"decoder.condition_encoder.\")\n .replace(\".norm_out.\", \".conv_norm_out.\")\n .replace(\".up.0.\", \".up_blocks.3.\")\n .replace(\".up.1.\", \".up_blocks.2.\")\n .replace(\".up.2.\", \".up_blocks.1.\")\n .replace(\".up.3.\", \".up_blocks.0.\")\n .replace(\".block.\", \".resnets.\")\n .replace(\"mid\", \"mid_block\")\n .replace(\".0.upsample.\", \".0.upsamplers.0.\")\n .replace(\".1.upsample.\", \".1.upsamplers.0.\")\n .replace(\".2.upsample.\", \".2.upsamplers.0.\")\n .replace(\".nin_shortcut.\", \".conv_shortcut.\")\n .replace(\".block_1.\", \".resnets.0.\")\n .replace(\".block_2.\", \".resnets.1.\")\n .replace(\".attn_1.k.\", \".attentions.0.to_k.\")\n .replace(\".attn_1.q.\", \".attentions.0.to_q.\")\n .replace(\".attn_1.v.\", \".attentions.0.to_v.\")\n .replace(\".attn_1.proj_out.\", \".attentions.0.to_out.0.\")\n .replace(\".attn_1.norm.\", \".attentions.0.group_norm.\")\n ] = v\n elif k.startswith(\"quant_conv.\"):\n converted_state_dict[k] = v\n elif k.startswith(\"post_quant_conv.\"):\n converted_state_dict[k] = v\n else:\n print(f\" skipping key `{k}`\")\n # fix weights shape\n for k, v in converted_state_dict.items():\n if (\n (k.startswith(\"encoder.mid_block.attentions.0\") or k.startswith(\"decoder.mid_block.attentions.0\"))\n and k.endswith(\"weight\")\n and (\"to_q\" in k or \"to_k\" in k or \"to_v\" in k or \"to_out\" in k)\n ):\n converted_state_dict[k] = converted_state_dict[k][:, :, 0, 0]\n\n return converted_state_dict\n\n\ndef get_asymmetric_autoencoder_kl_from_original_checkpoint(\n scale: Literal[\"1.5\", \"2\"], original_checkpoint_path: str, map_location: torch.device\n) -> AsymmetricAutoencoderKL:\n print(\"Loading original state_dict\")\n original_state_dict = torch.load(original_checkpoint_path, map_location=map_location)\n original_state_dict = original_state_dict[\"state_dict\"]\n print(\"Converting state_dict\")\n converted_state_dict = convert_asymmetric_autoencoder_kl_state_dict(original_state_dict)\n kwargs = ASYMMETRIC_AUTOENCODER_KL_x_1_5_CONFIG if scale == \"1.5\" else ASYMMETRIC_AUTOENCODER_KL_x_2_CONFIG\n print(\"Initializing AsymmetricAutoencoderKL model\")\n asymmetric_autoencoder_kl = AsymmetricAutoencoderKL(**kwargs)\n print(\"Loading weight from converted state_dict\")\n asymmetric_autoencoder_kl.load_state_dict(converted_state_dict)\n asymmetric_autoencoder_kl.eval()\n print(\"AsymmetricAutoencoderKL successfully initialized\")\n return asymmetric_autoencoder_kl\n\n\nif __name__ == \"__main__\":\n start = time.time()\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--scale\",\n default=None,\n type=str,\n required=True,\n help=\"Asymmetric VQGAN scale: `1.5` or `2`\",\n )\n parser.add_argument(\n \"--original_checkpoint_path\",\n default=None,\n type=str,\n required=True,\n help=\"Path to the original Asymmetric VQGAN checkpoint\",\n )\n parser.add_argument(\n \"--output_path\",\n default=None,\n type=str,\n required=True,\n help=\"Path to save pretrained AsymmetricAutoencoderKL model\",\n )\n parser.add_argument(\n \"--map_location\",\n default=\"cpu\",\n type=str,\n required=False,\n help=\"The device passed to `map_location` when loading the checkpoint\",\n )\n args = parser.parse_args()\n\n assert args.scale in [\"1.5\", \"2\"], f\"{args.scale} should be `1.5` of `2`\"\n assert Path(args.original_checkpoint_path).is_file()\n\n asymmetric_autoencoder_kl = get_asymmetric_autoencoder_kl_from_original_checkpoint(\n scale=args.scale,\n original_checkpoint_path=args.original_checkpoint_path,\n map_location=torch.device(args.map_location),\n )\n print(\"Saving pretrained AsymmetricAutoencoderKL\")\n asymmetric_autoencoder_kl.save_pretrained(args.output_path)\n print(f\"Done in {time.time() - start:.2f} seconds\")\n" }, { "path": "diffusers/scripts/convert_blipdiffusion_to_diffusers.py", "content": "\"\"\"\nThis script requires you to build `LAVIS` from source, since the pip version doesn't have BLIP Diffusion. Follow instructions here: https://github.com/salesforce/LAVIS/tree/main.\n\"\"\"\n\nimport argparse\nimport os\nimport tempfile\n\nimport torch\nfrom lavis.models import load_model_and_preprocess\nfrom transformers import CLIPTokenizer\nfrom transformers.models.blip_2.configuration_blip_2 import Blip2Config\n\nfrom diffusers import (\n AutoencoderKL,\n PNDMScheduler,\n UNet2DConditionModel,\n)\nfrom diffusers.pipelines import BlipDiffusionPipeline\nfrom diffusers.pipelines.blip_diffusion.blip_image_processing import BlipImageProcessor\nfrom diffusers.pipelines.blip_diffusion.modeling_blip2 import Blip2QFormerModel\nfrom diffusers.pipelines.blip_diffusion.modeling_ctx_clip import ContextCLIPTextModel\n\n\nBLIP2_CONFIG = {\n \"vision_config\": {\n \"hidden_size\": 1024,\n \"num_hidden_layers\": 23,\n \"num_attention_heads\": 16,\n \"image_size\": 224,\n \"patch_size\": 14,\n \"intermediate_size\": 4096,\n \"hidden_act\": \"quick_gelu\",\n },\n \"qformer_config\": {\n \"cross_attention_frequency\": 1,\n \"encoder_hidden_size\": 1024,\n \"vocab_size\": 30523,\n },\n \"num_query_tokens\": 16,\n}\nblip2config = Blip2Config(**BLIP2_CONFIG)\n\n\ndef qformer_model_from_original_config():\n qformer = Blip2QFormerModel(blip2config)\n return qformer\n\n\ndef embeddings_from_original_checkpoint(model, diffuser_embeddings_prefix, original_embeddings_prefix):\n embeddings = {}\n embeddings.update(\n {\n f\"{diffuser_embeddings_prefix}.word_embeddings.weight\": model[\n f\"{original_embeddings_prefix}.word_embeddings.weight\"\n ]\n }\n )\n embeddings.update(\n {\n f\"{diffuser_embeddings_prefix}.position_embeddings.weight\": model[\n f\"{original_embeddings_prefix}.position_embeddings.weight\"\n ]\n }\n )\n embeddings.update(\n {f\"{diffuser_embeddings_prefix}.LayerNorm.weight\": model[f\"{original_embeddings_prefix}.LayerNorm.weight\"]}\n )\n embeddings.update(\n {f\"{diffuser_embeddings_prefix}.LayerNorm.bias\": model[f\"{original_embeddings_prefix}.LayerNorm.bias\"]}\n )\n return embeddings\n\n\ndef proj_layer_from_original_checkpoint(model, diffuser_proj_prefix, original_proj_prefix):\n proj_layer = {}\n proj_layer.update({f\"{diffuser_proj_prefix}.dense1.weight\": model[f\"{original_proj_prefix}.dense1.weight\"]})\n proj_layer.update({f\"{diffuser_proj_prefix}.dense1.bias\": model[f\"{original_proj_prefix}.dense1.bias\"]})\n proj_layer.update({f\"{diffuser_proj_prefix}.dense2.weight\": model[f\"{original_proj_prefix}.dense2.weight\"]})\n proj_layer.update({f\"{diffuser_proj_prefix}.dense2.bias\": model[f\"{original_proj_prefix}.dense2.bias\"]})\n proj_layer.update({f\"{diffuser_proj_prefix}.LayerNorm.weight\": model[f\"{original_proj_prefix}.LayerNorm.weight\"]})\n proj_layer.update({f\"{diffuser_proj_prefix}.LayerNorm.bias\": model[f\"{original_proj_prefix}.LayerNorm.bias\"]})\n return proj_layer\n\n\ndef attention_from_original_checkpoint(model, diffuser_attention_prefix, original_attention_prefix):\n attention = {}\n attention.update(\n {\n f\"{diffuser_attention_prefix}.attention.query.weight\": model[\n f\"{original_attention_prefix}.self.query.weight\"\n ]\n }\n )\n attention.update(\n {f\"{diffuser_attention_prefix}.attention.query.bias\": model[f\"{original_attention_prefix}.self.query.bias\"]}\n )\n attention.update(\n {f\"{diffuser_attention_prefix}.attention.key.weight\": model[f\"{original_attention_prefix}.self.key.weight\"]}\n )\n attention.update(\n {f\"{diffuser_attention_prefix}.attention.key.bias\": model[f\"{original_attention_prefix}.self.key.bias\"]}\n )\n attention.update(\n {\n f\"{diffuser_attention_prefix}.attention.value.weight\": model[\n f\"{original_attention_prefix}.self.value.weight\"\n ]\n }\n )\n attention.update(\n {f\"{diffuser_attention_prefix}.attention.value.bias\": model[f\"{original_attention_prefix}.self.value.bias\"]}\n )\n attention.update(\n {f\"{diffuser_attention_prefix}.output.dense.weight\": model[f\"{original_attention_prefix}.output.dense.weight\"]}\n )\n attention.update(\n {f\"{diffuser_attention_prefix}.output.dense.bias\": model[f\"{original_attention_prefix}.output.dense.bias\"]}\n )\n attention.update(\n {\n f\"{diffuser_attention_prefix}.output.LayerNorm.weight\": model[\n f\"{original_attention_prefix}.output.LayerNorm.weight\"\n ]\n }\n )\n attention.update(\n {\n f\"{diffuser_attention_prefix}.output.LayerNorm.bias\": model[\n f\"{original_attention_prefix}.output.LayerNorm.bias\"\n ]\n }\n )\n return attention\n\n\ndef output_layers_from_original_checkpoint(model, diffuser_output_prefix, original_output_prefix):\n output_layers = {}\n output_layers.update({f\"{diffuser_output_prefix}.dense.weight\": model[f\"{original_output_prefix}.dense.weight\"]})\n output_layers.update({f\"{diffuser_output_prefix}.dense.bias\": model[f\"{original_output_prefix}.dense.bias\"]})\n output_layers.update(\n {f\"{diffuser_output_prefix}.LayerNorm.weight\": model[f\"{original_output_prefix}.LayerNorm.weight\"]}\n )\n output_layers.update(\n {f\"{diffuser_output_prefix}.LayerNorm.bias\": model[f\"{original_output_prefix}.LayerNorm.bias\"]}\n )\n return output_layers\n\n\ndef encoder_from_original_checkpoint(model, diffuser_encoder_prefix, original_encoder_prefix):\n encoder = {}\n for i in range(blip2config.qformer_config.num_hidden_layers):\n encoder.update(\n attention_from_original_checkpoint(\n model, f\"{diffuser_encoder_prefix}.{i}.attention\", f\"{original_encoder_prefix}.{i}.attention\"\n )\n )\n encoder.update(\n attention_from_original_checkpoint(\n model, f\"{diffuser_encoder_prefix}.{i}.crossattention\", f\"{original_encoder_prefix}.{i}.crossattention\"\n )\n )\n\n encoder.update(\n {\n f\"{diffuser_encoder_prefix}.{i}.intermediate.dense.weight\": model[\n f\"{original_encoder_prefix}.{i}.intermediate.dense.weight\"\n ]\n }\n )\n encoder.update(\n {\n f\"{diffuser_encoder_prefix}.{i}.intermediate.dense.bias\": model[\n f\"{original_encoder_prefix}.{i}.intermediate.dense.bias\"\n ]\n }\n )\n encoder.update(\n {\n f\"{diffuser_encoder_prefix}.{i}.intermediate_query.dense.weight\": model[\n f\"{original_encoder_prefix}.{i}.intermediate_query.dense.weight\"\n ]\n }\n )\n encoder.update(\n {\n f\"{diffuser_encoder_prefix}.{i}.intermediate_query.dense.bias\": model[\n f\"{original_encoder_prefix}.{i}.intermediate_query.dense.bias\"\n ]\n }\n )\n\n encoder.update(\n output_layers_from_original_checkpoint(\n model, f\"{diffuser_encoder_prefix}.{i}.output\", f\"{original_encoder_prefix}.{i}.output\"\n )\n )\n encoder.update(\n output_layers_from_original_checkpoint(\n model, f\"{diffuser_encoder_prefix}.{i}.output_query\", f\"{original_encoder_prefix}.{i}.output_query\"\n )\n )\n return encoder\n\n\ndef visual_encoder_layer_from_original_checkpoint(model, diffuser_prefix, original_prefix):\n visual_encoder_layer = {}\n\n visual_encoder_layer.update({f\"{diffuser_prefix}.layer_norm1.weight\": model[f\"{original_prefix}.ln_1.weight\"]})\n visual_encoder_layer.update({f\"{diffuser_prefix}.layer_norm1.bias\": model[f\"{original_prefix}.ln_1.bias\"]})\n visual_encoder_layer.update({f\"{diffuser_prefix}.layer_norm2.weight\": model[f\"{original_prefix}.ln_2.weight\"]})\n visual_encoder_layer.update({f\"{diffuser_prefix}.layer_norm2.bias\": model[f\"{original_prefix}.ln_2.bias\"]})\n visual_encoder_layer.update(\n {f\"{diffuser_prefix}.self_attn.qkv.weight\": model[f\"{original_prefix}.attn.in_proj_weight\"]}\n )\n visual_encoder_layer.update(\n {f\"{diffuser_prefix}.self_attn.qkv.bias\": model[f\"{original_prefix}.attn.in_proj_bias\"]}\n )\n visual_encoder_layer.update(\n {f\"{diffuser_prefix}.self_attn.projection.weight\": model[f\"{original_prefix}.attn.out_proj.weight\"]}\n )\n visual_encoder_layer.update(\n {f\"{diffuser_prefix}.self_attn.projection.bias\": model[f\"{original_prefix}.attn.out_proj.bias\"]}\n )\n visual_encoder_layer.update({f\"{diffuser_prefix}.mlp.fc1.weight\": model[f\"{original_prefix}.mlp.c_fc.weight\"]})\n visual_encoder_layer.update({f\"{diffuser_prefix}.mlp.fc1.bias\": model[f\"{original_prefix}.mlp.c_fc.bias\"]})\n visual_encoder_layer.update({f\"{diffuser_prefix}.mlp.fc2.weight\": model[f\"{original_prefix}.mlp.c_proj.weight\"]})\n visual_encoder_layer.update({f\"{diffuser_prefix}.mlp.fc2.bias\": model[f\"{original_prefix}.mlp.c_proj.bias\"]})\n\n return visual_encoder_layer\n\n\ndef visual_encoder_from_original_checkpoint(model, diffuser_prefix, original_prefix):\n visual_encoder = {}\n\n visual_encoder.update(\n {\n f\"{diffuser_prefix}.embeddings.class_embedding\": model[f\"{original_prefix}.class_embedding\"]\n .unsqueeze(0)\n .unsqueeze(0)\n }\n )\n visual_encoder.update(\n {\n f\"{diffuser_prefix}.embeddings.position_embedding\": model[\n f\"{original_prefix}.positional_embedding\"\n ].unsqueeze(0)\n }\n )\n visual_encoder.update(\n {f\"{diffuser_prefix}.embeddings.patch_embedding.weight\": model[f\"{original_prefix}.conv1.weight\"]}\n )\n visual_encoder.update({f\"{diffuser_prefix}.pre_layernorm.weight\": model[f\"{original_prefix}.ln_pre.weight\"]})\n visual_encoder.update({f\"{diffuser_prefix}.pre_layernorm.bias\": model[f\"{original_prefix}.ln_pre.bias\"]})\n\n for i in range(blip2config.vision_config.num_hidden_layers):\n visual_encoder.update(\n visual_encoder_layer_from_original_checkpoint(\n model, f\"{diffuser_prefix}.encoder.layers.{i}\", f\"{original_prefix}.transformer.resblocks.{i}\"\n )\n )\n\n visual_encoder.update({f\"{diffuser_prefix}.post_layernorm.weight\": model[\"blip.ln_vision.weight\"]})\n visual_encoder.update({f\"{diffuser_prefix}.post_layernorm.bias\": model[\"blip.ln_vision.bias\"]})\n\n return visual_encoder\n\n\ndef qformer_original_checkpoint_to_diffusers_checkpoint(model):\n qformer_checkpoint = {}\n qformer_checkpoint.update(embeddings_from_original_checkpoint(model, \"embeddings\", \"blip.Qformer.bert.embeddings\"))\n qformer_checkpoint.update({\"query_tokens\": model[\"blip.query_tokens\"]})\n qformer_checkpoint.update(proj_layer_from_original_checkpoint(model, \"proj_layer\", \"proj_layer\"))\n qformer_checkpoint.update(\n encoder_from_original_checkpoint(model, \"encoder.layer\", \"blip.Qformer.bert.encoder.layer\")\n )\n qformer_checkpoint.update(visual_encoder_from_original_checkpoint(model, \"visual_encoder\", \"blip.visual_encoder\"))\n return qformer_checkpoint\n\n\ndef get_qformer(model):\n print(\"loading qformer\")\n\n qformer = qformer_model_from_original_config()\n qformer_diffusers_checkpoint = qformer_original_checkpoint_to_diffusers_checkpoint(model)\n\n load_checkpoint_to_model(qformer_diffusers_checkpoint, qformer)\n\n print(\"done loading qformer\")\n return qformer\n\n\ndef load_checkpoint_to_model(checkpoint, model):\n with tempfile.NamedTemporaryFile(delete=False) as file:\n torch.save(checkpoint, file.name)\n del checkpoint\n model.load_state_dict(torch.load(file.name), strict=False)\n\n os.remove(file.name)\n\n\ndef save_blip_diffusion_model(model, args):\n qformer = get_qformer(model)\n qformer.eval()\n\n text_encoder = ContextCLIPTextModel.from_pretrained(\"runwayml/stable-diffusion-v1-5\", subfolder=\"text_encoder\")\n vae = AutoencoderKL.from_pretrained(\"runwayml/stable-diffusion-v1-5\", subfolder=\"vae\")\n\n unet = UNet2DConditionModel.from_pretrained(\"runwayml/stable-diffusion-v1-5\", subfolder=\"unet\")\n vae.eval()\n text_encoder.eval()\n scheduler = PNDMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n set_alpha_to_one=False,\n skip_prk_steps=True,\n )\n tokenizer = CLIPTokenizer.from_pretrained(\"runwayml/stable-diffusion-v1-5\", subfolder=\"tokenizer\")\n image_processor = BlipImageProcessor()\n blip_diffusion = BlipDiffusionPipeline(\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n vae=vae,\n unet=unet,\n scheduler=scheduler,\n qformer=qformer,\n image_processor=image_processor,\n )\n blip_diffusion.save_pretrained(args.checkpoint_path)\n\n\ndef main(args):\n model, _, _ = load_model_and_preprocess(\"blip_diffusion\", \"base\", device=\"cpu\", is_eval=True)\n save_blip_diffusion_model(model.state_dict(), args)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--checkpoint_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n args = parser.parse_args()\n\n main(args)\n" }, { "path": "diffusers/scripts/convert_consistency_decoder.py", "content": "import math\nimport os\nimport urllib\nimport warnings\nfrom argparse import ArgumentParser\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom huggingface_hub.utils import insecure_hashlib\nfrom safetensors.torch import load_file as stl\nfrom tqdm import tqdm\n\nfrom diffusers import AutoencoderKL, ConsistencyDecoderVAE, DiffusionPipeline, StableDiffusionPipeline, UNet2DModel\nfrom diffusers.models.autoencoders.vae import Encoder\nfrom diffusers.models.embeddings import TimestepEmbedding\nfrom diffusers.models.unets.unet_2d_blocks import ResnetDownsampleBlock2D, ResnetUpsampleBlock2D, UNetMidBlock2D\n\n\nargs = ArgumentParser()\nargs.add_argument(\"--save_pretrained\", required=False, default=None, type=str)\nargs.add_argument(\"--test_image\", required=True, type=str)\nargs = args.parse_args()\n\n\ndef _extract_into_tensor(arr, timesteps, broadcast_shape):\n # from: https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/gaussian_diffusion.py#L895 \"\"\"\n res = arr[timesteps].float()\n dims_to_append = len(broadcast_shape) - len(res.shape)\n return res[(...,) + (None,) * dims_to_append]\n\n\ndef betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):\n # from: https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/gaussian_diffusion.py#L45\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))\n return torch.tensor(betas)\n\n\ndef _download(url: str, root: str):\n os.makedirs(root, exist_ok=True)\n filename = os.path.basename(url)\n\n expected_sha256 = url.split(\"/\")[-2]\n download_target = os.path.join(root, filename)\n\n if os.path.exists(download_target) and not os.path.isfile(download_target):\n raise RuntimeError(f\"{download_target} exists and is not a regular file\")\n\n if os.path.isfile(download_target):\n if insecure_hashlib.sha256(open(download_target, \"rb\").read()).hexdigest() == expected_sha256:\n return download_target\n else:\n warnings.warn(f\"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file\")\n\n with urllib.request.urlopen(url) as source, open(download_target, \"wb\") as output:\n with tqdm(\n total=int(source.info().get(\"Content-Length\")),\n ncols=80,\n unit=\"iB\",\n unit_scale=True,\n unit_divisor=1024,\n ) as loop:\n while True:\n buffer = source.read(8192)\n if not buffer:\n break\n\n output.write(buffer)\n loop.update(len(buffer))\n\n if insecure_hashlib.sha256(open(download_target, \"rb\").read()).hexdigest() != expected_sha256:\n raise RuntimeError(\"Model has been downloaded but the SHA256 checksum does not not match\")\n\n return download_target\n\n\nclass ConsistencyDecoder:\n def __init__(self, device=\"cuda:0\", download_root=os.path.expanduser(\"~/.cache/clip\")):\n self.n_distilled_steps = 64\n download_target = _download(\n \"https://openaipublic.azureedge.net/diff-vae/c9cebd3132dd9c42936d803e33424145a748843c8f716c0814838bdc8a2fe7cb/decoder.pt\",\n download_root,\n )\n self.ckpt = torch.jit.load(download_target).to(device)\n self.device = device\n sigma_data = 0.5\n betas = betas_for_alpha_bar(1024, lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2).to(device)\n alphas = 1.0 - betas\n alphas_cumprod = torch.cumprod(alphas, dim=0)\n self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod)\n self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1.0 - alphas_cumprod)\n sqrt_recip_alphas_cumprod = torch.sqrt(1.0 / alphas_cumprod)\n sigmas = torch.sqrt(1.0 / alphas_cumprod - 1)\n self.c_skip = sqrt_recip_alphas_cumprod * sigma_data**2 / (sigmas**2 + sigma_data**2)\n self.c_out = sigmas * sigma_data / (sigmas**2 + sigma_data**2) ** 0.5\n self.c_in = sqrt_recip_alphas_cumprod / (sigmas**2 + sigma_data**2) ** 0.5\n\n @staticmethod\n def round_timesteps(timesteps, total_timesteps, n_distilled_steps, truncate_start=True):\n with torch.no_grad():\n space = torch.div(total_timesteps, n_distilled_steps, rounding_mode=\"floor\")\n rounded_timesteps = (torch.div(timesteps, space, rounding_mode=\"floor\") + 1) * space\n if truncate_start:\n rounded_timesteps[rounded_timesteps == total_timesteps] -= space\n else:\n rounded_timesteps[rounded_timesteps == total_timesteps] -= space\n rounded_timesteps[rounded_timesteps == 0] += space\n return rounded_timesteps\n\n @staticmethod\n def ldm_transform_latent(z, extra_scale_factor=1):\n channel_means = [0.38862467, 0.02253063, 0.07381133, -0.0171294]\n channel_stds = [0.9654121, 1.0440036, 0.76147926, 0.77022034]\n\n if len(z.shape) != 4:\n raise ValueError()\n\n z = z * 0.18215\n channels = [z[:, i] for i in range(z.shape[1])]\n\n channels = [extra_scale_factor * (c - channel_means[i]) / channel_stds[i] for i, c in enumerate(channels)]\n return torch.stack(channels, dim=1)\n\n @torch.no_grad()\n def __call__(\n self,\n features: torch.Tensor,\n schedule=[1.0, 0.5],\n generator=None,\n ):\n features = self.ldm_transform_latent(features)\n ts = self.round_timesteps(\n torch.arange(0, 1024),\n 1024,\n self.n_distilled_steps,\n truncate_start=False,\n )\n shape = (\n features.size(0),\n 3,\n 8 * features.size(2),\n 8 * features.size(3),\n )\n x_start = torch.zeros(shape, device=features.device, dtype=features.dtype)\n schedule_timesteps = [int((1024 - 1) * s) for s in schedule]\n for i in schedule_timesteps:\n t = ts[i].item()\n t_ = torch.tensor([t] * features.shape[0]).to(self.device)\n # noise = torch.randn_like(x_start)\n noise = torch.randn(x_start.shape, dtype=x_start.dtype, generator=generator).to(device=x_start.device)\n x_start = (\n _extract_into_tensor(self.sqrt_alphas_cumprod, t_, x_start.shape) * x_start\n + _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t_, x_start.shape) * noise\n )\n c_in = _extract_into_tensor(self.c_in, t_, x_start.shape)\n\n import torch.nn.functional as F\n\n from diffusers import UNet2DModel\n\n if isinstance(self.ckpt, UNet2DModel):\n input = torch.concat([c_in * x_start, F.upsample_nearest(features, scale_factor=8)], dim=1)\n model_output = self.ckpt(input, t_).sample\n else:\n model_output = self.ckpt(c_in * x_start, t_, features=features)\n\n B, C = x_start.shape[:2]\n model_output, _ = torch.split(model_output, C, dim=1)\n pred_xstart = (\n _extract_into_tensor(self.c_out, t_, x_start.shape) * model_output\n + _extract_into_tensor(self.c_skip, t_, x_start.shape) * x_start\n ).clamp(-1, 1)\n x_start = pred_xstart\n return x_start\n\n\ndef save_image(image, name):\n import numpy as np\n from PIL import Image\n\n image = image[0].cpu().numpy()\n image = (image + 1.0) * 127.5\n image = image.clip(0, 255).astype(np.uint8)\n image = Image.fromarray(image.transpose(1, 2, 0))\n image.save(name)\n\n\ndef load_image(uri, size=None, center_crop=False):\n import numpy as np\n from PIL import Image\n\n image = Image.open(uri)\n if center_crop:\n image = image.crop(\n (\n (image.width - min(image.width, image.height)) // 2,\n (image.height - min(image.width, image.height)) // 2,\n (image.width + min(image.width, image.height)) // 2,\n (image.height + min(image.width, image.height)) // 2,\n )\n )\n if size is not None:\n image = image.resize(size)\n image = torch.tensor(np.array(image).transpose(2, 0, 1)).unsqueeze(0).float()\n image = image / 127.5 - 1.0\n return image\n\n\nclass TimestepEmbedding_(nn.Module):\n def __init__(self, n_time=1024, n_emb=320, n_out=1280) -> None:\n super().__init__()\n self.emb = nn.Embedding(n_time, n_emb)\n self.f_1 = nn.Linear(n_emb, n_out)\n self.f_2 = nn.Linear(n_out, n_out)\n\n def forward(self, x) -> torch.Tensor:\n x = self.emb(x)\n x = self.f_1(x)\n x = F.silu(x)\n return self.f_2(x)\n\n\nclass ImageEmbedding(nn.Module):\n def __init__(self, in_channels=7, out_channels=320) -> None:\n super().__init__()\n self.f = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)\n\n def forward(self, x) -> torch.Tensor:\n return self.f(x)\n\n\nclass ImageUnembedding(nn.Module):\n def __init__(self, in_channels=320, out_channels=6) -> None:\n super().__init__()\n self.gn = nn.GroupNorm(32, in_channels)\n self.f = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)\n\n def forward(self, x) -> torch.Tensor:\n return self.f(F.silu(self.gn(x)))\n\n\nclass ConvResblock(nn.Module):\n def __init__(self, in_features=320, out_features=320) -> None:\n super().__init__()\n self.f_t = nn.Linear(1280, out_features * 2)\n\n self.gn_1 = nn.GroupNorm(32, in_features)\n self.f_1 = nn.Conv2d(in_features, out_features, kernel_size=3, padding=1)\n\n self.gn_2 = nn.GroupNorm(32, out_features)\n self.f_2 = nn.Conv2d(out_features, out_features, kernel_size=3, padding=1)\n\n skip_conv = in_features != out_features\n self.f_s = nn.Conv2d(in_features, out_features, kernel_size=1, padding=0) if skip_conv else nn.Identity()\n\n def forward(self, x, t):\n x_skip = x\n t = self.f_t(F.silu(t))\n t = t.chunk(2, dim=1)\n t_1 = t[0].unsqueeze(dim=2).unsqueeze(dim=3) + 1\n t_2 = t[1].unsqueeze(dim=2).unsqueeze(dim=3)\n\n gn_1 = F.silu(self.gn_1(x))\n f_1 = self.f_1(gn_1)\n\n gn_2 = self.gn_2(f_1)\n\n return self.f_s(x_skip) + self.f_2(F.silu(gn_2 * t_1 + t_2))\n\n\n# Also ConvResblock\nclass Downsample(nn.Module):\n def __init__(self, in_channels=320) -> None:\n super().__init__()\n self.f_t = nn.Linear(1280, in_channels * 2)\n\n self.gn_1 = nn.GroupNorm(32, in_channels)\n self.f_1 = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1)\n self.gn_2 = nn.GroupNorm(32, in_channels)\n\n self.f_2 = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1)\n\n def forward(self, x, t) -> torch.Tensor:\n x_skip = x\n\n t = self.f_t(F.silu(t))\n t_1, t_2 = t.chunk(2, dim=1)\n t_1 = t_1.unsqueeze(2).unsqueeze(3) + 1\n t_2 = t_2.unsqueeze(2).unsqueeze(3)\n\n gn_1 = F.silu(self.gn_1(x))\n avg_pool2d = F.avg_pool2d(gn_1, kernel_size=(2, 2), stride=None)\n\n f_1 = self.f_1(avg_pool2d)\n gn_2 = self.gn_2(f_1)\n\n f_2 = self.f_2(F.silu(t_2 + (t_1 * gn_2)))\n\n return f_2 + F.avg_pool2d(x_skip, kernel_size=(2, 2), stride=None)\n\n\n# Also ConvResblock\nclass Upsample(nn.Module):\n def __init__(self, in_channels=1024) -> None:\n super().__init__()\n self.f_t = nn.Linear(1280, in_channels * 2)\n\n self.gn_1 = nn.GroupNorm(32, in_channels)\n self.f_1 = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1)\n self.gn_2 = nn.GroupNorm(32, in_channels)\n\n self.f_2 = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1)\n\n def forward(self, x, t) -> torch.Tensor:\n x_skip = x\n\n t = self.f_t(F.silu(t))\n t_1, t_2 = t.chunk(2, dim=1)\n t_1 = t_1.unsqueeze(2).unsqueeze(3) + 1\n t_2 = t_2.unsqueeze(2).unsqueeze(3)\n\n gn_1 = F.silu(self.gn_1(x))\n upsample = F.upsample_nearest(gn_1, scale_factor=2)\n f_1 = self.f_1(upsample)\n gn_2 = self.gn_2(f_1)\n\n f_2 = self.f_2(F.silu(t_2 + (t_1 * gn_2)))\n\n return f_2 + F.upsample_nearest(x_skip, scale_factor=2)\n\n\nclass ConvUNetVAE(nn.Module):\n def __init__(self) -> None:\n super().__init__()\n self.embed_image = ImageEmbedding()\n self.embed_time = TimestepEmbedding_()\n\n down_0 = nn.ModuleList(\n [\n ConvResblock(320, 320),\n ConvResblock(320, 320),\n ConvResblock(320, 320),\n Downsample(320),\n ]\n )\n down_1 = nn.ModuleList(\n [\n ConvResblock(320, 640),\n ConvResblock(640, 640),\n ConvResblock(640, 640),\n Downsample(640),\n ]\n )\n down_2 = nn.ModuleList(\n [\n ConvResblock(640, 1024),\n ConvResblock(1024, 1024),\n ConvResblock(1024, 1024),\n Downsample(1024),\n ]\n )\n down_3 = nn.ModuleList(\n [\n ConvResblock(1024, 1024),\n ConvResblock(1024, 1024),\n ConvResblock(1024, 1024),\n ]\n )\n self.down = nn.ModuleList(\n [\n down_0,\n down_1,\n down_2,\n down_3,\n ]\n )\n\n self.mid = nn.ModuleList(\n [\n ConvResblock(1024, 1024),\n ConvResblock(1024, 1024),\n ]\n )\n\n up_3 = nn.ModuleList(\n [\n ConvResblock(1024 * 2, 1024),\n ConvResblock(1024 * 2, 1024),\n ConvResblock(1024 * 2, 1024),\n ConvResblock(1024 * 2, 1024),\n Upsample(1024),\n ]\n )\n up_2 = nn.ModuleList(\n [\n ConvResblock(1024 * 2, 1024),\n ConvResblock(1024 * 2, 1024),\n ConvResblock(1024 * 2, 1024),\n ConvResblock(1024 + 640, 1024),\n Upsample(1024),\n ]\n )\n up_1 = nn.ModuleList(\n [\n ConvResblock(1024 + 640, 640),\n ConvResblock(640 * 2, 640),\n ConvResblock(640 * 2, 640),\n ConvResblock(320 + 640, 640),\n Upsample(640),\n ]\n )\n up_0 = nn.ModuleList(\n [\n ConvResblock(320 + 640, 320),\n ConvResblock(320 * 2, 320),\n ConvResblock(320 * 2, 320),\n ConvResblock(320 * 2, 320),\n ]\n )\n self.up = nn.ModuleList(\n [\n up_0,\n up_1,\n up_2,\n up_3,\n ]\n )\n\n self.output = ImageUnembedding()\n\n def forward(self, x, t, features) -> torch.Tensor:\n converted = hasattr(self, \"converted\") and self.converted\n\n x = torch.cat([x, F.upsample_nearest(features, scale_factor=8)], dim=1)\n\n if converted:\n t = self.time_embedding(self.time_proj(t))\n else:\n t = self.embed_time(t)\n\n x = self.embed_image(x)\n\n skips = [x]\n for i, down in enumerate(self.down):\n if converted and i in [0, 1, 2, 3]:\n x, skips_ = down(x, t)\n for skip in skips_:\n skips.append(skip)\n else:\n for block in down:\n x = block(x, t)\n skips.append(x)\n print(x.float().abs().sum())\n\n if converted:\n x = self.mid(x, t)\n else:\n for i in range(2):\n x = self.mid[i](x, t)\n print(x.float().abs().sum())\n\n for i, up in enumerate(self.up[::-1]):\n if converted and i in [0, 1, 2, 3]:\n skip_4 = skips.pop()\n skip_3 = skips.pop()\n skip_2 = skips.pop()\n skip_1 = skips.pop()\n skips_ = (skip_1, skip_2, skip_3, skip_4)\n x = up(x, skips_, t)\n else:\n for block in up:\n if isinstance(block, ConvResblock):\n x = torch.concat([x, skips.pop()], dim=1)\n x = block(x, t)\n\n return self.output(x)\n\n\ndef rename_state_dict_key(k):\n k = k.replace(\"blocks.\", \"\")\n for i in range(5):\n k = k.replace(f\"down_{i}_\", f\"down.{i}.\")\n k = k.replace(f\"conv_{i}.\", f\"{i}.\")\n k = k.replace(f\"up_{i}_\", f\"up.{i}.\")\n k = k.replace(f\"mid_{i}\", f\"mid.{i}\")\n k = k.replace(\"upsamp.\", \"4.\")\n k = k.replace(\"downsamp.\", \"3.\")\n k = k.replace(\"f_t.w\", \"f_t.weight\").replace(\"f_t.b\", \"f_t.bias\")\n k = k.replace(\"f_1.w\", \"f_1.weight\").replace(\"f_1.b\", \"f_1.bias\")\n k = k.replace(\"f_2.w\", \"f_2.weight\").replace(\"f_2.b\", \"f_2.bias\")\n k = k.replace(\"f_s.w\", \"f_s.weight\").replace(\"f_s.b\", \"f_s.bias\")\n k = k.replace(\"f.w\", \"f.weight\").replace(\"f.b\", \"f.bias\")\n k = k.replace(\"gn_1.g\", \"gn_1.weight\").replace(\"gn_1.b\", \"gn_1.bias\")\n k = k.replace(\"gn_2.g\", \"gn_2.weight\").replace(\"gn_2.b\", \"gn_2.bias\")\n k = k.replace(\"gn.g\", \"gn.weight\").replace(\"gn.b\", \"gn.bias\")\n return k\n\n\ndef rename_state_dict(sd, embedding):\n sd = {rename_state_dict_key(k): v for k, v in sd.items()}\n sd[\"embed_time.emb.weight\"] = embedding[\"weight\"]\n return sd\n\n\n# encode with stable diffusion vae\npipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16)\npipe.vae.cuda()\n\n# construct original decoder with jitted model\ndecoder_consistency = ConsistencyDecoder(device=\"cuda:0\")\n\n# construct UNet code, overwrite the decoder with conv_unet_vae\nmodel = ConvUNetVAE()\nmodel.load_state_dict(\n rename_state_dict(\n stl(\"consistency_decoder.safetensors\"),\n stl(\"embedding.safetensors\"),\n )\n)\nmodel = model.cuda()\n\ndecoder_consistency.ckpt = model\n\nimage = load_image(args.test_image, size=(256, 256), center_crop=True)\nlatent = pipe.vae.encode(image.half().cuda()).latent_dist.sample()\n\n# decode with gan\nsample_gan = pipe.vae.decode(latent).sample.detach()\nsave_image(sample_gan, \"gan.png\")\n\n# decode with conv_unet_vae\nsample_consistency_orig = decoder_consistency(latent, generator=torch.Generator(\"cpu\").manual_seed(0))\nsave_image(sample_consistency_orig, \"con_orig.png\")\n\n\n########### conversion\n\nprint(\"CONVERSION\")\n\nprint(\"DOWN BLOCK ONE\")\n\nblock_one_sd_orig = model.down[0].state_dict()\nblock_one_sd_new = {}\n\nfor i in range(3):\n block_one_sd_new[f\"resnets.{i}.norm1.weight\"] = block_one_sd_orig.pop(f\"{i}.gn_1.weight\")\n block_one_sd_new[f\"resnets.{i}.norm1.bias\"] = block_one_sd_orig.pop(f\"{i}.gn_1.bias\")\n block_one_sd_new[f\"resnets.{i}.conv1.weight\"] = block_one_sd_orig.pop(f\"{i}.f_1.weight\")\n block_one_sd_new[f\"resnets.{i}.conv1.bias\"] = block_one_sd_orig.pop(f\"{i}.f_1.bias\")\n block_one_sd_new[f\"resnets.{i}.time_emb_proj.weight\"] = block_one_sd_orig.pop(f\"{i}.f_t.weight\")\n block_one_sd_new[f\"resnets.{i}.time_emb_proj.bias\"] = block_one_sd_orig.pop(f\"{i}.f_t.bias\")\n block_one_sd_new[f\"resnets.{i}.norm2.weight\"] = block_one_sd_orig.pop(f\"{i}.gn_2.weight\")\n block_one_sd_new[f\"resnets.{i}.norm2.bias\"] = block_one_sd_orig.pop(f\"{i}.gn_2.bias\")\n block_one_sd_new[f\"resnets.{i}.conv2.weight\"] = block_one_sd_orig.pop(f\"{i}.f_2.weight\")\n block_one_sd_new[f\"resnets.{i}.conv2.bias\"] = block_one_sd_orig.pop(f\"{i}.f_2.bias\")\n\nblock_one_sd_new[\"downsamplers.0.norm1.weight\"] = block_one_sd_orig.pop(\"3.gn_1.weight\")\nblock_one_sd_new[\"downsamplers.0.norm1.bias\"] = block_one_sd_orig.pop(\"3.gn_1.bias\")\nblock_one_sd_new[\"downsamplers.0.conv1.weight\"] = block_one_sd_orig.pop(\"3.f_1.weight\")\nblock_one_sd_new[\"downsamplers.0.conv1.bias\"] = block_one_sd_orig.pop(\"3.f_1.bias\")\nblock_one_sd_new[\"downsamplers.0.time_emb_proj.weight\"] = block_one_sd_orig.pop(\"3.f_t.weight\")\nblock_one_sd_new[\"downsamplers.0.time_emb_proj.bias\"] = block_one_sd_orig.pop(\"3.f_t.bias\")\nblock_one_sd_new[\"downsamplers.0.norm2.weight\"] = block_one_sd_orig.pop(\"3.gn_2.weight\")\nblock_one_sd_new[\"downsamplers.0.norm2.bias\"] = block_one_sd_orig.pop(\"3.gn_2.bias\")\nblock_one_sd_new[\"downsamplers.0.conv2.weight\"] = block_one_sd_orig.pop(\"3.f_2.weight\")\nblock_one_sd_new[\"downsamplers.0.conv2.bias\"] = block_one_sd_orig.pop(\"3.f_2.bias\")\n\nassert len(block_one_sd_orig) == 0\n\nblock_one = ResnetDownsampleBlock2D(\n in_channels=320,\n out_channels=320,\n temb_channels=1280,\n num_layers=3,\n add_downsample=True,\n resnet_time_scale_shift=\"scale_shift\",\n resnet_eps=1e-5,\n)\n\nblock_one.load_state_dict(block_one_sd_new)\n\nprint(\"DOWN BLOCK TWO\")\n\nblock_two_sd_orig = model.down[1].state_dict()\nblock_two_sd_new = {}\n\nfor i in range(3):\n block_two_sd_new[f\"resnets.{i}.norm1.weight\"] = block_two_sd_orig.pop(f\"{i}.gn_1.weight\")\n block_two_sd_new[f\"resnets.{i}.norm1.bias\"] = block_two_sd_orig.pop(f\"{i}.gn_1.bias\")\n block_two_sd_new[f\"resnets.{i}.conv1.weight\"] = block_two_sd_orig.pop(f\"{i}.f_1.weight\")\n block_two_sd_new[f\"resnets.{i}.conv1.bias\"] = block_two_sd_orig.pop(f\"{i}.f_1.bias\")\n block_two_sd_new[f\"resnets.{i}.time_emb_proj.weight\"] = block_two_sd_orig.pop(f\"{i}.f_t.weight\")\n block_two_sd_new[f\"resnets.{i}.time_emb_proj.bias\"] = block_two_sd_orig.pop(f\"{i}.f_t.bias\")\n block_two_sd_new[f\"resnets.{i}.norm2.weight\"] = block_two_sd_orig.pop(f\"{i}.gn_2.weight\")\n block_two_sd_new[f\"resnets.{i}.norm2.bias\"] = block_two_sd_orig.pop(f\"{i}.gn_2.bias\")\n block_two_sd_new[f\"resnets.{i}.conv2.weight\"] = block_two_sd_orig.pop(f\"{i}.f_2.weight\")\n block_two_sd_new[f\"resnets.{i}.conv2.bias\"] = block_two_sd_orig.pop(f\"{i}.f_2.bias\")\n\n if i == 0:\n block_two_sd_new[f\"resnets.{i}.conv_shortcut.weight\"] = block_two_sd_orig.pop(f\"{i}.f_s.weight\")\n block_two_sd_new[f\"resnets.{i}.conv_shortcut.bias\"] = block_two_sd_orig.pop(f\"{i}.f_s.bias\")\n\nblock_two_sd_new[\"downsamplers.0.norm1.weight\"] = block_two_sd_orig.pop(\"3.gn_1.weight\")\nblock_two_sd_new[\"downsamplers.0.norm1.bias\"] = block_two_sd_orig.pop(\"3.gn_1.bias\")\nblock_two_sd_new[\"downsamplers.0.conv1.weight\"] = block_two_sd_orig.pop(\"3.f_1.weight\")\nblock_two_sd_new[\"downsamplers.0.conv1.bias\"] = block_two_sd_orig.pop(\"3.f_1.bias\")\nblock_two_sd_new[\"downsamplers.0.time_emb_proj.weight\"] = block_two_sd_orig.pop(\"3.f_t.weight\")\nblock_two_sd_new[\"downsamplers.0.time_emb_proj.bias\"] = block_two_sd_orig.pop(\"3.f_t.bias\")\nblock_two_sd_new[\"downsamplers.0.norm2.weight\"] = block_two_sd_orig.pop(\"3.gn_2.weight\")\nblock_two_sd_new[\"downsamplers.0.norm2.bias\"] = block_two_sd_orig.pop(\"3.gn_2.bias\")\nblock_two_sd_new[\"downsamplers.0.conv2.weight\"] = block_two_sd_orig.pop(\"3.f_2.weight\")\nblock_two_sd_new[\"downsamplers.0.conv2.bias\"] = block_two_sd_orig.pop(\"3.f_2.bias\")\n\nassert len(block_two_sd_orig) == 0\n\nblock_two = ResnetDownsampleBlock2D(\n in_channels=320,\n out_channels=640,\n temb_channels=1280,\n num_layers=3,\n add_downsample=True,\n resnet_time_scale_shift=\"scale_shift\",\n resnet_eps=1e-5,\n)\n\nblock_two.load_state_dict(block_two_sd_new)\n\nprint(\"DOWN BLOCK THREE\")\n\nblock_three_sd_orig = model.down[2].state_dict()\nblock_three_sd_new = {}\n\nfor i in range(3):\n block_three_sd_new[f\"resnets.{i}.norm1.weight\"] = block_three_sd_orig.pop(f\"{i}.gn_1.weight\")\n block_three_sd_new[f\"resnets.{i}.norm1.bias\"] = block_three_sd_orig.pop(f\"{i}.gn_1.bias\")\n block_three_sd_new[f\"resnets.{i}.conv1.weight\"] = block_three_sd_orig.pop(f\"{i}.f_1.weight\")\n block_three_sd_new[f\"resnets.{i}.conv1.bias\"] = block_three_sd_orig.pop(f\"{i}.f_1.bias\")\n block_three_sd_new[f\"resnets.{i}.time_emb_proj.weight\"] = block_three_sd_orig.pop(f\"{i}.f_t.weight\")\n block_three_sd_new[f\"resnets.{i}.time_emb_proj.bias\"] = block_three_sd_orig.pop(f\"{i}.f_t.bias\")\n block_three_sd_new[f\"resnets.{i}.norm2.weight\"] = block_three_sd_orig.pop(f\"{i}.gn_2.weight\")\n block_three_sd_new[f\"resnets.{i}.norm2.bias\"] = block_three_sd_orig.pop(f\"{i}.gn_2.bias\")\n block_three_sd_new[f\"resnets.{i}.conv2.weight\"] = block_three_sd_orig.pop(f\"{i}.f_2.weight\")\n block_three_sd_new[f\"resnets.{i}.conv2.bias\"] = block_three_sd_orig.pop(f\"{i}.f_2.bias\")\n\n if i == 0:\n block_three_sd_new[f\"resnets.{i}.conv_shortcut.weight\"] = block_three_sd_orig.pop(f\"{i}.f_s.weight\")\n block_three_sd_new[f\"resnets.{i}.conv_shortcut.bias\"] = block_three_sd_orig.pop(f\"{i}.f_s.bias\")\n\nblock_three_sd_new[\"downsamplers.0.norm1.weight\"] = block_three_sd_orig.pop(\"3.gn_1.weight\")\nblock_three_sd_new[\"downsamplers.0.norm1.bias\"] = block_three_sd_orig.pop(\"3.gn_1.bias\")\nblock_three_sd_new[\"downsamplers.0.conv1.weight\"] = block_three_sd_orig.pop(\"3.f_1.weight\")\nblock_three_sd_new[\"downsamplers.0.conv1.bias\"] = block_three_sd_orig.pop(\"3.f_1.bias\")\nblock_three_sd_new[\"downsamplers.0.time_emb_proj.weight\"] = block_three_sd_orig.pop(\"3.f_t.weight\")\nblock_three_sd_new[\"downsamplers.0.time_emb_proj.bias\"] = block_three_sd_orig.pop(\"3.f_t.bias\")\nblock_three_sd_new[\"downsamplers.0.norm2.weight\"] = block_three_sd_orig.pop(\"3.gn_2.weight\")\nblock_three_sd_new[\"downsamplers.0.norm2.bias\"] = block_three_sd_orig.pop(\"3.gn_2.bias\")\nblock_three_sd_new[\"downsamplers.0.conv2.weight\"] = block_three_sd_orig.pop(\"3.f_2.weight\")\nblock_three_sd_new[\"downsamplers.0.conv2.bias\"] = block_three_sd_orig.pop(\"3.f_2.bias\")\n\nassert len(block_three_sd_orig) == 0\n\nblock_three = ResnetDownsampleBlock2D(\n in_channels=640,\n out_channels=1024,\n temb_channels=1280,\n num_layers=3,\n add_downsample=True,\n resnet_time_scale_shift=\"scale_shift\",\n resnet_eps=1e-5,\n)\n\nblock_three.load_state_dict(block_three_sd_new)\n\nprint(\"DOWN BLOCK FOUR\")\n\nblock_four_sd_orig = model.down[3].state_dict()\nblock_four_sd_new = {}\n\nfor i in range(3):\n block_four_sd_new[f\"resnets.{i}.norm1.weight\"] = block_four_sd_orig.pop(f\"{i}.gn_1.weight\")\n block_four_sd_new[f\"resnets.{i}.norm1.bias\"] = block_four_sd_orig.pop(f\"{i}.gn_1.bias\")\n block_four_sd_new[f\"resnets.{i}.conv1.weight\"] = block_four_sd_orig.pop(f\"{i}.f_1.weight\")\n block_four_sd_new[f\"resnets.{i}.conv1.bias\"] = block_four_sd_orig.pop(f\"{i}.f_1.bias\")\n block_four_sd_new[f\"resnets.{i}.time_emb_proj.weight\"] = block_four_sd_orig.pop(f\"{i}.f_t.weight\")\n block_four_sd_new[f\"resnets.{i}.time_emb_proj.bias\"] = block_four_sd_orig.pop(f\"{i}.f_t.bias\")\n block_four_sd_new[f\"resnets.{i}.norm2.weight\"] = block_four_sd_orig.pop(f\"{i}.gn_2.weight\")\n block_four_sd_new[f\"resnets.{i}.norm2.bias\"] = block_four_sd_orig.pop(f\"{i}.gn_2.bias\")\n block_four_sd_new[f\"resnets.{i}.conv2.weight\"] = block_four_sd_orig.pop(f\"{i}.f_2.weight\")\n block_four_sd_new[f\"resnets.{i}.conv2.bias\"] = block_four_sd_orig.pop(f\"{i}.f_2.bias\")\n\nassert len(block_four_sd_orig) == 0\n\nblock_four = ResnetDownsampleBlock2D(\n in_channels=1024,\n out_channels=1024,\n temb_channels=1280,\n num_layers=3,\n add_downsample=False,\n resnet_time_scale_shift=\"scale_shift\",\n resnet_eps=1e-5,\n)\n\nblock_four.load_state_dict(block_four_sd_new)\n\n\nprint(\"MID BLOCK 1\")\n\nmid_block_one_sd_orig = model.mid.state_dict()\nmid_block_one_sd_new = {}\n\nfor i in range(2):\n mid_block_one_sd_new[f\"resnets.{i}.norm1.weight\"] = mid_block_one_sd_orig.pop(f\"{i}.gn_1.weight\")\n mid_block_one_sd_new[f\"resnets.{i}.norm1.bias\"] = mid_block_one_sd_orig.pop(f\"{i}.gn_1.bias\")\n mid_block_one_sd_new[f\"resnets.{i}.conv1.weight\"] = mid_block_one_sd_orig.pop(f\"{i}.f_1.weight\")\n mid_block_one_sd_new[f\"resnets.{i}.conv1.bias\"] = mid_block_one_sd_orig.pop(f\"{i}.f_1.bias\")\n mid_block_one_sd_new[f\"resnets.{i}.time_emb_proj.weight\"] = mid_block_one_sd_orig.pop(f\"{i}.f_t.weight\")\n mid_block_one_sd_new[f\"resnets.{i}.time_emb_proj.bias\"] = mid_block_one_sd_orig.pop(f\"{i}.f_t.bias\")\n mid_block_one_sd_new[f\"resnets.{i}.norm2.weight\"] = mid_block_one_sd_orig.pop(f\"{i}.gn_2.weight\")\n mid_block_one_sd_new[f\"resnets.{i}.norm2.bias\"] = mid_block_one_sd_orig.pop(f\"{i}.gn_2.bias\")\n mid_block_one_sd_new[f\"resnets.{i}.conv2.weight\"] = mid_block_one_sd_orig.pop(f\"{i}.f_2.weight\")\n mid_block_one_sd_new[f\"resnets.{i}.conv2.bias\"] = mid_block_one_sd_orig.pop(f\"{i}.f_2.bias\")\n\nassert len(mid_block_one_sd_orig) == 0\n\nmid_block_one = UNetMidBlock2D(\n in_channels=1024,\n temb_channels=1280,\n num_layers=1,\n resnet_time_scale_shift=\"scale_shift\",\n resnet_eps=1e-5,\n add_attention=False,\n)\n\nmid_block_one.load_state_dict(mid_block_one_sd_new)\n\nprint(\"UP BLOCK ONE\")\n\nup_block_one_sd_orig = model.up[-1].state_dict()\nup_block_one_sd_new = {}\n\nfor i in range(4):\n up_block_one_sd_new[f\"resnets.{i}.norm1.weight\"] = up_block_one_sd_orig.pop(f\"{i}.gn_1.weight\")\n up_block_one_sd_new[f\"resnets.{i}.norm1.bias\"] = up_block_one_sd_orig.pop(f\"{i}.gn_1.bias\")\n up_block_one_sd_new[f\"resnets.{i}.conv1.weight\"] = up_block_one_sd_orig.pop(f\"{i}.f_1.weight\")\n up_block_one_sd_new[f\"resnets.{i}.conv1.bias\"] = up_block_one_sd_orig.pop(f\"{i}.f_1.bias\")\n up_block_one_sd_new[f\"resnets.{i}.time_emb_proj.weight\"] = up_block_one_sd_orig.pop(f\"{i}.f_t.weight\")\n up_block_one_sd_new[f\"resnets.{i}.time_emb_proj.bias\"] = up_block_one_sd_orig.pop(f\"{i}.f_t.bias\")\n up_block_one_sd_new[f\"resnets.{i}.norm2.weight\"] = up_block_one_sd_orig.pop(f\"{i}.gn_2.weight\")\n up_block_one_sd_new[f\"resnets.{i}.norm2.bias\"] = up_block_one_sd_orig.pop(f\"{i}.gn_2.bias\")\n up_block_one_sd_new[f\"resnets.{i}.conv2.weight\"] = up_block_one_sd_orig.pop(f\"{i}.f_2.weight\")\n up_block_one_sd_new[f\"resnets.{i}.conv2.bias\"] = up_block_one_sd_orig.pop(f\"{i}.f_2.bias\")\n up_block_one_sd_new[f\"resnets.{i}.conv_shortcut.weight\"] = up_block_one_sd_orig.pop(f\"{i}.f_s.weight\")\n up_block_one_sd_new[f\"resnets.{i}.conv_shortcut.bias\"] = up_block_one_sd_orig.pop(f\"{i}.f_s.bias\")\n\nup_block_one_sd_new[\"upsamplers.0.norm1.weight\"] = up_block_one_sd_orig.pop(\"4.gn_1.weight\")\nup_block_one_sd_new[\"upsamplers.0.norm1.bias\"] = up_block_one_sd_orig.pop(\"4.gn_1.bias\")\nup_block_one_sd_new[\"upsamplers.0.conv1.weight\"] = up_block_one_sd_orig.pop(\"4.f_1.weight\")\nup_block_one_sd_new[\"upsamplers.0.conv1.bias\"] = up_block_one_sd_orig.pop(\"4.f_1.bias\")\nup_block_one_sd_new[\"upsamplers.0.time_emb_proj.weight\"] = up_block_one_sd_orig.pop(\"4.f_t.weight\")\nup_block_one_sd_new[\"upsamplers.0.time_emb_proj.bias\"] = up_block_one_sd_orig.pop(\"4.f_t.bias\")\nup_block_one_sd_new[\"upsamplers.0.norm2.weight\"] = up_block_one_sd_orig.pop(\"4.gn_2.weight\")\nup_block_one_sd_new[\"upsamplers.0.norm2.bias\"] = up_block_one_sd_orig.pop(\"4.gn_2.bias\")\nup_block_one_sd_new[\"upsamplers.0.conv2.weight\"] = up_block_one_sd_orig.pop(\"4.f_2.weight\")\nup_block_one_sd_new[\"upsamplers.0.conv2.bias\"] = up_block_one_sd_orig.pop(\"4.f_2.bias\")\n\nassert len(up_block_one_sd_orig) == 0\n\nup_block_one = ResnetUpsampleBlock2D(\n in_channels=1024,\n prev_output_channel=1024,\n out_channels=1024,\n temb_channels=1280,\n num_layers=4,\n add_upsample=True,\n resnet_time_scale_shift=\"scale_shift\",\n resnet_eps=1e-5,\n)\n\nup_block_one.load_state_dict(up_block_one_sd_new)\n\nprint(\"UP BLOCK TWO\")\n\nup_block_two_sd_orig = model.up[-2].state_dict()\nup_block_two_sd_new = {}\n\nfor i in range(4):\n up_block_two_sd_new[f\"resnets.{i}.norm1.weight\"] = up_block_two_sd_orig.pop(f\"{i}.gn_1.weight\")\n up_block_two_sd_new[f\"resnets.{i}.norm1.bias\"] = up_block_two_sd_orig.pop(f\"{i}.gn_1.bias\")\n up_block_two_sd_new[f\"resnets.{i}.conv1.weight\"] = up_block_two_sd_orig.pop(f\"{i}.f_1.weight\")\n up_block_two_sd_new[f\"resnets.{i}.conv1.bias\"] = up_block_two_sd_orig.pop(f\"{i}.f_1.bias\")\n up_block_two_sd_new[f\"resnets.{i}.time_emb_proj.weight\"] = up_block_two_sd_orig.pop(f\"{i}.f_t.weight\")\n up_block_two_sd_new[f\"resnets.{i}.time_emb_proj.bias\"] = up_block_two_sd_orig.pop(f\"{i}.f_t.bias\")\n up_block_two_sd_new[f\"resnets.{i}.norm2.weight\"] = up_block_two_sd_orig.pop(f\"{i}.gn_2.weight\")\n up_block_two_sd_new[f\"resnets.{i}.norm2.bias\"] = up_block_two_sd_orig.pop(f\"{i}.gn_2.bias\")\n up_block_two_sd_new[f\"resnets.{i}.conv2.weight\"] = up_block_two_sd_orig.pop(f\"{i}.f_2.weight\")\n up_block_two_sd_new[f\"resnets.{i}.conv2.bias\"] = up_block_two_sd_orig.pop(f\"{i}.f_2.bias\")\n up_block_two_sd_new[f\"resnets.{i}.conv_shortcut.weight\"] = up_block_two_sd_orig.pop(f\"{i}.f_s.weight\")\n up_block_two_sd_new[f\"resnets.{i}.conv_shortcut.bias\"] = up_block_two_sd_orig.pop(f\"{i}.f_s.bias\")\n\nup_block_two_sd_new[\"upsamplers.0.norm1.weight\"] = up_block_two_sd_orig.pop(\"4.gn_1.weight\")\nup_block_two_sd_new[\"upsamplers.0.norm1.bias\"] = up_block_two_sd_orig.pop(\"4.gn_1.bias\")\nup_block_two_sd_new[\"upsamplers.0.conv1.weight\"] = up_block_two_sd_orig.pop(\"4.f_1.weight\")\nup_block_two_sd_new[\"upsamplers.0.conv1.bias\"] = up_block_two_sd_orig.pop(\"4.f_1.bias\")\nup_block_two_sd_new[\"upsamplers.0.time_emb_proj.weight\"] = up_block_two_sd_orig.pop(\"4.f_t.weight\")\nup_block_two_sd_new[\"upsamplers.0.time_emb_proj.bias\"] = up_block_two_sd_orig.pop(\"4.f_t.bias\")\nup_block_two_sd_new[\"upsamplers.0.norm2.weight\"] = up_block_two_sd_orig.pop(\"4.gn_2.weight\")\nup_block_two_sd_new[\"upsamplers.0.norm2.bias\"] = up_block_two_sd_orig.pop(\"4.gn_2.bias\")\nup_block_two_sd_new[\"upsamplers.0.conv2.weight\"] = up_block_two_sd_orig.pop(\"4.f_2.weight\")\nup_block_two_sd_new[\"upsamplers.0.conv2.bias\"] = up_block_two_sd_orig.pop(\"4.f_2.bias\")\n\nassert len(up_block_two_sd_orig) == 0\n\nup_block_two = ResnetUpsampleBlock2D(\n in_channels=640,\n prev_output_channel=1024,\n out_channels=1024,\n temb_channels=1280,\n num_layers=4,\n add_upsample=True,\n resnet_time_scale_shift=\"scale_shift\",\n resnet_eps=1e-5,\n)\n\nup_block_two.load_state_dict(up_block_two_sd_new)\n\nprint(\"UP BLOCK THREE\")\n\nup_block_three_sd_orig = model.up[-3].state_dict()\nup_block_three_sd_new = {}\n\nfor i in range(4):\n up_block_three_sd_new[f\"resnets.{i}.norm1.weight\"] = up_block_three_sd_orig.pop(f\"{i}.gn_1.weight\")\n up_block_three_sd_new[f\"resnets.{i}.norm1.bias\"] = up_block_three_sd_orig.pop(f\"{i}.gn_1.bias\")\n up_block_three_sd_new[f\"resnets.{i}.conv1.weight\"] = up_block_three_sd_orig.pop(f\"{i}.f_1.weight\")\n up_block_three_sd_new[f\"resnets.{i}.conv1.bias\"] = up_block_three_sd_orig.pop(f\"{i}.f_1.bias\")\n up_block_three_sd_new[f\"resnets.{i}.time_emb_proj.weight\"] = up_block_three_sd_orig.pop(f\"{i}.f_t.weight\")\n up_block_three_sd_new[f\"resnets.{i}.time_emb_proj.bias\"] = up_block_three_sd_orig.pop(f\"{i}.f_t.bias\")\n up_block_three_sd_new[f\"resnets.{i}.norm2.weight\"] = up_block_three_sd_orig.pop(f\"{i}.gn_2.weight\")\n up_block_three_sd_new[f\"resnets.{i}.norm2.bias\"] = up_block_three_sd_orig.pop(f\"{i}.gn_2.bias\")\n up_block_three_sd_new[f\"resnets.{i}.conv2.weight\"] = up_block_three_sd_orig.pop(f\"{i}.f_2.weight\")\n up_block_three_sd_new[f\"resnets.{i}.conv2.bias\"] = up_block_three_sd_orig.pop(f\"{i}.f_2.bias\")\n up_block_three_sd_new[f\"resnets.{i}.conv_shortcut.weight\"] = up_block_three_sd_orig.pop(f\"{i}.f_s.weight\")\n up_block_three_sd_new[f\"resnets.{i}.conv_shortcut.bias\"] = up_block_three_sd_orig.pop(f\"{i}.f_s.bias\")\n\nup_block_three_sd_new[\"upsamplers.0.norm1.weight\"] = up_block_three_sd_orig.pop(\"4.gn_1.weight\")\nup_block_three_sd_new[\"upsamplers.0.norm1.bias\"] = up_block_three_sd_orig.pop(\"4.gn_1.bias\")\nup_block_three_sd_new[\"upsamplers.0.conv1.weight\"] = up_block_three_sd_orig.pop(\"4.f_1.weight\")\nup_block_three_sd_new[\"upsamplers.0.conv1.bias\"] = up_block_three_sd_orig.pop(\"4.f_1.bias\")\nup_block_three_sd_new[\"upsamplers.0.time_emb_proj.weight\"] = up_block_three_sd_orig.pop(\"4.f_t.weight\")\nup_block_three_sd_new[\"upsamplers.0.time_emb_proj.bias\"] = up_block_three_sd_orig.pop(\"4.f_t.bias\")\nup_block_three_sd_new[\"upsamplers.0.norm2.weight\"] = up_block_three_sd_orig.pop(\"4.gn_2.weight\")\nup_block_three_sd_new[\"upsamplers.0.norm2.bias\"] = up_block_three_sd_orig.pop(\"4.gn_2.bias\")\nup_block_three_sd_new[\"upsamplers.0.conv2.weight\"] = up_block_three_sd_orig.pop(\"4.f_2.weight\")\nup_block_three_sd_new[\"upsamplers.0.conv2.bias\"] = up_block_three_sd_orig.pop(\"4.f_2.bias\")\n\nassert len(up_block_three_sd_orig) == 0\n\nup_block_three = ResnetUpsampleBlock2D(\n in_channels=320,\n prev_output_channel=1024,\n out_channels=640,\n temb_channels=1280,\n num_layers=4,\n add_upsample=True,\n resnet_time_scale_shift=\"scale_shift\",\n resnet_eps=1e-5,\n)\n\nup_block_three.load_state_dict(up_block_three_sd_new)\n\nprint(\"UP BLOCK FOUR\")\n\nup_block_four_sd_orig = model.up[-4].state_dict()\nup_block_four_sd_new = {}\n\nfor i in range(4):\n up_block_four_sd_new[f\"resnets.{i}.norm1.weight\"] = up_block_four_sd_orig.pop(f\"{i}.gn_1.weight\")\n up_block_four_sd_new[f\"resnets.{i}.norm1.bias\"] = up_block_four_sd_orig.pop(f\"{i}.gn_1.bias\")\n up_block_four_sd_new[f\"resnets.{i}.conv1.weight\"] = up_block_four_sd_orig.pop(f\"{i}.f_1.weight\")\n up_block_four_sd_new[f\"resnets.{i}.conv1.bias\"] = up_block_four_sd_orig.pop(f\"{i}.f_1.bias\")\n up_block_four_sd_new[f\"resnets.{i}.time_emb_proj.weight\"] = up_block_four_sd_orig.pop(f\"{i}.f_t.weight\")\n up_block_four_sd_new[f\"resnets.{i}.time_emb_proj.bias\"] = up_block_four_sd_orig.pop(f\"{i}.f_t.bias\")\n up_block_four_sd_new[f\"resnets.{i}.norm2.weight\"] = up_block_four_sd_orig.pop(f\"{i}.gn_2.weight\")\n up_block_four_sd_new[f\"resnets.{i}.norm2.bias\"] = up_block_four_sd_orig.pop(f\"{i}.gn_2.bias\")\n up_block_four_sd_new[f\"resnets.{i}.conv2.weight\"] = up_block_four_sd_orig.pop(f\"{i}.f_2.weight\")\n up_block_four_sd_new[f\"resnets.{i}.conv2.bias\"] = up_block_four_sd_orig.pop(f\"{i}.f_2.bias\")\n up_block_four_sd_new[f\"resnets.{i}.conv_shortcut.weight\"] = up_block_four_sd_orig.pop(f\"{i}.f_s.weight\")\n up_block_four_sd_new[f\"resnets.{i}.conv_shortcut.bias\"] = up_block_four_sd_orig.pop(f\"{i}.f_s.bias\")\n\nassert len(up_block_four_sd_orig) == 0\n\nup_block_four = ResnetUpsampleBlock2D(\n in_channels=320,\n prev_output_channel=640,\n out_channels=320,\n temb_channels=1280,\n num_layers=4,\n add_upsample=False,\n resnet_time_scale_shift=\"scale_shift\",\n resnet_eps=1e-5,\n)\n\nup_block_four.load_state_dict(up_block_four_sd_new)\n\nprint(\"initial projection (conv_in)\")\n\nconv_in_sd_orig = model.embed_image.state_dict()\nconv_in_sd_new = {}\n\nconv_in_sd_new[\"weight\"] = conv_in_sd_orig.pop(\"f.weight\")\nconv_in_sd_new[\"bias\"] = conv_in_sd_orig.pop(\"f.bias\")\n\nassert len(conv_in_sd_orig) == 0\n\nblock_out_channels = [320, 640, 1024, 1024]\n\nin_channels = 7\nconv_in_kernel = 3\nconv_in_padding = (conv_in_kernel - 1) // 2\nconv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding)\n\nconv_in.load_state_dict(conv_in_sd_new)\n\nprint(\"out projection (conv_out) (conv_norm_out)\")\nout_channels = 6\nnorm_num_groups = 32\nnorm_eps = 1e-5\nact_fn = \"silu\"\nconv_out_kernel = 3\nconv_out_padding = (conv_out_kernel - 1) // 2\nconv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)\n# uses torch.functional in orig\n# conv_act = get_activation(act_fn)\nconv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding)\n\nconv_norm_out.load_state_dict(model.output.gn.state_dict())\nconv_out.load_state_dict(model.output.f.state_dict())\n\nprint(\"timestep projection (time_proj) (time_embedding)\")\n\nf1_sd = model.embed_time.f_1.state_dict()\nf2_sd = model.embed_time.f_2.state_dict()\n\ntime_embedding_sd = {\n \"linear_1.weight\": f1_sd.pop(\"weight\"),\n \"linear_1.bias\": f1_sd.pop(\"bias\"),\n \"linear_2.weight\": f2_sd.pop(\"weight\"),\n \"linear_2.bias\": f2_sd.pop(\"bias\"),\n}\n\nassert len(f1_sd) == 0\nassert len(f2_sd) == 0\n\ntime_embedding_type = \"learned\"\nnum_train_timesteps = 1024\ntime_embedding_dim = 1280\n\ntime_proj = nn.Embedding(num_train_timesteps, block_out_channels[0])\ntimestep_input_dim = block_out_channels[0]\n\ntime_embedding = TimestepEmbedding(timestep_input_dim, time_embedding_dim)\n\ntime_proj.load_state_dict(model.embed_time.emb.state_dict())\ntime_embedding.load_state_dict(time_embedding_sd)\n\nprint(\"CONVERT\")\n\ntime_embedding.to(\"cuda\")\ntime_proj.to(\"cuda\")\nconv_in.to(\"cuda\")\n\nblock_one.to(\"cuda\")\nblock_two.to(\"cuda\")\nblock_three.to(\"cuda\")\nblock_four.to(\"cuda\")\n\nmid_block_one.to(\"cuda\")\n\nup_block_one.to(\"cuda\")\nup_block_two.to(\"cuda\")\nup_block_three.to(\"cuda\")\nup_block_four.to(\"cuda\")\n\nconv_norm_out.to(\"cuda\")\nconv_out.to(\"cuda\")\n\nmodel.time_proj = time_proj\nmodel.time_embedding = time_embedding\nmodel.embed_image = conv_in\n\nmodel.down[0] = block_one\nmodel.down[1] = block_two\nmodel.down[2] = block_three\nmodel.down[3] = block_four\n\nmodel.mid = mid_block_one\n\nmodel.up[-1] = up_block_one\nmodel.up[-2] = up_block_two\nmodel.up[-3] = up_block_three\nmodel.up[-4] = up_block_four\n\nmodel.output.gn = conv_norm_out\nmodel.output.f = conv_out\n\nmodel.converted = True\n\nsample_consistency_new = decoder_consistency(latent, generator=torch.Generator(\"cpu\").manual_seed(0))\nsave_image(sample_consistency_new, \"con_new.png\")\n\nassert (sample_consistency_orig == sample_consistency_new).all()\n\nprint(\"making unet\")\n\nunet = UNet2DModel(\n in_channels=in_channels,\n out_channels=out_channels,\n down_block_types=(\n \"ResnetDownsampleBlock2D\",\n \"ResnetDownsampleBlock2D\",\n \"ResnetDownsampleBlock2D\",\n \"ResnetDownsampleBlock2D\",\n ),\n up_block_types=(\n \"ResnetUpsampleBlock2D\",\n \"ResnetUpsampleBlock2D\",\n \"ResnetUpsampleBlock2D\",\n \"ResnetUpsampleBlock2D\",\n ),\n block_out_channels=block_out_channels,\n layers_per_block=3,\n norm_num_groups=norm_num_groups,\n norm_eps=norm_eps,\n resnet_time_scale_shift=\"scale_shift\",\n time_embedding_type=\"learned\",\n num_train_timesteps=num_train_timesteps,\n add_attention=False,\n)\n\nunet_state_dict = {}\n\n\ndef add_state_dict(prefix, mod):\n for k, v in mod.state_dict().items():\n unet_state_dict[f\"{prefix}.{k}\"] = v\n\n\nadd_state_dict(\"conv_in\", conv_in)\nadd_state_dict(\"time_proj\", time_proj)\nadd_state_dict(\"time_embedding\", time_embedding)\nadd_state_dict(\"down_blocks.0\", block_one)\nadd_state_dict(\"down_blocks.1\", block_two)\nadd_state_dict(\"down_blocks.2\", block_three)\nadd_state_dict(\"down_blocks.3\", block_four)\nadd_state_dict(\"mid_block\", mid_block_one)\nadd_state_dict(\"up_blocks.0\", up_block_one)\nadd_state_dict(\"up_blocks.1\", up_block_two)\nadd_state_dict(\"up_blocks.2\", up_block_three)\nadd_state_dict(\"up_blocks.3\", up_block_four)\nadd_state_dict(\"conv_norm_out\", conv_norm_out)\nadd_state_dict(\"conv_out\", conv_out)\n\nunet.load_state_dict(unet_state_dict)\n\nprint(\"running with diffusers unet\")\n\nunet.to(\"cuda\")\n\ndecoder_consistency.ckpt = unet\n\nsample_consistency_new_2 = decoder_consistency(latent, generator=torch.Generator(\"cpu\").manual_seed(0))\nsave_image(sample_consistency_new_2, \"con_new_2.png\")\n\nassert (sample_consistency_orig == sample_consistency_new_2).all()\n\nprint(\"running with diffusers model\")\n\nEncoder.old_constructor = Encoder.__init__\n\n\ndef new_constructor(self, **kwargs):\n self.old_constructor(**kwargs)\n self.constructor_arguments = kwargs\n\n\nEncoder.__init__ = new_constructor\n\n\nvae = AutoencoderKL.from_pretrained(\"runwayml/stable-diffusion-v1-5\", subfolder=\"vae\")\nconsistency_vae = ConsistencyDecoderVAE(\n encoder_args=vae.encoder.constructor_arguments,\n decoder_args=unet.config,\n scaling_factor=vae.config.scaling_factor,\n block_out_channels=vae.config.block_out_channels,\n latent_channels=vae.config.latent_channels,\n)\nconsistency_vae.encoder.load_state_dict(vae.encoder.state_dict())\nconsistency_vae.quant_conv.load_state_dict(vae.quant_conv.state_dict())\nconsistency_vae.decoder_unet.load_state_dict(unet.state_dict())\n\nconsistency_vae.to(dtype=torch.float16, device=\"cuda\")\n\nsample_consistency_new_3 = consistency_vae.decode(\n 0.18215 * latent, generator=torch.Generator(\"cpu\").manual_seed(0)\n).sample\n\nprint(\"max difference\")\nprint((sample_consistency_orig - sample_consistency_new_3).abs().max())\nprint(\"total difference\")\nprint((sample_consistency_orig - sample_consistency_new_3).abs().sum())\n# assert (sample_consistency_orig == sample_consistency_new_3).all()\n\nprint(\"running with diffusers pipeline\")\n\npipe = DiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", vae=consistency_vae, torch_dtype=torch.float16\n)\npipe.to(\"cuda\")\n\npipe(\"horse\", generator=torch.Generator(\"cpu\").manual_seed(0)).images[0].save(\"horse.png\")\n\n\nif args.save_pretrained is not None:\n consistency_vae.save_pretrained(args.save_pretrained)\n" }, { "path": "diffusers/scripts/convert_consistency_to_diffusers.py", "content": "import argparse\nimport os\n\nimport torch\n\nfrom diffusers import (\n CMStochasticIterativeScheduler,\n ConsistencyModelPipeline,\n UNet2DModel,\n)\n\n\nTEST_UNET_CONFIG = {\n \"sample_size\": 32,\n \"in_channels\": 3,\n \"out_channels\": 3,\n \"layers_per_block\": 2,\n \"num_class_embeds\": 1000,\n \"block_out_channels\": [32, 64],\n \"attention_head_dim\": 8,\n \"down_block_types\": [\n \"ResnetDownsampleBlock2D\",\n \"AttnDownBlock2D\",\n ],\n \"up_block_types\": [\n \"AttnUpBlock2D\",\n \"ResnetUpsampleBlock2D\",\n ],\n \"resnet_time_scale_shift\": \"scale_shift\",\n \"attn_norm_num_groups\": 32,\n \"upsample_type\": \"resnet\",\n \"downsample_type\": \"resnet\",\n}\n\nIMAGENET_64_UNET_CONFIG = {\n \"sample_size\": 64,\n \"in_channels\": 3,\n \"out_channels\": 3,\n \"layers_per_block\": 3,\n \"num_class_embeds\": 1000,\n \"block_out_channels\": [192, 192 * 2, 192 * 3, 192 * 4],\n \"attention_head_dim\": 64,\n \"down_block_types\": [\n \"ResnetDownsampleBlock2D\",\n \"AttnDownBlock2D\",\n \"AttnDownBlock2D\",\n \"AttnDownBlock2D\",\n ],\n \"up_block_types\": [\n \"AttnUpBlock2D\",\n \"AttnUpBlock2D\",\n \"AttnUpBlock2D\",\n \"ResnetUpsampleBlock2D\",\n ],\n \"resnet_time_scale_shift\": \"scale_shift\",\n \"attn_norm_num_groups\": 32,\n \"upsample_type\": \"resnet\",\n \"downsample_type\": \"resnet\",\n}\n\nLSUN_256_UNET_CONFIG = {\n \"sample_size\": 256,\n \"in_channels\": 3,\n \"out_channels\": 3,\n \"layers_per_block\": 2,\n \"num_class_embeds\": None,\n \"block_out_channels\": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],\n \"attention_head_dim\": 64,\n \"down_block_types\": [\n \"ResnetDownsampleBlock2D\",\n \"ResnetDownsampleBlock2D\",\n \"ResnetDownsampleBlock2D\",\n \"AttnDownBlock2D\",\n \"AttnDownBlock2D\",\n \"AttnDownBlock2D\",\n ],\n \"up_block_types\": [\n \"AttnUpBlock2D\",\n \"AttnUpBlock2D\",\n \"AttnUpBlock2D\",\n \"ResnetUpsampleBlock2D\",\n \"ResnetUpsampleBlock2D\",\n \"ResnetUpsampleBlock2D\",\n ],\n \"resnet_time_scale_shift\": \"default\",\n \"upsample_type\": \"resnet\",\n \"downsample_type\": \"resnet\",\n}\n\nCD_SCHEDULER_CONFIG = {\n \"num_train_timesteps\": 40,\n \"sigma_min\": 0.002,\n \"sigma_max\": 80.0,\n}\n\nCT_IMAGENET_64_SCHEDULER_CONFIG = {\n \"num_train_timesteps\": 201,\n \"sigma_min\": 0.002,\n \"sigma_max\": 80.0,\n}\n\nCT_LSUN_256_SCHEDULER_CONFIG = {\n \"num_train_timesteps\": 151,\n \"sigma_min\": 0.002,\n \"sigma_max\": 80.0,\n}\n\n\ndef str2bool(v):\n \"\"\"\n https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse\n \"\"\"\n if isinstance(v, bool):\n return v\n if v.lower() in (\"yes\", \"true\", \"t\", \"y\", \"1\"):\n return True\n elif v.lower() in (\"no\", \"false\", \"f\", \"n\", \"0\"):\n return False\n else:\n raise argparse.ArgumentTypeError(\"boolean value expected\")\n\n\ndef convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=False):\n new_checkpoint[f\"{new_prefix}.norm1.weight\"] = checkpoint[f\"{old_prefix}.in_layers.0.weight\"]\n new_checkpoint[f\"{new_prefix}.norm1.bias\"] = checkpoint[f\"{old_prefix}.in_layers.0.bias\"]\n new_checkpoint[f\"{new_prefix}.conv1.weight\"] = checkpoint[f\"{old_prefix}.in_layers.2.weight\"]\n new_checkpoint[f\"{new_prefix}.conv1.bias\"] = checkpoint[f\"{old_prefix}.in_layers.2.bias\"]\n new_checkpoint[f\"{new_prefix}.time_emb_proj.weight\"] = checkpoint[f\"{old_prefix}.emb_layers.1.weight\"]\n new_checkpoint[f\"{new_prefix}.time_emb_proj.bias\"] = checkpoint[f\"{old_prefix}.emb_layers.1.bias\"]\n new_checkpoint[f\"{new_prefix}.norm2.weight\"] = checkpoint[f\"{old_prefix}.out_layers.0.weight\"]\n new_checkpoint[f\"{new_prefix}.norm2.bias\"] = checkpoint[f\"{old_prefix}.out_layers.0.bias\"]\n new_checkpoint[f\"{new_prefix}.conv2.weight\"] = checkpoint[f\"{old_prefix}.out_layers.3.weight\"]\n new_checkpoint[f\"{new_prefix}.conv2.bias\"] = checkpoint[f\"{old_prefix}.out_layers.3.bias\"]\n\n if has_skip:\n new_checkpoint[f\"{new_prefix}.conv_shortcut.weight\"] = checkpoint[f\"{old_prefix}.skip_connection.weight\"]\n new_checkpoint[f\"{new_prefix}.conv_shortcut.bias\"] = checkpoint[f\"{old_prefix}.skip_connection.bias\"]\n\n return new_checkpoint\n\n\ndef convert_attention(checkpoint, new_checkpoint, old_prefix, new_prefix, attention_dim=None):\n weight_q, weight_k, weight_v = checkpoint[f\"{old_prefix}.qkv.weight\"].chunk(3, dim=0)\n bias_q, bias_k, bias_v = checkpoint[f\"{old_prefix}.qkv.bias\"].chunk(3, dim=0)\n\n new_checkpoint[f\"{new_prefix}.group_norm.weight\"] = checkpoint[f\"{old_prefix}.norm.weight\"]\n new_checkpoint[f\"{new_prefix}.group_norm.bias\"] = checkpoint[f\"{old_prefix}.norm.bias\"]\n\n new_checkpoint[f\"{new_prefix}.to_q.weight\"] = weight_q.squeeze(-1).squeeze(-1)\n new_checkpoint[f\"{new_prefix}.to_q.bias\"] = bias_q.squeeze(-1).squeeze(-1)\n new_checkpoint[f\"{new_prefix}.to_k.weight\"] = weight_k.squeeze(-1).squeeze(-1)\n new_checkpoint[f\"{new_prefix}.to_k.bias\"] = bias_k.squeeze(-1).squeeze(-1)\n new_checkpoint[f\"{new_prefix}.to_v.weight\"] = weight_v.squeeze(-1).squeeze(-1)\n new_checkpoint[f\"{new_prefix}.to_v.bias\"] = bias_v.squeeze(-1).squeeze(-1)\n\n new_checkpoint[f\"{new_prefix}.to_out.0.weight\"] = (\n checkpoint[f\"{old_prefix}.proj_out.weight\"].squeeze(-1).squeeze(-1)\n )\n new_checkpoint[f\"{new_prefix}.to_out.0.bias\"] = checkpoint[f\"{old_prefix}.proj_out.bias\"].squeeze(-1).squeeze(-1)\n\n return new_checkpoint\n\n\ndef con_pt_to_diffuser(checkpoint_path: str, unet_config):\n checkpoint = torch.load(checkpoint_path, map_location=\"cpu\")\n new_checkpoint = {}\n\n new_checkpoint[\"time_embedding.linear_1.weight\"] = checkpoint[\"time_embed.0.weight\"]\n new_checkpoint[\"time_embedding.linear_1.bias\"] = checkpoint[\"time_embed.0.bias\"]\n new_checkpoint[\"time_embedding.linear_2.weight\"] = checkpoint[\"time_embed.2.weight\"]\n new_checkpoint[\"time_embedding.linear_2.bias\"] = checkpoint[\"time_embed.2.bias\"]\n\n if unet_config[\"num_class_embeds\"] is not None:\n new_checkpoint[\"class_embedding.weight\"] = checkpoint[\"label_emb.weight\"]\n\n new_checkpoint[\"conv_in.weight\"] = checkpoint[\"input_blocks.0.0.weight\"]\n new_checkpoint[\"conv_in.bias\"] = checkpoint[\"input_blocks.0.0.bias\"]\n\n down_block_types = unet_config[\"down_block_types\"]\n layers_per_block = unet_config[\"layers_per_block\"]\n attention_head_dim = unet_config[\"attention_head_dim\"]\n channels_list = unet_config[\"block_out_channels\"]\n current_layer = 1\n prev_channels = channels_list[0]\n\n for i, layer_type in enumerate(down_block_types):\n current_channels = channels_list[i]\n downsample_block_has_skip = current_channels != prev_channels\n if layer_type == \"ResnetDownsampleBlock2D\":\n for j in range(layers_per_block):\n new_prefix = f\"down_blocks.{i}.resnets.{j}\"\n old_prefix = f\"input_blocks.{current_layer}.0\"\n has_skip = True if j == 0 and downsample_block_has_skip else False\n new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=has_skip)\n current_layer += 1\n\n elif layer_type == \"AttnDownBlock2D\":\n for j in range(layers_per_block):\n new_prefix = f\"down_blocks.{i}.resnets.{j}\"\n old_prefix = f\"input_blocks.{current_layer}.0\"\n has_skip = True if j == 0 and downsample_block_has_skip else False\n new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=has_skip)\n new_prefix = f\"down_blocks.{i}.attentions.{j}\"\n old_prefix = f\"input_blocks.{current_layer}.1\"\n new_checkpoint = convert_attention(\n checkpoint, new_checkpoint, old_prefix, new_prefix, attention_head_dim\n )\n current_layer += 1\n\n if i != len(down_block_types) - 1:\n new_prefix = f\"down_blocks.{i}.downsamplers.0\"\n old_prefix = f\"input_blocks.{current_layer}.0\"\n new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix)\n current_layer += 1\n\n prev_channels = current_channels\n\n # hardcoded the mid-block for now\n new_prefix = \"mid_block.resnets.0\"\n old_prefix = \"middle_block.0\"\n new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix)\n new_prefix = \"mid_block.attentions.0\"\n old_prefix = \"middle_block.1\"\n new_checkpoint = convert_attention(checkpoint, new_checkpoint, old_prefix, new_prefix, attention_head_dim)\n new_prefix = \"mid_block.resnets.1\"\n old_prefix = \"middle_block.2\"\n new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix)\n\n current_layer = 0\n up_block_types = unet_config[\"up_block_types\"]\n\n for i, layer_type in enumerate(up_block_types):\n if layer_type == \"ResnetUpsampleBlock2D\":\n for j in range(layers_per_block + 1):\n new_prefix = f\"up_blocks.{i}.resnets.{j}\"\n old_prefix = f\"output_blocks.{current_layer}.0\"\n new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=True)\n current_layer += 1\n\n if i != len(up_block_types) - 1:\n new_prefix = f\"up_blocks.{i}.upsamplers.0\"\n old_prefix = f\"output_blocks.{current_layer-1}.1\"\n new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix)\n elif layer_type == \"AttnUpBlock2D\":\n for j in range(layers_per_block + 1):\n new_prefix = f\"up_blocks.{i}.resnets.{j}\"\n old_prefix = f\"output_blocks.{current_layer}.0\"\n new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=True)\n new_prefix = f\"up_blocks.{i}.attentions.{j}\"\n old_prefix = f\"output_blocks.{current_layer}.1\"\n new_checkpoint = convert_attention(\n checkpoint, new_checkpoint, old_prefix, new_prefix, attention_head_dim\n )\n current_layer += 1\n\n if i != len(up_block_types) - 1:\n new_prefix = f\"up_blocks.{i}.upsamplers.0\"\n old_prefix = f\"output_blocks.{current_layer-1}.2\"\n new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix)\n\n new_checkpoint[\"conv_norm_out.weight\"] = checkpoint[\"out.0.weight\"]\n new_checkpoint[\"conv_norm_out.bias\"] = checkpoint[\"out.0.bias\"]\n new_checkpoint[\"conv_out.weight\"] = checkpoint[\"out.2.weight\"]\n new_checkpoint[\"conv_out.bias\"] = checkpoint[\"out.2.bias\"]\n\n return new_checkpoint\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"--unet_path\", default=None, type=str, required=True, help=\"Path to the unet.pt to convert.\")\n parser.add_argument(\n \"--dump_path\", default=None, type=str, required=True, help=\"Path to output the converted UNet model.\"\n )\n parser.add_argument(\"--class_cond\", default=True, type=str, help=\"Whether the model is class-conditional.\")\n\n args = parser.parse_args()\n args.class_cond = str2bool(args.class_cond)\n\n ckpt_name = os.path.basename(args.unet_path)\n print(f\"Checkpoint: {ckpt_name}\")\n\n # Get U-Net config\n if \"imagenet64\" in ckpt_name:\n unet_config = IMAGENET_64_UNET_CONFIG\n elif \"256\" in ckpt_name and ((\"bedroom\" in ckpt_name) or (\"cat\" in ckpt_name)):\n unet_config = LSUN_256_UNET_CONFIG\n elif \"test\" in ckpt_name:\n unet_config = TEST_UNET_CONFIG\n else:\n raise ValueError(f\"Checkpoint type {ckpt_name} is not currently supported.\")\n\n if not args.class_cond:\n unet_config[\"num_class_embeds\"] = None\n\n converted_unet_ckpt = con_pt_to_diffuser(args.unet_path, unet_config)\n\n image_unet = UNet2DModel(**unet_config)\n image_unet.load_state_dict(converted_unet_ckpt)\n\n # Get scheduler config\n if \"cd\" in ckpt_name or \"test\" in ckpt_name:\n scheduler_config = CD_SCHEDULER_CONFIG\n elif \"ct\" in ckpt_name and \"imagenet64\" in ckpt_name:\n scheduler_config = CT_IMAGENET_64_SCHEDULER_CONFIG\n elif \"ct\" in ckpt_name and \"256\" in ckpt_name and ((\"bedroom\" in ckpt_name) or (\"cat\" in ckpt_name)):\n scheduler_config = CT_LSUN_256_SCHEDULER_CONFIG\n else:\n raise ValueError(f\"Checkpoint type {ckpt_name} is not currently supported.\")\n\n cm_scheduler = CMStochasticIterativeScheduler(**scheduler_config)\n\n consistency_model = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)\n consistency_model.save_pretrained(args.dump_path)\n" }, { "path": "diffusers/scripts/convert_dance_diffusion_to_diffusers.py", "content": "#!/usr/bin/env python3\nimport argparse\nimport math\nimport os\nfrom copy import deepcopy\n\nimport requests\nimport torch\nfrom audio_diffusion.models import DiffusionAttnUnet1D\nfrom diffusion import sampling\nfrom torch import nn\n\nfrom diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNet1DModel\n\n\nMODELS_MAP = {\n \"gwf-440k\": {\n \"url\": \"https://model-server.zqevans2.workers.dev/gwf-440k.ckpt\",\n \"sample_rate\": 48000,\n \"sample_size\": 65536,\n },\n \"jmann-small-190k\": {\n \"url\": \"https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt\",\n \"sample_rate\": 48000,\n \"sample_size\": 65536,\n },\n \"jmann-large-580k\": {\n \"url\": \"https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt\",\n \"sample_rate\": 48000,\n \"sample_size\": 131072,\n },\n \"maestro-uncond-150k\": {\n \"url\": \"https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt\",\n \"sample_rate\": 16000,\n \"sample_size\": 65536,\n },\n \"unlocked-uncond-250k\": {\n \"url\": \"https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt\",\n \"sample_rate\": 16000,\n \"sample_size\": 65536,\n },\n \"honk-140k\": {\n \"url\": \"https://model-server.zqevans2.workers.dev/honk-140k.ckpt\",\n \"sample_rate\": 16000,\n \"sample_size\": 65536,\n },\n}\n\n\ndef alpha_sigma_to_t(alpha, sigma):\n \"\"\"Returns a timestep, given the scaling factors for the clean image and for\n the noise.\"\"\"\n return torch.atan2(sigma, alpha) / math.pi * 2\n\n\ndef get_crash_schedule(t):\n sigma = torch.sin(t * math.pi / 2) ** 2\n alpha = (1 - sigma**2) ** 0.5\n return alpha_sigma_to_t(alpha, sigma)\n\n\nclass Object(object):\n pass\n\n\nclass DiffusionUncond(nn.Module):\n def __init__(self, global_args):\n super().__init__()\n\n self.diffusion = DiffusionAttnUnet1D(global_args, n_attn_layers=4)\n self.diffusion_ema = deepcopy(self.diffusion)\n self.rng = torch.quasirandom.SobolEngine(1, scramble=True)\n\n\ndef download(model_name):\n url = MODELS_MAP[model_name][\"url\"]\n r = requests.get(url, stream=True)\n\n local_filename = f\"./{model_name}.ckpt\"\n with open(local_filename, \"wb\") as fp:\n for chunk in r.iter_content(chunk_size=8192):\n fp.write(chunk)\n\n return local_filename\n\n\nDOWN_NUM_TO_LAYER = {\n \"1\": \"resnets.0\",\n \"2\": \"attentions.0\",\n \"3\": \"resnets.1\",\n \"4\": \"attentions.1\",\n \"5\": \"resnets.2\",\n \"6\": \"attentions.2\",\n}\nUP_NUM_TO_LAYER = {\n \"8\": \"resnets.0\",\n \"9\": \"attentions.0\",\n \"10\": \"resnets.1\",\n \"11\": \"attentions.1\",\n \"12\": \"resnets.2\",\n \"13\": \"attentions.2\",\n}\nMID_NUM_TO_LAYER = {\n \"1\": \"resnets.0\",\n \"2\": \"attentions.0\",\n \"3\": \"resnets.1\",\n \"4\": \"attentions.1\",\n \"5\": \"resnets.2\",\n \"6\": \"attentions.2\",\n \"8\": \"resnets.3\",\n \"9\": \"attentions.3\",\n \"10\": \"resnets.4\",\n \"11\": \"attentions.4\",\n \"12\": \"resnets.5\",\n \"13\": \"attentions.5\",\n}\nDEPTH_0_TO_LAYER = {\n \"0\": \"resnets.0\",\n \"1\": \"resnets.1\",\n \"2\": \"resnets.2\",\n \"4\": \"resnets.0\",\n \"5\": \"resnets.1\",\n \"6\": \"resnets.2\",\n}\n\nRES_CONV_MAP = {\n \"skip\": \"conv_skip\",\n \"main.0\": \"conv_1\",\n \"main.1\": \"group_norm_1\",\n \"main.3\": \"conv_2\",\n \"main.4\": \"group_norm_2\",\n}\n\nATTN_MAP = {\n \"norm\": \"group_norm\",\n \"qkv_proj\": [\"query\", \"key\", \"value\"],\n \"out_proj\": [\"proj_attn\"],\n}\n\n\ndef convert_resconv_naming(name):\n if name.startswith(\"skip\"):\n return name.replace(\"skip\", RES_CONV_MAP[\"skip\"])\n\n # name has to be of format main.{digit}\n if not name.startswith(\"main.\"):\n raise ValueError(f\"ResConvBlock error with {name}\")\n\n return name.replace(name[:6], RES_CONV_MAP[name[:6]])\n\n\ndef convert_attn_naming(name):\n for key, value in ATTN_MAP.items():\n if name.startswith(key) and not isinstance(value, list):\n return name.replace(key, value)\n elif name.startswith(key):\n return [name.replace(key, v) for v in value]\n raise ValueError(f\"Attn error with {name}\")\n\n\ndef rename(input_string, max_depth=13):\n string = input_string\n\n if string.split(\".\")[0] == \"timestep_embed\":\n return string.replace(\"timestep_embed\", \"time_proj\")\n\n depth = 0\n if string.startswith(\"net.3.\"):\n depth += 1\n string = string[6:]\n elif string.startswith(\"net.\"):\n string = string[4:]\n\n while string.startswith(\"main.7.\"):\n depth += 1\n string = string[7:]\n\n if string.startswith(\"main.\"):\n string = string[5:]\n\n # mid block\n if string[:2].isdigit():\n layer_num = string[:2]\n string_left = string[2:]\n else:\n layer_num = string[0]\n string_left = string[1:]\n\n if depth == max_depth:\n new_layer = MID_NUM_TO_LAYER[layer_num]\n prefix = \"mid_block\"\n elif depth > 0 and int(layer_num) < 7:\n new_layer = DOWN_NUM_TO_LAYER[layer_num]\n prefix = f\"down_blocks.{depth}\"\n elif depth > 0 and int(layer_num) > 7:\n new_layer = UP_NUM_TO_LAYER[layer_num]\n prefix = f\"up_blocks.{max_depth - depth - 1}\"\n elif depth == 0:\n new_layer = DEPTH_0_TO_LAYER[layer_num]\n prefix = f\"up_blocks.{max_depth - 1}\" if int(layer_num) > 3 else \"down_blocks.0\"\n\n if not string_left.startswith(\".\"):\n raise ValueError(f\"Naming error with {input_string} and string_left: {string_left}.\")\n\n string_left = string_left[1:]\n\n if \"resnets\" in new_layer:\n string_left = convert_resconv_naming(string_left)\n elif \"attentions\" in new_layer:\n new_string_left = convert_attn_naming(string_left)\n string_left = new_string_left\n\n if not isinstance(string_left, list):\n new_string = prefix + \".\" + new_layer + \".\" + string_left\n else:\n new_string = [prefix + \".\" + new_layer + \".\" + s for s in string_left]\n return new_string\n\n\ndef rename_orig_weights(state_dict):\n new_state_dict = {}\n for k, v in state_dict.items():\n if k.endswith(\"kernel\"):\n # up- and downsample layers, don't have trainable weights\n continue\n\n new_k = rename(k)\n\n # check if we need to transform from Conv => Linear for attention\n if isinstance(new_k, list):\n new_state_dict = transform_conv_attns(new_state_dict, new_k, v)\n else:\n new_state_dict[new_k] = v\n\n return new_state_dict\n\n\ndef transform_conv_attns(new_state_dict, new_k, v):\n if len(new_k) == 1:\n if len(v.shape) == 3:\n # weight\n new_state_dict[new_k[0]] = v[:, :, 0]\n else:\n # bias\n new_state_dict[new_k[0]] = v\n else:\n # qkv matrices\n trippled_shape = v.shape[0]\n single_shape = trippled_shape // 3\n for i in range(3):\n if len(v.shape) == 3:\n new_state_dict[new_k[i]] = v[i * single_shape : (i + 1) * single_shape, :, 0]\n else:\n new_state_dict[new_k[i]] = v[i * single_shape : (i + 1) * single_shape]\n return new_state_dict\n\n\ndef main(args):\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n model_name = args.model_path.split(\"/\")[-1].split(\".\")[0]\n if not os.path.isfile(args.model_path):\n assert (\n model_name == args.model_path\n ), f\"Make sure to provide one of the official model names {MODELS_MAP.keys()}\"\n args.model_path = download(model_name)\n\n sample_rate = MODELS_MAP[model_name][\"sample_rate\"]\n sample_size = MODELS_MAP[model_name][\"sample_size\"]\n\n config = Object()\n config.sample_size = sample_size\n config.sample_rate = sample_rate\n config.latent_dim = 0\n\n diffusers_model = UNet1DModel(sample_size=sample_size, sample_rate=sample_rate)\n diffusers_state_dict = diffusers_model.state_dict()\n\n orig_model = DiffusionUncond(config)\n orig_model.load_state_dict(torch.load(args.model_path, map_location=device)[\"state_dict\"])\n orig_model = orig_model.diffusion_ema.eval()\n orig_model_state_dict = orig_model.state_dict()\n renamed_state_dict = rename_orig_weights(orig_model_state_dict)\n\n renamed_minus_diffusers = set(renamed_state_dict.keys()) - set(diffusers_state_dict.keys())\n diffusers_minus_renamed = set(diffusers_state_dict.keys()) - set(renamed_state_dict.keys())\n\n assert len(renamed_minus_diffusers) == 0, f\"Problem with {renamed_minus_diffusers}\"\n assert all(k.endswith(\"kernel\") for k in list(diffusers_minus_renamed)), f\"Problem with {diffusers_minus_renamed}\"\n\n for key, value in renamed_state_dict.items():\n assert (\n diffusers_state_dict[key].squeeze().shape == value.squeeze().shape\n ), f\"Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}\"\n if key == \"time_proj.weight\":\n value = value.squeeze()\n\n diffusers_state_dict[key] = value\n\n diffusers_model.load_state_dict(diffusers_state_dict)\n\n steps = 100\n seed = 33\n\n diffusers_scheduler = IPNDMScheduler(num_train_timesteps=steps)\n\n generator = torch.manual_seed(seed)\n noise = torch.randn([1, 2, config.sample_size], generator=generator).to(device)\n\n t = torch.linspace(1, 0, steps + 1, device=device)[:-1]\n step_list = get_crash_schedule(t)\n\n pipe = DanceDiffusionPipeline(unet=diffusers_model, scheduler=diffusers_scheduler)\n\n generator = torch.manual_seed(33)\n audio = pipe(num_inference_steps=steps, generator=generator).audios\n\n generated = sampling.iplms_sample(orig_model, noise, step_list, {})\n generated = generated.clamp(-1, 1)\n\n diff_sum = (generated - audio).abs().sum()\n diff_max = (generated - audio).abs().max()\n\n if args.save:\n pipe.save_pretrained(args.checkpoint_path)\n\n print(\"Diff sum\", diff_sum)\n print(\"Diff max\", diff_max)\n\n assert diff_max < 1e-3, f\"Diff max: {diff_max} is too much :-/\"\n\n print(f\"Conversion for {model_name} successful!\")\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"--model_path\", default=None, type=str, required=True, help=\"Path to the model to convert.\")\n parser.add_argument(\n \"--save\", default=True, type=bool, required=False, help=\"Whether to save the converted model or not.\"\n )\n parser.add_argument(\"--checkpoint_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n args = parser.parse_args()\n\n main(args)\n" }, { "path": "diffusers/scripts/convert_ddpm_original_checkpoint_to_diffusers.py", "content": "import argparse\nimport json\n\nimport torch\n\nfrom diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNet2DModel, VQModel\n\n\ndef shave_segments(path, n_shave_prefix_segments=1):\n \"\"\"\n Removes segments. Positive values shave the first segments, negative shave the last segments.\n \"\"\"\n if n_shave_prefix_segments >= 0:\n return \".\".join(path.split(\".\")[n_shave_prefix_segments:])\n else:\n return \".\".join(path.split(\".\")[:n_shave_prefix_segments])\n\n\ndef renew_resnet_paths(old_list, n_shave_prefix_segments=0):\n mapping = []\n for old_item in old_list:\n new_item = old_item\n new_item = new_item.replace(\"block.\", \"resnets.\")\n new_item = new_item.replace(\"conv_shorcut\", \"conv1\")\n new_item = new_item.replace(\"in_shortcut\", \"conv_shortcut\")\n new_item = new_item.replace(\"temb_proj\", \"time_emb_proj\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef renew_attention_paths(old_list, n_shave_prefix_segments=0, in_mid=False):\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n # In `model.mid`, the layer is called `attn`.\n if not in_mid:\n new_item = new_item.replace(\"attn\", \"attentions\")\n new_item = new_item.replace(\".k.\", \".key.\")\n new_item = new_item.replace(\".v.\", \".value.\")\n new_item = new_item.replace(\".q.\", \".query.\")\n\n new_item = new_item.replace(\"proj_out\", \"proj_attn\")\n new_item = new_item.replace(\"norm\", \"group_norm\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef assign_to_checkpoint(\n paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None\n):\n assert isinstance(paths, list), \"Paths should be a list of dicts containing 'old' and 'new' keys.\"\n\n if attention_paths_to_split is not None:\n if config is None:\n raise ValueError(\"Please specify the config if setting 'attention_paths_to_split' to 'True'.\")\n\n for path, path_map in attention_paths_to_split.items():\n old_tensor = old_checkpoint[path]\n channels = old_tensor.shape[0] // 3\n\n target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)\n\n num_heads = old_tensor.shape[0] // config.get(\"num_head_channels\", 1) // 3\n\n old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])\n query, key, value = old_tensor.split(channels // num_heads, dim=1)\n\n checkpoint[path_map[\"query\"]] = query.reshape(target_shape).squeeze()\n checkpoint[path_map[\"key\"]] = key.reshape(target_shape).squeeze()\n checkpoint[path_map[\"value\"]] = value.reshape(target_shape).squeeze()\n\n for path in paths:\n new_path = path[\"new\"]\n\n if attention_paths_to_split is not None and new_path in attention_paths_to_split:\n continue\n\n new_path = new_path.replace(\"down.\", \"down_blocks.\")\n new_path = new_path.replace(\"up.\", \"up_blocks.\")\n\n if additional_replacements is not None:\n for replacement in additional_replacements:\n new_path = new_path.replace(replacement[\"old\"], replacement[\"new\"])\n\n if \"attentions\" in new_path:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]].squeeze()\n else:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]]\n\n\ndef convert_ddpm_checkpoint(checkpoint, config):\n \"\"\"\n Takes a state dict and a config, and returns a converted checkpoint.\n \"\"\"\n new_checkpoint = {}\n\n new_checkpoint[\"time_embedding.linear_1.weight\"] = checkpoint[\"temb.dense.0.weight\"]\n new_checkpoint[\"time_embedding.linear_1.bias\"] = checkpoint[\"temb.dense.0.bias\"]\n new_checkpoint[\"time_embedding.linear_2.weight\"] = checkpoint[\"temb.dense.1.weight\"]\n new_checkpoint[\"time_embedding.linear_2.bias\"] = checkpoint[\"temb.dense.1.bias\"]\n\n new_checkpoint[\"conv_norm_out.weight\"] = checkpoint[\"norm_out.weight\"]\n new_checkpoint[\"conv_norm_out.bias\"] = checkpoint[\"norm_out.bias\"]\n\n new_checkpoint[\"conv_in.weight\"] = checkpoint[\"conv_in.weight\"]\n new_checkpoint[\"conv_in.bias\"] = checkpoint[\"conv_in.bias\"]\n new_checkpoint[\"conv_out.weight\"] = checkpoint[\"conv_out.weight\"]\n new_checkpoint[\"conv_out.bias\"] = checkpoint[\"conv_out.bias\"]\n\n num_down_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in checkpoint if \"down\" in layer})\n down_blocks = {\n layer_id: [key for key in checkpoint if f\"down.{layer_id}\" in key] for layer_id in range(num_down_blocks)\n }\n\n num_up_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in checkpoint if \"up\" in layer})\n up_blocks = {layer_id: [key for key in checkpoint if f\"up.{layer_id}\" in key] for layer_id in range(num_up_blocks)}\n\n for i in range(num_down_blocks):\n block_id = (i - 1) // (config[\"layers_per_block\"] + 1)\n\n if any(\"downsample\" in layer for layer in down_blocks[i]):\n new_checkpoint[f\"down_blocks.{i}.downsamplers.0.conv.weight\"] = checkpoint[\n f\"down.{i}.downsample.op.weight\"\n ]\n new_checkpoint[f\"down_blocks.{i}.downsamplers.0.conv.bias\"] = checkpoint[f\"down.{i}.downsample.op.bias\"]\n # new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']\n # new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']\n\n if any(\"block\" in layer for layer in down_blocks[i]):\n num_blocks = len(\n {\".\".join(shave_segments(layer, 2).split(\".\")[:2]) for layer in down_blocks[i] if \"block\" in layer}\n )\n blocks = {\n layer_id: [key for key in down_blocks[i] if f\"block.{layer_id}\" in key]\n for layer_id in range(num_blocks)\n }\n\n if num_blocks > 0:\n for j in range(config[\"layers_per_block\"]):\n paths = renew_resnet_paths(blocks[j])\n assign_to_checkpoint(paths, new_checkpoint, checkpoint)\n\n if any(\"attn\" in layer for layer in down_blocks[i]):\n num_attn = len(\n {\".\".join(shave_segments(layer, 2).split(\".\")[:2]) for layer in down_blocks[i] if \"attn\" in layer}\n )\n attns = {\n layer_id: [key for key in down_blocks[i] if f\"attn.{layer_id}\" in key]\n for layer_id in range(num_blocks)\n }\n\n if num_attn > 0:\n for j in range(config[\"layers_per_block\"]):\n paths = renew_attention_paths(attns[j])\n assign_to_checkpoint(paths, new_checkpoint, checkpoint, config=config)\n\n mid_block_1_layers = [key for key in checkpoint if \"mid.block_1\" in key]\n mid_block_2_layers = [key for key in checkpoint if \"mid.block_2\" in key]\n mid_attn_1_layers = [key for key in checkpoint if \"mid.attn_1\" in key]\n\n # Mid new 2\n paths = renew_resnet_paths(mid_block_1_layers)\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n checkpoint,\n additional_replacements=[{\"old\": \"mid.\", \"new\": \"mid_new_2.\"}, {\"old\": \"block_1\", \"new\": \"resnets.0\"}],\n )\n\n paths = renew_resnet_paths(mid_block_2_layers)\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n checkpoint,\n additional_replacements=[{\"old\": \"mid.\", \"new\": \"mid_new_2.\"}, {\"old\": \"block_2\", \"new\": \"resnets.1\"}],\n )\n\n paths = renew_attention_paths(mid_attn_1_layers, in_mid=True)\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n checkpoint,\n additional_replacements=[{\"old\": \"mid.\", \"new\": \"mid_new_2.\"}, {\"old\": \"attn_1\", \"new\": \"attentions.0\"}],\n )\n\n for i in range(num_up_blocks):\n block_id = num_up_blocks - 1 - i\n\n if any(\"upsample\" in layer for layer in up_blocks[i]):\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.weight\"] = checkpoint[\n f\"up.{i}.upsample.conv.weight\"\n ]\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.bias\"] = checkpoint[f\"up.{i}.upsample.conv.bias\"]\n\n if any(\"block\" in layer for layer in up_blocks[i]):\n num_blocks = len(\n {\".\".join(shave_segments(layer, 2).split(\".\")[:2]) for layer in up_blocks[i] if \"block\" in layer}\n )\n blocks = {\n layer_id: [key for key in up_blocks[i] if f\"block.{layer_id}\" in key] for layer_id in range(num_blocks)\n }\n\n if num_blocks > 0:\n for j in range(config[\"layers_per_block\"] + 1):\n replace_indices = {\"old\": f\"up_blocks.{i}\", \"new\": f\"up_blocks.{block_id}\"}\n paths = renew_resnet_paths(blocks[j])\n assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices])\n\n if any(\"attn\" in layer for layer in up_blocks[i]):\n num_attn = len(\n {\".\".join(shave_segments(layer, 2).split(\".\")[:2]) for layer in up_blocks[i] if \"attn\" in layer}\n )\n attns = {\n layer_id: [key for key in up_blocks[i] if f\"attn.{layer_id}\" in key] for layer_id in range(num_blocks)\n }\n\n if num_attn > 0:\n for j in range(config[\"layers_per_block\"] + 1):\n replace_indices = {\"old\": f\"up_blocks.{i}\", \"new\": f\"up_blocks.{block_id}\"}\n paths = renew_attention_paths(attns[j])\n assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices])\n\n new_checkpoint = {k.replace(\"mid_new_2\", \"mid_block\"): v for k, v in new_checkpoint.items()}\n return new_checkpoint\n\n\ndef convert_vq_autoenc_checkpoint(checkpoint, config):\n \"\"\"\n Takes a state dict and a config, and returns a converted checkpoint.\n \"\"\"\n new_checkpoint = {}\n\n new_checkpoint[\"encoder.conv_norm_out.weight\"] = checkpoint[\"encoder.norm_out.weight\"]\n new_checkpoint[\"encoder.conv_norm_out.bias\"] = checkpoint[\"encoder.norm_out.bias\"]\n\n new_checkpoint[\"encoder.conv_in.weight\"] = checkpoint[\"encoder.conv_in.weight\"]\n new_checkpoint[\"encoder.conv_in.bias\"] = checkpoint[\"encoder.conv_in.bias\"]\n new_checkpoint[\"encoder.conv_out.weight\"] = checkpoint[\"encoder.conv_out.weight\"]\n new_checkpoint[\"encoder.conv_out.bias\"] = checkpoint[\"encoder.conv_out.bias\"]\n\n new_checkpoint[\"decoder.conv_norm_out.weight\"] = checkpoint[\"decoder.norm_out.weight\"]\n new_checkpoint[\"decoder.conv_norm_out.bias\"] = checkpoint[\"decoder.norm_out.bias\"]\n\n new_checkpoint[\"decoder.conv_in.weight\"] = checkpoint[\"decoder.conv_in.weight\"]\n new_checkpoint[\"decoder.conv_in.bias\"] = checkpoint[\"decoder.conv_in.bias\"]\n new_checkpoint[\"decoder.conv_out.weight\"] = checkpoint[\"decoder.conv_out.weight\"]\n new_checkpoint[\"decoder.conv_out.bias\"] = checkpoint[\"decoder.conv_out.bias\"]\n\n num_down_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in checkpoint if \"down\" in layer})\n down_blocks = {\n layer_id: [key for key in checkpoint if f\"down.{layer_id}\" in key] for layer_id in range(num_down_blocks)\n }\n\n num_up_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in checkpoint if \"up\" in layer})\n up_blocks = {layer_id: [key for key in checkpoint if f\"up.{layer_id}\" in key] for layer_id in range(num_up_blocks)}\n\n for i in range(num_down_blocks):\n block_id = (i - 1) // (config[\"layers_per_block\"] + 1)\n\n if any(\"downsample\" in layer for layer in down_blocks[i]):\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.weight\"] = checkpoint[\n f\"encoder.down.{i}.downsample.conv.weight\"\n ]\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.bias\"] = checkpoint[\n f\"encoder.down.{i}.downsample.conv.bias\"\n ]\n\n if any(\"block\" in layer for layer in down_blocks[i]):\n num_blocks = len(\n {\".\".join(shave_segments(layer, 3).split(\".\")[:3]) for layer in down_blocks[i] if \"block\" in layer}\n )\n blocks = {\n layer_id: [key for key in down_blocks[i] if f\"block.{layer_id}\" in key]\n for layer_id in range(num_blocks)\n }\n\n if num_blocks > 0:\n for j in range(config[\"layers_per_block\"]):\n paths = renew_resnet_paths(blocks[j])\n assign_to_checkpoint(paths, new_checkpoint, checkpoint)\n\n if any(\"attn\" in layer for layer in down_blocks[i]):\n num_attn = len(\n {\".\".join(shave_segments(layer, 3).split(\".\")[:3]) for layer in down_blocks[i] if \"attn\" in layer}\n )\n attns = {\n layer_id: [key for key in down_blocks[i] if f\"attn.{layer_id}\" in key]\n for layer_id in range(num_blocks)\n }\n\n if num_attn > 0:\n for j in range(config[\"layers_per_block\"]):\n paths = renew_attention_paths(attns[j])\n assign_to_checkpoint(paths, new_checkpoint, checkpoint, config=config)\n\n mid_block_1_layers = [key for key in checkpoint if \"mid.block_1\" in key]\n mid_block_2_layers = [key for key in checkpoint if \"mid.block_2\" in key]\n mid_attn_1_layers = [key for key in checkpoint if \"mid.attn_1\" in key]\n\n # Mid new 2\n paths = renew_resnet_paths(mid_block_1_layers)\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n checkpoint,\n additional_replacements=[{\"old\": \"mid.\", \"new\": \"mid_new_2.\"}, {\"old\": \"block_1\", \"new\": \"resnets.0\"}],\n )\n\n paths = renew_resnet_paths(mid_block_2_layers)\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n checkpoint,\n additional_replacements=[{\"old\": \"mid.\", \"new\": \"mid_new_2.\"}, {\"old\": \"block_2\", \"new\": \"resnets.1\"}],\n )\n\n paths = renew_attention_paths(mid_attn_1_layers, in_mid=True)\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n checkpoint,\n additional_replacements=[{\"old\": \"mid.\", \"new\": \"mid_new_2.\"}, {\"old\": \"attn_1\", \"new\": \"attentions.0\"}],\n )\n\n for i in range(num_up_blocks):\n block_id = num_up_blocks - 1 - i\n\n if any(\"upsample\" in layer for layer in up_blocks[i]):\n new_checkpoint[f\"decoder.up_blocks.{block_id}.upsamplers.0.conv.weight\"] = checkpoint[\n f\"decoder.up.{i}.upsample.conv.weight\"\n ]\n new_checkpoint[f\"decoder.up_blocks.{block_id}.upsamplers.0.conv.bias\"] = checkpoint[\n f\"decoder.up.{i}.upsample.conv.bias\"\n ]\n\n if any(\"block\" in layer for layer in up_blocks[i]):\n num_blocks = len(\n {\".\".join(shave_segments(layer, 3).split(\".\")[:3]) for layer in up_blocks[i] if \"block\" in layer}\n )\n blocks = {\n layer_id: [key for key in up_blocks[i] if f\"block.{layer_id}\" in key] for layer_id in range(num_blocks)\n }\n\n if num_blocks > 0:\n for j in range(config[\"layers_per_block\"] + 1):\n replace_indices = {\"old\": f\"up_blocks.{i}\", \"new\": f\"up_blocks.{block_id}\"}\n paths = renew_resnet_paths(blocks[j])\n assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices])\n\n if any(\"attn\" in layer for layer in up_blocks[i]):\n num_attn = len(\n {\".\".join(shave_segments(layer, 3).split(\".\")[:3]) for layer in up_blocks[i] if \"attn\" in layer}\n )\n attns = {\n layer_id: [key for key in up_blocks[i] if f\"attn.{layer_id}\" in key] for layer_id in range(num_blocks)\n }\n\n if num_attn > 0:\n for j in range(config[\"layers_per_block\"] + 1):\n replace_indices = {\"old\": f\"up_blocks.{i}\", \"new\": f\"up_blocks.{block_id}\"}\n paths = renew_attention_paths(attns[j])\n assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices])\n\n new_checkpoint = {k.replace(\"mid_new_2\", \"mid_block\"): v for k, v in new_checkpoint.items()}\n new_checkpoint[\"quant_conv.weight\"] = checkpoint[\"quant_conv.weight\"]\n new_checkpoint[\"quant_conv.bias\"] = checkpoint[\"quant_conv.bias\"]\n if \"quantize.embedding.weight\" in checkpoint:\n new_checkpoint[\"quantize.embedding.weight\"] = checkpoint[\"quantize.embedding.weight\"]\n new_checkpoint[\"post_quant_conv.weight\"] = checkpoint[\"post_quant_conv.weight\"]\n new_checkpoint[\"post_quant_conv.bias\"] = checkpoint[\"post_quant_conv.bias\"]\n\n return new_checkpoint\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--checkpoint_path\", default=None, type=str, required=True, help=\"Path to the checkpoint to convert.\"\n )\n\n parser.add_argument(\n \"--config_file\",\n default=None,\n type=str,\n required=True,\n help=\"The config json file corresponding to the architecture.\",\n )\n\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n\n args = parser.parse_args()\n checkpoint = torch.load(args.checkpoint_path)\n\n with open(args.config_file) as f:\n config = json.loads(f.read())\n\n # unet case\n key_prefix_set = {key.split(\".\")[0] for key in checkpoint.keys()}\n if \"encoder\" in key_prefix_set and \"decoder\" in key_prefix_set:\n converted_checkpoint = convert_vq_autoenc_checkpoint(checkpoint, config)\n else:\n converted_checkpoint = convert_ddpm_checkpoint(checkpoint, config)\n\n if \"ddpm\" in config:\n del config[\"ddpm\"]\n\n if config[\"_class_name\"] == \"VQModel\":\n model = VQModel(**config)\n model.load_state_dict(converted_checkpoint)\n model.save_pretrained(args.dump_path)\n elif config[\"_class_name\"] == \"AutoencoderKL\":\n model = AutoencoderKL(**config)\n model.load_state_dict(converted_checkpoint)\n model.save_pretrained(args.dump_path)\n else:\n model = UNet2DModel(**config)\n model.load_state_dict(converted_checkpoint)\n\n scheduler = DDPMScheduler.from_config(\"/\".join(args.checkpoint_path.split(\"/\")[:-1]))\n\n pipe = DDPMPipeline(unet=model, scheduler=scheduler)\n pipe.save_pretrained(args.dump_path)\n" }, { "path": "diffusers/scripts/convert_diffusers_sdxl_lora_to_webui.py", "content": "# Script for converting a Hugging Face Diffusers trained SDXL LoRAs to Kohya format\n# This means that you can input your diffusers-trained LoRAs and\n# Get the output to work with WebUIs such as AUTOMATIC1111, ComfyUI, SD.Next and others.\n\n# To get started you can find some cool `diffusers` trained LoRAs such as this cute Corgy\n# https://huggingface.co/ignasbud/corgy_dog_LoRA/, download its `pytorch_lora_weights.safetensors` file\n# and run the script:\n# python convert_diffusers_sdxl_lora_to_webui.py --input_lora pytorch_lora_weights.safetensors --output_lora corgy.safetensors\n# now you can use corgy.safetensors in your WebUI of choice!\n\n# To train your own, here are some diffusers training scripts and utils that you can use and then convert:\n# LoRA Ease - no code SDXL Dreambooth LoRA trainer: https://huggingface.co/spaces/multimodalart/lora-ease\n# Dreambooth Advanced Training Script - state of the art techniques such as pivotal tuning and prodigy optimizer:\n# - Script: https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py\n# - Colab (only on Pro): https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/SDXL_Dreambooth_LoRA_advanced_example.ipynb\n# Canonical diffusers training scripts:\n# - Script: https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora_sdxl.py\n# - Colab (runs on free tier): https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/SDXL_DreamBooth_LoRA_.ipynb\n\nimport argparse\nimport os\n\nfrom safetensors.torch import load_file, save_file\n\nfrom diffusers.utils import convert_all_state_dict_to_peft, convert_state_dict_to_kohya\n\n\ndef convert_and_save(input_lora, output_lora=None):\n if output_lora is None:\n base_name = os.path.splitext(input_lora)[0]\n output_lora = f\"{base_name}_webui.safetensors\"\n\n diffusers_state_dict = load_file(input_lora)\n peft_state_dict = convert_all_state_dict_to_peft(diffusers_state_dict)\n kohya_state_dict = convert_state_dict_to_kohya(peft_state_dict)\n save_file(kohya_state_dict, output_lora)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description=\"Convert LoRA model to PEFT and then to Kohya format.\")\n parser.add_argument(\n \"--input_lora\",\n type=str,\n required=True,\n help=\"Path to the input LoRA model file in the diffusers format.\",\n )\n parser.add_argument(\n \"--output_lora\",\n type=str,\n required=False,\n help=\"Path for the converted LoRA (safetensors format for AUTOMATIC1111, ComfyUI, etc.). Optional, defaults to input name with a _webui suffix.\",\n )\n\n args = parser.parse_args()\n\n convert_and_save(args.input_lora, args.output_lora)\n" }, { "path": "diffusers/scripts/convert_diffusers_to_original_sdxl.py", "content": "# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.\n# *Only* converts the UNet, VAE, and Text Encoder.\n# Does not convert optimizer state or any other thing.\n\nimport argparse\nimport os.path as osp\nimport re\n\nimport torch\nfrom safetensors.torch import load_file, save_file\n\n\n# =================#\n# UNet Conversion #\n# =================#\n\nunet_conversion_map = [\n # (stable-diffusion, HF Diffusers)\n (\"time_embed.0.weight\", \"time_embedding.linear_1.weight\"),\n (\"time_embed.0.bias\", \"time_embedding.linear_1.bias\"),\n (\"time_embed.2.weight\", \"time_embedding.linear_2.weight\"),\n (\"time_embed.2.bias\", \"time_embedding.linear_2.bias\"),\n (\"input_blocks.0.0.weight\", \"conv_in.weight\"),\n (\"input_blocks.0.0.bias\", \"conv_in.bias\"),\n (\"out.0.weight\", \"conv_norm_out.weight\"),\n (\"out.0.bias\", \"conv_norm_out.bias\"),\n (\"out.2.weight\", \"conv_out.weight\"),\n (\"out.2.bias\", \"conv_out.bias\"),\n # the following are for sdxl\n (\"label_emb.0.0.weight\", \"add_embedding.linear_1.weight\"),\n (\"label_emb.0.0.bias\", \"add_embedding.linear_1.bias\"),\n (\"label_emb.0.2.weight\", \"add_embedding.linear_2.weight\"),\n (\"label_emb.0.2.bias\", \"add_embedding.linear_2.bias\"),\n]\n\nunet_conversion_map_resnet = [\n # (stable-diffusion, HF Diffusers)\n (\"in_layers.0\", \"norm1\"),\n (\"in_layers.2\", \"conv1\"),\n (\"out_layers.0\", \"norm2\"),\n (\"out_layers.3\", \"conv2\"),\n (\"emb_layers.1\", \"time_emb_proj\"),\n (\"skip_connection\", \"conv_shortcut\"),\n]\n\nunet_conversion_map_layer = []\n# hardcoded number of downblocks and resnets/attentions...\n# would need smarter logic for other networks.\nfor i in range(3):\n # loop over downblocks/upblocks\n\n for j in range(2):\n # loop over resnets/attentions for downblocks\n hf_down_res_prefix = f\"down_blocks.{i}.resnets.{j}.\"\n sd_down_res_prefix = f\"input_blocks.{3*i + j + 1}.0.\"\n unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))\n\n if i > 0:\n hf_down_atn_prefix = f\"down_blocks.{i}.attentions.{j}.\"\n sd_down_atn_prefix = f\"input_blocks.{3*i + j + 1}.1.\"\n unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))\n\n for j in range(4):\n # loop over resnets/attentions for upblocks\n hf_up_res_prefix = f\"up_blocks.{i}.resnets.{j}.\"\n sd_up_res_prefix = f\"output_blocks.{3*i + j}.0.\"\n unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))\n\n if i < 2:\n # no attention layers in up_blocks.0\n hf_up_atn_prefix = f\"up_blocks.{i}.attentions.{j}.\"\n sd_up_atn_prefix = f\"output_blocks.{3 * i + j}.1.\"\n unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))\n\n if i < 3:\n # no downsample in down_blocks.3\n hf_downsample_prefix = f\"down_blocks.{i}.downsamplers.0.conv.\"\n sd_downsample_prefix = f\"input_blocks.{3*(i+1)}.0.op.\"\n unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))\n\n # no upsample in up_blocks.3\n hf_upsample_prefix = f\"up_blocks.{i}.upsamplers.0.\"\n sd_upsample_prefix = f\"output_blocks.{3*i + 2}.{1 if i == 0 else 2}.\"\n unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))\nunet_conversion_map_layer.append((\"output_blocks.2.2.conv.\", \"output_blocks.2.1.conv.\"))\n\nhf_mid_atn_prefix = \"mid_block.attentions.0.\"\nsd_mid_atn_prefix = \"middle_block.1.\"\nunet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))\nfor j in range(2):\n hf_mid_res_prefix = f\"mid_block.resnets.{j}.\"\n sd_mid_res_prefix = f\"middle_block.{2*j}.\"\n unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))\n\n\ndef convert_unet_state_dict(unet_state_dict):\n # buyer beware: this is a *brittle* function,\n # and correct output requires that all of these pieces interact in\n # the exact order in which I have arranged them.\n mapping = {k: k for k in unet_state_dict.keys()}\n for sd_name, hf_name in unet_conversion_map:\n mapping[hf_name] = sd_name\n for k, v in mapping.items():\n if \"resnets\" in k:\n for sd_part, hf_part in unet_conversion_map_resnet:\n v = v.replace(hf_part, sd_part)\n mapping[k] = v\n for k, v in mapping.items():\n for sd_part, hf_part in unet_conversion_map_layer:\n v = v.replace(hf_part, sd_part)\n mapping[k] = v\n new_state_dict = {sd_name: unet_state_dict[hf_name] for hf_name, sd_name in mapping.items()}\n return new_state_dict\n\n\n# ================#\n# VAE Conversion #\n# ================#\n\nvae_conversion_map = [\n # (stable-diffusion, HF Diffusers)\n (\"nin_shortcut\", \"conv_shortcut\"),\n (\"norm_out\", \"conv_norm_out\"),\n (\"mid.attn_1.\", \"mid_block.attentions.0.\"),\n]\n\nfor i in range(4):\n # down_blocks have two resnets\n for j in range(2):\n hf_down_prefix = f\"encoder.down_blocks.{i}.resnets.{j}.\"\n sd_down_prefix = f\"encoder.down.{i}.block.{j}.\"\n vae_conversion_map.append((sd_down_prefix, hf_down_prefix))\n\n if i < 3:\n hf_downsample_prefix = f\"down_blocks.{i}.downsamplers.0.\"\n sd_downsample_prefix = f\"down.{i}.downsample.\"\n vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))\n\n hf_upsample_prefix = f\"up_blocks.{i}.upsamplers.0.\"\n sd_upsample_prefix = f\"up.{3-i}.upsample.\"\n vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))\n\n # up_blocks have three resnets\n # also, up blocks in hf are numbered in reverse from sd\n for j in range(3):\n hf_up_prefix = f\"decoder.up_blocks.{i}.resnets.{j}.\"\n sd_up_prefix = f\"decoder.up.{3-i}.block.{j}.\"\n vae_conversion_map.append((sd_up_prefix, hf_up_prefix))\n\n# this part accounts for mid blocks in both the encoder and the decoder\nfor i in range(2):\n hf_mid_res_prefix = f\"mid_block.resnets.{i}.\"\n sd_mid_res_prefix = f\"mid.block_{i+1}.\"\n vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))\n\n\nvae_conversion_map_attn = [\n # (stable-diffusion, HF Diffusers)\n (\"norm.\", \"group_norm.\"),\n # the following are for SDXL\n (\"q.\", \"to_q.\"),\n (\"k.\", \"to_k.\"),\n (\"v.\", \"to_v.\"),\n (\"proj_out.\", \"to_out.0.\"),\n]\n\n\ndef reshape_weight_for_sd(w):\n # convert HF linear weights to SD conv2d weights\n if not w.ndim == 1:\n return w.reshape(*w.shape, 1, 1)\n else:\n return w\n\n\ndef convert_vae_state_dict(vae_state_dict):\n mapping = {k: k for k in vae_state_dict.keys()}\n for k, v in mapping.items():\n for sd_part, hf_part in vae_conversion_map:\n v = v.replace(hf_part, sd_part)\n mapping[k] = v\n for k, v in mapping.items():\n if \"attentions\" in k:\n for sd_part, hf_part in vae_conversion_map_attn:\n v = v.replace(hf_part, sd_part)\n mapping[k] = v\n new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}\n weights_to_convert = [\"q\", \"k\", \"v\", \"proj_out\"]\n for k, v in new_state_dict.items():\n for weight_name in weights_to_convert:\n if f\"mid.attn_1.{weight_name}.weight\" in k:\n print(f\"Reshaping {k} for SD format\")\n new_state_dict[k] = reshape_weight_for_sd(v)\n return new_state_dict\n\n\n# =========================#\n# Text Encoder Conversion #\n# =========================#\n\n\ntextenc_conversion_lst = [\n # (stable-diffusion, HF Diffusers)\n (\"transformer.resblocks.\", \"text_model.encoder.layers.\"),\n (\"ln_1\", \"layer_norm1\"),\n (\"ln_2\", \"layer_norm2\"),\n (\".c_fc.\", \".fc1.\"),\n (\".c_proj.\", \".fc2.\"),\n (\".attn\", \".self_attn\"),\n (\"ln_final.\", \"text_model.final_layer_norm.\"),\n (\"token_embedding.weight\", \"text_model.embeddings.token_embedding.weight\"),\n (\"positional_embedding\", \"text_model.embeddings.position_embedding.weight\"),\n]\nprotected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}\ntextenc_pattern = re.compile(\"|\".join(protected.keys()))\n\n# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp\ncode2idx = {\"q\": 0, \"k\": 1, \"v\": 2}\n\n\ndef convert_openclip_text_enc_state_dict(text_enc_dict):\n new_state_dict = {}\n capture_qkv_weight = {}\n capture_qkv_bias = {}\n for k, v in text_enc_dict.items():\n if (\n k.endswith(\".self_attn.q_proj.weight\")\n or k.endswith(\".self_attn.k_proj.weight\")\n or k.endswith(\".self_attn.v_proj.weight\")\n ):\n k_pre = k[: -len(\".q_proj.weight\")]\n k_code = k[-len(\"q_proj.weight\")]\n if k_pre not in capture_qkv_weight:\n capture_qkv_weight[k_pre] = [None, None, None]\n capture_qkv_weight[k_pre][code2idx[k_code]] = v\n continue\n\n if (\n k.endswith(\".self_attn.q_proj.bias\")\n or k.endswith(\".self_attn.k_proj.bias\")\n or k.endswith(\".self_attn.v_proj.bias\")\n ):\n k_pre = k[: -len(\".q_proj.bias\")]\n k_code = k[-len(\"q_proj.bias\")]\n if k_pre not in capture_qkv_bias:\n capture_qkv_bias[k_pre] = [None, None, None]\n capture_qkv_bias[k_pre][code2idx[k_code]] = v\n continue\n\n relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k)\n new_state_dict[relabelled_key] = v\n\n for k_pre, tensors in capture_qkv_weight.items():\n if None in tensors:\n raise Exception(\"CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing\")\n relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)\n new_state_dict[relabelled_key + \".in_proj_weight\"] = torch.cat(tensors)\n\n for k_pre, tensors in capture_qkv_bias.items():\n if None in tensors:\n raise Exception(\"CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing\")\n relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)\n new_state_dict[relabelled_key + \".in_proj_bias\"] = torch.cat(tensors)\n\n return new_state_dict\n\n\ndef convert_openai_text_enc_state_dict(text_enc_dict):\n return text_enc_dict\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"--model_path\", default=None, type=str, required=True, help=\"Path to the model to convert.\")\n parser.add_argument(\"--checkpoint_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n parser.add_argument(\"--half\", action=\"store_true\", help=\"Save weights in half precision.\")\n parser.add_argument(\n \"--use_safetensors\", action=\"store_true\", help=\"Save weights use safetensors, default is ckpt.\"\n )\n\n args = parser.parse_args()\n\n assert args.model_path is not None, \"Must provide a model path!\"\n\n assert args.checkpoint_path is not None, \"Must provide a checkpoint path!\"\n\n # Path for safetensors\n unet_path = osp.join(args.model_path, \"unet\", \"diffusion_pytorch_model.safetensors\")\n vae_path = osp.join(args.model_path, \"vae\", \"diffusion_pytorch_model.safetensors\")\n text_enc_path = osp.join(args.model_path, \"text_encoder\", \"model.safetensors\")\n text_enc_2_path = osp.join(args.model_path, \"text_encoder_2\", \"model.safetensors\")\n\n # Load models from safetensors if it exists, if it doesn't pytorch\n if osp.exists(unet_path):\n unet_state_dict = load_file(unet_path, device=\"cpu\")\n else:\n unet_path = osp.join(args.model_path, \"unet\", \"diffusion_pytorch_model.bin\")\n unet_state_dict = torch.load(unet_path, map_location=\"cpu\")\n\n if osp.exists(vae_path):\n vae_state_dict = load_file(vae_path, device=\"cpu\")\n else:\n vae_path = osp.join(args.model_path, \"vae\", \"diffusion_pytorch_model.bin\")\n vae_state_dict = torch.load(vae_path, map_location=\"cpu\")\n\n if osp.exists(text_enc_path):\n text_enc_dict = load_file(text_enc_path, device=\"cpu\")\n else:\n text_enc_path = osp.join(args.model_path, \"text_encoder\", \"pytorch_model.bin\")\n text_enc_dict = torch.load(text_enc_path, map_location=\"cpu\")\n\n if osp.exists(text_enc_2_path):\n text_enc_2_dict = load_file(text_enc_2_path, device=\"cpu\")\n else:\n text_enc_2_path = osp.join(args.model_path, \"text_encoder_2\", \"pytorch_model.bin\")\n text_enc_2_dict = torch.load(text_enc_2_path, map_location=\"cpu\")\n\n # Convert the UNet model\n unet_state_dict = convert_unet_state_dict(unet_state_dict)\n unet_state_dict = {\"model.diffusion_model.\" + k: v for k, v in unet_state_dict.items()}\n\n # Convert the VAE model\n vae_state_dict = convert_vae_state_dict(vae_state_dict)\n vae_state_dict = {\"first_stage_model.\" + k: v for k, v in vae_state_dict.items()}\n\n # Convert text encoder 1\n text_enc_dict = convert_openai_text_enc_state_dict(text_enc_dict)\n text_enc_dict = {\"conditioner.embedders.0.transformer.\" + k: v for k, v in text_enc_dict.items()}\n\n # Convert text encoder 2\n text_enc_2_dict = convert_openclip_text_enc_state_dict(text_enc_2_dict)\n text_enc_2_dict = {\"conditioner.embedders.1.model.\" + k: v for k, v in text_enc_2_dict.items()}\n # We call the `.T.contiguous()` to match what's done in\n # https://github.com/huggingface/diffusers/blob/84905ca7287876b925b6bf8e9bb92fec21c78764/src/diffusers/loaders/single_file_utils.py#L1085\n text_enc_2_dict[\"conditioner.embedders.1.model.text_projection\"] = text_enc_2_dict.pop(\n \"conditioner.embedders.1.model.text_projection.weight\"\n ).T.contiguous()\n\n # Put together new checkpoint\n state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict, **text_enc_2_dict}\n\n if args.half:\n state_dict = {k: v.half() for k, v in state_dict.items()}\n\n if args.use_safetensors:\n save_file(state_dict, args.checkpoint_path)\n else:\n state_dict = {\"state_dict\": state_dict}\n torch.save(state_dict, args.checkpoint_path)\n" }, { "path": "diffusers/scripts/convert_diffusers_to_original_stable_diffusion.py", "content": "# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.\n# *Only* converts the UNet, VAE, and Text Encoder.\n# Does not convert optimizer state or any other thing.\n\nimport argparse\nimport os.path as osp\nimport re\n\nimport torch\nfrom safetensors.torch import load_file, save_file\n\n\n# =================#\n# UNet Conversion #\n# =================#\n\nunet_conversion_map = [\n # (stable-diffusion, HF Diffusers)\n (\"time_embed.0.weight\", \"time_embedding.linear_1.weight\"),\n (\"time_embed.0.bias\", \"time_embedding.linear_1.bias\"),\n (\"time_embed.2.weight\", \"time_embedding.linear_2.weight\"),\n (\"time_embed.2.bias\", \"time_embedding.linear_2.bias\"),\n (\"input_blocks.0.0.weight\", \"conv_in.weight\"),\n (\"input_blocks.0.0.bias\", \"conv_in.bias\"),\n (\"out.0.weight\", \"conv_norm_out.weight\"),\n (\"out.0.bias\", \"conv_norm_out.bias\"),\n (\"out.2.weight\", \"conv_out.weight\"),\n (\"out.2.bias\", \"conv_out.bias\"),\n]\n\nunet_conversion_map_resnet = [\n # (stable-diffusion, HF Diffusers)\n (\"in_layers.0\", \"norm1\"),\n (\"in_layers.2\", \"conv1\"),\n (\"out_layers.0\", \"norm2\"),\n (\"out_layers.3\", \"conv2\"),\n (\"emb_layers.1\", \"time_emb_proj\"),\n (\"skip_connection\", \"conv_shortcut\"),\n]\n\nunet_conversion_map_layer = []\n# hardcoded number of downblocks and resnets/attentions...\n# would need smarter logic for other networks.\nfor i in range(4):\n # loop over downblocks/upblocks\n\n for j in range(2):\n # loop over resnets/attentions for downblocks\n hf_down_res_prefix = f\"down_blocks.{i}.resnets.{j}.\"\n sd_down_res_prefix = f\"input_blocks.{3*i + j + 1}.0.\"\n unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))\n\n if i < 3:\n # no attention layers in down_blocks.3\n hf_down_atn_prefix = f\"down_blocks.{i}.attentions.{j}.\"\n sd_down_atn_prefix = f\"input_blocks.{3*i + j + 1}.1.\"\n unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))\n\n for j in range(3):\n # loop over resnets/attentions for upblocks\n hf_up_res_prefix = f\"up_blocks.{i}.resnets.{j}.\"\n sd_up_res_prefix = f\"output_blocks.{3*i + j}.0.\"\n unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))\n\n if i > 0:\n # no attention layers in up_blocks.0\n hf_up_atn_prefix = f\"up_blocks.{i}.attentions.{j}.\"\n sd_up_atn_prefix = f\"output_blocks.{3*i + j}.1.\"\n unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))\n\n if i < 3:\n # no downsample in down_blocks.3\n hf_downsample_prefix = f\"down_blocks.{i}.downsamplers.0.conv.\"\n sd_downsample_prefix = f\"input_blocks.{3*(i+1)}.0.op.\"\n unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))\n\n # no upsample in up_blocks.3\n hf_upsample_prefix = f\"up_blocks.{i}.upsamplers.0.\"\n sd_upsample_prefix = f\"output_blocks.{3*i + 2}.{1 if i == 0 else 2}.\"\n unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))\n\nhf_mid_atn_prefix = \"mid_block.attentions.0.\"\nsd_mid_atn_prefix = \"middle_block.1.\"\nunet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))\n\nfor j in range(2):\n hf_mid_res_prefix = f\"mid_block.resnets.{j}.\"\n sd_mid_res_prefix = f\"middle_block.{2*j}.\"\n unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))\n\n\ndef convert_unet_state_dict(unet_state_dict):\n # buyer beware: this is a *brittle* function,\n # and correct output requires that all of these pieces interact in\n # the exact order in which I have arranged them.\n mapping = {k: k for k in unet_state_dict.keys()}\n for sd_name, hf_name in unet_conversion_map:\n mapping[hf_name] = sd_name\n for k, v in mapping.items():\n if \"resnets\" in k:\n for sd_part, hf_part in unet_conversion_map_resnet:\n v = v.replace(hf_part, sd_part)\n mapping[k] = v\n for k, v in mapping.items():\n for sd_part, hf_part in unet_conversion_map_layer:\n v = v.replace(hf_part, sd_part)\n mapping[k] = v\n new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}\n return new_state_dict\n\n\n# ================#\n# VAE Conversion #\n# ================#\n\nvae_conversion_map = [\n # (stable-diffusion, HF Diffusers)\n (\"nin_shortcut\", \"conv_shortcut\"),\n (\"norm_out\", \"conv_norm_out\"),\n (\"mid.attn_1.\", \"mid_block.attentions.0.\"),\n]\n\nfor i in range(4):\n # down_blocks have two resnets\n for j in range(2):\n hf_down_prefix = f\"encoder.down_blocks.{i}.resnets.{j}.\"\n sd_down_prefix = f\"encoder.down.{i}.block.{j}.\"\n vae_conversion_map.append((sd_down_prefix, hf_down_prefix))\n\n if i < 3:\n hf_downsample_prefix = f\"down_blocks.{i}.downsamplers.0.\"\n sd_downsample_prefix = f\"down.{i}.downsample.\"\n vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))\n\n hf_upsample_prefix = f\"up_blocks.{i}.upsamplers.0.\"\n sd_upsample_prefix = f\"up.{3-i}.upsample.\"\n vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))\n\n # up_blocks have three resnets\n # also, up blocks in hf are numbered in reverse from sd\n for j in range(3):\n hf_up_prefix = f\"decoder.up_blocks.{i}.resnets.{j}.\"\n sd_up_prefix = f\"decoder.up.{3-i}.block.{j}.\"\n vae_conversion_map.append((sd_up_prefix, hf_up_prefix))\n\n# this part accounts for mid blocks in both the encoder and the decoder\nfor i in range(2):\n hf_mid_res_prefix = f\"mid_block.resnets.{i}.\"\n sd_mid_res_prefix = f\"mid.block_{i+1}.\"\n vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))\n\n\nvae_conversion_map_attn = [\n # (stable-diffusion, HF Diffusers)\n (\"norm.\", \"group_norm.\"),\n (\"q.\", \"query.\"),\n (\"k.\", \"key.\"),\n (\"v.\", \"value.\"),\n (\"proj_out.\", \"proj_attn.\"),\n]\n\n# This is probably not the most ideal solution, but it does work.\nvae_extra_conversion_map = [\n (\"to_q\", \"q\"),\n (\"to_k\", \"k\"),\n (\"to_v\", \"v\"),\n (\"to_out.0\", \"proj_out\"),\n]\n\n\ndef reshape_weight_for_sd(w):\n # convert HF linear weights to SD conv2d weights\n if not w.ndim == 1:\n return w.reshape(*w.shape, 1, 1)\n else:\n return w\n\n\ndef convert_vae_state_dict(vae_state_dict):\n mapping = {k: k for k in vae_state_dict.keys()}\n for k, v in mapping.items():\n for sd_part, hf_part in vae_conversion_map:\n v = v.replace(hf_part, sd_part)\n mapping[k] = v\n for k, v in mapping.items():\n if \"attentions\" in k:\n for sd_part, hf_part in vae_conversion_map_attn:\n v = v.replace(hf_part, sd_part)\n mapping[k] = v\n new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}\n weights_to_convert = [\"q\", \"k\", \"v\", \"proj_out\"]\n keys_to_rename = {}\n for k, v in new_state_dict.items():\n for weight_name in weights_to_convert:\n if f\"mid.attn_1.{weight_name}.weight\" in k:\n print(f\"Reshaping {k} for SD format\")\n new_state_dict[k] = reshape_weight_for_sd(v)\n for weight_name, real_weight_name in vae_extra_conversion_map:\n if f\"mid.attn_1.{weight_name}.weight\" in k or f\"mid.attn_1.{weight_name}.bias\" in k:\n keys_to_rename[k] = k.replace(weight_name, real_weight_name)\n for k, v in keys_to_rename.items():\n if k in new_state_dict:\n print(f\"Renaming {k} to {v}\")\n new_state_dict[v] = reshape_weight_for_sd(new_state_dict[k])\n del new_state_dict[k]\n return new_state_dict\n\n\n# =========================#\n# Text Encoder Conversion #\n# =========================#\n\n\ntextenc_conversion_lst = [\n # (stable-diffusion, HF Diffusers)\n (\"resblocks.\", \"text_model.encoder.layers.\"),\n (\"ln_1\", \"layer_norm1\"),\n (\"ln_2\", \"layer_norm2\"),\n (\".c_fc.\", \".fc1.\"),\n (\".c_proj.\", \".fc2.\"),\n (\".attn\", \".self_attn\"),\n (\"ln_final.\", \"transformer.text_model.final_layer_norm.\"),\n (\"token_embedding.weight\", \"transformer.text_model.embeddings.token_embedding.weight\"),\n (\"positional_embedding\", \"transformer.text_model.embeddings.position_embedding.weight\"),\n]\nprotected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}\ntextenc_pattern = re.compile(\"|\".join(protected.keys()))\n\n# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp\ncode2idx = {\"q\": 0, \"k\": 1, \"v\": 2}\n\n\ndef convert_text_enc_state_dict_v20(text_enc_dict):\n new_state_dict = {}\n capture_qkv_weight = {}\n capture_qkv_bias = {}\n for k, v in text_enc_dict.items():\n if (\n k.endswith(\".self_attn.q_proj.weight\")\n or k.endswith(\".self_attn.k_proj.weight\")\n or k.endswith(\".self_attn.v_proj.weight\")\n ):\n k_pre = k[: -len(\".q_proj.weight\")]\n k_code = k[-len(\"q_proj.weight\")]\n if k_pre not in capture_qkv_weight:\n capture_qkv_weight[k_pre] = [None, None, None]\n capture_qkv_weight[k_pre][code2idx[k_code]] = v\n continue\n\n if (\n k.endswith(\".self_attn.q_proj.bias\")\n or k.endswith(\".self_attn.k_proj.bias\")\n or k.endswith(\".self_attn.v_proj.bias\")\n ):\n k_pre = k[: -len(\".q_proj.bias\")]\n k_code = k[-len(\"q_proj.bias\")]\n if k_pre not in capture_qkv_bias:\n capture_qkv_bias[k_pre] = [None, None, None]\n capture_qkv_bias[k_pre][code2idx[k_code]] = v\n continue\n\n relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k)\n new_state_dict[relabelled_key] = v\n\n for k_pre, tensors in capture_qkv_weight.items():\n if None in tensors:\n raise Exception(\"CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing\")\n relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)\n new_state_dict[relabelled_key + \".in_proj_weight\"] = torch.cat(tensors)\n\n for k_pre, tensors in capture_qkv_bias.items():\n if None in tensors:\n raise Exception(\"CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing\")\n relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)\n new_state_dict[relabelled_key + \".in_proj_bias\"] = torch.cat(tensors)\n\n return new_state_dict\n\n\ndef convert_text_enc_state_dict(text_enc_dict):\n return text_enc_dict\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"--model_path\", default=None, type=str, required=True, help=\"Path to the model to convert.\")\n parser.add_argument(\"--checkpoint_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n parser.add_argument(\"--half\", action=\"store_true\", help=\"Save weights in half precision.\")\n parser.add_argument(\n \"--use_safetensors\", action=\"store_true\", help=\"Save weights use safetensors, default is ckpt.\"\n )\n\n args = parser.parse_args()\n\n assert args.model_path is not None, \"Must provide a model path!\"\n\n assert args.checkpoint_path is not None, \"Must provide a checkpoint path!\"\n\n # Path for safetensors\n unet_path = osp.join(args.model_path, \"unet\", \"diffusion_pytorch_model.safetensors\")\n vae_path = osp.join(args.model_path, \"vae\", \"diffusion_pytorch_model.safetensors\")\n text_enc_path = osp.join(args.model_path, \"text_encoder\", \"model.safetensors\")\n\n # Load models from safetensors if it exists, if it doesn't pytorch\n if osp.exists(unet_path):\n unet_state_dict = load_file(unet_path, device=\"cpu\")\n else:\n unet_path = osp.join(args.model_path, \"unet\", \"diffusion_pytorch_model.bin\")\n unet_state_dict = torch.load(unet_path, map_location=\"cpu\")\n\n if osp.exists(vae_path):\n vae_state_dict = load_file(vae_path, device=\"cpu\")\n else:\n vae_path = osp.join(args.model_path, \"vae\", \"diffusion_pytorch_model.bin\")\n vae_state_dict = torch.load(vae_path, map_location=\"cpu\")\n\n if osp.exists(text_enc_path):\n text_enc_dict = load_file(text_enc_path, device=\"cpu\")\n else:\n text_enc_path = osp.join(args.model_path, \"text_encoder\", \"pytorch_model.bin\")\n text_enc_dict = torch.load(text_enc_path, map_location=\"cpu\")\n\n # Convert the UNet model\n unet_state_dict = convert_unet_state_dict(unet_state_dict)\n unet_state_dict = {\"model.diffusion_model.\" + k: v for k, v in unet_state_dict.items()}\n\n # Convert the VAE model\n vae_state_dict = convert_vae_state_dict(vae_state_dict)\n vae_state_dict = {\"first_stage_model.\" + k: v for k, v in vae_state_dict.items()}\n\n # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper\n is_v20_model = \"text_model.encoder.layers.22.layer_norm2.bias\" in text_enc_dict\n\n if is_v20_model:\n # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm\n text_enc_dict = {\"transformer.\" + k: v for k, v in text_enc_dict.items()}\n text_enc_dict = convert_text_enc_state_dict_v20(text_enc_dict)\n text_enc_dict = {\"cond_stage_model.model.\" + k: v for k, v in text_enc_dict.items()}\n else:\n text_enc_dict = convert_text_enc_state_dict(text_enc_dict)\n text_enc_dict = {\"cond_stage_model.transformer.\" + k: v for k, v in text_enc_dict.items()}\n\n # Put together new checkpoint\n state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict}\n if args.half:\n state_dict = {k: v.half() for k, v in state_dict.items()}\n\n if args.use_safetensors:\n save_file(state_dict, args.checkpoint_path)\n else:\n state_dict = {\"state_dict\": state_dict}\n torch.save(state_dict, args.checkpoint_path)\n" }, { "path": "diffusers/scripts/convert_dit_to_diffusers.py", "content": "import argparse\nimport os\n\nimport torch\nfrom torchvision.datasets.utils import download_url\n\nfrom diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, Transformer2DModel\n\n\npretrained_models = {512: \"DiT-XL-2-512x512.pt\", 256: \"DiT-XL-2-256x256.pt\"}\n\n\ndef download_model(model_name):\n \"\"\"\n Downloads a pre-trained DiT model from the web.\n \"\"\"\n local_path = f\"pretrained_models/{model_name}\"\n if not os.path.isfile(local_path):\n os.makedirs(\"pretrained_models\", exist_ok=True)\n web_path = f\"https://dl.fbaipublicfiles.com/DiT/models/{model_name}\"\n download_url(web_path, \"pretrained_models\")\n model = torch.load(local_path, map_location=lambda storage, loc: storage)\n return model\n\n\ndef main(args):\n state_dict = download_model(pretrained_models[args.image_size])\n\n state_dict[\"pos_embed.proj.weight\"] = state_dict[\"x_embedder.proj.weight\"]\n state_dict[\"pos_embed.proj.bias\"] = state_dict[\"x_embedder.proj.bias\"]\n state_dict.pop(\"x_embedder.proj.weight\")\n state_dict.pop(\"x_embedder.proj.bias\")\n\n for depth in range(28):\n state_dict[f\"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_1.weight\"] = state_dict[\n \"t_embedder.mlp.0.weight\"\n ]\n state_dict[f\"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_1.bias\"] = state_dict[\n \"t_embedder.mlp.0.bias\"\n ]\n state_dict[f\"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_2.weight\"] = state_dict[\n \"t_embedder.mlp.2.weight\"\n ]\n state_dict[f\"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_2.bias\"] = state_dict[\n \"t_embedder.mlp.2.bias\"\n ]\n state_dict[f\"transformer_blocks.{depth}.norm1.emb.class_embedder.embedding_table.weight\"] = state_dict[\n \"y_embedder.embedding_table.weight\"\n ]\n\n state_dict[f\"transformer_blocks.{depth}.norm1.linear.weight\"] = state_dict[\n f\"blocks.{depth}.adaLN_modulation.1.weight\"\n ]\n state_dict[f\"transformer_blocks.{depth}.norm1.linear.bias\"] = state_dict[\n f\"blocks.{depth}.adaLN_modulation.1.bias\"\n ]\n\n q, k, v = torch.chunk(state_dict[f\"blocks.{depth}.attn.qkv.weight\"], 3, dim=0)\n q_bias, k_bias, v_bias = torch.chunk(state_dict[f\"blocks.{depth}.attn.qkv.bias\"], 3, dim=0)\n\n state_dict[f\"transformer_blocks.{depth}.attn1.to_q.weight\"] = q\n state_dict[f\"transformer_blocks.{depth}.attn1.to_q.bias\"] = q_bias\n state_dict[f\"transformer_blocks.{depth}.attn1.to_k.weight\"] = k\n state_dict[f\"transformer_blocks.{depth}.attn1.to_k.bias\"] = k_bias\n state_dict[f\"transformer_blocks.{depth}.attn1.to_v.weight\"] = v\n state_dict[f\"transformer_blocks.{depth}.attn1.to_v.bias\"] = v_bias\n\n state_dict[f\"transformer_blocks.{depth}.attn1.to_out.0.weight\"] = state_dict[\n f\"blocks.{depth}.attn.proj.weight\"\n ]\n state_dict[f\"transformer_blocks.{depth}.attn1.to_out.0.bias\"] = state_dict[f\"blocks.{depth}.attn.proj.bias\"]\n\n state_dict[f\"transformer_blocks.{depth}.ff.net.0.proj.weight\"] = state_dict[f\"blocks.{depth}.mlp.fc1.weight\"]\n state_dict[f\"transformer_blocks.{depth}.ff.net.0.proj.bias\"] = state_dict[f\"blocks.{depth}.mlp.fc1.bias\"]\n state_dict[f\"transformer_blocks.{depth}.ff.net.2.weight\"] = state_dict[f\"blocks.{depth}.mlp.fc2.weight\"]\n state_dict[f\"transformer_blocks.{depth}.ff.net.2.bias\"] = state_dict[f\"blocks.{depth}.mlp.fc2.bias\"]\n\n state_dict.pop(f\"blocks.{depth}.attn.qkv.weight\")\n state_dict.pop(f\"blocks.{depth}.attn.qkv.bias\")\n state_dict.pop(f\"blocks.{depth}.attn.proj.weight\")\n state_dict.pop(f\"blocks.{depth}.attn.proj.bias\")\n state_dict.pop(f\"blocks.{depth}.mlp.fc1.weight\")\n state_dict.pop(f\"blocks.{depth}.mlp.fc1.bias\")\n state_dict.pop(f\"blocks.{depth}.mlp.fc2.weight\")\n state_dict.pop(f\"blocks.{depth}.mlp.fc2.bias\")\n state_dict.pop(f\"blocks.{depth}.adaLN_modulation.1.weight\")\n state_dict.pop(f\"blocks.{depth}.adaLN_modulation.1.bias\")\n\n state_dict.pop(\"t_embedder.mlp.0.weight\")\n state_dict.pop(\"t_embedder.mlp.0.bias\")\n state_dict.pop(\"t_embedder.mlp.2.weight\")\n state_dict.pop(\"t_embedder.mlp.2.bias\")\n state_dict.pop(\"y_embedder.embedding_table.weight\")\n\n state_dict[\"proj_out_1.weight\"] = state_dict[\"final_layer.adaLN_modulation.1.weight\"]\n state_dict[\"proj_out_1.bias\"] = state_dict[\"final_layer.adaLN_modulation.1.bias\"]\n state_dict[\"proj_out_2.weight\"] = state_dict[\"final_layer.linear.weight\"]\n state_dict[\"proj_out_2.bias\"] = state_dict[\"final_layer.linear.bias\"]\n\n state_dict.pop(\"final_layer.linear.weight\")\n state_dict.pop(\"final_layer.linear.bias\")\n state_dict.pop(\"final_layer.adaLN_modulation.1.weight\")\n state_dict.pop(\"final_layer.adaLN_modulation.1.bias\")\n\n # DiT XL/2\n transformer = Transformer2DModel(\n sample_size=args.image_size // 8,\n num_layers=28,\n attention_head_dim=72,\n in_channels=4,\n out_channels=8,\n patch_size=2,\n attention_bias=True,\n num_attention_heads=16,\n activation_fn=\"gelu-approximate\",\n num_embeds_ada_norm=1000,\n norm_type=\"ada_norm_zero\",\n norm_elementwise_affine=False,\n )\n transformer.load_state_dict(state_dict, strict=True)\n\n scheduler = DDIMScheduler(\n num_train_timesteps=1000,\n beta_schedule=\"linear\",\n prediction_type=\"epsilon\",\n clip_sample=False,\n )\n\n vae = AutoencoderKL.from_pretrained(args.vae_model)\n\n pipeline = DiTPipeline(transformer=transformer, vae=vae, scheduler=scheduler)\n\n if args.save:\n pipeline.save_pretrained(args.checkpoint_path)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--image_size\",\n default=256,\n type=int,\n required=False,\n help=\"Image size of pretrained model, either 256 or 512.\",\n )\n parser.add_argument(\n \"--vae_model\",\n default=\"stabilityai/sd-vae-ft-ema\",\n type=str,\n required=False,\n help=\"Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.\",\n )\n parser.add_argument(\n \"--save\", default=True, type=bool, required=False, help=\"Whether to save the converted pipeline or not.\"\n )\n parser.add_argument(\n \"--checkpoint_path\", default=None, type=str, required=True, help=\"Path to the output pipeline.\"\n )\n\n args = parser.parse_args()\n main(args)\n" }, { "path": "diffusers/scripts/convert_gligen_to_diffusers.py", "content": "import argparse\nimport re\n\nimport torch\nimport yaml\nfrom transformers import (\n CLIPProcessor,\n CLIPTextModel,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n StableDiffusionGLIGENPipeline,\n StableDiffusionGLIGENTextImagePipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.pipelines.stable_diffusion.convert_from_ckpt import (\n assign_to_checkpoint,\n conv_attn_to_linear,\n protected,\n renew_attention_paths,\n renew_resnet_paths,\n renew_vae_attention_paths,\n renew_vae_resnet_paths,\n shave_segments,\n textenc_conversion_map,\n textenc_pattern,\n)\n\n\ndef convert_open_clip_checkpoint(checkpoint):\n checkpoint = checkpoint[\"text_encoder\"]\n text_model = CLIPTextModel.from_pretrained(\"openai/clip-vit-large-patch14\")\n\n keys = list(checkpoint.keys())\n\n text_model_dict = {}\n\n if \"cond_stage_model.model.text_projection\" in checkpoint:\n d_model = int(checkpoint[\"cond_stage_model.model.text_projection\"].shape[0])\n else:\n d_model = 1024\n\n for key in keys:\n if \"resblocks.23\" in key: # Diffusers drops the final layer and only uses the penultimate layer\n continue\n if key in textenc_conversion_map:\n text_model_dict[textenc_conversion_map[key]] = checkpoint[key]\n # if key.startswith(\"cond_stage_model.model.transformer.\"):\n new_key = key[len(\"transformer.\") :]\n if new_key.endswith(\".in_proj_weight\"):\n new_key = new_key[: -len(\".in_proj_weight\")]\n new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)\n text_model_dict[new_key + \".q_proj.weight\"] = checkpoint[key][:d_model, :]\n text_model_dict[new_key + \".k_proj.weight\"] = checkpoint[key][d_model : d_model * 2, :]\n text_model_dict[new_key + \".v_proj.weight\"] = checkpoint[key][d_model * 2 :, :]\n elif new_key.endswith(\".in_proj_bias\"):\n new_key = new_key[: -len(\".in_proj_bias\")]\n new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)\n text_model_dict[new_key + \".q_proj.bias\"] = checkpoint[key][:d_model]\n text_model_dict[new_key + \".k_proj.bias\"] = checkpoint[key][d_model : d_model * 2]\n text_model_dict[new_key + \".v_proj.bias\"] = checkpoint[key][d_model * 2 :]\n else:\n if key != \"transformer.text_model.embeddings.position_ids\":\n new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)\n\n text_model_dict[new_key] = checkpoint[key]\n\n if key == \"transformer.text_model.embeddings.token_embedding.weight\":\n text_model_dict[\"text_model.embeddings.token_embedding.weight\"] = checkpoint[key]\n\n text_model_dict.pop(\"text_model.embeddings.transformer.text_model.embeddings.token_embedding.weight\")\n\n text_model.load_state_dict(text_model_dict)\n\n return text_model\n\n\ndef convert_gligen_vae_checkpoint(checkpoint, config):\n checkpoint = checkpoint[\"autoencoder\"]\n vae_state_dict = {}\n vae_key = \"first_stage_model.\"\n keys = list(checkpoint.keys())\n for key in keys:\n vae_state_dict[key.replace(vae_key, \"\")] = checkpoint.get(key)\n\n new_checkpoint = {}\n\n new_checkpoint[\"encoder.conv_in.weight\"] = vae_state_dict[\"encoder.conv_in.weight\"]\n new_checkpoint[\"encoder.conv_in.bias\"] = vae_state_dict[\"encoder.conv_in.bias\"]\n new_checkpoint[\"encoder.conv_out.weight\"] = vae_state_dict[\"encoder.conv_out.weight\"]\n new_checkpoint[\"encoder.conv_out.bias\"] = vae_state_dict[\"encoder.conv_out.bias\"]\n new_checkpoint[\"encoder.conv_norm_out.weight\"] = vae_state_dict[\"encoder.norm_out.weight\"]\n new_checkpoint[\"encoder.conv_norm_out.bias\"] = vae_state_dict[\"encoder.norm_out.bias\"]\n\n new_checkpoint[\"decoder.conv_in.weight\"] = vae_state_dict[\"decoder.conv_in.weight\"]\n new_checkpoint[\"decoder.conv_in.bias\"] = vae_state_dict[\"decoder.conv_in.bias\"]\n new_checkpoint[\"decoder.conv_out.weight\"] = vae_state_dict[\"decoder.conv_out.weight\"]\n new_checkpoint[\"decoder.conv_out.bias\"] = vae_state_dict[\"decoder.conv_out.bias\"]\n new_checkpoint[\"decoder.conv_norm_out.weight\"] = vae_state_dict[\"decoder.norm_out.weight\"]\n new_checkpoint[\"decoder.conv_norm_out.bias\"] = vae_state_dict[\"decoder.norm_out.bias\"]\n\n new_checkpoint[\"quant_conv.weight\"] = vae_state_dict[\"quant_conv.weight\"]\n new_checkpoint[\"quant_conv.bias\"] = vae_state_dict[\"quant_conv.bias\"]\n new_checkpoint[\"post_quant_conv.weight\"] = vae_state_dict[\"post_quant_conv.weight\"]\n new_checkpoint[\"post_quant_conv.bias\"] = vae_state_dict[\"post_quant_conv.bias\"]\n\n # Retrieves the keys for the encoder down blocks only\n num_down_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in vae_state_dict if \"encoder.down\" in layer})\n down_blocks = {\n layer_id: [key for key in vae_state_dict if f\"down.{layer_id}\" in key] for layer_id in range(num_down_blocks)\n }\n\n # Retrieves the keys for the decoder up blocks only\n num_up_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in vae_state_dict if \"decoder.up\" in layer})\n up_blocks = {\n layer_id: [key for key in vae_state_dict if f\"up.{layer_id}\" in key] for layer_id in range(num_up_blocks)\n }\n\n for i in range(num_down_blocks):\n resnets = [key for key in down_blocks[i] if f\"down.{i}\" in key and f\"down.{i}.downsample\" not in key]\n\n if f\"encoder.down.{i}.downsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.weight\"] = vae_state_dict.pop(\n f\"encoder.down.{i}.downsample.conv.weight\"\n )\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.bias\"] = vae_state_dict.pop(\n f\"encoder.down.{i}.downsample.conv.bias\"\n )\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"down.{i}.block\", \"new\": f\"down_blocks.{i}.resnets\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_resnets = [key for key in vae_state_dict if \"encoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"encoder.mid.block_{i}\" in key]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_attentions = [key for key in vae_state_dict if \"encoder.mid.attn\" in key]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n conv_attn_to_linear(new_checkpoint)\n\n for i in range(num_up_blocks):\n block_id = num_up_blocks - 1 - i\n resnets = [\n key for key in up_blocks[block_id] if f\"up.{block_id}\" in key and f\"up.{block_id}.upsample\" not in key\n ]\n\n if f\"decoder.up.{block_id}.upsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.weight\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.weight\"\n ]\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.bias\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.bias\"\n ]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"up.{block_id}.block\", \"new\": f\"up_blocks.{i}.resnets\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_resnets = [key for key in vae_state_dict if \"decoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"decoder.mid.block_{i}\" in key]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_attentions = [key for key in vae_state_dict if \"decoder.mid.attn\" in key]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n conv_attn_to_linear(new_checkpoint)\n\n for key in new_checkpoint.keys():\n if \"encoder.mid_block.attentions.0\" in key or \"decoder.mid_block.attentions.0\" in key:\n if \"query\" in key:\n new_checkpoint[key.replace(\"query\", \"to_q\")] = new_checkpoint.pop(key)\n if \"value\" in key:\n new_checkpoint[key.replace(\"value\", \"to_v\")] = new_checkpoint.pop(key)\n if \"key\" in key:\n new_checkpoint[key.replace(\"key\", \"to_k\")] = new_checkpoint.pop(key)\n if \"proj_attn\" in key:\n new_checkpoint[key.replace(\"proj_attn\", \"to_out.0\")] = new_checkpoint.pop(key)\n\n return new_checkpoint\n\n\ndef convert_gligen_unet_checkpoint(checkpoint, config, path=None, extract_ema=False):\n unet_state_dict = {}\n checkpoint = checkpoint[\"model\"]\n keys = list(checkpoint.keys())\n\n unet_key = \"model.diffusion_model.\"\n\n if sum(k.startswith(\"model_ema\") for k in keys) > 100 and extract_ema:\n print(f\"Checkpoint {path} has bot EMA and non-EMA weights.\")\n print(\n \"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA\"\n \" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.\"\n )\n for key in keys:\n if key.startswith(\"model.diffusion_model\"):\n flat_ema_key = \"model_ema.\" + \"\".join(key.split(\".\")[1:])\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint.pop(flat_ema_key)\n else:\n if sum(k.startswith(\"model_ema\") for k in keys) > 100:\n print(\n \"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA\"\n \" weights (usually better for inference), please make sure to add the `--extract_ema` flag.\"\n )\n for key in keys:\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint.pop(key)\n\n new_checkpoint = {}\n\n new_checkpoint[\"time_embedding.linear_1.weight\"] = unet_state_dict[\"time_embed.0.weight\"]\n new_checkpoint[\"time_embedding.linear_1.bias\"] = unet_state_dict[\"time_embed.0.bias\"]\n new_checkpoint[\"time_embedding.linear_2.weight\"] = unet_state_dict[\"time_embed.2.weight\"]\n new_checkpoint[\"time_embedding.linear_2.bias\"] = unet_state_dict[\"time_embed.2.bias\"]\n\n new_checkpoint[\"conv_in.weight\"] = unet_state_dict[\"input_blocks.0.0.weight\"]\n new_checkpoint[\"conv_in.bias\"] = unet_state_dict[\"input_blocks.0.0.bias\"]\n\n new_checkpoint[\"conv_norm_out.weight\"] = unet_state_dict[\"out.0.weight\"]\n new_checkpoint[\"conv_norm_out.bias\"] = unet_state_dict[\"out.0.bias\"]\n new_checkpoint[\"conv_out.weight\"] = unet_state_dict[\"out.2.weight\"]\n new_checkpoint[\"conv_out.bias\"] = unet_state_dict[\"out.2.bias\"]\n\n # Retrieves the keys for the input blocks only\n num_input_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"input_blocks\" in layer})\n input_blocks = {\n layer_id: [key for key in unet_state_dict if f\"input_blocks.{layer_id}\" in key]\n for layer_id in range(num_input_blocks)\n }\n\n # Retrieves the keys for the middle blocks only\n num_middle_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"middle_block\" in layer})\n middle_blocks = {\n layer_id: [key for key in unet_state_dict if f\"middle_block.{layer_id}\" in key]\n for layer_id in range(num_middle_blocks)\n }\n\n # Retrieves the keys for the output blocks only\n num_output_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"output_blocks\" in layer})\n output_blocks = {\n layer_id: [key for key in unet_state_dict if f\"output_blocks.{layer_id}\" in key]\n for layer_id in range(num_output_blocks)\n }\n\n for i in range(1, num_input_blocks):\n block_id = (i - 1) // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = (i - 1) % (config[\"layers_per_block\"] + 1)\n\n resnets = [\n key for key in input_blocks[i] if f\"input_blocks.{i}.0\" in key and f\"input_blocks.{i}.0.op\" not in key\n ]\n attentions = [key for key in input_blocks[i] if f\"input_blocks.{i}.1\" in key]\n\n if f\"input_blocks.{i}.0.op.weight\" in unet_state_dict:\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.weight\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.op.weight\"\n )\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.bias\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.op.bias\"\n )\n\n paths = renew_resnet_paths(resnets)\n meta_path = {\"old\": f\"input_blocks.{i}.0\", \"new\": f\"down_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n meta_path = {\"old\": f\"input_blocks.{i}.1\", \"new\": f\"down_blocks.{block_id}.attentions.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n resnet_0 = middle_blocks[0]\n attentions = middle_blocks[1]\n resnet_1 = middle_blocks[2]\n\n resnet_0_paths = renew_resnet_paths(resnet_0)\n assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)\n\n resnet_1_paths = renew_resnet_paths(resnet_1)\n assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)\n\n attentions_paths = renew_attention_paths(attentions)\n meta_path = {\"old\": \"middle_block.1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(\n attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n for i in range(num_output_blocks):\n block_id = i // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = i % (config[\"layers_per_block\"] + 1)\n output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]\n output_block_list = {}\n\n for layer in output_block_layers:\n layer_id, layer_name = layer.split(\".\")[0], shave_segments(layer, 1)\n if layer_id in output_block_list:\n output_block_list[layer_id].append(layer_name)\n else:\n output_block_list[layer_id] = [layer_name]\n\n if len(output_block_list) > 1:\n resnets = [key for key in output_blocks[i] if f\"output_blocks.{i}.0\" in key]\n attentions = [key for key in output_blocks[i] if f\"output_blocks.{i}.1\" in key]\n\n resnet_0_paths = renew_resnet_paths(resnets)\n paths = renew_resnet_paths(resnets)\n\n meta_path = {\"old\": f\"output_blocks.{i}.0\", \"new\": f\"up_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n output_block_list = {k: sorted(v) for k, v in output_block_list.items()}\n if [\"conv.bias\", \"conv.weight\"] in output_block_list.values():\n index = list(output_block_list.values()).index([\"conv.bias\", \"conv.weight\"])\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.weight\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.weight\"\n ]\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.bias\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.bias\"\n ]\n\n # Clear attentions as they have been attributed above.\n if len(attentions) == 2:\n attentions = []\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n meta_path = {\n \"old\": f\"output_blocks.{i}.1\",\n \"new\": f\"up_blocks.{block_id}.attentions.{layer_in_block_id}\",\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n else:\n resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)\n for path in resnet_0_paths:\n old_path = \".\".join([\"output_blocks\", str(i), path[\"old\"]])\n new_path = \".\".join([\"up_blocks\", str(block_id), \"resnets\", str(layer_in_block_id), path[\"new\"]])\n\n new_checkpoint[new_path] = unet_state_dict[old_path]\n\n for key in keys:\n if \"position_net\" in key:\n new_checkpoint[key] = unet_state_dict[key]\n\n return new_checkpoint\n\n\ndef create_vae_config(original_config, image_size: int):\n vae_params = original_config[\"autoencoder\"][\"params\"][\"ddconfig\"]\n _ = original_config[\"autoencoder\"][\"params\"][\"embed_dim\"]\n\n block_out_channels = [vae_params[\"ch\"] * mult for mult in vae_params[\"ch_mult\"]]\n down_block_types = [\"DownEncoderBlock2D\"] * len(block_out_channels)\n up_block_types = [\"UpDecoderBlock2D\"] * len(block_out_channels)\n\n config = {\n \"sample_size\": image_size,\n \"in_channels\": vae_params[\"in_channels\"],\n \"out_channels\": vae_params[\"out_ch\"],\n \"down_block_types\": tuple(down_block_types),\n \"up_block_types\": tuple(up_block_types),\n \"block_out_channels\": tuple(block_out_channels),\n \"latent_channels\": vae_params[\"z_channels\"],\n \"layers_per_block\": vae_params[\"num_res_blocks\"],\n }\n\n return config\n\n\ndef create_unet_config(original_config, image_size: int, attention_type):\n unet_params = original_config[\"model\"][\"params\"]\n vae_params = original_config[\"autoencoder\"][\"params\"][\"ddconfig\"]\n\n block_out_channels = [unet_params[\"model_channels\"] * mult for mult in unet_params[\"channel_mult\"]]\n\n down_block_types = []\n resolution = 1\n for i in range(len(block_out_channels)):\n block_type = \"CrossAttnDownBlock2D\" if resolution in unet_params[\"attention_resolutions\"] else \"DownBlock2D\"\n down_block_types.append(block_type)\n if i != len(block_out_channels) - 1:\n resolution *= 2\n\n up_block_types = []\n for i in range(len(block_out_channels)):\n block_type = \"CrossAttnUpBlock2D\" if resolution in unet_params[\"attention_resolutions\"] else \"UpBlock2D\"\n up_block_types.append(block_type)\n resolution //= 2\n\n vae_scale_factor = 2 ** (len(vae_params[\"ch_mult\"]) - 1)\n\n head_dim = unet_params[\"num_heads\"] if \"num_heads\" in unet_params else None\n use_linear_projection = (\n unet_params[\"use_linear_in_transformer\"] if \"use_linear_in_transformer\" in unet_params else False\n )\n if use_linear_projection:\n if head_dim is None:\n head_dim = [5, 10, 20, 20]\n\n config = {\n \"sample_size\": image_size // vae_scale_factor,\n \"in_channels\": unet_params[\"in_channels\"],\n \"down_block_types\": tuple(down_block_types),\n \"block_out_channels\": tuple(block_out_channels),\n \"layers_per_block\": unet_params[\"num_res_blocks\"],\n \"cross_attention_dim\": unet_params[\"context_dim\"],\n \"attention_head_dim\": head_dim,\n \"use_linear_projection\": use_linear_projection,\n \"attention_type\": attention_type,\n }\n\n return config\n\n\ndef convert_gligen_to_diffusers(\n checkpoint_path: str,\n original_config_file: str,\n attention_type: str,\n image_size: int = 512,\n extract_ema: bool = False,\n num_in_channels: int = None,\n device: str = None,\n):\n if device is None:\n device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n checkpoint = torch.load(checkpoint_path, map_location=device)\n else:\n checkpoint = torch.load(checkpoint_path, map_location=device)\n\n if \"global_step\" in checkpoint:\n checkpoint[\"global_step\"]\n else:\n print(\"global_step key not found in model\")\n\n original_config = yaml.safe_load(original_config_file)\n\n if num_in_channels is not None:\n original_config[\"model\"][\"params\"][\"in_channels\"] = num_in_channels\n\n num_train_timesteps = original_config[\"diffusion\"][\"params\"][\"timesteps\"]\n beta_start = original_config[\"diffusion\"][\"params\"][\"linear_start\"]\n beta_end = original_config[\"diffusion\"][\"params\"][\"linear_end\"]\n\n scheduler = DDIMScheduler(\n beta_end=beta_end,\n beta_schedule=\"scaled_linear\",\n beta_start=beta_start,\n num_train_timesteps=num_train_timesteps,\n steps_offset=1,\n clip_sample=False,\n set_alpha_to_one=False,\n prediction_type=\"epsilon\",\n )\n\n # Convert the UNet2DConditionalModel model\n unet_config = create_unet_config(original_config, image_size, attention_type)\n unet = UNet2DConditionModel(**unet_config)\n\n converted_unet_checkpoint = convert_gligen_unet_checkpoint(\n checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema\n )\n\n unet.load_state_dict(converted_unet_checkpoint)\n\n # Convert the VAE model\n vae_config = create_vae_config(original_config, image_size)\n converted_vae_checkpoint = convert_gligen_vae_checkpoint(checkpoint, vae_config)\n\n vae = AutoencoderKL(**vae_config)\n vae.load_state_dict(converted_vae_checkpoint)\n\n # Convert the text model\n text_encoder = convert_open_clip_checkpoint(checkpoint)\n tokenizer = CLIPTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\")\n\n if attention_type == \"gated-text-image\":\n image_encoder = CLIPVisionModelWithProjection.from_pretrained(\"openai/clip-vit-large-patch14\")\n processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-large-patch14\")\n\n pipe = StableDiffusionGLIGENTextImagePipeline(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n image_encoder=image_encoder,\n processor=processor,\n unet=unet,\n scheduler=scheduler,\n safety_checker=None,\n feature_extractor=None,\n )\n elif attention_type == \"gated\":\n pipe = StableDiffusionGLIGENPipeline(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=None,\n feature_extractor=None,\n )\n\n return pipe\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--checkpoint_path\", default=None, type=str, required=True, help=\"Path to the checkpoint to convert.\"\n )\n parser.add_argument(\n \"--original_config_file\",\n default=None,\n type=str,\n required=True,\n help=\"The YAML config file corresponding to the gligen architecture.\",\n )\n parser.add_argument(\n \"--num_in_channels\",\n default=None,\n type=int,\n help=\"The number of input channels. If `None` number of input channels will be automatically inferred.\",\n )\n parser.add_argument(\n \"--extract_ema\",\n action=\"store_true\",\n help=(\n \"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights\"\n \" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield\"\n \" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.\"\n ),\n )\n parser.add_argument(\n \"--attention_type\",\n default=None,\n type=str,\n required=True,\n help=\"Type of attention ex: gated or gated-text-image\",\n )\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n parser.add_argument(\"--device\", type=str, help=\"Device to use.\")\n parser.add_argument(\"--half\", action=\"store_true\", help=\"Save weights in half precision.\")\n\n args = parser.parse_args()\n\n pipe = convert_gligen_to_diffusers(\n checkpoint_path=args.checkpoint_path,\n original_config_file=args.original_config_file,\n attention_type=args.attention_type,\n extract_ema=args.extract_ema,\n num_in_channels=args.num_in_channels,\n device=args.device,\n )\n\n if args.half:\n pipe.to(dtype=torch.float16)\n\n pipe.save_pretrained(args.dump_path)\n" }, { "path": "diffusers/scripts/convert_i2vgen_to_diffusers.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Conversion script for the LDM checkpoints.\"\"\"\n\nimport argparse\n\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom diffusers import DDIMScheduler, I2VGenXLPipeline, I2VGenXLUNet, StableDiffusionPipeline\n\n\nCLIP_ID = \"laion/CLIP-ViT-H-14-laion2B-s32B-b79K\"\n\n\ndef assign_to_checkpoint(\n paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None\n):\n \"\"\"\n This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits\n attention layers, and takes into account additional replacements that may arise.\n\n Assigns the weights to the new checkpoint.\n \"\"\"\n assert isinstance(paths, list), \"Paths should be a list of dicts containing 'old' and 'new' keys.\"\n\n # Splits the attention layers into three variables.\n if attention_paths_to_split is not None:\n for path, path_map in attention_paths_to_split.items():\n old_tensor = old_checkpoint[path]\n channels = old_tensor.shape[0] // 3\n\n target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)\n\n num_heads = old_tensor.shape[0] // config[\"num_head_channels\"] // 3\n\n old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])\n query, key, value = old_tensor.split(channels // num_heads, dim=1)\n\n checkpoint[path_map[\"query\"]] = query.reshape(target_shape)\n checkpoint[path_map[\"key\"]] = key.reshape(target_shape)\n checkpoint[path_map[\"value\"]] = value.reshape(target_shape)\n\n for path in paths:\n new_path = path[\"new\"]\n\n # These have already been assigned\n if attention_paths_to_split is not None and new_path in attention_paths_to_split:\n continue\n\n if additional_replacements is not None:\n for replacement in additional_replacements:\n new_path = new_path.replace(replacement[\"old\"], replacement[\"new\"])\n\n # proj_attn.weight has to be converted from conv 1D to linear\n weight = old_checkpoint[path[\"old\"]]\n names = [\"proj_attn.weight\"]\n names_2 = [\"proj_out.weight\", \"proj_in.weight\"]\n if any(k in new_path for k in names):\n checkpoint[new_path] = weight[:, :, 0]\n elif any(k in new_path for k in names_2) and len(weight.shape) > 2 and \".attentions.\" not in new_path:\n checkpoint[new_path] = weight[:, :, 0]\n else:\n checkpoint[new_path] = weight\n\n\ndef renew_attention_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside attentions to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef shave_segments(path, n_shave_prefix_segments=1):\n \"\"\"\n Removes segments. Positive values shave the first segments, negative shave the last segments.\n \"\"\"\n if n_shave_prefix_segments >= 0:\n return \".\".join(path.split(\".\")[n_shave_prefix_segments:])\n else:\n return \".\".join(path.split(\".\")[:n_shave_prefix_segments])\n\n\ndef renew_temp_conv_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside resnets to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n mapping.append({\"old\": old_item, \"new\": old_item})\n\n return mapping\n\n\ndef renew_resnet_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside resnets to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item.replace(\"in_layers.0\", \"norm1\")\n new_item = new_item.replace(\"in_layers.2\", \"conv1\")\n\n new_item = new_item.replace(\"out_layers.0\", \"norm2\")\n new_item = new_item.replace(\"out_layers.3\", \"conv2\")\n\n new_item = new_item.replace(\"emb_layers.1\", \"time_emb_proj\")\n new_item = new_item.replace(\"skip_connection\", \"conv_shortcut\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n if \"temopral_conv\" not in old_item:\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False):\n \"\"\"\n Takes a state dict and a config, and returns a converted checkpoint.\n \"\"\"\n\n # extract state_dict for UNet\n unet_state_dict = {}\n keys = list(checkpoint.keys())\n\n unet_key = \"model.diffusion_model.\"\n\n # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA\n if sum(k.startswith(\"model_ema\") for k in keys) > 100 and extract_ema:\n print(f\"Checkpoint {path} has both EMA and non-EMA weights.\")\n print(\n \"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA\"\n \" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.\"\n )\n for key in keys:\n if key.startswith(\"model.diffusion_model\"):\n flat_ema_key = \"model_ema.\" + \"\".join(key.split(\".\")[1:])\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint.pop(flat_ema_key)\n else:\n if sum(k.startswith(\"model_ema\") for k in keys) > 100:\n print(\n \"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA\"\n \" weights (usually better for inference), please make sure to add the `--extract_ema` flag.\"\n )\n\n for key in keys:\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint.pop(key)\n\n new_checkpoint = {}\n\n new_checkpoint[\"time_embedding.linear_1.weight\"] = unet_state_dict[\"time_embed.0.weight\"]\n new_checkpoint[\"time_embedding.linear_1.bias\"] = unet_state_dict[\"time_embed.0.bias\"]\n new_checkpoint[\"time_embedding.linear_2.weight\"] = unet_state_dict[\"time_embed.2.weight\"]\n new_checkpoint[\"time_embedding.linear_2.bias\"] = unet_state_dict[\"time_embed.2.bias\"]\n\n additional_embedding_substrings = [\n \"local_image_concat\",\n \"context_embedding\",\n \"local_image_embedding\",\n \"fps_embedding\",\n ]\n for k in unet_state_dict:\n if any(substring in k for substring in additional_embedding_substrings):\n diffusers_key = k.replace(\"local_image_concat\", \"image_latents_proj_in\").replace(\n \"local_image_embedding\", \"image_latents_context_embedding\"\n )\n new_checkpoint[diffusers_key] = unet_state_dict[k]\n\n # temporal encoder.\n new_checkpoint[\"image_latents_temporal_encoder.norm1.weight\"] = unet_state_dict[\n \"local_temporal_encoder.layers.0.0.norm.weight\"\n ]\n new_checkpoint[\"image_latents_temporal_encoder.norm1.bias\"] = unet_state_dict[\n \"local_temporal_encoder.layers.0.0.norm.bias\"\n ]\n\n # attention\n qkv = unet_state_dict[\"local_temporal_encoder.layers.0.0.fn.to_qkv.weight\"]\n q, k, v = torch.chunk(qkv, 3, dim=0)\n new_checkpoint[\"image_latents_temporal_encoder.attn1.to_q.weight\"] = q\n new_checkpoint[\"image_latents_temporal_encoder.attn1.to_k.weight\"] = k\n new_checkpoint[\"image_latents_temporal_encoder.attn1.to_v.weight\"] = v\n new_checkpoint[\"image_latents_temporal_encoder.attn1.to_out.0.weight\"] = unet_state_dict[\n \"local_temporal_encoder.layers.0.0.fn.to_out.0.weight\"\n ]\n new_checkpoint[\"image_latents_temporal_encoder.attn1.to_out.0.bias\"] = unet_state_dict[\n \"local_temporal_encoder.layers.0.0.fn.to_out.0.bias\"\n ]\n\n # feedforward\n new_checkpoint[\"image_latents_temporal_encoder.ff.net.0.proj.weight\"] = unet_state_dict[\n \"local_temporal_encoder.layers.0.1.net.0.0.weight\"\n ]\n new_checkpoint[\"image_latents_temporal_encoder.ff.net.0.proj.bias\"] = unet_state_dict[\n \"local_temporal_encoder.layers.0.1.net.0.0.bias\"\n ]\n new_checkpoint[\"image_latents_temporal_encoder.ff.net.2.weight\"] = unet_state_dict[\n \"local_temporal_encoder.layers.0.1.net.2.weight\"\n ]\n new_checkpoint[\"image_latents_temporal_encoder.ff.net.2.bias\"] = unet_state_dict[\n \"local_temporal_encoder.layers.0.1.net.2.bias\"\n ]\n\n if \"class_embed_type\" in config:\n if config[\"class_embed_type\"] is None:\n # No parameters to port\n ...\n elif config[\"class_embed_type\"] == \"timestep\" or config[\"class_embed_type\"] == \"projection\":\n new_checkpoint[\"class_embedding.linear_1.weight\"] = unet_state_dict[\"label_emb.0.0.weight\"]\n new_checkpoint[\"class_embedding.linear_1.bias\"] = unet_state_dict[\"label_emb.0.0.bias\"]\n new_checkpoint[\"class_embedding.linear_2.weight\"] = unet_state_dict[\"label_emb.0.2.weight\"]\n new_checkpoint[\"class_embedding.linear_2.bias\"] = unet_state_dict[\"label_emb.0.2.bias\"]\n else:\n raise NotImplementedError(f\"Not implemented `class_embed_type`: {config['class_embed_type']}\")\n\n new_checkpoint[\"conv_in.weight\"] = unet_state_dict[\"input_blocks.0.0.weight\"]\n new_checkpoint[\"conv_in.bias\"] = unet_state_dict[\"input_blocks.0.0.bias\"]\n\n first_temp_attention = [v for v in unet_state_dict if v.startswith(\"input_blocks.0.1\")]\n paths = renew_attention_paths(first_temp_attention)\n meta_path = {\"old\": \"input_blocks.0.1\", \"new\": \"transformer_in\"}\n assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config)\n\n new_checkpoint[\"conv_norm_out.weight\"] = unet_state_dict[\"out.0.weight\"]\n new_checkpoint[\"conv_norm_out.bias\"] = unet_state_dict[\"out.0.bias\"]\n new_checkpoint[\"conv_out.weight\"] = unet_state_dict[\"out.2.weight\"]\n new_checkpoint[\"conv_out.bias\"] = unet_state_dict[\"out.2.bias\"]\n\n # Retrieves the keys for the input blocks only\n num_input_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"input_blocks\" in layer})\n input_blocks = {\n layer_id: [key for key in unet_state_dict if f\"input_blocks.{layer_id}\" in key]\n for layer_id in range(num_input_blocks)\n }\n\n # Retrieves the keys for the middle blocks only\n num_middle_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"middle_block\" in layer})\n middle_blocks = {\n layer_id: [key for key in unet_state_dict if f\"middle_block.{layer_id}\" in key]\n for layer_id in range(num_middle_blocks)\n }\n\n # Retrieves the keys for the output blocks only\n num_output_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"output_blocks\" in layer})\n output_blocks = {\n layer_id: [key for key in unet_state_dict if f\"output_blocks.{layer_id}\" in key]\n for layer_id in range(num_output_blocks)\n }\n\n for i in range(1, num_input_blocks):\n block_id = (i - 1) // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = (i - 1) % (config[\"layers_per_block\"] + 1)\n\n resnets = [\n key for key in input_blocks[i] if f\"input_blocks.{i}.0\" in key and f\"input_blocks.{i}.0.op\" not in key\n ]\n attentions = [key for key in input_blocks[i] if f\"input_blocks.{i}.1\" in key]\n temp_attentions = [key for key in input_blocks[i] if f\"input_blocks.{i}.2\" in key]\n\n if f\"input_blocks.{i}.op.weight\" in unet_state_dict:\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.weight\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.op.weight\"\n )\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.bias\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.op.bias\"\n )\n\n paths = renew_resnet_paths(resnets)\n meta_path = {\"old\": f\"input_blocks.{i}.0\", \"new\": f\"down_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n temporal_convs = [key for key in resnets if \"temopral_conv\" in key]\n paths = renew_temp_conv_paths(temporal_convs)\n meta_path = {\n \"old\": f\"input_blocks.{i}.0.temopral_conv\",\n \"new\": f\"down_blocks.{block_id}.temp_convs.{layer_in_block_id}\",\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n meta_path = {\"old\": f\"input_blocks.{i}.1\", \"new\": f\"down_blocks.{block_id}.attentions.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n if len(temp_attentions):\n paths = renew_attention_paths(temp_attentions)\n meta_path = {\n \"old\": f\"input_blocks.{i}.2\",\n \"new\": f\"down_blocks.{block_id}.temp_attentions.{layer_in_block_id}\",\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n resnet_0 = middle_blocks[0]\n temporal_convs_0 = [key for key in resnet_0 if \"temopral_conv\" in key]\n attentions = middle_blocks[1]\n temp_attentions = middle_blocks[2]\n resnet_1 = middle_blocks[3]\n temporal_convs_1 = [key for key in resnet_1 if \"temopral_conv\" in key]\n\n resnet_0_paths = renew_resnet_paths(resnet_0)\n meta_path = {\"old\": \"middle_block.0\", \"new\": \"mid_block.resnets.0\"}\n assign_to_checkpoint(\n resnet_0_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path]\n )\n\n temp_conv_0_paths = renew_temp_conv_paths(temporal_convs_0)\n meta_path = {\"old\": \"middle_block.0.temopral_conv\", \"new\": \"mid_block.temp_convs.0\"}\n assign_to_checkpoint(\n temp_conv_0_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path]\n )\n\n resnet_1_paths = renew_resnet_paths(resnet_1)\n meta_path = {\"old\": \"middle_block.3\", \"new\": \"mid_block.resnets.1\"}\n assign_to_checkpoint(\n resnet_1_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path]\n )\n\n temp_conv_1_paths = renew_temp_conv_paths(temporal_convs_1)\n meta_path = {\"old\": \"middle_block.3.temopral_conv\", \"new\": \"mid_block.temp_convs.1\"}\n assign_to_checkpoint(\n temp_conv_1_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path]\n )\n\n attentions_paths = renew_attention_paths(attentions)\n meta_path = {\"old\": \"middle_block.1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(\n attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n temp_attentions_paths = renew_attention_paths(temp_attentions)\n meta_path = {\"old\": \"middle_block.2\", \"new\": \"mid_block.temp_attentions.0\"}\n assign_to_checkpoint(\n temp_attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n for i in range(num_output_blocks):\n block_id = i // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = i % (config[\"layers_per_block\"] + 1)\n output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]\n output_block_list = {}\n\n for layer in output_block_layers:\n layer_id, layer_name = layer.split(\".\")[0], shave_segments(layer, 1)\n if layer_id in output_block_list:\n output_block_list[layer_id].append(layer_name)\n else:\n output_block_list[layer_id] = [layer_name]\n\n if len(output_block_list) > 1:\n resnets = [key for key in output_blocks[i] if f\"output_blocks.{i}.0\" in key]\n attentions = [key for key in output_blocks[i] if f\"output_blocks.{i}.1\" in key]\n temp_attentions = [key for key in output_blocks[i] if f\"output_blocks.{i}.2\" in key]\n\n resnet_0_paths = renew_resnet_paths(resnets)\n paths = renew_resnet_paths(resnets)\n\n meta_path = {\"old\": f\"output_blocks.{i}.0\", \"new\": f\"up_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n temporal_convs = [key for key in resnets if \"temopral_conv\" in key]\n paths = renew_temp_conv_paths(temporal_convs)\n meta_path = {\n \"old\": f\"output_blocks.{i}.0.temopral_conv\",\n \"new\": f\"up_blocks.{block_id}.temp_convs.{layer_in_block_id}\",\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n output_block_list = {k: sorted(v) for k, v in output_block_list.items()}\n if [\"conv.bias\", \"conv.weight\"] in output_block_list.values():\n index = list(output_block_list.values()).index([\"conv.bias\", \"conv.weight\"])\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.weight\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.weight\"\n ]\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.bias\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.bias\"\n ]\n\n # Clear attentions as they have been attributed above.\n if len(attentions) == 2:\n attentions = []\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n meta_path = {\n \"old\": f\"output_blocks.{i}.1\",\n \"new\": f\"up_blocks.{block_id}.attentions.{layer_in_block_id}\",\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n if len(temp_attentions):\n paths = renew_attention_paths(temp_attentions)\n meta_path = {\n \"old\": f\"output_blocks.{i}.2\",\n \"new\": f\"up_blocks.{block_id}.temp_attentions.{layer_in_block_id}\",\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n else:\n resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)\n for path in resnet_0_paths:\n old_path = \".\".join([\"output_blocks\", str(i), path[\"old\"]])\n new_path = \".\".join([\"up_blocks\", str(block_id), \"resnets\", str(layer_in_block_id), path[\"new\"]])\n new_checkpoint[new_path] = unet_state_dict[old_path]\n\n temopral_conv_paths = [l for l in output_block_layers if \"temopral_conv\" in l]\n for path in temopral_conv_paths:\n pruned_path = path.split(\"temopral_conv.\")[-1]\n old_path = \".\".join([\"output_blocks\", str(i), str(block_id), \"temopral_conv\", pruned_path])\n new_path = \".\".join([\"up_blocks\", str(block_id), \"temp_convs\", str(layer_in_block_id), pruned_path])\n new_checkpoint[new_path] = unet_state_dict[old_path]\n\n return new_checkpoint\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--unet_checkpoint_path\", default=None, type=str, required=True, help=\"Path to the checkpoint to convert.\"\n )\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\")\n args = parser.parse_args()\n\n # UNet\n unet_checkpoint = torch.load(args.unet_checkpoint_path, map_location=\"cpu\")\n unet_checkpoint = unet_checkpoint[\"state_dict\"]\n unet = I2VGenXLUNet(sample_size=32)\n\n converted_ckpt = convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)\n\n diff_0 = set(unet.state_dict().keys()) - set(converted_ckpt.keys())\n diff_1 = set(converted_ckpt.keys()) - set(unet.state_dict().keys())\n\n assert len(diff_0) == len(diff_1) == 0, \"Converted weights don't match\"\n\n unet.load_state_dict(converted_ckpt, strict=True)\n\n # vae\n temp_pipe = StableDiffusionPipeline.from_single_file(\n \"https://huggingface.co/ali-vilab/i2vgen-xl/blob/main/models/v2-1_512-ema-pruned.ckpt\"\n )\n vae = temp_pipe.vae\n del temp_pipe\n\n # text encoder and tokenizer\n text_encoder = CLIPTextModel.from_pretrained(CLIP_ID)\n tokenizer = CLIPTokenizer.from_pretrained(CLIP_ID)\n\n # image encoder and feature extractor\n image_encoder = CLIPVisionModelWithProjection.from_pretrained(CLIP_ID)\n feature_extractor = CLIPImageProcessor.from_pretrained(CLIP_ID)\n\n # scheduler\n # https://github.com/ali-vilab/i2vgen-xl/blob/main/configs/i2vgen_xl_train.yaml\n scheduler = DDIMScheduler(\n beta_schedule=\"squaredcos_cap_v2\",\n rescale_betas_zero_snr=True,\n set_alpha_to_one=True,\n clip_sample=False,\n steps_offset=1,\n timestep_spacing=\"leading\",\n prediction_type=\"v_prediction\",\n )\n\n # final\n pipeline = I2VGenXLPipeline(\n unet=unet,\n vae=vae,\n image_encoder=image_encoder,\n feature_extractor=feature_extractor,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n scheduler=scheduler,\n )\n\n pipeline.save_pretrained(args.dump_path, push_to_hub=args.push_to_hub)\n" }, { "path": "diffusers/scripts/convert_if.py", "content": "import argparse\nimport inspect\nimport os\n\nimport numpy as np\nimport torch\nimport yaml\nfrom torch.nn import functional as F\nfrom transformers import CLIPConfig, CLIPImageProcessor, CLIPVisionModelWithProjection, T5EncoderModel, T5Tokenizer\n\nfrom diffusers import DDPMScheduler, IFPipeline, IFSuperResolutionPipeline, UNet2DConditionModel\nfrom diffusers.pipelines.deepfloyd_if.safety_checker import IFSafetyChecker\n\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"--dump_path\", required=False, default=None, type=str)\n\n parser.add_argument(\"--dump_path_stage_2\", required=False, default=None, type=str)\n\n parser.add_argument(\"--dump_path_stage_3\", required=False, default=None, type=str)\n\n parser.add_argument(\"--unet_config\", required=False, default=None, type=str, help=\"Path to unet config file\")\n\n parser.add_argument(\n \"--unet_checkpoint_path\", required=False, default=None, type=str, help=\"Path to unet checkpoint file\"\n )\n\n parser.add_argument(\n \"--unet_checkpoint_path_stage_2\",\n required=False,\n default=None,\n type=str,\n help=\"Path to stage 2 unet checkpoint file\",\n )\n\n parser.add_argument(\n \"--unet_checkpoint_path_stage_3\",\n required=False,\n default=None,\n type=str,\n help=\"Path to stage 3 unet checkpoint file\",\n )\n\n parser.add_argument(\"--p_head_path\", type=str, required=True)\n\n parser.add_argument(\"--w_head_path\", type=str, required=True)\n\n args = parser.parse_args()\n\n return args\n\n\ndef main(args):\n tokenizer = T5Tokenizer.from_pretrained(\"google/t5-v1_1-xxl\")\n text_encoder = T5EncoderModel.from_pretrained(\"google/t5-v1_1-xxl\")\n\n feature_extractor = CLIPImageProcessor.from_pretrained(\"openai/clip-vit-large-patch14\")\n safety_checker = convert_safety_checker(p_head_path=args.p_head_path, w_head_path=args.w_head_path)\n\n if args.unet_config is not None and args.unet_checkpoint_path is not None and args.dump_path is not None:\n convert_stage_1_pipeline(tokenizer, text_encoder, feature_extractor, safety_checker, args)\n\n if args.unet_checkpoint_path_stage_2 is not None and args.dump_path_stage_2 is not None:\n convert_super_res_pipeline(tokenizer, text_encoder, feature_extractor, safety_checker, args, stage=2)\n\n if args.unet_checkpoint_path_stage_3 is not None and args.dump_path_stage_3 is not None:\n convert_super_res_pipeline(tokenizer, text_encoder, feature_extractor, safety_checker, args, stage=3)\n\n\ndef convert_stage_1_pipeline(tokenizer, text_encoder, feature_extractor, safety_checker, args):\n unet = get_stage_1_unet(args.unet_config, args.unet_checkpoint_path)\n\n scheduler = DDPMScheduler(\n variance_type=\"learned_range\",\n beta_schedule=\"squaredcos_cap_v2\",\n prediction_type=\"epsilon\",\n thresholding=True,\n dynamic_thresholding_ratio=0.95,\n sample_max_value=1.5,\n )\n\n pipe = IFPipeline(\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n requires_safety_checker=True,\n )\n\n pipe.save_pretrained(args.dump_path)\n\n\ndef convert_super_res_pipeline(tokenizer, text_encoder, feature_extractor, safety_checker, args, stage):\n if stage == 2:\n unet_checkpoint_path = args.unet_checkpoint_path_stage_2\n sample_size = None\n dump_path = args.dump_path_stage_2\n elif stage == 3:\n unet_checkpoint_path = args.unet_checkpoint_path_stage_3\n sample_size = 1024\n dump_path = args.dump_path_stage_3\n else:\n assert False\n\n unet = get_super_res_unet(unet_checkpoint_path, verify_param_count=False, sample_size=sample_size)\n\n image_noising_scheduler = DDPMScheduler(\n beta_schedule=\"squaredcos_cap_v2\",\n )\n\n scheduler = DDPMScheduler(\n variance_type=\"learned_range\",\n beta_schedule=\"squaredcos_cap_v2\",\n prediction_type=\"epsilon\",\n thresholding=True,\n dynamic_thresholding_ratio=0.95,\n sample_max_value=1.0,\n )\n\n pipe = IFSuperResolutionPipeline(\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n unet=unet,\n scheduler=scheduler,\n image_noising_scheduler=image_noising_scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n requires_safety_checker=True,\n )\n\n pipe.save_pretrained(dump_path)\n\n\ndef get_stage_1_unet(unet_config, unet_checkpoint_path):\n original_unet_config = yaml.safe_load(unet_config)\n original_unet_config = original_unet_config[\"params\"]\n\n unet_diffusers_config = create_unet_diffusers_config(original_unet_config)\n\n unet = UNet2DConditionModel(**unet_diffusers_config)\n\n device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n unet_checkpoint = torch.load(unet_checkpoint_path, map_location=device)\n\n converted_unet_checkpoint = convert_ldm_unet_checkpoint(\n unet_checkpoint, unet_diffusers_config, path=unet_checkpoint_path\n )\n\n unet.load_state_dict(converted_unet_checkpoint)\n\n return unet\n\n\ndef convert_safety_checker(p_head_path, w_head_path):\n state_dict = {}\n\n # p head\n\n p_head = np.load(p_head_path)\n\n p_head_weights = p_head[\"weights\"]\n p_head_weights = torch.from_numpy(p_head_weights)\n p_head_weights = p_head_weights.unsqueeze(0)\n\n p_head_biases = p_head[\"biases\"]\n p_head_biases = torch.from_numpy(p_head_biases)\n p_head_biases = p_head_biases.unsqueeze(0)\n\n state_dict[\"p_head.weight\"] = p_head_weights\n state_dict[\"p_head.bias\"] = p_head_biases\n\n # w head\n\n w_head = np.load(w_head_path)\n\n w_head_weights = w_head[\"weights\"]\n w_head_weights = torch.from_numpy(w_head_weights)\n w_head_weights = w_head_weights.unsqueeze(0)\n\n w_head_biases = w_head[\"biases\"]\n w_head_biases = torch.from_numpy(w_head_biases)\n w_head_biases = w_head_biases.unsqueeze(0)\n\n state_dict[\"w_head.weight\"] = w_head_weights\n state_dict[\"w_head.bias\"] = w_head_biases\n\n # vision model\n\n vision_model = CLIPVisionModelWithProjection.from_pretrained(\"openai/clip-vit-large-patch14\")\n vision_model_state_dict = vision_model.state_dict()\n\n for key, value in vision_model_state_dict.items():\n key = f\"vision_model.{key}\"\n state_dict[key] = value\n\n # full model\n\n config = CLIPConfig.from_pretrained(\"openai/clip-vit-large-patch14\")\n safety_checker = IFSafetyChecker(config)\n\n safety_checker.load_state_dict(state_dict)\n\n return safety_checker\n\n\ndef create_unet_diffusers_config(original_unet_config, class_embed_type=None):\n attention_resolutions = parse_list(original_unet_config[\"attention_resolutions\"])\n attention_resolutions = [original_unet_config[\"image_size\"] // int(res) for res in attention_resolutions]\n\n channel_mult = parse_list(original_unet_config[\"channel_mult\"])\n block_out_channels = [original_unet_config[\"model_channels\"] * mult for mult in channel_mult]\n\n down_block_types = []\n resolution = 1\n\n for i in range(len(block_out_channels)):\n if resolution in attention_resolutions:\n block_type = \"SimpleCrossAttnDownBlock2D\"\n elif original_unet_config[\"resblock_updown\"]:\n block_type = \"ResnetDownsampleBlock2D\"\n else:\n block_type = \"DownBlock2D\"\n\n down_block_types.append(block_type)\n\n if i != len(block_out_channels) - 1:\n resolution *= 2\n\n up_block_types = []\n for i in range(len(block_out_channels)):\n if resolution in attention_resolutions:\n block_type = \"SimpleCrossAttnUpBlock2D\"\n elif original_unet_config[\"resblock_updown\"]:\n block_type = \"ResnetUpsampleBlock2D\"\n else:\n block_type = \"UpBlock2D\"\n up_block_types.append(block_type)\n resolution //= 2\n\n head_dim = original_unet_config[\"num_head_channels\"]\n\n use_linear_projection = (\n original_unet_config[\"use_linear_in_transformer\"]\n if \"use_linear_in_transformer\" in original_unet_config\n else False\n )\n if use_linear_projection:\n # stable diffusion 2-base-512 and 2-768\n if head_dim is None:\n head_dim = [5, 10, 20, 20]\n\n projection_class_embeddings_input_dim = None\n\n if class_embed_type is None:\n if \"num_classes\" in original_unet_config:\n if original_unet_config[\"num_classes\"] == \"sequential\":\n class_embed_type = \"projection\"\n assert \"adm_in_channels\" in original_unet_config\n projection_class_embeddings_input_dim = original_unet_config[\"adm_in_channels\"]\n else:\n raise NotImplementedError(\n f\"Unknown conditional unet num_classes config: {original_unet_config['num_classes']}\"\n )\n\n config = {\n \"sample_size\": original_unet_config[\"image_size\"],\n \"in_channels\": original_unet_config[\"in_channels\"],\n \"down_block_types\": tuple(down_block_types),\n \"block_out_channels\": tuple(block_out_channels),\n \"layers_per_block\": original_unet_config[\"num_res_blocks\"],\n \"cross_attention_dim\": original_unet_config[\"encoder_channels\"],\n \"attention_head_dim\": head_dim,\n \"use_linear_projection\": use_linear_projection,\n \"class_embed_type\": class_embed_type,\n \"projection_class_embeddings_input_dim\": projection_class_embeddings_input_dim,\n \"out_channels\": original_unet_config[\"out_channels\"],\n \"up_block_types\": tuple(up_block_types),\n \"upcast_attention\": False, # TODO: guessing\n \"cross_attention_norm\": \"group_norm\",\n \"mid_block_type\": \"UNetMidBlock2DSimpleCrossAttn\",\n \"addition_embed_type\": \"text\",\n \"act_fn\": \"gelu\",\n }\n\n if original_unet_config[\"use_scale_shift_norm\"]:\n config[\"resnet_time_scale_shift\"] = \"scale_shift\"\n\n if \"encoder_dim\" in original_unet_config:\n config[\"encoder_hid_dim\"] = original_unet_config[\"encoder_dim\"]\n\n return config\n\n\ndef convert_ldm_unet_checkpoint(unet_state_dict, config, path=None):\n \"\"\"\n Takes a state dict and a config, and returns a converted checkpoint.\n \"\"\"\n new_checkpoint = {}\n\n new_checkpoint[\"time_embedding.linear_1.weight\"] = unet_state_dict[\"time_embed.0.weight\"]\n new_checkpoint[\"time_embedding.linear_1.bias\"] = unet_state_dict[\"time_embed.0.bias\"]\n new_checkpoint[\"time_embedding.linear_2.weight\"] = unet_state_dict[\"time_embed.2.weight\"]\n new_checkpoint[\"time_embedding.linear_2.bias\"] = unet_state_dict[\"time_embed.2.bias\"]\n\n if config[\"class_embed_type\"] in [None, \"identity\"]:\n # No parameters to port\n ...\n elif config[\"class_embed_type\"] == \"timestep\" or config[\"class_embed_type\"] == \"projection\":\n new_checkpoint[\"class_embedding.linear_1.weight\"] = unet_state_dict[\"label_emb.0.0.weight\"]\n new_checkpoint[\"class_embedding.linear_1.bias\"] = unet_state_dict[\"label_emb.0.0.bias\"]\n new_checkpoint[\"class_embedding.linear_2.weight\"] = unet_state_dict[\"label_emb.0.2.weight\"]\n new_checkpoint[\"class_embedding.linear_2.bias\"] = unet_state_dict[\"label_emb.0.2.bias\"]\n else:\n raise NotImplementedError(f\"Not implemented `class_embed_type`: {config['class_embed_type']}\")\n\n new_checkpoint[\"conv_in.weight\"] = unet_state_dict[\"input_blocks.0.0.weight\"]\n new_checkpoint[\"conv_in.bias\"] = unet_state_dict[\"input_blocks.0.0.bias\"]\n\n new_checkpoint[\"conv_norm_out.weight\"] = unet_state_dict[\"out.0.weight\"]\n new_checkpoint[\"conv_norm_out.bias\"] = unet_state_dict[\"out.0.bias\"]\n new_checkpoint[\"conv_out.weight\"] = unet_state_dict[\"out.2.weight\"]\n new_checkpoint[\"conv_out.bias\"] = unet_state_dict[\"out.2.bias\"]\n\n # Retrieves the keys for the input blocks only\n num_input_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"input_blocks\" in layer})\n input_blocks = {\n layer_id: [key for key in unet_state_dict if f\"input_blocks.{layer_id}.\" in key]\n for layer_id in range(num_input_blocks)\n }\n\n # Retrieves the keys for the middle blocks only\n num_middle_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"middle_block\" in layer})\n middle_blocks = {\n layer_id: [key for key in unet_state_dict if f\"middle_block.{layer_id}\" in key]\n for layer_id in range(num_middle_blocks)\n }\n\n # Retrieves the keys for the output blocks only\n num_output_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"output_blocks\" in layer})\n output_blocks = {\n layer_id: [key for key in unet_state_dict if f\"output_blocks.{layer_id}.\" in key]\n for layer_id in range(num_output_blocks)\n }\n\n for i in range(1, num_input_blocks):\n block_id = (i - 1) // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = (i - 1) % (config[\"layers_per_block\"] + 1)\n\n resnets = [\n key for key in input_blocks[i] if f\"input_blocks.{i}.0\" in key and f\"input_blocks.{i}.0.op\" not in key\n ]\n attentions = [key for key in input_blocks[i] if f\"input_blocks.{i}.1\" in key]\n\n if f\"input_blocks.{i}.0.op.weight\" in unet_state_dict:\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.weight\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.op.weight\"\n )\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.bias\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.op.bias\"\n )\n\n paths = renew_resnet_paths(resnets)\n\n # TODO need better check than i in [4, 8, 12, 16]\n block_type = config[\"down_block_types\"][block_id]\n if (block_type == \"ResnetDownsampleBlock2D\" or block_type == \"SimpleCrossAttnDownBlock2D\") and i in [\n 4,\n 8,\n 12,\n 16,\n ]:\n meta_path = {\"old\": f\"input_blocks.{i}.0\", \"new\": f\"down_blocks.{block_id}.downsamplers.0\"}\n else:\n meta_path = {\"old\": f\"input_blocks.{i}.0\", \"new\": f\"down_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n if len(attentions):\n old_path = f\"input_blocks.{i}.1\"\n new_path = f\"down_blocks.{block_id}.attentions.{layer_in_block_id}\"\n\n assign_attention_to_checkpoint(\n new_checkpoint=new_checkpoint,\n unet_state_dict=unet_state_dict,\n old_path=old_path,\n new_path=new_path,\n config=config,\n )\n\n paths = renew_attention_paths(attentions)\n meta_path = {\"old\": old_path, \"new\": new_path}\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n unet_state_dict,\n additional_replacements=[meta_path],\n config=config,\n )\n\n resnet_0 = middle_blocks[0]\n attentions = middle_blocks[1]\n resnet_1 = middle_blocks[2]\n\n resnet_0_paths = renew_resnet_paths(resnet_0)\n assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)\n\n resnet_1_paths = renew_resnet_paths(resnet_1)\n assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)\n\n old_path = \"middle_block.1\"\n new_path = \"mid_block.attentions.0\"\n\n assign_attention_to_checkpoint(\n new_checkpoint=new_checkpoint,\n unet_state_dict=unet_state_dict,\n old_path=old_path,\n new_path=new_path,\n config=config,\n )\n\n attentions_paths = renew_attention_paths(attentions)\n meta_path = {\"old\": \"middle_block.1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(\n attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n for i in range(num_output_blocks):\n block_id = i // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = i % (config[\"layers_per_block\"] + 1)\n output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]\n output_block_list = {}\n\n for layer in output_block_layers:\n layer_id, layer_name = layer.split(\".\")[0], shave_segments(layer, 1)\n if layer_id in output_block_list:\n output_block_list[layer_id].append(layer_name)\n else:\n output_block_list[layer_id] = [layer_name]\n\n # len(output_block_list) == 1 -> resnet\n # len(output_block_list) == 2 -> resnet, attention\n # len(output_block_list) == 3 -> resnet, attention, upscale resnet\n\n if len(output_block_list) > 1:\n resnets = [key for key in output_blocks[i] if f\"output_blocks.{i}.0\" in key]\n attentions = [key for key in output_blocks[i] if f\"output_blocks.{i}.1\" in key]\n\n paths = renew_resnet_paths(resnets)\n\n meta_path = {\"old\": f\"output_blocks.{i}.0\", \"new\": f\"up_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n output_block_list = {k: sorted(v) for k, v in output_block_list.items()}\n if [\"conv.bias\", \"conv.weight\"] in output_block_list.values():\n index = list(output_block_list.values()).index([\"conv.bias\", \"conv.weight\"])\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.weight\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.weight\"\n ]\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.bias\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.bias\"\n ]\n\n # Clear attentions as they have been attributed above.\n if len(attentions) == 2:\n attentions = []\n\n if len(attentions):\n old_path = f\"output_blocks.{i}.1\"\n new_path = f\"up_blocks.{block_id}.attentions.{layer_in_block_id}\"\n\n assign_attention_to_checkpoint(\n new_checkpoint=new_checkpoint,\n unet_state_dict=unet_state_dict,\n old_path=old_path,\n new_path=new_path,\n config=config,\n )\n\n paths = renew_attention_paths(attentions)\n meta_path = {\n \"old\": old_path,\n \"new\": new_path,\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n if len(output_block_list) == 3:\n resnets = [key for key in output_blocks[i] if f\"output_blocks.{i}.2\" in key]\n paths = renew_resnet_paths(resnets)\n meta_path = {\"old\": f\"output_blocks.{i}.2\", \"new\": f\"up_blocks.{block_id}.upsamplers.0\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n else:\n resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)\n for path in resnet_0_paths:\n old_path = \".\".join([\"output_blocks\", str(i), path[\"old\"]])\n new_path = \".\".join([\"up_blocks\", str(block_id), \"resnets\", str(layer_in_block_id), path[\"new\"]])\n\n new_checkpoint[new_path] = unet_state_dict[old_path]\n\n if \"encoder_proj.weight\" in unet_state_dict:\n new_checkpoint[\"encoder_hid_proj.weight\"] = unet_state_dict.pop(\"encoder_proj.weight\")\n new_checkpoint[\"encoder_hid_proj.bias\"] = unet_state_dict.pop(\"encoder_proj.bias\")\n\n if \"encoder_pooling.0.weight\" in unet_state_dict:\n new_checkpoint[\"add_embedding.norm1.weight\"] = unet_state_dict.pop(\"encoder_pooling.0.weight\")\n new_checkpoint[\"add_embedding.norm1.bias\"] = unet_state_dict.pop(\"encoder_pooling.0.bias\")\n\n new_checkpoint[\"add_embedding.pool.positional_embedding\"] = unet_state_dict.pop(\n \"encoder_pooling.1.positional_embedding\"\n )\n new_checkpoint[\"add_embedding.pool.k_proj.weight\"] = unet_state_dict.pop(\"encoder_pooling.1.k_proj.weight\")\n new_checkpoint[\"add_embedding.pool.k_proj.bias\"] = unet_state_dict.pop(\"encoder_pooling.1.k_proj.bias\")\n new_checkpoint[\"add_embedding.pool.q_proj.weight\"] = unet_state_dict.pop(\"encoder_pooling.1.q_proj.weight\")\n new_checkpoint[\"add_embedding.pool.q_proj.bias\"] = unet_state_dict.pop(\"encoder_pooling.1.q_proj.bias\")\n new_checkpoint[\"add_embedding.pool.v_proj.weight\"] = unet_state_dict.pop(\"encoder_pooling.1.v_proj.weight\")\n new_checkpoint[\"add_embedding.pool.v_proj.bias\"] = unet_state_dict.pop(\"encoder_pooling.1.v_proj.bias\")\n\n new_checkpoint[\"add_embedding.proj.weight\"] = unet_state_dict.pop(\"encoder_pooling.2.weight\")\n new_checkpoint[\"add_embedding.proj.bias\"] = unet_state_dict.pop(\"encoder_pooling.2.bias\")\n\n new_checkpoint[\"add_embedding.norm2.weight\"] = unet_state_dict.pop(\"encoder_pooling.3.weight\")\n new_checkpoint[\"add_embedding.norm2.bias\"] = unet_state_dict.pop(\"encoder_pooling.3.bias\")\n\n return new_checkpoint\n\n\ndef shave_segments(path, n_shave_prefix_segments=1):\n \"\"\"\n Removes segments. Positive values shave the first segments, negative shave the last segments.\n \"\"\"\n if n_shave_prefix_segments >= 0:\n return \".\".join(path.split(\".\")[n_shave_prefix_segments:])\n else:\n return \".\".join(path.split(\".\")[:n_shave_prefix_segments])\n\n\ndef renew_resnet_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside resnets to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item.replace(\"in_layers.0\", \"norm1\")\n new_item = new_item.replace(\"in_layers.2\", \"conv1\")\n\n new_item = new_item.replace(\"out_layers.0\", \"norm2\")\n new_item = new_item.replace(\"out_layers.3\", \"conv2\")\n\n new_item = new_item.replace(\"emb_layers.1\", \"time_emb_proj\")\n new_item = new_item.replace(\"skip_connection\", \"conv_shortcut\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef renew_attention_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside attentions to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n if \"qkv\" in new_item:\n continue\n\n if \"encoder_kv\" in new_item:\n continue\n\n new_item = new_item.replace(\"norm.weight\", \"group_norm.weight\")\n new_item = new_item.replace(\"norm.bias\", \"group_norm.bias\")\n\n new_item = new_item.replace(\"proj_out.weight\", \"to_out.0.weight\")\n new_item = new_item.replace(\"proj_out.bias\", \"to_out.0.bias\")\n\n new_item = new_item.replace(\"norm_encoder.weight\", \"norm_cross.weight\")\n new_item = new_item.replace(\"norm_encoder.bias\", \"norm_cross.bias\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef assign_attention_to_checkpoint(new_checkpoint, unet_state_dict, old_path, new_path, config):\n qkv_weight = unet_state_dict.pop(f\"{old_path}.qkv.weight\")\n qkv_weight = qkv_weight[:, :, 0]\n\n qkv_bias = unet_state_dict.pop(f\"{old_path}.qkv.bias\")\n\n is_cross_attn_only = \"only_cross_attention\" in config and config[\"only_cross_attention\"]\n\n split = 1 if is_cross_attn_only else 3\n\n weights, bias = split_attentions(\n weight=qkv_weight,\n bias=qkv_bias,\n split=split,\n chunk_size=config[\"attention_head_dim\"],\n )\n\n if is_cross_attn_only:\n query_weight, q_bias = weights, bias\n new_checkpoint[f\"{new_path}.to_q.weight\"] = query_weight[0]\n new_checkpoint[f\"{new_path}.to_q.bias\"] = q_bias[0]\n else:\n [query_weight, key_weight, value_weight], [q_bias, k_bias, v_bias] = weights, bias\n new_checkpoint[f\"{new_path}.to_q.weight\"] = query_weight\n new_checkpoint[f\"{new_path}.to_q.bias\"] = q_bias\n new_checkpoint[f\"{new_path}.to_k.weight\"] = key_weight\n new_checkpoint[f\"{new_path}.to_k.bias\"] = k_bias\n new_checkpoint[f\"{new_path}.to_v.weight\"] = value_weight\n new_checkpoint[f\"{new_path}.to_v.bias\"] = v_bias\n\n encoder_kv_weight = unet_state_dict.pop(f\"{old_path}.encoder_kv.weight\")\n encoder_kv_weight = encoder_kv_weight[:, :, 0]\n\n encoder_kv_bias = unet_state_dict.pop(f\"{old_path}.encoder_kv.bias\")\n\n [encoder_k_weight, encoder_v_weight], [encoder_k_bias, encoder_v_bias] = split_attentions(\n weight=encoder_kv_weight,\n bias=encoder_kv_bias,\n split=2,\n chunk_size=config[\"attention_head_dim\"],\n )\n\n new_checkpoint[f\"{new_path}.add_k_proj.weight\"] = encoder_k_weight\n new_checkpoint[f\"{new_path}.add_k_proj.bias\"] = encoder_k_bias\n new_checkpoint[f\"{new_path}.add_v_proj.weight\"] = encoder_v_weight\n new_checkpoint[f\"{new_path}.add_v_proj.bias\"] = encoder_v_bias\n\n\ndef assign_to_checkpoint(paths, checkpoint, old_checkpoint, additional_replacements=None, config=None):\n \"\"\"\n This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits\n attention layers, and takes into account additional replacements that may arise.\n\n Assigns the weights to the new checkpoint.\n \"\"\"\n assert isinstance(paths, list), \"Paths should be a list of dicts containing 'old' and 'new' keys.\"\n\n for path in paths:\n new_path = path[\"new\"]\n\n # Global renaming happens here\n new_path = new_path.replace(\"middle_block.0\", \"mid_block.resnets.0\")\n new_path = new_path.replace(\"middle_block.1\", \"mid_block.attentions.0\")\n new_path = new_path.replace(\"middle_block.2\", \"mid_block.resnets.1\")\n\n if additional_replacements is not None:\n for replacement in additional_replacements:\n new_path = new_path.replace(replacement[\"old\"], replacement[\"new\"])\n\n # proj_attn.weight has to be converted from conv 1D to linear\n if \"proj_attn.weight\" in new_path or \"to_out.0.weight\" in new_path:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]][:, :, 0]\n else:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]]\n\n\n# TODO maybe document and/or can do more efficiently (build indices in for loop and extract once for each split?)\ndef split_attentions(*, weight, bias, split, chunk_size):\n weights = [None] * split\n biases = [None] * split\n\n weights_biases_idx = 0\n\n for starting_row_index in range(0, weight.shape[0], chunk_size):\n row_indices = torch.arange(starting_row_index, starting_row_index + chunk_size)\n\n weight_rows = weight[row_indices, :]\n bias_rows = bias[row_indices]\n\n if weights[weights_biases_idx] is None:\n weights[weights_biases_idx] = weight_rows\n biases[weights_biases_idx] = bias_rows\n else:\n assert weights[weights_biases_idx] is not None\n weights[weights_biases_idx] = torch.concat([weights[weights_biases_idx], weight_rows])\n biases[weights_biases_idx] = torch.concat([biases[weights_biases_idx], bias_rows])\n\n weights_biases_idx = (weights_biases_idx + 1) % split\n\n return weights, biases\n\n\ndef parse_list(value):\n if isinstance(value, str):\n value = value.split(\",\")\n value = [int(v) for v in value]\n elif isinstance(value, list):\n pass\n else:\n raise ValueError(f\"Can't parse list for type: {type(value)}\")\n\n return value\n\n\n# below is copy and pasted from original convert_if_stage_2.py script\n\n\ndef get_super_res_unet(unet_checkpoint_path, verify_param_count=True, sample_size=None):\n orig_path = unet_checkpoint_path\n\n original_unet_config = yaml.safe_load(os.path.join(orig_path, \"config.yml\"))\n original_unet_config = original_unet_config[\"params\"]\n\n unet_diffusers_config = superres_create_unet_diffusers_config(original_unet_config)\n unet_diffusers_config[\"time_embedding_dim\"] = original_unet_config[\"model_channels\"] * int(\n original_unet_config[\"channel_mult\"].split(\",\")[-1]\n )\n if original_unet_config[\"encoder_dim\"] != original_unet_config[\"encoder_channels\"]:\n unet_diffusers_config[\"encoder_hid_dim\"] = original_unet_config[\"encoder_dim\"]\n unet_diffusers_config[\"class_embed_type\"] = \"timestep\"\n unet_diffusers_config[\"addition_embed_type\"] = \"text\"\n\n unet_diffusers_config[\"time_embedding_act_fn\"] = \"gelu\"\n unet_diffusers_config[\"resnet_skip_time_act\"] = True\n unet_diffusers_config[\"resnet_out_scale_factor\"] = 1 / 0.7071\n unet_diffusers_config[\"mid_block_scale_factor\"] = 1 / 0.7071\n unet_diffusers_config[\"only_cross_attention\"] = (\n bool(original_unet_config[\"disable_self_attentions\"])\n if (\n \"disable_self_attentions\" in original_unet_config\n and isinstance(original_unet_config[\"disable_self_attentions\"], int)\n )\n else True\n )\n\n if sample_size is None:\n unet_diffusers_config[\"sample_size\"] = original_unet_config[\"image_size\"]\n else:\n # The second upscaler unet's sample size is incorrectly specified\n # in the config and is instead hardcoded in source\n unet_diffusers_config[\"sample_size\"] = sample_size\n\n unet_checkpoint = torch.load(os.path.join(unet_checkpoint_path, \"pytorch_model.bin\"), map_location=\"cpu\")\n\n if verify_param_count:\n # check that architecture matches - is a bit slow\n verify_param_count(orig_path, unet_diffusers_config)\n\n converted_unet_checkpoint = superres_convert_ldm_unet_checkpoint(\n unet_checkpoint, unet_diffusers_config, path=unet_checkpoint_path\n )\n converted_keys = converted_unet_checkpoint.keys()\n\n model = UNet2DConditionModel(**unet_diffusers_config)\n expected_weights = model.state_dict().keys()\n\n diff_c_e = set(converted_keys) - set(expected_weights)\n diff_e_c = set(expected_weights) - set(converted_keys)\n\n assert len(diff_e_c) == 0, f\"Expected, but not converted: {diff_e_c}\"\n assert len(diff_c_e) == 0, f\"Converted, but not expected: {diff_c_e}\"\n\n model.load_state_dict(converted_unet_checkpoint)\n\n return model\n\n\ndef superres_create_unet_diffusers_config(original_unet_config):\n attention_resolutions = parse_list(original_unet_config[\"attention_resolutions\"])\n attention_resolutions = [original_unet_config[\"image_size\"] // int(res) for res in attention_resolutions]\n\n channel_mult = parse_list(original_unet_config[\"channel_mult\"])\n block_out_channels = [original_unet_config[\"model_channels\"] * mult for mult in channel_mult]\n\n down_block_types = []\n resolution = 1\n\n for i in range(len(block_out_channels)):\n if resolution in attention_resolutions:\n block_type = \"SimpleCrossAttnDownBlock2D\"\n elif original_unet_config[\"resblock_updown\"]:\n block_type = \"ResnetDownsampleBlock2D\"\n else:\n block_type = \"DownBlock2D\"\n\n down_block_types.append(block_type)\n\n if i != len(block_out_channels) - 1:\n resolution *= 2\n\n up_block_types = []\n for i in range(len(block_out_channels)):\n if resolution in attention_resolutions:\n block_type = \"SimpleCrossAttnUpBlock2D\"\n elif original_unet_config[\"resblock_updown\"]:\n block_type = \"ResnetUpsampleBlock2D\"\n else:\n block_type = \"UpBlock2D\"\n up_block_types.append(block_type)\n resolution //= 2\n\n head_dim = original_unet_config[\"num_head_channels\"]\n use_linear_projection = (\n original_unet_config[\"use_linear_in_transformer\"]\n if \"use_linear_in_transformer\" in original_unet_config\n else False\n )\n if use_linear_projection:\n # stable diffusion 2-base-512 and 2-768\n if head_dim is None:\n head_dim = [5, 10, 20, 20]\n\n class_embed_type = None\n projection_class_embeddings_input_dim = None\n\n if \"num_classes\" in original_unet_config:\n if original_unet_config[\"num_classes\"] == \"sequential\":\n class_embed_type = \"projection\"\n assert \"adm_in_channels\" in original_unet_config\n projection_class_embeddings_input_dim = original_unet_config[\"adm_in_channels\"]\n else:\n raise NotImplementedError(\n f\"Unknown conditional unet num_classes config: {original_unet_config['num_classes']}\"\n )\n\n config = {\n \"in_channels\": original_unet_config[\"in_channels\"],\n \"down_block_types\": tuple(down_block_types),\n \"block_out_channels\": tuple(block_out_channels),\n \"layers_per_block\": tuple(original_unet_config[\"num_res_blocks\"]),\n \"cross_attention_dim\": original_unet_config[\"encoder_channels\"],\n \"attention_head_dim\": head_dim,\n \"use_linear_projection\": use_linear_projection,\n \"class_embed_type\": class_embed_type,\n \"projection_class_embeddings_input_dim\": projection_class_embeddings_input_dim,\n \"out_channels\": original_unet_config[\"out_channels\"],\n \"up_block_types\": tuple(up_block_types),\n \"upcast_attention\": False, # TODO: guessing\n \"cross_attention_norm\": \"group_norm\",\n \"mid_block_type\": \"UNetMidBlock2DSimpleCrossAttn\",\n \"act_fn\": \"gelu\",\n }\n\n if original_unet_config[\"use_scale_shift_norm\"]:\n config[\"resnet_time_scale_shift\"] = \"scale_shift\"\n\n return config\n\n\ndef superres_convert_ldm_unet_checkpoint(unet_state_dict, config, path=None, extract_ema=False):\n \"\"\"\n Takes a state dict and a config, and returns a converted checkpoint.\n \"\"\"\n new_checkpoint = {}\n\n new_checkpoint[\"time_embedding.linear_1.weight\"] = unet_state_dict[\"time_embed.0.weight\"]\n new_checkpoint[\"time_embedding.linear_1.bias\"] = unet_state_dict[\"time_embed.0.bias\"]\n new_checkpoint[\"time_embedding.linear_2.weight\"] = unet_state_dict[\"time_embed.2.weight\"]\n new_checkpoint[\"time_embedding.linear_2.bias\"] = unet_state_dict[\"time_embed.2.bias\"]\n\n if config[\"class_embed_type\"] is None:\n # No parameters to port\n ...\n elif config[\"class_embed_type\"] == \"timestep\" or config[\"class_embed_type\"] == \"projection\":\n new_checkpoint[\"class_embedding.linear_1.weight\"] = unet_state_dict[\"aug_proj.0.weight\"]\n new_checkpoint[\"class_embedding.linear_1.bias\"] = unet_state_dict[\"aug_proj.0.bias\"]\n new_checkpoint[\"class_embedding.linear_2.weight\"] = unet_state_dict[\"aug_proj.2.weight\"]\n new_checkpoint[\"class_embedding.linear_2.bias\"] = unet_state_dict[\"aug_proj.2.bias\"]\n else:\n raise NotImplementedError(f\"Not implemented `class_embed_type`: {config['class_embed_type']}\")\n\n if \"encoder_proj.weight\" in unet_state_dict:\n new_checkpoint[\"encoder_hid_proj.weight\"] = unet_state_dict[\"encoder_proj.weight\"]\n new_checkpoint[\"encoder_hid_proj.bias\"] = unet_state_dict[\"encoder_proj.bias\"]\n\n if \"encoder_pooling.0.weight\" in unet_state_dict:\n mapping = {\n \"encoder_pooling.0\": \"add_embedding.norm1\",\n \"encoder_pooling.1\": \"add_embedding.pool\",\n \"encoder_pooling.2\": \"add_embedding.proj\",\n \"encoder_pooling.3\": \"add_embedding.norm2\",\n }\n for key in unet_state_dict.keys():\n if key.startswith(\"encoder_pooling\"):\n prefix = key[: len(\"encoder_pooling.0\")]\n new_key = key.replace(prefix, mapping[prefix])\n new_checkpoint[new_key] = unet_state_dict[key]\n\n new_checkpoint[\"conv_in.weight\"] = unet_state_dict[\"input_blocks.0.0.weight\"]\n new_checkpoint[\"conv_in.bias\"] = unet_state_dict[\"input_blocks.0.0.bias\"]\n\n new_checkpoint[\"conv_norm_out.weight\"] = unet_state_dict[\"out.0.weight\"]\n new_checkpoint[\"conv_norm_out.bias\"] = unet_state_dict[\"out.0.bias\"]\n new_checkpoint[\"conv_out.weight\"] = unet_state_dict[\"out.2.weight\"]\n new_checkpoint[\"conv_out.bias\"] = unet_state_dict[\"out.2.bias\"]\n\n # Retrieves the keys for the input blocks only\n num_input_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"input_blocks\" in layer})\n input_blocks = {\n layer_id: [key for key in unet_state_dict if f\"input_blocks.{layer_id}.\" in key]\n for layer_id in range(num_input_blocks)\n }\n\n # Retrieves the keys for the middle blocks only\n num_middle_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"middle_block\" in layer})\n middle_blocks = {\n layer_id: [key for key in unet_state_dict if f\"middle_block.{layer_id}\" in key]\n for layer_id in range(num_middle_blocks)\n }\n\n # Retrieves the keys for the output blocks only\n num_output_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"output_blocks\" in layer})\n output_blocks = {\n layer_id: [key for key in unet_state_dict if f\"output_blocks.{layer_id}.\" in key]\n for layer_id in range(num_output_blocks)\n }\n if not isinstance(config[\"layers_per_block\"], int):\n layers_per_block_list = [e + 1 for e in config[\"layers_per_block\"]]\n layers_per_block_cumsum = list(np.cumsum(layers_per_block_list))\n downsampler_ids = layers_per_block_cumsum\n else:\n # TODO need better check than i in [4, 8, 12, 16]\n downsampler_ids = [4, 8, 12, 16]\n\n for i in range(1, num_input_blocks):\n if isinstance(config[\"layers_per_block\"], int):\n layers_per_block = config[\"layers_per_block\"]\n block_id = (i - 1) // (layers_per_block + 1)\n layer_in_block_id = (i - 1) % (layers_per_block + 1)\n else:\n block_id = next(k for k, n in enumerate(layers_per_block_cumsum) if (i - 1) < n)\n passed_blocks = layers_per_block_cumsum[block_id - 1] if block_id > 0 else 0\n layer_in_block_id = (i - 1) - passed_blocks\n\n resnets = [\n key for key in input_blocks[i] if f\"input_blocks.{i}.0\" in key and f\"input_blocks.{i}.0.op\" not in key\n ]\n attentions = [key for key in input_blocks[i] if f\"input_blocks.{i}.1\" in key]\n\n if f\"input_blocks.{i}.0.op.weight\" in unet_state_dict:\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.weight\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.op.weight\"\n )\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.bias\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.op.bias\"\n )\n\n paths = renew_resnet_paths(resnets)\n\n block_type = config[\"down_block_types\"][block_id]\n if (\n block_type == \"ResnetDownsampleBlock2D\" or block_type == \"SimpleCrossAttnDownBlock2D\"\n ) and i in downsampler_ids:\n meta_path = {\"old\": f\"input_blocks.{i}.0\", \"new\": f\"down_blocks.{block_id}.downsamplers.0\"}\n else:\n meta_path = {\"old\": f\"input_blocks.{i}.0\", \"new\": f\"down_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n if len(attentions):\n old_path = f\"input_blocks.{i}.1\"\n new_path = f\"down_blocks.{block_id}.attentions.{layer_in_block_id}\"\n\n assign_attention_to_checkpoint(\n new_checkpoint=new_checkpoint,\n unet_state_dict=unet_state_dict,\n old_path=old_path,\n new_path=new_path,\n config=config,\n )\n\n paths = renew_attention_paths(attentions)\n meta_path = {\"old\": old_path, \"new\": new_path}\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n unet_state_dict,\n additional_replacements=[meta_path],\n config=config,\n )\n\n resnet_0 = middle_blocks[0]\n attentions = middle_blocks[1]\n resnet_1 = middle_blocks[2]\n\n resnet_0_paths = renew_resnet_paths(resnet_0)\n assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)\n\n resnet_1_paths = renew_resnet_paths(resnet_1)\n assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)\n\n old_path = \"middle_block.1\"\n new_path = \"mid_block.attentions.0\"\n\n assign_attention_to_checkpoint(\n new_checkpoint=new_checkpoint,\n unet_state_dict=unet_state_dict,\n old_path=old_path,\n new_path=new_path,\n config=config,\n )\n\n attentions_paths = renew_attention_paths(attentions)\n meta_path = {\"old\": \"middle_block.1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(\n attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n if not isinstance(config[\"layers_per_block\"], int):\n layers_per_block_list = list(reversed([e + 1 for e in config[\"layers_per_block\"]]))\n layers_per_block_cumsum = list(np.cumsum(layers_per_block_list))\n\n for i in range(num_output_blocks):\n if isinstance(config[\"layers_per_block\"], int):\n layers_per_block = config[\"layers_per_block\"]\n block_id = i // (layers_per_block + 1)\n layer_in_block_id = i % (layers_per_block + 1)\n else:\n block_id = next(k for k, n in enumerate(layers_per_block_cumsum) if i < n)\n passed_blocks = layers_per_block_cumsum[block_id - 1] if block_id > 0 else 0\n layer_in_block_id = i - passed_blocks\n\n output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]\n output_block_list = {}\n\n for layer in output_block_layers:\n layer_id, layer_name = layer.split(\".\")[0], shave_segments(layer, 1)\n if layer_id in output_block_list:\n output_block_list[layer_id].append(layer_name)\n else:\n output_block_list[layer_id] = [layer_name]\n\n # len(output_block_list) == 1 -> resnet\n # len(output_block_list) == 2 -> resnet, attention or resnet, upscale resnet\n # len(output_block_list) == 3 -> resnet, attention, upscale resnet\n\n if len(output_block_list) > 1:\n resnets = [key for key in output_blocks[i] if f\"output_blocks.{i}.0\" in key]\n\n has_attention = True\n if len(output_block_list) == 2 and any(\"in_layers\" in k for k in output_block_list[\"1\"]):\n has_attention = False\n\n maybe_attentions = [key for key in output_blocks[i] if f\"output_blocks.{i}.1\" in key]\n\n paths = renew_resnet_paths(resnets)\n\n meta_path = {\"old\": f\"output_blocks.{i}.0\", \"new\": f\"up_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n output_block_list = {k: sorted(v) for k, v in output_block_list.items()}\n if [\"conv.bias\", \"conv.weight\"] in output_block_list.values():\n index = list(output_block_list.values()).index([\"conv.bias\", \"conv.weight\"])\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.weight\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.weight\"\n ]\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.bias\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.bias\"\n ]\n\n # this layer was no attention\n has_attention = False\n maybe_attentions = []\n\n if has_attention:\n old_path = f\"output_blocks.{i}.1\"\n new_path = f\"up_blocks.{block_id}.attentions.{layer_in_block_id}\"\n\n assign_attention_to_checkpoint(\n new_checkpoint=new_checkpoint,\n unet_state_dict=unet_state_dict,\n old_path=old_path,\n new_path=new_path,\n config=config,\n )\n\n paths = renew_attention_paths(maybe_attentions)\n meta_path = {\n \"old\": old_path,\n \"new\": new_path,\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n if len(output_block_list) == 3 or (not has_attention and len(maybe_attentions) > 0):\n layer_id = len(output_block_list) - 1\n resnets = [key for key in output_blocks[i] if f\"output_blocks.{i}.{layer_id}\" in key]\n paths = renew_resnet_paths(resnets)\n meta_path = {\"old\": f\"output_blocks.{i}.{layer_id}\", \"new\": f\"up_blocks.{block_id}.upsamplers.0\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n else:\n resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)\n for path in resnet_0_paths:\n old_path = \".\".join([\"output_blocks\", str(i), path[\"old\"]])\n new_path = \".\".join([\"up_blocks\", str(block_id), \"resnets\", str(layer_in_block_id), path[\"new\"]])\n\n new_checkpoint[new_path] = unet_state_dict[old_path]\n\n return new_checkpoint\n\n\ndef verify_param_count(orig_path, unet_diffusers_config):\n if \"-II-\" in orig_path:\n from deepfloyd_if.modules import IFStageII\n\n if_II = IFStageII(device=\"cpu\", dir_or_name=orig_path)\n elif \"-III-\" in orig_path:\n from deepfloyd_if.modules import IFStageIII\n\n if_II = IFStageIII(device=\"cpu\", dir_or_name=orig_path)\n else:\n assert f\"Weird name. Should have -II- or -III- in path: {orig_path}\"\n\n unet = UNet2DConditionModel(**unet_diffusers_config)\n\n # in params\n assert_param_count(unet.time_embedding, if_II.model.time_embed)\n assert_param_count(unet.conv_in, if_II.model.input_blocks[:1])\n\n # downblocks\n assert_param_count(unet.down_blocks[0], if_II.model.input_blocks[1:4])\n assert_param_count(unet.down_blocks[1], if_II.model.input_blocks[4:7])\n assert_param_count(unet.down_blocks[2], if_II.model.input_blocks[7:11])\n\n if \"-II-\" in orig_path:\n assert_param_count(unet.down_blocks[3], if_II.model.input_blocks[11:17])\n assert_param_count(unet.down_blocks[4], if_II.model.input_blocks[17:])\n if \"-III-\" in orig_path:\n assert_param_count(unet.down_blocks[3], if_II.model.input_blocks[11:15])\n assert_param_count(unet.down_blocks[4], if_II.model.input_blocks[15:20])\n assert_param_count(unet.down_blocks[5], if_II.model.input_blocks[20:])\n\n # mid block\n assert_param_count(unet.mid_block, if_II.model.middle_block)\n\n # up block\n if \"-II-\" in orig_path:\n assert_param_count(unet.up_blocks[0], if_II.model.output_blocks[:6])\n assert_param_count(unet.up_blocks[1], if_II.model.output_blocks[6:12])\n assert_param_count(unet.up_blocks[2], if_II.model.output_blocks[12:16])\n assert_param_count(unet.up_blocks[3], if_II.model.output_blocks[16:19])\n assert_param_count(unet.up_blocks[4], if_II.model.output_blocks[19:])\n if \"-III-\" in orig_path:\n assert_param_count(unet.up_blocks[0], if_II.model.output_blocks[:5])\n assert_param_count(unet.up_blocks[1], if_II.model.output_blocks[5:10])\n assert_param_count(unet.up_blocks[2], if_II.model.output_blocks[10:14])\n assert_param_count(unet.up_blocks[3], if_II.model.output_blocks[14:18])\n assert_param_count(unet.up_blocks[4], if_II.model.output_blocks[18:21])\n assert_param_count(unet.up_blocks[5], if_II.model.output_blocks[21:24])\n\n # out params\n assert_param_count(unet.conv_norm_out, if_II.model.out[0])\n assert_param_count(unet.conv_out, if_II.model.out[2])\n\n # make sure all model architecture has same param count\n assert_param_count(unet, if_II.model)\n\n\ndef assert_param_count(model_1, model_2):\n count_1 = sum(p.numel() for p in model_1.parameters())\n count_2 = sum(p.numel() for p in model_2.parameters())\n assert count_1 == count_2, f\"{model_1.__class__}: {count_1} != {model_2.__class__}: {count_2}\"\n\n\ndef superres_check_against_original(dump_path, unet_checkpoint_path):\n model_path = dump_path\n model = UNet2DConditionModel.from_pretrained(model_path)\n model.to(\"cuda\")\n orig_path = unet_checkpoint_path\n\n if \"-II-\" in orig_path:\n from deepfloyd_if.modules import IFStageII\n\n if_II_model = IFStageII(device=\"cuda\", dir_or_name=orig_path, model_kwargs={\"precision\": \"fp32\"}).model\n elif \"-III-\" in orig_path:\n from deepfloyd_if.modules import IFStageIII\n\n if_II_model = IFStageIII(device=\"cuda\", dir_or_name=orig_path, model_kwargs={\"precision\": \"fp32\"}).model\n\n batch_size = 1\n channels = model.config.in_channels // 2\n height = model.config.sample_size\n width = model.config.sample_size\n height = 1024\n width = 1024\n\n torch.manual_seed(0)\n\n latents = torch.randn((batch_size, channels, height, width), device=model.device)\n image_small = torch.randn((batch_size, channels, height // 4, width // 4), device=model.device)\n\n interpolate_antialias = {}\n if \"antialias\" in inspect.signature(F.interpolate).parameters:\n interpolate_antialias[\"antialias\"] = True\n image_upscaled = F.interpolate(\n image_small, size=[height, width], mode=\"bicubic\", align_corners=False, **interpolate_antialias\n )\n\n latent_model_input = torch.cat([latents, image_upscaled], dim=1).to(model.dtype)\n t = torch.tensor([5], device=model.device).to(model.dtype)\n\n seq_len = 64\n encoder_hidden_states = torch.randn((batch_size, seq_len, model.config.encoder_hid_dim), device=model.device).to(\n model.dtype\n )\n\n fake_class_labels = torch.tensor([t], device=model.device).to(model.dtype)\n\n with torch.no_grad():\n out = if_II_model(latent_model_input, t, aug_steps=fake_class_labels, text_emb=encoder_hidden_states)\n\n if_II_model.to(\"cpu\")\n del if_II_model\n import gc\n\n torch.cuda.empty_cache()\n gc.collect()\n print(50 * \"=\")\n\n with torch.no_grad():\n noise_pred = model(\n sample=latent_model_input,\n encoder_hidden_states=encoder_hidden_states,\n class_labels=fake_class_labels,\n timestep=t,\n ).sample\n\n print(\"Out shape\", noise_pred.shape)\n print(\"Diff\", (out - noise_pred).abs().sum())\n\n\nif __name__ == \"__main__\":\n main(parse_args())\n" }, { "path": "diffusers/scripts/convert_k_upscaler_to_diffusers.py", "content": "import argparse\n\nimport huggingface_hub\nimport k_diffusion as K\nimport torch\n\nfrom diffusers import UNet2DConditionModel\n\n\nUPSCALER_REPO = \"pcuenq/k-upscaler\"\n\n\ndef resnet_to_diffusers_checkpoint(resnet, checkpoint, *, diffusers_resnet_prefix, resnet_prefix):\n rv = {\n # norm1\n f\"{diffusers_resnet_prefix}.norm1.linear.weight\": checkpoint[f\"{resnet_prefix}.main.0.mapper.weight\"],\n f\"{diffusers_resnet_prefix}.norm1.linear.bias\": checkpoint[f\"{resnet_prefix}.main.0.mapper.bias\"],\n # conv1\n f\"{diffusers_resnet_prefix}.conv1.weight\": checkpoint[f\"{resnet_prefix}.main.2.weight\"],\n f\"{diffusers_resnet_prefix}.conv1.bias\": checkpoint[f\"{resnet_prefix}.main.2.bias\"],\n # norm2\n f\"{diffusers_resnet_prefix}.norm2.linear.weight\": checkpoint[f\"{resnet_prefix}.main.4.mapper.weight\"],\n f\"{diffusers_resnet_prefix}.norm2.linear.bias\": checkpoint[f\"{resnet_prefix}.main.4.mapper.bias\"],\n # conv2\n f\"{diffusers_resnet_prefix}.conv2.weight\": checkpoint[f\"{resnet_prefix}.main.6.weight\"],\n f\"{diffusers_resnet_prefix}.conv2.bias\": checkpoint[f\"{resnet_prefix}.main.6.bias\"],\n }\n\n if resnet.conv_shortcut is not None:\n rv.update(\n {\n f\"{diffusers_resnet_prefix}.conv_shortcut.weight\": checkpoint[f\"{resnet_prefix}.skip.weight\"],\n }\n )\n\n return rv\n\n\ndef self_attn_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix):\n weight_q, weight_k, weight_v = checkpoint[f\"{attention_prefix}.qkv_proj.weight\"].chunk(3, dim=0)\n bias_q, bias_k, bias_v = checkpoint[f\"{attention_prefix}.qkv_proj.bias\"].chunk(3, dim=0)\n rv = {\n # norm\n f\"{diffusers_attention_prefix}.norm1.linear.weight\": checkpoint[f\"{attention_prefix}.norm_in.mapper.weight\"],\n f\"{diffusers_attention_prefix}.norm1.linear.bias\": checkpoint[f\"{attention_prefix}.norm_in.mapper.bias\"],\n # to_q\n f\"{diffusers_attention_prefix}.attn1.to_q.weight\": weight_q.squeeze(-1).squeeze(-1),\n f\"{diffusers_attention_prefix}.attn1.to_q.bias\": bias_q,\n # to_k\n f\"{diffusers_attention_prefix}.attn1.to_k.weight\": weight_k.squeeze(-1).squeeze(-1),\n f\"{diffusers_attention_prefix}.attn1.to_k.bias\": bias_k,\n # to_v\n f\"{diffusers_attention_prefix}.attn1.to_v.weight\": weight_v.squeeze(-1).squeeze(-1),\n f\"{diffusers_attention_prefix}.attn1.to_v.bias\": bias_v,\n # to_out\n f\"{diffusers_attention_prefix}.attn1.to_out.0.weight\": checkpoint[f\"{attention_prefix}.out_proj.weight\"]\n .squeeze(-1)\n .squeeze(-1),\n f\"{diffusers_attention_prefix}.attn1.to_out.0.bias\": checkpoint[f\"{attention_prefix}.out_proj.bias\"],\n }\n\n return rv\n\n\ndef cross_attn_to_diffusers_checkpoint(\n checkpoint, *, diffusers_attention_prefix, diffusers_attention_index, attention_prefix\n):\n weight_k, weight_v = checkpoint[f\"{attention_prefix}.kv_proj.weight\"].chunk(2, dim=0)\n bias_k, bias_v = checkpoint[f\"{attention_prefix}.kv_proj.bias\"].chunk(2, dim=0)\n\n rv = {\n # norm2 (ada groupnorm)\n f\"{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight\": checkpoint[\n f\"{attention_prefix}.norm_dec.mapper.weight\"\n ],\n f\"{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias\": checkpoint[\n f\"{attention_prefix}.norm_dec.mapper.bias\"\n ],\n # layernorm on encoder_hidden_state\n f\"{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight\": checkpoint[\n f\"{attention_prefix}.norm_enc.weight\"\n ],\n f\"{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias\": checkpoint[\n f\"{attention_prefix}.norm_enc.bias\"\n ],\n # to_q\n f\"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight\": checkpoint[\n f\"{attention_prefix}.q_proj.weight\"\n ]\n .squeeze(-1)\n .squeeze(-1),\n f\"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias\": checkpoint[\n f\"{attention_prefix}.q_proj.bias\"\n ],\n # to_k\n f\"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight\": weight_k.squeeze(-1).squeeze(-1),\n f\"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias\": bias_k,\n # to_v\n f\"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight\": weight_v.squeeze(-1).squeeze(-1),\n f\"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias\": bias_v,\n # to_out\n f\"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight\": checkpoint[\n f\"{attention_prefix}.out_proj.weight\"\n ]\n .squeeze(-1)\n .squeeze(-1),\n f\"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias\": checkpoint[\n f\"{attention_prefix}.out_proj.bias\"\n ],\n }\n\n return rv\n\n\ndef block_to_diffusers_checkpoint(block, checkpoint, block_idx, block_type):\n block_prefix = \"inner_model.u_net.u_blocks\" if block_type == \"up\" else \"inner_model.u_net.d_blocks\"\n block_prefix = f\"{block_prefix}.{block_idx}\"\n\n diffusers_checkpoint = {}\n\n if not hasattr(block, \"attentions\"):\n n = 1 # resnet only\n elif not block.attentions[0].add_self_attention:\n n = 2 # resnet -> cross-attention\n else:\n n = 3 # resnet -> self-attention -> cross-attention)\n\n for resnet_idx, resnet in enumerate(block.resnets):\n # diffusers_resnet_prefix = f\"{diffusers_up_block_prefix}.resnets.{resnet_idx}\"\n diffusers_resnet_prefix = f\"{block_type}_blocks.{block_idx}.resnets.{resnet_idx}\"\n idx = n * resnet_idx if block_type == \"up\" else n * resnet_idx + 1\n resnet_prefix = f\"{block_prefix}.{idx}\" if block_type == \"up\" else f\"{block_prefix}.{idx}\"\n\n diffusers_checkpoint.update(\n resnet_to_diffusers_checkpoint(\n resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix\n )\n )\n\n if hasattr(block, \"attentions\"):\n for attention_idx, attention in enumerate(block.attentions):\n diffusers_attention_prefix = f\"{block_type}_blocks.{block_idx}.attentions.{attention_idx}\"\n idx = n * attention_idx + 1 if block_type == \"up\" else n * attention_idx + 2\n self_attention_prefix = f\"{block_prefix}.{idx}\"\n cross_attention_prefix = f\"{block_prefix}.{idx }\"\n cross_attention_index = 1 if not attention.add_self_attention else 2\n idx = (\n n * attention_idx + cross_attention_index\n if block_type == \"up\"\n else n * attention_idx + cross_attention_index + 1\n )\n cross_attention_prefix = f\"{block_prefix}.{idx }\"\n\n diffusers_checkpoint.update(\n cross_attn_to_diffusers_checkpoint(\n checkpoint,\n diffusers_attention_prefix=diffusers_attention_prefix,\n diffusers_attention_index=2,\n attention_prefix=cross_attention_prefix,\n )\n )\n\n if attention.add_self_attention is True:\n diffusers_checkpoint.update(\n self_attn_to_diffusers_checkpoint(\n checkpoint,\n diffusers_attention_prefix=diffusers_attention_prefix,\n attention_prefix=self_attention_prefix,\n )\n )\n\n return diffusers_checkpoint\n\n\ndef unet_to_diffusers_checkpoint(model, checkpoint):\n diffusers_checkpoint = {}\n\n # pre-processing\n diffusers_checkpoint.update(\n {\n \"conv_in.weight\": checkpoint[\"inner_model.proj_in.weight\"],\n \"conv_in.bias\": checkpoint[\"inner_model.proj_in.bias\"],\n }\n )\n\n # timestep and class embedding\n diffusers_checkpoint.update(\n {\n \"time_proj.weight\": checkpoint[\"inner_model.timestep_embed.weight\"].squeeze(-1),\n \"time_embedding.linear_1.weight\": checkpoint[\"inner_model.mapping.0.weight\"],\n \"time_embedding.linear_1.bias\": checkpoint[\"inner_model.mapping.0.bias\"],\n \"time_embedding.linear_2.weight\": checkpoint[\"inner_model.mapping.2.weight\"],\n \"time_embedding.linear_2.bias\": checkpoint[\"inner_model.mapping.2.bias\"],\n \"time_embedding.cond_proj.weight\": checkpoint[\"inner_model.mapping_cond.weight\"],\n }\n )\n\n # down_blocks\n for down_block_idx, down_block in enumerate(model.down_blocks):\n diffusers_checkpoint.update(block_to_diffusers_checkpoint(down_block, checkpoint, down_block_idx, \"down\"))\n\n # up_blocks\n for up_block_idx, up_block in enumerate(model.up_blocks):\n diffusers_checkpoint.update(block_to_diffusers_checkpoint(up_block, checkpoint, up_block_idx, \"up\"))\n\n # post-processing\n diffusers_checkpoint.update(\n {\n \"conv_out.weight\": checkpoint[\"inner_model.proj_out.weight\"],\n \"conv_out.bias\": checkpoint[\"inner_model.proj_out.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\ndef unet_model_from_original_config(original_config):\n in_channels = original_config[\"input_channels\"] + original_config[\"unet_cond_dim\"]\n out_channels = original_config[\"input_channels\"] + (1 if original_config[\"has_variance\"] else 0)\n\n block_out_channels = original_config[\"channels\"]\n\n assert (\n len(set(original_config[\"depths\"])) == 1\n ), \"UNet2DConditionModel currently do not support blocks with different number of layers\"\n layers_per_block = original_config[\"depths\"][0]\n\n class_labels_dim = original_config[\"mapping_cond_dim\"]\n cross_attention_dim = original_config[\"cross_cond_dim\"]\n\n attn1_types = []\n attn2_types = []\n for s, c in zip(original_config[\"self_attn_depths\"], original_config[\"cross_attn_depths\"]):\n if s:\n a1 = \"self\"\n a2 = \"cross\" if c else None\n elif c:\n a1 = \"cross\"\n a2 = None\n else:\n a1 = None\n a2 = None\n attn1_types.append(a1)\n attn2_types.append(a2)\n\n unet = UNet2DConditionModel(\n in_channels=in_channels,\n out_channels=out_channels,\n down_block_types=(\"KDownBlock2D\", \"KCrossAttnDownBlock2D\", \"KCrossAttnDownBlock2D\", \"KCrossAttnDownBlock2D\"),\n mid_block_type=None,\n up_block_types=(\"KCrossAttnUpBlock2D\", \"KCrossAttnUpBlock2D\", \"KCrossAttnUpBlock2D\", \"KUpBlock2D\"),\n block_out_channels=block_out_channels,\n layers_per_block=layers_per_block,\n act_fn=\"gelu\",\n norm_num_groups=None,\n cross_attention_dim=cross_attention_dim,\n attention_head_dim=64,\n time_cond_proj_dim=class_labels_dim,\n resnet_time_scale_shift=\"scale_shift\",\n time_embedding_type=\"fourier\",\n timestep_post_act=\"gelu\",\n conv_in_kernel=1,\n conv_out_kernel=1,\n )\n\n return unet\n\n\ndef main(args):\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n orig_config_path = huggingface_hub.hf_hub_download(UPSCALER_REPO, \"config_laion_text_cond_latent_upscaler_2.json\")\n orig_weights_path = huggingface_hub.hf_hub_download(\n UPSCALER_REPO, \"laion_text_cond_latent_upscaler_2_1_00470000_slim.pth\"\n )\n print(f\"loading original model configuration from {orig_config_path}\")\n print(f\"loading original model checkpoint from {orig_weights_path}\")\n\n print(\"converting to diffusers unet\")\n orig_config = K.config.load_config(open(orig_config_path))[\"model\"]\n model = unet_model_from_original_config(orig_config)\n\n orig_checkpoint = torch.load(orig_weights_path, map_location=device)[\"model_ema\"]\n converted_checkpoint = unet_to_diffusers_checkpoint(model, orig_checkpoint)\n\n model.load_state_dict(converted_checkpoint, strict=True)\n model.save_pretrained(args.dump_path)\n print(f\"saving converted unet model in {args.dump_path}\")\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n args = parser.parse_args()\n\n main(args)\n" }, { "path": "diffusers/scripts/convert_kakao_brain_unclip_to_diffusers.py", "content": "import argparse\nimport tempfile\n\nimport torch\nfrom accelerate import load_checkpoint_and_dispatch\nfrom transformers import CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom diffusers import UnCLIPPipeline, UNet2DConditionModel, UNet2DModel\nfrom diffusers.models.transformers.prior_transformer import PriorTransformer\nfrom diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel\nfrom diffusers.schedulers.scheduling_unclip import UnCLIPScheduler\n\n\nr\"\"\"\nExample - From the diffusers root directory:\n\nDownload weights:\n```sh\n$ wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/efdf6206d8ed593961593dc029a8affa/decoder-ckpt-step%3D01000000-of-01000000.ckpt\n$ wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/4226b831ae0279020d134281f3c31590/improved-sr-ckpt-step%3D1.2M.ckpt\n$ wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/85626483eaca9f581e2a78d31ff905ca/prior-ckpt-step%3D01000000-of-01000000.ckpt\n$ wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/0b62380a75e56f073e2844ab5199153d/ViT-L-14_stats.th\n```\n\nConvert the model:\n```sh\n$ python scripts/convert_kakao_brain_unclip_to_diffusers.py \\\n --decoder_checkpoint_path ./decoder-ckpt-step\\=01000000-of-01000000.ckpt \\\n --super_res_unet_checkpoint_path ./improved-sr-ckpt-step\\=1.2M.ckpt \\\n --prior_checkpoint_path ./prior-ckpt-step\\=01000000-of-01000000.ckpt \\\n --clip_stat_path ./ViT-L-14_stats.th \\\n --dump_path \n```\n\"\"\"\n\n\n# prior\n\nPRIOR_ORIGINAL_PREFIX = \"model\"\n\n# Uses default arguments\nPRIOR_CONFIG = {}\n\n\ndef prior_model_from_original_config():\n model = PriorTransformer(**PRIOR_CONFIG)\n\n return model\n\n\ndef prior_original_checkpoint_to_diffusers_checkpoint(model, checkpoint, clip_stats_checkpoint):\n diffusers_checkpoint = {}\n\n # .time_embed.0 -> .time_embedding.linear_1\n diffusers_checkpoint.update(\n {\n \"time_embedding.linear_1.weight\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.time_embed.0.weight\"],\n \"time_embedding.linear_1.bias\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.time_embed.0.bias\"],\n }\n )\n\n # .clip_img_proj -> .proj_in\n diffusers_checkpoint.update(\n {\n \"proj_in.weight\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.clip_img_proj.weight\"],\n \"proj_in.bias\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.clip_img_proj.bias\"],\n }\n )\n\n # .text_emb_proj -> .embedding_proj\n diffusers_checkpoint.update(\n {\n \"embedding_proj.weight\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.text_emb_proj.weight\"],\n \"embedding_proj.bias\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.text_emb_proj.bias\"],\n }\n )\n\n # .text_enc_proj -> .encoder_hidden_states_proj\n diffusers_checkpoint.update(\n {\n \"encoder_hidden_states_proj.weight\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.text_enc_proj.weight\"],\n \"encoder_hidden_states_proj.bias\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.text_enc_proj.bias\"],\n }\n )\n\n # .positional_embedding -> .positional_embedding\n diffusers_checkpoint.update({\"positional_embedding\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.positional_embedding\"]})\n\n # .prd_emb -> .prd_embedding\n diffusers_checkpoint.update({\"prd_embedding\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.prd_emb\"]})\n\n # .time_embed.2 -> .time_embedding.linear_2\n diffusers_checkpoint.update(\n {\n \"time_embedding.linear_2.weight\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.time_embed.2.weight\"],\n \"time_embedding.linear_2.bias\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.time_embed.2.bias\"],\n }\n )\n\n # .resblocks. -> .transformer_blocks.\n for idx in range(len(model.transformer_blocks)):\n diffusers_transformer_prefix = f\"transformer_blocks.{idx}\"\n original_transformer_prefix = f\"{PRIOR_ORIGINAL_PREFIX}.transformer.resblocks.{idx}\"\n\n # .attn -> .attn1\n diffusers_attention_prefix = f\"{diffusers_transformer_prefix}.attn1\"\n original_attention_prefix = f\"{original_transformer_prefix}.attn\"\n diffusers_checkpoint.update(\n prior_attention_to_diffusers(\n checkpoint,\n diffusers_attention_prefix=diffusers_attention_prefix,\n original_attention_prefix=original_attention_prefix,\n attention_head_dim=model.attention_head_dim,\n )\n )\n\n # .mlp -> .ff\n diffusers_ff_prefix = f\"{diffusers_transformer_prefix}.ff\"\n original_ff_prefix = f\"{original_transformer_prefix}.mlp\"\n diffusers_checkpoint.update(\n prior_ff_to_diffusers(\n checkpoint, diffusers_ff_prefix=diffusers_ff_prefix, original_ff_prefix=original_ff_prefix\n )\n )\n\n # .ln_1 -> .norm1\n diffusers_checkpoint.update(\n {\n f\"{diffusers_transformer_prefix}.norm1.weight\": checkpoint[\n f\"{original_transformer_prefix}.ln_1.weight\"\n ],\n f\"{diffusers_transformer_prefix}.norm1.bias\": checkpoint[f\"{original_transformer_prefix}.ln_1.bias\"],\n }\n )\n\n # .ln_2 -> .norm3\n diffusers_checkpoint.update(\n {\n f\"{diffusers_transformer_prefix}.norm3.weight\": checkpoint[\n f\"{original_transformer_prefix}.ln_2.weight\"\n ],\n f\"{diffusers_transformer_prefix}.norm3.bias\": checkpoint[f\"{original_transformer_prefix}.ln_2.bias\"],\n }\n )\n\n # .final_ln -> .norm_out\n diffusers_checkpoint.update(\n {\n \"norm_out.weight\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.final_ln.weight\"],\n \"norm_out.bias\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.final_ln.bias\"],\n }\n )\n\n # .out_proj -> .proj_to_clip_embeddings\n diffusers_checkpoint.update(\n {\n \"proj_to_clip_embeddings.weight\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.out_proj.weight\"],\n \"proj_to_clip_embeddings.bias\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.out_proj.bias\"],\n }\n )\n\n # clip stats\n clip_mean, clip_std = clip_stats_checkpoint\n clip_mean = clip_mean[None, :]\n clip_std = clip_std[None, :]\n\n diffusers_checkpoint.update({\"clip_mean\": clip_mean, \"clip_std\": clip_std})\n\n return diffusers_checkpoint\n\n\ndef prior_attention_to_diffusers(\n checkpoint, *, diffusers_attention_prefix, original_attention_prefix, attention_head_dim\n):\n diffusers_checkpoint = {}\n\n # .c_qkv -> .{to_q, to_k, to_v}\n [q_weight, k_weight, v_weight], [q_bias, k_bias, v_bias] = split_attentions(\n weight=checkpoint[f\"{original_attention_prefix}.c_qkv.weight\"],\n bias=checkpoint[f\"{original_attention_prefix}.c_qkv.bias\"],\n split=3,\n chunk_size=attention_head_dim,\n )\n\n diffusers_checkpoint.update(\n {\n f\"{diffusers_attention_prefix}.to_q.weight\": q_weight,\n f\"{diffusers_attention_prefix}.to_q.bias\": q_bias,\n f\"{diffusers_attention_prefix}.to_k.weight\": k_weight,\n f\"{diffusers_attention_prefix}.to_k.bias\": k_bias,\n f\"{diffusers_attention_prefix}.to_v.weight\": v_weight,\n f\"{diffusers_attention_prefix}.to_v.bias\": v_bias,\n }\n )\n\n # .c_proj -> .to_out.0\n diffusers_checkpoint.update(\n {\n f\"{diffusers_attention_prefix}.to_out.0.weight\": checkpoint[f\"{original_attention_prefix}.c_proj.weight\"],\n f\"{diffusers_attention_prefix}.to_out.0.bias\": checkpoint[f\"{original_attention_prefix}.c_proj.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\ndef prior_ff_to_diffusers(checkpoint, *, diffusers_ff_prefix, original_ff_prefix):\n diffusers_checkpoint = {\n # .c_fc -> .net.0.proj\n f\"{diffusers_ff_prefix}.net.{0}.proj.weight\": checkpoint[f\"{original_ff_prefix}.c_fc.weight\"],\n f\"{diffusers_ff_prefix}.net.{0}.proj.bias\": checkpoint[f\"{original_ff_prefix}.c_fc.bias\"],\n # .c_proj -> .net.2\n f\"{diffusers_ff_prefix}.net.{2}.weight\": checkpoint[f\"{original_ff_prefix}.c_proj.weight\"],\n f\"{diffusers_ff_prefix}.net.{2}.bias\": checkpoint[f\"{original_ff_prefix}.c_proj.bias\"],\n }\n\n return diffusers_checkpoint\n\n\n# done prior\n\n\n# decoder\n\nDECODER_ORIGINAL_PREFIX = \"model\"\n\n# We are hardcoding the model configuration for now. If we need to generalize to more model configurations, we can\n# update then.\nDECODER_CONFIG = {\n \"sample_size\": 64,\n \"layers_per_block\": 3,\n \"down_block_types\": (\n \"ResnetDownsampleBlock2D\",\n \"SimpleCrossAttnDownBlock2D\",\n \"SimpleCrossAttnDownBlock2D\",\n \"SimpleCrossAttnDownBlock2D\",\n ),\n \"up_block_types\": (\n \"SimpleCrossAttnUpBlock2D\",\n \"SimpleCrossAttnUpBlock2D\",\n \"SimpleCrossAttnUpBlock2D\",\n \"ResnetUpsampleBlock2D\",\n ),\n \"mid_block_type\": \"UNetMidBlock2DSimpleCrossAttn\",\n \"block_out_channels\": (320, 640, 960, 1280),\n \"in_channels\": 3,\n \"out_channels\": 6,\n \"cross_attention_dim\": 1536,\n \"class_embed_type\": \"identity\",\n \"attention_head_dim\": 64,\n \"resnet_time_scale_shift\": \"scale_shift\",\n}\n\n\ndef decoder_model_from_original_config():\n model = UNet2DConditionModel(**DECODER_CONFIG)\n\n return model\n\n\ndef decoder_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):\n diffusers_checkpoint = {}\n\n original_unet_prefix = DECODER_ORIGINAL_PREFIX\n num_head_channels = DECODER_CONFIG[\"attention_head_dim\"]\n\n diffusers_checkpoint.update(unet_time_embeddings(checkpoint, original_unet_prefix))\n diffusers_checkpoint.update(unet_conv_in(checkpoint, original_unet_prefix))\n\n # .input_blocks -> .down_blocks\n\n original_down_block_idx = 1\n\n for diffusers_down_block_idx in range(len(model.down_blocks)):\n checkpoint_update, num_original_down_blocks = unet_downblock_to_diffusers_checkpoint(\n model,\n checkpoint,\n diffusers_down_block_idx=diffusers_down_block_idx,\n original_down_block_idx=original_down_block_idx,\n original_unet_prefix=original_unet_prefix,\n num_head_channels=num_head_channels,\n )\n\n original_down_block_idx += num_original_down_blocks\n\n diffusers_checkpoint.update(checkpoint_update)\n\n # done .input_blocks -> .down_blocks\n\n diffusers_checkpoint.update(\n unet_midblock_to_diffusers_checkpoint(\n model,\n checkpoint,\n original_unet_prefix=original_unet_prefix,\n num_head_channels=num_head_channels,\n )\n )\n\n # .output_blocks -> .up_blocks\n\n original_up_block_idx = 0\n\n for diffusers_up_block_idx in range(len(model.up_blocks)):\n checkpoint_update, num_original_up_blocks = unet_upblock_to_diffusers_checkpoint(\n model,\n checkpoint,\n diffusers_up_block_idx=diffusers_up_block_idx,\n original_up_block_idx=original_up_block_idx,\n original_unet_prefix=original_unet_prefix,\n num_head_channels=num_head_channels,\n )\n\n original_up_block_idx += num_original_up_blocks\n\n diffusers_checkpoint.update(checkpoint_update)\n\n # done .output_blocks -> .up_blocks\n\n diffusers_checkpoint.update(unet_conv_norm_out(checkpoint, original_unet_prefix))\n diffusers_checkpoint.update(unet_conv_out(checkpoint, original_unet_prefix))\n\n return diffusers_checkpoint\n\n\n# done decoder\n\n# text proj\n\n\ndef text_proj_from_original_config():\n # From the conditional unet constructor where the dimension of the projected time embeddings is\n # constructed\n time_embed_dim = DECODER_CONFIG[\"block_out_channels\"][0] * 4\n\n cross_attention_dim = DECODER_CONFIG[\"cross_attention_dim\"]\n\n model = UnCLIPTextProjModel(time_embed_dim=time_embed_dim, cross_attention_dim=cross_attention_dim)\n\n return model\n\n\n# Note that the input checkpoint is the original decoder checkpoint\ndef text_proj_original_checkpoint_to_diffusers_checkpoint(checkpoint):\n diffusers_checkpoint = {\n # .text_seq_proj.0 -> .encoder_hidden_states_proj\n \"encoder_hidden_states_proj.weight\": checkpoint[f\"{DECODER_ORIGINAL_PREFIX}.text_seq_proj.0.weight\"],\n \"encoder_hidden_states_proj.bias\": checkpoint[f\"{DECODER_ORIGINAL_PREFIX}.text_seq_proj.0.bias\"],\n # .text_seq_proj.1 -> .text_encoder_hidden_states_norm\n \"text_encoder_hidden_states_norm.weight\": checkpoint[f\"{DECODER_ORIGINAL_PREFIX}.text_seq_proj.1.weight\"],\n \"text_encoder_hidden_states_norm.bias\": checkpoint[f\"{DECODER_ORIGINAL_PREFIX}.text_seq_proj.1.bias\"],\n # .clip_tok_proj -> .clip_extra_context_tokens_proj\n \"clip_extra_context_tokens_proj.weight\": checkpoint[f\"{DECODER_ORIGINAL_PREFIX}.clip_tok_proj.weight\"],\n \"clip_extra_context_tokens_proj.bias\": checkpoint[f\"{DECODER_ORIGINAL_PREFIX}.clip_tok_proj.bias\"],\n # .text_feat_proj -> .embedding_proj\n \"embedding_proj.weight\": checkpoint[f\"{DECODER_ORIGINAL_PREFIX}.text_feat_proj.weight\"],\n \"embedding_proj.bias\": checkpoint[f\"{DECODER_ORIGINAL_PREFIX}.text_feat_proj.bias\"],\n # .cf_param -> .learned_classifier_free_guidance_embeddings\n \"learned_classifier_free_guidance_embeddings\": checkpoint[f\"{DECODER_ORIGINAL_PREFIX}.cf_param\"],\n # .clip_emb -> .clip_image_embeddings_project_to_time_embeddings\n \"clip_image_embeddings_project_to_time_embeddings.weight\": checkpoint[\n f\"{DECODER_ORIGINAL_PREFIX}.clip_emb.weight\"\n ],\n \"clip_image_embeddings_project_to_time_embeddings.bias\": checkpoint[\n f\"{DECODER_ORIGINAL_PREFIX}.clip_emb.bias\"\n ],\n }\n\n return diffusers_checkpoint\n\n\n# done text proj\n\n# super res unet first steps\n\nSUPER_RES_UNET_FIRST_STEPS_PREFIX = \"model_first_steps\"\n\nSUPER_RES_UNET_FIRST_STEPS_CONFIG = {\n \"sample_size\": 256,\n \"layers_per_block\": 3,\n \"down_block_types\": (\n \"ResnetDownsampleBlock2D\",\n \"ResnetDownsampleBlock2D\",\n \"ResnetDownsampleBlock2D\",\n \"ResnetDownsampleBlock2D\",\n ),\n \"up_block_types\": (\n \"ResnetUpsampleBlock2D\",\n \"ResnetUpsampleBlock2D\",\n \"ResnetUpsampleBlock2D\",\n \"ResnetUpsampleBlock2D\",\n ),\n \"block_out_channels\": (320, 640, 960, 1280),\n \"in_channels\": 6,\n \"out_channels\": 3,\n \"add_attention\": False,\n}\n\n\ndef super_res_unet_first_steps_model_from_original_config():\n model = UNet2DModel(**SUPER_RES_UNET_FIRST_STEPS_CONFIG)\n\n return model\n\n\ndef super_res_unet_first_steps_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):\n diffusers_checkpoint = {}\n\n original_unet_prefix = SUPER_RES_UNET_FIRST_STEPS_PREFIX\n\n diffusers_checkpoint.update(unet_time_embeddings(checkpoint, original_unet_prefix))\n diffusers_checkpoint.update(unet_conv_in(checkpoint, original_unet_prefix))\n\n # .input_blocks -> .down_blocks\n\n original_down_block_idx = 1\n\n for diffusers_down_block_idx in range(len(model.down_blocks)):\n checkpoint_update, num_original_down_blocks = unet_downblock_to_diffusers_checkpoint(\n model,\n checkpoint,\n diffusers_down_block_idx=diffusers_down_block_idx,\n original_down_block_idx=original_down_block_idx,\n original_unet_prefix=original_unet_prefix,\n num_head_channels=None,\n )\n\n original_down_block_idx += num_original_down_blocks\n\n diffusers_checkpoint.update(checkpoint_update)\n\n diffusers_checkpoint.update(\n unet_midblock_to_diffusers_checkpoint(\n model,\n checkpoint,\n original_unet_prefix=original_unet_prefix,\n num_head_channels=None,\n )\n )\n\n # .output_blocks -> .up_blocks\n\n original_up_block_idx = 0\n\n for diffusers_up_block_idx in range(len(model.up_blocks)):\n checkpoint_update, num_original_up_blocks = unet_upblock_to_diffusers_checkpoint(\n model,\n checkpoint,\n diffusers_up_block_idx=diffusers_up_block_idx,\n original_up_block_idx=original_up_block_idx,\n original_unet_prefix=original_unet_prefix,\n num_head_channels=None,\n )\n\n original_up_block_idx += num_original_up_blocks\n\n diffusers_checkpoint.update(checkpoint_update)\n\n # done .output_blocks -> .up_blocks\n\n diffusers_checkpoint.update(unet_conv_norm_out(checkpoint, original_unet_prefix))\n diffusers_checkpoint.update(unet_conv_out(checkpoint, original_unet_prefix))\n\n return diffusers_checkpoint\n\n\n# done super res unet first steps\n\n# super res unet last step\n\nSUPER_RES_UNET_LAST_STEP_PREFIX = \"model_last_step\"\n\nSUPER_RES_UNET_LAST_STEP_CONFIG = {\n \"sample_size\": 256,\n \"layers_per_block\": 3,\n \"down_block_types\": (\n \"ResnetDownsampleBlock2D\",\n \"ResnetDownsampleBlock2D\",\n \"ResnetDownsampleBlock2D\",\n \"ResnetDownsampleBlock2D\",\n ),\n \"up_block_types\": (\n \"ResnetUpsampleBlock2D\",\n \"ResnetUpsampleBlock2D\",\n \"ResnetUpsampleBlock2D\",\n \"ResnetUpsampleBlock2D\",\n ),\n \"block_out_channels\": (320, 640, 960, 1280),\n \"in_channels\": 6,\n \"out_channels\": 3,\n \"add_attention\": False,\n}\n\n\ndef super_res_unet_last_step_model_from_original_config():\n model = UNet2DModel(**SUPER_RES_UNET_LAST_STEP_CONFIG)\n\n return model\n\n\ndef super_res_unet_last_step_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):\n diffusers_checkpoint = {}\n\n original_unet_prefix = SUPER_RES_UNET_LAST_STEP_PREFIX\n\n diffusers_checkpoint.update(unet_time_embeddings(checkpoint, original_unet_prefix))\n diffusers_checkpoint.update(unet_conv_in(checkpoint, original_unet_prefix))\n\n # .input_blocks -> .down_blocks\n\n original_down_block_idx = 1\n\n for diffusers_down_block_idx in range(len(model.down_blocks)):\n checkpoint_update, num_original_down_blocks = unet_downblock_to_diffusers_checkpoint(\n model,\n checkpoint,\n diffusers_down_block_idx=diffusers_down_block_idx,\n original_down_block_idx=original_down_block_idx,\n original_unet_prefix=original_unet_prefix,\n num_head_channels=None,\n )\n\n original_down_block_idx += num_original_down_blocks\n\n diffusers_checkpoint.update(checkpoint_update)\n\n diffusers_checkpoint.update(\n unet_midblock_to_diffusers_checkpoint(\n model,\n checkpoint,\n original_unet_prefix=original_unet_prefix,\n num_head_channels=None,\n )\n )\n\n # .output_blocks -> .up_blocks\n\n original_up_block_idx = 0\n\n for diffusers_up_block_idx in range(len(model.up_blocks)):\n checkpoint_update, num_original_up_blocks = unet_upblock_to_diffusers_checkpoint(\n model,\n checkpoint,\n diffusers_up_block_idx=diffusers_up_block_idx,\n original_up_block_idx=original_up_block_idx,\n original_unet_prefix=original_unet_prefix,\n num_head_channels=None,\n )\n\n original_up_block_idx += num_original_up_blocks\n\n diffusers_checkpoint.update(checkpoint_update)\n\n # done .output_blocks -> .up_blocks\n\n diffusers_checkpoint.update(unet_conv_norm_out(checkpoint, original_unet_prefix))\n diffusers_checkpoint.update(unet_conv_out(checkpoint, original_unet_prefix))\n\n return diffusers_checkpoint\n\n\n# done super res unet last step\n\n\n# unet utils\n\n\n# .time_embed -> .time_embedding\ndef unet_time_embeddings(checkpoint, original_unet_prefix):\n diffusers_checkpoint = {}\n\n diffusers_checkpoint.update(\n {\n \"time_embedding.linear_1.weight\": checkpoint[f\"{original_unet_prefix}.time_embed.0.weight\"],\n \"time_embedding.linear_1.bias\": checkpoint[f\"{original_unet_prefix}.time_embed.0.bias\"],\n \"time_embedding.linear_2.weight\": checkpoint[f\"{original_unet_prefix}.time_embed.2.weight\"],\n \"time_embedding.linear_2.bias\": checkpoint[f\"{original_unet_prefix}.time_embed.2.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\n# .input_blocks.0 -> .conv_in\ndef unet_conv_in(checkpoint, original_unet_prefix):\n diffusers_checkpoint = {}\n\n diffusers_checkpoint.update(\n {\n \"conv_in.weight\": checkpoint[f\"{original_unet_prefix}.input_blocks.0.0.weight\"],\n \"conv_in.bias\": checkpoint[f\"{original_unet_prefix}.input_blocks.0.0.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\n# .out.0 -> .conv_norm_out\ndef unet_conv_norm_out(checkpoint, original_unet_prefix):\n diffusers_checkpoint = {}\n\n diffusers_checkpoint.update(\n {\n \"conv_norm_out.weight\": checkpoint[f\"{original_unet_prefix}.out.0.weight\"],\n \"conv_norm_out.bias\": checkpoint[f\"{original_unet_prefix}.out.0.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\n# .out.2 -> .conv_out\ndef unet_conv_out(checkpoint, original_unet_prefix):\n diffusers_checkpoint = {}\n\n diffusers_checkpoint.update(\n {\n \"conv_out.weight\": checkpoint[f\"{original_unet_prefix}.out.2.weight\"],\n \"conv_out.bias\": checkpoint[f\"{original_unet_prefix}.out.2.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\n# .input_blocks -> .down_blocks\ndef unet_downblock_to_diffusers_checkpoint(\n model, checkpoint, *, diffusers_down_block_idx, original_down_block_idx, original_unet_prefix, num_head_channels\n):\n diffusers_checkpoint = {}\n\n diffusers_resnet_prefix = f\"down_blocks.{diffusers_down_block_idx}.resnets\"\n original_down_block_prefix = f\"{original_unet_prefix}.input_blocks\"\n\n down_block = model.down_blocks[diffusers_down_block_idx]\n\n num_resnets = len(down_block.resnets)\n\n if down_block.downsamplers is None:\n downsampler = False\n else:\n assert len(down_block.downsamplers) == 1\n downsampler = True\n # The downsample block is also a resnet\n num_resnets += 1\n\n for resnet_idx_inc in range(num_resnets):\n full_resnet_prefix = f\"{original_down_block_prefix}.{original_down_block_idx + resnet_idx_inc}.0\"\n\n if downsampler and resnet_idx_inc == num_resnets - 1:\n # this is a downsample block\n full_diffusers_resnet_prefix = f\"down_blocks.{diffusers_down_block_idx}.downsamplers.0\"\n else:\n # this is a regular resnet block\n full_diffusers_resnet_prefix = f\"{diffusers_resnet_prefix}.{resnet_idx_inc}\"\n\n diffusers_checkpoint.update(\n resnet_to_diffusers_checkpoint(\n checkpoint, resnet_prefix=full_resnet_prefix, diffusers_resnet_prefix=full_diffusers_resnet_prefix\n )\n )\n\n if hasattr(down_block, \"attentions\"):\n num_attentions = len(down_block.attentions)\n diffusers_attention_prefix = f\"down_blocks.{diffusers_down_block_idx}.attentions\"\n\n for attention_idx_inc in range(num_attentions):\n full_attention_prefix = f\"{original_down_block_prefix}.{original_down_block_idx + attention_idx_inc}.1\"\n full_diffusers_attention_prefix = f\"{diffusers_attention_prefix}.{attention_idx_inc}\"\n\n diffusers_checkpoint.update(\n attention_to_diffusers_checkpoint(\n checkpoint,\n attention_prefix=full_attention_prefix,\n diffusers_attention_prefix=full_diffusers_attention_prefix,\n num_head_channels=num_head_channels,\n )\n )\n\n num_original_down_blocks = num_resnets\n\n return diffusers_checkpoint, num_original_down_blocks\n\n\n# .middle_block -> .mid_block\ndef unet_midblock_to_diffusers_checkpoint(model, checkpoint, *, original_unet_prefix, num_head_channels):\n diffusers_checkpoint = {}\n\n # block 0\n\n original_block_idx = 0\n\n diffusers_checkpoint.update(\n resnet_to_diffusers_checkpoint(\n checkpoint,\n diffusers_resnet_prefix=\"mid_block.resnets.0\",\n resnet_prefix=f\"{original_unet_prefix}.middle_block.{original_block_idx}\",\n )\n )\n\n original_block_idx += 1\n\n # optional block 1\n\n if hasattr(model.mid_block, \"attentions\") and model.mid_block.attentions[0] is not None:\n diffusers_checkpoint.update(\n attention_to_diffusers_checkpoint(\n checkpoint,\n diffusers_attention_prefix=\"mid_block.attentions.0\",\n attention_prefix=f\"{original_unet_prefix}.middle_block.{original_block_idx}\",\n num_head_channels=num_head_channels,\n )\n )\n original_block_idx += 1\n\n # block 1 or block 2\n\n diffusers_checkpoint.update(\n resnet_to_diffusers_checkpoint(\n checkpoint,\n diffusers_resnet_prefix=\"mid_block.resnets.1\",\n resnet_prefix=f\"{original_unet_prefix}.middle_block.{original_block_idx}\",\n )\n )\n\n return diffusers_checkpoint\n\n\n# .output_blocks -> .up_blocks\ndef unet_upblock_to_diffusers_checkpoint(\n model, checkpoint, *, diffusers_up_block_idx, original_up_block_idx, original_unet_prefix, num_head_channels\n):\n diffusers_checkpoint = {}\n\n diffusers_resnet_prefix = f\"up_blocks.{diffusers_up_block_idx}.resnets\"\n original_up_block_prefix = f\"{original_unet_prefix}.output_blocks\"\n\n up_block = model.up_blocks[diffusers_up_block_idx]\n\n num_resnets = len(up_block.resnets)\n\n if up_block.upsamplers is None:\n upsampler = False\n else:\n assert len(up_block.upsamplers) == 1\n upsampler = True\n # The upsample block is also a resnet\n num_resnets += 1\n\n has_attentions = hasattr(up_block, \"attentions\")\n\n for resnet_idx_inc in range(num_resnets):\n if upsampler and resnet_idx_inc == num_resnets - 1:\n # this is an upsample block\n if has_attentions:\n # There is a middle attention block that we skip\n original_resnet_block_idx = 2\n else:\n original_resnet_block_idx = 1\n\n # we add the `minus 1` because the last two resnets are stuck together in the same output block\n full_resnet_prefix = (\n f\"{original_up_block_prefix}.{original_up_block_idx + resnet_idx_inc - 1}.{original_resnet_block_idx}\"\n )\n\n full_diffusers_resnet_prefix = f\"up_blocks.{diffusers_up_block_idx}.upsamplers.0\"\n else:\n # this is a regular resnet block\n full_resnet_prefix = f\"{original_up_block_prefix}.{original_up_block_idx + resnet_idx_inc}.0\"\n full_diffusers_resnet_prefix = f\"{diffusers_resnet_prefix}.{resnet_idx_inc}\"\n\n diffusers_checkpoint.update(\n resnet_to_diffusers_checkpoint(\n checkpoint, resnet_prefix=full_resnet_prefix, diffusers_resnet_prefix=full_diffusers_resnet_prefix\n )\n )\n\n if has_attentions:\n num_attentions = len(up_block.attentions)\n diffusers_attention_prefix = f\"up_blocks.{diffusers_up_block_idx}.attentions\"\n\n for attention_idx_inc in range(num_attentions):\n full_attention_prefix = f\"{original_up_block_prefix}.{original_up_block_idx + attention_idx_inc}.1\"\n full_diffusers_attention_prefix = f\"{diffusers_attention_prefix}.{attention_idx_inc}\"\n\n diffusers_checkpoint.update(\n attention_to_diffusers_checkpoint(\n checkpoint,\n attention_prefix=full_attention_prefix,\n diffusers_attention_prefix=full_diffusers_attention_prefix,\n num_head_channels=num_head_channels,\n )\n )\n\n num_original_down_blocks = num_resnets - 1 if upsampler else num_resnets\n\n return diffusers_checkpoint, num_original_down_blocks\n\n\ndef resnet_to_diffusers_checkpoint(checkpoint, *, diffusers_resnet_prefix, resnet_prefix):\n diffusers_checkpoint = {\n f\"{diffusers_resnet_prefix}.norm1.weight\": checkpoint[f\"{resnet_prefix}.in_layers.0.weight\"],\n f\"{diffusers_resnet_prefix}.norm1.bias\": checkpoint[f\"{resnet_prefix}.in_layers.0.bias\"],\n f\"{diffusers_resnet_prefix}.conv1.weight\": checkpoint[f\"{resnet_prefix}.in_layers.2.weight\"],\n f\"{diffusers_resnet_prefix}.conv1.bias\": checkpoint[f\"{resnet_prefix}.in_layers.2.bias\"],\n f\"{diffusers_resnet_prefix}.time_emb_proj.weight\": checkpoint[f\"{resnet_prefix}.emb_layers.1.weight\"],\n f\"{diffusers_resnet_prefix}.time_emb_proj.bias\": checkpoint[f\"{resnet_prefix}.emb_layers.1.bias\"],\n f\"{diffusers_resnet_prefix}.norm2.weight\": checkpoint[f\"{resnet_prefix}.out_layers.0.weight\"],\n f\"{diffusers_resnet_prefix}.norm2.bias\": checkpoint[f\"{resnet_prefix}.out_layers.0.bias\"],\n f\"{diffusers_resnet_prefix}.conv2.weight\": checkpoint[f\"{resnet_prefix}.out_layers.3.weight\"],\n f\"{diffusers_resnet_prefix}.conv2.bias\": checkpoint[f\"{resnet_prefix}.out_layers.3.bias\"],\n }\n\n skip_connection_prefix = f\"{resnet_prefix}.skip_connection\"\n\n if f\"{skip_connection_prefix}.weight\" in checkpoint:\n diffusers_checkpoint.update(\n {\n f\"{diffusers_resnet_prefix}.conv_shortcut.weight\": checkpoint[f\"{skip_connection_prefix}.weight\"],\n f\"{diffusers_resnet_prefix}.conv_shortcut.bias\": checkpoint[f\"{skip_connection_prefix}.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\ndef attention_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix, num_head_channels):\n diffusers_checkpoint = {}\n\n # .norm -> .group_norm\n diffusers_checkpoint.update(\n {\n f\"{diffusers_attention_prefix}.group_norm.weight\": checkpoint[f\"{attention_prefix}.norm.weight\"],\n f\"{diffusers_attention_prefix}.group_norm.bias\": checkpoint[f\"{attention_prefix}.norm.bias\"],\n }\n )\n\n # .qkv -> .{query, key, value}\n [q_weight, k_weight, v_weight], [q_bias, k_bias, v_bias] = split_attentions(\n weight=checkpoint[f\"{attention_prefix}.qkv.weight\"][:, :, 0],\n bias=checkpoint[f\"{attention_prefix}.qkv.bias\"],\n split=3,\n chunk_size=num_head_channels,\n )\n\n diffusers_checkpoint.update(\n {\n f\"{diffusers_attention_prefix}.to_q.weight\": q_weight,\n f\"{diffusers_attention_prefix}.to_q.bias\": q_bias,\n f\"{diffusers_attention_prefix}.to_k.weight\": k_weight,\n f\"{diffusers_attention_prefix}.to_k.bias\": k_bias,\n f\"{diffusers_attention_prefix}.to_v.weight\": v_weight,\n f\"{diffusers_attention_prefix}.to_v.bias\": v_bias,\n }\n )\n\n # .encoder_kv -> .{context_key, context_value}\n [encoder_k_weight, encoder_v_weight], [encoder_k_bias, encoder_v_bias] = split_attentions(\n weight=checkpoint[f\"{attention_prefix}.encoder_kv.weight\"][:, :, 0],\n bias=checkpoint[f\"{attention_prefix}.encoder_kv.bias\"],\n split=2,\n chunk_size=num_head_channels,\n )\n\n diffusers_checkpoint.update(\n {\n f\"{diffusers_attention_prefix}.add_k_proj.weight\": encoder_k_weight,\n f\"{diffusers_attention_prefix}.add_k_proj.bias\": encoder_k_bias,\n f\"{diffusers_attention_prefix}.add_v_proj.weight\": encoder_v_weight,\n f\"{diffusers_attention_prefix}.add_v_proj.bias\": encoder_v_bias,\n }\n )\n\n # .proj_out (1d conv) -> .proj_attn (linear)\n diffusers_checkpoint.update(\n {\n f\"{diffusers_attention_prefix}.to_out.0.weight\": checkpoint[f\"{attention_prefix}.proj_out.weight\"][\n :, :, 0\n ],\n f\"{diffusers_attention_prefix}.to_out.0.bias\": checkpoint[f\"{attention_prefix}.proj_out.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\n# TODO maybe document and/or can do more efficiently (build indices in for loop and extract once for each split?)\ndef split_attentions(*, weight, bias, split, chunk_size):\n weights = [None] * split\n biases = [None] * split\n\n weights_biases_idx = 0\n\n for starting_row_index in range(0, weight.shape[0], chunk_size):\n row_indices = torch.arange(starting_row_index, starting_row_index + chunk_size)\n\n weight_rows = weight[row_indices, :]\n bias_rows = bias[row_indices]\n\n if weights[weights_biases_idx] is None:\n assert weights[weights_biases_idx] is None\n weights[weights_biases_idx] = weight_rows\n biases[weights_biases_idx] = bias_rows\n else:\n assert weights[weights_biases_idx] is not None\n weights[weights_biases_idx] = torch.concat([weights[weights_biases_idx], weight_rows])\n biases[weights_biases_idx] = torch.concat([biases[weights_biases_idx], bias_rows])\n\n weights_biases_idx = (weights_biases_idx + 1) % split\n\n return weights, biases\n\n\n# done unet utils\n\n\n# Driver functions\n\n\ndef text_encoder():\n print(\"loading CLIP text encoder\")\n\n clip_name = \"openai/clip-vit-large-patch14\"\n\n # sets pad_value to 0\n pad_token = \"!\"\n\n tokenizer_model = CLIPTokenizer.from_pretrained(clip_name, pad_token=pad_token, device_map=\"auto\")\n\n assert tokenizer_model.convert_tokens_to_ids(pad_token) == 0\n\n text_encoder_model = CLIPTextModelWithProjection.from_pretrained(\n clip_name,\n # `CLIPTextModel` does not support device_map=\"auto\"\n # device_map=\"auto\"\n )\n\n print(\"done loading CLIP text encoder\")\n\n return text_encoder_model, tokenizer_model\n\n\ndef prior(*, args, checkpoint_map_location):\n print(\"loading prior\")\n\n prior_checkpoint = torch.load(args.prior_checkpoint_path, map_location=checkpoint_map_location)\n prior_checkpoint = prior_checkpoint[\"state_dict\"]\n\n clip_stats_checkpoint = torch.load(args.clip_stat_path, map_location=checkpoint_map_location)\n\n prior_model = prior_model_from_original_config()\n\n prior_diffusers_checkpoint = prior_original_checkpoint_to_diffusers_checkpoint(\n prior_model, prior_checkpoint, clip_stats_checkpoint\n )\n\n del prior_checkpoint\n del clip_stats_checkpoint\n\n load_checkpoint_to_model(prior_diffusers_checkpoint, prior_model, strict=True)\n\n print(\"done loading prior\")\n\n return prior_model\n\n\ndef decoder(*, args, checkpoint_map_location):\n print(\"loading decoder\")\n\n decoder_checkpoint = torch.load(args.decoder_checkpoint_path, map_location=checkpoint_map_location)\n decoder_checkpoint = decoder_checkpoint[\"state_dict\"]\n\n decoder_model = decoder_model_from_original_config()\n\n decoder_diffusers_checkpoint = decoder_original_checkpoint_to_diffusers_checkpoint(\n decoder_model, decoder_checkpoint\n )\n\n # text proj interlude\n\n # The original decoder implementation includes a set of parameters that are used\n # for creating the `encoder_hidden_states` which are what the U-net is conditioned\n # on. The diffusers conditional unet directly takes the encoder_hidden_states. We pull\n # the parameters into the UnCLIPTextProjModel class\n text_proj_model = text_proj_from_original_config()\n\n text_proj_checkpoint = text_proj_original_checkpoint_to_diffusers_checkpoint(decoder_checkpoint)\n\n load_checkpoint_to_model(text_proj_checkpoint, text_proj_model, strict=True)\n\n # done text proj interlude\n\n del decoder_checkpoint\n\n load_checkpoint_to_model(decoder_diffusers_checkpoint, decoder_model, strict=True)\n\n print(\"done loading decoder\")\n\n return decoder_model, text_proj_model\n\n\ndef super_res_unet(*, args, checkpoint_map_location):\n print(\"loading super resolution unet\")\n\n super_res_checkpoint = torch.load(args.super_res_unet_checkpoint_path, map_location=checkpoint_map_location)\n super_res_checkpoint = super_res_checkpoint[\"state_dict\"]\n\n # model_first_steps\n\n super_res_first_model = super_res_unet_first_steps_model_from_original_config()\n\n super_res_first_steps_checkpoint = super_res_unet_first_steps_original_checkpoint_to_diffusers_checkpoint(\n super_res_first_model, super_res_checkpoint\n )\n\n # model_last_step\n super_res_last_model = super_res_unet_last_step_model_from_original_config()\n\n super_res_last_step_checkpoint = super_res_unet_last_step_original_checkpoint_to_diffusers_checkpoint(\n super_res_last_model, super_res_checkpoint\n )\n\n del super_res_checkpoint\n\n load_checkpoint_to_model(super_res_first_steps_checkpoint, super_res_first_model, strict=True)\n\n load_checkpoint_to_model(super_res_last_step_checkpoint, super_res_last_model, strict=True)\n\n print(\"done loading super resolution unet\")\n\n return super_res_first_model, super_res_last_model\n\n\ndef load_checkpoint_to_model(checkpoint, model, strict=False):\n with tempfile.NamedTemporaryFile() as file:\n torch.save(checkpoint, file.name)\n del checkpoint\n if strict:\n model.load_state_dict(torch.load(file.name), strict=True)\n else:\n load_checkpoint_and_dispatch(model, file.name, device_map=\"auto\")\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n\n parser.add_argument(\n \"--prior_checkpoint_path\",\n default=None,\n type=str,\n required=True,\n help=\"Path to the prior checkpoint to convert.\",\n )\n\n parser.add_argument(\n \"--decoder_checkpoint_path\",\n default=None,\n type=str,\n required=True,\n help=\"Path to the decoder checkpoint to convert.\",\n )\n\n parser.add_argument(\n \"--super_res_unet_checkpoint_path\",\n default=None,\n type=str,\n required=True,\n help=\"Path to the super resolution checkpoint to convert.\",\n )\n\n parser.add_argument(\n \"--clip_stat_path\", default=None, type=str, required=True, help=\"Path to the clip stats checkpoint to convert.\"\n )\n\n parser.add_argument(\n \"--checkpoint_load_device\",\n default=\"cpu\",\n type=str,\n required=False,\n help=\"The device passed to `map_location` when loading checkpoints.\",\n )\n\n parser.add_argument(\n \"--debug\",\n default=None,\n type=str,\n required=False,\n help=\"Only run a specific stage of the convert script. Used for debugging\",\n )\n\n args = parser.parse_args()\n\n print(f\"loading checkpoints to {args.checkpoint_load_device}\")\n\n checkpoint_map_location = torch.device(args.checkpoint_load_device)\n\n if args.debug is not None:\n print(f\"debug: only executing {args.debug}\")\n\n if args.debug is None:\n text_encoder_model, tokenizer_model = text_encoder()\n\n prior_model = prior(args=args, checkpoint_map_location=checkpoint_map_location)\n\n decoder_model, text_proj_model = decoder(args=args, checkpoint_map_location=checkpoint_map_location)\n\n super_res_first_model, super_res_last_model = super_res_unet(\n args=args, checkpoint_map_location=checkpoint_map_location\n )\n\n prior_scheduler = UnCLIPScheduler(\n variance_type=\"fixed_small_log\",\n prediction_type=\"sample\",\n num_train_timesteps=1000,\n clip_sample_range=5.0,\n )\n\n decoder_scheduler = UnCLIPScheduler(\n variance_type=\"learned_range\",\n prediction_type=\"epsilon\",\n num_train_timesteps=1000,\n )\n\n super_res_scheduler = UnCLIPScheduler(\n variance_type=\"fixed_small_log\",\n prediction_type=\"epsilon\",\n num_train_timesteps=1000,\n )\n\n print(f\"saving Kakao Brain unCLIP to {args.dump_path}\")\n\n pipe = UnCLIPPipeline(\n prior=prior_model,\n decoder=decoder_model,\n text_proj=text_proj_model,\n tokenizer=tokenizer_model,\n text_encoder=text_encoder_model,\n super_res_first=super_res_first_model,\n super_res_last=super_res_last_model,\n prior_scheduler=prior_scheduler,\n decoder_scheduler=decoder_scheduler,\n super_res_scheduler=super_res_scheduler,\n )\n pipe.save_pretrained(args.dump_path)\n\n print(\"done writing Kakao Brain unCLIP\")\n elif args.debug == \"text_encoder\":\n text_encoder_model, tokenizer_model = text_encoder()\n elif args.debug == \"prior\":\n prior_model = prior(args=args, checkpoint_map_location=checkpoint_map_location)\n elif args.debug == \"decoder\":\n decoder_model, text_proj_model = decoder(args=args, checkpoint_map_location=checkpoint_map_location)\n elif args.debug == \"super_res_unet\":\n super_res_first_model, super_res_last_model = super_res_unet(\n args=args, checkpoint_map_location=checkpoint_map_location\n )\n else:\n raise ValueError(f\"unknown debug value : {args.debug}\")\n" }, { "path": "diffusers/scripts/convert_kandinsky3_unet.py", "content": "#!/usr/bin/env python3\nimport argparse\nimport fnmatch\n\nfrom safetensors.torch import load_file\n\nfrom diffusers import Kandinsky3UNet\n\n\nMAPPING = {\n \"to_time_embed.1\": \"time_embedding.linear_1\",\n \"to_time_embed.3\": \"time_embedding.linear_2\",\n \"in_layer\": \"conv_in\",\n \"out_layer.0\": \"conv_norm_out\",\n \"out_layer.2\": \"conv_out\",\n \"down_samples\": \"down_blocks\",\n \"up_samples\": \"up_blocks\",\n \"projection_lin\": \"encoder_hid_proj.projection_linear\",\n \"projection_ln\": \"encoder_hid_proj.projection_norm\",\n \"feature_pooling\": \"add_time_condition\",\n \"to_query\": \"to_q\",\n \"to_key\": \"to_k\",\n \"to_value\": \"to_v\",\n \"output_layer\": \"to_out.0\",\n \"self_attention_block\": \"attentions.0\",\n}\n\nDYNAMIC_MAP = {\n \"resnet_attn_blocks.*.0\": \"resnets_in.*\",\n \"resnet_attn_blocks.*.1\": (\"attentions.*\", 1),\n \"resnet_attn_blocks.*.2\": \"resnets_out.*\",\n}\n# MAPPING = {}\n\n\ndef convert_state_dict(unet_state_dict):\n \"\"\"\n Convert the state dict of a U-Net model to match the key format expected by Kandinsky3UNet model.\n Args:\n unet_model (torch.nn.Module): The original U-Net model.\n unet_kandi3_model (torch.nn.Module): The Kandinsky3UNet model to match keys with.\n\n Returns:\n OrderedDict: The converted state dictionary.\n \"\"\"\n # Example of renaming logic (this will vary based on your model's architecture)\n converted_state_dict = {}\n for key in unet_state_dict:\n new_key = key\n for pattern, new_pattern in MAPPING.items():\n new_key = new_key.replace(pattern, new_pattern)\n\n for dyn_pattern, dyn_new_pattern in DYNAMIC_MAP.items():\n has_matched = False\n if fnmatch.fnmatch(new_key, f\"*.{dyn_pattern}.*\") and not has_matched:\n star = int(new_key.split(dyn_pattern.split(\".\")[0])[-1].split(\".\")[1])\n\n if isinstance(dyn_new_pattern, tuple):\n new_star = star + dyn_new_pattern[-1]\n dyn_new_pattern = dyn_new_pattern[0]\n else:\n new_star = star\n\n pattern = dyn_pattern.replace(\"*\", str(star))\n new_pattern = dyn_new_pattern.replace(\"*\", str(new_star))\n\n new_key = new_key.replace(pattern, new_pattern)\n has_matched = True\n\n converted_state_dict[new_key] = unet_state_dict[key]\n\n return converted_state_dict\n\n\ndef main(model_path, output_path):\n # Load your original U-Net model\n unet_state_dict = load_file(model_path)\n\n # Initialize your Kandinsky3UNet model\n config = {}\n\n # Convert the state dict\n converted_state_dict = convert_state_dict(unet_state_dict)\n\n unet = Kandinsky3UNet(config)\n unet.load_state_dict(converted_state_dict)\n\n unet.save_pretrained(output_path)\n print(f\"Converted model saved to {output_path}\")\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description=\"Convert U-Net PyTorch model to Kandinsky3UNet format\")\n parser.add_argument(\"--model_path\", type=str, required=True, help=\"Path to the original U-Net PyTorch model\")\n parser.add_argument(\"--output_path\", type=str, required=True, help=\"Path to save the converted model\")\n\n args = parser.parse_args()\n main(args.model_path, args.output_path)\n" }, { "path": "diffusers/scripts/convert_kandinsky_to_diffusers.py", "content": "import argparse\nimport os\nimport tempfile\n\nimport torch\nfrom accelerate import load_checkpoint_and_dispatch\n\nfrom diffusers import UNet2DConditionModel\nfrom diffusers.models.transformers.prior_transformer import PriorTransformer\nfrom diffusers.models.vq_model import VQModel\n\n\n\"\"\"\nExample - From the diffusers root directory:\n\nDownload weights:\n```sh\n$ wget https://huggingface.co/ai-forever/Kandinsky_2.1/blob/main/prior_fp16.ckpt\n```\n\nConvert the model:\n```sh\npython scripts/convert_kandinsky_to_diffusers.py \\\n --prior_checkpoint_path /home/yiyi_huggingface_co/Kandinsky-2/checkpoints_Kandinsky_2.1/prior_fp16.ckpt \\\n --clip_stat_path /home/yiyi_huggingface_co/Kandinsky-2/checkpoints_Kandinsky_2.1/ViT-L-14_stats.th \\\n --text2img_checkpoint_path /home/yiyi_huggingface_co/Kandinsky-2/checkpoints_Kandinsky_2.1/decoder_fp16.ckpt \\\n --inpaint_text2img_checkpoint_path /home/yiyi_huggingface_co/Kandinsky-2/checkpoints_Kandinsky_2.1/inpainting_fp16.ckpt \\\n --movq_checkpoint_path /home/yiyi_huggingface_co/Kandinsky-2/checkpoints_Kandinsky_2.1/movq_final.ckpt \\\n --dump_path /home/yiyi_huggingface_co/dump \\\n --debug decoder\n```\n\"\"\"\n\n\n# prior\n\nPRIOR_ORIGINAL_PREFIX = \"model\"\n\n# Uses default arguments\nPRIOR_CONFIG = {}\n\n\ndef prior_model_from_original_config():\n model = PriorTransformer(**PRIOR_CONFIG)\n\n return model\n\n\ndef prior_original_checkpoint_to_diffusers_checkpoint(model, checkpoint, clip_stats_checkpoint):\n diffusers_checkpoint = {}\n\n # .time_embed.0 -> .time_embedding.linear_1\n diffusers_checkpoint.update(\n {\n \"time_embedding.linear_1.weight\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.time_embed.0.weight\"],\n \"time_embedding.linear_1.bias\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.time_embed.0.bias\"],\n }\n )\n\n # .clip_img_proj -> .proj_in\n diffusers_checkpoint.update(\n {\n \"proj_in.weight\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.clip_img_proj.weight\"],\n \"proj_in.bias\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.clip_img_proj.bias\"],\n }\n )\n\n # .text_emb_proj -> .embedding_proj\n diffusers_checkpoint.update(\n {\n \"embedding_proj.weight\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.text_emb_proj.weight\"],\n \"embedding_proj.bias\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.text_emb_proj.bias\"],\n }\n )\n\n # .text_enc_proj -> .encoder_hidden_states_proj\n diffusers_checkpoint.update(\n {\n \"encoder_hidden_states_proj.weight\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.text_enc_proj.weight\"],\n \"encoder_hidden_states_proj.bias\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.text_enc_proj.bias\"],\n }\n )\n\n # .positional_embedding -> .positional_embedding\n diffusers_checkpoint.update({\"positional_embedding\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.positional_embedding\"]})\n\n # .prd_emb -> .prd_embedding\n diffusers_checkpoint.update({\"prd_embedding\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.prd_emb\"]})\n\n # .time_embed.2 -> .time_embedding.linear_2\n diffusers_checkpoint.update(\n {\n \"time_embedding.linear_2.weight\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.time_embed.2.weight\"],\n \"time_embedding.linear_2.bias\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.time_embed.2.bias\"],\n }\n )\n\n # .resblocks. -> .transformer_blocks.\n for idx in range(len(model.transformer_blocks)):\n diffusers_transformer_prefix = f\"transformer_blocks.{idx}\"\n original_transformer_prefix = f\"{PRIOR_ORIGINAL_PREFIX}.transformer.resblocks.{idx}\"\n\n # .attn -> .attn1\n diffusers_attention_prefix = f\"{diffusers_transformer_prefix}.attn1\"\n original_attention_prefix = f\"{original_transformer_prefix}.attn\"\n diffusers_checkpoint.update(\n prior_attention_to_diffusers(\n checkpoint,\n diffusers_attention_prefix=diffusers_attention_prefix,\n original_attention_prefix=original_attention_prefix,\n attention_head_dim=model.attention_head_dim,\n )\n )\n\n # .mlp -> .ff\n diffusers_ff_prefix = f\"{diffusers_transformer_prefix}.ff\"\n original_ff_prefix = f\"{original_transformer_prefix}.mlp\"\n diffusers_checkpoint.update(\n prior_ff_to_diffusers(\n checkpoint, diffusers_ff_prefix=diffusers_ff_prefix, original_ff_prefix=original_ff_prefix\n )\n )\n\n # .ln_1 -> .norm1\n diffusers_checkpoint.update(\n {\n f\"{diffusers_transformer_prefix}.norm1.weight\": checkpoint[\n f\"{original_transformer_prefix}.ln_1.weight\"\n ],\n f\"{diffusers_transformer_prefix}.norm1.bias\": checkpoint[f\"{original_transformer_prefix}.ln_1.bias\"],\n }\n )\n\n # .ln_2 -> .norm3\n diffusers_checkpoint.update(\n {\n f\"{diffusers_transformer_prefix}.norm3.weight\": checkpoint[\n f\"{original_transformer_prefix}.ln_2.weight\"\n ],\n f\"{diffusers_transformer_prefix}.norm3.bias\": checkpoint[f\"{original_transformer_prefix}.ln_2.bias\"],\n }\n )\n\n # .final_ln -> .norm_out\n diffusers_checkpoint.update(\n {\n \"norm_out.weight\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.final_ln.weight\"],\n \"norm_out.bias\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.final_ln.bias\"],\n }\n )\n\n # .out_proj -> .proj_to_clip_embeddings\n diffusers_checkpoint.update(\n {\n \"proj_to_clip_embeddings.weight\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.out_proj.weight\"],\n \"proj_to_clip_embeddings.bias\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.out_proj.bias\"],\n }\n )\n\n # clip stats\n clip_mean, clip_std = clip_stats_checkpoint\n clip_mean = clip_mean[None, :]\n clip_std = clip_std[None, :]\n\n diffusers_checkpoint.update({\"clip_mean\": clip_mean, \"clip_std\": clip_std})\n\n return diffusers_checkpoint\n\n\ndef prior_attention_to_diffusers(\n checkpoint, *, diffusers_attention_prefix, original_attention_prefix, attention_head_dim\n):\n diffusers_checkpoint = {}\n\n # .c_qkv -> .{to_q, to_k, to_v}\n [q_weight, k_weight, v_weight], [q_bias, k_bias, v_bias] = split_attentions(\n weight=checkpoint[f\"{original_attention_prefix}.c_qkv.weight\"],\n bias=checkpoint[f\"{original_attention_prefix}.c_qkv.bias\"],\n split=3,\n chunk_size=attention_head_dim,\n )\n\n diffusers_checkpoint.update(\n {\n f\"{diffusers_attention_prefix}.to_q.weight\": q_weight,\n f\"{diffusers_attention_prefix}.to_q.bias\": q_bias,\n f\"{diffusers_attention_prefix}.to_k.weight\": k_weight,\n f\"{diffusers_attention_prefix}.to_k.bias\": k_bias,\n f\"{diffusers_attention_prefix}.to_v.weight\": v_weight,\n f\"{diffusers_attention_prefix}.to_v.bias\": v_bias,\n }\n )\n\n # .c_proj -> .to_out.0\n diffusers_checkpoint.update(\n {\n f\"{diffusers_attention_prefix}.to_out.0.weight\": checkpoint[f\"{original_attention_prefix}.c_proj.weight\"],\n f\"{diffusers_attention_prefix}.to_out.0.bias\": checkpoint[f\"{original_attention_prefix}.c_proj.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\ndef prior_ff_to_diffusers(checkpoint, *, diffusers_ff_prefix, original_ff_prefix):\n diffusers_checkpoint = {\n # .c_fc -> .net.0.proj\n f\"{diffusers_ff_prefix}.net.{0}.proj.weight\": checkpoint[f\"{original_ff_prefix}.c_fc.weight\"],\n f\"{diffusers_ff_prefix}.net.{0}.proj.bias\": checkpoint[f\"{original_ff_prefix}.c_fc.bias\"],\n # .c_proj -> .net.2\n f\"{diffusers_ff_prefix}.net.{2}.weight\": checkpoint[f\"{original_ff_prefix}.c_proj.weight\"],\n f\"{diffusers_ff_prefix}.net.{2}.bias\": checkpoint[f\"{original_ff_prefix}.c_proj.bias\"],\n }\n\n return diffusers_checkpoint\n\n\n# done prior\n\n# unet\n\n# We are hardcoding the model configuration for now. If we need to generalize to more model configurations, we can\n# update then.\n\nUNET_CONFIG = {\n \"act_fn\": \"silu\",\n \"addition_embed_type\": \"text_image\",\n \"addition_embed_type_num_heads\": 64,\n \"attention_head_dim\": 64,\n \"block_out_channels\": [384, 768, 1152, 1536],\n \"center_input_sample\": False,\n \"class_embed_type\": None,\n \"class_embeddings_concat\": False,\n \"conv_in_kernel\": 3,\n \"conv_out_kernel\": 3,\n \"cross_attention_dim\": 768,\n \"cross_attention_norm\": None,\n \"down_block_types\": [\n \"ResnetDownsampleBlock2D\",\n \"SimpleCrossAttnDownBlock2D\",\n \"SimpleCrossAttnDownBlock2D\",\n \"SimpleCrossAttnDownBlock2D\",\n ],\n \"downsample_padding\": 1,\n \"dual_cross_attention\": False,\n \"encoder_hid_dim\": 1024,\n \"encoder_hid_dim_type\": \"text_image_proj\",\n \"flip_sin_to_cos\": True,\n \"freq_shift\": 0,\n \"in_channels\": 4,\n \"layers_per_block\": 3,\n \"mid_block_only_cross_attention\": None,\n \"mid_block_scale_factor\": 1,\n \"mid_block_type\": \"UNetMidBlock2DSimpleCrossAttn\",\n \"norm_eps\": 1e-05,\n \"norm_num_groups\": 32,\n \"num_class_embeds\": None,\n \"only_cross_attention\": False,\n \"out_channels\": 8,\n \"projection_class_embeddings_input_dim\": None,\n \"resnet_out_scale_factor\": 1.0,\n \"resnet_skip_time_act\": False,\n \"resnet_time_scale_shift\": \"scale_shift\",\n \"sample_size\": 64,\n \"time_cond_proj_dim\": None,\n \"time_embedding_act_fn\": None,\n \"time_embedding_dim\": None,\n \"time_embedding_type\": \"positional\",\n \"timestep_post_act\": None,\n \"up_block_types\": [\n \"SimpleCrossAttnUpBlock2D\",\n \"SimpleCrossAttnUpBlock2D\",\n \"SimpleCrossAttnUpBlock2D\",\n \"ResnetUpsampleBlock2D\",\n ],\n \"upcast_attention\": False,\n \"use_linear_projection\": False,\n}\n\n\ndef unet_model_from_original_config():\n model = UNet2DConditionModel(**UNET_CONFIG)\n\n return model\n\n\ndef unet_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):\n diffusers_checkpoint = {}\n\n num_head_channels = UNET_CONFIG[\"attention_head_dim\"]\n\n diffusers_checkpoint.update(unet_time_embeddings(checkpoint))\n diffusers_checkpoint.update(unet_conv_in(checkpoint))\n diffusers_checkpoint.update(unet_add_embedding(checkpoint))\n diffusers_checkpoint.update(unet_encoder_hid_proj(checkpoint))\n\n # .input_blocks -> .down_blocks\n\n original_down_block_idx = 1\n\n for diffusers_down_block_idx in range(len(model.down_blocks)):\n checkpoint_update, num_original_down_blocks = unet_downblock_to_diffusers_checkpoint(\n model,\n checkpoint,\n diffusers_down_block_idx=diffusers_down_block_idx,\n original_down_block_idx=original_down_block_idx,\n num_head_channels=num_head_channels,\n )\n\n original_down_block_idx += num_original_down_blocks\n\n diffusers_checkpoint.update(checkpoint_update)\n\n # done .input_blocks -> .down_blocks\n\n diffusers_checkpoint.update(\n unet_midblock_to_diffusers_checkpoint(\n model,\n checkpoint,\n num_head_channels=num_head_channels,\n )\n )\n\n # .output_blocks -> .up_blocks\n\n original_up_block_idx = 0\n\n for diffusers_up_block_idx in range(len(model.up_blocks)):\n checkpoint_update, num_original_up_blocks = unet_upblock_to_diffusers_checkpoint(\n model,\n checkpoint,\n diffusers_up_block_idx=diffusers_up_block_idx,\n original_up_block_idx=original_up_block_idx,\n num_head_channels=num_head_channels,\n )\n\n original_up_block_idx += num_original_up_blocks\n\n diffusers_checkpoint.update(checkpoint_update)\n\n # done .output_blocks -> .up_blocks\n\n diffusers_checkpoint.update(unet_conv_norm_out(checkpoint))\n diffusers_checkpoint.update(unet_conv_out(checkpoint))\n\n return diffusers_checkpoint\n\n\n# done unet\n\n# inpaint unet\n\n# We are hardcoding the model configuration for now. If we need to generalize to more model configurations, we can\n# update then.\n\nINPAINT_UNET_CONFIG = {\n \"act_fn\": \"silu\",\n \"addition_embed_type\": \"text_image\",\n \"addition_embed_type_num_heads\": 64,\n \"attention_head_dim\": 64,\n \"block_out_channels\": [384, 768, 1152, 1536],\n \"center_input_sample\": False,\n \"class_embed_type\": None,\n \"class_embeddings_concat\": None,\n \"conv_in_kernel\": 3,\n \"conv_out_kernel\": 3,\n \"cross_attention_dim\": 768,\n \"cross_attention_norm\": None,\n \"down_block_types\": [\n \"ResnetDownsampleBlock2D\",\n \"SimpleCrossAttnDownBlock2D\",\n \"SimpleCrossAttnDownBlock2D\",\n \"SimpleCrossAttnDownBlock2D\",\n ],\n \"downsample_padding\": 1,\n \"dual_cross_attention\": False,\n \"encoder_hid_dim\": 1024,\n \"encoder_hid_dim_type\": \"text_image_proj\",\n \"flip_sin_to_cos\": True,\n \"freq_shift\": 0,\n \"in_channels\": 9,\n \"layers_per_block\": 3,\n \"mid_block_only_cross_attention\": None,\n \"mid_block_scale_factor\": 1,\n \"mid_block_type\": \"UNetMidBlock2DSimpleCrossAttn\",\n \"norm_eps\": 1e-05,\n \"norm_num_groups\": 32,\n \"num_class_embeds\": None,\n \"only_cross_attention\": False,\n \"out_channels\": 8,\n \"projection_class_embeddings_input_dim\": None,\n \"resnet_out_scale_factor\": 1.0,\n \"resnet_skip_time_act\": False,\n \"resnet_time_scale_shift\": \"scale_shift\",\n \"sample_size\": 64,\n \"time_cond_proj_dim\": None,\n \"time_embedding_act_fn\": None,\n \"time_embedding_dim\": None,\n \"time_embedding_type\": \"positional\",\n \"timestep_post_act\": None,\n \"up_block_types\": [\n \"SimpleCrossAttnUpBlock2D\",\n \"SimpleCrossAttnUpBlock2D\",\n \"SimpleCrossAttnUpBlock2D\",\n \"ResnetUpsampleBlock2D\",\n ],\n \"upcast_attention\": False,\n \"use_linear_projection\": False,\n}\n\n\ndef inpaint_unet_model_from_original_config():\n model = UNet2DConditionModel(**INPAINT_UNET_CONFIG)\n\n return model\n\n\ndef inpaint_unet_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):\n diffusers_checkpoint = {}\n\n num_head_channels = INPAINT_UNET_CONFIG[\"attention_head_dim\"]\n\n diffusers_checkpoint.update(unet_time_embeddings(checkpoint))\n diffusers_checkpoint.update(unet_conv_in(checkpoint))\n diffusers_checkpoint.update(unet_add_embedding(checkpoint))\n diffusers_checkpoint.update(unet_encoder_hid_proj(checkpoint))\n\n # .input_blocks -> .down_blocks\n\n original_down_block_idx = 1\n\n for diffusers_down_block_idx in range(len(model.down_blocks)):\n checkpoint_update, num_original_down_blocks = unet_downblock_to_diffusers_checkpoint(\n model,\n checkpoint,\n diffusers_down_block_idx=diffusers_down_block_idx,\n original_down_block_idx=original_down_block_idx,\n num_head_channels=num_head_channels,\n )\n\n original_down_block_idx += num_original_down_blocks\n\n diffusers_checkpoint.update(checkpoint_update)\n\n # done .input_blocks -> .down_blocks\n\n diffusers_checkpoint.update(\n unet_midblock_to_diffusers_checkpoint(\n model,\n checkpoint,\n num_head_channels=num_head_channels,\n )\n )\n\n # .output_blocks -> .up_blocks\n\n original_up_block_idx = 0\n\n for diffusers_up_block_idx in range(len(model.up_blocks)):\n checkpoint_update, num_original_up_blocks = unet_upblock_to_diffusers_checkpoint(\n model,\n checkpoint,\n diffusers_up_block_idx=diffusers_up_block_idx,\n original_up_block_idx=original_up_block_idx,\n num_head_channels=num_head_channels,\n )\n\n original_up_block_idx += num_original_up_blocks\n\n diffusers_checkpoint.update(checkpoint_update)\n\n # done .output_blocks -> .up_blocks\n\n diffusers_checkpoint.update(unet_conv_norm_out(checkpoint))\n diffusers_checkpoint.update(unet_conv_out(checkpoint))\n\n return diffusers_checkpoint\n\n\n# done inpaint unet\n\n\n# unet utils\n\n\n# .time_embed -> .time_embedding\ndef unet_time_embeddings(checkpoint):\n diffusers_checkpoint = {}\n\n diffusers_checkpoint.update(\n {\n \"time_embedding.linear_1.weight\": checkpoint[\"time_embed.0.weight\"],\n \"time_embedding.linear_1.bias\": checkpoint[\"time_embed.0.bias\"],\n \"time_embedding.linear_2.weight\": checkpoint[\"time_embed.2.weight\"],\n \"time_embedding.linear_2.bias\": checkpoint[\"time_embed.2.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\n# .input_blocks.0 -> .conv_in\ndef unet_conv_in(checkpoint):\n diffusers_checkpoint = {}\n\n diffusers_checkpoint.update(\n {\n \"conv_in.weight\": checkpoint[\"input_blocks.0.0.weight\"],\n \"conv_in.bias\": checkpoint[\"input_blocks.0.0.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\ndef unet_add_embedding(checkpoint):\n diffusers_checkpoint = {}\n\n diffusers_checkpoint.update(\n {\n \"add_embedding.text_norm.weight\": checkpoint[\"ln_model_n.weight\"],\n \"add_embedding.text_norm.bias\": checkpoint[\"ln_model_n.bias\"],\n \"add_embedding.text_proj.weight\": checkpoint[\"proj_n.weight\"],\n \"add_embedding.text_proj.bias\": checkpoint[\"proj_n.bias\"],\n \"add_embedding.image_proj.weight\": checkpoint[\"img_layer.weight\"],\n \"add_embedding.image_proj.bias\": checkpoint[\"img_layer.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\ndef unet_encoder_hid_proj(checkpoint):\n diffusers_checkpoint = {}\n\n diffusers_checkpoint.update(\n {\n \"encoder_hid_proj.image_embeds.weight\": checkpoint[\"clip_to_seq.weight\"],\n \"encoder_hid_proj.image_embeds.bias\": checkpoint[\"clip_to_seq.bias\"],\n \"encoder_hid_proj.text_proj.weight\": checkpoint[\"to_model_dim_n.weight\"],\n \"encoder_hid_proj.text_proj.bias\": checkpoint[\"to_model_dim_n.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\n# .out.0 -> .conv_norm_out\ndef unet_conv_norm_out(checkpoint):\n diffusers_checkpoint = {}\n\n diffusers_checkpoint.update(\n {\n \"conv_norm_out.weight\": checkpoint[\"out.0.weight\"],\n \"conv_norm_out.bias\": checkpoint[\"out.0.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\n# .out.2 -> .conv_out\ndef unet_conv_out(checkpoint):\n diffusers_checkpoint = {}\n\n diffusers_checkpoint.update(\n {\n \"conv_out.weight\": checkpoint[\"out.2.weight\"],\n \"conv_out.bias\": checkpoint[\"out.2.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\n# .input_blocks -> .down_blocks\ndef unet_downblock_to_diffusers_checkpoint(\n model, checkpoint, *, diffusers_down_block_idx, original_down_block_idx, num_head_channels\n):\n diffusers_checkpoint = {}\n\n diffusers_resnet_prefix = f\"down_blocks.{diffusers_down_block_idx}.resnets\"\n original_down_block_prefix = \"input_blocks\"\n\n down_block = model.down_blocks[diffusers_down_block_idx]\n\n num_resnets = len(down_block.resnets)\n\n if down_block.downsamplers is None:\n downsampler = False\n else:\n assert len(down_block.downsamplers) == 1\n downsampler = True\n # The downsample block is also a resnet\n num_resnets += 1\n\n for resnet_idx_inc in range(num_resnets):\n full_resnet_prefix = f\"{original_down_block_prefix}.{original_down_block_idx + resnet_idx_inc}.0\"\n\n if downsampler and resnet_idx_inc == num_resnets - 1:\n # this is a downsample block\n full_diffusers_resnet_prefix = f\"down_blocks.{diffusers_down_block_idx}.downsamplers.0\"\n else:\n # this is a regular resnet block\n full_diffusers_resnet_prefix = f\"{diffusers_resnet_prefix}.{resnet_idx_inc}\"\n\n diffusers_checkpoint.update(\n resnet_to_diffusers_checkpoint(\n checkpoint, resnet_prefix=full_resnet_prefix, diffusers_resnet_prefix=full_diffusers_resnet_prefix\n )\n )\n\n if hasattr(down_block, \"attentions\"):\n num_attentions = len(down_block.attentions)\n diffusers_attention_prefix = f\"down_blocks.{diffusers_down_block_idx}.attentions\"\n\n for attention_idx_inc in range(num_attentions):\n full_attention_prefix = f\"{original_down_block_prefix}.{original_down_block_idx + attention_idx_inc}.1\"\n full_diffusers_attention_prefix = f\"{diffusers_attention_prefix}.{attention_idx_inc}\"\n\n diffusers_checkpoint.update(\n attention_to_diffusers_checkpoint(\n checkpoint,\n attention_prefix=full_attention_prefix,\n diffusers_attention_prefix=full_diffusers_attention_prefix,\n num_head_channels=num_head_channels,\n )\n )\n\n num_original_down_blocks = num_resnets\n\n return diffusers_checkpoint, num_original_down_blocks\n\n\n# .middle_block -> .mid_block\ndef unet_midblock_to_diffusers_checkpoint(model, checkpoint, *, num_head_channels):\n diffusers_checkpoint = {}\n\n # block 0\n\n original_block_idx = 0\n\n diffusers_checkpoint.update(\n resnet_to_diffusers_checkpoint(\n checkpoint,\n diffusers_resnet_prefix=\"mid_block.resnets.0\",\n resnet_prefix=f\"middle_block.{original_block_idx}\",\n )\n )\n\n original_block_idx += 1\n\n # optional block 1\n\n if hasattr(model.mid_block, \"attentions\") and model.mid_block.attentions[0] is not None:\n diffusers_checkpoint.update(\n attention_to_diffusers_checkpoint(\n checkpoint,\n diffusers_attention_prefix=\"mid_block.attentions.0\",\n attention_prefix=f\"middle_block.{original_block_idx}\",\n num_head_channels=num_head_channels,\n )\n )\n original_block_idx += 1\n\n # block 1 or block 2\n\n diffusers_checkpoint.update(\n resnet_to_diffusers_checkpoint(\n checkpoint,\n diffusers_resnet_prefix=\"mid_block.resnets.1\",\n resnet_prefix=f\"middle_block.{original_block_idx}\",\n )\n )\n\n return diffusers_checkpoint\n\n\n# .output_blocks -> .up_blocks\ndef unet_upblock_to_diffusers_checkpoint(\n model, checkpoint, *, diffusers_up_block_idx, original_up_block_idx, num_head_channels\n):\n diffusers_checkpoint = {}\n\n diffusers_resnet_prefix = f\"up_blocks.{diffusers_up_block_idx}.resnets\"\n original_up_block_prefix = \"output_blocks\"\n\n up_block = model.up_blocks[diffusers_up_block_idx]\n\n num_resnets = len(up_block.resnets)\n\n if up_block.upsamplers is None:\n upsampler = False\n else:\n assert len(up_block.upsamplers) == 1\n upsampler = True\n # The upsample block is also a resnet\n num_resnets += 1\n\n has_attentions = hasattr(up_block, \"attentions\")\n\n for resnet_idx_inc in range(num_resnets):\n if upsampler and resnet_idx_inc == num_resnets - 1:\n # this is an upsample block\n if has_attentions:\n # There is a middle attention block that we skip\n original_resnet_block_idx = 2\n else:\n original_resnet_block_idx = 1\n\n # we add the `minus 1` because the last two resnets are stuck together in the same output block\n full_resnet_prefix = (\n f\"{original_up_block_prefix}.{original_up_block_idx + resnet_idx_inc - 1}.{original_resnet_block_idx}\"\n )\n\n full_diffusers_resnet_prefix = f\"up_blocks.{diffusers_up_block_idx}.upsamplers.0\"\n else:\n # this is a regular resnet block\n full_resnet_prefix = f\"{original_up_block_prefix}.{original_up_block_idx + resnet_idx_inc}.0\"\n full_diffusers_resnet_prefix = f\"{diffusers_resnet_prefix}.{resnet_idx_inc}\"\n\n diffusers_checkpoint.update(\n resnet_to_diffusers_checkpoint(\n checkpoint, resnet_prefix=full_resnet_prefix, diffusers_resnet_prefix=full_diffusers_resnet_prefix\n )\n )\n\n if has_attentions:\n num_attentions = len(up_block.attentions)\n diffusers_attention_prefix = f\"up_blocks.{diffusers_up_block_idx}.attentions\"\n\n for attention_idx_inc in range(num_attentions):\n full_attention_prefix = f\"{original_up_block_prefix}.{original_up_block_idx + attention_idx_inc}.1\"\n full_diffusers_attention_prefix = f\"{diffusers_attention_prefix}.{attention_idx_inc}\"\n\n diffusers_checkpoint.update(\n attention_to_diffusers_checkpoint(\n checkpoint,\n attention_prefix=full_attention_prefix,\n diffusers_attention_prefix=full_diffusers_attention_prefix,\n num_head_channels=num_head_channels,\n )\n )\n\n num_original_down_blocks = num_resnets - 1 if upsampler else num_resnets\n\n return diffusers_checkpoint, num_original_down_blocks\n\n\ndef resnet_to_diffusers_checkpoint(checkpoint, *, diffusers_resnet_prefix, resnet_prefix):\n diffusers_checkpoint = {\n f\"{diffusers_resnet_prefix}.norm1.weight\": checkpoint[f\"{resnet_prefix}.in_layers.0.weight\"],\n f\"{diffusers_resnet_prefix}.norm1.bias\": checkpoint[f\"{resnet_prefix}.in_layers.0.bias\"],\n f\"{diffusers_resnet_prefix}.conv1.weight\": checkpoint[f\"{resnet_prefix}.in_layers.2.weight\"],\n f\"{diffusers_resnet_prefix}.conv1.bias\": checkpoint[f\"{resnet_prefix}.in_layers.2.bias\"],\n f\"{diffusers_resnet_prefix}.time_emb_proj.weight\": checkpoint[f\"{resnet_prefix}.emb_layers.1.weight\"],\n f\"{diffusers_resnet_prefix}.time_emb_proj.bias\": checkpoint[f\"{resnet_prefix}.emb_layers.1.bias\"],\n f\"{diffusers_resnet_prefix}.norm2.weight\": checkpoint[f\"{resnet_prefix}.out_layers.0.weight\"],\n f\"{diffusers_resnet_prefix}.norm2.bias\": checkpoint[f\"{resnet_prefix}.out_layers.0.bias\"],\n f\"{diffusers_resnet_prefix}.conv2.weight\": checkpoint[f\"{resnet_prefix}.out_layers.3.weight\"],\n f\"{diffusers_resnet_prefix}.conv2.bias\": checkpoint[f\"{resnet_prefix}.out_layers.3.bias\"],\n }\n\n skip_connection_prefix = f\"{resnet_prefix}.skip_connection\"\n\n if f\"{skip_connection_prefix}.weight\" in checkpoint:\n diffusers_checkpoint.update(\n {\n f\"{diffusers_resnet_prefix}.conv_shortcut.weight\": checkpoint[f\"{skip_connection_prefix}.weight\"],\n f\"{diffusers_resnet_prefix}.conv_shortcut.bias\": checkpoint[f\"{skip_connection_prefix}.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\ndef attention_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix, num_head_channels):\n diffusers_checkpoint = {}\n\n # .norm -> .group_norm\n diffusers_checkpoint.update(\n {\n f\"{diffusers_attention_prefix}.group_norm.weight\": checkpoint[f\"{attention_prefix}.norm.weight\"],\n f\"{diffusers_attention_prefix}.group_norm.bias\": checkpoint[f\"{attention_prefix}.norm.bias\"],\n }\n )\n\n # .qkv -> .{query, key, value}\n [q_weight, k_weight, v_weight], [q_bias, k_bias, v_bias] = split_attentions(\n weight=checkpoint[f\"{attention_prefix}.qkv.weight\"][:, :, 0],\n bias=checkpoint[f\"{attention_prefix}.qkv.bias\"],\n split=3,\n chunk_size=num_head_channels,\n )\n\n diffusers_checkpoint.update(\n {\n f\"{diffusers_attention_prefix}.to_q.weight\": q_weight,\n f\"{diffusers_attention_prefix}.to_q.bias\": q_bias,\n f\"{diffusers_attention_prefix}.to_k.weight\": k_weight,\n f\"{diffusers_attention_prefix}.to_k.bias\": k_bias,\n f\"{diffusers_attention_prefix}.to_v.weight\": v_weight,\n f\"{diffusers_attention_prefix}.to_v.bias\": v_bias,\n }\n )\n\n # .encoder_kv -> .{context_key, context_value}\n [encoder_k_weight, encoder_v_weight], [encoder_k_bias, encoder_v_bias] = split_attentions(\n weight=checkpoint[f\"{attention_prefix}.encoder_kv.weight\"][:, :, 0],\n bias=checkpoint[f\"{attention_prefix}.encoder_kv.bias\"],\n split=2,\n chunk_size=num_head_channels,\n )\n\n diffusers_checkpoint.update(\n {\n f\"{diffusers_attention_prefix}.add_k_proj.weight\": encoder_k_weight,\n f\"{diffusers_attention_prefix}.add_k_proj.bias\": encoder_k_bias,\n f\"{diffusers_attention_prefix}.add_v_proj.weight\": encoder_v_weight,\n f\"{diffusers_attention_prefix}.add_v_proj.bias\": encoder_v_bias,\n }\n )\n\n # .proj_out (1d conv) -> .proj_attn (linear)\n diffusers_checkpoint.update(\n {\n f\"{diffusers_attention_prefix}.to_out.0.weight\": checkpoint[f\"{attention_prefix}.proj_out.weight\"][\n :, :, 0\n ],\n f\"{diffusers_attention_prefix}.to_out.0.bias\": checkpoint[f\"{attention_prefix}.proj_out.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\n# TODO maybe document and/or can do more efficiently (build indices in for loop and extract once for each split?)\ndef split_attentions(*, weight, bias, split, chunk_size):\n weights = [None] * split\n biases = [None] * split\n\n weights_biases_idx = 0\n\n for starting_row_index in range(0, weight.shape[0], chunk_size):\n row_indices = torch.arange(starting_row_index, starting_row_index + chunk_size)\n\n weight_rows = weight[row_indices, :]\n bias_rows = bias[row_indices]\n\n if weights[weights_biases_idx] is None:\n assert weights[weights_biases_idx] is None\n weights[weights_biases_idx] = weight_rows\n biases[weights_biases_idx] = bias_rows\n else:\n assert weights[weights_biases_idx] is not None\n weights[weights_biases_idx] = torch.concat([weights[weights_biases_idx], weight_rows])\n biases[weights_biases_idx] = torch.concat([biases[weights_biases_idx], bias_rows])\n\n weights_biases_idx = (weights_biases_idx + 1) % split\n\n return weights, biases\n\n\n# done unet utils\n\n\ndef prior(*, args, checkpoint_map_location):\n print(\"loading prior\")\n\n prior_checkpoint = torch.load(args.prior_checkpoint_path, map_location=checkpoint_map_location)\n\n clip_stats_checkpoint = torch.load(args.clip_stat_path, map_location=checkpoint_map_location)\n\n prior_model = prior_model_from_original_config()\n\n prior_diffusers_checkpoint = prior_original_checkpoint_to_diffusers_checkpoint(\n prior_model, prior_checkpoint, clip_stats_checkpoint\n )\n\n del prior_checkpoint\n del clip_stats_checkpoint\n\n load_checkpoint_to_model(prior_diffusers_checkpoint, prior_model, strict=True)\n\n print(\"done loading prior\")\n\n return prior_model\n\n\ndef text2img(*, args, checkpoint_map_location):\n print(\"loading text2img\")\n\n text2img_checkpoint = torch.load(args.text2img_checkpoint_path, map_location=checkpoint_map_location)\n\n unet_model = unet_model_from_original_config()\n\n unet_diffusers_checkpoint = unet_original_checkpoint_to_diffusers_checkpoint(unet_model, text2img_checkpoint)\n\n del text2img_checkpoint\n\n load_checkpoint_to_model(unet_diffusers_checkpoint, unet_model, strict=True)\n\n print(\"done loading text2img\")\n\n return unet_model\n\n\ndef inpaint_text2img(*, args, checkpoint_map_location):\n print(\"loading inpaint text2img\")\n\n inpaint_text2img_checkpoint = torch.load(\n args.inpaint_text2img_checkpoint_path, map_location=checkpoint_map_location\n )\n\n inpaint_unet_model = inpaint_unet_model_from_original_config()\n\n inpaint_unet_diffusers_checkpoint = inpaint_unet_original_checkpoint_to_diffusers_checkpoint(\n inpaint_unet_model, inpaint_text2img_checkpoint\n )\n\n del inpaint_text2img_checkpoint\n\n load_checkpoint_to_model(inpaint_unet_diffusers_checkpoint, inpaint_unet_model, strict=True)\n\n print(\"done loading inpaint text2img\")\n\n return inpaint_unet_model\n\n\n# movq\n\nMOVQ_CONFIG = {\n \"in_channels\": 3,\n \"out_channels\": 3,\n \"latent_channels\": 4,\n \"down_block_types\": (\"DownEncoderBlock2D\", \"DownEncoderBlock2D\", \"DownEncoderBlock2D\", \"AttnDownEncoderBlock2D\"),\n \"up_block_types\": (\"AttnUpDecoderBlock2D\", \"UpDecoderBlock2D\", \"UpDecoderBlock2D\", \"UpDecoderBlock2D\"),\n \"num_vq_embeddings\": 16384,\n \"block_out_channels\": (128, 256, 256, 512),\n \"vq_embed_dim\": 4,\n \"layers_per_block\": 2,\n \"norm_type\": \"spatial\",\n}\n\n\ndef movq_model_from_original_config():\n movq = VQModel(**MOVQ_CONFIG)\n return movq\n\n\ndef movq_encoder_to_diffusers_checkpoint(model, checkpoint):\n diffusers_checkpoint = {}\n\n # conv_in\n diffusers_checkpoint.update(\n {\n \"encoder.conv_in.weight\": checkpoint[\"encoder.conv_in.weight\"],\n \"encoder.conv_in.bias\": checkpoint[\"encoder.conv_in.bias\"],\n }\n )\n\n # down_blocks\n for down_block_idx, down_block in enumerate(model.encoder.down_blocks):\n diffusers_down_block_prefix = f\"encoder.down_blocks.{down_block_idx}\"\n down_block_prefix = f\"encoder.down.{down_block_idx}\"\n\n # resnets\n for resnet_idx, resnet in enumerate(down_block.resnets):\n diffusers_resnet_prefix = f\"{diffusers_down_block_prefix}.resnets.{resnet_idx}\"\n resnet_prefix = f\"{down_block_prefix}.block.{resnet_idx}\"\n\n diffusers_checkpoint.update(\n movq_resnet_to_diffusers_checkpoint(\n resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix\n )\n )\n\n # downsample\n\n # do not include the downsample when on the last down block\n # There is no downsample on the last down block\n if down_block_idx != len(model.encoder.down_blocks) - 1:\n # There's a single downsample in the original checkpoint but a list of downsamples\n # in the diffusers model.\n diffusers_downsample_prefix = f\"{diffusers_down_block_prefix}.downsamplers.0.conv\"\n downsample_prefix = f\"{down_block_prefix}.downsample.conv\"\n diffusers_checkpoint.update(\n {\n f\"{diffusers_downsample_prefix}.weight\": checkpoint[f\"{downsample_prefix}.weight\"],\n f\"{diffusers_downsample_prefix}.bias\": checkpoint[f\"{downsample_prefix}.bias\"],\n }\n )\n\n # attentions\n\n if hasattr(down_block, \"attentions\"):\n for attention_idx, _ in enumerate(down_block.attentions):\n diffusers_attention_prefix = f\"{diffusers_down_block_prefix}.attentions.{attention_idx}\"\n attention_prefix = f\"{down_block_prefix}.attn.{attention_idx}\"\n diffusers_checkpoint.update(\n movq_attention_to_diffusers_checkpoint(\n checkpoint,\n diffusers_attention_prefix=diffusers_attention_prefix,\n attention_prefix=attention_prefix,\n )\n )\n\n # mid block\n\n # mid block attentions\n\n # There is a single hardcoded attention block in the middle of the VQ-diffusion encoder\n diffusers_attention_prefix = \"encoder.mid_block.attentions.0\"\n attention_prefix = \"encoder.mid.attn_1\"\n diffusers_checkpoint.update(\n movq_attention_to_diffusers_checkpoint(\n checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix\n )\n )\n\n # mid block resnets\n\n for diffusers_resnet_idx, resnet in enumerate(model.encoder.mid_block.resnets):\n diffusers_resnet_prefix = f\"encoder.mid_block.resnets.{diffusers_resnet_idx}\"\n\n # the hardcoded prefixes to `block_` are 1 and 2\n orig_resnet_idx = diffusers_resnet_idx + 1\n # There are two hardcoded resnets in the middle of the VQ-diffusion encoder\n resnet_prefix = f\"encoder.mid.block_{orig_resnet_idx}\"\n\n diffusers_checkpoint.update(\n movq_resnet_to_diffusers_checkpoint(\n resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix\n )\n )\n\n diffusers_checkpoint.update(\n {\n # conv_norm_out\n \"encoder.conv_norm_out.weight\": checkpoint[\"encoder.norm_out.weight\"],\n \"encoder.conv_norm_out.bias\": checkpoint[\"encoder.norm_out.bias\"],\n # conv_out\n \"encoder.conv_out.weight\": checkpoint[\"encoder.conv_out.weight\"],\n \"encoder.conv_out.bias\": checkpoint[\"encoder.conv_out.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\ndef movq_decoder_to_diffusers_checkpoint(model, checkpoint):\n diffusers_checkpoint = {}\n\n # conv in\n diffusers_checkpoint.update(\n {\n \"decoder.conv_in.weight\": checkpoint[\"decoder.conv_in.weight\"],\n \"decoder.conv_in.bias\": checkpoint[\"decoder.conv_in.bias\"],\n }\n )\n\n # up_blocks\n\n for diffusers_up_block_idx, up_block in enumerate(model.decoder.up_blocks):\n # up_blocks are stored in reverse order in the VQ-diffusion checkpoint\n orig_up_block_idx = len(model.decoder.up_blocks) - 1 - diffusers_up_block_idx\n\n diffusers_up_block_prefix = f\"decoder.up_blocks.{diffusers_up_block_idx}\"\n up_block_prefix = f\"decoder.up.{orig_up_block_idx}\"\n\n # resnets\n for resnet_idx, resnet in enumerate(up_block.resnets):\n diffusers_resnet_prefix = f\"{diffusers_up_block_prefix}.resnets.{resnet_idx}\"\n resnet_prefix = f\"{up_block_prefix}.block.{resnet_idx}\"\n\n diffusers_checkpoint.update(\n movq_resnet_to_diffusers_checkpoint_spatial_norm(\n resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix\n )\n )\n\n # upsample\n\n # there is no up sample on the last up block\n if diffusers_up_block_idx != len(model.decoder.up_blocks) - 1:\n # There's a single upsample in the VQ-diffusion checkpoint but a list of downsamples\n # in the diffusers model.\n diffusers_downsample_prefix = f\"{diffusers_up_block_prefix}.upsamplers.0.conv\"\n downsample_prefix = f\"{up_block_prefix}.upsample.conv\"\n diffusers_checkpoint.update(\n {\n f\"{diffusers_downsample_prefix}.weight\": checkpoint[f\"{downsample_prefix}.weight\"],\n f\"{diffusers_downsample_prefix}.bias\": checkpoint[f\"{downsample_prefix}.bias\"],\n }\n )\n\n # attentions\n\n if hasattr(up_block, \"attentions\"):\n for attention_idx, _ in enumerate(up_block.attentions):\n diffusers_attention_prefix = f\"{diffusers_up_block_prefix}.attentions.{attention_idx}\"\n attention_prefix = f\"{up_block_prefix}.attn.{attention_idx}\"\n diffusers_checkpoint.update(\n movq_attention_to_diffusers_checkpoint_spatial_norm(\n checkpoint,\n diffusers_attention_prefix=diffusers_attention_prefix,\n attention_prefix=attention_prefix,\n )\n )\n\n # mid block\n\n # mid block attentions\n\n # There is a single hardcoded attention block in the middle of the VQ-diffusion decoder\n diffusers_attention_prefix = \"decoder.mid_block.attentions.0\"\n attention_prefix = \"decoder.mid.attn_1\"\n diffusers_checkpoint.update(\n movq_attention_to_diffusers_checkpoint_spatial_norm(\n checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix\n )\n )\n\n # mid block resnets\n\n for diffusers_resnet_idx, resnet in enumerate(model.encoder.mid_block.resnets):\n diffusers_resnet_prefix = f\"decoder.mid_block.resnets.{diffusers_resnet_idx}\"\n\n # the hardcoded prefixes to `block_` are 1 and 2\n orig_resnet_idx = diffusers_resnet_idx + 1\n # There are two hardcoded resnets in the middle of the VQ-diffusion decoder\n resnet_prefix = f\"decoder.mid.block_{orig_resnet_idx}\"\n\n diffusers_checkpoint.update(\n movq_resnet_to_diffusers_checkpoint_spatial_norm(\n resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix\n )\n )\n\n diffusers_checkpoint.update(\n {\n # conv_norm_out\n \"decoder.conv_norm_out.norm_layer.weight\": checkpoint[\"decoder.norm_out.norm_layer.weight\"],\n \"decoder.conv_norm_out.norm_layer.bias\": checkpoint[\"decoder.norm_out.norm_layer.bias\"],\n \"decoder.conv_norm_out.conv_y.weight\": checkpoint[\"decoder.norm_out.conv_y.weight\"],\n \"decoder.conv_norm_out.conv_y.bias\": checkpoint[\"decoder.norm_out.conv_y.bias\"],\n \"decoder.conv_norm_out.conv_b.weight\": checkpoint[\"decoder.norm_out.conv_b.weight\"],\n \"decoder.conv_norm_out.conv_b.bias\": checkpoint[\"decoder.norm_out.conv_b.bias\"],\n # conv_out\n \"decoder.conv_out.weight\": checkpoint[\"decoder.conv_out.weight\"],\n \"decoder.conv_out.bias\": checkpoint[\"decoder.conv_out.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\ndef movq_resnet_to_diffusers_checkpoint(resnet, checkpoint, *, diffusers_resnet_prefix, resnet_prefix):\n rv = {\n # norm1\n f\"{diffusers_resnet_prefix}.norm1.weight\": checkpoint[f\"{resnet_prefix}.norm1.weight\"],\n f\"{diffusers_resnet_prefix}.norm1.bias\": checkpoint[f\"{resnet_prefix}.norm1.bias\"],\n # conv1\n f\"{diffusers_resnet_prefix}.conv1.weight\": checkpoint[f\"{resnet_prefix}.conv1.weight\"],\n f\"{diffusers_resnet_prefix}.conv1.bias\": checkpoint[f\"{resnet_prefix}.conv1.bias\"],\n # norm2\n f\"{diffusers_resnet_prefix}.norm2.weight\": checkpoint[f\"{resnet_prefix}.norm2.weight\"],\n f\"{diffusers_resnet_prefix}.norm2.bias\": checkpoint[f\"{resnet_prefix}.norm2.bias\"],\n # conv2\n f\"{diffusers_resnet_prefix}.conv2.weight\": checkpoint[f\"{resnet_prefix}.conv2.weight\"],\n f\"{diffusers_resnet_prefix}.conv2.bias\": checkpoint[f\"{resnet_prefix}.conv2.bias\"],\n }\n\n if resnet.conv_shortcut is not None:\n rv.update(\n {\n f\"{diffusers_resnet_prefix}.conv_shortcut.weight\": checkpoint[f\"{resnet_prefix}.nin_shortcut.weight\"],\n f\"{diffusers_resnet_prefix}.conv_shortcut.bias\": checkpoint[f\"{resnet_prefix}.nin_shortcut.bias\"],\n }\n )\n\n return rv\n\n\ndef movq_resnet_to_diffusers_checkpoint_spatial_norm(resnet, checkpoint, *, diffusers_resnet_prefix, resnet_prefix):\n rv = {\n # norm1\n f\"{diffusers_resnet_prefix}.norm1.norm_layer.weight\": checkpoint[f\"{resnet_prefix}.norm1.norm_layer.weight\"],\n f\"{diffusers_resnet_prefix}.norm1.norm_layer.bias\": checkpoint[f\"{resnet_prefix}.norm1.norm_layer.bias\"],\n f\"{diffusers_resnet_prefix}.norm1.conv_y.weight\": checkpoint[f\"{resnet_prefix}.norm1.conv_y.weight\"],\n f\"{diffusers_resnet_prefix}.norm1.conv_y.bias\": checkpoint[f\"{resnet_prefix}.norm1.conv_y.bias\"],\n f\"{diffusers_resnet_prefix}.norm1.conv_b.weight\": checkpoint[f\"{resnet_prefix}.norm1.conv_b.weight\"],\n f\"{diffusers_resnet_prefix}.norm1.conv_b.bias\": checkpoint[f\"{resnet_prefix}.norm1.conv_b.bias\"],\n # conv1\n f\"{diffusers_resnet_prefix}.conv1.weight\": checkpoint[f\"{resnet_prefix}.conv1.weight\"],\n f\"{diffusers_resnet_prefix}.conv1.bias\": checkpoint[f\"{resnet_prefix}.conv1.bias\"],\n # norm2\n f\"{diffusers_resnet_prefix}.norm2.norm_layer.weight\": checkpoint[f\"{resnet_prefix}.norm2.norm_layer.weight\"],\n f\"{diffusers_resnet_prefix}.norm2.norm_layer.bias\": checkpoint[f\"{resnet_prefix}.norm2.norm_layer.bias\"],\n f\"{diffusers_resnet_prefix}.norm2.conv_y.weight\": checkpoint[f\"{resnet_prefix}.norm2.conv_y.weight\"],\n f\"{diffusers_resnet_prefix}.norm2.conv_y.bias\": checkpoint[f\"{resnet_prefix}.norm2.conv_y.bias\"],\n f\"{diffusers_resnet_prefix}.norm2.conv_b.weight\": checkpoint[f\"{resnet_prefix}.norm2.conv_b.weight\"],\n f\"{diffusers_resnet_prefix}.norm2.conv_b.bias\": checkpoint[f\"{resnet_prefix}.norm2.conv_b.bias\"],\n # conv2\n f\"{diffusers_resnet_prefix}.conv2.weight\": checkpoint[f\"{resnet_prefix}.conv2.weight\"],\n f\"{diffusers_resnet_prefix}.conv2.bias\": checkpoint[f\"{resnet_prefix}.conv2.bias\"],\n }\n\n if resnet.conv_shortcut is not None:\n rv.update(\n {\n f\"{diffusers_resnet_prefix}.conv_shortcut.weight\": checkpoint[f\"{resnet_prefix}.nin_shortcut.weight\"],\n f\"{diffusers_resnet_prefix}.conv_shortcut.bias\": checkpoint[f\"{resnet_prefix}.nin_shortcut.bias\"],\n }\n )\n\n return rv\n\n\ndef movq_attention_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix):\n return {\n # norm\n f\"{diffusers_attention_prefix}.group_norm.weight\": checkpoint[f\"{attention_prefix}.norm.weight\"],\n f\"{diffusers_attention_prefix}.group_norm.bias\": checkpoint[f\"{attention_prefix}.norm.bias\"],\n # query\n f\"{diffusers_attention_prefix}.to_q.weight\": checkpoint[f\"{attention_prefix}.q.weight\"][:, :, 0, 0],\n f\"{diffusers_attention_prefix}.to_q.bias\": checkpoint[f\"{attention_prefix}.q.bias\"],\n # key\n f\"{diffusers_attention_prefix}.to_k.weight\": checkpoint[f\"{attention_prefix}.k.weight\"][:, :, 0, 0],\n f\"{diffusers_attention_prefix}.to_k.bias\": checkpoint[f\"{attention_prefix}.k.bias\"],\n # value\n f\"{diffusers_attention_prefix}.to_v.weight\": checkpoint[f\"{attention_prefix}.v.weight\"][:, :, 0, 0],\n f\"{diffusers_attention_prefix}.to_v.bias\": checkpoint[f\"{attention_prefix}.v.bias\"],\n # proj_attn\n f\"{diffusers_attention_prefix}.to_out.0.weight\": checkpoint[f\"{attention_prefix}.proj_out.weight\"][:, :, 0, 0],\n f\"{diffusers_attention_prefix}.to_out.0.bias\": checkpoint[f\"{attention_prefix}.proj_out.bias\"],\n }\n\n\ndef movq_attention_to_diffusers_checkpoint_spatial_norm(checkpoint, *, diffusers_attention_prefix, attention_prefix):\n return {\n # norm\n f\"{diffusers_attention_prefix}.spatial_norm.norm_layer.weight\": checkpoint[\n f\"{attention_prefix}.norm.norm_layer.weight\"\n ],\n f\"{diffusers_attention_prefix}.spatial_norm.norm_layer.bias\": checkpoint[\n f\"{attention_prefix}.norm.norm_layer.bias\"\n ],\n f\"{diffusers_attention_prefix}.spatial_norm.conv_y.weight\": checkpoint[\n f\"{attention_prefix}.norm.conv_y.weight\"\n ],\n f\"{diffusers_attention_prefix}.spatial_norm.conv_y.bias\": checkpoint[f\"{attention_prefix}.norm.conv_y.bias\"],\n f\"{diffusers_attention_prefix}.spatial_norm.conv_b.weight\": checkpoint[\n f\"{attention_prefix}.norm.conv_b.weight\"\n ],\n f\"{diffusers_attention_prefix}.spatial_norm.conv_b.bias\": checkpoint[f\"{attention_prefix}.norm.conv_b.bias\"],\n # query\n f\"{diffusers_attention_prefix}.to_q.weight\": checkpoint[f\"{attention_prefix}.q.weight\"][:, :, 0, 0],\n f\"{diffusers_attention_prefix}.to_q.bias\": checkpoint[f\"{attention_prefix}.q.bias\"],\n # key\n f\"{diffusers_attention_prefix}.to_k.weight\": checkpoint[f\"{attention_prefix}.k.weight\"][:, :, 0, 0],\n f\"{diffusers_attention_prefix}.to_k.bias\": checkpoint[f\"{attention_prefix}.k.bias\"],\n # value\n f\"{diffusers_attention_prefix}.to_v.weight\": checkpoint[f\"{attention_prefix}.v.weight\"][:, :, 0, 0],\n f\"{diffusers_attention_prefix}.to_v.bias\": checkpoint[f\"{attention_prefix}.v.bias\"],\n # proj_attn\n f\"{diffusers_attention_prefix}.to_out.0.weight\": checkpoint[f\"{attention_prefix}.proj_out.weight\"][:, :, 0, 0],\n f\"{diffusers_attention_prefix}.to_out.0.bias\": checkpoint[f\"{attention_prefix}.proj_out.bias\"],\n }\n\n\ndef movq_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):\n diffusers_checkpoint = {}\n diffusers_checkpoint.update(movq_encoder_to_diffusers_checkpoint(model, checkpoint))\n\n # quant_conv\n\n diffusers_checkpoint.update(\n {\n \"quant_conv.weight\": checkpoint[\"quant_conv.weight\"],\n \"quant_conv.bias\": checkpoint[\"quant_conv.bias\"],\n }\n )\n\n # quantize\n diffusers_checkpoint.update({\"quantize.embedding.weight\": checkpoint[\"quantize.embedding.weight\"]})\n\n # post_quant_conv\n diffusers_checkpoint.update(\n {\n \"post_quant_conv.weight\": checkpoint[\"post_quant_conv.weight\"],\n \"post_quant_conv.bias\": checkpoint[\"post_quant_conv.bias\"],\n }\n )\n\n # decoder\n diffusers_checkpoint.update(movq_decoder_to_diffusers_checkpoint(model, checkpoint))\n\n return diffusers_checkpoint\n\n\ndef movq(*, args, checkpoint_map_location):\n print(\"loading movq\")\n\n movq_checkpoint = torch.load(args.movq_checkpoint_path, map_location=checkpoint_map_location)\n\n movq_model = movq_model_from_original_config()\n\n movq_diffusers_checkpoint = movq_original_checkpoint_to_diffusers_checkpoint(movq_model, movq_checkpoint)\n\n del movq_checkpoint\n\n load_checkpoint_to_model(movq_diffusers_checkpoint, movq_model, strict=True)\n\n print(\"done loading movq\")\n\n return movq_model\n\n\ndef load_checkpoint_to_model(checkpoint, model, strict=False):\n with tempfile.NamedTemporaryFile(delete=False) as file:\n torch.save(checkpoint, file.name)\n del checkpoint\n if strict:\n model.load_state_dict(torch.load(file.name), strict=True)\n else:\n load_checkpoint_and_dispatch(model, file.name, device_map=\"auto\")\n os.remove(file.name)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n\n parser.add_argument(\n \"--prior_checkpoint_path\",\n default=None,\n type=str,\n required=False,\n help=\"Path to the prior checkpoint to convert.\",\n )\n parser.add_argument(\n \"--clip_stat_path\",\n default=None,\n type=str,\n required=False,\n help=\"Path to the clip stats checkpoint to convert.\",\n )\n parser.add_argument(\n \"--text2img_checkpoint_path\",\n default=None,\n type=str,\n required=False,\n help=\"Path to the text2img checkpoint to convert.\",\n )\n parser.add_argument(\n \"--movq_checkpoint_path\",\n default=None,\n type=str,\n required=False,\n help=\"Path to the text2img checkpoint to convert.\",\n )\n parser.add_argument(\n \"--inpaint_text2img_checkpoint_path\",\n default=None,\n type=str,\n required=False,\n help=\"Path to the inpaint text2img checkpoint to convert.\",\n )\n parser.add_argument(\n \"--checkpoint_load_device\",\n default=\"cpu\",\n type=str,\n required=False,\n help=\"The device passed to `map_location` when loading checkpoints.\",\n )\n\n parser.add_argument(\n \"--debug\",\n default=None,\n type=str,\n required=False,\n help=\"Only run a specific stage of the convert script. Used for debugging\",\n )\n\n args = parser.parse_args()\n\n print(f\"loading checkpoints to {args.checkpoint_load_device}\")\n\n checkpoint_map_location = torch.device(args.checkpoint_load_device)\n\n if args.debug is not None:\n print(f\"debug: only executing {args.debug}\")\n\n if args.debug is None:\n print(\"to-do\")\n elif args.debug == \"prior\":\n prior_model = prior(args=args, checkpoint_map_location=checkpoint_map_location)\n prior_model.save_pretrained(args.dump_path)\n elif args.debug == \"text2img\":\n unet_model = text2img(args=args, checkpoint_map_location=checkpoint_map_location)\n unet_model.save_pretrained(f\"{args.dump_path}/unet\")\n elif args.debug == \"inpaint_text2img\":\n inpaint_unet_model = inpaint_text2img(args=args, checkpoint_map_location=checkpoint_map_location)\n inpaint_unet_model.save_pretrained(f\"{args.dump_path}/inpaint_unet\")\n elif args.debug == \"decoder\":\n decoder = movq(args=args, checkpoint_map_location=checkpoint_map_location)\n decoder.save_pretrained(f\"{args.dump_path}/decoder\")\n else:\n raise ValueError(f\"unknown debug value : {args.debug}\")\n" }, { "path": "diffusers/scripts/convert_ldm_original_checkpoint_to_diffusers.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Conversion script for the LDM checkpoints.\"\"\"\n\nimport argparse\nimport json\n\nimport torch\n\nfrom diffusers import DDPMScheduler, LDMPipeline, UNet2DModel, VQModel\n\n\ndef shave_segments(path, n_shave_prefix_segments=1):\n \"\"\"\n Removes segments. Positive values shave the first segments, negative shave the last segments.\n \"\"\"\n if n_shave_prefix_segments >= 0:\n return \".\".join(path.split(\".\")[n_shave_prefix_segments:])\n else:\n return \".\".join(path.split(\".\")[:n_shave_prefix_segments])\n\n\ndef renew_resnet_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside resnets to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item.replace(\"in_layers.0\", \"norm1\")\n new_item = new_item.replace(\"in_layers.2\", \"conv1\")\n\n new_item = new_item.replace(\"out_layers.0\", \"norm2\")\n new_item = new_item.replace(\"out_layers.3\", \"conv2\")\n\n new_item = new_item.replace(\"emb_layers.1\", \"time_emb_proj\")\n new_item = new_item.replace(\"skip_connection\", \"conv_shortcut\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef renew_attention_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside attentions to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n new_item = new_item.replace(\"norm.weight\", \"group_norm.weight\")\n new_item = new_item.replace(\"norm.bias\", \"group_norm.bias\")\n\n new_item = new_item.replace(\"proj_out.weight\", \"proj_attn.weight\")\n new_item = new_item.replace(\"proj_out.bias\", \"proj_attn.bias\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef assign_to_checkpoint(\n paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None\n):\n \"\"\"\n This does the final conversion step: take locally converted weights and apply a global renaming\n to them. It splits attention layers, and takes into account additional replacements\n that may arise.\n\n Assigns the weights to the new checkpoint.\n \"\"\"\n assert isinstance(paths, list), \"Paths should be a list of dicts containing 'old' and 'new' keys.\"\n\n # Splits the attention layers into three variables.\n if attention_paths_to_split is not None:\n for path, path_map in attention_paths_to_split.items():\n old_tensor = old_checkpoint[path]\n channels = old_tensor.shape[0] // 3\n\n target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)\n\n num_heads = old_tensor.shape[0] // config[\"num_head_channels\"] // 3\n\n old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])\n query, key, value = old_tensor.split(channels // num_heads, dim=1)\n\n checkpoint[path_map[\"query\"]] = query.reshape(target_shape)\n checkpoint[path_map[\"key\"]] = key.reshape(target_shape)\n checkpoint[path_map[\"value\"]] = value.reshape(target_shape)\n\n for path in paths:\n new_path = path[\"new\"]\n\n # These have already been assigned\n if attention_paths_to_split is not None and new_path in attention_paths_to_split:\n continue\n\n # Global renaming happens here\n new_path = new_path.replace(\"middle_block.0\", \"mid_block.resnets.0\")\n new_path = new_path.replace(\"middle_block.1\", \"mid_block.attentions.0\")\n new_path = new_path.replace(\"middle_block.2\", \"mid_block.resnets.1\")\n\n if additional_replacements is not None:\n for replacement in additional_replacements:\n new_path = new_path.replace(replacement[\"old\"], replacement[\"new\"])\n\n # proj_attn.weight has to be converted from conv 1D to linear\n if \"proj_attn.weight\" in new_path:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]][:, :, 0]\n else:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]]\n\n\ndef convert_ldm_checkpoint(checkpoint, config):\n \"\"\"\n Takes a state dict and a config, and returns a converted checkpoint.\n \"\"\"\n new_checkpoint = {}\n\n new_checkpoint[\"time_embedding.linear_1.weight\"] = checkpoint[\"time_embed.0.weight\"]\n new_checkpoint[\"time_embedding.linear_1.bias\"] = checkpoint[\"time_embed.0.bias\"]\n new_checkpoint[\"time_embedding.linear_2.weight\"] = checkpoint[\"time_embed.2.weight\"]\n new_checkpoint[\"time_embedding.linear_2.bias\"] = checkpoint[\"time_embed.2.bias\"]\n\n new_checkpoint[\"conv_in.weight\"] = checkpoint[\"input_blocks.0.0.weight\"]\n new_checkpoint[\"conv_in.bias\"] = checkpoint[\"input_blocks.0.0.bias\"]\n\n new_checkpoint[\"conv_norm_out.weight\"] = checkpoint[\"out.0.weight\"]\n new_checkpoint[\"conv_norm_out.bias\"] = checkpoint[\"out.0.bias\"]\n new_checkpoint[\"conv_out.weight\"] = checkpoint[\"out.2.weight\"]\n new_checkpoint[\"conv_out.bias\"] = checkpoint[\"out.2.bias\"]\n\n # Retrieves the keys for the input blocks only\n num_input_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in checkpoint if \"input_blocks\" in layer})\n input_blocks = {\n layer_id: [key for key in checkpoint if f\"input_blocks.{layer_id}\" in key]\n for layer_id in range(num_input_blocks)\n }\n\n # Retrieves the keys for the middle blocks only\n num_middle_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in checkpoint if \"middle_block\" in layer})\n middle_blocks = {\n layer_id: [key for key in checkpoint if f\"middle_block.{layer_id}\" in key]\n for layer_id in range(num_middle_blocks)\n }\n\n # Retrieves the keys for the output blocks only\n num_output_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in checkpoint if \"output_blocks\" in layer})\n output_blocks = {\n layer_id: [key for key in checkpoint if f\"output_blocks.{layer_id}\" in key]\n for layer_id in range(num_output_blocks)\n }\n\n for i in range(1, num_input_blocks):\n block_id = (i - 1) // (config[\"num_res_blocks\"] + 1)\n layer_in_block_id = (i - 1) % (config[\"num_res_blocks\"] + 1)\n\n resnets = [key for key in input_blocks[i] if f\"input_blocks.{i}.0\" in key]\n attentions = [key for key in input_blocks[i] if f\"input_blocks.{i}.1\" in key]\n\n if f\"input_blocks.{i}.0.op.weight\" in checkpoint:\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.weight\"] = checkpoint[\n f\"input_blocks.{i}.0.op.weight\"\n ]\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.bias\"] = checkpoint[\n f\"input_blocks.{i}.0.op.bias\"\n ]\n continue\n\n paths = renew_resnet_paths(resnets)\n meta_path = {\"old\": f\"input_blocks.{i}.0\", \"new\": f\"down_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n resnet_op = {\"old\": \"resnets.2.op\", \"new\": \"downsamplers.0.op\"}\n assign_to_checkpoint(\n paths, new_checkpoint, checkpoint, additional_replacements=[meta_path, resnet_op], config=config\n )\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n meta_path = {\n \"old\": f\"input_blocks.{i}.1\",\n \"new\": f\"down_blocks.{block_id}.attentions.{layer_in_block_id}\",\n }\n to_split = {\n f\"input_blocks.{i}.1.qkv.bias\": {\n \"key\": f\"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias\",\n \"query\": f\"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias\",\n \"value\": f\"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias\",\n },\n f\"input_blocks.{i}.1.qkv.weight\": {\n \"key\": f\"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight\",\n \"query\": f\"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight\",\n \"value\": f\"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight\",\n },\n }\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n checkpoint,\n additional_replacements=[meta_path],\n attention_paths_to_split=to_split,\n config=config,\n )\n\n resnet_0 = middle_blocks[0]\n attentions = middle_blocks[1]\n resnet_1 = middle_blocks[2]\n\n resnet_0_paths = renew_resnet_paths(resnet_0)\n assign_to_checkpoint(resnet_0_paths, new_checkpoint, checkpoint, config=config)\n\n resnet_1_paths = renew_resnet_paths(resnet_1)\n assign_to_checkpoint(resnet_1_paths, new_checkpoint, checkpoint, config=config)\n\n attentions_paths = renew_attention_paths(attentions)\n to_split = {\n \"middle_block.1.qkv.bias\": {\n \"key\": \"mid_block.attentions.0.key.bias\",\n \"query\": \"mid_block.attentions.0.query.bias\",\n \"value\": \"mid_block.attentions.0.value.bias\",\n },\n \"middle_block.1.qkv.weight\": {\n \"key\": \"mid_block.attentions.0.key.weight\",\n \"query\": \"mid_block.attentions.0.query.weight\",\n \"value\": \"mid_block.attentions.0.value.weight\",\n },\n }\n assign_to_checkpoint(\n attentions_paths, new_checkpoint, checkpoint, attention_paths_to_split=to_split, config=config\n )\n\n for i in range(num_output_blocks):\n block_id = i // (config[\"num_res_blocks\"] + 1)\n layer_in_block_id = i % (config[\"num_res_blocks\"] + 1)\n output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]\n output_block_list = {}\n\n for layer in output_block_layers:\n layer_id, layer_name = layer.split(\".\")[0], shave_segments(layer, 1)\n if layer_id in output_block_list:\n output_block_list[layer_id].append(layer_name)\n else:\n output_block_list[layer_id] = [layer_name]\n\n if len(output_block_list) > 1:\n resnets = [key for key in output_blocks[i] if f\"output_blocks.{i}.0\" in key]\n attentions = [key for key in output_blocks[i] if f\"output_blocks.{i}.1\" in key]\n\n resnet_0_paths = renew_resnet_paths(resnets)\n paths = renew_resnet_paths(resnets)\n\n meta_path = {\"old\": f\"output_blocks.{i}.0\", \"new\": f\"up_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[meta_path], config=config)\n\n if [\"conv.weight\", \"conv.bias\"] in output_block_list.values():\n index = list(output_block_list.values()).index([\"conv.weight\", \"conv.bias\"])\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.weight\"] = checkpoint[\n f\"output_blocks.{i}.{index}.conv.weight\"\n ]\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.bias\"] = checkpoint[\n f\"output_blocks.{i}.{index}.conv.bias\"\n ]\n\n # Clear attentions as they have been attributed above.\n if len(attentions) == 2:\n attentions = []\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n meta_path = {\n \"old\": f\"output_blocks.{i}.1\",\n \"new\": f\"up_blocks.{block_id}.attentions.{layer_in_block_id}\",\n }\n to_split = {\n f\"output_blocks.{i}.1.qkv.bias\": {\n \"key\": f\"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias\",\n \"query\": f\"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias\",\n \"value\": f\"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias\",\n },\n f\"output_blocks.{i}.1.qkv.weight\": {\n \"key\": f\"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight\",\n \"query\": f\"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight\",\n \"value\": f\"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight\",\n },\n }\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n checkpoint,\n additional_replacements=[meta_path],\n attention_paths_to_split=to_split if any(\"qkv\" in key for key in attentions) else None,\n config=config,\n )\n else:\n resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)\n for path in resnet_0_paths:\n old_path = \".\".join([\"output_blocks\", str(i), path[\"old\"]])\n new_path = \".\".join([\"up_blocks\", str(block_id), \"resnets\", str(layer_in_block_id), path[\"new\"]])\n\n new_checkpoint[new_path] = checkpoint[old_path]\n\n return new_checkpoint\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--checkpoint_path\", default=None, type=str, required=True, help=\"Path to the checkpoint to convert.\"\n )\n\n parser.add_argument(\n \"--config_file\",\n default=None,\n type=str,\n required=True,\n help=\"The config json file corresponding to the architecture.\",\n )\n\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n\n args = parser.parse_args()\n\n checkpoint = torch.load(args.checkpoint_path)\n\n with open(args.config_file) as f:\n config = json.loads(f.read())\n\n converted_checkpoint = convert_ldm_checkpoint(checkpoint, config)\n\n if \"ldm\" in config:\n del config[\"ldm\"]\n\n model = UNet2DModel(**config)\n model.load_state_dict(converted_checkpoint)\n\n try:\n scheduler = DDPMScheduler.from_config(\"/\".join(args.checkpoint_path.split(\"/\")[:-1]))\n vqvae = VQModel.from_pretrained(\"/\".join(args.checkpoint_path.split(\"/\")[:-1]))\n\n pipe = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)\n pipe.save_pretrained(args.dump_path)\n except: # noqa: E722\n model.save_pretrained(args.dump_path)\n" }, { "path": "diffusers/scripts/convert_lora_safetensor_to_diffusers.py", "content": "# coding=utf-8\n# Copyright 2024, Haofan Wang, Qixun Wang, All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Conversion script for the LoRA's safetensors checkpoints.\"\"\"\n\nimport argparse\n\nimport torch\nfrom safetensors.torch import load_file\n\nfrom diffusers import StableDiffusionPipeline\n\n\ndef convert(base_model_path, checkpoint_path, LORA_PREFIX_UNET, LORA_PREFIX_TEXT_ENCODER, alpha):\n # load base model\n pipeline = StableDiffusionPipeline.from_pretrained(base_model_path, torch_dtype=torch.float32)\n\n # load LoRA weight from .safetensors\n state_dict = load_file(checkpoint_path)\n\n visited = []\n\n # directly update weight in diffusers model\n for key in state_dict:\n # it is suggested to print out the key, it usually will be something like below\n # \"lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight\"\n\n # as we have set the alpha beforehand, so just skip\n if \".alpha\" in key or key in visited:\n continue\n\n if \"text\" in key:\n layer_infos = key.split(\".\")[0].split(LORA_PREFIX_TEXT_ENCODER + \"_\")[-1].split(\"_\")\n curr_layer = pipeline.text_encoder\n else:\n layer_infos = key.split(\".\")[0].split(LORA_PREFIX_UNET + \"_\")[-1].split(\"_\")\n curr_layer = pipeline.unet\n\n # find the target layer\n temp_name = layer_infos.pop(0)\n while len(layer_infos) > -1:\n try:\n curr_layer = curr_layer.__getattr__(temp_name)\n if len(layer_infos) > 0:\n temp_name = layer_infos.pop(0)\n elif len(layer_infos) == 0:\n break\n except Exception:\n if len(temp_name) > 0:\n temp_name += \"_\" + layer_infos.pop(0)\n else:\n temp_name = layer_infos.pop(0)\n\n pair_keys = []\n if \"lora_down\" in key:\n pair_keys.append(key.replace(\"lora_down\", \"lora_up\"))\n pair_keys.append(key)\n else:\n pair_keys.append(key)\n pair_keys.append(key.replace(\"lora_up\", \"lora_down\"))\n\n # update weight\n if len(state_dict[pair_keys[0]].shape) == 4:\n weight_up = state_dict[pair_keys[0]].squeeze(3).squeeze(2).to(torch.float32)\n weight_down = state_dict[pair_keys[1]].squeeze(3).squeeze(2).to(torch.float32)\n curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3)\n else:\n weight_up = state_dict[pair_keys[0]].to(torch.float32)\n weight_down = state_dict[pair_keys[1]].to(torch.float32)\n curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down)\n\n # update visited list\n for item in pair_keys:\n visited.append(item)\n\n return pipeline\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--base_model_path\", default=None, type=str, required=True, help=\"Path to the base model in diffusers format.\"\n )\n parser.add_argument(\n \"--checkpoint_path\", default=None, type=str, required=True, help=\"Path to the checkpoint to convert.\"\n )\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n parser.add_argument(\n \"--lora_prefix_unet\", default=\"lora_unet\", type=str, help=\"The prefix of UNet weight in safetensors\"\n )\n parser.add_argument(\n \"--lora_prefix_text_encoder\",\n default=\"lora_te\",\n type=str,\n help=\"The prefix of text encoder weight in safetensors\",\n )\n parser.add_argument(\"--alpha\", default=0.75, type=float, help=\"The merging ratio in W = W0 + alpha * deltaW\")\n parser.add_argument(\n \"--to_safetensors\", action=\"store_true\", help=\"Whether to store pipeline in safetensors format or not.\"\n )\n parser.add_argument(\"--device\", type=str, help=\"Device to use (e.g. cpu, cuda:0, cuda:1, etc.)\")\n\n args = parser.parse_args()\n\n base_model_path = args.base_model_path\n checkpoint_path = args.checkpoint_path\n dump_path = args.dump_path\n lora_prefix_unet = args.lora_prefix_unet\n lora_prefix_text_encoder = args.lora_prefix_text_encoder\n alpha = args.alpha\n\n pipe = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)\n\n pipe = pipe.to(args.device)\n pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)\n" }, { "path": "diffusers/scripts/convert_models_diffuser_to_diffusers.py", "content": "import json\nimport os\n\nimport torch\n\nfrom diffusers import UNet1DModel\n\n\nos.makedirs(\"hub/hopper-medium-v2/unet/hor32\", exist_ok=True)\nos.makedirs(\"hub/hopper-medium-v2/unet/hor128\", exist_ok=True)\n\nos.makedirs(\"hub/hopper-medium-v2/value_function\", exist_ok=True)\n\n\ndef unet(hor):\n if hor == 128:\n down_block_types = (\"DownResnetBlock1D\", \"DownResnetBlock1D\", \"DownResnetBlock1D\")\n block_out_channels = (32, 128, 256)\n up_block_types = (\"UpResnetBlock1D\", \"UpResnetBlock1D\")\n\n elif hor == 32:\n down_block_types = (\"DownResnetBlock1D\", \"DownResnetBlock1D\", \"DownResnetBlock1D\", \"DownResnetBlock1D\")\n block_out_channels = (32, 64, 128, 256)\n up_block_types = (\"UpResnetBlock1D\", \"UpResnetBlock1D\", \"UpResnetBlock1D\")\n model = torch.load(f\"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch\")\n state_dict = model.state_dict()\n config = {\n \"down_block_types\": down_block_types,\n \"block_out_channels\": block_out_channels,\n \"up_block_types\": up_block_types,\n \"layers_per_block\": 1,\n \"use_timestep_embedding\": True,\n \"out_block_type\": \"OutConv1DBlock\",\n \"norm_num_groups\": 8,\n \"downsample_each_block\": False,\n \"in_channels\": 14,\n \"out_channels\": 14,\n \"extra_in_channels\": 0,\n \"time_embedding_type\": \"positional\",\n \"flip_sin_to_cos\": False,\n \"freq_shift\": 1,\n \"sample_size\": 65536,\n \"mid_block_type\": \"MidResTemporalBlock1D\",\n \"act_fn\": \"mish\",\n }\n hf_value_function = UNet1DModel(**config)\n print(f\"length of state dict: {len(state_dict.keys())}\")\n print(f\"length of value function dict: {len(hf_value_function.state_dict().keys())}\")\n mapping = dict(zip(model.state_dict().keys(), hf_value_function.state_dict().keys()))\n for k, v in mapping.items():\n state_dict[v] = state_dict.pop(k)\n hf_value_function.load_state_dict(state_dict)\n\n torch.save(hf_value_function.state_dict(), f\"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin\")\n with open(f\"hub/hopper-medium-v2/unet/hor{hor}/config.json\", \"w\") as f:\n json.dump(config, f)\n\n\ndef value_function():\n config = {\n \"in_channels\": 14,\n \"down_block_types\": (\"DownResnetBlock1D\", \"DownResnetBlock1D\", \"DownResnetBlock1D\", \"DownResnetBlock1D\"),\n \"up_block_types\": (),\n \"out_block_type\": \"ValueFunction\",\n \"mid_block_type\": \"ValueFunctionMidBlock1D\",\n \"block_out_channels\": (32, 64, 128, 256),\n \"layers_per_block\": 1,\n \"downsample_each_block\": True,\n \"sample_size\": 65536,\n \"out_channels\": 14,\n \"extra_in_channels\": 0,\n \"time_embedding_type\": \"positional\",\n \"use_timestep_embedding\": True,\n \"flip_sin_to_cos\": False,\n \"freq_shift\": 1,\n \"norm_num_groups\": 8,\n \"act_fn\": \"mish\",\n }\n\n model = torch.load(\"/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch\")\n state_dict = model\n hf_value_function = UNet1DModel(**config)\n print(f\"length of state dict: {len(state_dict.keys())}\")\n print(f\"length of value function dict: {len(hf_value_function.state_dict().keys())}\")\n\n mapping = dict(zip(state_dict.keys(), hf_value_function.state_dict().keys()))\n for k, v in mapping.items():\n state_dict[v] = state_dict.pop(k)\n\n hf_value_function.load_state_dict(state_dict)\n\n torch.save(hf_value_function.state_dict(), \"hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin\")\n with open(\"hub/hopper-medium-v2/value_function/config.json\", \"w\") as f:\n json.dump(config, f)\n\n\nif __name__ == \"__main__\":\n unet(32)\n # unet(128)\n value_function()\n" }, { "path": "diffusers/scripts/convert_ms_text_to_video_to_diffusers.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Conversion script for the LDM checkpoints.\"\"\"\n\nimport argparse\n\nimport torch\n\nfrom diffusers import UNet3DConditionModel\n\n\ndef assign_to_checkpoint(\n paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None\n):\n \"\"\"\n This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits\n attention layers, and takes into account additional replacements that may arise.\n\n Assigns the weights to the new checkpoint.\n \"\"\"\n assert isinstance(paths, list), \"Paths should be a list of dicts containing 'old' and 'new' keys.\"\n\n # Splits the attention layers into three variables.\n if attention_paths_to_split is not None:\n for path, path_map in attention_paths_to_split.items():\n old_tensor = old_checkpoint[path]\n channels = old_tensor.shape[0] // 3\n\n target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)\n\n num_heads = old_tensor.shape[0] // config[\"num_head_channels\"] // 3\n\n old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])\n query, key, value = old_tensor.split(channels // num_heads, dim=1)\n\n checkpoint[path_map[\"query\"]] = query.reshape(target_shape)\n checkpoint[path_map[\"key\"]] = key.reshape(target_shape)\n checkpoint[path_map[\"value\"]] = value.reshape(target_shape)\n\n for path in paths:\n new_path = path[\"new\"]\n\n # These have already been assigned\n if attention_paths_to_split is not None and new_path in attention_paths_to_split:\n continue\n\n if additional_replacements is not None:\n for replacement in additional_replacements:\n new_path = new_path.replace(replacement[\"old\"], replacement[\"new\"])\n\n # proj_attn.weight has to be converted from conv 1D to linear\n weight = old_checkpoint[path[\"old\"]]\n names = [\"proj_attn.weight\"]\n names_2 = [\"proj_out.weight\", \"proj_in.weight\"]\n if any(k in new_path for k in names):\n checkpoint[new_path] = weight[:, :, 0]\n elif any(k in new_path for k in names_2) and len(weight.shape) > 2 and \".attentions.\" not in new_path:\n checkpoint[new_path] = weight[:, :, 0]\n else:\n checkpoint[new_path] = weight\n\n\ndef renew_attention_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside attentions to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n # new_item = new_item.replace('norm.weight', 'group_norm.weight')\n # new_item = new_item.replace('norm.bias', 'group_norm.bias')\n\n # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')\n # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')\n\n # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef shave_segments(path, n_shave_prefix_segments=1):\n \"\"\"\n Removes segments. Positive values shave the first segments, negative shave the last segments.\n \"\"\"\n if n_shave_prefix_segments >= 0:\n return \".\".join(path.split(\".\")[n_shave_prefix_segments:])\n else:\n return \".\".join(path.split(\".\")[:n_shave_prefix_segments])\n\n\ndef renew_temp_conv_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside resnets to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n mapping.append({\"old\": old_item, \"new\": old_item})\n\n return mapping\n\n\ndef renew_resnet_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside resnets to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item.replace(\"in_layers.0\", \"norm1\")\n new_item = new_item.replace(\"in_layers.2\", \"conv1\")\n\n new_item = new_item.replace(\"out_layers.0\", \"norm2\")\n new_item = new_item.replace(\"out_layers.3\", \"conv2\")\n\n new_item = new_item.replace(\"emb_layers.1\", \"time_emb_proj\")\n new_item = new_item.replace(\"skip_connection\", \"conv_shortcut\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n if \"temopral_conv\" not in old_item:\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False):\n \"\"\"\n Takes a state dict and a config, and returns a converted checkpoint.\n \"\"\"\n\n # extract state_dict for UNet\n unet_state_dict = {}\n keys = list(checkpoint.keys())\n\n unet_key = \"model.diffusion_model.\"\n\n # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA\n if sum(k.startswith(\"model_ema\") for k in keys) > 100 and extract_ema:\n print(f\"Checkpoint {path} has both EMA and non-EMA weights.\")\n print(\n \"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA\"\n \" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.\"\n )\n for key in keys:\n if key.startswith(\"model.diffusion_model\"):\n flat_ema_key = \"model_ema.\" + \"\".join(key.split(\".\")[1:])\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint.pop(flat_ema_key)\n else:\n if sum(k.startswith(\"model_ema\") for k in keys) > 100:\n print(\n \"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA\"\n \" weights (usually better for inference), please make sure to add the `--extract_ema` flag.\"\n )\n\n for key in keys:\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint.pop(key)\n\n new_checkpoint = {}\n\n new_checkpoint[\"time_embedding.linear_1.weight\"] = unet_state_dict[\"time_embed.0.weight\"]\n new_checkpoint[\"time_embedding.linear_1.bias\"] = unet_state_dict[\"time_embed.0.bias\"]\n new_checkpoint[\"time_embedding.linear_2.weight\"] = unet_state_dict[\"time_embed.2.weight\"]\n new_checkpoint[\"time_embedding.linear_2.bias\"] = unet_state_dict[\"time_embed.2.bias\"]\n\n if config[\"class_embed_type\"] is None:\n # No parameters to port\n ...\n elif config[\"class_embed_type\"] == \"timestep\" or config[\"class_embed_type\"] == \"projection\":\n new_checkpoint[\"class_embedding.linear_1.weight\"] = unet_state_dict[\"label_emb.0.0.weight\"]\n new_checkpoint[\"class_embedding.linear_1.bias\"] = unet_state_dict[\"label_emb.0.0.bias\"]\n new_checkpoint[\"class_embedding.linear_2.weight\"] = unet_state_dict[\"label_emb.0.2.weight\"]\n new_checkpoint[\"class_embedding.linear_2.bias\"] = unet_state_dict[\"label_emb.0.2.bias\"]\n else:\n raise NotImplementedError(f\"Not implemented `class_embed_type`: {config['class_embed_type']}\")\n\n new_checkpoint[\"conv_in.weight\"] = unet_state_dict[\"input_blocks.0.0.weight\"]\n new_checkpoint[\"conv_in.bias\"] = unet_state_dict[\"input_blocks.0.0.bias\"]\n\n first_temp_attention = [v for v in unet_state_dict if v.startswith(\"input_blocks.0.1\")]\n paths = renew_attention_paths(first_temp_attention)\n meta_path = {\"old\": \"input_blocks.0.1\", \"new\": \"transformer_in\"}\n assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config)\n\n new_checkpoint[\"conv_norm_out.weight\"] = unet_state_dict[\"out.0.weight\"]\n new_checkpoint[\"conv_norm_out.bias\"] = unet_state_dict[\"out.0.bias\"]\n new_checkpoint[\"conv_out.weight\"] = unet_state_dict[\"out.2.weight\"]\n new_checkpoint[\"conv_out.bias\"] = unet_state_dict[\"out.2.bias\"]\n\n # Retrieves the keys for the input blocks only\n num_input_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"input_blocks\" in layer})\n input_blocks = {\n layer_id: [key for key in unet_state_dict if f\"input_blocks.{layer_id}\" in key]\n for layer_id in range(num_input_blocks)\n }\n\n # Retrieves the keys for the middle blocks only\n num_middle_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"middle_block\" in layer})\n middle_blocks = {\n layer_id: [key for key in unet_state_dict if f\"middle_block.{layer_id}\" in key]\n for layer_id in range(num_middle_blocks)\n }\n\n # Retrieves the keys for the output blocks only\n num_output_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"output_blocks\" in layer})\n output_blocks = {\n layer_id: [key for key in unet_state_dict if f\"output_blocks.{layer_id}\" in key]\n for layer_id in range(num_output_blocks)\n }\n\n for i in range(1, num_input_blocks):\n block_id = (i - 1) // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = (i - 1) % (config[\"layers_per_block\"] + 1)\n\n resnets = [\n key for key in input_blocks[i] if f\"input_blocks.{i}.0\" in key and f\"input_blocks.{i}.0.op\" not in key\n ]\n attentions = [key for key in input_blocks[i] if f\"input_blocks.{i}.1\" in key]\n temp_attentions = [key for key in input_blocks[i] if f\"input_blocks.{i}.2\" in key]\n\n if f\"input_blocks.{i}.op.weight\" in unet_state_dict:\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.weight\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.op.weight\"\n )\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.bias\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.op.bias\"\n )\n\n paths = renew_resnet_paths(resnets)\n meta_path = {\"old\": f\"input_blocks.{i}.0\", \"new\": f\"down_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n temporal_convs = [key for key in resnets if \"temopral_conv\" in key]\n paths = renew_temp_conv_paths(temporal_convs)\n meta_path = {\n \"old\": f\"input_blocks.{i}.0.temopral_conv\",\n \"new\": f\"down_blocks.{block_id}.temp_convs.{layer_in_block_id}\",\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n meta_path = {\"old\": f\"input_blocks.{i}.1\", \"new\": f\"down_blocks.{block_id}.attentions.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n if len(temp_attentions):\n paths = renew_attention_paths(temp_attentions)\n meta_path = {\n \"old\": f\"input_blocks.{i}.2\",\n \"new\": f\"down_blocks.{block_id}.temp_attentions.{layer_in_block_id}\",\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n resnet_0 = middle_blocks[0]\n temporal_convs_0 = [key for key in resnet_0 if \"temopral_conv\" in key]\n attentions = middle_blocks[1]\n temp_attentions = middle_blocks[2]\n resnet_1 = middle_blocks[3]\n temporal_convs_1 = [key for key in resnet_1 if \"temopral_conv\" in key]\n\n resnet_0_paths = renew_resnet_paths(resnet_0)\n meta_path = {\"old\": \"middle_block.0\", \"new\": \"mid_block.resnets.0\"}\n assign_to_checkpoint(\n resnet_0_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path]\n )\n\n temp_conv_0_paths = renew_temp_conv_paths(temporal_convs_0)\n meta_path = {\"old\": \"middle_block.0.temopral_conv\", \"new\": \"mid_block.temp_convs.0\"}\n assign_to_checkpoint(\n temp_conv_0_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path]\n )\n\n resnet_1_paths = renew_resnet_paths(resnet_1)\n meta_path = {\"old\": \"middle_block.3\", \"new\": \"mid_block.resnets.1\"}\n assign_to_checkpoint(\n resnet_1_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path]\n )\n\n temp_conv_1_paths = renew_temp_conv_paths(temporal_convs_1)\n meta_path = {\"old\": \"middle_block.3.temopral_conv\", \"new\": \"mid_block.temp_convs.1\"}\n assign_to_checkpoint(\n temp_conv_1_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path]\n )\n\n attentions_paths = renew_attention_paths(attentions)\n meta_path = {\"old\": \"middle_block.1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(\n attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n temp_attentions_paths = renew_attention_paths(temp_attentions)\n meta_path = {\"old\": \"middle_block.2\", \"new\": \"mid_block.temp_attentions.0\"}\n assign_to_checkpoint(\n temp_attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n for i in range(num_output_blocks):\n block_id = i // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = i % (config[\"layers_per_block\"] + 1)\n output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]\n output_block_list = {}\n\n for layer in output_block_layers:\n layer_id, layer_name = layer.split(\".\")[0], shave_segments(layer, 1)\n if layer_id in output_block_list:\n output_block_list[layer_id].append(layer_name)\n else:\n output_block_list[layer_id] = [layer_name]\n\n if len(output_block_list) > 1:\n resnets = [key for key in output_blocks[i] if f\"output_blocks.{i}.0\" in key]\n attentions = [key for key in output_blocks[i] if f\"output_blocks.{i}.1\" in key]\n temp_attentions = [key for key in output_blocks[i] if f\"output_blocks.{i}.2\" in key]\n\n resnet_0_paths = renew_resnet_paths(resnets)\n paths = renew_resnet_paths(resnets)\n\n meta_path = {\"old\": f\"output_blocks.{i}.0\", \"new\": f\"up_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n temporal_convs = [key for key in resnets if \"temopral_conv\" in key]\n paths = renew_temp_conv_paths(temporal_convs)\n meta_path = {\n \"old\": f\"output_blocks.{i}.0.temopral_conv\",\n \"new\": f\"up_blocks.{block_id}.temp_convs.{layer_in_block_id}\",\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n output_block_list = {k: sorted(v) for k, v in output_block_list.items()}\n if [\"conv.bias\", \"conv.weight\"] in output_block_list.values():\n index = list(output_block_list.values()).index([\"conv.bias\", \"conv.weight\"])\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.weight\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.weight\"\n ]\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.bias\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.bias\"\n ]\n\n # Clear attentions as they have been attributed above.\n if len(attentions) == 2:\n attentions = []\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n meta_path = {\n \"old\": f\"output_blocks.{i}.1\",\n \"new\": f\"up_blocks.{block_id}.attentions.{layer_in_block_id}\",\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n if len(temp_attentions):\n paths = renew_attention_paths(temp_attentions)\n meta_path = {\n \"old\": f\"output_blocks.{i}.2\",\n \"new\": f\"up_blocks.{block_id}.temp_attentions.{layer_in_block_id}\",\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n else:\n resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)\n for path in resnet_0_paths:\n old_path = \".\".join([\"output_blocks\", str(i), path[\"old\"]])\n new_path = \".\".join([\"up_blocks\", str(block_id), \"resnets\", str(layer_in_block_id), path[\"new\"]])\n new_checkpoint[new_path] = unet_state_dict[old_path]\n\n temopral_conv_paths = [l for l in output_block_layers if \"temopral_conv\" in l]\n for path in temopral_conv_paths:\n pruned_path = path.split(\"temopral_conv.\")[-1]\n old_path = \".\".join([\"output_blocks\", str(i), str(block_id), \"temopral_conv\", pruned_path])\n new_path = \".\".join([\"up_blocks\", str(block_id), \"temp_convs\", str(layer_in_block_id), pruned_path])\n new_checkpoint[new_path] = unet_state_dict[old_path]\n\n return new_checkpoint\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--checkpoint_path\", default=None, type=str, required=True, help=\"Path to the checkpoint to convert.\"\n )\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n args = parser.parse_args()\n\n unet_checkpoint = torch.load(args.checkpoint_path, map_location=\"cpu\")\n unet = UNet3DConditionModel()\n\n converted_ckpt = convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)\n\n diff_0 = set(unet.state_dict().keys()) - set(converted_ckpt.keys())\n diff_1 = set(converted_ckpt.keys()) - set(unet.state_dict().keys())\n\n assert len(diff_0) == len(diff_1) == 0, \"Converted weights don't match\"\n\n # load state_dict\n unet.load_state_dict(converted_ckpt)\n\n unet.save_pretrained(args.dump_path)\n\n # -- finish converting the unet --\n" }, { "path": "diffusers/scripts/convert_music_spectrogram_to_diffusers.py", "content": "#!/usr/bin/env python3\nimport argparse\nimport os\n\nimport jax as jnp\nimport numpy as onp\nimport torch\nimport torch.nn as nn\nfrom music_spectrogram_diffusion import inference\nfrom t5x import checkpoints\n\nfrom diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline\nfrom diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, T5FilmDecoder\n\n\nMODEL = \"base_with_context\"\n\n\ndef load_notes_encoder(weights, model):\n model.token_embedder.weight = nn.Parameter(torch.Tensor(weights[\"token_embedder\"][\"embedding\"]))\n model.position_encoding.weight = nn.Parameter(torch.Tensor(weights[\"Embed_0\"][\"embedding\"]), requires_grad=False)\n for lyr_num, lyr in enumerate(model.encoders):\n ly_weight = weights[f\"layers_{lyr_num}\"]\n lyr.layer[0].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight[\"pre_attention_layer_norm\"][\"scale\"]))\n\n attention_weights = ly_weight[\"attention\"]\n lyr.layer[0].SelfAttention.q.weight = nn.Parameter(torch.Tensor(attention_weights[\"query\"][\"kernel\"].T))\n lyr.layer[0].SelfAttention.k.weight = nn.Parameter(torch.Tensor(attention_weights[\"key\"][\"kernel\"].T))\n lyr.layer[0].SelfAttention.v.weight = nn.Parameter(torch.Tensor(attention_weights[\"value\"][\"kernel\"].T))\n lyr.layer[0].SelfAttention.o.weight = nn.Parameter(torch.Tensor(attention_weights[\"out\"][\"kernel\"].T))\n\n lyr.layer[1].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight[\"pre_mlp_layer_norm\"][\"scale\"]))\n\n lyr.layer[1].DenseReluDense.wi_0.weight = nn.Parameter(torch.Tensor(ly_weight[\"mlp\"][\"wi_0\"][\"kernel\"].T))\n lyr.layer[1].DenseReluDense.wi_1.weight = nn.Parameter(torch.Tensor(ly_weight[\"mlp\"][\"wi_1\"][\"kernel\"].T))\n lyr.layer[1].DenseReluDense.wo.weight = nn.Parameter(torch.Tensor(ly_weight[\"mlp\"][\"wo\"][\"kernel\"].T))\n\n model.layer_norm.weight = nn.Parameter(torch.Tensor(weights[\"encoder_norm\"][\"scale\"]))\n return model\n\n\ndef load_continuous_encoder(weights, model):\n model.input_proj.weight = nn.Parameter(torch.Tensor(weights[\"input_proj\"][\"kernel\"].T))\n\n model.position_encoding.weight = nn.Parameter(torch.Tensor(weights[\"Embed_0\"][\"embedding\"]), requires_grad=False)\n\n for lyr_num, lyr in enumerate(model.encoders):\n ly_weight = weights[f\"layers_{lyr_num}\"]\n attention_weights = ly_weight[\"attention\"]\n\n lyr.layer[0].SelfAttention.q.weight = nn.Parameter(torch.Tensor(attention_weights[\"query\"][\"kernel\"].T))\n lyr.layer[0].SelfAttention.k.weight = nn.Parameter(torch.Tensor(attention_weights[\"key\"][\"kernel\"].T))\n lyr.layer[0].SelfAttention.v.weight = nn.Parameter(torch.Tensor(attention_weights[\"value\"][\"kernel\"].T))\n lyr.layer[0].SelfAttention.o.weight = nn.Parameter(torch.Tensor(attention_weights[\"out\"][\"kernel\"].T))\n lyr.layer[0].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight[\"pre_attention_layer_norm\"][\"scale\"]))\n\n lyr.layer[1].DenseReluDense.wi_0.weight = nn.Parameter(torch.Tensor(ly_weight[\"mlp\"][\"wi_0\"][\"kernel\"].T))\n lyr.layer[1].DenseReluDense.wi_1.weight = nn.Parameter(torch.Tensor(ly_weight[\"mlp\"][\"wi_1\"][\"kernel\"].T))\n lyr.layer[1].DenseReluDense.wo.weight = nn.Parameter(torch.Tensor(ly_weight[\"mlp\"][\"wo\"][\"kernel\"].T))\n lyr.layer[1].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight[\"pre_mlp_layer_norm\"][\"scale\"]))\n\n model.layer_norm.weight = nn.Parameter(torch.Tensor(weights[\"encoder_norm\"][\"scale\"]))\n\n return model\n\n\ndef load_decoder(weights, model):\n model.conditioning_emb[0].weight = nn.Parameter(torch.Tensor(weights[\"time_emb_dense0\"][\"kernel\"].T))\n model.conditioning_emb[2].weight = nn.Parameter(torch.Tensor(weights[\"time_emb_dense1\"][\"kernel\"].T))\n\n model.position_encoding.weight = nn.Parameter(torch.Tensor(weights[\"Embed_0\"][\"embedding\"]), requires_grad=False)\n\n model.continuous_inputs_projection.weight = nn.Parameter(\n torch.Tensor(weights[\"continuous_inputs_projection\"][\"kernel\"].T)\n )\n\n for lyr_num, lyr in enumerate(model.decoders):\n ly_weight = weights[f\"layers_{lyr_num}\"]\n lyr.layer[0].layer_norm.weight = nn.Parameter(\n torch.Tensor(ly_weight[\"pre_self_attention_layer_norm\"][\"scale\"])\n )\n\n lyr.layer[0].FiLMLayer.scale_bias.weight = nn.Parameter(\n torch.Tensor(ly_weight[\"FiLMLayer_0\"][\"DenseGeneral_0\"][\"kernel\"].T)\n )\n\n attention_weights = ly_weight[\"self_attention\"]\n lyr.layer[0].attention.to_q.weight = nn.Parameter(torch.Tensor(attention_weights[\"query\"][\"kernel\"].T))\n lyr.layer[0].attention.to_k.weight = nn.Parameter(torch.Tensor(attention_weights[\"key\"][\"kernel\"].T))\n lyr.layer[0].attention.to_v.weight = nn.Parameter(torch.Tensor(attention_weights[\"value\"][\"kernel\"].T))\n lyr.layer[0].attention.to_out[0].weight = nn.Parameter(torch.Tensor(attention_weights[\"out\"][\"kernel\"].T))\n\n attention_weights = ly_weight[\"MultiHeadDotProductAttention_0\"]\n lyr.layer[1].attention.to_q.weight = nn.Parameter(torch.Tensor(attention_weights[\"query\"][\"kernel\"].T))\n lyr.layer[1].attention.to_k.weight = nn.Parameter(torch.Tensor(attention_weights[\"key\"][\"kernel\"].T))\n lyr.layer[1].attention.to_v.weight = nn.Parameter(torch.Tensor(attention_weights[\"value\"][\"kernel\"].T))\n lyr.layer[1].attention.to_out[0].weight = nn.Parameter(torch.Tensor(attention_weights[\"out\"][\"kernel\"].T))\n lyr.layer[1].layer_norm.weight = nn.Parameter(\n torch.Tensor(ly_weight[\"pre_cross_attention_layer_norm\"][\"scale\"])\n )\n\n lyr.layer[2].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight[\"pre_mlp_layer_norm\"][\"scale\"]))\n lyr.layer[2].film.scale_bias.weight = nn.Parameter(\n torch.Tensor(ly_weight[\"FiLMLayer_1\"][\"DenseGeneral_0\"][\"kernel\"].T)\n )\n lyr.layer[2].DenseReluDense.wi_0.weight = nn.Parameter(torch.Tensor(ly_weight[\"mlp\"][\"wi_0\"][\"kernel\"].T))\n lyr.layer[2].DenseReluDense.wi_1.weight = nn.Parameter(torch.Tensor(ly_weight[\"mlp\"][\"wi_1\"][\"kernel\"].T))\n lyr.layer[2].DenseReluDense.wo.weight = nn.Parameter(torch.Tensor(ly_weight[\"mlp\"][\"wo\"][\"kernel\"].T))\n\n model.decoder_norm.weight = nn.Parameter(torch.Tensor(weights[\"decoder_norm\"][\"scale\"]))\n\n model.spec_out.weight = nn.Parameter(torch.Tensor(weights[\"spec_out_dense\"][\"kernel\"].T))\n\n return model\n\n\ndef main(args):\n t5_checkpoint = checkpoints.load_t5x_checkpoint(args.checkpoint_path)\n t5_checkpoint = jnp.tree_util.tree_map(onp.array, t5_checkpoint)\n\n gin_overrides = [\n \"from __gin__ import dynamic_registration\",\n \"from music_spectrogram_diffusion.models.diffusion import diffusion_utils\",\n \"diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0\",\n \"diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()\",\n ]\n\n gin_file = os.path.join(args.checkpoint_path, \"..\", \"config.gin\")\n gin_config = inference.parse_training_gin_file(gin_file, gin_overrides)\n synth_model = inference.InferenceModel(args.checkpoint_path, gin_config)\n\n scheduler = DDPMScheduler(beta_schedule=\"squaredcos_cap_v2\", variance_type=\"fixed_large\")\n\n notes_encoder = SpectrogramNotesEncoder(\n max_length=synth_model.sequence_length[\"inputs\"],\n vocab_size=synth_model.model.module.config.vocab_size,\n d_model=synth_model.model.module.config.emb_dim,\n dropout_rate=synth_model.model.module.config.dropout_rate,\n num_layers=synth_model.model.module.config.num_encoder_layers,\n num_heads=synth_model.model.module.config.num_heads,\n d_kv=synth_model.model.module.config.head_dim,\n d_ff=synth_model.model.module.config.mlp_dim,\n feed_forward_proj=\"gated-gelu\",\n )\n\n continuous_encoder = SpectrogramContEncoder(\n input_dims=synth_model.audio_codec.n_dims,\n targets_context_length=synth_model.sequence_length[\"targets_context\"],\n d_model=synth_model.model.module.config.emb_dim,\n dropout_rate=synth_model.model.module.config.dropout_rate,\n num_layers=synth_model.model.module.config.num_encoder_layers,\n num_heads=synth_model.model.module.config.num_heads,\n d_kv=synth_model.model.module.config.head_dim,\n d_ff=synth_model.model.module.config.mlp_dim,\n feed_forward_proj=\"gated-gelu\",\n )\n\n decoder = T5FilmDecoder(\n input_dims=synth_model.audio_codec.n_dims,\n targets_length=synth_model.sequence_length[\"targets_context\"],\n max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time,\n d_model=synth_model.model.module.config.emb_dim,\n num_layers=synth_model.model.module.config.num_decoder_layers,\n num_heads=synth_model.model.module.config.num_heads,\n d_kv=synth_model.model.module.config.head_dim,\n d_ff=synth_model.model.module.config.mlp_dim,\n dropout_rate=synth_model.model.module.config.dropout_rate,\n )\n\n notes_encoder = load_notes_encoder(t5_checkpoint[\"target\"][\"token_encoder\"], notes_encoder)\n continuous_encoder = load_continuous_encoder(t5_checkpoint[\"target\"][\"continuous_encoder\"], continuous_encoder)\n decoder = load_decoder(t5_checkpoint[\"target\"][\"decoder\"], decoder)\n\n melgan = OnnxRuntimeModel.from_pretrained(\"kashif/soundstream_mel_decoder\")\n\n pipe = SpectrogramDiffusionPipeline(\n notes_encoder=notes_encoder,\n continuous_encoder=continuous_encoder,\n decoder=decoder,\n scheduler=scheduler,\n melgan=melgan,\n )\n if args.save:\n pipe.save_pretrained(args.output_path)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"--output_path\", default=None, type=str, required=True, help=\"Path to the converted model.\")\n parser.add_argument(\n \"--save\", default=True, type=bool, required=False, help=\"Whether to save the converted model or not.\"\n )\n parser.add_argument(\n \"--checkpoint_path\",\n default=f\"{MODEL}/checkpoint_500000\",\n type=str,\n required=False,\n help=\"Path to the original jax model checkpoint.\",\n )\n args = parser.parse_args()\n\n main(args)\n" }, { "path": "diffusers/scripts/convert_ncsnpp_original_checkpoint_to_diffusers.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Conversion script for the NCSNPP checkpoints.\"\"\"\n\nimport argparse\nimport json\n\nimport torch\n\nfrom diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNet2DModel\n\n\ndef convert_ncsnpp_checkpoint(checkpoint, config):\n \"\"\"\n Takes a state dict and the path to\n \"\"\"\n new_model_architecture = UNet2DModel(**config)\n new_model_architecture.time_proj.W.data = checkpoint[\"all_modules.0.W\"].data\n new_model_architecture.time_proj.weight.data = checkpoint[\"all_modules.0.W\"].data\n new_model_architecture.time_embedding.linear_1.weight.data = checkpoint[\"all_modules.1.weight\"].data\n new_model_architecture.time_embedding.linear_1.bias.data = checkpoint[\"all_modules.1.bias\"].data\n\n new_model_architecture.time_embedding.linear_2.weight.data = checkpoint[\"all_modules.2.weight\"].data\n new_model_architecture.time_embedding.linear_2.bias.data = checkpoint[\"all_modules.2.bias\"].data\n\n new_model_architecture.conv_in.weight.data = checkpoint[\"all_modules.3.weight\"].data\n new_model_architecture.conv_in.bias.data = checkpoint[\"all_modules.3.bias\"].data\n\n new_model_architecture.conv_norm_out.weight.data = checkpoint[list(checkpoint.keys())[-4]].data\n new_model_architecture.conv_norm_out.bias.data = checkpoint[list(checkpoint.keys())[-3]].data\n new_model_architecture.conv_out.weight.data = checkpoint[list(checkpoint.keys())[-2]].data\n new_model_architecture.conv_out.bias.data = checkpoint[list(checkpoint.keys())[-1]].data\n\n module_index = 4\n\n def set_attention_weights(new_layer, old_checkpoint, index):\n new_layer.query.weight.data = old_checkpoint[f\"all_modules.{index}.NIN_0.W\"].data.T\n new_layer.key.weight.data = old_checkpoint[f\"all_modules.{index}.NIN_1.W\"].data.T\n new_layer.value.weight.data = old_checkpoint[f\"all_modules.{index}.NIN_2.W\"].data.T\n\n new_layer.query.bias.data = old_checkpoint[f\"all_modules.{index}.NIN_0.b\"].data\n new_layer.key.bias.data = old_checkpoint[f\"all_modules.{index}.NIN_1.b\"].data\n new_layer.value.bias.data = old_checkpoint[f\"all_modules.{index}.NIN_2.b\"].data\n\n new_layer.proj_attn.weight.data = old_checkpoint[f\"all_modules.{index}.NIN_3.W\"].data.T\n new_layer.proj_attn.bias.data = old_checkpoint[f\"all_modules.{index}.NIN_3.b\"].data\n\n new_layer.group_norm.weight.data = old_checkpoint[f\"all_modules.{index}.GroupNorm_0.weight\"].data\n new_layer.group_norm.bias.data = old_checkpoint[f\"all_modules.{index}.GroupNorm_0.bias\"].data\n\n def set_resnet_weights(new_layer, old_checkpoint, index):\n new_layer.conv1.weight.data = old_checkpoint[f\"all_modules.{index}.Conv_0.weight\"].data\n new_layer.conv1.bias.data = old_checkpoint[f\"all_modules.{index}.Conv_0.bias\"].data\n new_layer.norm1.weight.data = old_checkpoint[f\"all_modules.{index}.GroupNorm_0.weight\"].data\n new_layer.norm1.bias.data = old_checkpoint[f\"all_modules.{index}.GroupNorm_0.bias\"].data\n\n new_layer.conv2.weight.data = old_checkpoint[f\"all_modules.{index}.Conv_1.weight\"].data\n new_layer.conv2.bias.data = old_checkpoint[f\"all_modules.{index}.Conv_1.bias\"].data\n new_layer.norm2.weight.data = old_checkpoint[f\"all_modules.{index}.GroupNorm_1.weight\"].data\n new_layer.norm2.bias.data = old_checkpoint[f\"all_modules.{index}.GroupNorm_1.bias\"].data\n\n new_layer.time_emb_proj.weight.data = old_checkpoint[f\"all_modules.{index}.Dense_0.weight\"].data\n new_layer.time_emb_proj.bias.data = old_checkpoint[f\"all_modules.{index}.Dense_0.bias\"].data\n\n if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:\n new_layer.conv_shortcut.weight.data = old_checkpoint[f\"all_modules.{index}.Conv_2.weight\"].data\n new_layer.conv_shortcut.bias.data = old_checkpoint[f\"all_modules.{index}.Conv_2.bias\"].data\n\n for i, block in enumerate(new_model_architecture.downsample_blocks):\n has_attentions = hasattr(block, \"attentions\")\n for j in range(len(block.resnets)):\n set_resnet_weights(block.resnets[j], checkpoint, module_index)\n module_index += 1\n if has_attentions:\n set_attention_weights(block.attentions[j], checkpoint, module_index)\n module_index += 1\n\n if hasattr(block, \"downsamplers\") and block.downsamplers is not None:\n set_resnet_weights(block.resnet_down, checkpoint, module_index)\n module_index += 1\n block.skip_conv.weight.data = checkpoint[f\"all_modules.{module_index}.Conv_0.weight\"].data\n block.skip_conv.bias.data = checkpoint[f\"all_modules.{module_index}.Conv_0.bias\"].data\n module_index += 1\n\n set_resnet_weights(new_model_architecture.mid_block.resnets[0], checkpoint, module_index)\n module_index += 1\n set_attention_weights(new_model_architecture.mid_block.attentions[0], checkpoint, module_index)\n module_index += 1\n set_resnet_weights(new_model_architecture.mid_block.resnets[1], checkpoint, module_index)\n module_index += 1\n\n for i, block in enumerate(new_model_architecture.up_blocks):\n has_attentions = hasattr(block, \"attentions\")\n for j in range(len(block.resnets)):\n set_resnet_weights(block.resnets[j], checkpoint, module_index)\n module_index += 1\n if has_attentions:\n set_attention_weights(\n block.attentions[0], checkpoint, module_index\n ) # why can there only be a single attention layer for up?\n module_index += 1\n\n if hasattr(block, \"resnet_up\") and block.resnet_up is not None:\n block.skip_norm.weight.data = checkpoint[f\"all_modules.{module_index}.weight\"].data\n block.skip_norm.bias.data = checkpoint[f\"all_modules.{module_index}.bias\"].data\n module_index += 1\n block.skip_conv.weight.data = checkpoint[f\"all_modules.{module_index}.weight\"].data\n block.skip_conv.bias.data = checkpoint[f\"all_modules.{module_index}.bias\"].data\n module_index += 1\n set_resnet_weights(block.resnet_up, checkpoint, module_index)\n module_index += 1\n\n new_model_architecture.conv_norm_out.weight.data = checkpoint[f\"all_modules.{module_index}.weight\"].data\n new_model_architecture.conv_norm_out.bias.data = checkpoint[f\"all_modules.{module_index}.bias\"].data\n module_index += 1\n new_model_architecture.conv_out.weight.data = checkpoint[f\"all_modules.{module_index}.weight\"].data\n new_model_architecture.conv_out.bias.data = checkpoint[f\"all_modules.{module_index}.bias\"].data\n\n return new_model_architecture.state_dict()\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--checkpoint_path\",\n default=\"/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin\",\n type=str,\n required=False,\n help=\"Path to the checkpoint to convert.\",\n )\n\n parser.add_argument(\n \"--config_file\",\n default=\"/Users/arthurzucker/Work/diffusers/ArthurZ/config.json\",\n type=str,\n required=False,\n help=\"The config json file corresponding to the architecture.\",\n )\n\n parser.add_argument(\n \"--dump_path\",\n default=\"/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt\",\n type=str,\n required=False,\n help=\"Path to the output model.\",\n )\n\n args = parser.parse_args()\n\n checkpoint = torch.load(args.checkpoint_path, map_location=\"cpu\")\n\n with open(args.config_file) as f:\n config = json.loads(f.read())\n\n converted_checkpoint = convert_ncsnpp_checkpoint(\n checkpoint,\n config,\n )\n\n if \"sde\" in config:\n del config[\"sde\"]\n\n model = UNet2DModel(**config)\n model.load_state_dict(converted_checkpoint)\n\n try:\n scheduler = ScoreSdeVeScheduler.from_config(\"/\".join(args.checkpoint_path.split(\"/\")[:-1]))\n\n pipe = ScoreSdeVePipeline(unet=model, scheduler=scheduler)\n pipe.save_pretrained(args.dump_path)\n except: # noqa: E722\n model.save_pretrained(args.dump_path)\n" }, { "path": "diffusers/scripts/convert_original_audioldm2_to_diffusers.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Conversion script for the AudioLDM2 checkpoints.\"\"\"\n\nimport argparse\nimport re\nfrom typing import List, Union\n\nimport torch\nimport yaml\nfrom transformers import (\n AutoFeatureExtractor,\n AutoTokenizer,\n ClapConfig,\n ClapModel,\n GPT2Config,\n GPT2Model,\n SpeechT5HifiGan,\n SpeechT5HifiGanConfig,\n T5Config,\n T5EncoderModel,\n)\n\nfrom diffusers import (\n AudioLDM2Pipeline,\n AudioLDM2ProjectionModel,\n AudioLDM2UNet2DConditionModel,\n AutoencoderKL,\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n HeunDiscreteScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n)\nfrom diffusers.utils import is_safetensors_available\nfrom diffusers.utils.import_utils import BACKENDS_MAPPING\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.shave_segments\ndef shave_segments(path, n_shave_prefix_segments=1):\n \"\"\"\n Removes segments. Positive values shave the first segments, negative shave the last segments.\n \"\"\"\n if n_shave_prefix_segments >= 0:\n return \".\".join(path.split(\".\")[n_shave_prefix_segments:])\n else:\n return \".\".join(path.split(\".\")[:n_shave_prefix_segments])\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_resnet_paths\ndef renew_resnet_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside resnets to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item.replace(\"in_layers.0\", \"norm1\")\n new_item = new_item.replace(\"in_layers.2\", \"conv1\")\n\n new_item = new_item.replace(\"out_layers.0\", \"norm2\")\n new_item = new_item.replace(\"out_layers.3\", \"conv2\")\n\n new_item = new_item.replace(\"emb_layers.1\", \"time_emb_proj\")\n new_item = new_item.replace(\"skip_connection\", \"conv_shortcut\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_vae_resnet_paths\ndef renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside resnets to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n new_item = new_item.replace(\"nin_shortcut\", \"conv_shortcut\")\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_attention_paths\ndef renew_attention_paths(old_list):\n \"\"\"\n Updates paths inside attentions to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n # new_item = new_item.replace('norm.weight', 'group_norm.weight')\n # new_item = new_item.replace('norm.bias', 'group_norm.bias')\n\n # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')\n # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')\n\n # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside attentions to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n new_item = new_item.replace(\"norm.weight\", \"group_norm.weight\")\n new_item = new_item.replace(\"norm.bias\", \"group_norm.bias\")\n\n new_item = new_item.replace(\"q.weight\", \"to_q.weight\")\n new_item = new_item.replace(\"q.bias\", \"to_q.bias\")\n\n new_item = new_item.replace(\"k.weight\", \"to_k.weight\")\n new_item = new_item.replace(\"k.bias\", \"to_k.bias\")\n\n new_item = new_item.replace(\"v.weight\", \"to_v.weight\")\n new_item = new_item.replace(\"v.bias\", \"to_v.bias\")\n\n new_item = new_item.replace(\"proj_out.weight\", \"to_out.0.weight\")\n new_item = new_item.replace(\"proj_out.bias\", \"to_out.0.bias\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef assign_to_checkpoint(\n paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None\n):\n \"\"\"\n This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits\n attention layers, and takes into account additional replacements that may arise.\n\n Assigns the weights to the new checkpoint.\n \"\"\"\n assert isinstance(paths, list), \"Paths should be a list of dicts containing 'old' and 'new' keys.\"\n\n # Splits the attention layers into three variables.\n if attention_paths_to_split is not None:\n for path, path_map in attention_paths_to_split.items():\n old_tensor = old_checkpoint[path]\n channels = old_tensor.shape[0] // 3\n\n target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)\n\n num_heads = old_tensor.shape[0] // config[\"num_head_channels\"] // 3\n\n old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])\n query, key, value = old_tensor.split(channels // num_heads, dim=1)\n\n checkpoint[path_map[\"query\"]] = query.reshape(target_shape)\n checkpoint[path_map[\"key\"]] = key.reshape(target_shape)\n checkpoint[path_map[\"value\"]] = value.reshape(target_shape)\n\n for path in paths:\n new_path = path[\"new\"]\n\n # These have already been assigned\n if attention_paths_to_split is not None and new_path in attention_paths_to_split:\n continue\n\n if additional_replacements is not None:\n for replacement in additional_replacements:\n new_path = new_path.replace(replacement[\"old\"], replacement[\"new\"])\n\n # proj_attn.weight has to be converted from conv 1D to linear\n if \"proj_attn.weight\" in new_path:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]][:, :, 0]\n else:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]]\n\n\ndef conv_attn_to_linear(checkpoint):\n keys = list(checkpoint.keys())\n attn_keys = [\"to_q.weight\", \"to_k.weight\", \"to_v.weight\"]\n proj_key = \"to_out.0.weight\"\n for key in keys:\n if \".\".join(key.split(\".\")[-2:]) in attn_keys or \".\".join(key.split(\".\")[-3:]) == proj_key:\n if checkpoint[key].ndim > 2:\n checkpoint[key] = checkpoint[key].squeeze()\n\n\ndef create_unet_diffusers_config(original_config, image_size: int):\n \"\"\"\n Creates a UNet config for diffusers based on the config of the original AudioLDM2 model.\n \"\"\"\n unet_params = original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"]\n vae_params = original_config[\"model\"][\"params\"][\"first_stage_config\"][\"params\"][\"ddconfig\"]\n\n block_out_channels = [unet_params[\"model_channels\"] * mult for mult in unet_params[\"channel_mult\"]]\n\n down_block_types = []\n resolution = 1\n for i in range(len(block_out_channels)):\n block_type = \"CrossAttnDownBlock2D\" if resolution in unet_params[\"attention_resolutions\"] else \"DownBlock2D\"\n down_block_types.append(block_type)\n if i != len(block_out_channels) - 1:\n resolution *= 2\n\n up_block_types = []\n for i in range(len(block_out_channels)):\n block_type = \"CrossAttnUpBlock2D\" if resolution in unet_params[\"attention_resolutions\"] else \"UpBlock2D\"\n up_block_types.append(block_type)\n resolution //= 2\n\n vae_scale_factor = 2 ** (len(vae_params[\"ch_mult\"]) - 1)\n\n cross_attention_dim = list(unet_params[\"context_dim\"]) if \"context_dim\" in unet_params else block_out_channels\n if len(cross_attention_dim) > 1:\n # require two or more cross-attention layers per-block, each of different dimension\n cross_attention_dim = [cross_attention_dim for _ in range(len(block_out_channels))]\n\n config = {\n \"sample_size\": image_size // vae_scale_factor,\n \"in_channels\": unet_params[\"in_channels\"],\n \"out_channels\": unet_params[\"out_channels\"],\n \"down_block_types\": tuple(down_block_types),\n \"up_block_types\": tuple(up_block_types),\n \"block_out_channels\": tuple(block_out_channels),\n \"layers_per_block\": unet_params[\"num_res_blocks\"],\n \"transformer_layers_per_block\": unet_params[\"transformer_depth\"],\n \"cross_attention_dim\": tuple(cross_attention_dim),\n }\n\n return config\n\n\n# Adapted from diffusers.pipelines.stable_diffusion.convert_from_ckpt.create_vae_diffusers_config\ndef create_vae_diffusers_config(original_config, checkpoint, image_size: int):\n \"\"\"\n Creates a VAE config for diffusers based on the config of the original AudioLDM2 model. Compared to the original\n Stable Diffusion conversion, this function passes a *learnt* VAE scaling factor to the diffusers VAE.\n \"\"\"\n vae_params = original_config[\"model\"][\"params\"][\"first_stage_config\"][\"params\"][\"ddconfig\"]\n _ = original_config[\"model\"][\"params\"][\"first_stage_config\"][\"params\"][\"embed_dim\"]\n\n block_out_channels = [vae_params[\"ch\"] * mult for mult in vae_params[\"ch_mult\"]]\n down_block_types = [\"DownEncoderBlock2D\"] * len(block_out_channels)\n up_block_types = [\"UpDecoderBlock2D\"] * len(block_out_channels)\n\n scaling_factor = checkpoint[\"scale_factor\"] if \"scale_by_std\" in original_config[\"model\"][\"params\"] else 0.18215\n\n config = {\n \"sample_size\": image_size,\n \"in_channels\": vae_params[\"in_channels\"],\n \"out_channels\": vae_params[\"out_ch\"],\n \"down_block_types\": tuple(down_block_types),\n \"up_block_types\": tuple(up_block_types),\n \"block_out_channels\": tuple(block_out_channels),\n \"latent_channels\": vae_params[\"z_channels\"],\n \"layers_per_block\": vae_params[\"num_res_blocks\"],\n \"scaling_factor\": float(scaling_factor),\n }\n return config\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.create_diffusers_schedular\ndef create_diffusers_schedular(original_config):\n schedular = DDIMScheduler(\n num_train_timesteps=original_config[\"model\"][\"params\"][\"timesteps\"],\n beta_start=original_config[\"model\"][\"params\"][\"linear_start\"],\n beta_end=original_config[\"model\"][\"params\"][\"linear_end\"],\n beta_schedule=\"scaled_linear\",\n )\n return schedular\n\n\ndef convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False):\n \"\"\"\n Takes a state dict and a config, and returns a converted UNet checkpoint.\n \"\"\"\n\n # extract state_dict for UNet\n unet_state_dict = {}\n keys = list(checkpoint.keys())\n\n unet_key = \"model.diffusion_model.\"\n # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA\n if sum(k.startswith(\"model_ema\") for k in keys) > 100 and extract_ema:\n print(f\"Checkpoint {path} has both EMA and non-EMA weights.\")\n print(\n \"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA\"\n \" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.\"\n )\n for key in keys:\n if key.startswith(\"model.diffusion_model\"):\n flat_ema_key = \"model_ema.\" + \"\".join(key.split(\".\")[1:])\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint.pop(flat_ema_key)\n else:\n if sum(k.startswith(\"model_ema\") for k in keys) > 100:\n print(\n \"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA\"\n \" weights (usually better for inference), please make sure to add the `--extract_ema` flag.\"\n )\n\n # strip the unet prefix from the weight names\n for key in keys:\n if key.startswith(unet_key):\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint.pop(key)\n\n new_checkpoint = {}\n\n new_checkpoint[\"time_embedding.linear_1.weight\"] = unet_state_dict[\"time_embed.0.weight\"]\n new_checkpoint[\"time_embedding.linear_1.bias\"] = unet_state_dict[\"time_embed.0.bias\"]\n new_checkpoint[\"time_embedding.linear_2.weight\"] = unet_state_dict[\"time_embed.2.weight\"]\n new_checkpoint[\"time_embedding.linear_2.bias\"] = unet_state_dict[\"time_embed.2.bias\"]\n\n new_checkpoint[\"conv_in.weight\"] = unet_state_dict[\"input_blocks.0.0.weight\"]\n new_checkpoint[\"conv_in.bias\"] = unet_state_dict[\"input_blocks.0.0.bias\"]\n\n new_checkpoint[\"conv_norm_out.weight\"] = unet_state_dict[\"out.0.weight\"]\n new_checkpoint[\"conv_norm_out.bias\"] = unet_state_dict[\"out.0.bias\"]\n new_checkpoint[\"conv_out.weight\"] = unet_state_dict[\"out.2.weight\"]\n new_checkpoint[\"conv_out.bias\"] = unet_state_dict[\"out.2.bias\"]\n\n # Retrieves the keys for the input blocks only\n num_input_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"input_blocks\" in layer})\n input_blocks = {\n layer_id: [key for key in unet_state_dict if f\"input_blocks.{layer_id}.\" in key]\n for layer_id in range(num_input_blocks)\n }\n\n # Retrieves the keys for the middle blocks only\n num_middle_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"middle_block\" in layer})\n middle_blocks = {\n layer_id: [key for key in unet_state_dict if f\"middle_block.{layer_id}.\" in key]\n for layer_id in range(num_middle_blocks)\n }\n\n # Retrieves the keys for the output blocks only\n num_output_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"output_blocks\" in layer})\n output_blocks = {\n layer_id: [key for key in unet_state_dict if f\"output_blocks.{layer_id}.\" in key]\n for layer_id in range(num_output_blocks)\n }\n\n # Check how many Transformer blocks we have per layer\n if isinstance(config.get(\"cross_attention_dim\"), (list, tuple)):\n if isinstance(config[\"cross_attention_dim\"][0], (list, tuple)):\n # in this case we have multiple cross-attention layers per-block\n num_attention_layers = len(config.get(\"cross_attention_dim\")[0])\n else:\n num_attention_layers = 1\n\n if config.get(\"extra_self_attn_layer\"):\n num_attention_layers += 1\n\n for i in range(1, num_input_blocks):\n block_id = (i - 1) // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = (i - 1) % (config[\"layers_per_block\"] + 1)\n\n resnets = [\n key for key in input_blocks[i] if f\"input_blocks.{i}.0\" in key and f\"input_blocks.{i}.0.op\" not in key\n ]\n attentions = [key for key in input_blocks[i] if f\"input_blocks.{i}.0\" not in key]\n\n if f\"input_blocks.{i}.0.op.weight\" in unet_state_dict:\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.weight\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.op.weight\"\n )\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.bias\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.op.bias\"\n )\n\n paths = renew_resnet_paths(resnets)\n meta_path = {\"old\": f\"input_blocks.{i}.0\", \"new\": f\"down_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n meta_path = [\n {\n \"old\": f\"input_blocks.{i}.{1 + layer_id}\",\n \"new\": f\"down_blocks.{block_id}.attentions.{layer_in_block_id * num_attention_layers + layer_id}\",\n }\n for layer_id in range(num_attention_layers)\n ]\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=meta_path, config=config\n )\n\n resnet_0 = middle_blocks[0]\n resnet_1 = middle_blocks[num_middle_blocks - 1]\n\n resnet_0_paths = renew_resnet_paths(resnet_0)\n meta_path = {\"old\": \"middle_block.0\", \"new\": \"mid_block.resnets.0\"}\n assign_to_checkpoint(\n resnet_0_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n resnet_1_paths = renew_resnet_paths(resnet_1)\n meta_path = {\"old\": f\"middle_block.{len(middle_blocks) - 1}\", \"new\": \"mid_block.resnets.1\"}\n assign_to_checkpoint(\n resnet_1_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n for i in range(1, num_middle_blocks - 1):\n attentions = middle_blocks[i]\n attentions_paths = renew_attention_paths(attentions)\n meta_path = {\"old\": f\"middle_block.{i}\", \"new\": f\"mid_block.attentions.{i - 1}\"}\n assign_to_checkpoint(\n attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n for i in range(num_output_blocks):\n block_id = i // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = i % (config[\"layers_per_block\"] + 1)\n output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]\n output_block_list = {}\n\n for layer in output_block_layers:\n layer_id, layer_name = layer.split(\".\")[0], shave_segments(layer, 1)\n if layer_id in output_block_list:\n output_block_list[layer_id].append(layer_name)\n else:\n output_block_list[layer_id] = [layer_name]\n\n if len(output_block_list) > 1:\n resnets = [key for key in output_blocks[i] if f\"output_blocks.{i}.0\" in key]\n attentions = [key for key in output_blocks[i] if f\"output_blocks.{i}.0\" not in key]\n\n paths = renew_resnet_paths(resnets)\n\n meta_path = {\"old\": f\"output_blocks.{i}.0\", \"new\": f\"up_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n output_block_list = {k: sorted(v) for k, v in output_block_list.items()}\n if [\"conv.bias\", \"conv.weight\"] in output_block_list.values():\n index = list(output_block_list.values()).index([\"conv.bias\", \"conv.weight\"])\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.weight\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.weight\"\n ]\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.bias\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.bias\"\n ]\n\n attentions.remove(f\"output_blocks.{i}.{index}.conv.bias\")\n attentions.remove(f\"output_blocks.{i}.{index}.conv.weight\")\n\n # Clear attentions as they have been attributed above.\n if len(attentions) == 2:\n attentions = []\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n meta_path = [\n {\n \"old\": f\"output_blocks.{i}.{1 + layer_id}\",\n \"new\": f\"up_blocks.{block_id}.attentions.{layer_in_block_id * num_attention_layers + layer_id}\",\n }\n for layer_id in range(num_attention_layers)\n ]\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=meta_path, config=config\n )\n else:\n resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)\n for path in resnet_0_paths:\n old_path = \".\".join([\"output_blocks\", str(i), path[\"old\"]])\n new_path = \".\".join([\"up_blocks\", str(block_id), \"resnets\", str(layer_in_block_id), path[\"new\"]])\n\n new_checkpoint[new_path] = unet_state_dict[old_path]\n\n return new_checkpoint\n\n\ndef convert_ldm_vae_checkpoint(checkpoint, config):\n # extract state dict for VAE\n vae_state_dict = {}\n vae_key = \"first_stage_model.\"\n keys = list(checkpoint.keys())\n for key in keys:\n if key.startswith(vae_key):\n vae_state_dict[key.replace(vae_key, \"\")] = checkpoint.get(key)\n\n new_checkpoint = {}\n\n new_checkpoint[\"encoder.conv_in.weight\"] = vae_state_dict[\"encoder.conv_in.weight\"]\n new_checkpoint[\"encoder.conv_in.bias\"] = vae_state_dict[\"encoder.conv_in.bias\"]\n new_checkpoint[\"encoder.conv_out.weight\"] = vae_state_dict[\"encoder.conv_out.weight\"]\n new_checkpoint[\"encoder.conv_out.bias\"] = vae_state_dict[\"encoder.conv_out.bias\"]\n new_checkpoint[\"encoder.conv_norm_out.weight\"] = vae_state_dict[\"encoder.norm_out.weight\"]\n new_checkpoint[\"encoder.conv_norm_out.bias\"] = vae_state_dict[\"encoder.norm_out.bias\"]\n\n new_checkpoint[\"decoder.conv_in.weight\"] = vae_state_dict[\"decoder.conv_in.weight\"]\n new_checkpoint[\"decoder.conv_in.bias\"] = vae_state_dict[\"decoder.conv_in.bias\"]\n new_checkpoint[\"decoder.conv_out.weight\"] = vae_state_dict[\"decoder.conv_out.weight\"]\n new_checkpoint[\"decoder.conv_out.bias\"] = vae_state_dict[\"decoder.conv_out.bias\"]\n new_checkpoint[\"decoder.conv_norm_out.weight\"] = vae_state_dict[\"decoder.norm_out.weight\"]\n new_checkpoint[\"decoder.conv_norm_out.bias\"] = vae_state_dict[\"decoder.norm_out.bias\"]\n\n new_checkpoint[\"quant_conv.weight\"] = vae_state_dict[\"quant_conv.weight\"]\n new_checkpoint[\"quant_conv.bias\"] = vae_state_dict[\"quant_conv.bias\"]\n new_checkpoint[\"post_quant_conv.weight\"] = vae_state_dict[\"post_quant_conv.weight\"]\n new_checkpoint[\"post_quant_conv.bias\"] = vae_state_dict[\"post_quant_conv.bias\"]\n\n # Retrieves the keys for the encoder down blocks only\n num_down_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in vae_state_dict if \"encoder.down\" in layer})\n down_blocks = {\n layer_id: [key for key in vae_state_dict if f\"down.{layer_id}\" in key] for layer_id in range(num_down_blocks)\n }\n\n # Retrieves the keys for the decoder up blocks only\n num_up_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in vae_state_dict if \"decoder.up\" in layer})\n up_blocks = {\n layer_id: [key for key in vae_state_dict if f\"up.{layer_id}\" in key] for layer_id in range(num_up_blocks)\n }\n\n for i in range(num_down_blocks):\n resnets = [key for key in down_blocks[i] if f\"down.{i}\" in key and f\"down.{i}.downsample\" not in key]\n\n if f\"encoder.down.{i}.downsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.weight\"] = vae_state_dict.pop(\n f\"encoder.down.{i}.downsample.conv.weight\"\n )\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.bias\"] = vae_state_dict.pop(\n f\"encoder.down.{i}.downsample.conv.bias\"\n )\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"down.{i}.block\", \"new\": f\"down_blocks.{i}.resnets\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_resnets = [key for key in vae_state_dict if \"encoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"encoder.mid.block_{i}\" in key]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_attentions = [key for key in vae_state_dict if \"encoder.mid.attn\" in key]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n conv_attn_to_linear(new_checkpoint)\n\n for i in range(num_up_blocks):\n block_id = num_up_blocks - 1 - i\n resnets = [\n key for key in up_blocks[block_id] if f\"up.{block_id}\" in key and f\"up.{block_id}.upsample\" not in key\n ]\n\n if f\"decoder.up.{block_id}.upsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.weight\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.weight\"\n ]\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.bias\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.bias\"\n ]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"up.{block_id}.block\", \"new\": f\"up_blocks.{i}.resnets\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_resnets = [key for key in vae_state_dict if \"decoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"decoder.mid.block_{i}\" in key]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_attentions = [key for key in vae_state_dict if \"decoder.mid.attn\" in key]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n conv_attn_to_linear(new_checkpoint)\n return new_checkpoint\n\n\nCLAP_KEYS_TO_MODIFY_MAPPING = {\n \"text_branch\": \"text_model\",\n \"audio_branch\": \"audio_model.audio_encoder\",\n \"attn\": \"attention.self\",\n \"self.proj\": \"output.dense\",\n \"attention.self_mask\": \"attn_mask\",\n \"mlp.fc1\": \"intermediate.dense\",\n \"mlp.fc2\": \"output.dense\",\n \"norm1\": \"layernorm_before\",\n \"norm2\": \"layernorm_after\",\n \"bn0\": \"batch_norm\",\n}\n\nCLAP_KEYS_TO_IGNORE = [\n \"text_transform\",\n \"audio_transform\",\n \"stft\",\n \"logmel_extractor\",\n \"tscam_conv\",\n \"head\",\n \"attn_mask\",\n]\n\nCLAP_EXPECTED_MISSING_KEYS = [\"text_model.embeddings.token_type_ids\"]\n\n\ndef convert_open_clap_checkpoint(checkpoint):\n \"\"\"\n Takes a state dict and returns a converted CLAP checkpoint.\n \"\"\"\n # extract state dict for CLAP text embedding model, discarding the audio component\n model_state_dict = {}\n model_key = \"clap.model.\"\n keys = list(checkpoint.keys())\n for key in keys:\n if key.startswith(model_key):\n model_state_dict[key.replace(model_key, \"\")] = checkpoint.get(key)\n\n new_checkpoint = {}\n\n sequential_layers_pattern = r\".*sequential.(\\d+).*\"\n text_projection_pattern = r\".*_projection.(\\d+).*\"\n\n for key, value in model_state_dict.items():\n # check if key should be ignored in mapping - if so map it to a key name that we'll filter out at the end\n for key_to_ignore in CLAP_KEYS_TO_IGNORE:\n if key_to_ignore in key:\n key = \"spectrogram\"\n\n # check if any key needs to be modified\n for key_to_modify, new_key in CLAP_KEYS_TO_MODIFY_MAPPING.items():\n if key_to_modify in key:\n key = key.replace(key_to_modify, new_key)\n\n if re.match(sequential_layers_pattern, key):\n # replace sequential layers with list\n sequential_layer = re.match(sequential_layers_pattern, key).group(1)\n\n key = key.replace(f\"sequential.{sequential_layer}.\", f\"layers.{int(sequential_layer)//3}.linear.\")\n elif re.match(text_projection_pattern, key):\n projecton_layer = int(re.match(text_projection_pattern, key).group(1))\n\n # Because in CLAP they use `nn.Sequential`...\n transformers_projection_layer = 1 if projecton_layer == 0 else 2\n\n key = key.replace(f\"_projection.{projecton_layer}.\", f\"_projection.linear{transformers_projection_layer}.\")\n\n if \"audio\" and \"qkv\" in key:\n # split qkv into query key and value\n mixed_qkv = value\n qkv_dim = mixed_qkv.size(0) // 3\n\n query_layer = mixed_qkv[:qkv_dim]\n key_layer = mixed_qkv[qkv_dim : qkv_dim * 2]\n value_layer = mixed_qkv[qkv_dim * 2 :]\n\n new_checkpoint[key.replace(\"qkv\", \"query\")] = query_layer\n new_checkpoint[key.replace(\"qkv\", \"key\")] = key_layer\n new_checkpoint[key.replace(\"qkv\", \"value\")] = value_layer\n elif key != \"spectrogram\":\n new_checkpoint[key] = value\n\n return new_checkpoint\n\n\ndef create_transformers_vocoder_config(original_config):\n \"\"\"\n Creates a config for transformers SpeechT5HifiGan based on the config of the vocoder model.\n \"\"\"\n vocoder_params = original_config[\"model\"][\"params\"][\"vocoder_config\"][\"params\"]\n\n config = {\n \"model_in_dim\": vocoder_params[\"num_mels\"],\n \"sampling_rate\": vocoder_params[\"sampling_rate\"],\n \"upsample_initial_channel\": vocoder_params[\"upsample_initial_channel\"],\n \"upsample_rates\": list(vocoder_params[\"upsample_rates\"]),\n \"upsample_kernel_sizes\": list(vocoder_params[\"upsample_kernel_sizes\"]),\n \"resblock_kernel_sizes\": list(vocoder_params[\"resblock_kernel_sizes\"]),\n \"resblock_dilation_sizes\": [\n list(resblock_dilation) for resblock_dilation in vocoder_params[\"resblock_dilation_sizes\"]\n ],\n \"normalize_before\": False,\n }\n\n return config\n\n\ndef extract_sub_model(checkpoint, key_prefix):\n \"\"\"\n Takes a state dict and returns the state dict for a particular sub-model.\n \"\"\"\n\n sub_model_state_dict = {}\n keys = list(checkpoint.keys())\n for key in keys:\n if key.startswith(key_prefix):\n sub_model_state_dict[key.replace(key_prefix, \"\")] = checkpoint.get(key)\n\n return sub_model_state_dict\n\n\ndef convert_hifigan_checkpoint(checkpoint, config):\n \"\"\"\n Takes a state dict and config, and returns a converted HiFiGAN vocoder checkpoint.\n \"\"\"\n # extract state dict for vocoder\n vocoder_state_dict = extract_sub_model(checkpoint, key_prefix=\"first_stage_model.vocoder.\")\n\n # fix upsampler keys, everything else is correct already\n for i in range(len(config.upsample_rates)):\n vocoder_state_dict[f\"upsampler.{i}.weight\"] = vocoder_state_dict.pop(f\"ups.{i}.weight\")\n vocoder_state_dict[f\"upsampler.{i}.bias\"] = vocoder_state_dict.pop(f\"ups.{i}.bias\")\n\n if not config.normalize_before:\n # if we don't set normalize_before then these variables are unused, so we set them to their initialised values\n vocoder_state_dict[\"mean\"] = torch.zeros(config.model_in_dim)\n vocoder_state_dict[\"scale\"] = torch.ones(config.model_in_dim)\n\n return vocoder_state_dict\n\n\ndef convert_projection_checkpoint(checkpoint):\n projection_state_dict = {}\n conditioner_state_dict = extract_sub_model(checkpoint, key_prefix=\"cond_stage_models.0.\")\n\n projection_state_dict[\"sos_embed\"] = conditioner_state_dict[\"start_of_sequence_tokens.weight\"][0]\n projection_state_dict[\"sos_embed_1\"] = conditioner_state_dict[\"start_of_sequence_tokens.weight\"][1]\n\n projection_state_dict[\"eos_embed\"] = conditioner_state_dict[\"end_of_sequence_tokens.weight\"][0]\n projection_state_dict[\"eos_embed_1\"] = conditioner_state_dict[\"end_of_sequence_tokens.weight\"][1]\n\n projection_state_dict[\"projection.weight\"] = conditioner_state_dict[\"input_sequence_embed_linear.0.weight\"]\n projection_state_dict[\"projection.bias\"] = conditioner_state_dict[\"input_sequence_embed_linear.0.bias\"]\n\n projection_state_dict[\"projection_1.weight\"] = conditioner_state_dict[\"input_sequence_embed_linear.1.weight\"]\n projection_state_dict[\"projection_1.bias\"] = conditioner_state_dict[\"input_sequence_embed_linear.1.bias\"]\n\n return projection_state_dict\n\n\n# Adapted from https://github.com/haoheliu/AudioLDM2/blob/81ad2c6ce015c1310387695e2dae975a7d2ed6fd/audioldm2/utils.py#L143\nDEFAULT_CONFIG = {\n \"model\": {\n \"params\": {\n \"linear_start\": 0.0015,\n \"linear_end\": 0.0195,\n \"timesteps\": 1000,\n \"channels\": 8,\n \"scale_by_std\": True,\n \"unet_config\": {\n \"target\": \"audioldm2.latent_diffusion.openaimodel.UNetModel\",\n \"params\": {\n \"context_dim\": [None, 768, 1024],\n \"in_channels\": 8,\n \"out_channels\": 8,\n \"model_channels\": 128,\n \"attention_resolutions\": [8, 4, 2],\n \"num_res_blocks\": 2,\n \"channel_mult\": [1, 2, 3, 5],\n \"num_head_channels\": 32,\n \"transformer_depth\": 1,\n },\n },\n \"first_stage_config\": {\n \"target\": \"audioldm2.variational_autoencoder.autoencoder.AutoencoderKL\",\n \"params\": {\n \"embed_dim\": 8,\n \"ddconfig\": {\n \"z_channels\": 8,\n \"resolution\": 256,\n \"in_channels\": 1,\n \"out_ch\": 1,\n \"ch\": 128,\n \"ch_mult\": [1, 2, 4],\n \"num_res_blocks\": 2,\n },\n },\n },\n \"cond_stage_config\": {\n \"crossattn_audiomae_generated\": {\n \"target\": \"audioldm2.latent_diffusion.modules.encoders.modules.SequenceGenAudioMAECond\",\n \"params\": {\n \"sequence_gen_length\": 8,\n \"sequence_input_embed_dim\": [512, 1024],\n },\n }\n },\n \"vocoder_config\": {\n \"target\": \"audioldm2.first_stage_model.vocoder\",\n \"params\": {\n \"upsample_rates\": [5, 4, 2, 2, 2],\n \"upsample_kernel_sizes\": [16, 16, 8, 4, 4],\n \"upsample_initial_channel\": 1024,\n \"resblock_kernel_sizes\": [3, 7, 11],\n \"resblock_dilation_sizes\": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],\n \"num_mels\": 64,\n \"sampling_rate\": 16000,\n },\n },\n },\n },\n}\n\n\ndef load_pipeline_from_original_AudioLDM2_ckpt(\n checkpoint_path: str,\n original_config_file: str = None,\n image_size: int = 1024,\n prediction_type: str = None,\n extract_ema: bool = False,\n scheduler_type: str = \"ddim\",\n cross_attention_dim: Union[List, List[List]] = None,\n transformer_layers_per_block: int = None,\n device: str = None,\n from_safetensors: bool = False,\n) -> AudioLDM2Pipeline:\n \"\"\"\n Load an AudioLDM2 pipeline object from a `.ckpt`/`.safetensors` file and (ideally) a `.yaml` config file.\n\n Although many of the arguments can be automatically inferred, some of these rely on brittle checks against the\n global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is\n recommended that you override the default values and/or supply an `original_config_file` wherever possible.\n\n Args:\n checkpoint_path (`str`): Path to `.ckpt` file.\n original_config_file (`str`):\n Path to `.yaml` config file corresponding to the original architecture. If `None`, will be automatically\n set to the AudioLDM2 base config.\n image_size (`int`, *optional*, defaults to 1024):\n The image size that the model was trained on.\n prediction_type (`str`, *optional*):\n The prediction type that the model was trained on. If `None`, will be automatically\n inferred by looking for a key in the config. For the default config, the prediction type is `'epsilon'`.\n scheduler_type (`str`, *optional*, defaults to 'ddim'):\n Type of scheduler to use. Should be one of `[\"pndm\", \"lms\", \"heun\", \"euler\", \"euler-ancestral\", \"dpm\",\n \"ddim\"]`.\n cross_attention_dim (`list`, *optional*, defaults to `None`):\n The dimension of the cross-attention layers. If `None`, the cross-attention dimension will be\n automatically inferred. Set to `[768, 1024]` for the base model, or `[768, 1024, None]` for the large model.\n transformer_layers_per_block (`int`, *optional*, defaults to `None`):\n The number of transformer layers in each transformer block. If `None`, number of layers will be \"\n \"automatically inferred. Set to `1` for the base model, or `2` for the large model.\n extract_ema (`bool`, *optional*, defaults to `False`): Only relevant for\n checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults to\n `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher quality images for\n inference. Non-EMA weights are usually better to continue fine-tuning.\n device (`str`, *optional*, defaults to `None`):\n The device to use. Pass `None` to determine automatically.\n from_safetensors (`str`, *optional*, defaults to `False`):\n If `checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.\n return: An AudioLDM2Pipeline object representing the passed-in `.ckpt`/`.safetensors` file.\n \"\"\"\n\n if from_safetensors:\n if not is_safetensors_available():\n raise ValueError(BACKENDS_MAPPING[\"safetensors\"][1])\n\n from safetensors import safe_open\n\n checkpoint = {}\n with safe_open(checkpoint_path, framework=\"pt\", device=\"cpu\") as f:\n for key in f.keys():\n checkpoint[key] = f.get_tensor(key)\n else:\n if device is None:\n device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n checkpoint = torch.load(checkpoint_path, map_location=device)\n else:\n checkpoint = torch.load(checkpoint_path, map_location=device)\n\n if \"state_dict\" in checkpoint:\n checkpoint = checkpoint[\"state_dict\"]\n\n if original_config_file is None:\n original_config = DEFAULT_CONFIG\n else:\n original_config = yaml.safe_load(original_config_file)\n\n if image_size is not None:\n original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"][\"image_size\"] = image_size\n\n if cross_attention_dim is not None:\n original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"][\"context_dim\"] = cross_attention_dim\n\n if transformer_layers_per_block is not None:\n original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"][\"transformer_depth\"] = transformer_layers_per_block\n\n if (\n \"parameterization\" in original_config[\"model\"][\"params\"]\n and original_config[\"model\"][\"params\"][\"parameterization\"] == \"v\"\n ):\n if prediction_type is None:\n prediction_type = \"v_prediction\"\n else:\n if prediction_type is None:\n prediction_type = \"epsilon\"\n\n num_train_timesteps = original_config[\"model\"][\"params\"][\"timesteps\"]\n beta_start = original_config[\"model\"][\"params\"][\"linear_start\"]\n beta_end = original_config[\"model\"][\"params\"][\"linear_end\"]\n\n scheduler = DDIMScheduler(\n beta_end=beta_end,\n beta_schedule=\"scaled_linear\",\n beta_start=beta_start,\n num_train_timesteps=num_train_timesteps,\n steps_offset=1,\n clip_sample=False,\n set_alpha_to_one=False,\n prediction_type=prediction_type,\n )\n # make sure scheduler works correctly with DDIM\n scheduler.register_to_config(clip_sample=False)\n\n if scheduler_type == \"pndm\":\n config = dict(scheduler.config)\n config[\"skip_prk_steps\"] = True\n scheduler = PNDMScheduler.from_config(config)\n elif scheduler_type == \"lms\":\n scheduler = LMSDiscreteScheduler.from_config(scheduler.config)\n elif scheduler_type == \"heun\":\n scheduler = HeunDiscreteScheduler.from_config(scheduler.config)\n elif scheduler_type == \"euler\":\n scheduler = EulerDiscreteScheduler.from_config(scheduler.config)\n elif scheduler_type == \"euler-ancestral\":\n scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config)\n elif scheduler_type == \"dpm\":\n scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)\n elif scheduler_type == \"ddim\":\n scheduler = scheduler\n else:\n raise ValueError(f\"Scheduler of type {scheduler_type} doesn't exist!\")\n\n # Convert the UNet2DModel\n unet_config = create_unet_diffusers_config(original_config, image_size=image_size)\n unet = AudioLDM2UNet2DConditionModel(**unet_config)\n\n converted_unet_checkpoint = convert_ldm_unet_checkpoint(\n checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema\n )\n\n unet.load_state_dict(converted_unet_checkpoint)\n\n # Convert the VAE model\n vae_config = create_vae_diffusers_config(original_config, checkpoint=checkpoint, image_size=image_size)\n converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)\n\n vae = AutoencoderKL(**vae_config)\n vae.load_state_dict(converted_vae_checkpoint)\n\n # Convert the joint audio-text encoding model\n clap_config = ClapConfig.from_pretrained(\"laion/clap-htsat-unfused\")\n clap_config.audio_config.update(\n {\n \"patch_embeds_hidden_size\": 128,\n \"hidden_size\": 1024,\n \"depths\": [2, 2, 12, 2],\n }\n )\n # AudioLDM2 uses the same tokenizer and feature extractor as the original CLAP model\n clap_tokenizer = AutoTokenizer.from_pretrained(\"laion/clap-htsat-unfused\")\n clap_feature_extractor = AutoFeatureExtractor.from_pretrained(\"laion/clap-htsat-unfused\")\n\n converted_clap_model = convert_open_clap_checkpoint(checkpoint)\n clap_model = ClapModel(clap_config)\n\n missing_keys, unexpected_keys = clap_model.load_state_dict(converted_clap_model, strict=False)\n # we expect not to have token_type_ids in our original state dict so let's ignore them\n missing_keys = list(set(missing_keys) - set(CLAP_EXPECTED_MISSING_KEYS))\n\n if len(unexpected_keys) > 0:\n raise ValueError(f\"Unexpected keys when loading CLAP model: {unexpected_keys}\")\n\n if len(missing_keys) > 0:\n raise ValueError(f\"Missing keys when loading CLAP model: {missing_keys}\")\n\n # Convert the vocoder model\n vocoder_config = create_transformers_vocoder_config(original_config)\n vocoder_config = SpeechT5HifiGanConfig(**vocoder_config)\n converted_vocoder_checkpoint = convert_hifigan_checkpoint(checkpoint, vocoder_config)\n\n vocoder = SpeechT5HifiGan(vocoder_config)\n vocoder.load_state_dict(converted_vocoder_checkpoint)\n\n # Convert the Flan-T5 encoder model: AudioLDM2 uses the same configuration and tokenizer as the original Flan-T5 large model\n t5_config = T5Config.from_pretrained(\"google/flan-t5-large\")\n converted_t5_checkpoint = extract_sub_model(checkpoint, key_prefix=\"cond_stage_models.1.model.\")\n\n t5_tokenizer = AutoTokenizer.from_pretrained(\"google/flan-t5-large\")\n # hard-coded in the original implementation (i.e. not retrievable from the config)\n t5_tokenizer.model_max_length = 128\n t5_model = T5EncoderModel(t5_config)\n t5_model.load_state_dict(converted_t5_checkpoint)\n\n # Convert the GPT2 encoder model: AudioLDM2 uses the same configuration as the original GPT2 base model\n gpt2_config = GPT2Config.from_pretrained(\"gpt2\")\n gpt2_model = GPT2Model(gpt2_config)\n gpt2_model.config.max_new_tokens = original_config[\"model\"][\"params\"][\"cond_stage_config\"][\n \"crossattn_audiomae_generated\"\n ][\"params\"][\"sequence_gen_length\"]\n\n converted_gpt2_checkpoint = extract_sub_model(checkpoint, key_prefix=\"cond_stage_models.0.model.\")\n gpt2_model.load_state_dict(converted_gpt2_checkpoint)\n\n # Convert the extra embedding / projection layers\n projection_model = AudioLDM2ProjectionModel(clap_config.projection_dim, t5_config.d_model, gpt2_config.n_embd)\n\n converted_projection_checkpoint = convert_projection_checkpoint(checkpoint)\n projection_model.load_state_dict(converted_projection_checkpoint)\n\n # Instantiate the diffusers pipeline\n pipe = AudioLDM2Pipeline(\n vae=vae,\n text_encoder=clap_model,\n text_encoder_2=t5_model,\n projection_model=projection_model,\n language_model=gpt2_model,\n tokenizer=clap_tokenizer,\n tokenizer_2=t5_tokenizer,\n feature_extractor=clap_feature_extractor,\n unet=unet,\n scheduler=scheduler,\n vocoder=vocoder,\n )\n\n return pipe\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--checkpoint_path\", default=None, type=str, required=True, help=\"Path to the checkpoint to convert.\"\n )\n parser.add_argument(\n \"--original_config_file\",\n default=None,\n type=str,\n help=\"The YAML config file corresponding to the original architecture.\",\n )\n parser.add_argument(\n \"--cross_attention_dim\",\n default=None,\n type=int,\n nargs=\"+\",\n help=\"The dimension of the cross-attention layers. If `None`, the cross-attention dimension will be \"\n \"automatically inferred. Set to `768+1024` for the base model, or `768+1024+640` for the large model\",\n )\n parser.add_argument(\n \"--transformer_layers_per_block\",\n default=None,\n type=int,\n help=\"The number of transformer layers in each transformer block. If `None`, number of layers will be \"\n \"automatically inferred. Set to `1` for the base model, or `2` for the large model.\",\n )\n parser.add_argument(\n \"--scheduler_type\",\n default=\"ddim\",\n type=str,\n help=\"Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']\",\n )\n parser.add_argument(\n \"--image_size\",\n default=1048,\n type=int,\n help=\"The image size that the model was trained on.\",\n )\n parser.add_argument(\n \"--prediction_type\",\n default=None,\n type=str,\n help=(\"The prediction type that the model was trained on.\"),\n )\n parser.add_argument(\n \"--extract_ema\",\n action=\"store_true\",\n help=(\n \"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights\"\n \" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield\"\n \" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.\"\n ),\n )\n parser.add_argument(\n \"--from_safetensors\",\n action=\"store_true\",\n help=\"If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.\",\n )\n parser.add_argument(\n \"--to_safetensors\",\n action=\"store_true\",\n help=\"Whether to store pipeline in safetensors format or not.\",\n )\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n parser.add_argument(\"--device\", type=str, help=\"Device to use (e.g. cpu, cuda:0, cuda:1, etc.)\")\n args = parser.parse_args()\n\n pipe = load_pipeline_from_original_AudioLDM2_ckpt(\n checkpoint_path=args.checkpoint_path,\n original_config_file=args.original_config_file,\n image_size=args.image_size,\n prediction_type=args.prediction_type,\n extract_ema=args.extract_ema,\n scheduler_type=args.scheduler_type,\n cross_attention_dim=args.cross_attention_dim,\n transformer_layers_per_block=args.transformer_layers_per_block,\n from_safetensors=args.from_safetensors,\n device=args.device,\n )\n pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)\n" }, { "path": "diffusers/scripts/convert_original_audioldm_to_diffusers.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Conversion script for the AudioLDM checkpoints.\"\"\"\n\nimport argparse\nimport re\n\nimport torch\nimport yaml\nfrom transformers import (\n AutoTokenizer,\n ClapTextConfig,\n ClapTextModelWithProjection,\n SpeechT5HifiGan,\n SpeechT5HifiGanConfig,\n)\n\nfrom diffusers import (\n AudioLDMPipeline,\n AutoencoderKL,\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n HeunDiscreteScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n UNet2DConditionModel,\n)\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.shave_segments\ndef shave_segments(path, n_shave_prefix_segments=1):\n \"\"\"\n Removes segments. Positive values shave the first segments, negative shave the last segments.\n \"\"\"\n if n_shave_prefix_segments >= 0:\n return \".\".join(path.split(\".\")[n_shave_prefix_segments:])\n else:\n return \".\".join(path.split(\".\")[:n_shave_prefix_segments])\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_resnet_paths\ndef renew_resnet_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside resnets to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item.replace(\"in_layers.0\", \"norm1\")\n new_item = new_item.replace(\"in_layers.2\", \"conv1\")\n\n new_item = new_item.replace(\"out_layers.0\", \"norm2\")\n new_item = new_item.replace(\"out_layers.3\", \"conv2\")\n\n new_item = new_item.replace(\"emb_layers.1\", \"time_emb_proj\")\n new_item = new_item.replace(\"skip_connection\", \"conv_shortcut\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_vae_resnet_paths\ndef renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside resnets to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n new_item = new_item.replace(\"nin_shortcut\", \"conv_shortcut\")\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_attention_paths\ndef renew_attention_paths(old_list):\n \"\"\"\n Updates paths inside attentions to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n # new_item = new_item.replace('norm.weight', 'group_norm.weight')\n # new_item = new_item.replace('norm.bias', 'group_norm.bias')\n\n # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')\n # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')\n\n # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_vae_attention_paths\ndef renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside attentions to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n new_item = new_item.replace(\"norm.weight\", \"group_norm.weight\")\n new_item = new_item.replace(\"norm.bias\", \"group_norm.bias\")\n\n new_item = new_item.replace(\"q.weight\", \"query.weight\")\n new_item = new_item.replace(\"q.bias\", \"query.bias\")\n\n new_item = new_item.replace(\"k.weight\", \"key.weight\")\n new_item = new_item.replace(\"k.bias\", \"key.bias\")\n\n new_item = new_item.replace(\"v.weight\", \"value.weight\")\n new_item = new_item.replace(\"v.bias\", \"value.bias\")\n\n new_item = new_item.replace(\"proj_out.weight\", \"proj_attn.weight\")\n new_item = new_item.replace(\"proj_out.bias\", \"proj_attn.bias\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.assign_to_checkpoint\ndef assign_to_checkpoint(\n paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None\n):\n \"\"\"\n This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits\n attention layers, and takes into account additional replacements that may arise.\n\n Assigns the weights to the new checkpoint.\n \"\"\"\n assert isinstance(paths, list), \"Paths should be a list of dicts containing 'old' and 'new' keys.\"\n\n # Splits the attention layers into three variables.\n if attention_paths_to_split is not None:\n for path, path_map in attention_paths_to_split.items():\n old_tensor = old_checkpoint[path]\n channels = old_tensor.shape[0] // 3\n\n target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)\n\n num_heads = old_tensor.shape[0] // config[\"num_head_channels\"] // 3\n\n old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])\n query, key, value = old_tensor.split(channels // num_heads, dim=1)\n\n checkpoint[path_map[\"query\"]] = query.reshape(target_shape)\n checkpoint[path_map[\"key\"]] = key.reshape(target_shape)\n checkpoint[path_map[\"value\"]] = value.reshape(target_shape)\n\n for path in paths:\n new_path = path[\"new\"]\n\n # These have already been assigned\n if attention_paths_to_split is not None and new_path in attention_paths_to_split:\n continue\n\n # Global renaming happens here\n new_path = new_path.replace(\"middle_block.0\", \"mid_block.resnets.0\")\n new_path = new_path.replace(\"middle_block.1\", \"mid_block.attentions.0\")\n new_path = new_path.replace(\"middle_block.2\", \"mid_block.resnets.1\")\n\n if additional_replacements is not None:\n for replacement in additional_replacements:\n new_path = new_path.replace(replacement[\"old\"], replacement[\"new\"])\n\n # proj_attn.weight has to be converted from conv 1D to linear\n if \"proj_attn.weight\" in new_path:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]][:, :, 0]\n else:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]]\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.conv_attn_to_linear\ndef conv_attn_to_linear(checkpoint):\n keys = list(checkpoint.keys())\n attn_keys = [\"query.weight\", \"key.weight\", \"value.weight\"]\n for key in keys:\n if \".\".join(key.split(\".\")[-2:]) in attn_keys:\n if checkpoint[key].ndim > 2:\n checkpoint[key] = checkpoint[key][:, :, 0, 0]\n elif \"proj_attn.weight\" in key:\n if checkpoint[key].ndim > 2:\n checkpoint[key] = checkpoint[key][:, :, 0]\n\n\ndef create_unet_diffusers_config(original_config, image_size: int):\n \"\"\"\n Creates a UNet config for diffusers based on the config of the original AudioLDM model.\n \"\"\"\n unet_params = original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"]\n vae_params = original_config[\"model\"][\"params\"][\"first_stage_config\"][\"params\"][\"ddconfig\"]\n\n block_out_channels = [unet_params[\"model_channels\"] * mult for mult in unet_params[\"channel_mult\"]]\n\n down_block_types = []\n resolution = 1\n for i in range(len(block_out_channels)):\n block_type = \"CrossAttnDownBlock2D\" if resolution in unet_params[\"attention_resolutions\"] else \"DownBlock2D\"\n down_block_types.append(block_type)\n if i != len(block_out_channels) - 1:\n resolution *= 2\n\n up_block_types = []\n for i in range(len(block_out_channels)):\n block_type = \"CrossAttnUpBlock2D\" if resolution in unet_params[\"attention_resolutions\"] else \"UpBlock2D\"\n up_block_types.append(block_type)\n resolution //= 2\n\n vae_scale_factor = 2 ** (len(vae_params[\"ch_mult\"]) - 1)\n\n cross_attention_dim = (\n unet_params[\"cross_attention_dim\"] if \"cross_attention_dim\" in unet_params else block_out_channels\n )\n\n class_embed_type = \"simple_projection\" if \"extra_film_condition_dim\" in unet_params else None\n projection_class_embeddings_input_dim = (\n unet_params[\"extra_film_condition_dim\"] if \"extra_film_condition_dim\" in unet_params else None\n )\n class_embeddings_concat = unet_params[\"extra_film_use_concat\"] if \"extra_film_use_concat\" in unet_params else None\n\n config = {\n \"sample_size\": image_size // vae_scale_factor,\n \"in_channels\": unet_params[\"in_channels\"],\n \"out_channels\": unet_params[\"out_channels\"],\n \"down_block_types\": tuple(down_block_types),\n \"up_block_types\": tuple(up_block_types),\n \"block_out_channels\": tuple(block_out_channels),\n \"layers_per_block\": unet_params[\"num_res_blocks\"],\n \"cross_attention_dim\": cross_attention_dim,\n \"class_embed_type\": class_embed_type,\n \"projection_class_embeddings_input_dim\": projection_class_embeddings_input_dim,\n \"class_embeddings_concat\": class_embeddings_concat,\n }\n\n return config\n\n\n# Adapted from diffusers.pipelines.stable_diffusion.convert_from_ckpt.create_vae_diffusers_config\ndef create_vae_diffusers_config(original_config, checkpoint, image_size: int):\n \"\"\"\n Creates a VAE config for diffusers based on the config of the original AudioLDM model. Compared to the original\n Stable Diffusion conversion, this function passes a *learnt* VAE scaling factor to the diffusers VAE.\n \"\"\"\n vae_params = original_config[\"model\"][\"params\"][\"first_stage_config\"][\"params\"][\"ddconfig\"]\n _ = original_config[\"model\"][\"params\"][\"first_stage_config\"][\"params\"][\"embed_dim\"]\n\n block_out_channels = [vae_params[\"ch\"] * mult for mult in vae_params[\"ch_mult\"]]\n down_block_types = [\"DownEncoderBlock2D\"] * len(block_out_channels)\n up_block_types = [\"UpDecoderBlock2D\"] * len(block_out_channels)\n\n scaling_factor = checkpoint[\"scale_factor\"] if \"scale_by_std\" in original_config[\"model\"][\"params\"] else 0.18215\n\n config = {\n \"sample_size\": image_size,\n \"in_channels\": vae_params[\"in_channels\"],\n \"out_channels\": vae_params[\"out_ch\"],\n \"down_block_types\": tuple(down_block_types),\n \"up_block_types\": tuple(up_block_types),\n \"block_out_channels\": tuple(block_out_channels),\n \"latent_channels\": vae_params[\"z_channels\"],\n \"layers_per_block\": vae_params[\"num_res_blocks\"],\n \"scaling_factor\": float(scaling_factor),\n }\n return config\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.create_diffusers_schedular\ndef create_diffusers_schedular(original_config):\n schedular = DDIMScheduler(\n num_train_timesteps=original_config[\"model\"][\"params\"][\"timesteps\"],\n beta_start=original_config[\"model\"][\"params\"][\"linear_start\"],\n beta_end=original_config[\"model\"][\"params\"][\"linear_end\"],\n beta_schedule=\"scaled_linear\",\n )\n return schedular\n\n\n# Adapted from diffusers.pipelines.stable_diffusion.convert_from_ckpt.convert_ldm_unet_checkpoint\ndef convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False):\n \"\"\"\n Takes a state dict and a config, and returns a converted checkpoint. Compared to the original Stable Diffusion\n conversion, this function additionally converts the learnt film embedding linear layer.\n \"\"\"\n\n # extract state_dict for UNet\n unet_state_dict = {}\n keys = list(checkpoint.keys())\n\n unet_key = \"model.diffusion_model.\"\n # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA\n if sum(k.startswith(\"model_ema\") for k in keys) > 100 and extract_ema:\n print(f\"Checkpoint {path} has both EMA and non-EMA weights.\")\n print(\n \"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA\"\n \" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.\"\n )\n for key in keys:\n if key.startswith(\"model.diffusion_model\"):\n flat_ema_key = \"model_ema.\" + \"\".join(key.split(\".\")[1:])\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint.pop(flat_ema_key)\n else:\n if sum(k.startswith(\"model_ema\") for k in keys) > 100:\n print(\n \"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA\"\n \" weights (usually better for inference), please make sure to add the `--extract_ema` flag.\"\n )\n\n for key in keys:\n if key.startswith(unet_key):\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint.pop(key)\n\n new_checkpoint = {}\n\n new_checkpoint[\"time_embedding.linear_1.weight\"] = unet_state_dict[\"time_embed.0.weight\"]\n new_checkpoint[\"time_embedding.linear_1.bias\"] = unet_state_dict[\"time_embed.0.bias\"]\n new_checkpoint[\"time_embedding.linear_2.weight\"] = unet_state_dict[\"time_embed.2.weight\"]\n new_checkpoint[\"time_embedding.linear_2.bias\"] = unet_state_dict[\"time_embed.2.bias\"]\n\n new_checkpoint[\"class_embedding.weight\"] = unet_state_dict[\"film_emb.weight\"]\n new_checkpoint[\"class_embedding.bias\"] = unet_state_dict[\"film_emb.bias\"]\n\n new_checkpoint[\"conv_in.weight\"] = unet_state_dict[\"input_blocks.0.0.weight\"]\n new_checkpoint[\"conv_in.bias\"] = unet_state_dict[\"input_blocks.0.0.bias\"]\n\n new_checkpoint[\"conv_norm_out.weight\"] = unet_state_dict[\"out.0.weight\"]\n new_checkpoint[\"conv_norm_out.bias\"] = unet_state_dict[\"out.0.bias\"]\n new_checkpoint[\"conv_out.weight\"] = unet_state_dict[\"out.2.weight\"]\n new_checkpoint[\"conv_out.bias\"] = unet_state_dict[\"out.2.bias\"]\n\n # Retrieves the keys for the input blocks only\n num_input_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"input_blocks\" in layer})\n input_blocks = {\n layer_id: [key for key in unet_state_dict if f\"input_blocks.{layer_id}\" in key]\n for layer_id in range(num_input_blocks)\n }\n\n # Retrieves the keys for the middle blocks only\n num_middle_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"middle_block\" in layer})\n middle_blocks = {\n layer_id: [key for key in unet_state_dict if f\"middle_block.{layer_id}\" in key]\n for layer_id in range(num_middle_blocks)\n }\n\n # Retrieves the keys for the output blocks only\n num_output_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"output_blocks\" in layer})\n output_blocks = {\n layer_id: [key for key in unet_state_dict if f\"output_blocks.{layer_id}\" in key]\n for layer_id in range(num_output_blocks)\n }\n\n for i in range(1, num_input_blocks):\n block_id = (i - 1) // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = (i - 1) % (config[\"layers_per_block\"] + 1)\n\n resnets = [\n key for key in input_blocks[i] if f\"input_blocks.{i}.0\" in key and f\"input_blocks.{i}.0.op\" not in key\n ]\n attentions = [key for key in input_blocks[i] if f\"input_blocks.{i}.1\" in key]\n\n if f\"input_blocks.{i}.0.op.weight\" in unet_state_dict:\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.weight\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.op.weight\"\n )\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.bias\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.op.bias\"\n )\n\n paths = renew_resnet_paths(resnets)\n meta_path = {\"old\": f\"input_blocks.{i}.0\", \"new\": f\"down_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n meta_path = {\"old\": f\"input_blocks.{i}.1\", \"new\": f\"down_blocks.{block_id}.attentions.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n resnet_0 = middle_blocks[0]\n attentions = middle_blocks[1]\n resnet_1 = middle_blocks[2]\n\n resnet_0_paths = renew_resnet_paths(resnet_0)\n assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)\n\n resnet_1_paths = renew_resnet_paths(resnet_1)\n assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)\n\n attentions_paths = renew_attention_paths(attentions)\n meta_path = {\"old\": \"middle_block.1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(\n attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n for i in range(num_output_blocks):\n block_id = i // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = i % (config[\"layers_per_block\"] + 1)\n output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]\n output_block_list = {}\n\n for layer in output_block_layers:\n layer_id, layer_name = layer.split(\".\")[0], shave_segments(layer, 1)\n if layer_id in output_block_list:\n output_block_list[layer_id].append(layer_name)\n else:\n output_block_list[layer_id] = [layer_name]\n\n if len(output_block_list) > 1:\n resnets = [key for key in output_blocks[i] if f\"output_blocks.{i}.0\" in key]\n attentions = [key for key in output_blocks[i] if f\"output_blocks.{i}.1\" in key]\n\n resnet_0_paths = renew_resnet_paths(resnets)\n paths = renew_resnet_paths(resnets)\n\n meta_path = {\"old\": f\"output_blocks.{i}.0\", \"new\": f\"up_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n output_block_list = {k: sorted(v) for k, v in output_block_list.items()}\n if [\"conv.bias\", \"conv.weight\"] in output_block_list.values():\n index = list(output_block_list.values()).index([\"conv.bias\", \"conv.weight\"])\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.weight\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.weight\"\n ]\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.bias\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.bias\"\n ]\n\n # Clear attentions as they have been attributed above.\n if len(attentions) == 2:\n attentions = []\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n meta_path = {\n \"old\": f\"output_blocks.{i}.1\",\n \"new\": f\"up_blocks.{block_id}.attentions.{layer_in_block_id}\",\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n else:\n resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)\n for path in resnet_0_paths:\n old_path = \".\".join([\"output_blocks\", str(i), path[\"old\"]])\n new_path = \".\".join([\"up_blocks\", str(block_id), \"resnets\", str(layer_in_block_id), path[\"new\"]])\n\n new_checkpoint[new_path] = unet_state_dict[old_path]\n\n return new_checkpoint\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.convert_ldm_vae_checkpoint\ndef convert_ldm_vae_checkpoint(checkpoint, config):\n # extract state dict for VAE\n vae_state_dict = {}\n vae_key = \"first_stage_model.\"\n keys = list(checkpoint.keys())\n for key in keys:\n if key.startswith(vae_key):\n vae_state_dict[key.replace(vae_key, \"\")] = checkpoint.get(key)\n\n new_checkpoint = {}\n\n new_checkpoint[\"encoder.conv_in.weight\"] = vae_state_dict[\"encoder.conv_in.weight\"]\n new_checkpoint[\"encoder.conv_in.bias\"] = vae_state_dict[\"encoder.conv_in.bias\"]\n new_checkpoint[\"encoder.conv_out.weight\"] = vae_state_dict[\"encoder.conv_out.weight\"]\n new_checkpoint[\"encoder.conv_out.bias\"] = vae_state_dict[\"encoder.conv_out.bias\"]\n new_checkpoint[\"encoder.conv_norm_out.weight\"] = vae_state_dict[\"encoder.norm_out.weight\"]\n new_checkpoint[\"encoder.conv_norm_out.bias\"] = vae_state_dict[\"encoder.norm_out.bias\"]\n\n new_checkpoint[\"decoder.conv_in.weight\"] = vae_state_dict[\"decoder.conv_in.weight\"]\n new_checkpoint[\"decoder.conv_in.bias\"] = vae_state_dict[\"decoder.conv_in.bias\"]\n new_checkpoint[\"decoder.conv_out.weight\"] = vae_state_dict[\"decoder.conv_out.weight\"]\n new_checkpoint[\"decoder.conv_out.bias\"] = vae_state_dict[\"decoder.conv_out.bias\"]\n new_checkpoint[\"decoder.conv_norm_out.weight\"] = vae_state_dict[\"decoder.norm_out.weight\"]\n new_checkpoint[\"decoder.conv_norm_out.bias\"] = vae_state_dict[\"decoder.norm_out.bias\"]\n\n new_checkpoint[\"quant_conv.weight\"] = vae_state_dict[\"quant_conv.weight\"]\n new_checkpoint[\"quant_conv.bias\"] = vae_state_dict[\"quant_conv.bias\"]\n new_checkpoint[\"post_quant_conv.weight\"] = vae_state_dict[\"post_quant_conv.weight\"]\n new_checkpoint[\"post_quant_conv.bias\"] = vae_state_dict[\"post_quant_conv.bias\"]\n\n # Retrieves the keys for the encoder down blocks only\n num_down_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in vae_state_dict if \"encoder.down\" in layer})\n down_blocks = {\n layer_id: [key for key in vae_state_dict if f\"down.{layer_id}\" in key] for layer_id in range(num_down_blocks)\n }\n\n # Retrieves the keys for the decoder up blocks only\n num_up_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in vae_state_dict if \"decoder.up\" in layer})\n up_blocks = {\n layer_id: [key for key in vae_state_dict if f\"up.{layer_id}\" in key] for layer_id in range(num_up_blocks)\n }\n\n for i in range(num_down_blocks):\n resnets = [key for key in down_blocks[i] if f\"down.{i}\" in key and f\"down.{i}.downsample\" not in key]\n\n if f\"encoder.down.{i}.downsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.weight\"] = vae_state_dict.pop(\n f\"encoder.down.{i}.downsample.conv.weight\"\n )\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.bias\"] = vae_state_dict.pop(\n f\"encoder.down.{i}.downsample.conv.bias\"\n )\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"down.{i}.block\", \"new\": f\"down_blocks.{i}.resnets\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_resnets = [key for key in vae_state_dict if \"encoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"encoder.mid.block_{i}\" in key]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_attentions = [key for key in vae_state_dict if \"encoder.mid.attn\" in key]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n conv_attn_to_linear(new_checkpoint)\n\n for i in range(num_up_blocks):\n block_id = num_up_blocks - 1 - i\n resnets = [\n key for key in up_blocks[block_id] if f\"up.{block_id}\" in key and f\"up.{block_id}.upsample\" not in key\n ]\n\n if f\"decoder.up.{block_id}.upsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.weight\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.weight\"\n ]\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.bias\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.bias\"\n ]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"up.{block_id}.block\", \"new\": f\"up_blocks.{i}.resnets\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_resnets = [key for key in vae_state_dict if \"decoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"decoder.mid.block_{i}\" in key]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_attentions = [key for key in vae_state_dict if \"decoder.mid.attn\" in key]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n conv_attn_to_linear(new_checkpoint)\n return new_checkpoint\n\n\nCLAP_KEYS_TO_MODIFY_MAPPING = {\n \"text_branch\": \"text_model\",\n \"attn\": \"attention.self\",\n \"self.proj\": \"output.dense\",\n \"attention.self_mask\": \"attn_mask\",\n \"mlp.fc1\": \"intermediate.dense\",\n \"mlp.fc2\": \"output.dense\",\n \"norm1\": \"layernorm_before\",\n \"norm2\": \"layernorm_after\",\n \"bn0\": \"batch_norm\",\n}\n\nCLAP_KEYS_TO_IGNORE = [\"text_transform\"]\n\nCLAP_EXPECTED_MISSING_KEYS = [\"text_model.embeddings.token_type_ids\"]\n\n\ndef convert_open_clap_checkpoint(checkpoint):\n \"\"\"\n Takes a state dict and returns a converted CLAP checkpoint.\n \"\"\"\n # extract state dict for CLAP text embedding model, discarding the audio component\n model_state_dict = {}\n model_key = \"cond_stage_model.model.text_\"\n keys = list(checkpoint.keys())\n for key in keys:\n if key.startswith(model_key):\n model_state_dict[key.replace(model_key, \"text_\")] = checkpoint.get(key)\n\n new_checkpoint = {}\n\n sequential_layers_pattern = r\".*sequential.(\\d+).*\"\n text_projection_pattern = r\".*_projection.(\\d+).*\"\n\n for key, value in model_state_dict.items():\n # check if key should be ignored in mapping\n if key.split(\".\")[0] in CLAP_KEYS_TO_IGNORE:\n continue\n\n # check if any key needs to be modified\n for key_to_modify, new_key in CLAP_KEYS_TO_MODIFY_MAPPING.items():\n if key_to_modify in key:\n key = key.replace(key_to_modify, new_key)\n\n if re.match(sequential_layers_pattern, key):\n # replace sequential layers with list\n sequential_layer = re.match(sequential_layers_pattern, key).group(1)\n\n key = key.replace(f\"sequential.{sequential_layer}.\", f\"layers.{int(sequential_layer)//3}.linear.\")\n elif re.match(text_projection_pattern, key):\n projecton_layer = int(re.match(text_projection_pattern, key).group(1))\n\n # Because in CLAP they use `nn.Sequential`...\n transformers_projection_layer = 1 if projecton_layer == 0 else 2\n\n key = key.replace(f\"_projection.{projecton_layer}.\", f\"_projection.linear{transformers_projection_layer}.\")\n\n if \"audio\" and \"qkv\" in key:\n # split qkv into query key and value\n mixed_qkv = value\n qkv_dim = mixed_qkv.size(0) // 3\n\n query_layer = mixed_qkv[:qkv_dim]\n key_layer = mixed_qkv[qkv_dim : qkv_dim * 2]\n value_layer = mixed_qkv[qkv_dim * 2 :]\n\n new_checkpoint[key.replace(\"qkv\", \"query\")] = query_layer\n new_checkpoint[key.replace(\"qkv\", \"key\")] = key_layer\n new_checkpoint[key.replace(\"qkv\", \"value\")] = value_layer\n else:\n new_checkpoint[key] = value\n\n return new_checkpoint\n\n\ndef create_transformers_vocoder_config(original_config):\n \"\"\"\n Creates a config for transformers SpeechT5HifiGan based on the config of the vocoder model.\n \"\"\"\n vocoder_params = original_config[\"model\"][\"params\"][\"vocoder_config\"][\"params\"]\n\n config = {\n \"model_in_dim\": vocoder_params[\"num_mels\"],\n \"sampling_rate\": vocoder_params[\"sampling_rate\"],\n \"upsample_initial_channel\": vocoder_params[\"upsample_initial_channel\"],\n \"upsample_rates\": list(vocoder_params[\"upsample_rates\"]),\n \"upsample_kernel_sizes\": list(vocoder_params[\"upsample_kernel_sizes\"]),\n \"resblock_kernel_sizes\": list(vocoder_params[\"resblock_kernel_sizes\"]),\n \"resblock_dilation_sizes\": [\n list(resblock_dilation) for resblock_dilation in vocoder_params[\"resblock_dilation_sizes\"]\n ],\n \"normalize_before\": False,\n }\n\n return config\n\n\ndef convert_hifigan_checkpoint(checkpoint, config):\n \"\"\"\n Takes a state dict and config, and returns a converted HiFiGAN vocoder checkpoint.\n \"\"\"\n # extract state dict for vocoder\n vocoder_state_dict = {}\n vocoder_key = \"first_stage_model.vocoder.\"\n keys = list(checkpoint.keys())\n for key in keys:\n if key.startswith(vocoder_key):\n vocoder_state_dict[key.replace(vocoder_key, \"\")] = checkpoint.get(key)\n\n # fix upsampler keys, everything else is correct already\n for i in range(len(config.upsample_rates)):\n vocoder_state_dict[f\"upsampler.{i}.weight\"] = vocoder_state_dict.pop(f\"ups.{i}.weight\")\n vocoder_state_dict[f\"upsampler.{i}.bias\"] = vocoder_state_dict.pop(f\"ups.{i}.bias\")\n\n if not config.normalize_before:\n # if we don't set normalize_before then these variables are unused, so we set them to their initialised values\n vocoder_state_dict[\"mean\"] = torch.zeros(config.model_in_dim)\n vocoder_state_dict[\"scale\"] = torch.ones(config.model_in_dim)\n\n return vocoder_state_dict\n\n\n# Adapted from https://huggingface.co/spaces/haoheliu/audioldm-text-to-audio-generation/blob/84a0384742a22bd80c44e903e241f0623e874f1d/audioldm/utils.py#L72-L73\nDEFAULT_CONFIG = {\n \"model\": {\n \"params\": {\n \"linear_start\": 0.0015,\n \"linear_end\": 0.0195,\n \"timesteps\": 1000,\n \"channels\": 8,\n \"scale_by_std\": True,\n \"unet_config\": {\n \"target\": \"audioldm.latent_diffusion.openaimodel.UNetModel\",\n \"params\": {\n \"extra_film_condition_dim\": 512,\n \"extra_film_use_concat\": True,\n \"in_channels\": 8,\n \"out_channels\": 8,\n \"model_channels\": 128,\n \"attention_resolutions\": [8, 4, 2],\n \"num_res_blocks\": 2,\n \"channel_mult\": [1, 2, 3, 5],\n \"num_head_channels\": 32,\n },\n },\n \"first_stage_config\": {\n \"target\": \"audioldm.variational_autoencoder.autoencoder.AutoencoderKL\",\n \"params\": {\n \"embed_dim\": 8,\n \"ddconfig\": {\n \"z_channels\": 8,\n \"resolution\": 256,\n \"in_channels\": 1,\n \"out_ch\": 1,\n \"ch\": 128,\n \"ch_mult\": [1, 2, 4],\n \"num_res_blocks\": 2,\n },\n },\n },\n \"vocoder_config\": {\n \"target\": \"audioldm.first_stage_model.vocoder\",\n \"params\": {\n \"upsample_rates\": [5, 4, 2, 2, 2],\n \"upsample_kernel_sizes\": [16, 16, 8, 4, 4],\n \"upsample_initial_channel\": 1024,\n \"resblock_kernel_sizes\": [3, 7, 11],\n \"resblock_dilation_sizes\": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],\n \"num_mels\": 64,\n \"sampling_rate\": 16000,\n },\n },\n },\n },\n}\n\n\ndef load_pipeline_from_original_audioldm_ckpt(\n checkpoint_path: str,\n original_config_file: str = None,\n image_size: int = 512,\n prediction_type: str = None,\n extract_ema: bool = False,\n scheduler_type: str = \"ddim\",\n num_in_channels: int = None,\n model_channels: int = None,\n num_head_channels: int = None,\n device: str = None,\n from_safetensors: bool = False,\n) -> AudioLDMPipeline:\n \"\"\"\n Load an AudioLDM pipeline object from a `.ckpt`/`.safetensors` file and (ideally) a `.yaml` config file.\n\n Although many of the arguments can be automatically inferred, some of these rely on brittle checks against the\n global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is\n recommended that you override the default values and/or supply an `original_config_file` wherever possible.\n\n Args:\n checkpoint_path (`str`): Path to `.ckpt` file.\n original_config_file (`str`):\n Path to `.yaml` config file corresponding to the original architecture. If `None`, will be automatically\n set to the audioldm-s-full-v2 config.\n image_size (`int`, *optional*, defaults to 512):\n The image size that the model was trained on.\n prediction_type (`str`, *optional*):\n The prediction type that the model was trained on. If `None`, will be automatically\n inferred by looking for a key in the config. For the default config, the prediction type is `'epsilon'`.\n num_in_channels (`int`, *optional*, defaults to None):\n The number of UNet input channels. If `None`, it will be automatically inferred from the config.\n model_channels (`int`, *optional*, defaults to None):\n The number of UNet model channels. If `None`, it will be automatically inferred from the config. Override\n to 128 for the small checkpoints, 192 for the medium checkpoints and 256 for the large.\n num_head_channels (`int`, *optional*, defaults to None):\n The number of UNet head channels. If `None`, it will be automatically inferred from the config. Override\n to 32 for the small and medium checkpoints, and 64 for the large.\n scheduler_type (`str`, *optional*, defaults to 'pndm'):\n Type of scheduler to use. Should be one of `[\"pndm\", \"lms\", \"heun\", \"euler\", \"euler-ancestral\", \"dpm\",\n \"ddim\"]`.\n extract_ema (`bool`, *optional*, defaults to `False`): Only relevant for\n checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults to\n `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher quality images for\n inference. Non-EMA weights are usually better to continue fine-tuning.\n device (`str`, *optional*, defaults to `None`):\n The device to use. Pass `None` to determine automatically.\n from_safetensors (`str`, *optional*, defaults to `False`):\n If `checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.\n return: An AudioLDMPipeline object representing the passed-in `.ckpt`/`.safetensors` file.\n \"\"\"\n\n if from_safetensors:\n from safetensors import safe_open\n\n checkpoint = {}\n with safe_open(checkpoint_path, framework=\"pt\", device=\"cpu\") as f:\n for key in f.keys():\n checkpoint[key] = f.get_tensor(key)\n else:\n if device is None:\n device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n checkpoint = torch.load(checkpoint_path, map_location=device)\n else:\n checkpoint = torch.load(checkpoint_path, map_location=device)\n\n if \"state_dict\" in checkpoint:\n checkpoint = checkpoint[\"state_dict\"]\n\n if original_config_file is None:\n original_config = DEFAULT_CONFIG\n else:\n original_config = yaml.safe_load(original_config_file)\n\n if num_in_channels is not None:\n original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"][\"in_channels\"] = num_in_channels\n\n if model_channels is not None:\n original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"][\"model_channels\"] = model_channels\n\n if num_head_channels is not None:\n original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"][\"num_head_channels\"] = num_head_channels\n\n if (\n \"parameterization\" in original_config[\"model\"][\"params\"]\n and original_config[\"model\"][\"params\"][\"parameterization\"] == \"v\"\n ):\n if prediction_type is None:\n prediction_type = \"v_prediction\"\n else:\n if prediction_type is None:\n prediction_type = \"epsilon\"\n\n if image_size is None:\n image_size = 512\n\n num_train_timesteps = original_config[\"model\"][\"params\"][\"timesteps\"]\n beta_start = original_config[\"model\"][\"params\"][\"linear_start\"]\n beta_end = original_config[\"model\"][\"params\"][\"linear_end\"]\n\n scheduler = DDIMScheduler(\n beta_end=beta_end,\n beta_schedule=\"scaled_linear\",\n beta_start=beta_start,\n num_train_timesteps=num_train_timesteps,\n steps_offset=1,\n clip_sample=False,\n set_alpha_to_one=False,\n prediction_type=prediction_type,\n )\n # make sure scheduler works correctly with DDIM\n scheduler.register_to_config(clip_sample=False)\n\n if scheduler_type == \"pndm\":\n config = dict(scheduler.config)\n config[\"skip_prk_steps\"] = True\n scheduler = PNDMScheduler.from_config(config)\n elif scheduler_type == \"lms\":\n scheduler = LMSDiscreteScheduler.from_config(scheduler.config)\n elif scheduler_type == \"heun\":\n scheduler = HeunDiscreteScheduler.from_config(scheduler.config)\n elif scheduler_type == \"euler\":\n scheduler = EulerDiscreteScheduler.from_config(scheduler.config)\n elif scheduler_type == \"euler-ancestral\":\n scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config)\n elif scheduler_type == \"dpm\":\n scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)\n elif scheduler_type == \"ddim\":\n scheduler = scheduler\n else:\n raise ValueError(f\"Scheduler of type {scheduler_type} doesn't exist!\")\n\n # Convert the UNet2DModel\n unet_config = create_unet_diffusers_config(original_config, image_size=image_size)\n unet = UNet2DConditionModel(**unet_config)\n\n converted_unet_checkpoint = convert_ldm_unet_checkpoint(\n checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema\n )\n\n unet.load_state_dict(converted_unet_checkpoint)\n\n # Convert the VAE model\n vae_config = create_vae_diffusers_config(original_config, checkpoint=checkpoint, image_size=image_size)\n converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)\n\n vae = AutoencoderKL(**vae_config)\n vae.load_state_dict(converted_vae_checkpoint)\n\n # Convert the text model\n # AudioLDM uses the same configuration and tokenizer as the original CLAP model\n config = ClapTextConfig.from_pretrained(\"laion/clap-htsat-unfused\")\n tokenizer = AutoTokenizer.from_pretrained(\"laion/clap-htsat-unfused\")\n\n converted_text_model = convert_open_clap_checkpoint(checkpoint)\n text_model = ClapTextModelWithProjection(config)\n\n missing_keys, unexpected_keys = text_model.load_state_dict(converted_text_model, strict=False)\n # we expect not to have token_type_ids in our original state dict so let's ignore them\n missing_keys = list(set(missing_keys) - set(CLAP_EXPECTED_MISSING_KEYS))\n\n if len(unexpected_keys) > 0:\n raise ValueError(f\"Unexpected keys when loading CLAP model: {unexpected_keys}\")\n\n if len(missing_keys) > 0:\n raise ValueError(f\"Missing keys when loading CLAP model: {missing_keys}\")\n\n # Convert the vocoder model\n vocoder_config = create_transformers_vocoder_config(original_config)\n vocoder_config = SpeechT5HifiGanConfig(**vocoder_config)\n converted_vocoder_checkpoint = convert_hifigan_checkpoint(checkpoint, vocoder_config)\n\n vocoder = SpeechT5HifiGan(vocoder_config)\n vocoder.load_state_dict(converted_vocoder_checkpoint)\n\n # Instantiate the diffusers pipeline\n pipe = AudioLDMPipeline(\n vae=vae,\n text_encoder=text_model,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n vocoder=vocoder,\n )\n\n return pipe\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--checkpoint_path\", default=None, type=str, required=True, help=\"Path to the checkpoint to convert.\"\n )\n parser.add_argument(\n \"--original_config_file\",\n default=None,\n type=str,\n help=\"The YAML config file corresponding to the original architecture.\",\n )\n parser.add_argument(\n \"--num_in_channels\",\n default=None,\n type=int,\n help=\"The number of input channels. If `None` number of input channels will be automatically inferred.\",\n )\n parser.add_argument(\n \"--model_channels\",\n default=None,\n type=int,\n help=\"The number of UNet model channels. If `None`, it will be automatically inferred from the config. Override\"\n \" to 128 for the small checkpoints, 192 for the medium checkpoints and 256 for the large.\",\n )\n parser.add_argument(\n \"--num_head_channels\",\n default=None,\n type=int,\n help=\"The number of UNet head channels. If `None`, it will be automatically inferred from the config. Override\"\n \" to 32 for the small and medium checkpoints, and 64 for the large.\",\n )\n parser.add_argument(\n \"--scheduler_type\",\n default=\"ddim\",\n type=str,\n help=\"Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']\",\n )\n parser.add_argument(\n \"--image_size\",\n default=None,\n type=int,\n help=(\"The image size that the model was trained on.\"),\n )\n parser.add_argument(\n \"--prediction_type\",\n default=None,\n type=str,\n help=(\"The prediction type that the model was trained on.\"),\n )\n parser.add_argument(\n \"--extract_ema\",\n action=\"store_true\",\n help=(\n \"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights\"\n \" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield\"\n \" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.\"\n ),\n )\n parser.add_argument(\n \"--from_safetensors\",\n action=\"store_true\",\n help=\"If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.\",\n )\n parser.add_argument(\n \"--to_safetensors\",\n action=\"store_true\",\n help=\"Whether to store pipeline in safetensors format or not.\",\n )\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n parser.add_argument(\"--device\", type=str, help=\"Device to use (e.g. cpu, cuda:0, cuda:1, etc.)\")\n args = parser.parse_args()\n\n pipe = load_pipeline_from_original_audioldm_ckpt(\n checkpoint_path=args.checkpoint_path,\n original_config_file=args.original_config_file,\n image_size=args.image_size,\n prediction_type=args.prediction_type,\n extract_ema=args.extract_ema,\n scheduler_type=args.scheduler_type,\n num_in_channels=args.num_in_channels,\n model_channels=args.model_channels,\n num_head_channels=args.num_head_channels,\n from_safetensors=args.from_safetensors,\n device=args.device,\n )\n pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)\n" }, { "path": "diffusers/scripts/convert_original_controlnet_to_diffusers.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Conversion script for stable diffusion checkpoints which _only_ contain a controlnet.\"\"\"\n\nimport argparse\n\nfrom diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--checkpoint_path\", default=None, type=str, required=True, help=\"Path to the checkpoint to convert.\"\n )\n parser.add_argument(\n \"--original_config_file\",\n type=str,\n required=True,\n help=\"The YAML config file corresponding to the original architecture.\",\n )\n parser.add_argument(\n \"--num_in_channels\",\n default=None,\n type=int,\n help=\"The number of input channels. If `None` number of input channels will be automatically inferred.\",\n )\n parser.add_argument(\n \"--image_size\",\n default=512,\n type=int,\n help=(\n \"The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2\"\n \" Base. Use 768 for Stable Diffusion v2.\"\n ),\n )\n parser.add_argument(\n \"--extract_ema\",\n action=\"store_true\",\n help=(\n \"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights\"\n \" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield\"\n \" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.\"\n ),\n )\n parser.add_argument(\n \"--upcast_attention\",\n action=\"store_true\",\n help=(\n \"Whether the attention computation should always be upcasted. This is necessary when running stable\"\n \" diffusion 2.1.\"\n ),\n )\n parser.add_argument(\n \"--from_safetensors\",\n action=\"store_true\",\n help=\"If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.\",\n )\n parser.add_argument(\n \"--to_safetensors\",\n action=\"store_true\",\n help=\"Whether to store pipeline in safetensors format or not.\",\n )\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n parser.add_argument(\"--device\", type=str, help=\"Device to use (e.g. cpu, cuda:0, cuda:1, etc.)\")\n\n # small workaround to get argparser to parse a boolean input as either true _or_ false\n def parse_bool(string):\n if string == \"True\":\n return True\n elif string == \"False\":\n return False\n else:\n raise ValueError(f\"could not parse string as bool {string}\")\n\n parser.add_argument(\n \"--use_linear_projection\", help=\"Override for use linear projection\", required=False, type=parse_bool\n )\n\n parser.add_argument(\"--cross_attention_dim\", help=\"Override for cross attention_dim\", required=False, type=int)\n\n args = parser.parse_args()\n\n controlnet = download_controlnet_from_original_ckpt(\n checkpoint_path=args.checkpoint_path,\n original_config_file=args.original_config_file,\n image_size=args.image_size,\n extract_ema=args.extract_ema,\n num_in_channels=args.num_in_channels,\n upcast_attention=args.upcast_attention,\n from_safetensors=args.from_safetensors,\n device=args.device,\n use_linear_projection=args.use_linear_projection,\n cross_attention_dim=args.cross_attention_dim,\n )\n\n controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)\n" }, { "path": "diffusers/scripts/convert_original_musicldm_to_diffusers.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Conversion script for the MusicLDM checkpoints.\"\"\"\n\nimport argparse\nimport re\n\nimport torch\nimport yaml\nfrom transformers import (\n AutoFeatureExtractor,\n AutoTokenizer,\n ClapConfig,\n ClapModel,\n SpeechT5HifiGan,\n SpeechT5HifiGanConfig,\n)\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n HeunDiscreteScheduler,\n LMSDiscreteScheduler,\n MusicLDMPipeline,\n PNDMScheduler,\n UNet2DConditionModel,\n)\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.shave_segments\ndef shave_segments(path, n_shave_prefix_segments=1):\n \"\"\"\n Removes segments. Positive values shave the first segments, negative shave the last segments.\n \"\"\"\n if n_shave_prefix_segments >= 0:\n return \".\".join(path.split(\".\")[n_shave_prefix_segments:])\n else:\n return \".\".join(path.split(\".\")[:n_shave_prefix_segments])\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_resnet_paths\ndef renew_resnet_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside resnets to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item.replace(\"in_layers.0\", \"norm1\")\n new_item = new_item.replace(\"in_layers.2\", \"conv1\")\n\n new_item = new_item.replace(\"out_layers.0\", \"norm2\")\n new_item = new_item.replace(\"out_layers.3\", \"conv2\")\n\n new_item = new_item.replace(\"emb_layers.1\", \"time_emb_proj\")\n new_item = new_item.replace(\"skip_connection\", \"conv_shortcut\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_vae_resnet_paths\ndef renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside resnets to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n new_item = new_item.replace(\"nin_shortcut\", \"conv_shortcut\")\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_attention_paths\ndef renew_attention_paths(old_list):\n \"\"\"\n Updates paths inside attentions to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n # new_item = new_item.replace('norm.weight', 'group_norm.weight')\n # new_item = new_item.replace('norm.bias', 'group_norm.bias')\n\n # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')\n # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')\n\n # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside attentions to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n new_item = new_item.replace(\"norm.weight\", \"group_norm.weight\")\n new_item = new_item.replace(\"norm.bias\", \"group_norm.bias\")\n\n new_item = new_item.replace(\"q.weight\", \"to_q.weight\")\n new_item = new_item.replace(\"q.bias\", \"to_q.bias\")\n\n new_item = new_item.replace(\"k.weight\", \"to_k.weight\")\n new_item = new_item.replace(\"k.bias\", \"to_k.bias\")\n\n new_item = new_item.replace(\"v.weight\", \"to_v.weight\")\n new_item = new_item.replace(\"v.bias\", \"to_v.bias\")\n\n new_item = new_item.replace(\"proj_out.weight\", \"to_out.0.weight\")\n new_item = new_item.replace(\"proj_out.bias\", \"to_out.0.bias\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.assign_to_checkpoint\ndef assign_to_checkpoint(\n paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None\n):\n \"\"\"\n This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits\n attention layers, and takes into account additional replacements that may arise.\n\n Assigns the weights to the new checkpoint.\n \"\"\"\n assert isinstance(paths, list), \"Paths should be a list of dicts containing 'old' and 'new' keys.\"\n\n # Splits the attention layers into three variables.\n if attention_paths_to_split is not None:\n for path, path_map in attention_paths_to_split.items():\n old_tensor = old_checkpoint[path]\n channels = old_tensor.shape[0] // 3\n\n target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)\n\n num_heads = old_tensor.shape[0] // config[\"num_head_channels\"] // 3\n\n old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])\n query, key, value = old_tensor.split(channels // num_heads, dim=1)\n\n checkpoint[path_map[\"query\"]] = query.reshape(target_shape)\n checkpoint[path_map[\"key\"]] = key.reshape(target_shape)\n checkpoint[path_map[\"value\"]] = value.reshape(target_shape)\n\n for path in paths:\n new_path = path[\"new\"]\n\n # These have already been assigned\n if attention_paths_to_split is not None and new_path in attention_paths_to_split:\n continue\n\n # Global renaming happens here\n new_path = new_path.replace(\"middle_block.0\", \"mid_block.resnets.0\")\n new_path = new_path.replace(\"middle_block.1\", \"mid_block.attentions.0\")\n new_path = new_path.replace(\"middle_block.2\", \"mid_block.resnets.1\")\n\n if additional_replacements is not None:\n for replacement in additional_replacements:\n new_path = new_path.replace(replacement[\"old\"], replacement[\"new\"])\n\n # proj_attn.weight has to be converted from conv 1D to linear\n if \"proj_attn.weight\" in new_path:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]][:, :, 0]\n else:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]]\n\n\ndef conv_attn_to_linear(checkpoint):\n keys = list(checkpoint.keys())\n attn_keys = [\"to_q.weight\", \"to_k.weight\", \"to_v.weight\"]\n proj_key = \"to_out.0.weight\"\n for key in keys:\n if \".\".join(key.split(\".\")[-2:]) in attn_keys or \".\".join(key.split(\".\")[-3:]) == proj_key:\n if checkpoint[key].ndim > 2:\n checkpoint[key] = checkpoint[key].squeeze()\n\n\ndef create_unet_diffusers_config(original_config, image_size: int):\n \"\"\"\n Creates a UNet config for diffusers based on the config of the original MusicLDM model.\n \"\"\"\n unet_params = original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"]\n vae_params = original_config[\"model\"][\"params\"][\"first_stage_config\"][\"params\"][\"ddconfig\"]\n\n block_out_channels = [unet_params[\"model_channels\"] * mult for mult in unet_params[\"channel_mult\"]]\n\n down_block_types = []\n resolution = 1\n for i in range(len(block_out_channels)):\n block_type = \"CrossAttnDownBlock2D\" if resolution in unet_params[\"attention_resolutions\"] else \"DownBlock2D\"\n down_block_types.append(block_type)\n if i != len(block_out_channels) - 1:\n resolution *= 2\n\n up_block_types = []\n for i in range(len(block_out_channels)):\n block_type = \"CrossAttnUpBlock2D\" if resolution in unet_params[\"attention_resolutions\"] else \"UpBlock2D\"\n up_block_types.append(block_type)\n resolution //= 2\n\n vae_scale_factor = 2 ** (len(vae_params[\"ch_mult\"]) - 1)\n\n cross_attention_dim = (\n unet_params[\"cross_attention_dim\"] if \"cross_attention_dim\" in unet_params else block_out_channels\n )\n\n class_embed_type = \"simple_projection\" if \"extra_film_condition_dim\" in unet_params else None\n projection_class_embeddings_input_dim = (\n unet_params[\"extra_film_condition_dim\"] if \"extra_film_condition_dim\" in unet_params else None\n )\n class_embeddings_concat = unet_params[\"extra_film_use_concat\"] if \"extra_film_use_concat\" in unet_params else None\n\n config = {\n \"sample_size\": image_size // vae_scale_factor,\n \"in_channels\": unet_params[\"in_channels\"],\n \"out_channels\": unet_params[\"out_channels\"],\n \"down_block_types\": tuple(down_block_types),\n \"up_block_types\": tuple(up_block_types),\n \"block_out_channels\": tuple(block_out_channels),\n \"layers_per_block\": unet_params[\"num_res_blocks\"],\n \"cross_attention_dim\": cross_attention_dim,\n \"class_embed_type\": class_embed_type,\n \"projection_class_embeddings_input_dim\": projection_class_embeddings_input_dim,\n \"class_embeddings_concat\": class_embeddings_concat,\n }\n\n return config\n\n\n# Adapted from diffusers.pipelines.stable_diffusion.convert_from_ckpt.create_vae_diffusers_config\ndef create_vae_diffusers_config(original_config, checkpoint, image_size: int):\n \"\"\"\n Creates a VAE config for diffusers based on the config of the original MusicLDM model. Compared to the original\n Stable Diffusion conversion, this function passes a *learnt* VAE scaling factor to the diffusers VAE.\n \"\"\"\n vae_params = original_config[\"model\"][\"params\"][\"first_stage_config\"][\"params\"][\"ddconfig\"]\n _ = original_config[\"model\"][\"params\"][\"first_stage_config\"][\"params\"][\"embed_dim\"]\n\n block_out_channels = [vae_params[\"ch\"] * mult for mult in vae_params[\"ch_mult\"]]\n down_block_types = [\"DownEncoderBlock2D\"] * len(block_out_channels)\n up_block_types = [\"UpDecoderBlock2D\"] * len(block_out_channels)\n\n scaling_factor = checkpoint[\"scale_factor\"] if \"scale_by_std\" in original_config[\"model\"][\"params\"] else 0.18215\n\n config = {\n \"sample_size\": image_size,\n \"in_channels\": vae_params[\"in_channels\"],\n \"out_channels\": vae_params[\"out_ch\"],\n \"down_block_types\": tuple(down_block_types),\n \"up_block_types\": tuple(up_block_types),\n \"block_out_channels\": tuple(block_out_channels),\n \"latent_channels\": vae_params[\"z_channels\"],\n \"layers_per_block\": vae_params[\"num_res_blocks\"],\n \"scaling_factor\": float(scaling_factor),\n }\n return config\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.create_diffusers_schedular\ndef create_diffusers_schedular(original_config):\n schedular = DDIMScheduler(\n num_train_timesteps=original_config[\"model\"][\"params\"][\"timesteps\"],\n beta_start=original_config[\"model\"][\"params\"][\"linear_start\"],\n beta_end=original_config[\"model\"][\"params\"][\"linear_end\"],\n beta_schedule=\"scaled_linear\",\n )\n return schedular\n\n\ndef convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False):\n \"\"\"\n Takes a state dict and a config, and returns a converted checkpoint. Compared to the original Stable Diffusion\n conversion, this function additionally converts the learnt film embedding linear layer.\n \"\"\"\n\n # extract state_dict for UNet\n unet_state_dict = {}\n keys = list(checkpoint.keys())\n\n unet_key = \"model.diffusion_model.\"\n # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA\n if sum(k.startswith(\"model_ema\") for k in keys) > 100 and extract_ema:\n print(f\"Checkpoint {path} has both EMA and non-EMA weights.\")\n print(\n \"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA\"\n \" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.\"\n )\n for key in keys:\n if key.startswith(\"model.diffusion_model\"):\n flat_ema_key = \"model_ema.\" + \"\".join(key.split(\".\")[1:])\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint.pop(flat_ema_key)\n else:\n if sum(k.startswith(\"model_ema\") for k in keys) > 100:\n print(\n \"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA\"\n \" weights (usually better for inference), please make sure to add the `--extract_ema` flag.\"\n )\n\n for key in keys:\n if key.startswith(unet_key):\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint.pop(key)\n\n new_checkpoint = {}\n\n new_checkpoint[\"time_embedding.linear_1.weight\"] = unet_state_dict[\"time_embed.0.weight\"]\n new_checkpoint[\"time_embedding.linear_1.bias\"] = unet_state_dict[\"time_embed.0.bias\"]\n new_checkpoint[\"time_embedding.linear_2.weight\"] = unet_state_dict[\"time_embed.2.weight\"]\n new_checkpoint[\"time_embedding.linear_2.bias\"] = unet_state_dict[\"time_embed.2.bias\"]\n\n new_checkpoint[\"class_embedding.weight\"] = unet_state_dict[\"film_emb.weight\"]\n new_checkpoint[\"class_embedding.bias\"] = unet_state_dict[\"film_emb.bias\"]\n\n new_checkpoint[\"conv_in.weight\"] = unet_state_dict[\"input_blocks.0.0.weight\"]\n new_checkpoint[\"conv_in.bias\"] = unet_state_dict[\"input_blocks.0.0.bias\"]\n\n new_checkpoint[\"conv_norm_out.weight\"] = unet_state_dict[\"out.0.weight\"]\n new_checkpoint[\"conv_norm_out.bias\"] = unet_state_dict[\"out.0.bias\"]\n new_checkpoint[\"conv_out.weight\"] = unet_state_dict[\"out.2.weight\"]\n new_checkpoint[\"conv_out.bias\"] = unet_state_dict[\"out.2.bias\"]\n\n # Retrieves the keys for the input blocks only\n num_input_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"input_blocks\" in layer})\n input_blocks = {\n layer_id: [key for key in unet_state_dict if f\"input_blocks.{layer_id}\" in key]\n for layer_id in range(num_input_blocks)\n }\n\n # Retrieves the keys for the middle blocks only\n num_middle_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"middle_block\" in layer})\n middle_blocks = {\n layer_id: [key for key in unet_state_dict if f\"middle_block.{layer_id}\" in key]\n for layer_id in range(num_middle_blocks)\n }\n\n # Retrieves the keys for the output blocks only\n num_output_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"output_blocks\" in layer})\n output_blocks = {\n layer_id: [key for key in unet_state_dict if f\"output_blocks.{layer_id}\" in key]\n for layer_id in range(num_output_blocks)\n }\n\n for i in range(1, num_input_blocks):\n block_id = (i - 1) // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = (i - 1) % (config[\"layers_per_block\"] + 1)\n\n resnets = [\n key for key in input_blocks[i] if f\"input_blocks.{i}.0\" in key and f\"input_blocks.{i}.0.op\" not in key\n ]\n attentions = [key for key in input_blocks[i] if f\"input_blocks.{i}.1\" in key]\n\n if f\"input_blocks.{i}.0.op.weight\" in unet_state_dict:\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.weight\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.op.weight\"\n )\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.bias\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.op.bias\"\n )\n\n paths = renew_resnet_paths(resnets)\n meta_path = {\"old\": f\"input_blocks.{i}.0\", \"new\": f\"down_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n meta_path = {\"old\": f\"input_blocks.{i}.1\", \"new\": f\"down_blocks.{block_id}.attentions.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n resnet_0 = middle_blocks[0]\n attentions = middle_blocks[1]\n resnet_1 = middle_blocks[2]\n\n resnet_0_paths = renew_resnet_paths(resnet_0)\n assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)\n\n resnet_1_paths = renew_resnet_paths(resnet_1)\n assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)\n\n attentions_paths = renew_attention_paths(attentions)\n meta_path = {\"old\": \"middle_block.1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(\n attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n for i in range(num_output_blocks):\n block_id = i // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = i % (config[\"layers_per_block\"] + 1)\n output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]\n output_block_list = {}\n\n for layer in output_block_layers:\n layer_id, layer_name = layer.split(\".\")[0], shave_segments(layer, 1)\n if layer_id in output_block_list:\n output_block_list[layer_id].append(layer_name)\n else:\n output_block_list[layer_id] = [layer_name]\n\n if len(output_block_list) > 1:\n resnets = [key for key in output_blocks[i] if f\"output_blocks.{i}.0\" in key]\n attentions = [key for key in output_blocks[i] if f\"output_blocks.{i}.1\" in key]\n\n resnet_0_paths = renew_resnet_paths(resnets)\n paths = renew_resnet_paths(resnets)\n\n meta_path = {\"old\": f\"output_blocks.{i}.0\", \"new\": f\"up_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n output_block_list = {k: sorted(v) for k, v in output_block_list.items()}\n if [\"conv.bias\", \"conv.weight\"] in output_block_list.values():\n index = list(output_block_list.values()).index([\"conv.bias\", \"conv.weight\"])\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.weight\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.weight\"\n ]\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.bias\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.bias\"\n ]\n\n # Clear attentions as they have been attributed above.\n if len(attentions) == 2:\n attentions = []\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n meta_path = {\n \"old\": f\"output_blocks.{i}.1\",\n \"new\": f\"up_blocks.{block_id}.attentions.{layer_in_block_id}\",\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n else:\n resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)\n for path in resnet_0_paths:\n old_path = \".\".join([\"output_blocks\", str(i), path[\"old\"]])\n new_path = \".\".join([\"up_blocks\", str(block_id), \"resnets\", str(layer_in_block_id), path[\"new\"]])\n\n new_checkpoint[new_path] = unet_state_dict[old_path]\n\n return new_checkpoint\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.convert_ldm_vae_checkpoint\ndef convert_ldm_vae_checkpoint(checkpoint, config):\n # extract state dict for VAE\n vae_state_dict = {}\n vae_key = \"first_stage_model.\"\n keys = list(checkpoint.keys())\n for key in keys:\n if key.startswith(vae_key):\n vae_state_dict[key.replace(vae_key, \"\")] = checkpoint.get(key)\n\n new_checkpoint = {}\n\n new_checkpoint[\"encoder.conv_in.weight\"] = vae_state_dict[\"encoder.conv_in.weight\"]\n new_checkpoint[\"encoder.conv_in.bias\"] = vae_state_dict[\"encoder.conv_in.bias\"]\n new_checkpoint[\"encoder.conv_out.weight\"] = vae_state_dict[\"encoder.conv_out.weight\"]\n new_checkpoint[\"encoder.conv_out.bias\"] = vae_state_dict[\"encoder.conv_out.bias\"]\n new_checkpoint[\"encoder.conv_norm_out.weight\"] = vae_state_dict[\"encoder.norm_out.weight\"]\n new_checkpoint[\"encoder.conv_norm_out.bias\"] = vae_state_dict[\"encoder.norm_out.bias\"]\n\n new_checkpoint[\"decoder.conv_in.weight\"] = vae_state_dict[\"decoder.conv_in.weight\"]\n new_checkpoint[\"decoder.conv_in.bias\"] = vae_state_dict[\"decoder.conv_in.bias\"]\n new_checkpoint[\"decoder.conv_out.weight\"] = vae_state_dict[\"decoder.conv_out.weight\"]\n new_checkpoint[\"decoder.conv_out.bias\"] = vae_state_dict[\"decoder.conv_out.bias\"]\n new_checkpoint[\"decoder.conv_norm_out.weight\"] = vae_state_dict[\"decoder.norm_out.weight\"]\n new_checkpoint[\"decoder.conv_norm_out.bias\"] = vae_state_dict[\"decoder.norm_out.bias\"]\n\n new_checkpoint[\"quant_conv.weight\"] = vae_state_dict[\"quant_conv.weight\"]\n new_checkpoint[\"quant_conv.bias\"] = vae_state_dict[\"quant_conv.bias\"]\n new_checkpoint[\"post_quant_conv.weight\"] = vae_state_dict[\"post_quant_conv.weight\"]\n new_checkpoint[\"post_quant_conv.bias\"] = vae_state_dict[\"post_quant_conv.bias\"]\n\n # Retrieves the keys for the encoder down blocks only\n num_down_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in vae_state_dict if \"encoder.down\" in layer})\n down_blocks = {\n layer_id: [key for key in vae_state_dict if f\"down.{layer_id}\" in key] for layer_id in range(num_down_blocks)\n }\n\n # Retrieves the keys for the decoder up blocks only\n num_up_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in vae_state_dict if \"decoder.up\" in layer})\n up_blocks = {\n layer_id: [key for key in vae_state_dict if f\"up.{layer_id}\" in key] for layer_id in range(num_up_blocks)\n }\n\n for i in range(num_down_blocks):\n resnets = [key for key in down_blocks[i] if f\"down.{i}\" in key and f\"down.{i}.downsample\" not in key]\n\n if f\"encoder.down.{i}.downsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.weight\"] = vae_state_dict.pop(\n f\"encoder.down.{i}.downsample.conv.weight\"\n )\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.bias\"] = vae_state_dict.pop(\n f\"encoder.down.{i}.downsample.conv.bias\"\n )\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"down.{i}.block\", \"new\": f\"down_blocks.{i}.resnets\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_resnets = [key for key in vae_state_dict if \"encoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"encoder.mid.block_{i}\" in key]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_attentions = [key for key in vae_state_dict if \"encoder.mid.attn\" in key]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n conv_attn_to_linear(new_checkpoint)\n\n for i in range(num_up_blocks):\n block_id = num_up_blocks - 1 - i\n resnets = [\n key for key in up_blocks[block_id] if f\"up.{block_id}\" in key and f\"up.{block_id}.upsample\" not in key\n ]\n\n if f\"decoder.up.{block_id}.upsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.weight\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.weight\"\n ]\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.bias\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.bias\"\n ]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"up.{block_id}.block\", \"new\": f\"up_blocks.{i}.resnets\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_resnets = [key for key in vae_state_dict if \"decoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"decoder.mid.block_{i}\" in key]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_attentions = [key for key in vae_state_dict if \"decoder.mid.attn\" in key]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n conv_attn_to_linear(new_checkpoint)\n return new_checkpoint\n\n\nCLAP_KEYS_TO_MODIFY_MAPPING = {\n \"text_branch\": \"text_model\",\n \"audio_branch\": \"audio_model.audio_encoder\",\n \"attn\": \"attention.self\",\n \"self.proj\": \"output.dense\",\n \"attention.self_mask\": \"attn_mask\",\n \"mlp.fc1\": \"intermediate.dense\",\n \"mlp.fc2\": \"output.dense\",\n \"norm1\": \"layernorm_before\",\n \"norm2\": \"layernorm_after\",\n \"bn0\": \"batch_norm\",\n}\n\nCLAP_KEYS_TO_IGNORE = [\n \"text_transform\",\n \"audio_transform\",\n \"stft\",\n \"logmel_extractor\",\n \"tscam_conv\",\n \"head\",\n \"attn_mask\",\n]\n\nCLAP_EXPECTED_MISSING_KEYS = [\"text_model.embeddings.token_type_ids\"]\n\n\ndef convert_open_clap_checkpoint(checkpoint):\n \"\"\"\n Takes a state dict and returns a converted CLAP checkpoint.\n \"\"\"\n # extract state dict for CLAP text embedding model, discarding the audio component\n model_state_dict = {}\n model_key = \"cond_stage_model.model.\"\n keys = list(checkpoint.keys())\n for key in keys:\n if key.startswith(model_key):\n model_state_dict[key.replace(model_key, \"\")] = checkpoint.get(key)\n\n new_checkpoint = {}\n\n sequential_layers_pattern = r\".*sequential.(\\d+).*\"\n text_projection_pattern = r\".*_projection.(\\d+).*\"\n\n for key, value in model_state_dict.items():\n # check if key should be ignored in mapping - if so map it to a key name that we'll filter out at the end\n for key_to_ignore in CLAP_KEYS_TO_IGNORE:\n if key_to_ignore in key:\n key = \"spectrogram\"\n\n # check if any key needs to be modified\n for key_to_modify, new_key in CLAP_KEYS_TO_MODIFY_MAPPING.items():\n if key_to_modify in key:\n key = key.replace(key_to_modify, new_key)\n\n if re.match(sequential_layers_pattern, key):\n # replace sequential layers with list\n sequential_layer = re.match(sequential_layers_pattern, key).group(1)\n\n key = key.replace(f\"sequential.{sequential_layer}.\", f\"layers.{int(sequential_layer)//3}.linear.\")\n elif re.match(text_projection_pattern, key):\n projecton_layer = int(re.match(text_projection_pattern, key).group(1))\n\n # Because in CLAP they use `nn.Sequential`...\n transformers_projection_layer = 1 if projecton_layer == 0 else 2\n\n key = key.replace(f\"_projection.{projecton_layer}.\", f\"_projection.linear{transformers_projection_layer}.\")\n\n if \"audio\" and \"qkv\" in key:\n # split qkv into query key and value\n mixed_qkv = value\n qkv_dim = mixed_qkv.size(0) // 3\n\n query_layer = mixed_qkv[:qkv_dim]\n key_layer = mixed_qkv[qkv_dim : qkv_dim * 2]\n value_layer = mixed_qkv[qkv_dim * 2 :]\n\n new_checkpoint[key.replace(\"qkv\", \"query\")] = query_layer\n new_checkpoint[key.replace(\"qkv\", \"key\")] = key_layer\n new_checkpoint[key.replace(\"qkv\", \"value\")] = value_layer\n elif key != \"spectrogram\":\n new_checkpoint[key] = value\n\n return new_checkpoint\n\n\ndef create_transformers_vocoder_config(original_config):\n \"\"\"\n Creates a config for transformers SpeechT5HifiGan based on the config of the vocoder model.\n \"\"\"\n vocoder_params = original_config[\"model\"][\"params\"][\"vocoder_config\"][\"params\"]\n\n config = {\n \"model_in_dim\": vocoder_params[\"num_mels\"],\n \"sampling_rate\": vocoder_params[\"sampling_rate\"],\n \"upsample_initial_channel\": vocoder_params[\"upsample_initial_channel\"],\n \"upsample_rates\": list(vocoder_params[\"upsample_rates\"]),\n \"upsample_kernel_sizes\": list(vocoder_params[\"upsample_kernel_sizes\"]),\n \"resblock_kernel_sizes\": list(vocoder_params[\"resblock_kernel_sizes\"]),\n \"resblock_dilation_sizes\": [\n list(resblock_dilation) for resblock_dilation in vocoder_params[\"resblock_dilation_sizes\"]\n ],\n \"normalize_before\": False,\n }\n\n return config\n\n\ndef convert_hifigan_checkpoint(checkpoint, config):\n \"\"\"\n Takes a state dict and config, and returns a converted HiFiGAN vocoder checkpoint.\n \"\"\"\n # extract state dict for vocoder\n vocoder_state_dict = {}\n vocoder_key = \"first_stage_model.vocoder.\"\n keys = list(checkpoint.keys())\n for key in keys:\n if key.startswith(vocoder_key):\n vocoder_state_dict[key.replace(vocoder_key, \"\")] = checkpoint.get(key)\n\n # fix upsampler keys, everything else is correct already\n for i in range(len(config.upsample_rates)):\n vocoder_state_dict[f\"upsampler.{i}.weight\"] = vocoder_state_dict.pop(f\"ups.{i}.weight\")\n vocoder_state_dict[f\"upsampler.{i}.bias\"] = vocoder_state_dict.pop(f\"ups.{i}.bias\")\n\n if not config.normalize_before:\n # if we don't set normalize_before then these variables are unused, so we set them to their initialised values\n vocoder_state_dict[\"mean\"] = torch.zeros(config.model_in_dim)\n vocoder_state_dict[\"scale\"] = torch.ones(config.model_in_dim)\n\n return vocoder_state_dict\n\n\n# Adapted from https://huggingface.co/spaces/haoheliu/MusicLDM-text-to-audio-generation/blob/84a0384742a22bd80c44e903e241f0623e874f1d/MusicLDM/utils.py#L72-L73\nDEFAULT_CONFIG = {\n \"model\": {\n \"params\": {\n \"linear_start\": 0.0015,\n \"linear_end\": 0.0195,\n \"timesteps\": 1000,\n \"channels\": 8,\n \"scale_by_std\": True,\n \"unet_config\": {\n \"target\": \"MusicLDM.latent_diffusion.openaimodel.UNetModel\",\n \"params\": {\n \"extra_film_condition_dim\": 512,\n \"extra_film_use_concat\": True,\n \"in_channels\": 8,\n \"out_channels\": 8,\n \"model_channels\": 128,\n \"attention_resolutions\": [8, 4, 2],\n \"num_res_blocks\": 2,\n \"channel_mult\": [1, 2, 3, 5],\n \"num_head_channels\": 32,\n },\n },\n \"first_stage_config\": {\n \"target\": \"MusicLDM.variational_autoencoder.autoencoder.AutoencoderKL\",\n \"params\": {\n \"embed_dim\": 8,\n \"ddconfig\": {\n \"z_channels\": 8,\n \"resolution\": 256,\n \"in_channels\": 1,\n \"out_ch\": 1,\n \"ch\": 128,\n \"ch_mult\": [1, 2, 4],\n \"num_res_blocks\": 2,\n },\n },\n },\n \"vocoder_config\": {\n \"target\": \"MusicLDM.first_stage_model.vocoder\",\n \"params\": {\n \"upsample_rates\": [5, 4, 2, 2, 2],\n \"upsample_kernel_sizes\": [16, 16, 8, 4, 4],\n \"upsample_initial_channel\": 1024,\n \"resblock_kernel_sizes\": [3, 7, 11],\n \"resblock_dilation_sizes\": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],\n \"num_mels\": 64,\n \"sampling_rate\": 16000,\n },\n },\n },\n },\n}\n\n\ndef load_pipeline_from_original_MusicLDM_ckpt(\n checkpoint_path: str,\n original_config_file: str = None,\n image_size: int = 1024,\n prediction_type: str = None,\n extract_ema: bool = False,\n scheduler_type: str = \"ddim\",\n num_in_channels: int = None,\n model_channels: int = None,\n num_head_channels: int = None,\n device: str = None,\n from_safetensors: bool = False,\n) -> MusicLDMPipeline:\n \"\"\"\n Load an MusicLDM pipeline object from a `.ckpt`/`.safetensors` file and (ideally) a `.yaml` config file.\n\n Although many of the arguments can be automatically inferred, some of these rely on brittle checks against the\n global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is\n recommended that you override the default values and/or supply an `original_config_file` wherever possible.\n\n Args:\n checkpoint_path (`str`): Path to `.ckpt` file.\n original_config_file (`str`):\n Path to `.yaml` config file corresponding to the original architecture. If `None`, will be automatically\n set to the MusicLDM-s-full-v2 config.\n image_size (`int`, *optional*, defaults to 1024):\n The image size that the model was trained on.\n prediction_type (`str`, *optional*):\n The prediction type that the model was trained on. If `None`, will be automatically\n inferred by looking for a key in the config. For the default config, the prediction type is `'epsilon'`.\n num_in_channels (`int`, *optional*, defaults to None):\n The number of UNet input channels. If `None`, it will be automatically inferred from the config.\n model_channels (`int`, *optional*, defaults to None):\n The number of UNet model channels. If `None`, it will be automatically inferred from the config. Override\n to 128 for the small checkpoints, 192 for the medium checkpoints and 256 for the large.\n num_head_channels (`int`, *optional*, defaults to None):\n The number of UNet head channels. If `None`, it will be automatically inferred from the config. Override\n to 32 for the small and medium checkpoints, and 64 for the large.\n scheduler_type (`str`, *optional*, defaults to 'pndm'):\n Type of scheduler to use. Should be one of `[\"pndm\", \"lms\", \"heun\", \"euler\", \"euler-ancestral\", \"dpm\",\n \"ddim\"]`.\n extract_ema (`bool`, *optional*, defaults to `False`): Only relevant for\n checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults to\n `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher quality images for\n inference. Non-EMA weights are usually better to continue fine-tuning.\n device (`str`, *optional*, defaults to `None`):\n The device to use. Pass `None` to determine automatically.\n from_safetensors (`str`, *optional*, defaults to `False`):\n If `checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.\n return: An MusicLDMPipeline object representing the passed-in `.ckpt`/`.safetensors` file.\n \"\"\"\n if from_safetensors:\n from safetensors import safe_open\n\n checkpoint = {}\n with safe_open(checkpoint_path, framework=\"pt\", device=\"cpu\") as f:\n for key in f.keys():\n checkpoint[key] = f.get_tensor(key)\n else:\n if device is None:\n device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n checkpoint = torch.load(checkpoint_path, map_location=device)\n else:\n checkpoint = torch.load(checkpoint_path, map_location=device)\n\n if \"state_dict\" in checkpoint:\n checkpoint = checkpoint[\"state_dict\"]\n\n if original_config_file is None:\n original_config = DEFAULT_CONFIG\n else:\n original_config = yaml.safe_load(original_config_file)\n\n if num_in_channels is not None:\n original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"][\"in_channels\"] = num_in_channels\n\n if model_channels is not None:\n original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"][\"model_channels\"] = model_channels\n\n if num_head_channels is not None:\n original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"][\"num_head_channels\"] = num_head_channels\n\n if (\n \"parameterization\" in original_config[\"model\"][\"params\"]\n and original_config[\"model\"][\"params\"][\"parameterization\"] == \"v\"\n ):\n if prediction_type is None:\n prediction_type = \"v_prediction\"\n else:\n if prediction_type is None:\n prediction_type = \"epsilon\"\n\n if image_size is None:\n image_size = 512\n\n num_train_timesteps = original_config[\"model\"][\"params\"][\"timesteps\"]\n beta_start = original_config[\"model\"][\"params\"][\"linear_start\"]\n beta_end = original_config[\"model\"][\"params\"][\"linear_end\"]\n\n scheduler = DDIMScheduler(\n beta_end=beta_end,\n beta_schedule=\"scaled_linear\",\n beta_start=beta_start,\n num_train_timesteps=num_train_timesteps,\n steps_offset=1,\n clip_sample=False,\n set_alpha_to_one=False,\n prediction_type=prediction_type,\n )\n # make sure scheduler works correctly with DDIM\n scheduler.register_to_config(clip_sample=False)\n\n if scheduler_type == \"pndm\":\n config = dict(scheduler.config)\n config[\"skip_prk_steps\"] = True\n scheduler = PNDMScheduler.from_config(config)\n elif scheduler_type == \"lms\":\n scheduler = LMSDiscreteScheduler.from_config(scheduler.config)\n elif scheduler_type == \"heun\":\n scheduler = HeunDiscreteScheduler.from_config(scheduler.config)\n elif scheduler_type == \"euler\":\n scheduler = EulerDiscreteScheduler.from_config(scheduler.config)\n elif scheduler_type == \"euler-ancestral\":\n scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config)\n elif scheduler_type == \"dpm\":\n scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)\n elif scheduler_type == \"ddim\":\n scheduler = scheduler\n else:\n raise ValueError(f\"Scheduler of type {scheduler_type} doesn't exist!\")\n\n # Convert the UNet2DModel\n unet_config = create_unet_diffusers_config(original_config, image_size=image_size)\n unet = UNet2DConditionModel(**unet_config)\n\n converted_unet_checkpoint = convert_ldm_unet_checkpoint(\n checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema\n )\n\n unet.load_state_dict(converted_unet_checkpoint)\n\n # Convert the VAE model\n vae_config = create_vae_diffusers_config(original_config, checkpoint=checkpoint, image_size=image_size)\n converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)\n\n vae = AutoencoderKL(**vae_config)\n vae.load_state_dict(converted_vae_checkpoint)\n\n # Convert the text model\n # MusicLDM uses the same tokenizer as the original CLAP model, but a slightly different configuration\n config = ClapConfig.from_pretrained(\"laion/clap-htsat-unfused\")\n config.audio_config.update(\n {\n \"patch_embeds_hidden_size\": 128,\n \"hidden_size\": 1024,\n \"depths\": [2, 2, 12, 2],\n }\n )\n tokenizer = AutoTokenizer.from_pretrained(\"laion/clap-htsat-unfused\")\n feature_extractor = AutoFeatureExtractor.from_pretrained(\"laion/clap-htsat-unfused\")\n\n converted_text_model = convert_open_clap_checkpoint(checkpoint)\n text_model = ClapModel(config)\n\n missing_keys, unexpected_keys = text_model.load_state_dict(converted_text_model, strict=False)\n # we expect not to have token_type_ids in our original state dict so let's ignore them\n missing_keys = list(set(missing_keys) - set(CLAP_EXPECTED_MISSING_KEYS))\n\n if len(unexpected_keys) > 0:\n raise ValueError(f\"Unexpected keys when loading CLAP model: {unexpected_keys}\")\n\n if len(missing_keys) > 0:\n raise ValueError(f\"Missing keys when loading CLAP model: {missing_keys}\")\n\n # Convert the vocoder model\n vocoder_config = create_transformers_vocoder_config(original_config)\n vocoder_config = SpeechT5HifiGanConfig(**vocoder_config)\n converted_vocoder_checkpoint = convert_hifigan_checkpoint(checkpoint, vocoder_config)\n\n vocoder = SpeechT5HifiGan(vocoder_config)\n vocoder.load_state_dict(converted_vocoder_checkpoint)\n\n # Instantiate the diffusers pipeline\n pipe = MusicLDMPipeline(\n vae=vae,\n text_encoder=text_model,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n vocoder=vocoder,\n feature_extractor=feature_extractor,\n )\n\n return pipe\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--checkpoint_path\", default=None, type=str, required=True, help=\"Path to the checkpoint to convert.\"\n )\n parser.add_argument(\n \"--original_config_file\",\n default=None,\n type=str,\n help=\"The YAML config file corresponding to the original architecture.\",\n )\n parser.add_argument(\n \"--num_in_channels\",\n default=None,\n type=int,\n help=\"The number of input channels. If `None` number of input channels will be automatically inferred.\",\n )\n parser.add_argument(\n \"--model_channels\",\n default=None,\n type=int,\n help=\"The number of UNet model channels. If `None`, it will be automatically inferred from the config. Override\"\n \" to 128 for the small checkpoints, 192 for the medium checkpoints and 256 for the large.\",\n )\n parser.add_argument(\n \"--num_head_channels\",\n default=None,\n type=int,\n help=\"The number of UNet head channels. If `None`, it will be automatically inferred from the config. Override\"\n \" to 32 for the small and medium checkpoints, and 64 for the large.\",\n )\n parser.add_argument(\n \"--scheduler_type\",\n default=\"ddim\",\n type=str,\n help=\"Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']\",\n )\n parser.add_argument(\n \"--image_size\",\n default=None,\n type=int,\n help=(\"The image size that the model was trained on.\"),\n )\n parser.add_argument(\n \"--prediction_type\",\n default=None,\n type=str,\n help=(\"The prediction type that the model was trained on.\"),\n )\n parser.add_argument(\n \"--extract_ema\",\n action=\"store_true\",\n help=(\n \"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights\"\n \" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield\"\n \" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.\"\n ),\n )\n parser.add_argument(\n \"--from_safetensors\",\n action=\"store_true\",\n help=\"If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.\",\n )\n parser.add_argument(\n \"--to_safetensors\",\n action=\"store_true\",\n help=\"Whether to store pipeline in safetensors format or not.\",\n )\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n parser.add_argument(\"--device\", type=str, help=\"Device to use (e.g. cpu, cuda:0, cuda:1, etc.)\")\n args = parser.parse_args()\n\n pipe = load_pipeline_from_original_MusicLDM_ckpt(\n checkpoint_path=args.checkpoint_path,\n original_config_file=args.original_config_file,\n image_size=args.image_size,\n prediction_type=args.prediction_type,\n extract_ema=args.extract_ema,\n scheduler_type=args.scheduler_type,\n num_in_channels=args.num_in_channels,\n model_channels=args.model_channels,\n num_head_channels=args.num_head_channels,\n from_safetensors=args.from_safetensors,\n device=args.device,\n )\n pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)\n" }, { "path": "diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Conversion script for the LDM checkpoints.\"\"\"\n\nimport argparse\nimport importlib\n\nimport torch\n\nfrom diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--checkpoint_path\", default=None, type=str, required=True, help=\"Path to the checkpoint to convert.\"\n )\n # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml\n parser.add_argument(\n \"--original_config_file\",\n default=None,\n type=str,\n help=\"The YAML config file corresponding to the original architecture.\",\n )\n parser.add_argument(\n \"--config_files\",\n default=None,\n type=str,\n help=\"The YAML config file corresponding to the architecture.\",\n )\n parser.add_argument(\n \"--num_in_channels\",\n default=None,\n type=int,\n help=\"The number of input channels. If `None` number of input channels will be automatically inferred.\",\n )\n parser.add_argument(\n \"--scheduler_type\",\n default=\"pndm\",\n type=str,\n help=\"Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']\",\n )\n parser.add_argument(\n \"--pipeline_type\",\n default=None,\n type=str,\n help=(\n \"The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'\"\n \". If `None` pipeline will be automatically inferred.\"\n ),\n )\n parser.add_argument(\n \"--image_size\",\n default=None,\n type=int,\n help=(\n \"The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2\"\n \" Base. Use 768 for Stable Diffusion v2.\"\n ),\n )\n parser.add_argument(\n \"--prediction_type\",\n default=None,\n type=str,\n help=(\n \"The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable\"\n \" Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2.\"\n ),\n )\n parser.add_argument(\n \"--extract_ema\",\n action=\"store_true\",\n help=(\n \"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights\"\n \" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield\"\n \" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.\"\n ),\n )\n parser.add_argument(\n \"--upcast_attention\",\n action=\"store_true\",\n help=(\n \"Whether the attention computation should always be upcasted. This is necessary when running stable\"\n \" diffusion 2.1.\"\n ),\n )\n parser.add_argument(\n \"--from_safetensors\",\n action=\"store_true\",\n help=\"If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.\",\n )\n parser.add_argument(\n \"--to_safetensors\",\n action=\"store_true\",\n help=\"Whether to store pipeline in safetensors format or not.\",\n )\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n parser.add_argument(\"--device\", type=str, help=\"Device to use (e.g. cpu, cuda:0, cuda:1, etc.)\")\n parser.add_argument(\n \"--stable_unclip\",\n type=str,\n default=None,\n required=False,\n help=\"Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.\",\n )\n parser.add_argument(\n \"--stable_unclip_prior\",\n type=str,\n default=None,\n required=False,\n help=\"Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.\",\n )\n parser.add_argument(\n \"--clip_stats_path\",\n type=str,\n help=\"Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.\",\n required=False,\n )\n parser.add_argument(\n \"--controlnet\", action=\"store_true\", default=None, help=\"Set flag if this is a controlnet checkpoint.\"\n )\n parser.add_argument(\"--half\", action=\"store_true\", help=\"Save weights in half precision.\")\n parser.add_argument(\n \"--vae_path\",\n type=str,\n default=None,\n required=False,\n help=\"Set to a path, hub id to an already converted vae to not convert it again.\",\n )\n parser.add_argument(\n \"--pipeline_class_name\",\n type=str,\n default=None,\n required=False,\n help=\"Specify the pipeline class name\",\n )\n\n args = parser.parse_args()\n\n if args.pipeline_class_name is not None:\n library = importlib.import_module(\"diffusers\")\n class_obj = getattr(library, args.pipeline_class_name)\n pipeline_class = class_obj\n else:\n pipeline_class = None\n\n pipe = download_from_original_stable_diffusion_ckpt(\n checkpoint_path_or_dict=args.checkpoint_path,\n original_config_file=args.original_config_file,\n config_files=args.config_files,\n image_size=args.image_size,\n prediction_type=args.prediction_type,\n model_type=args.pipeline_type,\n extract_ema=args.extract_ema,\n scheduler_type=args.scheduler_type,\n num_in_channels=args.num_in_channels,\n upcast_attention=args.upcast_attention,\n from_safetensors=args.from_safetensors,\n device=args.device,\n stable_unclip=args.stable_unclip,\n stable_unclip_prior=args.stable_unclip_prior,\n clip_stats_path=args.clip_stats_path,\n controlnet=args.controlnet,\n vae_path=args.vae_path,\n pipeline_class=pipeline_class,\n )\n\n if args.half:\n pipe.to(dtype=torch.float16)\n\n if args.controlnet:\n # only save the controlnet model\n pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)\n else:\n pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)\n" }, { "path": "diffusers/scripts/convert_original_t2i_adapter.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nConversion script for the T2I-Adapter checkpoints.\n\"\"\"\n\nimport argparse\n\nimport torch\n\nfrom diffusers import T2IAdapter\n\n\ndef convert_adapter(src_state, in_channels):\n original_body_length = max([int(x.split(\".\")[1]) for x in src_state.keys() if \"body.\" in x]) + 1\n\n assert original_body_length == 8\n\n # (0, 1) -> channels 1\n assert src_state[\"body.0.block1.weight\"].shape == (320, 320, 3, 3)\n\n # (2, 3) -> channels 2\n assert src_state[\"body.2.in_conv.weight\"].shape == (640, 320, 1, 1)\n\n # (4, 5) -> channels 3\n assert src_state[\"body.4.in_conv.weight\"].shape == (1280, 640, 1, 1)\n\n # (6, 7) -> channels 4\n assert src_state[\"body.6.block1.weight\"].shape == (1280, 1280, 3, 3)\n\n res_state = {\n \"adapter.conv_in.weight\": src_state.pop(\"conv_in.weight\"),\n \"adapter.conv_in.bias\": src_state.pop(\"conv_in.bias\"),\n # 0.resnets.0\n \"adapter.body.0.resnets.0.block1.weight\": src_state.pop(\"body.0.block1.weight\"),\n \"adapter.body.0.resnets.0.block1.bias\": src_state.pop(\"body.0.block1.bias\"),\n \"adapter.body.0.resnets.0.block2.weight\": src_state.pop(\"body.0.block2.weight\"),\n \"adapter.body.0.resnets.0.block2.bias\": src_state.pop(\"body.0.block2.bias\"),\n # 0.resnets.1\n \"adapter.body.0.resnets.1.block1.weight\": src_state.pop(\"body.1.block1.weight\"),\n \"adapter.body.0.resnets.1.block1.bias\": src_state.pop(\"body.1.block1.bias\"),\n \"adapter.body.0.resnets.1.block2.weight\": src_state.pop(\"body.1.block2.weight\"),\n \"adapter.body.0.resnets.1.block2.bias\": src_state.pop(\"body.1.block2.bias\"),\n # 1\n \"adapter.body.1.in_conv.weight\": src_state.pop(\"body.2.in_conv.weight\"),\n \"adapter.body.1.in_conv.bias\": src_state.pop(\"body.2.in_conv.bias\"),\n # 1.resnets.0\n \"adapter.body.1.resnets.0.block1.weight\": src_state.pop(\"body.2.block1.weight\"),\n \"adapter.body.1.resnets.0.block1.bias\": src_state.pop(\"body.2.block1.bias\"),\n \"adapter.body.1.resnets.0.block2.weight\": src_state.pop(\"body.2.block2.weight\"),\n \"adapter.body.1.resnets.0.block2.bias\": src_state.pop(\"body.2.block2.bias\"),\n # 1.resnets.1\n \"adapter.body.1.resnets.1.block1.weight\": src_state.pop(\"body.3.block1.weight\"),\n \"adapter.body.1.resnets.1.block1.bias\": src_state.pop(\"body.3.block1.bias\"),\n \"adapter.body.1.resnets.1.block2.weight\": src_state.pop(\"body.3.block2.weight\"),\n \"adapter.body.1.resnets.1.block2.bias\": src_state.pop(\"body.3.block2.bias\"),\n # 2\n \"adapter.body.2.in_conv.weight\": src_state.pop(\"body.4.in_conv.weight\"),\n \"adapter.body.2.in_conv.bias\": src_state.pop(\"body.4.in_conv.bias\"),\n # 2.resnets.0\n \"adapter.body.2.resnets.0.block1.weight\": src_state.pop(\"body.4.block1.weight\"),\n \"adapter.body.2.resnets.0.block1.bias\": src_state.pop(\"body.4.block1.bias\"),\n \"adapter.body.2.resnets.0.block2.weight\": src_state.pop(\"body.4.block2.weight\"),\n \"adapter.body.2.resnets.0.block2.bias\": src_state.pop(\"body.4.block2.bias\"),\n # 2.resnets.1\n \"adapter.body.2.resnets.1.block1.weight\": src_state.pop(\"body.5.block1.weight\"),\n \"adapter.body.2.resnets.1.block1.bias\": src_state.pop(\"body.5.block1.bias\"),\n \"adapter.body.2.resnets.1.block2.weight\": src_state.pop(\"body.5.block2.weight\"),\n \"adapter.body.2.resnets.1.block2.bias\": src_state.pop(\"body.5.block2.bias\"),\n # 3.resnets.0\n \"adapter.body.3.resnets.0.block1.weight\": src_state.pop(\"body.6.block1.weight\"),\n \"adapter.body.3.resnets.0.block1.bias\": src_state.pop(\"body.6.block1.bias\"),\n \"adapter.body.3.resnets.0.block2.weight\": src_state.pop(\"body.6.block2.weight\"),\n \"adapter.body.3.resnets.0.block2.bias\": src_state.pop(\"body.6.block2.bias\"),\n # 3.resnets.1\n \"adapter.body.3.resnets.1.block1.weight\": src_state.pop(\"body.7.block1.weight\"),\n \"adapter.body.3.resnets.1.block1.bias\": src_state.pop(\"body.7.block1.bias\"),\n \"adapter.body.3.resnets.1.block2.weight\": src_state.pop(\"body.7.block2.weight\"),\n \"adapter.body.3.resnets.1.block2.bias\": src_state.pop(\"body.7.block2.bias\"),\n }\n\n assert len(src_state) == 0\n\n adapter = T2IAdapter(in_channels=in_channels, adapter_type=\"full_adapter\")\n\n adapter.load_state_dict(res_state)\n\n return adapter\n\n\ndef convert_light_adapter(src_state):\n original_body_length = max([int(x.split(\".\")[1]) for x in src_state.keys() if \"body.\" in x]) + 1\n\n assert original_body_length == 4\n\n res_state = {\n # body.0.in_conv\n \"adapter.body.0.in_conv.weight\": src_state.pop(\"body.0.in_conv.weight\"),\n \"adapter.body.0.in_conv.bias\": src_state.pop(\"body.0.in_conv.bias\"),\n # body.0.resnets.0\n \"adapter.body.0.resnets.0.block1.weight\": src_state.pop(\"body.0.body.0.block1.weight\"),\n \"adapter.body.0.resnets.0.block1.bias\": src_state.pop(\"body.0.body.0.block1.bias\"),\n \"adapter.body.0.resnets.0.block2.weight\": src_state.pop(\"body.0.body.0.block2.weight\"),\n \"adapter.body.0.resnets.0.block2.bias\": src_state.pop(\"body.0.body.0.block2.bias\"),\n # body.0.resnets.1\n \"adapter.body.0.resnets.1.block1.weight\": src_state.pop(\"body.0.body.1.block1.weight\"),\n \"adapter.body.0.resnets.1.block1.bias\": src_state.pop(\"body.0.body.1.block1.bias\"),\n \"adapter.body.0.resnets.1.block2.weight\": src_state.pop(\"body.0.body.1.block2.weight\"),\n \"adapter.body.0.resnets.1.block2.bias\": src_state.pop(\"body.0.body.1.block2.bias\"),\n # body.0.resnets.2\n \"adapter.body.0.resnets.2.block1.weight\": src_state.pop(\"body.0.body.2.block1.weight\"),\n \"adapter.body.0.resnets.2.block1.bias\": src_state.pop(\"body.0.body.2.block1.bias\"),\n \"adapter.body.0.resnets.2.block2.weight\": src_state.pop(\"body.0.body.2.block2.weight\"),\n \"adapter.body.0.resnets.2.block2.bias\": src_state.pop(\"body.0.body.2.block2.bias\"),\n # body.0.resnets.3\n \"adapter.body.0.resnets.3.block1.weight\": src_state.pop(\"body.0.body.3.block1.weight\"),\n \"adapter.body.0.resnets.3.block1.bias\": src_state.pop(\"body.0.body.3.block1.bias\"),\n \"adapter.body.0.resnets.3.block2.weight\": src_state.pop(\"body.0.body.3.block2.weight\"),\n \"adapter.body.0.resnets.3.block2.bias\": src_state.pop(\"body.0.body.3.block2.bias\"),\n # body.0.out_conv\n \"adapter.body.0.out_conv.weight\": src_state.pop(\"body.0.out_conv.weight\"),\n \"adapter.body.0.out_conv.bias\": src_state.pop(\"body.0.out_conv.bias\"),\n # body.1.in_conv\n \"adapter.body.1.in_conv.weight\": src_state.pop(\"body.1.in_conv.weight\"),\n \"adapter.body.1.in_conv.bias\": src_state.pop(\"body.1.in_conv.bias\"),\n # body.1.resnets.0\n \"adapter.body.1.resnets.0.block1.weight\": src_state.pop(\"body.1.body.0.block1.weight\"),\n \"adapter.body.1.resnets.0.block1.bias\": src_state.pop(\"body.1.body.0.block1.bias\"),\n \"adapter.body.1.resnets.0.block2.weight\": src_state.pop(\"body.1.body.0.block2.weight\"),\n \"adapter.body.1.resnets.0.block2.bias\": src_state.pop(\"body.1.body.0.block2.bias\"),\n # body.1.resnets.1\n \"adapter.body.1.resnets.1.block1.weight\": src_state.pop(\"body.1.body.1.block1.weight\"),\n \"adapter.body.1.resnets.1.block1.bias\": src_state.pop(\"body.1.body.1.block1.bias\"),\n \"adapter.body.1.resnets.1.block2.weight\": src_state.pop(\"body.1.body.1.block2.weight\"),\n \"adapter.body.1.resnets.1.block2.bias\": src_state.pop(\"body.1.body.1.block2.bias\"),\n # body.1.body.2\n \"adapter.body.1.resnets.2.block1.weight\": src_state.pop(\"body.1.body.2.block1.weight\"),\n \"adapter.body.1.resnets.2.block1.bias\": src_state.pop(\"body.1.body.2.block1.bias\"),\n \"adapter.body.1.resnets.2.block2.weight\": src_state.pop(\"body.1.body.2.block2.weight\"),\n \"adapter.body.1.resnets.2.block2.bias\": src_state.pop(\"body.1.body.2.block2.bias\"),\n # body.1.body.3\n \"adapter.body.1.resnets.3.block1.weight\": src_state.pop(\"body.1.body.3.block1.weight\"),\n \"adapter.body.1.resnets.3.block1.bias\": src_state.pop(\"body.1.body.3.block1.bias\"),\n \"adapter.body.1.resnets.3.block2.weight\": src_state.pop(\"body.1.body.3.block2.weight\"),\n \"adapter.body.1.resnets.3.block2.bias\": src_state.pop(\"body.1.body.3.block2.bias\"),\n # body.1.out_conv\n \"adapter.body.1.out_conv.weight\": src_state.pop(\"body.1.out_conv.weight\"),\n \"adapter.body.1.out_conv.bias\": src_state.pop(\"body.1.out_conv.bias\"),\n # body.2.in_conv\n \"adapter.body.2.in_conv.weight\": src_state.pop(\"body.2.in_conv.weight\"),\n \"adapter.body.2.in_conv.bias\": src_state.pop(\"body.2.in_conv.bias\"),\n # body.2.body.0\n \"adapter.body.2.resnets.0.block1.weight\": src_state.pop(\"body.2.body.0.block1.weight\"),\n \"adapter.body.2.resnets.0.block1.bias\": src_state.pop(\"body.2.body.0.block1.bias\"),\n \"adapter.body.2.resnets.0.block2.weight\": src_state.pop(\"body.2.body.0.block2.weight\"),\n \"adapter.body.2.resnets.0.block2.bias\": src_state.pop(\"body.2.body.0.block2.bias\"),\n # body.2.body.1\n \"adapter.body.2.resnets.1.block1.weight\": src_state.pop(\"body.2.body.1.block1.weight\"),\n \"adapter.body.2.resnets.1.block1.bias\": src_state.pop(\"body.2.body.1.block1.bias\"),\n \"adapter.body.2.resnets.1.block2.weight\": src_state.pop(\"body.2.body.1.block2.weight\"),\n \"adapter.body.2.resnets.1.block2.bias\": src_state.pop(\"body.2.body.1.block2.bias\"),\n # body.2.body.2\n \"adapter.body.2.resnets.2.block1.weight\": src_state.pop(\"body.2.body.2.block1.weight\"),\n \"adapter.body.2.resnets.2.block1.bias\": src_state.pop(\"body.2.body.2.block1.bias\"),\n \"adapter.body.2.resnets.2.block2.weight\": src_state.pop(\"body.2.body.2.block2.weight\"),\n \"adapter.body.2.resnets.2.block2.bias\": src_state.pop(\"body.2.body.2.block2.bias\"),\n # body.2.body.3\n \"adapter.body.2.resnets.3.block1.weight\": src_state.pop(\"body.2.body.3.block1.weight\"),\n \"adapter.body.2.resnets.3.block1.bias\": src_state.pop(\"body.2.body.3.block1.bias\"),\n \"adapter.body.2.resnets.3.block2.weight\": src_state.pop(\"body.2.body.3.block2.weight\"),\n \"adapter.body.2.resnets.3.block2.bias\": src_state.pop(\"body.2.body.3.block2.bias\"),\n # body.2.out_conv\n \"adapter.body.2.out_conv.weight\": src_state.pop(\"body.2.out_conv.weight\"),\n \"adapter.body.2.out_conv.bias\": src_state.pop(\"body.2.out_conv.bias\"),\n # body.3.in_conv\n \"adapter.body.3.in_conv.weight\": src_state.pop(\"body.3.in_conv.weight\"),\n \"adapter.body.3.in_conv.bias\": src_state.pop(\"body.3.in_conv.bias\"),\n # body.3.body.0\n \"adapter.body.3.resnets.0.block1.weight\": src_state.pop(\"body.3.body.0.block1.weight\"),\n \"adapter.body.3.resnets.0.block1.bias\": src_state.pop(\"body.3.body.0.block1.bias\"),\n \"adapter.body.3.resnets.0.block2.weight\": src_state.pop(\"body.3.body.0.block2.weight\"),\n \"adapter.body.3.resnets.0.block2.bias\": src_state.pop(\"body.3.body.0.block2.bias\"),\n # body.3.body.1\n \"adapter.body.3.resnets.1.block1.weight\": src_state.pop(\"body.3.body.1.block1.weight\"),\n \"adapter.body.3.resnets.1.block1.bias\": src_state.pop(\"body.3.body.1.block1.bias\"),\n \"adapter.body.3.resnets.1.block2.weight\": src_state.pop(\"body.3.body.1.block2.weight\"),\n \"adapter.body.3.resnets.1.block2.bias\": src_state.pop(\"body.3.body.1.block2.bias\"),\n # body.3.body.2\n \"adapter.body.3.resnets.2.block1.weight\": src_state.pop(\"body.3.body.2.block1.weight\"),\n \"adapter.body.3.resnets.2.block1.bias\": src_state.pop(\"body.3.body.2.block1.bias\"),\n \"adapter.body.3.resnets.2.block2.weight\": src_state.pop(\"body.3.body.2.block2.weight\"),\n \"adapter.body.3.resnets.2.block2.bias\": src_state.pop(\"body.3.body.2.block2.bias\"),\n # body.3.body.3\n \"adapter.body.3.resnets.3.block1.weight\": src_state.pop(\"body.3.body.3.block1.weight\"),\n \"adapter.body.3.resnets.3.block1.bias\": src_state.pop(\"body.3.body.3.block1.bias\"),\n \"adapter.body.3.resnets.3.block2.weight\": src_state.pop(\"body.3.body.3.block2.weight\"),\n \"adapter.body.3.resnets.3.block2.bias\": src_state.pop(\"body.3.body.3.block2.bias\"),\n # body.3.out_conv\n \"adapter.body.3.out_conv.weight\": src_state.pop(\"body.3.out_conv.weight\"),\n \"adapter.body.3.out_conv.bias\": src_state.pop(\"body.3.out_conv.bias\"),\n }\n\n assert len(src_state) == 0\n\n adapter = T2IAdapter(in_channels=3, channels=[320, 640, 1280], num_res_blocks=4, adapter_type=\"light_adapter\")\n\n adapter.load_state_dict(res_state)\n\n return adapter\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--checkpoint_path\", default=None, type=str, required=True, help=\"Path to the checkpoint to convert.\"\n )\n parser.add_argument(\n \"--output_path\", default=None, type=str, required=True, help=\"Path to the store the result checkpoint.\"\n )\n parser.add_argument(\n \"--is_adapter_light\",\n action=\"store_true\",\n help=\"Is checkpoint come from Adapter-Light architecture. ex: color-adapter\",\n )\n parser.add_argument(\"--in_channels\", required=False, type=int, help=\"Input channels for non-light adapter\")\n\n args = parser.parse_args()\n src_state = torch.load(args.checkpoint_path)\n\n if args.is_adapter_light:\n adapter = convert_light_adapter(src_state)\n else:\n if args.in_channels is None:\n raise ValueError(\"set `--in_channels=`\")\n adapter = convert_adapter(src_state, args.in_channels)\n\n adapter.save_pretrained(args.output_path)\n" }, { "path": "diffusers/scripts/convert_pixart_alpha_to_diffusers.py", "content": "import argparse\nimport os\n\nimport torch\nfrom transformers import T5EncoderModel, T5Tokenizer\n\nfrom diffusers import AutoencoderKL, DPMSolverMultistepScheduler, PixArtAlphaPipeline, Transformer2DModel\n\n\nckpt_id = \"PixArt-alpha/PixArt-alpha\"\n# https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/scripts/inference.py#L125\ninterpolation_scale = {256: 0.5, 512: 1, 1024: 2}\n\n\ndef main(args):\n all_state_dict = torch.load(args.orig_ckpt_path, map_location=\"cpu\")\n state_dict = all_state_dict.pop(\"state_dict\")\n converted_state_dict = {}\n\n # Patch embeddings.\n converted_state_dict[\"pos_embed.proj.weight\"] = state_dict.pop(\"x_embedder.proj.weight\")\n converted_state_dict[\"pos_embed.proj.bias\"] = state_dict.pop(\"x_embedder.proj.bias\")\n\n # Caption projection.\n converted_state_dict[\"caption_projection.linear_1.weight\"] = state_dict.pop(\"y_embedder.y_proj.fc1.weight\")\n converted_state_dict[\"caption_projection.linear_1.bias\"] = state_dict.pop(\"y_embedder.y_proj.fc1.bias\")\n converted_state_dict[\"caption_projection.linear_2.weight\"] = state_dict.pop(\"y_embedder.y_proj.fc2.weight\")\n converted_state_dict[\"caption_projection.linear_2.bias\"] = state_dict.pop(\"y_embedder.y_proj.fc2.bias\")\n\n # AdaLN-single LN\n converted_state_dict[\"adaln_single.emb.timestep_embedder.linear_1.weight\"] = state_dict.pop(\n \"t_embedder.mlp.0.weight\"\n )\n converted_state_dict[\"adaln_single.emb.timestep_embedder.linear_1.bias\"] = state_dict.pop(\"t_embedder.mlp.0.bias\")\n converted_state_dict[\"adaln_single.emb.timestep_embedder.linear_2.weight\"] = state_dict.pop(\n \"t_embedder.mlp.2.weight\"\n )\n converted_state_dict[\"adaln_single.emb.timestep_embedder.linear_2.bias\"] = state_dict.pop(\"t_embedder.mlp.2.bias\")\n\n if args.image_size == 1024:\n # Resolution.\n converted_state_dict[\"adaln_single.emb.resolution_embedder.linear_1.weight\"] = state_dict.pop(\n \"csize_embedder.mlp.0.weight\"\n )\n converted_state_dict[\"adaln_single.emb.resolution_embedder.linear_1.bias\"] = state_dict.pop(\n \"csize_embedder.mlp.0.bias\"\n )\n converted_state_dict[\"adaln_single.emb.resolution_embedder.linear_2.weight\"] = state_dict.pop(\n \"csize_embedder.mlp.2.weight\"\n )\n converted_state_dict[\"adaln_single.emb.resolution_embedder.linear_2.bias\"] = state_dict.pop(\n \"csize_embedder.mlp.2.bias\"\n )\n # Aspect ratio.\n converted_state_dict[\"adaln_single.emb.aspect_ratio_embedder.linear_1.weight\"] = state_dict.pop(\n \"ar_embedder.mlp.0.weight\"\n )\n converted_state_dict[\"adaln_single.emb.aspect_ratio_embedder.linear_1.bias\"] = state_dict.pop(\n \"ar_embedder.mlp.0.bias\"\n )\n converted_state_dict[\"adaln_single.emb.aspect_ratio_embedder.linear_2.weight\"] = state_dict.pop(\n \"ar_embedder.mlp.2.weight\"\n )\n converted_state_dict[\"adaln_single.emb.aspect_ratio_embedder.linear_2.bias\"] = state_dict.pop(\n \"ar_embedder.mlp.2.bias\"\n )\n # Shared norm.\n converted_state_dict[\"adaln_single.linear.weight\"] = state_dict.pop(\"t_block.1.weight\")\n converted_state_dict[\"adaln_single.linear.bias\"] = state_dict.pop(\"t_block.1.bias\")\n\n for depth in range(28):\n # Transformer blocks.\n converted_state_dict[f\"transformer_blocks.{depth}.scale_shift_table\"] = state_dict.pop(\n f\"blocks.{depth}.scale_shift_table\"\n )\n\n # Attention is all you need 🤘\n\n # Self attention.\n q, k, v = torch.chunk(state_dict.pop(f\"blocks.{depth}.attn.qkv.weight\"), 3, dim=0)\n q_bias, k_bias, v_bias = torch.chunk(state_dict.pop(f\"blocks.{depth}.attn.qkv.bias\"), 3, dim=0)\n converted_state_dict[f\"transformer_blocks.{depth}.attn1.to_q.weight\"] = q\n converted_state_dict[f\"transformer_blocks.{depth}.attn1.to_q.bias\"] = q_bias\n converted_state_dict[f\"transformer_blocks.{depth}.attn1.to_k.weight\"] = k\n converted_state_dict[f\"transformer_blocks.{depth}.attn1.to_k.bias\"] = k_bias\n converted_state_dict[f\"transformer_blocks.{depth}.attn1.to_v.weight\"] = v\n converted_state_dict[f\"transformer_blocks.{depth}.attn1.to_v.bias\"] = v_bias\n # Projection.\n converted_state_dict[f\"transformer_blocks.{depth}.attn1.to_out.0.weight\"] = state_dict.pop(\n f\"blocks.{depth}.attn.proj.weight\"\n )\n converted_state_dict[f\"transformer_blocks.{depth}.attn1.to_out.0.bias\"] = state_dict.pop(\n f\"blocks.{depth}.attn.proj.bias\"\n )\n\n # Feed-forward.\n converted_state_dict[f\"transformer_blocks.{depth}.ff.net.0.proj.weight\"] = state_dict.pop(\n f\"blocks.{depth}.mlp.fc1.weight\"\n )\n converted_state_dict[f\"transformer_blocks.{depth}.ff.net.0.proj.bias\"] = state_dict.pop(\n f\"blocks.{depth}.mlp.fc1.bias\"\n )\n converted_state_dict[f\"transformer_blocks.{depth}.ff.net.2.weight\"] = state_dict.pop(\n f\"blocks.{depth}.mlp.fc2.weight\"\n )\n converted_state_dict[f\"transformer_blocks.{depth}.ff.net.2.bias\"] = state_dict.pop(\n f\"blocks.{depth}.mlp.fc2.bias\"\n )\n\n # Cross-attention.\n q = state_dict.pop(f\"blocks.{depth}.cross_attn.q_linear.weight\")\n q_bias = state_dict.pop(f\"blocks.{depth}.cross_attn.q_linear.bias\")\n k, v = torch.chunk(state_dict.pop(f\"blocks.{depth}.cross_attn.kv_linear.weight\"), 2, dim=0)\n k_bias, v_bias = torch.chunk(state_dict.pop(f\"blocks.{depth}.cross_attn.kv_linear.bias\"), 2, dim=0)\n\n converted_state_dict[f\"transformer_blocks.{depth}.attn2.to_q.weight\"] = q\n converted_state_dict[f\"transformer_blocks.{depth}.attn2.to_q.bias\"] = q_bias\n converted_state_dict[f\"transformer_blocks.{depth}.attn2.to_k.weight\"] = k\n converted_state_dict[f\"transformer_blocks.{depth}.attn2.to_k.bias\"] = k_bias\n converted_state_dict[f\"transformer_blocks.{depth}.attn2.to_v.weight\"] = v\n converted_state_dict[f\"transformer_blocks.{depth}.attn2.to_v.bias\"] = v_bias\n\n converted_state_dict[f\"transformer_blocks.{depth}.attn2.to_out.0.weight\"] = state_dict.pop(\n f\"blocks.{depth}.cross_attn.proj.weight\"\n )\n converted_state_dict[f\"transformer_blocks.{depth}.attn2.to_out.0.bias\"] = state_dict.pop(\n f\"blocks.{depth}.cross_attn.proj.bias\"\n )\n\n # Final block.\n converted_state_dict[\"proj_out.weight\"] = state_dict.pop(\"final_layer.linear.weight\")\n converted_state_dict[\"proj_out.bias\"] = state_dict.pop(\"final_layer.linear.bias\")\n converted_state_dict[\"scale_shift_table\"] = state_dict.pop(\"final_layer.scale_shift_table\")\n\n # DiT XL/2\n transformer = Transformer2DModel(\n sample_size=args.image_size // 8,\n num_layers=28,\n attention_head_dim=72,\n in_channels=4,\n out_channels=8,\n patch_size=2,\n attention_bias=True,\n num_attention_heads=16,\n cross_attention_dim=1152,\n activation_fn=\"gelu-approximate\",\n num_embeds_ada_norm=1000,\n norm_type=\"ada_norm_single\",\n norm_elementwise_affine=False,\n norm_eps=1e-6,\n caption_channels=4096,\n )\n transformer.load_state_dict(converted_state_dict, strict=True)\n\n assert transformer.pos_embed.pos_embed is not None\n state_dict.pop(\"pos_embed\")\n state_dict.pop(\"y_embedder.y_embedding\")\n assert len(state_dict) == 0, f\"State dict is not empty, {state_dict.keys()}\"\n\n num_model_params = sum(p.numel() for p in transformer.parameters())\n print(f\"Total number of transformer parameters: {num_model_params}\")\n\n if args.only_transformer:\n transformer.save_pretrained(os.path.join(args.dump_path, \"transformer\"))\n else:\n scheduler = DPMSolverMultistepScheduler()\n\n vae = AutoencoderKL.from_pretrained(ckpt_id, subfolder=\"sd-vae-ft-ema\")\n\n tokenizer = T5Tokenizer.from_pretrained(ckpt_id, subfolder=\"t5-v1_1-xxl\")\n text_encoder = T5EncoderModel.from_pretrained(ckpt_id, subfolder=\"t5-v1_1-xxl\")\n\n pipeline = PixArtAlphaPipeline(\n tokenizer=tokenizer, text_encoder=text_encoder, transformer=transformer, vae=vae, scheduler=scheduler\n )\n\n pipeline.save_pretrained(args.dump_path)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--orig_ckpt_path\", default=None, type=str, required=False, help=\"Path to the checkpoint to convert.\"\n )\n parser.add_argument(\n \"--image_size\",\n default=1024,\n type=int,\n choices=[256, 512, 1024],\n required=False,\n help=\"Image size of pretrained model, either 512 or 1024.\",\n )\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output pipeline.\")\n parser.add_argument(\"--only_transformer\", default=True, type=bool, required=True)\n\n args = parser.parse_args()\n main(args)\n" }, { "path": "diffusers/scripts/convert_pixart_sigma_to_diffusers.py", "content": "import argparse\nimport os\n\nimport torch\nfrom transformers import T5EncoderModel, T5Tokenizer\n\nfrom diffusers import AutoencoderKL, DPMSolverMultistepScheduler, PixArtSigmaPipeline, Transformer2DModel\n\n\nckpt_id = \"PixArt-alpha\"\n# https://github.com/PixArt-alpha/PixArt-sigma/blob/dd087141864e30ec44f12cb7448dd654be065e88/scripts/inference.py#L158\ninterpolation_scale = {256: 0.5, 512: 1, 1024: 2, 2048: 4}\n\n\ndef main(args):\n all_state_dict = torch.load(args.orig_ckpt_path)\n state_dict = all_state_dict.pop(\"state_dict\")\n converted_state_dict = {}\n\n # Patch embeddings.\n converted_state_dict[\"pos_embed.proj.weight\"] = state_dict.pop(\"x_embedder.proj.weight\")\n converted_state_dict[\"pos_embed.proj.bias\"] = state_dict.pop(\"x_embedder.proj.bias\")\n\n # Caption projection.\n converted_state_dict[\"caption_projection.linear_1.weight\"] = state_dict.pop(\"y_embedder.y_proj.fc1.weight\")\n converted_state_dict[\"caption_projection.linear_1.bias\"] = state_dict.pop(\"y_embedder.y_proj.fc1.bias\")\n converted_state_dict[\"caption_projection.linear_2.weight\"] = state_dict.pop(\"y_embedder.y_proj.fc2.weight\")\n converted_state_dict[\"caption_projection.linear_2.bias\"] = state_dict.pop(\"y_embedder.y_proj.fc2.bias\")\n\n # AdaLN-single LN\n converted_state_dict[\"adaln_single.emb.timestep_embedder.linear_1.weight\"] = state_dict.pop(\n \"t_embedder.mlp.0.weight\"\n )\n converted_state_dict[\"adaln_single.emb.timestep_embedder.linear_1.bias\"] = state_dict.pop(\"t_embedder.mlp.0.bias\")\n converted_state_dict[\"adaln_single.emb.timestep_embedder.linear_2.weight\"] = state_dict.pop(\n \"t_embedder.mlp.2.weight\"\n )\n converted_state_dict[\"adaln_single.emb.timestep_embedder.linear_2.bias\"] = state_dict.pop(\"t_embedder.mlp.2.bias\")\n\n if args.micro_condition:\n # Resolution.\n converted_state_dict[\"adaln_single.emb.resolution_embedder.linear_1.weight\"] = state_dict.pop(\n \"csize_embedder.mlp.0.weight\"\n )\n converted_state_dict[\"adaln_single.emb.resolution_embedder.linear_1.bias\"] = state_dict.pop(\n \"csize_embedder.mlp.0.bias\"\n )\n converted_state_dict[\"adaln_single.emb.resolution_embedder.linear_2.weight\"] = state_dict.pop(\n \"csize_embedder.mlp.2.weight\"\n )\n converted_state_dict[\"adaln_single.emb.resolution_embedder.linear_2.bias\"] = state_dict.pop(\n \"csize_embedder.mlp.2.bias\"\n )\n # Aspect ratio.\n converted_state_dict[\"adaln_single.emb.aspect_ratio_embedder.linear_1.weight\"] = state_dict.pop(\n \"ar_embedder.mlp.0.weight\"\n )\n converted_state_dict[\"adaln_single.emb.aspect_ratio_embedder.linear_1.bias\"] = state_dict.pop(\n \"ar_embedder.mlp.0.bias\"\n )\n converted_state_dict[\"adaln_single.emb.aspect_ratio_embedder.linear_2.weight\"] = state_dict.pop(\n \"ar_embedder.mlp.2.weight\"\n )\n converted_state_dict[\"adaln_single.emb.aspect_ratio_embedder.linear_2.bias\"] = state_dict.pop(\n \"ar_embedder.mlp.2.bias\"\n )\n # Shared norm.\n converted_state_dict[\"adaln_single.linear.weight\"] = state_dict.pop(\"t_block.1.weight\")\n converted_state_dict[\"adaln_single.linear.bias\"] = state_dict.pop(\"t_block.1.bias\")\n\n for depth in range(28):\n # Transformer blocks.\n converted_state_dict[f\"transformer_blocks.{depth}.scale_shift_table\"] = state_dict.pop(\n f\"blocks.{depth}.scale_shift_table\"\n )\n # Attention is all you need 🤘\n\n # Self attention.\n q, k, v = torch.chunk(state_dict.pop(f\"blocks.{depth}.attn.qkv.weight\"), 3, dim=0)\n q_bias, k_bias, v_bias = torch.chunk(state_dict.pop(f\"blocks.{depth}.attn.qkv.bias\"), 3, dim=0)\n converted_state_dict[f\"transformer_blocks.{depth}.attn1.to_q.weight\"] = q\n converted_state_dict[f\"transformer_blocks.{depth}.attn1.to_q.bias\"] = q_bias\n converted_state_dict[f\"transformer_blocks.{depth}.attn1.to_k.weight\"] = k\n converted_state_dict[f\"transformer_blocks.{depth}.attn1.to_k.bias\"] = k_bias\n converted_state_dict[f\"transformer_blocks.{depth}.attn1.to_v.weight\"] = v\n converted_state_dict[f\"transformer_blocks.{depth}.attn1.to_v.bias\"] = v_bias\n # Projection.\n converted_state_dict[f\"transformer_blocks.{depth}.attn1.to_out.0.weight\"] = state_dict.pop(\n f\"blocks.{depth}.attn.proj.weight\"\n )\n converted_state_dict[f\"transformer_blocks.{depth}.attn1.to_out.0.bias\"] = state_dict.pop(\n f\"blocks.{depth}.attn.proj.bias\"\n )\n if args.qk_norm:\n converted_state_dict[f\"transformer_blocks.{depth}.attn1.q_norm.weight\"] = state_dict.pop(\n f\"blocks.{depth}.attn.q_norm.weight\"\n )\n converted_state_dict[f\"transformer_blocks.{depth}.attn1.q_norm.bias\"] = state_dict.pop(\n f\"blocks.{depth}.attn.q_norm.bias\"\n )\n converted_state_dict[f\"transformer_blocks.{depth}.attn1.k_norm.weight\"] = state_dict.pop(\n f\"blocks.{depth}.attn.k_norm.weight\"\n )\n converted_state_dict[f\"transformer_blocks.{depth}.attn1.k_norm.bias\"] = state_dict.pop(\n f\"blocks.{depth}.attn.k_norm.bias\"\n )\n\n # Feed-forward.\n converted_state_dict[f\"transformer_blocks.{depth}.ff.net.0.proj.weight\"] = state_dict.pop(\n f\"blocks.{depth}.mlp.fc1.weight\"\n )\n converted_state_dict[f\"transformer_blocks.{depth}.ff.net.0.proj.bias\"] = state_dict.pop(\n f\"blocks.{depth}.mlp.fc1.bias\"\n )\n converted_state_dict[f\"transformer_blocks.{depth}.ff.net.2.weight\"] = state_dict.pop(\n f\"blocks.{depth}.mlp.fc2.weight\"\n )\n converted_state_dict[f\"transformer_blocks.{depth}.ff.net.2.bias\"] = state_dict.pop(\n f\"blocks.{depth}.mlp.fc2.bias\"\n )\n\n # Cross-attention.\n q = state_dict.pop(f\"blocks.{depth}.cross_attn.q_linear.weight\")\n q_bias = state_dict.pop(f\"blocks.{depth}.cross_attn.q_linear.bias\")\n k, v = torch.chunk(state_dict.pop(f\"blocks.{depth}.cross_attn.kv_linear.weight\"), 2, dim=0)\n k_bias, v_bias = torch.chunk(state_dict.pop(f\"blocks.{depth}.cross_attn.kv_linear.bias\"), 2, dim=0)\n\n converted_state_dict[f\"transformer_blocks.{depth}.attn2.to_q.weight\"] = q\n converted_state_dict[f\"transformer_blocks.{depth}.attn2.to_q.bias\"] = q_bias\n converted_state_dict[f\"transformer_blocks.{depth}.attn2.to_k.weight\"] = k\n converted_state_dict[f\"transformer_blocks.{depth}.attn2.to_k.bias\"] = k_bias\n converted_state_dict[f\"transformer_blocks.{depth}.attn2.to_v.weight\"] = v\n converted_state_dict[f\"transformer_blocks.{depth}.attn2.to_v.bias\"] = v_bias\n\n converted_state_dict[f\"transformer_blocks.{depth}.attn2.to_out.0.weight\"] = state_dict.pop(\n f\"blocks.{depth}.cross_attn.proj.weight\"\n )\n converted_state_dict[f\"transformer_blocks.{depth}.attn2.to_out.0.bias\"] = state_dict.pop(\n f\"blocks.{depth}.cross_attn.proj.bias\"\n )\n\n # Final block.\n converted_state_dict[\"proj_out.weight\"] = state_dict.pop(\"final_layer.linear.weight\")\n converted_state_dict[\"proj_out.bias\"] = state_dict.pop(\"final_layer.linear.bias\")\n converted_state_dict[\"scale_shift_table\"] = state_dict.pop(\"final_layer.scale_shift_table\")\n\n # PixArt XL/2\n transformer = Transformer2DModel(\n sample_size=args.image_size // 8,\n num_layers=28,\n attention_head_dim=72,\n in_channels=4,\n out_channels=8,\n patch_size=2,\n attention_bias=True,\n num_attention_heads=16,\n cross_attention_dim=1152,\n activation_fn=\"gelu-approximate\",\n num_embeds_ada_norm=1000,\n norm_type=\"ada_norm_single\",\n norm_elementwise_affine=False,\n norm_eps=1e-6,\n caption_channels=4096,\n interpolation_scale=interpolation_scale[args.image_size],\n use_additional_conditions=args.micro_condition,\n )\n transformer.load_state_dict(converted_state_dict, strict=True)\n\n assert transformer.pos_embed.pos_embed is not None\n try:\n state_dict.pop(\"y_embedder.y_embedding\")\n state_dict.pop(\"pos_embed\")\n except Exception as e:\n print(f\"Skipping {str(e)}\")\n pass\n assert len(state_dict) == 0, f\"State dict is not empty, {state_dict.keys()}\"\n\n num_model_params = sum(p.numel() for p in transformer.parameters())\n print(f\"Total number of transformer parameters: {num_model_params}\")\n\n if args.only_transformer:\n transformer.save_pretrained(os.path.join(args.dump_path, \"transformer\"))\n else:\n # pixart-Sigma vae link: https://huggingface.co/PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers/tree/main/vae\n vae = AutoencoderKL.from_pretrained(f\"{ckpt_id}/pixart_sigma_sdxlvae_T5_diffusers\", subfolder=\"vae\")\n\n scheduler = DPMSolverMultistepScheduler()\n\n tokenizer = T5Tokenizer.from_pretrained(f\"{ckpt_id}/pixart_sigma_sdxlvae_T5_diffusers\", subfolder=\"tokenizer\")\n text_encoder = T5EncoderModel.from_pretrained(\n f\"{ckpt_id}/pixart_sigma_sdxlvae_T5_diffusers\", subfolder=\"text_encoder\"\n )\n\n pipeline = PixArtSigmaPipeline(\n tokenizer=tokenizer, text_encoder=text_encoder, transformer=transformer, vae=vae, scheduler=scheduler\n )\n\n pipeline.save_pretrained(args.dump_path)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--micro_condition\", action=\"store_true\", help=\"If use Micro-condition in PixArtMS structure during training.\"\n )\n parser.add_argument(\"--qk_norm\", action=\"store_true\", help=\"If use qk norm during training.\")\n parser.add_argument(\n \"--orig_ckpt_path\", default=None, type=str, required=False, help=\"Path to the checkpoint to convert.\"\n )\n parser.add_argument(\n \"--image_size\",\n default=1024,\n type=int,\n choices=[256, 512, 1024, 2048],\n required=False,\n help=\"Image size of pretrained model, 256, 512, 1024, or 2048.\",\n )\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output pipeline.\")\n parser.add_argument(\"--only_transformer\", default=True, type=bool, required=True)\n\n args = parser.parse_args()\n main(args)\n" }, { "path": "diffusers/scripts/convert_shap_e_to_diffusers.py", "content": "import argparse\nimport tempfile\n\nimport torch\nfrom accelerate import load_checkpoint_and_dispatch\n\nfrom diffusers.models.transformers.prior_transformer import PriorTransformer\nfrom diffusers.pipelines.shap_e import ShapERenderer\n\n\n\"\"\"\nExample - From the diffusers root directory:\n\nDownload weights:\n```sh\n$ wget \"https://openaipublic.azureedge.net/main/shap-e/text_cond.pt\"\n```\n\nConvert the model:\n```sh\n$ python scripts/convert_shap_e_to_diffusers.py \\\n --prior_checkpoint_path /home/yiyi_huggingface_co/shap-e/shap_e_model_cache/text_cond.pt \\\n --prior_image_checkpoint_path /home/yiyi_huggingface_co/shap-e/shap_e_model_cache/image_cond.pt \\\n --transmitter_checkpoint_path /home/yiyi_huggingface_co/shap-e/shap_e_model_cache/transmitter.pt\\\n --dump_path /home/yiyi_huggingface_co/model_repo/shap-e-img2img/shap_e_renderer\\\n --debug renderer\n```\n\"\"\"\n\n\n# prior\n\nPRIOR_ORIGINAL_PREFIX = \"wrapped\"\n\nPRIOR_CONFIG = {\n \"num_attention_heads\": 16,\n \"attention_head_dim\": 1024 // 16,\n \"num_layers\": 24,\n \"embedding_dim\": 1024,\n \"num_embeddings\": 1024,\n \"additional_embeddings\": 0,\n \"time_embed_act_fn\": \"gelu\",\n \"norm_in_type\": \"layer\",\n \"encoder_hid_proj_type\": None,\n \"added_emb_type\": None,\n \"time_embed_dim\": 1024 * 4,\n \"embedding_proj_dim\": 768,\n \"clip_embed_dim\": 1024 * 2,\n}\n\n\ndef prior_model_from_original_config():\n model = PriorTransformer(**PRIOR_CONFIG)\n\n return model\n\n\ndef prior_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):\n diffusers_checkpoint = {}\n\n # .time_embed.c_fc -> .time_embedding.linear_1\n diffusers_checkpoint.update(\n {\n \"time_embedding.linear_1.weight\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.time_embed.c_fc.weight\"],\n \"time_embedding.linear_1.bias\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.time_embed.c_fc.bias\"],\n }\n )\n\n # .time_embed.c_proj -> .time_embedding.linear_2\n diffusers_checkpoint.update(\n {\n \"time_embedding.linear_2.weight\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.time_embed.c_proj.weight\"],\n \"time_embedding.linear_2.bias\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.time_embed.c_proj.bias\"],\n }\n )\n\n # .input_proj -> .proj_in\n diffusers_checkpoint.update(\n {\n \"proj_in.weight\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.input_proj.weight\"],\n \"proj_in.bias\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.input_proj.bias\"],\n }\n )\n\n # .clip_emb -> .embedding_proj\n diffusers_checkpoint.update(\n {\n \"embedding_proj.weight\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.clip_embed.weight\"],\n \"embedding_proj.bias\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.clip_embed.bias\"],\n }\n )\n\n # .pos_emb -> .positional_embedding\n diffusers_checkpoint.update({\"positional_embedding\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.pos_emb\"][None, :]})\n\n # .ln_pre -> .norm_in\n diffusers_checkpoint.update(\n {\n \"norm_in.weight\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.ln_pre.weight\"],\n \"norm_in.bias\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.ln_pre.bias\"],\n }\n )\n\n # .backbone.resblocks. -> .transformer_blocks.\n for idx in range(len(model.transformer_blocks)):\n diffusers_transformer_prefix = f\"transformer_blocks.{idx}\"\n original_transformer_prefix = f\"{PRIOR_ORIGINAL_PREFIX}.backbone.resblocks.{idx}\"\n\n # .attn -> .attn1\n diffusers_attention_prefix = f\"{diffusers_transformer_prefix}.attn1\"\n original_attention_prefix = f\"{original_transformer_prefix}.attn\"\n diffusers_checkpoint.update(\n prior_attention_to_diffusers(\n checkpoint,\n diffusers_attention_prefix=diffusers_attention_prefix,\n original_attention_prefix=original_attention_prefix,\n attention_head_dim=model.attention_head_dim,\n )\n )\n\n # .mlp -> .ff\n diffusers_ff_prefix = f\"{diffusers_transformer_prefix}.ff\"\n original_ff_prefix = f\"{original_transformer_prefix}.mlp\"\n diffusers_checkpoint.update(\n prior_ff_to_diffusers(\n checkpoint, diffusers_ff_prefix=diffusers_ff_prefix, original_ff_prefix=original_ff_prefix\n )\n )\n\n # .ln_1 -> .norm1\n diffusers_checkpoint.update(\n {\n f\"{diffusers_transformer_prefix}.norm1.weight\": checkpoint[\n f\"{original_transformer_prefix}.ln_1.weight\"\n ],\n f\"{diffusers_transformer_prefix}.norm1.bias\": checkpoint[f\"{original_transformer_prefix}.ln_1.bias\"],\n }\n )\n\n # .ln_2 -> .norm3\n diffusers_checkpoint.update(\n {\n f\"{diffusers_transformer_prefix}.norm3.weight\": checkpoint[\n f\"{original_transformer_prefix}.ln_2.weight\"\n ],\n f\"{diffusers_transformer_prefix}.norm3.bias\": checkpoint[f\"{original_transformer_prefix}.ln_2.bias\"],\n }\n )\n\n # .ln_post -> .norm_out\n diffusers_checkpoint.update(\n {\n \"norm_out.weight\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.ln_post.weight\"],\n \"norm_out.bias\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.ln_post.bias\"],\n }\n )\n\n # .output_proj -> .proj_to_clip_embeddings\n diffusers_checkpoint.update(\n {\n \"proj_to_clip_embeddings.weight\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.output_proj.weight\"],\n \"proj_to_clip_embeddings.bias\": checkpoint[f\"{PRIOR_ORIGINAL_PREFIX}.output_proj.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\ndef prior_attention_to_diffusers(\n checkpoint, *, diffusers_attention_prefix, original_attention_prefix, attention_head_dim\n):\n diffusers_checkpoint = {}\n\n # .c_qkv -> .{to_q, to_k, to_v}\n [q_weight, k_weight, v_weight], [q_bias, k_bias, v_bias] = split_attentions(\n weight=checkpoint[f\"{original_attention_prefix}.c_qkv.weight\"],\n bias=checkpoint[f\"{original_attention_prefix}.c_qkv.bias\"],\n split=3,\n chunk_size=attention_head_dim,\n )\n\n diffusers_checkpoint.update(\n {\n f\"{diffusers_attention_prefix}.to_q.weight\": q_weight,\n f\"{diffusers_attention_prefix}.to_q.bias\": q_bias,\n f\"{diffusers_attention_prefix}.to_k.weight\": k_weight,\n f\"{diffusers_attention_prefix}.to_k.bias\": k_bias,\n f\"{diffusers_attention_prefix}.to_v.weight\": v_weight,\n f\"{diffusers_attention_prefix}.to_v.bias\": v_bias,\n }\n )\n\n # .c_proj -> .to_out.0\n diffusers_checkpoint.update(\n {\n f\"{diffusers_attention_prefix}.to_out.0.weight\": checkpoint[f\"{original_attention_prefix}.c_proj.weight\"],\n f\"{diffusers_attention_prefix}.to_out.0.bias\": checkpoint[f\"{original_attention_prefix}.c_proj.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\ndef prior_ff_to_diffusers(checkpoint, *, diffusers_ff_prefix, original_ff_prefix):\n diffusers_checkpoint = {\n # .c_fc -> .net.0.proj\n f\"{diffusers_ff_prefix}.net.{0}.proj.weight\": checkpoint[f\"{original_ff_prefix}.c_fc.weight\"],\n f\"{diffusers_ff_prefix}.net.{0}.proj.bias\": checkpoint[f\"{original_ff_prefix}.c_fc.bias\"],\n # .c_proj -> .net.2\n f\"{diffusers_ff_prefix}.net.{2}.weight\": checkpoint[f\"{original_ff_prefix}.c_proj.weight\"],\n f\"{diffusers_ff_prefix}.net.{2}.bias\": checkpoint[f\"{original_ff_prefix}.c_proj.bias\"],\n }\n\n return diffusers_checkpoint\n\n\n# done prior\n\n\n# prior_image (only slightly different from prior)\n\n\nPRIOR_IMAGE_ORIGINAL_PREFIX = \"wrapped\"\n\n# Uses default arguments\nPRIOR_IMAGE_CONFIG = {\n \"num_attention_heads\": 8,\n \"attention_head_dim\": 1024 // 8,\n \"num_layers\": 24,\n \"embedding_dim\": 1024,\n \"num_embeddings\": 1024,\n \"additional_embeddings\": 0,\n \"time_embed_act_fn\": \"gelu\",\n \"norm_in_type\": \"layer\",\n \"embedding_proj_norm_type\": \"layer\",\n \"encoder_hid_proj_type\": None,\n \"added_emb_type\": None,\n \"time_embed_dim\": 1024 * 4,\n \"embedding_proj_dim\": 1024,\n \"clip_embed_dim\": 1024 * 2,\n}\n\n\ndef prior_image_model_from_original_config():\n model = PriorTransformer(**PRIOR_IMAGE_CONFIG)\n\n return model\n\n\ndef prior_image_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):\n diffusers_checkpoint = {}\n\n # .time_embed.c_fc -> .time_embedding.linear_1\n diffusers_checkpoint.update(\n {\n \"time_embedding.linear_1.weight\": checkpoint[f\"{PRIOR_IMAGE_ORIGINAL_PREFIX}.time_embed.c_fc.weight\"],\n \"time_embedding.linear_1.bias\": checkpoint[f\"{PRIOR_IMAGE_ORIGINAL_PREFIX}.time_embed.c_fc.bias\"],\n }\n )\n\n # .time_embed.c_proj -> .time_embedding.linear_2\n diffusers_checkpoint.update(\n {\n \"time_embedding.linear_2.weight\": checkpoint[f\"{PRIOR_IMAGE_ORIGINAL_PREFIX}.time_embed.c_proj.weight\"],\n \"time_embedding.linear_2.bias\": checkpoint[f\"{PRIOR_IMAGE_ORIGINAL_PREFIX}.time_embed.c_proj.bias\"],\n }\n )\n\n # .input_proj -> .proj_in\n diffusers_checkpoint.update(\n {\n \"proj_in.weight\": checkpoint[f\"{PRIOR_IMAGE_ORIGINAL_PREFIX}.input_proj.weight\"],\n \"proj_in.bias\": checkpoint[f\"{PRIOR_IMAGE_ORIGINAL_PREFIX}.input_proj.bias\"],\n }\n )\n\n # .clip_embed.0 -> .embedding_proj_norm\n diffusers_checkpoint.update(\n {\n \"embedding_proj_norm.weight\": checkpoint[f\"{PRIOR_IMAGE_ORIGINAL_PREFIX}.clip_embed.0.weight\"],\n \"embedding_proj_norm.bias\": checkpoint[f\"{PRIOR_IMAGE_ORIGINAL_PREFIX}.clip_embed.0.bias\"],\n }\n )\n\n # ..clip_embed.1 -> .embedding_proj\n diffusers_checkpoint.update(\n {\n \"embedding_proj.weight\": checkpoint[f\"{PRIOR_IMAGE_ORIGINAL_PREFIX}.clip_embed.1.weight\"],\n \"embedding_proj.bias\": checkpoint[f\"{PRIOR_IMAGE_ORIGINAL_PREFIX}.clip_embed.1.bias\"],\n }\n )\n\n # .pos_emb -> .positional_embedding\n diffusers_checkpoint.update(\n {\"positional_embedding\": checkpoint[f\"{PRIOR_IMAGE_ORIGINAL_PREFIX}.pos_emb\"][None, :]}\n )\n\n # .ln_pre -> .norm_in\n diffusers_checkpoint.update(\n {\n \"norm_in.weight\": checkpoint[f\"{PRIOR_IMAGE_ORIGINAL_PREFIX}.ln_pre.weight\"],\n \"norm_in.bias\": checkpoint[f\"{PRIOR_IMAGE_ORIGINAL_PREFIX}.ln_pre.bias\"],\n }\n )\n\n # .backbone.resblocks. -> .transformer_blocks.\n for idx in range(len(model.transformer_blocks)):\n diffusers_transformer_prefix = f\"transformer_blocks.{idx}\"\n original_transformer_prefix = f\"{PRIOR_IMAGE_ORIGINAL_PREFIX}.backbone.resblocks.{idx}\"\n\n # .attn -> .attn1\n diffusers_attention_prefix = f\"{diffusers_transformer_prefix}.attn1\"\n original_attention_prefix = f\"{original_transformer_prefix}.attn\"\n diffusers_checkpoint.update(\n prior_attention_to_diffusers(\n checkpoint,\n diffusers_attention_prefix=diffusers_attention_prefix,\n original_attention_prefix=original_attention_prefix,\n attention_head_dim=model.attention_head_dim,\n )\n )\n\n # .mlp -> .ff\n diffusers_ff_prefix = f\"{diffusers_transformer_prefix}.ff\"\n original_ff_prefix = f\"{original_transformer_prefix}.mlp\"\n diffusers_checkpoint.update(\n prior_ff_to_diffusers(\n checkpoint, diffusers_ff_prefix=diffusers_ff_prefix, original_ff_prefix=original_ff_prefix\n )\n )\n\n # .ln_1 -> .norm1\n diffusers_checkpoint.update(\n {\n f\"{diffusers_transformer_prefix}.norm1.weight\": checkpoint[\n f\"{original_transformer_prefix}.ln_1.weight\"\n ],\n f\"{diffusers_transformer_prefix}.norm1.bias\": checkpoint[f\"{original_transformer_prefix}.ln_1.bias\"],\n }\n )\n\n # .ln_2 -> .norm3\n diffusers_checkpoint.update(\n {\n f\"{diffusers_transformer_prefix}.norm3.weight\": checkpoint[\n f\"{original_transformer_prefix}.ln_2.weight\"\n ],\n f\"{diffusers_transformer_prefix}.norm3.bias\": checkpoint[f\"{original_transformer_prefix}.ln_2.bias\"],\n }\n )\n\n # .ln_post -> .norm_out\n diffusers_checkpoint.update(\n {\n \"norm_out.weight\": checkpoint[f\"{PRIOR_IMAGE_ORIGINAL_PREFIX}.ln_post.weight\"],\n \"norm_out.bias\": checkpoint[f\"{PRIOR_IMAGE_ORIGINAL_PREFIX}.ln_post.bias\"],\n }\n )\n\n # .output_proj -> .proj_to_clip_embeddings\n diffusers_checkpoint.update(\n {\n \"proj_to_clip_embeddings.weight\": checkpoint[f\"{PRIOR_IMAGE_ORIGINAL_PREFIX}.output_proj.weight\"],\n \"proj_to_clip_embeddings.bias\": checkpoint[f\"{PRIOR_IMAGE_ORIGINAL_PREFIX}.output_proj.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\n# done prior_image\n\n\n# renderer\n\n## create the lookup table for marching cubes method used in MeshDecoder\n\nMC_TABLE = [\n [],\n [[0, 1, 0, 2, 0, 4]],\n [[1, 0, 1, 5, 1, 3]],\n [[0, 4, 1, 5, 0, 2], [1, 5, 1, 3, 0, 2]],\n [[2, 0, 2, 3, 2, 6]],\n [[0, 1, 2, 3, 0, 4], [2, 3, 2, 6, 0, 4]],\n [[1, 0, 1, 5, 1, 3], [2, 6, 0, 2, 3, 2]],\n [[3, 2, 2, 6, 3, 1], [3, 1, 2, 6, 1, 5], [1, 5, 2, 6, 0, 4]],\n [[3, 1, 3, 7, 3, 2]],\n [[0, 2, 0, 4, 0, 1], [3, 7, 2, 3, 1, 3]],\n [[1, 5, 3, 7, 1, 0], [3, 7, 3, 2, 1, 0]],\n [[2, 0, 0, 4, 2, 3], [2, 3, 0, 4, 3, 7], [3, 7, 0, 4, 1, 5]],\n [[2, 0, 3, 1, 2, 6], [3, 1, 3, 7, 2, 6]],\n [[1, 3, 3, 7, 1, 0], [1, 0, 3, 7, 0, 4], [0, 4, 3, 7, 2, 6]],\n [[0, 1, 1, 5, 0, 2], [0, 2, 1, 5, 2, 6], [2, 6, 1, 5, 3, 7]],\n [[0, 4, 1, 5, 3, 7], [0, 4, 3, 7, 2, 6]],\n [[4, 0, 4, 6, 4, 5]],\n [[0, 2, 4, 6, 0, 1], [4, 6, 4, 5, 0, 1]],\n [[1, 5, 1, 3, 1, 0], [4, 6, 5, 4, 0, 4]],\n [[5, 1, 1, 3, 5, 4], [5, 4, 1, 3, 4, 6], [4, 6, 1, 3, 0, 2]],\n [[2, 0, 2, 3, 2, 6], [4, 5, 0, 4, 6, 4]],\n [[6, 4, 4, 5, 6, 2], [6, 2, 4, 5, 2, 3], [2, 3, 4, 5, 0, 1]],\n [[2, 6, 2, 0, 3, 2], [1, 0, 1, 5, 3, 1], [6, 4, 5, 4, 0, 4]],\n [[1, 3, 5, 4, 1, 5], [1, 3, 4, 6, 5, 4], [1, 3, 3, 2, 4, 6], [3, 2, 2, 6, 4, 6]],\n [[3, 1, 3, 7, 3, 2], [6, 4, 5, 4, 0, 4]],\n [[4, 5, 0, 1, 4, 6], [0, 1, 0, 2, 4, 6], [7, 3, 2, 3, 1, 3]],\n [[3, 2, 1, 0, 3, 7], [1, 0, 1, 5, 3, 7], [6, 4, 5, 4, 0, 4]],\n [[3, 7, 3, 2, 1, 5], [3, 2, 6, 4, 1, 5], [1, 5, 6, 4, 5, 4], [3, 2, 2, 0, 6, 4]],\n [[3, 7, 2, 6, 3, 1], [2, 6, 2, 0, 3, 1], [5, 4, 0, 4, 6, 4]],\n [[1, 0, 1, 3, 5, 4], [1, 3, 2, 6, 5, 4], [1, 3, 3, 7, 2, 6], [5, 4, 2, 6, 4, 6]],\n [[0, 1, 1, 5, 0, 2], [0, 2, 1, 5, 2, 6], [2, 6, 1, 5, 3, 7], [4, 5, 0, 4, 4, 6]],\n [[6, 2, 4, 6, 4, 5], [4, 5, 5, 1, 6, 2], [6, 2, 5, 1, 7, 3]],\n [[5, 1, 5, 4, 5, 7]],\n [[0, 1, 0, 2, 0, 4], [5, 7, 1, 5, 4, 5]],\n [[1, 0, 5, 4, 1, 3], [5, 4, 5, 7, 1, 3]],\n [[4, 5, 5, 7, 4, 0], [4, 0, 5, 7, 0, 2], [0, 2, 5, 7, 1, 3]],\n [[2, 0, 2, 3, 2, 6], [7, 5, 1, 5, 4, 5]],\n [[2, 6, 0, 4, 2, 3], [0, 4, 0, 1, 2, 3], [7, 5, 1, 5, 4, 5]],\n [[5, 7, 1, 3, 5, 4], [1, 3, 1, 0, 5, 4], [6, 2, 0, 2, 3, 2]],\n [[3, 1, 3, 2, 7, 5], [3, 2, 0, 4, 7, 5], [3, 2, 2, 6, 0, 4], [7, 5, 0, 4, 5, 4]],\n [[3, 7, 3, 2, 3, 1], [5, 4, 7, 5, 1, 5]],\n [[0, 4, 0, 1, 2, 0], [3, 1, 3, 7, 2, 3], [4, 5, 7, 5, 1, 5]],\n [[7, 3, 3, 2, 7, 5], [7, 5, 3, 2, 5, 4], [5, 4, 3, 2, 1, 0]],\n [[0, 4, 2, 3, 0, 2], [0, 4, 3, 7, 2, 3], [0, 4, 4, 5, 3, 7], [4, 5, 5, 7, 3, 7]],\n [[2, 0, 3, 1, 2, 6], [3, 1, 3, 7, 2, 6], [4, 5, 7, 5, 1, 5]],\n [[1, 3, 3, 7, 1, 0], [1, 0, 3, 7, 0, 4], [0, 4, 3, 7, 2, 6], [5, 7, 1, 5, 5, 4]],\n [[2, 6, 2, 0, 3, 7], [2, 0, 4, 5, 3, 7], [3, 7, 4, 5, 7, 5], [2, 0, 0, 1, 4, 5]],\n [[4, 0, 5, 4, 5, 7], [5, 7, 7, 3, 4, 0], [4, 0, 7, 3, 6, 2]],\n [[4, 6, 5, 7, 4, 0], [5, 7, 5, 1, 4, 0]],\n [[1, 0, 0, 2, 1, 5], [1, 5, 0, 2, 5, 7], [5, 7, 0, 2, 4, 6]],\n [[0, 4, 4, 6, 0, 1], [0, 1, 4, 6, 1, 3], [1, 3, 4, 6, 5, 7]],\n [[0, 2, 4, 6, 5, 7], [0, 2, 5, 7, 1, 3]],\n [[5, 1, 4, 0, 5, 7], [4, 0, 4, 6, 5, 7], [3, 2, 6, 2, 0, 2]],\n [[2, 3, 2, 6, 0, 1], [2, 6, 7, 5, 0, 1], [0, 1, 7, 5, 1, 5], [2, 6, 6, 4, 7, 5]],\n [[0, 4, 4, 6, 0, 1], [0, 1, 4, 6, 1, 3], [1, 3, 4, 6, 5, 7], [2, 6, 0, 2, 2, 3]],\n [[3, 1, 2, 3, 2, 6], [2, 6, 6, 4, 3, 1], [3, 1, 6, 4, 7, 5]],\n [[4, 6, 5, 7, 4, 0], [5, 7, 5, 1, 4, 0], [2, 3, 1, 3, 7, 3]],\n [[1, 0, 0, 2, 1, 5], [1, 5, 0, 2, 5, 7], [5, 7, 0, 2, 4, 6], [3, 2, 1, 3, 3, 7]],\n [[0, 1, 0, 4, 2, 3], [0, 4, 5, 7, 2, 3], [0, 4, 4, 6, 5, 7], [2, 3, 5, 7, 3, 7]],\n [[7, 5, 3, 7, 3, 2], [3, 2, 2, 0, 7, 5], [7, 5, 2, 0, 6, 4]],\n [[0, 4, 4, 6, 5, 7], [0, 4, 5, 7, 1, 5], [0, 2, 1, 3, 3, 7], [3, 7, 2, 6, 0, 2]],\n [\n [3, 1, 7, 3, 6, 2],\n [6, 2, 0, 1, 3, 1],\n [6, 4, 0, 1, 6, 2],\n [6, 4, 5, 1, 0, 1],\n [6, 4, 7, 5, 5, 1],\n ],\n [\n [4, 0, 6, 4, 7, 5],\n [7, 5, 1, 0, 4, 0],\n [7, 3, 1, 0, 7, 5],\n [7, 3, 2, 0, 1, 0],\n [7, 3, 6, 2, 2, 0],\n ],\n [[7, 3, 6, 2, 6, 4], [7, 5, 7, 3, 6, 4]],\n [[6, 2, 6, 7, 6, 4]],\n [[0, 4, 0, 1, 0, 2], [6, 7, 4, 6, 2, 6]],\n [[1, 0, 1, 5, 1, 3], [7, 6, 4, 6, 2, 6]],\n [[1, 3, 0, 2, 1, 5], [0, 2, 0, 4, 1, 5], [7, 6, 4, 6, 2, 6]],\n [[2, 3, 6, 7, 2, 0], [6, 7, 6, 4, 2, 0]],\n [[4, 0, 0, 1, 4, 6], [4, 6, 0, 1, 6, 7], [6, 7, 0, 1, 2, 3]],\n [[6, 4, 2, 0, 6, 7], [2, 0, 2, 3, 6, 7], [5, 1, 3, 1, 0, 1]],\n [[1, 5, 1, 3, 0, 4], [1, 3, 7, 6, 0, 4], [0, 4, 7, 6, 4, 6], [1, 3, 3, 2, 7, 6]],\n [[3, 2, 3, 1, 3, 7], [6, 4, 2, 6, 7, 6]],\n [[3, 7, 3, 2, 1, 3], [0, 2, 0, 4, 1, 0], [7, 6, 4, 6, 2, 6]],\n [[1, 5, 3, 7, 1, 0], [3, 7, 3, 2, 1, 0], [4, 6, 2, 6, 7, 6]],\n [[2, 0, 0, 4, 2, 3], [2, 3, 0, 4, 3, 7], [3, 7, 0, 4, 1, 5], [6, 4, 2, 6, 6, 7]],\n [[7, 6, 6, 4, 7, 3], [7, 3, 6, 4, 3, 1], [3, 1, 6, 4, 2, 0]],\n [[0, 1, 4, 6, 0, 4], [0, 1, 6, 7, 4, 6], [0, 1, 1, 3, 6, 7], [1, 3, 3, 7, 6, 7]],\n [[0, 2, 0, 1, 4, 6], [0, 1, 3, 7, 4, 6], [0, 1, 1, 5, 3, 7], [4, 6, 3, 7, 6, 7]],\n [[7, 3, 6, 7, 6, 4], [6, 4, 4, 0, 7, 3], [7, 3, 4, 0, 5, 1]],\n [[4, 0, 6, 2, 4, 5], [6, 2, 6, 7, 4, 5]],\n [[2, 6, 6, 7, 2, 0], [2, 0, 6, 7, 0, 1], [0, 1, 6, 7, 4, 5]],\n [[6, 7, 4, 5, 6, 2], [4, 5, 4, 0, 6, 2], [3, 1, 0, 1, 5, 1]],\n [[2, 0, 2, 6, 3, 1], [2, 6, 4, 5, 3, 1], [2, 6, 6, 7, 4, 5], [3, 1, 4, 5, 1, 5]],\n [[0, 2, 2, 3, 0, 4], [0, 4, 2, 3, 4, 5], [4, 5, 2, 3, 6, 7]],\n [[0, 1, 2, 3, 6, 7], [0, 1, 6, 7, 4, 5]],\n [[0, 2, 2, 3, 0, 4], [0, 4, 2, 3, 4, 5], [4, 5, 2, 3, 6, 7], [1, 3, 0, 1, 1, 5]],\n [[5, 4, 1, 5, 1, 3], [1, 3, 3, 2, 5, 4], [5, 4, 3, 2, 7, 6]],\n [[4, 0, 6, 2, 4, 5], [6, 2, 6, 7, 4, 5], [1, 3, 7, 3, 2, 3]],\n [[2, 6, 6, 7, 2, 0], [2, 0, 6, 7, 0, 1], [0, 1, 6, 7, 4, 5], [3, 7, 2, 3, 3, 1]],\n [[0, 1, 1, 5, 3, 7], [0, 1, 3, 7, 2, 3], [0, 4, 2, 6, 6, 7], [6, 7, 4, 5, 0, 4]],\n [\n [6, 2, 7, 6, 5, 4],\n [5, 4, 0, 2, 6, 2],\n [5, 1, 0, 2, 5, 4],\n [5, 1, 3, 2, 0, 2],\n [5, 1, 7, 3, 3, 2],\n ],\n [[3, 1, 3, 7, 2, 0], [3, 7, 5, 4, 2, 0], [2, 0, 5, 4, 0, 4], [3, 7, 7, 6, 5, 4]],\n [[1, 0, 3, 1, 3, 7], [3, 7, 7, 6, 1, 0], [1, 0, 7, 6, 5, 4]],\n [\n [1, 0, 5, 1, 7, 3],\n [7, 3, 2, 0, 1, 0],\n [7, 6, 2, 0, 7, 3],\n [7, 6, 4, 0, 2, 0],\n [7, 6, 5, 4, 4, 0],\n ],\n [[7, 6, 5, 4, 5, 1], [7, 3, 7, 6, 5, 1]],\n [[5, 7, 5, 1, 5, 4], [6, 2, 7, 6, 4, 6]],\n [[0, 2, 0, 4, 1, 0], [5, 4, 5, 7, 1, 5], [2, 6, 7, 6, 4, 6]],\n [[1, 0, 5, 4, 1, 3], [5, 4, 5, 7, 1, 3], [2, 6, 7, 6, 4, 6]],\n [[4, 5, 5, 7, 4, 0], [4, 0, 5, 7, 0, 2], [0, 2, 5, 7, 1, 3], [6, 7, 4, 6, 6, 2]],\n [[2, 3, 6, 7, 2, 0], [6, 7, 6, 4, 2, 0], [1, 5, 4, 5, 7, 5]],\n [[4, 0, 0, 1, 4, 6], [4, 6, 0, 1, 6, 7], [6, 7, 0, 1, 2, 3], [5, 1, 4, 5, 5, 7]],\n [[0, 2, 2, 3, 6, 7], [0, 2, 6, 7, 4, 6], [0, 1, 4, 5, 5, 7], [5, 7, 1, 3, 0, 1]],\n [\n [5, 4, 7, 5, 3, 1],\n [3, 1, 0, 4, 5, 4],\n [3, 2, 0, 4, 3, 1],\n [3, 2, 6, 4, 0, 4],\n [3, 2, 7, 6, 6, 4],\n ],\n [[5, 4, 5, 7, 1, 5], [3, 7, 3, 2, 1, 3], [4, 6, 2, 6, 7, 6]],\n [[1, 0, 0, 2, 0, 4], [1, 5, 5, 4, 5, 7], [3, 2, 1, 3, 3, 7], [2, 6, 7, 6, 4, 6]],\n [[7, 3, 3, 2, 7, 5], [7, 5, 3, 2, 5, 4], [5, 4, 3, 2, 1, 0], [6, 2, 7, 6, 6, 4]],\n [\n [0, 4, 2, 3, 0, 2],\n [0, 4, 3, 7, 2, 3],\n [0, 4, 4, 5, 3, 7],\n [4, 5, 5, 7, 3, 7],\n [6, 7, 4, 6, 2, 6],\n ],\n [[7, 6, 6, 4, 7, 3], [7, 3, 6, 4, 3, 1], [3, 1, 6, 4, 2, 0], [5, 4, 7, 5, 5, 1]],\n [\n [0, 1, 4, 6, 0, 4],\n [0, 1, 6, 7, 4, 6],\n [0, 1, 1, 3, 6, 7],\n [1, 3, 3, 7, 6, 7],\n [5, 7, 1, 5, 4, 5],\n ],\n [\n [6, 7, 4, 6, 0, 2],\n [0, 2, 3, 7, 6, 7],\n [0, 1, 3, 7, 0, 2],\n [0, 1, 5, 7, 3, 7],\n [0, 1, 4, 5, 5, 7],\n ],\n [[4, 0, 6, 7, 4, 6], [4, 0, 7, 3, 6, 7], [4, 0, 5, 7, 7, 3], [4, 5, 5, 7, 4, 0]],\n [[7, 5, 5, 1, 7, 6], [7, 6, 5, 1, 6, 2], [6, 2, 5, 1, 4, 0]],\n [[0, 2, 1, 5, 0, 1], [0, 2, 5, 7, 1, 5], [0, 2, 2, 6, 5, 7], [2, 6, 6, 7, 5, 7]],\n [[1, 3, 1, 0, 5, 7], [1, 0, 2, 6, 5, 7], [5, 7, 2, 6, 7, 6], [1, 0, 0, 4, 2, 6]],\n [[2, 0, 6, 2, 6, 7], [6, 7, 7, 5, 2, 0], [2, 0, 7, 5, 3, 1]],\n [[0, 4, 0, 2, 1, 5], [0, 2, 6, 7, 1, 5], [0, 2, 2, 3, 6, 7], [1, 5, 6, 7, 5, 7]],\n [[7, 6, 5, 7, 5, 1], [5, 1, 1, 0, 7, 6], [7, 6, 1, 0, 3, 2]],\n [\n [2, 0, 3, 2, 7, 6],\n [7, 6, 4, 0, 2, 0],\n [7, 5, 4, 0, 7, 6],\n [7, 5, 1, 0, 4, 0],\n [7, 5, 3, 1, 1, 0],\n ],\n [[7, 5, 3, 1, 3, 2], [7, 6, 7, 5, 3, 2]],\n [[7, 5, 5, 1, 7, 6], [7, 6, 5, 1, 6, 2], [6, 2, 5, 1, 4, 0], [3, 1, 7, 3, 3, 2]],\n [\n [0, 2, 1, 5, 0, 1],\n [0, 2, 5, 7, 1, 5],\n [0, 2, 2, 6, 5, 7],\n [2, 6, 6, 7, 5, 7],\n [3, 7, 2, 3, 1, 3],\n ],\n [\n [3, 7, 2, 3, 0, 1],\n [0, 1, 5, 7, 3, 7],\n [0, 4, 5, 7, 0, 1],\n [0, 4, 6, 7, 5, 7],\n [0, 4, 2, 6, 6, 7],\n ],\n [[2, 0, 3, 7, 2, 3], [2, 0, 7, 5, 3, 7], [2, 0, 6, 7, 7, 5], [2, 6, 6, 7, 2, 0]],\n [\n [5, 7, 1, 5, 0, 4],\n [0, 4, 6, 7, 5, 7],\n [0, 2, 6, 7, 0, 4],\n [0, 2, 3, 7, 6, 7],\n [0, 2, 1, 3, 3, 7],\n ],\n [[1, 0, 5, 7, 1, 5], [1, 0, 7, 6, 5, 7], [1, 0, 3, 7, 7, 6], [1, 3, 3, 7, 1, 0]],\n [[0, 2, 0, 1, 0, 4], [3, 7, 6, 7, 5, 7]],\n [[7, 5, 7, 3, 7, 6]],\n [[7, 3, 7, 5, 7, 6]],\n [[0, 1, 0, 2, 0, 4], [6, 7, 3, 7, 5, 7]],\n [[1, 3, 1, 0, 1, 5], [7, 6, 3, 7, 5, 7]],\n [[0, 4, 1, 5, 0, 2], [1, 5, 1, 3, 0, 2], [6, 7, 3, 7, 5, 7]],\n [[2, 6, 2, 0, 2, 3], [7, 5, 6, 7, 3, 7]],\n [[0, 1, 2, 3, 0, 4], [2, 3, 2, 6, 0, 4], [5, 7, 6, 7, 3, 7]],\n [[1, 5, 1, 3, 0, 1], [2, 3, 2, 6, 0, 2], [5, 7, 6, 7, 3, 7]],\n [[3, 2, 2, 6, 3, 1], [3, 1, 2, 6, 1, 5], [1, 5, 2, 6, 0, 4], [7, 6, 3, 7, 7, 5]],\n [[3, 1, 7, 5, 3, 2], [7, 5, 7, 6, 3, 2]],\n [[7, 6, 3, 2, 7, 5], [3, 2, 3, 1, 7, 5], [4, 0, 1, 0, 2, 0]],\n [[5, 7, 7, 6, 5, 1], [5, 1, 7, 6, 1, 0], [1, 0, 7, 6, 3, 2]],\n [[2, 3, 2, 0, 6, 7], [2, 0, 1, 5, 6, 7], [2, 0, 0, 4, 1, 5], [6, 7, 1, 5, 7, 5]],\n [[6, 2, 2, 0, 6, 7], [6, 7, 2, 0, 7, 5], [7, 5, 2, 0, 3, 1]],\n [[0, 4, 0, 1, 2, 6], [0, 1, 5, 7, 2, 6], [2, 6, 5, 7, 6, 7], [0, 1, 1, 3, 5, 7]],\n [[1, 5, 0, 2, 1, 0], [1, 5, 2, 6, 0, 2], [1, 5, 5, 7, 2, 6], [5, 7, 7, 6, 2, 6]],\n [[5, 1, 7, 5, 7, 6], [7, 6, 6, 2, 5, 1], [5, 1, 6, 2, 4, 0]],\n [[4, 5, 4, 0, 4, 6], [7, 3, 5, 7, 6, 7]],\n [[0, 2, 4, 6, 0, 1], [4, 6, 4, 5, 0, 1], [3, 7, 5, 7, 6, 7]],\n [[4, 6, 4, 5, 0, 4], [1, 5, 1, 3, 0, 1], [6, 7, 3, 7, 5, 7]],\n [[5, 1, 1, 3, 5, 4], [5, 4, 1, 3, 4, 6], [4, 6, 1, 3, 0, 2], [7, 3, 5, 7, 7, 6]],\n [[2, 3, 2, 6, 0, 2], [4, 6, 4, 5, 0, 4], [3, 7, 5, 7, 6, 7]],\n [[6, 4, 4, 5, 6, 2], [6, 2, 4, 5, 2, 3], [2, 3, 4, 5, 0, 1], [7, 5, 6, 7, 7, 3]],\n [[0, 1, 1, 5, 1, 3], [0, 2, 2, 3, 2, 6], [4, 5, 0, 4, 4, 6], [5, 7, 6, 7, 3, 7]],\n [\n [1, 3, 5, 4, 1, 5],\n [1, 3, 4, 6, 5, 4],\n [1, 3, 3, 2, 4, 6],\n [3, 2, 2, 6, 4, 6],\n [7, 6, 3, 7, 5, 7],\n ],\n [[3, 1, 7, 5, 3, 2], [7, 5, 7, 6, 3, 2], [0, 4, 6, 4, 5, 4]],\n [[1, 0, 0, 2, 4, 6], [1, 0, 4, 6, 5, 4], [1, 3, 5, 7, 7, 6], [7, 6, 3, 2, 1, 3]],\n [[5, 7, 7, 6, 5, 1], [5, 1, 7, 6, 1, 0], [1, 0, 7, 6, 3, 2], [4, 6, 5, 4, 4, 0]],\n [\n [7, 5, 6, 7, 2, 3],\n [2, 3, 1, 5, 7, 5],\n [2, 0, 1, 5, 2, 3],\n [2, 0, 4, 5, 1, 5],\n [2, 0, 6, 4, 4, 5],\n ],\n [[6, 2, 2, 0, 6, 7], [6, 7, 2, 0, 7, 5], [7, 5, 2, 0, 3, 1], [4, 0, 6, 4, 4, 5]],\n [\n [4, 6, 5, 4, 1, 0],\n [1, 0, 2, 6, 4, 6],\n [1, 3, 2, 6, 1, 0],\n [1, 3, 7, 6, 2, 6],\n [1, 3, 5, 7, 7, 6],\n ],\n [\n [1, 5, 0, 2, 1, 0],\n [1, 5, 2, 6, 0, 2],\n [1, 5, 5, 7, 2, 6],\n [5, 7, 7, 6, 2, 6],\n [4, 6, 5, 4, 0, 4],\n ],\n [[5, 1, 4, 6, 5, 4], [5, 1, 6, 2, 4, 6], [5, 1, 7, 6, 6, 2], [5, 7, 7, 6, 5, 1]],\n [[5, 4, 7, 6, 5, 1], [7, 6, 7, 3, 5, 1]],\n [[7, 3, 5, 1, 7, 6], [5, 1, 5, 4, 7, 6], [2, 0, 4, 0, 1, 0]],\n [[3, 1, 1, 0, 3, 7], [3, 7, 1, 0, 7, 6], [7, 6, 1, 0, 5, 4]],\n [[0, 2, 0, 4, 1, 3], [0, 4, 6, 7, 1, 3], [1, 3, 6, 7, 3, 7], [0, 4, 4, 5, 6, 7]],\n [[5, 4, 7, 6, 5, 1], [7, 6, 7, 3, 5, 1], [0, 2, 3, 2, 6, 2]],\n [[1, 5, 5, 4, 7, 6], [1, 5, 7, 6, 3, 7], [1, 0, 3, 2, 2, 6], [2, 6, 0, 4, 1, 0]],\n [[3, 1, 1, 0, 3, 7], [3, 7, 1, 0, 7, 6], [7, 6, 1, 0, 5, 4], [2, 0, 3, 2, 2, 6]],\n [\n [2, 3, 6, 2, 4, 0],\n [4, 0, 1, 3, 2, 3],\n [4, 5, 1, 3, 4, 0],\n [4, 5, 7, 3, 1, 3],\n [4, 5, 6, 7, 7, 3],\n ],\n [[1, 5, 5, 4, 1, 3], [1, 3, 5, 4, 3, 2], [3, 2, 5, 4, 7, 6]],\n [[1, 5, 5, 4, 1, 3], [1, 3, 5, 4, 3, 2], [3, 2, 5, 4, 7, 6], [0, 4, 1, 0, 0, 2]],\n [[1, 0, 5, 4, 7, 6], [1, 0, 7, 6, 3, 2]],\n [[2, 3, 0, 2, 0, 4], [0, 4, 4, 5, 2, 3], [2, 3, 4, 5, 6, 7]],\n [[1, 3, 1, 5, 0, 2], [1, 5, 7, 6, 0, 2], [1, 5, 5, 4, 7, 6], [0, 2, 7, 6, 2, 6]],\n [\n [5, 1, 4, 5, 6, 7],\n [6, 7, 3, 1, 5, 1],\n [6, 2, 3, 1, 6, 7],\n [6, 2, 0, 1, 3, 1],\n [6, 2, 4, 0, 0, 1],\n ],\n [[6, 7, 2, 6, 2, 0], [2, 0, 0, 1, 6, 7], [6, 7, 0, 1, 4, 5]],\n [[6, 2, 4, 0, 4, 5], [6, 7, 6, 2, 4, 5]],\n [[6, 7, 7, 3, 6, 4], [6, 4, 7, 3, 4, 0], [4, 0, 7, 3, 5, 1]],\n [[1, 5, 1, 0, 3, 7], [1, 0, 4, 6, 3, 7], [1, 0, 0, 2, 4, 6], [3, 7, 4, 6, 7, 6]],\n [[1, 0, 3, 7, 1, 3], [1, 0, 7, 6, 3, 7], [1, 0, 0, 4, 7, 6], [0, 4, 4, 6, 7, 6]],\n [[6, 4, 7, 6, 7, 3], [7, 3, 3, 1, 6, 4], [6, 4, 3, 1, 2, 0]],\n [[6, 7, 7, 3, 6, 4], [6, 4, 7, 3, 4, 0], [4, 0, 7, 3, 5, 1], [2, 3, 6, 2, 2, 0]],\n [\n [7, 6, 3, 7, 1, 5],\n [1, 5, 4, 6, 7, 6],\n [1, 0, 4, 6, 1, 5],\n [1, 0, 2, 6, 4, 6],\n [1, 0, 3, 2, 2, 6],\n ],\n [\n [1, 0, 3, 7, 1, 3],\n [1, 0, 7, 6, 3, 7],\n [1, 0, 0, 4, 7, 6],\n [0, 4, 4, 6, 7, 6],\n [2, 6, 0, 2, 3, 2],\n ],\n [[3, 1, 7, 6, 3, 7], [3, 1, 6, 4, 7, 6], [3, 1, 2, 6, 6, 4], [3, 2, 2, 6, 3, 1]],\n [[3, 2, 3, 1, 7, 6], [3, 1, 0, 4, 7, 6], [7, 6, 0, 4, 6, 4], [3, 1, 1, 5, 0, 4]],\n [\n [0, 1, 2, 0, 6, 4],\n [6, 4, 5, 1, 0, 1],\n [6, 7, 5, 1, 6, 4],\n [6, 7, 3, 1, 5, 1],\n [6, 7, 2, 3, 3, 1],\n ],\n [[0, 1, 4, 0, 4, 6], [4, 6, 6, 7, 0, 1], [0, 1, 6, 7, 2, 3]],\n [[6, 7, 2, 3, 2, 0], [6, 4, 6, 7, 2, 0]],\n [\n [2, 6, 0, 2, 1, 3],\n [1, 3, 7, 6, 2, 6],\n [1, 5, 7, 6, 1, 3],\n [1, 5, 4, 6, 7, 6],\n [1, 5, 0, 4, 4, 6],\n ],\n [[1, 5, 1, 0, 1, 3], [4, 6, 7, 6, 2, 6]],\n [[0, 1, 2, 6, 0, 2], [0, 1, 6, 7, 2, 6], [0, 1, 4, 6, 6, 7], [0, 4, 4, 6, 0, 1]],\n [[6, 7, 6, 2, 6, 4]],\n [[6, 2, 7, 3, 6, 4], [7, 3, 7, 5, 6, 4]],\n [[7, 5, 6, 4, 7, 3], [6, 4, 6, 2, 7, 3], [1, 0, 2, 0, 4, 0]],\n [[6, 2, 7, 3, 6, 4], [7, 3, 7, 5, 6, 4], [0, 1, 5, 1, 3, 1]],\n [[2, 0, 0, 4, 1, 5], [2, 0, 1, 5, 3, 1], [2, 6, 3, 7, 7, 5], [7, 5, 6, 4, 2, 6]],\n [[3, 7, 7, 5, 3, 2], [3, 2, 7, 5, 2, 0], [2, 0, 7, 5, 6, 4]],\n [[3, 2, 3, 7, 1, 0], [3, 7, 6, 4, 1, 0], [3, 7, 7, 5, 6, 4], [1, 0, 6, 4, 0, 4]],\n [[3, 7, 7, 5, 3, 2], [3, 2, 7, 5, 2, 0], [2, 0, 7, 5, 6, 4], [1, 5, 3, 1, 1, 0]],\n [\n [7, 3, 5, 7, 4, 6],\n [4, 6, 2, 3, 7, 3],\n [4, 0, 2, 3, 4, 6],\n [4, 0, 1, 3, 2, 3],\n [4, 0, 5, 1, 1, 3],\n ],\n [[2, 3, 3, 1, 2, 6], [2, 6, 3, 1, 6, 4], [6, 4, 3, 1, 7, 5]],\n [[2, 3, 3, 1, 2, 6], [2, 6, 3, 1, 6, 4], [6, 4, 3, 1, 7, 5], [0, 1, 2, 0, 0, 4]],\n [[1, 0, 1, 5, 3, 2], [1, 5, 4, 6, 3, 2], [3, 2, 4, 6, 2, 6], [1, 5, 5, 7, 4, 6]],\n [\n [0, 2, 4, 0, 5, 1],\n [5, 1, 3, 2, 0, 2],\n [5, 7, 3, 2, 5, 1],\n [5, 7, 6, 2, 3, 2],\n [5, 7, 4, 6, 6, 2],\n ],\n [[2, 0, 3, 1, 7, 5], [2, 0, 7, 5, 6, 4]],\n [[4, 6, 0, 4, 0, 1], [0, 1, 1, 3, 4, 6], [4, 6, 1, 3, 5, 7]],\n [[0, 2, 1, 0, 1, 5], [1, 5, 5, 7, 0, 2], [0, 2, 5, 7, 4, 6]],\n [[5, 7, 4, 6, 4, 0], [5, 1, 5, 7, 4, 0]],\n [[5, 4, 4, 0, 5, 7], [5, 7, 4, 0, 7, 3], [7, 3, 4, 0, 6, 2]],\n [[0, 1, 0, 2, 4, 5], [0, 2, 3, 7, 4, 5], [4, 5, 3, 7, 5, 7], [0, 2, 2, 6, 3, 7]],\n [[5, 4, 4, 0, 5, 7], [5, 7, 4, 0, 7, 3], [7, 3, 4, 0, 6, 2], [1, 0, 5, 1, 1, 3]],\n [\n [1, 5, 3, 1, 2, 0],\n [2, 0, 4, 5, 1, 5],\n [2, 6, 4, 5, 2, 0],\n [2, 6, 7, 5, 4, 5],\n [2, 6, 3, 7, 7, 5],\n ],\n [[2, 3, 0, 4, 2, 0], [2, 3, 4, 5, 0, 4], [2, 3, 3, 7, 4, 5], [3, 7, 7, 5, 4, 5]],\n [[3, 2, 7, 3, 7, 5], [7, 5, 5, 4, 3, 2], [3, 2, 5, 4, 1, 0]],\n [\n [2, 3, 0, 4, 2, 0],\n [2, 3, 4, 5, 0, 4],\n [2, 3, 3, 7, 4, 5],\n [3, 7, 7, 5, 4, 5],\n [1, 5, 3, 1, 0, 1],\n ],\n [[3, 2, 1, 5, 3, 1], [3, 2, 5, 4, 1, 5], [3, 2, 7, 5, 5, 4], [3, 7, 7, 5, 3, 2]],\n [[2, 6, 2, 3, 0, 4], [2, 3, 7, 5, 0, 4], [2, 3, 3, 1, 7, 5], [0, 4, 7, 5, 4, 5]],\n [\n [3, 2, 1, 3, 5, 7],\n [5, 7, 6, 2, 3, 2],\n [5, 4, 6, 2, 5, 7],\n [5, 4, 0, 2, 6, 2],\n [5, 4, 1, 0, 0, 2],\n ],\n [\n [4, 5, 0, 4, 2, 6],\n [2, 6, 7, 5, 4, 5],\n [2, 3, 7, 5, 2, 6],\n [2, 3, 1, 5, 7, 5],\n [2, 3, 0, 1, 1, 5],\n ],\n [[2, 3, 2, 0, 2, 6], [1, 5, 7, 5, 4, 5]],\n [[5, 7, 4, 5, 4, 0], [4, 0, 0, 2, 5, 7], [5, 7, 0, 2, 1, 3]],\n [[5, 4, 1, 0, 1, 3], [5, 7, 5, 4, 1, 3]],\n [[0, 2, 4, 5, 0, 4], [0, 2, 5, 7, 4, 5], [0, 2, 1, 5, 5, 7], [0, 1, 1, 5, 0, 2]],\n [[5, 4, 5, 1, 5, 7]],\n [[4, 6, 6, 2, 4, 5], [4, 5, 6, 2, 5, 1], [5, 1, 6, 2, 7, 3]],\n [[4, 6, 6, 2, 4, 5], [4, 5, 6, 2, 5, 1], [5, 1, 6, 2, 7, 3], [0, 2, 4, 0, 0, 1]],\n [[3, 7, 3, 1, 2, 6], [3, 1, 5, 4, 2, 6], [3, 1, 1, 0, 5, 4], [2, 6, 5, 4, 6, 4]],\n [\n [6, 4, 2, 6, 3, 7],\n [3, 7, 5, 4, 6, 4],\n [3, 1, 5, 4, 3, 7],\n [3, 1, 0, 4, 5, 4],\n [3, 1, 2, 0, 0, 4],\n ],\n [[2, 0, 2, 3, 6, 4], [2, 3, 1, 5, 6, 4], [6, 4, 1, 5, 4, 5], [2, 3, 3, 7, 1, 5]],\n [\n [0, 4, 1, 0, 3, 2],\n [3, 2, 6, 4, 0, 4],\n [3, 7, 6, 4, 3, 2],\n [3, 7, 5, 4, 6, 4],\n [3, 7, 1, 5, 5, 4],\n ],\n [\n [1, 3, 0, 1, 4, 5],\n [4, 5, 7, 3, 1, 3],\n [4, 6, 7, 3, 4, 5],\n [4, 6, 2, 3, 7, 3],\n [4, 6, 0, 2, 2, 3],\n ],\n [[3, 7, 3, 1, 3, 2], [5, 4, 6, 4, 0, 4]],\n [[3, 1, 2, 6, 3, 2], [3, 1, 6, 4, 2, 6], [3, 1, 1, 5, 6, 4], [1, 5, 5, 4, 6, 4]],\n [\n [3, 1, 2, 6, 3, 2],\n [3, 1, 6, 4, 2, 6],\n [3, 1, 1, 5, 6, 4],\n [1, 5, 5, 4, 6, 4],\n [0, 4, 1, 0, 2, 0],\n ],\n [[4, 5, 6, 4, 6, 2], [6, 2, 2, 3, 4, 5], [4, 5, 2, 3, 0, 1]],\n [[2, 3, 6, 4, 2, 6], [2, 3, 4, 5, 6, 4], [2, 3, 0, 4, 4, 5], [2, 0, 0, 4, 2, 3]],\n [[1, 3, 5, 1, 5, 4], [5, 4, 4, 6, 1, 3], [1, 3, 4, 6, 0, 2]],\n [[1, 3, 0, 4, 1, 0], [1, 3, 4, 6, 0, 4], [1, 3, 5, 4, 4, 6], [1, 5, 5, 4, 1, 3]],\n [[4, 6, 0, 2, 0, 1], [4, 5, 4, 6, 0, 1]],\n [[4, 6, 4, 0, 4, 5]],\n [[4, 0, 6, 2, 7, 3], [4, 0, 7, 3, 5, 1]],\n [[1, 5, 0, 1, 0, 2], [0, 2, 2, 6, 1, 5], [1, 5, 2, 6, 3, 7]],\n [[3, 7, 1, 3, 1, 0], [1, 0, 0, 4, 3, 7], [3, 7, 0, 4, 2, 6]],\n [[3, 1, 2, 0, 2, 6], [3, 7, 3, 1, 2, 6]],\n [[0, 4, 2, 0, 2, 3], [2, 3, 3, 7, 0, 4], [0, 4, 3, 7, 1, 5]],\n [[3, 7, 1, 5, 1, 0], [3, 2, 3, 7, 1, 0]],\n [[0, 4, 1, 3, 0, 1], [0, 4, 3, 7, 1, 3], [0, 4, 2, 3, 3, 7], [0, 2, 2, 3, 0, 4]],\n [[3, 7, 3, 1, 3, 2]],\n [[2, 6, 3, 2, 3, 1], [3, 1, 1, 5, 2, 6], [2, 6, 1, 5, 0, 4]],\n [[1, 5, 3, 2, 1, 3], [1, 5, 2, 6, 3, 2], [1, 5, 0, 2, 2, 6], [1, 0, 0, 2, 1, 5]],\n [[2, 3, 0, 1, 0, 4], [2, 6, 2, 3, 0, 4]],\n [[2, 3, 2, 0, 2, 6]],\n [[1, 5, 0, 4, 0, 2], [1, 3, 1, 5, 0, 2]],\n [[1, 5, 1, 0, 1, 3]],\n [[0, 2, 0, 1, 0, 4]],\n [],\n]\n\n\ndef create_mc_lookup_table():\n cases = torch.zeros(256, 5, 3, dtype=torch.long)\n masks = torch.zeros(256, 5, dtype=torch.bool)\n\n edge_to_index = {\n (0, 1): 0,\n (2, 3): 1,\n (4, 5): 2,\n (6, 7): 3,\n (0, 2): 4,\n (1, 3): 5,\n (4, 6): 6,\n (5, 7): 7,\n (0, 4): 8,\n (1, 5): 9,\n (2, 6): 10,\n (3, 7): 11,\n }\n\n for i, case in enumerate(MC_TABLE):\n for j, tri in enumerate(case):\n for k, (c1, c2) in enumerate(zip(tri[::2], tri[1::2])):\n cases[i, j, k] = edge_to_index[(c1, c2) if c1 < c2 else (c2, c1)]\n masks[i, j] = True\n return cases, masks\n\n\nRENDERER_CONFIG = {}\n\n\ndef renderer_model_from_original_config():\n model = ShapERenderer(**RENDERER_CONFIG)\n\n return model\n\n\nRENDERER_MLP_ORIGINAL_PREFIX = \"renderer.nerstf\"\n\nRENDERER_PARAMS_PROJ_ORIGINAL_PREFIX = \"encoder.params_proj\"\n\n\ndef renderer_model_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):\n diffusers_checkpoint = {}\n diffusers_checkpoint.update(\n {f\"mlp.{k}\": checkpoint[f\"{RENDERER_MLP_ORIGINAL_PREFIX}.{k}\"] for k in model.mlp.state_dict().keys()}\n )\n\n diffusers_checkpoint.update(\n {\n f\"params_proj.{k}\": checkpoint[f\"{RENDERER_PARAMS_PROJ_ORIGINAL_PREFIX}.{k}\"]\n for k in model.params_proj.state_dict().keys()\n }\n )\n\n diffusers_checkpoint.update({\"void.background\": model.state_dict()[\"void.background\"]})\n\n cases, masks = create_mc_lookup_table()\n\n diffusers_checkpoint.update({\"mesh_decoder.cases\": cases})\n diffusers_checkpoint.update({\"mesh_decoder.masks\": masks})\n\n return diffusers_checkpoint\n\n\n# done renderer\n\n\n# TODO maybe document and/or can do more efficiently (build indices in for loop and extract once for each split?)\ndef split_attentions(*, weight, bias, split, chunk_size):\n weights = [None] * split\n biases = [None] * split\n\n weights_biases_idx = 0\n\n for starting_row_index in range(0, weight.shape[0], chunk_size):\n row_indices = torch.arange(starting_row_index, starting_row_index + chunk_size)\n\n weight_rows = weight[row_indices, :]\n bias_rows = bias[row_indices]\n\n if weights[weights_biases_idx] is None:\n assert weights[weights_biases_idx] is None\n weights[weights_biases_idx] = weight_rows\n biases[weights_biases_idx] = bias_rows\n else:\n assert weights[weights_biases_idx] is not None\n weights[weights_biases_idx] = torch.concat([weights[weights_biases_idx], weight_rows])\n biases[weights_biases_idx] = torch.concat([biases[weights_biases_idx], bias_rows])\n\n weights_biases_idx = (weights_biases_idx + 1) % split\n\n return weights, biases\n\n\n# done unet utils\n\n\n# Driver functions\n\n\ndef prior(*, args, checkpoint_map_location):\n print(\"loading prior\")\n\n prior_checkpoint = torch.load(args.prior_checkpoint_path, map_location=checkpoint_map_location)\n\n prior_model = prior_model_from_original_config()\n\n prior_diffusers_checkpoint = prior_original_checkpoint_to_diffusers_checkpoint(prior_model, prior_checkpoint)\n\n del prior_checkpoint\n\n load_prior_checkpoint_to_model(prior_diffusers_checkpoint, prior_model)\n\n print(\"done loading prior\")\n\n return prior_model\n\n\ndef prior_image(*, args, checkpoint_map_location):\n print(\"loading prior_image\")\n\n print(f\"load checkpoint from {args.prior_image_checkpoint_path}\")\n prior_checkpoint = torch.load(args.prior_image_checkpoint_path, map_location=checkpoint_map_location)\n\n prior_model = prior_image_model_from_original_config()\n\n prior_diffusers_checkpoint = prior_image_original_checkpoint_to_diffusers_checkpoint(prior_model, prior_checkpoint)\n\n del prior_checkpoint\n\n load_prior_checkpoint_to_model(prior_diffusers_checkpoint, prior_model)\n\n print(\"done loading prior_image\")\n\n return prior_model\n\n\ndef renderer(*, args, checkpoint_map_location):\n print(\" loading renderer\")\n\n renderer_checkpoint = torch.load(args.transmitter_checkpoint_path, map_location=checkpoint_map_location)\n\n renderer_model = renderer_model_from_original_config()\n\n renderer_diffusers_checkpoint = renderer_model_original_checkpoint_to_diffusers_checkpoint(\n renderer_model, renderer_checkpoint\n )\n\n del renderer_checkpoint\n\n load_checkpoint_to_model(renderer_diffusers_checkpoint, renderer_model, strict=True)\n\n print(\"done loading renderer\")\n\n return renderer_model\n\n\n# prior model will expect clip_mean and clip_std, whic are missing from the state_dict\nPRIOR_EXPECTED_MISSING_KEYS = [\"clip_mean\", \"clip_std\"]\n\n\ndef load_prior_checkpoint_to_model(checkpoint, model):\n with tempfile.NamedTemporaryFile() as file:\n torch.save(checkpoint, file.name)\n del checkpoint\n missing_keys, unexpected_keys = model.load_state_dict(torch.load(file.name), strict=False)\n missing_keys = list(set(missing_keys) - set(PRIOR_EXPECTED_MISSING_KEYS))\n\n if len(unexpected_keys) > 0:\n raise ValueError(f\"Unexpected keys when loading prior model: {unexpected_keys}\")\n if len(missing_keys) > 0:\n raise ValueError(f\"Missing keys when loading prior model: {missing_keys}\")\n\n\ndef load_checkpoint_to_model(checkpoint, model, strict=False):\n with tempfile.NamedTemporaryFile() as file:\n torch.save(checkpoint, file.name)\n del checkpoint\n if strict:\n model.load_state_dict(torch.load(file.name), strict=True)\n else:\n load_checkpoint_and_dispatch(model, file.name, device_map=\"auto\")\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n\n parser.add_argument(\n \"--prior_checkpoint_path\",\n default=None,\n type=str,\n required=False,\n help=\"Path to the prior checkpoint to convert.\",\n )\n\n parser.add_argument(\n \"--prior_image_checkpoint_path\",\n default=None,\n type=str,\n required=False,\n help=\"Path to the prior_image checkpoint to convert.\",\n )\n\n parser.add_argument(\n \"--transmitter_checkpoint_path\",\n default=None,\n type=str,\n required=False,\n help=\"Path to the transmitter checkpoint to convert.\",\n )\n\n parser.add_argument(\n \"--checkpoint_load_device\",\n default=\"cpu\",\n type=str,\n required=False,\n help=\"The device passed to `map_location` when loading checkpoints.\",\n )\n\n parser.add_argument(\n \"--debug\",\n default=None,\n type=str,\n required=False,\n help=\"Only run a specific stage of the convert script. Used for debugging\",\n )\n\n args = parser.parse_args()\n\n print(f\"loading checkpoints to {args.checkpoint_load_device}\")\n\n checkpoint_map_location = torch.device(args.checkpoint_load_device)\n\n if args.debug is not None:\n print(f\"debug: only executing {args.debug}\")\n\n if args.debug is None:\n print(\"YiYi TO-DO\")\n elif args.debug == \"prior\":\n prior_model = prior(args=args, checkpoint_map_location=checkpoint_map_location)\n prior_model.save_pretrained(args.dump_path)\n elif args.debug == \"prior_image\":\n prior_model = prior_image(args=args, checkpoint_map_location=checkpoint_map_location)\n prior_model.save_pretrained(args.dump_path)\n elif args.debug == \"renderer\":\n renderer_model = renderer(args=args, checkpoint_map_location=checkpoint_map_location)\n renderer_model.save_pretrained(args.dump_path)\n else:\n raise ValueError(f\"unknown debug value : {args.debug}\")\n" }, { "path": "diffusers/scripts/convert_stable_cascade.py", "content": "# Run this script to convert the Stable Cascade model weights to a diffusers pipeline.\nimport argparse\nfrom contextlib import nullcontext\n\nimport torch\nfrom safetensors.torch import load_file\nfrom transformers import (\n AutoTokenizer,\n CLIPConfig,\n CLIPImageProcessor,\n CLIPTextModelWithProjection,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers import (\n DDPMWuerstchenScheduler,\n StableCascadeCombinedPipeline,\n StableCascadeDecoderPipeline,\n StableCascadePriorPipeline,\n)\nfrom diffusers.loaders.single_file_utils import convert_stable_cascade_unet_single_file_to_diffusers\nfrom diffusers.models import StableCascadeUNet\nfrom diffusers.models.modeling_utils import load_model_dict_into_meta\nfrom diffusers.pipelines.wuerstchen import PaellaVQModel\nfrom diffusers.utils import is_accelerate_available\n\n\nif is_accelerate_available():\n from accelerate import init_empty_weights\n\nparser = argparse.ArgumentParser(description=\"Convert Stable Cascade model weights to a diffusers pipeline\")\nparser.add_argument(\"--model_path\", type=str, help=\"Location of Stable Cascade weights\")\nparser.add_argument(\"--stage_c_name\", type=str, default=\"stage_c.safetensors\", help=\"Name of stage c checkpoint file\")\nparser.add_argument(\"--stage_b_name\", type=str, default=\"stage_b.safetensors\", help=\"Name of stage b checkpoint file\")\nparser.add_argument(\"--skip_stage_c\", action=\"store_true\", help=\"Skip converting stage c\")\nparser.add_argument(\"--skip_stage_b\", action=\"store_true\", help=\"Skip converting stage b\")\nparser.add_argument(\"--use_safetensors\", action=\"store_true\", help=\"Use SafeTensors for conversion\")\nparser.add_argument(\n \"--prior_output_path\", default=\"stable-cascade-prior\", type=str, help=\"Hub organization to save the pipelines to\"\n)\nparser.add_argument(\n \"--decoder_output_path\",\n type=str,\n default=\"stable-cascade-decoder\",\n help=\"Hub organization to save the pipelines to\",\n)\nparser.add_argument(\n \"--combined_output_path\",\n type=str,\n default=\"stable-cascade-combined\",\n help=\"Hub organization to save the pipelines to\",\n)\nparser.add_argument(\"--save_combined\", action=\"store_true\")\nparser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Push to hub\")\nparser.add_argument(\"--variant\", type=str, help=\"Set to bf16 to save bfloat16 weights\")\n\nargs = parser.parse_args()\n\nif args.skip_stage_b and args.skip_stage_c:\n raise ValueError(\"At least one stage should be converted\")\nif (args.skip_stage_b or args.skip_stage_c) and args.save_combined:\n raise ValueError(\"Cannot skip stages when creating a combined pipeline\")\n\nmodel_path = args.model_path\n\ndevice = \"cpu\"\nif args.variant == \"bf16\":\n dtype = torch.bfloat16\nelse:\n dtype = torch.float32\n\n# set paths to model weights\nprior_checkpoint_path = f\"{model_path}/{args.stage_c_name}\"\ndecoder_checkpoint_path = f\"{model_path}/{args.stage_b_name}\"\n\n# Clip Text encoder and tokenizer\nconfig = CLIPConfig.from_pretrained(\"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\")\nconfig.text_config.projection_dim = config.projection_dim\ntext_encoder = CLIPTextModelWithProjection.from_pretrained(\n \"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\", config=config.text_config\n)\ntokenizer = AutoTokenizer.from_pretrained(\"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\")\n\n# image processor\nfeature_extractor = CLIPImageProcessor()\nimage_encoder = CLIPVisionModelWithProjection.from_pretrained(\"openai/clip-vit-large-patch14\")\n\n# scheduler for prior and decoder\nscheduler = DDPMWuerstchenScheduler()\nctx = init_empty_weights if is_accelerate_available() else nullcontext\n\nif not args.skip_stage_c:\n # Prior\n if args.use_safetensors:\n prior_orig_state_dict = load_file(prior_checkpoint_path, device=device)\n else:\n prior_orig_state_dict = torch.load(prior_checkpoint_path, map_location=device)\n\n prior_state_dict = convert_stable_cascade_unet_single_file_to_diffusers(prior_orig_state_dict)\n\n with ctx():\n prior_model = StableCascadeUNet(\n in_channels=16,\n out_channels=16,\n timestep_ratio_embedding_dim=64,\n patch_size=1,\n conditioning_dim=2048,\n block_out_channels=[2048, 2048],\n num_attention_heads=[32, 32],\n down_num_layers_per_block=[8, 24],\n up_num_layers_per_block=[24, 8],\n down_blocks_repeat_mappers=[1, 1],\n up_blocks_repeat_mappers=[1, 1],\n block_types_per_layer=[\n [\"SDCascadeResBlock\", \"SDCascadeTimestepBlock\", \"SDCascadeAttnBlock\"],\n [\"SDCascadeResBlock\", \"SDCascadeTimestepBlock\", \"SDCascadeAttnBlock\"],\n ],\n clip_text_in_channels=1280,\n clip_text_pooled_in_channels=1280,\n clip_image_in_channels=768,\n clip_seq=4,\n kernel_size=3,\n dropout=[0.1, 0.1],\n self_attn=True,\n timestep_conditioning_type=[\"sca\", \"crp\"],\n switch_level=[False],\n )\n if is_accelerate_available():\n load_model_dict_into_meta(prior_model, prior_state_dict)\n else:\n prior_model.load_state_dict(prior_state_dict)\n\n # Prior pipeline\n prior_pipeline = StableCascadePriorPipeline(\n prior=prior_model,\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n image_encoder=image_encoder,\n scheduler=scheduler,\n feature_extractor=feature_extractor,\n )\n prior_pipeline.to(dtype).save_pretrained(\n args.prior_output_path, push_to_hub=args.push_to_hub, variant=args.variant\n )\n\nif not args.skip_stage_b:\n # Decoder\n if args.use_safetensors:\n decoder_orig_state_dict = load_file(decoder_checkpoint_path, device=device)\n else:\n decoder_orig_state_dict = torch.load(decoder_checkpoint_path, map_location=device)\n\n decoder_state_dict = convert_stable_cascade_unet_single_file_to_diffusers(decoder_orig_state_dict)\n with ctx():\n decoder = StableCascadeUNet(\n in_channels=4,\n out_channels=4,\n timestep_ratio_embedding_dim=64,\n patch_size=2,\n conditioning_dim=1280,\n block_out_channels=[320, 640, 1280, 1280],\n down_num_layers_per_block=[2, 6, 28, 6],\n up_num_layers_per_block=[6, 28, 6, 2],\n down_blocks_repeat_mappers=[1, 1, 1, 1],\n up_blocks_repeat_mappers=[3, 3, 2, 2],\n num_attention_heads=[0, 0, 20, 20],\n block_types_per_layer=[\n [\"SDCascadeResBlock\", \"SDCascadeTimestepBlock\"],\n [\"SDCascadeResBlock\", \"SDCascadeTimestepBlock\"],\n [\"SDCascadeResBlock\", \"SDCascadeTimestepBlock\", \"SDCascadeAttnBlock\"],\n [\"SDCascadeResBlock\", \"SDCascadeTimestepBlock\", \"SDCascadeAttnBlock\"],\n ],\n clip_text_pooled_in_channels=1280,\n clip_seq=4,\n effnet_in_channels=16,\n pixel_mapper_in_channels=3,\n kernel_size=3,\n dropout=[0, 0, 0.1, 0.1],\n self_attn=True,\n timestep_conditioning_type=[\"sca\"],\n )\n\n if is_accelerate_available():\n load_model_dict_into_meta(decoder, decoder_state_dict)\n else:\n decoder.load_state_dict(decoder_state_dict)\n\n # VQGAN from Wuerstchen-V2\n vqmodel = PaellaVQModel.from_pretrained(\"warp-ai/wuerstchen\", subfolder=\"vqgan\")\n\n # Decoder pipeline\n decoder_pipeline = StableCascadeDecoderPipeline(\n decoder=decoder, text_encoder=text_encoder, tokenizer=tokenizer, vqgan=vqmodel, scheduler=scheduler\n )\n decoder_pipeline.to(dtype).save_pretrained(\n args.decoder_output_path, push_to_hub=args.push_to_hub, variant=args.variant\n )\n\nif args.save_combined:\n # Stable Cascade combined pipeline\n stable_cascade_pipeline = StableCascadeCombinedPipeline(\n # Decoder\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n decoder=decoder,\n scheduler=scheduler,\n vqgan=vqmodel,\n # Prior\n prior_text_encoder=text_encoder,\n prior_tokenizer=tokenizer,\n prior_prior=prior_model,\n prior_scheduler=scheduler,\n prior_image_encoder=image_encoder,\n prior_feature_extractor=feature_extractor,\n )\n stable_cascade_pipeline.to(dtype).save_pretrained(\n args.combined_output_path, push_to_hub=args.push_to_hub, variant=args.variant\n )\n" }, { "path": "diffusers/scripts/convert_stable_cascade_lite.py", "content": "# Run this script to convert the Stable Cascade model weights to a diffusers pipeline.\nimport argparse\nfrom contextlib import nullcontext\n\nimport torch\nfrom safetensors.torch import load_file\nfrom transformers import (\n AutoTokenizer,\n CLIPConfig,\n CLIPImageProcessor,\n CLIPTextModelWithProjection,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers import (\n DDPMWuerstchenScheduler,\n StableCascadeCombinedPipeline,\n StableCascadeDecoderPipeline,\n StableCascadePriorPipeline,\n)\nfrom diffusers.loaders.single_file_utils import convert_stable_cascade_unet_single_file_to_diffusers\nfrom diffusers.models import StableCascadeUNet\nfrom diffusers.models.modeling_utils import load_model_dict_into_meta\nfrom diffusers.pipelines.wuerstchen import PaellaVQModel\nfrom diffusers.utils import is_accelerate_available\n\n\nif is_accelerate_available():\n from accelerate import init_empty_weights\n\nparser = argparse.ArgumentParser(description=\"Convert Stable Cascade model weights to a diffusers pipeline\")\nparser.add_argument(\"--model_path\", type=str, help=\"Location of Stable Cascade weights\")\nparser.add_argument(\n \"--stage_c_name\", type=str, default=\"stage_c_lite.safetensors\", help=\"Name of stage c checkpoint file\"\n)\nparser.add_argument(\n \"--stage_b_name\", type=str, default=\"stage_b_lite.safetensors\", help=\"Name of stage b checkpoint file\"\n)\nparser.add_argument(\"--skip_stage_c\", action=\"store_true\", help=\"Skip converting stage c\")\nparser.add_argument(\"--skip_stage_b\", action=\"store_true\", help=\"Skip converting stage b\")\nparser.add_argument(\"--use_safetensors\", action=\"store_true\", help=\"Use SafeTensors for conversion\")\nparser.add_argument(\n \"--prior_output_path\",\n default=\"stable-cascade-prior-lite\",\n type=str,\n help=\"Hub organization to save the pipelines to\",\n)\nparser.add_argument(\n \"--decoder_output_path\",\n type=str,\n default=\"stable-cascade-decoder-lite\",\n help=\"Hub organization to save the pipelines to\",\n)\nparser.add_argument(\n \"--combined_output_path\",\n type=str,\n default=\"stable-cascade-combined-lite\",\n help=\"Hub organization to save the pipelines to\",\n)\nparser.add_argument(\"--save_combined\", action=\"store_true\")\nparser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Push to hub\")\nparser.add_argument(\"--variant\", type=str, help=\"Set to bf16 to save bfloat16 weights\")\n\nargs = parser.parse_args()\n\nif args.skip_stage_b and args.skip_stage_c:\n raise ValueError(\"At least one stage should be converted\")\nif (args.skip_stage_b or args.skip_stage_c) and args.save_combined:\n raise ValueError(\"Cannot skip stages when creating a combined pipeline\")\n\nmodel_path = args.model_path\n\ndevice = \"cpu\"\nif args.variant == \"bf16\":\n dtype = torch.bfloat16\nelse:\n dtype = torch.float32\n\n# set paths to model weights\nprior_checkpoint_path = f\"{model_path}/{args.stage_c_name}\"\ndecoder_checkpoint_path = f\"{model_path}/{args.stage_b_name}\"\n\n# Clip Text encoder and tokenizer\nconfig = CLIPConfig.from_pretrained(\"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\")\nconfig.text_config.projection_dim = config.projection_dim\ntext_encoder = CLIPTextModelWithProjection.from_pretrained(\n \"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\", config=config.text_config\n)\ntokenizer = AutoTokenizer.from_pretrained(\"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\")\n\n# image processor\nfeature_extractor = CLIPImageProcessor()\nimage_encoder = CLIPVisionModelWithProjection.from_pretrained(\"openai/clip-vit-large-patch14\")\n# scheduler for prior and decoder\nscheduler = DDPMWuerstchenScheduler()\n\nctx = init_empty_weights if is_accelerate_available() else nullcontext\n\nif not args.skip_stage_c:\n # Prior\n if args.use_safetensors:\n prior_orig_state_dict = load_file(prior_checkpoint_path, device=device)\n else:\n prior_orig_state_dict = torch.load(prior_checkpoint_path, map_location=device)\n\n prior_state_dict = convert_stable_cascade_unet_single_file_to_diffusers(prior_orig_state_dict)\n with ctx():\n prior_model = StableCascadeUNet(\n in_channels=16,\n out_channels=16,\n timestep_ratio_embedding_dim=64,\n patch_size=1,\n conditioning_dim=1536,\n block_out_channels=[1536, 1536],\n num_attention_heads=[24, 24],\n down_num_layers_per_block=[4, 12],\n up_num_layers_per_block=[12, 4],\n down_blocks_repeat_mappers=[1, 1],\n up_blocks_repeat_mappers=[1, 1],\n block_types_per_layer=[\n [\"SDCascadeResBlock\", \"SDCascadeTimestepBlock\", \"SDCascadeAttnBlock\"],\n [\"SDCascadeResBlock\", \"SDCascadeTimestepBlock\", \"SDCascadeAttnBlock\"],\n ],\n clip_text_in_channels=1280,\n clip_text_pooled_in_channels=1280,\n clip_image_in_channels=768,\n clip_seq=4,\n kernel_size=3,\n dropout=[0.1, 0.1],\n self_attn=True,\n timestep_conditioning_type=[\"sca\", \"crp\"],\n switch_level=[False],\n )\n\n if is_accelerate_available():\n load_model_dict_into_meta(prior_model, prior_state_dict)\n else:\n prior_model.load_state_dict(prior_state_dict)\n\n # Prior pipeline\n prior_pipeline = StableCascadePriorPipeline(\n prior=prior_model,\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n image_encoder=image_encoder,\n scheduler=scheduler,\n feature_extractor=feature_extractor,\n )\n prior_pipeline.to(dtype).save_pretrained(\n args.prior_output_path, push_to_hub=args.push_to_hub, variant=args.variant\n )\n\nif not args.skip_stage_b:\n # Decoder\n if args.use_safetensors:\n decoder_orig_state_dict = load_file(decoder_checkpoint_path, device=device)\n else:\n decoder_orig_state_dict = torch.load(decoder_checkpoint_path, map_location=device)\n\n decoder_state_dict = convert_stable_cascade_unet_single_file_to_diffusers(decoder_orig_state_dict)\n\n with ctx():\n decoder = StableCascadeUNet(\n in_channels=4,\n out_channels=4,\n timestep_ratio_embedding_dim=64,\n patch_size=2,\n conditioning_dim=1280,\n block_out_channels=[320, 576, 1152, 1152],\n down_num_layers_per_block=[2, 4, 14, 4],\n up_num_layers_per_block=[4, 14, 4, 2],\n down_blocks_repeat_mappers=[1, 1, 1, 1],\n up_blocks_repeat_mappers=[2, 2, 2, 2],\n num_attention_heads=[0, 9, 18, 18],\n block_types_per_layer=[\n [\"SDCascadeResBlock\", \"SDCascadeTimestepBlock\"],\n [\"SDCascadeResBlock\", \"SDCascadeTimestepBlock\"],\n [\"SDCascadeResBlock\", \"SDCascadeTimestepBlock\", \"SDCascadeAttnBlock\"],\n [\"SDCascadeResBlock\", \"SDCascadeTimestepBlock\", \"SDCascadeAttnBlock\"],\n ],\n clip_text_pooled_in_channels=1280,\n clip_seq=4,\n effnet_in_channels=16,\n pixel_mapper_in_channels=3,\n kernel_size=3,\n dropout=[0, 0, 0.1, 0.1],\n self_attn=True,\n timestep_conditioning_type=[\"sca\"],\n )\n\n if is_accelerate_available():\n load_model_dict_into_meta(decoder, decoder_state_dict)\n else:\n decoder.load_state_dict(decoder_state_dict)\n\n # VQGAN from Wuerstchen-V2\n vqmodel = PaellaVQModel.from_pretrained(\"warp-ai/wuerstchen\", subfolder=\"vqgan\")\n\n # Decoder pipeline\n decoder_pipeline = StableCascadeDecoderPipeline(\n decoder=decoder, text_encoder=text_encoder, tokenizer=tokenizer, vqgan=vqmodel, scheduler=scheduler\n )\n decoder_pipeline.to(dtype).save_pretrained(\n args.decoder_output_path, push_to_hub=args.push_to_hub, variant=args.variant\n )\n\nif args.save_combined:\n # Stable Cascade combined pipeline\n stable_cascade_pipeline = StableCascadeCombinedPipeline(\n # Decoder\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n decoder=decoder,\n scheduler=scheduler,\n vqgan=vqmodel,\n # Prior\n prior_text_encoder=text_encoder,\n prior_tokenizer=tokenizer,\n prior_prior=prior_model,\n prior_scheduler=scheduler,\n prior_image_encoder=image_encoder,\n prior_feature_extractor=feature_extractor,\n )\n stable_cascade_pipeline.to(dtype).save_pretrained(\n args.combined_output_path, push_to_hub=args.push_to_hub, variant=args.variant\n )\n" }, { "path": "diffusers/scripts/convert_stable_diffusion_checkpoint_to_onnx.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport argparse\nimport os\nimport shutil\nfrom pathlib import Path\n\nimport onnx\nimport torch\nfrom packaging import version\nfrom torch.onnx import export\n\nfrom diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline\n\n\nis_torch_less_than_1_11 = version.parse(version.parse(torch.__version__).base_version) < version.parse(\"1.11\")\n\n\ndef onnx_export(\n model,\n model_args: tuple,\n output_path: Path,\n ordered_input_names,\n output_names,\n dynamic_axes,\n opset,\n use_external_data_format=False,\n):\n output_path.parent.mkdir(parents=True, exist_ok=True)\n # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,\n # so we check the torch version for backwards compatibility\n if is_torch_less_than_1_11:\n export(\n model,\n model_args,\n f=output_path.as_posix(),\n input_names=ordered_input_names,\n output_names=output_names,\n dynamic_axes=dynamic_axes,\n do_constant_folding=True,\n use_external_data_format=use_external_data_format,\n enable_onnx_checker=True,\n opset_version=opset,\n )\n else:\n export(\n model,\n model_args,\n f=output_path.as_posix(),\n input_names=ordered_input_names,\n output_names=output_names,\n dynamic_axes=dynamic_axes,\n do_constant_folding=True,\n opset_version=opset,\n )\n\n\n@torch.no_grad()\ndef convert_models(model_path: str, output_path: str, opset: int, fp16: bool = False):\n dtype = torch.float16 if fp16 else torch.float32\n if fp16 and torch.cuda.is_available():\n device = \"cuda\"\n elif fp16 and not torch.cuda.is_available():\n raise ValueError(\"`float16` model export is only supported on GPUs with CUDA\")\n else:\n device = \"cpu\"\n pipeline = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=dtype).to(device)\n output_path = Path(output_path)\n\n # TEXT ENCODER\n num_tokens = pipeline.text_encoder.config.max_position_embeddings\n text_hidden_size = pipeline.text_encoder.config.hidden_size\n text_input = pipeline.tokenizer(\n \"A sample prompt\",\n padding=\"max_length\",\n max_length=pipeline.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n onnx_export(\n pipeline.text_encoder,\n # casting to torch.int32 until the CLIP fix is released: https://github.com/huggingface/transformers/pull/18515/files\n model_args=(text_input.input_ids.to(device=device, dtype=torch.int32)),\n output_path=output_path / \"text_encoder\" / \"model.onnx\",\n ordered_input_names=[\"input_ids\"],\n output_names=[\"last_hidden_state\", \"pooler_output\"],\n dynamic_axes={\n \"input_ids\": {0: \"batch\", 1: \"sequence\"},\n },\n opset=opset,\n )\n del pipeline.text_encoder\n\n # UNET\n unet_in_channels = pipeline.unet.config.in_channels\n unet_sample_size = pipeline.unet.config.sample_size\n unet_path = output_path / \"unet\" / \"model.onnx\"\n onnx_export(\n pipeline.unet,\n model_args=(\n torch.randn(2, unet_in_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype),\n torch.randn(2).to(device=device, dtype=dtype),\n torch.randn(2, num_tokens, text_hidden_size).to(device=device, dtype=dtype),\n False,\n ),\n output_path=unet_path,\n ordered_input_names=[\"sample\", \"timestep\", \"encoder_hidden_states\", \"return_dict\"],\n output_names=[\"out_sample\"], # has to be different from \"sample\" for correct tracing\n dynamic_axes={\n \"sample\": {0: \"batch\", 1: \"channels\", 2: \"height\", 3: \"width\"},\n \"timestep\": {0: \"batch\"},\n \"encoder_hidden_states\": {0: \"batch\", 1: \"sequence\"},\n },\n opset=opset,\n use_external_data_format=True, # UNet is > 2GB, so the weights need to be split\n )\n unet_model_path = str(unet_path.absolute().as_posix())\n unet_dir = os.path.dirname(unet_model_path)\n unet = onnx.load(unet_model_path)\n # clean up existing tensor files\n shutil.rmtree(unet_dir)\n os.mkdir(unet_dir)\n # collate external tensor files into one\n onnx.save_model(\n unet,\n unet_model_path,\n save_as_external_data=True,\n all_tensors_to_one_file=True,\n location=\"weights.pb\",\n convert_attribute=False,\n )\n del pipeline.unet\n\n # VAE ENCODER\n vae_encoder = pipeline.vae\n vae_in_channels = vae_encoder.config.in_channels\n vae_sample_size = vae_encoder.config.sample_size\n # need to get the raw tensor output (sample) from the encoder\n vae_encoder.forward = lambda sample, return_dict: vae_encoder.encode(sample, return_dict)[0].sample()\n onnx_export(\n vae_encoder,\n model_args=(\n torch.randn(1, vae_in_channels, vae_sample_size, vae_sample_size).to(device=device, dtype=dtype),\n False,\n ),\n output_path=output_path / \"vae_encoder\" / \"model.onnx\",\n ordered_input_names=[\"sample\", \"return_dict\"],\n output_names=[\"latent_sample\"],\n dynamic_axes={\n \"sample\": {0: \"batch\", 1: \"channels\", 2: \"height\", 3: \"width\"},\n },\n opset=opset,\n )\n\n # VAE DECODER\n vae_decoder = pipeline.vae\n vae_latent_channels = vae_decoder.config.latent_channels\n vae_out_channels = vae_decoder.config.out_channels\n # forward only through the decoder part\n vae_decoder.forward = vae_encoder.decode\n onnx_export(\n vae_decoder,\n model_args=(\n torch.randn(1, vae_latent_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype),\n False,\n ),\n output_path=output_path / \"vae_decoder\" / \"model.onnx\",\n ordered_input_names=[\"latent_sample\", \"return_dict\"],\n output_names=[\"sample\"],\n dynamic_axes={\n \"latent_sample\": {0: \"batch\", 1: \"channels\", 2: \"height\", 3: \"width\"},\n },\n opset=opset,\n )\n del pipeline.vae\n\n # SAFETY CHECKER\n if pipeline.safety_checker is not None:\n safety_checker = pipeline.safety_checker\n clip_num_channels = safety_checker.config.vision_config.num_channels\n clip_image_size = safety_checker.config.vision_config.image_size\n safety_checker.forward = safety_checker.forward_onnx\n onnx_export(\n pipeline.safety_checker,\n model_args=(\n torch.randn(\n 1,\n clip_num_channels,\n clip_image_size,\n clip_image_size,\n ).to(device=device, dtype=dtype),\n torch.randn(1, vae_sample_size, vae_sample_size, vae_out_channels).to(device=device, dtype=dtype),\n ),\n output_path=output_path / \"safety_checker\" / \"model.onnx\",\n ordered_input_names=[\"clip_input\", \"images\"],\n output_names=[\"out_images\", \"has_nsfw_concepts\"],\n dynamic_axes={\n \"clip_input\": {0: \"batch\", 1: \"channels\", 2: \"height\", 3: \"width\"},\n \"images\": {0: \"batch\", 1: \"height\", 2: \"width\", 3: \"channels\"},\n },\n opset=opset,\n )\n del pipeline.safety_checker\n safety_checker = OnnxRuntimeModel.from_pretrained(output_path / \"safety_checker\")\n feature_extractor = pipeline.feature_extractor\n else:\n safety_checker = None\n feature_extractor = None\n\n onnx_pipeline = OnnxStableDiffusionPipeline(\n vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / \"vae_encoder\"),\n vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / \"vae_decoder\"),\n text_encoder=OnnxRuntimeModel.from_pretrained(output_path / \"text_encoder\"),\n tokenizer=pipeline.tokenizer,\n unet=OnnxRuntimeModel.from_pretrained(output_path / \"unet\"),\n scheduler=pipeline.scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n requires_safety_checker=safety_checker is not None,\n )\n\n onnx_pipeline.save_pretrained(output_path)\n print(\"ONNX pipeline saved to\", output_path)\n\n del pipeline\n del onnx_pipeline\n _ = OnnxStableDiffusionPipeline.from_pretrained(output_path, provider=\"CPUExecutionProvider\")\n print(\"ONNX pipeline is loadable\")\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--model_path\",\n type=str,\n required=True,\n help=\"Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).\",\n )\n\n parser.add_argument(\"--output_path\", type=str, required=True, help=\"Path to the output model.\")\n\n parser.add_argument(\n \"--opset\",\n default=14,\n type=int,\n help=\"The version of the ONNX operator set to use.\",\n )\n parser.add_argument(\"--fp16\", action=\"store_true\", default=False, help=\"Export the models in `float16` mode\")\n\n args = parser.parse_args()\n\n convert_models(args.model_path, args.output_path, args.opset, args.fp16)\n" }, { "path": "diffusers/scripts/convert_stable_diffusion_controlnet_to_onnx.py", "content": "import argparse\nimport os\nimport shutil\nfrom pathlib import Path\n\nimport onnx\nimport onnx_graphsurgeon as gs\nimport torch\nfrom onnx import shape_inference\nfrom packaging import version\nfrom polygraphy.backend.onnx.loader import fold_constants\nfrom torch.onnx import export\n\nfrom diffusers import (\n ControlNetModel,\n StableDiffusionControlNetImg2ImgPipeline,\n)\nfrom diffusers.models.attention_processor import AttnProcessor\nfrom diffusers.pipelines.controlnet.pipeline_controlnet_sd_xl import StableDiffusionXLControlNetPipeline\n\n\nis_torch_less_than_1_11 = version.parse(version.parse(torch.__version__).base_version) < version.parse(\"1.11\")\nis_torch_2_0_1 = version.parse(version.parse(torch.__version__).base_version) == version.parse(\"2.0.1\")\n\n\nclass Optimizer:\n def __init__(self, onnx_graph, verbose=False):\n self.graph = gs.import_onnx(onnx_graph)\n self.verbose = verbose\n\n def info(self, prefix):\n if self.verbose:\n print(\n f\"{prefix} .. {len(self.graph.nodes)} nodes, {len(self.graph.tensors().keys())} tensors, {len(self.graph.inputs)} inputs, {len(self.graph.outputs)} outputs\"\n )\n\n def cleanup(self, return_onnx=False):\n self.graph.cleanup().toposort()\n if return_onnx:\n return gs.export_onnx(self.graph)\n\n def select_outputs(self, keep, names=None):\n self.graph.outputs = [self.graph.outputs[o] for o in keep]\n if names:\n for i, name in enumerate(names):\n self.graph.outputs[i].name = name\n\n def fold_constants(self, return_onnx=False):\n onnx_graph = fold_constants(gs.export_onnx(self.graph), allow_onnxruntime_shape_inference=True)\n self.graph = gs.import_onnx(onnx_graph)\n if return_onnx:\n return onnx_graph\n\n def infer_shapes(self, return_onnx=False):\n onnx_graph = gs.export_onnx(self.graph)\n if onnx_graph.ByteSize() > 2147483648:\n raise TypeError(\"ERROR: model size exceeds supported 2GB limit\")\n else:\n onnx_graph = shape_inference.infer_shapes(onnx_graph)\n\n self.graph = gs.import_onnx(onnx_graph)\n if return_onnx:\n return onnx_graph\n\n\ndef optimize(onnx_graph, name, verbose):\n opt = Optimizer(onnx_graph, verbose=verbose)\n opt.info(name + \": original\")\n opt.cleanup()\n opt.info(name + \": cleanup\")\n opt.fold_constants()\n opt.info(name + \": fold constants\")\n # opt.infer_shapes()\n # opt.info(name + ': shape inference')\n onnx_opt_graph = opt.cleanup(return_onnx=True)\n opt.info(name + \": finished\")\n return onnx_opt_graph\n\n\nclass UNet2DConditionControlNetModel(torch.nn.Module):\n def __init__(\n self,\n unet,\n controlnets: ControlNetModel,\n ):\n super().__init__()\n self.unet = unet\n self.controlnets = controlnets\n\n def forward(\n self,\n sample,\n timestep,\n encoder_hidden_states,\n controlnet_conds,\n controlnet_scales,\n ):\n for i, (controlnet_cond, conditioning_scale, controlnet) in enumerate(\n zip(controlnet_conds, controlnet_scales, self.controlnets)\n ):\n down_samples, mid_sample = controlnet(\n sample,\n timestep,\n encoder_hidden_states=encoder_hidden_states,\n controlnet_cond=controlnet_cond,\n conditioning_scale=conditioning_scale,\n return_dict=False,\n )\n\n # merge samples\n if i == 0:\n down_block_res_samples, mid_block_res_sample = down_samples, mid_sample\n else:\n down_block_res_samples = [\n samples_prev + samples_curr\n for samples_prev, samples_curr in zip(down_block_res_samples, down_samples)\n ]\n mid_block_res_sample += mid_sample\n\n noise_pred = self.unet(\n sample,\n timestep,\n encoder_hidden_states=encoder_hidden_states,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n return_dict=False,\n )[0]\n return noise_pred\n\n\nclass UNet2DConditionXLControlNetModel(torch.nn.Module):\n def __init__(\n self,\n unet,\n controlnets: ControlNetModel,\n ):\n super().__init__()\n self.unet = unet\n self.controlnets = controlnets\n\n def forward(\n self,\n sample,\n timestep,\n encoder_hidden_states,\n controlnet_conds,\n controlnet_scales,\n text_embeds,\n time_ids,\n ):\n added_cond_kwargs = {\"text_embeds\": text_embeds, \"time_ids\": time_ids}\n for i, (controlnet_cond, conditioning_scale, controlnet) in enumerate(\n zip(controlnet_conds, controlnet_scales, self.controlnets)\n ):\n down_samples, mid_sample = controlnet(\n sample,\n timestep,\n encoder_hidden_states=encoder_hidden_states,\n controlnet_cond=controlnet_cond,\n conditioning_scale=conditioning_scale,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )\n\n # merge samples\n if i == 0:\n down_block_res_samples, mid_block_res_sample = down_samples, mid_sample\n else:\n down_block_res_samples = [\n samples_prev + samples_curr\n for samples_prev, samples_curr in zip(down_block_res_samples, down_samples)\n ]\n mid_block_res_sample += mid_sample\n\n noise_pred = self.unet(\n sample,\n timestep,\n encoder_hidden_states=encoder_hidden_states,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n return noise_pred\n\n\ndef onnx_export(\n model,\n model_args: tuple,\n output_path: Path,\n ordered_input_names,\n output_names,\n dynamic_axes,\n opset,\n use_external_data_format=False,\n):\n output_path.parent.mkdir(parents=True, exist_ok=True)\n # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,\n # so we check the torch version for backwards compatibility\n with torch.inference_mode(), torch.autocast(\"cuda\"):\n if is_torch_less_than_1_11:\n export(\n model,\n model_args,\n f=output_path.as_posix(),\n input_names=ordered_input_names,\n output_names=output_names,\n dynamic_axes=dynamic_axes,\n do_constant_folding=True,\n use_external_data_format=use_external_data_format,\n enable_onnx_checker=True,\n opset_version=opset,\n )\n else:\n export(\n model,\n model_args,\n f=output_path.as_posix(),\n input_names=ordered_input_names,\n output_names=output_names,\n dynamic_axes=dynamic_axes,\n do_constant_folding=True,\n opset_version=opset,\n )\n\n\n@torch.no_grad()\ndef convert_models(\n model_path: str, controlnet_path: list, output_path: str, opset: int, fp16: bool = False, sd_xl: bool = False\n):\n \"\"\"\n Function to convert models in stable diffusion controlnet pipeline into ONNX format\n\n Example:\n python convert_stable_diffusion_controlnet_to_onnx.py\n --model_path danbrown/RevAnimated-v1-2-2\n --controlnet_path lllyasviel/control_v11f1e_sd15_tile ioclab/brightness-controlnet\n --output_path path-to-models-stable_diffusion/RevAnimated-v1-2-2\n --fp16\n\n Example for SD XL:\n python convert_stable_diffusion_controlnet_to_onnx.py\n --model_path stabilityai/stable-diffusion-xl-base-1.0\n --controlnet_path SargeZT/sdxl-controlnet-seg\n --output_path path-to-models-stable_diffusion/stable-diffusion-xl-base-1.0\n --fp16\n --sd_xl\n\n Returns:\n create 4 onnx models in output path\n text_encoder/model.onnx\n unet/model.onnx + unet/weights.pb\n vae_encoder/model.onnx\n vae_decoder/model.onnx\n\n run test script in diffusers/examples/community\n python test_onnx_controlnet.py\n --sd_model danbrown/RevAnimated-v1-2-2\n --onnx_model_dir path-to-models-stable_diffusion/RevAnimated-v1-2-2\n --qr_img_path path-to-qr-code-image\n \"\"\"\n dtype = torch.float16 if fp16 else torch.float32\n if fp16 and torch.cuda.is_available():\n device = \"cuda\"\n elif fp16 and not torch.cuda.is_available():\n raise ValueError(\"`float16` model export is only supported on GPUs with CUDA\")\n else:\n device = \"cpu\"\n\n # init controlnet\n controlnets = []\n for path in controlnet_path:\n controlnet = ControlNetModel.from_pretrained(path, torch_dtype=dtype).to(device)\n if is_torch_2_0_1:\n controlnet.set_attn_processor(AttnProcessor())\n controlnets.append(controlnet)\n\n if sd_xl:\n if len(controlnets) == 1:\n controlnet = controlnets[0]\n else:\n raise ValueError(\"MultiControlNet is not yet supported.\")\n pipeline = StableDiffusionXLControlNetPipeline.from_pretrained(\n model_path, controlnet=controlnet, torch_dtype=dtype, variant=\"fp16\", use_safetensors=True\n ).to(device)\n else:\n pipeline = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(\n model_path, controlnet=controlnets, torch_dtype=dtype\n ).to(device)\n\n output_path = Path(output_path)\n if is_torch_2_0_1:\n pipeline.unet.set_attn_processor(AttnProcessor())\n pipeline.vae.set_attn_processor(AttnProcessor())\n\n # # TEXT ENCODER\n num_tokens = pipeline.text_encoder.config.max_position_embeddings\n text_hidden_size = pipeline.text_encoder.config.hidden_size\n text_input = pipeline.tokenizer(\n \"A sample prompt\",\n padding=\"max_length\",\n max_length=pipeline.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n onnx_export(\n pipeline.text_encoder,\n # casting to torch.int32 until the CLIP fix is released: https://github.com/huggingface/transformers/pull/18515/files\n model_args=(text_input.input_ids.to(device=device, dtype=torch.int32)),\n output_path=output_path / \"text_encoder\" / \"model.onnx\",\n ordered_input_names=[\"input_ids\"],\n output_names=[\"last_hidden_state\", \"pooler_output\"],\n dynamic_axes={\n \"input_ids\": {0: \"batch\", 1: \"sequence\"},\n },\n opset=opset,\n )\n del pipeline.text_encoder\n\n # # UNET\n if sd_xl:\n controlnets = torch.nn.ModuleList(controlnets)\n unet_controlnet = UNet2DConditionXLControlNetModel(pipeline.unet, controlnets)\n unet_in_channels = pipeline.unet.config.in_channels\n unet_sample_size = pipeline.unet.config.sample_size\n text_hidden_size = 2048\n img_size = 8 * unet_sample_size\n unet_path = output_path / \"unet\" / \"model.onnx\"\n\n onnx_export(\n unet_controlnet,\n model_args=(\n torch.randn(2, unet_in_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype),\n torch.tensor([1.0]).to(device=device, dtype=dtype),\n torch.randn(2, num_tokens, text_hidden_size).to(device=device, dtype=dtype),\n torch.randn(len(controlnets), 2, 3, img_size, img_size).to(device=device, dtype=dtype),\n torch.randn(len(controlnets), 1).to(device=device, dtype=dtype),\n torch.randn(2, 1280).to(device=device, dtype=dtype),\n torch.rand(2, 6).to(device=device, dtype=dtype),\n ),\n output_path=unet_path,\n ordered_input_names=[\n \"sample\",\n \"timestep\",\n \"encoder_hidden_states\",\n \"controlnet_conds\",\n \"conditioning_scales\",\n \"text_embeds\",\n \"time_ids\",\n ],\n output_names=[\"noise_pred\"], # has to be different from \"sample\" for correct tracing\n dynamic_axes={\n \"sample\": {0: \"2B\", 2: \"H\", 3: \"W\"},\n \"encoder_hidden_states\": {0: \"2B\"},\n \"controlnet_conds\": {1: \"2B\", 3: \"8H\", 4: \"8W\"},\n \"text_embeds\": {0: \"2B\"},\n \"time_ids\": {0: \"2B\"},\n },\n opset=opset,\n use_external_data_format=True, # UNet is > 2GB, so the weights need to be split\n )\n unet_model_path = str(unet_path.absolute().as_posix())\n unet_dir = os.path.dirname(unet_model_path)\n # optimize onnx\n shape_inference.infer_shapes_path(unet_model_path, unet_model_path)\n unet_opt_graph = optimize(onnx.load(unet_model_path), name=\"Unet\", verbose=True)\n # clean up existing tensor files\n shutil.rmtree(unet_dir)\n os.mkdir(unet_dir)\n # collate external tensor files into one\n onnx.save_model(\n unet_opt_graph,\n unet_model_path,\n save_as_external_data=True,\n all_tensors_to_one_file=True,\n location=\"weights.pb\",\n convert_attribute=False,\n )\n del pipeline.unet\n else:\n controlnets = torch.nn.ModuleList(controlnets)\n unet_controlnet = UNet2DConditionControlNetModel(pipeline.unet, controlnets)\n unet_in_channels = pipeline.unet.config.in_channels\n unet_sample_size = pipeline.unet.config.sample_size\n img_size = 8 * unet_sample_size\n unet_path = output_path / \"unet\" / \"model.onnx\"\n\n onnx_export(\n unet_controlnet,\n model_args=(\n torch.randn(2, unet_in_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype),\n torch.tensor([1.0]).to(device=device, dtype=dtype),\n torch.randn(2, num_tokens, text_hidden_size).to(device=device, dtype=dtype),\n torch.randn(len(controlnets), 2, 3, img_size, img_size).to(device=device, dtype=dtype),\n torch.randn(len(controlnets), 1).to(device=device, dtype=dtype),\n ),\n output_path=unet_path,\n ordered_input_names=[\n \"sample\",\n \"timestep\",\n \"encoder_hidden_states\",\n \"controlnet_conds\",\n \"conditioning_scales\",\n ],\n output_names=[\"noise_pred\"], # has to be different from \"sample\" for correct tracing\n dynamic_axes={\n \"sample\": {0: \"2B\", 2: \"H\", 3: \"W\"},\n \"encoder_hidden_states\": {0: \"2B\"},\n \"controlnet_conds\": {1: \"2B\", 3: \"8H\", 4: \"8W\"},\n },\n opset=opset,\n use_external_data_format=True, # UNet is > 2GB, so the weights need to be split\n )\n unet_model_path = str(unet_path.absolute().as_posix())\n unet_dir = os.path.dirname(unet_model_path)\n # optimize onnx\n shape_inference.infer_shapes_path(unet_model_path, unet_model_path)\n unet_opt_graph = optimize(onnx.load(unet_model_path), name=\"Unet\", verbose=True)\n # clean up existing tensor files\n shutil.rmtree(unet_dir)\n os.mkdir(unet_dir)\n # collate external tensor files into one\n onnx.save_model(\n unet_opt_graph,\n unet_model_path,\n save_as_external_data=True,\n all_tensors_to_one_file=True,\n location=\"weights.pb\",\n convert_attribute=False,\n )\n del pipeline.unet\n\n # VAE ENCODER\n vae_encoder = pipeline.vae\n vae_in_channels = vae_encoder.config.in_channels\n vae_sample_size = vae_encoder.config.sample_size\n # need to get the raw tensor output (sample) from the encoder\n vae_encoder.forward = lambda sample: vae_encoder.encode(sample).latent_dist.sample()\n onnx_export(\n vae_encoder,\n model_args=(torch.randn(1, vae_in_channels, vae_sample_size, vae_sample_size).to(device=device, dtype=dtype),),\n output_path=output_path / \"vae_encoder\" / \"model.onnx\",\n ordered_input_names=[\"sample\"],\n output_names=[\"latent_sample\"],\n dynamic_axes={\n \"sample\": {0: \"batch\", 1: \"channels\", 2: \"height\", 3: \"width\"},\n },\n opset=opset,\n )\n\n # VAE DECODER\n vae_decoder = pipeline.vae\n vae_latent_channels = vae_decoder.config.latent_channels\n # forward only through the decoder part\n vae_decoder.forward = vae_encoder.decode\n onnx_export(\n vae_decoder,\n model_args=(\n torch.randn(1, vae_latent_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype),\n ),\n output_path=output_path / \"vae_decoder\" / \"model.onnx\",\n ordered_input_names=[\"latent_sample\"],\n output_names=[\"sample\"],\n dynamic_axes={\n \"latent_sample\": {0: \"batch\", 1: \"channels\", 2: \"height\", 3: \"width\"},\n },\n opset=opset,\n )\n del pipeline.vae\n\n del pipeline\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"--sd_xl\", action=\"store_true\", default=False, help=\"SD XL pipeline\")\n\n parser.add_argument(\n \"--model_path\",\n type=str,\n required=True,\n help=\"Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).\",\n )\n\n parser.add_argument(\n \"--controlnet_path\",\n nargs=\"+\",\n required=True,\n help=\"Path to the `controlnet` checkpoint to convert (either a local directory or on the Hub).\",\n )\n\n parser.add_argument(\"--output_path\", type=str, required=True, help=\"Path to the output model.\")\n\n parser.add_argument(\n \"--opset\",\n default=14,\n type=int,\n help=\"The version of the ONNX operator set to use.\",\n )\n parser.add_argument(\"--fp16\", action=\"store_true\", default=False, help=\"Export the models in `float16` mode\")\n\n args = parser.parse_args()\n\n convert_models(args.model_path, args.controlnet_path, args.output_path, args.opset, args.fp16, args.sd_xl)\n" }, { "path": "diffusers/scripts/convert_stable_diffusion_controlnet_to_tensorrt.py", "content": "import argparse\nimport sys\n\nimport tensorrt as trt\n\n\ndef convert_models(onnx_path: str, num_controlnet: int, output_path: str, fp16: bool = False, sd_xl: bool = False):\n \"\"\"\n Function to convert models in stable diffusion controlnet pipeline into TensorRT format\n\n Example:\n python convert_stable_diffusion_controlnet_to_tensorrt.py\n --onnx_path path-to-models-stable_diffusion/RevAnimated-v1-2-2/unet/model.onnx\n --output_path path-to-models-stable_diffusion/RevAnimated-v1-2-2/unet/model.engine\n --fp16\n --num_controlnet 2\n\n Example for SD XL:\n python convert_stable_diffusion_controlnet_to_tensorrt.py\n --onnx_path path-to-models-stable_diffusion/stable-diffusion-xl-base-1.0/unet/model.onnx\n --output_path path-to-models-stable_diffusion/stable-diffusion-xl-base-1.0/unet/model.engine\n --fp16\n --num_controlnet 1\n --sd_xl\n\n Returns:\n unet/model.engine\n\n run test script in diffusers/examples/community\n python test_onnx_controlnet.py\n --sd_model danbrown/RevAnimated-v1-2-2\n --onnx_model_dir path-to-models-stable_diffusion/RevAnimated-v1-2-2\n --unet_engine_path path-to-models-stable_diffusion/stable-diffusion-xl-base-1.0/unet/model.engine\n --qr_img_path path-to-qr-code-image\n \"\"\"\n # UNET\n if sd_xl:\n batch_size = 1\n unet_in_channels = 4\n unet_sample_size = 64\n num_tokens = 77\n text_hidden_size = 2048\n img_size = 512\n\n text_embeds_shape = (2 * batch_size, 1280)\n time_ids_shape = (2 * batch_size, 6)\n else:\n batch_size = 1\n unet_in_channels = 4\n unet_sample_size = 64\n num_tokens = 77\n text_hidden_size = 768\n img_size = 512\n batch_size = 1\n\n latents_shape = (2 * batch_size, unet_in_channels, unet_sample_size, unet_sample_size)\n embed_shape = (2 * batch_size, num_tokens, text_hidden_size)\n controlnet_conds_shape = (num_controlnet, 2 * batch_size, 3, img_size, img_size)\n\n TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE)\n TRT_BUILDER = trt.Builder(TRT_LOGGER)\n TRT_RUNTIME = trt.Runtime(TRT_LOGGER)\n\n network = TRT_BUILDER.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))\n onnx_parser = trt.OnnxParser(network, TRT_LOGGER)\n\n parse_success = onnx_parser.parse_from_file(onnx_path)\n for idx in range(onnx_parser.num_errors):\n print(onnx_parser.get_error(idx))\n if not parse_success:\n sys.exit(\"ONNX model parsing failed\")\n print(\"Load Onnx model done\")\n\n profile = TRT_BUILDER.create_optimization_profile()\n\n profile.set_shape(\"sample\", latents_shape, latents_shape, latents_shape)\n profile.set_shape(\"encoder_hidden_states\", embed_shape, embed_shape, embed_shape)\n profile.set_shape(\"controlnet_conds\", controlnet_conds_shape, controlnet_conds_shape, controlnet_conds_shape)\n if sd_xl:\n profile.set_shape(\"text_embeds\", text_embeds_shape, text_embeds_shape, text_embeds_shape)\n profile.set_shape(\"time_ids\", time_ids_shape, time_ids_shape, time_ids_shape)\n\n config = TRT_BUILDER.create_builder_config()\n config.add_optimization_profile(profile)\n config.set_preview_feature(trt.PreviewFeature.DISABLE_EXTERNAL_TACTIC_SOURCES_FOR_CORE_0805, True)\n if fp16:\n config.set_flag(trt.BuilderFlag.FP16)\n\n plan = TRT_BUILDER.build_serialized_network(network, config)\n if plan is None:\n sys.exit(\"Failed building engine\")\n print(\"Succeeded building engine\")\n\n engine = TRT_RUNTIME.deserialize_cuda_engine(plan)\n\n ## save TRT engine\n with open(output_path, \"wb\") as f:\n f.write(engine.serialize())\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"--sd_xl\", action=\"store_true\", default=False, help=\"SD XL pipeline\")\n\n parser.add_argument(\n \"--onnx_path\",\n type=str,\n required=True,\n help=\"Path to the onnx checkpoint to convert\",\n )\n\n parser.add_argument(\"--num_controlnet\", type=int)\n\n parser.add_argument(\"--output_path\", type=str, required=True, help=\"Path to the output model.\")\n\n parser.add_argument(\"--fp16\", action=\"store_true\", default=False, help=\"Export the models in `float16` mode\")\n\n args = parser.parse_args()\n\n convert_models(args.onnx_path, args.num_controlnet, args.output_path, args.fp16, args.sd_xl)\n" }, { "path": "diffusers/scripts/convert_svd_to_diffusers.py", "content": "from diffusers.utils import is_accelerate_available, logging\n\n\nif is_accelerate_available():\n pass\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef create_unet_diffusers_config(original_config, image_size: int, controlnet=False):\n \"\"\"\n Creates a config for the diffusers based on the config of the LDM model.\n \"\"\"\n if controlnet:\n unet_params = original_config.model.params.control_stage_config.params\n else:\n if \"unet_config\" in original_config.model.params and original_config.model.params.unet_config is not None:\n unet_params = original_config.model.params.unet_config.params\n else:\n unet_params = original_config.model.params.network_config.params\n\n vae_params = original_config.model.params.first_stage_config.params.encoder_config.params\n\n block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]\n\n down_block_types = []\n resolution = 1\n for i in range(len(block_out_channels)):\n block_type = (\n \"CrossAttnDownBlockSpatioTemporal\"\n if resolution in unet_params.attention_resolutions\n else \"DownBlockSpatioTemporal\"\n )\n down_block_types.append(block_type)\n if i != len(block_out_channels) - 1:\n resolution *= 2\n\n up_block_types = []\n for i in range(len(block_out_channels)):\n block_type = (\n \"CrossAttnUpBlockSpatioTemporal\"\n if resolution in unet_params.attention_resolutions\n else \"UpBlockSpatioTemporal\"\n )\n up_block_types.append(block_type)\n resolution //= 2\n\n if unet_params.transformer_depth is not None:\n transformer_layers_per_block = (\n unet_params.transformer_depth\n if isinstance(unet_params.transformer_depth, int)\n else list(unet_params.transformer_depth)\n )\n else:\n transformer_layers_per_block = 1\n\n vae_scale_factor = 2 ** (len(vae_params.ch_mult) - 1)\n\n head_dim = unet_params.num_heads if \"num_heads\" in unet_params else None\n use_linear_projection = (\n unet_params.use_linear_in_transformer if \"use_linear_in_transformer\" in unet_params else False\n )\n if use_linear_projection:\n # stable diffusion 2-base-512 and 2-768\n if head_dim is None:\n head_dim_mult = unet_params.model_channels // unet_params.num_head_channels\n head_dim = [head_dim_mult * c for c in list(unet_params.channel_mult)]\n\n class_embed_type = None\n addition_embed_type = None\n addition_time_embed_dim = None\n projection_class_embeddings_input_dim = None\n context_dim = None\n\n if unet_params.context_dim is not None:\n context_dim = (\n unet_params.context_dim if isinstance(unet_params.context_dim, int) else unet_params.context_dim[0]\n )\n\n if \"num_classes\" in unet_params:\n if unet_params.num_classes == \"sequential\":\n addition_time_embed_dim = 256\n assert \"adm_in_channels\" in unet_params\n projection_class_embeddings_input_dim = unet_params.adm_in_channels\n\n config = {\n \"sample_size\": image_size // vae_scale_factor,\n \"in_channels\": unet_params.in_channels,\n \"down_block_types\": tuple(down_block_types),\n \"block_out_channels\": tuple(block_out_channels),\n \"layers_per_block\": unet_params.num_res_blocks,\n \"cross_attention_dim\": context_dim,\n \"attention_head_dim\": head_dim,\n \"use_linear_projection\": use_linear_projection,\n \"class_embed_type\": class_embed_type,\n \"addition_embed_type\": addition_embed_type,\n \"addition_time_embed_dim\": addition_time_embed_dim,\n \"projection_class_embeddings_input_dim\": projection_class_embeddings_input_dim,\n \"transformer_layers_per_block\": transformer_layers_per_block,\n }\n\n if \"disable_self_attentions\" in unet_params:\n config[\"only_cross_attention\"] = unet_params.disable_self_attentions\n\n if \"num_classes\" in unet_params and isinstance(unet_params.num_classes, int):\n config[\"num_class_embeds\"] = unet_params.num_classes\n\n if controlnet:\n config[\"conditioning_channels\"] = unet_params.hint_channels\n else:\n config[\"out_channels\"] = unet_params.out_channels\n config[\"up_block_types\"] = tuple(up_block_types)\n\n return config\n\n\ndef assign_to_checkpoint(\n paths,\n checkpoint,\n old_checkpoint,\n attention_paths_to_split=None,\n additional_replacements=None,\n config=None,\n mid_block_suffix=\"\",\n):\n \"\"\"\n This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits\n attention layers, and takes into account additional replacements that may arise.\n\n Assigns the weights to the new checkpoint.\n \"\"\"\n assert isinstance(paths, list), \"Paths should be a list of dicts containing 'old' and 'new' keys.\"\n\n # Splits the attention layers into three variables.\n if attention_paths_to_split is not None:\n for path, path_map in attention_paths_to_split.items():\n old_tensor = old_checkpoint[path]\n channels = old_tensor.shape[0] // 3\n\n target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)\n\n num_heads = old_tensor.shape[0] // config[\"num_head_channels\"] // 3\n\n old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])\n query, key, value = old_tensor.split(channels // num_heads, dim=1)\n\n checkpoint[path_map[\"query\"]] = query.reshape(target_shape)\n checkpoint[path_map[\"key\"]] = key.reshape(target_shape)\n checkpoint[path_map[\"value\"]] = value.reshape(target_shape)\n\n if mid_block_suffix is not None:\n mid_block_suffix = f\".{mid_block_suffix}\"\n else:\n mid_block_suffix = \"\"\n\n for path in paths:\n new_path = path[\"new\"]\n\n # These have already been assigned\n if attention_paths_to_split is not None and new_path in attention_paths_to_split:\n continue\n\n # Global renaming happens here\n new_path = new_path.replace(\"middle_block.0\", f\"mid_block.resnets.0{mid_block_suffix}\")\n new_path = new_path.replace(\"middle_block.1\", \"mid_block.attentions.0\")\n new_path = new_path.replace(\"middle_block.2\", f\"mid_block.resnets.1{mid_block_suffix}\")\n\n if additional_replacements is not None:\n for replacement in additional_replacements:\n new_path = new_path.replace(replacement[\"old\"], replacement[\"new\"])\n\n if new_path == \"mid_block.resnets.0.spatial_res_block.norm1.weight\":\n print(\"yeyy\")\n\n # proj_attn.weight has to be converted from conv 1D to linear\n is_attn_weight = \"proj_attn.weight\" in new_path or (\"attentions\" in new_path and \"to_\" in new_path)\n shape = old_checkpoint[path[\"old\"]].shape\n if is_attn_weight and len(shape) == 3:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]][:, :, 0]\n elif is_attn_weight and len(shape) == 4:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]][:, :, 0, 0]\n else:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]]\n\n\ndef renew_attention_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside attentions to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n # new_item = new_item.replace('norm.weight', 'group_norm.weight')\n # new_item = new_item.replace('norm.bias', 'group_norm.bias')\n\n # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')\n # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')\n\n # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n new_item = new_item.replace(\"time_stack\", \"temporal_transformer_blocks\")\n\n new_item = new_item.replace(\"time_pos_embed.0.bias\", \"time_pos_embed.linear_1.bias\")\n new_item = new_item.replace(\"time_pos_embed.0.weight\", \"time_pos_embed.linear_1.weight\")\n new_item = new_item.replace(\"time_pos_embed.2.bias\", \"time_pos_embed.linear_2.bias\")\n new_item = new_item.replace(\"time_pos_embed.2.weight\", \"time_pos_embed.linear_2.weight\")\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef shave_segments(path, n_shave_prefix_segments=1):\n \"\"\"\n Removes segments. Positive values shave the first segments, negative shave the last segments.\n \"\"\"\n if n_shave_prefix_segments >= 0:\n return \".\".join(path.split(\".\")[n_shave_prefix_segments:])\n else:\n return \".\".join(path.split(\".\")[:n_shave_prefix_segments])\n\n\ndef renew_resnet_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside resnets to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item.replace(\"in_layers.0\", \"norm1\")\n new_item = new_item.replace(\"in_layers.2\", \"conv1\")\n\n new_item = new_item.replace(\"out_layers.0\", \"norm2\")\n new_item = new_item.replace(\"out_layers.3\", \"conv2\")\n\n new_item = new_item.replace(\"emb_layers.1\", \"time_emb_proj\")\n new_item = new_item.replace(\"skip_connection\", \"conv_shortcut\")\n\n new_item = new_item.replace(\"time_stack.\", \"\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef convert_ldm_unet_checkpoint(\n checkpoint, config, path=None, extract_ema=False, controlnet=False, skip_extract_state_dict=False\n):\n \"\"\"\n Takes a state dict and a config, and returns a converted checkpoint.\n \"\"\"\n\n if skip_extract_state_dict:\n unet_state_dict = checkpoint\n else:\n # extract state_dict for UNet\n unet_state_dict = {}\n keys = list(checkpoint.keys())\n\n unet_key = \"model.diffusion_model.\"\n\n # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA\n if sum(k.startswith(\"model_ema\") for k in keys) > 100 and extract_ema:\n logger.warning(f\"Checkpoint {path} has both EMA and non-EMA weights.\")\n logger.warning(\n \"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA\"\n \" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.\"\n )\n for key in keys:\n if key.startswith(\"model.diffusion_model\"):\n flat_ema_key = \"model_ema.\" + \"\".join(key.split(\".\")[1:])\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint.pop(flat_ema_key)\n else:\n if sum(k.startswith(\"model_ema\") for k in keys) > 100:\n logger.warning(\n \"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA\"\n \" weights (usually better for inference), please make sure to add the `--extract_ema` flag.\"\n )\n\n for key in keys:\n if key.startswith(unet_key):\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint.pop(key)\n\n new_checkpoint = {}\n\n new_checkpoint[\"time_embedding.linear_1.weight\"] = unet_state_dict[\"time_embed.0.weight\"]\n new_checkpoint[\"time_embedding.linear_1.bias\"] = unet_state_dict[\"time_embed.0.bias\"]\n new_checkpoint[\"time_embedding.linear_2.weight\"] = unet_state_dict[\"time_embed.2.weight\"]\n new_checkpoint[\"time_embedding.linear_2.bias\"] = unet_state_dict[\"time_embed.2.bias\"]\n\n if config[\"class_embed_type\"] is None:\n # No parameters to port\n ...\n elif config[\"class_embed_type\"] == \"timestep\" or config[\"class_embed_type\"] == \"projection\":\n new_checkpoint[\"class_embedding.linear_1.weight\"] = unet_state_dict[\"label_emb.0.0.weight\"]\n new_checkpoint[\"class_embedding.linear_1.bias\"] = unet_state_dict[\"label_emb.0.0.bias\"]\n new_checkpoint[\"class_embedding.linear_2.weight\"] = unet_state_dict[\"label_emb.0.2.weight\"]\n new_checkpoint[\"class_embedding.linear_2.bias\"] = unet_state_dict[\"label_emb.0.2.bias\"]\n else:\n raise NotImplementedError(f\"Not implemented `class_embed_type`: {config['class_embed_type']}\")\n\n # if config[\"addition_embed_type\"] == \"text_time\":\n new_checkpoint[\"add_embedding.linear_1.weight\"] = unet_state_dict[\"label_emb.0.0.weight\"]\n new_checkpoint[\"add_embedding.linear_1.bias\"] = unet_state_dict[\"label_emb.0.0.bias\"]\n new_checkpoint[\"add_embedding.linear_2.weight\"] = unet_state_dict[\"label_emb.0.2.weight\"]\n new_checkpoint[\"add_embedding.linear_2.bias\"] = unet_state_dict[\"label_emb.0.2.bias\"]\n\n new_checkpoint[\"conv_in.weight\"] = unet_state_dict[\"input_blocks.0.0.weight\"]\n new_checkpoint[\"conv_in.bias\"] = unet_state_dict[\"input_blocks.0.0.bias\"]\n\n new_checkpoint[\"conv_norm_out.weight\"] = unet_state_dict[\"out.0.weight\"]\n new_checkpoint[\"conv_norm_out.bias\"] = unet_state_dict[\"out.0.bias\"]\n new_checkpoint[\"conv_out.weight\"] = unet_state_dict[\"out.2.weight\"]\n new_checkpoint[\"conv_out.bias\"] = unet_state_dict[\"out.2.bias\"]\n\n # Retrieves the keys for the input blocks only\n num_input_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"input_blocks\" in layer})\n input_blocks = {\n layer_id: [key for key in unet_state_dict if f\"input_blocks.{layer_id}\" in key]\n for layer_id in range(num_input_blocks)\n }\n\n # Retrieves the keys for the middle blocks only\n num_middle_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"middle_block\" in layer})\n middle_blocks = {\n layer_id: [key for key in unet_state_dict if f\"middle_block.{layer_id}\" in key]\n for layer_id in range(num_middle_blocks)\n }\n\n # Retrieves the keys for the output blocks only\n num_output_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"output_blocks\" in layer})\n output_blocks = {\n layer_id: [key for key in unet_state_dict if f\"output_blocks.{layer_id}\" in key]\n for layer_id in range(num_output_blocks)\n }\n\n for i in range(1, num_input_blocks):\n block_id = (i - 1) // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = (i - 1) % (config[\"layers_per_block\"] + 1)\n\n spatial_resnets = [\n key\n for key in input_blocks[i]\n if f\"input_blocks.{i}.0\" in key\n and (\n f\"input_blocks.{i}.0.op\" not in key\n and f\"input_blocks.{i}.0.time_stack\" not in key\n and f\"input_blocks.{i}.0.time_mixer\" not in key\n )\n ]\n temporal_resnets = [key for key in input_blocks[i] if f\"input_blocks.{i}.0.time_stack\" in key]\n # import ipdb; ipdb.set_trace()\n attentions = [key for key in input_blocks[i] if f\"input_blocks.{i}.1\" in key]\n\n if f\"input_blocks.{i}.0.op.weight\" in unet_state_dict:\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.weight\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.op.weight\"\n )\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.bias\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.op.bias\"\n )\n\n paths = renew_resnet_paths(spatial_resnets)\n meta_path = {\n \"old\": f\"input_blocks.{i}.0\",\n \"new\": f\"down_blocks.{block_id}.resnets.{layer_in_block_id}.spatial_res_block\",\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n paths = renew_resnet_paths(temporal_resnets)\n meta_path = {\n \"old\": f\"input_blocks.{i}.0\",\n \"new\": f\"down_blocks.{block_id}.resnets.{layer_in_block_id}.temporal_res_block\",\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n # TODO resnet time_mixer.mix_factor\n if f\"input_blocks.{i}.0.time_mixer.mix_factor\" in unet_state_dict:\n new_checkpoint[\n f\"down_blocks.{block_id}.resnets.{layer_in_block_id}.time_mixer.mix_factor\"\n ] = unet_state_dict[f\"input_blocks.{i}.0.time_mixer.mix_factor\"]\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n meta_path = {\"old\": f\"input_blocks.{i}.1\", \"new\": f\"down_blocks.{block_id}.attentions.{layer_in_block_id}\"}\n # import ipdb; ipdb.set_trace()\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n resnet_0 = middle_blocks[0]\n attentions = middle_blocks[1]\n resnet_1 = middle_blocks[2]\n\n resnet_0_spatial = [key for key in resnet_0 if \"time_stack\" not in key and \"time_mixer\" not in key]\n resnet_0_paths = renew_resnet_paths(resnet_0_spatial)\n # import ipdb; ipdb.set_trace()\n assign_to_checkpoint(\n resnet_0_paths, new_checkpoint, unet_state_dict, config=config, mid_block_suffix=\"spatial_res_block\"\n )\n\n resnet_0_temporal = [key for key in resnet_0 if \"time_stack\" in key and \"time_mixer\" not in key]\n resnet_0_paths = renew_resnet_paths(resnet_0_temporal)\n assign_to_checkpoint(\n resnet_0_paths, new_checkpoint, unet_state_dict, config=config, mid_block_suffix=\"temporal_res_block\"\n )\n\n resnet_1_spatial = [key for key in resnet_1 if \"time_stack\" not in key and \"time_mixer\" not in key]\n resnet_1_paths = renew_resnet_paths(resnet_1_spatial)\n assign_to_checkpoint(\n resnet_1_paths, new_checkpoint, unet_state_dict, config=config, mid_block_suffix=\"spatial_res_block\"\n )\n\n resnet_1_temporal = [key for key in resnet_1 if \"time_stack\" in key and \"time_mixer\" not in key]\n resnet_1_paths = renew_resnet_paths(resnet_1_temporal)\n assign_to_checkpoint(\n resnet_1_paths, new_checkpoint, unet_state_dict, config=config, mid_block_suffix=\"temporal_res_block\"\n )\n\n new_checkpoint[\"mid_block.resnets.0.time_mixer.mix_factor\"] = unet_state_dict[\n \"middle_block.0.time_mixer.mix_factor\"\n ]\n new_checkpoint[\"mid_block.resnets.1.time_mixer.mix_factor\"] = unet_state_dict[\n \"middle_block.2.time_mixer.mix_factor\"\n ]\n\n attentions_paths = renew_attention_paths(attentions)\n meta_path = {\"old\": \"middle_block.1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(\n attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n for i in range(num_output_blocks):\n block_id = i // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = i % (config[\"layers_per_block\"] + 1)\n output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]\n output_block_list = {}\n\n for layer in output_block_layers:\n layer_id, layer_name = layer.split(\".\")[0], shave_segments(layer, 1)\n if layer_id in output_block_list:\n output_block_list[layer_id].append(layer_name)\n else:\n output_block_list[layer_id] = [layer_name]\n\n if len(output_block_list) > 1:\n spatial_resnets = [\n key\n for key in output_blocks[i]\n if f\"output_blocks.{i}.0\" in key\n and (f\"output_blocks.{i}.0.time_stack\" not in key and \"time_mixer\" not in key)\n ]\n # import ipdb; ipdb.set_trace()\n\n temporal_resnets = [key for key in output_blocks[i] if f\"output_blocks.{i}.0.time_stack\" in key]\n\n paths = renew_resnet_paths(spatial_resnets)\n meta_path = {\n \"old\": f\"output_blocks.{i}.0\",\n \"new\": f\"up_blocks.{block_id}.resnets.{layer_in_block_id}.spatial_res_block\",\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n paths = renew_resnet_paths(temporal_resnets)\n meta_path = {\n \"old\": f\"output_blocks.{i}.0\",\n \"new\": f\"up_blocks.{block_id}.resnets.{layer_in_block_id}.temporal_res_block\",\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n if f\"output_blocks.{i}.0.time_mixer.mix_factor\" in unet_state_dict:\n new_checkpoint[\n f\"up_blocks.{block_id}.resnets.{layer_in_block_id}.time_mixer.mix_factor\"\n ] = unet_state_dict[f\"output_blocks.{i}.0.time_mixer.mix_factor\"]\n\n output_block_list = {k: sorted(v) for k, v in output_block_list.items()}\n if [\"conv.bias\", \"conv.weight\"] in output_block_list.values():\n index = list(output_block_list.values()).index([\"conv.bias\", \"conv.weight\"])\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.weight\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.weight\"\n ]\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.bias\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.bias\"\n ]\n\n # Clear attentions as they have been attributed above.\n if len(attentions) == 2:\n attentions = []\n\n attentions = [key for key in output_blocks[i] if f\"output_blocks.{i}.1\" in key and \"conv\" not in key]\n if len(attentions):\n paths = renew_attention_paths(attentions)\n # import ipdb; ipdb.set_trace()\n meta_path = {\n \"old\": f\"output_blocks.{i}.1\",\n \"new\": f\"up_blocks.{block_id}.attentions.{layer_in_block_id}\",\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n else:\n spatial_layers = [\n layer for layer in output_block_layers if \"time_stack\" not in layer and \"time_mixer\" not in layer\n ]\n resnet_0_paths = renew_resnet_paths(spatial_layers, n_shave_prefix_segments=1)\n # import ipdb; ipdb.set_trace()\n for path in resnet_0_paths:\n old_path = \".\".join([\"output_blocks\", str(i), path[\"old\"]])\n new_path = \".\".join(\n [\"up_blocks\", str(block_id), \"resnets\", str(layer_in_block_id), \"spatial_res_block\", path[\"new\"]]\n )\n\n new_checkpoint[new_path] = unet_state_dict[old_path]\n\n temporal_layers = [\n layer for layer in output_block_layers if \"time_stack\" in layer and \"time_mixer\" not in key\n ]\n resnet_0_paths = renew_resnet_paths(temporal_layers, n_shave_prefix_segments=1)\n # import ipdb; ipdb.set_trace()\n for path in resnet_0_paths:\n old_path = \".\".join([\"output_blocks\", str(i), path[\"old\"]])\n new_path = \".\".join(\n [\"up_blocks\", str(block_id), \"resnets\", str(layer_in_block_id), \"temporal_res_block\", path[\"new\"]]\n )\n\n new_checkpoint[new_path] = unet_state_dict[old_path]\n\n new_checkpoint[\"up_blocks.0.resnets.0.time_mixer.mix_factor\"] = unet_state_dict[\n f\"output_blocks.{str(i)}.0.time_mixer.mix_factor\"\n ]\n\n return new_checkpoint\n\n\ndef conv_attn_to_linear(checkpoint):\n keys = list(checkpoint.keys())\n attn_keys = [\"to_q.weight\", \"to_k.weight\", \"to_v.weight\"]\n for key in keys:\n if \".\".join(key.split(\".\")[-2:]) in attn_keys:\n if checkpoint[key].ndim > 2:\n checkpoint[key] = checkpoint[key][:, :, 0, 0]\n elif \"proj_attn.weight\" in key:\n if checkpoint[key].ndim > 2:\n checkpoint[key] = checkpoint[key][:, :, 0]\n\n\ndef renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0, is_temporal=False):\n \"\"\"\n Updates paths inside resnets to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n # Temporal resnet\n new_item = old_item.replace(\"in_layers.0\", \"norm1\")\n new_item = new_item.replace(\"in_layers.2\", \"conv1\")\n\n new_item = new_item.replace(\"out_layers.0\", \"norm2\")\n new_item = new_item.replace(\"out_layers.3\", \"conv2\")\n\n new_item = new_item.replace(\"skip_connection\", \"conv_shortcut\")\n\n new_item = new_item.replace(\"time_stack.\", \"temporal_res_block.\")\n\n # Spatial resnet\n new_item = new_item.replace(\"conv1\", \"spatial_res_block.conv1\")\n new_item = new_item.replace(\"norm1\", \"spatial_res_block.norm1\")\n\n new_item = new_item.replace(\"conv2\", \"spatial_res_block.conv2\")\n new_item = new_item.replace(\"norm2\", \"spatial_res_block.norm2\")\n\n new_item = new_item.replace(\"nin_shortcut\", \"spatial_res_block.conv_shortcut\")\n\n new_item = new_item.replace(\"mix_factor\", \"spatial_res_block.time_mixer.mix_factor\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside attentions to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n new_item = new_item.replace(\"norm.weight\", \"group_norm.weight\")\n new_item = new_item.replace(\"norm.bias\", \"group_norm.bias\")\n\n new_item = new_item.replace(\"q.weight\", \"to_q.weight\")\n new_item = new_item.replace(\"q.bias\", \"to_q.bias\")\n\n new_item = new_item.replace(\"k.weight\", \"to_k.weight\")\n new_item = new_item.replace(\"k.bias\", \"to_k.bias\")\n\n new_item = new_item.replace(\"v.weight\", \"to_v.weight\")\n new_item = new_item.replace(\"v.bias\", \"to_v.bias\")\n\n new_item = new_item.replace(\"proj_out.weight\", \"to_out.0.weight\")\n new_item = new_item.replace(\"proj_out.bias\", \"to_out.0.bias\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef convert_ldm_vae_checkpoint(checkpoint, config):\n # extract state dict for VAE\n vae_state_dict = {}\n keys = list(checkpoint.keys())\n vae_key = \"first_stage_model.\" if any(k.startswith(\"first_stage_model.\") for k in keys) else \"\"\n for key in keys:\n if key.startswith(vae_key):\n vae_state_dict[key.replace(vae_key, \"\")] = checkpoint.get(key)\n\n new_checkpoint = {}\n\n new_checkpoint[\"encoder.conv_in.weight\"] = vae_state_dict[\"encoder.conv_in.weight\"]\n new_checkpoint[\"encoder.conv_in.bias\"] = vae_state_dict[\"encoder.conv_in.bias\"]\n new_checkpoint[\"encoder.conv_out.weight\"] = vae_state_dict[\"encoder.conv_out.weight\"]\n new_checkpoint[\"encoder.conv_out.bias\"] = vae_state_dict[\"encoder.conv_out.bias\"]\n new_checkpoint[\"encoder.conv_norm_out.weight\"] = vae_state_dict[\"encoder.norm_out.weight\"]\n new_checkpoint[\"encoder.conv_norm_out.bias\"] = vae_state_dict[\"encoder.norm_out.bias\"]\n\n new_checkpoint[\"decoder.conv_in.weight\"] = vae_state_dict[\"decoder.conv_in.weight\"]\n new_checkpoint[\"decoder.conv_in.bias\"] = vae_state_dict[\"decoder.conv_in.bias\"]\n new_checkpoint[\"decoder.conv_out.weight\"] = vae_state_dict[\"decoder.conv_out.weight\"]\n new_checkpoint[\"decoder.conv_out.bias\"] = vae_state_dict[\"decoder.conv_out.bias\"]\n new_checkpoint[\"decoder.conv_norm_out.weight\"] = vae_state_dict[\"decoder.norm_out.weight\"]\n new_checkpoint[\"decoder.conv_norm_out.bias\"] = vae_state_dict[\"decoder.norm_out.bias\"]\n new_checkpoint[\"decoder.time_conv_out.weight\"] = vae_state_dict[\"decoder.time_mix_conv.weight\"]\n new_checkpoint[\"decoder.time_conv_out.bias\"] = vae_state_dict[\"decoder.time_mix_conv.bias\"]\n\n # new_checkpoint[\"quant_conv.weight\"] = vae_state_dict[\"quant_conv.weight\"]\n # new_checkpoint[\"quant_conv.bias\"] = vae_state_dict[\"quant_conv.bias\"]\n # new_checkpoint[\"post_quant_conv.weight\"] = vae_state_dict[\"post_quant_conv.weight\"]\n # new_checkpoint[\"post_quant_conv.bias\"] = vae_state_dict[\"post_quant_conv.bias\"]\n\n # Retrieves the keys for the encoder down blocks only\n num_down_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in vae_state_dict if \"encoder.down\" in layer})\n down_blocks = {\n layer_id: [key for key in vae_state_dict if f\"down.{layer_id}\" in key] for layer_id in range(num_down_blocks)\n }\n\n # Retrieves the keys for the decoder up blocks only\n num_up_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in vae_state_dict if \"decoder.up\" in layer})\n up_blocks = {\n layer_id: [key for key in vae_state_dict if f\"up.{layer_id}\" in key] for layer_id in range(num_up_blocks)\n }\n\n for i in range(num_down_blocks):\n resnets = [key for key in down_blocks[i] if f\"down.{i}\" in key and f\"down.{i}.downsample\" not in key]\n\n if f\"encoder.down.{i}.downsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.weight\"] = vae_state_dict.pop(\n f\"encoder.down.{i}.downsample.conv.weight\"\n )\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.bias\"] = vae_state_dict.pop(\n f\"encoder.down.{i}.downsample.conv.bias\"\n )\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"down.{i}.block\", \"new\": f\"down_blocks.{i}.resnets\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_resnets = [key for key in vae_state_dict if \"encoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"encoder.mid.block_{i}\" in key]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_attentions = [key for key in vae_state_dict if \"encoder.mid.attn\" in key]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n conv_attn_to_linear(new_checkpoint)\n\n for i in range(num_up_blocks):\n block_id = num_up_blocks - 1 - i\n\n resnets = [\n key for key in up_blocks[block_id] if f\"up.{block_id}\" in key and f\"up.{block_id}.upsample\" not in key\n ]\n\n if f\"decoder.up.{block_id}.upsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.weight\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.weight\"\n ]\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.bias\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.bias\"\n ]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"up.{block_id}.block\", \"new\": f\"up_blocks.{i}.resnets\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_resnets = [key for key in vae_state_dict if \"decoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"decoder.mid.block_{i}\" in key]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_attentions = [key for key in vae_state_dict if \"decoder.mid.attn\" in key]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n conv_attn_to_linear(new_checkpoint)\n return new_checkpoint\n" }, { "path": "diffusers/scripts/convert_tiny_autoencoder_to_diffusers.py", "content": "import argparse\n\nimport safetensors.torch\n\nfrom diffusers import AutoencoderTiny\n\n\n\"\"\"\nExample - From the diffusers root directory:\n\nDownload the weights:\n```sh\n$ wget -q https://huggingface.co/madebyollin/taesd/resolve/main/taesd_encoder.safetensors\n$ wget -q https://huggingface.co/madebyollin/taesd/resolve/main/taesd_decoder.safetensors\n```\n\nConvert the model:\n```sh\n$ python scripts/convert_tiny_autoencoder_to_diffusers.py \\\n --encoder_ckpt_path taesd_encoder.safetensors \\\n --decoder_ckpt_path taesd_decoder.safetensors \\\n --dump_path taesd-diffusers\n```\n\"\"\"\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n parser.add_argument(\n \"--encoder_ckpt_path\",\n default=None,\n type=str,\n required=True,\n help=\"Path to the encoder ckpt.\",\n )\n parser.add_argument(\n \"--decoder_ckpt_path\",\n default=None,\n type=str,\n required=True,\n help=\"Path to the decoder ckpt.\",\n )\n parser.add_argument(\n \"--use_safetensors\", action=\"store_true\", help=\"Whether to serialize in the safetensors format.\"\n )\n args = parser.parse_args()\n\n print(\"Loading the original state_dicts of the encoder and the decoder...\")\n encoder_state_dict = safetensors.torch.load_file(args.encoder_ckpt_path)\n decoder_state_dict = safetensors.torch.load_file(args.decoder_ckpt_path)\n\n print(\"Populating the state_dicts in the diffusers format...\")\n tiny_autoencoder = AutoencoderTiny()\n new_state_dict = {}\n\n # Modify the encoder state dict.\n for k in encoder_state_dict:\n new_state_dict.update({f\"encoder.layers.{k}\": encoder_state_dict[k]})\n\n # Modify the decoder state dict.\n for k in decoder_state_dict:\n layer_id = int(k.split(\".\")[0]) - 1\n new_k = str(layer_id) + \".\" + \".\".join(k.split(\".\")[1:])\n new_state_dict.update({f\"decoder.layers.{new_k}\": decoder_state_dict[k]})\n\n # Assertion tests with the original implementation can be found here:\n # https://gist.github.com/sayakpaul/337b0988f08bd2cf2b248206f760e28f\n tiny_autoencoder.load_state_dict(new_state_dict)\n print(\"Population successful, serializing...\")\n tiny_autoencoder.save_pretrained(args.dump_path, safe_serialization=args.use_safetensors)\n" }, { "path": "diffusers/scripts/convert_unclip_txt2img_to_image_variation.py", "content": "import argparse\n\nfrom transformers import CLIPImageProcessor, CLIPVisionModelWithProjection\n\nfrom diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n\n parser.add_argument(\n \"--txt2img_unclip\",\n default=\"kakaobrain/karlo-v1-alpha\",\n type=str,\n required=False,\n help=\"The pretrained txt2img unclip.\",\n )\n\n args = parser.parse_args()\n\n txt2img = UnCLIPPipeline.from_pretrained(args.txt2img_unclip)\n\n feature_extractor = CLIPImageProcessor()\n image_encoder = CLIPVisionModelWithProjection.from_pretrained(\"openai/clip-vit-large-patch14\")\n\n img2img = UnCLIPImageVariationPipeline(\n decoder=txt2img.decoder,\n text_encoder=txt2img.text_encoder,\n tokenizer=txt2img.tokenizer,\n text_proj=txt2img.text_proj,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n super_res_first=txt2img.super_res_first,\n super_res_last=txt2img.super_res_last,\n decoder_scheduler=txt2img.decoder_scheduler,\n super_res_scheduler=txt2img.super_res_scheduler,\n )\n\n img2img.save_pretrained(args.dump_path)\n" }, { "path": "diffusers/scripts/convert_unidiffuser_to_diffusers.py", "content": "# Convert the original UniDiffuser checkpoints into diffusers equivalents.\n\nimport argparse\nfrom argparse import Namespace\n\nimport torch\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextConfig,\n CLIPTextModel,\n CLIPTokenizer,\n CLIPVisionConfig,\n CLIPVisionModelWithProjection,\n GPT2Tokenizer,\n)\n\nfrom diffusers import (\n AutoencoderKL,\n DPMSolverMultistepScheduler,\n UniDiffuserModel,\n UniDiffuserPipeline,\n UniDiffuserTextDecoder,\n)\n\n\nSCHEDULER_CONFIG = Namespace(\n **{\n \"beta_start\": 0.00085,\n \"beta_end\": 0.012,\n \"beta_schedule\": \"scaled_linear\",\n \"solver_order\": 3,\n }\n)\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.shave_segments\ndef shave_segments(path, n_shave_prefix_segments=1):\n \"\"\"\n Removes segments. Positive values shave the first segments, negative shave the last segments.\n \"\"\"\n if n_shave_prefix_segments >= 0:\n return \".\".join(path.split(\".\")[n_shave_prefix_segments:])\n else:\n return \".\".join(path.split(\".\")[:n_shave_prefix_segments])\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_vae_resnet_paths\ndef renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside resnets to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n new_item = new_item.replace(\"nin_shortcut\", \"conv_shortcut\")\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_vae_attention_paths\ndef renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside attentions to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n new_item = new_item.replace(\"norm.weight\", \"group_norm.weight\")\n new_item = new_item.replace(\"norm.bias\", \"group_norm.bias\")\n\n new_item = new_item.replace(\"q.weight\", \"to_q.weight\")\n new_item = new_item.replace(\"q.bias\", \"to_q.bias\")\n\n new_item = new_item.replace(\"k.weight\", \"to_k.weight\")\n new_item = new_item.replace(\"k.bias\", \"to_k.bias\")\n\n new_item = new_item.replace(\"v.weight\", \"to_v.weight\")\n new_item = new_item.replace(\"v.bias\", \"to_v.bias\")\n\n new_item = new_item.replace(\"proj_out.weight\", \"to_out.0.weight\")\n new_item = new_item.replace(\"proj_out.bias\", \"to_out.0.bias\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.conv_attn_to_linear\ndef conv_attn_to_linear(checkpoint):\n keys = list(checkpoint.keys())\n attn_keys = [\"query.weight\", \"key.weight\", \"value.weight\"]\n for key in keys:\n if \".\".join(key.split(\".\")[-2:]) in attn_keys:\n if checkpoint[key].ndim > 2:\n checkpoint[key] = checkpoint[key][:, :, 0, 0]\n elif \"proj_attn.weight\" in key:\n if checkpoint[key].ndim > 2:\n checkpoint[key] = checkpoint[key][:, :, 0]\n\n\n# Modified from diffusers.pipelines.stable_diffusion.convert_from_ckpt.assign_to_checkpoint\n# config.num_head_channels => num_head_channels\ndef assign_to_checkpoint(\n paths,\n checkpoint,\n old_checkpoint,\n attention_paths_to_split=None,\n additional_replacements=None,\n num_head_channels=1,\n):\n \"\"\"\n This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits\n attention layers, and takes into account additional replacements that may arise.\n\n Assigns the weights to the new checkpoint.\n \"\"\"\n assert isinstance(paths, list), \"Paths should be a list of dicts containing 'old' and 'new' keys.\"\n\n # Splits the attention layers into three variables.\n if attention_paths_to_split is not None:\n for path, path_map in attention_paths_to_split.items():\n old_tensor = old_checkpoint[path]\n channels = old_tensor.shape[0] // 3\n\n target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)\n\n num_heads = old_tensor.shape[0] // num_head_channels // 3\n\n old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])\n query, key, value = old_tensor.split(channels // num_heads, dim=1)\n\n checkpoint[path_map[\"query\"]] = query.reshape(target_shape)\n checkpoint[path_map[\"key\"]] = key.reshape(target_shape)\n checkpoint[path_map[\"value\"]] = value.reshape(target_shape)\n\n for path in paths:\n new_path = path[\"new\"]\n\n # These have already been assigned\n if attention_paths_to_split is not None and new_path in attention_paths_to_split:\n continue\n\n # Global renaming happens here\n new_path = new_path.replace(\"middle_block.0\", \"mid_block.resnets.0\")\n new_path = new_path.replace(\"middle_block.1\", \"mid_block.attentions.0\")\n new_path = new_path.replace(\"middle_block.2\", \"mid_block.resnets.1\")\n\n if additional_replacements is not None:\n for replacement in additional_replacements:\n new_path = new_path.replace(replacement[\"old\"], replacement[\"new\"])\n\n # proj_attn.weight has to be converted from conv 1D to linear\n is_attn_weight = \"proj_attn.weight\" in new_path or (\"attentions\" in new_path and \"to_\" in new_path)\n shape = old_checkpoint[path[\"old\"]].shape\n if is_attn_weight and len(shape) == 3:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]][:, :, 0]\n elif is_attn_weight and len(shape) == 4:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]][:, :, 0, 0]\n else:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]]\n\n\ndef create_vae_diffusers_config(config_type):\n # Hardcoded for now\n if args.config_type == \"test\":\n vae_config = create_vae_diffusers_config_test()\n elif args.config_type == \"big\":\n vae_config = create_vae_diffusers_config_big()\n else:\n raise NotImplementedError(\n f\"Config type {config_type} is not implemented, currently only config types\"\n \" 'test' and 'big' are available.\"\n )\n return vae_config\n\n\ndef create_unidiffuser_unet_config(config_type, version):\n # Hardcoded for now\n if args.config_type == \"test\":\n unet_config = create_unidiffuser_unet_config_test()\n elif args.config_type == \"big\":\n unet_config = create_unidiffuser_unet_config_big()\n else:\n raise NotImplementedError(\n f\"Config type {config_type} is not implemented, currently only config types\"\n \" 'test' and 'big' are available.\"\n )\n # Unidiffuser-v1 uses data type embeddings\n if version == 1:\n unet_config[\"use_data_type_embedding\"] = True\n return unet_config\n\n\ndef create_text_decoder_config(config_type):\n # Hardcoded for now\n if args.config_type == \"test\":\n text_decoder_config = create_text_decoder_config_test()\n elif args.config_type == \"big\":\n text_decoder_config = create_text_decoder_config_big()\n else:\n raise NotImplementedError(\n f\"Config type {config_type} is not implemented, currently only config types\"\n \" 'test' and 'big' are available.\"\n )\n return text_decoder_config\n\n\n# Hardcoded configs for test versions of the UniDiffuser models, corresponding to those in the fast default tests.\ndef create_vae_diffusers_config_test():\n vae_config = {\n \"sample_size\": 32,\n \"in_channels\": 3,\n \"out_channels\": 3,\n \"down_block_types\": [\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n \"up_block_types\": [\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n \"block_out_channels\": [32, 64],\n \"latent_channels\": 4,\n \"layers_per_block\": 1,\n }\n return vae_config\n\n\ndef create_unidiffuser_unet_config_test():\n unet_config = {\n \"text_dim\": 32,\n \"clip_img_dim\": 32,\n \"num_text_tokens\": 77,\n \"num_attention_heads\": 2,\n \"attention_head_dim\": 8,\n \"in_channels\": 4,\n \"out_channels\": 4,\n \"num_layers\": 2,\n \"dropout\": 0.0,\n \"norm_num_groups\": 32,\n \"attention_bias\": False,\n \"sample_size\": 16,\n \"patch_size\": 2,\n \"activation_fn\": \"gelu\",\n \"num_embeds_ada_norm\": 1000,\n \"norm_type\": \"layer_norm\",\n \"block_type\": \"unidiffuser\",\n \"pre_layer_norm\": False,\n \"use_timestep_embedding\": False,\n \"norm_elementwise_affine\": True,\n \"use_patch_pos_embed\": False,\n \"ff_final_dropout\": True,\n \"use_data_type_embedding\": False,\n }\n return unet_config\n\n\ndef create_text_decoder_config_test():\n text_decoder_config = {\n \"prefix_length\": 77,\n \"prefix_inner_dim\": 32,\n \"prefix_hidden_dim\": 32,\n \"vocab_size\": 1025, # 1024 + 1 for new EOS token\n \"n_positions\": 1024,\n \"n_embd\": 32,\n \"n_layer\": 5,\n \"n_head\": 4,\n \"n_inner\": 37,\n \"activation_function\": \"gelu\",\n \"resid_pdrop\": 0.1,\n \"embd_pdrop\": 0.1,\n \"attn_pdrop\": 0.1,\n \"layer_norm_epsilon\": 1e-5,\n \"initializer_range\": 0.02,\n }\n return text_decoder_config\n\n\n# Hardcoded configs for the UniDiffuser V1 model at https://huggingface.co/thu-ml/unidiffuser-v1\n# See also https://github.com/thu-ml/unidiffuser/blob/main/configs/sample_unidiffuser_v1.py\ndef create_vae_diffusers_config_big():\n vae_config = {\n \"sample_size\": 256,\n \"in_channels\": 3,\n \"out_channels\": 3,\n \"down_block_types\": [\"DownEncoderBlock2D\", \"DownEncoderBlock2D\", \"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n \"up_block_types\": [\"UpDecoderBlock2D\", \"UpDecoderBlock2D\", \"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n \"block_out_channels\": [128, 256, 512, 512],\n \"latent_channels\": 4,\n \"layers_per_block\": 2,\n }\n return vae_config\n\n\ndef create_unidiffuser_unet_config_big():\n unet_config = {\n \"text_dim\": 64,\n \"clip_img_dim\": 512,\n \"num_text_tokens\": 77,\n \"num_attention_heads\": 24,\n \"attention_head_dim\": 64,\n \"in_channels\": 4,\n \"out_channels\": 4,\n \"num_layers\": 30,\n \"dropout\": 0.0,\n \"norm_num_groups\": 32,\n \"attention_bias\": False,\n \"sample_size\": 64,\n \"patch_size\": 2,\n \"activation_fn\": \"gelu\",\n \"num_embeds_ada_norm\": 1000,\n \"norm_type\": \"layer_norm\",\n \"block_type\": \"unidiffuser\",\n \"pre_layer_norm\": False,\n \"use_timestep_embedding\": False,\n \"norm_elementwise_affine\": True,\n \"use_patch_pos_embed\": False,\n \"ff_final_dropout\": True,\n \"use_data_type_embedding\": False,\n }\n return unet_config\n\n\n# From https://huggingface.co/gpt2/blob/main/config.json, the GPT2 checkpoint used by UniDiffuser\ndef create_text_decoder_config_big():\n text_decoder_config = {\n \"prefix_length\": 77,\n \"prefix_inner_dim\": 768,\n \"prefix_hidden_dim\": 64,\n \"vocab_size\": 50258, # 50257 + 1 for new EOS token\n \"n_positions\": 1024,\n \"n_embd\": 768,\n \"n_layer\": 12,\n \"n_head\": 12,\n \"n_inner\": 3072,\n \"activation_function\": \"gelu\",\n \"resid_pdrop\": 0.1,\n \"embd_pdrop\": 0.1,\n \"attn_pdrop\": 0.1,\n \"layer_norm_epsilon\": 1e-5,\n \"initializer_range\": 0.02,\n }\n return text_decoder_config\n\n\n# Based on diffusers.pipelines.stable_diffusion.convert_from_ckpt.convert_ldm_vae_checkpoint\ndef convert_vae_to_diffusers(ckpt, diffusers_model, num_head_channels=1):\n \"\"\"\n Converts a UniDiffuser autoencoder_kl.pth checkpoint to a diffusers AutoencoderKL.\n \"\"\"\n # autoencoder_kl.pth ckpt is a torch state dict\n vae_state_dict = torch.load(ckpt, map_location=\"cpu\")\n\n new_checkpoint = {}\n\n new_checkpoint[\"encoder.conv_in.weight\"] = vae_state_dict[\"encoder.conv_in.weight\"]\n new_checkpoint[\"encoder.conv_in.bias\"] = vae_state_dict[\"encoder.conv_in.bias\"]\n new_checkpoint[\"encoder.conv_out.weight\"] = vae_state_dict[\"encoder.conv_out.weight\"]\n new_checkpoint[\"encoder.conv_out.bias\"] = vae_state_dict[\"encoder.conv_out.bias\"]\n new_checkpoint[\"encoder.conv_norm_out.weight\"] = vae_state_dict[\"encoder.norm_out.weight\"]\n new_checkpoint[\"encoder.conv_norm_out.bias\"] = vae_state_dict[\"encoder.norm_out.bias\"]\n\n new_checkpoint[\"decoder.conv_in.weight\"] = vae_state_dict[\"decoder.conv_in.weight\"]\n new_checkpoint[\"decoder.conv_in.bias\"] = vae_state_dict[\"decoder.conv_in.bias\"]\n new_checkpoint[\"decoder.conv_out.weight\"] = vae_state_dict[\"decoder.conv_out.weight\"]\n new_checkpoint[\"decoder.conv_out.bias\"] = vae_state_dict[\"decoder.conv_out.bias\"]\n new_checkpoint[\"decoder.conv_norm_out.weight\"] = vae_state_dict[\"decoder.norm_out.weight\"]\n new_checkpoint[\"decoder.conv_norm_out.bias\"] = vae_state_dict[\"decoder.norm_out.bias\"]\n\n new_checkpoint[\"quant_conv.weight\"] = vae_state_dict[\"quant_conv.weight\"]\n new_checkpoint[\"quant_conv.bias\"] = vae_state_dict[\"quant_conv.bias\"]\n new_checkpoint[\"post_quant_conv.weight\"] = vae_state_dict[\"post_quant_conv.weight\"]\n new_checkpoint[\"post_quant_conv.bias\"] = vae_state_dict[\"post_quant_conv.bias\"]\n\n # Retrieves the keys for the encoder down blocks only\n num_down_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in vae_state_dict if \"encoder.down\" in layer})\n down_blocks = {\n layer_id: [key for key in vae_state_dict if f\"down.{layer_id}\" in key] for layer_id in range(num_down_blocks)\n }\n\n # Retrieves the keys for the decoder up blocks only\n num_up_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in vae_state_dict if \"decoder.up\" in layer})\n up_blocks = {\n layer_id: [key for key in vae_state_dict if f\"up.{layer_id}\" in key] for layer_id in range(num_up_blocks)\n }\n\n for i in range(num_down_blocks):\n resnets = [key for key in down_blocks[i] if f\"down.{i}\" in key and f\"down.{i}.downsample\" not in key]\n\n if f\"encoder.down.{i}.downsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.weight\"] = vae_state_dict.pop(\n f\"encoder.down.{i}.downsample.conv.weight\"\n )\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.bias\"] = vae_state_dict.pop(\n f\"encoder.down.{i}.downsample.conv.bias\"\n )\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"down.{i}.block\", \"new\": f\"down_blocks.{i}.resnets\"}\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n vae_state_dict,\n additional_replacements=[meta_path],\n num_head_channels=num_head_channels, # not used in vae\n )\n\n mid_resnets = [key for key in vae_state_dict if \"encoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"encoder.mid.block_{i}\" in key]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"}\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n vae_state_dict,\n additional_replacements=[meta_path],\n num_head_channels=num_head_channels, # not used in vae\n )\n\n mid_attentions = [key for key in vae_state_dict if \"encoder.mid.attn\" in key]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n vae_state_dict,\n additional_replacements=[meta_path],\n num_head_channels=num_head_channels, # not used in vae\n )\n conv_attn_to_linear(new_checkpoint)\n\n for i in range(num_up_blocks):\n block_id = num_up_blocks - 1 - i\n resnets = [\n key for key in up_blocks[block_id] if f\"up.{block_id}\" in key and f\"up.{block_id}.upsample\" not in key\n ]\n\n if f\"decoder.up.{block_id}.upsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.weight\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.weight\"\n ]\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.bias\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.bias\"\n ]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"up.{block_id}.block\", \"new\": f\"up_blocks.{i}.resnets\"}\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n vae_state_dict,\n additional_replacements=[meta_path],\n num_head_channels=num_head_channels, # not used in vae\n )\n\n mid_resnets = [key for key in vae_state_dict if \"decoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"decoder.mid.block_{i}\" in key]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"}\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n vae_state_dict,\n additional_replacements=[meta_path],\n num_head_channels=num_head_channels, # not used in vae\n )\n\n mid_attentions = [key for key in vae_state_dict if \"decoder.mid.attn\" in key]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(\n paths,\n new_checkpoint,\n vae_state_dict,\n additional_replacements=[meta_path],\n num_head_channels=num_head_channels, # not used in vae\n )\n conv_attn_to_linear(new_checkpoint)\n\n missing_keys, unexpected_keys = diffusers_model.load_state_dict(new_checkpoint)\n for missing_key in missing_keys:\n print(f\"Missing key: {missing_key}\")\n for unexpected_key in unexpected_keys:\n print(f\"Unexpected key: {unexpected_key}\")\n\n return diffusers_model\n\n\ndef convert_uvit_block_to_diffusers_block(\n uvit_state_dict,\n new_state_dict,\n block_prefix,\n new_prefix=\"transformer.transformer_\",\n skip_connection=False,\n):\n \"\"\"\n Maps the keys in a UniDiffuser transformer block (`Block`) to the keys in a diffusers transformer block\n (`UTransformerBlock`/`UniDiffuserBlock`).\n \"\"\"\n prefix = new_prefix + block_prefix\n if skip_connection:\n new_state_dict[prefix + \".skip.skip_linear.weight\"] = uvit_state_dict[block_prefix + \".skip_linear.weight\"]\n new_state_dict[prefix + \".skip.skip_linear.bias\"] = uvit_state_dict[block_prefix + \".skip_linear.bias\"]\n new_state_dict[prefix + \".skip.norm.weight\"] = uvit_state_dict[block_prefix + \".norm1.weight\"]\n new_state_dict[prefix + \".skip.norm.bias\"] = uvit_state_dict[block_prefix + \".norm1.bias\"]\n\n # Create the prefix string for out_blocks.\n prefix += \".block\"\n\n # Split up attention qkv.weight into to_q.weight, to_k.weight, to_v.weight\n qkv = uvit_state_dict[block_prefix + \".attn.qkv.weight\"]\n new_attn_keys = [\".attn1.to_q.weight\", \".attn1.to_k.weight\", \".attn1.to_v.weight\"]\n new_attn_keys = [prefix + key for key in new_attn_keys]\n shape = qkv.shape[0] // len(new_attn_keys)\n for i, attn_key in enumerate(new_attn_keys):\n new_state_dict[attn_key] = qkv[i * shape : (i + 1) * shape]\n\n new_state_dict[prefix + \".attn1.to_out.0.weight\"] = uvit_state_dict[block_prefix + \".attn.proj.weight\"]\n new_state_dict[prefix + \".attn1.to_out.0.bias\"] = uvit_state_dict[block_prefix + \".attn.proj.bias\"]\n new_state_dict[prefix + \".norm1.weight\"] = uvit_state_dict[block_prefix + \".norm2.weight\"]\n new_state_dict[prefix + \".norm1.bias\"] = uvit_state_dict[block_prefix + \".norm2.bias\"]\n new_state_dict[prefix + \".ff.net.0.proj.weight\"] = uvit_state_dict[block_prefix + \".mlp.fc1.weight\"]\n new_state_dict[prefix + \".ff.net.0.proj.bias\"] = uvit_state_dict[block_prefix + \".mlp.fc1.bias\"]\n new_state_dict[prefix + \".ff.net.2.weight\"] = uvit_state_dict[block_prefix + \".mlp.fc2.weight\"]\n new_state_dict[prefix + \".ff.net.2.bias\"] = uvit_state_dict[block_prefix + \".mlp.fc2.bias\"]\n new_state_dict[prefix + \".norm3.weight\"] = uvit_state_dict[block_prefix + \".norm3.weight\"]\n new_state_dict[prefix + \".norm3.bias\"] = uvit_state_dict[block_prefix + \".norm3.bias\"]\n\n return uvit_state_dict, new_state_dict\n\n\ndef convert_uvit_to_diffusers(ckpt, diffusers_model):\n \"\"\"\n Converts a UniDiffuser uvit_v*.pth checkpoint to a diffusers UniDiffusersModel.\n \"\"\"\n # uvit_v*.pth ckpt is a torch state dict\n uvit_state_dict = torch.load(ckpt, map_location=\"cpu\")\n\n new_state_dict = {}\n\n # Input layers\n new_state_dict[\"vae_img_in.proj.weight\"] = uvit_state_dict[\"patch_embed.proj.weight\"]\n new_state_dict[\"vae_img_in.proj.bias\"] = uvit_state_dict[\"patch_embed.proj.bias\"]\n new_state_dict[\"clip_img_in.weight\"] = uvit_state_dict[\"clip_img_embed.weight\"]\n new_state_dict[\"clip_img_in.bias\"] = uvit_state_dict[\"clip_img_embed.bias\"]\n new_state_dict[\"text_in.weight\"] = uvit_state_dict[\"text_embed.weight\"]\n new_state_dict[\"text_in.bias\"] = uvit_state_dict[\"text_embed.bias\"]\n\n new_state_dict[\"pos_embed\"] = uvit_state_dict[\"pos_embed\"]\n\n # Handle data type token embeddings for UniDiffuser-v1\n if \"token_embedding.weight\" in uvit_state_dict and diffusers_model.use_data_type_embedding:\n new_state_dict[\"data_type_pos_embed_token\"] = uvit_state_dict[\"pos_embed_token\"]\n new_state_dict[\"data_type_token_embedding.weight\"] = uvit_state_dict[\"token_embedding.weight\"]\n\n # Also initialize the PatchEmbedding in UTransformer2DModel with the PatchEmbedding from the checkpoint.\n # This isn't used in the current implementation, so might want to remove.\n new_state_dict[\"transformer.pos_embed.proj.weight\"] = uvit_state_dict[\"patch_embed.proj.weight\"]\n new_state_dict[\"transformer.pos_embed.proj.bias\"] = uvit_state_dict[\"patch_embed.proj.bias\"]\n\n # Output layers\n new_state_dict[\"transformer.norm_out.weight\"] = uvit_state_dict[\"norm.weight\"]\n new_state_dict[\"transformer.norm_out.bias\"] = uvit_state_dict[\"norm.bias\"]\n\n new_state_dict[\"vae_img_out.weight\"] = uvit_state_dict[\"decoder_pred.weight\"]\n new_state_dict[\"vae_img_out.bias\"] = uvit_state_dict[\"decoder_pred.bias\"]\n new_state_dict[\"clip_img_out.weight\"] = uvit_state_dict[\"clip_img_out.weight\"]\n new_state_dict[\"clip_img_out.bias\"] = uvit_state_dict[\"clip_img_out.bias\"]\n new_state_dict[\"text_out.weight\"] = uvit_state_dict[\"text_out.weight\"]\n new_state_dict[\"text_out.bias\"] = uvit_state_dict[\"text_out.bias\"]\n\n # in_blocks\n in_blocks_prefixes = {\".\".join(layer.split(\".\")[:2]) for layer in uvit_state_dict if \"in_blocks\" in layer}\n for in_block_prefix in list(in_blocks_prefixes):\n convert_uvit_block_to_diffusers_block(uvit_state_dict, new_state_dict, in_block_prefix)\n\n # mid_block\n # Assume there's only one mid block\n convert_uvit_block_to_diffusers_block(uvit_state_dict, new_state_dict, \"mid_block\")\n\n # out_blocks\n out_blocks_prefixes = {\".\".join(layer.split(\".\")[:2]) for layer in uvit_state_dict if \"out_blocks\" in layer}\n for out_block_prefix in list(out_blocks_prefixes):\n convert_uvit_block_to_diffusers_block(uvit_state_dict, new_state_dict, out_block_prefix, skip_connection=True)\n\n missing_keys, unexpected_keys = diffusers_model.load_state_dict(new_state_dict)\n for missing_key in missing_keys:\n print(f\"Missing key: {missing_key}\")\n for unexpected_key in unexpected_keys:\n print(f\"Unexpected key: {unexpected_key}\")\n\n return diffusers_model\n\n\ndef convert_caption_decoder_to_diffusers(ckpt, diffusers_model):\n \"\"\"\n Converts a UniDiffuser caption_decoder.pth checkpoint to a diffusers UniDiffuserTextDecoder.\n \"\"\"\n # caption_decoder.pth ckpt is a torch state dict\n checkpoint_state_dict = torch.load(ckpt, map_location=\"cpu\")\n decoder_state_dict = {}\n # Remove the \"module.\" prefix, if necessary\n caption_decoder_key = \"module.\"\n for key in checkpoint_state_dict:\n if key.startswith(caption_decoder_key):\n decoder_state_dict[key.replace(caption_decoder_key, \"\")] = checkpoint_state_dict.get(key)\n else:\n decoder_state_dict[key] = checkpoint_state_dict.get(key)\n\n new_state_dict = {}\n\n # Encoder and Decoder\n new_state_dict[\"encode_prefix.weight\"] = decoder_state_dict[\"encode_prefix.weight\"]\n new_state_dict[\"encode_prefix.bias\"] = decoder_state_dict[\"encode_prefix.bias\"]\n new_state_dict[\"decode_prefix.weight\"] = decoder_state_dict[\"decode_prefix.weight\"]\n new_state_dict[\"decode_prefix.bias\"] = decoder_state_dict[\"decode_prefix.bias\"]\n\n # Internal GPT2LMHeadModel transformer model\n for key, val in decoder_state_dict.items():\n if key.startswith(\"gpt\"):\n suffix = key[len(\"gpt\") :]\n new_state_dict[\"transformer\" + suffix] = val\n\n missing_keys, unexpected_keys = diffusers_model.load_state_dict(new_state_dict)\n for missing_key in missing_keys:\n print(f\"Missing key: {missing_key}\")\n for unexpected_key in unexpected_keys:\n print(f\"Unexpected key: {unexpected_key}\")\n\n return diffusers_model\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--caption_decoder_checkpoint_path\",\n default=None,\n type=str,\n required=False,\n help=\"Path to caption decoder checkpoint to convert.\",\n )\n parser.add_argument(\n \"--uvit_checkpoint_path\", default=None, type=str, required=False, help=\"Path to U-ViT checkpoint to convert.\"\n )\n parser.add_argument(\n \"--vae_checkpoint_path\",\n default=None,\n type=str,\n required=False,\n help=\"Path to VAE checkpoint to convert.\",\n )\n parser.add_argument(\n \"--pipeline_output_path\",\n default=None,\n type=str,\n required=True,\n help=\"Path to save the output pipeline to.\",\n )\n parser.add_argument(\n \"--config_type\",\n default=\"test\",\n type=str,\n help=(\n \"Config type to use. Should be 'test' to create small models for testing or 'big' to convert a full\"\n \" checkpoint.\"\n ),\n )\n parser.add_argument(\n \"--version\",\n default=0,\n type=int,\n help=\"The UniDiffuser model type to convert to. Should be 0 for UniDiffuser-v0 and 1 for UniDiffuser-v1.\",\n )\n parser.add_argument(\n \"--safe_serialization\",\n action=\"store_true\",\n help=\"Whether to use safetensors/safe seialization when saving the pipeline.\",\n )\n\n args = parser.parse_args()\n\n # Convert the VAE model.\n if args.vae_checkpoint_path is not None:\n vae_config = create_vae_diffusers_config(args.config_type)\n vae = AutoencoderKL(**vae_config)\n vae = convert_vae_to_diffusers(args.vae_checkpoint_path, vae)\n\n # Convert the U-ViT (\"unet\") model.\n if args.uvit_checkpoint_path is not None:\n unet_config = create_unidiffuser_unet_config(args.config_type, args.version)\n unet = UniDiffuserModel(**unet_config)\n unet = convert_uvit_to_diffusers(args.uvit_checkpoint_path, unet)\n\n # Convert the caption decoder (\"text_decoder\") model.\n if args.caption_decoder_checkpoint_path is not None:\n text_decoder_config = create_text_decoder_config(args.config_type)\n text_decoder = UniDiffuserTextDecoder(**text_decoder_config)\n text_decoder = convert_caption_decoder_to_diffusers(args.caption_decoder_checkpoint_path, text_decoder)\n\n # Scheduler is the same for both the test and big models.\n scheduler_config = SCHEDULER_CONFIG\n scheduler = DPMSolverMultistepScheduler(\n beta_start=scheduler_config.beta_start,\n beta_end=scheduler_config.beta_end,\n beta_schedule=scheduler_config.beta_schedule,\n solver_order=scheduler_config.solver_order,\n )\n\n if args.config_type == \"test\":\n # Make a small random CLIPTextModel\n torch.manual_seed(0)\n clip_text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(clip_text_encoder_config)\n clip_tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n # Make a small random CLIPVisionModel and accompanying CLIPImageProcessor\n torch.manual_seed(0)\n clip_image_encoder_config = CLIPVisionConfig(\n image_size=32,\n patch_size=2,\n num_channels=3,\n hidden_size=32,\n projection_dim=32,\n num_hidden_layers=5,\n num_attention_heads=4,\n intermediate_size=37,\n dropout=0.1,\n attention_dropout=0.1,\n initializer_range=0.02,\n )\n image_encoder = CLIPVisionModelWithProjection(clip_image_encoder_config)\n image_processor = CLIPImageProcessor(crop_size=32, size=32)\n\n # Note that the text_decoder should already have its token embeddings resized.\n text_tokenizer = GPT2Tokenizer.from_pretrained(\"hf-internal-testing/tiny-random-GPT2Model\")\n eos = \"<|EOS|>\"\n special_tokens_dict = {\"eos_token\": eos}\n text_tokenizer.add_special_tokens(special_tokens_dict)\n elif args.config_type == \"big\":\n text_encoder = CLIPTextModel.from_pretrained(\"openai/clip-vit-large-patch14\")\n clip_tokenizer = CLIPTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\")\n\n image_encoder = CLIPVisionModelWithProjection.from_pretrained(\"openai/clip-vit-base-patch32\")\n image_processor = CLIPImageProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")\n\n # Note that the text_decoder should already have its token embeddings resized.\n text_tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n eos = \"<|EOS|>\"\n special_tokens_dict = {\"eos_token\": eos}\n text_tokenizer.add_special_tokens(special_tokens_dict)\n else:\n raise NotImplementedError(\n f\"Config type {args.config_type} is not implemented, currently only config types\"\n \" 'test' and 'big' are available.\"\n )\n\n pipeline = UniDiffuserPipeline(\n vae=vae,\n text_encoder=text_encoder,\n image_encoder=image_encoder,\n clip_image_processor=image_processor,\n clip_tokenizer=clip_tokenizer,\n text_decoder=text_decoder,\n text_tokenizer=text_tokenizer,\n unet=unet,\n scheduler=scheduler,\n )\n pipeline.save_pretrained(args.pipeline_output_path, safe_serialization=args.safe_serialization)\n" }, { "path": "diffusers/scripts/convert_vae_diff_to_onnx.py", "content": "# Copyright 2022 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport argparse\nfrom pathlib import Path\n\nimport torch\nfrom packaging import version\nfrom torch.onnx import export\n\nfrom diffusers import AutoencoderKL\n\n\nis_torch_less_than_1_11 = version.parse(version.parse(torch.__version__).base_version) < version.parse(\"1.11\")\n\n\ndef onnx_export(\n model,\n model_args: tuple,\n output_path: Path,\n ordered_input_names,\n output_names,\n dynamic_axes,\n opset,\n use_external_data_format=False,\n):\n output_path.parent.mkdir(parents=True, exist_ok=True)\n # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,\n # so we check the torch version for backwards compatibility\n if is_torch_less_than_1_11:\n export(\n model,\n model_args,\n f=output_path.as_posix(),\n input_names=ordered_input_names,\n output_names=output_names,\n dynamic_axes=dynamic_axes,\n do_constant_folding=True,\n use_external_data_format=use_external_data_format,\n enable_onnx_checker=True,\n opset_version=opset,\n )\n else:\n export(\n model,\n model_args,\n f=output_path.as_posix(),\n input_names=ordered_input_names,\n output_names=output_names,\n dynamic_axes=dynamic_axes,\n do_constant_folding=True,\n opset_version=opset,\n )\n\n\n@torch.no_grad()\ndef convert_models(model_path: str, output_path: str, opset: int, fp16: bool = False):\n dtype = torch.float16 if fp16 else torch.float32\n if fp16 and torch.cuda.is_available():\n device = \"cuda\"\n elif fp16 and not torch.cuda.is_available():\n raise ValueError(\"`float16` model export is only supported on GPUs with CUDA\")\n else:\n device = \"cpu\"\n output_path = Path(output_path)\n\n # VAE DECODER\n vae_decoder = AutoencoderKL.from_pretrained(model_path + \"/vae\")\n vae_latent_channels = vae_decoder.config.latent_channels\n # forward only through the decoder part\n vae_decoder.forward = vae_decoder.decode\n onnx_export(\n vae_decoder,\n model_args=(\n torch.randn(1, vae_latent_channels, 25, 25).to(device=device, dtype=dtype),\n False,\n ),\n output_path=output_path / \"vae_decoder\" / \"model.onnx\",\n ordered_input_names=[\"latent_sample\", \"return_dict\"],\n output_names=[\"sample\"],\n dynamic_axes={\n \"latent_sample\": {0: \"batch\", 1: \"channels\", 2: \"height\", 3: \"width\"},\n },\n opset=opset,\n )\n del vae_decoder\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--model_path\",\n type=str,\n required=True,\n help=\"Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).\",\n )\n\n parser.add_argument(\"--output_path\", type=str, required=True, help=\"Path to the output model.\")\n parser.add_argument(\n \"--opset\",\n default=14,\n type=int,\n help=\"The version of the ONNX operator set to use.\",\n )\n parser.add_argument(\"--fp16\", action=\"store_true\", default=False, help=\"Export the models in `float16` mode\")\n\n args = parser.parse_args()\n print(args.output_path)\n convert_models(args.model_path, args.output_path, args.opset, args.fp16)\n print(\"SD: Done: ONNX\")\n" }, { "path": "diffusers/scripts/convert_vae_pt_to_diffusers.py", "content": "import argparse\nimport io\n\nimport requests\nimport torch\nimport yaml\n\nfrom diffusers import AutoencoderKL\nfrom diffusers.pipelines.stable_diffusion.convert_from_ckpt import (\n assign_to_checkpoint,\n conv_attn_to_linear,\n create_vae_diffusers_config,\n renew_vae_attention_paths,\n renew_vae_resnet_paths,\n)\n\n\ndef custom_convert_ldm_vae_checkpoint(checkpoint, config):\n vae_state_dict = checkpoint\n\n new_checkpoint = {}\n\n new_checkpoint[\"encoder.conv_in.weight\"] = vae_state_dict[\"encoder.conv_in.weight\"]\n new_checkpoint[\"encoder.conv_in.bias\"] = vae_state_dict[\"encoder.conv_in.bias\"]\n new_checkpoint[\"encoder.conv_out.weight\"] = vae_state_dict[\"encoder.conv_out.weight\"]\n new_checkpoint[\"encoder.conv_out.bias\"] = vae_state_dict[\"encoder.conv_out.bias\"]\n new_checkpoint[\"encoder.conv_norm_out.weight\"] = vae_state_dict[\"encoder.norm_out.weight\"]\n new_checkpoint[\"encoder.conv_norm_out.bias\"] = vae_state_dict[\"encoder.norm_out.bias\"]\n\n new_checkpoint[\"decoder.conv_in.weight\"] = vae_state_dict[\"decoder.conv_in.weight\"]\n new_checkpoint[\"decoder.conv_in.bias\"] = vae_state_dict[\"decoder.conv_in.bias\"]\n new_checkpoint[\"decoder.conv_out.weight\"] = vae_state_dict[\"decoder.conv_out.weight\"]\n new_checkpoint[\"decoder.conv_out.bias\"] = vae_state_dict[\"decoder.conv_out.bias\"]\n new_checkpoint[\"decoder.conv_norm_out.weight\"] = vae_state_dict[\"decoder.norm_out.weight\"]\n new_checkpoint[\"decoder.conv_norm_out.bias\"] = vae_state_dict[\"decoder.norm_out.bias\"]\n\n new_checkpoint[\"quant_conv.weight\"] = vae_state_dict[\"quant_conv.weight\"]\n new_checkpoint[\"quant_conv.bias\"] = vae_state_dict[\"quant_conv.bias\"]\n new_checkpoint[\"post_quant_conv.weight\"] = vae_state_dict[\"post_quant_conv.weight\"]\n new_checkpoint[\"post_quant_conv.bias\"] = vae_state_dict[\"post_quant_conv.bias\"]\n\n # Retrieves the keys for the encoder down blocks only\n num_down_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in vae_state_dict if \"encoder.down\" in layer})\n down_blocks = {\n layer_id: [key for key in vae_state_dict if f\"down.{layer_id}\" in key] for layer_id in range(num_down_blocks)\n }\n\n # Retrieves the keys for the decoder up blocks only\n num_up_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in vae_state_dict if \"decoder.up\" in layer})\n up_blocks = {\n layer_id: [key for key in vae_state_dict if f\"up.{layer_id}\" in key] for layer_id in range(num_up_blocks)\n }\n\n for i in range(num_down_blocks):\n resnets = [key for key in down_blocks[i] if f\"down.{i}\" in key and f\"down.{i}.downsample\" not in key]\n\n if f\"encoder.down.{i}.downsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.weight\"] = vae_state_dict.pop(\n f\"encoder.down.{i}.downsample.conv.weight\"\n )\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.bias\"] = vae_state_dict.pop(\n f\"encoder.down.{i}.downsample.conv.bias\"\n )\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"down.{i}.block\", \"new\": f\"down_blocks.{i}.resnets\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_resnets = [key for key in vae_state_dict if \"encoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"encoder.mid.block_{i}\" in key]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_attentions = [key for key in vae_state_dict if \"encoder.mid.attn\" in key]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n conv_attn_to_linear(new_checkpoint)\n\n for i in range(num_up_blocks):\n block_id = num_up_blocks - 1 - i\n resnets = [\n key for key in up_blocks[block_id] if f\"up.{block_id}\" in key and f\"up.{block_id}.upsample\" not in key\n ]\n\n if f\"decoder.up.{block_id}.upsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.weight\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.weight\"\n ]\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.bias\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.bias\"\n ]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"up.{block_id}.block\", \"new\": f\"up_blocks.{i}.resnets\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_resnets = [key for key in vae_state_dict if \"decoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"decoder.mid.block_{i}\" in key]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_attentions = [key for key in vae_state_dict if \"decoder.mid.attn\" in key]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n conv_attn_to_linear(new_checkpoint)\n return new_checkpoint\n\n\ndef vae_pt_to_vae_diffuser(\n checkpoint_path: str,\n output_path: str,\n):\n # Only support V1\n r = requests.get(\n \" https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml\"\n )\n io_obj = io.BytesIO(r.content)\n\n original_config = yaml.safe_load(io_obj)\n image_size = 512\n device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n if checkpoint_path.endswith(\"safetensors\"):\n from safetensors import safe_open\n\n checkpoint = {}\n with safe_open(checkpoint_path, framework=\"pt\", device=\"cpu\") as f:\n for key in f.keys():\n checkpoint[key] = f.get_tensor(key)\n else:\n checkpoint = torch.load(checkpoint_path, map_location=device)[\"state_dict\"]\n\n # Convert the VAE model.\n vae_config = create_vae_diffusers_config(original_config, image_size=image_size)\n converted_vae_checkpoint = custom_convert_ldm_vae_checkpoint(checkpoint, vae_config)\n\n vae = AutoencoderKL(**vae_config)\n vae.load_state_dict(converted_vae_checkpoint)\n vae.save_pretrained(output_path)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"--vae_pt_path\", default=None, type=str, required=True, help=\"Path to the VAE.pt to convert.\")\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the VAE.pt to convert.\")\n\n args = parser.parse_args()\n\n vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)\n" }, { "path": "diffusers/scripts/convert_versatile_diffusion_to_diffusers.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Conversion script for the Versatile Stable Diffusion checkpoints.\"\"\"\n\nimport argparse\nfrom argparse import Namespace\n\nimport torch\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n UNet2DConditionModel,\n VersatileDiffusionPipeline,\n)\nfrom diffusers.pipelines.versatile_diffusion.modeling_text_unet import UNetFlatConditionModel\n\n\nSCHEDULER_CONFIG = Namespace(\n **{\n \"beta_linear_start\": 0.00085,\n \"beta_linear_end\": 0.012,\n \"timesteps\": 1000,\n \"scale_factor\": 0.18215,\n }\n)\n\nIMAGE_UNET_CONFIG = Namespace(\n **{\n \"input_channels\": 4,\n \"model_channels\": 320,\n \"output_channels\": 4,\n \"num_noattn_blocks\": [2, 2, 2, 2],\n \"channel_mult\": [1, 2, 4, 4],\n \"with_attn\": [True, True, True, False],\n \"num_heads\": 8,\n \"context_dim\": 768,\n \"use_checkpoint\": True,\n }\n)\n\nTEXT_UNET_CONFIG = Namespace(\n **{\n \"input_channels\": 768,\n \"model_channels\": 320,\n \"output_channels\": 768,\n \"num_noattn_blocks\": [2, 2, 2, 2],\n \"channel_mult\": [1, 2, 4, 4],\n \"second_dim\": [4, 4, 4, 4],\n \"with_attn\": [True, True, True, False],\n \"num_heads\": 8,\n \"context_dim\": 768,\n \"use_checkpoint\": True,\n }\n)\n\nAUTOENCODER_CONFIG = Namespace(\n **{\n \"double_z\": True,\n \"z_channels\": 4,\n \"resolution\": 256,\n \"in_channels\": 3,\n \"out_ch\": 3,\n \"ch\": 128,\n \"ch_mult\": [1, 2, 4, 4],\n \"num_res_blocks\": 2,\n \"attn_resolutions\": [],\n \"dropout\": 0.0,\n }\n)\n\n\ndef shave_segments(path, n_shave_prefix_segments=1):\n \"\"\"\n Removes segments. Positive values shave the first segments, negative shave the last segments.\n \"\"\"\n if n_shave_prefix_segments >= 0:\n return \".\".join(path.split(\".\")[n_shave_prefix_segments:])\n else:\n return \".\".join(path.split(\".\")[:n_shave_prefix_segments])\n\n\ndef renew_resnet_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside resnets to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item.replace(\"in_layers.0\", \"norm1\")\n new_item = new_item.replace(\"in_layers.2\", \"conv1\")\n\n new_item = new_item.replace(\"out_layers.0\", \"norm2\")\n new_item = new_item.replace(\"out_layers.3\", \"conv2\")\n\n new_item = new_item.replace(\"emb_layers.1\", \"time_emb_proj\")\n new_item = new_item.replace(\"skip_connection\", \"conv_shortcut\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside resnets to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n new_item = new_item.replace(\"nin_shortcut\", \"conv_shortcut\")\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef renew_attention_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside attentions to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n # new_item = new_item.replace('norm.weight', 'group_norm.weight')\n # new_item = new_item.replace('norm.bias', 'group_norm.bias')\n\n # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')\n # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')\n\n # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside attentions to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n new_item = new_item.replace(\"norm.weight\", \"group_norm.weight\")\n new_item = new_item.replace(\"norm.bias\", \"group_norm.bias\")\n\n new_item = new_item.replace(\"q.weight\", \"query.weight\")\n new_item = new_item.replace(\"q.bias\", \"query.bias\")\n\n new_item = new_item.replace(\"k.weight\", \"key.weight\")\n new_item = new_item.replace(\"k.bias\", \"key.bias\")\n\n new_item = new_item.replace(\"v.weight\", \"value.weight\")\n new_item = new_item.replace(\"v.bias\", \"value.bias\")\n\n new_item = new_item.replace(\"proj_out.weight\", \"proj_attn.weight\")\n new_item = new_item.replace(\"proj_out.bias\", \"proj_attn.bias\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef assign_to_checkpoint(\n paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None\n):\n \"\"\"\n This does the final conversion step: take locally converted weights and apply a global renaming\n to them. It splits attention layers, and takes into account additional replacements\n that may arise.\n\n Assigns the weights to the new checkpoint.\n \"\"\"\n assert isinstance(paths, list), \"Paths should be a list of dicts containing 'old' and 'new' keys.\"\n\n # Splits the attention layers into three variables.\n if attention_paths_to_split is not None:\n for path, path_map in attention_paths_to_split.items():\n old_tensor = old_checkpoint[path]\n channels = old_tensor.shape[0] // 3\n\n target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)\n\n num_heads = old_tensor.shape[0] // config[\"num_head_channels\"] // 3\n\n old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])\n query, key, value = old_tensor.split(channels // num_heads, dim=1)\n\n checkpoint[path_map[\"query\"]] = query.reshape(target_shape)\n checkpoint[path_map[\"key\"]] = key.reshape(target_shape)\n checkpoint[path_map[\"value\"]] = value.reshape(target_shape)\n\n for path in paths:\n new_path = path[\"new\"]\n\n # These have already been assigned\n if attention_paths_to_split is not None and new_path in attention_paths_to_split:\n continue\n\n # Global renaming happens here\n new_path = new_path.replace(\"middle_block.0\", \"mid_block.resnets.0\")\n new_path = new_path.replace(\"middle_block.1\", \"mid_block.attentions.0\")\n new_path = new_path.replace(\"middle_block.2\", \"mid_block.resnets.1\")\n\n if additional_replacements is not None:\n for replacement in additional_replacements:\n new_path = new_path.replace(replacement[\"old\"], replacement[\"new\"])\n\n # proj_attn.weight has to be converted from conv 1D to linear\n if \"proj_attn.weight\" in new_path:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]][:, :, 0]\n elif path[\"old\"] in old_checkpoint:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]]\n\n\ndef conv_attn_to_linear(checkpoint):\n keys = list(checkpoint.keys())\n attn_keys = [\"query.weight\", \"key.weight\", \"value.weight\"]\n for key in keys:\n if \".\".join(key.split(\".\")[-2:]) in attn_keys:\n if checkpoint[key].ndim > 2:\n checkpoint[key] = checkpoint[key][:, :, 0, 0]\n elif \"proj_attn.weight\" in key:\n if checkpoint[key].ndim > 2:\n checkpoint[key] = checkpoint[key][:, :, 0]\n\n\ndef create_image_unet_diffusers_config(unet_params):\n \"\"\"\n Creates a config for the diffusers based on the config of the VD model.\n \"\"\"\n\n block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]\n\n down_block_types = []\n resolution = 1\n for i in range(len(block_out_channels)):\n block_type = \"CrossAttnDownBlock2D\" if unet_params.with_attn[i] else \"DownBlock2D\"\n down_block_types.append(block_type)\n if i != len(block_out_channels) - 1:\n resolution *= 2\n\n up_block_types = []\n for i in range(len(block_out_channels)):\n block_type = \"CrossAttnUpBlock2D\" if unet_params.with_attn[-i - 1] else \"UpBlock2D\"\n up_block_types.append(block_type)\n resolution //= 2\n\n if not all(n == unet_params.num_noattn_blocks[0] for n in unet_params.num_noattn_blocks):\n raise ValueError(\"Not all num_res_blocks are equal, which is not supported in this script.\")\n\n config = {\n \"sample_size\": None,\n \"in_channels\": unet_params.input_channels,\n \"out_channels\": unet_params.output_channels,\n \"down_block_types\": tuple(down_block_types),\n \"up_block_types\": tuple(up_block_types),\n \"block_out_channels\": tuple(block_out_channels),\n \"layers_per_block\": unet_params.num_noattn_blocks[0],\n \"cross_attention_dim\": unet_params.context_dim,\n \"attention_head_dim\": unet_params.num_heads,\n }\n\n return config\n\n\ndef create_text_unet_diffusers_config(unet_params):\n \"\"\"\n Creates a config for the diffusers based on the config of the VD model.\n \"\"\"\n\n block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]\n\n down_block_types = []\n resolution = 1\n for i in range(len(block_out_channels)):\n block_type = \"CrossAttnDownBlockFlat\" if unet_params.with_attn[i] else \"DownBlockFlat\"\n down_block_types.append(block_type)\n if i != len(block_out_channels) - 1:\n resolution *= 2\n\n up_block_types = []\n for i in range(len(block_out_channels)):\n block_type = \"CrossAttnUpBlockFlat\" if unet_params.with_attn[-i - 1] else \"UpBlockFlat\"\n up_block_types.append(block_type)\n resolution //= 2\n\n if not all(n == unet_params.num_noattn_blocks[0] for n in unet_params.num_noattn_blocks):\n raise ValueError(\"Not all num_res_blocks are equal, which is not supported in this script.\")\n\n config = {\n \"sample_size\": None,\n \"in_channels\": (unet_params.input_channels, 1, 1),\n \"out_channels\": (unet_params.output_channels, 1, 1),\n \"down_block_types\": tuple(down_block_types),\n \"up_block_types\": tuple(up_block_types),\n \"block_out_channels\": tuple(block_out_channels),\n \"layers_per_block\": unet_params.num_noattn_blocks[0],\n \"cross_attention_dim\": unet_params.context_dim,\n \"attention_head_dim\": unet_params.num_heads,\n }\n\n return config\n\n\ndef create_vae_diffusers_config(vae_params):\n \"\"\"\n Creates a config for the diffusers based on the config of the VD model.\n \"\"\"\n\n block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult]\n down_block_types = [\"DownEncoderBlock2D\"] * len(block_out_channels)\n up_block_types = [\"UpDecoderBlock2D\"] * len(block_out_channels)\n\n config = {\n \"sample_size\": vae_params.resolution,\n \"in_channels\": vae_params.in_channels,\n \"out_channels\": vae_params.out_ch,\n \"down_block_types\": tuple(down_block_types),\n \"up_block_types\": tuple(up_block_types),\n \"block_out_channels\": tuple(block_out_channels),\n \"latent_channels\": vae_params.z_channels,\n \"layers_per_block\": vae_params.num_res_blocks,\n }\n return config\n\n\ndef create_diffusers_scheduler(original_config):\n schedular = DDIMScheduler(\n num_train_timesteps=original_config.model.params.timesteps,\n beta_start=original_config.model.params.linear_start,\n beta_end=original_config.model.params.linear_end,\n beta_schedule=\"scaled_linear\",\n )\n return schedular\n\n\ndef convert_vd_unet_checkpoint(checkpoint, config, unet_key, extract_ema=False):\n \"\"\"\n Takes a state dict and a config, and returns a converted checkpoint.\n \"\"\"\n\n # extract state_dict for UNet\n unet_state_dict = {}\n keys = list(checkpoint.keys())\n\n # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA\n if sum(k.startswith(\"model_ema\") for k in keys) > 100:\n print(\"Checkpoint has both EMA and non-EMA weights.\")\n if extract_ema:\n print(\n \"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA\"\n \" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.\"\n )\n for key in keys:\n if key.startswith(\"model.diffusion_model\"):\n flat_ema_key = \"model_ema.\" + \"\".join(key.split(\".\")[1:])\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint.pop(flat_ema_key)\n else:\n print(\n \"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA\"\n \" weights (usually better for inference), please make sure to add the `--extract_ema` flag.\"\n )\n\n for key in keys:\n if key.startswith(unet_key):\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint.pop(key)\n\n new_checkpoint = {}\n\n new_checkpoint[\"time_embedding.linear_1.weight\"] = checkpoint[\"model.diffusion_model.time_embed.0.weight\"]\n new_checkpoint[\"time_embedding.linear_1.bias\"] = checkpoint[\"model.diffusion_model.time_embed.0.bias\"]\n new_checkpoint[\"time_embedding.linear_2.weight\"] = checkpoint[\"model.diffusion_model.time_embed.2.weight\"]\n new_checkpoint[\"time_embedding.linear_2.bias\"] = checkpoint[\"model.diffusion_model.time_embed.2.bias\"]\n\n new_checkpoint[\"conv_in.weight\"] = unet_state_dict[\"input_blocks.0.0.weight\"]\n new_checkpoint[\"conv_in.bias\"] = unet_state_dict[\"input_blocks.0.0.bias\"]\n\n new_checkpoint[\"conv_norm_out.weight\"] = unet_state_dict[\"out.0.weight\"]\n new_checkpoint[\"conv_norm_out.bias\"] = unet_state_dict[\"out.0.bias\"]\n new_checkpoint[\"conv_out.weight\"] = unet_state_dict[\"out.2.weight\"]\n new_checkpoint[\"conv_out.bias\"] = unet_state_dict[\"out.2.bias\"]\n\n # Retrieves the keys for the input blocks only\n num_input_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"input_blocks\" in layer})\n input_blocks = {\n layer_id: [key for key in unet_state_dict if f\"input_blocks.{layer_id}\" in key]\n for layer_id in range(num_input_blocks)\n }\n\n # Retrieves the keys for the middle blocks only\n num_middle_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"middle_block\" in layer})\n middle_blocks = {\n layer_id: [key for key in unet_state_dict if f\"middle_block.{layer_id}\" in key]\n for layer_id in range(num_middle_blocks)\n }\n\n # Retrieves the keys for the output blocks only\n num_output_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"output_blocks\" in layer})\n output_blocks = {\n layer_id: [key for key in unet_state_dict if f\"output_blocks.{layer_id}\" in key]\n for layer_id in range(num_output_blocks)\n }\n\n for i in range(1, num_input_blocks):\n block_id = (i - 1) // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = (i - 1) % (config[\"layers_per_block\"] + 1)\n\n resnets = [\n key for key in input_blocks[i] if f\"input_blocks.{i}.0\" in key and f\"input_blocks.{i}.0.op\" not in key\n ]\n attentions = [key for key in input_blocks[i] if f\"input_blocks.{i}.1\" in key]\n\n if f\"input_blocks.{i}.0.op.weight\" in unet_state_dict:\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.weight\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.op.weight\"\n )\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.bias\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.op.bias\"\n )\n elif f\"input_blocks.{i}.0.weight\" in unet_state_dict:\n # text_unet uses linear layers in place of downsamplers\n shape = unet_state_dict[f\"input_blocks.{i}.0.weight\"].shape\n if shape[0] != shape[1]:\n continue\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.weight\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.weight\"\n )\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.bias\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.bias\"\n )\n\n paths = renew_resnet_paths(resnets)\n meta_path = {\"old\": f\"input_blocks.{i}.0\", \"new\": f\"down_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n meta_path = {\"old\": f\"input_blocks.{i}.1\", \"new\": f\"down_blocks.{block_id}.attentions.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n resnet_0 = middle_blocks[0]\n attentions = middle_blocks[1]\n resnet_1 = middle_blocks[2]\n\n resnet_0_paths = renew_resnet_paths(resnet_0)\n assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)\n\n resnet_1_paths = renew_resnet_paths(resnet_1)\n assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)\n\n attentions_paths = renew_attention_paths(attentions)\n meta_path = {\"old\": \"middle_block.1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(\n attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n for i in range(num_output_blocks):\n block_id = i // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = i % (config[\"layers_per_block\"] + 1)\n output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]\n output_block_list = {}\n\n for layer in output_block_layers:\n layer_id, layer_name = layer.split(\".\")[0], shave_segments(layer, 1)\n if layer_id in output_block_list:\n output_block_list[layer_id].append(layer_name)\n else:\n output_block_list[layer_id] = [layer_name]\n\n if len(output_block_list) > 1:\n resnets = [key for key in output_blocks[i] if f\"output_blocks.{i}.0\" in key]\n attentions = [key for key in output_blocks[i] if f\"output_blocks.{i}.1\" in key]\n\n paths = renew_resnet_paths(resnets)\n\n meta_path = {\"old\": f\"output_blocks.{i}.0\", \"new\": f\"up_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n if [\"conv.weight\", \"conv.bias\"] in output_block_list.values():\n index = list(output_block_list.values()).index([\"conv.weight\", \"conv.bias\"])\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.weight\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.weight\"\n ]\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.bias\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.bias\"\n ]\n # Clear attentions as they have been attributed above.\n if len(attentions) == 2:\n attentions = []\n elif f\"output_blocks.{i}.1.weight\" in unet_state_dict:\n # text_unet uses linear layers in place of upsamplers\n shape = unet_state_dict[f\"output_blocks.{i}.1.weight\"].shape\n if shape[0] != shape[1]:\n continue\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.weight\"] = unet_state_dict.pop(\n f\"output_blocks.{i}.1.weight\"\n )\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.bias\"] = unet_state_dict.pop(\n f\"output_blocks.{i}.1.bias\"\n )\n # Clear attentions as they have been attributed above.\n if len(attentions) == 2:\n attentions = []\n elif f\"output_blocks.{i}.2.weight\" in unet_state_dict:\n # text_unet uses linear layers in place of upsamplers\n shape = unet_state_dict[f\"output_blocks.{i}.2.weight\"].shape\n if shape[0] != shape[1]:\n continue\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.weight\"] = unet_state_dict.pop(\n f\"output_blocks.{i}.2.weight\"\n )\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.bias\"] = unet_state_dict.pop(\n f\"output_blocks.{i}.2.bias\"\n )\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n meta_path = {\n \"old\": f\"output_blocks.{i}.1\",\n \"new\": f\"up_blocks.{block_id}.attentions.{layer_in_block_id}\",\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n else:\n resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)\n for path in resnet_0_paths:\n old_path = \".\".join([\"output_blocks\", str(i), path[\"old\"]])\n new_path = \".\".join([\"up_blocks\", str(block_id), \"resnets\", str(layer_in_block_id), path[\"new\"]])\n\n new_checkpoint[new_path] = unet_state_dict[old_path]\n\n return new_checkpoint\n\n\ndef convert_vd_vae_checkpoint(checkpoint, config):\n # extract state dict for VAE\n vae_state_dict = {}\n keys = list(checkpoint.keys())\n for key in keys:\n vae_state_dict[key] = checkpoint.get(key)\n\n new_checkpoint = {}\n\n new_checkpoint[\"encoder.conv_in.weight\"] = vae_state_dict[\"encoder.conv_in.weight\"]\n new_checkpoint[\"encoder.conv_in.bias\"] = vae_state_dict[\"encoder.conv_in.bias\"]\n new_checkpoint[\"encoder.conv_out.weight\"] = vae_state_dict[\"encoder.conv_out.weight\"]\n new_checkpoint[\"encoder.conv_out.bias\"] = vae_state_dict[\"encoder.conv_out.bias\"]\n new_checkpoint[\"encoder.conv_norm_out.weight\"] = vae_state_dict[\"encoder.norm_out.weight\"]\n new_checkpoint[\"encoder.conv_norm_out.bias\"] = vae_state_dict[\"encoder.norm_out.bias\"]\n\n new_checkpoint[\"decoder.conv_in.weight\"] = vae_state_dict[\"decoder.conv_in.weight\"]\n new_checkpoint[\"decoder.conv_in.bias\"] = vae_state_dict[\"decoder.conv_in.bias\"]\n new_checkpoint[\"decoder.conv_out.weight\"] = vae_state_dict[\"decoder.conv_out.weight\"]\n new_checkpoint[\"decoder.conv_out.bias\"] = vae_state_dict[\"decoder.conv_out.bias\"]\n new_checkpoint[\"decoder.conv_norm_out.weight\"] = vae_state_dict[\"decoder.norm_out.weight\"]\n new_checkpoint[\"decoder.conv_norm_out.bias\"] = vae_state_dict[\"decoder.norm_out.bias\"]\n\n new_checkpoint[\"quant_conv.weight\"] = vae_state_dict[\"quant_conv.weight\"]\n new_checkpoint[\"quant_conv.bias\"] = vae_state_dict[\"quant_conv.bias\"]\n new_checkpoint[\"post_quant_conv.weight\"] = vae_state_dict[\"post_quant_conv.weight\"]\n new_checkpoint[\"post_quant_conv.bias\"] = vae_state_dict[\"post_quant_conv.bias\"]\n\n # Retrieves the keys for the encoder down blocks only\n num_down_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in vae_state_dict if \"encoder.down\" in layer})\n down_blocks = {\n layer_id: [key for key in vae_state_dict if f\"down.{layer_id}\" in key] for layer_id in range(num_down_blocks)\n }\n\n # Retrieves the keys for the decoder up blocks only\n num_up_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in vae_state_dict if \"decoder.up\" in layer})\n up_blocks = {\n layer_id: [key for key in vae_state_dict if f\"up.{layer_id}\" in key] for layer_id in range(num_up_blocks)\n }\n\n for i in range(num_down_blocks):\n resnets = [key for key in down_blocks[i] if f\"down.{i}\" in key and f\"down.{i}.downsample\" not in key]\n\n if f\"encoder.down.{i}.downsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.weight\"] = vae_state_dict.pop(\n f\"encoder.down.{i}.downsample.conv.weight\"\n )\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.bias\"] = vae_state_dict.pop(\n f\"encoder.down.{i}.downsample.conv.bias\"\n )\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"down.{i}.block\", \"new\": f\"down_blocks.{i}.resnets\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_resnets = [key for key in vae_state_dict if \"encoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"encoder.mid.block_{i}\" in key]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_attentions = [key for key in vae_state_dict if \"encoder.mid.attn\" in key]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n conv_attn_to_linear(new_checkpoint)\n\n for i in range(num_up_blocks):\n block_id = num_up_blocks - 1 - i\n resnets = [\n key for key in up_blocks[block_id] if f\"up.{block_id}\" in key and f\"up.{block_id}.upsample\" not in key\n ]\n\n if f\"decoder.up.{block_id}.upsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.weight\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.weight\"\n ]\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.bias\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.bias\"\n ]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"up.{block_id}.block\", \"new\": f\"up_blocks.{i}.resnets\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_resnets = [key for key in vae_state_dict if \"decoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"decoder.mid.block_{i}\" in key]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_attentions = [key for key in vae_state_dict if \"decoder.mid.attn\" in key]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n conv_attn_to_linear(new_checkpoint)\n return new_checkpoint\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--unet_checkpoint_path\", default=None, type=str, required=False, help=\"Path to the checkpoint to convert.\"\n )\n parser.add_argument(\n \"--vae_checkpoint_path\", default=None, type=str, required=False, help=\"Path to the checkpoint to convert.\"\n )\n parser.add_argument(\n \"--optimus_checkpoint_path\", default=None, type=str, required=False, help=\"Path to the checkpoint to convert.\"\n )\n parser.add_argument(\n \"--scheduler_type\",\n default=\"pndm\",\n type=str,\n help=\"Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']\",\n )\n parser.add_argument(\n \"--extract_ema\",\n action=\"store_true\",\n help=(\n \"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights\"\n \" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield\"\n \" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.\"\n ),\n )\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n\n args = parser.parse_args()\n\n scheduler_config = SCHEDULER_CONFIG\n\n num_train_timesteps = scheduler_config.timesteps\n beta_start = scheduler_config.beta_linear_start\n beta_end = scheduler_config.beta_linear_end\n if args.scheduler_type == \"pndm\":\n scheduler = PNDMScheduler(\n beta_end=beta_end,\n beta_schedule=\"scaled_linear\",\n beta_start=beta_start,\n num_train_timesteps=num_train_timesteps,\n skip_prk_steps=True,\n steps_offset=1,\n )\n elif args.scheduler_type == \"lms\":\n scheduler = LMSDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule=\"scaled_linear\")\n elif args.scheduler_type == \"euler\":\n scheduler = EulerDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule=\"scaled_linear\")\n elif args.scheduler_type == \"euler-ancestral\":\n scheduler = EulerAncestralDiscreteScheduler(\n beta_start=beta_start, beta_end=beta_end, beta_schedule=\"scaled_linear\"\n )\n elif args.scheduler_type == \"dpm\":\n scheduler = DPMSolverMultistepScheduler(\n beta_start=beta_start, beta_end=beta_end, beta_schedule=\"scaled_linear\"\n )\n elif args.scheduler_type == \"ddim\":\n scheduler = DDIMScheduler(\n beta_start=beta_start,\n beta_end=beta_end,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n steps_offset=1,\n )\n else:\n raise ValueError(f\"Scheduler of type {args.scheduler_type} doesn't exist!\")\n\n # Convert the UNet2DConditionModel models.\n if args.unet_checkpoint_path is not None:\n # image UNet\n image_unet_config = create_image_unet_diffusers_config(IMAGE_UNET_CONFIG)\n checkpoint = torch.load(args.unet_checkpoint_path)\n converted_image_unet_checkpoint = convert_vd_unet_checkpoint(\n checkpoint, image_unet_config, unet_key=\"model.diffusion_model.unet_image.\", extract_ema=args.extract_ema\n )\n image_unet = UNet2DConditionModel(**image_unet_config)\n image_unet.load_state_dict(converted_image_unet_checkpoint)\n\n # text UNet\n text_unet_config = create_text_unet_diffusers_config(TEXT_UNET_CONFIG)\n converted_text_unet_checkpoint = convert_vd_unet_checkpoint(\n checkpoint, text_unet_config, unet_key=\"model.diffusion_model.unet_text.\", extract_ema=args.extract_ema\n )\n text_unet = UNetFlatConditionModel(**text_unet_config)\n text_unet.load_state_dict(converted_text_unet_checkpoint)\n\n # Convert the VAE model.\n if args.vae_checkpoint_path is not None:\n vae_config = create_vae_diffusers_config(AUTOENCODER_CONFIG)\n checkpoint = torch.load(args.vae_checkpoint_path)\n converted_vae_checkpoint = convert_vd_vae_checkpoint(checkpoint, vae_config)\n\n vae = AutoencoderKL(**vae_config)\n vae.load_state_dict(converted_vae_checkpoint)\n\n tokenizer = CLIPTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\")\n image_feature_extractor = CLIPImageProcessor.from_pretrained(\"openai/clip-vit-large-patch14\")\n text_encoder = CLIPTextModelWithProjection.from_pretrained(\"openai/clip-vit-large-patch14\")\n image_encoder = CLIPVisionModelWithProjection.from_pretrained(\"openai/clip-vit-large-patch14\")\n\n pipe = VersatileDiffusionPipeline(\n scheduler=scheduler,\n tokenizer=tokenizer,\n image_feature_extractor=image_feature_extractor,\n text_encoder=text_encoder,\n image_encoder=image_encoder,\n image_unet=image_unet,\n text_unet=text_unet,\n vae=vae,\n )\n pipe.save_pretrained(args.dump_path)\n" }, { "path": "diffusers/scripts/convert_vq_diffusion_to_diffusers.py", "content": "\"\"\"\nThis script ports models from VQ-diffusion (https://github.com/microsoft/VQ-Diffusion) to diffusers.\n\nIt currently only supports porting the ITHQ dataset.\n\nITHQ dataset:\n```sh\n# From the root directory of diffusers.\n\n# Download the VQVAE checkpoint\n$ wget https://facevcstandard.blob.core.windows.net/v-zhictang/Improved-VQ-Diffusion_model_release/ithq_vqvae.pth?sv=2020-10-02&st=2022-05-30T15%3A17%3A18Z&se=2030-05-31T15%3A17%3A00Z&sr=b&sp=r&sig=1jVavHFPpUjDs%2FTO1V3PTezaNbPp2Nx8MxiWI7y6fEY%3D -O ithq_vqvae.pth\n\n# Download the VQVAE config\n# NOTE that in VQ-diffusion the documented file is `configs/ithq.yaml` but the target class\n# `image_synthesis.modeling.codecs.image_codec.ema_vqvae.PatchVQVAE`\n# loads `OUTPUT/pretrained_model/taming_dvae/config.yaml`\n$ wget https://raw.githubusercontent.com/microsoft/VQ-Diffusion/main/OUTPUT/pretrained_model/taming_dvae/config.yaml -O ithq_vqvae.yaml\n\n# Download the main model checkpoint\n$ wget https://facevcstandard.blob.core.windows.net/v-zhictang/Improved-VQ-Diffusion_model_release/ithq_learnable.pth?sv=2020-10-02&st=2022-05-30T10%3A22%3A06Z&se=2030-05-31T10%3A22%3A00Z&sr=b&sp=r&sig=GOE%2Bza02%2FPnGxYVOOPtwrTR4RA3%2F5NVgMxdW4kjaEZ8%3D -O ithq_learnable.pth\n\n# Download the main model config\n$ wget https://raw.githubusercontent.com/microsoft/VQ-Diffusion/main/configs/ithq.yaml -O ithq.yaml\n\n# run the convert script\n$ python ./scripts/convert_vq_diffusion_to_diffusers.py \\\n --checkpoint_path ./ithq_learnable.pth \\\n --original_config_file ./ithq.yaml \\\n --vqvae_checkpoint_path ./ithq_vqvae.pth \\\n --vqvae_original_config_file ./ithq_vqvae.yaml \\\n --dump_path \n```\n\"\"\"\n\nimport argparse\nimport tempfile\n\nimport torch\nimport yaml\nfrom accelerate import init_empty_weights, load_checkpoint_and_dispatch\nfrom transformers import CLIPTextModel, CLIPTokenizer\nfrom yaml.loader import FullLoader\n\nfrom diffusers import Transformer2DModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel\nfrom diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings\n\n\n# vqvae model\n\nPORTED_VQVAES = [\"image_synthesis.modeling.codecs.image_codec.patch_vqgan.PatchVQGAN\"]\n\n\ndef vqvae_model_from_original_config(original_config):\n assert (\n original_config[\"target\"] in PORTED_VQVAES\n ), f\"{original_config['target']} has not yet been ported to diffusers.\"\n\n original_config = original_config[\"params\"]\n\n original_encoder_config = original_config[\"encoder_config\"][\"params\"]\n original_decoder_config = original_config[\"decoder_config\"][\"params\"]\n\n in_channels = original_encoder_config[\"in_channels\"]\n out_channels = original_decoder_config[\"out_ch\"]\n\n down_block_types = get_down_block_types(original_encoder_config)\n up_block_types = get_up_block_types(original_decoder_config)\n\n assert original_encoder_config[\"ch\"] == original_decoder_config[\"ch\"]\n assert original_encoder_config[\"ch_mult\"] == original_decoder_config[\"ch_mult\"]\n block_out_channels = tuple(\n [original_encoder_config[\"ch\"] * a_ch_mult for a_ch_mult in original_encoder_config[\"ch_mult\"]]\n )\n\n assert original_encoder_config[\"num_res_blocks\"] == original_decoder_config[\"num_res_blocks\"]\n layers_per_block = original_encoder_config[\"num_res_blocks\"]\n\n assert original_encoder_config[\"z_channels\"] == original_decoder_config[\"z_channels\"]\n latent_channels = original_encoder_config[\"z_channels\"]\n\n num_vq_embeddings = original_config[\"n_embed\"]\n\n # Hard coded value for ResnetBlock.GoupNorm(num_groups) in VQ-diffusion\n norm_num_groups = 32\n\n e_dim = original_config[\"embed_dim\"]\n\n model = VQModel(\n in_channels=in_channels,\n out_channels=out_channels,\n down_block_types=down_block_types,\n up_block_types=up_block_types,\n block_out_channels=block_out_channels,\n layers_per_block=layers_per_block,\n latent_channels=latent_channels,\n num_vq_embeddings=num_vq_embeddings,\n norm_num_groups=norm_num_groups,\n vq_embed_dim=e_dim,\n )\n\n return model\n\n\ndef get_down_block_types(original_encoder_config):\n attn_resolutions = coerce_attn_resolutions(original_encoder_config[\"attn_resolutions\"])\n num_resolutions = len(original_encoder_config[\"ch_mult\"])\n resolution = coerce_resolution(original_encoder_config[\"resolution\"])\n\n curr_res = resolution\n down_block_types = []\n\n for _ in range(num_resolutions):\n if curr_res in attn_resolutions:\n down_block_type = \"AttnDownEncoderBlock2D\"\n else:\n down_block_type = \"DownEncoderBlock2D\"\n\n down_block_types.append(down_block_type)\n\n curr_res = [r // 2 for r in curr_res]\n\n return down_block_types\n\n\ndef get_up_block_types(original_decoder_config):\n attn_resolutions = coerce_attn_resolutions(original_decoder_config[\"attn_resolutions\"])\n num_resolutions = len(original_decoder_config[\"ch_mult\"])\n resolution = coerce_resolution(original_decoder_config[\"resolution\"])\n\n curr_res = [r // 2 ** (num_resolutions - 1) for r in resolution]\n up_block_types = []\n\n for _ in reversed(range(num_resolutions)):\n if curr_res in attn_resolutions:\n up_block_type = \"AttnUpDecoderBlock2D\"\n else:\n up_block_type = \"UpDecoderBlock2D\"\n\n up_block_types.append(up_block_type)\n\n curr_res = [r * 2 for r in curr_res]\n\n return up_block_types\n\n\ndef coerce_attn_resolutions(attn_resolutions):\n attn_resolutions = list(attn_resolutions)\n attn_resolutions_ = []\n for ar in attn_resolutions:\n if isinstance(ar, (list, tuple)):\n attn_resolutions_.append(list(ar))\n else:\n attn_resolutions_.append([ar, ar])\n return attn_resolutions_\n\n\ndef coerce_resolution(resolution):\n if isinstance(resolution, int):\n resolution = [resolution, resolution] # H, W\n elif isinstance(resolution, (tuple, list)):\n resolution = list(resolution)\n else:\n raise ValueError(\"Unknown type of resolution:\", resolution)\n return resolution\n\n\n# done vqvae model\n\n# vqvae checkpoint\n\n\ndef vqvae_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):\n diffusers_checkpoint = {}\n\n diffusers_checkpoint.update(vqvae_encoder_to_diffusers_checkpoint(model, checkpoint))\n\n # quant_conv\n\n diffusers_checkpoint.update(\n {\n \"quant_conv.weight\": checkpoint[\"quant_conv.weight\"],\n \"quant_conv.bias\": checkpoint[\"quant_conv.bias\"],\n }\n )\n\n # quantize\n diffusers_checkpoint.update({\"quantize.embedding.weight\": checkpoint[\"quantize.embedding\"]})\n\n # post_quant_conv\n diffusers_checkpoint.update(\n {\n \"post_quant_conv.weight\": checkpoint[\"post_quant_conv.weight\"],\n \"post_quant_conv.bias\": checkpoint[\"post_quant_conv.bias\"],\n }\n )\n\n # decoder\n diffusers_checkpoint.update(vqvae_decoder_to_diffusers_checkpoint(model, checkpoint))\n\n return diffusers_checkpoint\n\n\ndef vqvae_encoder_to_diffusers_checkpoint(model, checkpoint):\n diffusers_checkpoint = {}\n\n # conv_in\n diffusers_checkpoint.update(\n {\n \"encoder.conv_in.weight\": checkpoint[\"encoder.conv_in.weight\"],\n \"encoder.conv_in.bias\": checkpoint[\"encoder.conv_in.bias\"],\n }\n )\n\n # down_blocks\n for down_block_idx, down_block in enumerate(model.encoder.down_blocks):\n diffusers_down_block_prefix = f\"encoder.down_blocks.{down_block_idx}\"\n down_block_prefix = f\"encoder.down.{down_block_idx}\"\n\n # resnets\n for resnet_idx, resnet in enumerate(down_block.resnets):\n diffusers_resnet_prefix = f\"{diffusers_down_block_prefix}.resnets.{resnet_idx}\"\n resnet_prefix = f\"{down_block_prefix}.block.{resnet_idx}\"\n\n diffusers_checkpoint.update(\n vqvae_resnet_to_diffusers_checkpoint(\n resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix\n )\n )\n\n # downsample\n\n # do not include the downsample when on the last down block\n # There is no downsample on the last down block\n if down_block_idx != len(model.encoder.down_blocks) - 1:\n # There's a single downsample in the original checkpoint but a list of downsamples\n # in the diffusers model.\n diffusers_downsample_prefix = f\"{diffusers_down_block_prefix}.downsamplers.0.conv\"\n downsample_prefix = f\"{down_block_prefix}.downsample.conv\"\n diffusers_checkpoint.update(\n {\n f\"{diffusers_downsample_prefix}.weight\": checkpoint[f\"{downsample_prefix}.weight\"],\n f\"{diffusers_downsample_prefix}.bias\": checkpoint[f\"{downsample_prefix}.bias\"],\n }\n )\n\n # attentions\n\n if hasattr(down_block, \"attentions\"):\n for attention_idx, _ in enumerate(down_block.attentions):\n diffusers_attention_prefix = f\"{diffusers_down_block_prefix}.attentions.{attention_idx}\"\n attention_prefix = f\"{down_block_prefix}.attn.{attention_idx}\"\n diffusers_checkpoint.update(\n vqvae_attention_to_diffusers_checkpoint(\n checkpoint,\n diffusers_attention_prefix=diffusers_attention_prefix,\n attention_prefix=attention_prefix,\n )\n )\n\n # mid block\n\n # mid block attentions\n\n # There is a single hardcoded attention block in the middle of the VQ-diffusion encoder\n diffusers_attention_prefix = \"encoder.mid_block.attentions.0\"\n attention_prefix = \"encoder.mid.attn_1\"\n diffusers_checkpoint.update(\n vqvae_attention_to_diffusers_checkpoint(\n checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix\n )\n )\n\n # mid block resnets\n\n for diffusers_resnet_idx, resnet in enumerate(model.encoder.mid_block.resnets):\n diffusers_resnet_prefix = f\"encoder.mid_block.resnets.{diffusers_resnet_idx}\"\n\n # the hardcoded prefixes to `block_` are 1 and 2\n orig_resnet_idx = diffusers_resnet_idx + 1\n # There are two hardcoded resnets in the middle of the VQ-diffusion encoder\n resnet_prefix = f\"encoder.mid.block_{orig_resnet_idx}\"\n\n diffusers_checkpoint.update(\n vqvae_resnet_to_diffusers_checkpoint(\n resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix\n )\n )\n\n diffusers_checkpoint.update(\n {\n # conv_norm_out\n \"encoder.conv_norm_out.weight\": checkpoint[\"encoder.norm_out.weight\"],\n \"encoder.conv_norm_out.bias\": checkpoint[\"encoder.norm_out.bias\"],\n # conv_out\n \"encoder.conv_out.weight\": checkpoint[\"encoder.conv_out.weight\"],\n \"encoder.conv_out.bias\": checkpoint[\"encoder.conv_out.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\ndef vqvae_decoder_to_diffusers_checkpoint(model, checkpoint):\n diffusers_checkpoint = {}\n\n # conv in\n diffusers_checkpoint.update(\n {\n \"decoder.conv_in.weight\": checkpoint[\"decoder.conv_in.weight\"],\n \"decoder.conv_in.bias\": checkpoint[\"decoder.conv_in.bias\"],\n }\n )\n\n # up_blocks\n\n for diffusers_up_block_idx, up_block in enumerate(model.decoder.up_blocks):\n # up_blocks are stored in reverse order in the VQ-diffusion checkpoint\n orig_up_block_idx = len(model.decoder.up_blocks) - 1 - diffusers_up_block_idx\n\n diffusers_up_block_prefix = f\"decoder.up_blocks.{diffusers_up_block_idx}\"\n up_block_prefix = f\"decoder.up.{orig_up_block_idx}\"\n\n # resnets\n for resnet_idx, resnet in enumerate(up_block.resnets):\n diffusers_resnet_prefix = f\"{diffusers_up_block_prefix}.resnets.{resnet_idx}\"\n resnet_prefix = f\"{up_block_prefix}.block.{resnet_idx}\"\n\n diffusers_checkpoint.update(\n vqvae_resnet_to_diffusers_checkpoint(\n resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix\n )\n )\n\n # upsample\n\n # there is no up sample on the last up block\n if diffusers_up_block_idx != len(model.decoder.up_blocks) - 1:\n # There's a single upsample in the VQ-diffusion checkpoint but a list of downsamples\n # in the diffusers model.\n diffusers_downsample_prefix = f\"{diffusers_up_block_prefix}.upsamplers.0.conv\"\n downsample_prefix = f\"{up_block_prefix}.upsample.conv\"\n diffusers_checkpoint.update(\n {\n f\"{diffusers_downsample_prefix}.weight\": checkpoint[f\"{downsample_prefix}.weight\"],\n f\"{diffusers_downsample_prefix}.bias\": checkpoint[f\"{downsample_prefix}.bias\"],\n }\n )\n\n # attentions\n\n if hasattr(up_block, \"attentions\"):\n for attention_idx, _ in enumerate(up_block.attentions):\n diffusers_attention_prefix = f\"{diffusers_up_block_prefix}.attentions.{attention_idx}\"\n attention_prefix = f\"{up_block_prefix}.attn.{attention_idx}\"\n diffusers_checkpoint.update(\n vqvae_attention_to_diffusers_checkpoint(\n checkpoint,\n diffusers_attention_prefix=diffusers_attention_prefix,\n attention_prefix=attention_prefix,\n )\n )\n\n # mid block\n\n # mid block attentions\n\n # There is a single hardcoded attention block in the middle of the VQ-diffusion decoder\n diffusers_attention_prefix = \"decoder.mid_block.attentions.0\"\n attention_prefix = \"decoder.mid.attn_1\"\n diffusers_checkpoint.update(\n vqvae_attention_to_diffusers_checkpoint(\n checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix\n )\n )\n\n # mid block resnets\n\n for diffusers_resnet_idx, resnet in enumerate(model.encoder.mid_block.resnets):\n diffusers_resnet_prefix = f\"decoder.mid_block.resnets.{diffusers_resnet_idx}\"\n\n # the hardcoded prefixes to `block_` are 1 and 2\n orig_resnet_idx = diffusers_resnet_idx + 1\n # There are two hardcoded resnets in the middle of the VQ-diffusion decoder\n resnet_prefix = f\"decoder.mid.block_{orig_resnet_idx}\"\n\n diffusers_checkpoint.update(\n vqvae_resnet_to_diffusers_checkpoint(\n resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix\n )\n )\n\n diffusers_checkpoint.update(\n {\n # conv_norm_out\n \"decoder.conv_norm_out.weight\": checkpoint[\"decoder.norm_out.weight\"],\n \"decoder.conv_norm_out.bias\": checkpoint[\"decoder.norm_out.bias\"],\n # conv_out\n \"decoder.conv_out.weight\": checkpoint[\"decoder.conv_out.weight\"],\n \"decoder.conv_out.bias\": checkpoint[\"decoder.conv_out.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\ndef vqvae_resnet_to_diffusers_checkpoint(resnet, checkpoint, *, diffusers_resnet_prefix, resnet_prefix):\n rv = {\n # norm1\n f\"{diffusers_resnet_prefix}.norm1.weight\": checkpoint[f\"{resnet_prefix}.norm1.weight\"],\n f\"{diffusers_resnet_prefix}.norm1.bias\": checkpoint[f\"{resnet_prefix}.norm1.bias\"],\n # conv1\n f\"{diffusers_resnet_prefix}.conv1.weight\": checkpoint[f\"{resnet_prefix}.conv1.weight\"],\n f\"{diffusers_resnet_prefix}.conv1.bias\": checkpoint[f\"{resnet_prefix}.conv1.bias\"],\n # norm2\n f\"{diffusers_resnet_prefix}.norm2.weight\": checkpoint[f\"{resnet_prefix}.norm2.weight\"],\n f\"{diffusers_resnet_prefix}.norm2.bias\": checkpoint[f\"{resnet_prefix}.norm2.bias\"],\n # conv2\n f\"{diffusers_resnet_prefix}.conv2.weight\": checkpoint[f\"{resnet_prefix}.conv2.weight\"],\n f\"{diffusers_resnet_prefix}.conv2.bias\": checkpoint[f\"{resnet_prefix}.conv2.bias\"],\n }\n\n if resnet.conv_shortcut is not None:\n rv.update(\n {\n f\"{diffusers_resnet_prefix}.conv_shortcut.weight\": checkpoint[f\"{resnet_prefix}.nin_shortcut.weight\"],\n f\"{diffusers_resnet_prefix}.conv_shortcut.bias\": checkpoint[f\"{resnet_prefix}.nin_shortcut.bias\"],\n }\n )\n\n return rv\n\n\ndef vqvae_attention_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix):\n return {\n # group_norm\n f\"{diffusers_attention_prefix}.group_norm.weight\": checkpoint[f\"{attention_prefix}.norm.weight\"],\n f\"{diffusers_attention_prefix}.group_norm.bias\": checkpoint[f\"{attention_prefix}.norm.bias\"],\n # query\n f\"{diffusers_attention_prefix}.query.weight\": checkpoint[f\"{attention_prefix}.q.weight\"][:, :, 0, 0],\n f\"{diffusers_attention_prefix}.query.bias\": checkpoint[f\"{attention_prefix}.q.bias\"],\n # key\n f\"{diffusers_attention_prefix}.key.weight\": checkpoint[f\"{attention_prefix}.k.weight\"][:, :, 0, 0],\n f\"{diffusers_attention_prefix}.key.bias\": checkpoint[f\"{attention_prefix}.k.bias\"],\n # value\n f\"{diffusers_attention_prefix}.value.weight\": checkpoint[f\"{attention_prefix}.v.weight\"][:, :, 0, 0],\n f\"{diffusers_attention_prefix}.value.bias\": checkpoint[f\"{attention_prefix}.v.bias\"],\n # proj_attn\n f\"{diffusers_attention_prefix}.proj_attn.weight\": checkpoint[f\"{attention_prefix}.proj_out.weight\"][\n :, :, 0, 0\n ],\n f\"{diffusers_attention_prefix}.proj_attn.bias\": checkpoint[f\"{attention_prefix}.proj_out.bias\"],\n }\n\n\n# done vqvae checkpoint\n\n# transformer model\n\nPORTED_DIFFUSIONS = [\"image_synthesis.modeling.transformers.diffusion_transformer.DiffusionTransformer\"]\nPORTED_TRANSFORMERS = [\"image_synthesis.modeling.transformers.transformer_utils.Text2ImageTransformer\"]\nPORTED_CONTENT_EMBEDDINGS = [\"image_synthesis.modeling.embeddings.dalle_mask_image_embedding.DalleMaskImageEmbedding\"]\n\n\ndef transformer_model_from_original_config(\n original_diffusion_config, original_transformer_config, original_content_embedding_config\n):\n assert (\n original_diffusion_config[\"target\"] in PORTED_DIFFUSIONS\n ), f\"{original_diffusion_config['target']} has not yet been ported to diffusers.\"\n assert (\n original_transformer_config[\"target\"] in PORTED_TRANSFORMERS\n ), f\"{original_transformer_config['target']} has not yet been ported to diffusers.\"\n assert (\n original_content_embedding_config[\"target\"] in PORTED_CONTENT_EMBEDDINGS\n ), f\"{original_content_embedding_config['target']} has not yet been ported to diffusers.\"\n\n original_diffusion_config = original_diffusion_config[\"params\"]\n original_transformer_config = original_transformer_config[\"params\"]\n original_content_embedding_config = original_content_embedding_config[\"params\"]\n\n inner_dim = original_transformer_config[\"n_embd\"]\n\n n_heads = original_transformer_config[\"n_head\"]\n\n # VQ-Diffusion gives dimension of the multi-headed attention layers as the\n # number of attention heads times the sequence length (the dimension) of a\n # single head. We want to specify our attention blocks with those values\n # specified separately\n assert inner_dim % n_heads == 0\n d_head = inner_dim // n_heads\n\n depth = original_transformer_config[\"n_layer\"]\n context_dim = original_transformer_config[\"condition_dim\"]\n\n num_embed = original_content_embedding_config[\"num_embed\"]\n # the number of embeddings in the transformer includes the mask embedding.\n # the content embedding (the vqvae) does not include the mask embedding.\n num_embed = num_embed + 1\n\n height = original_transformer_config[\"content_spatial_size\"][0]\n width = original_transformer_config[\"content_spatial_size\"][1]\n\n assert width == height, \"width has to be equal to height\"\n dropout = original_transformer_config[\"resid_pdrop\"]\n num_embeds_ada_norm = original_diffusion_config[\"diffusion_step\"]\n\n model_kwargs = {\n \"attention_bias\": True,\n \"cross_attention_dim\": context_dim,\n \"attention_head_dim\": d_head,\n \"num_layers\": depth,\n \"dropout\": dropout,\n \"num_attention_heads\": n_heads,\n \"num_vector_embeds\": num_embed,\n \"num_embeds_ada_norm\": num_embeds_ada_norm,\n \"norm_num_groups\": 32,\n \"sample_size\": width,\n \"activation_fn\": \"geglu-approximate\",\n }\n\n model = Transformer2DModel(**model_kwargs)\n return model\n\n\n# done transformer model\n\n# transformer checkpoint\n\n\ndef transformer_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):\n diffusers_checkpoint = {}\n\n transformer_prefix = \"transformer.transformer\"\n\n diffusers_latent_image_embedding_prefix = \"latent_image_embedding\"\n latent_image_embedding_prefix = f\"{transformer_prefix}.content_emb\"\n\n # DalleMaskImageEmbedding\n diffusers_checkpoint.update(\n {\n f\"{diffusers_latent_image_embedding_prefix}.emb.weight\": checkpoint[\n f\"{latent_image_embedding_prefix}.emb.weight\"\n ],\n f\"{diffusers_latent_image_embedding_prefix}.height_emb.weight\": checkpoint[\n f\"{latent_image_embedding_prefix}.height_emb.weight\"\n ],\n f\"{diffusers_latent_image_embedding_prefix}.width_emb.weight\": checkpoint[\n f\"{latent_image_embedding_prefix}.width_emb.weight\"\n ],\n }\n )\n\n # transformer blocks\n for transformer_block_idx, transformer_block in enumerate(model.transformer_blocks):\n diffusers_transformer_block_prefix = f\"transformer_blocks.{transformer_block_idx}\"\n transformer_block_prefix = f\"{transformer_prefix}.blocks.{transformer_block_idx}\"\n\n # ada norm block\n diffusers_ada_norm_prefix = f\"{diffusers_transformer_block_prefix}.norm1\"\n ada_norm_prefix = f\"{transformer_block_prefix}.ln1\"\n\n diffusers_checkpoint.update(\n transformer_ada_norm_to_diffusers_checkpoint(\n checkpoint, diffusers_ada_norm_prefix=diffusers_ada_norm_prefix, ada_norm_prefix=ada_norm_prefix\n )\n )\n\n # attention block\n diffusers_attention_prefix = f\"{diffusers_transformer_block_prefix}.attn1\"\n attention_prefix = f\"{transformer_block_prefix}.attn1\"\n\n diffusers_checkpoint.update(\n transformer_attention_to_diffusers_checkpoint(\n checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix\n )\n )\n\n # ada norm block\n diffusers_ada_norm_prefix = f\"{diffusers_transformer_block_prefix}.norm2\"\n ada_norm_prefix = f\"{transformer_block_prefix}.ln1_1\"\n\n diffusers_checkpoint.update(\n transformer_ada_norm_to_diffusers_checkpoint(\n checkpoint, diffusers_ada_norm_prefix=diffusers_ada_norm_prefix, ada_norm_prefix=ada_norm_prefix\n )\n )\n\n # attention block\n diffusers_attention_prefix = f\"{diffusers_transformer_block_prefix}.attn2\"\n attention_prefix = f\"{transformer_block_prefix}.attn2\"\n\n diffusers_checkpoint.update(\n transformer_attention_to_diffusers_checkpoint(\n checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix\n )\n )\n\n # norm block\n diffusers_norm_block_prefix = f\"{diffusers_transformer_block_prefix}.norm3\"\n norm_block_prefix = f\"{transformer_block_prefix}.ln2\"\n\n diffusers_checkpoint.update(\n {\n f\"{diffusers_norm_block_prefix}.weight\": checkpoint[f\"{norm_block_prefix}.weight\"],\n f\"{diffusers_norm_block_prefix}.bias\": checkpoint[f\"{norm_block_prefix}.bias\"],\n }\n )\n\n # feedforward block\n diffusers_feedforward_prefix = f\"{diffusers_transformer_block_prefix}.ff\"\n feedforward_prefix = f\"{transformer_block_prefix}.mlp\"\n\n diffusers_checkpoint.update(\n transformer_feedforward_to_diffusers_checkpoint(\n checkpoint,\n diffusers_feedforward_prefix=diffusers_feedforward_prefix,\n feedforward_prefix=feedforward_prefix,\n )\n )\n\n # to logits\n\n diffusers_norm_out_prefix = \"norm_out\"\n norm_out_prefix = f\"{transformer_prefix}.to_logits.0\"\n\n diffusers_checkpoint.update(\n {\n f\"{diffusers_norm_out_prefix}.weight\": checkpoint[f\"{norm_out_prefix}.weight\"],\n f\"{diffusers_norm_out_prefix}.bias\": checkpoint[f\"{norm_out_prefix}.bias\"],\n }\n )\n\n diffusers_out_prefix = \"out\"\n out_prefix = f\"{transformer_prefix}.to_logits.1\"\n\n diffusers_checkpoint.update(\n {\n f\"{diffusers_out_prefix}.weight\": checkpoint[f\"{out_prefix}.weight\"],\n f\"{diffusers_out_prefix}.bias\": checkpoint[f\"{out_prefix}.bias\"],\n }\n )\n\n return diffusers_checkpoint\n\n\ndef transformer_ada_norm_to_diffusers_checkpoint(checkpoint, *, diffusers_ada_norm_prefix, ada_norm_prefix):\n return {\n f\"{diffusers_ada_norm_prefix}.emb.weight\": checkpoint[f\"{ada_norm_prefix}.emb.weight\"],\n f\"{diffusers_ada_norm_prefix}.linear.weight\": checkpoint[f\"{ada_norm_prefix}.linear.weight\"],\n f\"{diffusers_ada_norm_prefix}.linear.bias\": checkpoint[f\"{ada_norm_prefix}.linear.bias\"],\n }\n\n\ndef transformer_attention_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix):\n return {\n # key\n f\"{diffusers_attention_prefix}.to_k.weight\": checkpoint[f\"{attention_prefix}.key.weight\"],\n f\"{diffusers_attention_prefix}.to_k.bias\": checkpoint[f\"{attention_prefix}.key.bias\"],\n # query\n f\"{diffusers_attention_prefix}.to_q.weight\": checkpoint[f\"{attention_prefix}.query.weight\"],\n f\"{diffusers_attention_prefix}.to_q.bias\": checkpoint[f\"{attention_prefix}.query.bias\"],\n # value\n f\"{diffusers_attention_prefix}.to_v.weight\": checkpoint[f\"{attention_prefix}.value.weight\"],\n f\"{diffusers_attention_prefix}.to_v.bias\": checkpoint[f\"{attention_prefix}.value.bias\"],\n # linear out\n f\"{diffusers_attention_prefix}.to_out.0.weight\": checkpoint[f\"{attention_prefix}.proj.weight\"],\n f\"{diffusers_attention_prefix}.to_out.0.bias\": checkpoint[f\"{attention_prefix}.proj.bias\"],\n }\n\n\ndef transformer_feedforward_to_diffusers_checkpoint(checkpoint, *, diffusers_feedforward_prefix, feedforward_prefix):\n return {\n f\"{diffusers_feedforward_prefix}.net.0.proj.weight\": checkpoint[f\"{feedforward_prefix}.0.weight\"],\n f\"{diffusers_feedforward_prefix}.net.0.proj.bias\": checkpoint[f\"{feedforward_prefix}.0.bias\"],\n f\"{diffusers_feedforward_prefix}.net.2.weight\": checkpoint[f\"{feedforward_prefix}.2.weight\"],\n f\"{diffusers_feedforward_prefix}.net.2.bias\": checkpoint[f\"{feedforward_prefix}.2.bias\"],\n }\n\n\n# done transformer checkpoint\n\n\ndef read_config_file(filename):\n # The yaml file contains annotations that certain values should\n # loaded as tuples.\n with open(filename) as f:\n original_config = yaml.load(f, FullLoader)\n\n return original_config\n\n\n# We take separate arguments for the vqvae because the ITHQ vqvae config file\n# is separate from the config file for the rest of the model.\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--vqvae_checkpoint_path\",\n default=None,\n type=str,\n required=True,\n help=\"Path to the vqvae checkpoint to convert.\",\n )\n\n parser.add_argument(\n \"--vqvae_original_config_file\",\n default=None,\n type=str,\n required=True,\n help=\"The YAML config file corresponding to the original architecture for the vqvae.\",\n )\n\n parser.add_argument(\n \"--checkpoint_path\", default=None, type=str, required=True, help=\"Path to the checkpoint to convert.\"\n )\n\n parser.add_argument(\n \"--original_config_file\",\n default=None,\n type=str,\n required=True,\n help=\"The YAML config file corresponding to the original architecture.\",\n )\n\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n\n parser.add_argument(\n \"--checkpoint_load_device\",\n default=\"cpu\",\n type=str,\n required=False,\n help=\"The device passed to `map_location` when loading checkpoints.\",\n )\n\n # See link for how ema weights are always selected\n # https://github.com/microsoft/VQ-Diffusion/blob/3c98e77f721db7c787b76304fa2c96a36c7b00af/inference_VQ_Diffusion.py#L65\n parser.add_argument(\n \"--no_use_ema\",\n action=\"store_true\",\n required=False,\n help=(\n \"Set to not use the ema weights from the original VQ-Diffusion checkpoint. You probably do not want to set\"\n \" it as the original VQ-Diffusion always uses the ema weights when loading models.\"\n ),\n )\n\n args = parser.parse_args()\n\n use_ema = not args.no_use_ema\n\n print(f\"loading checkpoints to {args.checkpoint_load_device}\")\n\n checkpoint_map_location = torch.device(args.checkpoint_load_device)\n\n # vqvae_model\n\n print(f\"loading vqvae, config: {args.vqvae_original_config_file}, checkpoint: {args.vqvae_checkpoint_path}\")\n\n vqvae_original_config = read_config_file(args.vqvae_original_config_file).model\n vqvae_checkpoint = torch.load(args.vqvae_checkpoint_path, map_location=checkpoint_map_location)[\"model\"]\n\n with init_empty_weights():\n vqvae_model = vqvae_model_from_original_config(vqvae_original_config)\n\n vqvae_diffusers_checkpoint = vqvae_original_checkpoint_to_diffusers_checkpoint(vqvae_model, vqvae_checkpoint)\n\n with tempfile.NamedTemporaryFile() as vqvae_diffusers_checkpoint_file:\n torch.save(vqvae_diffusers_checkpoint, vqvae_diffusers_checkpoint_file.name)\n del vqvae_diffusers_checkpoint\n del vqvae_checkpoint\n load_checkpoint_and_dispatch(vqvae_model, vqvae_diffusers_checkpoint_file.name, device_map=\"auto\")\n\n print(\"done loading vqvae\")\n\n # done vqvae_model\n\n # transformer_model\n\n print(\n f\"loading transformer, config: {args.original_config_file}, checkpoint: {args.checkpoint_path}, use ema:\"\n f\" {use_ema}\"\n )\n\n original_config = read_config_file(args.original_config_file).model\n\n diffusion_config = original_config[\"params\"][\"diffusion_config\"]\n transformer_config = original_config[\"params\"][\"diffusion_config\"][\"params\"][\"transformer_config\"]\n content_embedding_config = original_config[\"params\"][\"diffusion_config\"][\"params\"][\"content_emb_config\"]\n\n pre_checkpoint = torch.load(args.checkpoint_path, map_location=checkpoint_map_location)\n\n if use_ema:\n if \"ema\" in pre_checkpoint:\n checkpoint = {}\n for k, v in pre_checkpoint[\"model\"].items():\n checkpoint[k] = v\n\n for k, v in pre_checkpoint[\"ema\"].items():\n # The ema weights are only used on the transformer. To mimic their key as if they came\n # from the state_dict for the top level model, we prefix with an additional \"transformer.\"\n # See the source linked in the args.use_ema config for more information.\n checkpoint[f\"transformer.{k}\"] = v\n else:\n print(\"attempted to load ema weights but no ema weights are specified in the loaded checkpoint.\")\n checkpoint = pre_checkpoint[\"model\"]\n else:\n checkpoint = pre_checkpoint[\"model\"]\n\n del pre_checkpoint\n\n with init_empty_weights():\n transformer_model = transformer_model_from_original_config(\n diffusion_config, transformer_config, content_embedding_config\n )\n\n diffusers_transformer_checkpoint = transformer_original_checkpoint_to_diffusers_checkpoint(\n transformer_model, checkpoint\n )\n\n # classifier free sampling embeddings interlude\n\n # The learned embeddings are stored on the transformer in the original VQ-diffusion. We store them on a separate\n # model, so we pull them off the checkpoint before the checkpoint is deleted.\n\n learnable_classifier_free_sampling_embeddings = diffusion_config[\"params\"].learnable_cf\n\n if learnable_classifier_free_sampling_embeddings:\n learned_classifier_free_sampling_embeddings_embeddings = checkpoint[\"transformer.empty_text_embed\"]\n else:\n learned_classifier_free_sampling_embeddings_embeddings = None\n\n # done classifier free sampling embeddings interlude\n\n with tempfile.NamedTemporaryFile() as diffusers_transformer_checkpoint_file:\n torch.save(diffusers_transformer_checkpoint, diffusers_transformer_checkpoint_file.name)\n del diffusers_transformer_checkpoint\n del checkpoint\n load_checkpoint_and_dispatch(transformer_model, diffusers_transformer_checkpoint_file.name, device_map=\"auto\")\n\n print(\"done loading transformer\")\n\n # done transformer_model\n\n # text encoder\n\n print(\"loading CLIP text encoder\")\n\n clip_name = \"openai/clip-vit-base-patch32\"\n\n # The original VQ-Diffusion specifies the pad value by the int used in the\n # returned tokens. Each model uses `0` as the pad value. The transformers clip api\n # specifies the pad value via the token before it has been tokenized. The `!` pad\n # token is the same as padding with the `0` pad value.\n pad_token = \"!\"\n\n tokenizer_model = CLIPTokenizer.from_pretrained(clip_name, pad_token=pad_token, device_map=\"auto\")\n\n assert tokenizer_model.convert_tokens_to_ids(pad_token) == 0\n\n text_encoder_model = CLIPTextModel.from_pretrained(\n clip_name,\n # `CLIPTextModel` does not support device_map=\"auto\"\n # device_map=\"auto\"\n )\n\n print(\"done loading CLIP text encoder\")\n\n # done text encoder\n\n # scheduler\n\n scheduler_model = VQDiffusionScheduler(\n # the scheduler has the same number of embeddings as the transformer\n num_vec_classes=transformer_model.num_vector_embeds\n )\n\n # done scheduler\n\n # learned classifier free sampling embeddings\n\n with init_empty_weights():\n learned_classifier_free_sampling_embeddings_model = LearnedClassifierFreeSamplingEmbeddings(\n learnable_classifier_free_sampling_embeddings,\n hidden_size=text_encoder_model.config.hidden_size,\n length=tokenizer_model.model_max_length,\n )\n\n learned_classifier_free_sampling_checkpoint = {\n \"embeddings\": learned_classifier_free_sampling_embeddings_embeddings.float()\n }\n\n with tempfile.NamedTemporaryFile() as learned_classifier_free_sampling_checkpoint_file:\n torch.save(learned_classifier_free_sampling_checkpoint, learned_classifier_free_sampling_checkpoint_file.name)\n del learned_classifier_free_sampling_checkpoint\n del learned_classifier_free_sampling_embeddings_embeddings\n load_checkpoint_and_dispatch(\n learned_classifier_free_sampling_embeddings_model,\n learned_classifier_free_sampling_checkpoint_file.name,\n device_map=\"auto\",\n )\n\n # done learned classifier free sampling embeddings\n\n print(f\"saving VQ diffusion model, path: {args.dump_path}\")\n\n pipe = VQDiffusionPipeline(\n vqvae=vqvae_model,\n transformer=transformer_model,\n tokenizer=tokenizer_model,\n text_encoder=text_encoder_model,\n learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings_model,\n scheduler=scheduler_model,\n )\n pipe.save_pretrained(args.dump_path)\n\n print(\"done writing VQ diffusion model\")\n" }, { "path": "diffusers/scripts/convert_wuerstchen.py", "content": "# Run inside root directory of official source code: https://github.com/dome272/wuerstchen/\nimport os\n\nimport torch\nfrom transformers import AutoTokenizer, CLIPTextModel\nfrom vqgan import VQModel\n\nfrom diffusers import (\n DDPMWuerstchenScheduler,\n WuerstchenCombinedPipeline,\n WuerstchenDecoderPipeline,\n WuerstchenPriorPipeline,\n)\nfrom diffusers.pipelines.wuerstchen import PaellaVQModel, WuerstchenDiffNeXt, WuerstchenPrior\n\n\nmodel_path = \"models/\"\ndevice = \"cpu\"\n\npaella_vqmodel = VQModel()\nstate_dict = torch.load(os.path.join(model_path, \"vqgan_f4_v1_500k.pt\"), map_location=device)[\"state_dict\"]\npaella_vqmodel.load_state_dict(state_dict)\n\nstate_dict[\"vquantizer.embedding.weight\"] = state_dict[\"vquantizer.codebook.weight\"]\nstate_dict.pop(\"vquantizer.codebook.weight\")\nvqmodel = PaellaVQModel(num_vq_embeddings=paella_vqmodel.codebook_size, latent_channels=paella_vqmodel.c_latent)\nvqmodel.load_state_dict(state_dict)\n\n# Clip Text encoder and tokenizer\ntext_encoder = CLIPTextModel.from_pretrained(\"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\")\ntokenizer = AutoTokenizer.from_pretrained(\"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\")\n\n# Generator\ngen_text_encoder = CLIPTextModel.from_pretrained(\"laion/CLIP-ViT-H-14-laion2B-s32B-b79K\").to(\"cpu\")\ngen_tokenizer = AutoTokenizer.from_pretrained(\"laion/CLIP-ViT-H-14-laion2B-s32B-b79K\")\n\norig_state_dict = torch.load(os.path.join(model_path, \"model_v2_stage_b.pt\"), map_location=device)[\"state_dict\"]\nstate_dict = {}\nfor key in orig_state_dict.keys():\n if key.endswith(\"in_proj_weight\"):\n weights = orig_state_dict[key].chunk(3, 0)\n state_dict[key.replace(\"attn.in_proj_weight\", \"to_q.weight\")] = weights[0]\n state_dict[key.replace(\"attn.in_proj_weight\", \"to_k.weight\")] = weights[1]\n state_dict[key.replace(\"attn.in_proj_weight\", \"to_v.weight\")] = weights[2]\n elif key.endswith(\"in_proj_bias\"):\n weights = orig_state_dict[key].chunk(3, 0)\n state_dict[key.replace(\"attn.in_proj_bias\", \"to_q.bias\")] = weights[0]\n state_dict[key.replace(\"attn.in_proj_bias\", \"to_k.bias\")] = weights[1]\n state_dict[key.replace(\"attn.in_proj_bias\", \"to_v.bias\")] = weights[2]\n elif key.endswith(\"out_proj.weight\"):\n weights = orig_state_dict[key]\n state_dict[key.replace(\"attn.out_proj.weight\", \"to_out.0.weight\")] = weights\n elif key.endswith(\"out_proj.bias\"):\n weights = orig_state_dict[key]\n state_dict[key.replace(\"attn.out_proj.bias\", \"to_out.0.bias\")] = weights\n else:\n state_dict[key] = orig_state_dict[key]\ndeocder = WuerstchenDiffNeXt()\ndeocder.load_state_dict(state_dict)\n\n# Prior\norig_state_dict = torch.load(os.path.join(model_path, \"model_v3_stage_c.pt\"), map_location=device)[\"ema_state_dict\"]\nstate_dict = {}\nfor key in orig_state_dict.keys():\n if key.endswith(\"in_proj_weight\"):\n weights = orig_state_dict[key].chunk(3, 0)\n state_dict[key.replace(\"attn.in_proj_weight\", \"to_q.weight\")] = weights[0]\n state_dict[key.replace(\"attn.in_proj_weight\", \"to_k.weight\")] = weights[1]\n state_dict[key.replace(\"attn.in_proj_weight\", \"to_v.weight\")] = weights[2]\n elif key.endswith(\"in_proj_bias\"):\n weights = orig_state_dict[key].chunk(3, 0)\n state_dict[key.replace(\"attn.in_proj_bias\", \"to_q.bias\")] = weights[0]\n state_dict[key.replace(\"attn.in_proj_bias\", \"to_k.bias\")] = weights[1]\n state_dict[key.replace(\"attn.in_proj_bias\", \"to_v.bias\")] = weights[2]\n elif key.endswith(\"out_proj.weight\"):\n weights = orig_state_dict[key]\n state_dict[key.replace(\"attn.out_proj.weight\", \"to_out.0.weight\")] = weights\n elif key.endswith(\"out_proj.bias\"):\n weights = orig_state_dict[key]\n state_dict[key.replace(\"attn.out_proj.bias\", \"to_out.0.bias\")] = weights\n else:\n state_dict[key] = orig_state_dict[key]\nprior_model = WuerstchenPrior(c_in=16, c=1536, c_cond=1280, c_r=64, depth=32, nhead=24).to(device)\nprior_model.load_state_dict(state_dict)\n\n# scheduler\nscheduler = DDPMWuerstchenScheduler()\n\n# Prior pipeline\nprior_pipeline = WuerstchenPriorPipeline(\n prior=prior_model, text_encoder=text_encoder, tokenizer=tokenizer, scheduler=scheduler\n)\n\nprior_pipeline.save_pretrained(\"warp-ai/wuerstchen-prior\")\n\ndecoder_pipeline = WuerstchenDecoderPipeline(\n text_encoder=gen_text_encoder, tokenizer=gen_tokenizer, vqgan=vqmodel, decoder=deocder, scheduler=scheduler\n)\ndecoder_pipeline.save_pretrained(\"warp-ai/wuerstchen\")\n\n# Wuerstchen pipeline\nwuerstchen_pipeline = WuerstchenCombinedPipeline(\n # Decoder\n text_encoder=gen_text_encoder,\n tokenizer=gen_tokenizer,\n decoder=deocder,\n scheduler=scheduler,\n vqgan=vqmodel,\n # Prior\n prior_tokenizer=tokenizer,\n prior_text_encoder=text_encoder,\n prior=prior_model,\n prior_scheduler=scheduler,\n)\nwuerstchen_pipeline.save_pretrained(\"warp-ai/WuerstchenCombinedPipeline\")\n" }, { "path": "diffusers/scripts/convert_zero123_to_diffusers.py", "content": "\"\"\"\nThis script modified from\nhttps://github.com/huggingface/diffusers/blob/bc691231360a4cbc7d19a58742ebb8ed0f05e027/scripts/convert_original_stable_diffusion_to_diffusers.py\n\nConvert original Zero1to3 checkpoint to diffusers checkpoint.\n\n# run the convert script\n$ python convert_zero123_to_diffusers.py \\\n --checkpoint_path /path/zero123/105000.ckpt \\\n --dump_path ./zero1to3 \\\n --original_config_file /path/zero123/configs/sd-objaverse-finetune-c_concat-256.yaml\n```\n\"\"\"\n\nimport argparse\n\nimport torch\nimport yaml\nfrom accelerate import init_empty_weights\nfrom accelerate.utils import set_module_tensor_to_device\nfrom pipeline_zero1to3 import CCProjection, Zero1to3StableDiffusionPipeline\nfrom transformers import (\n CLIPImageProcessor,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers.models import (\n AutoencoderKL,\n UNet2DConditionModel,\n)\nfrom diffusers.schedulers import DDIMScheduler\nfrom diffusers.utils import logging\n\n\nlogger = logging.get_logger(__name__)\n\n\ndef create_unet_diffusers_config(original_config, image_size: int, controlnet=False):\n \"\"\"\n Creates a config for the diffusers based on the config of the LDM model.\n \"\"\"\n if controlnet:\n unet_params = original_config[\"model\"][\"params\"][\"control_stage_config\"][\"params\"]\n else:\n if (\n \"unet_config\" in original_config[\"model\"][\"params\"]\n and original_config[\"model\"][\"params\"][\"unet_config\"] is not None\n ):\n unet_params = original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"]\n else:\n unet_params = original_config[\"model\"][\"params\"][\"network_config\"][\"params\"]\n\n vae_params = original_config[\"model\"][\"params\"][\"first_stage_config\"][\"params\"][\"ddconfig\"]\n\n block_out_channels = [unet_params[\"model_channels\"] * mult for mult in unet_params[\"channel_mult\"]]\n\n down_block_types = []\n resolution = 1\n for i in range(len(block_out_channels)):\n block_type = \"CrossAttnDownBlock2D\" if resolution in unet_params[\"attention_resolutions\"] else \"DownBlock2D\"\n down_block_types.append(block_type)\n if i != len(block_out_channels) - 1:\n resolution *= 2\n\n up_block_types = []\n for i in range(len(block_out_channels)):\n block_type = \"CrossAttnUpBlock2D\" if resolution in unet_params[\"attention_resolutions\"] else \"UpBlock2D\"\n up_block_types.append(block_type)\n resolution //= 2\n\n if unet_params[\"transformer_depth\"] is not None:\n transformer_layers_per_block = (\n unet_params[\"transformer_depth\"]\n if isinstance(unet_params[\"transformer_depth\"], int)\n else list(unet_params[\"transformer_depth\"])\n )\n else:\n transformer_layers_per_block = 1\n\n vae_scale_factor = 2 ** (len(vae_params[\"ch_mult\"]) - 1)\n\n head_dim = unet_params[\"num_heads\"] if \"num_heads\" in unet_params else None\n use_linear_projection = (\n unet_params[\"use_linear_in_transformer\"] if \"use_linear_in_transformer\" in unet_params else False\n )\n if use_linear_projection:\n # stable diffusion 2-base-512 and 2-768\n if head_dim is None:\n head_dim_mult = unet_params[\"model_channels\"] // unet_params[\"num_head_channels\"]\n head_dim = [head_dim_mult * c for c in list(unet_params[\"channel_mult\"])]\n\n class_embed_type = None\n addition_embed_type = None\n addition_time_embed_dim = None\n projection_class_embeddings_input_dim = None\n context_dim = None\n\n if unet_params[\"context_dim\"] is not None:\n context_dim = (\n unet_params[\"context_dim\"]\n if isinstance(unet_params[\"context_dim\"], int)\n else unet_params[\"context_dim\"][0]\n )\n\n if \"num_classes\" in unet_params:\n if unet_params[\"num_classes\"] == \"sequential\":\n if context_dim in [2048, 1280]:\n # SDXL\n addition_embed_type = \"text_time\"\n addition_time_embed_dim = 256\n else:\n class_embed_type = \"projection\"\n assert \"adm_in_channels\" in unet_params\n projection_class_embeddings_input_dim = unet_params[\"adm_in_channels\"]\n else:\n raise NotImplementedError(f\"Unknown conditional unet num_classes config: {unet_params['num_classes']}\")\n\n config = {\n \"sample_size\": image_size // vae_scale_factor,\n \"in_channels\": unet_params[\"in_channels\"],\n \"down_block_types\": tuple(down_block_types),\n \"block_out_channels\": tuple(block_out_channels),\n \"layers_per_block\": unet_params[\"num_res_blocks\"],\n \"cross_attention_dim\": context_dim,\n \"attention_head_dim\": head_dim,\n \"use_linear_projection\": use_linear_projection,\n \"class_embed_type\": class_embed_type,\n \"addition_embed_type\": addition_embed_type,\n \"addition_time_embed_dim\": addition_time_embed_dim,\n \"projection_class_embeddings_input_dim\": projection_class_embeddings_input_dim,\n \"transformer_layers_per_block\": transformer_layers_per_block,\n }\n\n if controlnet:\n config[\"conditioning_channels\"] = unet_params[\"hint_channels\"]\n else:\n config[\"out_channels\"] = unet_params[\"out_channels\"]\n config[\"up_block_types\"] = tuple(up_block_types)\n\n return config\n\n\ndef assign_to_checkpoint(\n paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None\n):\n \"\"\"\n This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits\n attention layers, and takes into account additional replacements that may arise.\n\n Assigns the weights to the new checkpoint.\n \"\"\"\n assert isinstance(paths, list), \"Paths should be a list of dicts containing 'old' and 'new' keys.\"\n\n # Splits the attention layers into three variables.\n if attention_paths_to_split is not None:\n for path, path_map in attention_paths_to_split.items():\n old_tensor = old_checkpoint[path]\n channels = old_tensor.shape[0] // 3\n\n target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)\n\n num_heads = old_tensor.shape[0] // config[\"num_head_channels\"] // 3\n\n old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])\n query, key, value = old_tensor.split(channels // num_heads, dim=1)\n\n checkpoint[path_map[\"query\"]] = query.reshape(target_shape)\n checkpoint[path_map[\"key\"]] = key.reshape(target_shape)\n checkpoint[path_map[\"value\"]] = value.reshape(target_shape)\n\n for path in paths:\n new_path = path[\"new\"]\n\n # These have already been assigned\n if attention_paths_to_split is not None and new_path in attention_paths_to_split:\n continue\n\n # Global renaming happens here\n new_path = new_path.replace(\"middle_block.0\", \"mid_block.resnets.0\")\n new_path = new_path.replace(\"middle_block.1\", \"mid_block.attentions.0\")\n new_path = new_path.replace(\"middle_block.2\", \"mid_block.resnets.1\")\n\n if additional_replacements is not None:\n for replacement in additional_replacements:\n new_path = new_path.replace(replacement[\"old\"], replacement[\"new\"])\n\n # proj_attn.weight has to be converted from conv 1D to linear\n is_attn_weight = \"proj_attn.weight\" in new_path or (\"attentions\" in new_path and \"to_\" in new_path)\n shape = old_checkpoint[path[\"old\"]].shape\n if is_attn_weight and len(shape) == 3:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]][:, :, 0]\n elif is_attn_weight and len(shape) == 4:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]][:, :, 0, 0]\n else:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]]\n\n\ndef shave_segments(path, n_shave_prefix_segments=1):\n \"\"\"\n Removes segments. Positive values shave the first segments, negative shave the last segments.\n \"\"\"\n if n_shave_prefix_segments >= 0:\n return \".\".join(path.split(\".\")[n_shave_prefix_segments:])\n else:\n return \".\".join(path.split(\".\")[:n_shave_prefix_segments])\n\n\ndef renew_resnet_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside resnets to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item.replace(\"in_layers.0\", \"norm1\")\n new_item = new_item.replace(\"in_layers.2\", \"conv1\")\n\n new_item = new_item.replace(\"out_layers.0\", \"norm2\")\n new_item = new_item.replace(\"out_layers.3\", \"conv2\")\n\n new_item = new_item.replace(\"emb_layers.1\", \"time_emb_proj\")\n new_item = new_item.replace(\"skip_connection\", \"conv_shortcut\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef renew_attention_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside attentions to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n # new_item = new_item.replace('norm.weight', 'group_norm.weight')\n # new_item = new_item.replace('norm.bias', 'group_norm.bias')\n\n # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')\n # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')\n\n # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef convert_ldm_unet_checkpoint(\n checkpoint, config, path=None, extract_ema=False, controlnet=False, skip_extract_state_dict=False\n):\n \"\"\"\n Takes a state dict and a config, and returns a converted checkpoint.\n \"\"\"\n\n if skip_extract_state_dict:\n unet_state_dict = checkpoint\n else:\n # extract state_dict for UNet\n unet_state_dict = {}\n keys = list(checkpoint.keys())\n\n if controlnet:\n unet_key = \"control_model.\"\n else:\n unet_key = \"model.diffusion_model.\"\n\n # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA\n if sum(k.startswith(\"model_ema\") for k in keys) > 100 and extract_ema:\n logger.warning(f\"Checkpoint {path} has both EMA and non-EMA weights.\")\n logger.warning(\n \"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA\"\n \" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.\"\n )\n for key in keys:\n if key.startswith(\"model.diffusion_model\"):\n flat_ema_key = \"model_ema.\" + \"\".join(key.split(\".\")[1:])\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint[flat_ema_key]\n else:\n if sum(k.startswith(\"model_ema\") for k in keys) > 100:\n logger.warning(\n \"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA\"\n \" weights (usually better for inference), please make sure to add the `--extract_ema` flag.\"\n )\n\n for key in keys:\n if key.startswith(unet_key):\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint[key]\n\n new_checkpoint = {}\n\n new_checkpoint[\"time_embedding.linear_1.weight\"] = unet_state_dict[\"time_embed.0.weight\"]\n new_checkpoint[\"time_embedding.linear_1.bias\"] = unet_state_dict[\"time_embed.0.bias\"]\n new_checkpoint[\"time_embedding.linear_2.weight\"] = unet_state_dict[\"time_embed.2.weight\"]\n new_checkpoint[\"time_embedding.linear_2.bias\"] = unet_state_dict[\"time_embed.2.bias\"]\n\n if config[\"class_embed_type\"] is None:\n # No parameters to port\n ...\n elif config[\"class_embed_type\"] == \"timestep\" or config[\"class_embed_type\"] == \"projection\":\n new_checkpoint[\"class_embedding.linear_1.weight\"] = unet_state_dict[\"label_emb.0.0.weight\"]\n new_checkpoint[\"class_embedding.linear_1.bias\"] = unet_state_dict[\"label_emb.0.0.bias\"]\n new_checkpoint[\"class_embedding.linear_2.weight\"] = unet_state_dict[\"label_emb.0.2.weight\"]\n new_checkpoint[\"class_embedding.linear_2.bias\"] = unet_state_dict[\"label_emb.0.2.bias\"]\n else:\n raise NotImplementedError(f\"Not implemented `class_embed_type`: {config['class_embed_type']}\")\n\n if config[\"addition_embed_type\"] == \"text_time\":\n new_checkpoint[\"add_embedding.linear_1.weight\"] = unet_state_dict[\"label_emb.0.0.weight\"]\n new_checkpoint[\"add_embedding.linear_1.bias\"] = unet_state_dict[\"label_emb.0.0.bias\"]\n new_checkpoint[\"add_embedding.linear_2.weight\"] = unet_state_dict[\"label_emb.0.2.weight\"]\n new_checkpoint[\"add_embedding.linear_2.bias\"] = unet_state_dict[\"label_emb.0.2.bias\"]\n\n new_checkpoint[\"conv_in.weight\"] = unet_state_dict[\"input_blocks.0.0.weight\"]\n new_checkpoint[\"conv_in.bias\"] = unet_state_dict[\"input_blocks.0.0.bias\"]\n\n if not controlnet:\n new_checkpoint[\"conv_norm_out.weight\"] = unet_state_dict[\"out.0.weight\"]\n new_checkpoint[\"conv_norm_out.bias\"] = unet_state_dict[\"out.0.bias\"]\n new_checkpoint[\"conv_out.weight\"] = unet_state_dict[\"out.2.weight\"]\n new_checkpoint[\"conv_out.bias\"] = unet_state_dict[\"out.2.bias\"]\n\n # Retrieves the keys for the input blocks only\n num_input_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"input_blocks\" in layer})\n input_blocks = {\n layer_id: [key for key in unet_state_dict if f\"input_blocks.{layer_id}\" in key]\n for layer_id in range(num_input_blocks)\n }\n\n # Retrieves the keys for the middle blocks only\n num_middle_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"middle_block\" in layer})\n middle_blocks = {\n layer_id: [key for key in unet_state_dict if f\"middle_block.{layer_id}\" in key]\n for layer_id in range(num_middle_blocks)\n }\n\n # Retrieves the keys for the output blocks only\n num_output_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"output_blocks\" in layer})\n output_blocks = {\n layer_id: [key for key in unet_state_dict if f\"output_blocks.{layer_id}\" in key]\n for layer_id in range(num_output_blocks)\n }\n\n for i in range(1, num_input_blocks):\n block_id = (i - 1) // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = (i - 1) % (config[\"layers_per_block\"] + 1)\n\n resnets = [\n key for key in input_blocks[i] if f\"input_blocks.{i}.0\" in key and f\"input_blocks.{i}.0.op\" not in key\n ]\n attentions = [key for key in input_blocks[i] if f\"input_blocks.{i}.1\" in key]\n\n if f\"input_blocks.{i}.0.op.weight\" in unet_state_dict:\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.weight\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.op.weight\"\n )\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.bias\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.op.bias\"\n )\n\n paths = renew_resnet_paths(resnets)\n meta_path = {\"old\": f\"input_blocks.{i}.0\", \"new\": f\"down_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n meta_path = {\"old\": f\"input_blocks.{i}.1\", \"new\": f\"down_blocks.{block_id}.attentions.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n resnet_0 = middle_blocks[0]\n attentions = middle_blocks[1]\n resnet_1 = middle_blocks[2]\n\n resnet_0_paths = renew_resnet_paths(resnet_0)\n assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)\n\n resnet_1_paths = renew_resnet_paths(resnet_1)\n assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)\n\n attentions_paths = renew_attention_paths(attentions)\n meta_path = {\"old\": \"middle_block.1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(\n attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n for i in range(num_output_blocks):\n block_id = i // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = i % (config[\"layers_per_block\"] + 1)\n output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]\n output_block_list = {}\n\n for layer in output_block_layers:\n layer_id, layer_name = layer.split(\".\")[0], shave_segments(layer, 1)\n if layer_id in output_block_list:\n output_block_list[layer_id].append(layer_name)\n else:\n output_block_list[layer_id] = [layer_name]\n\n if len(output_block_list) > 1:\n resnets = [key for key in output_blocks[i] if f\"output_blocks.{i}.0\" in key]\n attentions = [key for key in output_blocks[i] if f\"output_blocks.{i}.1\" in key]\n\n resnet_0_paths = renew_resnet_paths(resnets)\n paths = renew_resnet_paths(resnets)\n\n meta_path = {\"old\": f\"output_blocks.{i}.0\", \"new\": f\"up_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n output_block_list = {k: sorted(v) for k, v in output_block_list.items()}\n if [\"conv.bias\", \"conv.weight\"] in output_block_list.values():\n index = list(output_block_list.values()).index([\"conv.bias\", \"conv.weight\"])\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.weight\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.weight\"\n ]\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.bias\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.bias\"\n ]\n\n # Clear attentions as they have been attributed above.\n if len(attentions) == 2:\n attentions = []\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n meta_path = {\n \"old\": f\"output_blocks.{i}.1\",\n \"new\": f\"up_blocks.{block_id}.attentions.{layer_in_block_id}\",\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n else:\n resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)\n for path in resnet_0_paths:\n old_path = \".\".join([\"output_blocks\", str(i), path[\"old\"]])\n new_path = \".\".join([\"up_blocks\", str(block_id), \"resnets\", str(layer_in_block_id), path[\"new\"]])\n\n new_checkpoint[new_path] = unet_state_dict[old_path]\n\n if controlnet:\n # conditioning embedding\n\n orig_index = 0\n\n new_checkpoint[\"controlnet_cond_embedding.conv_in.weight\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.weight\"\n )\n new_checkpoint[\"controlnet_cond_embedding.conv_in.bias\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.bias\"\n )\n\n orig_index += 2\n\n diffusers_index = 0\n\n while diffusers_index < 6:\n new_checkpoint[f\"controlnet_cond_embedding.blocks.{diffusers_index}.weight\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.weight\"\n )\n new_checkpoint[f\"controlnet_cond_embedding.blocks.{diffusers_index}.bias\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.bias\"\n )\n diffusers_index += 1\n orig_index += 2\n\n new_checkpoint[\"controlnet_cond_embedding.conv_out.weight\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.weight\"\n )\n new_checkpoint[\"controlnet_cond_embedding.conv_out.bias\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.bias\"\n )\n\n # down blocks\n for i in range(num_input_blocks):\n new_checkpoint[f\"controlnet_down_blocks.{i}.weight\"] = unet_state_dict.pop(f\"zero_convs.{i}.0.weight\")\n new_checkpoint[f\"controlnet_down_blocks.{i}.bias\"] = unet_state_dict.pop(f\"zero_convs.{i}.0.bias\")\n\n # mid block\n new_checkpoint[\"controlnet_mid_block.weight\"] = unet_state_dict.pop(\"middle_block_out.0.weight\")\n new_checkpoint[\"controlnet_mid_block.bias\"] = unet_state_dict.pop(\"middle_block_out.0.bias\")\n\n return new_checkpoint\n\n\ndef create_vae_diffusers_config(original_config, image_size: int):\n \"\"\"\n Creates a config for the diffusers based on the config of the LDM model.\n \"\"\"\n vae_params = original_config[\"model\"][\"params\"][\"first_stage_config\"][\"params\"][\"ddconfig\"]\n _ = original_config[\"model\"][\"params\"][\"first_stage_config\"][\"params\"][\"embed_dim\"]\n\n block_out_channels = [vae_params[\"ch\"] * mult for mult in vae_params[\"ch_mult\"]]\n down_block_types = [\"DownEncoderBlock2D\"] * len(block_out_channels)\n up_block_types = [\"UpDecoderBlock2D\"] * len(block_out_channels)\n\n config = {\n \"sample_size\": image_size,\n \"in_channels\": vae_params[\"in_channels\"],\n \"out_channels\": vae_params[\"out_ch\"],\n \"down_block_types\": tuple(down_block_types),\n \"up_block_types\": tuple(up_block_types),\n \"block_out_channels\": tuple(block_out_channels),\n \"latent_channels\": vae_params[\"z_channels\"],\n \"layers_per_block\": vae_params[\"num_res_blocks\"],\n }\n return config\n\n\ndef convert_ldm_vae_checkpoint(checkpoint, config):\n # extract state dict for VAE\n vae_state_dict = {}\n vae_key = \"first_stage_model.\"\n keys = list(checkpoint.keys())\n for key in keys:\n if key.startswith(vae_key):\n vae_state_dict[key.replace(vae_key, \"\")] = checkpoint.get(key)\n\n new_checkpoint = {}\n\n new_checkpoint[\"encoder.conv_in.weight\"] = vae_state_dict[\"encoder.conv_in.weight\"]\n new_checkpoint[\"encoder.conv_in.bias\"] = vae_state_dict[\"encoder.conv_in.bias\"]\n new_checkpoint[\"encoder.conv_out.weight\"] = vae_state_dict[\"encoder.conv_out.weight\"]\n new_checkpoint[\"encoder.conv_out.bias\"] = vae_state_dict[\"encoder.conv_out.bias\"]\n new_checkpoint[\"encoder.conv_norm_out.weight\"] = vae_state_dict[\"encoder.norm_out.weight\"]\n new_checkpoint[\"encoder.conv_norm_out.bias\"] = vae_state_dict[\"encoder.norm_out.bias\"]\n\n new_checkpoint[\"decoder.conv_in.weight\"] = vae_state_dict[\"decoder.conv_in.weight\"]\n new_checkpoint[\"decoder.conv_in.bias\"] = vae_state_dict[\"decoder.conv_in.bias\"]\n new_checkpoint[\"decoder.conv_out.weight\"] = vae_state_dict[\"decoder.conv_out.weight\"]\n new_checkpoint[\"decoder.conv_out.bias\"] = vae_state_dict[\"decoder.conv_out.bias\"]\n new_checkpoint[\"decoder.conv_norm_out.weight\"] = vae_state_dict[\"decoder.norm_out.weight\"]\n new_checkpoint[\"decoder.conv_norm_out.bias\"] = vae_state_dict[\"decoder.norm_out.bias\"]\n\n new_checkpoint[\"quant_conv.weight\"] = vae_state_dict[\"quant_conv.weight\"]\n new_checkpoint[\"quant_conv.bias\"] = vae_state_dict[\"quant_conv.bias\"]\n new_checkpoint[\"post_quant_conv.weight\"] = vae_state_dict[\"post_quant_conv.weight\"]\n new_checkpoint[\"post_quant_conv.bias\"] = vae_state_dict[\"post_quant_conv.bias\"]\n\n # Retrieves the keys for the encoder down blocks only\n num_down_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in vae_state_dict if \"encoder.down\" in layer})\n down_blocks = {\n layer_id: [key for key in vae_state_dict if f\"down.{layer_id}\" in key] for layer_id in range(num_down_blocks)\n }\n\n # Retrieves the keys for the decoder up blocks only\n num_up_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in vae_state_dict if \"decoder.up\" in layer})\n up_blocks = {\n layer_id: [key for key in vae_state_dict if f\"up.{layer_id}\" in key] for layer_id in range(num_up_blocks)\n }\n\n for i in range(num_down_blocks):\n resnets = [key for key in down_blocks[i] if f\"down.{i}\" in key and f\"down.{i}.downsample\" not in key]\n\n if f\"encoder.down.{i}.downsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.weight\"] = vae_state_dict.pop(\n f\"encoder.down.{i}.downsample.conv.weight\"\n )\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.bias\"] = vae_state_dict.pop(\n f\"encoder.down.{i}.downsample.conv.bias\"\n )\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"down.{i}.block\", \"new\": f\"down_blocks.{i}.resnets\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_resnets = [key for key in vae_state_dict if \"encoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"encoder.mid.block_{i}\" in key]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_attentions = [key for key in vae_state_dict if \"encoder.mid.attn\" in key]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n conv_attn_to_linear(new_checkpoint)\n\n for i in range(num_up_blocks):\n block_id = num_up_blocks - 1 - i\n resnets = [\n key for key in up_blocks[block_id] if f\"up.{block_id}\" in key and f\"up.{block_id}.upsample\" not in key\n ]\n\n if f\"decoder.up.{block_id}.upsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.weight\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.weight\"\n ]\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.bias\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.bias\"\n ]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"up.{block_id}.block\", \"new\": f\"up_blocks.{i}.resnets\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_resnets = [key for key in vae_state_dict if \"decoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"decoder.mid.block_{i}\" in key]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_attentions = [key for key in vae_state_dict if \"decoder.mid.attn\" in key]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n conv_attn_to_linear(new_checkpoint)\n return new_checkpoint\n\n\ndef renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside resnets to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n new_item = new_item.replace(\"nin_shortcut\", \"conv_shortcut\")\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside attentions to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n new_item = new_item.replace(\"norm.weight\", \"group_norm.weight\")\n new_item = new_item.replace(\"norm.bias\", \"group_norm.bias\")\n\n new_item = new_item.replace(\"q.weight\", \"to_q.weight\")\n new_item = new_item.replace(\"q.bias\", \"to_q.bias\")\n\n new_item = new_item.replace(\"k.weight\", \"to_k.weight\")\n new_item = new_item.replace(\"k.bias\", \"to_k.bias\")\n\n new_item = new_item.replace(\"v.weight\", \"to_v.weight\")\n new_item = new_item.replace(\"v.bias\", \"to_v.bias\")\n\n new_item = new_item.replace(\"proj_out.weight\", \"to_out.0.weight\")\n new_item = new_item.replace(\"proj_out.bias\", \"to_out.0.bias\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef conv_attn_to_linear(checkpoint):\n keys = list(checkpoint.keys())\n attn_keys = [\"query.weight\", \"key.weight\", \"value.weight\"]\n for key in keys:\n if \".\".join(key.split(\".\")[-2:]) in attn_keys:\n if checkpoint[key].ndim > 2:\n checkpoint[key] = checkpoint[key][:, :, 0, 0]\n elif \"proj_attn.weight\" in key:\n if checkpoint[key].ndim > 2:\n checkpoint[key] = checkpoint[key][:, :, 0]\n\n\ndef convert_from_original_zero123_ckpt(checkpoint_path, original_config_file, extract_ema, device):\n ckpt = torch.load(checkpoint_path, map_location=device)\n ckpt[\"global_step\"]\n checkpoint = ckpt[\"state_dict\"]\n del ckpt\n torch.cuda.empty_cache()\n\n original_config = yaml.safe_load(original_config_file)\n original_config[\"model\"][\"params\"][\"cond_stage_config\"][\"target\"].split(\".\")[-1]\n num_in_channels = 8\n original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"][\"in_channels\"] = num_in_channels\n prediction_type = \"epsilon\"\n image_size = 256\n num_train_timesteps = getattr(original_config[\"model\"][\"params\"], \"timesteps\", None) or 1000\n\n beta_start = getattr(original_config[\"model\"][\"params\"], \"linear_start\", None) or 0.02\n beta_end = getattr(original_config[\"model\"][\"params\"], \"linear_end\", None) or 0.085\n scheduler = DDIMScheduler(\n beta_end=beta_end,\n beta_schedule=\"scaled_linear\",\n beta_start=beta_start,\n num_train_timesteps=num_train_timesteps,\n steps_offset=1,\n clip_sample=False,\n set_alpha_to_one=False,\n prediction_type=prediction_type,\n )\n scheduler.register_to_config(clip_sample=False)\n\n # Convert the UNet2DConditionModel model.\n upcast_attention = None\n unet_config = create_unet_diffusers_config(original_config, image_size=image_size)\n unet_config[\"upcast_attention\"] = upcast_attention\n with init_empty_weights():\n unet = UNet2DConditionModel(**unet_config)\n converted_unet_checkpoint = convert_ldm_unet_checkpoint(\n checkpoint, unet_config, path=None, extract_ema=extract_ema\n )\n for param_name, param in converted_unet_checkpoint.items():\n set_module_tensor_to_device(unet, param_name, \"cpu\", value=param)\n\n # Convert the VAE model.\n vae_config = create_vae_diffusers_config(original_config, image_size=image_size)\n converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)\n\n if (\n \"model\" in original_config\n and \"params\" in original_config[\"model\"]\n and \"scale_factor\" in original_config[\"model\"][\"params\"]\n ):\n vae_scaling_factor = original_config[\"model\"][\"params\"][\"scale_factor\"]\n else:\n vae_scaling_factor = 0.18215 # default SD scaling factor\n\n vae_config[\"scaling_factor\"] = vae_scaling_factor\n\n with init_empty_weights():\n vae = AutoencoderKL(**vae_config)\n\n for param_name, param in converted_vae_checkpoint.items():\n set_module_tensor_to_device(vae, param_name, \"cpu\", value=param)\n\n feature_extractor = CLIPImageProcessor.from_pretrained(\n \"lambdalabs/sd-image-variations-diffusers\", subfolder=\"feature_extractor\"\n )\n image_encoder = CLIPVisionModelWithProjection.from_pretrained(\n \"lambdalabs/sd-image-variations-diffusers\", subfolder=\"image_encoder\"\n )\n\n cc_projection = CCProjection()\n cc_projection.load_state_dict(\n {\n \"projection.weight\": checkpoint[\"cc_projection.weight\"].cpu(),\n \"projection.bias\": checkpoint[\"cc_projection.bias\"].cpu(),\n }\n )\n\n pipe = Zero1to3StableDiffusionPipeline(\n vae, image_encoder, unet, scheduler, None, feature_extractor, cc_projection, requires_safety_checker=False\n )\n\n return pipe\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--checkpoint_path\", default=None, type=str, required=True, help=\"Path to the checkpoint to convert.\"\n )\n parser.add_argument(\n \"--original_config_file\",\n default=None,\n type=str,\n help=\"The YAML config file corresponding to the original architecture.\",\n )\n parser.add_argument(\n \"--extract_ema\",\n action=\"store_true\",\n help=(\n \"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights\"\n \" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield\"\n \" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.\"\n ),\n )\n parser.add_argument(\n \"--to_safetensors\",\n action=\"store_true\",\n help=\"Whether to store pipeline in safetensors format or not.\",\n )\n parser.add_argument(\"--half\", action=\"store_true\", help=\"Save weights in half precision.\")\n parser.add_argument(\"--dump_path\", default=None, type=str, required=True, help=\"Path to the output model.\")\n parser.add_argument(\"--device\", type=str, help=\"Device to use (e.g. cpu, cuda:0, cuda:1, etc.)\")\n args = parser.parse_args()\n\n pipe = convert_from_original_zero123_ckpt(\n checkpoint_path=args.checkpoint_path,\n original_config_file=args.original_config_file,\n extract_ema=args.extract_ema,\n device=args.device,\n )\n\n if args.half:\n pipe.to(dtype=torch.float16)\n\n pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)\n" }, { "path": "diffusers/scripts/generate_logits.py", "content": "import random\n\nimport torch\nfrom huggingface_hub import HfApi\n\nfrom diffusers import UNet2DModel\n\n\napi = HfApi()\n\nresults = {}\n# fmt: off\nresults[\"google_ddpm_cifar10_32\"] = torch.tensor([\n -0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,\n 1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,\n -1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,\n 0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557\n])\nresults[\"google_ddpm_ema_bedroom_256\"] = torch.tensor([\n -2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,\n 1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,\n -2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,\n 2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365\n])\nresults[\"CompVis_ldm_celebahq_256\"] = torch.tensor([\n -0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,\n -0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,\n -0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,\n 0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943\n])\nresults[\"google_ncsnpp_ffhq_1024\"] = torch.tensor([\n 0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,\n -0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,\n 0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,\n -0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505\n])\nresults[\"google_ncsnpp_bedroom_256\"] = torch.tensor([\n 0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,\n -0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,\n 0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,\n -0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386\n])\nresults[\"google_ncsnpp_celebahq_256\"] = torch.tensor([\n 0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,\n -0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,\n 0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,\n -0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431\n])\nresults[\"google_ncsnpp_church_256\"] = torch.tensor([\n 0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,\n -0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,\n 0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,\n -0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390\n])\nresults[\"google_ncsnpp_ffhq_256\"] = torch.tensor([\n 0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,\n -0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,\n 0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,\n -0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473\n])\nresults[\"google_ddpm_cat_256\"] = torch.tensor([\n -1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,\n 1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,\n -2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,\n 1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])\nresults[\"google_ddpm_celebahq_256\"] = torch.tensor([\n -1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,\n 0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,\n -2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,\n 1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266\n])\nresults[\"google_ddpm_ema_celebahq_256\"] = torch.tensor([\n -1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,\n 0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,\n -2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,\n 1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355\n])\nresults[\"google_ddpm_church_256\"] = torch.tensor([\n -2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,\n 1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,\n -3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,\n 3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066\n])\nresults[\"google_ddpm_bedroom_256\"] = torch.tensor([\n -2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,\n 1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,\n -2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,\n 2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243\n])\nresults[\"google_ddpm_ema_church_256\"] = torch.tensor([\n -2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,\n 1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,\n -3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,\n 3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343\n])\nresults[\"google_ddpm_ema_cat_256\"] = torch.tensor([\n -1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,\n 1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,\n -2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,\n 1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219\n])\n# fmt: on\n\nmodels = api.list_models(filter=\"diffusers\")\nfor mod in models:\n if \"google\" in mod.author or mod.modelId == \"CompVis/ldm-celebahq-256\":\n local_checkpoint = \"/home/patrick/google_checkpoints/\" + mod.modelId.split(\"/\")[-1]\n\n print(f\"Started running {mod.modelId}!!!\")\n\n if mod.modelId.startswith(\"CompVis\"):\n model = UNet2DModel.from_pretrained(local_checkpoint, subfolder=\"unet\")\n else:\n model = UNet2DModel.from_pretrained(local_checkpoint)\n\n torch.manual_seed(0)\n random.seed(0)\n\n noise = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)\n time_step = torch.tensor([10] * noise.shape[0])\n with torch.no_grad():\n logits = model(noise, time_step).sample\n\n assert torch.allclose(\n logits[0, 0, 0, :30], results[\"_\".join(\"_\".join(mod.modelId.split(\"/\")).split(\"-\"))], atol=1e-3\n )\n print(f\"{mod.modelId} has passed successfully!!!\")\n" }, { "path": "diffusers/scripts/log_reports.py", "content": "import argparse\nimport json\nimport os\nfrom datetime import date\nfrom pathlib import Path\n\nfrom slack_sdk import WebClient\nfrom tabulate import tabulate\n\n\nMAX_LEN_MESSAGE = 2900 # slack endpoint has a limit of 3001 characters\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--slack_channel_name\", default=\"diffusers-ci-nightly\")\n\n\ndef main(slack_channel_name=None):\n failed = []\n passed = []\n\n group_info = []\n\n total_num_failed = 0\n empty_file = False or len(list(Path().glob(\"*.log\"))) == 0\n\n total_empty_files = []\n\n for log in Path().glob(\"*.log\"):\n section_num_failed = 0\n i = 0\n with open(log) as f:\n for line in f:\n line = json.loads(line)\n i += 1\n if line.get(\"nodeid\", \"\") != \"\":\n test = line[\"nodeid\"]\n if line.get(\"duration\", None) is not None:\n duration = f'{line[\"duration\"]:.4f}'\n if line.get(\"outcome\", \"\") == \"failed\":\n section_num_failed += 1\n failed.append([test, duration, log.name.split(\"_\")[0]])\n total_num_failed += 1\n else:\n passed.append([test, duration, log.name.split(\"_\")[0]])\n empty_file = i == 0\n group_info.append([str(log), section_num_failed, failed])\n total_empty_files.append(empty_file)\n os.remove(log)\n failed = []\n text = (\n \"🌞 There were no failures!\"\n if not any(total_empty_files)\n else \"Something went wrong there is at least one empty file - please check GH action results.\"\n )\n no_error_payload = {\n \"type\": \"section\",\n \"text\": {\n \"type\": \"plain_text\",\n \"text\": text,\n \"emoji\": True,\n },\n }\n\n message = \"\"\n payload = [\n {\n \"type\": \"header\",\n \"text\": {\n \"type\": \"plain_text\",\n \"text\": \"🤗 Results of the Diffusers scheduled nightly tests.\",\n },\n },\n ]\n if total_num_failed > 0:\n for i, (name, num_failed, failed_tests) in enumerate(group_info):\n if num_failed > 0:\n if num_failed == 1:\n message += f\"*{name}: {num_failed} failed test*\\n\"\n else:\n message += f\"*{name}: {num_failed} failed tests*\\n\"\n failed_table = []\n for test in failed_tests:\n failed_table.append(test[0].split(\"::\"))\n failed_table = tabulate(\n failed_table,\n headers=[\"Test Location\", \"Test Case\", \"Test Name\"],\n showindex=\"always\",\n tablefmt=\"grid\",\n maxcolwidths=[12, 12, 12],\n )\n message += \"\\n```\\n\" + failed_table + \"\\n```\"\n\n if total_empty_files[i]:\n message += f\"\\n*{name}: Warning! Empty file - please check the GitHub action job *\\n\"\n print(f\"### {message}\")\n else:\n payload.append(no_error_payload)\n\n if len(message) > MAX_LEN_MESSAGE:\n print(f\"Truncating long message from {len(message)} to {MAX_LEN_MESSAGE}\")\n message = message[:MAX_LEN_MESSAGE] + \"...\"\n\n if len(message) != 0:\n md_report = {\n \"type\": \"section\",\n \"text\": {\"type\": \"mrkdwn\", \"text\": message},\n }\n payload.append(md_report)\n action_button = {\n \"type\": \"section\",\n \"text\": {\"type\": \"mrkdwn\", \"text\": \"*For more details:*\"},\n \"accessory\": {\n \"type\": \"button\",\n \"text\": {\"type\": \"plain_text\", \"text\": \"Check Action results\", \"emoji\": True},\n \"url\": f\"https://github.com/huggingface/diffusers/actions/runs/{os.environ['GITHUB_RUN_ID']}\",\n },\n }\n payload.append(action_button)\n\n date_report = {\n \"type\": \"context\",\n \"elements\": [\n {\n \"type\": \"plain_text\",\n \"text\": f\"Nightly test results for {date.today()}\",\n },\n ],\n }\n payload.append(date_report)\n\n print(payload)\n\n client = WebClient(token=os.environ.get(\"SLACK_API_TOKEN\"))\n client.chat_postMessage(channel=f\"#{slack_channel_name}\", text=message, blocks=payload)\n\n\nif __name__ == \"__main__\":\n args = parser.parse_args()\n main(args.slack_channel_name)\n" }, { "path": "diffusers/setup.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"\nSimple check list from AllenNLP repo: https://github.com/allenai/allennlp/blob/main/setup.py\n\nTo create the package for PyPI.\n\n1. Run `make pre-release` (or `make pre-patch` for a patch release) then run `make fix-copies` to fix the index of the\n documentation.\n\n If releasing on a special branch, copy the updated README.md on the main branch for the commit you will make\n for the post-release and run `make fix-copies` on the main branch as well.\n\n2. Unpin specific versions from setup.py that use a git install.\n\n3. Checkout the release branch (v-release, for example v4.19-release), and commit these changes with the\n message: \"Release: \" and push.\n\n4. Manually trigger the \"Nightly and release tests on main/release branch\" workflow from the release branch. Wait for\n the tests to complete. We can safely ignore the known test failures.\n\n5. Wait for the tests on main to be completed and be green (otherwise revert and fix bugs).\n\n6. Add a tag in git to mark the release: \"git tag v -m 'Adds tag v for PyPI'\"\n Push the tag to git: git push --tags origin v-release\n\n7. Build both the sources and the wheel. Do not change anything in setup.py between\n creating the wheel and the source distribution (obviously).\n\n For the wheel, run: \"python setup.py bdist_wheel\" in the top level directory\n (This will build a wheel for the Python version you use to build it).\n\n For the sources, run: \"python setup.py sdist\"\n You should now have a /dist directory with both .whl and .tar.gz source versions.\n\n Long story cut short, you need to run both before you can upload the distribution to the\n test PyPI and the actual PyPI servers:\n\n python setup.py bdist_wheel && python setup.py sdist\n\n8. Check that everything looks correct by uploading the package to the PyPI test server:\n\n twine upload dist/* -r pypitest\n (pypi suggests using twine as other methods upload files via plaintext.)\n You may have to specify the repository url, use the following command then:\n twine upload dist/* -r pypitest --repository-url=https://test.pypi.org/legacy/\n\n Check that you can install it in a virtualenv by running:\n pip install -i https://testpypi.python.org/pypi diffusers\n\n If you are testing from a Colab Notebook, for instance, then do:\n pip install diffusers && pip uninstall diffusers\n pip install -i https://testpypi.python.org/pypi diffusers\n\n Check you can run the following commands:\n python -c \"from diffusers import __version__; print(__version__)\"\n python -c \"from diffusers import DiffusionPipeline; pipe = DiffusionPipeline.from_pretrained('fusing/unet-ldm-dummy-update'); pipe()\"\n python -c \"from diffusers import DiffusionPipeline; pipe = DiffusionPipeline.from_pretrained('hf-internal-testing/tiny-stable-diffusion-pipe', safety_checker=None); pipe('ah suh du')\"\n python -c \"from diffusers import *\"\n\n9. Upload the final version to the actual PyPI:\n twine upload dist/* -r pypi\n\n10. Prepare the release notes and publish them on GitHub once everything is looking hunky-dory. You can use the following\n Space to fetch all the commits applicable for the release: https://huggingface.co/spaces/lysandre/github-release. Repo should\n be `huggingface/diffusers`. `tag` should be the previous release tag (v0.26.1, for example), and `branch` should be\n the latest release branch (v0.27.0-release, for example). It denotes all commits that have happened on branch\n v0.27.0-release after the tag v0.26.1 was created.\n\n11. Run `make post-release` (or, for a patch release, `make post-patch`). If you were on a branch for the release,\n you need to go back to main before executing this.\n\"\"\"\n\nimport os\nimport re\nimport sys\n\nfrom setuptools import Command, find_packages, setup\n\n\n# IMPORTANT:\n# 1. all dependencies should be listed here with their version requirements if any\n# 2. once modified, run: `make deps_table_update` to update src/diffusers/dependency_versions_table.py\n_deps = [\n \"Pillow\", # keep the PIL.Image.Resampling deprecation away\n \"accelerate>=0.31.0\",\n \"compel==0.1.8\",\n \"datasets\",\n \"filelock\",\n \"flax>=0.4.1\",\n \"hf-doc-builder>=0.3.0\",\n \"huggingface-hub>=0.23.2\",\n \"requests-mock==1.10.0\",\n \"importlib_metadata\",\n \"invisible-watermark>=0.2.0\",\n \"isort>=5.5.4\",\n \"jax>=0.4.1\",\n \"jaxlib>=0.4.1\",\n \"Jinja2\",\n \"k-diffusion>=0.0.12\",\n \"torchsde\",\n \"note_seq\",\n \"librosa\",\n \"numpy\",\n \"parameterized\",\n \"peft>=0.6.0\",\n \"protobuf>=3.20.3,<4\",\n \"pytest\",\n \"pytest-timeout\",\n \"pytest-xdist\",\n \"python>=3.8.0\",\n \"ruff==0.1.5\",\n \"safetensors>=0.3.1\",\n \"sentencepiece>=0.1.91,!=0.1.92\",\n \"GitPython<3.1.19\",\n \"scipy\",\n \"onnx\",\n \"regex!=2019.12.17\",\n \"requests\",\n \"tensorboard\",\n \"torch>=1.4\",\n \"torchvision\",\n \"transformers>=4.41.2\",\n \"urllib3<=2.0.0\",\n \"black\",\n]\n\n# this is a lookup table with items like:\n#\n# tokenizers: \"huggingface-hub==0.8.0\"\n# packaging: \"packaging\"\n#\n# some of the values are versioned whereas others aren't.\ndeps = {b: a for a, b in (re.findall(r\"^(([^!=<>~]+)(?:[!=<>~].*)?$)\", x)[0] for x in _deps)}\n\n# since we save this data in src/diffusers/dependency_versions_table.py it can be easily accessed from\n# anywhere. If you need to quickly access the data from this table in a shell, you can do so easily with:\n#\n# python -c 'import sys; from diffusers.dependency_versions_table import deps; \\\n# print(\" \".join([deps[x] for x in sys.argv[1:]]))' tokenizers datasets\n#\n# Just pass the desired package names to that script as it's shown with 2 packages above.\n#\n# If diffusers is not yet installed and the work is done from the cloned repo remember to add `PYTHONPATH=src` to the script above\n#\n# You can then feed this for example to `pip`:\n#\n# pip install -U $(python -c 'import sys; from diffusers.dependency_versions_table import deps; \\\n# print(\" \".join([deps[x] for x in sys.argv[1:]]))' tokenizers datasets)\n#\n\n\ndef deps_list(*pkgs):\n return [deps[pkg] for pkg in pkgs]\n\n\nclass DepsTableUpdateCommand(Command):\n \"\"\"\n A custom command that updates the dependency table.\n usage: python setup.py deps_table_update\n \"\"\"\n\n description = \"build runtime dependency table\"\n user_options = [\n # format: (long option, short option, description).\n (\n \"dep-table-update\",\n None,\n \"updates src/diffusers/dependency_versions_table.py\",\n ),\n ]\n\n def initialize_options(self):\n pass\n\n def finalize_options(self):\n pass\n\n def run(self):\n entries = \"\\n\".join([f' \"{k}\": \"{v}\",' for k, v in deps.items()])\n content = [\n \"# THIS FILE HAS BEEN AUTOGENERATED. To update:\",\n \"# 1. modify the `_deps` dict in setup.py\",\n \"# 2. run `make deps_table_update`\",\n \"deps = {\",\n entries,\n \"}\",\n \"\",\n ]\n target = \"src/diffusers/dependency_versions_table.py\"\n print(f\"updating {target}\")\n with open(target, \"w\", encoding=\"utf-8\", newline=\"\\n\") as f:\n f.write(\"\\n\".join(content))\n\n\nextras = {}\nextras[\"quality\"] = deps_list(\"urllib3\", \"isort\", \"ruff\", \"hf-doc-builder\")\nextras[\"docs\"] = deps_list(\"hf-doc-builder\")\nextras[\"training\"] = deps_list(\"accelerate\", \"datasets\", \"protobuf\", \"tensorboard\", \"Jinja2\", \"peft\")\nextras[\"test\"] = deps_list(\n \"compel\",\n \"GitPython\",\n \"datasets\",\n \"Jinja2\",\n \"invisible-watermark\",\n \"k-diffusion\",\n \"librosa\",\n \"parameterized\",\n \"pytest\",\n \"pytest-timeout\",\n \"pytest-xdist\",\n \"requests-mock\",\n \"safetensors\",\n \"sentencepiece\",\n \"scipy\",\n \"torchvision\",\n \"transformers\",\n)\nextras[\"torch\"] = deps_list(\"torch\", \"accelerate\")\n\nif os.name == \"nt\": # windows\n extras[\"flax\"] = [] # jax is not supported on windows\nelse:\n extras[\"flax\"] = deps_list(\"jax\", \"jaxlib\", \"flax\")\n\nextras[\"dev\"] = (\n extras[\"quality\"] + extras[\"test\"] + extras[\"training\"] + extras[\"docs\"] + extras[\"torch\"] + extras[\"flax\"]\n)\n\ninstall_requires = [\n deps[\"importlib_metadata\"],\n deps[\"filelock\"],\n deps[\"huggingface-hub\"],\n deps[\"numpy\"],\n deps[\"regex\"],\n deps[\"requests\"],\n deps[\"safetensors\"],\n deps[\"Pillow\"],\n]\n\nversion_range_max = max(sys.version_info[1], 10) + 1\n\nsetup(\n name=\"diffusers\",\n version=\"0.30.0.dev0\", # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)\n description=\"State-of-the-art diffusion in PyTorch and JAX.\",\n long_description=open(\"README.md\", \"r\", encoding=\"utf-8\").read(),\n long_description_content_type=\"text/markdown\",\n keywords=\"deep learning diffusion jax pytorch stable diffusion audioldm\",\n license=\"Apache 2.0 License\",\n author=\"The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/diffusers/graphs/contributors)\",\n author_email=\"diffusers@huggingface.co\",\n url=\"https://github.com/huggingface/diffusers\",\n package_dir={\"\": \"src\"},\n packages=find_packages(\"src\"),\n package_data={\"diffusers\": [\"py.typed\"]},\n include_package_data=True,\n python_requires=\">=3.8.0\",\n install_requires=list(install_requires),\n extras_require=extras,\n entry_points={\"console_scripts\": [\"diffusers-cli=diffusers.commands.diffusers_cli:main\"]},\n classifiers=[\n \"Development Status :: 5 - Production/Stable\",\n \"Intended Audience :: Developers\",\n \"Intended Audience :: Education\",\n \"Intended Audience :: Science/Research\",\n \"License :: OSI Approved :: Apache Software License\",\n \"Operating System :: OS Independent\",\n \"Topic :: Scientific/Engineering :: Artificial Intelligence\",\n \"Programming Language :: Python :: 3\",\n ]\n + [f\"Programming Language :: Python :: 3.{i}\" for i in range(8, version_range_max)],\n cmdclass={\"deps_table_update\": DepsTableUpdateCommand},\n)\n" }, { "path": "diffusers/src/diffusers/__init__.py", "content": "__version__ = \"0.30.0.dev0\"\n\nfrom typing import TYPE_CHECKING\n\nfrom .utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n is_flax_available,\n is_k_diffusion_available,\n is_librosa_available,\n is_note_seq_available,\n is_onnx_available,\n is_scipy_available,\n is_torch_available,\n is_torchsde_available,\n is_transformers_available,\n)\n\n\n# Lazy Import based on\n# https://github.com/huggingface/transformers/blob/main/src/transformers/__init__.py\n\n# When adding a new object to this init, please add it to `_import_structure`. The `_import_structure` is a dictionary submodule to list of object names,\n# and is used to defer the actual importing for when the objects are requested.\n# This way `import diffusers` provides the names in the namespace without actually importing anything (and especially none of the backends).\n\n_import_structure = {\n \"configuration_utils\": [\"ConfigMixin\"],\n \"loaders\": [\"FromOriginalModelMixin\"],\n \"models\": [],\n \"pipelines\": [],\n \"schedulers\": [],\n \"utils\": [\n \"OptionalDependencyNotAvailable\",\n \"is_flax_available\",\n \"is_inflect_available\",\n \"is_invisible_watermark_available\",\n \"is_k_diffusion_available\",\n \"is_k_diffusion_version\",\n \"is_librosa_available\",\n \"is_note_seq_available\",\n \"is_onnx_available\",\n \"is_scipy_available\",\n \"is_torch_available\",\n \"is_torchsde_available\",\n \"is_transformers_available\",\n \"is_transformers_version\",\n \"is_unidecode_available\",\n \"logging\",\n ],\n}\n\ntry:\n if not is_onnx_available():\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from .utils import dummy_onnx_objects # noqa F403\n\n _import_structure[\"utils.dummy_onnx_objects\"] = [\n name for name in dir(dummy_onnx_objects) if not name.startswith(\"_\")\n ]\n\nelse:\n _import_structure[\"pipelines\"].extend([\"OnnxRuntimeModel\"])\n\ntry:\n if not is_torch_available():\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from .utils import dummy_pt_objects # noqa F403\n\n _import_structure[\"utils.dummy_pt_objects\"] = [name for name in dir(dummy_pt_objects) if not name.startswith(\"_\")]\n\nelse:\n _import_structure[\"models\"].extend(\n [\n \"AsymmetricAutoencoderKL\",\n \"AutoencoderKL\",\n \"AutoencoderKLTemporalDecoder\",\n \"AutoencoderTiny\",\n \"ConsistencyDecoderVAE\",\n \"ControlNetModel\",\n \"ControlNetXSAdapter\",\n \"DiTTransformer2DModel\",\n \"HunyuanDiT2DModel\",\n \"I2VGenXLUNet\",\n \"Kandinsky3UNet\",\n \"ModelMixin\",\n \"MotionAdapter\",\n \"MultiAdapter\",\n \"PixArtTransformer2DModel\",\n \"PriorTransformer\",\n \"SD3Transformer2DModel\",\n \"StableCascadeUNet\",\n \"T2IAdapter\",\n \"T5FilmDecoder\",\n \"Transformer2DModel\",\n \"UNet1DModel\",\n \"UNet2DConditionModel\",\n \"UNet2DModel\",\n \"UNet3DConditionModel\",\n \"UNetControlNetXSModel\",\n \"UNetMotionModel\",\n \"UNetSpatioTemporalConditionModel\",\n \"UVit2DModel\",\n \"VQModel\",\n ]\n )\n\n _import_structure[\"optimization\"] = [\n \"get_constant_schedule\",\n \"get_constant_schedule_with_warmup\",\n \"get_cosine_schedule_with_warmup\",\n \"get_cosine_with_hard_restarts_schedule_with_warmup\",\n \"get_linear_schedule_with_warmup\",\n \"get_polynomial_decay_schedule_with_warmup\",\n \"get_scheduler\",\n ]\n _import_structure[\"pipelines\"].extend(\n [\n \"AudioPipelineOutput\",\n \"AutoPipelineForImage2Image\",\n \"AutoPipelineForInpainting\",\n \"AutoPipelineForText2Image\",\n \"ConsistencyModelPipeline\",\n \"DanceDiffusionPipeline\",\n \"DDIMPipeline\",\n \"DDPMPipeline\",\n \"DiffusionPipeline\",\n \"DiTPipeline\",\n \"ImagePipelineOutput\",\n \"KarrasVePipeline\",\n \"LDMPipeline\",\n \"LDMSuperResolutionPipeline\",\n \"PNDMPipeline\",\n \"RePaintPipeline\",\n \"ScoreSdeVePipeline\",\n \"StableDiffusionMixin\",\n ]\n )\n _import_structure[\"schedulers\"].extend(\n [\n \"AmusedScheduler\",\n \"CMStochasticIterativeScheduler\",\n \"DDIMInverseScheduler\",\n \"DDIMParallelScheduler\",\n \"DDIMScheduler\",\n \"DDPMParallelScheduler\",\n \"DDPMScheduler\",\n \"DDPMWuerstchenScheduler\",\n \"DEISMultistepScheduler\",\n \"DPMSolverMultistepInverseScheduler\",\n \"DPMSolverMultistepScheduler\",\n \"DPMSolverSinglestepScheduler\",\n \"EDMDPMSolverMultistepScheduler\",\n \"EDMEulerScheduler\",\n \"EulerAncestralDiscreteScheduler\",\n \"EulerDiscreteScheduler\",\n \"FlowMatchEulerDiscreteScheduler\",\n \"HeunDiscreteScheduler\",\n \"IPNDMScheduler\",\n \"KarrasVeScheduler\",\n \"KDPM2AncestralDiscreteScheduler\",\n \"KDPM2DiscreteScheduler\",\n \"LCMScheduler\",\n \"PNDMScheduler\",\n \"RePaintScheduler\",\n \"SASolverScheduler\",\n \"SchedulerMixin\",\n \"ScoreSdeVeScheduler\",\n \"TCDScheduler\",\n \"UnCLIPScheduler\",\n \"UniPCMultistepScheduler\",\n \"VQDiffusionScheduler\",\n ]\n )\n _import_structure[\"training_utils\"] = [\"EMAModel\"]\n\ntry:\n if not (is_torch_available() and is_scipy_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from .utils import dummy_torch_and_scipy_objects # noqa F403\n\n _import_structure[\"utils.dummy_torch_and_scipy_objects\"] = [\n name for name in dir(dummy_torch_and_scipy_objects) if not name.startswith(\"_\")\n ]\n\nelse:\n _import_structure[\"schedulers\"].extend([\"LMSDiscreteScheduler\"])\n\ntry:\n if not (is_torch_available() and is_torchsde_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from .utils import dummy_torch_and_torchsde_objects # noqa F403\n\n _import_structure[\"utils.dummy_torch_and_torchsde_objects\"] = [\n name for name in dir(dummy_torch_and_torchsde_objects) if not name.startswith(\"_\")\n ]\n\nelse:\n _import_structure[\"schedulers\"].extend([\"DPMSolverSDEScheduler\"])\n\ntry:\n if not (is_torch_available() and is_transformers_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from .utils import dummy_torch_and_transformers_objects # noqa F403\n\n _import_structure[\"utils.dummy_torch_and_transformers_objects\"] = [\n name for name in dir(dummy_torch_and_transformers_objects) if not name.startswith(\"_\")\n ]\n\nelse:\n _import_structure[\"pipelines\"].extend(\n [\n \"AltDiffusionImg2ImgPipeline\",\n \"AltDiffusionPipeline\",\n \"AmusedImg2ImgPipeline\",\n \"AmusedInpaintPipeline\",\n \"AmusedPipeline\",\n \"AnimateDiffPipeline\",\n \"AnimateDiffSDXLPipeline\",\n \"AnimateDiffVideoToVideoPipeline\",\n \"AudioLDM2Pipeline\",\n \"AudioLDM2ProjectionModel\",\n \"AudioLDM2UNet2DConditionModel\",\n \"AudioLDMPipeline\",\n \"BlipDiffusionControlNetPipeline\",\n \"BlipDiffusionPipeline\",\n \"CLIPImageProjection\",\n \"CycleDiffusionPipeline\",\n \"HunyuanDiTPipeline\",\n \"I2VGenXLPipeline\",\n \"IFImg2ImgPipeline\",\n \"IFImg2ImgSuperResolutionPipeline\",\n \"IFInpaintingPipeline\",\n \"IFInpaintingSuperResolutionPipeline\",\n \"IFPipeline\",\n \"IFSuperResolutionPipeline\",\n \"ImageTextPipelineOutput\",\n \"Kandinsky3Img2ImgPipeline\",\n \"Kandinsky3Pipeline\",\n \"KandinskyCombinedPipeline\",\n \"KandinskyImg2ImgCombinedPipeline\",\n \"KandinskyImg2ImgPipeline\",\n \"KandinskyInpaintCombinedPipeline\",\n \"KandinskyInpaintPipeline\",\n \"KandinskyPipeline\",\n \"KandinskyPriorPipeline\",\n \"KandinskyV22CombinedPipeline\",\n \"KandinskyV22ControlnetImg2ImgPipeline\",\n \"KandinskyV22ControlnetPipeline\",\n \"KandinskyV22Img2ImgCombinedPipeline\",\n \"KandinskyV22Img2ImgPipeline\",\n \"KandinskyV22InpaintCombinedPipeline\",\n \"KandinskyV22InpaintPipeline\",\n \"KandinskyV22Pipeline\",\n \"KandinskyV22PriorEmb2EmbPipeline\",\n \"KandinskyV22PriorPipeline\",\n \"LatentConsistencyModelImg2ImgPipeline\",\n \"LatentConsistencyModelPipeline\",\n \"LDMTextToImagePipeline\",\n \"LEditsPPPipelineStableDiffusion\",\n \"LEditsPPPipelineStableDiffusionXL\",\n \"MarigoldDepthPipeline\",\n \"MarigoldNormalsPipeline\",\n \"MusicLDMPipeline\",\n \"PaintByExamplePipeline\",\n \"PIAPipeline\",\n \"PixArtAlphaPipeline\",\n \"PixArtSigmaPipeline\",\n \"SemanticStableDiffusionPipeline\",\n \"ShapEImg2ImgPipeline\",\n \"ShapEPipeline\",\n \"StableCascadeCombinedPipeline\",\n \"StableCascadeDecoderPipeline\",\n \"StableCascadePriorPipeline\",\n \"StableDiffusion3Img2ImgPipeline\",\n \"StableDiffusion3Pipeline\",\n \"StableDiffusion3InstructPix2PixPipeline\",\n \"StableDiffusionAdapterPipeline\",\n \"StableDiffusionAttendAndExcitePipeline\",\n \"StableDiffusionControlNetImg2ImgPipeline\",\n \"StableDiffusionControlNetInpaintPipeline\",\n \"StableDiffusionControlNetPipeline\",\n \"StableDiffusionControlNetXSPipeline\",\n \"StableDiffusionDepth2ImgPipeline\",\n \"StableDiffusionDiffEditPipeline\",\n \"StableDiffusionGLIGENPipeline\",\n \"StableDiffusionGLIGENTextImagePipeline\",\n \"StableDiffusionImageVariationPipeline\",\n \"StableDiffusionImg2ImgPipeline\",\n \"StableDiffusionInpaintPipeline\",\n \"StableDiffusionInpaintPipelineLegacy\",\n \"StableDiffusionInstructPix2PixPipeline\",\n \"StableDiffusionLatentUpscalePipeline\",\n \"StableDiffusionLDM3DPipeline\",\n \"StableDiffusionModelEditingPipeline\",\n \"StableDiffusionPanoramaPipeline\",\n \"StableDiffusionParadigmsPipeline\",\n \"StableDiffusionPipeline\",\n \"StableDiffusionPipelineSafe\",\n \"StableDiffusionPix2PixZeroPipeline\",\n \"StableDiffusionSAGPipeline\",\n \"StableDiffusionUpscalePipeline\",\n \"StableDiffusionXLAdapterPipeline\",\n \"StableDiffusionXLControlNetImg2ImgPipeline\",\n \"StableDiffusionXLControlNetInpaintPipeline\",\n \"StableDiffusionXLControlNetPipeline\",\n \"StableDiffusionXLControlNetXSPipeline\",\n \"StableDiffusionXLImg2ImgPipeline\",\n \"StableDiffusionXLInpaintPipeline\",\n \"StableDiffusionXLInstructPix2PixPipeline\",\n \"StableDiffusionXLPipeline\",\n \"StableUnCLIPImg2ImgPipeline\",\n \"StableUnCLIPPipeline\",\n \"StableVideoDiffusionPipeline\",\n \"TextToVideoSDPipeline\",\n \"TextToVideoZeroPipeline\",\n \"TextToVideoZeroSDXLPipeline\",\n \"UnCLIPImageVariationPipeline\",\n \"UnCLIPPipeline\",\n \"UniDiffuserModel\",\n \"UniDiffuserPipeline\",\n \"UniDiffuserTextDecoder\",\n \"VersatileDiffusionDualGuidedPipeline\",\n \"VersatileDiffusionImageVariationPipeline\",\n \"VersatileDiffusionPipeline\",\n \"VersatileDiffusionTextToImagePipeline\",\n \"VideoToVideoSDPipeline\",\n \"VQDiffusionPipeline\",\n \"WuerstchenCombinedPipeline\",\n \"WuerstchenDecoderPipeline\",\n \"WuerstchenPriorPipeline\",\n ]\n )\n\ntry:\n if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from .utils import dummy_torch_and_transformers_and_k_diffusion_objects # noqa F403\n\n _import_structure[\"utils.dummy_torch_and_transformers_and_k_diffusion_objects\"] = [\n name for name in dir(dummy_torch_and_transformers_and_k_diffusion_objects) if not name.startswith(\"_\")\n ]\n\nelse:\n _import_structure[\"pipelines\"].extend([\"StableDiffusionKDiffusionPipeline\", \"StableDiffusionXLKDiffusionPipeline\"])\n\ntry:\n if not (is_torch_available() and is_transformers_available() and is_onnx_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from .utils import dummy_torch_and_transformers_and_onnx_objects # noqa F403\n\n _import_structure[\"utils.dummy_torch_and_transformers_and_onnx_objects\"] = [\n name for name in dir(dummy_torch_and_transformers_and_onnx_objects) if not name.startswith(\"_\")\n ]\n\nelse:\n _import_structure[\"pipelines\"].extend(\n [\n \"OnnxStableDiffusionImg2ImgPipeline\",\n \"OnnxStableDiffusionInpaintPipeline\",\n \"OnnxStableDiffusionInpaintPipelineLegacy\",\n \"OnnxStableDiffusionPipeline\",\n \"OnnxStableDiffusionUpscalePipeline\",\n \"StableDiffusionOnnxPipeline\",\n ]\n )\n\ntry:\n if not (is_torch_available() and is_librosa_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from .utils import dummy_torch_and_librosa_objects # noqa F403\n\n _import_structure[\"utils.dummy_torch_and_librosa_objects\"] = [\n name for name in dir(dummy_torch_and_librosa_objects) if not name.startswith(\"_\")\n ]\n\nelse:\n _import_structure[\"pipelines\"].extend([\"AudioDiffusionPipeline\", \"Mel\"])\n\ntry:\n if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from .utils import dummy_transformers_and_torch_and_note_seq_objects # noqa F403\n\n _import_structure[\"utils.dummy_transformers_and_torch_and_note_seq_objects\"] = [\n name for name in dir(dummy_transformers_and_torch_and_note_seq_objects) if not name.startswith(\"_\")\n ]\n\n\nelse:\n _import_structure[\"pipelines\"].extend([\"SpectrogramDiffusionPipeline\"])\n\ntry:\n if not is_flax_available():\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from .utils import dummy_flax_objects # noqa F403\n\n _import_structure[\"utils.dummy_flax_objects\"] = [\n name for name in dir(dummy_flax_objects) if not name.startswith(\"_\")\n ]\n\n\nelse:\n _import_structure[\"models.controlnet_flax\"] = [\"FlaxControlNetModel\"]\n _import_structure[\"models.modeling_flax_utils\"] = [\"FlaxModelMixin\"]\n _import_structure[\"models.unets.unet_2d_condition_flax\"] = [\"FlaxUNet2DConditionModel\"]\n _import_structure[\"models.vae_flax\"] = [\"FlaxAutoencoderKL\"]\n _import_structure[\"pipelines\"].extend([\"FlaxDiffusionPipeline\"])\n _import_structure[\"schedulers\"].extend(\n [\n \"FlaxDDIMScheduler\",\n \"FlaxDDPMScheduler\",\n \"FlaxDPMSolverMultistepScheduler\",\n \"FlaxEulerDiscreteScheduler\",\n \"FlaxKarrasVeScheduler\",\n \"FlaxLMSDiscreteScheduler\",\n \"FlaxPNDMScheduler\",\n \"FlaxSchedulerMixin\",\n \"FlaxScoreSdeVeScheduler\",\n ]\n )\n\n\ntry:\n if not (is_flax_available() and is_transformers_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from .utils import dummy_flax_and_transformers_objects # noqa F403\n\n _import_structure[\"utils.dummy_flax_and_transformers_objects\"] = [\n name for name in dir(dummy_flax_and_transformers_objects) if not name.startswith(\"_\")\n ]\n\n\nelse:\n _import_structure[\"pipelines\"].extend(\n [\n \"FlaxStableDiffusionControlNetPipeline\",\n \"FlaxStableDiffusionImg2ImgPipeline\",\n \"FlaxStableDiffusionInpaintPipeline\",\n \"FlaxStableDiffusionPipeline\",\n \"FlaxStableDiffusionXLPipeline\",\n ]\n )\n\ntry:\n if not (is_note_seq_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from .utils import dummy_note_seq_objects # noqa F403\n\n _import_structure[\"utils.dummy_note_seq_objects\"] = [\n name for name in dir(dummy_note_seq_objects) if not name.startswith(\"_\")\n ]\n\n\nelse:\n _import_structure[\"pipelines\"].extend([\"MidiProcessor\"])\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n from .configuration_utils import ConfigMixin\n\n try:\n if not is_onnx_available():\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from .utils.dummy_onnx_objects import * # noqa F403\n else:\n from .pipelines import OnnxRuntimeModel\n\n try:\n if not is_torch_available():\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from .utils.dummy_pt_objects import * # noqa F403\n else:\n from .models import (\n AsymmetricAutoencoderKL,\n AutoencoderKL,\n AutoencoderKLTemporalDecoder,\n AutoencoderTiny,\n ConsistencyDecoderVAE,\n ControlNetModel,\n ControlNetXSAdapter,\n DiTTransformer2DModel,\n HunyuanDiT2DModel,\n I2VGenXLUNet,\n Kandinsky3UNet,\n ModelMixin,\n MotionAdapter,\n MultiAdapter,\n PixArtTransformer2DModel,\n PriorTransformer,\n SD3Transformer2DModel,\n T2IAdapter,\n T5FilmDecoder,\n Transformer2DModel,\n UNet1DModel,\n UNet2DConditionModel,\n UNet2DModel,\n UNet3DConditionModel,\n UNetControlNetXSModel,\n UNetMotionModel,\n UNetSpatioTemporalConditionModel,\n UVit2DModel,\n VQModel,\n )\n from .optimization import (\n get_constant_schedule,\n get_constant_schedule_with_warmup,\n get_cosine_schedule_with_warmup,\n get_cosine_with_hard_restarts_schedule_with_warmup,\n get_linear_schedule_with_warmup,\n get_polynomial_decay_schedule_with_warmup,\n get_scheduler,\n )\n from .pipelines import (\n AudioPipelineOutput,\n AutoPipelineForImage2Image,\n AutoPipelineForInpainting,\n AutoPipelineForText2Image,\n BlipDiffusionControlNetPipeline,\n BlipDiffusionPipeline,\n CLIPImageProjection,\n ConsistencyModelPipeline,\n DanceDiffusionPipeline,\n DDIMPipeline,\n DDPMPipeline,\n DiffusionPipeline,\n DiTPipeline,\n ImagePipelineOutput,\n KarrasVePipeline,\n LDMPipeline,\n LDMSuperResolutionPipeline,\n PNDMPipeline,\n RePaintPipeline,\n ScoreSdeVePipeline,\n StableDiffusionMixin,\n )\n from .schedulers import (\n AmusedScheduler,\n CMStochasticIterativeScheduler,\n DDIMInverseScheduler,\n DDIMParallelScheduler,\n DDIMScheduler,\n DDPMParallelScheduler,\n DDPMScheduler,\n DDPMWuerstchenScheduler,\n DEISMultistepScheduler,\n DPMSolverMultistepInverseScheduler,\n DPMSolverMultistepScheduler,\n DPMSolverSinglestepScheduler,\n EDMDPMSolverMultistepScheduler,\n EDMEulerScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n FlowMatchEulerDiscreteScheduler,\n HeunDiscreteScheduler,\n IPNDMScheduler,\n KarrasVeScheduler,\n KDPM2AncestralDiscreteScheduler,\n KDPM2DiscreteScheduler,\n LCMScheduler,\n PNDMScheduler,\n RePaintScheduler,\n SASolverScheduler,\n SchedulerMixin,\n ScoreSdeVeScheduler,\n TCDScheduler,\n UnCLIPScheduler,\n UniPCMultistepScheduler,\n VQDiffusionScheduler,\n )\n from .training_utils import EMAModel\n\n try:\n if not (is_torch_available() and is_scipy_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from .utils.dummy_torch_and_scipy_objects import * # noqa F403\n else:\n from .schedulers import LMSDiscreteScheduler\n\n try:\n if not (is_torch_available() and is_torchsde_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from .utils.dummy_torch_and_torchsde_objects import * # noqa F403\n else:\n from .schedulers import DPMSolverSDEScheduler\n\n try:\n if not (is_torch_available() and is_transformers_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from .utils.dummy_torch_and_transformers_objects import * # noqa F403\n else:\n from .pipelines import (\n AltDiffusionImg2ImgPipeline,\n AltDiffusionPipeline,\n AmusedImg2ImgPipeline,\n AmusedInpaintPipeline,\n AmusedPipeline,\n AnimateDiffPipeline,\n AnimateDiffSDXLPipeline,\n AnimateDiffVideoToVideoPipeline,\n AudioLDM2Pipeline,\n AudioLDM2ProjectionModel,\n AudioLDM2UNet2DConditionModel,\n AudioLDMPipeline,\n CLIPImageProjection,\n CycleDiffusionPipeline,\n HunyuanDiTPipeline,\n I2VGenXLPipeline,\n IFImg2ImgPipeline,\n IFImg2ImgSuperResolutionPipeline,\n IFInpaintingPipeline,\n IFInpaintingSuperResolutionPipeline,\n IFPipeline,\n IFSuperResolutionPipeline,\n ImageTextPipelineOutput,\n Kandinsky3Img2ImgPipeline,\n Kandinsky3Pipeline,\n KandinskyCombinedPipeline,\n KandinskyImg2ImgCombinedPipeline,\n KandinskyImg2ImgPipeline,\n KandinskyInpaintCombinedPipeline,\n KandinskyInpaintPipeline,\n KandinskyPipeline,\n KandinskyPriorPipeline,\n KandinskyV22CombinedPipeline,\n KandinskyV22ControlnetImg2ImgPipeline,\n KandinskyV22ControlnetPipeline,\n KandinskyV22Img2ImgCombinedPipeline,\n KandinskyV22Img2ImgPipeline,\n KandinskyV22InpaintCombinedPipeline,\n KandinskyV22InpaintPipeline,\n KandinskyV22Pipeline,\n KandinskyV22PriorEmb2EmbPipeline,\n KandinskyV22PriorPipeline,\n LatentConsistencyModelImg2ImgPipeline,\n LatentConsistencyModelPipeline,\n LDMTextToImagePipeline,\n LEditsPPPipelineStableDiffusion,\n LEditsPPPipelineStableDiffusionXL,\n MarigoldDepthPipeline,\n MarigoldNormalsPipeline,\n MusicLDMPipeline,\n PaintByExamplePipeline,\n PIAPipeline,\n PixArtAlphaPipeline,\n PixArtSigmaPipeline,\n SemanticStableDiffusionPipeline,\n ShapEImg2ImgPipeline,\n ShapEPipeline,\n StableCascadeCombinedPipeline,\n StableCascadeDecoderPipeline,\n StableCascadePriorPipeline,\n StableDiffusion3Img2ImgPipeline,\n StableDiffusion3Pipeline,\n StableDiffusion3InstructPix2PixPipeline,\n StableDiffusionAdapterPipeline,\n StableDiffusionAttendAndExcitePipeline,\n StableDiffusionControlNetImg2ImgPipeline,\n StableDiffusionControlNetInpaintPipeline,\n StableDiffusionControlNetPipeline,\n StableDiffusionControlNetXSPipeline,\n StableDiffusionDepth2ImgPipeline,\n StableDiffusionDiffEditPipeline,\n StableDiffusionGLIGENPipeline,\n StableDiffusionGLIGENTextImagePipeline,\n StableDiffusionImageVariationPipeline,\n StableDiffusionImg2ImgPipeline,\n StableDiffusionInpaintPipeline,\n StableDiffusionInpaintPipelineLegacy,\n StableDiffusionInstructPix2PixPipeline,\n StableDiffusionLatentUpscalePipeline,\n StableDiffusionLDM3DPipeline,\n StableDiffusionModelEditingPipeline,\n StableDiffusionPanoramaPipeline,\n StableDiffusionParadigmsPipeline,\n StableDiffusionPipeline,\n StableDiffusionPipelineSafe,\n StableDiffusionPix2PixZeroPipeline,\n StableDiffusionSAGPipeline,\n StableDiffusionUpscalePipeline,\n StableDiffusionXLAdapterPipeline,\n StableDiffusionXLControlNetImg2ImgPipeline,\n StableDiffusionXLControlNetInpaintPipeline,\n StableDiffusionXLControlNetPipeline,\n StableDiffusionXLControlNetXSPipeline,\n StableDiffusionXLImg2ImgPipeline,\n StableDiffusionXLInpaintPipeline,\n StableDiffusionXLInstructPix2PixPipeline,\n StableDiffusionXLPipeline,\n StableUnCLIPImg2ImgPipeline,\n StableUnCLIPPipeline,\n StableVideoDiffusionPipeline,\n TextToVideoSDPipeline,\n TextToVideoZeroPipeline,\n TextToVideoZeroSDXLPipeline,\n UnCLIPImageVariationPipeline,\n UnCLIPPipeline,\n UniDiffuserModel,\n UniDiffuserPipeline,\n UniDiffuserTextDecoder,\n VersatileDiffusionDualGuidedPipeline,\n VersatileDiffusionImageVariationPipeline,\n VersatileDiffusionPipeline,\n VersatileDiffusionTextToImagePipeline,\n VideoToVideoSDPipeline,\n VQDiffusionPipeline,\n WuerstchenCombinedPipeline,\n WuerstchenDecoderPipeline,\n WuerstchenPriorPipeline,\n )\n\n try:\n if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403\n else:\n from .pipelines import StableDiffusionKDiffusionPipeline, StableDiffusionXLKDiffusionPipeline\n\n try:\n if not (is_torch_available() and is_transformers_available() and is_onnx_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from .utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403\n else:\n from .pipelines import (\n OnnxStableDiffusionImg2ImgPipeline,\n OnnxStableDiffusionInpaintPipeline,\n OnnxStableDiffusionInpaintPipelineLegacy,\n OnnxStableDiffusionPipeline,\n OnnxStableDiffusionUpscalePipeline,\n StableDiffusionOnnxPipeline,\n )\n\n try:\n if not (is_torch_available() and is_librosa_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from .utils.dummy_torch_and_librosa_objects import * # noqa F403\n else:\n from .pipelines import AudioDiffusionPipeline, Mel\n\n try:\n if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from .utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403\n else:\n from .pipelines import SpectrogramDiffusionPipeline\n\n try:\n if not is_flax_available():\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from .utils.dummy_flax_objects import * # noqa F403\n else:\n from .models.controlnet_flax import FlaxControlNetModel\n from .models.modeling_flax_utils import FlaxModelMixin\n from .models.unets.unet_2d_condition_flax import FlaxUNet2DConditionModel\n from .models.vae_flax import FlaxAutoencoderKL\n from .pipelines import FlaxDiffusionPipeline\n from .schedulers import (\n FlaxDDIMScheduler,\n FlaxDDPMScheduler,\n FlaxDPMSolverMultistepScheduler,\n FlaxEulerDiscreteScheduler,\n FlaxKarrasVeScheduler,\n FlaxLMSDiscreteScheduler,\n FlaxPNDMScheduler,\n FlaxSchedulerMixin,\n FlaxScoreSdeVeScheduler,\n )\n\n try:\n if not (is_flax_available() and is_transformers_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from .utils.dummy_flax_and_transformers_objects import * # noqa F403\n else:\n from .pipelines import (\n FlaxStableDiffusionControlNetPipeline,\n FlaxStableDiffusionImg2ImgPipeline,\n FlaxStableDiffusionInpaintPipeline,\n FlaxStableDiffusionPipeline,\n FlaxStableDiffusionXLPipeline,\n )\n\n try:\n if not (is_note_seq_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from .utils.dummy_note_seq_objects import * # noqa F403\n else:\n from .pipelines import MidiProcessor\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n extra_objects={\"__version__\": __version__},\n )\n" }, { "path": "diffusers/src/diffusers/callbacks.py", "content": "from typing import Any, Dict, List\n\nfrom .configuration_utils import ConfigMixin, register_to_config\nfrom .utils import CONFIG_NAME\n\n\nclass PipelineCallback(ConfigMixin):\n \"\"\"\n Base class for all the official callbacks used in a pipeline. This class provides a structure for implementing\n custom callbacks and ensures that all callbacks have a consistent interface.\n\n Please implement the following:\n `tensor_inputs`: This should return a list of tensor inputs specific to your callback. You will only be able to\n include\n variables listed in the `._callback_tensor_inputs` attribute of your pipeline class.\n `callback_fn`: This method defines the core functionality of your callback.\n \"\"\"\n\n config_name = CONFIG_NAME\n\n @register_to_config\n def __init__(self, cutoff_step_ratio=1.0, cutoff_step_index=None):\n super().__init__()\n\n if (cutoff_step_ratio is None and cutoff_step_index is None) or (\n cutoff_step_ratio is not None and cutoff_step_index is not None\n ):\n raise ValueError(\"Either cutoff_step_ratio or cutoff_step_index should be provided, not both or none.\")\n\n if cutoff_step_ratio is not None and (\n not isinstance(cutoff_step_ratio, float) or not (0.0 <= cutoff_step_ratio <= 1.0)\n ):\n raise ValueError(\"cutoff_step_ratio must be a float between 0.0 and 1.0.\")\n\n @property\n def tensor_inputs(self) -> List[str]:\n raise NotImplementedError(f\"You need to set the attribute `tensor_inputs` for {self.__class__}\")\n\n def callback_fn(self, pipeline, step_index, timesteps, callback_kwargs) -> Dict[str, Any]:\n raise NotImplementedError(f\"You need to implement the method `callback_fn` for {self.__class__}\")\n\n def __call__(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:\n return self.callback_fn(pipeline, step_index, timestep, callback_kwargs)\n\n\nclass MultiPipelineCallbacks:\n \"\"\"\n This class is designed to handle multiple pipeline callbacks. It accepts a list of PipelineCallback objects and\n provides a unified interface for calling all of them.\n \"\"\"\n\n def __init__(self, callbacks: List[PipelineCallback]):\n self.callbacks = callbacks\n\n @property\n def tensor_inputs(self) -> List[str]:\n return [input for callback in self.callbacks for input in callback.tensor_inputs]\n\n def __call__(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:\n \"\"\"\n Calls all the callbacks in order with the given arguments and returns the final callback_kwargs.\n \"\"\"\n for callback in self.callbacks:\n callback_kwargs = callback(pipeline, step_index, timestep, callback_kwargs)\n\n return callback_kwargs\n\n\nclass SDCFGCutoffCallback(PipelineCallback):\n \"\"\"\n Callback function for Stable Diffusion Pipelines. After certain number of steps (set by `cutoff_step_ratio` or\n `cutoff_step_index`), this callback will disable the CFG.\n\n Note: This callback mutates the pipeline by changing the `_guidance_scale` attribute to 0.0 after the cutoff step.\n \"\"\"\n\n tensor_inputs = [\"prompt_embeds\"]\n\n def callback_fn(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:\n cutoff_step_ratio = self.config.cutoff_step_ratio\n cutoff_step_index = self.config.cutoff_step_index\n\n # Use cutoff_step_index if it's not None, otherwise use cutoff_step_ratio\n cutoff_step = (\n cutoff_step_index if cutoff_step_index is not None else int(pipeline.num_timesteps * cutoff_step_ratio)\n )\n\n if step_index == cutoff_step:\n prompt_embeds = callback_kwargs[self.tensor_inputs[0]]\n prompt_embeds = prompt_embeds[-1:] # \"-1\" denotes the embeddings for conditional text tokens.\n\n pipeline._guidance_scale = 0.0\n\n callback_kwargs[self.tensor_inputs[0]] = prompt_embeds\n return callback_kwargs\n\n\nclass SDXLCFGCutoffCallback(PipelineCallback):\n \"\"\"\n Callback function for Stable Diffusion XL Pipelines. After certain number of steps (set by `cutoff_step_ratio` or\n `cutoff_step_index`), this callback will disable the CFG.\n\n Note: This callback mutates the pipeline by changing the `_guidance_scale` attribute to 0.0 after the cutoff step.\n \"\"\"\n\n tensor_inputs = [\"prompt_embeds\", \"add_text_embeds\", \"add_time_ids\"]\n\n def callback_fn(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:\n cutoff_step_ratio = self.config.cutoff_step_ratio\n cutoff_step_index = self.config.cutoff_step_index\n\n # Use cutoff_step_index if it's not None, otherwise use cutoff_step_ratio\n cutoff_step = (\n cutoff_step_index if cutoff_step_index is not None else int(pipeline.num_timesteps * cutoff_step_ratio)\n )\n\n if step_index == cutoff_step:\n prompt_embeds = callback_kwargs[self.tensor_inputs[0]]\n prompt_embeds = prompt_embeds[-1:] # \"-1\" denotes the embeddings for conditional text tokens.\n\n add_text_embeds = callback_kwargs[self.tensor_inputs[1]]\n add_text_embeds = add_text_embeds[-1:] # \"-1\" denotes the embeddings for conditional pooled text tokens\n\n add_time_ids = callback_kwargs[self.tensor_inputs[2]]\n add_time_ids = add_time_ids[-1:] # \"-1\" denotes the embeddings for conditional added time vector\n\n pipeline._guidance_scale = 0.0\n\n callback_kwargs[self.tensor_inputs[0]] = prompt_embeds\n callback_kwargs[self.tensor_inputs[1]] = add_text_embeds\n callback_kwargs[self.tensor_inputs[2]] = add_time_ids\n return callback_kwargs\n\n\nclass IPAdapterScaleCutoffCallback(PipelineCallback):\n \"\"\"\n Callback function for any pipeline that inherits `IPAdapterMixin`. After certain number of steps (set by\n `cutoff_step_ratio` or `cutoff_step_index`), this callback will set the IP Adapter scale to `0.0`.\n\n Note: This callback mutates the IP Adapter attention processors by setting the scale to 0.0 after the cutoff step.\n \"\"\"\n\n tensor_inputs = []\n\n def callback_fn(self, pipeline, step_index, timestep, callback_kwargs) -> Dict[str, Any]:\n cutoff_step_ratio = self.config.cutoff_step_ratio\n cutoff_step_index = self.config.cutoff_step_index\n\n # Use cutoff_step_index if it's not None, otherwise use cutoff_step_ratio\n cutoff_step = (\n cutoff_step_index if cutoff_step_index is not None else int(pipeline.num_timesteps * cutoff_step_ratio)\n )\n\n if step_index == cutoff_step:\n pipeline.set_ip_adapter_scale(0.0)\n return callback_kwargs\n" }, { "path": "diffusers/src/diffusers/commands/__init__.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom abc import ABC, abstractmethod\nfrom argparse import ArgumentParser\n\n\nclass BaseDiffusersCLICommand(ABC):\n @staticmethod\n @abstractmethod\n def register_subcommand(parser: ArgumentParser):\n raise NotImplementedError()\n\n @abstractmethod\n def run(self):\n raise NotImplementedError()\n" }, { "path": "diffusers/src/diffusers/commands/diffusers_cli.py", "content": "#!/usr/bin/env python\n# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom argparse import ArgumentParser\n\nfrom .env import EnvironmentCommand\nfrom .fp16_safetensors import FP16SafetensorsCommand\n\n\ndef main():\n parser = ArgumentParser(\"Diffusers CLI tool\", usage=\"diffusers-cli []\")\n commands_parser = parser.add_subparsers(help=\"diffusers-cli command helpers\")\n\n # Register commands\n EnvironmentCommand.register_subcommand(commands_parser)\n FP16SafetensorsCommand.register_subcommand(commands_parser)\n\n # Let's go\n args = parser.parse_args()\n\n if not hasattr(args, \"func\"):\n parser.print_help()\n exit(1)\n\n # Run\n service = args.func(args)\n service.run()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/src/diffusers/commands/env.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport platform\nimport subprocess\nfrom argparse import ArgumentParser\n\nimport huggingface_hub\n\nfrom .. import __version__ as version\nfrom ..utils import (\n is_accelerate_available,\n is_bitsandbytes_available,\n is_flax_available,\n is_google_colab,\n is_notebook,\n is_peft_available,\n is_safetensors_available,\n is_torch_available,\n is_transformers_available,\n is_xformers_available,\n)\nfrom . import BaseDiffusersCLICommand\n\n\ndef info_command_factory(_):\n return EnvironmentCommand()\n\n\nclass EnvironmentCommand(BaseDiffusersCLICommand):\n @staticmethod\n def register_subcommand(parser: ArgumentParser) -> None:\n download_parser = parser.add_parser(\"env\")\n download_parser.set_defaults(func=info_command_factory)\n\n def run(self) -> dict:\n hub_version = huggingface_hub.__version__\n\n safetensors_version = \"not installed\"\n if is_safetensors_available():\n import safetensors\n\n safetensors_version = safetensors.__version__\n\n pt_version = \"not installed\"\n pt_cuda_available = \"NA\"\n if is_torch_available():\n import torch\n\n pt_version = torch.__version__\n pt_cuda_available = torch.cuda.is_available()\n\n flax_version = \"not installed\"\n jax_version = \"not installed\"\n jaxlib_version = \"not installed\"\n jax_backend = \"NA\"\n if is_flax_available():\n import flax\n import jax\n import jaxlib\n\n flax_version = flax.__version__\n jax_version = jax.__version__\n jaxlib_version = jaxlib.__version__\n jax_backend = jax.lib.xla_bridge.get_backend().platform\n\n transformers_version = \"not installed\"\n if is_transformers_available():\n import transformers\n\n transformers_version = transformers.__version__\n\n accelerate_version = \"not installed\"\n if is_accelerate_available():\n import accelerate\n\n accelerate_version = accelerate.__version__\n\n peft_version = \"not installed\"\n if is_peft_available():\n import peft\n\n peft_version = peft.__version__\n\n bitsandbytes_version = \"not installed\"\n if is_bitsandbytes_available():\n import bitsandbytes\n\n bitsandbytes_version = bitsandbytes.__version__\n\n xformers_version = \"not installed\"\n if is_xformers_available():\n import xformers\n\n xformers_version = xformers.__version__\n\n platform_info = platform.platform()\n\n is_notebook_str = \"Yes\" if is_notebook() else \"No\"\n\n is_google_colab_str = \"Yes\" if is_google_colab() else \"No\"\n\n accelerator = \"NA\"\n if platform.system() in {\"Linux\", \"Windows\"}:\n try:\n sp = subprocess.Popen(\n [\"nvidia-smi\", \"--query-gpu=gpu_name,memory.total\", \"--format=csv,noheader\"],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n )\n out_str, _ = sp.communicate()\n out_str = out_str.decode(\"utf-8\")\n\n if len(out_str) > 0:\n accelerator = out_str.strip() + \" VRAM\"\n except FileNotFoundError:\n pass\n elif platform.system() == \"Darwin\": # Mac OS\n try:\n sp = subprocess.Popen(\n [\"system_profiler\", \"SPDisplaysDataType\"],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n )\n out_str, _ = sp.communicate()\n out_str = out_str.decode(\"utf-8\")\n\n start = out_str.find(\"Chipset Model:\")\n if start != -1:\n start += len(\"Chipset Model:\")\n end = out_str.find(\"\\n\", start)\n accelerator = out_str[start:end].strip()\n\n start = out_str.find(\"VRAM (Total):\")\n if start != -1:\n start += len(\"VRAM (Total):\")\n end = out_str.find(\"\\n\", start)\n accelerator += \" VRAM: \" + out_str[start:end].strip()\n except FileNotFoundError:\n pass\n else:\n print(\"It seems you are running an unusual OS. Could you fill in the accelerator manually?\")\n\n info = {\n \"🤗 Diffusers version\": version,\n \"Platform\": platform_info,\n \"Running on a notebook?\": is_notebook_str,\n \"Running on Google Colab?\": is_google_colab_str,\n \"Python version\": platform.python_version(),\n \"PyTorch version (GPU?)\": f\"{pt_version} ({pt_cuda_available})\",\n \"Flax version (CPU?/GPU?/TPU?)\": f\"{flax_version} ({jax_backend})\",\n \"Jax version\": jax_version,\n \"JaxLib version\": jaxlib_version,\n \"Huggingface_hub version\": hub_version,\n \"Transformers version\": transformers_version,\n \"Accelerate version\": accelerate_version,\n \"PEFT version\": peft_version,\n \"Bitsandbytes version\": bitsandbytes_version,\n \"Safetensors version\": safetensors_version,\n \"xFormers version\": xformers_version,\n \"Accelerator\": accelerator,\n \"Using GPU in script?\": \"\",\n \"Using distributed or parallel set-up in script?\": \"\",\n }\n\n print(\"\\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\\n\")\n print(self.format_dict(info))\n\n return info\n\n @staticmethod\n def format_dict(d: dict) -> str:\n return \"\\n\".join([f\"- {prop}: {val}\" for prop, val in d.items()]) + \"\\n\"\n" }, { "path": "diffusers/src/diffusers/commands/fp16_safetensors.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"\nUsage example:\n diffusers-cli fp16_safetensors --ckpt_id=openai/shap-e --fp16 --use_safetensors\n\"\"\"\n\nimport glob\nimport json\nimport warnings\nfrom argparse import ArgumentParser, Namespace\nfrom importlib import import_module\n\nimport huggingface_hub\nimport torch\nfrom huggingface_hub import hf_hub_download\nfrom packaging import version\n\nfrom ..utils import logging\nfrom . import BaseDiffusersCLICommand\n\n\ndef conversion_command_factory(args: Namespace):\n if args.use_auth_token:\n warnings.warn(\n \"The `--use_auth_token` flag is deprecated and will be removed in a future version. Authentication is now\"\n \" handled automatically if user is logged in.\"\n )\n return FP16SafetensorsCommand(args.ckpt_id, args.fp16, args.use_safetensors)\n\n\nclass FP16SafetensorsCommand(BaseDiffusersCLICommand):\n @staticmethod\n def register_subcommand(parser: ArgumentParser):\n conversion_parser = parser.add_parser(\"fp16_safetensors\")\n conversion_parser.add_argument(\n \"--ckpt_id\",\n type=str,\n help=\"Repo id of the checkpoints on which to run the conversion. Example: 'openai/shap-e'.\",\n )\n conversion_parser.add_argument(\n \"--fp16\", action=\"store_true\", help=\"If serializing the variables in FP16 precision.\"\n )\n conversion_parser.add_argument(\n \"--use_safetensors\", action=\"store_true\", help=\"If serializing in the safetensors format.\"\n )\n conversion_parser.add_argument(\n \"--use_auth_token\",\n action=\"store_true\",\n help=\"When working with checkpoints having private visibility. When used `huggingface-cli login` needs to be run beforehand.\",\n )\n conversion_parser.set_defaults(func=conversion_command_factory)\n\n def __init__(self, ckpt_id: str, fp16: bool, use_safetensors: bool):\n self.logger = logging.get_logger(\"diffusers-cli/fp16_safetensors\")\n self.ckpt_id = ckpt_id\n self.local_ckpt_dir = f\"/tmp/{ckpt_id}\"\n self.fp16 = fp16\n\n self.use_safetensors = use_safetensors\n\n if not self.use_safetensors and not self.fp16:\n raise NotImplementedError(\n \"When `use_safetensors` and `fp16` both are False, then this command is of no use.\"\n )\n\n def run(self):\n if version.parse(huggingface_hub.__version__) < version.parse(\"0.9.0\"):\n raise ImportError(\n \"The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub\"\n \" installation.\"\n )\n else:\n from huggingface_hub import create_commit\n from huggingface_hub._commit_api import CommitOperationAdd\n\n model_index = hf_hub_download(repo_id=self.ckpt_id, filename=\"model_index.json\")\n with open(model_index, \"r\") as f:\n pipeline_class_name = json.load(f)[\"_class_name\"]\n pipeline_class = getattr(import_module(\"diffusers\"), pipeline_class_name)\n self.logger.info(f\"Pipeline class imported: {pipeline_class_name}.\")\n\n # Load the appropriate pipeline. We could have use `DiffusionPipeline`\n # here, but just to avoid any rough edge cases.\n pipeline = pipeline_class.from_pretrained(\n self.ckpt_id, torch_dtype=torch.float16 if self.fp16 else torch.float32\n )\n pipeline.save_pretrained(\n self.local_ckpt_dir,\n safe_serialization=True if self.use_safetensors else False,\n variant=\"fp16\" if self.fp16 else None,\n )\n self.logger.info(f\"Pipeline locally saved to {self.local_ckpt_dir}.\")\n\n # Fetch all the paths.\n if self.fp16:\n modified_paths = glob.glob(f\"{self.local_ckpt_dir}/*/*.fp16.*\")\n elif self.use_safetensors:\n modified_paths = glob.glob(f\"{self.local_ckpt_dir}/*/*.safetensors\")\n\n # Prepare for the PR.\n commit_message = f\"Serialize variables with FP16: {self.fp16} and safetensors: {self.use_safetensors}.\"\n operations = []\n for path in modified_paths:\n operations.append(CommitOperationAdd(path_in_repo=\"/\".join(path.split(\"/\")[4:]), path_or_fileobj=path))\n\n # Open the PR.\n commit_description = (\n \"Variables converted by the [`diffusers`' `fp16_safetensors`\"\n \" CLI](https://github.com/huggingface/diffusers/blob/main/src/diffusers/commands/fp16_safetensors.py).\"\n )\n hub_pr_url = create_commit(\n repo_id=self.ckpt_id,\n operations=operations,\n commit_message=commit_message,\n commit_description=commit_description,\n repo_type=\"model\",\n create_pr=True,\n ).pr_url\n self.logger.info(f\"PR created here: {hub_pr_url}.\")\n" }, { "path": "diffusers/src/diffusers/configuration_utils.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"ConfigMixin base class and utilities.\"\"\"\n\nimport dataclasses\nimport functools\nimport importlib\nimport inspect\nimport json\nimport os\nimport re\nfrom collections import OrderedDict\nfrom pathlib import PosixPath\nfrom typing import Any, Dict, Tuple, Union\n\nimport numpy as np\nfrom huggingface_hub import create_repo, hf_hub_download\nfrom huggingface_hub.utils import (\n EntryNotFoundError,\n RepositoryNotFoundError,\n RevisionNotFoundError,\n validate_hf_hub_args,\n)\nfrom requests import HTTPError\n\nfrom . import __version__\nfrom .utils import (\n HUGGINGFACE_CO_RESOLVE_ENDPOINT,\n DummyObject,\n deprecate,\n extract_commit_hash,\n http_user_agent,\n logging,\n)\n\n\nlogger = logging.get_logger(__name__)\n\n_re_configuration_file = re.compile(r\"config\\.(.*)\\.json\")\n\n\nclass FrozenDict(OrderedDict):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n for key, value in self.items():\n setattr(self, key, value)\n\n self.__frozen = True\n\n def __delitem__(self, *args, **kwargs):\n raise Exception(f\"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.\")\n\n def setdefault(self, *args, **kwargs):\n raise Exception(f\"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.\")\n\n def pop(self, *args, **kwargs):\n raise Exception(f\"You cannot use ``pop`` on a {self.__class__.__name__} instance.\")\n\n def update(self, *args, **kwargs):\n raise Exception(f\"You cannot use ``update`` on a {self.__class__.__name__} instance.\")\n\n def __setattr__(self, name, value):\n if hasattr(self, \"__frozen\") and self.__frozen:\n raise Exception(f\"You cannot use ``__setattr__`` on a {self.__class__.__name__} instance.\")\n super().__setattr__(name, value)\n\n def __setitem__(self, name, value):\n if hasattr(self, \"__frozen\") and self.__frozen:\n raise Exception(f\"You cannot use ``__setattr__`` on a {self.__class__.__name__} instance.\")\n super().__setitem__(name, value)\n\n\nclass ConfigMixin:\n r\"\"\"\n Base class for all configuration classes. All configuration parameters are stored under `self.config`. Also\n provides the [`~ConfigMixin.from_config`] and [`~ConfigMixin.save_config`] methods for loading, downloading, and\n saving classes that inherit from [`ConfigMixin`].\n\n Class attributes:\n - **config_name** (`str`) -- A filename under which the config should stored when calling\n [`~ConfigMixin.save_config`] (should be overridden by parent class).\n - **ignore_for_config** (`List[str]`) -- A list of attributes that should not be saved in the config (should be\n overridden by subclass).\n - **has_compatibles** (`bool`) -- Whether the class has compatible classes (should be overridden by subclass).\n - **_deprecated_kwargs** (`List[str]`) -- Keyword arguments that are deprecated. Note that the `init` function\n should only have a `kwargs` argument if at least one argument is deprecated (should be overridden by\n subclass).\n \"\"\"\n\n config_name = None\n ignore_for_config = []\n has_compatibles = False\n\n _deprecated_kwargs = []\n\n def register_to_config(self, **kwargs):\n if self.config_name is None:\n raise NotImplementedError(f\"Make sure that {self.__class__} has defined a class name `config_name`\")\n # Special case for `kwargs` used in deprecation warning added to schedulers\n # TODO: remove this when we remove the deprecation warning, and the `kwargs` argument,\n # or solve in a more general way.\n kwargs.pop(\"kwargs\", None)\n\n if not hasattr(self, \"_internal_dict\"):\n internal_dict = kwargs\n else:\n previous_dict = dict(self._internal_dict)\n internal_dict = {**self._internal_dict, **kwargs}\n logger.debug(f\"Updating config from {previous_dict} to {internal_dict}\")\n\n self._internal_dict = FrozenDict(internal_dict)\n\n def __getattr__(self, name: str) -> Any:\n \"\"\"The only reason we overwrite `getattr` here is to gracefully deprecate accessing\n config attributes directly. See https://github.com/huggingface/diffusers/pull/3129\n\n This function is mostly copied from PyTorch's __getattr__ overwrite:\n https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module\n \"\"\"\n\n is_in_config = \"_internal_dict\" in self.__dict__ and hasattr(self.__dict__[\"_internal_dict\"], name)\n is_attribute = name in self.__dict__\n\n if is_in_config and not is_attribute:\n deprecation_message = f\"Accessing config attribute `{name}` directly via '{type(self).__name__}' object attribute is deprecated. Please access '{name}' over '{type(self).__name__}'s config object instead, e.g. 'scheduler.config.{name}'.\"\n deprecate(\"direct config name access\", \"1.0.0\", deprecation_message, standard_warn=False)\n return self._internal_dict[name]\n\n raise AttributeError(f\"'{type(self).__name__}' object has no attribute '{name}'\")\n\n def save_config(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):\n \"\"\"\n Save a configuration object to the directory specified in `save_directory` so that it can be reloaded using the\n [`~ConfigMixin.from_config`] class method.\n\n Args:\n save_directory (`str` or `os.PathLike`):\n Directory where the configuration JSON file is saved (will be created if it does not exist).\n push_to_hub (`bool`, *optional*, defaults to `False`):\n Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the\n repository you want to push to with `repo_id` (will default to the name of `save_directory` in your\n namespace).\n kwargs (`Dict[str, Any]`, *optional*):\n Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.\n \"\"\"\n if os.path.isfile(save_directory):\n raise AssertionError(f\"Provided path ({save_directory}) should be a directory, not a file\")\n\n os.makedirs(save_directory, exist_ok=True)\n\n # If we save using the predefined names, we can load using `from_config`\n output_config_file = os.path.join(save_directory, self.config_name)\n\n self.to_json_file(output_config_file)\n logger.info(f\"Configuration saved in {output_config_file}\")\n\n if push_to_hub:\n commit_message = kwargs.pop(\"commit_message\", None)\n private = kwargs.pop(\"private\", False)\n create_pr = kwargs.pop(\"create_pr\", False)\n token = kwargs.pop(\"token\", None)\n repo_id = kwargs.pop(\"repo_id\", save_directory.split(os.path.sep)[-1])\n repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id\n\n self._upload_folder(\n save_directory,\n repo_id,\n token=token,\n commit_message=commit_message,\n create_pr=create_pr,\n )\n\n @classmethod\n def from_config(cls, config: Union[FrozenDict, Dict[str, Any]] = None, return_unused_kwargs=False, **kwargs):\n r\"\"\"\n Instantiate a Python class from a config dictionary.\n\n Parameters:\n config (`Dict[str, Any]`):\n A config dictionary from which the Python class is instantiated. Make sure to only load configuration\n files of compatible classes.\n return_unused_kwargs (`bool`, *optional*, defaults to `False`):\n Whether kwargs that are not consumed by the Python class should be returned or not.\n kwargs (remaining dictionary of keyword arguments, *optional*):\n Can be used to update the configuration object (after it is loaded) and initiate the Python class.\n `**kwargs` are passed directly to the underlying scheduler/model's `__init__` method and eventually\n overwrite the same named arguments in `config`.\n\n Returns:\n [`ModelMixin`] or [`SchedulerMixin`]:\n A model or scheduler object instantiated from a config dictionary.\n\n Examples:\n\n ```python\n >>> from diffusers import DDPMScheduler, DDIMScheduler, PNDMScheduler\n\n >>> # Download scheduler from huggingface.co and cache.\n >>> scheduler = DDPMScheduler.from_pretrained(\"google/ddpm-cifar10-32\")\n\n >>> # Instantiate DDIM scheduler class with same config as DDPM\n >>> scheduler = DDIMScheduler.from_config(scheduler.config)\n\n >>> # Instantiate PNDM scheduler class with same config as DDPM\n >>> scheduler = PNDMScheduler.from_config(scheduler.config)\n ```\n \"\"\"\n # <===== TO BE REMOVED WITH DEPRECATION\n # TODO(Patrick) - make sure to remove the following lines when config==\"model_path\" is deprecated\n if \"pretrained_model_name_or_path\" in kwargs:\n config = kwargs.pop(\"pretrained_model_name_or_path\")\n\n if config is None:\n raise ValueError(\"Please make sure to provide a config as the first positional argument.\")\n # ======>\n\n if not isinstance(config, dict):\n deprecation_message = \"It is deprecated to pass a pretrained model name or path to `from_config`.\"\n if \"Scheduler\" in cls.__name__:\n deprecation_message += (\n f\"If you were trying to load a scheduler, please use {cls}.from_pretrained(...) instead.\"\n \" Otherwise, please make sure to pass a configuration dictionary instead. This functionality will\"\n \" be removed in v1.0.0.\"\n )\n elif \"Model\" in cls.__name__:\n deprecation_message += (\n f\"If you were trying to load a model, please use {cls}.load_config(...) followed by\"\n f\" {cls}.from_config(...) instead. Otherwise, please make sure to pass a configuration dictionary\"\n \" instead. This functionality will be removed in v1.0.0.\"\n )\n deprecate(\"config-passed-as-path\", \"1.0.0\", deprecation_message, standard_warn=False)\n config, kwargs = cls.load_config(pretrained_model_name_or_path=config, return_unused_kwargs=True, **kwargs)\n\n init_dict, unused_kwargs, hidden_dict = cls.extract_init_dict(config, **kwargs)\n\n # Allow dtype to be specified on initialization\n if \"dtype\" in unused_kwargs:\n init_dict[\"dtype\"] = unused_kwargs.pop(\"dtype\")\n\n # add possible deprecated kwargs\n for deprecated_kwarg in cls._deprecated_kwargs:\n if deprecated_kwarg in unused_kwargs:\n init_dict[deprecated_kwarg] = unused_kwargs.pop(deprecated_kwarg)\n\n # Return model and optionally state and/or unused_kwargs\n model = cls(**init_dict)\n\n # make sure to also save config parameters that might be used for compatible classes\n # update _class_name\n if \"_class_name\" in hidden_dict:\n hidden_dict[\"_class_name\"] = cls.__name__\n\n model.register_to_config(**hidden_dict)\n\n # add hidden kwargs of compatible classes to unused_kwargs\n unused_kwargs = {**unused_kwargs, **hidden_dict}\n\n if return_unused_kwargs:\n return (model, unused_kwargs)\n else:\n return model\n\n @classmethod\n def get_config_dict(cls, *args, **kwargs):\n deprecation_message = (\n f\" The function get_config_dict is deprecated. Please use {cls}.load_config instead. This function will be\"\n \" removed in version v1.0.0\"\n )\n deprecate(\"get_config_dict\", \"1.0.0\", deprecation_message, standard_warn=False)\n return cls.load_config(*args, **kwargs)\n\n @classmethod\n @validate_hf_hub_args\n def load_config(\n cls,\n pretrained_model_name_or_path: Union[str, os.PathLike],\n return_unused_kwargs=False,\n return_commit_hash=False,\n **kwargs,\n ) -> Tuple[Dict[str, Any], Dict[str, Any]]:\n r\"\"\"\n Load a model or scheduler configuration.\n\n Parameters:\n pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):\n Can be either:\n\n - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on\n the Hub.\n - A path to a *directory* (for example `./my_model_directory`) containing model weights saved with\n [`~ConfigMixin.save_config`].\n\n cache_dir (`Union[str, os.PathLike]`, *optional*):\n Path to a directory where a downloaded pretrained model configuration is cached if the standard cache\n is not used.\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\n cached versions if they exist.\n resume_download:\n Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1\n of Diffusers.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\n output_loading_info(`bool`, *optional*, defaults to `False`):\n Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.\n local_files_only (`bool`, *optional*, defaults to `False`):\n Whether to only load local model weights and configuration files or not. If set to `True`, the model\n won't be downloaded from the Hub.\n token (`str` or *bool*, *optional*):\n The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from\n `diffusers-cli login` (stored in `~/.huggingface`) is used.\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier\n allowed by Git.\n subfolder (`str`, *optional*, defaults to `\"\"`):\n The subfolder location of a model file within a larger model repository on the Hub or locally.\n return_unused_kwargs (`bool`, *optional*, defaults to `False):\n Whether unused keyword arguments of the config are returned.\n return_commit_hash (`bool`, *optional*, defaults to `False):\n Whether the `commit_hash` of the loaded configuration are returned.\n\n Returns:\n `dict`:\n A dictionary of all the parameters stored in a JSON configuration file.\n\n \"\"\"\n cache_dir = kwargs.pop(\"cache_dir\", None)\n local_dir = kwargs.pop(\"local_dir\", None)\n local_dir_use_symlinks = kwargs.pop(\"local_dir_use_symlinks\", \"auto\")\n force_download = kwargs.pop(\"force_download\", False)\n resume_download = kwargs.pop(\"resume_download\", None)\n proxies = kwargs.pop(\"proxies\", None)\n token = kwargs.pop(\"token\", None)\n local_files_only = kwargs.pop(\"local_files_only\", False)\n revision = kwargs.pop(\"revision\", None)\n _ = kwargs.pop(\"mirror\", None)\n subfolder = kwargs.pop(\"subfolder\", None)\n user_agent = kwargs.pop(\"user_agent\", {})\n\n user_agent = {**user_agent, \"file_type\": \"config\"}\n user_agent = http_user_agent(user_agent)\n\n pretrained_model_name_or_path = str(pretrained_model_name_or_path)\n\n if cls.config_name is None:\n raise ValueError(\n \"`self.config_name` is not defined. Note that one should not load a config from \"\n \"`ConfigMixin`. Please make sure to define `config_name` in a class inheriting from `ConfigMixin`\"\n )\n\n if os.path.isfile(pretrained_model_name_or_path):\n config_file = pretrained_model_name_or_path\n elif os.path.isdir(pretrained_model_name_or_path):\n if subfolder is not None and os.path.isfile(\n os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)\n ):\n config_file = os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)\n elif os.path.isfile(os.path.join(pretrained_model_name_or_path, cls.config_name)):\n # Load from a PyTorch checkpoint\n config_file = os.path.join(pretrained_model_name_or_path, cls.config_name)\n else:\n raise EnvironmentError(\n f\"Error no file named {cls.config_name} found in directory {pretrained_model_name_or_path}.\"\n )\n else:\n try:\n # Load from URL or cache if already cached\n config_file = hf_hub_download(\n pretrained_model_name_or_path,\n filename=cls.config_name,\n cache_dir=cache_dir,\n force_download=force_download,\n proxies=proxies,\n resume_download=resume_download,\n local_files_only=local_files_only,\n token=token,\n user_agent=user_agent,\n subfolder=subfolder,\n revision=revision,\n local_dir=local_dir,\n local_dir_use_symlinks=local_dir_use_symlinks,\n )\n except RepositoryNotFoundError:\n raise EnvironmentError(\n f\"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier\"\n \" listed on 'https://huggingface.co/models'\\nIf this is a private repository, make sure to pass a\"\n \" token having permission to this repo with `token` or log in with `huggingface-cli login`.\"\n )\n except RevisionNotFoundError:\n raise EnvironmentError(\n f\"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for\"\n \" this model name. Check the model page at\"\n f\" 'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions.\"\n )\n except EntryNotFoundError:\n raise EnvironmentError(\n f\"{pretrained_model_name_or_path} does not appear to have a file named {cls.config_name}.\"\n )\n except HTTPError as err:\n raise EnvironmentError(\n \"There was a specific connection error when trying to load\"\n f\" {pretrained_model_name_or_path}:\\n{err}\"\n )\n except ValueError:\n raise EnvironmentError(\n f\"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it\"\n f\" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a\"\n f\" directory containing a {cls.config_name} file.\\nCheckout your internet connection or see how to\"\n \" run the library in offline mode at\"\n \" 'https://huggingface.co/docs/diffusers/installation#offline-mode'.\"\n )\n except EnvironmentError:\n raise EnvironmentError(\n f\"Can't load config for '{pretrained_model_name_or_path}'. If you were trying to load it from \"\n \"'https://huggingface.co/models', make sure you don't have a local directory with the same name. \"\n f\"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory \"\n f\"containing a {cls.config_name} file\"\n )\n\n try:\n # Load config dict\n config_dict = cls._dict_from_json_file(config_file)\n\n commit_hash = extract_commit_hash(config_file)\n except (json.JSONDecodeError, UnicodeDecodeError):\n raise EnvironmentError(f\"It looks like the config file at '{config_file}' is not a valid JSON file.\")\n\n if not (return_unused_kwargs or return_commit_hash):\n return config_dict\n\n outputs = (config_dict,)\n\n if return_unused_kwargs:\n outputs += (kwargs,)\n\n if return_commit_hash:\n outputs += (commit_hash,)\n\n return outputs\n\n @staticmethod\n def _get_init_keys(input_class):\n return set(dict(inspect.signature(input_class.__init__).parameters).keys())\n\n @classmethod\n def extract_init_dict(cls, config_dict, **kwargs):\n # Skip keys that were not present in the original config, so default __init__ values were used\n used_defaults = config_dict.get(\"_use_default_values\", [])\n config_dict = {k: v for k, v in config_dict.items() if k not in used_defaults and k != \"_use_default_values\"}\n\n # 0. Copy origin config dict\n original_dict = dict(config_dict.items())\n\n # 1. Retrieve expected config attributes from __init__ signature\n expected_keys = cls._get_init_keys(cls)\n expected_keys.remove(\"self\")\n # remove general kwargs if present in dict\n if \"kwargs\" in expected_keys:\n expected_keys.remove(\"kwargs\")\n # remove flax internal keys\n if hasattr(cls, \"_flax_internal_args\"):\n for arg in cls._flax_internal_args:\n expected_keys.remove(arg)\n\n # 2. Remove attributes that cannot be expected from expected config attributes\n # remove keys to be ignored\n if len(cls.ignore_for_config) > 0:\n expected_keys = expected_keys - set(cls.ignore_for_config)\n\n # load diffusers library to import compatible and original scheduler\n diffusers_library = importlib.import_module(__name__.split(\".\")[0])\n\n if cls.has_compatibles:\n compatible_classes = [c for c in cls._get_compatibles() if not isinstance(c, DummyObject)]\n else:\n compatible_classes = []\n\n expected_keys_comp_cls = set()\n for c in compatible_classes:\n expected_keys_c = cls._get_init_keys(c)\n expected_keys_comp_cls = expected_keys_comp_cls.union(expected_keys_c)\n expected_keys_comp_cls = expected_keys_comp_cls - cls._get_init_keys(cls)\n config_dict = {k: v for k, v in config_dict.items() if k not in expected_keys_comp_cls}\n\n # remove attributes from orig class that cannot be expected\n orig_cls_name = config_dict.pop(\"_class_name\", cls.__name__)\n if (\n isinstance(orig_cls_name, str)\n and orig_cls_name != cls.__name__\n and hasattr(diffusers_library, orig_cls_name)\n ):\n orig_cls = getattr(diffusers_library, orig_cls_name)\n unexpected_keys_from_orig = cls._get_init_keys(orig_cls) - expected_keys\n config_dict = {k: v for k, v in config_dict.items() if k not in unexpected_keys_from_orig}\n elif not isinstance(orig_cls_name, str) and not isinstance(orig_cls_name, (list, tuple)):\n raise ValueError(\n \"Make sure that the `_class_name` is of type string or list of string (for custom pipelines).\"\n )\n\n # remove private attributes\n config_dict = {k: v for k, v in config_dict.items() if not k.startswith(\"_\")}\n\n # 3. Create keyword arguments that will be passed to __init__ from expected keyword arguments\n init_dict = {}\n for key in expected_keys:\n # if config param is passed to kwarg and is present in config dict\n # it should overwrite existing config dict key\n if key in kwargs and key in config_dict:\n config_dict[key] = kwargs.pop(key)\n\n if key in kwargs:\n # overwrite key\n init_dict[key] = kwargs.pop(key)\n elif key in config_dict:\n # use value from config dict\n init_dict[key] = config_dict.pop(key)\n\n # 4. Give nice warning if unexpected values have been passed\n if len(config_dict) > 0:\n logger.warning(\n f\"The config attributes {config_dict} were passed to {cls.__name__}, \"\n \"but are not expected and will be ignored. Please verify your \"\n f\"{cls.config_name} configuration file.\"\n )\n\n # 5. Give nice info if config attributes are initialized to default because they have not been passed\n passed_keys = set(init_dict.keys())\n if len(expected_keys - passed_keys) > 0:\n logger.info(\n f\"{expected_keys - passed_keys} was not found in config. Values will be initialized to default values.\"\n )\n\n # 6. Define unused keyword arguments\n unused_kwargs = {**config_dict, **kwargs}\n\n # 7. Define \"hidden\" config parameters that were saved for compatible classes\n hidden_config_dict = {k: v for k, v in original_dict.items() if k not in init_dict}\n\n return init_dict, unused_kwargs, hidden_config_dict\n\n @classmethod\n def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]):\n with open(json_file, \"r\", encoding=\"utf-8\") as reader:\n text = reader.read()\n return json.loads(text)\n\n def __repr__(self):\n return f\"{self.__class__.__name__} {self.to_json_string()}\"\n\n @property\n def config(self) -> Dict[str, Any]:\n \"\"\"\n Returns the config of the class as a frozen dictionary\n\n Returns:\n `Dict[str, Any]`: Config of the class.\n \"\"\"\n return self._internal_dict\n\n def to_json_string(self) -> str:\n \"\"\"\n Serializes the configuration instance to a JSON string.\n\n Returns:\n `str`:\n String containing all the attributes that make up the configuration instance in JSON format.\n \"\"\"\n config_dict = self._internal_dict if hasattr(self, \"_internal_dict\") else {}\n config_dict[\"_class_name\"] = self.__class__.__name__\n config_dict[\"_diffusers_version\"] = __version__\n\n def to_json_saveable(value):\n if isinstance(value, np.ndarray):\n value = value.tolist()\n elif isinstance(value, PosixPath):\n value = str(value)\n return value\n\n config_dict = {k: to_json_saveable(v) for k, v in config_dict.items()}\n # Don't save \"_ignore_files\" or \"_use_default_values\"\n config_dict.pop(\"_ignore_files\", None)\n config_dict.pop(\"_use_default_values\", None)\n\n return json.dumps(config_dict, indent=2, sort_keys=True) + \"\\n\"\n\n def to_json_file(self, json_file_path: Union[str, os.PathLike]):\n \"\"\"\n Save the configuration instance's parameters to a JSON file.\n\n Args:\n json_file_path (`str` or `os.PathLike`):\n Path to the JSON file to save a configuration instance's parameters.\n \"\"\"\n with open(json_file_path, \"w\", encoding=\"utf-8\") as writer:\n writer.write(self.to_json_string())\n\n\ndef register_to_config(init):\n r\"\"\"\n Decorator to apply on the init of classes inheriting from [`ConfigMixin`] so that all the arguments are\n automatically sent to `self.register_for_config`. To ignore a specific argument accepted by the init but that\n shouldn't be registered in the config, use the `ignore_for_config` class variable\n\n Warning: Once decorated, all private arguments (beginning with an underscore) are trashed and not sent to the init!\n \"\"\"\n\n @functools.wraps(init)\n def inner_init(self, *args, **kwargs):\n # Ignore private kwargs in the init.\n init_kwargs = {k: v for k, v in kwargs.items() if not k.startswith(\"_\")}\n config_init_kwargs = {k: v for k, v in kwargs.items() if k.startswith(\"_\")}\n if not isinstance(self, ConfigMixin):\n raise RuntimeError(\n f\"`@register_for_config` was applied to {self.__class__.__name__} init method, but this class does \"\n \"not inherit from `ConfigMixin`.\"\n )\n\n ignore = getattr(self, \"ignore_for_config\", [])\n # Get positional arguments aligned with kwargs\n new_kwargs = {}\n signature = inspect.signature(init)\n parameters = {\n name: p.default for i, (name, p) in enumerate(signature.parameters.items()) if i > 0 and name not in ignore\n }\n for arg, name in zip(args, parameters.keys()):\n new_kwargs[name] = arg\n\n # Then add all kwargs\n new_kwargs.update(\n {\n k: init_kwargs.get(k, default)\n for k, default in parameters.items()\n if k not in ignore and k not in new_kwargs\n }\n )\n\n # Take note of the parameters that were not present in the loaded config\n if len(set(new_kwargs.keys()) - set(init_kwargs)) > 0:\n new_kwargs[\"_use_default_values\"] = list(set(new_kwargs.keys()) - set(init_kwargs))\n\n new_kwargs = {**config_init_kwargs, **new_kwargs}\n getattr(self, \"register_to_config\")(**new_kwargs)\n init(self, *args, **init_kwargs)\n\n return inner_init\n\n\ndef flax_register_to_config(cls):\n original_init = cls.__init__\n\n @functools.wraps(original_init)\n def init(self, *args, **kwargs):\n if not isinstance(self, ConfigMixin):\n raise RuntimeError(\n f\"`@register_for_config` was applied to {self.__class__.__name__} init method, but this class does \"\n \"not inherit from `ConfigMixin`.\"\n )\n\n # Ignore private kwargs in the init. Retrieve all passed attributes\n init_kwargs = dict(kwargs.items())\n\n # Retrieve default values\n fields = dataclasses.fields(self)\n default_kwargs = {}\n for field in fields:\n # ignore flax specific attributes\n if field.name in self._flax_internal_args:\n continue\n if type(field.default) == dataclasses._MISSING_TYPE:\n default_kwargs[field.name] = None\n else:\n default_kwargs[field.name] = getattr(self, field.name)\n\n # Make sure init_kwargs override default kwargs\n new_kwargs = {**default_kwargs, **init_kwargs}\n # dtype should be part of `init_kwargs`, but not `new_kwargs`\n if \"dtype\" in new_kwargs:\n new_kwargs.pop(\"dtype\")\n\n # Get positional arguments aligned with kwargs\n for i, arg in enumerate(args):\n name = fields[i].name\n new_kwargs[name] = arg\n\n # Take note of the parameters that were not present in the loaded config\n if len(set(new_kwargs.keys()) - set(init_kwargs)) > 0:\n new_kwargs[\"_use_default_values\"] = list(set(new_kwargs.keys()) - set(init_kwargs))\n\n getattr(self, \"register_to_config\")(**new_kwargs)\n original_init(self, *args, **kwargs)\n\n cls.__init__ = init\n return cls\n\n\nclass LegacyConfigMixin(ConfigMixin):\n r\"\"\"\n A subclass of `ConfigMixin` to resolve class mapping from legacy classes (like `Transformer2DModel`) to more\n pipeline-specific classes (like `DiTTransformer2DModel`).\n \"\"\"\n\n @classmethod\n def from_config(cls, config: Union[FrozenDict, Dict[str, Any]] = None, return_unused_kwargs=False, **kwargs):\n # To prevent depedency import problem.\n from .models.model_loading_utils import _fetch_remapped_cls_from_config\n\n # resolve remapping\n remapped_class = _fetch_remapped_cls_from_config(config, cls)\n\n return remapped_class.from_config(config, return_unused_kwargs, **kwargs)\n" }, { "path": "diffusers/src/diffusers/dependency_versions_check.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom .dependency_versions_table import deps\nfrom .utils.versions import require_version, require_version_core\n\n\n# define which module versions we always want to check at run time\n# (usually the ones defined in `install_requires` in setup.py)\n#\n# order specific notes:\n# - tqdm must be checked before tokenizers\n\npkgs_to_check_at_runtime = \"python requests filelock numpy\".split()\nfor pkg in pkgs_to_check_at_runtime:\n if pkg in deps:\n require_version_core(deps[pkg])\n else:\n raise ValueError(f\"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py\")\n\n\ndef dep_version_check(pkg, hint=None):\n require_version(deps[pkg], hint)\n" }, { "path": "diffusers/src/diffusers/dependency_versions_table.py", "content": "# THIS FILE HAS BEEN AUTOGENERATED. To update:\n# 1. modify the `_deps` dict in setup.py\n# 2. run `make deps_table_update`\ndeps = {\n \"Pillow\": \"Pillow\",\n \"accelerate\": \"accelerate>=0.31.0\",\n \"compel\": \"compel==0.1.8\",\n \"datasets\": \"datasets\",\n \"filelock\": \"filelock\",\n \"flax\": \"flax>=0.4.1\",\n \"hf-doc-builder\": \"hf-doc-builder>=0.3.0\",\n \"huggingface-hub\": \"huggingface-hub>=0.23.2\",\n \"requests-mock\": \"requests-mock==1.10.0\",\n \"importlib_metadata\": \"importlib_metadata\",\n \"invisible-watermark\": \"invisible-watermark>=0.2.0\",\n \"isort\": \"isort>=5.5.4\",\n \"jax\": \"jax>=0.4.1\",\n \"jaxlib\": \"jaxlib>=0.4.1\",\n \"Jinja2\": \"Jinja2\",\n \"k-diffusion\": \"k-diffusion>=0.0.12\",\n \"torchsde\": \"torchsde\",\n \"note_seq\": \"note_seq\",\n \"librosa\": \"librosa\",\n \"numpy\": \"numpy\",\n \"parameterized\": \"parameterized\",\n \"peft\": \"peft>=0.6.0\",\n \"protobuf\": \"protobuf>=3.20.3,<4\",\n \"pytest\": \"pytest\",\n \"pytest-timeout\": \"pytest-timeout\",\n \"pytest-xdist\": \"pytest-xdist\",\n \"python\": \"python>=3.8.0\",\n \"ruff\": \"ruff==0.1.5\",\n \"safetensors\": \"safetensors>=0.3.1\",\n \"sentencepiece\": \"sentencepiece>=0.1.91,!=0.1.92\",\n \"GitPython\": \"GitPython<3.1.19\",\n \"scipy\": \"scipy\",\n \"onnx\": \"onnx\",\n \"regex\": \"regex!=2019.12.17\",\n \"requests\": \"requests\",\n \"tensorboard\": \"tensorboard\",\n \"torch\": \"torch>=1.4\",\n \"torchvision\": \"torchvision\",\n \"transformers\": \"transformers>=4.41.2\",\n \"urllib3\": \"urllib3<=2.0.0\",\n \"black\": \"black\",\n}\n" }, { "path": "diffusers/src/diffusers/experimental/README.md", "content": "# 🧨 Diffusers Experimental\n\nWe are adding experimental code to support novel applications and usages of the Diffusers library.\nCurrently, the following experiments are supported:\n* Reinforcement learning via an implementation of the [Diffuser](https://arxiv.org/abs/2205.09991) model." }, { "path": "diffusers/src/diffusers/experimental/__init__.py", "content": "from .rl import ValueGuidedRLPipeline\n" }, { "path": "diffusers/src/diffusers/experimental/rl/__init__.py", "content": "from .value_guided_sampling import ValueGuidedRLPipeline\n" }, { "path": "diffusers/src/diffusers/experimental/rl/value_guided_sampling.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nimport torch\nimport tqdm\n\nfrom ...models.unets.unet_1d import UNet1DModel\nfrom ...pipelines import DiffusionPipeline\nfrom ...utils.dummy_pt_objects import DDPMScheduler\nfrom ...utils.torch_utils import randn_tensor\n\n\nclass ValueGuidedRLPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for value-guided sampling from a diffusion model trained to predict sequences of states.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Parameters:\n value_function ([`UNet1DModel`]):\n A specialized UNet for fine-tuning trajectories base on reward.\n unet ([`UNet1DModel`]):\n UNet architecture to denoise the encoded trajectories.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded trajectories. Default for this\n application is [`DDPMScheduler`].\n env ():\n An environment following the OpenAI gym API to act in. For now only Hopper has pretrained models.\n \"\"\"\n\n def __init__(\n self,\n value_function: UNet1DModel,\n unet: UNet1DModel,\n scheduler: DDPMScheduler,\n env,\n ):\n super().__init__()\n\n self.register_modules(value_function=value_function, unet=unet, scheduler=scheduler, env=env)\n\n self.data = env.get_dataset()\n self.means = {}\n for key in self.data.keys():\n try:\n self.means[key] = self.data[key].mean()\n except: # noqa: E722\n pass\n self.stds = {}\n for key in self.data.keys():\n try:\n self.stds[key] = self.data[key].std()\n except: # noqa: E722\n pass\n self.state_dim = env.observation_space.shape[0]\n self.action_dim = env.action_space.shape[0]\n\n def normalize(self, x_in, key):\n return (x_in - self.means[key]) / self.stds[key]\n\n def de_normalize(self, x_in, key):\n return x_in * self.stds[key] + self.means[key]\n\n def to_torch(self, x_in):\n if isinstance(x_in, dict):\n return {k: self.to_torch(v) for k, v in x_in.items()}\n elif torch.is_tensor(x_in):\n return x_in.to(self.unet.device)\n return torch.tensor(x_in, device=self.unet.device)\n\n def reset_x0(self, x_in, cond, act_dim):\n for key, val in cond.items():\n x_in[:, key, act_dim:] = val.clone()\n return x_in\n\n def run_diffusion(self, x, conditions, n_guide_steps, scale):\n batch_size = x.shape[0]\n y = None\n for i in tqdm.tqdm(self.scheduler.timesteps):\n # create batch of timesteps to pass into model\n timesteps = torch.full((batch_size,), i, device=self.unet.device, dtype=torch.long)\n for _ in range(n_guide_steps):\n with torch.enable_grad():\n x.requires_grad_()\n\n # permute to match dimension for pre-trained models\n y = self.value_function(x.permute(0, 2, 1), timesteps).sample\n grad = torch.autograd.grad([y.sum()], [x])[0]\n\n posterior_variance = self.scheduler._get_variance(i)\n model_std = torch.exp(0.5 * posterior_variance)\n grad = model_std * grad\n\n grad[timesteps < 2] = 0\n x = x.detach()\n x = x + scale * grad\n x = self.reset_x0(x, conditions, self.action_dim)\n\n prev_x = self.unet(x.permute(0, 2, 1), timesteps).sample.permute(0, 2, 1)\n\n # TODO: verify deprecation of this kwarg\n x = self.scheduler.step(prev_x, i, x)[\"prev_sample\"]\n\n # apply conditions to the trajectory (set the initial state)\n x = self.reset_x0(x, conditions, self.action_dim)\n x = self.to_torch(x)\n return x, y\n\n def __call__(self, obs, batch_size=64, planning_horizon=32, n_guide_steps=2, scale=0.1):\n # normalize the observations and create batch dimension\n obs = self.normalize(obs, \"observations\")\n obs = obs[None].repeat(batch_size, axis=0)\n\n conditions = {0: self.to_torch(obs)}\n shape = (batch_size, planning_horizon, self.state_dim + self.action_dim)\n\n # generate initial noise and apply our conditions (to make the trajectories start at current state)\n x1 = randn_tensor(shape, device=self.unet.device)\n x = self.reset_x0(x1, conditions, self.action_dim)\n x = self.to_torch(x)\n\n # run the diffusion process\n x, y = self.run_diffusion(x, conditions, n_guide_steps, scale)\n\n # sort output trajectories by value\n sorted_idx = y.argsort(0, descending=True).squeeze()\n sorted_values = x[sorted_idx]\n actions = sorted_values[:, :, : self.action_dim]\n actions = actions.detach().cpu().numpy()\n denorm_actions = self.de_normalize(actions, key=\"actions\")\n\n # select the action with the highest value\n if y is not None:\n selected_index = 0\n else:\n # if we didn't run value guiding, select a random action\n selected_index = np.random.randint(0, batch_size)\n\n denorm_actions = denorm_actions[selected_index, 0]\n return denorm_actions\n" }, { "path": "diffusers/src/diffusers/image_processor.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport math\nimport warnings\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom PIL import Image, ImageFilter, ImageOps\n\nfrom .configuration_utils import ConfigMixin, register_to_config\nfrom .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate\n\n\nPipelineImageInput = Union[\n PIL.Image.Image,\n np.ndarray,\n torch.Tensor,\n List[PIL.Image.Image],\n List[np.ndarray],\n List[torch.Tensor],\n]\n\nPipelineDepthInput = PipelineImageInput\n\n\ndef is_valid_image(image):\n return isinstance(image, PIL.Image.Image) or isinstance(image, (np.ndarray, torch.Tensor)) and image.ndim in (2, 3)\n\n\ndef is_valid_image_imagelist(images):\n # check if the image input is one of the supported formats for image and image list:\n # it can be either one of below 3\n # (1) a 4d pytorch tensor or numpy array,\n # (2) a valid image: PIL.Image.Image, 2-d np.ndarray or torch.Tensor (grayscale image), 3-d np.ndarray or torch.Tensor\n # (3) a list of valid image\n if isinstance(images, (np.ndarray, torch.Tensor)) and images.ndim == 4:\n return True\n elif is_valid_image(images):\n return True\n elif isinstance(images, list):\n return all(is_valid_image(image) for image in images)\n return False\n\n\nclass VaeImageProcessor(ConfigMixin):\n \"\"\"\n Image processor for VAE.\n\n Args:\n do_resize (`bool`, *optional*, defaults to `True`):\n Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`. Can accept\n `height` and `width` arguments from [`image_processor.VaeImageProcessor.preprocess`] method.\n vae_scale_factor (`int`, *optional*, defaults to `8`):\n VAE scale factor. If `do_resize` is `True`, the image is automatically resized to multiples of this factor.\n resample (`str`, *optional*, defaults to `lanczos`):\n Resampling filter to use when resizing the image.\n do_normalize (`bool`, *optional*, defaults to `True`):\n Whether to normalize the image to [-1,1].\n do_binarize (`bool`, *optional*, defaults to `False`):\n Whether to binarize the image to 0/1.\n do_convert_rgb (`bool`, *optional*, defaults to be `False`):\n Whether to convert the images to RGB format.\n do_convert_grayscale (`bool`, *optional*, defaults to be `False`):\n Whether to convert the images to grayscale format.\n \"\"\"\n\n config_name = CONFIG_NAME\n\n @register_to_config\n def __init__(\n self,\n do_resize: bool = True,\n vae_scale_factor: int = 8,\n vae_latent_channels: int = 4,\n resample: str = \"lanczos\",\n do_normalize: bool = True,\n do_binarize: bool = False,\n do_convert_rgb: bool = False,\n do_convert_grayscale: bool = False,\n ):\n super().__init__()\n if do_convert_rgb and do_convert_grayscale:\n raise ValueError(\n \"`do_convert_rgb` and `do_convert_grayscale` can not both be set to `True`,\"\n \" if you intended to convert the image into RGB format, please set `do_convert_grayscale = False`.\",\n \" if you intended to convert the image into grayscale format, please set `do_convert_rgb = False`\",\n )\n\n @staticmethod\n def numpy_to_pil(images: np.ndarray) -> List[PIL.Image.Image]:\n \"\"\"\n Convert a numpy image or a batch of images to a PIL image.\n \"\"\"\n if images.ndim == 3:\n images = images[None, ...]\n images = (images * 255).round().astype(\"uint8\")\n if images.shape[-1] == 1:\n # special case for grayscale (single channel) images\n pil_images = [Image.fromarray(image.squeeze(), mode=\"L\") for image in images]\n else:\n pil_images = [Image.fromarray(image) for image in images]\n\n return pil_images\n\n @staticmethod\n def pil_to_numpy(images: Union[List[PIL.Image.Image], PIL.Image.Image]) -> np.ndarray:\n \"\"\"\n Convert a PIL image or a list of PIL images to NumPy arrays.\n \"\"\"\n if not isinstance(images, list):\n images = [images]\n images = [np.array(image).astype(np.float32) / 255.0 for image in images]\n images = np.stack(images, axis=0)\n\n return images\n\n @staticmethod\n def numpy_to_pt(images: np.ndarray) -> torch.Tensor:\n \"\"\"\n Convert a NumPy image to a PyTorch tensor.\n \"\"\"\n if images.ndim == 3:\n images = images[..., None]\n\n images = torch.from_numpy(images.transpose(0, 3, 1, 2))\n return images\n\n @staticmethod\n def pt_to_numpy(images: torch.Tensor) -> np.ndarray:\n \"\"\"\n Convert a PyTorch tensor to a NumPy image.\n \"\"\"\n images = images.cpu().permute(0, 2, 3, 1).float().numpy()\n return images\n\n @staticmethod\n def normalize(images: Union[np.ndarray, torch.Tensor]) -> Union[np.ndarray, torch.Tensor]:\n \"\"\"\n Normalize an image array to [-1,1].\n \"\"\"\n return 2.0 * images - 1.0\n\n @staticmethod\n def denormalize(images: Union[np.ndarray, torch.Tensor]) -> Union[np.ndarray, torch.Tensor]:\n \"\"\"\n Denormalize an image array to [0,1].\n \"\"\"\n return (images / 2 + 0.5).clamp(0, 1)\n\n @staticmethod\n def convert_to_rgb(image: PIL.Image.Image) -> PIL.Image.Image:\n \"\"\"\n Converts a PIL image to RGB format.\n \"\"\"\n image = image.convert(\"RGB\")\n\n return image\n\n @staticmethod\n def convert_to_grayscale(image: PIL.Image.Image) -> PIL.Image.Image:\n \"\"\"\n Converts a PIL image to grayscale format.\n \"\"\"\n image = image.convert(\"L\")\n\n return image\n\n @staticmethod\n def blur(image: PIL.Image.Image, blur_factor: int = 4) -> PIL.Image.Image:\n \"\"\"\n Applies Gaussian blur to an image.\n \"\"\"\n image = image.filter(ImageFilter.GaussianBlur(blur_factor))\n\n return image\n\n @staticmethod\n def get_crop_region(mask_image: PIL.Image.Image, width: int, height: int, pad=0):\n \"\"\"\n Finds a rectangular region that contains all masked ares in an image, and expands region to match the aspect\n ratio of the original image; for example, if user drew mask in a 128x32 region, and the dimensions for\n processing are 512x512, the region will be expanded to 128x128.\n\n Args:\n mask_image (PIL.Image.Image): Mask image.\n width (int): Width of the image to be processed.\n height (int): Height of the image to be processed.\n pad (int, optional): Padding to be added to the crop region. Defaults to 0.\n\n Returns:\n tuple: (x1, y1, x2, y2) represent a rectangular region that contains all masked ares in an image and\n matches the original aspect ratio.\n \"\"\"\n\n mask_image = mask_image.convert(\"L\")\n mask = np.array(mask_image)\n\n # 1. find a rectangular region that contains all masked ares in an image\n h, w = mask.shape\n crop_left = 0\n for i in range(w):\n if not (mask[:, i] == 0).all():\n break\n crop_left += 1\n\n crop_right = 0\n for i in reversed(range(w)):\n if not (mask[:, i] == 0).all():\n break\n crop_right += 1\n\n crop_top = 0\n for i in range(h):\n if not (mask[i] == 0).all():\n break\n crop_top += 1\n\n crop_bottom = 0\n for i in reversed(range(h)):\n if not (mask[i] == 0).all():\n break\n crop_bottom += 1\n\n # 2. add padding to the crop region\n x1, y1, x2, y2 = (\n int(max(crop_left - pad, 0)),\n int(max(crop_top - pad, 0)),\n int(min(w - crop_right + pad, w)),\n int(min(h - crop_bottom + pad, h)),\n )\n\n # 3. expands crop region to match the aspect ratio of the image to be processed\n ratio_crop_region = (x2 - x1) / (y2 - y1)\n ratio_processing = width / height\n\n if ratio_crop_region > ratio_processing:\n desired_height = (x2 - x1) / ratio_processing\n desired_height_diff = int(desired_height - (y2 - y1))\n y1 -= desired_height_diff // 2\n y2 += desired_height_diff - desired_height_diff // 2\n if y2 >= mask_image.height:\n diff = y2 - mask_image.height\n y2 -= diff\n y1 -= diff\n if y1 < 0:\n y2 -= y1\n y1 -= y1\n if y2 >= mask_image.height:\n y2 = mask_image.height\n else:\n desired_width = (y2 - y1) * ratio_processing\n desired_width_diff = int(desired_width - (x2 - x1))\n x1 -= desired_width_diff // 2\n x2 += desired_width_diff - desired_width_diff // 2\n if x2 >= mask_image.width:\n diff = x2 - mask_image.width\n x2 -= diff\n x1 -= diff\n if x1 < 0:\n x2 -= x1\n x1 -= x1\n if x2 >= mask_image.width:\n x2 = mask_image.width\n\n return x1, y1, x2, y2\n\n def _resize_and_fill(\n self,\n image: PIL.Image.Image,\n width: int,\n height: int,\n ) -> PIL.Image.Image:\n \"\"\"\n Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center\n the image within the dimensions, filling empty with data from image.\n\n Args:\n image: The image to resize.\n width: The width to resize the image to.\n height: The height to resize the image to.\n \"\"\"\n\n ratio = width / height\n src_ratio = image.width / image.height\n\n src_w = width if ratio < src_ratio else image.width * height // image.height\n src_h = height if ratio >= src_ratio else image.height * width // image.width\n\n resized = image.resize((src_w, src_h), resample=PIL_INTERPOLATION[\"lanczos\"])\n res = Image.new(\"RGB\", (width, height))\n res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))\n\n if ratio < src_ratio:\n fill_height = height // 2 - src_h // 2\n if fill_height > 0:\n res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))\n res.paste(\n resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)),\n box=(0, fill_height + src_h),\n )\n elif ratio > src_ratio:\n fill_width = width // 2 - src_w // 2\n if fill_width > 0:\n res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))\n res.paste(\n resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)),\n box=(fill_width + src_w, 0),\n )\n\n return res\n\n def _resize_and_crop(\n self,\n image: PIL.Image.Image,\n width: int,\n height: int,\n ) -> PIL.Image.Image:\n \"\"\"\n Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center\n the image within the dimensions, cropping the excess.\n\n Args:\n image: The image to resize.\n width: The width to resize the image to.\n height: The height to resize the image to.\n \"\"\"\n ratio = width / height\n src_ratio = image.width / image.height\n\n src_w = width if ratio > src_ratio else image.width * height // image.height\n src_h = height if ratio <= src_ratio else image.height * width // image.width\n\n resized = image.resize((src_w, src_h), resample=PIL_INTERPOLATION[\"lanczos\"])\n res = Image.new(\"RGB\", (width, height))\n res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))\n return res\n\n def resize(\n self,\n image: Union[PIL.Image.Image, np.ndarray, torch.Tensor],\n height: int,\n width: int,\n resize_mode: str = \"default\", # \"default\", \"fill\", \"crop\"\n ) -> Union[PIL.Image.Image, np.ndarray, torch.Tensor]:\n \"\"\"\n Resize image.\n\n Args:\n image (`PIL.Image.Image`, `np.ndarray` or `torch.Tensor`):\n The image input, can be a PIL image, numpy array or pytorch tensor.\n height (`int`):\n The height to resize to.\n width (`int`):\n The width to resize to.\n resize_mode (`str`, *optional*, defaults to `default`):\n The resize mode to use, can be one of `default` or `fill`. If `default`, will resize the image to fit\n within the specified width and height, and it may not maintaining the original aspect ratio. If `fill`,\n will resize the image to fit within the specified width and height, maintaining the aspect ratio, and\n then center the image within the dimensions, filling empty with data from image. If `crop`, will resize\n the image to fit within the specified width and height, maintaining the aspect ratio, and then center\n the image within the dimensions, cropping the excess. Note that resize_mode `fill` and `crop` are only\n supported for PIL image input.\n\n Returns:\n `PIL.Image.Image`, `np.ndarray` or `torch.Tensor`:\n The resized image.\n \"\"\"\n if resize_mode != \"default\" and not isinstance(image, PIL.Image.Image):\n raise ValueError(f\"Only PIL image input is supported for resize_mode {resize_mode}\")\n if isinstance(image, PIL.Image.Image):\n if resize_mode == \"default\":\n image = image.resize((width, height), resample=PIL_INTERPOLATION[self.config.resample])\n elif resize_mode == \"fill\":\n image = self._resize_and_fill(image, width, height)\n elif resize_mode == \"crop\":\n image = self._resize_and_crop(image, width, height)\n else:\n raise ValueError(f\"resize_mode {resize_mode} is not supported\")\n\n elif isinstance(image, torch.Tensor):\n image = torch.nn.functional.interpolate(\n image,\n size=(height, width),\n )\n elif isinstance(image, np.ndarray):\n image = self.numpy_to_pt(image)\n image = torch.nn.functional.interpolate(\n image,\n size=(height, width),\n )\n image = self.pt_to_numpy(image)\n return image\n\n def binarize(self, image: PIL.Image.Image) -> PIL.Image.Image:\n \"\"\"\n Create a mask.\n\n Args:\n image (`PIL.Image.Image`):\n The image input, should be a PIL image.\n\n Returns:\n `PIL.Image.Image`:\n The binarized image. Values less than 0.5 are set to 0, values greater than 0.5 are set to 1.\n \"\"\"\n image[image < 0.5] = 0\n image[image >= 0.5] = 1\n\n return image\n\n def get_default_height_width(\n self,\n image: Union[PIL.Image.Image, np.ndarray, torch.Tensor],\n height: Optional[int] = None,\n width: Optional[int] = None,\n ) -> Tuple[int, int]:\n \"\"\"\n This function return the height and width that are downscaled to the next integer multiple of\n `vae_scale_factor`.\n\n Args:\n image(`PIL.Image.Image`, `np.ndarray` or `torch.Tensor`):\n The image input, can be a PIL image, numpy array or pytorch tensor. if it is a numpy array, should have\n shape `[batch, height, width]` or `[batch, height, width, channel]` if it is a pytorch tensor, should\n have shape `[batch, channel, height, width]`.\n height (`int`, *optional*, defaults to `None`):\n The height in preprocessed image. If `None`, will use the height of `image` input.\n width (`int`, *optional*`, defaults to `None`):\n The width in preprocessed. If `None`, will use the width of the `image` input.\n \"\"\"\n\n if height is None:\n if isinstance(image, PIL.Image.Image):\n height = image.height\n elif isinstance(image, torch.Tensor):\n height = image.shape[2]\n else:\n height = image.shape[1]\n\n if width is None:\n if isinstance(image, PIL.Image.Image):\n width = image.width\n elif isinstance(image, torch.Tensor):\n width = image.shape[3]\n else:\n width = image.shape[2]\n\n width, height = (\n x - x % self.config.vae_scale_factor for x in (width, height)\n ) # resize to integer multiple of vae_scale_factor\n\n return height, width\n\n def preprocess(\n self,\n image: PipelineImageInput,\n height: Optional[int] = None,\n width: Optional[int] = None,\n resize_mode: str = \"default\", # \"default\", \"fill\", \"crop\"\n crops_coords: Optional[Tuple[int, int, int, int]] = None,\n ) -> torch.Tensor:\n \"\"\"\n Preprocess the image input.\n\n Args:\n image (`pipeline_image_input`):\n The image input, accepted formats are PIL images, NumPy arrays, PyTorch tensors; Also accept list of\n supported formats.\n height (`int`, *optional*, defaults to `None`):\n The height in preprocessed image. If `None`, will use the `get_default_height_width()` to get default\n height.\n width (`int`, *optional*`, defaults to `None`):\n The width in preprocessed. If `None`, will use get_default_height_width()` to get the default width.\n resize_mode (`str`, *optional*, defaults to `default`):\n The resize mode, can be one of `default` or `fill`. If `default`, will resize the image to fit within\n the specified width and height, and it may not maintaining the original aspect ratio. If `fill`, will\n resize the image to fit within the specified width and height, maintaining the aspect ratio, and then\n center the image within the dimensions, filling empty with data from image. If `crop`, will resize the\n image to fit within the specified width and height, maintaining the aspect ratio, and then center the\n image within the dimensions, cropping the excess. Note that resize_mode `fill` and `crop` are only\n supported for PIL image input.\n crops_coords (`List[Tuple[int, int, int, int]]`, *optional*, defaults to `None`):\n The crop coordinates for each image in the batch. If `None`, will not crop the image.\n \"\"\"\n supported_formats = (PIL.Image.Image, np.ndarray, torch.Tensor)\n\n # Expand the missing dimension for 3-dimensional pytorch tensor or numpy array that represents grayscale image\n if self.config.do_convert_grayscale and isinstance(image, (torch.Tensor, np.ndarray)) and image.ndim == 3:\n if isinstance(image, torch.Tensor):\n # if image is a pytorch tensor could have 2 possible shapes:\n # 1. batch x height x width: we should insert the channel dimension at position 1\n # 2. channel x height x width: we should insert batch dimension at position 0,\n # however, since both channel and batch dimension has same size 1, it is same to insert at position 1\n # for simplicity, we insert a dimension of size 1 at position 1 for both cases\n image = image.unsqueeze(1)\n else:\n # if it is a numpy array, it could have 2 possible shapes:\n # 1. batch x height x width: insert channel dimension on last position\n # 2. height x width x channel: insert batch dimension on first position\n if image.shape[-1] == 1:\n image = np.expand_dims(image, axis=0)\n else:\n image = np.expand_dims(image, axis=-1)\n\n if isinstance(image, list) and isinstance(image[0], np.ndarray) and image[0].ndim == 4:\n warnings.warn(\n \"Passing `image` as a list of 4d np.ndarray is deprecated.\"\n \"Please concatenate the list along the batch dimension and pass it as a single 4d np.ndarray\",\n FutureWarning,\n )\n image = np.concatenate(image, axis=0)\n if isinstance(image, list) and isinstance(image[0], torch.Tensor) and image[0].ndim == 4:\n warnings.warn(\n \"Passing `image` as a list of 4d torch.Tensor is deprecated.\"\n \"Please concatenate the list along the batch dimension and pass it as a single 4d torch.Tensor\",\n FutureWarning,\n )\n image = torch.cat(image, axis=0)\n\n if not is_valid_image_imagelist(image):\n raise ValueError(\n f\"Input is in incorrect format. Currently, we only support {', '.join(str(x) for x in supported_formats)}\"\n )\n if not isinstance(image, list):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n if crops_coords is not None:\n image = [i.crop(crops_coords) for i in image]\n if self.config.do_resize:\n height, width = self.get_default_height_width(image[0], height, width)\n image = [self.resize(i, height, width, resize_mode=resize_mode) for i in image]\n if self.config.do_convert_rgb:\n image = [self.convert_to_rgb(i) for i in image]\n elif self.config.do_convert_grayscale:\n image = [self.convert_to_grayscale(i) for i in image]\n image = self.pil_to_numpy(image) # to np\n image = self.numpy_to_pt(image) # to pt\n\n elif isinstance(image[0], np.ndarray):\n image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0)\n\n image = self.numpy_to_pt(image)\n\n height, width = self.get_default_height_width(image, height, width)\n if self.config.do_resize:\n image = self.resize(image, height, width)\n\n elif isinstance(image[0], torch.Tensor):\n image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0)\n\n if self.config.do_convert_grayscale and image.ndim == 3:\n image = image.unsqueeze(1)\n\n channel = image.shape[1]\n # don't need any preprocess if the image is latents\n if channel == self.vae_latent_channels:\n return image\n\n height, width = self.get_default_height_width(image, height, width)\n if self.config.do_resize:\n image = self.resize(image, height, width)\n\n # expected range [0,1], normalize to [-1,1]\n do_normalize = self.config.do_normalize\n if do_normalize and image.min() < 0:\n warnings.warn(\n \"Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] \"\n f\"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]\",\n FutureWarning,\n )\n do_normalize = False\n if do_normalize:\n image = self.normalize(image)\n\n if self.config.do_binarize:\n image = self.binarize(image)\n\n return image\n\n def postprocess(\n self,\n image: torch.Tensor,\n output_type: str = \"pil\",\n do_denormalize: Optional[List[bool]] = None,\n ) -> Union[PIL.Image.Image, np.ndarray, torch.Tensor]:\n \"\"\"\n Postprocess the image output from tensor to `output_type`.\n\n Args:\n image (`torch.Tensor`):\n The image input, should be a pytorch tensor with shape `B x C x H x W`.\n output_type (`str`, *optional*, defaults to `pil`):\n The output type of the image, can be one of `pil`, `np`, `pt`, `latent`.\n do_denormalize (`List[bool]`, *optional*, defaults to `None`):\n Whether to denormalize the image to [0,1]. If `None`, will use the value of `do_normalize` in the\n `VaeImageProcessor` config.\n\n Returns:\n `PIL.Image.Image`, `np.ndarray` or `torch.Tensor`:\n The postprocessed image.\n \"\"\"\n if not isinstance(image, torch.Tensor):\n raise ValueError(\n f\"Input for postprocessing is in incorrect format: {type(image)}. We only support pytorch tensor\"\n )\n if output_type not in [\"latent\", \"pt\", \"np\", \"pil\"]:\n deprecation_message = (\n f\"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: \"\n \"`pil`, `np`, `pt`, `latent`\"\n )\n deprecate(\"Unsupported output_type\", \"1.0.0\", deprecation_message, standard_warn=False)\n output_type = \"np\"\n\n if output_type == \"latent\":\n return image\n\n if do_denormalize is None:\n do_denormalize = [self.config.do_normalize] * image.shape[0]\n\n image = torch.stack(\n [self.denormalize(image[i]) if do_denormalize[i] else image[i] for i in range(image.shape[0])]\n )\n\n if output_type == \"pt\":\n return image\n\n image = self.pt_to_numpy(image)\n\n if output_type == \"np\":\n return image\n\n if output_type == \"pil\":\n return self.numpy_to_pil(image)\n\n def apply_overlay(\n self,\n mask: PIL.Image.Image,\n init_image: PIL.Image.Image,\n image: PIL.Image.Image,\n crop_coords: Optional[Tuple[int, int, int, int]] = None,\n ) -> PIL.Image.Image:\n \"\"\"\n overlay the inpaint output to the original image\n \"\"\"\n\n width, height = image.width, image.height\n\n init_image = self.resize(init_image, width=width, height=height)\n mask = self.resize(mask, width=width, height=height)\n\n init_image_masked = PIL.Image.new(\"RGBa\", (width, height))\n init_image_masked.paste(init_image.convert(\"RGBA\").convert(\"RGBa\"), mask=ImageOps.invert(mask.convert(\"L\")))\n init_image_masked = init_image_masked.convert(\"RGBA\")\n\n if crop_coords is not None:\n x, y, x2, y2 = crop_coords\n w = x2 - x\n h = y2 - y\n base_image = PIL.Image.new(\"RGBA\", (width, height))\n image = self.resize(image, height=h, width=w, resize_mode=\"crop\")\n base_image.paste(image, (x, y))\n image = base_image.convert(\"RGB\")\n\n image = image.convert(\"RGBA\")\n image.alpha_composite(init_image_masked)\n image = image.convert(\"RGB\")\n\n return image\n\n\nclass VaeImageProcessorLDM3D(VaeImageProcessor):\n \"\"\"\n Image processor for VAE LDM3D.\n\n Args:\n do_resize (`bool`, *optional*, defaults to `True`):\n Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`.\n vae_scale_factor (`int`, *optional*, defaults to `8`):\n VAE scale factor. If `do_resize` is `True`, the image is automatically resized to multiples of this factor.\n resample (`str`, *optional*, defaults to `lanczos`):\n Resampling filter to use when resizing the image.\n do_normalize (`bool`, *optional*, defaults to `True`):\n Whether to normalize the image to [-1,1].\n \"\"\"\n\n config_name = CONFIG_NAME\n\n @register_to_config\n def __init__(\n self,\n do_resize: bool = True,\n vae_scale_factor: int = 8,\n resample: str = \"lanczos\",\n do_normalize: bool = True,\n ):\n super().__init__()\n\n @staticmethod\n def numpy_to_pil(images: np.ndarray) -> List[PIL.Image.Image]:\n \"\"\"\n Convert a NumPy image or a batch of images to a PIL image.\n \"\"\"\n if images.ndim == 3:\n images = images[None, ...]\n images = (images * 255).round().astype(\"uint8\")\n if images.shape[-1] == 1:\n # special case for grayscale (single channel) images\n pil_images = [Image.fromarray(image.squeeze(), mode=\"L\") for image in images]\n else:\n pil_images = [Image.fromarray(image[:, :, :3]) for image in images]\n\n return pil_images\n\n @staticmethod\n def depth_pil_to_numpy(images: Union[List[PIL.Image.Image], PIL.Image.Image]) -> np.ndarray:\n \"\"\"\n Convert a PIL image or a list of PIL images to NumPy arrays.\n \"\"\"\n if not isinstance(images, list):\n images = [images]\n\n images = [np.array(image).astype(np.float32) / (2**16 - 1) for image in images]\n images = np.stack(images, axis=0)\n return images\n\n @staticmethod\n def rgblike_to_depthmap(image: Union[np.ndarray, torch.Tensor]) -> Union[np.ndarray, torch.Tensor]:\n \"\"\"\n Args:\n image: RGB-like depth image\n\n Returns: depth map\n\n \"\"\"\n return image[:, :, 1] * 2**8 + image[:, :, 2]\n\n def numpy_to_depth(self, images: np.ndarray) -> List[PIL.Image.Image]:\n \"\"\"\n Convert a NumPy depth image or a batch of images to a PIL image.\n \"\"\"\n if images.ndim == 3:\n images = images[None, ...]\n images_depth = images[:, :, :, 3:]\n if images.shape[-1] == 6:\n images_depth = (images_depth * 255).round().astype(\"uint8\")\n pil_images = [\n Image.fromarray(self.rgblike_to_depthmap(image_depth), mode=\"I;16\") for image_depth in images_depth\n ]\n elif images.shape[-1] == 4:\n images_depth = (images_depth * 65535.0).astype(np.uint16)\n pil_images = [Image.fromarray(image_depth, mode=\"I;16\") for image_depth in images_depth]\n else:\n raise Exception(\"Not supported\")\n\n return pil_images\n\n def postprocess(\n self,\n image: torch.Tensor,\n output_type: str = \"pil\",\n do_denormalize: Optional[List[bool]] = None,\n ) -> Union[PIL.Image.Image, np.ndarray, torch.Tensor]:\n \"\"\"\n Postprocess the image output from tensor to `output_type`.\n\n Args:\n image (`torch.Tensor`):\n The image input, should be a pytorch tensor with shape `B x C x H x W`.\n output_type (`str`, *optional*, defaults to `pil`):\n The output type of the image, can be one of `pil`, `np`, `pt`, `latent`.\n do_denormalize (`List[bool]`, *optional*, defaults to `None`):\n Whether to denormalize the image to [0,1]. If `None`, will use the value of `do_normalize` in the\n `VaeImageProcessor` config.\n\n Returns:\n `PIL.Image.Image`, `np.ndarray` or `torch.Tensor`:\n The postprocessed image.\n \"\"\"\n if not isinstance(image, torch.Tensor):\n raise ValueError(\n f\"Input for postprocessing is in incorrect format: {type(image)}. We only support pytorch tensor\"\n )\n if output_type not in [\"latent\", \"pt\", \"np\", \"pil\"]:\n deprecation_message = (\n f\"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: \"\n \"`pil`, `np`, `pt`, `latent`\"\n )\n deprecate(\"Unsupported output_type\", \"1.0.0\", deprecation_message, standard_warn=False)\n output_type = \"np\"\n\n if do_denormalize is None:\n do_denormalize = [self.config.do_normalize] * image.shape[0]\n\n image = torch.stack(\n [self.denormalize(image[i]) if do_denormalize[i] else image[i] for i in range(image.shape[0])]\n )\n\n image = self.pt_to_numpy(image)\n\n if output_type == \"np\":\n if image.shape[-1] == 6:\n image_depth = np.stack([self.rgblike_to_depthmap(im[:, :, 3:]) for im in image], axis=0)\n else:\n image_depth = image[:, :, :, 3:]\n return image[:, :, :, :3], image_depth\n\n if output_type == \"pil\":\n return self.numpy_to_pil(image), self.numpy_to_depth(image)\n else:\n raise Exception(f\"This type {output_type} is not supported\")\n\n def preprocess(\n self,\n rgb: Union[torch.Tensor, PIL.Image.Image, np.ndarray],\n depth: Union[torch.Tensor, PIL.Image.Image, np.ndarray],\n height: Optional[int] = None,\n width: Optional[int] = None,\n target_res: Optional[int] = None,\n ) -> torch.Tensor:\n \"\"\"\n Preprocess the image input. Accepted formats are PIL images, NumPy arrays or PyTorch tensors.\n \"\"\"\n supported_formats = (PIL.Image.Image, np.ndarray, torch.Tensor)\n\n # Expand the missing dimension for 3-dimensional pytorch tensor or numpy array that represents grayscale image\n if self.config.do_convert_grayscale and isinstance(rgb, (torch.Tensor, np.ndarray)) and rgb.ndim == 3:\n raise Exception(\"This is not yet supported\")\n\n if isinstance(rgb, supported_formats):\n rgb = [rgb]\n depth = [depth]\n elif not (isinstance(rgb, list) and all(isinstance(i, supported_formats) for i in rgb)):\n raise ValueError(\n f\"Input is in incorrect format: {[type(i) for i in rgb]}. Currently, we only support {', '.join(supported_formats)}\"\n )\n\n if isinstance(rgb[0], PIL.Image.Image):\n if self.config.do_convert_rgb:\n raise Exception(\"This is not yet supported\")\n # rgb = [self.convert_to_rgb(i) for i in rgb]\n # depth = [self.convert_to_depth(i) for i in depth] #TODO define convert_to_depth\n if self.config.do_resize or target_res:\n height, width = self.get_default_height_width(rgb[0], height, width) if not target_res else target_res\n rgb = [self.resize(i, height, width) for i in rgb]\n depth = [self.resize(i, height, width) for i in depth]\n rgb = self.pil_to_numpy(rgb) # to np\n rgb = self.numpy_to_pt(rgb) # to pt\n\n depth = self.depth_pil_to_numpy(depth) # to np\n depth = self.numpy_to_pt(depth) # to pt\n\n elif isinstance(rgb[0], np.ndarray):\n rgb = np.concatenate(rgb, axis=0) if rgb[0].ndim == 4 else np.stack(rgb, axis=0)\n rgb = self.numpy_to_pt(rgb)\n height, width = self.get_default_height_width(rgb, height, width)\n if self.config.do_resize:\n rgb = self.resize(rgb, height, width)\n\n depth = np.concatenate(depth, axis=0) if rgb[0].ndim == 4 else np.stack(depth, axis=0)\n depth = self.numpy_to_pt(depth)\n height, width = self.get_default_height_width(depth, height, width)\n if self.config.do_resize:\n depth = self.resize(depth, height, width)\n\n elif isinstance(rgb[0], torch.Tensor):\n raise Exception(\"This is not yet supported\")\n # rgb = torch.cat(rgb, axis=0) if rgb[0].ndim == 4 else torch.stack(rgb, axis=0)\n\n # if self.config.do_convert_grayscale and rgb.ndim == 3:\n # rgb = rgb.unsqueeze(1)\n\n # channel = rgb.shape[1]\n\n # height, width = self.get_default_height_width(rgb, height, width)\n # if self.config.do_resize:\n # rgb = self.resize(rgb, height, width)\n\n # depth = torch.cat(depth, axis=0) if depth[0].ndim == 4 else torch.stack(depth, axis=0)\n\n # if self.config.do_convert_grayscale and depth.ndim == 3:\n # depth = depth.unsqueeze(1)\n\n # channel = depth.shape[1]\n # # don't need any preprocess if the image is latents\n # if depth == 4:\n # return rgb, depth\n\n # height, width = self.get_default_height_width(depth, height, width)\n # if self.config.do_resize:\n # depth = self.resize(depth, height, width)\n # expected range [0,1], normalize to [-1,1]\n do_normalize = self.config.do_normalize\n if rgb.min() < 0 and do_normalize:\n warnings.warn(\n \"Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] \"\n f\"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{rgb.min()},{rgb.max()}]\",\n FutureWarning,\n )\n do_normalize = False\n\n if do_normalize:\n rgb = self.normalize(rgb)\n depth = self.normalize(depth)\n\n if self.config.do_binarize:\n rgb = self.binarize(rgb)\n depth = self.binarize(depth)\n\n return rgb, depth\n\n\nclass IPAdapterMaskProcessor(VaeImageProcessor):\n \"\"\"\n Image processor for IP Adapter image masks.\n\n Args:\n do_resize (`bool`, *optional*, defaults to `True`):\n Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`.\n vae_scale_factor (`int`, *optional*, defaults to `8`):\n VAE scale factor. If `do_resize` is `True`, the image is automatically resized to multiples of this factor.\n resample (`str`, *optional*, defaults to `lanczos`):\n Resampling filter to use when resizing the image.\n do_normalize (`bool`, *optional*, defaults to `False`):\n Whether to normalize the image to [-1,1].\n do_binarize (`bool`, *optional*, defaults to `True`):\n Whether to binarize the image to 0/1.\n do_convert_grayscale (`bool`, *optional*, defaults to be `True`):\n Whether to convert the images to grayscale format.\n\n \"\"\"\n\n config_name = CONFIG_NAME\n\n @register_to_config\n def __init__(\n self,\n do_resize: bool = True,\n vae_scale_factor: int = 8,\n resample: str = \"lanczos\",\n do_normalize: bool = False,\n do_binarize: bool = True,\n do_convert_grayscale: bool = True,\n ):\n super().__init__(\n do_resize=do_resize,\n vae_scale_factor=vae_scale_factor,\n resample=resample,\n do_normalize=do_normalize,\n do_binarize=do_binarize,\n do_convert_grayscale=do_convert_grayscale,\n )\n\n @staticmethod\n def downsample(mask: torch.Tensor, batch_size: int, num_queries: int, value_embed_dim: int):\n \"\"\"\n Downsamples the provided mask tensor to match the expected dimensions for scaled dot-product attention. If the\n aspect ratio of the mask does not match the aspect ratio of the output image, a warning is issued.\n\n Args:\n mask (`torch.Tensor`):\n The input mask tensor generated with `IPAdapterMaskProcessor.preprocess()`.\n batch_size (`int`):\n The batch size.\n num_queries (`int`):\n The number of queries.\n value_embed_dim (`int`):\n The dimensionality of the value embeddings.\n\n Returns:\n `torch.Tensor`:\n The downsampled mask tensor.\n\n \"\"\"\n o_h = mask.shape[1]\n o_w = mask.shape[2]\n ratio = o_w / o_h\n mask_h = int(math.sqrt(num_queries / ratio))\n mask_h = int(mask_h) + int((num_queries % int(mask_h)) != 0)\n mask_w = num_queries // mask_h\n\n mask_downsample = F.interpolate(mask.unsqueeze(0), size=(mask_h, mask_w), mode=\"bicubic\").squeeze(0)\n\n # Repeat batch_size times\n if mask_downsample.shape[0] < batch_size:\n mask_downsample = mask_downsample.repeat(batch_size, 1, 1)\n\n mask_downsample = mask_downsample.view(mask_downsample.shape[0], -1)\n\n downsampled_area = mask_h * mask_w\n # If the output image and the mask do not have the same aspect ratio, tensor shapes will not match\n # Pad tensor if downsampled_mask.shape[1] is smaller than num_queries\n if downsampled_area < num_queries:\n warnings.warn(\n \"The aspect ratio of the mask does not match the aspect ratio of the output image. \"\n \"Please update your masks or adjust the output size for optimal performance.\",\n UserWarning,\n )\n mask_downsample = F.pad(mask_downsample, (0, num_queries - mask_downsample.shape[1]), value=0.0)\n # Discard last embeddings if downsampled_mask.shape[1] is bigger than num_queries\n if downsampled_area > num_queries:\n warnings.warn(\n \"The aspect ratio of the mask does not match the aspect ratio of the output image. \"\n \"Please update your masks or adjust the output size for optimal performance.\",\n UserWarning,\n )\n mask_downsample = mask_downsample[:, :num_queries]\n\n # Repeat last dimension to match SDPA output shape\n mask_downsample = mask_downsample.view(mask_downsample.shape[0], mask_downsample.shape[1], 1).repeat(\n 1, 1, value_embed_dim\n )\n\n return mask_downsample\n\n\nclass PixArtImageProcessor(VaeImageProcessor):\n \"\"\"\n Image processor for PixArt image resize and crop.\n\n Args:\n do_resize (`bool`, *optional*, defaults to `True`):\n Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`. Can accept\n `height` and `width` arguments from [`image_processor.VaeImageProcessor.preprocess`] method.\n vae_scale_factor (`int`, *optional*, defaults to `8`):\n VAE scale factor. If `do_resize` is `True`, the image is automatically resized to multiples of this factor.\n resample (`str`, *optional*, defaults to `lanczos`):\n Resampling filter to use when resizing the image.\n do_normalize (`bool`, *optional*, defaults to `True`):\n Whether to normalize the image to [-1,1].\n do_binarize (`bool`, *optional*, defaults to `False`):\n Whether to binarize the image to 0/1.\n do_convert_rgb (`bool`, *optional*, defaults to be `False`):\n Whether to convert the images to RGB format.\n do_convert_grayscale (`bool`, *optional*, defaults to be `False`):\n Whether to convert the images to grayscale format.\n \"\"\"\n\n @register_to_config\n def __init__(\n self,\n do_resize: bool = True,\n vae_scale_factor: int = 8,\n resample: str = \"lanczos\",\n do_normalize: bool = True,\n do_binarize: bool = False,\n do_convert_grayscale: bool = False,\n ):\n super().__init__(\n do_resize=do_resize,\n vae_scale_factor=vae_scale_factor,\n resample=resample,\n do_normalize=do_normalize,\n do_binarize=do_binarize,\n do_convert_grayscale=do_convert_grayscale,\n )\n\n @staticmethod\n def classify_height_width_bin(height: int, width: int, ratios: dict) -> Tuple[int, int]:\n \"\"\"Returns binned height and width.\"\"\"\n ar = float(height / width)\n closest_ratio = min(ratios.keys(), key=lambda ratio: abs(float(ratio) - ar))\n default_hw = ratios[closest_ratio]\n return int(default_hw[0]), int(default_hw[1])\n\n @staticmethod\n def resize_and_crop_tensor(samples: torch.Tensor, new_width: int, new_height: int) -> torch.Tensor:\n orig_height, orig_width = samples.shape[2], samples.shape[3]\n\n # Check if resizing is needed\n if orig_height != new_height or orig_width != new_width:\n ratio = max(new_height / orig_height, new_width / orig_width)\n resized_width = int(orig_width * ratio)\n resized_height = int(orig_height * ratio)\n\n # Resize\n samples = F.interpolate(\n samples, size=(resized_height, resized_width), mode=\"bilinear\", align_corners=False\n )\n\n # Center Crop\n start_x = (resized_width - new_width) // 2\n end_x = start_x + new_width\n start_y = (resized_height - new_height) // 2\n end_y = start_y + new_height\n samples = samples[:, :, start_y:end_y, start_x:end_x]\n\n return samples\n" }, { "path": "diffusers/src/diffusers/loaders/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ..utils import DIFFUSERS_SLOW_IMPORT, _LazyModule, deprecate\nfrom ..utils.import_utils import is_peft_available, is_torch_available, is_transformers_available\n\n\ndef text_encoder_lora_state_dict(text_encoder):\n deprecate(\n \"text_encoder_load_state_dict in `models`\",\n \"0.27.0\",\n \"`text_encoder_lora_state_dict` is deprecated and will be removed in 0.27.0. Make sure to retrieve the weights using `get_peft_model`. See https://huggingface.co/docs/peft/v0.6.2/en/quicktour#peftmodel for more information.\",\n )\n state_dict = {}\n\n for name, module in text_encoder_attn_modules(text_encoder):\n for k, v in module.q_proj.lora_linear_layer.state_dict().items():\n state_dict[f\"{name}.q_proj.lora_linear_layer.{k}\"] = v\n\n for k, v in module.k_proj.lora_linear_layer.state_dict().items():\n state_dict[f\"{name}.k_proj.lora_linear_layer.{k}\"] = v\n\n for k, v in module.v_proj.lora_linear_layer.state_dict().items():\n state_dict[f\"{name}.v_proj.lora_linear_layer.{k}\"] = v\n\n for k, v in module.out_proj.lora_linear_layer.state_dict().items():\n state_dict[f\"{name}.out_proj.lora_linear_layer.{k}\"] = v\n\n return state_dict\n\n\nif is_transformers_available():\n\n def text_encoder_attn_modules(text_encoder):\n deprecate(\n \"text_encoder_attn_modules in `models`\",\n \"0.27.0\",\n \"`text_encoder_lora_state_dict` is deprecated and will be removed in 0.27.0. Make sure to retrieve the weights using `get_peft_model`. See https://huggingface.co/docs/peft/v0.6.2/en/quicktour#peftmodel for more information.\",\n )\n from transformers import CLIPTextModel, CLIPTextModelWithProjection\n\n attn_modules = []\n\n if isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection)):\n for i, layer in enumerate(text_encoder.text_model.encoder.layers):\n name = f\"text_model.encoder.layers.{i}.self_attn\"\n mod = layer.self_attn\n attn_modules.append((name, mod))\n else:\n raise ValueError(f\"do not know how to get attention modules for: {text_encoder.__class__.__name__}\")\n\n return attn_modules\n\n\n_import_structure = {}\n\nif is_torch_available():\n _import_structure[\"single_file_model\"] = [\"FromOriginalModelMixin\"]\n _import_structure[\"unet\"] = [\"UNet2DConditionLoadersMixin\"]\n _import_structure[\"utils\"] = [\"AttnProcsLayers\"]\n if is_transformers_available():\n _import_structure[\"single_file\"] = [\"FromSingleFileMixin\"]\n _import_structure[\"lora\"] = [\"LoraLoaderMixin\", \"StableDiffusionXLLoraLoaderMixin\", \"SD3LoraLoaderMixin\"]\n _import_structure[\"textual_inversion\"] = [\"TextualInversionLoaderMixin\"]\n _import_structure[\"ip_adapter\"] = [\"IPAdapterMixin\"]\n\n_import_structure[\"peft\"] = [\"PeftAdapterMixin\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n if is_torch_available():\n from .single_file_model import FromOriginalModelMixin\n from .unet import UNet2DConditionLoadersMixin\n from .utils import AttnProcsLayers\n\n if is_transformers_available():\n from .ip_adapter import IPAdapterMixin\n from .lora import LoraLoaderMixin, SD3LoraLoaderMixin, StableDiffusionXLLoraLoaderMixin\n from .single_file import FromSingleFileMixin\n from .textual_inversion import TextualInversionLoaderMixin\n\n from .peft import PeftAdapterMixin\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(__name__, globals()[\"__file__\"], _import_structure, module_spec=__spec__)\n" }, { "path": "diffusers/src/diffusers/loaders/autoencoder.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom huggingface_hub.utils import validate_hf_hub_args\n\nfrom .single_file_utils import (\n create_diffusers_vae_model_from_ldm,\n fetch_ldm_config_and_checkpoint,\n)\n\n\nclass FromOriginalVAEMixin:\n \"\"\"\n Load pretrained AutoencoderKL weights saved in the `.ckpt` or `.safetensors` format into a [`AutoencoderKL`].\n \"\"\"\n\n @classmethod\n @validate_hf_hub_args\n def from_single_file(cls, pretrained_model_link_or_path, **kwargs):\n r\"\"\"\n Instantiate a [`AutoencoderKL`] from pretrained ControlNet weights saved in the original `.ckpt` or\n `.safetensors` format. The pipeline is set in evaluation mode (`model.eval()`) by default.\n\n Parameters:\n pretrained_model_link_or_path (`str` or `os.PathLike`, *optional*):\n Can be either:\n - A link to the `.ckpt` file (for example\n `\"https://huggingface.co//blob/main/.ckpt\"`) on the Hub.\n - A path to a *file* containing all pipeline weights.\n config_file (`str`, *optional*):\n Filepath to the configuration YAML file associated with the model. If not provided it will default to:\n https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml\n torch_dtype (`str` or `torch.dtype`, *optional*):\n Override the default `torch.dtype` and load the model with another dtype. If `\"auto\"` is passed, the\n dtype is automatically derived from the model's weights.\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\n cached versions if they exist.\n cache_dir (`Union[str, os.PathLike]`, *optional*):\n Path to a directory where a downloaded pretrained model configuration is cached if the standard cache\n is not used.\n resume_download:\n Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1\n of Diffusers.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\n local_files_only (`bool`, *optional*, defaults to `False`):\n Whether to only load local model weights and configuration files or not. If set to True, the model\n won't be downloaded from the Hub.\n token (`str` or *bool*, *optional*):\n The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from\n `diffusers-cli login` (stored in `~/.huggingface`) is used.\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier\n allowed by Git.\n image_size (`int`, *optional*, defaults to 512):\n The image size the model was trained on. Use 512 for all Stable Diffusion v1 models and the Stable\n Diffusion v2 base model. Use 768 for Stable Diffusion v2.\n scaling_factor (`float`, *optional*, defaults to 0.18215):\n The component-wise standard deviation of the trained latent space computed using the first batch of the\n training set. This is used to scale the latent space to have unit variance when training the diffusion\n model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the\n diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z\n = 1 / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution\n Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.\n kwargs (remaining dictionary of keyword arguments, *optional*):\n Can be used to overwrite load and saveable variables (for example the pipeline components of the\n specific pipeline class). The overwritten components are directly passed to the pipelines `__init__`\n method. See example below for more information.\n\n \n\n Make sure to pass both `image_size` and `scaling_factor` to `from_single_file()` if you're loading\n a VAE from SDXL or a Stable Diffusion v2 model or higher.\n\n \n\n Examples:\n\n ```py\n from diffusers import AutoencoderKL\n\n url = \"https://huggingface.co/stabilityai/sd-vae-ft-mse-original/blob/main/vae-ft-mse-840000-ema-pruned.safetensors\" # can also be local file\n model = AutoencoderKL.from_single_file(url)\n ```\n \"\"\"\n\n original_config_file = kwargs.pop(\"original_config_file\", None)\n config_file = kwargs.pop(\"config_file\", None)\n resume_download = kwargs.pop(\"resume_download\", None)\n force_download = kwargs.pop(\"force_download\", False)\n proxies = kwargs.pop(\"proxies\", None)\n token = kwargs.pop(\"token\", None)\n cache_dir = kwargs.pop(\"cache_dir\", None)\n local_files_only = kwargs.pop(\"local_files_only\", None)\n revision = kwargs.pop(\"revision\", None)\n torch_dtype = kwargs.pop(\"torch_dtype\", None)\n\n class_name = cls.__name__\n\n if (config_file is not None) and (original_config_file is not None):\n raise ValueError(\n \"You cannot pass both `config_file` and `original_config_file` to `from_single_file`. Please use only one of these arguments.\"\n )\n\n original_config_file = original_config_file or config_file\n original_config, checkpoint = fetch_ldm_config_and_checkpoint(\n pretrained_model_link_or_path=pretrained_model_link_or_path,\n class_name=class_name,\n original_config_file=original_config_file,\n resume_download=resume_download,\n force_download=force_download,\n proxies=proxies,\n token=token,\n revision=revision,\n local_files_only=local_files_only,\n cache_dir=cache_dir,\n )\n\n image_size = kwargs.pop(\"image_size\", None)\n scaling_factor = kwargs.pop(\"scaling_factor\", None)\n component = create_diffusers_vae_model_from_ldm(\n class_name,\n original_config,\n checkpoint,\n image_size=image_size,\n scaling_factor=scaling_factor,\n torch_dtype=torch_dtype,\n )\n vae = component[\"vae\"]\n if torch_dtype is not None:\n vae = vae.to(torch_dtype)\n\n return vae\n" }, { "path": "diffusers/src/diffusers/loaders/controlnet.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom huggingface_hub.utils import validate_hf_hub_args\n\nfrom .single_file_utils import (\n create_diffusers_controlnet_model_from_ldm,\n fetch_ldm_config_and_checkpoint,\n)\n\n\nclass FromOriginalControlNetMixin:\n \"\"\"\n Load pretrained ControlNet weights saved in the `.ckpt` or `.safetensors` format into a [`ControlNetModel`].\n \"\"\"\n\n @classmethod\n @validate_hf_hub_args\n def from_single_file(cls, pretrained_model_link_or_path, **kwargs):\n r\"\"\"\n Instantiate a [`ControlNetModel`] from pretrained ControlNet weights saved in the original `.ckpt` or\n `.safetensors` format. The pipeline is set in evaluation mode (`model.eval()`) by default.\n\n Parameters:\n pretrained_model_link_or_path (`str` or `os.PathLike`, *optional*):\n Can be either:\n - A link to the `.ckpt` file (for example\n `\"https://huggingface.co//blob/main/.ckpt\"`) on the Hub.\n - A path to a *file* containing all pipeline weights.\n config_file (`str`, *optional*):\n Filepath to the configuration YAML file associated with the model. If not provided it will default to:\n https://raw.githubusercontent.com/lllyasviel/ControlNet/main/models/cldm_v15.yaml\n torch_dtype (`str` or `torch.dtype`, *optional*):\n Override the default `torch.dtype` and load the model with another dtype. If `\"auto\"` is passed, the\n dtype is automatically derived from the model's weights.\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\n cached versions if they exist.\n cache_dir (`Union[str, os.PathLike]`, *optional*):\n Path to a directory where a downloaded pretrained model configuration is cached if the standard cache\n is not used.\n resume_download:\n Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1\n of Diffusers.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\n local_files_only (`bool`, *optional*, defaults to `False`):\n Whether to only load local model weights and configuration files or not. If set to True, the model\n won't be downloaded from the Hub.\n token (`str` or *bool*, *optional*):\n The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from\n `diffusers-cli login` (stored in `~/.huggingface`) is used.\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier\n allowed by Git.\n image_size (`int`, *optional*, defaults to 512):\n The image size the model was trained on. Use 512 for all Stable Diffusion v1 models and the Stable\n Diffusion v2 base model. Use 768 for Stable Diffusion v2.\n upcast_attention (`bool`, *optional*, defaults to `None`):\n Whether the attention computation should always be upcasted.\n kwargs (remaining dictionary of keyword arguments, *optional*):\n Can be used to overwrite load and saveable variables (for example the pipeline components of the\n specific pipeline class). The overwritten components are directly passed to the pipelines `__init__`\n method. See example below for more information.\n\n Examples:\n\n ```py\n from diffusers import StableDiffusionControlNetPipeline, ControlNetModel\n\n url = \"https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth\" # can also be a local path\n model = ControlNetModel.from_single_file(url)\n\n url = \"https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned.safetensors\" # can also be a local path\n pipe = StableDiffusionControlNetPipeline.from_single_file(url, controlnet=controlnet)\n ```\n \"\"\"\n original_config_file = kwargs.pop(\"original_config_file\", None)\n config_file = kwargs.pop(\"config_file\", None)\n resume_download = kwargs.pop(\"resume_download\", None)\n force_download = kwargs.pop(\"force_download\", False)\n proxies = kwargs.pop(\"proxies\", None)\n token = kwargs.pop(\"token\", None)\n cache_dir = kwargs.pop(\"cache_dir\", None)\n local_files_only = kwargs.pop(\"local_files_only\", None)\n revision = kwargs.pop(\"revision\", None)\n torch_dtype = kwargs.pop(\"torch_dtype\", None)\n\n class_name = cls.__name__\n if (config_file is not None) and (original_config_file is not None):\n raise ValueError(\n \"You cannot pass both `config_file` and `original_config_file` to `from_single_file`. Please use only one of these arguments.\"\n )\n\n original_config_file = config_file or original_config_file\n original_config, checkpoint = fetch_ldm_config_and_checkpoint(\n pretrained_model_link_or_path=pretrained_model_link_or_path,\n class_name=class_name,\n original_config_file=original_config_file,\n resume_download=resume_download,\n force_download=force_download,\n proxies=proxies,\n token=token,\n revision=revision,\n local_files_only=local_files_only,\n cache_dir=cache_dir,\n )\n\n upcast_attention = kwargs.pop(\"upcast_attention\", False)\n image_size = kwargs.pop(\"image_size\", None)\n\n component = create_diffusers_controlnet_model_from_ldm(\n class_name,\n original_config,\n checkpoint,\n upcast_attention=upcast_attention,\n image_size=image_size,\n torch_dtype=torch_dtype,\n )\n controlnet = component[\"controlnet\"]\n if torch_dtype is not None:\n controlnet = controlnet.to(torch_dtype)\n\n return controlnet\n" }, { "path": "diffusers/src/diffusers/loaders/ip_adapter.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom pathlib import Path\nfrom typing import Dict, List, Optional, Union\n\nimport torch\nimport torch.nn.functional as F\nfrom huggingface_hub.utils import validate_hf_hub_args\nfrom safetensors import safe_open\n\nfrom ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, load_state_dict\nfrom ..utils import (\n USE_PEFT_BACKEND,\n _get_model_file,\n is_accelerate_available,\n is_torch_version,\n is_transformers_available,\n logging,\n)\nfrom .unet_loader_utils import _maybe_expand_lora_scales\n\n\nif is_transformers_available():\n from transformers import (\n CLIPImageProcessor,\n CLIPVisionModelWithProjection,\n )\n\n from ..models.attention_processor import (\n AttnProcessor,\n AttnProcessor2_0,\n IPAdapterAttnProcessor,\n IPAdapterAttnProcessor2_0,\n )\n\nlogger = logging.get_logger(__name__)\n\n\nclass IPAdapterMixin:\n \"\"\"Mixin for handling IP Adapters.\"\"\"\n\n @validate_hf_hub_args\n def load_ip_adapter(\n self,\n pretrained_model_name_or_path_or_dict: Union[str, List[str], Dict[str, torch.Tensor]],\n subfolder: Union[str, List[str]],\n weight_name: Union[str, List[str]],\n image_encoder_folder: Optional[str] = \"image_encoder\",\n **kwargs,\n ):\n \"\"\"\n Parameters:\n pretrained_model_name_or_path_or_dict (`str` or `List[str]` or `os.PathLike` or `List[os.PathLike]` or `dict` or `List[dict]`):\n Can be either:\n\n - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on\n the Hub.\n - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved\n with [`ModelMixin.save_pretrained`].\n - A [torch state\n dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).\n subfolder (`str` or `List[str]`):\n The subfolder location of a model file within a larger model repository on the Hub or locally. If a\n list is passed, it should have the same length as `weight_name`.\n weight_name (`str` or `List[str]`):\n The name of the weight file to load. If a list is passed, it should have the same length as\n `weight_name`.\n image_encoder_folder (`str`, *optional*, defaults to `image_encoder`):\n The subfolder location of the image encoder within a larger model repository on the Hub or locally.\n Pass `None` to not load the image encoder. If the image encoder is located in a folder inside\n `subfolder`, you only need to pass the name of the folder that contains image encoder weights, e.g.\n `image_encoder_folder=\"image_encoder\"`. If the image encoder is located in a folder other than\n `subfolder`, you should pass the path to the folder that contains image encoder weights, for example,\n `image_encoder_folder=\"different_subfolder/image_encoder\"`.\n cache_dir (`Union[str, os.PathLike]`, *optional*):\n Path to a directory where a downloaded pretrained model configuration is cached if the standard cache\n is not used.\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\n cached versions if they exist.\n resume_download:\n Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1\n of Diffusers.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\n local_files_only (`bool`, *optional*, defaults to `False`):\n Whether to only load local model weights and configuration files or not. If set to `True`, the model\n won't be downloaded from the Hub.\n token (`str` or *bool*, *optional*):\n The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from\n `diffusers-cli login` (stored in `~/.huggingface`) is used.\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier\n allowed by Git.\n low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):\n Speed up model loading only loading the pretrained weights and not initializing the weights. This also\n tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.\n Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this\n argument to `True` will raise an error.\n \"\"\"\n\n # handle the list inputs for multiple IP Adapters\n if not isinstance(weight_name, list):\n weight_name = [weight_name]\n\n if not isinstance(pretrained_model_name_or_path_or_dict, list):\n pretrained_model_name_or_path_or_dict = [pretrained_model_name_or_path_or_dict]\n if len(pretrained_model_name_or_path_or_dict) == 1:\n pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict * len(weight_name)\n\n if not isinstance(subfolder, list):\n subfolder = [subfolder]\n if len(subfolder) == 1:\n subfolder = subfolder * len(weight_name)\n\n if len(weight_name) != len(pretrained_model_name_or_path_or_dict):\n raise ValueError(\"`weight_name` and `pretrained_model_name_or_path_or_dict` must have the same length.\")\n\n if len(weight_name) != len(subfolder):\n raise ValueError(\"`weight_name` and `subfolder` must have the same length.\")\n\n # Load the main state dict first.\n cache_dir = kwargs.pop(\"cache_dir\", None)\n force_download = kwargs.pop(\"force_download\", False)\n resume_download = kwargs.pop(\"resume_download\", None)\n proxies = kwargs.pop(\"proxies\", None)\n local_files_only = kwargs.pop(\"local_files_only\", None)\n token = kwargs.pop(\"token\", None)\n revision = kwargs.pop(\"revision\", None)\n low_cpu_mem_usage = kwargs.pop(\"low_cpu_mem_usage\", _LOW_CPU_MEM_USAGE_DEFAULT)\n\n if low_cpu_mem_usage and not is_accelerate_available():\n low_cpu_mem_usage = False\n logger.warning(\n \"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the\"\n \" environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install\"\n \" `accelerate` for faster and less memory-intense model loading. You can do so with: \\n```\\npip\"\n \" install accelerate\\n```\\n.\"\n )\n\n if low_cpu_mem_usage is True and not is_torch_version(\">=\", \"1.9.0\"):\n raise NotImplementedError(\n \"Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set\"\n \" `low_cpu_mem_usage=False`.\"\n )\n\n user_agent = {\n \"file_type\": \"attn_procs_weights\",\n \"framework\": \"pytorch\",\n }\n state_dicts = []\n for pretrained_model_name_or_path_or_dict, weight_name, subfolder in zip(\n pretrained_model_name_or_path_or_dict, weight_name, subfolder\n ):\n if not isinstance(pretrained_model_name_or_path_or_dict, dict):\n model_file = _get_model_file(\n pretrained_model_name_or_path_or_dict,\n weights_name=weight_name,\n cache_dir=cache_dir,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n subfolder=subfolder,\n user_agent=user_agent,\n )\n if weight_name.endswith(\".safetensors\"):\n state_dict = {\"image_proj\": {}, \"ip_adapter\": {}}\n with safe_open(model_file, framework=\"pt\", device=\"cpu\") as f:\n for key in f.keys():\n if key.startswith(\"image_proj.\"):\n state_dict[\"image_proj\"][key.replace(\"image_proj.\", \"\")] = f.get_tensor(key)\n elif key.startswith(\"ip_adapter.\"):\n state_dict[\"ip_adapter\"][key.replace(\"ip_adapter.\", \"\")] = f.get_tensor(key)\n else:\n state_dict = load_state_dict(model_file)\n else:\n state_dict = pretrained_model_name_or_path_or_dict\n\n keys = list(state_dict.keys())\n if keys != [\"image_proj\", \"ip_adapter\"]:\n raise ValueError(\"Required keys are (`image_proj` and `ip_adapter`) missing from the state dict.\")\n\n state_dicts.append(state_dict)\n\n # load CLIP image encoder here if it has not been registered to the pipeline yet\n if hasattr(self, \"image_encoder\") and getattr(self, \"image_encoder\", None) is None:\n if image_encoder_folder is not None:\n if not isinstance(pretrained_model_name_or_path_or_dict, dict):\n logger.info(f\"loading image_encoder from {pretrained_model_name_or_path_or_dict}\")\n if image_encoder_folder.count(\"/\") == 0:\n image_encoder_subfolder = Path(subfolder, image_encoder_folder).as_posix()\n else:\n image_encoder_subfolder = Path(image_encoder_folder).as_posix()\n\n image_encoder = CLIPVisionModelWithProjection.from_pretrained(\n pretrained_model_name_or_path_or_dict,\n subfolder=image_encoder_subfolder,\n low_cpu_mem_usage=low_cpu_mem_usage,\n ).to(self.device, dtype=self.dtype)\n self.register_modules(image_encoder=image_encoder)\n else:\n raise ValueError(\n \"`image_encoder` cannot be loaded because `pretrained_model_name_or_path_or_dict` is a state dict.\"\n )\n else:\n logger.warning(\n \"image_encoder is not loaded since `image_encoder_folder=None` passed. You will not be able to use `ip_adapter_image` when calling the pipeline with IP-Adapter.\"\n \"Use `ip_adapter_image_embeds` to pass pre-generated image embedding instead.\"\n )\n\n # create feature extractor if it has not been registered to the pipeline yet\n if hasattr(self, \"feature_extractor\") and getattr(self, \"feature_extractor\", None) is None:\n feature_extractor = CLIPImageProcessor()\n self.register_modules(feature_extractor=feature_extractor)\n\n # load ip-adapter into unet\n unet = getattr(self, self.unet_name) if not hasattr(self, \"unet\") else self.unet\n unet._load_ip_adapter_weights(state_dicts, low_cpu_mem_usage=low_cpu_mem_usage)\n\n extra_loras = unet._load_ip_adapter_loras(state_dicts)\n if extra_loras != {}:\n if not USE_PEFT_BACKEND:\n logger.warning(\"PEFT backend is required to load these weights.\")\n else:\n # apply the IP Adapter Face ID LoRA weights\n peft_config = getattr(unet, \"peft_config\", {})\n for k, lora in extra_loras.items():\n if f\"faceid_{k}\" not in peft_config:\n self.load_lora_weights(lora, adapter_name=f\"faceid_{k}\")\n self.set_adapters([f\"faceid_{k}\"], adapter_weights=[1.0])\n\n def set_ip_adapter_scale(self, scale):\n \"\"\"\n Set IP-Adapter scales per-transformer block. Input `scale` could be a single config or a list of configs for\n granular control over each IP-Adapter behavior. A config can be a float or a dictionary.\n\n Example:\n\n ```py\n # To use original IP-Adapter\n scale = 1.0\n pipeline.set_ip_adapter_scale(scale)\n\n # To use style block only\n scale = {\n \"up\": {\"block_0\": [0.0, 1.0, 0.0]},\n }\n pipeline.set_ip_adapter_scale(scale)\n\n # To use style+layout blocks\n scale = {\n \"down\": {\"block_2\": [0.0, 1.0]},\n \"up\": {\"block_0\": [0.0, 1.0, 0.0]},\n }\n pipeline.set_ip_adapter_scale(scale)\n\n # To use style and layout from 2 reference images\n scales = [{\"down\": {\"block_2\": [0.0, 1.0]}}, {\"up\": {\"block_0\": [0.0, 1.0, 0.0]}}]\n pipeline.set_ip_adapter_scale(scales)\n ```\n \"\"\"\n unet = getattr(self, self.unet_name) if not hasattr(self, \"unet\") else self.unet\n if not isinstance(scale, list):\n scale = [scale]\n scale_configs = _maybe_expand_lora_scales(unet, scale, default_scale=0.0)\n\n for attn_name, attn_processor in unet.attn_processors.items():\n if isinstance(attn_processor, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0)):\n if len(scale_configs) != len(attn_processor.scale):\n raise ValueError(\n f\"Cannot assign {len(scale_configs)} scale_configs to \"\n f\"{len(attn_processor.scale)} IP-Adapter.\"\n )\n elif len(scale_configs) == 1:\n scale_configs = scale_configs * len(attn_processor.scale)\n for i, scale_config in enumerate(scale_configs):\n if isinstance(scale_config, dict):\n for k, s in scale_config.items():\n if attn_name.startswith(k):\n attn_processor.scale[i] = s\n else:\n attn_processor.scale[i] = scale_config\n\n def unload_ip_adapter(self):\n \"\"\"\n Unloads the IP Adapter weights\n\n Examples:\n\n ```python\n >>> # Assuming `pipeline` is already loaded with the IP Adapter weights.\n >>> pipeline.unload_ip_adapter()\n >>> ...\n ```\n \"\"\"\n # remove CLIP image encoder\n if hasattr(self, \"image_encoder\") and getattr(self, \"image_encoder\", None) is not None:\n self.image_encoder = None\n self.register_to_config(image_encoder=[None, None])\n\n # remove feature extractor only when safety_checker is None as safety_checker uses\n # the feature_extractor later\n if not hasattr(self, \"safety_checker\"):\n if hasattr(self, \"feature_extractor\") and getattr(self, \"feature_extractor\", None) is not None:\n self.feature_extractor = None\n self.register_to_config(feature_extractor=[None, None])\n\n # remove hidden encoder\n self.unet.encoder_hid_proj = None\n self.config.encoder_hid_dim_type = None\n\n # restore original Unet attention processors layers\n attn_procs = {}\n for name, value in self.unet.attn_processors.items():\n attn_processor_class = (\n AttnProcessor2_0() if hasattr(F, \"scaled_dot_product_attention\") else AttnProcessor()\n )\n attn_procs[name] = (\n attn_processor_class\n if isinstance(value, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0))\n else value.__class__()\n )\n self.unet.set_attn_processor(attn_procs)\n" }, { "path": "diffusers/src/diffusers/loaders/lora.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport copy\nimport inspect\nimport os\nfrom pathlib import Path\nfrom typing import Callable, Dict, List, Optional, Union\n\nimport safetensors\nimport torch\nfrom huggingface_hub import model_info\nfrom huggingface_hub.constants import HF_HUB_OFFLINE\nfrom huggingface_hub.utils import validate_hf_hub_args\nfrom torch import nn\n\nfrom ..models.modeling_utils import load_state_dict\nfrom ..utils import (\n USE_PEFT_BACKEND,\n _get_model_file,\n convert_state_dict_to_diffusers,\n convert_state_dict_to_peft,\n delete_adapter_layers,\n get_adapter_name,\n get_peft_kwargs,\n is_accelerate_available,\n is_peft_version,\n is_transformers_available,\n logging,\n recurse_remove_peft_layers,\n scale_lora_layers,\n set_adapter_layers,\n set_weights_and_activate_adapters,\n)\nfrom .lora_conversion_utils import _convert_kohya_lora_to_diffusers, _maybe_map_sgm_blocks_to_diffusers\n\n\nif is_transformers_available():\n from transformers import PreTrainedModel\n\n from ..models.lora import text_encoder_attn_modules, text_encoder_mlp_modules\n\nif is_accelerate_available():\n from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module\n\nlogger = logging.get_logger(__name__)\n\nTEXT_ENCODER_NAME = \"text_encoder\"\nUNET_NAME = \"unet\"\nTRANSFORMER_NAME = \"transformer\"\n\nLORA_WEIGHT_NAME = \"pytorch_lora_weights.bin\"\nLORA_WEIGHT_NAME_SAFE = \"pytorch_lora_weights.safetensors\"\n\nLORA_DEPRECATION_MESSAGE = \"You are using an old version of LoRA backend. This will be deprecated in the next releases in favor of PEFT make sure to install the latest PEFT and transformers packages in the future.\"\n\n\nclass LoraLoaderMixin:\n r\"\"\"\n Load LoRA layers into [`UNet2DConditionModel`] and\n [`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel).\n \"\"\"\n\n text_encoder_name = TEXT_ENCODER_NAME\n unet_name = UNET_NAME\n transformer_name = TRANSFORMER_NAME\n num_fused_loras = 0\n\n def load_lora_weights(\n self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], adapter_name=None, **kwargs\n ):\n \"\"\"\n Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.unet` and\n `self.text_encoder`.\n\n All kwargs are forwarded to `self.lora_state_dict`.\n\n See [`~loaders.LoraLoaderMixin.lora_state_dict`] for more details on how the state dict is loaded.\n\n See [`~loaders.LoraLoaderMixin.load_lora_into_unet`] for more details on how the state dict is loaded into\n `self.unet`.\n\n See [`~loaders.LoraLoaderMixin.load_lora_into_text_encoder`] for more details on how the state dict is loaded\n into `self.text_encoder`.\n\n Parameters:\n pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):\n See [`~loaders.LoraLoaderMixin.lora_state_dict`].\n kwargs (`dict`, *optional*):\n See [`~loaders.LoraLoaderMixin.lora_state_dict`].\n adapter_name (`str`, *optional*):\n Adapter name to be used for referencing the loaded adapter model. If not specified, it will use\n `default_{i}` where i is the total number of adapters being loaded.\n \"\"\"\n if not USE_PEFT_BACKEND:\n raise ValueError(\"PEFT backend is required for this method.\")\n\n # if a dict is passed, copy it instead of modifying it inplace\n if isinstance(pretrained_model_name_or_path_or_dict, dict):\n pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict.copy()\n\n # First, ensure that the checkpoint is a compatible one and can be successfully loaded.\n state_dict, network_alphas = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)\n\n is_correct_format = all(\"lora\" in key or \"dora_scale\" in key for key in state_dict.keys())\n if not is_correct_format:\n raise ValueError(\"Invalid LoRA checkpoint.\")\n\n self.load_lora_into_unet(\n state_dict,\n network_alphas=network_alphas,\n unet=getattr(self, self.unet_name) if not hasattr(self, \"unet\") else self.unet,\n adapter_name=adapter_name,\n _pipeline=self,\n )\n self.load_lora_into_text_encoder(\n state_dict,\n network_alphas=network_alphas,\n text_encoder=getattr(self, self.text_encoder_name)\n if not hasattr(self, \"text_encoder\")\n else self.text_encoder,\n lora_scale=self.lora_scale,\n adapter_name=adapter_name,\n _pipeline=self,\n )\n\n @classmethod\n @validate_hf_hub_args\n def lora_state_dict(\n cls,\n pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],\n **kwargs,\n ):\n r\"\"\"\n Return state dict for lora weights and the network alphas.\n\n \n\n We support loading A1111 formatted LoRA checkpoints in a limited capacity.\n\n This function is experimental and might change in the future.\n\n \n\n Parameters:\n pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):\n Can be either:\n\n - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on\n the Hub.\n - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved\n with [`ModelMixin.save_pretrained`].\n - A [torch state\n dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).\n\n cache_dir (`Union[str, os.PathLike]`, *optional*):\n Path to a directory where a downloaded pretrained model configuration is cached if the standard cache\n is not used.\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\n cached versions if they exist.\n resume_download:\n Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1\n of Diffusers.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\n local_files_only (`bool`, *optional*, defaults to `False`):\n Whether to only load local model weights and configuration files or not. If set to `True`, the model\n won't be downloaded from the Hub.\n token (`str` or *bool*, *optional*):\n The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from\n `diffusers-cli login` (stored in `~/.huggingface`) is used.\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier\n allowed by Git.\n subfolder (`str`, *optional*, defaults to `\"\"`):\n The subfolder location of a model file within a larger model repository on the Hub or locally.\n weight_name (`str`, *optional*, defaults to None):\n Name of the serialized state dict file.\n \"\"\"\n # Load the main state dict first which has the LoRA layers for either of\n # UNet and text encoder or both.\n cache_dir = kwargs.pop(\"cache_dir\", None)\n force_download = kwargs.pop(\"force_download\", False)\n resume_download = kwargs.pop(\"resume_download\", None)\n proxies = kwargs.pop(\"proxies\", None)\n local_files_only = kwargs.pop(\"local_files_only\", None)\n token = kwargs.pop(\"token\", None)\n revision = kwargs.pop(\"revision\", None)\n subfolder = kwargs.pop(\"subfolder\", None)\n weight_name = kwargs.pop(\"weight_name\", None)\n unet_config = kwargs.pop(\"unet_config\", None)\n use_safetensors = kwargs.pop(\"use_safetensors\", None)\n\n allow_pickle = False\n if use_safetensors is None:\n use_safetensors = True\n allow_pickle = True\n\n user_agent = {\n \"file_type\": \"attn_procs_weights\",\n \"framework\": \"pytorch\",\n }\n\n model_file = None\n if not isinstance(pretrained_model_name_or_path_or_dict, dict):\n # Let's first try to load .safetensors weights\n if (use_safetensors and weight_name is None) or (\n weight_name is not None and weight_name.endswith(\".safetensors\")\n ):\n try:\n # Here we're relaxing the loading check to enable more Inference API\n # friendliness where sometimes, it's not at all possible to automatically\n # determine `weight_name`.\n if weight_name is None:\n weight_name = cls._best_guess_weight_name(\n pretrained_model_name_or_path_or_dict,\n file_extension=\".safetensors\",\n local_files_only=local_files_only,\n )\n model_file = _get_model_file(\n pretrained_model_name_or_path_or_dict,\n weights_name=weight_name or LORA_WEIGHT_NAME_SAFE,\n cache_dir=cache_dir,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n subfolder=subfolder,\n user_agent=user_agent,\n )\n state_dict = safetensors.torch.load_file(model_file, device=\"cpu\")\n except (IOError, safetensors.SafetensorError) as e:\n if not allow_pickle:\n raise e\n # try loading non-safetensors weights\n model_file = None\n pass\n\n if model_file is None:\n if weight_name is None:\n weight_name = cls._best_guess_weight_name(\n pretrained_model_name_or_path_or_dict, file_extension=\".bin\", local_files_only=local_files_only\n )\n model_file = _get_model_file(\n pretrained_model_name_or_path_or_dict,\n weights_name=weight_name or LORA_WEIGHT_NAME,\n cache_dir=cache_dir,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n subfolder=subfolder,\n user_agent=user_agent,\n )\n state_dict = load_state_dict(model_file)\n else:\n state_dict = pretrained_model_name_or_path_or_dict\n\n network_alphas = None\n # TODO: replace it with a method from `state_dict_utils`\n if all(\n (\n k.startswith(\"lora_te_\")\n or k.startswith(\"lora_unet_\")\n or k.startswith(\"lora_te1_\")\n or k.startswith(\"lora_te2_\")\n )\n for k in state_dict.keys()\n ):\n # Map SDXL blocks correctly.\n if unet_config is not None:\n # use unet config to remap block numbers\n state_dict = _maybe_map_sgm_blocks_to_diffusers(state_dict, unet_config)\n state_dict, network_alphas = _convert_kohya_lora_to_diffusers(state_dict)\n\n return state_dict, network_alphas\n\n @classmethod\n def _best_guess_weight_name(\n cls, pretrained_model_name_or_path_or_dict, file_extension=\".safetensors\", local_files_only=False\n ):\n if local_files_only or HF_HUB_OFFLINE:\n raise ValueError(\"When using the offline mode, you must specify a `weight_name`.\")\n\n targeted_files = []\n\n if os.path.isfile(pretrained_model_name_or_path_or_dict):\n return\n elif os.path.isdir(pretrained_model_name_or_path_or_dict):\n targeted_files = [\n f for f in os.listdir(pretrained_model_name_or_path_or_dict) if f.endswith(file_extension)\n ]\n else:\n files_in_repo = model_info(pretrained_model_name_or_path_or_dict).siblings\n targeted_files = [f.rfilename for f in files_in_repo if f.rfilename.endswith(file_extension)]\n if len(targeted_files) == 0:\n return\n\n # \"scheduler\" does not correspond to a LoRA checkpoint.\n # \"optimizer\" does not correspond to a LoRA checkpoint\n # only top-level checkpoints are considered and not the other ones, hence \"checkpoint\".\n unallowed_substrings = {\"scheduler\", \"optimizer\", \"checkpoint\"}\n targeted_files = list(\n filter(lambda x: all(substring not in x for substring in unallowed_substrings), targeted_files)\n )\n\n if any(f.endswith(LORA_WEIGHT_NAME) for f in targeted_files):\n targeted_files = list(filter(lambda x: x.endswith(LORA_WEIGHT_NAME), targeted_files))\n elif any(f.endswith(LORA_WEIGHT_NAME_SAFE) for f in targeted_files):\n targeted_files = list(filter(lambda x: x.endswith(LORA_WEIGHT_NAME_SAFE), targeted_files))\n\n if len(targeted_files) > 1:\n raise ValueError(\n f\"Provided path contains more than one weights file in the {file_extension} format. Either specify `weight_name` in `load_lora_weights` or make sure there's only one `.safetensors` or `.bin` file in {pretrained_model_name_or_path_or_dict}.\"\n )\n weight_name = targeted_files[0]\n return weight_name\n\n @classmethod\n def _optionally_disable_offloading(cls, _pipeline):\n \"\"\"\n Optionally removes offloading in case the pipeline has been already sequentially offloaded to CPU.\n\n Args:\n _pipeline (`DiffusionPipeline`):\n The pipeline to disable offloading for.\n\n Returns:\n tuple:\n A tuple indicating if `is_model_cpu_offload` or `is_sequential_cpu_offload` is True.\n \"\"\"\n is_model_cpu_offload = False\n is_sequential_cpu_offload = False\n\n if _pipeline is not None and _pipeline.hf_device_map is None:\n for _, component in _pipeline.components.items():\n if isinstance(component, nn.Module) and hasattr(component, \"_hf_hook\"):\n if not is_model_cpu_offload:\n is_model_cpu_offload = isinstance(component._hf_hook, CpuOffload)\n if not is_sequential_cpu_offload:\n is_sequential_cpu_offload = (\n isinstance(component._hf_hook, AlignDevicesHook)\n or hasattr(component._hf_hook, \"hooks\")\n and isinstance(component._hf_hook.hooks[0], AlignDevicesHook)\n )\n\n logger.info(\n \"Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again.\"\n )\n remove_hook_from_module(component, recurse=is_sequential_cpu_offload)\n\n return (is_model_cpu_offload, is_sequential_cpu_offload)\n\n @classmethod\n def load_lora_into_unet(cls, state_dict, network_alphas, unet, adapter_name=None, _pipeline=None):\n \"\"\"\n This will load the LoRA layers specified in `state_dict` into `unet`.\n\n Parameters:\n state_dict (`dict`):\n A standard state dict containing the lora layer parameters. The keys can either be indexed directly\n into the unet or prefixed with an additional `unet` which can be used to distinguish between text\n encoder lora layers.\n network_alphas (`Dict[str, float]`):\n The value of the network alpha used for stable learning and preventing underflow. This value has the\n same meaning as the `--network_alpha` option in the kohya-ss trainer script. Refer to [this\n link](https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning).\n unet (`UNet2DConditionModel`):\n The UNet model to load the LoRA layers into.\n adapter_name (`str`, *optional*):\n Adapter name to be used for referencing the loaded adapter model. If not specified, it will use\n `default_{i}` where i is the total number of adapters being loaded.\n \"\"\"\n if not USE_PEFT_BACKEND:\n raise ValueError(\"PEFT backend is required for this method.\")\n\n # If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918),\n # then the `state_dict` keys should have `cls.unet_name` and/or `cls.text_encoder_name` as\n # their prefixes.\n keys = list(state_dict.keys())\n only_text_encoder = all(key.startswith(cls.text_encoder_name) for key in keys)\n\n if any(key.startswith(cls.unet_name) for key in keys) and not only_text_encoder:\n # Load the layers corresponding to UNet.\n logger.info(f\"Loading {cls.unet_name}.\")\n unet.load_attn_procs(\n state_dict, network_alphas=network_alphas, adapter_name=adapter_name, _pipeline=_pipeline\n )\n\n @classmethod\n def load_lora_into_text_encoder(\n cls,\n state_dict,\n network_alphas,\n text_encoder,\n prefix=None,\n lora_scale=1.0,\n adapter_name=None,\n _pipeline=None,\n ):\n \"\"\"\n This will load the LoRA layers specified in `state_dict` into `text_encoder`\n\n Parameters:\n state_dict (`dict`):\n A standard state dict containing the lora layer parameters. The key should be prefixed with an\n additional `text_encoder` to distinguish between unet lora layers.\n network_alphas (`Dict[str, float]`):\n See `LoRALinearLayer` for more details.\n text_encoder (`CLIPTextModel`):\n The text encoder model to load the LoRA layers into.\n prefix (`str`):\n Expected prefix of the `text_encoder` in the `state_dict`.\n lora_scale (`float`):\n How much to scale the output of the lora linear layer before it is added with the output of the regular\n lora layer.\n adapter_name (`str`, *optional*):\n Adapter name to be used for referencing the loaded adapter model. If not specified, it will use\n `default_{i}` where i is the total number of adapters being loaded.\n \"\"\"\n if not USE_PEFT_BACKEND:\n raise ValueError(\"PEFT backend is required for this method.\")\n\n from peft import LoraConfig\n\n # If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918),\n # then the `state_dict` keys should have `self.unet_name` and/or `self.text_encoder_name` as\n # their prefixes.\n keys = list(state_dict.keys())\n prefix = cls.text_encoder_name if prefix is None else prefix\n\n # Safe prefix to check with.\n if any(cls.text_encoder_name in key for key in keys):\n # Load the layers corresponding to text encoder and make necessary adjustments.\n text_encoder_keys = [k for k in keys if k.startswith(prefix) and k.split(\".\")[0] == prefix]\n text_encoder_lora_state_dict = {\n k.replace(f\"{prefix}.\", \"\"): v for k, v in state_dict.items() if k in text_encoder_keys\n }\n\n if len(text_encoder_lora_state_dict) > 0:\n logger.info(f\"Loading {prefix}.\")\n rank = {}\n text_encoder_lora_state_dict = convert_state_dict_to_diffusers(text_encoder_lora_state_dict)\n\n # convert state dict\n text_encoder_lora_state_dict = convert_state_dict_to_peft(text_encoder_lora_state_dict)\n\n for name, _ in text_encoder_attn_modules(text_encoder):\n rank_key = f\"{name}.out_proj.lora_B.weight\"\n rank[rank_key] = text_encoder_lora_state_dict[rank_key].shape[1]\n\n patch_mlp = any(\".mlp.\" in key for key in text_encoder_lora_state_dict.keys())\n if patch_mlp:\n for name, _ in text_encoder_mlp_modules(text_encoder):\n rank_key_fc1 = f\"{name}.fc1.lora_B.weight\"\n rank_key_fc2 = f\"{name}.fc2.lora_B.weight\"\n\n rank[rank_key_fc1] = text_encoder_lora_state_dict[rank_key_fc1].shape[1]\n rank[rank_key_fc2] = text_encoder_lora_state_dict[rank_key_fc2].shape[1]\n\n if network_alphas is not None:\n alpha_keys = [\n k for k in network_alphas.keys() if k.startswith(prefix) and k.split(\".\")[0] == prefix\n ]\n network_alphas = {\n k.replace(f\"{prefix}.\", \"\"): v for k, v in network_alphas.items() if k in alpha_keys\n }\n\n lora_config_kwargs = get_peft_kwargs(rank, network_alphas, text_encoder_lora_state_dict, is_unet=False)\n if \"use_dora\" in lora_config_kwargs:\n if lora_config_kwargs[\"use_dora\"]:\n if is_peft_version(\"<\", \"0.9.0\"):\n raise ValueError(\n \"You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`.\"\n )\n else:\n if is_peft_version(\"<\", \"0.9.0\"):\n lora_config_kwargs.pop(\"use_dora\")\n lora_config = LoraConfig(**lora_config_kwargs)\n\n # adapter_name\n if adapter_name is None:\n adapter_name = get_adapter_name(text_encoder)\n\n is_model_cpu_offload, is_sequential_cpu_offload = cls._optionally_disable_offloading(_pipeline)\n\n # inject LoRA layers and load the state dict\n # in transformers we automatically check whether the adapter name is already in use or not\n text_encoder.load_adapter(\n adapter_name=adapter_name,\n adapter_state_dict=text_encoder_lora_state_dict,\n peft_config=lora_config,\n )\n\n # scale LoRA layers with `lora_scale`\n scale_lora_layers(text_encoder, weight=lora_scale)\n\n text_encoder.to(device=text_encoder.device, dtype=text_encoder.dtype)\n\n # Offload back.\n if is_model_cpu_offload:\n _pipeline.enable_model_cpu_offload()\n elif is_sequential_cpu_offload:\n _pipeline.enable_sequential_cpu_offload()\n # Unsafe code />\n\n @classmethod\n def load_lora_into_transformer(cls, state_dict, network_alphas, transformer, adapter_name=None, _pipeline=None):\n \"\"\"\n This will load the LoRA layers specified in `state_dict` into `transformer`.\n\n Parameters:\n state_dict (`dict`):\n A standard state dict containing the lora layer parameters. The keys can either be indexed directly\n into the unet or prefixed with an additional `unet` which can be used to distinguish between text\n encoder lora layers.\n network_alphas (`Dict[str, float]`):\n See `LoRALinearLayer` for more details.\n unet (`UNet2DConditionModel`):\n The UNet model to load the LoRA layers into.\n adapter_name (`str`, *optional*):\n Adapter name to be used for referencing the loaded adapter model. If not specified, it will use\n `default_{i}` where i is the total number of adapters being loaded.\n \"\"\"\n from peft import LoraConfig, inject_adapter_in_model, set_peft_model_state_dict\n\n keys = list(state_dict.keys())\n\n transformer_keys = [k for k in keys if k.startswith(cls.transformer_name)]\n state_dict = {\n k.replace(f\"{cls.transformer_name}.\", \"\"): v for k, v in state_dict.items() if k in transformer_keys\n }\n\n if network_alphas is not None:\n alpha_keys = [k for k in network_alphas.keys() if k.startswith(cls.transformer_name)]\n network_alphas = {\n k.replace(f\"{cls.transformer_name}.\", \"\"): v for k, v in network_alphas.items() if k in alpha_keys\n }\n\n if len(state_dict.keys()) > 0:\n if adapter_name in getattr(transformer, \"peft_config\", {}):\n raise ValueError(\n f\"Adapter name {adapter_name} already in use in the transformer - please select a new adapter name.\"\n )\n\n rank = {}\n for key, val in state_dict.items():\n if \"lora_B\" in key:\n rank[key] = val.shape[1]\n\n lora_config_kwargs = get_peft_kwargs(rank, network_alphas, state_dict)\n if \"use_dora\" in lora_config_kwargs:\n if lora_config_kwargs[\"use_dora\"] and is_peft_version(\"<\", \"0.9.0\"):\n raise ValueError(\n \"You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`.\"\n )\n else:\n lora_config_kwargs.pop(\"use_dora\")\n lora_config = LoraConfig(**lora_config_kwargs)\n\n # adapter_name\n if adapter_name is None:\n adapter_name = get_adapter_name(transformer)\n\n # In case the pipeline has been already offloaded to CPU - temporarily remove the hooks\n # otherwise loading LoRA weights will lead to an error\n is_model_cpu_offload, is_sequential_cpu_offload = cls._optionally_disable_offloading(_pipeline)\n\n inject_adapter_in_model(lora_config, transformer, adapter_name=adapter_name)\n incompatible_keys = set_peft_model_state_dict(transformer, state_dict, adapter_name)\n\n if incompatible_keys is not None:\n # check only for unexpected keys\n unexpected_keys = getattr(incompatible_keys, \"unexpected_keys\", None)\n if unexpected_keys:\n logger.warning(\n f\"Loading adapter weights from state_dict led to unexpected keys not found in the model: \"\n f\" {unexpected_keys}. \"\n )\n\n # Offload back.\n if is_model_cpu_offload:\n _pipeline.enable_model_cpu_offload()\n elif is_sequential_cpu_offload:\n _pipeline.enable_sequential_cpu_offload()\n # Unsafe code />\n\n @property\n def lora_scale(self) -> float:\n # property function that returns the lora scale which can be set at run time by the pipeline.\n # if _lora_scale has not been set, return 1\n return self._lora_scale if hasattr(self, \"_lora_scale\") else 1.0\n\n def _remove_text_encoder_monkey_patch(self):\n remove_method = recurse_remove_peft_layers\n if hasattr(self, \"text_encoder\"):\n remove_method(self.text_encoder)\n # In case text encoder have no Lora attached\n if getattr(self.text_encoder, \"peft_config\", None) is not None:\n del self.text_encoder.peft_config\n self.text_encoder._hf_peft_config_loaded = None\n\n if hasattr(self, \"text_encoder_2\"):\n remove_method(self.text_encoder_2)\n if getattr(self.text_encoder_2, \"peft_config\", None) is not None:\n del self.text_encoder_2.peft_config\n self.text_encoder_2._hf_peft_config_loaded = None\n\n @classmethod\n def save_lora_weights(\n cls,\n save_directory: Union[str, os.PathLike],\n unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,\n text_encoder_lora_layers: Dict[str, torch.nn.Module] = None,\n transformer_lora_layers: Dict[str, torch.nn.Module] = None,\n is_main_process: bool = True,\n weight_name: str = None,\n save_function: Callable = None,\n safe_serialization: bool = True,\n ):\n r\"\"\"\n Save the LoRA parameters corresponding to the UNet and text encoder.\n\n Arguments:\n save_directory (`str` or `os.PathLike`):\n Directory to save LoRA parameters to. Will be created if it doesn't exist.\n unet_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):\n State dict of the LoRA layers corresponding to the `unet`.\n text_encoder_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):\n State dict of the LoRA layers corresponding to the `text_encoder`. Must explicitly pass the text\n encoder LoRA state dict because it comes from 🤗 Transformers.\n is_main_process (`bool`, *optional*, defaults to `True`):\n Whether the process calling this is the main process or not. Useful during distributed training and you\n need to call this function on all processes. In this case, set `is_main_process=True` only on the main\n process to avoid race conditions.\n save_function (`Callable`):\n The function to use to save the state dictionary. Useful during distributed training when you need to\n replace `torch.save` with another method. Can be configured with the environment variable\n `DIFFUSERS_SAVE_MODE`.\n safe_serialization (`bool`, *optional*, defaults to `True`):\n Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.\n \"\"\"\n state_dict = {}\n\n def pack_weights(layers, prefix):\n layers_weights = layers.state_dict() if isinstance(layers, torch.nn.Module) else layers\n layers_state_dict = {f\"{prefix}.{module_name}\": param for module_name, param in layers_weights.items()}\n return layers_state_dict\n\n if not (unet_lora_layers or text_encoder_lora_layers or transformer_lora_layers):\n raise ValueError(\n \"You must pass at least one of `unet_lora_layers`, `text_encoder_lora_layers`, or `transformer_lora_layers`.\"\n )\n\n if unet_lora_layers:\n state_dict.update(pack_weights(unet_lora_layers, cls.unet_name))\n\n if text_encoder_lora_layers:\n state_dict.update(pack_weights(text_encoder_lora_layers, cls.text_encoder_name))\n\n if transformer_lora_layers:\n state_dict.update(pack_weights(transformer_lora_layers, \"transformer\"))\n\n # Save the model\n cls.write_lora_layers(\n state_dict=state_dict,\n save_directory=save_directory,\n is_main_process=is_main_process,\n weight_name=weight_name,\n save_function=save_function,\n safe_serialization=safe_serialization,\n )\n\n @staticmethod\n def write_lora_layers(\n state_dict: Dict[str, torch.Tensor],\n save_directory: str,\n is_main_process: bool,\n weight_name: str,\n save_function: Callable,\n safe_serialization: bool,\n ):\n if os.path.isfile(save_directory):\n logger.error(f\"Provided path ({save_directory}) should be a directory, not a file\")\n return\n\n if save_function is None:\n if safe_serialization:\n\n def save_function(weights, filename):\n return safetensors.torch.save_file(weights, filename, metadata={\"format\": \"pt\"})\n\n else:\n save_function = torch.save\n\n os.makedirs(save_directory, exist_ok=True)\n\n if weight_name is None:\n if safe_serialization:\n weight_name = LORA_WEIGHT_NAME_SAFE\n else:\n weight_name = LORA_WEIGHT_NAME\n\n save_path = Path(save_directory, weight_name).as_posix()\n save_function(state_dict, save_path)\n logger.info(f\"Model weights saved in {save_path}\")\n\n def unload_lora_weights(self):\n \"\"\"\n Unloads the LoRA parameters.\n\n Examples:\n\n ```python\n >>> # Assuming `pipeline` is already loaded with the LoRA parameters.\n >>> pipeline.unload_lora_weights()\n >>> ...\n ```\n \"\"\"\n if not USE_PEFT_BACKEND:\n raise ValueError(\"PEFT backend is required for this method.\")\n\n unet = getattr(self, self.unet_name) if not hasattr(self, \"unet\") else self.unet\n unet.unload_lora()\n\n # Safe to call the following regardless of LoRA.\n self._remove_text_encoder_monkey_patch()\n\n def fuse_lora(\n self,\n fuse_unet: bool = True,\n fuse_text_encoder: bool = True,\n lora_scale: float = 1.0,\n safe_fusing: bool = False,\n adapter_names: Optional[List[str]] = None,\n ):\n r\"\"\"\n Fuses the LoRA parameters into the original parameters of the corresponding blocks.\n\n \n\n This is an experimental API.\n\n \n\n Args:\n fuse_unet (`bool`, defaults to `True`): Whether to fuse the UNet LoRA parameters.\n fuse_text_encoder (`bool`, defaults to `True`):\n Whether to fuse the text encoder LoRA parameters. If the text encoder wasn't monkey-patched with the\n LoRA parameters then it won't have any effect.\n lora_scale (`float`, defaults to 1.0):\n Controls how much to influence the outputs with the LoRA parameters.\n safe_fusing (`bool`, defaults to `False`):\n Whether to check fused weights for NaN values before fusing and if values are NaN not fusing them.\n adapter_names (`List[str]`, *optional*):\n Adapter names to be used for fusing. If nothing is passed, all active adapters will be fused.\n\n Example:\n\n ```py\n from diffusers import DiffusionPipeline\n import torch\n\n pipeline = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16\n ).to(\"cuda\")\n pipeline.load_lora_weights(\"nerijs/pixel-art-xl\", weight_name=\"pixel-art-xl.safetensors\", adapter_name=\"pixel\")\n pipeline.fuse_lora(lora_scale=0.7)\n ```\n \"\"\"\n from peft.tuners.tuners_utils import BaseTunerLayer\n\n if fuse_unet or fuse_text_encoder:\n self.num_fused_loras += 1\n if self.num_fused_loras > 1:\n logger.warning(\n \"The current API is supported for operating with a single LoRA file. You are trying to load and fuse more than one LoRA which is not well-supported.\",\n )\n\n if fuse_unet:\n unet = getattr(self, self.unet_name) if not hasattr(self, \"unet\") else self.unet\n unet.fuse_lora(lora_scale, safe_fusing=safe_fusing, adapter_names=adapter_names)\n\n def fuse_text_encoder_lora(text_encoder, lora_scale=1.0, safe_fusing=False, adapter_names=None):\n merge_kwargs = {\"safe_merge\": safe_fusing}\n\n for module in text_encoder.modules():\n if isinstance(module, BaseTunerLayer):\n if lora_scale != 1.0:\n module.scale_layer(lora_scale)\n\n # For BC with previous PEFT versions, we need to check the signature\n # of the `merge` method to see if it supports the `adapter_names` argument.\n supported_merge_kwargs = list(inspect.signature(module.merge).parameters)\n if \"adapter_names\" in supported_merge_kwargs:\n merge_kwargs[\"adapter_names\"] = adapter_names\n elif \"adapter_names\" not in supported_merge_kwargs and adapter_names is not None:\n raise ValueError(\n \"The `adapter_names` argument is not supported with your PEFT version. \"\n \"Please upgrade to the latest version of PEFT. `pip install -U peft`\"\n )\n\n module.merge(**merge_kwargs)\n\n if fuse_text_encoder:\n if hasattr(self, \"text_encoder\"):\n fuse_text_encoder_lora(self.text_encoder, lora_scale, safe_fusing, adapter_names=adapter_names)\n if hasattr(self, \"text_encoder_2\"):\n fuse_text_encoder_lora(self.text_encoder_2, lora_scale, safe_fusing, adapter_names=adapter_names)\n\n def unfuse_lora(self, unfuse_unet: bool = True, unfuse_text_encoder: bool = True):\n r\"\"\"\n Reverses the effect of\n [`pipe.fuse_lora()`](https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraLoaderMixin.fuse_lora).\n\n \n\n This is an experimental API.\n\n \n\n Args:\n unfuse_unet (`bool`, defaults to `True`): Whether to unfuse the UNet LoRA parameters.\n unfuse_text_encoder (`bool`, defaults to `True`):\n Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn't monkey-patched with the\n LoRA parameters then it won't have any effect.\n \"\"\"\n from peft.tuners.tuners_utils import BaseTunerLayer\n\n unet = getattr(self, self.unet_name) if not hasattr(self, \"unet\") else self.unet\n if unfuse_unet:\n for module in unet.modules():\n if isinstance(module, BaseTunerLayer):\n module.unmerge()\n\n def unfuse_text_encoder_lora(text_encoder):\n for module in text_encoder.modules():\n if isinstance(module, BaseTunerLayer):\n module.unmerge()\n\n if unfuse_text_encoder:\n if hasattr(self, \"text_encoder\"):\n unfuse_text_encoder_lora(self.text_encoder)\n if hasattr(self, \"text_encoder_2\"):\n unfuse_text_encoder_lora(self.text_encoder_2)\n\n self.num_fused_loras -= 1\n\n def set_adapters_for_text_encoder(\n self,\n adapter_names: Union[List[str], str],\n text_encoder: Optional[\"PreTrainedModel\"] = None, # noqa: F821\n text_encoder_weights: Optional[Union[float, List[float], List[None]]] = None,\n ):\n \"\"\"\n Sets the adapter layers for the text encoder.\n\n Args:\n adapter_names (`List[str]` or `str`):\n The names of the adapters to use.\n text_encoder (`torch.nn.Module`, *optional*):\n The text encoder module to set the adapter layers for. If `None`, it will try to get the `text_encoder`\n attribute.\n text_encoder_weights (`List[float]`, *optional*):\n The weights to use for the text encoder. If `None`, the weights are set to `1.0` for all the adapters.\n \"\"\"\n if not USE_PEFT_BACKEND:\n raise ValueError(\"PEFT backend is required for this method.\")\n\n def process_weights(adapter_names, weights):\n # Expand weights into a list, one entry per adapter\n # e.g. for 2 adapters: 7 -> [7,7] ; [3, None] -> [3, None]\n if not isinstance(weights, list):\n weights = [weights] * len(adapter_names)\n\n if len(adapter_names) != len(weights):\n raise ValueError(\n f\"Length of adapter names {len(adapter_names)} is not equal to the length of the weights {len(weights)}\"\n )\n\n # Set None values to default of 1.0\n # e.g. [7,7] -> [7,7] ; [3, None] -> [3,1]\n weights = [w if w is not None else 1.0 for w in weights]\n\n return weights\n\n adapter_names = [adapter_names] if isinstance(adapter_names, str) else adapter_names\n text_encoder_weights = process_weights(adapter_names, text_encoder_weights)\n text_encoder = text_encoder or getattr(self, \"text_encoder\", None)\n if text_encoder is None:\n raise ValueError(\n \"The pipeline does not have a default `pipe.text_encoder` class. Please make sure to pass a `text_encoder` instead.\"\n )\n set_weights_and_activate_adapters(text_encoder, adapter_names, text_encoder_weights)\n\n def disable_lora_for_text_encoder(self, text_encoder: Optional[\"PreTrainedModel\"] = None):\n \"\"\"\n Disables the LoRA layers for the text encoder.\n\n Args:\n text_encoder (`torch.nn.Module`, *optional*):\n The text encoder module to disable the LoRA layers for. If `None`, it will try to get the\n `text_encoder` attribute.\n \"\"\"\n if not USE_PEFT_BACKEND:\n raise ValueError(\"PEFT backend is required for this method.\")\n\n text_encoder = text_encoder or getattr(self, \"text_encoder\", None)\n if text_encoder is None:\n raise ValueError(\"Text Encoder not found.\")\n set_adapter_layers(text_encoder, enabled=False)\n\n def enable_lora_for_text_encoder(self, text_encoder: Optional[\"PreTrainedModel\"] = None):\n \"\"\"\n Enables the LoRA layers for the text encoder.\n\n Args:\n text_encoder (`torch.nn.Module`, *optional*):\n The text encoder module to enable the LoRA layers for. If `None`, it will try to get the `text_encoder`\n attribute.\n \"\"\"\n if not USE_PEFT_BACKEND:\n raise ValueError(\"PEFT backend is required for this method.\")\n text_encoder = text_encoder or getattr(self, \"text_encoder\", None)\n if text_encoder is None:\n raise ValueError(\"Text Encoder not found.\")\n set_adapter_layers(self.text_encoder, enabled=True)\n\n def set_adapters(\n self,\n adapter_names: Union[List[str], str],\n adapter_weights: Optional[Union[float, Dict, List[float], List[Dict]]] = None,\n ):\n adapter_names = [adapter_names] if isinstance(adapter_names, str) else adapter_names\n\n adapter_weights = copy.deepcopy(adapter_weights)\n\n # Expand weights into a list, one entry per adapter\n if not isinstance(adapter_weights, list):\n adapter_weights = [adapter_weights] * len(adapter_names)\n\n if len(adapter_names) != len(adapter_weights):\n raise ValueError(\n f\"Length of adapter names {len(adapter_names)} is not equal to the length of the weights {len(adapter_weights)}\"\n )\n\n # Decompose weights into weights for unet, text_encoder and text_encoder_2\n unet_lora_weights, text_encoder_lora_weights, text_encoder_2_lora_weights = [], [], []\n\n list_adapters = self.get_list_adapters() # eg {\"unet\": [\"adapter1\", \"adapter2\"], \"text_encoder\": [\"adapter2\"]}\n all_adapters = {\n adapter for adapters in list_adapters.values() for adapter in adapters\n } # eg [\"adapter1\", \"adapter2\"]\n invert_list_adapters = {\n adapter: [part for part, adapters in list_adapters.items() if adapter in adapters]\n for adapter in all_adapters\n } # eg {\"adapter1\": [\"unet\"], \"adapter2\": [\"unet\", \"text_encoder\"]}\n\n for adapter_name, weights in zip(adapter_names, adapter_weights):\n if isinstance(weights, dict):\n unet_lora_weight = weights.pop(\"unet\", None)\n text_encoder_lora_weight = weights.pop(\"text_encoder\", None)\n text_encoder_2_lora_weight = weights.pop(\"text_encoder_2\", None)\n\n if len(weights) > 0:\n raise ValueError(\n f\"Got invalid key '{weights.keys()}' in lora weight dict for adapter {adapter_name}.\"\n )\n\n if text_encoder_2_lora_weight is not None and not hasattr(self, \"text_encoder_2\"):\n logger.warning(\n \"Lora weight dict contains text_encoder_2 weights but will be ignored because pipeline does not have text_encoder_2.\"\n )\n\n # warn if adapter doesn't have parts specified by adapter_weights\n for part_weight, part_name in zip(\n [unet_lora_weight, text_encoder_lora_weight, text_encoder_2_lora_weight],\n [\"unet\", \"text_encoder\", \"text_encoder_2\"],\n ):\n if part_weight is not None and part_name not in invert_list_adapters[adapter_name]:\n logger.warning(\n f\"Lora weight dict for adapter '{adapter_name}' contains {part_name}, but this will be ignored because {adapter_name} does not contain weights for {part_name}. Valid parts for {adapter_name} are: {invert_list_adapters[adapter_name]}.\"\n )\n\n else:\n unet_lora_weight = weights\n text_encoder_lora_weight = weights\n text_encoder_2_lora_weight = weights\n\n unet_lora_weights.append(unet_lora_weight)\n text_encoder_lora_weights.append(text_encoder_lora_weight)\n text_encoder_2_lora_weights.append(text_encoder_2_lora_weight)\n\n unet = getattr(self, self.unet_name) if not hasattr(self, \"unet\") else self.unet\n # Handle the UNET\n unet.set_adapters(adapter_names, unet_lora_weights)\n\n # Handle the Text Encoder\n if hasattr(self, \"text_encoder\"):\n self.set_adapters_for_text_encoder(adapter_names, self.text_encoder, text_encoder_lora_weights)\n if hasattr(self, \"text_encoder_2\"):\n self.set_adapters_for_text_encoder(adapter_names, self.text_encoder_2, text_encoder_2_lora_weights)\n\n def disable_lora(self):\n if not USE_PEFT_BACKEND:\n raise ValueError(\"PEFT backend is required for this method.\")\n\n # Disable unet adapters\n unet = getattr(self, self.unet_name) if not hasattr(self, \"unet\") else self.unet\n unet.disable_lora()\n\n # Disable text encoder adapters\n if hasattr(self, \"text_encoder\"):\n self.disable_lora_for_text_encoder(self.text_encoder)\n if hasattr(self, \"text_encoder_2\"):\n self.disable_lora_for_text_encoder(self.text_encoder_2)\n\n def enable_lora(self):\n if not USE_PEFT_BACKEND:\n raise ValueError(\"PEFT backend is required for this method.\")\n\n # Enable unet adapters\n unet = getattr(self, self.unet_name) if not hasattr(self, \"unet\") else self.unet\n unet.enable_lora()\n\n # Enable text encoder adapters\n if hasattr(self, \"text_encoder\"):\n self.enable_lora_for_text_encoder(self.text_encoder)\n if hasattr(self, \"text_encoder_2\"):\n self.enable_lora_for_text_encoder(self.text_encoder_2)\n\n def delete_adapters(self, adapter_names: Union[List[str], str]):\n \"\"\"\n Args:\n Deletes the LoRA layers of `adapter_name` for the unet and text-encoder(s).\n adapter_names (`Union[List[str], str]`):\n The names of the adapter to delete. Can be a single string or a list of strings\n \"\"\"\n if not USE_PEFT_BACKEND:\n raise ValueError(\"PEFT backend is required for this method.\")\n\n if isinstance(adapter_names, str):\n adapter_names = [adapter_names]\n\n # Delete unet adapters\n unet = getattr(self, self.unet_name) if not hasattr(self, \"unet\") else self.unet\n unet.delete_adapters(adapter_names)\n\n for adapter_name in adapter_names:\n # Delete text encoder adapters\n if hasattr(self, \"text_encoder\"):\n delete_adapter_layers(self.text_encoder, adapter_name)\n if hasattr(self, \"text_encoder_2\"):\n delete_adapter_layers(self.text_encoder_2, adapter_name)\n\n def get_active_adapters(self) -> List[str]:\n \"\"\"\n Gets the list of the current active adapters.\n\n Example:\n\n ```python\n from diffusers import DiffusionPipeline\n\n pipeline = DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n ).to(\"cuda\")\n pipeline.load_lora_weights(\"CiroN2022/toy-face\", weight_name=\"toy_face_sdxl.safetensors\", adapter_name=\"toy\")\n pipeline.get_active_adapters()\n ```\n \"\"\"\n if not USE_PEFT_BACKEND:\n raise ValueError(\n \"PEFT backend is required for this method. Please install the latest version of PEFT `pip install -U peft`\"\n )\n\n from peft.tuners.tuners_utils import BaseTunerLayer\n\n active_adapters = []\n unet = getattr(self, self.unet_name) if not hasattr(self, \"unet\") else self.unet\n for module in unet.modules():\n if isinstance(module, BaseTunerLayer):\n active_adapters = module.active_adapters\n break\n\n return active_adapters\n\n def get_list_adapters(self) -> Dict[str, List[str]]:\n \"\"\"\n Gets the current list of all available adapters in the pipeline.\n \"\"\"\n if not USE_PEFT_BACKEND:\n raise ValueError(\n \"PEFT backend is required for this method. Please install the latest version of PEFT `pip install -U peft`\"\n )\n\n set_adapters = {}\n\n if hasattr(self, \"text_encoder\") and hasattr(self.text_encoder, \"peft_config\"):\n set_adapters[\"text_encoder\"] = list(self.text_encoder.peft_config.keys())\n\n if hasattr(self, \"text_encoder_2\") and hasattr(self.text_encoder_2, \"peft_config\"):\n set_adapters[\"text_encoder_2\"] = list(self.text_encoder_2.peft_config.keys())\n\n unet = getattr(self, self.unet_name) if not hasattr(self, \"unet\") else self.unet\n if hasattr(self, self.unet_name) and hasattr(unet, \"peft_config\"):\n set_adapters[self.unet_name] = list(self.unet.peft_config.keys())\n\n return set_adapters\n\n def set_lora_device(self, adapter_names: List[str], device: Union[torch.device, str, int]) -> None:\n \"\"\"\n Moves the LoRAs listed in `adapter_names` to a target device. Useful for offloading the LoRA to the CPU in case\n you want to load multiple adapters and free some GPU memory.\n\n Args:\n adapter_names (`List[str]`):\n List of adapters to send device to.\n device (`Union[torch.device, str, int]`):\n Device to send the adapters to. Can be either a torch device, a str or an integer.\n \"\"\"\n if not USE_PEFT_BACKEND:\n raise ValueError(\"PEFT backend is required for this method.\")\n\n from peft.tuners.tuners_utils import BaseTunerLayer\n\n # Handle the UNET\n unet = getattr(self, self.unet_name) if not hasattr(self, \"unet\") else self.unet\n for unet_module in unet.modules():\n if isinstance(unet_module, BaseTunerLayer):\n for adapter_name in adapter_names:\n unet_module.lora_A[adapter_name].to(device)\n unet_module.lora_B[adapter_name].to(device)\n # this is a param, not a module, so device placement is not in-place -> re-assign\n if hasattr(unet_module, \"lora_magnitude_vector\") and unet_module.lora_magnitude_vector is not None:\n unet_module.lora_magnitude_vector[adapter_name] = unet_module.lora_magnitude_vector[\n adapter_name\n ].to(device)\n\n # Handle the text encoder\n modules_to_process = []\n if hasattr(self, \"text_encoder\"):\n modules_to_process.append(self.text_encoder)\n\n if hasattr(self, \"text_encoder_2\"):\n modules_to_process.append(self.text_encoder_2)\n\n for text_encoder in modules_to_process:\n # loop over submodules\n for text_encoder_module in text_encoder.modules():\n if isinstance(text_encoder_module, BaseTunerLayer):\n for adapter_name in adapter_names:\n text_encoder_module.lora_A[adapter_name].to(device)\n text_encoder_module.lora_B[adapter_name].to(device)\n # this is a param, not a module, so device placement is not in-place -> re-assign\n if (\n hasattr(text_encoder_module, \"lora_magnitude_vector\")\n and text_encoder_module.lora_magnitude_vector is not None\n ):\n text_encoder_module.lora_magnitude_vector[\n adapter_name\n ] = text_encoder_module.lora_magnitude_vector[adapter_name].to(device)\n\n\nclass StableDiffusionXLLoraLoaderMixin(LoraLoaderMixin):\n \"\"\"This class overrides `LoraLoaderMixin` with LoRA loading/saving code that's specific to SDXL\"\"\"\n\n # Override to properly handle the loading and unloading of the additional text encoder.\n def load_lora_weights(\n self,\n pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],\n adapter_name: Optional[str] = None,\n **kwargs,\n ):\n \"\"\"\n Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.unet` and\n `self.text_encoder`.\n\n All kwargs are forwarded to `self.lora_state_dict`.\n\n See [`~loaders.LoraLoaderMixin.lora_state_dict`] for more details on how the state dict is loaded.\n\n See [`~loaders.LoraLoaderMixin.load_lora_into_unet`] for more details on how the state dict is loaded into\n `self.unet`.\n\n See [`~loaders.LoraLoaderMixin.load_lora_into_text_encoder`] for more details on how the state dict is loaded\n into `self.text_encoder`.\n\n Parameters:\n pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):\n See [`~loaders.LoraLoaderMixin.lora_state_dict`].\n adapter_name (`str`, *optional*):\n Adapter name to be used for referencing the loaded adapter model. If not specified, it will use\n `default_{i}` where i is the total number of adapters being loaded.\n kwargs (`dict`, *optional*):\n See [`~loaders.LoraLoaderMixin.lora_state_dict`].\n \"\"\"\n if not USE_PEFT_BACKEND:\n raise ValueError(\"PEFT backend is required for this method.\")\n\n # We could have accessed the unet config from `lora_state_dict()` too. We pass\n # it here explicitly to be able to tell that it's coming from an SDXL\n # pipeline.\n\n # if a dict is passed, copy it instead of modifying it inplace\n if isinstance(pretrained_model_name_or_path_or_dict, dict):\n pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict.copy()\n\n # First, ensure that the checkpoint is a compatible one and can be successfully loaded.\n state_dict, network_alphas = self.lora_state_dict(\n pretrained_model_name_or_path_or_dict,\n unet_config=self.unet.config,\n **kwargs,\n )\n is_correct_format = all(\"lora\" in key or \"dora_scale\" in key for key in state_dict.keys())\n if not is_correct_format:\n raise ValueError(\"Invalid LoRA checkpoint.\")\n\n self.load_lora_into_unet(\n state_dict, network_alphas=network_alphas, unet=self.unet, adapter_name=adapter_name, _pipeline=self\n )\n text_encoder_state_dict = {k: v for k, v in state_dict.items() if \"text_encoder.\" in k}\n if len(text_encoder_state_dict) > 0:\n self.load_lora_into_text_encoder(\n text_encoder_state_dict,\n network_alphas=network_alphas,\n text_encoder=self.text_encoder,\n prefix=\"text_encoder\",\n lora_scale=self.lora_scale,\n adapter_name=adapter_name,\n _pipeline=self,\n )\n\n text_encoder_2_state_dict = {k: v for k, v in state_dict.items() if \"text_encoder_2.\" in k}\n if len(text_encoder_2_state_dict) > 0:\n self.load_lora_into_text_encoder(\n text_encoder_2_state_dict,\n network_alphas=network_alphas,\n text_encoder=self.text_encoder_2,\n prefix=\"text_encoder_2\",\n lora_scale=self.lora_scale,\n adapter_name=adapter_name,\n _pipeline=self,\n )\n\n @classmethod\n def save_lora_weights(\n cls,\n save_directory: Union[str, os.PathLike],\n unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,\n text_encoder_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,\n text_encoder_2_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,\n is_main_process: bool = True,\n weight_name: str = None,\n save_function: Callable = None,\n safe_serialization: bool = True,\n ):\n r\"\"\"\n Save the LoRA parameters corresponding to the UNet and text encoder.\n\n Arguments:\n save_directory (`str` or `os.PathLike`):\n Directory to save LoRA parameters to. Will be created if it doesn't exist.\n unet_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):\n State dict of the LoRA layers corresponding to the `unet`.\n text_encoder_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):\n State dict of the LoRA layers corresponding to the `text_encoder`. Must explicitly pass the text\n encoder LoRA state dict because it comes from 🤗 Transformers.\n text_encoder_2_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):\n State dict of the LoRA layers corresponding to the `text_encoder_2`. Must explicitly pass the text\n encoder LoRA state dict because it comes from 🤗 Transformers.\n is_main_process (`bool`, *optional*, defaults to `True`):\n Whether the process calling this is the main process or not. Useful during distributed training and you\n need to call this function on all processes. In this case, set `is_main_process=True` only on the main\n process to avoid race conditions.\n save_function (`Callable`):\n The function to use to save the state dictionary. Useful during distributed training when you need to\n replace `torch.save` with another method. Can be configured with the environment variable\n `DIFFUSERS_SAVE_MODE`.\n safe_serialization (`bool`, *optional*, defaults to `True`):\n Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.\n \"\"\"\n state_dict = {}\n\n def pack_weights(layers, prefix):\n layers_weights = layers.state_dict() if isinstance(layers, torch.nn.Module) else layers\n layers_state_dict = {f\"{prefix}.{module_name}\": param for module_name, param in layers_weights.items()}\n return layers_state_dict\n\n if not (unet_lora_layers or text_encoder_lora_layers or text_encoder_2_lora_layers):\n raise ValueError(\n \"You must pass at least one of `unet_lora_layers`, `text_encoder_lora_layers` or `text_encoder_2_lora_layers`.\"\n )\n\n if unet_lora_layers:\n state_dict.update(pack_weights(unet_lora_layers, \"unet\"))\n\n if text_encoder_lora_layers:\n state_dict.update(pack_weights(text_encoder_lora_layers, \"text_encoder\"))\n\n if text_encoder_2_lora_layers:\n state_dict.update(pack_weights(text_encoder_2_lora_layers, \"text_encoder_2\"))\n\n cls.write_lora_layers(\n state_dict=state_dict,\n save_directory=save_directory,\n is_main_process=is_main_process,\n weight_name=weight_name,\n save_function=save_function,\n safe_serialization=safe_serialization,\n )\n\n def _remove_text_encoder_monkey_patch(self):\n recurse_remove_peft_layers(self.text_encoder)\n # TODO: @younesbelkada handle this in transformers side\n if getattr(self.text_encoder, \"peft_config\", None) is not None:\n del self.text_encoder.peft_config\n self.text_encoder._hf_peft_config_loaded = None\n\n recurse_remove_peft_layers(self.text_encoder_2)\n if getattr(self.text_encoder_2, \"peft_config\", None) is not None:\n del self.text_encoder_2.peft_config\n self.text_encoder_2._hf_peft_config_loaded = None\n\n\nclass SD3LoraLoaderMixin:\n r\"\"\"\n Load LoRA layers into [`SD3Transformer2DModel`].\n \"\"\"\n\n transformer_name = TRANSFORMER_NAME\n num_fused_loras = 0\n\n def load_lora_weights(\n self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], adapter_name=None, **kwargs\n ):\n \"\"\"\n Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.unet` and\n `self.text_encoder`.\n\n All kwargs are forwarded to `self.lora_state_dict`.\n\n See [`~loaders.LoraLoaderMixin.lora_state_dict`] for more details on how the state dict is loaded.\n\n See [`~loaders.LoraLoaderMixin.load_lora_into_transformer`] for more details on how the state dict is loaded\n into `self.transformer`.\n\n Parameters:\n pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):\n See [`~loaders.LoraLoaderMixin.lora_state_dict`].\n kwargs (`dict`, *optional*):\n See [`~loaders.LoraLoaderMixin.lora_state_dict`].\n adapter_name (`str`, *optional*):\n Adapter name to be used for referencing the loaded adapter model. If not specified, it will use\n `default_{i}` where i is the total number of adapters being loaded.\n \"\"\"\n if not USE_PEFT_BACKEND:\n raise ValueError(\"PEFT backend is required for this method.\")\n\n # if a dict is passed, copy it instead of modifying it inplace\n if isinstance(pretrained_model_name_or_path_or_dict, dict):\n pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict.copy()\n\n # First, ensure that the checkpoint is a compatible one and can be successfully loaded.\n state_dict = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)\n\n is_correct_format = all(\"lora\" in key or \"dora_scale\" in key for key in state_dict.keys())\n if not is_correct_format:\n raise ValueError(\"Invalid LoRA checkpoint.\")\n\n self.load_lora_into_transformer(\n state_dict,\n transformer=getattr(self, self.transformer_name) if not hasattr(self, \"transformer\") else self.transformer,\n adapter_name=adapter_name,\n _pipeline=self,\n )\n\n @classmethod\n @validate_hf_hub_args\n def lora_state_dict(\n cls,\n pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],\n **kwargs,\n ):\n r\"\"\"\n Return state dict for lora weights and the network alphas.\n\n \n\n We support loading A1111 formatted LoRA checkpoints in a limited capacity.\n\n This function is experimental and might change in the future.\n\n \n\n Parameters:\n pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):\n Can be either:\n\n - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on\n the Hub.\n - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved\n with [`ModelMixin.save_pretrained`].\n - A [torch state\n dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).\n\n cache_dir (`Union[str, os.PathLike]`, *optional*):\n Path to a directory where a downloaded pretrained model configuration is cached if the standard cache\n is not used.\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\n cached versions if they exist.\n resume_download (`bool`, *optional*, defaults to `False`):\n Whether or not to resume downloading the model weights and configuration files. If set to `False`, any\n incompletely downloaded files are deleted.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\n local_files_only (`bool`, *optional*, defaults to `False`):\n Whether to only load local model weights and configuration files or not. If set to `True`, the model\n won't be downloaded from the Hub.\n token (`str` or *bool*, *optional*):\n The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from\n `diffusers-cli login` (stored in `~/.huggingface`) is used.\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier\n allowed by Git.\n subfolder (`str`, *optional*, defaults to `\"\"`):\n The subfolder location of a model file within a larger model repository on the Hub or locally.\n\n \"\"\"\n # Load the main state dict first which has the LoRA layers for either of\n # UNet and text encoder or both.\n cache_dir = kwargs.pop(\"cache_dir\", None)\n force_download = kwargs.pop(\"force_download\", False)\n resume_download = kwargs.pop(\"resume_download\", False)\n proxies = kwargs.pop(\"proxies\", None)\n local_files_only = kwargs.pop(\"local_files_only\", None)\n token = kwargs.pop(\"token\", None)\n revision = kwargs.pop(\"revision\", None)\n subfolder = kwargs.pop(\"subfolder\", None)\n weight_name = kwargs.pop(\"weight_name\", None)\n use_safetensors = kwargs.pop(\"use_safetensors\", None)\n\n allow_pickle = False\n if use_safetensors is None:\n use_safetensors = True\n allow_pickle = True\n\n user_agent = {\n \"file_type\": \"attn_procs_weights\",\n \"framework\": \"pytorch\",\n }\n\n model_file = None\n if not isinstance(pretrained_model_name_or_path_or_dict, dict):\n # Let's first try to load .safetensors weights\n if (use_safetensors and weight_name is None) or (\n weight_name is not None and weight_name.endswith(\".safetensors\")\n ):\n try:\n model_file = _get_model_file(\n pretrained_model_name_or_path_or_dict,\n weights_name=weight_name or LORA_WEIGHT_NAME_SAFE,\n cache_dir=cache_dir,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n subfolder=subfolder,\n user_agent=user_agent,\n )\n state_dict = safetensors.torch.load_file(model_file, device=\"cpu\")\n except (IOError, safetensors.SafetensorError) as e:\n if not allow_pickle:\n raise e\n # try loading non-safetensors weights\n model_file = None\n pass\n\n if model_file is None:\n model_file = _get_model_file(\n pretrained_model_name_or_path_or_dict,\n weights_name=weight_name or LORA_WEIGHT_NAME,\n cache_dir=cache_dir,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n subfolder=subfolder,\n user_agent=user_agent,\n )\n state_dict = load_state_dict(model_file)\n else:\n state_dict = pretrained_model_name_or_path_or_dict\n\n return state_dict\n\n @classmethod\n def load_lora_into_transformer(cls, state_dict, transformer, adapter_name=None, _pipeline=None):\n \"\"\"\n This will load the LoRA layers specified in `state_dict` into `transformer`.\n\n Parameters:\n state_dict (`dict`):\n A standard state dict containing the lora layer parameters. The keys can either be indexed directly\n into the unet or prefixed with an additional `unet` which can be used to distinguish between text\n encoder lora layers.\n transformer (`SD3Transformer2DModel`):\n The Transformer model to load the LoRA layers into.\n adapter_name (`str`, *optional*):\n Adapter name to be used for referencing the loaded adapter model. If not specified, it will use\n `default_{i}` where i is the total number of adapters being loaded.\n \"\"\"\n from peft import LoraConfig, inject_adapter_in_model, set_peft_model_state_dict\n\n keys = list(state_dict.keys())\n\n transformer_keys = [k for k in keys if k.startswith(cls.transformer_name)]\n state_dict = {\n k.replace(f\"{cls.transformer_name}.\", \"\"): v for k, v in state_dict.items() if k in transformer_keys\n }\n\n if len(state_dict.keys()) > 0:\n if adapter_name in getattr(transformer, \"peft_config\", {}):\n raise ValueError(\n f\"Adapter name {adapter_name} already in use in the transformer - please select a new adapter name.\"\n )\n\n rank = {}\n for key, val in state_dict.items():\n if \"lora_B\" in key:\n rank[key] = val.shape[1]\n\n lora_config_kwargs = get_peft_kwargs(rank, network_alpha_dict=None, peft_state_dict=state_dict)\n if \"use_dora\" in lora_config_kwargs:\n if lora_config_kwargs[\"use_dora\"] and is_peft_version(\"<\", \"0.9.0\"):\n raise ValueError(\n \"You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`.\"\n )\n else:\n lora_config_kwargs.pop(\"use_dora\")\n lora_config = LoraConfig(**lora_config_kwargs)\n\n # adapter_name\n if adapter_name is None:\n adapter_name = get_adapter_name(transformer)\n\n # In case the pipeline has been already offloaded to CPU - temporarily remove the hooks\n # otherwise loading LoRA weights will lead to an error\n is_model_cpu_offload, is_sequential_cpu_offload = cls._optionally_disable_offloading(_pipeline)\n\n inject_adapter_in_model(lora_config, transformer, adapter_name=adapter_name)\n incompatible_keys = set_peft_model_state_dict(transformer, state_dict, adapter_name)\n\n if incompatible_keys is not None:\n # check only for unexpected keys\n unexpected_keys = getattr(incompatible_keys, \"unexpected_keys\", None)\n if unexpected_keys:\n logger.warning(\n f\"Loading adapter weights from state_dict led to unexpected keys not found in the model: \"\n f\" {unexpected_keys}. \"\n )\n\n # Offload back.\n if is_model_cpu_offload:\n _pipeline.enable_model_cpu_offload()\n elif is_sequential_cpu_offload:\n _pipeline.enable_sequential_cpu_offload()\n # Unsafe code />\n\n @classmethod\n def save_lora_weights(\n cls,\n save_directory: Union[str, os.PathLike],\n transformer_lora_layers: Dict[str, torch.nn.Module] = None,\n is_main_process: bool = True,\n weight_name: str = None,\n save_function: Callable = None,\n safe_serialization: bool = True,\n ):\n r\"\"\"\n Save the LoRA parameters corresponding to the UNet and text encoder.\n\n Arguments:\n save_directory (`str` or `os.PathLike`):\n Directory to save LoRA parameters to. Will be created if it doesn't exist.\n transformer_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):\n State dict of the LoRA layers corresponding to the `transformer`.\n is_main_process (`bool`, *optional*, defaults to `True`):\n Whether the process calling this is the main process or not. Useful during distributed training and you\n need to call this function on all processes. In this case, set `is_main_process=True` only on the main\n process to avoid race conditions.\n save_function (`Callable`):\n The function to use to save the state dictionary. Useful during distributed training when you need to\n replace `torch.save` with another method. Can be configured with the environment variable\n `DIFFUSERS_SAVE_MODE`.\n safe_serialization (`bool`, *optional*, defaults to `True`):\n Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.\n \"\"\"\n state_dict = {}\n\n def pack_weights(layers, prefix):\n layers_weights = layers.state_dict() if isinstance(layers, torch.nn.Module) else layers\n layers_state_dict = {f\"{prefix}.{module_name}\": param for module_name, param in layers_weights.items()}\n return layers_state_dict\n\n if not transformer_lora_layers:\n raise ValueError(\"You must pass `transformer_lora_layers`.\")\n\n if transformer_lora_layers:\n state_dict.update(pack_weights(transformer_lora_layers, cls.transformer_name))\n\n # Save the model\n cls.write_lora_layers(\n state_dict=state_dict,\n save_directory=save_directory,\n is_main_process=is_main_process,\n weight_name=weight_name,\n save_function=save_function,\n safe_serialization=safe_serialization,\n )\n\n @staticmethod\n def write_lora_layers(\n state_dict: Dict[str, torch.Tensor],\n save_directory: str,\n is_main_process: bool,\n weight_name: str,\n save_function: Callable,\n safe_serialization: bool,\n ):\n if os.path.isfile(save_directory):\n logger.error(f\"Provided path ({save_directory}) should be a directory, not a file\")\n return\n\n if save_function is None:\n if safe_serialization:\n\n def save_function(weights, filename):\n return safetensors.torch.save_file(weights, filename, metadata={\"format\": \"pt\"})\n\n else:\n save_function = torch.save\n\n os.makedirs(save_directory, exist_ok=True)\n\n if weight_name is None:\n if safe_serialization:\n weight_name = LORA_WEIGHT_NAME_SAFE\n else:\n weight_name = LORA_WEIGHT_NAME\n\n save_path = Path(save_directory, weight_name).as_posix()\n save_function(state_dict, save_path)\n logger.info(f\"Model weights saved in {save_path}\")\n\n def unload_lora_weights(self):\n \"\"\"\n Unloads the LoRA parameters.\n\n Examples:\n\n ```python\n >>> # Assuming `pipeline` is already loaded with the LoRA parameters.\n >>> pipeline.unload_lora_weights()\n >>> ...\n ```\n \"\"\"\n transformer = getattr(self, self.transformer_name) if not hasattr(self, \"transformer\") else self.transformer\n recurse_remove_peft_layers(transformer)\n if hasattr(transformer, \"peft_config\"):\n del transformer.peft_config\n\n @classmethod\n # Copied from diffusers.loaders.lora.LoraLoaderMixin._optionally_disable_offloading\n def _optionally_disable_offloading(cls, _pipeline):\n \"\"\"\n Optionally removes offloading in case the pipeline has been already sequentially offloaded to CPU.\n\n Args:\n _pipeline (`DiffusionPipeline`):\n The pipeline to disable offloading for.\n\n Returns:\n tuple:\n A tuple indicating if `is_model_cpu_offload` or `is_sequential_cpu_offload` is True.\n \"\"\"\n is_model_cpu_offload = False\n is_sequential_cpu_offload = False\n\n if _pipeline is not None and _pipeline.hf_device_map is None:\n for _, component in _pipeline.components.items():\n if isinstance(component, nn.Module) and hasattr(component, \"_hf_hook\"):\n if not is_model_cpu_offload:\n is_model_cpu_offload = isinstance(component._hf_hook, CpuOffload)\n if not is_sequential_cpu_offload:\n is_sequential_cpu_offload = (\n isinstance(component._hf_hook, AlignDevicesHook)\n or hasattr(component._hf_hook, \"hooks\")\n and isinstance(component._hf_hook.hooks[0], AlignDevicesHook)\n )\n\n logger.info(\n \"Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again.\"\n )\n remove_hook_from_module(component, recurse=is_sequential_cpu_offload)\n\n return (is_model_cpu_offload, is_sequential_cpu_offload)\n" }, { "path": "diffusers/src/diffusers/loaders/lora_conversion_utils.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport re\n\nfrom ..utils import is_peft_version, logging\n\n\nlogger = logging.get_logger(__name__)\n\n\ndef _maybe_map_sgm_blocks_to_diffusers(state_dict, unet_config, delimiter=\"_\", block_slice_pos=5):\n # 1. get all state_dict_keys\n all_keys = list(state_dict.keys())\n sgm_patterns = [\"input_blocks\", \"middle_block\", \"output_blocks\"]\n\n # 2. check if needs remapping, if not return original dict\n is_in_sgm_format = False\n for key in all_keys:\n if any(p in key for p in sgm_patterns):\n is_in_sgm_format = True\n break\n\n if not is_in_sgm_format:\n return state_dict\n\n # 3. Else remap from SGM patterns\n new_state_dict = {}\n inner_block_map = [\"resnets\", \"attentions\", \"upsamplers\"]\n\n # Retrieves # of down, mid and up blocks\n input_block_ids, middle_block_ids, output_block_ids = set(), set(), set()\n\n for layer in all_keys:\n if \"text\" in layer:\n new_state_dict[layer] = state_dict.pop(layer)\n else:\n layer_id = int(layer.split(delimiter)[:block_slice_pos][-1])\n if sgm_patterns[0] in layer:\n input_block_ids.add(layer_id)\n elif sgm_patterns[1] in layer:\n middle_block_ids.add(layer_id)\n elif sgm_patterns[2] in layer:\n output_block_ids.add(layer_id)\n else:\n raise ValueError(f\"Checkpoint not supported because layer {layer} not supported.\")\n\n input_blocks = {\n layer_id: [key for key in state_dict if f\"input_blocks{delimiter}{layer_id}\" in key]\n for layer_id in input_block_ids\n }\n middle_blocks = {\n layer_id: [key for key in state_dict if f\"middle_block{delimiter}{layer_id}\" in key]\n for layer_id in middle_block_ids\n }\n output_blocks = {\n layer_id: [key for key in state_dict if f\"output_blocks{delimiter}{layer_id}\" in key]\n for layer_id in output_block_ids\n }\n\n # Rename keys accordingly\n for i in input_block_ids:\n block_id = (i - 1) // (unet_config.layers_per_block + 1)\n layer_in_block_id = (i - 1) % (unet_config.layers_per_block + 1)\n\n for key in input_blocks[i]:\n inner_block_id = int(key.split(delimiter)[block_slice_pos])\n inner_block_key = inner_block_map[inner_block_id] if \"op\" not in key else \"downsamplers\"\n inner_layers_in_block = str(layer_in_block_id) if \"op\" not in key else \"0\"\n new_key = delimiter.join(\n key.split(delimiter)[: block_slice_pos - 1]\n + [str(block_id), inner_block_key, inner_layers_in_block]\n + key.split(delimiter)[block_slice_pos + 1 :]\n )\n new_state_dict[new_key] = state_dict.pop(key)\n\n for i in middle_block_ids:\n key_part = None\n if i == 0:\n key_part = [inner_block_map[0], \"0\"]\n elif i == 1:\n key_part = [inner_block_map[1], \"0\"]\n elif i == 2:\n key_part = [inner_block_map[0], \"1\"]\n else:\n raise ValueError(f\"Invalid middle block id {i}.\")\n\n for key in middle_blocks[i]:\n new_key = delimiter.join(\n key.split(delimiter)[: block_slice_pos - 1] + key_part + key.split(delimiter)[block_slice_pos:]\n )\n new_state_dict[new_key] = state_dict.pop(key)\n\n for i in output_block_ids:\n block_id = i // (unet_config.layers_per_block + 1)\n layer_in_block_id = i % (unet_config.layers_per_block + 1)\n\n for key in output_blocks[i]:\n inner_block_id = int(key.split(delimiter)[block_slice_pos])\n inner_block_key = inner_block_map[inner_block_id]\n inner_layers_in_block = str(layer_in_block_id) if inner_block_id < 2 else \"0\"\n new_key = delimiter.join(\n key.split(delimiter)[: block_slice_pos - 1]\n + [str(block_id), inner_block_key, inner_layers_in_block]\n + key.split(delimiter)[block_slice_pos + 1 :]\n )\n new_state_dict[new_key] = state_dict.pop(key)\n\n if len(state_dict) > 0:\n raise ValueError(\"At this point all state dict entries have to be converted.\")\n\n return new_state_dict\n\n\ndef _convert_kohya_lora_to_diffusers(state_dict, unet_name=\"unet\", text_encoder_name=\"text_encoder\"):\n unet_state_dict = {}\n te_state_dict = {}\n te2_state_dict = {}\n network_alphas = {}\n is_unet_dora_lora = any(\"dora_scale\" in k and \"lora_unet_\" in k for k in state_dict)\n is_te_dora_lora = any(\"dora_scale\" in k and (\"lora_te_\" in k or \"lora_te1_\" in k) for k in state_dict)\n is_te2_dora_lora = any(\"dora_scale\" in k and \"lora_te2_\" in k for k in state_dict)\n\n if is_unet_dora_lora or is_te_dora_lora or is_te2_dora_lora:\n if is_peft_version(\"<\", \"0.9.0\"):\n raise ValueError(\n \"You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`.\"\n )\n\n # every down weight has a corresponding up weight and potentially an alpha weight\n lora_keys = [k for k in state_dict.keys() if k.endswith(\"lora_down.weight\")]\n for key in lora_keys:\n lora_name = key.split(\".\")[0]\n lora_name_up = lora_name + \".lora_up.weight\"\n lora_name_alpha = lora_name + \".alpha\"\n\n if lora_name.startswith(\"lora_unet_\"):\n diffusers_name = key.replace(\"lora_unet_\", \"\").replace(\"_\", \".\")\n\n if \"input.blocks\" in diffusers_name:\n diffusers_name = diffusers_name.replace(\"input.blocks\", \"down_blocks\")\n else:\n diffusers_name = diffusers_name.replace(\"down.blocks\", \"down_blocks\")\n\n if \"middle.block\" in diffusers_name:\n diffusers_name = diffusers_name.replace(\"middle.block\", \"mid_block\")\n else:\n diffusers_name = diffusers_name.replace(\"mid.block\", \"mid_block\")\n if \"output.blocks\" in diffusers_name:\n diffusers_name = diffusers_name.replace(\"output.blocks\", \"up_blocks\")\n else:\n diffusers_name = diffusers_name.replace(\"up.blocks\", \"up_blocks\")\n\n diffusers_name = diffusers_name.replace(\"transformer.blocks\", \"transformer_blocks\")\n diffusers_name = diffusers_name.replace(\"to.q.lora\", \"to_q_lora\")\n diffusers_name = diffusers_name.replace(\"to.k.lora\", \"to_k_lora\")\n diffusers_name = diffusers_name.replace(\"to.v.lora\", \"to_v_lora\")\n diffusers_name = diffusers_name.replace(\"to.out.0.lora\", \"to_out_lora\")\n diffusers_name = diffusers_name.replace(\"proj.in\", \"proj_in\")\n diffusers_name = diffusers_name.replace(\"proj.out\", \"proj_out\")\n diffusers_name = diffusers_name.replace(\"emb.layers\", \"time_emb_proj\")\n\n # SDXL specificity.\n if \"emb\" in diffusers_name and \"time.emb.proj\" not in diffusers_name:\n pattern = r\"\\.\\d+(?=\\D*$)\"\n diffusers_name = re.sub(pattern, \"\", diffusers_name, count=1)\n if \".in.\" in diffusers_name:\n diffusers_name = diffusers_name.replace(\"in.layers.2\", \"conv1\")\n if \".out.\" in diffusers_name:\n diffusers_name = diffusers_name.replace(\"out.layers.3\", \"conv2\")\n if \"downsamplers\" in diffusers_name or \"upsamplers\" in diffusers_name:\n diffusers_name = diffusers_name.replace(\"op\", \"conv\")\n if \"skip\" in diffusers_name:\n diffusers_name = diffusers_name.replace(\"skip.connection\", \"conv_shortcut\")\n\n # LyCORIS specificity.\n if \"time.emb.proj\" in diffusers_name:\n diffusers_name = diffusers_name.replace(\"time.emb.proj\", \"time_emb_proj\")\n if \"conv.shortcut\" in diffusers_name:\n diffusers_name = diffusers_name.replace(\"conv.shortcut\", \"conv_shortcut\")\n\n # General coverage.\n if \"transformer_blocks\" in diffusers_name:\n if \"attn1\" in diffusers_name or \"attn2\" in diffusers_name:\n diffusers_name = diffusers_name.replace(\"attn1\", \"attn1.processor\")\n diffusers_name = diffusers_name.replace(\"attn2\", \"attn2.processor\")\n unet_state_dict[diffusers_name] = state_dict.pop(key)\n unet_state_dict[diffusers_name.replace(\".down.\", \".up.\")] = state_dict.pop(lora_name_up)\n elif \"ff\" in diffusers_name:\n unet_state_dict[diffusers_name] = state_dict.pop(key)\n unet_state_dict[diffusers_name.replace(\".down.\", \".up.\")] = state_dict.pop(lora_name_up)\n elif any(key in diffusers_name for key in (\"proj_in\", \"proj_out\")):\n unet_state_dict[diffusers_name] = state_dict.pop(key)\n unet_state_dict[diffusers_name.replace(\".down.\", \".up.\")] = state_dict.pop(lora_name_up)\n else:\n unet_state_dict[diffusers_name] = state_dict.pop(key)\n unet_state_dict[diffusers_name.replace(\".down.\", \".up.\")] = state_dict.pop(lora_name_up)\n\n if is_unet_dora_lora:\n dora_scale_key_to_replace = \"_lora.down.\" if \"_lora.down.\" in diffusers_name else \".lora.down.\"\n unet_state_dict[\n diffusers_name.replace(dora_scale_key_to_replace, \".lora_magnitude_vector.\")\n ] = state_dict.pop(key.replace(\"lora_down.weight\", \"dora_scale\"))\n\n elif lora_name.startswith((\"lora_te_\", \"lora_te1_\", \"lora_te2_\")):\n if lora_name.startswith((\"lora_te_\", \"lora_te1_\")):\n key_to_replace = \"lora_te_\" if lora_name.startswith(\"lora_te_\") else \"lora_te1_\"\n else:\n key_to_replace = \"lora_te2_\"\n\n diffusers_name = key.replace(key_to_replace, \"\").replace(\"_\", \".\")\n diffusers_name = diffusers_name.replace(\"text.model\", \"text_model\")\n diffusers_name = diffusers_name.replace(\"self.attn\", \"self_attn\")\n diffusers_name = diffusers_name.replace(\"q.proj.lora\", \"to_q_lora\")\n diffusers_name = diffusers_name.replace(\"k.proj.lora\", \"to_k_lora\")\n diffusers_name = diffusers_name.replace(\"v.proj.lora\", \"to_v_lora\")\n diffusers_name = diffusers_name.replace(\"out.proj.lora\", \"to_out_lora\")\n diffusers_name = diffusers_name.replace(\"text.projection\", \"text_projection\")\n\n if \"self_attn\" in diffusers_name:\n if lora_name.startswith((\"lora_te_\", \"lora_te1_\")):\n te_state_dict[diffusers_name] = state_dict.pop(key)\n te_state_dict[diffusers_name.replace(\".down.\", \".up.\")] = state_dict.pop(lora_name_up)\n else:\n te2_state_dict[diffusers_name] = state_dict.pop(key)\n te2_state_dict[diffusers_name.replace(\".down.\", \".up.\")] = state_dict.pop(lora_name_up)\n elif \"mlp\" in diffusers_name:\n # Be aware that this is the new diffusers convention and the rest of the code might\n # not utilize it yet.\n diffusers_name = diffusers_name.replace(\".lora.\", \".lora_linear_layer.\")\n if lora_name.startswith((\"lora_te_\", \"lora_te1_\")):\n te_state_dict[diffusers_name] = state_dict.pop(key)\n te_state_dict[diffusers_name.replace(\".down.\", \".up.\")] = state_dict.pop(lora_name_up)\n else:\n te2_state_dict[diffusers_name] = state_dict.pop(key)\n te2_state_dict[diffusers_name.replace(\".down.\", \".up.\")] = state_dict.pop(lora_name_up)\n # OneTrainer specificity\n elif \"text_projection\" in diffusers_name and lora_name.startswith(\"lora_te2_\"):\n te2_state_dict[diffusers_name] = state_dict.pop(key)\n te2_state_dict[diffusers_name.replace(\".down.\", \".up.\")] = state_dict.pop(lora_name_up)\n\n if (is_te_dora_lora or is_te2_dora_lora) and lora_name.startswith((\"lora_te_\", \"lora_te1_\", \"lora_te2_\")):\n dora_scale_key_to_replace_te = (\n \"_lora.down.\" if \"_lora.down.\" in diffusers_name else \".lora_linear_layer.\"\n )\n if lora_name.startswith((\"lora_te_\", \"lora_te1_\")):\n te_state_dict[\n diffusers_name.replace(dora_scale_key_to_replace_te, \".lora_magnitude_vector.\")\n ] = state_dict.pop(key.replace(\"lora_down.weight\", \"dora_scale\"))\n elif lora_name.startswith(\"lora_te2_\"):\n te2_state_dict[\n diffusers_name.replace(dora_scale_key_to_replace_te, \".lora_magnitude_vector.\")\n ] = state_dict.pop(key.replace(\"lora_down.weight\", \"dora_scale\"))\n\n # Rename the alphas so that they can be mapped appropriately.\n if lora_name_alpha in state_dict:\n alpha = state_dict.pop(lora_name_alpha).item()\n if lora_name_alpha.startswith(\"lora_unet_\"):\n prefix = \"unet.\"\n elif lora_name_alpha.startswith((\"lora_te_\", \"lora_te1_\")):\n prefix = \"text_encoder.\"\n else:\n prefix = \"text_encoder_2.\"\n new_name = prefix + diffusers_name.split(\".lora.\")[0] + \".alpha\"\n network_alphas.update({new_name: alpha})\n\n if len(state_dict) > 0:\n raise ValueError(f\"The following keys have not been correctly renamed: \\n\\n {', '.join(state_dict.keys())}\")\n\n logger.info(\"Kohya-style checkpoint detected.\")\n unet_state_dict = {f\"{unet_name}.{module_name}\": params for module_name, params in unet_state_dict.items()}\n te_state_dict = {f\"{text_encoder_name}.{module_name}\": params for module_name, params in te_state_dict.items()}\n te2_state_dict = (\n {f\"text_encoder_2.{module_name}\": params for module_name, params in te2_state_dict.items()}\n if len(te2_state_dict) > 0\n else None\n )\n if te2_state_dict is not None:\n te_state_dict.update(te2_state_dict)\n\n new_state_dict = {**unet_state_dict, **te_state_dict}\n return new_state_dict, network_alphas\n" }, { "path": "diffusers/src/diffusers/loaders/peft.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import List, Union\n\nfrom ..utils import MIN_PEFT_VERSION, check_peft_version, is_peft_available\n\n\nclass PeftAdapterMixin:\n \"\"\"\n A class containing all functions for loading and using adapters weights that are supported in PEFT library. For\n more details about adapters and injecting them in a transformer-based model, check out the PEFT\n [documentation](https://huggingface.co/docs/peft/index).\n\n Install the latest version of PEFT, and use this mixin to:\n\n - Attach new adapters in the model.\n - Attach multiple adapters and iteratively activate/deactivate them.\n - Activate/deactivate all adapters from the model.\n - Get a list of the active adapters.\n \"\"\"\n\n _hf_peft_config_loaded = False\n\n def add_adapter(self, adapter_config, adapter_name: str = \"default\") -> None:\n r\"\"\"\n Adds a new adapter to the current model for training. If no adapter name is passed, a default name is assigned\n to the adapter to follow the convention of the PEFT library.\n\n If you are not familiar with adapters and PEFT methods, we invite you to read more about them in the PEFT\n [documentation](https://huggingface.co/docs/peft).\n\n Args:\n adapter_config (`[~peft.PeftConfig]`):\n The configuration of the adapter to add; supported adapters are non-prefix tuning and adaption prompt\n methods.\n adapter_name (`str`, *optional*, defaults to `\"default\"`):\n The name of the adapter to add. If no name is passed, a default name is assigned to the adapter.\n \"\"\"\n check_peft_version(min_version=MIN_PEFT_VERSION)\n\n if not is_peft_available():\n raise ImportError(\"PEFT is not available. Please install PEFT to use this function: `pip install peft`.\")\n\n from peft import PeftConfig, inject_adapter_in_model\n\n if not self._hf_peft_config_loaded:\n self._hf_peft_config_loaded = True\n elif adapter_name in self.peft_config:\n raise ValueError(f\"Adapter with name {adapter_name} already exists. Please use a different name.\")\n\n if not isinstance(adapter_config, PeftConfig):\n raise ValueError(\n f\"adapter_config should be an instance of PeftConfig. Got {type(adapter_config)} instead.\"\n )\n\n # Unlike transformers, here we don't need to retrieve the name_or_path of the unet as the loading logic is\n # handled by the `load_lora_layers` or `LoraLoaderMixin`. Therefore we set it to `None` here.\n adapter_config.base_model_name_or_path = None\n inject_adapter_in_model(adapter_config, self, adapter_name)\n self.set_adapter(adapter_name)\n\n def set_adapter(self, adapter_name: Union[str, List[str]]) -> None:\n \"\"\"\n Sets a specific adapter by forcing the model to only use that adapter and disables the other adapters.\n\n If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT\n [documentation](https://huggingface.co/docs/peft).\n\n Args:\n adapter_name (Union[str, List[str]])):\n The list of adapters to set or the adapter name in the case of a single adapter.\n \"\"\"\n check_peft_version(min_version=MIN_PEFT_VERSION)\n\n if not self._hf_peft_config_loaded:\n raise ValueError(\"No adapter loaded. Please load an adapter first.\")\n\n if isinstance(adapter_name, str):\n adapter_name = [adapter_name]\n\n missing = set(adapter_name) - set(self.peft_config)\n if len(missing) > 0:\n raise ValueError(\n f\"Following adapter(s) could not be found: {', '.join(missing)}. Make sure you are passing the correct adapter name(s).\"\n f\" current loaded adapters are: {list(self.peft_config.keys())}\"\n )\n\n from peft.tuners.tuners_utils import BaseTunerLayer\n\n _adapters_has_been_set = False\n\n for _, module in self.named_modules():\n if isinstance(module, BaseTunerLayer):\n if hasattr(module, \"set_adapter\"):\n module.set_adapter(adapter_name)\n # Previous versions of PEFT does not support multi-adapter inference\n elif not hasattr(module, \"set_adapter\") and len(adapter_name) != 1:\n raise ValueError(\n \"You are trying to set multiple adapters and you have a PEFT version that does not support multi-adapter inference. Please upgrade to the latest version of PEFT.\"\n \" `pip install -U peft` or `pip install -U git+https://github.com/huggingface/peft.git`\"\n )\n else:\n module.active_adapter = adapter_name\n _adapters_has_been_set = True\n\n if not _adapters_has_been_set:\n raise ValueError(\n \"Did not succeeded in setting the adapter. Please make sure you are using a model that supports adapters.\"\n )\n\n def disable_adapters(self) -> None:\n r\"\"\"\n Disable all adapters attached to the model and fallback to inference with the base model only.\n\n If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT\n [documentation](https://huggingface.co/docs/peft).\n \"\"\"\n check_peft_version(min_version=MIN_PEFT_VERSION)\n\n if not self._hf_peft_config_loaded:\n raise ValueError(\"No adapter loaded. Please load an adapter first.\")\n\n from peft.tuners.tuners_utils import BaseTunerLayer\n\n for _, module in self.named_modules():\n if isinstance(module, BaseTunerLayer):\n if hasattr(module, \"enable_adapters\"):\n module.enable_adapters(enabled=False)\n else:\n # support for older PEFT versions\n module.disable_adapters = True\n\n def enable_adapters(self) -> None:\n \"\"\"\n Enable adapters that are attached to the model. The model uses `self.active_adapters()` to retrieve the list of\n adapters to enable.\n\n If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT\n [documentation](https://huggingface.co/docs/peft).\n \"\"\"\n check_peft_version(min_version=MIN_PEFT_VERSION)\n\n if not self._hf_peft_config_loaded:\n raise ValueError(\"No adapter loaded. Please load an adapter first.\")\n\n from peft.tuners.tuners_utils import BaseTunerLayer\n\n for _, module in self.named_modules():\n if isinstance(module, BaseTunerLayer):\n if hasattr(module, \"enable_adapters\"):\n module.enable_adapters(enabled=True)\n else:\n # support for older PEFT versions\n module.disable_adapters = False\n\n def active_adapters(self) -> List[str]:\n \"\"\"\n Gets the current list of active adapters of the model.\n\n If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT\n [documentation](https://huggingface.co/docs/peft).\n \"\"\"\n check_peft_version(min_version=MIN_PEFT_VERSION)\n\n if not is_peft_available():\n raise ImportError(\"PEFT is not available. Please install PEFT to use this function: `pip install peft`.\")\n\n if not self._hf_peft_config_loaded:\n raise ValueError(\"No adapter loaded. Please load an adapter first.\")\n\n from peft.tuners.tuners_utils import BaseTunerLayer\n\n for _, module in self.named_modules():\n if isinstance(module, BaseTunerLayer):\n return module.active_adapter\n" }, { "path": "diffusers/src/diffusers/loaders/single_file.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport importlib\nimport inspect\nimport os\n\nimport torch\nfrom huggingface_hub import snapshot_download\nfrom huggingface_hub.utils import LocalEntryNotFoundError, validate_hf_hub_args\nfrom packaging import version\n\nfrom ..utils import deprecate, is_transformers_available, logging\nfrom .single_file_utils import (\n SingleFileComponentError,\n _is_model_weights_in_cached_folder,\n _legacy_load_clip_tokenizer,\n _legacy_load_safety_checker,\n _legacy_load_scheduler,\n create_diffusers_clip_model_from_ldm,\n create_diffusers_t5_model_from_checkpoint,\n fetch_diffusers_config,\n fetch_original_config,\n is_clip_model_in_single_file,\n is_t5_in_single_file,\n load_single_file_checkpoint,\n)\n\n\nlogger = logging.get_logger(__name__)\n\n# Legacy behaviour. `from_single_file` does not load the safety checker unless explicitly provided\nSINGLE_FILE_OPTIONAL_COMPONENTS = [\"safety_checker\"]\n\n\nif is_transformers_available():\n import transformers\n from transformers import PreTrainedModel, PreTrainedTokenizer\n\n\ndef load_single_file_sub_model(\n library_name,\n class_name,\n name,\n checkpoint,\n pipelines,\n is_pipeline_module,\n cached_model_config_path,\n original_config=None,\n local_files_only=False,\n torch_dtype=None,\n is_legacy_loading=False,\n **kwargs,\n):\n if is_pipeline_module:\n pipeline_module = getattr(pipelines, library_name)\n class_obj = getattr(pipeline_module, class_name)\n else:\n # else we just import it from the library.\n library = importlib.import_module(library_name)\n class_obj = getattr(library, class_name)\n\n if is_transformers_available():\n transformers_version = version.parse(version.parse(transformers.__version__).base_version)\n else:\n transformers_version = \"N/A\"\n\n is_transformers_model = (\n is_transformers_available()\n and issubclass(class_obj, PreTrainedModel)\n and transformers_version >= version.parse(\"4.20.0\")\n )\n is_tokenizer = (\n is_transformers_available()\n and issubclass(class_obj, PreTrainedTokenizer)\n and transformers_version >= version.parse(\"4.20.0\")\n )\n\n diffusers_module = importlib.import_module(__name__.split(\".\")[0])\n is_diffusers_single_file_model = issubclass(class_obj, diffusers_module.FromOriginalModelMixin)\n is_diffusers_model = issubclass(class_obj, diffusers_module.ModelMixin)\n is_diffusers_scheduler = issubclass(class_obj, diffusers_module.SchedulerMixin)\n\n if is_diffusers_single_file_model:\n load_method = getattr(class_obj, \"from_single_file\")\n\n # We cannot provide two different config options to the `from_single_file` method\n # Here we have to ignore loading the config from `cached_model_config_path` if `original_config` is provided\n if original_config:\n cached_model_config_path = None\n\n loaded_sub_model = load_method(\n pretrained_model_link_or_path_or_dict=checkpoint,\n original_config=original_config,\n config=cached_model_config_path,\n subfolder=name,\n torch_dtype=torch_dtype,\n local_files_only=local_files_only,\n **kwargs,\n )\n\n elif is_transformers_model and is_clip_model_in_single_file(class_obj, checkpoint):\n loaded_sub_model = create_diffusers_clip_model_from_ldm(\n class_obj,\n checkpoint=checkpoint,\n config=cached_model_config_path,\n subfolder=name,\n torch_dtype=torch_dtype,\n local_files_only=local_files_only,\n is_legacy_loading=is_legacy_loading,\n )\n\n elif is_transformers_model and is_t5_in_single_file(checkpoint):\n loaded_sub_model = create_diffusers_t5_model_from_checkpoint(\n class_obj,\n checkpoint=checkpoint,\n config=cached_model_config_path,\n subfolder=name,\n torch_dtype=torch_dtype,\n local_files_only=local_files_only,\n )\n\n elif is_tokenizer and is_legacy_loading:\n loaded_sub_model = _legacy_load_clip_tokenizer(\n class_obj, checkpoint=checkpoint, config=cached_model_config_path, local_files_only=local_files_only\n )\n\n elif is_diffusers_scheduler and is_legacy_loading:\n loaded_sub_model = _legacy_load_scheduler(\n class_obj, checkpoint=checkpoint, component_name=name, original_config=original_config, **kwargs\n )\n\n else:\n if not hasattr(class_obj, \"from_pretrained\"):\n raise ValueError(\n (\n f\"The component {class_obj.__name__} cannot be loaded as it does not seem to have\"\n \" a supported loading method.\"\n )\n )\n\n loading_kwargs = {}\n loading_kwargs.update(\n {\n \"pretrained_model_name_or_path\": cached_model_config_path,\n \"subfolder\": name,\n \"local_files_only\": local_files_only,\n }\n )\n\n # Schedulers and Tokenizers don't make use of torch_dtype\n # Skip passing it to those objects\n if issubclass(class_obj, torch.nn.Module):\n loading_kwargs.update({\"torch_dtype\": torch_dtype})\n\n if is_diffusers_model or is_transformers_model:\n if not _is_model_weights_in_cached_folder(cached_model_config_path, name):\n raise SingleFileComponentError(\n f\"Failed to load {class_name}. Weights for this component appear to be missing in the checkpoint.\"\n )\n\n load_method = getattr(class_obj, \"from_pretrained\")\n loaded_sub_model = load_method(**loading_kwargs)\n\n return loaded_sub_model\n\n\ndef _map_component_types_to_config_dict(component_types):\n diffusers_module = importlib.import_module(__name__.split(\".\")[0])\n config_dict = {}\n component_types.pop(\"self\", None)\n\n if is_transformers_available():\n transformers_version = version.parse(version.parse(transformers.__version__).base_version)\n else:\n transformers_version = \"N/A\"\n\n for component_name, component_value in component_types.items():\n is_diffusers_model = issubclass(component_value[0], diffusers_module.ModelMixin)\n is_scheduler_enum = component_value[0].__name__ == \"KarrasDiffusionSchedulers\"\n is_scheduler = issubclass(component_value[0], diffusers_module.SchedulerMixin)\n\n is_transformers_model = (\n is_transformers_available()\n and issubclass(component_value[0], PreTrainedModel)\n and transformers_version >= version.parse(\"4.20.0\")\n )\n is_transformers_tokenizer = (\n is_transformers_available()\n and issubclass(component_value[0], PreTrainedTokenizer)\n and transformers_version >= version.parse(\"4.20.0\")\n )\n\n if is_diffusers_model and component_name not in SINGLE_FILE_OPTIONAL_COMPONENTS:\n config_dict[component_name] = [\"diffusers\", component_value[0].__name__]\n\n elif is_scheduler_enum or is_scheduler:\n if is_scheduler_enum:\n # Since we cannot fetch a scheduler config from the hub, we default to DDIMScheduler\n # if the type hint is a KarrassDiffusionSchedulers enum\n config_dict[component_name] = [\"diffusers\", \"DDIMScheduler\"]\n\n elif is_scheduler:\n config_dict[component_name] = [\"diffusers\", component_value[0].__name__]\n\n elif (\n is_transformers_model or is_transformers_tokenizer\n ) and component_name not in SINGLE_FILE_OPTIONAL_COMPONENTS:\n config_dict[component_name] = [\"transformers\", component_value[0].__name__]\n\n else:\n config_dict[component_name] = [None, None]\n\n return config_dict\n\n\ndef _infer_pipeline_config_dict(pipeline_class):\n parameters = inspect.signature(pipeline_class.__init__).parameters\n required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty}\n component_types = pipeline_class._get_signature_types()\n\n # Ignore parameters that are not required for the pipeline\n component_types = {k: v for k, v in component_types.items() if k in required_parameters}\n config_dict = _map_component_types_to_config_dict(component_types)\n\n return config_dict\n\n\ndef _download_diffusers_model_config_from_hub(\n pretrained_model_name_or_path,\n cache_dir,\n revision,\n proxies,\n force_download=None,\n resume_download=None,\n local_files_only=None,\n token=None,\n):\n allow_patterns = [\"**/*.json\", \"*.json\", \"*.txt\", \"**/*.txt\", \"**/*.model\"]\n cached_model_path = snapshot_download(\n pretrained_model_name_or_path,\n cache_dir=cache_dir,\n revision=revision,\n proxies=proxies,\n force_download=force_download,\n resume_download=resume_download,\n local_files_only=local_files_only,\n token=token,\n allow_patterns=allow_patterns,\n )\n\n return cached_model_path\n\n\nclass FromSingleFileMixin:\n \"\"\"\n Load model weights saved in the `.ckpt` format into a [`DiffusionPipeline`].\n \"\"\"\n\n @classmethod\n @validate_hf_hub_args\n def from_single_file(cls, pretrained_model_link_or_path, **kwargs):\n r\"\"\"\n Instantiate a [`DiffusionPipeline`] from pretrained pipeline weights saved in the `.ckpt` or `.safetensors`\n format. The pipeline is set in evaluation mode (`model.eval()`) by default.\n\n Parameters:\n pretrained_model_link_or_path (`str` or `os.PathLike`, *optional*):\n Can be either:\n - A link to the `.ckpt` file (for example\n `\"https://huggingface.co//blob/main/.ckpt\"`) on the Hub.\n - A path to a *file* containing all pipeline weights.\n torch_dtype (`str` or `torch.dtype`, *optional*):\n Override the default `torch.dtype` and load the model with another dtype.\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\n cached versions if they exist.\n cache_dir (`Union[str, os.PathLike]`, *optional*):\n Path to a directory where a downloaded pretrained model configuration is cached if the standard cache\n is not used.\n resume_download:\n Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1\n of Diffusers.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\n local_files_only (`bool`, *optional*, defaults to `False`):\n Whether to only load local model weights and configuration files or not. If set to `True`, the model\n won't be downloaded from the Hub.\n token (`str` or *bool*, *optional*):\n The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from\n `diffusers-cli login` (stored in `~/.huggingface`) is used.\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier\n allowed by Git.\n original_config_file (`str`, *optional*):\n The path to the original config file that was used to train the model. If not provided, the config file\n will be inferred from the checkpoint file.\n config (`str`, *optional*):\n Can be either:\n - A string, the *repo id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline\n hosted on the Hub.\n - A path to a *directory* (for example `./my_pipeline_directory/`) containing the pipeline\n component configs in Diffusers format.\n kwargs (remaining dictionary of keyword arguments, *optional*):\n Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline\n class). The overwritten components are passed directly to the pipelines `__init__` method. See example\n below for more information.\n\n Examples:\n\n ```py\n >>> from diffusers import StableDiffusionPipeline\n\n >>> # Download pipeline from huggingface.co and cache.\n >>> pipeline = StableDiffusionPipeline.from_single_file(\n ... \"https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix/AbyssOrangeMix.safetensors\"\n ... )\n\n >>> # Download pipeline from local file\n >>> # file is downloaded under ./v1-5-pruned-emaonly.ckpt\n >>> pipeline = StableDiffusionPipeline.from_single_file(\"./v1-5-pruned-emaonly.ckpt\")\n\n >>> # Enable float16 and move to GPU\n >>> pipeline = StableDiffusionPipeline.from_single_file(\n ... \"https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt\",\n ... torch_dtype=torch.float16,\n ... )\n >>> pipeline.to(\"cuda\")\n ```\n\n \"\"\"\n original_config_file = kwargs.pop(\"original_config_file\", None)\n config = kwargs.pop(\"config\", None)\n original_config = kwargs.pop(\"original_config\", None)\n\n if original_config_file is not None:\n deprecation_message = (\n \"`original_config_file` argument is deprecated and will be removed in future versions.\"\n \"please use the `original_config` argument instead.\"\n )\n deprecate(\"original_config_file\", \"1.0.0\", deprecation_message)\n original_config = original_config_file\n\n resume_download = kwargs.pop(\"resume_download\", None)\n force_download = kwargs.pop(\"force_download\", False)\n proxies = kwargs.pop(\"proxies\", None)\n token = kwargs.pop(\"token\", None)\n cache_dir = kwargs.pop(\"cache_dir\", None)\n local_files_only = kwargs.pop(\"local_files_only\", False)\n revision = kwargs.pop(\"revision\", None)\n torch_dtype = kwargs.pop(\"torch_dtype\", None)\n\n is_legacy_loading = False\n\n # We shouldn't allow configuring individual models components through a Pipeline creation method\n # These model kwargs should be deprecated\n scaling_factor = kwargs.get(\"scaling_factor\", None)\n if scaling_factor is not None:\n deprecation_message = (\n \"Passing the `scaling_factor` argument to `from_single_file is deprecated \"\n \"and will be ignored in future versions.\"\n )\n deprecate(\"scaling_factor\", \"1.0.0\", deprecation_message)\n\n if original_config is not None:\n original_config = fetch_original_config(original_config, local_files_only=local_files_only)\n\n from ..pipelines.pipeline_utils import _get_pipeline_class\n\n pipeline_class = _get_pipeline_class(cls, config=None)\n\n checkpoint = load_single_file_checkpoint(\n pretrained_model_link_or_path,\n resume_download=resume_download,\n force_download=force_download,\n proxies=proxies,\n token=token,\n cache_dir=cache_dir,\n local_files_only=local_files_only,\n revision=revision,\n )\n\n if config is None:\n config = fetch_diffusers_config(checkpoint)\n default_pretrained_model_config_name = config[\"pretrained_model_name_or_path\"]\n else:\n default_pretrained_model_config_name = config\n\n if not os.path.isdir(default_pretrained_model_config_name):\n # Provided config is a repo_id\n if default_pretrained_model_config_name.count(\"/\") > 1:\n raise ValueError(\n f'The provided config \"{config}\"'\n \" is neither a valid local path nor a valid repo id. Please check the parameter.\"\n )\n try:\n # Attempt to download the config files for the pipeline\n cached_model_config_path = _download_diffusers_model_config_from_hub(\n default_pretrained_model_config_name,\n cache_dir=cache_dir,\n revision=revision,\n proxies=proxies,\n force_download=force_download,\n resume_download=resume_download,\n local_files_only=local_files_only,\n token=token,\n )\n config_dict = pipeline_class.load_config(cached_model_config_path)\n\n except LocalEntryNotFoundError:\n # `local_files_only=True` but a local diffusers format model config is not available in the cache\n # If `original_config` is not provided, we need override `local_files_only` to False\n # to fetch the config files from the hub so that we have a way\n # to configure the pipeline components.\n\n if original_config is None:\n logger.warning(\n \"`local_files_only` is True but no local configs were found for this checkpoint.\\n\"\n \"Attempting to download the necessary config files for this pipeline.\\n\"\n )\n cached_model_config_path = _download_diffusers_model_config_from_hub(\n default_pretrained_model_config_name,\n cache_dir=cache_dir,\n revision=revision,\n proxies=proxies,\n force_download=force_download,\n resume_download=resume_download,\n local_files_only=False,\n token=token,\n )\n config_dict = pipeline_class.load_config(cached_model_config_path)\n\n else:\n # For backwards compatibility\n # If `original_config` is provided, then we need to assume we are using legacy loading for pipeline components\n logger.warning(\n \"Detected legacy `from_single_file` loading behavior. Attempting to create the pipeline based on inferred components.\\n\"\n \"This may lead to errors if the model components are not correctly inferred. \\n\"\n \"To avoid this warning, please explicity pass the `config` argument to `from_single_file` with a path to a local diffusers model repo \\n\"\n \"e.g. `from_single_file(, config=) \\n\"\n \"or run `from_single_file` with `local_files_only=False` first to update the local cache directory with \"\n \"the necessary config files.\\n\"\n )\n is_legacy_loading = True\n cached_model_config_path = None\n\n config_dict = _infer_pipeline_config_dict(pipeline_class)\n config_dict[\"_class_name\"] = pipeline_class.__name__\n\n else:\n # Provided config is a path to a local directory attempt to load directly.\n cached_model_config_path = default_pretrained_model_config_name\n config_dict = pipeline_class.load_config(cached_model_config_path)\n\n # pop out \"_ignore_files\" as it is only needed for download\n config_dict.pop(\"_ignore_files\", None)\n\n expected_modules, optional_kwargs = pipeline_class._get_signature_keys(cls)\n passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}\n passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}\n\n init_dict, unused_kwargs, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)\n init_kwargs = {k: init_dict.pop(k) for k in optional_kwargs if k in init_dict}\n init_kwargs = {**init_kwargs, **passed_pipe_kwargs}\n\n from diffusers import pipelines\n\n # remove `null` components\n def load_module(name, value):\n if value[0] is None:\n return False\n if name in passed_class_obj and passed_class_obj[name] is None:\n return False\n if name in SINGLE_FILE_OPTIONAL_COMPONENTS:\n return False\n\n return True\n\n init_dict = {k: v for k, v in init_dict.items() if load_module(k, v)}\n\n for name, (library_name, class_name) in logging.tqdm(\n sorted(init_dict.items()), desc=\"Loading pipeline components...\"\n ):\n loaded_sub_model = None\n is_pipeline_module = hasattr(pipelines, library_name)\n\n if name in passed_class_obj:\n loaded_sub_model = passed_class_obj[name]\n\n else:\n try:\n loaded_sub_model = load_single_file_sub_model(\n library_name=library_name,\n class_name=class_name,\n name=name,\n checkpoint=checkpoint,\n is_pipeline_module=is_pipeline_module,\n cached_model_config_path=cached_model_config_path,\n pipelines=pipelines,\n torch_dtype=torch_dtype,\n original_config=original_config,\n local_files_only=local_files_only,\n is_legacy_loading=is_legacy_loading,\n **kwargs,\n )\n except SingleFileComponentError as e:\n raise SingleFileComponentError(\n (\n f\"{e.message}\\n\"\n f\"Please load the component before passing it in as an argument to `from_single_file`.\\n\"\n f\"\\n\"\n f\"{name} = {class_name}.from_pretrained('...')\\n\"\n f\"pipe = {pipeline_class.__name__}.from_single_file(, {name}={name})\\n\"\n f\"\\n\"\n )\n )\n\n init_kwargs[name] = loaded_sub_model\n\n missing_modules = set(expected_modules) - set(init_kwargs.keys())\n passed_modules = list(passed_class_obj.keys())\n optional_modules = pipeline_class._optional_components\n\n if len(missing_modules) > 0 and missing_modules <= set(passed_modules + optional_modules):\n for module in missing_modules:\n init_kwargs[module] = passed_class_obj.get(module, None)\n elif len(missing_modules) > 0:\n passed_modules = set(list(init_kwargs.keys()) + list(passed_class_obj.keys())) - optional_kwargs\n raise ValueError(\n f\"Pipeline {pipeline_class} expected {expected_modules}, but only {passed_modules} were passed.\"\n )\n\n # deprecated kwargs\n load_safety_checker = kwargs.pop(\"load_safety_checker\", None)\n if load_safety_checker is not None:\n deprecation_message = (\n \"Please pass instances of `StableDiffusionSafetyChecker` and `AutoImageProcessor`\"\n \"using the `safety_checker` and `feature_extractor` arguments in `from_single_file`\"\n )\n deprecate(\"load_safety_checker\", \"1.0.0\", deprecation_message)\n\n safety_checker_components = _legacy_load_safety_checker(local_files_only, torch_dtype)\n init_kwargs.update(safety_checker_components)\n\n pipe = pipeline_class(**init_kwargs)\n\n if torch_dtype is not None:\n pipe.to(dtype=torch_dtype)\n\n return pipe\n" }, { "path": "diffusers/src/diffusers/loaders/single_file_model.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport inspect\nimport re\nfrom contextlib import nullcontext\nfrom typing import Optional\n\nfrom huggingface_hub.utils import validate_hf_hub_args\n\nfrom ..utils import deprecate, is_accelerate_available, logging\nfrom .single_file_utils import (\n SingleFileComponentError,\n convert_controlnet_checkpoint,\n convert_ldm_unet_checkpoint,\n convert_ldm_vae_checkpoint,\n convert_sd3_transformer_checkpoint_to_diffusers,\n convert_stable_cascade_unet_single_file_to_diffusers,\n create_controlnet_diffusers_config_from_ldm,\n create_unet_diffusers_config_from_ldm,\n create_vae_diffusers_config_from_ldm,\n fetch_diffusers_config,\n fetch_original_config,\n load_single_file_checkpoint,\n)\n\n\nlogger = logging.get_logger(__name__)\n\n\nif is_accelerate_available():\n from accelerate import init_empty_weights\n\n from ..models.modeling_utils import load_model_dict_into_meta\n\n\nSINGLE_FILE_LOADABLE_CLASSES = {\n \"StableCascadeUNet\": {\n \"checkpoint_mapping_fn\": convert_stable_cascade_unet_single_file_to_diffusers,\n },\n \"UNet2DConditionModel\": {\n \"checkpoint_mapping_fn\": convert_ldm_unet_checkpoint,\n \"config_mapping_fn\": create_unet_diffusers_config_from_ldm,\n \"default_subfolder\": \"unet\",\n \"legacy_kwargs\": {\n \"num_in_channels\": \"in_channels\", # Legacy kwargs supported by `from_single_file` mapped to new args\n },\n },\n \"AutoencoderKL\": {\n \"checkpoint_mapping_fn\": convert_ldm_vae_checkpoint,\n \"config_mapping_fn\": create_vae_diffusers_config_from_ldm,\n \"default_subfolder\": \"vae\",\n },\n \"ControlNetModel\": {\n \"checkpoint_mapping_fn\": convert_controlnet_checkpoint,\n \"config_mapping_fn\": create_controlnet_diffusers_config_from_ldm,\n },\n \"SD3Transformer2DModel\": {\n \"checkpoint_mapping_fn\": convert_sd3_transformer_checkpoint_to_diffusers,\n \"default_subfolder\": \"transformer\",\n },\n}\n\n\ndef _get_mapping_function_kwargs(mapping_fn, **kwargs):\n parameters = inspect.signature(mapping_fn).parameters\n\n mapping_kwargs = {}\n for parameter in parameters:\n if parameter in kwargs:\n mapping_kwargs[parameter] = kwargs[parameter]\n\n return mapping_kwargs\n\n\nclass FromOriginalModelMixin:\n \"\"\"\n Load pretrained weights saved in the `.ckpt` or `.safetensors` format into a model.\n \"\"\"\n\n @classmethod\n @validate_hf_hub_args\n def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] = None, **kwargs):\n r\"\"\"\n Instantiate a model from pretrained weights saved in the original `.ckpt` or `.safetensors` format. The model\n is set in evaluation mode (`model.eval()`) by default.\n\n Parameters:\n pretrained_model_link_or_path_or_dict (`str`, *optional*):\n Can be either:\n - A link to the `.safetensors` or `.ckpt` file (for example\n `\"https://huggingface.co//blob/main/.safetensors\"`) on the Hub.\n - A path to a local *file* containing the weights of the component model.\n - A state dict containing the component model weights.\n config (`str`, *optional*):\n - A string, the *repo id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline hosted\n on the Hub.\n - A path to a *directory* (for example `./my_pipeline_directory/`) containing the pipeline component\n configs in Diffusers format.\n subfolder (`str`, *optional*, defaults to `\"\"`):\n The subfolder location of a model file within a larger model repository on the Hub or locally.\n original_config (`str`, *optional*):\n Dict or path to a yaml file containing the configuration for the model in its original format.\n If a dict is provided, it will be used to initialize the model configuration.\n torch_dtype (`str` or `torch.dtype`, *optional*):\n Override the default `torch.dtype` and load the model with another dtype. If `\"auto\"` is passed, the\n dtype is automatically derived from the model's weights.\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\n cached versions if they exist.\n cache_dir (`Union[str, os.PathLike]`, *optional*):\n Path to a directory where a downloaded pretrained model configuration is cached if the standard cache\n is not used.\n resume_download (`bool`, *optional*, defaults to `False`):\n Whether or not to resume downloading the model weights and configuration files. If set to `False`, any\n incompletely downloaded files are deleted.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\n local_files_only (`bool`, *optional*, defaults to `False`):\n Whether to only load local model weights and configuration files or not. If set to True, the model\n won't be downloaded from the Hub.\n token (`str` or *bool*, *optional*):\n The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from\n `diffusers-cli login` (stored in `~/.huggingface`) is used.\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier\n allowed by Git.\n kwargs (remaining dictionary of keyword arguments, *optional*):\n Can be used to overwrite load and saveable variables (for example the pipeline components of the\n specific pipeline class). The overwritten components are directly passed to the pipelines `__init__`\n method. See example below for more information.\n\n ```py\n >>> from diffusers import StableCascadeUNet\n\n >>> ckpt_path = \"https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b_lite.safetensors\"\n >>> model = StableCascadeUNet.from_single_file(ckpt_path)\n ```\n \"\"\"\n\n class_name = cls.__name__\n if class_name not in SINGLE_FILE_LOADABLE_CLASSES:\n raise ValueError(\n f\"FromOriginalModelMixin is currently only compatible with {', '.join(SINGLE_FILE_LOADABLE_CLASSES.keys())}\"\n )\n\n pretrained_model_link_or_path = kwargs.get(\"pretrained_model_link_or_path\", None)\n if pretrained_model_link_or_path is not None:\n deprecation_message = (\n \"Please use `pretrained_model_link_or_path_or_dict` argument instead for model classes\"\n )\n deprecate(\"pretrained_model_link_or_path\", \"1.0.0\", deprecation_message)\n pretrained_model_link_or_path_or_dict = pretrained_model_link_or_path\n\n config = kwargs.pop(\"config\", None)\n original_config = kwargs.pop(\"original_config\", None)\n\n if config is not None and original_config is not None:\n raise ValueError(\n \"`from_single_file` cannot accept both `config` and `original_config` arguments. Please provide only one of these arguments\"\n )\n\n resume_download = kwargs.pop(\"resume_download\", None)\n force_download = kwargs.pop(\"force_download\", False)\n proxies = kwargs.pop(\"proxies\", None)\n token = kwargs.pop(\"token\", None)\n cache_dir = kwargs.pop(\"cache_dir\", None)\n local_files_only = kwargs.pop(\"local_files_only\", None)\n subfolder = kwargs.pop(\"subfolder\", None)\n revision = kwargs.pop(\"revision\", None)\n torch_dtype = kwargs.pop(\"torch_dtype\", None)\n\n if isinstance(pretrained_model_link_or_path_or_dict, dict):\n checkpoint = pretrained_model_link_or_path_or_dict\n else:\n checkpoint = load_single_file_checkpoint(\n pretrained_model_link_or_path_or_dict,\n resume_download=resume_download,\n force_download=force_download,\n proxies=proxies,\n token=token,\n cache_dir=cache_dir,\n local_files_only=local_files_only,\n revision=revision,\n )\n\n mapping_functions = SINGLE_FILE_LOADABLE_CLASSES[class_name]\n\n checkpoint_mapping_fn = mapping_functions[\"checkpoint_mapping_fn\"]\n if original_config:\n if \"config_mapping_fn\" in mapping_functions:\n config_mapping_fn = mapping_functions[\"config_mapping_fn\"]\n else:\n config_mapping_fn = None\n\n if config_mapping_fn is None:\n raise ValueError(\n (\n f\"`original_config` has been provided for {class_name} but no mapping function\"\n \"was found to convert the original config to a Diffusers config in\"\n \"`diffusers.loaders.single_file_utils`\"\n )\n )\n\n if isinstance(original_config, str):\n # If original_config is a URL or filepath fetch the original_config dict\n original_config = fetch_original_config(original_config, local_files_only=local_files_only)\n\n config_mapping_kwargs = _get_mapping_function_kwargs(config_mapping_fn, **kwargs)\n diffusers_model_config = config_mapping_fn(\n original_config=original_config, checkpoint=checkpoint, **config_mapping_kwargs\n )\n else:\n if config:\n if isinstance(config, str):\n default_pretrained_model_config_name = config\n else:\n raise ValueError(\n (\n \"Invalid `config` argument. Please provide a string representing a repo id\"\n \"or path to a local Diffusers model repo.\"\n )\n )\n\n else:\n config = fetch_diffusers_config(checkpoint)\n default_pretrained_model_config_name = config[\"pretrained_model_name_or_path\"]\n\n if \"default_subfolder\" in mapping_functions:\n subfolder = mapping_functions[\"default_subfolder\"]\n\n subfolder = subfolder or config.pop(\n \"subfolder\", None\n ) # some configs contain a subfolder key, e.g. StableCascadeUNet\n\n diffusers_model_config = cls.load_config(\n pretrained_model_name_or_path=default_pretrained_model_config_name,\n subfolder=subfolder,\n local_files_only=local_files_only,\n )\n expected_kwargs, optional_kwargs = cls._get_signature_keys(cls)\n\n # Map legacy kwargs to new kwargs\n if \"legacy_kwargs\" in mapping_functions:\n legacy_kwargs = mapping_functions[\"legacy_kwargs\"]\n for legacy_key, new_key in legacy_kwargs.items():\n if legacy_key in kwargs:\n kwargs[new_key] = kwargs.pop(legacy_key)\n\n model_kwargs = {k: kwargs.get(k) for k in kwargs if k in expected_kwargs or k in optional_kwargs}\n diffusers_model_config.update(model_kwargs)\n\n checkpoint_mapping_kwargs = _get_mapping_function_kwargs(checkpoint_mapping_fn, **kwargs)\n diffusers_format_checkpoint = checkpoint_mapping_fn(\n config=diffusers_model_config, checkpoint=checkpoint, **checkpoint_mapping_kwargs\n )\n if not diffusers_format_checkpoint:\n raise SingleFileComponentError(\n f\"Failed to load {class_name}. Weights for this component appear to be missing in the checkpoint.\"\n )\n\n ctx = init_empty_weights if is_accelerate_available() else nullcontext\n with ctx():\n model = cls.from_config(diffusers_model_config)\n\n if is_accelerate_available():\n unexpected_keys = load_model_dict_into_meta(model, diffusers_format_checkpoint, dtype=torch_dtype)\n if model._keys_to_ignore_on_load_unexpected is not None:\n for pat in model._keys_to_ignore_on_load_unexpected:\n unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]\n\n if len(unexpected_keys) > 0:\n logger.warning(\n f\"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \\n {[', '.join(unexpected_keys)]}\"\n )\n else:\n model.load_state_dict(diffusers_format_checkpoint)\n\n if torch_dtype is not None:\n model.to(torch_dtype)\n\n model.eval()\n\n return model\n" }, { "path": "diffusers/src/diffusers/loaders/single_file_utils.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Conversion script for the Stable Diffusion checkpoints.\"\"\"\n\nimport os\nimport re\nfrom contextlib import nullcontext\nfrom io import BytesIO\nfrom urllib.parse import urlparse\n\nimport requests\nimport torch\nimport yaml\n\nfrom ..models.modeling_utils import load_state_dict\nfrom ..schedulers import (\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EDMDPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n HeunDiscreteScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n)\nfrom ..utils import (\n SAFETENSORS_WEIGHTS_NAME,\n WEIGHTS_NAME,\n deprecate,\n is_accelerate_available,\n is_transformers_available,\n logging,\n)\nfrom ..utils.hub_utils import _get_model_file\n\n\nif is_transformers_available():\n from transformers import AutoImageProcessor\n\nif is_accelerate_available():\n from accelerate import init_empty_weights\n\n from ..models.modeling_utils import load_model_dict_into_meta\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nCHECKPOINT_KEY_NAMES = {\n \"v2\": \"model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight\",\n \"xl_base\": \"conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.bias\",\n \"xl_refiner\": \"conditioner.embedders.0.model.transformer.resblocks.9.mlp.c_proj.bias\",\n \"upscale\": \"model.diffusion_model.input_blocks.10.0.skip_connection.bias\",\n \"controlnet\": \"control_model.time_embed.0.weight\",\n \"playground-v2-5\": \"edm_mean\",\n \"inpainting\": \"model.diffusion_model.input_blocks.0.0.weight\",\n \"clip\": \"cond_stage_model.transformer.text_model.embeddings.position_embedding.weight\",\n \"clip_sdxl\": \"conditioner.embedders.0.transformer.text_model.embeddings.position_embedding.weight\",\n \"clip_sd3\": \"text_encoders.clip_l.transformer.text_model.embeddings.position_embedding.weight\",\n \"open_clip\": \"cond_stage_model.model.token_embedding.weight\",\n \"open_clip_sdxl\": \"conditioner.embedders.1.model.positional_embedding\",\n \"open_clip_sdxl_refiner\": \"conditioner.embedders.0.model.text_projection\",\n \"open_clip_sd3\": \"text_encoders.clip_g.transformer.text_model.embeddings.position_embedding.weight\",\n \"stable_cascade_stage_b\": \"down_blocks.1.0.channelwise.0.weight\",\n \"stable_cascade_stage_c\": \"clip_txt_mapper.weight\",\n \"sd3\": \"model.diffusion_model.joint_blocks.0.context_block.adaLN_modulation.1.bias\",\n}\n\nDIFFUSERS_DEFAULT_PIPELINE_PATHS = {\n \"xl_base\": {\"pretrained_model_name_or_path\": \"stabilityai/stable-diffusion-xl-base-1.0\"},\n \"xl_refiner\": {\"pretrained_model_name_or_path\": \"stabilityai/stable-diffusion-xl-refiner-1.0\"},\n \"xl_inpaint\": {\"pretrained_model_name_or_path\": \"diffusers/stable-diffusion-xl-1.0-inpainting-0.1\"},\n \"playground-v2-5\": {\"pretrained_model_name_or_path\": \"playgroundai/playground-v2.5-1024px-aesthetic\"},\n \"upscale\": {\"pretrained_model_name_or_path\": \"stabilityai/stable-diffusion-x4-upscaler\"},\n \"inpainting\": {\"pretrained_model_name_or_path\": \"runwayml/stable-diffusion-inpainting\"},\n \"inpainting_v2\": {\"pretrained_model_name_or_path\": \"stabilityai/stable-diffusion-2-inpainting\"},\n \"controlnet\": {\"pretrained_model_name_or_path\": \"lllyasviel/control_v11p_sd15_canny\"},\n \"v2\": {\"pretrained_model_name_or_path\": \"stabilityai/stable-diffusion-2-1\"},\n \"v1\": {\"pretrained_model_name_or_path\": \"runwayml/stable-diffusion-v1-5\"},\n \"stable_cascade_stage_b\": {\"pretrained_model_name_or_path\": \"stabilityai/stable-cascade\", \"subfolder\": \"decoder\"},\n \"stable_cascade_stage_b_lite\": {\n \"pretrained_model_name_or_path\": \"stabilityai/stable-cascade\",\n \"subfolder\": \"decoder_lite\",\n },\n \"stable_cascade_stage_c\": {\n \"pretrained_model_name_or_path\": \"stabilityai/stable-cascade-prior\",\n \"subfolder\": \"prior\",\n },\n \"stable_cascade_stage_c_lite\": {\n \"pretrained_model_name_or_path\": \"stabilityai/stable-cascade-prior\",\n \"subfolder\": \"prior_lite\",\n },\n \"sd3\": {\n \"pretrained_model_name_or_path\": \"stabilityai/stable-diffusion-3-medium-diffusers\",\n },\n}\n\n# Use to configure model sample size when original config is provided\nDIFFUSERS_TO_LDM_DEFAULT_IMAGE_SIZE_MAP = {\n \"xl_base\": 1024,\n \"xl_refiner\": 1024,\n \"xl_inpaint\": 1024,\n \"playground-v2-5\": 1024,\n \"upscale\": 512,\n \"inpainting\": 512,\n \"inpainting_v2\": 512,\n \"controlnet\": 512,\n \"v2\": 768,\n \"v1\": 512,\n}\n\n\nDIFFUSERS_TO_LDM_MAPPING = {\n \"unet\": {\n \"layers\": {\n \"time_embedding.linear_1.weight\": \"time_embed.0.weight\",\n \"time_embedding.linear_1.bias\": \"time_embed.0.bias\",\n \"time_embedding.linear_2.weight\": \"time_embed.2.weight\",\n \"time_embedding.linear_2.bias\": \"time_embed.2.bias\",\n \"conv_in.weight\": \"input_blocks.0.0.weight\",\n \"conv_in.bias\": \"input_blocks.0.0.bias\",\n \"conv_norm_out.weight\": \"out.0.weight\",\n \"conv_norm_out.bias\": \"out.0.bias\",\n \"conv_out.weight\": \"out.2.weight\",\n \"conv_out.bias\": \"out.2.bias\",\n },\n \"class_embed_type\": {\n \"class_embedding.linear_1.weight\": \"label_emb.0.0.weight\",\n \"class_embedding.linear_1.bias\": \"label_emb.0.0.bias\",\n \"class_embedding.linear_2.weight\": \"label_emb.0.2.weight\",\n \"class_embedding.linear_2.bias\": \"label_emb.0.2.bias\",\n },\n \"addition_embed_type\": {\n \"add_embedding.linear_1.weight\": \"label_emb.0.0.weight\",\n \"add_embedding.linear_1.bias\": \"label_emb.0.0.bias\",\n \"add_embedding.linear_2.weight\": \"label_emb.0.2.weight\",\n \"add_embedding.linear_2.bias\": \"label_emb.0.2.bias\",\n },\n },\n \"controlnet\": {\n \"layers\": {\n \"time_embedding.linear_1.weight\": \"time_embed.0.weight\",\n \"time_embedding.linear_1.bias\": \"time_embed.0.bias\",\n \"time_embedding.linear_2.weight\": \"time_embed.2.weight\",\n \"time_embedding.linear_2.bias\": \"time_embed.2.bias\",\n \"conv_in.weight\": \"input_blocks.0.0.weight\",\n \"conv_in.bias\": \"input_blocks.0.0.bias\",\n \"controlnet_cond_embedding.conv_in.weight\": \"input_hint_block.0.weight\",\n \"controlnet_cond_embedding.conv_in.bias\": \"input_hint_block.0.bias\",\n \"controlnet_cond_embedding.conv_out.weight\": \"input_hint_block.14.weight\",\n \"controlnet_cond_embedding.conv_out.bias\": \"input_hint_block.14.bias\",\n },\n \"class_embed_type\": {\n \"class_embedding.linear_1.weight\": \"label_emb.0.0.weight\",\n \"class_embedding.linear_1.bias\": \"label_emb.0.0.bias\",\n \"class_embedding.linear_2.weight\": \"label_emb.0.2.weight\",\n \"class_embedding.linear_2.bias\": \"label_emb.0.2.bias\",\n },\n \"addition_embed_type\": {\n \"add_embedding.linear_1.weight\": \"label_emb.0.0.weight\",\n \"add_embedding.linear_1.bias\": \"label_emb.0.0.bias\",\n \"add_embedding.linear_2.weight\": \"label_emb.0.2.weight\",\n \"add_embedding.linear_2.bias\": \"label_emb.0.2.bias\",\n },\n },\n \"vae\": {\n \"encoder.conv_in.weight\": \"encoder.conv_in.weight\",\n \"encoder.conv_in.bias\": \"encoder.conv_in.bias\",\n \"encoder.conv_out.weight\": \"encoder.conv_out.weight\",\n \"encoder.conv_out.bias\": \"encoder.conv_out.bias\",\n \"encoder.conv_norm_out.weight\": \"encoder.norm_out.weight\",\n \"encoder.conv_norm_out.bias\": \"encoder.norm_out.bias\",\n \"decoder.conv_in.weight\": \"decoder.conv_in.weight\",\n \"decoder.conv_in.bias\": \"decoder.conv_in.bias\",\n \"decoder.conv_out.weight\": \"decoder.conv_out.weight\",\n \"decoder.conv_out.bias\": \"decoder.conv_out.bias\",\n \"decoder.conv_norm_out.weight\": \"decoder.norm_out.weight\",\n \"decoder.conv_norm_out.bias\": \"decoder.norm_out.bias\",\n \"quant_conv.weight\": \"quant_conv.weight\",\n \"quant_conv.bias\": \"quant_conv.bias\",\n \"post_quant_conv.weight\": \"post_quant_conv.weight\",\n \"post_quant_conv.bias\": \"post_quant_conv.bias\",\n },\n \"openclip\": {\n \"layers\": {\n \"text_model.embeddings.position_embedding.weight\": \"positional_embedding\",\n \"text_model.embeddings.token_embedding.weight\": \"token_embedding.weight\",\n \"text_model.final_layer_norm.weight\": \"ln_final.weight\",\n \"text_model.final_layer_norm.bias\": \"ln_final.bias\",\n \"text_projection.weight\": \"text_projection\",\n },\n \"transformer\": {\n \"text_model.encoder.layers.\": \"resblocks.\",\n \"layer_norm1\": \"ln_1\",\n \"layer_norm2\": \"ln_2\",\n \".fc1.\": \".c_fc.\",\n \".fc2.\": \".c_proj.\",\n \".self_attn\": \".attn\",\n \"transformer.text_model.final_layer_norm.\": \"ln_final.\",\n \"transformer.text_model.embeddings.token_embedding.weight\": \"token_embedding.weight\",\n \"transformer.text_model.embeddings.position_embedding.weight\": \"positional_embedding\",\n },\n },\n}\n\nSD_2_TEXT_ENCODER_KEYS_TO_IGNORE = [\n \"cond_stage_model.model.transformer.resblocks.23.attn.in_proj_bias\",\n \"cond_stage_model.model.transformer.resblocks.23.attn.in_proj_weight\",\n \"cond_stage_model.model.transformer.resblocks.23.attn.out_proj.bias\",\n \"cond_stage_model.model.transformer.resblocks.23.attn.out_proj.weight\",\n \"cond_stage_model.model.transformer.resblocks.23.ln_1.bias\",\n \"cond_stage_model.model.transformer.resblocks.23.ln_1.weight\",\n \"cond_stage_model.model.transformer.resblocks.23.ln_2.bias\",\n \"cond_stage_model.model.transformer.resblocks.23.ln_2.weight\",\n \"cond_stage_model.model.transformer.resblocks.23.mlp.c_fc.bias\",\n \"cond_stage_model.model.transformer.resblocks.23.mlp.c_fc.weight\",\n \"cond_stage_model.model.transformer.resblocks.23.mlp.c_proj.bias\",\n \"cond_stage_model.model.transformer.resblocks.23.mlp.c_proj.weight\",\n \"cond_stage_model.model.text_projection\",\n]\n\n# To support legacy scheduler_type argument\nSCHEDULER_DEFAULT_CONFIG = {\n \"beta_schedule\": \"scaled_linear\",\n \"beta_start\": 0.00085,\n \"beta_end\": 0.012,\n \"interpolation_type\": \"linear\",\n \"num_train_timesteps\": 1000,\n \"prediction_type\": \"epsilon\",\n \"sample_max_value\": 1.0,\n \"set_alpha_to_one\": False,\n \"skip_prk_steps\": True,\n \"steps_offset\": 1,\n \"timestep_spacing\": \"leading\",\n}\n\nLDM_VAE_KEY = \"first_stage_model.\"\nLDM_VAE_DEFAULT_SCALING_FACTOR = 0.18215\nPLAYGROUND_VAE_SCALING_FACTOR = 0.5\nLDM_UNET_KEY = \"model.diffusion_model.\"\nLDM_CONTROLNET_KEY = \"control_model.\"\nLDM_CLIP_PREFIX_TO_REMOVE = [\n \"cond_stage_model.transformer.\",\n \"conditioner.embedders.0.transformer.\",\n]\nOPEN_CLIP_PREFIX = \"conditioner.embedders.0.model.\"\nLDM_OPEN_CLIP_TEXT_PROJECTION_DIM = 1024\n\nVALID_URL_PREFIXES = [\"https://huggingface.co/\", \"huggingface.co/\", \"hf.co/\", \"https://hf.co/\"]\n\n\nclass SingleFileComponentError(Exception):\n def __init__(self, message=None):\n self.message = message\n super().__init__(self.message)\n\n\ndef is_valid_url(url):\n result = urlparse(url)\n if result.scheme and result.netloc:\n return True\n\n return False\n\n\ndef _extract_repo_id_and_weights_name(pretrained_model_name_or_path):\n if not is_valid_url(pretrained_model_name_or_path):\n raise ValueError(\"Invalid `pretrained_model_name_or_path` provided. Please set it to a valid URL.\")\n\n pattern = r\"([^/]+)/([^/]+)/(?:blob/main/)?(.+)\"\n weights_name = None\n repo_id = (None,)\n for prefix in VALID_URL_PREFIXES:\n pretrained_model_name_or_path = pretrained_model_name_or_path.replace(prefix, \"\")\n match = re.match(pattern, pretrained_model_name_or_path)\n if not match:\n logger.warning(\"Unable to identify the repo_id and weights_name from the provided URL.\")\n return repo_id, weights_name\n\n repo_id = f\"{match.group(1)}/{match.group(2)}\"\n weights_name = match.group(3)\n\n return repo_id, weights_name\n\n\ndef _is_model_weights_in_cached_folder(cached_folder, name):\n pretrained_model_name_or_path = os.path.join(cached_folder, name)\n weights_exist = False\n\n for weights_name in [WEIGHTS_NAME, SAFETENSORS_WEIGHTS_NAME]:\n if os.path.isfile(os.path.join(pretrained_model_name_or_path, weights_name)):\n weights_exist = True\n\n return weights_exist\n\n\ndef load_single_file_checkpoint(\n pretrained_model_link_or_path,\n resume_download=False,\n force_download=False,\n proxies=None,\n token=None,\n cache_dir=None,\n local_files_only=None,\n revision=None,\n):\n if os.path.isfile(pretrained_model_link_or_path):\n pretrained_model_link_or_path = pretrained_model_link_or_path\n\n else:\n repo_id, weights_name = _extract_repo_id_and_weights_name(pretrained_model_link_or_path)\n pretrained_model_link_or_path = _get_model_file(\n repo_id,\n weights_name=weights_name,\n force_download=force_download,\n cache_dir=cache_dir,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n )\n\n checkpoint = load_state_dict(pretrained_model_link_or_path)\n\n # some checkpoints contain the model state dict under a \"state_dict\" key\n while \"state_dict\" in checkpoint:\n checkpoint = checkpoint[\"state_dict\"]\n\n return checkpoint\n\n\ndef fetch_original_config(original_config_file, local_files_only=False):\n if os.path.isfile(original_config_file):\n with open(original_config_file, \"r\") as fp:\n original_config_file = fp.read()\n\n elif is_valid_url(original_config_file):\n if local_files_only:\n raise ValueError(\n \"`local_files_only` is set to True, but a URL was provided as `original_config_file`. \"\n \"Please provide a valid local file path.\"\n )\n\n original_config_file = BytesIO(requests.get(original_config_file).content)\n\n else:\n raise ValueError(\"Invalid `original_config_file` provided. Please set it to a valid file path or URL.\")\n\n original_config = yaml.safe_load(original_config_file)\n\n return original_config\n\n\ndef is_clip_model(checkpoint):\n if CHECKPOINT_KEY_NAMES[\"clip\"] in checkpoint:\n return True\n\n return False\n\n\ndef is_clip_sdxl_model(checkpoint):\n if CHECKPOINT_KEY_NAMES[\"clip_sdxl\"] in checkpoint:\n return True\n\n return False\n\n\ndef is_clip_sd3_model(checkpoint):\n if CHECKPOINT_KEY_NAMES[\"clip_sd3\"] in checkpoint:\n return True\n\n return False\n\n\ndef is_open_clip_model(checkpoint):\n if CHECKPOINT_KEY_NAMES[\"open_clip\"] in checkpoint:\n return True\n\n return False\n\n\ndef is_open_clip_sdxl_model(checkpoint):\n if CHECKPOINT_KEY_NAMES[\"open_clip_sdxl\"] in checkpoint:\n return True\n\n return False\n\n\ndef is_open_clip_sd3_model(checkpoint):\n if CHECKPOINT_KEY_NAMES[\"open_clip_sd3\"] in checkpoint:\n return True\n\n return False\n\n\ndef is_open_clip_sdxl_refiner_model(checkpoint):\n if CHECKPOINT_KEY_NAMES[\"open_clip_sdxl_refiner\"] in checkpoint:\n return True\n\n return False\n\n\ndef is_clip_model_in_single_file(class_obj, checkpoint):\n is_clip_in_checkpoint = any(\n [\n is_clip_model(checkpoint),\n is_clip_sd3_model(checkpoint),\n is_open_clip_model(checkpoint),\n is_open_clip_sdxl_model(checkpoint),\n is_open_clip_sdxl_refiner_model(checkpoint),\n is_open_clip_sd3_model(checkpoint),\n ]\n )\n if (\n class_obj.__name__ == \"CLIPTextModel\" or class_obj.__name__ == \"CLIPTextModelWithProjection\"\n ) and is_clip_in_checkpoint:\n return True\n\n return False\n\n\ndef infer_diffusers_model_type(checkpoint):\n if (\n CHECKPOINT_KEY_NAMES[\"inpainting\"] in checkpoint\n and checkpoint[CHECKPOINT_KEY_NAMES[\"inpainting\"]].shape[1] == 9\n ):\n if CHECKPOINT_KEY_NAMES[\"v2\"] in checkpoint and checkpoint[CHECKPOINT_KEY_NAMES[\"v2\"]].shape[-1] == 1024:\n model_type = \"inpainting_v2\"\n else:\n model_type = \"inpainting\"\n\n elif CHECKPOINT_KEY_NAMES[\"v2\"] in checkpoint and checkpoint[CHECKPOINT_KEY_NAMES[\"v2\"]].shape[-1] == 1024:\n model_type = \"v2\"\n\n elif CHECKPOINT_KEY_NAMES[\"playground-v2-5\"] in checkpoint:\n model_type = \"playground-v2-5\"\n\n elif CHECKPOINT_KEY_NAMES[\"xl_base\"] in checkpoint:\n model_type = \"xl_base\"\n\n elif CHECKPOINT_KEY_NAMES[\"xl_refiner\"] in checkpoint:\n model_type = \"xl_refiner\"\n\n elif CHECKPOINT_KEY_NAMES[\"upscale\"] in checkpoint:\n model_type = \"upscale\"\n\n elif CHECKPOINT_KEY_NAMES[\"controlnet\"] in checkpoint:\n model_type = \"controlnet\"\n\n elif (\n CHECKPOINT_KEY_NAMES[\"stable_cascade_stage_c\"] in checkpoint\n and checkpoint[CHECKPOINT_KEY_NAMES[\"stable_cascade_stage_c\"]].shape[0] == 1536\n ):\n model_type = \"stable_cascade_stage_c_lite\"\n\n elif (\n CHECKPOINT_KEY_NAMES[\"stable_cascade_stage_c\"] in checkpoint\n and checkpoint[CHECKPOINT_KEY_NAMES[\"stable_cascade_stage_c\"]].shape[0] == 2048\n ):\n model_type = \"stable_cascade_stage_c\"\n\n elif (\n CHECKPOINT_KEY_NAMES[\"stable_cascade_stage_b\"] in checkpoint\n and checkpoint[CHECKPOINT_KEY_NAMES[\"stable_cascade_stage_b\"]].shape[-1] == 576\n ):\n model_type = \"stable_cascade_stage_b_lite\"\n\n elif (\n CHECKPOINT_KEY_NAMES[\"stable_cascade_stage_b\"] in checkpoint\n and checkpoint[CHECKPOINT_KEY_NAMES[\"stable_cascade_stage_b\"]].shape[-1] == 640\n ):\n model_type = \"stable_cascade_stage_b\"\n\n elif CHECKPOINT_KEY_NAMES[\"sd3\"] in checkpoint:\n model_type = \"sd3\"\n\n else:\n model_type = \"v1\"\n\n return model_type\n\n\ndef fetch_diffusers_config(checkpoint):\n model_type = infer_diffusers_model_type(checkpoint)\n model_path = DIFFUSERS_DEFAULT_PIPELINE_PATHS[model_type]\n\n return model_path\n\n\ndef set_image_size(checkpoint, image_size=None):\n if image_size:\n return image_size\n\n model_type = infer_diffusers_model_type(checkpoint)\n image_size = DIFFUSERS_TO_LDM_DEFAULT_IMAGE_SIZE_MAP[model_type]\n\n return image_size\n\n\n# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.conv_attn_to_linear\ndef conv_attn_to_linear(checkpoint):\n keys = list(checkpoint.keys())\n attn_keys = [\"query.weight\", \"key.weight\", \"value.weight\"]\n for key in keys:\n if \".\".join(key.split(\".\")[-2:]) in attn_keys:\n if checkpoint[key].ndim > 2:\n checkpoint[key] = checkpoint[key][:, :, 0, 0]\n elif \"proj_attn.weight\" in key:\n if checkpoint[key].ndim > 2:\n checkpoint[key] = checkpoint[key][:, :, 0]\n\n\ndef create_unet_diffusers_config_from_ldm(\n original_config, checkpoint, image_size=None, upcast_attention=None, num_in_channels=None\n):\n \"\"\"\n Creates a config for the diffusers based on the config of the LDM model.\n \"\"\"\n if image_size is not None:\n deprecation_message = (\n \"Configuring UNet2DConditionModel with the `image_size` argument to `from_single_file`\"\n \"is deprecated and will be ignored in future versions.\"\n )\n deprecate(\"image_size\", \"1.0.0\", deprecation_message)\n\n image_size = set_image_size(checkpoint, image_size=image_size)\n\n if (\n \"unet_config\" in original_config[\"model\"][\"params\"]\n and original_config[\"model\"][\"params\"][\"unet_config\"] is not None\n ):\n unet_params = original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"]\n else:\n unet_params = original_config[\"model\"][\"params\"][\"network_config\"][\"params\"]\n\n if num_in_channels is not None:\n deprecation_message = (\n \"Configuring UNet2DConditionModel with the `num_in_channels` argument to `from_single_file`\"\n \"is deprecated and will be ignored in future versions.\"\n )\n deprecate(\"image_size\", \"1.0.0\", deprecation_message)\n in_channels = num_in_channels\n else:\n in_channels = unet_params[\"in_channels\"]\n\n vae_params = original_config[\"model\"][\"params\"][\"first_stage_config\"][\"params\"][\"ddconfig\"]\n block_out_channels = [unet_params[\"model_channels\"] * mult for mult in unet_params[\"channel_mult\"]]\n\n down_block_types = []\n resolution = 1\n for i in range(len(block_out_channels)):\n block_type = \"CrossAttnDownBlock2D\" if resolution in unet_params[\"attention_resolutions\"] else \"DownBlock2D\"\n down_block_types.append(block_type)\n if i != len(block_out_channels) - 1:\n resolution *= 2\n\n up_block_types = []\n for i in range(len(block_out_channels)):\n block_type = \"CrossAttnUpBlock2D\" if resolution in unet_params[\"attention_resolutions\"] else \"UpBlock2D\"\n up_block_types.append(block_type)\n resolution //= 2\n\n if unet_params[\"transformer_depth\"] is not None:\n transformer_layers_per_block = (\n unet_params[\"transformer_depth\"]\n if isinstance(unet_params[\"transformer_depth\"], int)\n else list(unet_params[\"transformer_depth\"])\n )\n else:\n transformer_layers_per_block = 1\n\n vae_scale_factor = 2 ** (len(vae_params[\"ch_mult\"]) - 1)\n\n head_dim = unet_params[\"num_heads\"] if \"num_heads\" in unet_params else None\n use_linear_projection = (\n unet_params[\"use_linear_in_transformer\"] if \"use_linear_in_transformer\" in unet_params else False\n )\n if use_linear_projection:\n # stable diffusion 2-base-512 and 2-768\n if head_dim is None:\n head_dim_mult = unet_params[\"model_channels\"] // unet_params[\"num_head_channels\"]\n head_dim = [head_dim_mult * c for c in list(unet_params[\"channel_mult\"])]\n\n class_embed_type = None\n addition_embed_type = None\n addition_time_embed_dim = None\n projection_class_embeddings_input_dim = None\n context_dim = None\n\n if unet_params[\"context_dim\"] is not None:\n context_dim = (\n unet_params[\"context_dim\"]\n if isinstance(unet_params[\"context_dim\"], int)\n else unet_params[\"context_dim\"][0]\n )\n\n if \"num_classes\" in unet_params:\n if unet_params[\"num_classes\"] == \"sequential\":\n if context_dim in [2048, 1280]:\n # SDXL\n addition_embed_type = \"text_time\"\n addition_time_embed_dim = 256\n else:\n class_embed_type = \"projection\"\n assert \"adm_in_channels\" in unet_params\n projection_class_embeddings_input_dim = unet_params[\"adm_in_channels\"]\n\n config = {\n \"sample_size\": image_size // vae_scale_factor,\n \"in_channels\": in_channels,\n \"down_block_types\": down_block_types,\n \"block_out_channels\": block_out_channels,\n \"layers_per_block\": unet_params[\"num_res_blocks\"],\n \"cross_attention_dim\": context_dim,\n \"attention_head_dim\": head_dim,\n \"use_linear_projection\": use_linear_projection,\n \"class_embed_type\": class_embed_type,\n \"addition_embed_type\": addition_embed_type,\n \"addition_time_embed_dim\": addition_time_embed_dim,\n \"projection_class_embeddings_input_dim\": projection_class_embeddings_input_dim,\n \"transformer_layers_per_block\": transformer_layers_per_block,\n }\n\n if upcast_attention is not None:\n deprecation_message = (\n \"Configuring UNet2DConditionModel with the `upcast_attention` argument to `from_single_file`\"\n \"is deprecated and will be ignored in future versions.\"\n )\n deprecate(\"image_size\", \"1.0.0\", deprecation_message)\n config[\"upcast_attention\"] = upcast_attention\n\n if \"disable_self_attentions\" in unet_params:\n config[\"only_cross_attention\"] = unet_params[\"disable_self_attentions\"]\n\n if \"num_classes\" in unet_params and isinstance(unet_params[\"num_classes\"], int):\n config[\"num_class_embeds\"] = unet_params[\"num_classes\"]\n\n config[\"out_channels\"] = unet_params[\"out_channels\"]\n config[\"up_block_types\"] = up_block_types\n\n return config\n\n\ndef create_controlnet_diffusers_config_from_ldm(original_config, checkpoint, image_size=None, **kwargs):\n if image_size is not None:\n deprecation_message = (\n \"Configuring ControlNetModel with the `image_size` argument\"\n \"is deprecated and will be ignored in future versions.\"\n )\n deprecate(\"image_size\", \"1.0.0\", deprecation_message)\n\n image_size = set_image_size(checkpoint, image_size=image_size)\n\n unet_params = original_config[\"model\"][\"params\"][\"control_stage_config\"][\"params\"]\n diffusers_unet_config = create_unet_diffusers_config_from_ldm(original_config, image_size=image_size)\n\n controlnet_config = {\n \"conditioning_channels\": unet_params[\"hint_channels\"],\n \"in_channels\": diffusers_unet_config[\"in_channels\"],\n \"down_block_types\": diffusers_unet_config[\"down_block_types\"],\n \"block_out_channels\": diffusers_unet_config[\"block_out_channels\"],\n \"layers_per_block\": diffusers_unet_config[\"layers_per_block\"],\n \"cross_attention_dim\": diffusers_unet_config[\"cross_attention_dim\"],\n \"attention_head_dim\": diffusers_unet_config[\"attention_head_dim\"],\n \"use_linear_projection\": diffusers_unet_config[\"use_linear_projection\"],\n \"class_embed_type\": diffusers_unet_config[\"class_embed_type\"],\n \"addition_embed_type\": diffusers_unet_config[\"addition_embed_type\"],\n \"addition_time_embed_dim\": diffusers_unet_config[\"addition_time_embed_dim\"],\n \"projection_class_embeddings_input_dim\": diffusers_unet_config[\"projection_class_embeddings_input_dim\"],\n \"transformer_layers_per_block\": diffusers_unet_config[\"transformer_layers_per_block\"],\n }\n\n return controlnet_config\n\n\ndef create_vae_diffusers_config_from_ldm(original_config, checkpoint, image_size=None, scaling_factor=None):\n \"\"\"\n Creates a config for the diffusers based on the config of the LDM model.\n \"\"\"\n if image_size is not None:\n deprecation_message = (\n \"Configuring AutoencoderKL with the `image_size` argument\"\n \"is deprecated and will be ignored in future versions.\"\n )\n deprecate(\"image_size\", \"1.0.0\", deprecation_message)\n\n image_size = set_image_size(checkpoint, image_size=image_size)\n\n if \"edm_mean\" in checkpoint and \"edm_std\" in checkpoint:\n latents_mean = checkpoint[\"edm_mean\"]\n latents_std = checkpoint[\"edm_std\"]\n else:\n latents_mean = None\n latents_std = None\n\n vae_params = original_config[\"model\"][\"params\"][\"first_stage_config\"][\"params\"][\"ddconfig\"]\n if (scaling_factor is None) and (latents_mean is not None) and (latents_std is not None):\n scaling_factor = PLAYGROUND_VAE_SCALING_FACTOR\n\n elif (scaling_factor is None) and (\"scale_factor\" in original_config[\"model\"][\"params\"]):\n scaling_factor = original_config[\"model\"][\"params\"][\"scale_factor\"]\n\n elif scaling_factor is None:\n scaling_factor = LDM_VAE_DEFAULT_SCALING_FACTOR\n\n block_out_channels = [vae_params[\"ch\"] * mult for mult in vae_params[\"ch_mult\"]]\n down_block_types = [\"DownEncoderBlock2D\"] * len(block_out_channels)\n up_block_types = [\"UpDecoderBlock2D\"] * len(block_out_channels)\n\n config = {\n \"sample_size\": image_size,\n \"in_channels\": vae_params[\"in_channels\"],\n \"out_channels\": vae_params[\"out_ch\"],\n \"down_block_types\": down_block_types,\n \"up_block_types\": up_block_types,\n \"block_out_channels\": block_out_channels,\n \"latent_channels\": vae_params[\"z_channels\"],\n \"layers_per_block\": vae_params[\"num_res_blocks\"],\n \"scaling_factor\": scaling_factor,\n }\n if latents_mean is not None and latents_std is not None:\n config.update({\"latents_mean\": latents_mean, \"latents_std\": latents_std})\n\n return config\n\n\ndef update_unet_resnet_ldm_to_diffusers(ldm_keys, new_checkpoint, checkpoint, mapping=None):\n for ldm_key in ldm_keys:\n diffusers_key = (\n ldm_key.replace(\"in_layers.0\", \"norm1\")\n .replace(\"in_layers.2\", \"conv1\")\n .replace(\"out_layers.0\", \"norm2\")\n .replace(\"out_layers.3\", \"conv2\")\n .replace(\"emb_layers.1\", \"time_emb_proj\")\n .replace(\"skip_connection\", \"conv_shortcut\")\n )\n if mapping:\n diffusers_key = diffusers_key.replace(mapping[\"old\"], mapping[\"new\"])\n new_checkpoint[diffusers_key] = checkpoint.get(ldm_key)\n\n\ndef update_unet_attention_ldm_to_diffusers(ldm_keys, new_checkpoint, checkpoint, mapping):\n for ldm_key in ldm_keys:\n diffusers_key = ldm_key.replace(mapping[\"old\"], mapping[\"new\"])\n new_checkpoint[diffusers_key] = checkpoint.get(ldm_key)\n\n\ndef update_vae_resnet_ldm_to_diffusers(keys, new_checkpoint, checkpoint, mapping):\n for ldm_key in keys:\n diffusers_key = ldm_key.replace(mapping[\"old\"], mapping[\"new\"]).replace(\"nin_shortcut\", \"conv_shortcut\")\n new_checkpoint[diffusers_key] = checkpoint.get(ldm_key)\n\n\ndef update_vae_attentions_ldm_to_diffusers(keys, new_checkpoint, checkpoint, mapping):\n for ldm_key in keys:\n diffusers_key = (\n ldm_key.replace(mapping[\"old\"], mapping[\"new\"])\n .replace(\"norm.weight\", \"group_norm.weight\")\n .replace(\"norm.bias\", \"group_norm.bias\")\n .replace(\"q.weight\", \"to_q.weight\")\n .replace(\"q.bias\", \"to_q.bias\")\n .replace(\"k.weight\", \"to_k.weight\")\n .replace(\"k.bias\", \"to_k.bias\")\n .replace(\"v.weight\", \"to_v.weight\")\n .replace(\"v.bias\", \"to_v.bias\")\n .replace(\"proj_out.weight\", \"to_out.0.weight\")\n .replace(\"proj_out.bias\", \"to_out.0.bias\")\n )\n new_checkpoint[diffusers_key] = checkpoint.get(ldm_key)\n\n # proj_attn.weight has to be converted from conv 1D to linear\n shape = new_checkpoint[diffusers_key].shape\n\n if len(shape) == 3:\n new_checkpoint[diffusers_key] = new_checkpoint[diffusers_key][:, :, 0]\n elif len(shape) == 4:\n new_checkpoint[diffusers_key] = new_checkpoint[diffusers_key][:, :, 0, 0]\n\n\ndef convert_stable_cascade_unet_single_file_to_diffusers(checkpoint, **kwargs):\n is_stage_c = \"clip_txt_mapper.weight\" in checkpoint\n\n if is_stage_c:\n state_dict = {}\n for key in checkpoint.keys():\n if key.endswith(\"in_proj_weight\"):\n weights = checkpoint[key].chunk(3, 0)\n state_dict[key.replace(\"attn.in_proj_weight\", \"to_q.weight\")] = weights[0]\n state_dict[key.replace(\"attn.in_proj_weight\", \"to_k.weight\")] = weights[1]\n state_dict[key.replace(\"attn.in_proj_weight\", \"to_v.weight\")] = weights[2]\n elif key.endswith(\"in_proj_bias\"):\n weights = checkpoint[key].chunk(3, 0)\n state_dict[key.replace(\"attn.in_proj_bias\", \"to_q.bias\")] = weights[0]\n state_dict[key.replace(\"attn.in_proj_bias\", \"to_k.bias\")] = weights[1]\n state_dict[key.replace(\"attn.in_proj_bias\", \"to_v.bias\")] = weights[2]\n elif key.endswith(\"out_proj.weight\"):\n weights = checkpoint[key]\n state_dict[key.replace(\"attn.out_proj.weight\", \"to_out.0.weight\")] = weights\n elif key.endswith(\"out_proj.bias\"):\n weights = checkpoint[key]\n state_dict[key.replace(\"attn.out_proj.bias\", \"to_out.0.bias\")] = weights\n else:\n state_dict[key] = checkpoint[key]\n else:\n state_dict = {}\n for key in checkpoint.keys():\n if key.endswith(\"in_proj_weight\"):\n weights = checkpoint[key].chunk(3, 0)\n state_dict[key.replace(\"attn.in_proj_weight\", \"to_q.weight\")] = weights[0]\n state_dict[key.replace(\"attn.in_proj_weight\", \"to_k.weight\")] = weights[1]\n state_dict[key.replace(\"attn.in_proj_weight\", \"to_v.weight\")] = weights[2]\n elif key.endswith(\"in_proj_bias\"):\n weights = checkpoint[key].chunk(3, 0)\n state_dict[key.replace(\"attn.in_proj_bias\", \"to_q.bias\")] = weights[0]\n state_dict[key.replace(\"attn.in_proj_bias\", \"to_k.bias\")] = weights[1]\n state_dict[key.replace(\"attn.in_proj_bias\", \"to_v.bias\")] = weights[2]\n elif key.endswith(\"out_proj.weight\"):\n weights = checkpoint[key]\n state_dict[key.replace(\"attn.out_proj.weight\", \"to_out.0.weight\")] = weights\n elif key.endswith(\"out_proj.bias\"):\n weights = checkpoint[key]\n state_dict[key.replace(\"attn.out_proj.bias\", \"to_out.0.bias\")] = weights\n # rename clip_mapper to clip_txt_pooled_mapper\n elif key.endswith(\"clip_mapper.weight\"):\n weights = checkpoint[key]\n state_dict[key.replace(\"clip_mapper.weight\", \"clip_txt_pooled_mapper.weight\")] = weights\n elif key.endswith(\"clip_mapper.bias\"):\n weights = checkpoint[key]\n state_dict[key.replace(\"clip_mapper.bias\", \"clip_txt_pooled_mapper.bias\")] = weights\n else:\n state_dict[key] = checkpoint[key]\n\n return state_dict\n\n\ndef convert_ldm_unet_checkpoint(checkpoint, config, extract_ema=False, **kwargs):\n \"\"\"\n Takes a state dict and a config, and returns a converted checkpoint.\n \"\"\"\n # extract state_dict for UNet\n unet_state_dict = {}\n keys = list(checkpoint.keys())\n unet_key = LDM_UNET_KEY\n\n # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA\n if sum(k.startswith(\"model_ema\") for k in keys) > 100 and extract_ema:\n logger.warning(\"Checkpoint has both EMA and non-EMA weights.\")\n logger.warning(\n \"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA\"\n \" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.\"\n )\n for key in keys:\n if key.startswith(\"model.diffusion_model\"):\n flat_ema_key = \"model_ema.\" + \"\".join(key.split(\".\")[1:])\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint.get(flat_ema_key)\n else:\n if sum(k.startswith(\"model_ema\") for k in keys) > 100:\n logger.warning(\n \"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA\"\n \" weights (usually better for inference), please make sure to add the `--extract_ema` flag.\"\n )\n for key in keys:\n if key.startswith(unet_key):\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint.get(key)\n\n new_checkpoint = {}\n ldm_unet_keys = DIFFUSERS_TO_LDM_MAPPING[\"unet\"][\"layers\"]\n for diffusers_key, ldm_key in ldm_unet_keys.items():\n if ldm_key not in unet_state_dict:\n continue\n new_checkpoint[diffusers_key] = unet_state_dict[ldm_key]\n\n if (\"class_embed_type\" in config) and (config[\"class_embed_type\"] in [\"timestep\", \"projection\"]):\n class_embed_keys = DIFFUSERS_TO_LDM_MAPPING[\"unet\"][\"class_embed_type\"]\n for diffusers_key, ldm_key in class_embed_keys.items():\n new_checkpoint[diffusers_key] = unet_state_dict[ldm_key]\n\n if (\"addition_embed_type\" in config) and (config[\"addition_embed_type\"] == \"text_time\"):\n addition_embed_keys = DIFFUSERS_TO_LDM_MAPPING[\"unet\"][\"addition_embed_type\"]\n for diffusers_key, ldm_key in addition_embed_keys.items():\n new_checkpoint[diffusers_key] = unet_state_dict[ldm_key]\n\n # Relevant to StableDiffusionUpscalePipeline\n if \"num_class_embeds\" in config:\n if (config[\"num_class_embeds\"] is not None) and (\"label_emb.weight\" in unet_state_dict):\n new_checkpoint[\"class_embedding.weight\"] = unet_state_dict[\"label_emb.weight\"]\n\n # Retrieves the keys for the input blocks only\n num_input_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"input_blocks\" in layer})\n input_blocks = {\n layer_id: [key for key in unet_state_dict if f\"input_blocks.{layer_id}\" in key]\n for layer_id in range(num_input_blocks)\n }\n\n # Retrieves the keys for the middle blocks only\n num_middle_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"middle_block\" in layer})\n middle_blocks = {\n layer_id: [key for key in unet_state_dict if f\"middle_block.{layer_id}\" in key]\n for layer_id in range(num_middle_blocks)\n }\n\n # Retrieves the keys for the output blocks only\n num_output_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"output_blocks\" in layer})\n output_blocks = {\n layer_id: [key for key in unet_state_dict if f\"output_blocks.{layer_id}\" in key]\n for layer_id in range(num_output_blocks)\n }\n\n # Down blocks\n for i in range(1, num_input_blocks):\n block_id = (i - 1) // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = (i - 1) % (config[\"layers_per_block\"] + 1)\n\n resnets = [\n key for key in input_blocks[i] if f\"input_blocks.{i}.0\" in key and f\"input_blocks.{i}.0.op\" not in key\n ]\n update_unet_resnet_ldm_to_diffusers(\n resnets,\n new_checkpoint,\n unet_state_dict,\n {\"old\": f\"input_blocks.{i}.0\", \"new\": f\"down_blocks.{block_id}.resnets.{layer_in_block_id}\"},\n )\n\n if f\"input_blocks.{i}.0.op.weight\" in unet_state_dict:\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.weight\"] = unet_state_dict.get(\n f\"input_blocks.{i}.0.op.weight\"\n )\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.bias\"] = unet_state_dict.get(\n f\"input_blocks.{i}.0.op.bias\"\n )\n\n attentions = [key for key in input_blocks[i] if f\"input_blocks.{i}.1\" in key]\n if attentions:\n update_unet_attention_ldm_to_diffusers(\n attentions,\n new_checkpoint,\n unet_state_dict,\n {\"old\": f\"input_blocks.{i}.1\", \"new\": f\"down_blocks.{block_id}.attentions.{layer_in_block_id}\"},\n )\n\n # Mid blocks\n for key in middle_blocks.keys():\n diffusers_key = max(key - 1, 0)\n if key % 2 == 0:\n update_unet_resnet_ldm_to_diffusers(\n middle_blocks[key],\n new_checkpoint,\n unet_state_dict,\n mapping={\"old\": f\"middle_block.{key}\", \"new\": f\"mid_block.resnets.{diffusers_key}\"},\n )\n else:\n update_unet_attention_ldm_to_diffusers(\n middle_blocks[key],\n new_checkpoint,\n unet_state_dict,\n mapping={\"old\": f\"middle_block.{key}\", \"new\": f\"mid_block.attentions.{diffusers_key}\"},\n )\n\n # Up Blocks\n for i in range(num_output_blocks):\n block_id = i // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = i % (config[\"layers_per_block\"] + 1)\n\n resnets = [\n key for key in output_blocks[i] if f\"output_blocks.{i}.0\" in key and f\"output_blocks.{i}.0.op\" not in key\n ]\n update_unet_resnet_ldm_to_diffusers(\n resnets,\n new_checkpoint,\n unet_state_dict,\n {\"old\": f\"output_blocks.{i}.0\", \"new\": f\"up_blocks.{block_id}.resnets.{layer_in_block_id}\"},\n )\n\n attentions = [\n key for key in output_blocks[i] if f\"output_blocks.{i}.1\" in key and f\"output_blocks.{i}.1.conv\" not in key\n ]\n if attentions:\n update_unet_attention_ldm_to_diffusers(\n attentions,\n new_checkpoint,\n unet_state_dict,\n {\"old\": f\"output_blocks.{i}.1\", \"new\": f\"up_blocks.{block_id}.attentions.{layer_in_block_id}\"},\n )\n\n if f\"output_blocks.{i}.1.conv.weight\" in unet_state_dict:\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.weight\"] = unet_state_dict[\n f\"output_blocks.{i}.1.conv.weight\"\n ]\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.bias\"] = unet_state_dict[\n f\"output_blocks.{i}.1.conv.bias\"\n ]\n if f\"output_blocks.{i}.2.conv.weight\" in unet_state_dict:\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.weight\"] = unet_state_dict[\n f\"output_blocks.{i}.2.conv.weight\"\n ]\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.bias\"] = unet_state_dict[\n f\"output_blocks.{i}.2.conv.bias\"\n ]\n\n return new_checkpoint\n\n\ndef convert_controlnet_checkpoint(\n checkpoint,\n config,\n **kwargs,\n):\n # Some controlnet ckpt files are distributed independently from the rest of the\n # model components i.e. https://huggingface.co/thibaud/controlnet-sd21/\n if \"time_embed.0.weight\" in checkpoint:\n controlnet_state_dict = checkpoint\n\n else:\n controlnet_state_dict = {}\n keys = list(checkpoint.keys())\n controlnet_key = LDM_CONTROLNET_KEY\n for key in keys:\n if key.startswith(controlnet_key):\n controlnet_state_dict[key.replace(controlnet_key, \"\")] = checkpoint.get(key)\n\n new_checkpoint = {}\n ldm_controlnet_keys = DIFFUSERS_TO_LDM_MAPPING[\"controlnet\"][\"layers\"]\n for diffusers_key, ldm_key in ldm_controlnet_keys.items():\n if ldm_key not in controlnet_state_dict:\n continue\n new_checkpoint[diffusers_key] = controlnet_state_dict[ldm_key]\n\n # Retrieves the keys for the input blocks only\n num_input_blocks = len(\n {\".\".join(layer.split(\".\")[:2]) for layer in controlnet_state_dict if \"input_blocks\" in layer}\n )\n input_blocks = {\n layer_id: [key for key in controlnet_state_dict if f\"input_blocks.{layer_id}\" in key]\n for layer_id in range(num_input_blocks)\n }\n\n # Down blocks\n for i in range(1, num_input_blocks):\n block_id = (i - 1) // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = (i - 1) % (config[\"layers_per_block\"] + 1)\n\n resnets = [\n key for key in input_blocks[i] if f\"input_blocks.{i}.0\" in key and f\"input_blocks.{i}.0.op\" not in key\n ]\n update_unet_resnet_ldm_to_diffusers(\n resnets,\n new_checkpoint,\n controlnet_state_dict,\n {\"old\": f\"input_blocks.{i}.0\", \"new\": f\"down_blocks.{block_id}.resnets.{layer_in_block_id}\"},\n )\n\n if f\"input_blocks.{i}.0.op.weight\" in controlnet_state_dict:\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.weight\"] = controlnet_state_dict.get(\n f\"input_blocks.{i}.0.op.weight\"\n )\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.bias\"] = controlnet_state_dict.get(\n f\"input_blocks.{i}.0.op.bias\"\n )\n\n attentions = [key for key in input_blocks[i] if f\"input_blocks.{i}.1\" in key]\n if attentions:\n update_unet_attention_ldm_to_diffusers(\n attentions,\n new_checkpoint,\n controlnet_state_dict,\n {\"old\": f\"input_blocks.{i}.1\", \"new\": f\"down_blocks.{block_id}.attentions.{layer_in_block_id}\"},\n )\n\n # controlnet down blocks\n for i in range(num_input_blocks):\n new_checkpoint[f\"controlnet_down_blocks.{i}.weight\"] = controlnet_state_dict.get(f\"zero_convs.{i}.0.weight\")\n new_checkpoint[f\"controlnet_down_blocks.{i}.bias\"] = controlnet_state_dict.get(f\"zero_convs.{i}.0.bias\")\n\n # Retrieves the keys for the middle blocks only\n num_middle_blocks = len(\n {\".\".join(layer.split(\".\")[:2]) for layer in controlnet_state_dict if \"middle_block\" in layer}\n )\n middle_blocks = {\n layer_id: [key for key in controlnet_state_dict if f\"middle_block.{layer_id}\" in key]\n for layer_id in range(num_middle_blocks)\n }\n\n # Mid blocks\n for key in middle_blocks.keys():\n diffusers_key = max(key - 1, 0)\n if key % 2 == 0:\n update_unet_resnet_ldm_to_diffusers(\n middle_blocks[key],\n new_checkpoint,\n controlnet_state_dict,\n mapping={\"old\": f\"middle_block.{key}\", \"new\": f\"mid_block.resnets.{diffusers_key}\"},\n )\n else:\n update_unet_attention_ldm_to_diffusers(\n middle_blocks[key],\n new_checkpoint,\n controlnet_state_dict,\n mapping={\"old\": f\"middle_block.{key}\", \"new\": f\"mid_block.attentions.{diffusers_key}\"},\n )\n\n # mid block\n new_checkpoint[\"controlnet_mid_block.weight\"] = controlnet_state_dict.get(\"middle_block_out.0.weight\")\n new_checkpoint[\"controlnet_mid_block.bias\"] = controlnet_state_dict.get(\"middle_block_out.0.bias\")\n\n # controlnet cond embedding blocks\n cond_embedding_blocks = {\n \".\".join(layer.split(\".\")[:2])\n for layer in controlnet_state_dict\n if \"input_hint_block\" in layer and (\"input_hint_block.0\" not in layer) and (\"input_hint_block.14\" not in layer)\n }\n num_cond_embedding_blocks = len(cond_embedding_blocks)\n\n for idx in range(1, num_cond_embedding_blocks + 1):\n diffusers_idx = idx - 1\n cond_block_id = 2 * idx\n\n new_checkpoint[f\"controlnet_cond_embedding.blocks.{diffusers_idx}.weight\"] = controlnet_state_dict.get(\n f\"input_hint_block.{cond_block_id}.weight\"\n )\n new_checkpoint[f\"controlnet_cond_embedding.blocks.{diffusers_idx}.bias\"] = controlnet_state_dict.get(\n f\"input_hint_block.{cond_block_id}.bias\"\n )\n\n return new_checkpoint\n\n\ndef convert_ldm_vae_checkpoint(checkpoint, config):\n # extract state dict for VAE\n # remove the LDM_VAE_KEY prefix from the ldm checkpoint keys so that it is easier to map them to diffusers keys\n vae_state_dict = {}\n keys = list(checkpoint.keys())\n vae_key = LDM_VAE_KEY if any(k.startswith(LDM_VAE_KEY) for k in keys) else \"\"\n for key in keys:\n if key.startswith(vae_key):\n vae_state_dict[key.replace(vae_key, \"\")] = checkpoint.get(key)\n\n new_checkpoint = {}\n vae_diffusers_ldm_map = DIFFUSERS_TO_LDM_MAPPING[\"vae\"]\n for diffusers_key, ldm_key in vae_diffusers_ldm_map.items():\n if ldm_key not in vae_state_dict:\n continue\n new_checkpoint[diffusers_key] = vae_state_dict[ldm_key]\n\n # Retrieves the keys for the encoder down blocks only\n num_down_blocks = len(config[\"down_block_types\"])\n down_blocks = {\n layer_id: [key for key in vae_state_dict if f\"down.{layer_id}\" in key] for layer_id in range(num_down_blocks)\n }\n\n for i in range(num_down_blocks):\n resnets = [key for key in down_blocks[i] if f\"down.{i}\" in key and f\"down.{i}.downsample\" not in key]\n update_vae_resnet_ldm_to_diffusers(\n resnets,\n new_checkpoint,\n vae_state_dict,\n mapping={\"old\": f\"down.{i}.block\", \"new\": f\"down_blocks.{i}.resnets\"},\n )\n if f\"encoder.down.{i}.downsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.weight\"] = vae_state_dict.get(\n f\"encoder.down.{i}.downsample.conv.weight\"\n )\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.bias\"] = vae_state_dict.get(\n f\"encoder.down.{i}.downsample.conv.bias\"\n )\n\n mid_resnets = [key for key in vae_state_dict if \"encoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"encoder.mid.block_{i}\" in key]\n update_vae_resnet_ldm_to_diffusers(\n resnets,\n new_checkpoint,\n vae_state_dict,\n mapping={\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"},\n )\n\n mid_attentions = [key for key in vae_state_dict if \"encoder.mid.attn\" in key]\n update_vae_attentions_ldm_to_diffusers(\n mid_attentions, new_checkpoint, vae_state_dict, mapping={\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n )\n\n # Retrieves the keys for the decoder up blocks only\n num_up_blocks = len(config[\"up_block_types\"])\n up_blocks = {\n layer_id: [key for key in vae_state_dict if f\"up.{layer_id}\" in key] for layer_id in range(num_up_blocks)\n }\n\n for i in range(num_up_blocks):\n block_id = num_up_blocks - 1 - i\n resnets = [\n key for key in up_blocks[block_id] if f\"up.{block_id}\" in key and f\"up.{block_id}.upsample\" not in key\n ]\n update_vae_resnet_ldm_to_diffusers(\n resnets,\n new_checkpoint,\n vae_state_dict,\n mapping={\"old\": f\"up.{block_id}.block\", \"new\": f\"up_blocks.{i}.resnets\"},\n )\n if f\"decoder.up.{block_id}.upsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.weight\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.weight\"\n ]\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.bias\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.bias\"\n ]\n\n mid_resnets = [key for key in vae_state_dict if \"decoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"decoder.mid.block_{i}\" in key]\n update_vae_resnet_ldm_to_diffusers(\n resnets,\n new_checkpoint,\n vae_state_dict,\n mapping={\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"},\n )\n\n mid_attentions = [key for key in vae_state_dict if \"decoder.mid.attn\" in key]\n update_vae_attentions_ldm_to_diffusers(\n mid_attentions, new_checkpoint, vae_state_dict, mapping={\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n )\n conv_attn_to_linear(new_checkpoint)\n\n return new_checkpoint\n\n\ndef convert_ldm_clip_checkpoint(checkpoint, remove_prefix=None):\n keys = list(checkpoint.keys())\n text_model_dict = {}\n\n remove_prefixes = []\n remove_prefixes.extend(LDM_CLIP_PREFIX_TO_REMOVE)\n if remove_prefix:\n remove_prefixes.append(remove_prefix)\n\n for key in keys:\n for prefix in remove_prefixes:\n if key.startswith(prefix):\n diffusers_key = key.replace(prefix, \"\")\n text_model_dict[diffusers_key] = checkpoint.get(key)\n\n return text_model_dict\n\n\ndef convert_open_clip_checkpoint(\n text_model,\n checkpoint,\n prefix=\"cond_stage_model.model.\",\n):\n text_model_dict = {}\n text_proj_key = prefix + \"text_projection\"\n\n if text_proj_key in checkpoint:\n text_proj_dim = int(checkpoint[text_proj_key].shape[0])\n elif hasattr(text_model.config, \"projection_dim\"):\n text_proj_dim = text_model.config.projection_dim\n else:\n text_proj_dim = LDM_OPEN_CLIP_TEXT_PROJECTION_DIM\n\n text_model_dict[\"text_model.embeddings.position_ids\"] = text_model.text_model.embeddings.get_buffer(\"position_ids\")\n\n keys = list(checkpoint.keys())\n keys_to_ignore = SD_2_TEXT_ENCODER_KEYS_TO_IGNORE\n\n openclip_diffusers_ldm_map = DIFFUSERS_TO_LDM_MAPPING[\"openclip\"][\"layers\"]\n for diffusers_key, ldm_key in openclip_diffusers_ldm_map.items():\n ldm_key = prefix + ldm_key\n if ldm_key not in checkpoint:\n continue\n if ldm_key in keys_to_ignore:\n continue\n if ldm_key.endswith(\"text_projection\"):\n text_model_dict[diffusers_key] = checkpoint[ldm_key].T.contiguous()\n else:\n text_model_dict[diffusers_key] = checkpoint[ldm_key]\n\n for key in keys:\n if key in keys_to_ignore:\n continue\n\n if not key.startswith(prefix + \"transformer.\"):\n continue\n\n diffusers_key = key.replace(prefix + \"transformer.\", \"\")\n transformer_diffusers_to_ldm_map = DIFFUSERS_TO_LDM_MAPPING[\"openclip\"][\"transformer\"]\n for new_key, old_key in transformer_diffusers_to_ldm_map.items():\n diffusers_key = (\n diffusers_key.replace(old_key, new_key).replace(\".in_proj_weight\", \"\").replace(\".in_proj_bias\", \"\")\n )\n\n if key.endswith(\".in_proj_weight\"):\n weight_value = checkpoint.get(key)\n\n text_model_dict[diffusers_key + \".q_proj.weight\"] = weight_value[:text_proj_dim, :].clone().detach()\n text_model_dict[diffusers_key + \".k_proj.weight\"] = (\n weight_value[text_proj_dim : text_proj_dim * 2, :].clone().detach()\n )\n text_model_dict[diffusers_key + \".v_proj.weight\"] = weight_value[text_proj_dim * 2 :, :].clone().detach()\n\n elif key.endswith(\".in_proj_bias\"):\n weight_value = checkpoint.get(key)\n text_model_dict[diffusers_key + \".q_proj.bias\"] = weight_value[:text_proj_dim].clone().detach()\n text_model_dict[diffusers_key + \".k_proj.bias\"] = (\n weight_value[text_proj_dim : text_proj_dim * 2].clone().detach()\n )\n text_model_dict[diffusers_key + \".v_proj.bias\"] = weight_value[text_proj_dim * 2 :].clone().detach()\n else:\n text_model_dict[diffusers_key] = checkpoint.get(key)\n\n if not (hasattr(text_model, \"embeddings\") and hasattr(text_model.embeddings.position_ids)):\n text_model_dict.pop(\"text_model.embeddings.position_ids\", None)\n\n return text_model_dict\n\n\ndef create_diffusers_clip_model_from_ldm(\n cls,\n checkpoint,\n subfolder=\"\",\n config=None,\n torch_dtype=None,\n local_files_only=None,\n is_legacy_loading=False,\n):\n if config:\n config = {\"pretrained_model_name_or_path\": config}\n else:\n config = fetch_diffusers_config(checkpoint)\n\n # For backwards compatibility\n # Older versions of `from_single_file` expected CLIP configs to be placed in their original transformers model repo\n # in the cache_dir, rather than in a subfolder of the Diffusers model\n if is_legacy_loading:\n logger.warning(\n (\n \"Detected legacy CLIP loading behavior. Please run `from_single_file` with `local_files_only=False once to update \"\n \"the local cache directory with the necessary CLIP model config files. \"\n \"Attempting to load CLIP model from legacy cache directory.\"\n )\n )\n\n if is_clip_model(checkpoint) or is_clip_sdxl_model(checkpoint):\n clip_config = \"openai/clip-vit-large-patch14\"\n config[\"pretrained_model_name_or_path\"] = clip_config\n subfolder = \"\"\n\n elif is_open_clip_model(checkpoint):\n clip_config = \"stabilityai/stable-diffusion-2\"\n config[\"pretrained_model_name_or_path\"] = clip_config\n subfolder = \"text_encoder\"\n\n else:\n clip_config = \"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\"\n config[\"pretrained_model_name_or_path\"] = clip_config\n subfolder = \"\"\n\n model_config = cls.config_class.from_pretrained(**config, subfolder=subfolder, local_files_only=local_files_only)\n ctx = init_empty_weights if is_accelerate_available() else nullcontext\n with ctx():\n model = cls(model_config)\n\n position_embedding_dim = model.text_model.embeddings.position_embedding.weight.shape[-1]\n\n if is_clip_model(checkpoint):\n diffusers_format_checkpoint = convert_ldm_clip_checkpoint(checkpoint)\n\n elif (\n is_clip_sdxl_model(checkpoint)\n and checkpoint[CHECKPOINT_KEY_NAMES[\"clip_sdxl\"]].shape[-1] == position_embedding_dim\n ):\n diffusers_format_checkpoint = convert_ldm_clip_checkpoint(checkpoint)\n\n elif (\n is_clip_sd3_model(checkpoint)\n and checkpoint[CHECKPOINT_KEY_NAMES[\"clip_sd3\"]].shape[-1] == position_embedding_dim\n ):\n diffusers_format_checkpoint = convert_ldm_clip_checkpoint(checkpoint, \"text_encoders.clip_l.transformer.\")\n diffusers_format_checkpoint[\"text_projection.weight\"] = torch.eye(position_embedding_dim)\n\n elif is_open_clip_model(checkpoint):\n prefix = \"cond_stage_model.model.\"\n diffusers_format_checkpoint = convert_open_clip_checkpoint(model, checkpoint, prefix=prefix)\n\n elif (\n is_open_clip_sdxl_model(checkpoint)\n and checkpoint[CHECKPOINT_KEY_NAMES[\"open_clip_sdxl\"]].shape[-1] == position_embedding_dim\n ):\n prefix = \"conditioner.embedders.1.model.\"\n diffusers_format_checkpoint = convert_open_clip_checkpoint(model, checkpoint, prefix=prefix)\n\n elif is_open_clip_sdxl_refiner_model(checkpoint):\n prefix = \"conditioner.embedders.0.model.\"\n diffusers_format_checkpoint = convert_open_clip_checkpoint(model, checkpoint, prefix=prefix)\n\n elif (\n is_open_clip_sd3_model(checkpoint)\n and checkpoint[CHECKPOINT_KEY_NAMES[\"open_clip_sd3\"]].shape[-1] == position_embedding_dim\n ):\n diffusers_format_checkpoint = convert_ldm_clip_checkpoint(checkpoint, \"text_encoders.clip_g.transformer.\")\n\n else:\n raise ValueError(\"The provided checkpoint does not seem to contain a valid CLIP model.\")\n\n if is_accelerate_available():\n unexpected_keys = load_model_dict_into_meta(model, diffusers_format_checkpoint, dtype=torch_dtype)\n if model._keys_to_ignore_on_load_unexpected is not None:\n for pat in model._keys_to_ignore_on_load_unexpected:\n unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]\n\n if len(unexpected_keys) > 0:\n logger.warning(\n f\"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \\n {[', '.join(unexpected_keys)]}\"\n )\n\n else:\n model.load_state_dict(diffusers_format_checkpoint)\n\n if torch_dtype is not None:\n model.to(torch_dtype)\n\n model.eval()\n\n return model\n\n\ndef _legacy_load_scheduler(\n cls,\n checkpoint,\n component_name,\n original_config=None,\n **kwargs,\n):\n scheduler_type = kwargs.get(\"scheduler_type\", None)\n prediction_type = kwargs.get(\"prediction_type\", None)\n\n if scheduler_type is not None:\n deprecation_message = (\n \"Please pass an instance of a Scheduler object directly to the `scheduler` argument in `from_single_file`.\"\n )\n deprecate(\"scheduler_type\", \"1.0.0\", deprecation_message)\n\n if prediction_type is not None:\n deprecation_message = (\n \"Please configure an instance of a Scheduler with the appropriate `prediction_type` \"\n \"and pass the object directly to the `scheduler` argument in `from_single_file`.\"\n )\n deprecate(\"prediction_type\", \"1.0.0\", deprecation_message)\n\n scheduler_config = SCHEDULER_DEFAULT_CONFIG\n model_type = infer_diffusers_model_type(checkpoint=checkpoint)\n\n global_step = checkpoint[\"global_step\"] if \"global_step\" in checkpoint else None\n\n if original_config:\n num_train_timesteps = getattr(original_config[\"model\"][\"params\"], \"timesteps\", 1000)\n else:\n num_train_timesteps = 1000\n\n scheduler_config[\"num_train_timesteps\"] = num_train_timesteps\n\n if model_type == \"v2\":\n if prediction_type is None:\n # NOTE: For stable diffusion 2 base it is recommended to pass `prediction_type==\"epsilon\"` # as it relies on a brittle global step parameter here\n prediction_type = \"epsilon\" if global_step == 875000 else \"v_prediction\"\n\n else:\n prediction_type = prediction_type or \"epsilon\"\n\n scheduler_config[\"prediction_type\"] = prediction_type\n\n if model_type in [\"xl_base\", \"xl_refiner\"]:\n scheduler_type = \"euler\"\n elif model_type == \"playground\":\n scheduler_type = \"edm_dpm_solver_multistep\"\n else:\n if original_config:\n beta_start = original_config[\"model\"][\"params\"].get(\"linear_start\")\n beta_end = original_config[\"model\"][\"params\"].get(\"linear_end\")\n\n else:\n beta_start = 0.02\n beta_end = 0.085\n\n scheduler_config[\"beta_start\"] = beta_start\n scheduler_config[\"beta_end\"] = beta_end\n scheduler_config[\"beta_schedule\"] = \"scaled_linear\"\n scheduler_config[\"clip_sample\"] = False\n scheduler_config[\"set_alpha_to_one\"] = False\n\n # to deal with an edge case StableDiffusionUpscale pipeline has two schedulers\n if component_name == \"low_res_scheduler\":\n return cls.from_config(\n {\n \"beta_end\": 0.02,\n \"beta_schedule\": \"scaled_linear\",\n \"beta_start\": 0.0001,\n \"clip_sample\": True,\n \"num_train_timesteps\": 1000,\n \"prediction_type\": \"epsilon\",\n \"trained_betas\": None,\n \"variance_type\": \"fixed_small\",\n }\n )\n\n if scheduler_type is None:\n return cls.from_config(scheduler_config)\n\n elif scheduler_type == \"pndm\":\n scheduler_config[\"skip_prk_steps\"] = True\n scheduler = PNDMScheduler.from_config(scheduler_config)\n\n elif scheduler_type == \"lms\":\n scheduler = LMSDiscreteScheduler.from_config(scheduler_config)\n\n elif scheduler_type == \"heun\":\n scheduler = HeunDiscreteScheduler.from_config(scheduler_config)\n\n elif scheduler_type == \"euler\":\n scheduler = EulerDiscreteScheduler.from_config(scheduler_config)\n\n elif scheduler_type == \"euler-ancestral\":\n scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler_config)\n\n elif scheduler_type == \"dpm\":\n scheduler = DPMSolverMultistepScheduler.from_config(scheduler_config)\n\n elif scheduler_type == \"ddim\":\n scheduler = DDIMScheduler.from_config(scheduler_config)\n\n elif scheduler_type == \"edm_dpm_solver_multistep\":\n scheduler_config = {\n \"algorithm_type\": \"dpmsolver++\",\n \"dynamic_thresholding_ratio\": 0.995,\n \"euler_at_final\": False,\n \"final_sigmas_type\": \"zero\",\n \"lower_order_final\": True,\n \"num_train_timesteps\": 1000,\n \"prediction_type\": \"epsilon\",\n \"rho\": 7.0,\n \"sample_max_value\": 1.0,\n \"sigma_data\": 0.5,\n \"sigma_max\": 80.0,\n \"sigma_min\": 0.002,\n \"solver_order\": 2,\n \"solver_type\": \"midpoint\",\n \"thresholding\": False,\n }\n scheduler = EDMDPMSolverMultistepScheduler(**scheduler_config)\n\n else:\n raise ValueError(f\"Scheduler of type {scheduler_type} doesn't exist!\")\n\n return scheduler\n\n\ndef _legacy_load_clip_tokenizer(cls, checkpoint, config=None, local_files_only=False):\n if config:\n config = {\"pretrained_model_name_or_path\": config}\n else:\n config = fetch_diffusers_config(checkpoint)\n\n if is_clip_model(checkpoint) or is_clip_sdxl_model(checkpoint):\n clip_config = \"openai/clip-vit-large-patch14\"\n config[\"pretrained_model_name_or_path\"] = clip_config\n subfolder = \"\"\n\n elif is_open_clip_model(checkpoint):\n clip_config = \"stabilityai/stable-diffusion-2\"\n config[\"pretrained_model_name_or_path\"] = clip_config\n subfolder = \"tokenizer\"\n\n else:\n clip_config = \"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\"\n config[\"pretrained_model_name_or_path\"] = clip_config\n subfolder = \"\"\n\n tokenizer = cls.from_pretrained(**config, subfolder=subfolder, local_files_only=local_files_only)\n\n return tokenizer\n\n\ndef _legacy_load_safety_checker(local_files_only, torch_dtype):\n # Support for loading safety checker components using the deprecated\n # `load_safety_checker` argument.\n\n from ..pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\n\n feature_extractor = AutoImageProcessor.from_pretrained(\n \"CompVis/stable-diffusion-safety-checker\", local_files_only=local_files_only, torch_dtype=torch_dtype\n )\n safety_checker = StableDiffusionSafetyChecker.from_pretrained(\n \"CompVis/stable-diffusion-safety-checker\", local_files_only=local_files_only, torch_dtype=torch_dtype\n )\n\n return {\"safety_checker\": safety_checker, \"feature_extractor\": feature_extractor}\n\n\n# in SD3 original implementation of AdaLayerNormContinuous, it split linear projection output into shift, scale;\n# while in diffusers it split into scale, shift. Here we swap the linear projection weights in order to be able to use diffusers implementation\ndef swap_scale_shift(weight, dim):\n shift, scale = weight.chunk(2, dim=0)\n new_weight = torch.cat([scale, shift], dim=0)\n return new_weight\n\n\ndef convert_sd3_transformer_checkpoint_to_diffusers(checkpoint, **kwargs):\n converted_state_dict = {}\n keys = list(checkpoint.keys())\n for k in keys:\n if \"model.diffusion_model.\" in k:\n checkpoint[k.replace(\"model.diffusion_model.\", \"\")] = checkpoint.pop(k)\n\n num_layers = list(set(int(k.split(\".\", 2)[1]) for k in checkpoint if \"joint_blocks\" in k))[-1] + 1 # noqa: C401\n caption_projection_dim = 1536\n\n # Positional and patch embeddings.\n converted_state_dict[\"pos_embed.pos_embed\"] = checkpoint.pop(\"pos_embed\")\n converted_state_dict[\"pos_embed.proj.weight\"] = checkpoint.pop(\"x_embedder.proj.weight\")\n converted_state_dict[\"pos_embed.proj.bias\"] = checkpoint.pop(\"x_embedder.proj.bias\")\n\n # Timestep embeddings.\n converted_state_dict[\"time_text_embed.timestep_embedder.linear_1.weight\"] = checkpoint.pop(\n \"t_embedder.mlp.0.weight\"\n )\n converted_state_dict[\"time_text_embed.timestep_embedder.linear_1.bias\"] = checkpoint.pop(\"t_embedder.mlp.0.bias\")\n converted_state_dict[\"time_text_embed.timestep_embedder.linear_2.weight\"] = checkpoint.pop(\n \"t_embedder.mlp.2.weight\"\n )\n converted_state_dict[\"time_text_embed.timestep_embedder.linear_2.bias\"] = checkpoint.pop(\"t_embedder.mlp.2.bias\")\n\n # Context projections.\n converted_state_dict[\"context_embedder.weight\"] = checkpoint.pop(\"context_embedder.weight\")\n converted_state_dict[\"context_embedder.bias\"] = checkpoint.pop(\"context_embedder.bias\")\n\n # Pooled context projection.\n converted_state_dict[\"time_text_embed.text_embedder.linear_1.weight\"] = checkpoint.pop(\"y_embedder.mlp.0.weight\")\n converted_state_dict[\"time_text_embed.text_embedder.linear_1.bias\"] = checkpoint.pop(\"y_embedder.mlp.0.bias\")\n converted_state_dict[\"time_text_embed.text_embedder.linear_2.weight\"] = checkpoint.pop(\"y_embedder.mlp.2.weight\")\n converted_state_dict[\"time_text_embed.text_embedder.linear_2.bias\"] = checkpoint.pop(\"y_embedder.mlp.2.bias\")\n\n # Transformer blocks 🎸.\n for i in range(num_layers):\n # Q, K, V\n sample_q, sample_k, sample_v = torch.chunk(\n checkpoint.pop(f\"joint_blocks.{i}.x_block.attn.qkv.weight\"), 3, dim=0\n )\n context_q, context_k, context_v = torch.chunk(\n checkpoint.pop(f\"joint_blocks.{i}.context_block.attn.qkv.weight\"), 3, dim=0\n )\n sample_q_bias, sample_k_bias, sample_v_bias = torch.chunk(\n checkpoint.pop(f\"joint_blocks.{i}.x_block.attn.qkv.bias\"), 3, dim=0\n )\n context_q_bias, context_k_bias, context_v_bias = torch.chunk(\n checkpoint.pop(f\"joint_blocks.{i}.context_block.attn.qkv.bias\"), 3, dim=0\n )\n\n converted_state_dict[f\"transformer_blocks.{i}.attn.to_q.weight\"] = torch.cat([sample_q])\n converted_state_dict[f\"transformer_blocks.{i}.attn.to_q.bias\"] = torch.cat([sample_q_bias])\n converted_state_dict[f\"transformer_blocks.{i}.attn.to_k.weight\"] = torch.cat([sample_k])\n converted_state_dict[f\"transformer_blocks.{i}.attn.to_k.bias\"] = torch.cat([sample_k_bias])\n converted_state_dict[f\"transformer_blocks.{i}.attn.to_v.weight\"] = torch.cat([sample_v])\n converted_state_dict[f\"transformer_blocks.{i}.attn.to_v.bias\"] = torch.cat([sample_v_bias])\n\n converted_state_dict[f\"transformer_blocks.{i}.attn.add_q_proj.weight\"] = torch.cat([context_q])\n converted_state_dict[f\"transformer_blocks.{i}.attn.add_q_proj.bias\"] = torch.cat([context_q_bias])\n converted_state_dict[f\"transformer_blocks.{i}.attn.add_k_proj.weight\"] = torch.cat([context_k])\n converted_state_dict[f\"transformer_blocks.{i}.attn.add_k_proj.bias\"] = torch.cat([context_k_bias])\n converted_state_dict[f\"transformer_blocks.{i}.attn.add_v_proj.weight\"] = torch.cat([context_v])\n converted_state_dict[f\"transformer_blocks.{i}.attn.add_v_proj.bias\"] = torch.cat([context_v_bias])\n\n # output projections.\n converted_state_dict[f\"transformer_blocks.{i}.attn.to_out.0.weight\"] = checkpoint.pop(\n f\"joint_blocks.{i}.x_block.attn.proj.weight\"\n )\n converted_state_dict[f\"transformer_blocks.{i}.attn.to_out.0.bias\"] = checkpoint.pop(\n f\"joint_blocks.{i}.x_block.attn.proj.bias\"\n )\n if not (i == num_layers - 1):\n converted_state_dict[f\"transformer_blocks.{i}.attn.to_add_out.weight\"] = checkpoint.pop(\n f\"joint_blocks.{i}.context_block.attn.proj.weight\"\n )\n converted_state_dict[f\"transformer_blocks.{i}.attn.to_add_out.bias\"] = checkpoint.pop(\n f\"joint_blocks.{i}.context_block.attn.proj.bias\"\n )\n\n # norms.\n converted_state_dict[f\"transformer_blocks.{i}.norm1.linear.weight\"] = checkpoint.pop(\n f\"joint_blocks.{i}.x_block.adaLN_modulation.1.weight\"\n )\n converted_state_dict[f\"transformer_blocks.{i}.norm1.linear.bias\"] = checkpoint.pop(\n f\"joint_blocks.{i}.x_block.adaLN_modulation.1.bias\"\n )\n if not (i == num_layers - 1):\n converted_state_dict[f\"transformer_blocks.{i}.norm1_context.linear.weight\"] = checkpoint.pop(\n f\"joint_blocks.{i}.context_block.adaLN_modulation.1.weight\"\n )\n converted_state_dict[f\"transformer_blocks.{i}.norm1_context.linear.bias\"] = checkpoint.pop(\n f\"joint_blocks.{i}.context_block.adaLN_modulation.1.bias\"\n )\n else:\n converted_state_dict[f\"transformer_blocks.{i}.norm1_context.linear.weight\"] = swap_scale_shift(\n checkpoint.pop(f\"joint_blocks.{i}.context_block.adaLN_modulation.1.weight\"),\n dim=caption_projection_dim,\n )\n converted_state_dict[f\"transformer_blocks.{i}.norm1_context.linear.bias\"] = swap_scale_shift(\n checkpoint.pop(f\"joint_blocks.{i}.context_block.adaLN_modulation.1.bias\"),\n dim=caption_projection_dim,\n )\n\n # ffs.\n converted_state_dict[f\"transformer_blocks.{i}.ff.net.0.proj.weight\"] = checkpoint.pop(\n f\"joint_blocks.{i}.x_block.mlp.fc1.weight\"\n )\n converted_state_dict[f\"transformer_blocks.{i}.ff.net.0.proj.bias\"] = checkpoint.pop(\n f\"joint_blocks.{i}.x_block.mlp.fc1.bias\"\n )\n converted_state_dict[f\"transformer_blocks.{i}.ff.net.2.weight\"] = checkpoint.pop(\n f\"joint_blocks.{i}.x_block.mlp.fc2.weight\"\n )\n converted_state_dict[f\"transformer_blocks.{i}.ff.net.2.bias\"] = checkpoint.pop(\n f\"joint_blocks.{i}.x_block.mlp.fc2.bias\"\n )\n if not (i == num_layers - 1):\n converted_state_dict[f\"transformer_blocks.{i}.ff_context.net.0.proj.weight\"] = checkpoint.pop(\n f\"joint_blocks.{i}.context_block.mlp.fc1.weight\"\n )\n converted_state_dict[f\"transformer_blocks.{i}.ff_context.net.0.proj.bias\"] = checkpoint.pop(\n f\"joint_blocks.{i}.context_block.mlp.fc1.bias\"\n )\n converted_state_dict[f\"transformer_blocks.{i}.ff_context.net.2.weight\"] = checkpoint.pop(\n f\"joint_blocks.{i}.context_block.mlp.fc2.weight\"\n )\n converted_state_dict[f\"transformer_blocks.{i}.ff_context.net.2.bias\"] = checkpoint.pop(\n f\"joint_blocks.{i}.context_block.mlp.fc2.bias\"\n )\n\n # Final blocks.\n converted_state_dict[\"proj_out.weight\"] = checkpoint.pop(\"final_layer.linear.weight\")\n converted_state_dict[\"proj_out.bias\"] = checkpoint.pop(\"final_layer.linear.bias\")\n converted_state_dict[\"norm_out.linear.weight\"] = swap_scale_shift(\n checkpoint.pop(\"final_layer.adaLN_modulation.1.weight\"), dim=caption_projection_dim\n )\n converted_state_dict[\"norm_out.linear.bias\"] = swap_scale_shift(\n checkpoint.pop(\"final_layer.adaLN_modulation.1.bias\"), dim=caption_projection_dim\n )\n\n return converted_state_dict\n\n\ndef is_t5_in_single_file(checkpoint):\n if \"text_encoders.t5xxl.transformer.shared.weight\" in checkpoint:\n return True\n\n return False\n\n\ndef convert_sd3_t5_checkpoint_to_diffusers(checkpoint):\n keys = list(checkpoint.keys())\n text_model_dict = {}\n\n remove_prefixes = [\"text_encoders.t5xxl.transformer.\"]\n\n for key in keys:\n for prefix in remove_prefixes:\n if key.startswith(prefix):\n diffusers_key = key.replace(prefix, \"\")\n text_model_dict[diffusers_key] = checkpoint.get(key)\n\n return text_model_dict\n\n\ndef create_diffusers_t5_model_from_checkpoint(\n cls,\n checkpoint,\n subfolder=\"\",\n config=None,\n torch_dtype=None,\n local_files_only=None,\n):\n if config:\n config = {\"pretrained_model_name_or_path\": config}\n else:\n config = fetch_diffusers_config(checkpoint)\n\n model_config = cls.config_class.from_pretrained(**config, subfolder=subfolder, local_files_only=local_files_only)\n ctx = init_empty_weights if is_accelerate_available() else nullcontext\n with ctx():\n model = cls(model_config)\n\n diffusers_format_checkpoint = convert_sd3_t5_checkpoint_to_diffusers(checkpoint)\n\n if is_accelerate_available():\n unexpected_keys = load_model_dict_into_meta(model, diffusers_format_checkpoint, dtype=torch_dtype)\n if model._keys_to_ignore_on_load_unexpected is not None:\n for pat in model._keys_to_ignore_on_load_unexpected:\n unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]\n\n if len(unexpected_keys) > 0:\n logger.warning(\n f\"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \\n {[', '.join(unexpected_keys)]}\"\n )\n\n else:\n model.load_state_dict(diffusers_format_checkpoint)\n" }, { "path": "diffusers/src/diffusers/loaders/textual_inversion.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Dict, List, Optional, Union\n\nimport safetensors\nimport torch\nfrom huggingface_hub.utils import validate_hf_hub_args\nfrom torch import nn\n\nfrom ..models.modeling_utils import load_state_dict\nfrom ..utils import _get_model_file, is_accelerate_available, is_transformers_available, logging\n\n\nif is_transformers_available():\n from transformers import PreTrainedModel, PreTrainedTokenizer\n\nif is_accelerate_available():\n from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module\n\nlogger = logging.get_logger(__name__)\n\nTEXT_INVERSION_NAME = \"learned_embeds.bin\"\nTEXT_INVERSION_NAME_SAFE = \"learned_embeds.safetensors\"\n\n\n@validate_hf_hub_args\ndef load_textual_inversion_state_dicts(pretrained_model_name_or_paths, **kwargs):\n cache_dir = kwargs.pop(\"cache_dir\", None)\n force_download = kwargs.pop(\"force_download\", False)\n resume_download = kwargs.pop(\"resume_download\", None)\n proxies = kwargs.pop(\"proxies\", None)\n local_files_only = kwargs.pop(\"local_files_only\", None)\n token = kwargs.pop(\"token\", None)\n revision = kwargs.pop(\"revision\", None)\n subfolder = kwargs.pop(\"subfolder\", None)\n weight_name = kwargs.pop(\"weight_name\", None)\n use_safetensors = kwargs.pop(\"use_safetensors\", None)\n\n allow_pickle = False\n if use_safetensors is None:\n use_safetensors = True\n allow_pickle = True\n\n user_agent = {\n \"file_type\": \"text_inversion\",\n \"framework\": \"pytorch\",\n }\n state_dicts = []\n for pretrained_model_name_or_path in pretrained_model_name_or_paths:\n if not isinstance(pretrained_model_name_or_path, (dict, torch.Tensor)):\n # 3.1. Load textual inversion file\n model_file = None\n\n # Let's first try to load .safetensors weights\n if (use_safetensors and weight_name is None) or (\n weight_name is not None and weight_name.endswith(\".safetensors\")\n ):\n try:\n model_file = _get_model_file(\n pretrained_model_name_or_path,\n weights_name=weight_name or TEXT_INVERSION_NAME_SAFE,\n cache_dir=cache_dir,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n subfolder=subfolder,\n user_agent=user_agent,\n )\n state_dict = safetensors.torch.load_file(model_file, device=\"cpu\")\n except Exception as e:\n if not allow_pickle:\n raise e\n\n model_file = None\n\n if model_file is None:\n model_file = _get_model_file(\n pretrained_model_name_or_path,\n weights_name=weight_name or TEXT_INVERSION_NAME,\n cache_dir=cache_dir,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n subfolder=subfolder,\n user_agent=user_agent,\n )\n state_dict = load_state_dict(model_file)\n else:\n state_dict = pretrained_model_name_or_path\n\n state_dicts.append(state_dict)\n\n return state_dicts\n\n\nclass TextualInversionLoaderMixin:\n r\"\"\"\n Load Textual Inversion tokens and embeddings to the tokenizer and text encoder.\n \"\"\"\n\n def maybe_convert_prompt(self, prompt: Union[str, List[str]], tokenizer: \"PreTrainedTokenizer\"): # noqa: F821\n r\"\"\"\n Processes prompts that include a special token corresponding to a multi-vector textual inversion embedding to\n be replaced with multiple special tokens each corresponding to one of the vectors. If the prompt has no textual\n inversion token or if the textual inversion token is a single vector, the input prompt is returned.\n\n Parameters:\n prompt (`str` or list of `str`):\n The prompt or prompts to guide the image generation.\n tokenizer (`PreTrainedTokenizer`):\n The tokenizer responsible for encoding the prompt into input tokens.\n\n Returns:\n `str` or list of `str`: The converted prompt\n \"\"\"\n if not isinstance(prompt, List):\n prompts = [prompt]\n else:\n prompts = prompt\n\n prompts = [self._maybe_convert_prompt(p, tokenizer) for p in prompts]\n\n if not isinstance(prompt, List):\n return prompts[0]\n\n return prompts\n\n def _maybe_convert_prompt(self, prompt: str, tokenizer: \"PreTrainedTokenizer\"): # noqa: F821\n r\"\"\"\n Maybe convert a prompt into a \"multi vector\"-compatible prompt. If the prompt includes a token that corresponds\n to a multi-vector textual inversion embedding, this function will process the prompt so that the special token\n is replaced with multiple special tokens each corresponding to one of the vectors. If the prompt has no textual\n inversion token or a textual inversion token that is a single vector, the input prompt is simply returned.\n\n Parameters:\n prompt (`str`):\n The prompt to guide the image generation.\n tokenizer (`PreTrainedTokenizer`):\n The tokenizer responsible for encoding the prompt into input tokens.\n\n Returns:\n `str`: The converted prompt\n \"\"\"\n tokens = tokenizer.tokenize(prompt)\n unique_tokens = set(tokens)\n for token in unique_tokens:\n if token in tokenizer.added_tokens_encoder:\n replacement = token\n i = 1\n while f\"{token}_{i}\" in tokenizer.added_tokens_encoder:\n replacement += f\" {token}_{i}\"\n i += 1\n\n prompt = prompt.replace(token, replacement)\n\n return prompt\n\n def _check_text_inv_inputs(self, tokenizer, text_encoder, pretrained_model_name_or_paths, tokens):\n if tokenizer is None:\n raise ValueError(\n f\"{self.__class__.__name__} requires `self.tokenizer` or passing a `tokenizer` of type `PreTrainedTokenizer` for calling\"\n f\" `{self.load_textual_inversion.__name__}`\"\n )\n\n if text_encoder is None:\n raise ValueError(\n f\"{self.__class__.__name__} requires `self.text_encoder` or passing a `text_encoder` of type `PreTrainedModel` for calling\"\n f\" `{self.load_textual_inversion.__name__}`\"\n )\n\n if len(pretrained_model_name_or_paths) > 1 and len(pretrained_model_name_or_paths) != len(tokens):\n raise ValueError(\n f\"You have passed a list of models of length {len(pretrained_model_name_or_paths)}, and list of tokens of length {len(tokens)} \"\n f\"Make sure both lists have the same length.\"\n )\n\n valid_tokens = [t for t in tokens if t is not None]\n if len(set(valid_tokens)) < len(valid_tokens):\n raise ValueError(f\"You have passed a list of tokens that contains duplicates: {tokens}\")\n\n @staticmethod\n def _retrieve_tokens_and_embeddings(tokens, state_dicts, tokenizer):\n all_tokens = []\n all_embeddings = []\n for state_dict, token in zip(state_dicts, tokens):\n if isinstance(state_dict, torch.Tensor):\n if token is None:\n raise ValueError(\n \"You are trying to load a textual inversion embedding that has been saved as a PyTorch tensor. Make sure to pass the name of the corresponding token in this case: `token=...`.\"\n )\n loaded_token = token\n embedding = state_dict\n elif len(state_dict) == 1:\n # diffusers\n loaded_token, embedding = next(iter(state_dict.items()))\n elif \"string_to_param\" in state_dict:\n # A1111\n loaded_token = state_dict[\"name\"]\n embedding = state_dict[\"string_to_param\"][\"*\"]\n else:\n raise ValueError(\n f\"Loaded state dictionary is incorrect: {state_dict}. \\n\\n\"\n \"Please verify that the loaded state dictionary of the textual embedding either only has a single key or includes the `string_to_param`\"\n \" input key.\"\n )\n\n if token is not None and loaded_token != token:\n logger.info(f\"The loaded token: {loaded_token} is overwritten by the passed token {token}.\")\n else:\n token = loaded_token\n\n if token in tokenizer.get_vocab():\n raise ValueError(\n f\"Token {token} already in tokenizer vocabulary. Please choose a different token name or remove {token} and embedding from the tokenizer and text encoder.\"\n )\n\n all_tokens.append(token)\n all_embeddings.append(embedding)\n\n return all_tokens, all_embeddings\n\n @staticmethod\n def _extend_tokens_and_embeddings(tokens, embeddings, tokenizer):\n all_tokens = []\n all_embeddings = []\n\n for embedding, token in zip(embeddings, tokens):\n if f\"{token}_1\" in tokenizer.get_vocab():\n multi_vector_tokens = [token]\n i = 1\n while f\"{token}_{i}\" in tokenizer.added_tokens_encoder:\n multi_vector_tokens.append(f\"{token}_{i}\")\n i += 1\n\n raise ValueError(\n f\"Multi-vector Token {multi_vector_tokens} already in tokenizer vocabulary. Please choose a different token name or remove the {multi_vector_tokens} and embedding from the tokenizer and text encoder.\"\n )\n\n is_multi_vector = len(embedding.shape) > 1 and embedding.shape[0] > 1\n if is_multi_vector:\n all_tokens += [token] + [f\"{token}_{i}\" for i in range(1, embedding.shape[0])]\n all_embeddings += [e for e in embedding] # noqa: C416\n else:\n all_tokens += [token]\n all_embeddings += [embedding[0]] if len(embedding.shape) > 1 else [embedding]\n\n return all_tokens, all_embeddings\n\n @validate_hf_hub_args\n def load_textual_inversion(\n self,\n pretrained_model_name_or_path: Union[str, List[str], Dict[str, torch.Tensor], List[Dict[str, torch.Tensor]]],\n token: Optional[Union[str, List[str]]] = None,\n tokenizer: Optional[\"PreTrainedTokenizer\"] = None, # noqa: F821\n text_encoder: Optional[\"PreTrainedModel\"] = None, # noqa: F821\n **kwargs,\n ):\n r\"\"\"\n Load Textual Inversion embeddings into the text encoder of [`StableDiffusionPipeline`] (both 🤗 Diffusers and\n Automatic1111 formats are supported).\n\n Parameters:\n pretrained_model_name_or_path (`str` or `os.PathLike` or `List[str or os.PathLike]` or `Dict` or `List[Dict]`):\n Can be either one of the following or a list of them:\n\n - A string, the *model id* (for example `sd-concepts-library/low-poly-hd-logos-icons`) of a\n pretrained model hosted on the Hub.\n - A path to a *directory* (for example `./my_text_inversion_directory/`) containing the textual\n inversion weights.\n - A path to a *file* (for example `./my_text_inversions.pt`) containing textual inversion weights.\n - A [torch state\n dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).\n\n token (`str` or `List[str]`, *optional*):\n Override the token to use for the textual inversion weights. If `pretrained_model_name_or_path` is a\n list, then `token` must also be a list of equal length.\n text_encoder ([`~transformers.CLIPTextModel`], *optional*):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n If not specified, function will take self.tokenizer.\n tokenizer ([`~transformers.CLIPTokenizer`], *optional*):\n A `CLIPTokenizer` to tokenize text. If not specified, function will take self.tokenizer.\n weight_name (`str`, *optional*):\n Name of a custom weight file. This should be used when:\n\n - The saved textual inversion file is in 🤗 Diffusers format, but was saved under a specific weight\n name such as `text_inv.bin`.\n - The saved textual inversion file is in the Automatic1111 format.\n cache_dir (`Union[str, os.PathLike]`, *optional*):\n Path to a directory where a downloaded pretrained model configuration is cached if the standard cache\n is not used.\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\n cached versions if they exist.\n resume_download:\n Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1\n of Diffusers.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\n local_files_only (`bool`, *optional*, defaults to `False`):\n Whether to only load local model weights and configuration files or not. If set to `True`, the model\n won't be downloaded from the Hub.\n token (`str` or *bool*, *optional*):\n The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from\n `diffusers-cli login` (stored in `~/.huggingface`) is used.\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier\n allowed by Git.\n subfolder (`str`, *optional*, defaults to `\"\"`):\n The subfolder location of a model file within a larger model repository on the Hub or locally.\n mirror (`str`, *optional*):\n Mirror source to resolve accessibility issues if you're downloading a model in China. We do not\n guarantee the timeliness or safety of the source, and you should refer to the mirror site for more\n information.\n\n Example:\n\n To load a Textual Inversion embedding vector in 🤗 Diffusers format:\n\n ```py\n from diffusers import StableDiffusionPipeline\n import torch\n\n model_id = \"runwayml/stable-diffusion-v1-5\"\n pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(\"cuda\")\n\n pipe.load_textual_inversion(\"sd-concepts-library/cat-toy\")\n\n prompt = \"A backpack\"\n\n image = pipe(prompt, num_inference_steps=50).images[0]\n image.save(\"cat-backpack.png\")\n ```\n\n To load a Textual Inversion embedding vector in Automatic1111 format, make sure to download the vector first\n (for example from [civitAI](https://civitai.com/models/3036?modelVersionId=9857)) and then load the vector\n locally:\n\n ```py\n from diffusers import StableDiffusionPipeline\n import torch\n\n model_id = \"runwayml/stable-diffusion-v1-5\"\n pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(\"cuda\")\n\n pipe.load_textual_inversion(\"./charturnerv2.pt\", token=\"charturnerv2\")\n\n prompt = \"charturnerv2, multiple views of the same character in the same outfit, a character turnaround of a woman wearing a black jacket and red shirt, best quality, intricate details.\"\n\n image = pipe(prompt, num_inference_steps=50).images[0]\n image.save(\"character.png\")\n ```\n\n \"\"\"\n # 1. Set correct tokenizer and text encoder\n tokenizer = tokenizer or getattr(self, \"tokenizer\", None)\n text_encoder = text_encoder or getattr(self, \"text_encoder\", None)\n\n # 2. Normalize inputs\n pretrained_model_name_or_paths = (\n [pretrained_model_name_or_path]\n if not isinstance(pretrained_model_name_or_path, list)\n else pretrained_model_name_or_path\n )\n tokens = [token] if not isinstance(token, list) else token\n if tokens[0] is None:\n tokens = tokens * len(pretrained_model_name_or_paths)\n\n # 3. Check inputs\n self._check_text_inv_inputs(tokenizer, text_encoder, pretrained_model_name_or_paths, tokens)\n\n # 4. Load state dicts of textual embeddings\n state_dicts = load_textual_inversion_state_dicts(pretrained_model_name_or_paths, **kwargs)\n\n # 4.1 Handle the special case when state_dict is a tensor that contains n embeddings for n tokens\n if len(tokens) > 1 and len(state_dicts) == 1:\n if isinstance(state_dicts[0], torch.Tensor):\n state_dicts = list(state_dicts[0])\n if len(tokens) != len(state_dicts):\n raise ValueError(\n f\"You have passed a state_dict contains {len(state_dicts)} embeddings, and list of tokens of length {len(tokens)} \"\n f\"Make sure both have the same length.\"\n )\n\n # 4. Retrieve tokens and embeddings\n tokens, embeddings = self._retrieve_tokens_and_embeddings(tokens, state_dicts, tokenizer)\n\n # 5. Extend tokens and embeddings for multi vector\n tokens, embeddings = self._extend_tokens_and_embeddings(tokens, embeddings, tokenizer)\n\n # 6. Make sure all embeddings have the correct size\n expected_emb_dim = text_encoder.get_input_embeddings().weight.shape[-1]\n if any(expected_emb_dim != emb.shape[-1] for emb in embeddings):\n raise ValueError(\n \"Loaded embeddings are of incorrect shape. Expected each textual inversion embedding \"\n \"to be of shape {input_embeddings.shape[-1]}, but are {embeddings.shape[-1]} \"\n )\n\n # 7. Now we can be sure that loading the embedding matrix works\n # < Unsafe code:\n\n # 7.1 Offload all hooks in case the pipeline was cpu offloaded before make sure, we offload and onload again\n is_model_cpu_offload = False\n is_sequential_cpu_offload = False\n if self.hf_device_map is None:\n for _, component in self.components.items():\n if isinstance(component, nn.Module):\n if hasattr(component, \"_hf_hook\"):\n is_model_cpu_offload = isinstance(getattr(component, \"_hf_hook\"), CpuOffload)\n is_sequential_cpu_offload = (\n isinstance(getattr(component, \"_hf_hook\"), AlignDevicesHook)\n or hasattr(component._hf_hook, \"hooks\")\n and isinstance(component._hf_hook.hooks[0], AlignDevicesHook)\n )\n logger.info(\n \"Accelerate hooks detected. Since you have called `load_textual_inversion()`, the previous hooks will be first removed. Then the textual inversion parameters will be loaded and the hooks will be applied again.\"\n )\n remove_hook_from_module(component, recurse=is_sequential_cpu_offload)\n\n # 7.2 save expected device and dtype\n device = text_encoder.device\n dtype = text_encoder.dtype\n\n # 7.3 Increase token embedding matrix\n text_encoder.resize_token_embeddings(len(tokenizer) + len(tokens))\n input_embeddings = text_encoder.get_input_embeddings().weight\n\n # 7.4 Load token and embedding\n for token, embedding in zip(tokens, embeddings):\n # add tokens and get ids\n tokenizer.add_tokens(token)\n token_id = tokenizer.convert_tokens_to_ids(token)\n input_embeddings.data[token_id] = embedding\n logger.info(f\"Loaded textual inversion embedding for {token}.\")\n\n input_embeddings.to(dtype=dtype, device=device)\n\n # 7.5 Offload the model again\n if is_model_cpu_offload:\n self.enable_model_cpu_offload()\n elif is_sequential_cpu_offload:\n self.enable_sequential_cpu_offload()\n\n # / Unsafe Code >\n\n def unload_textual_inversion(\n self,\n tokens: Optional[Union[str, List[str]]] = None,\n tokenizer: Optional[\"PreTrainedTokenizer\"] = None,\n text_encoder: Optional[\"PreTrainedModel\"] = None,\n ):\n r\"\"\"\n Unload Textual Inversion embeddings from the text encoder of [`StableDiffusionPipeline`]\n\n Example:\n ```py\n from diffusers import AutoPipelineForText2Image\n import torch\n\n pipeline = AutoPipelineForText2Image.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n\n # Example 1\n pipeline.load_textual_inversion(\"sd-concepts-library/gta5-artwork\")\n pipeline.load_textual_inversion(\"sd-concepts-library/moeb-style\")\n\n # Remove all token embeddings\n pipeline.unload_textual_inversion()\n\n # Example 2\n pipeline.load_textual_inversion(\"sd-concepts-library/moeb-style\")\n pipeline.load_textual_inversion(\"sd-concepts-library/gta5-artwork\")\n\n # Remove just one token\n pipeline.unload_textual_inversion(\"\")\n\n # Example 3: unload from SDXL\n pipeline = AutoPipelineForText2Image.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\")\n embedding_path = hf_hub_download(\n repo_id=\"linoyts/web_y2k\", filename=\"web_y2k_emb.safetensors\", repo_type=\"model\"\n )\n\n # load embeddings to the text encoders\n state_dict = load_file(embedding_path)\n\n # load embeddings of text_encoder 1 (CLIP ViT-L/14)\n pipeline.load_textual_inversion(\n state_dict[\"clip_l\"],\n token=[\"\", \"\"],\n text_encoder=pipeline.text_encoder,\n tokenizer=pipeline.tokenizer,\n )\n # load embeddings of text_encoder 2 (CLIP ViT-G/14)\n pipeline.load_textual_inversion(\n state_dict[\"clip_g\"],\n token=[\"\", \"\"],\n text_encoder=pipeline.text_encoder_2,\n tokenizer=pipeline.tokenizer_2,\n )\n\n # Unload explicitly from both text encoders abd tokenizers\n pipeline.unload_textual_inversion(\n tokens=[\"\", \"\"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer\n )\n pipeline.unload_textual_inversion(\n tokens=[\"\", \"\"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2\n )\n ```\n \"\"\"\n\n tokenizer = tokenizer or getattr(self, \"tokenizer\", None)\n text_encoder = text_encoder or getattr(self, \"text_encoder\", None)\n\n # Get textual inversion tokens and ids\n token_ids = []\n last_special_token_id = None\n\n if tokens:\n if isinstance(tokens, str):\n tokens = [tokens]\n for added_token_id, added_token in tokenizer.added_tokens_decoder.items():\n if not added_token.special:\n if added_token.content in tokens:\n token_ids.append(added_token_id)\n else:\n last_special_token_id = added_token_id\n if len(token_ids) == 0:\n raise ValueError(\"No tokens to remove found\")\n else:\n tokens = []\n for added_token_id, added_token in tokenizer.added_tokens_decoder.items():\n if not added_token.special:\n token_ids.append(added_token_id)\n tokens.append(added_token.content)\n else:\n last_special_token_id = added_token_id\n\n # Delete from tokenizer\n for token_id, token_to_remove in zip(token_ids, tokens):\n del tokenizer._added_tokens_decoder[token_id]\n del tokenizer._added_tokens_encoder[token_to_remove]\n\n # Make all token ids sequential in tokenizer\n key_id = 1\n for token_id in tokenizer.added_tokens_decoder:\n if token_id > last_special_token_id and token_id > last_special_token_id + key_id:\n token = tokenizer._added_tokens_decoder[token_id]\n tokenizer._added_tokens_decoder[last_special_token_id + key_id] = token\n del tokenizer._added_tokens_decoder[token_id]\n tokenizer._added_tokens_encoder[token.content] = last_special_token_id + key_id\n key_id += 1\n tokenizer._update_trie()\n\n # Delete from text encoder\n text_embedding_dim = text_encoder.get_input_embeddings().embedding_dim\n temp_text_embedding_weights = text_encoder.get_input_embeddings().weight\n text_embedding_weights = temp_text_embedding_weights[: last_special_token_id + 1]\n to_append = []\n for i in range(last_special_token_id + 1, temp_text_embedding_weights.shape[0]):\n if i not in token_ids:\n to_append.append(temp_text_embedding_weights[i].unsqueeze(0))\n if len(to_append) > 0:\n to_append = torch.cat(to_append, dim=0)\n text_embedding_weights = torch.cat([text_embedding_weights, to_append], dim=0)\n text_embeddings_filtered = nn.Embedding(text_embedding_weights.shape[0], text_embedding_dim)\n text_embeddings_filtered.weight.data = text_embedding_weights\n text_encoder.set_input_embeddings(text_embeddings_filtered)\n" }, { "path": "diffusers/src/diffusers/loaders/unet.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport inspect\nimport os\nfrom collections import defaultdict\nfrom contextlib import nullcontext\nfrom functools import partial\nfrom pathlib import Path\nfrom typing import Callable, Dict, List, Optional, Union\n\nimport safetensors\nimport torch\nimport torch.nn.functional as F\nfrom huggingface_hub.utils import validate_hf_hub_args\nfrom torch import nn\n\nfrom ..models.embeddings import (\n ImageProjection,\n IPAdapterFaceIDImageProjection,\n IPAdapterFaceIDPlusImageProjection,\n IPAdapterFullImageProjection,\n IPAdapterPlusImageProjection,\n MultiIPAdapterImageProjection,\n)\nfrom ..models.modeling_utils import load_model_dict_into_meta, load_state_dict\nfrom ..utils import (\n USE_PEFT_BACKEND,\n _get_model_file,\n convert_unet_state_dict_to_peft,\n delete_adapter_layers,\n get_adapter_name,\n get_peft_kwargs,\n is_accelerate_available,\n is_peft_version,\n is_torch_version,\n logging,\n set_adapter_layers,\n set_weights_and_activate_adapters,\n)\nfrom .lora import LORA_WEIGHT_NAME, LORA_WEIGHT_NAME_SAFE, TEXT_ENCODER_NAME, UNET_NAME\nfrom .unet_loader_utils import _maybe_expand_lora_scales\nfrom .utils import AttnProcsLayers\n\n\nif is_accelerate_available():\n from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module\n\nlogger = logging.get_logger(__name__)\n\n\nCUSTOM_DIFFUSION_WEIGHT_NAME = \"pytorch_custom_diffusion_weights.bin\"\nCUSTOM_DIFFUSION_WEIGHT_NAME_SAFE = \"pytorch_custom_diffusion_weights.safetensors\"\n\n\nclass UNet2DConditionLoadersMixin:\n \"\"\"\n Load LoRA layers into a [`UNet2DCondtionModel`].\n \"\"\"\n\n text_encoder_name = TEXT_ENCODER_NAME\n unet_name = UNET_NAME\n\n @validate_hf_hub_args\n def load_attn_procs(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):\n r\"\"\"\n Load pretrained attention processor layers into [`UNet2DConditionModel`]. Attention processor layers have to be\n defined in\n [`attention_processor.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py)\n and be a `torch.nn.Module` class. Currently supported: LoRA, Custom Diffusion. For LoRA, one must install\n `peft`: `pip install -U peft`.\n\n Parameters:\n pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):\n Can be either:\n\n - A string, the model id (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on\n the Hub.\n - A path to a directory (for example `./my_model_directory`) containing the model weights saved\n with [`ModelMixin.save_pretrained`].\n - A [torch state\n dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).\n\n cache_dir (`Union[str, os.PathLike]`, *optional*):\n Path to a directory where a downloaded pretrained model configuration is cached if the standard cache\n is not used.\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\n cached versions if they exist.\n resume_download:\n Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1\n of Diffusers.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\n local_files_only (`bool`, *optional*, defaults to `False`):\n Whether to only load local model weights and configuration files or not. If set to `True`, the model\n won't be downloaded from the Hub.\n token (`str` or *bool*, *optional*):\n The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from\n `diffusers-cli login` (stored in `~/.huggingface`) is used.\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier\n allowed by Git.\n subfolder (`str`, *optional*, defaults to `\"\"`):\n The subfolder location of a model file within a larger model repository on the Hub or locally.\n network_alphas (`Dict[str, float]`):\n The value of the network alpha used for stable learning and preventing underflow. This value has the\n same meaning as the `--network_alpha` option in the kohya-ss trainer script. Refer to [this\n link](https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning).\n adapter_name (`str`, *optional*, defaults to None):\n Adapter name to be used for referencing the loaded adapter model. If not specified, it will use\n `default_{i}` where i is the total number of adapters being loaded.\n weight_name (`str`, *optional*, defaults to None):\n Name of the serialized state dict file.\n\n Example:\n\n ```py\n from diffusers import AutoPipelineForText2Image\n import torch\n\n pipeline = AutoPipelineForText2Image.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16\n ).to(\"cuda\")\n pipeline.unet.load_attn_procs(\n \"jbilcke-hf/sdxl-cinematic-1\", weight_name=\"pytorch_lora_weights.safetensors\", adapter_name=\"cinematic\"\n )\n ```\n \"\"\"\n cache_dir = kwargs.pop(\"cache_dir\", None)\n force_download = kwargs.pop(\"force_download\", False)\n resume_download = kwargs.pop(\"resume_download\", None)\n proxies = kwargs.pop(\"proxies\", None)\n local_files_only = kwargs.pop(\"local_files_only\", None)\n token = kwargs.pop(\"token\", None)\n revision = kwargs.pop(\"revision\", None)\n subfolder = kwargs.pop(\"subfolder\", None)\n weight_name = kwargs.pop(\"weight_name\", None)\n use_safetensors = kwargs.pop(\"use_safetensors\", None)\n adapter_name = kwargs.pop(\"adapter_name\", None)\n _pipeline = kwargs.pop(\"_pipeline\", None)\n network_alphas = kwargs.pop(\"network_alphas\", None)\n allow_pickle = False\n\n if use_safetensors is None:\n use_safetensors = True\n allow_pickle = True\n\n user_agent = {\n \"file_type\": \"attn_procs_weights\",\n \"framework\": \"pytorch\",\n }\n\n model_file = None\n if not isinstance(pretrained_model_name_or_path_or_dict, dict):\n # Let's first try to load .safetensors weights\n if (use_safetensors and weight_name is None) or (\n weight_name is not None and weight_name.endswith(\".safetensors\")\n ):\n try:\n model_file = _get_model_file(\n pretrained_model_name_or_path_or_dict,\n weights_name=weight_name or LORA_WEIGHT_NAME_SAFE,\n cache_dir=cache_dir,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n subfolder=subfolder,\n user_agent=user_agent,\n )\n state_dict = safetensors.torch.load_file(model_file, device=\"cpu\")\n except IOError as e:\n if not allow_pickle:\n raise e\n # try loading non-safetensors weights\n pass\n if model_file is None:\n model_file = _get_model_file(\n pretrained_model_name_or_path_or_dict,\n weights_name=weight_name or LORA_WEIGHT_NAME,\n cache_dir=cache_dir,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n subfolder=subfolder,\n user_agent=user_agent,\n )\n state_dict = load_state_dict(model_file)\n else:\n state_dict = pretrained_model_name_or_path_or_dict\n\n is_custom_diffusion = any(\"custom_diffusion\" in k for k in state_dict.keys())\n is_lora = all((\"lora\" in k or k.endswith(\".alpha\")) for k in state_dict.keys())\n is_model_cpu_offload = False\n is_sequential_cpu_offload = False\n\n if is_custom_diffusion:\n attn_processors = self._process_custom_diffusion(state_dict=state_dict)\n elif is_lora:\n is_model_cpu_offload, is_sequential_cpu_offload = self._process_lora(\n state_dict=state_dict,\n unet_identifier_key=self.unet_name,\n network_alphas=network_alphas,\n adapter_name=adapter_name,\n _pipeline=_pipeline,\n )\n else:\n raise ValueError(\n f\"{model_file} does not seem to be in the correct format expected by Custom Diffusion training.\"\n )\n\n # \n\n def _process_custom_diffusion(self, state_dict):\n from ..models.attention_processor import CustomDiffusionAttnProcessor\n\n attn_processors = {}\n custom_diffusion_grouped_dict = defaultdict(dict)\n for key, value in state_dict.items():\n if len(value) == 0:\n custom_diffusion_grouped_dict[key] = {}\n else:\n if \"to_out\" in key:\n attn_processor_key, sub_key = \".\".join(key.split(\".\")[:-3]), \".\".join(key.split(\".\")[-3:])\n else:\n attn_processor_key, sub_key = \".\".join(key.split(\".\")[:-2]), \".\".join(key.split(\".\")[-2:])\n custom_diffusion_grouped_dict[attn_processor_key][sub_key] = value\n\n for key, value_dict in custom_diffusion_grouped_dict.items():\n if len(value_dict) == 0:\n attn_processors[key] = CustomDiffusionAttnProcessor(\n train_kv=False, train_q_out=False, hidden_size=None, cross_attention_dim=None\n )\n else:\n cross_attention_dim = value_dict[\"to_k_custom_diffusion.weight\"].shape[1]\n hidden_size = value_dict[\"to_k_custom_diffusion.weight\"].shape[0]\n train_q_out = True if \"to_q_custom_diffusion.weight\" in value_dict else False\n attn_processors[key] = CustomDiffusionAttnProcessor(\n train_kv=True,\n train_q_out=train_q_out,\n hidden_size=hidden_size,\n cross_attention_dim=cross_attention_dim,\n )\n attn_processors[key].load_state_dict(value_dict)\n\n return attn_processors\n\n def _process_lora(self, state_dict, unet_identifier_key, network_alphas, adapter_name, _pipeline):\n # This method does the following things:\n # 1. Filters the `state_dict` with keys matching `unet_identifier_key` when using the non-legacy\n # format. For legacy format no filtering is applied.\n # 2. Converts the `state_dict` to the `peft` compatible format.\n # 3. Creates a `LoraConfig` and then injects the converted `state_dict` into the UNet per the\n # `LoraConfig` specs.\n # 4. It also reports if the underlying `_pipeline` has any kind of offloading inside of it.\n if not USE_PEFT_BACKEND:\n raise ValueError(\"PEFT backend is required for this method.\")\n\n from peft import LoraConfig, inject_adapter_in_model, set_peft_model_state_dict\n\n keys = list(state_dict.keys())\n\n unet_keys = [k for k in keys if k.startswith(unet_identifier_key)]\n unet_state_dict = {\n k.replace(f\"{unet_identifier_key}.\", \"\"): v for k, v in state_dict.items() if k in unet_keys\n }\n\n if network_alphas is not None:\n alpha_keys = [k for k in network_alphas.keys() if k.startswith(unet_identifier_key)]\n network_alphas = {\n k.replace(f\"{unet_identifier_key}.\", \"\"): v for k, v in network_alphas.items() if k in alpha_keys\n }\n\n is_model_cpu_offload = False\n is_sequential_cpu_offload = False\n state_dict_to_be_used = unet_state_dict if len(unet_state_dict) > 0 else state_dict\n\n if len(state_dict_to_be_used) > 0:\n if adapter_name in getattr(self, \"peft_config\", {}):\n raise ValueError(\n f\"Adapter name {adapter_name} already in use in the Unet - please select a new adapter name.\"\n )\n\n state_dict = convert_unet_state_dict_to_peft(state_dict_to_be_used)\n\n if network_alphas is not None:\n # The alphas state dict have the same structure as Unet, thus we convert it to peft format using\n # `convert_unet_state_dict_to_peft` method.\n network_alphas = convert_unet_state_dict_to_peft(network_alphas)\n\n rank = {}\n for key, val in state_dict.items():\n if \"lora_B\" in key:\n rank[key] = val.shape[1]\n\n lora_config_kwargs = get_peft_kwargs(rank, network_alphas, state_dict, is_unet=True)\n if \"use_dora\" in lora_config_kwargs:\n if lora_config_kwargs[\"use_dora\"]:\n if is_peft_version(\"<\", \"0.9.0\"):\n raise ValueError(\n \"You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`.\"\n )\n else:\n if is_peft_version(\"<\", \"0.9.0\"):\n lora_config_kwargs.pop(\"use_dora\")\n lora_config = LoraConfig(**lora_config_kwargs)\n\n # adapter_name\n if adapter_name is None:\n adapter_name = get_adapter_name(self)\n\n # In case the pipeline has been already offloaded to CPU - temporarily remove the hooks\n # otherwise loading LoRA weights will lead to an error\n is_model_cpu_offload, is_sequential_cpu_offload = self._optionally_disable_offloading(_pipeline)\n\n inject_adapter_in_model(lora_config, self, adapter_name=adapter_name)\n incompatible_keys = set_peft_model_state_dict(self, state_dict, adapter_name)\n\n if incompatible_keys is not None:\n # check only for unexpected keys\n unexpected_keys = getattr(incompatible_keys, \"unexpected_keys\", None)\n if unexpected_keys:\n logger.warning(\n f\"Loading adapter weights from state_dict led to unexpected keys not found in the model: \"\n f\" {unexpected_keys}. \"\n )\n\n return is_model_cpu_offload, is_sequential_cpu_offload\n\n @classmethod\n # Copied from diffusers.loaders.lora.LoraLoaderMixin._optionally_disable_offloading\n def _optionally_disable_offloading(cls, _pipeline):\n \"\"\"\n Optionally removes offloading in case the pipeline has been already sequentially offloaded to CPU.\n\n Args:\n _pipeline (`DiffusionPipeline`):\n The pipeline to disable offloading for.\n\n Returns:\n tuple:\n A tuple indicating if `is_model_cpu_offload` or `is_sequential_cpu_offload` is True.\n \"\"\"\n is_model_cpu_offload = False\n is_sequential_cpu_offload = False\n\n if _pipeline is not None and _pipeline.hf_device_map is None:\n for _, component in _pipeline.components.items():\n if isinstance(component, nn.Module) and hasattr(component, \"_hf_hook\"):\n if not is_model_cpu_offload:\n is_model_cpu_offload = isinstance(component._hf_hook, CpuOffload)\n if not is_sequential_cpu_offload:\n is_sequential_cpu_offload = (\n isinstance(component._hf_hook, AlignDevicesHook)\n or hasattr(component._hf_hook, \"hooks\")\n and isinstance(component._hf_hook.hooks[0], AlignDevicesHook)\n )\n\n logger.info(\n \"Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again.\"\n )\n remove_hook_from_module(component, recurse=is_sequential_cpu_offload)\n\n return (is_model_cpu_offload, is_sequential_cpu_offload)\n\n def save_attn_procs(\n self,\n save_directory: Union[str, os.PathLike],\n is_main_process: bool = True,\n weight_name: str = None,\n save_function: Callable = None,\n safe_serialization: bool = True,\n **kwargs,\n ):\n r\"\"\"\n Save attention processor layers to a directory so that it can be reloaded with the\n [`~loaders.UNet2DConditionLoadersMixin.load_attn_procs`] method.\n\n Arguments:\n save_directory (`str` or `os.PathLike`):\n Directory to save an attention processor to (will be created if it doesn't exist).\n is_main_process (`bool`, *optional*, defaults to `True`):\n Whether the process calling this is the main process or not. Useful during distributed training and you\n need to call this function on all processes. In this case, set `is_main_process=True` only on the main\n process to avoid race conditions.\n save_function (`Callable`):\n The function to use to save the state dictionary. Useful during distributed training when you need to\n replace `torch.save` with another method. Can be configured with the environment variable\n `DIFFUSERS_SAVE_MODE`.\n safe_serialization (`bool`, *optional*, defaults to `True`):\n Whether to save the model using `safetensors` or with `pickle`.\n\n Example:\n\n ```py\n import torch\n from diffusers import DiffusionPipeline\n\n pipeline = DiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n torch_dtype=torch.float16,\n ).to(\"cuda\")\n pipeline.unet.load_attn_procs(\"path-to-save-model\", weight_name=\"pytorch_custom_diffusion_weights.bin\")\n pipeline.unet.save_attn_procs(\"path-to-save-model\", weight_name=\"pytorch_custom_diffusion_weights.bin\")\n ```\n \"\"\"\n from ..models.attention_processor import (\n CustomDiffusionAttnProcessor,\n CustomDiffusionAttnProcessor2_0,\n CustomDiffusionXFormersAttnProcessor,\n )\n\n if os.path.isfile(save_directory):\n logger.error(f\"Provided path ({save_directory}) should be a directory, not a file\")\n return\n\n is_custom_diffusion = any(\n isinstance(\n x,\n (CustomDiffusionAttnProcessor, CustomDiffusionAttnProcessor2_0, CustomDiffusionXFormersAttnProcessor),\n )\n for (_, x) in self.attn_processors.items()\n )\n if is_custom_diffusion:\n state_dict = self._get_custom_diffusion_state_dict()\n else:\n if not USE_PEFT_BACKEND:\n raise ValueError(\"PEFT backend is required for saving LoRAs using the `save_attn_procs()` method.\")\n\n from peft.utils import get_peft_model_state_dict\n\n state_dict = get_peft_model_state_dict(self)\n\n if save_function is None:\n if safe_serialization:\n\n def save_function(weights, filename):\n return safetensors.torch.save_file(weights, filename, metadata={\"format\": \"pt\"})\n\n else:\n save_function = torch.save\n\n os.makedirs(save_directory, exist_ok=True)\n\n if weight_name is None:\n if safe_serialization:\n weight_name = CUSTOM_DIFFUSION_WEIGHT_NAME_SAFE if is_custom_diffusion else LORA_WEIGHT_NAME_SAFE\n else:\n weight_name = CUSTOM_DIFFUSION_WEIGHT_NAME if is_custom_diffusion else LORA_WEIGHT_NAME\n\n # Save the model\n save_path = Path(save_directory, weight_name).as_posix()\n save_function(state_dict, save_path)\n logger.info(f\"Model weights saved in {save_path}\")\n\n def _get_custom_diffusion_state_dict(self):\n from ..models.attention_processor import (\n CustomDiffusionAttnProcessor,\n CustomDiffusionAttnProcessor2_0,\n CustomDiffusionXFormersAttnProcessor,\n )\n\n model_to_save = AttnProcsLayers(\n {\n y: x\n for (y, x) in self.attn_processors.items()\n if isinstance(\n x,\n (\n CustomDiffusionAttnProcessor,\n CustomDiffusionAttnProcessor2_0,\n CustomDiffusionXFormersAttnProcessor,\n ),\n )\n }\n )\n state_dict = model_to_save.state_dict()\n for name, attn in self.attn_processors.items():\n if len(attn.state_dict()) == 0:\n state_dict[name] = {}\n\n return state_dict\n\n def fuse_lora(self, lora_scale=1.0, safe_fusing=False, adapter_names=None):\n if not USE_PEFT_BACKEND:\n raise ValueError(\"PEFT backend is required for `fuse_lora()`.\")\n\n self.lora_scale = lora_scale\n self._safe_fusing = safe_fusing\n self.apply(partial(self._fuse_lora_apply, adapter_names=adapter_names))\n\n def _fuse_lora_apply(self, module, adapter_names=None):\n from peft.tuners.tuners_utils import BaseTunerLayer\n\n merge_kwargs = {\"safe_merge\": self._safe_fusing}\n\n if isinstance(module, BaseTunerLayer):\n if self.lora_scale != 1.0:\n module.scale_layer(self.lora_scale)\n\n # For BC with prevous PEFT versions, we need to check the signature\n # of the `merge` method to see if it supports the `adapter_names` argument.\n supported_merge_kwargs = list(inspect.signature(module.merge).parameters)\n if \"adapter_names\" in supported_merge_kwargs:\n merge_kwargs[\"adapter_names\"] = adapter_names\n elif \"adapter_names\" not in supported_merge_kwargs and adapter_names is not None:\n raise ValueError(\n \"The `adapter_names` argument is not supported with your PEFT version. Please upgrade\"\n \" to the latest version of PEFT. `pip install -U peft`\"\n )\n\n module.merge(**merge_kwargs)\n\n def unfuse_lora(self):\n if not USE_PEFT_BACKEND:\n raise ValueError(\"PEFT backend is required for `unfuse_lora()`.\")\n self.apply(self._unfuse_lora_apply)\n\n def _unfuse_lora_apply(self, module):\n from peft.tuners.tuners_utils import BaseTunerLayer\n\n if isinstance(module, BaseTunerLayer):\n module.unmerge()\n\n def unload_lora(self):\n if not USE_PEFT_BACKEND:\n raise ValueError(\"PEFT backend is required for `unload_lora()`.\")\n\n from ..utils import recurse_remove_peft_layers\n\n recurse_remove_peft_layers(self)\n if hasattr(self, \"peft_config\"):\n del self.peft_config\n\n def set_adapters(\n self,\n adapter_names: Union[List[str], str],\n weights: Optional[Union[float, Dict, List[float], List[Dict], List[None]]] = None,\n ):\n \"\"\"\n Set the currently active adapters for use in the UNet.\n\n Args:\n adapter_names (`List[str]` or `str`):\n The names of the adapters to use.\n adapter_weights (`Union[List[float], float]`, *optional*):\n The adapter(s) weights to use with the UNet. If `None`, the weights are set to `1.0` for all the\n adapters.\n\n Example:\n\n ```py\n from diffusers import AutoPipelineForText2Image\n import torch\n\n pipeline = AutoPipelineForText2Image.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16\n ).to(\"cuda\")\n pipeline.load_lora_weights(\n \"jbilcke-hf/sdxl-cinematic-1\", weight_name=\"pytorch_lora_weights.safetensors\", adapter_name=\"cinematic\"\n )\n pipeline.load_lora_weights(\"nerijs/pixel-art-xl\", weight_name=\"pixel-art-xl.safetensors\", adapter_name=\"pixel\")\n pipeline.set_adapters([\"cinematic\", \"pixel\"], adapter_weights=[0.5, 0.5])\n ```\n \"\"\"\n if not USE_PEFT_BACKEND:\n raise ValueError(\"PEFT backend is required for `set_adapters()`.\")\n\n adapter_names = [adapter_names] if isinstance(adapter_names, str) else adapter_names\n\n # Expand weights into a list, one entry per adapter\n # examples for e.g. 2 adapters: [{...}, 7] -> [7,7] ; None -> [None, None]\n if not isinstance(weights, list):\n weights = [weights] * len(adapter_names)\n\n if len(adapter_names) != len(weights):\n raise ValueError(\n f\"Length of adapter names {len(adapter_names)} is not equal to the length of their weights {len(weights)}.\"\n )\n\n # Set None values to default of 1.0\n # e.g. [{...}, 7] -> [{...}, 7] ; [None, None] -> [1.0, 1.0]\n weights = [w if w is not None else 1.0 for w in weights]\n\n # e.g. [{...}, 7] -> [{expanded dict...}, 7]\n weights = _maybe_expand_lora_scales(self, weights)\n\n set_weights_and_activate_adapters(self, adapter_names, weights)\n\n def disable_lora(self):\n \"\"\"\n Disable the UNet's active LoRA layers.\n\n Example:\n\n ```py\n from diffusers import AutoPipelineForText2Image\n import torch\n\n pipeline = AutoPipelineForText2Image.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16\n ).to(\"cuda\")\n pipeline.load_lora_weights(\n \"jbilcke-hf/sdxl-cinematic-1\", weight_name=\"pytorch_lora_weights.safetensors\", adapter_name=\"cinematic\"\n )\n pipeline.disable_lora()\n ```\n \"\"\"\n if not USE_PEFT_BACKEND:\n raise ValueError(\"PEFT backend is required for this method.\")\n set_adapter_layers(self, enabled=False)\n\n def enable_lora(self):\n \"\"\"\n Enable the UNet's active LoRA layers.\n\n Example:\n\n ```py\n from diffusers import AutoPipelineForText2Image\n import torch\n\n pipeline = AutoPipelineForText2Image.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16\n ).to(\"cuda\")\n pipeline.load_lora_weights(\n \"jbilcke-hf/sdxl-cinematic-1\", weight_name=\"pytorch_lora_weights.safetensors\", adapter_name=\"cinematic\"\n )\n pipeline.enable_lora()\n ```\n \"\"\"\n if not USE_PEFT_BACKEND:\n raise ValueError(\"PEFT backend is required for this method.\")\n set_adapter_layers(self, enabled=True)\n\n def delete_adapters(self, adapter_names: Union[List[str], str]):\n \"\"\"\n Delete an adapter's LoRA layers from the UNet.\n\n Args:\n adapter_names (`Union[List[str], str]`):\n The names (single string or list of strings) of the adapter to delete.\n\n Example:\n\n ```py\n from diffusers import AutoPipelineForText2Image\n import torch\n\n pipeline = AutoPipelineForText2Image.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16\n ).to(\"cuda\")\n pipeline.load_lora_weights(\n \"jbilcke-hf/sdxl-cinematic-1\", weight_name=\"pytorch_lora_weights.safetensors\", adapter_names=\"cinematic\"\n )\n pipeline.delete_adapters(\"cinematic\")\n ```\n \"\"\"\n if not USE_PEFT_BACKEND:\n raise ValueError(\"PEFT backend is required for this method.\")\n\n if isinstance(adapter_names, str):\n adapter_names = [adapter_names]\n\n for adapter_name in adapter_names:\n delete_adapter_layers(self, adapter_name)\n\n # Pop also the corresponding adapter from the config\n if hasattr(self, \"peft_config\"):\n self.peft_config.pop(adapter_name, None)\n\n def _convert_ip_adapter_image_proj_to_diffusers(self, state_dict, low_cpu_mem_usage=False):\n if low_cpu_mem_usage:\n if is_accelerate_available():\n from accelerate import init_empty_weights\n\n else:\n low_cpu_mem_usage = False\n logger.warning(\n \"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the\"\n \" environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install\"\n \" `accelerate` for faster and less memory-intense model loading. You can do so with: \\n```\\npip\"\n \" install accelerate\\n```\\n.\"\n )\n\n if low_cpu_mem_usage is True and not is_torch_version(\">=\", \"1.9.0\"):\n raise NotImplementedError(\n \"Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set\"\n \" `low_cpu_mem_usage=False`.\"\n )\n\n updated_state_dict = {}\n image_projection = None\n init_context = init_empty_weights if low_cpu_mem_usage else nullcontext\n\n if \"proj.weight\" in state_dict:\n # IP-Adapter\n num_image_text_embeds = 4\n clip_embeddings_dim = state_dict[\"proj.weight\"].shape[-1]\n cross_attention_dim = state_dict[\"proj.weight\"].shape[0] // 4\n\n with init_context():\n image_projection = ImageProjection(\n cross_attention_dim=cross_attention_dim,\n image_embed_dim=clip_embeddings_dim,\n num_image_text_embeds=num_image_text_embeds,\n )\n\n for key, value in state_dict.items():\n diffusers_name = key.replace(\"proj\", \"image_embeds\")\n updated_state_dict[diffusers_name] = value\n\n elif \"proj.3.weight\" in state_dict:\n # IP-Adapter Full\n clip_embeddings_dim = state_dict[\"proj.0.weight\"].shape[0]\n cross_attention_dim = state_dict[\"proj.3.weight\"].shape[0]\n\n with init_context():\n image_projection = IPAdapterFullImageProjection(\n cross_attention_dim=cross_attention_dim, image_embed_dim=clip_embeddings_dim\n )\n\n for key, value in state_dict.items():\n diffusers_name = key.replace(\"proj.0\", \"ff.net.0.proj\")\n diffusers_name = diffusers_name.replace(\"proj.2\", \"ff.net.2\")\n diffusers_name = diffusers_name.replace(\"proj.3\", \"norm\")\n updated_state_dict[diffusers_name] = value\n\n elif \"perceiver_resampler.proj_in.weight\" in state_dict:\n # IP-Adapter Face ID Plus\n id_embeddings_dim = state_dict[\"proj.0.weight\"].shape[1]\n embed_dims = state_dict[\"perceiver_resampler.proj_in.weight\"].shape[0]\n hidden_dims = state_dict[\"perceiver_resampler.proj_in.weight\"].shape[1]\n output_dims = state_dict[\"perceiver_resampler.proj_out.weight\"].shape[0]\n heads = state_dict[\"perceiver_resampler.layers.0.0.to_q.weight\"].shape[0] // 64\n\n with init_context():\n image_projection = IPAdapterFaceIDPlusImageProjection(\n embed_dims=embed_dims,\n output_dims=output_dims,\n hidden_dims=hidden_dims,\n heads=heads,\n id_embeddings_dim=id_embeddings_dim,\n )\n\n for key, value in state_dict.items():\n diffusers_name = key.replace(\"perceiver_resampler.\", \"\")\n diffusers_name = diffusers_name.replace(\"0.to\", \"attn.to\")\n diffusers_name = diffusers_name.replace(\"0.1.0.\", \"0.ff.0.\")\n diffusers_name = diffusers_name.replace(\"0.1.1.weight\", \"0.ff.1.net.0.proj.weight\")\n diffusers_name = diffusers_name.replace(\"0.1.3.weight\", \"0.ff.1.net.2.weight\")\n diffusers_name = diffusers_name.replace(\"1.1.0.\", \"1.ff.0.\")\n diffusers_name = diffusers_name.replace(\"1.1.1.weight\", \"1.ff.1.net.0.proj.weight\")\n diffusers_name = diffusers_name.replace(\"1.1.3.weight\", \"1.ff.1.net.2.weight\")\n diffusers_name = diffusers_name.replace(\"2.1.0.\", \"2.ff.0.\")\n diffusers_name = diffusers_name.replace(\"2.1.1.weight\", \"2.ff.1.net.0.proj.weight\")\n diffusers_name = diffusers_name.replace(\"2.1.3.weight\", \"2.ff.1.net.2.weight\")\n diffusers_name = diffusers_name.replace(\"3.1.0.\", \"3.ff.0.\")\n diffusers_name = diffusers_name.replace(\"3.1.1.weight\", \"3.ff.1.net.0.proj.weight\")\n diffusers_name = diffusers_name.replace(\"3.1.3.weight\", \"3.ff.1.net.2.weight\")\n diffusers_name = diffusers_name.replace(\"layers.0.0\", \"layers.0.ln0\")\n diffusers_name = diffusers_name.replace(\"layers.0.1\", \"layers.0.ln1\")\n diffusers_name = diffusers_name.replace(\"layers.1.0\", \"layers.1.ln0\")\n diffusers_name = diffusers_name.replace(\"layers.1.1\", \"layers.1.ln1\")\n diffusers_name = diffusers_name.replace(\"layers.2.0\", \"layers.2.ln0\")\n diffusers_name = diffusers_name.replace(\"layers.2.1\", \"layers.2.ln1\")\n diffusers_name = diffusers_name.replace(\"layers.3.0\", \"layers.3.ln0\")\n diffusers_name = diffusers_name.replace(\"layers.3.1\", \"layers.3.ln1\")\n\n if \"norm1\" in diffusers_name:\n updated_state_dict[diffusers_name.replace(\"0.norm1\", \"0\")] = value\n elif \"norm2\" in diffusers_name:\n updated_state_dict[diffusers_name.replace(\"0.norm2\", \"1\")] = value\n elif \"to_kv\" in diffusers_name:\n v_chunk = value.chunk(2, dim=0)\n updated_state_dict[diffusers_name.replace(\"to_kv\", \"to_k\")] = v_chunk[0]\n updated_state_dict[diffusers_name.replace(\"to_kv\", \"to_v\")] = v_chunk[1]\n elif \"to_out\" in diffusers_name:\n updated_state_dict[diffusers_name.replace(\"to_out\", \"to_out.0\")] = value\n elif \"proj.0.weight\" == diffusers_name:\n updated_state_dict[\"proj.net.0.proj.weight\"] = value\n elif \"proj.0.bias\" == diffusers_name:\n updated_state_dict[\"proj.net.0.proj.bias\"] = value\n elif \"proj.2.weight\" == diffusers_name:\n updated_state_dict[\"proj.net.2.weight\"] = value\n elif \"proj.2.bias\" == diffusers_name:\n updated_state_dict[\"proj.net.2.bias\"] = value\n else:\n updated_state_dict[diffusers_name] = value\n\n elif \"norm.weight\" in state_dict:\n # IP-Adapter Face ID\n id_embeddings_dim_in = state_dict[\"proj.0.weight\"].shape[1]\n id_embeddings_dim_out = state_dict[\"proj.0.weight\"].shape[0]\n multiplier = id_embeddings_dim_out // id_embeddings_dim_in\n norm_layer = \"norm.weight\"\n cross_attention_dim = state_dict[norm_layer].shape[0]\n num_tokens = state_dict[\"proj.2.weight\"].shape[0] // cross_attention_dim\n\n with init_context():\n image_projection = IPAdapterFaceIDImageProjection(\n cross_attention_dim=cross_attention_dim,\n image_embed_dim=id_embeddings_dim_in,\n mult=multiplier,\n num_tokens=num_tokens,\n )\n\n for key, value in state_dict.items():\n diffusers_name = key.replace(\"proj.0\", \"ff.net.0.proj\")\n diffusers_name = diffusers_name.replace(\"proj.2\", \"ff.net.2\")\n updated_state_dict[diffusers_name] = value\n\n else:\n # IP-Adapter Plus\n num_image_text_embeds = state_dict[\"latents\"].shape[1]\n embed_dims = state_dict[\"proj_in.weight\"].shape[1]\n output_dims = state_dict[\"proj_out.weight\"].shape[0]\n hidden_dims = state_dict[\"latents\"].shape[2]\n attn_key_present = any(\"attn\" in k for k in state_dict)\n heads = (\n state_dict[\"layers.0.attn.to_q.weight\"].shape[0] // 64\n if attn_key_present\n else state_dict[\"layers.0.0.to_q.weight\"].shape[0] // 64\n )\n\n with init_context():\n image_projection = IPAdapterPlusImageProjection(\n embed_dims=embed_dims,\n output_dims=output_dims,\n hidden_dims=hidden_dims,\n heads=heads,\n num_queries=num_image_text_embeds,\n )\n\n for key, value in state_dict.items():\n diffusers_name = key.replace(\"0.to\", \"2.to\")\n\n diffusers_name = diffusers_name.replace(\"0.0.norm1\", \"0.ln0\")\n diffusers_name = diffusers_name.replace(\"0.0.norm2\", \"0.ln1\")\n diffusers_name = diffusers_name.replace(\"1.0.norm1\", \"1.ln0\")\n diffusers_name = diffusers_name.replace(\"1.0.norm2\", \"1.ln1\")\n diffusers_name = diffusers_name.replace(\"2.0.norm1\", \"2.ln0\")\n diffusers_name = diffusers_name.replace(\"2.0.norm2\", \"2.ln1\")\n diffusers_name = diffusers_name.replace(\"3.0.norm1\", \"3.ln0\")\n diffusers_name = diffusers_name.replace(\"3.0.norm2\", \"3.ln1\")\n\n if \"to_kv\" in diffusers_name:\n parts = diffusers_name.split(\".\")\n parts[2] = \"attn\"\n diffusers_name = \".\".join(parts)\n v_chunk = value.chunk(2, dim=0)\n updated_state_dict[diffusers_name.replace(\"to_kv\", \"to_k\")] = v_chunk[0]\n updated_state_dict[diffusers_name.replace(\"to_kv\", \"to_v\")] = v_chunk[1]\n elif \"to_q\" in diffusers_name:\n parts = diffusers_name.split(\".\")\n parts[2] = \"attn\"\n diffusers_name = \".\".join(parts)\n updated_state_dict[diffusers_name] = value\n elif \"to_out\" in diffusers_name:\n parts = diffusers_name.split(\".\")\n parts[2] = \"attn\"\n diffusers_name = \".\".join(parts)\n updated_state_dict[diffusers_name.replace(\"to_out\", \"to_out.0\")] = value\n else:\n diffusers_name = diffusers_name.replace(\"0.1.0\", \"0.ff.0\")\n diffusers_name = diffusers_name.replace(\"0.1.1\", \"0.ff.1.net.0.proj\")\n diffusers_name = diffusers_name.replace(\"0.1.3\", \"0.ff.1.net.2\")\n\n diffusers_name = diffusers_name.replace(\"1.1.0\", \"1.ff.0\")\n diffusers_name = diffusers_name.replace(\"1.1.1\", \"1.ff.1.net.0.proj\")\n diffusers_name = diffusers_name.replace(\"1.1.3\", \"1.ff.1.net.2\")\n\n diffusers_name = diffusers_name.replace(\"2.1.0\", \"2.ff.0\")\n diffusers_name = diffusers_name.replace(\"2.1.1\", \"2.ff.1.net.0.proj\")\n diffusers_name = diffusers_name.replace(\"2.1.3\", \"2.ff.1.net.2\")\n\n diffusers_name = diffusers_name.replace(\"3.1.0\", \"3.ff.0\")\n diffusers_name = diffusers_name.replace(\"3.1.1\", \"3.ff.1.net.0.proj\")\n diffusers_name = diffusers_name.replace(\"3.1.3\", \"3.ff.1.net.2\")\n updated_state_dict[diffusers_name] = value\n\n if not low_cpu_mem_usage:\n image_projection.load_state_dict(updated_state_dict, strict=True)\n else:\n load_model_dict_into_meta(image_projection, updated_state_dict, device=self.device, dtype=self.dtype)\n\n return image_projection\n\n def _convert_ip_adapter_attn_to_diffusers(self, state_dicts, low_cpu_mem_usage=False):\n from ..models.attention_processor import (\n AttnProcessor,\n AttnProcessor2_0,\n IPAdapterAttnProcessor,\n IPAdapterAttnProcessor2_0,\n )\n\n if low_cpu_mem_usage:\n if is_accelerate_available():\n from accelerate import init_empty_weights\n\n else:\n low_cpu_mem_usage = False\n logger.warning(\n \"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the\"\n \" environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install\"\n \" `accelerate` for faster and less memory-intense model loading. You can do so with: \\n```\\npip\"\n \" install accelerate\\n```\\n.\"\n )\n\n if low_cpu_mem_usage is True and not is_torch_version(\">=\", \"1.9.0\"):\n raise NotImplementedError(\n \"Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set\"\n \" `low_cpu_mem_usage=False`.\"\n )\n\n # set ip-adapter cross-attention processors & load state_dict\n attn_procs = {}\n key_id = 1\n init_context = init_empty_weights if low_cpu_mem_usage else nullcontext\n for name in self.attn_processors.keys():\n cross_attention_dim = None if name.endswith(\"attn1.processor\") else self.config.cross_attention_dim\n if name.startswith(\"mid_block\"):\n hidden_size = self.config.block_out_channels[-1]\n elif name.startswith(\"up_blocks\"):\n block_id = int(name[len(\"up_blocks.\")])\n hidden_size = list(reversed(self.config.block_out_channels))[block_id]\n elif name.startswith(\"down_blocks\"):\n block_id = int(name[len(\"down_blocks.\")])\n hidden_size = self.config.block_out_channels[block_id]\n\n if cross_attention_dim is None or \"motion_modules\" in name:\n attn_processor_class = (\n AttnProcessor2_0 if hasattr(F, \"scaled_dot_product_attention\") else AttnProcessor\n )\n attn_procs[name] = attn_processor_class()\n\n else:\n attn_processor_class = (\n IPAdapterAttnProcessor2_0 if hasattr(F, \"scaled_dot_product_attention\") else IPAdapterAttnProcessor\n )\n num_image_text_embeds = []\n for state_dict in state_dicts:\n if \"proj.weight\" in state_dict[\"image_proj\"]:\n # IP-Adapter\n num_image_text_embeds += [4]\n elif \"proj.3.weight\" in state_dict[\"image_proj\"]:\n # IP-Adapter Full Face\n num_image_text_embeds += [257] # 256 CLIP tokens + 1 CLS token\n elif \"perceiver_resampler.proj_in.weight\" in state_dict[\"image_proj\"]:\n # IP-Adapter Face ID Plus\n num_image_text_embeds += [4]\n elif \"norm.weight\" in state_dict[\"image_proj\"]:\n # IP-Adapter Face ID\n num_image_text_embeds += [4]\n else:\n # IP-Adapter Plus\n num_image_text_embeds += [state_dict[\"image_proj\"][\"latents\"].shape[1]]\n\n with init_context():\n attn_procs[name] = attn_processor_class(\n hidden_size=hidden_size,\n cross_attention_dim=cross_attention_dim,\n scale=1.0,\n num_tokens=num_image_text_embeds,\n )\n\n value_dict = {}\n for i, state_dict in enumerate(state_dicts):\n value_dict.update({f\"to_k_ip.{i}.weight\": state_dict[\"ip_adapter\"][f\"{key_id}.to_k_ip.weight\"]})\n value_dict.update({f\"to_v_ip.{i}.weight\": state_dict[\"ip_adapter\"][f\"{key_id}.to_v_ip.weight\"]})\n\n if not low_cpu_mem_usage:\n attn_procs[name].load_state_dict(value_dict)\n else:\n device = next(iter(value_dict.values())).device\n dtype = next(iter(value_dict.values())).dtype\n load_model_dict_into_meta(attn_procs[name], value_dict, device=device, dtype=dtype)\n\n key_id += 2\n\n return attn_procs\n\n def _load_ip_adapter_weights(self, state_dicts, low_cpu_mem_usage=False):\n if not isinstance(state_dicts, list):\n state_dicts = [state_dicts]\n # Set encoder_hid_proj after loading ip_adapter weights,\n # because `IPAdapterPlusImageProjection` also has `attn_processors`.\n self.encoder_hid_proj = None\n\n attn_procs = self._convert_ip_adapter_attn_to_diffusers(state_dicts, low_cpu_mem_usage=low_cpu_mem_usage)\n self.set_attn_processor(attn_procs)\n\n # convert IP-Adapter Image Projection layers to diffusers\n image_projection_layers = []\n for state_dict in state_dicts:\n image_projection_layer = self._convert_ip_adapter_image_proj_to_diffusers(\n state_dict[\"image_proj\"], low_cpu_mem_usage=low_cpu_mem_usage\n )\n image_projection_layers.append(image_projection_layer)\n\n self.encoder_hid_proj = MultiIPAdapterImageProjection(image_projection_layers)\n self.config.encoder_hid_dim_type = \"ip_image_proj\"\n\n self.to(dtype=self.dtype, device=self.device)\n\n def _load_ip_adapter_loras(self, state_dicts):\n lora_dicts = {}\n for key_id, name in enumerate(self.attn_processors.keys()):\n for i, state_dict in enumerate(state_dicts):\n if f\"{key_id}.to_k_lora.down.weight\" in state_dict[\"ip_adapter\"]:\n if i not in lora_dicts:\n lora_dicts[i] = {}\n lora_dicts[i].update(\n {\n f\"unet.{name}.to_k_lora.down.weight\": state_dict[\"ip_adapter\"][\n f\"{key_id}.to_k_lora.down.weight\"\n ]\n }\n )\n lora_dicts[i].update(\n {\n f\"unet.{name}.to_q_lora.down.weight\": state_dict[\"ip_adapter\"][\n f\"{key_id}.to_q_lora.down.weight\"\n ]\n }\n )\n lora_dicts[i].update(\n {\n f\"unet.{name}.to_v_lora.down.weight\": state_dict[\"ip_adapter\"][\n f\"{key_id}.to_v_lora.down.weight\"\n ]\n }\n )\n lora_dicts[i].update(\n {\n f\"unet.{name}.to_out_lora.down.weight\": state_dict[\"ip_adapter\"][\n f\"{key_id}.to_out_lora.down.weight\"\n ]\n }\n )\n lora_dicts[i].update(\n {f\"unet.{name}.to_k_lora.up.weight\": state_dict[\"ip_adapter\"][f\"{key_id}.to_k_lora.up.weight\"]}\n )\n lora_dicts[i].update(\n {f\"unet.{name}.to_q_lora.up.weight\": state_dict[\"ip_adapter\"][f\"{key_id}.to_q_lora.up.weight\"]}\n )\n lora_dicts[i].update(\n {f\"unet.{name}.to_v_lora.up.weight\": state_dict[\"ip_adapter\"][f\"{key_id}.to_v_lora.up.weight\"]}\n )\n lora_dicts[i].update(\n {\n f\"unet.{name}.to_out_lora.up.weight\": state_dict[\"ip_adapter\"][\n f\"{key_id}.to_out_lora.up.weight\"\n ]\n }\n )\n return lora_dicts\n" }, { "path": "diffusers/src/diffusers/loaders/unet_loader_utils.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport copy\nfrom typing import TYPE_CHECKING, Dict, List, Union\n\nfrom ..utils import logging\n\n\nif TYPE_CHECKING:\n # import here to avoid circular imports\n from ..models import UNet2DConditionModel\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef _translate_into_actual_layer_name(name):\n \"\"\"Translate user-friendly name (e.g. 'mid') into actual layer name (e.g. 'mid_block.attentions.0')\"\"\"\n if name == \"mid\":\n return \"mid_block.attentions.0\"\n\n updown, block, attn = name.split(\".\")\n\n updown = updown.replace(\"down\", \"down_blocks\").replace(\"up\", \"up_blocks\")\n block = block.replace(\"block_\", \"\")\n attn = \"attentions.\" + attn\n\n return \".\".join((updown, block, attn))\n\n\ndef _maybe_expand_lora_scales(\n unet: \"UNet2DConditionModel\", weight_scales: List[Union[float, Dict]], default_scale=1.0\n):\n blocks_with_transformer = {\n \"down\": [i for i, block in enumerate(unet.down_blocks) if hasattr(block, \"attentions\")],\n \"up\": [i for i, block in enumerate(unet.up_blocks) if hasattr(block, \"attentions\")],\n }\n transformer_per_block = {\"down\": unet.config.layers_per_block, \"up\": unet.config.layers_per_block + 1}\n\n expanded_weight_scales = [\n _maybe_expand_lora_scales_for_one_adapter(\n weight_for_adapter,\n blocks_with_transformer,\n transformer_per_block,\n unet.state_dict(),\n default_scale=default_scale,\n )\n for weight_for_adapter in weight_scales\n ]\n\n return expanded_weight_scales\n\n\ndef _maybe_expand_lora_scales_for_one_adapter(\n scales: Union[float, Dict],\n blocks_with_transformer: Dict[str, int],\n transformer_per_block: Dict[str, int],\n state_dict: None,\n default_scale: float = 1.0,\n):\n \"\"\"\n Expands the inputs into a more granular dictionary. See the example below for more details.\n\n Parameters:\n scales (`Union[float, Dict]`):\n Scales dict to expand.\n blocks_with_transformer (`Dict[str, int]`):\n Dict with keys 'up' and 'down', showing which blocks have transformer layers\n transformer_per_block (`Dict[str, int]`):\n Dict with keys 'up' and 'down', showing how many transformer layers each block has\n\n E.g. turns\n ```python\n scales = {\"down\": 2, \"mid\": 3, \"up\": {\"block_0\": 4, \"block_1\": [5, 6, 7]}}\n blocks_with_transformer = {\"down\": [1, 2], \"up\": [0, 1]}\n transformer_per_block = {\"down\": 2, \"up\": 3}\n ```\n into\n ```python\n {\n \"down.block_1.0\": 2,\n \"down.block_1.1\": 2,\n \"down.block_2.0\": 2,\n \"down.block_2.1\": 2,\n \"mid\": 3,\n \"up.block_0.0\": 4,\n \"up.block_0.1\": 4,\n \"up.block_0.2\": 4,\n \"up.block_1.0\": 5,\n \"up.block_1.1\": 6,\n \"up.block_1.2\": 7,\n }\n ```\n \"\"\"\n if sorted(blocks_with_transformer.keys()) != [\"down\", \"up\"]:\n raise ValueError(\"blocks_with_transformer needs to be a dict with keys `'down' and `'up'`\")\n\n if sorted(transformer_per_block.keys()) != [\"down\", \"up\"]:\n raise ValueError(\"transformer_per_block needs to be a dict with keys `'down' and `'up'`\")\n\n if not isinstance(scales, dict):\n # don't expand if scales is a single number\n return scales\n\n scales = copy.deepcopy(scales)\n\n if \"mid\" not in scales:\n scales[\"mid\"] = default_scale\n elif isinstance(scales[\"mid\"], list):\n if len(scales[\"mid\"]) == 1:\n scales[\"mid\"] = scales[\"mid\"][0]\n else:\n raise ValueError(f\"Expected 1 scales for mid, got {len(scales['mid'])}.\")\n\n for updown in [\"up\", \"down\"]:\n if updown not in scales:\n scales[updown] = default_scale\n\n # eg {\"down\": 1} to {\"down\": {\"block_1\": 1, \"block_2\": 1}}}\n if not isinstance(scales[updown], dict):\n scales[updown] = {f\"block_{i}\": copy.deepcopy(scales[updown]) for i in blocks_with_transformer[updown]}\n\n # eg {\"down\": {\"block_1\": 1}} to {\"down\": {\"block_1\": [1, 1]}}\n for i in blocks_with_transformer[updown]:\n block = f\"block_{i}\"\n # set not assigned blocks to default scale\n if block not in scales[updown]:\n scales[updown][block] = default_scale\n if not isinstance(scales[updown][block], list):\n scales[updown][block] = [scales[updown][block] for _ in range(transformer_per_block[updown])]\n elif len(scales[updown][block]) == 1:\n # a list specifying scale to each masked IP input\n scales[updown][block] = scales[updown][block] * transformer_per_block[updown]\n elif len(scales[updown][block]) != transformer_per_block[updown]:\n raise ValueError(\n f\"Expected {transformer_per_block[updown]} scales for {updown}.{block}, got {len(scales[updown][block])}.\"\n )\n\n # eg {\"down\": \"block_1\": [1, 1]}} to {\"down.block_1.0\": 1, \"down.block_1.1\": 1}\n for i in blocks_with_transformer[updown]:\n block = f\"block_{i}\"\n for tf_idx, value in enumerate(scales[updown][block]):\n scales[f\"{updown}.{block}.{tf_idx}\"] = value\n\n del scales[updown]\n\n for layer in scales.keys():\n if not any(_translate_into_actual_layer_name(layer) in module for module in state_dict.keys()):\n raise ValueError(\n f\"Can't set lora scale for layer {layer}. It either doesn't exist in this unet or it has no attentions.\"\n )\n\n return {_translate_into_actual_layer_name(name): weight for name, weight in scales.items()}\n" }, { "path": "diffusers/src/diffusers/loaders/utils.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Dict\n\nimport torch\n\n\nclass AttnProcsLayers(torch.nn.Module):\n def __init__(self, state_dict: Dict[str, torch.Tensor]):\n super().__init__()\n self.layers = torch.nn.ModuleList(state_dict.values())\n self.mapping = dict(enumerate(state_dict.keys()))\n self.rev_mapping = {v: k for k, v in enumerate(state_dict.keys())}\n\n # .processor for unet, .self_attn for text encoder\n self.split_keys = [\".processor\", \".self_attn\"]\n\n # we add a hook to state_dict() and load_state_dict() so that the\n # naming fits with `unet.attn_processors`\n def map_to(module, state_dict, *args, **kwargs):\n new_state_dict = {}\n for key, value in state_dict.items():\n num = int(key.split(\".\")[1]) # 0 is always \"layers\"\n new_key = key.replace(f\"layers.{num}\", module.mapping[num])\n new_state_dict[new_key] = value\n\n return new_state_dict\n\n def remap_key(key, state_dict):\n for k in self.split_keys:\n if k in key:\n return key.split(k)[0] + k\n\n raise ValueError(\n f\"There seems to be a problem with the state_dict: {set(state_dict.keys())}. {key} has to have one of {self.split_keys}.\"\n )\n\n def map_from(module, state_dict, *args, **kwargs):\n all_keys = list(state_dict.keys())\n for key in all_keys:\n replace_key = remap_key(key, state_dict)\n new_key = key.replace(replace_key, f\"layers.{module.rev_mapping[replace_key]}\")\n state_dict[new_key] = state_dict[key]\n del state_dict[key]\n\n self._register_state_dict_hook(map_to)\n self._register_load_state_dict_pre_hook(map_from, with_module=True)\n" }, { "path": "diffusers/src/diffusers/models/README.md", "content": "# Models\n\nFor more detail on the models, please refer to the [docs](https://huggingface.co/docs/diffusers/api/models/overview)." }, { "path": "diffusers/src/diffusers/models/__init__.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import TYPE_CHECKING\n\nfrom ..utils import (\n DIFFUSERS_SLOW_IMPORT,\n _LazyModule,\n is_flax_available,\n is_torch_available,\n)\n\n\n_import_structure = {}\n\nif is_torch_available():\n _import_structure[\"adapter\"] = [\"MultiAdapter\", \"T2IAdapter\"]\n _import_structure[\"autoencoders.autoencoder_asym_kl\"] = [\"AsymmetricAutoencoderKL\"]\n _import_structure[\"autoencoders.autoencoder_kl\"] = [\"AutoencoderKL\"]\n _import_structure[\"autoencoders.autoencoder_kl_temporal_decoder\"] = [\"AutoencoderKLTemporalDecoder\"]\n _import_structure[\"autoencoders.autoencoder_tiny\"] = [\"AutoencoderTiny\"]\n _import_structure[\"autoencoders.consistency_decoder_vae\"] = [\"ConsistencyDecoderVAE\"]\n _import_structure[\"autoencoders.vq_model\"] = [\"VQModel\"]\n _import_structure[\"controlnet\"] = [\"ControlNetModel\"]\n _import_structure[\"controlnet_xs\"] = [\"ControlNetXSAdapter\", \"UNetControlNetXSModel\"]\n _import_structure[\"embeddings\"] = [\"ImageProjection\"]\n _import_structure[\"modeling_utils\"] = [\"ModelMixin\"]\n _import_structure[\"transformers.dit_transformer_2d\"] = [\"DiTTransformer2DModel\"]\n _import_structure[\"transformers.dual_transformer_2d\"] = [\"DualTransformer2DModel\"]\n _import_structure[\"transformers.hunyuan_transformer_2d\"] = [\"HunyuanDiT2DModel\"]\n _import_structure[\"transformers.pixart_transformer_2d\"] = [\"PixArtTransformer2DModel\"]\n _import_structure[\"transformers.prior_transformer\"] = [\"PriorTransformer\"]\n _import_structure[\"transformers.t5_film_transformer\"] = [\"T5FilmDecoder\"]\n _import_structure[\"transformers.transformer_2d\"] = [\"Transformer2DModel\"]\n _import_structure[\"transformers.transformer_sd3\"] = [\"SD3Transformer2DModel\"]\n _import_structure[\"transformers.transformer_temporal\"] = [\"TransformerTemporalModel\"]\n _import_structure[\"unets.unet_1d\"] = [\"UNet1DModel\"]\n _import_structure[\"unets.unet_2d\"] = [\"UNet2DModel\"]\n _import_structure[\"unets.unet_2d_condition\"] = [\"UNet2DConditionModel\"]\n _import_structure[\"unets.unet_3d_condition\"] = [\"UNet3DConditionModel\"]\n _import_structure[\"unets.unet_i2vgen_xl\"] = [\"I2VGenXLUNet\"]\n _import_structure[\"unets.unet_kandinsky3\"] = [\"Kandinsky3UNet\"]\n _import_structure[\"unets.unet_motion_model\"] = [\"MotionAdapter\", \"UNetMotionModel\"]\n _import_structure[\"unets.unet_spatio_temporal_condition\"] = [\"UNetSpatioTemporalConditionModel\"]\n _import_structure[\"unets.unet_stable_cascade\"] = [\"StableCascadeUNet\"]\n _import_structure[\"unets.uvit_2d\"] = [\"UVit2DModel\"]\n\nif is_flax_available():\n _import_structure[\"controlnet_flax\"] = [\"FlaxControlNetModel\"]\n _import_structure[\"unets.unet_2d_condition_flax\"] = [\"FlaxUNet2DConditionModel\"]\n _import_structure[\"vae_flax\"] = [\"FlaxAutoencoderKL\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n if is_torch_available():\n from .adapter import MultiAdapter, T2IAdapter\n from .autoencoders import (\n AsymmetricAutoencoderKL,\n AutoencoderKL,\n AutoencoderKLTemporalDecoder,\n AutoencoderTiny,\n ConsistencyDecoderVAE,\n VQModel,\n )\n from .controlnet import ControlNetModel\n from .controlnet_xs import ControlNetXSAdapter, UNetControlNetXSModel\n from .embeddings import ImageProjection\n from .modeling_utils import ModelMixin\n from .transformers import (\n DiTTransformer2DModel,\n DualTransformer2DModel,\n HunyuanDiT2DModel,\n PixArtTransformer2DModel,\n PriorTransformer,\n SD3Transformer2DModel,\n T5FilmDecoder,\n Transformer2DModel,\n TransformerTemporalModel,\n )\n from .unets import (\n I2VGenXLUNet,\n Kandinsky3UNet,\n MotionAdapter,\n StableCascadeUNet,\n UNet1DModel,\n UNet2DConditionModel,\n UNet2DModel,\n UNet3DConditionModel,\n UNetMotionModel,\n UNetSpatioTemporalConditionModel,\n UVit2DModel,\n )\n\n if is_flax_available():\n from .controlnet_flax import FlaxControlNetModel\n from .unets import FlaxUNet2DConditionModel\n from .vae_flax import FlaxAutoencoderKL\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(__name__, globals()[\"__file__\"], _import_structure, module_spec=__spec__)\n" }, { "path": "diffusers/src/diffusers/models/activations.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom ..utils import deprecate\nfrom ..utils.import_utils import is_torch_npu_available\n\n\nif is_torch_npu_available():\n import torch_npu\n\nACTIVATION_FUNCTIONS = {\n \"swish\": nn.SiLU(),\n \"silu\": nn.SiLU(),\n \"mish\": nn.Mish(),\n \"gelu\": nn.GELU(),\n \"relu\": nn.ReLU(),\n}\n\n\ndef get_activation(act_fn: str) -> nn.Module:\n \"\"\"Helper function to get activation function from string.\n\n Args:\n act_fn (str): Name of activation function.\n\n Returns:\n nn.Module: Activation function.\n \"\"\"\n\n act_fn = act_fn.lower()\n if act_fn in ACTIVATION_FUNCTIONS:\n return ACTIVATION_FUNCTIONS[act_fn]\n else:\n raise ValueError(f\"Unsupported activation function: {act_fn}\")\n\n\nclass FP32SiLU(nn.Module):\n r\"\"\"\n SiLU activation function with input upcasted to torch.float32.\n \"\"\"\n\n def __init__(self):\n super().__init__()\n\n def forward(self, inputs: torch.Tensor) -> torch.Tensor:\n return F.silu(inputs.float(), inplace=False).to(inputs.dtype)\n\n\nclass GELU(nn.Module):\n r\"\"\"\n GELU activation function with tanh approximation support with `approximate=\"tanh\"`.\n\n Parameters:\n dim_in (`int`): The number of channels in the input.\n dim_out (`int`): The number of channels in the output.\n approximate (`str`, *optional*, defaults to `\"none\"`): If `\"tanh\"`, use tanh approximation.\n bias (`bool`, defaults to True): Whether to use a bias in the linear layer.\n \"\"\"\n\n def __init__(self, dim_in: int, dim_out: int, approximate: str = \"none\", bias: bool = True):\n super().__init__()\n self.proj = nn.Linear(dim_in, dim_out, bias=bias)\n self.approximate = approximate\n\n def gelu(self, gate: torch.Tensor) -> torch.Tensor:\n if gate.device.type != \"mps\":\n return F.gelu(gate, approximate=self.approximate)\n # mps: gelu is not implemented for float16\n return F.gelu(gate.to(dtype=torch.float32), approximate=self.approximate).to(dtype=gate.dtype)\n\n def forward(self, hidden_states):\n hidden_states = self.proj(hidden_states)\n hidden_states = self.gelu(hidden_states)\n return hidden_states\n\n\nclass GEGLU(nn.Module):\n r\"\"\"\n A [variant](https://arxiv.org/abs/2002.05202) of the gated linear unit activation function.\n\n Parameters:\n dim_in (`int`): The number of channels in the input.\n dim_out (`int`): The number of channels in the output.\n bias (`bool`, defaults to True): Whether to use a bias in the linear layer.\n \"\"\"\n\n def __init__(self, dim_in: int, dim_out: int, bias: bool = True):\n super().__init__()\n self.proj = nn.Linear(dim_in, dim_out * 2, bias=bias)\n\n def gelu(self, gate: torch.Tensor) -> torch.Tensor:\n if gate.device.type != \"mps\":\n return F.gelu(gate)\n # mps: gelu is not implemented for float16\n return F.gelu(gate.to(dtype=torch.float32)).to(dtype=gate.dtype)\n\n def forward(self, hidden_states, *args, **kwargs):\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n hidden_states = self.proj(hidden_states)\n if is_torch_npu_available():\n # using torch_npu.npu_geglu can run faster and save memory on NPU.\n return torch_npu.npu_geglu(hidden_states, dim=-1, approximate=1)[0]\n else:\n hidden_states, gate = hidden_states.chunk(2, dim=-1)\n return hidden_states * self.gelu(gate)\n\n\nclass ApproximateGELU(nn.Module):\n r\"\"\"\n The approximate form of the Gaussian Error Linear Unit (GELU). For more details, see section 2 of this\n [paper](https://arxiv.org/abs/1606.08415).\n\n Parameters:\n dim_in (`int`): The number of channels in the input.\n dim_out (`int`): The number of channels in the output.\n bias (`bool`, defaults to True): Whether to use a bias in the linear layer.\n \"\"\"\n\n def __init__(self, dim_in: int, dim_out: int, bias: bool = True):\n super().__init__()\n self.proj = nn.Linear(dim_in, dim_out, bias=bias)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n x = self.proj(x)\n return x * torch.sigmoid(1.702 * x)\n" }, { "path": "diffusers/src/diffusers/models/adapter.py", "content": "# Copyright 2022 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport os\nfrom typing import Callable, List, Optional, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import logging\nfrom .modeling_utils import ModelMixin\n\n\nlogger = logging.get_logger(__name__)\n\n\nclass MultiAdapter(ModelMixin):\n r\"\"\"\n MultiAdapter is a wrapper model that contains multiple adapter models and merges their outputs according to\n user-assigned weighting.\n\n This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library\n implements for all the model (such as downloading or saving, etc.)\n\n Parameters:\n adapters (`List[T2IAdapter]`, *optional*, defaults to None):\n A list of `T2IAdapter` model instances.\n \"\"\"\n\n def __init__(self, adapters: List[\"T2IAdapter\"]):\n super(MultiAdapter, self).__init__()\n\n self.num_adapter = len(adapters)\n self.adapters = nn.ModuleList(adapters)\n\n if len(adapters) == 0:\n raise ValueError(\"Expecting at least one adapter\")\n\n if len(adapters) == 1:\n raise ValueError(\"For a single adapter, please use the `T2IAdapter` class instead of `MultiAdapter`\")\n\n # The outputs from each adapter are added together with a weight.\n # This means that the change in dimensions from downsampling must\n # be the same for all adapters. Inductively, it also means the\n # downscale_factor and total_downscale_factor must be the same for all\n # adapters.\n first_adapter_total_downscale_factor = adapters[0].total_downscale_factor\n first_adapter_downscale_factor = adapters[0].downscale_factor\n for idx in range(1, len(adapters)):\n if (\n adapters[idx].total_downscale_factor != first_adapter_total_downscale_factor\n or adapters[idx].downscale_factor != first_adapter_downscale_factor\n ):\n raise ValueError(\n f\"Expecting all adapters to have the same downscaling behavior, but got:\\n\"\n f\"adapters[0].total_downscale_factor={first_adapter_total_downscale_factor}\\n\"\n f\"adapters[0].downscale_factor={first_adapter_downscale_factor}\\n\"\n f\"adapter[`{idx}`].total_downscale_factor={adapters[idx].total_downscale_factor}\\n\"\n f\"adapter[`{idx}`].downscale_factor={adapters[idx].downscale_factor}\"\n )\n\n self.total_downscale_factor = first_adapter_total_downscale_factor\n self.downscale_factor = first_adapter_downscale_factor\n\n def forward(self, xs: torch.Tensor, adapter_weights: Optional[List[float]] = None) -> List[torch.Tensor]:\n r\"\"\"\n Args:\n xs (`torch.Tensor`):\n (batch, channel, height, width) input images for multiple adapter models concated along dimension 1,\n `channel` should equal to `num_adapter` * \"number of channel of image\".\n adapter_weights (`List[float]`, *optional*, defaults to None):\n List of floats representing the weight which will be multiply to each adapter's output before adding\n them together.\n \"\"\"\n if adapter_weights is None:\n adapter_weights = torch.tensor([1 / self.num_adapter] * self.num_adapter)\n else:\n adapter_weights = torch.tensor(adapter_weights)\n\n accume_state = None\n for x, w, adapter in zip(xs, adapter_weights, self.adapters):\n features = adapter(x)\n if accume_state is None:\n accume_state = features\n for i in range(len(accume_state)):\n accume_state[i] = w * accume_state[i]\n else:\n for i in range(len(features)):\n accume_state[i] += w * features[i]\n return accume_state\n\n def save_pretrained(\n self,\n save_directory: Union[str, os.PathLike],\n is_main_process: bool = True,\n save_function: Callable = None,\n safe_serialization: bool = True,\n variant: Optional[str] = None,\n ):\n \"\"\"\n Save a model and its configuration file to a directory, so that it can be re-loaded using the\n `[`~models.adapter.MultiAdapter.from_pretrained`]` class method.\n\n Arguments:\n save_directory (`str` or `os.PathLike`):\n Directory to which to save. Will be created if it doesn't exist.\n is_main_process (`bool`, *optional*, defaults to `True`):\n Whether the process calling this is the main process or not. Useful when in distributed training like\n TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on\n the main process to avoid race conditions.\n save_function (`Callable`):\n The function to use to save the state dictionary. Useful on distributed training like TPUs when one\n need to replace `torch.save` by another method. Can be configured with the environment variable\n `DIFFUSERS_SAVE_MODE`.\n safe_serialization (`bool`, *optional*, defaults to `True`):\n Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).\n variant (`str`, *optional*):\n If specified, weights are saved in the format pytorch_model..bin.\n \"\"\"\n idx = 0\n model_path_to_save = save_directory\n for adapter in self.adapters:\n adapter.save_pretrained(\n model_path_to_save,\n is_main_process=is_main_process,\n save_function=save_function,\n safe_serialization=safe_serialization,\n variant=variant,\n )\n\n idx += 1\n model_path_to_save = model_path_to_save + f\"_{idx}\"\n\n @classmethod\n def from_pretrained(cls, pretrained_model_path: Optional[Union[str, os.PathLike]], **kwargs):\n r\"\"\"\n Instantiate a pretrained MultiAdapter model from multiple pre-trained adapter models.\n\n The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train\n the model, you should first set it back in training mode with `model.train()`.\n\n The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come\n pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning\n task.\n\n The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those\n weights are discarded.\n\n Parameters:\n pretrained_model_path (`os.PathLike`):\n A path to a *directory* containing model weights saved using\n [`~diffusers.models.adapter.MultiAdapter.save_pretrained`], e.g., `./my_model_directory/adapter`.\n torch_dtype (`str` or `torch.dtype`, *optional*):\n Override the default `torch.dtype` and load the model under this dtype. If `\"auto\"` is passed the dtype\n will be automatically derived from the model's weights.\n output_loading_info(`bool`, *optional*, defaults to `False`):\n Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.\n device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):\n A map that specifies where each submodule should go. It doesn't need to be refined to each\n parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the\n same device.\n\n To have Accelerate compute the most optimized `device_map` automatically, set `device_map=\"auto\"`. For\n more information about each option see [designing a device\n map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).\n max_memory (`Dict`, *optional*):\n A dictionary device identifier to maximum memory. Will default to the maximum memory available for each\n GPU and the available CPU RAM if unset.\n low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):\n Speed up model loading by not initializing the weights and only loading the pre-trained weights. This\n also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the\n model. This is only supported when torch version >= 1.9.0. If you are using an older version of torch,\n setting this argument to `True` will raise an error.\n variant (`str`, *optional*):\n If specified load weights from `variant` filename, *e.g.* pytorch_model..bin. `variant` is\n ignored when using `from_flax`.\n use_safetensors (`bool`, *optional*, defaults to `None`):\n If set to `None`, the `safetensors` weights will be downloaded if they're available **and** if the\n `safetensors` library is installed. If set to `True`, the model will be forcibly loaded from\n `safetensors` weights. If set to `False`, loading will *not* use `safetensors`.\n \"\"\"\n idx = 0\n adapters = []\n\n # load adapter and append to list until no adapter directory exists anymore\n # first adapter has to be saved under `./mydirectory/adapter` to be compliant with `DiffusionPipeline.from_pretrained`\n # second, third, ... adapters have to be saved under `./mydirectory/adapter_1`, `./mydirectory/adapter_2`, ...\n model_path_to_load = pretrained_model_path\n while os.path.isdir(model_path_to_load):\n adapter = T2IAdapter.from_pretrained(model_path_to_load, **kwargs)\n adapters.append(adapter)\n\n idx += 1\n model_path_to_load = pretrained_model_path + f\"_{idx}\"\n\n logger.info(f\"{len(adapters)} adapters loaded from {pretrained_model_path}.\")\n\n if len(adapters) == 0:\n raise ValueError(\n f\"No T2IAdapters found under {os.path.dirname(pretrained_model_path)}. Expected at least {pretrained_model_path + '_0'}.\"\n )\n\n return cls(adapters)\n\n\nclass T2IAdapter(ModelMixin, ConfigMixin):\n r\"\"\"\n A simple ResNet-like model that accepts images containing control signals such as keyposes and depth. The model\n generates multiple feature maps that are used as additional conditioning in [`UNet2DConditionModel`]. The model's\n architecture follows the original implementation of\n [Adapter](https://github.com/TencentARC/T2I-Adapter/blob/686de4681515662c0ac2ffa07bf5dda83af1038a/ldm/modules/encoders/adapter.py#L97)\n and\n [AdapterLight](https://github.com/TencentARC/T2I-Adapter/blob/686de4681515662c0ac2ffa07bf5dda83af1038a/ldm/modules/encoders/adapter.py#L235).\n\n This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library\n implements for all the model (such as downloading or saving, etc.)\n\n Parameters:\n in_channels (`int`, *optional*, defaults to 3):\n Number of channels of Aapter's input(*control image*). Set this parameter to 1 if you're using gray scale\n image as *control image*.\n channels (`List[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):\n The number of channel of each downsample block's output hidden state. The `len(block_out_channels)` will\n also determine the number of downsample blocks in the Adapter.\n num_res_blocks (`int`, *optional*, defaults to 2):\n Number of ResNet blocks in each downsample block.\n downscale_factor (`int`, *optional*, defaults to 8):\n A factor that determines the total downscale factor of the Adapter.\n adapter_type (`str`, *optional*, defaults to `full_adapter`):\n The type of Adapter to use. Choose either `full_adapter` or `full_adapter_xl` or `light_adapter`.\n \"\"\"\n\n @register_to_config\n def __init__(\n self,\n in_channels: int = 3,\n channels: List[int] = [320, 640, 1280, 1280],\n num_res_blocks: int = 2,\n downscale_factor: int = 8,\n adapter_type: str = \"full_adapter\",\n ):\n super().__init__()\n\n if adapter_type == \"full_adapter\":\n self.adapter = FullAdapter(in_channels, channels, num_res_blocks, downscale_factor)\n elif adapter_type == \"full_adapter_xl\":\n self.adapter = FullAdapterXL(in_channels, channels, num_res_blocks, downscale_factor)\n elif adapter_type == \"light_adapter\":\n self.adapter = LightAdapter(in_channels, channels, num_res_blocks, downscale_factor)\n else:\n raise ValueError(\n f\"Unsupported adapter_type: '{adapter_type}'. Choose either 'full_adapter' or \"\n \"'full_adapter_xl' or 'light_adapter'.\"\n )\n\n def forward(self, x: torch.Tensor) -> List[torch.Tensor]:\n r\"\"\"\n This function processes the input tensor `x` through the adapter model and returns a list of feature tensors,\n each representing information extracted at a different scale from the input. The length of the list is\n determined by the number of downsample blocks in the Adapter, as specified by the `channels` and\n `num_res_blocks` parameters during initialization.\n \"\"\"\n return self.adapter(x)\n\n @property\n def total_downscale_factor(self):\n return self.adapter.total_downscale_factor\n\n @property\n def downscale_factor(self):\n \"\"\"The downscale factor applied in the T2I-Adapter's initial pixel unshuffle operation. If an input image's dimensions are\n not evenly divisible by the downscale_factor then an exception will be raised.\n \"\"\"\n return self.adapter.unshuffle.downscale_factor\n\n\n# full adapter\n\n\nclass FullAdapter(nn.Module):\n r\"\"\"\n See [`T2IAdapter`] for more information.\n \"\"\"\n\n def __init__(\n self,\n in_channels: int = 3,\n channels: List[int] = [320, 640, 1280, 1280],\n num_res_blocks: int = 2,\n downscale_factor: int = 8,\n ):\n super().__init__()\n\n in_channels = in_channels * downscale_factor**2\n\n self.unshuffle = nn.PixelUnshuffle(downscale_factor)\n self.conv_in = nn.Conv2d(in_channels, channels[0], kernel_size=3, padding=1)\n\n self.body = nn.ModuleList(\n [\n AdapterBlock(channels[0], channels[0], num_res_blocks),\n *[\n AdapterBlock(channels[i - 1], channels[i], num_res_blocks, down=True)\n for i in range(1, len(channels))\n ],\n ]\n )\n\n self.total_downscale_factor = downscale_factor * 2 ** (len(channels) - 1)\n\n def forward(self, x: torch.Tensor) -> List[torch.Tensor]:\n r\"\"\"\n This method processes the input tensor `x` through the FullAdapter model and performs operations including\n pixel unshuffling, convolution, and a stack of AdapterBlocks. It returns a list of feature tensors, each\n capturing information at a different stage of processing within the FullAdapter model. The number of feature\n tensors in the list is determined by the number of downsample blocks specified during initialization.\n \"\"\"\n x = self.unshuffle(x)\n x = self.conv_in(x)\n\n features = []\n\n for block in self.body:\n x = block(x)\n features.append(x)\n\n return features\n\n\nclass FullAdapterXL(nn.Module):\n r\"\"\"\n See [`T2IAdapter`] for more information.\n \"\"\"\n\n def __init__(\n self,\n in_channels: int = 3,\n channels: List[int] = [320, 640, 1280, 1280],\n num_res_blocks: int = 2,\n downscale_factor: int = 16,\n ):\n super().__init__()\n\n in_channels = in_channels * downscale_factor**2\n\n self.unshuffle = nn.PixelUnshuffle(downscale_factor)\n self.conv_in = nn.Conv2d(in_channels, channels[0], kernel_size=3, padding=1)\n\n self.body = []\n # blocks to extract XL features with dimensions of [320, 64, 64], [640, 64, 64], [1280, 32, 32], [1280, 32, 32]\n for i in range(len(channels)):\n if i == 1:\n self.body.append(AdapterBlock(channels[i - 1], channels[i], num_res_blocks))\n elif i == 2:\n self.body.append(AdapterBlock(channels[i - 1], channels[i], num_res_blocks, down=True))\n else:\n self.body.append(AdapterBlock(channels[i], channels[i], num_res_blocks))\n\n self.body = nn.ModuleList(self.body)\n # XL has only one downsampling AdapterBlock.\n self.total_downscale_factor = downscale_factor * 2\n\n def forward(self, x: torch.Tensor) -> List[torch.Tensor]:\n r\"\"\"\n This method takes the tensor x as input and processes it through FullAdapterXL model. It consists of operations\n including unshuffling pixels, applying convolution layer and appending each block into list of feature tensors.\n \"\"\"\n x = self.unshuffle(x)\n x = self.conv_in(x)\n\n features = []\n\n for block in self.body:\n x = block(x)\n features.append(x)\n\n return features\n\n\nclass AdapterBlock(nn.Module):\n r\"\"\"\n An AdapterBlock is a helper model that contains multiple ResNet-like blocks. It is used in the `FullAdapter` and\n `FullAdapterXL` models.\n\n Parameters:\n in_channels (`int`):\n Number of channels of AdapterBlock's input.\n out_channels (`int`):\n Number of channels of AdapterBlock's output.\n num_res_blocks (`int`):\n Number of ResNet blocks in the AdapterBlock.\n down (`bool`, *optional*, defaults to `False`):\n Whether to perform downsampling on AdapterBlock's input.\n \"\"\"\n\n def __init__(self, in_channels: int, out_channels: int, num_res_blocks: int, down: bool = False):\n super().__init__()\n\n self.downsample = None\n if down:\n self.downsample = nn.AvgPool2d(kernel_size=2, stride=2, ceil_mode=True)\n\n self.in_conv = None\n if in_channels != out_channels:\n self.in_conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)\n\n self.resnets = nn.Sequential(\n *[AdapterResnetBlock(out_channels) for _ in range(num_res_blocks)],\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n r\"\"\"\n This method takes tensor x as input and performs operations downsampling and convolutional layers if the\n self.downsample and self.in_conv properties of AdapterBlock model are specified. Then it applies a series of\n residual blocks to the input tensor.\n \"\"\"\n if self.downsample is not None:\n x = self.downsample(x)\n\n if self.in_conv is not None:\n x = self.in_conv(x)\n\n x = self.resnets(x)\n\n return x\n\n\nclass AdapterResnetBlock(nn.Module):\n r\"\"\"\n An `AdapterResnetBlock` is a helper model that implements a ResNet-like block.\n\n Parameters:\n channels (`int`):\n Number of channels of AdapterResnetBlock's input and output.\n \"\"\"\n\n def __init__(self, channels: int):\n super().__init__()\n self.block1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)\n self.act = nn.ReLU()\n self.block2 = nn.Conv2d(channels, channels, kernel_size=1)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n r\"\"\"\n This method takes input tensor x and applies a convolutional layer, ReLU activation, and another convolutional\n layer on the input tensor. It returns addition with the input tensor.\n \"\"\"\n\n h = self.act(self.block1(x))\n h = self.block2(h)\n\n return h + x\n\n\n# light adapter\n\n\nclass LightAdapter(nn.Module):\n r\"\"\"\n See [`T2IAdapter`] for more information.\n \"\"\"\n\n def __init__(\n self,\n in_channels: int = 3,\n channels: List[int] = [320, 640, 1280],\n num_res_blocks: int = 4,\n downscale_factor: int = 8,\n ):\n super().__init__()\n\n in_channels = in_channels * downscale_factor**2\n\n self.unshuffle = nn.PixelUnshuffle(downscale_factor)\n\n self.body = nn.ModuleList(\n [\n LightAdapterBlock(in_channels, channels[0], num_res_blocks),\n *[\n LightAdapterBlock(channels[i], channels[i + 1], num_res_blocks, down=True)\n for i in range(len(channels) - 1)\n ],\n LightAdapterBlock(channels[-1], channels[-1], num_res_blocks, down=True),\n ]\n )\n\n self.total_downscale_factor = downscale_factor * (2 ** len(channels))\n\n def forward(self, x: torch.Tensor) -> List[torch.Tensor]:\n r\"\"\"\n This method takes the input tensor x and performs downscaling and appends it in list of feature tensors. Each\n feature tensor corresponds to a different level of processing within the LightAdapter.\n \"\"\"\n x = self.unshuffle(x)\n\n features = []\n\n for block in self.body:\n x = block(x)\n features.append(x)\n\n return features\n\n\nclass LightAdapterBlock(nn.Module):\n r\"\"\"\n A `LightAdapterBlock` is a helper model that contains multiple `LightAdapterResnetBlocks`. It is used in the\n `LightAdapter` model.\n\n Parameters:\n in_channels (`int`):\n Number of channels of LightAdapterBlock's input.\n out_channels (`int`):\n Number of channels of LightAdapterBlock's output.\n num_res_blocks (`int`):\n Number of LightAdapterResnetBlocks in the LightAdapterBlock.\n down (`bool`, *optional*, defaults to `False`):\n Whether to perform downsampling on LightAdapterBlock's input.\n \"\"\"\n\n def __init__(self, in_channels: int, out_channels: int, num_res_blocks: int, down: bool = False):\n super().__init__()\n mid_channels = out_channels // 4\n\n self.downsample = None\n if down:\n self.downsample = nn.AvgPool2d(kernel_size=2, stride=2, ceil_mode=True)\n\n self.in_conv = nn.Conv2d(in_channels, mid_channels, kernel_size=1)\n self.resnets = nn.Sequential(*[LightAdapterResnetBlock(mid_channels) for _ in range(num_res_blocks)])\n self.out_conv = nn.Conv2d(mid_channels, out_channels, kernel_size=1)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n r\"\"\"\n This method takes tensor x as input and performs downsampling if required. Then it applies in convolution\n layer, a sequence of residual blocks, and out convolutional layer.\n \"\"\"\n if self.downsample is not None:\n x = self.downsample(x)\n\n x = self.in_conv(x)\n x = self.resnets(x)\n x = self.out_conv(x)\n\n return x\n\n\nclass LightAdapterResnetBlock(nn.Module):\n \"\"\"\n A `LightAdapterResnetBlock` is a helper model that implements a ResNet-like block with a slightly different\n architecture than `AdapterResnetBlock`.\n\n Parameters:\n channels (`int`):\n Number of channels of LightAdapterResnetBlock's input and output.\n \"\"\"\n\n def __init__(self, channels: int):\n super().__init__()\n self.block1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)\n self.act = nn.ReLU()\n self.block2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n r\"\"\"\n This function takes input tensor x and processes it through one convolutional layer, ReLU activation, and\n another convolutional layer and adds it to input tensor.\n \"\"\"\n\n h = self.act(self.block1(x))\n h = self.block2(h)\n\n return h + x\n" }, { "path": "diffusers/src/diffusers/models/attention.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Any, Dict, Optional\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom ..utils import deprecate, logging\nfrom ..utils.torch_utils import maybe_allow_in_graph\nfrom .activations import GEGLU, GELU, ApproximateGELU\nfrom .attention_processor import Attention, JointAttnProcessor2_0\nfrom .embeddings import SinusoidalPositionalEmbedding\nfrom .normalization import AdaLayerNorm, AdaLayerNormContinuous, AdaLayerNormZero, RMSNorm\n\n\nlogger = logging.get_logger(__name__)\n\n\ndef _chunked_feed_forward(ff: nn.Module, hidden_states: torch.Tensor, chunk_dim: int, chunk_size: int):\n # \"feed_forward_chunk_size\" can be used to save memory\n if hidden_states.shape[chunk_dim] % chunk_size != 0:\n raise ValueError(\n f\"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]} has to be divisible by chunk size: {chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.\"\n )\n\n num_chunks = hidden_states.shape[chunk_dim] // chunk_size\n ff_output = torch.cat(\n [ff(hid_slice) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)],\n dim=chunk_dim,\n )\n return ff_output\n\n\n@maybe_allow_in_graph\nclass GatedSelfAttentionDense(nn.Module):\n r\"\"\"\n A gated self-attention dense layer that combines visual features and object features.\n\n Parameters:\n query_dim (`int`): The number of channels in the query.\n context_dim (`int`): The number of channels in the context.\n n_heads (`int`): The number of heads to use for attention.\n d_head (`int`): The number of channels in each head.\n \"\"\"\n\n def __init__(self, query_dim: int, context_dim: int, n_heads: int, d_head: int):\n super().__init__()\n\n # we need a linear projection since we need cat visual feature and obj feature\n self.linear = nn.Linear(context_dim, query_dim)\n\n self.attn = Attention(query_dim=query_dim, heads=n_heads, dim_head=d_head)\n self.ff = FeedForward(query_dim, activation_fn=\"geglu\")\n\n self.norm1 = nn.LayerNorm(query_dim)\n self.norm2 = nn.LayerNorm(query_dim)\n\n self.register_parameter(\"alpha_attn\", nn.Parameter(torch.tensor(0.0)))\n self.register_parameter(\"alpha_dense\", nn.Parameter(torch.tensor(0.0)))\n\n self.enabled = True\n\n def forward(self, x: torch.Tensor, objs: torch.Tensor) -> torch.Tensor:\n if not self.enabled:\n return x\n\n n_visual = x.shape[1]\n objs = self.linear(objs)\n\n x = x + self.alpha_attn.tanh() * self.attn(self.norm1(torch.cat([x, objs], dim=1)))[:, :n_visual, :]\n x = x + self.alpha_dense.tanh() * self.ff(self.norm2(x))\n\n return x\n\n\n@maybe_allow_in_graph\nclass JointTransformerBlock(nn.Module):\n r\"\"\"\n A Transformer block following the MMDiT architecture, introduced in Stable Diffusion 3.\n\n Reference: https://arxiv.org/abs/2403.03206\n\n Parameters:\n dim (`int`): The number of channels in the input and output.\n num_attention_heads (`int`): The number of heads to use for multi-head attention.\n attention_head_dim (`int`): The number of channels in each head.\n context_pre_only (`bool`): Boolean to determine if we should add some blocks associated with the\n processing of `context` conditions.\n \"\"\"\n\n def __init__(self, dim, num_attention_heads, attention_head_dim, context_pre_only=False):\n super().__init__()\n\n self.context_pre_only = context_pre_only\n context_norm_type = \"ada_norm_continous\" if context_pre_only else \"ada_norm_zero\"\n\n self.norm1 = AdaLayerNormZero(dim)\n\n if context_norm_type == \"ada_norm_continous\":\n self.norm1_context = AdaLayerNormContinuous(\n dim, dim, elementwise_affine=False, eps=1e-6, bias=True, norm_type=\"layer_norm\"\n )\n elif context_norm_type == \"ada_norm_zero\":\n self.norm1_context = AdaLayerNormZero(dim)\n else:\n raise ValueError(\n f\"Unknown context_norm_type: {context_norm_type}, currently only support `ada_norm_continous`, `ada_norm_zero`\"\n )\n if hasattr(F, \"scaled_dot_product_attention\"):\n processor = JointAttnProcessor2_0()\n else:\n raise ValueError(\n \"The current PyTorch version does not support the `scaled_dot_product_attention` function.\"\n )\n self.attn = Attention(\n query_dim=dim,\n cross_attention_dim=None,\n added_kv_proj_dim=dim,\n dim_head=attention_head_dim // num_attention_heads,\n heads=num_attention_heads,\n out_dim=attention_head_dim,\n context_pre_only=context_pre_only,\n bias=True,\n processor=processor,\n )\n\n self.norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)\n self.ff = FeedForward(dim=dim, dim_out=dim, activation_fn=\"gelu-approximate\")\n\n if not context_pre_only:\n self.norm2_context = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)\n self.ff_context = FeedForward(dim=dim, dim_out=dim, activation_fn=\"gelu-approximate\")\n else:\n self.norm2_context = None\n self.ff_context = None\n\n # let chunk size default to None\n self._chunk_size = None\n self._chunk_dim = 0\n\n # Copied from diffusers.models.attention.BasicTransformerBlock.set_chunk_feed_forward\n def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0):\n # Sets chunk feed-forward\n self._chunk_size = chunk_size\n self._chunk_dim = dim\n\n def forward(\n self, hidden_states: torch.FloatTensor, encoder_hidden_states: torch.FloatTensor, temb: torch.FloatTensor\n ):\n norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)\n\n if self.context_pre_only:\n norm_encoder_hidden_states = self.norm1_context(encoder_hidden_states, temb)\n else:\n norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context(\n encoder_hidden_states, emb=temb\n )\n\n # Attention.\n attn_output, context_attn_output = self.attn(\n hidden_states=norm_hidden_states, encoder_hidden_states=norm_encoder_hidden_states\n )\n\n # Process attention outputs for the `hidden_states`.\n attn_output = gate_msa.unsqueeze(1) * attn_output\n hidden_states = hidden_states + attn_output\n\n norm_hidden_states = self.norm2(hidden_states)\n norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]\n if self._chunk_size is not None:\n # \"feed_forward_chunk_size\" can be used to save memory\n ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)\n else:\n ff_output = self.ff(norm_hidden_states)\n ff_output = gate_mlp.unsqueeze(1) * ff_output\n\n hidden_states = hidden_states + ff_output\n\n # Process attention outputs for the `encoder_hidden_states`.\n if self.context_pre_only:\n encoder_hidden_states = None\n else:\n context_attn_output = c_gate_msa.unsqueeze(1) * context_attn_output\n encoder_hidden_states = encoder_hidden_states + context_attn_output\n\n norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)\n norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]\n if self._chunk_size is not None:\n # \"feed_forward_chunk_size\" can be used to save memory\n context_ff_output = _chunked_feed_forward(\n self.ff_context, norm_encoder_hidden_states, self._chunk_dim, self._chunk_size\n )\n else:\n context_ff_output = self.ff_context(norm_encoder_hidden_states)\n encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output\n\n return encoder_hidden_states, hidden_states\n\n\n@maybe_allow_in_graph\nclass BasicTransformerBlock(nn.Module):\n r\"\"\"\n A basic Transformer block.\n\n Parameters:\n dim (`int`): The number of channels in the input and output.\n num_attention_heads (`int`): The number of heads to use for multi-head attention.\n attention_head_dim (`int`): The number of channels in each head.\n dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.\n cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.\n activation_fn (`str`, *optional*, defaults to `\"geglu\"`): Activation function to be used in feed-forward.\n num_embeds_ada_norm (:\n obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.\n attention_bias (:\n obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.\n only_cross_attention (`bool`, *optional*):\n Whether to use only cross-attention layers. In this case two cross attention layers are used.\n double_self_attention (`bool`, *optional*):\n Whether to use two self-attention layers. In this case no cross attention layers are used.\n upcast_attention (`bool`, *optional*):\n Whether to upcast the attention computation to float32. This is useful for mixed precision training.\n norm_elementwise_affine (`bool`, *optional*, defaults to `True`):\n Whether to use learnable elementwise affine parameters for normalization.\n norm_type (`str`, *optional*, defaults to `\"layer_norm\"`):\n The normalization layer to use. Can be `\"layer_norm\"`, `\"ada_norm\"` or `\"ada_norm_zero\"`.\n final_dropout (`bool` *optional*, defaults to False):\n Whether to apply a final dropout after the last feed-forward layer.\n attention_type (`str`, *optional*, defaults to `\"default\"`):\n The type of attention to use. Can be `\"default\"` or `\"gated\"` or `\"gated-text-image\"`.\n positional_embeddings (`str`, *optional*, defaults to `None`):\n The type of positional embeddings to apply to.\n num_positional_embeddings (`int`, *optional*, defaults to `None`):\n The maximum number of positional embeddings to apply.\n \"\"\"\n\n def __init__(\n self,\n dim: int,\n num_attention_heads: int,\n attention_head_dim: int,\n dropout=0.0,\n cross_attention_dim: Optional[int] = None,\n activation_fn: str = \"geglu\",\n num_embeds_ada_norm: Optional[int] = None,\n attention_bias: bool = False,\n only_cross_attention: bool = False,\n double_self_attention: bool = False,\n upcast_attention: bool = False,\n norm_elementwise_affine: bool = True,\n norm_type: str = \"layer_norm\", # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single', 'ada_norm_continuous', 'layer_norm_i2vgen'\n norm_eps: float = 1e-5,\n final_dropout: bool = False,\n attention_type: str = \"default\",\n positional_embeddings: Optional[str] = None,\n num_positional_embeddings: Optional[int] = None,\n ada_norm_continous_conditioning_embedding_dim: Optional[int] = None,\n ada_norm_bias: Optional[int] = None,\n ff_inner_dim: Optional[int] = None,\n ff_bias: bool = True,\n attention_out_bias: bool = True,\n ):\n super().__init__()\n self.only_cross_attention = only_cross_attention\n\n # We keep these boolean flags for backward-compatibility.\n self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == \"ada_norm_zero\"\n self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == \"ada_norm\"\n self.use_ada_layer_norm_single = norm_type == \"ada_norm_single\"\n self.use_layer_norm = norm_type == \"layer_norm\"\n self.use_ada_layer_norm_continuous = norm_type == \"ada_norm_continuous\"\n\n if norm_type in (\"ada_norm\", \"ada_norm_zero\") and num_embeds_ada_norm is None:\n raise ValueError(\n f\"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to\"\n f\" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.\"\n )\n\n self.norm_type = norm_type\n self.num_embeds_ada_norm = num_embeds_ada_norm\n\n if positional_embeddings and (num_positional_embeddings is None):\n raise ValueError(\n \"If `positional_embedding` type is defined, `num_positition_embeddings` must also be defined.\"\n )\n\n if positional_embeddings == \"sinusoidal\":\n self.pos_embed = SinusoidalPositionalEmbedding(dim, max_seq_length=num_positional_embeddings)\n else:\n self.pos_embed = None\n\n # Define 3 blocks. Each block has its own normalization layer.\n # 1. Self-Attn\n if norm_type == \"ada_norm\":\n self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)\n elif norm_type == \"ada_norm_zero\":\n self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)\n elif norm_type == \"ada_norm_continuous\":\n self.norm1 = AdaLayerNormContinuous(\n dim,\n ada_norm_continous_conditioning_embedding_dim,\n norm_elementwise_affine,\n norm_eps,\n ada_norm_bias,\n \"rms_norm\",\n )\n else:\n self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)\n\n self.attn1 = Attention(\n query_dim=dim,\n heads=num_attention_heads,\n dim_head=attention_head_dim,\n dropout=dropout,\n bias=attention_bias,\n cross_attention_dim=cross_attention_dim if only_cross_attention else None,\n upcast_attention=upcast_attention,\n out_bias=attention_out_bias,\n )\n\n # 2. Cross-Attn\n if cross_attention_dim is not None or double_self_attention:\n # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.\n # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during\n # the second cross attention block.\n if norm_type == \"ada_norm\":\n self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm)\n elif norm_type == \"ada_norm_continuous\":\n self.norm2 = AdaLayerNormContinuous(\n dim,\n ada_norm_continous_conditioning_embedding_dim,\n norm_elementwise_affine,\n norm_eps,\n ada_norm_bias,\n \"rms_norm\",\n )\n else:\n self.norm2 = nn.LayerNorm(dim, norm_eps, norm_elementwise_affine)\n\n self.attn2 = Attention(\n query_dim=dim,\n cross_attention_dim=cross_attention_dim if not double_self_attention else None,\n heads=num_attention_heads,\n dim_head=attention_head_dim,\n dropout=dropout,\n bias=attention_bias,\n upcast_attention=upcast_attention,\n out_bias=attention_out_bias,\n ) # is self-attn if encoder_hidden_states is none\n else:\n self.norm2 = None\n self.attn2 = None\n\n # 3. Feed-forward\n if norm_type == \"ada_norm_continuous\":\n self.norm3 = AdaLayerNormContinuous(\n dim,\n ada_norm_continous_conditioning_embedding_dim,\n norm_elementwise_affine,\n norm_eps,\n ada_norm_bias,\n \"layer_norm\",\n )\n\n elif norm_type in [\"ada_norm_zero\", \"ada_norm\", \"layer_norm\", \"ada_norm_continuous\"]:\n self.norm3 = nn.LayerNorm(dim, norm_eps, norm_elementwise_affine)\n elif norm_type == \"layer_norm_i2vgen\":\n self.norm3 = None\n\n self.ff = FeedForward(\n dim,\n dropout=dropout,\n activation_fn=activation_fn,\n final_dropout=final_dropout,\n inner_dim=ff_inner_dim,\n bias=ff_bias,\n )\n\n # 4. Fuser\n if attention_type == \"gated\" or attention_type == \"gated-text-image\":\n self.fuser = GatedSelfAttentionDense(dim, cross_attention_dim, num_attention_heads, attention_head_dim)\n\n # 5. Scale-shift for PixArt-Alpha.\n if norm_type == \"ada_norm_single\":\n self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5)\n\n # let chunk size default to None\n self._chunk_size = None\n self._chunk_dim = 0\n\n def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0):\n # Sets chunk feed-forward\n self._chunk_size = chunk_size\n self._chunk_dim = dim\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n attention_mask: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n timestep: Optional[torch.LongTensor] = None,\n cross_attention_kwargs: Dict[str, Any] = None,\n class_labels: Optional[torch.LongTensor] = None,\n added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,\n ) -> torch.Tensor:\n if cross_attention_kwargs is not None:\n if cross_attention_kwargs.get(\"scale\", None) is not None:\n logger.warning(\"Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.\")\n\n # Notice that normalization is always applied before the real computation in the following blocks.\n # 0. Self-Attention\n batch_size = hidden_states.shape[0]\n\n if self.norm_type == \"ada_norm\":\n norm_hidden_states = self.norm1(hidden_states, timestep)\n elif self.norm_type == \"ada_norm_zero\":\n norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(\n hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype\n )\n elif self.norm_type in [\"layer_norm\", \"layer_norm_i2vgen\"]:\n norm_hidden_states = self.norm1(hidden_states)\n elif self.norm_type == \"ada_norm_continuous\":\n norm_hidden_states = self.norm1(hidden_states, added_cond_kwargs[\"pooled_text_emb\"])\n elif self.norm_type == \"ada_norm_single\":\n shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (\n self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)\n ).chunk(6, dim=1)\n norm_hidden_states = self.norm1(hidden_states)\n norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa\n norm_hidden_states = norm_hidden_states.squeeze(1)\n else:\n raise ValueError(\"Incorrect norm used\")\n\n if self.pos_embed is not None:\n norm_hidden_states = self.pos_embed(norm_hidden_states)\n\n # 1. Prepare GLIGEN inputs\n cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}\n gligen_kwargs = cross_attention_kwargs.pop(\"gligen\", None)\n\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n if self.norm_type == \"ada_norm_zero\":\n attn_output = gate_msa.unsqueeze(1) * attn_output\n elif self.norm_type == \"ada_norm_single\":\n attn_output = gate_msa * attn_output\n\n hidden_states = attn_output + hidden_states\n if hidden_states.ndim == 4:\n hidden_states = hidden_states.squeeze(1)\n\n # 1.2 GLIGEN Control\n if gligen_kwargs is not None:\n hidden_states = self.fuser(hidden_states, gligen_kwargs[\"objs\"])\n\n # 3. Cross-Attention\n if self.attn2 is not None:\n if self.norm_type == \"ada_norm\":\n norm_hidden_states = self.norm2(hidden_states, timestep)\n elif self.norm_type in [\"ada_norm_zero\", \"layer_norm\", \"layer_norm_i2vgen\"]:\n norm_hidden_states = self.norm2(hidden_states)\n elif self.norm_type == \"ada_norm_single\":\n # For PixArt norm2 isn't applied here:\n # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103\n norm_hidden_states = hidden_states\n elif self.norm_type == \"ada_norm_continuous\":\n norm_hidden_states = self.norm2(hidden_states, added_cond_kwargs[\"pooled_text_emb\"])\n else:\n raise ValueError(\"Incorrect norm\")\n\n if self.pos_embed is not None and self.norm_type != \"ada_norm_single\":\n norm_hidden_states = self.pos_embed(norm_hidden_states)\n\n attn_output = self.attn2(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=encoder_attention_mask,\n **cross_attention_kwargs,\n )\n hidden_states = attn_output + hidden_states\n\n # 4. Feed-forward\n # i2vgen doesn't have this norm 🤷‍♂️\n if self.norm_type == \"ada_norm_continuous\":\n norm_hidden_states = self.norm3(hidden_states, added_cond_kwargs[\"pooled_text_emb\"])\n elif not self.norm_type == \"ada_norm_single\":\n norm_hidden_states = self.norm3(hidden_states)\n\n if self.norm_type == \"ada_norm_zero\":\n norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]\n\n if self.norm_type == \"ada_norm_single\":\n norm_hidden_states = self.norm2(hidden_states)\n norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp\n\n if self._chunk_size is not None:\n # \"feed_forward_chunk_size\" can be used to save memory\n ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)\n else:\n ff_output = self.ff(norm_hidden_states)\n\n if self.norm_type == \"ada_norm_zero\":\n ff_output = gate_mlp.unsqueeze(1) * ff_output\n elif self.norm_type == \"ada_norm_single\":\n ff_output = gate_mlp * ff_output\n\n hidden_states = ff_output + hidden_states\n if hidden_states.ndim == 4:\n hidden_states = hidden_states.squeeze(1)\n\n return hidden_states\n\n\n@maybe_allow_in_graph\nclass TemporalBasicTransformerBlock(nn.Module):\n r\"\"\"\n A basic Transformer block for video like data.\n\n Parameters:\n dim (`int`): The number of channels in the input and output.\n time_mix_inner_dim (`int`): The number of channels for temporal attention.\n num_attention_heads (`int`): The number of heads to use for multi-head attention.\n attention_head_dim (`int`): The number of channels in each head.\n cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.\n \"\"\"\n\n def __init__(\n self,\n dim: int,\n time_mix_inner_dim: int,\n num_attention_heads: int,\n attention_head_dim: int,\n cross_attention_dim: Optional[int] = None,\n ):\n super().__init__()\n self.is_res = dim == time_mix_inner_dim\n\n self.norm_in = nn.LayerNorm(dim)\n\n # Define 3 blocks. Each block has its own normalization layer.\n # 1. Self-Attn\n self.ff_in = FeedForward(\n dim,\n dim_out=time_mix_inner_dim,\n activation_fn=\"geglu\",\n )\n\n self.norm1 = nn.LayerNorm(time_mix_inner_dim)\n self.attn1 = Attention(\n query_dim=time_mix_inner_dim,\n heads=num_attention_heads,\n dim_head=attention_head_dim,\n cross_attention_dim=None,\n )\n\n # 2. Cross-Attn\n if cross_attention_dim is not None:\n # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.\n # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during\n # the second cross attention block.\n self.norm2 = nn.LayerNorm(time_mix_inner_dim)\n self.attn2 = Attention(\n query_dim=time_mix_inner_dim,\n cross_attention_dim=cross_attention_dim,\n heads=num_attention_heads,\n dim_head=attention_head_dim,\n ) # is self-attn if encoder_hidden_states is none\n else:\n self.norm2 = None\n self.attn2 = None\n\n # 3. Feed-forward\n self.norm3 = nn.LayerNorm(time_mix_inner_dim)\n self.ff = FeedForward(time_mix_inner_dim, activation_fn=\"geglu\")\n\n # let chunk size default to None\n self._chunk_size = None\n self._chunk_dim = None\n\n def set_chunk_feed_forward(self, chunk_size: Optional[int], **kwargs):\n # Sets chunk feed-forward\n self._chunk_size = chunk_size\n # chunk dim should be hardcoded to 1 to have better speed vs. memory trade-off\n self._chunk_dim = 1\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n num_frames: int,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n # Notice that normalization is always applied before the real computation in the following blocks.\n # 0. Self-Attention\n batch_size = hidden_states.shape[0]\n\n batch_frames, seq_length, channels = hidden_states.shape\n batch_size = batch_frames // num_frames\n\n hidden_states = hidden_states[None, :].reshape(batch_size, num_frames, seq_length, channels)\n hidden_states = hidden_states.permute(0, 2, 1, 3)\n hidden_states = hidden_states.reshape(batch_size * seq_length, num_frames, channels)\n\n residual = hidden_states\n hidden_states = self.norm_in(hidden_states)\n\n if self._chunk_size is not None:\n hidden_states = _chunked_feed_forward(self.ff_in, hidden_states, self._chunk_dim, self._chunk_size)\n else:\n hidden_states = self.ff_in(hidden_states)\n\n if self.is_res:\n hidden_states = hidden_states + residual\n\n norm_hidden_states = self.norm1(hidden_states)\n attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None)\n hidden_states = attn_output + hidden_states\n\n # 3. Cross-Attention\n if self.attn2 is not None:\n norm_hidden_states = self.norm2(hidden_states)\n attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=encoder_hidden_states)\n hidden_states = attn_output + hidden_states\n\n # 4. Feed-forward\n norm_hidden_states = self.norm3(hidden_states)\n\n if self._chunk_size is not None:\n ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)\n else:\n ff_output = self.ff(norm_hidden_states)\n\n if self.is_res:\n hidden_states = ff_output + hidden_states\n else:\n hidden_states = ff_output\n\n hidden_states = hidden_states[None, :].reshape(batch_size, seq_length, num_frames, channels)\n hidden_states = hidden_states.permute(0, 2, 1, 3)\n hidden_states = hidden_states.reshape(batch_size * num_frames, seq_length, channels)\n\n return hidden_states\n\n\nclass SkipFFTransformerBlock(nn.Module):\n def __init__(\n self,\n dim: int,\n num_attention_heads: int,\n attention_head_dim: int,\n kv_input_dim: int,\n kv_input_dim_proj_use_bias: bool,\n dropout=0.0,\n cross_attention_dim: Optional[int] = None,\n attention_bias: bool = False,\n attention_out_bias: bool = True,\n ):\n super().__init__()\n if kv_input_dim != dim:\n self.kv_mapper = nn.Linear(kv_input_dim, dim, kv_input_dim_proj_use_bias)\n else:\n self.kv_mapper = None\n\n self.norm1 = RMSNorm(dim, 1e-06)\n\n self.attn1 = Attention(\n query_dim=dim,\n heads=num_attention_heads,\n dim_head=attention_head_dim,\n dropout=dropout,\n bias=attention_bias,\n cross_attention_dim=cross_attention_dim,\n out_bias=attention_out_bias,\n )\n\n self.norm2 = RMSNorm(dim, 1e-06)\n\n self.attn2 = Attention(\n query_dim=dim,\n cross_attention_dim=cross_attention_dim,\n heads=num_attention_heads,\n dim_head=attention_head_dim,\n dropout=dropout,\n bias=attention_bias,\n out_bias=attention_out_bias,\n )\n\n def forward(self, hidden_states, encoder_hidden_states, cross_attention_kwargs):\n cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}\n\n if self.kv_mapper is not None:\n encoder_hidden_states = self.kv_mapper(F.silu(encoder_hidden_states))\n\n norm_hidden_states = self.norm1(hidden_states)\n\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n **cross_attention_kwargs,\n )\n\n hidden_states = attn_output + hidden_states\n\n norm_hidden_states = self.norm2(hidden_states)\n\n attn_output = self.attn2(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n **cross_attention_kwargs,\n )\n\n hidden_states = attn_output + hidden_states\n\n return hidden_states\n\n\nclass FeedForward(nn.Module):\n r\"\"\"\n A feed-forward layer.\n\n Parameters:\n dim (`int`): The number of channels in the input.\n dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.\n mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.\n dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.\n activation_fn (`str`, *optional*, defaults to `\"geglu\"`): Activation function to be used in feed-forward.\n final_dropout (`bool` *optional*, defaults to False): Apply a final dropout.\n bias (`bool`, defaults to True): Whether to use a bias in the linear layer.\n \"\"\"\n\n def __init__(\n self,\n dim: int,\n dim_out: Optional[int] = None,\n mult: int = 4,\n dropout: float = 0.0,\n activation_fn: str = \"geglu\",\n final_dropout: bool = False,\n inner_dim=None,\n bias: bool = True,\n ):\n super().__init__()\n if inner_dim is None:\n inner_dim = int(dim * mult)\n dim_out = dim_out if dim_out is not None else dim\n\n if activation_fn == \"gelu\":\n act_fn = GELU(dim, inner_dim, bias=bias)\n if activation_fn == \"gelu-approximate\":\n act_fn = GELU(dim, inner_dim, approximate=\"tanh\", bias=bias)\n elif activation_fn == \"geglu\":\n act_fn = GEGLU(dim, inner_dim, bias=bias)\n elif activation_fn == \"geglu-approximate\":\n act_fn = ApproximateGELU(dim, inner_dim, bias=bias)\n\n self.net = nn.ModuleList([])\n # project in\n self.net.append(act_fn)\n # project dropout\n self.net.append(nn.Dropout(dropout))\n # project out\n self.net.append(nn.Linear(inner_dim, dim_out, bias=bias))\n # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout\n if final_dropout:\n self.net.append(nn.Dropout(dropout))\n\n def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n for module in self.net:\n hidden_states = module(hidden_states)\n return hidden_states\n" }, { "path": "diffusers/src/diffusers/models/attention_flax.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport functools\nimport math\n\nimport flax.linen as nn\nimport jax\nimport jax.numpy as jnp\n\n\ndef _query_chunk_attention(query, key, value, precision, key_chunk_size: int = 4096):\n \"\"\"Multi-head dot product attention with a limited number of queries.\"\"\"\n num_kv, num_heads, k_features = key.shape[-3:]\n v_features = value.shape[-1]\n key_chunk_size = min(key_chunk_size, num_kv)\n query = query / jnp.sqrt(k_features)\n\n @functools.partial(jax.checkpoint, prevent_cse=False)\n def summarize_chunk(query, key, value):\n attn_weights = jnp.einsum(\"...qhd,...khd->...qhk\", query, key, precision=precision)\n\n max_score = jnp.max(attn_weights, axis=-1, keepdims=True)\n max_score = jax.lax.stop_gradient(max_score)\n exp_weights = jnp.exp(attn_weights - max_score)\n\n exp_values = jnp.einsum(\"...vhf,...qhv->...qhf\", value, exp_weights, precision=precision)\n max_score = jnp.einsum(\"...qhk->...qh\", max_score)\n\n return (exp_values, exp_weights.sum(axis=-1), max_score)\n\n def chunk_scanner(chunk_idx):\n # julienne key array\n key_chunk = jax.lax.dynamic_slice(\n operand=key,\n start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0], # [...,k,h,d]\n slice_sizes=list(key.shape[:-3]) + [key_chunk_size, num_heads, k_features], # [...,k,h,d]\n )\n\n # julienne value array\n value_chunk = jax.lax.dynamic_slice(\n operand=value,\n start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0], # [...,v,h,d]\n slice_sizes=list(value.shape[:-3]) + [key_chunk_size, num_heads, v_features], # [...,v,h,d]\n )\n\n return summarize_chunk(query, key_chunk, value_chunk)\n\n chunk_values, chunk_weights, chunk_max = jax.lax.map(f=chunk_scanner, xs=jnp.arange(0, num_kv, key_chunk_size))\n\n global_max = jnp.max(chunk_max, axis=0, keepdims=True)\n max_diffs = jnp.exp(chunk_max - global_max)\n\n chunk_values *= jnp.expand_dims(max_diffs, axis=-1)\n chunk_weights *= max_diffs\n\n all_values = chunk_values.sum(axis=0)\n all_weights = jnp.expand_dims(chunk_weights, -1).sum(axis=0)\n\n return all_values / all_weights\n\n\ndef jax_memory_efficient_attention(\n query, key, value, precision=jax.lax.Precision.HIGHEST, query_chunk_size: int = 1024, key_chunk_size: int = 4096\n):\n r\"\"\"\n Flax Memory-efficient multi-head dot product attention. https://arxiv.org/abs/2112.05682v2\n https://github.com/AminRezaei0x443/memory-efficient-attention\n\n Args:\n query (`jnp.ndarray`): (batch..., query_length, head, query_key_depth_per_head)\n key (`jnp.ndarray`): (batch..., key_value_length, head, query_key_depth_per_head)\n value (`jnp.ndarray`): (batch..., key_value_length, head, value_depth_per_head)\n precision (`jax.lax.Precision`, *optional*, defaults to `jax.lax.Precision.HIGHEST`):\n numerical precision for computation\n query_chunk_size (`int`, *optional*, defaults to 1024):\n chunk size to divide query array value must divide query_length equally without remainder\n key_chunk_size (`int`, *optional*, defaults to 4096):\n chunk size to divide key and value array value must divide key_value_length equally without remainder\n\n Returns:\n (`jnp.ndarray`) with shape of (batch..., query_length, head, value_depth_per_head)\n \"\"\"\n num_q, num_heads, q_features = query.shape[-3:]\n\n def chunk_scanner(chunk_idx, _):\n # julienne query array\n query_chunk = jax.lax.dynamic_slice(\n operand=query,\n start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0], # [...,q,h,d]\n slice_sizes=list(query.shape[:-3]) + [min(query_chunk_size, num_q), num_heads, q_features], # [...,q,h,d]\n )\n\n return (\n chunk_idx + query_chunk_size, # unused ignore it\n _query_chunk_attention(\n query=query_chunk, key=key, value=value, precision=precision, key_chunk_size=key_chunk_size\n ),\n )\n\n _, res = jax.lax.scan(\n f=chunk_scanner,\n init=0,\n xs=None,\n length=math.ceil(num_q / query_chunk_size), # start counter # stop counter\n )\n\n return jnp.concatenate(res, axis=-3) # fuse the chunked result back\n\n\nclass FlaxAttention(nn.Module):\n r\"\"\"\n A Flax multi-head attention module as described in: https://arxiv.org/abs/1706.03762\n\n Parameters:\n query_dim (:obj:`int`):\n Input hidden states dimension\n heads (:obj:`int`, *optional*, defaults to 8):\n Number of heads\n dim_head (:obj:`int`, *optional*, defaults to 64):\n Hidden states dimension inside each head\n dropout (:obj:`float`, *optional*, defaults to 0.0):\n Dropout rate\n use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):\n enable memory efficient attention https://arxiv.org/abs/2112.05682\n split_head_dim (`bool`, *optional*, defaults to `False`):\n Whether to split the head dimension into a new axis for the self-attention computation. In most cases,\n enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL.\n dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):\n Parameters `dtype`\n\n \"\"\"\n\n query_dim: int\n heads: int = 8\n dim_head: int = 64\n dropout: float = 0.0\n use_memory_efficient_attention: bool = False\n split_head_dim: bool = False\n dtype: jnp.dtype = jnp.float32\n\n def setup(self):\n inner_dim = self.dim_head * self.heads\n self.scale = self.dim_head**-0.5\n\n # Weights were exported with old names {to_q, to_k, to_v, to_out}\n self.query = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, name=\"to_q\")\n self.key = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, name=\"to_k\")\n self.value = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, name=\"to_v\")\n\n self.proj_attn = nn.Dense(self.query_dim, dtype=self.dtype, name=\"to_out_0\")\n self.dropout_layer = nn.Dropout(rate=self.dropout)\n\n def reshape_heads_to_batch_dim(self, tensor):\n batch_size, seq_len, dim = tensor.shape\n head_size = self.heads\n tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)\n tensor = jnp.transpose(tensor, (0, 2, 1, 3))\n tensor = tensor.reshape(batch_size * head_size, seq_len, dim // head_size)\n return tensor\n\n def reshape_batch_dim_to_heads(self, tensor):\n batch_size, seq_len, dim = tensor.shape\n head_size = self.heads\n tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)\n tensor = jnp.transpose(tensor, (0, 2, 1, 3))\n tensor = tensor.reshape(batch_size // head_size, seq_len, dim * head_size)\n return tensor\n\n def __call__(self, hidden_states, context=None, deterministic=True):\n context = hidden_states if context is None else context\n\n query_proj = self.query(hidden_states)\n key_proj = self.key(context)\n value_proj = self.value(context)\n\n if self.split_head_dim:\n b = hidden_states.shape[0]\n query_states = jnp.reshape(query_proj, (b, -1, self.heads, self.dim_head))\n key_states = jnp.reshape(key_proj, (b, -1, self.heads, self.dim_head))\n value_states = jnp.reshape(value_proj, (b, -1, self.heads, self.dim_head))\n else:\n query_states = self.reshape_heads_to_batch_dim(query_proj)\n key_states = self.reshape_heads_to_batch_dim(key_proj)\n value_states = self.reshape_heads_to_batch_dim(value_proj)\n\n if self.use_memory_efficient_attention:\n query_states = query_states.transpose(1, 0, 2)\n key_states = key_states.transpose(1, 0, 2)\n value_states = value_states.transpose(1, 0, 2)\n\n # this if statement create a chunk size for each layer of the unet\n # the chunk size is equal to the query_length dimension of the deepest layer of the unet\n\n flatten_latent_dim = query_states.shape[-3]\n if flatten_latent_dim % 64 == 0:\n query_chunk_size = int(flatten_latent_dim / 64)\n elif flatten_latent_dim % 16 == 0:\n query_chunk_size = int(flatten_latent_dim / 16)\n elif flatten_latent_dim % 4 == 0:\n query_chunk_size = int(flatten_latent_dim / 4)\n else:\n query_chunk_size = int(flatten_latent_dim)\n\n hidden_states = jax_memory_efficient_attention(\n query_states, key_states, value_states, query_chunk_size=query_chunk_size, key_chunk_size=4096 * 4\n )\n\n hidden_states = hidden_states.transpose(1, 0, 2)\n else:\n # compute attentions\n if self.split_head_dim:\n attention_scores = jnp.einsum(\"b t n h, b f n h -> b n f t\", key_states, query_states)\n else:\n attention_scores = jnp.einsum(\"b i d, b j d->b i j\", query_states, key_states)\n\n attention_scores = attention_scores * self.scale\n attention_probs = nn.softmax(attention_scores, axis=-1 if self.split_head_dim else 2)\n\n # attend to values\n if self.split_head_dim:\n hidden_states = jnp.einsum(\"b n f t, b t n h -> b f n h\", attention_probs, value_states)\n b = hidden_states.shape[0]\n hidden_states = jnp.reshape(hidden_states, (b, -1, self.heads * self.dim_head))\n else:\n hidden_states = jnp.einsum(\"b i j, b j d -> b i d\", attention_probs, value_states)\n hidden_states = self.reshape_batch_dim_to_heads(hidden_states)\n\n hidden_states = self.proj_attn(hidden_states)\n return self.dropout_layer(hidden_states, deterministic=deterministic)\n\n\nclass FlaxBasicTransformerBlock(nn.Module):\n r\"\"\"\n A Flax transformer block layer with `GLU` (Gated Linear Unit) activation function as described in:\n https://arxiv.org/abs/1706.03762\n\n\n Parameters:\n dim (:obj:`int`):\n Inner hidden states dimension\n n_heads (:obj:`int`):\n Number of heads\n d_head (:obj:`int`):\n Hidden states dimension inside each head\n dropout (:obj:`float`, *optional*, defaults to 0.0):\n Dropout rate\n only_cross_attention (`bool`, defaults to `False`):\n Whether to only apply cross attention.\n dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):\n Parameters `dtype`\n use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):\n enable memory efficient attention https://arxiv.org/abs/2112.05682\n split_head_dim (`bool`, *optional*, defaults to `False`):\n Whether to split the head dimension into a new axis for the self-attention computation. In most cases,\n enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL.\n \"\"\"\n\n dim: int\n n_heads: int\n d_head: int\n dropout: float = 0.0\n only_cross_attention: bool = False\n dtype: jnp.dtype = jnp.float32\n use_memory_efficient_attention: bool = False\n split_head_dim: bool = False\n\n def setup(self):\n # self attention (or cross_attention if only_cross_attention is True)\n self.attn1 = FlaxAttention(\n self.dim,\n self.n_heads,\n self.d_head,\n self.dropout,\n self.use_memory_efficient_attention,\n self.split_head_dim,\n dtype=self.dtype,\n )\n # cross attention\n self.attn2 = FlaxAttention(\n self.dim,\n self.n_heads,\n self.d_head,\n self.dropout,\n self.use_memory_efficient_attention,\n self.split_head_dim,\n dtype=self.dtype,\n )\n self.ff = FlaxFeedForward(dim=self.dim, dropout=self.dropout, dtype=self.dtype)\n self.norm1 = nn.LayerNorm(epsilon=1e-5, dtype=self.dtype)\n self.norm2 = nn.LayerNorm(epsilon=1e-5, dtype=self.dtype)\n self.norm3 = nn.LayerNorm(epsilon=1e-5, dtype=self.dtype)\n self.dropout_layer = nn.Dropout(rate=self.dropout)\n\n def __call__(self, hidden_states, context, deterministic=True):\n # self attention\n residual = hidden_states\n if self.only_cross_attention:\n hidden_states = self.attn1(self.norm1(hidden_states), context, deterministic=deterministic)\n else:\n hidden_states = self.attn1(self.norm1(hidden_states), deterministic=deterministic)\n hidden_states = hidden_states + residual\n\n # cross attention\n residual = hidden_states\n hidden_states = self.attn2(self.norm2(hidden_states), context, deterministic=deterministic)\n hidden_states = hidden_states + residual\n\n # feed forward\n residual = hidden_states\n hidden_states = self.ff(self.norm3(hidden_states), deterministic=deterministic)\n hidden_states = hidden_states + residual\n\n return self.dropout_layer(hidden_states, deterministic=deterministic)\n\n\nclass FlaxTransformer2DModel(nn.Module):\n r\"\"\"\n A Spatial Transformer layer with Gated Linear Unit (GLU) activation function as described in:\n https://arxiv.org/pdf/1506.02025.pdf\n\n\n Parameters:\n in_channels (:obj:`int`):\n Input number of channels\n n_heads (:obj:`int`):\n Number of heads\n d_head (:obj:`int`):\n Hidden states dimension inside each head\n depth (:obj:`int`, *optional*, defaults to 1):\n Number of transformers block\n dropout (:obj:`float`, *optional*, defaults to 0.0):\n Dropout rate\n use_linear_projection (`bool`, defaults to `False`): tbd\n only_cross_attention (`bool`, defaults to `False`): tbd\n dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):\n Parameters `dtype`\n use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):\n enable memory efficient attention https://arxiv.org/abs/2112.05682\n split_head_dim (`bool`, *optional*, defaults to `False`):\n Whether to split the head dimension into a new axis for the self-attention computation. In most cases,\n enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL.\n \"\"\"\n\n in_channels: int\n n_heads: int\n d_head: int\n depth: int = 1\n dropout: float = 0.0\n use_linear_projection: bool = False\n only_cross_attention: bool = False\n dtype: jnp.dtype = jnp.float32\n use_memory_efficient_attention: bool = False\n split_head_dim: bool = False\n\n def setup(self):\n self.norm = nn.GroupNorm(num_groups=32, epsilon=1e-5)\n\n inner_dim = self.n_heads * self.d_head\n if self.use_linear_projection:\n self.proj_in = nn.Dense(inner_dim, dtype=self.dtype)\n else:\n self.proj_in = nn.Conv(\n inner_dim,\n kernel_size=(1, 1),\n strides=(1, 1),\n padding=\"VALID\",\n dtype=self.dtype,\n )\n\n self.transformer_blocks = [\n FlaxBasicTransformerBlock(\n inner_dim,\n self.n_heads,\n self.d_head,\n dropout=self.dropout,\n only_cross_attention=self.only_cross_attention,\n dtype=self.dtype,\n use_memory_efficient_attention=self.use_memory_efficient_attention,\n split_head_dim=self.split_head_dim,\n )\n for _ in range(self.depth)\n ]\n\n if self.use_linear_projection:\n self.proj_out = nn.Dense(inner_dim, dtype=self.dtype)\n else:\n self.proj_out = nn.Conv(\n inner_dim,\n kernel_size=(1, 1),\n strides=(1, 1),\n padding=\"VALID\",\n dtype=self.dtype,\n )\n\n self.dropout_layer = nn.Dropout(rate=self.dropout)\n\n def __call__(self, hidden_states, context, deterministic=True):\n batch, height, width, channels = hidden_states.shape\n residual = hidden_states\n hidden_states = self.norm(hidden_states)\n if self.use_linear_projection:\n hidden_states = hidden_states.reshape(batch, height * width, channels)\n hidden_states = self.proj_in(hidden_states)\n else:\n hidden_states = self.proj_in(hidden_states)\n hidden_states = hidden_states.reshape(batch, height * width, channels)\n\n for transformer_block in self.transformer_blocks:\n hidden_states = transformer_block(hidden_states, context, deterministic=deterministic)\n\n if self.use_linear_projection:\n hidden_states = self.proj_out(hidden_states)\n hidden_states = hidden_states.reshape(batch, height, width, channels)\n else:\n hidden_states = hidden_states.reshape(batch, height, width, channels)\n hidden_states = self.proj_out(hidden_states)\n\n hidden_states = hidden_states + residual\n return self.dropout_layer(hidden_states, deterministic=deterministic)\n\n\nclass FlaxFeedForward(nn.Module):\n r\"\"\"\n Flax module that encapsulates two Linear layers separated by a non-linearity. It is the counterpart of PyTorch's\n [`FeedForward`] class, with the following simplifications:\n - The activation function is currently hardcoded to a gated linear unit from:\n https://arxiv.org/abs/2002.05202\n - `dim_out` is equal to `dim`.\n - The number of hidden dimensions is hardcoded to `dim * 4` in [`FlaxGELU`].\n\n Parameters:\n dim (:obj:`int`):\n Inner hidden states dimension\n dropout (:obj:`float`, *optional*, defaults to 0.0):\n Dropout rate\n dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):\n Parameters `dtype`\n \"\"\"\n\n dim: int\n dropout: float = 0.0\n dtype: jnp.dtype = jnp.float32\n\n def setup(self):\n # The second linear layer needs to be called\n # net_2 for now to match the index of the Sequential layer\n self.net_0 = FlaxGEGLU(self.dim, self.dropout, self.dtype)\n self.net_2 = nn.Dense(self.dim, dtype=self.dtype)\n\n def __call__(self, hidden_states, deterministic=True):\n hidden_states = self.net_0(hidden_states, deterministic=deterministic)\n hidden_states = self.net_2(hidden_states)\n return hidden_states\n\n\nclass FlaxGEGLU(nn.Module):\n r\"\"\"\n Flax implementation of a Linear layer followed by the variant of the gated linear unit activation function from\n https://arxiv.org/abs/2002.05202.\n\n Parameters:\n dim (:obj:`int`):\n Input hidden states dimension\n dropout (:obj:`float`, *optional*, defaults to 0.0):\n Dropout rate\n dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):\n Parameters `dtype`\n \"\"\"\n\n dim: int\n dropout: float = 0.0\n dtype: jnp.dtype = jnp.float32\n\n def setup(self):\n inner_dim = self.dim * 4\n self.proj = nn.Dense(inner_dim * 2, dtype=self.dtype)\n self.dropout_layer = nn.Dropout(rate=self.dropout)\n\n def __call__(self, hidden_states, deterministic=True):\n hidden_states = self.proj(hidden_states)\n hidden_linear, hidden_gelu = jnp.split(hidden_states, 2, axis=2)\n return self.dropout_layer(hidden_linear * nn.gelu(hidden_gelu), deterministic=deterministic)\n" }, { "path": "diffusers/src/diffusers/models/attention_processor.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport inspect\nimport math\nfrom importlib import import_module\nfrom typing import Callable, List, Optional, Union\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom ..image_processor import IPAdapterMaskProcessor\nfrom ..utils import deprecate, logging\nfrom ..utils.import_utils import is_torch_npu_available, is_xformers_available\nfrom ..utils.torch_utils import maybe_allow_in_graph\nfrom .lora import LoRALinearLayer\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nif is_torch_npu_available():\n import torch_npu\n\nif is_xformers_available():\n import xformers\n import xformers.ops\nelse:\n xformers = None\n\n\n@maybe_allow_in_graph\nclass Attention(nn.Module):\n r\"\"\"\n A cross attention layer.\n\n Parameters:\n query_dim (`int`):\n The number of channels in the query.\n cross_attention_dim (`int`, *optional*):\n The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`.\n heads (`int`, *optional*, defaults to 8):\n The number of heads to use for multi-head attention.\n dim_head (`int`, *optional*, defaults to 64):\n The number of channels in each head.\n dropout (`float`, *optional*, defaults to 0.0):\n The dropout probability to use.\n bias (`bool`, *optional*, defaults to False):\n Set to `True` for the query, key, and value linear layers to contain a bias parameter.\n upcast_attention (`bool`, *optional*, defaults to False):\n Set to `True` to upcast the attention computation to `float32`.\n upcast_softmax (`bool`, *optional*, defaults to False):\n Set to `True` to upcast the softmax computation to `float32`.\n cross_attention_norm (`str`, *optional*, defaults to `None`):\n The type of normalization to use for the cross attention. Can be `None`, `layer_norm`, or `group_norm`.\n cross_attention_norm_num_groups (`int`, *optional*, defaults to 32):\n The number of groups to use for the group norm in the cross attention.\n added_kv_proj_dim (`int`, *optional*, defaults to `None`):\n The number of channels to use for the added key and value projections. If `None`, no projection is used.\n norm_num_groups (`int`, *optional*, defaults to `None`):\n The number of groups to use for the group norm in the attention.\n spatial_norm_dim (`int`, *optional*, defaults to `None`):\n The number of channels to use for the spatial normalization.\n out_bias (`bool`, *optional*, defaults to `True`):\n Set to `True` to use a bias in the output linear layer.\n scale_qk (`bool`, *optional*, defaults to `True`):\n Set to `True` to scale the query and key by `1 / sqrt(dim_head)`.\n only_cross_attention (`bool`, *optional*, defaults to `False`):\n Set to `True` to only use cross attention and not added_kv_proj_dim. Can only be set to `True` if\n `added_kv_proj_dim` is not `None`.\n eps (`float`, *optional*, defaults to 1e-5):\n An additional value added to the denominator in group normalization that is used for numerical stability.\n rescale_output_factor (`float`, *optional*, defaults to 1.0):\n A factor to rescale the output by dividing it with this value.\n residual_connection (`bool`, *optional*, defaults to `False`):\n Set to `True` to add the residual connection to the output.\n _from_deprecated_attn_block (`bool`, *optional*, defaults to `False`):\n Set to `True` if the attention block is loaded from a deprecated state dict.\n processor (`AttnProcessor`, *optional*, defaults to `None`):\n The attention processor to use. If `None`, defaults to `AttnProcessor2_0` if `torch 2.x` is used and\n `AttnProcessor` otherwise.\n \"\"\"\n\n def __init__(\n self,\n query_dim: int,\n cross_attention_dim: Optional[int] = None,\n heads: int = 8,\n dim_head: int = 64,\n dropout: float = 0.0,\n bias: bool = False,\n upcast_attention: bool = False,\n upcast_softmax: bool = False,\n cross_attention_norm: Optional[str] = None,\n cross_attention_norm_num_groups: int = 32,\n qk_norm: Optional[str] = None,\n added_kv_proj_dim: Optional[int] = None,\n norm_num_groups: Optional[int] = None,\n spatial_norm_dim: Optional[int] = None,\n out_bias: bool = True,\n scale_qk: bool = True,\n only_cross_attention: bool = False,\n eps: float = 1e-5,\n rescale_output_factor: float = 1.0,\n residual_connection: bool = False,\n _from_deprecated_attn_block: bool = False,\n processor: Optional[\"AttnProcessor\"] = None,\n out_dim: int = None,\n context_pre_only=None,\n ):\n super().__init__()\n self.inner_dim = out_dim if out_dim is not None else dim_head * heads\n self.query_dim = query_dim\n self.use_bias = bias\n self.is_cross_attention = cross_attention_dim is not None\n self.cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim\n self.upcast_attention = upcast_attention\n self.upcast_softmax = upcast_softmax\n self.rescale_output_factor = rescale_output_factor\n self.residual_connection = residual_connection\n self.dropout = dropout\n self.fused_projections = False\n self.out_dim = out_dim if out_dim is not None else query_dim\n self.context_pre_only = context_pre_only\n\n # we make use of this private variable to know whether this class is loaded\n # with an deprecated state dict so that we can convert it on the fly\n self._from_deprecated_attn_block = _from_deprecated_attn_block\n\n self.scale_qk = scale_qk\n self.scale = dim_head**-0.5 if self.scale_qk else 1.0\n\n self.heads = out_dim // dim_head if out_dim is not None else heads\n # for slice_size > 0 the attention score computation\n # is split across the batch axis to save memory\n # You can set slice_size with `set_attention_slice`\n self.sliceable_head_dim = heads\n\n self.added_kv_proj_dim = added_kv_proj_dim\n self.only_cross_attention = only_cross_attention\n\n if self.added_kv_proj_dim is None and self.only_cross_attention:\n raise ValueError(\n \"`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`.\"\n )\n\n if norm_num_groups is not None:\n self.group_norm = nn.GroupNorm(num_channels=query_dim, num_groups=norm_num_groups, eps=eps, affine=True)\n else:\n self.group_norm = None\n\n if spatial_norm_dim is not None:\n self.spatial_norm = SpatialNorm(f_channels=query_dim, zq_channels=spatial_norm_dim)\n else:\n self.spatial_norm = None\n\n if qk_norm is None:\n self.norm_q = None\n self.norm_k = None\n elif qk_norm == \"layer_norm\":\n self.norm_q = nn.LayerNorm(dim_head, eps=eps)\n self.norm_k = nn.LayerNorm(dim_head, eps=eps)\n else:\n raise ValueError(f\"unknown qk_norm: {qk_norm}. Should be None or 'layer_norm'\")\n\n if cross_attention_norm is None:\n self.norm_cross = None\n elif cross_attention_norm == \"layer_norm\":\n self.norm_cross = nn.LayerNorm(self.cross_attention_dim)\n elif cross_attention_norm == \"group_norm\":\n if self.added_kv_proj_dim is not None:\n # The given `encoder_hidden_states` are initially of shape\n # (batch_size, seq_len, added_kv_proj_dim) before being projected\n # to (batch_size, seq_len, cross_attention_dim). The norm is applied\n # before the projection, so we need to use `added_kv_proj_dim` as\n # the number of channels for the group norm.\n norm_cross_num_channels = added_kv_proj_dim\n else:\n norm_cross_num_channels = self.cross_attention_dim\n\n self.norm_cross = nn.GroupNorm(\n num_channels=norm_cross_num_channels, num_groups=cross_attention_norm_num_groups, eps=1e-5, affine=True\n )\n else:\n raise ValueError(\n f\"unknown cross_attention_norm: {cross_attention_norm}. Should be None, 'layer_norm' or 'group_norm'\"\n )\n\n self.to_q = nn.Linear(query_dim, self.inner_dim, bias=bias)\n\n if not self.only_cross_attention:\n # only relevant for the `AddedKVProcessor` classes\n self.to_k = nn.Linear(self.cross_attention_dim, self.inner_dim, bias=bias)\n self.to_v = nn.Linear(self.cross_attention_dim, self.inner_dim, bias=bias)\n else:\n self.to_k = None\n self.to_v = None\n\n if self.added_kv_proj_dim is not None:\n self.add_k_proj = nn.Linear(added_kv_proj_dim, self.inner_dim)\n self.add_v_proj = nn.Linear(added_kv_proj_dim, self.inner_dim)\n if self.context_pre_only is not None:\n self.add_q_proj = nn.Linear(added_kv_proj_dim, self.inner_dim)\n\n self.to_out = nn.ModuleList([])\n self.to_out.append(nn.Linear(self.inner_dim, self.out_dim, bias=out_bias))\n self.to_out.append(nn.Dropout(dropout))\n\n if self.context_pre_only is not None and not self.context_pre_only:\n self.to_add_out = nn.Linear(self.inner_dim, self.out_dim, bias=out_bias)\n\n # set attention processor\n # We use the AttnProcessor2_0 by default when torch 2.x is used which uses\n # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention\n # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1\n if processor is None:\n processor = (\n AttnProcessor2_0() if hasattr(F, \"scaled_dot_product_attention\") and self.scale_qk else AttnProcessor()\n )\n self.set_processor(processor)\n\n def set_use_npu_flash_attention(self, use_npu_flash_attention: bool) -> None:\n r\"\"\"\n Set whether to use npu flash attention from `torch_npu` or not.\n\n \"\"\"\n if use_npu_flash_attention:\n processor = AttnProcessorNPU()\n else:\n # set attention processor\n # We use the AttnProcessor2_0 by default when torch 2.x is used which uses\n # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention\n # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1\n processor = (\n AttnProcessor2_0() if hasattr(F, \"scaled_dot_product_attention\") and self.scale_qk else AttnProcessor()\n )\n self.set_processor(processor)\n\n def set_use_memory_efficient_attention_xformers(\n self, use_memory_efficient_attention_xformers: bool, attention_op: Optional[Callable] = None\n ) -> None:\n r\"\"\"\n Set whether to use memory efficient attention from `xformers` or not.\n\n Args:\n use_memory_efficient_attention_xformers (`bool`):\n Whether to use memory efficient attention from `xformers` or not.\n attention_op (`Callable`, *optional*):\n The attention operation to use. Defaults to `None` which uses the default attention operation from\n `xformers`.\n \"\"\"\n is_lora = hasattr(self, \"processor\") and isinstance(\n self.processor,\n LORA_ATTENTION_PROCESSORS,\n )\n is_custom_diffusion = hasattr(self, \"processor\") and isinstance(\n self.processor,\n (CustomDiffusionAttnProcessor, CustomDiffusionXFormersAttnProcessor, CustomDiffusionAttnProcessor2_0),\n )\n is_added_kv_processor = hasattr(self, \"processor\") and isinstance(\n self.processor,\n (\n AttnAddedKVProcessor,\n AttnAddedKVProcessor2_0,\n SlicedAttnAddedKVProcessor,\n XFormersAttnAddedKVProcessor,\n LoRAAttnAddedKVProcessor,\n ),\n )\n\n if use_memory_efficient_attention_xformers:\n if is_added_kv_processor and (is_lora or is_custom_diffusion):\n raise NotImplementedError(\n f\"Memory efficient attention is currently not supported for LoRA or custom diffusion for attention processor type {self.processor}\"\n )\n if not is_xformers_available():\n raise ModuleNotFoundError(\n (\n \"Refer to https://github.com/facebookresearch/xformers for more information on how to install\"\n \" xformers\"\n ),\n name=\"xformers\",\n )\n elif not torch.cuda.is_available():\n raise ValueError(\n \"torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is\"\n \" only available for GPU \"\n )\n else:\n try:\n # Make sure we can run the memory efficient attention\n _ = xformers.ops.memory_efficient_attention(\n torch.randn((1, 2, 40), device=\"cuda\"),\n torch.randn((1, 2, 40), device=\"cuda\"),\n torch.randn((1, 2, 40), device=\"cuda\"),\n )\n except Exception as e:\n raise e\n\n if is_lora:\n # TODO (sayakpaul): should we throw a warning if someone wants to use the xformers\n # variant when using PT 2.0 now that we have LoRAAttnProcessor2_0?\n processor = LoRAXFormersAttnProcessor(\n hidden_size=self.processor.hidden_size,\n cross_attention_dim=self.processor.cross_attention_dim,\n rank=self.processor.rank,\n attention_op=attention_op,\n )\n processor.load_state_dict(self.processor.state_dict())\n processor.to(self.processor.to_q_lora.up.weight.device)\n elif is_custom_diffusion:\n processor = CustomDiffusionXFormersAttnProcessor(\n train_kv=self.processor.train_kv,\n train_q_out=self.processor.train_q_out,\n hidden_size=self.processor.hidden_size,\n cross_attention_dim=self.processor.cross_attention_dim,\n attention_op=attention_op,\n )\n processor.load_state_dict(self.processor.state_dict())\n if hasattr(self.processor, \"to_k_custom_diffusion\"):\n processor.to(self.processor.to_k_custom_diffusion.weight.device)\n elif is_added_kv_processor:\n # TODO(Patrick, Suraj, William) - currently xformers doesn't work for UnCLIP\n # which uses this type of cross attention ONLY because the attention mask of format\n # [0, ..., -10.000, ..., 0, ...,] is not supported\n # throw warning\n logger.info(\n \"Memory efficient attention with `xformers` might currently not work correctly if an attention mask is required for the attention operation.\"\n )\n processor = XFormersAttnAddedKVProcessor(attention_op=attention_op)\n else:\n processor = XFormersAttnProcessor(attention_op=attention_op)\n else:\n if is_lora:\n attn_processor_class = (\n LoRAAttnProcessor2_0 if hasattr(F, \"scaled_dot_product_attention\") else LoRAAttnProcessor\n )\n processor = attn_processor_class(\n hidden_size=self.processor.hidden_size,\n cross_attention_dim=self.processor.cross_attention_dim,\n rank=self.processor.rank,\n )\n processor.load_state_dict(self.processor.state_dict())\n processor.to(self.processor.to_q_lora.up.weight.device)\n elif is_custom_diffusion:\n attn_processor_class = (\n CustomDiffusionAttnProcessor2_0\n if hasattr(F, \"scaled_dot_product_attention\")\n else CustomDiffusionAttnProcessor\n )\n processor = attn_processor_class(\n train_kv=self.processor.train_kv,\n train_q_out=self.processor.train_q_out,\n hidden_size=self.processor.hidden_size,\n cross_attention_dim=self.processor.cross_attention_dim,\n )\n processor.load_state_dict(self.processor.state_dict())\n if hasattr(self.processor, \"to_k_custom_diffusion\"):\n processor.to(self.processor.to_k_custom_diffusion.weight.device)\n else:\n # set attention processor\n # We use the AttnProcessor2_0 by default when torch 2.x is used which uses\n # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention\n # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1\n processor = (\n AttnProcessor2_0()\n if hasattr(F, \"scaled_dot_product_attention\") and self.scale_qk\n else AttnProcessor()\n )\n\n self.set_processor(processor)\n\n def set_attention_slice(self, slice_size: int) -> None:\n r\"\"\"\n Set the slice size for attention computation.\n\n Args:\n slice_size (`int`):\n The slice size for attention computation.\n \"\"\"\n if slice_size is not None and slice_size > self.sliceable_head_dim:\n raise ValueError(f\"slice_size {slice_size} has to be smaller or equal to {self.sliceable_head_dim}.\")\n\n if slice_size is not None and self.added_kv_proj_dim is not None:\n processor = SlicedAttnAddedKVProcessor(slice_size)\n elif slice_size is not None:\n processor = SlicedAttnProcessor(slice_size)\n elif self.added_kv_proj_dim is not None:\n processor = AttnAddedKVProcessor()\n else:\n # set attention processor\n # We use the AttnProcessor2_0 by default when torch 2.x is used which uses\n # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention\n # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1\n processor = (\n AttnProcessor2_0() if hasattr(F, \"scaled_dot_product_attention\") and self.scale_qk else AttnProcessor()\n )\n\n self.set_processor(processor)\n\n def set_processor(self, processor: \"AttnProcessor\") -> None:\n r\"\"\"\n Set the attention processor to use.\n\n Args:\n processor (`AttnProcessor`):\n The attention processor to use.\n \"\"\"\n # if current processor is in `self._modules` and if passed `processor` is not, we need to\n # pop `processor` from `self._modules`\n if (\n hasattr(self, \"processor\")\n and isinstance(self.processor, torch.nn.Module)\n and not isinstance(processor, torch.nn.Module)\n ):\n logger.info(f\"You are removing possibly trained weights of {self.processor} with {processor}\")\n self._modules.pop(\"processor\")\n\n self.processor = processor\n\n def get_processor(self, return_deprecated_lora: bool = False) -> \"AttentionProcessor\":\n r\"\"\"\n Get the attention processor in use.\n\n Args:\n return_deprecated_lora (`bool`, *optional*, defaults to `False`):\n Set to `True` to return the deprecated LoRA attention processor.\n\n Returns:\n \"AttentionProcessor\": The attention processor in use.\n \"\"\"\n if not return_deprecated_lora:\n return self.processor\n\n # TODO(Sayak, Patrick). The rest of the function is needed to ensure backwards compatible\n # serialization format for LoRA Attention Processors. It should be deleted once the integration\n # with PEFT is completed.\n is_lora_activated = {\n name: module.lora_layer is not None\n for name, module in self.named_modules()\n if hasattr(module, \"lora_layer\")\n }\n\n # 1. if no layer has a LoRA activated we can return the processor as usual\n if not any(is_lora_activated.values()):\n return self.processor\n\n # If doesn't apply LoRA do `add_k_proj` or `add_v_proj`\n is_lora_activated.pop(\"add_k_proj\", None)\n is_lora_activated.pop(\"add_v_proj\", None)\n # 2. else it is not possible that only some layers have LoRA activated\n if not all(is_lora_activated.values()):\n raise ValueError(\n f\"Make sure that either all layers or no layers have LoRA activated, but have {is_lora_activated}\"\n )\n\n # 3. And we need to merge the current LoRA layers into the corresponding LoRA attention processor\n non_lora_processor_cls_name = self.processor.__class__.__name__\n lora_processor_cls = getattr(import_module(__name__), \"LoRA\" + non_lora_processor_cls_name)\n\n hidden_size = self.inner_dim\n\n # now create a LoRA attention processor from the LoRA layers\n if lora_processor_cls in [LoRAAttnProcessor, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor]:\n kwargs = {\n \"cross_attention_dim\": self.cross_attention_dim,\n \"rank\": self.to_q.lora_layer.rank,\n \"network_alpha\": self.to_q.lora_layer.network_alpha,\n \"q_rank\": self.to_q.lora_layer.rank,\n \"q_hidden_size\": self.to_q.lora_layer.out_features,\n \"k_rank\": self.to_k.lora_layer.rank,\n \"k_hidden_size\": self.to_k.lora_layer.out_features,\n \"v_rank\": self.to_v.lora_layer.rank,\n \"v_hidden_size\": self.to_v.lora_layer.out_features,\n \"out_rank\": self.to_out[0].lora_layer.rank,\n \"out_hidden_size\": self.to_out[0].lora_layer.out_features,\n }\n\n if hasattr(self.processor, \"attention_op\"):\n kwargs[\"attention_op\"] = self.processor.attention_op\n\n lora_processor = lora_processor_cls(hidden_size, **kwargs)\n lora_processor.to_q_lora.load_state_dict(self.to_q.lora_layer.state_dict())\n lora_processor.to_k_lora.load_state_dict(self.to_k.lora_layer.state_dict())\n lora_processor.to_v_lora.load_state_dict(self.to_v.lora_layer.state_dict())\n lora_processor.to_out_lora.load_state_dict(self.to_out[0].lora_layer.state_dict())\n elif lora_processor_cls == LoRAAttnAddedKVProcessor:\n lora_processor = lora_processor_cls(\n hidden_size,\n cross_attention_dim=self.add_k_proj.weight.shape[0],\n rank=self.to_q.lora_layer.rank,\n network_alpha=self.to_q.lora_layer.network_alpha,\n )\n lora_processor.to_q_lora.load_state_dict(self.to_q.lora_layer.state_dict())\n lora_processor.to_k_lora.load_state_dict(self.to_k.lora_layer.state_dict())\n lora_processor.to_v_lora.load_state_dict(self.to_v.lora_layer.state_dict())\n lora_processor.to_out_lora.load_state_dict(self.to_out[0].lora_layer.state_dict())\n\n # only save if used\n if self.add_k_proj.lora_layer is not None:\n lora_processor.add_k_proj_lora.load_state_dict(self.add_k_proj.lora_layer.state_dict())\n lora_processor.add_v_proj_lora.load_state_dict(self.add_v_proj.lora_layer.state_dict())\n else:\n lora_processor.add_k_proj_lora = None\n lora_processor.add_v_proj_lora = None\n else:\n raise ValueError(f\"{lora_processor_cls} does not exist.\")\n\n return lora_processor\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n **cross_attention_kwargs,\n ) -> torch.Tensor:\n r\"\"\"\n The forward method of the `Attention` class.\n\n Args:\n hidden_states (`torch.Tensor`):\n The hidden states of the query.\n encoder_hidden_states (`torch.Tensor`, *optional*):\n The hidden states of the encoder.\n attention_mask (`torch.Tensor`, *optional*):\n The attention mask to use. If `None`, no mask is applied.\n **cross_attention_kwargs:\n Additional keyword arguments to pass along to the cross attention.\n\n Returns:\n `torch.Tensor`: The output of the attention layer.\n \"\"\"\n # The `Attention` class can call different attention processors / attention functions\n # here we simply pass along all tensors to the selected processor class\n # For standard processors that are defined here, `**cross_attention_kwargs` is empty\n\n attn_parameters = set(inspect.signature(self.processor.__call__).parameters.keys())\n quiet_attn_parameters = {\"ip_adapter_masks\"}\n unused_kwargs = [\n k for k, _ in cross_attention_kwargs.items() if k not in attn_parameters and k not in quiet_attn_parameters\n ]\n if len(unused_kwargs) > 0:\n logger.warning(\n f\"cross_attention_kwargs {unused_kwargs} are not expected by {self.processor.__class__.__name__} and will be ignored.\"\n )\n cross_attention_kwargs = {k: w for k, w in cross_attention_kwargs.items() if k in attn_parameters}\n\n return self.processor(\n self,\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n\n def batch_to_head_dim(self, tensor: torch.Tensor) -> torch.Tensor:\n r\"\"\"\n Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size // heads, seq_len, dim * heads]`. `heads`\n is the number of heads initialized while constructing the `Attention` class.\n\n Args:\n tensor (`torch.Tensor`): The tensor to reshape.\n\n Returns:\n `torch.Tensor`: The reshaped tensor.\n \"\"\"\n head_size = self.heads\n batch_size, seq_len, dim = tensor.shape\n tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)\n tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)\n return tensor\n\n def head_to_batch_dim(self, tensor: torch.Tensor, out_dim: int = 3) -> torch.Tensor:\n r\"\"\"\n Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size, seq_len, heads, dim // heads]` `heads` is\n the number of heads initialized while constructing the `Attention` class.\n\n Args:\n tensor (`torch.Tensor`): The tensor to reshape.\n out_dim (`int`, *optional*, defaults to `3`): The output dimension of the tensor. If `3`, the tensor is\n reshaped to `[batch_size * heads, seq_len, dim // heads]`.\n\n Returns:\n `torch.Tensor`: The reshaped tensor.\n \"\"\"\n head_size = self.heads\n if tensor.ndim == 3:\n batch_size, seq_len, dim = tensor.shape\n extra_dim = 1\n else:\n batch_size, extra_dim, seq_len, dim = tensor.shape\n tensor = tensor.reshape(batch_size, seq_len * extra_dim, head_size, dim // head_size)\n tensor = tensor.permute(0, 2, 1, 3)\n\n if out_dim == 3:\n tensor = tensor.reshape(batch_size * head_size, seq_len * extra_dim, dim // head_size)\n\n return tensor\n\n def get_attention_scores(\n self, query: torch.Tensor, key: torch.Tensor, attention_mask: torch.Tensor = None\n ) -> torch.Tensor:\n r\"\"\"\n Compute the attention scores.\n\n Args:\n query (`torch.Tensor`): The query tensor.\n key (`torch.Tensor`): The key tensor.\n attention_mask (`torch.Tensor`, *optional*): The attention mask to use. If `None`, no mask is applied.\n\n Returns:\n `torch.Tensor`: The attention probabilities/scores.\n \"\"\"\n dtype = query.dtype\n if self.upcast_attention:\n query = query.float()\n key = key.float()\n\n if attention_mask is None:\n baddbmm_input = torch.empty(\n query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device\n )\n beta = 0\n else:\n baddbmm_input = attention_mask\n beta = 1\n\n attention_scores = torch.baddbmm(\n baddbmm_input,\n query,\n key.transpose(-1, -2),\n beta=beta,\n alpha=self.scale,\n )\n del baddbmm_input\n\n if self.upcast_softmax:\n attention_scores = attention_scores.float()\n\n attention_probs = attention_scores.softmax(dim=-1)\n del attention_scores\n\n attention_probs = attention_probs.to(dtype)\n\n return attention_probs\n\n def prepare_attention_mask(\n self, attention_mask: torch.Tensor, target_length: int, batch_size: int, out_dim: int = 3\n ) -> torch.Tensor:\n r\"\"\"\n Prepare the attention mask for the attention computation.\n\n Args:\n attention_mask (`torch.Tensor`):\n The attention mask to prepare.\n target_length (`int`):\n The target length of the attention mask. This is the length of the attention mask after padding.\n batch_size (`int`):\n The batch size, which is used to repeat the attention mask.\n out_dim (`int`, *optional*, defaults to `3`):\n The output dimension of the attention mask. Can be either `3` or `4`.\n\n Returns:\n `torch.Tensor`: The prepared attention mask.\n \"\"\"\n head_size = self.heads\n if attention_mask is None:\n return attention_mask\n\n current_length: int = attention_mask.shape[-1]\n if current_length != target_length:\n if attention_mask.device.type == \"mps\":\n # HACK: MPS: Does not support padding by greater than dimension of input tensor.\n # Instead, we can manually construct the padding tensor.\n padding_shape = (attention_mask.shape[0], attention_mask.shape[1], target_length)\n padding = torch.zeros(padding_shape, dtype=attention_mask.dtype, device=attention_mask.device)\n attention_mask = torch.cat([attention_mask, padding], dim=2)\n else:\n # TODO: for pipelines such as stable-diffusion, padding cross-attn mask:\n # we want to instead pad by (0, remaining_length), where remaining_length is:\n # remaining_length: int = target_length - current_length\n # TODO: re-enable tests/models/test_models_unet_2d_condition.py#test_model_xattn_padding\n attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)\n\n if out_dim == 3:\n if attention_mask.shape[0] < batch_size * head_size:\n attention_mask = attention_mask.repeat_interleave(head_size, dim=0)\n elif out_dim == 4:\n attention_mask = attention_mask.unsqueeze(1)\n attention_mask = attention_mask.repeat_interleave(head_size, dim=1)\n\n return attention_mask\n\n def norm_encoder_hidden_states(self, encoder_hidden_states: torch.Tensor) -> torch.Tensor:\n r\"\"\"\n Normalize the encoder hidden states. Requires `self.norm_cross` to be specified when constructing the\n `Attention` class.\n\n Args:\n encoder_hidden_states (`torch.Tensor`): Hidden states of the encoder.\n\n Returns:\n `torch.Tensor`: The normalized encoder hidden states.\n \"\"\"\n assert self.norm_cross is not None, \"self.norm_cross must be defined to call self.norm_encoder_hidden_states\"\n\n if isinstance(self.norm_cross, nn.LayerNorm):\n encoder_hidden_states = self.norm_cross(encoder_hidden_states)\n elif isinstance(self.norm_cross, nn.GroupNorm):\n # Group norm norms along the channels dimension and expects\n # input to be in the shape of (N, C, *). In this case, we want\n # to norm along the hidden dimension, so we need to move\n # (batch_size, sequence_length, hidden_size) ->\n # (batch_size, hidden_size, sequence_length)\n encoder_hidden_states = encoder_hidden_states.transpose(1, 2)\n encoder_hidden_states = self.norm_cross(encoder_hidden_states)\n encoder_hidden_states = encoder_hidden_states.transpose(1, 2)\n else:\n assert False\n\n return encoder_hidden_states\n\n @torch.no_grad()\n def fuse_projections(self, fuse=True):\n device = self.to_q.weight.data.device\n dtype = self.to_q.weight.data.dtype\n\n if not self.is_cross_attention:\n # fetch weight matrices.\n concatenated_weights = torch.cat([self.to_q.weight.data, self.to_k.weight.data, self.to_v.weight.data])\n in_features = concatenated_weights.shape[1]\n out_features = concatenated_weights.shape[0]\n\n # create a new single projection layer and copy over the weights.\n self.to_qkv = nn.Linear(in_features, out_features, bias=self.use_bias, device=device, dtype=dtype)\n self.to_qkv.weight.copy_(concatenated_weights)\n if self.use_bias:\n concatenated_bias = torch.cat([self.to_q.bias.data, self.to_k.bias.data, self.to_v.bias.data])\n self.to_qkv.bias.copy_(concatenated_bias)\n\n else:\n concatenated_weights = torch.cat([self.to_k.weight.data, self.to_v.weight.data])\n in_features = concatenated_weights.shape[1]\n out_features = concatenated_weights.shape[0]\n\n self.to_kv = nn.Linear(in_features, out_features, bias=self.use_bias, device=device, dtype=dtype)\n self.to_kv.weight.copy_(concatenated_weights)\n if self.use_bias:\n concatenated_bias = torch.cat([self.to_k.bias.data, self.to_v.bias.data])\n self.to_kv.bias.copy_(concatenated_bias)\n\n self.fused_projections = fuse\n\n\nclass AttnProcessor:\n r\"\"\"\n Default processor for performing attention-related computations.\n \"\"\"\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n temb: Optional[torch.Tensor] = None,\n *args,\n **kwargs,\n ) -> torch.Tensor:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n residual = hidden_states\n\n if attn.spatial_norm is not None:\n hidden_states = attn.spatial_norm(hidden_states, temb)\n\n input_ndim = hidden_states.ndim\n\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n\n if attn.group_norm is not None:\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n\n query = attn.to_q(hidden_states)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n attention_probs = attn.get_attention_scores(query, key, attention_mask)\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n if input_ndim == 4:\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n if attn.residual_connection:\n hidden_states = hidden_states + residual\n\n hidden_states = hidden_states / attn.rescale_output_factor\n\n return hidden_states\n\n\nclass CustomDiffusionAttnProcessor(nn.Module):\n r\"\"\"\n Processor for implementing attention for the Custom Diffusion method.\n\n Args:\n train_kv (`bool`, defaults to `True`):\n Whether to newly train the key and value matrices corresponding to the text features.\n train_q_out (`bool`, defaults to `True`):\n Whether to newly train query matrices corresponding to the latent image features.\n hidden_size (`int`, *optional*, defaults to `None`):\n The hidden size of the attention layer.\n cross_attention_dim (`int`, *optional*, defaults to `None`):\n The number of channels in the `encoder_hidden_states`.\n out_bias (`bool`, defaults to `True`):\n Whether to include the bias parameter in `train_q_out`.\n dropout (`float`, *optional*, defaults to 0.0):\n The dropout probability to use.\n \"\"\"\n\n def __init__(\n self,\n train_kv: bool = True,\n train_q_out: bool = True,\n hidden_size: Optional[int] = None,\n cross_attention_dim: Optional[int] = None,\n out_bias: bool = True,\n dropout: float = 0.0,\n ):\n super().__init__()\n self.train_kv = train_kv\n self.train_q_out = train_q_out\n\n self.hidden_size = hidden_size\n self.cross_attention_dim = cross_attention_dim\n\n # `_custom_diffusion` id for easy serialization and loading.\n if self.train_kv:\n self.to_k_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)\n self.to_v_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)\n if self.train_q_out:\n self.to_q_custom_diffusion = nn.Linear(hidden_size, hidden_size, bias=False)\n self.to_out_custom_diffusion = nn.ModuleList([])\n self.to_out_custom_diffusion.append(nn.Linear(hidden_size, hidden_size, bias=out_bias))\n self.to_out_custom_diffusion.append(nn.Dropout(dropout))\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n batch_size, sequence_length, _ = hidden_states.shape\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n if self.train_q_out:\n query = self.to_q_custom_diffusion(hidden_states).to(attn.to_q.weight.dtype)\n else:\n query = attn.to_q(hidden_states.to(attn.to_q.weight.dtype))\n\n if encoder_hidden_states is None:\n crossattn = False\n encoder_hidden_states = hidden_states\n else:\n crossattn = True\n if attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n if self.train_kv:\n key = self.to_k_custom_diffusion(encoder_hidden_states.to(self.to_k_custom_diffusion.weight.dtype))\n value = self.to_v_custom_diffusion(encoder_hidden_states.to(self.to_v_custom_diffusion.weight.dtype))\n key = key.to(attn.to_q.weight.dtype)\n value = value.to(attn.to_q.weight.dtype)\n else:\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n if crossattn:\n detach = torch.ones_like(key)\n detach[:, :1, :] = detach[:, :1, :] * 0.0\n key = detach * key + (1 - detach) * key.detach()\n value = detach * value + (1 - detach) * value.detach()\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n attention_probs = attn.get_attention_scores(query, key, attention_mask)\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n if self.train_q_out:\n # linear proj\n hidden_states = self.to_out_custom_diffusion[0](hidden_states)\n # dropout\n hidden_states = self.to_out_custom_diffusion[1](hidden_states)\n else:\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n return hidden_states\n\n\nclass AttnAddedKVProcessor:\n r\"\"\"\n Processor for performing attention-related computations with extra learnable key and value matrices for the text\n encoder.\n \"\"\"\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n *args,\n **kwargs,\n ) -> torch.Tensor:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n residual = hidden_states\n\n hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)\n batch_size, sequence_length, _ = hidden_states.shape\n\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n\n query = attn.to_q(hidden_states)\n query = attn.head_to_batch_dim(query)\n\n encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)\n encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)\n encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj)\n encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj)\n\n if not attn.only_cross_attention:\n key = attn.to_k(hidden_states)\n value = attn.to_v(hidden_states)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)\n value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)\n else:\n key = encoder_hidden_states_key_proj\n value = encoder_hidden_states_value_proj\n\n attention_probs = attn.get_attention_scores(query, key, attention_mask)\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)\n hidden_states = hidden_states + residual\n\n return hidden_states\n\n\nclass AttnAddedKVProcessor2_0:\n r\"\"\"\n Processor for performing scaled dot-product attention (enabled by default if you're using PyTorch 2.0), with extra\n learnable key and value matrices for the text encoder.\n \"\"\"\n\n def __init__(self):\n if not hasattr(F, \"scaled_dot_product_attention\"):\n raise ImportError(\n \"AttnAddedKVProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.\"\n )\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n *args,\n **kwargs,\n ) -> torch.Tensor:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n residual = hidden_states\n\n hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)\n batch_size, sequence_length, _ = hidden_states.shape\n\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size, out_dim=4)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n\n query = attn.to_q(hidden_states)\n query = attn.head_to_batch_dim(query, out_dim=4)\n\n encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)\n encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)\n encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj, out_dim=4)\n encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj, out_dim=4)\n\n if not attn.only_cross_attention:\n key = attn.to_k(hidden_states)\n value = attn.to_v(hidden_states)\n key = attn.head_to_batch_dim(key, out_dim=4)\n value = attn.head_to_batch_dim(value, out_dim=4)\n key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)\n value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)\n else:\n key = encoder_hidden_states_key_proj\n value = encoder_hidden_states_value_proj\n\n # the output of sdp = (batch, num_heads, seq_len, head_dim)\n # TODO: add support for attn.scale when we move to Torch 2.1\n hidden_states = F.scaled_dot_product_attention(\n query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False\n )\n hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, residual.shape[1])\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)\n hidden_states = hidden_states + residual\n\n return hidden_states\n\n\nclass JointAttnProcessor2_0:\n \"\"\"Attention processor used typically in processing the SD3-like self-attention projections.\"\"\"\n\n def __init__(self):\n if not hasattr(F, \"scaled_dot_product_attention\"):\n raise ImportError(\"AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.\")\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.FloatTensor,\n encoder_hidden_states: torch.FloatTensor = None,\n attention_mask: Optional[torch.FloatTensor] = None,\n *args,\n **kwargs,\n ) -> torch.FloatTensor:\n residual = hidden_states\n\n input_ndim = hidden_states.ndim\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n context_input_ndim = encoder_hidden_states.ndim\n if context_input_ndim == 4:\n batch_size, channel, height, width = encoder_hidden_states.shape\n encoder_hidden_states = encoder_hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n\n batch_size = encoder_hidden_states.shape[0]\n\n # `sample` projections.\n query = attn.to_q(hidden_states)\n key = attn.to_k(hidden_states)\n value = attn.to_v(hidden_states)\n\n # `context` projections.\n encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)\n encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)\n encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)\n\n # attention\n query = torch.cat([query, encoder_hidden_states_query_proj], dim=1)\n key = torch.cat([key, encoder_hidden_states_key_proj], dim=1)\n value = torch.cat([value, encoder_hidden_states_value_proj], dim=1)\n\n inner_dim = key.shape[-1]\n head_dim = inner_dim // attn.heads\n query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)\n hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)\n hidden_states = hidden_states.to(query.dtype)\n\n # Split the attention outputs.\n hidden_states, encoder_hidden_states = (\n hidden_states[:, : residual.shape[1]],\n hidden_states[:, residual.shape[1] :],\n )\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n if not attn.context_pre_only:\n encoder_hidden_states = attn.to_add_out(encoder_hidden_states)\n\n if input_ndim == 4:\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n if context_input_ndim == 4:\n encoder_hidden_states = encoder_hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n return hidden_states, encoder_hidden_states\n\n\nclass FusedJointAttnProcessor2_0:\n \"\"\"Attention processor used typically in processing the SD3-like self-attention projections.\"\"\"\n\n def __init__(self):\n if not hasattr(F, \"scaled_dot_product_attention\"):\n raise ImportError(\"AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.\")\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.FloatTensor,\n encoder_hidden_states: torch.FloatTensor = None,\n attention_mask: Optional[torch.FloatTensor] = None,\n *args,\n **kwargs,\n ) -> torch.FloatTensor:\n residual = hidden_states\n\n input_ndim = hidden_states.ndim\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n context_input_ndim = encoder_hidden_states.ndim\n if context_input_ndim == 4:\n batch_size, channel, height, width = encoder_hidden_states.shape\n encoder_hidden_states = encoder_hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n\n batch_size = encoder_hidden_states.shape[0]\n\n # `sample` projections.\n qkv = attn.to_qkv(hidden_states)\n split_size = qkv.shape[-1] // 3\n query, key, value = torch.split(qkv, split_size, dim=-1)\n\n # `context` projections.\n encoder_qkv = attn.to_added_qkv(encoder_hidden_states)\n split_size = encoder_qkv.shape[-1] // 3\n (\n encoder_hidden_states_query_proj,\n encoder_hidden_states_key_proj,\n encoder_hidden_states_value_proj,\n ) = torch.split(encoder_qkv, split_size, dim=-1)\n\n # attention\n query = torch.cat([query, encoder_hidden_states_query_proj], dim=1)\n key = torch.cat([key, encoder_hidden_states_key_proj], dim=1)\n value = torch.cat([value, encoder_hidden_states_value_proj], dim=1)\n\n inner_dim = key.shape[-1]\n head_dim = inner_dim // attn.heads\n query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n hidden_states = hidden_states = F.scaled_dot_product_attention(\n query, key, value, dropout_p=0.0, is_causal=False\n )\n hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)\n hidden_states = hidden_states.to(query.dtype)\n\n # Split the attention outputs.\n hidden_states, encoder_hidden_states = (\n hidden_states[:, : residual.shape[1]],\n hidden_states[:, residual.shape[1] :],\n )\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n if not attn.context_pre_only:\n encoder_hidden_states = attn.to_add_out(encoder_hidden_states)\n\n if input_ndim == 4:\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n if context_input_ndim == 4:\n encoder_hidden_states = encoder_hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n return hidden_states, encoder_hidden_states\n\n\nclass XFormersAttnAddedKVProcessor:\n r\"\"\"\n Processor for implementing memory efficient attention using xFormers.\n\n Args:\n attention_op (`Callable`, *optional*, defaults to `None`):\n The base\n [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to\n use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best\n operator.\n \"\"\"\n\n def __init__(self, attention_op: Optional[Callable] = None):\n self.attention_op = attention_op\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n residual = hidden_states\n hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)\n batch_size, sequence_length, _ = hidden_states.shape\n\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n\n query = attn.to_q(hidden_states)\n query = attn.head_to_batch_dim(query)\n\n encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)\n encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)\n encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj)\n encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj)\n\n if not attn.only_cross_attention:\n key = attn.to_k(hidden_states)\n value = attn.to_v(hidden_states)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)\n value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)\n else:\n key = encoder_hidden_states_key_proj\n value = encoder_hidden_states_value_proj\n\n hidden_states = xformers.ops.memory_efficient_attention(\n query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale\n )\n hidden_states = hidden_states.to(query.dtype)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)\n hidden_states = hidden_states + residual\n\n return hidden_states\n\n\nclass XFormersAttnProcessor:\n r\"\"\"\n Processor for implementing memory efficient attention using xFormers.\n\n Args:\n attention_op (`Callable`, *optional*, defaults to `None`):\n The base\n [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to\n use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best\n operator.\n \"\"\"\n\n def __init__(self, attention_op: Optional[Callable] = None):\n self.attention_op = attention_op\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n temb: Optional[torch.Tensor] = None,\n *args,\n **kwargs,\n ) -> torch.Tensor:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n residual = hidden_states\n\n if attn.spatial_norm is not None:\n hidden_states = attn.spatial_norm(hidden_states, temb)\n\n input_ndim = hidden_states.ndim\n\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n\n batch_size, key_tokens, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n\n attention_mask = attn.prepare_attention_mask(attention_mask, key_tokens, batch_size)\n if attention_mask is not None:\n # expand our mask's singleton query_tokens dimension:\n # [batch*heads, 1, key_tokens] ->\n # [batch*heads, query_tokens, key_tokens]\n # so that it can be added as a bias onto the attention scores that xformers computes:\n # [batch*heads, query_tokens, key_tokens]\n # we do this explicitly because xformers doesn't broadcast the singleton dimension for us.\n _, query_tokens, _ = hidden_states.shape\n attention_mask = attention_mask.expand(-1, query_tokens, -1)\n\n if attn.group_norm is not None:\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n\n query = attn.to_q(hidden_states)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n query = attn.head_to_batch_dim(query).contiguous()\n key = attn.head_to_batch_dim(key).contiguous()\n value = attn.head_to_batch_dim(value).contiguous()\n\n hidden_states = xformers.ops.memory_efficient_attention(\n query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale\n )\n hidden_states = hidden_states.to(query.dtype)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n if input_ndim == 4:\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n if attn.residual_connection:\n hidden_states = hidden_states + residual\n\n hidden_states = hidden_states / attn.rescale_output_factor\n\n return hidden_states\n\n\nclass AttnProcessorNPU:\n r\"\"\"\n Processor for implementing flash attention using torch_npu. Torch_npu supports only fp16 and bf16 data types. If\n fp32 is used, F.scaled_dot_product_attention will be used for computation, but the acceleration effect on NPU is\n not significant.\n\n \"\"\"\n\n def __init__(self):\n if not is_torch_npu_available():\n raise ImportError(\"AttnProcessorNPU requires torch_npu extensions and is supported only on npu devices.\")\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n temb: Optional[torch.Tensor] = None,\n *args,\n **kwargs,\n ) -> torch.Tensor:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n residual = hidden_states\n if attn.spatial_norm is not None:\n hidden_states = attn.spatial_norm(hidden_states, temb)\n\n input_ndim = hidden_states.ndim\n\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n\n if attention_mask is not None:\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n # scaled_dot_product_attention expects attention_mask shape to be\n # (batch, heads, source_length, target_length)\n attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])\n\n if attn.group_norm is not None:\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n\n query = attn.to_q(hidden_states)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n inner_dim = key.shape[-1]\n head_dim = inner_dim // attn.heads\n\n query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n # the output of sdp = (batch, num_heads, seq_len, head_dim)\n if query.dtype in (torch.float16, torch.bfloat16):\n hidden_states = torch_npu.npu_fusion_attention(\n query,\n key,\n value,\n attn.heads,\n input_layout=\"BNSD\",\n pse=None,\n atten_mask=attention_mask,\n scale=1.0 / math.sqrt(query.shape[-1]),\n pre_tockens=65536,\n next_tockens=65536,\n keep_prob=1.0,\n sync=False,\n inner_precise=0,\n )[0]\n else:\n # TODO: add support for attn.scale when we move to Torch 2.1\n hidden_states = F.scaled_dot_product_attention(\n query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False\n )\n\n hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)\n hidden_states = hidden_states.to(query.dtype)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n if input_ndim == 4:\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n if attn.residual_connection:\n hidden_states = hidden_states + residual\n\n hidden_states = hidden_states / attn.rescale_output_factor\n\n return hidden_states\n\n\nclass AttnProcessor2_0:\n r\"\"\"\n Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).\n \"\"\"\n\n def __init__(self):\n if not hasattr(F, \"scaled_dot_product_attention\"):\n raise ImportError(\"AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.\")\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n temb: Optional[torch.Tensor] = None,\n *args,\n **kwargs,\n ) -> torch.Tensor:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n residual = hidden_states\n if attn.spatial_norm is not None:\n hidden_states = attn.spatial_norm(hidden_states, temb)\n\n input_ndim = hidden_states.ndim\n\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n\n if attention_mask is not None:\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n # scaled_dot_product_attention expects attention_mask shape to be\n # (batch, heads, source_length, target_length)\n attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])\n\n if attn.group_norm is not None:\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n\n query = attn.to_q(hidden_states)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n inner_dim = key.shape[-1]\n head_dim = inner_dim // attn.heads\n\n query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n # the output of sdp = (batch, num_heads, seq_len, head_dim)\n # TODO: add support for attn.scale when we move to Torch 2.1\n hidden_states = F.scaled_dot_product_attention(\n query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False\n )\n\n hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)\n hidden_states = hidden_states.to(query.dtype)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n if input_ndim == 4:\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n if attn.residual_connection:\n hidden_states = hidden_states + residual\n\n hidden_states = hidden_states / attn.rescale_output_factor\n\n return hidden_states\n\n\nclass HunyuanAttnProcessor2_0:\n r\"\"\"\n Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is\n used in the HunyuanDiT model. It applies a s normalization layer and rotary embedding on query and key vector.\n \"\"\"\n\n def __init__(self):\n if not hasattr(F, \"scaled_dot_product_attention\"):\n raise ImportError(\"AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.\")\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n temb: Optional[torch.Tensor] = None,\n image_rotary_emb: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n from .embeddings import apply_rotary_emb\n\n residual = hidden_states\n if attn.spatial_norm is not None:\n hidden_states = attn.spatial_norm(hidden_states, temb)\n\n input_ndim = hidden_states.ndim\n\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n\n if attention_mask is not None:\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n # scaled_dot_product_attention expects attention_mask shape to be\n # (batch, heads, source_length, target_length)\n attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])\n\n if attn.group_norm is not None:\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n\n query = attn.to_q(hidden_states)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n inner_dim = key.shape[-1]\n head_dim = inner_dim // attn.heads\n\n query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n if attn.norm_q is not None:\n query = attn.norm_q(query)\n if attn.norm_k is not None:\n key = attn.norm_k(key)\n\n # Apply RoPE if needed\n if image_rotary_emb is not None:\n query = apply_rotary_emb(query, image_rotary_emb)\n if not attn.is_cross_attention:\n key = apply_rotary_emb(key, image_rotary_emb)\n\n # the output of sdp = (batch, num_heads, seq_len, head_dim)\n # TODO: add support for attn.scale when we move to Torch 2.1\n hidden_states = F.scaled_dot_product_attention(\n query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False\n )\n\n hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)\n hidden_states = hidden_states.to(query.dtype)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n if input_ndim == 4:\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n if attn.residual_connection:\n hidden_states = hidden_states + residual\n\n hidden_states = hidden_states / attn.rescale_output_factor\n\n return hidden_states\n\n\nclass FusedAttnProcessor2_0:\n r\"\"\"\n Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). It uses\n fused projection layers. For self-attention modules, all projection matrices (i.e., query, key, value) are fused.\n For cross-attention modules, key and value projection matrices are fused.\n\n \n\n This API is currently 🧪 experimental in nature and can change in future.\n\n \n \"\"\"\n\n def __init__(self):\n if not hasattr(F, \"scaled_dot_product_attention\"):\n raise ImportError(\n \"FusedAttnProcessor2_0 requires at least PyTorch 2.0, to use it. Please upgrade PyTorch to > 2.0.\"\n )\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n temb: Optional[torch.Tensor] = None,\n *args,\n **kwargs,\n ) -> torch.Tensor:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n residual = hidden_states\n if attn.spatial_norm is not None:\n hidden_states = attn.spatial_norm(hidden_states, temb)\n\n input_ndim = hidden_states.ndim\n\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n\n if attention_mask is not None:\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n # scaled_dot_product_attention expects attention_mask shape to be\n # (batch, heads, source_length, target_length)\n attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])\n\n if attn.group_norm is not None:\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n\n if encoder_hidden_states is None:\n qkv = attn.to_qkv(hidden_states)\n split_size = qkv.shape[-1] // 3\n query, key, value = torch.split(qkv, split_size, dim=-1)\n else:\n if attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n query = attn.to_q(hidden_states)\n\n kv = attn.to_kv(encoder_hidden_states)\n split_size = kv.shape[-1] // 2\n key, value = torch.split(kv, split_size, dim=-1)\n\n inner_dim = key.shape[-1]\n head_dim = inner_dim // attn.heads\n\n query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n # the output of sdp = (batch, num_heads, seq_len, head_dim)\n # TODO: add support for attn.scale when we move to Torch 2.1\n hidden_states = F.scaled_dot_product_attention(\n query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False\n )\n\n hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)\n hidden_states = hidden_states.to(query.dtype)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n if input_ndim == 4:\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n if attn.residual_connection:\n hidden_states = hidden_states + residual\n\n hidden_states = hidden_states / attn.rescale_output_factor\n\n return hidden_states\n\n\nclass CustomDiffusionXFormersAttnProcessor(nn.Module):\n r\"\"\"\n Processor for implementing memory efficient attention using xFormers for the Custom Diffusion method.\n\n Args:\n train_kv (`bool`, defaults to `True`):\n Whether to newly train the key and value matrices corresponding to the text features.\n train_q_out (`bool`, defaults to `True`):\n Whether to newly train query matrices corresponding to the latent image features.\n hidden_size (`int`, *optional*, defaults to `None`):\n The hidden size of the attention layer.\n cross_attention_dim (`int`, *optional*, defaults to `None`):\n The number of channels in the `encoder_hidden_states`.\n out_bias (`bool`, defaults to `True`):\n Whether to include the bias parameter in `train_q_out`.\n dropout (`float`, *optional*, defaults to 0.0):\n The dropout probability to use.\n attention_op (`Callable`, *optional*, defaults to `None`):\n The base\n [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to use\n as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best operator.\n \"\"\"\n\n def __init__(\n self,\n train_kv: bool = True,\n train_q_out: bool = False,\n hidden_size: Optional[int] = None,\n cross_attention_dim: Optional[int] = None,\n out_bias: bool = True,\n dropout: float = 0.0,\n attention_op: Optional[Callable] = None,\n ):\n super().__init__()\n self.train_kv = train_kv\n self.train_q_out = train_q_out\n\n self.hidden_size = hidden_size\n self.cross_attention_dim = cross_attention_dim\n self.attention_op = attention_op\n\n # `_custom_diffusion` id for easy serialization and loading.\n if self.train_kv:\n self.to_k_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)\n self.to_v_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)\n if self.train_q_out:\n self.to_q_custom_diffusion = nn.Linear(hidden_size, hidden_size, bias=False)\n self.to_out_custom_diffusion = nn.ModuleList([])\n self.to_out_custom_diffusion.append(nn.Linear(hidden_size, hidden_size, bias=out_bias))\n self.to_out_custom_diffusion.append(nn.Dropout(dropout))\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n\n if self.train_q_out:\n query = self.to_q_custom_diffusion(hidden_states).to(attn.to_q.weight.dtype)\n else:\n query = attn.to_q(hidden_states.to(attn.to_q.weight.dtype))\n\n if encoder_hidden_states is None:\n crossattn = False\n encoder_hidden_states = hidden_states\n else:\n crossattn = True\n if attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n if self.train_kv:\n key = self.to_k_custom_diffusion(encoder_hidden_states.to(self.to_k_custom_diffusion.weight.dtype))\n value = self.to_v_custom_diffusion(encoder_hidden_states.to(self.to_v_custom_diffusion.weight.dtype))\n key = key.to(attn.to_q.weight.dtype)\n value = value.to(attn.to_q.weight.dtype)\n else:\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n if crossattn:\n detach = torch.ones_like(key)\n detach[:, :1, :] = detach[:, :1, :] * 0.0\n key = detach * key + (1 - detach) * key.detach()\n value = detach * value + (1 - detach) * value.detach()\n\n query = attn.head_to_batch_dim(query).contiguous()\n key = attn.head_to_batch_dim(key).contiguous()\n value = attn.head_to_batch_dim(value).contiguous()\n\n hidden_states = xformers.ops.memory_efficient_attention(\n query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale\n )\n hidden_states = hidden_states.to(query.dtype)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n if self.train_q_out:\n # linear proj\n hidden_states = self.to_out_custom_diffusion[0](hidden_states)\n # dropout\n hidden_states = self.to_out_custom_diffusion[1](hidden_states)\n else:\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n return hidden_states\n\n\nclass CustomDiffusionAttnProcessor2_0(nn.Module):\n r\"\"\"\n Processor for implementing attention for the Custom Diffusion method using PyTorch 2.0’s memory-efficient scaled\n dot-product attention.\n\n Args:\n train_kv (`bool`, defaults to `True`):\n Whether to newly train the key and value matrices corresponding to the text features.\n train_q_out (`bool`, defaults to `True`):\n Whether to newly train query matrices corresponding to the latent image features.\n hidden_size (`int`, *optional*, defaults to `None`):\n The hidden size of the attention layer.\n cross_attention_dim (`int`, *optional*, defaults to `None`):\n The number of channels in the `encoder_hidden_states`.\n out_bias (`bool`, defaults to `True`):\n Whether to include the bias parameter in `train_q_out`.\n dropout (`float`, *optional*, defaults to 0.0):\n The dropout probability to use.\n \"\"\"\n\n def __init__(\n self,\n train_kv: bool = True,\n train_q_out: bool = True,\n hidden_size: Optional[int] = None,\n cross_attention_dim: Optional[int] = None,\n out_bias: bool = True,\n dropout: float = 0.0,\n ):\n super().__init__()\n self.train_kv = train_kv\n self.train_q_out = train_q_out\n\n self.hidden_size = hidden_size\n self.cross_attention_dim = cross_attention_dim\n\n # `_custom_diffusion` id for easy serialization and loading.\n if self.train_kv:\n self.to_k_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)\n self.to_v_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)\n if self.train_q_out:\n self.to_q_custom_diffusion = nn.Linear(hidden_size, hidden_size, bias=False)\n self.to_out_custom_diffusion = nn.ModuleList([])\n self.to_out_custom_diffusion.append(nn.Linear(hidden_size, hidden_size, bias=out_bias))\n self.to_out_custom_diffusion.append(nn.Dropout(dropout))\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n batch_size, sequence_length, _ = hidden_states.shape\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n if self.train_q_out:\n query = self.to_q_custom_diffusion(hidden_states)\n else:\n query = attn.to_q(hidden_states)\n\n if encoder_hidden_states is None:\n crossattn = False\n encoder_hidden_states = hidden_states\n else:\n crossattn = True\n if attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n if self.train_kv:\n key = self.to_k_custom_diffusion(encoder_hidden_states.to(self.to_k_custom_diffusion.weight.dtype))\n value = self.to_v_custom_diffusion(encoder_hidden_states.to(self.to_v_custom_diffusion.weight.dtype))\n key = key.to(attn.to_q.weight.dtype)\n value = value.to(attn.to_q.weight.dtype)\n\n else:\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n if crossattn:\n detach = torch.ones_like(key)\n detach[:, :1, :] = detach[:, :1, :] * 0.0\n key = detach * key + (1 - detach) * key.detach()\n value = detach * value + (1 - detach) * value.detach()\n\n inner_dim = hidden_states.shape[-1]\n\n head_dim = inner_dim // attn.heads\n query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n # the output of sdp = (batch, num_heads, seq_len, head_dim)\n # TODO: add support for attn.scale when we move to Torch 2.1\n hidden_states = F.scaled_dot_product_attention(\n query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False\n )\n\n hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)\n hidden_states = hidden_states.to(query.dtype)\n\n if self.train_q_out:\n # linear proj\n hidden_states = self.to_out_custom_diffusion[0](hidden_states)\n # dropout\n hidden_states = self.to_out_custom_diffusion[1](hidden_states)\n else:\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n return hidden_states\n\n\nclass SlicedAttnProcessor:\n r\"\"\"\n Processor for implementing sliced attention.\n\n Args:\n slice_size (`int`, *optional*):\n The number of steps to compute attention. Uses as many slices as `attention_head_dim // slice_size`, and\n `attention_head_dim` must be a multiple of the `slice_size`.\n \"\"\"\n\n def __init__(self, slice_size: int):\n self.slice_size = slice_size\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n residual = hidden_states\n\n input_ndim = hidden_states.ndim\n\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n\n if attn.group_norm is not None:\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n\n query = attn.to_q(hidden_states)\n dim = query.shape[-1]\n query = attn.head_to_batch_dim(query)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n batch_size_attention, query_tokens, _ = query.shape\n hidden_states = torch.zeros(\n (batch_size_attention, query_tokens, dim // attn.heads), device=query.device, dtype=query.dtype\n )\n\n for i in range(batch_size_attention // self.slice_size):\n start_idx = i * self.slice_size\n end_idx = (i + 1) * self.slice_size\n\n query_slice = query[start_idx:end_idx]\n key_slice = key[start_idx:end_idx]\n attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None\n\n attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice)\n\n attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx])\n\n hidden_states[start_idx:end_idx] = attn_slice\n\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n if input_ndim == 4:\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n if attn.residual_connection:\n hidden_states = hidden_states + residual\n\n hidden_states = hidden_states / attn.rescale_output_factor\n\n return hidden_states\n\n\nclass SlicedAttnAddedKVProcessor:\n r\"\"\"\n Processor for implementing sliced attention with extra learnable key and value matrices for the text encoder.\n\n Args:\n slice_size (`int`, *optional*):\n The number of steps to compute attention. Uses as many slices as `attention_head_dim // slice_size`, and\n `attention_head_dim` must be a multiple of the `slice_size`.\n \"\"\"\n\n def __init__(self, slice_size):\n self.slice_size = slice_size\n\n def __call__(\n self,\n attn: \"Attention\",\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n temb: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n residual = hidden_states\n\n if attn.spatial_norm is not None:\n hidden_states = attn.spatial_norm(hidden_states, temb)\n\n hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)\n\n batch_size, sequence_length, _ = hidden_states.shape\n\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n\n query = attn.to_q(hidden_states)\n dim = query.shape[-1]\n query = attn.head_to_batch_dim(query)\n\n encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)\n encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)\n\n encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj)\n encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj)\n\n if not attn.only_cross_attention:\n key = attn.to_k(hidden_states)\n value = attn.to_v(hidden_states)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)\n value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)\n else:\n key = encoder_hidden_states_key_proj\n value = encoder_hidden_states_value_proj\n\n batch_size_attention, query_tokens, _ = query.shape\n hidden_states = torch.zeros(\n (batch_size_attention, query_tokens, dim // attn.heads), device=query.device, dtype=query.dtype\n )\n\n for i in range(batch_size_attention // self.slice_size):\n start_idx = i * self.slice_size\n end_idx = (i + 1) * self.slice_size\n\n query_slice = query[start_idx:end_idx]\n key_slice = key[start_idx:end_idx]\n attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None\n\n attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice)\n\n attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx])\n\n hidden_states[start_idx:end_idx] = attn_slice\n\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)\n hidden_states = hidden_states + residual\n\n return hidden_states\n\n\nclass SpatialNorm(nn.Module):\n \"\"\"\n Spatially conditioned normalization as defined in https://arxiv.org/abs/2209.09002.\n\n Args:\n f_channels (`int`):\n The number of channels for input to group normalization layer, and output of the spatial norm layer.\n zq_channels (`int`):\n The number of channels for the quantized vector as described in the paper.\n \"\"\"\n\n def __init__(\n self,\n f_channels: int,\n zq_channels: int,\n ):\n super().__init__()\n self.norm_layer = nn.GroupNorm(num_channels=f_channels, num_groups=32, eps=1e-6, affine=True)\n self.conv_y = nn.Conv2d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0)\n self.conv_b = nn.Conv2d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0)\n\n def forward(self, f: torch.Tensor, zq: torch.Tensor) -> torch.Tensor:\n f_size = f.shape[-2:]\n zq = F.interpolate(zq, size=f_size, mode=\"nearest\")\n norm_f = self.norm_layer(f)\n new_f = norm_f * self.conv_y(zq) + self.conv_b(zq)\n return new_f\n\n\nclass LoRAAttnProcessor(nn.Module):\n def __init__(\n self,\n hidden_size: int,\n cross_attention_dim: Optional[int] = None,\n rank: int = 4,\n network_alpha: Optional[int] = None,\n **kwargs,\n ):\n deprecation_message = \"Using LoRAAttnProcessor is deprecated. Please use the PEFT backend for all things LoRA. You can install PEFT by running `pip install peft`.\"\n deprecate(\"LoRAAttnProcessor\", \"0.30.0\", deprecation_message, standard_warn=False)\n\n super().__init__()\n\n self.hidden_size = hidden_size\n self.cross_attention_dim = cross_attention_dim\n self.rank = rank\n\n q_rank = kwargs.pop(\"q_rank\", None)\n q_hidden_size = kwargs.pop(\"q_hidden_size\", None)\n q_rank = q_rank if q_rank is not None else rank\n q_hidden_size = q_hidden_size if q_hidden_size is not None else hidden_size\n\n v_rank = kwargs.pop(\"v_rank\", None)\n v_hidden_size = kwargs.pop(\"v_hidden_size\", None)\n v_rank = v_rank if v_rank is not None else rank\n v_hidden_size = v_hidden_size if v_hidden_size is not None else hidden_size\n\n out_rank = kwargs.pop(\"out_rank\", None)\n out_hidden_size = kwargs.pop(\"out_hidden_size\", None)\n out_rank = out_rank if out_rank is not None else rank\n out_hidden_size = out_hidden_size if out_hidden_size is not None else hidden_size\n\n self.to_q_lora = LoRALinearLayer(q_hidden_size, q_hidden_size, q_rank, network_alpha)\n self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)\n self.to_v_lora = LoRALinearLayer(cross_attention_dim or v_hidden_size, v_hidden_size, v_rank, network_alpha)\n self.to_out_lora = LoRALinearLayer(out_hidden_size, out_hidden_size, out_rank, network_alpha)\n\n def __call__(self, attn: Attention, hidden_states: torch.Tensor, **kwargs) -> torch.Tensor:\n self_cls_name = self.__class__.__name__\n deprecate(\n self_cls_name,\n \"0.26.0\",\n (\n f\"Make sure use {self_cls_name[4:]} instead by setting\"\n \"LoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using\"\n \" `LoraLoaderMixin.load_lora_weights`\"\n ),\n )\n attn.to_q.lora_layer = self.to_q_lora.to(hidden_states.device)\n attn.to_k.lora_layer = self.to_k_lora.to(hidden_states.device)\n attn.to_v.lora_layer = self.to_v_lora.to(hidden_states.device)\n attn.to_out[0].lora_layer = self.to_out_lora.to(hidden_states.device)\n\n attn._modules.pop(\"processor\")\n attn.processor = AttnProcessor()\n return attn.processor(attn, hidden_states, **kwargs)\n\n\nclass LoRAAttnProcessor2_0(nn.Module):\n def __init__(\n self,\n hidden_size: int,\n cross_attention_dim: Optional[int] = None,\n rank: int = 4,\n network_alpha: Optional[int] = None,\n **kwargs,\n ):\n deprecation_message = \"Using LoRAAttnProcessor is deprecated. Please use the PEFT backend for all things LoRA. You can install PEFT by running `pip install peft`.\"\n deprecate(\"LoRAAttnProcessor2_0\", \"0.30.0\", deprecation_message, standard_warn=False)\n\n super().__init__()\n if not hasattr(F, \"scaled_dot_product_attention\"):\n raise ImportError(\"AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.\")\n\n self.hidden_size = hidden_size\n self.cross_attention_dim = cross_attention_dim\n self.rank = rank\n\n q_rank = kwargs.pop(\"q_rank\", None)\n q_hidden_size = kwargs.pop(\"q_hidden_size\", None)\n q_rank = q_rank if q_rank is not None else rank\n q_hidden_size = q_hidden_size if q_hidden_size is not None else hidden_size\n\n v_rank = kwargs.pop(\"v_rank\", None)\n v_hidden_size = kwargs.pop(\"v_hidden_size\", None)\n v_rank = v_rank if v_rank is not None else rank\n v_hidden_size = v_hidden_size if v_hidden_size is not None else hidden_size\n\n out_rank = kwargs.pop(\"out_rank\", None)\n out_hidden_size = kwargs.pop(\"out_hidden_size\", None)\n out_rank = out_rank if out_rank is not None else rank\n out_hidden_size = out_hidden_size if out_hidden_size is not None else hidden_size\n\n self.to_q_lora = LoRALinearLayer(q_hidden_size, q_hidden_size, q_rank, network_alpha)\n self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)\n self.to_v_lora = LoRALinearLayer(cross_attention_dim or v_hidden_size, v_hidden_size, v_rank, network_alpha)\n self.to_out_lora = LoRALinearLayer(out_hidden_size, out_hidden_size, out_rank, network_alpha)\n\n def __call__(self, attn: Attention, hidden_states: torch.Tensor, **kwargs) -> torch.Tensor:\n self_cls_name = self.__class__.__name__\n deprecate(\n self_cls_name,\n \"0.26.0\",\n (\n f\"Make sure use {self_cls_name[4:]} instead by setting\"\n \"LoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using\"\n \" `LoraLoaderMixin.load_lora_weights`\"\n ),\n )\n attn.to_q.lora_layer = self.to_q_lora.to(hidden_states.device)\n attn.to_k.lora_layer = self.to_k_lora.to(hidden_states.device)\n attn.to_v.lora_layer = self.to_v_lora.to(hidden_states.device)\n attn.to_out[0].lora_layer = self.to_out_lora.to(hidden_states.device)\n\n attn._modules.pop(\"processor\")\n attn.processor = AttnProcessor2_0()\n return attn.processor(attn, hidden_states, **kwargs)\n\n\nclass LoRAXFormersAttnProcessor(nn.Module):\n r\"\"\"\n Processor for implementing the LoRA attention mechanism with memory efficient attention using xFormers.\n\n Args:\n hidden_size (`int`, *optional*):\n The hidden size of the attention layer.\n cross_attention_dim (`int`, *optional*):\n The number of channels in the `encoder_hidden_states`.\n rank (`int`, defaults to 4):\n The dimension of the LoRA update matrices.\n attention_op (`Callable`, *optional*, defaults to `None`):\n The base\n [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to\n use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best\n operator.\n network_alpha (`int`, *optional*):\n Equivalent to `alpha` but it's usage is specific to Kohya (A1111) style LoRAs.\n kwargs (`dict`):\n Additional keyword arguments to pass to the `LoRALinearLayer` layers.\n \"\"\"\n\n def __init__(\n self,\n hidden_size: int,\n cross_attention_dim: int,\n rank: int = 4,\n attention_op: Optional[Callable] = None,\n network_alpha: Optional[int] = None,\n **kwargs,\n ):\n super().__init__()\n\n self.hidden_size = hidden_size\n self.cross_attention_dim = cross_attention_dim\n self.rank = rank\n self.attention_op = attention_op\n\n q_rank = kwargs.pop(\"q_rank\", None)\n q_hidden_size = kwargs.pop(\"q_hidden_size\", None)\n q_rank = q_rank if q_rank is not None else rank\n q_hidden_size = q_hidden_size if q_hidden_size is not None else hidden_size\n\n v_rank = kwargs.pop(\"v_rank\", None)\n v_hidden_size = kwargs.pop(\"v_hidden_size\", None)\n v_rank = v_rank if v_rank is not None else rank\n v_hidden_size = v_hidden_size if v_hidden_size is not None else hidden_size\n\n out_rank = kwargs.pop(\"out_rank\", None)\n out_hidden_size = kwargs.pop(\"out_hidden_size\", None)\n out_rank = out_rank if out_rank is not None else rank\n out_hidden_size = out_hidden_size if out_hidden_size is not None else hidden_size\n\n self.to_q_lora = LoRALinearLayer(q_hidden_size, q_hidden_size, q_rank, network_alpha)\n self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)\n self.to_v_lora = LoRALinearLayer(cross_attention_dim or v_hidden_size, v_hidden_size, v_rank, network_alpha)\n self.to_out_lora = LoRALinearLayer(out_hidden_size, out_hidden_size, out_rank, network_alpha)\n\n def __call__(self, attn: Attention, hidden_states: torch.Tensor, **kwargs) -> torch.Tensor:\n self_cls_name = self.__class__.__name__\n deprecate(\n self_cls_name,\n \"0.26.0\",\n (\n f\"Make sure use {self_cls_name[4:]} instead by setting\"\n \"LoRA layers to `self.{to_q,to_k,to_v,add_k_proj,add_v_proj,to_out[0]}.lora_layer` respectively. This will be done automatically when using\"\n \" `LoraLoaderMixin.load_lora_weights`\"\n ),\n )\n attn.to_q.lora_layer = self.to_q_lora.to(hidden_states.device)\n attn.to_k.lora_layer = self.to_k_lora.to(hidden_states.device)\n attn.to_v.lora_layer = self.to_v_lora.to(hidden_states.device)\n attn.to_out[0].lora_layer = self.to_out_lora.to(hidden_states.device)\n\n attn._modules.pop(\"processor\")\n attn.processor = XFormersAttnProcessor()\n return attn.processor(attn, hidden_states, **kwargs)\n\n\nclass LoRAAttnAddedKVProcessor(nn.Module):\n r\"\"\"\n Processor for implementing the LoRA attention mechanism with extra learnable key and value matrices for the text\n encoder.\n\n Args:\n hidden_size (`int`, *optional*):\n The hidden size of the attention layer.\n cross_attention_dim (`int`, *optional*, defaults to `None`):\n The number of channels in the `encoder_hidden_states`.\n rank (`int`, defaults to 4):\n The dimension of the LoRA update matrices.\n network_alpha (`int`, *optional*):\n Equivalent to `alpha` but it's usage is specific to Kohya (A1111) style LoRAs.\n kwargs (`dict`):\n Additional keyword arguments to pass to the `LoRALinearLayer` layers.\n \"\"\"\n\n def __init__(\n self,\n hidden_size: int,\n cross_attention_dim: Optional[int] = None,\n rank: int = 4,\n network_alpha: Optional[int] = None,\n ):\n super().__init__()\n\n self.hidden_size = hidden_size\n self.cross_attention_dim = cross_attention_dim\n self.rank = rank\n\n self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)\n self.add_k_proj_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)\n self.add_v_proj_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)\n self.to_k_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)\n self.to_v_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)\n self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)\n\n def __call__(self, attn: Attention, hidden_states: torch.Tensor, **kwargs) -> torch.Tensor:\n self_cls_name = self.__class__.__name__\n deprecate(\n self_cls_name,\n \"0.26.0\",\n (\n f\"Make sure use {self_cls_name[4:]} instead by setting\"\n \"LoRA layers to `self.{to_q,to_k,to_v,add_k_proj,add_v_proj,to_out[0]}.lora_layer` respectively. This will be done automatically when using\"\n \" `LoraLoaderMixin.load_lora_weights`\"\n ),\n )\n attn.to_q.lora_layer = self.to_q_lora.to(hidden_states.device)\n attn.to_k.lora_layer = self.to_k_lora.to(hidden_states.device)\n attn.to_v.lora_layer = self.to_v_lora.to(hidden_states.device)\n attn.to_out[0].lora_layer = self.to_out_lora.to(hidden_states.device)\n\n attn._modules.pop(\"processor\")\n attn.processor = AttnAddedKVProcessor()\n return attn.processor(attn, hidden_states, **kwargs)\n\n\nclass IPAdapterAttnProcessor(nn.Module):\n r\"\"\"\n Attention processor for Multiple IP-Adapters.\n\n Args:\n hidden_size (`int`):\n The hidden size of the attention layer.\n cross_attention_dim (`int`):\n The number of channels in the `encoder_hidden_states`.\n num_tokens (`int`, `Tuple[int]` or `List[int]`, defaults to `(4,)`):\n The context length of the image features.\n scale (`float` or List[`float`], defaults to 1.0):\n the weight scale of image prompt.\n \"\"\"\n\n def __init__(self, hidden_size, cross_attention_dim=None, num_tokens=(4,), scale=1.0):\n super().__init__()\n\n self.hidden_size = hidden_size\n self.cross_attention_dim = cross_attention_dim\n\n if not isinstance(num_tokens, (tuple, list)):\n num_tokens = [num_tokens]\n self.num_tokens = num_tokens\n\n if not isinstance(scale, list):\n scale = [scale] * len(num_tokens)\n if len(scale) != len(num_tokens):\n raise ValueError(\"`scale` should be a list of integers with the same length as `num_tokens`.\")\n self.scale = scale\n\n self.to_k_ip = nn.ModuleList(\n [nn.Linear(cross_attention_dim, hidden_size, bias=False) for _ in range(len(num_tokens))]\n )\n self.to_v_ip = nn.ModuleList(\n [nn.Linear(cross_attention_dim, hidden_size, bias=False) for _ in range(len(num_tokens))]\n )\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n temb: Optional[torch.Tensor] = None,\n scale: float = 1.0,\n ip_adapter_masks: Optional[torch.Tensor] = None,\n ):\n residual = hidden_states\n\n # separate ip_hidden_states from encoder_hidden_states\n if encoder_hidden_states is not None:\n if isinstance(encoder_hidden_states, tuple):\n encoder_hidden_states, ip_hidden_states = encoder_hidden_states\n else:\n deprecation_message = (\n \"You have passed a tensor as `encoder_hidden_states`. This is deprecated and will be removed in a future release.\"\n \" Please make sure to update your script to pass `encoder_hidden_states` as a tuple to suppress this warning.\"\n )\n deprecate(\"encoder_hidden_states not a tuple\", \"1.0.0\", deprecation_message, standard_warn=False)\n end_pos = encoder_hidden_states.shape[1] - self.num_tokens[0]\n encoder_hidden_states, ip_hidden_states = (\n encoder_hidden_states[:, :end_pos, :],\n [encoder_hidden_states[:, end_pos:, :]],\n )\n\n if attn.spatial_norm is not None:\n hidden_states = attn.spatial_norm(hidden_states, temb)\n\n input_ndim = hidden_states.ndim\n\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n\n if attn.group_norm is not None:\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n\n query = attn.to_q(hidden_states)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n attention_probs = attn.get_attention_scores(query, key, attention_mask)\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n if ip_adapter_masks is not None:\n if not isinstance(ip_adapter_masks, List):\n # for backward compatibility, we accept `ip_adapter_mask` as a tensor of shape [num_ip_adapter, 1, height, width]\n ip_adapter_masks = list(ip_adapter_masks.unsqueeze(1))\n if not (len(ip_adapter_masks) == len(self.scale) == len(ip_hidden_states)):\n raise ValueError(\n f\"Length of ip_adapter_masks array ({len(ip_adapter_masks)}) must match \"\n f\"length of self.scale array ({len(self.scale)}) and number of ip_hidden_states \"\n f\"({len(ip_hidden_states)})\"\n )\n else:\n for index, (mask, scale, ip_state) in enumerate(zip(ip_adapter_masks, self.scale, ip_hidden_states)):\n if not isinstance(mask, torch.Tensor) or mask.ndim != 4:\n raise ValueError(\n \"Each element of the ip_adapter_masks array should be a tensor with shape \"\n \"[1, num_images_for_ip_adapter, height, width].\"\n \" Please use `IPAdapterMaskProcessor` to preprocess your mask\"\n )\n if mask.shape[1] != ip_state.shape[1]:\n raise ValueError(\n f\"Number of masks ({mask.shape[1]}) does not match \"\n f\"number of ip images ({ip_state.shape[1]}) at index {index}\"\n )\n if isinstance(scale, list) and not len(scale) == mask.shape[1]:\n raise ValueError(\n f\"Number of masks ({mask.shape[1]}) does not match \"\n f\"number of scales ({len(scale)}) at index {index}\"\n )\n else:\n ip_adapter_masks = [None] * len(self.scale)\n\n # for ip-adapter\n for current_ip_hidden_states, scale, to_k_ip, to_v_ip, mask in zip(\n ip_hidden_states, self.scale, self.to_k_ip, self.to_v_ip, ip_adapter_masks\n ):\n skip = False\n if isinstance(scale, list):\n if all(s == 0 for s in scale):\n skip = True\n elif scale == 0:\n skip = True\n if not skip:\n if mask is not None:\n if not isinstance(scale, list):\n scale = [scale] * mask.shape[1]\n\n current_num_images = mask.shape[1]\n for i in range(current_num_images):\n ip_key = to_k_ip(current_ip_hidden_states[:, i, :, :])\n ip_value = to_v_ip(current_ip_hidden_states[:, i, :, :])\n\n ip_key = attn.head_to_batch_dim(ip_key)\n ip_value = attn.head_to_batch_dim(ip_value)\n\n ip_attention_probs = attn.get_attention_scores(query, ip_key, None)\n _current_ip_hidden_states = torch.bmm(ip_attention_probs, ip_value)\n _current_ip_hidden_states = attn.batch_to_head_dim(_current_ip_hidden_states)\n\n mask_downsample = IPAdapterMaskProcessor.downsample(\n mask[:, i, :, :],\n batch_size,\n _current_ip_hidden_states.shape[1],\n _current_ip_hidden_states.shape[2],\n )\n\n mask_downsample = mask_downsample.to(dtype=query.dtype, device=query.device)\n\n hidden_states = hidden_states + scale[i] * (_current_ip_hidden_states * mask_downsample)\n else:\n ip_key = to_k_ip(current_ip_hidden_states)\n ip_value = to_v_ip(current_ip_hidden_states)\n\n ip_key = attn.head_to_batch_dim(ip_key)\n ip_value = attn.head_to_batch_dim(ip_value)\n\n ip_attention_probs = attn.get_attention_scores(query, ip_key, None)\n current_ip_hidden_states = torch.bmm(ip_attention_probs, ip_value)\n current_ip_hidden_states = attn.batch_to_head_dim(current_ip_hidden_states)\n\n hidden_states = hidden_states + scale * current_ip_hidden_states\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n if input_ndim == 4:\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n if attn.residual_connection:\n hidden_states = hidden_states + residual\n\n hidden_states = hidden_states / attn.rescale_output_factor\n\n return hidden_states\n\n\nclass IPAdapterAttnProcessor2_0(torch.nn.Module):\n r\"\"\"\n Attention processor for IP-Adapter for PyTorch 2.0.\n\n Args:\n hidden_size (`int`):\n The hidden size of the attention layer.\n cross_attention_dim (`int`):\n The number of channels in the `encoder_hidden_states`.\n num_tokens (`int`, `Tuple[int]` or `List[int]`, defaults to `(4,)`):\n The context length of the image features.\n scale (`float` or `List[float]`, defaults to 1.0):\n the weight scale of image prompt.\n \"\"\"\n\n def __init__(self, hidden_size, cross_attention_dim=None, num_tokens=(4,), scale=1.0):\n super().__init__()\n\n if not hasattr(F, \"scaled_dot_product_attention\"):\n raise ImportError(\n f\"{self.__class__.__name__} requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.\"\n )\n\n self.hidden_size = hidden_size\n self.cross_attention_dim = cross_attention_dim\n\n if not isinstance(num_tokens, (tuple, list)):\n num_tokens = [num_tokens]\n self.num_tokens = num_tokens\n\n if not isinstance(scale, list):\n scale = [scale] * len(num_tokens)\n if len(scale) != len(num_tokens):\n raise ValueError(\"`scale` should be a list of integers with the same length as `num_tokens`.\")\n self.scale = scale\n\n self.to_k_ip = nn.ModuleList(\n [nn.Linear(cross_attention_dim, hidden_size, bias=False) for _ in range(len(num_tokens))]\n )\n self.to_v_ip = nn.ModuleList(\n [nn.Linear(cross_attention_dim, hidden_size, bias=False) for _ in range(len(num_tokens))]\n )\n\n def __call__(\n self,\n attn: Attention,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n temb: Optional[torch.Tensor] = None,\n scale: float = 1.0,\n ip_adapter_masks: Optional[torch.Tensor] = None,\n ):\n residual = hidden_states\n\n # separate ip_hidden_states from encoder_hidden_states\n if encoder_hidden_states is not None:\n if isinstance(encoder_hidden_states, tuple):\n encoder_hidden_states, ip_hidden_states = encoder_hidden_states\n else:\n deprecation_message = (\n \"You have passed a tensor as `encoder_hidden_states`. This is deprecated and will be removed in a future release.\"\n \" Please make sure to update your script to pass `encoder_hidden_states` as a tuple to suppress this warning.\"\n )\n deprecate(\"encoder_hidden_states not a tuple\", \"1.0.0\", deprecation_message, standard_warn=False)\n end_pos = encoder_hidden_states.shape[1] - self.num_tokens[0]\n encoder_hidden_states, ip_hidden_states = (\n encoder_hidden_states[:, :end_pos, :],\n [encoder_hidden_states[:, end_pos:, :]],\n )\n\n if attn.spatial_norm is not None:\n hidden_states = attn.spatial_norm(hidden_states, temb)\n\n input_ndim = hidden_states.ndim\n\n if input_ndim == 4:\n batch_size, channel, height, width = hidden_states.shape\n hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)\n\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n\n if attention_mask is not None:\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n # scaled_dot_product_attention expects attention_mask shape to be\n # (batch, heads, source_length, target_length)\n attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])\n\n if attn.group_norm is not None:\n hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)\n\n query = attn.to_q(hidden_states)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n inner_dim = key.shape[-1]\n head_dim = inner_dim // attn.heads\n\n query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n # the output of sdp = (batch, num_heads, seq_len, head_dim)\n # TODO: add support for attn.scale when we move to Torch 2.1\n hidden_states = F.scaled_dot_product_attention(\n query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False\n )\n\n hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)\n hidden_states = hidden_states.to(query.dtype)\n\n if ip_adapter_masks is not None:\n if not isinstance(ip_adapter_masks, List):\n # for backward compatibility, we accept `ip_adapter_mask` as a tensor of shape [num_ip_adapter, 1, height, width]\n ip_adapter_masks = list(ip_adapter_masks.unsqueeze(1))\n if not (len(ip_adapter_masks) == len(self.scale) == len(ip_hidden_states)):\n raise ValueError(\n f\"Length of ip_adapter_masks array ({len(ip_adapter_masks)}) must match \"\n f\"length of self.scale array ({len(self.scale)}) and number of ip_hidden_states \"\n f\"({len(ip_hidden_states)})\"\n )\n else:\n for index, (mask, scale, ip_state) in enumerate(zip(ip_adapter_masks, self.scale, ip_hidden_states)):\n if not isinstance(mask, torch.Tensor) or mask.ndim != 4:\n raise ValueError(\n \"Each element of the ip_adapter_masks array should be a tensor with shape \"\n \"[1, num_images_for_ip_adapter, height, width].\"\n \" Please use `IPAdapterMaskProcessor` to preprocess your mask\"\n )\n if mask.shape[1] != ip_state.shape[1]:\n raise ValueError(\n f\"Number of masks ({mask.shape[1]}) does not match \"\n f\"number of ip images ({ip_state.shape[1]}) at index {index}\"\n )\n if isinstance(scale, list) and not len(scale) == mask.shape[1]:\n raise ValueError(\n f\"Number of masks ({mask.shape[1]}) does not match \"\n f\"number of scales ({len(scale)}) at index {index}\"\n )\n else:\n ip_adapter_masks = [None] * len(self.scale)\n\n # for ip-adapter\n for current_ip_hidden_states, scale, to_k_ip, to_v_ip, mask in zip(\n ip_hidden_states, self.scale, self.to_k_ip, self.to_v_ip, ip_adapter_masks\n ):\n skip = False\n if isinstance(scale, list):\n if all(s == 0 for s in scale):\n skip = True\n elif scale == 0:\n skip = True\n if not skip:\n if mask is not None:\n if not isinstance(scale, list):\n scale = [scale] * mask.shape[1]\n\n current_num_images = mask.shape[1]\n for i in range(current_num_images):\n ip_key = to_k_ip(current_ip_hidden_states[:, i, :, :])\n ip_value = to_v_ip(current_ip_hidden_states[:, i, :, :])\n\n ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n # the output of sdp = (batch, num_heads, seq_len, head_dim)\n # TODO: add support for attn.scale when we move to Torch 2.1\n _current_ip_hidden_states = F.scaled_dot_product_attention(\n query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False\n )\n\n _current_ip_hidden_states = _current_ip_hidden_states.transpose(1, 2).reshape(\n batch_size, -1, attn.heads * head_dim\n )\n _current_ip_hidden_states = _current_ip_hidden_states.to(query.dtype)\n\n mask_downsample = IPAdapterMaskProcessor.downsample(\n mask[:, i, :, :],\n batch_size,\n _current_ip_hidden_states.shape[1],\n _current_ip_hidden_states.shape[2],\n )\n\n mask_downsample = mask_downsample.to(dtype=query.dtype, device=query.device)\n hidden_states = hidden_states + scale[i] * (_current_ip_hidden_states * mask_downsample)\n else:\n ip_key = to_k_ip(current_ip_hidden_states)\n ip_value = to_v_ip(current_ip_hidden_states)\n\n ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n # the output of sdp = (batch, num_heads, seq_len, head_dim)\n # TODO: add support for attn.scale when we move to Torch 2.1\n current_ip_hidden_states = F.scaled_dot_product_attention(\n query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False\n )\n\n current_ip_hidden_states = current_ip_hidden_states.transpose(1, 2).reshape(\n batch_size, -1, attn.heads * head_dim\n )\n current_ip_hidden_states = current_ip_hidden_states.to(query.dtype)\n\n hidden_states = hidden_states + scale * current_ip_hidden_states\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n if input_ndim == 4:\n hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)\n\n if attn.residual_connection:\n hidden_states = hidden_states + residual\n\n hidden_states = hidden_states / attn.rescale_output_factor\n\n return hidden_states\n\n\nLORA_ATTENTION_PROCESSORS = (\n LoRAAttnProcessor,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n LoRAAttnAddedKVProcessor,\n)\n\nADDED_KV_ATTENTION_PROCESSORS = (\n AttnAddedKVProcessor,\n SlicedAttnAddedKVProcessor,\n AttnAddedKVProcessor2_0,\n XFormersAttnAddedKVProcessor,\n LoRAAttnAddedKVProcessor,\n)\n\nCROSS_ATTENTION_PROCESSORS = (\n AttnProcessor,\n AttnProcessor2_0,\n XFormersAttnProcessor,\n SlicedAttnProcessor,\n LoRAAttnProcessor,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n IPAdapterAttnProcessor,\n IPAdapterAttnProcessor2_0,\n)\n\nAttentionProcessor = Union[\n AttnProcessor,\n AttnProcessor2_0,\n FusedAttnProcessor2_0,\n XFormersAttnProcessor,\n SlicedAttnProcessor,\n AttnAddedKVProcessor,\n SlicedAttnAddedKVProcessor,\n AttnAddedKVProcessor2_0,\n XFormersAttnAddedKVProcessor,\n CustomDiffusionAttnProcessor,\n CustomDiffusionXFormersAttnProcessor,\n CustomDiffusionAttnProcessor2_0,\n # deprecated\n LoRAAttnProcessor,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n LoRAAttnAddedKVProcessor,\n]\n" }, { "path": "diffusers/src/diffusers/models/autoencoders/__init__.py", "content": "from .autoencoder_asym_kl import AsymmetricAutoencoderKL\nfrom .autoencoder_kl import AutoencoderKL\nfrom .autoencoder_kl_temporal_decoder import AutoencoderKLTemporalDecoder\nfrom .autoencoder_tiny import AutoencoderTiny\nfrom .consistency_decoder_vae import ConsistencyDecoderVAE\nfrom .vq_model import VQModel\n" }, { "path": "diffusers/src/diffusers/models/autoencoders/autoencoder_asym_kl.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...utils.accelerate_utils import apply_forward_hook\nfrom ..modeling_outputs import AutoencoderKLOutput\nfrom ..modeling_utils import ModelMixin\nfrom .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder, MaskConditionDecoder\n\n\nclass AsymmetricAutoencoderKL(ModelMixin, ConfigMixin):\n r\"\"\"\n Designing a Better Asymmetric VQGAN for StableDiffusion https://arxiv.org/abs/2306.04632 . A VAE model with KL loss\n for encoding images into latents and decoding latent representations into images.\n\n This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented\n for all models (such as downloading or saving).\n\n Parameters:\n in_channels (int, *optional*, defaults to 3): Number of channels in the input image.\n out_channels (int, *optional*, defaults to 3): Number of channels in the output.\n down_block_types (`Tuple[str]`, *optional*, defaults to `(\"DownEncoderBlock2D\",)`):\n Tuple of downsample block types.\n down_block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):\n Tuple of down block output channels.\n layers_per_down_block (`int`, *optional*, defaults to `1`):\n Number layers for down block.\n up_block_types (`Tuple[str]`, *optional*, defaults to `(\"UpDecoderBlock2D\",)`):\n Tuple of upsample block types.\n up_block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):\n Tuple of up block output channels.\n layers_per_up_block (`int`, *optional*, defaults to `1`):\n Number layers for up block.\n act_fn (`str`, *optional*, defaults to `\"silu\"`): The activation function to use.\n latent_channels (`int`, *optional*, defaults to 4): Number of channels in the latent space.\n sample_size (`int`, *optional*, defaults to `32`): Sample input size.\n norm_num_groups (`int`, *optional*, defaults to `32`):\n Number of groups to use for the first normalization layer in ResNet blocks.\n scaling_factor (`float`, *optional*, defaults to 0.18215):\n The component-wise standard deviation of the trained latent space computed using the first batch of the\n training set. This is used to scale the latent space to have unit variance when training the diffusion\n model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the\n diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1\n / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image\n Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.\n \"\"\"\n\n @register_to_config\n def __init__(\n self,\n in_channels: int = 3,\n out_channels: int = 3,\n down_block_types: Tuple[str, ...] = (\"DownEncoderBlock2D\",),\n down_block_out_channels: Tuple[int, ...] = (64,),\n layers_per_down_block: int = 1,\n up_block_types: Tuple[str, ...] = (\"UpDecoderBlock2D\",),\n up_block_out_channels: Tuple[int, ...] = (64,),\n layers_per_up_block: int = 1,\n act_fn: str = \"silu\",\n latent_channels: int = 4,\n norm_num_groups: int = 32,\n sample_size: int = 32,\n scaling_factor: float = 0.18215,\n ) -> None:\n super().__init__()\n\n # pass init params to Encoder\n self.encoder = Encoder(\n in_channels=in_channels,\n out_channels=latent_channels,\n down_block_types=down_block_types,\n block_out_channels=down_block_out_channels,\n layers_per_block=layers_per_down_block,\n act_fn=act_fn,\n norm_num_groups=norm_num_groups,\n double_z=True,\n )\n\n # pass init params to Decoder\n self.decoder = MaskConditionDecoder(\n in_channels=latent_channels,\n out_channels=out_channels,\n up_block_types=up_block_types,\n block_out_channels=up_block_out_channels,\n layers_per_block=layers_per_up_block,\n act_fn=act_fn,\n norm_num_groups=norm_num_groups,\n )\n\n self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1)\n self.post_quant_conv = nn.Conv2d(latent_channels, latent_channels, 1)\n\n self.use_slicing = False\n self.use_tiling = False\n\n self.register_to_config(block_out_channels=up_block_out_channels)\n self.register_to_config(force_upcast=False)\n\n @apply_forward_hook\n def encode(self, x: torch.Tensor, return_dict: bool = True) -> Union[AutoencoderKLOutput, Tuple[torch.Tensor]]:\n h = self.encoder(x)\n moments = self.quant_conv(h)\n posterior = DiagonalGaussianDistribution(moments)\n\n if not return_dict:\n return (posterior,)\n\n return AutoencoderKLOutput(latent_dist=posterior)\n\n def _decode(\n self,\n z: torch.Tensor,\n image: Optional[torch.Tensor] = None,\n mask: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ) -> Union[DecoderOutput, Tuple[torch.Tensor]]:\n z = self.post_quant_conv(z)\n dec = self.decoder(z, image, mask)\n\n if not return_dict:\n return (dec,)\n\n return DecoderOutput(sample=dec)\n\n @apply_forward_hook\n def decode(\n self,\n z: torch.Tensor,\n generator: Optional[torch.Generator] = None,\n image: Optional[torch.Tensor] = None,\n mask: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ) -> Union[DecoderOutput, Tuple[torch.Tensor]]:\n decoded = self._decode(z, image, mask).sample\n\n if not return_dict:\n return (decoded,)\n\n return DecoderOutput(sample=decoded)\n\n def forward(\n self,\n sample: torch.Tensor,\n mask: Optional[torch.Tensor] = None,\n sample_posterior: bool = False,\n return_dict: bool = True,\n generator: Optional[torch.Generator] = None,\n ) -> Union[DecoderOutput, Tuple[torch.Tensor]]:\n r\"\"\"\n Args:\n sample (`torch.Tensor`): Input sample.\n mask (`torch.Tensor`, *optional*, defaults to `None`): Optional inpainting mask.\n sample_posterior (`bool`, *optional*, defaults to `False`):\n Whether to sample from the posterior.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`DecoderOutput`] instead of a plain tuple.\n \"\"\"\n x = sample\n posterior = self.encode(x).latent_dist\n if sample_posterior:\n z = posterior.sample(generator=generator)\n else:\n z = posterior.mode()\n dec = self.decode(z, generator, sample, mask).sample\n\n if not return_dict:\n return (dec,)\n\n return DecoderOutput(sample=dec)\n" }, { "path": "diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Dict, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...loaders.single_file_model import FromOriginalModelMixin\nfrom ...utils.accelerate_utils import apply_forward_hook\nfrom ..attention_processor import (\n ADDED_KV_ATTENTION_PROCESSORS,\n CROSS_ATTENTION_PROCESSORS,\n Attention,\n AttentionProcessor,\n AttnAddedKVProcessor,\n AttnProcessor,\n)\nfrom ..modeling_outputs import AutoencoderKLOutput\nfrom ..modeling_utils import ModelMixin\nfrom .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder\n\n\nclass AutoencoderKL(ModelMixin, ConfigMixin, FromOriginalModelMixin):\n r\"\"\"\n A VAE model with KL loss for encoding images into latents and decoding latent representations into images.\n\n This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented\n for all models (such as downloading or saving).\n\n Parameters:\n in_channels (int, *optional*, defaults to 3): Number of channels in the input image.\n out_channels (int, *optional*, defaults to 3): Number of channels in the output.\n down_block_types (`Tuple[str]`, *optional*, defaults to `(\"DownEncoderBlock2D\",)`):\n Tuple of downsample block types.\n up_block_types (`Tuple[str]`, *optional*, defaults to `(\"UpDecoderBlock2D\",)`):\n Tuple of upsample block types.\n block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):\n Tuple of block output channels.\n act_fn (`str`, *optional*, defaults to `\"silu\"`): The activation function to use.\n latent_channels (`int`, *optional*, defaults to 4): Number of channels in the latent space.\n sample_size (`int`, *optional*, defaults to `32`): Sample input size.\n scaling_factor (`float`, *optional*, defaults to 0.18215):\n The component-wise standard deviation of the trained latent space computed using the first batch of the\n training set. This is used to scale the latent space to have unit variance when training the diffusion\n model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the\n diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1\n / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image\n Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.\n force_upcast (`bool`, *optional*, default to `True`):\n If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE\n can be fine-tuned / trained to a lower range without loosing too much precision in which case\n `force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix\n \"\"\"\n\n _supports_gradient_checkpointing = True\n _no_split_modules = [\"BasicTransformerBlock\", \"ResnetBlock2D\"]\n\n @register_to_config\n def __init__(\n self,\n in_channels: int = 3,\n out_channels: int = 3,\n down_block_types: Tuple[str] = (\"DownEncoderBlock2D\",),\n up_block_types: Tuple[str] = (\"UpDecoderBlock2D\",),\n block_out_channels: Tuple[int] = (64,),\n layers_per_block: int = 1,\n act_fn: str = \"silu\",\n latent_channels: int = 4,\n norm_num_groups: int = 32,\n sample_size: int = 32,\n scaling_factor: float = 0.18215,\n shift_factor: Optional[float] = None,\n latents_mean: Optional[Tuple[float]] = None,\n latents_std: Optional[Tuple[float]] = None,\n force_upcast: float = True,\n use_quant_conv: bool = True,\n use_post_quant_conv: bool = True,\n ):\n super().__init__()\n\n # pass init params to Encoder\n self.encoder = Encoder(\n in_channels=in_channels,\n out_channels=latent_channels,\n down_block_types=down_block_types,\n block_out_channels=block_out_channels,\n layers_per_block=layers_per_block,\n act_fn=act_fn,\n norm_num_groups=norm_num_groups,\n double_z=True,\n )\n\n # pass init params to Decoder\n self.decoder = Decoder(\n in_channels=latent_channels,\n out_channels=out_channels,\n up_block_types=up_block_types,\n block_out_channels=block_out_channels,\n layers_per_block=layers_per_block,\n norm_num_groups=norm_num_groups,\n act_fn=act_fn,\n )\n\n self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1) if use_quant_conv else None\n self.post_quant_conv = nn.Conv2d(latent_channels, latent_channels, 1) if use_post_quant_conv else None\n\n self.use_slicing = False\n self.use_tiling = False\n\n # only relevant if vae tiling is enabled\n self.tile_sample_min_size = self.config.sample_size\n sample_size = (\n self.config.sample_size[0]\n if isinstance(self.config.sample_size, (list, tuple))\n else self.config.sample_size\n )\n self.tile_latent_min_size = int(sample_size / (2 ** (len(self.config.block_out_channels) - 1)))\n self.tile_overlap_factor = 0.25\n\n def _set_gradient_checkpointing(self, module, value=False):\n if isinstance(module, (Encoder, Decoder)):\n module.gradient_checkpointing = value\n\n def enable_tiling(self, use_tiling: bool = True):\n r\"\"\"\n Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to\n compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow\n processing larger images.\n \"\"\"\n self.use_tiling = use_tiling\n\n def disable_tiling(self):\n r\"\"\"\n Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing\n decoding in one step.\n \"\"\"\n self.enable_tiling(False)\n\n def enable_slicing(self):\n r\"\"\"\n Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to\n compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.\n \"\"\"\n self.use_slicing = True\n\n def disable_slicing(self):\n r\"\"\"\n Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing\n decoding in one step.\n \"\"\"\n self.use_slicing = False\n\n @property\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors\n def attn_processors(self) -> Dict[str, AttentionProcessor]:\n r\"\"\"\n Returns:\n `dict` of attention processors: A dictionary containing all attention processors used in the model with\n indexed by its weight name.\n \"\"\"\n # set recursively\n processors = {}\n\n def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\n if hasattr(module, \"get_processor\"):\n processors[f\"{name}.processor\"] = module.get_processor(return_deprecated_lora=True)\n\n for sub_name, child in module.named_children():\n fn_recursive_add_processors(f\"{name}.{sub_name}\", child, processors)\n\n return processors\n\n for name, module in self.named_children():\n fn_recursive_add_processors(name, module, processors)\n\n return processors\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor\n def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\n r\"\"\"\n Sets the attention processor to use to compute attention.\n\n Parameters:\n processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):\n The instantiated processor class or a dictionary of processor classes that will be set as the processor\n for **all** `Attention` layers.\n\n If `processor` is a dict, the key needs to define the path to the corresponding cross attention\n processor. This is strongly recommended when setting trainable attention processors.\n\n \"\"\"\n count = len(self.attn_processors.keys())\n\n if isinstance(processor, dict) and len(processor) != count:\n raise ValueError(\n f\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\"\n f\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\"\n )\n\n def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\n if hasattr(module, \"set_processor\"):\n if not isinstance(processor, dict):\n module.set_processor(processor)\n else:\n module.set_processor(processor.pop(f\"{name}.processor\"))\n\n for sub_name, child in module.named_children():\n fn_recursive_attn_processor(f\"{name}.{sub_name}\", child, processor)\n\n for name, module in self.named_children():\n fn_recursive_attn_processor(name, module, processor)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor\n def set_default_attn_processor(self):\n \"\"\"\n Disables custom attention processors and sets the default attention implementation.\n \"\"\"\n if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnAddedKVProcessor()\n elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnProcessor()\n else:\n raise ValueError(\n f\"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}\"\n )\n\n self.set_attn_processor(processor)\n\n @apply_forward_hook\n def encode(\n self, x: torch.Tensor, return_dict: bool = True\n ) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:\n \"\"\"\n Encode a batch of images into latents.\n\n Args:\n x (`torch.Tensor`): Input batch of images.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.\n\n Returns:\n The latent representations of the encoded images. If `return_dict` is True, a\n [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.\n \"\"\"\n if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):\n return self.tiled_encode(x, return_dict=return_dict)\n\n if self.use_slicing and x.shape[0] > 1:\n encoded_slices = [self.encoder(x_slice) for x_slice in x.split(1)]\n h = torch.cat(encoded_slices)\n else:\n h = self.encoder(x)\n\n if self.quant_conv is not None:\n moments = self.quant_conv(h)\n else:\n moments = h\n\n posterior = DiagonalGaussianDistribution(moments)\n\n if not return_dict:\n return (posterior,)\n\n return AutoencoderKLOutput(latent_dist=posterior)\n\n def _decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:\n if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):\n return self.tiled_decode(z, return_dict=return_dict)\n\n if self.post_quant_conv is not None:\n z = self.post_quant_conv(z)\n\n dec = self.decoder(z)\n\n if not return_dict:\n return (dec,)\n\n return DecoderOutput(sample=dec)\n\n @apply_forward_hook\n def decode(\n self, z: torch.FloatTensor, return_dict: bool = True, generator=None\n ) -> Union[DecoderOutput, torch.FloatTensor]:\n \"\"\"\n Decode a batch of images.\n\n Args:\n z (`torch.Tensor`): Input batch of latent vectors.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.\n\n Returns:\n [`~models.vae.DecoderOutput`] or `tuple`:\n If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is\n returned.\n\n \"\"\"\n if self.use_slicing and z.shape[0] > 1:\n decoded_slices = [self._decode(z_slice).sample for z_slice in z.split(1)]\n decoded = torch.cat(decoded_slices)\n else:\n decoded = self._decode(z).sample\n\n if not return_dict:\n return (decoded,)\n\n return DecoderOutput(sample=decoded)\n\n def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:\n blend_extent = min(a.shape[2], b.shape[2], blend_extent)\n for y in range(blend_extent):\n b[:, :, y, :] = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)\n return b\n\n def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:\n blend_extent = min(a.shape[3], b.shape[3], blend_extent)\n for x in range(blend_extent):\n b[:, :, :, x] = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)\n return b\n\n def tiled_encode(self, x: torch.Tensor, return_dict: bool = True) -> AutoencoderKLOutput:\n r\"\"\"Encode a batch of images using a tiled encoder.\n\n When this option is enabled, the VAE will split the input tensor into tiles to compute encoding in several\n steps. This is useful to keep memory use constant regardless of image size. The end result of tiled encoding is\n different from non-tiled encoding because each tile uses a different encoder. To avoid tiling artifacts, the\n tiles overlap and are blended together to form a smooth output. You may still see tile-sized changes in the\n output, but they should be much less noticeable.\n\n Args:\n x (`torch.Tensor`): Input batch of images.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.\n\n Returns:\n [`~models.autoencoder_kl.AutoencoderKLOutput`] or `tuple`:\n If return_dict is True, a [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain\n `tuple` is returned.\n \"\"\"\n overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))\n blend_extent = int(self.tile_latent_min_size * self.tile_overlap_factor)\n row_limit = self.tile_latent_min_size - blend_extent\n\n # Split the image into 512x512 tiles and encode them separately.\n rows = []\n for i in range(0, x.shape[2], overlap_size):\n row = []\n for j in range(0, x.shape[3], overlap_size):\n tile = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]\n tile = self.encoder(tile)\n tile = self.quant_conv(tile)\n row.append(tile)\n rows.append(row)\n result_rows = []\n for i, row in enumerate(rows):\n result_row = []\n for j, tile in enumerate(row):\n # blend the above tile and the left tile\n # to the current tile and add the current tile to the result row\n if i > 0:\n tile = self.blend_v(rows[i - 1][j], tile, blend_extent)\n if j > 0:\n tile = self.blend_h(row[j - 1], tile, blend_extent)\n result_row.append(tile[:, :, :row_limit, :row_limit])\n result_rows.append(torch.cat(result_row, dim=3))\n\n moments = torch.cat(result_rows, dim=2)\n posterior = DiagonalGaussianDistribution(moments)\n\n if not return_dict:\n return (posterior,)\n\n return AutoencoderKLOutput(latent_dist=posterior)\n\n def tiled_decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:\n r\"\"\"\n Decode a batch of images using a tiled decoder.\n\n Args:\n z (`torch.Tensor`): Input batch of latent vectors.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.\n\n Returns:\n [`~models.vae.DecoderOutput`] or `tuple`:\n If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is\n returned.\n \"\"\"\n overlap_size = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor))\n blend_extent = int(self.tile_sample_min_size * self.tile_overlap_factor)\n row_limit = self.tile_sample_min_size - blend_extent\n\n # Split z into overlapping 64x64 tiles and decode them separately.\n # The tiles have an overlap to avoid seams between tiles.\n rows = []\n for i in range(0, z.shape[2], overlap_size):\n row = []\n for j in range(0, z.shape[3], overlap_size):\n tile = z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]\n tile = self.post_quant_conv(tile)\n decoded = self.decoder(tile)\n row.append(decoded)\n rows.append(row)\n result_rows = []\n for i, row in enumerate(rows):\n result_row = []\n for j, tile in enumerate(row):\n # blend the above tile and the left tile\n # to the current tile and add the current tile to the result row\n if i > 0:\n tile = self.blend_v(rows[i - 1][j], tile, blend_extent)\n if j > 0:\n tile = self.blend_h(row[j - 1], tile, blend_extent)\n result_row.append(tile[:, :, :row_limit, :row_limit])\n result_rows.append(torch.cat(result_row, dim=3))\n\n dec = torch.cat(result_rows, dim=2)\n if not return_dict:\n return (dec,)\n\n return DecoderOutput(sample=dec)\n\n def forward(\n self,\n sample: torch.Tensor,\n sample_posterior: bool = False,\n return_dict: bool = True,\n generator: Optional[torch.Generator] = None,\n ) -> Union[DecoderOutput, torch.Tensor]:\n r\"\"\"\n Args:\n sample (`torch.Tensor`): Input sample.\n sample_posterior (`bool`, *optional*, defaults to `False`):\n Whether to sample from the posterior.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`DecoderOutput`] instead of a plain tuple.\n \"\"\"\n x = sample\n posterior = self.encode(x).latent_dist\n if sample_posterior:\n z = posterior.sample(generator=generator)\n else:\n z = posterior.mode()\n dec = self.decode(z).sample\n\n if not return_dict:\n return (dec,)\n\n return DecoderOutput(sample=dec)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections\n def fuse_qkv_projections(self):\n \"\"\"\n Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)\n are fused. For cross-attention modules, key and value projection matrices are fused.\n\n \n\n This API is 🧪 experimental.\n\n \n \"\"\"\n self.original_attn_processors = None\n\n for _, attn_processor in self.attn_processors.items():\n if \"Added\" in str(attn_processor.__class__.__name__):\n raise ValueError(\"`fuse_qkv_projections()` is not supported for models having added KV projections.\")\n\n self.original_attn_processors = self.attn_processors\n\n for module in self.modules():\n if isinstance(module, Attention):\n module.fuse_projections(fuse=True)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections\n def unfuse_qkv_projections(self):\n \"\"\"Disables the fused QKV projection if enabled.\n\n \n\n This API is 🧪 experimental.\n\n \n\n \"\"\"\n if self.original_attn_processors is not None:\n self.set_attn_processor(self.original_attn_processors)\n" }, { "path": "diffusers/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Dict, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...utils import is_torch_version\nfrom ...utils.accelerate_utils import apply_forward_hook\nfrom ..attention_processor import CROSS_ATTENTION_PROCESSORS, AttentionProcessor, AttnProcessor\nfrom ..modeling_outputs import AutoencoderKLOutput\nfrom ..modeling_utils import ModelMixin\nfrom ..unets.unet_3d_blocks import MidBlockTemporalDecoder, UpBlockTemporalDecoder\nfrom .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder\n\n\nclass TemporalDecoder(nn.Module):\n def __init__(\n self,\n in_channels: int = 4,\n out_channels: int = 3,\n block_out_channels: Tuple[int] = (128, 256, 512, 512),\n layers_per_block: int = 2,\n ):\n super().__init__()\n self.layers_per_block = layers_per_block\n\n self.conv_in = nn.Conv2d(in_channels, block_out_channels[-1], kernel_size=3, stride=1, padding=1)\n self.mid_block = MidBlockTemporalDecoder(\n num_layers=self.layers_per_block,\n in_channels=block_out_channels[-1],\n out_channels=block_out_channels[-1],\n attention_head_dim=block_out_channels[-1],\n )\n\n # up\n self.up_blocks = nn.ModuleList([])\n reversed_block_out_channels = list(reversed(block_out_channels))\n output_channel = reversed_block_out_channels[0]\n for i in range(len(block_out_channels)):\n prev_output_channel = output_channel\n output_channel = reversed_block_out_channels[i]\n\n is_final_block = i == len(block_out_channels) - 1\n up_block = UpBlockTemporalDecoder(\n num_layers=self.layers_per_block + 1,\n in_channels=prev_output_channel,\n out_channels=output_channel,\n add_upsample=not is_final_block,\n )\n self.up_blocks.append(up_block)\n prev_output_channel = output_channel\n\n self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=32, eps=1e-6)\n\n self.conv_act = nn.SiLU()\n self.conv_out = torch.nn.Conv2d(\n in_channels=block_out_channels[0],\n out_channels=out_channels,\n kernel_size=3,\n padding=1,\n )\n\n conv_out_kernel_size = (3, 1, 1)\n padding = [int(k // 2) for k in conv_out_kernel_size]\n self.time_conv_out = torch.nn.Conv3d(\n in_channels=out_channels,\n out_channels=out_channels,\n kernel_size=conv_out_kernel_size,\n padding=padding,\n )\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n sample: torch.Tensor,\n image_only_indicator: torch.Tensor,\n num_frames: int = 1,\n ) -> torch.Tensor:\n r\"\"\"The forward method of the `Decoder` class.\"\"\"\n\n sample = self.conv_in(sample)\n\n upscale_dtype = next(iter(self.up_blocks.parameters())).dtype\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs)\n\n return custom_forward\n\n if is_torch_version(\">=\", \"1.11.0\"):\n # middle\n sample = torch.utils.checkpoint.checkpoint(\n create_custom_forward(self.mid_block),\n sample,\n image_only_indicator,\n use_reentrant=False,\n )\n sample = sample.to(upscale_dtype)\n\n # up\n for up_block in self.up_blocks:\n sample = torch.utils.checkpoint.checkpoint(\n create_custom_forward(up_block),\n sample,\n image_only_indicator,\n use_reentrant=False,\n )\n else:\n # middle\n sample = torch.utils.checkpoint.checkpoint(\n create_custom_forward(self.mid_block),\n sample,\n image_only_indicator,\n )\n sample = sample.to(upscale_dtype)\n\n # up\n for up_block in self.up_blocks:\n sample = torch.utils.checkpoint.checkpoint(\n create_custom_forward(up_block),\n sample,\n image_only_indicator,\n )\n else:\n # middle\n sample = self.mid_block(sample, image_only_indicator=image_only_indicator)\n sample = sample.to(upscale_dtype)\n\n # up\n for up_block in self.up_blocks:\n sample = up_block(sample, image_only_indicator=image_only_indicator)\n\n # post-process\n sample = self.conv_norm_out(sample)\n sample = self.conv_act(sample)\n sample = self.conv_out(sample)\n\n batch_frames, channels, height, width = sample.shape\n batch_size = batch_frames // num_frames\n sample = sample[None, :].reshape(batch_size, num_frames, channels, height, width).permute(0, 2, 1, 3, 4)\n sample = self.time_conv_out(sample)\n\n sample = sample.permute(0, 2, 1, 3, 4).reshape(batch_frames, channels, height, width)\n\n return sample\n\n\nclass AutoencoderKLTemporalDecoder(ModelMixin, ConfigMixin):\n r\"\"\"\n A VAE model with KL loss for encoding images into latents and decoding latent representations into images.\n\n This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented\n for all models (such as downloading or saving).\n\n Parameters:\n in_channels (int, *optional*, defaults to 3): Number of channels in the input image.\n out_channels (int, *optional*, defaults to 3): Number of channels in the output.\n down_block_types (`Tuple[str]`, *optional*, defaults to `(\"DownEncoderBlock2D\",)`):\n Tuple of downsample block types.\n block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):\n Tuple of block output channels.\n layers_per_block: (`int`, *optional*, defaults to 1): Number of layers per block.\n latent_channels (`int`, *optional*, defaults to 4): Number of channels in the latent space.\n sample_size (`int`, *optional*, defaults to `32`): Sample input size.\n scaling_factor (`float`, *optional*, defaults to 0.18215):\n The component-wise standard deviation of the trained latent space computed using the first batch of the\n training set. This is used to scale the latent space to have unit variance when training the diffusion\n model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the\n diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1\n / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image\n Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.\n force_upcast (`bool`, *optional*, default to `True`):\n If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE\n can be fine-tuned / trained to a lower range without loosing too much precision in which case\n `force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix\n \"\"\"\n\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n in_channels: int = 3,\n out_channels: int = 3,\n down_block_types: Tuple[str] = (\"DownEncoderBlock2D\",),\n block_out_channels: Tuple[int] = (64,),\n layers_per_block: int = 1,\n latent_channels: int = 4,\n sample_size: int = 32,\n scaling_factor: float = 0.18215,\n force_upcast: float = True,\n ):\n super().__init__()\n\n # pass init params to Encoder\n self.encoder = Encoder(\n in_channels=in_channels,\n out_channels=latent_channels,\n down_block_types=down_block_types,\n block_out_channels=block_out_channels,\n layers_per_block=layers_per_block,\n double_z=True,\n )\n\n # pass init params to Decoder\n self.decoder = TemporalDecoder(\n in_channels=latent_channels,\n out_channels=out_channels,\n block_out_channels=block_out_channels,\n layers_per_block=layers_per_block,\n )\n\n self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1)\n\n sample_size = (\n self.config.sample_size[0]\n if isinstance(self.config.sample_size, (list, tuple))\n else self.config.sample_size\n )\n self.tile_latent_min_size = int(sample_size / (2 ** (len(self.config.block_out_channels) - 1)))\n self.tile_overlap_factor = 0.25\n\n def _set_gradient_checkpointing(self, module, value=False):\n if isinstance(module, (Encoder, TemporalDecoder)):\n module.gradient_checkpointing = value\n\n @property\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors\n def attn_processors(self) -> Dict[str, AttentionProcessor]:\n r\"\"\"\n Returns:\n `dict` of attention processors: A dictionary containing all attention processors used in the model with\n indexed by its weight name.\n \"\"\"\n # set recursively\n processors = {}\n\n def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\n if hasattr(module, \"get_processor\"):\n processors[f\"{name}.processor\"] = module.get_processor(return_deprecated_lora=True)\n\n for sub_name, child in module.named_children():\n fn_recursive_add_processors(f\"{name}.{sub_name}\", child, processors)\n\n return processors\n\n for name, module in self.named_children():\n fn_recursive_add_processors(name, module, processors)\n\n return processors\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor\n def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\n r\"\"\"\n Sets the attention processor to use to compute attention.\n\n Parameters:\n processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):\n The instantiated processor class or a dictionary of processor classes that will be set as the processor\n for **all** `Attention` layers.\n\n If `processor` is a dict, the key needs to define the path to the corresponding cross attention\n processor. This is strongly recommended when setting trainable attention processors.\n\n \"\"\"\n count = len(self.attn_processors.keys())\n\n if isinstance(processor, dict) and len(processor) != count:\n raise ValueError(\n f\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\"\n f\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\"\n )\n\n def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\n if hasattr(module, \"set_processor\"):\n if not isinstance(processor, dict):\n module.set_processor(processor)\n else:\n module.set_processor(processor.pop(f\"{name}.processor\"))\n\n for sub_name, child in module.named_children():\n fn_recursive_attn_processor(f\"{name}.{sub_name}\", child, processor)\n\n for name, module in self.named_children():\n fn_recursive_attn_processor(name, module, processor)\n\n def set_default_attn_processor(self):\n \"\"\"\n Disables custom attention processors and sets the default attention implementation.\n \"\"\"\n if all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnProcessor()\n else:\n raise ValueError(\n f\"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}\"\n )\n\n self.set_attn_processor(processor)\n\n @apply_forward_hook\n def encode(\n self, x: torch.Tensor, return_dict: bool = True\n ) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:\n \"\"\"\n Encode a batch of images into latents.\n\n Args:\n x (`torch.Tensor`): Input batch of images.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether to return a [`~models.autoencoders.autoencoder_kl.AutoencoderKLOutput`] instead of a plain\n tuple.\n\n Returns:\n The latent representations of the encoded images. If `return_dict` is True, a\n [`~models.autoencoders.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is\n returned.\n \"\"\"\n h = self.encoder(x)\n moments = self.quant_conv(h)\n posterior = DiagonalGaussianDistribution(moments)\n\n if not return_dict:\n return (posterior,)\n\n return AutoencoderKLOutput(latent_dist=posterior)\n\n @apply_forward_hook\n def decode(\n self,\n z: torch.Tensor,\n num_frames: int,\n return_dict: bool = True,\n ) -> Union[DecoderOutput, torch.Tensor]:\n \"\"\"\n Decode a batch of images.\n\n Args:\n z (`torch.Tensor`): Input batch of latent vectors.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.\n\n Returns:\n [`~models.vae.DecoderOutput`] or `tuple`:\n If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is\n returned.\n\n \"\"\"\n batch_size = z.shape[0] // num_frames\n image_only_indicator = torch.zeros(batch_size, num_frames, dtype=z.dtype, device=z.device)\n decoded = self.decoder(z, num_frames=num_frames, image_only_indicator=image_only_indicator)\n\n if not return_dict:\n return (decoded,)\n\n return DecoderOutput(sample=decoded)\n\n def forward(\n self,\n sample: torch.Tensor,\n sample_posterior: bool = False,\n return_dict: bool = True,\n generator: Optional[torch.Generator] = None,\n num_frames: int = 1,\n ) -> Union[DecoderOutput, torch.Tensor]:\n r\"\"\"\n Args:\n sample (`torch.Tensor`): Input sample.\n sample_posterior (`bool`, *optional*, defaults to `False`):\n Whether to sample from the posterior.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`DecoderOutput`] instead of a plain tuple.\n \"\"\"\n x = sample\n posterior = self.encode(x).latent_dist\n if sample_posterior:\n z = posterior.sample(generator=generator)\n else:\n z = posterior.mode()\n\n dec = self.decode(z, num_frames=num_frames).sample\n\n if not return_dict:\n return (dec,)\n\n return DecoderOutput(sample=dec)\n" }, { "path": "diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.py", "content": "# Copyright 2024 Ollin Boer Bohan and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple, Union\n\nimport torch\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...utils import BaseOutput\nfrom ...utils.accelerate_utils import apply_forward_hook\nfrom ..modeling_utils import ModelMixin\nfrom .vae import DecoderOutput, DecoderTiny, EncoderTiny\n\n\n@dataclass\nclass AutoencoderTinyOutput(BaseOutput):\n \"\"\"\n Output of AutoencoderTiny encoding method.\n\n Args:\n latents (`torch.Tensor`): Encoded outputs of the `Encoder`.\n\n \"\"\"\n\n latents: torch.Tensor\n\n\nclass AutoencoderTiny(ModelMixin, ConfigMixin):\n r\"\"\"\n A tiny distilled VAE model for encoding images into latents and decoding latent representations into images.\n\n [`AutoencoderTiny`] is a wrapper around the original implementation of `TAESD`.\n\n This model inherits from [`ModelMixin`]. Check the superclass documentation for its generic methods implemented for\n all models (such as downloading or saving).\n\n Parameters:\n in_channels (`int`, *optional*, defaults to 3): Number of channels in the input image.\n out_channels (`int`, *optional*, defaults to 3): Number of channels in the output.\n encoder_block_out_channels (`Tuple[int]`, *optional*, defaults to `(64, 64, 64, 64)`):\n Tuple of integers representing the number of output channels for each encoder block. The length of the\n tuple should be equal to the number of encoder blocks.\n decoder_block_out_channels (`Tuple[int]`, *optional*, defaults to `(64, 64, 64, 64)`):\n Tuple of integers representing the number of output channels for each decoder block. The length of the\n tuple should be equal to the number of decoder blocks.\n act_fn (`str`, *optional*, defaults to `\"relu\"`):\n Activation function to be used throughout the model.\n latent_channels (`int`, *optional*, defaults to 4):\n Number of channels in the latent representation. The latent space acts as a compressed representation of\n the input image.\n upsampling_scaling_factor (`int`, *optional*, defaults to 2):\n Scaling factor for upsampling in the decoder. It determines the size of the output image during the\n upsampling process.\n num_encoder_blocks (`Tuple[int]`, *optional*, defaults to `(1, 3, 3, 3)`):\n Tuple of integers representing the number of encoder blocks at each stage of the encoding process. The\n length of the tuple should be equal to the number of stages in the encoder. Each stage has a different\n number of encoder blocks.\n num_decoder_blocks (`Tuple[int]`, *optional*, defaults to `(3, 3, 3, 1)`):\n Tuple of integers representing the number of decoder blocks at each stage of the decoding process. The\n length of the tuple should be equal to the number of stages in the decoder. Each stage has a different\n number of decoder blocks.\n latent_magnitude (`float`, *optional*, defaults to 3.0):\n Magnitude of the latent representation. This parameter scales the latent representation values to control\n the extent of information preservation.\n latent_shift (float, *optional*, defaults to 0.5):\n Shift applied to the latent representation. This parameter controls the center of the latent space.\n scaling_factor (`float`, *optional*, defaults to 1.0):\n The component-wise standard deviation of the trained latent space computed using the first batch of the\n training set. This is used to scale the latent space to have unit variance when training the diffusion\n model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the\n diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1\n / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image\n Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper. For this Autoencoder,\n however, no such scaling factor was used, hence the value of 1.0 as the default.\n force_upcast (`bool`, *optional*, default to `False`):\n If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE\n can be fine-tuned / trained to a lower range without losing too much precision, in which case\n `force_upcast` can be set to `False` (see this fp16-friendly\n [AutoEncoder](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix)).\n \"\"\"\n\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n in_channels: int = 3,\n out_channels: int = 3,\n encoder_block_out_channels: Tuple[int, ...] = (64, 64, 64, 64),\n decoder_block_out_channels: Tuple[int, ...] = (64, 64, 64, 64),\n act_fn: str = \"relu\",\n upsample_fn: str = \"nearest\",\n latent_channels: int = 4,\n upsampling_scaling_factor: int = 2,\n num_encoder_blocks: Tuple[int, ...] = (1, 3, 3, 3),\n num_decoder_blocks: Tuple[int, ...] = (3, 3, 3, 1),\n latent_magnitude: int = 3,\n latent_shift: float = 0.5,\n force_upcast: bool = False,\n scaling_factor: float = 1.0,\n ):\n super().__init__()\n\n if len(encoder_block_out_channels) != len(num_encoder_blocks):\n raise ValueError(\"`encoder_block_out_channels` should have the same length as `num_encoder_blocks`.\")\n if len(decoder_block_out_channels) != len(num_decoder_blocks):\n raise ValueError(\"`decoder_block_out_channels` should have the same length as `num_decoder_blocks`.\")\n\n self.encoder = EncoderTiny(\n in_channels=in_channels,\n out_channels=latent_channels,\n num_blocks=num_encoder_blocks,\n block_out_channels=encoder_block_out_channels,\n act_fn=act_fn,\n )\n\n self.decoder = DecoderTiny(\n in_channels=latent_channels,\n out_channels=out_channels,\n num_blocks=num_decoder_blocks,\n block_out_channels=decoder_block_out_channels,\n upsampling_scaling_factor=upsampling_scaling_factor,\n act_fn=act_fn,\n upsample_fn=upsample_fn,\n )\n\n self.latent_magnitude = latent_magnitude\n self.latent_shift = latent_shift\n self.scaling_factor = scaling_factor\n\n self.use_slicing = False\n self.use_tiling = False\n\n # only relevant if vae tiling is enabled\n self.spatial_scale_factor = 2**out_channels\n self.tile_overlap_factor = 0.125\n self.tile_sample_min_size = 512\n self.tile_latent_min_size = self.tile_sample_min_size // self.spatial_scale_factor\n\n self.register_to_config(block_out_channels=decoder_block_out_channels)\n self.register_to_config(force_upcast=False)\n\n def _set_gradient_checkpointing(self, module, value: bool = False) -> None:\n if isinstance(module, (EncoderTiny, DecoderTiny)):\n module.gradient_checkpointing = value\n\n def scale_latents(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"raw latents -> [0, 1]\"\"\"\n return x.div(2 * self.latent_magnitude).add(self.latent_shift).clamp(0, 1)\n\n def unscale_latents(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"[0, 1] -> raw latents\"\"\"\n return x.sub(self.latent_shift).mul(2 * self.latent_magnitude)\n\n def enable_slicing(self) -> None:\n r\"\"\"\n Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to\n compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.\n \"\"\"\n self.use_slicing = True\n\n def disable_slicing(self) -> None:\n r\"\"\"\n Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing\n decoding in one step.\n \"\"\"\n self.use_slicing = False\n\n def enable_tiling(self, use_tiling: bool = True) -> None:\n r\"\"\"\n Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to\n compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow\n processing larger images.\n \"\"\"\n self.use_tiling = use_tiling\n\n def disable_tiling(self) -> None:\n r\"\"\"\n Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing\n decoding in one step.\n \"\"\"\n self.enable_tiling(False)\n\n def _tiled_encode(self, x: torch.Tensor) -> torch.Tensor:\n r\"\"\"Encode a batch of images using a tiled encoder.\n\n When this option is enabled, the VAE will split the input tensor into tiles to compute encoding in several\n steps. This is useful to keep memory use constant regardless of image size. To avoid tiling artifacts, the\n tiles overlap and are blended together to form a smooth output.\n\n Args:\n x (`torch.Tensor`): Input batch of images.\n\n Returns:\n `torch.Tensor`: Encoded batch of images.\n \"\"\"\n # scale of encoder output relative to input\n sf = self.spatial_scale_factor\n tile_size = self.tile_sample_min_size\n\n # number of pixels to blend and to traverse between tile\n blend_size = int(tile_size * self.tile_overlap_factor)\n traverse_size = tile_size - blend_size\n\n # tiles index (up/left)\n ti = range(0, x.shape[-2], traverse_size)\n tj = range(0, x.shape[-1], traverse_size)\n\n # mask for blending\n blend_masks = torch.stack(\n torch.meshgrid([torch.arange(tile_size / sf) / (blend_size / sf - 1)] * 2, indexing=\"ij\")\n )\n blend_masks = blend_masks.clamp(0, 1).to(x.device)\n\n # output array\n out = torch.zeros(x.shape[0], 4, x.shape[-2] // sf, x.shape[-1] // sf, device=x.device)\n for i in ti:\n for j in tj:\n tile_in = x[..., i : i + tile_size, j : j + tile_size]\n # tile result\n tile_out = out[..., i // sf : (i + tile_size) // sf, j // sf : (j + tile_size) // sf]\n tile = self.encoder(tile_in)\n h, w = tile.shape[-2], tile.shape[-1]\n # blend tile result into output\n blend_mask_i = torch.ones_like(blend_masks[0]) if i == 0 else blend_masks[0]\n blend_mask_j = torch.ones_like(blend_masks[1]) if j == 0 else blend_masks[1]\n blend_mask = blend_mask_i * blend_mask_j\n tile, blend_mask = tile[..., :h, :w], blend_mask[..., :h, :w]\n tile_out.copy_(blend_mask * tile + (1 - blend_mask) * tile_out)\n return out\n\n def _tiled_decode(self, x: torch.Tensor) -> torch.Tensor:\n r\"\"\"Encode a batch of images using a tiled encoder.\n\n When this option is enabled, the VAE will split the input tensor into tiles to compute encoding in several\n steps. This is useful to keep memory use constant regardless of image size. To avoid tiling artifacts, the\n tiles overlap and are blended together to form a smooth output.\n\n Args:\n x (`torch.Tensor`): Input batch of images.\n\n Returns:\n `torch.Tensor`: Encoded batch of images.\n \"\"\"\n # scale of decoder output relative to input\n sf = self.spatial_scale_factor\n tile_size = self.tile_latent_min_size\n\n # number of pixels to blend and to traverse between tiles\n blend_size = int(tile_size * self.tile_overlap_factor)\n traverse_size = tile_size - blend_size\n\n # tiles index (up/left)\n ti = range(0, x.shape[-2], traverse_size)\n tj = range(0, x.shape[-1], traverse_size)\n\n # mask for blending\n blend_masks = torch.stack(\n torch.meshgrid([torch.arange(tile_size * sf) / (blend_size * sf - 1)] * 2, indexing=\"ij\")\n )\n blend_masks = blend_masks.clamp(0, 1).to(x.device)\n\n # output array\n out = torch.zeros(x.shape[0], 3, x.shape[-2] * sf, x.shape[-1] * sf, device=x.device)\n for i in ti:\n for j in tj:\n tile_in = x[..., i : i + tile_size, j : j + tile_size]\n # tile result\n tile_out = out[..., i * sf : (i + tile_size) * sf, j * sf : (j + tile_size) * sf]\n tile = self.decoder(tile_in)\n h, w = tile.shape[-2], tile.shape[-1]\n # blend tile result into output\n blend_mask_i = torch.ones_like(blend_masks[0]) if i == 0 else blend_masks[0]\n blend_mask_j = torch.ones_like(blend_masks[1]) if j == 0 else blend_masks[1]\n blend_mask = (blend_mask_i * blend_mask_j)[..., :h, :w]\n tile_out.copy_(blend_mask * tile + (1 - blend_mask) * tile_out)\n return out\n\n @apply_forward_hook\n def encode(self, x: torch.Tensor, return_dict: bool = True) -> Union[AutoencoderTinyOutput, Tuple[torch.Tensor]]:\n if self.use_slicing and x.shape[0] > 1:\n output = [\n self._tiled_encode(x_slice) if self.use_tiling else self.encoder(x_slice) for x_slice in x.split(1)\n ]\n output = torch.cat(output)\n else:\n output = self._tiled_encode(x) if self.use_tiling else self.encoder(x)\n\n if not return_dict:\n return (output,)\n\n return AutoencoderTinyOutput(latents=output)\n\n @apply_forward_hook\n def decode(\n self, x: torch.Tensor, generator: Optional[torch.Generator] = None, return_dict: bool = True\n ) -> Union[DecoderOutput, Tuple[torch.Tensor]]:\n if self.use_slicing and x.shape[0] > 1:\n output = [self._tiled_decode(x_slice) if self.use_tiling else self.decoder(x) for x_slice in x.split(1)]\n output = torch.cat(output)\n else:\n output = self._tiled_decode(x) if self.use_tiling else self.decoder(x)\n\n if not return_dict:\n return (output,)\n\n return DecoderOutput(sample=output)\n\n def forward(\n self,\n sample: torch.Tensor,\n return_dict: bool = True,\n ) -> Union[DecoderOutput, Tuple[torch.Tensor]]:\n r\"\"\"\n Args:\n sample (`torch.Tensor`): Input sample.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`DecoderOutput`] instead of a plain tuple.\n \"\"\"\n enc = self.encode(sample).latents\n\n # scale latents to be in [0, 1], then quantize latents to a byte tensor,\n # as if we were storing the latents in an RGBA uint8 image.\n scaled_enc = self.scale_latents(enc).mul_(255).round_().byte()\n\n # unquantize latents back into [0, 1], then unscale latents back to their original range,\n # as if we were loading the latents from an RGBA uint8 image.\n unscaled_enc = self.unscale_latents(scaled_enc / 255.0)\n\n dec = self.decode(unscaled_enc)\n\n if not return_dict:\n return (dec,)\n return DecoderOutput(sample=dec)\n" }, { "path": "diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom dataclasses import dataclass\nfrom typing import Dict, Optional, Tuple, Union\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...schedulers import ConsistencyDecoderScheduler\nfrom ...utils import BaseOutput\nfrom ...utils.accelerate_utils import apply_forward_hook\nfrom ...utils.torch_utils import randn_tensor\nfrom ..attention_processor import (\n ADDED_KV_ATTENTION_PROCESSORS,\n CROSS_ATTENTION_PROCESSORS,\n AttentionProcessor,\n AttnAddedKVProcessor,\n AttnProcessor,\n)\nfrom ..modeling_utils import ModelMixin\nfrom ..unets.unet_2d import UNet2DModel\nfrom .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder\n\n\n@dataclass\nclass ConsistencyDecoderVAEOutput(BaseOutput):\n \"\"\"\n Output of encoding method.\n\n Args:\n latent_dist (`DiagonalGaussianDistribution`):\n Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.\n `DiagonalGaussianDistribution` allows for sampling latents from the distribution.\n \"\"\"\n\n latent_dist: \"DiagonalGaussianDistribution\"\n\n\nclass ConsistencyDecoderVAE(ModelMixin, ConfigMixin):\n r\"\"\"\n The consistency decoder used with DALL-E 3.\n\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusionPipeline, ConsistencyDecoderVAE\n\n >>> vae = ConsistencyDecoderVAE.from_pretrained(\"openai/consistency-decoder\", torch_dtype=torch.float16)\n >>> pipe = StableDiffusionPipeline.from_pretrained(\n ... \"runwayml/stable-diffusion-v1-5\", vae=vae, torch_dtype=torch.float16\n ... ).to(\"cuda\")\n\n >>> image = pipe(\"horse\", generator=torch.manual_seed(0)).images[0]\n >>> image\n ```\n \"\"\"\n\n @register_to_config\n def __init__(\n self,\n scaling_factor: float = 0.18215,\n latent_channels: int = 4,\n sample_size: int = 32,\n encoder_act_fn: str = \"silu\",\n encoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 512),\n encoder_double_z: bool = True,\n encoder_down_block_types: Tuple[str, ...] = (\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n ),\n encoder_in_channels: int = 3,\n encoder_layers_per_block: int = 2,\n encoder_norm_num_groups: int = 32,\n encoder_out_channels: int = 4,\n decoder_add_attention: bool = False,\n decoder_block_out_channels: Tuple[int, ...] = (320, 640, 1024, 1024),\n decoder_down_block_types: Tuple[str, ...] = (\n \"ResnetDownsampleBlock2D\",\n \"ResnetDownsampleBlock2D\",\n \"ResnetDownsampleBlock2D\",\n \"ResnetDownsampleBlock2D\",\n ),\n decoder_downsample_padding: int = 1,\n decoder_in_channels: int = 7,\n decoder_layers_per_block: int = 3,\n decoder_norm_eps: float = 1e-05,\n decoder_norm_num_groups: int = 32,\n decoder_num_train_timesteps: int = 1024,\n decoder_out_channels: int = 6,\n decoder_resnet_time_scale_shift: str = \"scale_shift\",\n decoder_time_embedding_type: str = \"learned\",\n decoder_up_block_types: Tuple[str, ...] = (\n \"ResnetUpsampleBlock2D\",\n \"ResnetUpsampleBlock2D\",\n \"ResnetUpsampleBlock2D\",\n \"ResnetUpsampleBlock2D\",\n ),\n ):\n super().__init__()\n self.encoder = Encoder(\n act_fn=encoder_act_fn,\n block_out_channels=encoder_block_out_channels,\n double_z=encoder_double_z,\n down_block_types=encoder_down_block_types,\n in_channels=encoder_in_channels,\n layers_per_block=encoder_layers_per_block,\n norm_num_groups=encoder_norm_num_groups,\n out_channels=encoder_out_channels,\n )\n\n self.decoder_unet = UNet2DModel(\n add_attention=decoder_add_attention,\n block_out_channels=decoder_block_out_channels,\n down_block_types=decoder_down_block_types,\n downsample_padding=decoder_downsample_padding,\n in_channels=decoder_in_channels,\n layers_per_block=decoder_layers_per_block,\n norm_eps=decoder_norm_eps,\n norm_num_groups=decoder_norm_num_groups,\n num_train_timesteps=decoder_num_train_timesteps,\n out_channels=decoder_out_channels,\n resnet_time_scale_shift=decoder_resnet_time_scale_shift,\n time_embedding_type=decoder_time_embedding_type,\n up_block_types=decoder_up_block_types,\n )\n self.decoder_scheduler = ConsistencyDecoderScheduler()\n self.register_to_config(block_out_channels=encoder_block_out_channels)\n self.register_to_config(force_upcast=False)\n self.register_buffer(\n \"means\",\n torch.tensor([0.38862467, 0.02253063, 0.07381133, -0.0171294])[None, :, None, None],\n persistent=False,\n )\n self.register_buffer(\n \"stds\", torch.tensor([0.9654121, 1.0440036, 0.76147926, 0.77022034])[None, :, None, None], persistent=False\n )\n\n self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1)\n\n self.use_slicing = False\n self.use_tiling = False\n\n # only relevant if vae tiling is enabled\n self.tile_sample_min_size = self.config.sample_size\n sample_size = (\n self.config.sample_size[0]\n if isinstance(self.config.sample_size, (list, tuple))\n else self.config.sample_size\n )\n self.tile_latent_min_size = int(sample_size / (2 ** (len(self.config.block_out_channels) - 1)))\n self.tile_overlap_factor = 0.25\n\n # Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.enable_tiling\n def enable_tiling(self, use_tiling: bool = True):\n r\"\"\"\n Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to\n compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow\n processing larger images.\n \"\"\"\n self.use_tiling = use_tiling\n\n # Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.disable_tiling\n def disable_tiling(self):\n r\"\"\"\n Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing\n decoding in one step.\n \"\"\"\n self.enable_tiling(False)\n\n # Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.enable_slicing\n def enable_slicing(self):\n r\"\"\"\n Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to\n compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.\n \"\"\"\n self.use_slicing = True\n\n # Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.disable_slicing\n def disable_slicing(self):\n r\"\"\"\n Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing\n decoding in one step.\n \"\"\"\n self.use_slicing = False\n\n @property\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors\n def attn_processors(self) -> Dict[str, AttentionProcessor]:\n r\"\"\"\n Returns:\n `dict` of attention processors: A dictionary containing all attention processors used in the model with\n indexed by its weight name.\n \"\"\"\n # set recursively\n processors = {}\n\n def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\n if hasattr(module, \"get_processor\"):\n processors[f\"{name}.processor\"] = module.get_processor(return_deprecated_lora=True)\n\n for sub_name, child in module.named_children():\n fn_recursive_add_processors(f\"{name}.{sub_name}\", child, processors)\n\n return processors\n\n for name, module in self.named_children():\n fn_recursive_add_processors(name, module, processors)\n\n return processors\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor\n def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\n r\"\"\"\n Sets the attention processor to use to compute attention.\n\n Parameters:\n processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):\n The instantiated processor class or a dictionary of processor classes that will be set as the processor\n for **all** `Attention` layers.\n\n If `processor` is a dict, the key needs to define the path to the corresponding cross attention\n processor. This is strongly recommended when setting trainable attention processors.\n\n \"\"\"\n count = len(self.attn_processors.keys())\n\n if isinstance(processor, dict) and len(processor) != count:\n raise ValueError(\n f\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\"\n f\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\"\n )\n\n def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\n if hasattr(module, \"set_processor\"):\n if not isinstance(processor, dict):\n module.set_processor(processor)\n else:\n module.set_processor(processor.pop(f\"{name}.processor\"))\n\n for sub_name, child in module.named_children():\n fn_recursive_attn_processor(f\"{name}.{sub_name}\", child, processor)\n\n for name, module in self.named_children():\n fn_recursive_attn_processor(name, module, processor)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor\n def set_default_attn_processor(self):\n \"\"\"\n Disables custom attention processors and sets the default attention implementation.\n \"\"\"\n if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnAddedKVProcessor()\n elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnProcessor()\n else:\n raise ValueError(\n f\"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}\"\n )\n\n self.set_attn_processor(processor)\n\n @apply_forward_hook\n def encode(\n self, x: torch.Tensor, return_dict: bool = True\n ) -> Union[ConsistencyDecoderVAEOutput, Tuple[DiagonalGaussianDistribution]]:\n \"\"\"\n Encode a batch of images into latents.\n\n Args:\n x (`torch.Tensor`): Input batch of images.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether to return a [`~models.autoencoders.consistency_decoder_vae.ConsistencyDecoderVAEOutput`]\n instead of a plain tuple.\n\n Returns:\n The latent representations of the encoded images. If `return_dict` is True, a\n [`~models.autoencoders.consistency_decoder_vae.ConsistencyDecoderVAEOutput`] is returned, otherwise a\n plain `tuple` is returned.\n \"\"\"\n if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):\n return self.tiled_encode(x, return_dict=return_dict)\n\n if self.use_slicing and x.shape[0] > 1:\n encoded_slices = [self.encoder(x_slice) for x_slice in x.split(1)]\n h = torch.cat(encoded_slices)\n else:\n h = self.encoder(x)\n\n moments = self.quant_conv(h)\n posterior = DiagonalGaussianDistribution(moments)\n\n if not return_dict:\n return (posterior,)\n\n return ConsistencyDecoderVAEOutput(latent_dist=posterior)\n\n @apply_forward_hook\n def decode(\n self,\n z: torch.Tensor,\n generator: Optional[torch.Generator] = None,\n return_dict: bool = True,\n num_inference_steps: int = 2,\n ) -> Union[DecoderOutput, Tuple[torch.Tensor]]:\n \"\"\"\n Decodes the input latent vector `z` using the consistency decoder VAE model.\n\n Args:\n z (torch.Tensor): The input latent vector.\n generator (Optional[torch.Generator]): The random number generator. Default is None.\n return_dict (bool): Whether to return the output as a dictionary. Default is True.\n num_inference_steps (int): The number of inference steps. Default is 2.\n\n Returns:\n Union[DecoderOutput, Tuple[torch.Tensor]]: The decoded output.\n\n \"\"\"\n z = (z * self.config.scaling_factor - self.means) / self.stds\n\n scale_factor = 2 ** (len(self.config.block_out_channels) - 1)\n z = F.interpolate(z, mode=\"nearest\", scale_factor=scale_factor)\n\n batch_size, _, height, width = z.shape\n\n self.decoder_scheduler.set_timesteps(num_inference_steps, device=self.device)\n\n x_t = self.decoder_scheduler.init_noise_sigma * randn_tensor(\n (batch_size, 3, height, width), generator=generator, dtype=z.dtype, device=z.device\n )\n\n for t in self.decoder_scheduler.timesteps:\n model_input = torch.concat([self.decoder_scheduler.scale_model_input(x_t, t), z], dim=1)\n model_output = self.decoder_unet(model_input, t).sample[:, :3, :, :]\n prev_sample = self.decoder_scheduler.step(model_output, t, x_t, generator).prev_sample\n x_t = prev_sample\n\n x_0 = x_t\n\n if not return_dict:\n return (x_0,)\n\n return DecoderOutput(sample=x_0)\n\n # Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.blend_v\n def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:\n blend_extent = min(a.shape[2], b.shape[2], blend_extent)\n for y in range(blend_extent):\n b[:, :, y, :] = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)\n return b\n\n # Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.blend_h\n def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:\n blend_extent = min(a.shape[3], b.shape[3], blend_extent)\n for x in range(blend_extent):\n b[:, :, :, x] = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)\n return b\n\n def tiled_encode(self, x: torch.Tensor, return_dict: bool = True) -> Union[ConsistencyDecoderVAEOutput, Tuple]:\n r\"\"\"Encode a batch of images using a tiled encoder.\n\n When this option is enabled, the VAE will split the input tensor into tiles to compute encoding in several\n steps. This is useful to keep memory use constant regardless of image size. The end result of tiled encoding is\n different from non-tiled encoding because each tile uses a different encoder. To avoid tiling artifacts, the\n tiles overlap and are blended together to form a smooth output. You may still see tile-sized changes in the\n output, but they should be much less noticeable.\n\n Args:\n x (`torch.Tensor`): Input batch of images.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~models.autoencoders.consistency_decoder_vae.ConsistencyDecoderVAEOutput`]\n instead of a plain tuple.\n\n Returns:\n [`~models.autoencoders.consistency_decoder_vae.ConsistencyDecoderVAEOutput`] or `tuple`:\n If return_dict is True, a [`~models.autoencoders.consistency_decoder_vae.ConsistencyDecoderVAEOutput`]\n is returned, otherwise a plain `tuple` is returned.\n \"\"\"\n overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))\n blend_extent = int(self.tile_latent_min_size * self.tile_overlap_factor)\n row_limit = self.tile_latent_min_size - blend_extent\n\n # Split the image into 512x512 tiles and encode them separately.\n rows = []\n for i in range(0, x.shape[2], overlap_size):\n row = []\n for j in range(0, x.shape[3], overlap_size):\n tile = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]\n tile = self.encoder(tile)\n tile = self.quant_conv(tile)\n row.append(tile)\n rows.append(row)\n result_rows = []\n for i, row in enumerate(rows):\n result_row = []\n for j, tile in enumerate(row):\n # blend the above tile and the left tile\n # to the current tile and add the current tile to the result row\n if i > 0:\n tile = self.blend_v(rows[i - 1][j], tile, blend_extent)\n if j > 0:\n tile = self.blend_h(row[j - 1], tile, blend_extent)\n result_row.append(tile[:, :, :row_limit, :row_limit])\n result_rows.append(torch.cat(result_row, dim=3))\n\n moments = torch.cat(result_rows, dim=2)\n posterior = DiagonalGaussianDistribution(moments)\n\n if not return_dict:\n return (posterior,)\n\n return ConsistencyDecoderVAEOutput(latent_dist=posterior)\n\n def forward(\n self,\n sample: torch.Tensor,\n sample_posterior: bool = False,\n return_dict: bool = True,\n generator: Optional[torch.Generator] = None,\n ) -> Union[DecoderOutput, Tuple[torch.Tensor]]:\n r\"\"\"\n Args:\n sample (`torch.Tensor`): Input sample.\n sample_posterior (`bool`, *optional*, defaults to `False`):\n Whether to sample from the posterior.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`DecoderOutput`] instead of a plain tuple.\n generator (`torch.Generator`, *optional*, defaults to `None`):\n Generator to use for sampling.\n\n Returns:\n [`DecoderOutput`] or `tuple`:\n If return_dict is True, a [`DecoderOutput`] is returned, otherwise a plain `tuple` is returned.\n \"\"\"\n x = sample\n posterior = self.encode(x).latent_dist\n if sample_posterior:\n z = posterior.sample(generator=generator)\n else:\n z = posterior.mode()\n dec = self.decode(z, generator=generator).sample\n\n if not return_dict:\n return (dec,)\n\n return DecoderOutput(sample=dec)\n" }, { "path": "diffusers/src/diffusers/models/autoencoders/vae.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom ...utils import BaseOutput, is_torch_version\nfrom ...utils.torch_utils import randn_tensor\nfrom ..activations import get_activation\nfrom ..attention_processor import SpatialNorm\nfrom ..unets.unet_2d_blocks import (\n AutoencoderTinyBlock,\n UNetMidBlock2D,\n get_down_block,\n get_up_block,\n)\n\n\n@dataclass\nclass DecoderOutput(BaseOutput):\n r\"\"\"\n Output of decoding method.\n\n Args:\n sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):\n The decoded output sample from the last layer of the model.\n \"\"\"\n\n sample: torch.Tensor\n commit_loss: Optional[torch.FloatTensor] = None\n\n\nclass Encoder(nn.Module):\n r\"\"\"\n The `Encoder` layer of a variational autoencoder that encodes its input into a latent representation.\n\n Args:\n in_channels (`int`, *optional*, defaults to 3):\n The number of input channels.\n out_channels (`int`, *optional*, defaults to 3):\n The number of output channels.\n down_block_types (`Tuple[str, ...]`, *optional*, defaults to `(\"DownEncoderBlock2D\",)`):\n The types of down blocks to use. See `~diffusers.models.unet_2d_blocks.get_down_block` for available\n options.\n block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(64,)`):\n The number of output channels for each block.\n layers_per_block (`int`, *optional*, defaults to 2):\n The number of layers per block.\n norm_num_groups (`int`, *optional*, defaults to 32):\n The number of groups for normalization.\n act_fn (`str`, *optional*, defaults to `\"silu\"`):\n The activation function to use. See `~diffusers.models.activations.get_activation` for available options.\n double_z (`bool`, *optional*, defaults to `True`):\n Whether to double the number of output channels for the last block.\n \"\"\"\n\n def __init__(\n self,\n in_channels: int = 3,\n out_channels: int = 3,\n down_block_types: Tuple[str, ...] = (\"DownEncoderBlock2D\",),\n block_out_channels: Tuple[int, ...] = (64,),\n layers_per_block: int = 2,\n norm_num_groups: int = 32,\n act_fn: str = \"silu\",\n double_z: bool = True,\n mid_block_add_attention=True,\n ):\n super().__init__()\n self.layers_per_block = layers_per_block\n\n self.conv_in = nn.Conv2d(\n in_channels,\n block_out_channels[0],\n kernel_size=3,\n stride=1,\n padding=1,\n )\n\n self.down_blocks = nn.ModuleList([])\n\n # down\n output_channel = block_out_channels[0]\n for i, down_block_type in enumerate(down_block_types):\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n down_block = get_down_block(\n down_block_type,\n num_layers=self.layers_per_block,\n in_channels=input_channel,\n out_channels=output_channel,\n add_downsample=not is_final_block,\n resnet_eps=1e-6,\n downsample_padding=0,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n attention_head_dim=output_channel,\n temb_channels=None,\n )\n self.down_blocks.append(down_block)\n\n # mid\n self.mid_block = UNetMidBlock2D(\n in_channels=block_out_channels[-1],\n resnet_eps=1e-6,\n resnet_act_fn=act_fn,\n output_scale_factor=1,\n resnet_time_scale_shift=\"default\",\n attention_head_dim=block_out_channels[-1],\n resnet_groups=norm_num_groups,\n temb_channels=None,\n add_attention=mid_block_add_attention,\n )\n\n # out\n self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], num_groups=norm_num_groups, eps=1e-6)\n self.conv_act = nn.SiLU()\n\n conv_out_channels = 2 * out_channels if double_z else out_channels\n self.conv_out = nn.Conv2d(block_out_channels[-1], conv_out_channels, 3, padding=1)\n\n self.gradient_checkpointing = False\n\n def forward(self, sample: torch.Tensor) -> torch.Tensor:\n r\"\"\"The forward method of the `Encoder` class.\"\"\"\n\n sample = self.conv_in(sample)\n\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs)\n\n return custom_forward\n\n # down\n if is_torch_version(\">=\", \"1.11.0\"):\n for down_block in self.down_blocks:\n sample = torch.utils.checkpoint.checkpoint(\n create_custom_forward(down_block), sample, use_reentrant=False\n )\n # middle\n sample = torch.utils.checkpoint.checkpoint(\n create_custom_forward(self.mid_block), sample, use_reentrant=False\n )\n else:\n for down_block in self.down_blocks:\n sample = torch.utils.checkpoint.checkpoint(create_custom_forward(down_block), sample)\n # middle\n sample = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block), sample)\n\n else:\n # down\n for down_block in self.down_blocks:\n sample = down_block(sample)\n\n # middle\n sample = self.mid_block(sample)\n\n # post-process\n sample = self.conv_norm_out(sample)\n sample = self.conv_act(sample)\n sample = self.conv_out(sample)\n\n return sample\n\n\nclass Decoder(nn.Module):\n r\"\"\"\n The `Decoder` layer of a variational autoencoder that decodes its latent representation into an output sample.\n\n Args:\n in_channels (`int`, *optional*, defaults to 3):\n The number of input channels.\n out_channels (`int`, *optional*, defaults to 3):\n The number of output channels.\n up_block_types (`Tuple[str, ...]`, *optional*, defaults to `(\"UpDecoderBlock2D\",)`):\n The types of up blocks to use. See `~diffusers.models.unet_2d_blocks.get_up_block` for available options.\n block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(64,)`):\n The number of output channels for each block.\n layers_per_block (`int`, *optional*, defaults to 2):\n The number of layers per block.\n norm_num_groups (`int`, *optional*, defaults to 32):\n The number of groups for normalization.\n act_fn (`str`, *optional*, defaults to `\"silu\"`):\n The activation function to use. See `~diffusers.models.activations.get_activation` for available options.\n norm_type (`str`, *optional*, defaults to `\"group\"`):\n The normalization type to use. Can be either `\"group\"` or `\"spatial\"`.\n \"\"\"\n\n def __init__(\n self,\n in_channels: int = 3,\n out_channels: int = 3,\n up_block_types: Tuple[str, ...] = (\"UpDecoderBlock2D\",),\n block_out_channels: Tuple[int, ...] = (64,),\n layers_per_block: int = 2,\n norm_num_groups: int = 32,\n act_fn: str = \"silu\",\n norm_type: str = \"group\", # group, spatial\n mid_block_add_attention=True,\n ):\n super().__init__()\n self.layers_per_block = layers_per_block\n\n self.conv_in = nn.Conv2d(\n in_channels,\n block_out_channels[-1],\n kernel_size=3,\n stride=1,\n padding=1,\n )\n\n self.up_blocks = nn.ModuleList([])\n\n temb_channels = in_channels if norm_type == \"spatial\" else None\n\n # mid\n self.mid_block = UNetMidBlock2D(\n in_channels=block_out_channels[-1],\n resnet_eps=1e-6,\n resnet_act_fn=act_fn,\n output_scale_factor=1,\n resnet_time_scale_shift=\"default\" if norm_type == \"group\" else norm_type,\n attention_head_dim=block_out_channels[-1],\n resnet_groups=norm_num_groups,\n temb_channels=temb_channels,\n add_attention=mid_block_add_attention,\n )\n\n # up\n reversed_block_out_channels = list(reversed(block_out_channels))\n output_channel = reversed_block_out_channels[0]\n for i, up_block_type in enumerate(up_block_types):\n prev_output_channel = output_channel\n output_channel = reversed_block_out_channels[i]\n\n is_final_block = i == len(block_out_channels) - 1\n\n up_block = get_up_block(\n up_block_type,\n num_layers=self.layers_per_block + 1,\n in_channels=prev_output_channel,\n out_channels=output_channel,\n prev_output_channel=None,\n add_upsample=not is_final_block,\n resnet_eps=1e-6,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n attention_head_dim=output_channel,\n temb_channels=temb_channels,\n resnet_time_scale_shift=norm_type,\n )\n self.up_blocks.append(up_block)\n prev_output_channel = output_channel\n\n # out\n if norm_type == \"spatial\":\n self.conv_norm_out = SpatialNorm(block_out_channels[0], temb_channels)\n else:\n self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6)\n self.conv_act = nn.SiLU()\n self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1)\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n sample: torch.Tensor,\n latent_embeds: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n r\"\"\"The forward method of the `Decoder` class.\"\"\"\n\n sample = self.conv_in(sample)\n\n upscale_dtype = next(iter(self.up_blocks.parameters())).dtype\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs)\n\n return custom_forward\n\n if is_torch_version(\">=\", \"1.11.0\"):\n # middle\n sample = torch.utils.checkpoint.checkpoint(\n create_custom_forward(self.mid_block),\n sample,\n latent_embeds,\n use_reentrant=False,\n )\n sample = sample.to(upscale_dtype)\n\n # up\n for up_block in self.up_blocks:\n sample = torch.utils.checkpoint.checkpoint(\n create_custom_forward(up_block),\n sample,\n latent_embeds,\n use_reentrant=False,\n )\n else:\n # middle\n sample = torch.utils.checkpoint.checkpoint(\n create_custom_forward(self.mid_block), sample, latent_embeds\n )\n sample = sample.to(upscale_dtype)\n\n # up\n for up_block in self.up_blocks:\n sample = torch.utils.checkpoint.checkpoint(create_custom_forward(up_block), sample, latent_embeds)\n else:\n # middle\n sample = self.mid_block(sample, latent_embeds)\n sample = sample.to(upscale_dtype)\n\n # up\n for up_block in self.up_blocks:\n sample = up_block(sample, latent_embeds)\n\n # post-process\n if latent_embeds is None:\n sample = self.conv_norm_out(sample)\n else:\n sample = self.conv_norm_out(sample, latent_embeds)\n sample = self.conv_act(sample)\n sample = self.conv_out(sample)\n\n return sample\n\n\nclass UpSample(nn.Module):\n r\"\"\"\n The `UpSample` layer of a variational autoencoder that upsamples its input.\n\n Args:\n in_channels (`int`, *optional*, defaults to 3):\n The number of input channels.\n out_channels (`int`, *optional*, defaults to 3):\n The number of output channels.\n \"\"\"\n\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n ) -> None:\n super().__init__()\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.deconv = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n r\"\"\"The forward method of the `UpSample` class.\"\"\"\n x = torch.relu(x)\n x = self.deconv(x)\n return x\n\n\nclass MaskConditionEncoder(nn.Module):\n \"\"\"\n used in AsymmetricAutoencoderKL\n \"\"\"\n\n def __init__(\n self,\n in_ch: int,\n out_ch: int = 192,\n res_ch: int = 768,\n stride: int = 16,\n ) -> None:\n super().__init__()\n\n channels = []\n while stride > 1:\n stride = stride // 2\n in_ch_ = out_ch * 2\n if out_ch > res_ch:\n out_ch = res_ch\n if stride == 1:\n in_ch_ = res_ch\n channels.append((in_ch_, out_ch))\n out_ch *= 2\n\n out_channels = []\n for _in_ch, _out_ch in channels:\n out_channels.append(_out_ch)\n out_channels.append(channels[-1][0])\n\n layers = []\n in_ch_ = in_ch\n for l in range(len(out_channels)):\n out_ch_ = out_channels[l]\n if l == 0 or l == 1:\n layers.append(nn.Conv2d(in_ch_, out_ch_, kernel_size=3, stride=1, padding=1))\n else:\n layers.append(nn.Conv2d(in_ch_, out_ch_, kernel_size=4, stride=2, padding=1))\n in_ch_ = out_ch_\n\n self.layers = nn.Sequential(*layers)\n\n def forward(self, x: torch.Tensor, mask=None) -> torch.Tensor:\n r\"\"\"The forward method of the `MaskConditionEncoder` class.\"\"\"\n out = {}\n for l in range(len(self.layers)):\n layer = self.layers[l]\n x = layer(x)\n out[str(tuple(x.shape))] = x\n x = torch.relu(x)\n return out\n\n\nclass MaskConditionDecoder(nn.Module):\n r\"\"\"The `MaskConditionDecoder` should be used in combination with [`AsymmetricAutoencoderKL`] to enhance the model's\n decoder with a conditioner on the mask and masked image.\n\n Args:\n in_channels (`int`, *optional*, defaults to 3):\n The number of input channels.\n out_channels (`int`, *optional*, defaults to 3):\n The number of output channels.\n up_block_types (`Tuple[str, ...]`, *optional*, defaults to `(\"UpDecoderBlock2D\",)`):\n The types of up blocks to use. See `~diffusers.models.unet_2d_blocks.get_up_block` for available options.\n block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(64,)`):\n The number of output channels for each block.\n layers_per_block (`int`, *optional*, defaults to 2):\n The number of layers per block.\n norm_num_groups (`int`, *optional*, defaults to 32):\n The number of groups for normalization.\n act_fn (`str`, *optional*, defaults to `\"silu\"`):\n The activation function to use. See `~diffusers.models.activations.get_activation` for available options.\n norm_type (`str`, *optional*, defaults to `\"group\"`):\n The normalization type to use. Can be either `\"group\"` or `\"spatial\"`.\n \"\"\"\n\n def __init__(\n self,\n in_channels: int = 3,\n out_channels: int = 3,\n up_block_types: Tuple[str, ...] = (\"UpDecoderBlock2D\",),\n block_out_channels: Tuple[int, ...] = (64,),\n layers_per_block: int = 2,\n norm_num_groups: int = 32,\n act_fn: str = \"silu\",\n norm_type: str = \"group\", # group, spatial\n ):\n super().__init__()\n self.layers_per_block = layers_per_block\n\n self.conv_in = nn.Conv2d(\n in_channels,\n block_out_channels[-1],\n kernel_size=3,\n stride=1,\n padding=1,\n )\n\n self.up_blocks = nn.ModuleList([])\n\n temb_channels = in_channels if norm_type == \"spatial\" else None\n\n # mid\n self.mid_block = UNetMidBlock2D(\n in_channels=block_out_channels[-1],\n resnet_eps=1e-6,\n resnet_act_fn=act_fn,\n output_scale_factor=1,\n resnet_time_scale_shift=\"default\" if norm_type == \"group\" else norm_type,\n attention_head_dim=block_out_channels[-1],\n resnet_groups=norm_num_groups,\n temb_channels=temb_channels,\n )\n\n # up\n reversed_block_out_channels = list(reversed(block_out_channels))\n output_channel = reversed_block_out_channels[0]\n for i, up_block_type in enumerate(up_block_types):\n prev_output_channel = output_channel\n output_channel = reversed_block_out_channels[i]\n\n is_final_block = i == len(block_out_channels) - 1\n\n up_block = get_up_block(\n up_block_type,\n num_layers=self.layers_per_block + 1,\n in_channels=prev_output_channel,\n out_channels=output_channel,\n prev_output_channel=None,\n add_upsample=not is_final_block,\n resnet_eps=1e-6,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n attention_head_dim=output_channel,\n temb_channels=temb_channels,\n resnet_time_scale_shift=norm_type,\n )\n self.up_blocks.append(up_block)\n prev_output_channel = output_channel\n\n # condition encoder\n self.condition_encoder = MaskConditionEncoder(\n in_ch=out_channels,\n out_ch=block_out_channels[0],\n res_ch=block_out_channels[-1],\n )\n\n # out\n if norm_type == \"spatial\":\n self.conv_norm_out = SpatialNorm(block_out_channels[0], temb_channels)\n else:\n self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6)\n self.conv_act = nn.SiLU()\n self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1)\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n z: torch.Tensor,\n image: Optional[torch.Tensor] = None,\n mask: Optional[torch.Tensor] = None,\n latent_embeds: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n r\"\"\"The forward method of the `MaskConditionDecoder` class.\"\"\"\n sample = z\n sample = self.conv_in(sample)\n\n upscale_dtype = next(iter(self.up_blocks.parameters())).dtype\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs)\n\n return custom_forward\n\n if is_torch_version(\">=\", \"1.11.0\"):\n # middle\n sample = torch.utils.checkpoint.checkpoint(\n create_custom_forward(self.mid_block),\n sample,\n latent_embeds,\n use_reentrant=False,\n )\n sample = sample.to(upscale_dtype)\n\n # condition encoder\n if image is not None and mask is not None:\n masked_image = (1 - mask) * image\n im_x = torch.utils.checkpoint.checkpoint(\n create_custom_forward(self.condition_encoder),\n masked_image,\n mask,\n use_reentrant=False,\n )\n\n # up\n for up_block in self.up_blocks:\n if image is not None and mask is not None:\n sample_ = im_x[str(tuple(sample.shape))]\n mask_ = nn.functional.interpolate(mask, size=sample.shape[-2:], mode=\"nearest\")\n sample = sample * mask_ + sample_ * (1 - mask_)\n sample = torch.utils.checkpoint.checkpoint(\n create_custom_forward(up_block),\n sample,\n latent_embeds,\n use_reentrant=False,\n )\n if image is not None and mask is not None:\n sample = sample * mask + im_x[str(tuple(sample.shape))] * (1 - mask)\n else:\n # middle\n sample = torch.utils.checkpoint.checkpoint(\n create_custom_forward(self.mid_block), sample, latent_embeds\n )\n sample = sample.to(upscale_dtype)\n\n # condition encoder\n if image is not None and mask is not None:\n masked_image = (1 - mask) * image\n im_x = torch.utils.checkpoint.checkpoint(\n create_custom_forward(self.condition_encoder),\n masked_image,\n mask,\n )\n\n # up\n for up_block in self.up_blocks:\n if image is not None and mask is not None:\n sample_ = im_x[str(tuple(sample.shape))]\n mask_ = nn.functional.interpolate(mask, size=sample.shape[-2:], mode=\"nearest\")\n sample = sample * mask_ + sample_ * (1 - mask_)\n sample = torch.utils.checkpoint.checkpoint(create_custom_forward(up_block), sample, latent_embeds)\n if image is not None and mask is not None:\n sample = sample * mask + im_x[str(tuple(sample.shape))] * (1 - mask)\n else:\n # middle\n sample = self.mid_block(sample, latent_embeds)\n sample = sample.to(upscale_dtype)\n\n # condition encoder\n if image is not None and mask is not None:\n masked_image = (1 - mask) * image\n im_x = self.condition_encoder(masked_image, mask)\n\n # up\n for up_block in self.up_blocks:\n if image is not None and mask is not None:\n sample_ = im_x[str(tuple(sample.shape))]\n mask_ = nn.functional.interpolate(mask, size=sample.shape[-2:], mode=\"nearest\")\n sample = sample * mask_ + sample_ * (1 - mask_)\n sample = up_block(sample, latent_embeds)\n if image is not None and mask is not None:\n sample = sample * mask + im_x[str(tuple(sample.shape))] * (1 - mask)\n\n # post-process\n if latent_embeds is None:\n sample = self.conv_norm_out(sample)\n else:\n sample = self.conv_norm_out(sample, latent_embeds)\n sample = self.conv_act(sample)\n sample = self.conv_out(sample)\n\n return sample\n\n\nclass VectorQuantizer(nn.Module):\n \"\"\"\n Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly avoids costly matrix\n multiplications and allows for post-hoc remapping of indices.\n \"\"\"\n\n # NOTE: due to a bug the beta term was applied to the wrong term. for\n # backwards compatibility we use the buggy version by default, but you can\n # specify legacy=False to fix it.\n def __init__(\n self,\n n_e: int,\n vq_embed_dim: int,\n beta: float,\n remap=None,\n unknown_index: str = \"random\",\n sane_index_shape: bool = False,\n legacy: bool = True,\n ):\n super().__init__()\n self.n_e = n_e\n self.vq_embed_dim = vq_embed_dim\n self.beta = beta\n self.legacy = legacy\n\n self.embedding = nn.Embedding(self.n_e, self.vq_embed_dim)\n self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)\n\n self.remap = remap\n if self.remap is not None:\n self.register_buffer(\"used\", torch.tensor(np.load(self.remap)))\n self.used: torch.Tensor\n self.re_embed = self.used.shape[0]\n self.unknown_index = unknown_index # \"random\" or \"extra\" or integer\n if self.unknown_index == \"extra\":\n self.unknown_index = self.re_embed\n self.re_embed = self.re_embed + 1\n print(\n f\"Remapping {self.n_e} indices to {self.re_embed} indices. \"\n f\"Using {self.unknown_index} for unknown indices.\"\n )\n else:\n self.re_embed = n_e\n\n self.sane_index_shape = sane_index_shape\n\n def remap_to_used(self, inds: torch.LongTensor) -> torch.LongTensor:\n ishape = inds.shape\n assert len(ishape) > 1\n inds = inds.reshape(ishape[0], -1)\n used = self.used.to(inds)\n match = (inds[:, :, None] == used[None, None, ...]).long()\n new = match.argmax(-1)\n unknown = match.sum(2) < 1\n if self.unknown_index == \"random\":\n new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(device=new.device)\n else:\n new[unknown] = self.unknown_index\n return new.reshape(ishape)\n\n def unmap_to_all(self, inds: torch.LongTensor) -> torch.LongTensor:\n ishape = inds.shape\n assert len(ishape) > 1\n inds = inds.reshape(ishape[0], -1)\n used = self.used.to(inds)\n if self.re_embed > self.used.shape[0]: # extra token\n inds[inds >= self.used.shape[0]] = 0 # simply set to zero\n back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds)\n return back.reshape(ishape)\n\n def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, Tuple]:\n # reshape z -> (batch, height, width, channel) and flatten\n z = z.permute(0, 2, 3, 1).contiguous()\n z_flattened = z.view(-1, self.vq_embed_dim)\n\n # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z\n min_encoding_indices = torch.argmin(torch.cdist(z_flattened, self.embedding.weight), dim=1)\n\n z_q = self.embedding(min_encoding_indices).view(z.shape)\n perplexity = None\n min_encodings = None\n\n # compute loss for embedding\n if not self.legacy:\n loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + torch.mean((z_q - z.detach()) ** 2)\n else:\n loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean((z_q - z.detach()) ** 2)\n\n # preserve gradients\n z_q: torch.Tensor = z + (z_q - z).detach()\n\n # reshape back to match original input shape\n z_q = z_q.permute(0, 3, 1, 2).contiguous()\n\n if self.remap is not None:\n min_encoding_indices = min_encoding_indices.reshape(z.shape[0], -1) # add batch axis\n min_encoding_indices = self.remap_to_used(min_encoding_indices)\n min_encoding_indices = min_encoding_indices.reshape(-1, 1) # flatten\n\n if self.sane_index_shape:\n min_encoding_indices = min_encoding_indices.reshape(z_q.shape[0], z_q.shape[2], z_q.shape[3])\n\n return z_q, loss, (perplexity, min_encodings, min_encoding_indices)\n\n def get_codebook_entry(self, indices: torch.LongTensor, shape: Tuple[int, ...]) -> torch.Tensor:\n # shape specifying (batch, height, width, channel)\n if self.remap is not None:\n indices = indices.reshape(shape[0], -1) # add batch axis\n indices = self.unmap_to_all(indices)\n indices = indices.reshape(-1) # flatten again\n\n # get quantized latent vectors\n z_q: torch.Tensor = self.embedding(indices)\n\n if shape is not None:\n z_q = z_q.view(shape)\n # reshape back to match original input shape\n z_q = z_q.permute(0, 3, 1, 2).contiguous()\n\n return z_q\n\n\nclass DiagonalGaussianDistribution(object):\n def __init__(self, parameters: torch.Tensor, deterministic: bool = False):\n self.parameters = parameters\n self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)\n self.logvar = torch.clamp(self.logvar, -30.0, 20.0)\n self.deterministic = deterministic\n self.std = torch.exp(0.5 * self.logvar)\n self.var = torch.exp(self.logvar)\n if self.deterministic:\n self.var = self.std = torch.zeros_like(\n self.mean, device=self.parameters.device, dtype=self.parameters.dtype\n )\n\n def sample(self, generator: Optional[torch.Generator] = None) -> torch.Tensor:\n # make sure sample is on the same device as the parameters and has same dtype\n sample = randn_tensor(\n self.mean.shape,\n generator=generator,\n device=self.parameters.device,\n dtype=self.parameters.dtype,\n )\n x = self.mean + self.std * sample\n return x\n\n def kl(self, other: \"DiagonalGaussianDistribution\" = None) -> torch.Tensor:\n if self.deterministic:\n return torch.Tensor([0.0])\n else:\n if other is None:\n return 0.5 * torch.sum(\n torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,\n dim=[1, 2, 3],\n )\n else:\n return 0.5 * torch.sum(\n torch.pow(self.mean - other.mean, 2) / other.var\n + self.var / other.var\n - 1.0\n - self.logvar\n + other.logvar,\n dim=[1, 2, 3],\n )\n\n def nll(self, sample: torch.Tensor, dims: Tuple[int, ...] = [1, 2, 3]) -> torch.Tensor:\n if self.deterministic:\n return torch.Tensor([0.0])\n logtwopi = np.log(2.0 * np.pi)\n return 0.5 * torch.sum(\n logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,\n dim=dims,\n )\n\n def mode(self) -> torch.Tensor:\n return self.mean\n\n\nclass EncoderTiny(nn.Module):\n r\"\"\"\n The `EncoderTiny` layer is a simpler version of the `Encoder` layer.\n\n Args:\n in_channels (`int`):\n The number of input channels.\n out_channels (`int`):\n The number of output channels.\n num_blocks (`Tuple[int, ...]`):\n Each value of the tuple represents a Conv2d layer followed by `value` number of `AutoencoderTinyBlock`'s to\n use.\n block_out_channels (`Tuple[int, ...]`):\n The number of output channels for each block.\n act_fn (`str`):\n The activation function to use. See `~diffusers.models.activations.get_activation` for available options.\n \"\"\"\n\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n num_blocks: Tuple[int, ...],\n block_out_channels: Tuple[int, ...],\n act_fn: str,\n ):\n super().__init__()\n\n layers = []\n for i, num_block in enumerate(num_blocks):\n num_channels = block_out_channels[i]\n\n if i == 0:\n layers.append(nn.Conv2d(in_channels, num_channels, kernel_size=3, padding=1))\n else:\n layers.append(\n nn.Conv2d(\n num_channels,\n num_channels,\n kernel_size=3,\n padding=1,\n stride=2,\n bias=False,\n )\n )\n\n for _ in range(num_block):\n layers.append(AutoencoderTinyBlock(num_channels, num_channels, act_fn))\n\n layers.append(nn.Conv2d(block_out_channels[-1], out_channels, kernel_size=3, padding=1))\n\n self.layers = nn.Sequential(*layers)\n self.gradient_checkpointing = False\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n r\"\"\"The forward method of the `EncoderTiny` class.\"\"\"\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs)\n\n return custom_forward\n\n if is_torch_version(\">=\", \"1.11.0\"):\n x = torch.utils.checkpoint.checkpoint(create_custom_forward(self.layers), x, use_reentrant=False)\n else:\n x = torch.utils.checkpoint.checkpoint(create_custom_forward(self.layers), x)\n\n else:\n # scale image from [-1, 1] to [0, 1] to match TAESD convention\n x = self.layers(x.add(1).div(2))\n\n return x\n\n\nclass DecoderTiny(nn.Module):\n r\"\"\"\n The `DecoderTiny` layer is a simpler version of the `Decoder` layer.\n\n Args:\n in_channels (`int`):\n The number of input channels.\n out_channels (`int`):\n The number of output channels.\n num_blocks (`Tuple[int, ...]`):\n Each value of the tuple represents a Conv2d layer followed by `value` number of `AutoencoderTinyBlock`'s to\n use.\n block_out_channels (`Tuple[int, ...]`):\n The number of output channels for each block.\n upsampling_scaling_factor (`int`):\n The scaling factor to use for upsampling.\n act_fn (`str`):\n The activation function to use. See `~diffusers.models.activations.get_activation` for available options.\n \"\"\"\n\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n num_blocks: Tuple[int, ...],\n block_out_channels: Tuple[int, ...],\n upsampling_scaling_factor: int,\n act_fn: str,\n upsample_fn: str,\n ):\n super().__init__()\n\n layers = [\n nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=1),\n get_activation(act_fn),\n ]\n\n for i, num_block in enumerate(num_blocks):\n is_final_block = i == (len(num_blocks) - 1)\n num_channels = block_out_channels[i]\n\n for _ in range(num_block):\n layers.append(AutoencoderTinyBlock(num_channels, num_channels, act_fn))\n\n if not is_final_block:\n layers.append(nn.Upsample(scale_factor=upsampling_scaling_factor, mode=upsample_fn))\n\n conv_out_channel = num_channels if not is_final_block else out_channels\n layers.append(\n nn.Conv2d(\n num_channels,\n conv_out_channel,\n kernel_size=3,\n padding=1,\n bias=is_final_block,\n )\n )\n\n self.layers = nn.Sequential(*layers)\n self.gradient_checkpointing = False\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n r\"\"\"The forward method of the `DecoderTiny` class.\"\"\"\n # Clamp.\n x = torch.tanh(x / 3) * 3\n\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs)\n\n return custom_forward\n\n if is_torch_version(\">=\", \"1.11.0\"):\n x = torch.utils.checkpoint.checkpoint(create_custom_forward(self.layers), x, use_reentrant=False)\n else:\n x = torch.utils.checkpoint.checkpoint(create_custom_forward(self.layers), x)\n\n else:\n x = self.layers(x)\n\n # scale image from [0, 1] to [-1, 1] to match diffusers convention\n return x.mul(2).sub(1)\n" }, { "path": "diffusers/src/diffusers/models/autoencoders/vq_model.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...utils import BaseOutput\nfrom ...utils.accelerate_utils import apply_forward_hook\nfrom ..autoencoders.vae import Decoder, DecoderOutput, Encoder, VectorQuantizer\nfrom ..modeling_utils import ModelMixin\n\n\n@dataclass\nclass VQEncoderOutput(BaseOutput):\n \"\"\"\n Output of VQModel encoding method.\n\n Args:\n latents (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):\n The encoded output sample from the last layer of the model.\n \"\"\"\n\n latents: torch.Tensor\n\n\nclass VQModel(ModelMixin, ConfigMixin):\n r\"\"\"\n A VQ-VAE model for decoding latent representations.\n\n This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented\n for all models (such as downloading or saving).\n\n Parameters:\n in_channels (int, *optional*, defaults to 3): Number of channels in the input image.\n out_channels (int, *optional*, defaults to 3): Number of channels in the output.\n down_block_types (`Tuple[str]`, *optional*, defaults to `(\"DownEncoderBlock2D\",)`):\n Tuple of downsample block types.\n up_block_types (`Tuple[str]`, *optional*, defaults to `(\"UpDecoderBlock2D\",)`):\n Tuple of upsample block types.\n block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):\n Tuple of block output channels.\n layers_per_block (`int`, *optional*, defaults to `1`): Number of layers per block.\n act_fn (`str`, *optional*, defaults to `\"silu\"`): The activation function to use.\n latent_channels (`int`, *optional*, defaults to `3`): Number of channels in the latent space.\n sample_size (`int`, *optional*, defaults to `32`): Sample input size.\n num_vq_embeddings (`int`, *optional*, defaults to `256`): Number of codebook vectors in the VQ-VAE.\n norm_num_groups (`int`, *optional*, defaults to `32`): Number of groups for normalization layers.\n vq_embed_dim (`int`, *optional*): Hidden dim of codebook vectors in the VQ-VAE.\n scaling_factor (`float`, *optional*, defaults to `0.18215`):\n The component-wise standard deviation of the trained latent space computed using the first batch of the\n training set. This is used to scale the latent space to have unit variance when training the diffusion\n model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the\n diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1\n / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image\n Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.\n norm_type (`str`, *optional*, defaults to `\"group\"`):\n Type of normalization layer to use. Can be one of `\"group\"` or `\"spatial\"`.\n \"\"\"\n\n @register_to_config\n def __init__(\n self,\n in_channels: int = 3,\n out_channels: int = 3,\n down_block_types: Tuple[str, ...] = (\"DownEncoderBlock2D\",),\n up_block_types: Tuple[str, ...] = (\"UpDecoderBlock2D\",),\n block_out_channels: Tuple[int, ...] = (64,),\n layers_per_block: int = 1,\n act_fn: str = \"silu\",\n latent_channels: int = 3,\n sample_size: int = 32,\n num_vq_embeddings: int = 256,\n norm_num_groups: int = 32,\n vq_embed_dim: Optional[int] = None,\n scaling_factor: float = 0.18215,\n norm_type: str = \"group\", # group, spatial\n mid_block_add_attention=True,\n lookup_from_codebook=False,\n force_upcast=False,\n ):\n super().__init__()\n\n # pass init params to Encoder\n self.encoder = Encoder(\n in_channels=in_channels,\n out_channels=latent_channels,\n down_block_types=down_block_types,\n block_out_channels=block_out_channels,\n layers_per_block=layers_per_block,\n act_fn=act_fn,\n norm_num_groups=norm_num_groups,\n double_z=False,\n mid_block_add_attention=mid_block_add_attention,\n )\n\n vq_embed_dim = vq_embed_dim if vq_embed_dim is not None else latent_channels\n\n self.quant_conv = nn.Conv2d(latent_channels, vq_embed_dim, 1)\n self.quantize = VectorQuantizer(num_vq_embeddings, vq_embed_dim, beta=0.25, remap=None, sane_index_shape=False)\n self.post_quant_conv = nn.Conv2d(vq_embed_dim, latent_channels, 1)\n\n # pass init params to Decoder\n self.decoder = Decoder(\n in_channels=latent_channels,\n out_channels=out_channels,\n up_block_types=up_block_types,\n block_out_channels=block_out_channels,\n layers_per_block=layers_per_block,\n act_fn=act_fn,\n norm_num_groups=norm_num_groups,\n norm_type=norm_type,\n mid_block_add_attention=mid_block_add_attention,\n )\n\n @apply_forward_hook\n def encode(self, x: torch.Tensor, return_dict: bool = True) -> VQEncoderOutput:\n h = self.encoder(x)\n h = self.quant_conv(h)\n\n if not return_dict:\n return (h,)\n\n return VQEncoderOutput(latents=h)\n\n @apply_forward_hook\n def decode(\n self, h: torch.Tensor, force_not_quantize: bool = False, return_dict: bool = True, shape=None\n ) -> Union[DecoderOutput, torch.Tensor]:\n # also go through quantization layer\n if not force_not_quantize:\n quant, commit_loss, _ = self.quantize(h)\n elif self.config.lookup_from_codebook:\n quant = self.quantize.get_codebook_entry(h, shape)\n commit_loss = torch.zeros((h.shape[0])).to(h.device, dtype=h.dtype)\n else:\n quant = h\n commit_loss = torch.zeros((h.shape[0])).to(h.device, dtype=h.dtype)\n quant2 = self.post_quant_conv(quant)\n dec = self.decoder(quant2, quant if self.config.norm_type == \"spatial\" else None)\n\n if not return_dict:\n return dec, commit_loss\n\n return DecoderOutput(sample=dec, commit_loss=commit_loss)\n\n def forward(\n self, sample: torch.Tensor, return_dict: bool = True\n ) -> Union[DecoderOutput, Tuple[torch.Tensor, ...]]:\n r\"\"\"\n The [`VQModel`] forward method.\n\n Args:\n sample (`torch.Tensor`): Input sample.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`models.vq_model.VQEncoderOutput`] instead of a plain tuple.\n\n Returns:\n [`~models.vq_model.VQEncoderOutput`] or `tuple`:\n If return_dict is True, a [`~models.vq_model.VQEncoderOutput`] is returned, otherwise a plain `tuple`\n is returned.\n \"\"\"\n\n h = self.encode(sample).latents\n dec = self.decode(h)\n\n if not return_dict:\n return dec.sample, dec.commit_loss\n return dec\n" }, { "path": "diffusers/src/diffusers/models/controlnet.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, List, Optional, Tuple, Union\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..loaders.single_file_model import FromOriginalModelMixin\nfrom ..utils import BaseOutput, logging\nfrom .attention_processor import (\n ADDED_KV_ATTENTION_PROCESSORS,\n CROSS_ATTENTION_PROCESSORS,\n AttentionProcessor,\n AttnAddedKVProcessor,\n AttnProcessor,\n)\nfrom .embeddings import TextImageProjection, TextImageTimeEmbedding, TextTimeEmbedding, TimestepEmbedding, Timesteps\nfrom .modeling_utils import ModelMixin\nfrom .unets.unet_2d_blocks import (\n CrossAttnDownBlock2D,\n DownBlock2D,\n UNetMidBlock2D,\n UNetMidBlock2DCrossAttn,\n get_down_block,\n)\nfrom .unets.unet_2d_condition import UNet2DConditionModel\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n@dataclass\nclass ControlNetOutput(BaseOutput):\n \"\"\"\n The output of [`ControlNetModel`].\n\n Args:\n down_block_res_samples (`tuple[torch.Tensor]`):\n A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should\n be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can be\n used to condition the original UNet's downsampling activations.\n mid_down_block_re_sample (`torch.Tensor`):\n The activation of the midde block (the lowest sample resolution). Each tensor should be of shape\n `(batch_size, channel * lowest_resolution, height // lowest_resolution, width // lowest_resolution)`.\n Output can be used to condition the original UNet's middle block activation.\n \"\"\"\n\n down_block_res_samples: Tuple[torch.Tensor]\n mid_block_res_sample: torch.Tensor\n\n\nclass ControlNetConditioningEmbedding(nn.Module):\n \"\"\"\n Quoting from https://arxiv.org/abs/2302.05543: \"Stable Diffusion uses a pre-processing method similar to VQ-GAN\n [11] to convert the entire dataset of 512 × 512 images into smaller 64 × 64 “latent images” for stabilized\n training. This requires ControlNets to convert image-based conditions to 64 × 64 feature space to match the\n convolution size. We use a tiny network E(·) of four convolution layers with 4 × 4 kernels and 2 × 2 strides\n (activated by ReLU, channels are 16, 32, 64, 128, initialized with Gaussian weights, trained jointly with the full\n model) to encode image-space conditions ... into feature maps ...\"\n \"\"\"\n\n def __init__(\n self,\n conditioning_embedding_channels: int,\n conditioning_channels: int = 3,\n block_out_channels: Tuple[int, ...] = (16, 32, 96, 256),\n ):\n super().__init__()\n\n self.conv_in = nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1)\n\n self.blocks = nn.ModuleList([])\n\n for i in range(len(block_out_channels) - 1):\n channel_in = block_out_channels[i]\n channel_out = block_out_channels[i + 1]\n self.blocks.append(nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1))\n self.blocks.append(nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2))\n\n self.conv_out = zero_module(\n nn.Conv2d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1)\n )\n\n def forward(self, conditioning):\n embedding = self.conv_in(conditioning)\n embedding = F.silu(embedding)\n\n for block in self.blocks:\n embedding = block(embedding)\n embedding = F.silu(embedding)\n\n embedding = self.conv_out(embedding)\n\n return embedding\n\n\nclass ControlNetModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):\n \"\"\"\n A ControlNet model.\n\n Args:\n in_channels (`int`, defaults to 4):\n The number of channels in the input sample.\n flip_sin_to_cos (`bool`, defaults to `True`):\n Whether to flip the sin to cos in the time embedding.\n freq_shift (`int`, defaults to 0):\n The frequency shift to apply to the time embedding.\n down_block_types (`tuple[str]`, defaults to `(\"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\", \"DownBlock2D\")`):\n The tuple of downsample blocks to use.\n only_cross_attention (`Union[bool, Tuple[bool]]`, defaults to `False`):\n block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`):\n The tuple of output channels for each block.\n layers_per_block (`int`, defaults to 2):\n The number of layers per block.\n downsample_padding (`int`, defaults to 1):\n The padding to use for the downsampling convolution.\n mid_block_scale_factor (`float`, defaults to 1):\n The scale factor to use for the mid block.\n act_fn (`str`, defaults to \"silu\"):\n The activation function to use.\n norm_num_groups (`int`, *optional*, defaults to 32):\n The number of groups to use for the normalization. If None, normalization and activation layers is skipped\n in post-processing.\n norm_eps (`float`, defaults to 1e-5):\n The epsilon to use for the normalization.\n cross_attention_dim (`int`, defaults to 1280):\n The dimension of the cross attention features.\n transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):\n The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for\n [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],\n [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].\n encoder_hid_dim (`int`, *optional*, defaults to None):\n If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`\n dimension to `cross_attention_dim`.\n encoder_hid_dim_type (`str`, *optional*, defaults to `None`):\n If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text\n embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.\n attention_head_dim (`Union[int, Tuple[int]]`, defaults to 8):\n The dimension of the attention heads.\n use_linear_projection (`bool`, defaults to `False`):\n class_embed_type (`str`, *optional*, defaults to `None`):\n The type of class embedding to use which is ultimately summed with the time embeddings. Choose from None,\n `\"timestep\"`, `\"identity\"`, `\"projection\"`, or `\"simple_projection\"`.\n addition_embed_type (`str`, *optional*, defaults to `None`):\n Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or\n \"text\". \"text\" will use the `TextTimeEmbedding` layer.\n num_class_embeds (`int`, *optional*, defaults to 0):\n Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing\n class conditioning with `class_embed_type` equal to `None`.\n upcast_attention (`bool`, defaults to `False`):\n resnet_time_scale_shift (`str`, defaults to `\"default\"`):\n Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`.\n projection_class_embeddings_input_dim (`int`, *optional*, defaults to `None`):\n The dimension of the `class_labels` input when `class_embed_type=\"projection\"`. Required when\n `class_embed_type=\"projection\"`.\n controlnet_conditioning_channel_order (`str`, defaults to `\"rgb\"`):\n The channel order of conditional image. Will convert to `rgb` if it's `bgr`.\n conditioning_embedding_out_channels (`tuple[int]`, *optional*, defaults to `(16, 32, 96, 256)`):\n The tuple of output channel for each block in the `conditioning_embedding` layer.\n global_pool_conditions (`bool`, defaults to `False`):\n TODO(Patrick) - unused parameter.\n addition_embed_type_num_heads (`int`, defaults to 64):\n The number of heads to use for the `TextTimeEmbedding` layer.\n \"\"\"\n\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n in_channels: int = 4,\n conditioning_channels: int = 3,\n flip_sin_to_cos: bool = True,\n freq_shift: int = 0,\n down_block_types: Tuple[str, ...] = (\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"DownBlock2D\",\n ),\n mid_block_type: Optional[str] = \"UNetMidBlock2DCrossAttn\",\n only_cross_attention: Union[bool, Tuple[bool]] = False,\n block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),\n layers_per_block: int = 2,\n downsample_padding: int = 1,\n mid_block_scale_factor: float = 1,\n act_fn: str = \"silu\",\n norm_num_groups: Optional[int] = 32,\n norm_eps: float = 1e-5,\n cross_attention_dim: int = 1280,\n transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1,\n encoder_hid_dim: Optional[int] = None,\n encoder_hid_dim_type: Optional[str] = None,\n attention_head_dim: Union[int, Tuple[int, ...]] = 8,\n num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None,\n use_linear_projection: bool = False,\n class_embed_type: Optional[str] = None,\n addition_embed_type: Optional[str] = None,\n addition_time_embed_dim: Optional[int] = None,\n num_class_embeds: Optional[int] = None,\n upcast_attention: bool = False,\n resnet_time_scale_shift: str = \"default\",\n projection_class_embeddings_input_dim: Optional[int] = None,\n controlnet_conditioning_channel_order: str = \"rgb\",\n conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),\n global_pool_conditions: bool = False,\n addition_embed_type_num_heads: int = 64,\n ):\n super().__init__()\n\n # If `num_attention_heads` is not defined (which is the case for most models)\n # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.\n # The reason for this behavior is to correct for incorrectly named variables that were introduced\n # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131\n # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking\n # which is why we correct for the naming here.\n num_attention_heads = num_attention_heads or attention_head_dim\n\n # Check inputs\n if len(block_out_channels) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}.\"\n )\n\n if isinstance(transformer_layers_per_block, int):\n transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)\n\n # input\n conv_in_kernel = 3\n conv_in_padding = (conv_in_kernel - 1) // 2\n self.conv_in = nn.Conv2d(\n in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding\n )\n\n # time\n time_embed_dim = block_out_channels[0] * 4\n self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)\n timestep_input_dim = block_out_channels[0]\n self.time_embedding = TimestepEmbedding(\n timestep_input_dim,\n time_embed_dim,\n act_fn=act_fn,\n )\n\n if encoder_hid_dim_type is None and encoder_hid_dim is not None:\n encoder_hid_dim_type = \"text_proj\"\n self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)\n logger.info(\"encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.\")\n\n if encoder_hid_dim is None and encoder_hid_dim_type is not None:\n raise ValueError(\n f\"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}.\"\n )\n\n if encoder_hid_dim_type == \"text_proj\":\n self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)\n elif encoder_hid_dim_type == \"text_image_proj\":\n # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much\n # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use\n # case when `addition_embed_type == \"text_image_proj\"` (Kandinsky 2.1)`\n self.encoder_hid_proj = TextImageProjection(\n text_embed_dim=encoder_hid_dim,\n image_embed_dim=cross_attention_dim,\n cross_attention_dim=cross_attention_dim,\n )\n\n elif encoder_hid_dim_type is not None:\n raise ValueError(\n f\"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'.\"\n )\n else:\n self.encoder_hid_proj = None\n\n # class embedding\n if class_embed_type is None and num_class_embeds is not None:\n self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)\n elif class_embed_type == \"timestep\":\n self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\n elif class_embed_type == \"identity\":\n self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)\n elif class_embed_type == \"projection\":\n if projection_class_embeddings_input_dim is None:\n raise ValueError(\n \"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set\"\n )\n # The projection `class_embed_type` is the same as the timestep `class_embed_type` except\n # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings\n # 2. it projects from an arbitrary input dimension.\n #\n # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.\n # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.\n # As a result, `TimestepEmbedding` can be passed arbitrary vectors.\n self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)\n else:\n self.class_embedding = None\n\n if addition_embed_type == \"text\":\n if encoder_hid_dim is not None:\n text_time_embedding_from_dim = encoder_hid_dim\n else:\n text_time_embedding_from_dim = cross_attention_dim\n\n self.add_embedding = TextTimeEmbedding(\n text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads\n )\n elif addition_embed_type == \"text_image\":\n # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much\n # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use\n # case when `addition_embed_type == \"text_image\"` (Kandinsky 2.1)`\n self.add_embedding = TextImageTimeEmbedding(\n text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim\n )\n elif addition_embed_type == \"text_time\":\n self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)\n self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)\n\n elif addition_embed_type is not None:\n raise ValueError(f\"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.\")\n\n # control net conditioning embedding\n self.controlnet_cond_embedding = ControlNetConditioningEmbedding(\n conditioning_embedding_channels=block_out_channels[0],\n block_out_channels=conditioning_embedding_out_channels,\n conditioning_channels=conditioning_channels,\n )\n\n self.down_blocks = nn.ModuleList([])\n self.controlnet_down_blocks = nn.ModuleList([])\n\n if isinstance(only_cross_attention, bool):\n only_cross_attention = [only_cross_attention] * len(down_block_types)\n\n if isinstance(attention_head_dim, int):\n attention_head_dim = (attention_head_dim,) * len(down_block_types)\n\n if isinstance(num_attention_heads, int):\n num_attention_heads = (num_attention_heads,) * len(down_block_types)\n\n # down\n output_channel = block_out_channels[0]\n\n controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)\n controlnet_block = zero_module(controlnet_block)\n self.controlnet_down_blocks.append(controlnet_block)\n\n for i, down_block_type in enumerate(down_block_types):\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n down_block = get_down_block(\n down_block_type,\n num_layers=layers_per_block,\n transformer_layers_per_block=transformer_layers_per_block[i],\n in_channels=input_channel,\n out_channels=output_channel,\n temb_channels=time_embed_dim,\n add_downsample=not is_final_block,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=num_attention_heads[i],\n attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,\n downsample_padding=downsample_padding,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention[i],\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n self.down_blocks.append(down_block)\n\n for _ in range(layers_per_block):\n controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)\n controlnet_block = zero_module(controlnet_block)\n self.controlnet_down_blocks.append(controlnet_block)\n\n if not is_final_block:\n controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)\n controlnet_block = zero_module(controlnet_block)\n self.controlnet_down_blocks.append(controlnet_block)\n\n # mid\n mid_block_channel = block_out_channels[-1]\n\n controlnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1)\n controlnet_block = zero_module(controlnet_block)\n self.controlnet_mid_block = controlnet_block\n\n if mid_block_type == \"UNetMidBlock2DCrossAttn\":\n self.mid_block = UNetMidBlock2DCrossAttn(\n transformer_layers_per_block=transformer_layers_per_block[-1],\n in_channels=mid_block_channel,\n temb_channels=time_embed_dim,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n output_scale_factor=mid_block_scale_factor,\n resnet_time_scale_shift=resnet_time_scale_shift,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=num_attention_heads[-1],\n resnet_groups=norm_num_groups,\n use_linear_projection=use_linear_projection,\n upcast_attention=upcast_attention,\n )\n elif mid_block_type == \"UNetMidBlock2D\":\n self.mid_block = UNetMidBlock2D(\n in_channels=block_out_channels[-1],\n temb_channels=time_embed_dim,\n num_layers=0,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n output_scale_factor=mid_block_scale_factor,\n resnet_groups=norm_num_groups,\n resnet_time_scale_shift=resnet_time_scale_shift,\n add_attention=False,\n )\n else:\n raise ValueError(f\"unknown mid_block_type : {mid_block_type}\")\n\n @classmethod\n def from_unet(\n cls,\n unet: UNet2DConditionModel,\n controlnet_conditioning_channel_order: str = \"rgb\",\n conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),\n load_weights_from_unet: bool = True,\n conditioning_channels: int = 3,\n ):\n r\"\"\"\n Instantiate a [`ControlNetModel`] from [`UNet2DConditionModel`].\n\n Parameters:\n unet (`UNet2DConditionModel`):\n The UNet model weights to copy to the [`ControlNetModel`]. All configuration options are also copied\n where applicable.\n \"\"\"\n transformer_layers_per_block = (\n unet.config.transformer_layers_per_block if \"transformer_layers_per_block\" in unet.config else 1\n )\n encoder_hid_dim = unet.config.encoder_hid_dim if \"encoder_hid_dim\" in unet.config else None\n encoder_hid_dim_type = unet.config.encoder_hid_dim_type if \"encoder_hid_dim_type\" in unet.config else None\n addition_embed_type = unet.config.addition_embed_type if \"addition_embed_type\" in unet.config else None\n addition_time_embed_dim = (\n unet.config.addition_time_embed_dim if \"addition_time_embed_dim\" in unet.config else None\n )\n\n controlnet = cls(\n encoder_hid_dim=encoder_hid_dim,\n encoder_hid_dim_type=encoder_hid_dim_type,\n addition_embed_type=addition_embed_type,\n addition_time_embed_dim=addition_time_embed_dim,\n transformer_layers_per_block=transformer_layers_per_block,\n in_channels=unet.config.in_channels,\n flip_sin_to_cos=unet.config.flip_sin_to_cos,\n freq_shift=unet.config.freq_shift,\n down_block_types=unet.config.down_block_types,\n only_cross_attention=unet.config.only_cross_attention,\n block_out_channels=unet.config.block_out_channels,\n layers_per_block=unet.config.layers_per_block,\n downsample_padding=unet.config.downsample_padding,\n mid_block_scale_factor=unet.config.mid_block_scale_factor,\n act_fn=unet.config.act_fn,\n norm_num_groups=unet.config.norm_num_groups,\n norm_eps=unet.config.norm_eps,\n cross_attention_dim=unet.config.cross_attention_dim,\n attention_head_dim=unet.config.attention_head_dim,\n num_attention_heads=unet.config.num_attention_heads,\n use_linear_projection=unet.config.use_linear_projection,\n class_embed_type=unet.config.class_embed_type,\n num_class_embeds=unet.config.num_class_embeds,\n upcast_attention=unet.config.upcast_attention,\n resnet_time_scale_shift=unet.config.resnet_time_scale_shift,\n projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim,\n mid_block_type=unet.config.mid_block_type,\n controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,\n conditioning_embedding_out_channels=conditioning_embedding_out_channels,\n conditioning_channels=conditioning_channels,\n )\n\n if load_weights_from_unet:\n controlnet.conv_in.load_state_dict(unet.conv_in.state_dict())\n controlnet.time_proj.load_state_dict(unet.time_proj.state_dict())\n controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())\n\n if controlnet.class_embedding:\n controlnet.class_embedding.load_state_dict(unet.class_embedding.state_dict())\n\n if hasattr(controlnet, \"add_embedding\"):\n controlnet.add_embedding.load_state_dict(unet.add_embedding.state_dict())\n\n controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict())\n controlnet.mid_block.load_state_dict(unet.mid_block.state_dict())\n\n return controlnet\n\n @property\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors\n def attn_processors(self) -> Dict[str, AttentionProcessor]:\n r\"\"\"\n Returns:\n `dict` of attention processors: A dictionary containing all attention processors used in the model with\n indexed by its weight name.\n \"\"\"\n # set recursively\n processors = {}\n\n def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\n if hasattr(module, \"get_processor\"):\n processors[f\"{name}.processor\"] = module.get_processor(return_deprecated_lora=True)\n\n for sub_name, child in module.named_children():\n fn_recursive_add_processors(f\"{name}.{sub_name}\", child, processors)\n\n return processors\n\n for name, module in self.named_children():\n fn_recursive_add_processors(name, module, processors)\n\n return processors\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor\n def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\n r\"\"\"\n Sets the attention processor to use to compute attention.\n\n Parameters:\n processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):\n The instantiated processor class or a dictionary of processor classes that will be set as the processor\n for **all** `Attention` layers.\n\n If `processor` is a dict, the key needs to define the path to the corresponding cross attention\n processor. This is strongly recommended when setting trainable attention processors.\n\n \"\"\"\n count = len(self.attn_processors.keys())\n\n if isinstance(processor, dict) and len(processor) != count:\n raise ValueError(\n f\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\"\n f\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\"\n )\n\n def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\n if hasattr(module, \"set_processor\"):\n if not isinstance(processor, dict):\n module.set_processor(processor)\n else:\n module.set_processor(processor.pop(f\"{name}.processor\"))\n\n for sub_name, child in module.named_children():\n fn_recursive_attn_processor(f\"{name}.{sub_name}\", child, processor)\n\n for name, module in self.named_children():\n fn_recursive_attn_processor(name, module, processor)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor\n def set_default_attn_processor(self):\n \"\"\"\n Disables custom attention processors and sets the default attention implementation.\n \"\"\"\n if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnAddedKVProcessor()\n elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnProcessor()\n else:\n raise ValueError(\n f\"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}\"\n )\n\n self.set_attn_processor(processor)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attention_slice\n def set_attention_slice(self, slice_size: Union[str, int, List[int]]) -> None:\n r\"\"\"\n Enable sliced attention computation.\n\n When this option is enabled, the attention module splits the input tensor in slices to compute attention in\n several steps. This is useful for saving some memory in exchange for a small decrease in speed.\n\n Args:\n slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `\"auto\"`):\n When `\"auto\"`, input to the attention heads is halved, so attention is computed in two steps. If\n `\"max\"`, maximum amount of memory is saved by running only one slice at a time. If a number is\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\n must be a multiple of `slice_size`.\n \"\"\"\n sliceable_head_dims = []\n\n def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):\n if hasattr(module, \"set_attention_slice\"):\n sliceable_head_dims.append(module.sliceable_head_dim)\n\n for child in module.children():\n fn_recursive_retrieve_sliceable_dims(child)\n\n # retrieve number of attention layers\n for module in self.children():\n fn_recursive_retrieve_sliceable_dims(module)\n\n num_sliceable_layers = len(sliceable_head_dims)\n\n if slice_size == \"auto\":\n # half the attention head size is usually a good trade-off between\n # speed and memory\n slice_size = [dim // 2 for dim in sliceable_head_dims]\n elif slice_size == \"max\":\n # make smallest slice possible\n slice_size = num_sliceable_layers * [1]\n\n slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\n\n if len(slice_size) != len(sliceable_head_dims):\n raise ValueError(\n f\"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different\"\n f\" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}.\"\n )\n\n for i in range(len(slice_size)):\n size = slice_size[i]\n dim = sliceable_head_dims[i]\n if size is not None and size > dim:\n raise ValueError(f\"size {size} has to be smaller or equal to {dim}.\")\n\n # Recursively walk through all the children.\n # Any children which exposes the set_attention_slice method\n # gets the message\n def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):\n if hasattr(module, \"set_attention_slice\"):\n module.set_attention_slice(slice_size.pop())\n\n for child in module.children():\n fn_recursive_set_attention_slice(child, slice_size)\n\n reversed_slice_size = list(reversed(slice_size))\n for module in self.children():\n fn_recursive_set_attention_slice(module, reversed_slice_size)\n\n def _set_gradient_checkpointing(self, module, value: bool = False) -> None:\n if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)):\n module.gradient_checkpointing = value\n\n def forward(\n self,\n sample: torch.Tensor,\n timestep: Union[torch.Tensor, float, int],\n encoder_hidden_states: torch.Tensor,\n controlnet_cond: torch.Tensor,\n conditioning_scale: float = 1.0,\n class_labels: Optional[torch.Tensor] = None,\n timestep_cond: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guess_mode: bool = False,\n return_dict: bool = True,\n ) -> Union[ControlNetOutput, Tuple[Tuple[torch.Tensor, ...], torch.Tensor]]:\n \"\"\"\n The [`ControlNetModel`] forward method.\n\n Args:\n sample (`torch.Tensor`):\n The noisy input tensor.\n timestep (`Union[torch.Tensor, float, int]`):\n The number of timesteps to denoise an input.\n encoder_hidden_states (`torch.Tensor`):\n The encoder hidden states.\n controlnet_cond (`torch.Tensor`):\n The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.\n conditioning_scale (`float`, defaults to `1.0`):\n The scale factor for ControlNet outputs.\n class_labels (`torch.Tensor`, *optional*, defaults to `None`):\n Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.\n timestep_cond (`torch.Tensor`, *optional*, defaults to `None`):\n Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the\n timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep\n embeddings.\n attention_mask (`torch.Tensor`, *optional*, defaults to `None`):\n An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask\n is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large\n negative values to the attention scores corresponding to \"discard\" tokens.\n added_cond_kwargs (`dict`):\n Additional conditions for the Stable Diffusion XL UNet.\n cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`):\n A kwargs dictionary that if specified is passed along to the `AttnProcessor`.\n guess_mode (`bool`, defaults to `False`):\n In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if\n you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.\n return_dict (`bool`, defaults to `True`):\n Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.\n\n Returns:\n [`~models.controlnet.ControlNetOutput`] **or** `tuple`:\n If `return_dict` is `True`, a [`~models.controlnet.ControlNetOutput`] is returned, otherwise a tuple is\n returned where the first element is the sample tensor.\n \"\"\"\n # check channel order\n channel_order = self.config.controlnet_conditioning_channel_order\n\n if channel_order == \"rgb\":\n # in rgb order by default\n ...\n elif channel_order == \"bgr\":\n controlnet_cond = torch.flip(controlnet_cond, dims=[1])\n else:\n raise ValueError(f\"unknown `controlnet_conditioning_channel_order`: {channel_order}\")\n\n # prepare attention_mask\n if attention_mask is not None:\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n # 1. time\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can\n # This would be a good case for the `match` statement (Python 3.10+)\n is_mps = sample.device.type == \"mps\"\n if isinstance(timestep, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\n elif len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(sample.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timesteps = timesteps.expand(sample.shape[0])\n\n t_emb = self.time_proj(timesteps)\n\n # timesteps does not contain any weights and will always return f32 tensors\n # but time_embedding might actually be running in fp16. so we need to cast here.\n # there might be better ways to encapsulate this.\n t_emb = t_emb.to(dtype=sample.dtype)\n\n emb = self.time_embedding(t_emb, timestep_cond)\n aug_emb = None\n\n if self.class_embedding is not None:\n if class_labels is None:\n raise ValueError(\"class_labels should be provided when num_class_embeds > 0\")\n\n if self.config.class_embed_type == \"timestep\":\n class_labels = self.time_proj(class_labels)\n\n class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)\n emb = emb + class_emb\n\n if self.config.addition_embed_type is not None:\n if self.config.addition_embed_type == \"text\":\n aug_emb = self.add_embedding(encoder_hidden_states)\n\n elif self.config.addition_embed_type == \"text_time\":\n if \"text_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`\"\n )\n text_embeds = added_cond_kwargs.get(\"text_embeds\")\n if \"time_ids\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`\"\n )\n time_ids = added_cond_kwargs.get(\"time_ids\")\n time_embeds = self.add_time_proj(time_ids.flatten())\n time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))\n\n add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)\n add_embeds = add_embeds.to(emb.dtype)\n aug_emb = self.add_embedding(add_embeds)\n\n emb = emb + aug_emb if aug_emb is not None else emb\n\n # 2. pre-process\n sample = self.conv_in(sample)\n\n controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)\n sample = sample + controlnet_cond\n\n # 3. down\n down_block_res_samples = (sample,)\n for downsample_block in self.down_blocks:\n if hasattr(downsample_block, \"has_cross_attention\") and downsample_block.has_cross_attention:\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n else:\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb)\n\n down_block_res_samples += res_samples\n\n # 4. mid\n if self.mid_block is not None:\n if hasattr(self.mid_block, \"has_cross_attention\") and self.mid_block.has_cross_attention:\n sample = self.mid_block(\n sample,\n emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n else:\n sample = self.mid_block(sample, emb)\n\n # 5. Control net blocks\n\n controlnet_down_block_res_samples = ()\n\n for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):\n down_block_res_sample = controlnet_block(down_block_res_sample)\n controlnet_down_block_res_samples = controlnet_down_block_res_samples + (down_block_res_sample,)\n\n down_block_res_samples = controlnet_down_block_res_samples\n\n mid_block_res_sample = self.controlnet_mid_block(sample)\n\n # 6. scaling\n if guess_mode and not self.config.global_pool_conditions:\n scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device) # 0.1 to 1.0\n scales = scales * conditioning_scale\n down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)]\n mid_block_res_sample = mid_block_res_sample * scales[-1] # last one\n else:\n down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]\n mid_block_res_sample = mid_block_res_sample * conditioning_scale\n\n if self.config.global_pool_conditions:\n down_block_res_samples = [\n torch.mean(sample, dim=(2, 3), keepdim=True) for sample in down_block_res_samples\n ]\n mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True)\n\n if not return_dict:\n return (down_block_res_samples, mid_block_res_sample)\n\n return ControlNetOutput(\n down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample\n )\n\n\ndef zero_module(module):\n for p in module.parameters():\n nn.init.zeros_(p)\n return module\n" }, { "path": "diffusers/src/diffusers/models/controlnet_flax.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Optional, Tuple, Union\n\nimport flax\nimport flax.linen as nn\nimport jax\nimport jax.numpy as jnp\nfrom flax.core.frozen_dict import FrozenDict\n\nfrom ..configuration_utils import ConfigMixin, flax_register_to_config\nfrom ..utils import BaseOutput\nfrom .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps\nfrom .modeling_flax_utils import FlaxModelMixin\nfrom .unets.unet_2d_blocks_flax import (\n FlaxCrossAttnDownBlock2D,\n FlaxDownBlock2D,\n FlaxUNetMidBlock2DCrossAttn,\n)\n\n\n@flax.struct.dataclass\nclass FlaxControlNetOutput(BaseOutput):\n \"\"\"\n The output of [`FlaxControlNetModel`].\n\n Args:\n down_block_res_samples (`jnp.ndarray`):\n mid_block_res_sample (`jnp.ndarray`):\n \"\"\"\n\n down_block_res_samples: jnp.ndarray\n mid_block_res_sample: jnp.ndarray\n\n\nclass FlaxControlNetConditioningEmbedding(nn.Module):\n conditioning_embedding_channels: int\n block_out_channels: Tuple[int, ...] = (16, 32, 96, 256)\n dtype: jnp.dtype = jnp.float32\n\n def setup(self) -> None:\n self.conv_in = nn.Conv(\n self.block_out_channels[0],\n kernel_size=(3, 3),\n padding=((1, 1), (1, 1)),\n dtype=self.dtype,\n )\n\n blocks = []\n for i in range(len(self.block_out_channels) - 1):\n channel_in = self.block_out_channels[i]\n channel_out = self.block_out_channels[i + 1]\n conv1 = nn.Conv(\n channel_in,\n kernel_size=(3, 3),\n padding=((1, 1), (1, 1)),\n dtype=self.dtype,\n )\n blocks.append(conv1)\n conv2 = nn.Conv(\n channel_out,\n kernel_size=(3, 3),\n strides=(2, 2),\n padding=((1, 1), (1, 1)),\n dtype=self.dtype,\n )\n blocks.append(conv2)\n self.blocks = blocks\n\n self.conv_out = nn.Conv(\n self.conditioning_embedding_channels,\n kernel_size=(3, 3),\n padding=((1, 1), (1, 1)),\n kernel_init=nn.initializers.zeros_init(),\n bias_init=nn.initializers.zeros_init(),\n dtype=self.dtype,\n )\n\n def __call__(self, conditioning: jnp.ndarray) -> jnp.ndarray:\n embedding = self.conv_in(conditioning)\n embedding = nn.silu(embedding)\n\n for block in self.blocks:\n embedding = block(embedding)\n embedding = nn.silu(embedding)\n\n embedding = self.conv_out(embedding)\n\n return embedding\n\n\n@flax_register_to_config\nclass FlaxControlNetModel(nn.Module, FlaxModelMixin, ConfigMixin):\n r\"\"\"\n A ControlNet model.\n\n This model inherits from [`FlaxModelMixin`]. Check the superclass documentation for it’s generic methods\n implemented for all models (such as downloading or saving).\n\n This model is also a Flax Linen [`flax.linen.Module`](https://flax.readthedocs.io/en/latest/flax.linen.html#module)\n subclass. Use it as a regular Flax Linen module and refer to the Flax documentation for all matters related to its\n general usage and behavior.\n\n Inherent JAX features such as the following are supported:\n\n - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)\n - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)\n - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)\n - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)\n\n Parameters:\n sample_size (`int`, *optional*):\n The size of the input sample.\n in_channels (`int`, *optional*, defaults to 4):\n The number of channels in the input sample.\n down_block_types (`Tuple[str]`, *optional*, defaults to `(\"FlaxCrossAttnDownBlock2D\", \"FlaxCrossAttnDownBlock2D\", \"FlaxCrossAttnDownBlock2D\", \"FlaxDownBlock2D\")`):\n The tuple of downsample blocks to use.\n block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):\n The tuple of output channels for each block.\n layers_per_block (`int`, *optional*, defaults to 2):\n The number of layers per block.\n attention_head_dim (`int` or `Tuple[int]`, *optional*, defaults to 8):\n The dimension of the attention heads.\n num_attention_heads (`int` or `Tuple[int]`, *optional*):\n The number of attention heads.\n cross_attention_dim (`int`, *optional*, defaults to 768):\n The dimension of the cross attention features.\n dropout (`float`, *optional*, defaults to 0):\n Dropout probability for down, up and bottleneck blocks.\n flip_sin_to_cos (`bool`, *optional*, defaults to `True`):\n Whether to flip the sin to cos in the time embedding.\n freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.\n controlnet_conditioning_channel_order (`str`, *optional*, defaults to `rgb`):\n The channel order of conditional image. Will convert to `rgb` if it's `bgr`.\n conditioning_embedding_out_channels (`tuple`, *optional*, defaults to `(16, 32, 96, 256)`):\n The tuple of output channel for each block in the `conditioning_embedding` layer.\n \"\"\"\n\n sample_size: int = 32\n in_channels: int = 4\n down_block_types: Tuple[str, ...] = (\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"DownBlock2D\",\n )\n only_cross_attention: Union[bool, Tuple[bool, ...]] = False\n block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280)\n layers_per_block: int = 2\n attention_head_dim: Union[int, Tuple[int, ...]] = 8\n num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None\n cross_attention_dim: int = 1280\n dropout: float = 0.0\n use_linear_projection: bool = False\n dtype: jnp.dtype = jnp.float32\n flip_sin_to_cos: bool = True\n freq_shift: int = 0\n controlnet_conditioning_channel_order: str = \"rgb\"\n conditioning_embedding_out_channels: Tuple[int, ...] = (16, 32, 96, 256)\n\n def init_weights(self, rng: jax.Array) -> FrozenDict:\n # init input tensors\n sample_shape = (1, self.in_channels, self.sample_size, self.sample_size)\n sample = jnp.zeros(sample_shape, dtype=jnp.float32)\n timesteps = jnp.ones((1,), dtype=jnp.int32)\n encoder_hidden_states = jnp.zeros((1, 1, self.cross_attention_dim), dtype=jnp.float32)\n controlnet_cond_shape = (1, 3, self.sample_size * 8, self.sample_size * 8)\n controlnet_cond = jnp.zeros(controlnet_cond_shape, dtype=jnp.float32)\n\n params_rng, dropout_rng = jax.random.split(rng)\n rngs = {\"params\": params_rng, \"dropout\": dropout_rng}\n\n return self.init(rngs, sample, timesteps, encoder_hidden_states, controlnet_cond)[\"params\"]\n\n def setup(self) -> None:\n block_out_channels = self.block_out_channels\n time_embed_dim = block_out_channels[0] * 4\n\n # If `num_attention_heads` is not defined (which is the case for most models)\n # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.\n # The reason for this behavior is to correct for incorrectly named variables that were introduced\n # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131\n # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking\n # which is why we correct for the naming here.\n num_attention_heads = self.num_attention_heads or self.attention_head_dim\n\n # input\n self.conv_in = nn.Conv(\n block_out_channels[0],\n kernel_size=(3, 3),\n strides=(1, 1),\n padding=((1, 1), (1, 1)),\n dtype=self.dtype,\n )\n\n # time\n self.time_proj = FlaxTimesteps(\n block_out_channels[0], flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.config.freq_shift\n )\n self.time_embedding = FlaxTimestepEmbedding(time_embed_dim, dtype=self.dtype)\n\n self.controlnet_cond_embedding = FlaxControlNetConditioningEmbedding(\n conditioning_embedding_channels=block_out_channels[0],\n block_out_channels=self.conditioning_embedding_out_channels,\n )\n\n only_cross_attention = self.only_cross_attention\n if isinstance(only_cross_attention, bool):\n only_cross_attention = (only_cross_attention,) * len(self.down_block_types)\n\n if isinstance(num_attention_heads, int):\n num_attention_heads = (num_attention_heads,) * len(self.down_block_types)\n\n # down\n down_blocks = []\n controlnet_down_blocks = []\n\n output_channel = block_out_channels[0]\n\n controlnet_block = nn.Conv(\n output_channel,\n kernel_size=(1, 1),\n padding=\"VALID\",\n kernel_init=nn.initializers.zeros_init(),\n bias_init=nn.initializers.zeros_init(),\n dtype=self.dtype,\n )\n controlnet_down_blocks.append(controlnet_block)\n\n for i, down_block_type in enumerate(self.down_block_types):\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n if down_block_type == \"CrossAttnDownBlock2D\":\n down_block = FlaxCrossAttnDownBlock2D(\n in_channels=input_channel,\n out_channels=output_channel,\n dropout=self.dropout,\n num_layers=self.layers_per_block,\n num_attention_heads=num_attention_heads[i],\n add_downsample=not is_final_block,\n use_linear_projection=self.use_linear_projection,\n only_cross_attention=only_cross_attention[i],\n dtype=self.dtype,\n )\n else:\n down_block = FlaxDownBlock2D(\n in_channels=input_channel,\n out_channels=output_channel,\n dropout=self.dropout,\n num_layers=self.layers_per_block,\n add_downsample=not is_final_block,\n dtype=self.dtype,\n )\n\n down_blocks.append(down_block)\n\n for _ in range(self.layers_per_block):\n controlnet_block = nn.Conv(\n output_channel,\n kernel_size=(1, 1),\n padding=\"VALID\",\n kernel_init=nn.initializers.zeros_init(),\n bias_init=nn.initializers.zeros_init(),\n dtype=self.dtype,\n )\n controlnet_down_blocks.append(controlnet_block)\n\n if not is_final_block:\n controlnet_block = nn.Conv(\n output_channel,\n kernel_size=(1, 1),\n padding=\"VALID\",\n kernel_init=nn.initializers.zeros_init(),\n bias_init=nn.initializers.zeros_init(),\n dtype=self.dtype,\n )\n controlnet_down_blocks.append(controlnet_block)\n\n self.down_blocks = down_blocks\n self.controlnet_down_blocks = controlnet_down_blocks\n\n # mid\n mid_block_channel = block_out_channels[-1]\n self.mid_block = FlaxUNetMidBlock2DCrossAttn(\n in_channels=mid_block_channel,\n dropout=self.dropout,\n num_attention_heads=num_attention_heads[-1],\n use_linear_projection=self.use_linear_projection,\n dtype=self.dtype,\n )\n\n self.controlnet_mid_block = nn.Conv(\n mid_block_channel,\n kernel_size=(1, 1),\n padding=\"VALID\",\n kernel_init=nn.initializers.zeros_init(),\n bias_init=nn.initializers.zeros_init(),\n dtype=self.dtype,\n )\n\n def __call__(\n self,\n sample: jnp.ndarray,\n timesteps: Union[jnp.ndarray, float, int],\n encoder_hidden_states: jnp.ndarray,\n controlnet_cond: jnp.ndarray,\n conditioning_scale: float = 1.0,\n return_dict: bool = True,\n train: bool = False,\n ) -> Union[FlaxControlNetOutput, Tuple[Tuple[jnp.ndarray, ...], jnp.ndarray]]:\n r\"\"\"\n Args:\n sample (`jnp.ndarray`): (batch, channel, height, width) noisy inputs tensor\n timestep (`jnp.ndarray` or `float` or `int`): timesteps\n encoder_hidden_states (`jnp.ndarray`): (batch_size, sequence_length, hidden_size) encoder hidden states\n controlnet_cond (`jnp.ndarray`): (batch, channel, height, width) the conditional input tensor\n conditioning_scale (`float`, *optional*, defaults to `1.0`): the scale factor for controlnet outputs\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] instead of\n a plain tuple.\n train (`bool`, *optional*, defaults to `False`):\n Use deterministic functions and disable dropout when not training.\n\n Returns:\n [`~models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] or `tuple`:\n [`~models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] if `return_dict` is True, otherwise\n a `tuple`. When returning a tuple, the first element is the sample tensor.\n \"\"\"\n channel_order = self.controlnet_conditioning_channel_order\n if channel_order == \"bgr\":\n controlnet_cond = jnp.flip(controlnet_cond, axis=1)\n\n # 1. time\n if not isinstance(timesteps, jnp.ndarray):\n timesteps = jnp.array([timesteps], dtype=jnp.int32)\n elif isinstance(timesteps, jnp.ndarray) and len(timesteps.shape) == 0:\n timesteps = timesteps.astype(dtype=jnp.float32)\n timesteps = jnp.expand_dims(timesteps, 0)\n\n t_emb = self.time_proj(timesteps)\n t_emb = self.time_embedding(t_emb)\n\n # 2. pre-process\n sample = jnp.transpose(sample, (0, 2, 3, 1))\n sample = self.conv_in(sample)\n\n controlnet_cond = jnp.transpose(controlnet_cond, (0, 2, 3, 1))\n controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)\n sample += controlnet_cond\n\n # 3. down\n down_block_res_samples = (sample,)\n for down_block in self.down_blocks:\n if isinstance(down_block, FlaxCrossAttnDownBlock2D):\n sample, res_samples = down_block(sample, t_emb, encoder_hidden_states, deterministic=not train)\n else:\n sample, res_samples = down_block(sample, t_emb, deterministic=not train)\n down_block_res_samples += res_samples\n\n # 4. mid\n sample = self.mid_block(sample, t_emb, encoder_hidden_states, deterministic=not train)\n\n # 5. contronet blocks\n controlnet_down_block_res_samples = ()\n for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):\n down_block_res_sample = controlnet_block(down_block_res_sample)\n controlnet_down_block_res_samples += (down_block_res_sample,)\n\n down_block_res_samples = controlnet_down_block_res_samples\n\n mid_block_res_sample = self.controlnet_mid_block(sample)\n\n # 6. scaling\n down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]\n mid_block_res_sample *= conditioning_scale\n\n if not return_dict:\n return (down_block_res_samples, mid_block_res_sample)\n\n return FlaxControlNetOutput(\n down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample\n )\n" }, { "path": "diffusers/src/diffusers/models/controlnet_xs.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom dataclasses import dataclass\nfrom math import gcd\nfrom typing import Any, Dict, List, Optional, Tuple, Union\n\nimport torch\nimport torch.utils.checkpoint\nfrom torch import Tensor, nn\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import BaseOutput, is_torch_version, logging\nfrom ..utils.torch_utils import apply_freeu\nfrom .attention_processor import (\n ADDED_KV_ATTENTION_PROCESSORS,\n CROSS_ATTENTION_PROCESSORS,\n Attention,\n AttentionProcessor,\n AttnAddedKVProcessor,\n AttnProcessor,\n)\nfrom .controlnet import ControlNetConditioningEmbedding\nfrom .embeddings import TimestepEmbedding, Timesteps\nfrom .modeling_utils import ModelMixin\nfrom .unets.unet_2d_blocks import (\n CrossAttnDownBlock2D,\n CrossAttnUpBlock2D,\n Downsample2D,\n ResnetBlock2D,\n Transformer2DModel,\n UNetMidBlock2DCrossAttn,\n Upsample2D,\n)\nfrom .unets.unet_2d_condition import UNet2DConditionModel\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n@dataclass\nclass ControlNetXSOutput(BaseOutput):\n \"\"\"\n The output of [`UNetControlNetXSModel`].\n\n Args:\n sample (`Tensor` of shape `(batch_size, num_channels, height, width)`):\n The output of the `UNetControlNetXSModel`. Unlike `ControlNetOutput` this is NOT to be added to the base\n model output, but is already the final output.\n \"\"\"\n\n sample: Tensor = None\n\n\nclass DownBlockControlNetXSAdapter(nn.Module):\n \"\"\"Components that together with corresponding components from the base model will form a\n `ControlNetXSCrossAttnDownBlock2D`\"\"\"\n\n def __init__(\n self,\n resnets: nn.ModuleList,\n base_to_ctrl: nn.ModuleList,\n ctrl_to_base: nn.ModuleList,\n attentions: Optional[nn.ModuleList] = None,\n downsampler: Optional[nn.Conv2d] = None,\n ):\n super().__init__()\n self.resnets = resnets\n self.base_to_ctrl = base_to_ctrl\n self.ctrl_to_base = ctrl_to_base\n self.attentions = attentions\n self.downsamplers = downsampler\n\n\nclass MidBlockControlNetXSAdapter(nn.Module):\n \"\"\"Components that together with corresponding components from the base model will form a\n `ControlNetXSCrossAttnMidBlock2D`\"\"\"\n\n def __init__(self, midblock: UNetMidBlock2DCrossAttn, base_to_ctrl: nn.ModuleList, ctrl_to_base: nn.ModuleList):\n super().__init__()\n self.midblock = midblock\n self.base_to_ctrl = base_to_ctrl\n self.ctrl_to_base = ctrl_to_base\n\n\nclass UpBlockControlNetXSAdapter(nn.Module):\n \"\"\"Components that together with corresponding components from the base model will form a `ControlNetXSCrossAttnUpBlock2D`\"\"\"\n\n def __init__(self, ctrl_to_base: nn.ModuleList):\n super().__init__()\n self.ctrl_to_base = ctrl_to_base\n\n\ndef get_down_block_adapter(\n base_in_channels: int,\n base_out_channels: int,\n ctrl_in_channels: int,\n ctrl_out_channels: int,\n temb_channels: int,\n max_norm_num_groups: Optional[int] = 32,\n has_crossattn=True,\n transformer_layers_per_block: Optional[Union[int, Tuple[int]]] = 1,\n num_attention_heads: Optional[int] = 1,\n cross_attention_dim: Optional[int] = 1024,\n add_downsample: bool = True,\n upcast_attention: Optional[bool] = False,\n):\n num_layers = 2 # only support sd + sdxl\n\n resnets = []\n attentions = []\n ctrl_to_base = []\n base_to_ctrl = []\n\n if isinstance(transformer_layers_per_block, int):\n transformer_layers_per_block = [transformer_layers_per_block] * num_layers\n\n for i in range(num_layers):\n base_in_channels = base_in_channels if i == 0 else base_out_channels\n ctrl_in_channels = ctrl_in_channels if i == 0 else ctrl_out_channels\n\n # Before the resnet/attention application, information is concatted from base to control.\n # Concat doesn't require change in number of channels\n base_to_ctrl.append(make_zero_conv(base_in_channels, base_in_channels))\n\n resnets.append(\n ResnetBlock2D(\n in_channels=ctrl_in_channels + base_in_channels, # information from base is concatted to ctrl\n out_channels=ctrl_out_channels,\n temb_channels=temb_channels,\n groups=find_largest_factor(ctrl_in_channels + base_in_channels, max_factor=max_norm_num_groups),\n groups_out=find_largest_factor(ctrl_out_channels, max_factor=max_norm_num_groups),\n eps=1e-5,\n )\n )\n\n if has_crossattn:\n attentions.append(\n Transformer2DModel(\n num_attention_heads,\n ctrl_out_channels // num_attention_heads,\n in_channels=ctrl_out_channels,\n num_layers=transformer_layers_per_block[i],\n cross_attention_dim=cross_attention_dim,\n use_linear_projection=True,\n upcast_attention=upcast_attention,\n norm_num_groups=find_largest_factor(ctrl_out_channels, max_factor=max_norm_num_groups),\n )\n )\n\n # After the resnet/attention application, information is added from control to base\n # Addition requires change in number of channels\n ctrl_to_base.append(make_zero_conv(ctrl_out_channels, base_out_channels))\n\n if add_downsample:\n # Before the downsampler application, information is concatted from base to control\n # Concat doesn't require change in number of channels\n base_to_ctrl.append(make_zero_conv(base_out_channels, base_out_channels))\n\n downsamplers = Downsample2D(\n ctrl_out_channels + base_out_channels, use_conv=True, out_channels=ctrl_out_channels, name=\"op\"\n )\n\n # After the downsampler application, information is added from control to base\n # Addition requires change in number of channels\n ctrl_to_base.append(make_zero_conv(ctrl_out_channels, base_out_channels))\n else:\n downsamplers = None\n\n down_block_components = DownBlockControlNetXSAdapter(\n resnets=nn.ModuleList(resnets),\n base_to_ctrl=nn.ModuleList(base_to_ctrl),\n ctrl_to_base=nn.ModuleList(ctrl_to_base),\n )\n\n if has_crossattn:\n down_block_components.attentions = nn.ModuleList(attentions)\n if downsamplers is not None:\n down_block_components.downsamplers = downsamplers\n\n return down_block_components\n\n\ndef get_mid_block_adapter(\n base_channels: int,\n ctrl_channels: int,\n temb_channels: Optional[int] = None,\n max_norm_num_groups: Optional[int] = 32,\n transformer_layers_per_block: int = 1,\n num_attention_heads: Optional[int] = 1,\n cross_attention_dim: Optional[int] = 1024,\n upcast_attention: bool = False,\n):\n # Before the midblock application, information is concatted from base to control.\n # Concat doesn't require change in number of channels\n base_to_ctrl = make_zero_conv(base_channels, base_channels)\n\n midblock = UNetMidBlock2DCrossAttn(\n transformer_layers_per_block=transformer_layers_per_block,\n in_channels=ctrl_channels + base_channels,\n out_channels=ctrl_channels,\n temb_channels=temb_channels,\n # number or norm groups must divide both in_channels and out_channels\n resnet_groups=find_largest_factor(gcd(ctrl_channels, ctrl_channels + base_channels), max_norm_num_groups),\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=num_attention_heads,\n use_linear_projection=True,\n upcast_attention=upcast_attention,\n )\n\n # After the midblock application, information is added from control to base\n # Addition requires change in number of channels\n ctrl_to_base = make_zero_conv(ctrl_channels, base_channels)\n\n return MidBlockControlNetXSAdapter(base_to_ctrl=base_to_ctrl, midblock=midblock, ctrl_to_base=ctrl_to_base)\n\n\ndef get_up_block_adapter(\n out_channels: int,\n prev_output_channel: int,\n ctrl_skip_channels: List[int],\n):\n ctrl_to_base = []\n num_layers = 3 # only support sd + sdxl\n for i in range(num_layers):\n resnet_in_channels = prev_output_channel if i == 0 else out_channels\n ctrl_to_base.append(make_zero_conv(ctrl_skip_channels[i], resnet_in_channels))\n\n return UpBlockControlNetXSAdapter(ctrl_to_base=nn.ModuleList(ctrl_to_base))\n\n\nclass ControlNetXSAdapter(ModelMixin, ConfigMixin):\n r\"\"\"\n A `ControlNetXSAdapter` model. To use it, pass it into a `UNetControlNetXSModel` (together with a\n `UNet2DConditionModel` base model).\n\n This model inherits from [`ModelMixin`] and [`ConfigMixin`]. Check the superclass documentation for it's generic\n methods implemented for all models (such as downloading or saving).\n\n Like `UNetControlNetXSModel`, `ControlNetXSAdapter` is compatible with StableDiffusion and StableDiffusion-XL. It's\n default parameters are compatible with StableDiffusion.\n\n Parameters:\n conditioning_channels (`int`, defaults to 3):\n Number of channels of conditioning input (e.g. an image)\n conditioning_channel_order (`str`, defaults to `\"rgb\"`):\n The channel order of conditional image. Will convert to `rgb` if it's `bgr`.\n conditioning_embedding_out_channels (`tuple[int]`, defaults to `(16, 32, 96, 256)`):\n The tuple of output channels for each block in the `controlnet_cond_embedding` layer.\n time_embedding_mix (`float`, defaults to 1.0):\n If 0, then only the control adapters's time embedding is used. If 1, then only the base unet's time\n embedding is used. Otherwise, both are combined.\n learn_time_embedding (`bool`, defaults to `False`):\n Whether a time embedding should be learned. If yes, `UNetControlNetXSModel` will combine the time\n embeddings of the base model and the control adapter. If no, `UNetControlNetXSModel` will use the base\n model's time embedding.\n num_attention_heads (`list[int]`, defaults to `[4]`):\n The number of attention heads.\n block_out_channels (`list[int]`, defaults to `[4, 8, 16, 16]`):\n The tuple of output channels for each block.\n base_block_out_channels (`list[int]`, defaults to `[320, 640, 1280, 1280]`):\n The tuple of output channels for each block in the base unet.\n cross_attention_dim (`int`, defaults to 1024):\n The dimension of the cross attention features.\n down_block_types (`list[str]`, defaults to `[\"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\", \"DownBlock2D\"]`):\n The tuple of downsample blocks to use.\n sample_size (`int`, defaults to 96):\n Height and width of input/output sample.\n transformer_layers_per_block (`Union[int, Tuple[int]]`, defaults to 1):\n The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for\n [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].\n upcast_attention (`bool`, defaults to `True`):\n Whether the attention computation should always be upcasted.\n max_norm_num_groups (`int`, defaults to 32):\n Maximum number of groups in group normal. The actual number will the the largest divisor of the respective\n channels, that is <= max_norm_num_groups.\n \"\"\"\n\n @register_to_config\n def __init__(\n self,\n conditioning_channels: int = 3,\n conditioning_channel_order: str = \"rgb\",\n conditioning_embedding_out_channels: Tuple[int] = (16, 32, 96, 256),\n time_embedding_mix: float = 1.0,\n learn_time_embedding: bool = False,\n num_attention_heads: Union[int, Tuple[int]] = 4,\n block_out_channels: Tuple[int] = (4, 8, 16, 16),\n base_block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\n cross_attention_dim: int = 1024,\n down_block_types: Tuple[str] = (\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"DownBlock2D\",\n ),\n sample_size: Optional[int] = 96,\n transformer_layers_per_block: Union[int, Tuple[int]] = 1,\n upcast_attention: bool = True,\n max_norm_num_groups: int = 32,\n ):\n super().__init__()\n\n time_embedding_input_dim = base_block_out_channels[0]\n time_embedding_dim = base_block_out_channels[0] * 4\n\n # Check inputs\n if conditioning_channel_order not in [\"rgb\", \"bgr\"]:\n raise ValueError(f\"unknown `conditioning_channel_order`: {conditioning_channel_order}\")\n\n if len(block_out_channels) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(transformer_layers_per_block, (list, tuple)):\n transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)\n if not isinstance(cross_attention_dim, (list, tuple)):\n cross_attention_dim = [cross_attention_dim] * len(down_block_types)\n # see https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 for why `ControlNetXSAdapter` takes `num_attention_heads` instead of `attention_head_dim`\n if not isinstance(num_attention_heads, (list, tuple)):\n num_attention_heads = [num_attention_heads] * len(down_block_types)\n\n if len(num_attention_heads) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}.\"\n )\n\n # 5 - Create conditioning hint embedding\n self.controlnet_cond_embedding = ControlNetConditioningEmbedding(\n conditioning_embedding_channels=block_out_channels[0],\n block_out_channels=conditioning_embedding_out_channels,\n conditioning_channels=conditioning_channels,\n )\n\n # time\n if learn_time_embedding:\n self.time_embedding = TimestepEmbedding(time_embedding_input_dim, time_embedding_dim)\n else:\n self.time_embedding = None\n\n self.down_blocks = nn.ModuleList([])\n self.up_connections = nn.ModuleList([])\n\n # input\n self.conv_in = nn.Conv2d(4, block_out_channels[0], kernel_size=3, padding=1)\n self.control_to_base_for_conv_in = make_zero_conv(block_out_channels[0], base_block_out_channels[0])\n\n # down\n base_out_channels = base_block_out_channels[0]\n ctrl_out_channels = block_out_channels[0]\n for i, down_block_type in enumerate(down_block_types):\n base_in_channels = base_out_channels\n base_out_channels = base_block_out_channels[i]\n ctrl_in_channels = ctrl_out_channels\n ctrl_out_channels = block_out_channels[i]\n has_crossattn = \"CrossAttn\" in down_block_type\n is_final_block = i == len(down_block_types) - 1\n\n self.down_blocks.append(\n get_down_block_adapter(\n base_in_channels=base_in_channels,\n base_out_channels=base_out_channels,\n ctrl_in_channels=ctrl_in_channels,\n ctrl_out_channels=ctrl_out_channels,\n temb_channels=time_embedding_dim,\n max_norm_num_groups=max_norm_num_groups,\n has_crossattn=has_crossattn,\n transformer_layers_per_block=transformer_layers_per_block[i],\n num_attention_heads=num_attention_heads[i],\n cross_attention_dim=cross_attention_dim[i],\n add_downsample=not is_final_block,\n upcast_attention=upcast_attention,\n )\n )\n\n # mid\n self.mid_block = get_mid_block_adapter(\n base_channels=base_block_out_channels[-1],\n ctrl_channels=block_out_channels[-1],\n temb_channels=time_embedding_dim,\n transformer_layers_per_block=transformer_layers_per_block[-1],\n num_attention_heads=num_attention_heads[-1],\n cross_attention_dim=cross_attention_dim[-1],\n upcast_attention=upcast_attention,\n )\n\n # up\n # The skip connection channels are the output of the conv_in and of all the down subblocks\n ctrl_skip_channels = [block_out_channels[0]]\n for i, out_channels in enumerate(block_out_channels):\n number_of_subblocks = (\n 3 if i < len(block_out_channels) - 1 else 2\n ) # every block has 3 subblocks, except last one, which has 2 as it has no downsampler\n ctrl_skip_channels.extend([out_channels] * number_of_subblocks)\n\n reversed_base_block_out_channels = list(reversed(base_block_out_channels))\n\n base_out_channels = reversed_base_block_out_channels[0]\n for i in range(len(down_block_types)):\n prev_base_output_channel = base_out_channels\n base_out_channels = reversed_base_block_out_channels[i]\n ctrl_skip_channels_ = [ctrl_skip_channels.pop() for _ in range(3)]\n\n self.up_connections.append(\n get_up_block_adapter(\n out_channels=base_out_channels,\n prev_output_channel=prev_base_output_channel,\n ctrl_skip_channels=ctrl_skip_channels_,\n )\n )\n\n @classmethod\n def from_unet(\n cls,\n unet: UNet2DConditionModel,\n size_ratio: Optional[float] = None,\n block_out_channels: Optional[List[int]] = None,\n num_attention_heads: Optional[List[int]] = None,\n learn_time_embedding: bool = False,\n time_embedding_mix: int = 1.0,\n conditioning_channels: int = 3,\n conditioning_channel_order: str = \"rgb\",\n conditioning_embedding_out_channels: Tuple[int] = (16, 32, 96, 256),\n ):\n r\"\"\"\n Instantiate a [`ControlNetXSAdapter`] from a [`UNet2DConditionModel`].\n\n Parameters:\n unet (`UNet2DConditionModel`):\n The UNet model we want to control. The dimensions of the ControlNetXSAdapter will be adapted to it.\n size_ratio (float, *optional*, defaults to `None`):\n When given, block_out_channels is set to a fraction of the base model's block_out_channels. Either this\n or `block_out_channels` must be given.\n block_out_channels (`List[int]`, *optional*, defaults to `None`):\n Down blocks output channels in control model. Either this or `size_ratio` must be given.\n num_attention_heads (`List[int]`, *optional*, defaults to `None`):\n The dimension of the attention heads. The naming seems a bit confusing and it is, see\n https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 for why.\n learn_time_embedding (`bool`, defaults to `False`):\n Whether the `ControlNetXSAdapter` should learn a time embedding.\n time_embedding_mix (`float`, defaults to 1.0):\n If 0, then only the control adapter's time embedding is used. If 1, then only the base unet's time\n embedding is used. Otherwise, both are combined.\n conditioning_channels (`int`, defaults to 3):\n Number of channels of conditioning input (e.g. an image)\n conditioning_channel_order (`str`, defaults to `\"rgb\"`):\n The channel order of conditional image. Will convert to `rgb` if it's `bgr`.\n conditioning_embedding_out_channels (`Tuple[int]`, defaults to `(16, 32, 96, 256)`):\n The tuple of output channel for each block in the `controlnet_cond_embedding` layer.\n \"\"\"\n\n # Check input\n fixed_size = block_out_channels is not None\n relative_size = size_ratio is not None\n if not (fixed_size ^ relative_size):\n raise ValueError(\n \"Pass exactly one of `block_out_channels` (for absolute sizing) or `size_ratio` (for relative sizing).\"\n )\n\n # Create model\n block_out_channels = block_out_channels or [int(b * size_ratio) for b in unet.config.block_out_channels]\n if num_attention_heads is None:\n # The naming seems a bit confusing and it is, see https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 for why.\n num_attention_heads = unet.config.attention_head_dim\n\n model = cls(\n conditioning_channels=conditioning_channels,\n conditioning_channel_order=conditioning_channel_order,\n conditioning_embedding_out_channels=conditioning_embedding_out_channels,\n time_embedding_mix=time_embedding_mix,\n learn_time_embedding=learn_time_embedding,\n num_attention_heads=num_attention_heads,\n block_out_channels=block_out_channels,\n base_block_out_channels=unet.config.block_out_channels,\n cross_attention_dim=unet.config.cross_attention_dim,\n down_block_types=unet.config.down_block_types,\n sample_size=unet.config.sample_size,\n transformer_layers_per_block=unet.config.transformer_layers_per_block,\n upcast_attention=unet.config.upcast_attention,\n max_norm_num_groups=unet.config.norm_num_groups,\n )\n\n # ensure that the ControlNetXSAdapter is the same dtype as the UNet2DConditionModel\n model.to(unet.dtype)\n\n return model\n\n def forward(self, *args, **kwargs):\n raise ValueError(\n \"A ControlNetXSAdapter cannot be run by itself. Use it together with a UNet2DConditionModel to instantiate a UNetControlNetXSModel.\"\n )\n\n\nclass UNetControlNetXSModel(ModelMixin, ConfigMixin):\n r\"\"\"\n A UNet fused with a ControlNet-XS adapter model\n\n This model inherits from [`ModelMixin`] and [`ConfigMixin`]. Check the superclass documentation for it's generic\n methods implemented for all models (such as downloading or saving).\n\n `UNetControlNetXSModel` is compatible with StableDiffusion and StableDiffusion-XL. It's default parameters are\n compatible with StableDiffusion.\n\n It's parameters are either passed to the underlying `UNet2DConditionModel` or used exactly like in\n `ControlNetXSAdapter` . See their documentation for details.\n \"\"\"\n\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n # unet configs\n sample_size: Optional[int] = 96,\n down_block_types: Tuple[str] = (\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"DownBlock2D\",\n ),\n up_block_types: Tuple[str] = (\"UpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\"),\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\n norm_num_groups: Optional[int] = 32,\n cross_attention_dim: Union[int, Tuple[int]] = 1024,\n transformer_layers_per_block: Union[int, Tuple[int]] = 1,\n num_attention_heads: Union[int, Tuple[int]] = 8,\n addition_embed_type: Optional[str] = None,\n addition_time_embed_dim: Optional[int] = None,\n upcast_attention: bool = True,\n time_cond_proj_dim: Optional[int] = None,\n projection_class_embeddings_input_dim: Optional[int] = None,\n # additional controlnet configs\n time_embedding_mix: float = 1.0,\n ctrl_conditioning_channels: int = 3,\n ctrl_conditioning_embedding_out_channels: Tuple[int] = (16, 32, 96, 256),\n ctrl_conditioning_channel_order: str = \"rgb\",\n ctrl_learn_time_embedding: bool = False,\n ctrl_block_out_channels: Tuple[int] = (4, 8, 16, 16),\n ctrl_num_attention_heads: Union[int, Tuple[int]] = 4,\n ctrl_max_norm_num_groups: int = 32,\n ):\n super().__init__()\n\n if time_embedding_mix < 0 or time_embedding_mix > 1:\n raise ValueError(\"`time_embedding_mix` needs to be between 0 and 1.\")\n if time_embedding_mix < 1 and not ctrl_learn_time_embedding:\n raise ValueError(\"To use `time_embedding_mix` < 1, `ctrl_learn_time_embedding` must be `True`\")\n\n if addition_embed_type is not None and addition_embed_type != \"text_time\":\n raise ValueError(\n \"As `UNetControlNetXSModel` currently only supports StableDiffusion and StableDiffusion-XL, `addition_embed_type` must be `None` or `'text_time'`.\"\n )\n\n if not isinstance(transformer_layers_per_block, (list, tuple)):\n transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)\n if not isinstance(cross_attention_dim, (list, tuple)):\n cross_attention_dim = [cross_attention_dim] * len(down_block_types)\n if not isinstance(num_attention_heads, (list, tuple)):\n num_attention_heads = [num_attention_heads] * len(down_block_types)\n if not isinstance(ctrl_num_attention_heads, (list, tuple)):\n ctrl_num_attention_heads = [ctrl_num_attention_heads] * len(down_block_types)\n\n base_num_attention_heads = num_attention_heads\n\n self.in_channels = 4\n\n # # Input\n self.base_conv_in = nn.Conv2d(4, block_out_channels[0], kernel_size=3, padding=1)\n self.controlnet_cond_embedding = ControlNetConditioningEmbedding(\n conditioning_embedding_channels=ctrl_block_out_channels[0],\n block_out_channels=ctrl_conditioning_embedding_out_channels,\n conditioning_channels=ctrl_conditioning_channels,\n )\n self.ctrl_conv_in = nn.Conv2d(4, ctrl_block_out_channels[0], kernel_size=3, padding=1)\n self.control_to_base_for_conv_in = make_zero_conv(ctrl_block_out_channels[0], block_out_channels[0])\n\n # # Time\n time_embed_input_dim = block_out_channels[0]\n time_embed_dim = block_out_channels[0] * 4\n\n self.base_time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos=True, downscale_freq_shift=0)\n self.base_time_embedding = TimestepEmbedding(\n time_embed_input_dim,\n time_embed_dim,\n cond_proj_dim=time_cond_proj_dim,\n )\n self.ctrl_time_embedding = TimestepEmbedding(in_channels=time_embed_input_dim, time_embed_dim=time_embed_dim)\n\n if addition_embed_type is None:\n self.base_add_time_proj = None\n self.base_add_embedding = None\n else:\n self.base_add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos=True, downscale_freq_shift=0)\n self.base_add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)\n\n # # Create down blocks\n down_blocks = []\n base_out_channels = block_out_channels[0]\n ctrl_out_channels = ctrl_block_out_channels[0]\n for i, down_block_type in enumerate(down_block_types):\n base_in_channels = base_out_channels\n base_out_channels = block_out_channels[i]\n ctrl_in_channels = ctrl_out_channels\n ctrl_out_channels = ctrl_block_out_channels[i]\n has_crossattn = \"CrossAttn\" in down_block_type\n is_final_block = i == len(down_block_types) - 1\n\n down_blocks.append(\n ControlNetXSCrossAttnDownBlock2D(\n base_in_channels=base_in_channels,\n base_out_channels=base_out_channels,\n ctrl_in_channels=ctrl_in_channels,\n ctrl_out_channels=ctrl_out_channels,\n temb_channels=time_embed_dim,\n norm_num_groups=norm_num_groups,\n ctrl_max_norm_num_groups=ctrl_max_norm_num_groups,\n has_crossattn=has_crossattn,\n transformer_layers_per_block=transformer_layers_per_block[i],\n base_num_attention_heads=base_num_attention_heads[i],\n ctrl_num_attention_heads=ctrl_num_attention_heads[i],\n cross_attention_dim=cross_attention_dim[i],\n add_downsample=not is_final_block,\n upcast_attention=upcast_attention,\n )\n )\n\n # # Create mid block\n self.mid_block = ControlNetXSCrossAttnMidBlock2D(\n base_channels=block_out_channels[-1],\n ctrl_channels=ctrl_block_out_channels[-1],\n temb_channels=time_embed_dim,\n norm_num_groups=norm_num_groups,\n ctrl_max_norm_num_groups=ctrl_max_norm_num_groups,\n transformer_layers_per_block=transformer_layers_per_block[-1],\n base_num_attention_heads=base_num_attention_heads[-1],\n ctrl_num_attention_heads=ctrl_num_attention_heads[-1],\n cross_attention_dim=cross_attention_dim[-1],\n upcast_attention=upcast_attention,\n )\n\n # # Create up blocks\n up_blocks = []\n rev_transformer_layers_per_block = list(reversed(transformer_layers_per_block))\n rev_num_attention_heads = list(reversed(base_num_attention_heads))\n rev_cross_attention_dim = list(reversed(cross_attention_dim))\n\n # The skip connection channels are the output of the conv_in and of all the down subblocks\n ctrl_skip_channels = [ctrl_block_out_channels[0]]\n for i, out_channels in enumerate(ctrl_block_out_channels):\n number_of_subblocks = (\n 3 if i < len(ctrl_block_out_channels) - 1 else 2\n ) # every block has 3 subblocks, except last one, which has 2 as it has no downsampler\n ctrl_skip_channels.extend([out_channels] * number_of_subblocks)\n\n reversed_block_out_channels = list(reversed(block_out_channels))\n\n out_channels = reversed_block_out_channels[0]\n for i, up_block_type in enumerate(up_block_types):\n prev_output_channel = out_channels\n out_channels = reversed_block_out_channels[i]\n in_channels = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]\n ctrl_skip_channels_ = [ctrl_skip_channels.pop() for _ in range(3)]\n\n has_crossattn = \"CrossAttn\" in up_block_type\n is_final_block = i == len(block_out_channels) - 1\n\n up_blocks.append(\n ControlNetXSCrossAttnUpBlock2D(\n in_channels=in_channels,\n out_channels=out_channels,\n prev_output_channel=prev_output_channel,\n ctrl_skip_channels=ctrl_skip_channels_,\n temb_channels=time_embed_dim,\n resolution_idx=i,\n has_crossattn=has_crossattn,\n transformer_layers_per_block=rev_transformer_layers_per_block[i],\n num_attention_heads=rev_num_attention_heads[i],\n cross_attention_dim=rev_cross_attention_dim[i],\n add_upsample=not is_final_block,\n upcast_attention=upcast_attention,\n norm_num_groups=norm_num_groups,\n )\n )\n\n self.down_blocks = nn.ModuleList(down_blocks)\n self.up_blocks = nn.ModuleList(up_blocks)\n\n self.base_conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups)\n self.base_conv_act = nn.SiLU()\n self.base_conv_out = nn.Conv2d(block_out_channels[0], 4, kernel_size=3, padding=1)\n\n @classmethod\n def from_unet(\n cls,\n unet: UNet2DConditionModel,\n controlnet: Optional[ControlNetXSAdapter] = None,\n size_ratio: Optional[float] = None,\n ctrl_block_out_channels: Optional[List[float]] = None,\n time_embedding_mix: Optional[float] = None,\n ctrl_optional_kwargs: Optional[Dict] = None,\n ):\n r\"\"\"\n Instantiate a [`UNetControlNetXSModel`] from a [`UNet2DConditionModel`] and an optional [`ControlNetXSAdapter`]\n .\n\n Parameters:\n unet (`UNet2DConditionModel`):\n The UNet model we want to control.\n controlnet (`ControlNetXSAdapter`):\n The ConntrolNet-XS adapter with which the UNet will be fused. If none is given, a new ConntrolNet-XS\n adapter will be created.\n size_ratio (float, *optional*, defaults to `None`):\n Used to contruct the controlnet if none is given. See [`ControlNetXSAdapter.from_unet`] for details.\n ctrl_block_out_channels (`List[int]`, *optional*, defaults to `None`):\n Used to contruct the controlnet if none is given. See [`ControlNetXSAdapter.from_unet`] for details,\n where this parameter is called `block_out_channels`.\n time_embedding_mix (`float`, *optional*, defaults to None):\n Used to contruct the controlnet if none is given. See [`ControlNetXSAdapter.from_unet`] for details.\n ctrl_optional_kwargs (`Dict`, *optional*, defaults to `None`):\n Passed to the `init` of the new controlent if no controlent was given.\n \"\"\"\n if controlnet is None:\n controlnet = ControlNetXSAdapter.from_unet(\n unet, size_ratio, ctrl_block_out_channels, **ctrl_optional_kwargs\n )\n else:\n if any(\n o is not None for o in (size_ratio, ctrl_block_out_channels, time_embedding_mix, ctrl_optional_kwargs)\n ):\n raise ValueError(\n \"When a controlnet is passed, none of these parameters should be passed: size_ratio, ctrl_block_out_channels, time_embedding_mix, ctrl_optional_kwargs.\"\n )\n\n # # get params\n params_for_unet = [\n \"sample_size\",\n \"down_block_types\",\n \"up_block_types\",\n \"block_out_channels\",\n \"norm_num_groups\",\n \"cross_attention_dim\",\n \"transformer_layers_per_block\",\n \"addition_embed_type\",\n \"addition_time_embed_dim\",\n \"upcast_attention\",\n \"time_cond_proj_dim\",\n \"projection_class_embeddings_input_dim\",\n ]\n params_for_unet = {k: v for k, v in unet.config.items() if k in params_for_unet}\n # The naming seems a bit confusing and it is, see https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 for why.\n params_for_unet[\"num_attention_heads\"] = unet.config.attention_head_dim\n\n params_for_controlnet = [\n \"conditioning_channels\",\n \"conditioning_embedding_out_channels\",\n \"conditioning_channel_order\",\n \"learn_time_embedding\",\n \"block_out_channels\",\n \"num_attention_heads\",\n \"max_norm_num_groups\",\n ]\n params_for_controlnet = {\"ctrl_\" + k: v for k, v in controlnet.config.items() if k in params_for_controlnet}\n params_for_controlnet[\"time_embedding_mix\"] = controlnet.config.time_embedding_mix\n\n # # create model\n model = cls.from_config({**params_for_unet, **params_for_controlnet})\n\n # # load weights\n # from unet\n modules_from_unet = [\n \"time_embedding\",\n \"conv_in\",\n \"conv_norm_out\",\n \"conv_out\",\n ]\n for m in modules_from_unet:\n getattr(model, \"base_\" + m).load_state_dict(getattr(unet, m).state_dict())\n\n optional_modules_from_unet = [\n \"add_time_proj\",\n \"add_embedding\",\n ]\n for m in optional_modules_from_unet:\n if hasattr(unet, m) and getattr(unet, m) is not None:\n getattr(model, \"base_\" + m).load_state_dict(getattr(unet, m).state_dict())\n\n # from controlnet\n model.controlnet_cond_embedding.load_state_dict(controlnet.controlnet_cond_embedding.state_dict())\n model.ctrl_conv_in.load_state_dict(controlnet.conv_in.state_dict())\n if controlnet.time_embedding is not None:\n model.ctrl_time_embedding.load_state_dict(controlnet.time_embedding.state_dict())\n model.control_to_base_for_conv_in.load_state_dict(controlnet.control_to_base_for_conv_in.state_dict())\n\n # from both\n model.down_blocks = nn.ModuleList(\n ControlNetXSCrossAttnDownBlock2D.from_modules(b, c)\n for b, c in zip(unet.down_blocks, controlnet.down_blocks)\n )\n model.mid_block = ControlNetXSCrossAttnMidBlock2D.from_modules(unet.mid_block, controlnet.mid_block)\n model.up_blocks = nn.ModuleList(\n ControlNetXSCrossAttnUpBlock2D.from_modules(b, c)\n for b, c in zip(unet.up_blocks, controlnet.up_connections)\n )\n\n # ensure that the UNetControlNetXSModel is the same dtype as the UNet2DConditionModel\n model.to(unet.dtype)\n\n return model\n\n def freeze_unet_params(self) -> None:\n \"\"\"Freeze the weights of the parts belonging to the base UNet2DConditionModel, and leave everything else unfrozen for fine\n tuning.\"\"\"\n # Freeze everything\n for param in self.parameters():\n param.requires_grad = True\n\n # Unfreeze ControlNetXSAdapter\n base_parts = [\n \"base_time_proj\",\n \"base_time_embedding\",\n \"base_add_time_proj\",\n \"base_add_embedding\",\n \"base_conv_in\",\n \"base_conv_norm_out\",\n \"base_conv_act\",\n \"base_conv_out\",\n ]\n base_parts = [getattr(self, part) for part in base_parts if getattr(self, part) is not None]\n for part in base_parts:\n for param in part.parameters():\n param.requires_grad = False\n\n for d in self.down_blocks:\n d.freeze_base_params()\n self.mid_block.freeze_base_params()\n for u in self.up_blocks:\n u.freeze_base_params()\n\n def _set_gradient_checkpointing(self, module, value=False):\n if hasattr(module, \"gradient_checkpointing\"):\n module.gradient_checkpointing = value\n\n @property\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors\n def attn_processors(self) -> Dict[str, AttentionProcessor]:\n r\"\"\"\n Returns:\n `dict` of attention processors: A dictionary containing all attention processors used in the model with\n indexed by its weight name.\n \"\"\"\n # set recursively\n processors = {}\n\n def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\n if hasattr(module, \"get_processor\"):\n processors[f\"{name}.processor\"] = module.get_processor(return_deprecated_lora=True)\n\n for sub_name, child in module.named_children():\n fn_recursive_add_processors(f\"{name}.{sub_name}\", child, processors)\n\n return processors\n\n for name, module in self.named_children():\n fn_recursive_add_processors(name, module, processors)\n\n return processors\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor\n def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\n r\"\"\"\n Sets the attention processor to use to compute attention.\n\n Parameters:\n processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):\n The instantiated processor class or a dictionary of processor classes that will be set as the processor\n for **all** `Attention` layers.\n\n If `processor` is a dict, the key needs to define the path to the corresponding cross attention\n processor. This is strongly recommended when setting trainable attention processors.\n\n \"\"\"\n count = len(self.attn_processors.keys())\n\n if isinstance(processor, dict) and len(processor) != count:\n raise ValueError(\n f\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\"\n f\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\"\n )\n\n def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\n if hasattr(module, \"set_processor\"):\n if not isinstance(processor, dict):\n module.set_processor(processor)\n else:\n module.set_processor(processor.pop(f\"{name}.processor\"))\n\n for sub_name, child in module.named_children():\n fn_recursive_attn_processor(f\"{name}.{sub_name}\", child, processor)\n\n for name, module in self.named_children():\n fn_recursive_attn_processor(name, module, processor)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor\n def set_default_attn_processor(self):\n \"\"\"\n Disables custom attention processors and sets the default attention implementation.\n \"\"\"\n if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnAddedKVProcessor()\n elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnProcessor()\n else:\n raise ValueError(\n f\"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}\"\n )\n\n self.set_attn_processor(processor)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.enable_freeu\n def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):\n r\"\"\"Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.\n\n The suffixes after the scaling factors represent the stage blocks where they are being applied.\n\n Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that\n are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.\n\n Args:\n s1 (`float`):\n Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to\n mitigate the \"oversmoothing effect\" in the enhanced denoising process.\n s2 (`float`):\n Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to\n mitigate the \"oversmoothing effect\" in the enhanced denoising process.\n b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.\n b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.\n \"\"\"\n for i, upsample_block in enumerate(self.up_blocks):\n setattr(upsample_block, \"s1\", s1)\n setattr(upsample_block, \"s2\", s2)\n setattr(upsample_block, \"b1\", b1)\n setattr(upsample_block, \"b2\", b2)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.disable_freeu\n def disable_freeu(self):\n \"\"\"Disables the FreeU mechanism.\"\"\"\n freeu_keys = {\"s1\", \"s2\", \"b1\", \"b2\"}\n for i, upsample_block in enumerate(self.up_blocks):\n for k in freeu_keys:\n if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None:\n setattr(upsample_block, k, None)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections\n def fuse_qkv_projections(self):\n \"\"\"\n Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)\n are fused. For cross-attention modules, key and value projection matrices are fused.\n\n \n\n This API is 🧪 experimental.\n\n \n \"\"\"\n self.original_attn_processors = None\n\n for _, attn_processor in self.attn_processors.items():\n if \"Added\" in str(attn_processor.__class__.__name__):\n raise ValueError(\"`fuse_qkv_projections()` is not supported for models having added KV projections.\")\n\n self.original_attn_processors = self.attn_processors\n\n for module in self.modules():\n if isinstance(module, Attention):\n module.fuse_projections(fuse=True)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections\n def unfuse_qkv_projections(self):\n \"\"\"Disables the fused QKV projection if enabled.\n\n \n\n This API is 🧪 experimental.\n\n \n\n \"\"\"\n if self.original_attn_processors is not None:\n self.set_attn_processor(self.original_attn_processors)\n\n def forward(\n self,\n sample: Tensor,\n timestep: Union[torch.Tensor, float, int],\n encoder_hidden_states: torch.Tensor,\n controlnet_cond: Optional[torch.Tensor] = None,\n conditioning_scale: Optional[float] = 1.0,\n class_labels: Optional[torch.Tensor] = None,\n timestep_cond: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,\n return_dict: bool = True,\n apply_control: bool = True,\n ) -> Union[ControlNetXSOutput, Tuple]:\n \"\"\"\n The [`ControlNetXSModel`] forward method.\n\n Args:\n sample (`Tensor`):\n The noisy input tensor.\n timestep (`Union[torch.Tensor, float, int]`):\n The number of timesteps to denoise an input.\n encoder_hidden_states (`torch.Tensor`):\n The encoder hidden states.\n controlnet_cond (`Tensor`):\n The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.\n conditioning_scale (`float`, defaults to `1.0`):\n How much the control model affects the base model outputs.\n class_labels (`torch.Tensor`, *optional*, defaults to `None`):\n Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.\n timestep_cond (`torch.Tensor`, *optional*, defaults to `None`):\n Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the\n timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep\n embeddings.\n attention_mask (`torch.Tensor`, *optional*, defaults to `None`):\n An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask\n is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large\n negative values to the attention scores corresponding to \"discard\" tokens.\n cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`):\n A kwargs dictionary that if specified is passed along to the `AttnProcessor`.\n added_cond_kwargs (`dict`):\n Additional conditions for the Stable Diffusion XL UNet.\n return_dict (`bool`, defaults to `True`):\n Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.\n apply_control (`bool`, defaults to `True`):\n If `False`, the input is run only through the base model.\n\n Returns:\n [`~models.controlnetxs.ControlNetXSOutput`] **or** `tuple`:\n If `return_dict` is `True`, a [`~models.controlnetxs.ControlNetXSOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n \"\"\"\n\n # check channel order\n if self.config.ctrl_conditioning_channel_order == \"bgr\":\n controlnet_cond = torch.flip(controlnet_cond, dims=[1])\n\n # prepare attention_mask\n if attention_mask is not None:\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n # 1. time\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can\n # This would be a good case for the `match` statement (Python 3.10+)\n is_mps = sample.device.type == \"mps\"\n if isinstance(timestep, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\n elif len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(sample.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timesteps = timesteps.expand(sample.shape[0])\n\n t_emb = self.base_time_proj(timesteps)\n\n # timesteps does not contain any weights and will always return f32 tensors\n # but time_embedding might actually be running in fp16. so we need to cast here.\n # there might be better ways to encapsulate this.\n t_emb = t_emb.to(dtype=sample.dtype)\n\n if self.config.ctrl_learn_time_embedding and apply_control:\n ctrl_temb = self.ctrl_time_embedding(t_emb, timestep_cond)\n base_temb = self.base_time_embedding(t_emb, timestep_cond)\n interpolation_param = self.config.time_embedding_mix**0.3\n\n temb = ctrl_temb * interpolation_param + base_temb * (1 - interpolation_param)\n else:\n temb = self.base_time_embedding(t_emb)\n\n # added time & text embeddings\n aug_emb = None\n\n if self.config.addition_embed_type is None:\n pass\n elif self.config.addition_embed_type == \"text_time\":\n # SDXL - style\n if \"text_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`\"\n )\n text_embeds = added_cond_kwargs.get(\"text_embeds\")\n if \"time_ids\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`\"\n )\n time_ids = added_cond_kwargs.get(\"time_ids\")\n time_embeds = self.base_add_time_proj(time_ids.flatten())\n time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))\n add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)\n add_embeds = add_embeds.to(temb.dtype)\n aug_emb = self.base_add_embedding(add_embeds)\n else:\n raise ValueError(\n f\"ControlNet-XS currently only supports StableDiffusion and StableDiffusion-XL, so addition_embed_type = {self.config.addition_embed_type} is currently not supported.\"\n )\n\n temb = temb + aug_emb if aug_emb is not None else temb\n\n # text embeddings\n cemb = encoder_hidden_states\n\n # Preparation\n h_ctrl = h_base = sample\n hs_base, hs_ctrl = [], []\n\n # Cross Control\n guided_hint = self.controlnet_cond_embedding(controlnet_cond)\n\n # 1 - conv in & down\n\n h_base = self.base_conv_in(h_base)\n h_ctrl = self.ctrl_conv_in(h_ctrl)\n if guided_hint is not None:\n h_ctrl += guided_hint\n if apply_control:\n h_base = h_base + self.control_to_base_for_conv_in(h_ctrl) * conditioning_scale # add ctrl -> base\n\n hs_base.append(h_base)\n hs_ctrl.append(h_ctrl)\n\n for down in self.down_blocks:\n h_base, h_ctrl, residual_hb, residual_hc = down(\n hidden_states_base=h_base,\n hidden_states_ctrl=h_ctrl,\n temb=temb,\n encoder_hidden_states=cemb,\n conditioning_scale=conditioning_scale,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n apply_control=apply_control,\n )\n hs_base.extend(residual_hb)\n hs_ctrl.extend(residual_hc)\n\n # 2 - mid\n h_base, h_ctrl = self.mid_block(\n hidden_states_base=h_base,\n hidden_states_ctrl=h_ctrl,\n temb=temb,\n encoder_hidden_states=cemb,\n conditioning_scale=conditioning_scale,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n apply_control=apply_control,\n )\n\n # 3 - up\n for up in self.up_blocks:\n n_resnets = len(up.resnets)\n skips_hb = hs_base[-n_resnets:]\n skips_hc = hs_ctrl[-n_resnets:]\n hs_base = hs_base[:-n_resnets]\n hs_ctrl = hs_ctrl[:-n_resnets]\n h_base = up(\n hidden_states=h_base,\n res_hidden_states_tuple_base=skips_hb,\n res_hidden_states_tuple_ctrl=skips_hc,\n temb=temb,\n encoder_hidden_states=cemb,\n conditioning_scale=conditioning_scale,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n apply_control=apply_control,\n )\n\n # 4 - conv out\n h_base = self.base_conv_norm_out(h_base)\n h_base = self.base_conv_act(h_base)\n h_base = self.base_conv_out(h_base)\n\n if not return_dict:\n return (h_base,)\n\n return ControlNetXSOutput(sample=h_base)\n\n\nclass ControlNetXSCrossAttnDownBlock2D(nn.Module):\n def __init__(\n self,\n base_in_channels: int,\n base_out_channels: int,\n ctrl_in_channels: int,\n ctrl_out_channels: int,\n temb_channels: int,\n norm_num_groups: int = 32,\n ctrl_max_norm_num_groups: int = 32,\n has_crossattn=True,\n transformer_layers_per_block: Optional[Union[int, Tuple[int]]] = 1,\n base_num_attention_heads: Optional[int] = 1,\n ctrl_num_attention_heads: Optional[int] = 1,\n cross_attention_dim: Optional[int] = 1024,\n add_downsample: bool = True,\n upcast_attention: Optional[bool] = False,\n ):\n super().__init__()\n base_resnets = []\n base_attentions = []\n ctrl_resnets = []\n ctrl_attentions = []\n ctrl_to_base = []\n base_to_ctrl = []\n\n num_layers = 2 # only support sd + sdxl\n\n if isinstance(transformer_layers_per_block, int):\n transformer_layers_per_block = [transformer_layers_per_block] * num_layers\n\n for i in range(num_layers):\n base_in_channels = base_in_channels if i == 0 else base_out_channels\n ctrl_in_channels = ctrl_in_channels if i == 0 else ctrl_out_channels\n\n # Before the resnet/attention application, information is concatted from base to control.\n # Concat doesn't require change in number of channels\n base_to_ctrl.append(make_zero_conv(base_in_channels, base_in_channels))\n\n base_resnets.append(\n ResnetBlock2D(\n in_channels=base_in_channels,\n out_channels=base_out_channels,\n temb_channels=temb_channels,\n groups=norm_num_groups,\n )\n )\n ctrl_resnets.append(\n ResnetBlock2D(\n in_channels=ctrl_in_channels + base_in_channels, # information from base is concatted to ctrl\n out_channels=ctrl_out_channels,\n temb_channels=temb_channels,\n groups=find_largest_factor(\n ctrl_in_channels + base_in_channels, max_factor=ctrl_max_norm_num_groups\n ),\n groups_out=find_largest_factor(ctrl_out_channels, max_factor=ctrl_max_norm_num_groups),\n eps=1e-5,\n )\n )\n\n if has_crossattn:\n base_attentions.append(\n Transformer2DModel(\n base_num_attention_heads,\n base_out_channels // base_num_attention_heads,\n in_channels=base_out_channels,\n num_layers=transformer_layers_per_block[i],\n cross_attention_dim=cross_attention_dim,\n use_linear_projection=True,\n upcast_attention=upcast_attention,\n norm_num_groups=norm_num_groups,\n )\n )\n ctrl_attentions.append(\n Transformer2DModel(\n ctrl_num_attention_heads,\n ctrl_out_channels // ctrl_num_attention_heads,\n in_channels=ctrl_out_channels,\n num_layers=transformer_layers_per_block[i],\n cross_attention_dim=cross_attention_dim,\n use_linear_projection=True,\n upcast_attention=upcast_attention,\n norm_num_groups=find_largest_factor(ctrl_out_channels, max_factor=ctrl_max_norm_num_groups),\n )\n )\n\n # After the resnet/attention application, information is added from control to base\n # Addition requires change in number of channels\n ctrl_to_base.append(make_zero_conv(ctrl_out_channels, base_out_channels))\n\n if add_downsample:\n # Before the downsampler application, information is concatted from base to control\n # Concat doesn't require change in number of channels\n base_to_ctrl.append(make_zero_conv(base_out_channels, base_out_channels))\n\n self.base_downsamplers = Downsample2D(\n base_out_channels, use_conv=True, out_channels=base_out_channels, name=\"op\"\n )\n self.ctrl_downsamplers = Downsample2D(\n ctrl_out_channels + base_out_channels, use_conv=True, out_channels=ctrl_out_channels, name=\"op\"\n )\n\n # After the downsampler application, information is added from control to base\n # Addition requires change in number of channels\n ctrl_to_base.append(make_zero_conv(ctrl_out_channels, base_out_channels))\n else:\n self.base_downsamplers = None\n self.ctrl_downsamplers = None\n\n self.base_resnets = nn.ModuleList(base_resnets)\n self.ctrl_resnets = nn.ModuleList(ctrl_resnets)\n self.base_attentions = nn.ModuleList(base_attentions) if has_crossattn else [None] * num_layers\n self.ctrl_attentions = nn.ModuleList(ctrl_attentions) if has_crossattn else [None] * num_layers\n self.base_to_ctrl = nn.ModuleList(base_to_ctrl)\n self.ctrl_to_base = nn.ModuleList(ctrl_to_base)\n\n self.gradient_checkpointing = False\n\n @classmethod\n def from_modules(cls, base_downblock: CrossAttnDownBlock2D, ctrl_downblock: DownBlockControlNetXSAdapter):\n # get params\n def get_first_cross_attention(block):\n return block.attentions[0].transformer_blocks[0].attn2\n\n base_in_channels = base_downblock.resnets[0].in_channels\n base_out_channels = base_downblock.resnets[0].out_channels\n ctrl_in_channels = (\n ctrl_downblock.resnets[0].in_channels - base_in_channels\n ) # base channels are concatted to ctrl channels in init\n ctrl_out_channels = ctrl_downblock.resnets[0].out_channels\n temb_channels = base_downblock.resnets[0].time_emb_proj.in_features\n num_groups = base_downblock.resnets[0].norm1.num_groups\n ctrl_num_groups = ctrl_downblock.resnets[0].norm1.num_groups\n if hasattr(base_downblock, \"attentions\"):\n has_crossattn = True\n transformer_layers_per_block = len(base_downblock.attentions[0].transformer_blocks)\n base_num_attention_heads = get_first_cross_attention(base_downblock).heads\n ctrl_num_attention_heads = get_first_cross_attention(ctrl_downblock).heads\n cross_attention_dim = get_first_cross_attention(base_downblock).cross_attention_dim\n upcast_attention = get_first_cross_attention(base_downblock).upcast_attention\n else:\n has_crossattn = False\n transformer_layers_per_block = None\n base_num_attention_heads = None\n ctrl_num_attention_heads = None\n cross_attention_dim = None\n upcast_attention = None\n add_downsample = base_downblock.downsamplers is not None\n\n # create model\n model = cls(\n base_in_channels=base_in_channels,\n base_out_channels=base_out_channels,\n ctrl_in_channels=ctrl_in_channels,\n ctrl_out_channels=ctrl_out_channels,\n temb_channels=temb_channels,\n norm_num_groups=num_groups,\n ctrl_max_norm_num_groups=ctrl_num_groups,\n has_crossattn=has_crossattn,\n transformer_layers_per_block=transformer_layers_per_block,\n base_num_attention_heads=base_num_attention_heads,\n ctrl_num_attention_heads=ctrl_num_attention_heads,\n cross_attention_dim=cross_attention_dim,\n add_downsample=add_downsample,\n upcast_attention=upcast_attention,\n )\n\n # # load weights\n model.base_resnets.load_state_dict(base_downblock.resnets.state_dict())\n model.ctrl_resnets.load_state_dict(ctrl_downblock.resnets.state_dict())\n if has_crossattn:\n model.base_attentions.load_state_dict(base_downblock.attentions.state_dict())\n model.ctrl_attentions.load_state_dict(ctrl_downblock.attentions.state_dict())\n if add_downsample:\n model.base_downsamplers.load_state_dict(base_downblock.downsamplers[0].state_dict())\n model.ctrl_downsamplers.load_state_dict(ctrl_downblock.downsamplers.state_dict())\n model.base_to_ctrl.load_state_dict(ctrl_downblock.base_to_ctrl.state_dict())\n model.ctrl_to_base.load_state_dict(ctrl_downblock.ctrl_to_base.state_dict())\n\n return model\n\n def freeze_base_params(self) -> None:\n \"\"\"Freeze the weights of the parts belonging to the base UNet2DConditionModel, and leave everything else unfrozen for fine\n tuning.\"\"\"\n # Unfreeze everything\n for param in self.parameters():\n param.requires_grad = True\n\n # Freeze base part\n base_parts = [self.base_resnets]\n if isinstance(self.base_attentions, nn.ModuleList): # attentions can be a list of Nones\n base_parts.append(self.base_attentions)\n if self.base_downsamplers is not None:\n base_parts.append(self.base_downsamplers)\n for part in base_parts:\n for param in part.parameters():\n param.requires_grad = False\n\n def forward(\n self,\n hidden_states_base: Tensor,\n temb: Tensor,\n encoder_hidden_states: Optional[Tensor] = None,\n hidden_states_ctrl: Optional[Tensor] = None,\n conditioning_scale: Optional[float] = 1.0,\n attention_mask: Optional[Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[Tensor] = None,\n apply_control: bool = True,\n ) -> Tuple[Tensor, Tensor, Tuple[Tensor, ...], Tuple[Tensor, ...]]:\n if cross_attention_kwargs is not None:\n if cross_attention_kwargs.get(\"scale\", None) is not None:\n logger.warning(\"Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.\")\n\n h_base = hidden_states_base\n h_ctrl = hidden_states_ctrl\n\n base_output_states = ()\n ctrl_output_states = ()\n\n base_blocks = list(zip(self.base_resnets, self.base_attentions))\n ctrl_blocks = list(zip(self.ctrl_resnets, self.ctrl_attentions))\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n for (b_res, b_attn), (c_res, c_attn), b2c, c2b in zip(\n base_blocks, ctrl_blocks, self.base_to_ctrl, self.ctrl_to_base\n ):\n # concat base -> ctrl\n if apply_control:\n h_ctrl = torch.cat([h_ctrl, b2c(h_base)], dim=1)\n\n # apply base subblock\n if self.training and self.gradient_checkpointing:\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n h_base = torch.utils.checkpoint.checkpoint(\n create_custom_forward(b_res),\n h_base,\n temb,\n **ckpt_kwargs,\n )\n else:\n h_base = b_res(h_base, temb)\n\n if b_attn is not None:\n h_base = b_attn(\n h_base,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n\n # apply ctrl subblock\n if apply_control:\n if self.training and self.gradient_checkpointing:\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n h_ctrl = torch.utils.checkpoint.checkpoint(\n create_custom_forward(c_res),\n h_ctrl,\n temb,\n **ckpt_kwargs,\n )\n else:\n h_ctrl = c_res(h_ctrl, temb)\n if c_attn is not None:\n h_ctrl = c_attn(\n h_ctrl,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n\n # add ctrl -> base\n if apply_control:\n h_base = h_base + c2b(h_ctrl) * conditioning_scale\n\n base_output_states = base_output_states + (h_base,)\n ctrl_output_states = ctrl_output_states + (h_ctrl,)\n\n if self.base_downsamplers is not None: # if we have a base_downsampler, then also a ctrl_downsampler\n b2c = self.base_to_ctrl[-1]\n c2b = self.ctrl_to_base[-1]\n\n # concat base -> ctrl\n if apply_control:\n h_ctrl = torch.cat([h_ctrl, b2c(h_base)], dim=1)\n # apply base subblock\n h_base = self.base_downsamplers(h_base)\n # apply ctrl subblock\n if apply_control:\n h_ctrl = self.ctrl_downsamplers(h_ctrl)\n # add ctrl -> base\n if apply_control:\n h_base = h_base + c2b(h_ctrl) * conditioning_scale\n\n base_output_states = base_output_states + (h_base,)\n ctrl_output_states = ctrl_output_states + (h_ctrl,)\n\n return h_base, h_ctrl, base_output_states, ctrl_output_states\n\n\nclass ControlNetXSCrossAttnMidBlock2D(nn.Module):\n def __init__(\n self,\n base_channels: int,\n ctrl_channels: int,\n temb_channels: Optional[int] = None,\n norm_num_groups: int = 32,\n ctrl_max_norm_num_groups: int = 32,\n transformer_layers_per_block: int = 1,\n base_num_attention_heads: Optional[int] = 1,\n ctrl_num_attention_heads: Optional[int] = 1,\n cross_attention_dim: Optional[int] = 1024,\n upcast_attention: bool = False,\n ):\n super().__init__()\n\n # Before the midblock application, information is concatted from base to control.\n # Concat doesn't require change in number of channels\n self.base_to_ctrl = make_zero_conv(base_channels, base_channels)\n\n self.base_midblock = UNetMidBlock2DCrossAttn(\n transformer_layers_per_block=transformer_layers_per_block,\n in_channels=base_channels,\n temb_channels=temb_channels,\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=base_num_attention_heads,\n use_linear_projection=True,\n upcast_attention=upcast_attention,\n )\n\n self.ctrl_midblock = UNetMidBlock2DCrossAttn(\n transformer_layers_per_block=transformer_layers_per_block,\n in_channels=ctrl_channels + base_channels,\n out_channels=ctrl_channels,\n temb_channels=temb_channels,\n # number or norm groups must divide both in_channels and out_channels\n resnet_groups=find_largest_factor(\n gcd(ctrl_channels, ctrl_channels + base_channels), ctrl_max_norm_num_groups\n ),\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=ctrl_num_attention_heads,\n use_linear_projection=True,\n upcast_attention=upcast_attention,\n )\n\n # After the midblock application, information is added from control to base\n # Addition requires change in number of channels\n self.ctrl_to_base = make_zero_conv(ctrl_channels, base_channels)\n\n self.gradient_checkpointing = False\n\n @classmethod\n def from_modules(\n cls,\n base_midblock: UNetMidBlock2DCrossAttn,\n ctrl_midblock: MidBlockControlNetXSAdapter,\n ):\n base_to_ctrl = ctrl_midblock.base_to_ctrl\n ctrl_to_base = ctrl_midblock.ctrl_to_base\n ctrl_midblock = ctrl_midblock.midblock\n\n # get params\n def get_first_cross_attention(midblock):\n return midblock.attentions[0].transformer_blocks[0].attn2\n\n base_channels = ctrl_to_base.out_channels\n ctrl_channels = ctrl_to_base.in_channels\n transformer_layers_per_block = len(base_midblock.attentions[0].transformer_blocks)\n temb_channels = base_midblock.resnets[0].time_emb_proj.in_features\n num_groups = base_midblock.resnets[0].norm1.num_groups\n ctrl_num_groups = ctrl_midblock.resnets[0].norm1.num_groups\n base_num_attention_heads = get_first_cross_attention(base_midblock).heads\n ctrl_num_attention_heads = get_first_cross_attention(ctrl_midblock).heads\n cross_attention_dim = get_first_cross_attention(base_midblock).cross_attention_dim\n upcast_attention = get_first_cross_attention(base_midblock).upcast_attention\n\n # create model\n model = cls(\n base_channels=base_channels,\n ctrl_channels=ctrl_channels,\n temb_channels=temb_channels,\n norm_num_groups=num_groups,\n ctrl_max_norm_num_groups=ctrl_num_groups,\n transformer_layers_per_block=transformer_layers_per_block,\n base_num_attention_heads=base_num_attention_heads,\n ctrl_num_attention_heads=ctrl_num_attention_heads,\n cross_attention_dim=cross_attention_dim,\n upcast_attention=upcast_attention,\n )\n\n # load weights\n model.base_to_ctrl.load_state_dict(base_to_ctrl.state_dict())\n model.base_midblock.load_state_dict(base_midblock.state_dict())\n model.ctrl_midblock.load_state_dict(ctrl_midblock.state_dict())\n model.ctrl_to_base.load_state_dict(ctrl_to_base.state_dict())\n\n return model\n\n def freeze_base_params(self) -> None:\n \"\"\"Freeze the weights of the parts belonging to the base UNet2DConditionModel, and leave everything else unfrozen for fine\n tuning.\"\"\"\n # Unfreeze everything\n for param in self.parameters():\n param.requires_grad = True\n\n # Freeze base part\n for param in self.base_midblock.parameters():\n param.requires_grad = False\n\n def forward(\n self,\n hidden_states_base: Tensor,\n temb: Tensor,\n encoder_hidden_states: Tensor,\n hidden_states_ctrl: Optional[Tensor] = None,\n conditioning_scale: Optional[float] = 1.0,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n attention_mask: Optional[Tensor] = None,\n encoder_attention_mask: Optional[Tensor] = None,\n apply_control: bool = True,\n ) -> Tuple[Tensor, Tensor]:\n if cross_attention_kwargs is not None:\n if cross_attention_kwargs.get(\"scale\", None) is not None:\n logger.warning(\"Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.\")\n\n h_base = hidden_states_base\n h_ctrl = hidden_states_ctrl\n\n joint_args = {\n \"temb\": temb,\n \"encoder_hidden_states\": encoder_hidden_states,\n \"attention_mask\": attention_mask,\n \"cross_attention_kwargs\": cross_attention_kwargs,\n \"encoder_attention_mask\": encoder_attention_mask,\n }\n\n if apply_control:\n h_ctrl = torch.cat([h_ctrl, self.base_to_ctrl(h_base)], dim=1) # concat base -> ctrl\n h_base = self.base_midblock(h_base, **joint_args) # apply base mid block\n if apply_control:\n h_ctrl = self.ctrl_midblock(h_ctrl, **joint_args) # apply ctrl mid block\n h_base = h_base + self.ctrl_to_base(h_ctrl) * conditioning_scale # add ctrl -> base\n\n return h_base, h_ctrl\n\n\nclass ControlNetXSCrossAttnUpBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n prev_output_channel: int,\n ctrl_skip_channels: List[int],\n temb_channels: int,\n norm_num_groups: int = 32,\n resolution_idx: Optional[int] = None,\n has_crossattn=True,\n transformer_layers_per_block: int = 1,\n num_attention_heads: int = 1,\n cross_attention_dim: int = 1024,\n add_upsample: bool = True,\n upcast_attention: bool = False,\n ):\n super().__init__()\n resnets = []\n attentions = []\n ctrl_to_base = []\n\n num_layers = 3 # only support sd + sdxl\n\n self.has_cross_attention = has_crossattn\n self.num_attention_heads = num_attention_heads\n\n if isinstance(transformer_layers_per_block, int):\n transformer_layers_per_block = [transformer_layers_per_block] * num_layers\n\n for i in range(num_layers):\n res_skip_channels = in_channels if (i == num_layers - 1) else out_channels\n resnet_in_channels = prev_output_channel if i == 0 else out_channels\n\n ctrl_to_base.append(make_zero_conv(ctrl_skip_channels[i], resnet_in_channels))\n\n resnets.append(\n ResnetBlock2D(\n in_channels=resnet_in_channels + res_skip_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n groups=norm_num_groups,\n )\n )\n\n if has_crossattn:\n attentions.append(\n Transformer2DModel(\n num_attention_heads,\n out_channels // num_attention_heads,\n in_channels=out_channels,\n num_layers=transformer_layers_per_block[i],\n cross_attention_dim=cross_attention_dim,\n use_linear_projection=True,\n upcast_attention=upcast_attention,\n norm_num_groups=norm_num_groups,\n )\n )\n\n self.resnets = nn.ModuleList(resnets)\n self.attentions = nn.ModuleList(attentions) if has_crossattn else [None] * num_layers\n self.ctrl_to_base = nn.ModuleList(ctrl_to_base)\n\n if add_upsample:\n self.upsamplers = Upsample2D(out_channels, use_conv=True, out_channels=out_channels)\n else:\n self.upsamplers = None\n\n self.gradient_checkpointing = False\n self.resolution_idx = resolution_idx\n\n @classmethod\n def from_modules(cls, base_upblock: CrossAttnUpBlock2D, ctrl_upblock: UpBlockControlNetXSAdapter):\n ctrl_to_base_skip_connections = ctrl_upblock.ctrl_to_base\n\n # get params\n def get_first_cross_attention(block):\n return block.attentions[0].transformer_blocks[0].attn2\n\n out_channels = base_upblock.resnets[0].out_channels\n in_channels = base_upblock.resnets[-1].in_channels - out_channels\n prev_output_channels = base_upblock.resnets[0].in_channels - out_channels\n ctrl_skip_channelss = [c.in_channels for c in ctrl_to_base_skip_connections]\n temb_channels = base_upblock.resnets[0].time_emb_proj.in_features\n num_groups = base_upblock.resnets[0].norm1.num_groups\n resolution_idx = base_upblock.resolution_idx\n if hasattr(base_upblock, \"attentions\"):\n has_crossattn = True\n transformer_layers_per_block = len(base_upblock.attentions[0].transformer_blocks)\n num_attention_heads = get_first_cross_attention(base_upblock).heads\n cross_attention_dim = get_first_cross_attention(base_upblock).cross_attention_dim\n upcast_attention = get_first_cross_attention(base_upblock).upcast_attention\n else:\n has_crossattn = False\n transformer_layers_per_block = None\n num_attention_heads = None\n cross_attention_dim = None\n upcast_attention = None\n add_upsample = base_upblock.upsamplers is not None\n\n # create model\n model = cls(\n in_channels=in_channels,\n out_channels=out_channels,\n prev_output_channel=prev_output_channels,\n ctrl_skip_channels=ctrl_skip_channelss,\n temb_channels=temb_channels,\n norm_num_groups=num_groups,\n resolution_idx=resolution_idx,\n has_crossattn=has_crossattn,\n transformer_layers_per_block=transformer_layers_per_block,\n num_attention_heads=num_attention_heads,\n cross_attention_dim=cross_attention_dim,\n add_upsample=add_upsample,\n upcast_attention=upcast_attention,\n )\n\n # load weights\n model.resnets.load_state_dict(base_upblock.resnets.state_dict())\n if has_crossattn:\n model.attentions.load_state_dict(base_upblock.attentions.state_dict())\n if add_upsample:\n model.upsamplers.load_state_dict(base_upblock.upsamplers[0].state_dict())\n model.ctrl_to_base.load_state_dict(ctrl_to_base_skip_connections.state_dict())\n\n return model\n\n def freeze_base_params(self) -> None:\n \"\"\"Freeze the weights of the parts belonging to the base UNet2DConditionModel, and leave everything else unfrozen for fine\n tuning.\"\"\"\n # Unfreeze everything\n for param in self.parameters():\n param.requires_grad = True\n\n # Freeze base part\n base_parts = [self.resnets]\n if isinstance(self.attentions, nn.ModuleList): # attentions can be a list of Nones\n base_parts.append(self.attentions)\n if self.upsamplers is not None:\n base_parts.append(self.upsamplers)\n for part in base_parts:\n for param in part.parameters():\n param.requires_grad = False\n\n def forward(\n self,\n hidden_states: Tensor,\n res_hidden_states_tuple_base: Tuple[Tensor, ...],\n res_hidden_states_tuple_ctrl: Tuple[Tensor, ...],\n temb: Tensor,\n encoder_hidden_states: Optional[Tensor] = None,\n conditioning_scale: Optional[float] = 1.0,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n attention_mask: Optional[Tensor] = None,\n upsample_size: Optional[int] = None,\n encoder_attention_mask: Optional[Tensor] = None,\n apply_control: bool = True,\n ) -> Tensor:\n if cross_attention_kwargs is not None:\n if cross_attention_kwargs.get(\"scale\", None) is not None:\n logger.warning(\"Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.\")\n\n is_freeu_enabled = (\n getattr(self, \"s1\", None)\n and getattr(self, \"s2\", None)\n and getattr(self, \"b1\", None)\n and getattr(self, \"b2\", None)\n )\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n def maybe_apply_freeu_to_subblock(hidden_states, res_h_base):\n # FreeU: Only operate on the first two stages\n if is_freeu_enabled:\n return apply_freeu(\n self.resolution_idx,\n hidden_states,\n res_h_base,\n s1=self.s1,\n s2=self.s2,\n b1=self.b1,\n b2=self.b2,\n )\n else:\n return hidden_states, res_h_base\n\n for resnet, attn, c2b, res_h_base, res_h_ctrl in zip(\n self.resnets,\n self.attentions,\n self.ctrl_to_base,\n reversed(res_hidden_states_tuple_base),\n reversed(res_hidden_states_tuple_ctrl),\n ):\n if apply_control:\n hidden_states += c2b(res_h_ctrl) * conditioning_scale\n\n hidden_states, res_h_base = maybe_apply_freeu_to_subblock(hidden_states, res_h_base)\n hidden_states = torch.cat([hidden_states, res_h_base], dim=1)\n\n if self.training and self.gradient_checkpointing:\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet),\n hidden_states,\n temb,\n **ckpt_kwargs,\n )\n else:\n hidden_states = resnet(hidden_states, temb)\n\n if attn is not None:\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n\n if self.upsamplers is not None:\n hidden_states = self.upsamplers(hidden_states, upsample_size)\n\n return hidden_states\n\n\ndef make_zero_conv(in_channels, out_channels=None):\n return zero_module(nn.Conv2d(in_channels, out_channels, 1, padding=0))\n\n\ndef zero_module(module):\n for p in module.parameters():\n nn.init.zeros_(p)\n return module\n\n\ndef find_largest_factor(number, max_factor):\n factor = max_factor\n if factor >= number:\n return number\n while factor != 0:\n residual = number % factor\n if residual == 0:\n return factor\n factor -= 1\n" }, { "path": "diffusers/src/diffusers/models/downsampling.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom ..utils import deprecate\nfrom .normalization import RMSNorm\nfrom .upsampling import upfirdn2d_native\n\n\nclass Downsample1D(nn.Module):\n \"\"\"A 1D downsampling layer with an optional convolution.\n\n Parameters:\n channels (`int`):\n number of channels in the inputs and outputs.\n use_conv (`bool`, default `False`):\n option to use a convolution.\n out_channels (`int`, optional):\n number of output channels. Defaults to `channels`.\n padding (`int`, default `1`):\n padding for the convolution.\n name (`str`, default `conv`):\n name of the downsampling 1D layer.\n \"\"\"\n\n def __init__(\n self,\n channels: int,\n use_conv: bool = False,\n out_channels: Optional[int] = None,\n padding: int = 1,\n name: str = \"conv\",\n ):\n super().__init__()\n self.channels = channels\n self.out_channels = out_channels or channels\n self.use_conv = use_conv\n self.padding = padding\n stride = 2\n self.name = name\n\n if use_conv:\n self.conv = nn.Conv1d(self.channels, self.out_channels, 3, stride=stride, padding=padding)\n else:\n assert self.channels == self.out_channels\n self.conv = nn.AvgPool1d(kernel_size=stride, stride=stride)\n\n def forward(self, inputs: torch.Tensor) -> torch.Tensor:\n assert inputs.shape[1] == self.channels\n return self.conv(inputs)\n\n\nclass Downsample2D(nn.Module):\n \"\"\"A 2D downsampling layer with an optional convolution.\n\n Parameters:\n channels (`int`):\n number of channels in the inputs and outputs.\n use_conv (`bool`, default `False`):\n option to use a convolution.\n out_channels (`int`, optional):\n number of output channels. Defaults to `channels`.\n padding (`int`, default `1`):\n padding for the convolution.\n name (`str`, default `conv`):\n name of the downsampling 2D layer.\n \"\"\"\n\n def __init__(\n self,\n channels: int,\n use_conv: bool = False,\n out_channels: Optional[int] = None,\n padding: int = 1,\n name: str = \"conv\",\n kernel_size=3,\n norm_type=None,\n eps=None,\n elementwise_affine=None,\n bias=True,\n ):\n super().__init__()\n self.channels = channels\n self.out_channels = out_channels or channels\n self.use_conv = use_conv\n self.padding = padding\n stride = 2\n self.name = name\n\n if norm_type == \"ln_norm\":\n self.norm = nn.LayerNorm(channels, eps, elementwise_affine)\n elif norm_type == \"rms_norm\":\n self.norm = RMSNorm(channels, eps, elementwise_affine)\n elif norm_type is None:\n self.norm = None\n else:\n raise ValueError(f\"unknown norm_type: {norm_type}\")\n\n if use_conv:\n conv = nn.Conv2d(\n self.channels, self.out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias\n )\n else:\n assert self.channels == self.out_channels\n conv = nn.AvgPool2d(kernel_size=stride, stride=stride)\n\n # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed\n if name == \"conv\":\n self.Conv2d_0 = conv\n self.conv = conv\n elif name == \"Conv2d_0\":\n self.conv = conv\n else:\n self.conv = conv\n\n def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n assert hidden_states.shape[1] == self.channels\n\n if self.norm is not None:\n hidden_states = self.norm(hidden_states.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)\n\n if self.use_conv and self.padding == 0:\n pad = (0, 1, 0, 1)\n hidden_states = F.pad(hidden_states, pad, mode=\"constant\", value=0)\n\n assert hidden_states.shape[1] == self.channels\n\n hidden_states = self.conv(hidden_states)\n\n return hidden_states\n\n\nclass FirDownsample2D(nn.Module):\n \"\"\"A 2D FIR downsampling layer with an optional convolution.\n\n Parameters:\n channels (`int`):\n number of channels in the inputs and outputs.\n use_conv (`bool`, default `False`):\n option to use a convolution.\n out_channels (`int`, optional):\n number of output channels. Defaults to `channels`.\n fir_kernel (`tuple`, default `(1, 3, 3, 1)`):\n kernel for the FIR filter.\n \"\"\"\n\n def __init__(\n self,\n channels: Optional[int] = None,\n out_channels: Optional[int] = None,\n use_conv: bool = False,\n fir_kernel: Tuple[int, int, int, int] = (1, 3, 3, 1),\n ):\n super().__init__()\n out_channels = out_channels if out_channels else channels\n if use_conv:\n self.Conv2d_0 = nn.Conv2d(channels, out_channels, kernel_size=3, stride=1, padding=1)\n self.fir_kernel = fir_kernel\n self.use_conv = use_conv\n self.out_channels = out_channels\n\n def _downsample_2d(\n self,\n hidden_states: torch.Tensor,\n weight: Optional[torch.Tensor] = None,\n kernel: Optional[torch.Tensor] = None,\n factor: int = 2,\n gain: float = 1,\n ) -> torch.Tensor:\n \"\"\"Fused `Conv2d()` followed by `downsample_2d()`.\n Padding is performed only once at the beginning, not between the operations. The fused op is considerably more\n efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of\n arbitrary order.\n\n Args:\n hidden_states (`torch.Tensor`):\n Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.\n weight (`torch.Tensor`, *optional*):\n Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`. Grouped convolution can be\n performed by `inChannels = x.shape[0] // numGroups`.\n kernel (`torch.Tensor`, *optional*):\n FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] * factor`, which\n corresponds to average pooling.\n factor (`int`, *optional*, default to `2`):\n Integer downsampling factor.\n gain (`float`, *optional*, default to `1.0`):\n Scaling factor for signal magnitude.\n\n Returns:\n output (`torch.Tensor`):\n Tensor of the shape `[N, C, H // factor, W // factor]` or `[N, H // factor, W // factor, C]`, and same\n datatype as `x`.\n \"\"\"\n\n assert isinstance(factor, int) and factor >= 1\n if kernel is None:\n kernel = [1] * factor\n\n # setup kernel\n kernel = torch.tensor(kernel, dtype=torch.float32)\n if kernel.ndim == 1:\n kernel = torch.outer(kernel, kernel)\n kernel /= torch.sum(kernel)\n\n kernel = kernel * gain\n\n if self.use_conv:\n _, _, convH, convW = weight.shape\n pad_value = (kernel.shape[0] - factor) + (convW - 1)\n stride_value = [factor, factor]\n upfirdn_input = upfirdn2d_native(\n hidden_states,\n torch.tensor(kernel, device=hidden_states.device),\n pad=((pad_value + 1) // 2, pad_value // 2),\n )\n output = F.conv2d(upfirdn_input, weight, stride=stride_value, padding=0)\n else:\n pad_value = kernel.shape[0] - factor\n output = upfirdn2d_native(\n hidden_states,\n torch.tensor(kernel, device=hidden_states.device),\n down=factor,\n pad=((pad_value + 1) // 2, pad_value // 2),\n )\n\n return output\n\n def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n if self.use_conv:\n downsample_input = self._downsample_2d(hidden_states, weight=self.Conv2d_0.weight, kernel=self.fir_kernel)\n hidden_states = downsample_input + self.Conv2d_0.bias.reshape(1, -1, 1, 1)\n else:\n hidden_states = self._downsample_2d(hidden_states, kernel=self.fir_kernel, factor=2)\n\n return hidden_states\n\n\n# downsample/upsample layer used in k-upscaler, might be able to use FirDownsample2D/DirUpsample2D instead\nclass KDownsample2D(nn.Module):\n r\"\"\"A 2D K-downsampling layer.\n\n Parameters:\n pad_mode (`str`, *optional*, default to `\"reflect\"`): the padding mode to use.\n \"\"\"\n\n def __init__(self, pad_mode: str = \"reflect\"):\n super().__init__()\n self.pad_mode = pad_mode\n kernel_1d = torch.tensor([[1 / 8, 3 / 8, 3 / 8, 1 / 8]])\n self.pad = kernel_1d.shape[1] // 2 - 1\n self.register_buffer(\"kernel\", kernel_1d.T @ kernel_1d, persistent=False)\n\n def forward(self, inputs: torch.Tensor) -> torch.Tensor:\n inputs = F.pad(inputs, (self.pad,) * 4, self.pad_mode)\n weight = inputs.new_zeros(\n [\n inputs.shape[1],\n inputs.shape[1],\n self.kernel.shape[0],\n self.kernel.shape[1],\n ]\n )\n indices = torch.arange(inputs.shape[1], device=inputs.device)\n kernel = self.kernel.to(weight)[None, :].expand(inputs.shape[1], -1, -1)\n weight[indices, indices] = kernel\n return F.conv2d(inputs, weight, stride=2)\n\n\ndef downsample_2d(\n hidden_states: torch.Tensor,\n kernel: Optional[torch.Tensor] = None,\n factor: int = 2,\n gain: float = 1,\n) -> torch.Tensor:\n r\"\"\"Downsample2D a batch of 2D images with the given filter.\n Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and downsamples each image with the\n given filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the\n specified `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its\n shape is a multiple of the downsampling factor.\n\n Args:\n hidden_states (`torch.Tensor`)\n Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.\n kernel (`torch.Tensor`, *optional*):\n FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] * factor`, which\n corresponds to average pooling.\n factor (`int`, *optional*, default to `2`):\n Integer downsampling factor.\n gain (`float`, *optional*, default to `1.0`):\n Scaling factor for signal magnitude.\n\n Returns:\n output (`torch.Tensor`):\n Tensor of the shape `[N, C, H // factor, W // factor]`\n \"\"\"\n\n assert isinstance(factor, int) and factor >= 1\n if kernel is None:\n kernel = [1] * factor\n\n kernel = torch.tensor(kernel, dtype=torch.float32)\n if kernel.ndim == 1:\n kernel = torch.outer(kernel, kernel)\n kernel /= torch.sum(kernel)\n\n kernel = kernel * gain\n pad_value = kernel.shape[0] - factor\n output = upfirdn2d_native(\n hidden_states,\n kernel.to(device=hidden_states.device),\n down=factor,\n pad=((pad_value + 1) // 2, pad_value // 2),\n )\n return output\n" }, { "path": "diffusers/src/diffusers/models/embeddings.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport math\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom ..utils import deprecate\nfrom .activations import FP32SiLU, get_activation\nfrom .attention_processor import Attention\n\n\ndef get_timestep_embedding(\n timesteps: torch.Tensor,\n embedding_dim: int,\n flip_sin_to_cos: bool = False,\n downscale_freq_shift: float = 1,\n scale: float = 1,\n max_period: int = 10000,\n):\n \"\"\"\n This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.\n\n :param timesteps: a 1-D Tensor of N indices, one per batch element.\n These may be fractional.\n :param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the\n embeddings. :return: an [N x dim] Tensor of positional embeddings.\n \"\"\"\n assert len(timesteps.shape) == 1, \"Timesteps should be a 1d-array\"\n\n half_dim = embedding_dim // 2\n exponent = -math.log(max_period) * torch.arange(\n start=0, end=half_dim, dtype=torch.float32, device=timesteps.device\n )\n exponent = exponent / (half_dim - downscale_freq_shift)\n\n emb = torch.exp(exponent)\n emb = timesteps[:, None].float() * emb[None, :]\n\n # scale embeddings\n emb = scale * emb\n\n # concat sine and cosine embeddings\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)\n\n # flip sine and cosine embeddings\n if flip_sin_to_cos:\n emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)\n\n # zero pad\n if embedding_dim % 2 == 1:\n emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))\n return emb\n\n\ndef get_2d_sincos_pos_embed(\n embed_dim, grid_size, cls_token=False, extra_tokens=0, interpolation_scale=1.0, base_size=16\n):\n \"\"\"\n grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or\n [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)\n \"\"\"\n if isinstance(grid_size, int):\n grid_size = (grid_size, grid_size)\n\n grid_h = np.arange(grid_size[0], dtype=np.float32) / (grid_size[0] / base_size) / interpolation_scale\n grid_w = np.arange(grid_size[1], dtype=np.float32) / (grid_size[1] / base_size) / interpolation_scale\n grid = np.meshgrid(grid_w, grid_h) # here w goes first\n grid = np.stack(grid, axis=0)\n\n grid = grid.reshape([2, 1, grid_size[1], grid_size[0]])\n pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)\n if cls_token and extra_tokens > 0:\n pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)\n return pos_embed\n\n\ndef get_2d_sincos_pos_embed_from_grid(embed_dim, grid):\n if embed_dim % 2 != 0:\n raise ValueError(\"embed_dim must be divisible by 2\")\n\n # use half of dimensions to encode grid_h\n emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)\n emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)\n\n emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)\n return emb\n\n\ndef get_1d_sincos_pos_embed_from_grid(embed_dim, pos):\n \"\"\"\n embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D)\n \"\"\"\n if embed_dim % 2 != 0:\n raise ValueError(\"embed_dim must be divisible by 2\")\n\n omega = np.arange(embed_dim // 2, dtype=np.float64)\n omega /= embed_dim / 2.0\n omega = 1.0 / 10000**omega # (D/2,)\n\n pos = pos.reshape(-1) # (M,)\n out = np.einsum(\"m,d->md\", pos, omega) # (M, D/2), outer product\n\n emb_sin = np.sin(out) # (M, D/2)\n emb_cos = np.cos(out) # (M, D/2)\n\n emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)\n return emb\n\n\nclass PatchEmbed(nn.Module):\n \"\"\"2D Image to Patch Embedding with support for SD3 cropping.\"\"\"\n\n def __init__(\n self,\n height=224,\n width=224,\n patch_size=16,\n in_channels=3,\n embed_dim=768,\n layer_norm=False,\n flatten=True,\n bias=True,\n interpolation_scale=1,\n pos_embed_type=\"sincos\",\n pos_embed_max_size=None, # For SD3 cropping\n ):\n super().__init__()\n\n num_patches = (height // patch_size) * (width // patch_size)\n self.flatten = flatten\n self.layer_norm = layer_norm\n self.pos_embed_max_size = pos_embed_max_size\n\n self.proj = nn.Conv2d(\n in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias\n )\n if layer_norm:\n self.norm = nn.LayerNorm(embed_dim, elementwise_affine=False, eps=1e-6)\n else:\n self.norm = None\n\n self.patch_size = patch_size\n self.height, self.width = height // patch_size, width // patch_size\n self.base_size = height // patch_size\n self.interpolation_scale = interpolation_scale\n\n # Calculate positional embeddings based on max size or default\n if pos_embed_max_size:\n grid_size = pos_embed_max_size\n else:\n grid_size = int(num_patches**0.5)\n\n if pos_embed_type is None:\n self.pos_embed = None\n elif pos_embed_type == \"sincos\":\n pos_embed = get_2d_sincos_pos_embed(\n embed_dim, grid_size, base_size=self.base_size, interpolation_scale=self.interpolation_scale\n )\n persistent = True if pos_embed_max_size else False\n self.register_buffer(\"pos_embed\", torch.from_numpy(pos_embed).float().unsqueeze(0), persistent=persistent)\n else:\n raise ValueError(f\"Unsupported pos_embed_type: {pos_embed_type}\")\n\n def cropped_pos_embed(self, height, width):\n \"\"\"Crops positional embeddings for SD3 compatibility.\"\"\"\n if self.pos_embed_max_size is None:\n raise ValueError(\"`pos_embed_max_size` must be set for cropping.\")\n\n height = height // self.patch_size\n width = width // self.patch_size\n if height > self.pos_embed_max_size:\n raise ValueError(\n f\"Height ({height}) cannot be greater than `pos_embed_max_size`: {self.pos_embed_max_size}.\"\n )\n if width > self.pos_embed_max_size:\n raise ValueError(\n f\"Width ({width}) cannot be greater than `pos_embed_max_size`: {self.pos_embed_max_size}.\"\n )\n\n top = (self.pos_embed_max_size - height) // 2\n left = (self.pos_embed_max_size - width) // 2\n spatial_pos_embed = self.pos_embed.reshape(1, self.pos_embed_max_size, self.pos_embed_max_size, -1)\n spatial_pos_embed = spatial_pos_embed[:, top : top + height, left : left + width, :]\n spatial_pos_embed = spatial_pos_embed.reshape(1, -1, spatial_pos_embed.shape[-1])\n return spatial_pos_embed\n\n def forward(self, latent):\n if self.pos_embed_max_size is not None:\n height, width = latent.shape[-2:]\n else:\n height, width = latent.shape[-2] // self.patch_size, latent.shape[-1] // self.patch_size\n\n latent = self.proj(latent)\n if self.flatten:\n latent = latent.flatten(2).transpose(1, 2) # BCHW -> BNC\n if self.layer_norm:\n latent = self.norm(latent)\n if self.pos_embed is None:\n return latent.to(latent.dtype)\n # Interpolate or crop positional embeddings as needed\n if self.pos_embed_max_size:\n pos_embed = self.cropped_pos_embed(height, width)\n else:\n if self.height != height or self.width != width:\n pos_embed = get_2d_sincos_pos_embed(\n embed_dim=self.pos_embed.shape[-1],\n grid_size=(height, width),\n base_size=self.base_size,\n interpolation_scale=self.interpolation_scale,\n )\n pos_embed = torch.from_numpy(pos_embed).float().unsqueeze(0).to(latent.device)\n else:\n pos_embed = self.pos_embed\n\n return (latent + pos_embed).to(latent.dtype)\n\n\ndef get_2d_rotary_pos_embed(embed_dim, crops_coords, grid_size, use_real=True):\n \"\"\"\n RoPE for image tokens with 2d structure.\n\n Args:\n embed_dim: (`int`):\n The embedding dimension size\n crops_coords (`Tuple[int]`)\n The top-left and bottom-right coordinates of the crop.\n grid_size (`Tuple[int]`):\n The grid size of the positional embedding.\n use_real (`bool`):\n If True, return real part and imaginary part separately. Otherwise, return complex numbers.\n\n Returns:\n `torch.Tensor`: positional embdding with shape `( grid_size * grid_size, embed_dim/2)`.\n \"\"\"\n start, stop = crops_coords\n grid_h = np.linspace(start[0], stop[0], grid_size[0], endpoint=False, dtype=np.float32)\n grid_w = np.linspace(start[1], stop[1], grid_size[1], endpoint=False, dtype=np.float32)\n grid = np.meshgrid(grid_w, grid_h) # here w goes first\n grid = np.stack(grid, axis=0) # [2, W, H]\n\n grid = grid.reshape([2, 1, *grid.shape[1:]])\n pos_embed = get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=use_real)\n return pos_embed\n\n\ndef get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=False):\n assert embed_dim % 4 == 0\n\n # use half of dimensions to encode grid_h\n emb_h = get_1d_rotary_pos_embed(embed_dim // 2, grid[0].reshape(-1), use_real=use_real) # (H*W, D/4)\n emb_w = get_1d_rotary_pos_embed(embed_dim // 2, grid[1].reshape(-1), use_real=use_real) # (H*W, D/4)\n\n if use_real:\n cos = torch.cat([emb_h[0], emb_w[0]], dim=1) # (H*W, D/2)\n sin = torch.cat([emb_h[1], emb_w[1]], dim=1) # (H*W, D/2)\n return cos, sin\n else:\n emb = torch.cat([emb_h, emb_w], dim=1) # (H*W, D/2)\n return emb\n\n\ndef get_1d_rotary_pos_embed(dim: int, pos: Union[np.ndarray, int], theta: float = 10000.0, use_real=False):\n \"\"\"\n Precompute the frequency tensor for complex exponentials (cis) with given dimensions.\n\n This function calculates a frequency tensor with complex exponentials using the given dimension 'dim' and the end\n index 'end'. The 'theta' parameter scales the frequencies. The returned tensor contains complex values in complex64\n data type.\n\n Args:\n dim (`int`): Dimension of the frequency tensor.\n pos (`np.ndarray` or `int`): Position indices for the frequency tensor. [S] or scalar\n theta (`float`, *optional*, defaults to 10000.0):\n Scaling factor for frequency computation. Defaults to 10000.0.\n use_real (`bool`, *optional*):\n If True, return real part and imaginary part separately. Otherwise, return complex numbers.\n\n Returns:\n `torch.Tensor`: Precomputed frequency tensor with complex exponentials. [S, D/2]\n \"\"\"\n if isinstance(pos, int):\n pos = np.arange(pos)\n freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) # [D/2]\n t = torch.from_numpy(pos).to(freqs.device) # type: ignore # [S]\n freqs = torch.outer(t, freqs).float() # type: ignore # [S, D/2]\n if use_real:\n freqs_cos = freqs.cos().repeat_interleave(2, dim=1) # [S, D]\n freqs_sin = freqs.sin().repeat_interleave(2, dim=1) # [S, D]\n return freqs_cos, freqs_sin\n else:\n freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 # [S, D/2]\n return freqs_cis\n\n\ndef apply_rotary_emb(\n x: torch.Tensor,\n freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]],\n) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"\n Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings\n to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are\n reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting\n tensors contain rotary embeddings and are returned as real tensors.\n\n Args:\n x (`torch.Tensor`):\n Query or key tensor to apply rotary embeddings. [B, H, S, D] xk (torch.Tensor): Key tensor to apply\n freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],)\n\n Returns:\n Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.\n \"\"\"\n cos, sin = freqs_cis # [S, D]\n cos = cos[None, None]\n sin = sin[None, None]\n cos, sin = cos.to(x.device), sin.to(x.device)\n\n x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2]\n x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)\n out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype)\n\n return out\n\n\nclass TimestepEmbedding(nn.Module):\n def __init__(\n self,\n in_channels: int,\n time_embed_dim: int,\n act_fn: str = \"silu\",\n out_dim: int = None,\n post_act_fn: Optional[str] = None,\n cond_proj_dim=None,\n sample_proj_bias=True,\n ):\n super().__init__()\n\n self.linear_1 = nn.Linear(in_channels, time_embed_dim, sample_proj_bias)\n\n if cond_proj_dim is not None:\n self.cond_proj = nn.Linear(cond_proj_dim, in_channels, bias=False)\n else:\n self.cond_proj = None\n\n self.act = get_activation(act_fn)\n\n if out_dim is not None:\n time_embed_dim_out = out_dim\n else:\n time_embed_dim_out = time_embed_dim\n self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim_out, sample_proj_bias)\n\n if post_act_fn is None:\n self.post_act = None\n else:\n self.post_act = get_activation(post_act_fn)\n\n def forward(self, sample, condition=None):\n if condition is not None:\n sample = sample + self.cond_proj(condition)\n sample = self.linear_1(sample)\n\n if self.act is not None:\n sample = self.act(sample)\n\n sample = self.linear_2(sample)\n\n if self.post_act is not None:\n sample = self.post_act(sample)\n return sample\n\n\nclass Timesteps(nn.Module):\n def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float):\n super().__init__()\n self.num_channels = num_channels\n self.flip_sin_to_cos = flip_sin_to_cos\n self.downscale_freq_shift = downscale_freq_shift\n\n def forward(self, timesteps):\n t_emb = get_timestep_embedding(\n timesteps,\n self.num_channels,\n flip_sin_to_cos=self.flip_sin_to_cos,\n downscale_freq_shift=self.downscale_freq_shift,\n )\n return t_emb\n\n\nclass GaussianFourierProjection(nn.Module):\n \"\"\"Gaussian Fourier embeddings for noise levels.\"\"\"\n\n def __init__(\n self, embedding_size: int = 256, scale: float = 1.0, set_W_to_weight=True, log=True, flip_sin_to_cos=False\n ):\n super().__init__()\n self.weight = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False)\n self.log = log\n self.flip_sin_to_cos = flip_sin_to_cos\n\n if set_W_to_weight:\n # to delete later\n self.W = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False)\n\n self.weight = self.W\n\n def forward(self, x):\n if self.log:\n x = torch.log(x)\n\n x_proj = x[:, None] * self.weight[None, :] * 2 * np.pi\n\n if self.flip_sin_to_cos:\n out = torch.cat([torch.cos(x_proj), torch.sin(x_proj)], dim=-1)\n else:\n out = torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1)\n return out\n\n\nclass SinusoidalPositionalEmbedding(nn.Module):\n \"\"\"Apply positional information to a sequence of embeddings.\n\n Takes in a sequence of embeddings with shape (batch_size, seq_length, embed_dim) and adds positional embeddings to\n them\n\n Args:\n embed_dim: (int): Dimension of the positional embedding.\n max_seq_length: Maximum sequence length to apply positional embeddings\n\n \"\"\"\n\n def __init__(self, embed_dim: int, max_seq_length: int = 32):\n super().__init__()\n position = torch.arange(max_seq_length).unsqueeze(1)\n div_term = torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0) / embed_dim))\n pe = torch.zeros(1, max_seq_length, embed_dim)\n pe[0, :, 0::2] = torch.sin(position * div_term)\n pe[0, :, 1::2] = torch.cos(position * div_term)\n self.register_buffer(\"pe\", pe)\n\n def forward(self, x):\n _, seq_length, _ = x.shape\n x = x + self.pe[:, :seq_length]\n return x\n\n\nclass ImagePositionalEmbeddings(nn.Module):\n \"\"\"\n Converts latent image classes into vector embeddings. Sums the vector embeddings with positional embeddings for the\n height and width of the latent space.\n\n For more details, see figure 10 of the dall-e paper: https://arxiv.org/abs/2102.12092\n\n For VQ-diffusion:\n\n Output vector embeddings are used as input for the transformer.\n\n Note that the vector embeddings for the transformer are different than the vector embeddings from the VQVAE.\n\n Args:\n num_embed (`int`):\n Number of embeddings for the latent pixels embeddings.\n height (`int`):\n Height of the latent image i.e. the number of height embeddings.\n width (`int`):\n Width of the latent image i.e. the number of width embeddings.\n embed_dim (`int`):\n Dimension of the produced vector embeddings. Used for the latent pixel, height, and width embeddings.\n \"\"\"\n\n def __init__(\n self,\n num_embed: int,\n height: int,\n width: int,\n embed_dim: int,\n ):\n super().__init__()\n\n self.height = height\n self.width = width\n self.num_embed = num_embed\n self.embed_dim = embed_dim\n\n self.emb = nn.Embedding(self.num_embed, embed_dim)\n self.height_emb = nn.Embedding(self.height, embed_dim)\n self.width_emb = nn.Embedding(self.width, embed_dim)\n\n def forward(self, index):\n emb = self.emb(index)\n\n height_emb = self.height_emb(torch.arange(self.height, device=index.device).view(1, self.height))\n\n # 1 x H x D -> 1 x H x 1 x D\n height_emb = height_emb.unsqueeze(2)\n\n width_emb = self.width_emb(torch.arange(self.width, device=index.device).view(1, self.width))\n\n # 1 x W x D -> 1 x 1 x W x D\n width_emb = width_emb.unsqueeze(1)\n\n pos_emb = height_emb + width_emb\n\n # 1 x H x W x D -> 1 x L xD\n pos_emb = pos_emb.view(1, self.height * self.width, -1)\n\n emb = emb + pos_emb[:, : emb.shape[1], :]\n\n return emb\n\n\nclass LabelEmbedding(nn.Module):\n \"\"\"\n Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.\n\n Args:\n num_classes (`int`): The number of classes.\n hidden_size (`int`): The size of the vector embeddings.\n dropout_prob (`float`): The probability of dropping a label.\n \"\"\"\n\n def __init__(self, num_classes, hidden_size, dropout_prob):\n super().__init__()\n use_cfg_embedding = dropout_prob > 0\n self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)\n self.num_classes = num_classes\n self.dropout_prob = dropout_prob\n\n def token_drop(self, labels, force_drop_ids=None):\n \"\"\"\n Drops labels to enable classifier-free guidance.\n \"\"\"\n if force_drop_ids is None:\n drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob\n else:\n drop_ids = torch.tensor(force_drop_ids == 1)\n labels = torch.where(drop_ids, self.num_classes, labels)\n return labels\n\n def forward(self, labels: torch.LongTensor, force_drop_ids=None):\n use_dropout = self.dropout_prob > 0\n if (self.training and use_dropout) or (force_drop_ids is not None):\n labels = self.token_drop(labels, force_drop_ids)\n embeddings = self.embedding_table(labels)\n return embeddings\n\n\nclass TextImageProjection(nn.Module):\n def __init__(\n self,\n text_embed_dim: int = 1024,\n image_embed_dim: int = 768,\n cross_attention_dim: int = 768,\n num_image_text_embeds: int = 10,\n ):\n super().__init__()\n\n self.num_image_text_embeds = num_image_text_embeds\n self.image_embeds = nn.Linear(image_embed_dim, self.num_image_text_embeds * cross_attention_dim)\n self.text_proj = nn.Linear(text_embed_dim, cross_attention_dim)\n\n def forward(self, text_embeds: torch.Tensor, image_embeds: torch.Tensor):\n batch_size = text_embeds.shape[0]\n\n # image\n image_text_embeds = self.image_embeds(image_embeds)\n image_text_embeds = image_text_embeds.reshape(batch_size, self.num_image_text_embeds, -1)\n\n # text\n text_embeds = self.text_proj(text_embeds)\n\n return torch.cat([image_text_embeds, text_embeds], dim=1)\n\n\nclass ImageProjection(nn.Module):\n def __init__(\n self,\n image_embed_dim: int = 768,\n cross_attention_dim: int = 768,\n num_image_text_embeds: int = 32,\n ):\n super().__init__()\n\n self.num_image_text_embeds = num_image_text_embeds\n self.image_embeds = nn.Linear(image_embed_dim, self.num_image_text_embeds * cross_attention_dim)\n self.norm = nn.LayerNorm(cross_attention_dim)\n\n def forward(self, image_embeds: torch.Tensor):\n batch_size = image_embeds.shape[0]\n\n # image\n image_embeds = self.image_embeds(image_embeds)\n image_embeds = image_embeds.reshape(batch_size, self.num_image_text_embeds, -1)\n image_embeds = self.norm(image_embeds)\n return image_embeds\n\n\nclass IPAdapterFullImageProjection(nn.Module):\n def __init__(self, image_embed_dim=1024, cross_attention_dim=1024):\n super().__init__()\n from .attention import FeedForward\n\n self.ff = FeedForward(image_embed_dim, cross_attention_dim, mult=1, activation_fn=\"gelu\")\n self.norm = nn.LayerNorm(cross_attention_dim)\n\n def forward(self, image_embeds: torch.Tensor):\n return self.norm(self.ff(image_embeds))\n\n\nclass IPAdapterFaceIDImageProjection(nn.Module):\n def __init__(self, image_embed_dim=1024, cross_attention_dim=1024, mult=1, num_tokens=1):\n super().__init__()\n from .attention import FeedForward\n\n self.num_tokens = num_tokens\n self.cross_attention_dim = cross_attention_dim\n self.ff = FeedForward(image_embed_dim, cross_attention_dim * num_tokens, mult=mult, activation_fn=\"gelu\")\n self.norm = nn.LayerNorm(cross_attention_dim)\n\n def forward(self, image_embeds: torch.Tensor):\n x = self.ff(image_embeds)\n x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)\n return self.norm(x)\n\n\nclass CombinedTimestepLabelEmbeddings(nn.Module):\n def __init__(self, num_classes, embedding_dim, class_dropout_prob=0.1):\n super().__init__()\n\n self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=1)\n self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)\n self.class_embedder = LabelEmbedding(num_classes, embedding_dim, class_dropout_prob)\n\n def forward(self, timestep, class_labels, hidden_dtype=None):\n timesteps_proj = self.time_proj(timestep)\n timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype)) # (N, D)\n\n class_labels = self.class_embedder(class_labels) # (N, D)\n\n conditioning = timesteps_emb + class_labels # (N, D)\n\n return conditioning\n\n\nclass CombinedTimestepTextProjEmbeddings(nn.Module):\n def __init__(self, embedding_dim, pooled_projection_dim):\n super().__init__()\n\n self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)\n self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)\n self.text_embedder = PixArtAlphaTextProjection(pooled_projection_dim, embedding_dim, act_fn=\"silu\")\n\n def forward(self, timestep, pooled_projection):\n timesteps_proj = self.time_proj(timestep)\n timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=pooled_projection.dtype)) # (N, D)\n\n pooled_projections = self.text_embedder(pooled_projection)\n\n conditioning = timesteps_emb + pooled_projections\n\n return conditioning\n\n\nclass HunyuanDiTAttentionPool(nn.Module):\n # Copied from https://github.com/Tencent/HunyuanDiT/blob/cb709308d92e6c7e8d59d0dff41b74d35088db6a/hydit/modules/poolers.py#L6\n\n def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):\n super().__init__()\n self.positional_embedding = nn.Parameter(torch.randn(spacial_dim + 1, embed_dim) / embed_dim**0.5)\n self.k_proj = nn.Linear(embed_dim, embed_dim)\n self.q_proj = nn.Linear(embed_dim, embed_dim)\n self.v_proj = nn.Linear(embed_dim, embed_dim)\n self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)\n self.num_heads = num_heads\n\n def forward(self, x):\n x = x.permute(1, 0, 2) # NLC -> LNC\n x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (L+1)NC\n x = x + self.positional_embedding[:, None, :].to(x.dtype) # (L+1)NC\n x, _ = F.multi_head_attention_forward(\n query=x[:1],\n key=x,\n value=x,\n embed_dim_to_check=x.shape[-1],\n num_heads=self.num_heads,\n q_proj_weight=self.q_proj.weight,\n k_proj_weight=self.k_proj.weight,\n v_proj_weight=self.v_proj.weight,\n in_proj_weight=None,\n in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),\n bias_k=None,\n bias_v=None,\n add_zero_attn=False,\n dropout_p=0,\n out_proj_weight=self.c_proj.weight,\n out_proj_bias=self.c_proj.bias,\n use_separate_proj_weight=True,\n training=self.training,\n need_weights=False,\n )\n return x.squeeze(0)\n\n\nclass HunyuanCombinedTimestepTextSizeStyleEmbedding(nn.Module):\n def __init__(self, embedding_dim, pooled_projection_dim=1024, seq_len=256, cross_attention_dim=2048):\n super().__init__()\n\n self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)\n self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)\n\n self.pooler = HunyuanDiTAttentionPool(\n seq_len, cross_attention_dim, num_heads=8, output_dim=pooled_projection_dim\n )\n # Here we use a default learned embedder layer for future extension.\n self.style_embedder = nn.Embedding(1, embedding_dim)\n extra_in_dim = 256 * 6 + embedding_dim + pooled_projection_dim\n self.extra_embedder = PixArtAlphaTextProjection(\n in_features=extra_in_dim,\n hidden_size=embedding_dim * 4,\n out_features=embedding_dim,\n act_fn=\"silu_fp32\",\n )\n\n def forward(self, timestep, encoder_hidden_states, image_meta_size, style, hidden_dtype=None):\n timesteps_proj = self.time_proj(timestep)\n timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype)) # (N, 256)\n\n # extra condition1: text\n pooled_projections = self.pooler(encoder_hidden_states) # (N, 1024)\n\n # extra condition2: image meta size embdding\n image_meta_size = get_timestep_embedding(image_meta_size.view(-1), 256, True, 0)\n image_meta_size = image_meta_size.to(dtype=hidden_dtype)\n image_meta_size = image_meta_size.view(-1, 6 * 256) # (N, 1536)\n\n # extra condition3: style embedding\n style_embedding = self.style_embedder(style) # (N, embedding_dim)\n\n # Concatenate all extra vectors\n extra_cond = torch.cat([pooled_projections, image_meta_size, style_embedding], dim=1)\n conditioning = timesteps_emb + self.extra_embedder(extra_cond) # [B, D]\n\n return conditioning\n\n\nclass TextTimeEmbedding(nn.Module):\n def __init__(self, encoder_dim: int, time_embed_dim: int, num_heads: int = 64):\n super().__init__()\n self.norm1 = nn.LayerNorm(encoder_dim)\n self.pool = AttentionPooling(num_heads, encoder_dim)\n self.proj = nn.Linear(encoder_dim, time_embed_dim)\n self.norm2 = nn.LayerNorm(time_embed_dim)\n\n def forward(self, hidden_states):\n hidden_states = self.norm1(hidden_states)\n hidden_states = self.pool(hidden_states)\n hidden_states = self.proj(hidden_states)\n hidden_states = self.norm2(hidden_states)\n return hidden_states\n\n\nclass TextImageTimeEmbedding(nn.Module):\n def __init__(self, text_embed_dim: int = 768, image_embed_dim: int = 768, time_embed_dim: int = 1536):\n super().__init__()\n self.text_proj = nn.Linear(text_embed_dim, time_embed_dim)\n self.text_norm = nn.LayerNorm(time_embed_dim)\n self.image_proj = nn.Linear(image_embed_dim, time_embed_dim)\n\n def forward(self, text_embeds: torch.Tensor, image_embeds: torch.Tensor):\n # text\n time_text_embeds = self.text_proj(text_embeds)\n time_text_embeds = self.text_norm(time_text_embeds)\n\n # image\n time_image_embeds = self.image_proj(image_embeds)\n\n return time_image_embeds + time_text_embeds\n\n\nclass ImageTimeEmbedding(nn.Module):\n def __init__(self, image_embed_dim: int = 768, time_embed_dim: int = 1536):\n super().__init__()\n self.image_proj = nn.Linear(image_embed_dim, time_embed_dim)\n self.image_norm = nn.LayerNorm(time_embed_dim)\n\n def forward(self, image_embeds: torch.Tensor):\n # image\n time_image_embeds = self.image_proj(image_embeds)\n time_image_embeds = self.image_norm(time_image_embeds)\n return time_image_embeds\n\n\nclass ImageHintTimeEmbedding(nn.Module):\n def __init__(self, image_embed_dim: int = 768, time_embed_dim: int = 1536):\n super().__init__()\n self.image_proj = nn.Linear(image_embed_dim, time_embed_dim)\n self.image_norm = nn.LayerNorm(time_embed_dim)\n self.input_hint_block = nn.Sequential(\n nn.Conv2d(3, 16, 3, padding=1),\n nn.SiLU(),\n nn.Conv2d(16, 16, 3, padding=1),\n nn.SiLU(),\n nn.Conv2d(16, 32, 3, padding=1, stride=2),\n nn.SiLU(),\n nn.Conv2d(32, 32, 3, padding=1),\n nn.SiLU(),\n nn.Conv2d(32, 96, 3, padding=1, stride=2),\n nn.SiLU(),\n nn.Conv2d(96, 96, 3, padding=1),\n nn.SiLU(),\n nn.Conv2d(96, 256, 3, padding=1, stride=2),\n nn.SiLU(),\n nn.Conv2d(256, 4, 3, padding=1),\n )\n\n def forward(self, image_embeds: torch.Tensor, hint: torch.Tensor):\n # image\n time_image_embeds = self.image_proj(image_embeds)\n time_image_embeds = self.image_norm(time_image_embeds)\n hint = self.input_hint_block(hint)\n return time_image_embeds, hint\n\n\nclass AttentionPooling(nn.Module):\n # Copied from https://github.com/deep-floyd/IF/blob/2f91391f27dd3c468bf174be5805b4cc92980c0b/deepfloyd_if/model/nn.py#L54\n\n def __init__(self, num_heads, embed_dim, dtype=None):\n super().__init__()\n self.dtype = dtype\n self.positional_embedding = nn.Parameter(torch.randn(1, embed_dim) / embed_dim**0.5)\n self.k_proj = nn.Linear(embed_dim, embed_dim, dtype=self.dtype)\n self.q_proj = nn.Linear(embed_dim, embed_dim, dtype=self.dtype)\n self.v_proj = nn.Linear(embed_dim, embed_dim, dtype=self.dtype)\n self.num_heads = num_heads\n self.dim_per_head = embed_dim // self.num_heads\n\n def forward(self, x):\n bs, length, width = x.size()\n\n def shape(x):\n # (bs, length, width) --> (bs, length, n_heads, dim_per_head)\n x = x.view(bs, -1, self.num_heads, self.dim_per_head)\n # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)\n x = x.transpose(1, 2)\n # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)\n x = x.reshape(bs * self.num_heads, -1, self.dim_per_head)\n # (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)\n x = x.transpose(1, 2)\n return x\n\n class_token = x.mean(dim=1, keepdim=True) + self.positional_embedding.to(x.dtype)\n x = torch.cat([class_token, x], dim=1) # (bs, length+1, width)\n\n # (bs*n_heads, class_token_length, dim_per_head)\n q = shape(self.q_proj(class_token))\n # (bs*n_heads, length+class_token_length, dim_per_head)\n k = shape(self.k_proj(x))\n v = shape(self.v_proj(x))\n\n # (bs*n_heads, class_token_length, length+class_token_length):\n scale = 1 / math.sqrt(math.sqrt(self.dim_per_head))\n weight = torch.einsum(\"bct,bcs->bts\", q * scale, k * scale) # More stable with f16 than dividing afterwards\n weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)\n\n # (bs*n_heads, dim_per_head, class_token_length)\n a = torch.einsum(\"bts,bcs->bct\", weight, v)\n\n # (bs, length+1, width)\n a = a.reshape(bs, -1, 1).transpose(1, 2)\n\n return a[:, 0, :] # cls_token\n\n\ndef get_fourier_embeds_from_boundingbox(embed_dim, box):\n \"\"\"\n Args:\n embed_dim: int\n box: a 3-D tensor [B x N x 4] representing the bounding boxes for GLIGEN pipeline\n Returns:\n [B x N x embed_dim] tensor of positional embeddings\n \"\"\"\n\n batch_size, num_boxes = box.shape[:2]\n\n emb = 100 ** (torch.arange(embed_dim) / embed_dim)\n emb = emb[None, None, None].to(device=box.device, dtype=box.dtype)\n emb = emb * box.unsqueeze(-1)\n\n emb = torch.stack((emb.sin(), emb.cos()), dim=-1)\n emb = emb.permute(0, 1, 3, 4, 2).reshape(batch_size, num_boxes, embed_dim * 2 * 4)\n\n return emb\n\n\nclass GLIGENTextBoundingboxProjection(nn.Module):\n def __init__(self, positive_len, out_dim, feature_type=\"text-only\", fourier_freqs=8):\n super().__init__()\n self.positive_len = positive_len\n self.out_dim = out_dim\n\n self.fourier_embedder_dim = fourier_freqs\n self.position_dim = fourier_freqs * 2 * 4 # 2: sin/cos, 4: xyxy\n\n if isinstance(out_dim, tuple):\n out_dim = out_dim[0]\n\n if feature_type == \"text-only\":\n self.linears = nn.Sequential(\n nn.Linear(self.positive_len + self.position_dim, 512),\n nn.SiLU(),\n nn.Linear(512, 512),\n nn.SiLU(),\n nn.Linear(512, out_dim),\n )\n self.null_positive_feature = torch.nn.Parameter(torch.zeros([self.positive_len]))\n\n elif feature_type == \"text-image\":\n self.linears_text = nn.Sequential(\n nn.Linear(self.positive_len + self.position_dim, 512),\n nn.SiLU(),\n nn.Linear(512, 512),\n nn.SiLU(),\n nn.Linear(512, out_dim),\n )\n self.linears_image = nn.Sequential(\n nn.Linear(self.positive_len + self.position_dim, 512),\n nn.SiLU(),\n nn.Linear(512, 512),\n nn.SiLU(),\n nn.Linear(512, out_dim),\n )\n self.null_text_feature = torch.nn.Parameter(torch.zeros([self.positive_len]))\n self.null_image_feature = torch.nn.Parameter(torch.zeros([self.positive_len]))\n\n self.null_position_feature = torch.nn.Parameter(torch.zeros([self.position_dim]))\n\n def forward(\n self,\n boxes,\n masks,\n positive_embeddings=None,\n phrases_masks=None,\n image_masks=None,\n phrases_embeddings=None,\n image_embeddings=None,\n ):\n masks = masks.unsqueeze(-1)\n\n # embedding position (it may includes padding as placeholder)\n xyxy_embedding = get_fourier_embeds_from_boundingbox(self.fourier_embedder_dim, boxes) # B*N*4 -> B*N*C\n\n # learnable null embedding\n xyxy_null = self.null_position_feature.view(1, 1, -1)\n\n # replace padding with learnable null embedding\n xyxy_embedding = xyxy_embedding * masks + (1 - masks) * xyxy_null\n\n # positionet with text only information\n if positive_embeddings is not None:\n # learnable null embedding\n positive_null = self.null_positive_feature.view(1, 1, -1)\n\n # replace padding with learnable null embedding\n positive_embeddings = positive_embeddings * masks + (1 - masks) * positive_null\n\n objs = self.linears(torch.cat([positive_embeddings, xyxy_embedding], dim=-1))\n\n # positionet with text and image infomation\n else:\n phrases_masks = phrases_masks.unsqueeze(-1)\n image_masks = image_masks.unsqueeze(-1)\n\n # learnable null embedding\n text_null = self.null_text_feature.view(1, 1, -1)\n image_null = self.null_image_feature.view(1, 1, -1)\n\n # replace padding with learnable null embedding\n phrases_embeddings = phrases_embeddings * phrases_masks + (1 - phrases_masks) * text_null\n image_embeddings = image_embeddings * image_masks + (1 - image_masks) * image_null\n\n objs_text = self.linears_text(torch.cat([phrases_embeddings, xyxy_embedding], dim=-1))\n objs_image = self.linears_image(torch.cat([image_embeddings, xyxy_embedding], dim=-1))\n objs = torch.cat([objs_text, objs_image], dim=1)\n\n return objs\n\n\nclass PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module):\n \"\"\"\n For PixArt-Alpha.\n\n Reference:\n https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L164C9-L168C29\n \"\"\"\n\n def __init__(self, embedding_dim, size_emb_dim, use_additional_conditions: bool = False):\n super().__init__()\n\n self.outdim = size_emb_dim\n self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)\n self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)\n\n self.use_additional_conditions = use_additional_conditions\n if use_additional_conditions:\n self.additional_condition_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)\n self.resolution_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=size_emb_dim)\n self.aspect_ratio_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=size_emb_dim)\n\n def forward(self, timestep, resolution, aspect_ratio, batch_size, hidden_dtype):\n timesteps_proj = self.time_proj(timestep)\n timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype)) # (N, D)\n\n if self.use_additional_conditions:\n resolution_emb = self.additional_condition_proj(resolution.flatten()).to(hidden_dtype)\n resolution_emb = self.resolution_embedder(resolution_emb).reshape(batch_size, -1)\n aspect_ratio_emb = self.additional_condition_proj(aspect_ratio.flatten()).to(hidden_dtype)\n aspect_ratio_emb = self.aspect_ratio_embedder(aspect_ratio_emb).reshape(batch_size, -1)\n conditioning = timesteps_emb + torch.cat([resolution_emb, aspect_ratio_emb], dim=1)\n else:\n conditioning = timesteps_emb\n\n return conditioning\n\n\nclass PixArtAlphaTextProjection(nn.Module):\n \"\"\"\n Projects caption embeddings. Also handles dropout for classifier-free guidance.\n\n Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py\n \"\"\"\n\n def __init__(self, in_features, hidden_size, out_features=None, act_fn=\"gelu_tanh\"):\n super().__init__()\n if out_features is None:\n out_features = hidden_size\n self.linear_1 = nn.Linear(in_features=in_features, out_features=hidden_size, bias=True)\n if act_fn == \"gelu_tanh\":\n self.act_1 = nn.GELU(approximate=\"tanh\")\n elif act_fn == \"silu\":\n self.act_1 = nn.SiLU()\n elif act_fn == \"silu_fp32\":\n self.act_1 = FP32SiLU()\n else:\n raise ValueError(f\"Unknown activation function: {act_fn}\")\n self.linear_2 = nn.Linear(in_features=hidden_size, out_features=out_features, bias=True)\n\n def forward(self, caption):\n hidden_states = self.linear_1(caption)\n hidden_states = self.act_1(hidden_states)\n hidden_states = self.linear_2(hidden_states)\n return hidden_states\n\n\nclass IPAdapterPlusImageProjectionBlock(nn.Module):\n def __init__(\n self,\n embed_dims: int = 768,\n dim_head: int = 64,\n heads: int = 16,\n ffn_ratio: float = 4,\n ) -> None:\n super().__init__()\n from .attention import FeedForward\n\n self.ln0 = nn.LayerNorm(embed_dims)\n self.ln1 = nn.LayerNorm(embed_dims)\n self.attn = Attention(\n query_dim=embed_dims,\n dim_head=dim_head,\n heads=heads,\n out_bias=False,\n )\n self.ff = nn.Sequential(\n nn.LayerNorm(embed_dims),\n FeedForward(embed_dims, embed_dims, activation_fn=\"gelu\", mult=ffn_ratio, bias=False),\n )\n\n def forward(self, x, latents, residual):\n encoder_hidden_states = self.ln0(x)\n latents = self.ln1(latents)\n encoder_hidden_states = torch.cat([encoder_hidden_states, latents], dim=-2)\n latents = self.attn(latents, encoder_hidden_states) + residual\n latents = self.ff(latents) + latents\n return latents\n\n\nclass IPAdapterPlusImageProjection(nn.Module):\n \"\"\"Resampler of IP-Adapter Plus.\n\n Args:\n embed_dims (int): The feature dimension. Defaults to 768. output_dims (int): The number of output channels,\n that is the same\n number of the channels in the `unet.config.cross_attention_dim`. Defaults to 1024.\n hidden_dims (int):\n The number of hidden channels. Defaults to 1280. depth (int): The number of blocks. Defaults\n to 8. dim_head (int): The number of head channels. Defaults to 64. heads (int): Parallel attention heads.\n Defaults to 16. num_queries (int):\n The number of queries. Defaults to 8. ffn_ratio (float): The expansion ratio\n of feedforward network hidden\n layer channels. Defaults to 4.\n \"\"\"\n\n def __init__(\n self,\n embed_dims: int = 768,\n output_dims: int = 1024,\n hidden_dims: int = 1280,\n depth: int = 4,\n dim_head: int = 64,\n heads: int = 16,\n num_queries: int = 8,\n ffn_ratio: float = 4,\n ) -> None:\n super().__init__()\n self.latents = nn.Parameter(torch.randn(1, num_queries, hidden_dims) / hidden_dims**0.5)\n\n self.proj_in = nn.Linear(embed_dims, hidden_dims)\n\n self.proj_out = nn.Linear(hidden_dims, output_dims)\n self.norm_out = nn.LayerNorm(output_dims)\n\n self.layers = nn.ModuleList(\n [IPAdapterPlusImageProjectionBlock(hidden_dims, dim_head, heads, ffn_ratio) for _ in range(depth)]\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"Forward pass.\n\n Args:\n x (torch.Tensor): Input Tensor.\n Returns:\n torch.Tensor: Output Tensor.\n \"\"\"\n latents = self.latents.repeat(x.size(0), 1, 1)\n\n x = self.proj_in(x)\n\n for block in self.layers:\n residual = latents\n latents = block(x, latents, residual)\n\n latents = self.proj_out(latents)\n return self.norm_out(latents)\n\n\nclass IPAdapterFaceIDPlusImageProjection(nn.Module):\n \"\"\"FacePerceiverResampler of IP-Adapter Plus.\n\n Args:\n embed_dims (int): The feature dimension. Defaults to 768. output_dims (int): The number of output channels,\n that is the same\n number of the channels in the `unet.config.cross_attention_dim`. Defaults to 1024.\n hidden_dims (int):\n The number of hidden channels. Defaults to 1280. depth (int): The number of blocks. Defaults\n to 8. dim_head (int): The number of head channels. Defaults to 64. heads (int): Parallel attention heads.\n Defaults to 16. num_tokens (int): Number of tokens num_queries (int): The number of queries. Defaults to 8.\n ffn_ratio (float): The expansion ratio of feedforward network hidden\n layer channels. Defaults to 4.\n ffproj_ratio (float): The expansion ratio of feedforward network hidden\n layer channels (for ID embeddings). Defaults to 4.\n \"\"\"\n\n def __init__(\n self,\n embed_dims: int = 768,\n output_dims: int = 768,\n hidden_dims: int = 1280,\n id_embeddings_dim: int = 512,\n depth: int = 4,\n dim_head: int = 64,\n heads: int = 16,\n num_tokens: int = 4,\n num_queries: int = 8,\n ffn_ratio: float = 4,\n ffproj_ratio: int = 2,\n ) -> None:\n super().__init__()\n from .attention import FeedForward\n\n self.num_tokens = num_tokens\n self.embed_dim = embed_dims\n self.clip_embeds = None\n self.shortcut = False\n self.shortcut_scale = 1.0\n\n self.proj = FeedForward(id_embeddings_dim, embed_dims * num_tokens, activation_fn=\"gelu\", mult=ffproj_ratio)\n self.norm = nn.LayerNorm(embed_dims)\n\n self.proj_in = nn.Linear(hidden_dims, embed_dims)\n\n self.proj_out = nn.Linear(embed_dims, output_dims)\n self.norm_out = nn.LayerNorm(output_dims)\n\n self.layers = nn.ModuleList(\n [IPAdapterPlusImageProjectionBlock(embed_dims, dim_head, heads, ffn_ratio) for _ in range(depth)]\n )\n\n def forward(self, id_embeds: torch.Tensor) -> torch.Tensor:\n \"\"\"Forward pass.\n\n Args:\n id_embeds (torch.Tensor): Input Tensor (ID embeds).\n Returns:\n torch.Tensor: Output Tensor.\n \"\"\"\n id_embeds = id_embeds.to(self.clip_embeds.dtype)\n id_embeds = self.proj(id_embeds)\n id_embeds = id_embeds.reshape(-1, self.num_tokens, self.embed_dim)\n id_embeds = self.norm(id_embeds)\n latents = id_embeds\n\n clip_embeds = self.proj_in(self.clip_embeds)\n x = clip_embeds.reshape(-1, clip_embeds.shape[2], clip_embeds.shape[3])\n\n for block in self.layers:\n residual = latents\n latents = block(x, latents, residual)\n\n latents = self.proj_out(latents)\n out = self.norm_out(latents)\n if self.shortcut:\n out = id_embeds + self.shortcut_scale * out\n return out\n\n\nclass MultiIPAdapterImageProjection(nn.Module):\n def __init__(self, IPAdapterImageProjectionLayers: Union[List[nn.Module], Tuple[nn.Module]]):\n super().__init__()\n self.image_projection_layers = nn.ModuleList(IPAdapterImageProjectionLayers)\n\n def forward(self, image_embeds: List[torch.Tensor]):\n projected_image_embeds = []\n\n # currently, we accept `image_embeds` as\n # 1. a tensor (deprecated) with shape [batch_size, embed_dim] or [batch_size, sequence_length, embed_dim]\n # 2. list of `n` tensors where `n` is number of ip-adapters, each tensor can hae shape [batch_size, num_images, embed_dim] or [batch_size, num_images, sequence_length, embed_dim]\n if not isinstance(image_embeds, list):\n deprecation_message = (\n \"You have passed a tensor as `image_embeds`.This is deprecated and will be removed in a future release.\"\n \" Please make sure to update your script to pass `image_embeds` as a list of tensors to supress this warning.\"\n )\n deprecate(\"image_embeds not a list\", \"1.0.0\", deprecation_message, standard_warn=False)\n image_embeds = [image_embeds.unsqueeze(1)]\n\n if len(image_embeds) != len(self.image_projection_layers):\n raise ValueError(\n f\"image_embeds must have the same length as image_projection_layers, got {len(image_embeds)} and {len(self.image_projection_layers)}\"\n )\n\n for image_embed, image_projection_layer in zip(image_embeds, self.image_projection_layers):\n batch_size, num_images = image_embed.shape[0], image_embed.shape[1]\n image_embed = image_embed.reshape((batch_size * num_images,) + image_embed.shape[2:])\n image_embed = image_projection_layer(image_embed)\n image_embed = image_embed.reshape((batch_size, num_images) + image_embed.shape[1:])\n\n projected_image_embeds.append(image_embed)\n\n return projected_image_embeds\n" }, { "path": "diffusers/src/diffusers/models/embeddings_flax.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport math\n\nimport flax.linen as nn\nimport jax.numpy as jnp\n\n\ndef get_sinusoidal_embeddings(\n timesteps: jnp.ndarray,\n embedding_dim: int,\n freq_shift: float = 1,\n min_timescale: float = 1,\n max_timescale: float = 1.0e4,\n flip_sin_to_cos: bool = False,\n scale: float = 1.0,\n) -> jnp.ndarray:\n \"\"\"Returns the positional encoding (same as Tensor2Tensor).\n\n Args:\n timesteps: a 1-D Tensor of N indices, one per batch element.\n These may be fractional.\n embedding_dim: The number of output channels.\n min_timescale: The smallest time unit (should probably be 0.0).\n max_timescale: The largest time unit.\n Returns:\n a Tensor of timing signals [N, num_channels]\n \"\"\"\n assert timesteps.ndim == 1, \"Timesteps should be a 1d-array\"\n assert embedding_dim % 2 == 0, f\"Embedding dimension {embedding_dim} should be even\"\n num_timescales = float(embedding_dim // 2)\n log_timescale_increment = math.log(max_timescale / min_timescale) / (num_timescales - freq_shift)\n inv_timescales = min_timescale * jnp.exp(jnp.arange(num_timescales, dtype=jnp.float32) * -log_timescale_increment)\n emb = jnp.expand_dims(timesteps, 1) * jnp.expand_dims(inv_timescales, 0)\n\n # scale embeddings\n scaled_time = scale * emb\n\n if flip_sin_to_cos:\n signal = jnp.concatenate([jnp.cos(scaled_time), jnp.sin(scaled_time)], axis=1)\n else:\n signal = jnp.concatenate([jnp.sin(scaled_time), jnp.cos(scaled_time)], axis=1)\n signal = jnp.reshape(signal, [jnp.shape(timesteps)[0], embedding_dim])\n return signal\n\n\nclass FlaxTimestepEmbedding(nn.Module):\n r\"\"\"\n Time step Embedding Module. Learns embeddings for input time steps.\n\n Args:\n time_embed_dim (`int`, *optional*, defaults to `32`):\n Time step embedding dimension\n dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):\n Parameters `dtype`\n \"\"\"\n\n time_embed_dim: int = 32\n dtype: jnp.dtype = jnp.float32\n\n @nn.compact\n def __call__(self, temb):\n temb = nn.Dense(self.time_embed_dim, dtype=self.dtype, name=\"linear_1\")(temb)\n temb = nn.silu(temb)\n temb = nn.Dense(self.time_embed_dim, dtype=self.dtype, name=\"linear_2\")(temb)\n return temb\n\n\nclass FlaxTimesteps(nn.Module):\n r\"\"\"\n Wrapper Module for sinusoidal Time step Embeddings as described in https://arxiv.org/abs/2006.11239\n\n Args:\n dim (`int`, *optional*, defaults to `32`):\n Time step embedding dimension\n \"\"\"\n\n dim: int = 32\n flip_sin_to_cos: bool = False\n freq_shift: float = 1\n\n @nn.compact\n def __call__(self, timesteps):\n return get_sinusoidal_embeddings(\n timesteps, embedding_dim=self.dim, flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.freq_shift\n )\n" }, { "path": "diffusers/src/diffusers/models/lora.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\n# IMPORTANT: #\n###################################################################\n# ----------------------------------------------------------------#\n# This file is deprecated and will be removed soon #\n# (as soon as PEFT will become a required dependency for LoRA) #\n# ----------------------------------------------------------------#\n###################################################################\n\nfrom typing import Optional, Tuple, Union\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom ..utils import deprecate, logging\nfrom ..utils.import_utils import is_transformers_available\n\n\nif is_transformers_available():\n from transformers import CLIPTextModel, CLIPTextModelWithProjection\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef text_encoder_attn_modules(text_encoder):\n attn_modules = []\n\n if isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection)):\n for i, layer in enumerate(text_encoder.text_model.encoder.layers):\n name = f\"text_model.encoder.layers.{i}.self_attn\"\n mod = layer.self_attn\n attn_modules.append((name, mod))\n else:\n raise ValueError(f\"do not know how to get attention modules for: {text_encoder.__class__.__name__}\")\n\n return attn_modules\n\n\ndef text_encoder_mlp_modules(text_encoder):\n mlp_modules = []\n\n if isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection)):\n for i, layer in enumerate(text_encoder.text_model.encoder.layers):\n mlp_mod = layer.mlp\n name = f\"text_model.encoder.layers.{i}.mlp\"\n mlp_modules.append((name, mlp_mod))\n else:\n raise ValueError(f\"do not know how to get mlp modules for: {text_encoder.__class__.__name__}\")\n\n return mlp_modules\n\n\ndef adjust_lora_scale_text_encoder(text_encoder, lora_scale: float = 1.0):\n for _, attn_module in text_encoder_attn_modules(text_encoder):\n if isinstance(attn_module.q_proj, PatchedLoraProjection):\n attn_module.q_proj.lora_scale = lora_scale\n attn_module.k_proj.lora_scale = lora_scale\n attn_module.v_proj.lora_scale = lora_scale\n attn_module.out_proj.lora_scale = lora_scale\n\n for _, mlp_module in text_encoder_mlp_modules(text_encoder):\n if isinstance(mlp_module.fc1, PatchedLoraProjection):\n mlp_module.fc1.lora_scale = lora_scale\n mlp_module.fc2.lora_scale = lora_scale\n\n\nclass PatchedLoraProjection(torch.nn.Module):\n def __init__(self, regular_linear_layer, lora_scale=1, network_alpha=None, rank=4, dtype=None):\n deprecation_message = \"Use of `PatchedLoraProjection` is deprecated. Please switch to PEFT backend by installing PEFT: `pip install peft`.\"\n deprecate(\"PatchedLoraProjection\", \"1.0.0\", deprecation_message)\n\n super().__init__()\n from ..models.lora import LoRALinearLayer\n\n self.regular_linear_layer = regular_linear_layer\n\n device = self.regular_linear_layer.weight.device\n\n if dtype is None:\n dtype = self.regular_linear_layer.weight.dtype\n\n self.lora_linear_layer = LoRALinearLayer(\n self.regular_linear_layer.in_features,\n self.regular_linear_layer.out_features,\n network_alpha=network_alpha,\n device=device,\n dtype=dtype,\n rank=rank,\n )\n\n self.lora_scale = lora_scale\n\n # overwrite PyTorch's `state_dict` to be sure that only the 'regular_linear_layer' weights are saved\n # when saving the whole text encoder model and when LoRA is unloaded or fused\n def state_dict(self, *args, destination=None, prefix=\"\", keep_vars=False):\n if self.lora_linear_layer is None:\n return self.regular_linear_layer.state_dict(\n *args, destination=destination, prefix=prefix, keep_vars=keep_vars\n )\n\n return super().state_dict(*args, destination=destination, prefix=prefix, keep_vars=keep_vars)\n\n def _fuse_lora(self, lora_scale=1.0, safe_fusing=False):\n if self.lora_linear_layer is None:\n return\n\n dtype, device = self.regular_linear_layer.weight.data.dtype, self.regular_linear_layer.weight.data.device\n\n w_orig = self.regular_linear_layer.weight.data.float()\n w_up = self.lora_linear_layer.up.weight.data.float()\n w_down = self.lora_linear_layer.down.weight.data.float()\n\n if self.lora_linear_layer.network_alpha is not None:\n w_up = w_up * self.lora_linear_layer.network_alpha / self.lora_linear_layer.rank\n\n fused_weight = w_orig + (lora_scale * torch.bmm(w_up[None, :], w_down[None, :])[0])\n\n if safe_fusing and torch.isnan(fused_weight).any().item():\n raise ValueError(\n \"This LoRA weight seems to be broken. \"\n f\"Encountered NaN values when trying to fuse LoRA weights for {self}.\"\n \"LoRA weights will not be fused.\"\n )\n\n self.regular_linear_layer.weight.data = fused_weight.to(device=device, dtype=dtype)\n\n # we can drop the lora layer now\n self.lora_linear_layer = None\n\n # offload the up and down matrices to CPU to not blow the memory\n self.w_up = w_up.cpu()\n self.w_down = w_down.cpu()\n self.lora_scale = lora_scale\n\n def _unfuse_lora(self):\n if not (getattr(self, \"w_up\", None) is not None and getattr(self, \"w_down\", None) is not None):\n return\n\n fused_weight = self.regular_linear_layer.weight.data\n dtype, device = fused_weight.dtype, fused_weight.device\n\n w_up = self.w_up.to(device=device).float()\n w_down = self.w_down.to(device).float()\n\n unfused_weight = fused_weight.float() - (self.lora_scale * torch.bmm(w_up[None, :], w_down[None, :])[0])\n self.regular_linear_layer.weight.data = unfused_weight.to(device=device, dtype=dtype)\n\n self.w_up = None\n self.w_down = None\n\n def forward(self, input):\n if self.lora_scale is None:\n self.lora_scale = 1.0\n if self.lora_linear_layer is None:\n return self.regular_linear_layer(input)\n return self.regular_linear_layer(input) + (self.lora_scale * self.lora_linear_layer(input))\n\n\nclass LoRALinearLayer(nn.Module):\n r\"\"\"\n A linear layer that is used with LoRA.\n\n Parameters:\n in_features (`int`):\n Number of input features.\n out_features (`int`):\n Number of output features.\n rank (`int`, `optional`, defaults to 4):\n The rank of the LoRA layer.\n network_alpha (`float`, `optional`, defaults to `None`):\n The value of the network alpha used for stable learning and preventing underflow. This value has the same\n meaning as the `--network_alpha` option in the kohya-ss trainer script. See\n https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning\n device (`torch.device`, `optional`, defaults to `None`):\n The device to use for the layer's weights.\n dtype (`torch.dtype`, `optional`, defaults to `None`):\n The dtype to use for the layer's weights.\n \"\"\"\n\n def __init__(\n self,\n in_features: int,\n out_features: int,\n rank: int = 4,\n network_alpha: Optional[float] = None,\n device: Optional[Union[torch.device, str]] = None,\n dtype: Optional[torch.dtype] = None,\n ):\n super().__init__()\n\n deprecation_message = \"Use of `LoRALinearLayer` is deprecated. Please switch to PEFT backend by installing PEFT: `pip install peft`.\"\n deprecate(\"LoRALinearLayer\", \"1.0.0\", deprecation_message)\n\n self.down = nn.Linear(in_features, rank, bias=False, device=device, dtype=dtype)\n self.up = nn.Linear(rank, out_features, bias=False, device=device, dtype=dtype)\n # This value has the same meaning as the `--network_alpha` option in the kohya-ss trainer script.\n # See https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning\n self.network_alpha = network_alpha\n self.rank = rank\n self.out_features = out_features\n self.in_features = in_features\n\n nn.init.normal_(self.down.weight, std=1 / rank)\n nn.init.zeros_(self.up.weight)\n\n def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n orig_dtype = hidden_states.dtype\n dtype = self.down.weight.dtype\n\n down_hidden_states = self.down(hidden_states.to(dtype))\n up_hidden_states = self.up(down_hidden_states)\n\n if self.network_alpha is not None:\n up_hidden_states *= self.network_alpha / self.rank\n\n return up_hidden_states.to(orig_dtype)\n\n\nclass LoRAConv2dLayer(nn.Module):\n r\"\"\"\n A convolutional layer that is used with LoRA.\n\n Parameters:\n in_features (`int`):\n Number of input features.\n out_features (`int`):\n Number of output features.\n rank (`int`, `optional`, defaults to 4):\n The rank of the LoRA layer.\n kernel_size (`int` or `tuple` of two `int`, `optional`, defaults to 1):\n The kernel size of the convolution.\n stride (`int` or `tuple` of two `int`, `optional`, defaults to 1):\n The stride of the convolution.\n padding (`int` or `tuple` of two `int` or `str`, `optional`, defaults to 0):\n The padding of the convolution.\n network_alpha (`float`, `optional`, defaults to `None`):\n The value of the network alpha used for stable learning and preventing underflow. This value has the same\n meaning as the `--network_alpha` option in the kohya-ss trainer script. See\n https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning\n \"\"\"\n\n def __init__(\n self,\n in_features: int,\n out_features: int,\n rank: int = 4,\n kernel_size: Union[int, Tuple[int, int]] = (1, 1),\n stride: Union[int, Tuple[int, int]] = (1, 1),\n padding: Union[int, Tuple[int, int], str] = 0,\n network_alpha: Optional[float] = None,\n ):\n super().__init__()\n\n deprecation_message = \"Use of `LoRAConv2dLayer` is deprecated. Please switch to PEFT backend by installing PEFT: `pip install peft`.\"\n deprecate(\"LoRAConv2dLayer\", \"1.0.0\", deprecation_message)\n\n self.down = nn.Conv2d(in_features, rank, kernel_size=kernel_size, stride=stride, padding=padding, bias=False)\n # according to the official kohya_ss trainer kernel_size are always fixed for the up layer\n # # see: https://github.com/bmaltais/kohya_ss/blob/2accb1305979ba62f5077a23aabac23b4c37e935/networks/lora_diffusers.py#L129\n self.up = nn.Conv2d(rank, out_features, kernel_size=(1, 1), stride=(1, 1), bias=False)\n\n # This value has the same meaning as the `--network_alpha` option in the kohya-ss trainer script.\n # See https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning\n self.network_alpha = network_alpha\n self.rank = rank\n\n nn.init.normal_(self.down.weight, std=1 / rank)\n nn.init.zeros_(self.up.weight)\n\n def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n orig_dtype = hidden_states.dtype\n dtype = self.down.weight.dtype\n\n down_hidden_states = self.down(hidden_states.to(dtype))\n up_hidden_states = self.up(down_hidden_states)\n\n if self.network_alpha is not None:\n up_hidden_states *= self.network_alpha / self.rank\n\n return up_hidden_states.to(orig_dtype)\n\n\nclass LoRACompatibleConv(nn.Conv2d):\n \"\"\"\n A convolutional layer that can be used with LoRA.\n \"\"\"\n\n def __init__(self, *args, lora_layer: Optional[LoRAConv2dLayer] = None, **kwargs):\n deprecation_message = \"Use of `LoRACompatibleConv` is deprecated. Please switch to PEFT backend by installing PEFT: `pip install peft`.\"\n deprecate(\"LoRACompatibleConv\", \"1.0.0\", deprecation_message)\n\n super().__init__(*args, **kwargs)\n self.lora_layer = lora_layer\n\n def set_lora_layer(self, lora_layer: Optional[LoRAConv2dLayer]):\n deprecation_message = \"Use of `set_lora_layer()` is deprecated. Please switch to PEFT backend by installing PEFT: `pip install peft`.\"\n deprecate(\"set_lora_layer\", \"1.0.0\", deprecation_message)\n\n self.lora_layer = lora_layer\n\n def _fuse_lora(self, lora_scale: float = 1.0, safe_fusing: bool = False):\n if self.lora_layer is None:\n return\n\n dtype, device = self.weight.data.dtype, self.weight.data.device\n\n w_orig = self.weight.data.float()\n w_up = self.lora_layer.up.weight.data.float()\n w_down = self.lora_layer.down.weight.data.float()\n\n if self.lora_layer.network_alpha is not None:\n w_up = w_up * self.lora_layer.network_alpha / self.lora_layer.rank\n\n fusion = torch.mm(w_up.flatten(start_dim=1), w_down.flatten(start_dim=1))\n fusion = fusion.reshape((w_orig.shape))\n fused_weight = w_orig + (lora_scale * fusion)\n\n if safe_fusing and torch.isnan(fused_weight).any().item():\n raise ValueError(\n \"This LoRA weight seems to be broken. \"\n f\"Encountered NaN values when trying to fuse LoRA weights for {self}.\"\n \"LoRA weights will not be fused.\"\n )\n\n self.weight.data = fused_weight.to(device=device, dtype=dtype)\n\n # we can drop the lora layer now\n self.lora_layer = None\n\n # offload the up and down matrices to CPU to not blow the memory\n self.w_up = w_up.cpu()\n self.w_down = w_down.cpu()\n self._lora_scale = lora_scale\n\n def _unfuse_lora(self):\n if not (getattr(self, \"w_up\", None) is not None and getattr(self, \"w_down\", None) is not None):\n return\n\n fused_weight = self.weight.data\n dtype, device = fused_weight.data.dtype, fused_weight.data.device\n\n self.w_up = self.w_up.to(device=device).float()\n self.w_down = self.w_down.to(device).float()\n\n fusion = torch.mm(self.w_up.flatten(start_dim=1), self.w_down.flatten(start_dim=1))\n fusion = fusion.reshape((fused_weight.shape))\n unfused_weight = fused_weight.float() - (self._lora_scale * fusion)\n self.weight.data = unfused_weight.to(device=device, dtype=dtype)\n\n self.w_up = None\n self.w_down = None\n\n def forward(self, hidden_states: torch.Tensor, scale: float = 1.0) -> torch.Tensor:\n if self.padding_mode != \"zeros\":\n hidden_states = F.pad(hidden_states, self._reversed_padding_repeated_twice, mode=self.padding_mode)\n padding = (0, 0)\n else:\n padding = self.padding\n\n original_outputs = F.conv2d(\n hidden_states, self.weight, self.bias, self.stride, padding, self.dilation, self.groups\n )\n\n if self.lora_layer is None:\n return original_outputs\n else:\n return original_outputs + (scale * self.lora_layer(hidden_states))\n\n\nclass LoRACompatibleLinear(nn.Linear):\n \"\"\"\n A Linear layer that can be used with LoRA.\n \"\"\"\n\n def __init__(self, *args, lora_layer: Optional[LoRALinearLayer] = None, **kwargs):\n deprecation_message = \"Use of `LoRACompatibleLinear` is deprecated. Please switch to PEFT backend by installing PEFT: `pip install peft`.\"\n deprecate(\"LoRACompatibleLinear\", \"1.0.0\", deprecation_message)\n\n super().__init__(*args, **kwargs)\n self.lora_layer = lora_layer\n\n def set_lora_layer(self, lora_layer: Optional[LoRALinearLayer]):\n deprecation_message = \"Use of `set_lora_layer()` is deprecated. Please switch to PEFT backend by installing PEFT: `pip install peft`.\"\n deprecate(\"set_lora_layer\", \"1.0.0\", deprecation_message)\n self.lora_layer = lora_layer\n\n def _fuse_lora(self, lora_scale: float = 1.0, safe_fusing: bool = False):\n if self.lora_layer is None:\n return\n\n dtype, device = self.weight.data.dtype, self.weight.data.device\n\n w_orig = self.weight.data.float()\n w_up = self.lora_layer.up.weight.data.float()\n w_down = self.lora_layer.down.weight.data.float()\n\n if self.lora_layer.network_alpha is not None:\n w_up = w_up * self.lora_layer.network_alpha / self.lora_layer.rank\n\n fused_weight = w_orig + (lora_scale * torch.bmm(w_up[None, :], w_down[None, :])[0])\n\n if safe_fusing and torch.isnan(fused_weight).any().item():\n raise ValueError(\n \"This LoRA weight seems to be broken. \"\n f\"Encountered NaN values when trying to fuse LoRA weights for {self}.\"\n \"LoRA weights will not be fused.\"\n )\n\n self.weight.data = fused_weight.to(device=device, dtype=dtype)\n\n # we can drop the lora layer now\n self.lora_layer = None\n\n # offload the up and down matrices to CPU to not blow the memory\n self.w_up = w_up.cpu()\n self.w_down = w_down.cpu()\n self._lora_scale = lora_scale\n\n def _unfuse_lora(self):\n if not (getattr(self, \"w_up\", None) is not None and getattr(self, \"w_down\", None) is not None):\n return\n\n fused_weight = self.weight.data\n dtype, device = fused_weight.dtype, fused_weight.device\n\n w_up = self.w_up.to(device=device).float()\n w_down = self.w_down.to(device).float()\n\n unfused_weight = fused_weight.float() - (self._lora_scale * torch.bmm(w_up[None, :], w_down[None, :])[0])\n self.weight.data = unfused_weight.to(device=device, dtype=dtype)\n\n self.w_up = None\n self.w_down = None\n\n def forward(self, hidden_states: torch.Tensor, scale: float = 1.0) -> torch.Tensor:\n if self.lora_layer is None:\n out = super().forward(hidden_states)\n return out\n else:\n out = super().forward(hidden_states) + (scale * self.lora_layer(hidden_states))\n return out\n" }, { "path": "diffusers/src/diffusers/models/model_loading_utils.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport importlib\nimport inspect\nimport os\nfrom collections import OrderedDict\nfrom pathlib import Path\nfrom typing import List, Optional, Union\n\nimport safetensors\nimport torch\nfrom huggingface_hub.utils import EntryNotFoundError\n\nfrom ..utils import (\n SAFE_WEIGHTS_INDEX_NAME,\n SAFETENSORS_FILE_EXTENSION,\n WEIGHTS_INDEX_NAME,\n _add_variant,\n _get_model_file,\n is_accelerate_available,\n is_torch_version,\n logging,\n)\n\n\nlogger = logging.get_logger(__name__)\n\n_CLASS_REMAPPING_DICT = {\n \"Transformer2DModel\": {\n \"ada_norm_zero\": \"DiTTransformer2DModel\",\n \"ada_norm_single\": \"PixArtTransformer2DModel\",\n }\n}\n\n\nif is_accelerate_available():\n from accelerate import infer_auto_device_map\n from accelerate.utils import get_balanced_memory, get_max_memory, set_module_tensor_to_device\n\n\n# Adapted from `transformers` (see modeling_utils.py)\ndef _determine_device_map(model: torch.nn.Module, device_map, max_memory, torch_dtype):\n if isinstance(device_map, str):\n no_split_modules = model._get_no_split_modules(device_map)\n device_map_kwargs = {\"no_split_module_classes\": no_split_modules}\n\n if device_map != \"sequential\":\n max_memory = get_balanced_memory(\n model,\n dtype=torch_dtype,\n low_zero=(device_map == \"balanced_low_0\"),\n max_memory=max_memory,\n **device_map_kwargs,\n )\n else:\n max_memory = get_max_memory(max_memory)\n\n device_map_kwargs[\"max_memory\"] = max_memory\n device_map = infer_auto_device_map(model, dtype=torch_dtype, **device_map_kwargs)\n\n return device_map\n\n\ndef _fetch_remapped_cls_from_config(config, old_class):\n previous_class_name = old_class.__name__\n remapped_class_name = _CLASS_REMAPPING_DICT.get(previous_class_name).get(config[\"norm_type\"], None)\n\n # Details:\n # https://github.com/huggingface/diffusers/pull/7647#discussion_r1621344818\n if remapped_class_name:\n # load diffusers library to import compatible and original scheduler\n diffusers_library = importlib.import_module(__name__.split(\".\")[0])\n remapped_class = getattr(diffusers_library, remapped_class_name)\n logger.info(\n f\"Changing class object to be of `{remapped_class_name}` type from `{previous_class_name}` type.\"\n f\"This is because `{previous_class_name}` is scheduled to be deprecated in a future version. Note that this\"\n \" DOESN'T affect the final results.\"\n )\n return remapped_class\n else:\n return old_class\n\n\ndef load_state_dict(checkpoint_file: Union[str, os.PathLike], variant: Optional[str] = None):\n \"\"\"\n Reads a checkpoint file, returning properly formatted errors if they arise.\n \"\"\"\n try:\n file_extension = os.path.basename(checkpoint_file).split(\".\")[-1]\n if file_extension == SAFETENSORS_FILE_EXTENSION:\n return safetensors.torch.load_file(checkpoint_file, device=\"cpu\")\n else:\n weights_only_kwarg = {\"weights_only\": True} if is_torch_version(\">=\", \"1.13\") else {}\n return torch.load(\n checkpoint_file,\n map_location=\"cpu\",\n **weights_only_kwarg,\n )\n except Exception as e:\n try:\n with open(checkpoint_file) as f:\n if f.read().startswith(\"version\"):\n raise OSError(\n \"You seem to have cloned a repository without having git-lfs installed. Please install \"\n \"git-lfs and run `git lfs install` followed by `git lfs pull` in the folder \"\n \"you cloned.\"\n )\n else:\n raise ValueError(\n f\"Unable to locate the file {checkpoint_file} which is necessary to load this pretrained \"\n \"model. Make sure you have saved the model properly.\"\n ) from e\n except (UnicodeDecodeError, ValueError):\n raise OSError(\n f\"Unable to load weights from checkpoint file for '{checkpoint_file}' \" f\"at '{checkpoint_file}'. \"\n )\n\n\ndef load_model_dict_into_meta(\n model,\n state_dict: OrderedDict,\n device: Optional[Union[str, torch.device]] = None,\n dtype: Optional[Union[str, torch.dtype]] = None,\n model_name_or_path: Optional[str] = None,\n) -> List[str]:\n device = device or torch.device(\"cpu\")\n dtype = dtype or torch.float32\n\n accepts_dtype = \"dtype\" in set(inspect.signature(set_module_tensor_to_device).parameters.keys())\n\n unexpected_keys = []\n empty_state_dict = model.state_dict()\n for param_name, param in state_dict.items():\n if param_name not in empty_state_dict:\n unexpected_keys.append(param_name)\n continue\n\n if empty_state_dict[param_name].shape != param.shape:\n model_name_or_path_str = f\"{model_name_or_path} \" if model_name_or_path is not None else \"\"\n raise ValueError(\n f\"Cannot load {model_name_or_path_str}because {param_name} expected shape {empty_state_dict[param_name]}, but got {param.shape}. If you want to instead overwrite randomly initialized weights, please make sure to pass both `low_cpu_mem_usage=False` and `ignore_mismatched_sizes=True`. For more information, see also: https://github.com/huggingface/diffusers/issues/1619#issuecomment-1345604389 as an example.\"\n )\n\n if accepts_dtype:\n set_module_tensor_to_device(model, param_name, device, value=param, dtype=dtype)\n else:\n set_module_tensor_to_device(model, param_name, device, value=param)\n return unexpected_keys\n\n\ndef _load_state_dict_into_model(model_to_load, state_dict: OrderedDict) -> List[str]:\n # Convert old format to new format if needed from a PyTorch state_dict\n # copy state_dict so _load_from_state_dict can modify it\n state_dict = state_dict.copy()\n error_msgs = []\n\n # PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants\n # so we need to apply the function recursively.\n def load(module: torch.nn.Module, prefix: str = \"\"):\n args = (state_dict, prefix, {}, True, [], [], error_msgs)\n module._load_from_state_dict(*args)\n\n for name, child in module._modules.items():\n if child is not None:\n load(child, prefix + name + \".\")\n\n load(model_to_load)\n\n return error_msgs\n\n\ndef _fetch_index_file(\n is_local,\n pretrained_model_name_or_path,\n subfolder,\n use_safetensors,\n cache_dir,\n variant,\n force_download,\n resume_download,\n proxies,\n local_files_only,\n token,\n revision,\n user_agent,\n commit_hash,\n):\n if is_local:\n index_file = Path(\n pretrained_model_name_or_path,\n subfolder or \"\",\n _add_variant(SAFE_WEIGHTS_INDEX_NAME if use_safetensors else WEIGHTS_INDEX_NAME, variant),\n )\n else:\n index_file_in_repo = Path(\n subfolder or \"\",\n _add_variant(SAFE_WEIGHTS_INDEX_NAME if use_safetensors else WEIGHTS_INDEX_NAME, variant),\n ).as_posix()\n try:\n index_file = _get_model_file(\n pretrained_model_name_or_path,\n weights_name=index_file_in_repo,\n cache_dir=cache_dir,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n subfolder=subfolder,\n user_agent=user_agent,\n commit_hash=commit_hash,\n )\n index_file = Path(index_file)\n except (EntryNotFoundError, EnvironmentError):\n index_file = None\n\n return index_file\n" }, { "path": "diffusers/src/diffusers/models/modeling_flax_pytorch_utils.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"PyTorch - Flax general utilities.\"\"\"\n\nimport re\n\nimport jax.numpy as jnp\nfrom flax.traverse_util import flatten_dict, unflatten_dict\nfrom jax.random import PRNGKey\n\nfrom ..utils import logging\n\n\nlogger = logging.get_logger(__name__)\n\n\ndef rename_key(key):\n regex = r\"\\w+[.]\\d+\"\n pats = re.findall(regex, key)\n for pat in pats:\n key = key.replace(pat, \"_\".join(pat.split(\".\")))\n return key\n\n\n#####################\n# PyTorch => Flax #\n#####################\n\n\n# Adapted from https://github.com/huggingface/transformers/blob/c603c80f46881ae18b2ca50770ef65fa4033eacd/src/transformers/modeling_flax_pytorch_utils.py#L69\n# and https://github.com/patil-suraj/stable-diffusion-jax/blob/main/stable_diffusion_jax/convert_diffusers_to_jax.py\ndef rename_key_and_reshape_tensor(pt_tuple_key, pt_tensor, random_flax_state_dict):\n \"\"\"Rename PT weight names to corresponding Flax weight names and reshape tensor if necessary\"\"\"\n # conv norm or layer norm\n renamed_pt_tuple_key = pt_tuple_key[:-1] + (\"scale\",)\n\n # rename attention layers\n if len(pt_tuple_key) > 1:\n for rename_from, rename_to in (\n (\"to_out_0\", \"proj_attn\"),\n (\"to_k\", \"key\"),\n (\"to_v\", \"value\"),\n (\"to_q\", \"query\"),\n ):\n if pt_tuple_key[-2] == rename_from:\n weight_name = pt_tuple_key[-1]\n weight_name = \"kernel\" if weight_name == \"weight\" else weight_name\n renamed_pt_tuple_key = pt_tuple_key[:-2] + (rename_to, weight_name)\n if renamed_pt_tuple_key in random_flax_state_dict:\n assert random_flax_state_dict[renamed_pt_tuple_key].shape == pt_tensor.T.shape\n return renamed_pt_tuple_key, pt_tensor.T\n\n if (\n any(\"norm\" in str_ for str_ in pt_tuple_key)\n and (pt_tuple_key[-1] == \"bias\")\n and (pt_tuple_key[:-1] + (\"bias\",) not in random_flax_state_dict)\n and (pt_tuple_key[:-1] + (\"scale\",) in random_flax_state_dict)\n ):\n renamed_pt_tuple_key = pt_tuple_key[:-1] + (\"scale\",)\n return renamed_pt_tuple_key, pt_tensor\n elif pt_tuple_key[-1] in [\"weight\", \"gamma\"] and pt_tuple_key[:-1] + (\"scale\",) in random_flax_state_dict:\n renamed_pt_tuple_key = pt_tuple_key[:-1] + (\"scale\",)\n return renamed_pt_tuple_key, pt_tensor\n\n # embedding\n if pt_tuple_key[-1] == \"weight\" and pt_tuple_key[:-1] + (\"embedding\",) in random_flax_state_dict:\n pt_tuple_key = pt_tuple_key[:-1] + (\"embedding\",)\n return renamed_pt_tuple_key, pt_tensor\n\n # conv layer\n renamed_pt_tuple_key = pt_tuple_key[:-1] + (\"kernel\",)\n if pt_tuple_key[-1] == \"weight\" and pt_tensor.ndim == 4:\n pt_tensor = pt_tensor.transpose(2, 3, 1, 0)\n return renamed_pt_tuple_key, pt_tensor\n\n # linear layer\n renamed_pt_tuple_key = pt_tuple_key[:-1] + (\"kernel\",)\n if pt_tuple_key[-1] == \"weight\":\n pt_tensor = pt_tensor.T\n return renamed_pt_tuple_key, pt_tensor\n\n # old PyTorch layer norm weight\n renamed_pt_tuple_key = pt_tuple_key[:-1] + (\"weight\",)\n if pt_tuple_key[-1] == \"gamma\":\n return renamed_pt_tuple_key, pt_tensor\n\n # old PyTorch layer norm bias\n renamed_pt_tuple_key = pt_tuple_key[:-1] + (\"bias\",)\n if pt_tuple_key[-1] == \"beta\":\n return renamed_pt_tuple_key, pt_tensor\n\n return pt_tuple_key, pt_tensor\n\n\ndef convert_pytorch_state_dict_to_flax(pt_state_dict, flax_model, init_key=42):\n # Step 1: Convert pytorch tensor to numpy\n pt_state_dict = {k: v.numpy() for k, v in pt_state_dict.items()}\n\n # Step 2: Since the model is stateless, get random Flax params\n random_flax_params = flax_model.init_weights(PRNGKey(init_key))\n\n random_flax_state_dict = flatten_dict(random_flax_params)\n flax_state_dict = {}\n\n # Need to change some parameters name to match Flax names\n for pt_key, pt_tensor in pt_state_dict.items():\n renamed_pt_key = rename_key(pt_key)\n pt_tuple_key = tuple(renamed_pt_key.split(\".\"))\n\n # Correctly rename weight parameters\n flax_key, flax_tensor = rename_key_and_reshape_tensor(pt_tuple_key, pt_tensor, random_flax_state_dict)\n\n if flax_key in random_flax_state_dict:\n if flax_tensor.shape != random_flax_state_dict[flax_key].shape:\n raise ValueError(\n f\"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape \"\n f\"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.\"\n )\n\n # also add unexpected weight so that warning is thrown\n flax_state_dict[flax_key] = jnp.asarray(flax_tensor)\n\n return unflatten_dict(flax_state_dict)\n" }, { "path": "diffusers/src/diffusers/models/modeling_flax_utils.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os\nfrom pickle import UnpicklingError\nfrom typing import Any, Dict, Union\n\nimport jax\nimport jax.numpy as jnp\nimport msgpack.exceptions\nfrom flax.core.frozen_dict import FrozenDict, unfreeze\nfrom flax.serialization import from_bytes, to_bytes\nfrom flax.traverse_util import flatten_dict, unflatten_dict\nfrom huggingface_hub import create_repo, hf_hub_download\nfrom huggingface_hub.utils import (\n EntryNotFoundError,\n RepositoryNotFoundError,\n RevisionNotFoundError,\n validate_hf_hub_args,\n)\nfrom requests import HTTPError\n\nfrom .. import __version__, is_torch_available\nfrom ..utils import (\n CONFIG_NAME,\n FLAX_WEIGHTS_NAME,\n HUGGINGFACE_CO_RESOLVE_ENDPOINT,\n WEIGHTS_NAME,\n PushToHubMixin,\n logging,\n)\nfrom .modeling_flax_pytorch_utils import convert_pytorch_state_dict_to_flax\n\n\nlogger = logging.get_logger(__name__)\n\n\nclass FlaxModelMixin(PushToHubMixin):\n r\"\"\"\n Base class for all Flax models.\n\n [`FlaxModelMixin`] takes care of storing the model configuration and provides methods for loading, downloading and\n saving models.\n\n - **config_name** ([`str`]) -- Filename to save a model to when calling [`~FlaxModelMixin.save_pretrained`].\n \"\"\"\n\n config_name = CONFIG_NAME\n _automatically_saved_args = [\"_diffusers_version\", \"_class_name\", \"_name_or_path\"]\n _flax_internal_args = [\"name\", \"parent\", \"dtype\"]\n\n @classmethod\n def _from_config(cls, config, **kwargs):\n \"\"\"\n All context managers that the model should be initialized under go here.\n \"\"\"\n return cls(config, **kwargs)\n\n def _cast_floating_to(self, params: Union[Dict, FrozenDict], dtype: jnp.dtype, mask: Any = None) -> Any:\n \"\"\"\n Helper method to cast floating-point values of given parameter `PyTree` to given `dtype`.\n \"\"\"\n\n # taken from https://github.com/deepmind/jmp/blob/3a8318abc3292be38582794dbf7b094e6583b192/jmp/_src/policy.py#L27\n def conditional_cast(param):\n if isinstance(param, jnp.ndarray) and jnp.issubdtype(param.dtype, jnp.floating):\n param = param.astype(dtype)\n return param\n\n if mask is None:\n return jax.tree_map(conditional_cast, params)\n\n flat_params = flatten_dict(params)\n flat_mask, _ = jax.tree_flatten(mask)\n\n for masked, key in zip(flat_mask, flat_params.keys()):\n if masked:\n param = flat_params[key]\n flat_params[key] = conditional_cast(param)\n\n return unflatten_dict(flat_params)\n\n def to_bf16(self, params: Union[Dict, FrozenDict], mask: Any = None):\n r\"\"\"\n Cast the floating-point `params` to `jax.numpy.bfloat16`. This returns a new `params` tree and does not cast\n the `params` in place.\n\n This method can be used on a TPU to explicitly convert the model parameters to bfloat16 precision to do full\n half-precision training or to save weights in bfloat16 for inference in order to save memory and improve speed.\n\n Arguments:\n params (`Union[Dict, FrozenDict]`):\n A `PyTree` of model parameters.\n mask (`Union[Dict, FrozenDict]`):\n A `PyTree` with same structure as the `params` tree. The leaves should be booleans. It should be `True`\n for params you want to cast, and `False` for those you want to skip.\n\n Examples:\n\n ```python\n >>> from diffusers import FlaxUNet2DConditionModel\n\n >>> # load model\n >>> model, params = FlaxUNet2DConditionModel.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n >>> # By default, the model parameters will be in fp32 precision, to cast these to bfloat16 precision\n >>> params = model.to_bf16(params)\n >>> # If you don't want to cast certain parameters (for example layer norm bias and scale)\n >>> # then pass the mask as follows\n >>> from flax import traverse_util\n\n >>> model, params = FlaxUNet2DConditionModel.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n >>> flat_params = traverse_util.flatten_dict(params)\n >>> mask = {\n ... path: (path[-2] != (\"LayerNorm\", \"bias\") and path[-2:] != (\"LayerNorm\", \"scale\"))\n ... for path in flat_params\n ... }\n >>> mask = traverse_util.unflatten_dict(mask)\n >>> params = model.to_bf16(params, mask)\n ```\"\"\"\n return self._cast_floating_to(params, jnp.bfloat16, mask)\n\n def to_fp32(self, params: Union[Dict, FrozenDict], mask: Any = None):\n r\"\"\"\n Cast the floating-point `params` to `jax.numpy.float32`. This method can be used to explicitly convert the\n model parameters to fp32 precision. This returns a new `params` tree and does not cast the `params` in place.\n\n Arguments:\n params (`Union[Dict, FrozenDict]`):\n A `PyTree` of model parameters.\n mask (`Union[Dict, FrozenDict]`):\n A `PyTree` with same structure as the `params` tree. The leaves should be booleans. It should be `True`\n for params you want to cast, and `False` for those you want to skip.\n\n Examples:\n\n ```python\n >>> from diffusers import FlaxUNet2DConditionModel\n\n >>> # Download model and configuration from huggingface.co\n >>> model, params = FlaxUNet2DConditionModel.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n >>> # By default, the model params will be in fp32, to illustrate the use of this method,\n >>> # we'll first cast to fp16 and back to fp32\n >>> params = model.to_f16(params)\n >>> # now cast back to fp32\n >>> params = model.to_fp32(params)\n ```\"\"\"\n return self._cast_floating_to(params, jnp.float32, mask)\n\n def to_fp16(self, params: Union[Dict, FrozenDict], mask: Any = None):\n r\"\"\"\n Cast the floating-point `params` to `jax.numpy.float16`. This returns a new `params` tree and does not cast the\n `params` in place.\n\n This method can be used on a GPU to explicitly convert the model parameters to float16 precision to do full\n half-precision training or to save weights in float16 for inference in order to save memory and improve speed.\n\n Arguments:\n params (`Union[Dict, FrozenDict]`):\n A `PyTree` of model parameters.\n mask (`Union[Dict, FrozenDict]`):\n A `PyTree` with same structure as the `params` tree. The leaves should be booleans. It should be `True`\n for params you want to cast, and `False` for those you want to skip.\n\n Examples:\n\n ```python\n >>> from diffusers import FlaxUNet2DConditionModel\n\n >>> # load model\n >>> model, params = FlaxUNet2DConditionModel.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n >>> # By default, the model params will be in fp32, to cast these to float16\n >>> params = model.to_fp16(params)\n >>> # If you want don't want to cast certain parameters (for example layer norm bias and scale)\n >>> # then pass the mask as follows\n >>> from flax import traverse_util\n\n >>> model, params = FlaxUNet2DConditionModel.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n >>> flat_params = traverse_util.flatten_dict(params)\n >>> mask = {\n ... path: (path[-2] != (\"LayerNorm\", \"bias\") and path[-2:] != (\"LayerNorm\", \"scale\"))\n ... for path in flat_params\n ... }\n >>> mask = traverse_util.unflatten_dict(mask)\n >>> params = model.to_fp16(params, mask)\n ```\"\"\"\n return self._cast_floating_to(params, jnp.float16, mask)\n\n def init_weights(self, rng: jax.Array) -> Dict:\n raise NotImplementedError(f\"init_weights method has to be implemented for {self}\")\n\n @classmethod\n @validate_hf_hub_args\n def from_pretrained(\n cls,\n pretrained_model_name_or_path: Union[str, os.PathLike],\n dtype: jnp.dtype = jnp.float32,\n *model_args,\n **kwargs,\n ):\n r\"\"\"\n Instantiate a pretrained Flax model from a pretrained model configuration.\n\n Parameters:\n pretrained_model_name_or_path (`str` or `os.PathLike`):\n Can be either:\n\n - A string, the *model id* (for example `runwayml/stable-diffusion-v1-5`) of a pretrained model\n hosted on the Hub.\n - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved\n using [`~FlaxModelMixin.save_pretrained`].\n dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):\n The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and\n `jax.numpy.bfloat16` (on TPUs).\n\n This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If\n specified, all the computation will be performed with the given `dtype`.\n\n \n\n This only specifies the dtype of the *computation* and does not influence the dtype of model\n parameters.\n\n If you wish to change the dtype of the model parameters, see [`~FlaxModelMixin.to_fp16`] and\n [`~FlaxModelMixin.to_bf16`].\n\n \n\n model_args (sequence of positional arguments, *optional*):\n All remaining positional arguments are passed to the underlying model's `__init__` method.\n cache_dir (`Union[str, os.PathLike]`, *optional*):\n Path to a directory where a downloaded pretrained model configuration is cached if the standard cache\n is not used.\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\n cached versions if they exist.\n resume_download:\n Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1\n of Diffusers.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\n local_files_only(`bool`, *optional*, defaults to `False`):\n Whether to only load local model weights and configuration files or not. If set to `True`, the model\n won't be downloaded from the Hub.\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier\n allowed by Git.\n from_pt (`bool`, *optional*, defaults to `False`):\n Load the model weights from a PyTorch checkpoint save file.\n kwargs (remaining dictionary of keyword arguments, *optional*):\n Can be used to update the configuration object (after it is loaded) and initiate the model (for\n example, `output_attentions=True`). Behaves differently depending on whether a `config` is provided or\n automatically loaded:\n\n - If a configuration is provided with `config`, `kwargs` are directly passed to the underlying\n model's `__init__` method (we assume all relevant updates to the configuration have already been\n done).\n - If a configuration is not provided, `kwargs` are first passed to the configuration class\n initialization function [`~ConfigMixin.from_config`]. Each key of the `kwargs` that corresponds\n to a configuration attribute is used to override said attribute with the supplied `kwargs` value.\n Remaining keys that do not correspond to any configuration attribute are passed to the underlying\n model's `__init__` function.\n\n Examples:\n\n ```python\n >>> from diffusers import FlaxUNet2DConditionModel\n\n >>> # Download model and configuration from huggingface.co and cache.\n >>> model, params = FlaxUNet2DConditionModel.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n >>> # Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).\n >>> model, params = FlaxUNet2DConditionModel.from_pretrained(\"./test/saved_model/\")\n ```\n\n If you get the error message below, you need to finetune the weights for your downstream task:\n\n ```bash\n Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:\n - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated\n You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n ```\n \"\"\"\n config = kwargs.pop(\"config\", None)\n cache_dir = kwargs.pop(\"cache_dir\", None)\n force_download = kwargs.pop(\"force_download\", False)\n from_pt = kwargs.pop(\"from_pt\", False)\n resume_download = kwargs.pop(\"resume_download\", None)\n proxies = kwargs.pop(\"proxies\", None)\n local_files_only = kwargs.pop(\"local_files_only\", False)\n token = kwargs.pop(\"token\", None)\n revision = kwargs.pop(\"revision\", None)\n subfolder = kwargs.pop(\"subfolder\", None)\n\n user_agent = {\n \"diffusers\": __version__,\n \"file_type\": \"model\",\n \"framework\": \"flax\",\n }\n\n # Load config if we don't provide one\n if config is None:\n config, unused_kwargs = cls.load_config(\n pretrained_model_name_or_path,\n cache_dir=cache_dir,\n return_unused_kwargs=True,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n subfolder=subfolder,\n **kwargs,\n )\n\n model, model_kwargs = cls.from_config(config, dtype=dtype, return_unused_kwargs=True, **unused_kwargs)\n\n # Load model\n pretrained_path_with_subfolder = (\n pretrained_model_name_or_path\n if subfolder is None\n else os.path.join(pretrained_model_name_or_path, subfolder)\n )\n if os.path.isdir(pretrained_path_with_subfolder):\n if from_pt:\n if not os.path.isfile(os.path.join(pretrained_path_with_subfolder, WEIGHTS_NAME)):\n raise EnvironmentError(\n f\"Error no file named {WEIGHTS_NAME} found in directory {pretrained_path_with_subfolder} \"\n )\n model_file = os.path.join(pretrained_path_with_subfolder, WEIGHTS_NAME)\n elif os.path.isfile(os.path.join(pretrained_path_with_subfolder, FLAX_WEIGHTS_NAME)):\n # Load from a Flax checkpoint\n model_file = os.path.join(pretrained_path_with_subfolder, FLAX_WEIGHTS_NAME)\n # Check if pytorch weights exist instead\n elif os.path.isfile(os.path.join(pretrained_path_with_subfolder, WEIGHTS_NAME)):\n raise EnvironmentError(\n f\"{WEIGHTS_NAME} file found in directory {pretrained_path_with_subfolder}. Please load the model\"\n \" using `from_pt=True`.\"\n )\n else:\n raise EnvironmentError(\n f\"Error no file named {FLAX_WEIGHTS_NAME} or {WEIGHTS_NAME} found in directory \"\n f\"{pretrained_path_with_subfolder}.\"\n )\n else:\n try:\n model_file = hf_hub_download(\n pretrained_model_name_or_path,\n filename=FLAX_WEIGHTS_NAME if not from_pt else WEIGHTS_NAME,\n cache_dir=cache_dir,\n force_download=force_download,\n proxies=proxies,\n resume_download=resume_download,\n local_files_only=local_files_only,\n token=token,\n user_agent=user_agent,\n subfolder=subfolder,\n revision=revision,\n )\n\n except RepositoryNotFoundError:\n raise EnvironmentError(\n f\"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier \"\n \"listed on 'https://huggingface.co/models'\\nIf this is a private repository, make sure to pass a \"\n \"token having permission to this repo with `token` or log in with `huggingface-cli \"\n \"login`.\"\n )\n except RevisionNotFoundError:\n raise EnvironmentError(\n f\"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for \"\n \"this model name. Check the model page at \"\n f\"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions.\"\n )\n except EntryNotFoundError:\n raise EnvironmentError(\n f\"{pretrained_model_name_or_path} does not appear to have a file named {FLAX_WEIGHTS_NAME}.\"\n )\n except HTTPError as err:\n raise EnvironmentError(\n f\"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\\n\"\n f\"{err}\"\n )\n except ValueError:\n raise EnvironmentError(\n f\"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it\"\n f\" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a\"\n f\" directory containing a file named {FLAX_WEIGHTS_NAME} or {WEIGHTS_NAME}.\\nCheckout your\"\n \" internet connection or see how to run the library in offline mode at\"\n \" 'https://huggingface.co/docs/transformers/installation#offline-mode'.\"\n )\n except EnvironmentError:\n raise EnvironmentError(\n f\"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from \"\n \"'https://huggingface.co/models', make sure you don't have a local directory with the same name. \"\n f\"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory \"\n f\"containing a file named {FLAX_WEIGHTS_NAME} or {WEIGHTS_NAME}.\"\n )\n\n if from_pt:\n if is_torch_available():\n from .modeling_utils import load_state_dict\n else:\n raise EnvironmentError(\n \"Can't load the model in PyTorch format because PyTorch is not installed. \"\n \"Please, install PyTorch or use native Flax weights.\"\n )\n\n # Step 1: Get the pytorch file\n pytorch_model_file = load_state_dict(model_file)\n\n # Step 2: Convert the weights\n state = convert_pytorch_state_dict_to_flax(pytorch_model_file, model)\n else:\n try:\n with open(model_file, \"rb\") as state_f:\n state = from_bytes(cls, state_f.read())\n except (UnpicklingError, msgpack.exceptions.ExtraData) as e:\n try:\n with open(model_file) as f:\n if f.read().startswith(\"version\"):\n raise OSError(\n \"You seem to have cloned a repository without having git-lfs installed. Please\"\n \" install git-lfs and run `git lfs install` followed by `git lfs pull` in the\"\n \" folder you cloned.\"\n )\n else:\n raise ValueError from e\n except (UnicodeDecodeError, ValueError):\n raise EnvironmentError(f\"Unable to convert {model_file} to Flax deserializable object. \")\n # make sure all arrays are stored as jnp.ndarray\n # NOTE: This is to prevent a bug this will be fixed in Flax >= v0.3.4:\n # https://github.com/google/flax/issues/1261\n state = jax.tree_util.tree_map(lambda x: jax.device_put(x, jax.local_devices(backend=\"cpu\")[0]), state)\n\n # flatten dicts\n state = flatten_dict(state)\n\n params_shape_tree = jax.eval_shape(model.init_weights, rng=jax.random.PRNGKey(0))\n required_params = set(flatten_dict(unfreeze(params_shape_tree)).keys())\n\n shape_state = flatten_dict(unfreeze(params_shape_tree))\n\n missing_keys = required_params - set(state.keys())\n unexpected_keys = set(state.keys()) - required_params\n\n if missing_keys:\n logger.warning(\n f\"The checkpoint {pretrained_model_name_or_path} is missing required keys: {missing_keys}. \"\n \"Make sure to call model.init_weights to initialize the missing weights.\"\n )\n cls._missing_keys = missing_keys\n\n for key in state.keys():\n if key in shape_state and state[key].shape != shape_state[key].shape:\n raise ValueError(\n f\"Trying to load the pretrained weight for {key} failed: checkpoint has shape \"\n f\"{state[key].shape} which is incompatible with the model shape {shape_state[key].shape}. \"\n )\n\n # remove unexpected keys to not be saved again\n for unexpected_key in unexpected_keys:\n del state[unexpected_key]\n\n if len(unexpected_keys) > 0:\n logger.warning(\n f\"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when\"\n f\" initializing {model.__class__.__name__}: {unexpected_keys}\\n- This IS expected if you are\"\n f\" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task or\"\n \" with another architecture.\"\n )\n else:\n logger.info(f\"All model checkpoint weights were used when initializing {model.__class__.__name__}.\\n\")\n\n if len(missing_keys) > 0:\n logger.warning(\n f\"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at\"\n f\" {pretrained_model_name_or_path} and are newly initialized: {missing_keys}\\nYou should probably\"\n \" TRAIN this model on a down-stream task to be able to use it for predictions and inference.\"\n )\n else:\n logger.info(\n f\"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at\"\n f\" {pretrained_model_name_or_path}.\\nIf your task is similar to the task the model of the checkpoint\"\n f\" was trained on, you can already use {model.__class__.__name__} for predictions without further\"\n \" training.\"\n )\n\n return model, unflatten_dict(state)\n\n def save_pretrained(\n self,\n save_directory: Union[str, os.PathLike],\n params: Union[Dict, FrozenDict],\n is_main_process: bool = True,\n push_to_hub: bool = False,\n **kwargs,\n ):\n \"\"\"\n Save a model and its configuration file to a directory so that it can be reloaded using the\n [`~FlaxModelMixin.from_pretrained`] class method.\n\n Arguments:\n save_directory (`str` or `os.PathLike`):\n Directory to save a model and its configuration file to. Will be created if it doesn't exist.\n params (`Union[Dict, FrozenDict]`):\n A `PyTree` of model parameters.\n is_main_process (`bool`, *optional*, defaults to `True`):\n Whether the process calling this is the main process or not. Useful during distributed training and you\n need to call this function on all processes. In this case, set `is_main_process=True` only on the main\n process to avoid race conditions.\n push_to_hub (`bool`, *optional*, defaults to `False`):\n Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the\n repository you want to push to with `repo_id` (will default to the name of `save_directory` in your\n namespace).\n kwargs (`Dict[str, Any]`, *optional*):\n Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.\n \"\"\"\n if os.path.isfile(save_directory):\n logger.error(f\"Provided path ({save_directory}) should be a directory, not a file\")\n return\n\n os.makedirs(save_directory, exist_ok=True)\n\n if push_to_hub:\n commit_message = kwargs.pop(\"commit_message\", None)\n private = kwargs.pop(\"private\", False)\n create_pr = kwargs.pop(\"create_pr\", False)\n token = kwargs.pop(\"token\", None)\n repo_id = kwargs.pop(\"repo_id\", save_directory.split(os.path.sep)[-1])\n repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id\n\n model_to_save = self\n\n # Attach architecture to the config\n # Save the config\n if is_main_process:\n model_to_save.save_config(save_directory)\n\n # save model\n output_model_file = os.path.join(save_directory, FLAX_WEIGHTS_NAME)\n with open(output_model_file, \"wb\") as f:\n model_bytes = to_bytes(params)\n f.write(model_bytes)\n\n logger.info(f\"Model weights saved in {output_model_file}\")\n\n if push_to_hub:\n self._upload_folder(\n save_directory,\n repo_id,\n token=token,\n commit_message=commit_message,\n create_pr=create_pr,\n )\n" }, { "path": "diffusers/src/diffusers/models/modeling_outputs.py", "content": "from dataclasses import dataclass\n\nfrom ..utils import BaseOutput\n\n\n@dataclass\nclass AutoencoderKLOutput(BaseOutput):\n \"\"\"\n Output of AutoencoderKL encoding method.\n\n Args:\n latent_dist (`DiagonalGaussianDistribution`):\n Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.\n `DiagonalGaussianDistribution` allows for sampling latents from the distribution.\n \"\"\"\n\n latent_dist: \"DiagonalGaussianDistribution\" # noqa: F821\n\n\n@dataclass\nclass Transformer2DModelOutput(BaseOutput):\n \"\"\"\n The output of [`Transformer2DModel`].\n\n Args:\n sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):\n The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability\n distributions for the unnoised latent pixels.\n \"\"\"\n\n sample: \"torch.Tensor\" # noqa: F821\n" }, { "path": "diffusers/src/diffusers/models/modeling_pytorch_flax_utils.py", "content": "# coding=utf-8\r\n# Copyright 2024 The HuggingFace Inc. team.\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n# http://www.apache.org/licenses/LICENSE-2.0\r\n#\r\n# Unless required by applicable law or agreed to in writing, software\r\n# distributed under the License is distributed on an \"AS IS\" BASIS,\r\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n# See the License for the specific language governing permissions and\r\n# limitations under the License.\r\n\"\"\"PyTorch - Flax general utilities.\"\"\"\r\n\r\nfrom pickle import UnpicklingError\r\n\r\nimport jax\r\nimport jax.numpy as jnp\r\nimport numpy as np\r\nfrom flax.serialization import from_bytes\r\nfrom flax.traverse_util import flatten_dict\r\n\r\nfrom ..utils import logging\r\n\r\n\r\nlogger = logging.get_logger(__name__)\r\n\r\n\r\n#####################\r\n# Flax => PyTorch #\r\n#####################\r\n\r\n\r\n# from https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_flax_pytorch_utils.py#L224-L352\r\ndef load_flax_checkpoint_in_pytorch_model(pt_model, model_file):\r\n try:\r\n with open(model_file, \"rb\") as flax_state_f:\r\n flax_state = from_bytes(None, flax_state_f.read())\r\n except UnpicklingError as e:\r\n try:\r\n with open(model_file) as f:\r\n if f.read().startswith(\"version\"):\r\n raise OSError(\r\n \"You seem to have cloned a repository without having git-lfs installed. Please\"\r\n \" install git-lfs and run `git lfs install` followed by `git lfs pull` in the\"\r\n \" folder you cloned.\"\r\n )\r\n else:\r\n raise ValueError from e\r\n except (UnicodeDecodeError, ValueError):\r\n raise EnvironmentError(f\"Unable to convert {model_file} to Flax deserializable object. \")\r\n\r\n return load_flax_weights_in_pytorch_model(pt_model, flax_state)\r\n\r\n\r\ndef load_flax_weights_in_pytorch_model(pt_model, flax_state):\r\n \"\"\"Load flax checkpoints in a PyTorch model\"\"\"\r\n\r\n try:\r\n import torch # noqa: F401\r\n except ImportError:\r\n logger.error(\r\n \"Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see\"\r\n \" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation\"\r\n \" instructions.\"\r\n )\r\n raise\r\n\r\n # check if we have bf16 weights\r\n is_type_bf16 = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype == jnp.bfloat16, flax_state)).values()\r\n if any(is_type_bf16):\r\n # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16\r\n\r\n # and bf16 is not fully supported in PT yet.\r\n logger.warning(\r\n \"Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` \"\r\n \"before loading those in PyTorch model.\"\r\n )\r\n flax_state = jax.tree_util.tree_map(\r\n lambda params: params.astype(np.float32) if params.dtype == jnp.bfloat16 else params, flax_state\r\n )\r\n\r\n pt_model.base_model_prefix = \"\"\r\n\r\n flax_state_dict = flatten_dict(flax_state, sep=\".\")\r\n pt_model_dict = pt_model.state_dict()\r\n\r\n # keep track of unexpected & missing keys\r\n unexpected_keys = []\r\n missing_keys = set(pt_model_dict.keys())\r\n\r\n for flax_key_tuple, flax_tensor in flax_state_dict.items():\r\n flax_key_tuple_array = flax_key_tuple.split(\".\")\r\n\r\n if flax_key_tuple_array[-1] == \"kernel\" and flax_tensor.ndim == 4:\r\n flax_key_tuple_array = flax_key_tuple_array[:-1] + [\"weight\"]\r\n flax_tensor = jnp.transpose(flax_tensor, (3, 2, 0, 1))\r\n elif flax_key_tuple_array[-1] == \"kernel\":\r\n flax_key_tuple_array = flax_key_tuple_array[:-1] + [\"weight\"]\r\n flax_tensor = flax_tensor.T\r\n elif flax_key_tuple_array[-1] == \"scale\":\r\n flax_key_tuple_array = flax_key_tuple_array[:-1] + [\"weight\"]\r\n\r\n if \"time_embedding\" not in flax_key_tuple_array:\r\n for i, flax_key_tuple_string in enumerate(flax_key_tuple_array):\r\n flax_key_tuple_array[i] = (\r\n flax_key_tuple_string.replace(\"_0\", \".0\")\r\n .replace(\"_1\", \".1\")\r\n .replace(\"_2\", \".2\")\r\n .replace(\"_3\", \".3\")\r\n .replace(\"_4\", \".4\")\r\n .replace(\"_5\", \".5\")\r\n .replace(\"_6\", \".6\")\r\n .replace(\"_7\", \".7\")\r\n .replace(\"_8\", \".8\")\r\n .replace(\"_9\", \".9\")\r\n )\r\n\r\n flax_key = \".\".join(flax_key_tuple_array)\r\n\r\n if flax_key in pt_model_dict:\r\n if flax_tensor.shape != pt_model_dict[flax_key].shape:\r\n raise ValueError(\r\n f\"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected \"\r\n f\"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.\"\r\n )\r\n else:\r\n # add weight to pytorch dict\r\n flax_tensor = np.asarray(flax_tensor) if not isinstance(flax_tensor, np.ndarray) else flax_tensor\r\n pt_model_dict[flax_key] = torch.from_numpy(flax_tensor)\r\n # remove from missing keys\r\n missing_keys.remove(flax_key)\r\n else:\r\n # weight is not expected by PyTorch model\r\n unexpected_keys.append(flax_key)\r\n\r\n pt_model.load_state_dict(pt_model_dict)\r\n\r\n # re-transform missing_keys to list\r\n missing_keys = list(missing_keys)\r\n\r\n if len(unexpected_keys) > 0:\r\n logger.warning(\r\n \"Some weights of the Flax model were not used when initializing the PyTorch model\"\r\n f\" {pt_model.__class__.__name__}: {unexpected_keys}\\n- This IS expected if you are initializing\"\r\n f\" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture\"\r\n \" (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\\n- This\"\r\n f\" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect\"\r\n \" to be exactly identical (e.g. initializing a BertForSequenceClassification model from a\"\r\n \" FlaxBertForSequenceClassification model).\"\r\n )\r\n if len(missing_keys) > 0:\r\n logger.warning(\r\n f\"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly\"\r\n f\" initialized: {missing_keys}\\nYou should probably TRAIN this model on a down-stream task to be able to\"\r\n \" use it for predictions and inference.\"\r\n )\r\n\r\n return pt_model\r\n" }, { "path": "diffusers/src/diffusers/models/modeling_utils.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nimport itertools\nimport json\nimport os\nimport re\nfrom collections import OrderedDict\nfrom functools import partial\nfrom pathlib import Path\nfrom typing import Any, Callable, List, Optional, Tuple, Union\n\nimport safetensors\nimport torch\nfrom huggingface_hub import create_repo, split_torch_state_dict_into_shards\nfrom huggingface_hub.utils import validate_hf_hub_args\nfrom torch import Tensor, nn\n\nfrom .. import __version__\nfrom ..utils import (\n CONFIG_NAME,\n FLAX_WEIGHTS_NAME,\n SAFE_WEIGHTS_INDEX_NAME,\n SAFETENSORS_WEIGHTS_NAME,\n WEIGHTS_INDEX_NAME,\n WEIGHTS_NAME,\n _add_variant,\n _get_checkpoint_shard_files,\n _get_model_file,\n deprecate,\n is_accelerate_available,\n is_torch_version,\n logging,\n)\nfrom ..utils.hub_utils import (\n PushToHubMixin,\n load_or_create_model_card,\n populate_model_card,\n)\nfrom .model_loading_utils import (\n _determine_device_map,\n _fetch_index_file,\n _load_state_dict_into_model,\n load_model_dict_into_meta,\n load_state_dict,\n)\n\n\nlogger = logging.get_logger(__name__)\n\n_REGEX_SHARD = re.compile(r\"(.*?)-\\d{5}-of-\\d{5}\")\n\n\nif is_torch_version(\">=\", \"1.9.0\"):\n _LOW_CPU_MEM_USAGE_DEFAULT = True\nelse:\n _LOW_CPU_MEM_USAGE_DEFAULT = False\n\n\nif is_accelerate_available():\n import accelerate\n\n\ndef get_parameter_device(parameter: torch.nn.Module) -> torch.device:\n try:\n parameters_and_buffers = itertools.chain(parameter.parameters(), parameter.buffers())\n return next(parameters_and_buffers).device\n except StopIteration:\n # For torch.nn.DataParallel compatibility in PyTorch 1.5\n\n def find_tensor_attributes(module: torch.nn.Module) -> List[Tuple[str, Tensor]]:\n tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]\n return tuples\n\n gen = parameter._named_members(get_members_fn=find_tensor_attributes)\n first_tuple = next(gen)\n return first_tuple[1].device\n\n\ndef get_parameter_dtype(parameter: torch.nn.Module) -> torch.dtype:\n try:\n params = tuple(parameter.parameters())\n if len(params) > 0:\n return params[0].dtype\n\n buffers = tuple(parameter.buffers())\n if len(buffers) > 0:\n return buffers[0].dtype\n\n except StopIteration:\n # For torch.nn.DataParallel compatibility in PyTorch 1.5\n\n def find_tensor_attributes(module: torch.nn.Module) -> List[Tuple[str, Tensor]]:\n tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]\n return tuples\n\n gen = parameter._named_members(get_members_fn=find_tensor_attributes)\n first_tuple = next(gen)\n return first_tuple[1].dtype\n\n\nclass ModelMixin(torch.nn.Module, PushToHubMixin):\n r\"\"\"\n Base class for all models.\n\n [`ModelMixin`] takes care of storing the model configuration and provides methods for loading, downloading and\n saving models.\n\n - **config_name** ([`str`]) -- Filename to save a model to when calling [`~models.ModelMixin.save_pretrained`].\n \"\"\"\n\n config_name = CONFIG_NAME\n _automatically_saved_args = [\"_diffusers_version\", \"_class_name\", \"_name_or_path\"]\n _supports_gradient_checkpointing = False\n _keys_to_ignore_on_load_unexpected = None\n _no_split_modules = None\n\n def __init__(self):\n super().__init__()\n\n def __getattr__(self, name: str) -> Any:\n \"\"\"The only reason we overwrite `getattr` here is to gracefully deprecate accessing\n config attributes directly. See https://github.com/huggingface/diffusers/pull/3129 We need to overwrite\n __getattr__ here in addition so that we don't trigger `torch.nn.Module`'s __getattr__':\n https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module\n \"\"\"\n\n is_in_config = \"_internal_dict\" in self.__dict__ and hasattr(self.__dict__[\"_internal_dict\"], name)\n is_attribute = name in self.__dict__\n\n if is_in_config and not is_attribute:\n deprecation_message = f\"Accessing config attribute `{name}` directly via '{type(self).__name__}' object attribute is deprecated. Please access '{name}' over '{type(self).__name__}'s config object instead, e.g. 'unet.config.{name}'.\"\n deprecate(\"direct config name access\", \"1.0.0\", deprecation_message, standard_warn=False, stacklevel=3)\n return self._internal_dict[name]\n\n # call PyTorch's https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module\n return super().__getattr__(name)\n\n @property\n def is_gradient_checkpointing(self) -> bool:\n \"\"\"\n Whether gradient checkpointing is activated for this model or not.\n \"\"\"\n return any(hasattr(m, \"gradient_checkpointing\") and m.gradient_checkpointing for m in self.modules())\n\n def enable_gradient_checkpointing(self) -> None:\n \"\"\"\n Activates gradient checkpointing for the current model (may be referred to as *activation checkpointing* or\n *checkpoint activations* in other frameworks).\n \"\"\"\n if not self._supports_gradient_checkpointing:\n raise ValueError(f\"{self.__class__.__name__} does not support gradient checkpointing.\")\n self.apply(partial(self._set_gradient_checkpointing, value=True))\n\n def disable_gradient_checkpointing(self) -> None:\n \"\"\"\n Deactivates gradient checkpointing for the current model (may be referred to as *activation checkpointing* or\n *checkpoint activations* in other frameworks).\n \"\"\"\n if self._supports_gradient_checkpointing:\n self.apply(partial(self._set_gradient_checkpointing, value=False))\n\n def set_use_npu_flash_attention(self, valid: bool) -> None:\n r\"\"\"\n Set the switch for the npu flash attention.\n \"\"\"\n\n def fn_recursive_set_npu_flash_attention(module: torch.nn.Module):\n if hasattr(module, \"set_use_npu_flash_attention\"):\n module.set_use_npu_flash_attention(valid)\n\n for child in module.children():\n fn_recursive_set_npu_flash_attention(child)\n\n for module in self.children():\n if isinstance(module, torch.nn.Module):\n fn_recursive_set_npu_flash_attention(module)\n\n def enable_npu_flash_attention(self) -> None:\n r\"\"\"\n Enable npu flash attention from torch_npu\n\n \"\"\"\n self.set_use_npu_flash_attention(True)\n\n def disable_npu_flash_attention(self) -> None:\n r\"\"\"\n disable npu flash attention from torch_npu\n\n \"\"\"\n self.set_use_npu_flash_attention(False)\n\n def set_use_memory_efficient_attention_xformers(\n self, valid: bool, attention_op: Optional[Callable] = None\n ) -> None:\n # Recursively walk through all the children.\n # Any children which exposes the set_use_memory_efficient_attention_xformers method\n # gets the message\n def fn_recursive_set_mem_eff(module: torch.nn.Module):\n if hasattr(module, \"set_use_memory_efficient_attention_xformers\"):\n module.set_use_memory_efficient_attention_xformers(valid, attention_op)\n\n for child in module.children():\n fn_recursive_set_mem_eff(child)\n\n for module in self.children():\n if isinstance(module, torch.nn.Module):\n fn_recursive_set_mem_eff(module)\n\n def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None) -> None:\n r\"\"\"\n Enable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).\n\n When this option is enabled, you should observe lower GPU memory usage and a potential speed up during\n inference. Speed up during training is not guaranteed.\n\n \n\n ⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes\n precedent.\n\n \n\n Parameters:\n attention_op (`Callable`, *optional*):\n Override the default `None` operator for use as `op` argument to the\n [`memory_efficient_attention()`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention)\n function of xFormers.\n\n Examples:\n\n ```py\n >>> import torch\n >>> from diffusers import UNet2DConditionModel\n >>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp\n\n >>> model = UNet2DConditionModel.from_pretrained(\n ... \"stabilityai/stable-diffusion-2-1\", subfolder=\"unet\", torch_dtype=torch.float16\n ... )\n >>> model = model.to(\"cuda\")\n >>> model.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)\n ```\n \"\"\"\n self.set_use_memory_efficient_attention_xformers(True, attention_op)\n\n def disable_xformers_memory_efficient_attention(self) -> None:\n r\"\"\"\n Disable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).\n \"\"\"\n self.set_use_memory_efficient_attention_xformers(False)\n\n def save_pretrained(\n self,\n save_directory: Union[str, os.PathLike],\n is_main_process: bool = True,\n save_function: Optional[Callable] = None,\n safe_serialization: bool = True,\n variant: Optional[str] = None,\n max_shard_size: Union[int, str] = \"10GB\",\n push_to_hub: bool = False,\n **kwargs,\n ):\n \"\"\"\n Save a model and its configuration file to a directory so that it can be reloaded using the\n [`~models.ModelMixin.from_pretrained`] class method.\n\n Arguments:\n save_directory (`str` or `os.PathLike`):\n Directory to save a model and its configuration file to. Will be created if it doesn't exist.\n is_main_process (`bool`, *optional*, defaults to `True`):\n Whether the process calling this is the main process or not. Useful during distributed training and you\n need to call this function on all processes. In this case, set `is_main_process=True` only on the main\n process to avoid race conditions.\n save_function (`Callable`):\n The function to use to save the state dictionary. Useful during distributed training when you need to\n replace `torch.save` with another method. Can be configured with the environment variable\n `DIFFUSERS_SAVE_MODE`.\n safe_serialization (`bool`, *optional*, defaults to `True`):\n Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.\n variant (`str`, *optional*):\n If specified, weights are saved in the format `pytorch_model..bin`.\n max_shard_size (`int` or `str`, defaults to `\"10GB\"`):\n The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size\n lower than this size. If expressed as a string, needs to be digits followed by a unit (like `\"5GB\"`).\n If expressed as an integer, the unit is bytes. Note that this limit will be decreased after a certain\n period of time (starting from Oct 2024) to allow users to upgrade to the latest version of `diffusers`.\n This is to establish a common default size for this argument across different libraries in the Hugging\n Face ecosystem (`transformers`, and `accelerate`, for example).\n push_to_hub (`bool`, *optional*, defaults to `False`):\n Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the\n repository you want to push to with `repo_id` (will default to the name of `save_directory` in your\n namespace).\n kwargs (`Dict[str, Any]`, *optional*):\n Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.\n \"\"\"\n if os.path.isfile(save_directory):\n logger.error(f\"Provided path ({save_directory}) should be a directory, not a file\")\n return\n\n weights_name = SAFETENSORS_WEIGHTS_NAME if safe_serialization else WEIGHTS_NAME\n weights_name = _add_variant(weights_name, variant)\n weight_name_split = weights_name.split(\".\")\n if len(weight_name_split) in [2, 3]:\n weights_name_pattern = weight_name_split[0] + \"{suffix}.\" + \".\".join(weight_name_split[1:])\n else:\n raise ValueError(f\"Invalid {weights_name} provided.\")\n\n os.makedirs(save_directory, exist_ok=True)\n\n if push_to_hub:\n commit_message = kwargs.pop(\"commit_message\", None)\n private = kwargs.pop(\"private\", False)\n create_pr = kwargs.pop(\"create_pr\", False)\n token = kwargs.pop(\"token\", None)\n repo_id = kwargs.pop(\"repo_id\", save_directory.split(os.path.sep)[-1])\n repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id\n\n # Only save the model itself if we are using distributed training\n model_to_save = self\n\n # Attach architecture to the config\n # Save the config\n if is_main_process:\n model_to_save.save_config(save_directory)\n\n # Save the model\n state_dict = model_to_save.state_dict()\n\n # Save the model\n state_dict_split = split_torch_state_dict_into_shards(\n state_dict, max_shard_size=max_shard_size, filename_pattern=weights_name_pattern\n )\n\n # Clean the folder from a previous save\n if is_main_process:\n for filename in os.listdir(save_directory):\n if filename in state_dict_split.filename_to_tensors.keys():\n continue\n full_filename = os.path.join(save_directory, filename)\n if not os.path.isfile(full_filename):\n continue\n weights_without_ext = weights_name_pattern.replace(\".bin\", \"\").replace(\".safetensors\", \"\")\n weights_without_ext = weights_without_ext.replace(\"{suffix}\", \"\")\n filename_without_ext = filename.replace(\".bin\", \"\").replace(\".safetensors\", \"\")\n # make sure that file to be deleted matches format of sharded file, e.g. pytorch_model-00001-of-00005\n if (\n filename.startswith(weights_without_ext)\n and _REGEX_SHARD.fullmatch(filename_without_ext) is not None\n ):\n os.remove(full_filename)\n\n for filename, tensors in state_dict_split.filename_to_tensors.items():\n shard = {tensor: state_dict[tensor] for tensor in tensors}\n filepath = os.path.join(save_directory, filename)\n if safe_serialization:\n # At some point we will need to deal better with save_function (used for TPU and other distributed\n # joyfulness), but for now this enough.\n safetensors.torch.save_file(shard, filepath, metadata={\"format\": \"pt\"})\n else:\n torch.save(shard, filepath)\n\n if state_dict_split.is_sharded:\n index = {\n \"metadata\": state_dict_split.metadata,\n \"weight_map\": state_dict_split.tensor_to_filename,\n }\n save_index_file = SAFE_WEIGHTS_INDEX_NAME if safe_serialization else WEIGHTS_INDEX_NAME\n save_index_file = os.path.join(save_directory, _add_variant(save_index_file, variant))\n # Save the index as well\n with open(save_index_file, \"w\", encoding=\"utf-8\") as f:\n content = json.dumps(index, indent=2, sort_keys=True) + \"\\n\"\n f.write(content)\n logger.info(\n f\"The model is bigger than the maximum size per checkpoint ({max_shard_size}) and is going to be \"\n f\"split in {len(state_dict_split.filename_to_tensors)} checkpoint shards. You can find where each parameters has been saved in the \"\n f\"index located at {save_index_file}.\"\n )\n else:\n path_to_weights = os.path.join(save_directory, weights_name)\n logger.info(f\"Model weights saved in {path_to_weights}\")\n\n if push_to_hub:\n # Create a new empty model card and eventually tag it\n model_card = load_or_create_model_card(repo_id, token=token)\n model_card = populate_model_card(model_card)\n model_card.save(Path(save_directory, \"README.md\").as_posix())\n\n self._upload_folder(\n save_directory,\n repo_id,\n token=token,\n commit_message=commit_message,\n create_pr=create_pr,\n )\n\n @classmethod\n @validate_hf_hub_args\n def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):\n r\"\"\"\n Instantiate a pretrained PyTorch model from a pretrained model configuration.\n\n The model is set in evaluation mode - `model.eval()` - by default, and dropout modules are deactivated. To\n train the model, set it back in training mode with `model.train()`.\n\n Parameters:\n pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):\n Can be either:\n\n - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on\n the Hub.\n - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved\n with [`~ModelMixin.save_pretrained`].\n\n cache_dir (`Union[str, os.PathLike]`, *optional*):\n Path to a directory where a downloaded pretrained model configuration is cached if the standard cache\n is not used.\n torch_dtype (`str` or `torch.dtype`, *optional*):\n Override the default `torch.dtype` and load the model with another dtype. If `\"auto\"` is passed, the\n dtype is automatically derived from the model's weights.\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\n cached versions if they exist.\n resume_download:\n Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1\n of Diffusers.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\n output_loading_info (`bool`, *optional*, defaults to `False`):\n Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.\n local_files_only(`bool`, *optional*, defaults to `False`):\n Whether to only load local model weights and configuration files or not. If set to `True`, the model\n won't be downloaded from the Hub.\n token (`str` or *bool*, *optional*):\n The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from\n `diffusers-cli login` (stored in `~/.huggingface`) is used.\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier\n allowed by Git.\n from_flax (`bool`, *optional*, defaults to `False`):\n Load the model weights from a Flax checkpoint save file.\n subfolder (`str`, *optional*, defaults to `\"\"`):\n The subfolder location of a model file within a larger model repository on the Hub or locally.\n mirror (`str`, *optional*):\n Mirror source to resolve accessibility issues if you're downloading a model in China. We do not\n guarantee the timeliness or safety of the source, and you should refer to the mirror site for more\n information.\n device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):\n A map that specifies where each submodule should go. It doesn't need to be defined for each\n parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the\n same device.\n\n Set `device_map=\"auto\"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For\n more information about each option see [designing a device\n map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).\n max_memory (`Dict`, *optional*):\n A dictionary device identifier for the maximum memory. Will default to the maximum memory available for\n each GPU and the available CPU RAM if unset.\n offload_folder (`str` or `os.PathLike`, *optional*):\n The path to offload weights if `device_map` contains the value `\"disk\"`.\n offload_state_dict (`bool`, *optional*):\n If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if\n the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`\n when there is some disk offload.\n low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):\n Speed up model loading only loading the pretrained weights and not initializing the weights. This also\n tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.\n Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this\n argument to `True` will raise an error.\n variant (`str`, *optional*):\n Load weights from a specified `variant` filename such as `\"fp16\"` or `\"ema\"`. This is ignored when\n loading `from_flax`.\n use_safetensors (`bool`, *optional*, defaults to `None`):\n If set to `None`, the `safetensors` weights are downloaded if they're available **and** if the\n `safetensors` library is installed. If set to `True`, the model is forcibly loaded from `safetensors`\n weights. If set to `False`, `safetensors` weights are not loaded.\n\n \n\n To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with\n `huggingface-cli login`. You can also activate the special\n [\"offline-mode\"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a\n firewalled environment.\n\n \n\n Example:\n\n ```py\n from diffusers import UNet2DConditionModel\n\n unet = UNet2DConditionModel.from_pretrained(\"runwayml/stable-diffusion-v1-5\", subfolder=\"unet\")\n ```\n\n If you get the error message below, you need to finetune the weights for your downstream task:\n\n ```bash\n Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:\n - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated\n You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n ```\n \"\"\"\n cache_dir = kwargs.pop(\"cache_dir\", None)\n ignore_mismatched_sizes = kwargs.pop(\"ignore_mismatched_sizes\", False)\n force_download = kwargs.pop(\"force_download\", False)\n from_flax = kwargs.pop(\"from_flax\", False)\n resume_download = kwargs.pop(\"resume_download\", None)\n proxies = kwargs.pop(\"proxies\", None)\n output_loading_info = kwargs.pop(\"output_loading_info\", False)\n local_files_only = kwargs.pop(\"local_files_only\", None)\n token = kwargs.pop(\"token\", None)\n revision = kwargs.pop(\"revision\", None)\n torch_dtype = kwargs.pop(\"torch_dtype\", None)\n subfolder = kwargs.pop(\"subfolder\", None)\n device_map = kwargs.pop(\"device_map\", None)\n max_memory = kwargs.pop(\"max_memory\", None)\n offload_folder = kwargs.pop(\"offload_folder\", None)\n offload_state_dict = kwargs.pop(\"offload_state_dict\", False)\n low_cpu_mem_usage = kwargs.pop(\"low_cpu_mem_usage\", _LOW_CPU_MEM_USAGE_DEFAULT)\n variant = kwargs.pop(\"variant\", None)\n use_safetensors = kwargs.pop(\"use_safetensors\", None)\n\n allow_pickle = False\n if use_safetensors is None:\n use_safetensors = True\n allow_pickle = True\n\n if low_cpu_mem_usage and not is_accelerate_available():\n low_cpu_mem_usage = False\n logger.warning(\n \"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the\"\n \" environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install\"\n \" `accelerate` for faster and less memory-intense model loading. You can do so with: \\n```\\npip\"\n \" install accelerate\\n```\\n.\"\n )\n\n if device_map is not None and not is_accelerate_available():\n raise NotImplementedError(\n \"Loading and dispatching requires `accelerate`. Please make sure to install accelerate or set\"\n \" `device_map=None`. You can install accelerate with `pip install accelerate`.\"\n )\n\n # Check if we can handle device_map and dispatching the weights\n if device_map is not None and not is_torch_version(\">=\", \"1.9.0\"):\n raise NotImplementedError(\n \"Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set\"\n \" `device_map=None`.\"\n )\n\n if low_cpu_mem_usage is True and not is_torch_version(\">=\", \"1.9.0\"):\n raise NotImplementedError(\n \"Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set\"\n \" `low_cpu_mem_usage=False`.\"\n )\n\n if low_cpu_mem_usage is False and device_map is not None:\n raise ValueError(\n f\"You cannot set `low_cpu_mem_usage` to `False` while using device_map={device_map} for loading and\"\n \" dispatching. Please make sure to set `low_cpu_mem_usage=True`.\"\n )\n\n # change device_map into a map if we passed an int, a str or a torch.device\n if isinstance(device_map, torch.device):\n device_map = {\"\": device_map}\n elif isinstance(device_map, str) and device_map not in [\"auto\", \"balanced\", \"balanced_low_0\", \"sequential\"]:\n try:\n device_map = {\"\": torch.device(device_map)}\n except RuntimeError:\n raise ValueError(\n \"When passing device_map as a string, the value needs to be a device name (e.g. cpu, cuda:0) or \"\n f\"'auto', 'balanced', 'balanced_low_0', 'sequential' but found {device_map}.\"\n )\n elif isinstance(device_map, int):\n if device_map < 0:\n raise ValueError(\n \"You can't pass device_map as a negative int. If you want to put the model on the cpu, pass device_map = 'cpu' \"\n )\n else:\n device_map = {\"\": device_map}\n\n if device_map is not None:\n if low_cpu_mem_usage is None:\n low_cpu_mem_usage = True\n elif not low_cpu_mem_usage:\n raise ValueError(\"Passing along a `device_map` requires `low_cpu_mem_usage=True`\")\n\n if low_cpu_mem_usage:\n if device_map is not None and not is_torch_version(\">=\", \"1.10\"):\n # The max memory utils require PyTorch >= 1.10 to have torch.cuda.mem_get_info.\n raise ValueError(\"`low_cpu_mem_usage` and `device_map` require PyTorch >= 1.10.\")\n\n # Load config if we don't provide a configuration\n config_path = pretrained_model_name_or_path\n\n user_agent = {\n \"diffusers\": __version__,\n \"file_type\": \"model\",\n \"framework\": \"pytorch\",\n }\n\n # load config\n config, unused_kwargs, commit_hash = cls.load_config(\n config_path,\n cache_dir=cache_dir,\n return_unused_kwargs=True,\n return_commit_hash=True,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n subfolder=subfolder,\n user_agent=user_agent,\n **kwargs,\n )\n\n # Determine if we're loading from a directory of sharded checkpoints.\n is_sharded = False\n index_file = None\n is_local = os.path.isdir(pretrained_model_name_or_path)\n index_file = _fetch_index_file(\n is_local=is_local,\n pretrained_model_name_or_path=pretrained_model_name_or_path,\n subfolder=subfolder or \"\",\n use_safetensors=use_safetensors,\n cache_dir=cache_dir,\n variant=variant,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n user_agent=user_agent,\n commit_hash=commit_hash,\n )\n if index_file is not None and index_file.is_file():\n is_sharded = True\n\n if is_sharded and from_flax:\n raise ValueError(\"Loading of sharded checkpoints is not supported when `from_flax=True`.\")\n\n # load model\n model_file = None\n if from_flax:\n model_file = _get_model_file(\n pretrained_model_name_or_path,\n weights_name=FLAX_WEIGHTS_NAME,\n cache_dir=cache_dir,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n subfolder=subfolder,\n user_agent=user_agent,\n commit_hash=commit_hash,\n )\n model = cls.from_config(config, **unused_kwargs)\n\n # Convert the weights\n from .modeling_pytorch_flax_utils import load_flax_checkpoint_in_pytorch_model\n\n model = load_flax_checkpoint_in_pytorch_model(model, model_file)\n else:\n if is_sharded:\n sharded_ckpt_cached_folder, sharded_metadata = _get_checkpoint_shard_files(\n pretrained_model_name_or_path,\n index_file,\n cache_dir=cache_dir,\n proxies=proxies,\n resume_download=resume_download,\n local_files_only=local_files_only,\n token=token,\n user_agent=user_agent,\n revision=revision,\n subfolder=subfolder or \"\",\n )\n\n elif use_safetensors and not is_sharded:\n try:\n model_file = _get_model_file(\n pretrained_model_name_or_path,\n weights_name=_add_variant(SAFETENSORS_WEIGHTS_NAME, variant),\n cache_dir=cache_dir,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n subfolder=subfolder,\n user_agent=user_agent,\n commit_hash=commit_hash,\n )\n\n except IOError as e:\n logger.error(f\"An error occurred while trying to fetch {pretrained_model_name_or_path}: {e}\")\n if not allow_pickle:\n raise\n logger.warning(\n \"Defaulting to unsafe serialization. Pass `allow_pickle=False` to raise an error instead.\"\n )\n\n if model_file is None and not is_sharded:\n model_file = _get_model_file(\n pretrained_model_name_or_path,\n weights_name=_add_variant(WEIGHTS_NAME, variant),\n cache_dir=cache_dir,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n subfolder=subfolder,\n user_agent=user_agent,\n commit_hash=commit_hash,\n )\n\n if low_cpu_mem_usage:\n # Instantiate model with empty weights\n with accelerate.init_empty_weights():\n model = cls.from_config(config, **unused_kwargs)\n\n # if device_map is None, load the state dict and move the params from meta device to the cpu\n if device_map is None and not is_sharded:\n param_device = \"cpu\"\n state_dict = load_state_dict(model_file, variant=variant)\n model._convert_deprecated_attention_blocks(state_dict)\n # move the params from meta device to cpu\n missing_keys = set(model.state_dict().keys()) - set(state_dict.keys())\n if len(missing_keys) > 0:\n raise ValueError(\n f\"Cannot load {cls} from {pretrained_model_name_or_path} because the following keys are\"\n f\" missing: \\n {', '.join(missing_keys)}. \\n Please make sure to pass\"\n \" `low_cpu_mem_usage=False` and `device_map=None` if you want to randomly initialize\"\n \" those weights or else make sure your checkpoint file is correct.\"\n )\n\n unexpected_keys = load_model_dict_into_meta(\n model,\n state_dict,\n device=param_device,\n dtype=torch_dtype,\n model_name_or_path=pretrained_model_name_or_path,\n )\n\n if cls._keys_to_ignore_on_load_unexpected is not None:\n for pat in cls._keys_to_ignore_on_load_unexpected:\n unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]\n\n if len(unexpected_keys) > 0:\n logger.warning(\n f\"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \\n {[', '.join(unexpected_keys)]}\"\n )\n\n else: # else let accelerate handle loading and dispatching.\n # Load weights and dispatch according to the device_map\n # by default the device_map is None and the weights are loaded on the CPU\n device_map = _determine_device_map(model, device_map, max_memory, torch_dtype)\n try:\n accelerate.load_checkpoint_and_dispatch(\n model,\n model_file if not is_sharded else sharded_ckpt_cached_folder,\n device_map,\n max_memory=max_memory,\n offload_folder=offload_folder,\n offload_state_dict=offload_state_dict,\n dtype=torch_dtype,\n force_hooks=True,\n strict=True,\n )\n except AttributeError as e:\n # When using accelerate loading, we do not have the ability to load the state\n # dict and rename the weight names manually. Additionally, accelerate skips\n # torch loading conventions and directly writes into `module.{_buffers, _parameters}`\n # (which look like they should be private variables?), so we can't use the standard hooks\n # to rename parameters on load. We need to mimic the original weight names so the correct\n # attributes are available. After we have loaded the weights, we convert the deprecated\n # names to the new non-deprecated names. Then we _greatly encourage_ the user to convert\n # the weights so we don't have to do this again.\n\n if \"'Attention' object has no attribute\" in str(e):\n logger.warning(\n f\"Taking `{str(e)}` while using `accelerate.load_checkpoint_and_dispatch` to mean {pretrained_model_name_or_path}\"\n \" was saved with deprecated attention block weight names. We will load it with the deprecated attention block\"\n \" names and convert them on the fly to the new attention block format. Please re-save the model after this conversion,\"\n \" so we don't have to do the on the fly renaming in the future. If the model is from a hub checkpoint,\"\n \" please also re-upload it or open a PR on the original repository.\"\n )\n model._temp_convert_self_to_deprecated_attention_blocks()\n accelerate.load_checkpoint_and_dispatch(\n model,\n model_file,\n device_map,\n max_memory=max_memory,\n offload_folder=offload_folder,\n offload_state_dict=offload_state_dict,\n dtype=torch_dtype,\n )\n model._undo_temp_convert_self_to_deprecated_attention_blocks()\n else:\n raise e\n\n loading_info = {\n \"missing_keys\": [],\n \"unexpected_keys\": [],\n \"mismatched_keys\": [],\n \"error_msgs\": [],\n }\n else:\n model = cls.from_config(config, **unused_kwargs)\n\n state_dict = load_state_dict(model_file, variant=variant)\n model._convert_deprecated_attention_blocks(state_dict)\n\n model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model(\n model,\n state_dict,\n model_file,\n pretrained_model_name_or_path,\n ignore_mismatched_sizes=ignore_mismatched_sizes,\n )\n\n loading_info = {\n \"missing_keys\": missing_keys,\n \"unexpected_keys\": unexpected_keys,\n \"mismatched_keys\": mismatched_keys,\n \"error_msgs\": error_msgs,\n }\n\n if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):\n raise ValueError(\n f\"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}.\"\n )\n elif torch_dtype is not None:\n model = model.to(torch_dtype)\n\n model.register_to_config(_name_or_path=pretrained_model_name_or_path)\n\n # Set model in evaluation mode to deactivate DropOut modules by default\n model.eval()\n if output_loading_info:\n return model, loading_info\n\n return model\n\n @classmethod\n def _load_pretrained_model(\n cls,\n model,\n state_dict: OrderedDict,\n resolved_archive_file,\n pretrained_model_name_or_path: Union[str, os.PathLike],\n ignore_mismatched_sizes: bool = False,\n ):\n # Retrieve missing & unexpected_keys\n model_state_dict = model.state_dict()\n loaded_keys = list(state_dict.keys())\n\n expected_keys = list(model_state_dict.keys())\n\n original_loaded_keys = loaded_keys\n\n missing_keys = list(set(expected_keys) - set(loaded_keys))\n unexpected_keys = list(set(loaded_keys) - set(expected_keys))\n\n # Make sure we are able to load base models as well as derived models (with heads)\n model_to_load = model\n\n def _find_mismatched_keys(\n state_dict,\n model_state_dict,\n loaded_keys,\n ignore_mismatched_sizes,\n ):\n mismatched_keys = []\n if ignore_mismatched_sizes:\n for checkpoint_key in loaded_keys:\n model_key = checkpoint_key\n\n if (\n model_key in model_state_dict\n and state_dict[checkpoint_key].shape != model_state_dict[model_key].shape\n ):\n mismatched_keys.append(\n (checkpoint_key, state_dict[checkpoint_key].shape, model_state_dict[model_key].shape)\n )\n del state_dict[checkpoint_key]\n return mismatched_keys\n\n if state_dict is not None:\n # Whole checkpoint\n mismatched_keys = _find_mismatched_keys(\n state_dict,\n model_state_dict,\n original_loaded_keys,\n ignore_mismatched_sizes,\n )\n error_msgs = _load_state_dict_into_model(model_to_load, state_dict)\n\n if len(error_msgs) > 0:\n error_msg = \"\\n\\t\".join(error_msgs)\n if \"size mismatch\" in error_msg:\n error_msg += (\n \"\\n\\tYou may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method.\"\n )\n raise RuntimeError(f\"Error(s) in loading state_dict for {model.__class__.__name__}:\\n\\t{error_msg}\")\n\n if len(unexpected_keys) > 0:\n logger.warning(\n f\"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when\"\n f\" initializing {model.__class__.__name__}: {unexpected_keys}\\n- This IS expected if you are\"\n f\" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task\"\n \" or with another architecture (e.g. initializing a BertForSequenceClassification model from a\"\n \" BertForPreTraining model).\\n- This IS NOT expected if you are initializing\"\n f\" {model.__class__.__name__} from the checkpoint of a model that you expect to be exactly\"\n \" identical (initializing a BertForSequenceClassification model from a\"\n \" BertForSequenceClassification model).\"\n )\n else:\n logger.info(f\"All model checkpoint weights were used when initializing {model.__class__.__name__}.\\n\")\n if len(missing_keys) > 0:\n logger.warning(\n f\"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at\"\n f\" {pretrained_model_name_or_path} and are newly initialized: {missing_keys}\\nYou should probably\"\n \" TRAIN this model on a down-stream task to be able to use it for predictions and inference.\"\n )\n elif len(mismatched_keys) == 0:\n logger.info(\n f\"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at\"\n f\" {pretrained_model_name_or_path}.\\nIf your task is similar to the task the model of the\"\n f\" checkpoint was trained on, you can already use {model.__class__.__name__} for predictions\"\n \" without further training.\"\n )\n if len(mismatched_keys) > 0:\n mismatched_warning = \"\\n\".join(\n [\n f\"- {key}: found shape {shape1} in the checkpoint and {shape2} in the model instantiated\"\n for key, shape1, shape2 in mismatched_keys\n ]\n )\n logger.warning(\n f\"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at\"\n f\" {pretrained_model_name_or_path} and are newly initialized because the shapes did not\"\n f\" match:\\n{mismatched_warning}\\nYou should probably TRAIN this model on a down-stream task to be\"\n \" able to use it for predictions and inference.\"\n )\n\n return model, missing_keys, unexpected_keys, mismatched_keys, error_msgs\n\n @classmethod\n def _get_signature_keys(cls, obj):\n parameters = inspect.signature(obj.__init__).parameters\n required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty}\n optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty})\n expected_modules = set(required_parameters.keys()) - {\"self\"}\n\n return expected_modules, optional_parameters\n\n # Adapted from `transformers` modeling_utils.py\n def _get_no_split_modules(self, device_map: str):\n \"\"\"\n Get the modules of the model that should not be spit when using device_map. We iterate through the modules to\n get the underlying `_no_split_modules`.\n\n Args:\n device_map (`str`):\n The device map value. Options are [\"auto\", \"balanced\", \"balanced_low_0\", \"sequential\"]\n\n Returns:\n `List[str]`: List of modules that should not be split\n \"\"\"\n _no_split_modules = set()\n modules_to_check = [self]\n while len(modules_to_check) > 0:\n module = modules_to_check.pop(-1)\n # if the module does not appear in _no_split_modules, we also check the children\n if module.__class__.__name__ not in _no_split_modules:\n if isinstance(module, ModelMixin):\n if module._no_split_modules is None:\n raise ValueError(\n f\"{module.__class__.__name__} does not support `device_map='{device_map}'`. To implement support, the model \"\n \"class needs to implement the `_no_split_modules` attribute.\"\n )\n else:\n _no_split_modules = _no_split_modules | set(module._no_split_modules)\n modules_to_check += list(module.children())\n return list(_no_split_modules)\n\n @property\n def device(self) -> torch.device:\n \"\"\"\n `torch.device`: The device on which the module is (assuming that all the module parameters are on the same\n device).\n \"\"\"\n return get_parameter_device(self)\n\n @property\n def dtype(self) -> torch.dtype:\n \"\"\"\n `torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype).\n \"\"\"\n return get_parameter_dtype(self)\n\n def num_parameters(self, only_trainable: bool = False, exclude_embeddings: bool = False) -> int:\n \"\"\"\n Get number of (trainable or non-embedding) parameters in the module.\n\n Args:\n only_trainable (`bool`, *optional*, defaults to `False`):\n Whether or not to return only the number of trainable parameters.\n exclude_embeddings (`bool`, *optional*, defaults to `False`):\n Whether or not to return only the number of non-embedding parameters.\n\n Returns:\n `int`: The number of parameters.\n\n Example:\n\n ```py\n from diffusers import UNet2DConditionModel\n\n model_id = \"runwayml/stable-diffusion-v1-5\"\n unet = UNet2DConditionModel.from_pretrained(model_id, subfolder=\"unet\")\n unet.num_parameters(only_trainable=True)\n 859520964\n ```\n \"\"\"\n\n if exclude_embeddings:\n embedding_param_names = [\n f\"{name}.weight\"\n for name, module_type in self.named_modules()\n if isinstance(module_type, torch.nn.Embedding)\n ]\n non_embedding_parameters = [\n parameter for name, parameter in self.named_parameters() if name not in embedding_param_names\n ]\n return sum(p.numel() for p in non_embedding_parameters if p.requires_grad or not only_trainable)\n else:\n return sum(p.numel() for p in self.parameters() if p.requires_grad or not only_trainable)\n\n def _convert_deprecated_attention_blocks(self, state_dict: OrderedDict) -> None:\n deprecated_attention_block_paths = []\n\n def recursive_find_attn_block(name, module):\n if hasattr(module, \"_from_deprecated_attn_block\") and module._from_deprecated_attn_block:\n deprecated_attention_block_paths.append(name)\n\n for sub_name, sub_module in module.named_children():\n sub_name = sub_name if name == \"\" else f\"{name}.{sub_name}\"\n recursive_find_attn_block(sub_name, sub_module)\n\n recursive_find_attn_block(\"\", self)\n\n # NOTE: we have to check if the deprecated parameters are in the state dict\n # because it is possible we are loading from a state dict that was already\n # converted\n\n for path in deprecated_attention_block_paths:\n # group_norm path stays the same\n\n # query -> to_q\n if f\"{path}.query.weight\" in state_dict:\n state_dict[f\"{path}.to_q.weight\"] = state_dict.pop(f\"{path}.query.weight\")\n if f\"{path}.query.bias\" in state_dict:\n state_dict[f\"{path}.to_q.bias\"] = state_dict.pop(f\"{path}.query.bias\")\n\n # key -> to_k\n if f\"{path}.key.weight\" in state_dict:\n state_dict[f\"{path}.to_k.weight\"] = state_dict.pop(f\"{path}.key.weight\")\n if f\"{path}.key.bias\" in state_dict:\n state_dict[f\"{path}.to_k.bias\"] = state_dict.pop(f\"{path}.key.bias\")\n\n # value -> to_v\n if f\"{path}.value.weight\" in state_dict:\n state_dict[f\"{path}.to_v.weight\"] = state_dict.pop(f\"{path}.value.weight\")\n if f\"{path}.value.bias\" in state_dict:\n state_dict[f\"{path}.to_v.bias\"] = state_dict.pop(f\"{path}.value.bias\")\n\n # proj_attn -> to_out.0\n if f\"{path}.proj_attn.weight\" in state_dict:\n state_dict[f\"{path}.to_out.0.weight\"] = state_dict.pop(f\"{path}.proj_attn.weight\")\n if f\"{path}.proj_attn.bias\" in state_dict:\n state_dict[f\"{path}.to_out.0.bias\"] = state_dict.pop(f\"{path}.proj_attn.bias\")\n\n def _temp_convert_self_to_deprecated_attention_blocks(self) -> None:\n deprecated_attention_block_modules = []\n\n def recursive_find_attn_block(module):\n if hasattr(module, \"_from_deprecated_attn_block\") and module._from_deprecated_attn_block:\n deprecated_attention_block_modules.append(module)\n\n for sub_module in module.children():\n recursive_find_attn_block(sub_module)\n\n recursive_find_attn_block(self)\n\n for module in deprecated_attention_block_modules:\n module.query = module.to_q\n module.key = module.to_k\n module.value = module.to_v\n module.proj_attn = module.to_out[0]\n\n # We don't _have_ to delete the old attributes, but it's helpful to ensure\n # that _all_ the weights are loaded into the new attributes and we're not\n # making an incorrect assumption that this model should be converted when\n # it really shouldn't be.\n del module.to_q\n del module.to_k\n del module.to_v\n del module.to_out\n\n def _undo_temp_convert_self_to_deprecated_attention_blocks(self) -> None:\n deprecated_attention_block_modules = []\n\n def recursive_find_attn_block(module) -> None:\n if hasattr(module, \"_from_deprecated_attn_block\") and module._from_deprecated_attn_block:\n deprecated_attention_block_modules.append(module)\n\n for sub_module in module.children():\n recursive_find_attn_block(sub_module)\n\n recursive_find_attn_block(self)\n\n for module in deprecated_attention_block_modules:\n module.to_q = module.query\n module.to_k = module.key\n module.to_v = module.value\n module.to_out = nn.ModuleList([module.proj_attn, nn.Dropout(module.dropout)])\n\n del module.query\n del module.key\n del module.value\n del module.proj_attn\n\n\nclass LegacyModelMixin(ModelMixin):\n r\"\"\"\n A subclass of `ModelMixin` to resolve class mapping from legacy classes (like `Transformer2DModel`) to more\n pipeline-specific classes (like `DiTTransformer2DModel`).\n \"\"\"\n\n @classmethod\n @validate_hf_hub_args\n def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):\n # To prevent depedency import problem.\n from .model_loading_utils import _fetch_remapped_cls_from_config\n\n # Create a copy of the kwargs so that we don't mess with the keyword arguments in the downstream calls.\n kwargs_copy = kwargs.copy()\n\n cache_dir = kwargs.pop(\"cache_dir\", None)\n force_download = kwargs.pop(\"force_download\", False)\n resume_download = kwargs.pop(\"resume_download\", None)\n proxies = kwargs.pop(\"proxies\", None)\n local_files_only = kwargs.pop(\"local_files_only\", None)\n token = kwargs.pop(\"token\", None)\n revision = kwargs.pop(\"revision\", None)\n subfolder = kwargs.pop(\"subfolder\", None)\n\n # Load config if we don't provide a configuration\n config_path = pretrained_model_name_or_path\n\n user_agent = {\n \"diffusers\": __version__,\n \"file_type\": \"model\",\n \"framework\": \"pytorch\",\n }\n\n # load config\n config, _, _ = cls.load_config(\n config_path,\n cache_dir=cache_dir,\n return_unused_kwargs=True,\n return_commit_hash=True,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n subfolder=subfolder,\n user_agent=user_agent,\n **kwargs,\n )\n # resolve remapping\n remapped_class = _fetch_remapped_cls_from_config(config, cls)\n\n return remapped_class.from_pretrained(pretrained_model_name_or_path, **kwargs_copy)\n" }, { "path": "diffusers/src/diffusers/models/normalization.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numbers\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom ..utils import is_torch_version\nfrom .activations import get_activation\nfrom .embeddings import CombinedTimestepLabelEmbeddings, PixArtAlphaCombinedTimestepSizeEmbeddings\n\n\nclass AdaLayerNorm(nn.Module):\n r\"\"\"\n Norm layer modified to incorporate timestep embeddings.\n\n Parameters:\n embedding_dim (`int`): The size of each embedding vector.\n num_embeddings (`int`): The size of the embeddings dictionary.\n \"\"\"\n\n def __init__(self, embedding_dim: int, num_embeddings: int):\n super().__init__()\n self.emb = nn.Embedding(num_embeddings, embedding_dim)\n self.silu = nn.SiLU()\n self.linear = nn.Linear(embedding_dim, embedding_dim * 2)\n self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False)\n\n def forward(self, x: torch.Tensor, timestep: torch.Tensor) -> torch.Tensor:\n emb = self.linear(self.silu(self.emb(timestep)))\n scale, shift = torch.chunk(emb, 2)\n x = self.norm(x) * (1 + scale) + shift\n return x\n\n\nclass AdaLayerNormZero(nn.Module):\n r\"\"\"\n Norm layer adaptive layer norm zero (adaLN-Zero).\n\n Parameters:\n embedding_dim (`int`): The size of each embedding vector.\n num_embeddings (`int`): The size of the embeddings dictionary.\n \"\"\"\n\n def __init__(self, embedding_dim: int, num_embeddings: Optional[int] = None):\n super().__init__()\n if num_embeddings is not None:\n self.emb = CombinedTimestepLabelEmbeddings(num_embeddings, embedding_dim)\n else:\n self.emb = None\n\n self.silu = nn.SiLU()\n self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True)\n self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6)\n\n def forward(\n self,\n x: torch.Tensor,\n timestep: Optional[torch.Tensor] = None,\n class_labels: Optional[torch.LongTensor] = None,\n hidden_dtype: Optional[torch.dtype] = None,\n emb: Optional[torch.Tensor] = None,\n ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:\n if self.emb is not None:\n emb = self.emb(timestep, class_labels, hidden_dtype=hidden_dtype)\n emb = self.linear(self.silu(emb))\n shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.chunk(6, dim=1)\n x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]\n return x, gate_msa, shift_mlp, scale_mlp, gate_mlp\n\n\nclass AdaLayerNormSingle(nn.Module):\n r\"\"\"\n Norm layer adaptive layer norm single (adaLN-single).\n\n As proposed in PixArt-Alpha (see: https://arxiv.org/abs/2310.00426; Section 2.3).\n\n Parameters:\n embedding_dim (`int`): The size of each embedding vector.\n use_additional_conditions (`bool`): To use additional conditions for normalization or not.\n \"\"\"\n\n def __init__(self, embedding_dim: int, use_additional_conditions: bool = False):\n super().__init__()\n\n self.emb = PixArtAlphaCombinedTimestepSizeEmbeddings(\n embedding_dim, size_emb_dim=embedding_dim // 3, use_additional_conditions=use_additional_conditions\n )\n\n self.silu = nn.SiLU()\n self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True)\n\n def forward(\n self,\n timestep: torch.Tensor,\n added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,\n batch_size: Optional[int] = None,\n hidden_dtype: Optional[torch.dtype] = None,\n ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:\n # No modulation happening here.\n embedded_timestep = self.emb(timestep, **added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_dtype)\n return self.linear(self.silu(embedded_timestep)), embedded_timestep\n\n\nclass AdaGroupNorm(nn.Module):\n r\"\"\"\n GroupNorm layer modified to incorporate timestep embeddings.\n\n Parameters:\n embedding_dim (`int`): The size of each embedding vector.\n num_embeddings (`int`): The size of the embeddings dictionary.\n num_groups (`int`): The number of groups to separate the channels into.\n act_fn (`str`, *optional*, defaults to `None`): The activation function to use.\n eps (`float`, *optional*, defaults to `1e-5`): The epsilon value to use for numerical stability.\n \"\"\"\n\n def __init__(\n self, embedding_dim: int, out_dim: int, num_groups: int, act_fn: Optional[str] = None, eps: float = 1e-5\n ):\n super().__init__()\n self.num_groups = num_groups\n self.eps = eps\n\n if act_fn is None:\n self.act = None\n else:\n self.act = get_activation(act_fn)\n\n self.linear = nn.Linear(embedding_dim, out_dim * 2)\n\n def forward(self, x: torch.Tensor, emb: torch.Tensor) -> torch.Tensor:\n if self.act:\n emb = self.act(emb)\n emb = self.linear(emb)\n emb = emb[:, :, None, None]\n scale, shift = emb.chunk(2, dim=1)\n\n x = F.group_norm(x, self.num_groups, eps=self.eps)\n x = x * (1 + scale) + shift\n return x\n\n\nclass AdaLayerNormContinuous(nn.Module):\n def __init__(\n self,\n embedding_dim: int,\n conditioning_embedding_dim: int,\n # NOTE: It is a bit weird that the norm layer can be configured to have scale and shift parameters\n # because the output is immediately scaled and shifted by the projected conditioning embeddings.\n # Note that AdaLayerNorm does not let the norm layer have scale and shift parameters.\n # However, this is how it was implemented in the original code, and it's rather likely you should\n # set `elementwise_affine` to False.\n elementwise_affine=True,\n eps=1e-5,\n bias=True,\n norm_type=\"layer_norm\",\n ):\n super().__init__()\n self.silu = nn.SiLU()\n self.linear = nn.Linear(conditioning_embedding_dim, embedding_dim * 2, bias=bias)\n if norm_type == \"layer_norm\":\n self.norm = LayerNorm(embedding_dim, eps, elementwise_affine, bias)\n elif norm_type == \"rms_norm\":\n self.norm = RMSNorm(embedding_dim, eps, elementwise_affine)\n else:\n raise ValueError(f\"unknown norm_type {norm_type}\")\n\n def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor:\n # convert back to the original dtype in case `conditioning_embedding`` is upcasted to float32 (needed for hunyuanDiT)\n emb = self.linear(self.silu(conditioning_embedding).to(x.dtype))\n scale, shift = torch.chunk(emb, 2, dim=1)\n x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]\n return x\n\n\nif is_torch_version(\">=\", \"2.1.0\"):\n LayerNorm = nn.LayerNorm\nelse:\n # Has optional bias parameter compared to torch layer norm\n # TODO: replace with torch layernorm once min required torch version >= 2.1\n class LayerNorm(nn.Module):\n def __init__(self, dim, eps: float = 1e-5, elementwise_affine: bool = True, bias: bool = True):\n super().__init__()\n\n self.eps = eps\n\n if isinstance(dim, numbers.Integral):\n dim = (dim,)\n\n self.dim = torch.Size(dim)\n\n if elementwise_affine:\n self.weight = nn.Parameter(torch.ones(dim))\n self.bias = nn.Parameter(torch.zeros(dim)) if bias else None\n else:\n self.weight = None\n self.bias = None\n\n def forward(self, input):\n return F.layer_norm(input, self.dim, self.weight, self.bias, self.eps)\n\n\nclass RMSNorm(nn.Module):\n def __init__(self, dim, eps: float, elementwise_affine: bool = True):\n super().__init__()\n\n self.eps = eps\n\n if isinstance(dim, numbers.Integral):\n dim = (dim,)\n\n self.dim = torch.Size(dim)\n\n if elementwise_affine:\n self.weight = nn.Parameter(torch.ones(dim))\n else:\n self.weight = None\n\n def forward(self, hidden_states):\n input_dtype = hidden_states.dtype\n variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)\n hidden_states = hidden_states * torch.rsqrt(variance + self.eps)\n\n if self.weight is not None:\n # convert into half-precision if necessary\n if self.weight.dtype in [torch.float16, torch.bfloat16]:\n hidden_states = hidden_states.to(self.weight.dtype)\n hidden_states = hidden_states * self.weight\n else:\n hidden_states = hidden_states.to(input_dtype)\n\n return hidden_states\n\n\nclass GlobalResponseNorm(nn.Module):\n # Taken from https://github.com/facebookresearch/ConvNeXt-V2/blob/3608f67cc1dae164790c5d0aead7bf2d73d9719b/models/utils.py#L105\n def __init__(self, dim):\n super().__init__()\n self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim))\n self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim))\n\n def forward(self, x):\n gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True)\n nx = gx / (gx.mean(dim=-1, keepdim=True) + 1e-6)\n return self.gamma * (x * nx) + self.beta + x\n" }, { "path": "diffusers/src/diffusers/models/resnet.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n# `TemporalConvLayer` Copyright 2024 Alibaba DAMO-VILAB, The ModelScope Team and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom functools import partial\nfrom typing import Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom ..utils import deprecate\nfrom .activations import get_activation\nfrom .attention_processor import SpatialNorm\nfrom .downsampling import ( # noqa\n Downsample1D,\n Downsample2D,\n FirDownsample2D,\n KDownsample2D,\n downsample_2d,\n)\nfrom .normalization import AdaGroupNorm\nfrom .upsampling import ( # noqa\n FirUpsample2D,\n KUpsample2D,\n Upsample1D,\n Upsample2D,\n upfirdn2d_native,\n upsample_2d,\n)\n\n\nclass ResnetBlockCondNorm2D(nn.Module):\n r\"\"\"\n A Resnet block that use normalization layer that incorporate conditioning information.\n\n Parameters:\n in_channels (`int`): The number of channels in the input.\n out_channels (`int`, *optional*, default to be `None`):\n The number of output channels for the first conv2d layer. If None, same as `in_channels`.\n dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.\n temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.\n groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer.\n groups_out (`int`, *optional*, default to None):\n The number of groups to use for the second normalization layer. if set to None, same as `groups`.\n eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.\n non_linearity (`str`, *optional*, default to `\"swish\"`): the activation function to use.\n time_embedding_norm (`str`, *optional*, default to `\"ada_group\"` ):\n The normalization layer for time embedding `temb`. Currently only support \"ada_group\" or \"spatial\".\n kernel (`torch.Tensor`, optional, default to None): FIR filter, see\n [`~models.resnet.FirUpsample2D`] and [`~models.resnet.FirDownsample2D`].\n output_scale_factor (`float`, *optional*, default to be `1.0`): the scale factor to use for the output.\n use_in_shortcut (`bool`, *optional*, default to `True`):\n If `True`, add a 1x1 nn.conv2d layer for skip-connection.\n up (`bool`, *optional*, default to `False`): If `True`, add an upsample layer.\n down (`bool`, *optional*, default to `False`): If `True`, add a downsample layer.\n conv_shortcut_bias (`bool`, *optional*, default to `True`): If `True`, adds a learnable bias to the\n `conv_shortcut` output.\n conv_2d_out_channels (`int`, *optional*, default to `None`): the number of channels in the output.\n If None, same as `out_channels`.\n \"\"\"\n\n def __init__(\n self,\n *,\n in_channels: int,\n out_channels: Optional[int] = None,\n conv_shortcut: bool = False,\n dropout: float = 0.0,\n temb_channels: int = 512,\n groups: int = 32,\n groups_out: Optional[int] = None,\n eps: float = 1e-6,\n non_linearity: str = \"swish\",\n time_embedding_norm: str = \"ada_group\", # ada_group, spatial\n output_scale_factor: float = 1.0,\n use_in_shortcut: Optional[bool] = None,\n up: bool = False,\n down: bool = False,\n conv_shortcut_bias: bool = True,\n conv_2d_out_channels: Optional[int] = None,\n ):\n super().__init__()\n self.in_channels = in_channels\n out_channels = in_channels if out_channels is None else out_channels\n self.out_channels = out_channels\n self.use_conv_shortcut = conv_shortcut\n self.up = up\n self.down = down\n self.output_scale_factor = output_scale_factor\n self.time_embedding_norm = time_embedding_norm\n\n if groups_out is None:\n groups_out = groups\n\n if self.time_embedding_norm == \"ada_group\": # ada_group\n self.norm1 = AdaGroupNorm(temb_channels, in_channels, groups, eps=eps)\n elif self.time_embedding_norm == \"spatial\":\n self.norm1 = SpatialNorm(in_channels, temb_channels)\n else:\n raise ValueError(f\" unsupported time_embedding_norm: {self.time_embedding_norm}\")\n\n self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)\n\n if self.time_embedding_norm == \"ada_group\": # ada_group\n self.norm2 = AdaGroupNorm(temb_channels, out_channels, groups_out, eps=eps)\n elif self.time_embedding_norm == \"spatial\": # spatial\n self.norm2 = SpatialNorm(out_channels, temb_channels)\n else:\n raise ValueError(f\" unsupported time_embedding_norm: {self.time_embedding_norm}\")\n\n self.dropout = torch.nn.Dropout(dropout)\n\n conv_2d_out_channels = conv_2d_out_channels or out_channels\n self.conv2 = nn.Conv2d(out_channels, conv_2d_out_channels, kernel_size=3, stride=1, padding=1)\n\n self.nonlinearity = get_activation(non_linearity)\n\n self.upsample = self.downsample = None\n if self.up:\n self.upsample = Upsample2D(in_channels, use_conv=False)\n elif self.down:\n self.downsample = Downsample2D(in_channels, use_conv=False, padding=1, name=\"op\")\n\n self.use_in_shortcut = self.in_channels != conv_2d_out_channels if use_in_shortcut is None else use_in_shortcut\n\n self.conv_shortcut = None\n if self.use_in_shortcut:\n self.conv_shortcut = nn.Conv2d(\n in_channels,\n conv_2d_out_channels,\n kernel_size=1,\n stride=1,\n padding=0,\n bias=conv_shortcut_bias,\n )\n\n def forward(self, input_tensor: torch.Tensor, temb: torch.Tensor, *args, **kwargs) -> torch.Tensor:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n hidden_states = input_tensor\n\n hidden_states = self.norm1(hidden_states, temb)\n\n hidden_states = self.nonlinearity(hidden_states)\n\n if self.upsample is not None:\n # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984\n if hidden_states.shape[0] >= 64:\n input_tensor = input_tensor.contiguous()\n hidden_states = hidden_states.contiguous()\n input_tensor = self.upsample(input_tensor)\n hidden_states = self.upsample(hidden_states)\n\n elif self.downsample is not None:\n input_tensor = self.downsample(input_tensor)\n hidden_states = self.downsample(hidden_states)\n\n hidden_states = self.conv1(hidden_states)\n\n hidden_states = self.norm2(hidden_states, temb)\n\n hidden_states = self.nonlinearity(hidden_states)\n\n hidden_states = self.dropout(hidden_states)\n hidden_states = self.conv2(hidden_states)\n\n if self.conv_shortcut is not None:\n input_tensor = self.conv_shortcut(input_tensor)\n\n output_tensor = (input_tensor + hidden_states) / self.output_scale_factor\n\n return output_tensor\n\n\nclass ResnetBlock2D(nn.Module):\n r\"\"\"\n A Resnet block.\n\n Parameters:\n in_channels (`int`): The number of channels in the input.\n out_channels (`int`, *optional*, default to be `None`):\n The number of output channels for the first conv2d layer. If None, same as `in_channels`.\n dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.\n temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.\n groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer.\n groups_out (`int`, *optional*, default to None):\n The number of groups to use for the second normalization layer. if set to None, same as `groups`.\n eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.\n non_linearity (`str`, *optional*, default to `\"swish\"`): the activation function to use.\n time_embedding_norm (`str`, *optional*, default to `\"default\"` ): Time scale shift config.\n By default, apply timestep embedding conditioning with a simple shift mechanism. Choose \"scale_shift\" for a\n stronger conditioning with scale and shift.\n kernel (`torch.Tensor`, optional, default to None): FIR filter, see\n [`~models.resnet.FirUpsample2D`] and [`~models.resnet.FirDownsample2D`].\n output_scale_factor (`float`, *optional*, default to be `1.0`): the scale factor to use for the output.\n use_in_shortcut (`bool`, *optional*, default to `True`):\n If `True`, add a 1x1 nn.conv2d layer for skip-connection.\n up (`bool`, *optional*, default to `False`): If `True`, add an upsample layer.\n down (`bool`, *optional*, default to `False`): If `True`, add a downsample layer.\n conv_shortcut_bias (`bool`, *optional*, default to `True`): If `True`, adds a learnable bias to the\n `conv_shortcut` output.\n conv_2d_out_channels (`int`, *optional*, default to `None`): the number of channels in the output.\n If None, same as `out_channels`.\n \"\"\"\n\n def __init__(\n self,\n *,\n in_channels: int,\n out_channels: Optional[int] = None,\n conv_shortcut: bool = False,\n dropout: float = 0.0,\n temb_channels: int = 512,\n groups: int = 32,\n groups_out: Optional[int] = None,\n pre_norm: bool = True,\n eps: float = 1e-6,\n non_linearity: str = \"swish\",\n skip_time_act: bool = False,\n time_embedding_norm: str = \"default\", # default, scale_shift,\n kernel: Optional[torch.Tensor] = None,\n output_scale_factor: float = 1.0,\n use_in_shortcut: Optional[bool] = None,\n up: bool = False,\n down: bool = False,\n conv_shortcut_bias: bool = True,\n conv_2d_out_channels: Optional[int] = None,\n ):\n super().__init__()\n if time_embedding_norm == \"ada_group\":\n raise ValueError(\n \"This class cannot be used with `time_embedding_norm==ada_group`, please use `ResnetBlockCondNorm2D` instead\",\n )\n if time_embedding_norm == \"spatial\":\n raise ValueError(\n \"This class cannot be used with `time_embedding_norm==spatial`, please use `ResnetBlockCondNorm2D` instead\",\n )\n\n self.pre_norm = True\n self.in_channels = in_channels\n out_channels = in_channels if out_channels is None else out_channels\n self.out_channels = out_channels\n self.use_conv_shortcut = conv_shortcut\n self.up = up\n self.down = down\n self.output_scale_factor = output_scale_factor\n self.time_embedding_norm = time_embedding_norm\n self.skip_time_act = skip_time_act\n\n if groups_out is None:\n groups_out = groups\n\n self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)\n\n self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)\n\n if temb_channels is not None:\n if self.time_embedding_norm == \"default\":\n self.time_emb_proj = nn.Linear(temb_channels, out_channels)\n elif self.time_embedding_norm == \"scale_shift\":\n self.time_emb_proj = nn.Linear(temb_channels, 2 * out_channels)\n else:\n raise ValueError(f\"unknown time_embedding_norm : {self.time_embedding_norm} \")\n else:\n self.time_emb_proj = None\n\n self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)\n\n self.dropout = torch.nn.Dropout(dropout)\n conv_2d_out_channels = conv_2d_out_channels or out_channels\n self.conv2 = nn.Conv2d(out_channels, conv_2d_out_channels, kernel_size=3, stride=1, padding=1)\n\n self.nonlinearity = get_activation(non_linearity)\n\n self.upsample = self.downsample = None\n if self.up:\n if kernel == \"fir\":\n fir_kernel = (1, 3, 3, 1)\n self.upsample = lambda x: upsample_2d(x, kernel=fir_kernel)\n elif kernel == \"sde_vp\":\n self.upsample = partial(F.interpolate, scale_factor=2.0, mode=\"nearest\")\n else:\n self.upsample = Upsample2D(in_channels, use_conv=False)\n elif self.down:\n if kernel == \"fir\":\n fir_kernel = (1, 3, 3, 1)\n self.downsample = lambda x: downsample_2d(x, kernel=fir_kernel)\n elif kernel == \"sde_vp\":\n self.downsample = partial(F.avg_pool2d, kernel_size=2, stride=2)\n else:\n self.downsample = Downsample2D(in_channels, use_conv=False, padding=1, name=\"op\")\n\n self.use_in_shortcut = self.in_channels != conv_2d_out_channels if use_in_shortcut is None else use_in_shortcut\n\n self.conv_shortcut = None\n if self.use_in_shortcut:\n self.conv_shortcut = nn.Conv2d(\n in_channels,\n conv_2d_out_channels,\n kernel_size=1,\n stride=1,\n padding=0,\n bias=conv_shortcut_bias,\n )\n\n def forward(self, input_tensor: torch.Tensor, temb: torch.Tensor, *args, **kwargs) -> torch.Tensor:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n hidden_states = input_tensor\n\n hidden_states = self.norm1(hidden_states)\n hidden_states = self.nonlinearity(hidden_states)\n\n if self.upsample is not None:\n # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984\n if hidden_states.shape[0] >= 64:\n input_tensor = input_tensor.contiguous()\n hidden_states = hidden_states.contiguous()\n input_tensor = self.upsample(input_tensor)\n hidden_states = self.upsample(hidden_states)\n elif self.downsample is not None:\n input_tensor = self.downsample(input_tensor)\n hidden_states = self.downsample(hidden_states)\n\n hidden_states = self.conv1(hidden_states)\n\n if self.time_emb_proj is not None:\n if not self.skip_time_act:\n temb = self.nonlinearity(temb)\n temb = self.time_emb_proj(temb)[:, :, None, None]\n\n if self.time_embedding_norm == \"default\":\n if temb is not None:\n hidden_states = hidden_states + temb\n hidden_states = self.norm2(hidden_states)\n elif self.time_embedding_norm == \"scale_shift\":\n if temb is None:\n raise ValueError(\n f\" `temb` should not be None when `time_embedding_norm` is {self.time_embedding_norm}\"\n )\n time_scale, time_shift = torch.chunk(temb, 2, dim=1)\n hidden_states = self.norm2(hidden_states)\n hidden_states = hidden_states * (1 + time_scale) + time_shift\n else:\n hidden_states = self.norm2(hidden_states)\n\n hidden_states = self.nonlinearity(hidden_states)\n\n hidden_states = self.dropout(hidden_states)\n hidden_states = self.conv2(hidden_states)\n\n if self.conv_shortcut is not None:\n input_tensor = self.conv_shortcut(input_tensor)\n\n output_tensor = (input_tensor + hidden_states) / self.output_scale_factor\n\n return output_tensor\n\n\n# unet_rl.py\ndef rearrange_dims(tensor: torch.Tensor) -> torch.Tensor:\n if len(tensor.shape) == 2:\n return tensor[:, :, None]\n if len(tensor.shape) == 3:\n return tensor[:, :, None, :]\n elif len(tensor.shape) == 4:\n return tensor[:, :, 0, :]\n else:\n raise ValueError(f\"`len(tensor)`: {len(tensor)} has to be 2, 3 or 4.\")\n\n\nclass Conv1dBlock(nn.Module):\n \"\"\"\n Conv1d --> GroupNorm --> Mish\n\n Parameters:\n inp_channels (`int`): Number of input channels.\n out_channels (`int`): Number of output channels.\n kernel_size (`int` or `tuple`): Size of the convolving kernel.\n n_groups (`int`, default `8`): Number of groups to separate the channels into.\n activation (`str`, defaults to `mish`): Name of the activation function.\n \"\"\"\n\n def __init__(\n self,\n inp_channels: int,\n out_channels: int,\n kernel_size: Union[int, Tuple[int, int]],\n n_groups: int = 8,\n activation: str = \"mish\",\n ):\n super().__init__()\n\n self.conv1d = nn.Conv1d(inp_channels, out_channels, kernel_size, padding=kernel_size // 2)\n self.group_norm = nn.GroupNorm(n_groups, out_channels)\n self.mish = get_activation(activation)\n\n def forward(self, inputs: torch.Tensor) -> torch.Tensor:\n intermediate_repr = self.conv1d(inputs)\n intermediate_repr = rearrange_dims(intermediate_repr)\n intermediate_repr = self.group_norm(intermediate_repr)\n intermediate_repr = rearrange_dims(intermediate_repr)\n output = self.mish(intermediate_repr)\n return output\n\n\n# unet_rl.py\nclass ResidualTemporalBlock1D(nn.Module):\n \"\"\"\n Residual 1D block with temporal convolutions.\n\n Parameters:\n inp_channels (`int`): Number of input channels.\n out_channels (`int`): Number of output channels.\n embed_dim (`int`): Embedding dimension.\n kernel_size (`int` or `tuple`): Size of the convolving kernel.\n activation (`str`, defaults `mish`): It is possible to choose the right activation function.\n \"\"\"\n\n def __init__(\n self,\n inp_channels: int,\n out_channels: int,\n embed_dim: int,\n kernel_size: Union[int, Tuple[int, int]] = 5,\n activation: str = \"mish\",\n ):\n super().__init__()\n self.conv_in = Conv1dBlock(inp_channels, out_channels, kernel_size)\n self.conv_out = Conv1dBlock(out_channels, out_channels, kernel_size)\n\n self.time_emb_act = get_activation(activation)\n self.time_emb = nn.Linear(embed_dim, out_channels)\n\n self.residual_conv = (\n nn.Conv1d(inp_channels, out_channels, 1) if inp_channels != out_channels else nn.Identity()\n )\n\n def forward(self, inputs: torch.Tensor, t: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Args:\n inputs : [ batch_size x inp_channels x horizon ]\n t : [ batch_size x embed_dim ]\n\n returns:\n out : [ batch_size x out_channels x horizon ]\n \"\"\"\n t = self.time_emb_act(t)\n t = self.time_emb(t)\n out = self.conv_in(inputs) + rearrange_dims(t)\n out = self.conv_out(out)\n return out + self.residual_conv(inputs)\n\n\nclass TemporalConvLayer(nn.Module):\n \"\"\"\n Temporal convolutional layer that can be used for video (sequence of images) input Code mostly copied from:\n https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/models/multi_modal/video_synthesis/unet_sd.py#L1016\n\n Parameters:\n in_dim (`int`): Number of input channels.\n out_dim (`int`): Number of output channels.\n dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.\n \"\"\"\n\n def __init__(\n self,\n in_dim: int,\n out_dim: Optional[int] = None,\n dropout: float = 0.0,\n norm_num_groups: int = 32,\n ):\n super().__init__()\n out_dim = out_dim or in_dim\n self.in_dim = in_dim\n self.out_dim = out_dim\n\n # conv layers\n self.conv1 = nn.Sequential(\n nn.GroupNorm(norm_num_groups, in_dim),\n nn.SiLU(),\n nn.Conv3d(in_dim, out_dim, (3, 1, 1), padding=(1, 0, 0)),\n )\n self.conv2 = nn.Sequential(\n nn.GroupNorm(norm_num_groups, out_dim),\n nn.SiLU(),\n nn.Dropout(dropout),\n nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),\n )\n self.conv3 = nn.Sequential(\n nn.GroupNorm(norm_num_groups, out_dim),\n nn.SiLU(),\n nn.Dropout(dropout),\n nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),\n )\n self.conv4 = nn.Sequential(\n nn.GroupNorm(norm_num_groups, out_dim),\n nn.SiLU(),\n nn.Dropout(dropout),\n nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),\n )\n\n # zero out the last layer params,so the conv block is identity\n nn.init.zeros_(self.conv4[-1].weight)\n nn.init.zeros_(self.conv4[-1].bias)\n\n def forward(self, hidden_states: torch.Tensor, num_frames: int = 1) -> torch.Tensor:\n hidden_states = (\n hidden_states[None, :].reshape((-1, num_frames) + hidden_states.shape[1:]).permute(0, 2, 1, 3, 4)\n )\n\n identity = hidden_states\n hidden_states = self.conv1(hidden_states)\n hidden_states = self.conv2(hidden_states)\n hidden_states = self.conv3(hidden_states)\n hidden_states = self.conv4(hidden_states)\n\n hidden_states = identity + hidden_states\n\n hidden_states = hidden_states.permute(0, 2, 1, 3, 4).reshape(\n (hidden_states.shape[0] * hidden_states.shape[2], -1) + hidden_states.shape[3:]\n )\n return hidden_states\n\n\nclass TemporalResnetBlock(nn.Module):\n r\"\"\"\n A Resnet block.\n\n Parameters:\n in_channels (`int`): The number of channels in the input.\n out_channels (`int`, *optional*, default to be `None`):\n The number of output channels for the first conv2d layer. If None, same as `in_channels`.\n temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.\n eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.\n \"\"\"\n\n def __init__(\n self,\n in_channels: int,\n out_channels: Optional[int] = None,\n temb_channels: int = 512,\n eps: float = 1e-6,\n ):\n super().__init__()\n self.in_channels = in_channels\n out_channels = in_channels if out_channels is None else out_channels\n self.out_channels = out_channels\n\n kernel_size = (3, 1, 1)\n padding = [k // 2 for k in kernel_size]\n\n self.norm1 = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=eps, affine=True)\n self.conv1 = nn.Conv3d(\n in_channels,\n out_channels,\n kernel_size=kernel_size,\n stride=1,\n padding=padding,\n )\n\n if temb_channels is not None:\n self.time_emb_proj = nn.Linear(temb_channels, out_channels)\n else:\n self.time_emb_proj = None\n\n self.norm2 = torch.nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=eps, affine=True)\n\n self.dropout = torch.nn.Dropout(0.0)\n self.conv2 = nn.Conv3d(\n out_channels,\n out_channels,\n kernel_size=kernel_size,\n stride=1,\n padding=padding,\n )\n\n self.nonlinearity = get_activation(\"silu\")\n\n self.use_in_shortcut = self.in_channels != out_channels\n\n self.conv_shortcut = None\n if self.use_in_shortcut:\n self.conv_shortcut = nn.Conv3d(\n in_channels,\n out_channels,\n kernel_size=1,\n stride=1,\n padding=0,\n )\n\n def forward(self, input_tensor: torch.Tensor, temb: torch.Tensor) -> torch.Tensor:\n hidden_states = input_tensor\n\n hidden_states = self.norm1(hidden_states)\n hidden_states = self.nonlinearity(hidden_states)\n hidden_states = self.conv1(hidden_states)\n\n if self.time_emb_proj is not None:\n temb = self.nonlinearity(temb)\n temb = self.time_emb_proj(temb)[:, :, :, None, None]\n temb = temb.permute(0, 2, 1, 3, 4)\n hidden_states = hidden_states + temb\n\n hidden_states = self.norm2(hidden_states)\n hidden_states = self.nonlinearity(hidden_states)\n hidden_states = self.dropout(hidden_states)\n hidden_states = self.conv2(hidden_states)\n\n if self.conv_shortcut is not None:\n input_tensor = self.conv_shortcut(input_tensor)\n\n output_tensor = input_tensor + hidden_states\n\n return output_tensor\n\n\n# VideoResBlock\nclass SpatioTemporalResBlock(nn.Module):\n r\"\"\"\n A SpatioTemporal Resnet block.\n\n Parameters:\n in_channels (`int`): The number of channels in the input.\n out_channels (`int`, *optional*, default to be `None`):\n The number of output channels for the first conv2d layer. If None, same as `in_channels`.\n temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.\n eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the spatial resenet.\n temporal_eps (`float`, *optional*, defaults to `eps`): The epsilon to use for the temporal resnet.\n merge_factor (`float`, *optional*, defaults to `0.5`): The merge factor to use for the temporal mixing.\n merge_strategy (`str`, *optional*, defaults to `learned_with_images`):\n The merge strategy to use for the temporal mixing.\n switch_spatial_to_temporal_mix (`bool`, *optional*, defaults to `False`):\n If `True`, switch the spatial and temporal mixing.\n \"\"\"\n\n def __init__(\n self,\n in_channels: int,\n out_channels: Optional[int] = None,\n temb_channels: int = 512,\n eps: float = 1e-6,\n temporal_eps: Optional[float] = None,\n merge_factor: float = 0.5,\n merge_strategy=\"learned_with_images\",\n switch_spatial_to_temporal_mix: bool = False,\n ):\n super().__init__()\n\n self.spatial_res_block = ResnetBlock2D(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=eps,\n )\n\n self.temporal_res_block = TemporalResnetBlock(\n in_channels=out_channels if out_channels is not None else in_channels,\n out_channels=out_channels if out_channels is not None else in_channels,\n temb_channels=temb_channels,\n eps=temporal_eps if temporal_eps is not None else eps,\n )\n\n self.time_mixer = AlphaBlender(\n alpha=merge_factor,\n merge_strategy=merge_strategy,\n switch_spatial_to_temporal_mix=switch_spatial_to_temporal_mix,\n )\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n image_only_indicator: Optional[torch.Tensor] = None,\n ):\n num_frames = image_only_indicator.shape[-1]\n hidden_states = self.spatial_res_block(hidden_states, temb)\n\n batch_frames, channels, height, width = hidden_states.shape\n batch_size = batch_frames // num_frames\n\n hidden_states_mix = (\n hidden_states[None, :].reshape(batch_size, num_frames, channels, height, width).permute(0, 2, 1, 3, 4)\n )\n hidden_states = (\n hidden_states[None, :].reshape(batch_size, num_frames, channels, height, width).permute(0, 2, 1, 3, 4)\n )\n\n if temb is not None:\n temb = temb.reshape(batch_size, num_frames, -1)\n\n hidden_states = self.temporal_res_block(hidden_states, temb)\n hidden_states = self.time_mixer(\n x_spatial=hidden_states_mix,\n x_temporal=hidden_states,\n image_only_indicator=image_only_indicator,\n )\n\n hidden_states = hidden_states.permute(0, 2, 1, 3, 4).reshape(batch_frames, channels, height, width)\n return hidden_states\n\n\nclass AlphaBlender(nn.Module):\n r\"\"\"\n A module to blend spatial and temporal features.\n\n Parameters:\n alpha (`float`): The initial value of the blending factor.\n merge_strategy (`str`, *optional*, defaults to `learned_with_images`):\n The merge strategy to use for the temporal mixing.\n switch_spatial_to_temporal_mix (`bool`, *optional*, defaults to `False`):\n If `True`, switch the spatial and temporal mixing.\n \"\"\"\n\n strategies = [\"learned\", \"fixed\", \"learned_with_images\"]\n\n def __init__(\n self,\n alpha: float,\n merge_strategy: str = \"learned_with_images\",\n switch_spatial_to_temporal_mix: bool = False,\n ):\n super().__init__()\n self.merge_strategy = merge_strategy\n self.switch_spatial_to_temporal_mix = switch_spatial_to_temporal_mix # For TemporalVAE\n\n if merge_strategy not in self.strategies:\n raise ValueError(f\"merge_strategy needs to be in {self.strategies}\")\n\n if self.merge_strategy == \"fixed\":\n self.register_buffer(\"mix_factor\", torch.Tensor([alpha]))\n elif self.merge_strategy == \"learned\" or self.merge_strategy == \"learned_with_images\":\n self.register_parameter(\"mix_factor\", torch.nn.Parameter(torch.Tensor([alpha])))\n else:\n raise ValueError(f\"Unknown merge strategy {self.merge_strategy}\")\n\n def get_alpha(self, image_only_indicator: torch.Tensor, ndims: int) -> torch.Tensor:\n if self.merge_strategy == \"fixed\":\n alpha = self.mix_factor\n\n elif self.merge_strategy == \"learned\":\n alpha = torch.sigmoid(self.mix_factor)\n\n elif self.merge_strategy == \"learned_with_images\":\n if image_only_indicator is None:\n raise ValueError(\"Please provide image_only_indicator to use learned_with_images merge strategy\")\n\n alpha = torch.where(\n image_only_indicator.bool(),\n torch.ones(1, 1, device=image_only_indicator.device),\n torch.sigmoid(self.mix_factor)[..., None],\n )\n\n # (batch, channel, frames, height, width)\n if ndims == 5:\n alpha = alpha[:, None, :, None, None]\n # (batch*frames, height*width, channels)\n elif ndims == 3:\n alpha = alpha.reshape(-1)[:, None, None]\n else:\n raise ValueError(f\"Unexpected ndims {ndims}. Dimensions should be 3 or 5\")\n\n else:\n raise NotImplementedError\n\n return alpha\n\n def forward(\n self,\n x_spatial: torch.Tensor,\n x_temporal: torch.Tensor,\n image_only_indicator: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n alpha = self.get_alpha(image_only_indicator, x_spatial.ndim)\n alpha = alpha.to(x_spatial.dtype)\n\n if self.switch_spatial_to_temporal_mix:\n alpha = 1.0 - alpha\n\n x = alpha * x_spatial + (1.0 - alpha) * x_temporal\n return x\n" }, { "path": "diffusers/src/diffusers/models/resnet_flax.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport flax.linen as nn\nimport jax\nimport jax.numpy as jnp\n\n\nclass FlaxUpsample2D(nn.Module):\n out_channels: int\n dtype: jnp.dtype = jnp.float32\n\n def setup(self):\n self.conv = nn.Conv(\n self.out_channels,\n kernel_size=(3, 3),\n strides=(1, 1),\n padding=((1, 1), (1, 1)),\n dtype=self.dtype,\n )\n\n def __call__(self, hidden_states):\n batch, height, width, channels = hidden_states.shape\n hidden_states = jax.image.resize(\n hidden_states,\n shape=(batch, height * 2, width * 2, channels),\n method=\"nearest\",\n )\n hidden_states = self.conv(hidden_states)\n return hidden_states\n\n\nclass FlaxDownsample2D(nn.Module):\n out_channels: int\n dtype: jnp.dtype = jnp.float32\n\n def setup(self):\n self.conv = nn.Conv(\n self.out_channels,\n kernel_size=(3, 3),\n strides=(2, 2),\n padding=((1, 1), (1, 1)), # padding=\"VALID\",\n dtype=self.dtype,\n )\n\n def __call__(self, hidden_states):\n # pad = ((0, 0), (0, 1), (0, 1), (0, 0)) # pad height and width dim\n # hidden_states = jnp.pad(hidden_states, pad_width=pad)\n hidden_states = self.conv(hidden_states)\n return hidden_states\n\n\nclass FlaxResnetBlock2D(nn.Module):\n in_channels: int\n out_channels: int = None\n dropout_prob: float = 0.0\n use_nin_shortcut: bool = None\n dtype: jnp.dtype = jnp.float32\n\n def setup(self):\n out_channels = self.in_channels if self.out_channels is None else self.out_channels\n\n self.norm1 = nn.GroupNorm(num_groups=32, epsilon=1e-5)\n self.conv1 = nn.Conv(\n out_channels,\n kernel_size=(3, 3),\n strides=(1, 1),\n padding=((1, 1), (1, 1)),\n dtype=self.dtype,\n )\n\n self.time_emb_proj = nn.Dense(out_channels, dtype=self.dtype)\n\n self.norm2 = nn.GroupNorm(num_groups=32, epsilon=1e-5)\n self.dropout = nn.Dropout(self.dropout_prob)\n self.conv2 = nn.Conv(\n out_channels,\n kernel_size=(3, 3),\n strides=(1, 1),\n padding=((1, 1), (1, 1)),\n dtype=self.dtype,\n )\n\n use_nin_shortcut = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut\n\n self.conv_shortcut = None\n if use_nin_shortcut:\n self.conv_shortcut = nn.Conv(\n out_channels,\n kernel_size=(1, 1),\n strides=(1, 1),\n padding=\"VALID\",\n dtype=self.dtype,\n )\n\n def __call__(self, hidden_states, temb, deterministic=True):\n residual = hidden_states\n hidden_states = self.norm1(hidden_states)\n hidden_states = nn.swish(hidden_states)\n hidden_states = self.conv1(hidden_states)\n\n temb = self.time_emb_proj(nn.swish(temb))\n temb = jnp.expand_dims(jnp.expand_dims(temb, 1), 1)\n hidden_states = hidden_states + temb\n\n hidden_states = self.norm2(hidden_states)\n hidden_states = nn.swish(hidden_states)\n hidden_states = self.dropout(hidden_states, deterministic)\n hidden_states = self.conv2(hidden_states)\n\n if self.conv_shortcut is not None:\n residual = self.conv_shortcut(residual)\n\n return hidden_states + residual\n" }, { "path": "diffusers/src/diffusers/models/transformers/__init__.py", "content": "from ...utils import is_torch_available\n\n\nif is_torch_available():\n from .dit_transformer_2d import DiTTransformer2DModel\n from .dual_transformer_2d import DualTransformer2DModel\n from .hunyuan_transformer_2d import HunyuanDiT2DModel\n from .pixart_transformer_2d import PixArtTransformer2DModel\n from .prior_transformer import PriorTransformer\n from .t5_film_transformer import T5FilmDecoder\n from .transformer_2d import Transformer2DModel\n from .transformer_sd3 import SD3Transformer2DModel\n from .transformer_temporal import TransformerTemporalModel\n" }, { "path": "diffusers/src/diffusers/models/transformers/dit_transformer_2d.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Any, Dict, Optional\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...utils import is_torch_version, logging\nfrom ..attention import BasicTransformerBlock\nfrom ..embeddings import PatchEmbed\nfrom ..modeling_outputs import Transformer2DModelOutput\nfrom ..modeling_utils import ModelMixin\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass DiTTransformer2DModel(ModelMixin, ConfigMixin):\n r\"\"\"\n A 2D Transformer model as introduced in DiT (https://arxiv.org/abs/2212.09748).\n\n Parameters:\n num_attention_heads (int, optional, defaults to 16): The number of heads to use for multi-head attention.\n attention_head_dim (int, optional, defaults to 72): The number of channels in each head.\n in_channels (int, defaults to 4): The number of channels in the input.\n out_channels (int, optional):\n The number of channels in the output. Specify this parameter if the output channel number differs from the\n input.\n num_layers (int, optional, defaults to 28): The number of layers of Transformer blocks to use.\n dropout (float, optional, defaults to 0.0): The dropout probability to use within the Transformer blocks.\n norm_num_groups (int, optional, defaults to 32):\n Number of groups for group normalization within Transformer blocks.\n attention_bias (bool, optional, defaults to True):\n Configure if the Transformer blocks' attention should contain a bias parameter.\n sample_size (int, defaults to 32):\n The width of the latent images. This parameter is fixed during training.\n patch_size (int, defaults to 2):\n Size of the patches the model processes, relevant for architectures working on non-sequential data.\n activation_fn (str, optional, defaults to \"gelu-approximate\"):\n Activation function to use in feed-forward networks within Transformer blocks.\n num_embeds_ada_norm (int, optional, defaults to 1000):\n Number of embeddings for AdaLayerNorm, fixed during training and affects the maximum denoising steps during\n inference.\n upcast_attention (bool, optional, defaults to False):\n If true, upcasts the attention mechanism dimensions for potentially improved performance.\n norm_type (str, optional, defaults to \"ada_norm_zero\"):\n Specifies the type of normalization used, can be 'ada_norm_zero'.\n norm_elementwise_affine (bool, optional, defaults to False):\n If true, enables element-wise affine parameters in the normalization layers.\n norm_eps (float, optional, defaults to 1e-5):\n A small constant added to the denominator in normalization layers to prevent division by zero.\n \"\"\"\n\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n num_attention_heads: int = 16,\n attention_head_dim: int = 72,\n in_channels: int = 4,\n out_channels: Optional[int] = None,\n num_layers: int = 28,\n dropout: float = 0.0,\n norm_num_groups: int = 32,\n attention_bias: bool = True,\n sample_size: int = 32,\n patch_size: int = 2,\n activation_fn: str = \"gelu-approximate\",\n num_embeds_ada_norm: Optional[int] = 1000,\n upcast_attention: bool = False,\n norm_type: str = \"ada_norm_zero\",\n norm_elementwise_affine: bool = False,\n norm_eps: float = 1e-5,\n ):\n super().__init__()\n\n # Validate inputs.\n if norm_type != \"ada_norm_zero\":\n raise NotImplementedError(\n f\"Forward pass is not implemented when `patch_size` is not None and `norm_type` is '{norm_type}'.\"\n )\n elif norm_type == \"ada_norm_zero\" and num_embeds_ada_norm is None:\n raise ValueError(\n f\"When using a `patch_size` and this `norm_type` ({norm_type}), `num_embeds_ada_norm` cannot be None.\"\n )\n\n # Set some common variables used across the board.\n self.attention_head_dim = attention_head_dim\n self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim\n self.out_channels = in_channels if out_channels is None else out_channels\n self.gradient_checkpointing = False\n\n # 2. Initialize the position embedding and transformer blocks.\n self.height = self.config.sample_size\n self.width = self.config.sample_size\n\n self.patch_size = self.config.patch_size\n self.pos_embed = PatchEmbed(\n height=self.config.sample_size,\n width=self.config.sample_size,\n patch_size=self.config.patch_size,\n in_channels=self.config.in_channels,\n embed_dim=self.inner_dim,\n )\n\n self.transformer_blocks = nn.ModuleList(\n [\n BasicTransformerBlock(\n self.inner_dim,\n self.config.num_attention_heads,\n self.config.attention_head_dim,\n dropout=self.config.dropout,\n activation_fn=self.config.activation_fn,\n num_embeds_ada_norm=self.config.num_embeds_ada_norm,\n attention_bias=self.config.attention_bias,\n upcast_attention=self.config.upcast_attention,\n norm_type=norm_type,\n norm_elementwise_affine=self.config.norm_elementwise_affine,\n norm_eps=self.config.norm_eps,\n )\n for _ in range(self.config.num_layers)\n ]\n )\n\n # 3. Output blocks.\n self.norm_out = nn.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6)\n self.proj_out_1 = nn.Linear(self.inner_dim, 2 * self.inner_dim)\n self.proj_out_2 = nn.Linear(\n self.inner_dim, self.config.patch_size * self.config.patch_size * self.out_channels\n )\n\n def _set_gradient_checkpointing(self, module, value=False):\n if hasattr(module, \"gradient_checkpointing\"):\n module.gradient_checkpointing = value\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n timestep: Optional[torch.LongTensor] = None,\n class_labels: Optional[torch.LongTensor] = None,\n cross_attention_kwargs: Dict[str, Any] = None,\n return_dict: bool = True,\n ):\n \"\"\"\n The [`DiTTransformer2DModel`] forward method.\n\n Args:\n hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):\n Input `hidden_states`.\n timestep ( `torch.LongTensor`, *optional*):\n Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.\n class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):\n Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in\n `AdaLayerZeroNorm`.\n cross_attention_kwargs ( `Dict[str, Any]`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain\n tuple.\n\n Returns:\n If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a\n `tuple` where the first element is the sample tensor.\n \"\"\"\n # 1. Input\n height, width = hidden_states.shape[-2] // self.patch_size, hidden_states.shape[-1] // self.patch_size\n hidden_states = self.pos_embed(hidden_states)\n\n # 2. Blocks\n for block in self.transformer_blocks:\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(block),\n hidden_states,\n None,\n None,\n None,\n timestep,\n cross_attention_kwargs,\n class_labels,\n **ckpt_kwargs,\n )\n else:\n hidden_states = block(\n hidden_states,\n attention_mask=None,\n encoder_hidden_states=None,\n encoder_attention_mask=None,\n timestep=timestep,\n cross_attention_kwargs=cross_attention_kwargs,\n class_labels=class_labels,\n )\n\n # 3. Output\n conditioning = self.transformer_blocks[0].norm1.emb(timestep, class_labels, hidden_dtype=hidden_states.dtype)\n shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1)\n hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None]\n hidden_states = self.proj_out_2(hidden_states)\n\n # unpatchify\n height = width = int(hidden_states.shape[1] ** 0.5)\n hidden_states = hidden_states.reshape(\n shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)\n )\n hidden_states = torch.einsum(\"nhwpqc->nchpwq\", hidden_states)\n output = hidden_states.reshape(\n shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)\n )\n\n if not return_dict:\n return (output,)\n\n return Transformer2DModelOutput(sample=output)\n" }, { "path": "diffusers/src/diffusers/models/transformers/dual_transformer_2d.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Optional\n\nfrom torch import nn\n\nfrom ..modeling_outputs import Transformer2DModelOutput\nfrom .transformer_2d import Transformer2DModel\n\n\nclass DualTransformer2DModel(nn.Module):\n \"\"\"\n Dual transformer wrapper that combines two `Transformer2DModel`s for mixed inference.\n\n Parameters:\n num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.\n attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.\n in_channels (`int`, *optional*):\n Pass if the input is continuous. The number of channels in the input and output.\n num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.\n dropout (`float`, *optional*, defaults to 0.1): The dropout probability to use.\n cross_attention_dim (`int`, *optional*): The number of encoder_hidden_states dimensions to use.\n sample_size (`int`, *optional*): Pass if the input is discrete. The width of the latent images.\n Note that this is fixed at training time as it is used for learning a number of position embeddings. See\n `ImagePositionalEmbeddings`.\n num_vector_embeds (`int`, *optional*):\n Pass if the input is discrete. The number of classes of the vector embeddings of the latent pixels.\n Includes the class for the masked latent pixel.\n activation_fn (`str`, *optional*, defaults to `\"geglu\"`): Activation function to be used in feed-forward.\n num_embeds_ada_norm ( `int`, *optional*): Pass if at least one of the norm_layers is `AdaLayerNorm`.\n The number of diffusion steps used during training. Note that this is fixed at training time as it is used\n to learn a number of embeddings that are added to the hidden states. During inference, you can denoise for\n up to but not more than steps than `num_embeds_ada_norm`.\n attention_bias (`bool`, *optional*):\n Configure if the TransformerBlocks' attention should contain a bias parameter.\n \"\"\"\n\n def __init__(\n self,\n num_attention_heads: int = 16,\n attention_head_dim: int = 88,\n in_channels: Optional[int] = None,\n num_layers: int = 1,\n dropout: float = 0.0,\n norm_num_groups: int = 32,\n cross_attention_dim: Optional[int] = None,\n attention_bias: bool = False,\n sample_size: Optional[int] = None,\n num_vector_embeds: Optional[int] = None,\n activation_fn: str = \"geglu\",\n num_embeds_ada_norm: Optional[int] = None,\n ):\n super().__init__()\n self.transformers = nn.ModuleList(\n [\n Transformer2DModel(\n num_attention_heads=num_attention_heads,\n attention_head_dim=attention_head_dim,\n in_channels=in_channels,\n num_layers=num_layers,\n dropout=dropout,\n norm_num_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n attention_bias=attention_bias,\n sample_size=sample_size,\n num_vector_embeds=num_vector_embeds,\n activation_fn=activation_fn,\n num_embeds_ada_norm=num_embeds_ada_norm,\n )\n for _ in range(2)\n ]\n )\n\n # Variables that can be set by a pipeline:\n\n # The ratio of transformer1 to transformer2's output states to be combined during inference\n self.mix_ratio = 0.5\n\n # The shape of `encoder_hidden_states` is expected to be\n # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`\n self.condition_lengths = [77, 257]\n\n # Which transformer to use to encode which condition.\n # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`\n self.transformer_index_for_condition = [1, 0]\n\n def forward(\n self,\n hidden_states,\n encoder_hidden_states,\n timestep=None,\n attention_mask=None,\n cross_attention_kwargs=None,\n return_dict: bool = True,\n ):\n \"\"\"\n Args:\n hidden_states ( When discrete, `torch.LongTensor` of shape `(batch size, num latent pixels)`.\n When continuous, `torch.Tensor` of shape `(batch size, channel, height, width)`): Input hidden_states.\n encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):\n Conditional embeddings for cross attention layer. If not given, cross-attention defaults to\n self-attention.\n timestep ( `torch.long`, *optional*):\n Optional timestep to be applied as an embedding in AdaLayerNorm's. Used to indicate denoising step.\n attention_mask (`torch.Tensor`, *optional*):\n Optional attention mask to be applied in Attention.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain\n tuple.\n\n Returns:\n [`~models.transformers.transformer_2d.Transformer2DModelOutput`] or `tuple`:\n [`~models.transformers.transformer_2d.Transformer2DModelOutput`] if `return_dict` is True, otherwise a\n `tuple`. When returning a tuple, the first element is the sample tensor.\n \"\"\"\n input_states = hidden_states\n\n encoded_states = []\n tokens_start = 0\n # attention_mask is not used yet\n for i in range(2):\n # for each of the two transformers, pass the corresponding condition tokens\n condition_state = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]\n transformer_index = self.transformer_index_for_condition[i]\n encoded_state = self.transformers[transformer_index](\n input_states,\n encoder_hidden_states=condition_state,\n timestep=timestep,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n encoded_states.append(encoded_state - input_states)\n tokens_start += self.condition_lengths[i]\n\n output_states = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)\n output_states = output_states + input_states\n\n if not return_dict:\n return (output_states,)\n\n return Transformer2DModelOutput(sample=output_states)\n" }, { "path": "diffusers/src/diffusers/models/transformers/hunyuan_transformer_2d.py", "content": "# Copyright 2024 HunyuanDiT Authors and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Dict, Optional, Union\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...utils import logging\nfrom ...utils.torch_utils import maybe_allow_in_graph\nfrom ..attention import FeedForward\nfrom ..attention_processor import Attention, AttentionProcessor, HunyuanAttnProcessor2_0\nfrom ..embeddings import (\n HunyuanCombinedTimestepTextSizeStyleEmbedding,\n PatchEmbed,\n PixArtAlphaTextProjection,\n)\nfrom ..modeling_outputs import Transformer2DModelOutput\nfrom ..modeling_utils import ModelMixin\nfrom ..normalization import AdaLayerNormContinuous\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass FP32LayerNorm(nn.LayerNorm):\n def forward(self, inputs: torch.Tensor) -> torch.Tensor:\n origin_dtype = inputs.dtype\n return F.layer_norm(\n inputs.float(), self.normalized_shape, self.weight.float(), self.bias.float(), self.eps\n ).to(origin_dtype)\n\n\nclass AdaLayerNormShift(nn.Module):\n r\"\"\"\n Norm layer modified to incorporate timestep embeddings.\n\n Parameters:\n embedding_dim (`int`): The size of each embedding vector.\n num_embeddings (`int`): The size of the embeddings dictionary.\n \"\"\"\n\n def __init__(self, embedding_dim: int, elementwise_affine=True, eps=1e-6):\n super().__init__()\n self.silu = nn.SiLU()\n self.linear = nn.Linear(embedding_dim, embedding_dim)\n self.norm = FP32LayerNorm(embedding_dim, elementwise_affine=elementwise_affine, eps=eps)\n\n def forward(self, x: torch.Tensor, emb: torch.Tensor) -> torch.Tensor:\n shift = self.linear(self.silu(emb.to(torch.float32)).to(emb.dtype))\n x = self.norm(x) + shift.unsqueeze(dim=1)\n return x\n\n\n@maybe_allow_in_graph\nclass HunyuanDiTBlock(nn.Module):\n r\"\"\"\n Transformer block used in Hunyuan-DiT model (https://github.com/Tencent/HunyuanDiT). Allow skip connection and\n QKNorm\n\n Parameters:\n dim (`int`):\n The number of channels in the input and output.\n num_attention_heads (`int`):\n The number of headsto use for multi-head attention.\n cross_attention_dim (`int`,*optional*):\n The size of the encoder_hidden_states vector for cross attention.\n dropout(`float`, *optional*, defaults to 0.0):\n The dropout probability to use.\n activation_fn (`str`,*optional*, defaults to `\"geglu\"`):\n Activation function to be used in feed-forward. .\n norm_elementwise_affine (`bool`, *optional*, defaults to `True`):\n Whether to use learnable elementwise affine parameters for normalization.\n norm_eps (`float`, *optional*, defaults to 1e-6):\n A small constant added to the denominator in normalization layers to prevent division by zero.\n final_dropout (`bool` *optional*, defaults to False):\n Whether to apply a final dropout after the last feed-forward layer.\n ff_inner_dim (`int`, *optional*):\n The size of the hidden layer in the feed-forward block. Defaults to `None`.\n ff_bias (`bool`, *optional*, defaults to `True`):\n Whether to use bias in the feed-forward block.\n skip (`bool`, *optional*, defaults to `False`):\n Whether to use skip connection. Defaults to `False` for down-blocks and mid-blocks.\n qk_norm (`bool`, *optional*, defaults to `True`):\n Whether to use normalization in QK calculation. Defaults to `True`.\n \"\"\"\n\n def __init__(\n self,\n dim: int,\n num_attention_heads: int,\n cross_attention_dim: int = 1024,\n dropout=0.0,\n activation_fn: str = \"geglu\",\n norm_elementwise_affine: bool = True,\n norm_eps: float = 1e-6,\n final_dropout: bool = False,\n ff_inner_dim: Optional[int] = None,\n ff_bias: bool = True,\n skip: bool = False,\n qk_norm: bool = True,\n ):\n super().__init__()\n\n # Define 3 blocks. Each block has its own normalization layer.\n # NOTE: when new version comes, check norm2 and norm 3\n # 1. Self-Attn\n self.norm1 = AdaLayerNormShift(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)\n\n self.attn1 = Attention(\n query_dim=dim,\n cross_attention_dim=None,\n dim_head=dim // num_attention_heads,\n heads=num_attention_heads,\n qk_norm=\"layer_norm\" if qk_norm else None,\n eps=1e-6,\n bias=True,\n processor=HunyuanAttnProcessor2_0(),\n )\n\n # 2. Cross-Attn\n self.norm2 = FP32LayerNorm(dim, norm_eps, norm_elementwise_affine)\n\n self.attn2 = Attention(\n query_dim=dim,\n cross_attention_dim=cross_attention_dim,\n dim_head=dim // num_attention_heads,\n heads=num_attention_heads,\n qk_norm=\"layer_norm\" if qk_norm else None,\n eps=1e-6,\n bias=True,\n processor=HunyuanAttnProcessor2_0(),\n )\n # 3. Feed-forward\n self.norm3 = FP32LayerNorm(dim, norm_eps, norm_elementwise_affine)\n\n self.ff = FeedForward(\n dim,\n dropout=dropout, ### 0.0\n activation_fn=activation_fn, ### approx GeLU\n final_dropout=final_dropout, ### 0.0\n inner_dim=ff_inner_dim, ### int(dim * mlp_ratio)\n bias=ff_bias,\n )\n\n # 4. Skip Connection\n if skip:\n self.skip_norm = FP32LayerNorm(2 * dim, norm_eps, elementwise_affine=True)\n self.skip_linear = nn.Linear(2 * dim, dim)\n else:\n self.skip_linear = None\n\n # let chunk size default to None\n self._chunk_size = None\n self._chunk_dim = 0\n\n # Copied from diffusers.models.attention.BasicTransformerBlock.set_chunk_feed_forward\n def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0):\n # Sets chunk feed-forward\n self._chunk_size = chunk_size\n self._chunk_dim = dim\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n temb: Optional[torch.Tensor] = None,\n image_rotary_emb=None,\n skip=None,\n ) -> torch.Tensor:\n # Notice that normalization is always applied before the real computation in the following blocks.\n # 0. Long Skip Connection\n if self.skip_linear is not None:\n cat = torch.cat([hidden_states, skip], dim=-1)\n cat = self.skip_norm(cat)\n hidden_states = self.skip_linear(cat)\n\n # 1. Self-Attention\n norm_hidden_states = self.norm1(hidden_states, temb) ### checked: self.norm1 is correct\n attn_output = self.attn1(\n norm_hidden_states,\n image_rotary_emb=image_rotary_emb,\n )\n hidden_states = hidden_states + attn_output\n\n # 2. Cross-Attention\n hidden_states = hidden_states + self.attn2(\n self.norm2(hidden_states),\n encoder_hidden_states=encoder_hidden_states,\n image_rotary_emb=image_rotary_emb,\n )\n\n # FFN Layer ### TODO: switch norm2 and norm3 in the state dict\n mlp_inputs = self.norm3(hidden_states)\n hidden_states = hidden_states + self.ff(mlp_inputs)\n\n return hidden_states\n\n\nclass HunyuanDiT2DModel(ModelMixin, ConfigMixin):\n \"\"\"\n HunYuanDiT: Diffusion model with a Transformer backbone.\n\n Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.\n\n Parameters:\n num_attention_heads (`int`, *optional*, defaults to 16):\n The number of heads to use for multi-head attention.\n attention_head_dim (`int`, *optional*, defaults to 88):\n The number of channels in each head.\n in_channels (`int`, *optional*):\n The number of channels in the input and output (specify if the input is **continuous**).\n patch_size (`int`, *optional*):\n The size of the patch to use for the input.\n activation_fn (`str`, *optional*, defaults to `\"geglu\"`):\n Activation function to use in feed-forward.\n sample_size (`int`, *optional*):\n The width of the latent images. This is fixed during training since it is used to learn a number of\n position embeddings.\n dropout (`float`, *optional*, defaults to 0.0):\n The dropout probability to use.\n cross_attention_dim (`int`, *optional*):\n The number of dimension in the clip text embedding.\n hidden_size (`int`, *optional*):\n The size of hidden layer in the conditioning embedding layers.\n num_layers (`int`, *optional*, defaults to 1):\n The number of layers of Transformer blocks to use.\n mlp_ratio (`float`, *optional*, defaults to 4.0):\n The ratio of the hidden layer size to the input size.\n learn_sigma (`bool`, *optional*, defaults to `True`):\n Whether to predict variance.\n cross_attention_dim_t5 (`int`, *optional*):\n The number dimensions in t5 text embedding.\n pooled_projection_dim (`int`, *optional*):\n The size of the pooled projection.\n text_len (`int`, *optional*):\n The length of the clip text embedding.\n text_len_t5 (`int`, *optional*):\n The length of the T5 text embedding.\n \"\"\"\n\n @register_to_config\n def __init__(\n self,\n num_attention_heads: int = 16,\n attention_head_dim: int = 88,\n in_channels: Optional[int] = None,\n patch_size: Optional[int] = None,\n activation_fn: str = \"gelu-approximate\",\n sample_size=32,\n hidden_size=1152,\n num_layers: int = 28,\n mlp_ratio: float = 4.0,\n learn_sigma: bool = True,\n cross_attention_dim: int = 1024,\n norm_type: str = \"layer_norm\",\n cross_attention_dim_t5: int = 2048,\n pooled_projection_dim: int = 1024,\n text_len: int = 77,\n text_len_t5: int = 256,\n ):\n super().__init__()\n self.out_channels = in_channels * 2 if learn_sigma else in_channels\n self.num_heads = num_attention_heads\n self.inner_dim = num_attention_heads * attention_head_dim\n\n self.text_embedder = PixArtAlphaTextProjection(\n in_features=cross_attention_dim_t5,\n hidden_size=cross_attention_dim_t5 * 4,\n out_features=cross_attention_dim,\n act_fn=\"silu_fp32\",\n )\n\n self.text_embedding_padding = nn.Parameter(\n torch.randn(text_len + text_len_t5, cross_attention_dim, dtype=torch.float32)\n )\n\n self.pos_embed = PatchEmbed(\n height=sample_size,\n width=sample_size,\n in_channels=in_channels,\n embed_dim=hidden_size,\n patch_size=patch_size,\n pos_embed_type=None,\n )\n\n self.time_extra_emb = HunyuanCombinedTimestepTextSizeStyleEmbedding(\n hidden_size,\n pooled_projection_dim=pooled_projection_dim,\n seq_len=text_len_t5,\n cross_attention_dim=cross_attention_dim_t5,\n )\n\n # HunyuanDiT Blocks\n self.blocks = nn.ModuleList(\n [\n HunyuanDiTBlock(\n dim=self.inner_dim,\n num_attention_heads=self.config.num_attention_heads,\n activation_fn=activation_fn,\n ff_inner_dim=int(self.inner_dim * mlp_ratio),\n cross_attention_dim=cross_attention_dim,\n qk_norm=True, # See http://arxiv.org/abs/2302.05442 for details.\n skip=layer > num_layers // 2,\n )\n for layer in range(num_layers)\n ]\n )\n\n self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6)\n self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections\n def fuse_qkv_projections(self):\n \"\"\"\n Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)\n are fused. For cross-attention modules, key and value projection matrices are fused.\n\n \n\n This API is 🧪 experimental.\n\n \n \"\"\"\n self.original_attn_processors = None\n\n for _, attn_processor in self.attn_processors.items():\n if \"Added\" in str(attn_processor.__class__.__name__):\n raise ValueError(\"`fuse_qkv_projections()` is not supported for models having added KV projections.\")\n\n self.original_attn_processors = self.attn_processors\n\n for module in self.modules():\n if isinstance(module, Attention):\n module.fuse_projections(fuse=True)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections\n def unfuse_qkv_projections(self):\n \"\"\"Disables the fused QKV projection if enabled.\n\n \n\n This API is 🧪 experimental.\n\n \n\n \"\"\"\n if self.original_attn_processors is not None:\n self.set_attn_processor(self.original_attn_processors)\n\n @property\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors\n def attn_processors(self) -> Dict[str, AttentionProcessor]:\n r\"\"\"\n Returns:\n `dict` of attention processors: A dictionary containing all attention processors used in the model with\n indexed by its weight name.\n \"\"\"\n # set recursively\n processors = {}\n\n def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\n if hasattr(module, \"get_processor\"):\n processors[f\"{name}.processor\"] = module.get_processor(return_deprecated_lora=True)\n\n for sub_name, child in module.named_children():\n fn_recursive_add_processors(f\"{name}.{sub_name}\", child, processors)\n\n return processors\n\n for name, module in self.named_children():\n fn_recursive_add_processors(name, module, processors)\n\n return processors\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor\n def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\n r\"\"\"\n Sets the attention processor to use to compute attention.\n\n Parameters:\n processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):\n The instantiated processor class or a dictionary of processor classes that will be set as the processor\n for **all** `Attention` layers.\n\n If `processor` is a dict, the key needs to define the path to the corresponding cross attention\n processor. This is strongly recommended when setting trainable attention processors.\n\n \"\"\"\n count = len(self.attn_processors.keys())\n\n if isinstance(processor, dict) and len(processor) != count:\n raise ValueError(\n f\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\"\n f\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\"\n )\n\n def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\n if hasattr(module, \"set_processor\"):\n if not isinstance(processor, dict):\n module.set_processor(processor)\n else:\n module.set_processor(processor.pop(f\"{name}.processor\"))\n\n for sub_name, child in module.named_children():\n fn_recursive_attn_processor(f\"{name}.{sub_name}\", child, processor)\n\n for name, module in self.named_children():\n fn_recursive_attn_processor(name, module, processor)\n\n def set_default_attn_processor(self):\n \"\"\"\n Disables custom attention processors and sets the default attention implementation.\n \"\"\"\n self.set_attn_processor(HunyuanAttnProcessor2_0())\n\n def forward(\n self,\n hidden_states,\n timestep,\n encoder_hidden_states=None,\n text_embedding_mask=None,\n encoder_hidden_states_t5=None,\n text_embedding_mask_t5=None,\n image_meta_size=None,\n style=None,\n image_rotary_emb=None,\n return_dict=True,\n ):\n \"\"\"\n The [`HunyuanDiT2DModel`] forward method.\n\n Args:\n hidden_states (`torch.Tensor` of shape `(batch size, dim, height, width)`):\n The input tensor.\n timestep ( `torch.LongTensor`, *optional*):\n Used to indicate denoising step.\n encoder_hidden_states ( `torch.Tensor` of shape `(batch size, sequence len, embed dims)`, *optional*):\n Conditional embeddings for cross attention layer. This is the output of `BertModel`.\n text_embedding_mask: torch.Tensor\n An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. This is the output\n of `BertModel`.\n encoder_hidden_states_t5 ( `torch.Tensor` of shape `(batch size, sequence len, embed dims)`, *optional*):\n Conditional embeddings for cross attention layer. This is the output of T5 Text Encoder.\n text_embedding_mask_t5: torch.Tensor\n An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. This is the output\n of T5 Text Encoder.\n image_meta_size (torch.Tensor):\n Conditional embedding indicate the image sizes\n style: torch.Tensor:\n Conditional embedding indicate the style\n image_rotary_emb (`torch.Tensor`):\n The image rotary embeddings to apply on query and key tensors during attention calculation.\n return_dict: bool\n Whether to return a dictionary.\n \"\"\"\n\n height, width = hidden_states.shape[-2:]\n\n hidden_states = self.pos_embed(hidden_states)\n\n temb = self.time_extra_emb(\n timestep, encoder_hidden_states_t5, image_meta_size, style, hidden_dtype=timestep.dtype\n ) # [B, D]\n\n # text projection\n batch_size, sequence_length, _ = encoder_hidden_states_t5.shape\n encoder_hidden_states_t5 = self.text_embedder(\n encoder_hidden_states_t5.view(-1, encoder_hidden_states_t5.shape[-1])\n )\n encoder_hidden_states_t5 = encoder_hidden_states_t5.view(batch_size, sequence_length, -1)\n\n encoder_hidden_states = torch.cat([encoder_hidden_states, encoder_hidden_states_t5], dim=1)\n text_embedding_mask = torch.cat([text_embedding_mask, text_embedding_mask_t5], dim=-1)\n text_embedding_mask = text_embedding_mask.unsqueeze(2).bool()\n\n encoder_hidden_states = torch.where(text_embedding_mask, encoder_hidden_states, self.text_embedding_padding)\n\n skips = []\n for layer, block in enumerate(self.blocks):\n if layer > self.config.num_layers // 2:\n skip = skips.pop()\n hidden_states = block(\n hidden_states,\n temb=temb,\n encoder_hidden_states=encoder_hidden_states,\n image_rotary_emb=image_rotary_emb,\n skip=skip,\n ) # (N, L, D)\n else:\n hidden_states = block(\n hidden_states,\n temb=temb,\n encoder_hidden_states=encoder_hidden_states,\n image_rotary_emb=image_rotary_emb,\n ) # (N, L, D)\n\n if layer < (self.config.num_layers // 2 - 1):\n skips.append(hidden_states)\n\n # final layer\n hidden_states = self.norm_out(hidden_states, temb.to(torch.float32))\n hidden_states = self.proj_out(hidden_states)\n # (N, L, patch_size ** 2 * out_channels)\n\n # unpatchify: (N, out_channels, H, W)\n patch_size = self.pos_embed.patch_size\n height = height // patch_size\n width = width // patch_size\n\n hidden_states = hidden_states.reshape(\n shape=(hidden_states.shape[0], height, width, patch_size, patch_size, self.out_channels)\n )\n hidden_states = torch.einsum(\"nhwpqc->nchpwq\", hidden_states)\n output = hidden_states.reshape(\n shape=(hidden_states.shape[0], self.out_channels, height * patch_size, width * patch_size)\n )\n if not return_dict:\n return (output,)\n return Transformer2DModelOutput(sample=output)\n\n # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking\n def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:\n \"\"\"\n Sets the attention processor to use [feed forward\n chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).\n\n Parameters:\n chunk_size (`int`, *optional*):\n The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually\n over each tensor of dim=`dim`.\n dim (`int`, *optional*, defaults to `0`):\n The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)\n or dim=1 (sequence length).\n \"\"\"\n if dim not in [0, 1]:\n raise ValueError(f\"Make sure to set `dim` to either 0 or 1, not {dim}\")\n\n # By default chunk size is 1\n chunk_size = chunk_size or 1\n\n def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):\n if hasattr(module, \"set_chunk_feed_forward\"):\n module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)\n\n for child in module.children():\n fn_recursive_feed_forward(child, chunk_size, dim)\n\n for module in self.children():\n fn_recursive_feed_forward(module, chunk_size, dim)\n\n # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.disable_forward_chunking\n def disable_forward_chunking(self):\n def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):\n if hasattr(module, \"set_chunk_feed_forward\"):\n module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)\n\n for child in module.children():\n fn_recursive_feed_forward(child, chunk_size, dim)\n\n for module in self.children():\n fn_recursive_feed_forward(module, None, 0)\n" }, { "path": "diffusers/src/diffusers/models/transformers/pixart_transformer_2d.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Any, Dict, Optional\n\nimport torch\nfrom torch import nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...utils import is_torch_version, logging\nfrom ..attention import BasicTransformerBlock\nfrom ..embeddings import PatchEmbed, PixArtAlphaTextProjection\nfrom ..modeling_outputs import Transformer2DModelOutput\nfrom ..modeling_utils import ModelMixin\nfrom ..normalization import AdaLayerNormSingle\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass PixArtTransformer2DModel(ModelMixin, ConfigMixin):\n r\"\"\"\n A 2D Transformer model as introduced in PixArt family of models (https://arxiv.org/abs/2310.00426,\n https://arxiv.org/abs/2403.04692).\n\n Parameters:\n num_attention_heads (int, optional, defaults to 16): The number of heads to use for multi-head attention.\n attention_head_dim (int, optional, defaults to 72): The number of channels in each head.\n in_channels (int, defaults to 4): The number of channels in the input.\n out_channels (int, optional):\n The number of channels in the output. Specify this parameter if the output channel number differs from the\n input.\n num_layers (int, optional, defaults to 28): The number of layers of Transformer blocks to use.\n dropout (float, optional, defaults to 0.0): The dropout probability to use within the Transformer blocks.\n norm_num_groups (int, optional, defaults to 32):\n Number of groups for group normalization within Transformer blocks.\n cross_attention_dim (int, optional):\n The dimensionality for cross-attention layers, typically matching the encoder's hidden dimension.\n attention_bias (bool, optional, defaults to True):\n Configure if the Transformer blocks' attention should contain a bias parameter.\n sample_size (int, defaults to 128):\n The width of the latent images. This parameter is fixed during training.\n patch_size (int, defaults to 2):\n Size of the patches the model processes, relevant for architectures working on non-sequential data.\n activation_fn (str, optional, defaults to \"gelu-approximate\"):\n Activation function to use in feed-forward networks within Transformer blocks.\n num_embeds_ada_norm (int, optional, defaults to 1000):\n Number of embeddings for AdaLayerNorm, fixed during training and affects the maximum denoising steps during\n inference.\n upcast_attention (bool, optional, defaults to False):\n If true, upcasts the attention mechanism dimensions for potentially improved performance.\n norm_type (str, optional, defaults to \"ada_norm_zero\"):\n Specifies the type of normalization used, can be 'ada_norm_zero'.\n norm_elementwise_affine (bool, optional, defaults to False):\n If true, enables element-wise affine parameters in the normalization layers.\n norm_eps (float, optional, defaults to 1e-6):\n A small constant added to the denominator in normalization layers to prevent division by zero.\n interpolation_scale (int, optional): Scale factor to use during interpolating the position embeddings.\n use_additional_conditions (bool, optional): If we're using additional conditions as inputs.\n attention_type (str, optional, defaults to \"default\"): Kind of attention mechanism to be used.\n caption_channels (int, optional, defaults to None):\n Number of channels to use for projecting the caption embeddings.\n use_linear_projection (bool, optional, defaults to False):\n Deprecated argument. Will be removed in a future version.\n num_vector_embeds (bool, optional, defaults to False):\n Deprecated argument. Will be removed in a future version.\n \"\"\"\n\n _supports_gradient_checkpointing = True\n _no_split_modules = [\"BasicTransformerBlock\", \"PatchEmbed\"]\n\n @register_to_config\n def __init__(\n self,\n num_attention_heads: int = 16,\n attention_head_dim: int = 72,\n in_channels: int = 4,\n out_channels: Optional[int] = 8,\n num_layers: int = 28,\n dropout: float = 0.0,\n norm_num_groups: int = 32,\n cross_attention_dim: Optional[int] = 1152,\n attention_bias: bool = True,\n sample_size: int = 128,\n patch_size: int = 2,\n activation_fn: str = \"gelu-approximate\",\n num_embeds_ada_norm: Optional[int] = 1000,\n upcast_attention: bool = False,\n norm_type: str = \"ada_norm_single\",\n norm_elementwise_affine: bool = False,\n norm_eps: float = 1e-6,\n interpolation_scale: Optional[int] = None,\n use_additional_conditions: Optional[bool] = None,\n caption_channels: Optional[int] = None,\n attention_type: Optional[str] = \"default\",\n ):\n super().__init__()\n\n # Validate inputs.\n if norm_type != \"ada_norm_single\":\n raise NotImplementedError(\n f\"Forward pass is not implemented when `patch_size` is not None and `norm_type` is '{norm_type}'.\"\n )\n elif norm_type == \"ada_norm_single\" and num_embeds_ada_norm is None:\n raise ValueError(\n f\"When using a `patch_size` and this `norm_type` ({norm_type}), `num_embeds_ada_norm` cannot be None.\"\n )\n\n # Set some common variables used across the board.\n self.attention_head_dim = attention_head_dim\n self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim\n self.out_channels = in_channels if out_channels is None else out_channels\n if use_additional_conditions is None:\n if sample_size == 128:\n use_additional_conditions = True\n else:\n use_additional_conditions = False\n self.use_additional_conditions = use_additional_conditions\n\n self.gradient_checkpointing = False\n\n # 2. Initialize the position embedding and transformer blocks.\n self.height = self.config.sample_size\n self.width = self.config.sample_size\n\n interpolation_scale = (\n self.config.interpolation_scale\n if self.config.interpolation_scale is not None\n else max(self.config.sample_size // 64, 1)\n )\n self.pos_embed = PatchEmbed(\n height=self.config.sample_size,\n width=self.config.sample_size,\n patch_size=self.config.patch_size,\n in_channels=self.config.in_channels,\n embed_dim=self.inner_dim,\n interpolation_scale=interpolation_scale,\n )\n\n self.transformer_blocks = nn.ModuleList(\n [\n BasicTransformerBlock(\n self.inner_dim,\n self.config.num_attention_heads,\n self.config.attention_head_dim,\n dropout=self.config.dropout,\n cross_attention_dim=self.config.cross_attention_dim,\n activation_fn=self.config.activation_fn,\n num_embeds_ada_norm=self.config.num_embeds_ada_norm,\n attention_bias=self.config.attention_bias,\n upcast_attention=self.config.upcast_attention,\n norm_type=norm_type,\n norm_elementwise_affine=self.config.norm_elementwise_affine,\n norm_eps=self.config.norm_eps,\n attention_type=self.config.attention_type,\n )\n for _ in range(self.config.num_layers)\n ]\n )\n\n # 3. Output blocks.\n self.norm_out = nn.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6)\n self.scale_shift_table = nn.Parameter(torch.randn(2, self.inner_dim) / self.inner_dim**0.5)\n self.proj_out = nn.Linear(self.inner_dim, self.config.patch_size * self.config.patch_size * self.out_channels)\n\n self.adaln_single = AdaLayerNormSingle(\n self.inner_dim, use_additional_conditions=self.use_additional_conditions\n )\n self.caption_projection = None\n if self.config.caption_channels is not None:\n self.caption_projection = PixArtAlphaTextProjection(\n in_features=self.config.caption_channels, hidden_size=self.inner_dim\n )\n\n def _set_gradient_checkpointing(self, module, value=False):\n if hasattr(module, \"gradient_checkpointing\"):\n module.gradient_checkpointing = value\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n timestep: Optional[torch.LongTensor] = None,\n added_cond_kwargs: Dict[str, torch.Tensor] = None,\n cross_attention_kwargs: Dict[str, Any] = None,\n attention_mask: Optional[torch.Tensor] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ):\n \"\"\"\n The [`PixArtTransformer2DModel`] forward method.\n\n Args:\n hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):\n Input `hidden_states`.\n encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*):\n Conditional embeddings for cross attention layer. If not given, cross-attention defaults to\n self-attention.\n timestep (`torch.LongTensor`, *optional*):\n Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.\n added_cond_kwargs: (`Dict[str, Any]`, *optional*): Additional conditions to be used as inputs.\n cross_attention_kwargs ( `Dict[str, Any]`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n attention_mask ( `torch.Tensor`, *optional*):\n An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask\n is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large\n negative values to the attention scores corresponding to \"discard\" tokens.\n encoder_attention_mask ( `torch.Tensor`, *optional*):\n Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:\n\n * Mask `(batch, sequence_length)` True = keep, False = discard.\n * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.\n\n If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format\n above. This bias will be added to the cross-attention scores.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain\n tuple.\n\n Returns:\n If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a\n `tuple` where the first element is the sample tensor.\n \"\"\"\n if self.use_additional_conditions and added_cond_kwargs is None:\n raise ValueError(\"`added_cond_kwargs` cannot be None when using additional conditions for `adaln_single`.\")\n\n # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.\n # we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.\n # we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.\n # expects mask of shape:\n # [batch, key_tokens]\n # adds singleton query_tokens dimension:\n # [batch, 1, key_tokens]\n # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:\n # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)\n # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)\n if attention_mask is not None and attention_mask.ndim == 2:\n # assume that mask is expressed as:\n # (1 = keep, 0 = discard)\n # convert mask into a bias that can be added to attention scores:\n # (keep = +0, discard = -10000.0)\n attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n # convert encoder_attention_mask to a bias the same way we do for attention_mask\n if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:\n encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0\n encoder_attention_mask = encoder_attention_mask.unsqueeze(1)\n\n # 1. Input\n batch_size = hidden_states.shape[0]\n height, width = (\n hidden_states.shape[-2] // self.config.patch_size,\n hidden_states.shape[-1] // self.config.patch_size,\n )\n hidden_states = self.pos_embed(hidden_states)\n\n timestep, embedded_timestep = self.adaln_single(\n timestep, added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_states.dtype\n )\n\n if self.caption_projection is not None:\n encoder_hidden_states = self.caption_projection(encoder_hidden_states)\n encoder_hidden_states = encoder_hidden_states.view(batch_size, -1, hidden_states.shape[-1])\n\n # 2. Blocks\n for block in self.transformer_blocks:\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(block),\n hidden_states,\n attention_mask,\n encoder_hidden_states,\n encoder_attention_mask,\n timestep,\n cross_attention_kwargs,\n None,\n **ckpt_kwargs,\n )\n else:\n hidden_states = block(\n hidden_states,\n attention_mask=attention_mask,\n encoder_hidden_states=encoder_hidden_states,\n encoder_attention_mask=encoder_attention_mask,\n timestep=timestep,\n cross_attention_kwargs=cross_attention_kwargs,\n class_labels=None,\n )\n\n # 3. Output\n shift, scale = (\n self.scale_shift_table[None] + embedded_timestep[:, None].to(self.scale_shift_table.device)\n ).chunk(2, dim=1)\n hidden_states = self.norm_out(hidden_states)\n # Modulation\n hidden_states = hidden_states * (1 + scale.to(hidden_states.device)) + shift.to(hidden_states.device)\n hidden_states = self.proj_out(hidden_states)\n hidden_states = hidden_states.squeeze(1)\n\n # unpatchify\n hidden_states = hidden_states.reshape(\n shape=(-1, height, width, self.config.patch_size, self.config.patch_size, self.out_channels)\n )\n hidden_states = torch.einsum(\"nhwpqc->nchpwq\", hidden_states)\n output = hidden_states.reshape(\n shape=(-1, self.out_channels, height * self.config.patch_size, width * self.config.patch_size)\n )\n\n if not return_dict:\n return (output,)\n\n return Transformer2DModelOutput(sample=output)\n" }, { "path": "diffusers/src/diffusers/models/transformers/prior_transformer.py", "content": "from dataclasses import dataclass\nfrom typing import Dict, Optional, Union\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...loaders import PeftAdapterMixin, UNet2DConditionLoadersMixin\nfrom ...utils import BaseOutput\nfrom ..attention import BasicTransformerBlock\nfrom ..attention_processor import (\n ADDED_KV_ATTENTION_PROCESSORS,\n CROSS_ATTENTION_PROCESSORS,\n AttentionProcessor,\n AttnAddedKVProcessor,\n AttnProcessor,\n)\nfrom ..embeddings import TimestepEmbedding, Timesteps\nfrom ..modeling_utils import ModelMixin\n\n\n@dataclass\nclass PriorTransformerOutput(BaseOutput):\n \"\"\"\n The output of [`PriorTransformer`].\n\n Args:\n predicted_image_embedding (`torch.Tensor` of shape `(batch_size, embedding_dim)`):\n The predicted CLIP image embedding conditioned on the CLIP text embedding input.\n \"\"\"\n\n predicted_image_embedding: torch.Tensor\n\n\nclass PriorTransformer(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin):\n \"\"\"\n A Prior Transformer model.\n\n Parameters:\n num_attention_heads (`int`, *optional*, defaults to 32): The number of heads to use for multi-head attention.\n attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.\n num_layers (`int`, *optional*, defaults to 20): The number of layers of Transformer blocks to use.\n embedding_dim (`int`, *optional*, defaults to 768): The dimension of the model input `hidden_states`\n num_embeddings (`int`, *optional*, defaults to 77):\n The number of embeddings of the model input `hidden_states`\n additional_embeddings (`int`, *optional*, defaults to 4): The number of additional tokens appended to the\n projected `hidden_states`. The actual length of the used `hidden_states` is `num_embeddings +\n additional_embeddings`.\n dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.\n time_embed_act_fn (`str`, *optional*, defaults to 'silu'):\n The activation function to use to create timestep embeddings.\n norm_in_type (`str`, *optional*, defaults to None): The normalization layer to apply on hidden states before\n passing to Transformer blocks. Set it to `None` if normalization is not needed.\n embedding_proj_norm_type (`str`, *optional*, defaults to None):\n The normalization layer to apply on the input `proj_embedding`. Set it to `None` if normalization is not\n needed.\n encoder_hid_proj_type (`str`, *optional*, defaults to `linear`):\n The projection layer to apply on the input `encoder_hidden_states`. Set it to `None` if\n `encoder_hidden_states` is `None`.\n added_emb_type (`str`, *optional*, defaults to `prd`): Additional embeddings to condition the model.\n Choose from `prd` or `None`. if choose `prd`, it will prepend a token indicating the (quantized) dot\n product between the text embedding and image embedding as proposed in the unclip paper\n https://arxiv.org/abs/2204.06125 If it is `None`, no additional embeddings will be prepended.\n time_embed_dim (`int, *optional*, defaults to None): The dimension of timestep embeddings.\n If None, will be set to `num_attention_heads * attention_head_dim`\n embedding_proj_dim (`int`, *optional*, default to None):\n The dimension of `proj_embedding`. If None, will be set to `embedding_dim`.\n clip_embed_dim (`int`, *optional*, default to None):\n The dimension of the output. If None, will be set to `embedding_dim`.\n \"\"\"\n\n @register_to_config\n def __init__(\n self,\n num_attention_heads: int = 32,\n attention_head_dim: int = 64,\n num_layers: int = 20,\n embedding_dim: int = 768,\n num_embeddings=77,\n additional_embeddings=4,\n dropout: float = 0.0,\n time_embed_act_fn: str = \"silu\",\n norm_in_type: Optional[str] = None, # layer\n embedding_proj_norm_type: Optional[str] = None, # layer\n encoder_hid_proj_type: Optional[str] = \"linear\", # linear\n added_emb_type: Optional[str] = \"prd\", # prd\n time_embed_dim: Optional[int] = None,\n embedding_proj_dim: Optional[int] = None,\n clip_embed_dim: Optional[int] = None,\n ):\n super().__init__()\n self.num_attention_heads = num_attention_heads\n self.attention_head_dim = attention_head_dim\n inner_dim = num_attention_heads * attention_head_dim\n self.additional_embeddings = additional_embeddings\n\n time_embed_dim = time_embed_dim or inner_dim\n embedding_proj_dim = embedding_proj_dim or embedding_dim\n clip_embed_dim = clip_embed_dim or embedding_dim\n\n self.time_proj = Timesteps(inner_dim, True, 0)\n self.time_embedding = TimestepEmbedding(inner_dim, time_embed_dim, out_dim=inner_dim, act_fn=time_embed_act_fn)\n\n self.proj_in = nn.Linear(embedding_dim, inner_dim)\n\n if embedding_proj_norm_type is None:\n self.embedding_proj_norm = None\n elif embedding_proj_norm_type == \"layer\":\n self.embedding_proj_norm = nn.LayerNorm(embedding_proj_dim)\n else:\n raise ValueError(f\"unsupported embedding_proj_norm_type: {embedding_proj_norm_type}\")\n\n self.embedding_proj = nn.Linear(embedding_proj_dim, inner_dim)\n\n if encoder_hid_proj_type is None:\n self.encoder_hidden_states_proj = None\n elif encoder_hid_proj_type == \"linear\":\n self.encoder_hidden_states_proj = nn.Linear(embedding_dim, inner_dim)\n else:\n raise ValueError(f\"unsupported encoder_hid_proj_type: {encoder_hid_proj_type}\")\n\n self.positional_embedding = nn.Parameter(torch.zeros(1, num_embeddings + additional_embeddings, inner_dim))\n\n if added_emb_type == \"prd\":\n self.prd_embedding = nn.Parameter(torch.zeros(1, 1, inner_dim))\n elif added_emb_type is None:\n self.prd_embedding = None\n else:\n raise ValueError(\n f\"`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`.\"\n )\n\n self.transformer_blocks = nn.ModuleList(\n [\n BasicTransformerBlock(\n inner_dim,\n num_attention_heads,\n attention_head_dim,\n dropout=dropout,\n activation_fn=\"gelu\",\n attention_bias=True,\n )\n for d in range(num_layers)\n ]\n )\n\n if norm_in_type == \"layer\":\n self.norm_in = nn.LayerNorm(inner_dim)\n elif norm_in_type is None:\n self.norm_in = None\n else:\n raise ValueError(f\"Unsupported norm_in_type: {norm_in_type}.\")\n\n self.norm_out = nn.LayerNorm(inner_dim)\n\n self.proj_to_clip_embeddings = nn.Linear(inner_dim, clip_embed_dim)\n\n causal_attention_mask = torch.full(\n [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings], -10000.0\n )\n causal_attention_mask.triu_(1)\n causal_attention_mask = causal_attention_mask[None, ...]\n self.register_buffer(\"causal_attention_mask\", causal_attention_mask, persistent=False)\n\n self.clip_mean = nn.Parameter(torch.zeros(1, clip_embed_dim))\n self.clip_std = nn.Parameter(torch.zeros(1, clip_embed_dim))\n\n @property\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors\n def attn_processors(self) -> Dict[str, AttentionProcessor]:\n r\"\"\"\n Returns:\n `dict` of attention processors: A dictionary containing all attention processors used in the model with\n indexed by its weight name.\n \"\"\"\n # set recursively\n processors = {}\n\n def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\n if hasattr(module, \"get_processor\"):\n processors[f\"{name}.processor\"] = module.get_processor(return_deprecated_lora=True)\n\n for sub_name, child in module.named_children():\n fn_recursive_add_processors(f\"{name}.{sub_name}\", child, processors)\n\n return processors\n\n for name, module in self.named_children():\n fn_recursive_add_processors(name, module, processors)\n\n return processors\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor\n def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\n r\"\"\"\n Sets the attention processor to use to compute attention.\n\n Parameters:\n processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):\n The instantiated processor class or a dictionary of processor classes that will be set as the processor\n for **all** `Attention` layers.\n\n If `processor` is a dict, the key needs to define the path to the corresponding cross attention\n processor. This is strongly recommended when setting trainable attention processors.\n\n \"\"\"\n count = len(self.attn_processors.keys())\n\n if isinstance(processor, dict) and len(processor) != count:\n raise ValueError(\n f\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\"\n f\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\"\n )\n\n def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\n if hasattr(module, \"set_processor\"):\n if not isinstance(processor, dict):\n module.set_processor(processor)\n else:\n module.set_processor(processor.pop(f\"{name}.processor\"))\n\n for sub_name, child in module.named_children():\n fn_recursive_attn_processor(f\"{name}.{sub_name}\", child, processor)\n\n for name, module in self.named_children():\n fn_recursive_attn_processor(name, module, processor)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor\n def set_default_attn_processor(self):\n \"\"\"\n Disables custom attention processors and sets the default attention implementation.\n \"\"\"\n if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnAddedKVProcessor()\n elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnProcessor()\n else:\n raise ValueError(\n f\"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}\"\n )\n\n self.set_attn_processor(processor)\n\n def forward(\n self,\n hidden_states,\n timestep: Union[torch.Tensor, float, int],\n proj_embedding: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.BoolTensor] = None,\n return_dict: bool = True,\n ):\n \"\"\"\n The [`PriorTransformer`] forward method.\n\n Args:\n hidden_states (`torch.Tensor` of shape `(batch_size, embedding_dim)`):\n The currently predicted image embeddings.\n timestep (`torch.LongTensor`):\n Current denoising step.\n proj_embedding (`torch.Tensor` of shape `(batch_size, embedding_dim)`):\n Projected embedding vector the denoising process is conditioned on.\n encoder_hidden_states (`torch.Tensor` of shape `(batch_size, num_embeddings, embedding_dim)`):\n Hidden states of the text embeddings the denoising process is conditioned on.\n attention_mask (`torch.BoolTensor` of shape `(batch_size, num_embeddings)`):\n Text mask for the text embeddings.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~models.transformers.prior_transformer.PriorTransformerOutput`] instead of\n a plain tuple.\n\n Returns:\n [`~models.transformers.prior_transformer.PriorTransformerOutput`] or `tuple`:\n If return_dict is True, a [`~models.transformers.prior_transformer.PriorTransformerOutput`] is\n returned, otherwise a tuple is returned where the first element is the sample tensor.\n \"\"\"\n batch_size = hidden_states.shape[0]\n\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n timesteps = torch.tensor([timesteps], dtype=torch.long, device=hidden_states.device)\n elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(hidden_states.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timesteps = timesteps * torch.ones(batch_size, dtype=timesteps.dtype, device=timesteps.device)\n\n timesteps_projected = self.time_proj(timesteps)\n\n # timesteps does not contain any weights and will always return f32 tensors\n # but time_embedding might be fp16, so we need to cast here.\n timesteps_projected = timesteps_projected.to(dtype=self.dtype)\n time_embeddings = self.time_embedding(timesteps_projected)\n\n if self.embedding_proj_norm is not None:\n proj_embedding = self.embedding_proj_norm(proj_embedding)\n\n proj_embeddings = self.embedding_proj(proj_embedding)\n if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:\n encoder_hidden_states = self.encoder_hidden_states_proj(encoder_hidden_states)\n elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:\n raise ValueError(\"`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set\")\n\n hidden_states = self.proj_in(hidden_states)\n\n positional_embeddings = self.positional_embedding.to(hidden_states.dtype)\n\n additional_embeds = []\n additional_embeddings_len = 0\n\n if encoder_hidden_states is not None:\n additional_embeds.append(encoder_hidden_states)\n additional_embeddings_len += encoder_hidden_states.shape[1]\n\n if len(proj_embeddings.shape) == 2:\n proj_embeddings = proj_embeddings[:, None, :]\n\n if len(hidden_states.shape) == 2:\n hidden_states = hidden_states[:, None, :]\n\n additional_embeds = additional_embeds + [\n proj_embeddings,\n time_embeddings[:, None, :],\n hidden_states,\n ]\n\n if self.prd_embedding is not None:\n prd_embedding = self.prd_embedding.to(hidden_states.dtype).expand(batch_size, -1, -1)\n additional_embeds.append(prd_embedding)\n\n hidden_states = torch.cat(\n additional_embeds,\n dim=1,\n )\n\n # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens\n additional_embeddings_len = additional_embeddings_len + proj_embeddings.shape[1] + 1\n if positional_embeddings.shape[1] < hidden_states.shape[1]:\n positional_embeddings = F.pad(\n positional_embeddings,\n (\n 0,\n 0,\n additional_embeddings_len,\n self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,\n ),\n value=0.0,\n )\n\n hidden_states = hidden_states + positional_embeddings\n\n if attention_mask is not None:\n attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0\n attention_mask = F.pad(attention_mask, (0, self.additional_embeddings), value=0.0)\n attention_mask = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype)\n attention_mask = attention_mask.repeat_interleave(self.config.num_attention_heads, dim=0)\n\n if self.norm_in is not None:\n hidden_states = self.norm_in(hidden_states)\n\n for block in self.transformer_blocks:\n hidden_states = block(hidden_states, attention_mask=attention_mask)\n\n hidden_states = self.norm_out(hidden_states)\n\n if self.prd_embedding is not None:\n hidden_states = hidden_states[:, -1]\n else:\n hidden_states = hidden_states[:, additional_embeddings_len:]\n\n predicted_image_embedding = self.proj_to_clip_embeddings(hidden_states)\n\n if not return_dict:\n return (predicted_image_embedding,)\n\n return PriorTransformerOutput(predicted_image_embedding=predicted_image_embedding)\n\n def post_process_latents(self, prior_latents):\n prior_latents = (prior_latents * self.clip_std) + self.clip_mean\n return prior_latents\n" }, { "path": "diffusers/src/diffusers/models/transformers/t5_film_transformer.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport math\nfrom typing import Optional, Tuple\n\nimport torch\nfrom torch import nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ..attention_processor import Attention\nfrom ..embeddings import get_timestep_embedding\nfrom ..modeling_utils import ModelMixin\n\n\nclass T5FilmDecoder(ModelMixin, ConfigMixin):\n r\"\"\"\n T5 style decoder with FiLM conditioning.\n\n Args:\n input_dims (`int`, *optional*, defaults to `128`):\n The number of input dimensions.\n targets_length (`int`, *optional*, defaults to `256`):\n The length of the targets.\n d_model (`int`, *optional*, defaults to `768`):\n Size of the input hidden states.\n num_layers (`int`, *optional*, defaults to `12`):\n The number of `DecoderLayer`'s to use.\n num_heads (`int`, *optional*, defaults to `12`):\n The number of attention heads to use.\n d_kv (`int`, *optional*, defaults to `64`):\n Size of the key-value projection vectors.\n d_ff (`int`, *optional*, defaults to `2048`):\n The number of dimensions in the intermediate feed-forward layer of `DecoderLayer`'s.\n dropout_rate (`float`, *optional*, defaults to `0.1`):\n Dropout probability.\n \"\"\"\n\n @register_to_config\n def __init__(\n self,\n input_dims: int = 128,\n targets_length: int = 256,\n max_decoder_noise_time: float = 2000.0,\n d_model: int = 768,\n num_layers: int = 12,\n num_heads: int = 12,\n d_kv: int = 64,\n d_ff: int = 2048,\n dropout_rate: float = 0.1,\n ):\n super().__init__()\n\n self.conditioning_emb = nn.Sequential(\n nn.Linear(d_model, d_model * 4, bias=False),\n nn.SiLU(),\n nn.Linear(d_model * 4, d_model * 4, bias=False),\n nn.SiLU(),\n )\n\n self.position_encoding = nn.Embedding(targets_length, d_model)\n self.position_encoding.weight.requires_grad = False\n\n self.continuous_inputs_projection = nn.Linear(input_dims, d_model, bias=False)\n\n self.dropout = nn.Dropout(p=dropout_rate)\n\n self.decoders = nn.ModuleList()\n for lyr_num in range(num_layers):\n # FiLM conditional T5 decoder\n lyr = DecoderLayer(d_model=d_model, d_kv=d_kv, num_heads=num_heads, d_ff=d_ff, dropout_rate=dropout_rate)\n self.decoders.append(lyr)\n\n self.decoder_norm = T5LayerNorm(d_model)\n\n self.post_dropout = nn.Dropout(p=dropout_rate)\n self.spec_out = nn.Linear(d_model, input_dims, bias=False)\n\n def encoder_decoder_mask(self, query_input: torch.Tensor, key_input: torch.Tensor) -> torch.Tensor:\n mask = torch.mul(query_input.unsqueeze(-1), key_input.unsqueeze(-2))\n return mask.unsqueeze(-3)\n\n def forward(self, encodings_and_masks, decoder_input_tokens, decoder_noise_time):\n batch, _, _ = decoder_input_tokens.shape\n assert decoder_noise_time.shape == (batch,)\n\n # decoder_noise_time is in [0, 1), so rescale to expected timing range.\n time_steps = get_timestep_embedding(\n decoder_noise_time * self.config.max_decoder_noise_time,\n embedding_dim=self.config.d_model,\n max_period=self.config.max_decoder_noise_time,\n ).to(dtype=self.dtype)\n\n conditioning_emb = self.conditioning_emb(time_steps).unsqueeze(1)\n\n assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)\n\n seq_length = decoder_input_tokens.shape[1]\n\n # If we want to use relative positions for audio context, we can just offset\n # this sequence by the length of encodings_and_masks.\n decoder_positions = torch.broadcast_to(\n torch.arange(seq_length, device=decoder_input_tokens.device),\n (batch, seq_length),\n )\n\n position_encodings = self.position_encoding(decoder_positions)\n\n inputs = self.continuous_inputs_projection(decoder_input_tokens)\n inputs += position_encodings\n y = self.dropout(inputs)\n\n # decoder: No padding present.\n decoder_mask = torch.ones(\n decoder_input_tokens.shape[:2], device=decoder_input_tokens.device, dtype=inputs.dtype\n )\n\n # Translate encoding masks to encoder-decoder masks.\n encodings_and_encdec_masks = [(x, self.encoder_decoder_mask(decoder_mask, y)) for x, y in encodings_and_masks]\n\n # cross attend style: concat encodings\n encoded = torch.cat([x[0] for x in encodings_and_encdec_masks], dim=1)\n encoder_decoder_mask = torch.cat([x[1] for x in encodings_and_encdec_masks], dim=-1)\n\n for lyr in self.decoders:\n y = lyr(\n y,\n conditioning_emb=conditioning_emb,\n encoder_hidden_states=encoded,\n encoder_attention_mask=encoder_decoder_mask,\n )[0]\n\n y = self.decoder_norm(y)\n y = self.post_dropout(y)\n\n spec_out = self.spec_out(y)\n return spec_out\n\n\nclass DecoderLayer(nn.Module):\n r\"\"\"\n T5 decoder layer.\n\n Args:\n d_model (`int`):\n Size of the input hidden states.\n d_kv (`int`):\n Size of the key-value projection vectors.\n num_heads (`int`):\n Number of attention heads.\n d_ff (`int`):\n Size of the intermediate feed-forward layer.\n dropout_rate (`float`):\n Dropout probability.\n layer_norm_epsilon (`float`, *optional*, defaults to `1e-6`):\n A small value used for numerical stability to avoid dividing by zero.\n \"\"\"\n\n def __init__(\n self, d_model: int, d_kv: int, num_heads: int, d_ff: int, dropout_rate: float, layer_norm_epsilon: float = 1e-6\n ):\n super().__init__()\n self.layer = nn.ModuleList()\n\n # cond self attention: layer 0\n self.layer.append(\n T5LayerSelfAttentionCond(d_model=d_model, d_kv=d_kv, num_heads=num_heads, dropout_rate=dropout_rate)\n )\n\n # cross attention: layer 1\n self.layer.append(\n T5LayerCrossAttention(\n d_model=d_model,\n d_kv=d_kv,\n num_heads=num_heads,\n dropout_rate=dropout_rate,\n layer_norm_epsilon=layer_norm_epsilon,\n )\n )\n\n # Film Cond MLP + dropout: last layer\n self.layer.append(\n T5LayerFFCond(d_model=d_model, d_ff=d_ff, dropout_rate=dropout_rate, layer_norm_epsilon=layer_norm_epsilon)\n )\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n conditioning_emb: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n encoder_decoder_position_bias=None,\n ) -> Tuple[torch.Tensor]:\n hidden_states = self.layer[0](\n hidden_states,\n conditioning_emb=conditioning_emb,\n attention_mask=attention_mask,\n )\n\n if encoder_hidden_states is not None:\n encoder_extended_attention_mask = torch.where(encoder_attention_mask > 0, 0, -1e10).to(\n encoder_hidden_states.dtype\n )\n\n hidden_states = self.layer[1](\n hidden_states,\n key_value_states=encoder_hidden_states,\n attention_mask=encoder_extended_attention_mask,\n )\n\n # Apply Film Conditional Feed Forward layer\n hidden_states = self.layer[-1](hidden_states, conditioning_emb)\n\n return (hidden_states,)\n\n\nclass T5LayerSelfAttentionCond(nn.Module):\n r\"\"\"\n T5 style self-attention layer with conditioning.\n\n Args:\n d_model (`int`):\n Size of the input hidden states.\n d_kv (`int`):\n Size of the key-value projection vectors.\n num_heads (`int`):\n Number of attention heads.\n dropout_rate (`float`):\n Dropout probability.\n \"\"\"\n\n def __init__(self, d_model: int, d_kv: int, num_heads: int, dropout_rate: float):\n super().__init__()\n self.layer_norm = T5LayerNorm(d_model)\n self.FiLMLayer = T5FiLMLayer(in_features=d_model * 4, out_features=d_model)\n self.attention = Attention(query_dim=d_model, heads=num_heads, dim_head=d_kv, out_bias=False, scale_qk=False)\n self.dropout = nn.Dropout(dropout_rate)\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n conditioning_emb: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n # pre_self_attention_layer_norm\n normed_hidden_states = self.layer_norm(hidden_states)\n\n if conditioning_emb is not None:\n normed_hidden_states = self.FiLMLayer(normed_hidden_states, conditioning_emb)\n\n # Self-attention block\n attention_output = self.attention(normed_hidden_states)\n\n hidden_states = hidden_states + self.dropout(attention_output)\n\n return hidden_states\n\n\nclass T5LayerCrossAttention(nn.Module):\n r\"\"\"\n T5 style cross-attention layer.\n\n Args:\n d_model (`int`):\n Size of the input hidden states.\n d_kv (`int`):\n Size of the key-value projection vectors.\n num_heads (`int`):\n Number of attention heads.\n dropout_rate (`float`):\n Dropout probability.\n layer_norm_epsilon (`float`):\n A small value used for numerical stability to avoid dividing by zero.\n \"\"\"\n\n def __init__(self, d_model: int, d_kv: int, num_heads: int, dropout_rate: float, layer_norm_epsilon: float):\n super().__init__()\n self.attention = Attention(query_dim=d_model, heads=num_heads, dim_head=d_kv, out_bias=False, scale_qk=False)\n self.layer_norm = T5LayerNorm(d_model, eps=layer_norm_epsilon)\n self.dropout = nn.Dropout(dropout_rate)\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n key_value_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n normed_hidden_states = self.layer_norm(hidden_states)\n attention_output = self.attention(\n normed_hidden_states,\n encoder_hidden_states=key_value_states,\n attention_mask=attention_mask.squeeze(1),\n )\n layer_output = hidden_states + self.dropout(attention_output)\n return layer_output\n\n\nclass T5LayerFFCond(nn.Module):\n r\"\"\"\n T5 style feed-forward conditional layer.\n\n Args:\n d_model (`int`):\n Size of the input hidden states.\n d_ff (`int`):\n Size of the intermediate feed-forward layer.\n dropout_rate (`float`):\n Dropout probability.\n layer_norm_epsilon (`float`):\n A small value used for numerical stability to avoid dividing by zero.\n \"\"\"\n\n def __init__(self, d_model: int, d_ff: int, dropout_rate: float, layer_norm_epsilon: float):\n super().__init__()\n self.DenseReluDense = T5DenseGatedActDense(d_model=d_model, d_ff=d_ff, dropout_rate=dropout_rate)\n self.film = T5FiLMLayer(in_features=d_model * 4, out_features=d_model)\n self.layer_norm = T5LayerNorm(d_model, eps=layer_norm_epsilon)\n self.dropout = nn.Dropout(dropout_rate)\n\n def forward(self, hidden_states: torch.Tensor, conditioning_emb: Optional[torch.Tensor] = None) -> torch.Tensor:\n forwarded_states = self.layer_norm(hidden_states)\n if conditioning_emb is not None:\n forwarded_states = self.film(forwarded_states, conditioning_emb)\n\n forwarded_states = self.DenseReluDense(forwarded_states)\n hidden_states = hidden_states + self.dropout(forwarded_states)\n return hidden_states\n\n\nclass T5DenseGatedActDense(nn.Module):\n r\"\"\"\n T5 style feed-forward layer with gated activations and dropout.\n\n Args:\n d_model (`int`):\n Size of the input hidden states.\n d_ff (`int`):\n Size of the intermediate feed-forward layer.\n dropout_rate (`float`):\n Dropout probability.\n \"\"\"\n\n def __init__(self, d_model: int, d_ff: int, dropout_rate: float):\n super().__init__()\n self.wi_0 = nn.Linear(d_model, d_ff, bias=False)\n self.wi_1 = nn.Linear(d_model, d_ff, bias=False)\n self.wo = nn.Linear(d_ff, d_model, bias=False)\n self.dropout = nn.Dropout(dropout_rate)\n self.act = NewGELUActivation()\n\n def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n hidden_gelu = self.act(self.wi_0(hidden_states))\n hidden_linear = self.wi_1(hidden_states)\n hidden_states = hidden_gelu * hidden_linear\n hidden_states = self.dropout(hidden_states)\n\n hidden_states = self.wo(hidden_states)\n return hidden_states\n\n\nclass T5LayerNorm(nn.Module):\n r\"\"\"\n T5 style layer normalization module.\n\n Args:\n hidden_size (`int`):\n Size of the input hidden states.\n eps (`float`, `optional`, defaults to `1e-6`):\n A small value used for numerical stability to avoid dividing by zero.\n \"\"\"\n\n def __init__(self, hidden_size: int, eps: float = 1e-6):\n \"\"\"\n Construct a layernorm module in the T5 style. No bias and no subtraction of mean.\n \"\"\"\n super().__init__()\n self.weight = nn.Parameter(torch.ones(hidden_size))\n self.variance_epsilon = eps\n\n def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean\n # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated\n # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for\n # half-precision inputs is done in fp32\n\n variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)\n hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)\n\n # convert into half-precision if necessary\n if self.weight.dtype in [torch.float16, torch.bfloat16]:\n hidden_states = hidden_states.to(self.weight.dtype)\n\n return self.weight * hidden_states\n\n\nclass NewGELUActivation(nn.Module):\n \"\"\"\n Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also see\n the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415\n \"\"\"\n\n def forward(self, input: torch.Tensor) -> torch.Tensor:\n return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044715 * torch.pow(input, 3.0))))\n\n\nclass T5FiLMLayer(nn.Module):\n \"\"\"\n T5 style FiLM Layer.\n\n Args:\n in_features (`int`):\n Number of input features.\n out_features (`int`):\n Number of output features.\n \"\"\"\n\n def __init__(self, in_features: int, out_features: int):\n super().__init__()\n self.scale_bias = nn.Linear(in_features, out_features * 2, bias=False)\n\n def forward(self, x: torch.Tensor, conditioning_emb: torch.Tensor) -> torch.Tensor:\n emb = self.scale_bias(conditioning_emb)\n scale, shift = torch.chunk(emb, 2, -1)\n x = x * (1 + scale) + shift\n return x\n" }, { "path": "diffusers/src/diffusers/models/transformers/transformer_2d.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Any, Dict, Optional\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom ...configuration_utils import LegacyConfigMixin, register_to_config\nfrom ...utils import deprecate, is_torch_version, logging\nfrom ..attention import BasicTransformerBlock\nfrom ..embeddings import ImagePositionalEmbeddings, PatchEmbed, PixArtAlphaTextProjection\nfrom ..modeling_outputs import Transformer2DModelOutput\nfrom ..modeling_utils import LegacyModelMixin\nfrom ..normalization import AdaLayerNormSingle\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass Transformer2DModelOutput(Transformer2DModelOutput):\n deprecation_message = \"Importing `Transformer2DModelOutput` from `diffusers.models.transformer_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.modeling_outputs import Transformer2DModelOutput`, instead.\"\n deprecate(\"Transformer2DModelOutput\", \"1.0.0\", deprecation_message)\n\n\nclass Transformer2DModel(LegacyModelMixin, LegacyConfigMixin):\n \"\"\"\n A 2D Transformer model for image-like data.\n\n Parameters:\n num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.\n attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.\n in_channels (`int`, *optional*):\n The number of channels in the input and output (specify if the input is **continuous**).\n num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.\n dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.\n cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.\n sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).\n This is fixed during training since it is used to learn a number of position embeddings.\n num_vector_embeds (`int`, *optional*):\n The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**).\n Includes the class for the masked latent pixel.\n activation_fn (`str`, *optional*, defaults to `\"geglu\"`): Activation function to use in feed-forward.\n num_embeds_ada_norm ( `int`, *optional*):\n The number of diffusion steps used during training. Pass if at least one of the norm_layers is\n `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are\n added to the hidden states.\n\n During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`.\n attention_bias (`bool`, *optional*):\n Configure if the `TransformerBlocks` attention should contain a bias parameter.\n \"\"\"\n\n _supports_gradient_checkpointing = True\n _no_split_modules = [\"BasicTransformerBlock\"]\n\n @register_to_config\n def __init__(\n self,\n num_attention_heads: int = 16,\n attention_head_dim: int = 88,\n in_channels: Optional[int] = None,\n out_channels: Optional[int] = None,\n num_layers: int = 1,\n dropout: float = 0.0,\n norm_num_groups: int = 32,\n cross_attention_dim: Optional[int] = None,\n attention_bias: bool = False,\n sample_size: Optional[int] = None,\n num_vector_embeds: Optional[int] = None,\n patch_size: Optional[int] = None,\n activation_fn: str = \"geglu\",\n num_embeds_ada_norm: Optional[int] = None,\n use_linear_projection: bool = False,\n only_cross_attention: bool = False,\n double_self_attention: bool = False,\n upcast_attention: bool = False,\n norm_type: str = \"layer_norm\", # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single', 'ada_norm_continuous', 'layer_norm_i2vgen'\n norm_elementwise_affine: bool = True,\n norm_eps: float = 1e-5,\n attention_type: str = \"default\",\n caption_channels: int = None,\n interpolation_scale: float = None,\n use_additional_conditions: Optional[bool] = None,\n ):\n super().__init__()\n\n # Validate inputs.\n if patch_size is not None:\n if norm_type not in [\"ada_norm\", \"ada_norm_zero\", \"ada_norm_single\"]:\n raise NotImplementedError(\n f\"Forward pass is not implemented when `patch_size` is not None and `norm_type` is '{norm_type}'.\"\n )\n elif norm_type in [\"ada_norm\", \"ada_norm_zero\"] and num_embeds_ada_norm is None:\n raise ValueError(\n f\"When using a `patch_size` and this `norm_type` ({norm_type}), `num_embeds_ada_norm` cannot be None.\"\n )\n\n # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`\n # Define whether input is continuous or discrete depending on configuration\n self.is_input_continuous = (in_channels is not None) and (patch_size is None)\n self.is_input_vectorized = num_vector_embeds is not None\n self.is_input_patches = in_channels is not None and patch_size is not None\n\n if self.is_input_continuous and self.is_input_vectorized:\n raise ValueError(\n f\"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make\"\n \" sure that either `in_channels` or `num_vector_embeds` is None.\"\n )\n elif self.is_input_vectorized and self.is_input_patches:\n raise ValueError(\n f\"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make\"\n \" sure that either `num_vector_embeds` or `num_patches` is None.\"\n )\n elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:\n raise ValueError(\n f\"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:\"\n f\" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.\"\n )\n\n if norm_type == \"layer_norm\" and num_embeds_ada_norm is not None:\n deprecation_message = (\n f\"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or\"\n \" incorrectly set to `'layer_norm'`. Make sure to set `norm_type` to `'ada_norm'` in the config.\"\n \" Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect\"\n \" results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it\"\n \" would be very nice if you could open a Pull request for the `transformer/config.json` file\"\n )\n deprecate(\"norm_type!=num_embeds_ada_norm\", \"1.0.0\", deprecation_message, standard_warn=False)\n norm_type = \"ada_norm\"\n\n # Set some common variables used across the board.\n self.use_linear_projection = use_linear_projection\n self.interpolation_scale = interpolation_scale\n self.caption_channels = caption_channels\n self.num_attention_heads = num_attention_heads\n self.attention_head_dim = attention_head_dim\n self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim\n self.in_channels = in_channels\n self.out_channels = in_channels if out_channels is None else out_channels\n self.gradient_checkpointing = False\n\n if use_additional_conditions is None:\n if norm_type == \"ada_norm_single\" and sample_size == 128:\n use_additional_conditions = True\n else:\n use_additional_conditions = False\n self.use_additional_conditions = use_additional_conditions\n\n # 2. Initialize the right blocks.\n # These functions follow a common structure:\n # a. Initialize the input blocks. b. Initialize the transformer blocks.\n # c. Initialize the output blocks and other projection blocks when necessary.\n if self.is_input_continuous:\n self._init_continuous_input(norm_type=norm_type)\n elif self.is_input_vectorized:\n self._init_vectorized_inputs(norm_type=norm_type)\n elif self.is_input_patches:\n self._init_patched_inputs(norm_type=norm_type)\n\n def _init_continuous_input(self, norm_type):\n self.norm = torch.nn.GroupNorm(\n num_groups=self.config.norm_num_groups, num_channels=self.in_channels, eps=1e-6, affine=True\n )\n if self.use_linear_projection:\n self.proj_in = torch.nn.Linear(self.in_channels, self.inner_dim)\n else:\n self.proj_in = torch.nn.Conv2d(self.in_channels, self.inner_dim, kernel_size=1, stride=1, padding=0)\n\n self.transformer_blocks = nn.ModuleList(\n [\n BasicTransformerBlock(\n self.inner_dim,\n self.config.num_attention_heads,\n self.config.attention_head_dim,\n dropout=self.config.dropout,\n cross_attention_dim=self.config.cross_attention_dim,\n activation_fn=self.config.activation_fn,\n num_embeds_ada_norm=self.config.num_embeds_ada_norm,\n attention_bias=self.config.attention_bias,\n only_cross_attention=self.config.only_cross_attention,\n double_self_attention=self.config.double_self_attention,\n upcast_attention=self.config.upcast_attention,\n norm_type=norm_type,\n norm_elementwise_affine=self.config.norm_elementwise_affine,\n norm_eps=self.config.norm_eps,\n attention_type=self.config.attention_type,\n )\n for _ in range(self.config.num_layers)\n ]\n )\n\n if self.use_linear_projection:\n self.proj_out = torch.nn.Linear(self.inner_dim, self.out_channels)\n else:\n self.proj_out = torch.nn.Conv2d(self.inner_dim, self.out_channels, kernel_size=1, stride=1, padding=0)\n\n def _init_vectorized_inputs(self, norm_type):\n assert self.config.sample_size is not None, \"Transformer2DModel over discrete input must provide sample_size\"\n assert (\n self.config.num_vector_embeds is not None\n ), \"Transformer2DModel over discrete input must provide num_embed\"\n\n self.height = self.config.sample_size\n self.width = self.config.sample_size\n self.num_latent_pixels = self.height * self.width\n\n self.latent_image_embedding = ImagePositionalEmbeddings(\n num_embed=self.config.num_vector_embeds, embed_dim=self.inner_dim, height=self.height, width=self.width\n )\n\n self.transformer_blocks = nn.ModuleList(\n [\n BasicTransformerBlock(\n self.inner_dim,\n self.config.num_attention_heads,\n self.config.attention_head_dim,\n dropout=self.config.dropout,\n cross_attention_dim=self.config.cross_attention_dim,\n activation_fn=self.config.activation_fn,\n num_embeds_ada_norm=self.config.num_embeds_ada_norm,\n attention_bias=self.config.attention_bias,\n only_cross_attention=self.config.only_cross_attention,\n double_self_attention=self.config.double_self_attention,\n upcast_attention=self.config.upcast_attention,\n norm_type=norm_type,\n norm_elementwise_affine=self.config.norm_elementwise_affine,\n norm_eps=self.config.norm_eps,\n attention_type=self.config.attention_type,\n )\n for _ in range(self.config.num_layers)\n ]\n )\n\n self.norm_out = nn.LayerNorm(self.inner_dim)\n self.out = nn.Linear(self.inner_dim, self.config.num_vector_embeds - 1)\n\n def _init_patched_inputs(self, norm_type):\n assert self.config.sample_size is not None, \"Transformer2DModel over patched input must provide sample_size\"\n\n self.height = self.config.sample_size\n self.width = self.config.sample_size\n\n self.patch_size = self.config.patch_size\n interpolation_scale = (\n self.config.interpolation_scale\n if self.config.interpolation_scale is not None\n else max(self.config.sample_size // 64, 1)\n )\n self.pos_embed = PatchEmbed(\n height=self.config.sample_size,\n width=self.config.sample_size,\n patch_size=self.config.patch_size,\n in_channels=self.in_channels,\n embed_dim=self.inner_dim,\n interpolation_scale=interpolation_scale,\n )\n\n self.transformer_blocks = nn.ModuleList(\n [\n BasicTransformerBlock(\n self.inner_dim,\n self.config.num_attention_heads,\n self.config.attention_head_dim,\n dropout=self.config.dropout,\n cross_attention_dim=self.config.cross_attention_dim,\n activation_fn=self.config.activation_fn,\n num_embeds_ada_norm=self.config.num_embeds_ada_norm,\n attention_bias=self.config.attention_bias,\n only_cross_attention=self.config.only_cross_attention,\n double_self_attention=self.config.double_self_attention,\n upcast_attention=self.config.upcast_attention,\n norm_type=norm_type,\n norm_elementwise_affine=self.config.norm_elementwise_affine,\n norm_eps=self.config.norm_eps,\n attention_type=self.config.attention_type,\n )\n for _ in range(self.config.num_layers)\n ]\n )\n\n if self.config.norm_type != \"ada_norm_single\":\n self.norm_out = nn.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6)\n self.proj_out_1 = nn.Linear(self.inner_dim, 2 * self.inner_dim)\n self.proj_out_2 = nn.Linear(\n self.inner_dim, self.config.patch_size * self.config.patch_size * self.out_channels\n )\n elif self.config.norm_type == \"ada_norm_single\":\n self.norm_out = nn.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6)\n self.scale_shift_table = nn.Parameter(torch.randn(2, self.inner_dim) / self.inner_dim**0.5)\n self.proj_out = nn.Linear(\n self.inner_dim, self.config.patch_size * self.config.patch_size * self.out_channels\n )\n\n # PixArt-Alpha blocks.\n self.adaln_single = None\n if self.config.norm_type == \"ada_norm_single\":\n # TODO(Sayak, PVP) clean this, for now we use sample size to determine whether to use\n # additional conditions until we find better name\n self.adaln_single = AdaLayerNormSingle(\n self.inner_dim, use_additional_conditions=self.use_additional_conditions\n )\n\n self.caption_projection = None\n if self.caption_channels is not None:\n self.caption_projection = PixArtAlphaTextProjection(\n in_features=self.caption_channels, hidden_size=self.inner_dim\n )\n\n def _set_gradient_checkpointing(self, module, value=False):\n if hasattr(module, \"gradient_checkpointing\"):\n module.gradient_checkpointing = value\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n timestep: Optional[torch.LongTensor] = None,\n added_cond_kwargs: Dict[str, torch.Tensor] = None,\n class_labels: Optional[torch.LongTensor] = None,\n cross_attention_kwargs: Dict[str, Any] = None,\n attention_mask: Optional[torch.Tensor] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ):\n \"\"\"\n The [`Transformer2DModel`] forward method.\n\n Args:\n hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.Tensor` of shape `(batch size, channel, height, width)` if continuous):\n Input `hidden_states`.\n encoder_hidden_states ( `torch.Tensor` of shape `(batch size, sequence len, embed dims)`, *optional*):\n Conditional embeddings for cross attention layer. If not given, cross-attention defaults to\n self-attention.\n timestep ( `torch.LongTensor`, *optional*):\n Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.\n class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):\n Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in\n `AdaLayerZeroNorm`.\n cross_attention_kwargs ( `Dict[str, Any]`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n attention_mask ( `torch.Tensor`, *optional*):\n An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask\n is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large\n negative values to the attention scores corresponding to \"discard\" tokens.\n encoder_attention_mask ( `torch.Tensor`, *optional*):\n Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:\n\n * Mask `(batch, sequence_length)` True = keep, False = discard.\n * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.\n\n If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format\n above. This bias will be added to the cross-attention scores.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain\n tuple.\n\n Returns:\n If `return_dict` is True, an [`~models.transformers.transformer_2d.Transformer2DModelOutput`] is returned,\n otherwise a `tuple` where the first element is the sample tensor.\n \"\"\"\n if cross_attention_kwargs is not None:\n if cross_attention_kwargs.get(\"scale\", None) is not None:\n logger.warning(\"Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.\")\n # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.\n # we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.\n # we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.\n # expects mask of shape:\n # [batch, key_tokens]\n # adds singleton query_tokens dimension:\n # [batch, 1, key_tokens]\n # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:\n # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)\n # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)\n if attention_mask is not None and attention_mask.ndim == 2:\n # assume that mask is expressed as:\n # (1 = keep, 0 = discard)\n # convert mask into a bias that can be added to attention scores:\n # (keep = +0, discard = -10000.0)\n attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n # convert encoder_attention_mask to a bias the same way we do for attention_mask\n if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:\n encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0\n encoder_attention_mask = encoder_attention_mask.unsqueeze(1)\n\n # 1. Input\n if self.is_input_continuous:\n batch_size, _, height, width = hidden_states.shape\n residual = hidden_states\n hidden_states, inner_dim = self._operate_on_continuous_inputs(hidden_states)\n elif self.is_input_vectorized:\n hidden_states = self.latent_image_embedding(hidden_states)\n elif self.is_input_patches:\n height, width = hidden_states.shape[-2] // self.patch_size, hidden_states.shape[-1] // self.patch_size\n hidden_states, encoder_hidden_states, timestep, embedded_timestep = self._operate_on_patched_inputs(\n hidden_states, encoder_hidden_states, timestep, added_cond_kwargs\n )\n\n # 2. Blocks\n for block in self.transformer_blocks:\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(block),\n hidden_states,\n attention_mask,\n encoder_hidden_states,\n encoder_attention_mask,\n timestep,\n cross_attention_kwargs,\n class_labels,\n **ckpt_kwargs,\n )\n else:\n hidden_states = block(\n hidden_states,\n attention_mask=attention_mask,\n encoder_hidden_states=encoder_hidden_states,\n encoder_attention_mask=encoder_attention_mask,\n timestep=timestep,\n cross_attention_kwargs=cross_attention_kwargs,\n class_labels=class_labels,\n )\n\n # 3. Output\n if self.is_input_continuous:\n output = self._get_output_for_continuous_inputs(\n hidden_states=hidden_states,\n residual=residual,\n batch_size=batch_size,\n height=height,\n width=width,\n inner_dim=inner_dim,\n )\n elif self.is_input_vectorized:\n output = self._get_output_for_vectorized_inputs(hidden_states)\n elif self.is_input_patches:\n output = self._get_output_for_patched_inputs(\n hidden_states=hidden_states,\n timestep=timestep,\n class_labels=class_labels,\n embedded_timestep=embedded_timestep,\n height=height,\n width=width,\n )\n\n if not return_dict:\n return (output,)\n\n return Transformer2DModelOutput(sample=output)\n\n def _operate_on_continuous_inputs(self, hidden_states):\n batch, _, height, width = hidden_states.shape\n hidden_states = self.norm(hidden_states)\n\n if not self.use_linear_projection:\n hidden_states = self.proj_in(hidden_states)\n inner_dim = hidden_states.shape[1]\n hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)\n else:\n inner_dim = hidden_states.shape[1]\n hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)\n hidden_states = self.proj_in(hidden_states)\n\n return hidden_states, inner_dim\n\n def _operate_on_patched_inputs(self, hidden_states, encoder_hidden_states, timestep, added_cond_kwargs):\n batch_size = hidden_states.shape[0]\n hidden_states = self.pos_embed(hidden_states)\n embedded_timestep = None\n\n if self.adaln_single is not None:\n if self.use_additional_conditions and added_cond_kwargs is None:\n raise ValueError(\n \"`added_cond_kwargs` cannot be None when using additional conditions for `adaln_single`.\"\n )\n timestep, embedded_timestep = self.adaln_single(\n timestep, added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_states.dtype\n )\n\n if self.caption_projection is not None:\n encoder_hidden_states = self.caption_projection(encoder_hidden_states)\n encoder_hidden_states = encoder_hidden_states.view(batch_size, -1, hidden_states.shape[-1])\n\n return hidden_states, encoder_hidden_states, timestep, embedded_timestep\n\n def _get_output_for_continuous_inputs(self, hidden_states, residual, batch_size, height, width, inner_dim):\n if not self.use_linear_projection:\n hidden_states = (\n hidden_states.reshape(batch_size, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()\n )\n hidden_states = self.proj_out(hidden_states)\n else:\n hidden_states = self.proj_out(hidden_states)\n hidden_states = (\n hidden_states.reshape(batch_size, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()\n )\n\n output = hidden_states + residual\n return output\n\n def _get_output_for_vectorized_inputs(self, hidden_states):\n hidden_states = self.norm_out(hidden_states)\n logits = self.out(hidden_states)\n # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)\n logits = logits.permute(0, 2, 1)\n # log(p(x_0))\n output = F.log_softmax(logits.double(), dim=1).float()\n return output\n\n def _get_output_for_patched_inputs(\n self, hidden_states, timestep, class_labels, embedded_timestep, height=None, width=None\n ):\n if self.config.norm_type != \"ada_norm_single\":\n conditioning = self.transformer_blocks[0].norm1.emb(\n timestep, class_labels, hidden_dtype=hidden_states.dtype\n )\n shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1)\n hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None]\n hidden_states = self.proj_out_2(hidden_states)\n elif self.config.norm_type == \"ada_norm_single\":\n shift, scale = (self.scale_shift_table[None] + embedded_timestep[:, None]).chunk(2, dim=1)\n hidden_states = self.norm_out(hidden_states)\n # Modulation\n hidden_states = hidden_states * (1 + scale) + shift\n hidden_states = self.proj_out(hidden_states)\n hidden_states = hidden_states.squeeze(1)\n\n # unpatchify\n if self.adaln_single is None:\n height = width = int(hidden_states.shape[1] ** 0.5)\n hidden_states = hidden_states.reshape(\n shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)\n )\n hidden_states = torch.einsum(\"nhwpqc->nchpwq\", hidden_states)\n output = hidden_states.reshape(\n shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)\n )\n return output\n" }, { "path": "diffusers/src/diffusers/models/transformers/transformer_sd3.py", "content": "# Copyright 2024 Stability AI and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nfrom typing import Any, Dict, Optional, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...loaders import FromOriginalModelMixin, PeftAdapterMixin\nfrom ...models.attention import JointTransformerBlock\nfrom ...models.attention_processor import Attention, AttentionProcessor\nfrom ...models.modeling_utils import ModelMixin\nfrom ...models.normalization import AdaLayerNormContinuous\nfrom ...utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers\nfrom ..embeddings import CombinedTimestepTextProjEmbeddings, PatchEmbed\nfrom .transformer_2d import Transformer2DModelOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass SD3Transformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):\n \"\"\"\n The Transformer model introduced in Stable Diffusion 3.\n\n Reference: https://arxiv.org/abs/2403.03206\n\n Parameters:\n sample_size (`int`): The width of the latent images. This is fixed during training since\n it is used to learn a number of position embeddings.\n patch_size (`int`): Patch size to turn the input data into small patches.\n in_channels (`int`, *optional*, defaults to 16): The number of channels in the input.\n num_layers (`int`, *optional*, defaults to 18): The number of layers of Transformer blocks to use.\n attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.\n num_attention_heads (`int`, *optional*, defaults to 18): The number of heads to use for multi-head attention.\n cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.\n caption_projection_dim (`int`): Number of dimensions to use when projecting the `encoder_hidden_states`.\n pooled_projection_dim (`int`): Number of dimensions to use when projecting the `pooled_projections`.\n out_channels (`int`, defaults to 16): Number of output channels.\n\n \"\"\"\n\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n sample_size: int = 128,\n patch_size: int = 2,\n in_channels: int = 16,\n num_layers: int = 18,\n attention_head_dim: int = 64,\n num_attention_heads: int = 18,\n joint_attention_dim: int = 4096,\n caption_projection_dim: int = 1152,\n pooled_projection_dim: int = 2048,\n out_channels: int = 16,\n pos_embed_max_size: int = 96,\n ):\n super().__init__()\n default_out_channels = in_channels\n self.out_channels = out_channels if out_channels is not None else default_out_channels\n self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim\n\n self.pos_embed = PatchEmbed(\n height=self.config.sample_size,\n width=self.config.sample_size,\n patch_size=self.config.patch_size,\n in_channels=self.config.in_channels,\n embed_dim=self.inner_dim,\n pos_embed_max_size=pos_embed_max_size, # hard-code for now.\n )\n self.time_text_embed = CombinedTimestepTextProjEmbeddings(\n embedding_dim=self.inner_dim, pooled_projection_dim=self.config.pooled_projection_dim\n )\n self.context_embedder = nn.Linear(self.config.joint_attention_dim, self.config.caption_projection_dim)\n\n # `attention_head_dim` is doubled to account for the mixing.\n # It needs to crafted when we get the actual checkpoints.\n self.transformer_blocks = nn.ModuleList(\n [\n JointTransformerBlock(\n dim=self.inner_dim,\n num_attention_heads=self.config.num_attention_heads,\n attention_head_dim=self.inner_dim,\n context_pre_only=i == num_layers - 1,\n )\n for i in range(self.config.num_layers)\n ]\n )\n\n self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6)\n self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True)\n\n self.gradient_checkpointing = False\n\n # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking\n def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:\n \"\"\"\n Sets the attention processor to use [feed forward\n chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).\n\n Parameters:\n chunk_size (`int`, *optional*):\n The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually\n over each tensor of dim=`dim`.\n dim (`int`, *optional*, defaults to `0`):\n The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)\n or dim=1 (sequence length).\n \"\"\"\n if dim not in [0, 1]:\n raise ValueError(f\"Make sure to set `dim` to either 0 or 1, not {dim}\")\n\n # By default chunk size is 1\n chunk_size = chunk_size or 1\n\n def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):\n if hasattr(module, \"set_chunk_feed_forward\"):\n module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)\n\n for child in module.children():\n fn_recursive_feed_forward(child, chunk_size, dim)\n\n for module in self.children():\n fn_recursive_feed_forward(module, chunk_size, dim)\n\n @property\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors\n def attn_processors(self) -> Dict[str, AttentionProcessor]:\n r\"\"\"\n Returns:\n `dict` of attention processors: A dictionary containing all attention processors used in the model with\n indexed by its weight name.\n \"\"\"\n # set recursively\n processors = {}\n\n def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\n if hasattr(module, \"get_processor\"):\n processors[f\"{name}.processor\"] = module.get_processor(return_deprecated_lora=True)\n\n for sub_name, child in module.named_children():\n fn_recursive_add_processors(f\"{name}.{sub_name}\", child, processors)\n\n return processors\n\n for name, module in self.named_children():\n fn_recursive_add_processors(name, module, processors)\n\n return processors\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor\n def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\n r\"\"\"\n Sets the attention processor to use to compute attention.\n\n Parameters:\n processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):\n The instantiated processor class or a dictionary of processor classes that will be set as the processor\n for **all** `Attention` layers.\n\n If `processor` is a dict, the key needs to define the path to the corresponding cross attention\n processor. This is strongly recommended when setting trainable attention processors.\n\n \"\"\"\n count = len(self.attn_processors.keys())\n\n if isinstance(processor, dict) and len(processor) != count:\n raise ValueError(\n f\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\"\n f\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\"\n )\n\n def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\n if hasattr(module, \"set_processor\"):\n if not isinstance(processor, dict):\n module.set_processor(processor)\n else:\n module.set_processor(processor.pop(f\"{name}.processor\"))\n\n for sub_name, child in module.named_children():\n fn_recursive_attn_processor(f\"{name}.{sub_name}\", child, processor)\n\n for name, module in self.named_children():\n fn_recursive_attn_processor(name, module, processor)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections\n def fuse_qkv_projections(self):\n \"\"\"\n Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)\n are fused. For cross-attention modules, key and value projection matrices are fused.\n\n \n\n This API is 🧪 experimental.\n\n \n \"\"\"\n self.original_attn_processors = None\n\n for _, attn_processor in self.attn_processors.items():\n if \"Added\" in str(attn_processor.__class__.__name__):\n raise ValueError(\"`fuse_qkv_projections()` is not supported for models having added KV projections.\")\n\n self.original_attn_processors = self.attn_processors\n\n for module in self.modules():\n if isinstance(module, Attention):\n module.fuse_projections(fuse=True)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections\n def unfuse_qkv_projections(self):\n \"\"\"Disables the fused QKV projection if enabled.\n\n \n\n This API is 🧪 experimental.\n\n \n\n \"\"\"\n if self.original_attn_processors is not None:\n self.set_attn_processor(self.original_attn_processors)\n\n def _set_gradient_checkpointing(self, module, value=False):\n if hasattr(module, \"gradient_checkpointing\"):\n module.gradient_checkpointing = value\n\n def forward(\n self,\n hidden_states: torch.FloatTensor,\n encoder_hidden_states: torch.FloatTensor = None,\n pooled_projections: torch.FloatTensor = None,\n timestep: torch.LongTensor = None,\n joint_attention_kwargs: Optional[Dict[str, Any]] = None,\n return_dict: bool = True,\n ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:\n \"\"\"\n The [`SD3Transformer2DModel`] forward method.\n\n Args:\n hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):\n Input `hidden_states`.\n encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):\n Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.\n pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected\n from the embeddings of input conditions.\n timestep ( `torch.LongTensor`):\n Used to indicate denoising step.\n joint_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain\n tuple.\n\n Returns:\n If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a\n `tuple` where the first element is the sample tensor.\n \"\"\"\n if joint_attention_kwargs is not None:\n joint_attention_kwargs = joint_attention_kwargs.copy()\n lora_scale = joint_attention_kwargs.pop(\"scale\", 1.0)\n else:\n lora_scale = 1.0\n\n if USE_PEFT_BACKEND:\n # weight the lora layers by setting `lora_scale` for each PEFT layer\n scale_lora_layers(self, lora_scale)\n else:\n if joint_attention_kwargs is not None and joint_attention_kwargs.get(\"scale\", None) is not None:\n logger.warning(\n \"Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective.\"\n )\n\n height, width = hidden_states.shape[-2:]\n\n hidden_states = self.pos_embed(hidden_states) # takes care of adding positional embeddings too.\n temb = self.time_text_embed(timestep, pooled_projections)\n encoder_hidden_states = self.context_embedder(encoder_hidden_states)\n\n for block in self.transformer_blocks:\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(block),\n hidden_states,\n encoder_hidden_states,\n temb,\n **ckpt_kwargs,\n )\n\n else:\n encoder_hidden_states, hidden_states = block(\n hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states, temb=temb\n )\n\n hidden_states = self.norm_out(hidden_states, temb)\n hidden_states = self.proj_out(hidden_states)\n\n # unpatchify\n patch_size = self.config.patch_size\n height = height // patch_size\n width = width // patch_size\n\n hidden_states = hidden_states.reshape(\n shape=(hidden_states.shape[0], height, width, patch_size, patch_size, self.out_channels)\n )\n hidden_states = torch.einsum(\"nhwpqc->nchpwq\", hidden_states)\n output = hidden_states.reshape(\n shape=(hidden_states.shape[0], self.out_channels, height * patch_size, width * patch_size)\n )\n\n if USE_PEFT_BACKEND:\n # remove `lora_scale` from each PEFT layer\n unscale_lora_layers(self, lora_scale)\n\n if not return_dict:\n return (output,)\n\n return Transformer2DModelOutput(sample=output)\n" }, { "path": "diffusers/src/diffusers/models/transformers/transformer_temporal.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, Optional\n\nimport torch\nfrom torch import nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...utils import BaseOutput\nfrom ..attention import BasicTransformerBlock, TemporalBasicTransformerBlock\nfrom ..embeddings import TimestepEmbedding, Timesteps\nfrom ..modeling_utils import ModelMixin\nfrom ..resnet import AlphaBlender\n\n\n@dataclass\nclass TransformerTemporalModelOutput(BaseOutput):\n \"\"\"\n The output of [`TransformerTemporalModel`].\n\n Args:\n sample (`torch.Tensor` of shape `(batch_size x num_frames, num_channels, height, width)`):\n The hidden states output conditioned on `encoder_hidden_states` input.\n \"\"\"\n\n sample: torch.Tensor\n\n\nclass TransformerTemporalModel(ModelMixin, ConfigMixin):\n \"\"\"\n A Transformer model for video-like data.\n\n Parameters:\n num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.\n attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.\n in_channels (`int`, *optional*):\n The number of channels in the input and output (specify if the input is **continuous**).\n num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.\n dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.\n cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.\n attention_bias (`bool`, *optional*):\n Configure if the `TransformerBlock` attention should contain a bias parameter.\n sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).\n This is fixed during training since it is used to learn a number of position embeddings.\n activation_fn (`str`, *optional*, defaults to `\"geglu\"`):\n Activation function to use in feed-forward. See `diffusers.models.activations.get_activation` for supported\n activation functions.\n norm_elementwise_affine (`bool`, *optional*):\n Configure if the `TransformerBlock` should use learnable elementwise affine parameters for normalization.\n double_self_attention (`bool`, *optional*):\n Configure if each `TransformerBlock` should contain two self-attention layers.\n positional_embeddings: (`str`, *optional*):\n The type of positional embeddings to apply to the sequence input before passing use.\n num_positional_embeddings: (`int`, *optional*):\n The maximum length of the sequence over which to apply positional embeddings.\n \"\"\"\n\n @register_to_config\n def __init__(\n self,\n num_attention_heads: int = 16,\n attention_head_dim: int = 88,\n in_channels: Optional[int] = None,\n out_channels: Optional[int] = None,\n num_layers: int = 1,\n dropout: float = 0.0,\n norm_num_groups: int = 32,\n cross_attention_dim: Optional[int] = None,\n attention_bias: bool = False,\n sample_size: Optional[int] = None,\n activation_fn: str = \"geglu\",\n norm_elementwise_affine: bool = True,\n double_self_attention: bool = True,\n positional_embeddings: Optional[str] = None,\n num_positional_embeddings: Optional[int] = None,\n ):\n super().__init__()\n self.num_attention_heads = num_attention_heads\n self.attention_head_dim = attention_head_dim\n inner_dim = num_attention_heads * attention_head_dim\n\n self.in_channels = in_channels\n\n self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)\n self.proj_in = nn.Linear(in_channels, inner_dim)\n\n # 3. Define transformers blocks\n self.transformer_blocks = nn.ModuleList(\n [\n BasicTransformerBlock(\n inner_dim,\n num_attention_heads,\n attention_head_dim,\n dropout=dropout,\n cross_attention_dim=cross_attention_dim,\n activation_fn=activation_fn,\n attention_bias=attention_bias,\n double_self_attention=double_self_attention,\n norm_elementwise_affine=norm_elementwise_affine,\n positional_embeddings=positional_embeddings,\n num_positional_embeddings=num_positional_embeddings,\n )\n for d in range(num_layers)\n ]\n )\n\n self.proj_out = nn.Linear(inner_dim, in_channels)\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.LongTensor] = None,\n timestep: Optional[torch.LongTensor] = None,\n class_labels: torch.LongTensor = None,\n num_frames: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n return_dict: bool = True,\n ) -> TransformerTemporalModelOutput:\n \"\"\"\n The [`TransformerTemporal`] forward method.\n\n Args:\n hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.Tensor` of shape `(batch size, channel, height, width)` if continuous):\n Input hidden_states.\n encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):\n Conditional embeddings for cross attention layer. If not given, cross-attention defaults to\n self-attention.\n timestep ( `torch.LongTensor`, *optional*):\n Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.\n class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):\n Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in\n `AdaLayerZeroNorm`.\n num_frames (`int`, *optional*, defaults to 1):\n The number of frames to be processed per batch. This is used to reshape the hidden states.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~models.transformers.transformer_temporal.TransformerTemporalModelOutput`]\n instead of a plain tuple.\n\n Returns:\n [`~models.transformers.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`:\n If `return_dict` is True, an\n [`~models.transformers.transformer_temporal.TransformerTemporalModelOutput`] is returned, otherwise a\n `tuple` where the first element is the sample tensor.\n \"\"\"\n # 1. Input\n batch_frames, channel, height, width = hidden_states.shape\n batch_size = batch_frames // num_frames\n\n residual = hidden_states\n\n hidden_states = hidden_states[None, :].reshape(batch_size, num_frames, channel, height, width)\n hidden_states = hidden_states.permute(0, 2, 1, 3, 4)\n\n hidden_states = self.norm(hidden_states)\n hidden_states = hidden_states.permute(0, 3, 4, 2, 1).reshape(batch_size * height * width, num_frames, channel)\n\n hidden_states = self.proj_in(hidden_states)\n\n # 2. Blocks\n for block in self.transformer_blocks:\n hidden_states = block(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n timestep=timestep,\n cross_attention_kwargs=cross_attention_kwargs,\n class_labels=class_labels,\n )\n\n # 3. Output\n hidden_states = self.proj_out(hidden_states)\n hidden_states = (\n hidden_states[None, None, :]\n .reshape(batch_size, height, width, num_frames, channel)\n .permute(0, 3, 4, 1, 2)\n .contiguous()\n )\n hidden_states = hidden_states.reshape(batch_frames, channel, height, width)\n\n output = hidden_states + residual\n\n if not return_dict:\n return (output,)\n\n return TransformerTemporalModelOutput(sample=output)\n\n\nclass TransformerSpatioTemporalModel(nn.Module):\n \"\"\"\n A Transformer model for video-like data.\n\n Parameters:\n num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.\n attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.\n in_channels (`int`, *optional*):\n The number of channels in the input and output (specify if the input is **continuous**).\n out_channels (`int`, *optional*):\n The number of channels in the output (specify if the input is **continuous**).\n num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.\n cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.\n \"\"\"\n\n def __init__(\n self,\n num_attention_heads: int = 16,\n attention_head_dim: int = 88,\n in_channels: int = 320,\n out_channels: Optional[int] = None,\n num_layers: int = 1,\n cross_attention_dim: Optional[int] = None,\n ):\n super().__init__()\n self.num_attention_heads = num_attention_heads\n self.attention_head_dim = attention_head_dim\n\n inner_dim = num_attention_heads * attention_head_dim\n self.inner_dim = inner_dim\n\n # 2. Define input layers\n self.in_channels = in_channels\n self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6)\n self.proj_in = nn.Linear(in_channels, inner_dim)\n\n # 3. Define transformers blocks\n self.transformer_blocks = nn.ModuleList(\n [\n BasicTransformerBlock(\n inner_dim,\n num_attention_heads,\n attention_head_dim,\n cross_attention_dim=cross_attention_dim,\n )\n for d in range(num_layers)\n ]\n )\n\n time_mix_inner_dim = inner_dim\n self.temporal_transformer_blocks = nn.ModuleList(\n [\n TemporalBasicTransformerBlock(\n inner_dim,\n time_mix_inner_dim,\n num_attention_heads,\n attention_head_dim,\n cross_attention_dim=cross_attention_dim,\n )\n for _ in range(num_layers)\n ]\n )\n\n time_embed_dim = in_channels * 4\n self.time_pos_embed = TimestepEmbedding(in_channels, time_embed_dim, out_dim=in_channels)\n self.time_proj = Timesteps(in_channels, True, 0)\n self.time_mixer = AlphaBlender(alpha=0.5, merge_strategy=\"learned_with_images\")\n\n # 4. Define output layers\n self.out_channels = in_channels if out_channels is None else out_channels\n # TODO: should use out_channels for continuous projections\n self.proj_out = nn.Linear(inner_dim, in_channels)\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n image_only_indicator: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ):\n \"\"\"\n Args:\n hidden_states (`torch.Tensor` of shape `(batch size, channel, height, width)`):\n Input hidden_states.\n num_frames (`int`):\n The number of frames to be processed per batch. This is used to reshape the hidden states.\n encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):\n Conditional embeddings for cross attention layer. If not given, cross-attention defaults to\n self-attention.\n image_only_indicator (`torch.LongTensor` of shape `(batch size, num_frames)`, *optional*):\n A tensor indicating whether the input contains only images. 1 indicates that the input contains only\n images, 0 indicates that the input contains video frames.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~models.transformers.transformer_temporal.TransformerTemporalModelOutput`]\n instead of a plain tuple.\n\n Returns:\n [`~models.transformers.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`:\n If `return_dict` is True, an\n [`~models.transformers.transformer_temporal.TransformerTemporalModelOutput`] is returned, otherwise a\n `tuple` where the first element is the sample tensor.\n \"\"\"\n # 1. Input\n batch_frames, _, height, width = hidden_states.shape\n num_frames = image_only_indicator.shape[-1]\n batch_size = batch_frames // num_frames\n\n time_context = encoder_hidden_states\n time_context_first_timestep = time_context[None, :].reshape(\n batch_size, num_frames, -1, time_context.shape[-1]\n )[:, 0]\n time_context = time_context_first_timestep[:, None].broadcast_to(\n batch_size, height * width, time_context.shape[-2], time_context.shape[-1]\n )\n time_context = time_context.reshape(batch_size * height * width, -1, time_context.shape[-1])\n\n residual = hidden_states\n\n hidden_states = self.norm(hidden_states)\n inner_dim = hidden_states.shape[1]\n hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch_frames, height * width, inner_dim)\n hidden_states = self.proj_in(hidden_states)\n\n num_frames_emb = torch.arange(num_frames, device=hidden_states.device)\n num_frames_emb = num_frames_emb.repeat(batch_size, 1)\n num_frames_emb = num_frames_emb.reshape(-1)\n t_emb = self.time_proj(num_frames_emb)\n\n # `Timesteps` does not contain any weights and will always return f32 tensors\n # but time_embedding might actually be running in fp16. so we need to cast here.\n # there might be better ways to encapsulate this.\n t_emb = t_emb.to(dtype=hidden_states.dtype)\n\n emb = self.time_pos_embed(t_emb)\n emb = emb[:, None, :]\n\n # 2. Blocks\n for block, temporal_block in zip(self.transformer_blocks, self.temporal_transformer_blocks):\n if self.training and self.gradient_checkpointing:\n hidden_states = torch.utils.checkpoint.checkpoint(\n block,\n hidden_states,\n None,\n encoder_hidden_states,\n None,\n use_reentrant=False,\n )\n else:\n hidden_states = block(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n )\n\n hidden_states_mix = hidden_states\n hidden_states_mix = hidden_states_mix + emb\n\n hidden_states_mix = temporal_block(\n hidden_states_mix,\n num_frames=num_frames,\n encoder_hidden_states=time_context,\n )\n hidden_states = self.time_mixer(\n x_spatial=hidden_states,\n x_temporal=hidden_states_mix,\n image_only_indicator=image_only_indicator,\n )\n\n # 3. Output\n hidden_states = self.proj_out(hidden_states)\n hidden_states = hidden_states.reshape(batch_frames, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()\n\n output = hidden_states + residual\n\n if not return_dict:\n return (output,)\n\n return TransformerTemporalModelOutput(sample=output)\n" }, { "path": "diffusers/src/diffusers/models/unets/__init__.py", "content": "from ...utils import is_flax_available, is_torch_available\n\n\nif is_torch_available():\n from .unet_1d import UNet1DModel\n from .unet_2d import UNet2DModel\n from .unet_2d_condition import UNet2DConditionModel\n from .unet_3d_condition import UNet3DConditionModel\n from .unet_i2vgen_xl import I2VGenXLUNet\n from .unet_kandinsky3 import Kandinsky3UNet\n from .unet_motion_model import MotionAdapter, UNetMotionModel\n from .unet_spatio_temporal_condition import UNetSpatioTemporalConditionModel\n from .unet_stable_cascade import StableCascadeUNet\n from .uvit_2d import UVit2DModel\n\n\nif is_flax_available():\n from .unet_2d_condition_flax import FlaxUNet2DConditionModel\n" }, { "path": "diffusers/src/diffusers/models/unets/unet_1d.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...utils import BaseOutput\nfrom ..embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps\nfrom ..modeling_utils import ModelMixin\nfrom .unet_1d_blocks import get_down_block, get_mid_block, get_out_block, get_up_block\n\n\n@dataclass\nclass UNet1DOutput(BaseOutput):\n \"\"\"\n The output of [`UNet1DModel`].\n\n Args:\n sample (`torch.Tensor` of shape `(batch_size, num_channels, sample_size)`):\n The hidden states output from the last layer of the model.\n \"\"\"\n\n sample: torch.Tensor\n\n\nclass UNet1DModel(ModelMixin, ConfigMixin):\n r\"\"\"\n A 1D UNet model that takes a noisy sample and a timestep and returns a sample shaped output.\n\n This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented\n for all models (such as downloading or saving).\n\n Parameters:\n sample_size (`int`, *optional*): Default length of sample. Should be adaptable at runtime.\n in_channels (`int`, *optional*, defaults to 2): Number of channels in the input sample.\n out_channels (`int`, *optional*, defaults to 2): Number of channels in the output.\n extra_in_channels (`int`, *optional*, defaults to 0):\n Number of additional channels to be added to the input of the first down block. Useful for cases where the\n input data has more channels than what the model was initially designed for.\n time_embedding_type (`str`, *optional*, defaults to `\"fourier\"`): Type of time embedding to use.\n freq_shift (`float`, *optional*, defaults to 0.0): Frequency shift for Fourier time embedding.\n flip_sin_to_cos (`bool`, *optional*, defaults to `False`):\n Whether to flip sin to cos for Fourier time embedding.\n down_block_types (`Tuple[str]`, *optional*, defaults to `(\"DownBlock1DNoSkip\", \"DownBlock1D\", \"AttnDownBlock1D\")`):\n Tuple of downsample block types.\n up_block_types (`Tuple[str]`, *optional*, defaults to `(\"AttnUpBlock1D\", \"UpBlock1D\", \"UpBlock1DNoSkip\")`):\n Tuple of upsample block types.\n block_out_channels (`Tuple[int]`, *optional*, defaults to `(32, 32, 64)`):\n Tuple of block output channels.\n mid_block_type (`str`, *optional*, defaults to `\"UNetMidBlock1D\"`): Block type for middle of UNet.\n out_block_type (`str`, *optional*, defaults to `None`): Optional output processing block of UNet.\n act_fn (`str`, *optional*, defaults to `None`): Optional activation function in UNet blocks.\n norm_num_groups (`int`, *optional*, defaults to 8): The number of groups for normalization.\n layers_per_block (`int`, *optional*, defaults to 1): The number of layers per block.\n downsample_each_block (`int`, *optional*, defaults to `False`):\n Experimental feature for using a UNet without upsampling.\n \"\"\"\n\n @register_to_config\n def __init__(\n self,\n sample_size: int = 65536,\n sample_rate: Optional[int] = None,\n in_channels: int = 2,\n out_channels: int = 2,\n extra_in_channels: int = 0,\n time_embedding_type: str = \"fourier\",\n flip_sin_to_cos: bool = True,\n use_timestep_embedding: bool = False,\n freq_shift: float = 0.0,\n down_block_types: Tuple[str] = (\"DownBlock1DNoSkip\", \"DownBlock1D\", \"AttnDownBlock1D\"),\n up_block_types: Tuple[str] = (\"AttnUpBlock1D\", \"UpBlock1D\", \"UpBlock1DNoSkip\"),\n mid_block_type: Tuple[str] = \"UNetMidBlock1D\",\n out_block_type: str = None,\n block_out_channels: Tuple[int] = (32, 32, 64),\n act_fn: str = None,\n norm_num_groups: int = 8,\n layers_per_block: int = 1,\n downsample_each_block: bool = False,\n ):\n super().__init__()\n self.sample_size = sample_size\n\n # time\n if time_embedding_type == \"fourier\":\n self.time_proj = GaussianFourierProjection(\n embedding_size=8, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos\n )\n timestep_input_dim = 2 * block_out_channels[0]\n elif time_embedding_type == \"positional\":\n self.time_proj = Timesteps(\n block_out_channels[0], flip_sin_to_cos=flip_sin_to_cos, downscale_freq_shift=freq_shift\n )\n timestep_input_dim = block_out_channels[0]\n\n if use_timestep_embedding:\n time_embed_dim = block_out_channels[0] * 4\n self.time_mlp = TimestepEmbedding(\n in_channels=timestep_input_dim,\n time_embed_dim=time_embed_dim,\n act_fn=act_fn,\n out_dim=block_out_channels[0],\n )\n\n self.down_blocks = nn.ModuleList([])\n self.mid_block = None\n self.up_blocks = nn.ModuleList([])\n self.out_block = None\n\n # down\n output_channel = in_channels\n for i, down_block_type in enumerate(down_block_types):\n input_channel = output_channel\n output_channel = block_out_channels[i]\n\n if i == 0:\n input_channel += extra_in_channels\n\n is_final_block = i == len(block_out_channels) - 1\n\n down_block = get_down_block(\n down_block_type,\n num_layers=layers_per_block,\n in_channels=input_channel,\n out_channels=output_channel,\n temb_channels=block_out_channels[0],\n add_downsample=not is_final_block or downsample_each_block,\n )\n self.down_blocks.append(down_block)\n\n # mid\n self.mid_block = get_mid_block(\n mid_block_type,\n in_channels=block_out_channels[-1],\n mid_channels=block_out_channels[-1],\n out_channels=block_out_channels[-1],\n embed_dim=block_out_channels[0],\n num_layers=layers_per_block,\n add_downsample=downsample_each_block,\n )\n\n # up\n reversed_block_out_channels = list(reversed(block_out_channels))\n output_channel = reversed_block_out_channels[0]\n if out_block_type is None:\n final_upsample_channels = out_channels\n else:\n final_upsample_channels = block_out_channels[0]\n\n for i, up_block_type in enumerate(up_block_types):\n prev_output_channel = output_channel\n output_channel = (\n reversed_block_out_channels[i + 1] if i < len(up_block_types) - 1 else final_upsample_channels\n )\n\n is_final_block = i == len(block_out_channels) - 1\n\n up_block = get_up_block(\n up_block_type,\n num_layers=layers_per_block,\n in_channels=prev_output_channel,\n out_channels=output_channel,\n temb_channels=block_out_channels[0],\n add_upsample=not is_final_block,\n )\n self.up_blocks.append(up_block)\n prev_output_channel = output_channel\n\n # out\n num_groups_out = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4, 32)\n self.out_block = get_out_block(\n out_block_type=out_block_type,\n num_groups_out=num_groups_out,\n embed_dim=block_out_channels[0],\n out_channels=out_channels,\n act_fn=act_fn,\n fc_dim=block_out_channels[-1] // 4,\n )\n\n def forward(\n self,\n sample: torch.Tensor,\n timestep: Union[torch.Tensor, float, int],\n return_dict: bool = True,\n ) -> Union[UNet1DOutput, Tuple]:\n r\"\"\"\n The [`UNet1DModel`] forward method.\n\n Args:\n sample (`torch.Tensor`):\n The noisy input tensor with the following shape `(batch_size, num_channels, sample_size)`.\n timestep (`torch.Tensor` or `float` or `int`): The number of timesteps to denoise an input.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~models.unets.unet_1d.UNet1DOutput`] instead of a plain tuple.\n\n Returns:\n [`~models.unets.unet_1d.UNet1DOutput`] or `tuple`:\n If `return_dict` is True, an [`~models.unets.unet_1d.UNet1DOutput`] is returned, otherwise a `tuple` is\n returned where the first element is the sample tensor.\n \"\"\"\n\n # 1. time\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device)\n elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(sample.device)\n\n timestep_embed = self.time_proj(timesteps)\n if self.config.use_timestep_embedding:\n timestep_embed = self.time_mlp(timestep_embed)\n else:\n timestep_embed = timestep_embed[..., None]\n timestep_embed = timestep_embed.repeat([1, 1, sample.shape[2]]).to(sample.dtype)\n timestep_embed = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]))\n\n # 2. down\n down_block_res_samples = ()\n for downsample_block in self.down_blocks:\n sample, res_samples = downsample_block(hidden_states=sample, temb=timestep_embed)\n down_block_res_samples += res_samples\n\n # 3. mid\n if self.mid_block:\n sample = self.mid_block(sample, timestep_embed)\n\n # 4. up\n for i, upsample_block in enumerate(self.up_blocks):\n res_samples = down_block_res_samples[-1:]\n down_block_res_samples = down_block_res_samples[:-1]\n sample = upsample_block(sample, res_hidden_states_tuple=res_samples, temb=timestep_embed)\n\n # 5. post-process\n if self.out_block:\n sample = self.out_block(sample, timestep_embed)\n\n if not return_dict:\n return (sample,)\n\n return UNet1DOutput(sample=sample)\n" }, { "path": "diffusers/src/diffusers/models/unets/unet_1d_blocks.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport math\nfrom typing import Optional, Tuple, Union\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom ..activations import get_activation\nfrom ..resnet import Downsample1D, ResidualTemporalBlock1D, Upsample1D, rearrange_dims\n\n\nclass DownResnetBlock1D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: Optional[int] = None,\n num_layers: int = 1,\n conv_shortcut: bool = False,\n temb_channels: int = 32,\n groups: int = 32,\n groups_out: Optional[int] = None,\n non_linearity: Optional[str] = None,\n time_embedding_norm: str = \"default\",\n output_scale_factor: float = 1.0,\n add_downsample: bool = True,\n ):\n super().__init__()\n self.in_channels = in_channels\n out_channels = in_channels if out_channels is None else out_channels\n self.out_channels = out_channels\n self.use_conv_shortcut = conv_shortcut\n self.time_embedding_norm = time_embedding_norm\n self.add_downsample = add_downsample\n self.output_scale_factor = output_scale_factor\n\n if groups_out is None:\n groups_out = groups\n\n # there will always be at least one resnet\n resnets = [ResidualTemporalBlock1D(in_channels, out_channels, embed_dim=temb_channels)]\n\n for _ in range(num_layers):\n resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=temb_channels))\n\n self.resnets = nn.ModuleList(resnets)\n\n if non_linearity is None:\n self.nonlinearity = None\n else:\n self.nonlinearity = get_activation(non_linearity)\n\n self.downsample = None\n if add_downsample:\n self.downsample = Downsample1D(out_channels, use_conv=True, padding=1)\n\n def forward(self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:\n output_states = ()\n\n hidden_states = self.resnets[0](hidden_states, temb)\n for resnet in self.resnets[1:]:\n hidden_states = resnet(hidden_states, temb)\n\n output_states += (hidden_states,)\n\n if self.nonlinearity is not None:\n hidden_states = self.nonlinearity(hidden_states)\n\n if self.downsample is not None:\n hidden_states = self.downsample(hidden_states)\n\n return hidden_states, output_states\n\n\nclass UpResnetBlock1D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: Optional[int] = None,\n num_layers: int = 1,\n temb_channels: int = 32,\n groups: int = 32,\n groups_out: Optional[int] = None,\n non_linearity: Optional[str] = None,\n time_embedding_norm: str = \"default\",\n output_scale_factor: float = 1.0,\n add_upsample: bool = True,\n ):\n super().__init__()\n self.in_channels = in_channels\n out_channels = in_channels if out_channels is None else out_channels\n self.out_channels = out_channels\n self.time_embedding_norm = time_embedding_norm\n self.add_upsample = add_upsample\n self.output_scale_factor = output_scale_factor\n\n if groups_out is None:\n groups_out = groups\n\n # there will always be at least one resnet\n resnets = [ResidualTemporalBlock1D(2 * in_channels, out_channels, embed_dim=temb_channels)]\n\n for _ in range(num_layers):\n resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=temb_channels))\n\n self.resnets = nn.ModuleList(resnets)\n\n if non_linearity is None:\n self.nonlinearity = None\n else:\n self.nonlinearity = get_activation(non_linearity)\n\n self.upsample = None\n if add_upsample:\n self.upsample = Upsample1D(out_channels, use_conv_transpose=True)\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Optional[Tuple[torch.Tensor, ...]] = None,\n temb: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n if res_hidden_states_tuple is not None:\n res_hidden_states = res_hidden_states_tuple[-1]\n hidden_states = torch.cat((hidden_states, res_hidden_states), dim=1)\n\n hidden_states = self.resnets[0](hidden_states, temb)\n for resnet in self.resnets[1:]:\n hidden_states = resnet(hidden_states, temb)\n\n if self.nonlinearity is not None:\n hidden_states = self.nonlinearity(hidden_states)\n\n if self.upsample is not None:\n hidden_states = self.upsample(hidden_states)\n\n return hidden_states\n\n\nclass ValueFunctionMidBlock1D(nn.Module):\n def __init__(self, in_channels: int, out_channels: int, embed_dim: int):\n super().__init__()\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.embed_dim = embed_dim\n\n self.res1 = ResidualTemporalBlock1D(in_channels, in_channels // 2, embed_dim=embed_dim)\n self.down1 = Downsample1D(out_channels // 2, use_conv=True)\n self.res2 = ResidualTemporalBlock1D(in_channels // 2, in_channels // 4, embed_dim=embed_dim)\n self.down2 = Downsample1D(out_channels // 4, use_conv=True)\n\n def forward(self, x: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:\n x = self.res1(x, temb)\n x = self.down1(x)\n x = self.res2(x, temb)\n x = self.down2(x)\n return x\n\n\nclass MidResTemporalBlock1D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n embed_dim: int,\n num_layers: int = 1,\n add_downsample: bool = False,\n add_upsample: bool = False,\n non_linearity: Optional[str] = None,\n ):\n super().__init__()\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.add_downsample = add_downsample\n\n # there will always be at least one resnet\n resnets = [ResidualTemporalBlock1D(in_channels, out_channels, embed_dim=embed_dim)]\n\n for _ in range(num_layers):\n resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=embed_dim))\n\n self.resnets = nn.ModuleList(resnets)\n\n if non_linearity is None:\n self.nonlinearity = None\n else:\n self.nonlinearity = get_activation(non_linearity)\n\n self.upsample = None\n if add_upsample:\n self.upsample = Downsample1D(out_channels, use_conv=True)\n\n self.downsample = None\n if add_downsample:\n self.downsample = Downsample1D(out_channels, use_conv=True)\n\n if self.upsample and self.downsample:\n raise ValueError(\"Block cannot downsample and upsample\")\n\n def forward(self, hidden_states: torch.Tensor, temb: torch.Tensor) -> torch.Tensor:\n hidden_states = self.resnets[0](hidden_states, temb)\n for resnet in self.resnets[1:]:\n hidden_states = resnet(hidden_states, temb)\n\n if self.upsample:\n hidden_states = self.upsample(hidden_states)\n if self.downsample:\n self.downsample = self.downsample(hidden_states)\n\n return hidden_states\n\n\nclass OutConv1DBlock(nn.Module):\n def __init__(self, num_groups_out: int, out_channels: int, embed_dim: int, act_fn: str):\n super().__init__()\n self.final_conv1d_1 = nn.Conv1d(embed_dim, embed_dim, 5, padding=2)\n self.final_conv1d_gn = nn.GroupNorm(num_groups_out, embed_dim)\n self.final_conv1d_act = get_activation(act_fn)\n self.final_conv1d_2 = nn.Conv1d(embed_dim, out_channels, 1)\n\n def forward(self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:\n hidden_states = self.final_conv1d_1(hidden_states)\n hidden_states = rearrange_dims(hidden_states)\n hidden_states = self.final_conv1d_gn(hidden_states)\n hidden_states = rearrange_dims(hidden_states)\n hidden_states = self.final_conv1d_act(hidden_states)\n hidden_states = self.final_conv1d_2(hidden_states)\n return hidden_states\n\n\nclass OutValueFunctionBlock(nn.Module):\n def __init__(self, fc_dim: int, embed_dim: int, act_fn: str = \"mish\"):\n super().__init__()\n self.final_block = nn.ModuleList(\n [\n nn.Linear(fc_dim + embed_dim, fc_dim // 2),\n get_activation(act_fn),\n nn.Linear(fc_dim // 2, 1),\n ]\n )\n\n def forward(self, hidden_states: torch.Tensor, temb: torch.Tensor) -> torch.Tensor:\n hidden_states = hidden_states.view(hidden_states.shape[0], -1)\n hidden_states = torch.cat((hidden_states, temb), dim=-1)\n for layer in self.final_block:\n hidden_states = layer(hidden_states)\n\n return hidden_states\n\n\n_kernels = {\n \"linear\": [1 / 8, 3 / 8, 3 / 8, 1 / 8],\n \"cubic\": [-0.01171875, -0.03515625, 0.11328125, 0.43359375, 0.43359375, 0.11328125, -0.03515625, -0.01171875],\n \"lanczos3\": [\n 0.003689131001010537,\n 0.015056144446134567,\n -0.03399861603975296,\n -0.066637322306633,\n 0.13550527393817902,\n 0.44638532400131226,\n 0.44638532400131226,\n 0.13550527393817902,\n -0.066637322306633,\n -0.03399861603975296,\n 0.015056144446134567,\n 0.003689131001010537,\n ],\n}\n\n\nclass Downsample1d(nn.Module):\n def __init__(self, kernel: str = \"linear\", pad_mode: str = \"reflect\"):\n super().__init__()\n self.pad_mode = pad_mode\n kernel_1d = torch.tensor(_kernels[kernel])\n self.pad = kernel_1d.shape[0] // 2 - 1\n self.register_buffer(\"kernel\", kernel_1d)\n\n def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n hidden_states = F.pad(hidden_states, (self.pad,) * 2, self.pad_mode)\n weight = hidden_states.new_zeros([hidden_states.shape[1], hidden_states.shape[1], self.kernel.shape[0]])\n indices = torch.arange(hidden_states.shape[1], device=hidden_states.device)\n kernel = self.kernel.to(weight)[None, :].expand(hidden_states.shape[1], -1)\n weight[indices, indices] = kernel\n return F.conv1d(hidden_states, weight, stride=2)\n\n\nclass Upsample1d(nn.Module):\n def __init__(self, kernel: str = \"linear\", pad_mode: str = \"reflect\"):\n super().__init__()\n self.pad_mode = pad_mode\n kernel_1d = torch.tensor(_kernels[kernel]) * 2\n self.pad = kernel_1d.shape[0] // 2 - 1\n self.register_buffer(\"kernel\", kernel_1d)\n\n def forward(self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:\n hidden_states = F.pad(hidden_states, ((self.pad + 1) // 2,) * 2, self.pad_mode)\n weight = hidden_states.new_zeros([hidden_states.shape[1], hidden_states.shape[1], self.kernel.shape[0]])\n indices = torch.arange(hidden_states.shape[1], device=hidden_states.device)\n kernel = self.kernel.to(weight)[None, :].expand(hidden_states.shape[1], -1)\n weight[indices, indices] = kernel\n return F.conv_transpose1d(hidden_states, weight, stride=2, padding=self.pad * 2 + 1)\n\n\nclass SelfAttention1d(nn.Module):\n def __init__(self, in_channels: int, n_head: int = 1, dropout_rate: float = 0.0):\n super().__init__()\n self.channels = in_channels\n self.group_norm = nn.GroupNorm(1, num_channels=in_channels)\n self.num_heads = n_head\n\n self.query = nn.Linear(self.channels, self.channels)\n self.key = nn.Linear(self.channels, self.channels)\n self.value = nn.Linear(self.channels, self.channels)\n\n self.proj_attn = nn.Linear(self.channels, self.channels, bias=True)\n\n self.dropout = nn.Dropout(dropout_rate, inplace=True)\n\n def transpose_for_scores(self, projection: torch.Tensor) -> torch.Tensor:\n new_projection_shape = projection.size()[:-1] + (self.num_heads, -1)\n # move heads to 2nd position (B, T, H * D) -> (B, T, H, D) -> (B, H, T, D)\n new_projection = projection.view(new_projection_shape).permute(0, 2, 1, 3)\n return new_projection\n\n def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n residual = hidden_states\n batch, channel_dim, seq = hidden_states.shape\n\n hidden_states = self.group_norm(hidden_states)\n hidden_states = hidden_states.transpose(1, 2)\n\n query_proj = self.query(hidden_states)\n key_proj = self.key(hidden_states)\n value_proj = self.value(hidden_states)\n\n query_states = self.transpose_for_scores(query_proj)\n key_states = self.transpose_for_scores(key_proj)\n value_states = self.transpose_for_scores(value_proj)\n\n scale = 1 / math.sqrt(math.sqrt(key_states.shape[-1]))\n\n attention_scores = torch.matmul(query_states * scale, key_states.transpose(-1, -2) * scale)\n attention_probs = torch.softmax(attention_scores, dim=-1)\n\n # compute attention output\n hidden_states = torch.matmul(attention_probs, value_states)\n\n hidden_states = hidden_states.permute(0, 2, 1, 3).contiguous()\n new_hidden_states_shape = hidden_states.size()[:-2] + (self.channels,)\n hidden_states = hidden_states.view(new_hidden_states_shape)\n\n # compute next hidden_states\n hidden_states = self.proj_attn(hidden_states)\n hidden_states = hidden_states.transpose(1, 2)\n hidden_states = self.dropout(hidden_states)\n\n output = hidden_states + residual\n\n return output\n\n\nclass ResConvBlock(nn.Module):\n def __init__(self, in_channels: int, mid_channels: int, out_channels: int, is_last: bool = False):\n super().__init__()\n self.is_last = is_last\n self.has_conv_skip = in_channels != out_channels\n\n if self.has_conv_skip:\n self.conv_skip = nn.Conv1d(in_channels, out_channels, 1, bias=False)\n\n self.conv_1 = nn.Conv1d(in_channels, mid_channels, 5, padding=2)\n self.group_norm_1 = nn.GroupNorm(1, mid_channels)\n self.gelu_1 = nn.GELU()\n self.conv_2 = nn.Conv1d(mid_channels, out_channels, 5, padding=2)\n\n if not self.is_last:\n self.group_norm_2 = nn.GroupNorm(1, out_channels)\n self.gelu_2 = nn.GELU()\n\n def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n residual = self.conv_skip(hidden_states) if self.has_conv_skip else hidden_states\n\n hidden_states = self.conv_1(hidden_states)\n hidden_states = self.group_norm_1(hidden_states)\n hidden_states = self.gelu_1(hidden_states)\n hidden_states = self.conv_2(hidden_states)\n\n if not self.is_last:\n hidden_states = self.group_norm_2(hidden_states)\n hidden_states = self.gelu_2(hidden_states)\n\n output = hidden_states + residual\n return output\n\n\nclass UNetMidBlock1D(nn.Module):\n def __init__(self, mid_channels: int, in_channels: int, out_channels: Optional[int] = None):\n super().__init__()\n\n out_channels = in_channels if out_channels is None else out_channels\n\n # there is always at least one resnet\n self.down = Downsample1d(\"cubic\")\n resnets = [\n ResConvBlock(in_channels, mid_channels, mid_channels),\n ResConvBlock(mid_channels, mid_channels, mid_channels),\n ResConvBlock(mid_channels, mid_channels, mid_channels),\n ResConvBlock(mid_channels, mid_channels, mid_channels),\n ResConvBlock(mid_channels, mid_channels, mid_channels),\n ResConvBlock(mid_channels, mid_channels, out_channels),\n ]\n attentions = [\n SelfAttention1d(mid_channels, mid_channels // 32),\n SelfAttention1d(mid_channels, mid_channels // 32),\n SelfAttention1d(mid_channels, mid_channels // 32),\n SelfAttention1d(mid_channels, mid_channels // 32),\n SelfAttention1d(mid_channels, mid_channels // 32),\n SelfAttention1d(out_channels, out_channels // 32),\n ]\n self.up = Upsample1d(kernel=\"cubic\")\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n def forward(self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:\n hidden_states = self.down(hidden_states)\n for attn, resnet in zip(self.attentions, self.resnets):\n hidden_states = resnet(hidden_states)\n hidden_states = attn(hidden_states)\n\n hidden_states = self.up(hidden_states)\n\n return hidden_states\n\n\nclass AttnDownBlock1D(nn.Module):\n def __init__(self, out_channels: int, in_channels: int, mid_channels: Optional[int] = None):\n super().__init__()\n mid_channels = out_channels if mid_channels is None else mid_channels\n\n self.down = Downsample1d(\"cubic\")\n resnets = [\n ResConvBlock(in_channels, mid_channels, mid_channels),\n ResConvBlock(mid_channels, mid_channels, mid_channels),\n ResConvBlock(mid_channels, mid_channels, out_channels),\n ]\n attentions = [\n SelfAttention1d(mid_channels, mid_channels // 32),\n SelfAttention1d(mid_channels, mid_channels // 32),\n SelfAttention1d(out_channels, out_channels // 32),\n ]\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n def forward(self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:\n hidden_states = self.down(hidden_states)\n\n for resnet, attn in zip(self.resnets, self.attentions):\n hidden_states = resnet(hidden_states)\n hidden_states = attn(hidden_states)\n\n return hidden_states, (hidden_states,)\n\n\nclass DownBlock1D(nn.Module):\n def __init__(self, out_channels: int, in_channels: int, mid_channels: Optional[int] = None):\n super().__init__()\n mid_channels = out_channels if mid_channels is None else mid_channels\n\n self.down = Downsample1d(\"cubic\")\n resnets = [\n ResConvBlock(in_channels, mid_channels, mid_channels),\n ResConvBlock(mid_channels, mid_channels, mid_channels),\n ResConvBlock(mid_channels, mid_channels, out_channels),\n ]\n\n self.resnets = nn.ModuleList(resnets)\n\n def forward(self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:\n hidden_states = self.down(hidden_states)\n\n for resnet in self.resnets:\n hidden_states = resnet(hidden_states)\n\n return hidden_states, (hidden_states,)\n\n\nclass DownBlock1DNoSkip(nn.Module):\n def __init__(self, out_channels: int, in_channels: int, mid_channels: Optional[int] = None):\n super().__init__()\n mid_channels = out_channels if mid_channels is None else mid_channels\n\n resnets = [\n ResConvBlock(in_channels, mid_channels, mid_channels),\n ResConvBlock(mid_channels, mid_channels, mid_channels),\n ResConvBlock(mid_channels, mid_channels, out_channels),\n ]\n\n self.resnets = nn.ModuleList(resnets)\n\n def forward(self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:\n hidden_states = torch.cat([hidden_states, temb], dim=1)\n for resnet in self.resnets:\n hidden_states = resnet(hidden_states)\n\n return hidden_states, (hidden_states,)\n\n\nclass AttnUpBlock1D(nn.Module):\n def __init__(self, in_channels: int, out_channels: int, mid_channels: Optional[int] = None):\n super().__init__()\n mid_channels = out_channels if mid_channels is None else mid_channels\n\n resnets = [\n ResConvBlock(2 * in_channels, mid_channels, mid_channels),\n ResConvBlock(mid_channels, mid_channels, mid_channels),\n ResConvBlock(mid_channels, mid_channels, out_channels),\n ]\n attentions = [\n SelfAttention1d(mid_channels, mid_channels // 32),\n SelfAttention1d(mid_channels, mid_channels // 32),\n SelfAttention1d(out_channels, out_channels // 32),\n ]\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n self.up = Upsample1d(kernel=\"cubic\")\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n res_hidden_states = res_hidden_states_tuple[-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n\n for resnet, attn in zip(self.resnets, self.attentions):\n hidden_states = resnet(hidden_states)\n hidden_states = attn(hidden_states)\n\n hidden_states = self.up(hidden_states)\n\n return hidden_states\n\n\nclass UpBlock1D(nn.Module):\n def __init__(self, in_channels: int, out_channels: int, mid_channels: Optional[int] = None):\n super().__init__()\n mid_channels = in_channels if mid_channels is None else mid_channels\n\n resnets = [\n ResConvBlock(2 * in_channels, mid_channels, mid_channels),\n ResConvBlock(mid_channels, mid_channels, mid_channels),\n ResConvBlock(mid_channels, mid_channels, out_channels),\n ]\n\n self.resnets = nn.ModuleList(resnets)\n self.up = Upsample1d(kernel=\"cubic\")\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n res_hidden_states = res_hidden_states_tuple[-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n\n for resnet in self.resnets:\n hidden_states = resnet(hidden_states)\n\n hidden_states = self.up(hidden_states)\n\n return hidden_states\n\n\nclass UpBlock1DNoSkip(nn.Module):\n def __init__(self, in_channels: int, out_channels: int, mid_channels: Optional[int] = None):\n super().__init__()\n mid_channels = in_channels if mid_channels is None else mid_channels\n\n resnets = [\n ResConvBlock(2 * in_channels, mid_channels, mid_channels),\n ResConvBlock(mid_channels, mid_channels, mid_channels),\n ResConvBlock(mid_channels, mid_channels, out_channels, is_last=True),\n ]\n\n self.resnets = nn.ModuleList(resnets)\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n res_hidden_states = res_hidden_states_tuple[-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n\n for resnet in self.resnets:\n hidden_states = resnet(hidden_states)\n\n return hidden_states\n\n\nDownBlockType = Union[DownResnetBlock1D, DownBlock1D, AttnDownBlock1D, DownBlock1DNoSkip]\nMidBlockType = Union[MidResTemporalBlock1D, ValueFunctionMidBlock1D, UNetMidBlock1D]\nOutBlockType = Union[OutConv1DBlock, OutValueFunctionBlock]\nUpBlockType = Union[UpResnetBlock1D, UpBlock1D, AttnUpBlock1D, UpBlock1DNoSkip]\n\n\ndef get_down_block(\n down_block_type: str,\n num_layers: int,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n add_downsample: bool,\n) -> DownBlockType:\n if down_block_type == \"DownResnetBlock1D\":\n return DownResnetBlock1D(\n in_channels=in_channels,\n num_layers=num_layers,\n out_channels=out_channels,\n temb_channels=temb_channels,\n add_downsample=add_downsample,\n )\n elif down_block_type == \"DownBlock1D\":\n return DownBlock1D(out_channels=out_channels, in_channels=in_channels)\n elif down_block_type == \"AttnDownBlock1D\":\n return AttnDownBlock1D(out_channels=out_channels, in_channels=in_channels)\n elif down_block_type == \"DownBlock1DNoSkip\":\n return DownBlock1DNoSkip(out_channels=out_channels, in_channels=in_channels)\n raise ValueError(f\"{down_block_type} does not exist.\")\n\n\ndef get_up_block(\n up_block_type: str, num_layers: int, in_channels: int, out_channels: int, temb_channels: int, add_upsample: bool\n) -> UpBlockType:\n if up_block_type == \"UpResnetBlock1D\":\n return UpResnetBlock1D(\n in_channels=in_channels,\n num_layers=num_layers,\n out_channels=out_channels,\n temb_channels=temb_channels,\n add_upsample=add_upsample,\n )\n elif up_block_type == \"UpBlock1D\":\n return UpBlock1D(in_channels=in_channels, out_channels=out_channels)\n elif up_block_type == \"AttnUpBlock1D\":\n return AttnUpBlock1D(in_channels=in_channels, out_channels=out_channels)\n elif up_block_type == \"UpBlock1DNoSkip\":\n return UpBlock1DNoSkip(in_channels=in_channels, out_channels=out_channels)\n raise ValueError(f\"{up_block_type} does not exist.\")\n\n\ndef get_mid_block(\n mid_block_type: str,\n num_layers: int,\n in_channels: int,\n mid_channels: int,\n out_channels: int,\n embed_dim: int,\n add_downsample: bool,\n) -> MidBlockType:\n if mid_block_type == \"MidResTemporalBlock1D\":\n return MidResTemporalBlock1D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n embed_dim=embed_dim,\n add_downsample=add_downsample,\n )\n elif mid_block_type == \"ValueFunctionMidBlock1D\":\n return ValueFunctionMidBlock1D(in_channels=in_channels, out_channels=out_channels, embed_dim=embed_dim)\n elif mid_block_type == \"UNetMidBlock1D\":\n return UNetMidBlock1D(in_channels=in_channels, mid_channels=mid_channels, out_channels=out_channels)\n raise ValueError(f\"{mid_block_type} does not exist.\")\n\n\ndef get_out_block(\n *, out_block_type: str, num_groups_out: int, embed_dim: int, out_channels: int, act_fn: str, fc_dim: int\n) -> Optional[OutBlockType]:\n if out_block_type == \"OutConv1DBlock\":\n return OutConv1DBlock(num_groups_out, out_channels, embed_dim, act_fn)\n elif out_block_type == \"ValueFunction\":\n return OutValueFunctionBlock(fc_dim, embed_dim, act_fn)\n return None\n" }, { "path": "diffusers/src/diffusers/models/unets/unet_2d.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...utils import BaseOutput\nfrom ..embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps\nfrom ..modeling_utils import ModelMixin\nfrom .unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block\n\n\n@dataclass\nclass UNet2DOutput(BaseOutput):\n \"\"\"\n The output of [`UNet2DModel`].\n\n Args:\n sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):\n The hidden states output from the last layer of the model.\n \"\"\"\n\n sample: torch.Tensor\n\n\nclass UNet2DModel(ModelMixin, ConfigMixin):\n r\"\"\"\n A 2D UNet model that takes a noisy sample and a timestep and returns a sample shaped output.\n\n This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented\n for all models (such as downloading or saving).\n\n Parameters:\n sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):\n Height and width of input/output sample. Dimensions must be a multiple of `2 ** (len(block_out_channels) -\n 1)`.\n in_channels (`int`, *optional*, defaults to 3): Number of channels in the input sample.\n out_channels (`int`, *optional*, defaults to 3): Number of channels in the output.\n center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.\n time_embedding_type (`str`, *optional*, defaults to `\"positional\"`): Type of time embedding to use.\n freq_shift (`int`, *optional*, defaults to 0): Frequency shift for Fourier time embedding.\n flip_sin_to_cos (`bool`, *optional*, defaults to `True`):\n Whether to flip sin to cos for Fourier time embedding.\n down_block_types (`Tuple[str]`, *optional*, defaults to `(\"DownBlock2D\", \"AttnDownBlock2D\", \"AttnDownBlock2D\", \"AttnDownBlock2D\")`):\n Tuple of downsample block types.\n mid_block_type (`str`, *optional*, defaults to `\"UNetMidBlock2D\"`):\n Block type for middle of UNet, it can be either `UNetMidBlock2D` or `UnCLIPUNetMidBlock2D`.\n up_block_types (`Tuple[str]`, *optional*, defaults to `(\"AttnUpBlock2D\", \"AttnUpBlock2D\", \"AttnUpBlock2D\", \"UpBlock2D\")`):\n Tuple of upsample block types.\n block_out_channels (`Tuple[int]`, *optional*, defaults to `(224, 448, 672, 896)`):\n Tuple of block output channels.\n layers_per_block (`int`, *optional*, defaults to `2`): The number of layers per block.\n mid_block_scale_factor (`float`, *optional*, defaults to `1`): The scale factor for the mid block.\n downsample_padding (`int`, *optional*, defaults to `1`): The padding for the downsample convolution.\n downsample_type (`str`, *optional*, defaults to `conv`):\n The downsample type for downsampling layers. Choose between \"conv\" and \"resnet\"\n upsample_type (`str`, *optional*, defaults to `conv`):\n The upsample type for upsampling layers. Choose between \"conv\" and \"resnet\"\n dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.\n act_fn (`str`, *optional*, defaults to `\"silu\"`): The activation function to use.\n attention_head_dim (`int`, *optional*, defaults to `8`): The attention head dimension.\n norm_num_groups (`int`, *optional*, defaults to `32`): The number of groups for normalization.\n attn_norm_num_groups (`int`, *optional*, defaults to `None`):\n If set to an integer, a group norm layer will be created in the mid block's [`Attention`] layer with the\n given number of groups. If left as `None`, the group norm layer will only be created if\n `resnet_time_scale_shift` is set to `default`, and if created will have `norm_num_groups` groups.\n norm_eps (`float`, *optional*, defaults to `1e-5`): The epsilon for normalization.\n resnet_time_scale_shift (`str`, *optional*, defaults to `\"default\"`): Time scale shift config\n for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.\n class_embed_type (`str`, *optional*, defaults to `None`):\n The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,\n `\"timestep\"`, or `\"identity\"`.\n num_class_embeds (`int`, *optional*, defaults to `None`):\n Input dimension of the learnable embedding matrix to be projected to `time_embed_dim` when performing class\n conditioning with `class_embed_type` equal to `None`.\n \"\"\"\n\n @register_to_config\n def __init__(\n self,\n sample_size: Optional[Union[int, Tuple[int, int]]] = None,\n in_channels: int = 3,\n out_channels: int = 3,\n center_input_sample: bool = False,\n time_embedding_type: str = \"positional\",\n freq_shift: int = 0,\n flip_sin_to_cos: bool = True,\n down_block_types: Tuple[str, ...] = (\"DownBlock2D\", \"AttnDownBlock2D\", \"AttnDownBlock2D\", \"AttnDownBlock2D\"),\n up_block_types: Tuple[str, ...] = (\"AttnUpBlock2D\", \"AttnUpBlock2D\", \"AttnUpBlock2D\", \"UpBlock2D\"),\n block_out_channels: Tuple[int, ...] = (224, 448, 672, 896),\n layers_per_block: int = 2,\n mid_block_scale_factor: float = 1,\n downsample_padding: int = 1,\n downsample_type: str = \"conv\",\n upsample_type: str = \"conv\",\n dropout: float = 0.0,\n act_fn: str = \"silu\",\n attention_head_dim: Optional[int] = 8,\n norm_num_groups: int = 32,\n attn_norm_num_groups: Optional[int] = None,\n norm_eps: float = 1e-5,\n resnet_time_scale_shift: str = \"default\",\n add_attention: bool = True,\n class_embed_type: Optional[str] = None,\n num_class_embeds: Optional[int] = None,\n num_train_timesteps: Optional[int] = None,\n ):\n super().__init__()\n\n self.sample_size = sample_size\n time_embed_dim = block_out_channels[0] * 4\n\n # Check inputs\n if len(down_block_types) != len(up_block_types):\n raise ValueError(\n f\"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.\"\n )\n\n if len(block_out_channels) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.\"\n )\n\n # input\n self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))\n\n # time\n if time_embedding_type == \"fourier\":\n self.time_proj = GaussianFourierProjection(embedding_size=block_out_channels[0], scale=16)\n timestep_input_dim = 2 * block_out_channels[0]\n elif time_embedding_type == \"positional\":\n self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)\n timestep_input_dim = block_out_channels[0]\n elif time_embedding_type == \"learned\":\n self.time_proj = nn.Embedding(num_train_timesteps, block_out_channels[0])\n timestep_input_dim = block_out_channels[0]\n\n self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\n\n # class embedding\n if class_embed_type is None and num_class_embeds is not None:\n self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)\n elif class_embed_type == \"timestep\":\n self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\n elif class_embed_type == \"identity\":\n self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)\n else:\n self.class_embedding = None\n\n self.down_blocks = nn.ModuleList([])\n self.mid_block = None\n self.up_blocks = nn.ModuleList([])\n\n # down\n output_channel = block_out_channels[0]\n for i, down_block_type in enumerate(down_block_types):\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n down_block = get_down_block(\n down_block_type,\n num_layers=layers_per_block,\n in_channels=input_channel,\n out_channels=output_channel,\n temb_channels=time_embed_dim,\n add_downsample=not is_final_block,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel,\n downsample_padding=downsample_padding,\n resnet_time_scale_shift=resnet_time_scale_shift,\n downsample_type=downsample_type,\n dropout=dropout,\n )\n self.down_blocks.append(down_block)\n\n # mid\n self.mid_block = UNetMidBlock2D(\n in_channels=block_out_channels[-1],\n temb_channels=time_embed_dim,\n dropout=dropout,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n output_scale_factor=mid_block_scale_factor,\n resnet_time_scale_shift=resnet_time_scale_shift,\n attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1],\n resnet_groups=norm_num_groups,\n attn_groups=attn_norm_num_groups,\n add_attention=add_attention,\n )\n\n # up\n reversed_block_out_channels = list(reversed(block_out_channels))\n output_channel = reversed_block_out_channels[0]\n for i, up_block_type in enumerate(up_block_types):\n prev_output_channel = output_channel\n output_channel = reversed_block_out_channels[i]\n input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]\n\n is_final_block = i == len(block_out_channels) - 1\n\n up_block = get_up_block(\n up_block_type,\n num_layers=layers_per_block + 1,\n in_channels=input_channel,\n out_channels=output_channel,\n prev_output_channel=prev_output_channel,\n temb_channels=time_embed_dim,\n add_upsample=not is_final_block,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel,\n resnet_time_scale_shift=resnet_time_scale_shift,\n upsample_type=upsample_type,\n dropout=dropout,\n )\n self.up_blocks.append(up_block)\n prev_output_channel = output_channel\n\n # out\n num_groups_out = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4, 32)\n self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=num_groups_out, eps=norm_eps)\n self.conv_act = nn.SiLU()\n self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=3, padding=1)\n\n def forward(\n self,\n sample: torch.Tensor,\n timestep: Union[torch.Tensor, float, int],\n class_labels: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ) -> Union[UNet2DOutput, Tuple]:\n r\"\"\"\n The [`UNet2DModel`] forward method.\n\n Args:\n sample (`torch.Tensor`):\n The noisy input tensor with the following shape `(batch, channel, height, width)`.\n timestep (`torch.Tensor` or `float` or `int`): The number of timesteps to denoise an input.\n class_labels (`torch.Tensor`, *optional*, defaults to `None`):\n Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~models.unets.unet_2d.UNet2DOutput`] instead of a plain tuple.\n\n Returns:\n [`~models.unets.unet_2d.UNet2DOutput`] or `tuple`:\n If `return_dict` is True, an [`~models.unets.unet_2d.UNet2DOutput`] is returned, otherwise a `tuple` is\n returned where the first element is the sample tensor.\n \"\"\"\n # 0. center input if necessary\n if self.config.center_input_sample:\n sample = 2 * sample - 1.0\n\n # 1. time\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device)\n elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(sample.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timesteps = timesteps * torch.ones(sample.shape[0], dtype=timesteps.dtype, device=timesteps.device)\n\n t_emb = self.time_proj(timesteps)\n\n # timesteps does not contain any weights and will always return f32 tensors\n # but time_embedding might actually be running in fp16. so we need to cast here.\n # there might be better ways to encapsulate this.\n t_emb = t_emb.to(dtype=self.dtype)\n emb = self.time_embedding(t_emb)\n\n if self.class_embedding is not None:\n if class_labels is None:\n raise ValueError(\"class_labels should be provided when doing class conditioning\")\n\n if self.config.class_embed_type == \"timestep\":\n class_labels = self.time_proj(class_labels)\n\n class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)\n emb = emb + class_emb\n elif self.class_embedding is None and class_labels is not None:\n raise ValueError(\"class_embedding needs to be initialized in order to use class conditioning\")\n\n # 2. pre-process\n skip_sample = sample\n sample = self.conv_in(sample)\n\n # 3. down\n down_block_res_samples = (sample,)\n for downsample_block in self.down_blocks:\n if hasattr(downsample_block, \"skip_conv\"):\n sample, res_samples, skip_sample = downsample_block(\n hidden_states=sample, temb=emb, skip_sample=skip_sample\n )\n else:\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb)\n\n down_block_res_samples += res_samples\n\n # 4. mid\n sample = self.mid_block(sample, emb)\n\n # 5. up\n skip_sample = None\n for upsample_block in self.up_blocks:\n res_samples = down_block_res_samples[-len(upsample_block.resnets) :]\n down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]\n\n if hasattr(upsample_block, \"skip_conv\"):\n sample, skip_sample = upsample_block(sample, res_samples, emb, skip_sample)\n else:\n sample = upsample_block(sample, res_samples, emb)\n\n # 6. post-process\n sample = self.conv_norm_out(sample)\n sample = self.conv_act(sample)\n sample = self.conv_out(sample)\n\n if skip_sample is not None:\n sample += skip_sample\n\n if self.config.time_embedding_type == \"fourier\":\n timesteps = timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]))))\n sample = sample / timesteps\n\n if not return_dict:\n return (sample,)\n\n return UNet2DOutput(sample=sample)\n" }, { "path": "diffusers/src/diffusers/models/unets/unet_2d_blocks.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Any, Dict, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom ...utils import deprecate, is_torch_version, logging\nfrom ...utils.torch_utils import apply_freeu\nfrom ..activations import get_activation\nfrom ..attention_processor import Attention, AttnAddedKVProcessor, AttnAddedKVProcessor2_0\nfrom ..normalization import AdaGroupNorm\nfrom ..resnet import (\n Downsample2D,\n FirDownsample2D,\n FirUpsample2D,\n KDownsample2D,\n KUpsample2D,\n ResnetBlock2D,\n ResnetBlockCondNorm2D,\n Upsample2D,\n)\nfrom ..transformers.dual_transformer_2d import DualTransformer2DModel\nfrom ..transformers.transformer_2d import Transformer2DModel\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef get_down_block(\n down_block_type: str,\n num_layers: int,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n add_downsample: bool,\n resnet_eps: float,\n resnet_act_fn: str,\n transformer_layers_per_block: int = 1,\n num_attention_heads: Optional[int] = None,\n resnet_groups: Optional[int] = None,\n cross_attention_dim: Optional[int] = None,\n downsample_padding: Optional[int] = None,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = False,\n only_cross_attention: bool = False,\n upcast_attention: bool = False,\n resnet_time_scale_shift: str = \"default\",\n attention_type: str = \"default\",\n resnet_skip_time_act: bool = False,\n resnet_out_scale_factor: float = 1.0,\n cross_attention_norm: Optional[str] = None,\n attention_head_dim: Optional[int] = None,\n downsample_type: Optional[str] = None,\n dropout: float = 0.0,\n):\n # If attn head dim is not defined, we default it to the number of heads\n if attention_head_dim is None:\n logger.warning(\n f\"It is recommended to provide `attention_head_dim` when calling `get_down_block`. Defaulting `attention_head_dim` to {num_attention_heads}.\"\n )\n attention_head_dim = num_attention_heads\n\n down_block_type = down_block_type[7:] if down_block_type.startswith(\"UNetRes\") else down_block_type\n if down_block_type == \"DownBlock2D\":\n return DownBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n dropout=dropout,\n add_downsample=add_downsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n downsample_padding=downsample_padding,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n elif down_block_type == \"ResnetDownsampleBlock2D\":\n return ResnetDownsampleBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n dropout=dropout,\n add_downsample=add_downsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n resnet_time_scale_shift=resnet_time_scale_shift,\n skip_time_act=resnet_skip_time_act,\n output_scale_factor=resnet_out_scale_factor,\n )\n elif down_block_type == \"AttnDownBlock2D\":\n if add_downsample is False:\n downsample_type = None\n else:\n downsample_type = downsample_type or \"conv\" # default to 'conv'\n return AttnDownBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n dropout=dropout,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n downsample_padding=downsample_padding,\n attention_head_dim=attention_head_dim,\n resnet_time_scale_shift=resnet_time_scale_shift,\n downsample_type=downsample_type,\n )\n elif down_block_type == \"CrossAttnDownBlock2D\":\n if cross_attention_dim is None:\n raise ValueError(\"cross_attention_dim must be specified for CrossAttnDownBlock2D\")\n return CrossAttnDownBlock2D(\n num_layers=num_layers,\n transformer_layers_per_block=transformer_layers_per_block,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n dropout=dropout,\n add_downsample=add_downsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n downsample_padding=downsample_padding,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=num_attention_heads,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n attention_type=attention_type,\n )\n elif down_block_type == \"SimpleCrossAttnDownBlock2D\":\n if cross_attention_dim is None:\n raise ValueError(\"cross_attention_dim must be specified for SimpleCrossAttnDownBlock2D\")\n return SimpleCrossAttnDownBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n dropout=dropout,\n add_downsample=add_downsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n cross_attention_dim=cross_attention_dim,\n attention_head_dim=attention_head_dim,\n resnet_time_scale_shift=resnet_time_scale_shift,\n skip_time_act=resnet_skip_time_act,\n output_scale_factor=resnet_out_scale_factor,\n only_cross_attention=only_cross_attention,\n cross_attention_norm=cross_attention_norm,\n )\n elif down_block_type == \"SkipDownBlock2D\":\n return SkipDownBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n dropout=dropout,\n add_downsample=add_downsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n downsample_padding=downsample_padding,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n elif down_block_type == \"AttnSkipDownBlock2D\":\n return AttnSkipDownBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n dropout=dropout,\n add_downsample=add_downsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n attention_head_dim=attention_head_dim,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n elif down_block_type == \"DownEncoderBlock2D\":\n return DownEncoderBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n dropout=dropout,\n add_downsample=add_downsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n downsample_padding=downsample_padding,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n elif down_block_type == \"AttnDownEncoderBlock2D\":\n return AttnDownEncoderBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n dropout=dropout,\n add_downsample=add_downsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n downsample_padding=downsample_padding,\n attention_head_dim=attention_head_dim,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n elif down_block_type == \"KDownBlock2D\":\n return KDownBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n dropout=dropout,\n add_downsample=add_downsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n )\n elif down_block_type == \"KCrossAttnDownBlock2D\":\n return KCrossAttnDownBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n dropout=dropout,\n add_downsample=add_downsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n cross_attention_dim=cross_attention_dim,\n attention_head_dim=attention_head_dim,\n add_self_attention=True if not add_downsample else False,\n )\n raise ValueError(f\"{down_block_type} does not exist.\")\n\n\ndef get_mid_block(\n mid_block_type: str,\n temb_channels: int,\n in_channels: int,\n resnet_eps: float,\n resnet_act_fn: str,\n resnet_groups: int,\n output_scale_factor: float = 1.0,\n transformer_layers_per_block: int = 1,\n num_attention_heads: Optional[int] = None,\n cross_attention_dim: Optional[int] = None,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = False,\n mid_block_only_cross_attention: bool = False,\n upcast_attention: bool = False,\n resnet_time_scale_shift: str = \"default\",\n attention_type: str = \"default\",\n resnet_skip_time_act: bool = False,\n cross_attention_norm: Optional[str] = None,\n attention_head_dim: Optional[int] = 1,\n dropout: float = 0.0,\n):\n if mid_block_type == \"UNetMidBlock2DCrossAttn\":\n return UNetMidBlock2DCrossAttn(\n transformer_layers_per_block=transformer_layers_per_block,\n in_channels=in_channels,\n temb_channels=temb_channels,\n dropout=dropout,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n resnet_time_scale_shift=resnet_time_scale_shift,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=num_attention_heads,\n resnet_groups=resnet_groups,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n upcast_attention=upcast_attention,\n attention_type=attention_type,\n )\n elif mid_block_type == \"UNetMidBlock2DSimpleCrossAttn\":\n return UNetMidBlock2DSimpleCrossAttn(\n in_channels=in_channels,\n temb_channels=temb_channels,\n dropout=dropout,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n cross_attention_dim=cross_attention_dim,\n attention_head_dim=attention_head_dim,\n resnet_groups=resnet_groups,\n resnet_time_scale_shift=resnet_time_scale_shift,\n skip_time_act=resnet_skip_time_act,\n only_cross_attention=mid_block_only_cross_attention,\n cross_attention_norm=cross_attention_norm,\n )\n elif mid_block_type == \"UNetMidBlock2D\":\n return UNetMidBlock2D(\n in_channels=in_channels,\n temb_channels=temb_channels,\n dropout=dropout,\n num_layers=0,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n resnet_groups=resnet_groups,\n resnet_time_scale_shift=resnet_time_scale_shift,\n add_attention=False,\n )\n elif mid_block_type is None:\n return None\n else:\n raise ValueError(f\"unknown mid_block_type : {mid_block_type}\")\n\n\ndef get_up_block(\n up_block_type: str,\n num_layers: int,\n in_channels: int,\n out_channels: int,\n prev_output_channel: int,\n temb_channels: int,\n add_upsample: bool,\n resnet_eps: float,\n resnet_act_fn: str,\n resolution_idx: Optional[int] = None,\n transformer_layers_per_block: int = 1,\n num_attention_heads: Optional[int] = None,\n resnet_groups: Optional[int] = None,\n cross_attention_dim: Optional[int] = None,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = False,\n only_cross_attention: bool = False,\n upcast_attention: bool = False,\n resnet_time_scale_shift: str = \"default\",\n attention_type: str = \"default\",\n resnet_skip_time_act: bool = False,\n resnet_out_scale_factor: float = 1.0,\n cross_attention_norm: Optional[str] = None,\n attention_head_dim: Optional[int] = None,\n upsample_type: Optional[str] = None,\n dropout: float = 0.0,\n) -> nn.Module:\n # If attn head dim is not defined, we default it to the number of heads\n if attention_head_dim is None:\n logger.warning(\n f\"It is recommended to provide `attention_head_dim` when calling `get_up_block`. Defaulting `attention_head_dim` to {num_attention_heads}.\"\n )\n attention_head_dim = num_attention_heads\n\n up_block_type = up_block_type[7:] if up_block_type.startswith(\"UNetRes\") else up_block_type\n if up_block_type == \"UpBlock2D\":\n return UpBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n prev_output_channel=prev_output_channel,\n temb_channels=temb_channels,\n resolution_idx=resolution_idx,\n dropout=dropout,\n add_upsample=add_upsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n elif up_block_type == \"ResnetUpsampleBlock2D\":\n return ResnetUpsampleBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n prev_output_channel=prev_output_channel,\n temb_channels=temb_channels,\n resolution_idx=resolution_idx,\n dropout=dropout,\n add_upsample=add_upsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n resnet_time_scale_shift=resnet_time_scale_shift,\n skip_time_act=resnet_skip_time_act,\n output_scale_factor=resnet_out_scale_factor,\n )\n elif up_block_type == \"CrossAttnUpBlock2D\":\n if cross_attention_dim is None:\n raise ValueError(\"cross_attention_dim must be specified for CrossAttnUpBlock2D\")\n return CrossAttnUpBlock2D(\n num_layers=num_layers,\n transformer_layers_per_block=transformer_layers_per_block,\n in_channels=in_channels,\n out_channels=out_channels,\n prev_output_channel=prev_output_channel,\n temb_channels=temb_channels,\n resolution_idx=resolution_idx,\n dropout=dropout,\n add_upsample=add_upsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=num_attention_heads,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n attention_type=attention_type,\n )\n elif up_block_type == \"SimpleCrossAttnUpBlock2D\":\n if cross_attention_dim is None:\n raise ValueError(\"cross_attention_dim must be specified for SimpleCrossAttnUpBlock2D\")\n return SimpleCrossAttnUpBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n prev_output_channel=prev_output_channel,\n temb_channels=temb_channels,\n resolution_idx=resolution_idx,\n dropout=dropout,\n add_upsample=add_upsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n cross_attention_dim=cross_attention_dim,\n attention_head_dim=attention_head_dim,\n resnet_time_scale_shift=resnet_time_scale_shift,\n skip_time_act=resnet_skip_time_act,\n output_scale_factor=resnet_out_scale_factor,\n only_cross_attention=only_cross_attention,\n cross_attention_norm=cross_attention_norm,\n )\n elif up_block_type == \"AttnUpBlock2D\":\n if add_upsample is False:\n upsample_type = None\n else:\n upsample_type = upsample_type or \"conv\" # default to 'conv'\n\n return AttnUpBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n prev_output_channel=prev_output_channel,\n temb_channels=temb_channels,\n resolution_idx=resolution_idx,\n dropout=dropout,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n attention_head_dim=attention_head_dim,\n resnet_time_scale_shift=resnet_time_scale_shift,\n upsample_type=upsample_type,\n )\n elif up_block_type == \"SkipUpBlock2D\":\n return SkipUpBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n prev_output_channel=prev_output_channel,\n temb_channels=temb_channels,\n resolution_idx=resolution_idx,\n dropout=dropout,\n add_upsample=add_upsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n elif up_block_type == \"AttnSkipUpBlock2D\":\n return AttnSkipUpBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n prev_output_channel=prev_output_channel,\n temb_channels=temb_channels,\n resolution_idx=resolution_idx,\n dropout=dropout,\n add_upsample=add_upsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n attention_head_dim=attention_head_dim,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n elif up_block_type == \"UpDecoderBlock2D\":\n return UpDecoderBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n resolution_idx=resolution_idx,\n dropout=dropout,\n add_upsample=add_upsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n resnet_time_scale_shift=resnet_time_scale_shift,\n temb_channels=temb_channels,\n )\n elif up_block_type == \"AttnUpDecoderBlock2D\":\n return AttnUpDecoderBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n resolution_idx=resolution_idx,\n dropout=dropout,\n add_upsample=add_upsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n attention_head_dim=attention_head_dim,\n resnet_time_scale_shift=resnet_time_scale_shift,\n temb_channels=temb_channels,\n )\n elif up_block_type == \"KUpBlock2D\":\n return KUpBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n resolution_idx=resolution_idx,\n dropout=dropout,\n add_upsample=add_upsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n )\n elif up_block_type == \"KCrossAttnUpBlock2D\":\n return KCrossAttnUpBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n resolution_idx=resolution_idx,\n dropout=dropout,\n add_upsample=add_upsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n cross_attention_dim=cross_attention_dim,\n attention_head_dim=attention_head_dim,\n )\n\n raise ValueError(f\"{up_block_type} does not exist.\")\n\n\nclass AutoencoderTinyBlock(nn.Module):\n \"\"\"\n Tiny Autoencoder block used in [`AutoencoderTiny`]. It is a mini residual module consisting of plain conv + ReLU\n blocks.\n\n Args:\n in_channels (`int`): The number of input channels.\n out_channels (`int`): The number of output channels.\n act_fn (`str`):\n ` The activation function to use. Supported values are `\"swish\"`, `\"mish\"`, `\"gelu\"`, and `\"relu\"`.\n\n Returns:\n `torch.Tensor`: A tensor with the same shape as the input tensor, but with the number of channels equal to\n `out_channels`.\n \"\"\"\n\n def __init__(self, in_channels: int, out_channels: int, act_fn: str):\n super().__init__()\n act_fn = get_activation(act_fn)\n self.conv = nn.Sequential(\n nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),\n act_fn,\n nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),\n act_fn,\n nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),\n )\n self.skip = (\n nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)\n if in_channels != out_channels\n else nn.Identity()\n )\n self.fuse = nn.ReLU()\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return self.fuse(self.conv(x) + self.skip(x))\n\n\nclass UNetMidBlock2D(nn.Module):\n \"\"\"\n A 2D UNet mid-block [`UNetMidBlock2D`] with multiple residual blocks and optional attention blocks.\n\n Args:\n in_channels (`int`): The number of input channels.\n temb_channels (`int`): The number of temporal embedding channels.\n dropout (`float`, *optional*, defaults to 0.0): The dropout rate.\n num_layers (`int`, *optional*, defaults to 1): The number of residual blocks.\n resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks.\n resnet_time_scale_shift (`str`, *optional*, defaults to `default`):\n The type of normalization to apply to the time embeddings. This can help to improve the performance of the\n model on tasks with long-range temporal dependencies.\n resnet_act_fn (`str`, *optional*, defaults to `swish`): The activation function for the resnet blocks.\n resnet_groups (`int`, *optional*, defaults to 32):\n The number of groups to use in the group normalization layers of the resnet blocks.\n attn_groups (`Optional[int]`, *optional*, defaults to None): The number of groups for the attention blocks.\n resnet_pre_norm (`bool`, *optional*, defaults to `True`):\n Whether to use pre-normalization for the resnet blocks.\n add_attention (`bool`, *optional*, defaults to `True`): Whether to add attention blocks.\n attention_head_dim (`int`, *optional*, defaults to 1):\n Dimension of a single attention head. The number of attention heads is determined based on this value and\n the number of input channels.\n output_scale_factor (`float`, *optional*, defaults to 1.0): The output scale factor.\n\n Returns:\n `torch.Tensor`: The output of the last residual block, which is a tensor of shape `(batch_size, in_channels,\n height, width)`.\n\n \"\"\"\n\n def __init__(\n self,\n in_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\", # default, spatial\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n attn_groups: Optional[int] = None,\n resnet_pre_norm: bool = True,\n add_attention: bool = True,\n attention_head_dim: int = 1,\n output_scale_factor: float = 1.0,\n ):\n super().__init__()\n resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)\n self.add_attention = add_attention\n\n if attn_groups is None:\n attn_groups = resnet_groups if resnet_time_scale_shift == \"default\" else None\n\n # there is always at least one resnet\n if resnet_time_scale_shift == \"spatial\":\n resnets = [\n ResnetBlockCondNorm2D(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=\"spatial\",\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n )\n ]\n else:\n resnets = [\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n ]\n attentions = []\n\n if attention_head_dim is None:\n logger.warning(\n f\"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {in_channels}.\"\n )\n attention_head_dim = in_channels\n\n for _ in range(num_layers):\n if self.add_attention:\n attentions.append(\n Attention(\n in_channels,\n heads=in_channels // attention_head_dim,\n dim_head=attention_head_dim,\n rescale_output_factor=output_scale_factor,\n eps=resnet_eps,\n norm_num_groups=attn_groups,\n spatial_norm_dim=temb_channels if resnet_time_scale_shift == \"spatial\" else None,\n residual_connection=True,\n bias=True,\n upcast_softmax=True,\n _from_deprecated_attn_block=True,\n )\n )\n else:\n attentions.append(None)\n\n if resnet_time_scale_shift == \"spatial\":\n resnets.append(\n ResnetBlockCondNorm2D(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=\"spatial\",\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n )\n )\n else:\n resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n def forward(self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:\n hidden_states = self.resnets[0](hidden_states, temb)\n for attn, resnet in zip(self.attentions, self.resnets[1:]):\n if attn is not None:\n hidden_states = attn(hidden_states, temb=temb)\n hidden_states = resnet(hidden_states, temb)\n\n return hidden_states\n\n\nclass UNetMidBlock2DCrossAttn(nn.Module):\n def __init__(\n self,\n in_channels: int,\n temb_channels: int,\n out_channels: Optional[int] = None,\n dropout: float = 0.0,\n num_layers: int = 1,\n transformer_layers_per_block: Union[int, Tuple[int]] = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_groups_out: Optional[int] = None,\n resnet_pre_norm: bool = True,\n num_attention_heads: int = 1,\n output_scale_factor: float = 1.0,\n cross_attention_dim: int = 1280,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = False,\n upcast_attention: bool = False,\n attention_type: str = \"default\",\n ):\n super().__init__()\n\n out_channels = out_channels or in_channels\n self.in_channels = in_channels\n self.out_channels = out_channels\n\n self.has_cross_attention = True\n self.num_attention_heads = num_attention_heads\n resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)\n\n # support for variable transformer layers per block\n if isinstance(transformer_layers_per_block, int):\n transformer_layers_per_block = [transformer_layers_per_block] * num_layers\n\n resnet_groups_out = resnet_groups_out or resnet_groups\n\n # there is always at least one resnet\n resnets = [\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n groups_out=resnet_groups_out,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n ]\n attentions = []\n\n for i in range(num_layers):\n if not dual_cross_attention:\n attentions.append(\n Transformer2DModel(\n num_attention_heads,\n out_channels // num_attention_heads,\n in_channels=out_channels,\n num_layers=transformer_layers_per_block[i],\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups_out,\n use_linear_projection=use_linear_projection,\n upcast_attention=upcast_attention,\n attention_type=attention_type,\n )\n )\n else:\n attentions.append(\n DualTransformer2DModel(\n num_attention_heads,\n out_channels // num_attention_heads,\n in_channels=out_channels,\n num_layers=1,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n )\n )\n resnets.append(\n ResnetBlock2D(\n in_channels=out_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups_out,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n if cross_attention_kwargs is not None:\n if cross_attention_kwargs.get(\"scale\", None) is not None:\n logger.warning(\"Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.\")\n\n hidden_states = self.resnets[0](hidden_states, temb)\n for attn, resnet in zip(self.attentions, self.resnets[1:]):\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet),\n hidden_states,\n temb,\n **ckpt_kwargs,\n )\n else:\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n hidden_states = resnet(hidden_states, temb)\n\n return hidden_states\n\n\nclass UNetMidBlock2DSimpleCrossAttn(nn.Module):\n def __init__(\n self,\n in_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n attention_head_dim: int = 1,\n output_scale_factor: float = 1.0,\n cross_attention_dim: int = 1280,\n skip_time_act: bool = False,\n only_cross_attention: bool = False,\n cross_attention_norm: Optional[str] = None,\n ):\n super().__init__()\n\n self.has_cross_attention = True\n\n self.attention_head_dim = attention_head_dim\n resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)\n\n self.num_heads = in_channels // self.attention_head_dim\n\n # there is always at least one resnet\n resnets = [\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n skip_time_act=skip_time_act,\n )\n ]\n attentions = []\n\n for _ in range(num_layers):\n processor = (\n AttnAddedKVProcessor2_0() if hasattr(F, \"scaled_dot_product_attention\") else AttnAddedKVProcessor()\n )\n\n attentions.append(\n Attention(\n query_dim=in_channels,\n cross_attention_dim=in_channels,\n heads=self.num_heads,\n dim_head=self.attention_head_dim,\n added_kv_proj_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n bias=True,\n upcast_softmax=True,\n only_cross_attention=only_cross_attention,\n cross_attention_norm=cross_attention_norm,\n processor=processor,\n )\n )\n resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n skip_time_act=skip_time_act,\n )\n )\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}\n if cross_attention_kwargs.get(\"scale\", None) is not None:\n logger.warning(\"Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.\")\n\n if attention_mask is None:\n # if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask.\n mask = None if encoder_hidden_states is None else encoder_attention_mask\n else:\n # when attention_mask is defined: we don't even check for encoder_attention_mask.\n # this is to maintain compatibility with UnCLIP, which uses 'attention_mask' param for cross-attn masks.\n # TODO: UnCLIP should express cross-attn mask via encoder_attention_mask param instead of via attention_mask.\n # then we can simplify this whole if/else block to:\n # mask = attention_mask if encoder_hidden_states is None else encoder_attention_mask\n mask = attention_mask\n\n hidden_states = self.resnets[0](hidden_states, temb)\n for attn, resnet in zip(self.attentions, self.resnets[1:]):\n # attn\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=mask,\n **cross_attention_kwargs,\n )\n\n # resnet\n hidden_states = resnet(hidden_states, temb)\n\n return hidden_states\n\n\nclass AttnDownBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n attention_head_dim: int = 1,\n output_scale_factor: float = 1.0,\n downsample_padding: int = 1,\n downsample_type: str = \"conv\",\n ):\n super().__init__()\n resnets = []\n attentions = []\n self.downsample_type = downsample_type\n\n if attention_head_dim is None:\n logger.warning(\n f\"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}.\"\n )\n attention_head_dim = out_channels\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n attentions.append(\n Attention(\n out_channels,\n heads=out_channels // attention_head_dim,\n dim_head=attention_head_dim,\n rescale_output_factor=output_scale_factor,\n eps=resnet_eps,\n norm_num_groups=resnet_groups,\n residual_connection=True,\n bias=True,\n upcast_softmax=True,\n _from_deprecated_attn_block=True,\n )\n )\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n if downsample_type == \"conv\":\n self.downsamplers = nn.ModuleList(\n [\n Downsample2D(\n out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name=\"op\"\n )\n ]\n )\n elif downsample_type == \"resnet\":\n self.downsamplers = nn.ModuleList(\n [\n ResnetBlock2D(\n in_channels=out_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n down=True,\n )\n ]\n )\n else:\n self.downsamplers = None\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n upsample_size: Optional[int] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, ...]]:\n cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}\n if cross_attention_kwargs.get(\"scale\", None) is not None:\n logger.warning(\"Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.\")\n\n output_states = ()\n\n for resnet, attn in zip(self.resnets, self.attentions):\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(hidden_states, **cross_attention_kwargs)\n output_states = output_states + (hidden_states,)\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n if self.downsample_type == \"resnet\":\n hidden_states = downsampler(hidden_states, temb=temb)\n else:\n hidden_states = downsampler(hidden_states)\n\n output_states += (hidden_states,)\n\n return hidden_states, output_states\n\n\nclass CrossAttnDownBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n transformer_layers_per_block: Union[int, Tuple[int]] = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n num_attention_heads: int = 1,\n cross_attention_dim: int = 1280,\n output_scale_factor: float = 1.0,\n downsample_padding: int = 1,\n add_downsample: bool = True,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = False,\n only_cross_attention: bool = False,\n upcast_attention: bool = False,\n attention_type: str = \"default\",\n ):\n super().__init__()\n resnets = []\n attentions = []\n\n self.has_cross_attention = True\n self.num_attention_heads = num_attention_heads\n if isinstance(transformer_layers_per_block, int):\n transformer_layers_per_block = [transformer_layers_per_block] * num_layers\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n if not dual_cross_attention:\n attentions.append(\n Transformer2DModel(\n num_attention_heads,\n out_channels // num_attention_heads,\n in_channels=out_channels,\n num_layers=transformer_layers_per_block[i],\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n attention_type=attention_type,\n )\n )\n else:\n attentions.append(\n DualTransformer2DModel(\n num_attention_heads,\n out_channels // num_attention_heads,\n in_channels=out_channels,\n num_layers=1,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n )\n )\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n if add_downsample:\n self.downsamplers = nn.ModuleList(\n [\n Downsample2D(\n out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name=\"op\"\n )\n ]\n )\n else:\n self.downsamplers = None\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n additional_residuals: Optional[torch.Tensor] = None,\n ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, ...]]:\n if cross_attention_kwargs is not None:\n if cross_attention_kwargs.get(\"scale\", None) is not None:\n logger.warning(\"Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.\")\n\n output_states = ()\n\n blocks = list(zip(self.resnets, self.attentions))\n\n for i, (resnet, attn) in enumerate(blocks):\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet),\n hidden_states,\n temb,\n **ckpt_kwargs,\n )\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n else:\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n\n # apply additional residuals to the output of the last pair of resnet and attention blocks\n if i == len(blocks) - 1 and additional_residuals is not None:\n hidden_states = hidden_states + additional_residuals\n\n output_states = output_states + (hidden_states,)\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n\nclass DownBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n output_scale_factor: float = 1.0,\n add_downsample: bool = True,\n downsample_padding: int = 1,\n ):\n super().__init__()\n resnets = []\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n\n self.resnets = nn.ModuleList(resnets)\n\n if add_downsample:\n self.downsamplers = nn.ModuleList(\n [\n Downsample2D(\n out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name=\"op\"\n )\n ]\n )\n else:\n self.downsamplers = None\n\n self.gradient_checkpointing = False\n\n def forward(\n self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None, *args, **kwargs\n ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, ...]]:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n output_states = ()\n\n for resnet in self.resnets:\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs)\n\n return custom_forward\n\n if is_torch_version(\">=\", \"1.11.0\"):\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet), hidden_states, temb, use_reentrant=False\n )\n else:\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet), hidden_states, temb\n )\n else:\n hidden_states = resnet(hidden_states, temb)\n\n output_states = output_states + (hidden_states,)\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n\nclass DownEncoderBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n output_scale_factor: float = 1.0,\n add_downsample: bool = True,\n downsample_padding: int = 1,\n ):\n super().__init__()\n resnets = []\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n if resnet_time_scale_shift == \"spatial\":\n resnets.append(\n ResnetBlockCondNorm2D(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=None,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=\"spatial\",\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n )\n )\n else:\n resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=None,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n\n self.resnets = nn.ModuleList(resnets)\n\n if add_downsample:\n self.downsamplers = nn.ModuleList(\n [\n Downsample2D(\n out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name=\"op\"\n )\n ]\n )\n else:\n self.downsamplers = None\n\n def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n for resnet in self.resnets:\n hidden_states = resnet(hidden_states, temb=None)\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n return hidden_states\n\n\nclass AttnDownEncoderBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n attention_head_dim: int = 1,\n output_scale_factor: float = 1.0,\n add_downsample: bool = True,\n downsample_padding: int = 1,\n ):\n super().__init__()\n resnets = []\n attentions = []\n\n if attention_head_dim is None:\n logger.warning(\n f\"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}.\"\n )\n attention_head_dim = out_channels\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n if resnet_time_scale_shift == \"spatial\":\n resnets.append(\n ResnetBlockCondNorm2D(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=None,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=\"spatial\",\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n )\n )\n else:\n resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=None,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n attentions.append(\n Attention(\n out_channels,\n heads=out_channels // attention_head_dim,\n dim_head=attention_head_dim,\n rescale_output_factor=output_scale_factor,\n eps=resnet_eps,\n norm_num_groups=resnet_groups,\n residual_connection=True,\n bias=True,\n upcast_softmax=True,\n _from_deprecated_attn_block=True,\n )\n )\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n if add_downsample:\n self.downsamplers = nn.ModuleList(\n [\n Downsample2D(\n out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name=\"op\"\n )\n ]\n )\n else:\n self.downsamplers = None\n\n def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n for resnet, attn in zip(self.resnets, self.attentions):\n hidden_states = resnet(hidden_states, temb=None)\n hidden_states = attn(hidden_states)\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n return hidden_states\n\n\nclass AttnSkipDownBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_pre_norm: bool = True,\n attention_head_dim: int = 1,\n output_scale_factor: float = np.sqrt(2.0),\n add_downsample: bool = True,\n ):\n super().__init__()\n self.attentions = nn.ModuleList([])\n self.resnets = nn.ModuleList([])\n\n if attention_head_dim is None:\n logger.warning(\n f\"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}.\"\n )\n attention_head_dim = out_channels\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n self.resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=min(in_channels // 4, 32),\n groups_out=min(out_channels // 4, 32),\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n self.attentions.append(\n Attention(\n out_channels,\n heads=out_channels // attention_head_dim,\n dim_head=attention_head_dim,\n rescale_output_factor=output_scale_factor,\n eps=resnet_eps,\n norm_num_groups=32,\n residual_connection=True,\n bias=True,\n upcast_softmax=True,\n _from_deprecated_attn_block=True,\n )\n )\n\n if add_downsample:\n self.resnet_down = ResnetBlock2D(\n in_channels=out_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=min(out_channels // 4, 32),\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n use_in_shortcut=True,\n down=True,\n kernel=\"fir\",\n )\n self.downsamplers = nn.ModuleList([FirDownsample2D(out_channels, out_channels=out_channels)])\n self.skip_conv = nn.Conv2d(3, out_channels, kernel_size=(1, 1), stride=(1, 1))\n else:\n self.resnet_down = None\n self.downsamplers = None\n self.skip_conv = None\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n skip_sample: Optional[torch.Tensor] = None,\n *args,\n **kwargs,\n ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, ...], torch.Tensor]:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n output_states = ()\n\n for resnet, attn in zip(self.resnets, self.attentions):\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(hidden_states)\n output_states += (hidden_states,)\n\n if self.downsamplers is not None:\n hidden_states = self.resnet_down(hidden_states, temb)\n for downsampler in self.downsamplers:\n skip_sample = downsampler(skip_sample)\n\n hidden_states = self.skip_conv(skip_sample) + hidden_states\n\n output_states += (hidden_states,)\n\n return hidden_states, output_states, skip_sample\n\n\nclass SkipDownBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_pre_norm: bool = True,\n output_scale_factor: float = np.sqrt(2.0),\n add_downsample: bool = True,\n downsample_padding: int = 1,\n ):\n super().__init__()\n self.resnets = nn.ModuleList([])\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n self.resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=min(in_channels // 4, 32),\n groups_out=min(out_channels // 4, 32),\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n\n if add_downsample:\n self.resnet_down = ResnetBlock2D(\n in_channels=out_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=min(out_channels // 4, 32),\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n use_in_shortcut=True,\n down=True,\n kernel=\"fir\",\n )\n self.downsamplers = nn.ModuleList([FirDownsample2D(out_channels, out_channels=out_channels)])\n self.skip_conv = nn.Conv2d(3, out_channels, kernel_size=(1, 1), stride=(1, 1))\n else:\n self.resnet_down = None\n self.downsamplers = None\n self.skip_conv = None\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n skip_sample: Optional[torch.Tensor] = None,\n *args,\n **kwargs,\n ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, ...], torch.Tensor]:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n output_states = ()\n\n for resnet in self.resnets:\n hidden_states = resnet(hidden_states, temb)\n output_states += (hidden_states,)\n\n if self.downsamplers is not None:\n hidden_states = self.resnet_down(hidden_states, temb)\n for downsampler in self.downsamplers:\n skip_sample = downsampler(skip_sample)\n\n hidden_states = self.skip_conv(skip_sample) + hidden_states\n\n output_states += (hidden_states,)\n\n return hidden_states, output_states, skip_sample\n\n\nclass ResnetDownsampleBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n output_scale_factor: float = 1.0,\n add_downsample: bool = True,\n skip_time_act: bool = False,\n ):\n super().__init__()\n resnets = []\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n skip_time_act=skip_time_act,\n )\n )\n\n self.resnets = nn.ModuleList(resnets)\n\n if add_downsample:\n self.downsamplers = nn.ModuleList(\n [\n ResnetBlock2D(\n in_channels=out_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n skip_time_act=skip_time_act,\n down=True,\n )\n ]\n )\n else:\n self.downsamplers = None\n\n self.gradient_checkpointing = False\n\n def forward(\n self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None, *args, **kwargs\n ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, ...]]:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n output_states = ()\n\n for resnet in self.resnets:\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs)\n\n return custom_forward\n\n if is_torch_version(\">=\", \"1.11.0\"):\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet), hidden_states, temb, use_reentrant=False\n )\n else:\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet), hidden_states, temb\n )\n else:\n hidden_states = resnet(hidden_states, temb)\n\n output_states = output_states + (hidden_states,)\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states, temb)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n\nclass SimpleCrossAttnDownBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n attention_head_dim: int = 1,\n cross_attention_dim: int = 1280,\n output_scale_factor: float = 1.0,\n add_downsample: bool = True,\n skip_time_act: bool = False,\n only_cross_attention: bool = False,\n cross_attention_norm: Optional[str] = None,\n ):\n super().__init__()\n\n self.has_cross_attention = True\n\n resnets = []\n attentions = []\n\n self.attention_head_dim = attention_head_dim\n self.num_heads = out_channels // self.attention_head_dim\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n skip_time_act=skip_time_act,\n )\n )\n\n processor = (\n AttnAddedKVProcessor2_0() if hasattr(F, \"scaled_dot_product_attention\") else AttnAddedKVProcessor()\n )\n\n attentions.append(\n Attention(\n query_dim=out_channels,\n cross_attention_dim=out_channels,\n heads=self.num_heads,\n dim_head=attention_head_dim,\n added_kv_proj_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n bias=True,\n upcast_softmax=True,\n only_cross_attention=only_cross_attention,\n cross_attention_norm=cross_attention_norm,\n processor=processor,\n )\n )\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n if add_downsample:\n self.downsamplers = nn.ModuleList(\n [\n ResnetBlock2D(\n in_channels=out_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n skip_time_act=skip_time_act,\n down=True,\n )\n ]\n )\n else:\n self.downsamplers = None\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, ...]]:\n cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}\n if cross_attention_kwargs.get(\"scale\", None) is not None:\n logger.warning(\"Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.\")\n\n output_states = ()\n\n if attention_mask is None:\n # if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask.\n mask = None if encoder_hidden_states is None else encoder_attention_mask\n else:\n # when attention_mask is defined: we don't even check for encoder_attention_mask.\n # this is to maintain compatibility with UnCLIP, which uses 'attention_mask' param for cross-attn masks.\n # TODO: UnCLIP should express cross-attn mask via encoder_attention_mask param instead of via attention_mask.\n # then we can simplify this whole if/else block to:\n # mask = attention_mask if encoder_hidden_states is None else encoder_attention_mask\n mask = attention_mask\n\n for resnet, attn in zip(self.resnets, self.attentions):\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=mask,\n **cross_attention_kwargs,\n )\n else:\n hidden_states = resnet(hidden_states, temb)\n\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=mask,\n **cross_attention_kwargs,\n )\n\n output_states = output_states + (hidden_states,)\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states, temb)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n\nclass KDownBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 4,\n resnet_eps: float = 1e-5,\n resnet_act_fn: str = \"gelu\",\n resnet_group_size: int = 32,\n add_downsample: bool = False,\n ):\n super().__init__()\n resnets = []\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n groups = in_channels // resnet_group_size\n groups_out = out_channels // resnet_group_size\n\n resnets.append(\n ResnetBlockCondNorm2D(\n in_channels=in_channels,\n out_channels=out_channels,\n dropout=dropout,\n temb_channels=temb_channels,\n groups=groups,\n groups_out=groups_out,\n eps=resnet_eps,\n non_linearity=resnet_act_fn,\n time_embedding_norm=\"ada_group\",\n conv_shortcut_bias=False,\n )\n )\n\n self.resnets = nn.ModuleList(resnets)\n\n if add_downsample:\n # YiYi's comments- might be able to use FirDownsample2D, look into details later\n self.downsamplers = nn.ModuleList([KDownsample2D()])\n else:\n self.downsamplers = None\n\n self.gradient_checkpointing = False\n\n def forward(\n self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None, *args, **kwargs\n ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, ...]]:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n output_states = ()\n\n for resnet in self.resnets:\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs)\n\n return custom_forward\n\n if is_torch_version(\">=\", \"1.11.0\"):\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet), hidden_states, temb, use_reentrant=False\n )\n else:\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet), hidden_states, temb\n )\n else:\n hidden_states = resnet(hidden_states, temb)\n\n output_states += (hidden_states,)\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n return hidden_states, output_states\n\n\nclass KCrossAttnDownBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n cross_attention_dim: int,\n dropout: float = 0.0,\n num_layers: int = 4,\n resnet_group_size: int = 32,\n add_downsample: bool = True,\n attention_head_dim: int = 64,\n add_self_attention: bool = False,\n resnet_eps: float = 1e-5,\n resnet_act_fn: str = \"gelu\",\n ):\n super().__init__()\n resnets = []\n attentions = []\n\n self.has_cross_attention = True\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n groups = in_channels // resnet_group_size\n groups_out = out_channels // resnet_group_size\n\n resnets.append(\n ResnetBlockCondNorm2D(\n in_channels=in_channels,\n out_channels=out_channels,\n dropout=dropout,\n temb_channels=temb_channels,\n groups=groups,\n groups_out=groups_out,\n eps=resnet_eps,\n non_linearity=resnet_act_fn,\n time_embedding_norm=\"ada_group\",\n conv_shortcut_bias=False,\n )\n )\n attentions.append(\n KAttentionBlock(\n out_channels,\n out_channels // attention_head_dim,\n attention_head_dim,\n cross_attention_dim=cross_attention_dim,\n temb_channels=temb_channels,\n attention_bias=True,\n add_self_attention=add_self_attention,\n cross_attention_norm=\"layer_norm\",\n group_size=resnet_group_size,\n )\n )\n\n self.resnets = nn.ModuleList(resnets)\n self.attentions = nn.ModuleList(attentions)\n\n if add_downsample:\n self.downsamplers = nn.ModuleList([KDownsample2D()])\n else:\n self.downsamplers = None\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, ...]]:\n cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}\n if cross_attention_kwargs.get(\"scale\", None) is not None:\n logger.warning(\"Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.\")\n\n output_states = ()\n\n for resnet, attn in zip(self.resnets, self.attentions):\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet),\n hidden_states,\n temb,\n **ckpt_kwargs,\n )\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n emb=temb,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n encoder_attention_mask=encoder_attention_mask,\n )\n else:\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n emb=temb,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n encoder_attention_mask=encoder_attention_mask,\n )\n\n if self.downsamplers is None:\n output_states += (None,)\n else:\n output_states += (hidden_states,)\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n return hidden_states, output_states\n\n\nclass AttnUpBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n prev_output_channel: int,\n out_channels: int,\n temb_channels: int,\n resolution_idx: int = None,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n attention_head_dim: int = 1,\n output_scale_factor: float = 1.0,\n upsample_type: str = \"conv\",\n ):\n super().__init__()\n resnets = []\n attentions = []\n\n self.upsample_type = upsample_type\n\n if attention_head_dim is None:\n logger.warning(\n f\"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}.\"\n )\n attention_head_dim = out_channels\n\n for i in range(num_layers):\n res_skip_channels = in_channels if (i == num_layers - 1) else out_channels\n resnet_in_channels = prev_output_channel if i == 0 else out_channels\n\n resnets.append(\n ResnetBlock2D(\n in_channels=resnet_in_channels + res_skip_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n attentions.append(\n Attention(\n out_channels,\n heads=out_channels // attention_head_dim,\n dim_head=attention_head_dim,\n rescale_output_factor=output_scale_factor,\n eps=resnet_eps,\n norm_num_groups=resnet_groups,\n residual_connection=True,\n bias=True,\n upcast_softmax=True,\n _from_deprecated_attn_block=True,\n )\n )\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n if upsample_type == \"conv\":\n self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])\n elif upsample_type == \"resnet\":\n self.upsamplers = nn.ModuleList(\n [\n ResnetBlock2D(\n in_channels=out_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n up=True,\n )\n ]\n )\n else:\n self.upsamplers = None\n\n self.resolution_idx = resolution_idx\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n upsample_size: Optional[int] = None,\n *args,\n **kwargs,\n ) -> torch.Tensor:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n for resnet, attn in zip(self.resnets, self.attentions):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(hidden_states)\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n if self.upsample_type == \"resnet\":\n hidden_states = upsampler(hidden_states, temb=temb)\n else:\n hidden_states = upsampler(hidden_states)\n\n return hidden_states\n\n\nclass CrossAttnUpBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n prev_output_channel: int,\n temb_channels: int,\n resolution_idx: Optional[int] = None,\n dropout: float = 0.0,\n num_layers: int = 1,\n transformer_layers_per_block: Union[int, Tuple[int]] = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n num_attention_heads: int = 1,\n cross_attention_dim: int = 1280,\n output_scale_factor: float = 1.0,\n add_upsample: bool = True,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = False,\n only_cross_attention: bool = False,\n upcast_attention: bool = False,\n attention_type: str = \"default\",\n ):\n super().__init__()\n resnets = []\n attentions = []\n\n self.has_cross_attention = True\n self.num_attention_heads = num_attention_heads\n\n if isinstance(transformer_layers_per_block, int):\n transformer_layers_per_block = [transformer_layers_per_block] * num_layers\n\n for i in range(num_layers):\n res_skip_channels = in_channels if (i == num_layers - 1) else out_channels\n resnet_in_channels = prev_output_channel if i == 0 else out_channels\n\n resnets.append(\n ResnetBlock2D(\n in_channels=resnet_in_channels + res_skip_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n if not dual_cross_attention:\n attentions.append(\n Transformer2DModel(\n num_attention_heads,\n out_channels // num_attention_heads,\n in_channels=out_channels,\n num_layers=transformer_layers_per_block[i],\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n attention_type=attention_type,\n )\n )\n else:\n attentions.append(\n DualTransformer2DModel(\n num_attention_heads,\n out_channels // num_attention_heads,\n in_channels=out_channels,\n num_layers=1,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n )\n )\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n if add_upsample:\n self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])\n else:\n self.upsamplers = None\n\n self.gradient_checkpointing = False\n self.resolution_idx = resolution_idx\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n upsample_size: Optional[int] = None,\n attention_mask: Optional[torch.Tensor] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n if cross_attention_kwargs is not None:\n if cross_attention_kwargs.get(\"scale\", None) is not None:\n logger.warning(\"Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.\")\n\n is_freeu_enabled = (\n getattr(self, \"s1\", None)\n and getattr(self, \"s2\", None)\n and getattr(self, \"b1\", None)\n and getattr(self, \"b2\", None)\n )\n\n for resnet, attn in zip(self.resnets, self.attentions):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n\n # FreeU: Only operate on the first two stages\n if is_freeu_enabled:\n hidden_states, res_hidden_states = apply_freeu(\n self.resolution_idx,\n hidden_states,\n res_hidden_states,\n s1=self.s1,\n s2=self.s2,\n b1=self.b1,\n b2=self.b2,\n )\n\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet),\n hidden_states,\n temb,\n **ckpt_kwargs,\n )\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n else:\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n\nclass UpBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n prev_output_channel: int,\n out_channels: int,\n temb_channels: int,\n resolution_idx: Optional[int] = None,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n output_scale_factor: float = 1.0,\n add_upsample: bool = True,\n ):\n super().__init__()\n resnets = []\n\n for i in range(num_layers):\n res_skip_channels = in_channels if (i == num_layers - 1) else out_channels\n resnet_in_channels = prev_output_channel if i == 0 else out_channels\n\n resnets.append(\n ResnetBlock2D(\n in_channels=resnet_in_channels + res_skip_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n\n self.resnets = nn.ModuleList(resnets)\n\n if add_upsample:\n self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])\n else:\n self.upsamplers = None\n\n self.gradient_checkpointing = False\n self.resolution_idx = resolution_idx\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n upsample_size: Optional[int] = None,\n *args,\n **kwargs,\n ) -> torch.Tensor:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n is_freeu_enabled = (\n getattr(self, \"s1\", None)\n and getattr(self, \"s2\", None)\n and getattr(self, \"b1\", None)\n and getattr(self, \"b2\", None)\n )\n\n for resnet in self.resnets:\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n\n # FreeU: Only operate on the first two stages\n if is_freeu_enabled:\n hidden_states, res_hidden_states = apply_freeu(\n self.resolution_idx,\n hidden_states,\n res_hidden_states,\n s1=self.s1,\n s2=self.s2,\n b1=self.b1,\n b2=self.b2,\n )\n\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs)\n\n return custom_forward\n\n if is_torch_version(\">=\", \"1.11.0\"):\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet), hidden_states, temb, use_reentrant=False\n )\n else:\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet), hidden_states, temb\n )\n else:\n hidden_states = resnet(hidden_states, temb)\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n\nclass UpDecoderBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n resolution_idx: Optional[int] = None,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\", # default, spatial\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n output_scale_factor: float = 1.0,\n add_upsample: bool = True,\n temb_channels: Optional[int] = None,\n ):\n super().__init__()\n resnets = []\n\n for i in range(num_layers):\n input_channels = in_channels if i == 0 else out_channels\n\n if resnet_time_scale_shift == \"spatial\":\n resnets.append(\n ResnetBlockCondNorm2D(\n in_channels=input_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=\"spatial\",\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n )\n )\n else:\n resnets.append(\n ResnetBlock2D(\n in_channels=input_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n\n self.resnets = nn.ModuleList(resnets)\n\n if add_upsample:\n self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])\n else:\n self.upsamplers = None\n\n self.resolution_idx = resolution_idx\n\n def forward(self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:\n for resnet in self.resnets:\n hidden_states = resnet(hidden_states, temb=temb)\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states)\n\n return hidden_states\n\n\nclass AttnUpDecoderBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n resolution_idx: Optional[int] = None,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n attention_head_dim: int = 1,\n output_scale_factor: float = 1.0,\n add_upsample: bool = True,\n temb_channels: Optional[int] = None,\n ):\n super().__init__()\n resnets = []\n attentions = []\n\n if attention_head_dim is None:\n logger.warning(\n f\"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `out_channels`: {out_channels}.\"\n )\n attention_head_dim = out_channels\n\n for i in range(num_layers):\n input_channels = in_channels if i == 0 else out_channels\n\n if resnet_time_scale_shift == \"spatial\":\n resnets.append(\n ResnetBlockCondNorm2D(\n in_channels=input_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=\"spatial\",\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n )\n )\n else:\n resnets.append(\n ResnetBlock2D(\n in_channels=input_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n\n attentions.append(\n Attention(\n out_channels,\n heads=out_channels // attention_head_dim,\n dim_head=attention_head_dim,\n rescale_output_factor=output_scale_factor,\n eps=resnet_eps,\n norm_num_groups=resnet_groups if resnet_time_scale_shift != \"spatial\" else None,\n spatial_norm_dim=temb_channels if resnet_time_scale_shift == \"spatial\" else None,\n residual_connection=True,\n bias=True,\n upcast_softmax=True,\n _from_deprecated_attn_block=True,\n )\n )\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n if add_upsample:\n self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])\n else:\n self.upsamplers = None\n\n self.resolution_idx = resolution_idx\n\n def forward(self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:\n for resnet, attn in zip(self.resnets, self.attentions):\n hidden_states = resnet(hidden_states, temb=temb)\n hidden_states = attn(hidden_states, temb=temb)\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states)\n\n return hidden_states\n\n\nclass AttnSkipUpBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n prev_output_channel: int,\n out_channels: int,\n temb_channels: int,\n resolution_idx: Optional[int] = None,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_pre_norm: bool = True,\n attention_head_dim: int = 1,\n output_scale_factor: float = np.sqrt(2.0),\n add_upsample: bool = True,\n ):\n super().__init__()\n self.attentions = nn.ModuleList([])\n self.resnets = nn.ModuleList([])\n\n for i in range(num_layers):\n res_skip_channels = in_channels if (i == num_layers - 1) else out_channels\n resnet_in_channels = prev_output_channel if i == 0 else out_channels\n\n self.resnets.append(\n ResnetBlock2D(\n in_channels=resnet_in_channels + res_skip_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=min(resnet_in_channels + res_skip_channels // 4, 32),\n groups_out=min(out_channels // 4, 32),\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n\n if attention_head_dim is None:\n logger.warning(\n f\"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `out_channels`: {out_channels}.\"\n )\n attention_head_dim = out_channels\n\n self.attentions.append(\n Attention(\n out_channels,\n heads=out_channels // attention_head_dim,\n dim_head=attention_head_dim,\n rescale_output_factor=output_scale_factor,\n eps=resnet_eps,\n norm_num_groups=32,\n residual_connection=True,\n bias=True,\n upcast_softmax=True,\n _from_deprecated_attn_block=True,\n )\n )\n\n self.upsampler = FirUpsample2D(in_channels, out_channels=out_channels)\n if add_upsample:\n self.resnet_up = ResnetBlock2D(\n in_channels=out_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=min(out_channels // 4, 32),\n groups_out=min(out_channels // 4, 32),\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n use_in_shortcut=True,\n up=True,\n kernel=\"fir\",\n )\n self.skip_conv = nn.Conv2d(out_channels, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n self.skip_norm = torch.nn.GroupNorm(\n num_groups=min(out_channels // 4, 32), num_channels=out_channels, eps=resnet_eps, affine=True\n )\n self.act = nn.SiLU()\n else:\n self.resnet_up = None\n self.skip_conv = None\n self.skip_norm = None\n self.act = None\n\n self.resolution_idx = resolution_idx\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n skip_sample=None,\n *args,\n **kwargs,\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n for resnet in self.resnets:\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n\n hidden_states = resnet(hidden_states, temb)\n\n hidden_states = self.attentions[0](hidden_states)\n\n if skip_sample is not None:\n skip_sample = self.upsampler(skip_sample)\n else:\n skip_sample = 0\n\n if self.resnet_up is not None:\n skip_sample_states = self.skip_norm(hidden_states)\n skip_sample_states = self.act(skip_sample_states)\n skip_sample_states = self.skip_conv(skip_sample_states)\n\n skip_sample = skip_sample + skip_sample_states\n\n hidden_states = self.resnet_up(hidden_states, temb)\n\n return hidden_states, skip_sample\n\n\nclass SkipUpBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n prev_output_channel: int,\n out_channels: int,\n temb_channels: int,\n resolution_idx: Optional[int] = None,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_pre_norm: bool = True,\n output_scale_factor: float = np.sqrt(2.0),\n add_upsample: bool = True,\n upsample_padding: int = 1,\n ):\n super().__init__()\n self.resnets = nn.ModuleList([])\n\n for i in range(num_layers):\n res_skip_channels = in_channels if (i == num_layers - 1) else out_channels\n resnet_in_channels = prev_output_channel if i == 0 else out_channels\n\n self.resnets.append(\n ResnetBlock2D(\n in_channels=resnet_in_channels + res_skip_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=min((resnet_in_channels + res_skip_channels) // 4, 32),\n groups_out=min(out_channels // 4, 32),\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n\n self.upsampler = FirUpsample2D(in_channels, out_channels=out_channels)\n if add_upsample:\n self.resnet_up = ResnetBlock2D(\n in_channels=out_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=min(out_channels // 4, 32),\n groups_out=min(out_channels // 4, 32),\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n use_in_shortcut=True,\n up=True,\n kernel=\"fir\",\n )\n self.skip_conv = nn.Conv2d(out_channels, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n self.skip_norm = torch.nn.GroupNorm(\n num_groups=min(out_channels // 4, 32), num_channels=out_channels, eps=resnet_eps, affine=True\n )\n self.act = nn.SiLU()\n else:\n self.resnet_up = None\n self.skip_conv = None\n self.skip_norm = None\n self.act = None\n\n self.resolution_idx = resolution_idx\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n skip_sample=None,\n *args,\n **kwargs,\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n for resnet in self.resnets:\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n\n hidden_states = resnet(hidden_states, temb)\n\n if skip_sample is not None:\n skip_sample = self.upsampler(skip_sample)\n else:\n skip_sample = 0\n\n if self.resnet_up is not None:\n skip_sample_states = self.skip_norm(hidden_states)\n skip_sample_states = self.act(skip_sample_states)\n skip_sample_states = self.skip_conv(skip_sample_states)\n\n skip_sample = skip_sample + skip_sample_states\n\n hidden_states = self.resnet_up(hidden_states, temb)\n\n return hidden_states, skip_sample\n\n\nclass ResnetUpsampleBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n prev_output_channel: int,\n out_channels: int,\n temb_channels: int,\n resolution_idx: Optional[int] = None,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n output_scale_factor: float = 1.0,\n add_upsample: bool = True,\n skip_time_act: bool = False,\n ):\n super().__init__()\n resnets = []\n\n for i in range(num_layers):\n res_skip_channels = in_channels if (i == num_layers - 1) else out_channels\n resnet_in_channels = prev_output_channel if i == 0 else out_channels\n\n resnets.append(\n ResnetBlock2D(\n in_channels=resnet_in_channels + res_skip_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n skip_time_act=skip_time_act,\n )\n )\n\n self.resnets = nn.ModuleList(resnets)\n\n if add_upsample:\n self.upsamplers = nn.ModuleList(\n [\n ResnetBlock2D(\n in_channels=out_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n skip_time_act=skip_time_act,\n up=True,\n )\n ]\n )\n else:\n self.upsamplers = None\n\n self.gradient_checkpointing = False\n self.resolution_idx = resolution_idx\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n upsample_size: Optional[int] = None,\n *args,\n **kwargs,\n ) -> torch.Tensor:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n for resnet in self.resnets:\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs)\n\n return custom_forward\n\n if is_torch_version(\">=\", \"1.11.0\"):\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet), hidden_states, temb, use_reentrant=False\n )\n else:\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet), hidden_states, temb\n )\n else:\n hidden_states = resnet(hidden_states, temb)\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, temb)\n\n return hidden_states\n\n\nclass SimpleCrossAttnUpBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n prev_output_channel: int,\n temb_channels: int,\n resolution_idx: Optional[int] = None,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n attention_head_dim: int = 1,\n cross_attention_dim: int = 1280,\n output_scale_factor: float = 1.0,\n add_upsample: bool = True,\n skip_time_act: bool = False,\n only_cross_attention: bool = False,\n cross_attention_norm: Optional[str] = None,\n ):\n super().__init__()\n resnets = []\n attentions = []\n\n self.has_cross_attention = True\n self.attention_head_dim = attention_head_dim\n\n self.num_heads = out_channels // self.attention_head_dim\n\n for i in range(num_layers):\n res_skip_channels = in_channels if (i == num_layers - 1) else out_channels\n resnet_in_channels = prev_output_channel if i == 0 else out_channels\n\n resnets.append(\n ResnetBlock2D(\n in_channels=resnet_in_channels + res_skip_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n skip_time_act=skip_time_act,\n )\n )\n\n processor = (\n AttnAddedKVProcessor2_0() if hasattr(F, \"scaled_dot_product_attention\") else AttnAddedKVProcessor()\n )\n\n attentions.append(\n Attention(\n query_dim=out_channels,\n cross_attention_dim=out_channels,\n heads=self.num_heads,\n dim_head=self.attention_head_dim,\n added_kv_proj_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n bias=True,\n upcast_softmax=True,\n only_cross_attention=only_cross_attention,\n cross_attention_norm=cross_attention_norm,\n processor=processor,\n )\n )\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n if add_upsample:\n self.upsamplers = nn.ModuleList(\n [\n ResnetBlock2D(\n in_channels=out_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n skip_time_act=skip_time_act,\n up=True,\n )\n ]\n )\n else:\n self.upsamplers = None\n\n self.gradient_checkpointing = False\n self.resolution_idx = resolution_idx\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n upsample_size: Optional[int] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}\n if cross_attention_kwargs.get(\"scale\", None) is not None:\n logger.warning(\"Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.\")\n\n if attention_mask is None:\n # if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask.\n mask = None if encoder_hidden_states is None else encoder_attention_mask\n else:\n # when attention_mask is defined: we don't even check for encoder_attention_mask.\n # this is to maintain compatibility with UnCLIP, which uses 'attention_mask' param for cross-attn masks.\n # TODO: UnCLIP should express cross-attn mask via encoder_attention_mask param instead of via attention_mask.\n # then we can simplify this whole if/else block to:\n # mask = attention_mask if encoder_hidden_states is None else encoder_attention_mask\n mask = attention_mask\n\n for resnet, attn in zip(self.resnets, self.attentions):\n # resnet\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=mask,\n **cross_attention_kwargs,\n )\n else:\n hidden_states = resnet(hidden_states, temb)\n\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=mask,\n **cross_attention_kwargs,\n )\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, temb)\n\n return hidden_states\n\n\nclass KUpBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n resolution_idx: int,\n dropout: float = 0.0,\n num_layers: int = 5,\n resnet_eps: float = 1e-5,\n resnet_act_fn: str = \"gelu\",\n resnet_group_size: Optional[int] = 32,\n add_upsample: bool = True,\n ):\n super().__init__()\n resnets = []\n k_in_channels = 2 * out_channels\n k_out_channels = in_channels\n num_layers = num_layers - 1\n\n for i in range(num_layers):\n in_channels = k_in_channels if i == 0 else out_channels\n groups = in_channels // resnet_group_size\n groups_out = out_channels // resnet_group_size\n\n resnets.append(\n ResnetBlockCondNorm2D(\n in_channels=in_channels,\n out_channels=k_out_channels if (i == num_layers - 1) else out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=groups,\n groups_out=groups_out,\n dropout=dropout,\n non_linearity=resnet_act_fn,\n time_embedding_norm=\"ada_group\",\n conv_shortcut_bias=False,\n )\n )\n\n self.resnets = nn.ModuleList(resnets)\n\n if add_upsample:\n self.upsamplers = nn.ModuleList([KUpsample2D()])\n else:\n self.upsamplers = None\n\n self.gradient_checkpointing = False\n self.resolution_idx = resolution_idx\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n upsample_size: Optional[int] = None,\n *args,\n **kwargs,\n ) -> torch.Tensor:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n res_hidden_states_tuple = res_hidden_states_tuple[-1]\n if res_hidden_states_tuple is not None:\n hidden_states = torch.cat([hidden_states, res_hidden_states_tuple], dim=1)\n\n for resnet in self.resnets:\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs)\n\n return custom_forward\n\n if is_torch_version(\">=\", \"1.11.0\"):\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet), hidden_states, temb, use_reentrant=False\n )\n else:\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet), hidden_states, temb\n )\n else:\n hidden_states = resnet(hidden_states, temb)\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states)\n\n return hidden_states\n\n\nclass KCrossAttnUpBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n resolution_idx: int,\n dropout: float = 0.0,\n num_layers: int = 4,\n resnet_eps: float = 1e-5,\n resnet_act_fn: str = \"gelu\",\n resnet_group_size: int = 32,\n attention_head_dim: int = 1, # attention dim_head\n cross_attention_dim: int = 768,\n add_upsample: bool = True,\n upcast_attention: bool = False,\n ):\n super().__init__()\n resnets = []\n attentions = []\n\n is_first_block = in_channels == out_channels == temb_channels\n is_middle_block = in_channels != out_channels\n add_self_attention = True if is_first_block else False\n\n self.has_cross_attention = True\n self.attention_head_dim = attention_head_dim\n\n # in_channels, and out_channels for the block (k-unet)\n k_in_channels = out_channels if is_first_block else 2 * out_channels\n k_out_channels = in_channels\n\n num_layers = num_layers - 1\n\n for i in range(num_layers):\n in_channels = k_in_channels if i == 0 else out_channels\n groups = in_channels // resnet_group_size\n groups_out = out_channels // resnet_group_size\n\n if is_middle_block and (i == num_layers - 1):\n conv_2d_out_channels = k_out_channels\n else:\n conv_2d_out_channels = None\n\n resnets.append(\n ResnetBlockCondNorm2D(\n in_channels=in_channels,\n out_channels=out_channels,\n conv_2d_out_channels=conv_2d_out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=groups,\n groups_out=groups_out,\n dropout=dropout,\n non_linearity=resnet_act_fn,\n time_embedding_norm=\"ada_group\",\n conv_shortcut_bias=False,\n )\n )\n attentions.append(\n KAttentionBlock(\n k_out_channels if (i == num_layers - 1) else out_channels,\n k_out_channels // attention_head_dim\n if (i == num_layers - 1)\n else out_channels // attention_head_dim,\n attention_head_dim,\n cross_attention_dim=cross_attention_dim,\n temb_channels=temb_channels,\n attention_bias=True,\n add_self_attention=add_self_attention,\n cross_attention_norm=\"layer_norm\",\n upcast_attention=upcast_attention,\n )\n )\n\n self.resnets = nn.ModuleList(resnets)\n self.attentions = nn.ModuleList(attentions)\n\n if add_upsample:\n self.upsamplers = nn.ModuleList([KUpsample2D()])\n else:\n self.upsamplers = None\n\n self.gradient_checkpointing = False\n self.resolution_idx = resolution_idx\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n upsample_size: Optional[int] = None,\n attention_mask: Optional[torch.Tensor] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n res_hidden_states_tuple = res_hidden_states_tuple[-1]\n if res_hidden_states_tuple is not None:\n hidden_states = torch.cat([hidden_states, res_hidden_states_tuple], dim=1)\n\n for resnet, attn in zip(self.resnets, self.attentions):\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet),\n hidden_states,\n temb,\n **ckpt_kwargs,\n )\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n emb=temb,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n encoder_attention_mask=encoder_attention_mask,\n )\n else:\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n emb=temb,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n encoder_attention_mask=encoder_attention_mask,\n )\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states)\n\n return hidden_states\n\n\n# can potentially later be renamed to `No-feed-forward` attention\nclass KAttentionBlock(nn.Module):\n r\"\"\"\n A basic Transformer block.\n\n Parameters:\n dim (`int`): The number of channels in the input and output.\n num_attention_heads (`int`): The number of heads to use for multi-head attention.\n attention_head_dim (`int`): The number of channels in each head.\n dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.\n cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.\n attention_bias (`bool`, *optional*, defaults to `False`):\n Configure if the attention layers should contain a bias parameter.\n upcast_attention (`bool`, *optional*, defaults to `False`):\n Set to `True` to upcast the attention computation to `float32`.\n temb_channels (`int`, *optional*, defaults to 768):\n The number of channels in the token embedding.\n add_self_attention (`bool`, *optional*, defaults to `False`):\n Set to `True` to add self-attention to the block.\n cross_attention_norm (`str`, *optional*, defaults to `None`):\n The type of normalization to use for the cross attention. Can be `None`, `layer_norm`, or `group_norm`.\n group_size (`int`, *optional*, defaults to 32):\n The number of groups to separate the channels into for group normalization.\n \"\"\"\n\n def __init__(\n self,\n dim: int,\n num_attention_heads: int,\n attention_head_dim: int,\n dropout: float = 0.0,\n cross_attention_dim: Optional[int] = None,\n attention_bias: bool = False,\n upcast_attention: bool = False,\n temb_channels: int = 768, # for ada_group_norm\n add_self_attention: bool = False,\n cross_attention_norm: Optional[str] = None,\n group_size: int = 32,\n ):\n super().__init__()\n self.add_self_attention = add_self_attention\n\n # 1. Self-Attn\n if add_self_attention:\n self.norm1 = AdaGroupNorm(temb_channels, dim, max(1, dim // group_size))\n self.attn1 = Attention(\n query_dim=dim,\n heads=num_attention_heads,\n dim_head=attention_head_dim,\n dropout=dropout,\n bias=attention_bias,\n cross_attention_dim=None,\n cross_attention_norm=None,\n )\n\n # 2. Cross-Attn\n self.norm2 = AdaGroupNorm(temb_channels, dim, max(1, dim // group_size))\n self.attn2 = Attention(\n query_dim=dim,\n cross_attention_dim=cross_attention_dim,\n heads=num_attention_heads,\n dim_head=attention_head_dim,\n dropout=dropout,\n bias=attention_bias,\n upcast_attention=upcast_attention,\n cross_attention_norm=cross_attention_norm,\n )\n\n def _to_3d(self, hidden_states: torch.Tensor, height: int, weight: int) -> torch.Tensor:\n return hidden_states.permute(0, 2, 3, 1).reshape(hidden_states.shape[0], height * weight, -1)\n\n def _to_4d(self, hidden_states: torch.Tensor, height: int, weight: int) -> torch.Tensor:\n return hidden_states.permute(0, 2, 1).reshape(hidden_states.shape[0], -1, height, weight)\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n # TODO: mark emb as non-optional (self.norm2 requires it).\n # requires assessing impact of change to positional param interface.\n emb: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}\n if cross_attention_kwargs.get(\"scale\", None) is not None:\n logger.warning(\"Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.\")\n\n # 1. Self-Attention\n if self.add_self_attention:\n norm_hidden_states = self.norm1(hidden_states, emb)\n\n height, weight = norm_hidden_states.shape[2:]\n norm_hidden_states = self._to_3d(norm_hidden_states, height, weight)\n\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=None,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n attn_output = self._to_4d(attn_output, height, weight)\n\n hidden_states = attn_output + hidden_states\n\n # 2. Cross-Attention/None\n norm_hidden_states = self.norm2(hidden_states, emb)\n\n height, weight = norm_hidden_states.shape[2:]\n norm_hidden_states = self._to_3d(norm_hidden_states, height, weight)\n attn_output = self.attn2(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask if encoder_hidden_states is None else encoder_attention_mask,\n **cross_attention_kwargs,\n )\n attn_output = self._to_4d(attn_output, height, weight)\n\n hidden_states = attn_output + hidden_states\n\n return hidden_states\n" }, { "path": "diffusers/src/diffusers/models/unets/unet_2d_blocks_flax.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport flax.linen as nn\nimport jax.numpy as jnp\n\nfrom ..attention_flax import FlaxTransformer2DModel\nfrom ..resnet_flax import FlaxDownsample2D, FlaxResnetBlock2D, FlaxUpsample2D\n\n\nclass FlaxCrossAttnDownBlock2D(nn.Module):\n r\"\"\"\n Cross Attention 2D Downsizing block - original architecture from Unet transformers:\n https://arxiv.org/abs/2103.06104\n\n Parameters:\n in_channels (:obj:`int`):\n Input channels\n out_channels (:obj:`int`):\n Output channels\n dropout (:obj:`float`, *optional*, defaults to 0.0):\n Dropout rate\n num_layers (:obj:`int`, *optional*, defaults to 1):\n Number of attention blocks layers\n num_attention_heads (:obj:`int`, *optional*, defaults to 1):\n Number of attention heads of each spatial transformer block\n add_downsample (:obj:`bool`, *optional*, defaults to `True`):\n Whether to add downsampling layer before each final output\n use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):\n enable memory efficient attention https://arxiv.org/abs/2112.05682\n split_head_dim (`bool`, *optional*, defaults to `False`):\n Whether to split the head dimension into a new axis for the self-attention computation. In most cases,\n enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL.\n dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):\n Parameters `dtype`\n \"\"\"\n\n in_channels: int\n out_channels: int\n dropout: float = 0.0\n num_layers: int = 1\n num_attention_heads: int = 1\n add_downsample: bool = True\n use_linear_projection: bool = False\n only_cross_attention: bool = False\n use_memory_efficient_attention: bool = False\n split_head_dim: bool = False\n dtype: jnp.dtype = jnp.float32\n transformer_layers_per_block: int = 1\n\n def setup(self):\n resnets = []\n attentions = []\n\n for i in range(self.num_layers):\n in_channels = self.in_channels if i == 0 else self.out_channels\n\n res_block = FlaxResnetBlock2D(\n in_channels=in_channels,\n out_channels=self.out_channels,\n dropout_prob=self.dropout,\n dtype=self.dtype,\n )\n resnets.append(res_block)\n\n attn_block = FlaxTransformer2DModel(\n in_channels=self.out_channels,\n n_heads=self.num_attention_heads,\n d_head=self.out_channels // self.num_attention_heads,\n depth=self.transformer_layers_per_block,\n use_linear_projection=self.use_linear_projection,\n only_cross_attention=self.only_cross_attention,\n use_memory_efficient_attention=self.use_memory_efficient_attention,\n split_head_dim=self.split_head_dim,\n dtype=self.dtype,\n )\n attentions.append(attn_block)\n\n self.resnets = resnets\n self.attentions = attentions\n\n if self.add_downsample:\n self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype)\n\n def __call__(self, hidden_states, temb, encoder_hidden_states, deterministic=True):\n output_states = ()\n\n for resnet, attn in zip(self.resnets, self.attentions):\n hidden_states = resnet(hidden_states, temb, deterministic=deterministic)\n hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic)\n output_states += (hidden_states,)\n\n if self.add_downsample:\n hidden_states = self.downsamplers_0(hidden_states)\n output_states += (hidden_states,)\n\n return hidden_states, output_states\n\n\nclass FlaxDownBlock2D(nn.Module):\n r\"\"\"\n Flax 2D downsizing block\n\n Parameters:\n in_channels (:obj:`int`):\n Input channels\n out_channels (:obj:`int`):\n Output channels\n dropout (:obj:`float`, *optional*, defaults to 0.0):\n Dropout rate\n num_layers (:obj:`int`, *optional*, defaults to 1):\n Number of attention blocks layers\n add_downsample (:obj:`bool`, *optional*, defaults to `True`):\n Whether to add downsampling layer before each final output\n dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):\n Parameters `dtype`\n \"\"\"\n\n in_channels: int\n out_channels: int\n dropout: float = 0.0\n num_layers: int = 1\n add_downsample: bool = True\n dtype: jnp.dtype = jnp.float32\n\n def setup(self):\n resnets = []\n\n for i in range(self.num_layers):\n in_channels = self.in_channels if i == 0 else self.out_channels\n\n res_block = FlaxResnetBlock2D(\n in_channels=in_channels,\n out_channels=self.out_channels,\n dropout_prob=self.dropout,\n dtype=self.dtype,\n )\n resnets.append(res_block)\n self.resnets = resnets\n\n if self.add_downsample:\n self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype)\n\n def __call__(self, hidden_states, temb, deterministic=True):\n output_states = ()\n\n for resnet in self.resnets:\n hidden_states = resnet(hidden_states, temb, deterministic=deterministic)\n output_states += (hidden_states,)\n\n if self.add_downsample:\n hidden_states = self.downsamplers_0(hidden_states)\n output_states += (hidden_states,)\n\n return hidden_states, output_states\n\n\nclass FlaxCrossAttnUpBlock2D(nn.Module):\n r\"\"\"\n Cross Attention 2D Upsampling block - original architecture from Unet transformers:\n https://arxiv.org/abs/2103.06104\n\n Parameters:\n in_channels (:obj:`int`):\n Input channels\n out_channels (:obj:`int`):\n Output channels\n dropout (:obj:`float`, *optional*, defaults to 0.0):\n Dropout rate\n num_layers (:obj:`int`, *optional*, defaults to 1):\n Number of attention blocks layers\n num_attention_heads (:obj:`int`, *optional*, defaults to 1):\n Number of attention heads of each spatial transformer block\n add_upsample (:obj:`bool`, *optional*, defaults to `True`):\n Whether to add upsampling layer before each final output\n use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):\n enable memory efficient attention https://arxiv.org/abs/2112.05682\n split_head_dim (`bool`, *optional*, defaults to `False`):\n Whether to split the head dimension into a new axis for the self-attention computation. In most cases,\n enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL.\n dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):\n Parameters `dtype`\n \"\"\"\n\n in_channels: int\n out_channels: int\n prev_output_channel: int\n dropout: float = 0.0\n num_layers: int = 1\n num_attention_heads: int = 1\n add_upsample: bool = True\n use_linear_projection: bool = False\n only_cross_attention: bool = False\n use_memory_efficient_attention: bool = False\n split_head_dim: bool = False\n dtype: jnp.dtype = jnp.float32\n transformer_layers_per_block: int = 1\n\n def setup(self):\n resnets = []\n attentions = []\n\n for i in range(self.num_layers):\n res_skip_channels = self.in_channels if (i == self.num_layers - 1) else self.out_channels\n resnet_in_channels = self.prev_output_channel if i == 0 else self.out_channels\n\n res_block = FlaxResnetBlock2D(\n in_channels=resnet_in_channels + res_skip_channels,\n out_channels=self.out_channels,\n dropout_prob=self.dropout,\n dtype=self.dtype,\n )\n resnets.append(res_block)\n\n attn_block = FlaxTransformer2DModel(\n in_channels=self.out_channels,\n n_heads=self.num_attention_heads,\n d_head=self.out_channels // self.num_attention_heads,\n depth=self.transformer_layers_per_block,\n use_linear_projection=self.use_linear_projection,\n only_cross_attention=self.only_cross_attention,\n use_memory_efficient_attention=self.use_memory_efficient_attention,\n split_head_dim=self.split_head_dim,\n dtype=self.dtype,\n )\n attentions.append(attn_block)\n\n self.resnets = resnets\n self.attentions = attentions\n\n if self.add_upsample:\n self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype)\n\n def __call__(self, hidden_states, res_hidden_states_tuple, temb, encoder_hidden_states, deterministic=True):\n for resnet, attn in zip(self.resnets, self.attentions):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = jnp.concatenate((hidden_states, res_hidden_states), axis=-1)\n\n hidden_states = resnet(hidden_states, temb, deterministic=deterministic)\n hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic)\n\n if self.add_upsample:\n hidden_states = self.upsamplers_0(hidden_states)\n\n return hidden_states\n\n\nclass FlaxUpBlock2D(nn.Module):\n r\"\"\"\n Flax 2D upsampling block\n\n Parameters:\n in_channels (:obj:`int`):\n Input channels\n out_channels (:obj:`int`):\n Output channels\n prev_output_channel (:obj:`int`):\n Output channels from the previous block\n dropout (:obj:`float`, *optional*, defaults to 0.0):\n Dropout rate\n num_layers (:obj:`int`, *optional*, defaults to 1):\n Number of attention blocks layers\n add_downsample (:obj:`bool`, *optional*, defaults to `True`):\n Whether to add downsampling layer before each final output\n dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):\n Parameters `dtype`\n \"\"\"\n\n in_channels: int\n out_channels: int\n prev_output_channel: int\n dropout: float = 0.0\n num_layers: int = 1\n add_upsample: bool = True\n dtype: jnp.dtype = jnp.float32\n\n def setup(self):\n resnets = []\n\n for i in range(self.num_layers):\n res_skip_channels = self.in_channels if (i == self.num_layers - 1) else self.out_channels\n resnet_in_channels = self.prev_output_channel if i == 0 else self.out_channels\n\n res_block = FlaxResnetBlock2D(\n in_channels=resnet_in_channels + res_skip_channels,\n out_channels=self.out_channels,\n dropout_prob=self.dropout,\n dtype=self.dtype,\n )\n resnets.append(res_block)\n\n self.resnets = resnets\n\n if self.add_upsample:\n self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype)\n\n def __call__(self, hidden_states, res_hidden_states_tuple, temb, deterministic=True):\n for resnet in self.resnets:\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = jnp.concatenate((hidden_states, res_hidden_states), axis=-1)\n\n hidden_states = resnet(hidden_states, temb, deterministic=deterministic)\n\n if self.add_upsample:\n hidden_states = self.upsamplers_0(hidden_states)\n\n return hidden_states\n\n\nclass FlaxUNetMidBlock2DCrossAttn(nn.Module):\n r\"\"\"\n Cross Attention 2D Mid-level block - original architecture from Unet transformers: https://arxiv.org/abs/2103.06104\n\n Parameters:\n in_channels (:obj:`int`):\n Input channels\n dropout (:obj:`float`, *optional*, defaults to 0.0):\n Dropout rate\n num_layers (:obj:`int`, *optional*, defaults to 1):\n Number of attention blocks layers\n num_attention_heads (:obj:`int`, *optional*, defaults to 1):\n Number of attention heads of each spatial transformer block\n use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):\n enable memory efficient attention https://arxiv.org/abs/2112.05682\n split_head_dim (`bool`, *optional*, defaults to `False`):\n Whether to split the head dimension into a new axis for the self-attention computation. In most cases,\n enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL.\n dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):\n Parameters `dtype`\n \"\"\"\n\n in_channels: int\n dropout: float = 0.0\n num_layers: int = 1\n num_attention_heads: int = 1\n use_linear_projection: bool = False\n use_memory_efficient_attention: bool = False\n split_head_dim: bool = False\n dtype: jnp.dtype = jnp.float32\n transformer_layers_per_block: int = 1\n\n def setup(self):\n # there is always at least one resnet\n resnets = [\n FlaxResnetBlock2D(\n in_channels=self.in_channels,\n out_channels=self.in_channels,\n dropout_prob=self.dropout,\n dtype=self.dtype,\n )\n ]\n\n attentions = []\n\n for _ in range(self.num_layers):\n attn_block = FlaxTransformer2DModel(\n in_channels=self.in_channels,\n n_heads=self.num_attention_heads,\n d_head=self.in_channels // self.num_attention_heads,\n depth=self.transformer_layers_per_block,\n use_linear_projection=self.use_linear_projection,\n use_memory_efficient_attention=self.use_memory_efficient_attention,\n split_head_dim=self.split_head_dim,\n dtype=self.dtype,\n )\n attentions.append(attn_block)\n\n res_block = FlaxResnetBlock2D(\n in_channels=self.in_channels,\n out_channels=self.in_channels,\n dropout_prob=self.dropout,\n dtype=self.dtype,\n )\n resnets.append(res_block)\n\n self.resnets = resnets\n self.attentions = attentions\n\n def __call__(self, hidden_states, temb, encoder_hidden_states, deterministic=True):\n hidden_states = self.resnets[0](hidden_states, temb)\n for attn, resnet in zip(self.attentions, self.resnets[1:]):\n hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic)\n hidden_states = resnet(hidden_states, temb, deterministic=deterministic)\n\n return hidden_states\n" }, { "path": "diffusers/src/diffusers/models/unets/unet_2d_condition.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, List, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.utils.checkpoint\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...loaders import PeftAdapterMixin, UNet2DConditionLoadersMixin\nfrom ...loaders.single_file_model import FromOriginalModelMixin\nfrom ...utils import USE_PEFT_BACKEND, BaseOutput, deprecate, logging, scale_lora_layers, unscale_lora_layers\nfrom ..activations import get_activation\nfrom ..attention_processor import (\n ADDED_KV_ATTENTION_PROCESSORS,\n CROSS_ATTENTION_PROCESSORS,\n Attention,\n AttentionProcessor,\n AttnAddedKVProcessor,\n AttnProcessor,\n)\nfrom ..embeddings import (\n GaussianFourierProjection,\n GLIGENTextBoundingboxProjection,\n ImageHintTimeEmbedding,\n ImageProjection,\n ImageTimeEmbedding,\n TextImageProjection,\n TextImageTimeEmbedding,\n TextTimeEmbedding,\n TimestepEmbedding,\n Timesteps,\n)\nfrom ..modeling_utils import ModelMixin\nfrom .unet_2d_blocks import (\n get_down_block,\n get_mid_block,\n get_up_block,\n)\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n@dataclass\nclass UNet2DConditionOutput(BaseOutput):\n \"\"\"\n The output of [`UNet2DConditionModel`].\n\n Args:\n sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):\n The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.\n \"\"\"\n\n sample: torch.Tensor = None\n\n\nclass UNet2DConditionModel(\n ModelMixin, ConfigMixin, FromOriginalModelMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin\n):\n r\"\"\"\n A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample\n shaped output.\n\n This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented\n for all models (such as downloading or saving).\n\n Parameters:\n sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):\n Height and width of input/output sample.\n in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.\n out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.\n center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.\n flip_sin_to_cos (`bool`, *optional*, defaults to `True`):\n Whether to flip the sin to cos in the time embedding.\n freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.\n down_block_types (`Tuple[str]`, *optional*, defaults to `(\"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\", \"DownBlock2D\")`):\n The tuple of downsample blocks to use.\n mid_block_type (`str`, *optional*, defaults to `\"UNetMidBlock2DCrossAttn\"`):\n Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or\n `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.\n up_block_types (`Tuple[str]`, *optional*, defaults to `(\"UpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\")`):\n The tuple of upsample blocks to use.\n only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):\n Whether to include self-attention in the basic transformer blocks, see\n [`~models.attention.BasicTransformerBlock`].\n block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):\n The tuple of output channels for each block.\n layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.\n downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.\n mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.\n dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.\n act_fn (`str`, *optional*, defaults to `\"silu\"`): The activation function to use.\n norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.\n If `None`, normalization and activation layers is skipped in post-processing.\n norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.\n cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):\n The dimension of the cross attention features.\n transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):\n The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for\n [`~models.unets.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unets.unet_2d_blocks.CrossAttnUpBlock2D`],\n [`~models.unets.unet_2d_blocks.UNetMidBlock2DCrossAttn`].\n reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None):\n The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling\n blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for\n [`~models.unets.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unets.unet_2d_blocks.CrossAttnUpBlock2D`],\n [`~models.unets.unet_2d_blocks.UNetMidBlock2DCrossAttn`].\n encoder_hid_dim (`int`, *optional*, defaults to None):\n If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`\n dimension to `cross_attention_dim`.\n encoder_hid_dim_type (`str`, *optional*, defaults to `None`):\n If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text\n embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.\n attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.\n num_attention_heads (`int`, *optional*):\n The number of attention heads. If not defined, defaults to `attention_head_dim`\n resnet_time_scale_shift (`str`, *optional*, defaults to `\"default\"`): Time scale shift config\n for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.\n class_embed_type (`str`, *optional*, defaults to `None`):\n The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,\n `\"timestep\"`, `\"identity\"`, `\"projection\"`, or `\"simple_projection\"`.\n addition_embed_type (`str`, *optional*, defaults to `None`):\n Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or\n \"text\". \"text\" will use the `TextTimeEmbedding` layer.\n addition_time_embed_dim: (`int`, *optional*, defaults to `None`):\n Dimension for the timestep embeddings.\n num_class_embeds (`int`, *optional*, defaults to `None`):\n Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing\n class conditioning with `class_embed_type` equal to `None`.\n time_embedding_type (`str`, *optional*, defaults to `positional`):\n The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.\n time_embedding_dim (`int`, *optional*, defaults to `None`):\n An optional override for the dimension of the projected time embedding.\n time_embedding_act_fn (`str`, *optional*, defaults to `None`):\n Optional activation function to use only once on the time embeddings before they are passed to the rest of\n the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.\n timestep_post_act (`str`, *optional*, defaults to `None`):\n The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.\n time_cond_proj_dim (`int`, *optional*, defaults to `None`):\n The dimension of `cond_proj` layer in the timestep embedding.\n conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer.\n conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer.\n projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when\n `class_embed_type=\"projection\"`. Required when `class_embed_type=\"projection\"`.\n class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time\n embeddings with the class embeddings.\n mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):\n Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If\n `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the\n `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`\n otherwise.\n \"\"\"\n\n _supports_gradient_checkpointing = True\n _no_split_modules = [\"BasicTransformerBlock\", \"ResnetBlock2D\", \"CrossAttnUpBlock2D\"]\n\n @register_to_config\n def __init__(\n self,\n sample_size: Optional[int] = None,\n in_channels: int = 4,\n out_channels: int = 4,\n center_input_sample: bool = False,\n flip_sin_to_cos: bool = True,\n freq_shift: int = 0,\n down_block_types: Tuple[str] = (\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"DownBlock2D\",\n ),\n mid_block_type: Optional[str] = \"UNetMidBlock2DCrossAttn\",\n up_block_types: Tuple[str] = (\"UpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\"),\n only_cross_attention: Union[bool, Tuple[bool]] = False,\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\n layers_per_block: Union[int, Tuple[int]] = 2,\n downsample_padding: int = 1,\n mid_block_scale_factor: float = 1,\n dropout: float = 0.0,\n act_fn: str = \"silu\",\n norm_num_groups: Optional[int] = 32,\n norm_eps: float = 1e-5,\n cross_attention_dim: Union[int, Tuple[int]] = 1280,\n transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,\n reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None,\n encoder_hid_dim: Optional[int] = None,\n encoder_hid_dim_type: Optional[str] = None,\n attention_head_dim: Union[int, Tuple[int]] = 8,\n num_attention_heads: Optional[Union[int, Tuple[int]]] = None,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = False,\n class_embed_type: Optional[str] = None,\n addition_embed_type: Optional[str] = None,\n addition_time_embed_dim: Optional[int] = None,\n num_class_embeds: Optional[int] = None,\n upcast_attention: bool = False,\n resnet_time_scale_shift: str = \"default\",\n resnet_skip_time_act: bool = False,\n resnet_out_scale_factor: float = 1.0,\n time_embedding_type: str = \"positional\",\n time_embedding_dim: Optional[int] = None,\n time_embedding_act_fn: Optional[str] = None,\n timestep_post_act: Optional[str] = None,\n time_cond_proj_dim: Optional[int] = None,\n conv_in_kernel: int = 3,\n conv_out_kernel: int = 3,\n projection_class_embeddings_input_dim: Optional[int] = None,\n attention_type: str = \"default\",\n class_embeddings_concat: bool = False,\n mid_block_only_cross_attention: Optional[bool] = None,\n cross_attention_norm: Optional[str] = None,\n addition_embed_type_num_heads: int = 64,\n ):\n super().__init__()\n\n self.sample_size = sample_size\n\n if num_attention_heads is not None:\n raise ValueError(\n \"At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.\"\n )\n\n # If `num_attention_heads` is not defined (which is the case for most models)\n # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.\n # The reason for this behavior is to correct for incorrectly named variables that were introduced\n # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131\n # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking\n # which is why we correct for the naming here.\n num_attention_heads = num_attention_heads or attention_head_dim\n\n # Check inputs\n self._check_config(\n down_block_types=down_block_types,\n up_block_types=up_block_types,\n only_cross_attention=only_cross_attention,\n block_out_channels=block_out_channels,\n layers_per_block=layers_per_block,\n cross_attention_dim=cross_attention_dim,\n transformer_layers_per_block=transformer_layers_per_block,\n reverse_transformer_layers_per_block=reverse_transformer_layers_per_block,\n attention_head_dim=attention_head_dim,\n num_attention_heads=num_attention_heads,\n )\n\n # input\n conv_in_padding = (conv_in_kernel - 1) // 2\n self.conv_in = nn.Conv2d(\n in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding\n )\n\n # time\n time_embed_dim, timestep_input_dim = self._set_time_proj(\n time_embedding_type,\n block_out_channels=block_out_channels,\n flip_sin_to_cos=flip_sin_to_cos,\n freq_shift=freq_shift,\n time_embedding_dim=time_embedding_dim,\n )\n\n self.time_embedding = TimestepEmbedding(\n timestep_input_dim,\n time_embed_dim,\n act_fn=act_fn,\n post_act_fn=timestep_post_act,\n cond_proj_dim=time_cond_proj_dim,\n )\n\n self._set_encoder_hid_proj(\n encoder_hid_dim_type,\n cross_attention_dim=cross_attention_dim,\n encoder_hid_dim=encoder_hid_dim,\n )\n\n # class embedding\n self._set_class_embedding(\n class_embed_type,\n act_fn=act_fn,\n num_class_embeds=num_class_embeds,\n projection_class_embeddings_input_dim=projection_class_embeddings_input_dim,\n time_embed_dim=time_embed_dim,\n timestep_input_dim=timestep_input_dim,\n )\n\n self._set_add_embedding(\n addition_embed_type,\n addition_embed_type_num_heads=addition_embed_type_num_heads,\n addition_time_embed_dim=addition_time_embed_dim,\n cross_attention_dim=cross_attention_dim,\n encoder_hid_dim=encoder_hid_dim,\n flip_sin_to_cos=flip_sin_to_cos,\n freq_shift=freq_shift,\n projection_class_embeddings_input_dim=projection_class_embeddings_input_dim,\n time_embed_dim=time_embed_dim,\n )\n\n if time_embedding_act_fn is None:\n self.time_embed_act = None\n else:\n self.time_embed_act = get_activation(time_embedding_act_fn)\n\n self.down_blocks = nn.ModuleList([])\n self.up_blocks = nn.ModuleList([])\n\n if isinstance(only_cross_attention, bool):\n if mid_block_only_cross_attention is None:\n mid_block_only_cross_attention = only_cross_attention\n\n only_cross_attention = [only_cross_attention] * len(down_block_types)\n\n if mid_block_only_cross_attention is None:\n mid_block_only_cross_attention = False\n\n if isinstance(num_attention_heads, int):\n num_attention_heads = (num_attention_heads,) * len(down_block_types)\n\n if isinstance(attention_head_dim, int):\n attention_head_dim = (attention_head_dim,) * len(down_block_types)\n\n if isinstance(cross_attention_dim, int):\n cross_attention_dim = (cross_attention_dim,) * len(down_block_types)\n\n if isinstance(layers_per_block, int):\n layers_per_block = [layers_per_block] * len(down_block_types)\n\n if isinstance(transformer_layers_per_block, int):\n transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)\n\n if class_embeddings_concat:\n # The time embeddings are concatenated with the class embeddings. The dimension of the\n # time embeddings passed to the down, middle, and up blocks is twice the dimension of the\n # regular time embeddings\n blocks_time_embed_dim = time_embed_dim * 2\n else:\n blocks_time_embed_dim = time_embed_dim\n\n # down\n output_channel = block_out_channels[0]\n for i, down_block_type in enumerate(down_block_types):\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n down_block = get_down_block(\n down_block_type,\n num_layers=layers_per_block[i],\n transformer_layers_per_block=transformer_layers_per_block[i],\n in_channels=input_channel,\n out_channels=output_channel,\n temb_channels=blocks_time_embed_dim,\n add_downsample=not is_final_block,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim[i],\n num_attention_heads=num_attention_heads[i],\n downsample_padding=downsample_padding,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention[i],\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n attention_type=attention_type,\n resnet_skip_time_act=resnet_skip_time_act,\n resnet_out_scale_factor=resnet_out_scale_factor,\n cross_attention_norm=cross_attention_norm,\n attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,\n dropout=dropout,\n )\n self.down_blocks.append(down_block)\n\n # mid\n self.mid_block = get_mid_block(\n mid_block_type,\n temb_channels=blocks_time_embed_dim,\n in_channels=block_out_channels[-1],\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n output_scale_factor=mid_block_scale_factor,\n transformer_layers_per_block=transformer_layers_per_block[-1],\n num_attention_heads=num_attention_heads[-1],\n cross_attention_dim=cross_attention_dim[-1],\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n mid_block_only_cross_attention=mid_block_only_cross_attention,\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n attention_type=attention_type,\n resnet_skip_time_act=resnet_skip_time_act,\n cross_attention_norm=cross_attention_norm,\n attention_head_dim=attention_head_dim[-1],\n dropout=dropout,\n )\n\n # count how many layers upsample the images\n self.num_upsamplers = 0\n\n # up\n reversed_block_out_channels = list(reversed(block_out_channels))\n reversed_num_attention_heads = list(reversed(num_attention_heads))\n reversed_layers_per_block = list(reversed(layers_per_block))\n reversed_cross_attention_dim = list(reversed(cross_attention_dim))\n reversed_transformer_layers_per_block = (\n list(reversed(transformer_layers_per_block))\n if reverse_transformer_layers_per_block is None\n else reverse_transformer_layers_per_block\n )\n only_cross_attention = list(reversed(only_cross_attention))\n\n output_channel = reversed_block_out_channels[0]\n for i, up_block_type in enumerate(up_block_types):\n is_final_block = i == len(block_out_channels) - 1\n\n prev_output_channel = output_channel\n output_channel = reversed_block_out_channels[i]\n input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]\n\n # add upsample block for all BUT final layer\n if not is_final_block:\n add_upsample = True\n self.num_upsamplers += 1\n else:\n add_upsample = False\n\n up_block = get_up_block(\n up_block_type,\n num_layers=reversed_layers_per_block[i] + 1,\n transformer_layers_per_block=reversed_transformer_layers_per_block[i],\n in_channels=input_channel,\n out_channels=output_channel,\n prev_output_channel=prev_output_channel,\n temb_channels=blocks_time_embed_dim,\n add_upsample=add_upsample,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resolution_idx=i,\n resnet_groups=norm_num_groups,\n cross_attention_dim=reversed_cross_attention_dim[i],\n num_attention_heads=reversed_num_attention_heads[i],\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention[i],\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n attention_type=attention_type,\n resnet_skip_time_act=resnet_skip_time_act,\n resnet_out_scale_factor=resnet_out_scale_factor,\n cross_attention_norm=cross_attention_norm,\n attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,\n dropout=dropout,\n )\n self.up_blocks.append(up_block)\n prev_output_channel = output_channel\n\n # out\n if norm_num_groups is not None:\n self.conv_norm_out = nn.GroupNorm(\n num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps\n )\n\n self.conv_act = get_activation(act_fn)\n\n else:\n self.conv_norm_out = None\n self.conv_act = None\n\n conv_out_padding = (conv_out_kernel - 1) // 2\n self.conv_out = nn.Conv2d(\n block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding\n )\n\n self._set_pos_net_if_use_gligen(attention_type=attention_type, cross_attention_dim=cross_attention_dim)\n\n def _check_config(\n self,\n down_block_types: Tuple[str],\n up_block_types: Tuple[str],\n only_cross_attention: Union[bool, Tuple[bool]],\n block_out_channels: Tuple[int],\n layers_per_block: Union[int, Tuple[int]],\n cross_attention_dim: Union[int, Tuple[int]],\n transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple[int]]],\n reverse_transformer_layers_per_block: bool,\n attention_head_dim: int,\n num_attention_heads: Optional[Union[int, Tuple[int]]],\n ):\n if len(down_block_types) != len(up_block_types):\n raise ValueError(\n f\"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.\"\n )\n\n if len(block_out_channels) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}.\"\n )\n\n if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}.\"\n )\n if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None:\n for layer_number_per_block in transformer_layers_per_block:\n if isinstance(layer_number_per_block, list):\n raise ValueError(\"Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.\")\n\n def _set_time_proj(\n self,\n time_embedding_type: str,\n block_out_channels: int,\n flip_sin_to_cos: bool,\n freq_shift: float,\n time_embedding_dim: int,\n ) -> Tuple[int, int]:\n if time_embedding_type == \"fourier\":\n time_embed_dim = time_embedding_dim or block_out_channels[0] * 2\n if time_embed_dim % 2 != 0:\n raise ValueError(f\"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.\")\n self.time_proj = GaussianFourierProjection(\n time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos\n )\n timestep_input_dim = time_embed_dim\n elif time_embedding_type == \"positional\":\n time_embed_dim = time_embedding_dim or block_out_channels[0] * 4\n\n self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)\n timestep_input_dim = block_out_channels[0]\n else:\n raise ValueError(\n f\"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`.\"\n )\n\n return time_embed_dim, timestep_input_dim\n\n def _set_encoder_hid_proj(\n self,\n encoder_hid_dim_type: Optional[str],\n cross_attention_dim: Union[int, Tuple[int]],\n encoder_hid_dim: Optional[int],\n ):\n if encoder_hid_dim_type is None and encoder_hid_dim is not None:\n encoder_hid_dim_type = \"text_proj\"\n self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)\n logger.info(\"encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.\")\n\n if encoder_hid_dim is None and encoder_hid_dim_type is not None:\n raise ValueError(\n f\"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}.\"\n )\n\n if encoder_hid_dim_type == \"text_proj\":\n self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)\n elif encoder_hid_dim_type == \"text_image_proj\":\n # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much\n # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use\n # case when `addition_embed_type == \"text_image_proj\"` (Kandinsky 2.1)`\n self.encoder_hid_proj = TextImageProjection(\n text_embed_dim=encoder_hid_dim,\n image_embed_dim=cross_attention_dim,\n cross_attention_dim=cross_attention_dim,\n )\n elif encoder_hid_dim_type == \"image_proj\":\n # Kandinsky 2.2\n self.encoder_hid_proj = ImageProjection(\n image_embed_dim=encoder_hid_dim,\n cross_attention_dim=cross_attention_dim,\n )\n elif encoder_hid_dim_type is not None:\n raise ValueError(\n f\"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'.\"\n )\n else:\n self.encoder_hid_proj = None\n\n def _set_class_embedding(\n self,\n class_embed_type: Optional[str],\n act_fn: str,\n num_class_embeds: Optional[int],\n projection_class_embeddings_input_dim: Optional[int],\n time_embed_dim: int,\n timestep_input_dim: int,\n ):\n if class_embed_type is None and num_class_embeds is not None:\n self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)\n elif class_embed_type == \"timestep\":\n self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)\n elif class_embed_type == \"identity\":\n self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)\n elif class_embed_type == \"projection\":\n if projection_class_embeddings_input_dim is None:\n raise ValueError(\n \"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set\"\n )\n # The projection `class_embed_type` is the same as the timestep `class_embed_type` except\n # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings\n # 2. it projects from an arbitrary input dimension.\n #\n # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.\n # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.\n # As a result, `TimestepEmbedding` can be passed arbitrary vectors.\n self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)\n elif class_embed_type == \"simple_projection\":\n if projection_class_embeddings_input_dim is None:\n raise ValueError(\n \"`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set\"\n )\n self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)\n else:\n self.class_embedding = None\n\n def _set_add_embedding(\n self,\n addition_embed_type: str,\n addition_embed_type_num_heads: int,\n addition_time_embed_dim: Optional[int],\n flip_sin_to_cos: bool,\n freq_shift: float,\n cross_attention_dim: Optional[int],\n encoder_hid_dim: Optional[int],\n projection_class_embeddings_input_dim: Optional[int],\n time_embed_dim: int,\n ):\n if addition_embed_type == \"text\":\n if encoder_hid_dim is not None:\n text_time_embedding_from_dim = encoder_hid_dim\n else:\n text_time_embedding_from_dim = cross_attention_dim\n\n self.add_embedding = TextTimeEmbedding(\n text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads\n )\n elif addition_embed_type == \"text_image\":\n # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much\n # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use\n # case when `addition_embed_type == \"text_image\"` (Kandinsky 2.1)`\n self.add_embedding = TextImageTimeEmbedding(\n text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim\n )\n elif addition_embed_type == \"text_time\":\n self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)\n self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)\n elif addition_embed_type == \"image\":\n # Kandinsky 2.2\n self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)\n elif addition_embed_type == \"image_hint\":\n # Kandinsky 2.2 ControlNet\n self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)\n elif addition_embed_type is not None:\n raise ValueError(f\"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.\")\n\n def _set_pos_net_if_use_gligen(self, attention_type: str, cross_attention_dim: int):\n if attention_type in [\"gated\", \"gated-text-image\"]:\n positive_len = 768\n if isinstance(cross_attention_dim, int):\n positive_len = cross_attention_dim\n elif isinstance(cross_attention_dim, (list, tuple)):\n positive_len = cross_attention_dim[0]\n\n feature_type = \"text-only\" if attention_type == \"gated\" else \"text-image\"\n self.position_net = GLIGENTextBoundingboxProjection(\n positive_len=positive_len, out_dim=cross_attention_dim, feature_type=feature_type\n )\n\n @property\n def attn_processors(self) -> Dict[str, AttentionProcessor]:\n r\"\"\"\n Returns:\n `dict` of attention processors: A dictionary containing all attention processors used in the model with\n indexed by its weight name.\n \"\"\"\n # set recursively\n processors = {}\n\n def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\n if hasattr(module, \"get_processor\"):\n processors[f\"{name}.processor\"] = module.get_processor(return_deprecated_lora=True)\n\n for sub_name, child in module.named_children():\n fn_recursive_add_processors(f\"{name}.{sub_name}\", child, processors)\n\n return processors\n\n for name, module in self.named_children():\n fn_recursive_add_processors(name, module, processors)\n\n return processors\n\n def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\n r\"\"\"\n Sets the attention processor to use to compute attention.\n\n Parameters:\n processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):\n The instantiated processor class or a dictionary of processor classes that will be set as the processor\n for **all** `Attention` layers.\n\n If `processor` is a dict, the key needs to define the path to the corresponding cross attention\n processor. This is strongly recommended when setting trainable attention processors.\n\n \"\"\"\n count = len(self.attn_processors.keys())\n\n if isinstance(processor, dict) and len(processor) != count:\n raise ValueError(\n f\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\"\n f\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\"\n )\n\n def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\n if hasattr(module, \"set_processor\"):\n if not isinstance(processor, dict):\n module.set_processor(processor)\n else:\n module.set_processor(processor.pop(f\"{name}.processor\"))\n\n for sub_name, child in module.named_children():\n fn_recursive_attn_processor(f\"{name}.{sub_name}\", child, processor)\n\n for name, module in self.named_children():\n fn_recursive_attn_processor(name, module, processor)\n\n def set_default_attn_processor(self):\n \"\"\"\n Disables custom attention processors and sets the default attention implementation.\n \"\"\"\n if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnAddedKVProcessor()\n elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnProcessor()\n else:\n raise ValueError(\n f\"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}\"\n )\n\n self.set_attn_processor(processor)\n\n def set_attention_slice(self, slice_size: Union[str, int, List[int]] = \"auto\"):\n r\"\"\"\n Enable sliced attention computation.\n\n When this option is enabled, the attention module splits the input tensor in slices to compute attention in\n several steps. This is useful for saving some memory in exchange for a small decrease in speed.\n\n Args:\n slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `\"auto\"`):\n When `\"auto\"`, input to the attention heads is halved, so attention is computed in two steps. If\n `\"max\"`, maximum amount of memory is saved by running only one slice at a time. If a number is\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\n must be a multiple of `slice_size`.\n \"\"\"\n sliceable_head_dims = []\n\n def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):\n if hasattr(module, \"set_attention_slice\"):\n sliceable_head_dims.append(module.sliceable_head_dim)\n\n for child in module.children():\n fn_recursive_retrieve_sliceable_dims(child)\n\n # retrieve number of attention layers\n for module in self.children():\n fn_recursive_retrieve_sliceable_dims(module)\n\n num_sliceable_layers = len(sliceable_head_dims)\n\n if slice_size == \"auto\":\n # half the attention head size is usually a good trade-off between\n # speed and memory\n slice_size = [dim // 2 for dim in sliceable_head_dims]\n elif slice_size == \"max\":\n # make smallest slice possible\n slice_size = num_sliceable_layers * [1]\n\n slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\n\n if len(slice_size) != len(sliceable_head_dims):\n raise ValueError(\n f\"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different\"\n f\" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}.\"\n )\n\n for i in range(len(slice_size)):\n size = slice_size[i]\n dim = sliceable_head_dims[i]\n if size is not None and size > dim:\n raise ValueError(f\"size {size} has to be smaller or equal to {dim}.\")\n\n # Recursively walk through all the children.\n # Any children which exposes the set_attention_slice method\n # gets the message\n def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):\n if hasattr(module, \"set_attention_slice\"):\n module.set_attention_slice(slice_size.pop())\n\n for child in module.children():\n fn_recursive_set_attention_slice(child, slice_size)\n\n reversed_slice_size = list(reversed(slice_size))\n for module in self.children():\n fn_recursive_set_attention_slice(module, reversed_slice_size)\n\n def _set_gradient_checkpointing(self, module, value=False):\n if hasattr(module, \"gradient_checkpointing\"):\n module.gradient_checkpointing = value\n\n def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):\n r\"\"\"Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.\n\n The suffixes after the scaling factors represent the stage blocks where they are being applied.\n\n Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that\n are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.\n\n Args:\n s1 (`float`):\n Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to\n mitigate the \"oversmoothing effect\" in the enhanced denoising process.\n s2 (`float`):\n Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to\n mitigate the \"oversmoothing effect\" in the enhanced denoising process.\n b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.\n b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.\n \"\"\"\n for i, upsample_block in enumerate(self.up_blocks):\n setattr(upsample_block, \"s1\", s1)\n setattr(upsample_block, \"s2\", s2)\n setattr(upsample_block, \"b1\", b1)\n setattr(upsample_block, \"b2\", b2)\n\n def disable_freeu(self):\n \"\"\"Disables the FreeU mechanism.\"\"\"\n freeu_keys = {\"s1\", \"s2\", \"b1\", \"b2\"}\n for i, upsample_block in enumerate(self.up_blocks):\n for k in freeu_keys:\n if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None:\n setattr(upsample_block, k, None)\n\n def fuse_qkv_projections(self):\n \"\"\"\n Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)\n are fused. For cross-attention modules, key and value projection matrices are fused.\n\n \n\n This API is 🧪 experimental.\n\n \n \"\"\"\n self.original_attn_processors = None\n\n for _, attn_processor in self.attn_processors.items():\n if \"Added\" in str(attn_processor.__class__.__name__):\n raise ValueError(\"`fuse_qkv_projections()` is not supported for models having added KV projections.\")\n\n self.original_attn_processors = self.attn_processors\n\n for module in self.modules():\n if isinstance(module, Attention):\n module.fuse_projections(fuse=True)\n\n def unfuse_qkv_projections(self):\n \"\"\"Disables the fused QKV projection if enabled.\n\n \n\n This API is 🧪 experimental.\n\n \n\n \"\"\"\n if self.original_attn_processors is not None:\n self.set_attn_processor(self.original_attn_processors)\n\n def get_time_embed(\n self, sample: torch.Tensor, timestep: Union[torch.Tensor, float, int]\n ) -> Optional[torch.Tensor]:\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can\n # This would be a good case for the `match` statement (Python 3.10+)\n is_mps = sample.device.type == \"mps\"\n if isinstance(timestep, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\n elif len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(sample.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timesteps = timesteps.expand(sample.shape[0])\n\n t_emb = self.time_proj(timesteps)\n # `Timesteps` does not contain any weights and will always return f32 tensors\n # but time_embedding might actually be running in fp16. so we need to cast here.\n # there might be better ways to encapsulate this.\n t_emb = t_emb.to(dtype=sample.dtype)\n return t_emb\n\n def get_class_embed(self, sample: torch.Tensor, class_labels: Optional[torch.Tensor]) -> Optional[torch.Tensor]:\n class_emb = None\n if self.class_embedding is not None:\n if class_labels is None:\n raise ValueError(\"class_labels should be provided when num_class_embeds > 0\")\n\n if self.config.class_embed_type == \"timestep\":\n class_labels = self.time_proj(class_labels)\n\n # `Timesteps` does not contain any weights and will always return f32 tensors\n # there might be better ways to encapsulate this.\n class_labels = class_labels.to(dtype=sample.dtype)\n\n class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)\n return class_emb\n\n def get_aug_embed(\n self, emb: torch.Tensor, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any]\n ) -> Optional[torch.Tensor]:\n aug_emb = None\n if self.config.addition_embed_type == \"text\":\n aug_emb = self.add_embedding(encoder_hidden_states)\n elif self.config.addition_embed_type == \"text_image\":\n # Kandinsky 2.1 - style\n if \"image_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`\"\n )\n\n image_embs = added_cond_kwargs.get(\"image_embeds\")\n text_embs = added_cond_kwargs.get(\"text_embeds\", encoder_hidden_states)\n aug_emb = self.add_embedding(text_embs, image_embs)\n elif self.config.addition_embed_type == \"text_time\":\n # SDXL - style\n if \"text_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`\"\n )\n text_embeds = added_cond_kwargs.get(\"text_embeds\")\n if \"time_ids\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`\"\n )\n time_ids = added_cond_kwargs.get(\"time_ids\")\n time_embeds = self.add_time_proj(time_ids.flatten())\n time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))\n add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)\n add_embeds = add_embeds.to(emb.dtype)\n aug_emb = self.add_embedding(add_embeds)\n elif self.config.addition_embed_type == \"image\":\n # Kandinsky 2.2 - style\n if \"image_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`\"\n )\n image_embs = added_cond_kwargs.get(\"image_embeds\")\n aug_emb = self.add_embedding(image_embs)\n elif self.config.addition_embed_type == \"image_hint\":\n # Kandinsky 2.2 - style\n if \"image_embeds\" not in added_cond_kwargs or \"hint\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`\"\n )\n image_embs = added_cond_kwargs.get(\"image_embeds\")\n hint = added_cond_kwargs.get(\"hint\")\n aug_emb = self.add_embedding(image_embs, hint)\n return aug_emb\n\n def process_encoder_hidden_states(\n self, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any]\n ) -> torch.Tensor:\n if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == \"text_proj\":\n encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)\n elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == \"text_image_proj\":\n # Kandinsky 2.1 - style\n if \"image_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`\"\n )\n\n image_embeds = added_cond_kwargs.get(\"image_embeds\")\n encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)\n elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == \"image_proj\":\n # Kandinsky 2.2 - style\n if \"image_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`\"\n )\n image_embeds = added_cond_kwargs.get(\"image_embeds\")\n encoder_hidden_states = self.encoder_hid_proj(image_embeds)\n elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == \"ip_image_proj\":\n if \"image_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`\"\n )\n image_embeds = added_cond_kwargs.get(\"image_embeds\")\n image_embeds = self.encoder_hid_proj(image_embeds)\n encoder_hidden_states = (encoder_hidden_states, image_embeds)\n return encoder_hidden_states\n\n def forward(\n self,\n sample: torch.Tensor,\n timestep: Union[torch.Tensor, float, int],\n encoder_hidden_states: torch.Tensor,\n class_labels: Optional[torch.Tensor] = None,\n timestep_cond: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,\n down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\n mid_block_additional_residual: Optional[torch.Tensor] = None,\n down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ) -> Union[UNet2DConditionOutput, Tuple]:\n r\"\"\"\n The [`UNet2DConditionModel`] forward method.\n\n Args:\n sample (`torch.Tensor`):\n The noisy input tensor with the following shape `(batch, channel, height, width)`.\n timestep (`torch.Tensor` or `float` or `int`): The number of timesteps to denoise an input.\n encoder_hidden_states (`torch.Tensor`):\n The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.\n class_labels (`torch.Tensor`, *optional*, defaults to `None`):\n Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.\n timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`):\n Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed\n through the `self.time_embedding` layer to obtain the timestep embeddings.\n attention_mask (`torch.Tensor`, *optional*, defaults to `None`):\n An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask\n is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large\n negative values to the attention scores corresponding to \"discard\" tokens.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n added_cond_kwargs: (`dict`, *optional*):\n A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that\n are passed along to the UNet blocks.\n down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):\n A tuple of tensors that if specified are added to the residuals of down unet blocks.\n mid_block_additional_residual: (`torch.Tensor`, *optional*):\n A tensor that if specified is added to the residual of the middle unet block.\n down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*):\n additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s)\n encoder_attention_mask (`torch.Tensor`):\n A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If\n `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,\n which adds large negative values to the attention scores corresponding to \"discard\" tokens.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain\n tuple.\n\n Returns:\n [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:\n If `return_dict` is True, an [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] is returned,\n otherwise a `tuple` is returned where the first element is the sample tensor.\n \"\"\"\n # By default samples have to be AT least a multiple of the overall upsampling factor.\n # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).\n # However, the upsampling interpolation output size can be forced to fit any upsampling size\n # on the fly if necessary.\n default_overall_up_factor = 2**self.num_upsamplers\n\n # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`\n forward_upsample_size = False\n upsample_size = None\n\n for dim in sample.shape[-2:]:\n if dim % default_overall_up_factor != 0:\n # Forward upsample size to force interpolation output size.\n forward_upsample_size = True\n break\n\n # ensure attention_mask is a bias, and give it a singleton query_tokens dimension\n # expects mask of shape:\n # [batch, key_tokens]\n # adds singleton query_tokens dimension:\n # [batch, 1, key_tokens]\n # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:\n # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)\n # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)\n if attention_mask is not None:\n # assume that mask is expressed as:\n # (1 = keep, 0 = discard)\n # convert mask into a bias that can be added to attention scores:\n # (keep = +0, discard = -10000.0)\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n # convert encoder_attention_mask to a bias the same way we do for attention_mask\n if encoder_attention_mask is not None:\n encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0\n encoder_attention_mask = encoder_attention_mask.unsqueeze(1)\n\n # 0. center input if necessary\n if self.config.center_input_sample:\n sample = 2 * sample - 1.0\n\n # 1. time\n t_emb = self.get_time_embed(sample=sample, timestep=timestep)\n emb = self.time_embedding(t_emb, timestep_cond)\n aug_emb = None\n\n class_emb = self.get_class_embed(sample=sample, class_labels=class_labels)\n if class_emb is not None:\n if self.config.class_embeddings_concat:\n emb = torch.cat([emb, class_emb], dim=-1)\n else:\n emb = emb + class_emb\n\n aug_emb = self.get_aug_embed(\n emb=emb, encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs\n )\n if self.config.addition_embed_type == \"image_hint\":\n aug_emb, hint = aug_emb\n sample = torch.cat([sample, hint], dim=1)\n\n emb = emb + aug_emb if aug_emb is not None else emb\n\n if self.time_embed_act is not None:\n emb = self.time_embed_act(emb)\n\n encoder_hidden_states = self.process_encoder_hidden_states(\n encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs\n )\n\n # 2. pre-process\n sample = self.conv_in(sample)\n\n # 2.5 GLIGEN position net\n if cross_attention_kwargs is not None and cross_attention_kwargs.get(\"gligen\", None) is not None:\n cross_attention_kwargs = cross_attention_kwargs.copy()\n gligen_args = cross_attention_kwargs.pop(\"gligen\")\n cross_attention_kwargs[\"gligen\"] = {\"objs\": self.position_net(**gligen_args)}\n\n # 3. down\n # we're popping the `scale` instead of getting it because otherwise `scale` will be propagated\n # to the internal blocks and will raise deprecation warnings. this will be confusing for our users.\n if cross_attention_kwargs is not None:\n cross_attention_kwargs = cross_attention_kwargs.copy()\n lora_scale = cross_attention_kwargs.pop(\"scale\", 1.0)\n else:\n lora_scale = 1.0\n\n if USE_PEFT_BACKEND:\n # weight the lora layers by setting `lora_scale` for each PEFT layer\n scale_lora_layers(self, lora_scale)\n\n is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None\n # using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets\n is_adapter = down_intrablock_additional_residuals is not None\n # maintain backward compatibility for legacy usage, where\n # T2I-Adapter and ControlNet both use down_block_additional_residuals arg\n # but can only use one or the other\n if not is_adapter and mid_block_additional_residual is None and down_block_additional_residuals is not None:\n deprecate(\n \"T2I should not use down_block_additional_residuals\",\n \"1.3.0\",\n \"Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \\\n and will be removed in diffusers 1.3.0. `down_block_additional_residuals` should only be used \\\n for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. \",\n standard_warn=False,\n )\n down_intrablock_additional_residuals = down_block_additional_residuals\n is_adapter = True\n\n down_block_res_samples = (sample,)\n for downsample_block in self.down_blocks:\n if hasattr(downsample_block, \"has_cross_attention\") and downsample_block.has_cross_attention:\n # For t2i-adapter CrossAttnDownBlock2D\n additional_residuals = {}\n if is_adapter and len(down_intrablock_additional_residuals) > 0:\n additional_residuals[\"additional_residuals\"] = down_intrablock_additional_residuals.pop(0)\n\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n encoder_attention_mask=encoder_attention_mask,\n **additional_residuals,\n )\n else:\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb)\n if is_adapter and len(down_intrablock_additional_residuals) > 0:\n sample += down_intrablock_additional_residuals.pop(0)\n\n down_block_res_samples += res_samples\n\n if is_controlnet:\n new_down_block_res_samples = ()\n\n for down_block_res_sample, down_block_additional_residual in zip(\n down_block_res_samples, down_block_additional_residuals\n ):\n down_block_res_sample = down_block_res_sample + down_block_additional_residual\n new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)\n\n down_block_res_samples = new_down_block_res_samples\n\n # 4. mid\n if self.mid_block is not None:\n if hasattr(self.mid_block, \"has_cross_attention\") and self.mid_block.has_cross_attention:\n sample = self.mid_block(\n sample,\n emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n encoder_attention_mask=encoder_attention_mask,\n )\n else:\n sample = self.mid_block(sample, emb)\n\n # To support T2I-Adapter-XL\n if (\n is_adapter\n and len(down_intrablock_additional_residuals) > 0\n and sample.shape == down_intrablock_additional_residuals[0].shape\n ):\n sample += down_intrablock_additional_residuals.pop(0)\n\n if is_controlnet:\n sample = sample + mid_block_additional_residual\n\n # 5. up\n for i, upsample_block in enumerate(self.up_blocks):\n is_final_block = i == len(self.up_blocks) - 1\n\n res_samples = down_block_res_samples[-len(upsample_block.resnets) :]\n down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]\n\n # if we have not reached the final block and need to forward the\n # upsample size, we do it here\n if not is_final_block and forward_upsample_size:\n upsample_size = down_block_res_samples[-1].shape[2:]\n\n if hasattr(upsample_block, \"has_cross_attention\") and upsample_block.has_cross_attention:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n upsample_size=upsample_size,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n )\n else:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n upsample_size=upsample_size,\n )\n\n # 6. post-process\n if self.conv_norm_out:\n sample = self.conv_norm_out(sample)\n sample = self.conv_act(sample)\n sample = self.conv_out(sample)\n\n if USE_PEFT_BACKEND:\n # remove `lora_scale` from each PEFT layer\n unscale_lora_layers(self, lora_scale)\n\n if not return_dict:\n return (sample,)\n\n return UNet2DConditionOutput(sample=sample)\n" }, { "path": "diffusers/src/diffusers/models/unets/unet_2d_condition_flax.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Dict, Optional, Tuple, Union\n\nimport flax\nimport flax.linen as nn\nimport jax\nimport jax.numpy as jnp\nfrom flax.core.frozen_dict import FrozenDict\n\nfrom ...configuration_utils import ConfigMixin, flax_register_to_config\nfrom ...utils import BaseOutput\nfrom ..embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps\nfrom ..modeling_flax_utils import FlaxModelMixin\nfrom .unet_2d_blocks_flax import (\n FlaxCrossAttnDownBlock2D,\n FlaxCrossAttnUpBlock2D,\n FlaxDownBlock2D,\n FlaxUNetMidBlock2DCrossAttn,\n FlaxUpBlock2D,\n)\n\n\n@flax.struct.dataclass\nclass FlaxUNet2DConditionOutput(BaseOutput):\n \"\"\"\n The output of [`FlaxUNet2DConditionModel`].\n\n Args:\n sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`):\n The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.\n \"\"\"\n\n sample: jnp.ndarray\n\n\n@flax_register_to_config\nclass FlaxUNet2DConditionModel(nn.Module, FlaxModelMixin, ConfigMixin):\n r\"\"\"\n A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample\n shaped output.\n\n This model inherits from [`FlaxModelMixin`]. Check the superclass documentation for it's generic methods\n implemented for all models (such as downloading or saving).\n\n This model is also a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)\n subclass. Use it as a regular Flax Linen module and refer to the Flax documentation for all matters related to its\n general usage and behavior.\n\n Inherent JAX features such as the following are supported:\n - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)\n - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)\n - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)\n - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)\n\n Parameters:\n sample_size (`int`, *optional*):\n The size of the input sample.\n in_channels (`int`, *optional*, defaults to 4):\n The number of channels in the input sample.\n out_channels (`int`, *optional*, defaults to 4):\n The number of channels in the output.\n down_block_types (`Tuple[str]`, *optional*, defaults to `(\"FlaxCrossAttnDownBlock2D\", \"FlaxCrossAttnDownBlock2D\", \"FlaxCrossAttnDownBlock2D\", \"FlaxDownBlock2D\")`):\n The tuple of downsample blocks to use.\n up_block_types (`Tuple[str]`, *optional*, defaults to `(\"FlaxUpBlock2D\", \"FlaxCrossAttnUpBlock2D\", \"FlaxCrossAttnUpBlock2D\", \"FlaxCrossAttnUpBlock2D\")`):\n The tuple of upsample blocks to use.\n mid_block_type (`str`, *optional*, defaults to `\"UNetMidBlock2DCrossAttn\"`):\n Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`. If `None`, the mid block layer\n is skipped.\n block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):\n The tuple of output channels for each block.\n layers_per_block (`int`, *optional*, defaults to 2):\n The number of layers per block.\n attention_head_dim (`int` or `Tuple[int]`, *optional*, defaults to 8):\n The dimension of the attention heads.\n num_attention_heads (`int` or `Tuple[int]`, *optional*):\n The number of attention heads.\n cross_attention_dim (`int`, *optional*, defaults to 768):\n The dimension of the cross attention features.\n dropout (`float`, *optional*, defaults to 0):\n Dropout probability for down, up and bottleneck blocks.\n flip_sin_to_cos (`bool`, *optional*, defaults to `True`):\n Whether to flip the sin to cos in the time embedding.\n freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.\n use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):\n Enable memory efficient attention as described [here](https://arxiv.org/abs/2112.05682).\n split_head_dim (`bool`, *optional*, defaults to `False`):\n Whether to split the head dimension into a new axis for the self-attention computation. In most cases,\n enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL.\n \"\"\"\n\n sample_size: int = 32\n in_channels: int = 4\n out_channels: int = 4\n down_block_types: Tuple[str, ...] = (\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"DownBlock2D\",\n )\n up_block_types: Tuple[str, ...] = (\"UpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\")\n mid_block_type: Optional[str] = \"UNetMidBlock2DCrossAttn\"\n only_cross_attention: Union[bool, Tuple[bool]] = False\n block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280)\n layers_per_block: int = 2\n attention_head_dim: Union[int, Tuple[int, ...]] = 8\n num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None\n cross_attention_dim: int = 1280\n dropout: float = 0.0\n use_linear_projection: bool = False\n dtype: jnp.dtype = jnp.float32\n flip_sin_to_cos: bool = True\n freq_shift: int = 0\n use_memory_efficient_attention: bool = False\n split_head_dim: bool = False\n transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1\n addition_embed_type: Optional[str] = None\n addition_time_embed_dim: Optional[int] = None\n addition_embed_type_num_heads: int = 64\n projection_class_embeddings_input_dim: Optional[int] = None\n\n def init_weights(self, rng: jax.Array) -> FrozenDict:\n # init input tensors\n sample_shape = (1, self.in_channels, self.sample_size, self.sample_size)\n sample = jnp.zeros(sample_shape, dtype=jnp.float32)\n timesteps = jnp.ones((1,), dtype=jnp.int32)\n encoder_hidden_states = jnp.zeros((1, 1, self.cross_attention_dim), dtype=jnp.float32)\n\n params_rng, dropout_rng = jax.random.split(rng)\n rngs = {\"params\": params_rng, \"dropout\": dropout_rng}\n\n added_cond_kwargs = None\n if self.addition_embed_type == \"text_time\":\n # we retrieve the expected `text_embeds_dim` by first checking if the architecture is a refiner\n # or non-refiner architecture and then by \"reverse-computing\" from `projection_class_embeddings_input_dim`\n is_refiner = (\n 5 * self.config.addition_time_embed_dim + self.config.cross_attention_dim\n == self.config.projection_class_embeddings_input_dim\n )\n num_micro_conditions = 5 if is_refiner else 6\n\n text_embeds_dim = self.config.projection_class_embeddings_input_dim - (\n num_micro_conditions * self.config.addition_time_embed_dim\n )\n\n time_ids_channels = self.projection_class_embeddings_input_dim - text_embeds_dim\n time_ids_dims = time_ids_channels // self.addition_time_embed_dim\n added_cond_kwargs = {\n \"text_embeds\": jnp.zeros((1, text_embeds_dim), dtype=jnp.float32),\n \"time_ids\": jnp.zeros((1, time_ids_dims), dtype=jnp.float32),\n }\n return self.init(rngs, sample, timesteps, encoder_hidden_states, added_cond_kwargs)[\"params\"]\n\n def setup(self) -> None:\n block_out_channels = self.block_out_channels\n time_embed_dim = block_out_channels[0] * 4\n\n if self.num_attention_heads is not None:\n raise ValueError(\n \"At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.\"\n )\n\n # If `num_attention_heads` is not defined (which is the case for most models)\n # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.\n # The reason for this behavior is to correct for incorrectly named variables that were introduced\n # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131\n # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking\n # which is why we correct for the naming here.\n num_attention_heads = self.num_attention_heads or self.attention_head_dim\n\n # input\n self.conv_in = nn.Conv(\n block_out_channels[0],\n kernel_size=(3, 3),\n strides=(1, 1),\n padding=((1, 1), (1, 1)),\n dtype=self.dtype,\n )\n\n # time\n self.time_proj = FlaxTimesteps(\n block_out_channels[0], flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.config.freq_shift\n )\n self.time_embedding = FlaxTimestepEmbedding(time_embed_dim, dtype=self.dtype)\n\n only_cross_attention = self.only_cross_attention\n if isinstance(only_cross_attention, bool):\n only_cross_attention = (only_cross_attention,) * len(self.down_block_types)\n\n if isinstance(num_attention_heads, int):\n num_attention_heads = (num_attention_heads,) * len(self.down_block_types)\n\n # transformer layers per block\n transformer_layers_per_block = self.transformer_layers_per_block\n if isinstance(transformer_layers_per_block, int):\n transformer_layers_per_block = [transformer_layers_per_block] * len(self.down_block_types)\n\n # addition embed types\n if self.addition_embed_type is None:\n self.add_embedding = None\n elif self.addition_embed_type == \"text_time\":\n if self.addition_time_embed_dim is None:\n raise ValueError(\n f\"addition_embed_type {self.addition_embed_type} requires `addition_time_embed_dim` to not be None\"\n )\n self.add_time_proj = FlaxTimesteps(self.addition_time_embed_dim, self.flip_sin_to_cos, self.freq_shift)\n self.add_embedding = FlaxTimestepEmbedding(time_embed_dim, dtype=self.dtype)\n else:\n raise ValueError(f\"addition_embed_type: {self.addition_embed_type} must be None or `text_time`.\")\n\n # down\n down_blocks = []\n output_channel = block_out_channels[0]\n for i, down_block_type in enumerate(self.down_block_types):\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n if down_block_type == \"CrossAttnDownBlock2D\":\n down_block = FlaxCrossAttnDownBlock2D(\n in_channels=input_channel,\n out_channels=output_channel,\n dropout=self.dropout,\n num_layers=self.layers_per_block,\n transformer_layers_per_block=transformer_layers_per_block[i],\n num_attention_heads=num_attention_heads[i],\n add_downsample=not is_final_block,\n use_linear_projection=self.use_linear_projection,\n only_cross_attention=only_cross_attention[i],\n use_memory_efficient_attention=self.use_memory_efficient_attention,\n split_head_dim=self.split_head_dim,\n dtype=self.dtype,\n )\n else:\n down_block = FlaxDownBlock2D(\n in_channels=input_channel,\n out_channels=output_channel,\n dropout=self.dropout,\n num_layers=self.layers_per_block,\n add_downsample=not is_final_block,\n dtype=self.dtype,\n )\n\n down_blocks.append(down_block)\n self.down_blocks = down_blocks\n\n # mid\n if self.config.mid_block_type == \"UNetMidBlock2DCrossAttn\":\n self.mid_block = FlaxUNetMidBlock2DCrossAttn(\n in_channels=block_out_channels[-1],\n dropout=self.dropout,\n num_attention_heads=num_attention_heads[-1],\n transformer_layers_per_block=transformer_layers_per_block[-1],\n use_linear_projection=self.use_linear_projection,\n use_memory_efficient_attention=self.use_memory_efficient_attention,\n split_head_dim=self.split_head_dim,\n dtype=self.dtype,\n )\n elif self.config.mid_block_type is None:\n self.mid_block = None\n else:\n raise ValueError(f\"Unexpected mid_block_type {self.config.mid_block_type}\")\n\n # up\n up_blocks = []\n reversed_block_out_channels = list(reversed(block_out_channels))\n reversed_num_attention_heads = list(reversed(num_attention_heads))\n only_cross_attention = list(reversed(only_cross_attention))\n output_channel = reversed_block_out_channels[0]\n reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))\n for i, up_block_type in enumerate(self.up_block_types):\n prev_output_channel = output_channel\n output_channel = reversed_block_out_channels[i]\n input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]\n\n is_final_block = i == len(block_out_channels) - 1\n\n if up_block_type == \"CrossAttnUpBlock2D\":\n up_block = FlaxCrossAttnUpBlock2D(\n in_channels=input_channel,\n out_channels=output_channel,\n prev_output_channel=prev_output_channel,\n num_layers=self.layers_per_block + 1,\n transformer_layers_per_block=reversed_transformer_layers_per_block[i],\n num_attention_heads=reversed_num_attention_heads[i],\n add_upsample=not is_final_block,\n dropout=self.dropout,\n use_linear_projection=self.use_linear_projection,\n only_cross_attention=only_cross_attention[i],\n use_memory_efficient_attention=self.use_memory_efficient_attention,\n split_head_dim=self.split_head_dim,\n dtype=self.dtype,\n )\n else:\n up_block = FlaxUpBlock2D(\n in_channels=input_channel,\n out_channels=output_channel,\n prev_output_channel=prev_output_channel,\n num_layers=self.layers_per_block + 1,\n add_upsample=not is_final_block,\n dropout=self.dropout,\n dtype=self.dtype,\n )\n\n up_blocks.append(up_block)\n prev_output_channel = output_channel\n self.up_blocks = up_blocks\n\n # out\n self.conv_norm_out = nn.GroupNorm(num_groups=32, epsilon=1e-5)\n self.conv_out = nn.Conv(\n self.out_channels,\n kernel_size=(3, 3),\n strides=(1, 1),\n padding=((1, 1), (1, 1)),\n dtype=self.dtype,\n )\n\n def __call__(\n self,\n sample: jnp.ndarray,\n timesteps: Union[jnp.ndarray, float, int],\n encoder_hidden_states: jnp.ndarray,\n added_cond_kwargs: Optional[Union[Dict, FrozenDict]] = None,\n down_block_additional_residuals: Optional[Tuple[jnp.ndarray, ...]] = None,\n mid_block_additional_residual: Optional[jnp.ndarray] = None,\n return_dict: bool = True,\n train: bool = False,\n ) -> Union[FlaxUNet2DConditionOutput, Tuple[jnp.ndarray]]:\n r\"\"\"\n Args:\n sample (`jnp.ndarray`): (batch, channel, height, width) noisy inputs tensor\n timestep (`jnp.ndarray` or `float` or `int`): timesteps\n encoder_hidden_states (`jnp.ndarray`): (batch_size, sequence_length, hidden_size) encoder hidden states\n added_cond_kwargs: (`dict`, *optional*):\n A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that\n are passed along to the UNet blocks.\n down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):\n A tuple of tensors that if specified are added to the residuals of down unet blocks.\n mid_block_additional_residual: (`torch.Tensor`, *optional*):\n A tensor that if specified is added to the residual of the middle unet block.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] instead of\n a plain tuple.\n train (`bool`, *optional*, defaults to `False`):\n Use deterministic functions and disable dropout when not training.\n\n Returns:\n [`~models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] or `tuple`:\n [`~models.unets.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] if `return_dict` is True, otherwise a\n `tuple`. When returning a tuple, the first element is the sample tensor.\n \"\"\"\n # 1. time\n if not isinstance(timesteps, jnp.ndarray):\n timesteps = jnp.array([timesteps], dtype=jnp.int32)\n elif isinstance(timesteps, jnp.ndarray) and len(timesteps.shape) == 0:\n timesteps = timesteps.astype(dtype=jnp.float32)\n timesteps = jnp.expand_dims(timesteps, 0)\n\n t_emb = self.time_proj(timesteps)\n t_emb = self.time_embedding(t_emb)\n\n # additional embeddings\n aug_emb = None\n if self.addition_embed_type == \"text_time\":\n if added_cond_kwargs is None:\n raise ValueError(\n f\"Need to provide argument `added_cond_kwargs` for {self.__class__} when using `addition_embed_type={self.addition_embed_type}`\"\n )\n text_embeds = added_cond_kwargs.get(\"text_embeds\")\n if text_embeds is None:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`\"\n )\n time_ids = added_cond_kwargs.get(\"time_ids\")\n if time_ids is None:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`\"\n )\n # compute time embeds\n time_embeds = self.add_time_proj(jnp.ravel(time_ids)) # (1, 6) => (6,) => (6, 256)\n time_embeds = jnp.reshape(time_embeds, (text_embeds.shape[0], -1))\n add_embeds = jnp.concatenate([text_embeds, time_embeds], axis=-1)\n aug_emb = self.add_embedding(add_embeds)\n\n t_emb = t_emb + aug_emb if aug_emb is not None else t_emb\n\n # 2. pre-process\n sample = jnp.transpose(sample, (0, 2, 3, 1))\n sample = self.conv_in(sample)\n\n # 3. down\n down_block_res_samples = (sample,)\n for down_block in self.down_blocks:\n if isinstance(down_block, FlaxCrossAttnDownBlock2D):\n sample, res_samples = down_block(sample, t_emb, encoder_hidden_states, deterministic=not train)\n else:\n sample, res_samples = down_block(sample, t_emb, deterministic=not train)\n down_block_res_samples += res_samples\n\n if down_block_additional_residuals is not None:\n new_down_block_res_samples = ()\n\n for down_block_res_sample, down_block_additional_residual in zip(\n down_block_res_samples, down_block_additional_residuals\n ):\n down_block_res_sample += down_block_additional_residual\n new_down_block_res_samples += (down_block_res_sample,)\n\n down_block_res_samples = new_down_block_res_samples\n\n # 4. mid\n if self.mid_block is not None:\n sample = self.mid_block(sample, t_emb, encoder_hidden_states, deterministic=not train)\n\n if mid_block_additional_residual is not None:\n sample += mid_block_additional_residual\n\n # 5. up\n for up_block in self.up_blocks:\n res_samples = down_block_res_samples[-(self.layers_per_block + 1) :]\n down_block_res_samples = down_block_res_samples[: -(self.layers_per_block + 1)]\n if isinstance(up_block, FlaxCrossAttnUpBlock2D):\n sample = up_block(\n sample,\n temb=t_emb,\n encoder_hidden_states=encoder_hidden_states,\n res_hidden_states_tuple=res_samples,\n deterministic=not train,\n )\n else:\n sample = up_block(sample, temb=t_emb, res_hidden_states_tuple=res_samples, deterministic=not train)\n\n # 6. post-process\n sample = self.conv_norm_out(sample)\n sample = nn.silu(sample)\n sample = self.conv_out(sample)\n sample = jnp.transpose(sample, (0, 3, 1, 2))\n\n if not return_dict:\n return (sample,)\n\n return FlaxUNet2DConditionOutput(sample=sample)\n" }, { "path": "diffusers/src/diffusers/models/unets/unet_3d_blocks.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Any, Dict, Optional, Tuple, Union\n\nimport torch\nfrom torch import nn\n\nfrom ...utils import deprecate, is_torch_version, logging\nfrom ...utils.torch_utils import apply_freeu\nfrom ..attention import Attention\nfrom ..resnet import (\n Downsample2D,\n ResnetBlock2D,\n SpatioTemporalResBlock,\n TemporalConvLayer,\n Upsample2D,\n)\nfrom ..transformers.dual_transformer_2d import DualTransformer2DModel\nfrom ..transformers.transformer_2d import Transformer2DModel\nfrom ..transformers.transformer_temporal import (\n TransformerSpatioTemporalModel,\n TransformerTemporalModel,\n)\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef get_down_block(\n down_block_type: str,\n num_layers: int,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n add_downsample: bool,\n resnet_eps: float,\n resnet_act_fn: str,\n num_attention_heads: int,\n resnet_groups: Optional[int] = None,\n cross_attention_dim: Optional[int] = None,\n downsample_padding: Optional[int] = None,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = True,\n only_cross_attention: bool = False,\n upcast_attention: bool = False,\n resnet_time_scale_shift: str = \"default\",\n temporal_num_attention_heads: int = 8,\n temporal_max_seq_length: int = 32,\n transformer_layers_per_block: int = 1,\n) -> Union[\n \"DownBlock3D\",\n \"CrossAttnDownBlock3D\",\n \"DownBlockMotion\",\n \"CrossAttnDownBlockMotion\",\n \"DownBlockSpatioTemporal\",\n \"CrossAttnDownBlockSpatioTemporal\",\n]:\n if down_block_type == \"DownBlock3D\":\n return DownBlock3D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n add_downsample=add_downsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n downsample_padding=downsample_padding,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n elif down_block_type == \"CrossAttnDownBlock3D\":\n if cross_attention_dim is None:\n raise ValueError(\"cross_attention_dim must be specified for CrossAttnDownBlock3D\")\n return CrossAttnDownBlock3D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n add_downsample=add_downsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n downsample_padding=downsample_padding,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=num_attention_heads,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n if down_block_type == \"DownBlockMotion\":\n return DownBlockMotion(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n add_downsample=add_downsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n downsample_padding=downsample_padding,\n resnet_time_scale_shift=resnet_time_scale_shift,\n temporal_num_attention_heads=temporal_num_attention_heads,\n temporal_max_seq_length=temporal_max_seq_length,\n )\n elif down_block_type == \"CrossAttnDownBlockMotion\":\n if cross_attention_dim is None:\n raise ValueError(\"cross_attention_dim must be specified for CrossAttnDownBlockMotion\")\n return CrossAttnDownBlockMotion(\n num_layers=num_layers,\n transformer_layers_per_block=transformer_layers_per_block,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n add_downsample=add_downsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n downsample_padding=downsample_padding,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=num_attention_heads,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n temporal_num_attention_heads=temporal_num_attention_heads,\n temporal_max_seq_length=temporal_max_seq_length,\n )\n elif down_block_type == \"DownBlockSpatioTemporal\":\n # added for SDV\n return DownBlockSpatioTemporal(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n add_downsample=add_downsample,\n )\n elif down_block_type == \"CrossAttnDownBlockSpatioTemporal\":\n # added for SDV\n if cross_attention_dim is None:\n raise ValueError(\"cross_attention_dim must be specified for CrossAttnDownBlockSpatioTemporal\")\n return CrossAttnDownBlockSpatioTemporal(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n num_layers=num_layers,\n transformer_layers_per_block=transformer_layers_per_block,\n add_downsample=add_downsample,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=num_attention_heads,\n )\n\n raise ValueError(f\"{down_block_type} does not exist.\")\n\n\ndef get_up_block(\n up_block_type: str,\n num_layers: int,\n in_channels: int,\n out_channels: int,\n prev_output_channel: int,\n temb_channels: int,\n add_upsample: bool,\n resnet_eps: float,\n resnet_act_fn: str,\n num_attention_heads: int,\n resolution_idx: Optional[int] = None,\n resnet_groups: Optional[int] = None,\n cross_attention_dim: Optional[int] = None,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = True,\n only_cross_attention: bool = False,\n upcast_attention: bool = False,\n resnet_time_scale_shift: str = \"default\",\n temporal_num_attention_heads: int = 8,\n temporal_cross_attention_dim: Optional[int] = None,\n temporal_max_seq_length: int = 32,\n transformer_layers_per_block: int = 1,\n dropout: float = 0.0,\n) -> Union[\n \"UpBlock3D\",\n \"CrossAttnUpBlock3D\",\n \"UpBlockMotion\",\n \"CrossAttnUpBlockMotion\",\n \"UpBlockSpatioTemporal\",\n \"CrossAttnUpBlockSpatioTemporal\",\n]:\n if up_block_type == \"UpBlock3D\":\n return UpBlock3D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n prev_output_channel=prev_output_channel,\n temb_channels=temb_channels,\n add_upsample=add_upsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n resnet_time_scale_shift=resnet_time_scale_shift,\n resolution_idx=resolution_idx,\n )\n elif up_block_type == \"CrossAttnUpBlock3D\":\n if cross_attention_dim is None:\n raise ValueError(\"cross_attention_dim must be specified for CrossAttnUpBlock3D\")\n return CrossAttnUpBlock3D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n prev_output_channel=prev_output_channel,\n temb_channels=temb_channels,\n add_upsample=add_upsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=num_attention_heads,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n resolution_idx=resolution_idx,\n )\n if up_block_type == \"UpBlockMotion\":\n return UpBlockMotion(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n prev_output_channel=prev_output_channel,\n temb_channels=temb_channels,\n add_upsample=add_upsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n resnet_time_scale_shift=resnet_time_scale_shift,\n resolution_idx=resolution_idx,\n temporal_num_attention_heads=temporal_num_attention_heads,\n temporal_max_seq_length=temporal_max_seq_length,\n )\n elif up_block_type == \"CrossAttnUpBlockMotion\":\n if cross_attention_dim is None:\n raise ValueError(\"cross_attention_dim must be specified for CrossAttnUpBlockMotion\")\n return CrossAttnUpBlockMotion(\n num_layers=num_layers,\n transformer_layers_per_block=transformer_layers_per_block,\n in_channels=in_channels,\n out_channels=out_channels,\n prev_output_channel=prev_output_channel,\n temb_channels=temb_channels,\n add_upsample=add_upsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=num_attention_heads,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n resolution_idx=resolution_idx,\n temporal_num_attention_heads=temporal_num_attention_heads,\n temporal_max_seq_length=temporal_max_seq_length,\n )\n elif up_block_type == \"UpBlockSpatioTemporal\":\n # added for SDV\n return UpBlockSpatioTemporal(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n prev_output_channel=prev_output_channel,\n temb_channels=temb_channels,\n resolution_idx=resolution_idx,\n add_upsample=add_upsample,\n )\n elif up_block_type == \"CrossAttnUpBlockSpatioTemporal\":\n # added for SDV\n if cross_attention_dim is None:\n raise ValueError(\"cross_attention_dim must be specified for CrossAttnUpBlockSpatioTemporal\")\n return CrossAttnUpBlockSpatioTemporal(\n in_channels=in_channels,\n out_channels=out_channels,\n prev_output_channel=prev_output_channel,\n temb_channels=temb_channels,\n num_layers=num_layers,\n transformer_layers_per_block=transformer_layers_per_block,\n add_upsample=add_upsample,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=num_attention_heads,\n resolution_idx=resolution_idx,\n )\n\n raise ValueError(f\"{up_block_type} does not exist.\")\n\n\nclass UNetMidBlock3DCrossAttn(nn.Module):\n def __init__(\n self,\n in_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n num_attention_heads: int = 1,\n output_scale_factor: float = 1.0,\n cross_attention_dim: int = 1280,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = True,\n upcast_attention: bool = False,\n ):\n super().__init__()\n\n self.has_cross_attention = True\n self.num_attention_heads = num_attention_heads\n resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)\n\n # there is always at least one resnet\n resnets = [\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n ]\n temp_convs = [\n TemporalConvLayer(\n in_channels,\n in_channels,\n dropout=0.1,\n norm_num_groups=resnet_groups,\n )\n ]\n attentions = []\n temp_attentions = []\n\n for _ in range(num_layers):\n attentions.append(\n Transformer2DModel(\n in_channels // num_attention_heads,\n num_attention_heads,\n in_channels=in_channels,\n num_layers=1,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n use_linear_projection=use_linear_projection,\n upcast_attention=upcast_attention,\n )\n )\n temp_attentions.append(\n TransformerTemporalModel(\n in_channels // num_attention_heads,\n num_attention_heads,\n in_channels=in_channels,\n num_layers=1,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n )\n )\n resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n temp_convs.append(\n TemporalConvLayer(\n in_channels,\n in_channels,\n dropout=0.1,\n norm_num_groups=resnet_groups,\n )\n )\n\n self.resnets = nn.ModuleList(resnets)\n self.temp_convs = nn.ModuleList(temp_convs)\n self.attentions = nn.ModuleList(attentions)\n self.temp_attentions = nn.ModuleList(temp_attentions)\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n num_frames: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n ) -> torch.Tensor:\n hidden_states = self.resnets[0](hidden_states, temb)\n hidden_states = self.temp_convs[0](hidden_states, num_frames=num_frames)\n for attn, temp_attn, resnet, temp_conv in zip(\n self.attentions, self.temp_attentions, self.resnets[1:], self.temp_convs[1:]\n ):\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n hidden_states = temp_attn(\n hidden_states,\n num_frames=num_frames,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n hidden_states = resnet(hidden_states, temb)\n hidden_states = temp_conv(hidden_states, num_frames=num_frames)\n\n return hidden_states\n\n\nclass CrossAttnDownBlock3D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n num_attention_heads: int = 1,\n cross_attention_dim: int = 1280,\n output_scale_factor: float = 1.0,\n downsample_padding: int = 1,\n add_downsample: bool = True,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = False,\n only_cross_attention: bool = False,\n upcast_attention: bool = False,\n ):\n super().__init__()\n resnets = []\n attentions = []\n temp_attentions = []\n temp_convs = []\n\n self.has_cross_attention = True\n self.num_attention_heads = num_attention_heads\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n temp_convs.append(\n TemporalConvLayer(\n out_channels,\n out_channels,\n dropout=0.1,\n norm_num_groups=resnet_groups,\n )\n )\n attentions.append(\n Transformer2DModel(\n out_channels // num_attention_heads,\n num_attention_heads,\n in_channels=out_channels,\n num_layers=1,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n )\n )\n temp_attentions.append(\n TransformerTemporalModel(\n out_channels // num_attention_heads,\n num_attention_heads,\n in_channels=out_channels,\n num_layers=1,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n )\n )\n self.resnets = nn.ModuleList(resnets)\n self.temp_convs = nn.ModuleList(temp_convs)\n self.attentions = nn.ModuleList(attentions)\n self.temp_attentions = nn.ModuleList(temp_attentions)\n\n if add_downsample:\n self.downsamplers = nn.ModuleList(\n [\n Downsample2D(\n out_channels,\n use_conv=True,\n out_channels=out_channels,\n padding=downsample_padding,\n name=\"op\",\n )\n ]\n )\n else:\n self.downsamplers = None\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n num_frames: int = 1,\n cross_attention_kwargs: Dict[str, Any] = None,\n ) -> Union[torch.Tensor, Tuple[torch.Tensor, ...]]:\n # TODO(Patrick, William) - attention mask is not used\n output_states = ()\n\n for resnet, temp_conv, attn, temp_attn in zip(\n self.resnets, self.temp_convs, self.attentions, self.temp_attentions\n ):\n hidden_states = resnet(hidden_states, temb)\n hidden_states = temp_conv(hidden_states, num_frames=num_frames)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n hidden_states = temp_attn(\n hidden_states,\n num_frames=num_frames,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n output_states += (hidden_states,)\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states += (hidden_states,)\n\n return hidden_states, output_states\n\n\nclass DownBlock3D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n output_scale_factor: float = 1.0,\n add_downsample: bool = True,\n downsample_padding: int = 1,\n ):\n super().__init__()\n resnets = []\n temp_convs = []\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n temp_convs.append(\n TemporalConvLayer(\n out_channels,\n out_channels,\n dropout=0.1,\n norm_num_groups=resnet_groups,\n )\n )\n\n self.resnets = nn.ModuleList(resnets)\n self.temp_convs = nn.ModuleList(temp_convs)\n\n if add_downsample:\n self.downsamplers = nn.ModuleList(\n [\n Downsample2D(\n out_channels,\n use_conv=True,\n out_channels=out_channels,\n padding=downsample_padding,\n name=\"op\",\n )\n ]\n )\n else:\n self.downsamplers = None\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n num_frames: int = 1,\n ) -> Union[torch.Tensor, Tuple[torch.Tensor, ...]]:\n output_states = ()\n\n for resnet, temp_conv in zip(self.resnets, self.temp_convs):\n hidden_states = resnet(hidden_states, temb)\n hidden_states = temp_conv(hidden_states, num_frames=num_frames)\n\n output_states += (hidden_states,)\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states += (hidden_states,)\n\n return hidden_states, output_states\n\n\nclass CrossAttnUpBlock3D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n prev_output_channel: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n num_attention_heads: int = 1,\n cross_attention_dim: int = 1280,\n output_scale_factor: float = 1.0,\n add_upsample: bool = True,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = False,\n only_cross_attention: bool = False,\n upcast_attention: bool = False,\n resolution_idx: Optional[int] = None,\n ):\n super().__init__()\n resnets = []\n temp_convs = []\n attentions = []\n temp_attentions = []\n\n self.has_cross_attention = True\n self.num_attention_heads = num_attention_heads\n\n for i in range(num_layers):\n res_skip_channels = in_channels if (i == num_layers - 1) else out_channels\n resnet_in_channels = prev_output_channel if i == 0 else out_channels\n\n resnets.append(\n ResnetBlock2D(\n in_channels=resnet_in_channels + res_skip_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n temp_convs.append(\n TemporalConvLayer(\n out_channels,\n out_channels,\n dropout=0.1,\n norm_num_groups=resnet_groups,\n )\n )\n attentions.append(\n Transformer2DModel(\n out_channels // num_attention_heads,\n num_attention_heads,\n in_channels=out_channels,\n num_layers=1,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n )\n )\n temp_attentions.append(\n TransformerTemporalModel(\n out_channels // num_attention_heads,\n num_attention_heads,\n in_channels=out_channels,\n num_layers=1,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n )\n )\n self.resnets = nn.ModuleList(resnets)\n self.temp_convs = nn.ModuleList(temp_convs)\n self.attentions = nn.ModuleList(attentions)\n self.temp_attentions = nn.ModuleList(temp_attentions)\n\n if add_upsample:\n self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])\n else:\n self.upsamplers = None\n\n self.gradient_checkpointing = False\n self.resolution_idx = resolution_idx\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n upsample_size: Optional[int] = None,\n attention_mask: Optional[torch.Tensor] = None,\n num_frames: int = 1,\n cross_attention_kwargs: Dict[str, Any] = None,\n ) -> torch.Tensor:\n is_freeu_enabled = (\n getattr(self, \"s1\", None)\n and getattr(self, \"s2\", None)\n and getattr(self, \"b1\", None)\n and getattr(self, \"b2\", None)\n )\n\n # TODO(Patrick, William) - attention mask is not used\n for resnet, temp_conv, attn, temp_attn in zip(\n self.resnets, self.temp_convs, self.attentions, self.temp_attentions\n ):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n\n # FreeU: Only operate on the first two stages\n if is_freeu_enabled:\n hidden_states, res_hidden_states = apply_freeu(\n self.resolution_idx,\n hidden_states,\n res_hidden_states,\n s1=self.s1,\n s2=self.s2,\n b1=self.b1,\n b2=self.b2,\n )\n\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n\n hidden_states = resnet(hidden_states, temb)\n hidden_states = temp_conv(hidden_states, num_frames=num_frames)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n hidden_states = temp_attn(\n hidden_states,\n num_frames=num_frames,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n\nclass UpBlock3D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n prev_output_channel: int,\n out_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n output_scale_factor: float = 1.0,\n add_upsample: bool = True,\n resolution_idx: Optional[int] = None,\n ):\n super().__init__()\n resnets = []\n temp_convs = []\n\n for i in range(num_layers):\n res_skip_channels = in_channels if (i == num_layers - 1) else out_channels\n resnet_in_channels = prev_output_channel if i == 0 else out_channels\n\n resnets.append(\n ResnetBlock2D(\n in_channels=resnet_in_channels + res_skip_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n temp_convs.append(\n TemporalConvLayer(\n out_channels,\n out_channels,\n dropout=0.1,\n norm_num_groups=resnet_groups,\n )\n )\n\n self.resnets = nn.ModuleList(resnets)\n self.temp_convs = nn.ModuleList(temp_convs)\n\n if add_upsample:\n self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])\n else:\n self.upsamplers = None\n\n self.gradient_checkpointing = False\n self.resolution_idx = resolution_idx\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n upsample_size: Optional[int] = None,\n num_frames: int = 1,\n ) -> torch.Tensor:\n is_freeu_enabled = (\n getattr(self, \"s1\", None)\n and getattr(self, \"s2\", None)\n and getattr(self, \"b1\", None)\n and getattr(self, \"b2\", None)\n )\n for resnet, temp_conv in zip(self.resnets, self.temp_convs):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n\n # FreeU: Only operate on the first two stages\n if is_freeu_enabled:\n hidden_states, res_hidden_states = apply_freeu(\n self.resolution_idx,\n hidden_states,\n res_hidden_states,\n s1=self.s1,\n s2=self.s2,\n b1=self.b1,\n b2=self.b2,\n )\n\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n\n hidden_states = resnet(hidden_states, temb)\n hidden_states = temp_conv(hidden_states, num_frames=num_frames)\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n\nclass DownBlockMotion(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n output_scale_factor: float = 1.0,\n add_downsample: bool = True,\n downsample_padding: int = 1,\n temporal_num_attention_heads: int = 1,\n temporal_cross_attention_dim: Optional[int] = None,\n temporal_max_seq_length: int = 32,\n ):\n super().__init__()\n resnets = []\n motion_modules = []\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n motion_modules.append(\n TransformerTemporalModel(\n num_attention_heads=temporal_num_attention_heads,\n in_channels=out_channels,\n norm_num_groups=resnet_groups,\n cross_attention_dim=temporal_cross_attention_dim,\n attention_bias=False,\n activation_fn=\"geglu\",\n positional_embeddings=\"sinusoidal\",\n num_positional_embeddings=temporal_max_seq_length,\n attention_head_dim=out_channels // temporal_num_attention_heads,\n )\n )\n\n self.resnets = nn.ModuleList(resnets)\n self.motion_modules = nn.ModuleList(motion_modules)\n\n if add_downsample:\n self.downsamplers = nn.ModuleList(\n [\n Downsample2D(\n out_channels,\n use_conv=True,\n out_channels=out_channels,\n padding=downsample_padding,\n name=\"op\",\n )\n ]\n )\n else:\n self.downsamplers = None\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n num_frames: int = 1,\n *args,\n **kwargs,\n ) -> Union[torch.Tensor, Tuple[torch.Tensor, ...]]:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n output_states = ()\n\n blocks = zip(self.resnets, self.motion_modules)\n for resnet, motion_module in blocks:\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs)\n\n return custom_forward\n\n if is_torch_version(\">=\", \"1.11.0\"):\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet),\n hidden_states,\n temb,\n use_reentrant=False,\n )\n else:\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet), hidden_states, temb\n )\n\n else:\n hidden_states = resnet(hidden_states, temb)\n hidden_states = motion_module(hidden_states, num_frames=num_frames)[0]\n\n output_states = output_states + (hidden_states,)\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n\nclass CrossAttnDownBlockMotion(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n transformer_layers_per_block: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n num_attention_heads: int = 1,\n cross_attention_dim: int = 1280,\n output_scale_factor: float = 1.0,\n downsample_padding: int = 1,\n add_downsample: bool = True,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = False,\n only_cross_attention: bool = False,\n upcast_attention: bool = False,\n attention_type: str = \"default\",\n temporal_cross_attention_dim: Optional[int] = None,\n temporal_num_attention_heads: int = 8,\n temporal_max_seq_length: int = 32,\n ):\n super().__init__()\n resnets = []\n attentions = []\n motion_modules = []\n\n self.has_cross_attention = True\n self.num_attention_heads = num_attention_heads\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n\n if not dual_cross_attention:\n attentions.append(\n Transformer2DModel(\n num_attention_heads,\n out_channels // num_attention_heads,\n in_channels=out_channels,\n num_layers=transformer_layers_per_block,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n attention_type=attention_type,\n )\n )\n else:\n attentions.append(\n DualTransformer2DModel(\n num_attention_heads,\n out_channels // num_attention_heads,\n in_channels=out_channels,\n num_layers=1,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n )\n )\n\n motion_modules.append(\n TransformerTemporalModel(\n num_attention_heads=temporal_num_attention_heads,\n in_channels=out_channels,\n norm_num_groups=resnet_groups,\n cross_attention_dim=temporal_cross_attention_dim,\n attention_bias=False,\n activation_fn=\"geglu\",\n positional_embeddings=\"sinusoidal\",\n num_positional_embeddings=temporal_max_seq_length,\n attention_head_dim=out_channels // temporal_num_attention_heads,\n )\n )\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n self.motion_modules = nn.ModuleList(motion_modules)\n\n if add_downsample:\n self.downsamplers = nn.ModuleList(\n [\n Downsample2D(\n out_channels,\n use_conv=True,\n out_channels=out_channels,\n padding=downsample_padding,\n name=\"op\",\n )\n ]\n )\n else:\n self.downsamplers = None\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n num_frames: int = 1,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n additional_residuals: Optional[torch.Tensor] = None,\n ):\n if cross_attention_kwargs is not None:\n if cross_attention_kwargs.get(\"scale\", None) is not None:\n logger.warning(\"Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.\")\n\n output_states = ()\n\n blocks = list(zip(self.resnets, self.attentions, self.motion_modules))\n for i, (resnet, attn, motion_module) in enumerate(blocks):\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet),\n hidden_states,\n temb,\n **ckpt_kwargs,\n )\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n else:\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n hidden_states = motion_module(\n hidden_states,\n num_frames=num_frames,\n )[0]\n\n # apply additional residuals to the output of the last pair of resnet and attention blocks\n if i == len(blocks) - 1 and additional_residuals is not None:\n hidden_states = hidden_states + additional_residuals\n\n output_states = output_states + (hidden_states,)\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n\nclass CrossAttnUpBlockMotion(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n prev_output_channel: int,\n temb_channels: int,\n resolution_idx: Optional[int] = None,\n dropout: float = 0.0,\n num_layers: int = 1,\n transformer_layers_per_block: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n num_attention_heads: int = 1,\n cross_attention_dim: int = 1280,\n output_scale_factor: float = 1.0,\n add_upsample: bool = True,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = False,\n only_cross_attention: bool = False,\n upcast_attention: bool = False,\n attention_type: str = \"default\",\n temporal_cross_attention_dim: Optional[int] = None,\n temporal_num_attention_heads: int = 8,\n temporal_max_seq_length: int = 32,\n ):\n super().__init__()\n resnets = []\n attentions = []\n motion_modules = []\n\n self.has_cross_attention = True\n self.num_attention_heads = num_attention_heads\n\n for i in range(num_layers):\n res_skip_channels = in_channels if (i == num_layers - 1) else out_channels\n resnet_in_channels = prev_output_channel if i == 0 else out_channels\n\n resnets.append(\n ResnetBlock2D(\n in_channels=resnet_in_channels + res_skip_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n\n if not dual_cross_attention:\n attentions.append(\n Transformer2DModel(\n num_attention_heads,\n out_channels // num_attention_heads,\n in_channels=out_channels,\n num_layers=transformer_layers_per_block,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n attention_type=attention_type,\n )\n )\n else:\n attentions.append(\n DualTransformer2DModel(\n num_attention_heads,\n out_channels // num_attention_heads,\n in_channels=out_channels,\n num_layers=1,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n )\n )\n motion_modules.append(\n TransformerTemporalModel(\n num_attention_heads=temporal_num_attention_heads,\n in_channels=out_channels,\n norm_num_groups=resnet_groups,\n cross_attention_dim=temporal_cross_attention_dim,\n attention_bias=False,\n activation_fn=\"geglu\",\n positional_embeddings=\"sinusoidal\",\n num_positional_embeddings=temporal_max_seq_length,\n attention_head_dim=out_channels // temporal_num_attention_heads,\n )\n )\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n self.motion_modules = nn.ModuleList(motion_modules)\n\n if add_upsample:\n self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])\n else:\n self.upsamplers = None\n\n self.gradient_checkpointing = False\n self.resolution_idx = resolution_idx\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n upsample_size: Optional[int] = None,\n attention_mask: Optional[torch.Tensor] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n num_frames: int = 1,\n ) -> torch.Tensor:\n if cross_attention_kwargs is not None:\n if cross_attention_kwargs.get(\"scale\", None) is not None:\n logger.warning(\"Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.\")\n\n is_freeu_enabled = (\n getattr(self, \"s1\", None)\n and getattr(self, \"s2\", None)\n and getattr(self, \"b1\", None)\n and getattr(self, \"b2\", None)\n )\n\n blocks = zip(self.resnets, self.attentions, self.motion_modules)\n for resnet, attn, motion_module in blocks:\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n\n # FreeU: Only operate on the first two stages\n if is_freeu_enabled:\n hidden_states, res_hidden_states = apply_freeu(\n self.resolution_idx,\n hidden_states,\n res_hidden_states,\n s1=self.s1,\n s2=self.s2,\n b1=self.b1,\n b2=self.b2,\n )\n\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet),\n hidden_states,\n temb,\n **ckpt_kwargs,\n )\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n else:\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n hidden_states = motion_module(\n hidden_states,\n num_frames=num_frames,\n )[0]\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n\nclass UpBlockMotion(nn.Module):\n def __init__(\n self,\n in_channels: int,\n prev_output_channel: int,\n out_channels: int,\n temb_channels: int,\n resolution_idx: Optional[int] = None,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n output_scale_factor: float = 1.0,\n add_upsample: bool = True,\n temporal_norm_num_groups: int = 32,\n temporal_cross_attention_dim: Optional[int] = None,\n temporal_num_attention_heads: int = 8,\n temporal_max_seq_length: int = 32,\n ):\n super().__init__()\n resnets = []\n motion_modules = []\n\n for i in range(num_layers):\n res_skip_channels = in_channels if (i == num_layers - 1) else out_channels\n resnet_in_channels = prev_output_channel if i == 0 else out_channels\n\n resnets.append(\n ResnetBlock2D(\n in_channels=resnet_in_channels + res_skip_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n\n motion_modules.append(\n TransformerTemporalModel(\n num_attention_heads=temporal_num_attention_heads,\n in_channels=out_channels,\n norm_num_groups=temporal_norm_num_groups,\n cross_attention_dim=temporal_cross_attention_dim,\n attention_bias=False,\n activation_fn=\"geglu\",\n positional_embeddings=\"sinusoidal\",\n num_positional_embeddings=temporal_max_seq_length,\n attention_head_dim=out_channels // temporal_num_attention_heads,\n )\n )\n\n self.resnets = nn.ModuleList(resnets)\n self.motion_modules = nn.ModuleList(motion_modules)\n\n if add_upsample:\n self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])\n else:\n self.upsamplers = None\n\n self.gradient_checkpointing = False\n self.resolution_idx = resolution_idx\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n upsample_size=None,\n num_frames: int = 1,\n *args,\n **kwargs,\n ) -> torch.Tensor:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n is_freeu_enabled = (\n getattr(self, \"s1\", None)\n and getattr(self, \"s2\", None)\n and getattr(self, \"b1\", None)\n and getattr(self, \"b2\", None)\n )\n\n blocks = zip(self.resnets, self.motion_modules)\n\n for resnet, motion_module in blocks:\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n\n # FreeU: Only operate on the first two stages\n if is_freeu_enabled:\n hidden_states, res_hidden_states = apply_freeu(\n self.resolution_idx,\n hidden_states,\n res_hidden_states,\n s1=self.s1,\n s2=self.s2,\n b1=self.b1,\n b2=self.b2,\n )\n\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs)\n\n return custom_forward\n\n if is_torch_version(\">=\", \"1.11.0\"):\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet),\n hidden_states,\n temb,\n use_reentrant=False,\n )\n else:\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet), hidden_states, temb\n )\n\n else:\n hidden_states = resnet(hidden_states, temb)\n hidden_states = motion_module(hidden_states, num_frames=num_frames)[0]\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n\nclass UNetMidBlockCrossAttnMotion(nn.Module):\n def __init__(\n self,\n in_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n transformer_layers_per_block: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n num_attention_heads: int = 1,\n output_scale_factor: float = 1.0,\n cross_attention_dim: int = 1280,\n dual_cross_attention: float = False,\n use_linear_projection: float = False,\n upcast_attention: float = False,\n attention_type: str = \"default\",\n temporal_num_attention_heads: int = 1,\n temporal_cross_attention_dim: Optional[int] = None,\n temporal_max_seq_length: int = 32,\n ):\n super().__init__()\n\n self.has_cross_attention = True\n self.num_attention_heads = num_attention_heads\n resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)\n\n # there is always at least one resnet\n resnets = [\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n ]\n attentions = []\n motion_modules = []\n\n for _ in range(num_layers):\n if not dual_cross_attention:\n attentions.append(\n Transformer2DModel(\n num_attention_heads,\n in_channels // num_attention_heads,\n in_channels=in_channels,\n num_layers=transformer_layers_per_block,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n use_linear_projection=use_linear_projection,\n upcast_attention=upcast_attention,\n attention_type=attention_type,\n )\n )\n else:\n attentions.append(\n DualTransformer2DModel(\n num_attention_heads,\n in_channels // num_attention_heads,\n in_channels=in_channels,\n num_layers=1,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n )\n )\n resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n motion_modules.append(\n TransformerTemporalModel(\n num_attention_heads=temporal_num_attention_heads,\n attention_head_dim=in_channels // temporal_num_attention_heads,\n in_channels=in_channels,\n norm_num_groups=resnet_groups,\n cross_attention_dim=temporal_cross_attention_dim,\n attention_bias=False,\n positional_embeddings=\"sinusoidal\",\n num_positional_embeddings=temporal_max_seq_length,\n activation_fn=\"geglu\",\n )\n )\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n self.motion_modules = nn.ModuleList(motion_modules)\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n num_frames: int = 1,\n ) -> torch.Tensor:\n if cross_attention_kwargs is not None:\n if cross_attention_kwargs.get(\"scale\", None) is not None:\n logger.warning(\"Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.\")\n\n hidden_states = self.resnets[0](hidden_states, temb)\n\n blocks = zip(self.attentions, self.resnets[1:], self.motion_modules)\n for attn, resnet, motion_module in blocks:\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(motion_module),\n hidden_states,\n temb,\n **ckpt_kwargs,\n )\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet),\n hidden_states,\n temb,\n **ckpt_kwargs,\n )\n else:\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n hidden_states = motion_module(\n hidden_states,\n num_frames=num_frames,\n )[0]\n hidden_states = resnet(hidden_states, temb)\n\n return hidden_states\n\n\nclass MidBlockTemporalDecoder(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n attention_head_dim: int = 512,\n num_layers: int = 1,\n upcast_attention: bool = False,\n ):\n super().__init__()\n\n resnets = []\n attentions = []\n for i in range(num_layers):\n input_channels = in_channels if i == 0 else out_channels\n resnets.append(\n SpatioTemporalResBlock(\n in_channels=input_channels,\n out_channels=out_channels,\n temb_channels=None,\n eps=1e-6,\n temporal_eps=1e-5,\n merge_factor=0.0,\n merge_strategy=\"learned\",\n switch_spatial_to_temporal_mix=True,\n )\n )\n\n attentions.append(\n Attention(\n query_dim=in_channels,\n heads=in_channels // attention_head_dim,\n dim_head=attention_head_dim,\n eps=1e-6,\n upcast_attention=upcast_attention,\n norm_num_groups=32,\n bias=True,\n residual_connection=True,\n )\n )\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n image_only_indicator: torch.Tensor,\n ):\n hidden_states = self.resnets[0](\n hidden_states,\n image_only_indicator=image_only_indicator,\n )\n for resnet, attn in zip(self.resnets[1:], self.attentions):\n hidden_states = attn(hidden_states)\n hidden_states = resnet(\n hidden_states,\n image_only_indicator=image_only_indicator,\n )\n\n return hidden_states\n\n\nclass UpBlockTemporalDecoder(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n num_layers: int = 1,\n add_upsample: bool = True,\n ):\n super().__init__()\n resnets = []\n for i in range(num_layers):\n input_channels = in_channels if i == 0 else out_channels\n\n resnets.append(\n SpatioTemporalResBlock(\n in_channels=input_channels,\n out_channels=out_channels,\n temb_channels=None,\n eps=1e-6,\n temporal_eps=1e-5,\n merge_factor=0.0,\n merge_strategy=\"learned\",\n switch_spatial_to_temporal_mix=True,\n )\n )\n self.resnets = nn.ModuleList(resnets)\n\n if add_upsample:\n self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])\n else:\n self.upsamplers = None\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n image_only_indicator: torch.Tensor,\n ) -> torch.Tensor:\n for resnet in self.resnets:\n hidden_states = resnet(\n hidden_states,\n image_only_indicator=image_only_indicator,\n )\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states)\n\n return hidden_states\n\n\nclass UNetMidBlockSpatioTemporal(nn.Module):\n def __init__(\n self,\n in_channels: int,\n temb_channels: int,\n num_layers: int = 1,\n transformer_layers_per_block: Union[int, Tuple[int]] = 1,\n num_attention_heads: int = 1,\n cross_attention_dim: int = 1280,\n ):\n super().__init__()\n\n self.has_cross_attention = True\n self.num_attention_heads = num_attention_heads\n\n # support for variable transformer layers per block\n if isinstance(transformer_layers_per_block, int):\n transformer_layers_per_block = [transformer_layers_per_block] * num_layers\n\n # there is always at least one resnet\n resnets = [\n SpatioTemporalResBlock(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=1e-5,\n )\n ]\n attentions = []\n\n for i in range(num_layers):\n attentions.append(\n TransformerSpatioTemporalModel(\n num_attention_heads,\n in_channels // num_attention_heads,\n in_channels=in_channels,\n num_layers=transformer_layers_per_block[i],\n cross_attention_dim=cross_attention_dim,\n )\n )\n\n resnets.append(\n SpatioTemporalResBlock(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=1e-5,\n )\n )\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n image_only_indicator: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n hidden_states = self.resnets[0](\n hidden_states,\n temb,\n image_only_indicator=image_only_indicator,\n )\n\n for attn, resnet in zip(self.attentions, self.resnets[1:]):\n if self.training and self.gradient_checkpointing: # TODO\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n image_only_indicator=image_only_indicator,\n return_dict=False,\n )[0]\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet),\n hidden_states,\n temb,\n image_only_indicator,\n **ckpt_kwargs,\n )\n else:\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n image_only_indicator=image_only_indicator,\n return_dict=False,\n )[0]\n hidden_states = resnet(\n hidden_states,\n temb,\n image_only_indicator=image_only_indicator,\n )\n\n return hidden_states\n\n\nclass DownBlockSpatioTemporal(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n num_layers: int = 1,\n add_downsample: bool = True,\n ):\n super().__init__()\n resnets = []\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n resnets.append(\n SpatioTemporalResBlock(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=1e-5,\n )\n )\n\n self.resnets = nn.ModuleList(resnets)\n\n if add_downsample:\n self.downsamplers = nn.ModuleList(\n [\n Downsample2D(\n out_channels,\n use_conv=True,\n out_channels=out_channels,\n name=\"op\",\n )\n ]\n )\n else:\n self.downsamplers = None\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n image_only_indicator: Optional[torch.Tensor] = None,\n ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, ...]]:\n output_states = ()\n for resnet in self.resnets:\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs)\n\n return custom_forward\n\n if is_torch_version(\">=\", \"1.11.0\"):\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet),\n hidden_states,\n temb,\n image_only_indicator,\n use_reentrant=False,\n )\n else:\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet),\n hidden_states,\n temb,\n image_only_indicator,\n )\n else:\n hidden_states = resnet(\n hidden_states,\n temb,\n image_only_indicator=image_only_indicator,\n )\n\n output_states = output_states + (hidden_states,)\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n\nclass CrossAttnDownBlockSpatioTemporal(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n num_layers: int = 1,\n transformer_layers_per_block: Union[int, Tuple[int]] = 1,\n num_attention_heads: int = 1,\n cross_attention_dim: int = 1280,\n add_downsample: bool = True,\n ):\n super().__init__()\n resnets = []\n attentions = []\n\n self.has_cross_attention = True\n self.num_attention_heads = num_attention_heads\n if isinstance(transformer_layers_per_block, int):\n transformer_layers_per_block = [transformer_layers_per_block] * num_layers\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n resnets.append(\n SpatioTemporalResBlock(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=1e-6,\n )\n )\n attentions.append(\n TransformerSpatioTemporalModel(\n num_attention_heads,\n out_channels // num_attention_heads,\n in_channels=out_channels,\n num_layers=transformer_layers_per_block[i],\n cross_attention_dim=cross_attention_dim,\n )\n )\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n if add_downsample:\n self.downsamplers = nn.ModuleList(\n [\n Downsample2D(\n out_channels,\n use_conv=True,\n out_channels=out_channels,\n padding=1,\n name=\"op\",\n )\n ]\n )\n else:\n self.downsamplers = None\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n image_only_indicator: Optional[torch.Tensor] = None,\n ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, ...]]:\n output_states = ()\n\n blocks = list(zip(self.resnets, self.attentions))\n for resnet, attn in blocks:\n if self.training and self.gradient_checkpointing: # TODO\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet),\n hidden_states,\n temb,\n image_only_indicator,\n **ckpt_kwargs,\n )\n\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n image_only_indicator=image_only_indicator,\n return_dict=False,\n )[0]\n else:\n hidden_states = resnet(\n hidden_states,\n temb,\n image_only_indicator=image_only_indicator,\n )\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n image_only_indicator=image_only_indicator,\n return_dict=False,\n )[0]\n\n output_states = output_states + (hidden_states,)\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n\nclass UpBlockSpatioTemporal(nn.Module):\n def __init__(\n self,\n in_channels: int,\n prev_output_channel: int,\n out_channels: int,\n temb_channels: int,\n resolution_idx: Optional[int] = None,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n add_upsample: bool = True,\n ):\n super().__init__()\n resnets = []\n\n for i in range(num_layers):\n res_skip_channels = in_channels if (i == num_layers - 1) else out_channels\n resnet_in_channels = prev_output_channel if i == 0 else out_channels\n\n resnets.append(\n SpatioTemporalResBlock(\n in_channels=resnet_in_channels + res_skip_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n )\n )\n\n self.resnets = nn.ModuleList(resnets)\n\n if add_upsample:\n self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])\n else:\n self.upsamplers = None\n\n self.gradient_checkpointing = False\n self.resolution_idx = resolution_idx\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n image_only_indicator: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n for resnet in self.resnets:\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs)\n\n return custom_forward\n\n if is_torch_version(\">=\", \"1.11.0\"):\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet),\n hidden_states,\n temb,\n image_only_indicator,\n use_reentrant=False,\n )\n else:\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet),\n hidden_states,\n temb,\n image_only_indicator,\n )\n else:\n hidden_states = resnet(\n hidden_states,\n temb,\n image_only_indicator=image_only_indicator,\n )\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states)\n\n return hidden_states\n\n\nclass CrossAttnUpBlockSpatioTemporal(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n prev_output_channel: int,\n temb_channels: int,\n resolution_idx: Optional[int] = None,\n num_layers: int = 1,\n transformer_layers_per_block: Union[int, Tuple[int]] = 1,\n resnet_eps: float = 1e-6,\n num_attention_heads: int = 1,\n cross_attention_dim: int = 1280,\n add_upsample: bool = True,\n ):\n super().__init__()\n resnets = []\n attentions = []\n\n self.has_cross_attention = True\n self.num_attention_heads = num_attention_heads\n\n if isinstance(transformer_layers_per_block, int):\n transformer_layers_per_block = [transformer_layers_per_block] * num_layers\n\n for i in range(num_layers):\n res_skip_channels = in_channels if (i == num_layers - 1) else out_channels\n resnet_in_channels = prev_output_channel if i == 0 else out_channels\n\n resnets.append(\n SpatioTemporalResBlock(\n in_channels=resnet_in_channels + res_skip_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n )\n )\n attentions.append(\n TransformerSpatioTemporalModel(\n num_attention_heads,\n out_channels // num_attention_heads,\n in_channels=out_channels,\n num_layers=transformer_layers_per_block[i],\n cross_attention_dim=cross_attention_dim,\n )\n )\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n if add_upsample:\n self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])\n else:\n self.upsamplers = None\n\n self.gradient_checkpointing = False\n self.resolution_idx = resolution_idx\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n image_only_indicator: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n for resnet, attn in zip(self.resnets, self.attentions):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n\n if self.training and self.gradient_checkpointing: # TODO\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet),\n hidden_states,\n temb,\n image_only_indicator,\n **ckpt_kwargs,\n )\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n image_only_indicator=image_only_indicator,\n return_dict=False,\n )[0]\n else:\n hidden_states = resnet(\n hidden_states,\n temb,\n image_only_indicator=image_only_indicator,\n )\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n image_only_indicator=image_only_indicator,\n return_dict=False,\n )[0]\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states)\n\n return hidden_states\n" }, { "path": "diffusers/src/diffusers/models/unets/unet_3d_condition.py", "content": "# Copyright 2024 Alibaba DAMO-VILAB and The HuggingFace Team. All rights reserved.\n# Copyright 2024 The ModelScope Team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, List, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.utils.checkpoint\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...loaders import UNet2DConditionLoadersMixin\nfrom ...utils import BaseOutput, logging\nfrom ..activations import get_activation\nfrom ..attention_processor import (\n ADDED_KV_ATTENTION_PROCESSORS,\n CROSS_ATTENTION_PROCESSORS,\n Attention,\n AttentionProcessor,\n AttnAddedKVProcessor,\n AttnProcessor,\n)\nfrom ..embeddings import TimestepEmbedding, Timesteps\nfrom ..modeling_utils import ModelMixin\nfrom ..transformers.transformer_temporal import TransformerTemporalModel\nfrom .unet_3d_blocks import (\n CrossAttnDownBlock3D,\n CrossAttnUpBlock3D,\n DownBlock3D,\n UNetMidBlock3DCrossAttn,\n UpBlock3D,\n get_down_block,\n get_up_block,\n)\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n@dataclass\nclass UNet3DConditionOutput(BaseOutput):\n \"\"\"\n The output of [`UNet3DConditionModel`].\n\n Args:\n sample (`torch.Tensor` of shape `(batch_size, num_channels, num_frames, height, width)`):\n The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.\n \"\"\"\n\n sample: torch.Tensor\n\n\nclass UNet3DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):\n r\"\"\"\n A conditional 3D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample\n shaped output.\n\n This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented\n for all models (such as downloading or saving).\n\n Parameters:\n sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):\n Height and width of input/output sample.\n in_channels (`int`, *optional*, defaults to 4): The number of channels in the input sample.\n out_channels (`int`, *optional*, defaults to 4): The number of channels in the output.\n down_block_types (`Tuple[str]`, *optional*, defaults to `(\"CrossAttnDownBlock3D\", \"CrossAttnDownBlock3D\", \"CrossAttnDownBlock3D\", \"DownBlock3D\")`):\n The tuple of downsample blocks to use.\n up_block_types (`Tuple[str]`, *optional*, defaults to `(\"UpBlock3D\", \"CrossAttnUpBlock3D\", \"CrossAttnUpBlock3D\", \"CrossAttnUpBlock3D\")`):\n The tuple of upsample blocks to use.\n block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):\n The tuple of output channels for each block.\n layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.\n downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.\n mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.\n act_fn (`str`, *optional*, defaults to `\"silu\"`): The activation function to use.\n norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.\n If `None`, normalization and activation layers is skipped in post-processing.\n norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.\n cross_attention_dim (`int`, *optional*, defaults to 1024): The dimension of the cross attention features.\n attention_head_dim (`int`, *optional*, defaults to 64): The dimension of the attention heads.\n num_attention_heads (`int`, *optional*): The number of attention heads.\n time_cond_proj_dim (`int`, *optional*, defaults to `None`):\n The dimension of `cond_proj` layer in the timestep embedding.\n \"\"\"\n\n _supports_gradient_checkpointing = False\n\n @register_to_config\n def __init__(\n self,\n sample_size: Optional[int] = None,\n in_channels: int = 4,\n out_channels: int = 4,\n down_block_types: Tuple[str, ...] = (\n \"CrossAttnDownBlock3D\",\n \"CrossAttnDownBlock3D\",\n \"CrossAttnDownBlock3D\",\n \"DownBlock3D\",\n ),\n up_block_types: Tuple[str, ...] = (\n \"UpBlock3D\",\n \"CrossAttnUpBlock3D\",\n \"CrossAttnUpBlock3D\",\n \"CrossAttnUpBlock3D\",\n ),\n block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),\n layers_per_block: int = 2,\n downsample_padding: int = 1,\n mid_block_scale_factor: float = 1,\n act_fn: str = \"silu\",\n norm_num_groups: Optional[int] = 32,\n norm_eps: float = 1e-5,\n cross_attention_dim: int = 1024,\n attention_head_dim: Union[int, Tuple[int]] = 64,\n num_attention_heads: Optional[Union[int, Tuple[int]]] = None,\n time_cond_proj_dim: Optional[int] = None,\n ):\n super().__init__()\n\n self.sample_size = sample_size\n\n if num_attention_heads is not None:\n raise NotImplementedError(\n \"At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.\"\n )\n\n # If `num_attention_heads` is not defined (which is the case for most models)\n # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.\n # The reason for this behavior is to correct for incorrectly named variables that were introduced\n # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131\n # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking\n # which is why we correct for the naming here.\n num_attention_heads = num_attention_heads or attention_head_dim\n\n # Check inputs\n if len(down_block_types) != len(up_block_types):\n raise ValueError(\n f\"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.\"\n )\n\n if len(block_out_channels) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}.\"\n )\n\n # input\n conv_in_kernel = 3\n conv_out_kernel = 3\n conv_in_padding = (conv_in_kernel - 1) // 2\n self.conv_in = nn.Conv2d(\n in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding\n )\n\n # time\n time_embed_dim = block_out_channels[0] * 4\n self.time_proj = Timesteps(block_out_channels[0], True, 0)\n timestep_input_dim = block_out_channels[0]\n\n self.time_embedding = TimestepEmbedding(\n timestep_input_dim,\n time_embed_dim,\n act_fn=act_fn,\n cond_proj_dim=time_cond_proj_dim,\n )\n\n self.transformer_in = TransformerTemporalModel(\n num_attention_heads=8,\n attention_head_dim=attention_head_dim,\n in_channels=block_out_channels[0],\n num_layers=1,\n norm_num_groups=norm_num_groups,\n )\n\n # class embedding\n self.down_blocks = nn.ModuleList([])\n self.up_blocks = nn.ModuleList([])\n\n if isinstance(num_attention_heads, int):\n num_attention_heads = (num_attention_heads,) * len(down_block_types)\n\n # down\n output_channel = block_out_channels[0]\n for i, down_block_type in enumerate(down_block_types):\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n down_block = get_down_block(\n down_block_type,\n num_layers=layers_per_block,\n in_channels=input_channel,\n out_channels=output_channel,\n temb_channels=time_embed_dim,\n add_downsample=not is_final_block,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=num_attention_heads[i],\n downsample_padding=downsample_padding,\n dual_cross_attention=False,\n )\n self.down_blocks.append(down_block)\n\n # mid\n self.mid_block = UNetMidBlock3DCrossAttn(\n in_channels=block_out_channels[-1],\n temb_channels=time_embed_dim,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n output_scale_factor=mid_block_scale_factor,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=num_attention_heads[-1],\n resnet_groups=norm_num_groups,\n dual_cross_attention=False,\n )\n\n # count how many layers upsample the images\n self.num_upsamplers = 0\n\n # up\n reversed_block_out_channels = list(reversed(block_out_channels))\n reversed_num_attention_heads = list(reversed(num_attention_heads))\n\n output_channel = reversed_block_out_channels[0]\n for i, up_block_type in enumerate(up_block_types):\n is_final_block = i == len(block_out_channels) - 1\n\n prev_output_channel = output_channel\n output_channel = reversed_block_out_channels[i]\n input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]\n\n # add upsample block for all BUT final layer\n if not is_final_block:\n add_upsample = True\n self.num_upsamplers += 1\n else:\n add_upsample = False\n\n up_block = get_up_block(\n up_block_type,\n num_layers=layers_per_block + 1,\n in_channels=input_channel,\n out_channels=output_channel,\n prev_output_channel=prev_output_channel,\n temb_channels=time_embed_dim,\n add_upsample=add_upsample,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=reversed_num_attention_heads[i],\n dual_cross_attention=False,\n resolution_idx=i,\n )\n self.up_blocks.append(up_block)\n prev_output_channel = output_channel\n\n # out\n if norm_num_groups is not None:\n self.conv_norm_out = nn.GroupNorm(\n num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps\n )\n self.conv_act = get_activation(\"silu\")\n else:\n self.conv_norm_out = None\n self.conv_act = None\n\n conv_out_padding = (conv_out_kernel - 1) // 2\n self.conv_out = nn.Conv2d(\n block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding\n )\n\n @property\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors\n def attn_processors(self) -> Dict[str, AttentionProcessor]:\n r\"\"\"\n Returns:\n `dict` of attention processors: A dictionary containing all attention processors used in the model with\n indexed by its weight name.\n \"\"\"\n # set recursively\n processors = {}\n\n def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\n if hasattr(module, \"get_processor\"):\n processors[f\"{name}.processor\"] = module.get_processor(return_deprecated_lora=True)\n\n for sub_name, child in module.named_children():\n fn_recursive_add_processors(f\"{name}.{sub_name}\", child, processors)\n\n return processors\n\n for name, module in self.named_children():\n fn_recursive_add_processors(name, module, processors)\n\n return processors\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attention_slice\n def set_attention_slice(self, slice_size: Union[str, int, List[int]]) -> None:\n r\"\"\"\n Enable sliced attention computation.\n\n When this option is enabled, the attention module splits the input tensor in slices to compute attention in\n several steps. This is useful for saving some memory in exchange for a small decrease in speed.\n\n Args:\n slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `\"auto\"`):\n When `\"auto\"`, input to the attention heads is halved, so attention is computed in two steps. If\n `\"max\"`, maximum amount of memory is saved by running only one slice at a time. If a number is\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\n must be a multiple of `slice_size`.\n \"\"\"\n sliceable_head_dims = []\n\n def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):\n if hasattr(module, \"set_attention_slice\"):\n sliceable_head_dims.append(module.sliceable_head_dim)\n\n for child in module.children():\n fn_recursive_retrieve_sliceable_dims(child)\n\n # retrieve number of attention layers\n for module in self.children():\n fn_recursive_retrieve_sliceable_dims(module)\n\n num_sliceable_layers = len(sliceable_head_dims)\n\n if slice_size == \"auto\":\n # half the attention head size is usually a good trade-off between\n # speed and memory\n slice_size = [dim // 2 for dim in sliceable_head_dims]\n elif slice_size == \"max\":\n # make smallest slice possible\n slice_size = num_sliceable_layers * [1]\n\n slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\n\n if len(slice_size) != len(sliceable_head_dims):\n raise ValueError(\n f\"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different\"\n f\" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}.\"\n )\n\n for i in range(len(slice_size)):\n size = slice_size[i]\n dim = sliceable_head_dims[i]\n if size is not None and size > dim:\n raise ValueError(f\"size {size} has to be smaller or equal to {dim}.\")\n\n # Recursively walk through all the children.\n # Any children which exposes the set_attention_slice method\n # gets the message\n def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):\n if hasattr(module, \"set_attention_slice\"):\n module.set_attention_slice(slice_size.pop())\n\n for child in module.children():\n fn_recursive_set_attention_slice(child, slice_size)\n\n reversed_slice_size = list(reversed(slice_size))\n for module in self.children():\n fn_recursive_set_attention_slice(module, reversed_slice_size)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor\n def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\n r\"\"\"\n Sets the attention processor to use to compute attention.\n\n Parameters:\n processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):\n The instantiated processor class or a dictionary of processor classes that will be set as the processor\n for **all** `Attention` layers.\n\n If `processor` is a dict, the key needs to define the path to the corresponding cross attention\n processor. This is strongly recommended when setting trainable attention processors.\n\n \"\"\"\n count = len(self.attn_processors.keys())\n\n if isinstance(processor, dict) and len(processor) != count:\n raise ValueError(\n f\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\"\n f\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\"\n )\n\n def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\n if hasattr(module, \"set_processor\"):\n if not isinstance(processor, dict):\n module.set_processor(processor)\n else:\n module.set_processor(processor.pop(f\"{name}.processor\"))\n\n for sub_name, child in module.named_children():\n fn_recursive_attn_processor(f\"{name}.{sub_name}\", child, processor)\n\n for name, module in self.named_children():\n fn_recursive_attn_processor(name, module, processor)\n\n def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:\n \"\"\"\n Sets the attention processor to use [feed forward\n chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).\n\n Parameters:\n chunk_size (`int`, *optional*):\n The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually\n over each tensor of dim=`dim`.\n dim (`int`, *optional*, defaults to `0`):\n The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)\n or dim=1 (sequence length).\n \"\"\"\n if dim not in [0, 1]:\n raise ValueError(f\"Make sure to set `dim` to either 0 or 1, not {dim}\")\n\n # By default chunk size is 1\n chunk_size = chunk_size or 1\n\n def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):\n if hasattr(module, \"set_chunk_feed_forward\"):\n module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)\n\n for child in module.children():\n fn_recursive_feed_forward(child, chunk_size, dim)\n\n for module in self.children():\n fn_recursive_feed_forward(module, chunk_size, dim)\n\n def disable_forward_chunking(self):\n def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):\n if hasattr(module, \"set_chunk_feed_forward\"):\n module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)\n\n for child in module.children():\n fn_recursive_feed_forward(child, chunk_size, dim)\n\n for module in self.children():\n fn_recursive_feed_forward(module, None, 0)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor\n def set_default_attn_processor(self):\n \"\"\"\n Disables custom attention processors and sets the default attention implementation.\n \"\"\"\n if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnAddedKVProcessor()\n elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnProcessor()\n else:\n raise ValueError(\n f\"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}\"\n )\n\n self.set_attn_processor(processor)\n\n def _set_gradient_checkpointing(self, module, value: bool = False) -> None:\n if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):\n module.gradient_checkpointing = value\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.enable_freeu\n def enable_freeu(self, s1, s2, b1, b2):\n r\"\"\"Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.\n\n The suffixes after the scaling factors represent the stage blocks where they are being applied.\n\n Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that\n are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.\n\n Args:\n s1 (`float`):\n Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to\n mitigate the \"oversmoothing effect\" in the enhanced denoising process.\n s2 (`float`):\n Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to\n mitigate the \"oversmoothing effect\" in the enhanced denoising process.\n b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.\n b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.\n \"\"\"\n for i, upsample_block in enumerate(self.up_blocks):\n setattr(upsample_block, \"s1\", s1)\n setattr(upsample_block, \"s2\", s2)\n setattr(upsample_block, \"b1\", b1)\n setattr(upsample_block, \"b2\", b2)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.disable_freeu\n def disable_freeu(self):\n \"\"\"Disables the FreeU mechanism.\"\"\"\n freeu_keys = {\"s1\", \"s2\", \"b1\", \"b2\"}\n for i, upsample_block in enumerate(self.up_blocks):\n for k in freeu_keys:\n if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None:\n setattr(upsample_block, k, None)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections\n def fuse_qkv_projections(self):\n \"\"\"\n Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)\n are fused. For cross-attention modules, key and value projection matrices are fused.\n\n \n\n This API is 🧪 experimental.\n\n \n \"\"\"\n self.original_attn_processors = None\n\n for _, attn_processor in self.attn_processors.items():\n if \"Added\" in str(attn_processor.__class__.__name__):\n raise ValueError(\"`fuse_qkv_projections()` is not supported for models having added KV projections.\")\n\n self.original_attn_processors = self.attn_processors\n\n for module in self.modules():\n if isinstance(module, Attention):\n module.fuse_projections(fuse=True)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections\n def unfuse_qkv_projections(self):\n \"\"\"Disables the fused QKV projection if enabled.\n\n \n\n This API is 🧪 experimental.\n\n \n\n \"\"\"\n if self.original_attn_processors is not None:\n self.set_attn_processor(self.original_attn_processors)\n\n def forward(\n self,\n sample: torch.Tensor,\n timestep: Union[torch.Tensor, float, int],\n encoder_hidden_states: torch.Tensor,\n class_labels: Optional[torch.Tensor] = None,\n timestep_cond: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\n mid_block_additional_residual: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ) -> Union[UNet3DConditionOutput, Tuple[torch.Tensor]]:\n r\"\"\"\n The [`UNet3DConditionModel`] forward method.\n\n Args:\n sample (`torch.Tensor`):\n The noisy input tensor with the following shape `(batch, num_channels, num_frames, height, width`.\n timestep (`torch.Tensor` or `float` or `int`): The number of timesteps to denoise an input.\n encoder_hidden_states (`torch.Tensor`):\n The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.\n class_labels (`torch.Tensor`, *optional*, defaults to `None`):\n Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.\n timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`):\n Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed\n through the `self.time_embedding` layer to obtain the timestep embeddings.\n attention_mask (`torch.Tensor`, *optional*, defaults to `None`):\n An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask\n is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large\n negative values to the attention scores corresponding to \"discard\" tokens.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):\n A tuple of tensors that if specified are added to the residuals of down unet blocks.\n mid_block_additional_residual: (`torch.Tensor`, *optional*):\n A tensor that if specified is added to the residual of the middle unet block.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~models.unets.unet_3d_condition.UNet3DConditionOutput`] instead of a plain\n tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].\n\n Returns:\n [`~models.unets.unet_3d_condition.UNet3DConditionOutput`] or `tuple`:\n If `return_dict` is True, an [`~models.unets.unet_3d_condition.UNet3DConditionOutput`] is returned,\n otherwise a `tuple` is returned where the first element is the sample tensor.\n \"\"\"\n # By default samples have to be AT least a multiple of the overall upsampling factor.\n # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).\n # However, the upsampling interpolation output size can be forced to fit any upsampling size\n # on the fly if necessary.\n default_overall_up_factor = 2**self.num_upsamplers\n\n # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`\n forward_upsample_size = False\n upsample_size = None\n\n if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):\n logger.info(\"Forward upsample size to force interpolation output size.\")\n forward_upsample_size = True\n\n # prepare attention_mask\n if attention_mask is not None:\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n # 1. time\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can\n # This would be a good case for the `match` statement (Python 3.10+)\n is_mps = sample.device.type == \"mps\"\n if isinstance(timestep, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\n elif len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(sample.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n num_frames = sample.shape[2]\n timesteps = timesteps.expand(sample.shape[0])\n\n t_emb = self.time_proj(timesteps)\n\n # timesteps does not contain any weights and will always return f32 tensors\n # but time_embedding might actually be running in fp16. so we need to cast here.\n # there might be better ways to encapsulate this.\n t_emb = t_emb.to(dtype=self.dtype)\n\n emb = self.time_embedding(t_emb, timestep_cond)\n emb = emb.repeat_interleave(repeats=num_frames, dim=0)\n encoder_hidden_states = encoder_hidden_states.repeat_interleave(repeats=num_frames, dim=0)\n\n # 2. pre-process\n sample = sample.permute(0, 2, 1, 3, 4).reshape((sample.shape[0] * num_frames, -1) + sample.shape[3:])\n sample = self.conv_in(sample)\n\n sample = self.transformer_in(\n sample,\n num_frames=num_frames,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # 3. down\n down_block_res_samples = (sample,)\n for downsample_block in self.down_blocks:\n if hasattr(downsample_block, \"has_cross_attention\") and downsample_block.has_cross_attention:\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n num_frames=num_frames,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n else:\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb, num_frames=num_frames)\n\n down_block_res_samples += res_samples\n\n if down_block_additional_residuals is not None:\n new_down_block_res_samples = ()\n\n for down_block_res_sample, down_block_additional_residual in zip(\n down_block_res_samples, down_block_additional_residuals\n ):\n down_block_res_sample = down_block_res_sample + down_block_additional_residual\n new_down_block_res_samples += (down_block_res_sample,)\n\n down_block_res_samples = new_down_block_res_samples\n\n # 4. mid\n if self.mid_block is not None:\n sample = self.mid_block(\n sample,\n emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n num_frames=num_frames,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n\n if mid_block_additional_residual is not None:\n sample = sample + mid_block_additional_residual\n\n # 5. up\n for i, upsample_block in enumerate(self.up_blocks):\n is_final_block = i == len(self.up_blocks) - 1\n\n res_samples = down_block_res_samples[-len(upsample_block.resnets) :]\n down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]\n\n # if we have not reached the final block and need to forward the\n # upsample size, we do it here\n if not is_final_block and forward_upsample_size:\n upsample_size = down_block_res_samples[-1].shape[2:]\n\n if hasattr(upsample_block, \"has_cross_attention\") and upsample_block.has_cross_attention:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n encoder_hidden_states=encoder_hidden_states,\n upsample_size=upsample_size,\n attention_mask=attention_mask,\n num_frames=num_frames,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n else:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n upsample_size=upsample_size,\n num_frames=num_frames,\n )\n\n # 6. post-process\n if self.conv_norm_out:\n sample = self.conv_norm_out(sample)\n sample = self.conv_act(sample)\n\n sample = self.conv_out(sample)\n\n # reshape to (batch, channel, framerate, width, height)\n sample = sample[None, :].reshape((-1, num_frames) + sample.shape[1:]).permute(0, 2, 1, 3, 4)\n\n if not return_dict:\n return (sample,)\n\n return UNet3DConditionOutput(sample=sample)\n" }, { "path": "diffusers/src/diffusers/models/unets/unet_i2vgen_xl.py", "content": "# Copyright 2024 Alibaba DAMO-VILAB and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Any, Dict, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.utils.checkpoint\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...loaders import UNet2DConditionLoadersMixin\nfrom ...utils import logging\nfrom ..activations import get_activation\nfrom ..attention import Attention, FeedForward\nfrom ..attention_processor import (\n ADDED_KV_ATTENTION_PROCESSORS,\n CROSS_ATTENTION_PROCESSORS,\n AttentionProcessor,\n AttnAddedKVProcessor,\n AttnProcessor,\n)\nfrom ..embeddings import TimestepEmbedding, Timesteps\nfrom ..modeling_utils import ModelMixin\nfrom ..transformers.transformer_temporal import TransformerTemporalModel\nfrom .unet_3d_blocks import (\n CrossAttnDownBlock3D,\n CrossAttnUpBlock3D,\n DownBlock3D,\n UNetMidBlock3DCrossAttn,\n UpBlock3D,\n get_down_block,\n get_up_block,\n)\nfrom .unet_3d_condition import UNet3DConditionOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass I2VGenXLTransformerTemporalEncoder(nn.Module):\n def __init__(\n self,\n dim: int,\n num_attention_heads: int,\n attention_head_dim: int,\n activation_fn: str = \"geglu\",\n upcast_attention: bool = False,\n ff_inner_dim: Optional[int] = None,\n dropout: int = 0.0,\n ):\n super().__init__()\n self.norm1 = nn.LayerNorm(dim, elementwise_affine=True, eps=1e-5)\n self.attn1 = Attention(\n query_dim=dim,\n heads=num_attention_heads,\n dim_head=attention_head_dim,\n dropout=dropout,\n bias=False,\n upcast_attention=upcast_attention,\n out_bias=True,\n )\n self.ff = FeedForward(\n dim,\n dropout=dropout,\n activation_fn=activation_fn,\n final_dropout=False,\n inner_dim=ff_inner_dim,\n bias=True,\n )\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n ) -> torch.Tensor:\n norm_hidden_states = self.norm1(hidden_states)\n attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None)\n hidden_states = attn_output + hidden_states\n if hidden_states.ndim == 4:\n hidden_states = hidden_states.squeeze(1)\n\n ff_output = self.ff(hidden_states)\n hidden_states = ff_output + hidden_states\n if hidden_states.ndim == 4:\n hidden_states = hidden_states.squeeze(1)\n\n return hidden_states\n\n\nclass I2VGenXLUNet(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):\n r\"\"\"\n I2VGenXL UNet. It is a conditional 3D UNet model that takes a noisy sample, conditional state, and a timestep and\n returns a sample-shaped output.\n\n This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented\n for all models (such as downloading or saving).\n\n Parameters:\n sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):\n Height and width of input/output sample.\n in_channels (`int`, *optional*, defaults to 4): The number of channels in the input sample.\n out_channels (`int`, *optional*, defaults to 4): The number of channels in the output.\n down_block_types (`Tuple[str]`, *optional*, defaults to `(\"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\", \"DownBlock2D\")`):\n The tuple of downsample blocks to use.\n up_block_types (`Tuple[str]`, *optional*, defaults to `(\"UpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\")`):\n The tuple of upsample blocks to use.\n block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):\n The tuple of output channels for each block.\n layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.\n norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.\n If `None`, normalization and activation layers is skipped in post-processing.\n cross_attention_dim (`int`, *optional*, defaults to 1280): The dimension of the cross attention features.\n attention_head_dim (`int`, *optional*, defaults to 64): Attention head dim.\n num_attention_heads (`int`, *optional*): The number of attention heads.\n \"\"\"\n\n _supports_gradient_checkpointing = False\n\n @register_to_config\n def __init__(\n self,\n sample_size: Optional[int] = None,\n in_channels: int = 4,\n out_channels: int = 4,\n down_block_types: Tuple[str, ...] = (\n \"CrossAttnDownBlock3D\",\n \"CrossAttnDownBlock3D\",\n \"CrossAttnDownBlock3D\",\n \"DownBlock3D\",\n ),\n up_block_types: Tuple[str, ...] = (\n \"UpBlock3D\",\n \"CrossAttnUpBlock3D\",\n \"CrossAttnUpBlock3D\",\n \"CrossAttnUpBlock3D\",\n ),\n block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),\n layers_per_block: int = 2,\n norm_num_groups: Optional[int] = 32,\n cross_attention_dim: int = 1024,\n attention_head_dim: Union[int, Tuple[int]] = 64,\n num_attention_heads: Optional[Union[int, Tuple[int]]] = None,\n ):\n super().__init__()\n\n # When we first integrated the UNet into the library, we didn't have `attention_head_dim`. As a consequence\n # of that, we used `num_attention_heads` for arguments that actually denote attention head dimension. This\n # is why we ignore `num_attention_heads` and calculate it from `attention_head_dims` below.\n # This is still an incorrect way of calculating `num_attention_heads` but we need to stick to it\n # without running proper depcrecation cycles for the {down,mid,up} blocks which are a\n # part of the public API.\n num_attention_heads = attention_head_dim\n\n # Check inputs\n if len(down_block_types) != len(up_block_types):\n raise ValueError(\n f\"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.\"\n )\n\n if len(block_out_channels) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}.\"\n )\n\n # input\n self.conv_in = nn.Conv2d(in_channels + in_channels, block_out_channels[0], kernel_size=3, padding=1)\n\n self.transformer_in = TransformerTemporalModel(\n num_attention_heads=8,\n attention_head_dim=num_attention_heads,\n in_channels=block_out_channels[0],\n num_layers=1,\n norm_num_groups=norm_num_groups,\n )\n\n # image embedding\n self.image_latents_proj_in = nn.Sequential(\n nn.Conv2d(4, in_channels * 4, 3, padding=1),\n nn.SiLU(),\n nn.Conv2d(in_channels * 4, in_channels * 4, 3, stride=1, padding=1),\n nn.SiLU(),\n nn.Conv2d(in_channels * 4, in_channels, 3, stride=1, padding=1),\n )\n self.image_latents_temporal_encoder = I2VGenXLTransformerTemporalEncoder(\n dim=in_channels,\n num_attention_heads=2,\n ff_inner_dim=in_channels * 4,\n attention_head_dim=in_channels,\n activation_fn=\"gelu\",\n )\n self.image_latents_context_embedding = nn.Sequential(\n nn.Conv2d(4, in_channels * 8, 3, padding=1),\n nn.SiLU(),\n nn.AdaptiveAvgPool2d((32, 32)),\n nn.Conv2d(in_channels * 8, in_channels * 16, 3, stride=2, padding=1),\n nn.SiLU(),\n nn.Conv2d(in_channels * 16, cross_attention_dim, 3, stride=2, padding=1),\n )\n\n # other embeddings -- time, context, fps, etc.\n time_embed_dim = block_out_channels[0] * 4\n self.time_proj = Timesteps(block_out_channels[0], True, 0)\n timestep_input_dim = block_out_channels[0]\n\n self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=\"silu\")\n self.context_embedding = nn.Sequential(\n nn.Linear(cross_attention_dim, time_embed_dim),\n nn.SiLU(),\n nn.Linear(time_embed_dim, cross_attention_dim * in_channels),\n )\n self.fps_embedding = nn.Sequential(\n nn.Linear(timestep_input_dim, time_embed_dim), nn.SiLU(), nn.Linear(time_embed_dim, time_embed_dim)\n )\n\n # blocks\n self.down_blocks = nn.ModuleList([])\n self.up_blocks = nn.ModuleList([])\n\n if isinstance(num_attention_heads, int):\n num_attention_heads = (num_attention_heads,) * len(down_block_types)\n\n # down\n output_channel = block_out_channels[0]\n for i, down_block_type in enumerate(down_block_types):\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n down_block = get_down_block(\n down_block_type,\n num_layers=layers_per_block,\n in_channels=input_channel,\n out_channels=output_channel,\n temb_channels=time_embed_dim,\n add_downsample=not is_final_block,\n resnet_eps=1e-05,\n resnet_act_fn=\"silu\",\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=num_attention_heads[i],\n downsample_padding=1,\n dual_cross_attention=False,\n )\n self.down_blocks.append(down_block)\n\n # mid\n self.mid_block = UNetMidBlock3DCrossAttn(\n in_channels=block_out_channels[-1],\n temb_channels=time_embed_dim,\n resnet_eps=1e-05,\n resnet_act_fn=\"silu\",\n output_scale_factor=1,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=num_attention_heads[-1],\n resnet_groups=norm_num_groups,\n dual_cross_attention=False,\n )\n\n # count how many layers upsample the images\n self.num_upsamplers = 0\n\n # up\n reversed_block_out_channels = list(reversed(block_out_channels))\n reversed_num_attention_heads = list(reversed(num_attention_heads))\n\n output_channel = reversed_block_out_channels[0]\n for i, up_block_type in enumerate(up_block_types):\n is_final_block = i == len(block_out_channels) - 1\n\n prev_output_channel = output_channel\n output_channel = reversed_block_out_channels[i]\n input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]\n\n # add upsample block for all BUT final layer\n if not is_final_block:\n add_upsample = True\n self.num_upsamplers += 1\n else:\n add_upsample = False\n\n up_block = get_up_block(\n up_block_type,\n num_layers=layers_per_block + 1,\n in_channels=input_channel,\n out_channels=output_channel,\n prev_output_channel=prev_output_channel,\n temb_channels=time_embed_dim,\n add_upsample=add_upsample,\n resnet_eps=1e-05,\n resnet_act_fn=\"silu\",\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=reversed_num_attention_heads[i],\n dual_cross_attention=False,\n resolution_idx=i,\n )\n self.up_blocks.append(up_block)\n prev_output_channel = output_channel\n\n # out\n self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-05)\n self.conv_act = get_activation(\"silu\")\n self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=3, padding=1)\n\n @property\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors\n def attn_processors(self) -> Dict[str, AttentionProcessor]:\n r\"\"\"\n Returns:\n `dict` of attention processors: A dictionary containing all attention processors used in the model with\n indexed by its weight name.\n \"\"\"\n # set recursively\n processors = {}\n\n def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\n if hasattr(module, \"get_processor\"):\n processors[f\"{name}.processor\"] = module.get_processor(return_deprecated_lora=True)\n\n for sub_name, child in module.named_children():\n fn_recursive_add_processors(f\"{name}.{sub_name}\", child, processors)\n\n return processors\n\n for name, module in self.named_children():\n fn_recursive_add_processors(name, module, processors)\n\n return processors\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor\n def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\n r\"\"\"\n Sets the attention processor to use to compute attention.\n\n Parameters:\n processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):\n The instantiated processor class or a dictionary of processor classes that will be set as the processor\n for **all** `Attention` layers.\n\n If `processor` is a dict, the key needs to define the path to the corresponding cross attention\n processor. This is strongly recommended when setting trainable attention processors.\n\n \"\"\"\n count = len(self.attn_processors.keys())\n\n if isinstance(processor, dict) and len(processor) != count:\n raise ValueError(\n f\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\"\n f\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\"\n )\n\n def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\n if hasattr(module, \"set_processor\"):\n if not isinstance(processor, dict):\n module.set_processor(processor)\n else:\n module.set_processor(processor.pop(f\"{name}.processor\"))\n\n for sub_name, child in module.named_children():\n fn_recursive_attn_processor(f\"{name}.{sub_name}\", child, processor)\n\n for name, module in self.named_children():\n fn_recursive_attn_processor(name, module, processor)\n\n # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking\n def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:\n \"\"\"\n Sets the attention processor to use [feed forward\n chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).\n\n Parameters:\n chunk_size (`int`, *optional*):\n The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually\n over each tensor of dim=`dim`.\n dim (`int`, *optional*, defaults to `0`):\n The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)\n or dim=1 (sequence length).\n \"\"\"\n if dim not in [0, 1]:\n raise ValueError(f\"Make sure to set `dim` to either 0 or 1, not {dim}\")\n\n # By default chunk size is 1\n chunk_size = chunk_size or 1\n\n def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):\n if hasattr(module, \"set_chunk_feed_forward\"):\n module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)\n\n for child in module.children():\n fn_recursive_feed_forward(child, chunk_size, dim)\n\n for module in self.children():\n fn_recursive_feed_forward(module, chunk_size, dim)\n\n # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.disable_forward_chunking\n def disable_forward_chunking(self):\n def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):\n if hasattr(module, \"set_chunk_feed_forward\"):\n module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)\n\n for child in module.children():\n fn_recursive_feed_forward(child, chunk_size, dim)\n\n for module in self.children():\n fn_recursive_feed_forward(module, None, 0)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor\n def set_default_attn_processor(self):\n \"\"\"\n Disables custom attention processors and sets the default attention implementation.\n \"\"\"\n if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnAddedKVProcessor()\n elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnProcessor()\n else:\n raise ValueError(\n f\"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}\"\n )\n\n self.set_attn_processor(processor)\n\n # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel._set_gradient_checkpointing\n def _set_gradient_checkpointing(self, module, value: bool = False) -> None:\n if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):\n module.gradient_checkpointing = value\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.enable_freeu\n def enable_freeu(self, s1, s2, b1, b2):\n r\"\"\"Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.\n\n The suffixes after the scaling factors represent the stage blocks where they are being applied.\n\n Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that\n are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.\n\n Args:\n s1 (`float`):\n Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to\n mitigate the \"oversmoothing effect\" in the enhanced denoising process.\n s2 (`float`):\n Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to\n mitigate the \"oversmoothing effect\" in the enhanced denoising process.\n b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.\n b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.\n \"\"\"\n for i, upsample_block in enumerate(self.up_blocks):\n setattr(upsample_block, \"s1\", s1)\n setattr(upsample_block, \"s2\", s2)\n setattr(upsample_block, \"b1\", b1)\n setattr(upsample_block, \"b2\", b2)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.disable_freeu\n def disable_freeu(self):\n \"\"\"Disables the FreeU mechanism.\"\"\"\n freeu_keys = {\"s1\", \"s2\", \"b1\", \"b2\"}\n for i, upsample_block in enumerate(self.up_blocks):\n for k in freeu_keys:\n if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None:\n setattr(upsample_block, k, None)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections\n def fuse_qkv_projections(self):\n \"\"\"\n Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)\n are fused. For cross-attention modules, key and value projection matrices are fused.\n\n \n\n This API is 🧪 experimental.\n\n \n \"\"\"\n self.original_attn_processors = None\n\n for _, attn_processor in self.attn_processors.items():\n if \"Added\" in str(attn_processor.__class__.__name__):\n raise ValueError(\"`fuse_qkv_projections()` is not supported for models having added KV projections.\")\n\n self.original_attn_processors = self.attn_processors\n\n for module in self.modules():\n if isinstance(module, Attention):\n module.fuse_projections(fuse=True)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections\n def unfuse_qkv_projections(self):\n \"\"\"Disables the fused QKV projection if enabled.\n\n \n\n This API is 🧪 experimental.\n\n \n\n \"\"\"\n if self.original_attn_processors is not None:\n self.set_attn_processor(self.original_attn_processors)\n\n def forward(\n self,\n sample: torch.Tensor,\n timestep: Union[torch.Tensor, float, int],\n fps: torch.Tensor,\n image_latents: torch.Tensor,\n image_embeddings: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n timestep_cond: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n return_dict: bool = True,\n ) -> Union[UNet3DConditionOutput, Tuple[torch.Tensor]]:\n r\"\"\"\n The [`I2VGenXLUNet`] forward method.\n\n Args:\n sample (`torch.Tensor`):\n The noisy input tensor with the following shape `(batch, num_frames, channel, height, width`.\n timestep (`torch.Tensor` or `float` or `int`): The number of timesteps to denoise an input.\n fps (`torch.Tensor`): Frames per second for the video being generated. Used as a \"micro-condition\".\n image_latents (`torch.Tensor`): Image encodings from the VAE.\n image_embeddings (`torch.Tensor`):\n Projection embeddings of the conditioning image computed with a vision encoder.\n encoder_hidden_states (`torch.Tensor`):\n The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~models.unets.unet_3d_condition.UNet3DConditionOutput`] instead of a plain\n tuple.\n\n Returns:\n [`~models.unets.unet_3d_condition.UNet3DConditionOutput`] or `tuple`:\n If `return_dict` is True, an [`~models.unets.unet_3d_condition.UNet3DConditionOutput`] is returned,\n otherwise a `tuple` is returned where the first element is the sample tensor.\n \"\"\"\n batch_size, channels, num_frames, height, width = sample.shape\n\n # By default samples have to be AT least a multiple of the overall upsampling factor.\n # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).\n # However, the upsampling interpolation output size can be forced to fit any upsampling size\n # on the fly if necessary.\n default_overall_up_factor = 2**self.num_upsamplers\n\n # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`\n forward_upsample_size = False\n upsample_size = None\n\n if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):\n logger.info(\"Forward upsample size to force interpolation output size.\")\n forward_upsample_size = True\n\n # 1. time\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n # TODO: this requires sync between CPU and GPU. So try to pass `timesteps` as tensors if you can\n # This would be a good case for the `match` statement (Python 3.10+)\n is_mps = sample.device.type == \"mps\"\n if isinstance(timesteps, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\n elif len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(sample.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timesteps = timesteps.expand(sample.shape[0])\n t_emb = self.time_proj(timesteps)\n\n # timesteps does not contain any weights and will always return f32 tensors\n # but time_embedding might actually be running in fp16. so we need to cast here.\n # there might be better ways to encapsulate this.\n t_emb = t_emb.to(dtype=self.dtype)\n t_emb = self.time_embedding(t_emb, timestep_cond)\n\n # 2. FPS\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n fps = fps.expand(fps.shape[0])\n fps_emb = self.fps_embedding(self.time_proj(fps).to(dtype=self.dtype))\n\n # 3. time + FPS embeddings.\n emb = t_emb + fps_emb\n emb = emb.repeat_interleave(repeats=num_frames, dim=0)\n\n # 4. context embeddings.\n # The context embeddings consist of both text embeddings from the input prompt\n # AND the image embeddings from the input image. For images, both VAE encodings\n # and the CLIP image embeddings are incorporated.\n # So the final `context_embeddings` becomes the query for cross-attention.\n context_emb = sample.new_zeros(batch_size, 0, self.config.cross_attention_dim)\n context_emb = torch.cat([context_emb, encoder_hidden_states], dim=1)\n\n image_latents_for_context_embds = image_latents[:, :, :1, :]\n image_latents_context_embs = image_latents_for_context_embds.permute(0, 2, 1, 3, 4).reshape(\n image_latents_for_context_embds.shape[0] * image_latents_for_context_embds.shape[2],\n image_latents_for_context_embds.shape[1],\n image_latents_for_context_embds.shape[3],\n image_latents_for_context_embds.shape[4],\n )\n image_latents_context_embs = self.image_latents_context_embedding(image_latents_context_embs)\n\n _batch_size, _channels, _height, _width = image_latents_context_embs.shape\n image_latents_context_embs = image_latents_context_embs.permute(0, 2, 3, 1).reshape(\n _batch_size, _height * _width, _channels\n )\n context_emb = torch.cat([context_emb, image_latents_context_embs], dim=1)\n\n image_emb = self.context_embedding(image_embeddings)\n image_emb = image_emb.view(-1, self.config.in_channels, self.config.cross_attention_dim)\n context_emb = torch.cat([context_emb, image_emb], dim=1)\n context_emb = context_emb.repeat_interleave(repeats=num_frames, dim=0)\n\n image_latents = image_latents.permute(0, 2, 1, 3, 4).reshape(\n image_latents.shape[0] * image_latents.shape[2],\n image_latents.shape[1],\n image_latents.shape[3],\n image_latents.shape[4],\n )\n image_latents = self.image_latents_proj_in(image_latents)\n image_latents = (\n image_latents[None, :]\n .reshape(batch_size, num_frames, channels, height, width)\n .permute(0, 3, 4, 1, 2)\n .reshape(batch_size * height * width, num_frames, channels)\n )\n image_latents = self.image_latents_temporal_encoder(image_latents)\n image_latents = image_latents.reshape(batch_size, height, width, num_frames, channels).permute(0, 4, 3, 1, 2)\n\n # 5. pre-process\n sample = torch.cat([sample, image_latents], dim=1)\n sample = sample.permute(0, 2, 1, 3, 4).reshape((sample.shape[0] * num_frames, -1) + sample.shape[3:])\n sample = self.conv_in(sample)\n sample = self.transformer_in(\n sample,\n num_frames=num_frames,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # 6. down\n down_block_res_samples = (sample,)\n for downsample_block in self.down_blocks:\n if hasattr(downsample_block, \"has_cross_attention\") and downsample_block.has_cross_attention:\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=context_emb,\n num_frames=num_frames,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n else:\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb, num_frames=num_frames)\n\n down_block_res_samples += res_samples\n\n # 7. mid\n if self.mid_block is not None:\n sample = self.mid_block(\n sample,\n emb,\n encoder_hidden_states=context_emb,\n num_frames=num_frames,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n # 8. up\n for i, upsample_block in enumerate(self.up_blocks):\n is_final_block = i == len(self.up_blocks) - 1\n\n res_samples = down_block_res_samples[-len(upsample_block.resnets) :]\n down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]\n\n # if we have not reached the final block and need to forward the\n # upsample size, we do it here\n if not is_final_block and forward_upsample_size:\n upsample_size = down_block_res_samples[-1].shape[2:]\n\n if hasattr(upsample_block, \"has_cross_attention\") and upsample_block.has_cross_attention:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n encoder_hidden_states=context_emb,\n upsample_size=upsample_size,\n num_frames=num_frames,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n else:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n upsample_size=upsample_size,\n num_frames=num_frames,\n )\n\n # 9. post-process\n sample = self.conv_norm_out(sample)\n sample = self.conv_act(sample)\n\n sample = self.conv_out(sample)\n\n # reshape to (batch, channel, framerate, width, height)\n sample = sample[None, :].reshape((-1, num_frames) + sample.shape[1:]).permute(0, 2, 1, 3, 4)\n\n if not return_dict:\n return (sample,)\n\n return UNet3DConditionOutput(sample=sample)\n" }, { "path": "diffusers/src/diffusers/models/unets/unet_kandinsky3.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom dataclasses import dataclass\nfrom typing import Dict, Tuple, Union\n\nimport torch\nimport torch.utils.checkpoint\nfrom torch import nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...utils import BaseOutput, logging\nfrom ..attention_processor import Attention, AttentionProcessor, AttnProcessor\nfrom ..embeddings import TimestepEmbedding, Timesteps\nfrom ..modeling_utils import ModelMixin\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n@dataclass\nclass Kandinsky3UNetOutput(BaseOutput):\n sample: torch.Tensor = None\n\n\nclass Kandinsky3EncoderProj(nn.Module):\n def __init__(self, encoder_hid_dim, cross_attention_dim):\n super().__init__()\n self.projection_linear = nn.Linear(encoder_hid_dim, cross_attention_dim, bias=False)\n self.projection_norm = nn.LayerNorm(cross_attention_dim)\n\n def forward(self, x):\n x = self.projection_linear(x)\n x = self.projection_norm(x)\n return x\n\n\nclass Kandinsky3UNet(ModelMixin, ConfigMixin):\n @register_to_config\n def __init__(\n self,\n in_channels: int = 4,\n time_embedding_dim: int = 1536,\n groups: int = 32,\n attention_head_dim: int = 64,\n layers_per_block: Union[int, Tuple[int]] = 3,\n block_out_channels: Tuple[int] = (384, 768, 1536, 3072),\n cross_attention_dim: Union[int, Tuple[int]] = 4096,\n encoder_hid_dim: int = 4096,\n ):\n super().__init__()\n\n # TOOD(Yiyi): Give better name and put into config for the following 4 parameters\n expansion_ratio = 4\n compression_ratio = 2\n add_cross_attention = (False, True, True, True)\n add_self_attention = (False, True, True, True)\n\n out_channels = in_channels\n init_channels = block_out_channels[0] // 2\n self.time_proj = Timesteps(init_channels, flip_sin_to_cos=False, downscale_freq_shift=1)\n\n self.time_embedding = TimestepEmbedding(\n init_channels,\n time_embedding_dim,\n )\n\n self.add_time_condition = Kandinsky3AttentionPooling(\n time_embedding_dim, cross_attention_dim, attention_head_dim\n )\n\n self.conv_in = nn.Conv2d(in_channels, init_channels, kernel_size=3, padding=1)\n\n self.encoder_hid_proj = Kandinsky3EncoderProj(encoder_hid_dim, cross_attention_dim)\n\n hidden_dims = [init_channels] + list(block_out_channels)\n in_out_dims = list(zip(hidden_dims[:-1], hidden_dims[1:]))\n text_dims = [cross_attention_dim if is_exist else None for is_exist in add_cross_attention]\n num_blocks = len(block_out_channels) * [layers_per_block]\n layer_params = [num_blocks, text_dims, add_self_attention]\n rev_layer_params = map(reversed, layer_params)\n\n cat_dims = []\n self.num_levels = len(in_out_dims)\n self.down_blocks = nn.ModuleList([])\n for level, ((in_dim, out_dim), res_block_num, text_dim, self_attention) in enumerate(\n zip(in_out_dims, *layer_params)\n ):\n down_sample = level != (self.num_levels - 1)\n cat_dims.append(out_dim if level != (self.num_levels - 1) else 0)\n self.down_blocks.append(\n Kandinsky3DownSampleBlock(\n in_dim,\n out_dim,\n time_embedding_dim,\n text_dim,\n res_block_num,\n groups,\n attention_head_dim,\n expansion_ratio,\n compression_ratio,\n down_sample,\n self_attention,\n )\n )\n\n self.up_blocks = nn.ModuleList([])\n for level, ((out_dim, in_dim), res_block_num, text_dim, self_attention) in enumerate(\n zip(reversed(in_out_dims), *rev_layer_params)\n ):\n up_sample = level != 0\n self.up_blocks.append(\n Kandinsky3UpSampleBlock(\n in_dim,\n cat_dims.pop(),\n out_dim,\n time_embedding_dim,\n text_dim,\n res_block_num,\n groups,\n attention_head_dim,\n expansion_ratio,\n compression_ratio,\n up_sample,\n self_attention,\n )\n )\n\n self.conv_norm_out = nn.GroupNorm(groups, init_channels)\n self.conv_act_out = nn.SiLU()\n self.conv_out = nn.Conv2d(init_channels, out_channels, kernel_size=3, padding=1)\n\n @property\n def attn_processors(self) -> Dict[str, AttentionProcessor]:\n r\"\"\"\n Returns:\n `dict` of attention processors: A dictionary containing all attention processors used in the model with\n indexed by its weight name.\n \"\"\"\n # set recursively\n processors = {}\n\n def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\n if hasattr(module, \"set_processor\"):\n processors[f\"{name}.processor\"] = module.processor\n\n for sub_name, child in module.named_children():\n fn_recursive_add_processors(f\"{name}.{sub_name}\", child, processors)\n\n return processors\n\n for name, module in self.named_children():\n fn_recursive_add_processors(name, module, processors)\n\n return processors\n\n def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\n r\"\"\"\n Sets the attention processor to use to compute attention.\n\n Parameters:\n processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):\n The instantiated processor class or a dictionary of processor classes that will be set as the processor\n for **all** `Attention` layers.\n\n If `processor` is a dict, the key needs to define the path to the corresponding cross attention\n processor. This is strongly recommended when setting trainable attention processors.\n\n \"\"\"\n count = len(self.attn_processors.keys())\n\n if isinstance(processor, dict) and len(processor) != count:\n raise ValueError(\n f\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\"\n f\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\"\n )\n\n def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\n if hasattr(module, \"set_processor\"):\n if not isinstance(processor, dict):\n module.set_processor(processor)\n else:\n module.set_processor(processor.pop(f\"{name}.processor\"))\n\n for sub_name, child in module.named_children():\n fn_recursive_attn_processor(f\"{name}.{sub_name}\", child, processor)\n\n for name, module in self.named_children():\n fn_recursive_attn_processor(name, module, processor)\n\n def set_default_attn_processor(self):\n \"\"\"\n Disables custom attention processors and sets the default attention implementation.\n \"\"\"\n self.set_attn_processor(AttnProcessor())\n\n def _set_gradient_checkpointing(self, module, value=False):\n if hasattr(module, \"gradient_checkpointing\"):\n module.gradient_checkpointing = value\n\n def forward(self, sample, timestep, encoder_hidden_states=None, encoder_attention_mask=None, return_dict=True):\n if encoder_attention_mask is not None:\n encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0\n encoder_attention_mask = encoder_attention_mask.unsqueeze(1)\n\n if not torch.is_tensor(timestep):\n dtype = torch.float32 if isinstance(timestep, float) else torch.int32\n timestep = torch.tensor([timestep], dtype=dtype, device=sample.device)\n elif len(timestep.shape) == 0:\n timestep = timestep[None].to(sample.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timestep = timestep.expand(sample.shape[0])\n time_embed_input = self.time_proj(timestep).to(sample.dtype)\n time_embed = self.time_embedding(time_embed_input)\n\n encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)\n\n if encoder_hidden_states is not None:\n time_embed = self.add_time_condition(time_embed, encoder_hidden_states, encoder_attention_mask)\n\n hidden_states = []\n sample = self.conv_in(sample)\n for level, down_sample in enumerate(self.down_blocks):\n sample = down_sample(sample, time_embed, encoder_hidden_states, encoder_attention_mask)\n if level != self.num_levels - 1:\n hidden_states.append(sample)\n\n for level, up_sample in enumerate(self.up_blocks):\n if level != 0:\n sample = torch.cat([sample, hidden_states.pop()], dim=1)\n sample = up_sample(sample, time_embed, encoder_hidden_states, encoder_attention_mask)\n\n sample = self.conv_norm_out(sample)\n sample = self.conv_act_out(sample)\n sample = self.conv_out(sample)\n\n if not return_dict:\n return (sample,)\n return Kandinsky3UNetOutput(sample=sample)\n\n\nclass Kandinsky3UpSampleBlock(nn.Module):\n def __init__(\n self,\n in_channels,\n cat_dim,\n out_channels,\n time_embed_dim,\n context_dim=None,\n num_blocks=3,\n groups=32,\n head_dim=64,\n expansion_ratio=4,\n compression_ratio=2,\n up_sample=True,\n self_attention=True,\n ):\n super().__init__()\n up_resolutions = [[None, True if up_sample else None, None, None]] + [[None] * 4] * (num_blocks - 1)\n hidden_channels = (\n [(in_channels + cat_dim, in_channels)]\n + [(in_channels, in_channels)] * (num_blocks - 2)\n + [(in_channels, out_channels)]\n )\n attentions = []\n resnets_in = []\n resnets_out = []\n\n self.self_attention = self_attention\n self.context_dim = context_dim\n\n if self_attention:\n attentions.append(\n Kandinsky3AttentionBlock(out_channels, time_embed_dim, None, groups, head_dim, expansion_ratio)\n )\n else:\n attentions.append(nn.Identity())\n\n for (in_channel, out_channel), up_resolution in zip(hidden_channels, up_resolutions):\n resnets_in.append(\n Kandinsky3ResNetBlock(in_channel, in_channel, time_embed_dim, groups, compression_ratio, up_resolution)\n )\n\n if context_dim is not None:\n attentions.append(\n Kandinsky3AttentionBlock(\n in_channel, time_embed_dim, context_dim, groups, head_dim, expansion_ratio\n )\n )\n else:\n attentions.append(nn.Identity())\n\n resnets_out.append(\n Kandinsky3ResNetBlock(in_channel, out_channel, time_embed_dim, groups, compression_ratio)\n )\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets_in = nn.ModuleList(resnets_in)\n self.resnets_out = nn.ModuleList(resnets_out)\n\n def forward(self, x, time_embed, context=None, context_mask=None, image_mask=None):\n for attention, resnet_in, resnet_out in zip(self.attentions[1:], self.resnets_in, self.resnets_out):\n x = resnet_in(x, time_embed)\n if self.context_dim is not None:\n x = attention(x, time_embed, context, context_mask, image_mask)\n x = resnet_out(x, time_embed)\n\n if self.self_attention:\n x = self.attentions[0](x, time_embed, image_mask=image_mask)\n return x\n\n\nclass Kandinsky3DownSampleBlock(nn.Module):\n def __init__(\n self,\n in_channels,\n out_channels,\n time_embed_dim,\n context_dim=None,\n num_blocks=3,\n groups=32,\n head_dim=64,\n expansion_ratio=4,\n compression_ratio=2,\n down_sample=True,\n self_attention=True,\n ):\n super().__init__()\n attentions = []\n resnets_in = []\n resnets_out = []\n\n self.self_attention = self_attention\n self.context_dim = context_dim\n\n if self_attention:\n attentions.append(\n Kandinsky3AttentionBlock(in_channels, time_embed_dim, None, groups, head_dim, expansion_ratio)\n )\n else:\n attentions.append(nn.Identity())\n\n up_resolutions = [[None] * 4] * (num_blocks - 1) + [[None, None, False if down_sample else None, None]]\n hidden_channels = [(in_channels, out_channels)] + [(out_channels, out_channels)] * (num_blocks - 1)\n for (in_channel, out_channel), up_resolution in zip(hidden_channels, up_resolutions):\n resnets_in.append(\n Kandinsky3ResNetBlock(in_channel, out_channel, time_embed_dim, groups, compression_ratio)\n )\n\n if context_dim is not None:\n attentions.append(\n Kandinsky3AttentionBlock(\n out_channel, time_embed_dim, context_dim, groups, head_dim, expansion_ratio\n )\n )\n else:\n attentions.append(nn.Identity())\n\n resnets_out.append(\n Kandinsky3ResNetBlock(\n out_channel, out_channel, time_embed_dim, groups, compression_ratio, up_resolution\n )\n )\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets_in = nn.ModuleList(resnets_in)\n self.resnets_out = nn.ModuleList(resnets_out)\n\n def forward(self, x, time_embed, context=None, context_mask=None, image_mask=None):\n if self.self_attention:\n x = self.attentions[0](x, time_embed, image_mask=image_mask)\n\n for attention, resnet_in, resnet_out in zip(self.attentions[1:], self.resnets_in, self.resnets_out):\n x = resnet_in(x, time_embed)\n if self.context_dim is not None:\n x = attention(x, time_embed, context, context_mask, image_mask)\n x = resnet_out(x, time_embed)\n return x\n\n\nclass Kandinsky3ConditionalGroupNorm(nn.Module):\n def __init__(self, groups, normalized_shape, context_dim):\n super().__init__()\n self.norm = nn.GroupNorm(groups, normalized_shape, affine=False)\n self.context_mlp = nn.Sequential(nn.SiLU(), nn.Linear(context_dim, 2 * normalized_shape))\n self.context_mlp[1].weight.data.zero_()\n self.context_mlp[1].bias.data.zero_()\n\n def forward(self, x, context):\n context = self.context_mlp(context)\n\n for _ in range(len(x.shape[2:])):\n context = context.unsqueeze(-1)\n\n scale, shift = context.chunk(2, dim=1)\n x = self.norm(x) * (scale + 1.0) + shift\n return x\n\n\nclass Kandinsky3Block(nn.Module):\n def __init__(self, in_channels, out_channels, time_embed_dim, kernel_size=3, norm_groups=32, up_resolution=None):\n super().__init__()\n self.group_norm = Kandinsky3ConditionalGroupNorm(norm_groups, in_channels, time_embed_dim)\n self.activation = nn.SiLU()\n if up_resolution is not None and up_resolution:\n self.up_sample = nn.ConvTranspose2d(in_channels, in_channels, kernel_size=2, stride=2)\n else:\n self.up_sample = nn.Identity()\n\n padding = int(kernel_size > 1)\n self.projection = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=padding)\n\n if up_resolution is not None and not up_resolution:\n self.down_sample = nn.Conv2d(out_channels, out_channels, kernel_size=2, stride=2)\n else:\n self.down_sample = nn.Identity()\n\n def forward(self, x, time_embed):\n x = self.group_norm(x, time_embed)\n x = self.activation(x)\n x = self.up_sample(x)\n x = self.projection(x)\n x = self.down_sample(x)\n return x\n\n\nclass Kandinsky3ResNetBlock(nn.Module):\n def __init__(\n self, in_channels, out_channels, time_embed_dim, norm_groups=32, compression_ratio=2, up_resolutions=4 * [None]\n ):\n super().__init__()\n kernel_sizes = [1, 3, 3, 1]\n hidden_channel = max(in_channels, out_channels) // compression_ratio\n hidden_channels = (\n [(in_channels, hidden_channel)] + [(hidden_channel, hidden_channel)] * 2 + [(hidden_channel, out_channels)]\n )\n self.resnet_blocks = nn.ModuleList(\n [\n Kandinsky3Block(in_channel, out_channel, time_embed_dim, kernel_size, norm_groups, up_resolution)\n for (in_channel, out_channel), kernel_size, up_resolution in zip(\n hidden_channels, kernel_sizes, up_resolutions\n )\n ]\n )\n self.shortcut_up_sample = (\n nn.ConvTranspose2d(in_channels, in_channels, kernel_size=2, stride=2)\n if True in up_resolutions\n else nn.Identity()\n )\n self.shortcut_projection = (\n nn.Conv2d(in_channels, out_channels, kernel_size=1) if in_channels != out_channels else nn.Identity()\n )\n self.shortcut_down_sample = (\n nn.Conv2d(out_channels, out_channels, kernel_size=2, stride=2)\n if False in up_resolutions\n else nn.Identity()\n )\n\n def forward(self, x, time_embed):\n out = x\n for resnet_block in self.resnet_blocks:\n out = resnet_block(out, time_embed)\n\n x = self.shortcut_up_sample(x)\n x = self.shortcut_projection(x)\n x = self.shortcut_down_sample(x)\n x = x + out\n return x\n\n\nclass Kandinsky3AttentionPooling(nn.Module):\n def __init__(self, num_channels, context_dim, head_dim=64):\n super().__init__()\n self.attention = Attention(\n context_dim,\n context_dim,\n dim_head=head_dim,\n out_dim=num_channels,\n out_bias=False,\n )\n\n def forward(self, x, context, context_mask=None):\n context_mask = context_mask.to(dtype=context.dtype)\n context = self.attention(context.mean(dim=1, keepdim=True), context, context_mask)\n return x + context.squeeze(1)\n\n\nclass Kandinsky3AttentionBlock(nn.Module):\n def __init__(self, num_channels, time_embed_dim, context_dim=None, norm_groups=32, head_dim=64, expansion_ratio=4):\n super().__init__()\n self.in_norm = Kandinsky3ConditionalGroupNorm(norm_groups, num_channels, time_embed_dim)\n self.attention = Attention(\n num_channels,\n context_dim or num_channels,\n dim_head=head_dim,\n out_dim=num_channels,\n out_bias=False,\n )\n\n hidden_channels = expansion_ratio * num_channels\n self.out_norm = Kandinsky3ConditionalGroupNorm(norm_groups, num_channels, time_embed_dim)\n self.feed_forward = nn.Sequential(\n nn.Conv2d(num_channels, hidden_channels, kernel_size=1, bias=False),\n nn.SiLU(),\n nn.Conv2d(hidden_channels, num_channels, kernel_size=1, bias=False),\n )\n\n def forward(self, x, time_embed, context=None, context_mask=None, image_mask=None):\n height, width = x.shape[-2:]\n out = self.in_norm(x, time_embed)\n out = out.reshape(x.shape[0], -1, height * width).permute(0, 2, 1)\n context = context if context is not None else out\n if context_mask is not None:\n context_mask = context_mask.to(dtype=context.dtype)\n\n out = self.attention(out, context, context_mask)\n out = out.permute(0, 2, 1).unsqueeze(-1).reshape(out.shape[0], -1, height, width)\n x = x + out\n\n out = self.out_norm(x, time_embed)\n out = self.feed_forward(out)\n x = x + out\n return x\n" }, { "path": "diffusers/src/diffusers/models/unets/unet_motion_model.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Any, Dict, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\n\nfrom ...configuration_utils import ConfigMixin, FrozenDict, register_to_config\nfrom ...loaders import UNet2DConditionLoadersMixin\nfrom ...utils import logging\nfrom ..attention_processor import (\n ADDED_KV_ATTENTION_PROCESSORS,\n CROSS_ATTENTION_PROCESSORS,\n Attention,\n AttentionProcessor,\n AttnAddedKVProcessor,\n AttnProcessor,\n AttnProcessor2_0,\n IPAdapterAttnProcessor,\n IPAdapterAttnProcessor2_0,\n)\nfrom ..embeddings import TimestepEmbedding, Timesteps\nfrom ..modeling_utils import ModelMixin\nfrom ..transformers.transformer_temporal import TransformerTemporalModel\nfrom .unet_2d_blocks import UNetMidBlock2DCrossAttn\nfrom .unet_2d_condition import UNet2DConditionModel\nfrom .unet_3d_blocks import (\n CrossAttnDownBlockMotion,\n CrossAttnUpBlockMotion,\n DownBlockMotion,\n UNetMidBlockCrossAttnMotion,\n UpBlockMotion,\n get_down_block,\n get_up_block,\n)\nfrom .unet_3d_condition import UNet3DConditionOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass MotionModules(nn.Module):\n def __init__(\n self,\n in_channels: int,\n layers_per_block: int = 2,\n num_attention_heads: int = 8,\n attention_bias: bool = False,\n cross_attention_dim: Optional[int] = None,\n activation_fn: str = \"geglu\",\n norm_num_groups: int = 32,\n max_seq_length: int = 32,\n ):\n super().__init__()\n self.motion_modules = nn.ModuleList([])\n\n for i in range(layers_per_block):\n self.motion_modules.append(\n TransformerTemporalModel(\n in_channels=in_channels,\n norm_num_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n activation_fn=activation_fn,\n attention_bias=attention_bias,\n num_attention_heads=num_attention_heads,\n attention_head_dim=in_channels // num_attention_heads,\n positional_embeddings=\"sinusoidal\",\n num_positional_embeddings=max_seq_length,\n )\n )\n\n\nclass MotionAdapter(ModelMixin, ConfigMixin):\n @register_to_config\n def __init__(\n self,\n block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),\n motion_layers_per_block: int = 2,\n motion_mid_block_layers_per_block: int = 1,\n motion_num_attention_heads: int = 8,\n motion_norm_num_groups: int = 32,\n motion_max_seq_length: int = 32,\n use_motion_mid_block: bool = True,\n conv_in_channels: Optional[int] = None,\n ):\n \"\"\"Container to store AnimateDiff Motion Modules\n\n Args:\n block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):\n The tuple of output channels for each UNet block.\n motion_layers_per_block (`int`, *optional*, defaults to 2):\n The number of motion layers per UNet block.\n motion_mid_block_layers_per_block (`int`, *optional*, defaults to 1):\n The number of motion layers in the middle UNet block.\n motion_num_attention_heads (`int`, *optional*, defaults to 8):\n The number of heads to use in each attention layer of the motion module.\n motion_norm_num_groups (`int`, *optional*, defaults to 32):\n The number of groups to use in each group normalization layer of the motion module.\n motion_max_seq_length (`int`, *optional*, defaults to 32):\n The maximum sequence length to use in the motion module.\n use_motion_mid_block (`bool`, *optional*, defaults to True):\n Whether to use a motion module in the middle of the UNet.\n \"\"\"\n\n super().__init__()\n down_blocks = []\n up_blocks = []\n\n if conv_in_channels:\n # input\n self.conv_in = nn.Conv2d(conv_in_channels, block_out_channels[0], kernel_size=3, padding=1)\n else:\n self.conv_in = None\n\n for i, channel in enumerate(block_out_channels):\n output_channel = block_out_channels[i]\n down_blocks.append(\n MotionModules(\n in_channels=output_channel,\n norm_num_groups=motion_norm_num_groups,\n cross_attention_dim=None,\n activation_fn=\"geglu\",\n attention_bias=False,\n num_attention_heads=motion_num_attention_heads,\n max_seq_length=motion_max_seq_length,\n layers_per_block=motion_layers_per_block,\n )\n )\n\n if use_motion_mid_block:\n self.mid_block = MotionModules(\n in_channels=block_out_channels[-1],\n norm_num_groups=motion_norm_num_groups,\n cross_attention_dim=None,\n activation_fn=\"geglu\",\n attention_bias=False,\n num_attention_heads=motion_num_attention_heads,\n layers_per_block=motion_mid_block_layers_per_block,\n max_seq_length=motion_max_seq_length,\n )\n else:\n self.mid_block = None\n\n reversed_block_out_channels = list(reversed(block_out_channels))\n output_channel = reversed_block_out_channels[0]\n for i, channel in enumerate(reversed_block_out_channels):\n output_channel = reversed_block_out_channels[i]\n up_blocks.append(\n MotionModules(\n in_channels=output_channel,\n norm_num_groups=motion_norm_num_groups,\n cross_attention_dim=None,\n activation_fn=\"geglu\",\n attention_bias=False,\n num_attention_heads=motion_num_attention_heads,\n max_seq_length=motion_max_seq_length,\n layers_per_block=motion_layers_per_block + 1,\n )\n )\n\n self.down_blocks = nn.ModuleList(down_blocks)\n self.up_blocks = nn.ModuleList(up_blocks)\n\n def forward(self, sample):\n pass\n\n\nclass UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):\n r\"\"\"\n A modified conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a\n sample shaped output.\n\n This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented\n for all models (such as downloading or saving).\n \"\"\"\n\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n sample_size: Optional[int] = None,\n in_channels: int = 4,\n out_channels: int = 4,\n down_block_types: Tuple[str, ...] = (\n \"CrossAttnDownBlockMotion\",\n \"CrossAttnDownBlockMotion\",\n \"CrossAttnDownBlockMotion\",\n \"DownBlockMotion\",\n ),\n up_block_types: Tuple[str, ...] = (\n \"UpBlockMotion\",\n \"CrossAttnUpBlockMotion\",\n \"CrossAttnUpBlockMotion\",\n \"CrossAttnUpBlockMotion\",\n ),\n block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),\n layers_per_block: int = 2,\n downsample_padding: int = 1,\n mid_block_scale_factor: float = 1,\n act_fn: str = \"silu\",\n norm_num_groups: int = 32,\n norm_eps: float = 1e-5,\n cross_attention_dim: int = 1280,\n transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,\n reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None,\n use_linear_projection: bool = False,\n num_attention_heads: Union[int, Tuple[int, ...]] = 8,\n motion_max_seq_length: int = 32,\n motion_num_attention_heads: int = 8,\n use_motion_mid_block: int = True,\n encoder_hid_dim: Optional[int] = None,\n encoder_hid_dim_type: Optional[str] = None,\n addition_embed_type: Optional[str] = None,\n addition_time_embed_dim: Optional[int] = None,\n projection_class_embeddings_input_dim: Optional[int] = None,\n time_cond_proj_dim: Optional[int] = None,\n ):\n super().__init__()\n\n self.sample_size = sample_size\n\n # Check inputs\n if len(down_block_types) != len(up_block_types):\n raise ValueError(\n f\"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.\"\n )\n\n if len(block_out_channels) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}.\"\n )\n\n if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}.\"\n )\n\n if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None:\n for layer_number_per_block in transformer_layers_per_block:\n if isinstance(layer_number_per_block, list):\n raise ValueError(\"Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.\")\n\n # input\n conv_in_kernel = 3\n conv_out_kernel = 3\n conv_in_padding = (conv_in_kernel - 1) // 2\n self.conv_in = nn.Conv2d(\n in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding\n )\n\n # time\n time_embed_dim = block_out_channels[0] * 4\n self.time_proj = Timesteps(block_out_channels[0], True, 0)\n timestep_input_dim = block_out_channels[0]\n\n self.time_embedding = TimestepEmbedding(\n timestep_input_dim, time_embed_dim, act_fn=act_fn, cond_proj_dim=time_cond_proj_dim\n )\n\n if encoder_hid_dim_type is None:\n self.encoder_hid_proj = None\n\n if addition_embed_type == \"text_time\":\n self.add_time_proj = Timesteps(addition_time_embed_dim, True, 0)\n self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)\n\n # class embedding\n self.down_blocks = nn.ModuleList([])\n self.up_blocks = nn.ModuleList([])\n\n if isinstance(num_attention_heads, int):\n num_attention_heads = (num_attention_heads,) * len(down_block_types)\n\n if isinstance(cross_attention_dim, int):\n cross_attention_dim = (cross_attention_dim,) * len(down_block_types)\n\n if isinstance(layers_per_block, int):\n layers_per_block = [layers_per_block] * len(down_block_types)\n\n if isinstance(transformer_layers_per_block, int):\n transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)\n\n # down\n output_channel = block_out_channels[0]\n for i, down_block_type in enumerate(down_block_types):\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n down_block = get_down_block(\n down_block_type,\n num_layers=layers_per_block[i],\n in_channels=input_channel,\n out_channels=output_channel,\n temb_channels=time_embed_dim,\n add_downsample=not is_final_block,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim[i],\n num_attention_heads=num_attention_heads[i],\n downsample_padding=downsample_padding,\n use_linear_projection=use_linear_projection,\n dual_cross_attention=False,\n temporal_num_attention_heads=motion_num_attention_heads,\n temporal_max_seq_length=motion_max_seq_length,\n transformer_layers_per_block=transformer_layers_per_block[i],\n )\n self.down_blocks.append(down_block)\n\n # mid\n if use_motion_mid_block:\n self.mid_block = UNetMidBlockCrossAttnMotion(\n in_channels=block_out_channels[-1],\n temb_channels=time_embed_dim,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n output_scale_factor=mid_block_scale_factor,\n cross_attention_dim=cross_attention_dim[-1],\n num_attention_heads=num_attention_heads[-1],\n resnet_groups=norm_num_groups,\n dual_cross_attention=False,\n use_linear_projection=use_linear_projection,\n temporal_num_attention_heads=motion_num_attention_heads,\n temporal_max_seq_length=motion_max_seq_length,\n transformer_layers_per_block=transformer_layers_per_block[-1],\n )\n\n else:\n self.mid_block = UNetMidBlock2DCrossAttn(\n in_channels=block_out_channels[-1],\n temb_channels=time_embed_dim,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n output_scale_factor=mid_block_scale_factor,\n cross_attention_dim=cross_attention_dim[-1],\n num_attention_heads=num_attention_heads[-1],\n resnet_groups=norm_num_groups,\n dual_cross_attention=False,\n use_linear_projection=use_linear_projection,\n transformer_layers_per_block=transformer_layers_per_block[-1],\n )\n\n # count how many layers upsample the images\n self.num_upsamplers = 0\n\n # up\n reversed_block_out_channels = list(reversed(block_out_channels))\n reversed_num_attention_heads = list(reversed(num_attention_heads))\n reversed_layers_per_block = list(reversed(layers_per_block))\n reversed_cross_attention_dim = list(reversed(cross_attention_dim))\n reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))\n\n output_channel = reversed_block_out_channels[0]\n for i, up_block_type in enumerate(up_block_types):\n is_final_block = i == len(block_out_channels) - 1\n\n prev_output_channel = output_channel\n output_channel = reversed_block_out_channels[i]\n input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]\n\n # add upsample block for all BUT final layer\n if not is_final_block:\n add_upsample = True\n self.num_upsamplers += 1\n else:\n add_upsample = False\n\n up_block = get_up_block(\n up_block_type,\n num_layers=reversed_layers_per_block[i] + 1,\n in_channels=input_channel,\n out_channels=output_channel,\n prev_output_channel=prev_output_channel,\n temb_channels=time_embed_dim,\n add_upsample=add_upsample,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=reversed_cross_attention_dim[i],\n num_attention_heads=reversed_num_attention_heads[i],\n dual_cross_attention=False,\n resolution_idx=i,\n use_linear_projection=use_linear_projection,\n temporal_num_attention_heads=motion_num_attention_heads,\n temporal_max_seq_length=motion_max_seq_length,\n transformer_layers_per_block=reversed_transformer_layers_per_block[i],\n )\n self.up_blocks.append(up_block)\n prev_output_channel = output_channel\n\n # out\n if norm_num_groups is not None:\n self.conv_norm_out = nn.GroupNorm(\n num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps\n )\n self.conv_act = nn.SiLU()\n else:\n self.conv_norm_out = None\n self.conv_act = None\n\n conv_out_padding = (conv_out_kernel - 1) // 2\n self.conv_out = nn.Conv2d(\n block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding\n )\n\n @classmethod\n def from_unet2d(\n cls,\n unet: UNet2DConditionModel,\n motion_adapter: Optional[MotionAdapter] = None,\n load_weights: bool = True,\n ):\n has_motion_adapter = motion_adapter is not None\n\n if has_motion_adapter:\n motion_adapter.to(device=unet.device)\n\n # based on https://github.com/guoyww/AnimateDiff/blob/895f3220c06318ea0760131ec70408b466c49333/animatediff/models/unet.py#L459\n config = dict(unet.config)\n config[\"_class_name\"] = cls.__name__\n\n down_blocks = []\n for down_blocks_type in config[\"down_block_types\"]:\n if \"CrossAttn\" in down_blocks_type:\n down_blocks.append(\"CrossAttnDownBlockMotion\")\n else:\n down_blocks.append(\"DownBlockMotion\")\n config[\"down_block_types\"] = down_blocks\n\n up_blocks = []\n for down_blocks_type in config[\"up_block_types\"]:\n if \"CrossAttn\" in down_blocks_type:\n up_blocks.append(\"CrossAttnUpBlockMotion\")\n else:\n up_blocks.append(\"UpBlockMotion\")\n\n config[\"up_block_types\"] = up_blocks\n\n if has_motion_adapter:\n config[\"motion_num_attention_heads\"] = motion_adapter.config[\"motion_num_attention_heads\"]\n config[\"motion_max_seq_length\"] = motion_adapter.config[\"motion_max_seq_length\"]\n config[\"use_motion_mid_block\"] = motion_adapter.config[\"use_motion_mid_block\"]\n\n # For PIA UNets we need to set the number input channels to 9\n if motion_adapter.config[\"conv_in_channels\"]:\n config[\"in_channels\"] = motion_adapter.config[\"conv_in_channels\"]\n\n # Need this for backwards compatibility with UNet2DConditionModel checkpoints\n if not config.get(\"num_attention_heads\"):\n config[\"num_attention_heads\"] = config[\"attention_head_dim\"]\n\n config = FrozenDict(config)\n model = cls.from_config(config)\n\n if not load_weights:\n return model\n\n # Logic for loading PIA UNets which allow the first 4 channels to be any UNet2DConditionModel conv_in weight\n # while the last 5 channels must be PIA conv_in weights.\n if has_motion_adapter and motion_adapter.config[\"conv_in_channels\"]:\n model.conv_in = motion_adapter.conv_in\n updated_conv_in_weight = torch.cat(\n [unet.conv_in.weight, motion_adapter.conv_in.weight[:, 4:, :, :]], dim=1\n )\n model.conv_in.load_state_dict({\"weight\": updated_conv_in_weight, \"bias\": unet.conv_in.bias})\n else:\n model.conv_in.load_state_dict(unet.conv_in.state_dict())\n\n model.time_proj.load_state_dict(unet.time_proj.state_dict())\n model.time_embedding.load_state_dict(unet.time_embedding.state_dict())\n\n if any(\n isinstance(proc, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0))\n for proc in unet.attn_processors.values()\n ):\n attn_procs = {}\n for name, processor in unet.attn_processors.items():\n if name.endswith(\"attn1.processor\"):\n attn_processor_class = (\n AttnProcessor2_0 if hasattr(F, \"scaled_dot_product_attention\") else AttnProcessor\n )\n attn_procs[name] = attn_processor_class()\n else:\n attn_processor_class = (\n IPAdapterAttnProcessor2_0\n if hasattr(F, \"scaled_dot_product_attention\")\n else IPAdapterAttnProcessor\n )\n attn_procs[name] = attn_processor_class(\n hidden_size=processor.hidden_size,\n cross_attention_dim=processor.cross_attention_dim,\n scale=processor.scale,\n num_tokens=processor.num_tokens,\n )\n for name, processor in model.attn_processors.items():\n if name not in attn_procs:\n attn_procs[name] = processor.__class__()\n model.set_attn_processor(attn_procs)\n model.config.encoder_hid_dim_type = \"ip_image_proj\"\n model.encoder_hid_proj = unet.encoder_hid_proj\n\n for i, down_block in enumerate(unet.down_blocks):\n model.down_blocks[i].resnets.load_state_dict(down_block.resnets.state_dict())\n if hasattr(model.down_blocks[i], \"attentions\"):\n model.down_blocks[i].attentions.load_state_dict(down_block.attentions.state_dict())\n if model.down_blocks[i].downsamplers:\n model.down_blocks[i].downsamplers.load_state_dict(down_block.downsamplers.state_dict())\n\n for i, up_block in enumerate(unet.up_blocks):\n model.up_blocks[i].resnets.load_state_dict(up_block.resnets.state_dict())\n if hasattr(model.up_blocks[i], \"attentions\"):\n model.up_blocks[i].attentions.load_state_dict(up_block.attentions.state_dict())\n if model.up_blocks[i].upsamplers:\n model.up_blocks[i].upsamplers.load_state_dict(up_block.upsamplers.state_dict())\n\n model.mid_block.resnets.load_state_dict(unet.mid_block.resnets.state_dict())\n model.mid_block.attentions.load_state_dict(unet.mid_block.attentions.state_dict())\n\n if unet.conv_norm_out is not None:\n model.conv_norm_out.load_state_dict(unet.conv_norm_out.state_dict())\n if unet.conv_act is not None:\n model.conv_act.load_state_dict(unet.conv_act.state_dict())\n model.conv_out.load_state_dict(unet.conv_out.state_dict())\n\n if has_motion_adapter:\n model.load_motion_modules(motion_adapter)\n\n # ensure that the Motion UNet is the same dtype as the UNet2DConditionModel\n model.to(unet.dtype)\n\n return model\n\n def freeze_unet2d_params(self) -> None:\n \"\"\"Freeze the weights of just the UNet2DConditionModel, and leave the motion modules\n unfrozen for fine tuning.\n \"\"\"\n # Freeze everything\n for param in self.parameters():\n param.requires_grad = False\n\n # Unfreeze Motion Modules\n for down_block in self.down_blocks:\n motion_modules = down_block.motion_modules\n for param in motion_modules.parameters():\n param.requires_grad = True\n\n for up_block in self.up_blocks:\n motion_modules = up_block.motion_modules\n for param in motion_modules.parameters():\n param.requires_grad = True\n\n if hasattr(self.mid_block, \"motion_modules\"):\n motion_modules = self.mid_block.motion_modules\n for param in motion_modules.parameters():\n param.requires_grad = True\n\n def load_motion_modules(self, motion_adapter: Optional[MotionAdapter]) -> None:\n for i, down_block in enumerate(motion_adapter.down_blocks):\n self.down_blocks[i].motion_modules.load_state_dict(down_block.motion_modules.state_dict())\n for i, up_block in enumerate(motion_adapter.up_blocks):\n self.up_blocks[i].motion_modules.load_state_dict(up_block.motion_modules.state_dict())\n\n # to support older motion modules that don't have a mid_block\n if hasattr(self.mid_block, \"motion_modules\"):\n self.mid_block.motion_modules.load_state_dict(motion_adapter.mid_block.motion_modules.state_dict())\n\n def save_motion_modules(\n self,\n save_directory: str,\n is_main_process: bool = True,\n safe_serialization: bool = True,\n variant: Optional[str] = None,\n push_to_hub: bool = False,\n **kwargs,\n ) -> None:\n state_dict = self.state_dict()\n\n # Extract all motion modules\n motion_state_dict = {}\n for k, v in state_dict.items():\n if \"motion_modules\" in k:\n motion_state_dict[k] = v\n\n adapter = MotionAdapter(\n block_out_channels=self.config[\"block_out_channels\"],\n motion_layers_per_block=self.config[\"layers_per_block\"],\n motion_norm_num_groups=self.config[\"norm_num_groups\"],\n motion_num_attention_heads=self.config[\"motion_num_attention_heads\"],\n motion_max_seq_length=self.config[\"motion_max_seq_length\"],\n use_motion_mid_block=self.config[\"use_motion_mid_block\"],\n )\n adapter.load_state_dict(motion_state_dict)\n adapter.save_pretrained(\n save_directory=save_directory,\n is_main_process=is_main_process,\n safe_serialization=safe_serialization,\n variant=variant,\n push_to_hub=push_to_hub,\n **kwargs,\n )\n\n @property\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors\n def attn_processors(self) -> Dict[str, AttentionProcessor]:\n r\"\"\"\n Returns:\n `dict` of attention processors: A dictionary containing all attention processors used in the model with\n indexed by its weight name.\n \"\"\"\n # set recursively\n processors = {}\n\n def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\n if hasattr(module, \"get_processor\"):\n processors[f\"{name}.processor\"] = module.get_processor(return_deprecated_lora=True)\n\n for sub_name, child in module.named_children():\n fn_recursive_add_processors(f\"{name}.{sub_name}\", child, processors)\n\n return processors\n\n for name, module in self.named_children():\n fn_recursive_add_processors(name, module, processors)\n\n return processors\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor\n def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\n r\"\"\"\n Sets the attention processor to use to compute attention.\n\n Parameters:\n processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):\n The instantiated processor class or a dictionary of processor classes that will be set as the processor\n for **all** `Attention` layers.\n\n If `processor` is a dict, the key needs to define the path to the corresponding cross attention\n processor. This is strongly recommended when setting trainable attention processors.\n\n \"\"\"\n count = len(self.attn_processors.keys())\n\n if isinstance(processor, dict) and len(processor) != count:\n raise ValueError(\n f\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\"\n f\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\"\n )\n\n def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\n if hasattr(module, \"set_processor\"):\n if not isinstance(processor, dict):\n module.set_processor(processor)\n else:\n module.set_processor(processor.pop(f\"{name}.processor\"))\n\n for sub_name, child in module.named_children():\n fn_recursive_attn_processor(f\"{name}.{sub_name}\", child, processor)\n\n for name, module in self.named_children():\n fn_recursive_attn_processor(name, module, processor)\n\n # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking\n def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:\n \"\"\"\n Sets the attention processor to use [feed forward\n chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).\n\n Parameters:\n chunk_size (`int`, *optional*):\n The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually\n over each tensor of dim=`dim`.\n dim (`int`, *optional*, defaults to `0`):\n The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)\n or dim=1 (sequence length).\n \"\"\"\n if dim not in [0, 1]:\n raise ValueError(f\"Make sure to set `dim` to either 0 or 1, not {dim}\")\n\n # By default chunk size is 1\n chunk_size = chunk_size or 1\n\n def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):\n if hasattr(module, \"set_chunk_feed_forward\"):\n module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)\n\n for child in module.children():\n fn_recursive_feed_forward(child, chunk_size, dim)\n\n for module in self.children():\n fn_recursive_feed_forward(module, chunk_size, dim)\n\n # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.disable_forward_chunking\n def disable_forward_chunking(self) -> None:\n def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):\n if hasattr(module, \"set_chunk_feed_forward\"):\n module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)\n\n for child in module.children():\n fn_recursive_feed_forward(child, chunk_size, dim)\n\n for module in self.children():\n fn_recursive_feed_forward(module, None, 0)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor\n def set_default_attn_processor(self) -> None:\n \"\"\"\n Disables custom attention processors and sets the default attention implementation.\n \"\"\"\n if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnAddedKVProcessor()\n elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnProcessor()\n else:\n raise ValueError(\n f\"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}\"\n )\n\n self.set_attn_processor(processor)\n\n def _set_gradient_checkpointing(self, module, value: bool = False) -> None:\n if isinstance(module, (CrossAttnDownBlockMotion, DownBlockMotion, CrossAttnUpBlockMotion, UpBlockMotion)):\n module.gradient_checkpointing = value\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.enable_freeu\n def enable_freeu(self, s1: float, s2: float, b1: float, b2: float) -> None:\n r\"\"\"Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.\n\n The suffixes after the scaling factors represent the stage blocks where they are being applied.\n\n Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that\n are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.\n\n Args:\n s1 (`float`):\n Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to\n mitigate the \"oversmoothing effect\" in the enhanced denoising process.\n s2 (`float`):\n Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to\n mitigate the \"oversmoothing effect\" in the enhanced denoising process.\n b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.\n b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.\n \"\"\"\n for i, upsample_block in enumerate(self.up_blocks):\n setattr(upsample_block, \"s1\", s1)\n setattr(upsample_block, \"s2\", s2)\n setattr(upsample_block, \"b1\", b1)\n setattr(upsample_block, \"b2\", b2)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.disable_freeu\n def disable_freeu(self) -> None:\n \"\"\"Disables the FreeU mechanism.\"\"\"\n freeu_keys = {\"s1\", \"s2\", \"b1\", \"b2\"}\n for i, upsample_block in enumerate(self.up_blocks):\n for k in freeu_keys:\n if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None:\n setattr(upsample_block, k, None)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections\n def fuse_qkv_projections(self):\n \"\"\"\n Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)\n are fused. For cross-attention modules, key and value projection matrices are fused.\n\n \n\n This API is 🧪 experimental.\n\n \n \"\"\"\n self.original_attn_processors = None\n\n for _, attn_processor in self.attn_processors.items():\n if \"Added\" in str(attn_processor.__class__.__name__):\n raise ValueError(\"`fuse_qkv_projections()` is not supported for models having added KV projections.\")\n\n self.original_attn_processors = self.attn_processors\n\n for module in self.modules():\n if isinstance(module, Attention):\n module.fuse_projections(fuse=True)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections\n def unfuse_qkv_projections(self):\n \"\"\"Disables the fused QKV projection if enabled.\n\n \n\n This API is 🧪 experimental.\n\n \n\n \"\"\"\n if self.original_attn_processors is not None:\n self.set_attn_processor(self.original_attn_processors)\n\n def forward(\n self,\n sample: torch.Tensor,\n timestep: Union[torch.Tensor, float, int],\n encoder_hidden_states: torch.Tensor,\n timestep_cond: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,\n down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\n mid_block_additional_residual: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ) -> Union[UNet3DConditionOutput, Tuple[torch.Tensor]]:\n r\"\"\"\n The [`UNetMotionModel`] forward method.\n\n Args:\n sample (`torch.Tensor`):\n The noisy input tensor with the following shape `(batch, num_frames, channel, height, width`.\n timestep (`torch.Tensor` or `float` or `int`): The number of timesteps to denoise an input.\n encoder_hidden_states (`torch.Tensor`):\n The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.\n timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`):\n Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed\n through the `self.time_embedding` layer to obtain the timestep embeddings.\n attention_mask (`torch.Tensor`, *optional*, defaults to `None`):\n An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask\n is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large\n negative values to the attention scores corresponding to \"discard\" tokens.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):\n A tuple of tensors that if specified are added to the residuals of down unet blocks.\n mid_block_additional_residual: (`torch.Tensor`, *optional*):\n A tensor that if specified is added to the residual of the middle unet block.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~models.unets.unet_3d_condition.UNet3DConditionOutput`] instead of a plain\n tuple.\n\n Returns:\n [`~models.unets.unet_3d_condition.UNet3DConditionOutput`] or `tuple`:\n If `return_dict` is True, an [`~models.unets.unet_3d_condition.UNet3DConditionOutput`] is returned,\n otherwise a `tuple` is returned where the first element is the sample tensor.\n \"\"\"\n # By default samples have to be AT least a multiple of the overall upsampling factor.\n # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).\n # However, the upsampling interpolation output size can be forced to fit any upsampling size\n # on the fly if necessary.\n default_overall_up_factor = 2**self.num_upsamplers\n\n # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`\n forward_upsample_size = False\n upsample_size = None\n\n if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):\n logger.info(\"Forward upsample size to force interpolation output size.\")\n forward_upsample_size = True\n\n # prepare attention_mask\n if attention_mask is not None:\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n # 1. time\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can\n # This would be a good case for the `match` statement (Python 3.10+)\n is_mps = sample.device.type == \"mps\"\n if isinstance(timestep, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\n elif len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(sample.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n num_frames = sample.shape[2]\n timesteps = timesteps.expand(sample.shape[0])\n\n t_emb = self.time_proj(timesteps)\n\n # timesteps does not contain any weights and will always return f32 tensors\n # but time_embedding might actually be running in fp16. so we need to cast here.\n # there might be better ways to encapsulate this.\n t_emb = t_emb.to(dtype=self.dtype)\n\n emb = self.time_embedding(t_emb, timestep_cond)\n aug_emb = None\n\n if self.config.addition_embed_type == \"text_time\":\n if \"text_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`\"\n )\n\n text_embeds = added_cond_kwargs.get(\"text_embeds\")\n if \"time_ids\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`\"\n )\n time_ids = added_cond_kwargs.get(\"time_ids\")\n time_embeds = self.add_time_proj(time_ids.flatten())\n time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))\n\n add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)\n add_embeds = add_embeds.to(emb.dtype)\n aug_emb = self.add_embedding(add_embeds)\n\n emb = emb if aug_emb is None else emb + aug_emb\n emb = emb.repeat_interleave(repeats=num_frames, dim=0)\n encoder_hidden_states = encoder_hidden_states.repeat_interleave(repeats=num_frames, dim=0)\n\n if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == \"ip_image_proj\":\n if \"image_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`\"\n )\n image_embeds = added_cond_kwargs.get(\"image_embeds\")\n image_embeds = self.encoder_hid_proj(image_embeds)\n image_embeds = [image_embed.repeat_interleave(repeats=num_frames, dim=0) for image_embed in image_embeds]\n encoder_hidden_states = (encoder_hidden_states, image_embeds)\n\n # 2. pre-process\n sample = sample.permute(0, 2, 1, 3, 4).reshape((sample.shape[0] * num_frames, -1) + sample.shape[3:])\n sample = self.conv_in(sample)\n\n # 3. down\n down_block_res_samples = (sample,)\n for downsample_block in self.down_blocks:\n if hasattr(downsample_block, \"has_cross_attention\") and downsample_block.has_cross_attention:\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n num_frames=num_frames,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n else:\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb, num_frames=num_frames)\n\n down_block_res_samples += res_samples\n\n if down_block_additional_residuals is not None:\n new_down_block_res_samples = ()\n\n for down_block_res_sample, down_block_additional_residual in zip(\n down_block_res_samples, down_block_additional_residuals\n ):\n down_block_res_sample = down_block_res_sample + down_block_additional_residual\n new_down_block_res_samples += (down_block_res_sample,)\n\n down_block_res_samples = new_down_block_res_samples\n\n # 4. mid\n if self.mid_block is not None:\n # To support older versions of motion modules that don't have a mid_block\n if hasattr(self.mid_block, \"motion_modules\"):\n sample = self.mid_block(\n sample,\n emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n num_frames=num_frames,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n else:\n sample = self.mid_block(\n sample,\n emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n\n if mid_block_additional_residual is not None:\n sample = sample + mid_block_additional_residual\n\n # 5. up\n for i, upsample_block in enumerate(self.up_blocks):\n is_final_block = i == len(self.up_blocks) - 1\n\n res_samples = down_block_res_samples[-len(upsample_block.resnets) :]\n down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]\n\n # if we have not reached the final block and need to forward the\n # upsample size, we do it here\n if not is_final_block and forward_upsample_size:\n upsample_size = down_block_res_samples[-1].shape[2:]\n\n if hasattr(upsample_block, \"has_cross_attention\") and upsample_block.has_cross_attention:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n encoder_hidden_states=encoder_hidden_states,\n upsample_size=upsample_size,\n attention_mask=attention_mask,\n num_frames=num_frames,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n else:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n upsample_size=upsample_size,\n num_frames=num_frames,\n )\n\n # 6. post-process\n if self.conv_norm_out:\n sample = self.conv_norm_out(sample)\n sample = self.conv_act(sample)\n\n sample = self.conv_out(sample)\n\n # reshape to (batch, channel, framerate, width, height)\n sample = sample[None, :].reshape((-1, num_frames) + sample.shape[1:]).permute(0, 2, 1, 3, 4)\n\n if not return_dict:\n return (sample,)\n\n return UNet3DConditionOutput(sample=sample)\n" }, { "path": "diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py", "content": "from dataclasses import dataclass\nfrom typing import Dict, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...loaders import UNet2DConditionLoadersMixin\nfrom ...utils import BaseOutput, logging\nfrom ..attention_processor import CROSS_ATTENTION_PROCESSORS, AttentionProcessor, AttnProcessor\nfrom ..embeddings import TimestepEmbedding, Timesteps\nfrom ..modeling_utils import ModelMixin\nfrom .unet_3d_blocks import UNetMidBlockSpatioTemporal, get_down_block, get_up_block\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n@dataclass\nclass UNetSpatioTemporalConditionOutput(BaseOutput):\n \"\"\"\n The output of [`UNetSpatioTemporalConditionModel`].\n\n Args:\n sample (`torch.Tensor` of shape `(batch_size, num_frames, num_channels, height, width)`):\n The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.\n \"\"\"\n\n sample: torch.Tensor = None\n\n\nclass UNetSpatioTemporalConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):\n r\"\"\"\n A conditional Spatio-Temporal UNet model that takes a noisy video frames, conditional state, and a timestep and\n returns a sample shaped output.\n\n This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented\n for all models (such as downloading or saving).\n\n Parameters:\n sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):\n Height and width of input/output sample.\n in_channels (`int`, *optional*, defaults to 8): Number of channels in the input sample.\n out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.\n down_block_types (`Tuple[str]`, *optional*, defaults to `(\"CrossAttnDownBlockSpatioTemporal\", \"CrossAttnDownBlockSpatioTemporal\", \"CrossAttnDownBlockSpatioTemporal\", \"DownBlockSpatioTemporal\")`):\n The tuple of downsample blocks to use.\n up_block_types (`Tuple[str]`, *optional*, defaults to `(\"UpBlockSpatioTemporal\", \"CrossAttnUpBlockSpatioTemporal\", \"CrossAttnUpBlockSpatioTemporal\", \"CrossAttnUpBlockSpatioTemporal\")`):\n The tuple of upsample blocks to use.\n block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):\n The tuple of output channels for each block.\n addition_time_embed_dim: (`int`, defaults to 256):\n Dimension to to encode the additional time ids.\n projection_class_embeddings_input_dim (`int`, defaults to 768):\n The dimension of the projection of encoded `added_time_ids`.\n layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.\n cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):\n The dimension of the cross attention features.\n transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):\n The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for\n [`~models.unets.unet_3d_blocks.CrossAttnDownBlockSpatioTemporal`],\n [`~models.unets.unet_3d_blocks.CrossAttnUpBlockSpatioTemporal`],\n [`~models.unets.unet_3d_blocks.UNetMidBlockSpatioTemporal`].\n num_attention_heads (`int`, `Tuple[int]`, defaults to `(5, 10, 10, 20)`):\n The number of attention heads.\n dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.\n \"\"\"\n\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n sample_size: Optional[int] = None,\n in_channels: int = 8,\n out_channels: int = 4,\n down_block_types: Tuple[str] = (\n \"CrossAttnDownBlockSpatioTemporal\",\n \"CrossAttnDownBlockSpatioTemporal\",\n \"CrossAttnDownBlockSpatioTemporal\",\n \"DownBlockSpatioTemporal\",\n ),\n up_block_types: Tuple[str] = (\n \"UpBlockSpatioTemporal\",\n \"CrossAttnUpBlockSpatioTemporal\",\n \"CrossAttnUpBlockSpatioTemporal\",\n \"CrossAttnUpBlockSpatioTemporal\",\n ),\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\n addition_time_embed_dim: int = 256,\n projection_class_embeddings_input_dim: int = 768,\n layers_per_block: Union[int, Tuple[int]] = 2,\n cross_attention_dim: Union[int, Tuple[int]] = 1024,\n transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,\n num_attention_heads: Union[int, Tuple[int]] = (5, 10, 20, 20),\n num_frames: int = 25,\n ):\n super().__init__()\n\n self.sample_size = sample_size\n\n # Check inputs\n if len(down_block_types) != len(up_block_types):\n raise ValueError(\n f\"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.\"\n )\n\n if len(block_out_channels) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}.\"\n )\n\n if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}.\"\n )\n\n # input\n self.conv_in = nn.Conv2d(\n in_channels,\n block_out_channels[0],\n kernel_size=3,\n padding=1,\n )\n\n # time\n time_embed_dim = block_out_channels[0] * 4\n\n self.time_proj = Timesteps(block_out_channels[0], True, downscale_freq_shift=0)\n timestep_input_dim = block_out_channels[0]\n\n self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\n\n self.add_time_proj = Timesteps(addition_time_embed_dim, True, downscale_freq_shift=0)\n self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)\n\n self.down_blocks = nn.ModuleList([])\n self.up_blocks = nn.ModuleList([])\n\n if isinstance(num_attention_heads, int):\n num_attention_heads = (num_attention_heads,) * len(down_block_types)\n\n if isinstance(cross_attention_dim, int):\n cross_attention_dim = (cross_attention_dim,) * len(down_block_types)\n\n if isinstance(layers_per_block, int):\n layers_per_block = [layers_per_block] * len(down_block_types)\n\n if isinstance(transformer_layers_per_block, int):\n transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)\n\n blocks_time_embed_dim = time_embed_dim\n\n # down\n output_channel = block_out_channels[0]\n for i, down_block_type in enumerate(down_block_types):\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n down_block = get_down_block(\n down_block_type,\n num_layers=layers_per_block[i],\n transformer_layers_per_block=transformer_layers_per_block[i],\n in_channels=input_channel,\n out_channels=output_channel,\n temb_channels=blocks_time_embed_dim,\n add_downsample=not is_final_block,\n resnet_eps=1e-5,\n cross_attention_dim=cross_attention_dim[i],\n num_attention_heads=num_attention_heads[i],\n resnet_act_fn=\"silu\",\n )\n self.down_blocks.append(down_block)\n\n # mid\n self.mid_block = UNetMidBlockSpatioTemporal(\n block_out_channels[-1],\n temb_channels=blocks_time_embed_dim,\n transformer_layers_per_block=transformer_layers_per_block[-1],\n cross_attention_dim=cross_attention_dim[-1],\n num_attention_heads=num_attention_heads[-1],\n )\n\n # count how many layers upsample the images\n self.num_upsamplers = 0\n\n # up\n reversed_block_out_channels = list(reversed(block_out_channels))\n reversed_num_attention_heads = list(reversed(num_attention_heads))\n reversed_layers_per_block = list(reversed(layers_per_block))\n reversed_cross_attention_dim = list(reversed(cross_attention_dim))\n reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))\n\n output_channel = reversed_block_out_channels[0]\n for i, up_block_type in enumerate(up_block_types):\n is_final_block = i == len(block_out_channels) - 1\n\n prev_output_channel = output_channel\n output_channel = reversed_block_out_channels[i]\n input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]\n\n # add upsample block for all BUT final layer\n if not is_final_block:\n add_upsample = True\n self.num_upsamplers += 1\n else:\n add_upsample = False\n\n up_block = get_up_block(\n up_block_type,\n num_layers=reversed_layers_per_block[i] + 1,\n transformer_layers_per_block=reversed_transformer_layers_per_block[i],\n in_channels=input_channel,\n out_channels=output_channel,\n prev_output_channel=prev_output_channel,\n temb_channels=blocks_time_embed_dim,\n add_upsample=add_upsample,\n resnet_eps=1e-5,\n resolution_idx=i,\n cross_attention_dim=reversed_cross_attention_dim[i],\n num_attention_heads=reversed_num_attention_heads[i],\n resnet_act_fn=\"silu\",\n )\n self.up_blocks.append(up_block)\n prev_output_channel = output_channel\n\n # out\n self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=32, eps=1e-5)\n self.conv_act = nn.SiLU()\n\n self.conv_out = nn.Conv2d(\n block_out_channels[0],\n out_channels,\n kernel_size=3,\n padding=1,\n )\n\n @property\n def attn_processors(self) -> Dict[str, AttentionProcessor]:\n r\"\"\"\n Returns:\n `dict` of attention processors: A dictionary containing all attention processors used in the model with\n indexed by its weight name.\n \"\"\"\n # set recursively\n processors = {}\n\n def fn_recursive_add_processors(\n name: str,\n module: torch.nn.Module,\n processors: Dict[str, AttentionProcessor],\n ):\n if hasattr(module, \"get_processor\"):\n processors[f\"{name}.processor\"] = module.get_processor(return_deprecated_lora=True)\n\n for sub_name, child in module.named_children():\n fn_recursive_add_processors(f\"{name}.{sub_name}\", child, processors)\n\n return processors\n\n for name, module in self.named_children():\n fn_recursive_add_processors(name, module, processors)\n\n return processors\n\n def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\n r\"\"\"\n Sets the attention processor to use to compute attention.\n\n Parameters:\n processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):\n The instantiated processor class or a dictionary of processor classes that will be set as the processor\n for **all** `Attention` layers.\n\n If `processor` is a dict, the key needs to define the path to the corresponding cross attention\n processor. This is strongly recommended when setting trainable attention processors.\n\n \"\"\"\n count = len(self.attn_processors.keys())\n\n if isinstance(processor, dict) and len(processor) != count:\n raise ValueError(\n f\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\"\n f\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\"\n )\n\n def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\n if hasattr(module, \"set_processor\"):\n if not isinstance(processor, dict):\n module.set_processor(processor)\n else:\n module.set_processor(processor.pop(f\"{name}.processor\"))\n\n for sub_name, child in module.named_children():\n fn_recursive_attn_processor(f\"{name}.{sub_name}\", child, processor)\n\n for name, module in self.named_children():\n fn_recursive_attn_processor(name, module, processor)\n\n def set_default_attn_processor(self):\n \"\"\"\n Disables custom attention processors and sets the default attention implementation.\n \"\"\"\n if all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnProcessor()\n else:\n raise ValueError(\n f\"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}\"\n )\n\n self.set_attn_processor(processor)\n\n def _set_gradient_checkpointing(self, module, value=False):\n if hasattr(module, \"gradient_checkpointing\"):\n module.gradient_checkpointing = value\n\n # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking\n def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:\n \"\"\"\n Sets the attention processor to use [feed forward\n chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).\n\n Parameters:\n chunk_size (`int`, *optional*):\n The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually\n over each tensor of dim=`dim`.\n dim (`int`, *optional*, defaults to `0`):\n The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)\n or dim=1 (sequence length).\n \"\"\"\n if dim not in [0, 1]:\n raise ValueError(f\"Make sure to set `dim` to either 0 or 1, not {dim}\")\n\n # By default chunk size is 1\n chunk_size = chunk_size or 1\n\n def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):\n if hasattr(module, \"set_chunk_feed_forward\"):\n module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)\n\n for child in module.children():\n fn_recursive_feed_forward(child, chunk_size, dim)\n\n for module in self.children():\n fn_recursive_feed_forward(module, chunk_size, dim)\n\n def forward(\n self,\n sample: torch.Tensor,\n timestep: Union[torch.Tensor, float, int],\n encoder_hidden_states: torch.Tensor,\n added_time_ids: torch.Tensor,\n return_dict: bool = True,\n ) -> Union[UNetSpatioTemporalConditionOutput, Tuple]:\n r\"\"\"\n The [`UNetSpatioTemporalConditionModel`] forward method.\n\n Args:\n sample (`torch.Tensor`):\n The noisy input tensor with the following shape `(batch, num_frames, channel, height, width)`.\n timestep (`torch.Tensor` or `float` or `int`): The number of timesteps to denoise an input.\n encoder_hidden_states (`torch.Tensor`):\n The encoder hidden states with shape `(batch, sequence_length, cross_attention_dim)`.\n added_time_ids: (`torch.Tensor`):\n The additional time ids with shape `(batch, num_additional_ids)`. These are encoded with sinusoidal\n embeddings and added to the time embeddings.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] instead\n of a plain tuple.\n Returns:\n [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] or `tuple`:\n If `return_dict` is True, an [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] is\n returned, otherwise a `tuple` is returned where the first element is the sample tensor.\n \"\"\"\n # 1. time\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can\n # This would be a good case for the `match` statement (Python 3.10+)\n is_mps = sample.device.type == \"mps\"\n if isinstance(timestep, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\n elif len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(sample.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n batch_size, num_frames = sample.shape[:2]\n timesteps = timesteps.expand(batch_size)\n\n t_emb = self.time_proj(timesteps)\n\n # `Timesteps` does not contain any weights and will always return f32 tensors\n # but time_embedding might actually be running in fp16. so we need to cast here.\n # there might be better ways to encapsulate this.\n t_emb = t_emb.to(dtype=sample.dtype)\n\n emb = self.time_embedding(t_emb)\n\n time_embeds = self.add_time_proj(added_time_ids.flatten())\n time_embeds = time_embeds.reshape((batch_size, -1))\n time_embeds = time_embeds.to(emb.dtype)\n aug_emb = self.add_embedding(time_embeds)\n emb = emb + aug_emb\n\n # Flatten the batch and frames dimensions\n # sample: [batch, frames, channels, height, width] -> [batch * frames, channels, height, width]\n sample = sample.flatten(0, 1)\n # Repeat the embeddings num_video_frames times\n # emb: [batch, channels] -> [batch * frames, channels]\n emb = emb.repeat_interleave(num_frames, dim=0)\n # encoder_hidden_states: [batch, 1, channels] -> [batch * frames, 1, channels]\n encoder_hidden_states = encoder_hidden_states.repeat_interleave(num_frames, dim=0)\n\n # 2. pre-process\n sample = self.conv_in(sample)\n\n image_only_indicator = torch.zeros(batch_size, num_frames, dtype=sample.dtype, device=sample.device)\n\n down_block_res_samples = (sample,)\n for downsample_block in self.down_blocks:\n if hasattr(downsample_block, \"has_cross_attention\") and downsample_block.has_cross_attention:\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states,\n image_only_indicator=image_only_indicator,\n )\n else:\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n image_only_indicator=image_only_indicator,\n )\n\n down_block_res_samples += res_samples\n\n # 4. mid\n sample = self.mid_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states,\n image_only_indicator=image_only_indicator,\n )\n\n # 5. up\n for i, upsample_block in enumerate(self.up_blocks):\n res_samples = down_block_res_samples[-len(upsample_block.resnets) :]\n down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]\n\n if hasattr(upsample_block, \"has_cross_attention\") and upsample_block.has_cross_attention:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n encoder_hidden_states=encoder_hidden_states,\n image_only_indicator=image_only_indicator,\n )\n else:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n image_only_indicator=image_only_indicator,\n )\n\n # 6. post-process\n sample = self.conv_norm_out(sample)\n sample = self.conv_act(sample)\n sample = self.conv_out(sample)\n\n # 7. Reshape back to original shape\n sample = sample.reshape(batch_size, num_frames, *sample.shape[1:])\n\n if not return_dict:\n return (sample,)\n\n return UNetSpatioTemporalConditionOutput(sample=sample)\n" }, { "path": "diffusers/src/diffusers/models/unets/unet_stable_cascade.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport math\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...loaders import FromOriginalModelMixin\nfrom ...utils import BaseOutput\nfrom ..attention_processor import Attention\nfrom ..modeling_utils import ModelMixin\n\n\n# Copied from diffusers.pipelines.wuerstchen.modeling_wuerstchen_common.WuerstchenLayerNorm with WuerstchenLayerNorm -> SDCascadeLayerNorm\nclass SDCascadeLayerNorm(nn.LayerNorm):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n def forward(self, x):\n x = x.permute(0, 2, 3, 1)\n x = super().forward(x)\n return x.permute(0, 3, 1, 2)\n\n\nclass SDCascadeTimestepBlock(nn.Module):\n def __init__(self, c, c_timestep, conds=[]):\n super().__init__()\n\n self.mapper = nn.Linear(c_timestep, c * 2)\n self.conds = conds\n for cname in conds:\n setattr(self, f\"mapper_{cname}\", nn.Linear(c_timestep, c * 2))\n\n def forward(self, x, t):\n t = t.chunk(len(self.conds) + 1, dim=1)\n a, b = self.mapper(t[0])[:, :, None, None].chunk(2, dim=1)\n for i, c in enumerate(self.conds):\n ac, bc = getattr(self, f\"mapper_{c}\")(t[i + 1])[:, :, None, None].chunk(2, dim=1)\n a, b = a + ac, b + bc\n return x * (1 + a) + b\n\n\nclass SDCascadeResBlock(nn.Module):\n def __init__(self, c, c_skip=0, kernel_size=3, dropout=0.0):\n super().__init__()\n self.depthwise = nn.Conv2d(c, c, kernel_size=kernel_size, padding=kernel_size // 2, groups=c)\n self.norm = SDCascadeLayerNorm(c, elementwise_affine=False, eps=1e-6)\n self.channelwise = nn.Sequential(\n nn.Linear(c + c_skip, c * 4),\n nn.GELU(),\n GlobalResponseNorm(c * 4),\n nn.Dropout(dropout),\n nn.Linear(c * 4, c),\n )\n\n def forward(self, x, x_skip=None):\n x_res = x\n x = self.norm(self.depthwise(x))\n if x_skip is not None:\n x = torch.cat([x, x_skip], dim=1)\n x = self.channelwise(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)\n return x + x_res\n\n\n# from https://github.com/facebookresearch/ConvNeXt-V2/blob/3608f67cc1dae164790c5d0aead7bf2d73d9719b/models/utils.py#L105\nclass GlobalResponseNorm(nn.Module):\n def __init__(self, dim):\n super().__init__()\n self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim))\n self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim))\n\n def forward(self, x):\n agg_norm = torch.norm(x, p=2, dim=(1, 2), keepdim=True)\n stand_div_norm = agg_norm / (agg_norm.mean(dim=-1, keepdim=True) + 1e-6)\n return self.gamma * (x * stand_div_norm) + self.beta + x\n\n\nclass SDCascadeAttnBlock(nn.Module):\n def __init__(self, c, c_cond, nhead, self_attn=True, dropout=0.0):\n super().__init__()\n\n self.self_attn = self_attn\n self.norm = SDCascadeLayerNorm(c, elementwise_affine=False, eps=1e-6)\n self.attention = Attention(query_dim=c, heads=nhead, dim_head=c // nhead, dropout=dropout, bias=True)\n self.kv_mapper = nn.Sequential(nn.SiLU(), nn.Linear(c_cond, c))\n\n def forward(self, x, kv):\n kv = self.kv_mapper(kv)\n norm_x = self.norm(x)\n if self.self_attn:\n batch_size, channel, _, _ = x.shape\n kv = torch.cat([norm_x.view(batch_size, channel, -1).transpose(1, 2), kv], dim=1)\n x = x + self.attention(norm_x, encoder_hidden_states=kv)\n return x\n\n\nclass UpDownBlock2d(nn.Module):\n def __init__(self, in_channels, out_channels, mode, enabled=True):\n super().__init__()\n if mode not in [\"up\", \"down\"]:\n raise ValueError(f\"{mode} not supported\")\n interpolation = (\n nn.Upsample(scale_factor=2 if mode == \"up\" else 0.5, mode=\"bilinear\", align_corners=True)\n if enabled\n else nn.Identity()\n )\n mapping = nn.Conv2d(in_channels, out_channels, kernel_size=1)\n self.blocks = nn.ModuleList([interpolation, mapping] if mode == \"up\" else [mapping, interpolation])\n\n def forward(self, x):\n for block in self.blocks:\n x = block(x)\n return x\n\n\n@dataclass\nclass StableCascadeUNetOutput(BaseOutput):\n sample: torch.Tensor = None\n\n\nclass StableCascadeUNet(ModelMixin, ConfigMixin, FromOriginalModelMixin):\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n in_channels: int = 16,\n out_channels: int = 16,\n timestep_ratio_embedding_dim: int = 64,\n patch_size: int = 1,\n conditioning_dim: int = 2048,\n block_out_channels: Tuple[int] = (2048, 2048),\n num_attention_heads: Tuple[int] = (32, 32),\n down_num_layers_per_block: Tuple[int] = (8, 24),\n up_num_layers_per_block: Tuple[int] = (24, 8),\n down_blocks_repeat_mappers: Optional[Tuple[int]] = (\n 1,\n 1,\n ),\n up_blocks_repeat_mappers: Optional[Tuple[int]] = (1, 1),\n block_types_per_layer: Tuple[Tuple[str]] = (\n (\"SDCascadeResBlock\", \"SDCascadeTimestepBlock\", \"SDCascadeAttnBlock\"),\n (\"SDCascadeResBlock\", \"SDCascadeTimestepBlock\", \"SDCascadeAttnBlock\"),\n ),\n clip_text_in_channels: Optional[int] = None,\n clip_text_pooled_in_channels=1280,\n clip_image_in_channels: Optional[int] = None,\n clip_seq=4,\n effnet_in_channels: Optional[int] = None,\n pixel_mapper_in_channels: Optional[int] = None,\n kernel_size=3,\n dropout: Union[float, Tuple[float]] = (0.1, 0.1),\n self_attn: Union[bool, Tuple[bool]] = True,\n timestep_conditioning_type: Tuple[str] = (\"sca\", \"crp\"),\n switch_level: Optional[Tuple[bool]] = None,\n ):\n \"\"\"\n\n Parameters:\n in_channels (`int`, defaults to 16):\n Number of channels in the input sample.\n out_channels (`int`, defaults to 16):\n Number of channels in the output sample.\n timestep_ratio_embedding_dim (`int`, defaults to 64):\n Dimension of the projected time embedding.\n patch_size (`int`, defaults to 1):\n Patch size to use for pixel unshuffling layer\n conditioning_dim (`int`, defaults to 2048):\n Dimension of the image and text conditional embedding.\n block_out_channels (Tuple[int], defaults to (2048, 2048)):\n Tuple of output channels for each block.\n num_attention_heads (Tuple[int], defaults to (32, 32)):\n Number of attention heads in each attention block. Set to -1 to if block types in a layer do not have\n attention.\n down_num_layers_per_block (Tuple[int], defaults to [8, 24]):\n Number of layers in each down block.\n up_num_layers_per_block (Tuple[int], defaults to [24, 8]):\n Number of layers in each up block.\n down_blocks_repeat_mappers (Tuple[int], optional, defaults to [1, 1]):\n Number of 1x1 Convolutional layers to repeat in each down block.\n up_blocks_repeat_mappers (Tuple[int], optional, defaults to [1, 1]):\n Number of 1x1 Convolutional layers to repeat in each up block.\n block_types_per_layer (Tuple[Tuple[str]], optional,\n defaults to (\n (\"SDCascadeResBlock\", \"SDCascadeTimestepBlock\", \"SDCascadeAttnBlock\"), (\"SDCascadeResBlock\",\n \"SDCascadeTimestepBlock\", \"SDCascadeAttnBlock\")\n ): Block types used in each layer of the up/down blocks.\n clip_text_in_channels (`int`, *optional*, defaults to `None`):\n Number of input channels for CLIP based text conditioning.\n clip_text_pooled_in_channels (`int`, *optional*, defaults to 1280):\n Number of input channels for pooled CLIP text embeddings.\n clip_image_in_channels (`int`, *optional*):\n Number of input channels for CLIP based image conditioning.\n clip_seq (`int`, *optional*, defaults to 4):\n effnet_in_channels (`int`, *optional*, defaults to `None`):\n Number of input channels for effnet conditioning.\n pixel_mapper_in_channels (`int`, defaults to `None`):\n Number of input channels for pixel mapper conditioning.\n kernel_size (`int`, *optional*, defaults to 3):\n Kernel size to use in the block convolutional layers.\n dropout (Tuple[float], *optional*, defaults to (0.1, 0.1)):\n Dropout to use per block.\n self_attn (Union[bool, Tuple[bool]]):\n Tuple of booleans that determine whether to use self attention in a block or not.\n timestep_conditioning_type (Tuple[str], defaults to (\"sca\", \"crp\")):\n Timestep conditioning type.\n switch_level (Optional[Tuple[bool]], *optional*, defaults to `None`):\n Tuple that indicates whether upsampling or downsampling should be applied in a block\n \"\"\"\n\n super().__init__()\n\n if len(block_out_channels) != len(down_num_layers_per_block):\n raise ValueError(\n f\"Number of elements in `down_num_layers_per_block` must match the length of `block_out_channels`: {len(block_out_channels)}\"\n )\n\n elif len(block_out_channels) != len(up_num_layers_per_block):\n raise ValueError(\n f\"Number of elements in `up_num_layers_per_block` must match the length of `block_out_channels`: {len(block_out_channels)}\"\n )\n\n elif len(block_out_channels) != len(down_blocks_repeat_mappers):\n raise ValueError(\n f\"Number of elements in `down_blocks_repeat_mappers` must match the length of `block_out_channels`: {len(block_out_channels)}\"\n )\n\n elif len(block_out_channels) != len(up_blocks_repeat_mappers):\n raise ValueError(\n f\"Number of elements in `up_blocks_repeat_mappers` must match the length of `block_out_channels`: {len(block_out_channels)}\"\n )\n\n elif len(block_out_channels) != len(block_types_per_layer):\n raise ValueError(\n f\"Number of elements in `block_types_per_layer` must match the length of `block_out_channels`: {len(block_out_channels)}\"\n )\n\n if isinstance(dropout, float):\n dropout = (dropout,) * len(block_out_channels)\n if isinstance(self_attn, bool):\n self_attn = (self_attn,) * len(block_out_channels)\n\n # CONDITIONING\n if effnet_in_channels is not None:\n self.effnet_mapper = nn.Sequential(\n nn.Conv2d(effnet_in_channels, block_out_channels[0] * 4, kernel_size=1),\n nn.GELU(),\n nn.Conv2d(block_out_channels[0] * 4, block_out_channels[0], kernel_size=1),\n SDCascadeLayerNorm(block_out_channels[0], elementwise_affine=False, eps=1e-6),\n )\n if pixel_mapper_in_channels is not None:\n self.pixels_mapper = nn.Sequential(\n nn.Conv2d(pixel_mapper_in_channels, block_out_channels[0] * 4, kernel_size=1),\n nn.GELU(),\n nn.Conv2d(block_out_channels[0] * 4, block_out_channels[0], kernel_size=1),\n SDCascadeLayerNorm(block_out_channels[0], elementwise_affine=False, eps=1e-6),\n )\n\n self.clip_txt_pooled_mapper = nn.Linear(clip_text_pooled_in_channels, conditioning_dim * clip_seq)\n if clip_text_in_channels is not None:\n self.clip_txt_mapper = nn.Linear(clip_text_in_channels, conditioning_dim)\n if clip_image_in_channels is not None:\n self.clip_img_mapper = nn.Linear(clip_image_in_channels, conditioning_dim * clip_seq)\n self.clip_norm = nn.LayerNorm(conditioning_dim, elementwise_affine=False, eps=1e-6)\n\n self.embedding = nn.Sequential(\n nn.PixelUnshuffle(patch_size),\n nn.Conv2d(in_channels * (patch_size**2), block_out_channels[0], kernel_size=1),\n SDCascadeLayerNorm(block_out_channels[0], elementwise_affine=False, eps=1e-6),\n )\n\n def get_block(block_type, in_channels, nhead, c_skip=0, dropout=0, self_attn=True):\n if block_type == \"SDCascadeResBlock\":\n return SDCascadeResBlock(in_channels, c_skip, kernel_size=kernel_size, dropout=dropout)\n elif block_type == \"SDCascadeAttnBlock\":\n return SDCascadeAttnBlock(in_channels, conditioning_dim, nhead, self_attn=self_attn, dropout=dropout)\n elif block_type == \"SDCascadeTimestepBlock\":\n return SDCascadeTimestepBlock(\n in_channels, timestep_ratio_embedding_dim, conds=timestep_conditioning_type\n )\n else:\n raise ValueError(f\"Block type {block_type} not supported\")\n\n # BLOCKS\n # -- down blocks\n self.down_blocks = nn.ModuleList()\n self.down_downscalers = nn.ModuleList()\n self.down_repeat_mappers = nn.ModuleList()\n for i in range(len(block_out_channels)):\n if i > 0:\n self.down_downscalers.append(\n nn.Sequential(\n SDCascadeLayerNorm(block_out_channels[i - 1], elementwise_affine=False, eps=1e-6),\n UpDownBlock2d(\n block_out_channels[i - 1], block_out_channels[i], mode=\"down\", enabled=switch_level[i - 1]\n )\n if switch_level is not None\n else nn.Conv2d(block_out_channels[i - 1], block_out_channels[i], kernel_size=2, stride=2),\n )\n )\n else:\n self.down_downscalers.append(nn.Identity())\n\n down_block = nn.ModuleList()\n for _ in range(down_num_layers_per_block[i]):\n for block_type in block_types_per_layer[i]:\n block = get_block(\n block_type,\n block_out_channels[i],\n num_attention_heads[i],\n dropout=dropout[i],\n self_attn=self_attn[i],\n )\n down_block.append(block)\n self.down_blocks.append(down_block)\n\n if down_blocks_repeat_mappers is not None:\n block_repeat_mappers = nn.ModuleList()\n for _ in range(down_blocks_repeat_mappers[i] - 1):\n block_repeat_mappers.append(nn.Conv2d(block_out_channels[i], block_out_channels[i], kernel_size=1))\n self.down_repeat_mappers.append(block_repeat_mappers)\n\n # -- up blocks\n self.up_blocks = nn.ModuleList()\n self.up_upscalers = nn.ModuleList()\n self.up_repeat_mappers = nn.ModuleList()\n for i in reversed(range(len(block_out_channels))):\n if i > 0:\n self.up_upscalers.append(\n nn.Sequential(\n SDCascadeLayerNorm(block_out_channels[i], elementwise_affine=False, eps=1e-6),\n UpDownBlock2d(\n block_out_channels[i], block_out_channels[i - 1], mode=\"up\", enabled=switch_level[i - 1]\n )\n if switch_level is not None\n else nn.ConvTranspose2d(\n block_out_channels[i], block_out_channels[i - 1], kernel_size=2, stride=2\n ),\n )\n )\n else:\n self.up_upscalers.append(nn.Identity())\n\n up_block = nn.ModuleList()\n for j in range(up_num_layers_per_block[::-1][i]):\n for k, block_type in enumerate(block_types_per_layer[i]):\n c_skip = block_out_channels[i] if i < len(block_out_channels) - 1 and j == k == 0 else 0\n block = get_block(\n block_type,\n block_out_channels[i],\n num_attention_heads[i],\n c_skip=c_skip,\n dropout=dropout[i],\n self_attn=self_attn[i],\n )\n up_block.append(block)\n self.up_blocks.append(up_block)\n\n if up_blocks_repeat_mappers is not None:\n block_repeat_mappers = nn.ModuleList()\n for _ in range(up_blocks_repeat_mappers[::-1][i] - 1):\n block_repeat_mappers.append(nn.Conv2d(block_out_channels[i], block_out_channels[i], kernel_size=1))\n self.up_repeat_mappers.append(block_repeat_mappers)\n\n # OUTPUT\n self.clf = nn.Sequential(\n SDCascadeLayerNorm(block_out_channels[0], elementwise_affine=False, eps=1e-6),\n nn.Conv2d(block_out_channels[0], out_channels * (patch_size**2), kernel_size=1),\n nn.PixelShuffle(patch_size),\n )\n\n self.gradient_checkpointing = False\n\n def _set_gradient_checkpointing(self, value=False):\n self.gradient_checkpointing = value\n\n def _init_weights(self, m):\n if isinstance(m, (nn.Conv2d, nn.Linear)):\n torch.nn.init.xavier_uniform_(m.weight)\n if m.bias is not None:\n nn.init.constant_(m.bias, 0)\n\n nn.init.normal_(self.clip_txt_pooled_mapper.weight, std=0.02)\n nn.init.normal_(self.clip_txt_mapper.weight, std=0.02) if hasattr(self, \"clip_txt_mapper\") else None\n nn.init.normal_(self.clip_img_mapper.weight, std=0.02) if hasattr(self, \"clip_img_mapper\") else None\n\n if hasattr(self, \"effnet_mapper\"):\n nn.init.normal_(self.effnet_mapper[0].weight, std=0.02) # conditionings\n nn.init.normal_(self.effnet_mapper[2].weight, std=0.02) # conditionings\n\n if hasattr(self, \"pixels_mapper\"):\n nn.init.normal_(self.pixels_mapper[0].weight, std=0.02) # conditionings\n nn.init.normal_(self.pixels_mapper[2].weight, std=0.02) # conditionings\n\n torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02) # inputs\n nn.init.constant_(self.clf[1].weight, 0) # outputs\n\n # blocks\n for level_block in self.down_blocks + self.up_blocks:\n for block in level_block:\n if isinstance(block, SDCascadeResBlock):\n block.channelwise[-1].weight.data *= np.sqrt(1 / sum(self.config.blocks[0]))\n elif isinstance(block, SDCascadeTimestepBlock):\n nn.init.constant_(block.mapper.weight, 0)\n\n def get_timestep_ratio_embedding(self, timestep_ratio, max_positions=10000):\n r = timestep_ratio * max_positions\n half_dim = self.config.timestep_ratio_embedding_dim // 2\n\n emb = math.log(max_positions) / (half_dim - 1)\n emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp()\n emb = r[:, None] * emb[None, :]\n emb = torch.cat([emb.sin(), emb.cos()], dim=1)\n\n if self.config.timestep_ratio_embedding_dim % 2 == 1: # zero pad\n emb = nn.functional.pad(emb, (0, 1), mode=\"constant\")\n\n return emb.to(dtype=r.dtype)\n\n def get_clip_embeddings(self, clip_txt_pooled, clip_txt=None, clip_img=None):\n if len(clip_txt_pooled.shape) == 2:\n clip_txt_pool = clip_txt_pooled.unsqueeze(1)\n clip_txt_pool = self.clip_txt_pooled_mapper(clip_txt_pooled).view(\n clip_txt_pooled.size(0), clip_txt_pooled.size(1) * self.config.clip_seq, -1\n )\n if clip_txt is not None and clip_img is not None:\n clip_txt = self.clip_txt_mapper(clip_txt)\n if len(clip_img.shape) == 2:\n clip_img = clip_img.unsqueeze(1)\n clip_img = self.clip_img_mapper(clip_img).view(\n clip_img.size(0), clip_img.size(1) * self.config.clip_seq, -1\n )\n clip = torch.cat([clip_txt, clip_txt_pool, clip_img], dim=1)\n else:\n clip = clip_txt_pool\n return self.clip_norm(clip)\n\n def _down_encode(self, x, r_embed, clip):\n level_outputs = []\n block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers)\n\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs)\n\n return custom_forward\n\n for down_block, downscaler, repmap in block_group:\n x = downscaler(x)\n for i in range(len(repmap) + 1):\n for block in down_block:\n if isinstance(block, SDCascadeResBlock):\n x = torch.utils.checkpoint.checkpoint(create_custom_forward(block), x, use_reentrant=False)\n elif isinstance(block, SDCascadeAttnBlock):\n x = torch.utils.checkpoint.checkpoint(\n create_custom_forward(block), x, clip, use_reentrant=False\n )\n elif isinstance(block, SDCascadeTimestepBlock):\n x = torch.utils.checkpoint.checkpoint(\n create_custom_forward(block), x, r_embed, use_reentrant=False\n )\n else:\n x = x = torch.utils.checkpoint.checkpoint(\n create_custom_forward(block), use_reentrant=False\n )\n if i < len(repmap):\n x = repmap[i](x)\n level_outputs.insert(0, x)\n else:\n for down_block, downscaler, repmap in block_group:\n x = downscaler(x)\n for i in range(len(repmap) + 1):\n for block in down_block:\n if isinstance(block, SDCascadeResBlock):\n x = block(x)\n elif isinstance(block, SDCascadeAttnBlock):\n x = block(x, clip)\n elif isinstance(block, SDCascadeTimestepBlock):\n x = block(x, r_embed)\n else:\n x = block(x)\n if i < len(repmap):\n x = repmap[i](x)\n level_outputs.insert(0, x)\n return level_outputs\n\n def _up_decode(self, level_outputs, r_embed, clip):\n x = level_outputs[0]\n block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers)\n\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs)\n\n return custom_forward\n\n for i, (up_block, upscaler, repmap) in enumerate(block_group):\n for j in range(len(repmap) + 1):\n for k, block in enumerate(up_block):\n if isinstance(block, SDCascadeResBlock):\n skip = level_outputs[i] if k == 0 and i > 0 else None\n if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)):\n orig_type = x.dtype\n x = torch.nn.functional.interpolate(\n x.float(), skip.shape[-2:], mode=\"bilinear\", align_corners=True\n )\n x = x.to(orig_type)\n x = torch.utils.checkpoint.checkpoint(\n create_custom_forward(block), x, skip, use_reentrant=False\n )\n elif isinstance(block, SDCascadeAttnBlock):\n x = torch.utils.checkpoint.checkpoint(\n create_custom_forward(block), x, clip, use_reentrant=False\n )\n elif isinstance(block, SDCascadeTimestepBlock):\n x = torch.utils.checkpoint.checkpoint(\n create_custom_forward(block), x, r_embed, use_reentrant=False\n )\n else:\n x = torch.utils.checkpoint.checkpoint(create_custom_forward(block), x, use_reentrant=False)\n if j < len(repmap):\n x = repmap[j](x)\n x = upscaler(x)\n else:\n for i, (up_block, upscaler, repmap) in enumerate(block_group):\n for j in range(len(repmap) + 1):\n for k, block in enumerate(up_block):\n if isinstance(block, SDCascadeResBlock):\n skip = level_outputs[i] if k == 0 and i > 0 else None\n if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)):\n orig_type = x.dtype\n x = torch.nn.functional.interpolate(\n x.float(), skip.shape[-2:], mode=\"bilinear\", align_corners=True\n )\n x = x.to(orig_type)\n x = block(x, skip)\n elif isinstance(block, SDCascadeAttnBlock):\n x = block(x, clip)\n elif isinstance(block, SDCascadeTimestepBlock):\n x = block(x, r_embed)\n else:\n x = block(x)\n if j < len(repmap):\n x = repmap[j](x)\n x = upscaler(x)\n return x\n\n def forward(\n self,\n sample,\n timestep_ratio,\n clip_text_pooled,\n clip_text=None,\n clip_img=None,\n effnet=None,\n pixels=None,\n sca=None,\n crp=None,\n return_dict=True,\n ):\n if pixels is None:\n pixels = sample.new_zeros(sample.size(0), 3, 8, 8)\n\n # Process the conditioning embeddings\n timestep_ratio_embed = self.get_timestep_ratio_embedding(timestep_ratio)\n for c in self.config.timestep_conditioning_type:\n if c == \"sca\":\n cond = sca\n elif c == \"crp\":\n cond = crp\n else:\n cond = None\n t_cond = cond or torch.zeros_like(timestep_ratio)\n timestep_ratio_embed = torch.cat([timestep_ratio_embed, self.get_timestep_ratio_embedding(t_cond)], dim=1)\n clip = self.get_clip_embeddings(clip_txt_pooled=clip_text_pooled, clip_txt=clip_text, clip_img=clip_img)\n\n # Model Blocks\n x = self.embedding(sample)\n if hasattr(self, \"effnet_mapper\") and effnet is not None:\n x = x + self.effnet_mapper(\n nn.functional.interpolate(effnet, size=x.shape[-2:], mode=\"bilinear\", align_corners=True)\n )\n if hasattr(self, \"pixels_mapper\"):\n x = x + nn.functional.interpolate(\n self.pixels_mapper(pixels), size=x.shape[-2:], mode=\"bilinear\", align_corners=True\n )\n level_outputs = self._down_encode(x, timestep_ratio_embed, clip)\n x = self._up_decode(level_outputs, timestep_ratio_embed, clip)\n sample = self.clf(x)\n\n if not return_dict:\n return (sample,)\n return StableCascadeUNetOutput(sample=sample)\n" }, { "path": "diffusers/src/diffusers/models/unets/uvit_2d.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Dict, Union\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\nfrom torch.utils.checkpoint import checkpoint\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...loaders import PeftAdapterMixin\nfrom ..attention import BasicTransformerBlock, SkipFFTransformerBlock\nfrom ..attention_processor import (\n ADDED_KV_ATTENTION_PROCESSORS,\n CROSS_ATTENTION_PROCESSORS,\n AttentionProcessor,\n AttnAddedKVProcessor,\n AttnProcessor,\n)\nfrom ..embeddings import TimestepEmbedding, get_timestep_embedding\nfrom ..modeling_utils import ModelMixin\nfrom ..normalization import GlobalResponseNorm, RMSNorm\nfrom ..resnet import Downsample2D, Upsample2D\n\n\nclass UVit2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n # global config\n hidden_size: int = 1024,\n use_bias: bool = False,\n hidden_dropout: float = 0.0,\n # conditioning dimensions\n cond_embed_dim: int = 768,\n micro_cond_encode_dim: int = 256,\n micro_cond_embed_dim: int = 1280,\n encoder_hidden_size: int = 768,\n # num tokens\n vocab_size: int = 8256, # codebook_size + 1 (for the mask token) rounded\n codebook_size: int = 8192,\n # `UVit2DConvEmbed`\n in_channels: int = 768,\n block_out_channels: int = 768,\n num_res_blocks: int = 3,\n downsample: bool = False,\n upsample: bool = False,\n block_num_heads: int = 12,\n # `TransformerLayer`\n num_hidden_layers: int = 22,\n num_attention_heads: int = 16,\n # `Attention`\n attention_dropout: float = 0.0,\n # `FeedForward`\n intermediate_size: int = 2816,\n # `Norm`\n layer_norm_eps: float = 1e-6,\n ln_elementwise_affine: bool = True,\n sample_size: int = 64,\n ):\n super().__init__()\n\n self.encoder_proj = nn.Linear(encoder_hidden_size, hidden_size, bias=use_bias)\n self.encoder_proj_layer_norm = RMSNorm(hidden_size, layer_norm_eps, ln_elementwise_affine)\n\n self.embed = UVit2DConvEmbed(\n in_channels, block_out_channels, vocab_size, ln_elementwise_affine, layer_norm_eps, use_bias\n )\n\n self.cond_embed = TimestepEmbedding(\n micro_cond_embed_dim + cond_embed_dim, hidden_size, sample_proj_bias=use_bias\n )\n\n self.down_block = UVitBlock(\n block_out_channels,\n num_res_blocks,\n hidden_size,\n hidden_dropout,\n ln_elementwise_affine,\n layer_norm_eps,\n use_bias,\n block_num_heads,\n attention_dropout,\n downsample,\n False,\n )\n\n self.project_to_hidden_norm = RMSNorm(block_out_channels, layer_norm_eps, ln_elementwise_affine)\n self.project_to_hidden = nn.Linear(block_out_channels, hidden_size, bias=use_bias)\n\n self.transformer_layers = nn.ModuleList(\n [\n BasicTransformerBlock(\n dim=hidden_size,\n num_attention_heads=num_attention_heads,\n attention_head_dim=hidden_size // num_attention_heads,\n dropout=hidden_dropout,\n cross_attention_dim=hidden_size,\n attention_bias=use_bias,\n norm_type=\"ada_norm_continuous\",\n ada_norm_continous_conditioning_embedding_dim=hidden_size,\n norm_elementwise_affine=ln_elementwise_affine,\n norm_eps=layer_norm_eps,\n ada_norm_bias=use_bias,\n ff_inner_dim=intermediate_size,\n ff_bias=use_bias,\n attention_out_bias=use_bias,\n )\n for _ in range(num_hidden_layers)\n ]\n )\n\n self.project_from_hidden_norm = RMSNorm(hidden_size, layer_norm_eps, ln_elementwise_affine)\n self.project_from_hidden = nn.Linear(hidden_size, block_out_channels, bias=use_bias)\n\n self.up_block = UVitBlock(\n block_out_channels,\n num_res_blocks,\n hidden_size,\n hidden_dropout,\n ln_elementwise_affine,\n layer_norm_eps,\n use_bias,\n block_num_heads,\n attention_dropout,\n downsample=False,\n upsample=upsample,\n )\n\n self.mlm_layer = ConvMlmLayer(\n block_out_channels, in_channels, use_bias, ln_elementwise_affine, layer_norm_eps, codebook_size\n )\n\n self.gradient_checkpointing = False\n\n def _set_gradient_checkpointing(self, module, value: bool = False) -> None:\n pass\n\n def forward(self, input_ids, encoder_hidden_states, pooled_text_emb, micro_conds, cross_attention_kwargs=None):\n encoder_hidden_states = self.encoder_proj(encoder_hidden_states)\n encoder_hidden_states = self.encoder_proj_layer_norm(encoder_hidden_states)\n\n micro_cond_embeds = get_timestep_embedding(\n micro_conds.flatten(), self.config.micro_cond_encode_dim, flip_sin_to_cos=True, downscale_freq_shift=0\n )\n\n micro_cond_embeds = micro_cond_embeds.reshape((input_ids.shape[0], -1))\n\n pooled_text_emb = torch.cat([pooled_text_emb, micro_cond_embeds], dim=1)\n pooled_text_emb = pooled_text_emb.to(dtype=self.dtype)\n pooled_text_emb = self.cond_embed(pooled_text_emb).to(encoder_hidden_states.dtype)\n\n hidden_states = self.embed(input_ids)\n\n hidden_states = self.down_block(\n hidden_states,\n pooled_text_emb=pooled_text_emb,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n\n batch_size, channels, height, width = hidden_states.shape\n hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch_size, height * width, channels)\n\n hidden_states = self.project_to_hidden_norm(hidden_states)\n hidden_states = self.project_to_hidden(hidden_states)\n\n for layer in self.transformer_layers:\n if self.training and self.gradient_checkpointing:\n\n def layer_(*args):\n return checkpoint(layer, *args)\n\n else:\n layer_ = layer\n\n hidden_states = layer_(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n added_cond_kwargs={\"pooled_text_emb\": pooled_text_emb},\n )\n\n hidden_states = self.project_from_hidden_norm(hidden_states)\n hidden_states = self.project_from_hidden(hidden_states)\n\n hidden_states = hidden_states.reshape(batch_size, height, width, channels).permute(0, 3, 1, 2)\n\n hidden_states = self.up_block(\n hidden_states,\n pooled_text_emb=pooled_text_emb,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n\n logits = self.mlm_layer(hidden_states)\n\n return logits\n\n @property\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors\n def attn_processors(self) -> Dict[str, AttentionProcessor]:\n r\"\"\"\n Returns:\n `dict` of attention processors: A dictionary containing all attention processors used in the model with\n indexed by its weight name.\n \"\"\"\n # set recursively\n processors = {}\n\n def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\n if hasattr(module, \"get_processor\"):\n processors[f\"{name}.processor\"] = module.get_processor(return_deprecated_lora=True)\n\n for sub_name, child in module.named_children():\n fn_recursive_add_processors(f\"{name}.{sub_name}\", child, processors)\n\n return processors\n\n for name, module in self.named_children():\n fn_recursive_add_processors(name, module, processors)\n\n return processors\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor\n def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\n r\"\"\"\n Sets the attention processor to use to compute attention.\n\n Parameters:\n processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):\n The instantiated processor class or a dictionary of processor classes that will be set as the processor\n for **all** `Attention` layers.\n\n If `processor` is a dict, the key needs to define the path to the corresponding cross attention\n processor. This is strongly recommended when setting trainable attention processors.\n\n \"\"\"\n count = len(self.attn_processors.keys())\n\n if isinstance(processor, dict) and len(processor) != count:\n raise ValueError(\n f\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\"\n f\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\"\n )\n\n def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\n if hasattr(module, \"set_processor\"):\n if not isinstance(processor, dict):\n module.set_processor(processor)\n else:\n module.set_processor(processor.pop(f\"{name}.processor\"))\n\n for sub_name, child in module.named_children():\n fn_recursive_attn_processor(f\"{name}.{sub_name}\", child, processor)\n\n for name, module in self.named_children():\n fn_recursive_attn_processor(name, module, processor)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor\n def set_default_attn_processor(self):\n \"\"\"\n Disables custom attention processors and sets the default attention implementation.\n \"\"\"\n if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnAddedKVProcessor()\n elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnProcessor()\n else:\n raise ValueError(\n f\"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}\"\n )\n\n self.set_attn_processor(processor)\n\n\nclass UVit2DConvEmbed(nn.Module):\n def __init__(self, in_channels, block_out_channels, vocab_size, elementwise_affine, eps, bias):\n super().__init__()\n self.embeddings = nn.Embedding(vocab_size, in_channels)\n self.layer_norm = RMSNorm(in_channels, eps, elementwise_affine)\n self.conv = nn.Conv2d(in_channels, block_out_channels, kernel_size=1, bias=bias)\n\n def forward(self, input_ids):\n embeddings = self.embeddings(input_ids)\n embeddings = self.layer_norm(embeddings)\n embeddings = embeddings.permute(0, 3, 1, 2)\n embeddings = self.conv(embeddings)\n return embeddings\n\n\nclass UVitBlock(nn.Module):\n def __init__(\n self,\n channels,\n num_res_blocks: int,\n hidden_size,\n hidden_dropout,\n ln_elementwise_affine,\n layer_norm_eps,\n use_bias,\n block_num_heads,\n attention_dropout,\n downsample: bool,\n upsample: bool,\n ):\n super().__init__()\n\n if downsample:\n self.downsample = Downsample2D(\n channels,\n use_conv=True,\n padding=0,\n name=\"Conv2d_0\",\n kernel_size=2,\n norm_type=\"rms_norm\",\n eps=layer_norm_eps,\n elementwise_affine=ln_elementwise_affine,\n bias=use_bias,\n )\n else:\n self.downsample = None\n\n self.res_blocks = nn.ModuleList(\n [\n ConvNextBlock(\n channels,\n layer_norm_eps,\n ln_elementwise_affine,\n use_bias,\n hidden_dropout,\n hidden_size,\n )\n for i in range(num_res_blocks)\n ]\n )\n\n self.attention_blocks = nn.ModuleList(\n [\n SkipFFTransformerBlock(\n channels,\n block_num_heads,\n channels // block_num_heads,\n hidden_size,\n use_bias,\n attention_dropout,\n channels,\n attention_bias=use_bias,\n attention_out_bias=use_bias,\n )\n for _ in range(num_res_blocks)\n ]\n )\n\n if upsample:\n self.upsample = Upsample2D(\n channels,\n use_conv_transpose=True,\n kernel_size=2,\n padding=0,\n name=\"conv\",\n norm_type=\"rms_norm\",\n eps=layer_norm_eps,\n elementwise_affine=ln_elementwise_affine,\n bias=use_bias,\n interpolate=False,\n )\n else:\n self.upsample = None\n\n def forward(self, x, pooled_text_emb, encoder_hidden_states, cross_attention_kwargs):\n if self.downsample is not None:\n x = self.downsample(x)\n\n for res_block, attention_block in zip(self.res_blocks, self.attention_blocks):\n x = res_block(x, pooled_text_emb)\n\n batch_size, channels, height, width = x.shape\n x = x.view(batch_size, channels, height * width).permute(0, 2, 1)\n x = attention_block(\n x, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs\n )\n x = x.permute(0, 2, 1).view(batch_size, channels, height, width)\n\n if self.upsample is not None:\n x = self.upsample(x)\n\n return x\n\n\nclass ConvNextBlock(nn.Module):\n def __init__(\n self, channels, layer_norm_eps, ln_elementwise_affine, use_bias, hidden_dropout, hidden_size, res_ffn_factor=4\n ):\n super().__init__()\n self.depthwise = nn.Conv2d(\n channels,\n channels,\n kernel_size=3,\n padding=1,\n groups=channels,\n bias=use_bias,\n )\n self.norm = RMSNorm(channels, layer_norm_eps, ln_elementwise_affine)\n self.channelwise_linear_1 = nn.Linear(channels, int(channels * res_ffn_factor), bias=use_bias)\n self.channelwise_act = nn.GELU()\n self.channelwise_norm = GlobalResponseNorm(int(channels * res_ffn_factor))\n self.channelwise_linear_2 = nn.Linear(int(channels * res_ffn_factor), channels, bias=use_bias)\n self.channelwise_dropout = nn.Dropout(hidden_dropout)\n self.cond_embeds_mapper = nn.Linear(hidden_size, channels * 2, use_bias)\n\n def forward(self, x, cond_embeds):\n x_res = x\n\n x = self.depthwise(x)\n\n x = x.permute(0, 2, 3, 1)\n x = self.norm(x)\n\n x = self.channelwise_linear_1(x)\n x = self.channelwise_act(x)\n x = self.channelwise_norm(x)\n x = self.channelwise_linear_2(x)\n x = self.channelwise_dropout(x)\n\n x = x.permute(0, 3, 1, 2)\n\n x = x + x_res\n\n scale, shift = self.cond_embeds_mapper(F.silu(cond_embeds)).chunk(2, dim=1)\n x = x * (1 + scale[:, :, None, None]) + shift[:, :, None, None]\n\n return x\n\n\nclass ConvMlmLayer(nn.Module):\n def __init__(\n self,\n block_out_channels: int,\n in_channels: int,\n use_bias: bool,\n ln_elementwise_affine: bool,\n layer_norm_eps: float,\n codebook_size: int,\n ):\n super().__init__()\n self.conv1 = nn.Conv2d(block_out_channels, in_channels, kernel_size=1, bias=use_bias)\n self.layer_norm = RMSNorm(in_channels, layer_norm_eps, ln_elementwise_affine)\n self.conv2 = nn.Conv2d(in_channels, codebook_size, kernel_size=1, bias=use_bias)\n\n def forward(self, hidden_states):\n hidden_states = self.conv1(hidden_states)\n hidden_states = self.layer_norm(hidden_states.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)\n logits = self.conv2(hidden_states)\n return logits\n" }, { "path": "diffusers/src/diffusers/models/upsampling.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom ..utils import deprecate\nfrom .normalization import RMSNorm\n\n\nclass Upsample1D(nn.Module):\n \"\"\"A 1D upsampling layer with an optional convolution.\n\n Parameters:\n channels (`int`):\n number of channels in the inputs and outputs.\n use_conv (`bool`, default `False`):\n option to use a convolution.\n use_conv_transpose (`bool`, default `False`):\n option to use a convolution transpose.\n out_channels (`int`, optional):\n number of output channels. Defaults to `channels`.\n name (`str`, default `conv`):\n name of the upsampling 1D layer.\n \"\"\"\n\n def __init__(\n self,\n channels: int,\n use_conv: bool = False,\n use_conv_transpose: bool = False,\n out_channels: Optional[int] = None,\n name: str = \"conv\",\n ):\n super().__init__()\n self.channels = channels\n self.out_channels = out_channels or channels\n self.use_conv = use_conv\n self.use_conv_transpose = use_conv_transpose\n self.name = name\n\n self.conv = None\n if use_conv_transpose:\n self.conv = nn.ConvTranspose1d(channels, self.out_channels, 4, 2, 1)\n elif use_conv:\n self.conv = nn.Conv1d(self.channels, self.out_channels, 3, padding=1)\n\n def forward(self, inputs: torch.Tensor) -> torch.Tensor:\n assert inputs.shape[1] == self.channels\n if self.use_conv_transpose:\n return self.conv(inputs)\n\n outputs = F.interpolate(inputs, scale_factor=2.0, mode=\"nearest\")\n\n if self.use_conv:\n outputs = self.conv(outputs)\n\n return outputs\n\n\nclass Upsample2D(nn.Module):\n \"\"\"A 2D upsampling layer with an optional convolution.\n\n Parameters:\n channels (`int`):\n number of channels in the inputs and outputs.\n use_conv (`bool`, default `False`):\n option to use a convolution.\n use_conv_transpose (`bool`, default `False`):\n option to use a convolution transpose.\n out_channels (`int`, optional):\n number of output channels. Defaults to `channels`.\n name (`str`, default `conv`):\n name of the upsampling 2D layer.\n \"\"\"\n\n def __init__(\n self,\n channels: int,\n use_conv: bool = False,\n use_conv_transpose: bool = False,\n out_channels: Optional[int] = None,\n name: str = \"conv\",\n kernel_size: Optional[int] = None,\n padding=1,\n norm_type=None,\n eps=None,\n elementwise_affine=None,\n bias=True,\n interpolate=True,\n ):\n super().__init__()\n self.channels = channels\n self.out_channels = out_channels or channels\n self.use_conv = use_conv\n self.use_conv_transpose = use_conv_transpose\n self.name = name\n self.interpolate = interpolate\n\n if norm_type == \"ln_norm\":\n self.norm = nn.LayerNorm(channels, eps, elementwise_affine)\n elif norm_type == \"rms_norm\":\n self.norm = RMSNorm(channels, eps, elementwise_affine)\n elif norm_type is None:\n self.norm = None\n else:\n raise ValueError(f\"unknown norm_type: {norm_type}\")\n\n conv = None\n if use_conv_transpose:\n if kernel_size is None:\n kernel_size = 4\n conv = nn.ConvTranspose2d(\n channels, self.out_channels, kernel_size=kernel_size, stride=2, padding=padding, bias=bias\n )\n elif use_conv:\n if kernel_size is None:\n kernel_size = 3\n conv = nn.Conv2d(self.channels, self.out_channels, kernel_size=kernel_size, padding=padding, bias=bias)\n\n # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed\n if name == \"conv\":\n self.conv = conv\n else:\n self.Conv2d_0 = conv\n\n def forward(self, hidden_states: torch.Tensor, output_size: Optional[int] = None, *args, **kwargs) -> torch.Tensor:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n assert hidden_states.shape[1] == self.channels\n\n if self.norm is not None:\n hidden_states = self.norm(hidden_states.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)\n\n if self.use_conv_transpose:\n return self.conv(hidden_states)\n\n # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16\n # TODO(Suraj): Remove this cast once the issue is fixed in PyTorch\n # https://github.com/pytorch/pytorch/issues/86679\n dtype = hidden_states.dtype\n if dtype == torch.bfloat16:\n hidden_states = hidden_states.to(torch.float32)\n\n # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984\n if hidden_states.shape[0] >= 64:\n hidden_states = hidden_states.contiguous()\n\n # if `output_size` is passed we force the interpolation output\n # size and do not make use of `scale_factor=2`\n if self.interpolate:\n if output_size is None:\n hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode=\"nearest\")\n else:\n hidden_states = F.interpolate(hidden_states, size=output_size, mode=\"nearest\")\n\n # If the input is bfloat16, we cast back to bfloat16\n if dtype == torch.bfloat16:\n hidden_states = hidden_states.to(dtype)\n\n # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed\n if self.use_conv:\n if self.name == \"conv\":\n hidden_states = self.conv(hidden_states)\n else:\n hidden_states = self.Conv2d_0(hidden_states)\n\n return hidden_states\n\n\nclass FirUpsample2D(nn.Module):\n \"\"\"A 2D FIR upsampling layer with an optional convolution.\n\n Parameters:\n channels (`int`, optional):\n number of channels in the inputs and outputs.\n use_conv (`bool`, default `False`):\n option to use a convolution.\n out_channels (`int`, optional):\n number of output channels. Defaults to `channels`.\n fir_kernel (`tuple`, default `(1, 3, 3, 1)`):\n kernel for the FIR filter.\n \"\"\"\n\n def __init__(\n self,\n channels: Optional[int] = None,\n out_channels: Optional[int] = None,\n use_conv: bool = False,\n fir_kernel: Tuple[int, int, int, int] = (1, 3, 3, 1),\n ):\n super().__init__()\n out_channels = out_channels if out_channels else channels\n if use_conv:\n self.Conv2d_0 = nn.Conv2d(channels, out_channels, kernel_size=3, stride=1, padding=1)\n self.use_conv = use_conv\n self.fir_kernel = fir_kernel\n self.out_channels = out_channels\n\n def _upsample_2d(\n self,\n hidden_states: torch.Tensor,\n weight: Optional[torch.Tensor] = None,\n kernel: Optional[torch.Tensor] = None,\n factor: int = 2,\n gain: float = 1,\n ) -> torch.Tensor:\n \"\"\"Fused `upsample_2d()` followed by `Conv2d()`.\n\n Padding is performed only once at the beginning, not between the operations. The fused op is considerably more\n efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of\n arbitrary order.\n\n Args:\n hidden_states (`torch.Tensor`):\n Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.\n weight (`torch.Tensor`, *optional*):\n Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`. Grouped convolution can be\n performed by `inChannels = x.shape[0] // numGroups`.\n kernel (`torch.Tensor`, *optional*):\n FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] * factor`, which\n corresponds to nearest-neighbor upsampling.\n factor (`int`, *optional*): Integer upsampling factor (default: 2).\n gain (`float`, *optional*): Scaling factor for signal magnitude (default: 1.0).\n\n Returns:\n output (`torch.Tensor`):\n Tensor of the shape `[N, C, H * factor, W * factor]` or `[N, H * factor, W * factor, C]`, and same\n datatype as `hidden_states`.\n \"\"\"\n\n assert isinstance(factor, int) and factor >= 1\n\n # Setup filter kernel.\n if kernel is None:\n kernel = [1] * factor\n\n # setup kernel\n kernel = torch.tensor(kernel, dtype=torch.float32)\n if kernel.ndim == 1:\n kernel = torch.outer(kernel, kernel)\n kernel /= torch.sum(kernel)\n\n kernel = kernel * (gain * (factor**2))\n\n if self.use_conv:\n convH = weight.shape[2]\n convW = weight.shape[3]\n inC = weight.shape[1]\n\n pad_value = (kernel.shape[0] - factor) - (convW - 1)\n\n stride = (factor, factor)\n # Determine data dimensions.\n output_shape = (\n (hidden_states.shape[2] - 1) * factor + convH,\n (hidden_states.shape[3] - 1) * factor + convW,\n )\n output_padding = (\n output_shape[0] - (hidden_states.shape[2] - 1) * stride[0] - convH,\n output_shape[1] - (hidden_states.shape[3] - 1) * stride[1] - convW,\n )\n assert output_padding[0] >= 0 and output_padding[1] >= 0\n num_groups = hidden_states.shape[1] // inC\n\n # Transpose weights.\n weight = torch.reshape(weight, (num_groups, -1, inC, convH, convW))\n weight = torch.flip(weight, dims=[3, 4]).permute(0, 2, 1, 3, 4)\n weight = torch.reshape(weight, (num_groups * inC, -1, convH, convW))\n\n inverse_conv = F.conv_transpose2d(\n hidden_states,\n weight,\n stride=stride,\n output_padding=output_padding,\n padding=0,\n )\n\n output = upfirdn2d_native(\n inverse_conv,\n torch.tensor(kernel, device=inverse_conv.device),\n pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2 + 1),\n )\n else:\n pad_value = kernel.shape[0] - factor\n output = upfirdn2d_native(\n hidden_states,\n torch.tensor(kernel, device=hidden_states.device),\n up=factor,\n pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2),\n )\n\n return output\n\n def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n if self.use_conv:\n height = self._upsample_2d(hidden_states, self.Conv2d_0.weight, kernel=self.fir_kernel)\n height = height + self.Conv2d_0.bias.reshape(1, -1, 1, 1)\n else:\n height = self._upsample_2d(hidden_states, kernel=self.fir_kernel, factor=2)\n\n return height\n\n\nclass KUpsample2D(nn.Module):\n r\"\"\"A 2D K-upsampling layer.\n\n Parameters:\n pad_mode (`str`, *optional*, default to `\"reflect\"`): the padding mode to use.\n \"\"\"\n\n def __init__(self, pad_mode: str = \"reflect\"):\n super().__init__()\n self.pad_mode = pad_mode\n kernel_1d = torch.tensor([[1 / 8, 3 / 8, 3 / 8, 1 / 8]]) * 2\n self.pad = kernel_1d.shape[1] // 2 - 1\n self.register_buffer(\"kernel\", kernel_1d.T @ kernel_1d, persistent=False)\n\n def forward(self, inputs: torch.Tensor) -> torch.Tensor:\n inputs = F.pad(inputs, ((self.pad + 1) // 2,) * 4, self.pad_mode)\n weight = inputs.new_zeros(\n [\n inputs.shape[1],\n inputs.shape[1],\n self.kernel.shape[0],\n self.kernel.shape[1],\n ]\n )\n indices = torch.arange(inputs.shape[1], device=inputs.device)\n kernel = self.kernel.to(weight)[None, :].expand(inputs.shape[1], -1, -1)\n weight[indices, indices] = kernel\n return F.conv_transpose2d(inputs, weight, stride=2, padding=self.pad * 2 + 1)\n\n\ndef upfirdn2d_native(\n tensor: torch.Tensor,\n kernel: torch.Tensor,\n up: int = 1,\n down: int = 1,\n pad: Tuple[int, int] = (0, 0),\n) -> torch.Tensor:\n up_x = up_y = up\n down_x = down_y = down\n pad_x0 = pad_y0 = pad[0]\n pad_x1 = pad_y1 = pad[1]\n\n _, channel, in_h, in_w = tensor.shape\n tensor = tensor.reshape(-1, in_h, in_w, 1)\n\n _, in_h, in_w, minor = tensor.shape\n kernel_h, kernel_w = kernel.shape\n\n out = tensor.view(-1, in_h, 1, in_w, 1, minor)\n out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])\n out = out.view(-1, in_h * up_y, in_w * up_x, minor)\n\n out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])\n out = out.to(tensor.device) # Move back to mps if necessary\n out = out[\n :,\n max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),\n max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),\n :,\n ]\n\n out = out.permute(0, 3, 1, 2)\n out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])\n w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)\n out = F.conv2d(out, w)\n out = out.reshape(\n -1,\n minor,\n in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,\n in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,\n )\n out = out.permute(0, 2, 3, 1)\n out = out[:, ::down_y, ::down_x, :]\n\n out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1\n out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1\n\n return out.view(-1, channel, out_h, out_w)\n\n\ndef upsample_2d(\n hidden_states: torch.Tensor,\n kernel: Optional[torch.Tensor] = None,\n factor: int = 2,\n gain: float = 1,\n) -> torch.Tensor:\n r\"\"\"Upsample2D a batch of 2D images with the given filter.\n Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and upsamples each image with the given\n filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the specified\n `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its shape is\n a: multiple of the upsampling factor.\n\n Args:\n hidden_states (`torch.Tensor`):\n Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.\n kernel (`torch.Tensor`, *optional*):\n FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] * factor`, which\n corresponds to nearest-neighbor upsampling.\n factor (`int`, *optional*, default to `2`):\n Integer upsampling factor.\n gain (`float`, *optional*, default to `1.0`):\n Scaling factor for signal magnitude (default: 1.0).\n\n Returns:\n output (`torch.Tensor`):\n Tensor of the shape `[N, C, H * factor, W * factor]`\n \"\"\"\n assert isinstance(factor, int) and factor >= 1\n if kernel is None:\n kernel = [1] * factor\n\n kernel = torch.tensor(kernel, dtype=torch.float32)\n if kernel.ndim == 1:\n kernel = torch.outer(kernel, kernel)\n kernel /= torch.sum(kernel)\n\n kernel = kernel * (gain * (factor**2))\n pad_value = kernel.shape[0] - factor\n output = upfirdn2d_native(\n hidden_states,\n kernel.to(device=hidden_states.device),\n up=factor,\n pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2),\n )\n return output\n" }, { "path": "diffusers/src/diffusers/models/vae_flax.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# JAX implementation of VQGAN from taming-transformers https://github.com/CompVis/taming-transformers\n\nimport math\nfrom functools import partial\nfrom typing import Tuple\n\nimport flax\nimport flax.linen as nn\nimport jax\nimport jax.numpy as jnp\nfrom flax.core.frozen_dict import FrozenDict\n\nfrom ..configuration_utils import ConfigMixin, flax_register_to_config\nfrom ..utils import BaseOutput\nfrom .modeling_flax_utils import FlaxModelMixin\n\n\n@flax.struct.dataclass\nclass FlaxDecoderOutput(BaseOutput):\n \"\"\"\n Output of decoding method.\n\n Args:\n sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`):\n The decoded output sample from the last layer of the model.\n dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):\n The `dtype` of the parameters.\n \"\"\"\n\n sample: jnp.ndarray\n\n\n@flax.struct.dataclass\nclass FlaxAutoencoderKLOutput(BaseOutput):\n \"\"\"\n Output of AutoencoderKL encoding method.\n\n Args:\n latent_dist (`FlaxDiagonalGaussianDistribution`):\n Encoded outputs of `Encoder` represented as the mean and logvar of `FlaxDiagonalGaussianDistribution`.\n `FlaxDiagonalGaussianDistribution` allows for sampling latents from the distribution.\n \"\"\"\n\n latent_dist: \"FlaxDiagonalGaussianDistribution\"\n\n\nclass FlaxUpsample2D(nn.Module):\n \"\"\"\n Flax implementation of 2D Upsample layer\n\n Args:\n in_channels (`int`):\n Input channels\n dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):\n Parameters `dtype`\n \"\"\"\n\n in_channels: int\n dtype: jnp.dtype = jnp.float32\n\n def setup(self):\n self.conv = nn.Conv(\n self.in_channels,\n kernel_size=(3, 3),\n strides=(1, 1),\n padding=((1, 1), (1, 1)),\n dtype=self.dtype,\n )\n\n def __call__(self, hidden_states):\n batch, height, width, channels = hidden_states.shape\n hidden_states = jax.image.resize(\n hidden_states,\n shape=(batch, height * 2, width * 2, channels),\n method=\"nearest\",\n )\n hidden_states = self.conv(hidden_states)\n return hidden_states\n\n\nclass FlaxDownsample2D(nn.Module):\n \"\"\"\n Flax implementation of 2D Downsample layer\n\n Args:\n in_channels (`int`):\n Input channels\n dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):\n Parameters `dtype`\n \"\"\"\n\n in_channels: int\n dtype: jnp.dtype = jnp.float32\n\n def setup(self):\n self.conv = nn.Conv(\n self.in_channels,\n kernel_size=(3, 3),\n strides=(2, 2),\n padding=\"VALID\",\n dtype=self.dtype,\n )\n\n def __call__(self, hidden_states):\n pad = ((0, 0), (0, 1), (0, 1), (0, 0)) # pad height and width dim\n hidden_states = jnp.pad(hidden_states, pad_width=pad)\n hidden_states = self.conv(hidden_states)\n return hidden_states\n\n\nclass FlaxResnetBlock2D(nn.Module):\n \"\"\"\n Flax implementation of 2D Resnet Block.\n\n Args:\n in_channels (`int`):\n Input channels\n out_channels (`int`):\n Output channels\n dropout (:obj:`float`, *optional*, defaults to 0.0):\n Dropout rate\n groups (:obj:`int`, *optional*, defaults to `32`):\n The number of groups to use for group norm.\n use_nin_shortcut (:obj:`bool`, *optional*, defaults to `None`):\n Whether to use `nin_shortcut`. This activates a new layer inside ResNet block\n dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):\n Parameters `dtype`\n \"\"\"\n\n in_channels: int\n out_channels: int = None\n dropout: float = 0.0\n groups: int = 32\n use_nin_shortcut: bool = None\n dtype: jnp.dtype = jnp.float32\n\n def setup(self):\n out_channels = self.in_channels if self.out_channels is None else self.out_channels\n\n self.norm1 = nn.GroupNorm(num_groups=self.groups, epsilon=1e-6)\n self.conv1 = nn.Conv(\n out_channels,\n kernel_size=(3, 3),\n strides=(1, 1),\n padding=((1, 1), (1, 1)),\n dtype=self.dtype,\n )\n\n self.norm2 = nn.GroupNorm(num_groups=self.groups, epsilon=1e-6)\n self.dropout_layer = nn.Dropout(self.dropout)\n self.conv2 = nn.Conv(\n out_channels,\n kernel_size=(3, 3),\n strides=(1, 1),\n padding=((1, 1), (1, 1)),\n dtype=self.dtype,\n )\n\n use_nin_shortcut = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut\n\n self.conv_shortcut = None\n if use_nin_shortcut:\n self.conv_shortcut = nn.Conv(\n out_channels,\n kernel_size=(1, 1),\n strides=(1, 1),\n padding=\"VALID\",\n dtype=self.dtype,\n )\n\n def __call__(self, hidden_states, deterministic=True):\n residual = hidden_states\n hidden_states = self.norm1(hidden_states)\n hidden_states = nn.swish(hidden_states)\n hidden_states = self.conv1(hidden_states)\n\n hidden_states = self.norm2(hidden_states)\n hidden_states = nn.swish(hidden_states)\n hidden_states = self.dropout_layer(hidden_states, deterministic)\n hidden_states = self.conv2(hidden_states)\n\n if self.conv_shortcut is not None:\n residual = self.conv_shortcut(residual)\n\n return hidden_states + residual\n\n\nclass FlaxAttentionBlock(nn.Module):\n r\"\"\"\n Flax Convolutional based multi-head attention block for diffusion-based VAE.\n\n Parameters:\n channels (:obj:`int`):\n Input channels\n num_head_channels (:obj:`int`, *optional*, defaults to `None`):\n Number of attention heads\n num_groups (:obj:`int`, *optional*, defaults to `32`):\n The number of groups to use for group norm\n dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):\n Parameters `dtype`\n\n \"\"\"\n\n channels: int\n num_head_channels: int = None\n num_groups: int = 32\n dtype: jnp.dtype = jnp.float32\n\n def setup(self):\n self.num_heads = self.channels // self.num_head_channels if self.num_head_channels is not None else 1\n\n dense = partial(nn.Dense, self.channels, dtype=self.dtype)\n\n self.group_norm = nn.GroupNorm(num_groups=self.num_groups, epsilon=1e-6)\n self.query, self.key, self.value = dense(), dense(), dense()\n self.proj_attn = dense()\n\n def transpose_for_scores(self, projection):\n new_projection_shape = projection.shape[:-1] + (self.num_heads, -1)\n # move heads to 2nd position (B, T, H * D) -> (B, T, H, D)\n new_projection = projection.reshape(new_projection_shape)\n # (B, T, H, D) -> (B, H, T, D)\n new_projection = jnp.transpose(new_projection, (0, 2, 1, 3))\n return new_projection\n\n def __call__(self, hidden_states):\n residual = hidden_states\n batch, height, width, channels = hidden_states.shape\n\n hidden_states = self.group_norm(hidden_states)\n\n hidden_states = hidden_states.reshape((batch, height * width, channels))\n\n query = self.query(hidden_states)\n key = self.key(hidden_states)\n value = self.value(hidden_states)\n\n # transpose\n query = self.transpose_for_scores(query)\n key = self.transpose_for_scores(key)\n value = self.transpose_for_scores(value)\n\n # compute attentions\n scale = 1 / math.sqrt(math.sqrt(self.channels / self.num_heads))\n attn_weights = jnp.einsum(\"...qc,...kc->...qk\", query * scale, key * scale)\n attn_weights = nn.softmax(attn_weights, axis=-1)\n\n # attend to values\n hidden_states = jnp.einsum(\"...kc,...qk->...qc\", value, attn_weights)\n\n hidden_states = jnp.transpose(hidden_states, (0, 2, 1, 3))\n new_hidden_states_shape = hidden_states.shape[:-2] + (self.channels,)\n hidden_states = hidden_states.reshape(new_hidden_states_shape)\n\n hidden_states = self.proj_attn(hidden_states)\n hidden_states = hidden_states.reshape((batch, height, width, channels))\n hidden_states = hidden_states + residual\n return hidden_states\n\n\nclass FlaxDownEncoderBlock2D(nn.Module):\n r\"\"\"\n Flax Resnet blocks-based Encoder block for diffusion-based VAE.\n\n Parameters:\n in_channels (:obj:`int`):\n Input channels\n out_channels (:obj:`int`):\n Output channels\n dropout (:obj:`float`, *optional*, defaults to 0.0):\n Dropout rate\n num_layers (:obj:`int`, *optional*, defaults to 1):\n Number of Resnet layer block\n resnet_groups (:obj:`int`, *optional*, defaults to `32`):\n The number of groups to use for the Resnet block group norm\n add_downsample (:obj:`bool`, *optional*, defaults to `True`):\n Whether to add downsample layer\n dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):\n Parameters `dtype`\n \"\"\"\n\n in_channels: int\n out_channels: int\n dropout: float = 0.0\n num_layers: int = 1\n resnet_groups: int = 32\n add_downsample: bool = True\n dtype: jnp.dtype = jnp.float32\n\n def setup(self):\n resnets = []\n for i in range(self.num_layers):\n in_channels = self.in_channels if i == 0 else self.out_channels\n\n res_block = FlaxResnetBlock2D(\n in_channels=in_channels,\n out_channels=self.out_channels,\n dropout=self.dropout,\n groups=self.resnet_groups,\n dtype=self.dtype,\n )\n resnets.append(res_block)\n self.resnets = resnets\n\n if self.add_downsample:\n self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype)\n\n def __call__(self, hidden_states, deterministic=True):\n for resnet in self.resnets:\n hidden_states = resnet(hidden_states, deterministic=deterministic)\n\n if self.add_downsample:\n hidden_states = self.downsamplers_0(hidden_states)\n\n return hidden_states\n\n\nclass FlaxUpDecoderBlock2D(nn.Module):\n r\"\"\"\n Flax Resnet blocks-based Decoder block for diffusion-based VAE.\n\n Parameters:\n in_channels (:obj:`int`):\n Input channels\n out_channels (:obj:`int`):\n Output channels\n dropout (:obj:`float`, *optional*, defaults to 0.0):\n Dropout rate\n num_layers (:obj:`int`, *optional*, defaults to 1):\n Number of Resnet layer block\n resnet_groups (:obj:`int`, *optional*, defaults to `32`):\n The number of groups to use for the Resnet block group norm\n add_upsample (:obj:`bool`, *optional*, defaults to `True`):\n Whether to add upsample layer\n dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):\n Parameters `dtype`\n \"\"\"\n\n in_channels: int\n out_channels: int\n dropout: float = 0.0\n num_layers: int = 1\n resnet_groups: int = 32\n add_upsample: bool = True\n dtype: jnp.dtype = jnp.float32\n\n def setup(self):\n resnets = []\n for i in range(self.num_layers):\n in_channels = self.in_channels if i == 0 else self.out_channels\n res_block = FlaxResnetBlock2D(\n in_channels=in_channels,\n out_channels=self.out_channels,\n dropout=self.dropout,\n groups=self.resnet_groups,\n dtype=self.dtype,\n )\n resnets.append(res_block)\n\n self.resnets = resnets\n\n if self.add_upsample:\n self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype)\n\n def __call__(self, hidden_states, deterministic=True):\n for resnet in self.resnets:\n hidden_states = resnet(hidden_states, deterministic=deterministic)\n\n if self.add_upsample:\n hidden_states = self.upsamplers_0(hidden_states)\n\n return hidden_states\n\n\nclass FlaxUNetMidBlock2D(nn.Module):\n r\"\"\"\n Flax Unet Mid-Block module.\n\n Parameters:\n in_channels (:obj:`int`):\n Input channels\n dropout (:obj:`float`, *optional*, defaults to 0.0):\n Dropout rate\n num_layers (:obj:`int`, *optional*, defaults to 1):\n Number of Resnet layer block\n resnet_groups (:obj:`int`, *optional*, defaults to `32`):\n The number of groups to use for the Resnet and Attention block group norm\n num_attention_heads (:obj:`int`, *optional*, defaults to `1`):\n Number of attention heads for each attention block\n dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):\n Parameters `dtype`\n \"\"\"\n\n in_channels: int\n dropout: float = 0.0\n num_layers: int = 1\n resnet_groups: int = 32\n num_attention_heads: int = 1\n dtype: jnp.dtype = jnp.float32\n\n def setup(self):\n resnet_groups = self.resnet_groups if self.resnet_groups is not None else min(self.in_channels // 4, 32)\n\n # there is always at least one resnet\n resnets = [\n FlaxResnetBlock2D(\n in_channels=self.in_channels,\n out_channels=self.in_channels,\n dropout=self.dropout,\n groups=resnet_groups,\n dtype=self.dtype,\n )\n ]\n\n attentions = []\n\n for _ in range(self.num_layers):\n attn_block = FlaxAttentionBlock(\n channels=self.in_channels,\n num_head_channels=self.num_attention_heads,\n num_groups=resnet_groups,\n dtype=self.dtype,\n )\n attentions.append(attn_block)\n\n res_block = FlaxResnetBlock2D(\n in_channels=self.in_channels,\n out_channels=self.in_channels,\n dropout=self.dropout,\n groups=resnet_groups,\n dtype=self.dtype,\n )\n resnets.append(res_block)\n\n self.resnets = resnets\n self.attentions = attentions\n\n def __call__(self, hidden_states, deterministic=True):\n hidden_states = self.resnets[0](hidden_states, deterministic=deterministic)\n for attn, resnet in zip(self.attentions, self.resnets[1:]):\n hidden_states = attn(hidden_states)\n hidden_states = resnet(hidden_states, deterministic=deterministic)\n\n return hidden_states\n\n\nclass FlaxEncoder(nn.Module):\n r\"\"\"\n Flax Implementation of VAE Encoder.\n\n This model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)\n subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to\n general usage and behavior.\n\n Finally, this model supports inherent JAX features such as:\n - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)\n - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)\n - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)\n - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)\n\n Parameters:\n in_channels (:obj:`int`, *optional*, defaults to 3):\n Input channels\n out_channels (:obj:`int`, *optional*, defaults to 3):\n Output channels\n down_block_types (:obj:`Tuple[str]`, *optional*, defaults to `(DownEncoderBlock2D)`):\n DownEncoder block type\n block_out_channels (:obj:`Tuple[str]`, *optional*, defaults to `(64,)`):\n Tuple containing the number of output channels for each block\n layers_per_block (:obj:`int`, *optional*, defaults to `2`):\n Number of Resnet layer for each block\n norm_num_groups (:obj:`int`, *optional*, defaults to `32`):\n norm num group\n act_fn (:obj:`str`, *optional*, defaults to `silu`):\n Activation function\n double_z (:obj:`bool`, *optional*, defaults to `False`):\n Whether to double the last output channels\n dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):\n Parameters `dtype`\n \"\"\"\n\n in_channels: int = 3\n out_channels: int = 3\n down_block_types: Tuple[str] = (\"DownEncoderBlock2D\",)\n block_out_channels: Tuple[int] = (64,)\n layers_per_block: int = 2\n norm_num_groups: int = 32\n act_fn: str = \"silu\"\n double_z: bool = False\n dtype: jnp.dtype = jnp.float32\n\n def setup(self):\n block_out_channels = self.block_out_channels\n # in\n self.conv_in = nn.Conv(\n block_out_channels[0],\n kernel_size=(3, 3),\n strides=(1, 1),\n padding=((1, 1), (1, 1)),\n dtype=self.dtype,\n )\n\n # downsampling\n down_blocks = []\n output_channel = block_out_channels[0]\n for i, _ in enumerate(self.down_block_types):\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n down_block = FlaxDownEncoderBlock2D(\n in_channels=input_channel,\n out_channels=output_channel,\n num_layers=self.layers_per_block,\n resnet_groups=self.norm_num_groups,\n add_downsample=not is_final_block,\n dtype=self.dtype,\n )\n down_blocks.append(down_block)\n self.down_blocks = down_blocks\n\n # middle\n self.mid_block = FlaxUNetMidBlock2D(\n in_channels=block_out_channels[-1],\n resnet_groups=self.norm_num_groups,\n num_attention_heads=None,\n dtype=self.dtype,\n )\n\n # end\n conv_out_channels = 2 * self.out_channels if self.double_z else self.out_channels\n self.conv_norm_out = nn.GroupNorm(num_groups=self.norm_num_groups, epsilon=1e-6)\n self.conv_out = nn.Conv(\n conv_out_channels,\n kernel_size=(3, 3),\n strides=(1, 1),\n padding=((1, 1), (1, 1)),\n dtype=self.dtype,\n )\n\n def __call__(self, sample, deterministic: bool = True):\n # in\n sample = self.conv_in(sample)\n\n # downsampling\n for block in self.down_blocks:\n sample = block(sample, deterministic=deterministic)\n\n # middle\n sample = self.mid_block(sample, deterministic=deterministic)\n\n # end\n sample = self.conv_norm_out(sample)\n sample = nn.swish(sample)\n sample = self.conv_out(sample)\n\n return sample\n\n\nclass FlaxDecoder(nn.Module):\n r\"\"\"\n Flax Implementation of VAE Decoder.\n\n This model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)\n subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to\n general usage and behavior.\n\n Finally, this model supports inherent JAX features such as:\n - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)\n - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)\n - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)\n - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)\n\n Parameters:\n in_channels (:obj:`int`, *optional*, defaults to 3):\n Input channels\n out_channels (:obj:`int`, *optional*, defaults to 3):\n Output channels\n up_block_types (:obj:`Tuple[str]`, *optional*, defaults to `(UpDecoderBlock2D)`):\n UpDecoder block type\n block_out_channels (:obj:`Tuple[str]`, *optional*, defaults to `(64,)`):\n Tuple containing the number of output channels for each block\n layers_per_block (:obj:`int`, *optional*, defaults to `2`):\n Number of Resnet layer for each block\n norm_num_groups (:obj:`int`, *optional*, defaults to `32`):\n norm num group\n act_fn (:obj:`str`, *optional*, defaults to `silu`):\n Activation function\n double_z (:obj:`bool`, *optional*, defaults to `False`):\n Whether to double the last output channels\n dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):\n parameters `dtype`\n \"\"\"\n\n in_channels: int = 3\n out_channels: int = 3\n up_block_types: Tuple[str] = (\"UpDecoderBlock2D\",)\n block_out_channels: int = (64,)\n layers_per_block: int = 2\n norm_num_groups: int = 32\n act_fn: str = \"silu\"\n dtype: jnp.dtype = jnp.float32\n\n def setup(self):\n block_out_channels = self.block_out_channels\n\n # z to block_in\n self.conv_in = nn.Conv(\n block_out_channels[-1],\n kernel_size=(3, 3),\n strides=(1, 1),\n padding=((1, 1), (1, 1)),\n dtype=self.dtype,\n )\n\n # middle\n self.mid_block = FlaxUNetMidBlock2D(\n in_channels=block_out_channels[-1],\n resnet_groups=self.norm_num_groups,\n num_attention_heads=None,\n dtype=self.dtype,\n )\n\n # upsampling\n reversed_block_out_channels = list(reversed(block_out_channels))\n output_channel = reversed_block_out_channels[0]\n up_blocks = []\n for i, _ in enumerate(self.up_block_types):\n prev_output_channel = output_channel\n output_channel = reversed_block_out_channels[i]\n\n is_final_block = i == len(block_out_channels) - 1\n\n up_block = FlaxUpDecoderBlock2D(\n in_channels=prev_output_channel,\n out_channels=output_channel,\n num_layers=self.layers_per_block + 1,\n resnet_groups=self.norm_num_groups,\n add_upsample=not is_final_block,\n dtype=self.dtype,\n )\n up_blocks.append(up_block)\n prev_output_channel = output_channel\n\n self.up_blocks = up_blocks\n\n # end\n self.conv_norm_out = nn.GroupNorm(num_groups=self.norm_num_groups, epsilon=1e-6)\n self.conv_out = nn.Conv(\n self.out_channels,\n kernel_size=(3, 3),\n strides=(1, 1),\n padding=((1, 1), (1, 1)),\n dtype=self.dtype,\n )\n\n def __call__(self, sample, deterministic: bool = True):\n # z to block_in\n sample = self.conv_in(sample)\n\n # middle\n sample = self.mid_block(sample, deterministic=deterministic)\n\n # upsampling\n for block in self.up_blocks:\n sample = block(sample, deterministic=deterministic)\n\n sample = self.conv_norm_out(sample)\n sample = nn.swish(sample)\n sample = self.conv_out(sample)\n\n return sample\n\n\nclass FlaxDiagonalGaussianDistribution(object):\n def __init__(self, parameters, deterministic=False):\n # Last axis to account for channels-last\n self.mean, self.logvar = jnp.split(parameters, 2, axis=-1)\n self.logvar = jnp.clip(self.logvar, -30.0, 20.0)\n self.deterministic = deterministic\n self.std = jnp.exp(0.5 * self.logvar)\n self.var = jnp.exp(self.logvar)\n if self.deterministic:\n self.var = self.std = jnp.zeros_like(self.mean)\n\n def sample(self, key):\n return self.mean + self.std * jax.random.normal(key, self.mean.shape)\n\n def kl(self, other=None):\n if self.deterministic:\n return jnp.array([0.0])\n\n if other is None:\n return 0.5 * jnp.sum(self.mean**2 + self.var - 1.0 - self.logvar, axis=[1, 2, 3])\n\n return 0.5 * jnp.sum(\n jnp.square(self.mean - other.mean) / other.var + self.var / other.var - 1.0 - self.logvar + other.logvar,\n axis=[1, 2, 3],\n )\n\n def nll(self, sample, axis=[1, 2, 3]):\n if self.deterministic:\n return jnp.array([0.0])\n\n logtwopi = jnp.log(2.0 * jnp.pi)\n return 0.5 * jnp.sum(logtwopi + self.logvar + jnp.square(sample - self.mean) / self.var, axis=axis)\n\n def mode(self):\n return self.mean\n\n\n@flax_register_to_config\nclass FlaxAutoencoderKL(nn.Module, FlaxModelMixin, ConfigMixin):\n r\"\"\"\n Flax implementation of a VAE model with KL loss for decoding latent representations.\n\n This model inherits from [`FlaxModelMixin`]. Check the superclass documentation for it's generic methods\n implemented for all models (such as downloading or saving).\n\n This model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)\n subclass. Use it as a regular Flax Linen module and refer to the Flax documentation for all matter related to its\n general usage and behavior.\n\n Inherent JAX features such as the following are supported:\n\n - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)\n - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)\n - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)\n - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)\n\n Parameters:\n in_channels (`int`, *optional*, defaults to 3):\n Number of channels in the input image.\n out_channels (`int`, *optional*, defaults to 3):\n Number of channels in the output.\n down_block_types (`Tuple[str]`, *optional*, defaults to `(DownEncoderBlock2D)`):\n Tuple of downsample block types.\n up_block_types (`Tuple[str]`, *optional*, defaults to `(UpDecoderBlock2D)`):\n Tuple of upsample block types.\n block_out_channels (`Tuple[str]`, *optional*, defaults to `(64,)`):\n Tuple of block output channels.\n layers_per_block (`int`, *optional*, defaults to `2`):\n Number of ResNet layer for each block.\n act_fn (`str`, *optional*, defaults to `silu`):\n The activation function to use.\n latent_channels (`int`, *optional*, defaults to `4`):\n Number of channels in the latent space.\n norm_num_groups (`int`, *optional*, defaults to `32`):\n The number of groups for normalization.\n sample_size (`int`, *optional*, defaults to 32):\n Sample input size.\n scaling_factor (`float`, *optional*, defaults to 0.18215):\n The component-wise standard deviation of the trained latent space computed using the first batch of the\n training set. This is used to scale the latent space to have unit variance when training the diffusion\n model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the\n diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1\n / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image\n Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.\n dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):\n The `dtype` of the parameters.\n \"\"\"\n\n in_channels: int = 3\n out_channels: int = 3\n down_block_types: Tuple[str] = (\"DownEncoderBlock2D\",)\n up_block_types: Tuple[str] = (\"UpDecoderBlock2D\",)\n block_out_channels: Tuple[int] = (64,)\n layers_per_block: int = 1\n act_fn: str = \"silu\"\n latent_channels: int = 4\n norm_num_groups: int = 32\n sample_size: int = 32\n scaling_factor: float = 0.18215\n dtype: jnp.dtype = jnp.float32\n\n def setup(self):\n self.encoder = FlaxEncoder(\n in_channels=self.config.in_channels,\n out_channels=self.config.latent_channels,\n down_block_types=self.config.down_block_types,\n block_out_channels=self.config.block_out_channels,\n layers_per_block=self.config.layers_per_block,\n act_fn=self.config.act_fn,\n norm_num_groups=self.config.norm_num_groups,\n double_z=True,\n dtype=self.dtype,\n )\n self.decoder = FlaxDecoder(\n in_channels=self.config.latent_channels,\n out_channels=self.config.out_channels,\n up_block_types=self.config.up_block_types,\n block_out_channels=self.config.block_out_channels,\n layers_per_block=self.config.layers_per_block,\n norm_num_groups=self.config.norm_num_groups,\n act_fn=self.config.act_fn,\n dtype=self.dtype,\n )\n self.quant_conv = nn.Conv(\n 2 * self.config.latent_channels,\n kernel_size=(1, 1),\n strides=(1, 1),\n padding=\"VALID\",\n dtype=self.dtype,\n )\n self.post_quant_conv = nn.Conv(\n self.config.latent_channels,\n kernel_size=(1, 1),\n strides=(1, 1),\n padding=\"VALID\",\n dtype=self.dtype,\n )\n\n def init_weights(self, rng: jax.Array) -> FrozenDict:\n # init input tensors\n sample_shape = (1, self.in_channels, self.sample_size, self.sample_size)\n sample = jnp.zeros(sample_shape, dtype=jnp.float32)\n\n params_rng, dropout_rng, gaussian_rng = jax.random.split(rng, 3)\n rngs = {\"params\": params_rng, \"dropout\": dropout_rng, \"gaussian\": gaussian_rng}\n\n return self.init(rngs, sample)[\"params\"]\n\n def encode(self, sample, deterministic: bool = True, return_dict: bool = True):\n sample = jnp.transpose(sample, (0, 2, 3, 1))\n\n hidden_states = self.encoder(sample, deterministic=deterministic)\n moments = self.quant_conv(hidden_states)\n posterior = FlaxDiagonalGaussianDistribution(moments)\n\n if not return_dict:\n return (posterior,)\n\n return FlaxAutoencoderKLOutput(latent_dist=posterior)\n\n def decode(self, latents, deterministic: bool = True, return_dict: bool = True):\n if latents.shape[-1] != self.config.latent_channels:\n latents = jnp.transpose(latents, (0, 2, 3, 1))\n\n hidden_states = self.post_quant_conv(latents)\n hidden_states = self.decoder(hidden_states, deterministic=deterministic)\n\n hidden_states = jnp.transpose(hidden_states, (0, 3, 1, 2))\n\n if not return_dict:\n return (hidden_states,)\n\n return FlaxDecoderOutput(sample=hidden_states)\n\n def __call__(self, sample, sample_posterior=False, deterministic: bool = True, return_dict: bool = True):\n posterior = self.encode(sample, deterministic=deterministic, return_dict=return_dict)\n if sample_posterior:\n rng = self.make_rng(\"gaussian\")\n hidden_states = posterior.latent_dist.sample(rng)\n else:\n hidden_states = posterior.latent_dist.mode()\n\n sample = self.decode(hidden_states, return_dict=return_dict).sample\n\n if not return_dict:\n return (sample,)\n\n return FlaxDecoderOutput(sample=sample)\n" }, { "path": "diffusers/src/diffusers/models/vq_model.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom ..utils import deprecate\nfrom .autoencoders.vq_model import VQEncoderOutput, VQModel\n\n\nclass VQEncoderOutput(VQEncoderOutput):\n deprecation_message = \"Importing `VQEncoderOutput` from `diffusers.models.vq_model` is deprecated and this will be removed in a future version. Please use `from diffusers.models.autoencoders.vq_model import VQEncoderOutput`, instead.\"\n deprecate(\"VQEncoderOutput\", \"0.31\", deprecation_message)\n\n\nclass VQModel(VQModel):\n deprecation_message = \"Importing `VQModel` from `diffusers.models.vq_model` is deprecated and this will be removed in a future version. Please use `from diffusers.models.autoencoders.vq_model import VQModel`, instead.\"\n deprecate(\"VQModel\", \"0.31\", deprecation_message)\n" }, { "path": "diffusers/src/diffusers/optimization.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"PyTorch optimization for diffusion models.\"\"\"\n\nimport math\nfrom enum import Enum\nfrom typing import Optional, Union\n\nfrom torch.optim import Optimizer\nfrom torch.optim.lr_scheduler import LambdaLR\n\nfrom .utils import logging\n\n\nlogger = logging.get_logger(__name__)\n\n\nclass SchedulerType(Enum):\n LINEAR = \"linear\"\n COSINE = \"cosine\"\n COSINE_WITH_RESTARTS = \"cosine_with_restarts\"\n POLYNOMIAL = \"polynomial\"\n CONSTANT = \"constant\"\n CONSTANT_WITH_WARMUP = \"constant_with_warmup\"\n PIECEWISE_CONSTANT = \"piecewise_constant\"\n\n\ndef get_constant_schedule(optimizer: Optimizer, last_epoch: int = -1) -> LambdaLR:\n \"\"\"\n Create a schedule with a constant learning rate, using the learning rate set in optimizer.\n\n Args:\n optimizer ([`~torch.optim.Optimizer`]):\n The optimizer for which to schedule the learning rate.\n last_epoch (`int`, *optional*, defaults to -1):\n The index of the last epoch when resuming training.\n\n Return:\n `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.\n \"\"\"\n return LambdaLR(optimizer, lambda _: 1, last_epoch=last_epoch)\n\n\ndef get_constant_schedule_with_warmup(optimizer: Optimizer, num_warmup_steps: int, last_epoch: int = -1) -> LambdaLR:\n \"\"\"\n Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate\n increases linearly between 0 and the initial lr set in the optimizer.\n\n Args:\n optimizer ([`~torch.optim.Optimizer`]):\n The optimizer for which to schedule the learning rate.\n num_warmup_steps (`int`):\n The number of steps for the warmup phase.\n last_epoch (`int`, *optional*, defaults to -1):\n The index of the last epoch when resuming training.\n\n Return:\n `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.\n \"\"\"\n\n def lr_lambda(current_step: int):\n if current_step < num_warmup_steps:\n return float(current_step) / float(max(1.0, num_warmup_steps))\n return 1.0\n\n return LambdaLR(optimizer, lr_lambda, last_epoch=last_epoch)\n\n\ndef get_piecewise_constant_schedule(optimizer: Optimizer, step_rules: str, last_epoch: int = -1) -> LambdaLR:\n \"\"\"\n Create a schedule with a constant learning rate, using the learning rate set in optimizer.\n\n Args:\n optimizer ([`~torch.optim.Optimizer`]):\n The optimizer for which to schedule the learning rate.\n step_rules (`string`):\n The rules for the learning rate. ex: rule_steps=\"1:10,0.1:20,0.01:30,0.005\" it means that the learning rate\n if multiple 1 for the first 10 steps, mutiple 0.1 for the next 20 steps, multiple 0.01 for the next 30\n steps and multiple 0.005 for the other steps.\n last_epoch (`int`, *optional*, defaults to -1):\n The index of the last epoch when resuming training.\n\n Return:\n `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.\n \"\"\"\n\n rules_dict = {}\n rule_list = step_rules.split(\",\")\n for rule_str in rule_list[:-1]:\n value_str, steps_str = rule_str.split(\":\")\n steps = int(steps_str)\n value = float(value_str)\n rules_dict[steps] = value\n last_lr_multiple = float(rule_list[-1])\n\n def create_rules_function(rules_dict, last_lr_multiple):\n def rule_func(steps: int) -> float:\n sorted_steps = sorted(rules_dict.keys())\n for i, sorted_step in enumerate(sorted_steps):\n if steps < sorted_step:\n return rules_dict[sorted_steps[i]]\n return last_lr_multiple\n\n return rule_func\n\n rules_func = create_rules_function(rules_dict, last_lr_multiple)\n\n return LambdaLR(optimizer, rules_func, last_epoch=last_epoch)\n\n\ndef get_linear_schedule_with_warmup(\n optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, last_epoch: int = -1\n) -> LambdaLR:\n \"\"\"\n Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after\n a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer.\n\n Args:\n optimizer ([`~torch.optim.Optimizer`]):\n The optimizer for which to schedule the learning rate.\n num_warmup_steps (`int`):\n The number of steps for the warmup phase.\n num_training_steps (`int`):\n The total number of training steps.\n last_epoch (`int`, *optional*, defaults to -1):\n The index of the last epoch when resuming training.\n\n Return:\n `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.\n \"\"\"\n\n def lr_lambda(current_step: int):\n if current_step < num_warmup_steps:\n return float(current_step) / float(max(1, num_warmup_steps))\n return max(\n 0.0, float(num_training_steps - current_step) / float(max(1, num_training_steps - num_warmup_steps))\n )\n\n return LambdaLR(optimizer, lr_lambda, last_epoch)\n\n\ndef get_cosine_schedule_with_warmup(\n optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: float = 0.5, last_epoch: int = -1\n) -> LambdaLR:\n \"\"\"\n Create a schedule with a learning rate that decreases following the values of the cosine function between the\n initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the\n initial lr set in the optimizer.\n\n Args:\n optimizer ([`~torch.optim.Optimizer`]):\n The optimizer for which to schedule the learning rate.\n num_warmup_steps (`int`):\n The number of steps for the warmup phase.\n num_training_steps (`int`):\n The total number of training steps.\n num_periods (`float`, *optional*, defaults to 0.5):\n The number of periods of the cosine function in a schedule (the default is to just decrease from the max\n value to 0 following a half-cosine).\n last_epoch (`int`, *optional*, defaults to -1):\n The index of the last epoch when resuming training.\n\n Return:\n `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.\n \"\"\"\n\n def lr_lambda(current_step):\n if current_step < num_warmup_steps:\n return float(current_step) / float(max(1, num_warmup_steps))\n progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))\n return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))\n\n return LambdaLR(optimizer, lr_lambda, last_epoch)\n\n\ndef get_cosine_with_hard_restarts_schedule_with_warmup(\n optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: int = 1, last_epoch: int = -1\n) -> LambdaLR:\n \"\"\"\n Create a schedule with a learning rate that decreases following the values of the cosine function between the\n initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases\n linearly between 0 and the initial lr set in the optimizer.\n\n Args:\n optimizer ([`~torch.optim.Optimizer`]):\n The optimizer for which to schedule the learning rate.\n num_warmup_steps (`int`):\n The number of steps for the warmup phase.\n num_training_steps (`int`):\n The total number of training steps.\n num_cycles (`int`, *optional*, defaults to 1):\n The number of hard restarts to use.\n last_epoch (`int`, *optional*, defaults to -1):\n The index of the last epoch when resuming training.\n\n Return:\n `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.\n \"\"\"\n\n def lr_lambda(current_step):\n if current_step < num_warmup_steps:\n return float(current_step) / float(max(1, num_warmup_steps))\n progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))\n if progress >= 1.0:\n return 0.0\n return max(0.0, 0.5 * (1.0 + math.cos(math.pi * ((float(num_cycles) * progress) % 1.0))))\n\n return LambdaLR(optimizer, lr_lambda, last_epoch)\n\n\ndef get_polynomial_decay_schedule_with_warmup(\n optimizer: Optimizer,\n num_warmup_steps: int,\n num_training_steps: int,\n lr_end: float = 1e-7,\n power: float = 1.0,\n last_epoch: int = -1,\n) -> LambdaLR:\n \"\"\"\n Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the\n optimizer to end lr defined by *lr_end*, after a warmup period during which it increases linearly from 0 to the\n initial lr set in the optimizer.\n\n Args:\n optimizer ([`~torch.optim.Optimizer`]):\n The optimizer for which to schedule the learning rate.\n num_warmup_steps (`int`):\n The number of steps for the warmup phase.\n num_training_steps (`int`):\n The total number of training steps.\n lr_end (`float`, *optional*, defaults to 1e-7):\n The end LR.\n power (`float`, *optional*, defaults to 1.0):\n Power factor.\n last_epoch (`int`, *optional*, defaults to -1):\n The index of the last epoch when resuming training.\n\n Note: *power* defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT\n implementation at\n https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37\n\n Return:\n `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.\n\n \"\"\"\n\n lr_init = optimizer.defaults[\"lr\"]\n if not (lr_init > lr_end):\n raise ValueError(f\"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})\")\n\n def lr_lambda(current_step: int):\n if current_step < num_warmup_steps:\n return float(current_step) / float(max(1, num_warmup_steps))\n elif current_step > num_training_steps:\n return lr_end / lr_init # as LambdaLR multiplies by lr_init\n else:\n lr_range = lr_init - lr_end\n decay_steps = num_training_steps - num_warmup_steps\n pct_remaining = 1 - (current_step - num_warmup_steps) / decay_steps\n decay = lr_range * pct_remaining**power + lr_end\n return decay / lr_init # as LambdaLR multiplies by lr_init\n\n return LambdaLR(optimizer, lr_lambda, last_epoch)\n\n\nTYPE_TO_SCHEDULER_FUNCTION = {\n SchedulerType.LINEAR: get_linear_schedule_with_warmup,\n SchedulerType.COSINE: get_cosine_schedule_with_warmup,\n SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,\n SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,\n SchedulerType.CONSTANT: get_constant_schedule,\n SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,\n SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,\n}\n\n\ndef get_scheduler(\n name: Union[str, SchedulerType],\n optimizer: Optimizer,\n step_rules: Optional[str] = None,\n num_warmup_steps: Optional[int] = None,\n num_training_steps: Optional[int] = None,\n num_cycles: int = 1,\n power: float = 1.0,\n last_epoch: int = -1,\n) -> LambdaLR:\n \"\"\"\n Unified API to get any scheduler from its name.\n\n Args:\n name (`str` or `SchedulerType`):\n The name of the scheduler to use.\n optimizer (`torch.optim.Optimizer`):\n The optimizer that will be used during training.\n step_rules (`str`, *optional*):\n A string representing the step rules to use. This is only used by the `PIECEWISE_CONSTANT` scheduler.\n num_warmup_steps (`int`, *optional*):\n The number of warmup steps to do. This is not required by all schedulers (hence the argument being\n optional), the function will raise an error if it's unset and the scheduler type requires it.\n num_training_steps (`int``, *optional*):\n The number of training steps to do. This is not required by all schedulers (hence the argument being\n optional), the function will raise an error if it's unset and the scheduler type requires it.\n num_cycles (`int`, *optional*):\n The number of hard restarts used in `COSINE_WITH_RESTARTS` scheduler.\n power (`float`, *optional*, defaults to 1.0):\n Power factor. See `POLYNOMIAL` scheduler\n last_epoch (`int`, *optional*, defaults to -1):\n The index of the last epoch when resuming training.\n \"\"\"\n name = SchedulerType(name)\n schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name]\n if name == SchedulerType.CONSTANT:\n return schedule_func(optimizer, last_epoch=last_epoch)\n\n if name == SchedulerType.PIECEWISE_CONSTANT:\n return schedule_func(optimizer, step_rules=step_rules, last_epoch=last_epoch)\n\n # All other schedulers require `num_warmup_steps`\n if num_warmup_steps is None:\n raise ValueError(f\"{name} requires `num_warmup_steps`, please provide that argument.\")\n\n if name == SchedulerType.CONSTANT_WITH_WARMUP:\n return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, last_epoch=last_epoch)\n\n # All other schedulers require `num_training_steps`\n if num_training_steps is None:\n raise ValueError(f\"{name} requires `num_training_steps`, please provide that argument.\")\n\n if name == SchedulerType.COSINE_WITH_RESTARTS:\n return schedule_func(\n optimizer,\n num_warmup_steps=num_warmup_steps,\n num_training_steps=num_training_steps,\n num_cycles=num_cycles,\n last_epoch=last_epoch,\n )\n\n if name == SchedulerType.POLYNOMIAL:\n return schedule_func(\n optimizer,\n num_warmup_steps=num_warmup_steps,\n num_training_steps=num_training_steps,\n power=power,\n last_epoch=last_epoch,\n )\n\n return schedule_func(\n optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, last_epoch=last_epoch\n )\n" }, { "path": "diffusers/src/diffusers/pipelines/README.md", "content": "# 🧨 Diffusers Pipelines\n\nPipelines provide a simple way to run state-of-the-art diffusion models in inference.\nMost diffusion systems consist of multiple independently-trained models and highly adaptable scheduler\ncomponents - all of which are needed to have a functioning end-to-end diffusion system.\n\nAs an example, [Stable Diffusion](https://huggingface.co/blog/stable_diffusion) has three independently trained models:\n- [Autoencoder](https://github.com/huggingface/diffusers/blob/5cbed8e0d157f65d3ddc2420dfd09f2df630e978/src/diffusers/models/vae.py#L392)\n- [Conditional Unet](https://github.com/huggingface/diffusers/blob/5cbed8e0d157f65d3ddc2420dfd09f2df630e978/src/diffusers/models/unet_2d_condition.py#L12)\n- [CLIP text encoder](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTextModel)\n- a scheduler component, [scheduler](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py),\n- a [CLIPImageProcessor](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPImageProcessor),\n- as well as a [safety checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py).\nAll of these components are necessary to run stable diffusion in inference even though they were trained\nor created independently from each other.\n\nTo that end, we strive to offer all open-sourced, state-of-the-art diffusion system under a unified API.\nMore specifically, we strive to provide pipelines that\n- 1. can load the officially published weights and yield 1-to-1 the same outputs as the original implementation according to the corresponding paper (*e.g.* [LDMTextToImagePipeline](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/latent_diffusion), uses the officially released weights of [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)),\n- 2. have a simple user interface to run the model in inference (see the [Pipelines API](#pipelines-api) section),\n- 3. are easy to understand with code that is self-explanatory and can be read along-side the official paper (see [Pipelines summary](#pipelines-summary)),\n- 4. can easily be contributed by the community (see the [Contribution](#contribution) section).\n\n**Note** that pipelines do not (and should not) offer any training functionality.\nIf you are looking for *official* training examples, please have a look at [examples](https://github.com/huggingface/diffusers/tree/main/examples).\n\n\n## Pipelines Summary\n\nThe following table summarizes all officially supported pipelines, their corresponding paper, and if\navailable a colab notebook to directly try them out.\n\n| Pipeline | Source | Tasks | Colab\n|-------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------|:---:|:---:|\n| [dance diffusion](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/dance_diffusion) | [**Dance Diffusion**](https://github.com/Harmonai-org/sample-generator) | *Unconditional Audio Generation* |\n| [ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/ddpm) | [**Denoising Diffusion Probabilistic Models**](https://arxiv.org/abs/2006.11239) | *Unconditional Image Generation* |\n| [ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/ddim) | [**Denoising Diffusion Implicit Models**](https://arxiv.org/abs/2010.02502) | *Unconditional Image Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)\n| [latent_diffusion](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/latent_diffusion) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752) | *Text-to-Image Generation* |\n| [latent_diffusion_uncond](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/latent_diffusion_uncond) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752) | *Unconditional Image Generation* |\n| [pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pndm) | [**Pseudo Numerical Methods for Diffusion Models on Manifolds**](https://arxiv.org/abs/2202.09778) | *Unconditional Image Generation* |\n| [score_sde_ve](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/score_sde_ve) | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) | *Unconditional Image Generation* |\n| [score_sde_vp](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/score_sde_vp) | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) | *Unconditional Image Generation* |\n| [stable_diffusion](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | *Text-to-Image Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb)\n| [stable_diffusion](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | *Image-to-Image Text-Guided Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb)\n| [stable_diffusion](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | *Text-Guided Image Inpainting* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb)\n| [stochastic_karras_ve](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stochastic_karras_ve) | [**Elucidating the Design Space of Diffusion-Based Generative Models**](https://arxiv.org/abs/2206.00364) | *Unconditional Image Generation* |\n\n**Note**: Pipelines are simple examples of how to play around with the diffusion systems as described in the corresponding papers.\nHowever, most of them can be adapted to use different scheduler components or even different model components. Some pipeline examples are shown in the [Examples](#examples) below.\n\n## Pipelines API\n\nDiffusion models often consist of multiple independently-trained models or other previously existing components.\n\n\nEach model has been trained independently on a different task and the scheduler can easily be swapped out and replaced with a different one.\nDuring inference, we however want to be able to easily load all components and use them in inference - even if one component, *e.g.* CLIP's text encoder, originates from a different library, such as [Transformers](https://github.com/huggingface/transformers). To that end, all pipelines provide the following functionality:\n\n- [`from_pretrained` method](https://github.com/huggingface/diffusers/blob/5cbed8e0d157f65d3ddc2420dfd09f2df630e978/src/diffusers/pipeline_utils.py#L139) that accepts a Hugging Face Hub repository id, *e.g.* [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) or a path to a local directory, *e.g.*\n\"./stable-diffusion\". To correctly retrieve which models and components should be loaded, one has to provide a `model_index.json` file, *e.g.* [runwayml/stable-diffusion-v1-5/model_index.json](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/model_index.json), which defines all components that should be\nloaded into the pipelines. More specifically, for each model/component one needs to define the format `: [\"\", \"\"]`. `` is the attribute name given to the loaded instance of `` which can be found in the library or pipeline folder called `\"\"`.\n- [`save_pretrained`](https://github.com/huggingface/diffusers/blob/5cbed8e0d157f65d3ddc2420dfd09f2df630e978/src/diffusers/pipeline_utils.py#L90) that accepts a local path, *e.g.* `./stable-diffusion` under which all models/components of the pipeline will be saved. For each component/model a folder is created inside the local path that is named after the given attribute name, *e.g.* `./stable_diffusion/unet`.\nIn addition, a `model_index.json` file is created at the root of the local path, *e.g.* `./stable_diffusion/model_index.json` so that the complete pipeline can again be instantiated\nfrom the local path.\n- [`to`](https://github.com/huggingface/diffusers/blob/5cbed8e0d157f65d3ddc2420dfd09f2df630e978/src/diffusers/pipeline_utils.py#L118) which accepts a `string` or `torch.device` to move all models that are of type `torch.nn.Module` to the passed device. The behavior is fully analogous to [PyTorch's `to` method](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.to).\n- [`__call__`] method to use the pipeline in inference. `__call__` defines inference logic of the pipeline and should ideally encompass all aspects of it, from pre-processing to forwarding tensors to the different models and schedulers, as well as post-processing. The API of the `__call__` method can strongly vary from pipeline to pipeline. *E.g.* a text-to-image pipeline, such as [`StableDiffusionPipeline`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py) should accept among other things the text prompt to generate the image. A pure image generation pipeline, such as [DDPMPipeline](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/ddpm) on the other hand can be run without providing any inputs. To better understand what inputs can be adapted for\neach pipeline, one should look directly into the respective pipeline.\n\n**Note**: All pipelines have PyTorch's autograd disabled by decorating the `__call__` method with a [`torch.no_grad`](https://pytorch.org/docs/stable/generated/torch.no_grad.html) decorator because pipelines should\nnot be used for training. If you want to store the gradients during the forward pass, we recommend writing your own pipeline, see also our [community-examples](https://github.com/huggingface/diffusers/tree/main/examples/community)\n\n## Contribution\n\nWe are more than happy about any contribution to the officially supported pipelines 🤗. We aspire\nall of our pipelines to be **self-contained**, **easy-to-tweak**, **beginner-friendly** and for **one-purpose-only**.\n\n- **Self-contained**: A pipeline shall be as self-contained as possible. More specifically, this means that all functionality should be either directly defined in the pipeline file itself, should be inherited from (and only from) the [`DiffusionPipeline` class](https://github.com/huggingface/diffusers/blob/5cbed8e0d157f65d3ddc2420dfd09f2df630e978/src/diffusers/pipeline_utils.py#L56) or be directly attached to the model and scheduler components of the pipeline.\n- **Easy-to-use**: Pipelines should be extremely easy to use - one should be able to load the pipeline and\nuse it for its designated task, *e.g.* text-to-image generation, in just a couple of lines of code. Most\nlogic including pre-processing, an unrolled diffusion loop, and post-processing should all happen inside the `__call__` method.\n- **Easy-to-tweak**: Certain pipelines will not be able to handle all use cases and tasks that you might like them to. If you want to use a certain pipeline for a specific use case that is not yet supported, you might have to copy the pipeline file and tweak the code to your needs. We try to make the pipeline code as readable as possible so that each part –from pre-processing to diffusing to post-processing– can easily be adapted. If you would like the community to benefit from your customized pipeline, we would love to see a contribution to our [community-examples](https://github.com/huggingface/diffusers/tree/main/examples/community). If you feel that an important pipeline should be part of the official pipelines but isn't, a contribution to the [official pipelines](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines) would be even better.\n- **One-purpose-only**: Pipelines should be used for one task and one task only. Even if two tasks are very similar from a modeling point of view, *e.g.* image2image translation and in-painting, pipelines shall be used for one task only to keep them *easy-to-tweak* and *readable*.\n\n## Examples\n\n### Text-to-Image generation with Stable Diffusion\n\n```python\n# make sure you're logged in with `huggingface-cli login`\nfrom diffusers import StableDiffusionPipeline, LMSDiscreteScheduler\n\npipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\npipe = pipe.to(\"cuda\")\n\nprompt = \"a photo of an astronaut riding a horse on mars\"\nimage = pipe(prompt).images[0]\n\nimage.save(\"astronaut_rides_horse.png\")\n```\n\n### Image-to-Image text-guided generation with Stable Diffusion\n\nThe `StableDiffusionImg2ImgPipeline` lets you pass a text prompt and an initial image to condition the generation of new images.\n\n```python\nimport requests\nfrom PIL import Image\nfrom io import BytesIO\n\nfrom diffusers import StableDiffusionImg2ImgPipeline\n\n# load the pipeline\ndevice = \"cuda\"\npipe = StableDiffusionImg2ImgPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n torch_dtype=torch.float16,\n).to(device)\n\n# let's download an initial image\nurl = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n\nresponse = requests.get(url)\ninit_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\ninit_image = init_image.resize((768, 512))\n\nprompt = \"A fantasy landscape, trending on artstation\"\n\nimages = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images\n\nimages[0].save(\"fantasy_landscape.png\")\n```\nYou can also run this example on colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb)\n\n### Tweak prompts reusing seeds and latents\n\nYou can generate your own latents to reproduce results, or tweak your prompt on a specific result you liked. [This notebook](https://github.com/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb) shows how to do it step by step. You can also run it in Google Colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb).\n\n\n### In-painting using Stable Diffusion\n\nThe `StableDiffusionInpaintPipeline` lets you edit specific parts of an image by providing a mask and text prompt.\n\n```python\nimport PIL\nimport requests\nimport torch\nfrom io import BytesIO\n\nfrom diffusers import StableDiffusionInpaintPipeline\n\ndef download_image(url):\n response = requests.get(url)\n return PIL.Image.open(BytesIO(response.content)).convert(\"RGB\")\n\nimg_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\nmask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\n\ninit_image = download_image(img_url).resize((512, 512))\nmask_image = download_image(mask_url).resize((512, 512))\n\npipe = StableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\",\n torch_dtype=torch.float16,\n)\npipe = pipe.to(\"cuda\")\n\nprompt = \"Face of a yellow cat, high resolution, sitting on a park bench\"\nimage = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]\n```\n\nYou can also run this example on colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb)\n" }, { "path": "diffusers/src/diffusers/pipelines/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ..utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_flax_available,\n is_k_diffusion_available,\n is_librosa_available,\n is_note_seq_available,\n is_onnx_available,\n is_torch_available,\n is_torch_npu_available,\n is_transformers_available,\n)\n\n\n# These modules contain pipelines from multiple libraries/frameworks\n_dummy_objects = {}\n_import_structure = {\n \"controlnet\": [],\n \"controlnet_xs\": [],\n \"deprecated\": [],\n \"latent_diffusion\": [],\n \"ledits_pp\": [],\n \"marigold\": [],\n \"stable_diffusion\": [],\n \"stable_diffusion_xl\": [],\n}\n\ntry:\n if not is_torch_available():\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ..utils import dummy_pt_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_pt_objects))\nelse:\n _import_structure[\"auto_pipeline\"] = [\n \"AutoPipelineForImage2Image\",\n \"AutoPipelineForInpainting\",\n \"AutoPipelineForText2Image\",\n ]\n _import_structure[\"consistency_models\"] = [\"ConsistencyModelPipeline\"]\n _import_structure[\"dance_diffusion\"] = [\"DanceDiffusionPipeline\"]\n _import_structure[\"ddim\"] = [\"DDIMPipeline\"]\n _import_structure[\"ddpm\"] = [\"DDPMPipeline\"]\n _import_structure[\"dit\"] = [\"DiTPipeline\"]\n _import_structure[\"latent_diffusion\"].extend([\"LDMSuperResolutionPipeline\"])\n _import_structure[\"pipeline_utils\"] = [\n \"AudioPipelineOutput\",\n \"DiffusionPipeline\",\n \"StableDiffusionMixin\",\n \"ImagePipelineOutput\",\n ]\n _import_structure[\"deprecated\"].extend(\n [\n \"PNDMPipeline\",\n \"LDMPipeline\",\n \"RePaintPipeline\",\n \"ScoreSdeVePipeline\",\n \"KarrasVePipeline\",\n ]\n )\ntry:\n if not (is_torch_available() and is_librosa_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ..utils import dummy_torch_and_librosa_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_librosa_objects))\nelse:\n _import_structure[\"deprecated\"].extend([\"AudioDiffusionPipeline\", \"Mel\"])\n\ntry:\n if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ..utils import dummy_transformers_and_torch_and_note_seq_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_transformers_and_torch_and_note_seq_objects))\nelse:\n _import_structure[\"deprecated\"].extend(\n [\n \"MidiProcessor\",\n \"SpectrogramDiffusionPipeline\",\n ]\n )\n\ntry:\n if not (is_torch_available() and is_transformers_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ..utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"deprecated\"].extend(\n [\n \"VQDiffusionPipeline\",\n \"AltDiffusionPipeline\",\n \"AltDiffusionImg2ImgPipeline\",\n \"CycleDiffusionPipeline\",\n \"StableDiffusionInpaintPipelineLegacy\",\n \"StableDiffusionPix2PixZeroPipeline\",\n \"StableDiffusionParadigmsPipeline\",\n \"StableDiffusionModelEditingPipeline\",\n \"VersatileDiffusionDualGuidedPipeline\",\n \"VersatileDiffusionImageVariationPipeline\",\n \"VersatileDiffusionPipeline\",\n \"VersatileDiffusionTextToImagePipeline\",\n ]\n )\n _import_structure[\"amused\"] = [\"AmusedImg2ImgPipeline\", \"AmusedInpaintPipeline\", \"AmusedPipeline\"]\n _import_structure[\"animatediff\"] = [\n \"AnimateDiffPipeline\",\n \"AnimateDiffSDXLPipeline\",\n \"AnimateDiffVideoToVideoPipeline\",\n ]\n _import_structure[\"audioldm\"] = [\"AudioLDMPipeline\"]\n _import_structure[\"audioldm2\"] = [\n \"AudioLDM2Pipeline\",\n \"AudioLDM2ProjectionModel\",\n \"AudioLDM2UNet2DConditionModel\",\n ]\n _import_structure[\"blip_diffusion\"] = [\"BlipDiffusionPipeline\"]\n _import_structure[\"controlnet\"].extend(\n [\n \"BlipDiffusionControlNetPipeline\",\n \"StableDiffusionControlNetImg2ImgPipeline\",\n \"StableDiffusionControlNetInpaintPipeline\",\n \"StableDiffusionControlNetPipeline\",\n \"StableDiffusionXLControlNetImg2ImgPipeline\",\n \"StableDiffusionXLControlNetInpaintPipeline\",\n \"StableDiffusionXLControlNetPipeline\",\n ]\n )\n _import_structure[\"controlnet_xs\"].extend(\n [\n \"StableDiffusionControlNetXSPipeline\",\n \"StableDiffusionXLControlNetXSPipeline\",\n ]\n )\n _import_structure[\"deepfloyd_if\"] = [\n \"IFImg2ImgPipeline\",\n \"IFImg2ImgSuperResolutionPipeline\",\n \"IFInpaintingPipeline\",\n \"IFInpaintingSuperResolutionPipeline\",\n \"IFPipeline\",\n \"IFSuperResolutionPipeline\",\n ]\n _import_structure[\"hunyuandit\"] = [\"HunyuanDiTPipeline\"]\n _import_structure[\"kandinsky\"] = [\n \"KandinskyCombinedPipeline\",\n \"KandinskyImg2ImgCombinedPipeline\",\n \"KandinskyImg2ImgPipeline\",\n \"KandinskyInpaintCombinedPipeline\",\n \"KandinskyInpaintPipeline\",\n \"KandinskyPipeline\",\n \"KandinskyPriorPipeline\",\n ]\n _import_structure[\"kandinsky2_2\"] = [\n \"KandinskyV22CombinedPipeline\",\n \"KandinskyV22ControlnetImg2ImgPipeline\",\n \"KandinskyV22ControlnetPipeline\",\n \"KandinskyV22Img2ImgCombinedPipeline\",\n \"KandinskyV22Img2ImgPipeline\",\n \"KandinskyV22InpaintCombinedPipeline\",\n \"KandinskyV22InpaintPipeline\",\n \"KandinskyV22Pipeline\",\n \"KandinskyV22PriorEmb2EmbPipeline\",\n \"KandinskyV22PriorPipeline\",\n ]\n _import_structure[\"kandinsky3\"] = [\n \"Kandinsky3Img2ImgPipeline\",\n \"Kandinsky3Pipeline\",\n ]\n _import_structure[\"latent_consistency_models\"] = [\n \"LatentConsistencyModelImg2ImgPipeline\",\n \"LatentConsistencyModelPipeline\",\n ]\n _import_structure[\"latent_diffusion\"].extend([\"LDMTextToImagePipeline\"])\n _import_structure[\"ledits_pp\"].extend(\n [\n \"LEditsPPPipelineStableDiffusion\",\n \"LEditsPPPipelineStableDiffusionXL\",\n ]\n )\n _import_structure[\"marigold\"].extend(\n [\n \"MarigoldDepthPipeline\",\n \"MarigoldNormalsPipeline\",\n ]\n )\n _import_structure[\"musicldm\"] = [\"MusicLDMPipeline\"]\n _import_structure[\"paint_by_example\"] = [\"PaintByExamplePipeline\"]\n _import_structure[\"pia\"] = [\"PIAPipeline\"]\n _import_structure[\"pixart_alpha\"] = [\"PixArtAlphaPipeline\", \"PixArtSigmaPipeline\"]\n _import_structure[\"semantic_stable_diffusion\"] = [\"SemanticStableDiffusionPipeline\"]\n _import_structure[\"shap_e\"] = [\"ShapEImg2ImgPipeline\", \"ShapEPipeline\"]\n _import_structure[\"stable_cascade\"] = [\n \"StableCascadeCombinedPipeline\",\n \"StableCascadeDecoderPipeline\",\n \"StableCascadePriorPipeline\",\n ]\n _import_structure[\"stable_diffusion\"].extend(\n [\n \"CLIPImageProjection\",\n \"StableDiffusionDepth2ImgPipeline\",\n \"StableDiffusionImageVariationPipeline\",\n \"StableDiffusionImg2ImgPipeline\",\n \"StableDiffusionInpaintPipeline\",\n \"StableDiffusionInstructPix2PixPipeline\",\n \"StableDiffusionLatentUpscalePipeline\",\n \"StableDiffusionPipeline\",\n \"StableDiffusionUpscalePipeline\",\n \"StableUnCLIPImg2ImgPipeline\",\n \"StableUnCLIPPipeline\",\n \"StableDiffusionLDM3DPipeline\",\n ]\n )\n _import_structure[\"stable_diffusion_3\"] = [\"StableDiffusion3Pipeline\", \"StableDiffusion3Img2ImgPipeline\",\"StableDiffusion3InstructPix2PixPipeline\"]\n _import_structure[\"stable_diffusion_attend_and_excite\"] = [\"StableDiffusionAttendAndExcitePipeline\"]\n _import_structure[\"stable_diffusion_safe\"] = [\"StableDiffusionPipelineSafe\"]\n _import_structure[\"stable_diffusion_sag\"] = [\"StableDiffusionSAGPipeline\"]\n _import_structure[\"stable_diffusion_gligen\"] = [\n \"StableDiffusionGLIGENPipeline\",\n \"StableDiffusionGLIGENTextImagePipeline\",\n ]\n _import_structure[\"stable_video_diffusion\"] = [\"StableVideoDiffusionPipeline\"]\n _import_structure[\"stable_diffusion_xl\"].extend(\n [\n \"StableDiffusionXLImg2ImgPipeline\",\n \"StableDiffusionXLInpaintPipeline\",\n \"StableDiffusionXLInstructPix2PixPipeline\",\n \"StableDiffusionXLPipeline\",\n ]\n )\n _import_structure[\"stable_diffusion_diffedit\"] = [\"StableDiffusionDiffEditPipeline\"]\n _import_structure[\"stable_diffusion_ldm3d\"] = [\"StableDiffusionLDM3DPipeline\"]\n _import_structure[\"stable_diffusion_panorama\"] = [\"StableDiffusionPanoramaPipeline\"]\n _import_structure[\"t2i_adapter\"] = [\n \"StableDiffusionAdapterPipeline\",\n \"StableDiffusionXLAdapterPipeline\",\n ]\n _import_structure[\"text_to_video_synthesis\"] = [\n \"TextToVideoSDPipeline\",\n \"TextToVideoZeroPipeline\",\n \"TextToVideoZeroSDXLPipeline\",\n \"VideoToVideoSDPipeline\",\n ]\n _import_structure[\"i2vgen_xl\"] = [\"I2VGenXLPipeline\"]\n _import_structure[\"unclip\"] = [\"UnCLIPImageVariationPipeline\", \"UnCLIPPipeline\"]\n _import_structure[\"unidiffuser\"] = [\n \"ImageTextPipelineOutput\",\n \"UniDiffuserModel\",\n \"UniDiffuserPipeline\",\n \"UniDiffuserTextDecoder\",\n ]\n _import_structure[\"wuerstchen\"] = [\n \"WuerstchenCombinedPipeline\",\n \"WuerstchenDecoderPipeline\",\n \"WuerstchenPriorPipeline\",\n ]\ntry:\n if not is_onnx_available():\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ..utils import dummy_onnx_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_onnx_objects))\nelse:\n _import_structure[\"onnx_utils\"] = [\"OnnxRuntimeModel\"]\ntry:\n if not (is_torch_available() and is_transformers_available() and is_onnx_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ..utils import dummy_torch_and_transformers_and_onnx_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_and_onnx_objects))\nelse:\n _import_structure[\"stable_diffusion\"].extend(\n [\n \"OnnxStableDiffusionImg2ImgPipeline\",\n \"OnnxStableDiffusionInpaintPipeline\",\n \"OnnxStableDiffusionPipeline\",\n \"OnnxStableDiffusionUpscalePipeline\",\n \"StableDiffusionOnnxPipeline\",\n ]\n )\n\ntry:\n if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ..utils import (\n dummy_torch_and_transformers_and_k_diffusion_objects,\n )\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_and_k_diffusion_objects))\nelse:\n _import_structure[\"stable_diffusion_k_diffusion\"] = [\n \"StableDiffusionKDiffusionPipeline\",\n \"StableDiffusionXLKDiffusionPipeline\",\n ]\ntry:\n if not is_flax_available():\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ..utils import dummy_flax_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_flax_objects))\nelse:\n _import_structure[\"pipeline_flax_utils\"] = [\"FlaxDiffusionPipeline\"]\ntry:\n if not (is_flax_available() and is_transformers_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ..utils import dummy_flax_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_flax_and_transformers_objects))\nelse:\n _import_structure[\"controlnet\"].extend([\"FlaxStableDiffusionControlNetPipeline\"])\n _import_structure[\"stable_diffusion\"].extend(\n [\n \"FlaxStableDiffusionImg2ImgPipeline\",\n \"FlaxStableDiffusionInpaintPipeline\",\n \"FlaxStableDiffusionPipeline\",\n ]\n )\n _import_structure[\"stable_diffusion_xl\"].extend(\n [\n \"FlaxStableDiffusionXLPipeline\",\n ]\n )\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not is_torch_available():\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ..utils.dummy_pt_objects import * # noqa F403\n\n else:\n from .auto_pipeline import (\n AutoPipelineForImage2Image,\n AutoPipelineForInpainting,\n AutoPipelineForText2Image,\n )\n from .consistency_models import ConsistencyModelPipeline\n from .dance_diffusion import DanceDiffusionPipeline\n from .ddim import DDIMPipeline\n from .ddpm import DDPMPipeline\n from .deprecated import KarrasVePipeline, LDMPipeline, PNDMPipeline, RePaintPipeline, ScoreSdeVePipeline\n from .dit import DiTPipeline\n from .latent_diffusion import LDMSuperResolutionPipeline\n from .pipeline_utils import (\n AudioPipelineOutput,\n DiffusionPipeline,\n ImagePipelineOutput,\n StableDiffusionMixin,\n )\n\n try:\n if not (is_torch_available() and is_librosa_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ..utils.dummy_torch_and_librosa_objects import *\n else:\n from .deprecated import AudioDiffusionPipeline, Mel\n\n try:\n if not (is_torch_available() and is_transformers_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ..utils.dummy_torch_and_transformers_objects import *\n else:\n from .amused import AmusedImg2ImgPipeline, AmusedInpaintPipeline, AmusedPipeline\n from .animatediff import AnimateDiffPipeline, AnimateDiffSDXLPipeline, AnimateDiffVideoToVideoPipeline\n from .audioldm import AudioLDMPipeline\n from .audioldm2 import (\n AudioLDM2Pipeline,\n AudioLDM2ProjectionModel,\n AudioLDM2UNet2DConditionModel,\n )\n from .blip_diffusion import BlipDiffusionPipeline\n from .controlnet import (\n BlipDiffusionControlNetPipeline,\n StableDiffusionControlNetImg2ImgPipeline,\n StableDiffusionControlNetInpaintPipeline,\n StableDiffusionControlNetPipeline,\n StableDiffusionXLControlNetImg2ImgPipeline,\n StableDiffusionXLControlNetInpaintPipeline,\n StableDiffusionXLControlNetPipeline,\n )\n from .controlnet_xs import (\n StableDiffusionControlNetXSPipeline,\n StableDiffusionXLControlNetXSPipeline,\n )\n from .deepfloyd_if import (\n IFImg2ImgPipeline,\n IFImg2ImgSuperResolutionPipeline,\n IFInpaintingPipeline,\n IFInpaintingSuperResolutionPipeline,\n IFPipeline,\n IFSuperResolutionPipeline,\n )\n from .deprecated import (\n AltDiffusionImg2ImgPipeline,\n AltDiffusionPipeline,\n CycleDiffusionPipeline,\n StableDiffusionInpaintPipelineLegacy,\n StableDiffusionModelEditingPipeline,\n StableDiffusionParadigmsPipeline,\n StableDiffusionPix2PixZeroPipeline,\n VersatileDiffusionDualGuidedPipeline,\n VersatileDiffusionImageVariationPipeline,\n VersatileDiffusionPipeline,\n VersatileDiffusionTextToImagePipeline,\n VQDiffusionPipeline,\n )\n from .hunyuandit import HunyuanDiTPipeline\n from .i2vgen_xl import I2VGenXLPipeline\n from .kandinsky import (\n KandinskyCombinedPipeline,\n KandinskyImg2ImgCombinedPipeline,\n KandinskyImg2ImgPipeline,\n KandinskyInpaintCombinedPipeline,\n KandinskyInpaintPipeline,\n KandinskyPipeline,\n KandinskyPriorPipeline,\n )\n from .kandinsky2_2 import (\n KandinskyV22CombinedPipeline,\n KandinskyV22ControlnetImg2ImgPipeline,\n KandinskyV22ControlnetPipeline,\n KandinskyV22Img2ImgCombinedPipeline,\n KandinskyV22Img2ImgPipeline,\n KandinskyV22InpaintCombinedPipeline,\n KandinskyV22InpaintPipeline,\n KandinskyV22Pipeline,\n KandinskyV22PriorEmb2EmbPipeline,\n KandinskyV22PriorPipeline,\n )\n from .kandinsky3 import (\n Kandinsky3Img2ImgPipeline,\n Kandinsky3Pipeline,\n )\n from .latent_consistency_models import (\n LatentConsistencyModelImg2ImgPipeline,\n LatentConsistencyModelPipeline,\n )\n from .latent_diffusion import LDMTextToImagePipeline\n from .ledits_pp import (\n LEditsPPDiffusionPipelineOutput,\n LEditsPPInversionPipelineOutput,\n LEditsPPPipelineStableDiffusion,\n LEditsPPPipelineStableDiffusionXL,\n )\n from .marigold import (\n MarigoldDepthPipeline,\n MarigoldNormalsPipeline,\n )\n from .musicldm import MusicLDMPipeline\n from .paint_by_example import PaintByExamplePipeline\n from .pia import PIAPipeline\n from .pixart_alpha import PixArtAlphaPipeline, PixArtSigmaPipeline\n from .semantic_stable_diffusion import SemanticStableDiffusionPipeline\n from .shap_e import ShapEImg2ImgPipeline, ShapEPipeline\n from .stable_cascade import (\n StableCascadeCombinedPipeline,\n StableCascadeDecoderPipeline,\n StableCascadePriorPipeline,\n )\n from .stable_diffusion import (\n CLIPImageProjection,\n StableDiffusionDepth2ImgPipeline,\n StableDiffusionImageVariationPipeline,\n StableDiffusionImg2ImgPipeline,\n StableDiffusionInpaintPipeline,\n StableDiffusionInstructPix2PixPipeline,\n StableDiffusionLatentUpscalePipeline,\n StableDiffusionPipeline,\n StableDiffusionUpscalePipeline,\n StableUnCLIPImg2ImgPipeline,\n StableUnCLIPPipeline,\n )\n from .stable_diffusion_3 import StableDiffusion3Img2ImgPipeline, StableDiffusion3Pipeline, StableDiffusion3InstructPix2PixPipeline\n from .stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline\n from .stable_diffusion_diffedit import StableDiffusionDiffEditPipeline\n from .stable_diffusion_gligen import StableDiffusionGLIGENPipeline, StableDiffusionGLIGENTextImagePipeline\n from .stable_diffusion_ldm3d import StableDiffusionLDM3DPipeline\n from .stable_diffusion_panorama import StableDiffusionPanoramaPipeline\n from .stable_diffusion_safe import StableDiffusionPipelineSafe\n from .stable_diffusion_sag import StableDiffusionSAGPipeline\n from .stable_diffusion_xl import (\n StableDiffusionXLImg2ImgPipeline,\n StableDiffusionXLInpaintPipeline,\n StableDiffusionXLInstructPix2PixPipeline,\n StableDiffusionXLPipeline,\n )\n from .stable_video_diffusion import StableVideoDiffusionPipeline\n from .t2i_adapter import (\n StableDiffusionAdapterPipeline,\n StableDiffusionXLAdapterPipeline,\n )\n from .text_to_video_synthesis import (\n TextToVideoSDPipeline,\n TextToVideoZeroPipeline,\n TextToVideoZeroSDXLPipeline,\n VideoToVideoSDPipeline,\n )\n from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline\n from .unidiffuser import (\n ImageTextPipelineOutput,\n UniDiffuserModel,\n UniDiffuserPipeline,\n UniDiffuserTextDecoder,\n )\n from .wuerstchen import (\n WuerstchenCombinedPipeline,\n WuerstchenDecoderPipeline,\n WuerstchenPriorPipeline,\n )\n\n try:\n if not is_onnx_available():\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ..utils.dummy_onnx_objects import * # noqa F403\n\n else:\n from .onnx_utils import OnnxRuntimeModel\n\n try:\n if not (is_torch_available() and is_transformers_available() and is_onnx_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ..utils.dummy_torch_and_transformers_and_onnx_objects import *\n else:\n from .stable_diffusion import (\n OnnxStableDiffusionImg2ImgPipeline,\n OnnxStableDiffusionInpaintPipeline,\n OnnxStableDiffusionPipeline,\n OnnxStableDiffusionUpscalePipeline,\n StableDiffusionOnnxPipeline,\n )\n\n try:\n if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *\n else:\n from .stable_diffusion_k_diffusion import (\n StableDiffusionKDiffusionPipeline,\n StableDiffusionXLKDiffusionPipeline,\n )\n\n try:\n if not is_flax_available():\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ..utils.dummy_flax_objects import * # noqa F403\n else:\n from .pipeline_flax_utils import FlaxDiffusionPipeline\n\n try:\n if not (is_flax_available() and is_transformers_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ..utils.dummy_flax_and_transformers_objects import *\n else:\n from .controlnet import FlaxStableDiffusionControlNetPipeline\n from .stable_diffusion import (\n FlaxStableDiffusionImg2ImgPipeline,\n FlaxStableDiffusionInpaintPipeline,\n FlaxStableDiffusionPipeline,\n )\n from .stable_diffusion_xl import (\n FlaxStableDiffusionXLPipeline,\n )\n\n try:\n if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ..utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403\n\n else:\n from .deprecated import (\n MidiProcessor,\n SpectrogramDiffusionPipeline,\n )\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/amused/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import (\n AmusedImg2ImgPipeline,\n AmusedInpaintPipeline,\n AmusedPipeline,\n )\n\n _dummy_objects.update(\n {\n \"AmusedPipeline\": AmusedPipeline,\n \"AmusedImg2ImgPipeline\": AmusedImg2ImgPipeline,\n \"AmusedInpaintPipeline\": AmusedInpaintPipeline,\n }\n )\nelse:\n _import_structure[\"pipeline_amused\"] = [\"AmusedPipeline\"]\n _import_structure[\"pipeline_amused_img2img\"] = [\"AmusedImg2ImgPipeline\"]\n _import_structure[\"pipeline_amused_inpaint\"] = [\"AmusedInpaintPipeline\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import (\n AmusedPipeline,\n )\n else:\n from .pipeline_amused import AmusedPipeline\n from .pipeline_amused_img2img import AmusedImg2ImgPipeline\n from .pipeline_amused_inpaint import AmusedInpaintPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/amused/pipeline_amused.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport torch\nfrom transformers import CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom ...image_processor import VaeImageProcessor\nfrom ...models import UVit2DModel, VQModel\nfrom ...schedulers import AmusedScheduler\nfrom ...utils import replace_example_docstring\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import AmusedPipeline\n\n >>> pipe = AmusedPipeline.from_pretrained(\"amused/amused-512\", variant=\"fp16\", torch_dtype=torch.float16)\n >>> pipe = pipe.to(\"cuda\")\n\n >>> prompt = \"a photo of an astronaut riding a horse on mars\"\n >>> image = pipe(prompt).images[0]\n ```\n\"\"\"\n\n\nclass AmusedPipeline(DiffusionPipeline):\n image_processor: VaeImageProcessor\n vqvae: VQModel\n tokenizer: CLIPTokenizer\n text_encoder: CLIPTextModelWithProjection\n transformer: UVit2DModel\n scheduler: AmusedScheduler\n\n model_cpu_offload_seq = \"text_encoder->transformer->vqvae\"\n\n def __init__(\n self,\n vqvae: VQModel,\n tokenizer: CLIPTokenizer,\n text_encoder: CLIPTextModelWithProjection,\n transformer: UVit2DModel,\n scheduler: AmusedScheduler,\n ):\n super().__init__()\n\n self.register_modules(\n vqvae=vqvae,\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n transformer=transformer,\n scheduler=scheduler,\n )\n self.vae_scale_factor = 2 ** (len(self.vqvae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_normalize=False)\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Optional[Union[List[str], str]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 12,\n guidance_scale: float = 10.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.IntTensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n negative_encoder_hidden_states: Optional[torch.Tensor] = None,\n output_type=\"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n micro_conditioning_aesthetic_score: int = 6,\n micro_conditioning_crop_coord: Tuple[int, int] = (0, 0),\n temperature: Union[int, Tuple[int, int], List[int]] = (2, 0),\n ):\n \"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to `self.transformer.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 16):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 10.0):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.IntTensor`, *optional*):\n Pre-generated tokens representing latent vectors in `self.vqvae`, to be used as inputs for image\n gneration. If not provided, the starting latents will be completely masked.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument. A single vector from the\n pooled and projected final hidden states.\n encoder_hidden_states (`torch.Tensor`, *optional*):\n Pre-generated penultimate hidden states from the text encoder providing additional text conditioning.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n negative_encoder_hidden_states (`torch.Tensor`, *optional*):\n Analogous to `encoder_hidden_states` for the positive prompt.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n micro_conditioning_aesthetic_score (`int`, *optional*, defaults to 6):\n The targeted aesthetic score according to the laion aesthetic classifier. See\n https://laion.ai/blog/laion-aesthetics/ and the micro-conditioning section of\n https://arxiv.org/abs/2307.01952.\n micro_conditioning_crop_coord (`Tuple[int]`, *optional*, defaults to (0, 0)):\n The targeted height, width crop coordinates. See the micro-conditioning section of\n https://arxiv.org/abs/2307.01952.\n temperature (`Union[int, Tuple[int, int], List[int]]`, *optional*, defaults to (2, 0)):\n Configures the temperature scheduler on `self.scheduler` see `AmusedScheduler#set_timesteps`.\n\n Examples:\n\n Returns:\n [`~pipelines.pipeline_utils.ImagePipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.pipeline_utils.ImagePipelineOutput`] is returned, otherwise a\n `tuple` is returned where the first element is a list with the generated images.\n \"\"\"\n if (prompt_embeds is not None and encoder_hidden_states is None) or (\n prompt_embeds is None and encoder_hidden_states is not None\n ):\n raise ValueError(\"pass either both `prompt_embeds` and `encoder_hidden_states` or neither\")\n\n if (negative_prompt_embeds is not None and negative_encoder_hidden_states is None) or (\n negative_prompt_embeds is None and negative_encoder_hidden_states is not None\n ):\n raise ValueError(\n \"pass either both `negatve_prompt_embeds` and `negative_encoder_hidden_states` or neither\"\n )\n\n if (prompt is None and prompt_embeds is None) or (prompt is not None and prompt_embeds is not None):\n raise ValueError(\"pass only one of `prompt` or `prompt_embeds`\")\n\n if isinstance(prompt, str):\n prompt = [prompt]\n\n if prompt is not None:\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n batch_size = batch_size * num_images_per_prompt\n\n if height is None:\n height = self.transformer.config.sample_size * self.vae_scale_factor\n\n if width is None:\n width = self.transformer.config.sample_size * self.vae_scale_factor\n\n if prompt_embeds is None:\n input_ids = self.tokenizer(\n prompt,\n return_tensors=\"pt\",\n padding=\"max_length\",\n truncation=True,\n max_length=self.tokenizer.model_max_length,\n ).input_ids.to(self._execution_device)\n\n outputs = self.text_encoder(input_ids, return_dict=True, output_hidden_states=True)\n prompt_embeds = outputs.text_embeds\n encoder_hidden_states = outputs.hidden_states[-2]\n\n prompt_embeds = prompt_embeds.repeat(num_images_per_prompt, 1)\n encoder_hidden_states = encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)\n\n if guidance_scale > 1.0:\n if negative_prompt_embeds is None:\n if negative_prompt is None:\n negative_prompt = [\"\"] * len(prompt)\n\n if isinstance(negative_prompt, str):\n negative_prompt = [negative_prompt]\n\n input_ids = self.tokenizer(\n negative_prompt,\n return_tensors=\"pt\",\n padding=\"max_length\",\n truncation=True,\n max_length=self.tokenizer.model_max_length,\n ).input_ids.to(self._execution_device)\n\n outputs = self.text_encoder(input_ids, return_dict=True, output_hidden_states=True)\n negative_prompt_embeds = outputs.text_embeds\n negative_encoder_hidden_states = outputs.hidden_states[-2]\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(num_images_per_prompt, 1)\n negative_encoder_hidden_states = negative_encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)\n\n prompt_embeds = torch.concat([negative_prompt_embeds, prompt_embeds])\n encoder_hidden_states = torch.concat([negative_encoder_hidden_states, encoder_hidden_states])\n\n # Note that the micro conditionings _do_ flip the order of width, height for the original size\n # and the crop coordinates. This is how it was done in the original code base\n micro_conds = torch.tensor(\n [\n width,\n height,\n micro_conditioning_crop_coord[0],\n micro_conditioning_crop_coord[1],\n micro_conditioning_aesthetic_score,\n ],\n device=self._execution_device,\n dtype=encoder_hidden_states.dtype,\n )\n micro_conds = micro_conds.unsqueeze(0)\n micro_conds = micro_conds.expand(2 * batch_size if guidance_scale > 1.0 else batch_size, -1)\n\n shape = (batch_size, height // self.vae_scale_factor, width // self.vae_scale_factor)\n\n if latents is None:\n latents = torch.full(\n shape, self.scheduler.config.mask_token_id, dtype=torch.long, device=self._execution_device\n )\n\n self.scheduler.set_timesteps(num_inference_steps, temperature, self._execution_device)\n\n num_warmup_steps = len(self.scheduler.timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, timestep in enumerate(self.scheduler.timesteps):\n if guidance_scale > 1.0:\n model_input = torch.cat([latents] * 2)\n else:\n model_input = latents\n\n model_output = self.transformer(\n model_input,\n micro_conds=micro_conds,\n pooled_text_emb=prompt_embeds,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n\n if guidance_scale > 1.0:\n uncond_logits, cond_logits = model_output.chunk(2)\n model_output = uncond_logits + guidance_scale * (cond_logits - uncond_logits)\n\n latents = self.scheduler.step(\n model_output=model_output,\n timestep=timestep,\n sample=latents,\n generator=generator,\n ).prev_sample\n\n if i == len(self.scheduler.timesteps) - 1 or (\n (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0\n ):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, timestep, latents)\n\n if output_type == \"latent\":\n output = latents\n else:\n needs_upcasting = self.vqvae.dtype == torch.float16 and self.vqvae.config.force_upcast\n\n if needs_upcasting:\n self.vqvae.float()\n\n output = self.vqvae.decode(\n latents,\n force_not_quantize=True,\n shape=(\n batch_size,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n self.vqvae.config.latent_channels,\n ),\n ).sample.clip(0, 1)\n output = self.image_processor.postprocess(output, output_type)\n\n if needs_upcasting:\n self.vqvae.half()\n\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (output,)\n\n return ImagePipelineOutput(output)\n" }, { "path": "diffusers/src/diffusers/pipelines/amused/pipeline_amused_img2img.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport torch\nfrom transformers import CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...models import UVit2DModel, VQModel\nfrom ...schedulers import AmusedScheduler\nfrom ...utils import replace_example_docstring\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import AmusedImg2ImgPipeline\n >>> from diffusers.utils import load_image\n\n >>> pipe = AmusedImg2ImgPipeline.from_pretrained(\n ... \"amused/amused-512\", variant=\"fp16\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> prompt = \"winter mountains\"\n >>> input_image = (\n ... load_image(\n ... \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains.jpg\"\n ... )\n ... .resize((512, 512))\n ... .convert(\"RGB\")\n ... )\n >>> image = pipe(prompt, input_image).images[0]\n ```\n\"\"\"\n\n\nclass AmusedImg2ImgPipeline(DiffusionPipeline):\n image_processor: VaeImageProcessor\n vqvae: VQModel\n tokenizer: CLIPTokenizer\n text_encoder: CLIPTextModelWithProjection\n transformer: UVit2DModel\n scheduler: AmusedScheduler\n\n model_cpu_offload_seq = \"text_encoder->transformer->vqvae\"\n\n # TODO - when calling self.vqvae.quantize, it uses self.vqvae.quantize.embedding.weight before\n # the forward method of self.vqvae.quantize, so the hook doesn't get called to move the parameter\n # off the meta device. There should be a way to fix this instead of just not offloading it\n _exclude_from_cpu_offload = [\"vqvae\"]\n\n def __init__(\n self,\n vqvae: VQModel,\n tokenizer: CLIPTokenizer,\n text_encoder: CLIPTextModelWithProjection,\n transformer: UVit2DModel,\n scheduler: AmusedScheduler,\n ):\n super().__init__()\n\n self.register_modules(\n vqvae=vqvae,\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n transformer=transformer,\n scheduler=scheduler,\n )\n self.vae_scale_factor = 2 ** (len(self.vqvae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_normalize=False)\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Optional[Union[List[str], str]] = None,\n image: PipelineImageInput = None,\n strength: float = 0.5,\n num_inference_steps: int = 12,\n guidance_scale: float = 10.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n generator: Optional[torch.Generator] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n negative_encoder_hidden_states: Optional[torch.Tensor] = None,\n output_type=\"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n micro_conditioning_aesthetic_score: int = 6,\n micro_conditioning_crop_coord: Tuple[int, int] = (0, 0),\n temperature: Union[int, Tuple[int, int], List[int]] = (2, 0),\n ):\n \"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both\n numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list\n or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a\n list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image\n latents as `image`, but if passing latents directly it is not encoded again.\n strength (`float`, *optional*, defaults to 0.5):\n Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a\n starting point and more noise is added the higher the `strength`. The number of denoising steps depends\n on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising\n process runs for the full number of iterations specified in `num_inference_steps`. A value of 1\n essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 12):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 10.0):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument. A single vector from the\n pooled and projected final hidden states.\n encoder_hidden_states (`torch.Tensor`, *optional*):\n Pre-generated penultimate hidden states from the text encoder providing additional text conditioning.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n negative_encoder_hidden_states (`torch.Tensor`, *optional*):\n Analogous to `encoder_hidden_states` for the positive prompt.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n micro_conditioning_aesthetic_score (`int`, *optional*, defaults to 6):\n The targeted aesthetic score according to the laion aesthetic classifier. See\n https://laion.ai/blog/laion-aesthetics/ and the micro-conditioning section of\n https://arxiv.org/abs/2307.01952.\n micro_conditioning_crop_coord (`Tuple[int]`, *optional*, defaults to (0, 0)):\n The targeted height, width crop coordinates. See the micro-conditioning section of\n https://arxiv.org/abs/2307.01952.\n temperature (`Union[int, Tuple[int, int], List[int]]`, *optional*, defaults to (2, 0)):\n Configures the temperature scheduler on `self.scheduler` see `AmusedScheduler#set_timesteps`.\n\n Examples:\n\n Returns:\n [`~pipelines.pipeline_utils.ImagePipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.pipeline_utils.ImagePipelineOutput`] is returned, otherwise a\n `tuple` is returned where the first element is a list with the generated images.\n \"\"\"\n\n if (prompt_embeds is not None and encoder_hidden_states is None) or (\n prompt_embeds is None and encoder_hidden_states is not None\n ):\n raise ValueError(\"pass either both `prompt_embeds` and `encoder_hidden_states` or neither\")\n\n if (negative_prompt_embeds is not None and negative_encoder_hidden_states is None) or (\n negative_prompt_embeds is None and negative_encoder_hidden_states is not None\n ):\n raise ValueError(\n \"pass either both `negative_prompt_embeds` and `negative_encoder_hidden_states` or neither\"\n )\n\n if (prompt is None and prompt_embeds is None) or (prompt is not None and prompt_embeds is not None):\n raise ValueError(\"pass only one of `prompt` or `prompt_embeds`\")\n\n if isinstance(prompt, str):\n prompt = [prompt]\n\n if prompt is not None:\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n batch_size = batch_size * num_images_per_prompt\n\n if prompt_embeds is None:\n input_ids = self.tokenizer(\n prompt,\n return_tensors=\"pt\",\n padding=\"max_length\",\n truncation=True,\n max_length=self.tokenizer.model_max_length,\n ).input_ids.to(self._execution_device)\n\n outputs = self.text_encoder(input_ids, return_dict=True, output_hidden_states=True)\n prompt_embeds = outputs.text_embeds\n encoder_hidden_states = outputs.hidden_states[-2]\n\n prompt_embeds = prompt_embeds.repeat(num_images_per_prompt, 1)\n encoder_hidden_states = encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)\n\n if guidance_scale > 1.0:\n if negative_prompt_embeds is None:\n if negative_prompt is None:\n negative_prompt = [\"\"] * len(prompt)\n\n if isinstance(negative_prompt, str):\n negative_prompt = [negative_prompt]\n\n input_ids = self.tokenizer(\n negative_prompt,\n return_tensors=\"pt\",\n padding=\"max_length\",\n truncation=True,\n max_length=self.tokenizer.model_max_length,\n ).input_ids.to(self._execution_device)\n\n outputs = self.text_encoder(input_ids, return_dict=True, output_hidden_states=True)\n negative_prompt_embeds = outputs.text_embeds\n negative_encoder_hidden_states = outputs.hidden_states[-2]\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(num_images_per_prompt, 1)\n negative_encoder_hidden_states = negative_encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)\n\n prompt_embeds = torch.concat([negative_prompt_embeds, prompt_embeds])\n encoder_hidden_states = torch.concat([negative_encoder_hidden_states, encoder_hidden_states])\n\n image = self.image_processor.preprocess(image)\n\n height, width = image.shape[-2:]\n\n # Note that the micro conditionings _do_ flip the order of width, height for the original size\n # and the crop coordinates. This is how it was done in the original code base\n micro_conds = torch.tensor(\n [\n width,\n height,\n micro_conditioning_crop_coord[0],\n micro_conditioning_crop_coord[1],\n micro_conditioning_aesthetic_score,\n ],\n device=self._execution_device,\n dtype=encoder_hidden_states.dtype,\n )\n\n micro_conds = micro_conds.unsqueeze(0)\n micro_conds = micro_conds.expand(2 * batch_size if guidance_scale > 1.0 else batch_size, -1)\n\n self.scheduler.set_timesteps(num_inference_steps, temperature, self._execution_device)\n num_inference_steps = int(len(self.scheduler.timesteps) * strength)\n start_timestep_idx = len(self.scheduler.timesteps) - num_inference_steps\n\n needs_upcasting = self.vqvae.dtype == torch.float16 and self.vqvae.config.force_upcast\n\n if needs_upcasting:\n self.vqvae.float()\n\n latents = self.vqvae.encode(image.to(dtype=self.vqvae.dtype, device=self._execution_device)).latents\n latents_bsz, channels, latents_height, latents_width = latents.shape\n latents = self.vqvae.quantize(latents)[2][2].reshape(latents_bsz, latents_height, latents_width)\n latents = self.scheduler.add_noise(\n latents, self.scheduler.timesteps[start_timestep_idx - 1], generator=generator\n )\n latents = latents.repeat(num_images_per_prompt, 1, 1)\n\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i in range(start_timestep_idx, len(self.scheduler.timesteps)):\n timestep = self.scheduler.timesteps[i]\n\n if guidance_scale > 1.0:\n model_input = torch.cat([latents] * 2)\n else:\n model_input = latents\n\n model_output = self.transformer(\n model_input,\n micro_conds=micro_conds,\n pooled_text_emb=prompt_embeds,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n\n if guidance_scale > 1.0:\n uncond_logits, cond_logits = model_output.chunk(2)\n model_output = uncond_logits + guidance_scale * (cond_logits - uncond_logits)\n\n latents = self.scheduler.step(\n model_output=model_output,\n timestep=timestep,\n sample=latents,\n generator=generator,\n ).prev_sample\n\n if i == len(self.scheduler.timesteps) - 1 or ((i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, timestep, latents)\n\n if output_type == \"latent\":\n output = latents\n else:\n output = self.vqvae.decode(\n latents,\n force_not_quantize=True,\n shape=(\n batch_size,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n self.vqvae.config.latent_channels,\n ),\n ).sample.clip(0, 1)\n output = self.image_processor.postprocess(output, output_type)\n\n if needs_upcasting:\n self.vqvae.half()\n\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (output,)\n\n return ImagePipelineOutput(output)\n" }, { "path": "diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport torch\nfrom transformers import CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...models import UVit2DModel, VQModel\nfrom ...schedulers import AmusedScheduler\nfrom ...utils import replace_example_docstring\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import AmusedInpaintPipeline\n >>> from diffusers.utils import load_image\n\n >>> pipe = AmusedInpaintPipeline.from_pretrained(\n ... \"amused/amused-512\", variant=\"fp16\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> prompt = \"fall mountains\"\n >>> input_image = (\n ... load_image(\n ... \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1.jpg\"\n ... )\n ... .resize((512, 512))\n ... .convert(\"RGB\")\n ... )\n >>> mask = (\n ... load_image(\n ... \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1_mask.png\"\n ... )\n ... .resize((512, 512))\n ... .convert(\"L\")\n ... )\n >>> pipe(prompt, input_image, mask).images[0].save(\"out.png\")\n ```\n\"\"\"\n\n\nclass AmusedInpaintPipeline(DiffusionPipeline):\n image_processor: VaeImageProcessor\n vqvae: VQModel\n tokenizer: CLIPTokenizer\n text_encoder: CLIPTextModelWithProjection\n transformer: UVit2DModel\n scheduler: AmusedScheduler\n\n model_cpu_offload_seq = \"text_encoder->transformer->vqvae\"\n\n # TODO - when calling self.vqvae.quantize, it uses self.vqvae.quantize.embedding.weight before\n # the forward method of self.vqvae.quantize, so the hook doesn't get called to move the parameter\n # off the meta device. There should be a way to fix this instead of just not offloading it\n _exclude_from_cpu_offload = [\"vqvae\"]\n\n def __init__(\n self,\n vqvae: VQModel,\n tokenizer: CLIPTokenizer,\n text_encoder: CLIPTextModelWithProjection,\n transformer: UVit2DModel,\n scheduler: AmusedScheduler,\n ):\n super().__init__()\n\n self.register_modules(\n vqvae=vqvae,\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n transformer=transformer,\n scheduler=scheduler,\n )\n self.vae_scale_factor = 2 ** (len(self.vqvae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_normalize=False)\n self.mask_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor,\n do_normalize=False,\n do_binarize=True,\n do_convert_grayscale=True,\n do_resize=True,\n )\n self.scheduler.register_to_config(masking_schedule=\"linear\")\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Optional[Union[List[str], str]] = None,\n image: PipelineImageInput = None,\n mask_image: PipelineImageInput = None,\n strength: float = 1.0,\n num_inference_steps: int = 12,\n guidance_scale: float = 10.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n generator: Optional[torch.Generator] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n negative_encoder_hidden_states: Optional[torch.Tensor] = None,\n output_type=\"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n micro_conditioning_aesthetic_score: int = 6,\n micro_conditioning_crop_coord: Tuple[int, int] = (0, 0),\n temperature: Union[int, Tuple[int, int], List[int]] = (2, 0),\n ):\n \"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both\n numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list\n or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a\n list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image\n latents as `image`, but if passing latents directly it is not encoded again.\n mask_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image`, numpy array or tensor representing an image batch to mask `image`. White pixels in the mask\n are repainted while black pixels are preserved. If `mask_image` is a PIL image, it is converted to a\n single channel (luminance) before use. If it's a numpy array or pytorch tensor, it should contain one\n color channel (L) instead of 3, so the expected shape for pytorch tensor would be `(B, 1, H, W)`, `(B,\n H, W)`, `(1, H, W)`, `(H, W)`. And for numpy array would be for `(B, H, W, 1)`, `(B, H, W)`, `(H, W,\n 1)`, or `(H, W)`.\n strength (`float`, *optional*, defaults to 1.0):\n Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a\n starting point and more noise is added the higher the `strength`. The number of denoising steps depends\n on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising\n process runs for the full number of iterations specified in `num_inference_steps`. A value of 1\n essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 16):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 10.0):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument. A single vector from the\n pooled and projected final hidden states.\n encoder_hidden_states (`torch.Tensor`, *optional*):\n Pre-generated penultimate hidden states from the text encoder providing additional text conditioning.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n negative_encoder_hidden_states (`torch.Tensor`, *optional*):\n Analogous to `encoder_hidden_states` for the positive prompt.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n micro_conditioning_aesthetic_score (`int`, *optional*, defaults to 6):\n The targeted aesthetic score according to the laion aesthetic classifier. See\n https://laion.ai/blog/laion-aesthetics/ and the micro-conditioning section of\n https://arxiv.org/abs/2307.01952.\n micro_conditioning_crop_coord (`Tuple[int]`, *optional*, defaults to (0, 0)):\n The targeted height, width crop coordinates. See the micro-conditioning section of\n https://arxiv.org/abs/2307.01952.\n temperature (`Union[int, Tuple[int, int], List[int]]`, *optional*, defaults to (2, 0)):\n Configures the temperature scheduler on `self.scheduler` see `AmusedScheduler#set_timesteps`.\n\n Examples:\n\n Returns:\n [`~pipelines.pipeline_utils.ImagePipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.pipeline_utils.ImagePipelineOutput`] is returned, otherwise a\n `tuple` is returned where the first element is a list with the generated images.\n \"\"\"\n\n if (prompt_embeds is not None and encoder_hidden_states is None) or (\n prompt_embeds is None and encoder_hidden_states is not None\n ):\n raise ValueError(\"pass either both `prompt_embeds` and `encoder_hidden_states` or neither\")\n\n if (negative_prompt_embeds is not None and negative_encoder_hidden_states is None) or (\n negative_prompt_embeds is None and negative_encoder_hidden_states is not None\n ):\n raise ValueError(\n \"pass either both `negatve_prompt_embeds` and `negative_encoder_hidden_states` or neither\"\n )\n\n if (prompt is None and prompt_embeds is None) or (prompt is not None and prompt_embeds is not None):\n raise ValueError(\"pass only one of `prompt` or `prompt_embeds`\")\n\n if isinstance(prompt, str):\n prompt = [prompt]\n\n if prompt is not None:\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n batch_size = batch_size * num_images_per_prompt\n\n if prompt_embeds is None:\n input_ids = self.tokenizer(\n prompt,\n return_tensors=\"pt\",\n padding=\"max_length\",\n truncation=True,\n max_length=self.tokenizer.model_max_length,\n ).input_ids.to(self._execution_device)\n\n outputs = self.text_encoder(input_ids, return_dict=True, output_hidden_states=True)\n prompt_embeds = outputs.text_embeds\n encoder_hidden_states = outputs.hidden_states[-2]\n\n prompt_embeds = prompt_embeds.repeat(num_images_per_prompt, 1)\n encoder_hidden_states = encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)\n\n if guidance_scale > 1.0:\n if negative_prompt_embeds is None:\n if negative_prompt is None:\n negative_prompt = [\"\"] * len(prompt)\n\n if isinstance(negative_prompt, str):\n negative_prompt = [negative_prompt]\n\n input_ids = self.tokenizer(\n negative_prompt,\n return_tensors=\"pt\",\n padding=\"max_length\",\n truncation=True,\n max_length=self.tokenizer.model_max_length,\n ).input_ids.to(self._execution_device)\n\n outputs = self.text_encoder(input_ids, return_dict=True, output_hidden_states=True)\n negative_prompt_embeds = outputs.text_embeds\n negative_encoder_hidden_states = outputs.hidden_states[-2]\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(num_images_per_prompt, 1)\n negative_encoder_hidden_states = negative_encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)\n\n prompt_embeds = torch.concat([negative_prompt_embeds, prompt_embeds])\n encoder_hidden_states = torch.concat([negative_encoder_hidden_states, encoder_hidden_states])\n\n image = self.image_processor.preprocess(image)\n\n height, width = image.shape[-2:]\n\n # Note that the micro conditionings _do_ flip the order of width, height for the original size\n # and the crop coordinates. This is how it was done in the original code base\n micro_conds = torch.tensor(\n [\n width,\n height,\n micro_conditioning_crop_coord[0],\n micro_conditioning_crop_coord[1],\n micro_conditioning_aesthetic_score,\n ],\n device=self._execution_device,\n dtype=encoder_hidden_states.dtype,\n )\n\n micro_conds = micro_conds.unsqueeze(0)\n micro_conds = micro_conds.expand(2 * batch_size if guidance_scale > 1.0 else batch_size, -1)\n\n self.scheduler.set_timesteps(num_inference_steps, temperature, self._execution_device)\n num_inference_steps = int(len(self.scheduler.timesteps) * strength)\n start_timestep_idx = len(self.scheduler.timesteps) - num_inference_steps\n\n needs_upcasting = self.vqvae.dtype == torch.float16 and self.vqvae.config.force_upcast\n\n if needs_upcasting:\n self.vqvae.float()\n\n latents = self.vqvae.encode(image.to(dtype=self.vqvae.dtype, device=self._execution_device)).latents\n latents_bsz, channels, latents_height, latents_width = latents.shape\n latents = self.vqvae.quantize(latents)[2][2].reshape(latents_bsz, latents_height, latents_width)\n\n mask = self.mask_processor.preprocess(\n mask_image, height // self.vae_scale_factor, width // self.vae_scale_factor\n )\n mask = mask.reshape(mask.shape[0], latents_height, latents_width).bool().to(latents.device)\n latents[mask] = self.scheduler.config.mask_token_id\n\n starting_mask_ratio = mask.sum() / latents.numel()\n\n latents = latents.repeat(num_images_per_prompt, 1, 1)\n\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i in range(start_timestep_idx, len(self.scheduler.timesteps)):\n timestep = self.scheduler.timesteps[i]\n\n if guidance_scale > 1.0:\n model_input = torch.cat([latents] * 2)\n else:\n model_input = latents\n\n model_output = self.transformer(\n model_input,\n micro_conds=micro_conds,\n pooled_text_emb=prompt_embeds,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n\n if guidance_scale > 1.0:\n uncond_logits, cond_logits = model_output.chunk(2)\n model_output = uncond_logits + guidance_scale * (cond_logits - uncond_logits)\n\n latents = self.scheduler.step(\n model_output=model_output,\n timestep=timestep,\n sample=latents,\n generator=generator,\n starting_mask_ratio=starting_mask_ratio,\n ).prev_sample\n\n if i == len(self.scheduler.timesteps) - 1 or ((i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, timestep, latents)\n\n if output_type == \"latent\":\n output = latents\n else:\n output = self.vqvae.decode(\n latents,\n force_not_quantize=True,\n shape=(\n batch_size,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n self.vqvae.config.latent_channels,\n ),\n ).sample.clip(0, 1)\n output = self.image_processor.postprocess(output, output_type)\n\n if needs_upcasting:\n self.vqvae.half()\n\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (output,)\n\n return ImagePipelineOutput(output)\n" }, { "path": "diffusers/src/diffusers/pipelines/animatediff/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {\"pipeline_output\": [\"AnimateDiffPipelineOutput\"]}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_animatediff\"] = [\"AnimateDiffPipeline\"]\n _import_structure[\"pipeline_animatediff_sdxl\"] = [\"AnimateDiffSDXLPipeline\"]\n _import_structure[\"pipeline_animatediff_video2video\"] = [\"AnimateDiffVideoToVideoPipeline\"]\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n\n else:\n from .pipeline_animatediff import AnimateDiffPipeline\n from .pipeline_animatediff_sdxl import AnimateDiffSDXLPipeline\n from .pipeline_animatediff_video2video import AnimateDiffVideoToVideoPipeline\n from .pipeline_output import AnimateDiffPipelineOutput\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...image_processor import PipelineImageInput\nfrom ...loaders import IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel, UNetMotionModel\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...models.unets.unet_motion_model import MotionAdapter\nfrom ...schedulers import (\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n)\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ...video_processor import VideoProcessor\nfrom ..free_init_utils import FreeInitMixin\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom .pipeline_output import AnimateDiffPipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler\n >>> from diffusers.utils import export_to_gif\n\n >>> adapter = MotionAdapter.from_pretrained(\"guoyww/animatediff-motion-adapter-v1-5-2\")\n >>> pipe = AnimateDiffPipeline.from_pretrained(\"frankjoshua/toonyou_beta6\", motion_adapter=adapter)\n >>> pipe.scheduler = DDIMScheduler(beta_schedule=\"linear\", steps_offset=1, clip_sample=False)\n >>> output = pipe(prompt=\"A corgi walking in the park\")\n >>> frames = output.frames[0]\n >>> export_to_gif(frames, \"animation.gif\")\n ```\n\"\"\"\n\n\nclass AnimateDiffPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n TextualInversionLoaderMixin,\n IPAdapterMixin,\n LoraLoaderMixin,\n FreeInitMixin,\n):\n r\"\"\"\n Pipeline for text-to-video generation.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer (`CLIPTokenizer`):\n A [`~transformers.CLIPTokenizer`] to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A [`UNet2DConditionModel`] used to create a UNetMotionModel to denoise the encoded video latents.\n motion_adapter ([`MotionAdapter`]):\n A [`MotionAdapter`] to be used in combination with `unet` to denoise the encoded video latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->image_encoder->unet->vae\"\n _optional_components = [\"feature_extractor\", \"image_encoder\", \"motion_adapter\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: Union[UNet2DConditionModel, UNetMotionModel],\n motion_adapter: MotionAdapter,\n scheduler: Union[\n DDIMScheduler,\n PNDMScheduler,\n LMSDiscreteScheduler,\n EulerDiscreteScheduler,\n EulerAncestralDiscreteScheduler,\n DPMSolverMultistepScheduler,\n ],\n feature_extractor: CLIPImageProcessor = None,\n image_encoder: CLIPVisionModelWithProjection = None,\n ):\n super().__init__()\n if isinstance(unet, UNet2DConditionModel):\n unet = UNetMotionModel.from_unet2d(unet, motion_adapter)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n motion_adapter=motion_adapter,\n scheduler=scheduler,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.video_processor = VideoProcessor(do_resize=False, vae_scale_factor=self.vae_scale_factor)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with num_images_per_prompt -> num_videos_per_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n # Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents\n def decode_latents(self, latents):\n latents = 1 / self.vae.config.scaling_factor * latents\n\n batch_size, channels, num_frames, height, width = latents.shape\n latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)\n\n image = self.vae.decode(latents).sample\n video = image[None, :].reshape((batch_size, num_frames, -1) + image.shape[2:]).permute(0, 2, 1, 3, 4)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n video = video.float()\n return video\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n if ip_adapter_image_embeds is not None:\n if not isinstance(ip_adapter_image_embeds, list):\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}\"\n )\n elif ip_adapter_image_embeds[0].ndim not in [3, 4]:\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D\"\n )\n\n # Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_synth.TextToVideoSDPipeline.prepare_latents\n def prepare_latents(\n self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None\n ):\n shape = (\n batch_size,\n num_channels_latents,\n num_frames,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n num_frames: Optional[int] = 16,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_videos_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated video.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated video.\n num_frames (`int`, *optional*, defaults to 16):\n The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds\n amounts to 2 seconds of video.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality videos at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`. Latents should be of shape\n `(batch_size, num_channel, num_frames, height, width)`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*):\n Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of\n IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should\n contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not\n provided, embeddings are computed from the `ip_adapter_image` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated video. Choose between `torch.Tensor`, `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] instead\n of a plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.animatediff.pipeline_output.AnimateDiffPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.animatediff.pipeline_output.AnimateDiffPipelineOutput`] is\n returned, otherwise a `tuple` is returned where the first element is a list with the generated frames.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n num_videos_per_prompt = 1\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n ip_adapter_image,\n ip_adapter_image_embeds,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_videos_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=self.clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_videos_per_prompt,\n self.do_classifier_free_guidance,\n )\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_videos_per_prompt,\n num_channels_latents,\n num_frames,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Add image embeds for IP-Adapter\n added_cond_kwargs = (\n {\"image_embeds\": image_embeds}\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None\n else None\n )\n\n num_free_init_iters = self._free_init_num_iters if self.free_init_enabled else 1\n for free_init_iter in range(num_free_init_iters):\n if self.free_init_enabled:\n latents, timesteps = self._apply_free_init(\n latents, free_init_iter, num_inference_steps, device, latents.dtype, generator\n )\n\n self._num_timesteps = len(timesteps)\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n\n # 8. Denoising loop\n with self.progress_bar(total=self._num_timesteps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n ).sample\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, latents)\n\n # 9. Post processing\n if output_type == \"latent\":\n video = latents\n else:\n video_tensor = self.decode_latents(latents)\n video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)\n\n # 10. Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (video,)\n\n return AnimateDiffPipelineOutput(frames=video)\n" }, { "path": "diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport torch\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModel,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n)\n\nfrom ...image_processor import PipelineImageInput\nfrom ...loaders import (\n FromSingleFileMixin,\n IPAdapterMixin,\n StableDiffusionXLLoraLoaderMixin,\n TextualInversionLoaderMixin,\n)\nfrom ...models import AutoencoderKL, ImageProjection, MotionAdapter, UNet2DConditionModel, UNetMotionModel\nfrom ...models.attention_processor import (\n AttnProcessor2_0,\n FusedAttnProcessor2_0,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n XFormersAttnProcessor,\n)\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import (\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n)\nfrom ...utils import (\n USE_PEFT_BACKEND,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ...video_processor import VideoProcessor\nfrom ..free_init_utils import FreeInitMixin\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom .pipeline_output import AnimateDiffPipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers.models import MotionAdapter\n >>> from diffusers import AnimateDiffSDXLPipeline, DDIMScheduler\n >>> from diffusers.utils import export_to_gif\n\n >>> adapter = MotionAdapter.from_pretrained(\n ... \"a-r-r-o-w/animatediff-motion-adapter-sdxl-beta\", torch_dtype=torch.float16\n ... )\n\n >>> model_id = \"stabilityai/stable-diffusion-xl-base-1.0\"\n >>> scheduler = DDIMScheduler.from_pretrained(\n ... model_id,\n ... subfolder=\"scheduler\",\n ... clip_sample=False,\n ... timestep_spacing=\"linspace\",\n ... beta_schedule=\"linear\",\n ... steps_offset=1,\n ... )\n >>> pipe = AnimateDiffSDXLPipeline.from_pretrained(\n ... model_id,\n ... motion_adapter=adapter,\n ... scheduler=scheduler,\n ... torch_dtype=torch.float16,\n ... variant=\"fp16\",\n ... ).to(\"cuda\")\n\n >>> # enable memory savings\n >>> pipe.enable_vae_slicing()\n >>> pipe.enable_vae_tiling()\n\n >>> output = pipe(\n ... prompt=\"a panda surfing in the ocean, realistic, high quality\",\n ... negative_prompt=\"low quality, worst quality\",\n ... num_inference_steps=20,\n ... guidance_scale=8,\n ... width=1024,\n ... height=1024,\n ... num_frames=16,\n ... )\n\n >>> frames = output.frames[0]\n >>> export_to_gif(frames, \"animation.gif\")\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass AnimateDiffSDXLPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n FromSingleFileMixin,\n StableDiffusionXLLoraLoaderMixin,\n TextualInversionLoaderMixin,\n IPAdapterMixin,\n FreeInitMixin,\n):\n r\"\"\"\n Pipeline for text-to-video generation using Stable Diffusion XL.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion XL uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n text_encoder_2 ([` CLIPTextModelWithProjection`]):\n Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),\n specifically the\n [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)\n variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n tokenizer_2 (`CLIPTokenizer`):\n Second Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]):\n Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `\"True\"`):\n Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of\n `stabilityai/stable-diffusion-xl-base-1-0`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->text_encoder_2->image_encoder->unet->vae\"\n _optional_components = [\n \"tokenizer\",\n \"tokenizer_2\",\n \"text_encoder\",\n \"text_encoder_2\",\n \"image_encoder\",\n \"feature_extractor\",\n ]\n _callback_tensor_inputs = [\n \"latents\",\n \"prompt_embeds\",\n \"negative_prompt_embeds\",\n \"add_text_embeds\",\n \"add_time_ids\",\n \"negative_pooled_prompt_embeds\",\n \"negative_add_time_ids\",\n ]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n text_encoder_2: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n tokenizer_2: CLIPTokenizer,\n unet: Union[UNet2DConditionModel, UNetMotionModel],\n motion_adapter: MotionAdapter,\n scheduler: Union[\n DDIMScheduler,\n PNDMScheduler,\n LMSDiscreteScheduler,\n EulerDiscreteScheduler,\n EulerAncestralDiscreteScheduler,\n DPMSolverMultistepScheduler,\n ],\n image_encoder: CLIPVisionModelWithProjection = None,\n feature_extractor: CLIPImageProcessor = None,\n force_zeros_for_empty_prompt: bool = True,\n ):\n super().__init__()\n\n if isinstance(unet, UNet2DConditionModel):\n unet = UNetMotionModel.from_unet2d(unet, motion_adapter)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n unet=unet,\n motion_adapter=motion_adapter,\n scheduler=scheduler,\n image_encoder=image_encoder,\n feature_extractor=feature_extractor,\n )\n self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor)\n\n self.default_sample_size = self.unet.config.sample_size\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt with num_images_per_prompt->num_videos_per_prompt\n def encode_prompt(\n self,\n prompt: str,\n prompt_2: Optional[str] = None,\n device: Optional[torch.device] = None,\n num_videos_per_prompt: int = 1,\n do_classifier_free_guidance: bool = True,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n device: (`torch.device`):\n torch device\n num_videos_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n lora_scale (`float`, *optional*):\n A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n device = device or self._execution_device\n\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if self.text_encoder is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)\n else:\n scale_lora_layers(self.text_encoder_2, lora_scale)\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n\n if prompt is not None:\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # Define tokenizers and text encoders\n tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]\n text_encoders = (\n [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]\n )\n\n if prompt_embeds is None:\n prompt_2 = prompt_2 or prompt\n prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2\n\n # textual inversion: process multi-vector tokens if necessary\n prompt_embeds_list = []\n prompts = [prompt, prompt_2]\n for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, tokenizer)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n if clip_skip is None:\n prompt_embeds = prompt_embeds.hidden_states[-2]\n else:\n # \"2\" because SDXL always indexes from the penultimate layer.\n prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]\n\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n\n # get unconditional embeddings for classifier free guidance\n zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt\n if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:\n negative_prompt_embeds = torch.zeros_like(prompt_embeds)\n negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)\n elif do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n\n # normalize str to list\n negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt\n negative_prompt_2 = (\n batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2\n )\n\n uncond_tokens: List[str]\n if prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = [negative_prompt, negative_prompt_2]\n\n negative_prompt_embeds_list = []\n for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n negative_prompt_embeds = text_encoder(\n uncond_input.input_ids.to(device),\n output_hidden_states=True,\n )\n # We are only ALWAYS interested in the pooled output of the final text encoder\n negative_pooled_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]\n\n negative_prompt_embeds_list.append(negative_prompt_embeds)\n\n negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)\n\n if self.text_encoder_2 is not None:\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_videos_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n if self.text_encoder_2 is not None:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_videos_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_videos_per_prompt).view(\n bs_embed * num_videos_per_prompt, -1\n )\n if do_classifier_free_guidance:\n negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_videos_per_prompt).view(\n bs_embed * num_videos_per_prompt, -1\n )\n\n if self.text_encoder is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder_2, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n # Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents\n def decode_latents(self, latents):\n latents = 1 / self.vae.config.scaling_factor * latents\n\n batch_size, channels, num_frames, height, width = latents.shape\n latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)\n\n image = self.vae.decode(latents).sample\n video = image[None, :].reshape((batch_size, num_frames, -1) + image.shape[2:]).permute(0, 2, 1, 3, 4)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n video = video.float()\n return video\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n prompt_2,\n height,\n width,\n negative_prompt=None,\n negative_prompt_2=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n pooled_prompt_embeds=None,\n negative_pooled_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if prompt_embeds is not None and pooled_prompt_embeds is None:\n raise ValueError(\n \"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.\"\n )\n\n if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:\n raise ValueError(\n \"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.\"\n )\n\n # Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_synth.TextToVideoSDPipeline.prepare_latents\n def prepare_latents(\n self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None\n ):\n shape = (\n batch_size,\n num_channels_latents,\n num_frames,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def _get_add_time_ids(\n self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None\n ):\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n\n passed_add_embed_dim = (\n self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim\n )\n expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features\n\n if expected_add_embed_dim != passed_add_embed_dim:\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.\"\n )\n\n add_time_ids = torch.tensor([add_time_ids], dtype=dtype)\n return add_time_ids\n\n def upcast_vae(self):\n dtype = self.vae.dtype\n self.vae.to(dtype=torch.float32)\n use_torch_2_0_or_xformers = isinstance(\n self.vae.decoder.mid_block.attentions[0].processor,\n (\n AttnProcessor2_0,\n XFormersAttnProcessor,\n LoRAXFormersAttnProcessor,\n LoRAAttnProcessor2_0,\n FusedAttnProcessor2_0,\n ),\n )\n # if xformers or torch_2_0 is used attention block does not need\n # to be in float32 which can save lots of memory\n if use_torch_2_0_or_xformers:\n self.vae.post_quant_conv.to(dtype)\n self.vae.decoder.conv_in.to(dtype)\n self.vae.decoder.mid_block.to(dtype)\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(\n self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32\n ) -> torch.Tensor:\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n w (`torch.Tensor`):\n Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.\n embedding_dim (`int`, *optional*, defaults to 512):\n Dimension of the embeddings to generate.\n dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):\n Data type of the generated embeddings.\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def guidance_rescale(self):\n return self._guidance_rescale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def denoising_end(self):\n return self._denoising_end\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @property\n def interrupt(self):\n return self._interrupt\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n num_frames: int = 16,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n sigmas: List[float] = None,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_videos_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Optional[Tuple[int, int]] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Optional[Tuple[int, int]] = None,\n negative_original_size: Optional[Tuple[int, int]] = None,\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\n negative_target_size: Optional[Tuple[int, int]] = None,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the video generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n num_frames:\n The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds\n amounts to 2 seconds of video.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated video. This is set to 1024 by default for the best results.\n Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated video. This is set to 1024 by default for the best results.\n Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality video at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n sigmas (`List[float]`, *optional*):\n Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in\n their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed\n will be used.\n denoising_end (`float`, *optional*):\n When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be\n completed before it is intentionally prematurely terminated. As a result, the returned sample will\n still retain a substantial amount of noise as determined by the discrete timesteps selected by the\n scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a\n \"Mixture of Denoisers\" multi-pipeline setup, as elaborated in [**Refining the Image\n Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)\n guidance_scale (`float`, *optional*, defaults to 5.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower video quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the video generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the video generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n num_videos_per_prompt (`int`, *optional*, defaults to 1):\n The number of videos to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for video\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*):\n Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. If not provided, embeddings are computed from the\n `ip_adapter_image` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated video. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion_xl.AnimateDiffPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of\n [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).\n Guidance rescale factor should fix overexposure when using zero terminal SNR.\n original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.\n `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as\n explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n `crops_coords_top_left` can be used to generate an image that appears to be \"cropped\" from the position\n `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n For most cases, `target_size` should be set to the desired height and width of the generated image. If\n not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a specific image resolution. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a target image resolution. It should be as same\n as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.animatediff.pipeline_output.AnimateDiffPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.animatediff.pipeline_output.AnimateDiffPipelineOutput`] is\n returned, otherwise a `tuple` is returned where the first element is a list with the generated frames.\n \"\"\"\n\n # 0. Default height and width to unet\n height = height or self.default_sample_size * self.vae_scale_factor\n width = width or self.default_sample_size * self.vae_scale_factor\n\n num_videos_per_prompt = 1\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n height,\n width,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._guidance_rescale = guidance_rescale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._denoising_end = denoising_end\n self._interrupt = False\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_videos_per_prompt=num_videos_per_prompt,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # 4. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(\n self.scheduler, num_inference_steps, device, timesteps, sigmas\n )\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_videos_per_prompt,\n num_channels_latents,\n num_frames,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Prepare added time ids & embeddings\n add_text_embeds = pooled_prompt_embeds\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n add_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n if negative_original_size is not None and negative_target_size is not None:\n negative_add_time_ids = self._get_add_time_ids(\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n else:\n negative_add_time_ids = add_time_ids\n\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(batch_size * num_videos_per_prompt, 1)\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_videos_per_prompt,\n self.do_classifier_free_guidance,\n )\n\n # 7.1 Apply denoising_end\n if (\n self.denoising_end is not None\n and isinstance(self.denoising_end, float)\n and self.denoising_end > 0\n and self.denoising_end < 1\n ):\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (self.denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n # 8. Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_videos_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n num_free_init_iters = self._free_init_num_iters if self.free_init_enabled else 1\n for free_init_iter in range(num_free_init_iters):\n if self.free_init_enabled:\n latents, timesteps = self._apply_free_init(\n latents, free_init_iter, num_inference_steps, device, latents.dtype, generator\n )\n\n self._num_timesteps = len(timesteps)\n\n # 9. Denoising loop\n with self.progress_bar(total=self._num_timesteps) as progress_bar:\n for i, t in enumerate(timesteps):\n if self.interrupt:\n continue\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n if ip_adapter_image is not None or ip_adapter_image_embeds:\n added_cond_kwargs[\"image_embeds\"] = image_embeds\n\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=self.cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(\n noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale\n )\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n add_text_embeds = callback_outputs.pop(\"add_text_embeds\", add_text_embeds)\n negative_pooled_prompt_embeds = callback_outputs.pop(\n \"negative_pooled_prompt_embeds\", negative_pooled_prompt_embeds\n )\n add_time_ids = callback_outputs.pop(\"add_time_ids\", add_time_ids)\n negative_add_time_ids = callback_outputs.pop(\"negative_add_time_ids\", negative_add_time_ids)\n\n progress_bar.update()\n\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n # 10. Post processing\n if output_type == \"latent\":\n video = latents\n else:\n video_tensor = self.decode_latents(latents)\n video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n\n # 11. Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (video,)\n\n return AnimateDiffPipelineOutput(frames=video)\n" }, { "path": "diffusers/src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...image_processor import PipelineImageInput\nfrom ...loaders import IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel, UNetMotionModel\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...models.unets.unet_motion_model import MotionAdapter\nfrom ...schedulers import (\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n)\nfrom ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers\nfrom ...utils.torch_utils import randn_tensor\nfrom ...video_processor import VideoProcessor\nfrom ..free_init_utils import FreeInitMixin\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom .pipeline_output import AnimateDiffPipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import imageio\n >>> import requests\n >>> import torch\n >>> from diffusers import AnimateDiffVideoToVideoPipeline, DDIMScheduler, MotionAdapter\n >>> from diffusers.utils import export_to_gif\n >>> from io import BytesIO\n >>> from PIL import Image\n\n >>> adapter = MotionAdapter.from_pretrained(\n ... \"guoyww/animatediff-motion-adapter-v1-5-2\", torch_dtype=torch.float16\n ... )\n >>> pipe = AnimateDiffVideoToVideoPipeline.from_pretrained(\n ... \"SG161222/Realistic_Vision_V5.1_noVAE\", motion_adapter=adapter\n ... ).to(\"cuda\")\n >>> pipe.scheduler = DDIMScheduler(\n ... beta_schedule=\"linear\", steps_offset=1, clip_sample=False, timespace_spacing=\"linspace\"\n ... )\n\n\n >>> def load_video(file_path: str):\n ... images = []\n\n ... if file_path.startswith((\"http://\", \"https://\")):\n ... # If the file_path is a URL\n ... response = requests.get(file_path)\n ... response.raise_for_status()\n ... content = BytesIO(response.content)\n ... vid = imageio.get_reader(content)\n ... else:\n ... # Assuming it's a local file path\n ... vid = imageio.get_reader(file_path)\n\n ... for frame in vid:\n ... pil_image = Image.fromarray(frame)\n ... images.append(pil_image)\n\n ... return images\n\n\n >>> video = load_video(\n ... \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-vid2vid-input-1.gif\"\n ... )\n >>> output = pipe(\n ... video=video, prompt=\"panda playing a guitar, on a boat, in the ocean, high quality\", strength=0.5\n ... )\n >>> frames = output.frames[0]\n >>> export_to_gif(frames, \"animation.gif\")\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass AnimateDiffVideoToVideoPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n TextualInversionLoaderMixin,\n IPAdapterMixin,\n LoraLoaderMixin,\n FreeInitMixin,\n):\n r\"\"\"\n Pipeline for video-to-video generation.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer (`CLIPTokenizer`):\n A [`~transformers.CLIPTokenizer`] to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A [`UNet2DConditionModel`] used to create a UNetMotionModel to denoise the encoded video latents.\n motion_adapter ([`MotionAdapter`]):\n A [`MotionAdapter`] to be used in combination with `unet` to denoise the encoded video latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->image_encoder->unet->vae\"\n _optional_components = [\"feature_extractor\", \"image_encoder\", \"motion_adapter\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n motion_adapter: MotionAdapter,\n scheduler: Union[\n DDIMScheduler,\n PNDMScheduler,\n LMSDiscreteScheduler,\n EulerDiscreteScheduler,\n EulerAncestralDiscreteScheduler,\n DPMSolverMultistepScheduler,\n ],\n feature_extractor: CLIPImageProcessor = None,\n image_encoder: CLIPVisionModelWithProjection = None,\n ):\n super().__init__()\n if isinstance(unet, UNet2DConditionModel):\n unet = UNetMotionModel.from_unet2d(unet, motion_adapter)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n motion_adapter=motion_adapter,\n scheduler=scheduler,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with num_images_per_prompt -> num_videos_per_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n # Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents\n def decode_latents(self, latents):\n latents = 1 / self.vae.config.scaling_factor * latents\n\n batch_size, channels, num_frames, height, width = latents.shape\n latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)\n\n image = self.vae.decode(latents).sample\n video = image[None, :].reshape((batch_size, num_frames, -1) + image.shape[2:]).permute(0, 2, 1, 3, 4)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n video = video.float()\n return video\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n strength,\n height,\n width,\n video=None,\n latents=None,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if video is not None and latents is not None:\n raise ValueError(\"Only one of `video` or `latents` should be provided\")\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n if ip_adapter_image_embeds is not None:\n if not isinstance(ip_adapter_image_embeds, list):\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}\"\n )\n elif ip_adapter_image_embeds[0].ndim not in [3, 4]:\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D\"\n )\n\n def get_timesteps(self, num_inference_steps, timesteps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = timesteps[t_start * self.scheduler.order :]\n\n return timesteps, num_inference_steps - t_start\n\n def prepare_latents(\n self,\n video,\n height,\n width,\n num_channels_latents,\n batch_size,\n timestep,\n dtype,\n device,\n generator,\n latents=None,\n ):\n if latents is None:\n num_frames = video.shape[1]\n else:\n num_frames = latents.shape[2]\n\n shape = (\n batch_size,\n num_channels_latents,\n num_frames,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n )\n\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n # make sure the VAE is in float32 mode, as it overflows in float16\n if self.vae.config.force_upcast:\n video = video.float()\n self.vae.to(dtype=torch.float32)\n\n if isinstance(generator, list):\n if len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n init_latents = [\n retrieve_latents(self.vae.encode(video[i]), generator=generator[i]).unsqueeze(0)\n for i in range(batch_size)\n ]\n else:\n init_latents = [\n retrieve_latents(self.vae.encode(vid), generator=generator).unsqueeze(0) for vid in video\n ]\n\n init_latents = torch.cat(init_latents, dim=0)\n\n # restore vae to original dtype\n if self.vae.config.force_upcast:\n self.vae.to(dtype)\n\n init_latents = init_latents.to(dtype)\n init_latents = self.vae.config.scaling_factor * init_latents\n\n if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:\n # expand init_latents for batch_size\n error_message = (\n f\"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial\"\n \" images (`image`). Please make sure to update your script to pass as many initial images as text prompts\"\n )\n raise ValueError(error_message)\n elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n init_latents = torch.cat([init_latents], dim=0)\n\n noise = randn_tensor(init_latents.shape, generator=generator, device=device, dtype=dtype)\n latents = self.scheduler.add_noise(init_latents, noise, timestep).permute(0, 2, 1, 3, 4)\n else:\n if shape != latents.shape:\n # [B, C, F, H, W]\n raise ValueError(f\"`latents` expected to have {shape=}, but found {latents.shape=}\")\n latents = latents.to(device, dtype=dtype)\n\n return latents\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n def __call__(\n self,\n video: List[List[PipelineImageInput]] = None,\n prompt: Optional[Union[str, List[str]]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n guidance_scale: float = 7.5,\n strength: float = 0.8,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_videos_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n video (`List[PipelineImageInput]`):\n The input video to condition the generation on. Must be a list of images/frames of the video.\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated video.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated video.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality videos at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n sigmas (`List[float]`, *optional*):\n Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in\n their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed\n will be used.\n strength (`float`, *optional*, defaults to 0.8):\n Higher strength leads to more differences between original video and generated video.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`. Latents should be of shape\n `(batch_size, num_channel, num_frames, height, width)`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*):\n Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of\n IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should\n contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not\n provided, embeddings are computed from the `ip_adapter_image` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated video. Choose between `torch.Tensor`, `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`AnimateDiffPipelineOutput`] instead of a plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`pipelines.animatediff.pipeline_output.AnimateDiffPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`pipelines.animatediff.pipeline_output.AnimateDiffPipelineOutput`] is\n returned, otherwise a `tuple` is returned where the first element is a list with the generated frames.\n \"\"\"\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n num_videos_per_prompt = 1\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt=prompt,\n strength=strength,\n height=height,\n width=width,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n video=video,\n latents=latents,\n ip_adapter_image=ip_adapter_image,\n ip_adapter_image_embeds=ip_adapter_image_embeds,\n callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_videos_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_videos_per_prompt,\n self.do_classifier_free_guidance,\n )\n\n # 4. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(\n self.scheduler, num_inference_steps, device, timesteps, sigmas\n )\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, timesteps, strength, device)\n latent_timestep = timesteps[:1].repeat(batch_size * num_videos_per_prompt)\n\n # 5. Prepare latent variables\n if latents is None:\n video = self.video_processor.preprocess_video(video, height=height, width=width)\n # Move the number of frames before the number of channels.\n video = video.permute(0, 2, 1, 3, 4)\n video = video.to(device=device, dtype=prompt_embeds.dtype)\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n video=video,\n height=height,\n width=width,\n num_channels_latents=num_channels_latents,\n batch_size=batch_size * num_videos_per_prompt,\n timestep=latent_timestep,\n dtype=prompt_embeds.dtype,\n device=device,\n generator=generator,\n latents=latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Add image embeds for IP-Adapter\n added_cond_kwargs = (\n {\"image_embeds\": image_embeds}\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None\n else None\n )\n\n num_free_init_iters = self._free_init_num_iters if self.free_init_enabled else 1\n for free_init_iter in range(num_free_init_iters):\n if self.free_init_enabled:\n latents, timesteps = self._apply_free_init(\n latents, free_init_iter, num_inference_steps, device, latents.dtype, generator\n )\n num_inference_steps = len(timesteps)\n # make sure to readjust timesteps based on strength\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, timesteps, strength, device)\n\n self._num_timesteps = len(timesteps)\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n\n # 8. Denoising loop\n with self.progress_bar(total=self._num_timesteps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=self.cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n ).sample\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n\n # 9. Post-processing\n if output_type == \"latent\":\n video = latents\n else:\n video_tensor = self.decode_latents(latents)\n video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)\n\n # 10. Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (video,)\n\n return AnimateDiffPipelineOutput(frames=video)\n" }, { "path": "diffusers/src/diffusers/pipelines/animatediff/pipeline_output.py", "content": "from dataclasses import dataclass\nfrom typing import List, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\n\nfrom ...utils import BaseOutput\n\n\n@dataclass\nclass AnimateDiffPipelineOutput(BaseOutput):\n r\"\"\"\n Output class for AnimateDiff pipelines.\n\n Args:\n frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):\n List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing\n denoised\n PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape\n `(batch_size, num_frames, channels, height, width)`\n \"\"\"\n\n frames: Union[torch.Tensor, np.ndarray, List[List[PIL.Image.Image]]]\n" }, { "path": "diffusers/src/diffusers/pipelines/audioldm/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n is_torch_available,\n is_transformers_available,\n is_transformers_version,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available() and is_transformers_version(\">=\", \"4.27.0\")):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import (\n AudioLDMPipeline,\n )\n\n _dummy_objects.update({\"AudioLDMPipeline\": AudioLDMPipeline})\nelse:\n _import_structure[\"pipeline_audioldm\"] = [\"AudioLDMPipeline\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available() and is_transformers_version(\">=\", \"4.27.0\")):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import (\n AudioLDMPipeline,\n )\n\n else:\n from .pipeline_audioldm import AudioLDMPipeline\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom transformers import ClapTextModelWithProjection, RobertaTokenizer, RobertaTokenizerFast, SpeechT5HifiGan\n\nfrom ...models import AutoencoderKL, UNet2DConditionModel\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import logging, replace_example_docstring\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline, StableDiffusionMixin\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import AudioLDMPipeline\n >>> import torch\n >>> import scipy\n\n >>> repo_id = \"cvssp/audioldm-s-full-v2\"\n >>> pipe = AudioLDMPipeline.from_pretrained(repo_id, torch_dtype=torch.float16)\n >>> pipe = pipe.to(\"cuda\")\n\n >>> prompt = \"Techno music with a strong, upbeat tempo and high melodic riffs\"\n >>> audio = pipe(prompt, num_inference_steps=10, audio_length_in_s=5.0).audios[0]\n\n >>> # save the audio sample as a .wav file\n >>> scipy.io.wavfile.write(\"techno.wav\", rate=16000, data=audio)\n ```\n\"\"\"\n\n\nclass AudioLDMPipeline(DiffusionPipeline, StableDiffusionMixin):\n r\"\"\"\n Pipeline for text-to-audio generation using AudioLDM.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.ClapTextModelWithProjection`]):\n Frozen text-encoder (`ClapTextModelWithProjection`, specifically the\n [laion/clap-htsat-unfused](https://huggingface.co/laion/clap-htsat-unfused) variant.\n tokenizer ([`PreTrainedTokenizer`]):\n A [`~transformers.RobertaTokenizer`] to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded audio latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n vocoder ([`~transformers.SpeechT5HifiGan`]):\n Vocoder of class `SpeechT5HifiGan`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: ClapTextModelWithProjection,\n tokenizer: Union[RobertaTokenizer, RobertaTokenizerFast],\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n vocoder: SpeechT5HifiGan,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n vocoder=vocoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_waveforms_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device (`torch.device`):\n torch device\n num_waveforms_per_prompt (`int`):\n number of waveforms that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the audio generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n \"\"\"\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n attention_mask = text_inputs.attention_mask\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLAP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask.to(device),\n )\n prompt_embeds = prompt_embeds.text_embeds\n # additional L_2 normalization over each hidden-state\n prompt_embeds = F.normalize(prompt_embeds, dim=-1)\n\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n\n (\n bs_embed,\n seq_len,\n ) = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_waveforms_per_prompt)\n prompt_embeds = prompt_embeds.view(bs_embed * num_waveforms_per_prompt, seq_len)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n uncond_input_ids = uncond_input.input_ids.to(device)\n attention_mask = uncond_input.attention_mask.to(device)\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input_ids,\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds.text_embeds\n # additional L_2 normalization over each hidden-state\n negative_prompt_embeds = F.normalize(negative_prompt_embeds, dim=-1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_waveforms_per_prompt)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_waveforms_per_prompt, seq_len)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n def decode_latents(self, latents):\n latents = 1 / self.vae.config.scaling_factor * latents\n mel_spectrogram = self.vae.decode(latents).sample\n return mel_spectrogram\n\n def mel_spectrogram_to_waveform(self, mel_spectrogram):\n if mel_spectrogram.dim() == 4:\n mel_spectrogram = mel_spectrogram.squeeze(1)\n\n waveform = self.vocoder(mel_spectrogram)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n waveform = waveform.cpu().float()\n return waveform\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n audio_length_in_s,\n vocoder_upsample_factor,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n min_audio_length_in_s = vocoder_upsample_factor * self.vae_scale_factor\n if audio_length_in_s < min_audio_length_in_s:\n raise ValueError(\n f\"`audio_length_in_s` has to be a positive value greater than or equal to {min_audio_length_in_s}, but \"\n f\"is {audio_length_in_s}.\"\n )\n\n if self.vocoder.config.model_in_dim % self.vae_scale_factor != 0:\n raise ValueError(\n f\"The number of frequency bins in the vocoder's log-mel spectrogram has to be divisible by the \"\n f\"VAE scale factor, but got {self.vocoder.config.model_in_dim} bins and a scale factor of \"\n f\"{self.vae_scale_factor}.\"\n )\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents with width->self.vocoder.config.model_in_dim\n def prepare_latents(self, batch_size, num_channels_latents, height, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(self.vocoder.config.model_in_dim) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n audio_length_in_s: Optional[float] = None,\n num_inference_steps: int = 10,\n guidance_scale: float = 2.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_waveforms_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: Optional[int] = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n output_type: Optional[str] = \"np\",\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide audio generation. If not defined, you need to pass `prompt_embeds`.\n audio_length_in_s (`int`, *optional*, defaults to 5.12):\n The length of the generated audio sample in seconds.\n num_inference_steps (`int`, *optional*, defaults to 10):\n The number of denoising steps. More denoising steps usually lead to a higher quality audio at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 2.5):\n A higher guidance scale value encourages the model to generate audio that is closely linked to the text\n `prompt` at the expense of lower sound quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in audio generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_waveforms_per_prompt (`int`, *optional*, defaults to 1):\n The number of waveforms to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.AudioPipelineOutput`] instead of a plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n output_type (`str`, *optional*, defaults to `\"np\"`):\n The output format of the generated image. Choose between `\"np\"` to return a NumPy `np.ndarray` or\n `\"pt\"` to return a PyTorch `torch.Tensor` object.\n\n Examples:\n\n Returns:\n [`~pipelines.AudioPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.AudioPipelineOutput`] is returned, otherwise a `tuple` is\n returned where the first element is a list with the generated audio.\n \"\"\"\n # 0. Convert audio input length from seconds to spectrogram height\n vocoder_upsample_factor = np.prod(self.vocoder.config.upsample_rates) / self.vocoder.config.sampling_rate\n\n if audio_length_in_s is None:\n audio_length_in_s = self.unet.config.sample_size * self.vae_scale_factor * vocoder_upsample_factor\n\n height = int(audio_length_in_s / vocoder_upsample_factor)\n\n original_waveform_length = int(audio_length_in_s * self.vocoder.config.sampling_rate)\n if height % self.vae_scale_factor != 0:\n height = int(np.ceil(height / self.vae_scale_factor)) * self.vae_scale_factor\n logger.info(\n f\"Audio length in seconds {audio_length_in_s} is increased to {height * vocoder_upsample_factor} \"\n f\"so that it can be handled by the model. It will be cut to {audio_length_in_s} after the \"\n f\"denoising process.\"\n )\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n audio_length_in_s,\n vocoder_upsample_factor,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n prompt_embeds = self._encode_prompt(\n prompt,\n device,\n num_waveforms_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_waveforms_per_prompt,\n num_channels_latents,\n height,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=None,\n class_labels=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # 8. Post-processing\n mel_spectrogram = self.decode_latents(latents)\n\n audio = self.mel_spectrogram_to_waveform(mel_spectrogram)\n\n audio = audio[:, :original_waveform_length]\n\n if output_type == \"np\":\n audio = audio.numpy()\n\n if not return_dict:\n return (audio,)\n\n return AudioPipelineOutput(audios=audio)\n" }, { "path": "diffusers/src/diffusers/pipelines/audioldm2/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n is_transformers_version,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available() and is_transformers_version(\">=\", \"4.27.0\")):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"modeling_audioldm2\"] = [\"AudioLDM2ProjectionModel\", \"AudioLDM2UNet2DConditionModel\"]\n _import_structure[\"pipeline_audioldm2\"] = [\"AudioLDM2Pipeline\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available() and is_transformers_version(\">=\", \"4.27.0\")):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n\n else:\n from .modeling_audioldm2 import AudioLDM2ProjectionModel, AudioLDM2UNet2DConditionModel\n from .pipeline_audioldm2 import AudioLDM2Pipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/audioldm2/modeling_audioldm2.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, List, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.utils.checkpoint\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...loaders import UNet2DConditionLoadersMixin\nfrom ...models.activations import get_activation\nfrom ...models.attention_processor import (\n ADDED_KV_ATTENTION_PROCESSORS,\n CROSS_ATTENTION_PROCESSORS,\n AttentionProcessor,\n AttnAddedKVProcessor,\n AttnProcessor,\n)\nfrom ...models.embeddings import (\n TimestepEmbedding,\n Timesteps,\n)\nfrom ...models.modeling_utils import ModelMixin\nfrom ...models.resnet import Downsample2D, ResnetBlock2D, Upsample2D\nfrom ...models.transformers.transformer_2d import Transformer2DModel\nfrom ...models.unets.unet_2d_blocks import DownBlock2D, UpBlock2D\nfrom ...models.unets.unet_2d_condition import UNet2DConditionOutput\nfrom ...utils import BaseOutput, is_torch_version, logging\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef add_special_tokens(hidden_states, attention_mask, sos_token, eos_token):\n batch_size = hidden_states.shape[0]\n\n if attention_mask is not None:\n # Add two more steps to attn mask\n new_attn_mask_step = attention_mask.new_ones((batch_size, 1))\n attention_mask = torch.concat([new_attn_mask_step, attention_mask, new_attn_mask_step], dim=-1)\n\n # Add the SOS / EOS tokens at the start / end of the sequence respectively\n sos_token = sos_token.expand(batch_size, 1, -1)\n eos_token = eos_token.expand(batch_size, 1, -1)\n hidden_states = torch.concat([sos_token, hidden_states, eos_token], dim=1)\n return hidden_states, attention_mask\n\n\n@dataclass\nclass AudioLDM2ProjectionModelOutput(BaseOutput):\n \"\"\"\n Args:\n Class for AudioLDM2 projection layer's outputs.\n hidden_states (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):\n Sequence of hidden-states obtained by linearly projecting the hidden-states for each of the text\n encoders and subsequently concatenating them together.\n attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):\n Mask to avoid performing attention on padding token indices, formed by concatenating the attention masks\n for the two text encoders together. Mask values selected in `[0, 1]`:\n\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n \"\"\"\n\n hidden_states: torch.Tensor\n attention_mask: Optional[torch.LongTensor] = None\n\n\nclass AudioLDM2ProjectionModel(ModelMixin, ConfigMixin):\n \"\"\"\n A simple linear projection model to map two text embeddings to a shared latent space. It also inserts learned\n embedding vectors at the start and end of each text embedding sequence respectively. Each variable appended with\n `_1` refers to that corresponding to the second text encoder. Otherwise, it is from the first.\n\n Args:\n text_encoder_dim (`int`):\n Dimensionality of the text embeddings from the first text encoder (CLAP).\n text_encoder_1_dim (`int`):\n Dimensionality of the text embeddings from the second text encoder (T5 or VITS).\n langauge_model_dim (`int`):\n Dimensionality of the text embeddings from the language model (GPT2).\n \"\"\"\n\n @register_to_config\n def __init__(\n self,\n text_encoder_dim,\n text_encoder_1_dim,\n langauge_model_dim,\n use_learned_position_embedding=None,\n max_seq_length=None,\n ):\n super().__init__()\n # additional projection layers for each text encoder\n self.projection = nn.Linear(text_encoder_dim, langauge_model_dim)\n self.projection_1 = nn.Linear(text_encoder_1_dim, langauge_model_dim)\n\n # learnable SOS / EOS token embeddings for each text encoder\n self.sos_embed = nn.Parameter(torch.ones(langauge_model_dim))\n self.eos_embed = nn.Parameter(torch.ones(langauge_model_dim))\n\n self.sos_embed_1 = nn.Parameter(torch.ones(langauge_model_dim))\n self.eos_embed_1 = nn.Parameter(torch.ones(langauge_model_dim))\n\n self.use_learned_position_embedding = use_learned_position_embedding\n\n # learable positional embedding for vits encoder\n if self.use_learned_position_embedding is not None:\n self.learnable_positional_embedding = torch.nn.Parameter(\n torch.zeros((1, text_encoder_1_dim, max_seq_length))\n )\n\n def forward(\n self,\n hidden_states: Optional[torch.Tensor] = None,\n hidden_states_1: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.LongTensor] = None,\n attention_mask_1: Optional[torch.LongTensor] = None,\n ):\n hidden_states = self.projection(hidden_states)\n hidden_states, attention_mask = add_special_tokens(\n hidden_states, attention_mask, sos_token=self.sos_embed, eos_token=self.eos_embed\n )\n\n # Add positional embedding for Vits hidden state\n if self.use_learned_position_embedding is not None:\n hidden_states_1 = (hidden_states_1.permute(0, 2, 1) + self.learnable_positional_embedding).permute(0, 2, 1)\n\n hidden_states_1 = self.projection_1(hidden_states_1)\n hidden_states_1, attention_mask_1 = add_special_tokens(\n hidden_states_1, attention_mask_1, sos_token=self.sos_embed_1, eos_token=self.eos_embed_1\n )\n\n # concatenate clap and t5 text encoding\n hidden_states = torch.cat([hidden_states, hidden_states_1], dim=1)\n\n # concatenate attention masks\n if attention_mask is None and attention_mask_1 is not None:\n attention_mask = attention_mask_1.new_ones((hidden_states[:2]))\n elif attention_mask is not None and attention_mask_1 is None:\n attention_mask_1 = attention_mask.new_ones((hidden_states_1[:2]))\n\n if attention_mask is not None and attention_mask_1 is not None:\n attention_mask = torch.cat([attention_mask, attention_mask_1], dim=-1)\n else:\n attention_mask = None\n\n return AudioLDM2ProjectionModelOutput(\n hidden_states=hidden_states,\n attention_mask=attention_mask,\n )\n\n\nclass AudioLDM2UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):\n r\"\"\"\n A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample\n shaped output. Compared to the vanilla [`UNet2DConditionModel`], this variant optionally includes an additional\n self-attention layer in each Transformer block, as well as multiple cross-attention layers. It also allows for up\n to two cross-attention embeddings, `encoder_hidden_states` and `encoder_hidden_states_1`.\n\n This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented\n for all models (such as downloading or saving).\n\n Parameters:\n sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):\n Height and width of input/output sample.\n in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.\n out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.\n flip_sin_to_cos (`bool`, *optional*, defaults to `False`):\n Whether to flip the sin to cos in the time embedding.\n freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.\n down_block_types (`Tuple[str]`, *optional*, defaults to `(\"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\", \"DownBlock2D\")`):\n The tuple of downsample blocks to use.\n mid_block_type (`str`, *optional*, defaults to `\"UNetMidBlock2DCrossAttn\"`):\n Block type for middle of UNet, it can only be `UNetMidBlock2DCrossAttn` for AudioLDM2.\n up_block_types (`Tuple[str]`, *optional*, defaults to `(\"UpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\")`):\n The tuple of upsample blocks to use.\n only_cross_attention (`bool` or `Tuple[bool]`, *optional*, default to `False`):\n Whether to include self-attention in the basic transformer blocks, see\n [`~models.attention.BasicTransformerBlock`].\n block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):\n The tuple of output channels for each block.\n layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.\n downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.\n mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.\n act_fn (`str`, *optional*, defaults to `\"silu\"`): The activation function to use.\n norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.\n If `None`, normalization and activation layers is skipped in post-processing.\n norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.\n cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):\n The dimension of the cross attention features.\n transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):\n The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for\n [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],\n [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].\n attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.\n num_attention_heads (`int`, *optional*):\n The number of attention heads. If not defined, defaults to `attention_head_dim`\n resnet_time_scale_shift (`str`, *optional*, defaults to `\"default\"`): Time scale shift config\n for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.\n class_embed_type (`str`, *optional*, defaults to `None`):\n The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,\n `\"timestep\"`, `\"identity\"`, `\"projection\"`, or `\"simple_projection\"`.\n num_class_embeds (`int`, *optional*, defaults to `None`):\n Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing\n class conditioning with `class_embed_type` equal to `None`.\n time_embedding_type (`str`, *optional*, defaults to `positional`):\n The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.\n time_embedding_dim (`int`, *optional*, defaults to `None`):\n An optional override for the dimension of the projected time embedding.\n time_embedding_act_fn (`str`, *optional*, defaults to `None`):\n Optional activation function to use only once on the time embeddings before they are passed to the rest of\n the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.\n timestep_post_act (`str`, *optional*, defaults to `None`):\n The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.\n time_cond_proj_dim (`int`, *optional*, defaults to `None`):\n The dimension of `cond_proj` layer in the timestep embedding.\n conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer.\n conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer.\n projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when\n `class_embed_type=\"projection\"`. Required when `class_embed_type=\"projection\"`.\n class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time\n embeddings with the class embeddings.\n \"\"\"\n\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n sample_size: Optional[int] = None,\n in_channels: int = 4,\n out_channels: int = 4,\n flip_sin_to_cos: bool = True,\n freq_shift: int = 0,\n down_block_types: Tuple[str] = (\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"DownBlock2D\",\n ),\n mid_block_type: Optional[str] = \"UNetMidBlock2DCrossAttn\",\n up_block_types: Tuple[str] = (\"UpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\"),\n only_cross_attention: Union[bool, Tuple[bool]] = False,\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\n layers_per_block: Union[int, Tuple[int]] = 2,\n downsample_padding: int = 1,\n mid_block_scale_factor: float = 1,\n act_fn: str = \"silu\",\n norm_num_groups: Optional[int] = 32,\n norm_eps: float = 1e-5,\n cross_attention_dim: Union[int, Tuple[int]] = 1280,\n transformer_layers_per_block: Union[int, Tuple[int]] = 1,\n attention_head_dim: Union[int, Tuple[int]] = 8,\n num_attention_heads: Optional[Union[int, Tuple[int]]] = None,\n use_linear_projection: bool = False,\n class_embed_type: Optional[str] = None,\n num_class_embeds: Optional[int] = None,\n upcast_attention: bool = False,\n resnet_time_scale_shift: str = \"default\",\n time_embedding_type: str = \"positional\",\n time_embedding_dim: Optional[int] = None,\n time_embedding_act_fn: Optional[str] = None,\n timestep_post_act: Optional[str] = None,\n time_cond_proj_dim: Optional[int] = None,\n conv_in_kernel: int = 3,\n conv_out_kernel: int = 3,\n projection_class_embeddings_input_dim: Optional[int] = None,\n class_embeddings_concat: bool = False,\n ):\n super().__init__()\n\n self.sample_size = sample_size\n\n if num_attention_heads is not None:\n raise ValueError(\n \"At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.\"\n )\n\n # If `num_attention_heads` is not defined (which is the case for most models)\n # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.\n # The reason for this behavior is to correct for incorrectly named variables that were introduced\n # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131\n # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking\n # which is why we correct for the naming here.\n num_attention_heads = num_attention_heads or attention_head_dim\n\n # Check inputs\n if len(down_block_types) != len(up_block_types):\n raise ValueError(\n f\"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.\"\n )\n\n if len(block_out_channels) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}.\"\n )\n\n if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}.\"\n )\n\n # input\n conv_in_padding = (conv_in_kernel - 1) // 2\n self.conv_in = nn.Conv2d(\n in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding\n )\n\n # time\n if time_embedding_type == \"positional\":\n time_embed_dim = time_embedding_dim or block_out_channels[0] * 4\n\n self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)\n timestep_input_dim = block_out_channels[0]\n else:\n raise ValueError(f\"{time_embedding_type} does not exist. Please make sure to use `positional`.\")\n\n self.time_embedding = TimestepEmbedding(\n timestep_input_dim,\n time_embed_dim,\n act_fn=act_fn,\n post_act_fn=timestep_post_act,\n cond_proj_dim=time_cond_proj_dim,\n )\n\n # class embedding\n if class_embed_type is None and num_class_embeds is not None:\n self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)\n elif class_embed_type == \"timestep\":\n self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)\n elif class_embed_type == \"identity\":\n self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)\n elif class_embed_type == \"projection\":\n if projection_class_embeddings_input_dim is None:\n raise ValueError(\n \"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set\"\n )\n # The projection `class_embed_type` is the same as the timestep `class_embed_type` except\n # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings\n # 2. it projects from an arbitrary input dimension.\n #\n # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.\n # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.\n # As a result, `TimestepEmbedding` can be passed arbitrary vectors.\n self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)\n elif class_embed_type == \"simple_projection\":\n if projection_class_embeddings_input_dim is None:\n raise ValueError(\n \"`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set\"\n )\n self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)\n else:\n self.class_embedding = None\n\n if time_embedding_act_fn is None:\n self.time_embed_act = None\n else:\n self.time_embed_act = get_activation(time_embedding_act_fn)\n\n self.down_blocks = nn.ModuleList([])\n self.up_blocks = nn.ModuleList([])\n\n if isinstance(only_cross_attention, bool):\n only_cross_attention = [only_cross_attention] * len(down_block_types)\n\n if isinstance(num_attention_heads, int):\n num_attention_heads = (num_attention_heads,) * len(down_block_types)\n\n if isinstance(cross_attention_dim, int):\n cross_attention_dim = (cross_attention_dim,) * len(down_block_types)\n\n if isinstance(layers_per_block, int):\n layers_per_block = [layers_per_block] * len(down_block_types)\n\n if isinstance(transformer_layers_per_block, int):\n transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)\n\n if class_embeddings_concat:\n # The time embeddings are concatenated with the class embeddings. The dimension of the\n # time embeddings passed to the down, middle, and up blocks is twice the dimension of the\n # regular time embeddings\n blocks_time_embed_dim = time_embed_dim * 2\n else:\n blocks_time_embed_dim = time_embed_dim\n\n # down\n output_channel = block_out_channels[0]\n for i, down_block_type in enumerate(down_block_types):\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n down_block = get_down_block(\n down_block_type,\n num_layers=layers_per_block[i],\n transformer_layers_per_block=transformer_layers_per_block[i],\n in_channels=input_channel,\n out_channels=output_channel,\n temb_channels=blocks_time_embed_dim,\n add_downsample=not is_final_block,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim[i],\n num_attention_heads=num_attention_heads[i],\n downsample_padding=downsample_padding,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention[i],\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n self.down_blocks.append(down_block)\n\n # mid\n if mid_block_type == \"UNetMidBlock2DCrossAttn\":\n self.mid_block = UNetMidBlock2DCrossAttn(\n transformer_layers_per_block=transformer_layers_per_block[-1],\n in_channels=block_out_channels[-1],\n temb_channels=blocks_time_embed_dim,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n output_scale_factor=mid_block_scale_factor,\n resnet_time_scale_shift=resnet_time_scale_shift,\n cross_attention_dim=cross_attention_dim[-1],\n num_attention_heads=num_attention_heads[-1],\n resnet_groups=norm_num_groups,\n use_linear_projection=use_linear_projection,\n upcast_attention=upcast_attention,\n )\n else:\n raise ValueError(\n f\"unknown mid_block_type : {mid_block_type}. Should be `UNetMidBlock2DCrossAttn` for AudioLDM2.\"\n )\n\n # count how many layers upsample the images\n self.num_upsamplers = 0\n\n # up\n reversed_block_out_channels = list(reversed(block_out_channels))\n reversed_num_attention_heads = list(reversed(num_attention_heads))\n reversed_layers_per_block = list(reversed(layers_per_block))\n reversed_cross_attention_dim = list(reversed(cross_attention_dim))\n reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))\n only_cross_attention = list(reversed(only_cross_attention))\n\n output_channel = reversed_block_out_channels[0]\n for i, up_block_type in enumerate(up_block_types):\n is_final_block = i == len(block_out_channels) - 1\n\n prev_output_channel = output_channel\n output_channel = reversed_block_out_channels[i]\n input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]\n\n # add upsample block for all BUT final layer\n if not is_final_block:\n add_upsample = True\n self.num_upsamplers += 1\n else:\n add_upsample = False\n\n up_block = get_up_block(\n up_block_type,\n num_layers=reversed_layers_per_block[i] + 1,\n transformer_layers_per_block=reversed_transformer_layers_per_block[i],\n in_channels=input_channel,\n out_channels=output_channel,\n prev_output_channel=prev_output_channel,\n temb_channels=blocks_time_embed_dim,\n add_upsample=add_upsample,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=reversed_cross_attention_dim[i],\n num_attention_heads=reversed_num_attention_heads[i],\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention[i],\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n self.up_blocks.append(up_block)\n prev_output_channel = output_channel\n\n # out\n if norm_num_groups is not None:\n self.conv_norm_out = nn.GroupNorm(\n num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps\n )\n\n self.conv_act = get_activation(act_fn)\n\n else:\n self.conv_norm_out = None\n self.conv_act = None\n\n conv_out_padding = (conv_out_kernel - 1) // 2\n self.conv_out = nn.Conv2d(\n block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding\n )\n\n @property\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors\n def attn_processors(self) -> Dict[str, AttentionProcessor]:\n r\"\"\"\n Returns:\n `dict` of attention processors: A dictionary containing all attention processors used in the model with\n indexed by its weight name.\n \"\"\"\n # set recursively\n processors = {}\n\n def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\n if hasattr(module, \"get_processor\"):\n processors[f\"{name}.processor\"] = module.get_processor(return_deprecated_lora=True)\n\n for sub_name, child in module.named_children():\n fn_recursive_add_processors(f\"{name}.{sub_name}\", child, processors)\n\n return processors\n\n for name, module in self.named_children():\n fn_recursive_add_processors(name, module, processors)\n\n return processors\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor\n def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\n r\"\"\"\n Sets the attention processor to use to compute attention.\n\n Parameters:\n processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):\n The instantiated processor class or a dictionary of processor classes that will be set as the processor\n for **all** `Attention` layers.\n\n If `processor` is a dict, the key needs to define the path to the corresponding cross attention\n processor. This is strongly recommended when setting trainable attention processors.\n\n \"\"\"\n count = len(self.attn_processors.keys())\n\n if isinstance(processor, dict) and len(processor) != count:\n raise ValueError(\n f\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\"\n f\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\"\n )\n\n def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\n if hasattr(module, \"set_processor\"):\n if not isinstance(processor, dict):\n module.set_processor(processor)\n else:\n module.set_processor(processor.pop(f\"{name}.processor\"))\n\n for sub_name, child in module.named_children():\n fn_recursive_attn_processor(f\"{name}.{sub_name}\", child, processor)\n\n for name, module in self.named_children():\n fn_recursive_attn_processor(name, module, processor)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor\n def set_default_attn_processor(self):\n \"\"\"\n Disables custom attention processors and sets the default attention implementation.\n \"\"\"\n if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnAddedKVProcessor()\n elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnProcessor()\n else:\n raise ValueError(\n f\"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}\"\n )\n\n self.set_attn_processor(processor)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attention_slice\n def set_attention_slice(self, slice_size):\n r\"\"\"\n Enable sliced attention computation.\n\n When this option is enabled, the attention module splits the input tensor in slices to compute attention in\n several steps. This is useful for saving some memory in exchange for a small decrease in speed.\n\n Args:\n slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `\"auto\"`):\n When `\"auto\"`, input to the attention heads is halved, so attention is computed in two steps. If\n `\"max\"`, maximum amount of memory is saved by running only one slice at a time. If a number is\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\n must be a multiple of `slice_size`.\n \"\"\"\n sliceable_head_dims = []\n\n def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):\n if hasattr(module, \"set_attention_slice\"):\n sliceable_head_dims.append(module.sliceable_head_dim)\n\n for child in module.children():\n fn_recursive_retrieve_sliceable_dims(child)\n\n # retrieve number of attention layers\n for module in self.children():\n fn_recursive_retrieve_sliceable_dims(module)\n\n num_sliceable_layers = len(sliceable_head_dims)\n\n if slice_size == \"auto\":\n # half the attention head size is usually a good trade-off between\n # speed and memory\n slice_size = [dim // 2 for dim in sliceable_head_dims]\n elif slice_size == \"max\":\n # make smallest slice possible\n slice_size = num_sliceable_layers * [1]\n\n slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\n\n if len(slice_size) != len(sliceable_head_dims):\n raise ValueError(\n f\"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different\"\n f\" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}.\"\n )\n\n for i in range(len(slice_size)):\n size = slice_size[i]\n dim = sliceable_head_dims[i]\n if size is not None and size > dim:\n raise ValueError(f\"size {size} has to be smaller or equal to {dim}.\")\n\n # Recursively walk through all the children.\n # Any children which exposes the set_attention_slice method\n # gets the message\n def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):\n if hasattr(module, \"set_attention_slice\"):\n module.set_attention_slice(slice_size.pop())\n\n for child in module.children():\n fn_recursive_set_attention_slice(child, slice_size)\n\n reversed_slice_size = list(reversed(slice_size))\n for module in self.children():\n fn_recursive_set_attention_slice(module, reversed_slice_size)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel._set_gradient_checkpointing\n def _set_gradient_checkpointing(self, module, value=False):\n if hasattr(module, \"gradient_checkpointing\"):\n module.gradient_checkpointing = value\n\n def forward(\n self,\n sample: torch.Tensor,\n timestep: Union[torch.Tensor, float, int],\n encoder_hidden_states: torch.Tensor,\n class_labels: Optional[torch.Tensor] = None,\n timestep_cond: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n encoder_hidden_states_1: Optional[torch.Tensor] = None,\n encoder_attention_mask_1: Optional[torch.Tensor] = None,\n ) -> Union[UNet2DConditionOutput, Tuple]:\n r\"\"\"\n The [`AudioLDM2UNet2DConditionModel`] forward method.\n\n Args:\n sample (`torch.Tensor`):\n The noisy input tensor with the following shape `(batch, channel, height, width)`.\n timestep (`torch.Tensor` or `float` or `int`): The number of timesteps to denoise an input.\n encoder_hidden_states (`torch.Tensor`):\n The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.\n encoder_attention_mask (`torch.Tensor`):\n A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If\n `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,\n which adds large negative values to the attention scores corresponding to \"discard\" tokens.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain\n tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].\n encoder_hidden_states_1 (`torch.Tensor`, *optional*):\n A second set of encoder hidden states with shape `(batch, sequence_length_2, feature_dim_2)`. Can be\n used to condition the model on a different set of embeddings to `encoder_hidden_states`.\n encoder_attention_mask_1 (`torch.Tensor`, *optional*):\n A cross-attention mask of shape `(batch, sequence_length_2)` is applied to `encoder_hidden_states_1`.\n If `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,\n which adds large negative values to the attention scores corresponding to \"discard\" tokens.\n\n Returns:\n [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:\n If `return_dict` is True, an [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] is returned,\n otherwise a `tuple` is returned where the first element is the sample tensor.\n \"\"\"\n # By default samples have to be AT least a multiple of the overall upsampling factor.\n # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).\n # However, the upsampling interpolation output size can be forced to fit any upsampling size\n # on the fly if necessary.\n default_overall_up_factor = 2**self.num_upsamplers\n\n # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`\n forward_upsample_size = False\n upsample_size = None\n\n if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):\n logger.info(\"Forward upsample size to force interpolation output size.\")\n forward_upsample_size = True\n\n # ensure attention_mask is a bias, and give it a singleton query_tokens dimension\n # expects mask of shape:\n # [batch, key_tokens]\n # adds singleton query_tokens dimension:\n # [batch, 1, key_tokens]\n # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:\n # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)\n # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)\n if attention_mask is not None:\n # assume that mask is expressed as:\n # (1 = keep, 0 = discard)\n # convert mask into a bias that can be added to attention scores:\n # (keep = +0, discard = -10000.0)\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n # convert encoder_attention_mask to a bias the same way we do for attention_mask\n if encoder_attention_mask is not None:\n encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0\n encoder_attention_mask = encoder_attention_mask.unsqueeze(1)\n\n if encoder_attention_mask_1 is not None:\n encoder_attention_mask_1 = (1 - encoder_attention_mask_1.to(sample.dtype)) * -10000.0\n encoder_attention_mask_1 = encoder_attention_mask_1.unsqueeze(1)\n\n # 1. time\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can\n # This would be a good case for the `match` statement (Python 3.10+)\n is_mps = sample.device.type == \"mps\"\n if isinstance(timestep, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\n elif len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(sample.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timesteps = timesteps.expand(sample.shape[0])\n\n t_emb = self.time_proj(timesteps)\n\n # `Timesteps` does not contain any weights and will always return f32 tensors\n # but time_embedding might actually be running in fp16. so we need to cast here.\n # there might be better ways to encapsulate this.\n t_emb = t_emb.to(dtype=sample.dtype)\n\n emb = self.time_embedding(t_emb, timestep_cond)\n aug_emb = None\n\n if self.class_embedding is not None:\n if class_labels is None:\n raise ValueError(\"class_labels should be provided when num_class_embeds > 0\")\n\n if self.config.class_embed_type == \"timestep\":\n class_labels = self.time_proj(class_labels)\n\n # `Timesteps` does not contain any weights and will always return f32 tensors\n # there might be better ways to encapsulate this.\n class_labels = class_labels.to(dtype=sample.dtype)\n\n class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)\n\n if self.config.class_embeddings_concat:\n emb = torch.cat([emb, class_emb], dim=-1)\n else:\n emb = emb + class_emb\n\n emb = emb + aug_emb if aug_emb is not None else emb\n\n if self.time_embed_act is not None:\n emb = self.time_embed_act(emb)\n\n # 2. pre-process\n sample = self.conv_in(sample)\n\n # 3. down\n down_block_res_samples = (sample,)\n for downsample_block in self.down_blocks:\n if hasattr(downsample_block, \"has_cross_attention\") and downsample_block.has_cross_attention:\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n encoder_attention_mask=encoder_attention_mask,\n encoder_hidden_states_1=encoder_hidden_states_1,\n encoder_attention_mask_1=encoder_attention_mask_1,\n )\n else:\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb)\n\n down_block_res_samples += res_samples\n\n # 4. mid\n if self.mid_block is not None:\n sample = self.mid_block(\n sample,\n emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n encoder_attention_mask=encoder_attention_mask,\n encoder_hidden_states_1=encoder_hidden_states_1,\n encoder_attention_mask_1=encoder_attention_mask_1,\n )\n\n # 5. up\n for i, upsample_block in enumerate(self.up_blocks):\n is_final_block = i == len(self.up_blocks) - 1\n\n res_samples = down_block_res_samples[-len(upsample_block.resnets) :]\n down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]\n\n # if we have not reached the final block and need to forward the\n # upsample size, we do it here\n if not is_final_block and forward_upsample_size:\n upsample_size = down_block_res_samples[-1].shape[2:]\n\n if hasattr(upsample_block, \"has_cross_attention\") and upsample_block.has_cross_attention:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n upsample_size=upsample_size,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n encoder_hidden_states_1=encoder_hidden_states_1,\n encoder_attention_mask_1=encoder_attention_mask_1,\n )\n else:\n sample = upsample_block(\n hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size\n )\n\n # 6. post-process\n if self.conv_norm_out:\n sample = self.conv_norm_out(sample)\n sample = self.conv_act(sample)\n sample = self.conv_out(sample)\n\n if not return_dict:\n return (sample,)\n\n return UNet2DConditionOutput(sample=sample)\n\n\ndef get_down_block(\n down_block_type,\n num_layers,\n in_channels,\n out_channels,\n temb_channels,\n add_downsample,\n resnet_eps,\n resnet_act_fn,\n transformer_layers_per_block=1,\n num_attention_heads=None,\n resnet_groups=None,\n cross_attention_dim=None,\n downsample_padding=None,\n use_linear_projection=False,\n only_cross_attention=False,\n upcast_attention=False,\n resnet_time_scale_shift=\"default\",\n):\n down_block_type = down_block_type[7:] if down_block_type.startswith(\"UNetRes\") else down_block_type\n if down_block_type == \"DownBlock2D\":\n return DownBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n add_downsample=add_downsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n downsample_padding=downsample_padding,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n elif down_block_type == \"CrossAttnDownBlock2D\":\n if cross_attention_dim is None:\n raise ValueError(\"cross_attention_dim must be specified for CrossAttnDownBlock2D\")\n return CrossAttnDownBlock2D(\n num_layers=num_layers,\n transformer_layers_per_block=transformer_layers_per_block,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n add_downsample=add_downsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n downsample_padding=downsample_padding,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=num_attention_heads,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n raise ValueError(f\"{down_block_type} does not exist.\")\n\n\ndef get_up_block(\n up_block_type,\n num_layers,\n in_channels,\n out_channels,\n prev_output_channel,\n temb_channels,\n add_upsample,\n resnet_eps,\n resnet_act_fn,\n transformer_layers_per_block=1,\n num_attention_heads=None,\n resnet_groups=None,\n cross_attention_dim=None,\n use_linear_projection=False,\n only_cross_attention=False,\n upcast_attention=False,\n resnet_time_scale_shift=\"default\",\n):\n up_block_type = up_block_type[7:] if up_block_type.startswith(\"UNetRes\") else up_block_type\n if up_block_type == \"UpBlock2D\":\n return UpBlock2D(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n prev_output_channel=prev_output_channel,\n temb_channels=temb_channels,\n add_upsample=add_upsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n elif up_block_type == \"CrossAttnUpBlock2D\":\n if cross_attention_dim is None:\n raise ValueError(\"cross_attention_dim must be specified for CrossAttnUpBlock2D\")\n return CrossAttnUpBlock2D(\n num_layers=num_layers,\n transformer_layers_per_block=transformer_layers_per_block,\n in_channels=in_channels,\n out_channels=out_channels,\n prev_output_channel=prev_output_channel,\n temb_channels=temb_channels,\n add_upsample=add_upsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=num_attention_heads,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n raise ValueError(f\"{up_block_type} does not exist.\")\n\n\nclass CrossAttnDownBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n transformer_layers_per_block: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n num_attention_heads=1,\n cross_attention_dim=1280,\n output_scale_factor=1.0,\n downsample_padding=1,\n add_downsample=True,\n use_linear_projection=False,\n only_cross_attention=False,\n upcast_attention=False,\n ):\n super().__init__()\n resnets = []\n attentions = []\n\n self.has_cross_attention = True\n self.num_attention_heads = num_attention_heads\n\n if isinstance(cross_attention_dim, int):\n cross_attention_dim = (cross_attention_dim,)\n if isinstance(cross_attention_dim, (list, tuple)) and len(cross_attention_dim) > 4:\n raise ValueError(\n \"Only up to 4 cross-attention layers are supported. Ensure that the length of cross-attention \"\n f\"dims is less than or equal to 4. Got cross-attention dims {cross_attention_dim} of length {len(cross_attention_dim)}\"\n )\n self.cross_attention_dim = cross_attention_dim\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n for j in range(len(cross_attention_dim)):\n attentions.append(\n Transformer2DModel(\n num_attention_heads,\n out_channels // num_attention_heads,\n in_channels=out_channels,\n num_layers=transformer_layers_per_block,\n cross_attention_dim=cross_attention_dim[j],\n norm_num_groups=resnet_groups,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n double_self_attention=True if cross_attention_dim[j] is None else False,\n )\n )\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n if add_downsample:\n self.downsamplers = nn.ModuleList(\n [\n Downsample2D(\n out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name=\"op\"\n )\n ]\n )\n else:\n self.downsamplers = None\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n encoder_hidden_states_1: Optional[torch.Tensor] = None,\n encoder_attention_mask_1: Optional[torch.Tensor] = None,\n ):\n output_states = ()\n num_layers = len(self.resnets)\n num_attention_per_layer = len(self.attentions) // num_layers\n\n encoder_hidden_states_1 = (\n encoder_hidden_states_1 if encoder_hidden_states_1 is not None else encoder_hidden_states\n )\n encoder_attention_mask_1 = (\n encoder_attention_mask_1 if encoder_hidden_states_1 is not None else encoder_attention_mask\n )\n\n for i in range(num_layers):\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(self.resnets[i]),\n hidden_states,\n temb,\n **ckpt_kwargs,\n )\n for idx, cross_attention_dim in enumerate(self.cross_attention_dim):\n if cross_attention_dim is not None and idx <= 1:\n forward_encoder_hidden_states = encoder_hidden_states\n forward_encoder_attention_mask = encoder_attention_mask\n elif cross_attention_dim is not None and idx > 1:\n forward_encoder_hidden_states = encoder_hidden_states_1\n forward_encoder_attention_mask = encoder_attention_mask_1\n else:\n forward_encoder_hidden_states = None\n forward_encoder_attention_mask = None\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(self.attentions[i * num_attention_per_layer + idx], return_dict=False),\n hidden_states,\n forward_encoder_hidden_states,\n None, # timestep\n None, # class_labels\n cross_attention_kwargs,\n attention_mask,\n forward_encoder_attention_mask,\n **ckpt_kwargs,\n )[0]\n else:\n hidden_states = self.resnets[i](hidden_states, temb)\n for idx, cross_attention_dim in enumerate(self.cross_attention_dim):\n if cross_attention_dim is not None and idx <= 1:\n forward_encoder_hidden_states = encoder_hidden_states\n forward_encoder_attention_mask = encoder_attention_mask\n elif cross_attention_dim is not None and idx > 1:\n forward_encoder_hidden_states = encoder_hidden_states_1\n forward_encoder_attention_mask = encoder_attention_mask_1\n else:\n forward_encoder_hidden_states = None\n forward_encoder_attention_mask = None\n hidden_states = self.attentions[i * num_attention_per_layer + idx](\n hidden_states,\n attention_mask=attention_mask,\n encoder_hidden_states=forward_encoder_hidden_states,\n encoder_attention_mask=forward_encoder_attention_mask,\n return_dict=False,\n )[0]\n\n output_states = output_states + (hidden_states,)\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n\nclass UNetMidBlock2DCrossAttn(nn.Module):\n def __init__(\n self,\n in_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n transformer_layers_per_block: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n num_attention_heads=1,\n output_scale_factor=1.0,\n cross_attention_dim=1280,\n use_linear_projection=False,\n upcast_attention=False,\n ):\n super().__init__()\n\n self.has_cross_attention = True\n self.num_attention_heads = num_attention_heads\n resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)\n\n if isinstance(cross_attention_dim, int):\n cross_attention_dim = (cross_attention_dim,)\n if isinstance(cross_attention_dim, (list, tuple)) and len(cross_attention_dim) > 4:\n raise ValueError(\n \"Only up to 4 cross-attention layers are supported. Ensure that the length of cross-attention \"\n f\"dims is less than or equal to 4. Got cross-attention dims {cross_attention_dim} of length {len(cross_attention_dim)}\"\n )\n self.cross_attention_dim = cross_attention_dim\n\n # there is always at least one resnet\n resnets = [\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n ]\n attentions = []\n\n for i in range(num_layers):\n for j in range(len(cross_attention_dim)):\n attentions.append(\n Transformer2DModel(\n num_attention_heads,\n in_channels // num_attention_heads,\n in_channels=in_channels,\n num_layers=transformer_layers_per_block,\n cross_attention_dim=cross_attention_dim[j],\n norm_num_groups=resnet_groups,\n use_linear_projection=use_linear_projection,\n upcast_attention=upcast_attention,\n double_self_attention=True if cross_attention_dim[j] is None else False,\n )\n )\n resnets.append(\n ResnetBlock2D(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n encoder_hidden_states_1: Optional[torch.Tensor] = None,\n encoder_attention_mask_1: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n hidden_states = self.resnets[0](hidden_states, temb)\n num_attention_per_layer = len(self.attentions) // (len(self.resnets) - 1)\n\n encoder_hidden_states_1 = (\n encoder_hidden_states_1 if encoder_hidden_states_1 is not None else encoder_hidden_states\n )\n encoder_attention_mask_1 = (\n encoder_attention_mask_1 if encoder_hidden_states_1 is not None else encoder_attention_mask\n )\n\n for i in range(len(self.resnets[1:])):\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n for idx, cross_attention_dim in enumerate(self.cross_attention_dim):\n if cross_attention_dim is not None and idx <= 1:\n forward_encoder_hidden_states = encoder_hidden_states\n forward_encoder_attention_mask = encoder_attention_mask\n elif cross_attention_dim is not None and idx > 1:\n forward_encoder_hidden_states = encoder_hidden_states_1\n forward_encoder_attention_mask = encoder_attention_mask_1\n else:\n forward_encoder_hidden_states = None\n forward_encoder_attention_mask = None\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(self.attentions[i * num_attention_per_layer + idx], return_dict=False),\n hidden_states,\n forward_encoder_hidden_states,\n None, # timestep\n None, # class_labels\n cross_attention_kwargs,\n attention_mask,\n forward_encoder_attention_mask,\n **ckpt_kwargs,\n )[0]\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(self.resnets[i + 1]),\n hidden_states,\n temb,\n **ckpt_kwargs,\n )\n else:\n for idx, cross_attention_dim in enumerate(self.cross_attention_dim):\n if cross_attention_dim is not None and idx <= 1:\n forward_encoder_hidden_states = encoder_hidden_states\n forward_encoder_attention_mask = encoder_attention_mask\n elif cross_attention_dim is not None and idx > 1:\n forward_encoder_hidden_states = encoder_hidden_states_1\n forward_encoder_attention_mask = encoder_attention_mask_1\n else:\n forward_encoder_hidden_states = None\n forward_encoder_attention_mask = None\n hidden_states = self.attentions[i * num_attention_per_layer + idx](\n hidden_states,\n attention_mask=attention_mask,\n encoder_hidden_states=forward_encoder_hidden_states,\n encoder_attention_mask=forward_encoder_attention_mask,\n return_dict=False,\n )[0]\n\n hidden_states = self.resnets[i + 1](hidden_states, temb)\n\n return hidden_states\n\n\nclass CrossAttnUpBlock2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n prev_output_channel: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n transformer_layers_per_block: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n num_attention_heads=1,\n cross_attention_dim=1280,\n output_scale_factor=1.0,\n add_upsample=True,\n use_linear_projection=False,\n only_cross_attention=False,\n upcast_attention=False,\n ):\n super().__init__()\n resnets = []\n attentions = []\n\n self.has_cross_attention = True\n self.num_attention_heads = num_attention_heads\n\n if isinstance(cross_attention_dim, int):\n cross_attention_dim = (cross_attention_dim,)\n if isinstance(cross_attention_dim, (list, tuple)) and len(cross_attention_dim) > 4:\n raise ValueError(\n \"Only up to 4 cross-attention layers are supported. Ensure that the length of cross-attention \"\n f\"dims is less than or equal to 4. Got cross-attention dims {cross_attention_dim} of length {len(cross_attention_dim)}\"\n )\n self.cross_attention_dim = cross_attention_dim\n\n for i in range(num_layers):\n res_skip_channels = in_channels if (i == num_layers - 1) else out_channels\n resnet_in_channels = prev_output_channel if i == 0 else out_channels\n\n resnets.append(\n ResnetBlock2D(\n in_channels=resnet_in_channels + res_skip_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n for j in range(len(cross_attention_dim)):\n attentions.append(\n Transformer2DModel(\n num_attention_heads,\n out_channels // num_attention_heads,\n in_channels=out_channels,\n num_layers=transformer_layers_per_block,\n cross_attention_dim=cross_attention_dim[j],\n norm_num_groups=resnet_groups,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n double_self_attention=True if cross_attention_dim[j] is None else False,\n )\n )\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n if add_upsample:\n self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])\n else:\n self.upsamplers = None\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n upsample_size: Optional[int] = None,\n attention_mask: Optional[torch.Tensor] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n encoder_hidden_states_1: Optional[torch.Tensor] = None,\n encoder_attention_mask_1: Optional[torch.Tensor] = None,\n ):\n num_layers = len(self.resnets)\n num_attention_per_layer = len(self.attentions) // num_layers\n\n encoder_hidden_states_1 = (\n encoder_hidden_states_1 if encoder_hidden_states_1 is not None else encoder_hidden_states\n )\n encoder_attention_mask_1 = (\n encoder_attention_mask_1 if encoder_hidden_states_1 is not None else encoder_attention_mask\n )\n\n for i in range(num_layers):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(self.resnets[i]),\n hidden_states,\n temb,\n **ckpt_kwargs,\n )\n for idx, cross_attention_dim in enumerate(self.cross_attention_dim):\n if cross_attention_dim is not None and idx <= 1:\n forward_encoder_hidden_states = encoder_hidden_states\n forward_encoder_attention_mask = encoder_attention_mask\n elif cross_attention_dim is not None and idx > 1:\n forward_encoder_hidden_states = encoder_hidden_states_1\n forward_encoder_attention_mask = encoder_attention_mask_1\n else:\n forward_encoder_hidden_states = None\n forward_encoder_attention_mask = None\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(self.attentions[i * num_attention_per_layer + idx], return_dict=False),\n hidden_states,\n forward_encoder_hidden_states,\n None, # timestep\n None, # class_labels\n cross_attention_kwargs,\n attention_mask,\n forward_encoder_attention_mask,\n **ckpt_kwargs,\n )[0]\n else:\n hidden_states = self.resnets[i](hidden_states, temb)\n for idx, cross_attention_dim in enumerate(self.cross_attention_dim):\n if cross_attention_dim is not None and idx <= 1:\n forward_encoder_hidden_states = encoder_hidden_states\n forward_encoder_attention_mask = encoder_attention_mask\n elif cross_attention_dim is not None and idx > 1:\n forward_encoder_hidden_states = encoder_hidden_states_1\n forward_encoder_attention_mask = encoder_attention_mask_1\n else:\n forward_encoder_hidden_states = None\n forward_encoder_attention_mask = None\n hidden_states = self.attentions[i * num_attention_per_layer + idx](\n hidden_states,\n attention_mask=attention_mask,\n encoder_hidden_states=forward_encoder_hidden_states,\n encoder_attention_mask=forward_encoder_attention_mask,\n return_dict=False,\n )[0]\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n" }, { "path": "diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py", "content": "# Copyright 2024 CVSSP, ByteDance and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport torch\nfrom transformers import (\n ClapFeatureExtractor,\n ClapModel,\n GPT2Model,\n RobertaTokenizer,\n RobertaTokenizerFast,\n SpeechT5HifiGan,\n T5EncoderModel,\n T5Tokenizer,\n T5TokenizerFast,\n VitsModel,\n VitsTokenizer,\n)\n\nfrom ...models import AutoencoderKL\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n is_accelerate_available,\n is_accelerate_version,\n is_librosa_available,\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline\nfrom .modeling_audioldm2 import AudioLDM2ProjectionModel, AudioLDM2UNet2DConditionModel\n\n\nif is_librosa_available():\n import librosa\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import scipy\n >>> import torch\n >>> from diffusers import AudioLDM2Pipeline\n\n >>> repo_id = \"cvssp/audioldm2\"\n >>> pipe = AudioLDM2Pipeline.from_pretrained(repo_id, torch_dtype=torch.float16)\n >>> pipe = pipe.to(\"cuda\")\n\n >>> # define the prompts\n >>> prompt = \"The sound of a hammer hitting a wooden surface.\"\n >>> negative_prompt = \"Low quality.\"\n\n >>> # set the seed for generator\n >>> generator = torch.Generator(\"cuda\").manual_seed(0)\n\n >>> # run the generation\n >>> audio = pipe(\n ... prompt,\n ... negative_prompt=negative_prompt,\n ... num_inference_steps=200,\n ... audio_length_in_s=10.0,\n ... num_waveforms_per_prompt=3,\n ... generator=generator,\n ... ).audios\n\n >>> # save the best audio sample (index 0) as a .wav file\n >>> scipy.io.wavfile.write(\"techno.wav\", rate=16000, data=audio[0])\n ```\n ```\n #Using AudioLDM2 for Text To Speech\n >>> import scipy\n >>> import torch\n >>> from diffusers import AudioLDM2Pipeline\n\n >>> repo_id = \"anhnct/audioldm2_gigaspeech\"\n >>> pipe = AudioLDM2Pipeline.from_pretrained(repo_id, torch_dtype=torch.float16)\n >>> pipe = pipe.to(\"cuda\")\n\n >>> # define the prompts\n >>> prompt = \"A female reporter is speaking\"\n >>> transcript = \"wish you have a good day\"\n\n >>> # set the seed for generator\n >>> generator = torch.Generator(\"cuda\").manual_seed(0)\n\n >>> # run the generation\n >>> audio = pipe(\n ... prompt,\n ... transcription=transcript,\n ... num_inference_steps=200,\n ... audio_length_in_s=10.0,\n ... num_waveforms_per_prompt=2,\n ... generator=generator,\n ... max_new_tokens=512, #Must set max_new_tokens equa to 512 for TTS\n ... ).audios\n\n >>> # save the best audio sample (index 0) as a .wav file\n >>> scipy.io.wavfile.write(\"tts.wav\", rate=16000, data=audio[0])\n ```\n\"\"\"\n\n\ndef prepare_inputs_for_generation(\n inputs_embeds,\n attention_mask=None,\n past_key_values=None,\n **kwargs,\n):\n if past_key_values is not None:\n # only last token for inputs_embeds if past is defined in kwargs\n inputs_embeds = inputs_embeds[:, -1:]\n\n return {\n \"inputs_embeds\": inputs_embeds,\n \"attention_mask\": attention_mask,\n \"past_key_values\": past_key_values,\n \"use_cache\": kwargs.get(\"use_cache\"),\n }\n\n\nclass AudioLDM2Pipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for text-to-audio generation using AudioLDM2.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.ClapModel`]):\n First frozen text-encoder. AudioLDM2 uses the joint audio-text embedding model\n [CLAP](https://huggingface.co/docs/transformers/model_doc/clap#transformers.CLAPTextModelWithProjection),\n specifically the [laion/clap-htsat-unfused](https://huggingface.co/laion/clap-htsat-unfused) variant. The\n text branch is used to encode the text prompt to a prompt embedding. The full audio-text model is used to\n rank generated waveforms against the text prompt by computing similarity scores.\n text_encoder_2 ([`~transformers.T5EncoderModel`, `~transformers.VitsModel`]):\n Second frozen text-encoder. AudioLDM2 uses the encoder of\n [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the\n [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) variant. Second frozen text-encoder use\n for TTS. AudioLDM2 uses the encoder of\n [Vits](https://huggingface.co/docs/transformers/model_doc/vits#transformers.VitsModel).\n projection_model ([`AudioLDM2ProjectionModel`]):\n A trained model used to linearly project the hidden-states from the first and second text encoder models\n and insert learned SOS and EOS token embeddings. The projected hidden-states from the two text encoders are\n concatenated to give the input to the language model. A Learned Position Embedding for the Vits\n hidden-states\n language_model ([`~transformers.GPT2Model`]):\n An auto-regressive language model used to generate a sequence of hidden-states conditioned on the projected\n outputs from the two text encoders.\n tokenizer ([`~transformers.RobertaTokenizer`]):\n Tokenizer to tokenize text for the first frozen text-encoder.\n tokenizer_2 ([`~transformers.T5Tokenizer`, `~transformers.VitsTokenizer`]):\n Tokenizer to tokenize text for the second frozen text-encoder.\n feature_extractor ([`~transformers.ClapFeatureExtractor`]):\n Feature extractor to pre-process generated audio waveforms to log-mel spectrograms for automatic scoring.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded audio latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n vocoder ([`~transformers.SpeechT5HifiGan`]):\n Vocoder of class `SpeechT5HifiGan` to convert the mel-spectrogram latents to the final audio waveform.\n \"\"\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: ClapModel,\n text_encoder_2: Union[T5EncoderModel, VitsModel],\n projection_model: AudioLDM2ProjectionModel,\n language_model: GPT2Model,\n tokenizer: Union[RobertaTokenizer, RobertaTokenizerFast],\n tokenizer_2: Union[T5Tokenizer, T5TokenizerFast, VitsTokenizer],\n feature_extractor: ClapFeatureExtractor,\n unet: AudioLDM2UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n vocoder: SpeechT5HifiGan,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n projection_model=projection_model,\n language_model=language_model,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n feature_extractor=feature_extractor,\n unet=unet,\n scheduler=scheduler,\n vocoder=vocoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n\n # Copied from diffusers.pipelines.pipeline_utils.StableDiffusionMixin.enable_vae_slicing\n def enable_vae_slicing(self):\n r\"\"\"\n Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to\n compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.\n \"\"\"\n self.vae.enable_slicing()\n\n # Copied from diffusers.pipelines.pipeline_utils.StableDiffusionMixin.disable_vae_slicing\n def disable_vae_slicing(self):\n r\"\"\"\n Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to\n computing decoding in one step.\n \"\"\"\n self.vae.disable_slicing()\n\n def enable_model_cpu_offload(self, gpu_id=0):\n r\"\"\"\n Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared\n to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`\n method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with\n `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.\n \"\"\"\n if is_accelerate_available() and is_accelerate_version(\">=\", \"0.17.0.dev0\"):\n from accelerate import cpu_offload_with_hook\n else:\n raise ImportError(\"`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.\")\n\n device = torch.device(f\"cuda:{gpu_id}\")\n\n if self.device.type != \"cpu\":\n self.to(\"cpu\", silence_dtype_warnings=True)\n torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)\n\n model_sequence = [\n self.text_encoder.text_model,\n self.text_encoder.text_projection,\n self.text_encoder_2,\n self.projection_model,\n self.language_model,\n self.unet,\n self.vae,\n self.vocoder,\n self.text_encoder,\n ]\n\n hook = None\n for cpu_offloaded_model in model_sequence:\n _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)\n\n # We'll offload the last model manually.\n self.final_offload_hook = hook\n\n def generate_language_model(\n self,\n inputs_embeds: torch.Tensor = None,\n max_new_tokens: int = 8,\n **model_kwargs,\n ):\n \"\"\"\n\n Generates a sequence of hidden-states from the language model, conditioned on the embedding inputs.\n\n Parameters:\n inputs_embeds (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):\n The sequence used as a prompt for the generation.\n max_new_tokens (`int`):\n Number of new tokens to generate.\n model_kwargs (`Dict[str, Any]`, *optional*):\n Ad hoc parametrization of additional model-specific kwargs that will be forwarded to the `forward`\n function of the model.\n\n Return:\n `inputs_embeds (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):\n The sequence of generated hidden-states.\n \"\"\"\n max_new_tokens = max_new_tokens if max_new_tokens is not None else self.language_model.config.max_new_tokens\n for _ in range(max_new_tokens):\n # prepare model inputs\n model_inputs = prepare_inputs_for_generation(inputs_embeds, **model_kwargs)\n\n # forward pass to get next hidden states\n output = self.language_model(**model_inputs, return_dict=True)\n\n next_hidden_states = output.last_hidden_state\n\n # Update the model input\n inputs_embeds = torch.cat([inputs_embeds, next_hidden_states[:, -1:, :]], dim=1)\n\n # Update generated hidden states, model inputs, and length for next step\n model_kwargs = self.language_model._update_model_kwargs_for_generation(output, model_kwargs)\n\n return inputs_embeds[:, -max_new_tokens:, :]\n\n def encode_prompt(\n self,\n prompt,\n device,\n num_waveforms_per_prompt,\n do_classifier_free_guidance,\n transcription=None,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n generated_prompt_embeds: Optional[torch.Tensor] = None,\n negative_generated_prompt_embeds: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.LongTensor] = None,\n negative_attention_mask: Optional[torch.LongTensor] = None,\n max_new_tokens: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n transcription (`str` or `List[str]`):\n transcription of text to speech\n device (`torch.device`):\n torch device\n num_waveforms_per_prompt (`int`):\n number of waveforms that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the audio generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-computed text embeddings from the Flan T5 model. Can be used to easily tweak text inputs, *e.g.*\n prompt weighting. If not provided, text embeddings will be computed from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-computed negative text embeddings from the Flan T5 model. Can be used to easily tweak text inputs,\n *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be computed from\n `negative_prompt` input argument.\n generated_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings from the GPT2 langauge model. Can be used to easily tweak text inputs,\n *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input\n argument.\n negative_generated_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings from the GPT2 language model. Can be used to easily tweak text\n inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be computed from\n `negative_prompt` input argument.\n attention_mask (`torch.LongTensor`, *optional*):\n Pre-computed attention mask to be applied to the `prompt_embeds`. If not provided, attention mask will\n be computed from `prompt` input argument.\n negative_attention_mask (`torch.LongTensor`, *optional*):\n Pre-computed attention mask to be applied to the `negative_prompt_embeds`. If not provided, attention\n mask will be computed from `negative_prompt` input argument.\n max_new_tokens (`int`, *optional*, defaults to None):\n The number of new tokens to generate with the GPT2 language model.\n Returns:\n prompt_embeds (`torch.Tensor`):\n Text embeddings from the Flan T5 model.\n attention_mask (`torch.LongTensor`):\n Attention mask to be applied to the `prompt_embeds`.\n generated_prompt_embeds (`torch.Tensor`):\n Text embeddings generated from the GPT2 langauge model.\n\n Example:\n\n ```python\n >>> import scipy\n >>> import torch\n >>> from diffusers import AudioLDM2Pipeline\n\n >>> repo_id = \"cvssp/audioldm2\"\n >>> pipe = AudioLDM2Pipeline.from_pretrained(repo_id, torch_dtype=torch.float16)\n >>> pipe = pipe.to(\"cuda\")\n\n >>> # Get text embedding vectors\n >>> prompt_embeds, attention_mask, generated_prompt_embeds = pipe.encode_prompt(\n ... prompt=\"Techno music with a strong, upbeat tempo and high melodic riffs\",\n ... device=\"cuda\",\n ... do_classifier_free_guidance=True,\n ... )\n\n >>> # Pass text embeddings to pipeline for text-conditional audio generation\n >>> audio = pipe(\n ... prompt_embeds=prompt_embeds,\n ... attention_mask=attention_mask,\n ... generated_prompt_embeds=generated_prompt_embeds,\n ... num_inference_steps=200,\n ... audio_length_in_s=10.0,\n ... ).audios[0]\n\n >>> # save generated audio sample\n >>> scipy.io.wavfile.write(\"techno.wav\", rate=16000, data=audio)\n ```\"\"\"\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # Define tokenizers and text encoders\n tokenizers = [self.tokenizer, self.tokenizer_2]\n is_vits_text_encoder = isinstance(self.text_encoder_2, VitsModel)\n\n if is_vits_text_encoder:\n text_encoders = [self.text_encoder, self.text_encoder_2.text_encoder]\n else:\n text_encoders = [self.text_encoder, self.text_encoder_2]\n\n if prompt_embeds is None:\n prompt_embeds_list = []\n attention_mask_list = []\n\n for tokenizer, text_encoder in zip(tokenizers, text_encoders):\n use_prompt = isinstance(\n tokenizer, (RobertaTokenizer, RobertaTokenizerFast, T5Tokenizer, T5TokenizerFast)\n )\n text_inputs = tokenizer(\n prompt if use_prompt else transcription,\n padding=\"max_length\"\n if isinstance(tokenizer, (RobertaTokenizer, RobertaTokenizerFast, VitsTokenizer))\n else True,\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n attention_mask = text_inputs.attention_mask\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])\n logger.warning(\n f\"The following part of your input was truncated because {text_encoder.config.model_type} can \"\n f\"only handle sequences up to {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n text_input_ids = text_input_ids.to(device)\n attention_mask = attention_mask.to(device)\n\n if text_encoder.config.model_type == \"clap\":\n prompt_embeds = text_encoder.get_text_features(\n text_input_ids,\n attention_mask=attention_mask,\n )\n # append the seq-len dim: (bs, hidden_size) -> (bs, seq_len, hidden_size)\n prompt_embeds = prompt_embeds[:, None, :]\n # make sure that we attend to this single hidden-state\n attention_mask = attention_mask.new_ones((batch_size, 1))\n elif is_vits_text_encoder:\n # Add end_token_id and attention mask in the end of sequence phonemes\n for text_input_id, text_attention_mask in zip(text_input_ids, attention_mask):\n for idx, phoneme_id in enumerate(text_input_id):\n if phoneme_id == 0:\n text_input_id[idx] = 182\n text_attention_mask[idx] = 1\n break\n prompt_embeds = text_encoder(\n text_input_ids, attention_mask=attention_mask, padding_mask=attention_mask.unsqueeze(-1)\n )\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = text_encoder(\n text_input_ids,\n attention_mask=attention_mask,\n )\n prompt_embeds = prompt_embeds[0]\n\n prompt_embeds_list.append(prompt_embeds)\n attention_mask_list.append(attention_mask)\n\n projection_output = self.projection_model(\n hidden_states=prompt_embeds_list[0],\n hidden_states_1=prompt_embeds_list[1],\n attention_mask=attention_mask_list[0],\n attention_mask_1=attention_mask_list[1],\n )\n projected_prompt_embeds = projection_output.hidden_states\n projected_attention_mask = projection_output.attention_mask\n\n generated_prompt_embeds = self.generate_language_model(\n projected_prompt_embeds,\n attention_mask=projected_attention_mask,\n max_new_tokens=max_new_tokens,\n )\n\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n attention_mask = (\n attention_mask.to(device=device)\n if attention_mask is not None\n else torch.ones(prompt_embeds.shape[:2], dtype=torch.long, device=device)\n )\n generated_prompt_embeds = generated_prompt_embeds.to(dtype=self.language_model.dtype, device=device)\n\n bs_embed, seq_len, hidden_size = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_waveforms_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_waveforms_per_prompt, seq_len, hidden_size)\n\n # duplicate attention mask for each generation per prompt\n attention_mask = attention_mask.repeat(1, num_waveforms_per_prompt)\n attention_mask = attention_mask.view(bs_embed * num_waveforms_per_prompt, seq_len)\n\n bs_embed, seq_len, hidden_size = generated_prompt_embeds.shape\n # duplicate generated embeddings for each generation per prompt, using mps friendly method\n generated_prompt_embeds = generated_prompt_embeds.repeat(1, num_waveforms_per_prompt, 1)\n generated_prompt_embeds = generated_prompt_embeds.view(\n bs_embed * num_waveforms_per_prompt, seq_len, hidden_size\n )\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n negative_prompt_embeds_list = []\n negative_attention_mask_list = []\n max_length = prompt_embeds.shape[1]\n for tokenizer, text_encoder in zip(tokenizers, text_encoders):\n uncond_input = tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length\n if isinstance(tokenizer, (RobertaTokenizer, RobertaTokenizerFast, VitsTokenizer))\n else max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n uncond_input_ids = uncond_input.input_ids.to(device)\n negative_attention_mask = uncond_input.attention_mask.to(device)\n\n if text_encoder.config.model_type == \"clap\":\n negative_prompt_embeds = text_encoder.get_text_features(\n uncond_input_ids,\n attention_mask=negative_attention_mask,\n )\n # append the seq-len dim: (bs, hidden_size) -> (bs, seq_len, hidden_size)\n negative_prompt_embeds = negative_prompt_embeds[:, None, :]\n # make sure that we attend to this single hidden-state\n negative_attention_mask = negative_attention_mask.new_ones((batch_size, 1))\n elif is_vits_text_encoder:\n negative_prompt_embeds = torch.zeros(\n batch_size,\n tokenizer.model_max_length,\n text_encoder.config.hidden_size,\n ).to(dtype=self.text_encoder_2.dtype, device=device)\n negative_attention_mask = torch.zeros(batch_size, tokenizer.model_max_length).to(\n dtype=self.text_encoder_2.dtype, device=device\n )\n else:\n negative_prompt_embeds = text_encoder(\n uncond_input_ids,\n attention_mask=negative_attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n negative_prompt_embeds_list.append(negative_prompt_embeds)\n negative_attention_mask_list.append(negative_attention_mask)\n\n projection_output = self.projection_model(\n hidden_states=negative_prompt_embeds_list[0],\n hidden_states_1=negative_prompt_embeds_list[1],\n attention_mask=negative_attention_mask_list[0],\n attention_mask_1=negative_attention_mask_list[1],\n )\n negative_projected_prompt_embeds = projection_output.hidden_states\n negative_projected_attention_mask = projection_output.attention_mask\n\n negative_generated_prompt_embeds = self.generate_language_model(\n negative_projected_prompt_embeds,\n attention_mask=negative_projected_attention_mask,\n max_new_tokens=max_new_tokens,\n )\n\n if do_classifier_free_guidance:\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n negative_attention_mask = (\n negative_attention_mask.to(device=device)\n if negative_attention_mask is not None\n else torch.ones(negative_prompt_embeds.shape[:2], dtype=torch.long, device=device)\n )\n negative_generated_prompt_embeds = negative_generated_prompt_embeds.to(\n dtype=self.language_model.dtype, device=device\n )\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_waveforms_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_waveforms_per_prompt, seq_len, -1)\n\n # duplicate unconditional attention mask for each generation per prompt\n negative_attention_mask = negative_attention_mask.repeat(1, num_waveforms_per_prompt)\n negative_attention_mask = negative_attention_mask.view(batch_size * num_waveforms_per_prompt, seq_len)\n\n # duplicate unconditional generated embeddings for each generation per prompt\n seq_len = negative_generated_prompt_embeds.shape[1]\n negative_generated_prompt_embeds = negative_generated_prompt_embeds.repeat(1, num_waveforms_per_prompt, 1)\n negative_generated_prompt_embeds = negative_generated_prompt_embeds.view(\n batch_size * num_waveforms_per_prompt, seq_len, -1\n )\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n attention_mask = torch.cat([negative_attention_mask, attention_mask])\n generated_prompt_embeds = torch.cat([negative_generated_prompt_embeds, generated_prompt_embeds])\n\n return prompt_embeds, attention_mask, generated_prompt_embeds\n\n # Copied from diffusers.pipelines.audioldm.pipeline_audioldm.AudioLDMPipeline.mel_spectrogram_to_waveform\n def mel_spectrogram_to_waveform(self, mel_spectrogram):\n if mel_spectrogram.dim() == 4:\n mel_spectrogram = mel_spectrogram.squeeze(1)\n\n waveform = self.vocoder(mel_spectrogram)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n waveform = waveform.cpu().float()\n return waveform\n\n def score_waveforms(self, text, audio, num_waveforms_per_prompt, device, dtype):\n if not is_librosa_available():\n logger.info(\n \"Automatic scoring of the generated audio waveforms against the input prompt text requires the \"\n \"`librosa` package to resample the generated waveforms. Returning the audios in the order they were \"\n \"generated. To enable automatic scoring, install `librosa` with: `pip install librosa`.\"\n )\n return audio\n inputs = self.tokenizer(text, return_tensors=\"pt\", padding=True)\n resampled_audio = librosa.resample(\n audio.numpy(), orig_sr=self.vocoder.config.sampling_rate, target_sr=self.feature_extractor.sampling_rate\n )\n inputs[\"input_features\"] = self.feature_extractor(\n list(resampled_audio), return_tensors=\"pt\", sampling_rate=self.feature_extractor.sampling_rate\n ).input_features.type(dtype)\n inputs = inputs.to(device)\n\n # compute the audio-text similarity score using the CLAP model\n logits_per_text = self.text_encoder(**inputs).logits_per_text\n # sort by the highest matching generations per prompt\n indices = torch.argsort(logits_per_text, dim=1, descending=True)[:, :num_waveforms_per_prompt]\n audio = torch.index_select(audio, 0, indices.reshape(-1).cpu())\n return audio\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n audio_length_in_s,\n vocoder_upsample_factor,\n callback_steps,\n transcription=None,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n generated_prompt_embeds=None,\n negative_generated_prompt_embeds=None,\n attention_mask=None,\n negative_attention_mask=None,\n ):\n min_audio_length_in_s = vocoder_upsample_factor * self.vae_scale_factor\n if audio_length_in_s < min_audio_length_in_s:\n raise ValueError(\n f\"`audio_length_in_s` has to be a positive value greater than or equal to {min_audio_length_in_s}, but \"\n f\"is {audio_length_in_s}.\"\n )\n\n if self.vocoder.config.model_in_dim % self.vae_scale_factor != 0:\n raise ValueError(\n f\"The number of frequency bins in the vocoder's log-mel spectrogram has to be divisible by the \"\n f\"VAE scale factor, but got {self.vocoder.config.model_in_dim} bins and a scale factor of \"\n f\"{self.vae_scale_factor}.\"\n )\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and (prompt_embeds is None or generated_prompt_embeds is None):\n raise ValueError(\n \"Provide either `prompt`, or `prompt_embeds` and `generated_prompt_embeds`. Cannot leave \"\n \"`prompt` undefined without specifying both `prompt_embeds` and `generated_prompt_embeds`.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_embeds is not None and negative_generated_prompt_embeds is None:\n raise ValueError(\n \"Cannot forward `negative_prompt_embeds` without `negative_generated_prompt_embeds`. Ensure that\"\n \"both arguments are specified\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n if attention_mask is not None and attention_mask.shape != prompt_embeds.shape[:2]:\n raise ValueError(\n \"`attention_mask should have the same batch size and sequence length as `prompt_embeds`, but got:\"\n f\"`attention_mask: {attention_mask.shape} != `prompt_embeds` {prompt_embeds.shape}\"\n )\n\n if transcription is None:\n if self.text_encoder_2.config.model_type == \"vits\":\n raise ValueError(\"Cannot forward without transcription. Please make sure to\" \" have transcription\")\n elif transcription is not None and (\n not isinstance(transcription, str) and not isinstance(transcription, list)\n ):\n raise ValueError(f\"`transcription` has to be of type `str` or `list` but is {type(transcription)}\")\n\n if generated_prompt_embeds is not None and negative_generated_prompt_embeds is not None:\n if generated_prompt_embeds.shape != negative_generated_prompt_embeds.shape:\n raise ValueError(\n \"`generated_prompt_embeds` and `negative_generated_prompt_embeds` must have the same shape when \"\n f\"passed directly, but got: `generated_prompt_embeds` {generated_prompt_embeds.shape} != \"\n f\"`negative_generated_prompt_embeds` {negative_generated_prompt_embeds.shape}.\"\n )\n if (\n negative_attention_mask is not None\n and negative_attention_mask.shape != negative_prompt_embeds.shape[:2]\n ):\n raise ValueError(\n \"`attention_mask should have the same batch size and sequence length as `prompt_embeds`, but got:\"\n f\"`attention_mask: {negative_attention_mask.shape} != `prompt_embeds` {negative_prompt_embeds.shape}\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents with width->self.vocoder.config.model_in_dim\n def prepare_latents(self, batch_size, num_channels_latents, height, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(self.vocoder.config.model_in_dim) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n transcription: Union[str, List[str]] = None,\n audio_length_in_s: Optional[float] = None,\n num_inference_steps: int = 200,\n guidance_scale: float = 3.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_waveforms_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n generated_prompt_embeds: Optional[torch.Tensor] = None,\n negative_generated_prompt_embeds: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.LongTensor] = None,\n negative_attention_mask: Optional[torch.LongTensor] = None,\n max_new_tokens: Optional[int] = None,\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: Optional[int] = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n output_type: Optional[str] = \"np\",\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide audio generation. If not defined, you need to pass `prompt_embeds`.\n transcription (`str` or `List[str]`, *optional*):\\\n The transcript for text to speech.\n audio_length_in_s (`int`, *optional*, defaults to 10.24):\n The length of the generated audio sample in seconds.\n num_inference_steps (`int`, *optional*, defaults to 200):\n The number of denoising steps. More denoising steps usually lead to a higher quality audio at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 3.5):\n A higher guidance scale value encourages the model to generate audio that is closely linked to the text\n `prompt` at the expense of lower sound quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in audio generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_waveforms_per_prompt (`int`, *optional*, defaults to 1):\n The number of waveforms to generate per prompt. If `num_waveforms_per_prompt > 1`, then automatic\n scoring is performed between the generated outputs and the text prompt. This scoring ranks the\n generated waveforms based on their cosine similarity with the text input in the joint text-audio\n embedding space.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for spectrogram\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n generated_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings from the GPT2 langauge model. Can be used to easily tweak text inputs,\n *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input\n argument.\n negative_generated_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings from the GPT2 language model. Can be used to easily tweak text\n inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be computed from\n `negative_prompt` input argument.\n attention_mask (`torch.LongTensor`, *optional*):\n Pre-computed attention mask to be applied to the `prompt_embeds`. If not provided, attention mask will\n be computed from `prompt` input argument.\n negative_attention_mask (`torch.LongTensor`, *optional*):\n Pre-computed attention mask to be applied to the `negative_prompt_embeds`. If not provided, attention\n mask will be computed from `negative_prompt` input argument.\n max_new_tokens (`int`, *optional*, defaults to None):\n Number of new tokens to generate with the GPT2 language model. If not provided, number of tokens will\n be taken from the config of the model.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n output_type (`str`, *optional*, defaults to `\"np\"`):\n The output format of the generated audio. Choose between `\"np\"` to return a NumPy `np.ndarray` or\n `\"pt\"` to return a PyTorch `torch.Tensor` object. Set to `\"latent\"` to return the latent diffusion\n model (LDM) output.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated audio.\n \"\"\"\n # 0. Convert audio input length from seconds to spectrogram height\n vocoder_upsample_factor = np.prod(self.vocoder.config.upsample_rates) / self.vocoder.config.sampling_rate\n\n if audio_length_in_s is None:\n audio_length_in_s = self.unet.config.sample_size * self.vae_scale_factor * vocoder_upsample_factor\n\n height = int(audio_length_in_s / vocoder_upsample_factor)\n\n original_waveform_length = int(audio_length_in_s * self.vocoder.config.sampling_rate)\n if height % self.vae_scale_factor != 0:\n height = int(np.ceil(height / self.vae_scale_factor)) * self.vae_scale_factor\n logger.info(\n f\"Audio length in seconds {audio_length_in_s} is increased to {height * vocoder_upsample_factor} \"\n f\"so that it can be handled by the model. It will be cut to {audio_length_in_s} after the \"\n f\"denoising process.\"\n )\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n audio_length_in_s,\n vocoder_upsample_factor,\n callback_steps,\n transcription,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n generated_prompt_embeds,\n negative_generated_prompt_embeds,\n attention_mask,\n negative_attention_mask,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n prompt_embeds, attention_mask, generated_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_waveforms_per_prompt,\n do_classifier_free_guidance,\n transcription,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n generated_prompt_embeds=generated_prompt_embeds,\n negative_generated_prompt_embeds=negative_generated_prompt_embeds,\n attention_mask=attention_mask,\n negative_attention_mask=negative_attention_mask,\n max_new_tokens=max_new_tokens,\n )\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_waveforms_per_prompt,\n num_channels_latents,\n height,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=generated_prompt_embeds,\n encoder_hidden_states_1=prompt_embeds,\n encoder_attention_mask_1=attention_mask,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n self.maybe_free_model_hooks()\n\n # 8. Post-processing\n if not output_type == \"latent\":\n latents = 1 / self.vae.config.scaling_factor * latents\n mel_spectrogram = self.vae.decode(latents).sample\n else:\n return AudioPipelineOutput(audios=latents)\n\n audio = self.mel_spectrogram_to_waveform(mel_spectrogram)\n\n audio = audio[:, :original_waveform_length]\n\n # 9. Automatic scoring\n if num_waveforms_per_prompt > 1 and prompt is not None:\n audio = self.score_waveforms(\n text=prompt,\n audio=audio,\n num_waveforms_per_prompt=num_waveforms_per_prompt,\n device=device,\n dtype=prompt_embeds.dtype,\n )\n\n if output_type == \"np\":\n audio = audio.numpy()\n\n if not return_dict:\n return (audio,)\n\n return AudioPipelineOutput(audios=audio)\n" }, { "path": "diffusers/src/diffusers/pipelines/auto_pipeline.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom collections import OrderedDict\n\nfrom huggingface_hub.utils import validate_hf_hub_args\n\nfrom ..configuration_utils import ConfigMixin\nfrom .controlnet import (\n StableDiffusionControlNetImg2ImgPipeline,\n StableDiffusionControlNetInpaintPipeline,\n StableDiffusionControlNetPipeline,\n StableDiffusionXLControlNetImg2ImgPipeline,\n StableDiffusionXLControlNetInpaintPipeline,\n StableDiffusionXLControlNetPipeline,\n)\nfrom .deepfloyd_if import IFImg2ImgPipeline, IFInpaintingPipeline, IFPipeline\nfrom .hunyuandit import HunyuanDiTPipeline\nfrom .kandinsky import (\n KandinskyCombinedPipeline,\n KandinskyImg2ImgCombinedPipeline,\n KandinskyImg2ImgPipeline,\n KandinskyInpaintCombinedPipeline,\n KandinskyInpaintPipeline,\n KandinskyPipeline,\n)\nfrom .kandinsky2_2 import (\n KandinskyV22CombinedPipeline,\n KandinskyV22Img2ImgCombinedPipeline,\n KandinskyV22Img2ImgPipeline,\n KandinskyV22InpaintCombinedPipeline,\n KandinskyV22InpaintPipeline,\n KandinskyV22Pipeline,\n)\nfrom .kandinsky3 import Kandinsky3Img2ImgPipeline, Kandinsky3Pipeline\nfrom .latent_consistency_models import LatentConsistencyModelImg2ImgPipeline, LatentConsistencyModelPipeline\nfrom .pixart_alpha import PixArtAlphaPipeline, PixArtSigmaPipeline\nfrom .stable_cascade import StableCascadeCombinedPipeline, StableCascadeDecoderPipeline\nfrom .stable_diffusion import (\n StableDiffusionImg2ImgPipeline,\n StableDiffusionInpaintPipeline,\n StableDiffusionPipeline,\n)\nfrom .stable_diffusion_3 import (\n StableDiffusion3Img2ImgPipeline,\n StableDiffusion3Pipeline,\n)\nfrom .stable_diffusion_xl import (\n StableDiffusionXLImg2ImgPipeline,\n StableDiffusionXLInpaintPipeline,\n StableDiffusionXLPipeline,\n)\nfrom .wuerstchen import WuerstchenCombinedPipeline, WuerstchenDecoderPipeline\n\n\nAUTO_TEXT2IMAGE_PIPELINES_MAPPING = OrderedDict(\n [\n (\"stable-diffusion\", StableDiffusionPipeline),\n (\"stable-diffusion-xl\", StableDiffusionXLPipeline),\n (\"stable-diffusion-3\", StableDiffusion3Pipeline),\n (\"if\", IFPipeline),\n (\"hunyuan\", HunyuanDiTPipeline),\n (\"kandinsky\", KandinskyCombinedPipeline),\n (\"kandinsky22\", KandinskyV22CombinedPipeline),\n (\"kandinsky3\", Kandinsky3Pipeline),\n (\"stable-diffusion-controlnet\", StableDiffusionControlNetPipeline),\n (\"stable-diffusion-xl-controlnet\", StableDiffusionXLControlNetPipeline),\n (\"wuerstchen\", WuerstchenCombinedPipeline),\n (\"cascade\", StableCascadeCombinedPipeline),\n (\"lcm\", LatentConsistencyModelPipeline),\n (\"pixart-alpha\", PixArtAlphaPipeline),\n (\"pixart-sigma\", PixArtSigmaPipeline),\n ]\n)\n\nAUTO_IMAGE2IMAGE_PIPELINES_MAPPING = OrderedDict(\n [\n (\"stable-diffusion\", StableDiffusionImg2ImgPipeline),\n (\"stable-diffusion-xl\", StableDiffusionXLImg2ImgPipeline),\n (\"stable-diffusion-3\", StableDiffusion3Img2ImgPipeline),\n (\"if\", IFImg2ImgPipeline),\n (\"kandinsky\", KandinskyImg2ImgCombinedPipeline),\n (\"kandinsky22\", KandinskyV22Img2ImgCombinedPipeline),\n (\"kandinsky3\", Kandinsky3Img2ImgPipeline),\n (\"stable-diffusion-controlnet\", StableDiffusionControlNetImg2ImgPipeline),\n (\"stable-diffusion-xl-controlnet\", StableDiffusionXLControlNetImg2ImgPipeline),\n (\"lcm\", LatentConsistencyModelImg2ImgPipeline),\n ]\n)\n\nAUTO_INPAINT_PIPELINES_MAPPING = OrderedDict(\n [\n (\"stable-diffusion\", StableDiffusionInpaintPipeline),\n (\"stable-diffusion-xl\", StableDiffusionXLInpaintPipeline),\n (\"if\", IFInpaintingPipeline),\n (\"kandinsky\", KandinskyInpaintCombinedPipeline),\n (\"kandinsky22\", KandinskyV22InpaintCombinedPipeline),\n (\"stable-diffusion-controlnet\", StableDiffusionControlNetInpaintPipeline),\n (\"stable-diffusion-xl-controlnet\", StableDiffusionXLControlNetInpaintPipeline),\n ]\n)\n\n_AUTO_TEXT2IMAGE_DECODER_PIPELINES_MAPPING = OrderedDict(\n [\n (\"kandinsky\", KandinskyPipeline),\n (\"kandinsky22\", KandinskyV22Pipeline),\n (\"wuerstchen\", WuerstchenDecoderPipeline),\n (\"cascade\", StableCascadeDecoderPipeline),\n ]\n)\n_AUTO_IMAGE2IMAGE_DECODER_PIPELINES_MAPPING = OrderedDict(\n [\n (\"kandinsky\", KandinskyImg2ImgPipeline),\n (\"kandinsky22\", KandinskyV22Img2ImgPipeline),\n ]\n)\n_AUTO_INPAINT_DECODER_PIPELINES_MAPPING = OrderedDict(\n [\n (\"kandinsky\", KandinskyInpaintPipeline),\n (\"kandinsky22\", KandinskyV22InpaintPipeline),\n ]\n)\n\nSUPPORTED_TASKS_MAPPINGS = [\n AUTO_TEXT2IMAGE_PIPELINES_MAPPING,\n AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,\n AUTO_INPAINT_PIPELINES_MAPPING,\n _AUTO_TEXT2IMAGE_DECODER_PIPELINES_MAPPING,\n _AUTO_IMAGE2IMAGE_DECODER_PIPELINES_MAPPING,\n _AUTO_INPAINT_DECODER_PIPELINES_MAPPING,\n]\n\n\ndef _get_connected_pipeline(pipeline_cls):\n # for now connected pipelines can only be loaded from decoder pipelines, such as kandinsky-community/kandinsky-2-2-decoder\n if pipeline_cls in _AUTO_TEXT2IMAGE_DECODER_PIPELINES_MAPPING.values():\n return _get_task_class(\n AUTO_TEXT2IMAGE_PIPELINES_MAPPING, pipeline_cls.__name__, throw_error_if_not_exist=False\n )\n if pipeline_cls in _AUTO_IMAGE2IMAGE_DECODER_PIPELINES_MAPPING.values():\n return _get_task_class(\n AUTO_IMAGE2IMAGE_PIPELINES_MAPPING, pipeline_cls.__name__, throw_error_if_not_exist=False\n )\n if pipeline_cls in _AUTO_INPAINT_DECODER_PIPELINES_MAPPING.values():\n return _get_task_class(AUTO_INPAINT_PIPELINES_MAPPING, pipeline_cls.__name__, throw_error_if_not_exist=False)\n\n\ndef _get_task_class(mapping, pipeline_class_name, throw_error_if_not_exist: bool = True):\n def get_model(pipeline_class_name):\n for task_mapping in SUPPORTED_TASKS_MAPPINGS:\n for model_name, pipeline in task_mapping.items():\n if pipeline.__name__ == pipeline_class_name:\n return model_name\n\n model_name = get_model(pipeline_class_name)\n\n if model_name is not None:\n task_class = mapping.get(model_name, None)\n if task_class is not None:\n return task_class\n\n if throw_error_if_not_exist:\n raise ValueError(f\"AutoPipeline can't find a pipeline linked to {pipeline_class_name} for {model_name}\")\n\n\nclass AutoPipelineForText2Image(ConfigMixin):\n r\"\"\"\n\n [`AutoPipelineForText2Image`] is a generic pipeline class that instantiates a text-to-image pipeline class. The\n specific underlying pipeline class is automatically selected from either the\n [`~AutoPipelineForText2Image.from_pretrained`] or [`~AutoPipelineForText2Image.from_pipe`] methods.\n\n This class cannot be instantiated using `__init__()` (throws an error).\n\n Class attributes:\n\n - **config_name** (`str`) -- The configuration filename that stores the class and module names of all the\n diffusion pipeline's components.\n\n \"\"\"\n\n config_name = \"model_index.json\"\n\n def __init__(self, *args, **kwargs):\n raise EnvironmentError(\n f\"{self.__class__.__name__} is designed to be instantiated \"\n f\"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path)` or \"\n f\"`{self.__class__.__name__}.from_pipe(pipeline)` methods.\"\n )\n\n @classmethod\n @validate_hf_hub_args\n def from_pretrained(cls, pretrained_model_or_path, **kwargs):\n r\"\"\"\n Instantiates a text-to-image Pytorch diffusion pipeline from pretrained pipeline weight.\n\n The from_pretrained() method takes care of returning the correct pipeline class instance by:\n 1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its\n config object\n 2. Find the text-to-image pipeline linked to the pipeline class using pattern matching on pipeline class\n name.\n\n If a `controlnet` argument is passed, it will instantiate a [`StableDiffusionControlNetPipeline`] object.\n\n The pipeline is set in evaluation mode (`model.eval()`) by default.\n\n If you get the error message below, you need to finetune the weights for your downstream task:\n\n ```\n Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:\n - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated\n You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n ```\n\n Parameters:\n pretrained_model_or_path (`str` or `os.PathLike`, *optional*):\n Can be either:\n\n - A string, the *repo id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline\n hosted on the Hub.\n - A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights\n saved using\n [`~DiffusionPipeline.save_pretrained`].\n torch_dtype (`str` or `torch.dtype`, *optional*):\n Override the default `torch.dtype` and load the model with another dtype. If \"auto\" is passed, the\n dtype is automatically derived from the model's weights.\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\n cached versions if they exist.\n cache_dir (`Union[str, os.PathLike]`, *optional*):\n Path to a directory where a downloaded pretrained model configuration is cached if the standard cache\n is not used.\n resume_download:\n Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1\n of Diffusers.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\n output_loading_info(`bool`, *optional*, defaults to `False`):\n Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.\n local_files_only (`bool`, *optional*, defaults to `False`):\n Whether to only load local model weights and configuration files or not. If set to `True`, the model\n won't be downloaded from the Hub.\n token (`str` or *bool*, *optional*):\n The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from\n `diffusers-cli login` (stored in `~/.huggingface`) is used.\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier\n allowed by Git.\n custom_revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, or a commit id similar to\n `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a\n custom pipeline from GitHub, otherwise it defaults to `\"main\"` when loading from the Hub.\n mirror (`str`, *optional*):\n Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not\n guarantee the timeliness or safety of the source, and you should refer to the mirror site for more\n information.\n device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):\n A map that specifies where each submodule should go. It doesn’t need to be defined for each\n parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the\n same device.\n\n Set `device_map=\"auto\"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For\n more information about each option see [designing a device\n map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).\n max_memory (`Dict`, *optional*):\n A dictionary device identifier for the maximum memory. Will default to the maximum memory available for\n each GPU and the available CPU RAM if unset.\n offload_folder (`str` or `os.PathLike`, *optional*):\n The path to offload weights if device_map contains the value `\"disk\"`.\n offload_state_dict (`bool`, *optional*):\n If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if\n the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`\n when there is some disk offload.\n low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):\n Speed up model loading only loading the pretrained weights and not initializing the weights. This also\n tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.\n Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this\n argument to `True` will raise an error.\n use_safetensors (`bool`, *optional*, defaults to `None`):\n If set to `None`, the safetensors weights are downloaded if they're available **and** if the\n safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors\n weights. If set to `False`, safetensors weights are not loaded.\n kwargs (remaining dictionary of keyword arguments, *optional*):\n Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline\n class). The overwritten components are passed directly to the pipelines `__init__` method. See example\n below for more information.\n variant (`str`, *optional*):\n Load weights from a specified variant filename such as `\"fp16\"` or `\"ema\"`. This is ignored when\n loading `from_flax`.\n\n \n\n To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with\n `huggingface-cli login`.\n\n \n\n Examples:\n\n ```py\n >>> from diffusers import AutoPipelineForText2Image\n\n >>> pipeline = AutoPipelineForText2Image.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n >>> image = pipeline(prompt).images[0]\n ```\n \"\"\"\n cache_dir = kwargs.pop(\"cache_dir\", None)\n force_download = kwargs.pop(\"force_download\", False)\n resume_download = kwargs.pop(\"resume_download\", None)\n proxies = kwargs.pop(\"proxies\", None)\n token = kwargs.pop(\"token\", None)\n local_files_only = kwargs.pop(\"local_files_only\", False)\n revision = kwargs.pop(\"revision\", None)\n\n load_config_kwargs = {\n \"cache_dir\": cache_dir,\n \"force_download\": force_download,\n \"resume_download\": resume_download,\n \"proxies\": proxies,\n \"token\": token,\n \"local_files_only\": local_files_only,\n \"revision\": revision,\n }\n\n config = cls.load_config(pretrained_model_or_path, **load_config_kwargs)\n orig_class_name = config[\"_class_name\"]\n\n if \"controlnet\" in kwargs:\n orig_class_name = config[\"_class_name\"].replace(\"Pipeline\", \"ControlNetPipeline\")\n\n text_2_image_cls = _get_task_class(AUTO_TEXT2IMAGE_PIPELINES_MAPPING, orig_class_name)\n\n kwargs = {**load_config_kwargs, **kwargs}\n return text_2_image_cls.from_pretrained(pretrained_model_or_path, **kwargs)\n\n @classmethod\n def from_pipe(cls, pipeline, **kwargs):\n r\"\"\"\n Instantiates a text-to-image Pytorch diffusion pipeline from another instantiated diffusion pipeline class.\n\n The from_pipe() method takes care of returning the correct pipeline class instance by finding the text-to-image\n pipeline linked to the pipeline class using pattern matching on pipeline class name.\n\n All the modules the pipeline contains will be used to initialize the new pipeline without reallocating\n additional memory.\n\n The pipeline is set in evaluation mode (`model.eval()`) by default.\n\n Parameters:\n pipeline (`DiffusionPipeline`):\n an instantiated `DiffusionPipeline` object\n\n ```py\n >>> from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image\n\n >>> pipe_i2i = AutoPipelineForImage2Image.from_pretrained(\n ... \"runwayml/stable-diffusion-v1-5\", requires_safety_checker=False\n ... )\n\n >>> pipe_t2i = AutoPipelineForText2Image.from_pipe(pipe_i2i)\n >>> image = pipe_t2i(prompt).images[0]\n ```\n \"\"\"\n\n original_config = dict(pipeline.config)\n original_cls_name = pipeline.__class__.__name__\n\n # derive the pipeline class to instantiate\n text_2_image_cls = _get_task_class(AUTO_TEXT2IMAGE_PIPELINES_MAPPING, original_cls_name)\n\n if \"controlnet\" in kwargs:\n if kwargs[\"controlnet\"] is not None:\n text_2_image_cls = _get_task_class(\n AUTO_TEXT2IMAGE_PIPELINES_MAPPING,\n text_2_image_cls.__name__.replace(\"ControlNet\", \"\").replace(\"Pipeline\", \"ControlNetPipeline\"),\n )\n else:\n text_2_image_cls = _get_task_class(\n AUTO_TEXT2IMAGE_PIPELINES_MAPPING,\n text_2_image_cls.__name__.replace(\"ControlNetPipeline\", \"Pipeline\"),\n )\n\n # define expected module and optional kwargs given the pipeline signature\n expected_modules, optional_kwargs = text_2_image_cls._get_signature_keys(text_2_image_cls)\n\n pretrained_model_name_or_path = original_config.pop(\"_name_or_path\", None)\n\n # allow users pass modules in `kwargs` to override the original pipeline's components\n passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}\n original_class_obj = {\n k: pipeline.components[k]\n for k, v in pipeline.components.items()\n if k in expected_modules and k not in passed_class_obj\n }\n\n # allow users pass optional kwargs to override the original pipelines config attribute\n passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}\n original_pipe_kwargs = {\n k: original_config[k]\n for k, v in original_config.items()\n if k in optional_kwargs and k not in passed_pipe_kwargs\n }\n\n # config that were not expected by original pipeline is stored as private attribute\n # we will pass them as optional arguments if they can be accepted by the pipeline\n additional_pipe_kwargs = [\n k[1:]\n for k in original_config.keys()\n if k.startswith(\"_\") and k[1:] in optional_kwargs and k[1:] not in passed_pipe_kwargs\n ]\n for k in additional_pipe_kwargs:\n original_pipe_kwargs[k] = original_config.pop(f\"_{k}\")\n\n text_2_image_kwargs = {**passed_class_obj, **original_class_obj, **passed_pipe_kwargs, **original_pipe_kwargs}\n\n # store unused config as private attribute\n unused_original_config = {\n f\"{'' if k.startswith('_') else '_'}{k}\": original_config[k]\n for k, v in original_config.items()\n if k not in text_2_image_kwargs\n }\n\n missing_modules = set(expected_modules) - set(pipeline._optional_components) - set(text_2_image_kwargs.keys())\n\n if len(missing_modules) > 0:\n raise ValueError(\n f\"Pipeline {text_2_image_cls} expected {expected_modules}, but only {set(list(passed_class_obj.keys()) + list(original_class_obj.keys()))} were passed\"\n )\n\n model = text_2_image_cls(**text_2_image_kwargs)\n model.register_to_config(_name_or_path=pretrained_model_name_or_path)\n model.register_to_config(**unused_original_config)\n\n return model\n\n\nclass AutoPipelineForImage2Image(ConfigMixin):\n r\"\"\"\n\n [`AutoPipelineForImage2Image`] is a generic pipeline class that instantiates an image-to-image pipeline class. The\n specific underlying pipeline class is automatically selected from either the\n [`~AutoPipelineForImage2Image.from_pretrained`] or [`~AutoPipelineForImage2Image.from_pipe`] methods.\n\n This class cannot be instantiated using `__init__()` (throws an error).\n\n Class attributes:\n\n - **config_name** (`str`) -- The configuration filename that stores the class and module names of all the\n diffusion pipeline's components.\n\n \"\"\"\n\n config_name = \"model_index.json\"\n\n def __init__(self, *args, **kwargs):\n raise EnvironmentError(\n f\"{self.__class__.__name__} is designed to be instantiated \"\n f\"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path)` or \"\n f\"`{self.__class__.__name__}.from_pipe(pipeline)` methods.\"\n )\n\n @classmethod\n @validate_hf_hub_args\n def from_pretrained(cls, pretrained_model_or_path, **kwargs):\n r\"\"\"\n Instantiates a image-to-image Pytorch diffusion pipeline from pretrained pipeline weight.\n\n The from_pretrained() method takes care of returning the correct pipeline class instance by:\n 1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its\n config object\n 2. Find the image-to-image pipeline linked to the pipeline class using pattern matching on pipeline class\n name.\n\n If a `controlnet` argument is passed, it will instantiate a [`StableDiffusionControlNetImg2ImgPipeline`]\n object.\n\n The pipeline is set in evaluation mode (`model.eval()`) by default.\n\n If you get the error message below, you need to finetune the weights for your downstream task:\n\n ```\n Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:\n - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated\n You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n ```\n\n Parameters:\n pretrained_model_or_path (`str` or `os.PathLike`, *optional*):\n Can be either:\n\n - A string, the *repo id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline\n hosted on the Hub.\n - A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights\n saved using\n [`~DiffusionPipeline.save_pretrained`].\n torch_dtype (`str` or `torch.dtype`, *optional*):\n Override the default `torch.dtype` and load the model with another dtype. If \"auto\" is passed, the\n dtype is automatically derived from the model's weights.\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\n cached versions if they exist.\n cache_dir (`Union[str, os.PathLike]`, *optional*):\n Path to a directory where a downloaded pretrained model configuration is cached if the standard cache\n is not used.\n resume_download:\n Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1\n of Diffusers.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\n output_loading_info(`bool`, *optional*, defaults to `False`):\n Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.\n local_files_only (`bool`, *optional*, defaults to `False`):\n Whether to only load local model weights and configuration files or not. If set to `True`, the model\n won't be downloaded from the Hub.\n token (`str` or *bool*, *optional*):\n The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from\n `diffusers-cli login` (stored in `~/.huggingface`) is used.\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier\n allowed by Git.\n custom_revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, or a commit id similar to\n `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a\n custom pipeline from GitHub, otherwise it defaults to `\"main\"` when loading from the Hub.\n mirror (`str`, *optional*):\n Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not\n guarantee the timeliness or safety of the source, and you should refer to the mirror site for more\n information.\n device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):\n A map that specifies where each submodule should go. It doesn’t need to be defined for each\n parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the\n same device.\n\n Set `device_map=\"auto\"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For\n more information about each option see [designing a device\n map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).\n max_memory (`Dict`, *optional*):\n A dictionary device identifier for the maximum memory. Will default to the maximum memory available for\n each GPU and the available CPU RAM if unset.\n offload_folder (`str` or `os.PathLike`, *optional*):\n The path to offload weights if device_map contains the value `\"disk\"`.\n offload_state_dict (`bool`, *optional*):\n If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if\n the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`\n when there is some disk offload.\n low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):\n Speed up model loading only loading the pretrained weights and not initializing the weights. This also\n tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.\n Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this\n argument to `True` will raise an error.\n use_safetensors (`bool`, *optional*, defaults to `None`):\n If set to `None`, the safetensors weights are downloaded if they're available **and** if the\n safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors\n weights. If set to `False`, safetensors weights are not loaded.\n kwargs (remaining dictionary of keyword arguments, *optional*):\n Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline\n class). The overwritten components are passed directly to the pipelines `__init__` method. See example\n below for more information.\n variant (`str`, *optional*):\n Load weights from a specified variant filename such as `\"fp16\"` or `\"ema\"`. This is ignored when\n loading `from_flax`.\n\n \n\n To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with\n `huggingface-cli login`.\n\n \n\n Examples:\n\n ```py\n >>> from diffusers import AutoPipelineForImage2Image\n\n >>> pipeline = AutoPipelineForImage2Image.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n >>> image = pipeline(prompt, image).images[0]\n ```\n \"\"\"\n cache_dir = kwargs.pop(\"cache_dir\", None)\n force_download = kwargs.pop(\"force_download\", False)\n resume_download = kwargs.pop(\"resume_download\", None)\n proxies = kwargs.pop(\"proxies\", None)\n token = kwargs.pop(\"token\", None)\n local_files_only = kwargs.pop(\"local_files_only\", False)\n revision = kwargs.pop(\"revision\", None)\n\n load_config_kwargs = {\n \"cache_dir\": cache_dir,\n \"force_download\": force_download,\n \"resume_download\": resume_download,\n \"proxies\": proxies,\n \"token\": token,\n \"local_files_only\": local_files_only,\n \"revision\": revision,\n }\n\n config = cls.load_config(pretrained_model_or_path, **load_config_kwargs)\n orig_class_name = config[\"_class_name\"]\n\n if \"controlnet\" in kwargs:\n orig_class_name = config[\"_class_name\"].replace(\"Pipeline\", \"ControlNetPipeline\")\n\n image_2_image_cls = _get_task_class(AUTO_IMAGE2IMAGE_PIPELINES_MAPPING, orig_class_name)\n\n kwargs = {**load_config_kwargs, **kwargs}\n return image_2_image_cls.from_pretrained(pretrained_model_or_path, **kwargs)\n\n @classmethod\n def from_pipe(cls, pipeline, **kwargs):\n r\"\"\"\n Instantiates a image-to-image Pytorch diffusion pipeline from another instantiated diffusion pipeline class.\n\n The from_pipe() method takes care of returning the correct pipeline class instance by finding the\n image-to-image pipeline linked to the pipeline class using pattern matching on pipeline class name.\n\n All the modules the pipeline contains will be used to initialize the new pipeline without reallocating\n additional memory.\n\n The pipeline is set in evaluation mode (`model.eval()`) by default.\n\n Parameters:\n pipeline (`DiffusionPipeline`):\n an instantiated `DiffusionPipeline` object\n\n Examples:\n\n ```py\n >>> from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image\n\n >>> pipe_t2i = AutoPipelineForText2Image.from_pretrained(\n ... \"runwayml/stable-diffusion-v1-5\", requires_safety_checker=False\n ... )\n\n >>> pipe_i2i = AutoPipelineForImage2Image.from_pipe(pipe_t2i)\n >>> image = pipe_i2i(prompt, image).images[0]\n ```\n \"\"\"\n\n original_config = dict(pipeline.config)\n original_cls_name = pipeline.__class__.__name__\n\n # derive the pipeline class to instantiate\n image_2_image_cls = _get_task_class(AUTO_IMAGE2IMAGE_PIPELINES_MAPPING, original_cls_name)\n\n if \"controlnet\" in kwargs:\n if kwargs[\"controlnet\"] is not None:\n image_2_image_cls = _get_task_class(\n AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,\n image_2_image_cls.__name__.replace(\"ControlNet\", \"\").replace(\n \"Img2ImgPipeline\", \"ControlNetImg2ImgPipeline\"\n ),\n )\n else:\n image_2_image_cls = _get_task_class(\n AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,\n image_2_image_cls.__name__.replace(\"ControlNetImg2ImgPipeline\", \"Img2ImgPipeline\"),\n )\n\n # define expected module and optional kwargs given the pipeline signature\n expected_modules, optional_kwargs = image_2_image_cls._get_signature_keys(image_2_image_cls)\n\n pretrained_model_name_or_path = original_config.pop(\"_name_or_path\", None)\n\n # allow users pass modules in `kwargs` to override the original pipeline's components\n passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}\n original_class_obj = {\n k: pipeline.components[k]\n for k, v in pipeline.components.items()\n if k in expected_modules and k not in passed_class_obj\n }\n\n # allow users pass optional kwargs to override the original pipelines config attribute\n passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}\n original_pipe_kwargs = {\n k: original_config[k]\n for k, v in original_config.items()\n if k in optional_kwargs and k not in passed_pipe_kwargs\n }\n\n # config attribute that were not expected by original pipeline is stored as its private attribute\n # we will pass them as optional arguments if they can be accepted by the pipeline\n additional_pipe_kwargs = [\n k[1:]\n for k in original_config.keys()\n if k.startswith(\"_\") and k[1:] in optional_kwargs and k[1:] not in passed_pipe_kwargs\n ]\n for k in additional_pipe_kwargs:\n original_pipe_kwargs[k] = original_config.pop(f\"_{k}\")\n\n image_2_image_kwargs = {**passed_class_obj, **original_class_obj, **passed_pipe_kwargs, **original_pipe_kwargs}\n\n # store unused config as private attribute\n unused_original_config = {\n f\"{'' if k.startswith('_') else '_'}{k}\": original_config[k]\n for k, v in original_config.items()\n if k not in image_2_image_kwargs\n }\n\n missing_modules = set(expected_modules) - set(pipeline._optional_components) - set(image_2_image_kwargs.keys())\n\n if len(missing_modules) > 0:\n raise ValueError(\n f\"Pipeline {image_2_image_cls} expected {expected_modules}, but only {set(list(passed_class_obj.keys()) + list(original_class_obj.keys()))} were passed\"\n )\n\n model = image_2_image_cls(**image_2_image_kwargs)\n model.register_to_config(_name_or_path=pretrained_model_name_or_path)\n model.register_to_config(**unused_original_config)\n\n return model\n\n\nclass AutoPipelineForInpainting(ConfigMixin):\n r\"\"\"\n\n [`AutoPipelineForInpainting`] is a generic pipeline class that instantiates an inpainting pipeline class. The\n specific underlying pipeline class is automatically selected from either the\n [`~AutoPipelineForInpainting.from_pretrained`] or [`~AutoPipelineForInpainting.from_pipe`] methods.\n\n This class cannot be instantiated using `__init__()` (throws an error).\n\n Class attributes:\n\n - **config_name** (`str`) -- The configuration filename that stores the class and module names of all the\n diffusion pipeline's components.\n\n \"\"\"\n\n config_name = \"model_index.json\"\n\n def __init__(self, *args, **kwargs):\n raise EnvironmentError(\n f\"{self.__class__.__name__} is designed to be instantiated \"\n f\"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path)` or \"\n f\"`{self.__class__.__name__}.from_pipe(pipeline)` methods.\"\n )\n\n @classmethod\n @validate_hf_hub_args\n def from_pretrained(cls, pretrained_model_or_path, **kwargs):\n r\"\"\"\n Instantiates a inpainting Pytorch diffusion pipeline from pretrained pipeline weight.\n\n The from_pretrained() method takes care of returning the correct pipeline class instance by:\n 1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its\n config object\n 2. Find the inpainting pipeline linked to the pipeline class using pattern matching on pipeline class name.\n\n If a `controlnet` argument is passed, it will instantiate a [`StableDiffusionControlNetInpaintPipeline`]\n object.\n\n The pipeline is set in evaluation mode (`model.eval()`) by default.\n\n If you get the error message below, you need to finetune the weights for your downstream task:\n\n ```\n Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:\n - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated\n You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n ```\n\n Parameters:\n pretrained_model_or_path (`str` or `os.PathLike`, *optional*):\n Can be either:\n\n - A string, the *repo id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline\n hosted on the Hub.\n - A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights\n saved using\n [`~DiffusionPipeline.save_pretrained`].\n torch_dtype (`str` or `torch.dtype`, *optional*):\n Override the default `torch.dtype` and load the model with another dtype. If \"auto\" is passed, the\n dtype is automatically derived from the model's weights.\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\n cached versions if they exist.\n cache_dir (`Union[str, os.PathLike]`, *optional*):\n Path to a directory where a downloaded pretrained model configuration is cached if the standard cache\n is not used.\n resume_download:\n Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1\n of Diffusers.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\n output_loading_info(`bool`, *optional*, defaults to `False`):\n Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.\n local_files_only (`bool`, *optional*, defaults to `False`):\n Whether to only load local model weights and configuration files or not. If set to `True`, the model\n won't be downloaded from the Hub.\n token (`str` or *bool*, *optional*):\n The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from\n `diffusers-cli login` (stored in `~/.huggingface`) is used.\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier\n allowed by Git.\n custom_revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, or a commit id similar to\n `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a\n custom pipeline from GitHub, otherwise it defaults to `\"main\"` when loading from the Hub.\n mirror (`str`, *optional*):\n Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not\n guarantee the timeliness or safety of the source, and you should refer to the mirror site for more\n information.\n device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):\n A map that specifies where each submodule should go. It doesn’t need to be defined for each\n parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the\n same device.\n\n Set `device_map=\"auto\"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For\n more information about each option see [designing a device\n map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).\n max_memory (`Dict`, *optional*):\n A dictionary device identifier for the maximum memory. Will default to the maximum memory available for\n each GPU and the available CPU RAM if unset.\n offload_folder (`str` or `os.PathLike`, *optional*):\n The path to offload weights if device_map contains the value `\"disk\"`.\n offload_state_dict (`bool`, *optional*):\n If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if\n the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`\n when there is some disk offload.\n low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):\n Speed up model loading only loading the pretrained weights and not initializing the weights. This also\n tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.\n Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this\n argument to `True` will raise an error.\n use_safetensors (`bool`, *optional*, defaults to `None`):\n If set to `None`, the safetensors weights are downloaded if they're available **and** if the\n safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors\n weights. If set to `False`, safetensors weights are not loaded.\n kwargs (remaining dictionary of keyword arguments, *optional*):\n Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline\n class). The overwritten components are passed directly to the pipelines `__init__` method. See example\n below for more information.\n variant (`str`, *optional*):\n Load weights from a specified variant filename such as `\"fp16\"` or `\"ema\"`. This is ignored when\n loading `from_flax`.\n\n \n\n To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with\n `huggingface-cli login`.\n\n \n\n Examples:\n\n ```py\n >>> from diffusers import AutoPipelineForInpainting\n\n >>> pipeline = AutoPipelineForInpainting.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n >>> image = pipeline(prompt, image=init_image, mask_image=mask_image).images[0]\n ```\n \"\"\"\n cache_dir = kwargs.pop(\"cache_dir\", None)\n force_download = kwargs.pop(\"force_download\", False)\n resume_download = kwargs.pop(\"resume_download\", None)\n proxies = kwargs.pop(\"proxies\", None)\n token = kwargs.pop(\"token\", None)\n local_files_only = kwargs.pop(\"local_files_only\", False)\n revision = kwargs.pop(\"revision\", None)\n\n load_config_kwargs = {\n \"cache_dir\": cache_dir,\n \"force_download\": force_download,\n \"resume_download\": resume_download,\n \"proxies\": proxies,\n \"token\": token,\n \"local_files_only\": local_files_only,\n \"revision\": revision,\n }\n\n config = cls.load_config(pretrained_model_or_path, **load_config_kwargs)\n orig_class_name = config[\"_class_name\"]\n\n if \"controlnet\" in kwargs:\n orig_class_name = config[\"_class_name\"].replace(\"Pipeline\", \"ControlNetPipeline\")\n\n inpainting_cls = _get_task_class(AUTO_INPAINT_PIPELINES_MAPPING, orig_class_name)\n\n kwargs = {**load_config_kwargs, **kwargs}\n return inpainting_cls.from_pretrained(pretrained_model_or_path, **kwargs)\n\n @classmethod\n def from_pipe(cls, pipeline, **kwargs):\n r\"\"\"\n Instantiates a inpainting Pytorch diffusion pipeline from another instantiated diffusion pipeline class.\n\n The from_pipe() method takes care of returning the correct pipeline class instance by finding the inpainting\n pipeline linked to the pipeline class using pattern matching on pipeline class name.\n\n All the modules the pipeline class contain will be used to initialize the new pipeline without reallocating\n additional memory.\n\n The pipeline is set in evaluation mode (`model.eval()`) by default.\n\n Parameters:\n pipeline (`DiffusionPipeline`):\n an instantiated `DiffusionPipeline` object\n\n Examples:\n\n ```py\n >>> from diffusers import AutoPipelineForText2Image, AutoPipelineForInpainting\n\n >>> pipe_t2i = AutoPipelineForText2Image.from_pretrained(\n ... \"DeepFloyd/IF-I-XL-v1.0\", requires_safety_checker=False\n ... )\n\n >>> pipe_inpaint = AutoPipelineForInpainting.from_pipe(pipe_t2i)\n >>> image = pipe_inpaint(prompt, image=init_image, mask_image=mask_image).images[0]\n ```\n \"\"\"\n original_config = dict(pipeline.config)\n original_cls_name = pipeline.__class__.__name__\n\n # derive the pipeline class to instantiate\n inpainting_cls = _get_task_class(AUTO_INPAINT_PIPELINES_MAPPING, original_cls_name)\n\n if \"controlnet\" in kwargs:\n if kwargs[\"controlnet\"] is not None:\n inpainting_cls = _get_task_class(\n AUTO_INPAINT_PIPELINES_MAPPING,\n inpainting_cls.__name__.replace(\"ControlNet\", \"\").replace(\n \"InpaintPipeline\", \"ControlNetInpaintPipeline\"\n ),\n )\n else:\n inpainting_cls = _get_task_class(\n AUTO_INPAINT_PIPELINES_MAPPING,\n inpainting_cls.__name__.replace(\"ControlNetInpaintPipeline\", \"InpaintPipeline\"),\n )\n\n # define expected module and optional kwargs given the pipeline signature\n expected_modules, optional_kwargs = inpainting_cls._get_signature_keys(inpainting_cls)\n\n pretrained_model_name_or_path = original_config.pop(\"_name_or_path\", None)\n\n # allow users pass modules in `kwargs` to override the original pipeline's components\n passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}\n original_class_obj = {\n k: pipeline.components[k]\n for k, v in pipeline.components.items()\n if k in expected_modules and k not in passed_class_obj\n }\n\n # allow users pass optional kwargs to override the original pipelines config attribute\n passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}\n original_pipe_kwargs = {\n k: original_config[k]\n for k, v in original_config.items()\n if k in optional_kwargs and k not in passed_pipe_kwargs\n }\n\n # config that were not expected by original pipeline is stored as private attribute\n # we will pass them as optional arguments if they can be accepted by the pipeline\n additional_pipe_kwargs = [\n k[1:]\n for k in original_config.keys()\n if k.startswith(\"_\") and k[1:] in optional_kwargs and k[1:] not in passed_pipe_kwargs\n ]\n for k in additional_pipe_kwargs:\n original_pipe_kwargs[k] = original_config.pop(f\"_{k}\")\n\n inpainting_kwargs = {**passed_class_obj, **original_class_obj, **passed_pipe_kwargs, **original_pipe_kwargs}\n\n # store unused config as private attribute\n unused_original_config = {\n f\"{'' if k.startswith('_') else '_'}{k}\": original_config[k]\n for k, v in original_config.items()\n if k not in inpainting_kwargs\n }\n\n missing_modules = set(expected_modules) - set(pipeline._optional_components) - set(inpainting_kwargs.keys())\n\n if len(missing_modules) > 0:\n raise ValueError(\n f\"Pipeline {inpainting_cls} expected {expected_modules}, but only {set(list(passed_class_obj.keys()) + list(original_class_obj.keys()))} were passed\"\n )\n\n model = inpainting_cls(**inpainting_kwargs)\n model.register_to_config(_name_or_path=pretrained_model_name_or_path)\n model.register_to_config(**unused_original_config)\n\n return model\n" }, { "path": "diffusers/src/diffusers/pipelines/blip_diffusion/__init__.py", "content": "from dataclasses import dataclass\nfrom typing import List, Optional, Union\n\nimport numpy as np\nimport PIL\nfrom PIL import Image\n\nfrom ...utils import OptionalDependencyNotAvailable, is_torch_available, is_transformers_available\n\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline\nelse:\n from .blip_image_processing import BlipImageProcessor\n from .modeling_blip2 import Blip2QFormerModel\n from .modeling_ctx_clip import ContextCLIPTextModel\n from .pipeline_blip_diffusion import BlipDiffusionPipeline\n" }, { "path": "diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Image processor class for BLIP.\"\"\"\n\nfrom typing import Dict, List, Optional, Union\n\nimport numpy as np\nimport torch\nfrom transformers.image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict\nfrom transformers.image_transforms import convert_to_rgb, resize, to_channel_dimension_format\nfrom transformers.image_utils import (\n OPENAI_CLIP_MEAN,\n OPENAI_CLIP_STD,\n ChannelDimension,\n ImageInput,\n PILImageResampling,\n infer_channel_dimension_format,\n is_scaled_image,\n make_list_of_images,\n to_numpy_array,\n valid_images,\n)\nfrom transformers.utils import TensorType, is_vision_available, logging\n\nfrom diffusers.utils import numpy_to_pil\n\n\nif is_vision_available():\n import PIL.Image\n\n\nlogger = logging.get_logger(__name__)\n\n\n# We needed some extra functions on top of the ones in transformers.image_processing_utils.BaseImageProcessor, namely center crop\n# Copy-pasted from transformers.models.blip.image_processing_blip.BlipImageProcessor\nclass BlipImageProcessor(BaseImageProcessor):\n r\"\"\"\n Constructs a BLIP image processor.\n\n Args:\n do_resize (`bool`, *optional*, defaults to `True`):\n Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the\n `do_resize` parameter in the `preprocess` method.\n size (`dict`, *optional*, defaults to `{\"height\": 384, \"width\": 384}`):\n Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`\n method.\n resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):\n Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`. Can be\n overridden by the `resample` parameter in the `preprocess` method.\n do_rescale (`bool`, *optional*, defaults to `True`):\n Wwhether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the\n `do_rescale` parameter in the `preprocess` method.\n rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):\n Scale factor to use if rescaling the image. Only has an effect if `do_rescale` is set to `True`. Can be\n overridden by the `rescale_factor` parameter in the `preprocess` method.\n do_normalize (`bool`, *optional*, defaults to `True`):\n Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`\n method. Can be overridden by the `do_normalize` parameter in the `preprocess` method.\n image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):\n Mean to use if normalizing the image. This is a float or list of floats the length of the number of\n channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be\n overridden by the `image_mean` parameter in the `preprocess` method.\n image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):\n Standard deviation to use if normalizing the image. This is a float or list of floats the length of the\n number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.\n Can be overridden by the `image_std` parameter in the `preprocess` method.\n do_convert_rgb (`bool`, *optional*, defaults to `True`):\n Whether to convert the image to RGB.\n \"\"\"\n\n model_input_names = [\"pixel_values\"]\n\n def __init__(\n self,\n do_resize: bool = True,\n size: Dict[str, int] = None,\n resample: PILImageResampling = PILImageResampling.BICUBIC,\n do_rescale: bool = True,\n rescale_factor: Union[int, float] = 1 / 255,\n do_normalize: bool = True,\n image_mean: Optional[Union[float, List[float]]] = None,\n image_std: Optional[Union[float, List[float]]] = None,\n do_convert_rgb: bool = True,\n do_center_crop: bool = True,\n **kwargs,\n ) -> None:\n super().__init__(**kwargs)\n size = size if size is not None else {\"height\": 224, \"width\": 224}\n size = get_size_dict(size, default_to_square=True)\n\n self.do_resize = do_resize\n self.size = size\n self.resample = resample\n self.do_rescale = do_rescale\n self.rescale_factor = rescale_factor\n self.do_normalize = do_normalize\n self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN\n self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD\n self.do_convert_rgb = do_convert_rgb\n self.do_center_crop = do_center_crop\n\n # Copy-pasted from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize with PILImageResampling.BILINEAR->PILImageResampling.BICUBIC\n def resize(\n self,\n image: np.ndarray,\n size: Dict[str, int],\n resample: PILImageResampling = PILImageResampling.BICUBIC,\n data_format: Optional[Union[str, ChannelDimension]] = None,\n input_data_format: Optional[Union[str, ChannelDimension]] = None,\n **kwargs,\n ) -> np.ndarray:\n \"\"\"\n Resize an image to `(size[\"height\"], size[\"width\"])`.\n\n Args:\n image (`np.ndarray`):\n Image to resize.\n size (`Dict[str, int]`):\n Dictionary in the format `{\"height\": int, \"width\": int}` specifying the size of the output image.\n resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):\n `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BICUBIC`.\n data_format (`ChannelDimension` or `str`, *optional*):\n The channel dimension format for the output image. If unset, the channel dimension format of the input\n image is used. Can be one of:\n - `\"channels_first\"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.\n - `\"channels_last\"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.\n - `\"none\"` or `ChannelDimension.NONE`: image in (height, width) format.\n input_data_format (`ChannelDimension` or `str`, *optional*):\n The channel dimension format for the input image. If unset, the channel dimension format is inferred\n from the input image. Can be one of:\n - `\"channels_first\"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.\n - `\"channels_last\"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.\n - `\"none\"` or `ChannelDimension.NONE`: image in (height, width) format.\n\n Returns:\n `np.ndarray`: The resized image.\n \"\"\"\n size = get_size_dict(size)\n if \"height\" not in size or \"width\" not in size:\n raise ValueError(f\"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}\")\n output_size = (size[\"height\"], size[\"width\"])\n return resize(\n image,\n size=output_size,\n resample=resample,\n data_format=data_format,\n input_data_format=input_data_format,\n **kwargs,\n )\n\n def preprocess(\n self,\n images: ImageInput,\n do_resize: Optional[bool] = None,\n size: Optional[Dict[str, int]] = None,\n resample: PILImageResampling = None,\n do_rescale: Optional[bool] = None,\n do_center_crop: Optional[bool] = None,\n rescale_factor: Optional[float] = None,\n do_normalize: Optional[bool] = None,\n image_mean: Optional[Union[float, List[float]]] = None,\n image_std: Optional[Union[float, List[float]]] = None,\n return_tensors: Optional[Union[str, TensorType]] = None,\n do_convert_rgb: bool = None,\n data_format: ChannelDimension = ChannelDimension.FIRST,\n input_data_format: Optional[Union[str, ChannelDimension]] = None,\n **kwargs,\n ) -> PIL.Image.Image:\n \"\"\"\n Preprocess an image or batch of images.\n\n Args:\n images (`ImageInput`):\n Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If\n passing in images with pixel values between 0 and 1, set `do_rescale=False`.\n do_resize (`bool`, *optional*, defaults to `self.do_resize`):\n Whether to resize the image.\n size (`Dict[str, int]`, *optional*, defaults to `self.size`):\n Controls the size of the image after `resize`. The shortest edge of the image is resized to\n `size[\"shortest_edge\"]` whilst preserving the aspect ratio. If the longest edge of this resized image\n is > `int(size[\"shortest_edge\"] * (1333 / 800))`, then the image is resized again to make the longest\n edge equal to `int(size[\"shortest_edge\"] * (1333 / 800))`.\n resample (`PILImageResampling`, *optional*, defaults to `self.resample`):\n Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`.\n do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):\n Whether to rescale the image values between [0 - 1].\n rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):\n Rescale factor to rescale the image by if `do_rescale` is set to `True`.\n do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):\n Whether to normalize the image.\n image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):\n Image mean to normalize the image by if `do_normalize` is set to `True`.\n image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):\n Image standard deviation to normalize the image by if `do_normalize` is set to `True`.\n do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):\n Whether to convert the image to RGB.\n return_tensors (`str` or `TensorType`, *optional*):\n The type of tensors to return. Can be one of:\n - Unset: Return a list of `np.ndarray`.\n - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.\n - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.\n - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.\n - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.\n data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):\n The channel dimension format for the output image. Can be one of:\n - `\"channels_first\"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.\n - `\"channels_last\"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.\n - Unset: Use the channel dimension format of the input image.\n input_data_format (`ChannelDimension` or `str`, *optional*):\n The channel dimension format for the input image. If unset, the channel dimension format is inferred\n from the input image. Can be one of:\n - `\"channels_first\"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.\n - `\"channels_last\"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.\n - `\"none\"` or `ChannelDimension.NONE`: image in (height, width) format.\n \"\"\"\n do_resize = do_resize if do_resize is not None else self.do_resize\n resample = resample if resample is not None else self.resample\n do_rescale = do_rescale if do_rescale is not None else self.do_rescale\n rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor\n do_normalize = do_normalize if do_normalize is not None else self.do_normalize\n image_mean = image_mean if image_mean is not None else self.image_mean\n image_std = image_std if image_std is not None else self.image_std\n do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb\n do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop\n\n size = size if size is not None else self.size\n size = get_size_dict(size, default_to_square=False)\n images = make_list_of_images(images)\n\n if not valid_images(images):\n raise ValueError(\n \"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, \"\n \"torch.Tensor, tf.Tensor or jax.ndarray.\"\n )\n\n if do_resize and size is None or resample is None:\n raise ValueError(\"Size and resample must be specified if do_resize is True.\")\n\n if do_rescale and rescale_factor is None:\n raise ValueError(\"Rescale factor must be specified if do_rescale is True.\")\n\n if do_normalize and (image_mean is None or image_std is None):\n raise ValueError(\"Image mean and std must be specified if do_normalize is True.\")\n\n # PIL RGBA images are converted to RGB\n if do_convert_rgb:\n images = [convert_to_rgb(image) for image in images]\n\n # All transformations expect numpy arrays.\n images = [to_numpy_array(image) for image in images]\n\n if is_scaled_image(images[0]) and do_rescale:\n logger.warning_once(\n \"It looks like you are trying to rescale already rescaled images. If the input\"\n \" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again.\"\n )\n if input_data_format is None:\n # We assume that all images have the same channel dimension format.\n input_data_format = infer_channel_dimension_format(images[0])\n\n if do_resize:\n images = [\n self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)\n for image in images\n ]\n\n if do_rescale:\n images = [\n self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)\n for image in images\n ]\n if do_normalize:\n images = [\n self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)\n for image in images\n ]\n if do_center_crop:\n images = [self.center_crop(image, size, input_data_format=input_data_format) for image in images]\n\n images = [\n to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images\n ]\n\n encoded_outputs = BatchFeature(data={\"pixel_values\": images}, tensor_type=return_tensors)\n return encoded_outputs\n\n # Follows diffusers.VaeImageProcessor.postprocess\n def postprocess(self, sample: torch.Tensor, output_type: str = \"pil\"):\n if output_type not in [\"pt\", \"np\", \"pil\"]:\n raise ValueError(\n f\"output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']\"\n )\n\n # Equivalent to diffusers.VaeImageProcessor.denormalize\n sample = (sample / 2 + 0.5).clamp(0, 1)\n if output_type == \"pt\":\n return sample\n\n # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy\n sample = sample.cpu().permute(0, 2, 3, 1).numpy()\n if output_type == \"np\":\n return sample\n # Output_type must be 'pil'\n sample = numpy_to_pil(sample)\n return sample\n" }, { "path": "diffusers/src/diffusers/pipelines/blip_diffusion/modeling_blip2.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Optional, Tuple, Union\n\nimport torch\nimport torch.utils.checkpoint\nfrom torch import nn\nfrom transformers import BertTokenizer\nfrom transformers.activations import QuickGELUActivation as QuickGELU\nfrom transformers.modeling_outputs import (\n BaseModelOutputWithPastAndCrossAttentions,\n BaseModelOutputWithPooling,\n BaseModelOutputWithPoolingAndCrossAttentions,\n)\nfrom transformers.models.blip_2.configuration_blip_2 import Blip2Config, Blip2VisionConfig\nfrom transformers.models.blip_2.modeling_blip_2 import (\n Blip2Encoder,\n Blip2PreTrainedModel,\n Blip2QFormerAttention,\n Blip2QFormerIntermediate,\n Blip2QFormerOutput,\n)\nfrom transformers.pytorch_utils import apply_chunking_to_forward\nfrom transformers.utils import (\n logging,\n replace_return_docstrings,\n)\n\n\nlogger = logging.get_logger(__name__)\n\n\n# There is an implementation of Blip2 in `transformers` : https://github.com/huggingface/transformers/blob/main/src/transformers/models/blip_2/modeling_blip_2.py.\n# But it doesn't support getting multimodal embeddings. So, this module can be\n# replaced with a future `transformers` version supports that.\nclass Blip2TextEmbeddings(nn.Module):\n \"\"\"Construct the embeddings from word and position embeddings.\"\"\"\n\n def __init__(self, config):\n super().__init__()\n self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)\n self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)\n\n # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load\n # any TensorFlow checkpoint file\n self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n self.dropout = nn.Dropout(config.hidden_dropout_prob)\n\n # position_ids (1, len position emb) is contiguous in memory and exported when serialized\n self.register_buffer(\"position_ids\", torch.arange(config.max_position_embeddings).expand((1, -1)))\n self.position_embedding_type = getattr(config, \"position_embedding_type\", \"absolute\")\n\n self.config = config\n\n def forward(\n self,\n input_ids=None,\n position_ids=None,\n query_embeds=None,\n past_key_values_length=0,\n ):\n if input_ids is not None:\n seq_length = input_ids.size()[1]\n else:\n seq_length = 0\n\n if position_ids is None:\n position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length].clone()\n\n if input_ids is not None:\n embeddings = self.word_embeddings(input_ids)\n if self.position_embedding_type == \"absolute\":\n position_embeddings = self.position_embeddings(position_ids)\n embeddings = embeddings + position_embeddings\n\n if query_embeds is not None:\n batch_size = embeddings.shape[0]\n # repeat the query embeddings for batch size\n query_embeds = query_embeds.repeat(batch_size, 1, 1)\n embeddings = torch.cat((query_embeds, embeddings), dim=1)\n else:\n embeddings = query_embeds\n embeddings = embeddings.to(query_embeds.dtype)\n embeddings = self.LayerNorm(embeddings)\n embeddings = self.dropout(embeddings)\n return embeddings\n\n\n# Copy-pasted from transformers.models.blip.modeling_blip.BlipVisionEmbeddings with Blip->Blip2\nclass Blip2VisionEmbeddings(nn.Module):\n def __init__(self, config: Blip2VisionConfig):\n super().__init__()\n self.config = config\n self.embed_dim = config.hidden_size\n self.image_size = config.image_size\n self.patch_size = config.patch_size\n\n self.class_embedding = nn.Parameter(torch.randn(1, 1, self.embed_dim))\n\n self.patch_embedding = nn.Conv2d(\n in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size, bias=False\n )\n\n self.num_patches = (self.image_size // self.patch_size) ** 2\n self.num_positions = self.num_patches + 1\n\n self.position_embedding = nn.Parameter(torch.randn(1, self.num_positions, self.embed_dim))\n\n def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:\n batch_size = pixel_values.shape[0]\n target_dtype = self.patch_embedding.weight.dtype\n patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid]\n patch_embeds = patch_embeds.flatten(2).transpose(1, 2)\n\n class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype)\n embeddings = torch.cat([class_embeds, patch_embeds], dim=1)\n embeddings = embeddings + self.position_embedding[:, : embeddings.size(1), :].to(target_dtype)\n return embeddings\n\n\n# The Qformer encoder, which takes the visual embeddings, and the text input, to get multimodal embeddings\nclass Blip2QFormerEncoder(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.config = config\n self.layer = nn.ModuleList(\n [Blip2QFormerLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]\n )\n self.gradient_checkpointing = False\n\n def forward(\n self,\n hidden_states,\n attention_mask=None,\n head_mask=None,\n encoder_hidden_states=None,\n encoder_attention_mask=None,\n past_key_values=None,\n use_cache=None,\n output_attentions=False,\n output_hidden_states=False,\n return_dict=True,\n query_length=0,\n ):\n all_hidden_states = () if output_hidden_states else None\n all_self_attentions = () if output_attentions else None\n all_cross_attentions = () if output_attentions else None\n\n next_decoder_cache = () if use_cache else None\n\n for i in range(self.config.num_hidden_layers):\n layer_module = self.layer[i]\n if output_hidden_states:\n all_hidden_states = all_hidden_states + (hidden_states,)\n\n layer_head_mask = head_mask[i] if head_mask is not None else None\n past_key_value = past_key_values[i] if past_key_values is not None else None\n\n if getattr(self.config, \"gradient_checkpointing\", False) and self.training:\n if use_cache:\n logger.warning(\n \"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\"\n )\n use_cache = False\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs, past_key_value, output_attentions, query_length)\n\n return custom_forward\n\n layer_outputs = torch.utils.checkpoint.checkpoint(\n create_custom_forward(layer_module),\n hidden_states,\n attention_mask,\n layer_head_mask,\n encoder_hidden_states,\n encoder_attention_mask,\n )\n else:\n layer_outputs = layer_module(\n hidden_states,\n attention_mask,\n layer_head_mask,\n encoder_hidden_states,\n encoder_attention_mask,\n past_key_value,\n output_attentions,\n query_length,\n )\n\n hidden_states = layer_outputs[0]\n if use_cache:\n next_decoder_cache += (layer_outputs[-1],)\n if output_attentions:\n all_self_attentions = all_self_attentions + (layer_outputs[1],)\n if layer_module.has_cross_attention:\n all_cross_attentions = all_cross_attentions + (layer_outputs[2],)\n\n if output_hidden_states:\n all_hidden_states = all_hidden_states + (hidden_states,)\n\n if not return_dict:\n return tuple(\n v\n for v in [\n hidden_states,\n next_decoder_cache,\n all_hidden_states,\n all_self_attentions,\n all_cross_attentions,\n ]\n if v is not None\n )\n return BaseModelOutputWithPastAndCrossAttentions(\n last_hidden_state=hidden_states,\n past_key_values=next_decoder_cache,\n hidden_states=all_hidden_states,\n attentions=all_self_attentions,\n cross_attentions=all_cross_attentions,\n )\n\n\n# The layers making up the Qformer encoder\nclass Blip2QFormerLayer(nn.Module):\n def __init__(self, config, layer_idx):\n super().__init__()\n self.chunk_size_feed_forward = config.chunk_size_feed_forward\n self.seq_len_dim = 1\n self.attention = Blip2QFormerAttention(config)\n\n self.layer_idx = layer_idx\n\n if layer_idx % config.cross_attention_frequency == 0:\n self.crossattention = Blip2QFormerAttention(config, is_cross_attention=True)\n self.has_cross_attention = True\n else:\n self.has_cross_attention = False\n\n self.intermediate = Blip2QFormerIntermediate(config)\n self.intermediate_query = Blip2QFormerIntermediate(config)\n self.output_query = Blip2QFormerOutput(config)\n self.output = Blip2QFormerOutput(config)\n\n def forward(\n self,\n hidden_states,\n attention_mask=None,\n head_mask=None,\n encoder_hidden_states=None,\n encoder_attention_mask=None,\n past_key_value=None,\n output_attentions=False,\n query_length=0,\n ):\n # decoder uni-directional self-attention cached key/values tuple is at positions 1,2\n self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None\n self_attention_outputs = self.attention(\n hidden_states,\n attention_mask,\n head_mask,\n output_attentions=output_attentions,\n past_key_value=self_attn_past_key_value,\n )\n attention_output = self_attention_outputs[0]\n outputs = self_attention_outputs[1:-1]\n\n present_key_value = self_attention_outputs[-1]\n\n if query_length > 0:\n query_attention_output = attention_output[:, :query_length, :]\n\n if self.has_cross_attention:\n if encoder_hidden_states is None:\n raise ValueError(\"encoder_hidden_states must be given for cross-attention layers\")\n cross_attention_outputs = self.crossattention(\n query_attention_output,\n attention_mask,\n head_mask,\n encoder_hidden_states,\n encoder_attention_mask,\n output_attentions=output_attentions,\n )\n query_attention_output = cross_attention_outputs[0]\n # add cross attentions if we output attention weights\n outputs = outputs + cross_attention_outputs[1:-1]\n\n layer_output = apply_chunking_to_forward(\n self.feed_forward_chunk_query,\n self.chunk_size_feed_forward,\n self.seq_len_dim,\n query_attention_output,\n )\n\n if attention_output.shape[1] > query_length:\n layer_output_text = apply_chunking_to_forward(\n self.feed_forward_chunk,\n self.chunk_size_feed_forward,\n self.seq_len_dim,\n attention_output[:, query_length:, :],\n )\n layer_output = torch.cat([layer_output, layer_output_text], dim=1)\n else:\n layer_output = apply_chunking_to_forward(\n self.feed_forward_chunk,\n self.chunk_size_feed_forward,\n self.seq_len_dim,\n attention_output,\n )\n outputs = (layer_output,) + outputs\n\n outputs = outputs + (present_key_value,)\n\n return outputs\n\n def feed_forward_chunk(self, attention_output):\n intermediate_output = self.intermediate(attention_output)\n layer_output = self.output(intermediate_output, attention_output)\n return layer_output\n\n def feed_forward_chunk_query(self, attention_output):\n intermediate_output = self.intermediate_query(attention_output)\n layer_output = self.output_query(intermediate_output, attention_output)\n return layer_output\n\n\n# ProjLayer used to project the multimodal Blip2 embeddings to be used in the text encoder\nclass ProjLayer(nn.Module):\n def __init__(self, in_dim, out_dim, hidden_dim, drop_p=0.1, eps=1e-12):\n super().__init__()\n\n # Dense1 -> Act -> Dense2 -> Drop -> Res -> Norm\n self.dense1 = nn.Linear(in_dim, hidden_dim)\n self.act_fn = QuickGELU()\n self.dense2 = nn.Linear(hidden_dim, out_dim)\n self.dropout = nn.Dropout(drop_p)\n\n self.LayerNorm = nn.LayerNorm(out_dim, eps=eps)\n\n def forward(self, x):\n x_in = x\n\n x = self.LayerNorm(x)\n x = self.dropout(self.dense2(self.act_fn(self.dense1(x)))) + x_in\n\n return x\n\n\n# Copy-pasted from transformers.models.blip.modeling_blip.BlipVisionModel with Blip->Blip2, BLIP->BLIP_2\nclass Blip2VisionModel(Blip2PreTrainedModel):\n main_input_name = \"pixel_values\"\n config_class = Blip2VisionConfig\n\n def __init__(self, config: Blip2VisionConfig):\n super().__init__(config)\n self.config = config\n embed_dim = config.hidden_size\n self.embeddings = Blip2VisionEmbeddings(config)\n self.pre_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)\n self.encoder = Blip2Encoder(config)\n self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)\n\n self.post_init()\n\n @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=Blip2VisionConfig)\n def forward(\n self,\n pixel_values: Optional[torch.Tensor] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, BaseModelOutputWithPooling]:\n r\"\"\"\n Returns:\n\n \"\"\"\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n if pixel_values is None:\n raise ValueError(\"You have to specify pixel_values\")\n\n hidden_states = self.embeddings(pixel_values)\n hidden_states = self.pre_layernorm(hidden_states)\n encoder_outputs = self.encoder(\n inputs_embeds=hidden_states,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n last_hidden_state = encoder_outputs[0]\n last_hidden_state = self.post_layernorm(last_hidden_state)\n\n pooled_output = last_hidden_state[:, 0, :]\n pooled_output = self.post_layernorm(pooled_output)\n\n if not return_dict:\n return (last_hidden_state, pooled_output) + encoder_outputs[1:]\n\n return BaseModelOutputWithPooling(\n last_hidden_state=last_hidden_state,\n pooler_output=pooled_output,\n hidden_states=encoder_outputs.hidden_states,\n attentions=encoder_outputs.attentions,\n )\n\n def get_input_embeddings(self):\n return self.embeddings\n\n\n# Qformer model, used to get multimodal embeddings from the text and image inputs\nclass Blip2QFormerModel(Blip2PreTrainedModel):\n \"\"\"\n Querying Transformer (Q-Former), used in BLIP-2.\n \"\"\"\n\n def __init__(self, config: Blip2Config):\n super().__init__(config)\n self.config = config\n self.embeddings = Blip2TextEmbeddings(config.qformer_config)\n self.visual_encoder = Blip2VisionModel(config.vision_config)\n self.query_tokens = nn.Parameter(torch.zeros(1, config.num_query_tokens, config.qformer_config.hidden_size))\n if not hasattr(config, \"tokenizer\") or config.tokenizer is None:\n self.tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\", truncation_side=\"right\")\n else:\n self.tokenizer = BertTokenizer.from_pretrained(config.tokenizer, truncation_side=\"right\")\n self.tokenizer.add_special_tokens({\"bos_token\": \"[DEC]\"})\n self.proj_layer = ProjLayer(\n in_dim=config.qformer_config.hidden_size,\n out_dim=config.qformer_config.hidden_size,\n hidden_dim=config.qformer_config.hidden_size * 4,\n drop_p=0.1,\n eps=1e-12,\n )\n\n self.encoder = Blip2QFormerEncoder(config.qformer_config)\n\n self.post_init()\n\n def get_input_embeddings(self):\n return self.embeddings.word_embeddings\n\n def set_input_embeddings(self, value):\n self.embeddings.word_embeddings = value\n\n def _prune_heads(self, heads_to_prune):\n \"\"\"\n Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base\n class PreTrainedModel\n \"\"\"\n for layer, heads in heads_to_prune.items():\n self.encoder.layer[layer].attention.prune_heads(heads)\n\n def get_extended_attention_mask(\n self,\n attention_mask: torch.Tensor,\n input_shape: Tuple[int],\n device: torch.device,\n has_query: bool = False,\n ) -> torch.Tensor:\n \"\"\"\n Makes broadcastable attention and causal masks so that future and masked tokens are ignored.\n\n Arguments:\n attention_mask (`torch.Tensor`):\n Mask with ones indicating tokens to attend to, zeros for tokens to ignore.\n input_shape (`Tuple[int]`):\n The shape of the input to the model.\n device (`torch.device`):\n The device of the input to the model.\n\n Returns:\n `torch.Tensor` The extended attention mask, with a the same dtype as `attention_mask.dtype`.\n \"\"\"\n # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]\n # ourselves in which case we just need to make it broadcastable to all heads.\n if attention_mask.dim() == 3:\n extended_attention_mask = attention_mask[:, None, :, :]\n elif attention_mask.dim() == 2:\n # Provided a padding mask of dimensions [batch_size, seq_length]\n # - the model is an encoder, so make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]\n extended_attention_mask = attention_mask[:, None, None, :]\n else:\n raise ValueError(\n \"Wrong shape for input_ids (shape {}) or attention_mask (shape {})\".format(\n input_shape, attention_mask.shape\n )\n )\n\n # Since attention_mask is 1.0 for positions we want to attend and 0.0 for\n # masked positions, this operation will create a tensor which is 0.0 for\n # positions we want to attend and -10000.0 for masked positions.\n # Since we are adding it to the raw scores before the softmax, this is\n # effectively the same as removing these entirely.\n extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility\n extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0\n return extended_attention_mask\n\n def forward(\n self,\n text_input=None,\n image_input=None,\n head_mask=None,\n encoder_hidden_states=None,\n encoder_attention_mask=None,\n past_key_values=None,\n use_cache=None,\n output_attentions=None,\n output_hidden_states=None,\n return_dict=None,\n ):\n r\"\"\"\n encoder_hidden_states (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, `optional`):\n Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if\n the model is configured as a decoder.\n encoder_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, `optional`):\n Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in\n the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n past_key_values (`tuple(tuple(torch.Tensor))` of length `config.n_layers` with each tuple having 4 tensors of:\n shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and\n value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are\n used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key\n value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape\n `(batch_size, sequence_length)`.\n use_cache (`bool`, `optional`):\n If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see\n `past_key_values`).\n \"\"\"\n\n text = self.tokenizer(text_input, return_tensors=\"pt\", padding=True)\n text = text.to(self.device)\n input_ids = text.input_ids\n batch_size = input_ids.shape[0]\n query_atts = torch.ones((batch_size, self.query_tokens.size()[1]), dtype=torch.long).to(self.device)\n attention_mask = torch.cat([query_atts, text.attention_mask], dim=1)\n\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n # past_key_values_length\n past_key_values_length = (\n past_key_values[0][0].shape[2] - self.config.query_length if past_key_values is not None else 0\n )\n\n query_length = self.query_tokens.shape[1]\n\n embedding_output = self.embeddings(\n input_ids=input_ids,\n query_embeds=self.query_tokens,\n past_key_values_length=past_key_values_length,\n )\n\n # embedding_output = self.layernorm(query_embeds)\n # embedding_output = self.dropout(embedding_output)\n\n input_shape = embedding_output.size()[:-1]\n batch_size, seq_length = input_shape\n device = embedding_output.device\n\n image_embeds_frozen = self.visual_encoder(image_input).last_hidden_state\n # image_embeds_frozen = torch.ones_like(image_embeds_frozen)\n encoder_hidden_states = image_embeds_frozen\n\n if attention_mask is None:\n attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)\n\n # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]\n # ourselves in which case we just need to make it broadcastable to all heads.\n extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, device)\n\n # If a 2D or 3D attention mask is provided for the cross-attention\n # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]\n if encoder_hidden_states is not None:\n if isinstance(encoder_hidden_states, list):\n encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size()\n else:\n encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()\n encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)\n\n if isinstance(encoder_attention_mask, list):\n encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]\n elif encoder_attention_mask is None:\n encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)\n encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)\n else:\n encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)\n else:\n encoder_extended_attention_mask = None\n\n # Prepare head mask if needed\n # 1.0 in head_mask indicate we keep the head\n # attention_probs has shape bsz x n_heads x N x N\n # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]\n # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]\n head_mask = self.get_head_mask(head_mask, self.config.qformer_config.num_hidden_layers)\n\n encoder_outputs = self.encoder(\n embedding_output,\n attention_mask=extended_attention_mask,\n head_mask=head_mask,\n encoder_hidden_states=encoder_hidden_states,\n encoder_attention_mask=encoder_extended_attention_mask,\n past_key_values=past_key_values,\n use_cache=use_cache,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n query_length=query_length,\n )\n sequence_output = encoder_outputs[0]\n pooled_output = sequence_output[:, 0, :]\n\n if not return_dict:\n return self.proj_layer(sequence_output[:, :query_length, :])\n\n return BaseModelOutputWithPoolingAndCrossAttentions(\n last_hidden_state=sequence_output,\n pooler_output=pooled_output,\n past_key_values=encoder_outputs.past_key_values,\n hidden_states=encoder_outputs.hidden_states,\n attentions=encoder_outputs.attentions,\n cross_attentions=encoder_outputs.cross_attentions,\n )\n" }, { "path": "diffusers/src/diffusers/pipelines/blip_diffusion/modeling_ctx_clip.py", "content": "# Copyright 2024 Salesforce.com, inc.\n# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Optional, Tuple, Union\n\nimport torch\nfrom torch import nn\nfrom transformers import CLIPPreTrainedModel\nfrom transformers.modeling_outputs import BaseModelOutputWithPooling\nfrom transformers.models.clip.configuration_clip import CLIPTextConfig\nfrom transformers.models.clip.modeling_clip import CLIPEncoder\n\n\ndef _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):\n \"\"\"\n Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.\n \"\"\"\n bsz, src_len = mask.size()\n tgt_len = tgt_len if tgt_len is not None else src_len\n\n expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)\n\n inverted_mask = 1.0 - expanded_mask\n\n return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)\n\n\n# This is a modified version of the CLIPTextModel from transformers.models.clip.modeling_clip\n# Which allows for an extra input of \"context embeddings\", which are the query embeddings used in Qformer\n# They pass through the clip model, along with the text embeddings, and interact with them using self attention\nclass ContextCLIPTextModel(CLIPPreTrainedModel):\n config_class = CLIPTextConfig\n\n _no_split_modules = [\"CLIPEncoderLayer\"]\n\n def __init__(self, config: CLIPTextConfig):\n super().__init__(config)\n self.text_model = ContextCLIPTextTransformer(config)\n # Initialize weights and apply final processing\n self.post_init()\n\n def forward(\n self,\n ctx_embeddings: torch.Tensor = None,\n ctx_begin_pos: list = None,\n input_ids: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.Tensor] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, BaseModelOutputWithPooling]:\n return self.text_model(\n ctx_embeddings=ctx_embeddings,\n ctx_begin_pos=ctx_begin_pos,\n input_ids=input_ids,\n attention_mask=attention_mask,\n position_ids=position_ids,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n\nclass ContextCLIPTextTransformer(nn.Module):\n def __init__(self, config: CLIPTextConfig):\n super().__init__()\n self.config = config\n embed_dim = config.hidden_size\n self.embeddings = ContextCLIPTextEmbeddings(config)\n self.encoder = CLIPEncoder(config)\n self.final_layer_norm = nn.LayerNorm(embed_dim)\n\n def forward(\n self,\n ctx_embeddings: torch.Tensor,\n ctx_begin_pos: list,\n input_ids: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.Tensor] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, BaseModelOutputWithPooling]:\n r\"\"\"\n Returns:\n\n \"\"\"\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n if input_ids is None:\n raise ValueError(\"You have to specify either input_ids\")\n\n input_shape = input_ids.size()\n input_ids = input_ids.view(-1, input_shape[-1])\n\n hidden_states = self.embeddings(\n input_ids=input_ids,\n position_ids=position_ids,\n ctx_embeddings=ctx_embeddings,\n ctx_begin_pos=ctx_begin_pos,\n )\n\n bsz, seq_len = input_shape\n if ctx_embeddings is not None:\n seq_len += ctx_embeddings.size(1)\n # CLIP's text model uses causal mask, prepare it here.\n # https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324\n causal_attention_mask = self._build_causal_attention_mask(bsz, seq_len, hidden_states.dtype).to(\n hidden_states.device\n )\n # expand attention_mask\n if attention_mask is not None:\n # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]\n attention_mask = _expand_mask(attention_mask, hidden_states.dtype)\n\n encoder_outputs = self.encoder(\n inputs_embeds=hidden_states,\n attention_mask=attention_mask,\n causal_attention_mask=causal_attention_mask,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n last_hidden_state = encoder_outputs[0]\n last_hidden_state = self.final_layer_norm(last_hidden_state)\n\n # text_embeds.shape = [batch_size, sequence_length, transformer.width]\n # take features from the eot embedding (eot_token is the highest number in each sequence)\n # casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14\n pooled_output = last_hidden_state[\n torch.arange(last_hidden_state.shape[0], device=input_ids.device),\n input_ids.to(torch.int).argmax(dim=-1),\n ]\n\n if not return_dict:\n return (last_hidden_state, pooled_output) + encoder_outputs[1:]\n\n return BaseModelOutputWithPooling(\n last_hidden_state=last_hidden_state,\n pooler_output=pooled_output,\n hidden_states=encoder_outputs.hidden_states,\n attentions=encoder_outputs.attentions,\n )\n\n def _build_causal_attention_mask(self, bsz, seq_len, dtype):\n # lazily create causal attention mask, with full attention between the vision tokens\n # pytorch uses additive attention mask; fill with -inf\n mask = torch.empty(bsz, seq_len, seq_len, dtype=dtype)\n mask.fill_(torch.tensor(torch.finfo(dtype).min))\n mask.triu_(1) # zero out the lower diagonal\n mask = mask.unsqueeze(1) # expand mask\n return mask\n\n\nclass ContextCLIPTextEmbeddings(nn.Module):\n def __init__(self, config: CLIPTextConfig):\n super().__init__()\n embed_dim = config.hidden_size\n\n self.token_embedding = nn.Embedding(config.vocab_size, embed_dim)\n self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim)\n\n # position_ids (1, len position emb) is contiguous in memory and exported when serialized\n self.register_buffer(\"position_ids\", torch.arange(config.max_position_embeddings).expand((1, -1)))\n\n def forward(\n self,\n ctx_embeddings: torch.Tensor,\n ctx_begin_pos: list,\n input_ids: Optional[torch.LongTensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n inputs_embeds: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n if ctx_embeddings is None:\n ctx_len = 0\n else:\n ctx_len = ctx_embeddings.shape[1]\n\n seq_length = (input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]) + ctx_len\n\n if position_ids is None:\n position_ids = self.position_ids[:, :seq_length]\n\n if inputs_embeds is None:\n inputs_embeds = self.token_embedding(input_ids)\n\n # for each input embeddings, add the ctx embeddings at the correct position\n input_embeds_ctx = []\n bsz = inputs_embeds.shape[0]\n\n if ctx_embeddings is not None:\n for i in range(bsz):\n cbp = ctx_begin_pos[i]\n\n prefix = inputs_embeds[i, :cbp]\n # remove the special token embedding\n suffix = inputs_embeds[i, cbp:]\n\n input_embeds_ctx.append(torch.cat([prefix, ctx_embeddings[i], suffix], dim=0))\n\n inputs_embeds = torch.stack(input_embeds_ctx, dim=0)\n\n position_embeddings = self.position_embedding(position_ids)\n embeddings = inputs_embeds + position_embeddings\n\n return embeddings\n" }, { "path": "diffusers/src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py", "content": "# Copyright 2024 Salesforce.com, inc.\n# Copyright 2024 The HuggingFace Team. All rights reserved.#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import List, Optional, Union\n\nimport PIL.Image\nimport torch\nfrom transformers import CLIPTokenizer\n\nfrom ...models import AutoencoderKL, UNet2DConditionModel\nfrom ...schedulers import PNDMScheduler\nfrom ...utils import (\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\nfrom .blip_image_processing import BlipImageProcessor\nfrom .modeling_blip2 import Blip2QFormerModel\nfrom .modeling_ctx_clip import ContextCLIPTextModel\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers.pipelines import BlipDiffusionPipeline\n >>> from diffusers.utils import load_image\n >>> import torch\n\n >>> blip_diffusion_pipe = BlipDiffusionPipeline.from_pretrained(\n ... \"Salesforce/blipdiffusion\", torch_dtype=torch.float16\n ... ).to(\"cuda\")\n\n\n >>> cond_subject = \"dog\"\n >>> tgt_subject = \"dog\"\n >>> text_prompt_input = \"swimming underwater\"\n\n >>> cond_image = load_image(\n ... \"https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/dog.jpg\"\n ... )\n >>> guidance_scale = 7.5\n >>> num_inference_steps = 25\n >>> negative_prompt = \"over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate\"\n\n\n >>> output = blip_diffusion_pipe(\n ... text_prompt_input,\n ... cond_image,\n ... cond_subject,\n ... tgt_subject,\n ... guidance_scale=guidance_scale,\n ... num_inference_steps=num_inference_steps,\n ... neg_prompt=negative_prompt,\n ... height=512,\n ... width=512,\n ... ).images\n >>> output[0].save(\"image.png\")\n ```\n\"\"\"\n\n\nclass BlipDiffusionPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for Zero-Shot Subject Driven Generation using Blip Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n tokenizer ([`CLIPTokenizer`]):\n Tokenizer for the text encoder\n text_encoder ([`ContextCLIPTextModel`]):\n Text encoder to encode the text prompt\n vae ([`AutoencoderKL`]):\n VAE model to map the latents to the image\n unet ([`UNet2DConditionModel`]):\n Conditional U-Net architecture to denoise the image embedding.\n scheduler ([`PNDMScheduler`]):\n A scheduler to be used in combination with `unet` to generate image latents.\n qformer ([`Blip2QFormerModel`]):\n QFormer model to get multi-modal embeddings from the text and image.\n image_processor ([`BlipImageProcessor`]):\n Image Processor to preprocess and postprocess the image.\n ctx_begin_pos (int, `optional`, defaults to 2):\n Position of the context token in the text encoder.\n \"\"\"\n\n model_cpu_offload_seq = \"qformer->text_encoder->unet->vae\"\n\n def __init__(\n self,\n tokenizer: CLIPTokenizer,\n text_encoder: ContextCLIPTextModel,\n vae: AutoencoderKL,\n unet: UNet2DConditionModel,\n scheduler: PNDMScheduler,\n qformer: Blip2QFormerModel,\n image_processor: BlipImageProcessor,\n ctx_begin_pos: int = 2,\n mean: List[float] = None,\n std: List[float] = None,\n ):\n super().__init__()\n\n self.register_modules(\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n vae=vae,\n unet=unet,\n scheduler=scheduler,\n qformer=qformer,\n image_processor=image_processor,\n )\n self.register_to_config(ctx_begin_pos=ctx_begin_pos, mean=mean, std=std)\n\n def get_query_embeddings(self, input_image, src_subject):\n return self.qformer(image_input=input_image, text_input=src_subject, return_dict=False)\n\n # from the original Blip Diffusion code, speciefies the target subject and augments the prompt by repeating it\n def _build_prompt(self, prompts, tgt_subjects, prompt_strength=1.0, prompt_reps=20):\n rv = []\n for prompt, tgt_subject in zip(prompts, tgt_subjects):\n prompt = f\"a {tgt_subject} {prompt.strip()}\"\n # a trick to amplify the prompt\n rv.append(\", \".join([prompt] * int(prompt_strength * prompt_reps)))\n\n return rv\n\n # Copied from diffusers.pipelines.consistency_models.pipeline_consistency_models.ConsistencyModelPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels, height, width, dtype, device, generator, latents=None):\n shape = (batch_size, num_channels, height, width)\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device=device, dtype=dtype)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def encode_prompt(self, query_embeds, prompt, device=None):\n device = device or self._execution_device\n\n # embeddings for prompt, with query_embeds as context\n max_len = self.text_encoder.text_model.config.max_position_embeddings\n max_len -= self.qformer.config.num_query_tokens\n\n tokenized_prompt = self.tokenizer(\n prompt,\n padding=\"max_length\",\n truncation=True,\n max_length=max_len,\n return_tensors=\"pt\",\n ).to(device)\n\n batch_size = query_embeds.shape[0]\n ctx_begin_pos = [self.config.ctx_begin_pos] * batch_size\n\n text_embeddings = self.text_encoder(\n input_ids=tokenized_prompt.input_ids,\n ctx_embeddings=query_embeds,\n ctx_begin_pos=ctx_begin_pos,\n )[0]\n\n return text_embeddings\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: List[str],\n reference_image: PIL.Image.Image,\n source_subject_category: List[str],\n target_subject_category: List[str],\n latents: Optional[torch.Tensor] = None,\n guidance_scale: float = 7.5,\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 50,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n neg_prompt: Optional[str] = \"\",\n prompt_strength: float = 1.0,\n prompt_reps: int = 20,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`List[str]`):\n The prompt or prompts to guide the image generation.\n reference_image (`PIL.Image.Image`):\n The reference image to condition the generation on.\n source_subject_category (`List[str]`):\n The source subject category.\n target_subject_category (`List[str]`):\n The target subject category.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by random sampling.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n height (`int`, *optional*, defaults to 512):\n The height of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n neg_prompt (`str`, *optional*, defaults to \"\"):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n prompt_strength (`float`, *optional*, defaults to 1.0):\n The strength of the prompt. Specifies the number of times the prompt is repeated along with prompt_reps\n to amplify the prompt.\n prompt_reps (`int`, *optional*, defaults to 20):\n The number of times the prompt is repeated along with prompt_strength to amplify the prompt.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between: `\"pil\"` (`PIL.Image.Image`), `\"np\"`\n (`np.array`) or `\"pt\"` (`torch.Tensor`).\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`\n \"\"\"\n device = self._execution_device\n\n reference_image = self.image_processor.preprocess(\n reference_image, image_mean=self.config.mean, image_std=self.config.std, return_tensors=\"pt\"\n )[\"pixel_values\"]\n reference_image = reference_image.to(device)\n\n if isinstance(prompt, str):\n prompt = [prompt]\n if isinstance(source_subject_category, str):\n source_subject_category = [source_subject_category]\n if isinstance(target_subject_category, str):\n target_subject_category = [target_subject_category]\n\n batch_size = len(prompt)\n\n prompt = self._build_prompt(\n prompts=prompt,\n tgt_subjects=target_subject_category,\n prompt_strength=prompt_strength,\n prompt_reps=prompt_reps,\n )\n query_embeds = self.get_query_embeddings(reference_image, source_subject_category)\n text_embeddings = self.encode_prompt(query_embeds, prompt, device)\n do_classifier_free_guidance = guidance_scale > 1.0\n if do_classifier_free_guidance:\n max_length = self.text_encoder.text_model.config.max_position_embeddings\n\n uncond_input = self.tokenizer(\n [neg_prompt] * batch_size,\n padding=\"max_length\",\n max_length=max_length,\n return_tensors=\"pt\",\n )\n uncond_embeddings = self.text_encoder(\n input_ids=uncond_input.input_ids.to(device),\n ctx_embeddings=None,\n )[0]\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n\n scale_down_factor = 2 ** (len(self.unet.config.block_out_channels) - 1)\n latents = self.prepare_latents(\n batch_size=batch_size,\n num_channels=self.unet.config.in_channels,\n height=height // scale_down_factor,\n width=width // scale_down_factor,\n generator=generator,\n latents=latents,\n dtype=self.unet.dtype,\n device=device,\n )\n # set timesteps\n extra_set_kwargs = {}\n self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)\n\n for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):\n # expand the latents if we are doing classifier free guidance\n do_classifier_free_guidance = guidance_scale > 1.0\n\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n\n noise_pred = self.unet(\n latent_model_input,\n timestep=t,\n encoder_hidden_states=text_embeddings,\n down_block_additional_residuals=None,\n mid_block_additional_residual=None,\n )[\"sample\"]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n latents = self.scheduler.step(\n noise_pred,\n t,\n latents,\n )[\"prev_sample\"]\n\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/consistency_models/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n _LazyModule,\n)\n\n\n_import_structure = {\n \"pipeline_consistency_models\": [\"ConsistencyModelPipeline\"],\n}\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n from .pipeline_consistency_models import ConsistencyModelPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n" }, { "path": "diffusers/src/diffusers/pipelines/consistency_models/pipeline_consistency_models.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Callable, List, Optional, Union\n\nimport torch\n\nfrom ...models import UNet2DModel\nfrom ...schedulers import CMStochasticIterativeScheduler\nfrom ...utils import (\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n\n >>> from diffusers import ConsistencyModelPipeline\n\n >>> device = \"cuda\"\n >>> # Load the cd_imagenet64_l2 checkpoint.\n >>> model_id_or_path = \"openai/diffusers-cd_imagenet64_l2\"\n >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n >>> pipe.to(device)\n\n >>> # Onestep Sampling\n >>> image = pipe(num_inference_steps=1).images[0]\n >>> image.save(\"cd_imagenet64_l2_onestep_sample.png\")\n\n >>> # Onestep sampling, class-conditional image generation\n >>> # ImageNet-64 class label 145 corresponds to king penguins\n >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n >>> image.save(\"cd_imagenet64_l2_onestep_sample_penguin.png\")\n\n >>> # Multistep sampling, class-conditional image generation\n >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n >>> image.save(\"cd_imagenet64_l2_multistep_sample_penguin.png\")\n ```\n\"\"\"\n\n\nclass ConsistencyModelPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for unconditional or class-conditional image generation.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Args:\n unet ([`UNet2DModel`]):\n A `UNet2DModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Currently only\n compatible with [`CMStochasticIterativeScheduler`].\n \"\"\"\n\n model_cpu_offload_seq = \"unet\"\n\n def __init__(self, unet: UNet2DModel, scheduler: CMStochasticIterativeScheduler) -> None:\n super().__init__()\n\n self.register_modules(\n unet=unet,\n scheduler=scheduler,\n )\n\n self.safety_checker = None\n\n def prepare_latents(self, batch_size, num_channels, height, width, dtype, device, generator, latents=None):\n shape = (batch_size, num_channels, height, width)\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device=device, dtype=dtype)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n # Follows diffusers.VaeImageProcessor.postprocess\n def postprocess_image(self, sample: torch.Tensor, output_type: str = \"pil\"):\n if output_type not in [\"pt\", \"np\", \"pil\"]:\n raise ValueError(\n f\"output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']\"\n )\n\n # Equivalent to diffusers.VaeImageProcessor.denormalize\n sample = (sample / 2 + 0.5).clamp(0, 1)\n if output_type == \"pt\":\n return sample\n\n # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy\n sample = sample.cpu().permute(0, 2, 3, 1).numpy()\n if output_type == \"np\":\n return sample\n\n # Output_type must be 'pil'\n sample = self.numpy_to_pil(sample)\n return sample\n\n def prepare_class_labels(self, batch_size, device, class_labels=None):\n if self.unet.config.num_class_embeds is not None:\n if isinstance(class_labels, list):\n class_labels = torch.tensor(class_labels, dtype=torch.int)\n elif isinstance(class_labels, int):\n assert batch_size == 1, \"Batch size must be 1 if classes is an int\"\n class_labels = torch.tensor([class_labels], dtype=torch.int)\n elif class_labels is None:\n # Randomly generate batch_size class labels\n # TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils\n class_labels = torch.randint(0, self.unet.config.num_class_embeds, size=(batch_size,))\n class_labels = class_labels.to(device)\n else:\n class_labels = None\n return class_labels\n\n def check_inputs(self, num_inference_steps, timesteps, latents, batch_size, img_size, callback_steps):\n if num_inference_steps is None and timesteps is None:\n raise ValueError(\"Exactly one of `num_inference_steps` or `timesteps` must be supplied.\")\n\n if num_inference_steps is not None and timesteps is not None:\n logger.warning(\n f\"Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;\"\n \" `timesteps` will be used over `num_inference_steps`.\"\n )\n\n if latents is not None:\n expected_shape = (batch_size, 3, img_size, img_size)\n if latents.shape != expected_shape:\n raise ValueError(f\"The shape of latents is {latents.shape} but is expected to be {expected_shape}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n batch_size: int = 1,\n class_labels: Optional[Union[torch.Tensor, List[int], int]] = None,\n num_inference_steps: int = 1,\n timesteps: List[int] = None,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n ):\n r\"\"\"\n Args:\n batch_size (`int`, *optional*, defaults to 1):\n The number of images to generate.\n class_labels (`torch.Tensor` or `List[int]` or `int`, *optional*):\n Optional class labels for conditioning class-conditional consistency models. Not used if the model is\n not class-conditional.\n num_inference_steps (`int`, *optional*, defaults to 1):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`\n timesteps are used. Must be in descending order.\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is\n returned where the first element is a list with the generated images.\n \"\"\"\n # 0. Prepare call parameters\n img_size = self.unet.config.sample_size\n device = self._execution_device\n\n # 1. Check inputs\n self.check_inputs(num_inference_steps, timesteps, latents, batch_size, img_size, callback_steps)\n\n # 2. Prepare image latents\n # Sample image latents x_0 ~ N(0, sigma_0^2 * I)\n sample = self.prepare_latents(\n batch_size=batch_size,\n num_channels=self.unet.config.in_channels,\n height=img_size,\n width=img_size,\n dtype=self.unet.dtype,\n device=device,\n generator=generator,\n latents=latents,\n )\n\n # 3. Handle class_labels for class-conditional models\n class_labels = self.prepare_class_labels(batch_size, device, class_labels=class_labels)\n\n # 4. Prepare timesteps\n if timesteps is not None:\n self.scheduler.set_timesteps(timesteps=timesteps, device=device)\n timesteps = self.scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n self.scheduler.set_timesteps(num_inference_steps)\n timesteps = self.scheduler.timesteps\n\n # 5. Denoising loop\n # Multistep sampling: implements Algorithm 1 in the paper\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n scaled_sample = self.scheduler.scale_model_input(sample, t)\n model_output = self.unet(scaled_sample, t, class_labels=class_labels, return_dict=False)[0]\n\n sample = self.scheduler.step(model_output, t, sample, generator=generator)[0]\n\n # call the callback, if provided\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, sample)\n\n # 6. Post-process image sample\n image = self.postprocess_image(sample, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/controlnet/__init__.py", "content": "from typing import TYPE_CHECKING\r\n\r\nfrom ...utils import (\r\n DIFFUSERS_SLOW_IMPORT,\r\n OptionalDependencyNotAvailable,\r\n _LazyModule,\r\n get_objects_from_module,\r\n is_flax_available,\r\n is_torch_available,\r\n is_transformers_available,\r\n)\r\n\r\n\r\n_dummy_objects = {}\r\n_import_structure = {}\r\n\r\ntry:\r\n if not (is_transformers_available() and is_torch_available()):\r\n raise OptionalDependencyNotAvailable()\r\nexcept OptionalDependencyNotAvailable:\r\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\r\n\r\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\r\nelse:\r\n _import_structure[\"multicontrolnet\"] = [\"MultiControlNetModel\"]\r\n _import_structure[\"pipeline_controlnet\"] = [\"StableDiffusionControlNetPipeline\"]\r\n _import_structure[\"pipeline_controlnet_blip_diffusion\"] = [\"BlipDiffusionControlNetPipeline\"]\r\n _import_structure[\"pipeline_controlnet_img2img\"] = [\"StableDiffusionControlNetImg2ImgPipeline\"]\r\n _import_structure[\"pipeline_controlnet_inpaint\"] = [\"StableDiffusionControlNetInpaintPipeline\"]\r\n _import_structure[\"pipeline_controlnet_inpaint_sd_xl\"] = [\"StableDiffusionXLControlNetInpaintPipeline\"]\r\n _import_structure[\"pipeline_controlnet_sd_xl\"] = [\"StableDiffusionXLControlNetPipeline\"]\r\n _import_structure[\"pipeline_controlnet_sd_xl_img2img\"] = [\"StableDiffusionXLControlNetImg2ImgPipeline\"]\r\ntry:\r\n if not (is_transformers_available() and is_flax_available()):\r\n raise OptionalDependencyNotAvailable()\r\nexcept OptionalDependencyNotAvailable:\r\n from ...utils import dummy_flax_and_transformers_objects # noqa F403\r\n\r\n _dummy_objects.update(get_objects_from_module(dummy_flax_and_transformers_objects))\r\nelse:\r\n _import_structure[\"pipeline_flax_controlnet\"] = [\"FlaxStableDiffusionControlNetPipeline\"]\r\n\r\n\r\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\r\n try:\r\n if not (is_transformers_available() and is_torch_available()):\r\n raise OptionalDependencyNotAvailable()\r\n\r\n except OptionalDependencyNotAvailable:\r\n from ...utils.dummy_torch_and_transformers_objects import *\r\n else:\r\n from .multicontrolnet import MultiControlNetModel\r\n from .pipeline_controlnet import StableDiffusionControlNetPipeline\r\n from .pipeline_controlnet_blip_diffusion import BlipDiffusionControlNetPipeline\r\n from .pipeline_controlnet_img2img import StableDiffusionControlNetImg2ImgPipeline\r\n from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline\r\n from .pipeline_controlnet_inpaint_sd_xl import StableDiffusionXLControlNetInpaintPipeline\r\n from .pipeline_controlnet_sd_xl import StableDiffusionXLControlNetPipeline\r\n from .pipeline_controlnet_sd_xl_img2img import StableDiffusionXLControlNetImg2ImgPipeline\r\n\r\n try:\r\n if not (is_transformers_available() and is_flax_available()):\r\n raise OptionalDependencyNotAvailable()\r\n except OptionalDependencyNotAvailable:\r\n from ...utils.dummy_flax_and_transformers_objects import * # noqa F403\r\n else:\r\n from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline\r\n\r\n\r\nelse:\r\n import sys\r\n\r\n sys.modules[__name__] = _LazyModule(\r\n __name__,\r\n globals()[\"__file__\"],\r\n _import_structure,\r\n module_spec=__spec__,\r\n )\r\n for name, value in _dummy_objects.items():\r\n setattr(sys.modules[__name__], name, value)\r\n" }, { "path": "diffusers/src/diffusers/pipelines/controlnet/multicontrolnet.py", "content": "import os\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport torch\nfrom torch import nn\n\nfrom ...models.controlnet import ControlNetModel, ControlNetOutput\nfrom ...models.modeling_utils import ModelMixin\nfrom ...utils import logging\n\n\nlogger = logging.get_logger(__name__)\n\n\nclass MultiControlNetModel(ModelMixin):\n r\"\"\"\n Multiple `ControlNetModel` wrapper class for Multi-ControlNet\n\n This module is a wrapper for multiple instances of the `ControlNetModel`. The `forward()` API is designed to be\n compatible with `ControlNetModel`.\n\n Args:\n controlnets (`List[ControlNetModel]`):\n Provides additional conditioning to the unet during the denoising process. You must set multiple\n `ControlNetModel` as a list.\n \"\"\"\n\n def __init__(self, controlnets: Union[List[ControlNetModel], Tuple[ControlNetModel]]):\n super().__init__()\n self.nets = nn.ModuleList(controlnets)\n\n def forward(\n self,\n sample: torch.Tensor,\n timestep: Union[torch.Tensor, float, int],\n encoder_hidden_states: torch.Tensor,\n controlnet_cond: List[torch.tensor],\n conditioning_scale: List[float],\n class_labels: Optional[torch.Tensor] = None,\n timestep_cond: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guess_mode: bool = False,\n return_dict: bool = True,\n ) -> Union[ControlNetOutput, Tuple]:\n for i, (image, scale, controlnet) in enumerate(zip(controlnet_cond, conditioning_scale, self.nets)):\n down_samples, mid_sample = controlnet(\n sample=sample,\n timestep=timestep,\n encoder_hidden_states=encoder_hidden_states,\n controlnet_cond=image,\n conditioning_scale=scale,\n class_labels=class_labels,\n timestep_cond=timestep_cond,\n attention_mask=attention_mask,\n added_cond_kwargs=added_cond_kwargs,\n cross_attention_kwargs=cross_attention_kwargs,\n guess_mode=guess_mode,\n return_dict=return_dict,\n )\n\n # merge samples\n if i == 0:\n down_block_res_samples, mid_block_res_sample = down_samples, mid_sample\n else:\n down_block_res_samples = [\n samples_prev + samples_curr\n for samples_prev, samples_curr in zip(down_block_res_samples, down_samples)\n ]\n mid_block_res_sample += mid_sample\n\n return down_block_res_samples, mid_block_res_sample\n\n def save_pretrained(\n self,\n save_directory: Union[str, os.PathLike],\n is_main_process: bool = True,\n save_function: Callable = None,\n safe_serialization: bool = True,\n variant: Optional[str] = None,\n ):\n \"\"\"\n Save a model and its configuration file to a directory, so that it can be re-loaded using the\n `[`~pipelines.controlnet.MultiControlNetModel.from_pretrained`]` class method.\n\n Arguments:\n save_directory (`str` or `os.PathLike`):\n Directory to which to save. Will be created if it doesn't exist.\n is_main_process (`bool`, *optional*, defaults to `True`):\n Whether the process calling this is the main process or not. Useful when in distributed training like\n TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on\n the main process to avoid race conditions.\n save_function (`Callable`):\n The function to use to save the state dictionary. Useful on distributed training like TPUs when one\n need to replace `torch.save` by another method. Can be configured with the environment variable\n `DIFFUSERS_SAVE_MODE`.\n safe_serialization (`bool`, *optional*, defaults to `True`):\n Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).\n variant (`str`, *optional*):\n If specified, weights are saved in the format pytorch_model..bin.\n \"\"\"\n for idx, controlnet in enumerate(self.nets):\n suffix = \"\" if idx == 0 else f\"_{idx}\"\n controlnet.save_pretrained(\n save_directory + suffix,\n is_main_process=is_main_process,\n save_function=save_function,\n safe_serialization=safe_serialization,\n variant=variant,\n )\n\n @classmethod\n def from_pretrained(cls, pretrained_model_path: Optional[Union[str, os.PathLike]], **kwargs):\n r\"\"\"\n Instantiate a pretrained MultiControlNet model from multiple pre-trained controlnet models.\n\n The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train\n the model, you should first set it back in training mode with `model.train()`.\n\n The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come\n pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning\n task.\n\n The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those\n weights are discarded.\n\n Parameters:\n pretrained_model_path (`os.PathLike`):\n A path to a *directory* containing model weights saved using\n [`~diffusers.pipelines.controlnet.MultiControlNetModel.save_pretrained`], e.g.,\n `./my_model_directory/controlnet`.\n torch_dtype (`str` or `torch.dtype`, *optional*):\n Override the default `torch.dtype` and load the model under this dtype. If `\"auto\"` is passed the dtype\n will be automatically derived from the model's weights.\n output_loading_info(`bool`, *optional*, defaults to `False`):\n Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.\n device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):\n A map that specifies where each submodule should go. It doesn't need to be refined to each\n parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the\n same device.\n\n To have Accelerate compute the most optimized `device_map` automatically, set `device_map=\"auto\"`. For\n more information about each option see [designing a device\n map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).\n max_memory (`Dict`, *optional*):\n A dictionary device identifier to maximum memory. Will default to the maximum memory available for each\n GPU and the available CPU RAM if unset.\n low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):\n Speed up model loading by not initializing the weights and only loading the pre-trained weights. This\n also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the\n model. This is only supported when torch version >= 1.9.0. If you are using an older version of torch,\n setting this argument to `True` will raise an error.\n variant (`str`, *optional*):\n If specified load weights from `variant` filename, *e.g.* pytorch_model..bin. `variant` is\n ignored when using `from_flax`.\n use_safetensors (`bool`, *optional*, defaults to `None`):\n If set to `None`, the `safetensors` weights will be downloaded if they're available **and** if the\n `safetensors` library is installed. If set to `True`, the model will be forcibly loaded from\n `safetensors` weights. If set to `False`, loading will *not* use `safetensors`.\n \"\"\"\n idx = 0\n controlnets = []\n\n # load controlnet and append to list until no controlnet directory exists anymore\n # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`\n # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...\n model_path_to_load = pretrained_model_path\n while os.path.isdir(model_path_to_load):\n controlnet = ControlNetModel.from_pretrained(model_path_to_load, **kwargs)\n controlnets.append(controlnet)\n\n idx += 1\n model_path_to_load = pretrained_model_path + f\"_{idx}\"\n\n logger.info(f\"{len(controlnets)} controlnets loaded from {pretrained_model_path}.\")\n\n if len(controlnets) == 0:\n raise ValueError(\n f\"No ControlNets found under {os.path.dirname(pretrained_model_path)}. Expected at least {pretrained_model_path + '_0'}.\"\n )\n\n return cls(controlnets)\n" }, { "path": "diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...callbacks import MultiPipelineCallbacks, PipelineCallback\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, ControlNetModel, ImageProjection, UNet2DConditionModel\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ..stable_diffusion.pipeline_output import StableDiffusionPipelineOutput\nfrom ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom .multicontrolnet import MultiControlNetModel\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> # !pip install opencv-python transformers accelerate\n >>> from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\n >>> from diffusers.utils import load_image\n >>> import numpy as np\n >>> import torch\n\n >>> import cv2\n >>> from PIL import Image\n\n >>> # download an image\n >>> image = load_image(\n ... \"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png\"\n ... )\n >>> image = np.array(image)\n\n >>> # get canny image\n >>> image = cv2.Canny(image, 100, 200)\n >>> image = image[:, :, None]\n >>> image = np.concatenate([image, image, image], axis=2)\n >>> canny_image = Image.fromarray(image)\n\n >>> # load control net and stable diffusion v1-5\n >>> controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\", torch_dtype=torch.float16)\n >>> pipe = StableDiffusionControlNetPipeline.from_pretrained(\n ... \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, torch_dtype=torch.float16\n ... )\n\n >>> # speed up diffusion process with faster scheduler and memory optimization\n >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n >>> # remove following line if xformers is not installed\n >>> pipe.enable_xformers_memory_efficient_attention()\n\n >>> pipe.enable_model_cpu_offload()\n\n >>> # generate image\n >>> generator = torch.manual_seed(0)\n >>> image = pipe(\n ... \"futuristic-looking woman\", num_inference_steps=20, generator=generator, image=canny_image\n ... ).images[0]\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass StableDiffusionControlNetPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n TextualInversionLoaderMixin,\n LoraLoaderMixin,\n IPAdapterMixin,\n FromSingleFileMixin,\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):\n Provides additional conditioning to the `unet` during the denoising process. If you set multiple\n ControlNets as a list, the outputs from each ControlNet are added together to create one combined\n additional conditioning.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->image_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"image_encoder\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder: CLIPVisionModelWithProjection = None,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n if isinstance(controlnet, (list, tuple)):\n controlnet = MultiControlNetModel(controlnet)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n controlnet=controlnet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)\n self.control_image_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False\n )\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n image,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n controlnet_conditioning_scale=1.0,\n control_guidance_start=0.0,\n control_guidance_end=1.0,\n callback_on_step_end_tensor_inputs=None,\n ):\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # Check `image`\n is_compiled = hasattr(F, \"scaled_dot_product_attention\") and isinstance(\n self.controlnet, torch._dynamo.eval_frame.OptimizedModule\n )\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n self.check_image(image, prompt, prompt_embeds)\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if not isinstance(image, list):\n raise TypeError(\"For multiple controlnets: `image` must be type `list`\")\n\n # When `image` is a nested list:\n # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])\n elif any(isinstance(i, list) for i in image):\n transposed_image = [list(t) for t in zip(*image)]\n if len(transposed_image) != len(self.controlnet.nets):\n raise ValueError(\n f\"For multiple controlnets: if you pass`image` as a list of list, each sublist must have the same length as the number of controlnets, but the sublists in `image` got {len(transposed_image)} images and {len(self.controlnet.nets)} ControlNets.\"\n )\n for image_ in transposed_image:\n self.check_image(image_, prompt, prompt_embeds)\n elif len(image) != len(self.controlnet.nets):\n raise ValueError(\n f\"For multiple controlnets: `image` must have the same length as the number of controlnets, but got {len(image)} images and {len(self.controlnet.nets)} ControlNets.\"\n )\n else:\n for image_ in image:\n self.check_image(image_, prompt, prompt_embeds)\n else:\n assert False\n\n # Check `controlnet_conditioning_scale`\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n if not isinstance(controlnet_conditioning_scale, float):\n raise TypeError(\"For single controlnet: `controlnet_conditioning_scale` must be type `float`.\")\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if isinstance(controlnet_conditioning_scale, list):\n if any(isinstance(i, list) for i in controlnet_conditioning_scale):\n raise ValueError(\n \"A single batch of varying conditioning scale settings (e.g. [[1.0, 0.5], [0.2, 0.8]]) is not supported at the moment. \"\n \"The conditioning scale must be fixed across the batch.\"\n )\n elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(\n self.controlnet.nets\n ):\n raise ValueError(\n \"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have\"\n \" the same length as the number of controlnets\"\n )\n else:\n assert False\n\n if not isinstance(control_guidance_start, (tuple, list)):\n control_guidance_start = [control_guidance_start]\n\n if not isinstance(control_guidance_end, (tuple, list)):\n control_guidance_end = [control_guidance_end]\n\n if len(control_guidance_start) != len(control_guidance_end):\n raise ValueError(\n f\"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list.\"\n )\n\n if isinstance(self.controlnet, MultiControlNetModel):\n if len(control_guidance_start) != len(self.controlnet.nets):\n raise ValueError(\n f\"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}.\"\n )\n\n for start, end in zip(control_guidance_start, control_guidance_end):\n if start >= end:\n raise ValueError(\n f\"control guidance start: {start} cannot be larger or equal to control guidance end: {end}.\"\n )\n if start < 0.0:\n raise ValueError(f\"control guidance start: {start} can't be smaller than 0.\")\n if end > 1.0:\n raise ValueError(f\"control guidance end: {end} can't be larger than 1.0.\")\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n if ip_adapter_image_embeds is not None:\n if not isinstance(ip_adapter_image_embeds, list):\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}\"\n )\n elif ip_adapter_image_embeds[0].ndim not in [3, 4]:\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D\"\n )\n\n def check_image(self, image, prompt, prompt_embeds):\n image_is_pil = isinstance(image, PIL.Image.Image)\n image_is_tensor = isinstance(image, torch.Tensor)\n image_is_np = isinstance(image, np.ndarray)\n image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)\n image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)\n image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)\n\n if (\n not image_is_pil\n and not image_is_tensor\n and not image_is_np\n and not image_is_pil_list\n and not image_is_tensor_list\n and not image_is_np_list\n ):\n raise TypeError(\n f\"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}\"\n )\n\n if image_is_pil:\n image_batch_size = 1\n else:\n image_batch_size = len(image)\n\n if prompt is not None and isinstance(prompt, str):\n prompt_batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n prompt_batch_size = len(prompt)\n elif prompt_embeds is not None:\n prompt_batch_size = prompt_embeds.shape[0]\n\n if image_batch_size != 1 and image_batch_size != prompt_batch_size:\n raise ValueError(\n f\"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}\"\n )\n\n def prepare_image(\n self,\n image,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype,\n do_classifier_free_guidance=False,\n guess_mode=False,\n ):\n image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)\n image_batch_size = image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n image = image.repeat_interleave(repeat_by, dim=0)\n\n image = image.to(device=device, dtype=dtype)\n\n if do_classifier_free_guidance and not guess_mode:\n image = torch.cat([image] * 2)\n\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(\n self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32\n ) -> torch.Tensor:\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n w (`torch.Tensor`):\n Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.\n embedding_dim (`int`, *optional*, defaults to 512):\n Dimension of the embeddings to generate.\n dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):\n Data type of the generated embeddings.\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: PipelineImageInput = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n sigmas: List[float] = None,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: Union[float, List[float]] = 1.0,\n guess_mode: bool = False,\n control_guidance_start: Union[float, List[float]] = 0.0,\n control_guidance_end: Union[float, List[float]] = 1.0,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[\n Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]\n ] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:\n `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):\n The ControlNet input condition to provide guidance to the `unet` for generation. If the type is\n specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be accepted\n as an image. The dimensions of the output image defaults to `image`'s dimensions. If height and/or\n width are passed, `image` is resized accordingly. If multiple ControlNets are specified in `init`,\n images must be passed as a list such that each element of the list can be correctly batched for input\n to a single ControlNet. When `prompt` is a list, and if a list of images is passed for a single\n ControlNet, each will be paired with each prompt in the `prompt` list. This also applies to multiple\n ControlNets, where a list of image lists can be passed to batch for each prompt and each ControlNet.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n sigmas (`List[float]`, *optional*):\n Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in\n their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed\n will be used.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of\n IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should\n contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not\n provided, embeddings are computed from the `ip_adapter_image` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):\n The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added\n to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set\n the corresponding scale as a list.\n guess_mode (`bool`, *optional*, defaults to `False`):\n The ControlNet encoder tries to recognize the content of the input image even if you remove all\n prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.\n control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):\n The percentage of total steps at which the ControlNet starts applying.\n control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):\n The percentage of total steps at which the ControlNet stops applying.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):\n A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of\n each denoising step during the inference. with the following arguments: `callback_on_step_end(self:\n DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a\n list of all tensors as specified by `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n\n if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):\n callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs\n\n controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet\n\n # align format for control guidance\n if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):\n control_guidance_start = len(control_guidance_end) * [control_guidance_start]\n elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):\n control_guidance_end = len(control_guidance_start) * [control_guidance_end]\n elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):\n mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1\n control_guidance_start, control_guidance_end = (\n mult * [control_guidance_start],\n mult * [control_guidance_end],\n )\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n image,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n ip_adapter_image,\n ip_adapter_image_embeds,\n controlnet_conditioning_scale,\n control_guidance_start,\n control_guidance_end,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):\n controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)\n\n global_pool_conditions = (\n controlnet.config.global_pool_conditions\n if isinstance(controlnet, ControlNetModel)\n else controlnet.nets[0].config.global_pool_conditions\n )\n guess_mode = guess_mode or global_pool_conditions\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=self.clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_images_per_prompt,\n self.do_classifier_free_guidance,\n )\n\n # 4. Prepare image\n if isinstance(controlnet, ControlNetModel):\n image = self.prepare_image(\n image=image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n height, width = image.shape[-2:]\n elif isinstance(controlnet, MultiControlNetModel):\n images = []\n\n # Nested lists as ControlNet condition\n if isinstance(image[0], list):\n # Transpose the nested image list\n image = [list(t) for t in zip(*image)]\n\n for image_ in image:\n image_ = self.prepare_image(\n image=image_,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n\n images.append(image_)\n\n image = images\n height, width = image[0].shape[-2:]\n else:\n assert False\n\n # 5. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(\n self.scheduler, num_inference_steps, device, timesteps, sigmas\n )\n self._num_timesteps = len(timesteps)\n\n # 6. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6.5 Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7.1 Add image embeds for IP-Adapter\n added_cond_kwargs = (\n {\"image_embeds\": image_embeds}\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None\n else None\n )\n\n # 7.2 Create tensor stating which controlnets to keep\n controlnet_keep = []\n for i in range(len(timesteps)):\n keeps = [\n 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)\n for s, e in zip(control_guidance_start, control_guidance_end)\n ]\n controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n is_unet_compiled = is_compiled_module(self.unet)\n is_controlnet_compiled = is_compiled_module(self.controlnet)\n is_torch_higher_equal_2_1 = is_torch_version(\">=\", \"2.1\")\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # Relevant thread:\n # https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428\n if (is_unet_compiled and is_controlnet_compiled) and is_torch_higher_equal_2_1:\n torch._inductor.cudagraph_mark_step_begin()\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # controlnet(s) inference\n if guess_mode and self.do_classifier_free_guidance:\n # Infer ControlNet only for the conditional batch.\n control_model_input = latents\n control_model_input = self.scheduler.scale_model_input(control_model_input, t)\n controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]\n else:\n control_model_input = latent_model_input\n controlnet_prompt_embeds = prompt_embeds\n\n if isinstance(controlnet_keep[i], list):\n cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]\n else:\n controlnet_cond_scale = controlnet_conditioning_scale\n if isinstance(controlnet_cond_scale, list):\n controlnet_cond_scale = controlnet_cond_scale[0]\n cond_scale = controlnet_cond_scale * controlnet_keep[i]\n\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n control_model_input,\n t,\n encoder_hidden_states=controlnet_prompt_embeds,\n controlnet_cond=image,\n conditioning_scale=cond_scale,\n guess_mode=guess_mode,\n return_dict=False,\n )\n\n if guess_mode and self.do_classifier_free_guidance:\n # Infered ControlNet only for the conditional batch.\n # To apply the output of ControlNet to both the unconditional and conditional batches,\n # add 0 to the unconditional batch to keep it unchanged.\n down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]\n mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=self.cross_attention_kwargs,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # If we do sequential model offloading, let's offload unet and controlnet\n # manually for max memory savings\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.unet.to(\"cpu\")\n self.controlnet.to(\"cpu\")\n torch.cuda.empty_cache()\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[\n 0\n ]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py", "content": "# Copyright 2024 Salesforce.com, inc.\n# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import List, Optional, Union\n\nimport PIL.Image\nimport torch\nfrom transformers import CLIPTokenizer\n\nfrom ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel\nfrom ...schedulers import PNDMScheduler\nfrom ...utils import (\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..blip_diffusion.blip_image_processing import BlipImageProcessor\nfrom ..blip_diffusion.modeling_blip2 import Blip2QFormerModel\nfrom ..blip_diffusion.modeling_ctx_clip import ContextCLIPTextModel\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers.pipelines import BlipDiffusionControlNetPipeline\n >>> from diffusers.utils import load_image\n >>> from controlnet_aux import CannyDetector\n >>> import torch\n\n >>> blip_diffusion_pipe = BlipDiffusionControlNetPipeline.from_pretrained(\n ... \"Salesforce/blipdiffusion-controlnet\", torch_dtype=torch.float16\n ... ).to(\"cuda\")\n\n >>> style_subject = \"flower\"\n >>> tgt_subject = \"teapot\"\n >>> text_prompt = \"on a marble table\"\n\n >>> cldm_cond_image = load_image(\n ... \"https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/kettle.jpg\"\n ... ).resize((512, 512))\n >>> canny = CannyDetector()\n >>> cldm_cond_image = canny(cldm_cond_image, 30, 70, output_type=\"pil\")\n >>> style_image = load_image(\n ... \"https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg\"\n ... )\n >>> guidance_scale = 7.5\n >>> num_inference_steps = 50\n >>> negative_prompt = \"over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate\"\n\n\n >>> output = blip_diffusion_pipe(\n ... text_prompt,\n ... style_image,\n ... cldm_cond_image,\n ... style_subject,\n ... tgt_subject,\n ... guidance_scale=guidance_scale,\n ... num_inference_steps=num_inference_steps,\n ... neg_prompt=negative_prompt,\n ... height=512,\n ... width=512,\n ... ).images\n >>> output[0].save(\"image.png\")\n ```\n\"\"\"\n\n\nclass BlipDiffusionControlNetPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for Canny Edge based Controlled subject-driven generation using Blip Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n tokenizer ([`CLIPTokenizer`]):\n Tokenizer for the text encoder\n text_encoder ([`ContextCLIPTextModel`]):\n Text encoder to encode the text prompt\n vae ([`AutoencoderKL`]):\n VAE model to map the latents to the image\n unet ([`UNet2DConditionModel`]):\n Conditional U-Net architecture to denoise the image embedding.\n scheduler ([`PNDMScheduler`]):\n A scheduler to be used in combination with `unet` to generate image latents.\n qformer ([`Blip2QFormerModel`]):\n QFormer model to get multi-modal embeddings from the text and image.\n controlnet ([`ControlNetModel`]):\n ControlNet model to get the conditioning image embedding.\n image_processor ([`BlipImageProcessor`]):\n Image Processor to preprocess and postprocess the image.\n ctx_begin_pos (int, `optional`, defaults to 2):\n Position of the context token in the text encoder.\n \"\"\"\n\n model_cpu_offload_seq = \"qformer->text_encoder->unet->vae\"\n\n def __init__(\n self,\n tokenizer: CLIPTokenizer,\n text_encoder: ContextCLIPTextModel,\n vae: AutoencoderKL,\n unet: UNet2DConditionModel,\n scheduler: PNDMScheduler,\n qformer: Blip2QFormerModel,\n controlnet: ControlNetModel,\n image_processor: BlipImageProcessor,\n ctx_begin_pos: int = 2,\n mean: List[float] = None,\n std: List[float] = None,\n ):\n super().__init__()\n\n self.register_modules(\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n vae=vae,\n unet=unet,\n scheduler=scheduler,\n qformer=qformer,\n controlnet=controlnet,\n image_processor=image_processor,\n )\n self.register_to_config(ctx_begin_pos=ctx_begin_pos, mean=mean, std=std)\n\n def get_query_embeddings(self, input_image, src_subject):\n return self.qformer(image_input=input_image, text_input=src_subject, return_dict=False)\n\n # from the original Blip Diffusion code, speciefies the target subject and augments the prompt by repeating it\n def _build_prompt(self, prompts, tgt_subjects, prompt_strength=1.0, prompt_reps=20):\n rv = []\n for prompt, tgt_subject in zip(prompts, tgt_subjects):\n prompt = f\"a {tgt_subject} {prompt.strip()}\"\n # a trick to amplify the prompt\n rv.append(\", \".join([prompt] * int(prompt_strength * prompt_reps)))\n\n return rv\n\n # Copied from diffusers.pipelines.consistency_models.pipeline_consistency_models.ConsistencyModelPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels, height, width, dtype, device, generator, latents=None):\n shape = (batch_size, num_channels, height, width)\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device=device, dtype=dtype)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def encode_prompt(self, query_embeds, prompt, device=None):\n device = device or self._execution_device\n\n # embeddings for prompt, with query_embeds as context\n max_len = self.text_encoder.text_model.config.max_position_embeddings\n max_len -= self.qformer.config.num_query_tokens\n\n tokenized_prompt = self.tokenizer(\n prompt,\n padding=\"max_length\",\n truncation=True,\n max_length=max_len,\n return_tensors=\"pt\",\n ).to(device)\n\n batch_size = query_embeds.shape[0]\n ctx_begin_pos = [self.config.ctx_begin_pos] * batch_size\n\n text_embeddings = self.text_encoder(\n input_ids=tokenized_prompt.input_ids,\n ctx_embeddings=query_embeds,\n ctx_begin_pos=ctx_begin_pos,\n )[0]\n\n return text_embeddings\n\n # Adapted from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image\n def prepare_control_image(\n self,\n image,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype,\n do_classifier_free_guidance=False,\n ):\n image = self.image_processor.preprocess(\n image,\n size={\"width\": width, \"height\": height},\n do_rescale=True,\n do_center_crop=False,\n do_normalize=False,\n return_tensors=\"pt\",\n )[\"pixel_values\"].to(device)\n image_batch_size = image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n image = image.repeat_interleave(repeat_by, dim=0)\n\n image = image.to(device=device, dtype=dtype)\n\n if do_classifier_free_guidance:\n image = torch.cat([image] * 2)\n\n return image\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: List[str],\n reference_image: PIL.Image.Image,\n condtioning_image: PIL.Image.Image,\n source_subject_category: List[str],\n target_subject_category: List[str],\n latents: Optional[torch.Tensor] = None,\n guidance_scale: float = 7.5,\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 50,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n neg_prompt: Optional[str] = \"\",\n prompt_strength: float = 1.0,\n prompt_reps: int = 20,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`List[str]`):\n The prompt or prompts to guide the image generation.\n reference_image (`PIL.Image.Image`):\n The reference image to condition the generation on.\n condtioning_image (`PIL.Image.Image`):\n The conditioning canny edge image to condition the generation on.\n source_subject_category (`List[str]`):\n The source subject category.\n target_subject_category (`List[str]`):\n The target subject category.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by random sampling.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n height (`int`, *optional*, defaults to 512):\n The height of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width of the generated image.\n seed (`int`, *optional*, defaults to 42):\n The seed to use for random generation.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n neg_prompt (`str`, *optional*, defaults to \"\"):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n prompt_strength (`float`, *optional*, defaults to 1.0):\n The strength of the prompt. Specifies the number of times the prompt is repeated along with prompt_reps\n to amplify the prompt.\n prompt_reps (`int`, *optional*, defaults to 20):\n The number of times the prompt is repeated along with prompt_strength to amplify the prompt.\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`\n \"\"\"\n device = self._execution_device\n\n reference_image = self.image_processor.preprocess(\n reference_image, image_mean=self.config.mean, image_std=self.config.std, return_tensors=\"pt\"\n )[\"pixel_values\"]\n reference_image = reference_image.to(device)\n\n if isinstance(prompt, str):\n prompt = [prompt]\n if isinstance(source_subject_category, str):\n source_subject_category = [source_subject_category]\n if isinstance(target_subject_category, str):\n target_subject_category = [target_subject_category]\n\n batch_size = len(prompt)\n\n prompt = self._build_prompt(\n prompts=prompt,\n tgt_subjects=target_subject_category,\n prompt_strength=prompt_strength,\n prompt_reps=prompt_reps,\n )\n query_embeds = self.get_query_embeddings(reference_image, source_subject_category)\n text_embeddings = self.encode_prompt(query_embeds, prompt, device)\n # 3. unconditional embedding\n do_classifier_free_guidance = guidance_scale > 1.0\n if do_classifier_free_guidance:\n max_length = self.text_encoder.text_model.config.max_position_embeddings\n\n uncond_input = self.tokenizer(\n [neg_prompt] * batch_size,\n padding=\"max_length\",\n max_length=max_length,\n return_tensors=\"pt\",\n )\n uncond_embeddings = self.text_encoder(\n input_ids=uncond_input.input_ids.to(device),\n ctx_embeddings=None,\n )[0]\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n scale_down_factor = 2 ** (len(self.unet.config.block_out_channels) - 1)\n latents = self.prepare_latents(\n batch_size=batch_size,\n num_channels=self.unet.config.in_channels,\n height=height // scale_down_factor,\n width=width // scale_down_factor,\n generator=generator,\n latents=latents,\n dtype=self.unet.dtype,\n device=device,\n )\n # set timesteps\n extra_set_kwargs = {}\n self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)\n\n cond_image = self.prepare_control_image(\n image=condtioning_image,\n width=width,\n height=height,\n batch_size=batch_size,\n num_images_per_prompt=1,\n device=device,\n dtype=self.controlnet.dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n )\n\n for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):\n # expand the latents if we are doing classifier free guidance\n do_classifier_free_guidance = guidance_scale > 1.0\n\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n latent_model_input,\n t,\n encoder_hidden_states=text_embeddings,\n controlnet_cond=cond_image,\n return_dict=False,\n )\n\n noise_pred = self.unet(\n latent_model_input,\n timestep=t,\n encoder_hidden_states=text_embeddings,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n )[\"sample\"]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n latents = self.scheduler.step(\n noise_pred,\n t,\n latents,\n )[\"prev_sample\"]\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...callbacks import MultiPipelineCallbacks, PipelineCallback\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, ControlNetModel, ImageProjection, UNet2DConditionModel\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import is_compiled_module, randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ..stable_diffusion import StableDiffusionPipelineOutput\nfrom ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom .multicontrolnet import MultiControlNetModel\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> # !pip install opencv-python transformers accelerate\n >>> from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, UniPCMultistepScheduler\n >>> from diffusers.utils import load_image\n >>> import numpy as np\n >>> import torch\n\n >>> import cv2\n >>> from PIL import Image\n\n >>> # download an image\n >>> image = load_image(\n ... \"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png\"\n ... )\n >>> np_image = np.array(image)\n\n >>> # get canny image\n >>> np_image = cv2.Canny(np_image, 100, 200)\n >>> np_image = np_image[:, :, None]\n >>> np_image = np.concatenate([np_image, np_image, np_image], axis=2)\n >>> canny_image = Image.fromarray(np_image)\n\n >>> # load control net and stable diffusion v1-5\n >>> controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\", torch_dtype=torch.float16)\n >>> pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(\n ... \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, torch_dtype=torch.float16\n ... )\n\n >>> # speed up diffusion process with faster scheduler and memory optimization\n >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n >>> pipe.enable_model_cpu_offload()\n\n >>> # generate image\n >>> generator = torch.manual_seed(0)\n >>> image = pipe(\n ... \"futuristic-looking woman\",\n ... num_inference_steps=20,\n ... generator=generator,\n ... image=image,\n ... control_image=canny_image,\n ... ).images[0]\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n\ndef prepare_image(image):\n if isinstance(image, torch.Tensor):\n # Batch single image\n if image.ndim == 3:\n image = image.unsqueeze(0)\n\n image = image.to(dtype=torch.float32)\n else:\n # preprocess image\n if isinstance(image, (PIL.Image.Image, np.ndarray)):\n image = [image]\n\n if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):\n image = [np.array(i.convert(\"RGB\"))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n elif isinstance(image, list) and isinstance(image[0], np.ndarray):\n image = np.concatenate([i[None, :] for i in image], axis=0)\n\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0\n\n return image\n\n\nclass StableDiffusionControlNetImg2ImgPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n TextualInversionLoaderMixin,\n LoraLoaderMixin,\n IPAdapterMixin,\n FromSingleFileMixin,\n):\n r\"\"\"\n Pipeline for image-to-image generation using Stable Diffusion with ControlNet guidance.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):\n Provides additional conditioning to the `unet` during the denoising process. If you set multiple\n ControlNets as a list, the outputs from each ControlNet are added together to create one combined\n additional conditioning.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"image_encoder\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder: CLIPVisionModelWithProjection = None,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n if isinstance(controlnet, (list, tuple)):\n controlnet = MultiControlNetModel(controlnet)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n controlnet=controlnet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)\n self.control_image_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False\n )\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n image,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n controlnet_conditioning_scale=1.0,\n control_guidance_start=0.0,\n control_guidance_end=1.0,\n callback_on_step_end_tensor_inputs=None,\n ):\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # `prompt` needs more sophisticated handling when there are multiple\n # conditionings.\n if isinstance(self.controlnet, MultiControlNetModel):\n if isinstance(prompt, list):\n logger.warning(\n f\"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}\"\n \" prompts. The conditionings will be fixed across the prompts.\"\n )\n\n # Check `image`\n is_compiled = hasattr(F, \"scaled_dot_product_attention\") and isinstance(\n self.controlnet, torch._dynamo.eval_frame.OptimizedModule\n )\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n self.check_image(image, prompt, prompt_embeds)\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if not isinstance(image, list):\n raise TypeError(\"For multiple controlnets: `image` must be type `list`\")\n\n # When `image` is a nested list:\n # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])\n elif any(isinstance(i, list) for i in image):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif len(image) != len(self.controlnet.nets):\n raise ValueError(\n f\"For multiple controlnets: `image` must have the same length as the number of controlnets, but got {len(image)} images and {len(self.controlnet.nets)} ControlNets.\"\n )\n\n for image_ in image:\n self.check_image(image_, prompt, prompt_embeds)\n else:\n assert False\n\n # Check `controlnet_conditioning_scale`\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n if not isinstance(controlnet_conditioning_scale, float):\n raise TypeError(\"For single controlnet: `controlnet_conditioning_scale` must be type `float`.\")\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if isinstance(controlnet_conditioning_scale, list):\n if any(isinstance(i, list) for i in controlnet_conditioning_scale):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(\n self.controlnet.nets\n ):\n raise ValueError(\n \"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have\"\n \" the same length as the number of controlnets\"\n )\n else:\n assert False\n\n if len(control_guidance_start) != len(control_guidance_end):\n raise ValueError(\n f\"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list.\"\n )\n\n if isinstance(self.controlnet, MultiControlNetModel):\n if len(control_guidance_start) != len(self.controlnet.nets):\n raise ValueError(\n f\"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}.\"\n )\n\n for start, end in zip(control_guidance_start, control_guidance_end):\n if start >= end:\n raise ValueError(\n f\"control guidance start: {start} cannot be larger or equal to control guidance end: {end}.\"\n )\n if start < 0.0:\n raise ValueError(f\"control guidance start: {start} can't be smaller than 0.\")\n if end > 1.0:\n raise ValueError(f\"control guidance end: {end} can't be larger than 1.0.\")\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n if ip_adapter_image_embeds is not None:\n if not isinstance(ip_adapter_image_embeds, list):\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}\"\n )\n elif ip_adapter_image_embeds[0].ndim not in [3, 4]:\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D\"\n )\n\n # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image\n def check_image(self, image, prompt, prompt_embeds):\n image_is_pil = isinstance(image, PIL.Image.Image)\n image_is_tensor = isinstance(image, torch.Tensor)\n image_is_np = isinstance(image, np.ndarray)\n image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)\n image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)\n image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)\n\n if (\n not image_is_pil\n and not image_is_tensor\n and not image_is_np\n and not image_is_pil_list\n and not image_is_tensor_list\n and not image_is_np_list\n ):\n raise TypeError(\n f\"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}\"\n )\n\n if image_is_pil:\n image_batch_size = 1\n else:\n image_batch_size = len(image)\n\n if prompt is not None and isinstance(prompt, str):\n prompt_batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n prompt_batch_size = len(prompt)\n elif prompt_embeds is not None:\n prompt_batch_size = prompt_embeds.shape[0]\n\n if image_batch_size != 1 and image_batch_size != prompt_batch_size:\n raise ValueError(\n f\"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}\"\n )\n\n # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image\n def prepare_control_image(\n self,\n image,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype,\n do_classifier_free_guidance=False,\n guess_mode=False,\n ):\n image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)\n image_batch_size = image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n image = image.repeat_interleave(repeat_by, dim=0)\n\n image = image.to(device=device, dtype=dtype)\n\n if do_classifier_free_guidance and not guess_mode:\n image = torch.cat([image] * 2)\n\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n if hasattr(self.scheduler, \"set_begin_index\"):\n self.scheduler.set_begin_index(t_start * self.scheduler.order)\n\n return timesteps, num_inference_steps - t_start\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.prepare_latents\n def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n\n if image.shape[1] == 4:\n init_latents = image\n\n else:\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n elif isinstance(generator, list):\n init_latents = [\n retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])\n for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = retrieve_latents(self.vae.encode(image), generator=generator)\n\n init_latents = self.vae.config.scaling_factor * init_latents\n\n if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:\n # expand init_latents for batch_size\n deprecation_message = (\n f\"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial\"\n \" images (`image`). Initial images are now duplicating to match the number of text prompts. Note\"\n \" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update\"\n \" your script to pass as many initial images as text prompts to suppress this warning.\"\n )\n deprecate(\"len(prompt) != len(image)\", \"1.0.0\", deprecation_message, standard_warn=False)\n additional_image_per_prompt = batch_size // init_latents.shape[0]\n init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)\n elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n init_latents = torch.cat([init_latents], dim=0)\n\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n latents = init_latents\n\n return latents\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: PipelineImageInput = None,\n control_image: PipelineImageInput = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n strength: float = 0.8,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: Union[float, List[float]] = 0.8,\n guess_mode: bool = False,\n control_guidance_start: Union[float, List[float]] = 0.0,\n control_guidance_end: Union[float, List[float]] = 1.0,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[\n Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]\n ] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:\n `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):\n The initial image to be used as the starting point for the image generation process. Can also accept\n image latents as `image`, and if passing latents directly they are not encoded again.\n control_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:\n `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):\n The ControlNet input condition to provide guidance to the `unet` for generation. If the type is\n specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be accepted\n as an image. The dimensions of the output image defaults to `image`'s dimensions. If height and/or\n width are passed, `image` is resized accordingly. If multiple ControlNets are specified in `init`,\n images must be passed as a list such that each element of the list can be correctly batched for input\n to a single ControlNet.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n strength (`float`, *optional*, defaults to 0.8):\n Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a\n starting point and more noise is added the higher the `strength`. The number of denoising steps depends\n on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising\n process runs for the full number of iterations specified in `num_inference_steps`. A value of 1\n essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of\n IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should\n contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not\n provided, embeddings are computed from the `ip_adapter_image` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):\n The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added\n to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set\n the corresponding scale as a list.\n guess_mode (`bool`, *optional*, defaults to `False`):\n The ControlNet encoder tries to recognize the content of the input image even if you remove all\n prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.\n control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):\n The percentage of total steps at which the ControlNet starts applying.\n control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):\n The percentage of total steps at which the ControlNet stops applying.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):\n A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of\n each denoising step during the inference. with the following arguments: `callback_on_step_end(self:\n DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a\n list of all tensors as specified by `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n\n if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):\n callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs\n\n controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet\n\n # align format for control guidance\n if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):\n control_guidance_start = len(control_guidance_end) * [control_guidance_start]\n elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):\n control_guidance_end = len(control_guidance_start) * [control_guidance_end]\n elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):\n mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1\n control_guidance_start, control_guidance_end = (\n mult * [control_guidance_start],\n mult * [control_guidance_end],\n )\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n control_image,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n ip_adapter_image,\n ip_adapter_image_embeds,\n controlnet_conditioning_scale,\n control_guidance_start,\n control_guidance_end,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):\n controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)\n\n global_pool_conditions = (\n controlnet.config.global_pool_conditions\n if isinstance(controlnet, ControlNetModel)\n else controlnet.nets[0].config.global_pool_conditions\n )\n guess_mode = guess_mode or global_pool_conditions\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=self.clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_images_per_prompt,\n self.do_classifier_free_guidance,\n )\n\n # 4. Prepare image\n image = self.image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)\n\n # 5. Prepare controlnet_conditioning_image\n if isinstance(controlnet, ControlNetModel):\n control_image = self.prepare_control_image(\n image=control_image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n elif isinstance(controlnet, MultiControlNetModel):\n control_images = []\n\n for control_image_ in control_image:\n control_image_ = self.prepare_control_image(\n image=control_image_,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n\n control_images.append(control_image_)\n\n control_image = control_images\n else:\n assert False\n\n # 5. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n self._num_timesteps = len(timesteps)\n\n # 6. Prepare latent variables\n if latents is None:\n latents = self.prepare_latents(\n image,\n latent_timestep,\n batch_size,\n num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n generator,\n )\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7.1 Add image embeds for IP-Adapter\n added_cond_kwargs = (\n {\"image_embeds\": image_embeds}\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None\n else None\n )\n\n # 7.2 Create tensor stating which controlnets to keep\n controlnet_keep = []\n for i in range(len(timesteps)):\n keeps = [\n 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)\n for s, e in zip(control_guidance_start, control_guidance_end)\n ]\n controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # controlnet(s) inference\n if guess_mode and self.do_classifier_free_guidance:\n # Infer ControlNet only for the conditional batch.\n control_model_input = latents\n control_model_input = self.scheduler.scale_model_input(control_model_input, t)\n controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]\n else:\n control_model_input = latent_model_input\n controlnet_prompt_embeds = prompt_embeds\n\n if isinstance(controlnet_keep[i], list):\n cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]\n else:\n controlnet_cond_scale = controlnet_conditioning_scale\n if isinstance(controlnet_cond_scale, list):\n controlnet_cond_scale = controlnet_cond_scale[0]\n cond_scale = controlnet_cond_scale * controlnet_keep[i]\n\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n control_model_input,\n t,\n encoder_hidden_states=controlnet_prompt_embeds,\n controlnet_cond=control_image,\n conditioning_scale=cond_scale,\n guess_mode=guess_mode,\n return_dict=False,\n )\n\n if guess_mode and self.do_classifier_free_guidance:\n # Infered ControlNet only for the conditional batch.\n # To apply the output of ControlNet to both the unconditional and conditional batches,\n # add 0 to the unconditional batch to keep it unchanged.\n down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]\n mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=self.cross_attention_kwargs,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # If we do sequential model offloading, let's offload unet and controlnet\n # manually for max memory savings\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.unet.to(\"cpu\")\n self.controlnet.to(\"cpu\")\n torch.cuda.empty_cache()\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[\n 0\n ]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...callbacks import MultiPipelineCallbacks, PipelineCallback\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, ControlNetModel, ImageProjection, UNet2DConditionModel\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import is_compiled_module, randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ..stable_diffusion import StableDiffusionPipelineOutput\nfrom ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom .multicontrolnet import MultiControlNetModel\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> # !pip install transformers accelerate\n >>> from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, DDIMScheduler\n >>> from diffusers.utils import load_image\n >>> import numpy as np\n >>> import torch\n\n >>> init_image = load_image(\n ... \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png\"\n ... )\n >>> init_image = init_image.resize((512, 512))\n\n >>> generator = torch.Generator(device=\"cpu\").manual_seed(1)\n\n >>> mask_image = load_image(\n ... \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png\"\n ... )\n >>> mask_image = mask_image.resize((512, 512))\n\n\n >>> def make_canny_condition(image):\n ... image = np.array(image)\n ... image = cv2.Canny(image, 100, 200)\n ... image = image[:, :, None]\n ... image = np.concatenate([image, image, image], axis=2)\n ... image = Image.fromarray(image)\n ... return image\n\n\n >>> control_image = make_canny_condition(init_image)\n\n >>> controlnet = ControlNetModel.from_pretrained(\n ... \"lllyasviel/control_v11p_sd15_inpaint\", torch_dtype=torch.float16\n ... )\n >>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(\n ... \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, torch_dtype=torch.float16\n ... )\n\n >>> pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\n >>> pipe.enable_model_cpu_offload()\n\n >>> # generate image\n >>> image = pipe(\n ... \"a handsome man with ray-ban sunglasses\",\n ... num_inference_steps=20,\n ... generator=generator,\n ... eta=1.0,\n ... image=init_image,\n ... mask_image=mask_image,\n ... control_image=control_image,\n ... ).images[0]\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n\nclass StableDiffusionControlNetInpaintPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n TextualInversionLoaderMixin,\n LoraLoaderMixin,\n IPAdapterMixin,\n FromSingleFileMixin,\n):\n r\"\"\"\n Pipeline for image inpainting using Stable Diffusion with ControlNet guidance.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n \n\n This pipeline can be used with checkpoints that have been specifically fine-tuned for inpainting\n ([runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting)) as well as\n default text-to-image Stable Diffusion checkpoints\n ([runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)). Default text-to-image\n Stable Diffusion checkpoints might be preferable for ControlNets that have been fine-tuned on those, such as\n [lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint).\n\n \n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):\n Provides additional conditioning to the `unet` during the denoising process. If you set multiple\n ControlNets as a list, the outputs from each ControlNet are added together to create one combined\n additional conditioning.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->image_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"image_encoder\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder: CLIPVisionModelWithProjection = None,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n if isinstance(controlnet, (list, tuple)):\n controlnet = MultiControlNetModel(controlnet)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n controlnet=controlnet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.mask_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True\n )\n self.control_image_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False\n )\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n if hasattr(self.scheduler, \"set_begin_index\"):\n self.scheduler.set_begin_index(t_start * self.scheduler.order)\n\n return timesteps, num_inference_steps - t_start\n\n def check_inputs(\n self,\n prompt,\n image,\n mask_image,\n height,\n width,\n callback_steps,\n output_type,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n controlnet_conditioning_scale=1.0,\n control_guidance_start=0.0,\n control_guidance_end=1.0,\n callback_on_step_end_tensor_inputs=None,\n padding_mask_crop=None,\n ):\n if height is not None and height % 8 != 0 or width is not None and width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if padding_mask_crop is not None:\n if not isinstance(image, PIL.Image.Image):\n raise ValueError(\n f\"The image should be a PIL image when inpainting mask crop, but is of type\" f\" {type(image)}.\"\n )\n if not isinstance(mask_image, PIL.Image.Image):\n raise ValueError(\n f\"The mask image should be a PIL image when inpainting mask crop, but is of type\"\n f\" {type(mask_image)}.\"\n )\n if output_type != \"pil\":\n raise ValueError(f\"The output type should be PIL when inpainting mask crop, but is\" f\" {output_type}.\")\n\n # `prompt` needs more sophisticated handling when there are multiple\n # conditionings.\n if isinstance(self.controlnet, MultiControlNetModel):\n if isinstance(prompt, list):\n logger.warning(\n f\"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}\"\n \" prompts. The conditionings will be fixed across the prompts.\"\n )\n\n # Check `image`\n is_compiled = hasattr(F, \"scaled_dot_product_attention\") and isinstance(\n self.controlnet, torch._dynamo.eval_frame.OptimizedModule\n )\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n self.check_image(image, prompt, prompt_embeds)\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if not isinstance(image, list):\n raise TypeError(\"For multiple controlnets: `image` must be type `list`\")\n\n # When `image` is a nested list:\n # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])\n elif any(isinstance(i, list) for i in image):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif len(image) != len(self.controlnet.nets):\n raise ValueError(\n f\"For multiple controlnets: `image` must have the same length as the number of controlnets, but got {len(image)} images and {len(self.controlnet.nets)} ControlNets.\"\n )\n\n for image_ in image:\n self.check_image(image_, prompt, prompt_embeds)\n else:\n assert False\n\n # Check `controlnet_conditioning_scale`\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n if not isinstance(controlnet_conditioning_scale, float):\n raise TypeError(\"For single controlnet: `controlnet_conditioning_scale` must be type `float`.\")\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if isinstance(controlnet_conditioning_scale, list):\n if any(isinstance(i, list) for i in controlnet_conditioning_scale):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(\n self.controlnet.nets\n ):\n raise ValueError(\n \"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have\"\n \" the same length as the number of controlnets\"\n )\n else:\n assert False\n\n if len(control_guidance_start) != len(control_guidance_end):\n raise ValueError(\n f\"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list.\"\n )\n\n if isinstance(self.controlnet, MultiControlNetModel):\n if len(control_guidance_start) != len(self.controlnet.nets):\n raise ValueError(\n f\"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}.\"\n )\n\n for start, end in zip(control_guidance_start, control_guidance_end):\n if start >= end:\n raise ValueError(\n f\"control guidance start: {start} cannot be larger or equal to control guidance end: {end}.\"\n )\n if start < 0.0:\n raise ValueError(f\"control guidance start: {start} can't be smaller than 0.\")\n if end > 1.0:\n raise ValueError(f\"control guidance end: {end} can't be larger than 1.0.\")\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n if ip_adapter_image_embeds is not None:\n if not isinstance(ip_adapter_image_embeds, list):\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}\"\n )\n elif ip_adapter_image_embeds[0].ndim not in [3, 4]:\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D\"\n )\n\n # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image\n def check_image(self, image, prompt, prompt_embeds):\n image_is_pil = isinstance(image, PIL.Image.Image)\n image_is_tensor = isinstance(image, torch.Tensor)\n image_is_np = isinstance(image, np.ndarray)\n image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)\n image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)\n image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)\n\n if (\n not image_is_pil\n and not image_is_tensor\n and not image_is_np\n and not image_is_pil_list\n and not image_is_tensor_list\n and not image_is_np_list\n ):\n raise TypeError(\n f\"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}\"\n )\n\n if image_is_pil:\n image_batch_size = 1\n else:\n image_batch_size = len(image)\n\n if prompt is not None and isinstance(prompt, str):\n prompt_batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n prompt_batch_size = len(prompt)\n elif prompt_embeds is not None:\n prompt_batch_size = prompt_embeds.shape[0]\n\n if image_batch_size != 1 and image_batch_size != prompt_batch_size:\n raise ValueError(\n f\"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}\"\n )\n\n def prepare_control_image(\n self,\n image,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype,\n crops_coords,\n resize_mode,\n do_classifier_free_guidance=False,\n guess_mode=False,\n ):\n image = self.control_image_processor.preprocess(\n image, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode\n ).to(dtype=torch.float32)\n image_batch_size = image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n image = image.repeat_interleave(repeat_by, dim=0)\n\n image = image.to(device=device, dtype=dtype)\n\n if do_classifier_free_guidance and not guess_mode:\n image = torch.cat([image] * 2)\n\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline.prepare_latents\n def prepare_latents(\n self,\n batch_size,\n num_channels_latents,\n height,\n width,\n dtype,\n device,\n generator,\n latents=None,\n image=None,\n timestep=None,\n is_strength_max=True,\n return_noise=False,\n return_image_latents=False,\n ):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if (image is None or timestep is None) and not is_strength_max:\n raise ValueError(\n \"Since strength < 1. initial latents are to be initialised as a combination of Image + Noise.\"\n \"However, either the image or the noise timestep has not been provided.\"\n )\n\n if return_image_latents or (latents is None and not is_strength_max):\n image = image.to(device=device, dtype=dtype)\n\n if image.shape[1] == 4:\n image_latents = image\n else:\n image_latents = self._encode_vae_image(image=image, generator=generator)\n image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)\n\n if latents is None:\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n # if strength is 1. then initialise the latents to noise, else initial to image + noise\n latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)\n # if pure noise then scale the initial latents by the Scheduler's init sigma\n latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents\n else:\n noise = latents.to(device)\n latents = noise * self.scheduler.init_noise_sigma\n\n outputs = (latents,)\n\n if return_noise:\n outputs += (noise,)\n\n if return_image_latents:\n outputs += (image_latents,)\n\n return outputs\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline.prepare_mask_latents\n def prepare_mask_latents(\n self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance\n ):\n # resize the mask to latents shape as we concatenate the mask to the latents\n # we do that before converting to dtype to avoid breaking in case we're using cpu_offload\n # and half precision\n mask = torch.nn.functional.interpolate(\n mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)\n )\n mask = mask.to(device=device, dtype=dtype)\n\n masked_image = masked_image.to(device=device, dtype=dtype)\n\n if masked_image.shape[1] == 4:\n masked_image_latents = masked_image\n else:\n masked_image_latents = self._encode_vae_image(masked_image, generator=generator)\n\n # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method\n if mask.shape[0] < batch_size:\n if not batch_size % mask.shape[0] == 0:\n raise ValueError(\n \"The passed mask and the required batch size don't match. Masks are supposed to be duplicated to\"\n f\" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number\"\n \" of masks that you pass is divisible by the total requested batch size.\"\n )\n mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)\n if masked_image_latents.shape[0] < batch_size:\n if not batch_size % masked_image_latents.shape[0] == 0:\n raise ValueError(\n \"The passed images and the required batch size don't match. Images are supposed to be duplicated\"\n f\" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed.\"\n \" Make sure the number of images that you pass is divisible by the total requested batch size.\"\n )\n masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)\n\n mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask\n masked_image_latents = (\n torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents\n )\n\n # aligning device to prevent device errors when concating it with the latent model input\n masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)\n return mask, masked_image_latents\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline._encode_vae_image\n def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):\n if isinstance(generator, list):\n image_latents = [\n retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])\n for i in range(image.shape[0])\n ]\n image_latents = torch.cat(image_latents, dim=0)\n else:\n image_latents = retrieve_latents(self.vae.encode(image), generator=generator)\n\n image_latents = self.vae.config.scaling_factor * image_latents\n\n return image_latents\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: PipelineImageInput = None,\n mask_image: PipelineImageInput = None,\n control_image: PipelineImageInput = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n padding_mask_crop: Optional[int] = None,\n strength: float = 1.0,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: Union[float, List[float]] = 0.5,\n guess_mode: bool = False,\n control_guidance_start: Union[float, List[float]] = 0.0,\n control_guidance_end: Union[float, List[float]] = 1.0,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[\n Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]\n ] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`,\n `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image`, NumPy array or tensor representing an image batch to be used as the starting point. For both\n NumPy array and PyTorch tensor, the expected value range is between `[0, 1]`. If it's a tensor or a\n list or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a NumPy array or\n a list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)`. It can also accept image\n latents as `image`, but if passing latents directly it is not encoded again.\n mask_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`,\n `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image`, NumPy array or tensor representing an image batch to mask `image`. White pixels in the mask\n are repainted while black pixels are preserved. If `mask_image` is a PIL image, it is converted to a\n single channel (luminance) before use. If it's a NumPy array or PyTorch tensor, it should contain one\n color channel (L) instead of 3, so the expected shape for PyTorch tensor would be `(B, 1, H, W)`, `(B,\n H, W)`, `(1, H, W)`, `(H, W)`. And for NumPy array, it would be for `(B, H, W, 1)`, `(B, H, W)`, `(H,\n W, 1)`, or `(H, W)`.\n control_image (`torch.Tensor`, `PIL.Image.Image`, `List[torch.Tensor]`, `List[PIL.Image.Image]`,\n `List[List[torch.Tensor]]`, or `List[List[PIL.Image.Image]]`):\n The ControlNet input condition to provide guidance to the `unet` for generation. If the type is\n specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be accepted\n as an image. The dimensions of the output image defaults to `image`'s dimensions. If height and/or\n width are passed, `image` is resized accordingly. If multiple ControlNets are specified in `init`,\n images must be passed as a list such that each element of the list can be correctly batched for input\n to a single ControlNet.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n padding_mask_crop (`int`, *optional*, defaults to `None`):\n The size of margin in the crop to be applied to the image and masking. If `None`, no crop is applied to\n image and mask_image. If `padding_mask_crop` is not `None`, it will first find a rectangular region\n with the same aspect ration of the image and contains all masked area, and then expand that area based\n on `padding_mask_crop`. The image and mask_image will then be cropped based on the expanded area before\n resizing to the original image size for inpainting. This is useful when the masked area is small while\n the image is large and contain information irrelevant for inpainting, such as background.\n strength (`float`, *optional*, defaults to 1.0):\n Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a\n starting point and more noise is added the higher the `strength`. The number of denoising steps depends\n on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising\n process runs for the full number of iterations specified in `num_inference_steps`. A value of 1\n essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of\n IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should\n contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not\n provided, embeddings are computed from the `ip_adapter_image` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 0.5):\n The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added\n to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set\n the corresponding scale as a list.\n guess_mode (`bool`, *optional*, defaults to `False`):\n The ControlNet encoder tries to recognize the content of the input image even if you remove all\n prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.\n control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):\n The percentage of total steps at which the ControlNet starts applying.\n control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):\n The percentage of total steps at which the ControlNet stops applying.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):\n A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of\n each denoising step during the inference. with the following arguments: `callback_on_step_end(self:\n DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a\n list of all tensors as specified by `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n\n if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):\n callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs\n\n controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet\n\n # align format for control guidance\n if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):\n control_guidance_start = len(control_guidance_end) * [control_guidance_start]\n elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):\n control_guidance_end = len(control_guidance_start) * [control_guidance_end]\n elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):\n mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1\n control_guidance_start, control_guidance_end = (\n mult * [control_guidance_start],\n mult * [control_guidance_end],\n )\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n control_image,\n mask_image,\n height,\n width,\n callback_steps,\n output_type,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n ip_adapter_image,\n ip_adapter_image_embeds,\n controlnet_conditioning_scale,\n control_guidance_start,\n control_guidance_end,\n callback_on_step_end_tensor_inputs,\n padding_mask_crop,\n )\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if padding_mask_crop is not None:\n height, width = self.image_processor.get_default_height_width(image, height, width)\n crops_coords = self.mask_processor.get_crop_region(mask_image, width, height, pad=padding_mask_crop)\n resize_mode = \"fill\"\n else:\n crops_coords = None\n resize_mode = \"default\"\n\n device = self._execution_device\n\n if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):\n controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)\n\n global_pool_conditions = (\n controlnet.config.global_pool_conditions\n if isinstance(controlnet, ControlNetModel)\n else controlnet.nets[0].config.global_pool_conditions\n )\n guess_mode = guess_mode or global_pool_conditions\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=self.clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_images_per_prompt,\n self.do_classifier_free_guidance,\n )\n\n # 4. Prepare image\n if isinstance(controlnet, ControlNetModel):\n control_image = self.prepare_control_image(\n image=control_image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n crops_coords=crops_coords,\n resize_mode=resize_mode,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n elif isinstance(controlnet, MultiControlNetModel):\n control_images = []\n\n for control_image_ in control_image:\n control_image_ = self.prepare_control_image(\n image=control_image_,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n crops_coords=crops_coords,\n resize_mode=resize_mode,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n\n control_images.append(control_image_)\n\n control_image = control_images\n else:\n assert False\n\n # 4.1 Preprocess mask and image - resizes image and mask w.r.t height and width\n original_image = image\n init_image = self.image_processor.preprocess(\n image, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode\n )\n init_image = init_image.to(dtype=torch.float32)\n\n mask = self.mask_processor.preprocess(\n mask_image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords\n )\n\n masked_image = init_image * (mask < 0.5)\n _, _, height, width = init_image.shape\n\n # 5. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(\n num_inference_steps=num_inference_steps, strength=strength, device=device\n )\n # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise\n is_strength_max = strength == 1.0\n self._num_timesteps = len(timesteps)\n\n # 6. Prepare latent variables\n num_channels_latents = self.vae.config.latent_channels\n num_channels_unet = self.unet.config.in_channels\n return_image_latents = num_channels_unet == 4\n latents_outputs = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n image=init_image,\n timestep=latent_timestep,\n is_strength_max=is_strength_max,\n return_noise=True,\n return_image_latents=return_image_latents,\n )\n\n if return_image_latents:\n latents, noise, image_latents = latents_outputs\n else:\n latents, noise = latents_outputs\n\n # 7. Prepare mask latent variables\n mask, masked_image_latents = self.prepare_mask_latents(\n mask,\n masked_image,\n batch_size * num_images_per_prompt,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n self.do_classifier_free_guidance,\n )\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7.1 Add image embeds for IP-Adapter\n added_cond_kwargs = (\n {\"image_embeds\": image_embeds}\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None\n else None\n )\n\n # 7.2 Create tensor stating which controlnets to keep\n controlnet_keep = []\n for i in range(len(timesteps)):\n keeps = [\n 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)\n for s, e in zip(control_guidance_start, control_guidance_end)\n ]\n controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # controlnet(s) inference\n if guess_mode and self.do_classifier_free_guidance:\n # Infer ControlNet only for the conditional batch.\n control_model_input = latents\n control_model_input = self.scheduler.scale_model_input(control_model_input, t)\n controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]\n else:\n control_model_input = latent_model_input\n controlnet_prompt_embeds = prompt_embeds\n\n if isinstance(controlnet_keep[i], list):\n cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]\n else:\n controlnet_cond_scale = controlnet_conditioning_scale\n if isinstance(controlnet_cond_scale, list):\n controlnet_cond_scale = controlnet_cond_scale[0]\n cond_scale = controlnet_cond_scale * controlnet_keep[i]\n\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n control_model_input,\n t,\n encoder_hidden_states=controlnet_prompt_embeds,\n controlnet_cond=control_image,\n conditioning_scale=cond_scale,\n guess_mode=guess_mode,\n return_dict=False,\n )\n\n if guess_mode and self.do_classifier_free_guidance:\n # Infered ControlNet only for the conditional batch.\n # To apply the output of ControlNet to both the unconditional and conditional batches,\n # add 0 to the unconditional batch to keep it unchanged.\n down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]\n mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])\n\n # predict the noise residual\n if num_channels_unet == 9:\n latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)\n\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=self.cross_attention_kwargs,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if num_channels_unet == 4:\n init_latents_proper = image_latents\n if self.do_classifier_free_guidance:\n init_mask, _ = mask.chunk(2)\n else:\n init_mask = mask\n\n if i < len(timesteps) - 1:\n noise_timestep = timesteps[i + 1]\n init_latents_proper = self.scheduler.add_noise(\n init_latents_proper, noise, torch.tensor([noise_timestep])\n )\n\n latents = (1 - init_mask) * init_latents_proper + init_mask * latents\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # If we do sequential model offloading, let's offload unet and controlnet\n # manually for max memory savings\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.unet.to(\"cpu\")\n self.controlnet.to(\"cpu\")\n torch.cuda.empty_cache()\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[\n 0\n ]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n if padding_mask_crop is not None:\n image = [self.image_processor.apply_overlay(mask_image, original_image, i, crops_coords) for i in image]\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py", "content": "# Copyright 2024 Harutatsu Akiyama, Jinbin Bai, and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModel,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n)\n\nfrom ...callbacks import MultiPipelineCallbacks, PipelineCallback\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import (\n FromSingleFileMixin,\n IPAdapterMixin,\n StableDiffusionXLLoraLoaderMixin,\n TextualInversionLoaderMixin,\n)\nfrom ...models import AutoencoderKL, ControlNetModel, ImageProjection, UNet2DConditionModel\nfrom ...models.attention_processor import (\n AttnProcessor2_0,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n XFormersAttnProcessor,\n)\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n is_invisible_watermark_available,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import is_compiled_module, randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ..stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput\nfrom .multicontrolnet import MultiControlNetModel\n\n\nif is_invisible_watermark_available():\n from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> # !pip install transformers accelerate\n >>> from diffusers import StableDiffusionXLControlNetInpaintPipeline, ControlNetModel, DDIMScheduler\n >>> from diffusers.utils import load_image\n >>> import numpy as np\n >>> import torch\n\n >>> init_image = load_image(\n ... \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png\"\n ... )\n >>> init_image = init_image.resize((1024, 1024))\n\n >>> generator = torch.Generator(device=\"cpu\").manual_seed(1)\n\n >>> mask_image = load_image(\n ... \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png\"\n ... )\n >>> mask_image = mask_image.resize((1024, 1024))\n\n\n >>> def make_canny_condition(image):\n ... image = np.array(image)\n ... image = cv2.Canny(image, 100, 200)\n ... image = image[:, :, None]\n ... image = np.concatenate([image, image, image], axis=2)\n ... image = Image.fromarray(image)\n ... return image\n\n\n >>> control_image = make_canny_condition(init_image)\n\n >>> controlnet = ControlNetModel.from_pretrained(\n ... \"diffusers/controlnet-canny-sdxl-1.0\", torch_dtype=torch.float16\n ... )\n >>> pipe = StableDiffusionXLControlNetInpaintPipeline.from_pretrained(\n ... \"stabilityai/stable-diffusion-xl-base-1.0\", controlnet=controlnet, torch_dtype=torch.float16\n ... )\n\n >>> pipe.enable_model_cpu_offload()\n\n >>> # generate image\n >>> image = pipe(\n ... \"a handsome man with ray-ban sunglasses\",\n ... num_inference_steps=20,\n ... generator=generator,\n ... eta=1.0,\n ... image=init_image,\n ... mask_image=mask_image,\n ... control_image=control_image,\n ... ).images[0]\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\nclass StableDiffusionXLControlNetInpaintPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n StableDiffusionXLLoraLoaderMixin,\n FromSingleFileMixin,\n IPAdapterMixin,\n TextualInversionLoaderMixin,\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion XL.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion XL uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n text_encoder_2 ([` CLIPTextModelWithProjection`]):\n Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),\n specifically the\n [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)\n variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n tokenizer_2 (`CLIPTokenizer`):\n Second Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->text_encoder_2->unet->vae\"\n\n _optional_components = [\n \"tokenizer\",\n \"tokenizer_2\",\n \"text_encoder\",\n \"text_encoder_2\",\n \"image_encoder\",\n \"feature_extractor\",\n ]\n _callback_tensor_inputs = [\n \"latents\",\n \"prompt_embeds\",\n \"negative_prompt_embeds\",\n \"add_text_embeds\",\n \"add_time_ids\",\n \"negative_pooled_prompt_embeds\",\n \"add_neg_time_ids\",\n \"mask\",\n \"masked_image_latents\",\n ]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n text_encoder_2: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n tokenizer_2: CLIPTokenizer,\n unet: UNet2DConditionModel,\n controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],\n scheduler: KarrasDiffusionSchedulers,\n requires_aesthetics_score: bool = False,\n force_zeros_for_empty_prompt: bool = True,\n add_watermarker: Optional[bool] = None,\n feature_extractor: Optional[CLIPImageProcessor] = None,\n image_encoder: Optional[CLIPVisionModelWithProjection] = None,\n ):\n super().__init__()\n\n if isinstance(controlnet, (list, tuple)):\n controlnet = MultiControlNetModel(controlnet)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n unet=unet,\n controlnet=controlnet,\n scheduler=scheduler,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)\n self.register_to_config(requires_aesthetics_score=requires_aesthetics_score)\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.mask_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True\n )\n self.control_image_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False\n )\n\n add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()\n\n if add_watermarker:\n self.watermark = StableDiffusionXLWatermarker()\n else:\n self.watermark = None\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt: str,\n prompt_2: Optional[str] = None,\n device: Optional[torch.device] = None,\n num_images_per_prompt: int = 1,\n do_classifier_free_guidance: bool = True,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n lora_scale (`float`, *optional*):\n A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n device = device or self._execution_device\n\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if self.text_encoder is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)\n else:\n scale_lora_layers(self.text_encoder_2, lora_scale)\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n\n if prompt is not None:\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # Define tokenizers and text encoders\n tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]\n text_encoders = (\n [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]\n )\n\n if prompt_embeds is None:\n prompt_2 = prompt_2 or prompt\n prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2\n\n # textual inversion: process multi-vector tokens if necessary\n prompt_embeds_list = []\n prompts = [prompt, prompt_2]\n for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, tokenizer)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n if clip_skip is None:\n prompt_embeds = prompt_embeds.hidden_states[-2]\n else:\n # \"2\" because SDXL always indexes from the penultimate layer.\n prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]\n\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n\n # get unconditional embeddings for classifier free guidance\n zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt\n if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:\n negative_prompt_embeds = torch.zeros_like(prompt_embeds)\n negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)\n elif do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n\n # normalize str to list\n negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt\n negative_prompt_2 = (\n batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2\n )\n\n uncond_tokens: List[str]\n if prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = [negative_prompt, negative_prompt_2]\n\n negative_prompt_embeds_list = []\n for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n negative_prompt_embeds = text_encoder(\n uncond_input.input_ids.to(device),\n output_hidden_states=True,\n )\n # We are only ALWAYS interested in the pooled output of the final text encoder\n negative_pooled_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]\n\n negative_prompt_embeds_list.append(negative_prompt_embeds)\n\n negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)\n\n if self.text_encoder_2 is not None:\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n if self.text_encoder_2 is not None:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n if do_classifier_free_guidance:\n negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n\n if self.text_encoder is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder_2, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_image(self, image, prompt, prompt_embeds):\n image_is_pil = isinstance(image, PIL.Image.Image)\n image_is_tensor = isinstance(image, torch.Tensor)\n image_is_np = isinstance(image, np.ndarray)\n image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)\n image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)\n image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)\n\n if (\n not image_is_pil\n and not image_is_tensor\n and not image_is_np\n and not image_is_pil_list\n and not image_is_tensor_list\n and not image_is_np_list\n ):\n raise TypeError(\n f\"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}\"\n )\n\n if image_is_pil:\n image_batch_size = 1\n else:\n image_batch_size = len(image)\n\n if prompt is not None and isinstance(prompt, str):\n prompt_batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n prompt_batch_size = len(prompt)\n elif prompt_embeds is not None:\n prompt_batch_size = prompt_embeds.shape[0]\n\n if image_batch_size != 1 and image_batch_size != prompt_batch_size:\n raise ValueError(\n f\"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}\"\n )\n\n def check_inputs(\n self,\n prompt,\n prompt_2,\n image,\n mask_image,\n strength,\n num_inference_steps,\n callback_steps,\n output_type,\n negative_prompt=None,\n negative_prompt_2=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n pooled_prompt_embeds=None,\n negative_pooled_prompt_embeds=None,\n controlnet_conditioning_scale=1.0,\n control_guidance_start=0.0,\n control_guidance_end=1.0,\n callback_on_step_end_tensor_inputs=None,\n padding_mask_crop=None,\n ):\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n if num_inference_steps is None:\n raise ValueError(\"`num_inference_steps` cannot be None.\")\n elif not isinstance(num_inference_steps, int) or num_inference_steps <= 0:\n raise ValueError(\n f\"`num_inference_steps` has to be a positive integer but is {num_inference_steps} of type\"\n f\" {type(num_inference_steps)}.\"\n )\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if padding_mask_crop is not None:\n if not isinstance(image, PIL.Image.Image):\n raise ValueError(\n f\"The image should be a PIL image when inpainting mask crop, but is of type\" f\" {type(image)}.\"\n )\n if not isinstance(mask_image, PIL.Image.Image):\n raise ValueError(\n f\"The mask image should be a PIL image when inpainting mask crop, but is of type\"\n f\" {type(mask_image)}.\"\n )\n if output_type != \"pil\":\n raise ValueError(f\"The output type should be PIL when inpainting mask crop, but is\" f\" {output_type}.\")\n\n if prompt_embeds is not None and pooled_prompt_embeds is None:\n raise ValueError(\n \"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.\"\n )\n\n if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:\n raise ValueError(\n \"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.\"\n )\n\n # `prompt` needs more sophisticated handling when there are multiple\n # conditionings.\n if isinstance(self.controlnet, MultiControlNetModel):\n if isinstance(prompt, list):\n logger.warning(\n f\"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}\"\n \" prompts. The conditionings will be fixed across the prompts.\"\n )\n\n # Check `image`\n is_compiled = hasattr(F, \"scaled_dot_product_attention\") and isinstance(\n self.controlnet, torch._dynamo.eval_frame.OptimizedModule\n )\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n self.check_image(image, prompt, prompt_embeds)\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if not isinstance(image, list):\n raise TypeError(\"For multiple controlnets: `image` must be type `list`\")\n\n # When `image` is a nested list:\n # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])\n elif any(isinstance(i, list) for i in image):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif len(image) != len(self.controlnet.nets):\n raise ValueError(\n f\"For multiple controlnets: `image` must have the same length as the number of controlnets, but got {len(image)} images and {len(self.controlnet.nets)} ControlNets.\"\n )\n\n for image_ in image:\n self.check_image(image_, prompt, prompt_embeds)\n else:\n assert False\n\n # Check `controlnet_conditioning_scale`\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n if not isinstance(controlnet_conditioning_scale, float):\n raise TypeError(\"For single controlnet: `controlnet_conditioning_scale` must be type `float`.\")\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if isinstance(controlnet_conditioning_scale, list):\n if any(isinstance(i, list) for i in controlnet_conditioning_scale):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(\n self.controlnet.nets\n ):\n raise ValueError(\n \"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have\"\n \" the same length as the number of controlnets\"\n )\n else:\n assert False\n\n if not isinstance(control_guidance_start, (tuple, list)):\n control_guidance_start = [control_guidance_start]\n\n if not isinstance(control_guidance_end, (tuple, list)):\n control_guidance_end = [control_guidance_end]\n\n if len(control_guidance_start) != len(control_guidance_end):\n raise ValueError(\n f\"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list.\"\n )\n\n if isinstance(self.controlnet, MultiControlNetModel):\n if len(control_guidance_start) != len(self.controlnet.nets):\n raise ValueError(\n f\"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}.\"\n )\n\n for start, end in zip(control_guidance_start, control_guidance_end):\n if start >= end:\n raise ValueError(\n f\"control guidance start: {start} cannot be larger or equal to control guidance end: {end}.\"\n )\n if start < 0.0:\n raise ValueError(f\"control guidance start: {start} can't be smaller than 0.\")\n if end > 1.0:\n raise ValueError(f\"control guidance end: {end} can't be larger than 1.0.\")\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n if ip_adapter_image_embeds is not None:\n if not isinstance(ip_adapter_image_embeds, list):\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}\"\n )\n elif ip_adapter_image_embeds[0].ndim not in [3, 4]:\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D\"\n )\n\n def prepare_control_image(\n self,\n image,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype,\n crops_coords,\n resize_mode,\n do_classifier_free_guidance=False,\n guess_mode=False,\n ):\n image = self.control_image_processor.preprocess(\n image, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode\n ).to(dtype=torch.float32)\n image_batch_size = image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n image = image.repeat_interleave(repeat_by, dim=0)\n\n image = image.to(device=device, dtype=dtype)\n\n if do_classifier_free_guidance and not guess_mode:\n image = torch.cat([image] * 2)\n\n return image\n\n def prepare_latents(\n self,\n batch_size,\n num_channels_latents,\n height,\n width,\n dtype,\n device,\n generator,\n latents=None,\n image=None,\n timestep=None,\n is_strength_max=True,\n add_noise=True,\n return_noise=False,\n return_image_latents=False,\n ):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if (image is None or timestep is None) and not is_strength_max:\n raise ValueError(\n \"Since strength < 1. initial latents are to be initialised as a combination of Image + Noise.\"\n \"However, either the image or the noise timestep has not been provided.\"\n )\n\n if return_image_latents or (latents is None and not is_strength_max):\n image = image.to(device=device, dtype=dtype)\n\n if image.shape[1] == 4:\n image_latents = image\n else:\n image_latents = self._encode_vae_image(image=image, generator=generator)\n image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)\n\n if latents is None and add_noise:\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n # if strength is 1. then initialise the latents to noise, else initial to image + noise\n latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)\n # if pure noise then scale the initial latents by the Scheduler's init sigma\n latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents\n elif add_noise:\n noise = latents.to(device)\n latents = noise * self.scheduler.init_noise_sigma\n else:\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n latents = image_latents.to(device)\n\n outputs = (latents,)\n\n if return_noise:\n outputs += (noise,)\n\n if return_image_latents:\n outputs += (image_latents,)\n\n return outputs\n\n def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):\n dtype = image.dtype\n if self.vae.config.force_upcast:\n image = image.float()\n self.vae.to(dtype=torch.float32)\n\n if isinstance(generator, list):\n image_latents = [\n retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])\n for i in range(image.shape[0])\n ]\n image_latents = torch.cat(image_latents, dim=0)\n else:\n image_latents = retrieve_latents(self.vae.encode(image), generator=generator)\n\n if self.vae.config.force_upcast:\n self.vae.to(dtype)\n\n image_latents = image_latents.to(dtype)\n image_latents = self.vae.config.scaling_factor * image_latents\n\n return image_latents\n\n def prepare_mask_latents(\n self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance\n ):\n # resize the mask to latents shape as we concatenate the mask to the latents\n # we do that before converting to dtype to avoid breaking in case we're using cpu_offload\n # and half precision\n mask = torch.nn.functional.interpolate(\n mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)\n )\n mask = mask.to(device=device, dtype=dtype)\n\n # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method\n if mask.shape[0] < batch_size:\n if not batch_size % mask.shape[0] == 0:\n raise ValueError(\n \"The passed mask and the required batch size don't match. Masks are supposed to be duplicated to\"\n f\" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number\"\n \" of masks that you pass is divisible by the total requested batch size.\"\n )\n mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)\n\n mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask\n\n masked_image_latents = None\n if masked_image is not None:\n masked_image = masked_image.to(device=device, dtype=dtype)\n masked_image_latents = self._encode_vae_image(masked_image, generator=generator)\n if masked_image_latents.shape[0] < batch_size:\n if not batch_size % masked_image_latents.shape[0] == 0:\n raise ValueError(\n \"The passed images and the required batch size don't match. Images are supposed to be duplicated\"\n f\" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed.\"\n \" Make sure the number of images that you pass is divisible by the total requested batch size.\"\n )\n masked_image_latents = masked_image_latents.repeat(\n batch_size // masked_image_latents.shape[0], 1, 1, 1\n )\n\n masked_image_latents = (\n torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents\n )\n\n # aligning device to prevent device errors when concating it with the latent model input\n masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)\n\n return mask, masked_image_latents\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength, device, denoising_start=None):\n # get the original timestep using init_timestep\n if denoising_start is None:\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n t_start = max(num_inference_steps - init_timestep, 0)\n else:\n t_start = 0\n\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n\n # Strength is irrelevant if we directly request a timestep to start at;\n # that is, strength is determined by the denoising_start instead.\n if denoising_start is not None:\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (denoising_start * self.scheduler.config.num_train_timesteps)\n )\n )\n\n num_inference_steps = (timesteps < discrete_timestep_cutoff).sum().item()\n if self.scheduler.order == 2 and num_inference_steps % 2 == 0:\n # if the scheduler is a 2nd order scheduler we might have to do +1\n # because `num_inference_steps` might be even given that every timestep\n # (except the highest one) is duplicated. If `num_inference_steps` is even it would\n # mean that we cut the timesteps in the middle of the denoising step\n # (between 1st and 2nd derivative) which leads to incorrect results. By adding 1\n # we ensure that the denoising process always ends after the 2nd derivate step of the scheduler\n num_inference_steps = num_inference_steps + 1\n\n # because t_n+1 >= t_n, we slice the timesteps starting from the end\n timesteps = timesteps[-num_inference_steps:]\n return timesteps, num_inference_steps\n\n return timesteps, num_inference_steps - t_start\n\n def _get_add_time_ids(\n self,\n original_size,\n crops_coords_top_left,\n target_size,\n aesthetic_score,\n negative_aesthetic_score,\n dtype,\n text_encoder_projection_dim=None,\n ):\n if self.config.requires_aesthetics_score:\n add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,))\n add_neg_time_ids = list(original_size + crops_coords_top_left + (negative_aesthetic_score,))\n else:\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n add_neg_time_ids = list(original_size + crops_coords_top_left + target_size)\n\n passed_add_embed_dim = (\n self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim\n )\n expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features\n\n if (\n expected_add_embed_dim > passed_add_embed_dim\n and (expected_add_embed_dim - passed_add_embed_dim) == self.unet.config.addition_time_embed_dim\n ):\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to enable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=True)` to make sure `aesthetic_score` {aesthetic_score} and `negative_aesthetic_score` {negative_aesthetic_score} is correctly used by the model.\"\n )\n elif (\n expected_add_embed_dim < passed_add_embed_dim\n and (passed_add_embed_dim - expected_add_embed_dim) == self.unet.config.addition_time_embed_dim\n ):\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the model.\"\n )\n elif expected_add_embed_dim != passed_add_embed_dim:\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.\"\n )\n\n add_time_ids = torch.tensor([add_time_ids], dtype=dtype)\n add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype)\n\n return add_time_ids, add_neg_time_ids\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae\n def upcast_vae(self):\n dtype = self.vae.dtype\n self.vae.to(dtype=torch.float32)\n use_torch_2_0_or_xformers = isinstance(\n self.vae.decoder.mid_block.attentions[0].processor,\n (\n AttnProcessor2_0,\n XFormersAttnProcessor,\n LoRAXFormersAttnProcessor,\n LoRAAttnProcessor2_0,\n ),\n )\n # if xformers or torch_2_0 is used attention block does not need\n # to be in float32 which can save lots of memory\n if use_torch_2_0_or_xformers:\n self.vae.post_quant_conv.to(dtype)\n self.vae.decoder.conv_in.to(dtype)\n self.vae.decoder.mid_block.to(dtype)\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n image: PipelineImageInput = None,\n mask_image: PipelineImageInput = None,\n control_image: Union[\n PipelineImageInput,\n List[PipelineImageInput],\n ] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n padding_mask_crop: Optional[int] = None,\n strength: float = 0.9999,\n num_inference_steps: int = 50,\n denoising_start: Optional[float] = None,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: Union[float, List[float]] = 1.0,\n guess_mode: bool = False,\n control_guidance_start: Union[float, List[float]] = 0.0,\n control_guidance_end: Union[float, List[float]] = 1.0,\n guidance_rescale: float = 0.0,\n original_size: Tuple[int, int] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Tuple[int, int] = None,\n aesthetic_score: float = 6.0,\n negative_aesthetic_score: float = 2.5,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[\n Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]\n ] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n image (`PIL.Image.Image`):\n `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will\n be masked out with `mask_image` and repainted according to `prompt`.\n mask_image (`PIL.Image.Image`):\n `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be\n repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted\n to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)\n instead of 3, so the expected shape would be `(B, H, W, 1)`.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n padding_mask_crop (`int`, *optional*, defaults to `None`):\n The size of margin in the crop to be applied to the image and masking. If `None`, no crop is applied to\n image and mask_image. If `padding_mask_crop` is not `None`, it will first find a rectangular region\n with the same aspect ration of the image and contains all masked area, and then expand that area based\n on `padding_mask_crop`. The image and mask_image will then be cropped based on the expanded area before\n resizing to the original image size for inpainting. This is useful when the masked area is small while\n the image is large and contain information irrelevant for inpainting, such as background.\n strength (`float`, *optional*, defaults to 0.9999):\n Conceptually, indicates how much to transform the masked portion of the reference `image`. Must be\n between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the\n `strength`. The number of denoising steps depends on the amount of noise initially added. When\n `strength` is 1, added noise will be maximum and the denoising process will run for the full number of\n iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores the masked\n portion of the reference `image`. Note that in the case of `denoising_start` being declared as an\n integer, the value of `strength` will be ignored.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n denoising_start (`float`, *optional*):\n When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be\n bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and\n it is assumed that the passed `image` is a partly denoised image. Note that when this is specified,\n strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline\n is integrated into a \"Mixture of Denoisers\" multi-pipeline setup, as detailed in [**Refining the Image\n Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).\n denoising_end (`float`, *optional*):\n When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be\n completed before it is intentionally prematurely terminated. As a result, the returned sample will\n still retain a substantial amount of noise (ca. final 20% of timesteps still needed) and should be\n denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the\n final 20% of the scheduler. The denoising_end parameter should ideally be utilized when this pipeline\n forms a part of a \"Mixture of Denoisers\" multi-pipeline setup, as elaborated in [**Refining the Image\n Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of\n IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should\n contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not\n provided, embeddings are computed from the `ip_adapter_image` input argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.\n `original_size` defaults to `(width, height)` if not specified. Part of SDXL's micro-conditioning as\n explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n `crops_coords_top_left` can be used to generate an image that appears to be \"cropped\" from the position\n `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n For most cases, `target_size` should be set to the desired height and width of the generated image. If\n not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n aesthetic_score (`float`, *optional*, defaults to 6.0):\n Used to simulate an aesthetic score of the generated image by influencing the positive text condition.\n Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n negative_aesthetic_score (`float`, *optional*, defaults to 2.5):\n Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). Can be used to\n simulate an aesthetic score of the generated image by influencing the negative text condition.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):\n A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of\n each denoising step during the inference. with the following arguments: `callback_on_step_end(self:\n DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a\n list of all tensors as specified by `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a\n `tuple. `tuple. When returning a tuple, the first element is a list with the generated images.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n\n if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):\n callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs\n\n controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet\n\n # align format for control guidance\n if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):\n control_guidance_start = len(control_guidance_end) * [control_guidance_start]\n elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):\n control_guidance_end = len(control_guidance_start) * [control_guidance_end]\n elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):\n mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1\n control_guidance_start, control_guidance_end = (\n mult * [control_guidance_start],\n mult * [control_guidance_end],\n )\n\n # # 0.0 Default height and width to unet\n # height = height or self.unet.config.sample_size * self.vae_scale_factor\n # width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 0.1 align format for control guidance\n if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):\n control_guidance_start = len(control_guidance_end) * [control_guidance_start]\n elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):\n control_guidance_end = len(control_guidance_start) * [control_guidance_end]\n elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):\n mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1\n control_guidance_start, control_guidance_end = (\n mult * [control_guidance_start],\n mult * [control_guidance_end],\n )\n\n # 1. Check inputs\n self.check_inputs(\n prompt,\n prompt_2,\n control_image,\n mask_image,\n strength,\n num_inference_steps,\n callback_steps,\n output_type,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n ip_adapter_image,\n ip_adapter_image_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n controlnet_conditioning_scale,\n control_guidance_start,\n control_guidance_end,\n callback_on_step_end_tensor_inputs,\n padding_mask_crop,\n )\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):\n controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # 3.1 Encode ip_adapter_image\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_images_per_prompt,\n self.do_classifier_free_guidance,\n )\n\n # 4. set timesteps\n def denoising_value_valid(dnv):\n return isinstance(dnv, float) and 0 < dnv < 1\n\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(\n num_inference_steps,\n strength,\n device,\n denoising_start=denoising_start if denoising_value_valid(denoising_start) else None,\n )\n # check that number of inference steps is not < 1 - as this doesn't make sense\n if num_inference_steps < 1:\n raise ValueError(\n f\"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline\"\n f\"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline.\"\n )\n # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise\n is_strength_max = strength == 1.0\n self._num_timesteps = len(timesteps)\n\n # 5. Preprocess mask and image - resizes image and mask w.r.t height and width\n # 5.1 Prepare init image\n if padding_mask_crop is not None:\n height, width = self.image_processor.get_default_height_width(image, height, width)\n crops_coords = self.mask_processor.get_crop_region(mask_image, width, height, pad=padding_mask_crop)\n resize_mode = \"fill\"\n else:\n crops_coords = None\n resize_mode = \"default\"\n\n original_image = image\n init_image = self.image_processor.preprocess(\n image, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode\n )\n init_image = init_image.to(dtype=torch.float32)\n\n # 5.2 Prepare control images\n if isinstance(controlnet, ControlNetModel):\n control_image = self.prepare_control_image(\n image=control_image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n crops_coords=crops_coords,\n resize_mode=resize_mode,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n elif isinstance(controlnet, MultiControlNetModel):\n control_images = []\n\n for control_image_ in control_image:\n control_image_ = self.prepare_control_image(\n image=control_image_,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n crops_coords=crops_coords,\n resize_mode=resize_mode,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n\n control_images.append(control_image_)\n\n control_image = control_images\n else:\n raise ValueError(f\"{controlnet.__class__} is not supported.\")\n\n # 5.3 Prepare mask\n mask = self.mask_processor.preprocess(\n mask_image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords\n )\n\n masked_image = init_image * (mask < 0.5)\n _, _, height, width = init_image.shape\n\n # 6. Prepare latent variables\n num_channels_latents = self.vae.config.latent_channels\n num_channels_unet = self.unet.config.in_channels\n return_image_latents = num_channels_unet == 4\n\n add_noise = True if denoising_start is None else False\n latents_outputs = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n image=init_image,\n timestep=latent_timestep,\n is_strength_max=is_strength_max,\n add_noise=add_noise,\n return_noise=True,\n return_image_latents=return_image_latents,\n )\n\n if return_image_latents:\n latents, noise, image_latents = latents_outputs\n else:\n latents, noise = latents_outputs\n\n # 7. Prepare mask latent variables\n mask, masked_image_latents = self.prepare_mask_latents(\n mask,\n masked_image,\n batch_size * num_images_per_prompt,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n self.do_classifier_free_guidance,\n )\n\n # 8. Check that sizes of mask, masked image and latents match\n if num_channels_unet == 9:\n # default case for runwayml/stable-diffusion-inpainting\n num_channels_mask = mask.shape[1]\n num_channels_masked_image = masked_image_latents.shape[1]\n if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:\n raise ValueError(\n f\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\"\n f\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\"\n f\" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}\"\n f\" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of\"\n \" `pipeline.unet` or your `mask_image` or `image` input.\"\n )\n elif num_channels_unet != 4:\n raise ValueError(\n f\"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}.\"\n )\n # 8.1 Prepare extra step kwargs.\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 8.2 Create tensor stating which controlnets to keep\n controlnet_keep = []\n for i in range(len(timesteps)):\n keeps = [\n 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)\n for s, e in zip(control_guidance_start, control_guidance_end)\n ]\n controlnet_keep.append(keeps if isinstance(controlnet, MultiControlNetModel) else keeps[0])\n\n # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n height, width = latents.shape[-2:]\n height = height * self.vae_scale_factor\n width = width * self.vae_scale_factor\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n # 10. Prepare added time ids & embeddings\n add_text_embeds = pooled_prompt_embeds\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n add_time_ids, add_neg_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n aesthetic_score,\n negative_aesthetic_score,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)\n\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1)\n add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device)\n\n # 11. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n\n if (\n denoising_end is not None\n and denoising_start is not None\n and denoising_value_valid(denoising_end)\n and denoising_value_valid(denoising_start)\n and denoising_start >= denoising_end\n ):\n raise ValueError(\n f\"`denoising_start`: {denoising_start} cannot be larger than or equal to `denoising_end`: \"\n + f\" {denoising_end} when using type float.\"\n )\n elif denoising_end is not None and denoising_value_valid(denoising_end):\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n\n # concat latents, mask, masked_image_latents in the channel dimension\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n\n # controlnet(s) inference\n if guess_mode and self.do_classifier_free_guidance:\n # Infer ControlNet only for the conditional batch.\n control_model_input = latents\n control_model_input = self.scheduler.scale_model_input(control_model_input, t)\n controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]\n controlnet_added_cond_kwargs = {\n \"text_embeds\": add_text_embeds.chunk(2)[1],\n \"time_ids\": add_time_ids.chunk(2)[1],\n }\n else:\n control_model_input = latent_model_input\n controlnet_prompt_embeds = prompt_embeds\n controlnet_added_cond_kwargs = added_cond_kwargs\n\n if isinstance(controlnet_keep[i], list):\n cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]\n else:\n controlnet_cond_scale = controlnet_conditioning_scale\n if isinstance(controlnet_cond_scale, list):\n controlnet_cond_scale = controlnet_cond_scale[0]\n cond_scale = controlnet_cond_scale * controlnet_keep[i]\n\n # # Resize control_image to match the size of the input to the controlnet\n # if control_image.shape[-2:] != control_model_input.shape[-2:]:\n # control_image = F.interpolate(control_image, size=control_model_input.shape[-2:], mode=\"bilinear\", align_corners=False)\n\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n control_model_input,\n t,\n encoder_hidden_states=controlnet_prompt_embeds,\n controlnet_cond=control_image,\n conditioning_scale=cond_scale,\n guess_mode=guess_mode,\n added_cond_kwargs=controlnet_added_cond_kwargs,\n return_dict=False,\n )\n\n if guess_mode and self.do_classifier_free_guidance:\n # Infered ControlNet only for the conditional batch.\n # To apply the output of ControlNet to both the unconditional and conditional batches,\n # add 0 to the unconditional batch to keep it unchanged.\n down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]\n mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n added_cond_kwargs[\"image_embeds\"] = image_embeds\n\n if num_channels_unet == 9:\n latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=self.cross_attention_kwargs,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if self.do_classifier_free_guidance and guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if num_channels_unet == 4:\n init_latents_proper = image_latents\n if self.do_classifier_free_guidance:\n init_mask, _ = mask.chunk(2)\n else:\n init_mask = mask\n\n if i < len(timesteps) - 1:\n noise_timestep = timesteps[i + 1]\n init_latents_proper = self.scheduler.add_noise(\n init_latents_proper, noise, torch.tensor([noise_timestep])\n )\n\n latents = (1 - init_mask) * init_latents_proper + init_mask * latents\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # make sure the VAE is in float32 mode, as it overflows in float16\n if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n # If we do sequential model offloading, let's offload unet and controlnet\n # manually for max memory savings\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.unet.to(\"cpu\")\n self.controlnet.to(\"cpu\")\n torch.cuda.empty_cache()\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n else:\n return StableDiffusionXLPipelineOutput(images=latents)\n\n # apply watermark if available\n if self.watermark is not None:\n image = self.watermark.apply_watermark(image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n if padding_mask_crop is not None:\n image = [self.image_processor.apply_overlay(mask_image, original_image, i, crops_coords) for i in image]\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModel,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers.utils.import_utils import is_invisible_watermark_available\n\nfrom ...callbacks import MultiPipelineCallbacks, PipelineCallback\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import (\n FromSingleFileMixin,\n IPAdapterMixin,\n StableDiffusionXLLoraLoaderMixin,\n TextualInversionLoaderMixin,\n)\nfrom ...models import AutoencoderKL, ControlNetModel, ImageProjection, UNet2DConditionModel\nfrom ...models.attention_processor import (\n AttnProcessor2_0,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n XFormersAttnProcessor,\n)\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ..stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput\n\n\nif is_invisible_watermark_available():\n from ..stable_diffusion_xl.watermark import StableDiffusionXLWatermarker\n\nfrom .multicontrolnet import MultiControlNetModel\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> # !pip install opencv-python transformers accelerate\n >>> from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL\n >>> from diffusers.utils import load_image\n >>> import numpy as np\n >>> import torch\n\n >>> import cv2\n >>> from PIL import Image\n\n >>> prompt = \"aerial view, a futuristic research complex in a bright foggy jungle, hard lighting\"\n >>> negative_prompt = \"low quality, bad quality, sketches\"\n\n >>> # download an image\n >>> image = load_image(\n ... \"https://hf.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png\"\n ... )\n\n >>> # initialize the models and pipeline\n >>> controlnet_conditioning_scale = 0.5 # recommended for good generalization\n >>> controlnet = ControlNetModel.from_pretrained(\n ... \"diffusers/controlnet-canny-sdxl-1.0\", torch_dtype=torch.float16\n ... )\n >>> vae = AutoencoderKL.from_pretrained(\"madebyollin/sdxl-vae-fp16-fix\", torch_dtype=torch.float16)\n >>> pipe = StableDiffusionXLControlNetPipeline.from_pretrained(\n ... \"stabilityai/stable-diffusion-xl-base-1.0\", controlnet=controlnet, vae=vae, torch_dtype=torch.float16\n ... )\n >>> pipe.enable_model_cpu_offload()\n\n >>> # get canny image\n >>> image = np.array(image)\n >>> image = cv2.Canny(image, 100, 200)\n >>> image = image[:, :, None]\n >>> image = np.concatenate([image, image, image], axis=2)\n >>> canny_image = Image.fromarray(image)\n\n >>> # generate image\n >>> image = pipe(\n ... prompt, controlnet_conditioning_scale=controlnet_conditioning_scale, image=canny_image\n ... ).images[0]\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass StableDiffusionXLControlNetPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n TextualInversionLoaderMixin,\n StableDiffusionXLLoraLoaderMixin,\n IPAdapterMixin,\n FromSingleFileMixin,\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet guidance.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n text_encoder_2 ([`~transformers.CLIPTextModelWithProjection`]):\n Second frozen text-encoder\n ([laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n tokenizer_2 ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):\n Provides additional conditioning to the `unet` during the denoising process. If you set multiple\n ControlNets as a list, the outputs from each ControlNet are added together to create one combined\n additional conditioning.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `\"True\"`):\n Whether the negative prompt embeddings should always be set to 0. Also see the config of\n `stabilityai/stable-diffusion-xl-base-1-0`.\n add_watermarker (`bool`, *optional*):\n Whether to use the [invisible_watermark](https://github.com/ShieldMnt/invisible-watermark/) library to\n watermark output images. If not defined, it defaults to `True` if the package is installed; otherwise no\n watermarker is used.\n \"\"\"\n\n # leave controlnet out on purpose because it iterates with unet\n model_cpu_offload_seq = \"text_encoder->text_encoder_2->image_encoder->unet->vae\"\n _optional_components = [\n \"tokenizer\",\n \"tokenizer_2\",\n \"text_encoder\",\n \"text_encoder_2\",\n \"feature_extractor\",\n \"image_encoder\",\n ]\n _callback_tensor_inputs = [\n \"latents\",\n \"prompt_embeds\",\n \"negative_prompt_embeds\",\n \"add_text_embeds\",\n \"add_time_ids\",\n \"negative_pooled_prompt_embeds\",\n \"negative_add_time_ids\",\n ]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n text_encoder_2: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n tokenizer_2: CLIPTokenizer,\n unet: UNet2DConditionModel,\n controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],\n scheduler: KarrasDiffusionSchedulers,\n force_zeros_for_empty_prompt: bool = True,\n add_watermarker: Optional[bool] = None,\n feature_extractor: CLIPImageProcessor = None,\n image_encoder: CLIPVisionModelWithProjection = None,\n ):\n super().__init__()\n\n if isinstance(controlnet, (list, tuple)):\n controlnet = MultiControlNetModel(controlnet)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n unet=unet,\n controlnet=controlnet,\n scheduler=scheduler,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)\n self.control_image_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False\n )\n add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()\n\n if add_watermarker:\n self.watermark = StableDiffusionXLWatermarker()\n else:\n self.watermark = None\n\n self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt: str,\n prompt_2: Optional[str] = None,\n device: Optional[torch.device] = None,\n num_images_per_prompt: int = 1,\n do_classifier_free_guidance: bool = True,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n lora_scale (`float`, *optional*):\n A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n device = device or self._execution_device\n\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if self.text_encoder is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)\n else:\n scale_lora_layers(self.text_encoder_2, lora_scale)\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n\n if prompt is not None:\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # Define tokenizers and text encoders\n tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]\n text_encoders = (\n [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]\n )\n\n if prompt_embeds is None:\n prompt_2 = prompt_2 or prompt\n prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2\n\n # textual inversion: process multi-vector tokens if necessary\n prompt_embeds_list = []\n prompts = [prompt, prompt_2]\n for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, tokenizer)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n if clip_skip is None:\n prompt_embeds = prompt_embeds.hidden_states[-2]\n else:\n # \"2\" because SDXL always indexes from the penultimate layer.\n prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]\n\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n\n # get unconditional embeddings for classifier free guidance\n zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt\n if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:\n negative_prompt_embeds = torch.zeros_like(prompt_embeds)\n negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)\n elif do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n\n # normalize str to list\n negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt\n negative_prompt_2 = (\n batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2\n )\n\n uncond_tokens: List[str]\n if prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = [negative_prompt, negative_prompt_2]\n\n negative_prompt_embeds_list = []\n for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n negative_prompt_embeds = text_encoder(\n uncond_input.input_ids.to(device),\n output_hidden_states=True,\n )\n # We are only ALWAYS interested in the pooled output of the final text encoder\n negative_pooled_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]\n\n negative_prompt_embeds_list.append(negative_prompt_embeds)\n\n negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)\n\n if self.text_encoder_2 is not None:\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n if self.text_encoder_2 is not None:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n if do_classifier_free_guidance:\n negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n\n if self.text_encoder is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder_2, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n prompt_2,\n image,\n callback_steps,\n negative_prompt=None,\n negative_prompt_2=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n pooled_prompt_embeds=None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n negative_pooled_prompt_embeds=None,\n controlnet_conditioning_scale=1.0,\n control_guidance_start=0.0,\n control_guidance_end=1.0,\n callback_on_step_end_tensor_inputs=None,\n ):\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if prompt_embeds is not None and pooled_prompt_embeds is None:\n raise ValueError(\n \"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.\"\n )\n\n if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:\n raise ValueError(\n \"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.\"\n )\n\n # `prompt` needs more sophisticated handling when there are multiple\n # conditionings.\n if isinstance(self.controlnet, MultiControlNetModel):\n if isinstance(prompt, list):\n logger.warning(\n f\"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}\"\n \" prompts. The conditionings will be fixed across the prompts.\"\n )\n\n # Check `image`\n is_compiled = hasattr(F, \"scaled_dot_product_attention\") and isinstance(\n self.controlnet, torch._dynamo.eval_frame.OptimizedModule\n )\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n self.check_image(image, prompt, prompt_embeds)\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if not isinstance(image, list):\n raise TypeError(\"For multiple controlnets: `image` must be type `list`\")\n\n # When `image` is a nested list:\n # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])\n elif any(isinstance(i, list) for i in image):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif len(image) != len(self.controlnet.nets):\n raise ValueError(\n f\"For multiple controlnets: `image` must have the same length as the number of controlnets, but got {len(image)} images and {len(self.controlnet.nets)} ControlNets.\"\n )\n\n for image_ in image:\n self.check_image(image_, prompt, prompt_embeds)\n else:\n assert False\n\n # Check `controlnet_conditioning_scale`\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n if not isinstance(controlnet_conditioning_scale, float):\n raise TypeError(\"For single controlnet: `controlnet_conditioning_scale` must be type `float`.\")\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if isinstance(controlnet_conditioning_scale, list):\n if any(isinstance(i, list) for i in controlnet_conditioning_scale):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(\n self.controlnet.nets\n ):\n raise ValueError(\n \"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have\"\n \" the same length as the number of controlnets\"\n )\n else:\n assert False\n\n if not isinstance(control_guidance_start, (tuple, list)):\n control_guidance_start = [control_guidance_start]\n\n if not isinstance(control_guidance_end, (tuple, list)):\n control_guidance_end = [control_guidance_end]\n\n if len(control_guidance_start) != len(control_guidance_end):\n raise ValueError(\n f\"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list.\"\n )\n\n if isinstance(self.controlnet, MultiControlNetModel):\n if len(control_guidance_start) != len(self.controlnet.nets):\n raise ValueError(\n f\"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}.\"\n )\n\n for start, end in zip(control_guidance_start, control_guidance_end):\n if start >= end:\n raise ValueError(\n f\"control guidance start: {start} cannot be larger or equal to control guidance end: {end}.\"\n )\n if start < 0.0:\n raise ValueError(f\"control guidance start: {start} can't be smaller than 0.\")\n if end > 1.0:\n raise ValueError(f\"control guidance end: {end} can't be larger than 1.0.\")\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n if ip_adapter_image_embeds is not None:\n if not isinstance(ip_adapter_image_embeds, list):\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}\"\n )\n elif ip_adapter_image_embeds[0].ndim not in [3, 4]:\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D\"\n )\n\n # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image\n def check_image(self, image, prompt, prompt_embeds):\n image_is_pil = isinstance(image, PIL.Image.Image)\n image_is_tensor = isinstance(image, torch.Tensor)\n image_is_np = isinstance(image, np.ndarray)\n image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)\n image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)\n image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)\n\n if (\n not image_is_pil\n and not image_is_tensor\n and not image_is_np\n and not image_is_pil_list\n and not image_is_tensor_list\n and not image_is_np_list\n ):\n raise TypeError(\n f\"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}\"\n )\n\n if image_is_pil:\n image_batch_size = 1\n else:\n image_batch_size = len(image)\n\n if prompt is not None and isinstance(prompt, str):\n prompt_batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n prompt_batch_size = len(prompt)\n elif prompt_embeds is not None:\n prompt_batch_size = prompt_embeds.shape[0]\n\n if image_batch_size != 1 and image_batch_size != prompt_batch_size:\n raise ValueError(\n f\"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}\"\n )\n\n # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image\n def prepare_image(\n self,\n image,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype,\n do_classifier_free_guidance=False,\n guess_mode=False,\n ):\n image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)\n image_batch_size = image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n image = image.repeat_interleave(repeat_by, dim=0)\n\n image = image.to(device=device, dtype=dtype)\n\n if do_classifier_free_guidance and not guess_mode:\n image = torch.cat([image] * 2)\n\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline._get_add_time_ids\n def _get_add_time_ids(\n self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None\n ):\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n\n passed_add_embed_dim = (\n self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim\n )\n expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features\n\n if expected_add_embed_dim != passed_add_embed_dim:\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.\"\n )\n\n add_time_ids = torch.tensor([add_time_ids], dtype=dtype)\n return add_time_ids\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae\n def upcast_vae(self):\n dtype = self.vae.dtype\n self.vae.to(dtype=torch.float32)\n use_torch_2_0_or_xformers = isinstance(\n self.vae.decoder.mid_block.attentions[0].processor,\n (\n AttnProcessor2_0,\n XFormersAttnProcessor,\n LoRAXFormersAttnProcessor,\n LoRAAttnProcessor2_0,\n ),\n )\n # if xformers or torch_2_0 is used attention block does not need\n # to be in float32 which can save lots of memory\n if use_torch_2_0_or_xformers:\n self.vae.post_quant_conv.to(dtype)\n self.vae.decoder.conv_in.to(dtype)\n self.vae.decoder.mid_block.to(dtype)\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(\n self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32\n ) -> torch.Tensor:\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n w (`torch.Tensor`):\n Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.\n embedding_dim (`int`, *optional*, defaults to 512):\n Dimension of the embeddings to generate.\n dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):\n Data type of the generated embeddings.\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def denoising_end(self):\n return self._denoising_end\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n image: PipelineImageInput = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n sigmas: List[float] = None,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: Union[float, List[float]] = 1.0,\n guess_mode: bool = False,\n control_guidance_start: Union[float, List[float]] = 0.0,\n control_guidance_end: Union[float, List[float]] = 1.0,\n original_size: Tuple[int, int] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Tuple[int, int] = None,\n negative_original_size: Optional[Tuple[int, int]] = None,\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\n negative_target_size: Optional[Tuple[int, int]] = None,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[\n Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]\n ] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:\n `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):\n The ControlNet input condition to provide guidance to the `unet` for generation. If the type is\n specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be accepted\n as an image. The dimensions of the output image defaults to `image`'s dimensions. If height and/or\n width are passed, `image` is resized accordingly. If multiple ControlNets are specified in `init`,\n images must be passed as a list such that each element of the list can be correctly batched for input\n to a single ControlNet.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image. Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image. Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n sigmas (`List[float]`, *optional*):\n Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in\n their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed\n will be used.\n denoising_end (`float`, *optional*):\n When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be\n completed before it is intentionally prematurely terminated. As a result, the returned sample will\n still retain a substantial amount of noise as determined by the discrete timesteps selected by the\n scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a\n \"Mixture of Denoisers\" multi-pipeline setup, as elaborated in [**Refining the Image\n Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)\n guidance_scale (`float`, *optional*, defaults to 5.0):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. This is sent to `tokenizer_2`\n and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, pooled text embeddings are generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs (prompt\n weighting). If not provided, pooled `negative_prompt_embeds` are generated from `negative_prompt` input\n argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of\n IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should\n contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not\n provided, embeddings are computed from the `ip_adapter_image` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):\n The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added\n to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set\n the corresponding scale as a list.\n guess_mode (`bool`, *optional*, defaults to `False`):\n The ControlNet encoder tries to recognize the content of the input image even if you remove all\n prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.\n control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):\n The percentage of total steps at which the ControlNet starts applying.\n control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):\n The percentage of total steps at which the ControlNet stops applying.\n original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.\n `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as\n explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n `crops_coords_top_left` can be used to generate an image that appears to be \"cropped\" from the position\n `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n For most cases, `target_size` should be set to the desired height and width of the generated image. If\n not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a specific image resolution. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a target image resolution. It should be as same\n as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):\n A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of\n each denoising step during the inference. with the following arguments: `callback_on_step_end(self:\n DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a\n list of all tensors as specified by `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned containing the output images.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n\n if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):\n callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs\n\n controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet\n\n # align format for control guidance\n if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):\n control_guidance_start = len(control_guidance_end) * [control_guidance_start]\n elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):\n control_guidance_end = len(control_guidance_start) * [control_guidance_end]\n elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):\n mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1\n control_guidance_start, control_guidance_end = (\n mult * [control_guidance_start],\n mult * [control_guidance_end],\n )\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n image,\n callback_steps,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n ip_adapter_image,\n ip_adapter_image_embeds,\n negative_pooled_prompt_embeds,\n controlnet_conditioning_scale,\n control_guidance_start,\n control_guidance_end,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._denoising_end = denoising_end\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):\n controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)\n\n global_pool_conditions = (\n controlnet.config.global_pool_conditions\n if isinstance(controlnet, ControlNetModel)\n else controlnet.nets[0].config.global_pool_conditions\n )\n guess_mode = guess_mode or global_pool_conditions\n\n # 3.1 Encode input prompt\n text_encoder_lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt,\n prompt_2,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # 3.2 Encode ip_adapter_image\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_images_per_prompt,\n self.do_classifier_free_guidance,\n )\n\n # 4. Prepare image\n if isinstance(controlnet, ControlNetModel):\n image = self.prepare_image(\n image=image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n height, width = image.shape[-2:]\n elif isinstance(controlnet, MultiControlNetModel):\n images = []\n\n for image_ in image:\n image_ = self.prepare_image(\n image=image_,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n\n images.append(image_)\n\n image = images\n height, width = image[0].shape[-2:]\n else:\n assert False\n\n # 5. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(\n self.scheduler, num_inference_steps, device, timesteps, sigmas\n )\n self._num_timesteps = len(timesteps)\n\n # 6. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6.5 Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7.1 Create tensor stating which controlnets to keep\n controlnet_keep = []\n for i in range(len(timesteps)):\n keeps = [\n 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)\n for s, e in zip(control_guidance_start, control_guidance_end)\n ]\n controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)\n\n # 7.2 Prepare added time ids & embeddings\n if isinstance(image, list):\n original_size = original_size or image[0].shape[-2:]\n else:\n original_size = original_size or image.shape[-2:]\n target_size = target_size or (height, width)\n\n add_text_embeds = pooled_prompt_embeds\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n add_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n\n if negative_original_size is not None and negative_target_size is not None:\n negative_add_time_ids = self._get_add_time_ids(\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n else:\n negative_add_time_ids = add_time_ids\n\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n\n # 8.1 Apply denoising_end\n if (\n self.denoising_end is not None\n and isinstance(self.denoising_end, float)\n and self.denoising_end > 0\n and self.denoising_end < 1\n ):\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (self.denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n is_unet_compiled = is_compiled_module(self.unet)\n is_controlnet_compiled = is_compiled_module(self.controlnet)\n is_torch_higher_equal_2_1 = is_torch_version(\">=\", \"2.1\")\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # Relevant thread:\n # https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428\n if (is_unet_compiled and is_controlnet_compiled) and is_torch_higher_equal_2_1:\n torch._inductor.cudagraph_mark_step_begin()\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n\n # controlnet(s) inference\n if guess_mode and self.do_classifier_free_guidance:\n # Infer ControlNet only for the conditional batch.\n control_model_input = latents\n control_model_input = self.scheduler.scale_model_input(control_model_input, t)\n controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]\n controlnet_added_cond_kwargs = {\n \"text_embeds\": add_text_embeds.chunk(2)[1],\n \"time_ids\": add_time_ids.chunk(2)[1],\n }\n else:\n control_model_input = latent_model_input\n controlnet_prompt_embeds = prompt_embeds\n controlnet_added_cond_kwargs = added_cond_kwargs\n\n if isinstance(controlnet_keep[i], list):\n cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]\n else:\n controlnet_cond_scale = controlnet_conditioning_scale\n if isinstance(controlnet_cond_scale, list):\n controlnet_cond_scale = controlnet_cond_scale[0]\n cond_scale = controlnet_cond_scale * controlnet_keep[i]\n\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n control_model_input,\n t,\n encoder_hidden_states=controlnet_prompt_embeds,\n controlnet_cond=image,\n conditioning_scale=cond_scale,\n guess_mode=guess_mode,\n added_cond_kwargs=controlnet_added_cond_kwargs,\n return_dict=False,\n )\n\n if guess_mode and self.do_classifier_free_guidance:\n # Infered ControlNet only for the conditional batch.\n # To apply the output of ControlNet to both the unconditional and conditional batches,\n # add 0 to the unconditional batch to keep it unchanged.\n down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]\n mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n added_cond_kwargs[\"image_embeds\"] = image_embeds\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=self.cross_attention_kwargs,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n add_text_embeds = callback_outputs.pop(\"add_text_embeds\", add_text_embeds)\n negative_pooled_prompt_embeds = callback_outputs.pop(\n \"negative_pooled_prompt_embeds\", negative_pooled_prompt_embeds\n )\n add_time_ids = callback_outputs.pop(\"add_time_ids\", add_time_ids)\n negative_add_time_ids = callback_outputs.pop(\"negative_add_time_ids\", negative_add_time_ids)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n # unscale/denormalize the latents\n # denormalize with the mean and std if available and not None\n has_latents_mean = hasattr(self.vae.config, \"latents_mean\") and self.vae.config.latents_mean is not None\n has_latents_std = hasattr(self.vae.config, \"latents_std\") and self.vae.config.latents_std is not None\n if has_latents_mean and has_latents_std:\n latents_mean = (\n torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)\n )\n latents_std = (\n torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)\n )\n latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean\n else:\n latents = latents / self.vae.config.scaling_factor\n\n image = self.vae.decode(latents, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n image = latents\n\n if not output_type == \"latent\":\n # apply watermark if available\n if self.watermark is not None:\n image = self.watermark.apply_watermark(image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl_img2img.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModel,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers.utils.import_utils import is_invisible_watermark_available\n\nfrom ...callbacks import MultiPipelineCallbacks, PipelineCallback\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import (\n FromSingleFileMixin,\n IPAdapterMixin,\n StableDiffusionXLLoraLoaderMixin,\n TextualInversionLoaderMixin,\n)\nfrom ...models import AutoencoderKL, ControlNetModel, ImageProjection, UNet2DConditionModel\nfrom ...models.attention_processor import (\n AttnProcessor2_0,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n XFormersAttnProcessor,\n)\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import is_compiled_module, randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ..stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput\n\n\nif is_invisible_watermark_available():\n from ..stable_diffusion_xl.watermark import StableDiffusionXLWatermarker\n\nfrom .multicontrolnet import MultiControlNetModel\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> # pip install accelerate transformers safetensors diffusers\n\n >>> import torch\n >>> import numpy as np\n >>> from PIL import Image\n\n >>> from transformers import DPTFeatureExtractor, DPTForDepthEstimation\n >>> from diffusers import ControlNetModel, StableDiffusionXLControlNetImg2ImgPipeline, AutoencoderKL\n >>> from diffusers.utils import load_image\n\n\n >>> depth_estimator = DPTForDepthEstimation.from_pretrained(\"Intel/dpt-hybrid-midas\").to(\"cuda\")\n >>> feature_extractor = DPTFeatureExtractor.from_pretrained(\"Intel/dpt-hybrid-midas\")\n >>> controlnet = ControlNetModel.from_pretrained(\n ... \"diffusers/controlnet-depth-sdxl-1.0-small\",\n ... variant=\"fp16\",\n ... use_safetensors=True,\n ... torch_dtype=torch.float16,\n ... )\n >>> vae = AutoencoderKL.from_pretrained(\"madebyollin/sdxl-vae-fp16-fix\", torch_dtype=torch.float16)\n >>> pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(\n ... \"stabilityai/stable-diffusion-xl-base-1.0\",\n ... controlnet=controlnet,\n ... vae=vae,\n ... variant=\"fp16\",\n ... use_safetensors=True,\n ... torch_dtype=torch.float16,\n ... )\n >>> pipe.enable_model_cpu_offload()\n\n\n >>> def get_depth_map(image):\n ... image = feature_extractor(images=image, return_tensors=\"pt\").pixel_values.to(\"cuda\")\n ... with torch.no_grad(), torch.autocast(\"cuda\"):\n ... depth_map = depth_estimator(image).predicted_depth\n\n ... depth_map = torch.nn.functional.interpolate(\n ... depth_map.unsqueeze(1),\n ... size=(1024, 1024),\n ... mode=\"bicubic\",\n ... align_corners=False,\n ... )\n ... depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)\n ... depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)\n ... depth_map = (depth_map - depth_min) / (depth_max - depth_min)\n ... image = torch.cat([depth_map] * 3, dim=1)\n ... image = image.permute(0, 2, 3, 1).cpu().numpy()[0]\n ... image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))\n ... return image\n\n\n >>> prompt = \"A robot, 4k photo\"\n >>> image = load_image(\n ... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n ... \"/kandinsky/cat.png\"\n ... ).resize((1024, 1024))\n >>> controlnet_conditioning_scale = 0.5 # recommended for good generalization\n >>> depth_image = get_depth_map(image)\n\n >>> images = pipe(\n ... prompt,\n ... image=image,\n ... control_image=depth_image,\n ... strength=0.99,\n ... num_inference_steps=50,\n ... controlnet_conditioning_scale=controlnet_conditioning_scale,\n ... ).images\n >>> images[0].save(f\"robot_cat.png\")\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n\nclass StableDiffusionXLControlNetImg2ImgPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n TextualInversionLoaderMixin,\n StableDiffusionXLLoraLoaderMixin,\n FromSingleFileMixin,\n IPAdapterMixin,\n):\n r\"\"\"\n Pipeline for image-to-image generation using Stable Diffusion XL with ControlNet guidance.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n text_encoder_2 ([` CLIPTextModelWithProjection`]):\n Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),\n specifically the\n [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)\n variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n tokenizer_2 (`CLIPTokenizer`):\n Second Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):\n Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets\n as a list, the outputs from each ControlNet are added together to create one combined additional\n conditioning.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n requires_aesthetics_score (`bool`, *optional*, defaults to `\"False\"`):\n Whether the `unet` requires an `aesthetic_score` condition to be passed during inference. Also see the\n config of `stabilityai/stable-diffusion-xl-refiner-1-0`.\n force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `\"True\"`):\n Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of\n `stabilityai/stable-diffusion-xl-base-1-0`.\n add_watermarker (`bool`, *optional*):\n Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to\n watermark output images. If not defined, it will default to True if the package is installed, otherwise no\n watermarker will be used.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->text_encoder_2->image_encoder->unet->vae\"\n _optional_components = [\n \"tokenizer\",\n \"tokenizer_2\",\n \"text_encoder\",\n \"text_encoder_2\",\n \"feature_extractor\",\n \"image_encoder\",\n ]\n _callback_tensor_inputs = [\n \"latents\",\n \"prompt_embeds\",\n \"negative_prompt_embeds\",\n \"add_text_embeds\",\n \"add_time_ids\",\n \"negative_pooled_prompt_embeds\",\n \"add_neg_time_ids\",\n ]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n text_encoder_2: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n tokenizer_2: CLIPTokenizer,\n unet: UNet2DConditionModel,\n controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],\n scheduler: KarrasDiffusionSchedulers,\n requires_aesthetics_score: bool = False,\n force_zeros_for_empty_prompt: bool = True,\n add_watermarker: Optional[bool] = None,\n feature_extractor: CLIPImageProcessor = None,\n image_encoder: CLIPVisionModelWithProjection = None,\n ):\n super().__init__()\n\n if isinstance(controlnet, (list, tuple)):\n controlnet = MultiControlNetModel(controlnet)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n unet=unet,\n controlnet=controlnet,\n scheduler=scheduler,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)\n self.control_image_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False\n )\n add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()\n\n if add_watermarker:\n self.watermark = StableDiffusionXLWatermarker()\n else:\n self.watermark = None\n\n self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)\n self.register_to_config(requires_aesthetics_score=requires_aesthetics_score)\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt: str,\n prompt_2: Optional[str] = None,\n device: Optional[torch.device] = None,\n num_images_per_prompt: int = 1,\n do_classifier_free_guidance: bool = True,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n lora_scale (`float`, *optional*):\n A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n device = device or self._execution_device\n\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if self.text_encoder is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)\n else:\n scale_lora_layers(self.text_encoder_2, lora_scale)\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n\n if prompt is not None:\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # Define tokenizers and text encoders\n tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]\n text_encoders = (\n [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]\n )\n\n if prompt_embeds is None:\n prompt_2 = prompt_2 or prompt\n prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2\n\n # textual inversion: process multi-vector tokens if necessary\n prompt_embeds_list = []\n prompts = [prompt, prompt_2]\n for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, tokenizer)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n if clip_skip is None:\n prompt_embeds = prompt_embeds.hidden_states[-2]\n else:\n # \"2\" because SDXL always indexes from the penultimate layer.\n prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]\n\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n\n # get unconditional embeddings for classifier free guidance\n zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt\n if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:\n negative_prompt_embeds = torch.zeros_like(prompt_embeds)\n negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)\n elif do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n\n # normalize str to list\n negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt\n negative_prompt_2 = (\n batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2\n )\n\n uncond_tokens: List[str]\n if prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = [negative_prompt, negative_prompt_2]\n\n negative_prompt_embeds_list = []\n for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n negative_prompt_embeds = text_encoder(\n uncond_input.input_ids.to(device),\n output_hidden_states=True,\n )\n # We are only ALWAYS interested in the pooled output of the final text encoder\n negative_pooled_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]\n\n negative_prompt_embeds_list.append(negative_prompt_embeds)\n\n negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)\n\n if self.text_encoder_2 is not None:\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n if self.text_encoder_2 is not None:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n if do_classifier_free_guidance:\n negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n\n if self.text_encoder is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder_2, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n prompt_2,\n image,\n strength,\n num_inference_steps,\n callback_steps,\n negative_prompt=None,\n negative_prompt_2=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n pooled_prompt_embeds=None,\n negative_pooled_prompt_embeds=None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n controlnet_conditioning_scale=1.0,\n control_guidance_start=0.0,\n control_guidance_end=1.0,\n callback_on_step_end_tensor_inputs=None,\n ):\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n if num_inference_steps is None:\n raise ValueError(\"`num_inference_steps` cannot be None.\")\n elif not isinstance(num_inference_steps, int) or num_inference_steps <= 0:\n raise ValueError(\n f\"`num_inference_steps` has to be a positive integer but is {num_inference_steps} of type\"\n f\" {type(num_inference_steps)}.\"\n )\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if prompt_embeds is not None and pooled_prompt_embeds is None:\n raise ValueError(\n \"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.\"\n )\n\n if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:\n raise ValueError(\n \"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.\"\n )\n\n # `prompt` needs more sophisticated handling when there are multiple\n # conditionings.\n if isinstance(self.controlnet, MultiControlNetModel):\n if isinstance(prompt, list):\n logger.warning(\n f\"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}\"\n \" prompts. The conditionings will be fixed across the prompts.\"\n )\n\n # Check `image`\n is_compiled = hasattr(F, \"scaled_dot_product_attention\") and isinstance(\n self.controlnet, torch._dynamo.eval_frame.OptimizedModule\n )\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n self.check_image(image, prompt, prompt_embeds)\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if not isinstance(image, list):\n raise TypeError(\"For multiple controlnets: `image` must be type `list`\")\n\n # When `image` is a nested list:\n # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])\n elif any(isinstance(i, list) for i in image):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif len(image) != len(self.controlnet.nets):\n raise ValueError(\n f\"For multiple controlnets: `image` must have the same length as the number of controlnets, but got {len(image)} images and {len(self.controlnet.nets)} ControlNets.\"\n )\n\n for image_ in image:\n self.check_image(image_, prompt, prompt_embeds)\n else:\n assert False\n\n # Check `controlnet_conditioning_scale`\n if (\n isinstance(self.controlnet, ControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, ControlNetModel)\n ):\n if not isinstance(controlnet_conditioning_scale, float):\n raise TypeError(\"For single controlnet: `controlnet_conditioning_scale` must be type `float`.\")\n elif (\n isinstance(self.controlnet, MultiControlNetModel)\n or is_compiled\n and isinstance(self.controlnet._orig_mod, MultiControlNetModel)\n ):\n if isinstance(controlnet_conditioning_scale, list):\n if any(isinstance(i, list) for i in controlnet_conditioning_scale):\n raise ValueError(\"A single batch of multiple conditionings are supported at the moment.\")\n elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(\n self.controlnet.nets\n ):\n raise ValueError(\n \"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have\"\n \" the same length as the number of controlnets\"\n )\n else:\n assert False\n\n if not isinstance(control_guidance_start, (tuple, list)):\n control_guidance_start = [control_guidance_start]\n\n if not isinstance(control_guidance_end, (tuple, list)):\n control_guidance_end = [control_guidance_end]\n\n if len(control_guidance_start) != len(control_guidance_end):\n raise ValueError(\n f\"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list.\"\n )\n\n if isinstance(self.controlnet, MultiControlNetModel):\n if len(control_guidance_start) != len(self.controlnet.nets):\n raise ValueError(\n f\"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}.\"\n )\n\n for start, end in zip(control_guidance_start, control_guidance_end):\n if start >= end:\n raise ValueError(\n f\"control guidance start: {start} cannot be larger or equal to control guidance end: {end}.\"\n )\n if start < 0.0:\n raise ValueError(f\"control guidance start: {start} can't be smaller than 0.\")\n if end > 1.0:\n raise ValueError(f\"control guidance end: {end} can't be larger than 1.0.\")\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n if ip_adapter_image_embeds is not None:\n if not isinstance(ip_adapter_image_embeds, list):\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}\"\n )\n elif ip_adapter_image_embeds[0].ndim not in [3, 4]:\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D\"\n )\n\n # Copied from diffusers.pipelines.controlnet.pipeline_controlnet_sd_xl.StableDiffusionXLControlNetPipeline.check_image\n def check_image(self, image, prompt, prompt_embeds):\n image_is_pil = isinstance(image, PIL.Image.Image)\n image_is_tensor = isinstance(image, torch.Tensor)\n image_is_np = isinstance(image, np.ndarray)\n image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)\n image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)\n image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)\n\n if (\n not image_is_pil\n and not image_is_tensor\n and not image_is_np\n and not image_is_pil_list\n and not image_is_tensor_list\n and not image_is_np_list\n ):\n raise TypeError(\n f\"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}\"\n )\n\n if image_is_pil:\n image_batch_size = 1\n else:\n image_batch_size = len(image)\n\n if prompt is not None and isinstance(prompt, str):\n prompt_batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n prompt_batch_size = len(prompt)\n elif prompt_embeds is not None:\n prompt_batch_size = prompt_embeds.shape[0]\n\n if image_batch_size != 1 and image_batch_size != prompt_batch_size:\n raise ValueError(\n f\"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}\"\n )\n\n # Copied from diffusers.pipelines.controlnet.pipeline_controlnet_sd_xl.StableDiffusionXLControlNetPipeline.prepare_image\n def prepare_control_image(\n self,\n image,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype,\n do_classifier_free_guidance=False,\n guess_mode=False,\n ):\n image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)\n image_batch_size = image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n image = image.repeat_interleave(repeat_by, dim=0)\n\n image = image.to(device=device, dtype=dtype)\n\n if do_classifier_free_guidance and not guess_mode:\n image = torch.cat([image] * 2)\n\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n if hasattr(self.scheduler, \"set_begin_index\"):\n self.scheduler.set_begin_index(t_start * self.scheduler.order)\n\n return timesteps, num_inference_steps - t_start\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline.prepare_latents\n def prepare_latents(\n self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None, add_noise=True\n ):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n latents_mean = latents_std = None\n if hasattr(self.vae.config, \"latents_mean\") and self.vae.config.latents_mean is not None:\n latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1)\n if hasattr(self.vae.config, \"latents_std\") and self.vae.config.latents_std is not None:\n latents_std = torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1)\n\n # Offload text encoder if `enable_model_cpu_offload` was enabled\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.text_encoder_2.to(\"cpu\")\n torch.cuda.empty_cache()\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n\n if image.shape[1] == 4:\n init_latents = image\n\n else:\n # make sure the VAE is in float32 mode, as it overflows in float16\n if self.vae.config.force_upcast:\n image = image.float()\n self.vae.to(dtype=torch.float32)\n\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n elif isinstance(generator, list):\n init_latents = [\n retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])\n for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = retrieve_latents(self.vae.encode(image), generator=generator)\n\n if self.vae.config.force_upcast:\n self.vae.to(dtype)\n\n init_latents = init_latents.to(dtype)\n if latents_mean is not None and latents_std is not None:\n latents_mean = latents_mean.to(device=self.device, dtype=dtype)\n latents_std = latents_std.to(device=self.device, dtype=dtype)\n init_latents = (init_latents - latents_mean) * self.vae.config.scaling_factor / latents_std\n else:\n init_latents = self.vae.config.scaling_factor * init_latents\n\n if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:\n # expand init_latents for batch_size\n additional_image_per_prompt = batch_size // init_latents.shape[0]\n init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)\n elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n init_latents = torch.cat([init_latents], dim=0)\n\n if add_noise:\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n\n latents = init_latents\n\n return latents\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline._get_add_time_ids\n def _get_add_time_ids(\n self,\n original_size,\n crops_coords_top_left,\n target_size,\n aesthetic_score,\n negative_aesthetic_score,\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype,\n text_encoder_projection_dim=None,\n ):\n if self.config.requires_aesthetics_score:\n add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,))\n add_neg_time_ids = list(\n negative_original_size + negative_crops_coords_top_left + (negative_aesthetic_score,)\n )\n else:\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n add_neg_time_ids = list(negative_original_size + crops_coords_top_left + negative_target_size)\n\n passed_add_embed_dim = (\n self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim\n )\n expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features\n\n if (\n expected_add_embed_dim > passed_add_embed_dim\n and (expected_add_embed_dim - passed_add_embed_dim) == self.unet.config.addition_time_embed_dim\n ):\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to enable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=True)` to make sure `aesthetic_score` {aesthetic_score} and `negative_aesthetic_score` {negative_aesthetic_score} is correctly used by the model.\"\n )\n elif (\n expected_add_embed_dim < passed_add_embed_dim\n and (passed_add_embed_dim - expected_add_embed_dim) == self.unet.config.addition_time_embed_dim\n ):\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the model.\"\n )\n elif expected_add_embed_dim != passed_add_embed_dim:\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.\"\n )\n\n add_time_ids = torch.tensor([add_time_ids], dtype=dtype)\n add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype)\n\n return add_time_ids, add_neg_time_ids\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae\n def upcast_vae(self):\n dtype = self.vae.dtype\n self.vae.to(dtype=torch.float32)\n use_torch_2_0_or_xformers = isinstance(\n self.vae.decoder.mid_block.attentions[0].processor,\n (\n AttnProcessor2_0,\n XFormersAttnProcessor,\n LoRAXFormersAttnProcessor,\n LoRAAttnProcessor2_0,\n ),\n )\n # if xformers or torch_2_0 is used attention block does not need\n # to be in float32 which can save lots of memory\n if use_torch_2_0_or_xformers:\n self.vae.post_quant_conv.to(dtype)\n self.vae.decoder.conv_in.to(dtype)\n self.vae.decoder.mid_block.to(dtype)\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n image: PipelineImageInput = None,\n control_image: PipelineImageInput = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n strength: float = 0.8,\n num_inference_steps: int = 50,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: Union[float, List[float]] = 0.8,\n guess_mode: bool = False,\n control_guidance_start: Union[float, List[float]] = 0.0,\n control_guidance_end: Union[float, List[float]] = 1.0,\n original_size: Tuple[int, int] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Tuple[int, int] = None,\n negative_original_size: Optional[Tuple[int, int]] = None,\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\n negative_target_size: Optional[Tuple[int, int]] = None,\n aesthetic_score: float = 6.0,\n negative_aesthetic_score: float = 2.5,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[\n Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]\n ] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:\n `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):\n The initial image will be used as the starting point for the image generation process. Can also accept\n image latents as `image`, if passing latents directly, it will not be encoded again.\n control_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:\n `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):\n The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If\n the type is specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also\n be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height\n and/or width are passed, `image` is resized according to them. If multiple ControlNets are specified in\n init, images must be passed as a list such that each element of the list can be correctly batched for\n input to a single controlnet.\n height (`int`, *optional*, defaults to the size of control_image):\n The height in pixels of the generated image. Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n width (`int`, *optional*, defaults to the size of control_image):\n The width in pixels of the generated image. Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n strength (`float`, *optional*, defaults to 0.8):\n Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a\n starting point and more noise is added the higher the `strength`. The number of denoising steps depends\n on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising\n process runs for the full number of iterations specified in `num_inference_steps`. A value of 1\n essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of\n IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should\n contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not\n provided, embeddings are computed from the `ip_adapter_image` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):\n The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added\n to the residual in the original unet. If multiple ControlNets are specified in init, you can set the\n corresponding scale as a list.\n guess_mode (`bool`, *optional*, defaults to `False`):\n In this mode, the ControlNet encoder will try best to recognize the content of the input image even if\n you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.\n control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):\n The percentage of total steps at which the controlnet starts applying.\n control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):\n The percentage of total steps at which the controlnet stops applying.\n original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.\n `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as\n explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n `crops_coords_top_left` can be used to generate an image that appears to be \"cropped\" from the position\n `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n For most cases, `target_size` should be set to the desired height and width of the generated image. If\n not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a specific image resolution. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a target image resolution. It should be as same\n as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n aesthetic_score (`float`, *optional*, defaults to 6.0):\n Used to simulate an aesthetic score of the generated image by influencing the positive text condition.\n Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n negative_aesthetic_score (`float`, *optional*, defaults to 2.5):\n Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). Can be used to\n simulate an aesthetic score of the generated image by influencing the negative text condition.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):\n A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of\n each denoising step during the inference. with the following arguments: `callback_on_step_end(self:\n DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a\n list of all tensors as specified by `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple`\n containing the output images.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n\n if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):\n callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs\n\n controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet\n\n # align format for control guidance\n if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):\n control_guidance_start = len(control_guidance_end) * [control_guidance_start]\n elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):\n control_guidance_end = len(control_guidance_start) * [control_guidance_end]\n elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):\n mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1\n control_guidance_start, control_guidance_end = (\n mult * [control_guidance_start],\n mult * [control_guidance_end],\n )\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n control_image,\n strength,\n num_inference_steps,\n callback_steps,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ip_adapter_image,\n ip_adapter_image_embeds,\n controlnet_conditioning_scale,\n control_guidance_start,\n control_guidance_end,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):\n controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)\n\n global_pool_conditions = (\n controlnet.config.global_pool_conditions\n if isinstance(controlnet, ControlNetModel)\n else controlnet.nets[0].config.global_pool_conditions\n )\n guess_mode = guess_mode or global_pool_conditions\n\n # 3.1. Encode input prompt\n text_encoder_lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt,\n prompt_2,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # 3.2 Encode ip_adapter_image\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_images_per_prompt,\n self.do_classifier_free_guidance,\n )\n\n # 4. Prepare image and controlnet_conditioning_image\n image = self.image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)\n\n if isinstance(controlnet, ControlNetModel):\n control_image = self.prepare_control_image(\n image=control_image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n height, width = control_image.shape[-2:]\n elif isinstance(controlnet, MultiControlNetModel):\n control_images = []\n\n for control_image_ in control_image:\n control_image_ = self.prepare_control_image(\n image=control_image_,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n\n control_images.append(control_image_)\n\n control_image = control_images\n height, width = control_image[0].shape[-2:]\n else:\n assert False\n\n # 5. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n self._num_timesteps = len(timesteps)\n\n # 6. Prepare latent variables\n if latents is None:\n latents = self.prepare_latents(\n image,\n latent_timestep,\n batch_size,\n num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n generator,\n True,\n )\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7.1 Create tensor stating which controlnets to keep\n controlnet_keep = []\n for i in range(len(timesteps)):\n keeps = [\n 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)\n for s, e in zip(control_guidance_start, control_guidance_end)\n ]\n controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)\n\n # 7.2 Prepare added time ids & embeddings\n if isinstance(control_image, list):\n original_size = original_size or control_image[0].shape[-2:]\n else:\n original_size = original_size or control_image.shape[-2:]\n target_size = target_size or (height, width)\n\n if negative_original_size is None:\n negative_original_size = original_size\n if negative_target_size is None:\n negative_target_size = target_size\n add_text_embeds = pooled_prompt_embeds\n\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n add_time_ids, add_neg_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n aesthetic_score,\n negative_aesthetic_score,\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)\n\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1)\n add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n\n # controlnet(s) inference\n if guess_mode and self.do_classifier_free_guidance:\n # Infer ControlNet only for the conditional batch.\n control_model_input = latents\n control_model_input = self.scheduler.scale_model_input(control_model_input, t)\n controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]\n controlnet_added_cond_kwargs = {\n \"text_embeds\": add_text_embeds.chunk(2)[1],\n \"time_ids\": add_time_ids.chunk(2)[1],\n }\n else:\n control_model_input = latent_model_input\n controlnet_prompt_embeds = prompt_embeds\n controlnet_added_cond_kwargs = added_cond_kwargs\n\n if isinstance(controlnet_keep[i], list):\n cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]\n else:\n controlnet_cond_scale = controlnet_conditioning_scale\n if isinstance(controlnet_cond_scale, list):\n controlnet_cond_scale = controlnet_cond_scale[0]\n cond_scale = controlnet_cond_scale * controlnet_keep[i]\n\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n control_model_input,\n t,\n encoder_hidden_states=controlnet_prompt_embeds,\n controlnet_cond=control_image,\n conditioning_scale=cond_scale,\n guess_mode=guess_mode,\n added_cond_kwargs=controlnet_added_cond_kwargs,\n return_dict=False,\n )\n\n if guess_mode and self.do_classifier_free_guidance:\n # Infered ControlNet only for the conditional batch.\n # To apply the output of ControlNet to both the unconditional and conditional batches,\n # add 0 to the unconditional batch to keep it unchanged.\n down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]\n mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n added_cond_kwargs[\"image_embeds\"] = image_embeds\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=self.cross_attention_kwargs,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n add_text_embeds = callback_outputs.pop(\"add_text_embeds\", add_text_embeds)\n negative_pooled_prompt_embeds = callback_outputs.pop(\n \"negative_pooled_prompt_embeds\", negative_pooled_prompt_embeds\n )\n add_time_ids = callback_outputs.pop(\"add_time_ids\", add_time_ids)\n add_neg_time_ids = callback_outputs.pop(\"add_neg_time_ids\", add_neg_time_ids)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # If we do sequential model offloading, let's offload unet and controlnet\n # manually for max memory savings\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.unet.to(\"cpu\")\n self.controlnet.to(\"cpu\")\n torch.cuda.empty_cache()\n\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n # unscale/denormalize the latents\n # denormalize with the mean and std if available and not None\n has_latents_mean = hasattr(self.vae.config, \"latents_mean\") and self.vae.config.latents_mean is not None\n has_latents_std = hasattr(self.vae.config, \"latents_std\") and self.vae.config.latents_std is not None\n if has_latents_mean and has_latents_std:\n latents_mean = (\n torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)\n )\n latents_std = (\n torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)\n )\n latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean\n else:\n latents = latents / self.vae.config.scaling_factor\n\n image = self.vae.decode(latents, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n image = latents\n return StableDiffusionXLPipelineOutput(images=image)\n\n # apply watermark if available\n if self.watermark is not None:\n image = self.watermark.apply_watermark(image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/controlnet/pipeline_flax_controlnet.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport warnings\nfrom functools import partial\nfrom typing import Dict, List, Optional, Union\n\nimport jax\nimport jax.numpy as jnp\nimport numpy as np\nfrom flax.core.frozen_dict import FrozenDict\nfrom flax.jax_utils import unreplicate\nfrom flax.training.common_utils import shard\nfrom PIL import Image\nfrom transformers import CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel\n\nfrom ...models import FlaxAutoencoderKL, FlaxControlNetModel, FlaxUNet2DConditionModel\nfrom ...schedulers import (\n FlaxDDIMScheduler,\n FlaxDPMSolverMultistepScheduler,\n FlaxLMSDiscreteScheduler,\n FlaxPNDMScheduler,\n)\nfrom ...utils import PIL_INTERPOLATION, logging, replace_example_docstring\nfrom ..pipeline_flax_utils import FlaxDiffusionPipeline\nfrom ..stable_diffusion import FlaxStableDiffusionPipelineOutput\nfrom ..stable_diffusion.safety_checker_flax import FlaxStableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n# Set to True to use python for loop instead of jax.fori_loop for easier debugging\nDEBUG = False\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import jax\n >>> import numpy as np\n >>> import jax.numpy as jnp\n >>> from flax.jax_utils import replicate\n >>> from flax.training.common_utils import shard\n >>> from diffusers.utils import load_image, make_image_grid\n >>> from PIL import Image\n >>> from diffusers import FlaxStableDiffusionControlNetPipeline, FlaxControlNetModel\n\n\n >>> def create_key(seed=0):\n ... return jax.random.PRNGKey(seed)\n\n\n >>> rng = create_key(0)\n\n >>> # get canny image\n >>> canny_image = load_image(\n ... \"https://huggingface.co/datasets/YiYiXu/test-doc-assets/resolve/main/blog_post_cell_10_output_0.jpeg\"\n ... )\n\n >>> prompts = \"best quality, extremely detailed\"\n >>> negative_prompts = \"monochrome, lowres, bad anatomy, worst quality, low quality\"\n\n >>> # load control net and stable diffusion v1-5\n >>> controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(\n ... \"lllyasviel/sd-controlnet-canny\", from_pt=True, dtype=jnp.float32\n ... )\n >>> pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(\n ... \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, revision=\"flax\", dtype=jnp.float32\n ... )\n >>> params[\"controlnet\"] = controlnet_params\n\n >>> num_samples = jax.device_count()\n >>> rng = jax.random.split(rng, jax.device_count())\n\n >>> prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples)\n >>> negative_prompt_ids = pipe.prepare_text_inputs([negative_prompts] * num_samples)\n >>> processed_image = pipe.prepare_image_inputs([canny_image] * num_samples)\n\n >>> p_params = replicate(params)\n >>> prompt_ids = shard(prompt_ids)\n >>> negative_prompt_ids = shard(negative_prompt_ids)\n >>> processed_image = shard(processed_image)\n\n >>> output = pipe(\n ... prompt_ids=prompt_ids,\n ... image=processed_image,\n ... params=p_params,\n ... prng_seed=rng,\n ... num_inference_steps=50,\n ... neg_prompt_ids=negative_prompt_ids,\n ... jit=True,\n ... ).images\n\n >>> output_images = pipe.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:])))\n >>> output_images = make_image_grid(output_images, num_samples // 4, 4)\n >>> output_images.save(\"generated_image.png\")\n ```\n\"\"\"\n\n\nclass FlaxStableDiffusionControlNetPipeline(FlaxDiffusionPipeline):\n r\"\"\"\n Flax-based pipeline for text-to-image generation using Stable Diffusion with ControlNet Guidance.\n\n This model inherits from [`FlaxDiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Args:\n vae ([`FlaxAutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.FlaxCLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`FlaxUNet2DConditionModel`]):\n A `FlaxUNet2DConditionModel` to denoise the encoded image latents.\n controlnet ([`FlaxControlNetModel`]:\n Provides additional conditioning to the `unet` during the denoising process.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`FlaxDDIMScheduler`], [`FlaxLMSDiscreteScheduler`], [`FlaxPNDMScheduler`], or\n [`FlaxDPMSolverMultistepScheduler`].\n safety_checker ([`FlaxStableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n def __init__(\n self,\n vae: FlaxAutoencoderKL,\n text_encoder: FlaxCLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: FlaxUNet2DConditionModel,\n controlnet: FlaxControlNetModel,\n scheduler: Union[\n FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler\n ],\n safety_checker: FlaxStableDiffusionSafetyChecker,\n feature_extractor: CLIPFeatureExtractor,\n dtype: jnp.dtype = jnp.float32,\n ):\n super().__init__()\n self.dtype = dtype\n\n if safety_checker is None:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n controlnet=controlnet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n\n def prepare_text_inputs(self, prompt: Union[str, List[str]]):\n if not isinstance(prompt, (str, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n text_input = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"np\",\n )\n\n return text_input.input_ids\n\n def prepare_image_inputs(self, image: Union[Image.Image, List[Image.Image]]):\n if not isinstance(image, (Image.Image, list)):\n raise ValueError(f\"image has to be of type `PIL.Image.Image` or list but is {type(image)}\")\n\n if isinstance(image, Image.Image):\n image = [image]\n\n processed_images = jnp.concatenate([preprocess(img, jnp.float32) for img in image])\n\n return processed_images\n\n def _get_has_nsfw_concepts(self, features, params):\n has_nsfw_concepts = self.safety_checker(features, params)\n return has_nsfw_concepts\n\n def _run_safety_checker(self, images, safety_model_params, jit=False):\n # safety_model_params should already be replicated when jit is True\n pil_images = [Image.fromarray(image) for image in images]\n features = self.feature_extractor(pil_images, return_tensors=\"np\").pixel_values\n\n if jit:\n features = shard(features)\n has_nsfw_concepts = _p_get_has_nsfw_concepts(self, features, safety_model_params)\n has_nsfw_concepts = unshard(has_nsfw_concepts)\n safety_model_params = unreplicate(safety_model_params)\n else:\n has_nsfw_concepts = self._get_has_nsfw_concepts(features, safety_model_params)\n\n images_was_copied = False\n for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):\n if has_nsfw_concept:\n if not images_was_copied:\n images_was_copied = True\n images = images.copy()\n\n images[idx] = np.zeros(images[idx].shape, dtype=np.uint8) # black image\n\n if any(has_nsfw_concepts):\n warnings.warn(\n \"Potential NSFW content was detected in one or more images. A black image will be returned\"\n \" instead. Try again with a different prompt and/or seed.\"\n )\n\n return images, has_nsfw_concepts\n\n def _generate(\n self,\n prompt_ids: jnp.ndarray,\n image: jnp.ndarray,\n params: Union[Dict, FrozenDict],\n prng_seed: jax.Array,\n num_inference_steps: int,\n guidance_scale: float,\n latents: Optional[jnp.ndarray] = None,\n neg_prompt_ids: Optional[jnp.ndarray] = None,\n controlnet_conditioning_scale: float = 1.0,\n ):\n height, width = image.shape[-2:]\n if height % 64 != 0 or width % 64 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 64 but are {height} and {width}.\")\n\n # get prompt text embeddings\n prompt_embeds = self.text_encoder(prompt_ids, params=params[\"text_encoder\"])[0]\n\n # TODO: currently it is assumed `do_classifier_free_guidance = guidance_scale > 1.0`\n # implement this conditional `do_classifier_free_guidance = guidance_scale > 1.0`\n batch_size = prompt_ids.shape[0]\n\n max_length = prompt_ids.shape[-1]\n\n if neg_prompt_ids is None:\n uncond_input = self.tokenizer(\n [\"\"] * batch_size, padding=\"max_length\", max_length=max_length, return_tensors=\"np\"\n ).input_ids\n else:\n uncond_input = neg_prompt_ids\n negative_prompt_embeds = self.text_encoder(uncond_input, params=params[\"text_encoder\"])[0]\n context = jnp.concatenate([negative_prompt_embeds, prompt_embeds])\n\n image = jnp.concatenate([image] * 2)\n\n latents_shape = (\n batch_size,\n self.unet.config.in_channels,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n )\n if latents is None:\n latents = jax.random.normal(prng_seed, shape=latents_shape, dtype=jnp.float32)\n else:\n if latents.shape != latents_shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {latents_shape}\")\n\n def loop_body(step, args):\n latents, scheduler_state = args\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n latents_input = jnp.concatenate([latents] * 2)\n\n t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step]\n timestep = jnp.broadcast_to(t, latents_input.shape[0])\n\n latents_input = self.scheduler.scale_model_input(scheduler_state, latents_input, t)\n\n down_block_res_samples, mid_block_res_sample = self.controlnet.apply(\n {\"params\": params[\"controlnet\"]},\n jnp.array(latents_input),\n jnp.array(timestep, dtype=jnp.int32),\n encoder_hidden_states=context,\n controlnet_cond=image,\n conditioning_scale=controlnet_conditioning_scale,\n return_dict=False,\n )\n\n # predict the noise residual\n noise_pred = self.unet.apply(\n {\"params\": params[\"unet\"]},\n jnp.array(latents_input),\n jnp.array(timestep, dtype=jnp.int32),\n encoder_hidden_states=context,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n ).sample\n\n # perform guidance\n noise_pred_uncond, noise_prediction_text = jnp.split(noise_pred, 2, axis=0)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents, scheduler_state = self.scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple()\n return latents, scheduler_state\n\n scheduler_state = self.scheduler.set_timesteps(\n params[\"scheduler\"], num_inference_steps=num_inference_steps, shape=latents_shape\n )\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * params[\"scheduler\"].init_noise_sigma\n\n if DEBUG:\n # run with python for loop\n for i in range(num_inference_steps):\n latents, scheduler_state = loop_body(i, (latents, scheduler_state))\n else:\n latents, _ = jax.lax.fori_loop(0, num_inference_steps, loop_body, (latents, scheduler_state))\n\n # scale and decode the image latents with vae\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.apply({\"params\": params[\"vae\"]}, latents, method=self.vae.decode).sample\n\n image = (image / 2 + 0.5).clip(0, 1).transpose(0, 2, 3, 1)\n return image\n\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt_ids: jnp.ndarray,\n image: jnp.ndarray,\n params: Union[Dict, FrozenDict],\n prng_seed: jax.Array,\n num_inference_steps: int = 50,\n guidance_scale: Union[float, jnp.ndarray] = 7.5,\n latents: jnp.ndarray = None,\n neg_prompt_ids: jnp.ndarray = None,\n controlnet_conditioning_scale: Union[float, jnp.ndarray] = 1.0,\n return_dict: bool = True,\n jit: bool = False,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt_ids (`jnp.ndarray`):\n The prompt or prompts to guide the image generation.\n image (`jnp.ndarray`):\n Array representing the ControlNet input condition to provide guidance to the `unet` for generation.\n params (`Dict` or `FrozenDict`):\n Dictionary containing the model parameters/weights.\n prng_seed (`jax.Array`):\n Array containing random number generator key.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n latents (`jnp.ndarray`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n array is generated by sampling using the supplied random `generator`.\n controlnet_conditioning_scale (`float` or `jnp.ndarray`, *optional*, defaults to 1.0):\n The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added\n to the residual in the original `unet`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of\n a plain tuple.\n jit (`bool`, defaults to `False`):\n Whether to run `pmap` versions of the generation and safety scoring functions.\n\n \n\n This argument exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a\n future release.\n\n \n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] is\n returned, otherwise a `tuple` is returned where the first element is a list with the generated images\n and the second element is a list of `bool`s indicating whether the corresponding generated image\n contains \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n\n height, width = image.shape[-2:]\n\n if isinstance(guidance_scale, float):\n # Convert to a tensor so each device gets a copy. Follow the prompt_ids for\n # shape information, as they may be sharded (when `jit` is `True`), or not.\n guidance_scale = jnp.array([guidance_scale] * prompt_ids.shape[0])\n if len(prompt_ids.shape) > 2:\n # Assume sharded\n guidance_scale = guidance_scale[:, None]\n\n if isinstance(controlnet_conditioning_scale, float):\n # Convert to a tensor so each device gets a copy. Follow the prompt_ids for\n # shape information, as they may be sharded (when `jit` is `True`), or not.\n controlnet_conditioning_scale = jnp.array([controlnet_conditioning_scale] * prompt_ids.shape[0])\n if len(prompt_ids.shape) > 2:\n # Assume sharded\n controlnet_conditioning_scale = controlnet_conditioning_scale[:, None]\n\n if jit:\n images = _p_generate(\n self,\n prompt_ids,\n image,\n params,\n prng_seed,\n num_inference_steps,\n guidance_scale,\n latents,\n neg_prompt_ids,\n controlnet_conditioning_scale,\n )\n else:\n images = self._generate(\n prompt_ids,\n image,\n params,\n prng_seed,\n num_inference_steps,\n guidance_scale,\n latents,\n neg_prompt_ids,\n controlnet_conditioning_scale,\n )\n\n if self.safety_checker is not None:\n safety_params = params[\"safety_checker\"]\n images_uint8_casted = (images * 255).round().astype(\"uint8\")\n num_devices, batch_size = images.shape[:2]\n\n images_uint8_casted = np.asarray(images_uint8_casted).reshape(num_devices * batch_size, height, width, 3)\n images_uint8_casted, has_nsfw_concept = self._run_safety_checker(images_uint8_casted, safety_params, jit)\n images = np.array(images)\n\n # block images\n if any(has_nsfw_concept):\n for i, is_nsfw in enumerate(has_nsfw_concept):\n if is_nsfw:\n images[i] = np.asarray(images_uint8_casted[i])\n\n images = images.reshape(num_devices, batch_size, height, width, 3)\n else:\n images = np.asarray(images)\n has_nsfw_concept = False\n\n if not return_dict:\n return (images, has_nsfw_concept)\n\n return FlaxStableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept)\n\n\n# Static argnums are pipe, num_inference_steps. A change would trigger recompilation.\n# Non-static args are (sharded) input tensors mapped over their first dimension (hence, `0`).\n@partial(\n jax.pmap,\n in_axes=(None, 0, 0, 0, 0, None, 0, 0, 0, 0),\n static_broadcasted_argnums=(0, 5),\n)\ndef _p_generate(\n pipe,\n prompt_ids,\n image,\n params,\n prng_seed,\n num_inference_steps,\n guidance_scale,\n latents,\n neg_prompt_ids,\n controlnet_conditioning_scale,\n):\n return pipe._generate(\n prompt_ids,\n image,\n params,\n prng_seed,\n num_inference_steps,\n guidance_scale,\n latents,\n neg_prompt_ids,\n controlnet_conditioning_scale,\n )\n\n\n@partial(jax.pmap, static_broadcasted_argnums=(0,))\ndef _p_get_has_nsfw_concepts(pipe, features, params):\n return pipe._get_has_nsfw_concepts(features, params)\n\n\ndef unshard(x: jnp.ndarray):\n # einops.rearrange(x, 'd b ... -> (d b) ...')\n num_devices, batch_size = x.shape[:2]\n rest = x.shape[2:]\n return x.reshape(num_devices * batch_size, *rest)\n\n\ndef preprocess(image, dtype):\n image = image.convert(\"RGB\")\n w, h = image.size\n w, h = (x - x % 64 for x in (w, h)) # resize to integer multiple of 64\n image = image.resize((w, h), resample=PIL_INTERPOLATION[\"lanczos\"])\n image = jnp.array(image).astype(dtype) / 255.0\n image = image[None].transpose(0, 3, 1, 2)\n return image\n" }, { "path": "diffusers/src/diffusers/pipelines/controlnet_xs/__init__.py", "content": "from typing import TYPE_CHECKING\r\n\r\nfrom ...utils import (\r\n DIFFUSERS_SLOW_IMPORT,\r\n OptionalDependencyNotAvailable,\r\n _LazyModule,\r\n get_objects_from_module,\r\n is_flax_available,\r\n is_torch_available,\r\n is_transformers_available,\r\n)\r\n\r\n\r\n_dummy_objects = {}\r\n_import_structure = {}\r\n\r\ntry:\r\n if not (is_transformers_available() and is_torch_available()):\r\n raise OptionalDependencyNotAvailable()\r\nexcept OptionalDependencyNotAvailable:\r\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\r\n\r\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\r\nelse:\r\n _import_structure[\"pipeline_controlnet_xs\"] = [\"StableDiffusionControlNetXSPipeline\"]\r\n _import_structure[\"pipeline_controlnet_xs_sd_xl\"] = [\"StableDiffusionXLControlNetXSPipeline\"]\r\ntry:\r\n if not (is_transformers_available() and is_flax_available()):\r\n raise OptionalDependencyNotAvailable()\r\nexcept OptionalDependencyNotAvailable:\r\n from ...utils import dummy_flax_and_transformers_objects # noqa F403\r\n\r\n _dummy_objects.update(get_objects_from_module(dummy_flax_and_transformers_objects))\r\nelse:\r\n pass # _import_structure[\"pipeline_flax_controlnet\"] = [\"FlaxStableDiffusionControlNetPipeline\"]\r\n\r\n\r\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\r\n try:\r\n if not (is_transformers_available() and is_torch_available()):\r\n raise OptionalDependencyNotAvailable()\r\n\r\n except OptionalDependencyNotAvailable:\r\n from ...utils.dummy_torch_and_transformers_objects import *\r\n else:\r\n from .pipeline_controlnet_xs import StableDiffusionControlNetXSPipeline\r\n from .pipeline_controlnet_xs_sd_xl import StableDiffusionXLControlNetXSPipeline\r\n\r\n try:\r\n if not (is_transformers_available() and is_flax_available()):\r\n raise OptionalDependencyNotAvailable()\r\n except OptionalDependencyNotAvailable:\r\n from ...utils.dummy_flax_and_transformers_objects import * # noqa F403\r\n else:\r\n pass # from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline\r\n\r\n\r\nelse:\r\n import sys\r\n\r\n sys.modules[__name__] = _LazyModule(\r\n __name__,\r\n globals()[\"__file__\"],\r\n _import_structure,\r\n module_spec=__spec__,\r\n )\r\n for name, value in _dummy_objects.items():\r\n setattr(sys.modules[__name__], name, value)\r\n" }, { "path": "diffusers/src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom ...callbacks import MultiPipelineCallbacks, PipelineCallback\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, ControlNetXSAdapter, UNet2DConditionModel, UNetControlNetXSModel\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ..stable_diffusion.pipeline_output import StableDiffusionPipelineOutput\nfrom ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> # !pip install opencv-python transformers accelerate\n >>> from diffusers import StableDiffusionControlNetXSPipeline, ControlNetXSAdapter\n >>> from diffusers.utils import load_image\n >>> import numpy as np\n >>> import torch\n\n >>> import cv2\n >>> from PIL import Image\n\n >>> prompt = \"aerial view, a futuristic research complex in a bright foggy jungle, hard lighting\"\n >>> negative_prompt = \"low quality, bad quality, sketches\"\n\n >>> # download an image\n >>> image = load_image(\n ... \"https://hf.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png\"\n ... )\n\n >>> # initialize the models and pipeline\n >>> controlnet_conditioning_scale = 0.5\n\n >>> controlnet = ControlNetXSAdapter.from_pretrained(\n ... \"UmerHA/Testing-ConrolNetXS-SD2.1-canny\", torch_dtype=torch.float16\n ... )\n >>> pipe = StableDiffusionControlNetXSPipeline.from_pretrained(\n ... \"stabilityai/stable-diffusion-2-1-base\", controlnet=controlnet, torch_dtype=torch.float16\n ... )\n >>> pipe.enable_model_cpu_offload()\n\n >>> # get canny image\n >>> image = np.array(image)\n >>> image = cv2.Canny(image, 100, 200)\n >>> image = image[:, :, None]\n >>> image = np.concatenate([image, image, image], axis=2)\n >>> canny_image = Image.fromarray(image)\n >>> # generate image\n >>> image = pipe(\n ... prompt, controlnet_conditioning_scale=controlnet_conditioning_scale, image=canny_image\n ... ).images[0]\n ```\n\"\"\"\n\n\nclass StableDiffusionControlNetXSPipeline(\n DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion with ControlNet-XS guidance.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A [`UNet2DConditionModel`] used to create a UNetControlNetXSModel to denoise the encoded image latents.\n controlnet ([`ControlNetXSAdapter`]):\n A [`ControlNetXSAdapter`] to be used in combination with `unet` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: Union[UNet2DConditionModel, UNetControlNetXSModel],\n controlnet: ControlNetXSAdapter,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if isinstance(unet, UNet2DConditionModel):\n unet = UNetControlNetXSModel.from_unet(unet, controlnet)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n controlnet=controlnet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)\n self.control_image_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False\n )\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n image,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n controlnet_conditioning_scale=1.0,\n control_guidance_start=0.0,\n control_guidance_end=1.0,\n callback_on_step_end_tensor_inputs=None,\n ):\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # Check `image` and `controlnet_conditioning_scale`\n is_compiled = hasattr(F, \"scaled_dot_product_attention\") and isinstance(\n self.unet, torch._dynamo.eval_frame.OptimizedModule\n )\n if (\n isinstance(self.unet, UNetControlNetXSModel)\n or is_compiled\n and isinstance(self.unet._orig_mod, UNetControlNetXSModel)\n ):\n self.check_image(image, prompt, prompt_embeds)\n if not isinstance(controlnet_conditioning_scale, float):\n raise TypeError(\"For single controlnet: `controlnet_conditioning_scale` must be type `float`.\")\n else:\n assert False\n\n start, end = control_guidance_start, control_guidance_end\n if start >= end:\n raise ValueError(\n f\"control guidance start: {start} cannot be larger or equal to control guidance end: {end}.\"\n )\n if start < 0.0:\n raise ValueError(f\"control guidance start: {start} can't be smaller than 0.\")\n if end > 1.0:\n raise ValueError(f\"control guidance end: {end} can't be larger than 1.0.\")\n\n def check_image(self, image, prompt, prompt_embeds):\n image_is_pil = isinstance(image, PIL.Image.Image)\n image_is_tensor = isinstance(image, torch.Tensor)\n image_is_np = isinstance(image, np.ndarray)\n image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)\n image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)\n image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)\n\n if (\n not image_is_pil\n and not image_is_tensor\n and not image_is_np\n and not image_is_pil_list\n and not image_is_tensor_list\n and not image_is_np_list\n ):\n raise TypeError(\n f\"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}\"\n )\n\n if image_is_pil:\n image_batch_size = 1\n else:\n image_batch_size = len(image)\n\n if prompt is not None and isinstance(prompt, str):\n prompt_batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n prompt_batch_size = len(prompt)\n elif prompt_embeds is not None:\n prompt_batch_size = prompt_embeds.shape[0]\n\n if image_batch_size != 1 and image_batch_size != prompt_batch_size:\n raise ValueError(\n f\"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}\"\n )\n\n def prepare_image(\n self,\n image,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype,\n do_classifier_free_guidance=False,\n ):\n image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)\n image_batch_size = image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n image = image.repeat_interleave(repeat_by, dim=0)\n\n image = image.to(device=device, dtype=dtype)\n\n if do_classifier_free_guidance:\n image = torch.cat([image] * 2)\n\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @property\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.guidance_scale\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.clip_skip\n def clip_skip(self):\n return self._clip_skip\n\n @property\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.do_classifier_free_guidance\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.cross_attention_kwargs\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.num_timesteps\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: PipelineImageInput = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: Union[float, List[float]] = 1.0,\n control_guidance_start: float = 0.0,\n control_guidance_end: float = 1.0,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[\n Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]\n ] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:\n `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):\n The ControlNet input condition to provide guidance to the `unet` for generation. If the type is\n specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be accepted\n as an image. The dimensions of the output image defaults to `image`'s dimensions. If height and/or\n width are passed, `image` is resized accordingly. If multiple ControlNets are specified in `init`,\n images must be passed as a list such that each element of the list can be correctly batched for input\n to a single ControlNet.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):\n The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added\n to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set\n the corresponding scale as a list.\n control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):\n The percentage of total steps at which the ControlNet starts applying.\n control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):\n The percentage of total steps at which the ControlNet stops applying.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):\n A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of\n each denoising step during the inference. with the following arguments: `callback_on_step_end(self:\n DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a\n list of all tensors as specified by `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeine class.\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n\n if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):\n callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs\n\n unet = self.unet._orig_mod if is_compiled_module(self.unet) else self.unet\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n image,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n controlnet_conditioning_scale,\n control_guidance_start,\n control_guidance_end,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._interrupt = False\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=clip_skip,\n )\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Prepare image\n image = self.prepare_image(\n image=image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=unet.dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n )\n height, width = image.shape[-2:]\n\n # 5. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 6. Prepare latent variables\n num_channels_latents = self.unet.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n self._num_timesteps = len(timesteps)\n is_controlnet_compiled = is_compiled_module(self.unet)\n is_torch_higher_equal_2_1 = is_torch_version(\">=\", \"2.1\")\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # Relevant thread:\n # https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428\n if is_controlnet_compiled and is_torch_higher_equal_2_1:\n torch._inductor.cudagraph_mark_step_begin()\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n apply_control = (\n i / len(timesteps) >= control_guidance_start and (i + 1) / len(timesteps) <= control_guidance_end\n )\n noise_pred = self.unet(\n sample=latent_model_input,\n timestep=t,\n encoder_hidden_states=prompt_embeds,\n controlnet_cond=image,\n conditioning_scale=controlnet_conditioning_scale,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=True,\n apply_control=apply_control,\n ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n\n # If we do sequential model offloading, let's offload unet and controlnet\n # manually for max memory savings\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.unet.to(\"cpu\")\n self.controlnet.to(\"cpu\")\n torch.cuda.empty_cache()\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[\n 0\n ]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs_sd_xl.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModel,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n)\n\nfrom diffusers.utils.import_utils import is_invisible_watermark_available\n\nfrom ...callbacks import MultiPipelineCallbacks, PipelineCallback\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, ControlNetXSAdapter, UNet2DConditionModel, UNetControlNetXSModel\nfrom ...models.attention_processor import (\n AttnProcessor2_0,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n XFormersAttnProcessor,\n)\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n USE_PEFT_BACKEND,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline\nfrom ..stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput\n\n\nif is_invisible_watermark_available():\n from ..stable_diffusion_xl.watermark import StableDiffusionXLWatermarker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> # !pip install opencv-python transformers accelerate\n >>> from diffusers import StableDiffusionXLControlNetXSPipeline, ControlNetXSAdapter, AutoencoderKL\n >>> from diffusers.utils import load_image\n >>> import numpy as np\n >>> import torch\n\n >>> import cv2\n >>> from PIL import Image\n\n >>> prompt = \"aerial view, a futuristic research complex in a bright foggy jungle, hard lighting\"\n >>> negative_prompt = \"low quality, bad quality, sketches\"\n\n >>> # download an image\n >>> image = load_image(\n ... \"https://hf.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png\"\n ... )\n\n >>> # initialize the models and pipeline\n >>> controlnet_conditioning_scale = 0.5\n >>> vae = AutoencoderKL.from_pretrained(\"madebyollin/sdxl-vae-fp16-fix\", torch_dtype=torch.float16)\n >>> controlnet = ControlNetXSAdapter.from_pretrained(\n ... \"UmerHA/Testing-ConrolNetXS-SDXL-canny\", torch_dtype=torch.float16\n ... )\n >>> pipe = StableDiffusionXLControlNetXSPipeline.from_pretrained(\n ... \"stabilityai/stable-diffusion-xl-base-1.0\", controlnet=controlnet, torch_dtype=torch.float16\n ... )\n >>> pipe.enable_model_cpu_offload()\n\n >>> # get canny image\n >>> image = np.array(image)\n >>> image = cv2.Canny(image, 100, 200)\n >>> image = image[:, :, None]\n >>> image = np.concatenate([image, image, image], axis=2)\n >>> canny_image = Image.fromarray(image)\n\n >>> # generate image\n >>> image = pipe(\n ... prompt, controlnet_conditioning_scale=controlnet_conditioning_scale, image=canny_image\n ... ).images[0]\n ```\n\"\"\"\n\n\nclass StableDiffusionXLControlNetXSPipeline(\n DiffusionPipeline,\n TextualInversionLoaderMixin,\n StableDiffusionXLLoraLoaderMixin,\n FromSingleFileMixin,\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet-XS guidance.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n text_encoder_2 ([`~transformers.CLIPTextModelWithProjection`]):\n Second frozen text-encoder\n ([laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n tokenizer_2 ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A [`UNet2DConditionModel`] used to create a UNetControlNetXSModel to denoise the encoded image latents.\n controlnet ([`ControlNetXSAdapter`]):\n A [`ControlNetXSAdapter`] to be used in combination with `unet` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `\"True\"`):\n Whether the negative prompt embeddings should always be set to 0. Also see the config of\n `stabilityai/stable-diffusion-xl-base-1-0`.\n add_watermarker (`bool`, *optional*):\n Whether to use the [invisible_watermark](https://github.com/ShieldMnt/invisible-watermark/) library to\n watermark output images. If not defined, it defaults to `True` if the package is installed; otherwise no\n watermarker is used.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->text_encoder_2->unet->vae\"\n _optional_components = [\n \"tokenizer\",\n \"tokenizer_2\",\n \"text_encoder\",\n \"text_encoder_2\",\n \"feature_extractor\",\n ]\n _callback_tensor_inputs = [\n \"latents\",\n \"prompt_embeds\",\n \"negative_prompt_embeds\",\n \"add_text_embeds\",\n \"add_time_ids\",\n \"negative_pooled_prompt_embeds\",\n \"negative_add_time_ids\",\n ]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n text_encoder_2: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n tokenizer_2: CLIPTokenizer,\n unet: Union[UNet2DConditionModel, UNetControlNetXSModel],\n controlnet: ControlNetXSAdapter,\n scheduler: KarrasDiffusionSchedulers,\n force_zeros_for_empty_prompt: bool = True,\n add_watermarker: Optional[bool] = None,\n feature_extractor: CLIPImageProcessor = None,\n ):\n super().__init__()\n\n if isinstance(unet, UNet2DConditionModel):\n unet = UNetControlNetXSModel.from_unet(unet, controlnet)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n unet=unet,\n controlnet=controlnet,\n scheduler=scheduler,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)\n self.control_image_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False\n )\n add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()\n\n if add_watermarker:\n self.watermark = StableDiffusionXLWatermarker()\n else:\n self.watermark = None\n\n self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt: str,\n prompt_2: Optional[str] = None,\n device: Optional[torch.device] = None,\n num_images_per_prompt: int = 1,\n do_classifier_free_guidance: bool = True,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n lora_scale (`float`, *optional*):\n A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n device = device or self._execution_device\n\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if self.text_encoder is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)\n else:\n scale_lora_layers(self.text_encoder_2, lora_scale)\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n\n if prompt is not None:\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # Define tokenizers and text encoders\n tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]\n text_encoders = (\n [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]\n )\n\n if prompt_embeds is None:\n prompt_2 = prompt_2 or prompt\n prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2\n\n # textual inversion: process multi-vector tokens if necessary\n prompt_embeds_list = []\n prompts = [prompt, prompt_2]\n for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, tokenizer)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n if clip_skip is None:\n prompt_embeds = prompt_embeds.hidden_states[-2]\n else:\n # \"2\" because SDXL always indexes from the penultimate layer.\n prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]\n\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n\n # get unconditional embeddings for classifier free guidance\n zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt\n if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:\n negative_prompt_embeds = torch.zeros_like(prompt_embeds)\n negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)\n elif do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n\n # normalize str to list\n negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt\n negative_prompt_2 = (\n batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2\n )\n\n uncond_tokens: List[str]\n if prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = [negative_prompt, negative_prompt_2]\n\n negative_prompt_embeds_list = []\n for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n negative_prompt_embeds = text_encoder(\n uncond_input.input_ids.to(device),\n output_hidden_states=True,\n )\n # We are only ALWAYS interested in the pooled output of the final text encoder\n negative_pooled_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]\n\n negative_prompt_embeds_list.append(negative_prompt_embeds)\n\n negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)\n\n if self.text_encoder_2 is not None:\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n if self.text_encoder_2 is not None:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n if do_classifier_free_guidance:\n negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n\n if self.text_encoder is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder_2, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n prompt_2,\n image,\n negative_prompt=None,\n negative_prompt_2=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n pooled_prompt_embeds=None,\n negative_pooled_prompt_embeds=None,\n controlnet_conditioning_scale=1.0,\n control_guidance_start=0.0,\n control_guidance_end=1.0,\n callback_on_step_end_tensor_inputs=None,\n ):\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if prompt_embeds is not None and pooled_prompt_embeds is None:\n raise ValueError(\n \"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.\"\n )\n\n if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:\n raise ValueError(\n \"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.\"\n )\n\n # Check `image` and ``controlnet_conditioning_scale``\n is_compiled = hasattr(F, \"scaled_dot_product_attention\") and isinstance(\n self.unet, torch._dynamo.eval_frame.OptimizedModule\n )\n if (\n isinstance(self.unet, UNetControlNetXSModel)\n or is_compiled\n and isinstance(self.unet._orig_mod, UNetControlNetXSModel)\n ):\n self.check_image(image, prompt, prompt_embeds)\n if not isinstance(controlnet_conditioning_scale, float):\n raise TypeError(\"For single controlnet: `controlnet_conditioning_scale` must be type `float`.\")\n else:\n assert False\n\n start, end = control_guidance_start, control_guidance_end\n if start >= end:\n raise ValueError(\n f\"control guidance start: {start} cannot be larger or equal to control guidance end: {end}.\"\n )\n if start < 0.0:\n raise ValueError(f\"control guidance start: {start} can't be smaller than 0.\")\n if end > 1.0:\n raise ValueError(f\"control guidance end: {end} can't be larger than 1.0.\")\n\n # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image\n def check_image(self, image, prompt, prompt_embeds):\n image_is_pil = isinstance(image, PIL.Image.Image)\n image_is_tensor = isinstance(image, torch.Tensor)\n image_is_np = isinstance(image, np.ndarray)\n image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)\n image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)\n image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)\n\n if (\n not image_is_pil\n and not image_is_tensor\n and not image_is_np\n and not image_is_pil_list\n and not image_is_tensor_list\n and not image_is_np_list\n ):\n raise TypeError(\n f\"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}\"\n )\n\n if image_is_pil:\n image_batch_size = 1\n else:\n image_batch_size = len(image)\n\n if prompt is not None and isinstance(prompt, str):\n prompt_batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n prompt_batch_size = len(prompt)\n elif prompt_embeds is not None:\n prompt_batch_size = prompt_embeds.shape[0]\n\n if image_batch_size != 1 and image_batch_size != prompt_batch_size:\n raise ValueError(\n f\"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}\"\n )\n\n def prepare_image(\n self,\n image,\n width,\n height,\n batch_size,\n num_images_per_prompt,\n device,\n dtype,\n do_classifier_free_guidance=False,\n ):\n image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)\n image_batch_size = image.shape[0]\n\n if image_batch_size == 1:\n repeat_by = batch_size\n else:\n # image batch size is the same as prompt batch size\n repeat_by = num_images_per_prompt\n\n image = image.repeat_interleave(repeat_by, dim=0)\n\n image = image.to(device=device, dtype=dtype)\n\n if do_classifier_free_guidance:\n image = torch.cat([image] * 2)\n\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def _get_add_time_ids(\n self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None\n ):\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n\n passed_add_embed_dim = (\n self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim\n )\n expected_add_embed_dim = self.unet.base_add_embedding.linear_1.in_features\n\n if expected_add_embed_dim != passed_add_embed_dim:\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.\"\n )\n\n add_time_ids = torch.tensor([add_time_ids], dtype=dtype)\n return add_time_ids\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae\n def upcast_vae(self):\n dtype = self.vae.dtype\n self.vae.to(dtype=torch.float32)\n use_torch_2_0_or_xformers = isinstance(\n self.vae.decoder.mid_block.attentions[0].processor,\n (\n AttnProcessor2_0,\n XFormersAttnProcessor,\n LoRAXFormersAttnProcessor,\n LoRAAttnProcessor2_0,\n ),\n )\n # if xformers or torch_2_0 is used attention block does not need\n # to be in float32 which can save lots of memory\n if use_torch_2_0_or_xformers:\n self.vae.post_quant_conv.to(dtype)\n self.vae.decoder.conv_in.to(dtype)\n self.vae.decoder.mid_block.to(dtype)\n\n @property\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.guidance_scale\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.clip_skip\n def clip_skip(self):\n return self._clip_skip\n\n @property\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.do_classifier_free_guidance\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.cross_attention_kwargs\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.num_timesteps\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n image: PipelineImageInput = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: Union[float, List[float]] = 1.0,\n control_guidance_start: float = 0.0,\n control_guidance_end: float = 1.0,\n original_size: Tuple[int, int] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Tuple[int, int] = None,\n negative_original_size: Optional[Tuple[int, int]] = None,\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\n negative_target_size: Optional[Tuple[int, int]] = None,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[\n Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]\n ] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:\n `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):\n The ControlNet input condition to provide guidance to the `unet` for generation. If the type is\n specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be accepted\n as an image. The dimensions of the output image defaults to `image`'s dimensions. If height and/or\n width are passed, `image` is resized accordingly. If multiple ControlNets are specified in `init`,\n images must be passed as a list such that each element of the list can be correctly batched for input\n to a single ControlNet.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image. Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image. Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 5.0):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. This is sent to `tokenizer_2`\n and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, pooled text embeddings are generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs (prompt\n weighting). If not provided, pooled `negative_prompt_embeds` are generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):\n The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added\n to the residual in the original `unet`.\n control_guidance_start (`float`, *optional*, defaults to 0.0):\n The percentage of total steps at which the ControlNet starts applying.\n control_guidance_end (`float`, *optional*, defaults to 1.0):\n The percentage of total steps at which the ControlNet stops applying.\n original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.\n `original_size` defaults to `(width, height)` if not specified. Part of SDXL's micro-conditioning as\n explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n `crops_coords_top_left` can be used to generate an image that appears to be \"cropped\" from the position\n `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n For most cases, `target_size` should be set to the desired height and width of the generated image. If\n not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a specific image resolution. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a target image resolution. It should be as same\n as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):\n A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of\n each denoising step during the inference. with the following arguments: `callback_on_step_end(self:\n DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a\n list of all tensors as specified by `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeine class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] is\n returned, otherwise a `tuple` is returned containing the output images.\n \"\"\"\n\n if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):\n callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs\n\n unet = self.unet._orig_mod if is_compiled_module(self.unet) else self.unet\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n image,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n controlnet_conditioning_scale,\n control_guidance_start,\n control_guidance_end,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._interrupt = False\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt,\n prompt_2,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=clip_skip,\n )\n\n # 4. Prepare image\n if isinstance(unet, UNetControlNetXSModel):\n image = self.prepare_image(\n image=image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=unet.dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n )\n height, width = image.shape[-2:]\n else:\n assert False\n\n # 5. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 6. Prepare latent variables\n num_channels_latents = self.unet.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7.1 Prepare added time ids & embeddings\n if isinstance(image, list):\n original_size = original_size or image[0].shape[-2:]\n else:\n original_size = original_size or image.shape[-2:]\n target_size = target_size or (height, width)\n\n add_text_embeds = pooled_prompt_embeds\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n add_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n\n if negative_original_size is not None and negative_target_size is not None:\n negative_add_time_ids = self._get_add_time_ids(\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n else:\n negative_add_time_ids = add_time_ids\n\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n self._num_timesteps = len(timesteps)\n is_controlnet_compiled = is_compiled_module(self.unet)\n is_torch_higher_equal_2_1 = is_torch_version(\">=\", \"2.1\")\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # Relevant thread:\n # https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428\n if is_controlnet_compiled and is_torch_higher_equal_2_1:\n torch._inductor.cudagraph_mark_step_begin()\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n\n # predict the noise residual\n apply_control = (\n i / len(timesteps) >= control_guidance_start and (i + 1) / len(timesteps) <= control_guidance_end\n )\n noise_pred = self.unet(\n sample=latent_model_input,\n timestep=t,\n encoder_hidden_states=prompt_embeds,\n controlnet_cond=image,\n conditioning_scale=controlnet_conditioning_scale,\n cross_attention_kwargs=cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=True,\n apply_control=apply_control,\n ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n\n # manually for max memory savings\n if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n image = latents\n\n if not output_type == \"latent\":\n # apply watermark if available\n if self.watermark is not None:\n image = self.watermark.apply_watermark(image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/dance_diffusion/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import DIFFUSERS_SLOW_IMPORT, _LazyModule\n\n\n_import_structure = {\"pipeline_dance_diffusion\": [\"DanceDiffusionPipeline\"]}\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n from .pipeline_dance_diffusion import DanceDiffusionPipeline\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n" }, { "path": "diffusers/src/diffusers/pipelines/dance_diffusion/pipeline_dance_diffusion.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nfrom typing import List, Optional, Tuple, Union\n\nimport torch\n\nfrom ...utils import logging\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass DanceDiffusionPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for audio generation.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Parameters:\n unet ([`UNet1DModel`]):\n A `UNet1DModel` to denoise the encoded audio.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of\n [`IPNDMScheduler`].\n \"\"\"\n\n model_cpu_offload_seq = \"unet\"\n\n def __init__(self, unet, scheduler):\n super().__init__()\n self.register_modules(unet=unet, scheduler=scheduler)\n\n @torch.no_grad()\n def __call__(\n self,\n batch_size: int = 1,\n num_inference_steps: int = 100,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n audio_length_in_s: Optional[float] = None,\n return_dict: bool = True,\n ) -> Union[AudioPipelineOutput, Tuple]:\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n batch_size (`int`, *optional*, defaults to 1):\n The number of audio samples to generate.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher-quality audio sample at\n the expense of slower inference.\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n audio_length_in_s (`float`, *optional*, defaults to `self.unet.config.sample_size/self.unet.config.sample_rate`):\n The length of the generated audio sample in seconds.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.AudioPipelineOutput`] instead of a plain tuple.\n\n Example:\n\n ```py\n from diffusers import DiffusionPipeline\n from scipy.io.wavfile import write\n\n model_id = \"harmonai/maestro-150k\"\n pipe = DiffusionPipeline.from_pretrained(model_id)\n pipe = pipe.to(\"cuda\")\n\n audios = pipe(audio_length_in_s=4.0).audios\n\n # To save locally\n for i, audio in enumerate(audios):\n write(f\"maestro_test_{i}.wav\", pipe.unet.sample_rate, audio.transpose())\n\n # To dislay in google colab\n import IPython.display as ipd\n\n for audio in audios:\n display(ipd.Audio(audio, rate=pipe.unet.sample_rate))\n ```\n\n Returns:\n [`~pipelines.AudioPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.AudioPipelineOutput`] is returned, otherwise a `tuple` is\n returned where the first element is a list with the generated audio.\n \"\"\"\n\n if audio_length_in_s is None:\n audio_length_in_s = self.unet.config.sample_size / self.unet.config.sample_rate\n\n sample_size = audio_length_in_s * self.unet.config.sample_rate\n\n down_scale_factor = 2 ** len(self.unet.up_blocks)\n if sample_size < 3 * down_scale_factor:\n raise ValueError(\n f\"{audio_length_in_s} is too small. Make sure it's bigger or equal to\"\n f\" {3 * down_scale_factor / self.unet.config.sample_rate}.\"\n )\n\n original_sample_size = int(sample_size)\n if sample_size % down_scale_factor != 0:\n sample_size = (\n (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1\n ) * down_scale_factor\n logger.info(\n f\"{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled\"\n f\" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising\"\n \" process.\"\n )\n sample_size = int(sample_size)\n\n dtype = next(self.unet.parameters()).dtype\n shape = (batch_size, self.unet.config.in_channels, sample_size)\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n audio = randn_tensor(shape, generator=generator, device=self._execution_device, dtype=dtype)\n\n # set step values\n self.scheduler.set_timesteps(num_inference_steps, device=audio.device)\n self.scheduler.timesteps = self.scheduler.timesteps.to(dtype)\n\n for t in self.progress_bar(self.scheduler.timesteps):\n # 1. predict noise model_output\n model_output = self.unet(audio, t).sample\n\n # 2. compute previous audio sample: x_t -> t_t-1\n audio = self.scheduler.step(model_output, t, audio).prev_sample\n\n audio = audio.clamp(-1, 1).float().cpu().numpy()\n\n audio = audio[:, :, :original_sample_size]\n\n if not return_dict:\n return (audio,)\n\n return AudioPipelineOutput(audios=audio)\n" }, { "path": "diffusers/src/diffusers/pipelines/ddim/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import DIFFUSERS_SLOW_IMPORT, _LazyModule\n\n\n_import_structure = {\"pipeline_ddim\": [\"DDIMPipeline\"]}\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n from .pipeline_ddim import DDIMPipeline\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n" }, { "path": "diffusers/src/diffusers/pipelines/ddim/pipeline_ddim.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import List, Optional, Tuple, Union\n\nimport torch\n\nfrom ...schedulers import DDIMScheduler\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nclass DDIMPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for image generation.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Parameters:\n unet ([`UNet2DModel`]):\n A `UNet2DModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of\n [`DDPMScheduler`], or [`DDIMScheduler`].\n \"\"\"\n\n model_cpu_offload_seq = \"unet\"\n\n def __init__(self, unet, scheduler):\n super().__init__()\n\n # make sure scheduler can always be converted to DDIM\n scheduler = DDIMScheduler.from_config(scheduler.config)\n\n self.register_modules(unet=unet, scheduler=scheduler)\n\n @torch.no_grad()\n def __call__(\n self,\n batch_size: int = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n eta: float = 0.0,\n num_inference_steps: int = 50,\n use_clipped_model_output: Optional[bool] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n ) -> Union[ImagePipelineOutput, Tuple]:\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n batch_size (`int`, *optional*, defaults to 1):\n The number of images to generate.\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers. A value of `0` corresponds to\n DDIM and `1` corresponds to DDPM.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n use_clipped_model_output (`bool`, *optional*, defaults to `None`):\n If `True` or `False`, see documentation for [`DDIMScheduler.step`]. If `None`, nothing is passed\n downstream to the scheduler (use `None` for schedulers which don't support this argument).\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n\n Example:\n\n ```py\n >>> from diffusers import DDIMPipeline\n >>> import PIL.Image\n >>> import numpy as np\n\n >>> # load model and scheduler\n >>> pipe = DDIMPipeline.from_pretrained(\"fusing/ddim-lsun-bedroom\")\n\n >>> # run pipeline in inference (sample random noise and denoise)\n >>> image = pipe(eta=0.0, num_inference_steps=50)\n\n >>> # process image to PIL\n >>> image_processed = image.cpu().permute(0, 2, 3, 1)\n >>> image_processed = (image_processed + 1.0) * 127.5\n >>> image_processed = image_processed.numpy().astype(np.uint8)\n >>> image_pil = PIL.Image.fromarray(image_processed[0])\n\n >>> # save image\n >>> image_pil.save(\"test.png\")\n ```\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is\n returned where the first element is a list with the generated images\n \"\"\"\n\n # Sample gaussian noise to begin loop\n if isinstance(self.unet.config.sample_size, int):\n image_shape = (\n batch_size,\n self.unet.config.in_channels,\n self.unet.config.sample_size,\n self.unet.config.sample_size,\n )\n else:\n image_shape = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)\n\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n image = randn_tensor(image_shape, generator=generator, device=self._execution_device, dtype=self.unet.dtype)\n\n # set step values\n self.scheduler.set_timesteps(num_inference_steps)\n\n for t in self.progress_bar(self.scheduler.timesteps):\n # 1. predict noise model_output\n model_output = self.unet(image, t).sample\n\n # 2. predict previous mean of image x_t-1 and add variance depending on eta\n # eta corresponds to η in paper and should be between [0, 1]\n # do x_t -> x_t-1\n image = self.scheduler.step(\n model_output, t, image, eta=eta, use_clipped_model_output=use_clipped_model_output, generator=generator\n ).prev_sample\n\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).numpy()\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/ddpm/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n _LazyModule,\n)\n\n\n_import_structure = {\"pipeline_ddpm\": [\"DDPMPipeline\"]}\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n from .pipeline_ddpm import DDPMPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n" }, { "path": "diffusers/src/diffusers/pipelines/ddpm/pipeline_ddpm.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nfrom typing import List, Optional, Tuple, Union\n\nimport torch\n\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nclass DDPMPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for image generation.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Parameters:\n unet ([`UNet2DModel`]):\n A `UNet2DModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of\n [`DDPMScheduler`], or [`DDIMScheduler`].\n \"\"\"\n\n model_cpu_offload_seq = \"unet\"\n\n def __init__(self, unet, scheduler):\n super().__init__()\n self.register_modules(unet=unet, scheduler=scheduler)\n\n @torch.no_grad()\n def __call__(\n self,\n batch_size: int = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n num_inference_steps: int = 1000,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n ) -> Union[ImagePipelineOutput, Tuple]:\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n batch_size (`int`, *optional*, defaults to 1):\n The number of images to generate.\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n num_inference_steps (`int`, *optional*, defaults to 1000):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n\n Example:\n\n ```py\n >>> from diffusers import DDPMPipeline\n\n >>> # load model and scheduler\n >>> pipe = DDPMPipeline.from_pretrained(\"google/ddpm-cat-256\")\n\n >>> # run pipeline in inference (sample random noise and denoise)\n >>> image = pipe().images[0]\n\n >>> # save image\n >>> image.save(\"ddpm_generated_image.png\")\n ```\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is\n returned where the first element is a list with the generated images\n \"\"\"\n # Sample gaussian noise to begin loop\n if isinstance(self.unet.config.sample_size, int):\n image_shape = (\n batch_size,\n self.unet.config.in_channels,\n self.unet.config.sample_size,\n self.unet.config.sample_size,\n )\n else:\n image_shape = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)\n\n if self.device.type == \"mps\":\n # randn does not work reproducibly on mps\n image = randn_tensor(image_shape, generator=generator)\n image = image.to(self.device)\n else:\n image = randn_tensor(image_shape, generator=generator, device=self.device)\n\n # set step values\n self.scheduler.set_timesteps(num_inference_steps)\n\n for t in self.progress_bar(self.scheduler.timesteps):\n # 1. predict noise model_output\n model_output = self.unet(image, t).sample\n\n # 2. compute previous image: x_t -> x_t-1\n image = self.scheduler.step(model_output, t, image, generator=generator).prev_sample\n\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).numpy()\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/deepfloyd_if/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {\n \"timesteps\": [\n \"fast27_timesteps\",\n \"smart100_timesteps\",\n \"smart185_timesteps\",\n \"smart27_timesteps\",\n \"smart50_timesteps\",\n \"super100_timesteps\",\n \"super27_timesteps\",\n \"super40_timesteps\",\n ]\n}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_if\"] = [\"IFPipeline\"]\n _import_structure[\"pipeline_if_img2img\"] = [\"IFImg2ImgPipeline\"]\n _import_structure[\"pipeline_if_img2img_superresolution\"] = [\"IFImg2ImgSuperResolutionPipeline\"]\n _import_structure[\"pipeline_if_inpainting\"] = [\"IFInpaintingPipeline\"]\n _import_structure[\"pipeline_if_inpainting_superresolution\"] = [\"IFInpaintingSuperResolutionPipeline\"]\n _import_structure[\"pipeline_if_superresolution\"] = [\"IFSuperResolutionPipeline\"]\n _import_structure[\"pipeline_output\"] = [\"IFPipelineOutput\"]\n _import_structure[\"safety_checker\"] = [\"IFSafetyChecker\"]\n _import_structure[\"watermark\"] = [\"IFWatermarker\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n else:\n from .pipeline_if import IFPipeline\n from .pipeline_if_img2img import IFImg2ImgPipeline\n from .pipeline_if_img2img_superresolution import IFImg2ImgSuperResolutionPipeline\n from .pipeline_if_inpainting import IFInpaintingPipeline\n from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline\n from .pipeline_if_superresolution import IFSuperResolutionPipeline\n from .pipeline_output import IFPipelineOutput\n from .safety_checker import IFSafetyChecker\n from .timesteps import (\n fast27_timesteps,\n smart27_timesteps,\n smart50_timesteps,\n smart100_timesteps,\n smart185_timesteps,\n super27_timesteps,\n super40_timesteps,\n super100_timesteps,\n )\n from .watermark import IFWatermarker\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py", "content": "import html\nimport inspect\nimport re\nimport urllib.parse as ul\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport torch\nfrom transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer\n\nfrom ...loaders import LoraLoaderMixin\nfrom ...models import UNet2DConditionModel\nfrom ...schedulers import DDPMScheduler\nfrom ...utils import (\n BACKENDS_MAPPING,\n is_bs4_available,\n is_ftfy_available,\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline\nfrom .pipeline_output import IFPipelineOutput\nfrom .safety_checker import IFSafetyChecker\nfrom .watermark import IFWatermarker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nif is_bs4_available():\n from bs4 import BeautifulSoup\n\nif is_ftfy_available():\n import ftfy\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import IFPipeline, IFSuperResolutionPipeline, DiffusionPipeline\n >>> from diffusers.utils import pt_to_pil\n >>> import torch\n\n >>> pipe = IFPipeline.from_pretrained(\"DeepFloyd/IF-I-XL-v1.0\", variant=\"fp16\", torch_dtype=torch.float16)\n >>> pipe.enable_model_cpu_offload()\n\n >>> prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says \"very deep learning\"'\n >>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)\n\n >>> image = pipe(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, output_type=\"pt\").images\n\n >>> # save intermediate image\n >>> pil_image = pt_to_pil(image)\n >>> pil_image[0].save(\"./if_stage_I.png\")\n\n >>> super_res_1_pipe = IFSuperResolutionPipeline.from_pretrained(\n ... \"DeepFloyd/IF-II-L-v1.0\", text_encoder=None, variant=\"fp16\", torch_dtype=torch.float16\n ... )\n >>> super_res_1_pipe.enable_model_cpu_offload()\n\n >>> image = super_res_1_pipe(\n ... image=image, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, output_type=\"pt\"\n ... ).images\n\n >>> # save intermediate image\n >>> pil_image = pt_to_pil(image)\n >>> pil_image[0].save(\"./if_stage_I.png\")\n\n >>> safety_modules = {\n ... \"feature_extractor\": pipe.feature_extractor,\n ... \"safety_checker\": pipe.safety_checker,\n ... \"watermarker\": pipe.watermarker,\n ... }\n >>> super_res_2_pipe = DiffusionPipeline.from_pretrained(\n ... \"stabilityai/stable-diffusion-x4-upscaler\", **safety_modules, torch_dtype=torch.float16\n ... )\n >>> super_res_2_pipe.enable_model_cpu_offload()\n\n >>> image = super_res_2_pipe(\n ... prompt=prompt,\n ... image=image,\n ... ).images\n >>> image[0].save(\"./if_stage_II.png\")\n ```\n\"\"\"\n\n\nclass IFPipeline(DiffusionPipeline, LoraLoaderMixin):\n tokenizer: T5Tokenizer\n text_encoder: T5EncoderModel\n\n unet: UNet2DConditionModel\n scheduler: DDPMScheduler\n\n feature_extractor: Optional[CLIPImageProcessor]\n safety_checker: Optional[IFSafetyChecker]\n\n watermarker: Optional[IFWatermarker]\n\n bad_punct_regex = re.compile(\n r\"[\"\n + \"#®•©™&@·º½¾¿¡§~\"\n + r\"\\)\"\n + r\"\\(\"\n + r\"\\]\"\n + r\"\\[\"\n + r\"\\}\"\n + r\"\\{\"\n + r\"\\|\"\n + \"\\\\\"\n + r\"\\/\"\n + r\"\\*\"\n + r\"]{1,}\"\n ) # noqa\n\n _optional_components = [\"tokenizer\", \"text_encoder\", \"safety_checker\", \"feature_extractor\", \"watermarker\"]\n model_cpu_offload_seq = \"text_encoder->unet\"\n _exclude_from_cpu_offload = [\"watermarker\"]\n\n def __init__(\n self,\n tokenizer: T5Tokenizer,\n text_encoder: T5EncoderModel,\n unet: UNet2DConditionModel,\n scheduler: DDPMScheduler,\n safety_checker: Optional[IFSafetyChecker],\n feature_extractor: Optional[CLIPImageProcessor],\n watermarker: Optional[IFWatermarker],\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the IF license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.register_modules(\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n watermarker=watermarker,\n )\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n @torch.no_grad()\n def encode_prompt(\n self,\n prompt: Union[str, List[str]],\n do_classifier_free_guidance: bool = True,\n num_images_per_prompt: int = 1,\n device: Optional[torch.device] = None,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n clean_caption: bool = False,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):\n whether to use classifier free guidance or not\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n number of images that should be generated per prompt\n device: (`torch.device`, *optional*):\n torch device to place the resulting embeddings on\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.\n Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n clean_caption (bool, defaults to `False`):\n If `True`, the function will preprocess and clean the provided caption before encoding.\n \"\"\"\n if prompt is not None and negative_prompt is not None:\n if type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n\n if device is None:\n device = self._execution_device\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF\n max_length = 77\n\n if prompt_embeds is None:\n prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {max_length} tokens: {removed_text}\"\n )\n\n attention_mask = text_inputs.attention_mask.to(device)\n\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n prompt_embeds = prompt_embeds[0]\n\n if self.text_encoder is not None:\n dtype = self.text_encoder.dtype\n elif self.unet is not None:\n dtype = self.unet.dtype\n else:\n dtype = None\n\n prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_attention_mask=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n attention_mask = uncond_input.attention_mask.to(device)\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n else:\n negative_prompt_embeds = None\n\n return prompt_embeds, negative_prompt_embeds\n\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors=\"pt\").to(device)\n image, nsfw_detected, watermark_detected = self.safety_checker(\n images=image,\n clip_input=safety_checker_input.pixel_values.to(dtype=dtype),\n )\n else:\n nsfw_detected = None\n watermark_detected = None\n\n return image, nsfw_detected, watermark_detected\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n def prepare_intermediate_images(self, batch_size, num_channels, height, width, dtype, device, generator):\n shape = (batch_size, num_channels, height, width)\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n intermediate_images = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n # scale the initial noise by the standard deviation required by the scheduler\n intermediate_images = intermediate_images * self.scheduler.init_noise_sigma\n return intermediate_images\n\n def _text_preprocessing(self, text, clean_caption=False):\n if clean_caption and not is_bs4_available():\n logger.warning(BACKENDS_MAPPING[\"bs4\"][-1].format(\"Setting `clean_caption=True`\"))\n logger.warning(\"Setting `clean_caption` to False...\")\n clean_caption = False\n\n if clean_caption and not is_ftfy_available():\n logger.warning(BACKENDS_MAPPING[\"ftfy\"][-1].format(\"Setting `clean_caption=True`\"))\n logger.warning(\"Setting `clean_caption` to False...\")\n clean_caption = False\n\n if not isinstance(text, (tuple, list)):\n text = [text]\n\n def process(text: str):\n if clean_caption:\n text = self._clean_caption(text)\n text = self._clean_caption(text)\n else:\n text = text.lower().strip()\n return text\n\n return [process(t) for t in text]\n\n def _clean_caption(self, caption):\n caption = str(caption)\n caption = ul.unquote_plus(caption)\n caption = caption.strip().lower()\n caption = re.sub(\"\", \"person\", caption)\n # urls:\n caption = re.sub(\n r\"\\b((?:https?:(?:\\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\\w/-]*\\b\\/?(?!@)))\", # noqa\n \"\",\n caption,\n ) # regex for urls\n caption = re.sub(\n r\"\\b((?:www:(?:\\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\\w/-]*\\b\\/?(?!@)))\", # noqa\n \"\",\n caption,\n ) # regex for urls\n # html:\n caption = BeautifulSoup(caption, features=\"html.parser\").text\n\n # @\n caption = re.sub(r\"@[\\w\\d]+\\b\", \"\", caption)\n\n # 31C0—31EF CJK Strokes\n # 31F0—31FF Katakana Phonetic Extensions\n # 3200—32FF Enclosed CJK Letters and Months\n # 3300—33FF CJK Compatibility\n # 3400—4DBF CJK Unified Ideographs Extension A\n # 4DC0—4DFF Yijing Hexagram Symbols\n # 4E00—9FFF CJK Unified Ideographs\n caption = re.sub(r\"[\\u31c0-\\u31ef]+\", \"\", caption)\n caption = re.sub(r\"[\\u31f0-\\u31ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3200-\\u32ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3300-\\u33ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3400-\\u4dbf]+\", \"\", caption)\n caption = re.sub(r\"[\\u4dc0-\\u4dff]+\", \"\", caption)\n caption = re.sub(r\"[\\u4e00-\\u9fff]+\", \"\", caption)\n #######################################################\n\n # все виды тире / all types of dash --> \"-\"\n caption = re.sub(\n r\"[\\u002D\\u058A\\u05BE\\u1400\\u1806\\u2010-\\u2015\\u2E17\\u2E1A\\u2E3A\\u2E3B\\u2E40\\u301C\\u3030\\u30A0\\uFE31\\uFE32\\uFE58\\uFE63\\uFF0D]+\", # noqa\n \"-\",\n caption,\n )\n\n # кавычки к одному стандарту\n caption = re.sub(r\"[`´«»“”¨]\", '\"', caption)\n caption = re.sub(r\"[‘’]\", \"'\", caption)\n\n # "\n caption = re.sub(r\""?\", \"\", caption)\n # &\n caption = re.sub(r\"&\", \"\", caption)\n\n # ip adresses:\n caption = re.sub(r\"\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\", \" \", caption)\n\n # article ids:\n caption = re.sub(r\"\\d:\\d\\d\\s+$\", \"\", caption)\n\n # \\n\n caption = re.sub(r\"\\\\n\", \" \", caption)\n\n # \"#123\"\n caption = re.sub(r\"#\\d{1,3}\\b\", \"\", caption)\n # \"#12345..\"\n caption = re.sub(r\"#\\d{5,}\\b\", \"\", caption)\n # \"123456..\"\n caption = re.sub(r\"\\b\\d{6,}\\b\", \"\", caption)\n # filenames:\n caption = re.sub(r\"[\\S]+\\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)\", \"\", caption)\n\n #\n caption = re.sub(r\"[\\\"\\']{2,}\", r'\"', caption) # \"\"\"AUSVERKAUFT\"\"\"\n caption = re.sub(r\"[\\.]{2,}\", r\" \", caption) # \"\"\"AUSVERKAUFT\"\"\"\n\n caption = re.sub(self.bad_punct_regex, r\" \", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT\n caption = re.sub(r\"\\s+\\.\\s+\", r\" \", caption) # \" . \"\n\n # this-is-my-cute-cat / this_is_my_cute_cat\n regex2 = re.compile(r\"(?:\\-|\\_)\")\n if len(re.findall(regex2, caption)) > 3:\n caption = re.sub(regex2, \" \", caption)\n\n caption = ftfy.fix_text(caption)\n caption = html.unescape(html.unescape(caption))\n\n caption = re.sub(r\"\\b[a-zA-Z]{1,3}\\d{3,15}\\b\", \"\", caption) # jc6640\n caption = re.sub(r\"\\b[a-zA-Z]+\\d+[a-zA-Z]+\\b\", \"\", caption) # jc6640vc\n caption = re.sub(r\"\\b\\d+[a-zA-Z]+\\d+\\b\", \"\", caption) # 6640vc231\n\n caption = re.sub(r\"(worldwide\\s+)?(free\\s+)?shipping\", \"\", caption)\n caption = re.sub(r\"(free\\s)?download(\\sfree)?\", \"\", caption)\n caption = re.sub(r\"\\bclick\\b\\s(?:for|on)\\s\\w+\", \"\", caption)\n caption = re.sub(r\"\\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\\simage[s]?)?\", \"\", caption)\n caption = re.sub(r\"\\bpage\\s+\\d+\\b\", \"\", caption)\n\n caption = re.sub(r\"\\b\\d*[a-zA-Z]+\\d+[a-zA-Z]+\\d+[a-zA-Z\\d]*\\b\", r\" \", caption) # j2d1a2a...\n\n caption = re.sub(r\"\\b\\d+\\.?\\d*[xх×]\\d+\\.?\\d*\\b\", \"\", caption)\n\n caption = re.sub(r\"\\b\\s+\\:\\s+\", r\": \", caption)\n caption = re.sub(r\"(\\D[,\\./])\\b\", r\"\\1 \", caption)\n caption = re.sub(r\"\\s+\", \" \", caption)\n\n caption.strip()\n\n caption = re.sub(r\"^[\\\"\\']([\\w\\W]+)[\\\"\\']$\", r\"\\1\", caption)\n caption = re.sub(r\"^[\\'\\_,\\-\\:;]\", r\"\", caption)\n caption = re.sub(r\"[\\'\\_,\\-\\:\\-\\+]$\", r\"\", caption)\n caption = re.sub(r\"^\\.\\S+$\", \"\", caption)\n\n return caption.strip()\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n num_inference_steps: int = 100,\n timesteps: List[int] = None,\n guidance_scale: float = 7.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n height: Optional[int] = None,\n width: Optional[int] = None,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n clean_caption: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`\n timesteps are used. Must be in descending order.\n guidance_scale (`float`, *optional*, defaults to 7.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n height (`int`, *optional*, defaults to self.unet.config.sample_size):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size):\n The width in pixels of the generated image.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n clean_caption (`bool`, *optional*, defaults to `True`):\n Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to\n be installed. If the dependencies are not installed, the embeddings will be created from the raw\n prompt.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When\n returning a tuple, the first element is a list with the generated images, and the second element is a list\n of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\" (nsfw)\n or watermarked content, according to the `safety_checker`.\n \"\"\"\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(prompt, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)\n\n # 2. Define call parameters\n height = height or self.unet.config.sample_size\n width = width or self.unet.config.sample_size\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n do_classifier_free_guidance,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n clean_caption=clean_caption,\n )\n\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Prepare timesteps\n if timesteps is not None:\n self.scheduler.set_timesteps(timesteps=timesteps, device=device)\n timesteps = self.scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n if hasattr(self.scheduler, \"set_begin_index\"):\n self.scheduler.set_begin_index(0)\n\n # 5. Prepare intermediate images\n intermediate_images = self.prepare_intermediate_images(\n batch_size * num_images_per_prompt,\n self.unet.config.in_channels,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # HACK: see comment in `enable_model_cpu_offload`\n if hasattr(self, \"text_encoder_offload_hook\") and self.text_encoder_offload_hook is not None:\n self.text_encoder_offload_hook.offload()\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n model_input = (\n torch.cat([intermediate_images] * 2) if do_classifier_free_guidance else intermediate_images\n )\n model_input = self.scheduler.scale_model_input(model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1], dim=1)\n noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1], dim=1)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)\n\n if self.scheduler.config.variance_type not in [\"learned\", \"learned_range\"]:\n noise_pred, _ = noise_pred.split(model_input.shape[1], dim=1)\n\n # compute the previous noisy sample x_t -> x_t-1\n intermediate_images = self.scheduler.step(\n noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False\n )[0]\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, intermediate_images)\n\n image = intermediate_images\n\n if output_type == \"pil\":\n # 8. Post-processing\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n # 9. Run safety checker\n image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # 10. Convert to PIL\n image = self.numpy_to_pil(image)\n\n # 11. Apply watermark\n if self.watermarker is not None:\n image = self.watermarker.apply_watermark(image, self.unet.config.sample_size)\n elif output_type == \"pt\":\n nsfw_detected = None\n watermark_detected = None\n\n if hasattr(self, \"unet_offload_hook\") and self.unet_offload_hook is not None:\n self.unet_offload_hook.offload()\n else:\n # 8. Post-processing\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n # 9. Run safety checker\n image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, nsfw_detected, watermark_detected)\n\n return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected)\n" }, { "path": "diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py", "content": "import html\nimport inspect\nimport re\nimport urllib.parse as ul\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer\n\nfrom ...loaders import LoraLoaderMixin\nfrom ...models import UNet2DConditionModel\nfrom ...schedulers import DDPMScheduler\nfrom ...utils import (\n BACKENDS_MAPPING,\n PIL_INTERPOLATION,\n is_bs4_available,\n is_ftfy_available,\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline\nfrom .pipeline_output import IFPipelineOutput\nfrom .safety_checker import IFSafetyChecker\nfrom .watermark import IFWatermarker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nif is_bs4_available():\n from bs4 import BeautifulSoup\n\nif is_ftfy_available():\n import ftfy\n\n\ndef resize(images: PIL.Image.Image, img_size: int) -> PIL.Image.Image:\n w, h = images.size\n\n coef = w / h\n\n w, h = img_size, img_size\n\n if coef >= 1:\n w = int(round(img_size / 8 * coef) * 8)\n else:\n h = int(round(img_size / 8 / coef) * 8)\n\n images = images.resize((w, h), resample=PIL_INTERPOLATION[\"bicubic\"], reducing_gap=None)\n\n return images\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline, DiffusionPipeline\n >>> from diffusers.utils import pt_to_pil\n >>> import torch\n >>> from PIL import Image\n >>> import requests\n >>> from io import BytesIO\n\n >>> url = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n >>> response = requests.get(url)\n >>> original_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n >>> original_image = original_image.resize((768, 512))\n\n >>> pipe = IFImg2ImgPipeline.from_pretrained(\n ... \"DeepFloyd/IF-I-XL-v1.0\",\n ... variant=\"fp16\",\n ... torch_dtype=torch.float16,\n ... )\n >>> pipe.enable_model_cpu_offload()\n\n >>> prompt = \"A fantasy landscape in style minecraft\"\n >>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)\n\n >>> image = pipe(\n ... image=original_image,\n ... prompt_embeds=prompt_embeds,\n ... negative_prompt_embeds=negative_embeds,\n ... output_type=\"pt\",\n ... ).images\n\n >>> # save intermediate image\n >>> pil_image = pt_to_pil(image)\n >>> pil_image[0].save(\"./if_stage_I.png\")\n\n >>> super_res_1_pipe = IFImg2ImgSuperResolutionPipeline.from_pretrained(\n ... \"DeepFloyd/IF-II-L-v1.0\",\n ... text_encoder=None,\n ... variant=\"fp16\",\n ... torch_dtype=torch.float16,\n ... )\n >>> super_res_1_pipe.enable_model_cpu_offload()\n\n >>> image = super_res_1_pipe(\n ... image=image,\n ... original_image=original_image,\n ... prompt_embeds=prompt_embeds,\n ... negative_prompt_embeds=negative_embeds,\n ... ).images\n >>> image[0].save(\"./if_stage_II.png\")\n ```\n\"\"\"\n\n\nclass IFImg2ImgPipeline(DiffusionPipeline, LoraLoaderMixin):\n tokenizer: T5Tokenizer\n text_encoder: T5EncoderModel\n\n unet: UNet2DConditionModel\n scheduler: DDPMScheduler\n\n feature_extractor: Optional[CLIPImageProcessor]\n safety_checker: Optional[IFSafetyChecker]\n\n watermarker: Optional[IFWatermarker]\n\n bad_punct_regex = re.compile(\n r\"[\"\n + \"#®•©™&@·º½¾¿¡§~\"\n + r\"\\)\"\n + r\"\\(\"\n + r\"\\]\"\n + r\"\\[\"\n + r\"\\}\"\n + r\"\\{\"\n + r\"\\|\"\n + \"\\\\\"\n + r\"\\/\"\n + r\"\\*\"\n + r\"]{1,}\"\n ) # noqa\n\n _optional_components = [\"tokenizer\", \"text_encoder\", \"safety_checker\", \"feature_extractor\", \"watermarker\"]\n model_cpu_offload_seq = \"text_encoder->unet\"\n _exclude_from_cpu_offload = [\"watermarker\"]\n\n def __init__(\n self,\n tokenizer: T5Tokenizer,\n text_encoder: T5EncoderModel,\n unet: UNet2DConditionModel,\n scheduler: DDPMScheduler,\n safety_checker: Optional[IFSafetyChecker],\n feature_extractor: Optional[CLIPImageProcessor],\n watermarker: Optional[IFWatermarker],\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the IF license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.register_modules(\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n watermarker=watermarker,\n )\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n @torch.no_grad()\n def encode_prompt(\n self,\n prompt: Union[str, List[str]],\n do_classifier_free_guidance: bool = True,\n num_images_per_prompt: int = 1,\n device: Optional[torch.device] = None,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n clean_caption: bool = False,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):\n whether to use classifier free guidance or not\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n number of images that should be generated per prompt\n device: (`torch.device`, *optional*):\n torch device to place the resulting embeddings on\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.\n Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n clean_caption (bool, defaults to `False`):\n If `True`, the function will preprocess and clean the provided caption before encoding.\n \"\"\"\n if prompt is not None and negative_prompt is not None:\n if type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n\n if device is None:\n device = self._execution_device\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF\n max_length = 77\n\n if prompt_embeds is None:\n prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {max_length} tokens: {removed_text}\"\n )\n\n attention_mask = text_inputs.attention_mask.to(device)\n\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n prompt_embeds = prompt_embeds[0]\n\n if self.text_encoder is not None:\n dtype = self.text_encoder.dtype\n elif self.unet is not None:\n dtype = self.unet.dtype\n else:\n dtype = None\n\n prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_attention_mask=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n attention_mask = uncond_input.attention_mask.to(device)\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n else:\n negative_prompt_embeds = None\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors=\"pt\").to(device)\n image, nsfw_detected, watermark_detected = self.safety_checker(\n images=image,\n clip_input=safety_checker_input.pixel_values.to(dtype=dtype),\n )\n else:\n nsfw_detected = None\n watermark_detected = None\n\n return image, nsfw_detected, watermark_detected\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n image,\n batch_size,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if isinstance(image, list):\n check_image_type = image[0]\n else:\n check_image_type = image\n\n if (\n not isinstance(check_image_type, torch.Tensor)\n and not isinstance(check_image_type, PIL.Image.Image)\n and not isinstance(check_image_type, np.ndarray)\n ):\n raise ValueError(\n \"`image` has to be of type `torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is\"\n f\" {type(check_image_type)}\"\n )\n\n if isinstance(image, list):\n image_batch_size = len(image)\n elif isinstance(image, torch.Tensor):\n image_batch_size = image.shape[0]\n elif isinstance(image, PIL.Image.Image):\n image_batch_size = 1\n elif isinstance(image, np.ndarray):\n image_batch_size = image.shape[0]\n else:\n assert False\n\n if batch_size != image_batch_size:\n raise ValueError(f\"image batch size: {image_batch_size} must be same as prompt batch size {batch_size}\")\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing\n def _text_preprocessing(self, text, clean_caption=False):\n if clean_caption and not is_bs4_available():\n logger.warning(BACKENDS_MAPPING[\"bs4\"][-1].format(\"Setting `clean_caption=True`\"))\n logger.warning(\"Setting `clean_caption` to False...\")\n clean_caption = False\n\n if clean_caption and not is_ftfy_available():\n logger.warning(BACKENDS_MAPPING[\"ftfy\"][-1].format(\"Setting `clean_caption=True`\"))\n logger.warning(\"Setting `clean_caption` to False...\")\n clean_caption = False\n\n if not isinstance(text, (tuple, list)):\n text = [text]\n\n def process(text: str):\n if clean_caption:\n text = self._clean_caption(text)\n text = self._clean_caption(text)\n else:\n text = text.lower().strip()\n return text\n\n return [process(t) for t in text]\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption\n def _clean_caption(self, caption):\n caption = str(caption)\n caption = ul.unquote_plus(caption)\n caption = caption.strip().lower()\n caption = re.sub(\"\", \"person\", caption)\n # urls:\n caption = re.sub(\n r\"\\b((?:https?:(?:\\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\\w/-]*\\b\\/?(?!@)))\", # noqa\n \"\",\n caption,\n ) # regex for urls\n caption = re.sub(\n r\"\\b((?:www:(?:\\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\\w/-]*\\b\\/?(?!@)))\", # noqa\n \"\",\n caption,\n ) # regex for urls\n # html:\n caption = BeautifulSoup(caption, features=\"html.parser\").text\n\n # @\n caption = re.sub(r\"@[\\w\\d]+\\b\", \"\", caption)\n\n # 31C0—31EF CJK Strokes\n # 31F0—31FF Katakana Phonetic Extensions\n # 3200—32FF Enclosed CJK Letters and Months\n # 3300—33FF CJK Compatibility\n # 3400—4DBF CJK Unified Ideographs Extension A\n # 4DC0—4DFF Yijing Hexagram Symbols\n # 4E00—9FFF CJK Unified Ideographs\n caption = re.sub(r\"[\\u31c0-\\u31ef]+\", \"\", caption)\n caption = re.sub(r\"[\\u31f0-\\u31ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3200-\\u32ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3300-\\u33ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3400-\\u4dbf]+\", \"\", caption)\n caption = re.sub(r\"[\\u4dc0-\\u4dff]+\", \"\", caption)\n caption = re.sub(r\"[\\u4e00-\\u9fff]+\", \"\", caption)\n #######################################################\n\n # все виды тире / all types of dash --> \"-\"\n caption = re.sub(\n r\"[\\u002D\\u058A\\u05BE\\u1400\\u1806\\u2010-\\u2015\\u2E17\\u2E1A\\u2E3A\\u2E3B\\u2E40\\u301C\\u3030\\u30A0\\uFE31\\uFE32\\uFE58\\uFE63\\uFF0D]+\", # noqa\n \"-\",\n caption,\n )\n\n # кавычки к одному стандарту\n caption = re.sub(r\"[`´«»“”¨]\", '\"', caption)\n caption = re.sub(r\"[‘’]\", \"'\", caption)\n\n # "\n caption = re.sub(r\""?\", \"\", caption)\n # &\n caption = re.sub(r\"&\", \"\", caption)\n\n # ip adresses:\n caption = re.sub(r\"\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\", \" \", caption)\n\n # article ids:\n caption = re.sub(r\"\\d:\\d\\d\\s+$\", \"\", caption)\n\n # \\n\n caption = re.sub(r\"\\\\n\", \" \", caption)\n\n # \"#123\"\n caption = re.sub(r\"#\\d{1,3}\\b\", \"\", caption)\n # \"#12345..\"\n caption = re.sub(r\"#\\d{5,}\\b\", \"\", caption)\n # \"123456..\"\n caption = re.sub(r\"\\b\\d{6,}\\b\", \"\", caption)\n # filenames:\n caption = re.sub(r\"[\\S]+\\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)\", \"\", caption)\n\n #\n caption = re.sub(r\"[\\\"\\']{2,}\", r'\"', caption) # \"\"\"AUSVERKAUFT\"\"\"\n caption = re.sub(r\"[\\.]{2,}\", r\" \", caption) # \"\"\"AUSVERKAUFT\"\"\"\n\n caption = re.sub(self.bad_punct_regex, r\" \", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT\n caption = re.sub(r\"\\s+\\.\\s+\", r\" \", caption) # \" . \"\n\n # this-is-my-cute-cat / this_is_my_cute_cat\n regex2 = re.compile(r\"(?:\\-|\\_)\")\n if len(re.findall(regex2, caption)) > 3:\n caption = re.sub(regex2, \" \", caption)\n\n caption = ftfy.fix_text(caption)\n caption = html.unescape(html.unescape(caption))\n\n caption = re.sub(r\"\\b[a-zA-Z]{1,3}\\d{3,15}\\b\", \"\", caption) # jc6640\n caption = re.sub(r\"\\b[a-zA-Z]+\\d+[a-zA-Z]+\\b\", \"\", caption) # jc6640vc\n caption = re.sub(r\"\\b\\d+[a-zA-Z]+\\d+\\b\", \"\", caption) # 6640vc231\n\n caption = re.sub(r\"(worldwide\\s+)?(free\\s+)?shipping\", \"\", caption)\n caption = re.sub(r\"(free\\s)?download(\\sfree)?\", \"\", caption)\n caption = re.sub(r\"\\bclick\\b\\s(?:for|on)\\s\\w+\", \"\", caption)\n caption = re.sub(r\"\\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\\simage[s]?)?\", \"\", caption)\n caption = re.sub(r\"\\bpage\\s+\\d+\\b\", \"\", caption)\n\n caption = re.sub(r\"\\b\\d*[a-zA-Z]+\\d+[a-zA-Z]+\\d+[a-zA-Z\\d]*\\b\", r\" \", caption) # j2d1a2a...\n\n caption = re.sub(r\"\\b\\d+\\.?\\d*[xх×]\\d+\\.?\\d*\\b\", \"\", caption)\n\n caption = re.sub(r\"\\b\\s+\\:\\s+\", r\": \", caption)\n caption = re.sub(r\"(\\D[,\\./])\\b\", r\"\\1 \", caption)\n caption = re.sub(r\"\\s+\", \" \", caption)\n\n caption.strip()\n\n caption = re.sub(r\"^[\\\"\\']([\\w\\W]+)[\\\"\\']$\", r\"\\1\", caption)\n caption = re.sub(r\"^[\\'\\_,\\-\\:;]\", r\"\", caption)\n caption = re.sub(r\"[\\'\\_,\\-\\:\\-\\+]$\", r\"\", caption)\n caption = re.sub(r\"^\\.\\S+$\", \"\", caption)\n\n return caption.strip()\n\n def preprocess_image(self, image: PIL.Image.Image) -> torch.Tensor:\n if not isinstance(image, list):\n image = [image]\n\n def numpy_to_pt(images):\n if images.ndim == 3:\n images = images[..., None]\n\n images = torch.from_numpy(images.transpose(0, 3, 1, 2))\n return images\n\n if isinstance(image[0], PIL.Image.Image):\n new_image = []\n\n for image_ in image:\n image_ = image_.convert(\"RGB\")\n image_ = resize(image_, self.unet.config.sample_size)\n image_ = np.array(image_)\n image_ = image_.astype(np.float32)\n image_ = image_ / 127.5 - 1\n new_image.append(image_)\n\n image = new_image\n\n image = np.stack(image, axis=0) # to np\n image = numpy_to_pt(image) # to pt\n\n elif isinstance(image[0], np.ndarray):\n image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0)\n image = numpy_to_pt(image)\n\n elif isinstance(image[0], torch.Tensor):\n image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0)\n\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n if hasattr(self.scheduler, \"set_begin_index\"):\n self.scheduler.set_begin_index(t_start * self.scheduler.order)\n\n return timesteps, num_inference_steps - t_start\n\n def prepare_intermediate_images(\n self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None\n ):\n _, channels, height, width = image.shape\n\n batch_size = batch_size * num_images_per_prompt\n\n shape = (batch_size, channels, height, width)\n\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n image = image.repeat_interleave(num_images_per_prompt, dim=0)\n image = self.scheduler.add_noise(image, noise, timestep)\n\n return image\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: Union[\n PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray]\n ] = None,\n strength: float = 0.7,\n num_inference_steps: int = 80,\n timesteps: List[int] = None,\n guidance_scale: float = 10.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n clean_caption: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n image (`torch.Tensor` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process.\n strength (`float`, *optional*, defaults to 0.7):\n Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`\n will be used as a starting point, adding more noise to it the larger the `strength`. The number of\n denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will\n be maximum and the denoising process will run for the full number of iterations specified in\n `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 80):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`\n timesteps are used. Must be in descending order.\n guidance_scale (`float`, *optional*, defaults to 10.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n clean_caption (`bool`, *optional*, defaults to `True`):\n Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to\n be installed. If the dependencies are not installed, the embeddings will be created from the raw\n prompt.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When\n returning a tuple, the first element is a list with the generated images, and the second element is a list\n of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\" (nsfw)\n or watermarked content, according to the `safety_checker`.\n \"\"\"\n # 1. Check inputs. Raise error if not correct\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n self.check_inputs(\n prompt, image, batch_size, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds\n )\n\n # 2. Define call parameters\n device = self._execution_device\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n do_classifier_free_guidance,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n clean_caption=clean_caption,\n )\n\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n dtype = prompt_embeds.dtype\n\n # 4. Prepare timesteps\n if timesteps is not None:\n self.scheduler.set_timesteps(timesteps=timesteps, device=device)\n timesteps = self.scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)\n\n # 5. Prepare intermediate images\n image = self.preprocess_image(image)\n image = image.to(device=device, dtype=dtype)\n\n noise_timestep = timesteps[0:1]\n noise_timestep = noise_timestep.repeat(batch_size * num_images_per_prompt)\n\n intermediate_images = self.prepare_intermediate_images(\n image, noise_timestep, batch_size, num_images_per_prompt, dtype, device, generator\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # HACK: see comment in `enable_model_cpu_offload`\n if hasattr(self, \"text_encoder_offload_hook\") and self.text_encoder_offload_hook is not None:\n self.text_encoder_offload_hook.offload()\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n model_input = (\n torch.cat([intermediate_images] * 2) if do_classifier_free_guidance else intermediate_images\n )\n model_input = self.scheduler.scale_model_input(model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1], dim=1)\n noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1], dim=1)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)\n\n if self.scheduler.config.variance_type not in [\"learned\", \"learned_range\"]:\n noise_pred, _ = noise_pred.split(model_input.shape[1], dim=1)\n\n # compute the previous noisy sample x_t -> x_t-1\n intermediate_images = self.scheduler.step(\n noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False\n )[0]\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, intermediate_images)\n\n image = intermediate_images\n\n if output_type == \"pil\":\n # 8. Post-processing\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n # 9. Run safety checker\n image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # 10. Convert to PIL\n image = self.numpy_to_pil(image)\n\n # 11. Apply watermark\n if self.watermarker is not None:\n self.watermarker.apply_watermark(image, self.unet.config.sample_size)\n elif output_type == \"pt\":\n nsfw_detected = None\n watermark_detected = None\n\n if hasattr(self, \"unet_offload_hook\") and self.unet_offload_hook is not None:\n self.unet_offload_hook.offload()\n else:\n # 8. Post-processing\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n # 9. Run safety checker\n image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, nsfw_detected, watermark_detected)\n\n return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected)\n" }, { "path": "diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py", "content": "import html\nimport inspect\nimport re\nimport urllib.parse as ul\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer\n\nfrom ...loaders import LoraLoaderMixin\nfrom ...models import UNet2DConditionModel\nfrom ...schedulers import DDPMScheduler\nfrom ...utils import (\n BACKENDS_MAPPING,\n PIL_INTERPOLATION,\n is_bs4_available,\n is_ftfy_available,\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline\nfrom .pipeline_output import IFPipelineOutput\nfrom .safety_checker import IFSafetyChecker\nfrom .watermark import IFWatermarker\n\n\nif is_bs4_available():\n from bs4 import BeautifulSoup\n\nif is_ftfy_available():\n import ftfy\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.resize\ndef resize(images: PIL.Image.Image, img_size: int) -> PIL.Image.Image:\n w, h = images.size\n\n coef = w / h\n\n w, h = img_size, img_size\n\n if coef >= 1:\n w = int(round(img_size / 8 * coef) * 8)\n else:\n h = int(round(img_size / 8 / coef) * 8)\n\n images = images.resize((w, h), resample=PIL_INTERPOLATION[\"bicubic\"], reducing_gap=None)\n\n return images\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline, DiffusionPipeline\n >>> from diffusers.utils import pt_to_pil\n >>> import torch\n >>> from PIL import Image\n >>> import requests\n >>> from io import BytesIO\n\n >>> url = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n >>> response = requests.get(url)\n >>> original_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n >>> original_image = original_image.resize((768, 512))\n\n >>> pipe = IFImg2ImgPipeline.from_pretrained(\n ... \"DeepFloyd/IF-I-XL-v1.0\",\n ... variant=\"fp16\",\n ... torch_dtype=torch.float16,\n ... )\n >>> pipe.enable_model_cpu_offload()\n\n >>> prompt = \"A fantasy landscape in style minecraft\"\n >>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)\n\n >>> image = pipe(\n ... image=original_image,\n ... prompt_embeds=prompt_embeds,\n ... negative_prompt_embeds=negative_embeds,\n ... output_type=\"pt\",\n ... ).images\n\n >>> # save intermediate image\n >>> pil_image = pt_to_pil(image)\n >>> pil_image[0].save(\"./if_stage_I.png\")\n\n >>> super_res_1_pipe = IFImg2ImgSuperResolutionPipeline.from_pretrained(\n ... \"DeepFloyd/IF-II-L-v1.0\",\n ... text_encoder=None,\n ... variant=\"fp16\",\n ... torch_dtype=torch.float16,\n ... )\n >>> super_res_1_pipe.enable_model_cpu_offload()\n\n >>> image = super_res_1_pipe(\n ... image=image,\n ... original_image=original_image,\n ... prompt_embeds=prompt_embeds,\n ... negative_prompt_embeds=negative_embeds,\n ... ).images\n >>> image[0].save(\"./if_stage_II.png\")\n ```\n\"\"\"\n\n\nclass IFImg2ImgSuperResolutionPipeline(DiffusionPipeline, LoraLoaderMixin):\n tokenizer: T5Tokenizer\n text_encoder: T5EncoderModel\n\n unet: UNet2DConditionModel\n scheduler: DDPMScheduler\n image_noising_scheduler: DDPMScheduler\n\n feature_extractor: Optional[CLIPImageProcessor]\n safety_checker: Optional[IFSafetyChecker]\n\n watermarker: Optional[IFWatermarker]\n\n bad_punct_regex = re.compile(\n r\"[\"\n + \"#®•©™&@·º½¾¿¡§~\"\n + r\"\\)\"\n + r\"\\(\"\n + r\"\\]\"\n + r\"\\[\"\n + r\"\\}\"\n + r\"\\{\"\n + r\"\\|\"\n + \"\\\\\"\n + r\"\\/\"\n + r\"\\*\"\n + r\"]{1,}\"\n ) # noqa\n\n _optional_components = [\"tokenizer\", \"text_encoder\", \"safety_checker\", \"feature_extractor\"]\n model_cpu_offload_seq = \"text_encoder->unet\"\n _exclude_from_cpu_offload = [\"watermarker\"]\n\n def __init__(\n self,\n tokenizer: T5Tokenizer,\n text_encoder: T5EncoderModel,\n unet: UNet2DConditionModel,\n scheduler: DDPMScheduler,\n image_noising_scheduler: DDPMScheduler,\n safety_checker: Optional[IFSafetyChecker],\n feature_extractor: Optional[CLIPImageProcessor],\n watermarker: Optional[IFWatermarker],\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the IF license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n if unet.config.in_channels != 6:\n logger.warning(\n \"It seems like you have loaded a checkpoint that shall not be used for super resolution from {unet.config._name_or_path} as it accepts {unet.config.in_channels} input channels instead of 6. Please make sure to pass a super resolution checkpoint as the `'unet'`: IFSuperResolutionPipeline.from_pretrained(unet=super_resolution_unet, ...)`.\"\n )\n\n self.register_modules(\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n unet=unet,\n scheduler=scheduler,\n image_noising_scheduler=image_noising_scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n watermarker=watermarker,\n )\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing\n def _text_preprocessing(self, text, clean_caption=False):\n if clean_caption and not is_bs4_available():\n logger.warning(BACKENDS_MAPPING[\"bs4\"][-1].format(\"Setting `clean_caption=True`\"))\n logger.warning(\"Setting `clean_caption` to False...\")\n clean_caption = False\n\n if clean_caption and not is_ftfy_available():\n logger.warning(BACKENDS_MAPPING[\"ftfy\"][-1].format(\"Setting `clean_caption=True`\"))\n logger.warning(\"Setting `clean_caption` to False...\")\n clean_caption = False\n\n if not isinstance(text, (tuple, list)):\n text = [text]\n\n def process(text: str):\n if clean_caption:\n text = self._clean_caption(text)\n text = self._clean_caption(text)\n else:\n text = text.lower().strip()\n return text\n\n return [process(t) for t in text]\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption\n def _clean_caption(self, caption):\n caption = str(caption)\n caption = ul.unquote_plus(caption)\n caption = caption.strip().lower()\n caption = re.sub(\"\", \"person\", caption)\n # urls:\n caption = re.sub(\n r\"\\b((?:https?:(?:\\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\\w/-]*\\b\\/?(?!@)))\", # noqa\n \"\",\n caption,\n ) # regex for urls\n caption = re.sub(\n r\"\\b((?:www:(?:\\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\\w/-]*\\b\\/?(?!@)))\", # noqa\n \"\",\n caption,\n ) # regex for urls\n # html:\n caption = BeautifulSoup(caption, features=\"html.parser\").text\n\n # @\n caption = re.sub(r\"@[\\w\\d]+\\b\", \"\", caption)\n\n # 31C0—31EF CJK Strokes\n # 31F0—31FF Katakana Phonetic Extensions\n # 3200—32FF Enclosed CJK Letters and Months\n # 3300—33FF CJK Compatibility\n # 3400—4DBF CJK Unified Ideographs Extension A\n # 4DC0—4DFF Yijing Hexagram Symbols\n # 4E00—9FFF CJK Unified Ideographs\n caption = re.sub(r\"[\\u31c0-\\u31ef]+\", \"\", caption)\n caption = re.sub(r\"[\\u31f0-\\u31ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3200-\\u32ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3300-\\u33ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3400-\\u4dbf]+\", \"\", caption)\n caption = re.sub(r\"[\\u4dc0-\\u4dff]+\", \"\", caption)\n caption = re.sub(r\"[\\u4e00-\\u9fff]+\", \"\", caption)\n #######################################################\n\n # все виды тире / all types of dash --> \"-\"\n caption = re.sub(\n r\"[\\u002D\\u058A\\u05BE\\u1400\\u1806\\u2010-\\u2015\\u2E17\\u2E1A\\u2E3A\\u2E3B\\u2E40\\u301C\\u3030\\u30A0\\uFE31\\uFE32\\uFE58\\uFE63\\uFF0D]+\", # noqa\n \"-\",\n caption,\n )\n\n # кавычки к одному стандарту\n caption = re.sub(r\"[`´«»“”¨]\", '\"', caption)\n caption = re.sub(r\"[‘’]\", \"'\", caption)\n\n # "\n caption = re.sub(r\""?\", \"\", caption)\n # &\n caption = re.sub(r\"&\", \"\", caption)\n\n # ip adresses:\n caption = re.sub(r\"\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\", \" \", caption)\n\n # article ids:\n caption = re.sub(r\"\\d:\\d\\d\\s+$\", \"\", caption)\n\n # \\n\n caption = re.sub(r\"\\\\n\", \" \", caption)\n\n # \"#123\"\n caption = re.sub(r\"#\\d{1,3}\\b\", \"\", caption)\n # \"#12345..\"\n caption = re.sub(r\"#\\d{5,}\\b\", \"\", caption)\n # \"123456..\"\n caption = re.sub(r\"\\b\\d{6,}\\b\", \"\", caption)\n # filenames:\n caption = re.sub(r\"[\\S]+\\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)\", \"\", caption)\n\n #\n caption = re.sub(r\"[\\\"\\']{2,}\", r'\"', caption) # \"\"\"AUSVERKAUFT\"\"\"\n caption = re.sub(r\"[\\.]{2,}\", r\" \", caption) # \"\"\"AUSVERKAUFT\"\"\"\n\n caption = re.sub(self.bad_punct_regex, r\" \", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT\n caption = re.sub(r\"\\s+\\.\\s+\", r\" \", caption) # \" . \"\n\n # this-is-my-cute-cat / this_is_my_cute_cat\n regex2 = re.compile(r\"(?:\\-|\\_)\")\n if len(re.findall(regex2, caption)) > 3:\n caption = re.sub(regex2, \" \", caption)\n\n caption = ftfy.fix_text(caption)\n caption = html.unescape(html.unescape(caption))\n\n caption = re.sub(r\"\\b[a-zA-Z]{1,3}\\d{3,15}\\b\", \"\", caption) # jc6640\n caption = re.sub(r\"\\b[a-zA-Z]+\\d+[a-zA-Z]+\\b\", \"\", caption) # jc6640vc\n caption = re.sub(r\"\\b\\d+[a-zA-Z]+\\d+\\b\", \"\", caption) # 6640vc231\n\n caption = re.sub(r\"(worldwide\\s+)?(free\\s+)?shipping\", \"\", caption)\n caption = re.sub(r\"(free\\s)?download(\\sfree)?\", \"\", caption)\n caption = re.sub(r\"\\bclick\\b\\s(?:for|on)\\s\\w+\", \"\", caption)\n caption = re.sub(r\"\\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\\simage[s]?)?\", \"\", caption)\n caption = re.sub(r\"\\bpage\\s+\\d+\\b\", \"\", caption)\n\n caption = re.sub(r\"\\b\\d*[a-zA-Z]+\\d+[a-zA-Z]+\\d+[a-zA-Z\\d]*\\b\", r\" \", caption) # j2d1a2a...\n\n caption = re.sub(r\"\\b\\d+\\.?\\d*[xх×]\\d+\\.?\\d*\\b\", \"\", caption)\n\n caption = re.sub(r\"\\b\\s+\\:\\s+\", r\": \", caption)\n caption = re.sub(r\"(\\D[,\\./])\\b\", r\"\\1 \", caption)\n caption = re.sub(r\"\\s+\", \" \", caption)\n\n caption.strip()\n\n caption = re.sub(r\"^[\\\"\\']([\\w\\W]+)[\\\"\\']$\", r\"\\1\", caption)\n caption = re.sub(r\"^[\\'\\_,\\-\\:;]\", r\"\", caption)\n caption = re.sub(r\"[\\'\\_,\\-\\:\\-\\+]$\", r\"\", caption)\n caption = re.sub(r\"^\\.\\S+$\", \"\", caption)\n\n return caption.strip()\n\n @torch.no_grad()\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt: Union[str, List[str]],\n do_classifier_free_guidance: bool = True,\n num_images_per_prompt: int = 1,\n device: Optional[torch.device] = None,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n clean_caption: bool = False,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):\n whether to use classifier free guidance or not\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n number of images that should be generated per prompt\n device: (`torch.device`, *optional*):\n torch device to place the resulting embeddings on\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.\n Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n clean_caption (bool, defaults to `False`):\n If `True`, the function will preprocess and clean the provided caption before encoding.\n \"\"\"\n if prompt is not None and negative_prompt is not None:\n if type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n\n if device is None:\n device = self._execution_device\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF\n max_length = 77\n\n if prompt_embeds is None:\n prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {max_length} tokens: {removed_text}\"\n )\n\n attention_mask = text_inputs.attention_mask.to(device)\n\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n prompt_embeds = prompt_embeds[0]\n\n if self.text_encoder is not None:\n dtype = self.text_encoder.dtype\n elif self.unet is not None:\n dtype = self.unet.dtype\n else:\n dtype = None\n\n prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_attention_mask=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n attention_mask = uncond_input.attention_mask.to(device)\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n else:\n negative_prompt_embeds = None\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors=\"pt\").to(device)\n image, nsfw_detected, watermark_detected = self.safety_checker(\n images=image,\n clip_input=safety_checker_input.pixel_values.to(dtype=dtype),\n )\n else:\n nsfw_detected = None\n watermark_detected = None\n\n return image, nsfw_detected, watermark_detected\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n image,\n original_image,\n batch_size,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # image\n\n if isinstance(image, list):\n check_image_type = image[0]\n else:\n check_image_type = image\n\n if (\n not isinstance(check_image_type, torch.Tensor)\n and not isinstance(check_image_type, PIL.Image.Image)\n and not isinstance(check_image_type, np.ndarray)\n ):\n raise ValueError(\n \"`image` has to be of type `torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is\"\n f\" {type(check_image_type)}\"\n )\n\n if isinstance(image, list):\n image_batch_size = len(image)\n elif isinstance(image, torch.Tensor):\n image_batch_size = image.shape[0]\n elif isinstance(image, PIL.Image.Image):\n image_batch_size = 1\n elif isinstance(image, np.ndarray):\n image_batch_size = image.shape[0]\n else:\n assert False\n\n if batch_size != image_batch_size:\n raise ValueError(f\"image batch size: {image_batch_size} must be same as prompt batch size {batch_size}\")\n\n # original_image\n\n if isinstance(original_image, list):\n check_image_type = original_image[0]\n else:\n check_image_type = original_image\n\n if (\n not isinstance(check_image_type, torch.Tensor)\n and not isinstance(check_image_type, PIL.Image.Image)\n and not isinstance(check_image_type, np.ndarray)\n ):\n raise ValueError(\n \"`original_image` has to be of type `torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is\"\n f\" {type(check_image_type)}\"\n )\n\n if isinstance(original_image, list):\n image_batch_size = len(original_image)\n elif isinstance(original_image, torch.Tensor):\n image_batch_size = original_image.shape[0]\n elif isinstance(original_image, PIL.Image.Image):\n image_batch_size = 1\n elif isinstance(original_image, np.ndarray):\n image_batch_size = original_image.shape[0]\n else:\n assert False\n\n if batch_size != image_batch_size:\n raise ValueError(\n f\"original_image batch size: {image_batch_size} must be same as prompt batch size {batch_size}\"\n )\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.preprocess_image with preprocess_image -> preprocess_original_image\n def preprocess_original_image(self, image: PIL.Image.Image) -> torch.Tensor:\n if not isinstance(image, list):\n image = [image]\n\n def numpy_to_pt(images):\n if images.ndim == 3:\n images = images[..., None]\n\n images = torch.from_numpy(images.transpose(0, 3, 1, 2))\n return images\n\n if isinstance(image[0], PIL.Image.Image):\n new_image = []\n\n for image_ in image:\n image_ = image_.convert(\"RGB\")\n image_ = resize(image_, self.unet.config.sample_size)\n image_ = np.array(image_)\n image_ = image_.astype(np.float32)\n image_ = image_ / 127.5 - 1\n new_image.append(image_)\n\n image = new_image\n\n image = np.stack(image, axis=0) # to np\n image = numpy_to_pt(image) # to pt\n\n elif isinstance(image[0], np.ndarray):\n image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0)\n image = numpy_to_pt(image)\n\n elif isinstance(image[0], torch.Tensor):\n image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0)\n\n return image\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_superresolution.IFSuperResolutionPipeline.preprocess_image\n def preprocess_image(self, image: PIL.Image.Image, num_images_per_prompt, device) -> torch.Tensor:\n if not isinstance(image, torch.Tensor) and not isinstance(image, list):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n image = [np.array(i).astype(np.float32) / 127.5 - 1.0 for i in image]\n\n image = np.stack(image, axis=0) # to np\n image = torch.from_numpy(image.transpose(0, 3, 1, 2))\n elif isinstance(image[0], np.ndarray):\n image = np.stack(image, axis=0) # to np\n if image.ndim == 5:\n image = image[0]\n\n image = torch.from_numpy(image.transpose(0, 3, 1, 2))\n elif isinstance(image, list) and isinstance(image[0], torch.Tensor):\n dims = image[0].ndim\n\n if dims == 3:\n image = torch.stack(image, dim=0)\n elif dims == 4:\n image = torch.concat(image, dim=0)\n else:\n raise ValueError(f\"Image must have 3 or 4 dimensions, instead got {dims}\")\n\n image = image.to(device=device, dtype=self.unet.dtype)\n\n image = image.repeat_interleave(num_images_per_prompt, dim=0)\n\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n if hasattr(self.scheduler, \"set_begin_index\"):\n self.scheduler.set_begin_index(t_start * self.scheduler.order)\n\n return timesteps, num_inference_steps - t_start\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.prepare_intermediate_images\n def prepare_intermediate_images(\n self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None\n ):\n _, channels, height, width = image.shape\n\n batch_size = batch_size * num_images_per_prompt\n\n shape = (batch_size, channels, height, width)\n\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n image = image.repeat_interleave(num_images_per_prompt, dim=0)\n image = self.scheduler.add_noise(image, noise, timestep)\n\n return image\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n image: Union[PIL.Image.Image, np.ndarray, torch.Tensor],\n original_image: Union[\n PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray]\n ] = None,\n strength: float = 0.8,\n prompt: Union[str, List[str]] = None,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n guidance_scale: float = 4.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n noise_level: int = 250,\n clean_caption: bool = True,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n image (`torch.Tensor` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process.\n original_image (`torch.Tensor` or `PIL.Image.Image`):\n The original image that `image` was varied from.\n strength (`float`, *optional*, defaults to 0.8):\n Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`\n will be used as a starting point, adding more noise to it the larger the `strength`. The number of\n denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will\n be maximum and the denoising process will run for the full number of iterations specified in\n `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`\n timesteps are used. Must be in descending order.\n guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n noise_level (`int`, *optional*, defaults to 250):\n The amount of noise to add to the upscaled image. Must be in the range `[0, 1000)`\n clean_caption (`bool`, *optional*, defaults to `True`):\n Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to\n be installed. If the dependencies are not installed, the embeddings will be created from the raw\n prompt.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When\n returning a tuple, the first element is a list with the generated images, and the second element is a list\n of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\" (nsfw)\n or watermarked content, according to the `safety_checker`.\n \"\"\"\n # 1. Check inputs. Raise error if not correct\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n self.check_inputs(\n prompt,\n image,\n original_image,\n batch_size,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n )\n\n # 2. Define call parameters\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n device = self._execution_device\n\n # 3. Encode input prompt\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n do_classifier_free_guidance,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n clean_caption=clean_caption,\n )\n\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n dtype = prompt_embeds.dtype\n\n # 4. Prepare timesteps\n if timesteps is not None:\n self.scheduler.set_timesteps(timesteps=timesteps, device=device)\n timesteps = self.scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)\n\n # 5. prepare original image\n original_image = self.preprocess_original_image(original_image)\n original_image = original_image.to(device=device, dtype=dtype)\n\n # 6. Prepare intermediate images\n noise_timestep = timesteps[0:1]\n noise_timestep = noise_timestep.repeat(batch_size * num_images_per_prompt)\n\n intermediate_images = self.prepare_intermediate_images(\n original_image,\n noise_timestep,\n batch_size,\n num_images_per_prompt,\n dtype,\n device,\n generator,\n )\n\n # 7. Prepare upscaled image and noise level\n _, _, height, width = original_image.shape\n\n image = self.preprocess_image(image, num_images_per_prompt, device)\n\n upscaled = F.interpolate(image, (height, width), mode=\"bilinear\", align_corners=True)\n\n noise_level = torch.tensor([noise_level] * upscaled.shape[0], device=upscaled.device)\n noise = randn_tensor(upscaled.shape, generator=generator, device=upscaled.device, dtype=upscaled.dtype)\n upscaled = self.image_noising_scheduler.add_noise(upscaled, noise, timesteps=noise_level)\n\n if do_classifier_free_guidance:\n noise_level = torch.cat([noise_level] * 2)\n\n # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # HACK: see comment in `enable_model_cpu_offload`\n if hasattr(self, \"text_encoder_offload_hook\") and self.text_encoder_offload_hook is not None:\n self.text_encoder_offload_hook.offload()\n\n # 9. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n model_input = torch.cat([intermediate_images, upscaled], dim=1)\n\n model_input = torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input\n model_input = self.scheduler.scale_model_input(model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n class_labels=noise_level,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1] // 2, dim=1)\n noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1] // 2, dim=1)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)\n\n if self.scheduler.config.variance_type not in [\"learned\", \"learned_range\"]:\n noise_pred, _ = noise_pred.split(intermediate_images.shape[1], dim=1)\n\n # compute the previous noisy sample x_t -> x_t-1\n intermediate_images = self.scheduler.step(\n noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False\n )[0]\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, intermediate_images)\n\n image = intermediate_images\n\n if output_type == \"pil\":\n # 10. Post-processing\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n # 11. Run safety checker\n image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # 12. Convert to PIL\n image = self.numpy_to_pil(image)\n\n # 13. Apply watermark\n if self.watermarker is not None:\n self.watermarker.apply_watermark(image, self.unet.config.sample_size)\n elif output_type == \"pt\":\n nsfw_detected = None\n watermark_detected = None\n\n else:\n # 10. Post-processing\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n # 11. Run safety checker\n image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, nsfw_detected, watermark_detected)\n\n return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected)\n" }, { "path": "diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py", "content": "import html\nimport inspect\nimport re\nimport urllib.parse as ul\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer\n\nfrom ...loaders import LoraLoaderMixin\nfrom ...models import UNet2DConditionModel\nfrom ...schedulers import DDPMScheduler\nfrom ...utils import (\n BACKENDS_MAPPING,\n PIL_INTERPOLATION,\n is_bs4_available,\n is_ftfy_available,\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline\nfrom .pipeline_output import IFPipelineOutput\nfrom .safety_checker import IFSafetyChecker\nfrom .watermark import IFWatermarker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nif is_bs4_available():\n from bs4 import BeautifulSoup\n\nif is_ftfy_available():\n import ftfy\n\n\n# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.resize\ndef resize(images: PIL.Image.Image, img_size: int) -> PIL.Image.Image:\n w, h = images.size\n\n coef = w / h\n\n w, h = img_size, img_size\n\n if coef >= 1:\n w = int(round(img_size / 8 * coef) * 8)\n else:\n h = int(round(img_size / 8 / coef) * 8)\n\n images = images.resize((w, h), resample=PIL_INTERPOLATION[\"bicubic\"], reducing_gap=None)\n\n return images\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline, DiffusionPipeline\n >>> from diffusers.utils import pt_to_pil\n >>> import torch\n >>> from PIL import Image\n >>> import requests\n >>> from io import BytesIO\n\n >>> url = \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/person.png\"\n >>> response = requests.get(url)\n >>> original_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n >>> original_image = original_image\n\n >>> url = \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/glasses_mask.png\"\n >>> response = requests.get(url)\n >>> mask_image = Image.open(BytesIO(response.content))\n >>> mask_image = mask_image\n\n >>> pipe = IFInpaintingPipeline.from_pretrained(\n ... \"DeepFloyd/IF-I-XL-v1.0\", variant=\"fp16\", torch_dtype=torch.float16\n ... )\n >>> pipe.enable_model_cpu_offload()\n\n >>> prompt = \"blue sunglasses\"\n >>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)\n\n >>> image = pipe(\n ... image=original_image,\n ... mask_image=mask_image,\n ... prompt_embeds=prompt_embeds,\n ... negative_prompt_embeds=negative_embeds,\n ... output_type=\"pt\",\n ... ).images\n\n >>> # save intermediate image\n >>> pil_image = pt_to_pil(image)\n >>> pil_image[0].save(\"./if_stage_I.png\")\n\n >>> super_res_1_pipe = IFInpaintingSuperResolutionPipeline.from_pretrained(\n ... \"DeepFloyd/IF-II-L-v1.0\", text_encoder=None, variant=\"fp16\", torch_dtype=torch.float16\n ... )\n >>> super_res_1_pipe.enable_model_cpu_offload()\n\n >>> image = super_res_1_pipe(\n ... image=image,\n ... mask_image=mask_image,\n ... original_image=original_image,\n ... prompt_embeds=prompt_embeds,\n ... negative_prompt_embeds=negative_embeds,\n ... ).images\n >>> image[0].save(\"./if_stage_II.png\")\n ```\n\"\"\"\n\n\nclass IFInpaintingPipeline(DiffusionPipeline, LoraLoaderMixin):\n tokenizer: T5Tokenizer\n text_encoder: T5EncoderModel\n\n unet: UNet2DConditionModel\n scheduler: DDPMScheduler\n\n feature_extractor: Optional[CLIPImageProcessor]\n safety_checker: Optional[IFSafetyChecker]\n\n watermarker: Optional[IFWatermarker]\n\n bad_punct_regex = re.compile(\n r\"[\"\n + \"#®•©™&@·º½¾¿¡§~\"\n + r\"\\)\"\n + r\"\\(\"\n + r\"\\]\"\n + r\"\\[\"\n + r\"\\}\"\n + r\"\\{\"\n + r\"\\|\"\n + \"\\\\\"\n + r\"\\/\"\n + r\"\\*\"\n + r\"]{1,}\"\n ) # noqa\n\n _optional_components = [\"tokenizer\", \"text_encoder\", \"safety_checker\", \"feature_extractor\", \"watermarker\"]\n model_cpu_offload_seq = \"text_encoder->unet\"\n _exclude_from_cpu_offload = [\"watermarker\"]\n\n def __init__(\n self,\n tokenizer: T5Tokenizer,\n text_encoder: T5EncoderModel,\n unet: UNet2DConditionModel,\n scheduler: DDPMScheduler,\n safety_checker: Optional[IFSafetyChecker],\n feature_extractor: Optional[CLIPImageProcessor],\n watermarker: Optional[IFWatermarker],\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the IF license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.register_modules(\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n watermarker=watermarker,\n )\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n @torch.no_grad()\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt: Union[str, List[str]],\n do_classifier_free_guidance: bool = True,\n num_images_per_prompt: int = 1,\n device: Optional[torch.device] = None,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n clean_caption: bool = False,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):\n whether to use classifier free guidance or not\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n number of images that should be generated per prompt\n device: (`torch.device`, *optional*):\n torch device to place the resulting embeddings on\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.\n Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n clean_caption (bool, defaults to `False`):\n If `True`, the function will preprocess and clean the provided caption before encoding.\n \"\"\"\n if prompt is not None and negative_prompt is not None:\n if type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n\n if device is None:\n device = self._execution_device\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF\n max_length = 77\n\n if prompt_embeds is None:\n prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {max_length} tokens: {removed_text}\"\n )\n\n attention_mask = text_inputs.attention_mask.to(device)\n\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n prompt_embeds = prompt_embeds[0]\n\n if self.text_encoder is not None:\n dtype = self.text_encoder.dtype\n elif self.unet is not None:\n dtype = self.unet.dtype\n else:\n dtype = None\n\n prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_attention_mask=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n attention_mask = uncond_input.attention_mask.to(device)\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n else:\n negative_prompt_embeds = None\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors=\"pt\").to(device)\n image, nsfw_detected, watermark_detected = self.safety_checker(\n images=image,\n clip_input=safety_checker_input.pixel_values.to(dtype=dtype),\n )\n else:\n nsfw_detected = None\n watermark_detected = None\n\n return image, nsfw_detected, watermark_detected\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n image,\n mask_image,\n batch_size,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # image\n\n if isinstance(image, list):\n check_image_type = image[0]\n else:\n check_image_type = image\n\n if (\n not isinstance(check_image_type, torch.Tensor)\n and not isinstance(check_image_type, PIL.Image.Image)\n and not isinstance(check_image_type, np.ndarray)\n ):\n raise ValueError(\n \"`image` has to be of type `torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is\"\n f\" {type(check_image_type)}\"\n )\n\n if isinstance(image, list):\n image_batch_size = len(image)\n elif isinstance(image, torch.Tensor):\n image_batch_size = image.shape[0]\n elif isinstance(image, PIL.Image.Image):\n image_batch_size = 1\n elif isinstance(image, np.ndarray):\n image_batch_size = image.shape[0]\n else:\n assert False\n\n if batch_size != image_batch_size:\n raise ValueError(f\"image batch size: {image_batch_size} must be same as prompt batch size {batch_size}\")\n\n # mask_image\n\n if isinstance(mask_image, list):\n check_image_type = mask_image[0]\n else:\n check_image_type = mask_image\n\n if (\n not isinstance(check_image_type, torch.Tensor)\n and not isinstance(check_image_type, PIL.Image.Image)\n and not isinstance(check_image_type, np.ndarray)\n ):\n raise ValueError(\n \"`mask_image` has to be of type `torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is\"\n f\" {type(check_image_type)}\"\n )\n\n if isinstance(mask_image, list):\n image_batch_size = len(mask_image)\n elif isinstance(mask_image, torch.Tensor):\n image_batch_size = mask_image.shape[0]\n elif isinstance(mask_image, PIL.Image.Image):\n image_batch_size = 1\n elif isinstance(mask_image, np.ndarray):\n image_batch_size = mask_image.shape[0]\n else:\n assert False\n\n if image_batch_size != 1 and batch_size != image_batch_size:\n raise ValueError(\n f\"mask_image batch size: {image_batch_size} must be `1` or the same as prompt batch size {batch_size}\"\n )\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing\n def _text_preprocessing(self, text, clean_caption=False):\n if clean_caption and not is_bs4_available():\n logger.warning(BACKENDS_MAPPING[\"bs4\"][-1].format(\"Setting `clean_caption=True`\"))\n logger.warning(\"Setting `clean_caption` to False...\")\n clean_caption = False\n\n if clean_caption and not is_ftfy_available():\n logger.warning(BACKENDS_MAPPING[\"ftfy\"][-1].format(\"Setting `clean_caption=True`\"))\n logger.warning(\"Setting `clean_caption` to False...\")\n clean_caption = False\n\n if not isinstance(text, (tuple, list)):\n text = [text]\n\n def process(text: str):\n if clean_caption:\n text = self._clean_caption(text)\n text = self._clean_caption(text)\n else:\n text = text.lower().strip()\n return text\n\n return [process(t) for t in text]\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption\n def _clean_caption(self, caption):\n caption = str(caption)\n caption = ul.unquote_plus(caption)\n caption = caption.strip().lower()\n caption = re.sub(\"\", \"person\", caption)\n # urls:\n caption = re.sub(\n r\"\\b((?:https?:(?:\\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\\w/-]*\\b\\/?(?!@)))\", # noqa\n \"\",\n caption,\n ) # regex for urls\n caption = re.sub(\n r\"\\b((?:www:(?:\\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\\w/-]*\\b\\/?(?!@)))\", # noqa\n \"\",\n caption,\n ) # regex for urls\n # html:\n caption = BeautifulSoup(caption, features=\"html.parser\").text\n\n # @\n caption = re.sub(r\"@[\\w\\d]+\\b\", \"\", caption)\n\n # 31C0—31EF CJK Strokes\n # 31F0—31FF Katakana Phonetic Extensions\n # 3200—32FF Enclosed CJK Letters and Months\n # 3300—33FF CJK Compatibility\n # 3400—4DBF CJK Unified Ideographs Extension A\n # 4DC0—4DFF Yijing Hexagram Symbols\n # 4E00—9FFF CJK Unified Ideographs\n caption = re.sub(r\"[\\u31c0-\\u31ef]+\", \"\", caption)\n caption = re.sub(r\"[\\u31f0-\\u31ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3200-\\u32ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3300-\\u33ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3400-\\u4dbf]+\", \"\", caption)\n caption = re.sub(r\"[\\u4dc0-\\u4dff]+\", \"\", caption)\n caption = re.sub(r\"[\\u4e00-\\u9fff]+\", \"\", caption)\n #######################################################\n\n # все виды тире / all types of dash --> \"-\"\n caption = re.sub(\n r\"[\\u002D\\u058A\\u05BE\\u1400\\u1806\\u2010-\\u2015\\u2E17\\u2E1A\\u2E3A\\u2E3B\\u2E40\\u301C\\u3030\\u30A0\\uFE31\\uFE32\\uFE58\\uFE63\\uFF0D]+\", # noqa\n \"-\",\n caption,\n )\n\n # кавычки к одному стандарту\n caption = re.sub(r\"[`´«»“”¨]\", '\"', caption)\n caption = re.sub(r\"[‘’]\", \"'\", caption)\n\n # "\n caption = re.sub(r\""?\", \"\", caption)\n # &\n caption = re.sub(r\"&\", \"\", caption)\n\n # ip adresses:\n caption = re.sub(r\"\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\", \" \", caption)\n\n # article ids:\n caption = re.sub(r\"\\d:\\d\\d\\s+$\", \"\", caption)\n\n # \\n\n caption = re.sub(r\"\\\\n\", \" \", caption)\n\n # \"#123\"\n caption = re.sub(r\"#\\d{1,3}\\b\", \"\", caption)\n # \"#12345..\"\n caption = re.sub(r\"#\\d{5,}\\b\", \"\", caption)\n # \"123456..\"\n caption = re.sub(r\"\\b\\d{6,}\\b\", \"\", caption)\n # filenames:\n caption = re.sub(r\"[\\S]+\\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)\", \"\", caption)\n\n #\n caption = re.sub(r\"[\\\"\\']{2,}\", r'\"', caption) # \"\"\"AUSVERKAUFT\"\"\"\n caption = re.sub(r\"[\\.]{2,}\", r\" \", caption) # \"\"\"AUSVERKAUFT\"\"\"\n\n caption = re.sub(self.bad_punct_regex, r\" \", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT\n caption = re.sub(r\"\\s+\\.\\s+\", r\" \", caption) # \" . \"\n\n # this-is-my-cute-cat / this_is_my_cute_cat\n regex2 = re.compile(r\"(?:\\-|\\_)\")\n if len(re.findall(regex2, caption)) > 3:\n caption = re.sub(regex2, \" \", caption)\n\n caption = ftfy.fix_text(caption)\n caption = html.unescape(html.unescape(caption))\n\n caption = re.sub(r\"\\b[a-zA-Z]{1,3}\\d{3,15}\\b\", \"\", caption) # jc6640\n caption = re.sub(r\"\\b[a-zA-Z]+\\d+[a-zA-Z]+\\b\", \"\", caption) # jc6640vc\n caption = re.sub(r\"\\b\\d+[a-zA-Z]+\\d+\\b\", \"\", caption) # 6640vc231\n\n caption = re.sub(r\"(worldwide\\s+)?(free\\s+)?shipping\", \"\", caption)\n caption = re.sub(r\"(free\\s)?download(\\sfree)?\", \"\", caption)\n caption = re.sub(r\"\\bclick\\b\\s(?:for|on)\\s\\w+\", \"\", caption)\n caption = re.sub(r\"\\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\\simage[s]?)?\", \"\", caption)\n caption = re.sub(r\"\\bpage\\s+\\d+\\b\", \"\", caption)\n\n caption = re.sub(r\"\\b\\d*[a-zA-Z]+\\d+[a-zA-Z]+\\d+[a-zA-Z\\d]*\\b\", r\" \", caption) # j2d1a2a...\n\n caption = re.sub(r\"\\b\\d+\\.?\\d*[xх×]\\d+\\.?\\d*\\b\", \"\", caption)\n\n caption = re.sub(r\"\\b\\s+\\:\\s+\", r\": \", caption)\n caption = re.sub(r\"(\\D[,\\./])\\b\", r\"\\1 \", caption)\n caption = re.sub(r\"\\s+\", \" \", caption)\n\n caption.strip()\n\n caption = re.sub(r\"^[\\\"\\']([\\w\\W]+)[\\\"\\']$\", r\"\\1\", caption)\n caption = re.sub(r\"^[\\'\\_,\\-\\:;]\", r\"\", caption)\n caption = re.sub(r\"[\\'\\_,\\-\\:\\-\\+]$\", r\"\", caption)\n caption = re.sub(r\"^\\.\\S+$\", \"\", caption)\n\n return caption.strip()\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.preprocess_image\n def preprocess_image(self, image: PIL.Image.Image) -> torch.Tensor:\n if not isinstance(image, list):\n image = [image]\n\n def numpy_to_pt(images):\n if images.ndim == 3:\n images = images[..., None]\n\n images = torch.from_numpy(images.transpose(0, 3, 1, 2))\n return images\n\n if isinstance(image[0], PIL.Image.Image):\n new_image = []\n\n for image_ in image:\n image_ = image_.convert(\"RGB\")\n image_ = resize(image_, self.unet.config.sample_size)\n image_ = np.array(image_)\n image_ = image_.astype(np.float32)\n image_ = image_ / 127.5 - 1\n new_image.append(image_)\n\n image = new_image\n\n image = np.stack(image, axis=0) # to np\n image = numpy_to_pt(image) # to pt\n\n elif isinstance(image[0], np.ndarray):\n image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0)\n image = numpy_to_pt(image)\n\n elif isinstance(image[0], torch.Tensor):\n image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0)\n\n return image\n\n def preprocess_mask_image(self, mask_image) -> torch.Tensor:\n if not isinstance(mask_image, list):\n mask_image = [mask_image]\n\n if isinstance(mask_image[0], torch.Tensor):\n mask_image = torch.cat(mask_image, axis=0) if mask_image[0].ndim == 4 else torch.stack(mask_image, axis=0)\n\n if mask_image.ndim == 2:\n # Batch and add channel dim for single mask\n mask_image = mask_image.unsqueeze(0).unsqueeze(0)\n elif mask_image.ndim == 3 and mask_image.shape[0] == 1:\n # Single mask, the 0'th dimension is considered to be\n # the existing batch size of 1\n mask_image = mask_image.unsqueeze(0)\n elif mask_image.ndim == 3 and mask_image.shape[0] != 1:\n # Batch of mask, the 0'th dimension is considered to be\n # the batching dimension\n mask_image = mask_image.unsqueeze(1)\n\n mask_image[mask_image < 0.5] = 0\n mask_image[mask_image >= 0.5] = 1\n\n elif isinstance(mask_image[0], PIL.Image.Image):\n new_mask_image = []\n\n for mask_image_ in mask_image:\n mask_image_ = mask_image_.convert(\"L\")\n mask_image_ = resize(mask_image_, self.unet.config.sample_size)\n mask_image_ = np.array(mask_image_)\n mask_image_ = mask_image_[None, None, :]\n new_mask_image.append(mask_image_)\n\n mask_image = new_mask_image\n\n mask_image = np.concatenate(mask_image, axis=0)\n mask_image = mask_image.astype(np.float32) / 255.0\n mask_image[mask_image < 0.5] = 0\n mask_image[mask_image >= 0.5] = 1\n mask_image = torch.from_numpy(mask_image)\n\n elif isinstance(mask_image[0], np.ndarray):\n mask_image = np.concatenate([m[None, None, :] for m in mask_image], axis=0)\n\n mask_image[mask_image < 0.5] = 0\n mask_image[mask_image >= 0.5] = 1\n mask_image = torch.from_numpy(mask_image)\n\n return mask_image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n if hasattr(self.scheduler, \"set_begin_index\"):\n self.scheduler.set_begin_index(t_start * self.scheduler.order)\n\n return timesteps, num_inference_steps - t_start\n\n def prepare_intermediate_images(\n self, image, timestep, batch_size, num_images_per_prompt, dtype, device, mask_image, generator=None\n ):\n image_batch_size, channels, height, width = image.shape\n\n batch_size = batch_size * num_images_per_prompt\n\n shape = (batch_size, channels, height, width)\n\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n image = image.repeat_interleave(num_images_per_prompt, dim=0)\n noised_image = self.scheduler.add_noise(image, noise, timestep)\n\n image = (1 - mask_image) * image + mask_image * noised_image\n\n return image\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: Union[\n PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray]\n ] = None,\n mask_image: Union[\n PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray]\n ] = None,\n strength: float = 1.0,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n guidance_scale: float = 7.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n clean_caption: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n image (`torch.Tensor` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process.\n mask_image (`PIL.Image.Image`):\n `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be\n repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted\n to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)\n instead of 3, so the expected shape would be `(B, H, W, 1)`.\n strength (`float`, *optional*, defaults to 1.0):\n Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`\n will be used as a starting point, adding more noise to it the larger the `strength`. The number of\n denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will\n be maximum and the denoising process will run for the full number of iterations specified in\n `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`\n timesteps are used. Must be in descending order.\n guidance_scale (`float`, *optional*, defaults to 7.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n clean_caption (`bool`, *optional*, defaults to `True`):\n Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to\n be installed. If the dependencies are not installed, the embeddings will be created from the raw\n prompt.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When\n returning a tuple, the first element is a list with the generated images, and the second element is a list\n of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\" (nsfw)\n or watermarked content, according to the `safety_checker`.\n \"\"\"\n # 1. Check inputs. Raise error if not correct\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n self.check_inputs(\n prompt,\n image,\n mask_image,\n batch_size,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n )\n\n # 2. Define call parameters\n device = self._execution_device\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n do_classifier_free_guidance,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n clean_caption=clean_caption,\n )\n\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n dtype = prompt_embeds.dtype\n\n # 4. Prepare timesteps\n if timesteps is not None:\n self.scheduler.set_timesteps(timesteps=timesteps, device=device)\n timesteps = self.scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)\n\n # 5. Prepare intermediate images\n image = self.preprocess_image(image)\n image = image.to(device=device, dtype=dtype)\n\n mask_image = self.preprocess_mask_image(mask_image)\n mask_image = mask_image.to(device=device, dtype=dtype)\n\n if mask_image.shape[0] == 1:\n mask_image = mask_image.repeat_interleave(batch_size * num_images_per_prompt, dim=0)\n else:\n mask_image = mask_image.repeat_interleave(num_images_per_prompt, dim=0)\n\n noise_timestep = timesteps[0:1]\n noise_timestep = noise_timestep.repeat(batch_size * num_images_per_prompt)\n\n intermediate_images = self.prepare_intermediate_images(\n image, noise_timestep, batch_size, num_images_per_prompt, dtype, device, mask_image, generator\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # HACK: see comment in `enable_model_cpu_offload`\n if hasattr(self, \"text_encoder_offload_hook\") and self.text_encoder_offload_hook is not None:\n self.text_encoder_offload_hook.offload()\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n model_input = (\n torch.cat([intermediate_images] * 2) if do_classifier_free_guidance else intermediate_images\n )\n model_input = self.scheduler.scale_model_input(model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1], dim=1)\n noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1], dim=1)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)\n\n if self.scheduler.config.variance_type not in [\"learned\", \"learned_range\"]:\n noise_pred, _ = noise_pred.split(model_input.shape[1], dim=1)\n\n # compute the previous noisy sample x_t -> x_t-1\n prev_intermediate_images = intermediate_images\n\n intermediate_images = self.scheduler.step(\n noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False\n )[0]\n\n intermediate_images = (1 - mask_image) * prev_intermediate_images + mask_image * intermediate_images\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, intermediate_images)\n\n image = intermediate_images\n\n if output_type == \"pil\":\n # 8. Post-processing\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n # 9. Run safety checker\n image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # 10. Convert to PIL\n image = self.numpy_to_pil(image)\n\n # 11. Apply watermark\n if self.watermarker is not None:\n self.watermarker.apply_watermark(image, self.unet.config.sample_size)\n elif output_type == \"pt\":\n nsfw_detected = None\n watermark_detected = None\n\n if hasattr(self, \"unet_offload_hook\") and self.unet_offload_hook is not None:\n self.unet_offload_hook.offload()\n else:\n # 8. Post-processing\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n # 9. Run safety checker\n image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, nsfw_detected, watermark_detected)\n\n return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected)\n" }, { "path": "diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting_superresolution.py", "content": "import html\nimport inspect\nimport re\nimport urllib.parse as ul\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer\n\nfrom ...loaders import LoraLoaderMixin\nfrom ...models import UNet2DConditionModel\nfrom ...schedulers import DDPMScheduler\nfrom ...utils import (\n BACKENDS_MAPPING,\n PIL_INTERPOLATION,\n is_bs4_available,\n is_ftfy_available,\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline\nfrom .pipeline_output import IFPipelineOutput\nfrom .safety_checker import IFSafetyChecker\nfrom .watermark import IFWatermarker\n\n\nif is_bs4_available():\n from bs4 import BeautifulSoup\n\nif is_ftfy_available():\n import ftfy\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.resize\ndef resize(images: PIL.Image.Image, img_size: int) -> PIL.Image.Image:\n w, h = images.size\n\n coef = w / h\n\n w, h = img_size, img_size\n\n if coef >= 1:\n w = int(round(img_size / 8 * coef) * 8)\n else:\n h = int(round(img_size / 8 / coef) * 8)\n\n images = images.resize((w, h), resample=PIL_INTERPOLATION[\"bicubic\"], reducing_gap=None)\n\n return images\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline, DiffusionPipeline\n >>> from diffusers.utils import pt_to_pil\n >>> import torch\n >>> from PIL import Image\n >>> import requests\n >>> from io import BytesIO\n\n >>> url = \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/person.png\"\n >>> response = requests.get(url)\n >>> original_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n >>> original_image = original_image\n\n >>> url = \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/glasses_mask.png\"\n >>> response = requests.get(url)\n >>> mask_image = Image.open(BytesIO(response.content))\n >>> mask_image = mask_image\n\n >>> pipe = IFInpaintingPipeline.from_pretrained(\n ... \"DeepFloyd/IF-I-XL-v1.0\", variant=\"fp16\", torch_dtype=torch.float16\n ... )\n >>> pipe.enable_model_cpu_offload()\n\n >>> prompt = \"blue sunglasses\"\n\n >>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)\n >>> image = pipe(\n ... image=original_image,\n ... mask_image=mask_image,\n ... prompt_embeds=prompt_embeds,\n ... negative_prompt_embeds=negative_embeds,\n ... output_type=\"pt\",\n ... ).images\n\n >>> # save intermediate image\n >>> pil_image = pt_to_pil(image)\n >>> pil_image[0].save(\"./if_stage_I.png\")\n\n >>> super_res_1_pipe = IFInpaintingSuperResolutionPipeline.from_pretrained(\n ... \"DeepFloyd/IF-II-L-v1.0\", text_encoder=None, variant=\"fp16\", torch_dtype=torch.float16\n ... )\n >>> super_res_1_pipe.enable_model_cpu_offload()\n\n >>> image = super_res_1_pipe(\n ... image=image,\n ... mask_image=mask_image,\n ... original_image=original_image,\n ... prompt_embeds=prompt_embeds,\n ... negative_prompt_embeds=negative_embeds,\n ... ).images\n >>> image[0].save(\"./if_stage_II.png\")\n ```\n \"\"\"\n\n\nclass IFInpaintingSuperResolutionPipeline(DiffusionPipeline, LoraLoaderMixin):\n tokenizer: T5Tokenizer\n text_encoder: T5EncoderModel\n\n unet: UNet2DConditionModel\n scheduler: DDPMScheduler\n image_noising_scheduler: DDPMScheduler\n\n feature_extractor: Optional[CLIPImageProcessor]\n safety_checker: Optional[IFSafetyChecker]\n\n watermarker: Optional[IFWatermarker]\n\n bad_punct_regex = re.compile(\n r\"[\"\n + \"#®•©™&@·º½¾¿¡§~\"\n + r\"\\)\"\n + r\"\\(\"\n + r\"\\]\"\n + r\"\\[\"\n + r\"\\}\"\n + r\"\\{\"\n + r\"\\|\"\n + \"\\\\\"\n + r\"\\/\"\n + r\"\\*\"\n + r\"]{1,}\"\n ) # noqa\n\n model_cpu_offload_seq = \"text_encoder->unet\"\n _optional_components = [\"tokenizer\", \"text_encoder\", \"safety_checker\", \"feature_extractor\", \"watermarker\"]\n _exclude_from_cpu_offload = [\"watermarker\"]\n\n def __init__(\n self,\n tokenizer: T5Tokenizer,\n text_encoder: T5EncoderModel,\n unet: UNet2DConditionModel,\n scheduler: DDPMScheduler,\n image_noising_scheduler: DDPMScheduler,\n safety_checker: Optional[IFSafetyChecker],\n feature_extractor: Optional[CLIPImageProcessor],\n watermarker: Optional[IFWatermarker],\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the IF license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n if unet.config.in_channels != 6:\n logger.warning(\n \"It seems like you have loaded a checkpoint that shall not be used for super resolution from {unet.config._name_or_path} as it accepts {unet.config.in_channels} input channels instead of 6. Please make sure to pass a super resolution checkpoint as the `'unet'`: IFSuperResolutionPipeline.from_pretrained(unet=super_resolution_unet, ...)`.\"\n )\n\n self.register_modules(\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n unet=unet,\n scheduler=scheduler,\n image_noising_scheduler=image_noising_scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n watermarker=watermarker,\n )\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing\n def _text_preprocessing(self, text, clean_caption=False):\n if clean_caption and not is_bs4_available():\n logger.warning(BACKENDS_MAPPING[\"bs4\"][-1].format(\"Setting `clean_caption=True`\"))\n logger.warning(\"Setting `clean_caption` to False...\")\n clean_caption = False\n\n if clean_caption and not is_ftfy_available():\n logger.warning(BACKENDS_MAPPING[\"ftfy\"][-1].format(\"Setting `clean_caption=True`\"))\n logger.warning(\"Setting `clean_caption` to False...\")\n clean_caption = False\n\n if not isinstance(text, (tuple, list)):\n text = [text]\n\n def process(text: str):\n if clean_caption:\n text = self._clean_caption(text)\n text = self._clean_caption(text)\n else:\n text = text.lower().strip()\n return text\n\n return [process(t) for t in text]\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption\n def _clean_caption(self, caption):\n caption = str(caption)\n caption = ul.unquote_plus(caption)\n caption = caption.strip().lower()\n caption = re.sub(\"\", \"person\", caption)\n # urls:\n caption = re.sub(\n r\"\\b((?:https?:(?:\\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\\w/-]*\\b\\/?(?!@)))\", # noqa\n \"\",\n caption,\n ) # regex for urls\n caption = re.sub(\n r\"\\b((?:www:(?:\\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\\w/-]*\\b\\/?(?!@)))\", # noqa\n \"\",\n caption,\n ) # regex for urls\n # html:\n caption = BeautifulSoup(caption, features=\"html.parser\").text\n\n # @\n caption = re.sub(r\"@[\\w\\d]+\\b\", \"\", caption)\n\n # 31C0—31EF CJK Strokes\n # 31F0—31FF Katakana Phonetic Extensions\n # 3200—32FF Enclosed CJK Letters and Months\n # 3300—33FF CJK Compatibility\n # 3400—4DBF CJK Unified Ideographs Extension A\n # 4DC0—4DFF Yijing Hexagram Symbols\n # 4E00—9FFF CJK Unified Ideographs\n caption = re.sub(r\"[\\u31c0-\\u31ef]+\", \"\", caption)\n caption = re.sub(r\"[\\u31f0-\\u31ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3200-\\u32ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3300-\\u33ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3400-\\u4dbf]+\", \"\", caption)\n caption = re.sub(r\"[\\u4dc0-\\u4dff]+\", \"\", caption)\n caption = re.sub(r\"[\\u4e00-\\u9fff]+\", \"\", caption)\n #######################################################\n\n # все виды тире / all types of dash --> \"-\"\n caption = re.sub(\n r\"[\\u002D\\u058A\\u05BE\\u1400\\u1806\\u2010-\\u2015\\u2E17\\u2E1A\\u2E3A\\u2E3B\\u2E40\\u301C\\u3030\\u30A0\\uFE31\\uFE32\\uFE58\\uFE63\\uFF0D]+\", # noqa\n \"-\",\n caption,\n )\n\n # кавычки к одному стандарту\n caption = re.sub(r\"[`´«»“”¨]\", '\"', caption)\n caption = re.sub(r\"[‘’]\", \"'\", caption)\n\n # "\n caption = re.sub(r\""?\", \"\", caption)\n # &\n caption = re.sub(r\"&\", \"\", caption)\n\n # ip adresses:\n caption = re.sub(r\"\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\", \" \", caption)\n\n # article ids:\n caption = re.sub(r\"\\d:\\d\\d\\s+$\", \"\", caption)\n\n # \\n\n caption = re.sub(r\"\\\\n\", \" \", caption)\n\n # \"#123\"\n caption = re.sub(r\"#\\d{1,3}\\b\", \"\", caption)\n # \"#12345..\"\n caption = re.sub(r\"#\\d{5,}\\b\", \"\", caption)\n # \"123456..\"\n caption = re.sub(r\"\\b\\d{6,}\\b\", \"\", caption)\n # filenames:\n caption = re.sub(r\"[\\S]+\\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)\", \"\", caption)\n\n #\n caption = re.sub(r\"[\\\"\\']{2,}\", r'\"', caption) # \"\"\"AUSVERKAUFT\"\"\"\n caption = re.sub(r\"[\\.]{2,}\", r\" \", caption) # \"\"\"AUSVERKAUFT\"\"\"\n\n caption = re.sub(self.bad_punct_regex, r\" \", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT\n caption = re.sub(r\"\\s+\\.\\s+\", r\" \", caption) # \" . \"\n\n # this-is-my-cute-cat / this_is_my_cute_cat\n regex2 = re.compile(r\"(?:\\-|\\_)\")\n if len(re.findall(regex2, caption)) > 3:\n caption = re.sub(regex2, \" \", caption)\n\n caption = ftfy.fix_text(caption)\n caption = html.unescape(html.unescape(caption))\n\n caption = re.sub(r\"\\b[a-zA-Z]{1,3}\\d{3,15}\\b\", \"\", caption) # jc6640\n caption = re.sub(r\"\\b[a-zA-Z]+\\d+[a-zA-Z]+\\b\", \"\", caption) # jc6640vc\n caption = re.sub(r\"\\b\\d+[a-zA-Z]+\\d+\\b\", \"\", caption) # 6640vc231\n\n caption = re.sub(r\"(worldwide\\s+)?(free\\s+)?shipping\", \"\", caption)\n caption = re.sub(r\"(free\\s)?download(\\sfree)?\", \"\", caption)\n caption = re.sub(r\"\\bclick\\b\\s(?:for|on)\\s\\w+\", \"\", caption)\n caption = re.sub(r\"\\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\\simage[s]?)?\", \"\", caption)\n caption = re.sub(r\"\\bpage\\s+\\d+\\b\", \"\", caption)\n\n caption = re.sub(r\"\\b\\d*[a-zA-Z]+\\d+[a-zA-Z]+\\d+[a-zA-Z\\d]*\\b\", r\" \", caption) # j2d1a2a...\n\n caption = re.sub(r\"\\b\\d+\\.?\\d*[xх×]\\d+\\.?\\d*\\b\", \"\", caption)\n\n caption = re.sub(r\"\\b\\s+\\:\\s+\", r\": \", caption)\n caption = re.sub(r\"(\\D[,\\./])\\b\", r\"\\1 \", caption)\n caption = re.sub(r\"\\s+\", \" \", caption)\n\n caption.strip()\n\n caption = re.sub(r\"^[\\\"\\']([\\w\\W]+)[\\\"\\']$\", r\"\\1\", caption)\n caption = re.sub(r\"^[\\'\\_,\\-\\:;]\", r\"\", caption)\n caption = re.sub(r\"[\\'\\_,\\-\\:\\-\\+]$\", r\"\", caption)\n caption = re.sub(r\"^\\.\\S+$\", \"\", caption)\n\n return caption.strip()\n\n @torch.no_grad()\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt: Union[str, List[str]],\n do_classifier_free_guidance: bool = True,\n num_images_per_prompt: int = 1,\n device: Optional[torch.device] = None,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n clean_caption: bool = False,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):\n whether to use classifier free guidance or not\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n number of images that should be generated per prompt\n device: (`torch.device`, *optional*):\n torch device to place the resulting embeddings on\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.\n Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n clean_caption (bool, defaults to `False`):\n If `True`, the function will preprocess and clean the provided caption before encoding.\n \"\"\"\n if prompt is not None and negative_prompt is not None:\n if type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n\n if device is None:\n device = self._execution_device\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF\n max_length = 77\n\n if prompt_embeds is None:\n prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {max_length} tokens: {removed_text}\"\n )\n\n attention_mask = text_inputs.attention_mask.to(device)\n\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n prompt_embeds = prompt_embeds[0]\n\n if self.text_encoder is not None:\n dtype = self.text_encoder.dtype\n elif self.unet is not None:\n dtype = self.unet.dtype\n else:\n dtype = None\n\n prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_attention_mask=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n attention_mask = uncond_input.attention_mask.to(device)\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n else:\n negative_prompt_embeds = None\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors=\"pt\").to(device)\n image, nsfw_detected, watermark_detected = self.safety_checker(\n images=image,\n clip_input=safety_checker_input.pixel_values.to(dtype=dtype),\n )\n else:\n nsfw_detected = None\n watermark_detected = None\n\n return image, nsfw_detected, watermark_detected\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n image,\n original_image,\n mask_image,\n batch_size,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # image\n\n if isinstance(image, list):\n check_image_type = image[0]\n else:\n check_image_type = image\n\n if (\n not isinstance(check_image_type, torch.Tensor)\n and not isinstance(check_image_type, PIL.Image.Image)\n and not isinstance(check_image_type, np.ndarray)\n ):\n raise ValueError(\n \"`image` has to be of type `torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is\"\n f\" {type(check_image_type)}\"\n )\n\n if isinstance(image, list):\n image_batch_size = len(image)\n elif isinstance(image, torch.Tensor):\n image_batch_size = image.shape[0]\n elif isinstance(image, PIL.Image.Image):\n image_batch_size = 1\n elif isinstance(image, np.ndarray):\n image_batch_size = image.shape[0]\n else:\n assert False\n\n if batch_size != image_batch_size:\n raise ValueError(f\"image batch size: {image_batch_size} must be same as prompt batch size {batch_size}\")\n\n # original_image\n\n if isinstance(original_image, list):\n check_image_type = original_image[0]\n else:\n check_image_type = original_image\n\n if (\n not isinstance(check_image_type, torch.Tensor)\n and not isinstance(check_image_type, PIL.Image.Image)\n and not isinstance(check_image_type, np.ndarray)\n ):\n raise ValueError(\n \"`original_image` has to be of type `torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is\"\n f\" {type(check_image_type)}\"\n )\n\n if isinstance(original_image, list):\n image_batch_size = len(original_image)\n elif isinstance(original_image, torch.Tensor):\n image_batch_size = original_image.shape[0]\n elif isinstance(original_image, PIL.Image.Image):\n image_batch_size = 1\n elif isinstance(original_image, np.ndarray):\n image_batch_size = original_image.shape[0]\n else:\n assert False\n\n if batch_size != image_batch_size:\n raise ValueError(\n f\"original_image batch size: {image_batch_size} must be same as prompt batch size {batch_size}\"\n )\n\n # mask_image\n\n if isinstance(mask_image, list):\n check_image_type = mask_image[0]\n else:\n check_image_type = mask_image\n\n if (\n not isinstance(check_image_type, torch.Tensor)\n and not isinstance(check_image_type, PIL.Image.Image)\n and not isinstance(check_image_type, np.ndarray)\n ):\n raise ValueError(\n \"`mask_image` has to be of type `torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is\"\n f\" {type(check_image_type)}\"\n )\n\n if isinstance(mask_image, list):\n image_batch_size = len(mask_image)\n elif isinstance(mask_image, torch.Tensor):\n image_batch_size = mask_image.shape[0]\n elif isinstance(mask_image, PIL.Image.Image):\n image_batch_size = 1\n elif isinstance(mask_image, np.ndarray):\n image_batch_size = mask_image.shape[0]\n else:\n assert False\n\n if image_batch_size != 1 and batch_size != image_batch_size:\n raise ValueError(\n f\"mask_image batch size: {image_batch_size} must be `1` or the same as prompt batch size {batch_size}\"\n )\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.preprocess_image with preprocess_image -> preprocess_original_image\n def preprocess_original_image(self, image: PIL.Image.Image) -> torch.Tensor:\n if not isinstance(image, list):\n image = [image]\n\n def numpy_to_pt(images):\n if images.ndim == 3:\n images = images[..., None]\n\n images = torch.from_numpy(images.transpose(0, 3, 1, 2))\n return images\n\n if isinstance(image[0], PIL.Image.Image):\n new_image = []\n\n for image_ in image:\n image_ = image_.convert(\"RGB\")\n image_ = resize(image_, self.unet.config.sample_size)\n image_ = np.array(image_)\n image_ = image_.astype(np.float32)\n image_ = image_ / 127.5 - 1\n new_image.append(image_)\n\n image = new_image\n\n image = np.stack(image, axis=0) # to np\n image = numpy_to_pt(image) # to pt\n\n elif isinstance(image[0], np.ndarray):\n image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0)\n image = numpy_to_pt(image)\n\n elif isinstance(image[0], torch.Tensor):\n image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0)\n\n return image\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_superresolution.IFSuperResolutionPipeline.preprocess_image\n def preprocess_image(self, image: PIL.Image.Image, num_images_per_prompt, device) -> torch.Tensor:\n if not isinstance(image, torch.Tensor) and not isinstance(image, list):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n image = [np.array(i).astype(np.float32) / 127.5 - 1.0 for i in image]\n\n image = np.stack(image, axis=0) # to np\n image = torch.from_numpy(image.transpose(0, 3, 1, 2))\n elif isinstance(image[0], np.ndarray):\n image = np.stack(image, axis=0) # to np\n if image.ndim == 5:\n image = image[0]\n\n image = torch.from_numpy(image.transpose(0, 3, 1, 2))\n elif isinstance(image, list) and isinstance(image[0], torch.Tensor):\n dims = image[0].ndim\n\n if dims == 3:\n image = torch.stack(image, dim=0)\n elif dims == 4:\n image = torch.concat(image, dim=0)\n else:\n raise ValueError(f\"Image must have 3 or 4 dimensions, instead got {dims}\")\n\n image = image.to(device=device, dtype=self.unet.dtype)\n\n image = image.repeat_interleave(num_images_per_prompt, dim=0)\n\n return image\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_inpainting.IFInpaintingPipeline.preprocess_mask_image\n def preprocess_mask_image(self, mask_image) -> torch.Tensor:\n if not isinstance(mask_image, list):\n mask_image = [mask_image]\n\n if isinstance(mask_image[0], torch.Tensor):\n mask_image = torch.cat(mask_image, axis=0) if mask_image[0].ndim == 4 else torch.stack(mask_image, axis=0)\n\n if mask_image.ndim == 2:\n # Batch and add channel dim for single mask\n mask_image = mask_image.unsqueeze(0).unsqueeze(0)\n elif mask_image.ndim == 3 and mask_image.shape[0] == 1:\n # Single mask, the 0'th dimension is considered to be\n # the existing batch size of 1\n mask_image = mask_image.unsqueeze(0)\n elif mask_image.ndim == 3 and mask_image.shape[0] != 1:\n # Batch of mask, the 0'th dimension is considered to be\n # the batching dimension\n mask_image = mask_image.unsqueeze(1)\n\n mask_image[mask_image < 0.5] = 0\n mask_image[mask_image >= 0.5] = 1\n\n elif isinstance(mask_image[0], PIL.Image.Image):\n new_mask_image = []\n\n for mask_image_ in mask_image:\n mask_image_ = mask_image_.convert(\"L\")\n mask_image_ = resize(mask_image_, self.unet.config.sample_size)\n mask_image_ = np.array(mask_image_)\n mask_image_ = mask_image_[None, None, :]\n new_mask_image.append(mask_image_)\n\n mask_image = new_mask_image\n\n mask_image = np.concatenate(mask_image, axis=0)\n mask_image = mask_image.astype(np.float32) / 255.0\n mask_image[mask_image < 0.5] = 0\n mask_image[mask_image >= 0.5] = 1\n mask_image = torch.from_numpy(mask_image)\n\n elif isinstance(mask_image[0], np.ndarray):\n mask_image = np.concatenate([m[None, None, :] for m in mask_image], axis=0)\n\n mask_image[mask_image < 0.5] = 0\n mask_image[mask_image >= 0.5] = 1\n mask_image = torch.from_numpy(mask_image)\n\n return mask_image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n if hasattr(self.scheduler, \"set_begin_index\"):\n self.scheduler.set_begin_index(t_start * self.scheduler.order)\n\n return timesteps, num_inference_steps - t_start\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_inpainting.IFInpaintingPipeline.prepare_intermediate_images\n def prepare_intermediate_images(\n self, image, timestep, batch_size, num_images_per_prompt, dtype, device, mask_image, generator=None\n ):\n image_batch_size, channels, height, width = image.shape\n\n batch_size = batch_size * num_images_per_prompt\n\n shape = (batch_size, channels, height, width)\n\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n image = image.repeat_interleave(num_images_per_prompt, dim=0)\n noised_image = self.scheduler.add_noise(image, noise, timestep)\n\n image = (1 - mask_image) * image + mask_image * noised_image\n\n return image\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n image: Union[PIL.Image.Image, np.ndarray, torch.Tensor],\n original_image: Union[\n PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray]\n ] = None,\n mask_image: Union[\n PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray]\n ] = None,\n strength: float = 0.8,\n prompt: Union[str, List[str]] = None,\n num_inference_steps: int = 100,\n timesteps: List[int] = None,\n guidance_scale: float = 4.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n noise_level: int = 0,\n clean_caption: bool = True,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n image (`torch.Tensor` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process.\n original_image (`torch.Tensor` or `PIL.Image.Image`):\n The original image that `image` was varied from.\n mask_image (`PIL.Image.Image`):\n `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be\n repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted\n to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)\n instead of 3, so the expected shape would be `(B, H, W, 1)`.\n strength (`float`, *optional*, defaults to 0.8):\n Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`\n will be used as a starting point, adding more noise to it the larger the `strength`. The number of\n denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will\n be maximum and the denoising process will run for the full number of iterations specified in\n `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`\n timesteps are used. Must be in descending order.\n guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n noise_level (`int`, *optional*, defaults to 0):\n The amount of noise to add to the upscaled image. Must be in the range `[0, 1000)`\n clean_caption (`bool`, *optional*, defaults to `True`):\n Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to\n be installed. If the dependencies are not installed, the embeddings will be created from the raw\n prompt.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When\n returning a tuple, the first element is a list with the generated images, and the second element is a list\n of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\" (nsfw)\n or watermarked content, according to the `safety_checker`.\n \"\"\"\n # 1. Check inputs. Raise error if not correct\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n self.check_inputs(\n prompt,\n image,\n original_image,\n mask_image,\n batch_size,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n )\n\n # 2. Define call parameters\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n device = self._execution_device\n\n # 3. Encode input prompt\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n do_classifier_free_guidance,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n clean_caption=clean_caption,\n )\n\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n dtype = prompt_embeds.dtype\n\n # 4. Prepare timesteps\n if timesteps is not None:\n self.scheduler.set_timesteps(timesteps=timesteps, device=device)\n timesteps = self.scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)\n\n # 5. prepare original image\n original_image = self.preprocess_original_image(original_image)\n original_image = original_image.to(device=device, dtype=dtype)\n\n # 6. prepare mask image\n mask_image = self.preprocess_mask_image(mask_image)\n mask_image = mask_image.to(device=device, dtype=dtype)\n\n if mask_image.shape[0] == 1:\n mask_image = mask_image.repeat_interleave(batch_size * num_images_per_prompt, dim=0)\n else:\n mask_image = mask_image.repeat_interleave(num_images_per_prompt, dim=0)\n\n # 6. Prepare intermediate images\n noise_timestep = timesteps[0:1]\n noise_timestep = noise_timestep.repeat(batch_size * num_images_per_prompt)\n\n intermediate_images = self.prepare_intermediate_images(\n original_image,\n noise_timestep,\n batch_size,\n num_images_per_prompt,\n dtype,\n device,\n mask_image,\n generator,\n )\n\n # 7. Prepare upscaled image and noise level\n _, _, height, width = original_image.shape\n\n image = self.preprocess_image(image, num_images_per_prompt, device)\n\n upscaled = F.interpolate(image, (height, width), mode=\"bilinear\", align_corners=True)\n\n noise_level = torch.tensor([noise_level] * upscaled.shape[0], device=upscaled.device)\n noise = randn_tensor(upscaled.shape, generator=generator, device=upscaled.device, dtype=upscaled.dtype)\n upscaled = self.image_noising_scheduler.add_noise(upscaled, noise, timesteps=noise_level)\n\n if do_classifier_free_guidance:\n noise_level = torch.cat([noise_level] * 2)\n\n # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # HACK: see comment in `enable_model_cpu_offload`\n if hasattr(self, \"text_encoder_offload_hook\") and self.text_encoder_offload_hook is not None:\n self.text_encoder_offload_hook.offload()\n\n # 9. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n model_input = torch.cat([intermediate_images, upscaled], dim=1)\n\n model_input = torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input\n model_input = self.scheduler.scale_model_input(model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n class_labels=noise_level,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1] // 2, dim=1)\n noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1] // 2, dim=1)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)\n\n if self.scheduler.config.variance_type not in [\"learned\", \"learned_range\"]:\n noise_pred, _ = noise_pred.split(intermediate_images.shape[1], dim=1)\n\n # compute the previous noisy sample x_t -> x_t-1\n prev_intermediate_images = intermediate_images\n\n intermediate_images = self.scheduler.step(\n noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False\n )[0]\n\n intermediate_images = (1 - mask_image) * prev_intermediate_images + mask_image * intermediate_images\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, intermediate_images)\n\n image = intermediate_images\n\n if output_type == \"pil\":\n # 10. Post-processing\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n # 11. Run safety checker\n image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # 12. Convert to PIL\n image = self.numpy_to_pil(image)\n\n # 13. Apply watermark\n if self.watermarker is not None:\n self.watermarker.apply_watermark(image, self.unet.config.sample_size)\n elif output_type == \"pt\":\n nsfw_detected = None\n watermark_detected = None\n\n else:\n # 10. Post-processing\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n # 11. Run safety checker\n image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, nsfw_detected, watermark_detected)\n\n return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected)\n" }, { "path": "diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py", "content": "import html\nimport inspect\nimport re\nimport urllib.parse as ul\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer\n\nfrom ...loaders import LoraLoaderMixin\nfrom ...models import UNet2DConditionModel\nfrom ...schedulers import DDPMScheduler\nfrom ...utils import (\n BACKENDS_MAPPING,\n is_bs4_available,\n is_ftfy_available,\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline\nfrom .pipeline_output import IFPipelineOutput\nfrom .safety_checker import IFSafetyChecker\nfrom .watermark import IFWatermarker\n\n\nif is_bs4_available():\n from bs4 import BeautifulSoup\n\nif is_ftfy_available():\n import ftfy\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import IFPipeline, IFSuperResolutionPipeline, DiffusionPipeline\n >>> from diffusers.utils import pt_to_pil\n >>> import torch\n\n >>> pipe = IFPipeline.from_pretrained(\"DeepFloyd/IF-I-XL-v1.0\", variant=\"fp16\", torch_dtype=torch.float16)\n >>> pipe.enable_model_cpu_offload()\n\n >>> prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says \"very deep learning\"'\n >>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)\n\n >>> image = pipe(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, output_type=\"pt\").images\n\n >>> # save intermediate image\n >>> pil_image = pt_to_pil(image)\n >>> pil_image[0].save(\"./if_stage_I.png\")\n\n >>> super_res_1_pipe = IFSuperResolutionPipeline.from_pretrained(\n ... \"DeepFloyd/IF-II-L-v1.0\", text_encoder=None, variant=\"fp16\", torch_dtype=torch.float16\n ... )\n >>> super_res_1_pipe.enable_model_cpu_offload()\n\n >>> image = super_res_1_pipe(\n ... image=image, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds\n ... ).images\n >>> image[0].save(\"./if_stage_II.png\")\n ```\n\"\"\"\n\n\nclass IFSuperResolutionPipeline(DiffusionPipeline, LoraLoaderMixin):\n tokenizer: T5Tokenizer\n text_encoder: T5EncoderModel\n\n unet: UNet2DConditionModel\n scheduler: DDPMScheduler\n image_noising_scheduler: DDPMScheduler\n\n feature_extractor: Optional[CLIPImageProcessor]\n safety_checker: Optional[IFSafetyChecker]\n\n watermarker: Optional[IFWatermarker]\n\n bad_punct_regex = re.compile(\n r\"[\"\n + \"#®•©™&@·º½¾¿¡§~\"\n + r\"\\)\"\n + r\"\\(\"\n + r\"\\]\"\n + r\"\\[\"\n + r\"\\}\"\n + r\"\\{\"\n + r\"\\|\"\n + \"\\\\\"\n + r\"\\/\"\n + r\"\\*\"\n + r\"]{1,}\"\n ) # noqa\n\n _optional_components = [\"tokenizer\", \"text_encoder\", \"safety_checker\", \"feature_extractor\", \"watermarker\"]\n model_cpu_offload_seq = \"text_encoder->unet\"\n _exclude_from_cpu_offload = [\"watermarker\"]\n\n def __init__(\n self,\n tokenizer: T5Tokenizer,\n text_encoder: T5EncoderModel,\n unet: UNet2DConditionModel,\n scheduler: DDPMScheduler,\n image_noising_scheduler: DDPMScheduler,\n safety_checker: Optional[IFSafetyChecker],\n feature_extractor: Optional[CLIPImageProcessor],\n watermarker: Optional[IFWatermarker],\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the IF license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n if unet.config.in_channels != 6:\n logger.warning(\n \"It seems like you have loaded a checkpoint that shall not be used for super resolution from {unet.config._name_or_path} as it accepts {unet.config.in_channels} input channels instead of 6. Please make sure to pass a super resolution checkpoint as the `'unet'`: IFSuperResolutionPipeline.from_pretrained(unet=super_resolution_unet, ...)`.\"\n )\n\n self.register_modules(\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n unet=unet,\n scheduler=scheduler,\n image_noising_scheduler=image_noising_scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n watermarker=watermarker,\n )\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing\n def _text_preprocessing(self, text, clean_caption=False):\n if clean_caption and not is_bs4_available():\n logger.warning(BACKENDS_MAPPING[\"bs4\"][-1].format(\"Setting `clean_caption=True`\"))\n logger.warning(\"Setting `clean_caption` to False...\")\n clean_caption = False\n\n if clean_caption and not is_ftfy_available():\n logger.warning(BACKENDS_MAPPING[\"ftfy\"][-1].format(\"Setting `clean_caption=True`\"))\n logger.warning(\"Setting `clean_caption` to False...\")\n clean_caption = False\n\n if not isinstance(text, (tuple, list)):\n text = [text]\n\n def process(text: str):\n if clean_caption:\n text = self._clean_caption(text)\n text = self._clean_caption(text)\n else:\n text = text.lower().strip()\n return text\n\n return [process(t) for t in text]\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption\n def _clean_caption(self, caption):\n caption = str(caption)\n caption = ul.unquote_plus(caption)\n caption = caption.strip().lower()\n caption = re.sub(\"\", \"person\", caption)\n # urls:\n caption = re.sub(\n r\"\\b((?:https?:(?:\\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\\w/-]*\\b\\/?(?!@)))\", # noqa\n \"\",\n caption,\n ) # regex for urls\n caption = re.sub(\n r\"\\b((?:www:(?:\\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\\w/-]*\\b\\/?(?!@)))\", # noqa\n \"\",\n caption,\n ) # regex for urls\n # html:\n caption = BeautifulSoup(caption, features=\"html.parser\").text\n\n # @\n caption = re.sub(r\"@[\\w\\d]+\\b\", \"\", caption)\n\n # 31C0—31EF CJK Strokes\n # 31F0—31FF Katakana Phonetic Extensions\n # 3200—32FF Enclosed CJK Letters and Months\n # 3300—33FF CJK Compatibility\n # 3400—4DBF CJK Unified Ideographs Extension A\n # 4DC0—4DFF Yijing Hexagram Symbols\n # 4E00—9FFF CJK Unified Ideographs\n caption = re.sub(r\"[\\u31c0-\\u31ef]+\", \"\", caption)\n caption = re.sub(r\"[\\u31f0-\\u31ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3200-\\u32ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3300-\\u33ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3400-\\u4dbf]+\", \"\", caption)\n caption = re.sub(r\"[\\u4dc0-\\u4dff]+\", \"\", caption)\n caption = re.sub(r\"[\\u4e00-\\u9fff]+\", \"\", caption)\n #######################################################\n\n # все виды тире / all types of dash --> \"-\"\n caption = re.sub(\n r\"[\\u002D\\u058A\\u05BE\\u1400\\u1806\\u2010-\\u2015\\u2E17\\u2E1A\\u2E3A\\u2E3B\\u2E40\\u301C\\u3030\\u30A0\\uFE31\\uFE32\\uFE58\\uFE63\\uFF0D]+\", # noqa\n \"-\",\n caption,\n )\n\n # кавычки к одному стандарту\n caption = re.sub(r\"[`´«»“”¨]\", '\"', caption)\n caption = re.sub(r\"[‘’]\", \"'\", caption)\n\n # "\n caption = re.sub(r\""?\", \"\", caption)\n # &\n caption = re.sub(r\"&\", \"\", caption)\n\n # ip adresses:\n caption = re.sub(r\"\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\", \" \", caption)\n\n # article ids:\n caption = re.sub(r\"\\d:\\d\\d\\s+$\", \"\", caption)\n\n # \\n\n caption = re.sub(r\"\\\\n\", \" \", caption)\n\n # \"#123\"\n caption = re.sub(r\"#\\d{1,3}\\b\", \"\", caption)\n # \"#12345..\"\n caption = re.sub(r\"#\\d{5,}\\b\", \"\", caption)\n # \"123456..\"\n caption = re.sub(r\"\\b\\d{6,}\\b\", \"\", caption)\n # filenames:\n caption = re.sub(r\"[\\S]+\\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)\", \"\", caption)\n\n #\n caption = re.sub(r\"[\\\"\\']{2,}\", r'\"', caption) # \"\"\"AUSVERKAUFT\"\"\"\n caption = re.sub(r\"[\\.]{2,}\", r\" \", caption) # \"\"\"AUSVERKAUFT\"\"\"\n\n caption = re.sub(self.bad_punct_regex, r\" \", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT\n caption = re.sub(r\"\\s+\\.\\s+\", r\" \", caption) # \" . \"\n\n # this-is-my-cute-cat / this_is_my_cute_cat\n regex2 = re.compile(r\"(?:\\-|\\_)\")\n if len(re.findall(regex2, caption)) > 3:\n caption = re.sub(regex2, \" \", caption)\n\n caption = ftfy.fix_text(caption)\n caption = html.unescape(html.unescape(caption))\n\n caption = re.sub(r\"\\b[a-zA-Z]{1,3}\\d{3,15}\\b\", \"\", caption) # jc6640\n caption = re.sub(r\"\\b[a-zA-Z]+\\d+[a-zA-Z]+\\b\", \"\", caption) # jc6640vc\n caption = re.sub(r\"\\b\\d+[a-zA-Z]+\\d+\\b\", \"\", caption) # 6640vc231\n\n caption = re.sub(r\"(worldwide\\s+)?(free\\s+)?shipping\", \"\", caption)\n caption = re.sub(r\"(free\\s)?download(\\sfree)?\", \"\", caption)\n caption = re.sub(r\"\\bclick\\b\\s(?:for|on)\\s\\w+\", \"\", caption)\n caption = re.sub(r\"\\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\\simage[s]?)?\", \"\", caption)\n caption = re.sub(r\"\\bpage\\s+\\d+\\b\", \"\", caption)\n\n caption = re.sub(r\"\\b\\d*[a-zA-Z]+\\d+[a-zA-Z]+\\d+[a-zA-Z\\d]*\\b\", r\" \", caption) # j2d1a2a...\n\n caption = re.sub(r\"\\b\\d+\\.?\\d*[xх×]\\d+\\.?\\d*\\b\", \"\", caption)\n\n caption = re.sub(r\"\\b\\s+\\:\\s+\", r\": \", caption)\n caption = re.sub(r\"(\\D[,\\./])\\b\", r\"\\1 \", caption)\n caption = re.sub(r\"\\s+\", \" \", caption)\n\n caption.strip()\n\n caption = re.sub(r\"^[\\\"\\']([\\w\\W]+)[\\\"\\']$\", r\"\\1\", caption)\n caption = re.sub(r\"^[\\'\\_,\\-\\:;]\", r\"\", caption)\n caption = re.sub(r\"[\\'\\_,\\-\\:\\-\\+]$\", r\"\", caption)\n caption = re.sub(r\"^\\.\\S+$\", \"\", caption)\n\n return caption.strip()\n\n @torch.no_grad()\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt: Union[str, List[str]],\n do_classifier_free_guidance: bool = True,\n num_images_per_prompt: int = 1,\n device: Optional[torch.device] = None,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n clean_caption: bool = False,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):\n whether to use classifier free guidance or not\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n number of images that should be generated per prompt\n device: (`torch.device`, *optional*):\n torch device to place the resulting embeddings on\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.\n Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n clean_caption (bool, defaults to `False`):\n If `True`, the function will preprocess and clean the provided caption before encoding.\n \"\"\"\n if prompt is not None and negative_prompt is not None:\n if type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n\n if device is None:\n device = self._execution_device\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF\n max_length = 77\n\n if prompt_embeds is None:\n prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {max_length} tokens: {removed_text}\"\n )\n\n attention_mask = text_inputs.attention_mask.to(device)\n\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n prompt_embeds = prompt_embeds[0]\n\n if self.text_encoder is not None:\n dtype = self.text_encoder.dtype\n elif self.unet is not None:\n dtype = self.unet.dtype\n else:\n dtype = None\n\n prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_attention_mask=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n attention_mask = uncond_input.attention_mask.to(device)\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n else:\n negative_prompt_embeds = None\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors=\"pt\").to(device)\n image, nsfw_detected, watermark_detected = self.safety_checker(\n images=image,\n clip_input=safety_checker_input.pixel_values.to(dtype=dtype),\n )\n else:\n nsfw_detected = None\n watermark_detected = None\n\n return image, nsfw_detected, watermark_detected\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n image,\n batch_size,\n noise_level,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if noise_level < 0 or noise_level >= self.image_noising_scheduler.config.num_train_timesteps:\n raise ValueError(\n f\"`noise_level`: {noise_level} must be a valid timestep in `self.noising_scheduler`, [0, {self.image_noising_scheduler.config.num_train_timesteps})\"\n )\n\n if isinstance(image, list):\n check_image_type = image[0]\n else:\n check_image_type = image\n\n if (\n not isinstance(check_image_type, torch.Tensor)\n and not isinstance(check_image_type, PIL.Image.Image)\n and not isinstance(check_image_type, np.ndarray)\n ):\n raise ValueError(\n \"`image` has to be of type `torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is\"\n f\" {type(check_image_type)}\"\n )\n\n if isinstance(image, list):\n image_batch_size = len(image)\n elif isinstance(image, torch.Tensor):\n image_batch_size = image.shape[0]\n elif isinstance(image, PIL.Image.Image):\n image_batch_size = 1\n elif isinstance(image, np.ndarray):\n image_batch_size = image.shape[0]\n else:\n assert False\n\n if batch_size != image_batch_size:\n raise ValueError(f\"image batch size: {image_batch_size} must be same as prompt batch size {batch_size}\")\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_intermediate_images\n def prepare_intermediate_images(self, batch_size, num_channels, height, width, dtype, device, generator):\n shape = (batch_size, num_channels, height, width)\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n intermediate_images = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n # scale the initial noise by the standard deviation required by the scheduler\n intermediate_images = intermediate_images * self.scheduler.init_noise_sigma\n return intermediate_images\n\n def preprocess_image(self, image, num_images_per_prompt, device):\n if not isinstance(image, torch.Tensor) and not isinstance(image, list):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n image = [np.array(i).astype(np.float32) / 127.5 - 1.0 for i in image]\n\n image = np.stack(image, axis=0) # to np\n image = torch.from_numpy(image.transpose(0, 3, 1, 2))\n elif isinstance(image[0], np.ndarray):\n image = np.stack(image, axis=0) # to np\n if image.ndim == 5:\n image = image[0]\n\n image = torch.from_numpy(image.transpose(0, 3, 1, 2))\n elif isinstance(image, list) and isinstance(image[0], torch.Tensor):\n dims = image[0].ndim\n\n if dims == 3:\n image = torch.stack(image, dim=0)\n elif dims == 4:\n image = torch.concat(image, dim=0)\n else:\n raise ValueError(f\"Image must have 3 or 4 dimensions, instead got {dims}\")\n\n image = image.to(device=device, dtype=self.unet.dtype)\n\n image = image.repeat_interleave(num_images_per_prompt, dim=0)\n\n return image\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n height: int = None,\n width: int = None,\n image: Union[PIL.Image.Image, np.ndarray, torch.Tensor] = None,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n guidance_scale: float = 4.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n noise_level: int = 250,\n clean_caption: bool = True,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n height (`int`, *optional*, defaults to None):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to None):\n The width in pixels of the generated image.\n image (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`):\n The image to be upscaled.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*, defaults to None):\n Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`\n timesteps are used. Must be in descending order.\n guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n noise_level (`int`, *optional*, defaults to 250):\n The amount of noise to add to the upscaled image. Must be in the range `[0, 1000)`\n clean_caption (`bool`, *optional*, defaults to `True`):\n Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to\n be installed. If the dependencies are not installed, the embeddings will be created from the raw\n prompt.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When\n returning a tuple, the first element is a list with the generated images, and the second element is a list\n of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\" (nsfw)\n or watermarked content, according to the `safety_checker`.\n \"\"\"\n # 1. Check inputs. Raise error if not correct\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n self.check_inputs(\n prompt,\n image,\n batch_size,\n noise_level,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n )\n\n # 2. Define call parameters\n\n height = height or self.unet.config.sample_size\n width = width or self.unet.config.sample_size\n\n device = self._execution_device\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n do_classifier_free_guidance,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n clean_caption=clean_caption,\n )\n\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Prepare timesteps\n if timesteps is not None:\n self.scheduler.set_timesteps(timesteps=timesteps, device=device)\n timesteps = self.scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n if hasattr(self.scheduler, \"set_begin_index\"):\n self.scheduler.set_begin_index(0)\n\n # 5. Prepare intermediate images\n num_channels = self.unet.config.in_channels // 2\n intermediate_images = self.prepare_intermediate_images(\n batch_size * num_images_per_prompt,\n num_channels,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Prepare upscaled image and noise level\n image = self.preprocess_image(image, num_images_per_prompt, device)\n upscaled = F.interpolate(image, (height, width), mode=\"bilinear\", align_corners=True)\n\n noise_level = torch.tensor([noise_level] * upscaled.shape[0], device=upscaled.device)\n noise = randn_tensor(upscaled.shape, generator=generator, device=upscaled.device, dtype=upscaled.dtype)\n upscaled = self.image_noising_scheduler.add_noise(upscaled, noise, timesteps=noise_level)\n\n if do_classifier_free_guidance:\n noise_level = torch.cat([noise_level] * 2)\n\n # HACK: see comment in `enable_model_cpu_offload`\n if hasattr(self, \"text_encoder_offload_hook\") and self.text_encoder_offload_hook is not None:\n self.text_encoder_offload_hook.offload()\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n model_input = torch.cat([intermediate_images, upscaled], dim=1)\n\n model_input = torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input\n model_input = self.scheduler.scale_model_input(model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n class_labels=noise_level,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1] // 2, dim=1)\n noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1] // 2, dim=1)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)\n\n if self.scheduler.config.variance_type not in [\"learned\", \"learned_range\"]:\n noise_pred, _ = noise_pred.split(intermediate_images.shape[1], dim=1)\n\n # compute the previous noisy sample x_t -> x_t-1\n intermediate_images = self.scheduler.step(\n noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False\n )[0]\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, intermediate_images)\n\n image = intermediate_images\n\n if output_type == \"pil\":\n # 9. Post-processing\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n # 10. Run safety checker\n image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # 11. Convert to PIL\n image = self.numpy_to_pil(image)\n\n # 12. Apply watermark\n if self.watermarker is not None:\n self.watermarker.apply_watermark(image, self.unet.config.sample_size)\n elif output_type == \"pt\":\n nsfw_detected = None\n watermark_detected = None\n\n if hasattr(self, \"unet_offload_hook\") and self.unet_offload_hook is not None:\n self.unet_offload_hook.offload()\n else:\n # 9. Post-processing\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n # 10. Run safety checker\n image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, nsfw_detected, watermark_detected)\n\n return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected)\n" }, { "path": "diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_output.py", "content": "from dataclasses import dataclass\nfrom typing import List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\n\nfrom ...utils import BaseOutput\n\n\n@dataclass\nclass IFPipelineOutput(BaseOutput):\n \"\"\"\n Args:\n Output class for Stable Diffusion pipelines.\n images (`List[PIL.Image.Image]` or `np.ndarray`)\n List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,\n num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.\n nsfw_detected (`List[bool]`)\n List of flags denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content or a watermark. `None` if safety checking could not be performed.\n watermark_detected (`List[bool]`)\n List of flags denoting whether the corresponding generated image likely has a watermark. `None` if safety\n checking could not be performed.\n \"\"\"\n\n images: Union[List[PIL.Image.Image], np.ndarray]\n nsfw_detected: Optional[List[bool]]\n watermark_detected: Optional[List[bool]]\n" }, { "path": "diffusers/src/diffusers/pipelines/deepfloyd_if/safety_checker.py", "content": "import numpy as np\nimport torch\nimport torch.nn as nn\nfrom transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel\n\nfrom ...utils import logging\n\n\nlogger = logging.get_logger(__name__)\n\n\nclass IFSafetyChecker(PreTrainedModel):\n config_class = CLIPConfig\n\n _no_split_modules = [\"CLIPEncoderLayer\"]\n\n def __init__(self, config: CLIPConfig):\n super().__init__(config)\n\n self.vision_model = CLIPVisionModelWithProjection(config.vision_config)\n\n self.p_head = nn.Linear(config.vision_config.projection_dim, 1)\n self.w_head = nn.Linear(config.vision_config.projection_dim, 1)\n\n @torch.no_grad()\n def forward(self, clip_input, images, p_threshold=0.5, w_threshold=0.5):\n image_embeds = self.vision_model(clip_input)[0]\n\n nsfw_detected = self.p_head(image_embeds)\n nsfw_detected = nsfw_detected.flatten()\n nsfw_detected = nsfw_detected > p_threshold\n nsfw_detected = nsfw_detected.tolist()\n\n if any(nsfw_detected):\n logger.warning(\n \"Potential NSFW content was detected in one or more images. A black image will be returned instead.\"\n \" Try again with a different prompt and/or seed.\"\n )\n\n for idx, nsfw_detected_ in enumerate(nsfw_detected):\n if nsfw_detected_:\n images[idx] = np.zeros(images[idx].shape)\n\n watermark_detected = self.w_head(image_embeds)\n watermark_detected = watermark_detected.flatten()\n watermark_detected = watermark_detected > w_threshold\n watermark_detected = watermark_detected.tolist()\n\n if any(watermark_detected):\n logger.warning(\n \"Potential watermarked content was detected in one or more images. A black image will be returned instead.\"\n \" Try again with a different prompt and/or seed.\"\n )\n\n for idx, watermark_detected_ in enumerate(watermark_detected):\n if watermark_detected_:\n images[idx] = np.zeros(images[idx].shape)\n\n return images, nsfw_detected, watermark_detected\n" }, { "path": "diffusers/src/diffusers/pipelines/deepfloyd_if/timesteps.py", "content": "fast27_timesteps = [\n 999,\n 800,\n 799,\n 600,\n 599,\n 500,\n 400,\n 399,\n 377,\n 355,\n 333,\n 311,\n 288,\n 266,\n 244,\n 222,\n 200,\n 199,\n 177,\n 155,\n 133,\n 111,\n 88,\n 66,\n 44,\n 22,\n 0,\n]\n\nsmart27_timesteps = [\n 999,\n 976,\n 952,\n 928,\n 905,\n 882,\n 858,\n 857,\n 810,\n 762,\n 715,\n 714,\n 572,\n 429,\n 428,\n 286,\n 285,\n 238,\n 190,\n 143,\n 142,\n 118,\n 95,\n 71,\n 47,\n 24,\n 0,\n]\n\nsmart50_timesteps = [\n 999,\n 988,\n 977,\n 966,\n 955,\n 944,\n 933,\n 922,\n 911,\n 900,\n 899,\n 879,\n 859,\n 840,\n 820,\n 800,\n 799,\n 766,\n 733,\n 700,\n 699,\n 650,\n 600,\n 599,\n 500,\n 499,\n 400,\n 399,\n 350,\n 300,\n 299,\n 266,\n 233,\n 200,\n 199,\n 179,\n 159,\n 140,\n 120,\n 100,\n 99,\n 88,\n 77,\n 66,\n 55,\n 44,\n 33,\n 22,\n 11,\n 0,\n]\n\nsmart100_timesteps = [\n 999,\n 995,\n 992,\n 989,\n 985,\n 981,\n 978,\n 975,\n 971,\n 967,\n 964,\n 961,\n 957,\n 956,\n 951,\n 947,\n 942,\n 937,\n 933,\n 928,\n 923,\n 919,\n 914,\n 913,\n 908,\n 903,\n 897,\n 892,\n 887,\n 881,\n 876,\n 871,\n 870,\n 864,\n 858,\n 852,\n 846,\n 840,\n 834,\n 828,\n 827,\n 820,\n 813,\n 806,\n 799,\n 792,\n 785,\n 784,\n 777,\n 770,\n 763,\n 756,\n 749,\n 742,\n 741,\n 733,\n 724,\n 716,\n 707,\n 699,\n 698,\n 688,\n 677,\n 666,\n 656,\n 655,\n 645,\n 634,\n 623,\n 613,\n 612,\n 598,\n 584,\n 570,\n 569,\n 555,\n 541,\n 527,\n 526,\n 505,\n 484,\n 483,\n 462,\n 440,\n 439,\n 396,\n 395,\n 352,\n 351,\n 308,\n 307,\n 264,\n 263,\n 220,\n 219,\n 176,\n 132,\n 88,\n 44,\n 0,\n]\n\nsmart185_timesteps = [\n 999,\n 997,\n 995,\n 992,\n 990,\n 988,\n 986,\n 984,\n 981,\n 979,\n 977,\n 975,\n 972,\n 970,\n 968,\n 966,\n 964,\n 961,\n 959,\n 957,\n 956,\n 954,\n 951,\n 949,\n 946,\n 944,\n 941,\n 939,\n 936,\n 934,\n 931,\n 929,\n 926,\n 924,\n 921,\n 919,\n 916,\n 914,\n 913,\n 910,\n 907,\n 905,\n 902,\n 899,\n 896,\n 893,\n 891,\n 888,\n 885,\n 882,\n 879,\n 877,\n 874,\n 871,\n 870,\n 867,\n 864,\n 861,\n 858,\n 855,\n 852,\n 849,\n 846,\n 843,\n 840,\n 837,\n 834,\n 831,\n 828,\n 827,\n 824,\n 821,\n 817,\n 814,\n 811,\n 808,\n 804,\n 801,\n 798,\n 795,\n 791,\n 788,\n 785,\n 784,\n 780,\n 777,\n 774,\n 770,\n 766,\n 763,\n 760,\n 756,\n 752,\n 749,\n 746,\n 742,\n 741,\n 737,\n 733,\n 730,\n 726,\n 722,\n 718,\n 714,\n 710,\n 707,\n 703,\n 699,\n 698,\n 694,\n 690,\n 685,\n 681,\n 677,\n 673,\n 669,\n 664,\n 660,\n 656,\n 655,\n 650,\n 646,\n 641,\n 636,\n 632,\n 627,\n 622,\n 618,\n 613,\n 612,\n 607,\n 602,\n 596,\n 591,\n 586,\n 580,\n 575,\n 570,\n 569,\n 563,\n 557,\n 551,\n 545,\n 539,\n 533,\n 527,\n 526,\n 519,\n 512,\n 505,\n 498,\n 491,\n 484,\n 483,\n 474,\n 466,\n 457,\n 449,\n 440,\n 439,\n 428,\n 418,\n 407,\n 396,\n 395,\n 381,\n 366,\n 352,\n 351,\n 330,\n 308,\n 307,\n 286,\n 264,\n 263,\n 242,\n 220,\n 219,\n 176,\n 175,\n 132,\n 131,\n 88,\n 44,\n 0,\n]\n\nsuper27_timesteps = [\n 999,\n 991,\n 982,\n 974,\n 966,\n 958,\n 950,\n 941,\n 933,\n 925,\n 916,\n 908,\n 900,\n 899,\n 874,\n 850,\n 825,\n 800,\n 799,\n 700,\n 600,\n 500,\n 400,\n 300,\n 200,\n 100,\n 0,\n]\n\nsuper40_timesteps = [\n 999,\n 992,\n 985,\n 978,\n 971,\n 964,\n 957,\n 949,\n 942,\n 935,\n 928,\n 921,\n 914,\n 907,\n 900,\n 899,\n 879,\n 859,\n 840,\n 820,\n 800,\n 799,\n 766,\n 733,\n 700,\n 699,\n 650,\n 600,\n 599,\n 500,\n 499,\n 400,\n 399,\n 300,\n 299,\n 200,\n 199,\n 100,\n 99,\n 0,\n]\n\nsuper100_timesteps = [\n 999,\n 996,\n 992,\n 989,\n 985,\n 982,\n 979,\n 975,\n 972,\n 968,\n 965,\n 961,\n 958,\n 955,\n 951,\n 948,\n 944,\n 941,\n 938,\n 934,\n 931,\n 927,\n 924,\n 920,\n 917,\n 914,\n 910,\n 907,\n 903,\n 900,\n 899,\n 891,\n 884,\n 876,\n 869,\n 861,\n 853,\n 846,\n 838,\n 830,\n 823,\n 815,\n 808,\n 800,\n 799,\n 788,\n 777,\n 766,\n 755,\n 744,\n 733,\n 722,\n 711,\n 700,\n 699,\n 688,\n 677,\n 666,\n 655,\n 644,\n 633,\n 622,\n 611,\n 600,\n 599,\n 585,\n 571,\n 557,\n 542,\n 528,\n 514,\n 500,\n 499,\n 485,\n 471,\n 457,\n 442,\n 428,\n 414,\n 400,\n 399,\n 379,\n 359,\n 340,\n 320,\n 300,\n 299,\n 279,\n 259,\n 240,\n 220,\n 200,\n 199,\n 166,\n 133,\n 100,\n 99,\n 66,\n 33,\n 0,\n]\n" }, { "path": "diffusers/src/diffusers/pipelines/deepfloyd_if/watermark.py", "content": "from typing import List\n\nimport PIL.Image\nimport torch\nfrom PIL import Image\n\nfrom ...configuration_utils import ConfigMixin\nfrom ...models.modeling_utils import ModelMixin\nfrom ...utils import PIL_INTERPOLATION\n\n\nclass IFWatermarker(ModelMixin, ConfigMixin):\n def __init__(self):\n super().__init__()\n\n self.register_buffer(\"watermark_image\", torch.zeros((62, 62, 4)))\n self.watermark_image_as_pil = None\n\n def apply_watermark(self, images: List[PIL.Image.Image], sample_size=None):\n # Copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287\n\n h = images[0].height\n w = images[0].width\n\n sample_size = sample_size or h\n\n coef = min(h / sample_size, w / sample_size)\n img_h, img_w = (int(h / coef), int(w / coef)) if coef < 1 else (h, w)\n\n S1, S2 = 1024**2, img_w * img_h\n K = (S2 / S1) ** 0.5\n wm_size, wm_x, wm_y = int(K * 62), img_w - int(14 * K), img_h - int(14 * K)\n\n if self.watermark_image_as_pil is None:\n watermark_image = self.watermark_image.to(torch.uint8).cpu().numpy()\n watermark_image = Image.fromarray(watermark_image, mode=\"RGBA\")\n self.watermark_image_as_pil = watermark_image\n\n wm_img = self.watermark_image_as_pil.resize(\n (wm_size, wm_size), PIL_INTERPOLATION[\"bicubic\"], reducing_gap=None\n )\n\n for pil_img in images:\n pil_img.paste(wm_img, box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y), mask=wm_img.split()[-1])\n\n return images\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/README.md", "content": "# Deprecated Pipelines\n\nThis folder contains pipelines that have very low usage as measured by model downloads, issues and PRs. While you can still use the pipelines just as before, we will stop testing the pipelines and will not accept any changes to existing files." }, { "path": "diffusers/src/diffusers/pipelines/deprecated/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_librosa_available,\n is_note_seq_available,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not is_torch_available():\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_pt_objects\n\n _dummy_objects.update(get_objects_from_module(dummy_pt_objects))\nelse:\n _import_structure[\"latent_diffusion_uncond\"] = [\"LDMPipeline\"]\n _import_structure[\"pndm\"] = [\"PNDMPipeline\"]\n _import_structure[\"repaint\"] = [\"RePaintPipeline\"]\n _import_structure[\"score_sde_ve\"] = [\"ScoreSdeVePipeline\"]\n _import_structure[\"stochastic_karras_ve\"] = [\"KarrasVePipeline\"]\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"alt_diffusion\"] = [\n \"AltDiffusionImg2ImgPipeline\",\n \"AltDiffusionPipeline\",\n \"AltDiffusionPipelineOutput\",\n ]\n _import_structure[\"versatile_diffusion\"] = [\n \"VersatileDiffusionDualGuidedPipeline\",\n \"VersatileDiffusionImageVariationPipeline\",\n \"VersatileDiffusionPipeline\",\n \"VersatileDiffusionTextToImagePipeline\",\n ]\n _import_structure[\"vq_diffusion\"] = [\"VQDiffusionPipeline\"]\n _import_structure[\"stable_diffusion_variants\"] = [\n \"CycleDiffusionPipeline\",\n \"StableDiffusionInpaintPipelineLegacy\",\n \"StableDiffusionPix2PixZeroPipeline\",\n \"StableDiffusionParadigmsPipeline\",\n \"StableDiffusionModelEditingPipeline\",\n ]\n\ntry:\n if not (is_torch_available() and is_librosa_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_librosa_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_librosa_objects))\n\nelse:\n _import_structure[\"audio_diffusion\"] = [\"AudioDiffusionPipeline\", \"Mel\"]\n\ntry:\n if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_transformers_and_torch_and_note_seq_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_transformers_and_torch_and_note_seq_objects))\n\nelse:\n _import_structure[\"spectrogram_diffusion\"] = [\"MidiProcessor\", \"SpectrogramDiffusionPipeline\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not is_torch_available():\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_pt_objects import *\n\n else:\n from .latent_diffusion_uncond import LDMPipeline\n from .pndm import PNDMPipeline\n from .repaint import RePaintPipeline\n from .score_sde_ve import ScoreSdeVePipeline\n from .stochastic_karras_ve import KarrasVePipeline\n\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n\n else:\n from .alt_diffusion import AltDiffusionImg2ImgPipeline, AltDiffusionPipeline, AltDiffusionPipelineOutput\n from .audio_diffusion import AudioDiffusionPipeline, Mel\n from .spectrogram_diffusion import SpectrogramDiffusionPipeline\n from .stable_diffusion_variants import (\n CycleDiffusionPipeline,\n StableDiffusionInpaintPipelineLegacy,\n StableDiffusionModelEditingPipeline,\n StableDiffusionParadigmsPipeline,\n StableDiffusionPix2PixZeroPipeline,\n )\n from .stochastic_karras_ve import KarrasVePipeline\n from .versatile_diffusion import (\n VersatileDiffusionDualGuidedPipeline,\n VersatileDiffusionImageVariationPipeline,\n VersatileDiffusionPipeline,\n VersatileDiffusionTextToImagePipeline,\n )\n from .vq_diffusion import VQDiffusionPipeline\n\n try:\n if not (is_torch_available() and is_librosa_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_librosa_objects import *\n else:\n from .audio_diffusion import AudioDiffusionPipeline, Mel\n\n try:\n if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403\n else:\n from .spectrogram_diffusion import (\n MidiProcessor,\n SpectrogramDiffusionPipeline,\n )\n\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ....utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ....utils import dummy_torch_and_transformers_objects\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"modeling_roberta_series\"] = [\"RobertaSeriesModelWithTransformation\"]\n _import_structure[\"pipeline_alt_diffusion\"] = [\"AltDiffusionPipeline\"]\n _import_structure[\"pipeline_alt_diffusion_img2img\"] = [\"AltDiffusionImg2ImgPipeline\"]\n\n _import_structure[\"pipeline_output\"] = [\"AltDiffusionPipelineOutput\"]\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ....utils.dummy_torch_and_transformers_objects import *\n\n else:\n from .modeling_roberta_series import RobertaSeriesModelWithTransformation\n from .pipeline_alt_diffusion import AltDiffusionPipeline\n from .pipeline_alt_diffusion_img2img import AltDiffusionImg2ImgPipeline\n from .pipeline_output import AltDiffusionPipelineOutput\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/modeling_roberta_series.py", "content": "from dataclasses import dataclass\nfrom typing import Optional, Tuple\n\nimport torch\nfrom torch import nn\nfrom transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel\nfrom transformers.utils import ModelOutput\n\n\n@dataclass\nclass TransformationModelOutput(ModelOutput):\n \"\"\"\n Base class for text model's outputs that also contains a pooling of the last hidden states.\n\n Args:\n text_embeds (`torch.Tensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):\n The text embeddings obtained by applying the projection layer to the pooler_output.\n last_hidden_state (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):\n Sequence of hidden-states at the output of the last layer of the model.\n hidden_states (`tuple(torch.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):\n Tuple of `torch.Tensor` (one for the output of the embeddings, if the model has an embedding layer, + one\n for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.\n\n Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.\n attentions (`tuple(torch.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):\n Tuple of `torch.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,\n sequence_length)`.\n\n Attentions weights after the attention softmax, used to compute the weighted average in the self-attention\n heads.\n \"\"\"\n\n projection_state: Optional[torch.Tensor] = None\n last_hidden_state: torch.Tensor = None\n hidden_states: Optional[Tuple[torch.Tensor]] = None\n attentions: Optional[Tuple[torch.Tensor]] = None\n\n\nclass RobertaSeriesConfig(XLMRobertaConfig):\n def __init__(\n self,\n pad_token_id=1,\n bos_token_id=0,\n eos_token_id=2,\n project_dim=512,\n pooler_fn=\"cls\",\n learn_encoder=False,\n use_attention_mask=True,\n **kwargs,\n ):\n super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)\n self.project_dim = project_dim\n self.pooler_fn = pooler_fn\n self.learn_encoder = learn_encoder\n self.use_attention_mask = use_attention_mask\n\n\nclass RobertaSeriesModelWithTransformation(RobertaPreTrainedModel):\n _keys_to_ignore_on_load_unexpected = [r\"pooler\", r\"logit_scale\"]\n _keys_to_ignore_on_load_missing = [r\"position_ids\", r\"predictions.decoder.bias\"]\n base_model_prefix = \"roberta\"\n config_class = RobertaSeriesConfig\n\n def __init__(self, config):\n super().__init__(config)\n self.roberta = XLMRobertaModel(config)\n self.transformation = nn.Linear(config.hidden_size, config.project_dim)\n self.has_pre_transformation = getattr(config, \"has_pre_transformation\", False)\n if self.has_pre_transformation:\n self.transformation_pre = nn.Linear(config.hidden_size, config.project_dim)\n self.pre_LN = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n self.post_init()\n\n def forward(\n self,\n input_ids: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n token_type_ids: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.Tensor] = None,\n head_mask: Optional[torch.Tensor] = None,\n inputs_embeds: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n output_attentions: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n ):\n r\"\"\" \"\"\"\n\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n outputs = self.base_model(\n input_ids=input_ids,\n attention_mask=attention_mask,\n token_type_ids=token_type_ids,\n position_ids=position_ids,\n head_mask=head_mask,\n inputs_embeds=inputs_embeds,\n encoder_hidden_states=encoder_hidden_states,\n encoder_attention_mask=encoder_attention_mask,\n output_attentions=output_attentions,\n output_hidden_states=True if self.has_pre_transformation else output_hidden_states,\n return_dict=return_dict,\n )\n\n if self.has_pre_transformation:\n sequence_output2 = outputs[\"hidden_states\"][-2]\n sequence_output2 = self.pre_LN(sequence_output2)\n projection_state2 = self.transformation_pre(sequence_output2)\n\n return TransformationModelOutput(\n projection_state=projection_state2,\n last_hidden_state=outputs.last_hidden_state,\n hidden_states=outputs.hidden_states,\n attentions=outputs.attentions,\n )\n else:\n projection_state = self.transformation(outputs.last_hidden_state)\n return TransformationModelOutput(\n projection_state=projection_state,\n last_hidden_state=outputs.last_hidden_state,\n hidden_states=outputs.hidden_states,\n attentions=outputs.attentions,\n )\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport torch\nfrom packaging import version\nfrom transformers import CLIPImageProcessor, CLIPVisionModelWithProjection, XLMRobertaTokenizer\n\nfrom ....configuration_utils import FrozenDict\nfrom ....image_processor import PipelineImageInput, VaeImageProcessor\nfrom ....loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ....models import AutoencoderKL, ImageProjection, UNet2DConditionModel\nfrom ....models.lora import adjust_lora_scale_text_encoder\nfrom ....schedulers import KarrasDiffusionSchedulers\nfrom ....utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ....utils.torch_utils import randn_tensor\nfrom ...pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ...stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom .modeling_roberta_series import RobertaSeriesModelWithTransformation\nfrom .pipeline_output import AltDiffusionPipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import AltDiffusionPipeline\n\n >>> pipe = AltDiffusionPipeline.from_pretrained(\"BAAI/AltDiffusion-m9\", torch_dtype=torch.float16)\n >>> pipe = pipe.to(\"cuda\")\n\n >>> # \"dark elf princess, highly detailed, d & d, fantasy, highly detailed, digital painting, trending on artstation, concept art, sharp focus, illustration, art by artgerm and greg rutkowski and fuji choko and viktoria gavrilenko and hoang lap\"\n >>> prompt = \"黑暗精灵公主，非常详细，幻想，非常详细，数字绘画，概念艺术，敏锐的焦点，插图\"\n >>> image = pipe(prompt).images[0]\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass AltDiffusionPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n TextualInversionLoaderMixin,\n LoraLoaderMixin,\n IPAdapterMixin,\n FromSingleFileMixin,\n):\n r\"\"\"\n Pipeline for text-to-image generation using Alt Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.RobertaSeriesModelWithTransformation`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.XLMRobertaTokenizer`]):\n A `XLMRobertaTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->image_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"image_encoder\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: RobertaSeriesModelWithTransformation,\n tokenizer: XLMRobertaTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder: CLIPVisionModelWithProjection = None,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"clip_sample\"] = False\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Alt Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n timesteps (`torch.Tensor`):\n generate embedding vectors at these timesteps\n embedding_dim (`int`, *optional*, defaults to 512):\n dimension of the embeddings to generate\n dtype:\n data type of the generated embeddings\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def guidance_rescale(self):\n return self._guidance_rescale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n sigmas: List[float] = None,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when\n using zero terminal SNR.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n # to deal with lora scaling and other possible forward hooks\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._guidance_rescale = guidance_rescale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n if ip_adapter_image is not None:\n output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True\n image_embeds, negative_image_embeds = self.encode_image(\n ip_adapter_image, device, num_images_per_prompt, output_hidden_state\n )\n if self.do_classifier_free_guidance:\n image_embeds = torch.cat([negative_image_embeds, image_embeds])\n\n # 4. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(\n self.scheduler, num_inference_steps, device, timesteps, sigmas\n )\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 6.1 Add image embeds for IP-Adapter\n added_cond_kwargs = {\"image_embeds\": image_embeds} if ip_adapter_image is not None else None\n\n # 6.2 Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=self.cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[\n 0\n ]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return AltDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom packaging import version\nfrom transformers import CLIPImageProcessor, CLIPVisionModelWithProjection, XLMRobertaTokenizer\n\nfrom ....configuration_utils import FrozenDict\nfrom ....image_processor import PipelineImageInput, VaeImageProcessor\nfrom ....loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ....models import AutoencoderKL, ImageProjection, UNet2DConditionModel\nfrom ....models.lora import adjust_lora_scale_text_encoder\nfrom ....schedulers import KarrasDiffusionSchedulers\nfrom ....utils import (\n PIL_INTERPOLATION,\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ....utils.torch_utils import randn_tensor\nfrom ...pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ...stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom .modeling_roberta_series import RobertaSeriesModelWithTransformation\nfrom .pipeline_output import AltDiffusionPipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import requests\n >>> import torch\n >>> from PIL import Image\n >>> from io import BytesIO\n\n >>> from diffusers import AltDiffusionImg2ImgPipeline\n\n >>> device = \"cuda\"\n >>> model_id_or_path = \"BAAI/AltDiffusion-m9\"\n >>> pipe = AltDiffusionImg2ImgPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n >>> pipe = pipe.to(device)\n\n >>> url = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n\n >>> response = requests.get(url)\n >>> init_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n >>> init_image = init_image.resize((768, 512))\n\n >>> # \"A fantasy landscape, trending on artstation\"\n >>> prompt = \"幻想风景, artstation\"\n\n >>> images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images\n >>> images[0].save(\"幻想风景.png\")\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess\ndef preprocess(image):\n deprecation_message = \"The preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead\"\n deprecate(\"preprocess\", \"1.0.0\", deprecation_message, standard_warn=False)\n if isinstance(image, torch.Tensor):\n return image\n elif isinstance(image, PIL.Image.Image):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n w, h = image[0].size\n w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8\n\n image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION[\"lanczos\"]))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n image = np.array(image).astype(np.float32) / 255.0\n image = image.transpose(0, 3, 1, 2)\n image = 2.0 * image - 1.0\n image = torch.from_numpy(image)\n elif isinstance(image[0], torch.Tensor):\n image = torch.cat(image, dim=0)\n return image\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass AltDiffusionImg2ImgPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n TextualInversionLoaderMixin,\n IPAdapterMixin,\n LoraLoaderMixin,\n FromSingleFileMixin,\n):\n r\"\"\"\n Pipeline for text-guided image-to-image generation using Alt Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.RobertaSeriesModelWithTransformation`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.XLMRobertaTokenizer`]):\n A `XLMRobertaTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->image_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"image_encoder\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: RobertaSeriesModelWithTransformation,\n tokenizer: XLMRobertaTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder: CLIPVisionModelWithProjection = None,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"clip_sample\"] = False\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Alt Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n strength,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n\n return timesteps, num_inference_steps - t_start\n\n def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n\n if image.shape[1] == 4:\n init_latents = image\n\n else:\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n elif isinstance(generator, list):\n init_latents = [\n retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])\n for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = retrieve_latents(self.vae.encode(image), generator=generator)\n\n init_latents = self.vae.config.scaling_factor * init_latents\n\n if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:\n # expand init_latents for batch_size\n deprecation_message = (\n f\"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial\"\n \" images (`image`). Initial images are now duplicating to match the number of text prompts. Note\"\n \" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update\"\n \" your script to pass as many initial images as text prompts to suppress this warning.\"\n )\n deprecate(\"len(prompt) != len(image)\", \"1.0.0\", deprecation_message, standard_warn=False)\n additional_image_per_prompt = batch_size // init_latents.shape[0]\n init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)\n elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n init_latents = torch.cat([init_latents], dim=0)\n\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n latents = init_latents\n\n return latents\n\n def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n timesteps (`torch.Tensor`):\n generate embedding vectors at these timesteps\n embedding_dim (`int`, *optional*, defaults to 512):\n dimension of the embeddings to generate\n dtype:\n data type of the generated embeddings\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: PipelineImageInput = None,\n strength: float = 0.8,\n num_inference_steps: Optional[int] = 50,\n timesteps: List[int] = None,\n sigmas: List[float] = None,\n guidance_scale: Optional[float] = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: Optional[float] = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: int = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both\n numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list\n or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a\n list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image\n latents as `image`, but if passing latents directly it is not encoded again.\n strength (`float`, *optional*, defaults to 0.8):\n Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a\n starting point and more noise is added the higher the `strength`. The number of denoising steps depends\n on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising\n process runs for the full number of iterations specified in `num_inference_steps`. A value of 1\n essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference. This parameter is modulated by `strength`.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n strength,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=self.clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n if ip_adapter_image is not None:\n output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True\n image_embeds, negative_image_embeds = self.encode_image(\n ip_adapter_image, device, num_images_per_prompt, output_hidden_state\n )\n if self.do_classifier_free_guidance:\n image_embeds = torch.cat([negative_image_embeds, image_embeds])\n\n # 4. Preprocess image\n image = self.image_processor.preprocess(image)\n\n # 5. set timesteps\n timesteps, num_inference_steps = retrieve_timesteps(\n self.scheduler, num_inference_steps, device, timesteps, sigmas\n )\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n\n # 6. Prepare latent variables\n latents = self.prepare_latents(\n image,\n latent_timestep,\n batch_size,\n num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n generator,\n )\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7.1 Add image embeds for IP-Adapter\n added_cond_kwargs = {\"image_embeds\": image_embeds} if ip_adapter_image is not None else None\n\n # 7.2 Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=self.cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[\n 0\n ]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return AltDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_output.py", "content": "from dataclasses import dataclass\nfrom typing import List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\n\nfrom ....utils import (\n BaseOutput,\n)\n\n\n@dataclass\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_output.StableDiffusionPipelineOutput with Stable->Alt\nclass AltDiffusionPipelineOutput(BaseOutput):\n \"\"\"\n Output class for Alt Diffusion pipelines.\n\n Args:\n images (`List[PIL.Image.Image]` or `np.ndarray`)\n List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,\n num_channels)`.\n nsfw_content_detected (`List[bool]`)\n List indicating whether the corresponding generated image contains \"not-safe-for-work\" (nsfw) content or\n `None` if safety checking could not be performed.\n \"\"\"\n\n images: Union[List[PIL.Image.Image], np.ndarray]\n nsfw_content_detected: Optional[List[bool]]\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ....utils import DIFFUSERS_SLOW_IMPORT, _LazyModule\n\n\n_import_structure = {\n \"mel\": [\"Mel\"],\n \"pipeline_audio_diffusion\": [\"AudioDiffusionPipeline\"],\n}\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n from .mel import Mel\n from .pipeline_audio_diffusion import AudioDiffusionPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/mel.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport numpy as np # noqa: E402\n\nfrom ....configuration_utils import ConfigMixin, register_to_config\nfrom ....schedulers.scheduling_utils import SchedulerMixin\n\n\ntry:\n import librosa # noqa: E402\n\n _librosa_can_be_imported = True\n _import_error = \"\"\nexcept Exception as e:\n _librosa_can_be_imported = False\n _import_error = (\n f\"Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.\"\n )\n\n\nfrom PIL import Image # noqa: E402\n\n\nclass Mel(ConfigMixin, SchedulerMixin):\n \"\"\"\n Parameters:\n x_res (`int`):\n x resolution of spectrogram (time).\n y_res (`int`):\n y resolution of spectrogram (frequency bins).\n sample_rate (`int`):\n Sample rate of audio.\n n_fft (`int`):\n Number of Fast Fourier Transforms.\n hop_length (`int`):\n Hop length (a higher number is recommended if `y_res` < 256).\n top_db (`int`):\n Loudest decibel value.\n n_iter (`int`):\n Number of iterations for Griffin-Lim Mel inversion.\n \"\"\"\n\n config_name = \"mel_config.json\"\n\n @register_to_config\n def __init__(\n self,\n x_res: int = 256,\n y_res: int = 256,\n sample_rate: int = 22050,\n n_fft: int = 2048,\n hop_length: int = 512,\n top_db: int = 80,\n n_iter: int = 32,\n ):\n self.hop_length = hop_length\n self.sr = sample_rate\n self.n_fft = n_fft\n self.top_db = top_db\n self.n_iter = n_iter\n self.set_resolution(x_res, y_res)\n self.audio = None\n\n if not _librosa_can_be_imported:\n raise ValueError(_import_error)\n\n def set_resolution(self, x_res: int, y_res: int):\n \"\"\"Set resolution.\n\n Args:\n x_res (`int`):\n x resolution of spectrogram (time).\n y_res (`int`):\n y resolution of spectrogram (frequency bins).\n \"\"\"\n self.x_res = x_res\n self.y_res = y_res\n self.n_mels = self.y_res\n self.slice_size = self.x_res * self.hop_length - 1\n\n def load_audio(self, audio_file: str = None, raw_audio: np.ndarray = None):\n \"\"\"Load audio.\n\n Args:\n audio_file (`str`):\n An audio file that must be on disk due to [Librosa](https://librosa.org/) limitation.\n raw_audio (`np.ndarray`):\n The raw audio file as a NumPy array.\n \"\"\"\n if audio_file is not None:\n self.audio, _ = librosa.load(audio_file, mono=True, sr=self.sr)\n else:\n self.audio = raw_audio\n\n # Pad with silence if necessary.\n if len(self.audio) < self.x_res * self.hop_length:\n self.audio = np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio),))])\n\n def get_number_of_slices(self) -> int:\n \"\"\"Get number of slices in audio.\n\n Returns:\n `int`:\n Number of spectograms audio can be sliced into.\n \"\"\"\n return len(self.audio) // self.slice_size\n\n def get_audio_slice(self, slice: int = 0) -> np.ndarray:\n \"\"\"Get slice of audio.\n\n Args:\n slice (`int`):\n Slice number of audio (out of `get_number_of_slices()`).\n\n Returns:\n `np.ndarray`:\n The audio slice as a NumPy array.\n \"\"\"\n return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]\n\n def get_sample_rate(self) -> int:\n \"\"\"Get sample rate.\n\n Returns:\n `int`:\n Sample rate of audio.\n \"\"\"\n return self.sr\n\n def audio_slice_to_image(self, slice: int) -> Image.Image:\n \"\"\"Convert slice of audio to spectrogram.\n\n Args:\n slice (`int`):\n Slice number of audio to convert (out of `get_number_of_slices()`).\n\n Returns:\n `PIL Image`:\n A grayscale image of `x_res x y_res`.\n \"\"\"\n S = librosa.feature.melspectrogram(\n y=self.get_audio_slice(slice), sr=self.sr, n_fft=self.n_fft, hop_length=self.hop_length, n_mels=self.n_mels\n )\n log_S = librosa.power_to_db(S, ref=np.max, top_db=self.top_db)\n bytedata = (((log_S + self.top_db) * 255 / self.top_db).clip(0, 255) + 0.5).astype(np.uint8)\n image = Image.fromarray(bytedata)\n return image\n\n def image_to_audio(self, image: Image.Image) -> np.ndarray:\n \"\"\"Converts spectrogram to audio.\n\n Args:\n image (`PIL Image`):\n An grayscale image of `x_res x y_res`.\n\n Returns:\n audio (`np.ndarray`):\n The audio as a NumPy array.\n \"\"\"\n bytedata = np.frombuffer(image.tobytes(), dtype=\"uint8\").reshape((image.height, image.width))\n log_S = bytedata.astype(\"float\") * self.top_db / 255 - self.top_db\n S = librosa.db_to_power(log_S)\n audio = librosa.feature.inverse.mel_to_audio(\n S, sr=self.sr, n_fft=self.n_fft, hop_length=self.hop_length, n_iter=self.n_iter\n )\n return audio\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/pipeline_audio_diffusion.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nfrom math import acos, sin\nfrom typing import List, Tuple, Union\n\nimport numpy as np\nimport torch\nfrom PIL import Image\n\nfrom ....models import AutoencoderKL, UNet2DConditionModel\nfrom ....schedulers import DDIMScheduler, DDPMScheduler\nfrom ....utils.torch_utils import randn_tensor\nfrom ...pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput\nfrom .mel import Mel\n\n\nclass AudioDiffusionPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for audio diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Parameters:\n vqae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n mel ([`Mel`]):\n Transform audio into a spectrogram.\n scheduler ([`DDIMScheduler`] or [`DDPMScheduler`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`] or [`DDPMScheduler`].\n \"\"\"\n\n _optional_components = [\"vqvae\"]\n\n def __init__(\n self,\n vqvae: AutoencoderKL,\n unet: UNet2DConditionModel,\n mel: Mel,\n scheduler: Union[DDIMScheduler, DDPMScheduler],\n ):\n super().__init__()\n self.register_modules(unet=unet, scheduler=scheduler, mel=mel, vqvae=vqvae)\n\n def get_default_steps(self) -> int:\n \"\"\"Returns default number of steps recommended for inference.\n\n Returns:\n `int`:\n The number of steps.\n \"\"\"\n return 50 if isinstance(self.scheduler, DDIMScheduler) else 1000\n\n @torch.no_grad()\n def __call__(\n self,\n batch_size: int = 1,\n audio_file: str = None,\n raw_audio: np.ndarray = None,\n slice: int = 0,\n start_step: int = 0,\n steps: int = None,\n generator: torch.Generator = None,\n mask_start_secs: float = 0,\n mask_end_secs: float = 0,\n step_generator: torch.Generator = None,\n eta: float = 0,\n noise: torch.Tensor = None,\n encoding: torch.Tensor = None,\n return_dict=True,\n ) -> Union[\n Union[AudioPipelineOutput, ImagePipelineOutput],\n Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],\n ]:\n \"\"\"\n The call function to the pipeline for generation.\n\n Args:\n batch_size (`int`):\n Number of samples to generate.\n audio_file (`str`):\n An audio file that must be on disk due to [Librosa](https://librosa.org/) limitation.\n raw_audio (`np.ndarray`):\n The raw audio file as a NumPy array.\n slice (`int`):\n Slice number of audio to convert.\n start_step (int):\n Step to start diffusion from.\n steps (`int`):\n Number of denoising steps (defaults to `50` for DDIM and `1000` for DDPM).\n generator (`torch.Generator`):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n mask_start_secs (`float`):\n Number of seconds of audio to mask (not generate) at start.\n mask_end_secs (`float`):\n Number of seconds of audio to mask (not generate) at end.\n step_generator (`torch.Generator`):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) used to denoise.\n None\n eta (`float`):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n noise (`torch.Tensor`):\n A noise tensor of shape `(batch_size, 1, height, width)` or `None`.\n encoding (`torch.Tensor`):\n A tensor for [`UNet2DConditionModel`] of shape `(batch_size, seq_length, cross_attention_dim)`.\n return_dict (`bool`):\n Whether or not to return a [`AudioPipelineOutput`], [`ImagePipelineOutput`] or a plain tuple.\n\n Examples:\n\n For audio diffusion:\n\n ```py\n import torch\n from IPython.display import Audio\n from diffusers import DiffusionPipeline\n\n device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n pipe = DiffusionPipeline.from_pretrained(\"teticio/audio-diffusion-256\").to(device)\n\n output = pipe()\n display(output.images[0])\n display(Audio(output.audios[0], rate=mel.get_sample_rate()))\n ```\n\n For latent audio diffusion:\n\n ```py\n import torch\n from IPython.display import Audio\n from diffusers import DiffusionPipeline\n\n device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n pipe = DiffusionPipeline.from_pretrained(\"teticio/latent-audio-diffusion-256\").to(device)\n\n output = pipe()\n display(output.images[0])\n display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))\n ```\n\n For other tasks like variation, inpainting, outpainting, etc:\n\n ```py\n output = pipe(\n raw_audio=output.audios[0, 0],\n start_step=int(pipe.get_default_steps() / 2),\n mask_start_secs=1,\n mask_end_secs=1,\n )\n display(output.images[0])\n display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))\n ```\n\n Returns:\n `List[PIL Image]`:\n A list of Mel spectrograms (`float`, `List[np.ndarray]`) with the sample rate and raw audio.\n \"\"\"\n\n steps = steps or self.get_default_steps()\n self.scheduler.set_timesteps(steps)\n step_generator = step_generator or generator\n # For backwards compatibility\n if isinstance(self.unet.config.sample_size, int):\n self.unet.config.sample_size = (self.unet.config.sample_size, self.unet.config.sample_size)\n if noise is None:\n noise = randn_tensor(\n (\n batch_size,\n self.unet.config.in_channels,\n self.unet.config.sample_size[0],\n self.unet.config.sample_size[1],\n ),\n generator=generator,\n device=self.device,\n )\n images = noise\n mask = None\n\n if audio_file is not None or raw_audio is not None:\n self.mel.load_audio(audio_file, raw_audio)\n input_image = self.mel.audio_slice_to_image(slice)\n input_image = np.frombuffer(input_image.tobytes(), dtype=\"uint8\").reshape(\n (input_image.height, input_image.width)\n )\n input_image = (input_image / 255) * 2 - 1\n input_images = torch.tensor(input_image[np.newaxis, :, :], dtype=torch.float).to(self.device)\n\n if self.vqvae is not None:\n input_images = self.vqvae.encode(torch.unsqueeze(input_images, 0)).latent_dist.sample(\n generator=generator\n )[0]\n input_images = self.vqvae.config.scaling_factor * input_images\n\n if start_step > 0:\n images[0, 0] = self.scheduler.add_noise(input_images, noise, self.scheduler.timesteps[start_step - 1])\n\n pixels_per_second = (\n self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length\n )\n mask_start = int(mask_start_secs * pixels_per_second)\n mask_end = int(mask_end_secs * pixels_per_second)\n mask = self.scheduler.add_noise(input_images, noise, torch.tensor(self.scheduler.timesteps[start_step:]))\n\n for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])):\n if isinstance(self.unet, UNet2DConditionModel):\n model_output = self.unet(images, t, encoding)[\"sample\"]\n else:\n model_output = self.unet(images, t)[\"sample\"]\n\n if isinstance(self.scheduler, DDIMScheduler):\n images = self.scheduler.step(\n model_output=model_output,\n timestep=t,\n sample=images,\n eta=eta,\n generator=step_generator,\n )[\"prev_sample\"]\n else:\n images = self.scheduler.step(\n model_output=model_output,\n timestep=t,\n sample=images,\n generator=step_generator,\n )[\"prev_sample\"]\n\n if mask is not None:\n if mask_start > 0:\n images[:, :, :, :mask_start] = mask[:, step, :, :mask_start]\n if mask_end > 0:\n images[:, :, :, -mask_end:] = mask[:, step, :, -mask_end:]\n\n if self.vqvae is not None:\n # 0.18215 was scaling factor used in training to ensure unit variance\n images = 1 / self.vqvae.config.scaling_factor * images\n images = self.vqvae.decode(images)[\"sample\"]\n\n images = (images / 2 + 0.5).clamp(0, 1)\n images = images.cpu().permute(0, 2, 3, 1).numpy()\n images = (images * 255).round().astype(\"uint8\")\n images = list(\n (Image.fromarray(_[:, :, 0]) for _ in images)\n if images.shape[3] == 1\n else (Image.fromarray(_, mode=\"RGB\").convert(\"L\") for _ in images)\n )\n\n audios = [self.mel.image_to_audio(_) for _ in images]\n if not return_dict:\n return images, (self.mel.get_sample_rate(), audios)\n\n return BaseOutput(**AudioPipelineOutput(np.array(audios)[:, np.newaxis, :]), **ImagePipelineOutput(images))\n\n @torch.no_grad()\n def encode(self, images: List[Image.Image], steps: int = 50) -> np.ndarray:\n \"\"\"\n Reverse the denoising step process to recover a noisy image from the generated image.\n\n Args:\n images (`List[PIL Image]`):\n List of images to encode.\n steps (`int`):\n Number of encoding steps to perform (defaults to `50`).\n\n Returns:\n `np.ndarray`:\n A noise tensor of shape `(batch_size, 1, height, width)`.\n \"\"\"\n\n # Only works with DDIM as this method is deterministic\n assert isinstance(self.scheduler, DDIMScheduler)\n self.scheduler.set_timesteps(steps)\n sample = np.array(\n [np.frombuffer(image.tobytes(), dtype=\"uint8\").reshape((1, image.height, image.width)) for image in images]\n )\n sample = (sample / 255) * 2 - 1\n sample = torch.Tensor(sample).to(self.device)\n\n for t in self.progress_bar(torch.flip(self.scheduler.timesteps, (0,))):\n prev_timestep = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps\n alpha_prod_t = self.scheduler.alphas_cumprod[t]\n alpha_prod_t_prev = (\n self.scheduler.alphas_cumprod[prev_timestep]\n if prev_timestep >= 0\n else self.scheduler.final_alpha_cumprod\n )\n beta_prod_t = 1 - alpha_prod_t\n model_output = self.unet(sample, t)[\"sample\"]\n pred_sample_direction = (1 - alpha_prod_t_prev) ** (0.5) * model_output\n sample = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)\n sample = sample * alpha_prod_t ** (0.5) + beta_prod_t ** (0.5) * model_output\n\n return sample\n\n @staticmethod\n def slerp(x0: torch.Tensor, x1: torch.Tensor, alpha: float) -> torch.Tensor:\n \"\"\"Spherical Linear intERPolation.\n\n Args:\n x0 (`torch.Tensor`):\n The first tensor to interpolate between.\n x1 (`torch.Tensor`):\n Second tensor to interpolate between.\n alpha (`float`):\n Interpolation between 0 and 1\n\n Returns:\n `torch.Tensor`:\n The interpolated tensor.\n \"\"\"\n\n theta = acos(torch.dot(torch.flatten(x0), torch.flatten(x1)) / torch.norm(x0) / torch.norm(x1))\n return sin((1 - alpha) * theta) * x0 / sin(theta) + sin(alpha * theta) * x1 / sin(theta)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/latent_diffusion_uncond/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ....utils import DIFFUSERS_SLOW_IMPORT, _LazyModule\n\n\n_import_structure = {\"pipeline_latent_diffusion_uncond\": [\"LDMPipeline\"]}\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n from .pipeline_latent_diffusion_uncond import LDMPipeline\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import List, Optional, Tuple, Union\n\nimport torch\n\nfrom ....models import UNet2DModel, VQModel\nfrom ....schedulers import DDIMScheduler\nfrom ....utils.torch_utils import randn_tensor\nfrom ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nclass LDMPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for unconditional image generation using latent diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Parameters:\n vqvae ([`VQModel`]):\n Vector-quantized (VQ) model to encode and decode images to and from latent representations.\n unet ([`UNet2DModel`]):\n A `UNet2DModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n [`DDIMScheduler`] is used in combination with `unet` to denoise the encoded image latents.\n \"\"\"\n\n def __init__(self, vqvae: VQModel, unet: UNet2DModel, scheduler: DDIMScheduler):\n super().__init__()\n self.register_modules(vqvae=vqvae, unet=unet, scheduler=scheduler)\n\n @torch.no_grad()\n def __call__(\n self,\n batch_size: int = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n eta: float = 0.0,\n num_inference_steps: int = 50,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n **kwargs,\n ) -> Union[Tuple, ImagePipelineOutput]:\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n batch_size (`int`, *optional*, defaults to 1):\n Number of images to generate.\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n\n Example:\n\n ```py\n >>> from diffusers import LDMPipeline\n\n >>> # load model and scheduler\n >>> pipe = LDMPipeline.from_pretrained(\"CompVis/ldm-celebahq-256\")\n\n >>> # run pipeline in inference (sample random noise and denoise)\n >>> image = pipe().images[0]\n ```\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is\n returned where the first element is a list with the generated images\n \"\"\"\n\n latents = randn_tensor(\n (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),\n generator=generator,\n )\n latents = latents.to(self.device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n\n self.scheduler.set_timesteps(num_inference_steps)\n\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n\n extra_kwargs = {}\n if accepts_eta:\n extra_kwargs[\"eta\"] = eta\n\n for t in self.progress_bar(self.scheduler.timesteps):\n latent_model_input = self.scheduler.scale_model_input(latents, t)\n # predict the noise residual\n noise_prediction = self.unet(latent_model_input, t).sample\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_prediction, t, latents, **extra_kwargs).prev_sample\n\n # adjust latents with inverse of vae scale\n latents = latents / self.vqvae.config.scaling_factor\n # decode the image latents with the VAE\n image = self.vqvae.decode(latents).sample\n\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).numpy()\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/pndm/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ....utils import DIFFUSERS_SLOW_IMPORT, _LazyModule\n\n\n_import_structure = {\"pipeline_pndm\": [\"PNDMPipeline\"]}\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n from .pipeline_pndm import PNDMPipeline\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/pndm/pipeline_pndm.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nfrom typing import List, Optional, Tuple, Union\n\nimport torch\n\nfrom ....models import UNet2DModel\nfrom ....schedulers import PNDMScheduler\nfrom ....utils.torch_utils import randn_tensor\nfrom ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nclass PNDMPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for unconditional image generation.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Parameters:\n unet ([`UNet2DModel`]):\n A `UNet2DModel` to denoise the encoded image latents.\n scheduler ([`PNDMScheduler`]):\n A `PNDMScheduler` to be used in combination with `unet` to denoise the encoded image.\n \"\"\"\n\n unet: UNet2DModel\n scheduler: PNDMScheduler\n\n def __init__(self, unet: UNet2DModel, scheduler: PNDMScheduler):\n super().__init__()\n\n scheduler = PNDMScheduler.from_config(scheduler.config)\n\n self.register_modules(unet=unet, scheduler=scheduler)\n\n @torch.no_grad()\n def __call__(\n self,\n batch_size: int = 1,\n num_inference_steps: int = 50,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n **kwargs,\n ) -> Union[ImagePipelineOutput, Tuple]:\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n batch_size (`int`, `optional`, defaults to 1):\n The number of images to generate.\n num_inference_steps (`int`, `optional`, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n generator (`torch.Generator`, `optional`):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n output_type (`str`, `optional`, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple.\n\n Example:\n\n ```py\n >>> from diffusers import PNDMPipeline\n\n >>> # load model and scheduler\n >>> pndm = PNDMPipeline.from_pretrained(\"google/ddpm-cifar10-32\")\n\n >>> # run pipeline in inference (sample random noise and denoise)\n >>> image = pndm().images[0]\n\n >>> # save image\n >>> image.save(\"pndm_generated_image.png\")\n ```\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is\n returned where the first element is a list with the generated images.\n \"\"\"\n # For more information on the sampling method you can take a look at Algorithm 2 of\n # the official paper: https://arxiv.org/pdf/2202.09778.pdf\n\n # Sample gaussian noise to begin loop\n image = randn_tensor(\n (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),\n generator=generator,\n device=self.device,\n )\n\n self.scheduler.set_timesteps(num_inference_steps)\n for t in self.progress_bar(self.scheduler.timesteps):\n model_output = self.unet(image, t).sample\n\n image = self.scheduler.step(model_output, t, image).prev_sample\n\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).numpy()\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/repaint/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ....utils import DIFFUSERS_SLOW_IMPORT, _LazyModule\n\n\n_import_structure = {\"pipeline_repaint\": [\"RePaintPipeline\"]}\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n from .pipeline_repaint import RePaintPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/repaint/pipeline_repaint.py", "content": "# Copyright 2024 ETH Zurich Computer Vision Lab and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\n\nfrom ....models import UNet2DModel\nfrom ....schedulers import RePaintScheduler\nfrom ....utils import PIL_INTERPOLATION, deprecate, logging\nfrom ....utils.torch_utils import randn_tensor\nfrom ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess\ndef _preprocess_image(image: Union[List, PIL.Image.Image, torch.Tensor]):\n deprecation_message = \"The preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead\"\n deprecate(\"preprocess\", \"1.0.0\", deprecation_message, standard_warn=False)\n if isinstance(image, torch.Tensor):\n return image\n elif isinstance(image, PIL.Image.Image):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n w, h = image[0].size\n w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8\n\n image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION[\"lanczos\"]))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n image = np.array(image).astype(np.float32) / 255.0\n image = image.transpose(0, 3, 1, 2)\n image = 2.0 * image - 1.0\n image = torch.from_numpy(image)\n elif isinstance(image[0], torch.Tensor):\n image = torch.cat(image, dim=0)\n return image\n\n\ndef _preprocess_mask(mask: Union[List, PIL.Image.Image, torch.Tensor]):\n if isinstance(mask, torch.Tensor):\n return mask\n elif isinstance(mask, PIL.Image.Image):\n mask = [mask]\n\n if isinstance(mask[0], PIL.Image.Image):\n w, h = mask[0].size\n w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32\n mask = [np.array(m.convert(\"L\").resize((w, h), resample=PIL_INTERPOLATION[\"nearest\"]))[None, :] for m in mask]\n mask = np.concatenate(mask, axis=0)\n mask = mask.astype(np.float32) / 255.0\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n mask = torch.from_numpy(mask)\n elif isinstance(mask[0], torch.Tensor):\n mask = torch.cat(mask, dim=0)\n return mask\n\n\nclass RePaintPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for image inpainting using RePaint.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Parameters:\n unet ([`UNet2DModel`]):\n A `UNet2DModel` to denoise the encoded image latents.\n scheduler ([`RePaintScheduler`]):\n A `RePaintScheduler` to be used in combination with `unet` to denoise the encoded image.\n \"\"\"\n\n unet: UNet2DModel\n scheduler: RePaintScheduler\n model_cpu_offload_seq = \"unet\"\n\n def __init__(self, unet, scheduler):\n super().__init__()\n self.register_modules(unet=unet, scheduler=scheduler)\n\n @torch.no_grad()\n def __call__(\n self,\n image: Union[torch.Tensor, PIL.Image.Image],\n mask_image: Union[torch.Tensor, PIL.Image.Image],\n num_inference_steps: int = 250,\n eta: float = 0.0,\n jump_length: int = 10,\n jump_n_sample: int = 10,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n ) -> Union[ImagePipelineOutput, Tuple]:\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n image (`torch.Tensor` or `PIL.Image.Image`):\n The original image to inpaint on.\n mask_image (`torch.Tensor` or `PIL.Image.Image`):\n The mask_image where 0.0 define which part of the original image to inpaint.\n num_inference_steps (`int`, *optional*, defaults to 1000):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n eta (`float`):\n The weight of the added noise in a diffusion step. Its value is between 0.0 and 1.0; 0.0 corresponds to\n DDIM and 1.0 is the DDPM scheduler.\n jump_length (`int`, *optional*, defaults to 10):\n The number of steps taken forward in time before going backward in time for a single jump (\"j\" in\n RePaint paper). Take a look at Figure 9 and 10 in the [paper](https://arxiv.org/pdf/2201.09865.pdf).\n jump_n_sample (`int`, *optional*, defaults to 10):\n The number of times to make a forward time jump for a given chosen time sample. Take a look at Figure 9\n and 10 in the [paper](https://arxiv.org/pdf/2201.09865.pdf).\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n output_type (`str`, `optional`, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple.\n\n Example:\n\n ```py\n >>> from io import BytesIO\n >>> import torch\n >>> import PIL\n >>> import requests\n >>> from diffusers import RePaintPipeline, RePaintScheduler\n\n\n >>> def download_image(url):\n ... response = requests.get(url)\n ... return PIL.Image.open(BytesIO(response.content)).convert(\"RGB\")\n\n\n >>> img_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/celeba_hq_256.png\"\n >>> mask_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png\"\n\n >>> # Load the original image and the mask as PIL images\n >>> original_image = download_image(img_url).resize((256, 256))\n >>> mask_image = download_image(mask_url).resize((256, 256))\n\n >>> # Load the RePaint scheduler and pipeline based on a pretrained DDPM model\n >>> scheduler = RePaintScheduler.from_pretrained(\"google/ddpm-ema-celebahq-256\")\n >>> pipe = RePaintPipeline.from_pretrained(\"google/ddpm-ema-celebahq-256\", scheduler=scheduler)\n >>> pipe = pipe.to(\"cuda\")\n\n >>> generator = torch.Generator(device=\"cuda\").manual_seed(0)\n >>> output = pipe(\n ... image=original_image,\n ... mask_image=mask_image,\n ... num_inference_steps=250,\n ... eta=0.0,\n ... jump_length=10,\n ... jump_n_sample=10,\n ... generator=generator,\n ... )\n >>> inpainted_image = output.images[0]\n ```\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is\n returned where the first element is a list with the generated images.\n \"\"\"\n\n original_image = image\n\n original_image = _preprocess_image(original_image)\n original_image = original_image.to(device=self._execution_device, dtype=self.unet.dtype)\n mask_image = _preprocess_mask(mask_image)\n mask_image = mask_image.to(device=self._execution_device, dtype=self.unet.dtype)\n\n batch_size = original_image.shape[0]\n\n # sample gaussian noise to begin the loop\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n image_shape = original_image.shape\n image = randn_tensor(image_shape, generator=generator, device=self._execution_device, dtype=self.unet.dtype)\n\n # set step values\n self.scheduler.set_timesteps(num_inference_steps, jump_length, jump_n_sample, self._execution_device)\n self.scheduler.eta = eta\n\n t_last = self.scheduler.timesteps[0] + 1\n generator = generator[0] if isinstance(generator, list) else generator\n for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):\n if t < t_last:\n # predict the noise residual\n model_output = self.unet(image, t).sample\n # compute previous image: x_t -> x_t-1\n image = self.scheduler.step(model_output, t, image, original_image, mask_image, generator).prev_sample\n\n else:\n # compute the reverse: x_t-1 -> x_t\n image = self.scheduler.undo_step(image, t_last, generator)\n t_last = t\n\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).numpy()\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/score_sde_ve/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ....utils import DIFFUSERS_SLOW_IMPORT, _LazyModule\n\n\n_import_structure = {\"pipeline_score_sde_ve\": [\"ScoreSdeVePipeline\"]}\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n from .pipeline_score_sde_ve import ScoreSdeVePipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/score_sde_ve/pipeline_score_sde_ve.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import List, Optional, Tuple, Union\n\nimport torch\n\nfrom ....models import UNet2DModel\nfrom ....schedulers import ScoreSdeVeScheduler\nfrom ....utils.torch_utils import randn_tensor\nfrom ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nclass ScoreSdeVePipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for unconditional image generation.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Parameters:\n unet ([`UNet2DModel`]):\n A `UNet2DModel` to denoise the encoded image.\n scheduler ([`ScoreSdeVeScheduler`]):\n A `ScoreSdeVeScheduler` to be used in combination with `unet` to denoise the encoded image.\n \"\"\"\n\n unet: UNet2DModel\n scheduler: ScoreSdeVeScheduler\n\n def __init__(self, unet: UNet2DModel, scheduler: ScoreSdeVeScheduler):\n super().__init__()\n self.register_modules(unet=unet, scheduler=scheduler)\n\n @torch.no_grad()\n def __call__(\n self,\n batch_size: int = 1,\n num_inference_steps: int = 2000,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n **kwargs,\n ) -> Union[ImagePipelineOutput, Tuple]:\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n batch_size (`int`, *optional*, defaults to 1):\n The number of images to generate.\n generator (`torch.Generator`, `optional`):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n output_type (`str`, `optional`, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple.\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is\n returned where the first element is a list with the generated images.\n \"\"\"\n\n img_size = self.unet.config.sample_size\n shape = (batch_size, 3, img_size, img_size)\n\n model = self.unet\n\n sample = randn_tensor(shape, generator=generator) * self.scheduler.init_noise_sigma\n sample = sample.to(self.device)\n\n self.scheduler.set_timesteps(num_inference_steps)\n self.scheduler.set_sigmas(num_inference_steps)\n\n for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):\n sigma_t = self.scheduler.sigmas[i] * torch.ones(shape[0], device=self.device)\n\n # correction step\n for _ in range(self.scheduler.config.correct_steps):\n model_output = self.unet(sample, sigma_t).sample\n sample = self.scheduler.step_correct(model_output, sample, generator=generator).prev_sample\n\n # prediction step\n model_output = model(sample, sigma_t).sample\n output = self.scheduler.step_pred(model_output, t, sample, generator=generator)\n\n sample, sample_mean = output.prev_sample, output.prev_sample_mean\n\n sample = sample_mean.clamp(0, 1)\n sample = sample.cpu().permute(0, 2, 3, 1).numpy()\n if output_type == \"pil\":\n sample = self.numpy_to_pil(sample)\n\n if not return_dict:\n return (sample,)\n\n return ImagePipelineOutput(images=sample)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/__init__.py", "content": "# flake8: noqa\nfrom typing import TYPE_CHECKING\nfrom ....utils import (\n DIFFUSERS_SLOW_IMPORT,\n _LazyModule,\n is_note_seq_available,\n OptionalDependencyNotAvailable,\n is_torch_available,\n is_transformers_available,\n get_objects_from_module,\n)\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ....utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"continous_encoder\"] = [\"SpectrogramContEncoder\"]\n _import_structure[\"notes_encoder\"] = [\"SpectrogramNotesEncoder\"]\n _import_structure[\"pipeline_spectrogram_diffusion\"] = [\n \"SpectrogramContEncoder\",\n \"SpectrogramDiffusionPipeline\",\n \"T5FilmDecoder\",\n ]\ntry:\n if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ....utils import dummy_transformers_and_torch_and_note_seq_objects\n\n _dummy_objects.update(get_objects_from_module(dummy_transformers_and_torch_and_note_seq_objects))\nelse:\n _import_structure[\"midi_utils\"] = [\"MidiProcessor\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n\n except OptionalDependencyNotAvailable:\n from ....utils.dummy_torch_and_transformers_objects import *\n else:\n from .pipeline_spectrogram_diffusion import SpectrogramDiffusionPipeline\n from .pipeline_spectrogram_diffusion import SpectrogramContEncoder\n from .pipeline_spectrogram_diffusion import SpectrogramNotesEncoder\n from .pipeline_spectrogram_diffusion import T5FilmDecoder\n\n try:\n if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ....utils.dummy_transformers_and_torch_and_note_seq_objects import *\n\n else:\n from .midi_utils import MidiProcessor\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/continuous_encoder.py", "content": "# Copyright 2022 The Music Spectrogram Diffusion Authors.\n# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport torch\nimport torch.nn as nn\nfrom transformers.modeling_utils import ModuleUtilsMixin\nfrom transformers.models.t5.modeling_t5 import (\n T5Block,\n T5Config,\n T5LayerNorm,\n)\n\nfrom ....configuration_utils import ConfigMixin, register_to_config\nfrom ....models import ModelMixin\n\n\nclass SpectrogramContEncoder(ModelMixin, ConfigMixin, ModuleUtilsMixin):\n @register_to_config\n def __init__(\n self,\n input_dims: int,\n targets_context_length: int,\n d_model: int,\n dropout_rate: float,\n num_layers: int,\n num_heads: int,\n d_kv: int,\n d_ff: int,\n feed_forward_proj: str,\n is_decoder: bool = False,\n ):\n super().__init__()\n\n self.input_proj = nn.Linear(input_dims, d_model, bias=False)\n\n self.position_encoding = nn.Embedding(targets_context_length, d_model)\n self.position_encoding.weight.requires_grad = False\n\n self.dropout_pre = nn.Dropout(p=dropout_rate)\n\n t5config = T5Config(\n d_model=d_model,\n num_heads=num_heads,\n d_kv=d_kv,\n d_ff=d_ff,\n feed_forward_proj=feed_forward_proj,\n dropout_rate=dropout_rate,\n is_decoder=is_decoder,\n is_encoder_decoder=False,\n )\n self.encoders = nn.ModuleList()\n for lyr_num in range(num_layers):\n lyr = T5Block(t5config)\n self.encoders.append(lyr)\n\n self.layer_norm = T5LayerNorm(d_model)\n self.dropout_post = nn.Dropout(p=dropout_rate)\n\n def forward(self, encoder_inputs, encoder_inputs_mask):\n x = self.input_proj(encoder_inputs)\n\n # terminal relative positional encodings\n max_positions = encoder_inputs.shape[1]\n input_positions = torch.arange(max_positions, device=encoder_inputs.device)\n\n seq_lens = encoder_inputs_mask.sum(-1)\n input_positions = torch.roll(input_positions.unsqueeze(0), tuple(seq_lens.tolist()), dims=0)\n x += self.position_encoding(input_positions)\n\n x = self.dropout_pre(x)\n\n # inverted the attention mask\n input_shape = encoder_inputs.size()\n extended_attention_mask = self.get_extended_attention_mask(encoder_inputs_mask, input_shape)\n\n for lyr in self.encoders:\n x = lyr(x, extended_attention_mask)[0]\n x = self.layer_norm(x)\n\n return self.dropout_post(x), encoder_inputs_mask\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/midi_utils.py", "content": "# Copyright 2022 The Music Spectrogram Diffusion Authors.\n# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport dataclasses\nimport math\nimport os\nfrom typing import Any, Callable, List, Mapping, MutableMapping, Optional, Sequence, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\n\nfrom ....utils import is_note_seq_available\nfrom .pipeline_spectrogram_diffusion import TARGET_FEATURE_LENGTH\n\n\nif is_note_seq_available():\n import note_seq\nelse:\n raise ImportError(\"Please install note-seq via `pip install note-seq`\")\n\n\nINPUT_FEATURE_LENGTH = 2048\n\nSAMPLE_RATE = 16000\nHOP_SIZE = 320\nFRAME_RATE = int(SAMPLE_RATE // HOP_SIZE)\n\nDEFAULT_STEPS_PER_SECOND = 100\nDEFAULT_MAX_SHIFT_SECONDS = 10\nDEFAULT_NUM_VELOCITY_BINS = 1\n\nSLAKH_CLASS_PROGRAMS = {\n \"Acoustic Piano\": 0,\n \"Electric Piano\": 4,\n \"Chromatic Percussion\": 8,\n \"Organ\": 16,\n \"Acoustic Guitar\": 24,\n \"Clean Electric Guitar\": 26,\n \"Distorted Electric Guitar\": 29,\n \"Acoustic Bass\": 32,\n \"Electric Bass\": 33,\n \"Violin\": 40,\n \"Viola\": 41,\n \"Cello\": 42,\n \"Contrabass\": 43,\n \"Orchestral Harp\": 46,\n \"Timpani\": 47,\n \"String Ensemble\": 48,\n \"Synth Strings\": 50,\n \"Choir and Voice\": 52,\n \"Orchestral Hit\": 55,\n \"Trumpet\": 56,\n \"Trombone\": 57,\n \"Tuba\": 58,\n \"French Horn\": 60,\n \"Brass Section\": 61,\n \"Soprano/Alto Sax\": 64,\n \"Tenor Sax\": 66,\n \"Baritone Sax\": 67,\n \"Oboe\": 68,\n \"English Horn\": 69,\n \"Bassoon\": 70,\n \"Clarinet\": 71,\n \"Pipe\": 73,\n \"Synth Lead\": 80,\n \"Synth Pad\": 88,\n}\n\n\n@dataclasses.dataclass\nclass NoteRepresentationConfig:\n \"\"\"Configuration note representations.\"\"\"\n\n onsets_only: bool\n include_ties: bool\n\n\n@dataclasses.dataclass\nclass NoteEventData:\n pitch: int\n velocity: Optional[int] = None\n program: Optional[int] = None\n is_drum: Optional[bool] = None\n instrument: Optional[int] = None\n\n\n@dataclasses.dataclass\nclass NoteEncodingState:\n \"\"\"Encoding state for note transcription, keeping track of active pitches.\"\"\"\n\n # velocity bin for active pitches and programs\n active_pitches: MutableMapping[Tuple[int, int], int] = dataclasses.field(default_factory=dict)\n\n\n@dataclasses.dataclass\nclass EventRange:\n type: str\n min_value: int\n max_value: int\n\n\n@dataclasses.dataclass\nclass Event:\n type: str\n value: int\n\n\nclass Tokenizer:\n def __init__(self, regular_ids: int):\n # The special tokens: 0=PAD, 1=EOS, and 2=UNK\n self._num_special_tokens = 3\n self._num_regular_tokens = regular_ids\n\n def encode(self, token_ids):\n encoded = []\n for token_id in token_ids:\n if not 0 <= token_id < self._num_regular_tokens:\n raise ValueError(\n f\"token_id {token_id} does not fall within valid range of [0, {self._num_regular_tokens})\"\n )\n encoded.append(token_id + self._num_special_tokens)\n\n # Add EOS token\n encoded.append(1)\n\n # Pad to till INPUT_FEATURE_LENGTH\n encoded = encoded + [0] * (INPUT_FEATURE_LENGTH - len(encoded))\n\n return encoded\n\n\nclass Codec:\n \"\"\"Encode and decode events.\n\n Useful for declaring what certain ranges of a vocabulary should be used for. This is intended to be used from\n Python before encoding or after decoding with GenericTokenVocabulary. This class is more lightweight and does not\n include things like EOS or UNK token handling.\n\n To ensure that 'shift' events are always the first block of the vocab and start at 0, that event type is required\n and specified separately.\n \"\"\"\n\n def __init__(self, max_shift_steps: int, steps_per_second: float, event_ranges: List[EventRange]):\n \"\"\"Define Codec.\n\n Args:\n max_shift_steps: Maximum number of shift steps that can be encoded.\n steps_per_second: Shift steps will be interpreted as having a duration of\n 1 / steps_per_second.\n event_ranges: Other supported event types and their ranges.\n \"\"\"\n self.steps_per_second = steps_per_second\n self._shift_range = EventRange(type=\"shift\", min_value=0, max_value=max_shift_steps)\n self._event_ranges = [self._shift_range] + event_ranges\n # Ensure all event types have unique names.\n assert len(self._event_ranges) == len({er.type for er in self._event_ranges})\n\n @property\n def num_classes(self) -> int:\n return sum(er.max_value - er.min_value + 1 for er in self._event_ranges)\n\n # The next couple methods are simplified special case methods just for shift\n # events that are intended to be used from within autograph functions.\n\n def is_shift_event_index(self, index: int) -> bool:\n return (self._shift_range.min_value <= index) and (index <= self._shift_range.max_value)\n\n @property\n def max_shift_steps(self) -> int:\n return self._shift_range.max_value\n\n def encode_event(self, event: Event) -> int:\n \"\"\"Encode an event to an index.\"\"\"\n offset = 0\n for er in self._event_ranges:\n if event.type == er.type:\n if not er.min_value <= event.value <= er.max_value:\n raise ValueError(\n f\"Event value {event.value} is not within valid range \"\n f\"[{er.min_value}, {er.max_value}] for type {event.type}\"\n )\n return offset + event.value - er.min_value\n offset += er.max_value - er.min_value + 1\n\n raise ValueError(f\"Unknown event type: {event.type}\")\n\n def event_type_range(self, event_type: str) -> Tuple[int, int]:\n \"\"\"Return [min_id, max_id] for an event type.\"\"\"\n offset = 0\n for er in self._event_ranges:\n if event_type == er.type:\n return offset, offset + (er.max_value - er.min_value)\n offset += er.max_value - er.min_value + 1\n\n raise ValueError(f\"Unknown event type: {event_type}\")\n\n def decode_event_index(self, index: int) -> Event:\n \"\"\"Decode an event index to an Event.\"\"\"\n offset = 0\n for er in self._event_ranges:\n if offset <= index <= offset + er.max_value - er.min_value:\n return Event(type=er.type, value=er.min_value + index - offset)\n offset += er.max_value - er.min_value + 1\n\n raise ValueError(f\"Unknown event index: {index}\")\n\n\n@dataclasses.dataclass\nclass ProgramGranularity:\n # both tokens_map_fn and program_map_fn should be idempotent\n tokens_map_fn: Callable[[Sequence[int], Codec], Sequence[int]]\n program_map_fn: Callable[[int], int]\n\n\ndef drop_programs(tokens, codec: Codec):\n \"\"\"Drops program change events from a token sequence.\"\"\"\n min_program_id, max_program_id = codec.event_type_range(\"program\")\n return tokens[(tokens < min_program_id) | (tokens > max_program_id)]\n\n\ndef programs_to_midi_classes(tokens, codec):\n \"\"\"Modifies program events to be the first program in the MIDI class.\"\"\"\n min_program_id, max_program_id = codec.event_type_range(\"program\")\n is_program = (tokens >= min_program_id) & (tokens <= max_program_id)\n return np.where(is_program, min_program_id + 8 * ((tokens - min_program_id) // 8), tokens)\n\n\nPROGRAM_GRANULARITIES = {\n # \"flat\" granularity; drop program change tokens and set NoteSequence\n # programs to zero\n \"flat\": ProgramGranularity(tokens_map_fn=drop_programs, program_map_fn=lambda program: 0),\n # map each program to the first program in its MIDI class\n \"midi_class\": ProgramGranularity(\n tokens_map_fn=programs_to_midi_classes, program_map_fn=lambda program: 8 * (program // 8)\n ),\n # leave programs as is\n \"full\": ProgramGranularity(tokens_map_fn=lambda tokens, codec: tokens, program_map_fn=lambda program: program),\n}\n\n\ndef frame(signal, frame_length, frame_step, pad_end=False, pad_value=0, axis=-1):\n \"\"\"\n equivalent of tf.signal.frame\n \"\"\"\n signal_length = signal.shape[axis]\n if pad_end:\n frames_overlap = frame_length - frame_step\n rest_samples = np.abs(signal_length - frames_overlap) % np.abs(frame_length - frames_overlap)\n pad_size = int(frame_length - rest_samples)\n\n if pad_size != 0:\n pad_axis = [0] * signal.ndim\n pad_axis[axis] = pad_size\n signal = F.pad(signal, pad_axis, \"constant\", pad_value)\n frames = signal.unfold(axis, frame_length, frame_step)\n return frames\n\n\ndef program_to_slakh_program(program):\n # this is done very hackily, probably should use a custom mapping\n for slakh_program in sorted(SLAKH_CLASS_PROGRAMS.values(), reverse=True):\n if program >= slakh_program:\n return slakh_program\n\n\ndef audio_to_frames(\n samples,\n hop_size: int,\n frame_rate: int,\n) -> Tuple[Sequence[Sequence[int]], torch.Tensor]:\n \"\"\"Convert audio samples to non-overlapping frames and frame times.\"\"\"\n frame_size = hop_size\n samples = np.pad(samples, [0, frame_size - len(samples) % frame_size], mode=\"constant\")\n\n # Split audio into frames.\n frames = frame(\n torch.Tensor(samples).unsqueeze(0),\n frame_length=frame_size,\n frame_step=frame_size,\n pad_end=False, # TODO check why its off by 1 here when True\n )\n\n num_frames = len(samples) // frame_size\n\n times = np.arange(num_frames) / frame_rate\n return frames, times\n\n\ndef note_sequence_to_onsets_and_offsets_and_programs(\n ns: note_seq.NoteSequence,\n) -> Tuple[Sequence[float], Sequence[NoteEventData]]:\n \"\"\"Extract onset & offset times and pitches & programs from a NoteSequence.\n\n The onset & offset times will not necessarily be in sorted order.\n\n Args:\n ns: NoteSequence from which to extract onsets and offsets.\n\n Returns:\n times: A list of note onset and offset times. values: A list of NoteEventData objects where velocity is zero for\n note\n offsets.\n \"\"\"\n # Sort by program and pitch and put offsets before onsets as a tiebreaker for\n # subsequent stable sort.\n notes = sorted(ns.notes, key=lambda note: (note.is_drum, note.program, note.pitch))\n times = [note.end_time for note in notes if not note.is_drum] + [note.start_time for note in notes]\n values = [\n NoteEventData(pitch=note.pitch, velocity=0, program=note.program, is_drum=False)\n for note in notes\n if not note.is_drum\n ] + [\n NoteEventData(pitch=note.pitch, velocity=note.velocity, program=note.program, is_drum=note.is_drum)\n for note in notes\n ]\n return times, values\n\n\ndef num_velocity_bins_from_codec(codec: Codec):\n \"\"\"Get number of velocity bins from event codec.\"\"\"\n lo, hi = codec.event_type_range(\"velocity\")\n return hi - lo\n\n\n# segment an array into segments of length n\ndef segment(a, n):\n return [a[i : i + n] for i in range(0, len(a), n)]\n\n\ndef velocity_to_bin(velocity, num_velocity_bins):\n if velocity == 0:\n return 0\n else:\n return math.ceil(num_velocity_bins * velocity / note_seq.MAX_MIDI_VELOCITY)\n\n\ndef note_event_data_to_events(\n state: Optional[NoteEncodingState],\n value: NoteEventData,\n codec: Codec,\n) -> Sequence[Event]:\n \"\"\"Convert note event data to a sequence of events.\"\"\"\n if value.velocity is None:\n # onsets only, no program or velocity\n return [Event(\"pitch\", value.pitch)]\n else:\n num_velocity_bins = num_velocity_bins_from_codec(codec)\n velocity_bin = velocity_to_bin(value.velocity, num_velocity_bins)\n if value.program is None:\n # onsets + offsets + velocities only, no programs\n if state is not None:\n state.active_pitches[(value.pitch, 0)] = velocity_bin\n return [Event(\"velocity\", velocity_bin), Event(\"pitch\", value.pitch)]\n else:\n if value.is_drum:\n # drum events use a separate vocabulary\n return [Event(\"velocity\", velocity_bin), Event(\"drum\", value.pitch)]\n else:\n # program + velocity + pitch\n if state is not None:\n state.active_pitches[(value.pitch, value.program)] = velocity_bin\n return [\n Event(\"program\", value.program),\n Event(\"velocity\", velocity_bin),\n Event(\"pitch\", value.pitch),\n ]\n\n\ndef note_encoding_state_to_events(state: NoteEncodingState) -> Sequence[Event]:\n \"\"\"Output program and pitch events for active notes plus a final tie event.\"\"\"\n events = []\n for pitch, program in sorted(state.active_pitches.keys(), key=lambda k: k[::-1]):\n if state.active_pitches[(pitch, program)]:\n events += [Event(\"program\", program), Event(\"pitch\", pitch)]\n events.append(Event(\"tie\", 0))\n return events\n\n\ndef encode_and_index_events(\n state, event_times, event_values, codec, frame_times, encode_event_fn, encoding_state_to_events_fn=None\n):\n \"\"\"Encode a sequence of timed events and index to audio frame times.\n\n Encodes time shifts as repeated single step shifts for later run length encoding.\n\n Optionally, also encodes a sequence of \"state events\", keeping track of the current encoding state at each audio\n frame. This can be used e.g. to prepend events representing the current state to a targets segment.\n\n Args:\n state: Initial event encoding state.\n event_times: Sequence of event times.\n event_values: Sequence of event values.\n encode_event_fn: Function that transforms event value into a sequence of one\n or more Event objects.\n codec: An Codec object that maps Event objects to indices.\n frame_times: Time for every audio frame.\n encoding_state_to_events_fn: Function that transforms encoding state into a\n sequence of one or more Event objects.\n\n Returns:\n events: Encoded events and shifts. event_start_indices: Corresponding start event index for every audio frame.\n Note: one event can correspond to multiple audio indices due to sampling rate differences. This makes\n splitting sequences tricky because the same event can appear at the end of one sequence and the beginning of\n another.\n event_end_indices: Corresponding end event index for every audio frame. Used\n to ensure when slicing that one chunk ends where the next begins. Should always be true that\n event_end_indices[i] = event_start_indices[i + 1].\n state_events: Encoded \"state\" events representing the encoding state before\n each event.\n state_event_indices: Corresponding state event index for every audio frame.\n \"\"\"\n indices = np.argsort(event_times, kind=\"stable\")\n event_steps = [round(event_times[i] * codec.steps_per_second) for i in indices]\n event_values = [event_values[i] for i in indices]\n\n events = []\n state_events = []\n event_start_indices = []\n state_event_indices = []\n\n cur_step = 0\n cur_event_idx = 0\n cur_state_event_idx = 0\n\n def fill_event_start_indices_to_cur_step():\n while (\n len(event_start_indices) < len(frame_times)\n and frame_times[len(event_start_indices)] < cur_step / codec.steps_per_second\n ):\n event_start_indices.append(cur_event_idx)\n state_event_indices.append(cur_state_event_idx)\n\n for event_step, event_value in zip(event_steps, event_values):\n while event_step > cur_step:\n events.append(codec.encode_event(Event(type=\"shift\", value=1)))\n cur_step += 1\n fill_event_start_indices_to_cur_step()\n cur_event_idx = len(events)\n cur_state_event_idx = len(state_events)\n if encoding_state_to_events_fn:\n # Dump state to state events *before* processing the next event, because\n # we want to capture the state prior to the occurrence of the event.\n for e in encoding_state_to_events_fn(state):\n state_events.append(codec.encode_event(e))\n\n for e in encode_event_fn(state, event_value, codec):\n events.append(codec.encode_event(e))\n\n # After the last event, continue filling out the event_start_indices array.\n # The inequality is not strict because if our current step lines up exactly\n # with (the start of) an audio frame, we need to add an additional shift event\n # to \"cover\" that frame.\n while cur_step / codec.steps_per_second <= frame_times[-1]:\n events.append(codec.encode_event(Event(type=\"shift\", value=1)))\n cur_step += 1\n fill_event_start_indices_to_cur_step()\n cur_event_idx = len(events)\n\n # Now fill in event_end_indices. We need this extra array to make sure that\n # when we slice events, each slice ends exactly where the subsequent slice\n # begins.\n event_end_indices = event_start_indices[1:] + [len(events)]\n\n events = np.array(events).astype(np.int32)\n state_events = np.array(state_events).astype(np.int32)\n event_start_indices = segment(np.array(event_start_indices).astype(np.int32), TARGET_FEATURE_LENGTH)\n event_end_indices = segment(np.array(event_end_indices).astype(np.int32), TARGET_FEATURE_LENGTH)\n state_event_indices = segment(np.array(state_event_indices).astype(np.int32), TARGET_FEATURE_LENGTH)\n\n outputs = []\n for start_indices, end_indices, event_indices in zip(event_start_indices, event_end_indices, state_event_indices):\n outputs.append(\n {\n \"inputs\": events,\n \"event_start_indices\": start_indices,\n \"event_end_indices\": end_indices,\n \"state_events\": state_events,\n \"state_event_indices\": event_indices,\n }\n )\n\n return outputs\n\n\ndef extract_sequence_with_indices(features, state_events_end_token=None, feature_key=\"inputs\"):\n \"\"\"Extract target sequence corresponding to audio token segment.\"\"\"\n features = features.copy()\n start_idx = features[\"event_start_indices\"][0]\n end_idx = features[\"event_end_indices\"][-1]\n\n features[feature_key] = features[feature_key][start_idx:end_idx]\n\n if state_events_end_token is not None:\n # Extract the state events corresponding to the audio start token, and\n # prepend them to the targets array.\n state_event_start_idx = features[\"state_event_indices\"][0]\n state_event_end_idx = state_event_start_idx + 1\n while features[\"state_events\"][state_event_end_idx - 1] != state_events_end_token:\n state_event_end_idx += 1\n features[feature_key] = np.concatenate(\n [\n features[\"state_events\"][state_event_start_idx:state_event_end_idx],\n features[feature_key],\n ],\n axis=0,\n )\n\n return features\n\n\ndef map_midi_programs(\n feature, codec: Codec, granularity_type: str = \"full\", feature_key: str = \"inputs\"\n) -> Mapping[str, Any]:\n \"\"\"Apply MIDI program map to token sequences.\"\"\"\n granularity = PROGRAM_GRANULARITIES[granularity_type]\n\n feature[feature_key] = granularity.tokens_map_fn(feature[feature_key], codec)\n return feature\n\n\ndef run_length_encode_shifts_fn(\n features,\n codec: Codec,\n feature_key: str = \"inputs\",\n state_change_event_types: Sequence[str] = (),\n) -> Callable[[Mapping[str, Any]], Mapping[str, Any]]:\n \"\"\"Return a function that run-length encodes shifts for a given codec.\n\n Args:\n codec: The Codec to use for shift events.\n feature_key: The feature key for which to run-length encode shifts.\n state_change_event_types: A list of event types that represent state\n changes; tokens corresponding to these event types will be interpreted as state changes and redundant ones\n will be removed.\n\n Returns:\n A preprocessing function that run-length encodes single-step shifts.\n \"\"\"\n state_change_event_ranges = [codec.event_type_range(event_type) for event_type in state_change_event_types]\n\n def run_length_encode_shifts(features: MutableMapping[str, Any]) -> Mapping[str, Any]:\n \"\"\"Combine leading/interior shifts, trim trailing shifts.\n\n Args:\n features: Dict of features to process.\n\n Returns:\n A dict of features.\n \"\"\"\n events = features[feature_key]\n\n shift_steps = 0\n total_shift_steps = 0\n output = np.array([], dtype=np.int32)\n\n current_state = np.zeros(len(state_change_event_ranges), dtype=np.int32)\n\n for event in events:\n if codec.is_shift_event_index(event):\n shift_steps += 1\n total_shift_steps += 1\n\n else:\n # If this event is a state change and has the same value as the current\n # state, we can skip it entirely.\n is_redundant = False\n for i, (min_index, max_index) in enumerate(state_change_event_ranges):\n if (min_index <= event) and (event <= max_index):\n if current_state[i] == event:\n is_redundant = True\n current_state[i] = event\n if is_redundant:\n continue\n\n # Once we've reached a non-shift event, RLE all previous shift events\n # before outputting the non-shift event.\n if shift_steps > 0:\n shift_steps = total_shift_steps\n while shift_steps > 0:\n output_steps = np.minimum(codec.max_shift_steps, shift_steps)\n output = np.concatenate([output, [output_steps]], axis=0)\n shift_steps -= output_steps\n output = np.concatenate([output, [event]], axis=0)\n\n features[feature_key] = output\n return features\n\n return run_length_encode_shifts(features)\n\n\ndef note_representation_processor_chain(features, codec: Codec, note_representation_config: NoteRepresentationConfig):\n tie_token = codec.encode_event(Event(\"tie\", 0))\n state_events_end_token = tie_token if note_representation_config.include_ties else None\n\n features = extract_sequence_with_indices(\n features, state_events_end_token=state_events_end_token, feature_key=\"inputs\"\n )\n\n features = map_midi_programs(features, codec)\n\n features = run_length_encode_shifts_fn(features, codec, state_change_event_types=[\"velocity\", \"program\"])\n\n return features\n\n\nclass MidiProcessor:\n def __init__(self):\n self.codec = Codec(\n max_shift_steps=DEFAULT_MAX_SHIFT_SECONDS * DEFAULT_STEPS_PER_SECOND,\n steps_per_second=DEFAULT_STEPS_PER_SECOND,\n event_ranges=[\n EventRange(\"pitch\", note_seq.MIN_MIDI_PITCH, note_seq.MAX_MIDI_PITCH),\n EventRange(\"velocity\", 0, DEFAULT_NUM_VELOCITY_BINS),\n EventRange(\"tie\", 0, 0),\n EventRange(\"program\", note_seq.MIN_MIDI_PROGRAM, note_seq.MAX_MIDI_PROGRAM),\n EventRange(\"drum\", note_seq.MIN_MIDI_PITCH, note_seq.MAX_MIDI_PITCH),\n ],\n )\n self.tokenizer = Tokenizer(self.codec.num_classes)\n self.note_representation_config = NoteRepresentationConfig(onsets_only=False, include_ties=True)\n\n def __call__(self, midi: Union[bytes, os.PathLike, str]):\n if not isinstance(midi, bytes):\n with open(midi, \"rb\") as f:\n midi = f.read()\n\n ns = note_seq.midi_to_note_sequence(midi)\n ns_sus = note_seq.apply_sustain_control_changes(ns)\n\n for note in ns_sus.notes:\n if not note.is_drum:\n note.program = program_to_slakh_program(note.program)\n\n samples = np.zeros(int(ns_sus.total_time * SAMPLE_RATE))\n\n _, frame_times = audio_to_frames(samples, HOP_SIZE, FRAME_RATE)\n times, values = note_sequence_to_onsets_and_offsets_and_programs(ns_sus)\n\n events = encode_and_index_events(\n state=NoteEncodingState(),\n event_times=times,\n event_values=values,\n frame_times=frame_times,\n codec=self.codec,\n encode_event_fn=note_event_data_to_events,\n encoding_state_to_events_fn=note_encoding_state_to_events,\n )\n\n events = [\n note_representation_processor_chain(event, self.codec, self.note_representation_config) for event in events\n ]\n input_tokens = [self.tokenizer.encode(event[\"inputs\"]) for event in events]\n\n return input_tokens\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/notes_encoder.py", "content": "# Copyright 2022 The Music Spectrogram Diffusion Authors.\n# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport torch\nimport torch.nn as nn\nfrom transformers.modeling_utils import ModuleUtilsMixin\nfrom transformers.models.t5.modeling_t5 import T5Block, T5Config, T5LayerNorm\n\nfrom ....configuration_utils import ConfigMixin, register_to_config\nfrom ....models import ModelMixin\n\n\nclass SpectrogramNotesEncoder(ModelMixin, ConfigMixin, ModuleUtilsMixin):\n @register_to_config\n def __init__(\n self,\n max_length: int,\n vocab_size: int,\n d_model: int,\n dropout_rate: float,\n num_layers: int,\n num_heads: int,\n d_kv: int,\n d_ff: int,\n feed_forward_proj: str,\n is_decoder: bool = False,\n ):\n super().__init__()\n\n self.token_embedder = nn.Embedding(vocab_size, d_model)\n\n self.position_encoding = nn.Embedding(max_length, d_model)\n self.position_encoding.weight.requires_grad = False\n\n self.dropout_pre = nn.Dropout(p=dropout_rate)\n\n t5config = T5Config(\n vocab_size=vocab_size,\n d_model=d_model,\n num_heads=num_heads,\n d_kv=d_kv,\n d_ff=d_ff,\n dropout_rate=dropout_rate,\n feed_forward_proj=feed_forward_proj,\n is_decoder=is_decoder,\n is_encoder_decoder=False,\n )\n\n self.encoders = nn.ModuleList()\n for lyr_num in range(num_layers):\n lyr = T5Block(t5config)\n self.encoders.append(lyr)\n\n self.layer_norm = T5LayerNorm(d_model)\n self.dropout_post = nn.Dropout(p=dropout_rate)\n\n def forward(self, encoder_input_tokens, encoder_inputs_mask):\n x = self.token_embedder(encoder_input_tokens)\n\n seq_length = encoder_input_tokens.shape[1]\n inputs_positions = torch.arange(seq_length, device=encoder_input_tokens.device)\n x += self.position_encoding(inputs_positions)\n\n x = self.dropout_pre(x)\n\n # inverted the attention mask\n input_shape = encoder_input_tokens.size()\n extended_attention_mask = self.get_extended_attention_mask(encoder_inputs_mask, input_shape)\n\n for lyr in self.encoders:\n x = lyr(x, extended_attention_mask)[0]\n x = self.layer_norm(x)\n\n return self.dropout_post(x), encoder_inputs_mask\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/pipeline_spectrogram_diffusion.py", "content": "# Copyright 2022 The Music Spectrogram Diffusion Authors.\n# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport math\nfrom typing import Any, Callable, List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ....models import T5FilmDecoder\nfrom ....schedulers import DDPMScheduler\nfrom ....utils import is_onnx_available, logging\nfrom ....utils.torch_utils import randn_tensor\n\n\nif is_onnx_available():\n from ...onnx_utils import OnnxRuntimeModel\n\nfrom ...pipeline_utils import AudioPipelineOutput, DiffusionPipeline\nfrom .continuous_encoder import SpectrogramContEncoder\nfrom .notes_encoder import SpectrogramNotesEncoder\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nTARGET_FEATURE_LENGTH = 256\n\n\nclass SpectrogramDiffusionPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for unconditional audio generation.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Args:\n notes_encoder ([`SpectrogramNotesEncoder`]):\n continuous_encoder ([`SpectrogramContEncoder`]):\n decoder ([`T5FilmDecoder`]):\n A [`T5FilmDecoder`] to denoise the encoded audio latents.\n scheduler ([`DDPMScheduler`]):\n A scheduler to be used in combination with `decoder` to denoise the encoded audio latents.\n melgan ([`OnnxRuntimeModel`]):\n \"\"\"\n\n _optional_components = [\"melgan\"]\n\n def __init__(\n self,\n notes_encoder: SpectrogramNotesEncoder,\n continuous_encoder: SpectrogramContEncoder,\n decoder: T5FilmDecoder,\n scheduler: DDPMScheduler,\n melgan: OnnxRuntimeModel if is_onnx_available() else Any,\n ) -> None:\n super().__init__()\n\n # From MELGAN\n self.min_value = math.log(1e-5) # Matches MelGAN training.\n self.max_value = 4.0 # Largest value for most examples\n self.n_dims = 128\n\n self.register_modules(\n notes_encoder=notes_encoder,\n continuous_encoder=continuous_encoder,\n decoder=decoder,\n scheduler=scheduler,\n melgan=melgan,\n )\n\n def scale_features(self, features, output_range=(-1.0, 1.0), clip=False):\n \"\"\"Linearly scale features to network outputs range.\"\"\"\n min_out, max_out = output_range\n if clip:\n features = torch.clip(features, self.min_value, self.max_value)\n # Scale to [0, 1].\n zero_one = (features - self.min_value) / (self.max_value - self.min_value)\n # Scale to [min_out, max_out].\n return zero_one * (max_out - min_out) + min_out\n\n def scale_to_features(self, outputs, input_range=(-1.0, 1.0), clip=False):\n \"\"\"Invert by linearly scaling network outputs to features range.\"\"\"\n min_out, max_out = input_range\n outputs = torch.clip(outputs, min_out, max_out) if clip else outputs\n # Scale to [0, 1].\n zero_one = (outputs - min_out) / (max_out - min_out)\n # Scale to [self.min_value, self.max_value].\n return zero_one * (self.max_value - self.min_value) + self.min_value\n\n def encode(self, input_tokens, continuous_inputs, continuous_mask):\n tokens_mask = input_tokens > 0\n tokens_encoded, tokens_mask = self.notes_encoder(\n encoder_input_tokens=input_tokens, encoder_inputs_mask=tokens_mask\n )\n\n continuous_encoded, continuous_mask = self.continuous_encoder(\n encoder_inputs=continuous_inputs, encoder_inputs_mask=continuous_mask\n )\n\n return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]\n\n def decode(self, encodings_and_masks, input_tokens, noise_time):\n timesteps = noise_time\n if not torch.is_tensor(timesteps):\n timesteps = torch.tensor([timesteps], dtype=torch.long, device=input_tokens.device)\n elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(input_tokens.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timesteps = timesteps * torch.ones(input_tokens.shape[0], dtype=timesteps.dtype, device=timesteps.device)\n\n logits = self.decoder(\n encodings_and_masks=encodings_and_masks, decoder_input_tokens=input_tokens, decoder_noise_time=timesteps\n )\n return logits\n\n @torch.no_grad()\n def __call__(\n self,\n input_tokens: List[List[int]],\n generator: Optional[torch.Generator] = None,\n num_inference_steps: int = 100,\n return_dict: bool = True,\n output_type: str = \"np\",\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n ) -> Union[AudioPipelineOutput, Tuple]:\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n input_tokens (`List[List[int]]`):\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality audio at the\n expense of slower inference.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.AudioPipelineOutput`] instead of a plain tuple.\n output_type (`str`, *optional*, defaults to `\"np\"`):\n The output format of the generated audio.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n\n Example:\n\n ```py\n >>> from diffusers import SpectrogramDiffusionPipeline, MidiProcessor\n\n >>> pipe = SpectrogramDiffusionPipeline.from_pretrained(\"google/music-spectrogram-diffusion\")\n >>> pipe = pipe.to(\"cuda\")\n >>> processor = MidiProcessor()\n\n >>> # Download MIDI from: wget http://www.piano-midi.de/midis/beethoven/beethoven_hammerklavier_2.mid\n >>> output = pipe(processor(\"beethoven_hammerklavier_2.mid\"))\n\n >>> audio = output.audios[0]\n ```\n\n Returns:\n [`pipelines.AudioPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`pipelines.AudioPipelineOutput`] is returned, otherwise a `tuple` is\n returned where the first element is a list with the generated audio.\n \"\"\"\n\n pred_mel = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims], dtype=np.float32)\n full_pred_mel = np.zeros([1, 0, self.n_dims], np.float32)\n ones = torch.ones((1, TARGET_FEATURE_LENGTH), dtype=bool, device=self.device)\n\n for i, encoder_input_tokens in enumerate(input_tokens):\n if i == 0:\n encoder_continuous_inputs = torch.from_numpy(pred_mel[:1].copy()).to(\n device=self.device, dtype=self.decoder.dtype\n )\n # The first chunk has no previous context.\n encoder_continuous_mask = torch.zeros((1, TARGET_FEATURE_LENGTH), dtype=bool, device=self.device)\n else:\n # The full song pipeline does not feed in a context feature, so the mask\n # will be all 0s after the feature converter. Because we know we're\n # feeding in a full context chunk from the previous prediction, set it\n # to all 1s.\n encoder_continuous_mask = ones\n\n encoder_continuous_inputs = self.scale_features(\n encoder_continuous_inputs, output_range=[-1.0, 1.0], clip=True\n )\n\n encodings_and_masks = self.encode(\n input_tokens=torch.IntTensor([encoder_input_tokens]).to(device=self.device),\n continuous_inputs=encoder_continuous_inputs,\n continuous_mask=encoder_continuous_mask,\n )\n\n # Sample encoder_continuous_inputs shaped gaussian noise to begin loop\n x = randn_tensor(\n shape=encoder_continuous_inputs.shape,\n generator=generator,\n device=self.device,\n dtype=self.decoder.dtype,\n )\n\n # set step values\n self.scheduler.set_timesteps(num_inference_steps)\n\n # Denoising diffusion loop\n for j, t in enumerate(self.progress_bar(self.scheduler.timesteps)):\n output = self.decode(\n encodings_and_masks=encodings_and_masks,\n input_tokens=x,\n noise_time=t / self.scheduler.config.num_train_timesteps, # rescale to [0, 1)\n )\n\n # Compute previous output: x_t -> x_t-1\n x = self.scheduler.step(output, t, x, generator=generator).prev_sample\n\n mel = self.scale_to_features(x, input_range=[-1.0, 1.0])\n encoder_continuous_inputs = mel[:1]\n pred_mel = mel.cpu().float().numpy()\n\n full_pred_mel = np.concatenate([full_pred_mel, pred_mel[:1]], axis=1)\n\n # call the callback, if provided\n if callback is not None and i % callback_steps == 0:\n callback(i, full_pred_mel)\n\n logger.info(\"Generated segment\", i)\n\n if output_type == \"np\" and not is_onnx_available():\n raise ValueError(\n \"Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'.\"\n )\n elif output_type == \"np\" and self.melgan is None:\n raise ValueError(\n \"Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'.\"\n )\n\n if output_type == \"np\":\n output = self.melgan(input_features=full_pred_mel.astype(np.float32))\n else:\n output = full_pred_mel\n\n if not return_dict:\n return (output,)\n\n return AudioPipelineOutput(audios=output)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ....utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ....utils import dummy_torch_and_transformers_objects\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_cycle_diffusion\"] = [\"CycleDiffusionPipeline\"]\n _import_structure[\"pipeline_stable_diffusion_inpaint_legacy\"] = [\"StableDiffusionInpaintPipelineLegacy\"]\n _import_structure[\"pipeline_stable_diffusion_model_editing\"] = [\"StableDiffusionModelEditingPipeline\"]\n\n _import_structure[\"pipeline_stable_diffusion_paradigms\"] = [\"StableDiffusionParadigmsPipeline\"]\n _import_structure[\"pipeline_stable_diffusion_pix2pix_zero\"] = [\"StableDiffusionPix2PixZeroPipeline\"]\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ....utils.dummy_torch_and_transformers_objects import *\n\n else:\n from .pipeline_cycle_diffusion import CycleDiffusionPipeline\n from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy\n from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline\n from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline\n from .pipeline_stable_diffusion_pix2pix_zero import StableDiffusionPix2PixZeroPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_cycle_diffusion.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom packaging import version\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom ....configuration_utils import FrozenDict\nfrom ....image_processor import PipelineImageInput, VaeImageProcessor\nfrom ....loaders import LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ....models import AutoencoderKL, UNet2DConditionModel\nfrom ....models.lora import adjust_lora_scale_text_encoder\nfrom ....schedulers import DDIMScheduler\nfrom ....utils import PIL_INTERPOLATION, USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers\nfrom ....utils.torch_utils import randn_tensor\nfrom ...pipeline_utils import DiffusionPipeline\nfrom ...stable_diffusion.pipeline_output import StableDiffusionPipelineOutput\nfrom ...stable_diffusion.safety_checker import StableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess\ndef preprocess(image):\n deprecation_message = \"The preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead\"\n deprecate(\"preprocess\", \"1.0.0\", deprecation_message, standard_warn=False)\n if isinstance(image, torch.Tensor):\n return image\n elif isinstance(image, PIL.Image.Image):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n w, h = image[0].size\n w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8\n\n image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION[\"lanczos\"]))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n image = np.array(image).astype(np.float32) / 255.0\n image = image.transpose(0, 3, 1, 2)\n image = 2.0 * image - 1.0\n image = torch.from_numpy(image)\n elif isinstance(image[0], torch.Tensor):\n image = torch.cat(image, dim=0)\n return image\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n\ndef posterior_sample(scheduler, latents, timestep, clean_latents, generator, eta):\n # 1. get previous step value (=t-1)\n prev_timestep = timestep - scheduler.config.num_train_timesteps // scheduler.num_inference_steps\n\n if prev_timestep <= 0:\n return clean_latents\n\n # 2. compute alphas, betas\n alpha_prod_t = scheduler.alphas_cumprod[timestep]\n alpha_prod_t_prev = (\n scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else scheduler.final_alpha_cumprod\n )\n\n variance = scheduler._get_variance(timestep, prev_timestep)\n std_dev_t = eta * variance ** (0.5)\n\n # direction pointing to x_t\n e_t = (latents - alpha_prod_t ** (0.5) * clean_latents) / (1 - alpha_prod_t) ** (0.5)\n dir_xt = (1.0 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * e_t\n noise = std_dev_t * randn_tensor(\n clean_latents.shape, dtype=clean_latents.dtype, device=clean_latents.device, generator=generator\n )\n prev_latents = alpha_prod_t_prev ** (0.5) * clean_latents + dir_xt + noise\n\n return prev_latents\n\n\ndef compute_noise(scheduler, prev_latents, latents, timestep, noise_pred, eta):\n # 1. get previous step value (=t-1)\n prev_timestep = timestep - scheduler.config.num_train_timesteps // scheduler.num_inference_steps\n\n # 2. compute alphas, betas\n alpha_prod_t = scheduler.alphas_cumprod[timestep]\n alpha_prod_t_prev = (\n scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else scheduler.final_alpha_cumprod\n )\n\n beta_prod_t = 1 - alpha_prod_t\n\n # 3. compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)\n\n # 4. Clip \"predicted x_0\"\n if scheduler.config.clip_sample:\n pred_original_sample = torch.clamp(pred_original_sample, -1, 1)\n\n # 5. compute variance: \"sigma_t(η)\" -> see formula (16)\n # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)\n variance = scheduler._get_variance(timestep, prev_timestep)\n std_dev_t = eta * variance ** (0.5)\n\n # 6. compute \"direction pointing to x_t\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * noise_pred\n\n noise = (prev_latents - (alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction)) / (\n variance ** (0.5) * eta\n )\n return noise\n\n\nclass CycleDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):\n r\"\"\"\n Pipeline for text-guided image to image generation using Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can only be an\n instance of [`DDIMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: DDIMScheduler,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n def check_inputs(\n self,\n prompt,\n strength,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n if hasattr(self.scheduler, \"set_begin_index\"):\n self.scheduler.set_begin_index(t_start * self.scheduler.order)\n\n return timesteps, num_inference_steps - t_start\n\n def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):\n image = image.to(device=device, dtype=dtype)\n\n batch_size = image.shape[0]\n\n if image.shape[1] == 4:\n init_latents = image\n\n else:\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if isinstance(generator, list):\n init_latents = [\n retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])\n for i in range(image.shape[0])\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = retrieve_latents(self.vae.encode(image), generator=generator)\n\n init_latents = self.vae.config.scaling_factor * init_latents\n\n if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:\n # expand init_latents for batch_size\n deprecation_message = (\n f\"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial\"\n \" images (`image`). Initial images are now duplicating to match the number of text prompts. Note\"\n \" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update\"\n \" your script to pass as many initial images as text prompts to suppress this warning.\"\n )\n deprecate(\"len(prompt) != len(image)\", \"1.0.0\", deprecation_message, standard_warn=False)\n additional_image_per_prompt = batch_size // init_latents.shape[0]\n init_latents = torch.cat([init_latents] * additional_image_per_prompt * num_images_per_prompt, dim=0)\n elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n init_latents = torch.cat([init_latents] * num_images_per_prompt, dim=0)\n\n # add noise to latents using the timestep\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n # get latents\n clean_latents = init_latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n latents = init_latents\n\n return latents, clean_latents\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n source_prompt: Union[str, List[str]],\n image: PipelineImageInput = None,\n strength: float = 0.8,\n num_inference_steps: Optional[int] = 50,\n guidance_scale: Optional[float] = 7.5,\n source_guidance_scale: Optional[float] = 1,\n num_images_per_prompt: Optional[int] = 1,\n eta: Optional[float] = 0.1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n image (`torch.Tensor` `np.ndarray`, `PIL.Image.Image`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image` or tensor representing an image batch to be used as the starting point. Can also accept image\n latents as `image`, but if passing latents directly it is not encoded again.\n strength (`float`, *optional*, defaults to 0.8):\n Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a\n starting point and more noise is added the higher the `strength`. The number of denoising steps depends\n on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising\n process runs for the full number of iterations specified in `num_inference_steps`. A value of 1\n essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference. This parameter is modulated by `strength`.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n source_guidance_scale (`float`, *optional*, defaults to 1):\n Guidance scale for the source prompt. This is useful to control the amount of influence the source\n prompt has for encoding.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n Example:\n\n ```py\n import requests\n import torch\n from PIL import Image\n from io import BytesIO\n\n from diffusers import CycleDiffusionPipeline, DDIMScheduler\n\n # load the pipeline\n # make sure you're logged in with `huggingface-cli login`\n model_id_or_path = \"CompVis/stable-diffusion-v1-4\"\n scheduler = DDIMScheduler.from_pretrained(model_id_or_path, subfolder=\"scheduler\")\n pipe = CycleDiffusionPipeline.from_pretrained(model_id_or_path, scheduler=scheduler).to(\"cuda\")\n\n # let's download an initial image\n url = \"https://raw.githubusercontent.com/ChenWu98/cycle-diffusion/main/data/dalle2/An%20astronaut%20riding%20a%20horse.png\"\n response = requests.get(url)\n init_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n init_image = init_image.resize((512, 512))\n init_image.save(\"horse.png\")\n\n # let's specify a prompt\n source_prompt = \"An astronaut riding a horse\"\n prompt = \"An astronaut riding an elephant\"\n\n # call the pipeline\n image = pipe(\n prompt=prompt,\n source_prompt=source_prompt,\n image=init_image,\n num_inference_steps=100,\n eta=0.1,\n strength=0.8,\n guidance_scale=2,\n source_guidance_scale=1,\n ).images[0]\n\n image.save(\"horse_to_elephant.png\")\n\n # let's try another example\n # See more samples at the original repo: https://github.com/ChenWu98/cycle-diffusion\n url = (\n \"https://raw.githubusercontent.com/ChenWu98/cycle-diffusion/main/data/dalle2/A%20black%20colored%20car.png\"\n )\n response = requests.get(url)\n init_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n init_image = init_image.resize((512, 512))\n init_image.save(\"black.png\")\n\n source_prompt = \"A black colored car\"\n prompt = \"A blue colored car\"\n\n # call the pipeline\n torch.manual_seed(0)\n image = pipe(\n prompt=prompt,\n source_prompt=source_prompt,\n image=init_image,\n num_inference_steps=100,\n eta=0.1,\n strength=0.85,\n guidance_scale=3,\n source_guidance_scale=1,\n ).images[0]\n\n image.save(\"black_to_blue.png\")\n ```\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n # 1. Check inputs\n self.check_inputs(prompt, strength, callback_steps)\n\n # 2. Define call parameters\n batch_size = 1 if isinstance(prompt, str) else len(prompt)\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n prompt_embeds_tuple = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n prompt_embeds=prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=clip_skip,\n )\n source_prompt_embeds_tuple = self.encode_prompt(\n source_prompt, device, num_images_per_prompt, do_classifier_free_guidance, None, clip_skip=clip_skip\n )\n if prompt_embeds_tuple[1] is not None:\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n else:\n prompt_embeds = prompt_embeds_tuple[0]\n if source_prompt_embeds_tuple[1] is not None:\n source_prompt_embeds = torch.cat([source_prompt_embeds_tuple[1], source_prompt_embeds_tuple[0]])\n else:\n source_prompt_embeds = source_prompt_embeds_tuple[0]\n\n # 4. Preprocess image\n image = self.image_processor.preprocess(image)\n\n # 5. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n\n # 6. Prepare latent variables\n latents, clean_latents = self.prepare_latents(\n image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator\n )\n source_latents = latents\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n generator = extra_step_kwargs.pop(\"generator\", None)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n source_latent_model_input = (\n torch.cat([source_latents] * 2) if do_classifier_free_guidance else source_latents\n )\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n source_latent_model_input = self.scheduler.scale_model_input(source_latent_model_input, t)\n\n # predict the noise residual\n if do_classifier_free_guidance:\n concat_latent_model_input = torch.stack(\n [\n source_latent_model_input[0],\n latent_model_input[0],\n source_latent_model_input[1],\n latent_model_input[1],\n ],\n dim=0,\n )\n concat_prompt_embeds = torch.stack(\n [\n source_prompt_embeds[0],\n prompt_embeds[0],\n source_prompt_embeds[1],\n prompt_embeds[1],\n ],\n dim=0,\n )\n else:\n concat_latent_model_input = torch.cat(\n [\n source_latent_model_input,\n latent_model_input,\n ],\n dim=0,\n )\n concat_prompt_embeds = torch.cat(\n [\n source_prompt_embeds,\n prompt_embeds,\n ],\n dim=0,\n )\n\n concat_noise_pred = self.unet(\n concat_latent_model_input,\n t,\n cross_attention_kwargs=cross_attention_kwargs,\n encoder_hidden_states=concat_prompt_embeds,\n ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n (\n source_noise_pred_uncond,\n noise_pred_uncond,\n source_noise_pred_text,\n noise_pred_text,\n ) = concat_noise_pred.chunk(4, dim=0)\n\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n source_noise_pred = source_noise_pred_uncond + source_guidance_scale * (\n source_noise_pred_text - source_noise_pred_uncond\n )\n\n else:\n (source_noise_pred, noise_pred) = concat_noise_pred.chunk(2, dim=0)\n\n # Sample source_latents from the posterior distribution.\n prev_source_latents = posterior_sample(\n self.scheduler, source_latents, t, clean_latents, generator=generator, **extra_step_kwargs\n )\n # Compute noise.\n noise = compute_noise(\n self.scheduler, prev_source_latents, source_latents, t, source_noise_pred, **extra_step_kwargs\n )\n source_latents = prev_source_latents\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(\n noise_pred, t, latents, variance_noise=noise, **extra_step_kwargs\n ).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # 9. Post-processing\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_onnx_stable_diffusion_inpaint_legacy.py", "content": "import inspect\nfrom typing import Callable, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTokenizer\n\nfrom ....configuration_utils import FrozenDict\nfrom ....schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler\nfrom ....utils import deprecate, logging\nfrom ...onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel\nfrom ...pipeline_utils import DiffusionPipeline\nfrom ...stable_diffusion.pipeline_output import StableDiffusionPipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef preprocess(image):\n w, h = image.size\n w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32\n image = image.resize((w, h), resample=PIL.Image.LANCZOS)\n image = np.array(image).astype(np.float32) / 255.0\n image = image[None].transpose(0, 3, 1, 2)\n return 2.0 * image - 1.0\n\n\ndef preprocess_mask(mask, scale_factor=8):\n mask = mask.convert(\"L\")\n w, h = mask.size\n w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32\n mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL.Image.NEAREST)\n mask = np.array(mask).astype(np.float32) / 255.0\n mask = np.tile(mask, (4, 1, 1))\n mask = mask[None].transpose(0, 1, 2, 3) # what does this step do?\n mask = 1 - mask # repaint white, keep black\n return mask\n\n\nclass OnnxStableDiffusionInpaintPipelineLegacy(DiffusionPipeline):\n r\"\"\"\n Pipeline for text-guided image inpainting using Stable Diffusion. This is a *legacy feature* for Onnx pipelines to\n provide compatibility with StableDiffusionInpaintPipelineLegacy and may be removed in the future.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n _is_onnx = True\n\n vae_encoder: OnnxRuntimeModel\n vae_decoder: OnnxRuntimeModel\n text_encoder: OnnxRuntimeModel\n tokenizer: CLIPTokenizer\n unet: OnnxRuntimeModel\n scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]\n safety_checker: OnnxRuntimeModel\n feature_extractor: CLIPImageProcessor\n\n def __init__(\n self,\n vae_encoder: OnnxRuntimeModel,\n vae_decoder: OnnxRuntimeModel,\n text_encoder: OnnxRuntimeModel,\n tokenizer: CLIPTokenizer,\n unet: OnnxRuntimeModel,\n scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],\n safety_checker: OnnxRuntimeModel,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"clip_sample\"] = False\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.register_modules(\n vae_encoder=vae_encoder,\n vae_decoder=vae_decoder,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion.OnnxStableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt: Union[str, List[str]],\n num_images_per_prompt: Optional[int],\n do_classifier_free_guidance: bool,\n negative_prompt: Optional[str],\n prompt_embeds: Optional[np.ndarray] = None,\n negative_prompt_embeds: Optional[np.ndarray] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`):\n prompt to be encoded\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n \"\"\"\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"np\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"max_length\", return_tensors=\"np\").input_ids\n\n if not np.array_equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]\n\n prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt] * batch_size\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"np\",\n )\n negative_prompt_embeds = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0]\n\n if do_classifier_free_guidance:\n negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n def check_inputs(\n self,\n prompt,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n def __call__(\n self,\n prompt: Union[str, List[str]],\n image: Union[np.ndarray, PIL.Image.Image] = None,\n mask_image: Union[np.ndarray, PIL.Image.Image] = None,\n strength: float = 0.8,\n num_inference_steps: Optional[int] = 50,\n guidance_scale: Optional[float] = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: Optional[float] = 0.0,\n generator: Optional[np.random.RandomState] = None,\n prompt_embeds: Optional[np.ndarray] = None,\n negative_prompt_embeds: Optional[np.ndarray] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, np.ndarray], None]] = None,\n callback_steps: int = 1,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n image (`nd.ndarray` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process. This is the image whose masked region will be inpainted.\n mask_image (`nd.ndarray` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be\n replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a\n PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should\n contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.uu\n strength (`float`, *optional*, defaults to 0.8):\n Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`\n will be used as a starting point, adding more noise to it the larger the `strength`. The number of\n denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will\n be maximum and the denoising process will run for the full number of iterations specified in\n `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference. This parameter will be modulated by `strength`.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (?) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`np.random.RandomState`, *optional*):\n A np.random.RandomState to make generation deterministic.\n prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n\n # check inputs. Raise error if not correct\n self.check_inputs(prompt, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)\n\n # define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n\n if generator is None:\n generator = np.random\n\n # set timesteps\n self.scheduler.set_timesteps(num_inference_steps)\n\n if isinstance(image, PIL.Image.Image):\n image = preprocess(image)\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n prompt_embeds = self._encode_prompt(\n prompt,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n\n latents_dtype = prompt_embeds.dtype\n image = image.astype(latents_dtype)\n\n # encode the init image into latents and scale the latents\n init_latents = self.vae_encoder(sample=image)[0]\n init_latents = 0.18215 * init_latents\n\n # Expand init_latents for batch_size and num_images_per_prompt\n init_latents = np.concatenate([init_latents] * num_images_per_prompt, axis=0)\n init_latents_orig = init_latents\n\n # preprocess mask\n if not isinstance(mask_image, np.ndarray):\n mask_image = preprocess_mask(mask_image, 8)\n mask_image = mask_image.astype(latents_dtype)\n mask = np.concatenate([mask_image] * num_images_per_prompt, axis=0)\n\n # check sizes\n if not mask.shape == init_latents.shape:\n raise ValueError(\"The mask and image should be the same size!\")\n\n # get the original timestep using init_timestep\n offset = self.scheduler.config.get(\"steps_offset\", 0)\n init_timestep = int(num_inference_steps * strength) + offset\n init_timestep = min(init_timestep, num_inference_steps)\n\n timesteps = self.scheduler.timesteps.numpy()[-init_timestep]\n timesteps = np.array([timesteps] * batch_size * num_images_per_prompt)\n\n # add noise to latents using the timesteps\n noise = generator.randn(*init_latents.shape).astype(latents_dtype)\n init_latents = self.scheduler.add_noise(\n torch.from_numpy(init_latents), torch.from_numpy(noise), torch.from_numpy(timesteps)\n )\n init_latents = init_latents.numpy()\n\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (?) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to ? in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n latents = init_latents\n\n t_start = max(num_inference_steps - init_timestep + offset, 0)\n timesteps = self.scheduler.timesteps[t_start:].numpy()\n timestep_dtype = next(\n (input.type for input in self.unet.model.get_inputs() if input.name == \"timestep\"), \"tensor(float)\"\n )\n timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]\n\n for i, t in enumerate(self.progress_bar(timesteps)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n timestep = np.array([t], dtype=timestep_dtype)\n noise_pred = self.unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds)[\n 0\n ]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(\n torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs\n ).prev_sample\n\n latents = latents.numpy()\n\n init_latents_proper = self.scheduler.add_noise(\n torch.from_numpy(init_latents_orig), torch.from_numpy(noise), torch.from_numpy(np.array([t]))\n )\n\n init_latents_proper = init_latents_proper.numpy()\n\n latents = (init_latents_proper * mask) + (latents * (1 - mask))\n\n # call the callback, if provided\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n latents = 1 / 0.18215 * latents\n # image = self.vae_decoder(latent_sample=latents)[0]\n # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1\n image = np.concatenate(\n [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]\n )\n\n image = np.clip(image / 2 + 0.5, 0, 1)\n image = image.transpose((0, 2, 3, 1))\n\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(\n self.numpy_to_pil(image), return_tensors=\"np\"\n ).pixel_values.astype(image.dtype)\n # There will throw an error if use safety_checker batchsize>1\n images, has_nsfw_concept = [], []\n for i in range(image.shape[0]):\n image_i, has_nsfw_concept_i = self.safety_checker(\n clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]\n )\n images.append(image_i)\n has_nsfw_concept.append(has_nsfw_concept_i[0])\n image = np.concatenate(images)\n else:\n has_nsfw_concept = None\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_inpaint_legacy.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom packaging import version\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom ....configuration_utils import FrozenDict\nfrom ....image_processor import VaeImageProcessor\nfrom ....loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ....models import AutoencoderKL, UNet2DConditionModel\nfrom ....models.lora import adjust_lora_scale_text_encoder\nfrom ....schedulers import KarrasDiffusionSchedulers\nfrom ....utils import PIL_INTERPOLATION, USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers\nfrom ....utils.torch_utils import randn_tensor\nfrom ...pipeline_utils import DiffusionPipeline\nfrom ...stable_diffusion import StableDiffusionPipelineOutput\nfrom ...stable_diffusion.safety_checker import StableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__)\n\n\ndef preprocess_image(image, batch_size):\n w, h = image.size\n w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8\n image = image.resize((w, h), resample=PIL_INTERPOLATION[\"lanczos\"])\n image = np.array(image).astype(np.float32) / 255.0\n image = np.vstack([image[None].transpose(0, 3, 1, 2)] * batch_size)\n image = torch.from_numpy(image)\n return 2.0 * image - 1.0\n\n\ndef preprocess_mask(mask, batch_size, scale_factor=8):\n if not isinstance(mask, torch.Tensor):\n mask = mask.convert(\"L\")\n w, h = mask.size\n w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8\n mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION[\"nearest\"])\n mask = np.array(mask).astype(np.float32) / 255.0\n mask = np.tile(mask, (4, 1, 1))\n mask = np.vstack([mask[None]] * batch_size)\n mask = 1 - mask # repaint white, keep black\n mask = torch.from_numpy(mask)\n return mask\n\n else:\n valid_mask_channel_sizes = [1, 3]\n # if mask channel is fourth tensor dimension, permute dimensions to pytorch standard (B, C, H, W)\n if mask.shape[3] in valid_mask_channel_sizes:\n mask = mask.permute(0, 3, 1, 2)\n elif mask.shape[1] not in valid_mask_channel_sizes:\n raise ValueError(\n f\"Mask channel dimension of size in {valid_mask_channel_sizes} should be second or fourth dimension,\"\n f\" but received mask of shape {tuple(mask.shape)}\"\n )\n # (potentially) reduce mask channel dimension from 3 to 1 for broadcasting to latent shape\n mask = mask.mean(dim=1, keepdim=True)\n h, w = mask.shape[-2:]\n h, w = (x - x % 8 for x in (h, w)) # resize to integer multiple of 8\n mask = torch.nn.functional.interpolate(mask, (h // scale_factor, w // scale_factor))\n return mask\n\n\nclass StableDiffusionInpaintPipelineLegacy(\n DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin\n):\n r\"\"\"\n Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n In addition the pipeline inherits the following loading methods:\n - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]\n - *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`]\n - *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`]\n\n as well as the following saving methods:\n - *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`]\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\"feature_extractor\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n deprecation_message = (\n f\"The class {self.__class__} is deprecated and will be removed in v1.0.0. You can achieve exactly the same functionality\"\n \"by loading your model into `StableDiffusionInpaintPipeline` instead. See https://github.com/huggingface/diffusers/pull/3533\"\n \"for more information.\"\n )\n deprecate(\"legacy is outdated\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"clip_sample\"] = False\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n strength,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n\n return timesteps, num_inference_steps - t_start\n\n def prepare_latents(self, image, timestep, num_images_per_prompt, dtype, device, generator):\n image = image.to(device=device, dtype=dtype)\n init_latent_dist = self.vae.encode(image).latent_dist\n init_latents = init_latent_dist.sample(generator=generator)\n init_latents = self.vae.config.scaling_factor * init_latents\n\n # Expand init_latents for batch_size and num_images_per_prompt\n init_latents = torch.cat([init_latents] * num_images_per_prompt, dim=0)\n init_latents_orig = init_latents\n\n # add noise to latents using the timesteps\n noise = randn_tensor(init_latents.shape, generator=generator, device=device, dtype=dtype)\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n latents = init_latents\n return latents, init_latents_orig, noise\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: Union[torch.Tensor, PIL.Image.Image] = None,\n mask_image: Union[torch.Tensor, PIL.Image.Image] = None,\n strength: float = 0.8,\n num_inference_steps: Optional[int] = 50,\n guidance_scale: Optional[float] = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n add_predicted_noise: Optional[bool] = False,\n eta: Optional[float] = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n image (`torch.Tensor` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process. This is the image whose masked region will be inpainted.\n mask_image (`torch.Tensor` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be\n replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a\n PIL image, it will be converted to a single channel (luminance) before use. If mask is a tensor, the\n expected shape should be either `(B, H, W, C)` or `(B, C, H, W)`, where C is 1 or 3.\n strength (`float`, *optional*, defaults to 0.8):\n Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`\n is 1, the denoising process will be run on the masked area for the full number of iterations specified\n in `num_inference_steps`. `image` will be used as a reference for the masked area, adding more noise to\n that region the larger the `strength`. If `strength` is 0, no inpainting will occur.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The reference number of denoising steps. More denoising steps usually lead to a higher quality image at\n the expense of slower inference. This parameter will be modulated by `strength`, as explained above.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale`\n is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n add_predicted_noise (`bool`, *optional*, defaults to True):\n Use predicted noise instead of random noise when constructing noisy versions of the original image in\n the reverse diffusion process\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n # 1. Check inputs\n self.check_inputs(prompt, strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Preprocess image and mask\n if not isinstance(image, torch.Tensor):\n image = preprocess_image(image, batch_size)\n\n mask_image = preprocess_mask(mask_image, batch_size, self.vae_scale_factor)\n\n # 5. set timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n\n # 6. Prepare latent variables\n # encode the init image into latents and scale the latents\n latents, init_latents_orig, noise = self.prepare_latents(\n image, latent_timestep, num_images_per_prompt, prompt_embeds.dtype, device, generator\n )\n\n # 7. Prepare mask latent\n mask = mask_image.to(device=device, dtype=latents.dtype)\n mask = torch.cat([mask] * num_images_per_prompt)\n\n # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 9. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n # masking\n if add_predicted_noise:\n init_latents_proper = self.scheduler.add_noise(\n init_latents_orig, noise_pred_uncond, torch.tensor([t])\n )\n else:\n init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t]))\n\n latents = (init_latents_proper * mask) + (latents * (1 - mask))\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # use original latents corresponding to unmasked portions of the image\n latents = (init_latents_orig * mask) + (latents * (1 - mask))\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_model_editing.py", "content": "# Copyright 2024 TIME Authors and The HuggingFace Team. All rights reserved.\"\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport copy\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport torch\nfrom transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer\n\nfrom ....image_processor import VaeImageProcessor\nfrom ....loaders import LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ....models import AutoencoderKL, UNet2DConditionModel\nfrom ....models.lora import adjust_lora_scale_text_encoder\nfrom ....schedulers import PNDMScheduler\nfrom ....schedulers.scheduling_utils import SchedulerMixin\nfrom ....utils import USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers\nfrom ....utils.torch_utils import randn_tensor\nfrom ...pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ...stable_diffusion.pipeline_output import StableDiffusionPipelineOutput\nfrom ...stable_diffusion.safety_checker import StableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nAUGS_CONST = [\"A photo of \", \"An image of \", \"A picture of \"]\n\n\nclass StableDiffusionModelEditingPipeline(\n DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, LoraLoaderMixin\n):\n r\"\"\"\n Pipeline for text-to-image model editing.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPFeatureExtractor`]):\n A `CLIPFeatureExtractor` to extract features from generated images; used as inputs to the `safety_checker`.\n with_to_k ([`bool`]):\n Whether to edit the key projection matrices along with the value projection matrices.\n with_augs ([`list`]):\n Textual augmentations to apply while editing the text-to-image model. Set to `[]` for no augmentations.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: SchedulerMixin,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPFeatureExtractor,\n requires_safety_checker: bool = True,\n with_to_k: bool = True,\n with_augs: list = AUGS_CONST,\n ):\n super().__init__()\n\n if isinstance(scheduler, PNDMScheduler):\n logger.error(\"PNDMScheduler for this pipeline is currently not supported.\")\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n self.with_to_k = with_to_k\n self.with_augs = with_augs\n\n # get cross-attention layers\n ca_layers = []\n\n def append_ca(net_):\n if net_.__class__.__name__ == \"CrossAttention\":\n ca_layers.append(net_)\n elif hasattr(net_, \"children\"):\n for net__ in net_.children():\n append_ca(net__)\n\n # recursively find all cross-attention layers in unet\n for net in self.unet.named_children():\n if \"down\" in net[0]:\n append_ca(net[1])\n elif \"up\" in net[0]:\n append_ca(net[1])\n elif \"mid\" in net[0]:\n append_ca(net[1])\n\n # get projection matrices\n self.ca_clip_layers = [l for l in ca_layers if l.to_v.in_features == 768]\n self.projection_matrices = [l.to_v for l in self.ca_clip_layers]\n self.og_matrices = [copy.deepcopy(l.to_v) for l in self.ca_clip_layers]\n if self.with_to_k:\n self.projection_matrices = self.projection_matrices + [l.to_k for l in self.ca_clip_layers]\n self.og_matrices = self.og_matrices + [copy.deepcopy(l.to_k) for l in self.ca_clip_layers]\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @torch.no_grad()\n def edit_model(\n self,\n source_prompt: str,\n destination_prompt: str,\n lamb: float = 0.1,\n restart_params: bool = True,\n ):\n r\"\"\"\n Apply model editing via closed-form solution (see Eq. 5 in the TIME [paper](https://arxiv.org/abs/2303.08084)).\n\n Args:\n source_prompt (`str`):\n The source prompt containing the concept to be edited.\n destination_prompt (`str`):\n The destination prompt. Must contain all words from `source_prompt` with additional ones to specify the\n target edit.\n lamb (`float`, *optional*, defaults to 0.1):\n The lambda parameter specifying the regularization intesity. Smaller values increase the editing power.\n restart_params (`bool`, *optional*, defaults to True):\n Restart the model parameters to their pre-trained version before editing. This is done to avoid edit\n compounding. When it is `False`, edits accumulate.\n \"\"\"\n\n # restart LDM parameters\n if restart_params:\n num_ca_clip_layers = len(self.ca_clip_layers)\n for idx_, l in enumerate(self.ca_clip_layers):\n l.to_v = copy.deepcopy(self.og_matrices[idx_])\n self.projection_matrices[idx_] = l.to_v\n if self.with_to_k:\n l.to_k = copy.deepcopy(self.og_matrices[num_ca_clip_layers + idx_])\n self.projection_matrices[num_ca_clip_layers + idx_] = l.to_k\n\n # set up sentences\n old_texts = [source_prompt]\n new_texts = [destination_prompt]\n # add augmentations\n base = old_texts[0] if old_texts[0][0:1] != \"A\" else \"a\" + old_texts[0][1:]\n for aug in self.with_augs:\n old_texts.append(aug + base)\n base = new_texts[0] if new_texts[0][0:1] != \"A\" else \"a\" + new_texts[0][1:]\n for aug in self.with_augs:\n new_texts.append(aug + base)\n\n # prepare input k* and v*\n old_embs, new_embs = [], []\n for old_text, new_text in zip(old_texts, new_texts):\n text_input = self.tokenizer(\n [old_text, new_text],\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]\n old_emb, new_emb = text_embeddings\n old_embs.append(old_emb)\n new_embs.append(new_emb)\n\n # identify corresponding destinations for each token in old_emb\n idxs_replaces = []\n for old_text, new_text in zip(old_texts, new_texts):\n tokens_a = self.tokenizer(old_text).input_ids\n tokens_b = self.tokenizer(new_text).input_ids\n tokens_a = [self.tokenizer.encode(\"a \")[1] if self.tokenizer.decode(t) == \"an\" else t for t in tokens_a]\n tokens_b = [self.tokenizer.encode(\"a \")[1] if self.tokenizer.decode(t) == \"an\" else t for t in tokens_b]\n num_orig_tokens = len(tokens_a)\n idxs_replace = []\n j = 0\n for i in range(num_orig_tokens):\n curr_token = tokens_a[i]\n while tokens_b[j] != curr_token:\n j += 1\n idxs_replace.append(j)\n j += 1\n while j < 77:\n idxs_replace.append(j)\n j += 1\n while len(idxs_replace) < 77:\n idxs_replace.append(76)\n idxs_replaces.append(idxs_replace)\n\n # prepare batch: for each pair of setences, old context and new values\n contexts, valuess = [], []\n for old_emb, new_emb, idxs_replace in zip(old_embs, new_embs, idxs_replaces):\n context = old_emb.detach()\n values = []\n with torch.no_grad():\n for layer in self.projection_matrices:\n values.append(layer(new_emb[idxs_replace]).detach())\n contexts.append(context)\n valuess.append(values)\n\n # edit the model\n for layer_num in range(len(self.projection_matrices)):\n # mat1 = \\lambda W + \\sum{v k^T}\n mat1 = lamb * self.projection_matrices[layer_num].weight\n\n # mat2 = \\lambda I + \\sum{k k^T}\n mat2 = lamb * torch.eye(\n self.projection_matrices[layer_num].weight.shape[1],\n device=self.projection_matrices[layer_num].weight.device,\n )\n\n # aggregate sums for mat1, mat2\n for context, values in zip(contexts, valuess):\n context_vector = context.reshape(context.shape[0], context.shape[1], 1)\n context_vector_T = context.reshape(context.shape[0], 1, context.shape[1])\n value_vector = values[layer_num].reshape(values[layer_num].shape[0], values[layer_num].shape[1], 1)\n for_mat1 = (value_vector @ context_vector_T).sum(dim=0)\n for_mat2 = (context_vector @ context_vector_T).sum(dim=0)\n mat1 += for_mat1\n mat2 += for_mat2\n\n # update projection matrix\n self.projection_matrices[layer_num].weight = torch.nn.Parameter(mat1 @ torch.inverse(mat2))\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n\n Examples:\n\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusionModelEditingPipeline\n\n >>> model_ckpt = \"CompVis/stable-diffusion-v1-4\"\n >>> pipe = StableDiffusionModelEditingPipeline.from_pretrained(model_ckpt)\n\n >>> pipe = pipe.to(\"cuda\")\n\n >>> source_prompt = \"A pack of roses\"\n >>> destination_prompt = \"A pack of blue roses\"\n >>> pipe.edit_model(source_prompt, destination_prompt)\n\n >>> prompt = \"A field of roses\"\n >>> image = pipe(prompt).images[0]\n ```\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_paradigms.py", "content": "# Copyright 2024 ParaDiGMS authors and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom ....image_processor import VaeImageProcessor\nfrom ....loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ....models import AutoencoderKL, UNet2DConditionModel\nfrom ....models.lora import adjust_lora_scale_text_encoder\nfrom ....schedulers import KarrasDiffusionSchedulers\nfrom ....utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ....utils.torch_utils import randn_tensor\nfrom ...pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ...stable_diffusion.pipeline_output import StableDiffusionPipelineOutput\nfrom ...stable_diffusion.safety_checker import StableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import DDPMParallelScheduler\n >>> from diffusers import StableDiffusionParadigmsPipeline\n\n >>> scheduler = DDPMParallelScheduler.from_pretrained(\"runwayml/stable-diffusion-v1-5\", subfolder=\"scheduler\")\n\n >>> pipe = StableDiffusionParadigmsPipeline.from_pretrained(\n ... \"runwayml/stable-diffusion-v1-5\", scheduler=scheduler, torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> ngpu, batch_per_device = torch.cuda.device_count(), 5\n >>> pipe.wrapped_unet = torch.nn.DataParallel(pipe.unet, device_ids=[d for d in range(ngpu)])\n\n >>> prompt = \"a photo of an astronaut riding a horse on mars\"\n >>> image = pipe(prompt, parallel=ngpu * batch_per_device, num_inference_steps=1000).images[0]\n ```\n\"\"\"\n\n\nclass StableDiffusionParadigmsPipeline(\n DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin\n):\n r\"\"\"\n Pipeline for text-to-image generation using a parallelized version of Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # attribute to wrap the unet with torch.nn.DataParallel when running multiple denoising steps on multiple GPUs\n self.wrapped_unet = self.unet\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def _cumsum(self, input, dim, debug=False):\n if debug:\n # cumsum_cuda_kernel does not have a deterministic implementation\n # so perform cumsum on cpu for debugging purposes\n return torch.cumsum(input.cpu().float(), dim=dim).to(input.device)\n else:\n return torch.cumsum(input, dim=dim)\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n parallel: int = 10,\n tolerance: float = 0.1,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n debug: bool = False,\n clip_skip: int = None,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n parallel (`int`, *optional*, defaults to 10):\n The batch size to use when doing parallel sampling. More parallelism may lead to faster inference but\n requires higher memory usage and can also require more total FLOPs.\n tolerance (`float`, *optional*, defaults to 0.1):\n The error tolerance for determining when to slide the batch window forward for parallel sampling. Lower\n tolerance usually leads to less or no degradation. Higher tolerance is faster but can risk degradation\n of sample quality. The tolerance is specified as a ratio of the scheduler's noise magnitude.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n debug (`bool`, *optional*, defaults to `False`):\n Whether or not to run in debug mode. In debug mode, `torch.cumsum` is evaluated using the CPU.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n clip_skip=clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n extra_step_kwargs.pop(\"generator\", None)\n\n # # 7. Denoising loop\n scheduler = self.scheduler\n parallel = min(parallel, len(scheduler.timesteps))\n\n begin_idx = 0\n end_idx = parallel\n latents_time_evolution_buffer = torch.stack([latents] * (len(scheduler.timesteps) + 1))\n\n # We must make sure the noise of stochastic schedulers such as DDPM is sampled only once per timestep.\n # Sampling inside the parallel denoising loop will mess this up, so we pre-sample the noise vectors outside the denoising loop.\n noise_array = torch.zeros_like(latents_time_evolution_buffer)\n for j in range(len(scheduler.timesteps)):\n base_noise = randn_tensor(\n shape=latents.shape, generator=generator, device=latents.device, dtype=prompt_embeds.dtype\n )\n noise = (self.scheduler._get_variance(scheduler.timesteps[j]) ** 0.5) * base_noise\n noise_array[j] = noise.clone()\n\n # We specify the error tolerance as a ratio of the scheduler's noise magnitude. We similarly compute the error tolerance\n # outside of the denoising loop to avoid recomputing it at every step.\n # We will be dividing the norm of the noise, so we store its inverse here to avoid a division at every step.\n inverse_variance_norm = 1.0 / torch.tensor(\n [scheduler._get_variance(scheduler.timesteps[j]) for j in range(len(scheduler.timesteps))] + [0]\n ).to(noise_array.device)\n latent_dim = noise_array[0, 0].numel()\n inverse_variance_norm = inverse_variance_norm[:, None] / latent_dim\n\n scaled_tolerance = tolerance**2\n\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n steps = 0\n while begin_idx < len(scheduler.timesteps):\n # these have shape (parallel_dim, 2*batch_size, ...)\n # parallel_len is at most parallel, but could be less if we are at the end of the timesteps\n # we are processing batch window of timesteps spanning [begin_idx, end_idx)\n parallel_len = end_idx - begin_idx\n\n block_prompt_embeds = torch.stack([prompt_embeds] * parallel_len)\n block_latents = latents_time_evolution_buffer[begin_idx:end_idx]\n block_t = scheduler.timesteps[begin_idx:end_idx, None].repeat(1, batch_size * num_images_per_prompt)\n t_vec = block_t\n if do_classifier_free_guidance:\n t_vec = t_vec.repeat(1, 2)\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = (\n torch.cat([block_latents] * 2, dim=1) if do_classifier_free_guidance else block_latents\n )\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t_vec)\n\n # if parallel_len is small, no need to use multiple GPUs\n net = self.wrapped_unet if parallel_len > 3 else self.unet\n # predict the noise residual, shape is now [parallel_len * 2 * batch_size * num_images_per_prompt, ...]\n model_output = net(\n latent_model_input.flatten(0, 1),\n t_vec.flatten(0, 1),\n encoder_hidden_states=block_prompt_embeds.flatten(0, 1),\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n per_latent_shape = model_output.shape[1:]\n if do_classifier_free_guidance:\n model_output = model_output.reshape(\n parallel_len, 2, batch_size * num_images_per_prompt, *per_latent_shape\n )\n noise_pred_uncond, noise_pred_text = model_output[:, 0], model_output[:, 1]\n model_output = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n model_output = model_output.reshape(\n parallel_len * batch_size * num_images_per_prompt, *per_latent_shape\n )\n\n block_latents_denoise = scheduler.batch_step_no_noise(\n model_output=model_output,\n timesteps=block_t.flatten(0, 1),\n sample=block_latents.flatten(0, 1),\n **extra_step_kwargs,\n ).reshape(block_latents.shape)\n\n # back to shape (parallel_dim, batch_size, ...)\n # now we want to add the pre-sampled noise\n # parallel sampling algorithm requires computing the cumulative drift from the beginning\n # of the window, so we need to compute cumulative sum of the deltas and the pre-sampled noises.\n delta = block_latents_denoise - block_latents\n cumulative_delta = self._cumsum(delta, dim=0, debug=debug)\n cumulative_noise = self._cumsum(noise_array[begin_idx:end_idx], dim=0, debug=debug)\n\n # if we are using an ODE-like scheduler (like DDIM), we don't want to add noise\n if scheduler._is_ode_scheduler:\n cumulative_noise = 0\n\n block_latents_new = (\n latents_time_evolution_buffer[begin_idx][None,] + cumulative_delta + cumulative_noise\n )\n cur_error = torch.linalg.norm(\n (block_latents_new - latents_time_evolution_buffer[begin_idx + 1 : end_idx + 1]).reshape(\n parallel_len, batch_size * num_images_per_prompt, -1\n ),\n dim=-1,\n ).pow(2)\n error_ratio = cur_error * inverse_variance_norm[begin_idx + 1 : end_idx + 1]\n\n # find the first index of the vector error_ratio that is greater than error tolerance\n # we can shift the window for the next iteration up to this index\n error_ratio = torch.nn.functional.pad(\n error_ratio, (0, 0, 0, 1), value=1e9\n ) # handle the case when everything is below ratio, by padding the end of parallel_len dimension\n any_error_at_time = torch.max(error_ratio > scaled_tolerance, dim=1).values.int()\n ind = torch.argmax(any_error_at_time).item()\n\n # compute the new begin and end idxs for the window\n new_begin_idx = begin_idx + min(1 + ind, parallel)\n new_end_idx = min(new_begin_idx + parallel, len(scheduler.timesteps))\n\n # store the computed latents for the current window in the global buffer\n latents_time_evolution_buffer[begin_idx + 1 : end_idx + 1] = block_latents_new\n # initialize the new sliding window latents with the end of the current window,\n # should be better than random initialization\n latents_time_evolution_buffer[end_idx : new_end_idx + 1] = latents_time_evolution_buffer[end_idx][\n None,\n ]\n\n steps += 1\n\n progress_bar.update(new_begin_idx - begin_idx)\n if callback is not None and steps % callback_steps == 0:\n callback(begin_idx, block_t[begin_idx], latents_time_evolution_buffer[begin_idx])\n\n begin_idx = new_begin_idx\n end_idx = new_end_idx\n\n latents = latents_time_evolution_buffer[-1]\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py", "content": "# Copyright 2024 Pix2Pix Zero Authors and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom dataclasses import dataclass\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom transformers import (\n BlipForConditionalGeneration,\n BlipProcessor,\n CLIPImageProcessor,\n CLIPTextModel,\n CLIPTokenizer,\n)\n\nfrom ....image_processor import PipelineImageInput, VaeImageProcessor\nfrom ....loaders import LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ....models import AutoencoderKL, UNet2DConditionModel\nfrom ....models.attention_processor import Attention\nfrom ....models.lora import adjust_lora_scale_text_encoder\nfrom ....schedulers import DDIMScheduler, DDPMScheduler, EulerAncestralDiscreteScheduler, LMSDiscreteScheduler\nfrom ....schedulers.scheduling_ddim_inverse import DDIMInverseScheduler\nfrom ....utils import (\n PIL_INTERPOLATION,\n USE_PEFT_BACKEND,\n BaseOutput,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ....utils.torch_utils import randn_tensor\nfrom ...pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ...stable_diffusion.pipeline_output import StableDiffusionPipelineOutput\nfrom ...stable_diffusion.safety_checker import StableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n@dataclass\nclass Pix2PixInversionPipelineOutput(BaseOutput, TextualInversionLoaderMixin):\n \"\"\"\n Output class for Stable Diffusion pipelines.\n\n Args:\n latents (`torch.Tensor`)\n inverted latents tensor\n images (`List[PIL.Image.Image]` or `np.ndarray`)\n List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,\n num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.\n \"\"\"\n\n latents: torch.Tensor\n images: Union[List[PIL.Image.Image], np.ndarray]\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import requests\n >>> import torch\n\n >>> from diffusers import DDIMScheduler, StableDiffusionPix2PixZeroPipeline\n\n\n >>> def download(embedding_url, local_filepath):\n ... r = requests.get(embedding_url)\n ... with open(local_filepath, \"wb\") as f:\n ... f.write(r.content)\n\n\n >>> model_ckpt = \"CompVis/stable-diffusion-v1-4\"\n >>> pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained(model_ckpt, torch_dtype=torch.float16)\n >>> pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)\n >>> pipeline.to(\"cuda\")\n\n >>> prompt = \"a high resolution painting of a cat in the style of van gough\"\n >>> source_emb_url = \"https://hf.co/datasets/sayakpaul/sample-datasets/resolve/main/cat.pt\"\n >>> target_emb_url = \"https://hf.co/datasets/sayakpaul/sample-datasets/resolve/main/dog.pt\"\n\n >>> for url in [source_emb_url, target_emb_url]:\n ... download(url, url.split(\"/\")[-1])\n\n >>> src_embeds = torch.load(source_emb_url.split(\"/\")[-1])\n >>> target_embeds = torch.load(target_emb_url.split(\"/\")[-1])\n >>> images = pipeline(\n ... prompt,\n ... source_embeds=src_embeds,\n ... target_embeds=target_embeds,\n ... num_inference_steps=50,\n ... cross_attention_guidance_amount=0.15,\n ... ).images\n\n >>> images[0].save(\"edited_image_dog.png\")\n ```\n\"\"\"\n\nEXAMPLE_INVERT_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from transformers import BlipForConditionalGeneration, BlipProcessor\n >>> from diffusers import DDIMScheduler, DDIMInverseScheduler, StableDiffusionPix2PixZeroPipeline\n\n >>> import requests\n >>> from PIL import Image\n\n >>> captioner_id = \"Salesforce/blip-image-captioning-base\"\n >>> processor = BlipProcessor.from_pretrained(captioner_id)\n >>> model = BlipForConditionalGeneration.from_pretrained(\n ... captioner_id, torch_dtype=torch.float16, low_cpu_mem_usage=True\n ... )\n\n >>> sd_model_ckpt = \"CompVis/stable-diffusion-v1-4\"\n >>> pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained(\n ... sd_model_ckpt,\n ... caption_generator=model,\n ... caption_processor=processor,\n ... torch_dtype=torch.float16,\n ... safety_checker=None,\n ... )\n\n >>> pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)\n >>> pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)\n >>> pipeline.enable_model_cpu_offload()\n\n >>> img_url = \"https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/test_images/cats/cat_6.png\"\n\n >>> raw_image = Image.open(requests.get(img_url, stream=True).raw).convert(\"RGB\").resize((512, 512))\n >>> # generate caption\n >>> caption = pipeline.generate_caption(raw_image)\n\n >>> # \"a photography of a cat with flowers and dai dai daie - daie - daie kasaii\"\n >>> inv_latents = pipeline.invert(caption, image=raw_image).latents\n >>> # we need to generate source and target embeds\n\n >>> source_prompts = [\"a cat sitting on the street\", \"a cat playing in the field\", \"a face of a cat\"]\n\n >>> target_prompts = [\"a dog sitting on the street\", \"a dog playing in the field\", \"a face of a dog\"]\n\n >>> source_embeds = pipeline.get_embeds(source_prompts)\n >>> target_embeds = pipeline.get_embeds(target_prompts)\n >>> # the latents can then be used to edit a real image\n >>> # when using Stable Diffusion 2 or other models that use v-prediction\n >>> # set `cross_attention_guidance_amount` to 0.01 or less to avoid input latent gradient explosion\n\n >>> image = pipeline(\n ... caption,\n ... source_embeds=source_embeds,\n ... target_embeds=target_embeds,\n ... num_inference_steps=50,\n ... cross_attention_guidance_amount=0.15,\n ... generator=generator,\n ... latents=inv_latents,\n ... negative_prompt=caption,\n ... ).images[0]\n >>> image.save(\"edited_image.png\")\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess\ndef preprocess(image):\n deprecation_message = \"The preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead\"\n deprecate(\"preprocess\", \"1.0.0\", deprecation_message, standard_warn=False)\n if isinstance(image, torch.Tensor):\n return image\n elif isinstance(image, PIL.Image.Image):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n w, h = image[0].size\n w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8\n\n image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION[\"lanczos\"]))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n image = np.array(image).astype(np.float32) / 255.0\n image = image.transpose(0, 3, 1, 2)\n image = 2.0 * image - 1.0\n image = torch.from_numpy(image)\n elif isinstance(image[0], torch.Tensor):\n image = torch.cat(image, dim=0)\n return image\n\n\ndef prepare_unet(unet: UNet2DConditionModel):\n \"\"\"Modifies the UNet (`unet`) to perform Pix2Pix Zero optimizations.\"\"\"\n pix2pix_zero_attn_procs = {}\n for name in unet.attn_processors.keys():\n module_name = name.replace(\".processor\", \"\")\n module = unet.get_submodule(module_name)\n if \"attn2\" in name:\n pix2pix_zero_attn_procs[name] = Pix2PixZeroAttnProcessor(is_pix2pix_zero=True)\n module.requires_grad_(True)\n else:\n pix2pix_zero_attn_procs[name] = Pix2PixZeroAttnProcessor(is_pix2pix_zero=False)\n module.requires_grad_(False)\n\n unet.set_attn_processor(pix2pix_zero_attn_procs)\n return unet\n\n\nclass Pix2PixZeroL2Loss:\n def __init__(self):\n self.loss = 0.0\n\n def compute_loss(self, predictions, targets):\n self.loss += ((predictions - targets) ** 2).sum((1, 2)).mean(0)\n\n\nclass Pix2PixZeroAttnProcessor:\n \"\"\"An attention processor class to store the attention weights.\n In Pix2Pix Zero, it happens during computations in the cross-attention blocks.\"\"\"\n\n def __init__(self, is_pix2pix_zero=False):\n self.is_pix2pix_zero = is_pix2pix_zero\n if self.is_pix2pix_zero:\n self.reference_cross_attn_map = {}\n\n def __call__(\n self,\n attn: Attention,\n hidden_states,\n encoder_hidden_states=None,\n attention_mask=None,\n timestep=None,\n loss=None,\n ):\n batch_size, sequence_length, _ = hidden_states.shape\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n query = attn.to_q(hidden_states)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n attention_probs = attn.get_attention_scores(query, key, attention_mask)\n if self.is_pix2pix_zero and timestep is not None:\n # new bookkeeping to save the attention weights.\n if loss is None:\n self.reference_cross_attn_map[timestep.item()] = attention_probs.detach().cpu()\n # compute loss\n elif loss is not None:\n prev_attn_probs = self.reference_cross_attn_map.pop(timestep.item())\n loss.compute_loss(attention_probs, prev_attn_probs.to(attention_probs.device))\n\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n return hidden_states\n\n\nclass StableDiffusionPix2PixZeroPipeline(DiffusionPipeline, StableDiffusionMixin):\n r\"\"\"\n Pipeline for pixel-level image editing using Pix2Pix Zero. Based on Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`], or [`DDPMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n requires_safety_checker (bool):\n Whether the pipeline requires a safety checker. We recommend setting it to True if you're using the\n pipeline publicly.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\n \"safety_checker\",\n \"feature_extractor\",\n \"caption_generator\",\n \"caption_processor\",\n \"inverse_scheduler\",\n ]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[DDPMScheduler, DDIMScheduler, EulerAncestralDiscreteScheduler, LMSDiscreteScheduler],\n feature_extractor: CLIPImageProcessor,\n safety_checker: StableDiffusionSafetyChecker,\n inverse_scheduler: DDIMInverseScheduler,\n caption_generator: BlipForConditionalGeneration,\n caption_processor: BlipProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n caption_processor=caption_processor,\n caption_generator=caption_generator,\n inverse_scheduler=inverse_scheduler,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n source_embeds,\n target_embeds,\n callback_steps,\n prompt_embeds=None,\n ):\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if source_embeds is None and target_embeds is None:\n raise ValueError(\"`source_embeds` and `target_embeds` cannot be undefined.\")\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @torch.no_grad()\n def generate_caption(self, images):\n \"\"\"Generates caption for a given image.\"\"\"\n text = \"a photography of\"\n\n prev_device = self.caption_generator.device\n\n device = self._execution_device\n inputs = self.caption_processor(images, text, return_tensors=\"pt\").to(\n device=device, dtype=self.caption_generator.dtype\n )\n self.caption_generator.to(device)\n outputs = self.caption_generator.generate(**inputs, max_new_tokens=128)\n\n # offload caption generator\n self.caption_generator.to(prev_device)\n\n caption = self.caption_processor.batch_decode(outputs, skip_special_tokens=True)[0]\n return caption\n\n def construct_direction(self, embs_source: torch.Tensor, embs_target: torch.Tensor):\n \"\"\"Constructs the edit direction to steer the image generation process semantically.\"\"\"\n return (embs_target.mean(0) - embs_source.mean(0)).unsqueeze(0)\n\n @torch.no_grad()\n def get_embeds(self, prompt: List[str], batch_size: int = 16) -> torch.Tensor:\n num_prompts = len(prompt)\n embeds = []\n for i in range(0, num_prompts, batch_size):\n prompt_slice = prompt[i : i + batch_size]\n\n input_ids = self.tokenizer(\n prompt_slice,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n ).input_ids\n\n input_ids = input_ids.to(self.text_encoder.device)\n embeds.append(self.text_encoder(input_ids)[0])\n\n return torch.cat(embeds, dim=0).mean(0)[None]\n\n def prepare_image_latents(self, image, batch_size, dtype, device, generator=None):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n image = image.to(device=device, dtype=dtype)\n\n if image.shape[1] == 4:\n latents = image\n\n else:\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if isinstance(generator, list):\n latents = [\n self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)\n ]\n latents = torch.cat(latents, dim=0)\n else:\n latents = self.vae.encode(image).latent_dist.sample(generator)\n\n latents = self.vae.config.scaling_factor * latents\n\n if batch_size != latents.shape[0]:\n if batch_size % latents.shape[0] == 0:\n # expand image_latents for batch_size\n deprecation_message = (\n f\"You have passed {batch_size} text prompts (`prompt`), but only {latents.shape[0]} initial\"\n \" images (`image`). Initial images are now duplicating to match the number of text prompts. Note\"\n \" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update\"\n \" your script to pass as many initial images as text prompts to suppress this warning.\"\n )\n deprecate(\"len(prompt) != len(image)\", \"1.0.0\", deprecation_message, standard_warn=False)\n additional_latents_per_image = batch_size // latents.shape[0]\n latents = torch.cat([latents] * additional_latents_per_image, dim=0)\n else:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n latents = torch.cat([latents], dim=0)\n\n return latents\n\n def get_epsilon(self, model_output: torch.Tensor, sample: torch.Tensor, timestep: int):\n pred_type = self.inverse_scheduler.config.prediction_type\n alpha_prod_t = self.inverse_scheduler.alphas_cumprod[timestep]\n\n beta_prod_t = 1 - alpha_prod_t\n\n if pred_type == \"epsilon\":\n return model_output\n elif pred_type == \"sample\":\n return (sample - alpha_prod_t ** (0.5) * model_output) / beta_prod_t ** (0.5)\n elif pred_type == \"v_prediction\":\n return (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample\n else:\n raise ValueError(\n f\"prediction_type given as {pred_type} must be one of `epsilon`, `sample`, or `v_prediction`\"\n )\n\n def auto_corr_loss(self, hidden_states, generator=None):\n reg_loss = 0.0\n for i in range(hidden_states.shape[0]):\n for j in range(hidden_states.shape[1]):\n noise = hidden_states[i : i + 1, j : j + 1, :, :]\n while True:\n roll_amount = torch.randint(noise.shape[2] // 2, (1,), generator=generator).item()\n reg_loss += (noise * torch.roll(noise, shifts=roll_amount, dims=2)).mean() ** 2\n reg_loss += (noise * torch.roll(noise, shifts=roll_amount, dims=3)).mean() ** 2\n\n if noise.shape[2] <= 8:\n break\n noise = F.avg_pool2d(noise, kernel_size=2)\n return reg_loss\n\n def kl_divergence(self, hidden_states):\n mean = hidden_states.mean()\n var = hidden_states.var()\n return var + mean**2 - 1 - torch.log(var + 1e-7)\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Optional[Union[str, List[str]]] = None,\n source_embeds: torch.Tensor = None,\n target_embeds: torch.Tensor = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n cross_attention_guidance_amount: float = 0.1,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: Optional[int] = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n source_embeds (`torch.Tensor`):\n Source concept embeddings. Generation of the embeddings as per the [original\n paper](https://arxiv.org/abs/2302.03027). Used in discovering the edit direction.\n target_embeds (`torch.Tensor`):\n Target concept embeddings. Generation of the embeddings as per the [original\n paper](https://arxiv.org/abs/2302.03027). Used in discovering the edit direction.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n cross_attention_guidance_amount (`float`, defaults to 0.1):\n Amount of guidance needed from the reference cross-attention maps.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n # 0. Define the spatial resolutions.\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n source_embeds,\n target_embeds,\n callback_steps,\n prompt_embeds,\n )\n\n # 3. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n if cross_attention_kwargs is None:\n cross_attention_kwargs = {}\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n clip_skip=clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Generate the inverted noise from the input image or any other image\n # generated from the input prompt.\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n latents_init = latents.clone()\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 8. Rejig the UNet so that we can obtain the cross-attenion maps and\n # use them for guiding the subsequent image generation.\n self.unet = prepare_unet(self.unet)\n\n # 7. Denoising loop where we obtain the cross-attention maps.\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs={\"timestep\": t},\n ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # 8. Compute the edit directions.\n edit_direction = self.construct_direction(source_embeds, target_embeds).to(prompt_embeds.device)\n\n # 9. Edit the prompt embeddings as per the edit directions discovered.\n prompt_embeds_edit = prompt_embeds.clone()\n prompt_embeds_edit[1:2] += edit_direction\n\n # 10. Second denoising loop to generate the edited image.\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n latents = latents_init\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # we want to learn the latent such that it steers the generation\n # process towards the edited direction, so make the make initial\n # noise learnable\n x_in = latent_model_input.detach().clone()\n x_in.requires_grad = True\n\n # optimizer\n opt = torch.optim.SGD([x_in], lr=cross_attention_guidance_amount)\n\n with torch.enable_grad():\n # initialize loss\n loss = Pix2PixZeroL2Loss()\n\n # predict the noise residual\n noise_pred = self.unet(\n x_in,\n t,\n encoder_hidden_states=prompt_embeds_edit.detach(),\n cross_attention_kwargs={\"timestep\": t, \"loss\": loss},\n ).sample\n\n loss.loss.backward(retain_graph=False)\n opt.step()\n\n # recompute the noise\n noise_pred = self.unet(\n x_in.detach(),\n t,\n encoder_hidden_states=prompt_embeds_edit,\n cross_attention_kwargs={\"timestep\": None},\n ).sample\n\n latents = x_in.detach().chunk(2)[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_INVERT_DOC_STRING)\n def invert(\n self,\n prompt: Optional[str] = None,\n image: PipelineImageInput = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n cross_attention_guidance_amount: float = 0.1,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: Optional[int] = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n lambda_auto_corr: float = 20.0,\n lambda_kl: float = 20.0,\n num_reg_steps: int = 5,\n num_auto_corr_rolls: int = 5,\n ):\n r\"\"\"\n Function used to generate inverted latents given a prompt and image.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n image (`torch.Tensor` `np.ndarray`, `PIL.Image.Image`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image`, or tensor representing an image batch which will be used for conditioning. Can also accept\n image latents as `image`, if passing latents directly, it will not be encoded again.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 1):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n cross_attention_guidance_amount (`float`, defaults to 0.1):\n Amount of guidance needed from the reference cross-attention maps.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n lambda_auto_corr (`float`, *optional*, defaults to 20.0):\n Lambda parameter to control auto correction\n lambda_kl (`float`, *optional*, defaults to 20.0):\n Lambda parameter to control Kullback–Leibler divergence output\n num_reg_steps (`int`, *optional*, defaults to 5):\n Number of regularization loss steps\n num_auto_corr_rolls (`int`, *optional*, defaults to 5):\n Number of auto correction roll steps\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.pipeline_stable_diffusion_pix2pix_zero.Pix2PixInversionPipelineOutput`] or\n `tuple`:\n [`~pipelines.stable_diffusion.pipeline_stable_diffusion_pix2pix_zero.Pix2PixInversionPipelineOutput`] if\n `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is the inverted\n latents tensor and then second is the corresponding decoded image.\n \"\"\"\n # 1. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n if cross_attention_kwargs is None:\n cross_attention_kwargs = {}\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Preprocess image\n image = self.image_processor.preprocess(image)\n\n # 4. Prepare latent variables\n latents = self.prepare_image_latents(image, batch_size, self.vae.dtype, device, generator)\n\n # 5. Encode input prompt\n num_images_per_prompt = 1\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n prompt_embeds=prompt_embeds,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Prepare timesteps\n self.inverse_scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.inverse_scheduler.timesteps\n\n # 6. Rejig the UNet so that we can obtain the cross-attenion maps and\n # use them for guiding the subsequent image generation.\n self.unet = prepare_unet(self.unet)\n\n # 7. Denoising loop where we obtain the cross-attention maps.\n num_warmup_steps = len(timesteps) - num_inference_steps * self.inverse_scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.inverse_scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs={\"timestep\": t},\n ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # regularization of the noise prediction\n with torch.enable_grad():\n for _ in range(num_reg_steps):\n if lambda_auto_corr > 0:\n for _ in range(num_auto_corr_rolls):\n var = torch.autograd.Variable(noise_pred.detach().clone(), requires_grad=True)\n\n # Derive epsilon from model output before regularizing to IID standard normal\n var_epsilon = self.get_epsilon(var, latent_model_input.detach(), t)\n\n l_ac = self.auto_corr_loss(var_epsilon, generator=generator)\n l_ac.backward()\n\n grad = var.grad.detach() / num_auto_corr_rolls\n noise_pred = noise_pred - lambda_auto_corr * grad\n\n if lambda_kl > 0:\n var = torch.autograd.Variable(noise_pred.detach().clone(), requires_grad=True)\n\n # Derive epsilon from model output before regularizing to IID standard normal\n var_epsilon = self.get_epsilon(var, latent_model_input.detach(), t)\n\n l_kld = self.kl_divergence(var_epsilon)\n l_kld.backward()\n\n grad = var.grad.detach()\n noise_pred = noise_pred - lambda_kl * grad\n\n noise_pred = noise_pred.detach()\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.inverse_scheduler.step(noise_pred, t, latents).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or (\n (i + 1) > num_warmup_steps and (i + 1) % self.inverse_scheduler.order == 0\n ):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n inverted_latents = latents.detach().clone()\n\n # 8. Post-processing\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (inverted_latents, image)\n\n return Pix2PixInversionPipelineOutput(latents=inverted_latents, images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/stochastic_karras_ve/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ....utils import DIFFUSERS_SLOW_IMPORT, _LazyModule\n\n\n_import_structure = {\"pipeline_stochastic_karras_ve\": [\"KarrasVePipeline\"]}\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n from .pipeline_stochastic_karras_ve import KarrasVePipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/stochastic_karras_ve/pipeline_stochastic_karras_ve.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import List, Optional, Tuple, Union\n\nimport torch\n\nfrom ....models import UNet2DModel\nfrom ....schedulers import KarrasVeScheduler\nfrom ....utils.torch_utils import randn_tensor\nfrom ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nclass KarrasVePipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for unconditional image generation.\n\n Parameters:\n unet ([`UNet2DModel`]):\n A `UNet2DModel` to denoise the encoded image.\n scheduler ([`KarrasVeScheduler`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image.\n \"\"\"\n\n # add type hints for linting\n unet: UNet2DModel\n scheduler: KarrasVeScheduler\n\n def __init__(self, unet: UNet2DModel, scheduler: KarrasVeScheduler):\n super().__init__()\n self.register_modules(unet=unet, scheduler=scheduler)\n\n @torch.no_grad()\n def __call__(\n self,\n batch_size: int = 1,\n num_inference_steps: int = 50,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n **kwargs,\n ) -> Union[Tuple, ImagePipelineOutput]:\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n batch_size (`int`, *optional*, defaults to 1):\n The number of images to generate.\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple.\n\n Example:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is\n returned where the first element is a list with the generated images.\n \"\"\"\n\n img_size = self.unet.config.sample_size\n shape = (batch_size, 3, img_size, img_size)\n\n model = self.unet\n\n # sample x_0 ~ N(0, sigma_0^2 * I)\n sample = randn_tensor(shape, generator=generator, device=self.device) * self.scheduler.init_noise_sigma\n\n self.scheduler.set_timesteps(num_inference_steps)\n\n for t in self.progress_bar(self.scheduler.timesteps):\n # here sigma_t == t_i from the paper\n sigma = self.scheduler.schedule[t]\n sigma_prev = self.scheduler.schedule[t - 1] if t > 0 else 0\n\n # 1. Select temporarily increased noise level sigma_hat\n # 2. Add new noise to move from sample_i to sample_hat\n sample_hat, sigma_hat = self.scheduler.add_noise_to_input(sample, sigma, generator=generator)\n\n # 3. Predict the noise residual given the noise magnitude `sigma_hat`\n # The model inputs and output are adjusted by following eq. (213) in [1].\n model_output = (sigma_hat / 2) * model((sample_hat + 1) / 2, sigma_hat / 2).sample\n\n # 4. Evaluate dx/dt at sigma_hat\n # 5. Take Euler step from sigma to sigma_prev\n step_output = self.scheduler.step(model_output, sigma_hat, sigma_prev, sample_hat)\n\n if sigma_prev != 0:\n # 6. Apply 2nd order correction\n # The model inputs and output are adjusted by following eq. (213) in [1].\n model_output = (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2, sigma_prev / 2).sample\n step_output = self.scheduler.step_correct(\n model_output,\n sigma_hat,\n sigma_prev,\n sample_hat,\n step_output.prev_sample,\n step_output[\"derivative\"],\n )\n sample = step_output.prev_sample\n\n sample = (sample / 2 + 0.5).clamp(0, 1)\n image = sample.cpu().permute(0, 2, 3, 1).numpy()\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ....utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n is_torch_available,\n is_transformers_available,\n is_transformers_version,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available() and is_transformers_version(\">=\", \"4.25.0\")):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ....utils.dummy_torch_and_transformers_objects import (\n VersatileDiffusionDualGuidedPipeline,\n VersatileDiffusionImageVariationPipeline,\n VersatileDiffusionPipeline,\n VersatileDiffusionTextToImagePipeline,\n )\n\n _dummy_objects.update(\n {\n \"VersatileDiffusionDualGuidedPipeline\": VersatileDiffusionDualGuidedPipeline,\n \"VersatileDiffusionImageVariationPipeline\": VersatileDiffusionImageVariationPipeline,\n \"VersatileDiffusionPipeline\": VersatileDiffusionPipeline,\n \"VersatileDiffusionTextToImagePipeline\": VersatileDiffusionTextToImagePipeline,\n }\n )\nelse:\n _import_structure[\"modeling_text_unet\"] = [\"UNetFlatConditionModel\"]\n _import_structure[\"pipeline_versatile_diffusion\"] = [\"VersatileDiffusionPipeline\"]\n _import_structure[\"pipeline_versatile_diffusion_dual_guided\"] = [\"VersatileDiffusionDualGuidedPipeline\"]\n _import_structure[\"pipeline_versatile_diffusion_image_variation\"] = [\"VersatileDiffusionImageVariationPipeline\"]\n _import_structure[\"pipeline_versatile_diffusion_text_to_image\"] = [\"VersatileDiffusionTextToImagePipeline\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available() and is_transformers_version(\">=\", \"4.25.0\")):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ....utils.dummy_torch_and_transformers_objects import (\n VersatileDiffusionDualGuidedPipeline,\n VersatileDiffusionImageVariationPipeline,\n VersatileDiffusionPipeline,\n VersatileDiffusionTextToImagePipeline,\n )\n else:\n from .pipeline_versatile_diffusion import VersatileDiffusionPipeline\n from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline\n from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline\n from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/modeling_text_unet.py", "content": "from typing import Any, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom diffusers.utils import deprecate\n\nfrom ....configuration_utils import ConfigMixin, register_to_config\nfrom ....models import ModelMixin\nfrom ....models.activations import get_activation\nfrom ....models.attention_processor import (\n ADDED_KV_ATTENTION_PROCESSORS,\n CROSS_ATTENTION_PROCESSORS,\n Attention,\n AttentionProcessor,\n AttnAddedKVProcessor,\n AttnAddedKVProcessor2_0,\n AttnProcessor,\n)\nfrom ....models.embeddings import (\n GaussianFourierProjection,\n ImageHintTimeEmbedding,\n ImageProjection,\n ImageTimeEmbedding,\n TextImageProjection,\n TextImageTimeEmbedding,\n TextTimeEmbedding,\n TimestepEmbedding,\n Timesteps,\n)\nfrom ....models.resnet import ResnetBlockCondNorm2D\nfrom ....models.transformers.dual_transformer_2d import DualTransformer2DModel\nfrom ....models.transformers.transformer_2d import Transformer2DModel\nfrom ....models.unets.unet_2d_condition import UNet2DConditionOutput\nfrom ....utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers\nfrom ....utils.torch_utils import apply_freeu\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef get_down_block(\n down_block_type,\n num_layers,\n in_channels,\n out_channels,\n temb_channels,\n add_downsample,\n resnet_eps,\n resnet_act_fn,\n num_attention_heads,\n transformer_layers_per_block,\n attention_type,\n attention_head_dim,\n resnet_groups=None,\n cross_attention_dim=None,\n downsample_padding=None,\n dual_cross_attention=False,\n use_linear_projection=False,\n only_cross_attention=False,\n upcast_attention=False,\n resnet_time_scale_shift=\"default\",\n resnet_skip_time_act=False,\n resnet_out_scale_factor=1.0,\n cross_attention_norm=None,\n dropout=0.0,\n):\n down_block_type = down_block_type[7:] if down_block_type.startswith(\"UNetRes\") else down_block_type\n if down_block_type == \"DownBlockFlat\":\n return DownBlockFlat(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n dropout=dropout,\n add_downsample=add_downsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n downsample_padding=downsample_padding,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n elif down_block_type == \"CrossAttnDownBlockFlat\":\n if cross_attention_dim is None:\n raise ValueError(\"cross_attention_dim must be specified for CrossAttnDownBlockFlat\")\n return CrossAttnDownBlockFlat(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n dropout=dropout,\n add_downsample=add_downsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n downsample_padding=downsample_padding,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=num_attention_heads,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n raise ValueError(f\"{down_block_type} is not supported.\")\n\n\ndef get_up_block(\n up_block_type,\n num_layers,\n in_channels,\n out_channels,\n prev_output_channel,\n temb_channels,\n add_upsample,\n resnet_eps,\n resnet_act_fn,\n num_attention_heads,\n transformer_layers_per_block,\n resolution_idx,\n attention_type,\n attention_head_dim,\n resnet_groups=None,\n cross_attention_dim=None,\n dual_cross_attention=False,\n use_linear_projection=False,\n only_cross_attention=False,\n upcast_attention=False,\n resnet_time_scale_shift=\"default\",\n resnet_skip_time_act=False,\n resnet_out_scale_factor=1.0,\n cross_attention_norm=None,\n dropout=0.0,\n):\n up_block_type = up_block_type[7:] if up_block_type.startswith(\"UNetRes\") else up_block_type\n if up_block_type == \"UpBlockFlat\":\n return UpBlockFlat(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n prev_output_channel=prev_output_channel,\n temb_channels=temb_channels,\n dropout=dropout,\n add_upsample=add_upsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n elif up_block_type == \"CrossAttnUpBlockFlat\":\n if cross_attention_dim is None:\n raise ValueError(\"cross_attention_dim must be specified for CrossAttnUpBlockFlat\")\n return CrossAttnUpBlockFlat(\n num_layers=num_layers,\n in_channels=in_channels,\n out_channels=out_channels,\n prev_output_channel=prev_output_channel,\n temb_channels=temb_channels,\n dropout=dropout,\n add_upsample=add_upsample,\n resnet_eps=resnet_eps,\n resnet_act_fn=resnet_act_fn,\n resnet_groups=resnet_groups,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=num_attention_heads,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n raise ValueError(f\"{up_block_type} is not supported.\")\n\n\nclass FourierEmbedder(nn.Module):\n def __init__(self, num_freqs=64, temperature=100):\n super().__init__()\n\n self.num_freqs = num_freqs\n self.temperature = temperature\n\n freq_bands = temperature ** (torch.arange(num_freqs) / num_freqs)\n freq_bands = freq_bands[None, None, None]\n self.register_buffer(\"freq_bands\", freq_bands, persistent=False)\n\n def __call__(self, x):\n x = self.freq_bands * x.unsqueeze(-1)\n return torch.stack((x.sin(), x.cos()), dim=-1).permute(0, 1, 3, 4, 2).reshape(*x.shape[:2], -1)\n\n\nclass GLIGENTextBoundingboxProjection(nn.Module):\n def __init__(self, positive_len, out_dim, feature_type, fourier_freqs=8):\n super().__init__()\n self.positive_len = positive_len\n self.out_dim = out_dim\n\n self.fourier_embedder = FourierEmbedder(num_freqs=fourier_freqs)\n self.position_dim = fourier_freqs * 2 * 4 # 2: sin/cos, 4: xyxy\n\n if isinstance(out_dim, tuple):\n out_dim = out_dim[0]\n\n if feature_type == \"text-only\":\n self.linears = nn.Sequential(\n nn.Linear(self.positive_len + self.position_dim, 512),\n nn.SiLU(),\n nn.Linear(512, 512),\n nn.SiLU(),\n nn.Linear(512, out_dim),\n )\n self.null_positive_feature = torch.nn.Parameter(torch.zeros([self.positive_len]))\n\n elif feature_type == \"text-image\":\n self.linears_text = nn.Sequential(\n nn.Linear(self.positive_len + self.position_dim, 512),\n nn.SiLU(),\n nn.Linear(512, 512),\n nn.SiLU(),\n nn.Linear(512, out_dim),\n )\n self.linears_image = nn.Sequential(\n nn.Linear(self.positive_len + self.position_dim, 512),\n nn.SiLU(),\n nn.Linear(512, 512),\n nn.SiLU(),\n nn.Linear(512, out_dim),\n )\n self.null_text_feature = torch.nn.Parameter(torch.zeros([self.positive_len]))\n self.null_image_feature = torch.nn.Parameter(torch.zeros([self.positive_len]))\n\n self.null_position_feature = torch.nn.Parameter(torch.zeros([self.position_dim]))\n\n def forward(\n self,\n boxes,\n masks,\n positive_embeddings=None,\n phrases_masks=None,\n image_masks=None,\n phrases_embeddings=None,\n image_embeddings=None,\n ):\n masks = masks.unsqueeze(-1)\n\n xyxy_embedding = self.fourier_embedder(boxes)\n xyxy_null = self.null_position_feature.view(1, 1, -1)\n xyxy_embedding = xyxy_embedding * masks + (1 - masks) * xyxy_null\n\n if positive_embeddings:\n positive_null = self.null_positive_feature.view(1, 1, -1)\n positive_embeddings = positive_embeddings * masks + (1 - masks) * positive_null\n\n objs = self.linears(torch.cat([positive_embeddings, xyxy_embedding], dim=-1))\n else:\n phrases_masks = phrases_masks.unsqueeze(-1)\n image_masks = image_masks.unsqueeze(-1)\n\n text_null = self.null_text_feature.view(1, 1, -1)\n image_null = self.null_image_feature.view(1, 1, -1)\n\n phrases_embeddings = phrases_embeddings * phrases_masks + (1 - phrases_masks) * text_null\n image_embeddings = image_embeddings * image_masks + (1 - image_masks) * image_null\n\n objs_text = self.linears_text(torch.cat([phrases_embeddings, xyxy_embedding], dim=-1))\n objs_image = self.linears_image(torch.cat([image_embeddings, xyxy_embedding], dim=-1))\n objs = torch.cat([objs_text, objs_image], dim=1)\n\n return objs\n\n\nclass UNetFlatConditionModel(ModelMixin, ConfigMixin):\n r\"\"\"\n A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample\n shaped output.\n\n This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented\n for all models (such as downloading or saving).\n\n Parameters:\n sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):\n Height and width of input/output sample.\n in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.\n out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.\n center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.\n flip_sin_to_cos (`bool`, *optional*, defaults to `False`):\n Whether to flip the sin to cos in the time embedding.\n freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.\n down_block_types (`Tuple[str]`, *optional*, defaults to `(\"CrossAttnDownBlockFlat\", \"CrossAttnDownBlockFlat\", \"CrossAttnDownBlockFlat\", \"DownBlockFlat\")`):\n The tuple of downsample blocks to use.\n mid_block_type (`str`, *optional*, defaults to `\"UNetMidBlockFlatCrossAttn\"`):\n Block type for middle of UNet, it can be one of `UNetMidBlockFlatCrossAttn`, `UNetMidBlockFlat`, or\n `UNetMidBlockFlatSimpleCrossAttn`. If `None`, the mid block layer is skipped.\n up_block_types (`Tuple[str]`, *optional*, defaults to `(\"UpBlockFlat\", \"CrossAttnUpBlockFlat\", \"CrossAttnUpBlockFlat\", \"CrossAttnUpBlockFlat\")`):\n The tuple of upsample blocks to use.\n only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):\n Whether to include self-attention in the basic transformer blocks, see\n [`~models.attention.BasicTransformerBlock`].\n block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):\n The tuple of output channels for each block.\n layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.\n downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.\n mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.\n dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.\n act_fn (`str`, *optional*, defaults to `\"silu\"`): The activation function to use.\n norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.\n If `None`, normalization and activation layers is skipped in post-processing.\n norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.\n cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):\n The dimension of the cross attention features.\n transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):\n The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for\n [`~models.unet_2d_blocks.CrossAttnDownBlockFlat`], [`~models.unet_2d_blocks.CrossAttnUpBlockFlat`],\n [`~models.unet_2d_blocks.UNetMidBlockFlatCrossAttn`].\n reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None):\n The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling\n blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for\n [`~models.unet_2d_blocks.CrossAttnDownBlockFlat`], [`~models.unet_2d_blocks.CrossAttnUpBlockFlat`],\n [`~models.unet_2d_blocks.UNetMidBlockFlatCrossAttn`].\n encoder_hid_dim (`int`, *optional*, defaults to None):\n If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`\n dimension to `cross_attention_dim`.\n encoder_hid_dim_type (`str`, *optional*, defaults to `None`):\n If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text\n embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.\n attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.\n num_attention_heads (`int`, *optional*):\n The number of attention heads. If not defined, defaults to `attention_head_dim`\n resnet_time_scale_shift (`str`, *optional*, defaults to `\"default\"`): Time scale shift config\n for ResNet blocks (see [`~models.resnet.ResnetBlockFlat`]). Choose from `default` or `scale_shift`.\n class_embed_type (`str`, *optional*, defaults to `None`):\n The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,\n `\"timestep\"`, `\"identity\"`, `\"projection\"`, or `\"simple_projection\"`.\n addition_embed_type (`str`, *optional*, defaults to `None`):\n Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or\n \"text\". \"text\" will use the `TextTimeEmbedding` layer.\n addition_time_embed_dim: (`int`, *optional*, defaults to `None`):\n Dimension for the timestep embeddings.\n num_class_embeds (`int`, *optional*, defaults to `None`):\n Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing\n class conditioning with `class_embed_type` equal to `None`.\n time_embedding_type (`str`, *optional*, defaults to `positional`):\n The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.\n time_embedding_dim (`int`, *optional*, defaults to `None`):\n An optional override for the dimension of the projected time embedding.\n time_embedding_act_fn (`str`, *optional*, defaults to `None`):\n Optional activation function to use only once on the time embeddings before they are passed to the rest of\n the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.\n timestep_post_act (`str`, *optional*, defaults to `None`):\n The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.\n time_cond_proj_dim (`int`, *optional*, defaults to `None`):\n The dimension of `cond_proj` layer in the timestep embedding.\n conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. conv_out_kernel (`int`,\n *optional*, default to `3`): The kernel size of `conv_out` layer. projection_class_embeddings_input_dim (`int`,\n *optional*): The dimension of the `class_labels` input when\n `class_embed_type=\"projection\"`. Required when `class_embed_type=\"projection\"`.\n class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time\n embeddings with the class embeddings.\n mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):\n Whether to use cross attention with the mid block when using the `UNetMidBlockFlatSimpleCrossAttn`. If\n `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the\n `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`\n otherwise.\n \"\"\"\n\n _supports_gradient_checkpointing = True\n _no_split_modules = [\"BasicTransformerBlock\", \"ResnetBlockFlat\", \"CrossAttnUpBlockFlat\"]\n\n @register_to_config\n def __init__(\n self,\n sample_size: Optional[int] = None,\n in_channels: int = 4,\n out_channels: int = 4,\n center_input_sample: bool = False,\n flip_sin_to_cos: bool = True,\n freq_shift: int = 0,\n down_block_types: Tuple[str] = (\n \"CrossAttnDownBlockFlat\",\n \"CrossAttnDownBlockFlat\",\n \"CrossAttnDownBlockFlat\",\n \"DownBlockFlat\",\n ),\n mid_block_type: Optional[str] = \"UNetMidBlockFlatCrossAttn\",\n up_block_types: Tuple[str] = (\n \"UpBlockFlat\",\n \"CrossAttnUpBlockFlat\",\n \"CrossAttnUpBlockFlat\",\n \"CrossAttnUpBlockFlat\",\n ),\n only_cross_attention: Union[bool, Tuple[bool]] = False,\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\n layers_per_block: Union[int, Tuple[int]] = 2,\n downsample_padding: int = 1,\n mid_block_scale_factor: float = 1,\n dropout: float = 0.0,\n act_fn: str = \"silu\",\n norm_num_groups: Optional[int] = 32,\n norm_eps: float = 1e-5,\n cross_attention_dim: Union[int, Tuple[int]] = 1280,\n transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,\n reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None,\n encoder_hid_dim: Optional[int] = None,\n encoder_hid_dim_type: Optional[str] = None,\n attention_head_dim: Union[int, Tuple[int]] = 8,\n num_attention_heads: Optional[Union[int, Tuple[int]]] = None,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = False,\n class_embed_type: Optional[str] = None,\n addition_embed_type: Optional[str] = None,\n addition_time_embed_dim: Optional[int] = None,\n num_class_embeds: Optional[int] = None,\n upcast_attention: bool = False,\n resnet_time_scale_shift: str = \"default\",\n resnet_skip_time_act: bool = False,\n resnet_out_scale_factor: int = 1.0,\n time_embedding_type: str = \"positional\",\n time_embedding_dim: Optional[int] = None,\n time_embedding_act_fn: Optional[str] = None,\n timestep_post_act: Optional[str] = None,\n time_cond_proj_dim: Optional[int] = None,\n conv_in_kernel: int = 3,\n conv_out_kernel: int = 3,\n projection_class_embeddings_input_dim: Optional[int] = None,\n attention_type: str = \"default\",\n class_embeddings_concat: bool = False,\n mid_block_only_cross_attention: Optional[bool] = None,\n cross_attention_norm: Optional[str] = None,\n addition_embed_type_num_heads=64,\n ):\n super().__init__()\n\n self.sample_size = sample_size\n\n if num_attention_heads is not None:\n raise ValueError(\n \"At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.\"\n )\n\n # If `num_attention_heads` is not defined (which is the case for most models)\n # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.\n # The reason for this behavior is to correct for incorrectly named variables that were introduced\n # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131\n # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking\n # which is why we correct for the naming here.\n num_attention_heads = num_attention_heads or attention_head_dim\n\n # Check inputs\n if len(down_block_types) != len(up_block_types):\n raise ValueError(\n f\"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.\"\n )\n\n if len(block_out_channels) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}.\"\n )\n\n if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}.\"\n )\n if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None:\n for layer_number_per_block in transformer_layers_per_block:\n if isinstance(layer_number_per_block, list):\n raise ValueError(\"Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.\")\n\n # input\n conv_in_padding = (conv_in_kernel - 1) // 2\n self.conv_in = LinearMultiDim(\n in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding\n )\n\n # time\n if time_embedding_type == \"fourier\":\n time_embed_dim = time_embedding_dim or block_out_channels[0] * 2\n if time_embed_dim % 2 != 0:\n raise ValueError(f\"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.\")\n self.time_proj = GaussianFourierProjection(\n time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos\n )\n timestep_input_dim = time_embed_dim\n elif time_embedding_type == \"positional\":\n time_embed_dim = time_embedding_dim or block_out_channels[0] * 4\n\n self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)\n timestep_input_dim = block_out_channels[0]\n else:\n raise ValueError(\n f\"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`.\"\n )\n\n self.time_embedding = TimestepEmbedding(\n timestep_input_dim,\n time_embed_dim,\n act_fn=act_fn,\n post_act_fn=timestep_post_act,\n cond_proj_dim=time_cond_proj_dim,\n )\n\n if encoder_hid_dim_type is None and encoder_hid_dim is not None:\n encoder_hid_dim_type = \"text_proj\"\n self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)\n logger.info(\"encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.\")\n\n if encoder_hid_dim is None and encoder_hid_dim_type is not None:\n raise ValueError(\n f\"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}.\"\n )\n\n if encoder_hid_dim_type == \"text_proj\":\n self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)\n elif encoder_hid_dim_type == \"text_image_proj\":\n # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much\n # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use\n # case when `addition_embed_type == \"text_image_proj\"` (Kandinsky 2.1)`\n self.encoder_hid_proj = TextImageProjection(\n text_embed_dim=encoder_hid_dim,\n image_embed_dim=cross_attention_dim,\n cross_attention_dim=cross_attention_dim,\n )\n elif encoder_hid_dim_type == \"image_proj\":\n # Kandinsky 2.2\n self.encoder_hid_proj = ImageProjection(\n image_embed_dim=encoder_hid_dim,\n cross_attention_dim=cross_attention_dim,\n )\n elif encoder_hid_dim_type is not None:\n raise ValueError(\n f\"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'.\"\n )\n else:\n self.encoder_hid_proj = None\n\n # class embedding\n if class_embed_type is None and num_class_embeds is not None:\n self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)\n elif class_embed_type == \"timestep\":\n self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)\n elif class_embed_type == \"identity\":\n self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)\n elif class_embed_type == \"projection\":\n if projection_class_embeddings_input_dim is None:\n raise ValueError(\n \"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set\"\n )\n # The projection `class_embed_type` is the same as the timestep `class_embed_type` except\n # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings\n # 2. it projects from an arbitrary input dimension.\n #\n # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.\n # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.\n # As a result, `TimestepEmbedding` can be passed arbitrary vectors.\n self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)\n elif class_embed_type == \"simple_projection\":\n if projection_class_embeddings_input_dim is None:\n raise ValueError(\n \"`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set\"\n )\n self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)\n else:\n self.class_embedding = None\n\n if addition_embed_type == \"text\":\n if encoder_hid_dim is not None:\n text_time_embedding_from_dim = encoder_hid_dim\n else:\n text_time_embedding_from_dim = cross_attention_dim\n\n self.add_embedding = TextTimeEmbedding(\n text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads\n )\n elif addition_embed_type == \"text_image\":\n # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much\n # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use\n # case when `addition_embed_type == \"text_image\"` (Kandinsky 2.1)`\n self.add_embedding = TextImageTimeEmbedding(\n text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim\n )\n elif addition_embed_type == \"text_time\":\n self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)\n self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)\n elif addition_embed_type == \"image\":\n # Kandinsky 2.2\n self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)\n elif addition_embed_type == \"image_hint\":\n # Kandinsky 2.2 ControlNet\n self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)\n elif addition_embed_type is not None:\n raise ValueError(f\"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.\")\n\n if time_embedding_act_fn is None:\n self.time_embed_act = None\n else:\n self.time_embed_act = get_activation(time_embedding_act_fn)\n\n self.down_blocks = nn.ModuleList([])\n self.up_blocks = nn.ModuleList([])\n\n if isinstance(only_cross_attention, bool):\n if mid_block_only_cross_attention is None:\n mid_block_only_cross_attention = only_cross_attention\n\n only_cross_attention = [only_cross_attention] * len(down_block_types)\n\n if mid_block_only_cross_attention is None:\n mid_block_only_cross_attention = False\n\n if isinstance(num_attention_heads, int):\n num_attention_heads = (num_attention_heads,) * len(down_block_types)\n\n if isinstance(attention_head_dim, int):\n attention_head_dim = (attention_head_dim,) * len(down_block_types)\n\n if isinstance(cross_attention_dim, int):\n cross_attention_dim = (cross_attention_dim,) * len(down_block_types)\n\n if isinstance(layers_per_block, int):\n layers_per_block = [layers_per_block] * len(down_block_types)\n\n if isinstance(transformer_layers_per_block, int):\n transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)\n\n if class_embeddings_concat:\n # The time embeddings are concatenated with the class embeddings. The dimension of the\n # time embeddings passed to the down, middle, and up blocks is twice the dimension of the\n # regular time embeddings\n blocks_time_embed_dim = time_embed_dim * 2\n else:\n blocks_time_embed_dim = time_embed_dim\n\n # down\n output_channel = block_out_channels[0]\n for i, down_block_type in enumerate(down_block_types):\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n down_block = get_down_block(\n down_block_type,\n num_layers=layers_per_block[i],\n transformer_layers_per_block=transformer_layers_per_block[i],\n in_channels=input_channel,\n out_channels=output_channel,\n temb_channels=blocks_time_embed_dim,\n add_downsample=not is_final_block,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim[i],\n num_attention_heads=num_attention_heads[i],\n downsample_padding=downsample_padding,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention[i],\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n attention_type=attention_type,\n resnet_skip_time_act=resnet_skip_time_act,\n resnet_out_scale_factor=resnet_out_scale_factor,\n cross_attention_norm=cross_attention_norm,\n attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,\n dropout=dropout,\n )\n self.down_blocks.append(down_block)\n\n # mid\n if mid_block_type == \"UNetMidBlockFlatCrossAttn\":\n self.mid_block = UNetMidBlockFlatCrossAttn(\n transformer_layers_per_block=transformer_layers_per_block[-1],\n in_channels=block_out_channels[-1],\n temb_channels=blocks_time_embed_dim,\n dropout=dropout,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n output_scale_factor=mid_block_scale_factor,\n resnet_time_scale_shift=resnet_time_scale_shift,\n cross_attention_dim=cross_attention_dim[-1],\n num_attention_heads=num_attention_heads[-1],\n resnet_groups=norm_num_groups,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n upcast_attention=upcast_attention,\n attention_type=attention_type,\n )\n elif mid_block_type == \"UNetMidBlockFlatSimpleCrossAttn\":\n self.mid_block = UNetMidBlockFlatSimpleCrossAttn(\n in_channels=block_out_channels[-1],\n temb_channels=blocks_time_embed_dim,\n dropout=dropout,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n output_scale_factor=mid_block_scale_factor,\n cross_attention_dim=cross_attention_dim[-1],\n attention_head_dim=attention_head_dim[-1],\n resnet_groups=norm_num_groups,\n resnet_time_scale_shift=resnet_time_scale_shift,\n skip_time_act=resnet_skip_time_act,\n only_cross_attention=mid_block_only_cross_attention,\n cross_attention_norm=cross_attention_norm,\n )\n elif mid_block_type == \"UNetMidBlockFlat\":\n self.mid_block = UNetMidBlockFlat(\n in_channels=block_out_channels[-1],\n temb_channels=blocks_time_embed_dim,\n dropout=dropout,\n num_layers=0,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n output_scale_factor=mid_block_scale_factor,\n resnet_groups=norm_num_groups,\n resnet_time_scale_shift=resnet_time_scale_shift,\n add_attention=False,\n )\n elif mid_block_type is None:\n self.mid_block = None\n else:\n raise ValueError(f\"unknown mid_block_type : {mid_block_type}\")\n\n # count how many layers upsample the images\n self.num_upsamplers = 0\n\n # up\n reversed_block_out_channels = list(reversed(block_out_channels))\n reversed_num_attention_heads = list(reversed(num_attention_heads))\n reversed_layers_per_block = list(reversed(layers_per_block))\n reversed_cross_attention_dim = list(reversed(cross_attention_dim))\n reversed_transformer_layers_per_block = (\n list(reversed(transformer_layers_per_block))\n if reverse_transformer_layers_per_block is None\n else reverse_transformer_layers_per_block\n )\n only_cross_attention = list(reversed(only_cross_attention))\n\n output_channel = reversed_block_out_channels[0]\n for i, up_block_type in enumerate(up_block_types):\n is_final_block = i == len(block_out_channels) - 1\n\n prev_output_channel = output_channel\n output_channel = reversed_block_out_channels[i]\n input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]\n\n # add upsample block for all BUT final layer\n if not is_final_block:\n add_upsample = True\n self.num_upsamplers += 1\n else:\n add_upsample = False\n\n up_block = get_up_block(\n up_block_type,\n num_layers=reversed_layers_per_block[i] + 1,\n transformer_layers_per_block=reversed_transformer_layers_per_block[i],\n in_channels=input_channel,\n out_channels=output_channel,\n prev_output_channel=prev_output_channel,\n temb_channels=blocks_time_embed_dim,\n add_upsample=add_upsample,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resolution_idx=i,\n resnet_groups=norm_num_groups,\n cross_attention_dim=reversed_cross_attention_dim[i],\n num_attention_heads=reversed_num_attention_heads[i],\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention[i],\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n attention_type=attention_type,\n resnet_skip_time_act=resnet_skip_time_act,\n resnet_out_scale_factor=resnet_out_scale_factor,\n cross_attention_norm=cross_attention_norm,\n attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,\n dropout=dropout,\n )\n self.up_blocks.append(up_block)\n prev_output_channel = output_channel\n\n # out\n if norm_num_groups is not None:\n self.conv_norm_out = nn.GroupNorm(\n num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps\n )\n\n self.conv_act = get_activation(act_fn)\n\n else:\n self.conv_norm_out = None\n self.conv_act = None\n\n conv_out_padding = (conv_out_kernel - 1) // 2\n self.conv_out = LinearMultiDim(\n block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding\n )\n\n if attention_type in [\"gated\", \"gated-text-image\"]:\n positive_len = 768\n if isinstance(cross_attention_dim, int):\n positive_len = cross_attention_dim\n elif isinstance(cross_attention_dim, (list, tuple)):\n positive_len = cross_attention_dim[0]\n\n feature_type = \"text-only\" if attention_type == \"gated\" else \"text-image\"\n self.position_net = GLIGENTextBoundingboxProjection(\n positive_len=positive_len, out_dim=cross_attention_dim, feature_type=feature_type\n )\n\n @property\n def attn_processors(self) -> Dict[str, AttentionProcessor]:\n r\"\"\"\n Returns:\n `dict` of attention processors: A dictionary containing all attention processors used in the model with\n indexed by its weight name.\n \"\"\"\n # set recursively\n processors = {}\n\n def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\n if hasattr(module, \"get_processor\"):\n processors[f\"{name}.processor\"] = module.get_processor(return_deprecated_lora=True)\n\n for sub_name, child in module.named_children():\n fn_recursive_add_processors(f\"{name}.{sub_name}\", child, processors)\n\n return processors\n\n for name, module in self.named_children():\n fn_recursive_add_processors(name, module, processors)\n\n return processors\n\n def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\n r\"\"\"\n Sets the attention processor to use to compute attention.\n\n Parameters:\n processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):\n The instantiated processor class or a dictionary of processor classes that will be set as the processor\n for **all** `Attention` layers.\n\n If `processor` is a dict, the key needs to define the path to the corresponding cross attention\n processor. This is strongly recommended when setting trainable attention processors.\n\n \"\"\"\n count = len(self.attn_processors.keys())\n\n if isinstance(processor, dict) and len(processor) != count:\n raise ValueError(\n f\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\"\n f\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\"\n )\n\n def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\n if hasattr(module, \"set_processor\"):\n if not isinstance(processor, dict):\n module.set_processor(processor)\n else:\n module.set_processor(processor.pop(f\"{name}.processor\"))\n\n for sub_name, child in module.named_children():\n fn_recursive_attn_processor(f\"{name}.{sub_name}\", child, processor)\n\n for name, module in self.named_children():\n fn_recursive_attn_processor(name, module, processor)\n\n def set_default_attn_processor(self):\n \"\"\"\n Disables custom attention processors and sets the default attention implementation.\n \"\"\"\n if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnAddedKVProcessor()\n elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnProcessor()\n else:\n raise ValueError(\n f\"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}\"\n )\n\n self.set_attn_processor(processor)\n\n def set_attention_slice(self, slice_size):\n r\"\"\"\n Enable sliced attention computation.\n\n When this option is enabled, the attention module splits the input tensor in slices to compute attention in\n several steps. This is useful for saving some memory in exchange for a small decrease in speed.\n\n Args:\n slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `\"auto\"`):\n When `\"auto\"`, input to the attention heads is halved, so attention is computed in two steps. If\n `\"max\"`, maximum amount of memory is saved by running only one slice at a time. If a number is\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\n must be a multiple of `slice_size`.\n \"\"\"\n sliceable_head_dims = []\n\n def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):\n if hasattr(module, \"set_attention_slice\"):\n sliceable_head_dims.append(module.sliceable_head_dim)\n\n for child in module.children():\n fn_recursive_retrieve_sliceable_dims(child)\n\n # retrieve number of attention layers\n for module in self.children():\n fn_recursive_retrieve_sliceable_dims(module)\n\n num_sliceable_layers = len(sliceable_head_dims)\n\n if slice_size == \"auto\":\n # half the attention head size is usually a good trade-off between\n # speed and memory\n slice_size = [dim // 2 for dim in sliceable_head_dims]\n elif slice_size == \"max\":\n # make smallest slice possible\n slice_size = num_sliceable_layers * [1]\n\n slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\n\n if len(slice_size) != len(sliceable_head_dims):\n raise ValueError(\n f\"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different\"\n f\" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}.\"\n )\n\n for i in range(len(slice_size)):\n size = slice_size[i]\n dim = sliceable_head_dims[i]\n if size is not None and size > dim:\n raise ValueError(f\"size {size} has to be smaller or equal to {dim}.\")\n\n # Recursively walk through all the children.\n # Any children which exposes the set_attention_slice method\n # gets the message\n def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):\n if hasattr(module, \"set_attention_slice\"):\n module.set_attention_slice(slice_size.pop())\n\n for child in module.children():\n fn_recursive_set_attention_slice(child, slice_size)\n\n reversed_slice_size = list(reversed(slice_size))\n for module in self.children():\n fn_recursive_set_attention_slice(module, reversed_slice_size)\n\n def _set_gradient_checkpointing(self, module, value=False):\n if hasattr(module, \"gradient_checkpointing\"):\n module.gradient_checkpointing = value\n\n def enable_freeu(self, s1, s2, b1, b2):\n r\"\"\"Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.\n\n The suffixes after the scaling factors represent the stage blocks where they are being applied.\n\n Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that\n are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.\n\n Args:\n s1 (`float`):\n Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to\n mitigate the \"oversmoothing effect\" in the enhanced denoising process.\n s2 (`float`):\n Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to\n mitigate the \"oversmoothing effect\" in the enhanced denoising process.\n b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.\n b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.\n \"\"\"\n for i, upsample_block in enumerate(self.up_blocks):\n setattr(upsample_block, \"s1\", s1)\n setattr(upsample_block, \"s2\", s2)\n setattr(upsample_block, \"b1\", b1)\n setattr(upsample_block, \"b2\", b2)\n\n def disable_freeu(self):\n \"\"\"Disables the FreeU mechanism.\"\"\"\n freeu_keys = {\"s1\", \"s2\", \"b1\", \"b2\"}\n for i, upsample_block in enumerate(self.up_blocks):\n for k in freeu_keys:\n if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None:\n setattr(upsample_block, k, None)\n\n def fuse_qkv_projections(self):\n \"\"\"\n Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)\n are fused. For cross-attention modules, key and value projection matrices are fused.\n\n \n\n This API is 🧪 experimental.\n\n \n \"\"\"\n self.original_attn_processors = None\n\n for _, attn_processor in self.attn_processors.items():\n if \"Added\" in str(attn_processor.__class__.__name__):\n raise ValueError(\"`fuse_qkv_projections()` is not supported for models having added KV projections.\")\n\n self.original_attn_processors = self.attn_processors\n\n for module in self.modules():\n if isinstance(module, Attention):\n module.fuse_projections(fuse=True)\n\n def unfuse_qkv_projections(self):\n \"\"\"Disables the fused QKV projection if enabled.\n\n \n\n This API is 🧪 experimental.\n\n \n\n \"\"\"\n if self.original_attn_processors is not None:\n self.set_attn_processor(self.original_attn_processors)\n\n def unload_lora(self):\n \"\"\"Unloads LoRA weights.\"\"\"\n deprecate(\n \"unload_lora\",\n \"0.28.0\",\n \"Calling `unload_lora()` is deprecated and will be removed in a future version. Please install `peft` and then call `disable_adapters().\",\n )\n for module in self.modules():\n if hasattr(module, \"set_lora_layer\"):\n module.set_lora_layer(None)\n\n def forward(\n self,\n sample: torch.Tensor,\n timestep: Union[torch.Tensor, float, int],\n encoder_hidden_states: torch.Tensor,\n class_labels: Optional[torch.Tensor] = None,\n timestep_cond: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,\n down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\n mid_block_additional_residual: Optional[torch.Tensor] = None,\n down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ) -> Union[UNet2DConditionOutput, Tuple]:\n r\"\"\"\n The [`UNetFlatConditionModel`] forward method.\n\n Args:\n sample (`torch.Tensor`):\n The noisy input tensor with the following shape `(batch, channel, height, width)`.\n timestep (`torch.Tensor` or `float` or `int`): The number of timesteps to denoise an input.\n encoder_hidden_states (`torch.Tensor`):\n The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.\n class_labels (`torch.Tensor`, *optional*, defaults to `None`):\n Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.\n timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`):\n Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed\n through the `self.time_embedding` layer to obtain the timestep embeddings.\n attention_mask (`torch.Tensor`, *optional*, defaults to `None`):\n An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask\n is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large\n negative values to the attention scores corresponding to \"discard\" tokens.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n added_cond_kwargs: (`dict`, *optional*):\n A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that\n are passed along to the UNet blocks.\n down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):\n A tuple of tensors that if specified are added to the residuals of down unet blocks.\n mid_block_additional_residual: (`torch.Tensor`, *optional*):\n A tensor that if specified is added to the residual of the middle unet block.\n encoder_attention_mask (`torch.Tensor`):\n A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If\n `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,\n which adds large negative values to the attention scores corresponding to \"discard\" tokens.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain\n tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].\n added_cond_kwargs: (`dict`, *optional*):\n A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that\n are passed along to the UNet blocks.\n down_block_additional_residuals (`tuple` of `torch.Tensor`, *optional*):\n additional residuals to be added to UNet long skip connections from down blocks to up blocks for\n example from ControlNet side model(s)\n mid_block_additional_residual (`torch.Tensor`, *optional*):\n additional residual to be added to UNet mid block output, for example from ControlNet side model\n down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*):\n additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s)\n\n Returns:\n [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:\n If `return_dict` is True, an [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] is returned,\n otherwise a `tuple` is returned where the first element is the sample tensor.\n \"\"\"\n # By default samples have to be AT least a multiple of the overall upsampling factor.\n # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).\n # However, the upsampling interpolation output size can be forced to fit any upsampling size\n # on the fly if necessary.\n default_overall_up_factor = 2**self.num_upsamplers\n\n # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`\n forward_upsample_size = False\n upsample_size = None\n\n for dim in sample.shape[-2:]:\n if dim % default_overall_up_factor != 0:\n # Forward upsample size to force interpolation output size.\n forward_upsample_size = True\n break\n\n # ensure attention_mask is a bias, and give it a singleton query_tokens dimension\n # expects mask of shape:\n # [batch, key_tokens]\n # adds singleton query_tokens dimension:\n # [batch, 1, key_tokens]\n # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:\n # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)\n # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)\n if attention_mask is not None:\n # assume that mask is expressed as:\n # (1 = keep, 0 = discard)\n # convert mask into a bias that can be added to attention scores:\n # (keep = +0, discard = -10000.0)\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n # convert encoder_attention_mask to a bias the same way we do for attention_mask\n if encoder_attention_mask is not None:\n encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0\n encoder_attention_mask = encoder_attention_mask.unsqueeze(1)\n\n # 0. center input if necessary\n if self.config.center_input_sample:\n sample = 2 * sample - 1.0\n\n # 1. time\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can\n # This would be a good case for the `match` statement (Python 3.10+)\n is_mps = sample.device.type == \"mps\"\n if isinstance(timestep, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\n elif len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(sample.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timesteps = timesteps.expand(sample.shape[0])\n\n t_emb = self.time_proj(timesteps)\n\n # `Timesteps` does not contain any weights and will always return f32 tensors\n # but time_embedding might actually be running in fp16. so we need to cast here.\n # there might be better ways to encapsulate this.\n t_emb = t_emb.to(dtype=sample.dtype)\n\n emb = self.time_embedding(t_emb, timestep_cond)\n aug_emb = None\n\n if self.class_embedding is not None:\n if class_labels is None:\n raise ValueError(\"class_labels should be provided when num_class_embeds > 0\")\n\n if self.config.class_embed_type == \"timestep\":\n class_labels = self.time_proj(class_labels)\n\n # `Timesteps` does not contain any weights and will always return f32 tensors\n # there might be better ways to encapsulate this.\n class_labels = class_labels.to(dtype=sample.dtype)\n\n class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)\n\n if self.config.class_embeddings_concat:\n emb = torch.cat([emb, class_emb], dim=-1)\n else:\n emb = emb + class_emb\n\n if self.config.addition_embed_type == \"text\":\n aug_emb = self.add_embedding(encoder_hidden_states)\n elif self.config.addition_embed_type == \"text_image\":\n # Kandinsky 2.1 - style\n if \"image_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`\"\n )\n\n image_embs = added_cond_kwargs.get(\"image_embeds\")\n text_embs = added_cond_kwargs.get(\"text_embeds\", encoder_hidden_states)\n aug_emb = self.add_embedding(text_embs, image_embs)\n elif self.config.addition_embed_type == \"text_time\":\n # SDXL - style\n if \"text_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`\"\n )\n text_embeds = added_cond_kwargs.get(\"text_embeds\")\n if \"time_ids\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`\"\n )\n time_ids = added_cond_kwargs.get(\"time_ids\")\n time_embeds = self.add_time_proj(time_ids.flatten())\n time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))\n add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)\n add_embeds = add_embeds.to(emb.dtype)\n aug_emb = self.add_embedding(add_embeds)\n elif self.config.addition_embed_type == \"image\":\n # Kandinsky 2.2 - style\n if \"image_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`\"\n )\n image_embs = added_cond_kwargs.get(\"image_embeds\")\n aug_emb = self.add_embedding(image_embs)\n elif self.config.addition_embed_type == \"image_hint\":\n # Kandinsky 2.2 - style\n if \"image_embeds\" not in added_cond_kwargs or \"hint\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`\"\n )\n image_embs = added_cond_kwargs.get(\"image_embeds\")\n hint = added_cond_kwargs.get(\"hint\")\n aug_emb, hint = self.add_embedding(image_embs, hint)\n sample = torch.cat([sample, hint], dim=1)\n\n emb = emb + aug_emb if aug_emb is not None else emb\n\n if self.time_embed_act is not None:\n emb = self.time_embed_act(emb)\n\n if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == \"text_proj\":\n encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)\n elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == \"text_image_proj\":\n # Kandinsky 2.1 - style\n if \"image_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`\"\n )\n\n image_embeds = added_cond_kwargs.get(\"image_embeds\")\n encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)\n elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == \"image_proj\":\n # Kandinsky 2.2 - style\n if \"image_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`\"\n )\n image_embeds = added_cond_kwargs.get(\"image_embeds\")\n encoder_hidden_states = self.encoder_hid_proj(image_embeds)\n elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == \"ip_image_proj\":\n if \"image_embeds\" not in added_cond_kwargs:\n raise ValueError(\n f\"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`\"\n )\n image_embeds = added_cond_kwargs.get(\"image_embeds\")\n image_embeds = self.encoder_hid_proj(image_embeds)\n encoder_hidden_states = (encoder_hidden_states, image_embeds)\n\n # 2. pre-process\n sample = self.conv_in(sample)\n\n # 2.5 GLIGEN position net\n if cross_attention_kwargs is not None and cross_attention_kwargs.get(\"gligen\", None) is not None:\n cross_attention_kwargs = cross_attention_kwargs.copy()\n gligen_args = cross_attention_kwargs.pop(\"gligen\")\n cross_attention_kwargs[\"gligen\"] = {\"objs\": self.position_net(**gligen_args)}\n\n # 3. down\n lora_scale = cross_attention_kwargs.get(\"scale\", 1.0) if cross_attention_kwargs is not None else 1.0\n if USE_PEFT_BACKEND:\n # weight the lora layers by setting `lora_scale` for each PEFT layer\n scale_lora_layers(self, lora_scale)\n\n is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None\n # using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets\n is_adapter = down_intrablock_additional_residuals is not None\n # maintain backward compatibility for legacy usage, where\n # T2I-Adapter and ControlNet both use down_block_additional_residuals arg\n # but can only use one or the other\n if not is_adapter and mid_block_additional_residual is None and down_block_additional_residuals is not None:\n deprecate(\n \"T2I should not use down_block_additional_residuals\",\n \"1.3.0\",\n \"Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \\\n and will be removed in diffusers 1.3.0. `down_block_additional_residuals` should only be used \\\n for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. \",\n standard_warn=False,\n )\n down_intrablock_additional_residuals = down_block_additional_residuals\n is_adapter = True\n\n down_block_res_samples = (sample,)\n for downsample_block in self.down_blocks:\n if hasattr(downsample_block, \"has_cross_attention\") and downsample_block.has_cross_attention:\n # For t2i-adapter CrossAttnDownBlockFlat\n additional_residuals = {}\n if is_adapter and len(down_intrablock_additional_residuals) > 0:\n additional_residuals[\"additional_residuals\"] = down_intrablock_additional_residuals.pop(0)\n\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n encoder_attention_mask=encoder_attention_mask,\n **additional_residuals,\n )\n else:\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb)\n if is_adapter and len(down_intrablock_additional_residuals) > 0:\n sample += down_intrablock_additional_residuals.pop(0)\n\n down_block_res_samples += res_samples\n\n if is_controlnet:\n new_down_block_res_samples = ()\n\n for down_block_res_sample, down_block_additional_residual in zip(\n down_block_res_samples, down_block_additional_residuals\n ):\n down_block_res_sample = down_block_res_sample + down_block_additional_residual\n new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)\n\n down_block_res_samples = new_down_block_res_samples\n\n # 4. mid\n if self.mid_block is not None:\n if hasattr(self.mid_block, \"has_cross_attention\") and self.mid_block.has_cross_attention:\n sample = self.mid_block(\n sample,\n emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n cross_attention_kwargs=cross_attention_kwargs,\n encoder_attention_mask=encoder_attention_mask,\n )\n else:\n sample = self.mid_block(sample, emb)\n\n # To support T2I-Adapter-XL\n if (\n is_adapter\n and len(down_intrablock_additional_residuals) > 0\n and sample.shape == down_intrablock_additional_residuals[0].shape\n ):\n sample += down_intrablock_additional_residuals.pop(0)\n\n if is_controlnet:\n sample = sample + mid_block_additional_residual\n\n # 5. up\n for i, upsample_block in enumerate(self.up_blocks):\n is_final_block = i == len(self.up_blocks) - 1\n\n res_samples = down_block_res_samples[-len(upsample_block.resnets) :]\n down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]\n\n # if we have not reached the final block and need to forward the\n # upsample size, we do it here\n if not is_final_block and forward_upsample_size:\n upsample_size = down_block_res_samples[-1].shape[2:]\n\n if hasattr(upsample_block, \"has_cross_attention\") and upsample_block.has_cross_attention:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n upsample_size=upsample_size,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n )\n else:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n upsample_size=upsample_size,\n scale=lora_scale,\n )\n\n # 6. post-process\n if self.conv_norm_out:\n sample = self.conv_norm_out(sample)\n sample = self.conv_act(sample)\n sample = self.conv_out(sample)\n\n if USE_PEFT_BACKEND:\n # remove `lora_scale` from each PEFT layer\n unscale_lora_layers(self, lora_scale)\n\n if not return_dict:\n return (sample,)\n\n return UNet2DConditionOutput(sample=sample)\n\n\nclass LinearMultiDim(nn.Linear):\n def __init__(self, in_features, out_features=None, second_dim=4, *args, **kwargs):\n in_features = [in_features, second_dim, 1] if isinstance(in_features, int) else list(in_features)\n if out_features is None:\n out_features = in_features\n out_features = [out_features, second_dim, 1] if isinstance(out_features, int) else list(out_features)\n self.in_features_multidim = in_features\n self.out_features_multidim = out_features\n super().__init__(np.array(in_features).prod(), np.array(out_features).prod())\n\n def forward(self, input_tensor, *args, **kwargs):\n shape = input_tensor.shape\n n_dim = len(self.in_features_multidim)\n input_tensor = input_tensor.reshape(*shape[0:-n_dim], self.in_features)\n output_tensor = super().forward(input_tensor)\n output_tensor = output_tensor.view(*shape[0:-n_dim], *self.out_features_multidim)\n return output_tensor\n\n\nclass ResnetBlockFlat(nn.Module):\n def __init__(\n self,\n *,\n in_channels,\n out_channels=None,\n dropout=0.0,\n temb_channels=512,\n groups=32,\n groups_out=None,\n pre_norm=True,\n eps=1e-6,\n time_embedding_norm=\"default\",\n use_in_shortcut=None,\n second_dim=4,\n **kwargs,\n ):\n super().__init__()\n self.pre_norm = pre_norm\n self.pre_norm = True\n\n in_channels = [in_channels, second_dim, 1] if isinstance(in_channels, int) else list(in_channels)\n self.in_channels_prod = np.array(in_channels).prod()\n self.channels_multidim = in_channels\n\n if out_channels is not None:\n out_channels = [out_channels, second_dim, 1] if isinstance(out_channels, int) else list(out_channels)\n out_channels_prod = np.array(out_channels).prod()\n self.out_channels_multidim = out_channels\n else:\n out_channels_prod = self.in_channels_prod\n self.out_channels_multidim = self.channels_multidim\n self.time_embedding_norm = time_embedding_norm\n\n if groups_out is None:\n groups_out = groups\n\n self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=self.in_channels_prod, eps=eps, affine=True)\n self.conv1 = torch.nn.Conv2d(self.in_channels_prod, out_channels_prod, kernel_size=1, padding=0)\n\n if temb_channels is not None:\n self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels_prod)\n else:\n self.time_emb_proj = None\n\n self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels_prod, eps=eps, affine=True)\n self.dropout = torch.nn.Dropout(dropout)\n self.conv2 = torch.nn.Conv2d(out_channels_prod, out_channels_prod, kernel_size=1, padding=0)\n\n self.nonlinearity = nn.SiLU()\n\n self.use_in_shortcut = (\n self.in_channels_prod != out_channels_prod if use_in_shortcut is None else use_in_shortcut\n )\n\n self.conv_shortcut = None\n if self.use_in_shortcut:\n self.conv_shortcut = torch.nn.Conv2d(\n self.in_channels_prod, out_channels_prod, kernel_size=1, stride=1, padding=0\n )\n\n def forward(self, input_tensor, temb):\n shape = input_tensor.shape\n n_dim = len(self.channels_multidim)\n input_tensor = input_tensor.reshape(*shape[0:-n_dim], self.in_channels_prod, 1, 1)\n input_tensor = input_tensor.view(-1, self.in_channels_prod, 1, 1)\n\n hidden_states = input_tensor\n\n hidden_states = self.norm1(hidden_states)\n hidden_states = self.nonlinearity(hidden_states)\n hidden_states = self.conv1(hidden_states)\n\n if temb is not None:\n temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None]\n hidden_states = hidden_states + temb\n\n hidden_states = self.norm2(hidden_states)\n hidden_states = self.nonlinearity(hidden_states)\n\n hidden_states = self.dropout(hidden_states)\n hidden_states = self.conv2(hidden_states)\n\n if self.conv_shortcut is not None:\n input_tensor = self.conv_shortcut(input_tensor)\n\n output_tensor = input_tensor + hidden_states\n\n output_tensor = output_tensor.view(*shape[0:-n_dim], -1)\n output_tensor = output_tensor.view(*shape[0:-n_dim], *self.out_channels_multidim)\n\n return output_tensor\n\n\nclass DownBlockFlat(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n output_scale_factor: float = 1.0,\n add_downsample: bool = True,\n downsample_padding: int = 1,\n ):\n super().__init__()\n resnets = []\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n resnets.append(\n ResnetBlockFlat(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n\n self.resnets = nn.ModuleList(resnets)\n\n if add_downsample:\n self.downsamplers = nn.ModuleList(\n [\n LinearMultiDim(\n out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name=\"op\"\n )\n ]\n )\n else:\n self.downsamplers = None\n\n self.gradient_checkpointing = False\n\n def forward(\n self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None\n ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, ...]]:\n output_states = ()\n\n for resnet in self.resnets:\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs)\n\n return custom_forward\n\n if is_torch_version(\">=\", \"1.11.0\"):\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet), hidden_states, temb, use_reentrant=False\n )\n else:\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet), hidden_states, temb\n )\n else:\n hidden_states = resnet(hidden_states, temb)\n\n output_states = output_states + (hidden_states,)\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n\nclass CrossAttnDownBlockFlat(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n transformer_layers_per_block: Union[int, Tuple[int]] = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n num_attention_heads: int = 1,\n cross_attention_dim: int = 1280,\n output_scale_factor: float = 1.0,\n downsample_padding: int = 1,\n add_downsample: bool = True,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = False,\n only_cross_attention: bool = False,\n upcast_attention: bool = False,\n attention_type: str = \"default\",\n ):\n super().__init__()\n resnets = []\n attentions = []\n\n self.has_cross_attention = True\n self.num_attention_heads = num_attention_heads\n if isinstance(transformer_layers_per_block, int):\n transformer_layers_per_block = [transformer_layers_per_block] * num_layers\n\n for i in range(num_layers):\n in_channels = in_channels if i == 0 else out_channels\n resnets.append(\n ResnetBlockFlat(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n if not dual_cross_attention:\n attentions.append(\n Transformer2DModel(\n num_attention_heads,\n out_channels // num_attention_heads,\n in_channels=out_channels,\n num_layers=transformer_layers_per_block[i],\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n attention_type=attention_type,\n )\n )\n else:\n attentions.append(\n DualTransformer2DModel(\n num_attention_heads,\n out_channels // num_attention_heads,\n in_channels=out_channels,\n num_layers=1,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n )\n )\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n if add_downsample:\n self.downsamplers = nn.ModuleList(\n [\n LinearMultiDim(\n out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name=\"op\"\n )\n ]\n )\n else:\n self.downsamplers = None\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n additional_residuals: Optional[torch.Tensor] = None,\n ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, ...]]:\n output_states = ()\n\n blocks = list(zip(self.resnets, self.attentions))\n\n for i, (resnet, attn) in enumerate(blocks):\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet),\n hidden_states,\n temb,\n **ckpt_kwargs,\n )\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n else:\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n\n # apply additional residuals to the output of the last pair of resnet and attention blocks\n if i == len(blocks) - 1 and additional_residuals is not None:\n hidden_states = hidden_states + additional_residuals\n\n output_states = output_states + (hidden_states,)\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n\n# Copied from diffusers.models.unets.unet_2d_blocks.UpBlock2D with UpBlock2D->UpBlockFlat, ResnetBlock2D->ResnetBlockFlat, Upsample2D->LinearMultiDim\nclass UpBlockFlat(nn.Module):\n def __init__(\n self,\n in_channels: int,\n prev_output_channel: int,\n out_channels: int,\n temb_channels: int,\n resolution_idx: Optional[int] = None,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n output_scale_factor: float = 1.0,\n add_upsample: bool = True,\n ):\n super().__init__()\n resnets = []\n\n for i in range(num_layers):\n res_skip_channels = in_channels if (i == num_layers - 1) else out_channels\n resnet_in_channels = prev_output_channel if i == 0 else out_channels\n\n resnets.append(\n ResnetBlockFlat(\n in_channels=resnet_in_channels + res_skip_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n\n self.resnets = nn.ModuleList(resnets)\n\n if add_upsample:\n self.upsamplers = nn.ModuleList([LinearMultiDim(out_channels, use_conv=True, out_channels=out_channels)])\n else:\n self.upsamplers = None\n\n self.gradient_checkpointing = False\n self.resolution_idx = resolution_idx\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n upsample_size: Optional[int] = None,\n *args,\n **kwargs,\n ) -> torch.Tensor:\n if len(args) > 0 or kwargs.get(\"scale\", None) is not None:\n deprecation_message = \"The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`.\"\n deprecate(\"scale\", \"1.0.0\", deprecation_message)\n\n is_freeu_enabled = (\n getattr(self, \"s1\", None)\n and getattr(self, \"s2\", None)\n and getattr(self, \"b1\", None)\n and getattr(self, \"b2\", None)\n )\n\n for resnet in self.resnets:\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n\n # FreeU: Only operate on the first two stages\n if is_freeu_enabled:\n hidden_states, res_hidden_states = apply_freeu(\n self.resolution_idx,\n hidden_states,\n res_hidden_states,\n s1=self.s1,\n s2=self.s2,\n b1=self.b1,\n b2=self.b2,\n )\n\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs)\n\n return custom_forward\n\n if is_torch_version(\">=\", \"1.11.0\"):\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet), hidden_states, temb, use_reentrant=False\n )\n else:\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet), hidden_states, temb\n )\n else:\n hidden_states = resnet(hidden_states, temb)\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n\n# Copied from diffusers.models.unets.unet_2d_blocks.CrossAttnUpBlock2D with CrossAttnUpBlock2D->CrossAttnUpBlockFlat, ResnetBlock2D->ResnetBlockFlat, Upsample2D->LinearMultiDim\nclass CrossAttnUpBlockFlat(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n prev_output_channel: int,\n temb_channels: int,\n resolution_idx: Optional[int] = None,\n dropout: float = 0.0,\n num_layers: int = 1,\n transformer_layers_per_block: Union[int, Tuple[int]] = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n num_attention_heads: int = 1,\n cross_attention_dim: int = 1280,\n output_scale_factor: float = 1.0,\n add_upsample: bool = True,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = False,\n only_cross_attention: bool = False,\n upcast_attention: bool = False,\n attention_type: str = \"default\",\n ):\n super().__init__()\n resnets = []\n attentions = []\n\n self.has_cross_attention = True\n self.num_attention_heads = num_attention_heads\n\n if isinstance(transformer_layers_per_block, int):\n transformer_layers_per_block = [transformer_layers_per_block] * num_layers\n\n for i in range(num_layers):\n res_skip_channels = in_channels if (i == num_layers - 1) else out_channels\n resnet_in_channels = prev_output_channel if i == 0 else out_channels\n\n resnets.append(\n ResnetBlockFlat(\n in_channels=resnet_in_channels + res_skip_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n if not dual_cross_attention:\n attentions.append(\n Transformer2DModel(\n num_attention_heads,\n out_channels // num_attention_heads,\n in_channels=out_channels,\n num_layers=transformer_layers_per_block[i],\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n attention_type=attention_type,\n )\n )\n else:\n attentions.append(\n DualTransformer2DModel(\n num_attention_heads,\n out_channels // num_attention_heads,\n in_channels=out_channels,\n num_layers=1,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n )\n )\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n if add_upsample:\n self.upsamplers = nn.ModuleList([LinearMultiDim(out_channels, use_conv=True, out_channels=out_channels)])\n else:\n self.upsamplers = None\n\n self.gradient_checkpointing = False\n self.resolution_idx = resolution_idx\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n res_hidden_states_tuple: Tuple[torch.Tensor, ...],\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n upsample_size: Optional[int] = None,\n attention_mask: Optional[torch.Tensor] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n if cross_attention_kwargs is not None:\n if cross_attention_kwargs.get(\"scale\", None) is not None:\n logger.warning(\"Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.\")\n\n is_freeu_enabled = (\n getattr(self, \"s1\", None)\n and getattr(self, \"s2\", None)\n and getattr(self, \"b1\", None)\n and getattr(self, \"b2\", None)\n )\n\n for resnet, attn in zip(self.resnets, self.attentions):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n\n # FreeU: Only operate on the first two stages\n if is_freeu_enabled:\n hidden_states, res_hidden_states = apply_freeu(\n self.resolution_idx,\n hidden_states,\n res_hidden_states,\n s1=self.s1,\n s2=self.s2,\n b1=self.b1,\n b2=self.b2,\n )\n\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet),\n hidden_states,\n temb,\n **ckpt_kwargs,\n )\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n else:\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n\n# Copied from diffusers.models.unets.unet_2d_blocks.UNetMidBlock2D with UNetMidBlock2D->UNetMidBlockFlat, ResnetBlock2D->ResnetBlockFlat\nclass UNetMidBlockFlat(nn.Module):\n \"\"\"\n A 2D UNet mid-block [`UNetMidBlockFlat`] with multiple residual blocks and optional attention blocks.\n\n Args:\n in_channels (`int`): The number of input channels.\n temb_channels (`int`): The number of temporal embedding channels.\n dropout (`float`, *optional*, defaults to 0.0): The dropout rate.\n num_layers (`int`, *optional*, defaults to 1): The number of residual blocks.\n resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks.\n resnet_time_scale_shift (`str`, *optional*, defaults to `default`):\n The type of normalization to apply to the time embeddings. This can help to improve the performance of the\n model on tasks with long-range temporal dependencies.\n resnet_act_fn (`str`, *optional*, defaults to `swish`): The activation function for the resnet blocks.\n resnet_groups (`int`, *optional*, defaults to 32):\n The number of groups to use in the group normalization layers of the resnet blocks.\n attn_groups (`Optional[int]`, *optional*, defaults to None): The number of groups for the attention blocks.\n resnet_pre_norm (`bool`, *optional*, defaults to `True`):\n Whether to use pre-normalization for the resnet blocks.\n add_attention (`bool`, *optional*, defaults to `True`): Whether to add attention blocks.\n attention_head_dim (`int`, *optional*, defaults to 1):\n Dimension of a single attention head. The number of attention heads is determined based on this value and\n the number of input channels.\n output_scale_factor (`float`, *optional*, defaults to 1.0): The output scale factor.\n\n Returns:\n `torch.Tensor`: The output of the last residual block, which is a tensor of shape `(batch_size, in_channels,\n height, width)`.\n\n \"\"\"\n\n def __init__(\n self,\n in_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\", # default, spatial\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n attn_groups: Optional[int] = None,\n resnet_pre_norm: bool = True,\n add_attention: bool = True,\n attention_head_dim: int = 1,\n output_scale_factor: float = 1.0,\n ):\n super().__init__()\n resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)\n self.add_attention = add_attention\n\n if attn_groups is None:\n attn_groups = resnet_groups if resnet_time_scale_shift == \"default\" else None\n\n # there is always at least one resnet\n if resnet_time_scale_shift == \"spatial\":\n resnets = [\n ResnetBlockCondNorm2D(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=\"spatial\",\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n )\n ]\n else:\n resnets = [\n ResnetBlockFlat(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n ]\n attentions = []\n\n if attention_head_dim is None:\n logger.warning(\n f\"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {in_channels}.\"\n )\n attention_head_dim = in_channels\n\n for _ in range(num_layers):\n if self.add_attention:\n attentions.append(\n Attention(\n in_channels,\n heads=in_channels // attention_head_dim,\n dim_head=attention_head_dim,\n rescale_output_factor=output_scale_factor,\n eps=resnet_eps,\n norm_num_groups=attn_groups,\n spatial_norm_dim=temb_channels if resnet_time_scale_shift == \"spatial\" else None,\n residual_connection=True,\n bias=True,\n upcast_softmax=True,\n _from_deprecated_attn_block=True,\n )\n )\n else:\n attentions.append(None)\n\n if resnet_time_scale_shift == \"spatial\":\n resnets.append(\n ResnetBlockCondNorm2D(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=\"spatial\",\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n )\n )\n else:\n resnets.append(\n ResnetBlockFlat(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n def forward(self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:\n hidden_states = self.resnets[0](hidden_states, temb)\n for attn, resnet in zip(self.attentions, self.resnets[1:]):\n if attn is not None:\n hidden_states = attn(hidden_states, temb=temb)\n hidden_states = resnet(hidden_states, temb)\n\n return hidden_states\n\n\n# Copied from diffusers.models.unets.unet_2d_blocks.UNetMidBlock2DCrossAttn with UNetMidBlock2DCrossAttn->UNetMidBlockFlatCrossAttn, ResnetBlock2D->ResnetBlockFlat\nclass UNetMidBlockFlatCrossAttn(nn.Module):\n def __init__(\n self,\n in_channels: int,\n temb_channels: int,\n out_channels: Optional[int] = None,\n dropout: float = 0.0,\n num_layers: int = 1,\n transformer_layers_per_block: Union[int, Tuple[int]] = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_groups_out: Optional[int] = None,\n resnet_pre_norm: bool = True,\n num_attention_heads: int = 1,\n output_scale_factor: float = 1.0,\n cross_attention_dim: int = 1280,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = False,\n upcast_attention: bool = False,\n attention_type: str = \"default\",\n ):\n super().__init__()\n\n out_channels = out_channels or in_channels\n self.in_channels = in_channels\n self.out_channels = out_channels\n\n self.has_cross_attention = True\n self.num_attention_heads = num_attention_heads\n resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)\n\n # support for variable transformer layers per block\n if isinstance(transformer_layers_per_block, int):\n transformer_layers_per_block = [transformer_layers_per_block] * num_layers\n\n resnet_groups_out = resnet_groups_out or resnet_groups\n\n # there is always at least one resnet\n resnets = [\n ResnetBlockFlat(\n in_channels=in_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n groups_out=resnet_groups_out,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n ]\n attentions = []\n\n for i in range(num_layers):\n if not dual_cross_attention:\n attentions.append(\n Transformer2DModel(\n num_attention_heads,\n out_channels // num_attention_heads,\n in_channels=out_channels,\n num_layers=transformer_layers_per_block[i],\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups_out,\n use_linear_projection=use_linear_projection,\n upcast_attention=upcast_attention,\n attention_type=attention_type,\n )\n )\n else:\n attentions.append(\n DualTransformer2DModel(\n num_attention_heads,\n out_channels // num_attention_heads,\n in_channels=out_channels,\n num_layers=1,\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n )\n )\n resnets.append(\n ResnetBlockFlat(\n in_channels=out_channels,\n out_channels=out_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups_out,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n )\n )\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n self.gradient_checkpointing = False\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n if cross_attention_kwargs is not None:\n if cross_attention_kwargs.get(\"scale\", None) is not None:\n logger.warning(\"Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.\")\n\n hidden_states = self.resnets[0](hidden_states, temb)\n for attn, resnet in zip(self.attentions, self.resnets[1:]):\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module, return_dict=None):\n def custom_forward(*inputs):\n if return_dict is not None:\n return module(*inputs, return_dict=return_dict)\n else:\n return module(*inputs)\n\n return custom_forward\n\n ckpt_kwargs: Dict[str, Any] = {\"use_reentrant\": False} if is_torch_version(\">=\", \"1.11.0\") else {}\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n hidden_states = torch.utils.checkpoint.checkpoint(\n create_custom_forward(resnet),\n hidden_states,\n temb,\n **ckpt_kwargs,\n )\n else:\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n hidden_states = resnet(hidden_states, temb)\n\n return hidden_states\n\n\n# Copied from diffusers.models.unets.unet_2d_blocks.UNetMidBlock2DSimpleCrossAttn with UNetMidBlock2DSimpleCrossAttn->UNetMidBlockFlatSimpleCrossAttn, ResnetBlock2D->ResnetBlockFlat\nclass UNetMidBlockFlatSimpleCrossAttn(nn.Module):\n def __init__(\n self,\n in_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n num_layers: int = 1,\n resnet_eps: float = 1e-6,\n resnet_time_scale_shift: str = \"default\",\n resnet_act_fn: str = \"swish\",\n resnet_groups: int = 32,\n resnet_pre_norm: bool = True,\n attention_head_dim: int = 1,\n output_scale_factor: float = 1.0,\n cross_attention_dim: int = 1280,\n skip_time_act: bool = False,\n only_cross_attention: bool = False,\n cross_attention_norm: Optional[str] = None,\n ):\n super().__init__()\n\n self.has_cross_attention = True\n\n self.attention_head_dim = attention_head_dim\n resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)\n\n self.num_heads = in_channels // self.attention_head_dim\n\n # there is always at least one resnet\n resnets = [\n ResnetBlockFlat(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n skip_time_act=skip_time_act,\n )\n ]\n attentions = []\n\n for _ in range(num_layers):\n processor = (\n AttnAddedKVProcessor2_0() if hasattr(F, \"scaled_dot_product_attention\") else AttnAddedKVProcessor()\n )\n\n attentions.append(\n Attention(\n query_dim=in_channels,\n cross_attention_dim=in_channels,\n heads=self.num_heads,\n dim_head=self.attention_head_dim,\n added_kv_proj_dim=cross_attention_dim,\n norm_num_groups=resnet_groups,\n bias=True,\n upcast_softmax=True,\n only_cross_attention=only_cross_attention,\n cross_attention_norm=cross_attention_norm,\n processor=processor,\n )\n )\n resnets.append(\n ResnetBlockFlat(\n in_channels=in_channels,\n out_channels=in_channels,\n temb_channels=temb_channels,\n eps=resnet_eps,\n groups=resnet_groups,\n dropout=dropout,\n time_embedding_norm=resnet_time_scale_shift,\n non_linearity=resnet_act_fn,\n output_scale_factor=output_scale_factor,\n pre_norm=resnet_pre_norm,\n skip_time_act=skip_time_act,\n )\n )\n\n self.attentions = nn.ModuleList(attentions)\n self.resnets = nn.ModuleList(resnets)\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n temb: Optional[torch.Tensor] = None,\n encoder_hidden_states: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}\n if cross_attention_kwargs.get(\"scale\", None) is not None:\n logger.warning(\"Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.\")\n\n if attention_mask is None:\n # if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask.\n mask = None if encoder_hidden_states is None else encoder_attention_mask\n else:\n # when attention_mask is defined: we don't even check for encoder_attention_mask.\n # this is to maintain compatibility with UnCLIP, which uses 'attention_mask' param for cross-attn masks.\n # TODO: UnCLIP should express cross-attn mask via encoder_attention_mask param instead of via attention_mask.\n # then we can simplify this whole if/else block to:\n # mask = attention_mask if encoder_hidden_states is None else encoder_attention_mask\n mask = attention_mask\n\n hidden_states = self.resnets[0](hidden_states, temb)\n for attn, resnet in zip(self.attentions, self.resnets[1:]):\n # attn\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=mask,\n **cross_attention_kwargs,\n )\n\n # resnet\n hidden_states = resnet(hidden_states, temb)\n\n return hidden_states\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py", "content": "import inspect\nfrom typing import Callable, List, Optional, Union\n\nimport PIL.Image\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModel\n\nfrom ....models import AutoencoderKL, UNet2DConditionModel\nfrom ....schedulers import KarrasDiffusionSchedulers\nfrom ....utils import logging\nfrom ...pipeline_utils import DiffusionPipeline\nfrom .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline\nfrom .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline\nfrom .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass VersatileDiffusionPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n tokenizer: CLIPTokenizer\n image_feature_extractor: CLIPImageProcessor\n text_encoder: CLIPTextModel\n image_encoder: CLIPVisionModel\n image_unet: UNet2DConditionModel\n text_unet: UNet2DConditionModel\n vae: AutoencoderKL\n scheduler: KarrasDiffusionSchedulers\n\n def __init__(\n self,\n tokenizer: CLIPTokenizer,\n image_feature_extractor: CLIPImageProcessor,\n text_encoder: CLIPTextModel,\n image_encoder: CLIPVisionModel,\n image_unet: UNet2DConditionModel,\n text_unet: UNet2DConditionModel,\n vae: AutoencoderKL,\n scheduler: KarrasDiffusionSchedulers,\n ):\n super().__init__()\n\n self.register_modules(\n tokenizer=tokenizer,\n image_feature_extractor=image_feature_extractor,\n text_encoder=text_encoder,\n image_encoder=image_encoder,\n image_unet=image_unet,\n text_unet=text_unet,\n vae=vae,\n scheduler=scheduler,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n\n @torch.no_grad()\n def image_variation(\n self,\n image: Union[torch.Tensor, PIL.Image.Image],\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n image (`PIL.Image.Image`, `List[PIL.Image.Image]` or `torch.Tensor`):\n The image prompt or prompts to guide the image generation.\n height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n\n Examples:\n\n ```py\n >>> from diffusers import VersatileDiffusionPipeline\n >>> import torch\n >>> import requests\n >>> from io import BytesIO\n >>> from PIL import Image\n\n >>> # let's download an initial image\n >>> url = \"https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg\"\n\n >>> response = requests.get(url)\n >>> image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n\n >>> pipe = VersatileDiffusionPipeline.from_pretrained(\n ... \"shi-labs/versatile-diffusion\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> generator = torch.Generator(device=\"cuda\").manual_seed(0)\n >>> image = pipe.image_variation(image, generator=generator).images[0]\n >>> image.save(\"./car_variation.png\")\n ```\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n expected_components = inspect.signature(VersatileDiffusionImageVariationPipeline.__init__).parameters.keys()\n components = {name: component for name, component in self.components.items() if name in expected_components}\n return VersatileDiffusionImageVariationPipeline(**components)(\n image=image,\n height=height,\n width=width,\n num_inference_steps=num_inference_steps,\n guidance_scale=guidance_scale,\n negative_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n latents=latents,\n output_type=output_type,\n return_dict=return_dict,\n callback=callback,\n callback_steps=callback_steps,\n )\n\n @torch.no_grad()\n def text_to_image(\n self,\n prompt: Union[str, List[str]],\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide image generation.\n height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n\n Examples:\n\n ```py\n >>> from diffusers import VersatileDiffusionPipeline\n >>> import torch\n\n >>> pipe = VersatileDiffusionPipeline.from_pretrained(\n ... \"shi-labs/versatile-diffusion\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> generator = torch.Generator(device=\"cuda\").manual_seed(0)\n >>> image = pipe.text_to_image(\"an astronaut riding on a horse on mars\", generator=generator).images[0]\n >>> image.save(\"./astronaut.png\")\n ```\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n expected_components = inspect.signature(VersatileDiffusionTextToImagePipeline.__init__).parameters.keys()\n components = {name: component for name, component in self.components.items() if name in expected_components}\n temp_pipeline = VersatileDiffusionTextToImagePipeline(**components)\n output = temp_pipeline(\n prompt=prompt,\n height=height,\n width=width,\n num_inference_steps=num_inference_steps,\n guidance_scale=guidance_scale,\n negative_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n latents=latents,\n output_type=output_type,\n return_dict=return_dict,\n callback=callback,\n callback_steps=callback_steps,\n )\n # swap the attention blocks back to the original state\n temp_pipeline._swap_unet_attention_blocks()\n\n return output\n\n @torch.no_grad()\n def dual_guided(\n self,\n prompt: Union[PIL.Image.Image, List[PIL.Image.Image]],\n image: Union[str, List[str]],\n text_to_image_strength: float = 0.5,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide image generation.\n height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n\n Examples:\n\n ```py\n >>> from diffusers import VersatileDiffusionPipeline\n >>> import torch\n >>> import requests\n >>> from io import BytesIO\n >>> from PIL import Image\n\n >>> # let's download an initial image\n >>> url = \"https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg\"\n\n >>> response = requests.get(url)\n >>> image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n >>> text = \"a red car in the sun\"\n\n >>> pipe = VersatileDiffusionPipeline.from_pretrained(\n ... \"shi-labs/versatile-diffusion\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> generator = torch.Generator(device=\"cuda\").manual_seed(0)\n >>> text_to_image_strength = 0.75\n\n >>> image = pipe.dual_guided(\n ... prompt=text, image=image, text_to_image_strength=text_to_image_strength, generator=generator\n ... ).images[0]\n >>> image.save(\"./car_variation.png\")\n ```\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is\n returned where the first element is a list with the generated images.\n \"\"\"\n\n expected_components = inspect.signature(VersatileDiffusionDualGuidedPipeline.__init__).parameters.keys()\n components = {name: component for name, component in self.components.items() if name in expected_components}\n temp_pipeline = VersatileDiffusionDualGuidedPipeline(**components)\n output = temp_pipeline(\n prompt=prompt,\n image=image,\n text_to_image_strength=text_to_image_strength,\n height=height,\n width=width,\n num_inference_steps=num_inference_steps,\n guidance_scale=guidance_scale,\n num_images_per_prompt=num_images_per_prompt,\n eta=eta,\n generator=generator,\n latents=latents,\n output_type=output_type,\n return_dict=return_dict,\n callback=callback,\n callback_steps=callback_steps,\n )\n temp_pipeline._revert_dual_attention()\n\n return output\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Callable, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.utils.checkpoint\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n)\n\nfrom ....image_processor import VaeImageProcessor\nfrom ....models import AutoencoderKL, DualTransformer2DModel, Transformer2DModel, UNet2DConditionModel\nfrom ....schedulers import KarrasDiffusionSchedulers\nfrom ....utils import deprecate, logging\nfrom ....utils.torch_utils import randn_tensor\nfrom ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput\nfrom .modeling_text_unet import UNetFlatConditionModel\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass VersatileDiffusionDualGuidedPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for image-text dual-guided generation using Versatile Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Parameters:\n vqvae ([`VQModel`]):\n Vector-quantized (VQ) model to encode and decode images to and from latent representations.\n bert ([`LDMBertModel`]):\n Text-encoder model based on [`~transformers.BERT`].\n tokenizer ([`~transformers.BertTokenizer`]):\n A `BertTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n \"\"\"\n\n model_cpu_offload_seq = \"bert->unet->vqvae\"\n\n tokenizer: CLIPTokenizer\n image_feature_extractor: CLIPImageProcessor\n text_encoder: CLIPTextModelWithProjection\n image_encoder: CLIPVisionModelWithProjection\n image_unet: UNet2DConditionModel\n text_unet: UNetFlatConditionModel\n vae: AutoencoderKL\n scheduler: KarrasDiffusionSchedulers\n\n _optional_components = [\"text_unet\"]\n\n def __init__(\n self,\n tokenizer: CLIPTokenizer,\n image_feature_extractor: CLIPImageProcessor,\n text_encoder: CLIPTextModelWithProjection,\n image_encoder: CLIPVisionModelWithProjection,\n image_unet: UNet2DConditionModel,\n text_unet: UNetFlatConditionModel,\n vae: AutoencoderKL,\n scheduler: KarrasDiffusionSchedulers,\n ):\n super().__init__()\n self.register_modules(\n tokenizer=tokenizer,\n image_feature_extractor=image_feature_extractor,\n text_encoder=text_encoder,\n image_encoder=image_encoder,\n image_unet=image_unet,\n text_unet=text_unet,\n vae=vae,\n scheduler=scheduler,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n if self.text_unet is not None and (\n \"dual_cross_attention\" not in self.image_unet.config or not self.image_unet.config.dual_cross_attention\n ):\n # if loading from a universal checkpoint rather than a saved dual-guided pipeline\n self._convert_to_dual_attention()\n\n def remove_unused_weights(self):\n self.register_modules(text_unet=None)\n\n def _convert_to_dual_attention(self):\n \"\"\"\n Replace image_unet's `Transformer2DModel` blocks with `DualTransformer2DModel` that contains transformer blocks\n from both `image_unet` and `text_unet`\n \"\"\"\n for name, module in self.image_unet.named_modules():\n if isinstance(module, Transformer2DModel):\n parent_name, index = name.rsplit(\".\", 1)\n index = int(index)\n\n image_transformer = self.image_unet.get_submodule(parent_name)[index]\n text_transformer = self.text_unet.get_submodule(parent_name)[index]\n\n config = image_transformer.config\n dual_transformer = DualTransformer2DModel(\n num_attention_heads=config.num_attention_heads,\n attention_head_dim=config.attention_head_dim,\n in_channels=config.in_channels,\n num_layers=config.num_layers,\n dropout=config.dropout,\n norm_num_groups=config.norm_num_groups,\n cross_attention_dim=config.cross_attention_dim,\n attention_bias=config.attention_bias,\n sample_size=config.sample_size,\n num_vector_embeds=config.num_vector_embeds,\n activation_fn=config.activation_fn,\n num_embeds_ada_norm=config.num_embeds_ada_norm,\n )\n dual_transformer.transformers[0] = image_transformer\n dual_transformer.transformers[1] = text_transformer\n\n self.image_unet.get_submodule(parent_name)[index] = dual_transformer\n self.image_unet.register_to_config(dual_cross_attention=True)\n\n def _revert_dual_attention(self):\n \"\"\"\n Revert the image_unet `DualTransformer2DModel` blocks back to `Transformer2DModel` with image_unet weights Call\n this function if you reuse `image_unet` in another pipeline, e.g. `VersatileDiffusionPipeline`\n \"\"\"\n for name, module in self.image_unet.named_modules():\n if isinstance(module, DualTransformer2DModel):\n parent_name, index = name.rsplit(\".\", 1)\n index = int(index)\n self.image_unet.get_submodule(parent_name)[index] = module.transformers[0]\n\n self.image_unet.register_to_config(dual_cross_attention=False)\n\n def _encode_text_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n \"\"\"\n\n def normalize_embeddings(encoder_output):\n embeds = self.text_encoder.text_projection(encoder_output.last_hidden_state)\n embeds_pooled = encoder_output.text_embeds\n embeds = embeds / torch.norm(embeds_pooled.unsqueeze(1), dim=-1, keepdim=True)\n return embeds\n\n batch_size = len(prompt)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"max_length\", return_tensors=\"pt\").input_ids\n\n if not torch.equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n prompt_embeds = normalize_embeddings(prompt_embeds)\n\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n uncond_tokens = [\"\"] * batch_size\n max_length = text_input_ids.shape[-1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = normalize_embeddings(negative_prompt_embeds)\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n def _encode_image_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n \"\"\"\n\n def normalize_embeddings(encoder_output):\n embeds = self.image_encoder.vision_model.post_layernorm(encoder_output.last_hidden_state)\n embeds = self.image_encoder.visual_projection(embeds)\n embeds_pooled = embeds[:, 0:1]\n embeds = embeds / torch.norm(embeds_pooled, dim=-1, keepdim=True)\n return embeds\n\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n\n # get prompt text embeddings\n image_input = self.image_feature_extractor(images=prompt, return_tensors=\"pt\")\n pixel_values = image_input.pixel_values.to(device).to(self.image_encoder.dtype)\n image_embeddings = self.image_encoder(pixel_values)\n image_embeddings = normalize_embeddings(image_embeddings)\n\n # duplicate image embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = image_embeddings.shape\n image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)\n image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n uncond_images = [np.zeros((512, 512, 3)) + 0.5] * batch_size\n uncond_images = self.image_feature_extractor(images=uncond_images, return_tensors=\"pt\")\n pixel_values = uncond_images.pixel_values.to(device).to(self.image_encoder.dtype)\n negative_prompt_embeds = self.image_encoder(pixel_values)\n negative_prompt_embeds = normalize_embeddings(negative_prompt_embeds)\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and conditional embeddings into a single batch\n # to avoid doing two forward passes\n image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings])\n\n return image_embeddings\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(self, prompt, image, height, width, callback_steps):\n if not isinstance(prompt, str) and not isinstance(prompt, PIL.Image.Image) and not isinstance(prompt, list):\n raise ValueError(f\"`prompt` has to be of type `str` `PIL.Image` or `list` but is {type(prompt)}\")\n if not isinstance(image, str) and not isinstance(image, PIL.Image.Image) and not isinstance(image, list):\n raise ValueError(f\"`image` has to be of type `str` `PIL.Image` or `list` but is {type(image)}\")\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def set_transformer_params(self, mix_ratio: float = 0.5, condition_types: Tuple = (\"text\", \"image\")):\n for name, module in self.image_unet.named_modules():\n if isinstance(module, DualTransformer2DModel):\n module.mix_ratio = mix_ratio\n\n for i, type in enumerate(condition_types):\n if type == \"text\":\n module.condition_lengths[i] = self.text_encoder.config.max_position_embeddings\n module.transformer_index_for_condition[i] = 1 # use the second (text) transformer\n else:\n module.condition_lengths[i] = 257\n module.transformer_index_for_condition[i] = 0 # use the first (image) transformer\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[PIL.Image.Image, List[PIL.Image.Image]],\n image: Union[str, List[str]],\n text_to_image_strength: float = 0.5,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide image generation.\n height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n\n Examples:\n\n ```py\n >>> from diffusers import VersatileDiffusionDualGuidedPipeline\n >>> import torch\n >>> import requests\n >>> from io import BytesIO\n >>> from PIL import Image\n\n >>> # let's download an initial image\n >>> url = \"https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg\"\n\n >>> response = requests.get(url)\n >>> image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n >>> text = \"a red car in the sun\"\n\n >>> pipe = VersatileDiffusionDualGuidedPipeline.from_pretrained(\n ... \"shi-labs/versatile-diffusion\", torch_dtype=torch.float16\n ... )\n >>> pipe.remove_unused_weights()\n >>> pipe = pipe.to(\"cuda\")\n\n >>> generator = torch.Generator(device=\"cuda\").manual_seed(0)\n >>> text_to_image_strength = 0.75\n\n >>> image = pipe(\n ... prompt=text, image=image, text_to_image_strength=text_to_image_strength, generator=generator\n ... ).images[0]\n >>> image.save(\"./car_variation.png\")\n ```\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is\n returned where the first element is a list with the generated images.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.image_unet.config.sample_size * self.vae_scale_factor\n width = width or self.image_unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(prompt, image, height, width, callback_steps)\n\n # 2. Define call parameters\n prompt = [prompt] if not isinstance(prompt, list) else prompt\n image = [image] if not isinstance(image, list) else image\n batch_size = len(prompt)\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompts\n prompt_embeds = self._encode_text_prompt(prompt, device, num_images_per_prompt, do_classifier_free_guidance)\n image_embeddings = self._encode_image_prompt(image, device, num_images_per_prompt, do_classifier_free_guidance)\n dual_prompt_embeddings = torch.cat([prompt_embeds, image_embeddings], dim=1)\n prompt_types = (\"text\", \"image\")\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.image_unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n dual_prompt_embeddings.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs.\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Combine the attention blocks of the image and text UNets\n self.set_transformer_params(text_to_image_strength, prompt_types)\n\n # 8. Denoising loop\n for i, t in enumerate(self.progress_bar(timesteps)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.image_unet(latent_model_input, t, encoder_hidden_states=dual_prompt_embeddings).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n else:\n image = latents\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Callable, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.utils.checkpoint\nfrom transformers import CLIPImageProcessor, CLIPVisionModelWithProjection\n\nfrom ....image_processor import VaeImageProcessor\nfrom ....models import AutoencoderKL, UNet2DConditionModel\nfrom ....schedulers import KarrasDiffusionSchedulers\nfrom ....utils import deprecate, logging\nfrom ....utils.torch_utils import randn_tensor\nfrom ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass VersatileDiffusionImageVariationPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for image variation using Versatile Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Parameters:\n vqvae ([`VQModel`]):\n Vector-quantized (VQ) model to encode and decode images to and from latent representations.\n bert ([`LDMBertModel`]):\n Text-encoder model based on [`~transformers.BERT`].\n tokenizer ([`~transformers.BertTokenizer`]):\n A `BertTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n \"\"\"\n\n model_cpu_offload_seq = \"bert->unet->vqvae\"\n\n image_feature_extractor: CLIPImageProcessor\n image_encoder: CLIPVisionModelWithProjection\n image_unet: UNet2DConditionModel\n vae: AutoencoderKL\n scheduler: KarrasDiffusionSchedulers\n\n def __init__(\n self,\n image_feature_extractor: CLIPImageProcessor,\n image_encoder: CLIPVisionModelWithProjection,\n image_unet: UNet2DConditionModel,\n vae: AutoencoderKL,\n scheduler: KarrasDiffusionSchedulers,\n ):\n super().__init__()\n self.register_modules(\n image_feature_extractor=image_feature_extractor,\n image_encoder=image_encoder,\n image_unet=image_unet,\n vae=vae,\n scheduler=scheduler,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n \"\"\"\n\n def normalize_embeddings(encoder_output):\n embeds = self.image_encoder.vision_model.post_layernorm(encoder_output.last_hidden_state)\n embeds = self.image_encoder.visual_projection(embeds)\n embeds_pooled = embeds[:, 0:1]\n embeds = embeds / torch.norm(embeds_pooled, dim=-1, keepdim=True)\n return embeds\n\n if isinstance(prompt, torch.Tensor) and len(prompt.shape) == 4:\n prompt = list(prompt)\n\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n\n # get prompt text embeddings\n image_input = self.image_feature_extractor(images=prompt, return_tensors=\"pt\")\n pixel_values = image_input.pixel_values.to(device).to(self.image_encoder.dtype)\n image_embeddings = self.image_encoder(pixel_values)\n image_embeddings = normalize_embeddings(image_embeddings)\n\n # duplicate image embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = image_embeddings.shape\n image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)\n image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n uncond_images: List[str]\n if negative_prompt is None:\n uncond_images = [np.zeros((512, 512, 3)) + 0.5] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, PIL.Image.Image):\n uncond_images = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_images = negative_prompt\n\n uncond_images = self.image_feature_extractor(images=uncond_images, return_tensors=\"pt\")\n pixel_values = uncond_images.pixel_values.to(device).to(self.image_encoder.dtype)\n negative_prompt_embeds = self.image_encoder(pixel_values)\n negative_prompt_embeds = normalize_embeddings(negative_prompt_embeds)\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and conditional embeddings into a single batch\n # to avoid doing two forward passes\n image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings])\n\n return image_embeddings\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_image_variation.StableDiffusionImageVariationPipeline.check_inputs\n def check_inputs(self, image, height, width, callback_steps):\n if (\n not isinstance(image, torch.Tensor)\n and not isinstance(image, PIL.Image.Image)\n and not isinstance(image, list)\n ):\n raise ValueError(\n \"`image` has to be of type `torch.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is\"\n f\" {type(image)}\"\n )\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @torch.no_grad()\n def __call__(\n self,\n image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.Tensor],\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n image (`PIL.Image.Image`, `List[PIL.Image.Image]` or `torch.Tensor`):\n The image prompt or prompts to guide the image generation.\n height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n\n Examples:\n\n ```py\n >>> from diffusers import VersatileDiffusionImageVariationPipeline\n >>> import torch\n >>> import requests\n >>> from io import BytesIO\n >>> from PIL import Image\n\n >>> # let's download an initial image\n >>> url = \"https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg\"\n\n >>> response = requests.get(url)\n >>> image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n\n >>> pipe = VersatileDiffusionImageVariationPipeline.from_pretrained(\n ... \"shi-labs/versatile-diffusion\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> generator = torch.Generator(device=\"cuda\").manual_seed(0)\n >>> image = pipe(image, generator=generator).images[0]\n >>> image.save(\"./car_variation.png\")\n ```\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.image_unet.config.sample_size * self.vae_scale_factor\n width = width or self.image_unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(image, height, width, callback_steps)\n\n # 2. Define call parameters\n batch_size = 1 if isinstance(image, PIL.Image.Image) else len(image)\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n image_embeddings = self._encode_prompt(\n image, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt\n )\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.image_unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n image_embeddings.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs.\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Denoising loop\n for i, t in enumerate(self.progress_bar(timesteps)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.image_unet(latent_model_input, t, encoder_hidden_states=image_embeddings).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n else:\n image = latents\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Callable, List, Optional, Union\n\nimport torch\nimport torch.utils.checkpoint\nfrom transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom ....image_processor import VaeImageProcessor\nfrom ....models import AutoencoderKL, Transformer2DModel, UNet2DConditionModel\nfrom ....schedulers import KarrasDiffusionSchedulers\nfrom ....utils import deprecate, logging\nfrom ....utils.torch_utils import randn_tensor\nfrom ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput\nfrom .modeling_text_unet import UNetFlatConditionModel\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass VersatileDiffusionTextToImagePipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for text-to-image generation using Versatile Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Parameters:\n vqvae ([`VQModel`]):\n Vector-quantized (VQ) model to encode and decode images to and from latent representations.\n bert ([`LDMBertModel`]):\n Text-encoder model based on [`~transformers.BERT`].\n tokenizer ([`~transformers.BertTokenizer`]):\n A `BertTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n \"\"\"\n\n model_cpu_offload_seq = \"bert->unet->vqvae\"\n\n tokenizer: CLIPTokenizer\n image_feature_extractor: CLIPImageProcessor\n text_encoder: CLIPTextModelWithProjection\n image_unet: UNet2DConditionModel\n text_unet: UNetFlatConditionModel\n vae: AutoencoderKL\n scheduler: KarrasDiffusionSchedulers\n\n _optional_components = [\"text_unet\"]\n\n def __init__(\n self,\n tokenizer: CLIPTokenizer,\n text_encoder: CLIPTextModelWithProjection,\n image_unet: UNet2DConditionModel,\n text_unet: UNetFlatConditionModel,\n vae: AutoencoderKL,\n scheduler: KarrasDiffusionSchedulers,\n ):\n super().__init__()\n self.register_modules(\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n image_unet=image_unet,\n text_unet=text_unet,\n vae=vae,\n scheduler=scheduler,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n if self.text_unet is not None:\n self._swap_unet_attention_blocks()\n\n def _swap_unet_attention_blocks(self):\n \"\"\"\n Swap the `Transformer2DModel` blocks between the image and text UNets\n \"\"\"\n for name, module in self.image_unet.named_modules():\n if isinstance(module, Transformer2DModel):\n parent_name, index = name.rsplit(\".\", 1)\n index = int(index)\n self.image_unet.get_submodule(parent_name)[index], self.text_unet.get_submodule(parent_name)[index] = (\n self.text_unet.get_submodule(parent_name)[index],\n self.image_unet.get_submodule(parent_name)[index],\n )\n\n def remove_unused_weights(self):\n self.register_modules(text_unet=None)\n\n def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n \"\"\"\n\n def normalize_embeddings(encoder_output):\n embeds = self.text_encoder.text_projection(encoder_output.last_hidden_state)\n embeds_pooled = encoder_output.text_embeds\n embeds = embeds / torch.norm(embeds_pooled.unsqueeze(1), dim=-1, keepdim=True)\n return embeds\n\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"max_length\", return_tensors=\"pt\").input_ids\n\n if not torch.equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n prompt_embeds = normalize_embeddings(prompt_embeds)\n\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = text_input_ids.shape[-1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = normalize_embeddings(negative_prompt_embeds)\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide image generation.\n height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n\n Examples:\n\n ```py\n >>> from diffusers import VersatileDiffusionTextToImagePipeline\n >>> import torch\n\n >>> pipe = VersatileDiffusionTextToImagePipeline.from_pretrained(\n ... \"shi-labs/versatile-diffusion\", torch_dtype=torch.float16\n ... )\n >>> pipe.remove_unused_weights()\n >>> pipe = pipe.to(\"cuda\")\n\n >>> generator = torch.Generator(device=\"cuda\").manual_seed(0)\n >>> image = pipe(\"an astronaut riding on a horse on mars\", generator=generator).images[0]\n >>> image.save(\"./astronaut.png\")\n ```\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.image_unet.config.sample_size * self.vae_scale_factor\n width = width or self.image_unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(prompt, height, width, callback_steps)\n\n # 2. Define call parameters\n batch_size = 1 if isinstance(prompt, str) else len(prompt)\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n prompt_embeds = self._encode_prompt(\n prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt\n )\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.image_unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs.\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Denoising loop\n for i, t in enumerate(self.progress_bar(timesteps)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.image_unet(latent_model_input, t, encoder_hidden_states=prompt_embeds).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n else:\n image = latents\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ....utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ....utils.dummy_torch_and_transformers_objects import (\n LearnedClassifierFreeSamplingEmbeddings,\n VQDiffusionPipeline,\n )\n\n _dummy_objects.update(\n {\n \"LearnedClassifierFreeSamplingEmbeddings\": LearnedClassifierFreeSamplingEmbeddings,\n \"VQDiffusionPipeline\": VQDiffusionPipeline,\n }\n )\nelse:\n _import_structure[\"pipeline_vq_diffusion\"] = [\"LearnedClassifierFreeSamplingEmbeddings\", \"VQDiffusionPipeline\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ....utils.dummy_torch_and_transformers_objects import (\n LearnedClassifierFreeSamplingEmbeddings,\n VQDiffusionPipeline,\n )\n else:\n from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py", "content": "# Copyright 2024 Microsoft and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Callable, List, Optional, Tuple, Union\n\nimport torch\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nfrom ....configuration_utils import ConfigMixin, register_to_config\nfrom ....models import ModelMixin, Transformer2DModel, VQModel\nfrom ....schedulers import VQDiffusionScheduler\nfrom ....utils import logging\nfrom ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass LearnedClassifierFreeSamplingEmbeddings(ModelMixin, ConfigMixin):\n \"\"\"\n Utility class for storing learned text embeddings for classifier free sampling\n \"\"\"\n\n @register_to_config\n def __init__(self, learnable: bool, hidden_size: Optional[int] = None, length: Optional[int] = None):\n super().__init__()\n\n self.learnable = learnable\n\n if self.learnable:\n assert hidden_size is not None, \"learnable=True requires `hidden_size` to be set\"\n assert length is not None, \"learnable=True requires `length` to be set\"\n\n embeddings = torch.zeros(length, hidden_size)\n else:\n embeddings = None\n\n self.embeddings = torch.nn.Parameter(embeddings)\n\n\nclass VQDiffusionPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for text-to-image generation using VQ Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Args:\n vqvae ([`VQModel`]):\n Vector Quantized Variational Auto-Encoder (VAE) model to encode and decode images to and from latent\n representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n transformer ([`Transformer2DModel`]):\n A conditional `Transformer2DModel` to denoise the encoded image latents.\n scheduler ([`VQDiffusionScheduler`]):\n A scheduler to be used in combination with `transformer` to denoise the encoded image latents.\n \"\"\"\n\n vqvae: VQModel\n text_encoder: CLIPTextModel\n tokenizer: CLIPTokenizer\n transformer: Transformer2DModel\n learned_classifier_free_sampling_embeddings: LearnedClassifierFreeSamplingEmbeddings\n scheduler: VQDiffusionScheduler\n\n def __init__(\n self,\n vqvae: VQModel,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n transformer: Transformer2DModel,\n scheduler: VQDiffusionScheduler,\n learned_classifier_free_sampling_embeddings: LearnedClassifierFreeSamplingEmbeddings,\n ):\n super().__init__()\n\n self.register_modules(\n vqvae=vqvae,\n transformer=transformer,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n scheduler=scheduler,\n learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings,\n )\n\n def _encode_prompt(self, prompt, num_images_per_prompt, do_classifier_free_guidance):\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n\n if text_input_ids.shape[-1] > self.tokenizer.model_max_length:\n removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]\n prompt_embeds = self.text_encoder(text_input_ids.to(self.device))[0]\n\n # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.\n # While CLIP does normalize the pooled output of the text transformer when combining\n # the image and text embeddings, CLIP does not directly normalize the last hidden state.\n #\n # CLIP normalizing the pooled output.\n # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053\n prompt_embeds = prompt_embeds / prompt_embeds.norm(dim=-1, keepdim=True)\n\n # duplicate text embeddings for each generation per prompt\n prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n\n if do_classifier_free_guidance:\n if self.learned_classifier_free_sampling_embeddings.learnable:\n negative_prompt_embeds = self.learned_classifier_free_sampling_embeddings.embeddings\n negative_prompt_embeds = negative_prompt_embeds.unsqueeze(0).repeat(batch_size, 1, 1)\n else:\n uncond_tokens = [\"\"] * batch_size\n\n max_length = text_input_ids.shape[-1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n negative_prompt_embeds = self.text_encoder(uncond_input.input_ids.to(self.device))[0]\n # See comment for normalizing text embeddings\n negative_prompt_embeds = negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1, keepdim=True)\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n num_inference_steps: int = 100,\n guidance_scale: float = 5.0,\n truncation_rate: float = 1.0,\n num_images_per_prompt: int = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n ) -> Union[ImagePipelineOutput, Tuple]:\n \"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide image generation.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n truncation_rate (`float`, *optional*, defaults to 1.0 (equivalent to no truncation)):\n Used to \"truncate\" the predicted classes for x_0 such that the cumulative probability for a pixel is at\n most `truncation_rate`. The lowest probabilities that would increase the cumulative probability above\n `truncation_rate` are set to zero.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor` of shape (batch), *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Must be valid embedding indices.If not provided, a latents tensor will be generated of\n completely masked latent pixels.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is\n returned where the first element is a list with the generated images.\n \"\"\"\n if isinstance(prompt, str):\n batch_size = 1\n elif isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n batch_size = batch_size * num_images_per_prompt\n\n do_classifier_free_guidance = guidance_scale > 1.0\n\n prompt_embeds = self._encode_prompt(prompt, num_images_per_prompt, do_classifier_free_guidance)\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n # get the initial completely masked latents unless the user supplied it\n\n latents_shape = (batch_size, self.transformer.num_latent_pixels)\n if latents is None:\n mask_class = self.transformer.num_vector_embeds - 1\n latents = torch.full(latents_shape, mask_class).to(self.device)\n else:\n if latents.shape != latents_shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {latents_shape}\")\n if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():\n raise ValueError(\n \"Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,\"\n f\" {self.transformer.num_vector_embeds - 1} (inclusive).\"\n )\n latents = latents.to(self.device)\n\n # set timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=self.device)\n\n timesteps_tensor = self.scheduler.timesteps.to(self.device)\n\n sample = latents\n\n for i, t in enumerate(self.progress_bar(timesteps_tensor)):\n # expand the sample if we are doing classifier free guidance\n latent_model_input = torch.cat([sample] * 2) if do_classifier_free_guidance else sample\n\n # predict the un-noised image\n # model_output == `log_p_x_0`\n model_output = self.transformer(latent_model_input, encoder_hidden_states=prompt_embeds, timestep=t).sample\n\n if do_classifier_free_guidance:\n model_output_uncond, model_output_text = model_output.chunk(2)\n model_output = model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)\n model_output -= torch.logsumexp(model_output, dim=1, keepdim=True)\n\n model_output = self.truncate(model_output, truncation_rate)\n\n # remove `log(0)`'s (`-inf`s)\n model_output = model_output.clamp(-70)\n\n # compute the previous noisy sample x_t -> x_t-1\n sample = self.scheduler.step(model_output, timestep=t, sample=sample, generator=generator).prev_sample\n\n # call the callback, if provided\n if callback is not None and i % callback_steps == 0:\n callback(i, t, sample)\n\n embedding_channels = self.vqvae.config.vq_embed_dim\n embeddings_shape = (batch_size, self.transformer.height, self.transformer.width, embedding_channels)\n embeddings = self.vqvae.quantize.get_codebook_entry(sample, shape=embeddings_shape)\n image = self.vqvae.decode(embeddings, force_not_quantize=True).sample\n\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).numpy()\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n\n def truncate(self, log_p_x_0: torch.Tensor, truncation_rate: float) -> torch.Tensor:\n \"\"\"\n Truncates `log_p_x_0` such that for each column vector, the total cumulative probability is `truncation_rate`\n The lowest probabilities that would increase the cumulative probability above `truncation_rate` are set to\n zero.\n \"\"\"\n sorted_log_p_x_0, indices = torch.sort(log_p_x_0, 1, descending=True)\n sorted_p_x_0 = torch.exp(sorted_log_p_x_0)\n keep_mask = sorted_p_x_0.cumsum(dim=1) < truncation_rate\n\n # Ensure that at least the largest probability is not zeroed out\n all_true = torch.full_like(keep_mask[:, 0:1, :], True)\n keep_mask = torch.cat((all_true, keep_mask), dim=1)\n keep_mask = keep_mask[:, :-1, :]\n\n keep_mask = keep_mask.gather(1, indices.argsort(1))\n\n rv = log_p_x_0.clone()\n\n rv[~keep_mask] = -torch.inf # -inf = log(0)\n\n return rv\n" }, { "path": "diffusers/src/diffusers/pipelines/dit/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import DIFFUSERS_SLOW_IMPORT, _LazyModule\n\n\n_import_structure = {\"pipeline_dit\": [\"DiTPipeline\"]}\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n from .pipeline_dit import DiTPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n" }, { "path": "diffusers/src/diffusers/pipelines/dit/pipeline_dit.py", "content": "# Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)\n# William Peebles and Saining Xie\n#\n# Copyright (c) 2021 OpenAI\n# MIT License\n#\n# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Dict, List, Optional, Tuple, Union\n\nimport torch\n\nfrom ...models import AutoencoderKL, DiTTransformer2DModel\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nclass DiTPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for image generation based on a Transformer backbone instead of a UNet.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Parameters:\n transformer ([`DiTTransformer2DModel`]):\n A class conditioned `DiTTransformer2DModel` to denoise the encoded image latents.\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n scheduler ([`DDIMScheduler`]):\n A scheduler to be used in combination with `transformer` to denoise the encoded image latents.\n \"\"\"\n\n model_cpu_offload_seq = \"transformer->vae\"\n\n def __init__(\n self,\n transformer: DiTTransformer2DModel,\n vae: AutoencoderKL,\n scheduler: KarrasDiffusionSchedulers,\n id2label: Optional[Dict[int, str]] = None,\n ):\n super().__init__()\n self.register_modules(transformer=transformer, vae=vae, scheduler=scheduler)\n\n # create a imagenet -> id dictionary for easier use\n self.labels = {}\n if id2label is not None:\n for key, value in id2label.items():\n for label in value.split(\",\"):\n self.labels[label.lstrip().rstrip()] = int(key)\n self.labels = dict(sorted(self.labels.items()))\n\n def get_label_ids(self, label: Union[str, List[str]]) -> List[int]:\n r\"\"\"\n\n Map label strings from ImageNet to corresponding class ids.\n\n Parameters:\n label (`str` or `dict` of `str`):\n Label strings to be mapped to class ids.\n\n Returns:\n `list` of `int`:\n Class ids to be processed by pipeline.\n \"\"\"\n\n if not isinstance(label, list):\n label = list(label)\n\n for l in label:\n if l not in self.labels:\n raise ValueError(\n f\"{l} does not exist. Please make sure to select one of the following labels: \\n {self.labels}.\"\n )\n\n return [self.labels[l] for l in label]\n\n @torch.no_grad()\n def __call__(\n self,\n class_labels: List[int],\n guidance_scale: float = 4.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n num_inference_steps: int = 50,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n ) -> Union[ImagePipelineOutput, Tuple]:\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n class_labels (List[int]):\n List of ImageNet class labels for the images to be generated.\n guidance_scale (`float`, *optional*, defaults to 4.0):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n num_inference_steps (`int`, *optional*, defaults to 250):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple.\n\n Examples:\n\n ```py\n >>> from diffusers import DiTPipeline, DPMSolverMultistepScheduler\n >>> import torch\n\n >>> pipe = DiTPipeline.from_pretrained(\"facebook/DiT-XL-2-256\", torch_dtype=torch.float16)\n >>> pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n >>> pipe = pipe.to(\"cuda\")\n\n >>> # pick words from Imagenet class labels\n >>> pipe.labels # to print all available words\n\n >>> # pick words that exist in ImageNet\n >>> words = [\"white shark\", \"umbrella\"]\n\n >>> class_ids = pipe.get_label_ids(words)\n\n >>> generator = torch.manual_seed(33)\n >>> output = pipe(class_labels=class_ids, num_inference_steps=25, generator=generator)\n\n >>> image = output.images[0] # label 'white shark'\n ```\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is\n returned where the first element is a list with the generated images\n \"\"\"\n\n batch_size = len(class_labels)\n latent_size = self.transformer.config.sample_size\n latent_channels = self.transformer.config.in_channels\n\n latents = randn_tensor(\n shape=(batch_size, latent_channels, latent_size, latent_size),\n generator=generator,\n device=self._execution_device,\n dtype=self.transformer.dtype,\n )\n latent_model_input = torch.cat([latents] * 2) if guidance_scale > 1 else latents\n\n class_labels = torch.tensor(class_labels, device=self._execution_device).reshape(-1)\n class_null = torch.tensor([1000] * batch_size, device=self._execution_device)\n class_labels_input = torch.cat([class_labels, class_null], 0) if guidance_scale > 1 else class_labels\n\n # set step values\n self.scheduler.set_timesteps(num_inference_steps)\n for t in self.progress_bar(self.scheduler.timesteps):\n if guidance_scale > 1:\n half = latent_model_input[: len(latent_model_input) // 2]\n latent_model_input = torch.cat([half, half], dim=0)\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n timesteps = t\n if not torch.is_tensor(timesteps):\n # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can\n # This would be a good case for the `match` statement (Python 3.10+)\n is_mps = latent_model_input.device.type == \"mps\"\n if isinstance(timesteps, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n timesteps = torch.tensor([timesteps], dtype=dtype, device=latent_model_input.device)\n elif len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(latent_model_input.device)\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timesteps = timesteps.expand(latent_model_input.shape[0])\n # predict noise model_output\n noise_pred = self.transformer(\n latent_model_input, timestep=timesteps, class_labels=class_labels_input\n ).sample\n\n # perform guidance\n if guidance_scale > 1:\n eps, rest = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]\n cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)\n\n half_eps = uncond_eps + guidance_scale * (cond_eps - uncond_eps)\n eps = torch.cat([half_eps, half_eps], dim=0)\n\n noise_pred = torch.cat([eps, rest], dim=1)\n\n # learned sigma\n if self.transformer.config.out_channels // 2 == latent_channels:\n model_output, _ = torch.split(noise_pred, latent_channels, dim=1)\n else:\n model_output = noise_pred\n\n # compute previous image: x_t -> x_t-1\n latent_model_input = self.scheduler.step(model_output, t, latent_model_input).prev_sample\n\n if guidance_scale > 1:\n latents, _ = latent_model_input.chunk(2, dim=0)\n else:\n latents = latent_model_input\n\n latents = 1 / self.vae.config.scaling_factor * latents\n samples = self.vae.decode(latents).sample\n\n samples = (samples / 2 + 0.5).clamp(0, 1)\n\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n samples = samples.cpu().permute(0, 2, 3, 1).float().numpy()\n\n if output_type == \"pil\":\n samples = self.numpy_to_pil(samples)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (samples,)\n\n return ImagePipelineOutput(images=samples)\n" }, { "path": "diffusers/src/diffusers/pipelines/free_init_utils.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport math\nfrom typing import Tuple, Union\n\nimport torch\nimport torch.fft as fft\n\nfrom ..utils.torch_utils import randn_tensor\n\n\nclass FreeInitMixin:\n r\"\"\"Mixin class for FreeInit.\"\"\"\n\n def enable_free_init(\n self,\n num_iters: int = 3,\n use_fast_sampling: bool = False,\n method: str = \"butterworth\",\n order: int = 4,\n spatial_stop_frequency: float = 0.25,\n temporal_stop_frequency: float = 0.25,\n ):\n \"\"\"Enables the FreeInit mechanism as in https://arxiv.org/abs/2312.07537.\n\n This implementation has been adapted from the [official repository](https://github.com/TianxingWu/FreeInit).\n\n Args:\n num_iters (`int`, *optional*, defaults to `3`):\n Number of FreeInit noise re-initialization iterations.\n use_fast_sampling (`bool`, *optional*, defaults to `False`):\n Whether or not to speedup sampling procedure at the cost of probably lower quality results. Enables the\n \"Coarse-to-Fine Sampling\" strategy, as mentioned in the paper, if set to `True`.\n method (`str`, *optional*, defaults to `butterworth`):\n Must be one of `butterworth`, `ideal` or `gaussian` to use as the filtering method for the FreeInit low\n pass filter.\n order (`int`, *optional*, defaults to `4`):\n Order of the filter used in `butterworth` method. Larger values lead to `ideal` method behaviour\n whereas lower values lead to `gaussian` method behaviour.\n spatial_stop_frequency (`float`, *optional*, defaults to `0.25`):\n Normalized stop frequency for spatial dimensions. Must be between 0 to 1. Referred to as `d_s` in the\n original implementation.\n temporal_stop_frequency (`float`, *optional*, defaults to `0.25`):\n Normalized stop frequency for temporal dimensions. Must be between 0 to 1. Referred to as `d_t` in the\n original implementation.\n \"\"\"\n self._free_init_num_iters = num_iters\n self._free_init_use_fast_sampling = use_fast_sampling\n self._free_init_method = method\n self._free_init_order = order\n self._free_init_spatial_stop_frequency = spatial_stop_frequency\n self._free_init_temporal_stop_frequency = temporal_stop_frequency\n\n def disable_free_init(self):\n \"\"\"Disables the FreeInit mechanism if enabled.\"\"\"\n self._free_init_num_iters = None\n\n @property\n def free_init_enabled(self):\n return hasattr(self, \"_free_init_num_iters\") and self._free_init_num_iters is not None\n\n def _get_free_init_freq_filter(\n self,\n shape: Tuple[int, ...],\n device: Union[str, torch.dtype],\n filter_type: str,\n order: float,\n spatial_stop_frequency: float,\n temporal_stop_frequency: float,\n ) -> torch.Tensor:\n r\"\"\"Returns the FreeInit filter based on filter type and other input conditions.\"\"\"\n\n time, height, width = shape[-3], shape[-2], shape[-1]\n mask = torch.zeros(shape)\n\n if spatial_stop_frequency == 0 or temporal_stop_frequency == 0:\n return mask\n\n if filter_type == \"butterworth\":\n\n def retrieve_mask(x):\n return 1 / (1 + (x / spatial_stop_frequency**2) ** order)\n elif filter_type == \"gaussian\":\n\n def retrieve_mask(x):\n return math.exp(-1 / (2 * spatial_stop_frequency**2) * x)\n elif filter_type == \"ideal\":\n\n def retrieve_mask(x):\n return 1 if x <= spatial_stop_frequency * 2 else 0\n else:\n raise NotImplementedError(\"`filter_type` must be one of gaussian, butterworth or ideal\")\n\n for t in range(time):\n for h in range(height):\n for w in range(width):\n d_square = (\n ((spatial_stop_frequency / temporal_stop_frequency) * (2 * t / time - 1)) ** 2\n + (2 * h / height - 1) ** 2\n + (2 * w / width - 1) ** 2\n )\n mask[..., t, h, w] = retrieve_mask(d_square)\n\n return mask.to(device)\n\n def _apply_freq_filter(self, x: torch.Tensor, noise: torch.Tensor, low_pass_filter: torch.Tensor) -> torch.Tensor:\n r\"\"\"Noise reinitialization.\"\"\"\n # FFT\n x_freq = fft.fftn(x, dim=(-3, -2, -1))\n x_freq = fft.fftshift(x_freq, dim=(-3, -2, -1))\n noise_freq = fft.fftn(noise, dim=(-3, -2, -1))\n noise_freq = fft.fftshift(noise_freq, dim=(-3, -2, -1))\n\n # frequency mix\n high_pass_filter = 1 - low_pass_filter\n x_freq_low = x_freq * low_pass_filter\n noise_freq_high = noise_freq * high_pass_filter\n x_freq_mixed = x_freq_low + noise_freq_high # mix in freq domain\n\n # IFFT\n x_freq_mixed = fft.ifftshift(x_freq_mixed, dim=(-3, -2, -1))\n x_mixed = fft.ifftn(x_freq_mixed, dim=(-3, -2, -1)).real\n\n return x_mixed\n\n def _apply_free_init(\n self,\n latents: torch.Tensor,\n free_init_iteration: int,\n num_inference_steps: int,\n device: torch.device,\n dtype: torch.dtype,\n generator: torch.Generator,\n ):\n if free_init_iteration == 0:\n self._free_init_initial_noise = latents.detach().clone()\n else:\n latent_shape = latents.shape\n\n free_init_filter_shape = (1, *latent_shape[1:])\n free_init_freq_filter = self._get_free_init_freq_filter(\n shape=free_init_filter_shape,\n device=device,\n filter_type=self._free_init_method,\n order=self._free_init_order,\n spatial_stop_frequency=self._free_init_spatial_stop_frequency,\n temporal_stop_frequency=self._free_init_temporal_stop_frequency,\n )\n\n current_diffuse_timestep = self.scheduler.config.num_train_timesteps - 1\n diffuse_timesteps = torch.full((latent_shape[0],), current_diffuse_timestep).long()\n\n z_t = self.scheduler.add_noise(\n original_samples=latents, noise=self._free_init_initial_noise, timesteps=diffuse_timesteps.to(device)\n ).to(dtype=torch.float32)\n\n z_rand = randn_tensor(\n shape=latent_shape,\n generator=generator,\n device=device,\n dtype=torch.float32,\n )\n latents = self._apply_freq_filter(z_t, z_rand, low_pass_filter=free_init_freq_filter)\n latents = latents.to(dtype)\n\n # Coarse-to-Fine Sampling for faster inference (can lead to lower quality)\n if self._free_init_use_fast_sampling:\n num_inference_steps = max(\n 1, int(num_inference_steps / self._free_init_num_iters * (free_init_iteration + 1))\n )\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n\n return latents, self.scheduler.timesteps\n" }, { "path": "diffusers/src/diffusers/pipelines/hunyuandit/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_hunyuandit\"] = [\"HunyuanDiTPipeline\"]\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n else:\n from .pipeline_hunyuandit import HunyuanDiTPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/hunyuandit/pipeline_hunyuandit.py", "content": "# Copyright 2024 HunyuanDiT Authors and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nfrom transformers import BertModel, BertTokenizer, CLIPImageProcessor, MT5Tokenizer, T5EncoderModel\n\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\n\nfrom ...callbacks import MultiPipelineCallbacks, PipelineCallback\nfrom ...image_processor import VaeImageProcessor\nfrom ...models import AutoencoderKL, HunyuanDiT2DModel\nfrom ...models.embeddings import get_2d_rotary_pos_embed\nfrom ...pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom ...schedulers import DDPMScheduler\nfrom ...utils import (\n is_torch_xla_available,\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline\n\n\nif is_torch_xla_available():\n import torch_xla.core.xla_model as xm\n\n XLA_AVAILABLE = True\nelse:\n XLA_AVAILABLE = False\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import HunyuanDiTPipeline\n\n >>> pipe = HunyuanDiTPipeline.from_pretrained(\n ... \"Tencent-Hunyuan/HunyuanDiT-Diffusers\", torch_dtype=torch.float16\n ... )\n >>> pipe.to(\"cuda\")\n\n >>> # You may also use English prompt as HunyuanDiT supports both English and Chinese\n >>> # prompt = \"An astronaut riding a horse\"\n >>> prompt = \"一个宇航员在骑马\"\n >>> image = pipe(prompt).images[0]\n ```\n\"\"\"\n\nSTANDARD_RATIO = np.array(\n [\n 1.0, # 1:1\n 4.0 / 3.0, # 4:3\n 3.0 / 4.0, # 3:4\n 16.0 / 9.0, # 16:9\n 9.0 / 16.0, # 9:16\n ]\n)\nSTANDARD_SHAPE = [\n [(1024, 1024), (1280, 1280)], # 1:1\n [(1024, 768), (1152, 864), (1280, 960)], # 4:3\n [(768, 1024), (864, 1152), (960, 1280)], # 3:4\n [(1280, 768)], # 16:9\n [(768, 1280)], # 9:16\n]\nSTANDARD_AREA = [np.array([w * h for w, h in shapes]) for shapes in STANDARD_SHAPE]\nSUPPORTED_SHAPE = [\n (1024, 1024),\n (1280, 1280), # 1:1\n (1024, 768),\n (1152, 864),\n (1280, 960), # 4:3\n (768, 1024),\n (864, 1152),\n (960, 1280), # 3:4\n (1280, 768), # 16:9\n (768, 1280), # 9:16\n]\n\n\ndef map_to_standard_shapes(target_width, target_height):\n target_ratio = target_width / target_height\n closest_ratio_idx = np.argmin(np.abs(STANDARD_RATIO - target_ratio))\n closest_area_idx = np.argmin(np.abs(STANDARD_AREA[closest_ratio_idx] - target_width * target_height))\n width, height = STANDARD_SHAPE[closest_ratio_idx][closest_area_idx]\n return width, height\n\n\ndef get_resize_crop_region_for_grid(src, tgt_size):\n th = tw = tgt_size\n h, w = src\n\n r = h / w\n\n # resize\n if r > 1:\n resize_height = th\n resize_width = int(round(th / h * w))\n else:\n resize_width = tw\n resize_height = int(round(tw / w * h))\n\n crop_top = int(round((th - resize_height) / 2.0))\n crop_left = int(round((tw - resize_width) / 2.0))\n\n return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\nclass HunyuanDiTPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for English/Chinese-to-image generation using HunyuanDiT.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n HunyuanDiT uses two text encoders: [mT5](https://huggingface.co/google/mt5-base) and [bilingual CLIP](fine-tuned by\n ourselves)\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. We use\n `sdxl-vae-fp16-fix`.\n text_encoder (Optional[`~transformers.BertModel`, `~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n HunyuanDiT uses a fine-tuned [bilingual CLIP].\n tokenizer (Optional[`~transformers.BertTokenizer`, `~transformers.CLIPTokenizer`]):\n A `BertTokenizer` or `CLIPTokenizer` to tokenize text.\n transformer ([`HunyuanDiT2DModel`]):\n The HunyuanDiT model designed by Tencent Hunyuan.\n text_encoder_2 (`T5EncoderModel`):\n The mT5 embedder. Specifically, it is 't5-v1_1-xxl'.\n tokenizer_2 (`MT5Tokenizer`):\n The tokenizer for the mT5 embedder.\n scheduler ([`DDPMScheduler`]):\n A scheduler to be used in combination with HunyuanDiT to denoise the encoded image latents.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->text_encoder_2->transformer->vae\"\n _optional_components = [\n \"safety_checker\",\n \"feature_extractor\",\n \"text_encoder_2\",\n \"tokenizer_2\",\n \"text_encoder\",\n \"tokenizer\",\n ]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\n \"latents\",\n \"prompt_embeds\",\n \"negative_prompt_embeds\",\n \"prompt_embeds_2\",\n \"negative_prompt_embeds_2\",\n ]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: BertModel,\n tokenizer: BertTokenizer,\n transformer: HunyuanDiT2DModel,\n scheduler: DDPMScheduler,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n text_encoder_2=T5EncoderModel,\n tokenizer_2=MT5Tokenizer,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n transformer=transformer,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n text_encoder_2=text_encoder_2,\n )\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.vae_scale_factor = (\n 2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, \"vae\") and self.vae is not None else 8\n )\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n self.default_sample_size = (\n self.transformer.config.sample_size\n if hasattr(self, \"transformer\") and self.transformer is not None\n else 128\n )\n\n def encode_prompt(\n self,\n prompt: str,\n device: torch.device = None,\n dtype: torch.dtype = None,\n num_images_per_prompt: int = 1,\n do_classifier_free_guidance: bool = True,\n negative_prompt: Optional[str] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n prompt_attention_mask: Optional[torch.Tensor] = None,\n negative_prompt_attention_mask: Optional[torch.Tensor] = None,\n max_sequence_length: Optional[int] = None,\n text_encoder_index: int = 0,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n dtype (`torch.dtype`):\n torch dtype\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n prompt_attention_mask (`torch.Tensor`, *optional*):\n Attention mask for the prompt. Required when `prompt_embeds` is passed directly.\n negative_prompt_attention_mask (`torch.Tensor`, *optional*):\n Attention mask for the negative prompt. Required when `negative_prompt_embeds` is passed directly.\n max_sequence_length (`int`, *optional*): maximum sequence length to use for the prompt.\n text_encoder_index (`int`, *optional*):\n Index of the text encoder to use. `0` for clip and `1` for T5.\n \"\"\"\n if dtype is None:\n if self.text_encoder_2 is not None:\n dtype = self.text_encoder_2.dtype\n elif self.transformer is not None:\n dtype = self.transformer.dtype\n else:\n dtype = None\n\n if device is None:\n device = self._execution_device\n\n tokenizers = [self.tokenizer, self.tokenizer_2]\n text_encoders = [self.text_encoder, self.text_encoder_2]\n\n tokenizer = tokenizers[text_encoder_index]\n text_encoder = text_encoders[text_encoder_index]\n\n if max_sequence_length is None:\n if text_encoder_index == 0:\n max_length = 77\n if text_encoder_index == 1:\n max_length = 256\n else:\n max_length = max_sequence_length\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_attention_mask=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_attention_mask = text_inputs.attention_mask.to(device)\n prompt_embeds = text_encoder(\n text_input_ids.to(device),\n attention_mask=prompt_attention_mask,\n )\n prompt_embeds = prompt_embeds[0]\n prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1)\n\n prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = prompt_embeds.shape[1]\n uncond_input = tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n negative_prompt_attention_mask = uncond_input.attention_mask.to(device)\n negative_prompt_embeds = text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=negative_prompt_attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n return prompt_embeds, negative_prompt_embeds, prompt_attention_mask, negative_prompt_attention_mask\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n height,\n width,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n prompt_attention_mask=None,\n negative_prompt_attention_mask=None,\n prompt_embeds_2=None,\n negative_prompt_embeds_2=None,\n prompt_attention_mask_2=None,\n negative_prompt_attention_mask_2=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is None and prompt_embeds_2 is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds_2`. Cannot leave both `prompt` and `prompt_embeds_2` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if prompt_embeds is not None and prompt_attention_mask is None:\n raise ValueError(\"Must provide `prompt_attention_mask` when specifying `prompt_embeds`.\")\n\n if prompt_embeds_2 is not None and prompt_attention_mask_2 is None:\n raise ValueError(\"Must provide `prompt_attention_mask_2` when specifying `prompt_embeds_2`.\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if negative_prompt_embeds is not None and negative_prompt_attention_mask is None:\n raise ValueError(\"Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.\")\n\n if negative_prompt_embeds_2 is not None and negative_prompt_attention_mask_2 is None:\n raise ValueError(\n \"Must provide `negative_prompt_attention_mask_2` when specifying `negative_prompt_embeds_2`.\"\n )\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n if prompt_embeds_2 is not None and negative_prompt_embeds_2 is not None:\n if prompt_embeds_2.shape != negative_prompt_embeds_2.shape:\n raise ValueError(\n \"`prompt_embeds_2` and `negative_prompt_embeds_2` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds_2` {prompt_embeds_2.shape} != `negative_prompt_embeds_2`\"\n f\" {negative_prompt_embeds_2.shape}.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def guidance_rescale(self):\n return self._guidance_rescale\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @property\n def interrupt(self):\n return self._interrupt\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: Optional[int] = 50,\n guidance_scale: Optional[float] = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: Optional[float] = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n prompt_embeds_2: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds_2: Optional[torch.Tensor] = None,\n prompt_attention_mask: Optional[torch.Tensor] = None,\n prompt_attention_mask_2: Optional[torch.Tensor] = None,\n negative_prompt_attention_mask: Optional[torch.Tensor] = None,\n negative_prompt_attention_mask_2: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback_on_step_end: Optional[\n Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]\n ] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n guidance_rescale: float = 0.0,\n original_size: Optional[Tuple[int, int]] = (1024, 1024),\n target_size: Optional[Tuple[int, int]] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n use_resolution_binning: bool = True,\n ):\n r\"\"\"\n The call function to the pipeline for generation with HunyuanDiT.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`):\n The height in pixels of the generated image.\n width (`int`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference. This parameter is modulated by `strength`.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n prompt_embeds_2 (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n negative_prompt_embeds_2 (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n prompt_attention_mask (`torch.Tensor`, *optional*):\n Attention mask for the prompt. Required when `prompt_embeds` is passed directly.\n prompt_attention_mask_2 (`torch.Tensor`, *optional*):\n Attention mask for the prompt. Required when `prompt_embeds_2` is passed directly.\n negative_prompt_attention_mask (`torch.Tensor`, *optional*):\n Attention mask for the negative prompt. Required when `negative_prompt_embeds` is passed directly.\n negative_prompt_attention_mask_2 (`torch.Tensor`, *optional*):\n Attention mask for the negative prompt. Required when `negative_prompt_embeds_2` is passed directly.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback_on_step_end (`Callable[[int, int, Dict], None]`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):\n A callback function or a list of callback functions to be called at the end of each denoising step.\n callback_on_step_end_tensor_inputs (`List[str]`, *optional*):\n A list of tensor inputs that should be passed to the callback function. If not defined, all tensor\n inputs will be passed.\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Rescale the noise_cfg according to `guidance_rescale`. Based on findings of [Common Diffusion Noise\n Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n original_size (`Tuple[int, int]`, *optional*, defaults to `(1024, 1024)`):\n The original size of the image. Used to calculate the time ids.\n target_size (`Tuple[int, int]`, *optional*):\n The target size of the image. Used to calculate the time ids.\n crops_coords_top_left (`Tuple[int, int]`, *optional*, defaults to `(0, 0)`):\n The top left coordinates of the crop. Used to calculate the time ids.\n use_resolution_binning (`bool`, *optional*, defaults to `True`):\n Whether to use resolution binning or not. If `True`, the input resolution will be mapped to the closest\n standard resolution. Supported resolutions are 1024x1024, 1280x1280, 1024x768, 1152x864, 1280x960,\n 768x1024, 864x1152, 960x1280, 1280x768, and 768x1280. It is recommended to set this to `True`.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n\n if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):\n callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs\n\n # 0. default height and width\n height = height or self.default_sample_size * self.vae_scale_factor\n width = width or self.default_sample_size * self.vae_scale_factor\n height = int((height // 16) * 16)\n width = int((width // 16) * 16)\n\n if use_resolution_binning and (height, width) not in SUPPORTED_SHAPE:\n width, height = map_to_standard_shapes(width, height)\n height = int(height)\n width = int(width)\n logger.warning(f\"Reshaped to (height, width)=({height}, {width}), Supported shapes are {SUPPORTED_SHAPE}\")\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n height,\n width,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n prompt_attention_mask,\n negative_prompt_attention_mask,\n prompt_embeds_2,\n negative_prompt_embeds_2,\n prompt_attention_mask_2,\n negative_prompt_attention_mask_2,\n callback_on_step_end_tensor_inputs,\n )\n self._guidance_scale = guidance_scale\n self._guidance_rescale = guidance_rescale\n self._interrupt = False\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n prompt_attention_mask,\n negative_prompt_attention_mask,\n ) = self.encode_prompt(\n prompt=prompt,\n device=device,\n dtype=self.transformer.dtype,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n prompt_attention_mask=prompt_attention_mask,\n negative_prompt_attention_mask=negative_prompt_attention_mask,\n max_sequence_length=77,\n text_encoder_index=0,\n )\n (\n prompt_embeds_2,\n negative_prompt_embeds_2,\n prompt_attention_mask_2,\n negative_prompt_attention_mask_2,\n ) = self.encode_prompt(\n prompt=prompt,\n device=device,\n dtype=self.transformer.dtype,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds_2,\n negative_prompt_embeds=negative_prompt_embeds_2,\n prompt_attention_mask=prompt_attention_mask_2,\n negative_prompt_attention_mask=negative_prompt_attention_mask_2,\n max_sequence_length=256,\n text_encoder_index=1,\n )\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.transformer.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7 create image_rotary_emb, style embedding & time ids\n grid_height = height // 8 // self.transformer.config.patch_size\n grid_width = width // 8 // self.transformer.config.patch_size\n base_size = 512 // 8 // self.transformer.config.patch_size\n grid_crops_coords = get_resize_crop_region_for_grid((grid_height, grid_width), base_size)\n image_rotary_emb = get_2d_rotary_pos_embed(\n self.transformer.inner_dim // self.transformer.num_heads, grid_crops_coords, (grid_height, grid_width)\n )\n\n style = torch.tensor([0], device=device)\n\n target_size = target_size or (height, width)\n add_time_ids = list(original_size + target_size + crops_coords_top_left)\n add_time_ids = torch.tensor([add_time_ids], dtype=prompt_embeds.dtype)\n\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask])\n prompt_embeds_2 = torch.cat([negative_prompt_embeds_2, prompt_embeds_2])\n prompt_attention_mask_2 = torch.cat([negative_prompt_attention_mask_2, prompt_attention_mask_2])\n add_time_ids = torch.cat([add_time_ids] * 2, dim=0)\n style = torch.cat([style] * 2, dim=0)\n\n prompt_embeds = prompt_embeds.to(device=device)\n prompt_attention_mask = prompt_attention_mask.to(device=device)\n prompt_embeds_2 = prompt_embeds_2.to(device=device)\n prompt_attention_mask_2 = prompt_attention_mask_2.to(device=device)\n add_time_ids = add_time_ids.to(dtype=prompt_embeds.dtype, device=device).repeat(\n batch_size * num_images_per_prompt, 1\n )\n style = style.to(device=device).repeat(batch_size * num_images_per_prompt)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n if self.interrupt:\n continue\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # expand scalar t to 1-D tensor to match the 1st dim of latent_model_input\n t_expand = torch.tensor([t] * latent_model_input.shape[0], device=device).to(\n dtype=latent_model_input.dtype\n )\n\n # predict the noise residual\n noise_pred = self.transformer(\n latent_model_input,\n t_expand,\n encoder_hidden_states=prompt_embeds,\n text_embedding_mask=prompt_attention_mask,\n encoder_hidden_states_t5=prompt_embeds_2,\n text_embedding_mask_t5=prompt_attention_mask_2,\n image_meta_size=add_time_ids,\n style=style,\n image_rotary_emb=image_rotary_emb,\n return_dict=False,\n )[0]\n\n noise_pred, _ = noise_pred.chunk(2, dim=1)\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if self.do_classifier_free_guidance and guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n prompt_embeds_2 = callback_outputs.pop(\"prompt_embeds_2\", prompt_embeds_2)\n negative_prompt_embeds_2 = callback_outputs.pop(\n \"negative_prompt_embeds_2\", negative_prompt_embeds_2\n )\n\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n\n if XLA_AVAILABLE:\n xm.mark_step()\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/i2vgen_xl/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_i2vgen_xl\"] = [\"I2VGenXLPipeline\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import * # noqa F403\n else:\n from .pipeline_i2vgen_xl import I2VGenXLPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py", "content": "# Copyright 2024 Alibaba DAMO-VILAB and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...models import AutoencoderKL\nfrom ...models.unets.unet_i2vgen_xl import I2VGenXLUNet\nfrom ...schedulers import DDIMScheduler\nfrom ...utils import (\n BaseOutput,\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ...video_processor import VideoProcessor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import I2VGenXLPipeline\n >>> from diffusers.utils import export_to_gif, load_image\n\n >>> pipeline = I2VGenXLPipeline.from_pretrained(\n ... \"ali-vilab/i2vgen-xl\", torch_dtype=torch.float16, variant=\"fp16\"\n ... )\n >>> pipeline.enable_model_cpu_offload()\n\n >>> image_url = (\n ... \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/i2vgen_xl_images/img_0009.png\"\n ... )\n >>> image = load_image(image_url).convert(\"RGB\")\n\n >>> prompt = \"Papers were floating in the air on a table in the library\"\n >>> negative_prompt = \"Distorted, discontinuous, Ugly, blurry, low resolution, motionless, static, disfigured, disconnected limbs, Ugly faces, incomplete arms\"\n >>> generator = torch.manual_seed(8888)\n\n >>> frames = pipeline(\n ... prompt=prompt,\n ... image=image,\n ... num_inference_steps=50,\n ... negative_prompt=negative_prompt,\n ... guidance_scale=9.0,\n ... generator=generator,\n ... ).frames[0]\n >>> video_path = export_to_gif(frames, \"i2v.gif\")\n ```\n\"\"\"\n\n\n@dataclass\nclass I2VGenXLPipelineOutput(BaseOutput):\n r\"\"\"\n Output class for image-to-video pipeline.\n\n Args:\n frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):\n List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing\n denoised\n PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape\n `(batch_size, num_frames, channels, height, width)`\n \"\"\"\n\n frames: Union[torch.Tensor, np.ndarray, List[List[PIL.Image.Image]]]\n\n\nclass I2VGenXLPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n):\n r\"\"\"\n Pipeline for image-to-video generation as proposed in [I2VGenXL](https://i2vgen-xl.github.io/).\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer (`CLIPTokenizer`):\n A [`~transformers.CLIPTokenizer`] to tokenize text.\n unet ([`I2VGenXLUNet`]):\n A [`I2VGenXLUNet`] to denoise the encoded video latents.\n scheduler ([`DDIMScheduler`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->image_encoder->unet->vae\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n image_encoder: CLIPVisionModelWithProjection,\n feature_extractor: CLIPImageProcessor,\n unet: I2VGenXLUNet,\n scheduler: DDIMScheduler,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n image_encoder=image_encoder,\n feature_extractor=feature_extractor,\n unet=unet,\n scheduler=scheduler,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n # `do_resize=False` as we do custom resizing.\n self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor, do_resize=False)\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n def encode_prompt(\n self,\n prompt,\n device,\n num_videos_per_prompt,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_videos_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_videos_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if self.do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n # Apply clip_skip to negative prompt embeds\n if clip_skip is None:\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n else:\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n negative_prompt_embeds = negative_prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n negative_prompt_embeds = self.text_encoder.text_model.final_layer_norm(negative_prompt_embeds)\n\n if self.do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_videos_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)\n\n return prompt_embeds, negative_prompt_embeds\n\n def _encode_image(self, image, device, num_videos_per_prompt):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.video_processor.pil_to_numpy(image)\n image = self.video_processor.numpy_to_pt(image)\n\n # Normalize the image with CLIP training stats.\n image = self.feature_extractor(\n images=image,\n do_normalize=True,\n do_center_crop=False,\n do_resize=False,\n do_rescale=False,\n return_tensors=\"pt\",\n ).pixel_values\n\n image = image.to(device=device, dtype=dtype)\n image_embeddings = self.image_encoder(image).image_embeds\n image_embeddings = image_embeddings.unsqueeze(1)\n\n # duplicate image embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = image_embeddings.shape\n image_embeddings = image_embeddings.repeat(1, num_videos_per_prompt, 1)\n image_embeddings = image_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1)\n\n if self.do_classifier_free_guidance:\n negative_image_embeddings = torch.zeros_like(image_embeddings)\n image_embeddings = torch.cat([negative_image_embeddings, image_embeddings])\n\n return image_embeddings\n\n def decode_latents(self, latents, decode_chunk_size=None):\n latents = 1 / self.vae.config.scaling_factor * latents\n\n batch_size, channels, num_frames, height, width = latents.shape\n latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)\n\n if decode_chunk_size is not None:\n frames = []\n for i in range(0, latents.shape[0], decode_chunk_size):\n frame = self.vae.decode(latents[i : i + decode_chunk_size]).sample\n frames.append(frame)\n image = torch.cat(frames, dim=0)\n else:\n image = self.vae.decode(latents).sample\n\n decode_shape = (batch_size, num_frames, -1) + image.shape[2:]\n video = image[None, :].reshape(decode_shape).permute(0, 2, 1, 3, 4)\n\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n video = video.float()\n return video\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n image,\n height,\n width,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if (\n not isinstance(image, torch.Tensor)\n and not isinstance(image, PIL.Image.Image)\n and not isinstance(image, list)\n ):\n raise ValueError(\n \"`image` has to be of type `torch.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is\"\n f\" {type(image)}\"\n )\n\n def prepare_image_latents(\n self,\n image,\n device,\n num_frames,\n num_videos_per_prompt,\n ):\n image = image.to(device=device)\n image_latents = self.vae.encode(image).latent_dist.sample()\n image_latents = image_latents * self.vae.config.scaling_factor\n\n # Add frames dimension to image latents\n image_latents = image_latents.unsqueeze(2)\n\n # Append a position mask for each subsequent frame\n # after the intial image latent frame\n frame_position_mask = []\n for frame_idx in range(num_frames - 1):\n scale = (frame_idx + 1) / (num_frames - 1)\n frame_position_mask.append(torch.ones_like(image_latents[:, :, :1]) * scale)\n if frame_position_mask:\n frame_position_mask = torch.cat(frame_position_mask, dim=2)\n image_latents = torch.cat([image_latents, frame_position_mask], dim=2)\n\n # duplicate image_latents for each generation per prompt, using mps friendly method\n image_latents = image_latents.repeat(num_videos_per_prompt, 1, 1, 1, 1)\n\n if self.do_classifier_free_guidance:\n image_latents = torch.cat([image_latents] * 2)\n\n return image_latents\n\n # Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_synth.TextToVideoSDPipeline.prepare_latents\n def prepare_latents(\n self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None\n ):\n shape = (\n batch_size,\n num_channels_latents,\n num_frames,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: PipelineImageInput = None,\n height: Optional[int] = 704,\n width: Optional[int] = 1280,\n target_fps: Optional[int] = 16,\n num_frames: int = 16,\n num_inference_steps: int = 50,\n guidance_scale: float = 9.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n eta: float = 0.0,\n num_videos_per_prompt: Optional[int] = 1,\n decode_chunk_size: Optional[int] = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: Optional[int] = 1,\n ):\n r\"\"\"\n The call function to the pipeline for image-to-video generation with [`I2VGenXLPipeline`].\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.Tensor`):\n Image or images to guide image generation. If you provide a tensor, it needs to be compatible with\n [`CLIPImageProcessor`](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json).\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n target_fps (`int`, *optional*):\n Frames per second. The rate at which the generated images shall be exported to a video after\n generation. This is also used as a \"micro-condition\" while generation.\n num_frames (`int`, *optional*):\n The number of video frames to generate.\n num_inference_steps (`int`, *optional*):\n The number of denoising steps.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n eta (`float`, *optional*):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n num_videos_per_prompt (`int`, *optional*):\n The number of images to generate per prompt.\n decode_chunk_size (`int`, *optional*):\n The number of frames to decode at a time. The higher the chunk size, the higher the temporal\n consistency between frames, but also the higher the memory consumption. By default, the decoder will\n decode all frames at once for maximal quality. Reduce `decode_chunk_size` to reduce memory usage.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n\n Examples:\n\n Returns:\n [`pipelines.i2vgen_xl.pipeline_i2vgen_xl.I2VGenXLPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`pipelines.i2vgen_xl.pipeline_i2vgen_xl.I2VGenXLPipelineOutput`] is\n returned, otherwise a `tuple` is returned where the first element is a list with the generated frames.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(prompt, image, height, width, negative_prompt, prompt_embeds, negative_prompt_embeds)\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n self._guidance_scale = guidance_scale\n\n # 3.1 Encode input text prompt\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_videos_per_prompt,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n clip_skip=clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 3.2 Encode image prompt\n # 3.2.1 Image encodings.\n # https://github.com/ali-vilab/i2vgen-xl/blob/2539c9262ff8a2a22fa9daecbfd13f0a2dbc32d0/tools/inferences/inference_i2vgen_entrance.py#L114\n cropped_image = _center_crop_wide(image, (width, width))\n cropped_image = _resize_bilinear(\n cropped_image, (self.feature_extractor.crop_size[\"width\"], self.feature_extractor.crop_size[\"height\"])\n )\n image_embeddings = self._encode_image(cropped_image, device, num_videos_per_prompt)\n\n # 3.2.2 Image latents.\n resized_image = _center_crop_wide(image, (width, height))\n image = self.video_processor.preprocess(resized_image).to(device=device, dtype=image_embeddings.dtype)\n image_latents = self.prepare_image_latents(\n image,\n device=device,\n num_frames=num_frames,\n num_videos_per_prompt=num_videos_per_prompt,\n )\n\n # 3.3 Prepare additional conditions for the UNet.\n if self.do_classifier_free_guidance:\n fps_tensor = torch.tensor([target_fps, target_fps]).to(device)\n else:\n fps_tensor = torch.tensor([target_fps]).to(device)\n fps_tensor = fps_tensor.repeat(batch_size * num_videos_per_prompt, 1).ravel()\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_videos_per_prompt,\n num_channels_latents,\n num_frames,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n fps=fps_tensor,\n image_latents=image_latents,\n image_embeddings=image_embeddings,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # reshape latents\n batch_size, channel, frames, width, height = latents.shape\n latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * frames, channel, width, height)\n noise_pred = noise_pred.permute(0, 2, 1, 3, 4).reshape(batch_size * frames, channel, width, height)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # reshape latents back\n latents = latents[None, :].reshape(batch_size, frames, channel, width, height).permute(0, 2, 1, 3, 4)\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n\n # 8. Post processing\n if output_type == \"latent\":\n video = latents\n else:\n video_tensor = self.decode_latents(latents, decode_chunk_size=decode_chunk_size)\n video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)\n\n # 9. Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (video,)\n\n return I2VGenXLPipelineOutput(frames=video)\n\n\n# The following utilities are taken and adapted from\n# https://github.com/ali-vilab/i2vgen-xl/blob/main/utils/transforms.py.\n\n\ndef _convert_pt_to_pil(image: Union[torch.Tensor, List[torch.Tensor]]):\n if isinstance(image, list) and isinstance(image[0], torch.Tensor):\n image = torch.cat(image, 0)\n\n if isinstance(image, torch.Tensor):\n if image.ndim == 3:\n image = image.unsqueeze(0)\n\n image_numpy = VaeImageProcessor.pt_to_numpy(image)\n image_pil = VaeImageProcessor.numpy_to_pil(image_numpy)\n image = image_pil\n\n return image\n\n\ndef _resize_bilinear(\n image: Union[torch.Tensor, List[torch.Tensor], PIL.Image.Image, List[PIL.Image.Image]], resolution: Tuple[int, int]\n):\n # First convert the images to PIL in case they are float tensors (only relevant for tests now).\n image = _convert_pt_to_pil(image)\n\n if isinstance(image, list):\n image = [u.resize(resolution, PIL.Image.BILINEAR) for u in image]\n else:\n image = image.resize(resolution, PIL.Image.BILINEAR)\n return image\n\n\ndef _center_crop_wide(\n image: Union[torch.Tensor, List[torch.Tensor], PIL.Image.Image, List[PIL.Image.Image]], resolution: Tuple[int, int]\n):\n # First convert the images to PIL in case they are float tensors (only relevant for tests now).\n image = _convert_pt_to_pil(image)\n\n if isinstance(image, list):\n scale = min(image[0].size[0] / resolution[0], image[0].size[1] / resolution[1])\n image = [u.resize((round(u.width // scale), round(u.height // scale)), resample=PIL.Image.BOX) for u in image]\n\n # center crop\n x1 = (image[0].width - resolution[0]) // 2\n y1 = (image[0].height - resolution[1]) // 2\n image = [u.crop((x1, y1, x1 + resolution[0], y1 + resolution[1])) for u in image]\n return image\n else:\n scale = min(image.size[0] / resolution[0], image.size[1] / resolution[1])\n image = image.resize((round(image.width // scale), round(image.height // scale)), resample=PIL.Image.BOX)\n x1 = (image.width - resolution[0]) // 2\n y1 = (image.height - resolution[1]) // 2\n image = image.crop((x1, y1, x1 + resolution[0], y1 + resolution[1]))\n return image\n" }, { "path": "diffusers/src/diffusers/pipelines/kandinsky/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_kandinsky\"] = [\"KandinskyPipeline\"]\n _import_structure[\"pipeline_kandinsky_combined\"] = [\n \"KandinskyCombinedPipeline\",\n \"KandinskyImg2ImgCombinedPipeline\",\n \"KandinskyInpaintCombinedPipeline\",\n ]\n _import_structure[\"pipeline_kandinsky_img2img\"] = [\"KandinskyImg2ImgPipeline\"]\n _import_structure[\"pipeline_kandinsky_inpaint\"] = [\"KandinskyInpaintPipeline\"]\n _import_structure[\"pipeline_kandinsky_prior\"] = [\"KandinskyPriorPipeline\", \"KandinskyPriorPipelineOutput\"]\n _import_structure[\"text_encoder\"] = [\"MultilingualCLIP\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n\n else:\n from .pipeline_kandinsky import KandinskyPipeline\n from .pipeline_kandinsky_combined import (\n KandinskyCombinedPipeline,\n KandinskyImg2ImgCombinedPipeline,\n KandinskyInpaintCombinedPipeline,\n )\n from .pipeline_kandinsky_img2img import KandinskyImg2ImgPipeline\n from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline\n from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput\n from .text_encoder import MultilingualCLIP\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Callable, List, Optional, Union\n\nimport torch\nfrom transformers import (\n XLMRobertaTokenizer,\n)\n\nfrom ...models import UNet2DConditionModel, VQModel\nfrom ...schedulers import DDIMScheduler, DDPMScheduler\nfrom ...utils import (\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\nfrom .text_encoder import MultilingualCLIP\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n >>> import torch\n\n >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/Kandinsky-2-1-prior\")\n >>> pipe_prior.to(\"cuda\")\n\n >>> prompt = \"red cat, 4k photo\"\n >>> out = pipe_prior(prompt)\n >>> image_emb = out.image_embeds\n >>> negative_image_emb = out.negative_image_embeds\n\n >>> pipe = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\")\n >>> pipe.to(\"cuda\")\n\n >>> image = pipe(\n ... prompt,\n ... image_embeds=image_emb,\n ... negative_image_embeds=negative_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=100,\n ... ).images\n\n >>> image[0].save(\"cat.png\")\n ```\n\"\"\"\n\n\ndef get_new_h_w(h, w, scale_factor=8):\n new_h = h // scale_factor**2\n if h % scale_factor**2 != 0:\n new_h += 1\n new_w = w // scale_factor**2\n if w % scale_factor**2 != 0:\n new_w += 1\n return new_h * scale_factor, new_w * scale_factor\n\n\nclass KandinskyPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for text-to-image generation using Kandinsky\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n text_encoder ([`MultilingualCLIP`]):\n Frozen text-encoder.\n tokenizer ([`XLMRobertaTokenizer`]):\n Tokenizer of class\n scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):\n A scheduler to be used in combination with `unet` to generate image latents.\n unet ([`UNet2DConditionModel`]):\n Conditional U-Net architecture to denoise the image embedding.\n movq ([`VQModel`]):\n MoVQ Decoder to generate the image from the latents.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->movq\"\n\n def __init__(\n self,\n text_encoder: MultilingualCLIP,\n tokenizer: XLMRobertaTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[DDIMScheduler, DDPMScheduler],\n movq: VQModel,\n ):\n super().__init__()\n\n self.register_modules(\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n movq=movq,\n )\n self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)\n\n # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents\n def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n latents = latents * scheduler.init_noise_sigma\n return latents\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n ):\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n truncation=True,\n max_length=77,\n return_attention_mask=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n text_input_ids = text_input_ids.to(device)\n text_mask = text_inputs.attention_mask.to(device)\n\n prompt_embeds, text_encoder_hidden_states = self.text_encoder(\n input_ids=text_input_ids, attention_mask=text_mask\n )\n\n prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=77,\n truncation=True,\n return_attention_mask=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n uncond_text_input_ids = uncond_input.input_ids.to(device)\n uncond_text_mask = uncond_input.attention_mask.to(device)\n\n negative_prompt_embeds, uncond_text_encoder_hidden_states = self.text_encoder(\n input_ids=uncond_text_input_ids, attention_mask=uncond_text_mask\n )\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)\n\n seq_len = uncond_text_encoder_hidden_states.shape[1]\n uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)\n uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(\n batch_size * num_images_per_prompt, seq_len, -1\n )\n uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n # done duplicates\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])\n\n text_mask = torch.cat([uncond_text_mask, text_mask])\n\n return prompt_embeds, text_encoder_hidden_states, text_mask\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]],\n image_embeds: Union[torch.Tensor, List[torch.Tensor]],\n negative_image_embeds: Union[torch.Tensor, List[torch.Tensor]],\n negative_prompt: Optional[Union[str, List[str]]] = None,\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 100,\n guidance_scale: float = 4.0,\n num_images_per_prompt: int = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n return_dict: bool = True,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n image_embeds (`torch.Tensor` or `List[torch.Tensor]`):\n The clip image embeddings for text prompt, that will be used to condition the image generation.\n negative_image_embeds (`torch.Tensor` or `List[torch.Tensor]`):\n The clip image embeddings for negative text prompt, will be used to condition the image generation.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between: `\"pil\"` (`PIL.Image.Image`), `\"np\"`\n (`np.array`) or `\"pt\"` (`torch.Tensor`).\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`\n \"\"\"\n\n if isinstance(prompt, str):\n batch_size = 1\n elif isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n device = self._execution_device\n\n batch_size = batch_size * num_images_per_prompt\n do_classifier_free_guidance = guidance_scale > 1.0\n\n prompt_embeds, text_encoder_hidden_states, _ = self._encode_prompt(\n prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt\n )\n\n if isinstance(image_embeds, list):\n image_embeds = torch.cat(image_embeds, dim=0)\n if isinstance(negative_image_embeds, list):\n negative_image_embeds = torch.cat(negative_image_embeds, dim=0)\n\n if do_classifier_free_guidance:\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n\n image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(\n dtype=prompt_embeds.dtype, device=device\n )\n\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps_tensor = self.scheduler.timesteps\n\n num_channels_latents = self.unet.config.in_channels\n\n height, width = get_new_h_w(height, width, self.movq_scale_factor)\n\n # create initial latent\n latents = self.prepare_latents(\n (batch_size, num_channels_latents, height, width),\n text_encoder_hidden_states.dtype,\n device,\n generator,\n latents,\n self.scheduler,\n )\n\n for i, t in enumerate(self.progress_bar(timesteps_tensor)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n\n added_cond_kwargs = {\"text_embeds\": prompt_embeds, \"image_embeds\": image_embeds}\n noise_pred = self.unet(\n sample=latent_model_input,\n timestep=t,\n encoder_hidden_states=text_encoder_hidden_states,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n if do_classifier_free_guidance:\n noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n _, variance_pred_text = variance_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n noise_pred = torch.cat([noise_pred, variance_pred_text], dim=1)\n\n if not (\n hasattr(self.scheduler.config, \"variance_type\")\n and self.scheduler.config.variance_type in [\"learned\", \"learned_range\"]\n ):\n noise_pred, _ = noise_pred.split(latents.shape[1], dim=1)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(\n noise_pred,\n t,\n latents,\n generator=generator,\n ).prev_sample\n\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # post-processing\n image = self.movq.decode(latents, force_not_quantize=True)[\"sample\"]\n\n self.maybe_free_model_hooks()\n\n if output_type not in [\"pt\", \"np\", \"pil\"]:\n raise ValueError(f\"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}\")\n\n if output_type in [\"np\", \"pil\"]:\n image = image * 0.5 + 0.5\n image = image.clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Callable, List, Optional, Union\n\nimport PIL.Image\nimport torch\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n XLMRobertaTokenizer,\n)\n\nfrom ...models import PriorTransformer, UNet2DConditionModel, VQModel\nfrom ...schedulers import DDIMScheduler, DDPMScheduler, UnCLIPScheduler\nfrom ...utils import (\n replace_example_docstring,\n)\nfrom ..pipeline_utils import DiffusionPipeline\nfrom .pipeline_kandinsky import KandinskyPipeline\nfrom .pipeline_kandinsky_img2img import KandinskyImg2ImgPipeline\nfrom .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline\nfrom .pipeline_kandinsky_prior import KandinskyPriorPipeline\nfrom .text_encoder import MultilingualCLIP\n\n\nTEXT2IMAGE_EXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n from diffusers import AutoPipelineForText2Image\n import torch\n\n pipe = AutoPipelineForText2Image.from_pretrained(\n \"kandinsky-community/kandinsky-2-1\", torch_dtype=torch.float16\n )\n pipe.enable_model_cpu_offload()\n\n prompt = \"A lion in galaxies, spirals, nebulae, stars, smoke, iridescent, intricate detail, octane render, 8k\"\n\n image = pipe(prompt=prompt, num_inference_steps=25).images[0]\n ```\n\"\"\"\n\nIMAGE2IMAGE_EXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n from diffusers import AutoPipelineForImage2Image\n import torch\n import requests\n from io import BytesIO\n from PIL import Image\n import os\n\n pipe = AutoPipelineForImage2Image.from_pretrained(\n \"kandinsky-community/kandinsky-2-1\", torch_dtype=torch.float16\n )\n pipe.enable_model_cpu_offload()\n\n prompt = \"A fantasy landscape, Cinematic lighting\"\n negative_prompt = \"low quality, bad quality\"\n\n url = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n\n response = requests.get(url)\n image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n image.thumbnail((768, 768))\n\n image = pipe(prompt=prompt, image=original_image, num_inference_steps=25).images[0]\n ```\n\"\"\"\n\nINPAINT_EXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n from diffusers import AutoPipelineForInpainting\n from diffusers.utils import load_image\n import torch\n import numpy as np\n\n pipe = AutoPipelineForInpainting.from_pretrained(\n \"kandinsky-community/kandinsky-2-1-inpaint\", torch_dtype=torch.float16\n )\n pipe.enable_model_cpu_offload()\n\n prompt = \"A fantasy landscape, Cinematic lighting\"\n negative_prompt = \"low quality, bad quality\"\n\n original_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\" \"/kandinsky/cat.png\"\n )\n\n mask = np.zeros((768, 768), dtype=np.float32)\n # Let's mask out an area above the cat's head\n mask[:250, 250:-250] = 1\n\n image = pipe(prompt=prompt, image=original_image, mask_image=mask, num_inference_steps=25).images[0]\n ```\n\"\"\"\n\n\nclass KandinskyCombinedPipeline(DiffusionPipeline):\n \"\"\"\n Combined Pipeline for text-to-image generation using Kandinsky\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n text_encoder ([`MultilingualCLIP`]):\n Frozen text-encoder.\n tokenizer ([`XLMRobertaTokenizer`]):\n Tokenizer of class\n scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):\n A scheduler to be used in combination with `unet` to generate image latents.\n unet ([`UNet2DConditionModel`]):\n Conditional U-Net architecture to denoise the image embedding.\n movq ([`VQModel`]):\n MoVQ Decoder to generate the image from the latents.\n prior_prior ([`PriorTransformer`]):\n The canonical unCLIP prior to approximate the image embedding from the text embedding.\n prior_image_encoder ([`CLIPVisionModelWithProjection`]):\n Frozen image-encoder.\n prior_text_encoder ([`CLIPTextModelWithProjection`]):\n Frozen text-encoder.\n prior_tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n prior_scheduler ([`UnCLIPScheduler`]):\n A scheduler to be used in combination with `prior` to generate image embedding.\n \"\"\"\n\n _load_connected_pipes = True\n model_cpu_offload_seq = \"text_encoder->unet->movq->prior_prior->prior_image_encoder->prior_text_encoder\"\n _exclude_from_cpu_offload = [\"prior_prior\"]\n\n def __init__(\n self,\n text_encoder: MultilingualCLIP,\n tokenizer: XLMRobertaTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[DDIMScheduler, DDPMScheduler],\n movq: VQModel,\n prior_prior: PriorTransformer,\n prior_image_encoder: CLIPVisionModelWithProjection,\n prior_text_encoder: CLIPTextModelWithProjection,\n prior_tokenizer: CLIPTokenizer,\n prior_scheduler: UnCLIPScheduler,\n prior_image_processor: CLIPImageProcessor,\n ):\n super().__init__()\n\n self.register_modules(\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n movq=movq,\n prior_prior=prior_prior,\n prior_image_encoder=prior_image_encoder,\n prior_text_encoder=prior_text_encoder,\n prior_tokenizer=prior_tokenizer,\n prior_scheduler=prior_scheduler,\n prior_image_processor=prior_image_processor,\n )\n self.prior_pipe = KandinskyPriorPipeline(\n prior=prior_prior,\n image_encoder=prior_image_encoder,\n text_encoder=prior_text_encoder,\n tokenizer=prior_tokenizer,\n scheduler=prior_scheduler,\n image_processor=prior_image_processor,\n )\n self.decoder_pipe = KandinskyPipeline(\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n movq=movq,\n )\n\n def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):\n self.decoder_pipe.enable_xformers_memory_efficient_attention(attention_op)\n\n def enable_sequential_cpu_offload(self, gpu_id=0):\n r\"\"\"\n Offloads all models (`unet`, `text_encoder`, `vae`, and `safety checker` state dicts) to CPU using 🤗\n Accelerate, significantly reducing memory usage. Models are moved to a `torch.device('meta')` and loaded on a\n GPU only when their specific submodule's `forward` method is called. Offloading happens on a submodule basis.\n Memory savings are higher than using `enable_model_cpu_offload`, but performance is lower.\n \"\"\"\n self.prior_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id)\n self.decoder_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id)\n\n def progress_bar(self, iterable=None, total=None):\n self.prior_pipe.progress_bar(iterable=iterable, total=total)\n self.decoder_pipe.progress_bar(iterable=iterable, total=total)\n self.decoder_pipe.enable_model_cpu_offload()\n\n def set_progress_bar_config(self, **kwargs):\n self.prior_pipe.set_progress_bar_config(**kwargs)\n self.decoder_pipe.set_progress_bar_config(**kwargs)\n\n @torch.no_grad()\n @replace_example_docstring(TEXT2IMAGE_EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]],\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_inference_steps: int = 100,\n guidance_scale: float = 4.0,\n num_images_per_prompt: int = 1,\n height: int = 512,\n width: int = 512,\n prior_guidance_scale: float = 4.0,\n prior_num_inference_steps: int = 25,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n return_dict: bool = True,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n prior_guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n prior_num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between: `\"pil\"` (`PIL.Image.Image`), `\"np\"`\n (`np.array`) or `\"pt\"` (`torch.Tensor`).\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`\n \"\"\"\n prior_outputs = self.prior_pipe(\n prompt=prompt,\n negative_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n num_inference_steps=prior_num_inference_steps,\n generator=generator,\n latents=latents,\n guidance_scale=prior_guidance_scale,\n output_type=\"pt\",\n return_dict=False,\n )\n image_embeds = prior_outputs[0]\n negative_image_embeds = prior_outputs[1]\n\n prompt = [prompt] if not isinstance(prompt, (list, tuple)) else prompt\n\n if len(prompt) < image_embeds.shape[0] and image_embeds.shape[0] % len(prompt) == 0:\n prompt = (image_embeds.shape[0] // len(prompt)) * prompt\n\n outputs = self.decoder_pipe(\n prompt=prompt,\n image_embeds=image_embeds,\n negative_image_embeds=negative_image_embeds,\n width=width,\n height=height,\n num_inference_steps=num_inference_steps,\n generator=generator,\n guidance_scale=guidance_scale,\n output_type=output_type,\n callback=callback,\n callback_steps=callback_steps,\n return_dict=return_dict,\n )\n\n self.maybe_free_model_hooks()\n\n return outputs\n\n\nclass KandinskyImg2ImgCombinedPipeline(DiffusionPipeline):\n \"\"\"\n Combined Pipeline for image-to-image generation using Kandinsky\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n text_encoder ([`MultilingualCLIP`]):\n Frozen text-encoder.\n tokenizer ([`XLMRobertaTokenizer`]):\n Tokenizer of class\n scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):\n A scheduler to be used in combination with `unet` to generate image latents.\n unet ([`UNet2DConditionModel`]):\n Conditional U-Net architecture to denoise the image embedding.\n movq ([`VQModel`]):\n MoVQ Decoder to generate the image from the latents.\n prior_prior ([`PriorTransformer`]):\n The canonical unCLIP prior to approximate the image embedding from the text embedding.\n prior_image_encoder ([`CLIPVisionModelWithProjection`]):\n Frozen image-encoder.\n prior_text_encoder ([`CLIPTextModelWithProjection`]):\n Frozen text-encoder.\n prior_tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n prior_scheduler ([`UnCLIPScheduler`]):\n A scheduler to be used in combination with `prior` to generate image embedding.\n \"\"\"\n\n _load_connected_pipes = True\n model_cpu_offload_seq = \"prior_text_encoder->prior_image_encoder->prior_prior->\" \"text_encoder->unet->movq\"\n _exclude_from_cpu_offload = [\"prior_prior\"]\n\n def __init__(\n self,\n text_encoder: MultilingualCLIP,\n tokenizer: XLMRobertaTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[DDIMScheduler, DDPMScheduler],\n movq: VQModel,\n prior_prior: PriorTransformer,\n prior_image_encoder: CLIPVisionModelWithProjection,\n prior_text_encoder: CLIPTextModelWithProjection,\n prior_tokenizer: CLIPTokenizer,\n prior_scheduler: UnCLIPScheduler,\n prior_image_processor: CLIPImageProcessor,\n ):\n super().__init__()\n\n self.register_modules(\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n movq=movq,\n prior_prior=prior_prior,\n prior_image_encoder=prior_image_encoder,\n prior_text_encoder=prior_text_encoder,\n prior_tokenizer=prior_tokenizer,\n prior_scheduler=prior_scheduler,\n prior_image_processor=prior_image_processor,\n )\n self.prior_pipe = KandinskyPriorPipeline(\n prior=prior_prior,\n image_encoder=prior_image_encoder,\n text_encoder=prior_text_encoder,\n tokenizer=prior_tokenizer,\n scheduler=prior_scheduler,\n image_processor=prior_image_processor,\n )\n self.decoder_pipe = KandinskyImg2ImgPipeline(\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n movq=movq,\n )\n\n def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):\n self.decoder_pipe.enable_xformers_memory_efficient_attention(attention_op)\n\n def enable_sequential_cpu_offload(self, gpu_id=0):\n r\"\"\"\n Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,\n text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a\n `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.\n Note that offloading happens on a submodule basis. Memory savings are higher than with\n `enable_model_cpu_offload`, but performance is lower.\n \"\"\"\n self.prior_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id)\n self.decoder_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id)\n\n def progress_bar(self, iterable=None, total=None):\n self.prior_pipe.progress_bar(iterable=iterable, total=total)\n self.decoder_pipe.progress_bar(iterable=iterable, total=total)\n self.decoder_pipe.enable_model_cpu_offload()\n\n def set_progress_bar_config(self, **kwargs):\n self.prior_pipe.set_progress_bar_config(**kwargs)\n self.decoder_pipe.set_progress_bar_config(**kwargs)\n\n @torch.no_grad()\n @replace_example_docstring(IMAGE2IMAGE_EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]],\n image: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]],\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_inference_steps: int = 100,\n guidance_scale: float = 4.0,\n num_images_per_prompt: int = 1,\n strength: float = 0.3,\n height: int = 512,\n width: int = 512,\n prior_guidance_scale: float = 4.0,\n prior_num_inference_steps: int = 25,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n return_dict: bool = True,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process. Can also accept image latents as `image`, if passing latents directly, it will not be encoded\n again.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n strength (`float`, *optional*, defaults to 0.3):\n Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`\n will be used as a starting point, adding more noise to it the larger the `strength`. The number of\n denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will\n be maximum and the denoising process will run for the full number of iterations specified in\n `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.\n prior_guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n prior_num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between: `\"pil\"` (`PIL.Image.Image`), `\"np\"`\n (`np.array`) or `\"pt\"` (`torch.Tensor`).\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`\n \"\"\"\n prior_outputs = self.prior_pipe(\n prompt=prompt,\n negative_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n num_inference_steps=prior_num_inference_steps,\n generator=generator,\n latents=latents,\n guidance_scale=prior_guidance_scale,\n output_type=\"pt\",\n return_dict=False,\n )\n image_embeds = prior_outputs[0]\n negative_image_embeds = prior_outputs[1]\n\n prompt = [prompt] if not isinstance(prompt, (list, tuple)) else prompt\n image = [image] if isinstance(prompt, PIL.Image.Image) else image\n\n if len(prompt) < image_embeds.shape[0] and image_embeds.shape[0] % len(prompt) == 0:\n prompt = (image_embeds.shape[0] // len(prompt)) * prompt\n\n if (\n isinstance(image, (list, tuple))\n and len(image) < image_embeds.shape[0]\n and image_embeds.shape[0] % len(image) == 0\n ):\n image = (image_embeds.shape[0] // len(image)) * image\n\n outputs = self.decoder_pipe(\n prompt=prompt,\n image=image,\n image_embeds=image_embeds,\n negative_image_embeds=negative_image_embeds,\n strength=strength,\n width=width,\n height=height,\n num_inference_steps=num_inference_steps,\n generator=generator,\n guidance_scale=guidance_scale,\n output_type=output_type,\n callback=callback,\n callback_steps=callback_steps,\n return_dict=return_dict,\n )\n\n self.maybe_free_model_hooks()\n\n return outputs\n\n\nclass KandinskyInpaintCombinedPipeline(DiffusionPipeline):\n \"\"\"\n Combined Pipeline for generation using Kandinsky\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n text_encoder ([`MultilingualCLIP`]):\n Frozen text-encoder.\n tokenizer ([`XLMRobertaTokenizer`]):\n Tokenizer of class\n scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):\n A scheduler to be used in combination with `unet` to generate image latents.\n unet ([`UNet2DConditionModel`]):\n Conditional U-Net architecture to denoise the image embedding.\n movq ([`VQModel`]):\n MoVQ Decoder to generate the image from the latents.\n prior_prior ([`PriorTransformer`]):\n The canonical unCLIP prior to approximate the image embedding from the text embedding.\n prior_image_encoder ([`CLIPVisionModelWithProjection`]):\n Frozen image-encoder.\n prior_text_encoder ([`CLIPTextModelWithProjection`]):\n Frozen text-encoder.\n prior_tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n prior_scheduler ([`UnCLIPScheduler`]):\n A scheduler to be used in combination with `prior` to generate image embedding.\n \"\"\"\n\n _load_connected_pipes = True\n model_cpu_offload_seq = \"prior_text_encoder->prior_image_encoder->prior_prior->text_encoder->unet->movq\"\n _exclude_from_cpu_offload = [\"prior_prior\"]\n\n def __init__(\n self,\n text_encoder: MultilingualCLIP,\n tokenizer: XLMRobertaTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[DDIMScheduler, DDPMScheduler],\n movq: VQModel,\n prior_prior: PriorTransformer,\n prior_image_encoder: CLIPVisionModelWithProjection,\n prior_text_encoder: CLIPTextModelWithProjection,\n prior_tokenizer: CLIPTokenizer,\n prior_scheduler: UnCLIPScheduler,\n prior_image_processor: CLIPImageProcessor,\n ):\n super().__init__()\n\n self.register_modules(\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n movq=movq,\n prior_prior=prior_prior,\n prior_image_encoder=prior_image_encoder,\n prior_text_encoder=prior_text_encoder,\n prior_tokenizer=prior_tokenizer,\n prior_scheduler=prior_scheduler,\n prior_image_processor=prior_image_processor,\n )\n self.prior_pipe = KandinskyPriorPipeline(\n prior=prior_prior,\n image_encoder=prior_image_encoder,\n text_encoder=prior_text_encoder,\n tokenizer=prior_tokenizer,\n scheduler=prior_scheduler,\n image_processor=prior_image_processor,\n )\n self.decoder_pipe = KandinskyInpaintPipeline(\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n movq=movq,\n )\n\n def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):\n self.decoder_pipe.enable_xformers_memory_efficient_attention(attention_op)\n\n def enable_sequential_cpu_offload(self, gpu_id=0):\n r\"\"\"\n Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,\n text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a\n `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.\n Note that offloading happens on a submodule basis. Memory savings are higher than with\n `enable_model_cpu_offload`, but performance is lower.\n \"\"\"\n self.prior_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id)\n self.decoder_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id)\n\n def progress_bar(self, iterable=None, total=None):\n self.prior_pipe.progress_bar(iterable=iterable, total=total)\n self.decoder_pipe.progress_bar(iterable=iterable, total=total)\n self.decoder_pipe.enable_model_cpu_offload()\n\n def set_progress_bar_config(self, **kwargs):\n self.prior_pipe.set_progress_bar_config(**kwargs)\n self.decoder_pipe.set_progress_bar_config(**kwargs)\n\n @torch.no_grad()\n @replace_example_docstring(INPAINT_EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]],\n image: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]],\n mask_image: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]],\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_inference_steps: int = 100,\n guidance_scale: float = 4.0,\n num_images_per_prompt: int = 1,\n height: int = 512,\n width: int = 512,\n prior_guidance_scale: float = 4.0,\n prior_num_inference_steps: int = 25,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n return_dict: bool = True,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process. Can also accept image latents as `image`, if passing latents directly, it will not be encoded\n again.\n mask_image (`np.array`):\n Tensor representing an image batch, to mask `image`. White pixels in the mask will be repainted, while\n black pixels will be preserved. If `mask_image` is a PIL image, it will be converted to a single\n channel (luminance) before use. If it's a tensor, it should contain one color channel (L) instead of 3,\n so the expected shape would be `(B, H, W, 1)`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n prior_guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n prior_num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between: `\"pil\"` (`PIL.Image.Image`), `\"np\"`\n (`np.array`) or `\"pt\"` (`torch.Tensor`).\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`\n \"\"\"\n prior_outputs = self.prior_pipe(\n prompt=prompt,\n negative_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n num_inference_steps=prior_num_inference_steps,\n generator=generator,\n latents=latents,\n guidance_scale=prior_guidance_scale,\n output_type=\"pt\",\n return_dict=False,\n )\n image_embeds = prior_outputs[0]\n negative_image_embeds = prior_outputs[1]\n\n prompt = [prompt] if not isinstance(prompt, (list, tuple)) else prompt\n image = [image] if isinstance(prompt, PIL.Image.Image) else image\n mask_image = [mask_image] if isinstance(mask_image, PIL.Image.Image) else mask_image\n\n if len(prompt) < image_embeds.shape[0] and image_embeds.shape[0] % len(prompt) == 0:\n prompt = (image_embeds.shape[0] // len(prompt)) * prompt\n\n if (\n isinstance(image, (list, tuple))\n and len(image) < image_embeds.shape[0]\n and image_embeds.shape[0] % len(image) == 0\n ):\n image = (image_embeds.shape[0] // len(image)) * image\n\n if (\n isinstance(mask_image, (list, tuple))\n and len(mask_image) < image_embeds.shape[0]\n and image_embeds.shape[0] % len(mask_image) == 0\n ):\n mask_image = (image_embeds.shape[0] // len(mask_image)) * mask_image\n\n outputs = self.decoder_pipe(\n prompt=prompt,\n image=image,\n mask_image=mask_image,\n image_embeds=image_embeds,\n negative_image_embeds=negative_image_embeds,\n width=width,\n height=height,\n num_inference_steps=num_inference_steps,\n generator=generator,\n guidance_scale=guidance_scale,\n output_type=output_type,\n callback=callback,\n callback_steps=callback_steps,\n return_dict=return_dict,\n )\n\n self.maybe_free_model_hooks()\n\n return outputs\n" }, { "path": "diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Callable, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom PIL import Image\nfrom transformers import (\n XLMRobertaTokenizer,\n)\n\nfrom ...models import UNet2DConditionModel, VQModel\nfrom ...schedulers import DDIMScheduler\nfrom ...utils import (\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\nfrom .text_encoder import MultilingualCLIP\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import KandinskyImg2ImgPipeline, KandinskyPriorPipeline\n >>> from diffusers.utils import load_image\n >>> import torch\n\n >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-1-prior\", torch_dtype=torch.float16\n ... )\n >>> pipe_prior.to(\"cuda\")\n\n >>> prompt = \"A red cartoon frog, 4k\"\n >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n >>> pipe = KandinskyImg2ImgPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-1\", torch_dtype=torch.float16\n ... )\n >>> pipe.to(\"cuda\")\n\n >>> init_image = load_image(\n ... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n ... \"/kandinsky/frog.png\"\n ... )\n\n >>> image = pipe(\n ... prompt,\n ... image=init_image,\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=100,\n ... strength=0.2,\n ... ).images\n\n >>> image[0].save(\"red_frog.png\")\n ```\n\"\"\"\n\n\ndef get_new_h_w(h, w, scale_factor=8):\n new_h = h // scale_factor**2\n if h % scale_factor**2 != 0:\n new_h += 1\n new_w = w // scale_factor**2\n if w % scale_factor**2 != 0:\n new_w += 1\n return new_h * scale_factor, new_w * scale_factor\n\n\ndef prepare_image(pil_image, w=512, h=512):\n pil_image = pil_image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)\n arr = np.array(pil_image.convert(\"RGB\"))\n arr = arr.astype(np.float32) / 127.5 - 1\n arr = np.transpose(arr, [2, 0, 1])\n image = torch.from_numpy(arr).unsqueeze(0)\n return image\n\n\nclass KandinskyImg2ImgPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for image-to-image generation using Kandinsky\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n text_encoder ([`MultilingualCLIP`]):\n Frozen text-encoder.\n tokenizer ([`XLMRobertaTokenizer`]):\n Tokenizer of class\n scheduler ([`DDIMScheduler`]):\n A scheduler to be used in combination with `unet` to generate image latents.\n unet ([`UNet2DConditionModel`]):\n Conditional U-Net architecture to denoise the image embedding.\n movq ([`VQModel`]):\n MoVQ image encoder and decoder\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->movq\"\n\n def __init__(\n self,\n text_encoder: MultilingualCLIP,\n movq: VQModel,\n tokenizer: XLMRobertaTokenizer,\n unet: UNet2DConditionModel,\n scheduler: DDIMScheduler,\n ):\n super().__init__()\n\n self.register_modules(\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n movq=movq,\n )\n self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)\n\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start:]\n\n return timesteps, num_inference_steps - t_start\n\n def prepare_latents(self, latents, latent_timestep, shape, dtype, device, generator, scheduler):\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n latents = latents * scheduler.init_noise_sigma\n\n shape = latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n latents = self.add_noise(latents, noise, latent_timestep)\n return latents\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n ):\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=77,\n truncation=True,\n return_attention_mask=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n text_input_ids = text_input_ids.to(device)\n text_mask = text_inputs.attention_mask.to(device)\n\n prompt_embeds, text_encoder_hidden_states = self.text_encoder(\n input_ids=text_input_ids, attention_mask=text_mask\n )\n\n prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=77,\n truncation=True,\n return_attention_mask=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n uncond_text_input_ids = uncond_input.input_ids.to(device)\n uncond_text_mask = uncond_input.attention_mask.to(device)\n\n negative_prompt_embeds, uncond_text_encoder_hidden_states = self.text_encoder(\n input_ids=uncond_text_input_ids, attention_mask=uncond_text_mask\n )\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)\n\n seq_len = uncond_text_encoder_hidden_states.shape[1]\n uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)\n uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(\n batch_size * num_images_per_prompt, seq_len, -1\n )\n uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n # done duplicates\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])\n\n text_mask = torch.cat([uncond_text_mask, text_mask])\n\n return prompt_embeds, text_encoder_hidden_states, text_mask\n\n # add_noise method to overwrite the one in schedule because it use a different beta schedule for adding noise vs sampling\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.IntTensor,\n ) -> torch.Tensor:\n betas = torch.linspace(0.0001, 0.02, 1000, dtype=torch.float32)\n alphas = 1.0 - betas\n alphas_cumprod = torch.cumprod(alphas, dim=0)\n alphas_cumprod = alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)\n timesteps = timesteps.to(original_samples.device)\n\n sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n while len(sqrt_alpha_prod.shape) < len(original_samples.shape):\n sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n\n noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise\n\n return noisy_samples\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]],\n image: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]],\n image_embeds: torch.Tensor,\n negative_image_embeds: torch.Tensor,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 100,\n strength: float = 0.3,\n guidance_scale: float = 7.0,\n num_images_per_prompt: int = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n output_type: Optional[str] = \"pil\",\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n return_dict: bool = True,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n image (`torch.Tensor`, `PIL.Image.Image`):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process.\n image_embeds (`torch.Tensor` or `List[torch.Tensor]`):\n The clip image embeddings for text prompt, that will be used to condition the image generation.\n negative_image_embeds (`torch.Tensor` or `List[torch.Tensor]`):\n The clip image embeddings for negative text prompt, will be used to condition the image generation.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n strength (`float`, *optional*, defaults to 0.3):\n Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`\n will be used as a starting point, adding more noise to it the larger the `strength`. The number of\n denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will\n be maximum and the denoising process will run for the full number of iterations specified in\n `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.\n guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between: `\"pil\"` (`PIL.Image.Image`), `\"np\"`\n (`np.array`) or `\"pt\"` (`torch.Tensor`).\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`\n \"\"\"\n # 1. Define call parameters\n if isinstance(prompt, str):\n batch_size = 1\n elif isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n device = self._execution_device\n\n batch_size = batch_size * num_images_per_prompt\n\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 2. get text and image embeddings\n prompt_embeds, text_encoder_hidden_states, _ = self._encode_prompt(\n prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt\n )\n\n if isinstance(image_embeds, list):\n image_embeds = torch.cat(image_embeds, dim=0)\n if isinstance(negative_image_embeds, list):\n negative_image_embeds = torch.cat(negative_image_embeds, dim=0)\n\n if do_classifier_free_guidance:\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n\n image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(\n dtype=prompt_embeds.dtype, device=device\n )\n\n # 3. pre-processing initial image\n if not isinstance(image, list):\n image = [image]\n if not all(isinstance(i, (PIL.Image.Image, torch.Tensor)) for i in image):\n raise ValueError(\n f\"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support PIL image and pytorch tensor\"\n )\n\n image = torch.cat([prepare_image(i, width, height) for i in image], dim=0)\n image = image.to(dtype=prompt_embeds.dtype, device=device)\n\n latents = self.movq.encode(image)[\"latents\"]\n latents = latents.repeat_interleave(num_images_per_prompt, dim=0)\n\n # 4. set timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n\n timesteps_tensor, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n\n # the formular to calculate timestep for add_noise is taken from the original kandinsky repo\n latent_timestep = int(self.scheduler.config.num_train_timesteps * strength) - 2\n\n latent_timestep = torch.tensor([latent_timestep] * batch_size, dtype=timesteps_tensor.dtype, device=device)\n\n num_channels_latents = self.unet.config.in_channels\n\n height, width = get_new_h_w(height, width, self.movq_scale_factor)\n\n # 5. Create initial latent\n latents = self.prepare_latents(\n latents,\n latent_timestep,\n (batch_size, num_channels_latents, height, width),\n text_encoder_hidden_states.dtype,\n device,\n generator,\n self.scheduler,\n )\n\n # 6. Denoising loop\n for i, t in enumerate(self.progress_bar(timesteps_tensor)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n\n added_cond_kwargs = {\"text_embeds\": prompt_embeds, \"image_embeds\": image_embeds}\n noise_pred = self.unet(\n sample=latent_model_input,\n timestep=t,\n encoder_hidden_states=text_encoder_hidden_states,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n if do_classifier_free_guidance:\n noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n _, variance_pred_text = variance_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n noise_pred = torch.cat([noise_pred, variance_pred_text], dim=1)\n\n if not (\n hasattr(self.scheduler.config, \"variance_type\")\n and self.scheduler.config.variance_type in [\"learned\", \"learned_range\"]\n ):\n noise_pred, _ = noise_pred.split(latents.shape[1], dim=1)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(\n noise_pred,\n t,\n latents,\n generator=generator,\n ).prev_sample\n\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # 7. post-processing\n image = self.movq.decode(latents, force_not_quantize=True)[\"sample\"]\n\n self.maybe_free_model_hooks()\n\n if output_type not in [\"pt\", \"np\", \"pil\"]:\n raise ValueError(f\"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}\")\n\n if output_type in [\"np\", \"pil\"]:\n image = image * 0.5 + 0.5\n image = image.clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom copy import deepcopy\nfrom typing import Callable, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom packaging import version\nfrom PIL import Image\nfrom transformers import (\n XLMRobertaTokenizer,\n)\n\nfrom ... import __version__\nfrom ...models import UNet2DConditionModel, VQModel\nfrom ...schedulers import DDIMScheduler\nfrom ...utils import (\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\nfrom .text_encoder import MultilingualCLIP\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import KandinskyInpaintPipeline, KandinskyPriorPipeline\n >>> from diffusers.utils import load_image\n >>> import torch\n >>> import numpy as np\n\n >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-1-prior\", torch_dtype=torch.float16\n ... )\n >>> pipe_prior.to(\"cuda\")\n\n >>> prompt = \"a hat\"\n >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n >>> pipe = KandinskyInpaintPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-1-inpaint\", torch_dtype=torch.float16\n ... )\n >>> pipe.to(\"cuda\")\n\n >>> init_image = load_image(\n ... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n ... \"/kandinsky/cat.png\"\n ... )\n\n >>> mask = np.zeros((768, 768), dtype=np.float32)\n >>> mask[:250, 250:-250] = 1\n\n >>> out = pipe(\n ... prompt,\n ... image=init_image,\n ... mask_image=mask,\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=50,\n ... )\n\n >>> image = out.images[0]\n >>> image.save(\"cat_with_hat.png\")\n ```\n\"\"\"\n\n\ndef get_new_h_w(h, w, scale_factor=8):\n new_h = h // scale_factor**2\n if h % scale_factor**2 != 0:\n new_h += 1\n new_w = w // scale_factor**2\n if w % scale_factor**2 != 0:\n new_w += 1\n return new_h * scale_factor, new_w * scale_factor\n\n\ndef prepare_mask(masks):\n prepared_masks = []\n for mask in masks:\n old_mask = deepcopy(mask)\n for i in range(mask.shape[1]):\n for j in range(mask.shape[2]):\n if old_mask[0][i][j] == 1:\n continue\n if i != 0:\n mask[:, i - 1, j] = 0\n if j != 0:\n mask[:, i, j - 1] = 0\n if i != 0 and j != 0:\n mask[:, i - 1, j - 1] = 0\n if i != mask.shape[1] - 1:\n mask[:, i + 1, j] = 0\n if j != mask.shape[2] - 1:\n mask[:, i, j + 1] = 0\n if i != mask.shape[1] - 1 and j != mask.shape[2] - 1:\n mask[:, i + 1, j + 1] = 0\n prepared_masks.append(mask)\n return torch.stack(prepared_masks, dim=0)\n\n\ndef prepare_mask_and_masked_image(image, mask, height, width):\n r\"\"\"\n Prepares a pair (mask, image) to be consumed by the Kandinsky inpaint pipeline. This means that those inputs will\n be converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for\n the ``image`` and ``1`` for the ``mask``.\n\n The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be\n binarized (``mask > 0.5``) and cast to ``torch.float32`` too.\n\n Args:\n image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.\n It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``\n ``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.\n mask (_type_): The mask to apply to the image, i.e. regions to inpaint.\n It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``\n ``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n\n\n Raises:\n ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask\n should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.\n TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not\n (ot the other way around).\n\n Returns:\n tuple[torch.Tensor]: The pair (mask, image) as ``torch.Tensor`` with 4\n dimensions: ``batch x channels x height x width``.\n \"\"\"\n\n if image is None:\n raise ValueError(\"`image` input cannot be undefined.\")\n\n if mask is None:\n raise ValueError(\"`mask_image` input cannot be undefined.\")\n\n if isinstance(image, torch.Tensor):\n if not isinstance(mask, torch.Tensor):\n raise TypeError(f\"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not\")\n\n # Batch single image\n if image.ndim == 3:\n assert image.shape[0] == 3, \"Image outside a batch should be of shape (3, H, W)\"\n image = image.unsqueeze(0)\n\n # Batch and add channel dim for single mask\n if mask.ndim == 2:\n mask = mask.unsqueeze(0).unsqueeze(0)\n\n # Batch single mask or add channel dim\n if mask.ndim == 3:\n # Single batched mask, no channel dim or single mask not batched but channel dim\n if mask.shape[0] == 1:\n mask = mask.unsqueeze(0)\n\n # Batched masks no channel dim\n else:\n mask = mask.unsqueeze(1)\n\n assert image.ndim == 4 and mask.ndim == 4, \"Image and Mask must have 4 dimensions\"\n assert image.shape[-2:] == mask.shape[-2:], \"Image and Mask must have the same spatial dimensions\"\n assert image.shape[0] == mask.shape[0], \"Image and Mask must have the same batch size\"\n\n # Check image is in [-1, 1]\n if image.min() < -1 or image.max() > 1:\n raise ValueError(\"Image should be in [-1, 1] range\")\n\n # Check mask is in [0, 1]\n if mask.min() < 0 or mask.max() > 1:\n raise ValueError(\"Mask should be in [0, 1] range\")\n\n # Binarize mask\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n\n # Image as float32\n image = image.to(dtype=torch.float32)\n elif isinstance(mask, torch.Tensor):\n raise TypeError(f\"`mask` is a torch.Tensor but `image` (type: {type(image)} is not\")\n else:\n # preprocess image\n if isinstance(image, (PIL.Image.Image, np.ndarray)):\n image = [image]\n\n if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):\n # resize all images w.r.t passed height an width\n image = [i.resize((width, height), resample=Image.BICUBIC, reducing_gap=1) for i in image]\n image = [np.array(i.convert(\"RGB\"))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n elif isinstance(image, list) and isinstance(image[0], np.ndarray):\n image = np.concatenate([i[None, :] for i in image], axis=0)\n\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0\n\n # preprocess mask\n if isinstance(mask, (PIL.Image.Image, np.ndarray)):\n mask = [mask]\n\n if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):\n mask = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in mask]\n mask = np.concatenate([np.array(m.convert(\"L\"))[None, None, :] for m in mask], axis=0)\n mask = mask.astype(np.float32) / 255.0\n elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):\n mask = np.concatenate([m[None, None, :] for m in mask], axis=0)\n\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n mask = torch.from_numpy(mask)\n\n mask = 1 - mask\n\n return mask, image\n\n\nclass KandinskyInpaintPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for text-guided image inpainting using Kandinsky2.1\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n text_encoder ([`MultilingualCLIP`]):\n Frozen text-encoder.\n tokenizer ([`XLMRobertaTokenizer`]):\n Tokenizer of class\n scheduler ([`DDIMScheduler`]):\n A scheduler to be used in combination with `unet` to generate image latents.\n unet ([`UNet2DConditionModel`]):\n Conditional U-Net architecture to denoise the image embedding.\n movq ([`VQModel`]):\n MoVQ image encoder and decoder\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->movq\"\n\n def __init__(\n self,\n text_encoder: MultilingualCLIP,\n movq: VQModel,\n tokenizer: XLMRobertaTokenizer,\n unet: UNet2DConditionModel,\n scheduler: DDIMScheduler,\n ):\n super().__init__()\n\n self.register_modules(\n text_encoder=text_encoder,\n movq=movq,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n )\n self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)\n self._warn_has_been_called = False\n\n # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents\n def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n latents = latents * scheduler.init_noise_sigma\n return latents\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n ):\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=77,\n truncation=True,\n return_attention_mask=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n text_input_ids = text_input_ids.to(device)\n text_mask = text_inputs.attention_mask.to(device)\n\n prompt_embeds, text_encoder_hidden_states = self.text_encoder(\n input_ids=text_input_ids, attention_mask=text_mask\n )\n\n prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=77,\n truncation=True,\n return_attention_mask=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n uncond_text_input_ids = uncond_input.input_ids.to(device)\n uncond_text_mask = uncond_input.attention_mask.to(device)\n\n negative_prompt_embeds, uncond_text_encoder_hidden_states = self.text_encoder(\n input_ids=uncond_text_input_ids, attention_mask=uncond_text_mask\n )\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)\n\n seq_len = uncond_text_encoder_hidden_states.shape[1]\n uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)\n uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(\n batch_size * num_images_per_prompt, seq_len, -1\n )\n uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n # done duplicates\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])\n\n text_mask = torch.cat([uncond_text_mask, text_mask])\n\n return prompt_embeds, text_encoder_hidden_states, text_mask\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]],\n image: Union[torch.Tensor, PIL.Image.Image],\n mask_image: Union[torch.Tensor, PIL.Image.Image, np.ndarray],\n image_embeds: torch.Tensor,\n negative_image_embeds: torch.Tensor,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 100,\n guidance_scale: float = 4.0,\n num_images_per_prompt: int = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n return_dict: bool = True,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n image (`torch.Tensor`, `PIL.Image.Image` or `np.ndarray`):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process.\n mask_image (`PIL.Image.Image`,`torch.Tensor` or `np.ndarray`):\n `Image`, or a tensor representing an image batch, to mask `image`. White pixels in the mask will be\n repainted, while black pixels will be preserved. You can pass a pytorch tensor as mask only if the\n image you passed is a pytorch tensor, and it should contain one color channel (L) instead of 3, so the\n expected shape would be either `(B, 1, H, W,)`, `(B, H, W)`, `(1, H, W)` or `(H, W)` If image is an PIL\n image or numpy array, mask should also be a either PIL image or numpy array. If it is a PIL image, it\n will be converted to a single channel (luminance) before use. If it is a nummpy array, the expected\n shape is `(H, W)`.\n image_embeds (`torch.Tensor` or `List[torch.Tensor]`):\n The clip image embeddings for text prompt, that will be used to condition the image generation.\n negative_image_embeds (`torch.Tensor` or `List[torch.Tensor]`):\n The clip image embeddings for negative text prompt, will be used to condition the image generation.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between: `\"pil\"` (`PIL.Image.Image`), `\"np\"`\n (`np.array`) or `\"pt\"` (`torch.Tensor`).\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`\n \"\"\"\n if not self._warn_has_been_called and version.parse(version.parse(__version__).base_version) < version.parse(\n \"0.23.0.dev0\"\n ):\n logger.warning(\n \"Please note that the expected format of `mask_image` has recently been changed. \"\n \"Before diffusers == 0.19.0, Kandinsky Inpainting pipelines repainted black pixels and preserved black pixels. \"\n \"As of diffusers==0.19.0 this behavior has been inverted. Now white pixels are repainted and black pixels are preserved. \"\n \"This way, Kandinsky's masking behavior is aligned with Stable Diffusion. \"\n \"THIS means that you HAVE to invert the input mask to have the same behavior as before as explained in https://github.com/huggingface/diffusers/pull/4207. \"\n \"This warning will be surpressed after the first inference call and will be removed in diffusers>0.23.0\"\n )\n self._warn_has_been_called = True\n\n # Define call parameters\n if isinstance(prompt, str):\n batch_size = 1\n elif isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n device = self._execution_device\n\n batch_size = batch_size * num_images_per_prompt\n do_classifier_free_guidance = guidance_scale > 1.0\n\n prompt_embeds, text_encoder_hidden_states, _ = self._encode_prompt(\n prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt\n )\n\n if isinstance(image_embeds, list):\n image_embeds = torch.cat(image_embeds, dim=0)\n if isinstance(negative_image_embeds, list):\n negative_image_embeds = torch.cat(negative_image_embeds, dim=0)\n\n if do_classifier_free_guidance:\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n\n image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(\n dtype=prompt_embeds.dtype, device=device\n )\n\n # preprocess image and mask\n mask_image, image = prepare_mask_and_masked_image(image, mask_image, height, width)\n\n image = image.to(dtype=prompt_embeds.dtype, device=device)\n image = self.movq.encode(image)[\"latents\"]\n\n mask_image = mask_image.to(dtype=prompt_embeds.dtype, device=device)\n\n image_shape = tuple(image.shape[-2:])\n mask_image = F.interpolate(\n mask_image,\n image_shape,\n mode=\"nearest\",\n )\n mask_image = prepare_mask(mask_image)\n masked_image = image * mask_image\n\n mask_image = mask_image.repeat_interleave(num_images_per_prompt, dim=0)\n masked_image = masked_image.repeat_interleave(num_images_per_prompt, dim=0)\n if do_classifier_free_guidance:\n mask_image = mask_image.repeat(2, 1, 1, 1)\n masked_image = masked_image.repeat(2, 1, 1, 1)\n\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps_tensor = self.scheduler.timesteps\n\n num_channels_latents = self.movq.config.latent_channels\n\n # get h, w for latents\n sample_height, sample_width = get_new_h_w(height, width, self.movq_scale_factor)\n\n # create initial latent\n latents = self.prepare_latents(\n (batch_size, num_channels_latents, sample_height, sample_width),\n text_encoder_hidden_states.dtype,\n device,\n generator,\n latents,\n self.scheduler,\n )\n\n # Check that sizes of mask, masked image and latents match with expected\n num_channels_mask = mask_image.shape[1]\n num_channels_masked_image = masked_image.shape[1]\n if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:\n raise ValueError(\n f\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\"\n f\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\"\n f\" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}\"\n f\" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of\"\n \" `pipeline.unet` or your `mask_image` or `image` input.\"\n )\n\n for i, t in enumerate(self.progress_bar(timesteps_tensor)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = torch.cat([latent_model_input, masked_image, mask_image], dim=1)\n\n added_cond_kwargs = {\"text_embeds\": prompt_embeds, \"image_embeds\": image_embeds}\n noise_pred = self.unet(\n sample=latent_model_input,\n timestep=t,\n encoder_hidden_states=text_encoder_hidden_states,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n if do_classifier_free_guidance:\n noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n _, variance_pred_text = variance_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n noise_pred = torch.cat([noise_pred, variance_pred_text], dim=1)\n\n if not (\n hasattr(self.scheduler.config, \"variance_type\")\n and self.scheduler.config.variance_type in [\"learned\", \"learned_range\"]\n ):\n noise_pred, _ = noise_pred.split(latents.shape[1], dim=1)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(\n noise_pred,\n t,\n latents,\n generator=generator,\n ).prev_sample\n\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # post-processing\n image = self.movq.decode(latents, force_not_quantize=True)[\"sample\"]\n\n self.maybe_free_model_hooks()\n\n if output_type not in [\"pt\", \"np\", \"pil\"]:\n raise ValueError(f\"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}\")\n\n if output_type in [\"np\", \"pil\"]:\n image = image * 0.5 + 0.5\n image = image.clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom dataclasses import dataclass\nfrom typing import List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...models import PriorTransformer\nfrom ...schedulers import UnCLIPScheduler\nfrom ...utils import (\n BaseOutput,\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n >>> import torch\n\n >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1-prior\")\n >>> pipe_prior.to(\"cuda\")\n\n >>> prompt = \"red cat, 4k photo\"\n >>> out = pipe_prior(prompt)\n >>> image_emb = out.image_embeds\n >>> negative_image_emb = out.negative_image_embeds\n\n >>> pipe = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\")\n >>> pipe.to(\"cuda\")\n\n >>> image = pipe(\n ... prompt,\n ... image_embeds=image_emb,\n ... negative_image_embeds=negative_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=100,\n ... ).images\n\n >>> image[0].save(\"cat.png\")\n ```\n\"\"\"\n\nEXAMPLE_INTERPOLATE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import KandinskyPriorPipeline, KandinskyPipeline\n >>> from diffusers.utils import load_image\n >>> import PIL\n\n >>> import torch\n >>> from torchvision import transforms\n\n >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-1-prior\", torch_dtype=torch.float16\n ... )\n >>> pipe_prior.to(\"cuda\")\n\n >>> img1 = load_image(\n ... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n ... \"/kandinsky/cat.png\"\n ... )\n\n >>> img2 = load_image(\n ... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n ... \"/kandinsky/starry_night.jpeg\"\n ... )\n\n >>> images_texts = [\"a cat\", img1, img2]\n >>> weights = [0.3, 0.3, 0.4]\n >>> image_emb, zero_image_emb = pipe_prior.interpolate(images_texts, weights)\n\n >>> pipe = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\", torch_dtype=torch.float16)\n >>> pipe.to(\"cuda\")\n\n >>> image = pipe(\n ... \"\",\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=150,\n ... ).images[0]\n\n >>> image.save(\"starry_cat.png\")\n ```\n\"\"\"\n\n\n@dataclass\nclass KandinskyPriorPipelineOutput(BaseOutput):\n \"\"\"\n Output class for KandinskyPriorPipeline.\n\n Args:\n image_embeds (`torch.Tensor`)\n clip image embeddings for text prompt\n negative_image_embeds (`List[PIL.Image.Image]` or `np.ndarray`)\n clip image embeddings for unconditional tokens\n \"\"\"\n\n image_embeds: Union[torch.Tensor, np.ndarray]\n negative_image_embeds: Union[torch.Tensor, np.ndarray]\n\n\nclass KandinskyPriorPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for generating image prior for Kandinsky\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n prior ([`PriorTransformer`]):\n The canonical unCLIP prior to approximate the image embedding from the text embedding.\n image_encoder ([`CLIPVisionModelWithProjection`]):\n Frozen image-encoder.\n text_encoder ([`CLIPTextModelWithProjection`]):\n Frozen text-encoder.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n scheduler ([`UnCLIPScheduler`]):\n A scheduler to be used in combination with `prior` to generate image embedding.\n \"\"\"\n\n _exclude_from_cpu_offload = [\"prior\"]\n model_cpu_offload_seq = \"text_encoder->prior\"\n\n def __init__(\n self,\n prior: PriorTransformer,\n image_encoder: CLIPVisionModelWithProjection,\n text_encoder: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n scheduler: UnCLIPScheduler,\n image_processor: CLIPImageProcessor,\n ):\n super().__init__()\n\n self.register_modules(\n prior=prior,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n scheduler=scheduler,\n image_encoder=image_encoder,\n image_processor=image_processor,\n )\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_INTERPOLATE_DOC_STRING)\n def interpolate(\n self,\n images_and_prompts: List[Union[str, PIL.Image.Image, torch.Tensor]],\n weights: List[float],\n num_images_per_prompt: int = 1,\n num_inference_steps: int = 25,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n negative_prior_prompt: Optional[str] = None,\n negative_prompt: str = \"\",\n guidance_scale: float = 4.0,\n device=None,\n ):\n \"\"\"\n Function invoked when using the prior pipeline for interpolation.\n\n Args:\n images_and_prompts (`List[Union[str, PIL.Image.Image, torch.Tensor]]`):\n list of prompts and images to guide the image generation.\n weights: (`List[float]`):\n list of weights for each condition in `images_and_prompts`\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n num_inference_steps (`int`, *optional*, defaults to 25):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n negative_prior_prompt (`str`, *optional*):\n The prompt not to guide the prior diffusion process. Ignored when not using guidance (i.e., ignored if\n `guidance_scale` is less than `1`).\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt not to guide the image generation. Ignored when not using guidance (i.e., ignored if\n `guidance_scale` is less than `1`).\n guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n\n Examples:\n\n Returns:\n [`KandinskyPriorPipelineOutput`] or `tuple`\n \"\"\"\n\n device = device or self.device\n\n if len(images_and_prompts) != len(weights):\n raise ValueError(\n f\"`images_and_prompts` contains {len(images_and_prompts)} items and `weights` contains {len(weights)} items - they should be lists of same length\"\n )\n\n image_embeddings = []\n for cond, weight in zip(images_and_prompts, weights):\n if isinstance(cond, str):\n image_emb = self(\n cond,\n num_inference_steps=num_inference_steps,\n num_images_per_prompt=num_images_per_prompt,\n generator=generator,\n latents=latents,\n negative_prompt=negative_prior_prompt,\n guidance_scale=guidance_scale,\n ).image_embeds\n\n elif isinstance(cond, (PIL.Image.Image, torch.Tensor)):\n if isinstance(cond, PIL.Image.Image):\n cond = (\n self.image_processor(cond, return_tensors=\"pt\")\n .pixel_values[0]\n .unsqueeze(0)\n .to(dtype=self.image_encoder.dtype, device=device)\n )\n\n image_emb = self.image_encoder(cond)[\"image_embeds\"]\n\n else:\n raise ValueError(\n f\"`images_and_prompts` can only contains elements to be of type `str`, `PIL.Image.Image` or `torch.Tensor` but is {type(cond)}\"\n )\n\n image_embeddings.append(image_emb * weight)\n\n image_emb = torch.cat(image_embeddings).sum(dim=0, keepdim=True)\n\n out_zero = self(\n negative_prompt,\n num_inference_steps=num_inference_steps,\n num_images_per_prompt=num_images_per_prompt,\n generator=generator,\n latents=latents,\n negative_prompt=negative_prior_prompt,\n guidance_scale=guidance_scale,\n )\n zero_image_emb = out_zero.negative_image_embeds if negative_prompt == \"\" else out_zero.image_embeds\n\n return KandinskyPriorPipelineOutput(image_embeds=image_emb, negative_image_embeds=zero_image_emb)\n\n # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents\n def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n latents = latents * scheduler.init_noise_sigma\n return latents\n\n def get_zero_embed(self, batch_size=1, device=None):\n device = device or self.device\n zero_img = torch.zeros(1, 3, self.image_encoder.config.image_size, self.image_encoder.config.image_size).to(\n device=device, dtype=self.image_encoder.dtype\n )\n zero_image_emb = self.image_encoder(zero_img)[\"image_embeds\"]\n zero_image_emb = zero_image_emb.repeat(batch_size, 1)\n return zero_image_emb\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n ):\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n text_mask = text_inputs.attention_mask.bool().to(device)\n\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]\n\n text_encoder_output = self.text_encoder(text_input_ids.to(device))\n\n prompt_embeds = text_encoder_output.text_embeds\n text_encoder_hidden_states = text_encoder_output.last_hidden_state\n\n prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n uncond_text_mask = uncond_input.attention_mask.bool().to(device)\n negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))\n\n negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds\n uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)\n\n seq_len = uncond_text_encoder_hidden_states.shape[1]\n uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)\n uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(\n batch_size * num_images_per_prompt, seq_len, -1\n )\n uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n # done duplicates\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])\n\n text_mask = torch.cat([uncond_text_mask, text_mask])\n\n return prompt_embeds, text_encoder_hidden_states, text_mask\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]],\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: int = 1,\n num_inference_steps: int = 25,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n guidance_scale: float = 4.0,\n output_type: Optional[str] = \"pt\",\n return_dict: bool = True,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n num_inference_steps (`int`, *optional*, defaults to 25):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n output_type (`str`, *optional*, defaults to `\"pt\"`):\n The output format of the generate image. Choose between: `\"np\"` (`np.array`) or `\"pt\"`\n (`torch.Tensor`).\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n\n Examples:\n\n Returns:\n [`KandinskyPriorPipelineOutput`] or `tuple`\n \"\"\"\n\n if isinstance(prompt, str):\n prompt = [prompt]\n elif not isinstance(prompt, list):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if isinstance(negative_prompt, str):\n negative_prompt = [negative_prompt]\n elif not isinstance(negative_prompt, list) and negative_prompt is not None:\n raise ValueError(f\"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}\")\n\n # if the negative prompt is defined we double the batch size to\n # directly retrieve the negative prompt embedding\n if negative_prompt is not None:\n prompt = prompt + negative_prompt\n negative_prompt = 2 * negative_prompt\n\n device = self._execution_device\n\n batch_size = len(prompt)\n batch_size = batch_size * num_images_per_prompt\n\n do_classifier_free_guidance = guidance_scale > 1.0\n prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt(\n prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt\n )\n\n # prior\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n prior_timesteps_tensor = self.scheduler.timesteps\n\n embedding_dim = self.prior.config.embedding_dim\n\n latents = self.prepare_latents(\n (batch_size, embedding_dim),\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n self.scheduler,\n )\n\n for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n\n predicted_image_embedding = self.prior(\n latent_model_input,\n timestep=t,\n proj_embedding=prompt_embeds,\n encoder_hidden_states=text_encoder_hidden_states,\n attention_mask=text_mask,\n ).predicted_image_embedding\n\n if do_classifier_free_guidance:\n predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)\n predicted_image_embedding = predicted_image_embedding_uncond + guidance_scale * (\n predicted_image_embedding_text - predicted_image_embedding_uncond\n )\n\n if i + 1 == prior_timesteps_tensor.shape[0]:\n prev_timestep = None\n else:\n prev_timestep = prior_timesteps_tensor[i + 1]\n\n latents = self.scheduler.step(\n predicted_image_embedding,\n timestep=t,\n sample=latents,\n generator=generator,\n prev_timestep=prev_timestep,\n ).prev_sample\n\n latents = self.prior.post_process_latents(latents)\n\n image_embeddings = latents\n\n # if negative prompt has been defined, we retrieve split the image embedding into two\n if negative_prompt is None:\n zero_embeds = self.get_zero_embed(latents.shape[0], device=latents.device)\n\n self.maybe_free_model_hooks()\n else:\n image_embeddings, zero_embeds = image_embeddings.chunk(2)\n\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.prior_hook.offload()\n\n if output_type not in [\"pt\", \"np\"]:\n raise ValueError(f\"Only the output types `pt` and `np` are supported not output_type={output_type}\")\n\n if output_type == \"np\":\n image_embeddings = image_embeddings.cpu().numpy()\n zero_embeds = zero_embeds.cpu().numpy()\n\n if not return_dict:\n return (image_embeddings, zero_embeds)\n\n return KandinskyPriorPipelineOutput(image_embeds=image_embeddings, negative_image_embeds=zero_embeds)\n" }, { "path": "diffusers/src/diffusers/pipelines/kandinsky/text_encoder.py", "content": "import torch\nfrom transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel\n\n\nclass MCLIPConfig(XLMRobertaConfig):\n model_type = \"M-CLIP\"\n\n def __init__(self, transformerDimSize=1024, imageDimSize=768, **kwargs):\n self.transformerDimensions = transformerDimSize\n self.numDims = imageDimSize\n super().__init__(**kwargs)\n\n\nclass MultilingualCLIP(PreTrainedModel):\n config_class = MCLIPConfig\n\n def __init__(self, config, *args, **kwargs):\n super().__init__(config, *args, **kwargs)\n self.transformer = XLMRobertaModel(config)\n self.LinearTransformation = torch.nn.Linear(\n in_features=config.transformerDimensions, out_features=config.numDims\n )\n\n def forward(self, input_ids, attention_mask):\n embs = self.transformer(input_ids=input_ids, attention_mask=attention_mask)[0]\n embs2 = (embs * attention_mask.unsqueeze(2)).sum(dim=1) / attention_mask.sum(dim=1)[:, None]\n return self.LinearTransformation(embs2), embs\n" }, { "path": "diffusers/src/diffusers/pipelines/kandinsky2_2/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_kandinsky2_2\"] = [\"KandinskyV22Pipeline\"]\n _import_structure[\"pipeline_kandinsky2_2_combined\"] = [\n \"KandinskyV22CombinedPipeline\",\n \"KandinskyV22Img2ImgCombinedPipeline\",\n \"KandinskyV22InpaintCombinedPipeline\",\n ]\n _import_structure[\"pipeline_kandinsky2_2_controlnet\"] = [\"KandinskyV22ControlnetPipeline\"]\n _import_structure[\"pipeline_kandinsky2_2_controlnet_img2img\"] = [\"KandinskyV22ControlnetImg2ImgPipeline\"]\n _import_structure[\"pipeline_kandinsky2_2_img2img\"] = [\"KandinskyV22Img2ImgPipeline\"]\n _import_structure[\"pipeline_kandinsky2_2_inpainting\"] = [\"KandinskyV22InpaintPipeline\"]\n _import_structure[\"pipeline_kandinsky2_2_prior\"] = [\"KandinskyV22PriorPipeline\"]\n _import_structure[\"pipeline_kandinsky2_2_prior_emb2emb\"] = [\"KandinskyV22PriorEmb2EmbPipeline\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n else:\n from .pipeline_kandinsky2_2 import KandinskyV22Pipeline\n from .pipeline_kandinsky2_2_combined import (\n KandinskyV22CombinedPipeline,\n KandinskyV22Img2ImgCombinedPipeline,\n KandinskyV22InpaintCombinedPipeline,\n )\n from .pipeline_kandinsky2_2_controlnet import KandinskyV22ControlnetPipeline\n from .pipeline_kandinsky2_2_controlnet_img2img import KandinskyV22ControlnetImg2ImgPipeline\n from .pipeline_kandinsky2_2_img2img import KandinskyV22Img2ImgPipeline\n from .pipeline_kandinsky2_2_inpainting import KandinskyV22InpaintPipeline\n from .pipeline_kandinsky2_2_prior import KandinskyV22PriorPipeline\n from .pipeline_kandinsky2_2_prior_emb2emb import KandinskyV22PriorEmb2EmbPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Callable, Dict, List, Optional, Union\n\nimport torch\n\nfrom ...models import UNet2DConditionModel, VQModel\nfrom ...schedulers import DDPMScheduler\nfrom ...utils import deprecate, logging, replace_example_docstring\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n >>> import torch\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n >>> pipe_prior.to(\"cuda\")\n >>> prompt = \"red cat, 4k photo\"\n >>> out = pipe_prior(prompt)\n >>> image_emb = out.image_embeds\n >>> zero_image_emb = out.negative_image_embeds\n >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n >>> pipe.to(\"cuda\")\n >>> image = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=50,\n ... ).images\n >>> image[0].save(\"cat.png\")\n ```\n\"\"\"\n\n\ndef downscale_height_and_width(height, width, scale_factor=8):\n new_height = height // scale_factor**2\n if height % scale_factor**2 != 0:\n new_height += 1\n new_width = width // scale_factor**2\n if width % scale_factor**2 != 0:\n new_width += 1\n return new_height * scale_factor, new_width * scale_factor\n\n\nclass KandinskyV22Pipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for text-to-image generation using Kandinsky\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):\n A scheduler to be used in combination with `unet` to generate image latents.\n unet ([`UNet2DConditionModel`]):\n Conditional U-Net architecture to denoise the image embedding.\n movq ([`VQModel`]):\n MoVQ Decoder to generate the image from the latents.\n \"\"\"\n\n model_cpu_offload_seq = \"unet->movq\"\n _callback_tensor_inputs = [\"latents\", \"image_embeds\", \"negative_image_embeds\"]\n\n def __init__(\n self,\n unet: UNet2DConditionModel,\n scheduler: DDPMScheduler,\n movq: VQModel,\n ):\n super().__init__()\n\n self.register_modules(\n unet=unet,\n scheduler=scheduler,\n movq=movq,\n )\n self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)\n\n # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents\n def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n latents = latents * scheduler.init_noise_sigma\n return latents\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n image_embeds: Union[torch.Tensor, List[torch.Tensor]],\n negative_image_embeds: Union[torch.Tensor, List[torch.Tensor]],\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 100,\n guidance_scale: float = 4.0,\n num_images_per_prompt: int = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n image_embeds (`torch.Tensor` or `List[torch.Tensor]`):\n The clip image embeddings for text prompt, that will be used to condition the image generation.\n negative_image_embeds (`torch.Tensor` or `List[torch.Tensor]`):\n The clip image embeddings for negative text prompt, will be used to condition the image generation.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between: `\"pil\"` (`PIL.Image.Image`), `\"np\"`\n (`np.array`) or `\"pt\"` (`torch.Tensor`).\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n device = self._execution_device\n\n self._guidance_scale = guidance_scale\n\n if isinstance(image_embeds, list):\n image_embeds = torch.cat(image_embeds, dim=0)\n batch_size = image_embeds.shape[0] * num_images_per_prompt\n if isinstance(negative_image_embeds, list):\n negative_image_embeds = torch.cat(negative_image_embeds, dim=0)\n\n if self.do_classifier_free_guidance:\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n\n image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(\n dtype=self.unet.dtype, device=device\n )\n\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n num_channels_latents = self.unet.config.in_channels\n\n height, width = downscale_height_and_width(height, width, self.movq_scale_factor)\n\n # create initial latent\n latents = self.prepare_latents(\n (batch_size, num_channels_latents, height, width),\n image_embeds.dtype,\n device,\n generator,\n latents,\n self.scheduler,\n )\n\n self._num_timesteps = len(timesteps)\n for i, t in enumerate(self.progress_bar(timesteps)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n\n added_cond_kwargs = {\"image_embeds\": image_embeds}\n noise_pred = self.unet(\n sample=latent_model_input,\n timestep=t,\n encoder_hidden_states=None,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n if self.do_classifier_free_guidance:\n noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n _, variance_pred_text = variance_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n noise_pred = torch.cat([noise_pred, variance_pred_text], dim=1)\n\n if not (\n hasattr(self.scheduler.config, \"variance_type\")\n and self.scheduler.config.variance_type in [\"learned\", \"learned_range\"]\n ):\n noise_pred, _ = noise_pred.split(latents.shape[1], dim=1)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(\n noise_pred,\n t,\n latents,\n generator=generator,\n )[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n image_embeds = callback_outputs.pop(\"image_embeds\", image_embeds)\n negative_image_embeds = callback_outputs.pop(\"negative_image_embeds\", negative_image_embeds)\n\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if output_type not in [\"pt\", \"np\", \"pil\", \"latent\"]:\n raise ValueError(f\"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}\")\n\n if not output_type == \"latent\":\n # post-processing\n image = self.movq.decode(latents, force_not_quantize=True)[\"sample\"]\n if output_type in [\"np\", \"pil\"]:\n image = image * 0.5 + 0.5\n image = image.clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n else:\n image = latents\n\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Callable, Dict, List, Optional, Union\n\nimport PIL.Image\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...models import PriorTransformer, UNet2DConditionModel, VQModel\nfrom ...schedulers import DDPMScheduler, UnCLIPScheduler\nfrom ...utils import deprecate, logging, replace_example_docstring\nfrom ..pipeline_utils import DiffusionPipeline\nfrom .pipeline_kandinsky2_2 import KandinskyV22Pipeline\nfrom .pipeline_kandinsky2_2_img2img import KandinskyV22Img2ImgPipeline\nfrom .pipeline_kandinsky2_2_inpainting import KandinskyV22InpaintPipeline\nfrom .pipeline_kandinsky2_2_prior import KandinskyV22PriorPipeline\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nTEXT2IMAGE_EXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n from diffusers import AutoPipelineForText2Image\n import torch\n\n pipe = AutoPipelineForText2Image.from_pretrained(\n \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n )\n pipe.enable_model_cpu_offload()\n\n prompt = \"A lion in galaxies, spirals, nebulae, stars, smoke, iridescent, intricate detail, octane render, 8k\"\n\n image = pipe(prompt=prompt, num_inference_steps=25).images[0]\n ```\n\"\"\"\n\nIMAGE2IMAGE_EXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n from diffusers import AutoPipelineForImage2Image\n import torch\n import requests\n from io import BytesIO\n from PIL import Image\n import os\n\n pipe = AutoPipelineForImage2Image.from_pretrained(\n \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n )\n pipe.enable_model_cpu_offload()\n\n prompt = \"A fantasy landscape, Cinematic lighting\"\n negative_prompt = \"low quality, bad quality\"\n\n url = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n\n response = requests.get(url)\n image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n image.thumbnail((768, 768))\n\n image = pipe(prompt=prompt, image=original_image, num_inference_steps=25).images[0]\n ```\n\"\"\"\n\nINPAINT_EXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n from diffusers import AutoPipelineForInpainting\n from diffusers.utils import load_image\n import torch\n import numpy as np\n\n pipe = AutoPipelineForInpainting.from_pretrained(\n \"kandinsky-community/kandinsky-2-2-decoder-inpaint\", torch_dtype=torch.float16\n )\n pipe.enable_model_cpu_offload()\n\n prompt = \"A fantasy landscape, Cinematic lighting\"\n negative_prompt = \"low quality, bad quality\"\n\n original_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\" \"/kandinsky/cat.png\"\n )\n\n mask = np.zeros((768, 768), dtype=np.float32)\n # Let's mask out an area above the cat's head\n mask[:250, 250:-250] = 1\n\n image = pipe(prompt=prompt, image=original_image, mask_image=mask, num_inference_steps=25).images[0]\n ```\n\"\"\"\n\n\nclass KandinskyV22CombinedPipeline(DiffusionPipeline):\n \"\"\"\n Combined Pipeline for text-to-image generation using Kandinsky\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):\n A scheduler to be used in combination with `unet` to generate image latents.\n unet ([`UNet2DConditionModel`]):\n Conditional U-Net architecture to denoise the image embedding.\n movq ([`VQModel`]):\n MoVQ Decoder to generate the image from the latents.\n prior_prior ([`PriorTransformer`]):\n The canonical unCLIP prior to approximate the image embedding from the text embedding.\n prior_image_encoder ([`CLIPVisionModelWithProjection`]):\n Frozen image-encoder.\n prior_text_encoder ([`CLIPTextModelWithProjection`]):\n Frozen text-encoder.\n prior_tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n prior_scheduler ([`UnCLIPScheduler`]):\n A scheduler to be used in combination with `prior` to generate image embedding.\n prior_image_processor ([`CLIPImageProcessor`]):\n A image_processor to be used to preprocess image from clip.\n \"\"\"\n\n model_cpu_offload_seq = \"prior_text_encoder->prior_image_encoder->unet->movq\"\n _load_connected_pipes = True\n _exclude_from_cpu_offload = [\"prior_prior\"]\n\n def __init__(\n self,\n unet: UNet2DConditionModel,\n scheduler: DDPMScheduler,\n movq: VQModel,\n prior_prior: PriorTransformer,\n prior_image_encoder: CLIPVisionModelWithProjection,\n prior_text_encoder: CLIPTextModelWithProjection,\n prior_tokenizer: CLIPTokenizer,\n prior_scheduler: UnCLIPScheduler,\n prior_image_processor: CLIPImageProcessor,\n ):\n super().__init__()\n\n self.register_modules(\n unet=unet,\n scheduler=scheduler,\n movq=movq,\n prior_prior=prior_prior,\n prior_image_encoder=prior_image_encoder,\n prior_text_encoder=prior_text_encoder,\n prior_tokenizer=prior_tokenizer,\n prior_scheduler=prior_scheduler,\n prior_image_processor=prior_image_processor,\n )\n self.prior_pipe = KandinskyV22PriorPipeline(\n prior=prior_prior,\n image_encoder=prior_image_encoder,\n text_encoder=prior_text_encoder,\n tokenizer=prior_tokenizer,\n scheduler=prior_scheduler,\n image_processor=prior_image_processor,\n )\n self.decoder_pipe = KandinskyV22Pipeline(\n unet=unet,\n scheduler=scheduler,\n movq=movq,\n )\n\n def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):\n self.decoder_pipe.enable_xformers_memory_efficient_attention(attention_op)\n\n def enable_sequential_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = \"cuda\"):\n r\"\"\"\n Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,\n text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a\n `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.\n Note that offloading happens on a submodule basis. Memory savings are higher than with\n `enable_model_cpu_offload`, but performance is lower.\n \"\"\"\n self.prior_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id, device=device)\n self.decoder_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id, device=device)\n\n def progress_bar(self, iterable=None, total=None):\n self.prior_pipe.progress_bar(iterable=iterable, total=total)\n self.decoder_pipe.progress_bar(iterable=iterable, total=total)\n self.decoder_pipe.enable_model_cpu_offload()\n\n def set_progress_bar_config(self, **kwargs):\n self.prior_pipe.set_progress_bar_config(**kwargs)\n self.decoder_pipe.set_progress_bar_config(**kwargs)\n\n @torch.no_grad()\n @replace_example_docstring(TEXT2IMAGE_EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]],\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_inference_steps: int = 100,\n guidance_scale: float = 4.0,\n num_images_per_prompt: int = 1,\n height: int = 512,\n width: int = 512,\n prior_guidance_scale: float = 4.0,\n prior_num_inference_steps: int = 25,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n return_dict: bool = True,\n prior_callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n prior_callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n prior_guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n prior_num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between: `\"pil\"` (`PIL.Image.Image`), `\"np\"`\n (`np.array`) or `\"pt\"` (`torch.Tensor`).\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n prior_callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference of the prior pipeline.\n The function is called with the following arguments: `prior_callback_on_step_end(self:\n DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`.\n prior_callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `prior_callback_on_step_end` function. The tensors specified in the\n list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in\n the `._callback_tensor_inputs` attribute of your prior pipeline class.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference of the decoder pipeline.\n The function is called with the following arguments: `callback_on_step_end(self: DiffusionPipeline,\n step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors\n as specified by `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`\n \"\"\"\n prior_outputs = self.prior_pipe(\n prompt=prompt,\n negative_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n num_inference_steps=prior_num_inference_steps,\n generator=generator,\n latents=latents,\n guidance_scale=prior_guidance_scale,\n output_type=\"pt\",\n return_dict=False,\n callback_on_step_end=prior_callback_on_step_end,\n callback_on_step_end_tensor_inputs=prior_callback_on_step_end_tensor_inputs,\n )\n image_embeds = prior_outputs[0]\n negative_image_embeds = prior_outputs[1]\n\n prompt = [prompt] if not isinstance(prompt, (list, tuple)) else prompt\n\n if len(prompt) < image_embeds.shape[0] and image_embeds.shape[0] % len(prompt) == 0:\n prompt = (image_embeds.shape[0] // len(prompt)) * prompt\n\n outputs = self.decoder_pipe(\n image_embeds=image_embeds,\n negative_image_embeds=negative_image_embeds,\n width=width,\n height=height,\n num_inference_steps=num_inference_steps,\n generator=generator,\n guidance_scale=guidance_scale,\n output_type=output_type,\n callback=callback,\n callback_steps=callback_steps,\n return_dict=return_dict,\n callback_on_step_end=callback_on_step_end,\n callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,\n )\n self.maybe_free_model_hooks()\n\n return outputs\n\n\nclass KandinskyV22Img2ImgCombinedPipeline(DiffusionPipeline):\n \"\"\"\n Combined Pipeline for image-to-image generation using Kandinsky\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):\n A scheduler to be used in combination with `unet` to generate image latents.\n unet ([`UNet2DConditionModel`]):\n Conditional U-Net architecture to denoise the image embedding.\n movq ([`VQModel`]):\n MoVQ Decoder to generate the image from the latents.\n prior_prior ([`PriorTransformer`]):\n The canonical unCLIP prior to approximate the image embedding from the text embedding.\n prior_image_encoder ([`CLIPVisionModelWithProjection`]):\n Frozen image-encoder.\n prior_text_encoder ([`CLIPTextModelWithProjection`]):\n Frozen text-encoder.\n prior_tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n prior_scheduler ([`UnCLIPScheduler`]):\n A scheduler to be used in combination with `prior` to generate image embedding.\n prior_image_processor ([`CLIPImageProcessor`]):\n A image_processor to be used to preprocess image from clip.\n \"\"\"\n\n model_cpu_offload_seq = \"prior_text_encoder->prior_image_encoder->unet->movq\"\n _load_connected_pipes = True\n _exclude_from_cpu_offload = [\"prior_prior\"]\n\n def __init__(\n self,\n unet: UNet2DConditionModel,\n scheduler: DDPMScheduler,\n movq: VQModel,\n prior_prior: PriorTransformer,\n prior_image_encoder: CLIPVisionModelWithProjection,\n prior_text_encoder: CLIPTextModelWithProjection,\n prior_tokenizer: CLIPTokenizer,\n prior_scheduler: UnCLIPScheduler,\n prior_image_processor: CLIPImageProcessor,\n ):\n super().__init__()\n\n self.register_modules(\n unet=unet,\n scheduler=scheduler,\n movq=movq,\n prior_prior=prior_prior,\n prior_image_encoder=prior_image_encoder,\n prior_text_encoder=prior_text_encoder,\n prior_tokenizer=prior_tokenizer,\n prior_scheduler=prior_scheduler,\n prior_image_processor=prior_image_processor,\n )\n self.prior_pipe = KandinskyV22PriorPipeline(\n prior=prior_prior,\n image_encoder=prior_image_encoder,\n text_encoder=prior_text_encoder,\n tokenizer=prior_tokenizer,\n scheduler=prior_scheduler,\n image_processor=prior_image_processor,\n )\n self.decoder_pipe = KandinskyV22Img2ImgPipeline(\n unet=unet,\n scheduler=scheduler,\n movq=movq,\n )\n\n def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):\n self.decoder_pipe.enable_xformers_memory_efficient_attention(attention_op)\n\n def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = \"cuda\"):\n r\"\"\"\n Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared\n to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`\n method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with\n `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.\n \"\"\"\n self.prior_pipe.enable_model_cpu_offload(gpu_id=gpu_id, device=device)\n self.decoder_pipe.enable_model_cpu_offload(gpu_id=gpu_id, device=device)\n\n def enable_sequential_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = \"cuda\"):\n r\"\"\"\n Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,\n text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a\n `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.\n Note that offloading happens on a submodule basis. Memory savings are higher than with\n `enable_model_cpu_offload`, but performance is lower.\n \"\"\"\n self.prior_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id, device=device)\n self.decoder_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id, device=device)\n\n def progress_bar(self, iterable=None, total=None):\n self.prior_pipe.progress_bar(iterable=iterable, total=total)\n self.decoder_pipe.progress_bar(iterable=iterable, total=total)\n self.decoder_pipe.enable_model_cpu_offload()\n\n def set_progress_bar_config(self, **kwargs):\n self.prior_pipe.set_progress_bar_config(**kwargs)\n self.decoder_pipe.set_progress_bar_config(**kwargs)\n\n @torch.no_grad()\n @replace_example_docstring(IMAGE2IMAGE_EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]],\n image: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]],\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_inference_steps: int = 100,\n guidance_scale: float = 4.0,\n strength: float = 0.3,\n num_images_per_prompt: int = 1,\n height: int = 512,\n width: int = 512,\n prior_guidance_scale: float = 4.0,\n prior_num_inference_steps: int = 25,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n return_dict: bool = True,\n prior_callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n prior_callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process. Can also accept image latents as `image`, if passing latents directly, it will not be encoded\n again.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n strength (`float`, *optional*, defaults to 0.3):\n Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`\n will be used as a starting point, adding more noise to it the larger the `strength`. The number of\n denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will\n be maximum and the denoising process will run for the full number of iterations specified in\n `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n prior_guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n prior_num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between: `\"pil\"` (`PIL.Image.Image`), `\"np\"`\n (`np.array`) or `\"pt\"` (`torch.Tensor`).\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`\n \"\"\"\n prior_outputs = self.prior_pipe(\n prompt=prompt,\n negative_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n num_inference_steps=prior_num_inference_steps,\n generator=generator,\n latents=latents,\n guidance_scale=prior_guidance_scale,\n output_type=\"pt\",\n return_dict=False,\n callback_on_step_end=prior_callback_on_step_end,\n callback_on_step_end_tensor_inputs=prior_callback_on_step_end_tensor_inputs,\n )\n image_embeds = prior_outputs[0]\n negative_image_embeds = prior_outputs[1]\n\n prompt = [prompt] if not isinstance(prompt, (list, tuple)) else prompt\n image = [image] if isinstance(prompt, PIL.Image.Image) else image\n\n if len(prompt) < image_embeds.shape[0] and image_embeds.shape[0] % len(prompt) == 0:\n prompt = (image_embeds.shape[0] // len(prompt)) * prompt\n\n if (\n isinstance(image, (list, tuple))\n and len(image) < image_embeds.shape[0]\n and image_embeds.shape[0] % len(image) == 0\n ):\n image = (image_embeds.shape[0] // len(image)) * image\n\n outputs = self.decoder_pipe(\n image=image,\n image_embeds=image_embeds,\n negative_image_embeds=negative_image_embeds,\n width=width,\n height=height,\n strength=strength,\n num_inference_steps=num_inference_steps,\n generator=generator,\n guidance_scale=guidance_scale,\n output_type=output_type,\n callback=callback,\n callback_steps=callback_steps,\n return_dict=return_dict,\n callback_on_step_end=callback_on_step_end,\n callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,\n )\n\n self.maybe_free_model_hooks()\n return outputs\n\n\nclass KandinskyV22InpaintCombinedPipeline(DiffusionPipeline):\n \"\"\"\n Combined Pipeline for inpainting generation using Kandinsky\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):\n A scheduler to be used in combination with `unet` to generate image latents.\n unet ([`UNet2DConditionModel`]):\n Conditional U-Net architecture to denoise the image embedding.\n movq ([`VQModel`]):\n MoVQ Decoder to generate the image from the latents.\n prior_prior ([`PriorTransformer`]):\n The canonical unCLIP prior to approximate the image embedding from the text embedding.\n prior_image_encoder ([`CLIPVisionModelWithProjection`]):\n Frozen image-encoder.\n prior_text_encoder ([`CLIPTextModelWithProjection`]):\n Frozen text-encoder.\n prior_tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n prior_scheduler ([`UnCLIPScheduler`]):\n A scheduler to be used in combination with `prior` to generate image embedding.\n prior_image_processor ([`CLIPImageProcessor`]):\n A image_processor to be used to preprocess image from clip.\n \"\"\"\n\n model_cpu_offload_seq = \"prior_text_encoder->prior_image_encoder->unet->movq\"\n _load_connected_pipes = True\n _exclude_from_cpu_offload = [\"prior_prior\"]\n\n def __init__(\n self,\n unet: UNet2DConditionModel,\n scheduler: DDPMScheduler,\n movq: VQModel,\n prior_prior: PriorTransformer,\n prior_image_encoder: CLIPVisionModelWithProjection,\n prior_text_encoder: CLIPTextModelWithProjection,\n prior_tokenizer: CLIPTokenizer,\n prior_scheduler: UnCLIPScheduler,\n prior_image_processor: CLIPImageProcessor,\n ):\n super().__init__()\n\n self.register_modules(\n unet=unet,\n scheduler=scheduler,\n movq=movq,\n prior_prior=prior_prior,\n prior_image_encoder=prior_image_encoder,\n prior_text_encoder=prior_text_encoder,\n prior_tokenizer=prior_tokenizer,\n prior_scheduler=prior_scheduler,\n prior_image_processor=prior_image_processor,\n )\n self.prior_pipe = KandinskyV22PriorPipeline(\n prior=prior_prior,\n image_encoder=prior_image_encoder,\n text_encoder=prior_text_encoder,\n tokenizer=prior_tokenizer,\n scheduler=prior_scheduler,\n image_processor=prior_image_processor,\n )\n self.decoder_pipe = KandinskyV22InpaintPipeline(\n unet=unet,\n scheduler=scheduler,\n movq=movq,\n )\n\n def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):\n self.decoder_pipe.enable_xformers_memory_efficient_attention(attention_op)\n\n def enable_sequential_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = \"cuda\"):\n r\"\"\"\n Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,\n text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a\n `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.\n Note that offloading happens on a submodule basis. Memory savings are higher than with\n `enable_model_cpu_offload`, but performance is lower.\n \"\"\"\n self.prior_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id, device=device)\n self.decoder_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id, device=device)\n\n def progress_bar(self, iterable=None, total=None):\n self.prior_pipe.progress_bar(iterable=iterable, total=total)\n self.decoder_pipe.progress_bar(iterable=iterable, total=total)\n self.decoder_pipe.enable_model_cpu_offload()\n\n def set_progress_bar_config(self, **kwargs):\n self.prior_pipe.set_progress_bar_config(**kwargs)\n self.decoder_pipe.set_progress_bar_config(**kwargs)\n\n @torch.no_grad()\n @replace_example_docstring(INPAINT_EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]],\n image: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]],\n mask_image: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]],\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_inference_steps: int = 100,\n guidance_scale: float = 4.0,\n num_images_per_prompt: int = 1,\n height: int = 512,\n width: int = 512,\n prior_guidance_scale: float = 4.0,\n prior_num_inference_steps: int = 25,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n prior_callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n prior_callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process. Can also accept image latents as `image`, if passing latents directly, it will not be encoded\n again.\n mask_image (`np.array`):\n Tensor representing an image batch, to mask `image`. White pixels in the mask will be repainted, while\n black pixels will be preserved. If `mask_image` is a PIL image, it will be converted to a single\n channel (luminance) before use. If it's a tensor, it should contain one color channel (L) instead of 3,\n so the expected shape would be `(B, H, W, 1)`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n prior_guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n prior_num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between: `\"pil\"` (`PIL.Image.Image`), `\"np\"`\n (`np.array`) or `\"pt\"` (`torch.Tensor`).\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n prior_callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `prior_callback_on_step_end(self: DiffusionPipeline, step: int, timestep:\n int, callback_kwargs: Dict)`.\n prior_callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `prior_callback_on_step_end` function. The tensors specified in the\n list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in\n the `._callback_tensor_inputs` attribute of your pipeline class.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`\n \"\"\"\n prior_kwargs = {}\n if kwargs.get(\"prior_callback\", None) is not None:\n prior_kwargs[\"callback\"] = kwargs.pop(\"prior_callback\")\n deprecate(\n \"prior_callback\",\n \"1.0.0\",\n \"Passing `prior_callback` as an input argument to `__call__` is deprecated, consider use `prior_callback_on_step_end`\",\n )\n if kwargs.get(\"prior_callback_steps\", None) is not None:\n deprecate(\n \"prior_callback_steps\",\n \"1.0.0\",\n \"Passing `prior_callback_steps` as an input argument to `__call__` is deprecated, consider use `prior_callback_on_step_end`\",\n )\n prior_kwargs[\"callback_steps\"] = kwargs.pop(\"prior_callback_steps\")\n\n prior_outputs = self.prior_pipe(\n prompt=prompt,\n negative_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n num_inference_steps=prior_num_inference_steps,\n generator=generator,\n latents=latents,\n guidance_scale=prior_guidance_scale,\n output_type=\"pt\",\n return_dict=False,\n callback_on_step_end=prior_callback_on_step_end,\n callback_on_step_end_tensor_inputs=prior_callback_on_step_end_tensor_inputs,\n **prior_kwargs,\n )\n image_embeds = prior_outputs[0]\n negative_image_embeds = prior_outputs[1]\n\n prompt = [prompt] if not isinstance(prompt, (list, tuple)) else prompt\n image = [image] if isinstance(prompt, PIL.Image.Image) else image\n mask_image = [mask_image] if isinstance(mask_image, PIL.Image.Image) else mask_image\n\n if len(prompt) < image_embeds.shape[0] and image_embeds.shape[0] % len(prompt) == 0:\n prompt = (image_embeds.shape[0] // len(prompt)) * prompt\n\n if (\n isinstance(image, (list, tuple))\n and len(image) < image_embeds.shape[0]\n and image_embeds.shape[0] % len(image) == 0\n ):\n image = (image_embeds.shape[0] // len(image)) * image\n\n if (\n isinstance(mask_image, (list, tuple))\n and len(mask_image) < image_embeds.shape[0]\n and image_embeds.shape[0] % len(mask_image) == 0\n ):\n mask_image = (image_embeds.shape[0] // len(mask_image)) * mask_image\n\n outputs = self.decoder_pipe(\n image=image,\n mask_image=mask_image,\n image_embeds=image_embeds,\n negative_image_embeds=negative_image_embeds,\n width=width,\n height=height,\n num_inference_steps=num_inference_steps,\n generator=generator,\n guidance_scale=guidance_scale,\n output_type=output_type,\n return_dict=return_dict,\n callback_on_step_end=callback_on_step_end,\n callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,\n **kwargs,\n )\n self.maybe_free_model_hooks()\n\n return outputs\n" }, { "path": "diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Callable, List, Optional, Union\n\nimport torch\n\nfrom ...models import UNet2DConditionModel, VQModel\nfrom ...schedulers import DDPMScheduler\nfrom ...utils import (\n logging,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> import numpy as np\n\n >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n >>> from transformers import pipeline\n >>> from diffusers.utils import load_image\n\n\n >>> def make_hint(image, depth_estimator):\n ... image = depth_estimator(image)[\"depth\"]\n ... image = np.array(image)\n ... image = image[:, :, None]\n ... image = np.concatenate([image, image, image], axis=2)\n ... detected_map = torch.from_numpy(image).float() / 255.0\n ... hint = detected_map.permute(2, 0, 1)\n ... return hint\n\n\n >>> depth_estimator = pipeline(\"depth-estimation\")\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n ... )\n >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n\n >>> img = load_image(\n ... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n ... \"/kandinsky/cat.png\"\n ... ).resize((768, 768))\n\n >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n >>> prompt = \"A robot, 4k photo\"\n >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n >>> image_emb, zero_image_emb = pipe_prior(\n ... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n ... ).to_tuple()\n\n >>> images = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... hint=hint,\n ... num_inference_steps=50,\n ... generator=generator,\n ... height=768,\n ... width=768,\n ... ).images\n\n >>> images[0].save(\"robot_cat.png\")\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.kandinsky2_2.pipeline_kandinsky2_2.downscale_height_and_width\ndef downscale_height_and_width(height, width, scale_factor=8):\n new_height = height // scale_factor**2\n if height % scale_factor**2 != 0:\n new_height += 1\n new_width = width // scale_factor**2\n if width % scale_factor**2 != 0:\n new_width += 1\n return new_height * scale_factor, new_width * scale_factor\n\n\nclass KandinskyV22ControlnetPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for text-to-image generation using Kandinsky\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n scheduler ([`DDIMScheduler`]):\n A scheduler to be used in combination with `unet` to generate image latents.\n unet ([`UNet2DConditionModel`]):\n Conditional U-Net architecture to denoise the image embedding.\n movq ([`VQModel`]):\n MoVQ Decoder to generate the image from the latents.\n \"\"\"\n\n model_cpu_offload_seq = \"unet->movq\"\n\n def __init__(\n self,\n unet: UNet2DConditionModel,\n scheduler: DDPMScheduler,\n movq: VQModel,\n ):\n super().__init__()\n\n self.register_modules(\n unet=unet,\n scheduler=scheduler,\n movq=movq,\n )\n self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)\n\n # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents\n def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n latents = latents * scheduler.init_noise_sigma\n return latents\n\n @torch.no_grad()\n def __call__(\n self,\n image_embeds: Union[torch.Tensor, List[torch.Tensor]],\n negative_image_embeds: Union[torch.Tensor, List[torch.Tensor]],\n hint: torch.Tensor,\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 100,\n guidance_scale: float = 4.0,\n num_images_per_prompt: int = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n return_dict: bool = True,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n hint (`torch.Tensor`):\n The controlnet condition.\n image_embeds (`torch.Tensor` or `List[torch.Tensor]`):\n The clip image embeddings for text prompt, that will be used to condition the image generation.\n negative_image_embeds (`torch.Tensor` or `List[torch.Tensor]`):\n The clip image embeddings for negative text prompt, will be used to condition the image generation.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between: `\"pil\"` (`PIL.Image.Image`), `\"np\"`\n (`np.array`) or `\"pt\"` (`torch.Tensor`).\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`\n \"\"\"\n device = self._execution_device\n\n do_classifier_free_guidance = guidance_scale > 1.0\n\n if isinstance(image_embeds, list):\n image_embeds = torch.cat(image_embeds, dim=0)\n if isinstance(negative_image_embeds, list):\n negative_image_embeds = torch.cat(negative_image_embeds, dim=0)\n if isinstance(hint, list):\n hint = torch.cat(hint, dim=0)\n\n batch_size = image_embeds.shape[0] * num_images_per_prompt\n\n if do_classifier_free_guidance:\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n hint = hint.repeat_interleave(num_images_per_prompt, dim=0)\n\n image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(\n dtype=self.unet.dtype, device=device\n )\n hint = torch.cat([hint, hint], dim=0).to(dtype=self.unet.dtype, device=device)\n\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps_tensor = self.scheduler.timesteps\n\n num_channels_latents = self.movq.config.latent_channels\n\n height, width = downscale_height_and_width(height, width, self.movq_scale_factor)\n\n # create initial latent\n latents = self.prepare_latents(\n (batch_size, num_channels_latents, height, width),\n image_embeds.dtype,\n device,\n generator,\n latents,\n self.scheduler,\n )\n\n for i, t in enumerate(self.progress_bar(timesteps_tensor)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n\n added_cond_kwargs = {\"image_embeds\": image_embeds, \"hint\": hint}\n noise_pred = self.unet(\n sample=latent_model_input,\n timestep=t,\n encoder_hidden_states=None,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n if do_classifier_free_guidance:\n noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n _, variance_pred_text = variance_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n noise_pred = torch.cat([noise_pred, variance_pred_text], dim=1)\n\n if not (\n hasattr(self.scheduler.config, \"variance_type\")\n and self.scheduler.config.variance_type in [\"learned\", \"learned_range\"]\n ):\n noise_pred, _ = noise_pred.split(latents.shape[1], dim=1)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(\n noise_pred,\n t,\n latents,\n generator=generator,\n )[0]\n\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n # post-processing\n image = self.movq.decode(latents, force_not_quantize=True)[\"sample\"]\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if output_type not in [\"pt\", \"np\", \"pil\"]:\n raise ValueError(f\"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}\")\n\n if output_type in [\"np\", \"pil\"]:\n image = image * 0.5 + 0.5\n image = image.clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Callable, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom PIL import Image\n\nfrom ...models import UNet2DConditionModel, VQModel\nfrom ...schedulers import DDPMScheduler\nfrom ...utils import (\n logging,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> import numpy as np\n\n >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n >>> from transformers import pipeline\n >>> from diffusers.utils import load_image\n\n\n >>> def make_hint(image, depth_estimator):\n ... image = depth_estimator(image)[\"depth\"]\n ... image = np.array(image)\n ... image = image[:, :, None]\n ... image = np.concatenate([image, image, image], axis=2)\n ... detected_map = torch.from_numpy(image).float() / 255.0\n ... hint = detected_map.permute(2, 0, 1)\n ... return hint\n\n\n >>> depth_estimator = pipeline(\"depth-estimation\")\n\n >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n ... )\n >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> img = load_image(\n ... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n ... \"/kandinsky/cat.png\"\n ... ).resize((768, 768))\n\n\n >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n >>> prompt = \"A robot, 4k photo\"\n >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n >>> images = pipe(\n ... image=img,\n ... strength=0.5,\n ... image_embeds=img_emb.image_embeds,\n ... negative_image_embeds=negative_emb.image_embeds,\n ... hint=hint,\n ... num_inference_steps=50,\n ... generator=generator,\n ... height=768,\n ... width=768,\n ... ).images\n\n >>> images[0].save(\"robot_cat.png\")\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.kandinsky2_2.pipeline_kandinsky2_2.downscale_height_and_width\ndef downscale_height_and_width(height, width, scale_factor=8):\n new_height = height // scale_factor**2\n if height % scale_factor**2 != 0:\n new_height += 1\n new_width = width // scale_factor**2\n if width % scale_factor**2 != 0:\n new_width += 1\n return new_height * scale_factor, new_width * scale_factor\n\n\n# Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_img2img.prepare_image\ndef prepare_image(pil_image, w=512, h=512):\n pil_image = pil_image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)\n arr = np.array(pil_image.convert(\"RGB\"))\n arr = arr.astype(np.float32) / 127.5 - 1\n arr = np.transpose(arr, [2, 0, 1])\n image = torch.from_numpy(arr).unsqueeze(0)\n return image\n\n\nclass KandinskyV22ControlnetImg2ImgPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for image-to-image generation using Kandinsky\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n scheduler ([`DDIMScheduler`]):\n A scheduler to be used in combination with `unet` to generate image latents.\n unet ([`UNet2DConditionModel`]):\n Conditional U-Net architecture to denoise the image embedding.\n movq ([`VQModel`]):\n MoVQ Decoder to generate the image from the latents.\n \"\"\"\n\n model_cpu_offload_seq = \"unet->movq\"\n\n def __init__(\n self,\n unet: UNet2DConditionModel,\n scheduler: DDPMScheduler,\n movq: VQModel,\n ):\n super().__init__()\n\n self.register_modules(\n unet=unet,\n scheduler=scheduler,\n movq=movq,\n )\n self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)\n\n # Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_img2img.KandinskyImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start:]\n\n return timesteps, num_inference_steps - t_start\n\n # Copied from diffusers.pipelines.kandinsky2_2.pipeline_kandinsky2_2_img2img.KandinskyV22Img2ImgPipeline.prepare_latents\n def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n\n if image.shape[1] == 4:\n init_latents = image\n\n else:\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n elif isinstance(generator, list):\n init_latents = [\n self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = self.movq.encode(image).latent_dist.sample(generator)\n\n init_latents = self.movq.config.scaling_factor * init_latents\n\n init_latents = torch.cat([init_latents], dim=0)\n\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n\n latents = init_latents\n\n return latents\n\n @torch.no_grad()\n def __call__(\n self,\n image_embeds: Union[torch.Tensor, List[torch.Tensor]],\n image: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]],\n negative_image_embeds: Union[torch.Tensor, List[torch.Tensor]],\n hint: torch.Tensor,\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 100,\n guidance_scale: float = 4.0,\n strength: float = 0.3,\n num_images_per_prompt: int = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n output_type: Optional[str] = \"pil\",\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n return_dict: bool = True,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n image_embeds (`torch.Tensor` or `List[torch.Tensor]`):\n The clip image embeddings for text prompt, that will be used to condition the image generation.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process. Can also accept image latents as `image`, if passing latents directly, it will not be encoded\n again.\n strength (`float`, *optional*, defaults to 0.8):\n Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`\n will be used as a starting point, adding more noise to it the larger the `strength`. The number of\n denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will\n be maximum and the denoising process will run for the full number of iterations specified in\n `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.\n hint (`torch.Tensor`):\n The controlnet condition.\n negative_image_embeds (`torch.Tensor` or `List[torch.Tensor]`):\n The clip image embeddings for negative text prompt, will be used to condition the image generation.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between: `\"pil\"` (`PIL.Image.Image`), `\"np\"`\n (`np.array`) or `\"pt\"` (`torch.Tensor`).\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`\n \"\"\"\n device = self._execution_device\n\n do_classifier_free_guidance = guidance_scale > 1.0\n\n if isinstance(image_embeds, list):\n image_embeds = torch.cat(image_embeds, dim=0)\n if isinstance(negative_image_embeds, list):\n negative_image_embeds = torch.cat(negative_image_embeds, dim=0)\n if isinstance(hint, list):\n hint = torch.cat(hint, dim=0)\n\n batch_size = image_embeds.shape[0]\n\n if do_classifier_free_guidance:\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n hint = hint.repeat_interleave(num_images_per_prompt, dim=0)\n\n image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(\n dtype=self.unet.dtype, device=device\n )\n hint = torch.cat([hint, hint], dim=0).to(dtype=self.unet.dtype, device=device)\n\n if not isinstance(image, list):\n image = [image]\n if not all(isinstance(i, (PIL.Image.Image, torch.Tensor)) for i in image):\n raise ValueError(\n f\"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support PIL image and pytorch tensor\"\n )\n\n image = torch.cat([prepare_image(i, width, height) for i in image], dim=0)\n image = image.to(dtype=image_embeds.dtype, device=device)\n\n latents = self.movq.encode(image)[\"latents\"]\n latents = latents.repeat_interleave(num_images_per_prompt, dim=0)\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n height, width = downscale_height_and_width(height, width, self.movq_scale_factor)\n latents = self.prepare_latents(\n latents, latent_timestep, batch_size, num_images_per_prompt, image_embeds.dtype, device, generator\n )\n for i, t in enumerate(self.progress_bar(timesteps)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n\n added_cond_kwargs = {\"image_embeds\": image_embeds, \"hint\": hint}\n noise_pred = self.unet(\n sample=latent_model_input,\n timestep=t,\n encoder_hidden_states=None,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n if do_classifier_free_guidance:\n noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n _, variance_pred_text = variance_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n noise_pred = torch.cat([noise_pred, variance_pred_text], dim=1)\n\n if not (\n hasattr(self.scheduler.config, \"variance_type\")\n and self.scheduler.config.variance_type in [\"learned\", \"learned_range\"]\n ):\n noise_pred, _ = noise_pred.split(latents.shape[1], dim=1)\n\n # compute the previous noisy sample x_t -> x_t-1\n\n latents = self.scheduler.step(\n noise_pred,\n t,\n latents,\n generator=generator,\n )[0]\n\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # post-processing\n image = self.movq.decode(latents, force_not_quantize=True)[\"sample\"]\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if output_type not in [\"pt\", \"np\", \"pil\"]:\n raise ValueError(f\"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}\")\n\n if output_type in [\"np\", \"pil\"]:\n image = image * 0.5 + 0.5\n image = image.clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom PIL import Image\n\nfrom ...models import UNet2DConditionModel, VQModel\nfrom ...schedulers import DDPMScheduler\nfrom ...utils import deprecate, logging\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n >>> from diffusers.utils import load_image\n >>> import torch\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n ... )\n >>> pipe_prior.to(\"cuda\")\n\n >>> prompt = \"A red cartoon frog, 4k\"\n >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n ... )\n >>> pipe.to(\"cuda\")\n\n >>> init_image = load_image(\n ... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n ... \"/kandinsky/frog.png\"\n ... )\n\n >>> image = pipe(\n ... image=init_image,\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=100,\n ... strength=0.2,\n ... ).images\n\n >>> image[0].save(\"red_frog.png\")\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.kandinsky2_2.pipeline_kandinsky2_2.downscale_height_and_width\ndef downscale_height_and_width(height, width, scale_factor=8):\n new_height = height // scale_factor**2\n if height % scale_factor**2 != 0:\n new_height += 1\n new_width = width // scale_factor**2\n if width % scale_factor**2 != 0:\n new_width += 1\n return new_height * scale_factor, new_width * scale_factor\n\n\n# Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_img2img.prepare_image\ndef prepare_image(pil_image, w=512, h=512):\n pil_image = pil_image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)\n arr = np.array(pil_image.convert(\"RGB\"))\n arr = arr.astype(np.float32) / 127.5 - 1\n arr = np.transpose(arr, [2, 0, 1])\n image = torch.from_numpy(arr).unsqueeze(0)\n return image\n\n\nclass KandinskyV22Img2ImgPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for image-to-image generation using Kandinsky\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n scheduler ([`DDIMScheduler`]):\n A scheduler to be used in combination with `unet` to generate image latents.\n unet ([`UNet2DConditionModel`]):\n Conditional U-Net architecture to denoise the image embedding.\n movq ([`VQModel`]):\n MoVQ Decoder to generate the image from the latents.\n \"\"\"\n\n model_cpu_offload_seq = \"unet->movq\"\n _callback_tensor_inputs = [\"latents\", \"image_embeds\", \"negative_image_embeds\"]\n\n def __init__(\n self,\n unet: UNet2DConditionModel,\n scheduler: DDPMScheduler,\n movq: VQModel,\n ):\n super().__init__()\n\n self.register_modules(\n unet=unet,\n scheduler=scheduler,\n movq=movq,\n )\n self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)\n\n # Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_img2img.KandinskyImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start:]\n\n return timesteps, num_inference_steps - t_start\n\n def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n\n if image.shape[1] == 4:\n init_latents = image\n\n else:\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n elif isinstance(generator, list):\n init_latents = [\n self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = self.movq.encode(image).latent_dist.sample(generator)\n\n init_latents = self.movq.config.scaling_factor * init_latents\n\n init_latents = torch.cat([init_latents], dim=0)\n\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n\n latents = init_latents\n\n return latents\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n def __call__(\n self,\n image_embeds: Union[torch.Tensor, List[torch.Tensor]],\n image: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]],\n negative_image_embeds: Union[torch.Tensor, List[torch.Tensor]],\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 100,\n guidance_scale: float = 4.0,\n strength: float = 0.3,\n num_images_per_prompt: int = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n image_embeds (`torch.Tensor` or `List[torch.Tensor]`):\n The clip image embeddings for text prompt, that will be used to condition the image generation.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process. Can also accept image latents as `image`, if passing latents directly, it will not be encoded\n again.\n strength (`float`, *optional*, defaults to 0.8):\n Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`\n will be used as a starting point, adding more noise to it the larger the `strength`. The number of\n denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will\n be maximum and the denoising process will run for the full number of iterations specified in\n `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.\n negative_image_embeds (`torch.Tensor` or `List[torch.Tensor]`):\n The clip image embeddings for negative text prompt, will be used to condition the image generation.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between: `\"pil\"` (`PIL.Image.Image`), `\"np\"`\n (`np.array`) or `\"pt\"` (`torch.Tensor`).\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n device = self._execution_device\n\n self._guidance_scale = guidance_scale\n\n if isinstance(image_embeds, list):\n image_embeds = torch.cat(image_embeds, dim=0)\n batch_size = image_embeds.shape[0]\n if isinstance(negative_image_embeds, list):\n negative_image_embeds = torch.cat(negative_image_embeds, dim=0)\n\n if self.do_classifier_free_guidance:\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n\n image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(\n dtype=self.unet.dtype, device=device\n )\n\n if not isinstance(image, list):\n image = [image]\n if not all(isinstance(i, (PIL.Image.Image, torch.Tensor)) for i in image):\n raise ValueError(\n f\"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support PIL image and pytorch tensor\"\n )\n\n image = torch.cat([prepare_image(i, width, height) for i in image], dim=0)\n image = image.to(dtype=image_embeds.dtype, device=device)\n\n latents = self.movq.encode(image)[\"latents\"]\n latents = latents.repeat_interleave(num_images_per_prompt, dim=0)\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n height, width = downscale_height_and_width(height, width, self.movq_scale_factor)\n latents = self.prepare_latents(\n latents, latent_timestep, batch_size, num_images_per_prompt, image_embeds.dtype, device, generator\n )\n self._num_timesteps = len(timesteps)\n for i, t in enumerate(self.progress_bar(timesteps)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n\n added_cond_kwargs = {\"image_embeds\": image_embeds}\n noise_pred = self.unet(\n sample=latent_model_input,\n timestep=t,\n encoder_hidden_states=None,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n if self.do_classifier_free_guidance:\n noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n _, variance_pred_text = variance_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n noise_pred = torch.cat([noise_pred, variance_pred_text], dim=1)\n\n if not (\n hasattr(self.scheduler.config, \"variance_type\")\n and self.scheduler.config.variance_type in [\"learned\", \"learned_range\"]\n ):\n noise_pred, _ = noise_pred.split(latents.shape[1], dim=1)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(\n noise_pred,\n t,\n latents,\n generator=generator,\n )[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n image_embeds = callback_outputs.pop(\"image_embeds\", image_embeds)\n negative_image_embeds = callback_outputs.pop(\"negative_image_embeds\", negative_image_embeds)\n\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if output_type not in [\"pt\", \"np\", \"pil\", \"latent\"]:\n raise ValueError(\n f\"Only the output types `pt`, `pil` ,`np` and `latent` are supported not output_type={output_type}\"\n )\n\n if not output_type == \"latent\":\n # post-processing\n image = self.movq.decode(latents, force_not_quantize=True)[\"sample\"]\n if output_type in [\"np\", \"pil\"]:\n image = image * 0.5 + 0.5\n image = image.clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n else:\n image = latents\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom copy import deepcopy\nfrom typing import Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom packaging import version\nfrom PIL import Image\n\nfrom ... import __version__\nfrom ...models import UNet2DConditionModel, VQModel\nfrom ...schedulers import DDPMScheduler\nfrom ...utils import deprecate, logging\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import KandinskyV22InpaintPipeline, KandinskyV22PriorPipeline\n >>> from diffusers.utils import load_image\n >>> import torch\n >>> import numpy as np\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n ... )\n >>> pipe_prior.to(\"cuda\")\n\n >>> prompt = \"a hat\"\n >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n >>> pipe = KandinskyV22InpaintPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-decoder-inpaint\", torch_dtype=torch.float16\n ... )\n >>> pipe.to(\"cuda\")\n\n >>> init_image = load_image(\n ... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n ... \"/kandinsky/cat.png\"\n ... )\n\n >>> mask = np.zeros((768, 768), dtype=np.float32)\n >>> mask[:250, 250:-250] = 1\n\n >>> out = pipe(\n ... image=init_image,\n ... mask_image=mask,\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=50,\n ... )\n\n >>> image = out.images[0]\n >>> image.save(\"cat_with_hat.png\")\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.kandinsky2_2.pipeline_kandinsky2_2.downscale_height_and_width\ndef downscale_height_and_width(height, width, scale_factor=8):\n new_height = height // scale_factor**2\n if height % scale_factor**2 != 0:\n new_height += 1\n new_width = width // scale_factor**2\n if width % scale_factor**2 != 0:\n new_width += 1\n return new_height * scale_factor, new_width * scale_factor\n\n\n# Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_inpaint.prepare_mask\ndef prepare_mask(masks):\n prepared_masks = []\n for mask in masks:\n old_mask = deepcopy(mask)\n for i in range(mask.shape[1]):\n for j in range(mask.shape[2]):\n if old_mask[0][i][j] == 1:\n continue\n if i != 0:\n mask[:, i - 1, j] = 0\n if j != 0:\n mask[:, i, j - 1] = 0\n if i != 0 and j != 0:\n mask[:, i - 1, j - 1] = 0\n if i != mask.shape[1] - 1:\n mask[:, i + 1, j] = 0\n if j != mask.shape[2] - 1:\n mask[:, i, j + 1] = 0\n if i != mask.shape[1] - 1 and j != mask.shape[2] - 1:\n mask[:, i + 1, j + 1] = 0\n prepared_masks.append(mask)\n return torch.stack(prepared_masks, dim=0)\n\n\n# Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_inpaint.prepare_mask_and_masked_image\ndef prepare_mask_and_masked_image(image, mask, height, width):\n r\"\"\"\n Prepares a pair (mask, image) to be consumed by the Kandinsky inpaint pipeline. This means that those inputs will\n be converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for\n the ``image`` and ``1`` for the ``mask``.\n\n The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be\n binarized (``mask > 0.5``) and cast to ``torch.float32`` too.\n\n Args:\n image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.\n It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``\n ``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.\n mask (_type_): The mask to apply to the image, i.e. regions to inpaint.\n It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``\n ``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n\n\n Raises:\n ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask\n should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.\n TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not\n (ot the other way around).\n\n Returns:\n tuple[torch.Tensor]: The pair (mask, image) as ``torch.Tensor`` with 4\n dimensions: ``batch x channels x height x width``.\n \"\"\"\n\n if image is None:\n raise ValueError(\"`image` input cannot be undefined.\")\n\n if mask is None:\n raise ValueError(\"`mask_image` input cannot be undefined.\")\n\n if isinstance(image, torch.Tensor):\n if not isinstance(mask, torch.Tensor):\n raise TypeError(f\"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not\")\n\n # Batch single image\n if image.ndim == 3:\n assert image.shape[0] == 3, \"Image outside a batch should be of shape (3, H, W)\"\n image = image.unsqueeze(0)\n\n # Batch and add channel dim for single mask\n if mask.ndim == 2:\n mask = mask.unsqueeze(0).unsqueeze(0)\n\n # Batch single mask or add channel dim\n if mask.ndim == 3:\n # Single batched mask, no channel dim or single mask not batched but channel dim\n if mask.shape[0] == 1:\n mask = mask.unsqueeze(0)\n\n # Batched masks no channel dim\n else:\n mask = mask.unsqueeze(1)\n\n assert image.ndim == 4 and mask.ndim == 4, \"Image and Mask must have 4 dimensions\"\n assert image.shape[-2:] == mask.shape[-2:], \"Image and Mask must have the same spatial dimensions\"\n assert image.shape[0] == mask.shape[0], \"Image and Mask must have the same batch size\"\n\n # Check image is in [-1, 1]\n if image.min() < -1 or image.max() > 1:\n raise ValueError(\"Image should be in [-1, 1] range\")\n\n # Check mask is in [0, 1]\n if mask.min() < 0 or mask.max() > 1:\n raise ValueError(\"Mask should be in [0, 1] range\")\n\n # Binarize mask\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n\n # Image as float32\n image = image.to(dtype=torch.float32)\n elif isinstance(mask, torch.Tensor):\n raise TypeError(f\"`mask` is a torch.Tensor but `image` (type: {type(image)} is not\")\n else:\n # preprocess image\n if isinstance(image, (PIL.Image.Image, np.ndarray)):\n image = [image]\n\n if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):\n # resize all images w.r.t passed height an width\n image = [i.resize((width, height), resample=Image.BICUBIC, reducing_gap=1) for i in image]\n image = [np.array(i.convert(\"RGB\"))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n elif isinstance(image, list) and isinstance(image[0], np.ndarray):\n image = np.concatenate([i[None, :] for i in image], axis=0)\n\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0\n\n # preprocess mask\n if isinstance(mask, (PIL.Image.Image, np.ndarray)):\n mask = [mask]\n\n if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):\n mask = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in mask]\n mask = np.concatenate([np.array(m.convert(\"L\"))[None, None, :] for m in mask], axis=0)\n mask = mask.astype(np.float32) / 255.0\n elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):\n mask = np.concatenate([m[None, None, :] for m in mask], axis=0)\n\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n mask = torch.from_numpy(mask)\n\n mask = 1 - mask\n\n return mask, image\n\n\nclass KandinskyV22InpaintPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for text-guided image inpainting using Kandinsky2.1\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n scheduler ([`DDIMScheduler`]):\n A scheduler to be used in combination with `unet` to generate image latents.\n unet ([`UNet2DConditionModel`]):\n Conditional U-Net architecture to denoise the image embedding.\n movq ([`VQModel`]):\n MoVQ Decoder to generate the image from the latents.\n \"\"\"\n\n model_cpu_offload_seq = \"unet->movq\"\n _callback_tensor_inputs = [\"latents\", \"image_embeds\", \"negative_image_embeds\", \"masked_image\", \"mask_image\"]\n\n def __init__(\n self,\n unet: UNet2DConditionModel,\n scheduler: DDPMScheduler,\n movq: VQModel,\n ):\n super().__init__()\n\n self.register_modules(\n unet=unet,\n scheduler=scheduler,\n movq=movq,\n )\n self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)\n self._warn_has_been_called = False\n\n # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents\n def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n latents = latents * scheduler.init_noise_sigma\n return latents\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n def __call__(\n self,\n image_embeds: Union[torch.Tensor, List[torch.Tensor]],\n image: Union[torch.Tensor, PIL.Image.Image],\n mask_image: Union[torch.Tensor, PIL.Image.Image, np.ndarray],\n negative_image_embeds: Union[torch.Tensor, List[torch.Tensor]],\n height: int = 512,\n width: int = 512,\n num_inference_steps: int = 100,\n guidance_scale: float = 4.0,\n num_images_per_prompt: int = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n image_embeds (`torch.Tensor` or `List[torch.Tensor]`):\n The clip image embeddings for text prompt, that will be used to condition the image generation.\n image (`PIL.Image.Image`):\n `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will\n be masked out with `mask_image` and repainted according to `prompt`.\n mask_image (`np.array`):\n Tensor representing an image batch, to mask `image`. White pixels in the mask will be repainted, while\n black pixels will be preserved. If `mask_image` is a PIL image, it will be converted to a single\n channel (luminance) before use. If it's a tensor, it should contain one color channel (L) instead of 3,\n so the expected shape would be `(B, H, W, 1)`.\n negative_image_embeds (`torch.Tensor` or `List[torch.Tensor]`):\n The clip image embeddings for negative text prompt, will be used to condition the image generation.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between: `\"pil\"` (`PIL.Image.Image`), `\"np\"`\n (`np.array`) or `\"pt\"` (`torch.Tensor`).\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`\n \"\"\"\n if not self._warn_has_been_called and version.parse(version.parse(__version__).base_version) < version.parse(\n \"0.23.0.dev0\"\n ):\n logger.warning(\n \"Please note that the expected format of `mask_image` has recently been changed. \"\n \"Before diffusers == 0.19.0, Kandinsky Inpainting pipelines repainted black pixels and preserved black pixels. \"\n \"As of diffusers==0.19.0 this behavior has been inverted. Now white pixels are repainted and black pixels are preserved. \"\n \"This way, Kandinsky's masking behavior is aligned with Stable Diffusion. \"\n \"THIS means that you HAVE to invert the input mask to have the same behavior as before as explained in https://github.com/huggingface/diffusers/pull/4207. \"\n \"This warning will be surpressed after the first inference call and will be removed in diffusers>0.23.0\"\n )\n self._warn_has_been_called = True\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n self._guidance_scale = guidance_scale\n\n device = self._execution_device\n\n if isinstance(image_embeds, list):\n image_embeds = torch.cat(image_embeds, dim=0)\n batch_size = image_embeds.shape[0] * num_images_per_prompt\n if isinstance(negative_image_embeds, list):\n negative_image_embeds = torch.cat(negative_image_embeds, dim=0)\n\n if self.do_classifier_free_guidance:\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n\n image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(\n dtype=self.unet.dtype, device=device\n )\n\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # preprocess image and mask\n mask_image, image = prepare_mask_and_masked_image(image, mask_image, height, width)\n\n image = image.to(dtype=image_embeds.dtype, device=device)\n image = self.movq.encode(image)[\"latents\"]\n\n mask_image = mask_image.to(dtype=image_embeds.dtype, device=device)\n\n image_shape = tuple(image.shape[-2:])\n mask_image = F.interpolate(\n mask_image,\n image_shape,\n mode=\"nearest\",\n )\n mask_image = prepare_mask(mask_image)\n masked_image = image * mask_image\n\n mask_image = mask_image.repeat_interleave(num_images_per_prompt, dim=0)\n masked_image = masked_image.repeat_interleave(num_images_per_prompt, dim=0)\n if self.do_classifier_free_guidance:\n mask_image = mask_image.repeat(2, 1, 1, 1)\n masked_image = masked_image.repeat(2, 1, 1, 1)\n\n num_channels_latents = self.movq.config.latent_channels\n\n height, width = downscale_height_and_width(height, width, self.movq_scale_factor)\n\n # create initial latent\n latents = self.prepare_latents(\n (batch_size, num_channels_latents, height, width),\n image_embeds.dtype,\n device,\n generator,\n latents,\n self.scheduler,\n )\n noise = torch.clone(latents)\n\n self._num_timesteps = len(timesteps)\n for i, t in enumerate(self.progress_bar(timesteps)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = torch.cat([latent_model_input, masked_image, mask_image], dim=1)\n\n added_cond_kwargs = {\"image_embeds\": image_embeds}\n noise_pred = self.unet(\n sample=latent_model_input,\n timestep=t,\n encoder_hidden_states=None,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n if self.do_classifier_free_guidance:\n noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n _, variance_pred_text = variance_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n noise_pred = torch.cat([noise_pred, variance_pred_text], dim=1)\n\n if not (\n hasattr(self.scheduler.config, \"variance_type\")\n and self.scheduler.config.variance_type in [\"learned\", \"learned_range\"]\n ):\n noise_pred, _ = noise_pred.split(latents.shape[1], dim=1)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(\n noise_pred,\n t,\n latents,\n generator=generator,\n )[0]\n init_latents_proper = image[:1]\n init_mask = mask_image[:1]\n\n if i < len(timesteps) - 1:\n noise_timestep = timesteps[i + 1]\n init_latents_proper = self.scheduler.add_noise(\n init_latents_proper, noise, torch.tensor([noise_timestep])\n )\n\n latents = init_mask * init_latents_proper + (1 - init_mask) * latents\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n image_embeds = callback_outputs.pop(\"image_embeds\", image_embeds)\n negative_image_embeds = callback_outputs.pop(\"negative_image_embeds\", negative_image_embeds)\n masked_image = callback_outputs.pop(\"masked_image\", masked_image)\n mask_image = callback_outputs.pop(\"mask_image\", mask_image)\n\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # post-processing\n latents = mask_image[:1] * image[:1] + (1 - mask_image[:1]) * latents\n\n if output_type not in [\"pt\", \"np\", \"pil\", \"latent\"]:\n raise ValueError(\n f\"Only the output types `pt`, `pil`, `np` and `latent` are supported not output_type={output_type}\"\n )\n\n if not output_type == \"latent\":\n image = self.movq.decode(latents, force_not_quantize=True)[\"sample\"]\n\n if output_type in [\"np\", \"pil\"]:\n image = image * 0.5 + 0.5\n image = image.clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n else:\n image = latents\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior.py", "content": "from typing import Callable, Dict, List, Optional, Union\n\nimport PIL.Image\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...models import PriorTransformer\nfrom ...schedulers import UnCLIPScheduler\nfrom ...utils import (\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..kandinsky import KandinskyPriorPipelineOutput\nfrom ..pipeline_utils import DiffusionPipeline\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n >>> import torch\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n >>> pipe_prior.to(\"cuda\")\n >>> prompt = \"red cat, 4k photo\"\n >>> image_emb, negative_image_emb = pipe_prior(prompt).to_tuple()\n\n >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n >>> pipe.to(\"cuda\")\n >>> image = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=negative_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=50,\n ... ).images\n >>> image[0].save(\"cat.png\")\n ```\n\"\"\"\n\nEXAMPLE_INTERPOLATE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22Pipeline\n >>> from diffusers.utils import load_image\n >>> import PIL\n >>> import torch\n >>> from torchvision import transforms\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n ... )\n >>> pipe_prior.to(\"cuda\")\n >>> img1 = load_image(\n ... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n ... \"/kandinsky/cat.png\"\n ... )\n >>> img2 = load_image(\n ... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n ... \"/kandinsky/starry_night.jpeg\"\n ... )\n >>> images_texts = [\"a cat\", img1, img2]\n >>> weights = [0.3, 0.3, 0.4]\n >>> out = pipe_prior.interpolate(images_texts, weights)\n >>> pipe = KandinskyV22Pipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n ... )\n >>> pipe.to(\"cuda\")\n >>> image = pipe(\n ... image_embeds=out.image_embeds,\n ... negative_image_embeds=out.negative_image_embeds,\n ... height=768,\n ... width=768,\n ... num_inference_steps=50,\n ... ).images[0]\n >>> image.save(\"starry_cat.png\")\n ```\n\"\"\"\n\n\nclass KandinskyV22PriorPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for generating image prior for Kandinsky\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n prior ([`PriorTransformer`]):\n The canonical unCLIP prior to approximate the image embedding from the text embedding.\n image_encoder ([`CLIPVisionModelWithProjection`]):\n Frozen image-encoder.\n text_encoder ([`CLIPTextModelWithProjection`]):\n Frozen text-encoder.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n scheduler ([`UnCLIPScheduler`]):\n A scheduler to be used in combination with `prior` to generate image embedding.\n image_processor ([`CLIPImageProcessor`]):\n A image_processor to be used to preprocess image from clip.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->image_encoder->prior\"\n _exclude_from_cpu_offload = [\"prior\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"text_encoder_hidden_states\", \"text_mask\"]\n\n def __init__(\n self,\n prior: PriorTransformer,\n image_encoder: CLIPVisionModelWithProjection,\n text_encoder: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n scheduler: UnCLIPScheduler,\n image_processor: CLIPImageProcessor,\n ):\n super().__init__()\n\n self.register_modules(\n prior=prior,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n scheduler=scheduler,\n image_encoder=image_encoder,\n image_processor=image_processor,\n )\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_INTERPOLATE_DOC_STRING)\n def interpolate(\n self,\n images_and_prompts: List[Union[str, PIL.Image.Image, torch.Tensor]],\n weights: List[float],\n num_images_per_prompt: int = 1,\n num_inference_steps: int = 25,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n negative_prior_prompt: Optional[str] = None,\n negative_prompt: str = \"\",\n guidance_scale: float = 4.0,\n device=None,\n ):\n \"\"\"\n Function invoked when using the prior pipeline for interpolation.\n\n Args:\n images_and_prompts (`List[Union[str, PIL.Image.Image, torch.Tensor]]`):\n list of prompts and images to guide the image generation.\n weights: (`List[float]`):\n list of weights for each condition in `images_and_prompts`\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n negative_prior_prompt (`str`, *optional*):\n The prompt not to guide the prior diffusion process. Ignored when not using guidance (i.e., ignored if\n `guidance_scale` is less than `1`).\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt not to guide the image generation. Ignored when not using guidance (i.e., ignored if\n `guidance_scale` is less than `1`).\n guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n\n Examples:\n\n Returns:\n [`KandinskyPriorPipelineOutput`] or `tuple`\n \"\"\"\n\n device = device or self.device\n\n if len(images_and_prompts) != len(weights):\n raise ValueError(\n f\"`images_and_prompts` contains {len(images_and_prompts)} items and `weights` contains {len(weights)} items - they should be lists of same length\"\n )\n\n image_embeddings = []\n for cond, weight in zip(images_and_prompts, weights):\n if isinstance(cond, str):\n image_emb = self(\n cond,\n num_inference_steps=num_inference_steps,\n num_images_per_prompt=num_images_per_prompt,\n generator=generator,\n latents=latents,\n negative_prompt=negative_prior_prompt,\n guidance_scale=guidance_scale,\n ).image_embeds.unsqueeze(0)\n\n elif isinstance(cond, (PIL.Image.Image, torch.Tensor)):\n if isinstance(cond, PIL.Image.Image):\n cond = (\n self.image_processor(cond, return_tensors=\"pt\")\n .pixel_values[0]\n .unsqueeze(0)\n .to(dtype=self.image_encoder.dtype, device=device)\n )\n\n image_emb = self.image_encoder(cond)[\"image_embeds\"].repeat(num_images_per_prompt, 1).unsqueeze(0)\n\n else:\n raise ValueError(\n f\"`images_and_prompts` can only contains elements to be of type `str`, `PIL.Image.Image` or `torch.Tensor` but is {type(cond)}\"\n )\n\n image_embeddings.append(image_emb * weight)\n\n image_emb = torch.cat(image_embeddings).sum(dim=0)\n\n out_zero = self(\n negative_prompt,\n num_inference_steps=num_inference_steps,\n num_images_per_prompt=num_images_per_prompt,\n generator=generator,\n latents=latents,\n negative_prompt=negative_prior_prompt,\n guidance_scale=guidance_scale,\n )\n zero_image_emb = out_zero.negative_image_embeds if negative_prompt == \"\" else out_zero.image_embeds\n\n return KandinskyPriorPipelineOutput(image_embeds=image_emb, negative_image_embeds=zero_image_emb)\n\n # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents\n def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n latents = latents * scheduler.init_noise_sigma\n return latents\n\n # Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_prior.KandinskyPriorPipeline.get_zero_embed\n def get_zero_embed(self, batch_size=1, device=None):\n device = device or self.device\n zero_img = torch.zeros(1, 3, self.image_encoder.config.image_size, self.image_encoder.config.image_size).to(\n device=device, dtype=self.image_encoder.dtype\n )\n zero_image_emb = self.image_encoder(zero_img)[\"image_embeds\"]\n zero_image_emb = zero_image_emb.repeat(batch_size, 1)\n return zero_image_emb\n\n # Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_prior.KandinskyPriorPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n ):\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n text_mask = text_inputs.attention_mask.bool().to(device)\n\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]\n\n text_encoder_output = self.text_encoder(text_input_ids.to(device))\n\n prompt_embeds = text_encoder_output.text_embeds\n text_encoder_hidden_states = text_encoder_output.last_hidden_state\n\n prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n uncond_text_mask = uncond_input.attention_mask.bool().to(device)\n negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))\n\n negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds\n uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)\n\n seq_len = uncond_text_encoder_hidden_states.shape[1]\n uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)\n uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(\n batch_size * num_images_per_prompt, seq_len, -1\n )\n uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n # done duplicates\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])\n\n text_mask = torch.cat([uncond_text_mask, text_mask])\n\n return prompt_embeds, text_encoder_hidden_states, text_mask\n\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]],\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: int = 1,\n num_inference_steps: int = 25,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n guidance_scale: float = 4.0,\n output_type: Optional[str] = \"pt\", # pt only\n return_dict: bool = True,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n output_type (`str`, *optional*, defaults to `\"pt\"`):\n The output format of the generate image. Choose between: `\"np\"` (`np.array`) or `\"pt\"`\n (`torch.Tensor`).\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`KandinskyPriorPipelineOutput`] or `tuple`\n \"\"\"\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if isinstance(prompt, str):\n prompt = [prompt]\n elif not isinstance(prompt, list):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if isinstance(negative_prompt, str):\n negative_prompt = [negative_prompt]\n elif not isinstance(negative_prompt, list) and negative_prompt is not None:\n raise ValueError(f\"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}\")\n\n # if the negative prompt is defined we double the batch size to\n # directly retrieve the negative prompt embedding\n if negative_prompt is not None:\n prompt = prompt + negative_prompt\n negative_prompt = 2 * negative_prompt\n\n device = self._execution_device\n\n batch_size = len(prompt)\n batch_size = batch_size * num_images_per_prompt\n\n self._guidance_scale = guidance_scale\n\n prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt(\n prompt, device, num_images_per_prompt, self.do_classifier_free_guidance, negative_prompt\n )\n\n # prior\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n embedding_dim = self.prior.config.embedding_dim\n\n latents = self.prepare_latents(\n (batch_size, embedding_dim),\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n self.scheduler,\n )\n self._num_timesteps = len(timesteps)\n for i, t in enumerate(self.progress_bar(timesteps)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n\n predicted_image_embedding = self.prior(\n latent_model_input,\n timestep=t,\n proj_embedding=prompt_embeds,\n encoder_hidden_states=text_encoder_hidden_states,\n attention_mask=text_mask,\n ).predicted_image_embedding\n\n if self.do_classifier_free_guidance:\n predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)\n predicted_image_embedding = predicted_image_embedding_uncond + self.guidance_scale * (\n predicted_image_embedding_text - predicted_image_embedding_uncond\n )\n\n if i + 1 == timesteps.shape[0]:\n prev_timestep = None\n else:\n prev_timestep = timesteps[i + 1]\n\n latents = self.scheduler.step(\n predicted_image_embedding,\n timestep=t,\n sample=latents,\n generator=generator,\n prev_timestep=prev_timestep,\n ).prev_sample\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n text_encoder_hidden_states = callback_outputs.pop(\n \"text_encoder_hidden_states\", text_encoder_hidden_states\n )\n text_mask = callback_outputs.pop(\"text_mask\", text_mask)\n\n latents = self.prior.post_process_latents(latents)\n\n image_embeddings = latents\n\n # if negative prompt has been defined, we retrieve split the image embedding into two\n if negative_prompt is None:\n zero_embeds = self.get_zero_embed(latents.shape[0], device=latents.device)\n else:\n image_embeddings, zero_embeds = image_embeddings.chunk(2)\n\n self.maybe_free_model_hooks()\n\n if output_type not in [\"pt\", \"np\"]:\n raise ValueError(f\"Only the output types `pt` and `np` are supported not output_type={output_type}\")\n\n if output_type == \"np\":\n image_embeddings = image_embeddings.cpu().numpy()\n zero_embeds = zero_embeds.cpu().numpy()\n\n if not return_dict:\n return (image_embeddings, zero_embeds)\n\n return KandinskyPriorPipelineOutput(image_embeds=image_embeddings, negative_image_embeds=zero_embeds)\n" }, { "path": "diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.py", "content": "from typing import List, Optional, Union\n\nimport PIL.Image\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...models import PriorTransformer\nfrom ...schedulers import UnCLIPScheduler\nfrom ...utils import (\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..kandinsky import KandinskyPriorPipelineOutput\nfrom ..pipeline_utils import DiffusionPipeline\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorEmb2EmbPipeline\n >>> import torch\n\n >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n ... )\n >>> pipe_prior.to(\"cuda\")\n\n >>> prompt = \"red cat, 4k photo\"\n >>> img = load_image(\n ... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n ... \"/kandinsky/cat.png\"\n ... )\n >>> image_emb, nagative_image_emb = pipe_prior(prompt, image=img, strength=0.2).to_tuple()\n\n >>> pipe = KandinskyPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-decoder, torch_dtype=torch.float16\"\n ... )\n >>> pipe.to(\"cuda\")\n\n >>> image = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=negative_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=100,\n ... ).images\n\n >>> image[0].save(\"cat.png\")\n ```\n\"\"\"\n\nEXAMPLE_INTERPOLATE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22Pipeline\n >>> from diffusers.utils import load_image\n >>> import PIL\n\n >>> import torch\n >>> from torchvision import transforms\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n ... )\n >>> pipe_prior.to(\"cuda\")\n\n >>> img1 = load_image(\n ... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n ... \"/kandinsky/cat.png\"\n ... )\n\n >>> img2 = load_image(\n ... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n ... \"/kandinsky/starry_night.jpeg\"\n ... )\n\n >>> images_texts = [\"a cat\", img1, img2]\n >>> weights = [0.3, 0.3, 0.4]\n >>> image_emb, zero_image_emb = pipe_prior.interpolate(images_texts, weights)\n\n >>> pipe = KandinskyV22Pipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n ... )\n >>> pipe.to(\"cuda\")\n\n >>> image = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=150,\n ... ).images[0]\n\n >>> image.save(\"starry_cat.png\")\n ```\n\"\"\"\n\n\nclass KandinskyV22PriorEmb2EmbPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for generating image prior for Kandinsky\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n prior ([`PriorTransformer`]):\n The canonical unCLIP prior to approximate the image embedding from the text embedding.\n image_encoder ([`CLIPVisionModelWithProjection`]):\n Frozen image-encoder.\n text_encoder ([`CLIPTextModelWithProjection`]):\n Frozen text-encoder.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n scheduler ([`UnCLIPScheduler`]):\n A scheduler to be used in combination with `prior` to generate image embedding.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->image_encoder->prior\"\n _exclude_from_cpu_offload = [\"prior\"]\n\n def __init__(\n self,\n prior: PriorTransformer,\n image_encoder: CLIPVisionModelWithProjection,\n text_encoder: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n scheduler: UnCLIPScheduler,\n image_processor: CLIPImageProcessor,\n ):\n super().__init__()\n\n self.register_modules(\n prior=prior,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n scheduler=scheduler,\n image_encoder=image_encoder,\n image_processor=image_processor,\n )\n\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start:]\n\n return timesteps, num_inference_steps - t_start\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_INTERPOLATE_DOC_STRING)\n def interpolate(\n self,\n images_and_prompts: List[Union[str, PIL.Image.Image, torch.Tensor]],\n weights: List[float],\n num_images_per_prompt: int = 1,\n num_inference_steps: int = 25,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n negative_prior_prompt: Optional[str] = None,\n negative_prompt: str = \"\",\n guidance_scale: float = 4.0,\n device=None,\n ):\n \"\"\"\n Function invoked when using the prior pipeline for interpolation.\n\n Args:\n images_and_prompts (`List[Union[str, PIL.Image.Image, torch.Tensor]]`):\n list of prompts and images to guide the image generation.\n weights: (`List[float]`):\n list of weights for each condition in `images_and_prompts`\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n negative_prior_prompt (`str`, *optional*):\n The prompt not to guide the prior diffusion process. Ignored when not using guidance (i.e., ignored if\n `guidance_scale` is less than `1`).\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt not to guide the image generation. Ignored when not using guidance (i.e., ignored if\n `guidance_scale` is less than `1`).\n guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n\n Examples:\n\n Returns:\n [`KandinskyPriorPipelineOutput`] or `tuple`\n \"\"\"\n\n device = device or self.device\n\n if len(images_and_prompts) != len(weights):\n raise ValueError(\n f\"`images_and_prompts` contains {len(images_and_prompts)} items and `weights` contains {len(weights)} items - they should be lists of same length\"\n )\n\n image_embeddings = []\n for cond, weight in zip(images_and_prompts, weights):\n if isinstance(cond, str):\n image_emb = self(\n cond,\n num_inference_steps=num_inference_steps,\n num_images_per_prompt=num_images_per_prompt,\n generator=generator,\n latents=latents,\n negative_prompt=negative_prior_prompt,\n guidance_scale=guidance_scale,\n ).image_embeds.unsqueeze(0)\n\n elif isinstance(cond, (PIL.Image.Image, torch.Tensor)):\n image_emb = self._encode_image(\n cond, device=device, num_images_per_prompt=num_images_per_prompt\n ).unsqueeze(0)\n\n else:\n raise ValueError(\n f\"`images_and_prompts` can only contains elements to be of type `str`, `PIL.Image.Image` or `torch.Tensor` but is {type(cond)}\"\n )\n\n image_embeddings.append(image_emb * weight)\n\n image_emb = torch.cat(image_embeddings).sum(dim=0)\n\n return KandinskyPriorPipelineOutput(image_embeds=image_emb, negative_image_embeds=torch.randn_like(image_emb))\n\n def _encode_image(\n self,\n image: Union[torch.Tensor, List[PIL.Image.Image]],\n device,\n num_images_per_prompt,\n ):\n if not isinstance(image, torch.Tensor):\n image = self.image_processor(image, return_tensors=\"pt\").pixel_values.to(\n dtype=self.image_encoder.dtype, device=device\n )\n\n image_emb = self.image_encoder(image)[\"image_embeds\"] # B, D\n image_emb = image_emb.repeat_interleave(num_images_per_prompt, dim=0)\n image_emb.to(device=device)\n\n return image_emb\n\n def prepare_latents(self, emb, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):\n emb = emb.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n\n init_latents = emb\n\n if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:\n additional_image_per_prompt = batch_size // init_latents.shape[0]\n init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)\n elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n init_latents = torch.cat([init_latents], dim=0)\n\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n latents = init_latents\n\n return latents\n\n # Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_prior.KandinskyPriorPipeline.get_zero_embed\n def get_zero_embed(self, batch_size=1, device=None):\n device = device or self.device\n zero_img = torch.zeros(1, 3, self.image_encoder.config.image_size, self.image_encoder.config.image_size).to(\n device=device, dtype=self.image_encoder.dtype\n )\n zero_image_emb = self.image_encoder(zero_img)[\"image_embeds\"]\n zero_image_emb = zero_image_emb.repeat(batch_size, 1)\n return zero_image_emb\n\n # Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_prior.KandinskyPriorPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n ):\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n text_mask = text_inputs.attention_mask.bool().to(device)\n\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]\n\n text_encoder_output = self.text_encoder(text_input_ids.to(device))\n\n prompt_embeds = text_encoder_output.text_embeds\n text_encoder_hidden_states = text_encoder_output.last_hidden_state\n\n prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n uncond_text_mask = uncond_input.attention_mask.bool().to(device)\n negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))\n\n negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds\n uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)\n\n seq_len = uncond_text_encoder_hidden_states.shape[1]\n uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)\n uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(\n batch_size * num_images_per_prompt, seq_len, -1\n )\n uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n # done duplicates\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])\n\n text_mask = torch.cat([uncond_text_mask, text_mask])\n\n return prompt_embeds, text_encoder_hidden_states, text_mask\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]],\n image: Union[torch.Tensor, List[torch.Tensor], PIL.Image.Image, List[PIL.Image.Image]],\n strength: float = 0.3,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: int = 1,\n num_inference_steps: int = 25,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n guidance_scale: float = 4.0,\n output_type: Optional[str] = \"pt\", # pt only\n return_dict: bool = True,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n strength (`float`, *optional*, defaults to 0.8):\n Conceptually, indicates how much to transform the reference `emb`. Must be between 0 and 1. `image`\n will be used as a starting point, adding more noise to it the larger the `strength`. The number of\n denoising steps depends on the amount of noise initially added.\n emb (`torch.Tensor`):\n The image embedding.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n output_type (`str`, *optional*, defaults to `\"pt\"`):\n The output format of the generate image. Choose between: `\"np\"` (`np.array`) or `\"pt\"`\n (`torch.Tensor`).\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n\n Examples:\n\n Returns:\n [`KandinskyPriorPipelineOutput`] or `tuple`\n \"\"\"\n\n if isinstance(prompt, str):\n prompt = [prompt]\n elif not isinstance(prompt, list):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if isinstance(negative_prompt, str):\n negative_prompt = [negative_prompt]\n elif not isinstance(negative_prompt, list) and negative_prompt is not None:\n raise ValueError(f\"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}\")\n\n # if the negative prompt is defined we double the batch size to\n # directly retrieve the negative prompt embedding\n if negative_prompt is not None:\n prompt = prompt + negative_prompt\n negative_prompt = 2 * negative_prompt\n\n device = self._execution_device\n\n batch_size = len(prompt)\n batch_size = batch_size * num_images_per_prompt\n\n do_classifier_free_guidance = guidance_scale > 1.0\n prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt(\n prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt\n )\n\n if not isinstance(image, List):\n image = [image]\n\n if isinstance(image[0], torch.Tensor):\n image = torch.cat(image, dim=0)\n\n if isinstance(image, torch.Tensor) and image.ndim == 2:\n # allow user to pass image_embeds directly\n image_embeds = image.repeat_interleave(num_images_per_prompt, dim=0)\n elif isinstance(image, torch.Tensor) and image.ndim != 4:\n raise ValueError(\n f\" if pass `image` as pytorch tensor, or a list of pytorch tensor, please make sure each tensor has shape [batch_size, channels, height, width], currently {image[0].unsqueeze(0).shape}\"\n )\n else:\n image_embeds = self._encode_image(image, device, num_images_per_prompt)\n\n # prior\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n\n latents = image_embeds\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n latent_timestep = timesteps[:1].repeat(batch_size)\n latents = self.prepare_latents(\n latents,\n latent_timestep,\n batch_size // num_images_per_prompt,\n num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n generator,\n )\n\n for i, t in enumerate(self.progress_bar(timesteps)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n\n predicted_image_embedding = self.prior(\n latent_model_input,\n timestep=t,\n proj_embedding=prompt_embeds,\n encoder_hidden_states=text_encoder_hidden_states,\n attention_mask=text_mask,\n ).predicted_image_embedding\n\n if do_classifier_free_guidance:\n predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)\n predicted_image_embedding = predicted_image_embedding_uncond + guidance_scale * (\n predicted_image_embedding_text - predicted_image_embedding_uncond\n )\n\n if i + 1 == timesteps.shape[0]:\n prev_timestep = None\n else:\n prev_timestep = timesteps[i + 1]\n\n latents = self.scheduler.step(\n predicted_image_embedding,\n timestep=t,\n sample=latents,\n generator=generator,\n prev_timestep=prev_timestep,\n ).prev_sample\n\n latents = self.prior.post_process_latents(latents)\n\n image_embeddings = latents\n\n # if negative prompt has been defined, we retrieve split the image embedding into two\n if negative_prompt is None:\n zero_embeds = self.get_zero_embed(latents.shape[0], device=latents.device)\n else:\n image_embeddings, zero_embeds = image_embeddings.chunk(2)\n\n self.maybe_free_model_hooks()\n\n if output_type not in [\"pt\", \"np\"]:\n raise ValueError(f\"Only the output types `pt` and `np` are supported not output_type={output_type}\")\n\n if output_type == \"np\":\n image_embeddings = image_embeddings.cpu().numpy()\n zero_embeds = zero_embeds.cpu().numpy()\n\n if not return_dict:\n return (image_embeddings, zero_embeds)\n\n return KandinskyPriorPipelineOutput(image_embeds=image_embeddings, negative_image_embeds=zero_embeds)\n" }, { "path": "diffusers/src/diffusers/pipelines/kandinsky3/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_kandinsky3\"] = [\"Kandinsky3Pipeline\"]\n _import_structure[\"pipeline_kandinsky3_img2img\"] = [\"Kandinsky3Img2ImgPipeline\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n else:\n from .pipeline_kandinsky3 import Kandinsky3Pipeline\n from .pipeline_kandinsky3_img2img import Kandinsky3Img2ImgPipeline\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/kandinsky3/convert_kandinsky3_unet.py", "content": "#!/usr/bin/env python3\nimport argparse\nimport fnmatch\n\nfrom safetensors.torch import load_file\n\nfrom diffusers import Kandinsky3UNet\n\n\nMAPPING = {\n \"to_time_embed.1\": \"time_embedding.linear_1\",\n \"to_time_embed.3\": \"time_embedding.linear_2\",\n \"in_layer\": \"conv_in\",\n \"out_layer.0\": \"conv_norm_out\",\n \"out_layer.2\": \"conv_out\",\n \"down_samples\": \"down_blocks\",\n \"up_samples\": \"up_blocks\",\n \"projection_lin\": \"encoder_hid_proj.projection_linear\",\n \"projection_ln\": \"encoder_hid_proj.projection_norm\",\n \"feature_pooling\": \"add_time_condition\",\n \"to_query\": \"to_q\",\n \"to_key\": \"to_k\",\n \"to_value\": \"to_v\",\n \"output_layer\": \"to_out.0\",\n \"self_attention_block\": \"attentions.0\",\n}\n\nDYNAMIC_MAP = {\n \"resnet_attn_blocks.*.0\": \"resnets_in.*\",\n \"resnet_attn_blocks.*.1\": (\"attentions.*\", 1),\n \"resnet_attn_blocks.*.2\": \"resnets_out.*\",\n}\n# MAPPING = {}\n\n\ndef convert_state_dict(unet_state_dict):\n \"\"\"\n Args:\n Convert the state dict of a U-Net model to match the key format expected by Kandinsky3UNet model.\n unet_model (torch.nn.Module): The original U-Net model. unet_kandi3_model (torch.nn.Module): The Kandinsky3UNet\n model to match keys with.\n\n Returns:\n OrderedDict: The converted state dictionary.\n \"\"\"\n # Example of renaming logic (this will vary based on your model's architecture)\n converted_state_dict = {}\n for key in unet_state_dict:\n new_key = key\n for pattern, new_pattern in MAPPING.items():\n new_key = new_key.replace(pattern, new_pattern)\n\n for dyn_pattern, dyn_new_pattern in DYNAMIC_MAP.items():\n has_matched = False\n if fnmatch.fnmatch(new_key, f\"*.{dyn_pattern}.*\") and not has_matched:\n star = int(new_key.split(dyn_pattern.split(\".\")[0])[-1].split(\".\")[1])\n\n if isinstance(dyn_new_pattern, tuple):\n new_star = star + dyn_new_pattern[-1]\n dyn_new_pattern = dyn_new_pattern[0]\n else:\n new_star = star\n\n pattern = dyn_pattern.replace(\"*\", str(star))\n new_pattern = dyn_new_pattern.replace(\"*\", str(new_star))\n\n new_key = new_key.replace(pattern, new_pattern)\n has_matched = True\n\n converted_state_dict[new_key] = unet_state_dict[key]\n\n return converted_state_dict\n\n\ndef main(model_path, output_path):\n # Load your original U-Net model\n unet_state_dict = load_file(model_path)\n\n # Initialize your Kandinsky3UNet model\n config = {}\n\n # Convert the state dict\n converted_state_dict = convert_state_dict(unet_state_dict)\n\n unet = Kandinsky3UNet(config)\n unet.load_state_dict(converted_state_dict)\n\n unet.save_pretrained(output_path)\n print(f\"Converted model saved to {output_path}\")\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description=\"Convert U-Net PyTorch model to Kandinsky3UNet format\")\n parser.add_argument(\"--model_path\", type=str, required=True, help=\"Path to the original U-Net PyTorch model\")\n parser.add_argument(\"--output_path\", type=str, required=True, help=\"Path to save the converted model\")\n\n args = parser.parse_args()\n main(args.model_path, args.output_path)\n" }, { "path": "diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3.py", "content": "from typing import Callable, Dict, List, Optional, Union\n\nimport torch\nfrom transformers import T5EncoderModel, T5Tokenizer\n\nfrom ...loaders import LoraLoaderMixin\nfrom ...models import Kandinsky3UNet, VQModel\nfrom ...schedulers import DDPMScheduler\nfrom ...utils import (\n deprecate,\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import AutoPipelineForText2Image\n >>> import torch\n\n >>> pipe = AutoPipelineForText2Image.from_pretrained(\n ... \"kandinsky-community/kandinsky-3\", variant=\"fp16\", torch_dtype=torch.float16\n ... )\n >>> pipe.enable_model_cpu_offload()\n\n >>> prompt = \"A photograph of the inside of a subway train. There are raccoons sitting on the seats. One of them is reading a newspaper. The window shows the city in the background.\"\n\n >>> generator = torch.Generator(device=\"cpu\").manual_seed(0)\n >>> image = pipe(prompt, num_inference_steps=25, generator=generator).images[0]\n ```\n\n\"\"\"\n\n\ndef downscale_height_and_width(height, width, scale_factor=8):\n new_height = height // scale_factor**2\n if height % scale_factor**2 != 0:\n new_height += 1\n new_width = width // scale_factor**2\n if width % scale_factor**2 != 0:\n new_width += 1\n return new_height * scale_factor, new_width * scale_factor\n\n\nclass Kandinsky3Pipeline(DiffusionPipeline, LoraLoaderMixin):\n model_cpu_offload_seq = \"text_encoder->unet->movq\"\n _callback_tensor_inputs = [\n \"latents\",\n \"prompt_embeds\",\n \"negative_prompt_embeds\",\n \"negative_attention_mask\",\n \"attention_mask\",\n ]\n\n def __init__(\n self,\n tokenizer: T5Tokenizer,\n text_encoder: T5EncoderModel,\n unet: Kandinsky3UNet,\n scheduler: DDPMScheduler,\n movq: VQModel,\n ):\n super().__init__()\n\n self.register_modules(\n tokenizer=tokenizer, text_encoder=text_encoder, unet=unet, scheduler=scheduler, movq=movq\n )\n\n def process_embeds(self, embeddings, attention_mask, cut_context):\n if cut_context:\n embeddings[attention_mask == 0] = torch.zeros_like(embeddings[attention_mask == 0])\n max_seq_length = attention_mask.sum(-1).max() + 1\n embeddings = embeddings[:, :max_seq_length]\n attention_mask = attention_mask[:, :max_seq_length]\n return embeddings, attention_mask\n\n @torch.no_grad()\n def encode_prompt(\n self,\n prompt,\n do_classifier_free_guidance=True,\n num_images_per_prompt=1,\n device=None,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n _cut_context=False,\n attention_mask: Optional[torch.Tensor] = None,\n negative_attention_mask: Optional[torch.Tensor] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`, *optional*):\n torch device to place the resulting embeddings on\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.\n Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n attention_mask (`torch.Tensor`, *optional*):\n Pre-generated attention mask. Must provide if passing `prompt_embeds` directly.\n negative_attention_mask (`torch.Tensor`, *optional*):\n Pre-generated negative attention mask. Must provide if passing `negative_prompt_embeds` directly.\n \"\"\"\n if prompt is not None and negative_prompt is not None:\n if type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n\n if device is None:\n device = self._execution_device\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n max_length = 128\n\n if prompt_embeds is None:\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids.to(device)\n attention_mask = text_inputs.attention_mask.to(device)\n prompt_embeds = self.text_encoder(\n text_input_ids,\n attention_mask=attention_mask,\n )\n prompt_embeds = prompt_embeds[0]\n prompt_embeds, attention_mask = self.process_embeds(prompt_embeds, attention_mask, _cut_context)\n prompt_embeds = prompt_embeds * attention_mask.unsqueeze(2)\n\n if self.text_encoder is not None:\n dtype = self.text_encoder.dtype\n else:\n dtype = None\n\n prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n attention_mask = attention_mask.repeat(num_images_per_prompt, 1)\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n if negative_prompt is not None:\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=128,\n truncation=True,\n return_attention_mask=True,\n return_tensors=\"pt\",\n )\n text_input_ids = uncond_input.input_ids.to(device)\n negative_attention_mask = uncond_input.attention_mask.to(device)\n\n negative_prompt_embeds = self.text_encoder(\n text_input_ids,\n attention_mask=negative_attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_embeds = negative_prompt_embeds[:, : prompt_embeds.shape[1]]\n negative_attention_mask = negative_attention_mask[:, : prompt_embeds.shape[1]]\n negative_prompt_embeds = negative_prompt_embeds * negative_attention_mask.unsqueeze(2)\n\n else:\n negative_prompt_embeds = torch.zeros_like(prompt_embeds)\n negative_attention_mask = torch.zeros_like(attention_mask)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)\n if negative_prompt_embeds.shape != prompt_embeds.shape:\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n negative_attention_mask = negative_attention_mask.repeat(num_images_per_prompt, 1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n else:\n negative_prompt_embeds = None\n negative_attention_mask = None\n return prompt_embeds, negative_prompt_embeds, attention_mask, negative_attention_mask\n\n def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n latents = latents * scheduler.init_noise_sigma\n return latents\n\n def check_inputs(\n self,\n prompt,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n attention_mask=None,\n negative_attention_mask=None,\n ):\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n if negative_prompt_embeds is not None and negative_attention_mask is None:\n raise ValueError(\"Please provide `negative_attention_mask` along with `negative_prompt_embeds`\")\n\n if negative_prompt_embeds is not None and negative_attention_mask is not None:\n if negative_prompt_embeds.shape[:2] != negative_attention_mask.shape:\n raise ValueError(\n \"`negative_prompt_embeds` and `negative_attention_mask` must have the same batch_size and token length when passed directly, but\"\n f\" got: `negative_prompt_embeds` {negative_prompt_embeds.shape[:2]} != `negative_attention_mask`\"\n f\" {negative_attention_mask.shape}.\"\n )\n\n if prompt_embeds is not None and attention_mask is None:\n raise ValueError(\"Please provide `attention_mask` along with `prompt_embeds`\")\n\n if prompt_embeds is not None and attention_mask is not None:\n if prompt_embeds.shape[:2] != attention_mask.shape:\n raise ValueError(\n \"`prompt_embeds` and `attention_mask` must have the same batch_size and token length when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape[:2]} != `attention_mask`\"\n f\" {attention_mask.shape}.\"\n )\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n num_inference_steps: int = 25,\n guidance_scale: float = 3.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n height: Optional[int] = 1024,\n width: Optional[int] = 1024,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n negative_attention_mask: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n latents=None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n num_inference_steps (`int`, *optional*, defaults to 25):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`\n timesteps are used. Must be in descending order.\n guidance_scale (`float`, *optional*, defaults to 3.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n height (`int`, *optional*, defaults to self.unet.config.sample_size):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size):\n The width in pixels of the generated image.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n attention_mask (`torch.Tensor`, *optional*):\n Pre-generated attention mask. Must provide if passing `prompt_embeds` directly.\n negative_attention_mask (`torch.Tensor`, *optional*):\n Pre-generated negative attention mask. Must provide if passing `negative_prompt_embeds` directly.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n clean_caption (`bool`, *optional*, defaults to `True`):\n Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to\n be installed. If the dependencies are not installed, the embeddings will be created from the raw\n prompt.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`\n\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n cut_context = True\n device = self._execution_device\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n callback_on_step_end_tensor_inputs,\n attention_mask,\n negative_attention_mask,\n )\n\n self._guidance_scale = guidance_scale\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # 3. Encode input prompt\n prompt_embeds, negative_prompt_embeds, attention_mask, negative_attention_mask = self.encode_prompt(\n prompt,\n self.do_classifier_free_guidance,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n _cut_context=cut_context,\n attention_mask=attention_mask,\n negative_attention_mask=negative_attention_mask,\n )\n\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n attention_mask = torch.cat([negative_attention_mask, attention_mask]).bool()\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latents\n height, width = downscale_height_and_width(height, width, 8)\n\n latents = self.prepare_latents(\n (batch_size * num_images_per_prompt, 4, height, width),\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n self.scheduler,\n )\n\n if hasattr(self, \"text_encoder_offload_hook\") and self.text_encoder_offload_hook is not None:\n self.text_encoder_offload_hook.offload()\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n encoder_attention_mask=attention_mask,\n return_dict=False,\n )[0]\n\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n\n noise_pred = (guidance_scale + 1.0) * noise_pred_text - guidance_scale * noise_pred_uncond\n # noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(\n noise_pred,\n t,\n latents,\n generator=generator,\n ).prev_sample\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n attention_mask = callback_outputs.pop(\"attention_mask\", attention_mask)\n negative_attention_mask = callback_outputs.pop(\"negative_attention_mask\", negative_attention_mask)\n\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # post-processing\n if output_type not in [\"pt\", \"np\", \"pil\", \"latent\"]:\n raise ValueError(\n f\"Only the output types `pt`, `pil`, `np` and `latent` are supported not output_type={output_type}\"\n )\n\n if not output_type == \"latent\":\n image = self.movq.decode(latents, force_not_quantize=True)[\"sample\"]\n\n if output_type in [\"np\", \"pil\"]:\n image = image * 0.5 + 0.5\n image = image.clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n else:\n image = latents\n\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py", "content": "import inspect\nfrom typing import Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL\nimport PIL.Image\nimport torch\nfrom transformers import T5EncoderModel, T5Tokenizer\n\nfrom ...loaders import LoraLoaderMixin\nfrom ...models import Kandinsky3UNet, VQModel\nfrom ...schedulers import DDPMScheduler\nfrom ...utils import (\n deprecate,\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import AutoPipelineForImage2Image\n >>> from diffusers.utils import load_image\n >>> import torch\n\n >>> pipe = AutoPipelineForImage2Image.from_pretrained(\n ... \"kandinsky-community/kandinsky-3\", variant=\"fp16\", torch_dtype=torch.float16\n ... )\n >>> pipe.enable_model_cpu_offload()\n\n >>> prompt = \"A painting of the inside of a subway train with tiny raccoons.\"\n >>> image = load_image(\n ... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky3/t2i.png\"\n ... )\n\n >>> generator = torch.Generator(device=\"cpu\").manual_seed(0)\n >>> image = pipe(prompt, image=image, strength=0.75, num_inference_steps=25, generator=generator).images[0]\n ```\n\"\"\"\n\n\ndef downscale_height_and_width(height, width, scale_factor=8):\n new_height = height // scale_factor**2\n if height % scale_factor**2 != 0:\n new_height += 1\n new_width = width // scale_factor**2\n if width % scale_factor**2 != 0:\n new_width += 1\n return new_height * scale_factor, new_width * scale_factor\n\n\ndef prepare_image(pil_image):\n arr = np.array(pil_image.convert(\"RGB\"))\n arr = arr.astype(np.float32) / 127.5 - 1\n arr = np.transpose(arr, [2, 0, 1])\n image = torch.from_numpy(arr).unsqueeze(0)\n return image\n\n\nclass Kandinsky3Img2ImgPipeline(DiffusionPipeline, LoraLoaderMixin):\n model_cpu_offload_seq = \"text_encoder->movq->unet->movq\"\n _callback_tensor_inputs = [\n \"latents\",\n \"prompt_embeds\",\n \"negative_prompt_embeds\",\n \"negative_attention_mask\",\n \"attention_mask\",\n ]\n\n def __init__(\n self,\n tokenizer: T5Tokenizer,\n text_encoder: T5EncoderModel,\n unet: Kandinsky3UNet,\n scheduler: DDPMScheduler,\n movq: VQModel,\n ):\n super().__init__()\n\n self.register_modules(\n tokenizer=tokenizer, text_encoder=text_encoder, unet=unet, scheduler=scheduler, movq=movq\n )\n\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start:]\n\n return timesteps, num_inference_steps - t_start\n\n def _process_embeds(self, embeddings, attention_mask, cut_context):\n # return embeddings, attention_mask\n if cut_context:\n embeddings[attention_mask == 0] = torch.zeros_like(embeddings[attention_mask == 0])\n max_seq_length = attention_mask.sum(-1).max() + 1\n embeddings = embeddings[:, :max_seq_length]\n attention_mask = attention_mask[:, :max_seq_length]\n return embeddings, attention_mask\n\n @torch.no_grad()\n def encode_prompt(\n self,\n prompt,\n do_classifier_free_guidance=True,\n num_images_per_prompt=1,\n device=None,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n _cut_context=False,\n attention_mask: Optional[torch.Tensor] = None,\n negative_attention_mask: Optional[torch.Tensor] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`, *optional*):\n torch device to place the resulting embeddings on\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.\n Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n attention_mask (`torch.Tensor`, *optional*):\n Pre-generated attention mask. Must provide if passing `prompt_embeds` directly.\n negative_attention_mask (`torch.Tensor`, *optional*):\n Pre-generated negative attention mask. Must provide if passing `negative_prompt_embeds` directly.\n \"\"\"\n if prompt is not None and negative_prompt is not None:\n if type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n\n if device is None:\n device = self._execution_device\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n max_length = 128\n\n if prompt_embeds is None:\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids.to(device)\n attention_mask = text_inputs.attention_mask.to(device)\n prompt_embeds = self.text_encoder(\n text_input_ids,\n attention_mask=attention_mask,\n )\n prompt_embeds = prompt_embeds[0]\n prompt_embeds, attention_mask = self._process_embeds(prompt_embeds, attention_mask, _cut_context)\n prompt_embeds = prompt_embeds * attention_mask.unsqueeze(2)\n\n if self.text_encoder is not None:\n dtype = self.text_encoder.dtype\n else:\n dtype = None\n\n prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n attention_mask = attention_mask.repeat(num_images_per_prompt, 1)\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n if negative_prompt is not None:\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=128,\n truncation=True,\n return_attention_mask=True,\n return_tensors=\"pt\",\n )\n text_input_ids = uncond_input.input_ids.to(device)\n negative_attention_mask = uncond_input.attention_mask.to(device)\n\n negative_prompt_embeds = self.text_encoder(\n text_input_ids,\n attention_mask=negative_attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_embeds = negative_prompt_embeds[:, : prompt_embeds.shape[1]]\n negative_attention_mask = negative_attention_mask[:, : prompt_embeds.shape[1]]\n negative_prompt_embeds = negative_prompt_embeds * negative_attention_mask.unsqueeze(2)\n\n else:\n negative_prompt_embeds = torch.zeros_like(prompt_embeds)\n negative_attention_mask = torch.zeros_like(attention_mask)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)\n if negative_prompt_embeds.shape != prompt_embeds.shape:\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n negative_attention_mask = negative_attention_mask.repeat(num_images_per_prompt, 1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n else:\n negative_prompt_embeds = None\n negative_attention_mask = None\n return prompt_embeds, negative_prompt_embeds, attention_mask, negative_attention_mask\n\n def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n\n if image.shape[1] == 4:\n init_latents = image\n\n else:\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n elif isinstance(generator, list):\n init_latents = [\n self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = self.movq.encode(image).latent_dist.sample(generator)\n\n init_latents = self.movq.config.scaling_factor * init_latents\n\n init_latents = torch.cat([init_latents], dim=0)\n\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n\n latents = init_latents\n\n return latents\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n attention_mask=None,\n negative_attention_mask=None,\n ):\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if negative_prompt_embeds is not None and negative_attention_mask is None:\n raise ValueError(\"Please provide `negative_attention_mask` along with `negative_prompt_embeds`\")\n\n if negative_prompt_embeds is not None and negative_attention_mask is not None:\n if negative_prompt_embeds.shape[:2] != negative_attention_mask.shape:\n raise ValueError(\n \"`negative_prompt_embeds` and `negative_attention_mask` must have the same batch_size and token length when passed directly, but\"\n f\" got: `negative_prompt_embeds` {negative_prompt_embeds.shape[:2]} != `negative_attention_mask`\"\n f\" {negative_attention_mask.shape}.\"\n )\n\n if prompt_embeds is not None and attention_mask is None:\n raise ValueError(\"Please provide `attention_mask` along with `prompt_embeds`\")\n\n if prompt_embeds is not None and attention_mask is not None:\n if prompt_embeds.shape[:2] != attention_mask.shape:\n raise ValueError(\n \"`prompt_embeds` and `attention_mask` must have the same batch_size and token length when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape[:2]} != `attention_mask`\"\n f\" {attention_mask.shape}.\"\n )\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]] = None,\n strength: float = 0.3,\n num_inference_steps: int = 25,\n guidance_scale: float = 3.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n negative_attention_mask: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process.\n strength (`float`, *optional*, defaults to 0.8):\n Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a\n starting point and more noise is added the higher the `strength`. The number of denoising steps depends\n on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising\n process runs for the full number of iterations specified in `num_inference_steps`. A value of 1\n essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 3.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n attention_mask (`torch.Tensor`, *optional*):\n Pre-generated attention mask. Must provide if passing `prompt_embeds` directly.\n negative_attention_mask (`torch.Tensor`, *optional*):\n Pre-generated negative attention mask. Must provide if passing `negative_prompt_embeds` directly.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`\n\n \"\"\"\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n cut_context = True\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n callback_on_step_end_tensor_inputs,\n attention_mask,\n negative_attention_mask,\n )\n\n self._guidance_scale = guidance_scale\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n prompt_embeds, negative_prompt_embeds, attention_mask, negative_attention_mask = self.encode_prompt(\n prompt,\n self.do_classifier_free_guidance,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n _cut_context=cut_context,\n attention_mask=attention_mask,\n negative_attention_mask=negative_attention_mask,\n )\n\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n attention_mask = torch.cat([negative_attention_mask, attention_mask]).bool()\n if not isinstance(image, list):\n image = [image]\n if not all(isinstance(i, (PIL.Image.Image, torch.Tensor)) for i in image):\n raise ValueError(\n f\"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support PIL image and pytorch tensor\"\n )\n\n image = torch.cat([prepare_image(i) for i in image], dim=0)\n image = image.to(dtype=prompt_embeds.dtype, device=device)\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n # 5. Prepare latents\n latents = self.movq.encode(image)[\"latents\"]\n latents = latents.repeat_interleave(num_images_per_prompt, dim=0)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n latents = self.prepare_latents(\n latents, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator\n )\n if hasattr(self, \"text_encoder_offload_hook\") and self.text_encoder_offload_hook is not None:\n self.text_encoder_offload_hook.offload()\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n encoder_attention_mask=attention_mask,\n )[0]\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n\n noise_pred = (guidance_scale + 1.0) * noise_pred_text - guidance_scale * noise_pred_uncond\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(\n noise_pred,\n t,\n latents,\n generator=generator,\n ).prev_sample\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n attention_mask = callback_outputs.pop(\"attention_mask\", attention_mask)\n negative_attention_mask = callback_outputs.pop(\"negative_attention_mask\", negative_attention_mask)\n\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # post-processing\n if output_type not in [\"pt\", \"np\", \"pil\", \"latent\"]:\n raise ValueError(\n f\"Only the output types `pt`, `pil`, `np` and `latent` are supported not output_type={output_type}\"\n )\n if not output_type == \"latent\":\n image = self.movq.decode(latents, force_not_quantize=True)[\"sample\"]\n\n if output_type in [\"np\", \"pil\"]:\n image = image * 0.5 + 0.5\n image = image.clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n else:\n image = latents\n\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/latent_consistency_models/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_latent_consistency_img2img\"] = [\"LatentConsistencyModelImg2ImgPipeline\"]\n _import_structure[\"pipeline_latent_consistency_text2img\"] = [\"LatentConsistencyModelPipeline\"]\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n else:\n from .pipeline_latent_consistency_img2img import LatentConsistencyModelImg2ImgPipeline\n from .pipeline_latent_consistency_text2img import LatentConsistencyModelPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py", "content": "# Copyright 2024 Stanford University Team and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion\n# and https://github.com/hojonathanho/diffusion\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport PIL.Image\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import LCMScheduler\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ..stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import AutoPipelineForImage2Image\n >>> import torch\n >>> import PIL\n\n >>> pipe = AutoPipelineForImage2Image.from_pretrained(\"SimianLuo/LCM_Dreamshaper_v7\")\n >>> # To save GPU memory, torch.float16 can be used, but it may compromise image quality.\n >>> pipe.to(torch_device=\"cuda\", torch_dtype=torch.float32)\n\n >>> prompt = \"High altitude snowy mountains\"\n >>> image = PIL.Image.open(\"./snowy_mountains.png\")\n\n >>> # Can be set to 1~50 steps. LCM support fast inference even <= 4 steps. Recommend: 1~8 steps.\n >>> num_inference_steps = 4\n >>> images = pipe(\n ... prompt=prompt, image=image, num_inference_steps=num_inference_steps, guidance_scale=8.0\n ... ).images\n\n >>> images[0].save(\"image.png\")\n ```\n\n\"\"\"\n\n\nclass LatentConsistencyModelImg2ImgPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n TextualInversionLoaderMixin,\n IPAdapterMixin,\n LoraLoaderMixin,\n FromSingleFileMixin,\n):\n r\"\"\"\n Pipeline for image-to-image generation using a latent consistency model.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Currently only\n supports [`LCMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n requires_safety_checker (`bool`, *optional*, defaults to `True`):\n Whether the pipeline requires a safety checker component.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"image_encoder\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"denoised\", \"prompt_embeds\", \"w_embedding\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: LCMScheduler,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder: Optional[CLIPVisionModelWithProjection] = None,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.prepare_latents\n def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n\n if image.shape[1] == 4:\n init_latents = image\n\n else:\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n elif isinstance(generator, list):\n init_latents = [\n retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])\n for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = retrieve_latents(self.vae.encode(image), generator=generator)\n\n init_latents = self.vae.config.scaling_factor * init_latents\n\n if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:\n # expand init_latents for batch_size\n deprecation_message = (\n f\"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial\"\n \" images (`image`). Initial images are now duplicating to match the number of text prompts. Note\"\n \" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update\"\n \" your script to pass as many initial images as text prompts to suppress this warning.\"\n )\n deprecate(\"len(prompt) != len(image)\", \"1.0.0\", deprecation_message, standard_warn=False)\n additional_image_per_prompt = batch_size // init_latents.shape[0]\n init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)\n elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n init_latents = torch.cat([init_latents], dim=0)\n\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n latents = init_latents\n\n return latents\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(\n self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32\n ) -> torch.Tensor:\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n w (`torch.Tensor`):\n Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.\n embedding_dim (`int`, *optional*, defaults to 512):\n Dimension of the embeddings to generate.\n dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):\n Data type of the generated embeddings.\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n if hasattr(self.scheduler, \"set_begin_index\"):\n self.scheduler.set_begin_index(t_start * self.scheduler.order)\n\n return timesteps, num_inference_steps - t_start\n\n def check_inputs(\n self,\n prompt: Union[str, List[str]],\n strength: float,\n callback_steps: int,\n prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n if ip_adapter_image_embeds is not None:\n if not isinstance(ip_adapter_image_embeds, list):\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}\"\n )\n elif ip_adapter_image_embeds[0].ndim not in [3, 4]:\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D\"\n )\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n @property\n def do_classifier_free_guidance(self):\n return False\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: PipelineImageInput = None,\n num_inference_steps: int = 4,\n strength: float = 0.8,\n original_inference_steps: int = None,\n timesteps: List[int] = None,\n guidance_scale: float = 8.5,\n num_images_per_prompt: Optional[int] = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n original_inference_steps (`int`, *optional*):\n The original number of inference steps use to generate a linearly-spaced timestep schedule, from which\n we will draw `num_inference_steps` evenly spaced timesteps from as our final timestep schedule,\n following the Skipping-Step method in the paper (see Section 4.3). If not set this will default to the\n scheduler's `original_inference_steps` attribute.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`\n timesteps on the original LCM training/distillation timestep schedule are used. Must be in descending\n order.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n Note that the original latent consistency models paper uses a different CFG formulation where the\n guidance scales are decreased by 1 (so in the paper formulation CFG is enabled when `guidance_scale >\n 0`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*):\n Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of\n IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should\n contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not\n provided, embeddings are computed from the `ip_adapter_image` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n strength,\n callback_steps,\n prompt_embeds,\n ip_adapter_image,\n ip_adapter_image_embeds,\n callback_on_step_end_tensor_inputs,\n )\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_images_per_prompt,\n self.do_classifier_free_guidance,\n )\n\n # 3. Encode input prompt\n lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n\n # NOTE: when a LCM is distilled from an LDM via latent consistency distillation (Algorithm 1) with guided\n # distillation, the forward pass of the LCM learns to approximate sampling from the LDM using CFG with the\n # unconditional prompt \"\" (the empty string). Due to this, LCMs currently do not support negative prompts.\n prompt_embeds, _ = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=None,\n lora_scale=lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # 4. Encode image\n image = self.image_processor.preprocess(image)\n\n # 5. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(\n self.scheduler,\n num_inference_steps,\n device,\n timesteps,\n original_inference_steps=original_inference_steps,\n strength=strength,\n )\n\n # 6. Prepare latent variables\n original_inference_steps = (\n original_inference_steps\n if original_inference_steps is not None\n else self.scheduler.config.original_inference_steps\n )\n latent_timestep = timesteps[:1]\n if latents is None:\n latents = self.prepare_latents(\n image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator\n )\n bs = batch_size * num_images_per_prompt\n\n # 6. Get Guidance Scale Embedding\n # NOTE: We use the Imagen CFG formulation that StableDiffusionPipeline uses rather than the original LCM paper\n # CFG formulation, so we need to subtract 1 from the input guidance_scale.\n # LCM CFG formulation: cfg_noise = noise_cond + cfg_scale * (noise_cond - noise_uncond), (cfg_scale > 0.0 using CFG)\n w = torch.tensor(self.guidance_scale - 1).repeat(bs)\n w_embedding = self.get_guidance_scale_embedding(w, embedding_dim=self.unet.config.time_cond_proj_dim).to(\n device=device, dtype=latents.dtype\n )\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, None)\n\n # 7.1 Add image embeds for IP-Adapter\n added_cond_kwargs = (\n {\"image_embeds\": image_embeds}\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None\n else None\n )\n\n # 8. LCM Multistep Sampling Loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n latents = latents.to(prompt_embeds.dtype)\n\n # model prediction (v-prediction, eps, x)\n model_pred = self.unet(\n latents,\n t,\n timestep_cond=w_embedding,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=self.cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # compute the previous noisy sample x_t -> x_t-1\n latents, denoised = self.scheduler.step(model_pred, t, latents, **extra_step_kwargs, return_dict=False)\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n w_embedding = callback_outputs.pop(\"w_embedding\", w_embedding)\n denoised = callback_outputs.pop(\"denoised\", denoised)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n denoised = denoised.to(prompt_embeds.dtype)\n if not output_type == \"latent\":\n image = self.vae.decode(denoised / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = denoised\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py", "content": "# Copyright 2024 Stanford University Team and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion\n# and https://github.com/hojonathanho/diffusion\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import LCMScheduler\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ..stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import DiffusionPipeline\n >>> import torch\n\n >>> pipe = DiffusionPipeline.from_pretrained(\"SimianLuo/LCM_Dreamshaper_v7\")\n >>> # To save GPU memory, torch.float16 can be used, but it may compromise image quality.\n >>> pipe.to(torch_device=\"cuda\", torch_dtype=torch.float32)\n\n >>> prompt = \"Self-portrait oil painting, a beautiful cyborg with golden hair, 8k\"\n\n >>> # Can be set to 1~50 steps. LCM support fast inference even <= 4 steps. Recommend: 1~8 steps.\n >>> num_inference_steps = 4\n >>> images = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=8.0).images\n >>> images[0].save(\"image.png\")\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass LatentConsistencyModelPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n TextualInversionLoaderMixin,\n IPAdapterMixin,\n LoraLoaderMixin,\n FromSingleFileMixin,\n):\n r\"\"\"\n Pipeline for text-to-image generation using a latent consistency model.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Currently only\n supports [`LCMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n requires_safety_checker (`bool`, *optional*, defaults to `True`):\n Whether the pipeline requires a safety checker component.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"image_encoder\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"denoised\", \"prompt_embeds\", \"w_embedding\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: LCMScheduler,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder: Optional[CLIPVisionModelWithProjection] = None,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def get_guidance_scale_embedding(\n self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32\n ) -> torch.Tensor:\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n w (`torch.Tensor`):\n Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.\n embedding_dim (`int`, *optional*, defaults to 512):\n Dimension of the embeddings to generate.\n dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):\n Data type of the generated embeddings.\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Currently StableDiffusionPipeline.check_inputs with negative prompt stuff removed\n def check_inputs(\n self,\n prompt: Union[str, List[str]],\n height: int,\n width: int,\n callback_steps: int,\n prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n if ip_adapter_image_embeds is not None:\n if not isinstance(ip_adapter_image_embeds, list):\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}\"\n )\n elif ip_adapter_image_embeds[0].ndim not in [3, 4]:\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D\"\n )\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n @property\n def do_classifier_free_guidance(self):\n return False\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 4,\n original_inference_steps: int = None,\n timesteps: List[int] = None,\n guidance_scale: float = 8.5,\n num_images_per_prompt: Optional[int] = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n original_inference_steps (`int`, *optional*):\n The original number of inference steps use to generate a linearly-spaced timestep schedule, from which\n we will draw `num_inference_steps` evenly spaced timesteps from as our final timestep schedule,\n following the Skipping-Step method in the paper (see Section 4.3). If not set this will default to the\n scheduler's `original_inference_steps` attribute.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`\n timesteps on the original LCM training/distillation timestep schedule are used. Must be in descending\n order.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n Note that the original latent consistency models paper uses a different CFG formulation where the\n guidance scales are decreased by 1 (so in the paper formulation CFG is enabled when `guidance_scale >\n 0`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*):\n Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of\n IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should\n contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not\n provided, embeddings are computed from the `ip_adapter_image` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n height,\n width,\n callback_steps,\n prompt_embeds,\n ip_adapter_image,\n ip_adapter_image_embeds,\n callback_on_step_end_tensor_inputs,\n )\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_images_per_prompt,\n self.do_classifier_free_guidance,\n )\n\n # 3. Encode input prompt\n lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n\n # NOTE: when a LCM is distilled from an LDM via latent consistency distillation (Algorithm 1) with guided\n # distillation, the forward pass of the LCM learns to approximate sampling from the LDM using CFG with the\n # unconditional prompt \"\" (the empty string). Due to this, LCMs currently do not support negative prompts.\n prompt_embeds, _ = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=None,\n lora_scale=lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # 4. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(\n self.scheduler, num_inference_steps, device, timesteps, original_inference_steps=original_inference_steps\n )\n\n # 5. Prepare latent variable\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n bs = batch_size * num_images_per_prompt\n\n # 6. Get Guidance Scale Embedding\n # NOTE: We use the Imagen CFG formulation that StableDiffusionPipeline uses rather than the original LCM paper\n # CFG formulation, so we need to subtract 1 from the input guidance_scale.\n # LCM CFG formulation: cfg_noise = noise_cond + cfg_scale * (noise_cond - noise_uncond), (cfg_scale > 0.0 using CFG)\n w = torch.tensor(self.guidance_scale - 1).repeat(bs)\n w_embedding = self.get_guidance_scale_embedding(w, embedding_dim=self.unet.config.time_cond_proj_dim).to(\n device=device, dtype=latents.dtype\n )\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, None)\n\n # 7.1 Add image embeds for IP-Adapter\n added_cond_kwargs = (\n {\"image_embeds\": image_embeds}\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None\n else None\n )\n\n # 8. LCM MultiStep Sampling Loop:\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n latents = latents.to(prompt_embeds.dtype)\n\n # model prediction (v-prediction, eps, x)\n model_pred = self.unet(\n latents,\n t,\n timestep_cond=w_embedding,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=self.cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # compute the previous noisy sample x_t -> x_t-1\n latents, denoised = self.scheduler.step(model_pred, t, latents, **extra_step_kwargs, return_dict=False)\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n w_embedding = callback_outputs.pop(\"w_embedding\", w_embedding)\n denoised = callback_outputs.pop(\"denoised\", denoised)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n denoised = denoised.to(prompt_embeds.dtype)\n if not output_type == \"latent\":\n image = self.vae.decode(denoised / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = denoised\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/latent_diffusion/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_latent_diffusion\"] = [\"LDMBertModel\", \"LDMTextToImagePipeline\"]\n _import_structure[\"pipeline_latent_diffusion_superresolution\"] = [\"LDMSuperResolutionPipeline\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n else:\n from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline\n from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import List, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.utils.checkpoint\nfrom transformers import PretrainedConfig, PreTrainedModel, PreTrainedTokenizer\nfrom transformers.activations import ACT2FN\nfrom transformers.modeling_outputs import BaseModelOutput\nfrom transformers.utils import logging\n\nfrom ...models import AutoencoderKL, UNet2DConditionModel, UNet2DModel, VQModel\nfrom ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nclass LDMTextToImagePipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for text-to-image generation using latent diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Parameters:\n vqvae ([`VQModel`]):\n Vector-quantized (VQ) model to encode and decode images to and from latent representations.\n bert ([`LDMBertModel`]):\n Text-encoder model based on [`~transformers.BERT`].\n tokenizer ([`~transformers.BertTokenizer`]):\n A `BertTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n \"\"\"\n\n model_cpu_offload_seq = \"bert->unet->vqvae\"\n\n def __init__(\n self,\n vqvae: Union[VQModel, AutoencoderKL],\n bert: PreTrainedModel,\n tokenizer: PreTrainedTokenizer,\n unet: Union[UNet2DModel, UNet2DConditionModel],\n scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],\n ):\n super().__init__()\n self.register_modules(vqvae=vqvae, bert=bert, tokenizer=tokenizer, unet=unet, scheduler=scheduler)\n self.vae_scale_factor = 2 ** (len(self.vqvae.config.block_out_channels) - 1)\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: Optional[int] = 50,\n guidance_scale: Optional[float] = 1.0,\n eta: Optional[float] = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n **kwargs,\n ) -> Union[Tuple, ImagePipelineOutput]:\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 1.0):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple.\n\n Example:\n\n ```py\n >>> from diffusers import DiffusionPipeline\n\n >>> # load model and scheduler\n >>> ldm = DiffusionPipeline.from_pretrained(\"CompVis/ldm-text2im-large-256\")\n\n >>> # run pipeline in inference (sample random noise and denoise)\n >>> prompt = \"A painting of a squirrel eating a burger\"\n >>> images = ldm([prompt], num_inference_steps=50, eta=0.3, guidance_scale=6).images\n\n >>> # save images\n >>> for idx, image in enumerate(images):\n ... image.save(f\"squirrel-{idx}.png\")\n ```\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is\n returned where the first element is a list with the generated images.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n if isinstance(prompt, str):\n batch_size = 1\n elif isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n # get unconditional embeddings for classifier free guidance\n if guidance_scale != 1.0:\n uncond_input = self.tokenizer(\n [\"\"] * batch_size, padding=\"max_length\", max_length=77, truncation=True, return_tensors=\"pt\"\n )\n negative_prompt_embeds = self.bert(uncond_input.input_ids.to(self._execution_device))[0]\n\n # get prompt text embeddings\n text_input = self.tokenizer(prompt, padding=\"max_length\", max_length=77, truncation=True, return_tensors=\"pt\")\n prompt_embeds = self.bert(text_input.input_ids.to(self._execution_device))[0]\n\n # get the initial random noise unless the user supplied it\n latents_shape = (batch_size, self.unet.config.in_channels, height // 8, width // 8)\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(\n latents_shape, generator=generator, device=self._execution_device, dtype=prompt_embeds.dtype\n )\n else:\n if latents.shape != latents_shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {latents_shape}\")\n latents = latents.to(self._execution_device)\n\n self.scheduler.set_timesteps(num_inference_steps)\n\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n\n extra_kwargs = {}\n if accepts_eta:\n extra_kwargs[\"eta\"] = eta\n\n for t in self.progress_bar(self.scheduler.timesteps):\n if guidance_scale == 1.0:\n # guidance_scale of 1 means no guidance\n latents_input = latents\n context = prompt_embeds\n else:\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n latents_input = torch.cat([latents] * 2)\n context = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # predict the noise residual\n noise_pred = self.unet(latents_input, t, encoder_hidden_states=context).sample\n # perform guidance\n if guidance_scale != 1.0:\n noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_kwargs).prev_sample\n\n # scale and decode the image latents with vae\n latents = 1 / self.vqvae.config.scaling_factor * latents\n image = self.vqvae.decode(latents).sample\n\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).numpy()\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n\n\n################################################################################\n# Code for the text transformer model\n################################################################################\n\"\"\" PyTorch LDMBERT model.\"\"\"\n\n\nlogger = logging.get_logger(__name__)\n\nLDMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [\n \"ldm-bert\",\n # See all LDMBert models at https://huggingface.co/models?filter=ldmbert\n]\n\n\nLDMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {\n \"ldm-bert\": \"https://huggingface.co/valhalla/ldm-bert/blob/main/config.json\",\n}\n\n\n\"\"\" LDMBERT model configuration\"\"\"\n\n\nclass LDMBertConfig(PretrainedConfig):\n model_type = \"ldmbert\"\n keys_to_ignore_at_inference = [\"past_key_values\"]\n attribute_map = {\"num_attention_heads\": \"encoder_attention_heads\", \"hidden_size\": \"d_model\"}\n\n def __init__(\n self,\n vocab_size=30522,\n max_position_embeddings=77,\n encoder_layers=32,\n encoder_ffn_dim=5120,\n encoder_attention_heads=8,\n head_dim=64,\n encoder_layerdrop=0.0,\n activation_function=\"gelu\",\n d_model=1280,\n dropout=0.1,\n attention_dropout=0.0,\n activation_dropout=0.0,\n init_std=0.02,\n classifier_dropout=0.0,\n scale_embedding=False,\n use_cache=True,\n pad_token_id=0,\n **kwargs,\n ):\n self.vocab_size = vocab_size\n self.max_position_embeddings = max_position_embeddings\n self.d_model = d_model\n self.encoder_ffn_dim = encoder_ffn_dim\n self.encoder_layers = encoder_layers\n self.encoder_attention_heads = encoder_attention_heads\n self.head_dim = head_dim\n self.dropout = dropout\n self.attention_dropout = attention_dropout\n self.activation_dropout = activation_dropout\n self.activation_function = activation_function\n self.init_std = init_std\n self.encoder_layerdrop = encoder_layerdrop\n self.classifier_dropout = classifier_dropout\n self.use_cache = use_cache\n self.num_hidden_layers = encoder_layers\n self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True\n\n super().__init__(pad_token_id=pad_token_id, **kwargs)\n\n\ndef _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):\n \"\"\"\n Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.\n \"\"\"\n bsz, src_len = mask.size()\n tgt_len = tgt_len if tgt_len is not None else src_len\n\n expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)\n\n inverted_mask = 1.0 - expanded_mask\n\n return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)\n\n\n# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->LDMBert\nclass LDMBertAttention(nn.Module):\n \"\"\"Multi-headed attention from 'Attention Is All You Need' paper\"\"\"\n\n def __init__(\n self,\n embed_dim: int,\n num_heads: int,\n head_dim: int,\n dropout: float = 0.0,\n is_decoder: bool = False,\n bias: bool = False,\n ):\n super().__init__()\n self.embed_dim = embed_dim\n self.num_heads = num_heads\n self.dropout = dropout\n self.head_dim = head_dim\n self.inner_dim = head_dim * num_heads\n\n self.scaling = self.head_dim**-0.5\n self.is_decoder = is_decoder\n\n self.k_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias)\n self.v_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias)\n self.q_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias)\n self.out_proj = nn.Linear(self.inner_dim, embed_dim)\n\n def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):\n return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n key_value_states: Optional[torch.Tensor] = None,\n past_key_value: Optional[Tuple[torch.Tensor]] = None,\n attention_mask: Optional[torch.Tensor] = None,\n layer_head_mask: Optional[torch.Tensor] = None,\n output_attentions: bool = False,\n ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:\n \"\"\"Input shape: Batch x Time x Channel\"\"\"\n\n # if key_value_states are provided this layer is used as a cross-attention layer\n # for the decoder\n is_cross_attention = key_value_states is not None\n\n bsz, tgt_len, _ = hidden_states.size()\n\n # get query proj\n query_states = self.q_proj(hidden_states) * self.scaling\n # get key, value proj\n if is_cross_attention and past_key_value is not None:\n # reuse k,v, cross_attentions\n key_states = past_key_value[0]\n value_states = past_key_value[1]\n elif is_cross_attention:\n # cross_attentions\n key_states = self._shape(self.k_proj(key_value_states), -1, bsz)\n value_states = self._shape(self.v_proj(key_value_states), -1, bsz)\n elif past_key_value is not None:\n # reuse k, v, self_attention\n key_states = self._shape(self.k_proj(hidden_states), -1, bsz)\n value_states = self._shape(self.v_proj(hidden_states), -1, bsz)\n key_states = torch.cat([past_key_value[0], key_states], dim=2)\n value_states = torch.cat([past_key_value[1], value_states], dim=2)\n else:\n # self_attention\n key_states = self._shape(self.k_proj(hidden_states), -1, bsz)\n value_states = self._shape(self.v_proj(hidden_states), -1, bsz)\n\n if self.is_decoder:\n # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.\n # Further calls to cross_attention layer can then reuse all cross-attention\n # key/value_states (first \"if\" case)\n # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of\n # all previous decoder key/value_states. Further calls to uni-directional self-attention\n # can concat previous decoder key/value_states to current projected key/value_states (third \"elif\" case)\n # if encoder bi-directional self-attention `past_key_value` is always `None`\n past_key_value = (key_states, value_states)\n\n proj_shape = (bsz * self.num_heads, -1, self.head_dim)\n query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)\n key_states = key_states.view(*proj_shape)\n value_states = value_states.view(*proj_shape)\n\n src_len = key_states.size(1)\n attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))\n\n if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):\n raise ValueError(\n f\"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is\"\n f\" {attn_weights.size()}\"\n )\n\n if attention_mask is not None:\n if attention_mask.size() != (bsz, 1, tgt_len, src_len):\n raise ValueError(\n f\"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}\"\n )\n attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask\n attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)\n\n attn_weights = nn.functional.softmax(attn_weights, dim=-1)\n\n if layer_head_mask is not None:\n if layer_head_mask.size() != (self.num_heads,):\n raise ValueError(\n f\"Head mask for a single layer should be of size {(self.num_heads,)}, but is\"\n f\" {layer_head_mask.size()}\"\n )\n attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)\n attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)\n\n if output_attentions:\n # this operation is a bit awkward, but it's required to\n # make sure that attn_weights keeps its gradient.\n # In order to do so, attn_weights have to be reshaped\n # twice and have to be reused in the following\n attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)\n attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)\n else:\n attn_weights_reshaped = None\n\n attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)\n\n attn_output = torch.bmm(attn_probs, value_states)\n\n if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):\n raise ValueError(\n f\"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is\"\n f\" {attn_output.size()}\"\n )\n\n attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)\n attn_output = attn_output.transpose(1, 2)\n\n # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be\n # partitioned across GPUs when using tensor-parallelism.\n attn_output = attn_output.reshape(bsz, tgt_len, self.inner_dim)\n\n attn_output = self.out_proj(attn_output)\n\n return attn_output, attn_weights_reshaped, past_key_value\n\n\nclass LDMBertEncoderLayer(nn.Module):\n def __init__(self, config: LDMBertConfig):\n super().__init__()\n self.embed_dim = config.d_model\n self.self_attn = LDMBertAttention(\n embed_dim=self.embed_dim,\n num_heads=config.encoder_attention_heads,\n head_dim=config.head_dim,\n dropout=config.attention_dropout,\n )\n self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)\n self.dropout = config.dropout\n self.activation_fn = ACT2FN[config.activation_function]\n self.activation_dropout = config.activation_dropout\n self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)\n self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)\n self.final_layer_norm = nn.LayerNorm(self.embed_dim)\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n attention_mask: torch.Tensor,\n layer_head_mask: torch.Tensor,\n output_attentions: Optional[bool] = False,\n ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:\n \"\"\"\n Args:\n hidden_states (`torch.Tensor`): input to the layer of shape `(seq_len, batch, embed_dim)`\n attention_mask (`torch.Tensor`): attention mask of size\n `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.\n layer_head_mask (`torch.Tensor`): mask for attention heads in a given layer of size\n `(encoder_attention_heads,)`.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under\n returned tensors for more detail.\n \"\"\"\n residual = hidden_states\n hidden_states = self.self_attn_layer_norm(hidden_states)\n hidden_states, attn_weights, _ = self.self_attn(\n hidden_states=hidden_states,\n attention_mask=attention_mask,\n layer_head_mask=layer_head_mask,\n output_attentions=output_attentions,\n )\n hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)\n hidden_states = residual + hidden_states\n\n residual = hidden_states\n hidden_states = self.final_layer_norm(hidden_states)\n hidden_states = self.activation_fn(self.fc1(hidden_states))\n hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)\n hidden_states = self.fc2(hidden_states)\n hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)\n hidden_states = residual + hidden_states\n\n if hidden_states.dtype == torch.float16 and (\n torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()\n ):\n clamp_value = torch.finfo(hidden_states.dtype).max - 1000\n hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)\n\n outputs = (hidden_states,)\n\n if output_attentions:\n outputs += (attn_weights,)\n\n return outputs\n\n\n# Copied from transformers.models.bart.modeling_bart.BartPretrainedModel with Bart->LDMBert\nclass LDMBertPreTrainedModel(PreTrainedModel):\n config_class = LDMBertConfig\n base_model_prefix = \"model\"\n _supports_gradient_checkpointing = True\n _keys_to_ignore_on_load_unexpected = [r\"encoder\\.version\", r\"decoder\\.version\"]\n\n def _init_weights(self, module):\n std = self.config.init_std\n if isinstance(module, nn.Linear):\n module.weight.data.normal_(mean=0.0, std=std)\n if module.bias is not None:\n module.bias.data.zero_()\n elif isinstance(module, nn.Embedding):\n module.weight.data.normal_(mean=0.0, std=std)\n if module.padding_idx is not None:\n module.weight.data[module.padding_idx].zero_()\n\n def _set_gradient_checkpointing(self, module, value=False):\n if isinstance(module, (LDMBertEncoder,)):\n module.gradient_checkpointing = value\n\n @property\n def dummy_inputs(self):\n pad_token = self.config.pad_token_id\n input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)\n dummy_inputs = {\n \"attention_mask\": input_ids.ne(pad_token),\n \"input_ids\": input_ids,\n }\n return dummy_inputs\n\n\nclass LDMBertEncoder(LDMBertPreTrainedModel):\n \"\"\"\n Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a\n [`LDMBertEncoderLayer`].\n\n Args:\n config: LDMBertConfig\n embed_tokens (nn.Embedding): output embedding\n \"\"\"\n\n def __init__(self, config: LDMBertConfig):\n super().__init__(config)\n\n self.dropout = config.dropout\n\n embed_dim = config.d_model\n self.padding_idx = config.pad_token_id\n self.max_source_positions = config.max_position_embeddings\n\n self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim)\n self.embed_positions = nn.Embedding(config.max_position_embeddings, embed_dim)\n self.layers = nn.ModuleList([LDMBertEncoderLayer(config) for _ in range(config.encoder_layers)])\n self.layer_norm = nn.LayerNorm(embed_dim)\n\n self.gradient_checkpointing = False\n # Initialize weights and apply final processing\n self.post_init()\n\n def get_input_embeddings(self):\n return self.embed_tokens\n\n def set_input_embeddings(self, value):\n self.embed_tokens = value\n\n def forward(\n self,\n input_ids: torch.LongTensor = None,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n head_mask: Optional[torch.Tensor] = None,\n inputs_embeds: Optional[torch.Tensor] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, BaseModelOutput]:\n r\"\"\"\n Args:\n input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you\n provide it.\n\n Indices can be obtained using [`BartTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n [`PreTrainedTokenizer.__call__`] for details.\n\n [What are input IDs?](../glossary#input-ids)\n attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):\n Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n\n [What are attention masks?](../glossary#attention-mask)\n head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):\n Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:\n\n - 1 indicates the head is **not masked**,\n - 0 indicates the head is **masked**.\n\n inputs_embeds (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):\n Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.\n This is useful if you want more control over how to convert `input_ids` indices into associated vectors\n than the model's internal embedding lookup matrix.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under\n returned tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors\n for more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.BaseModelOutput`] instead of a plain tuple.\n \"\"\"\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n # retrieve input_ids and inputs_embeds\n if input_ids is not None and inputs_embeds is not None:\n raise ValueError(\"You cannot specify both input_ids and inputs_embeds at the same time\")\n elif input_ids is not None:\n input_shape = input_ids.size()\n input_ids = input_ids.view(-1, input_shape[-1])\n elif inputs_embeds is not None:\n input_shape = inputs_embeds.size()[:-1]\n else:\n raise ValueError(\"You have to specify either input_ids or inputs_embeds\")\n\n if inputs_embeds is None:\n inputs_embeds = self.embed_tokens(input_ids)\n\n seq_len = input_shape[1]\n if position_ids is None:\n position_ids = torch.arange(seq_len, dtype=torch.long, device=inputs_embeds.device).expand((1, -1))\n embed_pos = self.embed_positions(position_ids)\n\n hidden_states = inputs_embeds + embed_pos\n hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)\n\n # expand attention_mask\n if attention_mask is not None:\n # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]\n attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)\n\n encoder_states = () if output_hidden_states else None\n all_attentions = () if output_attentions else None\n\n # check if head_mask has a correct number of layers specified if desired\n if head_mask is not None:\n if head_mask.size()[0] != (len(self.layers)):\n raise ValueError(\n f\"The head_mask should be specified for {len(self.layers)} layers, but it is for\"\n f\" {head_mask.size()[0]}.\"\n )\n\n for idx, encoder_layer in enumerate(self.layers):\n if output_hidden_states:\n encoder_states = encoder_states + (hidden_states,)\n if self.gradient_checkpointing and self.training:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs, output_attentions)\n\n return custom_forward\n\n layer_outputs = torch.utils.checkpoint.checkpoint(\n create_custom_forward(encoder_layer),\n hidden_states,\n attention_mask,\n (head_mask[idx] if head_mask is not None else None),\n )\n else:\n layer_outputs = encoder_layer(\n hidden_states,\n attention_mask,\n layer_head_mask=(head_mask[idx] if head_mask is not None else None),\n output_attentions=output_attentions,\n )\n\n hidden_states = layer_outputs[0]\n\n if output_attentions:\n all_attentions = all_attentions + (layer_outputs[1],)\n\n hidden_states = self.layer_norm(hidden_states)\n\n if output_hidden_states:\n encoder_states = encoder_states + (hidden_states,)\n\n if not return_dict:\n return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)\n return BaseModelOutput(\n last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions\n )\n\n\nclass LDMBertModel(LDMBertPreTrainedModel):\n _no_split_modules = []\n\n def __init__(self, config: LDMBertConfig):\n super().__init__(config)\n self.model = LDMBertEncoder(config)\n self.to_logits = nn.Linear(config.hidden_size, config.vocab_size)\n\n def forward(\n self,\n input_ids=None,\n attention_mask=None,\n position_ids=None,\n head_mask=None,\n inputs_embeds=None,\n output_attentions=None,\n output_hidden_states=None,\n return_dict=None,\n ):\n outputs = self.model(\n input_ids,\n attention_mask=attention_mask,\n position_ids=position_ids,\n head_mask=head_mask,\n inputs_embeds=inputs_embeds,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n return outputs\n" }, { "path": "diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py", "content": "import inspect\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.utils.checkpoint\n\nfrom ...models import UNet2DModel, VQModel\nfrom ...schedulers import (\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n)\nfrom ...utils import PIL_INTERPOLATION\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\ndef preprocess(image):\n w, h = image.size\n w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32\n image = image.resize((w, h), resample=PIL_INTERPOLATION[\"lanczos\"])\n image = np.array(image).astype(np.float32) / 255.0\n image = image[None].transpose(0, 3, 1, 2)\n image = torch.from_numpy(image)\n return 2.0 * image - 1.0\n\n\nclass LDMSuperResolutionPipeline(DiffusionPipeline):\n r\"\"\"\n A pipeline for image super-resolution using latent diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Parameters:\n vqvae ([`VQModel`]):\n Vector-quantized (VQ) model to encode and decode images to and from latent representations.\n unet ([`UNet2DModel`]):\n A `UNet2DModel` to denoise the encoded image.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], [`EulerDiscreteScheduler`],\n [`EulerAncestralDiscreteScheduler`], [`DPMSolverMultistepScheduler`], or [`PNDMScheduler`].\n \"\"\"\n\n def __init__(\n self,\n vqvae: VQModel,\n unet: UNet2DModel,\n scheduler: Union[\n DDIMScheduler,\n PNDMScheduler,\n LMSDiscreteScheduler,\n EulerDiscreteScheduler,\n EulerAncestralDiscreteScheduler,\n DPMSolverMultistepScheduler,\n ],\n ):\n super().__init__()\n self.register_modules(vqvae=vqvae, unet=unet, scheduler=scheduler)\n\n @torch.no_grad()\n def __call__(\n self,\n image: Union[torch.Tensor, PIL.Image.Image] = None,\n batch_size: Optional[int] = 1,\n num_inference_steps: Optional[int] = 100,\n eta: Optional[float] = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n ) -> Union[Tuple, ImagePipelineOutput]:\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n image (`torch.Tensor` or `PIL.Image.Image`):\n `Image` or tensor representing an image batch to be used as the starting point for the process.\n batch_size (`int`, *optional*, defaults to 1):\n Number of images to generate.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple.\n\n Example:\n\n ```py\n >>> import requests\n >>> from PIL import Image\n >>> from io import BytesIO\n >>> from diffusers import LDMSuperResolutionPipeline\n >>> import torch\n\n >>> # load model and scheduler\n >>> pipeline = LDMSuperResolutionPipeline.from_pretrained(\"CompVis/ldm-super-resolution-4x-openimages\")\n >>> pipeline = pipeline.to(\"cuda\")\n\n >>> # let's download an image\n >>> url = (\n ... \"https://user-images.githubusercontent.com/38061659/199705896-b48e17b8-b231-47cd-a270-4ffa5a93fa3e.png\"\n ... )\n >>> response = requests.get(url)\n >>> low_res_img = Image.open(BytesIO(response.content)).convert(\"RGB\")\n >>> low_res_img = low_res_img.resize((128, 128))\n\n >>> # run pipeline in inference (sample random noise and denoise)\n >>> upscaled_image = pipeline(low_res_img, num_inference_steps=100, eta=1).images[0]\n >>> # save image\n >>> upscaled_image.save(\"ldm_generated_image.png\")\n ```\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is\n returned where the first element is a list with the generated images\n \"\"\"\n if isinstance(image, PIL.Image.Image):\n batch_size = 1\n elif isinstance(image, torch.Tensor):\n batch_size = image.shape[0]\n else:\n raise ValueError(f\"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(image)}\")\n\n if isinstance(image, PIL.Image.Image):\n image = preprocess(image)\n\n height, width = image.shape[-2:]\n\n # in_channels should be 6: 3 for latents, 3 for low resolution image\n latents_shape = (batch_size, self.unet.config.in_channels // 2, height, width)\n latents_dtype = next(self.unet.parameters()).dtype\n\n latents = randn_tensor(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)\n\n image = image.to(device=self.device, dtype=latents_dtype)\n\n # set timesteps and move to the correct device\n self.scheduler.set_timesteps(num_inference_steps, device=self.device)\n timesteps_tensor = self.scheduler.timesteps\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_kwargs = {}\n if accepts_eta:\n extra_kwargs[\"eta\"] = eta\n\n for t in self.progress_bar(timesteps_tensor):\n # concat latents and low resolution image in the channel dimension.\n latents_input = torch.cat([latents, image], dim=1)\n latents_input = self.scheduler.scale_model_input(latents_input, t)\n # predict the noise residual\n noise_pred = self.unet(latents_input, t).sample\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_kwargs).prev_sample\n\n # decode the image latents with the VQVAE\n image = self.vqvae.decode(latents).sample\n image = torch.clamp(image, -1.0, 1.0)\n image = image / 2 + 0.5\n image = image.cpu().permute(0, 2, 3, 1).numpy()\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/ledits_pp/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_leditspp_stable_diffusion\"] = [\"LEditsPPPipelineStableDiffusion\"]\n _import_structure[\"pipeline_leditspp_stable_diffusion_xl\"] = [\"LEditsPPPipelineStableDiffusionXL\"]\n\n _import_structure[\"pipeline_output\"] = [\"LEditsPPDiffusionPipelineOutput\", \"LEditsPPDiffusionPipelineOutput\"]\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n else:\n from .pipeline_leditspp_stable_diffusion import (\n LEditsPPDiffusionPipelineOutput,\n LEditsPPInversionPipelineOutput,\n LEditsPPPipelineStableDiffusion,\n )\n from .pipeline_leditspp_stable_diffusion_xl import LEditsPPPipelineStableDiffusionXL\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py", "content": "import inspect\nimport math\nfrom itertools import repeat\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport torch\nimport torch.nn.functional as F\nfrom packaging import version\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom ...configuration_utils import FrozenDict\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, UNet2DConditionModel\nfrom ...models.attention_processor import Attention, AttnProcessor\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom ...schedulers import DDIMScheduler, DPMSolverMultistepScheduler\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline\nfrom .pipeline_output import LEditsPPDiffusionPipelineOutput, LEditsPPInversionPipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import PIL\n >>> import requests\n >>> import torch\n >>> from io import BytesIO\n\n >>> from diffusers import LEditsPPPipelineStableDiffusion\n >>> from diffusers.utils import load_image\n\n >>> pipe = LEditsPPPipelineStableDiffusion.from_pretrained(\n ... \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> img_url = \"https://www.aiml.informatik.tu-darmstadt.de/people/mbrack/cherry_blossom.png\"\n >>> image = load_image(img_url).convert(\"RGB\")\n\n >>> _ = pipe.invert(image=image, num_inversion_steps=50, skip=0.1)\n\n >>> edited_image = pipe(\n ... editing_prompt=[\"cherry blossom\"], edit_guidance_scale=10.0, edit_threshold=0.75\n ... ).images[0]\n ```\n\"\"\"\n\n\n# Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionAttendAndExcitePipeline.AttentionStore\nclass LeditsAttentionStore:\n @staticmethod\n def get_empty_store():\n return {\"down_cross\": [], \"mid_cross\": [], \"up_cross\": [], \"down_self\": [], \"mid_self\": [], \"up_self\": []}\n\n def __call__(self, attn, is_cross: bool, place_in_unet: str, editing_prompts, PnP=False):\n # attn.shape = batch_size * head_size, seq_len query, seq_len_key\n if attn.shape[1] <= self.max_size:\n bs = 1 + int(PnP) + editing_prompts\n skip = 2 if PnP else 1 # skip PnP & unconditional\n attn = torch.stack(attn.split(self.batch_size)).permute(1, 0, 2, 3)\n source_batch_size = int(attn.shape[1] // bs)\n self.forward(attn[:, skip * source_batch_size :], is_cross, place_in_unet)\n\n def forward(self, attn, is_cross: bool, place_in_unet: str):\n key = f\"{place_in_unet}_{'cross' if is_cross else 'self'}\"\n\n self.step_store[key].append(attn)\n\n def between_steps(self, store_step=True):\n if store_step:\n if self.average:\n if len(self.attention_store) == 0:\n self.attention_store = self.step_store\n else:\n for key in self.attention_store:\n for i in range(len(self.attention_store[key])):\n self.attention_store[key][i] += self.step_store[key][i]\n else:\n if len(self.attention_store) == 0:\n self.attention_store = [self.step_store]\n else:\n self.attention_store.append(self.step_store)\n\n self.cur_step += 1\n self.step_store = self.get_empty_store()\n\n def get_attention(self, step: int):\n if self.average:\n attention = {\n key: [item / self.cur_step for item in self.attention_store[key]] for key in self.attention_store\n }\n else:\n assert step is not None\n attention = self.attention_store[step]\n return attention\n\n def aggregate_attention(\n self, attention_maps, prompts, res: Union[int, Tuple[int]], from_where: List[str], is_cross: bool, select: int\n ):\n out = [[] for x in range(self.batch_size)]\n if isinstance(res, int):\n num_pixels = res**2\n resolution = (res, res)\n else:\n num_pixels = res[0] * res[1]\n resolution = res[:2]\n\n for location in from_where:\n for bs_item in attention_maps[f\"{location}_{'cross' if is_cross else 'self'}\"]:\n for batch, item in enumerate(bs_item):\n if item.shape[1] == num_pixels:\n cross_maps = item.reshape(len(prompts), -1, *resolution, item.shape[-1])[select]\n out[batch].append(cross_maps)\n\n out = torch.stack([torch.cat(x, dim=0) for x in out])\n # average over heads\n out = out.sum(1) / out.shape[1]\n return out\n\n def __init__(self, average: bool, batch_size=1, max_resolution=16, max_size: int = None):\n self.step_store = self.get_empty_store()\n self.attention_store = []\n self.cur_step = 0\n self.average = average\n self.batch_size = batch_size\n if max_size is None:\n self.max_size = max_resolution**2\n elif max_size is not None and max_resolution is None:\n self.max_size = max_size\n else:\n raise ValueError(\"Only allowed to set one of max_resolution or max_size\")\n\n\n# Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionAttendAndExcitePipeline.GaussianSmoothing\nclass LeditsGaussianSmoothing:\n def __init__(self, device):\n kernel_size = [3, 3]\n sigma = [0.5, 0.5]\n\n # The gaussian kernel is the product of the gaussian function of each dimension.\n kernel = 1\n meshgrids = torch.meshgrid([torch.arange(size, dtype=torch.float32) for size in kernel_size])\n for size, std, mgrid in zip(kernel_size, sigma, meshgrids):\n mean = (size - 1) / 2\n kernel *= 1 / (std * math.sqrt(2 * math.pi)) * torch.exp(-(((mgrid - mean) / (2 * std)) ** 2))\n\n # Make sure sum of values in gaussian kernel equals 1.\n kernel = kernel / torch.sum(kernel)\n\n # Reshape to depthwise convolutional weight\n kernel = kernel.view(1, 1, *kernel.size())\n kernel = kernel.repeat(1, *[1] * (kernel.dim() - 1))\n\n self.weight = kernel.to(device)\n\n def __call__(self, input):\n \"\"\"\n Arguments:\n Apply gaussian filter to input.\n input (torch.Tensor): Input to apply gaussian filter on.\n Returns:\n filtered (torch.Tensor): Filtered output.\n \"\"\"\n return F.conv2d(input, weight=self.weight.to(input.dtype))\n\n\nclass LEDITSCrossAttnProcessor:\n def __init__(self, attention_store, place_in_unet, pnp, editing_prompts):\n self.attnstore = attention_store\n self.place_in_unet = place_in_unet\n self.editing_prompts = editing_prompts\n self.pnp = pnp\n\n def __call__(\n self,\n attn: Attention,\n hidden_states,\n encoder_hidden_states,\n attention_mask=None,\n temb=None,\n ):\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n\n query = attn.to_q(hidden_states)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n attention_probs = attn.get_attention_scores(query, key, attention_mask)\n self.attnstore(\n attention_probs,\n is_cross=True,\n place_in_unet=self.place_in_unet,\n editing_prompts=self.editing_prompts,\n PnP=self.pnp,\n )\n\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n hidden_states = hidden_states / attn.rescale_output_factor\n return hidden_states\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\nclass LEditsPPPipelineStableDiffusion(\n DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin\n):\n \"\"\"\n Pipeline for textual image editing using LEDits++ with Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`] and builds on the [`StableDiffusionPipeline`]. Check the superclass\n documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular\n device, etc.).\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer ([`~transformers.CLIPTokenizer`]):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`DPMSolverMultistepScheduler`] or [`DDIMScheduler`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of\n [`DPMSolverMultistepScheduler`] or [`DDIMScheduler`]. If any other scheduler is passed it will\n automatically be set to [`DPMSolverMultistepScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\"]\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"image_encoder\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[DDIMScheduler, DPMSolverMultistepScheduler],\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if not isinstance(scheduler, DDIMScheduler) and not isinstance(scheduler, DPMSolverMultistepScheduler):\n scheduler = DPMSolverMultistepScheduler.from_config(\n scheduler.config, algorithm_type=\"sde-dpmsolver++\", solver_order=2\n )\n logger.warning(\n \"This pipeline only supports DDIMScheduler and DPMSolverMultistepScheduler. \"\n \"The scheduler has been changed to DPMSolverMultistepScheduler.\"\n )\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"clip_sample\"] = False\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n self.inversion_steps = None\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, eta, generator=None):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs\n def check_inputs(\n self,\n negative_prompt=None,\n editing_prompt_embeddings=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if editing_prompt_embeddings is not None and negative_prompt_embeds is not None:\n if editing_prompt_embeddings.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`editing_prompt_embeddings` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `editing_prompt_embeddings` {editing_prompt_embeddings.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, latents):\n # shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)\n\n # if latents.shape != shape:\n # raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def prepare_unet(self, attention_store, PnP: bool = False):\n attn_procs = {}\n for name in self.unet.attn_processors.keys():\n if name.startswith(\"mid_block\"):\n place_in_unet = \"mid\"\n elif name.startswith(\"up_blocks\"):\n place_in_unet = \"up\"\n elif name.startswith(\"down_blocks\"):\n place_in_unet = \"down\"\n else:\n continue\n\n if \"attn2\" in name and place_in_unet != \"mid\":\n attn_procs[name] = LEDITSCrossAttnProcessor(\n attention_store=attention_store,\n place_in_unet=place_in_unet,\n pnp=PnP,\n editing_prompts=self.enabled_editing_prompts,\n )\n else:\n attn_procs[name] = AttnProcessor()\n\n self.unet.set_attn_processor(attn_procs)\n\n def encode_prompt(\n self,\n device,\n num_images_per_prompt,\n enable_edit_guidance,\n negative_prompt=None,\n editing_prompt=None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n editing_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n enable_edit_guidance (`bool`):\n whether to perform any editing or reconstruct the input image instead\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n editing_prompt (`str` or `List[str]`, *optional*):\n Editing prompt(s) to be encoded. If not defined, one has to pass `editing_prompt_embeds` instead.\n editing_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n batch_size = self.batch_size\n num_edit_tokens = None\n\n if negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but exoected\"\n f\"{batch_size} based on the input images. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: procecss multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = negative_prompt_embeds.dtype\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n if enable_edit_guidance:\n if editing_prompt_embeds is None:\n # textual inversion: procecss multi-vector tokens if necessary\n # if isinstance(self, TextualInversionLoaderMixin):\n # prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n if isinstance(editing_prompt, str):\n editing_prompt = [editing_prompt]\n\n max_length = negative_prompt_embeds.shape[1]\n text_inputs = self.tokenizer(\n [x for item in editing_prompt for x in repeat(item, batch_size)],\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n return_length=True,\n )\n\n num_edit_tokens = text_inputs.length - 2 # not counting startoftext and endoftext\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(\n [x for item in editing_prompt for x in repeat(item, batch_size)],\n padding=\"longest\",\n return_tensors=\"pt\",\n ).input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if (\n hasattr(self.text_encoder.config, \"use_attention_mask\")\n and self.text_encoder.config.use_attention_mask\n ):\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n editing_prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n editing_prompt_embeds = editing_prompt_embeds[0]\n else:\n editing_prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n editing_prompt_embeds = editing_prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n editing_prompt_embeds = self.text_encoder.text_model.final_layer_norm(editing_prompt_embeds)\n\n editing_prompt_embeds = editing_prompt_embeds.to(dtype=negative_prompt_embeds.dtype, device=device)\n\n bs_embed_edit, seq_len, _ = editing_prompt_embeds.shape\n editing_prompt_embeds = editing_prompt_embeds.to(dtype=negative_prompt_embeds.dtype, device=device)\n editing_prompt_embeds = editing_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n editing_prompt_embeds = editing_prompt_embeds.view(bs_embed_edit * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return editing_prompt_embeds, negative_prompt_embeds, num_edit_tokens\n\n @property\n def guidance_rescale(self):\n return self._guidance_rescale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n editing_prompt: Optional[Union[str, List[str]]] = None,\n editing_prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n reverse_editing_direction: Optional[Union[bool, List[bool]]] = False,\n edit_guidance_scale: Optional[Union[float, List[float]]] = 5,\n edit_warmup_steps: Optional[Union[int, List[int]]] = 0,\n edit_cooldown_steps: Optional[Union[int, List[int]]] = None,\n edit_threshold: Optional[Union[float, List[float]]] = 0.9,\n user_mask: Optional[torch.Tensor] = None,\n sem_guidance: Optional[List[torch.Tensor]] = None,\n use_cross_attn_mask: bool = False,\n use_intersect_mask: bool = True,\n attn_store_steps: Optional[List[int]] = [],\n store_averaged_over_steps: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for editing. The\n [`~pipelines.ledits_pp.LEditsPPPipelineStableDiffusion.invert`] method has to be called beforehand. Edits will\n always be performed for the last inverted image(s).\n\n Args:\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n generator (`torch.Generator`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ledits_pp.LEditsPPDiffusionPipelineOutput`] instead of a plain\n tuple.\n editing_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. The image is reconstructed by setting\n `editing_prompt = None`. Guidance direction of prompt should be specified via\n `reverse_editing_direction`.\n editing_prompt_embeds (`torch.Tensor>`, *optional*):\n Pre-computed embeddings to use for guiding the image generation. Guidance direction of embedding should\n be specified via `reverse_editing_direction`.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n reverse_editing_direction (`bool` or `List[bool]`, *optional*, defaults to `False`):\n Whether the corresponding prompt in `editing_prompt` should be increased or decreased.\n edit_guidance_scale (`float` or `List[float]`, *optional*, defaults to 5):\n Guidance scale for guiding the image generation. If provided as list values should correspond to\n `editing_prompt`. `edit_guidance_scale` is defined as `s_e` of equation 12 of [LEDITS++\n Paper](https://arxiv.org/abs/2301.12247).\n edit_warmup_steps (`float` or `List[float]`, *optional*, defaults to 10):\n Number of diffusion steps (for each prompt) for which guidance will not be applied.\n edit_cooldown_steps (`float` or `List[float]`, *optional*, defaults to `None`):\n Number of diffusion steps (for each prompt) after which guidance will no longer be applied.\n edit_threshold (`float` or `List[float]`, *optional*, defaults to 0.9):\n Masking threshold of guidance. Threshold should be proportional to the image region that is modified.\n 'edit_threshold' is defined as 'λ' of equation 12 of [LEDITS++\n Paper](https://arxiv.org/abs/2301.12247).\n user_mask (`torch.Tensor`, *optional*):\n User-provided mask for even better control over the editing process. This is helpful when LEDITS++'s\n implicit masks do not meet user preferences.\n sem_guidance (`List[torch.Tensor]`, *optional*):\n List of pre-generated guidance vectors to be applied at generation. Length of the list has to\n correspond to `num_inference_steps`.\n use_cross_attn_mask (`bool`, defaults to `False`):\n Whether cross-attention masks are used. Cross-attention masks are always used when use_intersect_mask\n is set to true. Cross-attention masks are defined as 'M^1' of equation 12 of [LEDITS++\n paper](https://arxiv.org/pdf/2311.16711.pdf).\n use_intersect_mask (`bool`, defaults to `True`):\n Whether the masking term is calculated as intersection of cross-attention masks and masks derived from\n the noise estimate. Cross-attention mask are defined as 'M^1' and masks derived from the noise estimate\n are defined as 'M^2' of equation 12 of [LEDITS++ paper](https://arxiv.org/pdf/2311.16711.pdf).\n attn_store_steps (`List[int]`, *optional*):\n Steps for which the attention maps are stored in the AttentionStore. Just for visualization purposes.\n store_averaged_over_steps (`bool`, defaults to `True`):\n Whether the attention maps for the 'attn_store_steps' are stored averaged over the diffusion steps. If\n False, attention maps for each step are stores separately. Just for visualization purposes.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when\n using zero terminal SNR.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.ledits_pp.LEditsPPDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.ledits_pp.LEditsPPDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When\n returning a tuple, the first element is a list with the generated images, and the second element is a list\n of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\" (nsfw)\n content, according to the `safety_checker`.\n \"\"\"\n\n if self.inversion_steps is None:\n raise ValueError(\n \"You need to invert an input image first before calling the pipeline. The `invert` method has to be called beforehand. Edits will always be performed for the last inverted image(s).\"\n )\n\n eta = self.eta\n num_images_per_prompt = 1\n latents = self.init_latents\n\n zs = self.zs\n self.scheduler.set_timesteps(len(self.scheduler.timesteps))\n\n if use_intersect_mask:\n use_cross_attn_mask = True\n\n if use_cross_attn_mask:\n self.smoothing = LeditsGaussianSmoothing(self.device)\n\n if user_mask is not None:\n user_mask = user_mask.to(self.device)\n\n org_prompt = \"\"\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n negative_prompt,\n editing_prompt_embeds,\n negative_prompt_embeds,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_rescale = guidance_rescale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n\n # 2. Define call parameters\n batch_size = self.batch_size\n\n if editing_prompt:\n enable_edit_guidance = True\n if isinstance(editing_prompt, str):\n editing_prompt = [editing_prompt]\n self.enabled_editing_prompts = len(editing_prompt)\n elif editing_prompt_embeds is not None:\n enable_edit_guidance = True\n self.enabled_editing_prompts = editing_prompt_embeds.shape[0]\n else:\n self.enabled_editing_prompts = 0\n enable_edit_guidance = False\n\n # 3. Encode input prompt\n lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n\n edit_concepts, uncond_embeddings, num_edit_tokens = self.encode_prompt(\n editing_prompt=editing_prompt,\n device=self.device,\n num_images_per_prompt=num_images_per_prompt,\n enable_edit_guidance=enable_edit_guidance,\n negative_prompt=negative_prompt,\n editing_prompt_embeds=editing_prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if enable_edit_guidance:\n text_embeddings = torch.cat([uncond_embeddings, edit_concepts])\n self.text_cross_attention_maps = [editing_prompt] if isinstance(editing_prompt, str) else editing_prompt\n else:\n text_embeddings = torch.cat([uncond_embeddings])\n\n # 4. Prepare timesteps\n # self.scheduler.set_timesteps(num_inference_steps, device=self.device)\n timesteps = self.inversion_steps\n t_to_idx = {int(v): k for k, v in enumerate(timesteps[-zs.shape[0] :])}\n\n if use_cross_attn_mask:\n self.attention_store = LeditsAttentionStore(\n average=store_averaged_over_steps,\n batch_size=batch_size,\n max_size=(latents.shape[-2] / 4.0) * (latents.shape[-1] / 4.0),\n max_resolution=None,\n )\n self.prepare_unet(self.attention_store, PnP=False)\n resolution = latents.shape[-2:]\n att_res = (int(resolution[0] / 4), int(resolution[1] / 4))\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n None,\n None,\n text_embeddings.dtype,\n self.device,\n latents,\n )\n\n # 6. Prepare extra step kwargs.\n extra_step_kwargs = self.prepare_extra_step_kwargs(eta)\n\n self.sem_guidance = None\n self.activation_mask = None\n\n # 7. Denoising loop\n num_warmup_steps = 0\n with self.progress_bar(total=len(timesteps)) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n\n if enable_edit_guidance:\n latent_model_input = torch.cat([latents] * (1 + self.enabled_editing_prompts))\n else:\n latent_model_input = latents\n\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n text_embed_input = text_embeddings\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embed_input).sample\n\n noise_pred_out = noise_pred.chunk(1 + self.enabled_editing_prompts) # [b,4, 64, 64]\n noise_pred_uncond = noise_pred_out[0]\n noise_pred_edit_concepts = noise_pred_out[1:]\n\n noise_guidance_edit = torch.zeros(\n noise_pred_uncond.shape,\n device=self.device,\n dtype=noise_pred_uncond.dtype,\n )\n\n if sem_guidance is not None and len(sem_guidance) > i:\n noise_guidance_edit += sem_guidance[i].to(self.device)\n\n elif enable_edit_guidance:\n if self.activation_mask is None:\n self.activation_mask = torch.zeros(\n (len(timesteps), len(noise_pred_edit_concepts), *noise_pred_edit_concepts[0].shape)\n )\n\n if self.sem_guidance is None:\n self.sem_guidance = torch.zeros((len(timesteps), *noise_pred_uncond.shape))\n\n for c, noise_pred_edit_concept in enumerate(noise_pred_edit_concepts):\n if isinstance(edit_warmup_steps, list):\n edit_warmup_steps_c = edit_warmup_steps[c]\n else:\n edit_warmup_steps_c = edit_warmup_steps\n if i < edit_warmup_steps_c:\n continue\n\n if isinstance(edit_guidance_scale, list):\n edit_guidance_scale_c = edit_guidance_scale[c]\n else:\n edit_guidance_scale_c = edit_guidance_scale\n\n if isinstance(edit_threshold, list):\n edit_threshold_c = edit_threshold[c]\n else:\n edit_threshold_c = edit_threshold\n if isinstance(reverse_editing_direction, list):\n reverse_editing_direction_c = reverse_editing_direction[c]\n else:\n reverse_editing_direction_c = reverse_editing_direction\n\n if isinstance(edit_cooldown_steps, list):\n edit_cooldown_steps_c = edit_cooldown_steps[c]\n elif edit_cooldown_steps is None:\n edit_cooldown_steps_c = i + 1\n else:\n edit_cooldown_steps_c = edit_cooldown_steps\n\n if i >= edit_cooldown_steps_c:\n continue\n\n noise_guidance_edit_tmp = noise_pred_edit_concept - noise_pred_uncond\n\n if reverse_editing_direction_c:\n noise_guidance_edit_tmp = noise_guidance_edit_tmp * -1\n\n noise_guidance_edit_tmp = noise_guidance_edit_tmp * edit_guidance_scale_c\n\n if user_mask is not None:\n noise_guidance_edit_tmp = noise_guidance_edit_tmp * user_mask\n\n if use_cross_attn_mask:\n out = self.attention_store.aggregate_attention(\n attention_maps=self.attention_store.step_store,\n prompts=self.text_cross_attention_maps,\n res=att_res,\n from_where=[\"up\", \"down\"],\n is_cross=True,\n select=self.text_cross_attention_maps.index(editing_prompt[c]),\n )\n attn_map = out[:, :, :, 1 : 1 + num_edit_tokens[c]] # 0 -> startoftext\n\n # average over all tokens\n if attn_map.shape[3] != num_edit_tokens[c]:\n raise ValueError(\n f\"Incorrect shape of attention_map. Expected size {num_edit_tokens[c]}, but found {attn_map.shape[3]}!\"\n )\n\n attn_map = torch.sum(attn_map, dim=3)\n\n # gaussian_smoothing\n attn_map = F.pad(attn_map.unsqueeze(1), (1, 1, 1, 1), mode=\"reflect\")\n attn_map = self.smoothing(attn_map).squeeze(1)\n\n # torch.quantile function expects float32\n if attn_map.dtype == torch.float32:\n tmp = torch.quantile(attn_map.flatten(start_dim=1), edit_threshold_c, dim=1)\n else:\n tmp = torch.quantile(\n attn_map.flatten(start_dim=1).to(torch.float32), edit_threshold_c, dim=1\n ).to(attn_map.dtype)\n attn_mask = torch.where(\n attn_map >= tmp.unsqueeze(1).unsqueeze(1).repeat(1, *att_res), 1.0, 0.0\n )\n\n # resolution must match latent space dimension\n attn_mask = F.interpolate(\n attn_mask.unsqueeze(1),\n noise_guidance_edit_tmp.shape[-2:], # 64,64\n ).repeat(1, 4, 1, 1)\n self.activation_mask[i, c] = attn_mask.detach().cpu()\n if not use_intersect_mask:\n noise_guidance_edit_tmp = noise_guidance_edit_tmp * attn_mask\n\n if use_intersect_mask:\n if t <= 800:\n noise_guidance_edit_tmp_quantile = torch.abs(noise_guidance_edit_tmp)\n noise_guidance_edit_tmp_quantile = torch.sum(\n noise_guidance_edit_tmp_quantile, dim=1, keepdim=True\n )\n noise_guidance_edit_tmp_quantile = noise_guidance_edit_tmp_quantile.repeat(\n 1, self.unet.config.in_channels, 1, 1\n )\n\n # torch.quantile function expects float32\n if noise_guidance_edit_tmp_quantile.dtype == torch.float32:\n tmp = torch.quantile(\n noise_guidance_edit_tmp_quantile.flatten(start_dim=2),\n edit_threshold_c,\n dim=2,\n keepdim=False,\n )\n else:\n tmp = torch.quantile(\n noise_guidance_edit_tmp_quantile.flatten(start_dim=2).to(torch.float32),\n edit_threshold_c,\n dim=2,\n keepdim=False,\n ).to(noise_guidance_edit_tmp_quantile.dtype)\n\n intersect_mask = (\n torch.where(\n noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None],\n torch.ones_like(noise_guidance_edit_tmp),\n torch.zeros_like(noise_guidance_edit_tmp),\n )\n * attn_mask\n )\n\n self.activation_mask[i, c] = intersect_mask.detach().cpu()\n\n noise_guidance_edit_tmp = noise_guidance_edit_tmp * intersect_mask\n\n else:\n # print(f\"only attention mask for step {i}\")\n noise_guidance_edit_tmp = noise_guidance_edit_tmp * attn_mask\n\n elif not use_cross_attn_mask:\n # calculate quantile\n noise_guidance_edit_tmp_quantile = torch.abs(noise_guidance_edit_tmp)\n noise_guidance_edit_tmp_quantile = torch.sum(\n noise_guidance_edit_tmp_quantile, dim=1, keepdim=True\n )\n noise_guidance_edit_tmp_quantile = noise_guidance_edit_tmp_quantile.repeat(1, 4, 1, 1)\n\n # torch.quantile function expects float32\n if noise_guidance_edit_tmp_quantile.dtype == torch.float32:\n tmp = torch.quantile(\n noise_guidance_edit_tmp_quantile.flatten(start_dim=2),\n edit_threshold_c,\n dim=2,\n keepdim=False,\n )\n else:\n tmp = torch.quantile(\n noise_guidance_edit_tmp_quantile.flatten(start_dim=2).to(torch.float32),\n edit_threshold_c,\n dim=2,\n keepdim=False,\n ).to(noise_guidance_edit_tmp_quantile.dtype)\n\n self.activation_mask[i, c] = (\n torch.where(\n noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None],\n torch.ones_like(noise_guidance_edit_tmp),\n torch.zeros_like(noise_guidance_edit_tmp),\n )\n .detach()\n .cpu()\n )\n\n noise_guidance_edit_tmp = torch.where(\n noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None],\n noise_guidance_edit_tmp,\n torch.zeros_like(noise_guidance_edit_tmp),\n )\n\n noise_guidance_edit += noise_guidance_edit_tmp\n\n self.sem_guidance[i] = noise_guidance_edit.detach().cpu()\n\n noise_pred = noise_pred_uncond + noise_guidance_edit\n\n if enable_edit_guidance and self.guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(\n noise_pred,\n noise_pred_edit_concepts.mean(dim=0, keepdim=False),\n guidance_rescale=self.guidance_rescale,\n )\n\n idx = t_to_idx[int(t)]\n latents = self.scheduler.step(\n noise_pred, t, latents, variance_noise=zs[idx], **extra_step_kwargs\n ).prev_sample\n\n # step callback\n if use_cross_attn_mask:\n store_step = i in attn_store_steps\n self.attention_store.between_steps(store_step)\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n # prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n\n # 8. Post-processing\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[\n 0\n ]\n image, has_nsfw_concept = self.run_safety_checker(image, self.device, text_embeddings.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return LEditsPPDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n\n @torch.no_grad()\n def invert(\n self,\n image: PipelineImageInput,\n source_prompt: str = \"\",\n source_guidance_scale: float = 3.5,\n num_inversion_steps: int = 30,\n skip: float = 0.15,\n generator: Optional[torch.Generator] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: Optional[int] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n resize_mode: Optional[str] = \"default\",\n crops_coords: Optional[Tuple[int, int, int, int]] = None,\n ):\n r\"\"\"\n The function to the pipeline for image inversion as described by the [LEDITS++\n Paper](https://arxiv.org/abs/2301.12247). If the scheduler is set to [`~schedulers.DDIMScheduler`] the\n inversion proposed by [edit-friendly DPDM](https://arxiv.org/abs/2304.06140) will be performed instead.\n\n Args:\n image (`PipelineImageInput`):\n Input for the image(s) that are to be edited. Multiple input images have to default to the same aspect\n ratio.\n source_prompt (`str`, defaults to `\"\"`):\n Prompt describing the input image that will be used for guidance during inversion. Guidance is disabled\n if the `source_prompt` is `\"\"`.\n source_guidance_scale (`float`, defaults to `3.5`):\n Strength of guidance during inversion.\n num_inversion_steps (`int`, defaults to `30`):\n Number of total performed inversion steps after discarding the initial `skip` steps.\n skip (`float`, defaults to `0.15`):\n Portion of initial steps that will be ignored for inversion and subsequent generation. Lower values\n will lead to stronger changes to the input image. `skip` has to be between `0` and `1`.\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make inversion\n deterministic.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n height (`int`, *optional*, defaults to `None`):\n The height in preprocessed image. If `None`, will use the `get_default_height_width()` to get default\n height.\n width (`int`, *optional*`, defaults to `None`):\n The width in preprocessed. If `None`, will use get_default_height_width()` to get the default width.\n resize_mode (`str`, *optional*, defaults to `default`):\n The resize mode, can be one of `default` or `fill`. If `default`, will resize the image to fit within\n the specified width and height, and it may not maintaining the original aspect ratio. If `fill`, will\n resize the image to fit within the specified width and height, maintaining the aspect ratio, and then\n center the image within the dimensions, filling empty with data from image. If `crop`, will resize the\n image to fit within the specified width and height, maintaining the aspect ratio, and then center the\n image within the dimensions, cropping the excess. Note that resize_mode `fill` and `crop` are only\n supported for PIL image input.\n crops_coords (`List[Tuple[int, int, int, int]]`, *optional*, defaults to `None`):\n The crop coordinates for each image in the batch. If `None`, will not crop the image.\n\n Returns:\n [`~pipelines.ledits_pp.LEditsPPInversionPipelineOutput`]: Output will contain the resized input image(s)\n and respective VAE reconstruction(s).\n \"\"\"\n # Reset attn processor, we do not want to store attn maps during inversion\n self.unet.set_attn_processor(AttnProcessor())\n\n self.eta = 1.0\n\n self.scheduler.config.timestep_spacing = \"leading\"\n self.scheduler.set_timesteps(int(num_inversion_steps * (1 + skip)))\n self.inversion_steps = self.scheduler.timesteps[-num_inversion_steps:]\n timesteps = self.inversion_steps\n\n # 1. encode image\n x0, resized = self.encode_image(\n image,\n dtype=self.text_encoder.dtype,\n height=height,\n width=width,\n resize_mode=resize_mode,\n crops_coords=crops_coords,\n )\n self.batch_size = x0.shape[0]\n\n # autoencoder reconstruction\n image_rec = self.vae.decode(x0 / self.vae.config.scaling_factor, return_dict=False, generator=generator)[0]\n image_rec = self.image_processor.postprocess(image_rec, output_type=\"pil\")\n\n # 2. get embeddings\n do_classifier_free_guidance = source_guidance_scale > 1.0\n\n lora_scale = cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n\n uncond_embedding, text_embeddings, _ = self.encode_prompt(\n num_images_per_prompt=1,\n device=self.device,\n negative_prompt=None,\n enable_edit_guidance=do_classifier_free_guidance,\n editing_prompt=source_prompt,\n lora_scale=lora_scale,\n clip_skip=clip_skip,\n )\n\n # 3. find zs and xts\n variance_noise_shape = (num_inversion_steps, *x0.shape)\n\n # intermediate latents\n t_to_idx = {int(v): k for k, v in enumerate(timesteps)}\n xts = torch.zeros(size=variance_noise_shape, device=self.device, dtype=uncond_embedding.dtype)\n\n for t in reversed(timesteps):\n idx = num_inversion_steps - t_to_idx[int(t)] - 1\n noise = randn_tensor(shape=x0.shape, generator=generator, device=self.device, dtype=x0.dtype)\n xts[idx] = self.scheduler.add_noise(x0, noise, torch.Tensor([t]))\n xts = torch.cat([x0.unsqueeze(0), xts], dim=0)\n\n self.scheduler.set_timesteps(len(self.scheduler.timesteps))\n # noise maps\n zs = torch.zeros(size=variance_noise_shape, device=self.device, dtype=uncond_embedding.dtype)\n\n with self.progress_bar(total=len(timesteps)) as progress_bar:\n for t in timesteps:\n idx = num_inversion_steps - t_to_idx[int(t)] - 1\n # 1. predict noise residual\n xt = xts[idx + 1]\n\n noise_pred = self.unet(xt, timestep=t, encoder_hidden_states=uncond_embedding).sample\n\n if not source_prompt == \"\":\n noise_pred_cond = self.unet(xt, timestep=t, encoder_hidden_states=text_embeddings).sample\n noise_pred = noise_pred + source_guidance_scale * (noise_pred_cond - noise_pred)\n\n xtm1 = xts[idx]\n z, xtm1_corrected = compute_noise(self.scheduler, xtm1, xt, t, noise_pred, self.eta)\n zs[idx] = z\n\n # correction to avoid error accumulation\n xts[idx] = xtm1_corrected\n\n progress_bar.update()\n\n self.init_latents = xts[-1].expand(self.batch_size, -1, -1, -1)\n zs = zs.flip(0)\n self.zs = zs\n\n return LEditsPPInversionPipelineOutput(images=resized, vae_reconstruction_images=image_rec)\n\n @torch.no_grad()\n def encode_image(self, image, dtype=None, height=None, width=None, resize_mode=\"default\", crops_coords=None):\n image = self.image_processor.preprocess(\n image=image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords\n )\n resized = self.image_processor.postprocess(image=image, output_type=\"pil\")\n\n if max(image.shape[-2:]) > self.vae.config[\"sample_size\"] * 1.5:\n logger.warning(\n \"Your input images far exceed the default resolution of the underlying diffusion model. \"\n \"The output images may contain severe artifacts! \"\n \"Consider down-sampling the input using the `height` and `width` parameters\"\n )\n image = image.to(dtype)\n\n x0 = self.vae.encode(image.to(self.device)).latent_dist.mode()\n x0 = x0.to(dtype)\n x0 = self.vae.config.scaling_factor * x0\n return x0, resized\n\n\ndef compute_noise_ddim(scheduler, prev_latents, latents, timestep, noise_pred, eta):\n # 1. get previous step value (=t-1)\n prev_timestep = timestep - scheduler.config.num_train_timesteps // scheduler.num_inference_steps\n\n # 2. compute alphas, betas\n alpha_prod_t = scheduler.alphas_cumprod[timestep]\n alpha_prod_t_prev = (\n scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else scheduler.final_alpha_cumprod\n )\n\n beta_prod_t = 1 - alpha_prod_t\n\n # 3. compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)\n\n # 4. Clip \"predicted x_0\"\n if scheduler.config.clip_sample:\n pred_original_sample = torch.clamp(pred_original_sample, -1, 1)\n\n # 5. compute variance: \"sigma_t(η)\" -> see formula (16)\n # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)\n variance = scheduler._get_variance(timestep, prev_timestep)\n std_dev_t = eta * variance ** (0.5)\n\n # 6. compute \"direction pointing to x_t\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * noise_pred\n\n # modifed so that updated xtm1 is returned as well (to avoid error accumulation)\n mu_xt = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction\n if variance > 0.0:\n noise = (prev_latents - mu_xt) / (variance ** (0.5) * eta)\n else:\n noise = torch.tensor([0.0]).to(latents.device)\n\n return noise, mu_xt + (eta * variance**0.5) * noise\n\n\ndef compute_noise_sde_dpm_pp_2nd(scheduler, prev_latents, latents, timestep, noise_pred, eta):\n def first_order_update(model_output, sample): # timestep, prev_timestep, sample):\n sigma_t, sigma_s = scheduler.sigmas[scheduler.step_index + 1], scheduler.sigmas[scheduler.step_index]\n alpha_t, sigma_t = scheduler._sigma_to_alpha_sigma_t(sigma_t)\n alpha_s, sigma_s = scheduler._sigma_to_alpha_sigma_t(sigma_s)\n lambda_t = torch.log(alpha_t) - torch.log(sigma_t)\n lambda_s = torch.log(alpha_s) - torch.log(sigma_s)\n\n h = lambda_t - lambda_s\n\n mu_xt = (sigma_t / sigma_s * torch.exp(-h)) * sample + (alpha_t * (1 - torch.exp(-2.0 * h))) * model_output\n\n mu_xt = scheduler.dpm_solver_first_order_update(\n model_output=model_output, sample=sample, noise=torch.zeros_like(sample)\n )\n\n sigma = sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h))\n if sigma > 0.0:\n noise = (prev_latents - mu_xt) / sigma\n else:\n noise = torch.tensor([0.0]).to(sample.device)\n\n prev_sample = mu_xt + sigma * noise\n return noise, prev_sample\n\n def second_order_update(model_output_list, sample): # timestep_list, prev_timestep, sample):\n sigma_t, sigma_s0, sigma_s1 = (\n scheduler.sigmas[scheduler.step_index + 1],\n scheduler.sigmas[scheduler.step_index],\n scheduler.sigmas[scheduler.step_index - 1],\n )\n\n alpha_t, sigma_t = scheduler._sigma_to_alpha_sigma_t(sigma_t)\n alpha_s0, sigma_s0 = scheduler._sigma_to_alpha_sigma_t(sigma_s0)\n alpha_s1, sigma_s1 = scheduler._sigma_to_alpha_sigma_t(sigma_s1)\n\n lambda_t = torch.log(alpha_t) - torch.log(sigma_t)\n lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)\n lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1)\n\n m0, m1 = model_output_list[-1], model_output_list[-2]\n\n h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1\n r0 = h_0 / h\n D0, D1 = m0, (1.0 / r0) * (m0 - m1)\n\n mu_xt = (\n (sigma_t / sigma_s0 * torch.exp(-h)) * sample\n + (alpha_t * (1 - torch.exp(-2.0 * h))) * D0\n + 0.5 * (alpha_t * (1 - torch.exp(-2.0 * h))) * D1\n )\n\n sigma = sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h))\n if sigma > 0.0:\n noise = (prev_latents - mu_xt) / sigma\n else:\n noise = torch.tensor([0.0]).to(sample.device)\n\n prev_sample = mu_xt + sigma * noise\n\n return noise, prev_sample\n\n if scheduler.step_index is None:\n scheduler._init_step_index(timestep)\n\n model_output = scheduler.convert_model_output(model_output=noise_pred, sample=latents)\n for i in range(scheduler.config.solver_order - 1):\n scheduler.model_outputs[i] = scheduler.model_outputs[i + 1]\n scheduler.model_outputs[-1] = model_output\n\n if scheduler.lower_order_nums < 1:\n noise, prev_sample = first_order_update(model_output, latents)\n else:\n noise, prev_sample = second_order_update(scheduler.model_outputs, latents)\n\n if scheduler.lower_order_nums < scheduler.config.solver_order:\n scheduler.lower_order_nums += 1\n\n # upon completion increase step index by one\n scheduler._step_index += 1\n\n return noise, prev_sample\n\n\ndef compute_noise(scheduler, *args):\n if isinstance(scheduler, DDIMScheduler):\n return compute_noise_ddim(scheduler, *args)\n elif (\n isinstance(scheduler, DPMSolverMultistepScheduler)\n and scheduler.config.algorithm_type == \"sde-dpmsolver++\"\n and scheduler.config.solver_order == 2\n ):\n return compute_noise_sde_dpm_pp_2nd(scheduler, *args)\n else:\n raise NotImplementedError\n" }, { "path": "diffusers/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py", "content": "# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nimport math\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport torch\nimport torch.nn.functional as F\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModel,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n)\n\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import (\n FromSingleFileMixin,\n IPAdapterMixin,\n StableDiffusionXLLoraLoaderMixin,\n TextualInversionLoaderMixin,\n)\nfrom ...models import AutoencoderKL, UNet2DConditionModel\nfrom ...models.attention_processor import (\n Attention,\n AttnProcessor,\n AttnProcessor2_0,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n XFormersAttnProcessor,\n)\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import DDIMScheduler, DPMSolverMultistepScheduler\nfrom ...utils import (\n USE_PEFT_BACKEND,\n is_invisible_watermark_available,\n is_torch_xla_available,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline\nfrom .pipeline_output import LEditsPPDiffusionPipelineOutput, LEditsPPInversionPipelineOutput\n\n\nif is_invisible_watermark_available():\n from ..stable_diffusion_xl.watermark import StableDiffusionXLWatermarker\n\nif is_torch_xla_available():\n import torch_xla.core.xla_model as xm\n\n XLA_AVAILABLE = True\nelse:\n XLA_AVAILABLE = False\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> import PIL\n >>> import requests\n >>> from io import BytesIO\n\n >>> from diffusers import LEditsPPPipelineStableDiffusionXL\n\n >>> pipe = LEditsPPPipelineStableDiffusionXL.from_pretrained(\n ... \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n\n >>> def download_image(url):\n ... response = requests.get(url)\n ... return PIL.Image.open(BytesIO(response.content)).convert(\"RGB\")\n\n\n >>> img_url = \"https://www.aiml.informatik.tu-darmstadt.de/people/mbrack/tennis.jpg\"\n >>> image = download_image(img_url)\n\n >>> _ = pipe.invert(image=image, num_inversion_steps=50, skip=0.2)\n\n >>> edited_image = pipe(\n ... editing_prompt=[\"tennis ball\", \"tomato\"],\n ... reverse_editing_direction=[True, False],\n ... edit_guidance_scale=[5.0, 10.0],\n ... edit_threshold=[0.9, 0.85],\n ... ).images[0]\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LeditsAttentionStore\nclass LeditsAttentionStore:\n @staticmethod\n def get_empty_store():\n return {\"down_cross\": [], \"mid_cross\": [], \"up_cross\": [], \"down_self\": [], \"mid_self\": [], \"up_self\": []}\n\n def __call__(self, attn, is_cross: bool, place_in_unet: str, editing_prompts, PnP=False):\n # attn.shape = batch_size * head_size, seq_len query, seq_len_key\n if attn.shape[1] <= self.max_size:\n bs = 1 + int(PnP) + editing_prompts\n skip = 2 if PnP else 1 # skip PnP & unconditional\n attn = torch.stack(attn.split(self.batch_size)).permute(1, 0, 2, 3)\n source_batch_size = int(attn.shape[1] // bs)\n self.forward(attn[:, skip * source_batch_size :], is_cross, place_in_unet)\n\n def forward(self, attn, is_cross: bool, place_in_unet: str):\n key = f\"{place_in_unet}_{'cross' if is_cross else 'self'}\"\n\n self.step_store[key].append(attn)\n\n def between_steps(self, store_step=True):\n if store_step:\n if self.average:\n if len(self.attention_store) == 0:\n self.attention_store = self.step_store\n else:\n for key in self.attention_store:\n for i in range(len(self.attention_store[key])):\n self.attention_store[key][i] += self.step_store[key][i]\n else:\n if len(self.attention_store) == 0:\n self.attention_store = [self.step_store]\n else:\n self.attention_store.append(self.step_store)\n\n self.cur_step += 1\n self.step_store = self.get_empty_store()\n\n def get_attention(self, step: int):\n if self.average:\n attention = {\n key: [item / self.cur_step for item in self.attention_store[key]] for key in self.attention_store\n }\n else:\n assert step is not None\n attention = self.attention_store[step]\n return attention\n\n def aggregate_attention(\n self, attention_maps, prompts, res: Union[int, Tuple[int]], from_where: List[str], is_cross: bool, select: int\n ):\n out = [[] for x in range(self.batch_size)]\n if isinstance(res, int):\n num_pixels = res**2\n resolution = (res, res)\n else:\n num_pixels = res[0] * res[1]\n resolution = res[:2]\n\n for location in from_where:\n for bs_item in attention_maps[f\"{location}_{'cross' if is_cross else 'self'}\"]:\n for batch, item in enumerate(bs_item):\n if item.shape[1] == num_pixels:\n cross_maps = item.reshape(len(prompts), -1, *resolution, item.shape[-1])[select]\n out[batch].append(cross_maps)\n\n out = torch.stack([torch.cat(x, dim=0) for x in out])\n # average over heads\n out = out.sum(1) / out.shape[1]\n return out\n\n def __init__(self, average: bool, batch_size=1, max_resolution=16, max_size: int = None):\n self.step_store = self.get_empty_store()\n self.attention_store = []\n self.cur_step = 0\n self.average = average\n self.batch_size = batch_size\n if max_size is None:\n self.max_size = max_resolution**2\n elif max_size is not None and max_resolution is None:\n self.max_size = max_size\n else:\n raise ValueError(\"Only allowed to set one of max_resolution or max_size\")\n\n\n# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LeditsGaussianSmoothing\nclass LeditsGaussianSmoothing:\n def __init__(self, device):\n kernel_size = [3, 3]\n sigma = [0.5, 0.5]\n\n # The gaussian kernel is the product of the gaussian function of each dimension.\n kernel = 1\n meshgrids = torch.meshgrid([torch.arange(size, dtype=torch.float32) for size in kernel_size])\n for size, std, mgrid in zip(kernel_size, sigma, meshgrids):\n mean = (size - 1) / 2\n kernel *= 1 / (std * math.sqrt(2 * math.pi)) * torch.exp(-(((mgrid - mean) / (2 * std)) ** 2))\n\n # Make sure sum of values in gaussian kernel equals 1.\n kernel = kernel / torch.sum(kernel)\n\n # Reshape to depthwise convolutional weight\n kernel = kernel.view(1, 1, *kernel.size())\n kernel = kernel.repeat(1, *[1] * (kernel.dim() - 1))\n\n self.weight = kernel.to(device)\n\n def __call__(self, input):\n \"\"\"\n Arguments:\n Apply gaussian filter to input.\n input (torch.Tensor): Input to apply gaussian filter on.\n Returns:\n filtered (torch.Tensor): Filtered output.\n \"\"\"\n return F.conv2d(input, weight=self.weight.to(input.dtype))\n\n\n# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LEDITSCrossAttnProcessor\nclass LEDITSCrossAttnProcessor:\n def __init__(self, attention_store, place_in_unet, pnp, editing_prompts):\n self.attnstore = attention_store\n self.place_in_unet = place_in_unet\n self.editing_prompts = editing_prompts\n self.pnp = pnp\n\n def __call__(\n self,\n attn: Attention,\n hidden_states,\n encoder_hidden_states,\n attention_mask=None,\n temb=None,\n ):\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n\n query = attn.to_q(hidden_states)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n attention_probs = attn.get_attention_scores(query, key, attention_mask)\n self.attnstore(\n attention_probs,\n is_cross=True,\n place_in_unet=self.place_in_unet,\n editing_prompts=self.editing_prompts,\n PnP=self.pnp,\n )\n\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n hidden_states = hidden_states / attn.rescale_output_factor\n return hidden_states\n\n\nclass LEditsPPPipelineStableDiffusionXL(\n DiffusionPipeline,\n FromSingleFileMixin,\n StableDiffusionXLLoraLoaderMixin,\n TextualInversionLoaderMixin,\n IPAdapterMixin,\n):\n \"\"\"\n Pipeline for textual image editing using LEDits++ with Stable Diffusion XL.\n\n This model inherits from [`DiffusionPipeline`] and builds on the [`StableDiffusionXLPipeline`]. Check the\n superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a\n particular device, etc.).\n\n In addition the pipeline inherits the following loading methods:\n - *LoRA*: [`LEditsPPPipelineStableDiffusionXL.load_lora_weights`]\n - *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`]\n\n as well as the following saving methods:\n - *LoRA*: [`loaders.StableDiffusionXLPipeline.save_lora_weights`]\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion XL uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n text_encoder_2 ([`~transformers.CLIPTextModelWithProjection`]):\n Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),\n specifically the\n [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)\n variant.\n tokenizer ([`~transformers.CLIPTokenizer`]):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n tokenizer_2 ([`~transformers.CLIPTokenizer`]):\n Second Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`DPMSolverMultistepScheduler`] or [`DDIMScheduler`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of\n [`DPMSolverMultistepScheduler`] or [`DDIMScheduler`]. If any other scheduler is passed it will\n automatically be set to [`DPMSolverMultistepScheduler`].\n force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `\"True\"`):\n Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of\n `stabilityai/stable-diffusion-xl-base-1-0`.\n add_watermarker (`bool`, *optional*):\n Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to\n watermark output images. If not defined, it will default to True if the package is installed, otherwise no\n watermarker will be used.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->text_encoder_2->unet->vae\"\n _optional_components = [\n \"tokenizer\",\n \"tokenizer_2\",\n \"text_encoder\",\n \"text_encoder_2\",\n \"image_encoder\",\n \"feature_extractor\",\n ]\n _callback_tensor_inputs = [\n \"latents\",\n \"prompt_embeds\",\n \"negative_prompt_embeds\",\n \"add_text_embeds\",\n \"add_time_ids\",\n \"negative_pooled_prompt_embeds\",\n \"negative_add_time_ids\",\n ]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n text_encoder_2: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n tokenizer_2: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: Union[DPMSolverMultistepScheduler, DDIMScheduler],\n image_encoder: CLIPVisionModelWithProjection = None,\n feature_extractor: CLIPImageProcessor = None,\n force_zeros_for_empty_prompt: bool = True,\n add_watermarker: Optional[bool] = None,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n unet=unet,\n scheduler=scheduler,\n image_encoder=image_encoder,\n feature_extractor=feature_extractor,\n )\n self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n if not isinstance(scheduler, DDIMScheduler) and not isinstance(scheduler, DPMSolverMultistepScheduler):\n self.scheduler = DPMSolverMultistepScheduler.from_config(\n scheduler.config, algorithm_type=\"sde-dpmsolver++\", solver_order=2\n )\n logger.warning(\n \"This pipeline only supports DDIMScheduler and DPMSolverMultistepScheduler. \"\n \"The scheduler has been changed to DPMSolverMultistepScheduler.\"\n )\n\n self.default_sample_size = self.unet.config.sample_size\n\n add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()\n\n if add_watermarker:\n self.watermark = StableDiffusionXLWatermarker()\n else:\n self.watermark = None\n self.inversion_steps = None\n\n def encode_prompt(\n self,\n device: Optional[torch.device] = None,\n num_images_per_prompt: int = 1,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n enable_edit_guidance: bool = True,\n editing_prompt: Optional[str] = None,\n editing_prompt_embeds: Optional[torch.Tensor] = None,\n editing_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n ) -> object:\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead.\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n lora_scale (`float`, *optional*):\n A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n enable_edit_guidance (`bool`):\n Whether to guide towards an editing prompt or not.\n editing_prompt (`str` or `List[str]`, *optional*):\n Editing prompt(s) to be encoded. If not defined and 'enable_edit_guidance' is True, one has to pass\n `editing_prompt_embeds` instead.\n editing_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated edit text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided and 'enable_edit_guidance' is True, editing_prompt_embeds will be generated from\n `editing_prompt` input argument.\n editing_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated edit pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled editing_pooled_prompt_embeds will be generated from `editing_prompt`\n input argument.\n \"\"\"\n device = device or self._execution_device\n\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if self.text_encoder is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)\n else:\n scale_lora_layers(self.text_encoder_2, lora_scale)\n\n batch_size = self.batch_size\n\n # Define tokenizers and text encoders\n tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]\n text_encoders = (\n [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]\n )\n num_edit_tokens = 0\n\n # get unconditional embeddings for classifier free guidance\n zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt\n\n if negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n\n # normalize str to list\n negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt\n negative_prompt_2 = (\n batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2\n )\n\n uncond_tokens: List[str]\n\n if batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but image inversion \"\n f\" has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of the input images.\"\n )\n else:\n uncond_tokens = [negative_prompt, negative_prompt_2]\n\n negative_prompt_embeds_list = []\n for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)\n\n uncond_input = tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n negative_prompt_embeds = text_encoder(\n uncond_input.input_ids.to(device),\n output_hidden_states=True,\n )\n # We are only ALWAYS interested in the pooled output of the final text encoder\n negative_pooled_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]\n\n negative_prompt_embeds_list.append(negative_prompt_embeds)\n\n negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)\n\n if zero_out_negative_prompt:\n negative_prompt_embeds = torch.zeros_like(negative_prompt_embeds)\n negative_pooled_prompt_embeds = torch.zeros_like(negative_pooled_prompt_embeds)\n\n if enable_edit_guidance and editing_prompt_embeds is None:\n editing_prompt_2 = editing_prompt\n\n editing_prompts = [editing_prompt, editing_prompt_2]\n edit_prompt_embeds_list = []\n\n for editing_prompt, tokenizer, text_encoder in zip(editing_prompts, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n editing_prompt = self.maybe_convert_prompt(editing_prompt, tokenizer)\n\n max_length = negative_prompt_embeds.shape[1]\n edit_concepts_input = tokenizer(\n # [x for item in editing_prompt for x in repeat(item, batch_size)],\n editing_prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n return_length=True,\n )\n num_edit_tokens = edit_concepts_input.length - 2\n\n edit_concepts_embeds = text_encoder(\n edit_concepts_input.input_ids.to(device),\n output_hidden_states=True,\n )\n # We are only ALWAYS interested in the pooled output of the final text encoder\n editing_pooled_prompt_embeds = edit_concepts_embeds[0]\n if clip_skip is None:\n edit_concepts_embeds = edit_concepts_embeds.hidden_states[-2]\n else:\n # \"2\" because SDXL always indexes from the penultimate layer.\n edit_concepts_embeds = edit_concepts_embeds.hidden_states[-(clip_skip + 2)]\n\n edit_prompt_embeds_list.append(edit_concepts_embeds)\n\n edit_concepts_embeds = torch.concat(edit_prompt_embeds_list, dim=-1)\n elif not enable_edit_guidance:\n edit_concepts_embeds = None\n editing_pooled_prompt_embeds = None\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n bs_embed, seq_len, _ = negative_prompt_embeds.shape\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if enable_edit_guidance:\n bs_embed_edit, seq_len, _ = edit_concepts_embeds.shape\n edit_concepts_embeds = edit_concepts_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n edit_concepts_embeds = edit_concepts_embeds.repeat(1, num_images_per_prompt, 1)\n edit_concepts_embeds = edit_concepts_embeds.view(bs_embed_edit * num_images_per_prompt, seq_len, -1)\n\n negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n\n if enable_edit_guidance:\n editing_pooled_prompt_embeds = editing_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed_edit * num_images_per_prompt, -1\n )\n\n if self.text_encoder is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder_2, lora_scale)\n\n return (\n negative_prompt_embeds,\n edit_concepts_embeds,\n negative_pooled_prompt_embeds,\n editing_pooled_prompt_embeds,\n num_edit_tokens,\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, eta, generator=None):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n negative_prompt=None,\n negative_prompt_2=None,\n negative_prompt_embeds=None,\n negative_pooled_prompt_embeds=None,\n ):\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:\n raise ValueError(\n \"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.\"\n )\n\n # Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, device, latents):\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def _get_add_time_ids(\n self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None\n ):\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n\n passed_add_embed_dim = (\n self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim\n )\n expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features\n\n if expected_add_embed_dim != passed_add_embed_dim:\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.\"\n )\n\n add_time_ids = torch.tensor([add_time_ids], dtype=dtype)\n return add_time_ids\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae\n def upcast_vae(self):\n dtype = self.vae.dtype\n self.vae.to(dtype=torch.float32)\n use_torch_2_0_or_xformers = isinstance(\n self.vae.decoder.mid_block.attentions[0].processor,\n (\n AttnProcessor2_0,\n XFormersAttnProcessor,\n LoRAXFormersAttnProcessor,\n LoRAAttnProcessor2_0,\n ),\n )\n # if xformers or torch_2_0 is used attention block does not need\n # to be in float32 which can save lots of memory\n if use_torch_2_0_or_xformers:\n self.vae.post_quant_conv.to(dtype)\n self.vae.decoder.conv_in.to(dtype)\n self.vae.decoder.mid_block.to(dtype)\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(\n self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32\n ) -> torch.Tensor:\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n w (`torch.Tensor`):\n Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.\n embedding_dim (`int`, *optional*, defaults to 512):\n Dimension of the embeddings to generate.\n dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):\n Data type of the generated embeddings.\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def guidance_rescale(self):\n return self._guidance_rescale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def denoising_end(self):\n return self._denoising_end\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n # Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LEditsPPPipelineStableDiffusion.prepare_unet\n def prepare_unet(self, attention_store, PnP: bool = False):\n attn_procs = {}\n for name in self.unet.attn_processors.keys():\n if name.startswith(\"mid_block\"):\n place_in_unet = \"mid\"\n elif name.startswith(\"up_blocks\"):\n place_in_unet = \"up\"\n elif name.startswith(\"down_blocks\"):\n place_in_unet = \"down\"\n else:\n continue\n\n if \"attn2\" in name and place_in_unet != \"mid\":\n attn_procs[name] = LEDITSCrossAttnProcessor(\n attention_store=attention_store,\n place_in_unet=place_in_unet,\n pnp=PnP,\n editing_prompts=self.enabled_editing_prompts,\n )\n else:\n attn_procs[name] = AttnProcessor()\n\n self.unet.set_attn_processor(attn_procs)\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n denoising_end: Optional[float] = None,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Optional[Tuple[int, int]] = None,\n editing_prompt: Optional[Union[str, List[str]]] = None,\n editing_prompt_embeddings: Optional[torch.Tensor] = None,\n editing_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n reverse_editing_direction: Optional[Union[bool, List[bool]]] = False,\n edit_guidance_scale: Optional[Union[float, List[float]]] = 5,\n edit_warmup_steps: Optional[Union[int, List[int]]] = 0,\n edit_cooldown_steps: Optional[Union[int, List[int]]] = None,\n edit_threshold: Optional[Union[float, List[float]]] = 0.9,\n sem_guidance: Optional[List[torch.Tensor]] = None,\n use_cross_attn_mask: bool = False,\n use_intersect_mask: bool = False,\n user_mask: Optional[torch.Tensor] = None,\n attn_store_steps: Optional[List[int]] = [],\n store_averaged_over_steps: bool = True,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for editing. The\n [`~pipelines.ledits_pp.LEditsPPPipelineStableDiffusionXL.invert`] method has to be called beforehand. Edits\n will always be performed for the last inverted image(s).\n\n Args:\n denoising_end (`float`, *optional*):\n When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be\n completed before it is intentionally prematurely terminated. As a result, the returned sample will\n still retain a substantial amount of noise as determined by the discrete timesteps selected by the\n scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a\n \"Mixture of Denoisers\" multi-pipeline setup, as elaborated in [**Refining the Image\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*):\n Optional image input to work with IP Adapters.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead\n of a plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n guidance_rescale (`float`, *optional*, defaults to 0.7):\n Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of\n [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).\n Guidance rescale factor should fix overexposure when using zero terminal SNR.\n crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n `crops_coords_top_left` can be used to generate an image that appears to be \"cropped\" from the position\n `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n For most cases, `target_size` should be set to the desired height and width of the generated image. If\n not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n editing_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. The image is reconstructed by setting\n `editing_prompt = None`. Guidance direction of prompt should be specified via\n `reverse_editing_direction`.\n editing_prompt_embeddings (`torch.Tensor`, *optional*):\n Pre-generated edit text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, editing_prompt_embeddings will be generated from `editing_prompt` input argument.\n editing_pooled_prompt_embeddings (`torch.Tensor`, *optional*):\n Pre-generated pooled edit text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, editing_prompt_embeddings will be generated from `editing_prompt` input\n argument.\n reverse_editing_direction (`bool` or `List[bool]`, *optional*, defaults to `False`):\n Whether the corresponding prompt in `editing_prompt` should be increased or decreased.\n edit_guidance_scale (`float` or `List[float]`, *optional*, defaults to 5):\n Guidance scale for guiding the image generation. If provided as list values should correspond to\n `editing_prompt`. `edit_guidance_scale` is defined as `s_e` of equation 12 of [LEDITS++\n Paper](https://arxiv.org/abs/2301.12247).\n edit_warmup_steps (`float` or `List[float]`, *optional*, defaults to 10):\n Number of diffusion steps (for each prompt) for which guidance is not applied.\n edit_cooldown_steps (`float` or `List[float]`, *optional*, defaults to `None`):\n Number of diffusion steps (for each prompt) after which guidance is no longer applied.\n edit_threshold (`float` or `List[float]`, *optional*, defaults to 0.9):\n Masking threshold of guidance. Threshold should be proportional to the image region that is modified.\n 'edit_threshold' is defined as 'λ' of equation 12 of [LEDITS++\n Paper](https://arxiv.org/abs/2301.12247).\n sem_guidance (`List[torch.Tensor]`, *optional*):\n List of pre-generated guidance vectors to be applied at generation. Length of the list has to\n correspond to `num_inference_steps`.\n use_cross_attn_mask:\n Whether cross-attention masks are used. Cross-attention masks are always used when use_intersect_mask\n is set to true. Cross-attention masks are defined as 'M^1' of equation 12 of [LEDITS++\n paper](https://arxiv.org/pdf/2311.16711.pdf).\n use_intersect_mask:\n Whether the masking term is calculated as intersection of cross-attention masks and masks derived from\n the noise estimate. Cross-attention mask are defined as 'M^1' and masks derived from the noise estimate\n are defined as 'M^2' of equation 12 of [LEDITS++ paper](https://arxiv.org/pdf/2311.16711.pdf).\n user_mask:\n User-provided mask for even better control over the editing process. This is helpful when LEDITS++'s\n implicit masks do not meet user preferences.\n attn_store_steps:\n Steps for which the attention maps are stored in the AttentionStore. Just for visualization purposes.\n store_averaged_over_steps:\n Whether the attention maps for the 'attn_store_steps' are stored averaged over the diffusion steps. If\n False, attention maps for each step are stores separately. Just for visualization purposes.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.ledits_pp.LEditsPPDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.ledits_pp.LEditsPPDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When\n returning a tuple, the first element is a list with the generated images.\n \"\"\"\n if self.inversion_steps is None:\n raise ValueError(\n \"You need to invert an input image first before calling the pipeline. The `invert` method has to be called beforehand. Edits will always be performed for the last inverted image(s).\"\n )\n\n eta = self.eta\n num_images_per_prompt = 1\n latents = self.init_latents\n\n zs = self.zs\n self.scheduler.set_timesteps(len(self.scheduler.timesteps))\n\n if use_intersect_mask:\n use_cross_attn_mask = True\n\n if use_cross_attn_mask:\n self.smoothing = LeditsGaussianSmoothing(self.device)\n\n if user_mask is not None:\n user_mask = user_mask.to(self.device)\n\n # TODO: Check inputs\n # 1. Check inputs. Raise error if not correct\n # self.check_inputs(\n # callback_steps,\n # negative_prompt,\n # negative_prompt_2,\n # prompt_embeds,\n # negative_prompt_embeds,\n # pooled_prompt_embeds,\n # negative_pooled_prompt_embeds,\n # )\n self._guidance_rescale = guidance_rescale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._denoising_end = denoising_end\n\n # 2. Define call parameters\n batch_size = self.batch_size\n\n device = self._execution_device\n\n if editing_prompt:\n enable_edit_guidance = True\n if isinstance(editing_prompt, str):\n editing_prompt = [editing_prompt]\n self.enabled_editing_prompts = len(editing_prompt)\n elif editing_prompt_embeddings is not None:\n enable_edit_guidance = True\n self.enabled_editing_prompts = editing_prompt_embeddings.shape[0]\n else:\n self.enabled_editing_prompts = 0\n enable_edit_guidance = False\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n (\n prompt_embeds,\n edit_prompt_embeds,\n negative_pooled_prompt_embeds,\n pooled_edit_embeds,\n num_edit_tokens,\n ) = self.encode_prompt(\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n negative_prompt_embeds=negative_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=self.clip_skip,\n enable_edit_guidance=enable_edit_guidance,\n editing_prompt=editing_prompt,\n editing_prompt_embeds=editing_prompt_embeddings,\n editing_pooled_prompt_embeds=editing_pooled_prompt_embeds,\n )\n\n # 4. Prepare timesteps\n # self.scheduler.set_timesteps(num_inference_steps, device=device)\n\n timesteps = self.inversion_steps\n t_to_idx = {int(v): k for k, v in enumerate(timesteps)}\n\n if use_cross_attn_mask:\n self.attention_store = LeditsAttentionStore(\n average=store_averaged_over_steps,\n batch_size=batch_size,\n max_size=(latents.shape[-2] / 4.0) * (latents.shape[-1] / 4.0),\n max_resolution=None,\n )\n self.prepare_unet(self.attention_store)\n resolution = latents.shape[-2:]\n att_res = (int(resolution[0] / 4), int(resolution[1] / 4))\n\n # 5. Prepare latent variables\n latents = self.prepare_latents(device=device, latents=latents)\n\n # 6. Prepare extra step kwargs.\n extra_step_kwargs = self.prepare_extra_step_kwargs(eta)\n\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(negative_pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n # 7. Prepare added time ids & embeddings\n add_text_embeds = negative_pooled_prompt_embeds\n add_time_ids = self._get_add_time_ids(\n self.size,\n crops_coords_top_left,\n self.size,\n dtype=negative_pooled_prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n\n if enable_edit_guidance:\n prompt_embeds = torch.cat([prompt_embeds, edit_prompt_embeds], dim=0)\n add_text_embeds = torch.cat([add_text_embeds, pooled_edit_embeds], dim=0)\n edit_concepts_time_ids = add_time_ids.repeat(edit_prompt_embeds.shape[0], 1)\n add_time_ids = torch.cat([add_time_ids, edit_concepts_time_ids], dim=0)\n self.text_cross_attention_maps = [editing_prompt] if isinstance(editing_prompt, str) else editing_prompt\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)\n\n if ip_adapter_image is not None:\n # TODO: fix image encoding\n image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt)\n if self.do_classifier_free_guidance:\n image_embeds = torch.cat([negative_image_embeds, image_embeds])\n image_embeds = image_embeds.to(device)\n\n # 8. Denoising loop\n self.sem_guidance = None\n self.activation_mask = None\n\n if (\n self.denoising_end is not None\n and isinstance(self.denoising_end, float)\n and self.denoising_end > 0\n and self.denoising_end < 1\n ):\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (self.denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n # 9. Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=self._num_timesteps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * (1 + self.enabled_editing_prompts))\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n # predict the noise residual\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n if ip_adapter_image is not None:\n added_cond_kwargs[\"image_embeds\"] = image_embeds\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n noise_pred_out = noise_pred.chunk(1 + self.enabled_editing_prompts) # [b,4, 64, 64]\n noise_pred_uncond = noise_pred_out[0]\n noise_pred_edit_concepts = noise_pred_out[1:]\n\n noise_guidance_edit = torch.zeros(\n noise_pred_uncond.shape,\n device=self.device,\n dtype=noise_pred_uncond.dtype,\n )\n\n if sem_guidance is not None and len(sem_guidance) > i:\n noise_guidance_edit += sem_guidance[i].to(self.device)\n\n elif enable_edit_guidance:\n if self.activation_mask is None:\n self.activation_mask = torch.zeros(\n (len(timesteps), self.enabled_editing_prompts, *noise_pred_edit_concepts[0].shape)\n )\n if self.sem_guidance is None:\n self.sem_guidance = torch.zeros((len(timesteps), *noise_pred_uncond.shape))\n\n # noise_guidance_edit = torch.zeros_like(noise_guidance)\n for c, noise_pred_edit_concept in enumerate(noise_pred_edit_concepts):\n if isinstance(edit_warmup_steps, list):\n edit_warmup_steps_c = edit_warmup_steps[c]\n else:\n edit_warmup_steps_c = edit_warmup_steps\n if i < edit_warmup_steps_c:\n continue\n\n if isinstance(edit_guidance_scale, list):\n edit_guidance_scale_c = edit_guidance_scale[c]\n else:\n edit_guidance_scale_c = edit_guidance_scale\n\n if isinstance(edit_threshold, list):\n edit_threshold_c = edit_threshold[c]\n else:\n edit_threshold_c = edit_threshold\n if isinstance(reverse_editing_direction, list):\n reverse_editing_direction_c = reverse_editing_direction[c]\n else:\n reverse_editing_direction_c = reverse_editing_direction\n\n if isinstance(edit_cooldown_steps, list):\n edit_cooldown_steps_c = edit_cooldown_steps[c]\n elif edit_cooldown_steps is None:\n edit_cooldown_steps_c = i + 1\n else:\n edit_cooldown_steps_c = edit_cooldown_steps\n\n if i >= edit_cooldown_steps_c:\n continue\n\n noise_guidance_edit_tmp = noise_pred_edit_concept - noise_pred_uncond\n\n if reverse_editing_direction_c:\n noise_guidance_edit_tmp = noise_guidance_edit_tmp * -1\n\n noise_guidance_edit_tmp = noise_guidance_edit_tmp * edit_guidance_scale_c\n\n if user_mask is not None:\n noise_guidance_edit_tmp = noise_guidance_edit_tmp * user_mask\n\n if use_cross_attn_mask:\n out = self.attention_store.aggregate_attention(\n attention_maps=self.attention_store.step_store,\n prompts=self.text_cross_attention_maps,\n res=att_res,\n from_where=[\"up\", \"down\"],\n is_cross=True,\n select=self.text_cross_attention_maps.index(editing_prompt[c]),\n )\n attn_map = out[:, :, :, 1 : 1 + num_edit_tokens[c]] # 0 -> startoftext\n\n # average over all tokens\n if attn_map.shape[3] != num_edit_tokens[c]:\n raise ValueError(\n f\"Incorrect shape of attention_map. Expected size {num_edit_tokens[c]}, but found {attn_map.shape[3]}!\"\n )\n attn_map = torch.sum(attn_map, dim=3)\n\n # gaussian_smoothing\n attn_map = F.pad(attn_map.unsqueeze(1), (1, 1, 1, 1), mode=\"reflect\")\n attn_map = self.smoothing(attn_map).squeeze(1)\n\n # torch.quantile function expects float32\n if attn_map.dtype == torch.float32:\n tmp = torch.quantile(attn_map.flatten(start_dim=1), edit_threshold_c, dim=1)\n else:\n tmp = torch.quantile(\n attn_map.flatten(start_dim=1).to(torch.float32), edit_threshold_c, dim=1\n ).to(attn_map.dtype)\n attn_mask = torch.where(\n attn_map >= tmp.unsqueeze(1).unsqueeze(1).repeat(1, *att_res), 1.0, 0.0\n )\n\n # resolution must match latent space dimension\n attn_mask = F.interpolate(\n attn_mask.unsqueeze(1),\n noise_guidance_edit_tmp.shape[-2:], # 64,64\n ).repeat(1, 4, 1, 1)\n self.activation_mask[i, c] = attn_mask.detach().cpu()\n if not use_intersect_mask:\n noise_guidance_edit_tmp = noise_guidance_edit_tmp * attn_mask\n\n if use_intersect_mask:\n noise_guidance_edit_tmp_quantile = torch.abs(noise_guidance_edit_tmp)\n noise_guidance_edit_tmp_quantile = torch.sum(\n noise_guidance_edit_tmp_quantile, dim=1, keepdim=True\n )\n noise_guidance_edit_tmp_quantile = noise_guidance_edit_tmp_quantile.repeat(\n 1, self.unet.config.in_channels, 1, 1\n )\n\n # torch.quantile function expects float32\n if noise_guidance_edit_tmp_quantile.dtype == torch.float32:\n tmp = torch.quantile(\n noise_guidance_edit_tmp_quantile.flatten(start_dim=2),\n edit_threshold_c,\n dim=2,\n keepdim=False,\n )\n else:\n tmp = torch.quantile(\n noise_guidance_edit_tmp_quantile.flatten(start_dim=2).to(torch.float32),\n edit_threshold_c,\n dim=2,\n keepdim=False,\n ).to(noise_guidance_edit_tmp_quantile.dtype)\n\n intersect_mask = (\n torch.where(\n noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None],\n torch.ones_like(noise_guidance_edit_tmp),\n torch.zeros_like(noise_guidance_edit_tmp),\n )\n * attn_mask\n )\n\n self.activation_mask[i, c] = intersect_mask.detach().cpu()\n\n noise_guidance_edit_tmp = noise_guidance_edit_tmp * intersect_mask\n\n elif not use_cross_attn_mask:\n # calculate quantile\n noise_guidance_edit_tmp_quantile = torch.abs(noise_guidance_edit_tmp)\n noise_guidance_edit_tmp_quantile = torch.sum(\n noise_guidance_edit_tmp_quantile, dim=1, keepdim=True\n )\n noise_guidance_edit_tmp_quantile = noise_guidance_edit_tmp_quantile.repeat(1, 4, 1, 1)\n\n # torch.quantile function expects float32\n if noise_guidance_edit_tmp_quantile.dtype == torch.float32:\n tmp = torch.quantile(\n noise_guidance_edit_tmp_quantile.flatten(start_dim=2),\n edit_threshold_c,\n dim=2,\n keepdim=False,\n )\n else:\n tmp = torch.quantile(\n noise_guidance_edit_tmp_quantile.flatten(start_dim=2).to(torch.float32),\n edit_threshold_c,\n dim=2,\n keepdim=False,\n ).to(noise_guidance_edit_tmp_quantile.dtype)\n\n self.activation_mask[i, c] = (\n torch.where(\n noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None],\n torch.ones_like(noise_guidance_edit_tmp),\n torch.zeros_like(noise_guidance_edit_tmp),\n )\n .detach()\n .cpu()\n )\n\n noise_guidance_edit_tmp = torch.where(\n noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None],\n noise_guidance_edit_tmp,\n torch.zeros_like(noise_guidance_edit_tmp),\n )\n\n noise_guidance_edit += noise_guidance_edit_tmp\n\n self.sem_guidance[i] = noise_guidance_edit.detach().cpu()\n\n noise_pred = noise_pred_uncond + noise_guidance_edit\n\n # compute the previous noisy sample x_t -> x_t-1\n if enable_edit_guidance and self.guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(\n noise_pred,\n noise_pred_edit_concepts.mean(dim=0, keepdim=False),\n guidance_rescale=self.guidance_rescale,\n )\n\n idx = t_to_idx[int(t)]\n latents = self.scheduler.step(\n noise_pred, t, latents, variance_noise=zs[idx], **extra_step_kwargs, return_dict=False\n )[0]\n\n # step callback\n if use_cross_attn_mask:\n store_step = i in attn_store_steps\n self.attention_store.between_steps(store_step)\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n add_text_embeds = callback_outputs.pop(\"add_text_embeds\", add_text_embeds)\n negative_pooled_prompt_embeds = callback_outputs.pop(\n \"negative_pooled_prompt_embeds\", negative_pooled_prompt_embeds\n )\n add_time_ids = callback_outputs.pop(\"add_time_ids\", add_time_ids)\n # negative_add_time_ids = callback_outputs.pop(\"negative_add_time_ids\", negative_add_time_ids)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > 0 and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n\n if XLA_AVAILABLE:\n xm.mark_step()\n\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n image = latents\n\n if not output_type == \"latent\":\n # apply watermark if available\n if self.watermark is not None:\n image = self.watermark.apply_watermark(image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return LEditsPPDiffusionPipelineOutput(images=image, nsfw_content_detected=None)\n\n @torch.no_grad()\n # Modified from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LEditsPPPipelineStableDiffusion.encode_image\n def encode_image(self, image, dtype=None, height=None, width=None, resize_mode=\"default\", crops_coords=None):\n image = self.image_processor.preprocess(\n image=image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords\n )\n resized = self.image_processor.postprocess(image=image, output_type=\"pil\")\n\n if max(image.shape[-2:]) > self.vae.config[\"sample_size\"] * 1.5:\n logger.warning(\n \"Your input images far exceed the default resolution of the underlying diffusion model. \"\n \"The output images may contain severe artifacts! \"\n \"Consider down-sampling the input using the `height` and `width` parameters\"\n )\n image = image.to(self.device, dtype=dtype)\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n image = image.float()\n self.upcast_vae()\n\n x0 = self.vae.encode(image).latent_dist.mode()\n x0 = x0.to(dtype)\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n\n x0 = self.vae.config.scaling_factor * x0\n return x0, resized\n\n @torch.no_grad()\n def invert(\n self,\n image: PipelineImageInput,\n source_prompt: str = \"\",\n source_guidance_scale=3.5,\n negative_prompt: str = None,\n negative_prompt_2: str = None,\n num_inversion_steps: int = 50,\n skip: float = 0.15,\n generator: Optional[torch.Generator] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n num_zero_noise_steps: int = 3,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n ):\n r\"\"\"\n The function to the pipeline for image inversion as described by the [LEDITS++\n Paper](https://arxiv.org/abs/2301.12247). If the scheduler is set to [`~schedulers.DDIMScheduler`] the\n inversion proposed by [edit-friendly DPDM](https://arxiv.org/abs/2304.06140) will be performed instead.\n\n Args:\n image (`PipelineImageInput`):\n Input for the image(s) that are to be edited. Multiple input images have to default to the same aspect\n ratio.\n source_prompt (`str`, defaults to `\"\"`):\n Prompt describing the input image that will be used for guidance during inversion. Guidance is disabled\n if the `source_prompt` is `\"\"`.\n source_guidance_scale (`float`, defaults to `3.5`):\n Strength of guidance during inversion.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n num_inversion_steps (`int`, defaults to `50`):\n Number of total performed inversion steps after discarding the initial `skip` steps.\n skip (`float`, defaults to `0.15`):\n Portion of initial steps that will be ignored for inversion and subsequent generation. Lower values\n will lead to stronger changes to the input image. `skip` has to be between `0` and `1`.\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make inversion\n deterministic.\n crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n `crops_coords_top_left` can be used to generate an image that appears to be \"cropped\" from the position\n `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n num_zero_noise_steps (`int`, defaults to `3`):\n Number of final diffusion steps that will not renoise the current image. If no steps are set to zero\n SD-XL in combination with [`DPMSolverMultistepScheduler`] will produce noise artifacts.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n\n Returns:\n [`~pipelines.ledits_pp.LEditsPPInversionPipelineOutput`]: Output will contain the resized input image(s)\n and respective VAE reconstruction(s).\n \"\"\"\n\n # Reset attn processor, we do not want to store attn maps during inversion\n self.unet.set_attn_processor(AttnProcessor())\n\n self.eta = 1.0\n\n self.scheduler.config.timestep_spacing = \"leading\"\n self.scheduler.set_timesteps(int(num_inversion_steps * (1 + skip)))\n self.inversion_steps = self.scheduler.timesteps[-num_inversion_steps:]\n timesteps = self.inversion_steps\n\n num_images_per_prompt = 1\n\n device = self._execution_device\n\n # 0. Ensure that only uncond embedding is used if prompt = \"\"\n if source_prompt == \"\":\n # noise pred should only be noise_pred_uncond\n source_guidance_scale = 0.0\n do_classifier_free_guidance = False\n else:\n do_classifier_free_guidance = source_guidance_scale > 1.0\n\n # 1. prepare image\n x0, resized = self.encode_image(image, dtype=self.text_encoder_2.dtype)\n width = x0.shape[2] * self.vae_scale_factor\n height = x0.shape[3] * self.vae_scale_factor\n self.size = (height, width)\n\n self.batch_size = x0.shape[0]\n\n # 2. get embeddings\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n\n if isinstance(source_prompt, str):\n source_prompt = [source_prompt] * self.batch_size\n\n (\n negative_prompt_embeds,\n prompt_embeds,\n negative_pooled_prompt_embeds,\n edit_pooled_prompt_embeds,\n _,\n ) = self.encode_prompt(\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n editing_prompt=source_prompt,\n lora_scale=text_encoder_lora_scale,\n enable_edit_guidance=do_classifier_free_guidance,\n )\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(negative_pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n # 3. Prepare added time ids & embeddings\n add_text_embeds = negative_pooled_prompt_embeds\n add_time_ids = self._get_add_time_ids(\n self.size,\n crops_coords_top_left,\n self.size,\n dtype=negative_prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n\n if do_classifier_free_guidance:\n negative_prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([add_text_embeds, edit_pooled_prompt_embeds], dim=0)\n add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)\n\n negative_prompt_embeds = negative_prompt_embeds.to(device)\n\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(self.batch_size * num_images_per_prompt, 1)\n\n # autoencoder reconstruction\n if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:\n self.upcast_vae()\n x0_tmp = x0.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n image_rec = self.vae.decode(\n x0_tmp / self.vae.config.scaling_factor, return_dict=False, generator=generator\n )[0]\n elif self.vae.config.force_upcast:\n x0_tmp = x0.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n image_rec = self.vae.decode(\n x0_tmp / self.vae.config.scaling_factor, return_dict=False, generator=generator\n )[0]\n else:\n image_rec = self.vae.decode(x0 / self.vae.config.scaling_factor, return_dict=False, generator=generator)[0]\n\n image_rec = self.image_processor.postprocess(image_rec, output_type=\"pil\")\n\n # 5. find zs and xts\n variance_noise_shape = (num_inversion_steps, *x0.shape)\n\n # intermediate latents\n t_to_idx = {int(v): k for k, v in enumerate(timesteps)}\n xts = torch.zeros(size=variance_noise_shape, device=self.device, dtype=negative_prompt_embeds.dtype)\n\n for t in reversed(timesteps):\n idx = num_inversion_steps - t_to_idx[int(t)] - 1\n noise = randn_tensor(shape=x0.shape, generator=generator, device=self.device, dtype=x0.dtype)\n xts[idx] = self.scheduler.add_noise(x0, noise, t.unsqueeze(0))\n xts = torch.cat([x0.unsqueeze(0), xts], dim=0)\n\n # noise maps\n zs = torch.zeros(size=variance_noise_shape, device=self.device, dtype=negative_prompt_embeds.dtype)\n\n self.scheduler.set_timesteps(len(self.scheduler.timesteps))\n\n for t in self.progress_bar(timesteps):\n idx = num_inversion_steps - t_to_idx[int(t)] - 1\n # 1. predict noise residual\n xt = xts[idx + 1]\n\n latent_model_input = torch.cat([xt] * 2) if do_classifier_free_guidance else xt\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=negative_prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # 2. perform guidance\n if do_classifier_free_guidance:\n noise_pred_out = noise_pred.chunk(2)\n noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1]\n noise_pred = noise_pred_uncond + source_guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n xtm1 = xts[idx]\n z, xtm1_corrected = compute_noise(self.scheduler, xtm1, xt, t, noise_pred, self.eta)\n zs[idx] = z\n\n # correction to avoid error accumulation\n xts[idx] = xtm1_corrected\n\n self.init_latents = xts[-1]\n zs = zs.flip(0)\n\n if num_zero_noise_steps > 0:\n zs[-num_zero_noise_steps:] = torch.zeros_like(zs[-num_zero_noise_steps:])\n self.zs = zs\n return LEditsPPInversionPipelineOutput(images=resized, vae_reconstruction_images=image_rec)\n\n\n# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.rescale_noise_cfg\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\n# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.compute_noise_ddim\ndef compute_noise_ddim(scheduler, prev_latents, latents, timestep, noise_pred, eta):\n # 1. get previous step value (=t-1)\n prev_timestep = timestep - scheduler.config.num_train_timesteps // scheduler.num_inference_steps\n\n # 2. compute alphas, betas\n alpha_prod_t = scheduler.alphas_cumprod[timestep]\n alpha_prod_t_prev = (\n scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else scheduler.final_alpha_cumprod\n )\n\n beta_prod_t = 1 - alpha_prod_t\n\n # 3. compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)\n\n # 4. Clip \"predicted x_0\"\n if scheduler.config.clip_sample:\n pred_original_sample = torch.clamp(pred_original_sample, -1, 1)\n\n # 5. compute variance: \"sigma_t(η)\" -> see formula (16)\n # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)\n variance = scheduler._get_variance(timestep, prev_timestep)\n std_dev_t = eta * variance ** (0.5)\n\n # 6. compute \"direction pointing to x_t\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * noise_pred\n\n # modifed so that updated xtm1 is returned as well (to avoid error accumulation)\n mu_xt = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction\n if variance > 0.0:\n noise = (prev_latents - mu_xt) / (variance ** (0.5) * eta)\n else:\n noise = torch.tensor([0.0]).to(latents.device)\n\n return noise, mu_xt + (eta * variance**0.5) * noise\n\n\n# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.compute_noise_sde_dpm_pp_2nd\ndef compute_noise_sde_dpm_pp_2nd(scheduler, prev_latents, latents, timestep, noise_pred, eta):\n def first_order_update(model_output, sample): # timestep, prev_timestep, sample):\n sigma_t, sigma_s = scheduler.sigmas[scheduler.step_index + 1], scheduler.sigmas[scheduler.step_index]\n alpha_t, sigma_t = scheduler._sigma_to_alpha_sigma_t(sigma_t)\n alpha_s, sigma_s = scheduler._sigma_to_alpha_sigma_t(sigma_s)\n lambda_t = torch.log(alpha_t) - torch.log(sigma_t)\n lambda_s = torch.log(alpha_s) - torch.log(sigma_s)\n\n h = lambda_t - lambda_s\n\n mu_xt = (sigma_t / sigma_s * torch.exp(-h)) * sample + (alpha_t * (1 - torch.exp(-2.0 * h))) * model_output\n\n mu_xt = scheduler.dpm_solver_first_order_update(\n model_output=model_output, sample=sample, noise=torch.zeros_like(sample)\n )\n\n sigma = sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h))\n if sigma > 0.0:\n noise = (prev_latents - mu_xt) / sigma\n else:\n noise = torch.tensor([0.0]).to(sample.device)\n\n prev_sample = mu_xt + sigma * noise\n return noise, prev_sample\n\n def second_order_update(model_output_list, sample): # timestep_list, prev_timestep, sample):\n sigma_t, sigma_s0, sigma_s1 = (\n scheduler.sigmas[scheduler.step_index + 1],\n scheduler.sigmas[scheduler.step_index],\n scheduler.sigmas[scheduler.step_index - 1],\n )\n\n alpha_t, sigma_t = scheduler._sigma_to_alpha_sigma_t(sigma_t)\n alpha_s0, sigma_s0 = scheduler._sigma_to_alpha_sigma_t(sigma_s0)\n alpha_s1, sigma_s1 = scheduler._sigma_to_alpha_sigma_t(sigma_s1)\n\n lambda_t = torch.log(alpha_t) - torch.log(sigma_t)\n lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)\n lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1)\n\n m0, m1 = model_output_list[-1], model_output_list[-2]\n\n h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1\n r0 = h_0 / h\n D0, D1 = m0, (1.0 / r0) * (m0 - m1)\n\n mu_xt = (\n (sigma_t / sigma_s0 * torch.exp(-h)) * sample\n + (alpha_t * (1 - torch.exp(-2.0 * h))) * D0\n + 0.5 * (alpha_t * (1 - torch.exp(-2.0 * h))) * D1\n )\n\n sigma = sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h))\n if sigma > 0.0:\n noise = (prev_latents - mu_xt) / sigma\n else:\n noise = torch.tensor([0.0]).to(sample.device)\n\n prev_sample = mu_xt + sigma * noise\n\n return noise, prev_sample\n\n if scheduler.step_index is None:\n scheduler._init_step_index(timestep)\n\n model_output = scheduler.convert_model_output(model_output=noise_pred, sample=latents)\n for i in range(scheduler.config.solver_order - 1):\n scheduler.model_outputs[i] = scheduler.model_outputs[i + 1]\n scheduler.model_outputs[-1] = model_output\n\n if scheduler.lower_order_nums < 1:\n noise, prev_sample = first_order_update(model_output, latents)\n else:\n noise, prev_sample = second_order_update(scheduler.model_outputs, latents)\n\n if scheduler.lower_order_nums < scheduler.config.solver_order:\n scheduler.lower_order_nums += 1\n\n # upon completion increase step index by one\n scheduler._step_index += 1\n\n return noise, prev_sample\n\n\n# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.compute_noise\ndef compute_noise(scheduler, *args):\n if isinstance(scheduler, DDIMScheduler):\n return compute_noise_ddim(scheduler, *args)\n elif (\n isinstance(scheduler, DPMSolverMultistepScheduler)\n and scheduler.config.algorithm_type == \"sde-dpmsolver++\"\n and scheduler.config.solver_order == 2\n ):\n return compute_noise_sde_dpm_pp_2nd(scheduler, *args)\n else:\n raise NotImplementedError\n" }, { "path": "diffusers/src/diffusers/pipelines/ledits_pp/pipeline_output.py", "content": "from dataclasses import dataclass\nfrom typing import List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\n\nfrom ...utils import BaseOutput\n\n\n@dataclass\nclass LEditsPPDiffusionPipelineOutput(BaseOutput):\n \"\"\"\n Output class for LEdits++ Diffusion pipelines.\n\n Args:\n images (`List[PIL.Image.Image]` or `np.ndarray`)\n List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,\n num_channels)`.\n nsfw_content_detected (`List[bool]`)\n List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content or\n `None` if safety checking could not be performed.\n \"\"\"\n\n images: Union[List[PIL.Image.Image], np.ndarray]\n nsfw_content_detected: Optional[List[bool]]\n\n\n@dataclass\nclass LEditsPPInversionPipelineOutput(BaseOutput):\n \"\"\"\n Output class for LEdits++ Diffusion pipelines.\n\n Args:\n input_images (`List[PIL.Image.Image]` or `np.ndarray`)\n List of the cropped and resized input images as PIL images of length `batch_size` or NumPy array of shape `\n (batch_size, height, width, num_channels)`.\n vae_reconstruction_images (`List[PIL.Image.Image]` or `np.ndarray`)\n List of VAE reconstruction of all input images as PIL images of length `batch_size` or NumPy array of shape\n ` (batch_size, height, width, num_channels)`.\n \"\"\"\n\n images: Union[List[PIL.Image.Image], np.ndarray]\n vae_reconstruction_images: Union[List[PIL.Image.Image], np.ndarray]\n" }, { "path": "diffusers/src/diffusers/pipelines/marigold/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"marigold_image_processing\"] = [\"MarigoldImageProcessor\"]\n _import_structure[\"pipeline_marigold_depth\"] = [\"MarigoldDepthOutput\", \"MarigoldDepthPipeline\"]\n _import_structure[\"pipeline_marigold_normals\"] = [\"MarigoldNormalsOutput\", \"MarigoldNormalsPipeline\"]\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n else:\n from .marigold_image_processing import MarigoldImageProcessor\n from .pipeline_marigold_depth import MarigoldDepthOutput, MarigoldDepthPipeline\n from .pipeline_marigold_normals import MarigoldNormalsOutput, MarigoldNormalsPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/marigold/marigold_image_processing.py", "content": "from typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL\nimport torch\nimport torch.nn.functional as F\nfrom PIL import Image\n\nfrom ... import ConfigMixin\nfrom ...configuration_utils import register_to_config\nfrom ...image_processor import PipelineImageInput\nfrom ...utils import CONFIG_NAME, logging\nfrom ...utils.import_utils import is_matplotlib_available\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass MarigoldImageProcessor(ConfigMixin):\n config_name = CONFIG_NAME\n\n @register_to_config\n def __init__(\n self,\n vae_scale_factor: int = 8,\n do_normalize: bool = True,\n do_range_check: bool = True,\n ):\n super().__init__()\n\n @staticmethod\n def expand_tensor_or_array(images: Union[torch.Tensor, np.ndarray]) -> Union[torch.Tensor, np.ndarray]:\n \"\"\"\n Expand a tensor or array to a specified number of images.\n \"\"\"\n if isinstance(images, np.ndarray):\n if images.ndim == 2: # [H,W] -> [1,H,W,1]\n images = images[None, ..., None]\n if images.ndim == 3: # [H,W,C] -> [1,H,W,C]\n images = images[None]\n elif isinstance(images, torch.Tensor):\n if images.ndim == 2: # [H,W] -> [1,1,H,W]\n images = images[None, None]\n elif images.ndim == 3: # [1,H,W] -> [1,1,H,W]\n images = images[None]\n else:\n raise ValueError(f\"Unexpected input type: {type(images)}\")\n return images\n\n @staticmethod\n def pt_to_numpy(images: torch.Tensor) -> np.ndarray:\n \"\"\"\n Convert a PyTorch tensor to a NumPy image.\n \"\"\"\n images = images.cpu().permute(0, 2, 3, 1).float().numpy()\n return images\n\n @staticmethod\n def numpy_to_pt(images: np.ndarray) -> torch.Tensor:\n \"\"\"\n Convert a NumPy image to a PyTorch tensor.\n \"\"\"\n if np.issubdtype(images.dtype, np.integer) and not np.issubdtype(images.dtype, np.unsignedinteger):\n raise ValueError(f\"Input image dtype={images.dtype} cannot be a signed integer.\")\n if np.issubdtype(images.dtype, np.complexfloating):\n raise ValueError(f\"Input image dtype={images.dtype} cannot be complex.\")\n if np.issubdtype(images.dtype, bool):\n raise ValueError(f\"Input image dtype={images.dtype} cannot be boolean.\")\n\n images = torch.from_numpy(images.transpose(0, 3, 1, 2))\n return images\n\n @staticmethod\n def resize_antialias(\n image: torch.Tensor, size: Tuple[int, int], mode: str, is_aa: Optional[bool] = None\n ) -> torch.Tensor:\n if not torch.is_tensor(image):\n raise ValueError(f\"Invalid input type={type(image)}.\")\n if not torch.is_floating_point(image):\n raise ValueError(f\"Invalid input dtype={image.dtype}.\")\n if image.dim() != 4:\n raise ValueError(f\"Invalid input dimensions; shape={image.shape}.\")\n\n antialias = is_aa and mode in (\"bilinear\", \"bicubic\")\n image = F.interpolate(image, size, mode=mode, antialias=antialias)\n\n return image\n\n @staticmethod\n def resize_to_max_edge(image: torch.Tensor, max_edge_sz: int, mode: str) -> torch.Tensor:\n if not torch.is_tensor(image):\n raise ValueError(f\"Invalid input type={type(image)}.\")\n if not torch.is_floating_point(image):\n raise ValueError(f\"Invalid input dtype={image.dtype}.\")\n if image.dim() != 4:\n raise ValueError(f\"Invalid input dimensions; shape={image.shape}.\")\n\n h, w = image.shape[-2:]\n max_orig = max(h, w)\n new_h = h * max_edge_sz // max_orig\n new_w = w * max_edge_sz // max_orig\n\n if new_h == 0 or new_w == 0:\n raise ValueError(f\"Extreme aspect ratio of the input image: [{w} x {h}]\")\n\n image = MarigoldImageProcessor.resize_antialias(image, (new_h, new_w), mode, is_aa=True)\n\n return image\n\n @staticmethod\n def pad_image(image: torch.Tensor, align: int) -> Tuple[torch.Tensor, Tuple[int, int]]:\n if not torch.is_tensor(image):\n raise ValueError(f\"Invalid input type={type(image)}.\")\n if not torch.is_floating_point(image):\n raise ValueError(f\"Invalid input dtype={image.dtype}.\")\n if image.dim() != 4:\n raise ValueError(f\"Invalid input dimensions; shape={image.shape}.\")\n\n h, w = image.shape[-2:]\n ph, pw = -h % align, -w % align\n\n image = F.pad(image, (0, pw, 0, ph), mode=\"replicate\")\n\n return image, (ph, pw)\n\n @staticmethod\n def unpad_image(image: torch.Tensor, padding: Tuple[int, int]) -> torch.Tensor:\n if not torch.is_tensor(image):\n raise ValueError(f\"Invalid input type={type(image)}.\")\n if not torch.is_floating_point(image):\n raise ValueError(f\"Invalid input dtype={image.dtype}.\")\n if image.dim() != 4:\n raise ValueError(f\"Invalid input dimensions; shape={image.shape}.\")\n\n ph, pw = padding\n uh = None if ph == 0 else -ph\n uw = None if pw == 0 else -pw\n\n image = image[:, :, :uh, :uw]\n\n return image\n\n @staticmethod\n def load_image_canonical(\n image: Union[torch.Tensor, np.ndarray, Image.Image],\n device: torch.device = torch.device(\"cpu\"),\n dtype: torch.dtype = torch.float32,\n ) -> Tuple[torch.Tensor, int]:\n if isinstance(image, Image.Image):\n image = np.array(image)\n\n image_dtype_max = None\n if isinstance(image, (np.ndarray, torch.Tensor)):\n image = MarigoldImageProcessor.expand_tensor_or_array(image)\n if image.ndim != 4:\n raise ValueError(\"Input image is not 2-, 3-, or 4-dimensional.\")\n if isinstance(image, np.ndarray):\n if np.issubdtype(image.dtype, np.integer) and not np.issubdtype(image.dtype, np.unsignedinteger):\n raise ValueError(f\"Input image dtype={image.dtype} cannot be a signed integer.\")\n if np.issubdtype(image.dtype, np.complexfloating):\n raise ValueError(f\"Input image dtype={image.dtype} cannot be complex.\")\n if np.issubdtype(image.dtype, bool):\n raise ValueError(f\"Input image dtype={image.dtype} cannot be boolean.\")\n if np.issubdtype(image.dtype, np.unsignedinteger):\n image_dtype_max = np.iinfo(image.dtype).max\n image = image.astype(np.float32) # because torch does not have unsigned dtypes beyond torch.uint8\n image = MarigoldImageProcessor.numpy_to_pt(image)\n\n if torch.is_tensor(image) and not torch.is_floating_point(image) and image_dtype_max is None:\n if image.dtype != torch.uint8:\n raise ValueError(f\"Image dtype={image.dtype} is not supported.\")\n image_dtype_max = 255\n\n if not torch.is_tensor(image):\n raise ValueError(f\"Input type unsupported: {type(image)}.\")\n\n if image.shape[1] == 1:\n image = image.repeat(1, 3, 1, 1) # [N,1,H,W] -> [N,3,H,W]\n if image.shape[1] != 3:\n raise ValueError(f\"Input image is not 1- or 3-channel: {image.shape}.\")\n\n image = image.to(device=device, dtype=dtype)\n\n if image_dtype_max is not None:\n image = image / image_dtype_max\n\n return image\n\n @staticmethod\n def check_image_values_range(image: torch.Tensor) -> None:\n if not torch.is_tensor(image):\n raise ValueError(f\"Invalid input type={type(image)}.\")\n if not torch.is_floating_point(image):\n raise ValueError(f\"Invalid input dtype={image.dtype}.\")\n if image.min().item() < 0.0 or image.max().item() > 1.0:\n raise ValueError(\"Input image data is partially outside of the [0,1] range.\")\n\n def preprocess(\n self,\n image: PipelineImageInput,\n processing_resolution: Optional[int] = None,\n resample_method_input: str = \"bilinear\",\n device: torch.device = torch.device(\"cpu\"),\n dtype: torch.dtype = torch.float32,\n ):\n if isinstance(image, list):\n images = None\n for i, img in enumerate(image):\n img = self.load_image_canonical(img, device, dtype) # [N,3,H,W]\n if images is None:\n images = img\n else:\n if images.shape[2:] != img.shape[2:]:\n raise ValueError(\n f\"Input image[{i}] has incompatible dimensions {img.shape[2:]} with the previous images \"\n f\"{images.shape[2:]}\"\n )\n images = torch.cat((images, img), dim=0)\n image = images\n del images\n else:\n image = self.load_image_canonical(image, device, dtype) # [N,3,H,W]\n\n original_resolution = image.shape[2:]\n\n if self.config.do_range_check:\n self.check_image_values_range(image)\n\n if self.config.do_normalize:\n image = image * 2.0 - 1.0\n\n if processing_resolution is not None and processing_resolution > 0:\n image = self.resize_to_max_edge(image, processing_resolution, resample_method_input) # [N,3,PH,PW]\n\n image, padding = self.pad_image(image, self.config.vae_scale_factor) # [N,3,PPH,PPW]\n\n return image, padding, original_resolution\n\n @staticmethod\n def colormap(\n image: Union[np.ndarray, torch.Tensor],\n cmap: str = \"Spectral\",\n bytes: bool = False,\n _force_method: Optional[str] = None,\n ) -> Union[np.ndarray, torch.Tensor]:\n \"\"\"\n Converts a monochrome image into an RGB image by applying the specified colormap. This function mimics the\n behavior of matplotlib.colormaps, but allows the user to use the most discriminative color maps (\"Spectral\",\n \"binary\") without having to install or import matplotlib. For all other cases, the function will attempt to use\n the native implementation.\n\n Args:\n image: 2D tensor of values between 0 and 1, either as np.ndarray or torch.Tensor.\n cmap: Colormap name.\n bytes: Whether to return the output as uint8 or floating point image.\n _force_method:\n Can be used to specify whether to use the native implementation (`\"matplotlib\"`), the efficient custom\n implementation of the select color maps (`\"custom\"`), or rely on autodetection (`None`, default).\n\n Returns:\n An RGB-colorized tensor corresponding to the input image.\n \"\"\"\n if not (torch.is_tensor(image) or isinstance(image, np.ndarray)):\n raise ValueError(\"Argument must be a numpy array or torch tensor.\")\n if _force_method not in (None, \"matplotlib\", \"custom\"):\n raise ValueError(\"_force_method must be either `None`, `'matplotlib'` or `'custom'`.\")\n\n supported_cmaps = {\n \"binary\": [\n (1.0, 1.0, 1.0),\n (0.0, 0.0, 0.0),\n ],\n \"Spectral\": [ # Taken from matplotlib/_cm.py\n (0.61960784313725492, 0.003921568627450980, 0.25882352941176473), # 0.0 -> [0]\n (0.83529411764705885, 0.24313725490196078, 0.30980392156862746),\n (0.95686274509803926, 0.42745098039215684, 0.2627450980392157),\n (0.99215686274509807, 0.68235294117647061, 0.38039215686274508),\n (0.99607843137254903, 0.8784313725490196, 0.54509803921568623),\n (1.0, 1.0, 0.74901960784313726),\n (0.90196078431372551, 0.96078431372549022, 0.59607843137254901),\n (0.6705882352941176, 0.8666666666666667, 0.64313725490196083),\n (0.4, 0.76078431372549016, 0.6470588235294118),\n (0.19607843137254902, 0.53333333333333333, 0.74117647058823533),\n (0.36862745098039218, 0.30980392156862746, 0.63529411764705879), # 1.0 -> [K-1]\n ],\n }\n\n def method_matplotlib(image, cmap, bytes=False):\n if is_matplotlib_available():\n import matplotlib\n else:\n return None\n\n arg_is_pt, device = torch.is_tensor(image), None\n if arg_is_pt:\n image, device = image.cpu().numpy(), image.device\n\n if cmap not in matplotlib.colormaps:\n raise ValueError(\n f\"Unexpected color map {cmap}; available options are: {', '.join(list(matplotlib.colormaps.keys()))}\"\n )\n\n cmap = matplotlib.colormaps[cmap]\n out = cmap(image, bytes=bytes) # [?,4]\n out = out[..., :3] # [?,3]\n\n if arg_is_pt:\n out = torch.tensor(out, device=device)\n\n return out\n\n def method_custom(image, cmap, bytes=False):\n arg_is_np = isinstance(image, np.ndarray)\n if arg_is_np:\n image = torch.tensor(image)\n if image.dtype == torch.uint8:\n image = image.float() / 255\n else:\n image = image.float()\n\n is_cmap_reversed = cmap.endswith(\"_r\")\n if is_cmap_reversed:\n cmap = cmap[:-2]\n\n if cmap not in supported_cmaps:\n raise ValueError(\n f\"Only {list(supported_cmaps.keys())} color maps are available without installing matplotlib.\"\n )\n\n cmap = supported_cmaps[cmap]\n if is_cmap_reversed:\n cmap = cmap[::-1]\n cmap = torch.tensor(cmap, dtype=torch.float, device=image.device) # [K,3]\n K = cmap.shape[0]\n\n pos = image.clamp(min=0, max=1) * (K - 1)\n left = pos.long()\n right = (left + 1).clamp(max=K - 1)\n\n d = (pos - left.float()).unsqueeze(-1)\n left_colors = cmap[left]\n right_colors = cmap[right]\n\n out = (1 - d) * left_colors + d * right_colors\n\n if bytes:\n out = (out * 255).to(torch.uint8)\n\n if arg_is_np:\n out = out.numpy()\n\n return out\n\n if _force_method is None and torch.is_tensor(image) and cmap == \"Spectral\":\n return method_custom(image, cmap, bytes)\n\n out = None\n if _force_method != \"custom\":\n out = method_matplotlib(image, cmap, bytes)\n\n if _force_method == \"matplotlib\" and out is None:\n raise ImportError(\"Make sure to install matplotlib if you want to use a color map other than 'Spectral'.\")\n\n if out is None:\n out = method_custom(image, cmap, bytes)\n\n return out\n\n @staticmethod\n def visualize_depth(\n depth: Union[\n PIL.Image.Image,\n np.ndarray,\n torch.Tensor,\n List[PIL.Image.Image],\n List[np.ndarray],\n List[torch.Tensor],\n ],\n val_min: float = 0.0,\n val_max: float = 1.0,\n color_map: str = \"Spectral\",\n ) -> Union[PIL.Image.Image, List[PIL.Image.Image]]:\n \"\"\"\n Visualizes depth maps, such as predictions of the `MarigoldDepthPipeline`.\n\n Args:\n depth (`Union[PIL.Image.Image, np.ndarray, torch.Tensor, List[PIL.Image.Image], List[np.ndarray],\n List[torch.Tensor]]`): Depth maps.\n val_min (`float`, *optional*, defaults to `0.0`): Minimum value of the visualized depth range.\n val_max (`float`, *optional*, defaults to `1.0`): Maximum value of the visualized depth range.\n color_map (`str`, *optional*, defaults to `\"Spectral\"`): Color map used to convert a single-channel\n depth prediction into colored representation.\n\n Returns: `PIL.Image.Image` or `List[PIL.Image.Image]` with depth maps visualization.\n \"\"\"\n if val_max <= val_min:\n raise ValueError(f\"Invalid values range: [{val_min}, {val_max}].\")\n\n def visualize_depth_one(img, idx=None):\n prefix = \"Depth\" + (f\"[{idx}]\" if idx else \"\")\n if isinstance(img, PIL.Image.Image):\n if img.mode != \"I;16\":\n raise ValueError(f\"{prefix}: invalid PIL mode={img.mode}.\")\n img = np.array(img).astype(np.float32) / (2**16 - 1)\n if isinstance(img, np.ndarray) or torch.is_tensor(img):\n if img.ndim != 2:\n raise ValueError(f\"{prefix}: unexpected shape={img.shape}.\")\n if isinstance(img, np.ndarray):\n img = torch.from_numpy(img)\n if not torch.is_floating_point(img):\n raise ValueError(f\"{prefix}: unexected dtype={img.dtype}.\")\n else:\n raise ValueError(f\"{prefix}: unexpected type={type(img)}.\")\n if val_min != 0.0 or val_max != 1.0:\n img = (img - val_min) / (val_max - val_min)\n img = MarigoldImageProcessor.colormap(img, cmap=color_map, bytes=True) # [H,W,3]\n img = PIL.Image.fromarray(img.cpu().numpy())\n return img\n\n if depth is None or isinstance(depth, list) and any(o is None for o in depth):\n raise ValueError(\"Input depth is `None`\")\n if isinstance(depth, (np.ndarray, torch.Tensor)):\n depth = MarigoldImageProcessor.expand_tensor_or_array(depth)\n if isinstance(depth, np.ndarray):\n depth = MarigoldImageProcessor.numpy_to_pt(depth) # [N,H,W,1] -> [N,1,H,W]\n if not (depth.ndim == 4 and depth.shape[1] == 1): # [N,1,H,W]\n raise ValueError(f\"Unexpected input shape={depth.shape}, expecting [N,1,H,W].\")\n return [visualize_depth_one(img[0], idx) for idx, img in enumerate(depth)]\n elif isinstance(depth, list):\n return [visualize_depth_one(img, idx) for idx, img in enumerate(depth)]\n else:\n raise ValueError(f\"Unexpected input type: {type(depth)}\")\n\n @staticmethod\n def export_depth_to_16bit_png(\n depth: Union[np.ndarray, torch.Tensor, List[np.ndarray], List[torch.Tensor]],\n val_min: float = 0.0,\n val_max: float = 1.0,\n ) -> Union[PIL.Image.Image, List[PIL.Image.Image]]:\n def export_depth_to_16bit_png_one(img, idx=None):\n prefix = \"Depth\" + (f\"[{idx}]\" if idx else \"\")\n if not isinstance(img, np.ndarray) and not torch.is_tensor(img):\n raise ValueError(f\"{prefix}: unexpected type={type(img)}.\")\n if img.ndim != 2:\n raise ValueError(f\"{prefix}: unexpected shape={img.shape}.\")\n if torch.is_tensor(img):\n img = img.cpu().numpy()\n if not np.issubdtype(img.dtype, np.floating):\n raise ValueError(f\"{prefix}: unexected dtype={img.dtype}.\")\n if val_min != 0.0 or val_max != 1.0:\n img = (img - val_min) / (val_max - val_min)\n img = (img * (2**16 - 1)).astype(np.uint16)\n img = PIL.Image.fromarray(img, mode=\"I;16\")\n return img\n\n if depth is None or isinstance(depth, list) and any(o is None for o in depth):\n raise ValueError(\"Input depth is `None`\")\n if isinstance(depth, (np.ndarray, torch.Tensor)):\n depth = MarigoldImageProcessor.expand_tensor_or_array(depth)\n if isinstance(depth, np.ndarray):\n depth = MarigoldImageProcessor.numpy_to_pt(depth) # [N,H,W,1] -> [N,1,H,W]\n if not (depth.ndim == 4 and depth.shape[1] == 1):\n raise ValueError(f\"Unexpected input shape={depth.shape}, expecting [N,1,H,W].\")\n return [export_depth_to_16bit_png_one(img[0], idx) for idx, img in enumerate(depth)]\n elif isinstance(depth, list):\n return [export_depth_to_16bit_png_one(img, idx) for idx, img in enumerate(depth)]\n else:\n raise ValueError(f\"Unexpected input type: {type(depth)}\")\n\n @staticmethod\n def visualize_normals(\n normals: Union[\n np.ndarray,\n torch.Tensor,\n List[np.ndarray],\n List[torch.Tensor],\n ],\n flip_x: bool = False,\n flip_y: bool = False,\n flip_z: bool = False,\n ) -> Union[PIL.Image.Image, List[PIL.Image.Image]]:\n \"\"\"\n Visualizes surface normals, such as predictions of the `MarigoldNormalsPipeline`.\n\n Args:\n normals (`Union[np.ndarray, torch.Tensor, List[np.ndarray], List[torch.Tensor]]`):\n Surface normals.\n flip_x (`bool`, *optional*, defaults to `False`): Flips the X axis of the normals frame of reference.\n Default direction is right.\n flip_y (`bool`, *optional*, defaults to `False`): Flips the Y axis of the normals frame of reference.\n Default direction is top.\n flip_z (`bool`, *optional*, defaults to `False`): Flips the Z axis of the normals frame of reference.\n Default direction is facing the observer.\n\n Returns: `PIL.Image.Image` or `List[PIL.Image.Image]` with surface normals visualization.\n \"\"\"\n flip_vec = None\n if any((flip_x, flip_y, flip_z)):\n flip_vec = torch.tensor(\n [\n (-1) ** flip_x,\n (-1) ** flip_y,\n (-1) ** flip_z,\n ],\n dtype=torch.float32,\n )\n\n def visualize_normals_one(img, idx=None):\n img = img.permute(1, 2, 0)\n if flip_vec is not None:\n img *= flip_vec.to(img.device)\n img = (img + 1.0) * 0.5\n img = (img * 255).to(dtype=torch.uint8, device=\"cpu\").numpy()\n img = PIL.Image.fromarray(img)\n return img\n\n if normals is None or isinstance(normals, list) and any(o is None for o in normals):\n raise ValueError(\"Input normals is `None`\")\n if isinstance(normals, (np.ndarray, torch.Tensor)):\n normals = MarigoldImageProcessor.expand_tensor_or_array(normals)\n if isinstance(normals, np.ndarray):\n normals = MarigoldImageProcessor.numpy_to_pt(normals) # [N,3,H,W]\n if not (normals.ndim == 4 and normals.shape[1] == 3):\n raise ValueError(f\"Unexpected input shape={normals.shape}, expecting [N,3,H,W].\")\n return [visualize_normals_one(img, idx) for idx, img in enumerate(normals)]\n elif isinstance(normals, list):\n return [visualize_normals_one(img, idx) for idx, img in enumerate(normals)]\n else:\n raise ValueError(f\"Unexpected input type: {type(normals)}\")\n\n @staticmethod\n def visualize_uncertainty(\n uncertainty: Union[\n np.ndarray,\n torch.Tensor,\n List[np.ndarray],\n List[torch.Tensor],\n ],\n saturation_percentile=95,\n ) -> Union[PIL.Image.Image, List[PIL.Image.Image]]:\n \"\"\"\n Visualizes dense uncertainties, such as produced by `MarigoldDepthPipeline` or `MarigoldNormalsPipeline`.\n\n Args:\n uncertainty (`Union[np.ndarray, torch.Tensor, List[np.ndarray], List[torch.Tensor]]`):\n Uncertainty maps.\n saturation_percentile (`int`, *optional*, defaults to `95`):\n Specifies the percentile uncertainty value visualized with maximum intensity.\n\n Returns: `PIL.Image.Image` or `List[PIL.Image.Image]` with uncertainty visualization.\n \"\"\"\n\n def visualize_uncertainty_one(img, idx=None):\n prefix = \"Uncertainty\" + (f\"[{idx}]\" if idx else \"\")\n if img.min() < 0:\n raise ValueError(f\"{prefix}: unexected data range, min={img.min()}.\")\n img = img.squeeze(0).cpu().numpy()\n saturation_value = np.percentile(img, saturation_percentile)\n img = np.clip(img * 255 / saturation_value, 0, 255)\n img = img.astype(np.uint8)\n img = PIL.Image.fromarray(img)\n return img\n\n if uncertainty is None or isinstance(uncertainty, list) and any(o is None for o in uncertainty):\n raise ValueError(\"Input uncertainty is `None`\")\n if isinstance(uncertainty, (np.ndarray, torch.Tensor)):\n uncertainty = MarigoldImageProcessor.expand_tensor_or_array(uncertainty)\n if isinstance(uncertainty, np.ndarray):\n uncertainty = MarigoldImageProcessor.numpy_to_pt(uncertainty) # [N,1,H,W]\n if not (uncertainty.ndim == 4 and uncertainty.shape[1] == 1):\n raise ValueError(f\"Unexpected input shape={uncertainty.shape}, expecting [N,1,H,W].\")\n return [visualize_uncertainty_one(img, idx) for idx, img in enumerate(uncertainty)]\n elif isinstance(uncertainty, list):\n return [visualize_uncertainty_one(img, idx) for idx, img in enumerate(uncertainty)]\n else:\n raise ValueError(f\"Unexpected input type: {type(uncertainty)}\")\n" }, { "path": "diffusers/src/diffusers/pipelines/marigold/pipeline_marigold_depth.py", "content": "# Copyright 2024 Marigold authors, PRS ETH Zurich. All rights reserved.\n# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# --------------------------------------------------------------------------\n# More information and citation instructions are available on the\n# Marigold project website: https://marigoldmonodepth.github.io\n# --------------------------------------------------------------------------\nfrom dataclasses import dataclass\nfrom functools import partial\nfrom typing import Any, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nfrom ...image_processor import PipelineImageInput\nfrom ...models import (\n AutoencoderKL,\n UNet2DConditionModel,\n)\nfrom ...schedulers import (\n DDIMScheduler,\n LCMScheduler,\n)\nfrom ...utils import (\n BaseOutput,\n logging,\n replace_example_docstring,\n)\nfrom ...utils.import_utils import is_scipy_available\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline\nfrom .marigold_image_processing import MarigoldImageProcessor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nEXAMPLE_DOC_STRING = \"\"\"\nExamples:\n```py\n>>> import diffusers\n>>> import torch\n\n>>> pipe = diffusers.MarigoldDepthPipeline.from_pretrained(\n... \"prs-eth/marigold-depth-lcm-v1-0\", variant=\"fp16\", torch_dtype=torch.float16\n... ).to(\"cuda\")\n\n>>> image = diffusers.utils.load_image(\"https://marigoldmonodepth.github.io/images/einstein.jpg\")\n>>> depth = pipe(image)\n\n>>> vis = pipe.image_processor.visualize_depth(depth.prediction)\n>>> vis[0].save(\"einstein_depth.png\")\n\n>>> depth_16bit = pipe.image_processor.export_depth_to_16bit_png(depth.prediction)\n>>> depth_16bit[0].save(\"einstein_depth_16bit.png\")\n```\n\"\"\"\n\n\n@dataclass\nclass MarigoldDepthOutput(BaseOutput):\n \"\"\"\n Output class for Marigold monocular depth prediction pipeline.\n\n Args:\n prediction (`np.ndarray`, `torch.Tensor`):\n Predicted depth maps with values in the range [0, 1]. The shape is always $numimages \\times 1 \\times height\n \\times width$, regardless of whether the images were passed as a 4D array or a list.\n uncertainty (`None`, `np.ndarray`, `torch.Tensor`):\n Uncertainty maps computed from the ensemble, with values in the range [0, 1]. The shape is $numimages\n \\times 1 \\times height \\times width$.\n latent (`None`, `torch.Tensor`):\n Latent features corresponding to the predictions, compatible with the `latents` argument of the pipeline.\n The shape is $numimages * numensemble \\times 4 \\times latentheight \\times latentwidth$.\n \"\"\"\n\n prediction: Union[np.ndarray, torch.Tensor]\n uncertainty: Union[None, np.ndarray, torch.Tensor]\n latent: Union[None, torch.Tensor]\n\n\nclass MarigoldDepthPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for monocular depth estimation using the Marigold method: https://marigoldmonodepth.github.io.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n unet (`UNet2DConditionModel`):\n Conditional U-Net to denoise the depth latent, conditioned on image latent.\n vae (`AutoencoderKL`):\n Variational Auto-Encoder (VAE) Model to encode and decode images and predictions to and from latent\n representations.\n scheduler (`DDIMScheduler` or `LCMScheduler`):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents.\n text_encoder (`CLIPTextModel`):\n Text-encoder, for empty text embedding.\n tokenizer (`CLIPTokenizer`):\n CLIP tokenizer.\n prediction_type (`str`, *optional*):\n Type of predictions made by the model.\n scale_invariant (`bool`, *optional*):\n A model property specifying whether the predicted depth maps are scale-invariant. This value must be set in\n the model config. When used together with the `shift_invariant=True` flag, the model is also called\n \"affine-invariant\". NB: overriding this value is not supported.\n shift_invariant (`bool`, *optional*):\n A model property specifying whether the predicted depth maps are shift-invariant. This value must be set in\n the model config. When used together with the `scale_invariant=True` flag, the model is also called\n \"affine-invariant\". NB: overriding this value is not supported.\n default_denoising_steps (`int`, *optional*):\n The minimum number of denoising diffusion steps that are required to produce a prediction of reasonable\n quality with the given model. This value must be set in the model config. When the pipeline is called\n without explicitly setting `num_inference_steps`, the default value is used. This is required to ensure\n reasonable results with various model flavors compatible with the pipeline, such as those relying on very\n short denoising schedules (`LCMScheduler`) and those with full diffusion schedules (`DDIMScheduler`).\n default_processing_resolution (`int`, *optional*):\n The recommended value of the `processing_resolution` parameter of the pipeline. This value must be set in\n the model config. When the pipeline is called without explicitly setting `processing_resolution`, the\n default value is used. This is required to ensure reasonable results with various model flavors trained\n with varying optimal processing resolution values.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n supported_prediction_types = (\"depth\", \"disparity\")\n\n def __init__(\n self,\n unet: UNet2DConditionModel,\n vae: AutoencoderKL,\n scheduler: Union[DDIMScheduler, LCMScheduler],\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n prediction_type: Optional[str] = None,\n scale_invariant: Optional[bool] = True,\n shift_invariant: Optional[bool] = True,\n default_denoising_steps: Optional[int] = None,\n default_processing_resolution: Optional[int] = None,\n ):\n super().__init__()\n\n if prediction_type not in self.supported_prediction_types:\n logger.warning(\n f\"Potentially unsupported `prediction_type='{prediction_type}'`; values supported by the pipeline: \"\n f\"{self.supported_prediction_types}.\"\n )\n\n self.register_modules(\n unet=unet,\n vae=vae,\n scheduler=scheduler,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n )\n self.register_to_config(\n prediction_type=prediction_type,\n scale_invariant=scale_invariant,\n shift_invariant=shift_invariant,\n default_denoising_steps=default_denoising_steps,\n default_processing_resolution=default_processing_resolution,\n )\n\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n\n self.scale_invariant = scale_invariant\n self.shift_invariant = shift_invariant\n self.default_denoising_steps = default_denoising_steps\n self.default_processing_resolution = default_processing_resolution\n\n self.empty_text_embedding = None\n\n self.image_processor = MarigoldImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n def check_inputs(\n self,\n image: PipelineImageInput,\n num_inference_steps: int,\n ensemble_size: int,\n processing_resolution: int,\n resample_method_input: str,\n resample_method_output: str,\n batch_size: int,\n ensembling_kwargs: Optional[Dict[str, Any]],\n latents: Optional[torch.Tensor],\n generator: Optional[Union[torch.Generator, List[torch.Generator]]],\n output_type: str,\n output_uncertainty: bool,\n ) -> int:\n if num_inference_steps is None:\n raise ValueError(\"`num_inference_steps` is not specified and could not be resolved from the model config.\")\n if num_inference_steps < 1:\n raise ValueError(\"`num_inference_steps` must be positive.\")\n if ensemble_size < 1:\n raise ValueError(\"`ensemble_size` must be positive.\")\n if ensemble_size == 2:\n logger.warning(\n \"`ensemble_size` == 2 results are similar to no ensembling (1); \"\n \"consider increasing the value to at least 3.\"\n )\n if ensemble_size > 1 and (self.scale_invariant or self.shift_invariant) and not is_scipy_available():\n raise ImportError(\"Make sure to install scipy if you want to use ensembling.\")\n if ensemble_size == 1 and output_uncertainty:\n raise ValueError(\n \"Computing uncertainty by setting `output_uncertainty=True` also requires setting `ensemble_size` \"\n \"greater than 1.\"\n )\n if processing_resolution is None:\n raise ValueError(\n \"`processing_resolution` is not specified and could not be resolved from the model config.\"\n )\n if processing_resolution < 0:\n raise ValueError(\n \"`processing_resolution` must be non-negative: 0 for native resolution, or any positive value for \"\n \"downsampled processing.\"\n )\n if processing_resolution % self.vae_scale_factor != 0:\n raise ValueError(f\"`processing_resolution` must be a multiple of {self.vae_scale_factor}.\")\n if resample_method_input not in (\"nearest\", \"nearest-exact\", \"bilinear\", \"bicubic\", \"area\"):\n raise ValueError(\n \"`resample_method_input` takes string values compatible with PIL library: \"\n \"nearest, nearest-exact, bilinear, bicubic, area.\"\n )\n if resample_method_output not in (\"nearest\", \"nearest-exact\", \"bilinear\", \"bicubic\", \"area\"):\n raise ValueError(\n \"`resample_method_output` takes string values compatible with PIL library: \"\n \"nearest, nearest-exact, bilinear, bicubic, area.\"\n )\n if batch_size < 1:\n raise ValueError(\"`batch_size` must be positive.\")\n if output_type not in [\"pt\", \"np\"]:\n raise ValueError(\"`output_type` must be one of `pt` or `np`.\")\n if latents is not None and generator is not None:\n raise ValueError(\"`latents` and `generator` cannot be used together.\")\n if ensembling_kwargs is not None:\n if not isinstance(ensembling_kwargs, dict):\n raise ValueError(\"`ensembling_kwargs` must be a dictionary.\")\n if \"reduction\" in ensembling_kwargs and ensembling_kwargs[\"reduction\"] not in (\"mean\", \"median\"):\n raise ValueError(\"`ensembling_kwargs['reduction']` can be either `'mean'` or `'median'`.\")\n\n # image checks\n num_images = 0\n W, H = None, None\n if not isinstance(image, list):\n image = [image]\n for i, img in enumerate(image):\n if isinstance(img, np.ndarray) or torch.is_tensor(img):\n if img.ndim not in (2, 3, 4):\n raise ValueError(f\"`image[{i}]` has unsupported dimensions or shape: {img.shape}.\")\n H_i, W_i = img.shape[-2:]\n N_i = 1\n if img.ndim == 4:\n N_i = img.shape[0]\n elif isinstance(img, Image.Image):\n W_i, H_i = img.size\n N_i = 1\n else:\n raise ValueError(f\"Unsupported `image[{i}]` type: {type(img)}.\")\n if W is None:\n W, H = W_i, H_i\n elif (W, H) != (W_i, H_i):\n raise ValueError(\n f\"Input `image[{i}]` has incompatible dimensions {(W_i, H_i)} with the previous images {(W, H)}\"\n )\n num_images += N_i\n\n # latents checks\n if latents is not None:\n if not torch.is_tensor(latents):\n raise ValueError(\"`latents` must be a torch.Tensor.\")\n if latents.dim() != 4:\n raise ValueError(f\"`latents` has unsupported dimensions or shape: {latents.shape}.\")\n\n if processing_resolution > 0:\n max_orig = max(H, W)\n new_H = H * processing_resolution // max_orig\n new_W = W * processing_resolution // max_orig\n if new_H == 0 or new_W == 0:\n raise ValueError(f\"Extreme aspect ratio of the input image: [{W} x {H}]\")\n W, H = new_W, new_H\n w = (W + self.vae_scale_factor - 1) // self.vae_scale_factor\n h = (H + self.vae_scale_factor - 1) // self.vae_scale_factor\n shape_expected = (num_images * ensemble_size, self.vae.config.latent_channels, h, w)\n\n if latents.shape != shape_expected:\n raise ValueError(f\"`latents` has unexpected shape={latents.shape} expected={shape_expected}.\")\n\n # generator checks\n if generator is not None:\n if isinstance(generator, list):\n if len(generator) != num_images * ensemble_size:\n raise ValueError(\n \"The number of generators must match the total number of ensemble members for all input images.\"\n )\n if not all(g.device.type == generator[0].device.type for g in generator):\n raise ValueError(\"`generator` device placement is not consistent in the list.\")\n elif not isinstance(generator, torch.Generator):\n raise ValueError(f\"Unsupported generator type: {type(generator)}.\")\n\n return num_images\n\n def progress_bar(self, iterable=None, total=None, desc=None, leave=True):\n if not hasattr(self, \"_progress_bar_config\"):\n self._progress_bar_config = {}\n elif not isinstance(self._progress_bar_config, dict):\n raise ValueError(\n f\"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}.\"\n )\n\n progress_bar_config = dict(**self._progress_bar_config)\n progress_bar_config[\"desc\"] = progress_bar_config.get(\"desc\", desc)\n progress_bar_config[\"leave\"] = progress_bar_config.get(\"leave\", leave)\n if iterable is not None:\n return tqdm(iterable, **progress_bar_config)\n elif total is not None:\n return tqdm(total=total, **progress_bar_config)\n else:\n raise ValueError(\"Either `total` or `iterable` has to be defined.\")\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n image: PipelineImageInput,\n num_inference_steps: Optional[int] = None,\n ensemble_size: int = 1,\n processing_resolution: Optional[int] = None,\n match_input_resolution: bool = True,\n resample_method_input: str = \"bilinear\",\n resample_method_output: str = \"bilinear\",\n batch_size: int = 1,\n ensembling_kwargs: Optional[Dict[str, Any]] = None,\n latents: Optional[Union[torch.Tensor, List[torch.Tensor]]] = None,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n output_type: str = \"np\",\n output_uncertainty: bool = False,\n output_latent: bool = False,\n return_dict: bool = True,\n ):\n \"\"\"\n Function invoked when calling the pipeline.\n\n Args:\n image (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`),\n `List[torch.Tensor]`: An input image or images used as an input for the depth estimation task. For\n arrays and tensors, the expected value range is between `[0, 1]`. Passing a batch of images is possible\n by providing a four-dimensional array or a tensor. Additionally, a list of images of two- or\n three-dimensional arrays or tensors can be passed. In the latter case, all list elements must have the\n same width and height.\n num_inference_steps (`int`, *optional*, defaults to `None`):\n Number of denoising diffusion steps during inference. The default value `None` results in automatic\n selection. The number of steps should be at least 10 with the full Marigold models, and between 1 and 4\n for Marigold-LCM models.\n ensemble_size (`int`, defaults to `1`):\n Number of ensemble predictions. Recommended values are 5 and higher for better precision, or 1 for\n faster inference.\n processing_resolution (`int`, *optional*, defaults to `None`):\n Effective processing resolution. When set to `0`, matches the larger input image dimension. This\n produces crisper predictions, but may also lead to the overall loss of global context. The default\n value `None` resolves to the optimal value from the model config.\n match_input_resolution (`bool`, *optional*, defaults to `True`):\n When enabled, the output prediction is resized to match the input dimensions. When disabled, the longer\n side of the output will equal to `processing_resolution`.\n resample_method_input (`str`, *optional*, defaults to `\"bilinear\"`):\n Resampling method used to resize input images to `processing_resolution`. The accepted values are:\n `\"nearest\"`, `\"nearest-exact\"`, `\"bilinear\"`, `\"bicubic\"`, or `\"area\"`.\n resample_method_output (`str`, *optional*, defaults to `\"bilinear\"`):\n Resampling method used to resize output predictions to match the input resolution. The accepted values\n are `\"nearest\"`, `\"nearest-exact\"`, `\"bilinear\"`, `\"bicubic\"`, or `\"area\"`.\n batch_size (`int`, *optional*, defaults to `1`):\n Batch size; only matters when setting `ensemble_size` or passing a tensor of images.\n ensembling_kwargs (`dict`, *optional*, defaults to `None`)\n Extra dictionary with arguments for precise ensembling control. The following options are available:\n - reduction (`str`, *optional*, defaults to `\"median\"`): Defines the ensembling function applied in\n every pixel location, can be either `\"median\"` or `\"mean\"`.\n - regularizer_strength (`float`, *optional*, defaults to `0.02`): Strength of the regularizer that\n pulls the aligned predictions to the unit range from 0 to 1.\n - max_iter (`int`, *optional*, defaults to `2`): Maximum number of the alignment solver steps. Refer to\n `scipy.optimize.minimize` function, `options` argument.\n - tol (`float`, *optional*, defaults to `1e-3`): Alignment solver tolerance. The solver stops when the\n tolerance is reached.\n - max_res (`int`, *optional*, defaults to `None`): Resolution at which the alignment is performed;\n `None` matches the `processing_resolution`.\n latents (`torch.Tensor`, or `List[torch.Tensor]`, *optional*, defaults to `None`):\n Latent noise tensors to replace the random initialization. These can be taken from the previous\n function call's output.\n generator (`torch.Generator`, or `List[torch.Generator]`, *optional*, defaults to `None`):\n Random number generator object to ensure reproducibility.\n output_type (`str`, *optional*, defaults to `\"np\"`):\n Preferred format of the output's `prediction` and the optional `uncertainty` fields. The accepted\n values are: `\"np\"` (numpy array) or `\"pt\"` (torch tensor).\n output_uncertainty (`bool`, *optional*, defaults to `False`):\n When enabled, the output's `uncertainty` field contains the predictive uncertainty map, provided that\n the `ensemble_size` argument is set to a value above 2.\n output_latent (`bool`, *optional*, defaults to `False`):\n When enabled, the output's `latent` field contains the latent codes corresponding to the predictions\n within the ensemble. These codes can be saved, modified, and used for subsequent calls with the\n `latents` argument.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.marigold.MarigoldDepthOutput`] instead of a plain tuple.\n\n Examples:\n\n Returns:\n [`~pipelines.marigold.MarigoldDepthOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.marigold.MarigoldDepthOutput`] is returned, otherwise a\n `tuple` is returned where the first element is the prediction, the second element is the uncertainty\n (or `None`), and the third is the latent (or `None`).\n \"\"\"\n\n # 0. Resolving variables.\n device = self._execution_device\n dtype = self.dtype\n\n # Model-specific optimal default values leading to fast and reasonable results.\n if num_inference_steps is None:\n num_inference_steps = self.default_denoising_steps\n if processing_resolution is None:\n processing_resolution = self.default_processing_resolution\n\n # 1. Check inputs.\n num_images = self.check_inputs(\n image,\n num_inference_steps,\n ensemble_size,\n processing_resolution,\n resample_method_input,\n resample_method_output,\n batch_size,\n ensembling_kwargs,\n latents,\n generator,\n output_type,\n output_uncertainty,\n )\n\n # 2. Prepare empty text conditioning.\n # Model invocation: self.tokenizer, self.text_encoder.\n if self.empty_text_embedding is None:\n prompt = \"\"\n text_inputs = self.tokenizer(\n prompt,\n padding=\"do_not_pad\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids.to(device)\n self.empty_text_embedding = self.text_encoder(text_input_ids)[0] # [1,2,1024]\n\n # 3. Preprocess input images. This function loads input image or images of compatible dimensions `(H, W)`,\n # optionally downsamples them to the `processing_resolution` `(PH, PW)`, where\n # `max(PH, PW) == processing_resolution`, and pads the dimensions to `(PPH, PPW)` such that these values are\n # divisible by the latent space downscaling factor (typically 8 in Stable Diffusion). The default value `None`\n # of `processing_resolution` resolves to the optimal value from the model config. It is a recommended mode of\n # operation and leads to the most reasonable results. Using the native image resolution or any other processing\n # resolution can lead to loss of either fine details or global context in the output predictions.\n image, padding, original_resolution = self.image_processor.preprocess(\n image, processing_resolution, resample_method_input, device, dtype\n ) # [N,3,PPH,PPW]\n\n # 4. Encode input image into latent space. At this step, each of the `N` input images is represented with `E`\n # ensemble members. Each ensemble member is an independent diffused prediction, just initialized independently.\n # Latents of each such predictions across all input images and all ensemble members are represented in the\n # `pred_latent` variable. The variable `image_latent` is of the same shape: it contains each input image encoded\n # into latent space and replicated `E` times. The latents can be either generated (see `generator` to ensure\n # reproducibility), or passed explicitly via the `latents` argument. The latter can be set outside the pipeline\n # code. For example, in the Marigold-LCM video processing demo, the latents initialization of a frame is taken\n # as a convex combination of the latents output of the pipeline for the previous frame and a newly-sampled\n # noise. This behavior can be achieved by setting the `output_latent` argument to `True`. The latent space\n # dimensions are `(h, w)`. Encoding into latent space happens in batches of size `batch_size`.\n # Model invocation: self.vae.encoder.\n image_latent, pred_latent = self.prepare_latents(\n image, latents, generator, ensemble_size, batch_size\n ) # [N*E,4,h,w], [N*E,4,h,w]\n\n del image\n\n batch_empty_text_embedding = self.empty_text_embedding.to(device=device, dtype=dtype).repeat(\n batch_size, 1, 1\n ) # [B,1024,2]\n\n # 5. Process the denoising loop. All `N * E` latents are processed sequentially in batches of size `batch_size`.\n # The unet model takes concatenated latent spaces of the input image and the predicted modality as an input, and\n # outputs noise for the predicted modality's latent space. The number of denoising diffusion steps is defined by\n # `num_inference_steps`. It is either set directly, or resolves to the optimal value specific to the loaded\n # model.\n # Model invocation: self.unet.\n pred_latents = []\n\n for i in self.progress_bar(\n range(0, num_images * ensemble_size, batch_size), leave=True, desc=\"Marigold predictions...\"\n ):\n batch_image_latent = image_latent[i : i + batch_size] # [B,4,h,w]\n batch_pred_latent = pred_latent[i : i + batch_size] # [B,4,h,w]\n effective_batch_size = batch_image_latent.shape[0]\n text = batch_empty_text_embedding[:effective_batch_size] # [B,2,1024]\n\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n for t in self.progress_bar(self.scheduler.timesteps, leave=False, desc=\"Diffusion steps...\"):\n batch_latent = torch.cat([batch_image_latent, batch_pred_latent], dim=1) # [B,8,h,w]\n noise = self.unet(batch_latent, t, encoder_hidden_states=text, return_dict=False)[0] # [B,4,h,w]\n batch_pred_latent = self.scheduler.step(\n noise, t, batch_pred_latent, generator=generator\n ).prev_sample # [B,4,h,w]\n\n pred_latents.append(batch_pred_latent)\n\n pred_latent = torch.cat(pred_latents, dim=0) # [N*E,4,h,w]\n\n del (\n pred_latents,\n image_latent,\n batch_empty_text_embedding,\n batch_image_latent,\n batch_pred_latent,\n text,\n batch_latent,\n noise,\n )\n\n # 6. Decode predictions from latent into pixel space. The resulting `N * E` predictions have shape `(PPH, PPW)`,\n # which requires slight postprocessing. Decoding into pixel space happens in batches of size `batch_size`.\n # Model invocation: self.vae.decoder.\n prediction = torch.cat(\n [\n self.decode_prediction(pred_latent[i : i + batch_size])\n for i in range(0, pred_latent.shape[0], batch_size)\n ],\n dim=0,\n ) # [N*E,1,PPH,PPW]\n\n if not output_latent:\n pred_latent = None\n\n # 7. Remove padding. The output shape is (PH, PW).\n prediction = self.image_processor.unpad_image(prediction, padding) # [N*E,1,PH,PW]\n\n # 8. Ensemble and compute uncertainty (when `output_uncertainty` is set). This code treats each of the `N`\n # groups of `E` ensemble predictions independently. For each group it computes an ensembled prediction of shape\n # `(PH, PW)` and an optional uncertainty map of the same dimensions. After computing this pair of outputs for\n # each group independently, it stacks them respectively into batches of `N` almost final predictions and\n # uncertainty maps.\n uncertainty = None\n if ensemble_size > 1:\n prediction = prediction.reshape(num_images, ensemble_size, *prediction.shape[1:]) # [N,E,1,PH,PW]\n prediction = [\n self.ensemble_depth(\n prediction[i],\n self.scale_invariant,\n self.shift_invariant,\n output_uncertainty,\n **(ensembling_kwargs or {}),\n )\n for i in range(num_images)\n ] # [ [[1,1,PH,PW], [1,1,PH,PW]], ... ]\n prediction, uncertainty = zip(*prediction) # [[1,1,PH,PW], ... ], [[1,1,PH,PW], ... ]\n prediction = torch.cat(prediction, dim=0) # [N,1,PH,PW]\n if output_uncertainty:\n uncertainty = torch.cat(uncertainty, dim=0) # [N,1,PH,PW]\n else:\n uncertainty = None\n\n # 9. If `match_input_resolution` is set, the output prediction and the uncertainty are upsampled to match the\n # input resolution `(H, W)`. This step may introduce upsampling artifacts, and therefore can be disabled.\n # Depending on the downstream use-case, upsampling can be also chosen based on the tolerated artifacts by\n # setting the `resample_method_output` parameter (e.g., to `\"nearest\"`).\n if match_input_resolution:\n prediction = self.image_processor.resize_antialias(\n prediction, original_resolution, resample_method_output, is_aa=False\n ) # [N,1,H,W]\n if uncertainty is not None and output_uncertainty:\n uncertainty = self.image_processor.resize_antialias(\n uncertainty, original_resolution, resample_method_output, is_aa=False\n ) # [N,1,H,W]\n\n # 10. Prepare the final outputs.\n if output_type == \"np\":\n prediction = self.image_processor.pt_to_numpy(prediction) # [N,H,W,1]\n if uncertainty is not None and output_uncertainty:\n uncertainty = self.image_processor.pt_to_numpy(uncertainty) # [N,H,W,1]\n\n # 11. Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (prediction, uncertainty, pred_latent)\n\n return MarigoldDepthOutput(\n prediction=prediction,\n uncertainty=uncertainty,\n latent=pred_latent,\n )\n\n def prepare_latents(\n self,\n image: torch.Tensor,\n latents: Optional[torch.Tensor],\n generator: Optional[torch.Generator],\n ensemble_size: int,\n batch_size: int,\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n def retrieve_latents(encoder_output):\n if hasattr(encoder_output, \"latent_dist\"):\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n image_latent = torch.cat(\n [\n retrieve_latents(self.vae.encode(image[i : i + batch_size]))\n for i in range(0, image.shape[0], batch_size)\n ],\n dim=0,\n ) # [N,4,h,w]\n image_latent = image_latent * self.vae.config.scaling_factor\n image_latent = image_latent.repeat_interleave(ensemble_size, dim=0) # [N*E,4,h,w]\n\n pred_latent = latents\n if pred_latent is None:\n pred_latent = randn_tensor(\n image_latent.shape,\n generator=generator,\n device=image_latent.device,\n dtype=image_latent.dtype,\n ) # [N*E,4,h,w]\n\n return image_latent, pred_latent\n\n def decode_prediction(self, pred_latent: torch.Tensor) -> torch.Tensor:\n if pred_latent.dim() != 4 or pred_latent.shape[1] != self.vae.config.latent_channels:\n raise ValueError(\n f\"Expecting 4D tensor of shape [B,{self.vae.config.latent_channels},H,W]; got {pred_latent.shape}.\"\n )\n\n prediction = self.vae.decode(pred_latent / self.vae.config.scaling_factor, return_dict=False)[0] # [B,3,H,W]\n\n prediction = prediction.mean(dim=1, keepdim=True) # [B,1,H,W]\n prediction = torch.clip(prediction, -1.0, 1.0) # [B,1,H,W]\n prediction = (prediction + 1.0) / 2.0\n\n return prediction # [B,1,H,W]\n\n @staticmethod\n def ensemble_depth(\n depth: torch.Tensor,\n scale_invariant: bool = True,\n shift_invariant: bool = True,\n output_uncertainty: bool = False,\n reduction: str = \"median\",\n regularizer_strength: float = 0.02,\n max_iter: int = 2,\n tol: float = 1e-3,\n max_res: int = 1024,\n ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:\n \"\"\"\n Ensembles the depth maps represented by the `depth` tensor with expected shape `(B, 1, H, W)`, where B is the\n number of ensemble members for a given prediction of size `(H x W)`. Even though the function is designed for\n depth maps, it can also be used with disparity maps as long as the input tensor values are non-negative. The\n alignment happens when the predictions have one or more degrees of freedom, that is when they are either\n affine-invariant (`scale_invariant=True` and `shift_invariant=True`), or just scale-invariant (only\n `scale_invariant=True`). For absolute predictions (`scale_invariant=False` and `shift_invariant=False`)\n alignment is skipped and only ensembling is performed.\n\n Args:\n depth (`torch.Tensor`):\n Input ensemble depth maps.\n scale_invariant (`bool`, *optional*, defaults to `True`):\n Whether to treat predictions as scale-invariant.\n shift_invariant (`bool`, *optional*, defaults to `True`):\n Whether to treat predictions as shift-invariant.\n output_uncertainty (`bool`, *optional*, defaults to `False`):\n Whether to output uncertainty map.\n reduction (`str`, *optional*, defaults to `\"median\"`):\n Reduction method used to ensemble aligned predictions. The accepted values are: `\"mean\"` and\n `\"median\"`.\n regularizer_strength (`float`, *optional*, defaults to `0.02`):\n Strength of the regularizer that pulls the aligned predictions to the unit range from 0 to 1.\n max_iter (`int`, *optional*, defaults to `2`):\n Maximum number of the alignment solver steps. Refer to `scipy.optimize.minimize` function, `options`\n argument.\n tol (`float`, *optional*, defaults to `1e-3`):\n Alignment solver tolerance. The solver stops when the tolerance is reached.\n max_res (`int`, *optional*, defaults to `1024`):\n Resolution at which the alignment is performed; `None` matches the `processing_resolution`.\n Returns:\n A tensor of aligned and ensembled depth maps and optionally a tensor of uncertainties of the same shape:\n `(1, 1, H, W)`.\n \"\"\"\n if depth.dim() != 4 or depth.shape[1] != 1:\n raise ValueError(f\"Expecting 4D tensor of shape [B,1,H,W]; got {depth.shape}.\")\n if reduction not in (\"mean\", \"median\"):\n raise ValueError(f\"Unrecognized reduction method: {reduction}.\")\n if not scale_invariant and shift_invariant:\n raise ValueError(\"Pure shift-invariant ensembling is not supported.\")\n\n def init_param(depth: torch.Tensor):\n init_min = depth.reshape(ensemble_size, -1).min(dim=1).values\n init_max = depth.reshape(ensemble_size, -1).max(dim=1).values\n\n if scale_invariant and shift_invariant:\n init_s = 1.0 / (init_max - init_min).clamp(min=1e-6)\n init_t = -init_s * init_min\n param = torch.cat((init_s, init_t)).cpu().numpy()\n elif scale_invariant:\n init_s = 1.0 / init_max.clamp(min=1e-6)\n param = init_s.cpu().numpy()\n else:\n raise ValueError(\"Unrecognized alignment.\")\n\n return param\n\n def align(depth: torch.Tensor, param: np.ndarray) -> torch.Tensor:\n if scale_invariant and shift_invariant:\n s, t = np.split(param, 2)\n s = torch.from_numpy(s).to(depth).view(ensemble_size, 1, 1, 1)\n t = torch.from_numpy(t).to(depth).view(ensemble_size, 1, 1, 1)\n out = depth * s + t\n elif scale_invariant:\n s = torch.from_numpy(param).to(depth).view(ensemble_size, 1, 1, 1)\n out = depth * s\n else:\n raise ValueError(\"Unrecognized alignment.\")\n return out\n\n def ensemble(\n depth_aligned: torch.Tensor, return_uncertainty: bool = False\n ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:\n uncertainty = None\n if reduction == \"mean\":\n prediction = torch.mean(depth_aligned, dim=0, keepdim=True)\n if return_uncertainty:\n uncertainty = torch.std(depth_aligned, dim=0, keepdim=True)\n elif reduction == \"median\":\n prediction = torch.median(depth_aligned, dim=0, keepdim=True).values\n if return_uncertainty:\n uncertainty = torch.median(torch.abs(depth_aligned - prediction), dim=0, keepdim=True).values\n else:\n raise ValueError(f\"Unrecognized reduction method: {reduction}.\")\n return prediction, uncertainty\n\n def cost_fn(param: np.ndarray, depth: torch.Tensor) -> float:\n cost = 0.0\n depth_aligned = align(depth, param)\n\n for i, j in torch.combinations(torch.arange(ensemble_size)):\n diff = depth_aligned[i] - depth_aligned[j]\n cost += (diff**2).mean().sqrt().item()\n\n if regularizer_strength > 0:\n prediction, _ = ensemble(depth_aligned, return_uncertainty=False)\n err_near = (0.0 - prediction.min()).abs().item()\n err_far = (1.0 - prediction.max()).abs().item()\n cost += (err_near + err_far) * regularizer_strength\n\n return cost\n\n def compute_param(depth: torch.Tensor):\n import scipy\n\n depth_to_align = depth.to(torch.float32)\n if max_res is not None and max(depth_to_align.shape[2:]) > max_res:\n depth_to_align = MarigoldImageProcessor.resize_to_max_edge(depth_to_align, max_res, \"nearest-exact\")\n\n param = init_param(depth_to_align)\n\n res = scipy.optimize.minimize(\n partial(cost_fn, depth=depth_to_align),\n param,\n method=\"BFGS\",\n tol=tol,\n options={\"maxiter\": max_iter, \"disp\": False},\n )\n\n return res.x\n\n requires_aligning = scale_invariant or shift_invariant\n ensemble_size = depth.shape[0]\n\n if requires_aligning:\n param = compute_param(depth)\n depth = align(depth, param)\n\n depth, uncertainty = ensemble(depth, return_uncertainty=output_uncertainty)\n\n depth_max = depth.max()\n if scale_invariant and shift_invariant:\n depth_min = depth.min()\n elif scale_invariant:\n depth_min = 0\n else:\n raise ValueError(\"Unrecognized alignment.\")\n depth_range = (depth_max - depth_min).clamp(min=1e-6)\n depth = (depth - depth_min) / depth_range\n if output_uncertainty:\n uncertainty /= depth_range\n\n return depth, uncertainty # [1,1,H,W], [1,1,H,W]\n" }, { "path": "diffusers/src/diffusers/pipelines/marigold/pipeline_marigold_normals.py", "content": "# Copyright 2024 Marigold authors, PRS ETH Zurich. All rights reserved.\n# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# --------------------------------------------------------------------------\n# More information and citation instructions are available on the\n# Marigold project website: https://marigoldmonodepth.github.io\n# --------------------------------------------------------------------------\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nfrom ...image_processor import PipelineImageInput\nfrom ...models import (\n AutoencoderKL,\n UNet2DConditionModel,\n)\nfrom ...schedulers import (\n DDIMScheduler,\n LCMScheduler,\n)\nfrom ...utils import (\n BaseOutput,\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline\nfrom .marigold_image_processing import MarigoldImageProcessor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nEXAMPLE_DOC_STRING = \"\"\"\nExamples:\n```py\n>>> import diffusers\n>>> import torch\n\n>>> pipe = diffusers.MarigoldNormalsPipeline.from_pretrained(\n... \"prs-eth/marigold-normals-lcm-v0-1\", variant=\"fp16\", torch_dtype=torch.float16\n... ).to(\"cuda\")\n\n>>> image = diffusers.utils.load_image(\"https://marigoldmonodepth.github.io/images/einstein.jpg\")\n>>> normals = pipe(image)\n\n>>> vis = pipe.image_processor.visualize_normals(normals.prediction)\n>>> vis[0].save(\"einstein_normals.png\")\n```\n\"\"\"\n\n\n@dataclass\nclass MarigoldNormalsOutput(BaseOutput):\n \"\"\"\n Output class for Marigold monocular normals prediction pipeline.\n\n Args:\n prediction (`np.ndarray`, `torch.Tensor`):\n Predicted normals with values in the range [-1, 1]. The shape is always $numimages \\times 3 \\times height\n \\times width$, regardless of whether the images were passed as a 4D array or a list.\n uncertainty (`None`, `np.ndarray`, `torch.Tensor`):\n Uncertainty maps computed from the ensemble, with values in the range [0, 1]. The shape is $numimages\n \\times 1 \\times height \\times width$.\n latent (`None`, `torch.Tensor`):\n Latent features corresponding to the predictions, compatible with the `latents` argument of the pipeline.\n The shape is $numimages * numensemble \\times 4 \\times latentheight \\times latentwidth$.\n \"\"\"\n\n prediction: Union[np.ndarray, torch.Tensor]\n uncertainty: Union[None, np.ndarray, torch.Tensor]\n latent: Union[None, torch.Tensor]\n\n\nclass MarigoldNormalsPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for monocular normals estimation using the Marigold method: https://marigoldmonodepth.github.io.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n unet (`UNet2DConditionModel`):\n Conditional U-Net to denoise the normals latent, conditioned on image latent.\n vae (`AutoencoderKL`):\n Variational Auto-Encoder (VAE) Model to encode and decode images and predictions to and from latent\n representations.\n scheduler (`DDIMScheduler` or `LCMScheduler`):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents.\n text_encoder (`CLIPTextModel`):\n Text-encoder, for empty text embedding.\n tokenizer (`CLIPTokenizer`):\n CLIP tokenizer.\n prediction_type (`str`, *optional*):\n Type of predictions made by the model.\n use_full_z_range (`bool`, *optional*):\n Whether the normals predicted by this model utilize the full range of the Z dimension, or only its positive\n half.\n default_denoising_steps (`int`, *optional*):\n The minimum number of denoising diffusion steps that are required to produce a prediction of reasonable\n quality with the given model. This value must be set in the model config. When the pipeline is called\n without explicitly setting `num_inference_steps`, the default value is used. This is required to ensure\n reasonable results with various model flavors compatible with the pipeline, such as those relying on very\n short denoising schedules (`LCMScheduler`) and those with full diffusion schedules (`DDIMScheduler`).\n default_processing_resolution (`int`, *optional*):\n The recommended value of the `processing_resolution` parameter of the pipeline. This value must be set in\n the model config. When the pipeline is called without explicitly setting `processing_resolution`, the\n default value is used. This is required to ensure reasonable results with various model flavors trained\n with varying optimal processing resolution values.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n supported_prediction_types = (\"normals\",)\n\n def __init__(\n self,\n unet: UNet2DConditionModel,\n vae: AutoencoderKL,\n scheduler: Union[DDIMScheduler, LCMScheduler],\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n prediction_type: Optional[str] = None,\n use_full_z_range: Optional[bool] = True,\n default_denoising_steps: Optional[int] = None,\n default_processing_resolution: Optional[int] = None,\n ):\n super().__init__()\n\n if prediction_type not in self.supported_prediction_types:\n logger.warning(\n f\"Potentially unsupported `prediction_type='{prediction_type}'`; values supported by the pipeline: \"\n f\"{self.supported_prediction_types}.\"\n )\n\n self.register_modules(\n unet=unet,\n vae=vae,\n scheduler=scheduler,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n )\n self.register_to_config(\n use_full_z_range=use_full_z_range,\n default_denoising_steps=default_denoising_steps,\n default_processing_resolution=default_processing_resolution,\n )\n\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n\n self.use_full_z_range = use_full_z_range\n self.default_denoising_steps = default_denoising_steps\n self.default_processing_resolution = default_processing_resolution\n\n self.empty_text_embedding = None\n\n self.image_processor = MarigoldImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n def check_inputs(\n self,\n image: PipelineImageInput,\n num_inference_steps: int,\n ensemble_size: int,\n processing_resolution: int,\n resample_method_input: str,\n resample_method_output: str,\n batch_size: int,\n ensembling_kwargs: Optional[Dict[str, Any]],\n latents: Optional[torch.Tensor],\n generator: Optional[Union[torch.Generator, List[torch.Generator]]],\n output_type: str,\n output_uncertainty: bool,\n ) -> int:\n if num_inference_steps is None:\n raise ValueError(\"`num_inference_steps` is not specified and could not be resolved from the model config.\")\n if num_inference_steps < 1:\n raise ValueError(\"`num_inference_steps` must be positive.\")\n if ensemble_size < 1:\n raise ValueError(\"`ensemble_size` must be positive.\")\n if ensemble_size == 2:\n logger.warning(\n \"`ensemble_size` == 2 results are similar to no ensembling (1); \"\n \"consider increasing the value to at least 3.\"\n )\n if ensemble_size == 1 and output_uncertainty:\n raise ValueError(\n \"Computing uncertainty by setting `output_uncertainty=True` also requires setting `ensemble_size` \"\n \"greater than 1.\"\n )\n if processing_resolution is None:\n raise ValueError(\n \"`processing_resolution` is not specified and could not be resolved from the model config.\"\n )\n if processing_resolution < 0:\n raise ValueError(\n \"`processing_resolution` must be non-negative: 0 for native resolution, or any positive value for \"\n \"downsampled processing.\"\n )\n if processing_resolution % self.vae_scale_factor != 0:\n raise ValueError(f\"`processing_resolution` must be a multiple of {self.vae_scale_factor}.\")\n if resample_method_input not in (\"nearest\", \"nearest-exact\", \"bilinear\", \"bicubic\", \"area\"):\n raise ValueError(\n \"`resample_method_input` takes string values compatible with PIL library: \"\n \"nearest, nearest-exact, bilinear, bicubic, area.\"\n )\n if resample_method_output not in (\"nearest\", \"nearest-exact\", \"bilinear\", \"bicubic\", \"area\"):\n raise ValueError(\n \"`resample_method_output` takes string values compatible with PIL library: \"\n \"nearest, nearest-exact, bilinear, bicubic, area.\"\n )\n if batch_size < 1:\n raise ValueError(\"`batch_size` must be positive.\")\n if output_type not in [\"pt\", \"np\"]:\n raise ValueError(\"`output_type` must be one of `pt` or `np`.\")\n if latents is not None and generator is not None:\n raise ValueError(\"`latents` and `generator` cannot be used together.\")\n if ensembling_kwargs is not None:\n if not isinstance(ensembling_kwargs, dict):\n raise ValueError(\"`ensembling_kwargs` must be a dictionary.\")\n if \"reduction\" in ensembling_kwargs and ensembling_kwargs[\"reduction\"] not in (\"closest\", \"mean\"):\n raise ValueError(\"`ensembling_kwargs['reduction']` can be either `'closest'` or `'mean'`.\")\n\n # image checks\n num_images = 0\n W, H = None, None\n if not isinstance(image, list):\n image = [image]\n for i, img in enumerate(image):\n if isinstance(img, np.ndarray) or torch.is_tensor(img):\n if img.ndim not in (2, 3, 4):\n raise ValueError(f\"`image[{i}]` has unsupported dimensions or shape: {img.shape}.\")\n H_i, W_i = img.shape[-2:]\n N_i = 1\n if img.ndim == 4:\n N_i = img.shape[0]\n elif isinstance(img, Image.Image):\n W_i, H_i = img.size\n N_i = 1\n else:\n raise ValueError(f\"Unsupported `image[{i}]` type: {type(img)}.\")\n if W is None:\n W, H = W_i, H_i\n elif (W, H) != (W_i, H_i):\n raise ValueError(\n f\"Input `image[{i}]` has incompatible dimensions {(W_i, H_i)} with the previous images {(W, H)}\"\n )\n num_images += N_i\n\n # latents checks\n if latents is not None:\n if not torch.is_tensor(latents):\n raise ValueError(\"`latents` must be a torch.Tensor.\")\n if latents.dim() != 4:\n raise ValueError(f\"`latents` has unsupported dimensions or shape: {latents.shape}.\")\n\n if processing_resolution > 0:\n max_orig = max(H, W)\n new_H = H * processing_resolution // max_orig\n new_W = W * processing_resolution // max_orig\n if new_H == 0 or new_W == 0:\n raise ValueError(f\"Extreme aspect ratio of the input image: [{W} x {H}]\")\n W, H = new_W, new_H\n w = (W + self.vae_scale_factor - 1) // self.vae_scale_factor\n h = (H + self.vae_scale_factor - 1) // self.vae_scale_factor\n shape_expected = (num_images * ensemble_size, self.vae.config.latent_channels, h, w)\n\n if latents.shape != shape_expected:\n raise ValueError(f\"`latents` has unexpected shape={latents.shape} expected={shape_expected}.\")\n\n # generator checks\n if generator is not None:\n if isinstance(generator, list):\n if len(generator) != num_images * ensemble_size:\n raise ValueError(\n \"The number of generators must match the total number of ensemble members for all input images.\"\n )\n if not all(g.device.type == generator[0].device.type for g in generator):\n raise ValueError(\"`generator` device placement is not consistent in the list.\")\n elif not isinstance(generator, torch.Generator):\n raise ValueError(f\"Unsupported generator type: {type(generator)}.\")\n\n return num_images\n\n def progress_bar(self, iterable=None, total=None, desc=None, leave=True):\n if not hasattr(self, \"_progress_bar_config\"):\n self._progress_bar_config = {}\n elif not isinstance(self._progress_bar_config, dict):\n raise ValueError(\n f\"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}.\"\n )\n\n progress_bar_config = dict(**self._progress_bar_config)\n progress_bar_config[\"desc\"] = progress_bar_config.get(\"desc\", desc)\n progress_bar_config[\"leave\"] = progress_bar_config.get(\"leave\", leave)\n if iterable is not None:\n return tqdm(iterable, **progress_bar_config)\n elif total is not None:\n return tqdm(total=total, **progress_bar_config)\n else:\n raise ValueError(\"Either `total` or `iterable` has to be defined.\")\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n image: PipelineImageInput,\n num_inference_steps: Optional[int] = None,\n ensemble_size: int = 1,\n processing_resolution: Optional[int] = None,\n match_input_resolution: bool = True,\n resample_method_input: str = \"bilinear\",\n resample_method_output: str = \"bilinear\",\n batch_size: int = 1,\n ensembling_kwargs: Optional[Dict[str, Any]] = None,\n latents: Optional[Union[torch.Tensor, List[torch.Tensor]]] = None,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n output_type: str = \"np\",\n output_uncertainty: bool = False,\n output_latent: bool = False,\n return_dict: bool = True,\n ):\n \"\"\"\n Function invoked when calling the pipeline.\n\n Args:\n image (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`),\n `List[torch.Tensor]`: An input image or images used as an input for the normals estimation task. For\n arrays and tensors, the expected value range is between `[0, 1]`. Passing a batch of images is possible\n by providing a four-dimensional array or a tensor. Additionally, a list of images of two- or\n three-dimensional arrays or tensors can be passed. In the latter case, all list elements must have the\n same width and height.\n num_inference_steps (`int`, *optional*, defaults to `None`):\n Number of denoising diffusion steps during inference. The default value `None` results in automatic\n selection. The number of steps should be at least 10 with the full Marigold models, and between 1 and 4\n for Marigold-LCM models.\n ensemble_size (`int`, defaults to `1`):\n Number of ensemble predictions. Recommended values are 5 and higher for better precision, or 1 for\n faster inference.\n processing_resolution (`int`, *optional*, defaults to `None`):\n Effective processing resolution. When set to `0`, matches the larger input image dimension. This\n produces crisper predictions, but may also lead to the overall loss of global context. The default\n value `None` resolves to the optimal value from the model config.\n match_input_resolution (`bool`, *optional*, defaults to `True`):\n When enabled, the output prediction is resized to match the input dimensions. When disabled, the longer\n side of the output will equal to `processing_resolution`.\n resample_method_input (`str`, *optional*, defaults to `\"bilinear\"`):\n Resampling method used to resize input images to `processing_resolution`. The accepted values are:\n `\"nearest\"`, `\"nearest-exact\"`, `\"bilinear\"`, `\"bicubic\"`, or `\"area\"`.\n resample_method_output (`str`, *optional*, defaults to `\"bilinear\"`):\n Resampling method used to resize output predictions to match the input resolution. The accepted values\n are `\"nearest\"`, `\"nearest-exact\"`, `\"bilinear\"`, `\"bicubic\"`, or `\"area\"`.\n batch_size (`int`, *optional*, defaults to `1`):\n Batch size; only matters when setting `ensemble_size` or passing a tensor of images.\n ensembling_kwargs (`dict`, *optional*, defaults to `None`)\n Extra dictionary with arguments for precise ensembling control. The following options are available:\n - reduction (`str`, *optional*, defaults to `\"closest\"`): Defines the ensembling function applied in\n every pixel location, can be either `\"closest\"` or `\"mean\"`.\n latents (`torch.Tensor`, *optional*, defaults to `None`):\n Latent noise tensors to replace the random initialization. These can be taken from the previous\n function call's output.\n generator (`torch.Generator`, or `List[torch.Generator]`, *optional*, defaults to `None`):\n Random number generator object to ensure reproducibility.\n output_type (`str`, *optional*, defaults to `\"np\"`):\n Preferred format of the output's `prediction` and the optional `uncertainty` fields. The accepted\n values are: `\"np\"` (numpy array) or `\"pt\"` (torch tensor).\n output_uncertainty (`bool`, *optional*, defaults to `False`):\n When enabled, the output's `uncertainty` field contains the predictive uncertainty map, provided that\n the `ensemble_size` argument is set to a value above 2.\n output_latent (`bool`, *optional*, defaults to `False`):\n When enabled, the output's `latent` field contains the latent codes corresponding to the predictions\n within the ensemble. These codes can be saved, modified, and used for subsequent calls with the\n `latents` argument.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.marigold.MarigoldDepthOutput`] instead of a plain tuple.\n\n Examples:\n\n Returns:\n [`~pipelines.marigold.MarigoldNormalsOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.marigold.MarigoldNormalsOutput`] is returned, otherwise a\n `tuple` is returned where the first element is the prediction, the second element is the uncertainty\n (or `None`), and the third is the latent (or `None`).\n \"\"\"\n\n # 0. Resolving variables.\n device = self._execution_device\n dtype = self.dtype\n\n # Model-specific optimal default values leading to fast and reasonable results.\n if num_inference_steps is None:\n num_inference_steps = self.default_denoising_steps\n if processing_resolution is None:\n processing_resolution = self.default_processing_resolution\n\n # 1. Check inputs.\n num_images = self.check_inputs(\n image,\n num_inference_steps,\n ensemble_size,\n processing_resolution,\n resample_method_input,\n resample_method_output,\n batch_size,\n ensembling_kwargs,\n latents,\n generator,\n output_type,\n output_uncertainty,\n )\n\n # 2. Prepare empty text conditioning.\n # Model invocation: self.tokenizer, self.text_encoder.\n if self.empty_text_embedding is None:\n prompt = \"\"\n text_inputs = self.tokenizer(\n prompt,\n padding=\"do_not_pad\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids.to(device)\n self.empty_text_embedding = self.text_encoder(text_input_ids)[0] # [1,2,1024]\n\n # 3. Preprocess input images. This function loads input image or images of compatible dimensions `(H, W)`,\n # optionally downsamples them to the `processing_resolution` `(PH, PW)`, where\n # `max(PH, PW) == processing_resolution`, and pads the dimensions to `(PPH, PPW)` such that these values are\n # divisible by the latent space downscaling factor (typically 8 in Stable Diffusion). The default value `None`\n # of `processing_resolution` resolves to the optimal value from the model config. It is a recommended mode of\n # operation and leads to the most reasonable results. Using the native image resolution or any other processing\n # resolution can lead to loss of either fine details or global context in the output predictions.\n image, padding, original_resolution = self.image_processor.preprocess(\n image, processing_resolution, resample_method_input, device, dtype\n ) # [N,3,PPH,PPW]\n\n # 4. Encode input image into latent space. At this step, each of the `N` input images is represented with `E`\n # ensemble members. Each ensemble member is an independent diffused prediction, just initialized independently.\n # Latents of each such predictions across all input images and all ensemble members are represented in the\n # `pred_latent` variable. The variable `image_latent` is of the same shape: it contains each input image encoded\n # into latent space and replicated `E` times. The latents can be either generated (see `generator` to ensure\n # reproducibility), or passed explicitly via the `latents` argument. The latter can be set outside the pipeline\n # code. For example, in the Marigold-LCM video processing demo, the latents initialization of a frame is taken\n # as a convex combination of the latents output of the pipeline for the previous frame and a newly-sampled\n # noise. This behavior can be achieved by setting the `output_latent` argument to `True`. The latent space\n # dimensions are `(h, w)`. Encoding into latent space happens in batches of size `batch_size`.\n # Model invocation: self.vae.encoder.\n image_latent, pred_latent = self.prepare_latents(\n image, latents, generator, ensemble_size, batch_size\n ) # [N*E,4,h,w], [N*E,4,h,w]\n\n del image\n\n batch_empty_text_embedding = self.empty_text_embedding.to(device=device, dtype=dtype).repeat(\n batch_size, 1, 1\n ) # [B,1024,2]\n\n # 5. Process the denoising loop. All `N * E` latents are processed sequentially in batches of size `batch_size`.\n # The unet model takes concatenated latent spaces of the input image and the predicted modality as an input, and\n # outputs noise for the predicted modality's latent space. The number of denoising diffusion steps is defined by\n # `num_inference_steps`. It is either set directly, or resolves to the optimal value specific to the loaded\n # model.\n # Model invocation: self.unet.\n pred_latents = []\n\n for i in self.progress_bar(\n range(0, num_images * ensemble_size, batch_size), leave=True, desc=\"Marigold predictions...\"\n ):\n batch_image_latent = image_latent[i : i + batch_size] # [B,4,h,w]\n batch_pred_latent = pred_latent[i : i + batch_size] # [B,4,h,w]\n effective_batch_size = batch_image_latent.shape[0]\n text = batch_empty_text_embedding[:effective_batch_size] # [B,2,1024]\n\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n for t in self.progress_bar(self.scheduler.timesteps, leave=False, desc=\"Diffusion steps...\"):\n batch_latent = torch.cat([batch_image_latent, batch_pred_latent], dim=1) # [B,8,h,w]\n noise = self.unet(batch_latent, t, encoder_hidden_states=text, return_dict=False)[0] # [B,4,h,w]\n batch_pred_latent = self.scheduler.step(\n noise, t, batch_pred_latent, generator=generator\n ).prev_sample # [B,4,h,w]\n\n pred_latents.append(batch_pred_latent)\n\n pred_latent = torch.cat(pred_latents, dim=0) # [N*E,4,h,w]\n\n del (\n pred_latents,\n image_latent,\n batch_empty_text_embedding,\n batch_image_latent,\n batch_pred_latent,\n text,\n batch_latent,\n noise,\n )\n\n # 6. Decode predictions from latent into pixel space. The resulting `N * E` predictions have shape `(PPH, PPW)`,\n # which requires slight postprocessing. Decoding into pixel space happens in batches of size `batch_size`.\n # Model invocation: self.vae.decoder.\n prediction = torch.cat(\n [\n self.decode_prediction(pred_latent[i : i + batch_size])\n for i in range(0, pred_latent.shape[0], batch_size)\n ],\n dim=0,\n ) # [N*E,3,PPH,PPW]\n\n if not output_latent:\n pred_latent = None\n\n # 7. Remove padding. The output shape is (PH, PW).\n prediction = self.image_processor.unpad_image(prediction, padding) # [N*E,3,PH,PW]\n\n # 8. Ensemble and compute uncertainty (when `output_uncertainty` is set). This code treats each of the `N`\n # groups of `E` ensemble predictions independently. For each group it computes an ensembled prediction of shape\n # `(PH, PW)` and an optional uncertainty map of the same dimensions. After computing this pair of outputs for\n # each group independently, it stacks them respectively into batches of `N` almost final predictions and\n # uncertainty maps.\n uncertainty = None\n if ensemble_size > 1:\n prediction = prediction.reshape(num_images, ensemble_size, *prediction.shape[1:]) # [N,E,3,PH,PW]\n prediction = [\n self.ensemble_normals(prediction[i], output_uncertainty, **(ensembling_kwargs or {}))\n for i in range(num_images)\n ] # [ [[1,3,PH,PW], [1,1,PH,PW]], ... ]\n prediction, uncertainty = zip(*prediction) # [[1,3,PH,PW], ... ], [[1,1,PH,PW], ... ]\n prediction = torch.cat(prediction, dim=0) # [N,3,PH,PW]\n if output_uncertainty:\n uncertainty = torch.cat(uncertainty, dim=0) # [N,1,PH,PW]\n else:\n uncertainty = None\n\n # 9. If `match_input_resolution` is set, the output prediction and the uncertainty are upsampled to match the\n # input resolution `(H, W)`. This step may introduce upsampling artifacts, and therefore can be disabled.\n # After upsampling, the native resolution normal maps are renormalized to unit length to reduce the artifacts.\n # Depending on the downstream use-case, upsampling can be also chosen based on the tolerated artifacts by\n # setting the `resample_method_output` parameter (e.g., to `\"nearest\"`).\n if match_input_resolution:\n prediction = self.image_processor.resize_antialias(\n prediction, original_resolution, resample_method_output, is_aa=False\n ) # [N,3,H,W]\n prediction = self.normalize_normals(prediction) # [N,3,H,W]\n if uncertainty is not None and output_uncertainty:\n uncertainty = self.image_processor.resize_antialias(\n uncertainty, original_resolution, resample_method_output, is_aa=False\n ) # [N,1,H,W]\n\n # 10. Prepare the final outputs.\n if output_type == \"np\":\n prediction = self.image_processor.pt_to_numpy(prediction) # [N,H,W,3]\n if uncertainty is not None and output_uncertainty:\n uncertainty = self.image_processor.pt_to_numpy(uncertainty) # [N,H,W,1]\n\n # 11. Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (prediction, uncertainty, pred_latent)\n\n return MarigoldNormalsOutput(\n prediction=prediction,\n uncertainty=uncertainty,\n latent=pred_latent,\n )\n\n # Copied from diffusers.pipelines.marigold.pipeline_marigold_depth.MarigoldDepthPipeline.prepare_latents\n def prepare_latents(\n self,\n image: torch.Tensor,\n latents: Optional[torch.Tensor],\n generator: Optional[torch.Generator],\n ensemble_size: int,\n batch_size: int,\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n def retrieve_latents(encoder_output):\n if hasattr(encoder_output, \"latent_dist\"):\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n image_latent = torch.cat(\n [\n retrieve_latents(self.vae.encode(image[i : i + batch_size]))\n for i in range(0, image.shape[0], batch_size)\n ],\n dim=0,\n ) # [N,4,h,w]\n image_latent = image_latent * self.vae.config.scaling_factor\n image_latent = image_latent.repeat_interleave(ensemble_size, dim=0) # [N*E,4,h,w]\n\n pred_latent = latents\n if pred_latent is None:\n pred_latent = randn_tensor(\n image_latent.shape,\n generator=generator,\n device=image_latent.device,\n dtype=image_latent.dtype,\n ) # [N*E,4,h,w]\n\n return image_latent, pred_latent\n\n def decode_prediction(self, pred_latent: torch.Tensor) -> torch.Tensor:\n if pred_latent.dim() != 4 or pred_latent.shape[1] != self.vae.config.latent_channels:\n raise ValueError(\n f\"Expecting 4D tensor of shape [B,{self.vae.config.latent_channels},H,W]; got {pred_latent.shape}.\"\n )\n\n prediction = self.vae.decode(pred_latent / self.vae.config.scaling_factor, return_dict=False)[0] # [B,3,H,W]\n\n prediction = torch.clip(prediction, -1.0, 1.0)\n\n if not self.use_full_z_range:\n prediction[:, 2, :, :] *= 0.5\n prediction[:, 2, :, :] += 0.5\n\n prediction = self.normalize_normals(prediction) # [B,3,H,W]\n\n return prediction # [B,3,H,W]\n\n @staticmethod\n def normalize_normals(normals: torch.Tensor, eps: float = 1e-6) -> torch.Tensor:\n if normals.dim() != 4 or normals.shape[1] != 3:\n raise ValueError(f\"Expecting 4D tensor of shape [B,3,H,W]; got {normals.shape}.\")\n\n norm = torch.norm(normals, dim=1, keepdim=True)\n normals /= norm.clamp(min=eps)\n\n return normals\n\n @staticmethod\n def ensemble_normals(\n normals: torch.Tensor, output_uncertainty: bool, reduction: str = \"closest\"\n ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:\n \"\"\"\n Ensembles the normals maps represented by the `normals` tensor with expected shape `(B, 3, H, W)`, where B is\n the number of ensemble members for a given prediction of size `(H x W)`.\n\n Args:\n normals (`torch.Tensor`):\n Input ensemble normals maps.\n output_uncertainty (`bool`, *optional*, defaults to `False`):\n Whether to output uncertainty map.\n reduction (`str`, *optional*, defaults to `\"closest\"`):\n Reduction method used to ensemble aligned predictions. The accepted values are: `\"closest\"` and\n `\"mean\"`.\n\n Returns:\n A tensor of aligned and ensembled normals maps with shape `(1, 3, H, W)` and optionally a tensor of\n uncertainties of shape `(1, 1, H, W)`.\n \"\"\"\n if normals.dim() != 4 or normals.shape[1] != 3:\n raise ValueError(f\"Expecting 4D tensor of shape [B,3,H,W]; got {normals.shape}.\")\n if reduction not in (\"closest\", \"mean\"):\n raise ValueError(f\"Unrecognized reduction method: {reduction}.\")\n\n mean_normals = normals.mean(dim=0, keepdim=True) # [1,3,H,W]\n mean_normals = MarigoldNormalsPipeline.normalize_normals(mean_normals) # [1,3,H,W]\n\n sim_cos = (mean_normals * normals).sum(dim=1, keepdim=True) # [E,1,H,W]\n sim_cos = sim_cos.clamp(-1, 1) # required to avoid NaN in uncertainty with fp16\n\n uncertainty = None\n if output_uncertainty:\n uncertainty = sim_cos.arccos() # [E,1,H,W]\n uncertainty = uncertainty.mean(dim=0, keepdim=True) / np.pi # [1,1,H,W]\n\n if reduction == \"mean\":\n return mean_normals, uncertainty # [1,3,H,W], [1,1,H,W]\n\n closest_indices = sim_cos.argmax(dim=0, keepdim=True) # [1,1,H,W]\n closest_indices = closest_indices.repeat(1, 3, 1, 1) # [1,3,H,W]\n closest_normals = torch.gather(normals, 0, closest_indices) # [1,3,H,W]\n\n return closest_normals, uncertainty # [1,3,H,W], [1,1,H,W]\n" }, { "path": "diffusers/src/diffusers/pipelines/musicldm/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n is_transformers_version,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available() and is_transformers_version(\">=\", \"4.27.0\")):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_musicldm\"] = [\"MusicLDMPipeline\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available() and is_transformers_version(\">=\", \"4.27.0\")):\n raise OptionalDependencyNotAvailable()\n\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n else:\n from .pipeline_musicldm import MusicLDMPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/musicldm/pipeline_musicldm.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport torch\nfrom transformers import (\n ClapFeatureExtractor,\n ClapModel,\n ClapTextModelWithProjection,\n RobertaTokenizer,\n RobertaTokenizerFast,\n SpeechT5HifiGan,\n)\n\nfrom ...models import AutoencoderKL, UNet2DConditionModel\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n is_accelerate_available,\n is_accelerate_version,\n is_librosa_available,\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline, StableDiffusionMixin\n\n\nif is_librosa_available():\n import librosa\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import MusicLDMPipeline\n >>> import torch\n >>> import scipy\n\n >>> repo_id = \"ucsd-reach/musicldm\"\n >>> pipe = MusicLDMPipeline.from_pretrained(repo_id, torch_dtype=torch.float16)\n >>> pipe = pipe.to(\"cuda\")\n\n >>> prompt = \"Techno music with a strong, upbeat tempo and high melodic riffs\"\n >>> audio = pipe(prompt, num_inference_steps=10, audio_length_in_s=5.0).audios[0]\n\n >>> # save the audio sample as a .wav file\n >>> scipy.io.wavfile.write(\"techno.wav\", rate=16000, data=audio)\n ```\n\"\"\"\n\n\nclass MusicLDMPipeline(DiffusionPipeline, StableDiffusionMixin):\n r\"\"\"\n Pipeline for text-to-audio generation using MusicLDM.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.ClapModel`]):\n Frozen text-audio embedding model (`ClapTextModel`), specifically the\n [laion/clap-htsat-unfused](https://huggingface.co/laion/clap-htsat-unfused) variant.\n tokenizer ([`PreTrainedTokenizer`]):\n A [`~transformers.RobertaTokenizer`] to tokenize text.\n feature_extractor ([`~transformers.ClapFeatureExtractor`]):\n Feature extractor to compute mel-spectrograms from audio waveforms.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded audio latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n vocoder ([`~transformers.SpeechT5HifiGan`]):\n Vocoder of class `SpeechT5HifiGan`.\n \"\"\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: Union[ClapTextModelWithProjection, ClapModel],\n tokenizer: Union[RobertaTokenizer, RobertaTokenizerFast],\n feature_extractor: Optional[ClapFeatureExtractor],\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n vocoder: SpeechT5HifiGan,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n feature_extractor=feature_extractor,\n unet=unet,\n scheduler=scheduler,\n vocoder=vocoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_waveforms_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device (`torch.device`):\n torch device\n num_waveforms_per_prompt (`int`):\n number of waveforms that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the audio generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n \"\"\"\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n attention_mask = text_inputs.attention_mask\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLAP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = self.text_encoder.get_text_features(\n text_input_ids.to(device),\n attention_mask=attention_mask.to(device),\n )\n\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.text_model.dtype, device=device)\n\n (\n bs_embed,\n seq_len,\n ) = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_waveforms_per_prompt)\n prompt_embeds = prompt_embeds.view(bs_embed * num_waveforms_per_prompt, seq_len)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n uncond_input_ids = uncond_input.input_ids.to(device)\n attention_mask = uncond_input.attention_mask.to(device)\n\n negative_prompt_embeds = self.text_encoder.get_text_features(\n uncond_input_ids,\n attention_mask=attention_mask,\n )\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.text_model.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_waveforms_per_prompt)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_waveforms_per_prompt, seq_len)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.audioldm.pipeline_audioldm.AudioLDMPipeline.mel_spectrogram_to_waveform\n def mel_spectrogram_to_waveform(self, mel_spectrogram):\n if mel_spectrogram.dim() == 4:\n mel_spectrogram = mel_spectrogram.squeeze(1)\n\n waveform = self.vocoder(mel_spectrogram)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n waveform = waveform.cpu().float()\n return waveform\n\n # Copied from diffusers.pipelines.audioldm2.pipeline_audioldm2.AudioLDM2Pipeline.score_waveforms\n def score_waveforms(self, text, audio, num_waveforms_per_prompt, device, dtype):\n if not is_librosa_available():\n logger.info(\n \"Automatic scoring of the generated audio waveforms against the input prompt text requires the \"\n \"`librosa` package to resample the generated waveforms. Returning the audios in the order they were \"\n \"generated. To enable automatic scoring, install `librosa` with: `pip install librosa`.\"\n )\n return audio\n inputs = self.tokenizer(text, return_tensors=\"pt\", padding=True)\n resampled_audio = librosa.resample(\n audio.numpy(), orig_sr=self.vocoder.config.sampling_rate, target_sr=self.feature_extractor.sampling_rate\n )\n inputs[\"input_features\"] = self.feature_extractor(\n list(resampled_audio), return_tensors=\"pt\", sampling_rate=self.feature_extractor.sampling_rate\n ).input_features.type(dtype)\n inputs = inputs.to(device)\n\n # compute the audio-text similarity score using the CLAP model\n logits_per_text = self.text_encoder(**inputs).logits_per_text\n # sort by the highest matching generations per prompt\n indices = torch.argsort(logits_per_text, dim=1, descending=True)[:, :num_waveforms_per_prompt]\n audio = torch.index_select(audio, 0, indices.reshape(-1).cpu())\n return audio\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.audioldm.pipeline_audioldm.AudioLDMPipeline.check_inputs\n def check_inputs(\n self,\n prompt,\n audio_length_in_s,\n vocoder_upsample_factor,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n min_audio_length_in_s = vocoder_upsample_factor * self.vae_scale_factor\n if audio_length_in_s < min_audio_length_in_s:\n raise ValueError(\n f\"`audio_length_in_s` has to be a positive value greater than or equal to {min_audio_length_in_s}, but \"\n f\"is {audio_length_in_s}.\"\n )\n\n if self.vocoder.config.model_in_dim % self.vae_scale_factor != 0:\n raise ValueError(\n f\"The number of frequency bins in the vocoder's log-mel spectrogram has to be divisible by the \"\n f\"VAE scale factor, but got {self.vocoder.config.model_in_dim} bins and a scale factor of \"\n f\"{self.vae_scale_factor}.\"\n )\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # Copied from diffusers.pipelines.audioldm.pipeline_audioldm.AudioLDMPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(self.vocoder.config.model_in_dim) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def enable_model_cpu_offload(self, gpu_id=0):\n r\"\"\"\n Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared\n to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`\n method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with\n `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.\n \"\"\"\n if is_accelerate_available() and is_accelerate_version(\">=\", \"0.17.0.dev0\"):\n from accelerate import cpu_offload_with_hook\n else:\n raise ImportError(\"`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.\")\n\n device = torch.device(f\"cuda:{gpu_id}\")\n\n if self.device.type != \"cpu\":\n self.to(\"cpu\", silence_dtype_warnings=True)\n torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)\n\n model_sequence = [\n self.text_encoder.text_model,\n self.text_encoder.text_projection,\n self.unet,\n self.vae,\n self.vocoder,\n self.text_encoder,\n ]\n\n hook = None\n for cpu_offloaded_model in model_sequence:\n _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)\n\n # We'll offload the last model manually.\n self.final_offload_hook = hook\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n audio_length_in_s: Optional[float] = None,\n num_inference_steps: int = 200,\n guidance_scale: float = 2.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_waveforms_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: Optional[int] = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n output_type: Optional[str] = \"np\",\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide audio generation. If not defined, you need to pass `prompt_embeds`.\n audio_length_in_s (`int`, *optional*, defaults to 10.24):\n The length of the generated audio sample in seconds.\n num_inference_steps (`int`, *optional*, defaults to 200):\n The number of denoising steps. More denoising steps usually lead to a higher quality audio at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 2.0):\n A higher guidance scale value encourages the model to generate audio that is closely linked to the text\n `prompt` at the expense of lower sound quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in audio generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_waveforms_per_prompt (`int`, *optional*, defaults to 1):\n The number of waveforms to generate per prompt. If `num_waveforms_per_prompt > 1`, the text encoding\n model is a joint text-audio model ([`~transformers.ClapModel`]), and the tokenizer is a\n `[~transformers.ClapProcessor]`, then automatic scoring will be performed between the generated outputs\n and the input text. This scoring ranks the generated waveforms based on their cosine similarity to text\n input in the joint text-audio embedding space.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.AudioPipelineOutput`] instead of a plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n output_type (`str`, *optional*, defaults to `\"np\"`):\n The output format of the generated audio. Choose between `\"np\"` to return a NumPy `np.ndarray` or\n `\"pt\"` to return a PyTorch `torch.Tensor` object. Set to `\"latent\"` to return the latent diffusion\n model (LDM) output.\n\n Examples:\n\n Returns:\n [`~pipelines.AudioPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.AudioPipelineOutput`] is returned, otherwise a `tuple` is\n returned where the first element is a list with the generated audio.\n \"\"\"\n # 0. Convert audio input length from seconds to spectrogram height\n vocoder_upsample_factor = np.prod(self.vocoder.config.upsample_rates) / self.vocoder.config.sampling_rate\n\n if audio_length_in_s is None:\n audio_length_in_s = self.unet.config.sample_size * self.vae_scale_factor * vocoder_upsample_factor\n\n height = int(audio_length_in_s / vocoder_upsample_factor)\n\n original_waveform_length = int(audio_length_in_s * self.vocoder.config.sampling_rate)\n if height % self.vae_scale_factor != 0:\n height = int(np.ceil(height / self.vae_scale_factor)) * self.vae_scale_factor\n logger.info(\n f\"Audio length in seconds {audio_length_in_s} is increased to {height * vocoder_upsample_factor} \"\n f\"so that it can be handled by the model. It will be cut to {audio_length_in_s} after the \"\n f\"denoising process.\"\n )\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n audio_length_in_s,\n vocoder_upsample_factor,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n prompt_embeds = self._encode_prompt(\n prompt,\n device,\n num_waveforms_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_waveforms_per_prompt,\n num_channels_latents,\n height,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=None,\n class_labels=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n self.maybe_free_model_hooks()\n\n # 8. Post-processing\n if not output_type == \"latent\":\n latents = 1 / self.vae.config.scaling_factor * latents\n mel_spectrogram = self.vae.decode(latents).sample\n else:\n return AudioPipelineOutput(audios=latents)\n\n audio = self.mel_spectrogram_to_waveform(mel_spectrogram)\n\n audio = audio[:, :original_waveform_length]\n\n # 9. Automatic scoring\n if num_waveforms_per_prompt > 1 and prompt is not None:\n audio = self.score_waveforms(\n text=prompt,\n audio=audio,\n num_waveforms_per_prompt=num_waveforms_per_prompt,\n device=device,\n dtype=prompt_embeds.dtype,\n )\n\n if output_type == \"np\":\n audio = audio.numpy()\n\n if not return_dict:\n return (audio,)\n\n return AudioPipelineOutput(audios=audio)\n" }, { "path": "diffusers/src/diffusers/pipelines/onnx_utils.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport os\nimport shutil\nfrom pathlib import Path\nfrom typing import Optional, Union\n\nimport numpy as np\nfrom huggingface_hub import hf_hub_download\nfrom huggingface_hub.utils import validate_hf_hub_args\n\nfrom ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging\n\n\nif is_onnx_available():\n import onnxruntime as ort\n\n\nlogger = logging.get_logger(__name__)\n\nORT_TO_NP_TYPE = {\n \"tensor(bool)\": np.bool_,\n \"tensor(int8)\": np.int8,\n \"tensor(uint8)\": np.uint8,\n \"tensor(int16)\": np.int16,\n \"tensor(uint16)\": np.uint16,\n \"tensor(int32)\": np.int32,\n \"tensor(uint32)\": np.uint32,\n \"tensor(int64)\": np.int64,\n \"tensor(uint64)\": np.uint64,\n \"tensor(float16)\": np.float16,\n \"tensor(float)\": np.float32,\n \"tensor(double)\": np.float64,\n}\n\n\nclass OnnxRuntimeModel:\n def __init__(self, model=None, **kwargs):\n logger.info(\"`diffusers.OnnxRuntimeModel` is experimental and might change in the future.\")\n self.model = model\n self.model_save_dir = kwargs.get(\"model_save_dir\", None)\n self.latest_model_name = kwargs.get(\"latest_model_name\", ONNX_WEIGHTS_NAME)\n\n def __call__(self, **kwargs):\n inputs = {k: np.array(v) for k, v in kwargs.items()}\n return self.model.run(None, inputs)\n\n @staticmethod\n def load_model(path: Union[str, Path], provider=None, sess_options=None):\n \"\"\"\n Loads an ONNX Inference session with an ExecutionProvider. Default provider is `CPUExecutionProvider`\n\n Arguments:\n path (`str` or `Path`):\n Directory from which to load\n provider(`str`, *optional*):\n Onnxruntime execution provider to use for loading the model, defaults to `CPUExecutionProvider`\n \"\"\"\n if provider is None:\n logger.info(\"No onnxruntime provider specified, using CPUExecutionProvider\")\n provider = \"CPUExecutionProvider\"\n\n return ort.InferenceSession(path, providers=[provider], sess_options=sess_options)\n\n def _save_pretrained(self, save_directory: Union[str, Path], file_name: Optional[str] = None, **kwargs):\n \"\"\"\n Save a model and its configuration file to a directory, so that it can be re-loaded using the\n [`~optimum.onnxruntime.modeling_ort.ORTModel.from_pretrained`] class method. It will always save the\n latest_model_name.\n\n Arguments:\n save_directory (`str` or `Path`):\n Directory where to save the model file.\n file_name(`str`, *optional*):\n Overwrites the default model file name from `\"model.onnx\"` to `file_name`. This allows you to save the\n model with a different name.\n \"\"\"\n model_file_name = file_name if file_name is not None else ONNX_WEIGHTS_NAME\n\n src_path = self.model_save_dir.joinpath(self.latest_model_name)\n dst_path = Path(save_directory).joinpath(model_file_name)\n try:\n shutil.copyfile(src_path, dst_path)\n except shutil.SameFileError:\n pass\n\n # copy external weights (for models >2GB)\n src_path = self.model_save_dir.joinpath(ONNX_EXTERNAL_WEIGHTS_NAME)\n if src_path.exists():\n dst_path = Path(save_directory).joinpath(ONNX_EXTERNAL_WEIGHTS_NAME)\n try:\n shutil.copyfile(src_path, dst_path)\n except shutil.SameFileError:\n pass\n\n def save_pretrained(\n self,\n save_directory: Union[str, os.PathLike],\n **kwargs,\n ):\n \"\"\"\n Save a model to a directory, so that it can be re-loaded using the [`~OnnxModel.from_pretrained`] class\n method.:\n\n Arguments:\n save_directory (`str` or `os.PathLike`):\n Directory to which to save. Will be created if it doesn't exist.\n \"\"\"\n if os.path.isfile(save_directory):\n logger.error(f\"Provided path ({save_directory}) should be a directory, not a file\")\n return\n\n os.makedirs(save_directory, exist_ok=True)\n\n # saving model weights/files\n self._save_pretrained(save_directory, **kwargs)\n\n @classmethod\n @validate_hf_hub_args\n def _from_pretrained(\n cls,\n model_id: Union[str, Path],\n token: Optional[Union[bool, str, None]] = None,\n revision: Optional[Union[str, None]] = None,\n force_download: bool = False,\n cache_dir: Optional[str] = None,\n file_name: Optional[str] = None,\n provider: Optional[str] = None,\n sess_options: Optional[\"ort.SessionOptions\"] = None,\n **kwargs,\n ):\n \"\"\"\n Load a model from a directory or the HF Hub.\n\n Arguments:\n model_id (`str` or `Path`):\n Directory from which to load\n token (`str` or `bool`):\n Is needed to load models from a private or gated repository\n revision (`str`):\n Revision is the specific model version to use. It can be a branch name, a tag name, or a commit id\n cache_dir (`Union[str, Path]`, *optional*):\n Path to a directory in which a downloaded pretrained model configuration should be cached if the\n standard cache should not be used.\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\n cached versions if they exist.\n file_name(`str`):\n Overwrites the default model file name from `\"model.onnx\"` to `file_name`. This allows you to load\n different model files from the same repository or directory.\n provider(`str`):\n The ONNX runtime provider, e.g. `CPUExecutionProvider` or `CUDAExecutionProvider`.\n kwargs (`Dict`, *optional*):\n kwargs will be passed to the model during initialization\n \"\"\"\n model_file_name = file_name if file_name is not None else ONNX_WEIGHTS_NAME\n # load model from local directory\n if os.path.isdir(model_id):\n model = OnnxRuntimeModel.load_model(\n Path(model_id, model_file_name).as_posix(), provider=provider, sess_options=sess_options\n )\n kwargs[\"model_save_dir\"] = Path(model_id)\n # load model from hub\n else:\n # download model\n model_cache_path = hf_hub_download(\n repo_id=model_id,\n filename=model_file_name,\n token=token,\n revision=revision,\n cache_dir=cache_dir,\n force_download=force_download,\n )\n kwargs[\"model_save_dir\"] = Path(model_cache_path).parent\n kwargs[\"latest_model_name\"] = Path(model_cache_path).name\n model = OnnxRuntimeModel.load_model(model_cache_path, provider=provider, sess_options=sess_options)\n return cls(model=model, **kwargs)\n\n @classmethod\n @validate_hf_hub_args\n def from_pretrained(\n cls,\n model_id: Union[str, Path],\n force_download: bool = True,\n token: Optional[str] = None,\n cache_dir: Optional[str] = None,\n **model_kwargs,\n ):\n revision = None\n if len(str(model_id).split(\"@\")) == 2:\n model_id, revision = model_id.split(\"@\")\n\n return cls._from_pretrained(\n model_id=model_id,\n revision=revision,\n cache_dir=cache_dir,\n force_download=force_download,\n token=token,\n **model_kwargs,\n )\n" }, { "path": "diffusers/src/diffusers/pipelines/paint_by_example/__init__.py", "content": "from dataclasses import dataclass\nfrom typing import TYPE_CHECKING, List, Optional, Union\n\nimport numpy as np\nimport PIL\nfrom PIL import Image\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"image_encoder\"] = [\"PaintByExampleImageEncoder\"]\n _import_structure[\"pipeline_paint_by_example\"] = [\"PaintByExamplePipeline\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n else:\n from .image_encoder import PaintByExampleImageEncoder\n from .pipeline_paint_by_example import PaintByExamplePipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/paint_by_example/image_encoder.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport torch\nfrom torch import nn\nfrom transformers import CLIPPreTrainedModel, CLIPVisionModel\n\nfrom ...models.attention import BasicTransformerBlock\nfrom ...utils import logging\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass PaintByExampleImageEncoder(CLIPPreTrainedModel):\n def __init__(self, config, proj_size=None):\n super().__init__(config)\n self.proj_size = proj_size or getattr(config, \"projection_dim\", 768)\n\n self.model = CLIPVisionModel(config)\n self.mapper = PaintByExampleMapper(config)\n self.final_layer_norm = nn.LayerNorm(config.hidden_size)\n self.proj_out = nn.Linear(config.hidden_size, self.proj_size)\n\n # uncondition for scaling\n self.uncond_vector = nn.Parameter(torch.randn((1, 1, self.proj_size)))\n\n def forward(self, pixel_values, return_uncond_vector=False):\n clip_output = self.model(pixel_values=pixel_values)\n latent_states = clip_output.pooler_output\n latent_states = self.mapper(latent_states[:, None])\n latent_states = self.final_layer_norm(latent_states)\n latent_states = self.proj_out(latent_states)\n if return_uncond_vector:\n return latent_states, self.uncond_vector\n\n return latent_states\n\n\nclass PaintByExampleMapper(nn.Module):\n def __init__(self, config):\n super().__init__()\n num_layers = (config.num_hidden_layers + 1) // 5\n hid_size = config.hidden_size\n num_heads = 1\n self.blocks = nn.ModuleList(\n [\n BasicTransformerBlock(hid_size, num_heads, hid_size, activation_fn=\"gelu\", attention_bias=True)\n for _ in range(num_layers)\n ]\n )\n\n def forward(self, hidden_states):\n for block in self.blocks:\n hidden_states = block(hidden_states)\n\n return hidden_states\n" }, { "path": "diffusers/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Callable, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom transformers import CLIPImageProcessor\n\nfrom ...image_processor import VaeImageProcessor\nfrom ...models import AutoencoderKL, UNet2DConditionModel\nfrom ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler\nfrom ...utils import deprecate, logging\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ..stable_diffusion import StableDiffusionPipelineOutput\nfrom ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom .image_encoder import PaintByExampleImageEncoder\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n\ndef prepare_mask_and_masked_image(image, mask):\n \"\"\"\n Prepares a pair (image, mask) to be consumed by the Paint by Example pipeline. This means that those inputs will be\n converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the\n ``image`` and ``1`` for the ``mask``.\n\n The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be\n binarized (``mask > 0.5``) and cast to ``torch.float32`` too.\n\n Args:\n image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.\n It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``\n ``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.\n mask (_type_): The mask to apply to the image, i.e. regions to inpaint.\n It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``\n ``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.\n\n\n Raises:\n ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask\n should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.\n TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not\n (ot the other way around).\n\n Returns:\n tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4\n dimensions: ``batch x channels x height x width``.\n \"\"\"\n if isinstance(image, torch.Tensor):\n if not isinstance(mask, torch.Tensor):\n raise TypeError(f\"`image` is a torch.Tensor but `mask` (type: {type(mask)} is not\")\n\n # Batch single image\n if image.ndim == 3:\n assert image.shape[0] == 3, \"Image outside a batch should be of shape (3, H, W)\"\n image = image.unsqueeze(0)\n\n # Batch and add channel dim for single mask\n if mask.ndim == 2:\n mask = mask.unsqueeze(0).unsqueeze(0)\n\n # Batch single mask or add channel dim\n if mask.ndim == 3:\n # Batched mask\n if mask.shape[0] == image.shape[0]:\n mask = mask.unsqueeze(1)\n else:\n mask = mask.unsqueeze(0)\n\n assert image.ndim == 4 and mask.ndim == 4, \"Image and Mask must have 4 dimensions\"\n assert image.shape[-2:] == mask.shape[-2:], \"Image and Mask must have the same spatial dimensions\"\n assert image.shape[0] == mask.shape[0], \"Image and Mask must have the same batch size\"\n assert mask.shape[1] == 1, \"Mask image must have a single channel\"\n\n # Check image is in [-1, 1]\n if image.min() < -1 or image.max() > 1:\n raise ValueError(\"Image should be in [-1, 1] range\")\n\n # Check mask is in [0, 1]\n if mask.min() < 0 or mask.max() > 1:\n raise ValueError(\"Mask should be in [0, 1] range\")\n\n # paint-by-example inverses the mask\n mask = 1 - mask\n\n # Binarize mask\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n\n # Image as float32\n image = image.to(dtype=torch.float32)\n elif isinstance(mask, torch.Tensor):\n raise TypeError(f\"`mask` is a torch.Tensor but `image` (type: {type(image)} is not\")\n else:\n if isinstance(image, PIL.Image.Image):\n image = [image]\n\n image = np.concatenate([np.array(i.convert(\"RGB\"))[None, :] for i in image], axis=0)\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0\n\n # preprocess mask\n if isinstance(mask, PIL.Image.Image):\n mask = [mask]\n\n mask = np.concatenate([np.array(m.convert(\"L\"))[None, None, :] for m in mask], axis=0)\n mask = mask.astype(np.float32) / 255.0\n\n # paint-by-example inverses the mask\n mask = 1 - mask\n\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n mask = torch.from_numpy(mask)\n\n masked_image = image * mask\n\n return mask, masked_image\n\n\nclass PaintByExamplePipeline(DiffusionPipeline, StableDiffusionMixin):\n r\"\"\"\n \n\n 🧪 This is an experimental feature!\n\n \n\n Pipeline for image-guided image inpainting using Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n image_encoder ([`PaintByExampleImageEncoder`]):\n Encodes the example input image. The `unet` is conditioned on the example image instead of a text prompt.\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n\n \"\"\"\n\n # TODO: feature_extractor is required to encode initial images (if they are in PIL format),\n # we should give a descriptive message if the pipeline doesn't have one.\n\n model_cpu_offload_seq = \"unet->vae\"\n _exclude_from_cpu_offload = [\"image_encoder\"]\n _optional_components = [\"safety_checker\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n image_encoder: PaintByExampleImageEncoder,\n unet: UNet2DConditionModel,\n scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = False,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n image_encoder=image_encoder,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_image_variation.StableDiffusionImageVariationPipeline.check_inputs\n def check_inputs(self, image, height, width, callback_steps):\n if (\n not isinstance(image, torch.Tensor)\n and not isinstance(image, PIL.Image.Image)\n and not isinstance(image, list)\n ):\n raise ValueError(\n \"`image` has to be of type `torch.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is\"\n f\" {type(image)}\"\n )\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline.prepare_mask_latents\n def prepare_mask_latents(\n self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance\n ):\n # resize the mask to latents shape as we concatenate the mask to the latents\n # we do that before converting to dtype to avoid breaking in case we're using cpu_offload\n # and half precision\n mask = torch.nn.functional.interpolate(\n mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)\n )\n mask = mask.to(device=device, dtype=dtype)\n\n masked_image = masked_image.to(device=device, dtype=dtype)\n\n if masked_image.shape[1] == 4:\n masked_image_latents = masked_image\n else:\n masked_image_latents = self._encode_vae_image(masked_image, generator=generator)\n\n # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method\n if mask.shape[0] < batch_size:\n if not batch_size % mask.shape[0] == 0:\n raise ValueError(\n \"The passed mask and the required batch size don't match. Masks are supposed to be duplicated to\"\n f\" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number\"\n \" of masks that you pass is divisible by the total requested batch size.\"\n )\n mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)\n if masked_image_latents.shape[0] < batch_size:\n if not batch_size % masked_image_latents.shape[0] == 0:\n raise ValueError(\n \"The passed images and the required batch size don't match. Images are supposed to be duplicated\"\n f\" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed.\"\n \" Make sure the number of images that you pass is divisible by the total requested batch size.\"\n )\n masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)\n\n mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask\n masked_image_latents = (\n torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents\n )\n\n # aligning device to prevent device errors when concating it with the latent model input\n masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)\n return mask, masked_image_latents\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline._encode_vae_image\n def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):\n if isinstance(generator, list):\n image_latents = [\n retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])\n for i in range(image.shape[0])\n ]\n image_latents = torch.cat(image_latents, dim=0)\n else:\n image_latents = retrieve_latents(self.vae.encode(image), generator=generator)\n\n image_latents = self.vae.config.scaling_factor * image_latents\n\n return image_latents\n\n def _encode_image(self, image, device, num_images_per_prompt, do_classifier_free_guidance):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(images=image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n image_embeddings, negative_prompt_embeds = self.image_encoder(image, return_uncond_vector=True)\n\n # duplicate image embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = image_embeddings.shape\n image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)\n image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, image_embeddings.shape[0], 1)\n negative_prompt_embeds = negative_prompt_embeds.view(bs_embed * num_images_per_prompt, 1, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings])\n\n return image_embeddings\n\n @torch.no_grad()\n def __call__(\n self,\n example_image: Union[torch.Tensor, PIL.Image.Image],\n image: Union[torch.Tensor, PIL.Image.Image],\n mask_image: Union[torch.Tensor, PIL.Image.Image],\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n example_image (`torch.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]`):\n An example image to guide image generation.\n image (`torch.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]`):\n `Image` or tensor representing an image batch to be inpainted (parts of the image are masked out with\n `mask_image` and repainted according to `prompt`).\n mask_image (`torch.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]`):\n `Image` or tensor representing an image batch to mask `image`. White pixels in the mask are repainted,\n while black pixels are preserved. If `mask_image` is a PIL image, it is converted to a single channel\n (luminance) before use. If it's a tensor, it should contain one color channel (L) instead of 3, so the\n expected shape would be `(B, H, W, 1)`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n\n Example:\n\n ```py\n >>> import PIL\n >>> import requests\n >>> import torch\n >>> from io import BytesIO\n >>> from diffusers import PaintByExamplePipeline\n\n\n >>> def download_image(url):\n ... response = requests.get(url)\n ... return PIL.Image.open(BytesIO(response.content)).convert(\"RGB\")\n\n\n >>> img_url = (\n ... \"https://raw.githubusercontent.com/Fantasy-Studio/Paint-by-Example/main/examples/image/example_1.png\"\n ... )\n >>> mask_url = (\n ... \"https://raw.githubusercontent.com/Fantasy-Studio/Paint-by-Example/main/examples/mask/example_1.png\"\n ... )\n >>> example_url = \"https://raw.githubusercontent.com/Fantasy-Studio/Paint-by-Example/main/examples/reference/example_1.jpg\"\n\n >>> init_image = download_image(img_url).resize((512, 512))\n >>> mask_image = download_image(mask_url).resize((512, 512))\n >>> example_image = download_image(example_url).resize((512, 512))\n\n >>> pipe = PaintByExamplePipeline.from_pretrained(\n ... \"Fantasy-Studio/Paint-by-Example\",\n ... torch_dtype=torch.float16,\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> image = pipe(image=init_image, mask_image=mask_image, example_image=example_image).images[0]\n >>> image\n ```\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n # 1. Define call parameters\n if isinstance(image, PIL.Image.Image):\n batch_size = 1\n elif isinstance(image, list):\n batch_size = len(image)\n else:\n batch_size = image.shape[0]\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 2. Preprocess mask and image\n mask, masked_image = prepare_mask_and_masked_image(image, mask_image)\n height, width = masked_image.shape[-2:]\n\n # 3. Check inputs\n self.check_inputs(example_image, height, width, callback_steps)\n\n # 4. Encode input image\n image_embeddings = self._encode_image(\n example_image, device, num_images_per_prompt, do_classifier_free_guidance\n )\n\n # 5. set timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 6. Prepare latent variables\n num_channels_latents = self.vae.config.latent_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n image_embeddings.dtype,\n device,\n generator,\n latents,\n )\n\n # 7. Prepare mask latent variables\n mask, masked_image_latents = self.prepare_mask_latents(\n mask,\n masked_image,\n batch_size * num_images_per_prompt,\n height,\n width,\n image_embeddings.dtype,\n device,\n generator,\n do_classifier_free_guidance,\n )\n\n # 8. Check that sizes of mask, masked image and latents match\n num_channels_mask = mask.shape[1]\n num_channels_masked_image = masked_image_latents.shape[1]\n if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:\n raise ValueError(\n f\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\"\n f\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\"\n f\" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}\"\n f\" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of\"\n \" `pipeline.unet` or your `mask_image` or `image` input.\"\n )\n\n # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 10. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n\n # concat latents, mask, masked_image_latents in the channel dimension\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n latent_model_input = torch.cat([latent_model_input, masked_image_latents, mask], dim=1)\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=image_embeddings).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n self.maybe_free_model_hooks()\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, image_embeddings.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/pia/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_pia\"] = [\"PIAPipeline\", \"PIAPipelineOutput\"]\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n\n else:\n from .pipeline_pia import PIAPipeline, PIAPipelineOutput\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/pia/pipeline_pia.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom dataclasses import dataclass\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...image_processor import PipelineImageInput\nfrom ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel, UNetMotionModel\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...models.unets.unet_motion_model import MotionAdapter\nfrom ...schedulers import (\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n)\nfrom ...utils import (\n USE_PEFT_BACKEND,\n BaseOutput,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ...video_processor import VideoProcessor\nfrom ..free_init_utils import FreeInitMixin\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import (\n ... EulerDiscreteScheduler,\n ... MotionAdapter,\n ... PIAPipeline,\n ... )\n >>> from diffusers.utils import export_to_gif, load_image\n\n >>> adapter = MotionAdapter.from_pretrained(\"../checkpoints/pia-diffusers\")\n >>> pipe = PIAPipeline.from_pretrained(\"SG161222/Realistic_Vision_V6.0_B1_noVAE\", motion_adapter=adapter)\n >>> pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)\n >>> image = load_image(\n ... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png?download=true\"\n ... )\n >>> image = image.resize((512, 512))\n >>> prompt = \"cat in a hat\"\n >>> negative_prompt = \"wrong white balance, dark, sketches,worst quality,low quality, deformed, distorted, disfigured, bad eyes, wrong lips,weird mouth, bad teeth, mutated hands and fingers, bad anatomy,wrong anatomy, amputation, extra limb, missing limb, floating,limbs, disconnected limbs, mutation, ugly, disgusting, bad_pictures, negative_hand-neg\"\n >>> generator = torch.Generator(\"cpu\").manual_seed(0)\n >>> output = pipe(image=image, prompt=prompt, negative_prompt=negative_prompt, generator=generator)\n >>> frames = output.frames[0]\n >>> export_to_gif(frames, \"pia-animation.gif\")\n ```\n\"\"\"\n\nRANGE_LIST = [\n [1.0, 0.9, 0.85, 0.85, 0.85, 0.8], # 0 Small Motion\n [1.0, 0.8, 0.8, 0.8, 0.79, 0.78, 0.75], # Moderate Motion\n [1.0, 0.8, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.6, 0.5, 0.5], # Large Motion\n [1.0, 0.9, 0.85, 0.85, 0.85, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.85, 0.85, 0.9, 1.0], # Loop\n [1.0, 0.8, 0.8, 0.8, 0.79, 0.78, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78, 0.79, 0.8, 0.8, 1.0], # Loop\n [1.0, 0.8, 0.7, 0.7, 0.7, 0.7, 0.6, 0.5, 0.5, 0.6, 0.7, 0.7, 0.7, 0.7, 0.8, 1.0], # Loop\n [0.5, 0.4, 0.4, 0.4, 0.35, 0.3], # Style Transfer Candidate Small Motion\n [0.5, 0.4, 0.4, 0.4, 0.35, 0.35, 0.3, 0.25, 0.2], # Style Transfer Moderate Motion\n [0.5, 0.2], # Style Transfer Large Motion\n]\n\n\ndef prepare_mask_coef_by_statistics(num_frames: int, cond_frame: int, motion_scale: int):\n assert num_frames > 0, \"video_length should be greater than 0\"\n\n assert num_frames > cond_frame, \"video_length should be greater than cond_frame\"\n\n range_list = RANGE_LIST\n\n assert motion_scale < len(range_list), f\"motion_scale type{motion_scale} not implemented\"\n\n coef = range_list[motion_scale]\n coef = coef + ([coef[-1]] * (num_frames - len(coef)))\n\n order = [abs(i - cond_frame) for i in range(num_frames)]\n coef = [coef[order[i]] for i in range(num_frames)]\n\n return coef\n\n\n@dataclass\nclass PIAPipelineOutput(BaseOutput):\n r\"\"\"\n Output class for PIAPipeline.\n\n Args:\n frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):\n Nested list of length `batch_size` with denoised PIL image sequences of length `num_frames`, NumPy array of\n shape `(batch_size, num_frames, channels, height, width, Torch tensor of shape `(batch_size, num_frames,\n channels, height, width)`.\n \"\"\"\n\n frames: Union[torch.Tensor, np.ndarray, List[List[PIL.Image.Image]]]\n\n\nclass PIAPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n TextualInversionLoaderMixin,\n IPAdapterMixin,\n LoraLoaderMixin,\n FromSingleFileMixin,\n FreeInitMixin,\n):\n r\"\"\"\n Pipeline for text-to-video generation.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer (`CLIPTokenizer`):\n A [`~transformers.CLIPTokenizer`] to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A [`UNet2DConditionModel`] used to create a UNetMotionModel to denoise the encoded video latents.\n motion_adapter ([`MotionAdapter`]):\n A [`MotionAdapter`] to be used in combination with `unet` to denoise the encoded video latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->image_encoder->unet->vae\"\n _optional_components = [\"feature_extractor\", \"image_encoder\", \"motion_adapter\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: Union[UNet2DConditionModel, UNetMotionModel],\n scheduler: Union[\n DDIMScheduler,\n PNDMScheduler,\n LMSDiscreteScheduler,\n EulerDiscreteScheduler,\n EulerAncestralDiscreteScheduler,\n DPMSolverMultistepScheduler,\n ],\n motion_adapter: Optional[MotionAdapter] = None,\n feature_extractor: CLIPImageProcessor = None,\n image_encoder: CLIPVisionModelWithProjection = None,\n ):\n super().__init__()\n if isinstance(unet, UNet2DConditionModel):\n unet = UNetMotionModel.from_unet2d(unet, motion_adapter)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n motion_adapter=motion_adapter,\n scheduler=scheduler,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.video_processor = VideoProcessor(do_resize=False, vae_scale_factor=self.vae_scale_factor)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with num_images_per_prompt -> num_videos_per_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents\n def decode_latents(self, latents):\n latents = 1 / self.vae.config.scaling_factor * latents\n\n batch_size, channels, num_frames, height, width = latents.shape\n latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)\n\n image = self.vae.decode(latents).sample\n video = image[None, :].reshape((batch_size, num_frames, -1) + image.shape[2:]).permute(0, 2, 1, 3, 4)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n video = video.float()\n return video\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n height,\n width,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n if ip_adapter_image_embeds is not None:\n if not isinstance(ip_adapter_image_embeds, list):\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}\"\n )\n elif ip_adapter_image_embeds[0].ndim not in [3, 4]:\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n # Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_synth.TextToVideoSDPipeline.prepare_latents\n def prepare_latents(\n self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None\n ):\n shape = (\n batch_size,\n num_channels_latents,\n num_frames,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def prepare_masked_condition(\n self,\n image,\n batch_size,\n num_channels_latents,\n num_frames,\n height,\n width,\n dtype,\n device,\n generator,\n motion_scale=0,\n ):\n shape = (\n batch_size,\n num_channels_latents,\n num_frames,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n )\n _, _, _, scaled_height, scaled_width = shape\n\n image = self.video_processor.preprocess(image)\n image = image.to(device, dtype)\n\n if isinstance(generator, list):\n image_latent = [\n self.vae.encode(image[k : k + 1]).latent_dist.sample(generator[k]) for k in range(batch_size)\n ]\n image_latent = torch.cat(image_latent, dim=0)\n else:\n image_latent = self.vae.encode(image).latent_dist.sample(generator)\n\n image_latent = image_latent.to(device=device, dtype=dtype)\n image_latent = torch.nn.functional.interpolate(image_latent, size=[scaled_height, scaled_width])\n image_latent_padding = image_latent.clone() * self.vae.config.scaling_factor\n\n mask = torch.zeros((batch_size, 1, num_frames, scaled_height, scaled_width)).to(device=device, dtype=dtype)\n mask_coef = prepare_mask_coef_by_statistics(num_frames, 0, motion_scale)\n masked_image = torch.zeros(batch_size, 4, num_frames, scaled_height, scaled_width).to(\n device=device, dtype=self.unet.dtype\n )\n for f in range(num_frames):\n mask[:, :, f, :, :] = mask_coef[f]\n masked_image[:, :, f, :, :] = image_latent_padding.clone()\n\n mask = torch.cat([mask] * 2) if self.do_classifier_free_guidance else mask\n masked_image = torch.cat([masked_image] * 2) if self.do_classifier_free_guidance else masked_image\n\n return mask, masked_image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n if hasattr(self.scheduler, \"set_begin_index\"):\n self.scheduler.set_begin_index(t_start * self.scheduler.order)\n\n return timesteps, num_inference_steps - t_start\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n image: PipelineImageInput,\n prompt: Union[str, List[str]] = None,\n strength: float = 1.0,\n num_frames: Optional[int] = 16,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_videos_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n motion_scale: int = 0,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n image (`PipelineImageInput`):\n The input image to be used for video generation.\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n strength (`float`, *optional*, defaults to 1.0):\n Indicates extent to transform the reference `image`. Must be between 0 and 1.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated video.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated video.\n num_frames (`int`, *optional*, defaults to 16):\n The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds\n amounts to 2 seconds of video.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality videos at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`. Latents should be of shape\n `(batch_size, num_channel, num_frames, height, width)`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*):\n Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of\n IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should\n contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not\n provided, embeddings are computed from the `ip_adapter_image` input argument.\n motion_scale: (`int`, *optional*, defaults to 0):\n Parameter that controls the amount and type of motion that is added to the image. Increasing the value\n increases the amount of motion, while specific ranges of values control the type of motion that is\n added. Must be between 0 and 8. Set between 0-2 to only increase the amount of motion. Set between 3-5\n to create looping motion. Set between 6-8 to perform motion with image style transfer.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated video. Choose between `torch.Tensor`, `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] instead\n of a plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.pia.pipeline_pia.PIAPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.pia.pipeline_pia.PIAPipelineOutput`] is returned, otherwise a\n `tuple` is returned where the first element is a list with the generated frames.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n num_videos_per_prompt = 1\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n height,\n width,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n ip_adapter_image,\n ip_adapter_image_embeds,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_videos_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=self.clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_videos_per_prompt,\n self.do_classifier_free_guidance,\n )\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n latent_timestep = timesteps[:1].repeat(batch_size * num_videos_per_prompt)\n self._num_timesteps = len(timesteps)\n\n # 5. Prepare latent variables\n latents = self.prepare_latents(\n batch_size * num_videos_per_prompt,\n 4,\n num_frames,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents=latents,\n )\n mask, masked_image = self.prepare_masked_condition(\n image,\n batch_size * num_videos_per_prompt,\n 4,\n num_frames=num_frames,\n height=height,\n width=width,\n dtype=self.unet.dtype,\n device=device,\n generator=generator,\n motion_scale=motion_scale,\n )\n if strength < 1.0:\n noise = randn_tensor(latents.shape, generator=generator, device=device, dtype=latents.dtype)\n latents = self.scheduler.add_noise(masked_image[0], noise, latent_timestep)\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Add image embeds for IP-Adapter\n added_cond_kwargs = (\n {\"image_embeds\": image_embeds}\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None\n else None\n )\n\n # 8. Denoising loop\n num_free_init_iters = self._free_init_num_iters if self.free_init_enabled else 1\n for free_init_iter in range(num_free_init_iters):\n if self.free_init_enabled:\n latents, timesteps = self._apply_free_init(\n latents, free_init_iter, num_inference_steps, device, latents.dtype, generator\n )\n\n self._num_timesteps = len(timesteps)\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n\n with self.progress_bar(total=self._num_timesteps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n latent_model_input = torch.cat([latent_model_input, mask, masked_image], dim=1)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n ).sample\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n\n # 9. Post processing\n if output_type == \"latent\":\n video = latents\n else:\n video_tensor = self.decode_latents(latents)\n video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)\n\n # 10. Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (video,)\n\n return PIAPipelineOutput(frames=video)\n" }, { "path": "diffusers/src/diffusers/pipelines/pipeline_flax_utils.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport importlib\nimport inspect\nimport os\nfrom typing import Any, Dict, List, Optional, Union\n\nimport flax\nimport numpy as np\nimport PIL.Image\nfrom flax.core.frozen_dict import FrozenDict\nfrom huggingface_hub import create_repo, snapshot_download\nfrom huggingface_hub.utils import validate_hf_hub_args\nfrom PIL import Image\nfrom tqdm.auto import tqdm\n\nfrom ..configuration_utils import ConfigMixin\nfrom ..models.modeling_flax_utils import FLAX_WEIGHTS_NAME, FlaxModelMixin\nfrom ..schedulers.scheduling_utils_flax import SCHEDULER_CONFIG_NAME, FlaxSchedulerMixin\nfrom ..utils import (\n CONFIG_NAME,\n BaseOutput,\n PushToHubMixin,\n http_user_agent,\n is_transformers_available,\n logging,\n)\n\n\nif is_transformers_available():\n from transformers import FlaxPreTrainedModel\n\nINDEX_FILE = \"diffusion_flax_model.bin\"\n\n\nlogger = logging.get_logger(__name__)\n\n\nLOADABLE_CLASSES = {\n \"diffusers\": {\n \"FlaxModelMixin\": [\"save_pretrained\", \"from_pretrained\"],\n \"FlaxSchedulerMixin\": [\"save_pretrained\", \"from_pretrained\"],\n \"FlaxDiffusionPipeline\": [\"save_pretrained\", \"from_pretrained\"],\n },\n \"transformers\": {\n \"PreTrainedTokenizer\": [\"save_pretrained\", \"from_pretrained\"],\n \"PreTrainedTokenizerFast\": [\"save_pretrained\", \"from_pretrained\"],\n \"FlaxPreTrainedModel\": [\"save_pretrained\", \"from_pretrained\"],\n \"FeatureExtractionMixin\": [\"save_pretrained\", \"from_pretrained\"],\n \"ProcessorMixin\": [\"save_pretrained\", \"from_pretrained\"],\n \"ImageProcessingMixin\": [\"save_pretrained\", \"from_pretrained\"],\n },\n}\n\nALL_IMPORTABLE_CLASSES = {}\nfor library in LOADABLE_CLASSES:\n ALL_IMPORTABLE_CLASSES.update(LOADABLE_CLASSES[library])\n\n\ndef import_flax_or_no_model(module, class_name):\n try:\n # 1. First make sure that if a Flax object is present, import this one\n class_obj = getattr(module, \"Flax\" + class_name)\n except AttributeError:\n # 2. If this doesn't work, it's not a model and we don't append \"Flax\"\n class_obj = getattr(module, class_name)\n except AttributeError:\n raise ValueError(f\"Neither Flax{class_name} nor {class_name} exist in {module}\")\n\n return class_obj\n\n\n@flax.struct.dataclass\nclass FlaxImagePipelineOutput(BaseOutput):\n \"\"\"\n Output class for image pipelines.\n\n Args:\n images (`List[PIL.Image.Image]` or `np.ndarray`)\n List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,\n num_channels)`.\n \"\"\"\n\n images: Union[List[PIL.Image.Image], np.ndarray]\n\n\nclass FlaxDiffusionPipeline(ConfigMixin, PushToHubMixin):\n r\"\"\"\n Base class for Flax-based pipelines.\n\n [`FlaxDiffusionPipeline`] stores all components (models, schedulers, and processors) for diffusion pipelines and\n provides methods for loading, downloading and saving models. It also includes methods to:\n\n - enable/disable the progress bar for the denoising iteration\n\n Class attributes:\n\n - **config_name** ([`str`]) -- The configuration filename that stores the class and module names of all the\n diffusion pipeline's components.\n \"\"\"\n\n config_name = \"model_index.json\"\n\n def register_modules(self, **kwargs):\n # import it here to avoid circular import\n from diffusers import pipelines\n\n for name, module in kwargs.items():\n if module is None:\n register_dict = {name: (None, None)}\n else:\n # retrieve library\n library = module.__module__.split(\".\")[0]\n\n # check if the module is a pipeline module\n pipeline_dir = module.__module__.split(\".\")[-2]\n path = module.__module__.split(\".\")\n is_pipeline_module = pipeline_dir in path and hasattr(pipelines, pipeline_dir)\n\n # if library is not in LOADABLE_CLASSES, then it is a custom module.\n # Or if it's a pipeline module, then the module is inside the pipeline\n # folder so we set the library to module name.\n if library not in LOADABLE_CLASSES or is_pipeline_module:\n library = pipeline_dir\n\n # retrieve class_name\n class_name = module.__class__.__name__\n\n register_dict = {name: (library, class_name)}\n\n # save model index config\n self.register_to_config(**register_dict)\n\n # set models\n setattr(self, name, module)\n\n def save_pretrained(\n self,\n save_directory: Union[str, os.PathLike],\n params: Union[Dict, FrozenDict],\n push_to_hub: bool = False,\n **kwargs,\n ):\n # TODO: handle inference_state\n \"\"\"\n Save all saveable variables of the pipeline to a directory. A pipeline variable can be saved and loaded if its\n class implements both a save and loading method. The pipeline is easily reloaded using the\n [`~FlaxDiffusionPipeline.from_pretrained`] class method.\n\n Arguments:\n save_directory (`str` or `os.PathLike`):\n Directory to which to save. Will be created if it doesn't exist.\n push_to_hub (`bool`, *optional*, defaults to `False`):\n Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the\n repository you want to push to with `repo_id` (will default to the name of `save_directory` in your\n namespace).\n kwargs (`Dict[str, Any]`, *optional*):\n Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.\n \"\"\"\n self.save_config(save_directory)\n\n model_index_dict = dict(self.config)\n model_index_dict.pop(\"_class_name\")\n model_index_dict.pop(\"_diffusers_version\")\n model_index_dict.pop(\"_module\", None)\n\n if push_to_hub:\n commit_message = kwargs.pop(\"commit_message\", None)\n private = kwargs.pop(\"private\", False)\n create_pr = kwargs.pop(\"create_pr\", False)\n token = kwargs.pop(\"token\", None)\n repo_id = kwargs.pop(\"repo_id\", save_directory.split(os.path.sep)[-1])\n repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id\n\n for pipeline_component_name in model_index_dict.keys():\n sub_model = getattr(self, pipeline_component_name)\n if sub_model is None:\n # edge case for saving a pipeline with safety_checker=None\n continue\n\n model_cls = sub_model.__class__\n\n save_method_name = None\n # search for the model's base class in LOADABLE_CLASSES\n for library_name, library_classes in LOADABLE_CLASSES.items():\n library = importlib.import_module(library_name)\n for base_class, save_load_methods in library_classes.items():\n class_candidate = getattr(library, base_class, None)\n if class_candidate is not None and issubclass(model_cls, class_candidate):\n # if we found a suitable base class in LOADABLE_CLASSES then grab its save method\n save_method_name = save_load_methods[0]\n break\n if save_method_name is not None:\n break\n\n save_method = getattr(sub_model, save_method_name)\n expects_params = \"params\" in set(inspect.signature(save_method).parameters.keys())\n\n if expects_params:\n save_method(\n os.path.join(save_directory, pipeline_component_name), params=params[pipeline_component_name]\n )\n else:\n save_method(os.path.join(save_directory, pipeline_component_name))\n\n if push_to_hub:\n self._upload_folder(\n save_directory,\n repo_id,\n token=token,\n commit_message=commit_message,\n create_pr=create_pr,\n )\n\n @classmethod\n @validate_hf_hub_args\n def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):\n r\"\"\"\n Instantiate a Flax-based diffusion pipeline from pretrained pipeline weights.\n\n The pipeline is set in evaluation mode (`model.eval()) by default and dropout modules are deactivated.\n\n If you get the error message below, you need to finetune the weights for your downstream task:\n\n ```\n Some weights of FlaxUNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:\n ```\n\n Parameters:\n pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):\n Can be either:\n\n - A string, the *repo id* (for example `runwayml/stable-diffusion-v1-5`) of a pretrained pipeline\n hosted on the Hub.\n - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved\n using [`~FlaxDiffusionPipeline.save_pretrained`].\n dtype (`str` or `jnp.dtype`, *optional*):\n Override the default `jnp.dtype` and load the model under this dtype. If `\"auto\"`, the dtype is\n automatically derived from the model's weights.\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\n cached versions if they exist.\n resume_download:\n Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1\n of Diffusers.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\n output_loading_info(`bool`, *optional*, defaults to `False`):\n Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.\n local_files_only (`bool`, *optional*, defaults to `False`):\n Whether to only load local model weights and configuration files or not. If set to `True`, the model\n won't be downloaded from the Hub.\n token (`str` or *bool*, *optional*):\n The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from\n `diffusers-cli login` (stored in `~/.huggingface`) is used.\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier\n allowed by Git.\n mirror (`str`, *optional*):\n Mirror source to resolve accessibility issues if you're downloading a model in China. We do not\n guarantee the timeliness or safety of the source, and you should refer to the mirror site for more\n information.\n kwargs (remaining dictionary of keyword arguments, *optional*):\n Can be used to overwrite load and saveable variables (the pipeline components) of the specific pipeline\n class. The overwritten components are passed directly to the pipelines `__init__` method.\n\n \n\n To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with\n `huggingface-cli login`.\n\n \n\n Examples:\n\n ```py\n >>> from diffusers import FlaxDiffusionPipeline\n\n >>> # Download pipeline from huggingface.co and cache.\n >>> # Requires to be logged in to Hugging Face hub,\n >>> # see more in [the documentation](https://huggingface.co/docs/hub/security-tokens)\n >>> pipeline, params = FlaxDiffusionPipeline.from_pretrained(\n ... \"runwayml/stable-diffusion-v1-5\",\n ... revision=\"bf16\",\n ... dtype=jnp.bfloat16,\n ... )\n\n >>> # Download pipeline, but use a different scheduler\n >>> from diffusers import FlaxDPMSolverMultistepScheduler\n\n >>> model_id = \"runwayml/stable-diffusion-v1-5\"\n >>> dpmpp, dpmpp_state = FlaxDPMSolverMultistepScheduler.from_pretrained(\n ... model_id,\n ... subfolder=\"scheduler\",\n ... )\n\n >>> dpm_pipe, dpm_params = FlaxStableDiffusionPipeline.from_pretrained(\n ... model_id, revision=\"bf16\", dtype=jnp.bfloat16, scheduler=dpmpp\n ... )\n >>> dpm_params[\"scheduler\"] = dpmpp_state\n ```\n \"\"\"\n cache_dir = kwargs.pop(\"cache_dir\", None)\n resume_download = kwargs.pop(\"resume_download\", None)\n proxies = kwargs.pop(\"proxies\", None)\n local_files_only = kwargs.pop(\"local_files_only\", False)\n token = kwargs.pop(\"token\", None)\n revision = kwargs.pop(\"revision\", None)\n from_pt = kwargs.pop(\"from_pt\", False)\n use_memory_efficient_attention = kwargs.pop(\"use_memory_efficient_attention\", False)\n split_head_dim = kwargs.pop(\"split_head_dim\", False)\n dtype = kwargs.pop(\"dtype\", None)\n\n # 1. Download the checkpoints and configs\n # use snapshot download here to get it working from from_pretrained\n if not os.path.isdir(pretrained_model_name_or_path):\n config_dict = cls.load_config(\n pretrained_model_name_or_path,\n cache_dir=cache_dir,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n )\n # make sure we only download sub-folders and `diffusers` filenames\n folder_names = [k for k in config_dict.keys() if not k.startswith(\"_\")]\n allow_patterns = [os.path.join(k, \"*\") for k in folder_names]\n allow_patterns += [FLAX_WEIGHTS_NAME, SCHEDULER_CONFIG_NAME, CONFIG_NAME, cls.config_name]\n\n ignore_patterns = [\"*.bin\", \"*.safetensors\"] if not from_pt else []\n ignore_patterns += [\"*.onnx\", \"*.onnx_data\", \"*.xml\", \"*.pb\"]\n\n if cls != FlaxDiffusionPipeline:\n requested_pipeline_class = cls.__name__\n else:\n requested_pipeline_class = config_dict.get(\"_class_name\", cls.__name__)\n requested_pipeline_class = (\n requested_pipeline_class\n if requested_pipeline_class.startswith(\"Flax\")\n else \"Flax\" + requested_pipeline_class\n )\n\n user_agent = {\"pipeline_class\": requested_pipeline_class}\n user_agent = http_user_agent(user_agent)\n\n # download all allow_patterns\n cached_folder = snapshot_download(\n pretrained_model_name_or_path,\n cache_dir=cache_dir,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n allow_patterns=allow_patterns,\n ignore_patterns=ignore_patterns,\n user_agent=user_agent,\n )\n else:\n cached_folder = pretrained_model_name_or_path\n\n config_dict = cls.load_config(cached_folder)\n\n # 2. Load the pipeline class, if using custom module then load it from the hub\n # if we load from explicit class, let's use it\n if cls != FlaxDiffusionPipeline:\n pipeline_class = cls\n else:\n diffusers_module = importlib.import_module(cls.__module__.split(\".\")[0])\n class_name = (\n config_dict[\"_class_name\"]\n if config_dict[\"_class_name\"].startswith(\"Flax\")\n else \"Flax\" + config_dict[\"_class_name\"]\n )\n pipeline_class = getattr(diffusers_module, class_name)\n\n # some modules can be passed directly to the init\n # in this case they are already instantiated in `kwargs`\n # extract them here\n expected_modules, optional_kwargs = cls._get_signature_keys(pipeline_class)\n passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}\n passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}\n\n init_dict, unused_kwargs, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)\n\n # define init kwargs\n init_kwargs = {k: init_dict.pop(k) for k in optional_kwargs if k in init_dict}\n init_kwargs = {**init_kwargs, **passed_pipe_kwargs}\n\n # remove `null` components\n def load_module(name, value):\n if value[0] is None:\n return False\n if name in passed_class_obj and passed_class_obj[name] is None:\n return False\n return True\n\n init_dict = {k: v for k, v in init_dict.items() if load_module(k, v)}\n\n # Throw nice warnings / errors for fast accelerate loading\n if len(unused_kwargs) > 0:\n logger.warning(\n f\"Keyword arguments {unused_kwargs} are not expected by {pipeline_class.__name__} and will be ignored.\"\n )\n\n # inference_params\n params = {}\n\n # import it here to avoid circular import\n from diffusers import pipelines\n\n # 3. Load each module in the pipeline\n for name, (library_name, class_name) in init_dict.items():\n if class_name is None:\n # edge case for when the pipeline was saved with safety_checker=None\n init_kwargs[name] = None\n continue\n\n is_pipeline_module = hasattr(pipelines, library_name)\n loaded_sub_model = None\n sub_model_should_be_defined = True\n\n # if the model is in a pipeline module, then we load it from the pipeline\n if name in passed_class_obj:\n # 1. check that passed_class_obj has correct parent class\n if not is_pipeline_module:\n library = importlib.import_module(library_name)\n class_obj = getattr(library, class_name)\n importable_classes = LOADABLE_CLASSES[library_name]\n class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()}\n\n expected_class_obj = None\n for class_name, class_candidate in class_candidates.items():\n if class_candidate is not None and issubclass(class_obj, class_candidate):\n expected_class_obj = class_candidate\n\n if not issubclass(passed_class_obj[name].__class__, expected_class_obj):\n raise ValueError(\n f\"{passed_class_obj[name]} is of type: {type(passed_class_obj[name])}, but should be\"\n f\" {expected_class_obj}\"\n )\n elif passed_class_obj[name] is None:\n logger.warning(\n f\"You have passed `None` for {name} to disable its functionality in {pipeline_class}. Note\"\n f\" that this might lead to problems when using {pipeline_class} and is not recommended.\"\n )\n sub_model_should_be_defined = False\n else:\n logger.warning(\n f\"You have passed a non-standard module {passed_class_obj[name]}. We cannot verify whether it\"\n \" has the correct type\"\n )\n\n # set passed class object\n loaded_sub_model = passed_class_obj[name]\n elif is_pipeline_module:\n pipeline_module = getattr(pipelines, library_name)\n class_obj = import_flax_or_no_model(pipeline_module, class_name)\n\n importable_classes = ALL_IMPORTABLE_CLASSES\n class_candidates = {c: class_obj for c in importable_classes.keys()}\n else:\n # else we just import it from the library.\n library = importlib.import_module(library_name)\n class_obj = import_flax_or_no_model(library, class_name)\n\n importable_classes = LOADABLE_CLASSES[library_name]\n class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()}\n\n if loaded_sub_model is None and sub_model_should_be_defined:\n load_method_name = None\n for class_name, class_candidate in class_candidates.items():\n if class_candidate is not None and issubclass(class_obj, class_candidate):\n load_method_name = importable_classes[class_name][1]\n\n load_method = getattr(class_obj, load_method_name)\n\n # check if the module is in a subdirectory\n if os.path.isdir(os.path.join(cached_folder, name)):\n loadable_folder = os.path.join(cached_folder, name)\n else:\n loaded_sub_model = cached_folder\n\n if issubclass(class_obj, FlaxModelMixin):\n loaded_sub_model, loaded_params = load_method(\n loadable_folder,\n from_pt=from_pt,\n use_memory_efficient_attention=use_memory_efficient_attention,\n split_head_dim=split_head_dim,\n dtype=dtype,\n )\n params[name] = loaded_params\n elif is_transformers_available() and issubclass(class_obj, FlaxPreTrainedModel):\n if from_pt:\n # TODO(Suraj): Fix this in Transformers. We should be able to use `_do_init=False` here\n loaded_sub_model = load_method(loadable_folder, from_pt=from_pt)\n loaded_params = loaded_sub_model.params\n del loaded_sub_model._params\n else:\n loaded_sub_model, loaded_params = load_method(loadable_folder, _do_init=False)\n params[name] = loaded_params\n elif issubclass(class_obj, FlaxSchedulerMixin):\n loaded_sub_model, scheduler_state = load_method(loadable_folder)\n params[name] = scheduler_state\n else:\n loaded_sub_model = load_method(loadable_folder)\n\n init_kwargs[name] = loaded_sub_model # UNet(...), # DiffusionSchedule(...)\n\n # 4. Potentially add passed objects if expected\n missing_modules = set(expected_modules) - set(init_kwargs.keys())\n passed_modules = list(passed_class_obj.keys())\n\n if len(missing_modules) > 0 and missing_modules <= set(passed_modules):\n for module in missing_modules:\n init_kwargs[module] = passed_class_obj.get(module, None)\n elif len(missing_modules) > 0:\n passed_modules = set(list(init_kwargs.keys()) + list(passed_class_obj.keys())) - optional_kwargs\n raise ValueError(\n f\"Pipeline {pipeline_class} expected {expected_modules}, but only {passed_modules} were passed.\"\n )\n\n model = pipeline_class(**init_kwargs, dtype=dtype)\n return model, params\n\n @classmethod\n def _get_signature_keys(cls, obj):\n parameters = inspect.signature(obj.__init__).parameters\n required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty}\n optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty})\n expected_modules = set(required_parameters.keys()) - {\"self\"}\n\n return expected_modules, optional_parameters\n\n @property\n def components(self) -> Dict[str, Any]:\n r\"\"\"\n\n The `self.components` property can be useful to run different pipelines with the same weights and\n configurations to not have to re-allocate memory.\n\n Examples:\n\n ```py\n >>> from diffusers import (\n ... FlaxStableDiffusionPipeline,\n ... FlaxStableDiffusionImg2ImgPipeline,\n ... )\n\n >>> text2img = FlaxStableDiffusionPipeline.from_pretrained(\n ... \"runwayml/stable-diffusion-v1-5\", revision=\"bf16\", dtype=jnp.bfloat16\n ... )\n >>> img2img = FlaxStableDiffusionImg2ImgPipeline(**text2img.components)\n ```\n\n Returns:\n A dictionary containing all the modules needed to initialize the pipeline.\n \"\"\"\n expected_modules, optional_parameters = self._get_signature_keys(self)\n components = {\n k: getattr(self, k) for k in self.config.keys() if not k.startswith(\"_\") and k not in optional_parameters\n }\n\n if set(components.keys()) != expected_modules:\n raise ValueError(\n f\"{self} has been incorrectly initialized or {self.__class__} is incorrectly implemented. Expected\"\n f\" {expected_modules} to be defined, but {components} are defined.\"\n )\n\n return components\n\n @staticmethod\n def numpy_to_pil(images):\n \"\"\"\n Convert a NumPy image or a batch of images to a PIL image.\n \"\"\"\n if images.ndim == 3:\n images = images[None, ...]\n images = (images * 255).round().astype(\"uint8\")\n if images.shape[-1] == 1:\n # special case for grayscale (single channel) images\n pil_images = [Image.fromarray(image.squeeze(), mode=\"L\") for image in images]\n else:\n pil_images = [Image.fromarray(image) for image in images]\n\n return pil_images\n\n # TODO: make it compatible with jax.lax\n def progress_bar(self, iterable):\n if not hasattr(self, \"_progress_bar_config\"):\n self._progress_bar_config = {}\n elif not isinstance(self._progress_bar_config, dict):\n raise ValueError(\n f\"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}.\"\n )\n\n return tqdm(iterable, **self._progress_bar_config)\n\n def set_progress_bar_config(self, **kwargs):\n self._progress_bar_config = kwargs\n" }, { "path": "diffusers/src/diffusers/pipelines/pipeline_loading_utils.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport importlib\nimport os\nimport re\nimport warnings\nfrom pathlib import Path\nfrom typing import Any, Dict, List, Optional, Union\n\nimport torch\nfrom huggingface_hub import model_info\nfrom huggingface_hub.utils import validate_hf_hub_args\nfrom packaging import version\n\nfrom .. import __version__\nfrom ..utils import (\n FLAX_WEIGHTS_NAME,\n ONNX_EXTERNAL_WEIGHTS_NAME,\n ONNX_WEIGHTS_NAME,\n SAFETENSORS_WEIGHTS_NAME,\n WEIGHTS_NAME,\n get_class_from_dynamic_module,\n is_accelerate_available,\n is_peft_available,\n is_transformers_available,\n logging,\n)\nfrom ..utils.torch_utils import is_compiled_module\n\n\nif is_transformers_available():\n import transformers\n from transformers import PreTrainedModel\n from transformers.utils import FLAX_WEIGHTS_NAME as TRANSFORMERS_FLAX_WEIGHTS_NAME\n from transformers.utils import SAFE_WEIGHTS_NAME as TRANSFORMERS_SAFE_WEIGHTS_NAME\n from transformers.utils import WEIGHTS_NAME as TRANSFORMERS_WEIGHTS_NAME\n\nif is_accelerate_available():\n import accelerate\n from accelerate import dispatch_model\n from accelerate.hooks import remove_hook_from_module\n from accelerate.utils import compute_module_sizes, get_max_memory\n\n\nINDEX_FILE = \"diffusion_pytorch_model.bin\"\nCUSTOM_PIPELINE_FILE_NAME = \"pipeline.py\"\nDUMMY_MODULES_FOLDER = \"diffusers.utils\"\nTRANSFORMERS_DUMMY_MODULES_FOLDER = \"transformers.utils\"\nCONNECTED_PIPES_KEYS = [\"prior\"]\n\nlogger = logging.get_logger(__name__)\n\nLOADABLE_CLASSES = {\n \"diffusers\": {\n \"ModelMixin\": [\"save_pretrained\", \"from_pretrained\"],\n \"SchedulerMixin\": [\"save_pretrained\", \"from_pretrained\"],\n \"DiffusionPipeline\": [\"save_pretrained\", \"from_pretrained\"],\n \"OnnxRuntimeModel\": [\"save_pretrained\", \"from_pretrained\"],\n },\n \"transformers\": {\n \"PreTrainedTokenizer\": [\"save_pretrained\", \"from_pretrained\"],\n \"PreTrainedTokenizerFast\": [\"save_pretrained\", \"from_pretrained\"],\n \"PreTrainedModel\": [\"save_pretrained\", \"from_pretrained\"],\n \"FeatureExtractionMixin\": [\"save_pretrained\", \"from_pretrained\"],\n \"ProcessorMixin\": [\"save_pretrained\", \"from_pretrained\"],\n \"ImageProcessingMixin\": [\"save_pretrained\", \"from_pretrained\"],\n },\n \"onnxruntime.training\": {\n \"ORTModule\": [\"save_pretrained\", \"from_pretrained\"],\n },\n}\n\nALL_IMPORTABLE_CLASSES = {}\nfor library in LOADABLE_CLASSES:\n ALL_IMPORTABLE_CLASSES.update(LOADABLE_CLASSES[library])\n\n\ndef is_safetensors_compatible(filenames, variant=None, passed_components=None) -> bool:\n \"\"\"\n Checking for safetensors compatibility:\n - By default, all models are saved with the default pytorch serialization, so we use the list of default pytorch\n files to know which safetensors files are needed.\n - The model is safetensors compatible only if there is a matching safetensors file for every default pytorch file.\n\n Converting default pytorch serialized filenames to safetensors serialized filenames:\n - For models from the diffusers library, just replace the \".bin\" extension with \".safetensors\"\n - For models from the transformers library, the filename changes from \"pytorch_model\" to \"model\", and the \".bin\"\n extension is replaced with \".safetensors\"\n \"\"\"\n pt_filenames = []\n\n sf_filenames = set()\n\n passed_components = passed_components or []\n\n for filename in filenames:\n _, extension = os.path.splitext(filename)\n\n if len(filename.split(\"/\")) == 2 and filename.split(\"/\")[0] in passed_components:\n continue\n\n if extension == \".bin\":\n pt_filenames.append(os.path.normpath(filename))\n elif extension == \".safetensors\":\n sf_filenames.add(os.path.normpath(filename))\n\n for filename in pt_filenames:\n # filename = 'foo/bar/baz.bam' -> path = 'foo/bar', filename = 'baz', extension = '.bam'\n path, filename = os.path.split(filename)\n filename, extension = os.path.splitext(filename)\n\n if filename.startswith(\"pytorch_model\"):\n filename = filename.replace(\"pytorch_model\", \"model\")\n else:\n filename = filename\n\n expected_sf_filename = os.path.normpath(os.path.join(path, filename))\n expected_sf_filename = f\"{expected_sf_filename}.safetensors\"\n if expected_sf_filename not in sf_filenames:\n logger.warning(f\"{expected_sf_filename} not found\")\n return False\n\n return True\n\n\ndef variant_compatible_siblings(filenames, variant=None) -> Union[List[os.PathLike], str]:\n weight_names = [\n WEIGHTS_NAME,\n SAFETENSORS_WEIGHTS_NAME,\n FLAX_WEIGHTS_NAME,\n ONNX_WEIGHTS_NAME,\n ONNX_EXTERNAL_WEIGHTS_NAME,\n ]\n\n if is_transformers_available():\n weight_names += [TRANSFORMERS_WEIGHTS_NAME, TRANSFORMERS_SAFE_WEIGHTS_NAME, TRANSFORMERS_FLAX_WEIGHTS_NAME]\n\n # model_pytorch, diffusion_model_pytorch, ...\n weight_prefixes = [w.split(\".\")[0] for w in weight_names]\n # .bin, .safetensors, ...\n weight_suffixs = [w.split(\".\")[-1] for w in weight_names]\n # -00001-of-00002\n transformers_index_format = r\"\\d{5}-of-\\d{5}\"\n\n if variant is not None:\n # `diffusion_pytorch_model.fp16.bin` as well as `model.fp16-00001-of-00002.safetensors`\n variant_file_re = re.compile(\n rf\"({'|'.join(weight_prefixes)})\\.({variant}|{variant}-{transformers_index_format})\\.({'|'.join(weight_suffixs)})$\"\n )\n # `text_encoder/pytorch_model.bin.index.fp16.json`\n variant_index_re = re.compile(\n rf\"({'|'.join(weight_prefixes)})\\.({'|'.join(weight_suffixs)})\\.index\\.{variant}\\.json$\"\n )\n\n # `diffusion_pytorch_model.bin` as well as `model-00001-of-00002.safetensors`\n non_variant_file_re = re.compile(\n rf\"({'|'.join(weight_prefixes)})(-{transformers_index_format})?\\.({'|'.join(weight_suffixs)})$\"\n )\n # `text_encoder/pytorch_model.bin.index.json`\n non_variant_index_re = re.compile(rf\"({'|'.join(weight_prefixes)})\\.({'|'.join(weight_suffixs)})\\.index\\.json\")\n\n if variant is not None:\n variant_weights = {f for f in filenames if variant_file_re.match(f.split(\"/\")[-1]) is not None}\n variant_indexes = {f for f in filenames if variant_index_re.match(f.split(\"/\")[-1]) is not None}\n variant_filenames = variant_weights | variant_indexes\n else:\n variant_filenames = set()\n\n non_variant_weights = {f for f in filenames if non_variant_file_re.match(f.split(\"/\")[-1]) is not None}\n non_variant_indexes = {f for f in filenames if non_variant_index_re.match(f.split(\"/\")[-1]) is not None}\n non_variant_filenames = non_variant_weights | non_variant_indexes\n\n # all variant filenames will be used by default\n usable_filenames = set(variant_filenames)\n\n def convert_to_variant(filename):\n if \"index\" in filename:\n variant_filename = filename.replace(\"index\", f\"index.{variant}\")\n elif re.compile(f\"^(.*?){transformers_index_format}\").match(filename) is not None:\n variant_filename = f\"{filename.split('-')[0]}.{variant}-{'-'.join(filename.split('-')[1:])}\"\n else:\n variant_filename = f\"{filename.split('.')[0]}.{variant}.{filename.split('.')[1]}\"\n return variant_filename\n\n for f in non_variant_filenames:\n variant_filename = convert_to_variant(f)\n if variant_filename not in usable_filenames:\n usable_filenames.add(f)\n\n return usable_filenames, variant_filenames\n\n\n@validate_hf_hub_args\ndef warn_deprecated_model_variant(pretrained_model_name_or_path, token, variant, revision, model_filenames):\n info = model_info(\n pretrained_model_name_or_path,\n token=token,\n revision=None,\n )\n filenames = {sibling.rfilename for sibling in info.siblings}\n comp_model_filenames, _ = variant_compatible_siblings(filenames, variant=revision)\n comp_model_filenames = [\".\".join(f.split(\".\")[:1] + f.split(\".\")[2:]) for f in comp_model_filenames]\n\n if set(model_filenames).issubset(set(comp_model_filenames)):\n warnings.warn(\n f\"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` even though you can load it via `variant=`{revision}`. Loading model variants via `revision='{revision}'` is deprecated and will be removed in diffusers v1. Please use `variant='{revision}'` instead.\",\n FutureWarning,\n )\n else:\n warnings.warn(\n f\"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have the required variant filenames in the 'main' branch. \\n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {revision} files' so that the correct variant file can be added.\",\n FutureWarning,\n )\n\n\ndef _unwrap_model(model):\n \"\"\"Unwraps a model.\"\"\"\n if is_compiled_module(model):\n model = model._orig_mod\n\n if is_peft_available():\n from peft import PeftModel\n\n if isinstance(model, PeftModel):\n model = model.base_model.model\n\n return model\n\n\ndef maybe_raise_or_warn(\n library_name, library, class_name, importable_classes, passed_class_obj, name, is_pipeline_module\n):\n \"\"\"Simple helper method to raise or warn in case incorrect module has been passed\"\"\"\n if not is_pipeline_module:\n library = importlib.import_module(library_name)\n class_obj = getattr(library, class_name)\n class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()}\n\n expected_class_obj = None\n for class_name, class_candidate in class_candidates.items():\n if class_candidate is not None and issubclass(class_obj, class_candidate):\n expected_class_obj = class_candidate\n\n # Dynamo wraps the original model in a private class.\n # I didn't find a public API to get the original class.\n sub_model = passed_class_obj[name]\n unwrapped_sub_model = _unwrap_model(sub_model)\n model_cls = unwrapped_sub_model.__class__\n\n if not issubclass(model_cls, expected_class_obj):\n raise ValueError(\n f\"{passed_class_obj[name]} is of type: {model_cls}, but should be\" f\" {expected_class_obj}\"\n )\n else:\n logger.warning(\n f\"You have passed a non-standard module {passed_class_obj[name]}. We cannot verify whether it\"\n \" has the correct type\"\n )\n\n\ndef get_class_obj_and_candidates(\n library_name, class_name, importable_classes, pipelines, is_pipeline_module, component_name=None, cache_dir=None\n):\n \"\"\"Simple helper method to retrieve class object of module as well as potential parent class objects\"\"\"\n component_folder = os.path.join(cache_dir, component_name)\n\n if is_pipeline_module:\n pipeline_module = getattr(pipelines, library_name)\n\n class_obj = getattr(pipeline_module, class_name)\n class_candidates = {c: class_obj for c in importable_classes.keys()}\n elif os.path.isfile(os.path.join(component_folder, library_name + \".py\")):\n # load custom component\n class_obj = get_class_from_dynamic_module(\n component_folder, module_file=library_name + \".py\", class_name=class_name\n )\n class_candidates = {c: class_obj for c in importable_classes.keys()}\n else:\n # else we just import it from the library.\n library = importlib.import_module(library_name)\n\n class_obj = getattr(library, class_name)\n class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()}\n\n return class_obj, class_candidates\n\n\ndef _get_custom_pipeline_class(\n custom_pipeline,\n repo_id=None,\n hub_revision=None,\n class_name=None,\n cache_dir=None,\n revision=None,\n):\n if custom_pipeline.endswith(\".py\"):\n path = Path(custom_pipeline)\n # decompose into folder & file\n file_name = path.name\n custom_pipeline = path.parent.absolute()\n elif repo_id is not None:\n file_name = f\"{custom_pipeline}.py\"\n custom_pipeline = repo_id\n else:\n file_name = CUSTOM_PIPELINE_FILE_NAME\n\n if repo_id is not None and hub_revision is not None:\n # if we load the pipeline code from the Hub\n # make sure to overwrite the `revision`\n revision = hub_revision\n\n return get_class_from_dynamic_module(\n custom_pipeline,\n module_file=file_name,\n class_name=class_name,\n cache_dir=cache_dir,\n revision=revision,\n )\n\n\ndef _get_pipeline_class(\n class_obj,\n config=None,\n load_connected_pipeline=False,\n custom_pipeline=None,\n repo_id=None,\n hub_revision=None,\n class_name=None,\n cache_dir=None,\n revision=None,\n):\n if custom_pipeline is not None:\n return _get_custom_pipeline_class(\n custom_pipeline,\n repo_id=repo_id,\n hub_revision=hub_revision,\n class_name=class_name,\n cache_dir=cache_dir,\n revision=revision,\n )\n\n if class_obj.__name__ != \"DiffusionPipeline\":\n return class_obj\n\n diffusers_module = importlib.import_module(class_obj.__module__.split(\".\")[0])\n class_name = class_name or config[\"_class_name\"]\n if not class_name:\n raise ValueError(\n \"The class name could not be found in the configuration file. Please make sure to pass the correct `class_name`.\"\n )\n\n class_name = class_name[4:] if class_name.startswith(\"Flax\") else class_name\n\n pipeline_cls = getattr(diffusers_module, class_name)\n\n if load_connected_pipeline:\n from .auto_pipeline import _get_connected_pipeline\n\n connected_pipeline_cls = _get_connected_pipeline(pipeline_cls)\n if connected_pipeline_cls is not None:\n logger.info(\n f\"Loading connected pipeline {connected_pipeline_cls.__name__} instead of {pipeline_cls.__name__} as specified via `load_connected_pipeline=True`\"\n )\n else:\n logger.info(f\"{pipeline_cls.__name__} has no connected pipeline class. Loading {pipeline_cls.__name__}.\")\n\n pipeline_cls = connected_pipeline_cls or pipeline_cls\n\n return pipeline_cls\n\n\ndef _load_empty_model(\n library_name: str,\n class_name: str,\n importable_classes: List[Any],\n pipelines: Any,\n is_pipeline_module: bool,\n name: str,\n torch_dtype: Union[str, torch.dtype],\n cached_folder: Union[str, os.PathLike],\n **kwargs,\n):\n # retrieve class objects.\n class_obj, _ = get_class_obj_and_candidates(\n library_name,\n class_name,\n importable_classes,\n pipelines,\n is_pipeline_module,\n component_name=name,\n cache_dir=cached_folder,\n )\n\n if is_transformers_available():\n transformers_version = version.parse(version.parse(transformers.__version__).base_version)\n else:\n transformers_version = \"N/A\"\n\n # Determine library.\n is_transformers_model = (\n is_transformers_available()\n and issubclass(class_obj, PreTrainedModel)\n and transformers_version >= version.parse(\"4.20.0\")\n )\n diffusers_module = importlib.import_module(__name__.split(\".\")[0])\n is_diffusers_model = issubclass(class_obj, diffusers_module.ModelMixin)\n\n model = None\n config_path = cached_folder\n user_agent = {\n \"diffusers\": __version__,\n \"file_type\": \"model\",\n \"framework\": \"pytorch\",\n }\n\n if is_diffusers_model:\n # Load config and then the model on meta.\n config, unused_kwargs, commit_hash = class_obj.load_config(\n os.path.join(config_path, name),\n cache_dir=cached_folder,\n return_unused_kwargs=True,\n return_commit_hash=True,\n force_download=kwargs.pop(\"force_download\", False),\n resume_download=kwargs.pop(\"resume_download\", None),\n proxies=kwargs.pop(\"proxies\", None),\n local_files_only=kwargs.pop(\"local_files_only\", False),\n token=kwargs.pop(\"token\", None),\n revision=kwargs.pop(\"revision\", None),\n subfolder=kwargs.pop(\"subfolder\", None),\n user_agent=user_agent,\n )\n with accelerate.init_empty_weights():\n model = class_obj.from_config(config, **unused_kwargs)\n elif is_transformers_model:\n config_class = getattr(class_obj, \"config_class\", None)\n if config_class is None:\n raise ValueError(\"`config_class` cannot be None. Please double-check the model.\")\n\n config = config_class.from_pretrained(\n cached_folder,\n subfolder=name,\n force_download=kwargs.pop(\"force_download\", False),\n resume_download=kwargs.pop(\"resume_download\", None),\n proxies=kwargs.pop(\"proxies\", None),\n local_files_only=kwargs.pop(\"local_files_only\", False),\n token=kwargs.pop(\"token\", None),\n revision=kwargs.pop(\"revision\", None),\n user_agent=user_agent,\n )\n with accelerate.init_empty_weights():\n model = class_obj(config)\n\n if model is not None:\n model = model.to(dtype=torch_dtype)\n return model\n\n\ndef _assign_components_to_devices(\n module_sizes: Dict[str, float], device_memory: Dict[str, float], device_mapping_strategy: str = \"balanced\"\n):\n device_ids = list(device_memory.keys())\n device_cycle = device_ids + device_ids[::-1]\n device_memory = device_memory.copy()\n\n device_id_component_mapping = {}\n current_device_index = 0\n for component in module_sizes:\n device_id = device_cycle[current_device_index % len(device_cycle)]\n component_memory = module_sizes[component]\n curr_device_memory = device_memory[device_id]\n\n # If the GPU doesn't fit the current component offload to the CPU.\n if component_memory > curr_device_memory:\n device_id_component_mapping[\"cpu\"] = [component]\n else:\n if device_id not in device_id_component_mapping:\n device_id_component_mapping[device_id] = [component]\n else:\n device_id_component_mapping[device_id].append(component)\n\n # Update the device memory.\n device_memory[device_id] -= component_memory\n current_device_index += 1\n\n return device_id_component_mapping\n\n\ndef _get_final_device_map(device_map, pipeline_class, passed_class_obj, init_dict, library, max_memory, **kwargs):\n # To avoid circular import problem.\n from diffusers import pipelines\n\n torch_dtype = kwargs.get(\"torch_dtype\", torch.float32)\n\n # Load each module in the pipeline on a meta device so that we can derive the device map.\n init_empty_modules = {}\n for name, (library_name, class_name) in init_dict.items():\n if class_name.startswith(\"Flax\"):\n raise ValueError(\"Flax pipelines are not supported with `device_map`.\")\n\n # Define all importable classes\n is_pipeline_module = hasattr(pipelines, library_name)\n importable_classes = ALL_IMPORTABLE_CLASSES\n loaded_sub_model = None\n\n # Use passed sub model or load class_name from library_name\n if name in passed_class_obj:\n # if the model is in a pipeline module, then we load it from the pipeline\n # check that passed_class_obj has correct parent class\n maybe_raise_or_warn(\n library_name,\n library,\n class_name,\n importable_classes,\n passed_class_obj,\n name,\n is_pipeline_module,\n )\n with accelerate.init_empty_weights():\n loaded_sub_model = passed_class_obj[name]\n\n else:\n loaded_sub_model = _load_empty_model(\n library_name=library_name,\n class_name=class_name,\n importable_classes=importable_classes,\n pipelines=pipelines,\n is_pipeline_module=is_pipeline_module,\n pipeline_class=pipeline_class,\n name=name,\n torch_dtype=torch_dtype,\n cached_folder=kwargs.get(\"cached_folder\", None),\n force_download=kwargs.get(\"force_download\", None),\n resume_download=kwargs.get(\"resume_download\", None),\n proxies=kwargs.get(\"proxies\", None),\n local_files_only=kwargs.get(\"local_files_only\", None),\n token=kwargs.get(\"token\", None),\n revision=kwargs.get(\"revision\", None),\n )\n\n if loaded_sub_model is not None:\n init_empty_modules[name] = loaded_sub_model\n\n # determine device map\n # Obtain a sorted dictionary for mapping the model-level components\n # to their sizes.\n module_sizes = {\n module_name: compute_module_sizes(module, dtype=torch_dtype)[\"\"]\n for module_name, module in init_empty_modules.items()\n if isinstance(module, torch.nn.Module)\n }\n module_sizes = dict(sorted(module_sizes.items(), key=lambda item: item[1], reverse=True))\n\n # Obtain maximum memory available per device (GPUs only).\n max_memory = get_max_memory(max_memory)\n max_memory = dict(sorted(max_memory.items(), key=lambda item: item[1], reverse=True))\n max_memory = {k: v for k, v in max_memory.items() if k != \"cpu\"}\n\n # Obtain a dictionary mapping the model-level components to the available\n # devices based on the maximum memory and the model sizes.\n final_device_map = None\n if len(max_memory) > 0:\n device_id_component_mapping = _assign_components_to_devices(\n module_sizes, max_memory, device_mapping_strategy=device_map\n )\n\n # Obtain the final device map, e.g., `{\"unet\": 0, \"text_encoder\": 1, \"vae\": 1, ...}`\n final_device_map = {}\n for device_id, components in device_id_component_mapping.items():\n for component in components:\n final_device_map[component] = device_id\n\n return final_device_map\n\n\ndef load_sub_model(\n library_name: str,\n class_name: str,\n importable_classes: List[Any],\n pipelines: Any,\n is_pipeline_module: bool,\n pipeline_class: Any,\n torch_dtype: torch.dtype,\n provider: Any,\n sess_options: Any,\n device_map: Optional[Union[Dict[str, torch.device], str]],\n max_memory: Optional[Dict[Union[int, str], Union[int, str]]],\n offload_folder: Optional[Union[str, os.PathLike]],\n offload_state_dict: bool,\n model_variants: Dict[str, str],\n name: str,\n from_flax: bool,\n variant: str,\n low_cpu_mem_usage: bool,\n cached_folder: Union[str, os.PathLike],\n):\n \"\"\"Helper method to load the module `name` from `library_name` and `class_name`\"\"\"\n\n # retrieve class candidates\n\n class_obj, class_candidates = get_class_obj_and_candidates(\n library_name,\n class_name,\n importable_classes,\n pipelines,\n is_pipeline_module,\n component_name=name,\n cache_dir=cached_folder,\n )\n\n load_method_name = None\n # retrieve load method name\n for class_name, class_candidate in class_candidates.items():\n if class_candidate is not None and issubclass(class_obj, class_candidate):\n load_method_name = importable_classes[class_name][1]\n\n # if load method name is None, then we have a dummy module -> raise Error\n if load_method_name is None:\n none_module = class_obj.__module__\n is_dummy_path = none_module.startswith(DUMMY_MODULES_FOLDER) or none_module.startswith(\n TRANSFORMERS_DUMMY_MODULES_FOLDER\n )\n if is_dummy_path and \"dummy\" in none_module:\n # call class_obj for nice error message of missing requirements\n class_obj()\n\n raise ValueError(\n f\"The component {class_obj} of {pipeline_class} cannot be loaded as it does not seem to have\"\n f\" any of the loading methods defined in {ALL_IMPORTABLE_CLASSES}.\"\n )\n\n load_method = getattr(class_obj, load_method_name)\n\n # add kwargs to loading method\n diffusers_module = importlib.import_module(__name__.split(\".\")[0])\n loading_kwargs = {}\n if issubclass(class_obj, torch.nn.Module):\n loading_kwargs[\"torch_dtype\"] = torch_dtype\n if issubclass(class_obj, diffusers_module.OnnxRuntimeModel):\n loading_kwargs[\"provider\"] = provider\n loading_kwargs[\"sess_options\"] = sess_options\n\n is_diffusers_model = issubclass(class_obj, diffusers_module.ModelMixin)\n\n if is_transformers_available():\n transformers_version = version.parse(version.parse(transformers.__version__).base_version)\n else:\n transformers_version = \"N/A\"\n\n is_transformers_model = (\n is_transformers_available()\n and issubclass(class_obj, PreTrainedModel)\n and transformers_version >= version.parse(\"4.20.0\")\n )\n\n # When loading a transformers model, if the device_map is None, the weights will be initialized as opposed to diffusers.\n # To make default loading faster we set the `low_cpu_mem_usage=low_cpu_mem_usage` flag which is `True` by default.\n # This makes sure that the weights won't be initialized which significantly speeds up loading.\n if is_diffusers_model or is_transformers_model:\n loading_kwargs[\"device_map\"] = device_map\n loading_kwargs[\"max_memory\"] = max_memory\n loading_kwargs[\"offload_folder\"] = offload_folder\n loading_kwargs[\"offload_state_dict\"] = offload_state_dict\n loading_kwargs[\"variant\"] = model_variants.pop(name, None)\n\n if from_flax:\n loading_kwargs[\"from_flax\"] = True\n\n # the following can be deleted once the minimum required `transformers` version\n # is higher than 4.27\n if (\n is_transformers_model\n and loading_kwargs[\"variant\"] is not None\n and transformers_version < version.parse(\"4.27.0\")\n ):\n raise ImportError(\n f\"When passing `variant='{variant}'`, please make sure to upgrade your `transformers` version to at least 4.27.0.dev0\"\n )\n elif is_transformers_model and loading_kwargs[\"variant\"] is None:\n loading_kwargs.pop(\"variant\")\n\n # if `from_flax` and model is transformer model, can currently not load with `low_cpu_mem_usage`\n if not (from_flax and is_transformers_model):\n loading_kwargs[\"low_cpu_mem_usage\"] = low_cpu_mem_usage\n else:\n loading_kwargs[\"low_cpu_mem_usage\"] = False\n\n # check if the module is in a subdirectory\n if os.path.isdir(os.path.join(cached_folder, name)):\n loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs)\n else:\n # else load from the root directory\n loaded_sub_model = load_method(cached_folder, **loading_kwargs)\n\n if isinstance(loaded_sub_model, torch.nn.Module) and isinstance(device_map, dict):\n # remove hooks\n remove_hook_from_module(loaded_sub_model, recurse=True)\n needs_offloading_to_cpu = device_map[\"\"] == \"cpu\"\n\n if needs_offloading_to_cpu:\n dispatch_model(\n loaded_sub_model,\n state_dict=loaded_sub_model.state_dict(),\n device_map=device_map,\n force_hooks=True,\n main_device=0,\n )\n else:\n dispatch_model(loaded_sub_model, device_map=device_map, force_hooks=True)\n\n return loaded_sub_model\n\n\ndef _fetch_class_library_tuple(module):\n # import it here to avoid circular import\n diffusers_module = importlib.import_module(__name__.split(\".\")[0])\n pipelines = getattr(diffusers_module, \"pipelines\")\n\n # register the config from the original module, not the dynamo compiled one\n not_compiled_module = _unwrap_model(module)\n library = not_compiled_module.__module__.split(\".\")[0]\n\n # check if the module is a pipeline module\n module_path_items = not_compiled_module.__module__.split(\".\")\n pipeline_dir = module_path_items[-2] if len(module_path_items) > 2 else None\n\n path = not_compiled_module.__module__.split(\".\")\n is_pipeline_module = pipeline_dir in path and hasattr(pipelines, pipeline_dir)\n\n # if library is not in LOADABLE_CLASSES, then it is a custom module.\n # Or if it's a pipeline module, then the module is inside the pipeline\n # folder so we set the library to module name.\n if is_pipeline_module:\n library = pipeline_dir\n elif library not in LOADABLE_CLASSES:\n library = not_compiled_module.__module__\n\n # retrieve class_name\n class_name = not_compiled_module.__class__.__name__\n\n return (library, class_name)\n" }, { "path": "diffusers/src/diffusers/pipelines/pipeline_utils.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport fnmatch\nimport importlib\nimport inspect\nimport os\nimport re\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Any, Callable, Dict, List, Optional, Union, get_args, get_origin\n\nimport numpy as np\nimport PIL.Image\nimport requests\nimport torch\nfrom huggingface_hub import (\n ModelCard,\n create_repo,\n hf_hub_download,\n model_info,\n snapshot_download,\n)\nfrom huggingface_hub.utils import OfflineModeIsEnabled, validate_hf_hub_args\nfrom packaging import version\nfrom requests.exceptions import HTTPError\nfrom tqdm.auto import tqdm\n\nfrom .. import __version__\nfrom ..configuration_utils import ConfigMixin\nfrom ..models import AutoencoderKL\nfrom ..models.attention_processor import FusedAttnProcessor2_0\nfrom ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, ModelMixin\nfrom ..schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME\nfrom ..utils import (\n CONFIG_NAME,\n DEPRECATED_REVISION_ARGS,\n BaseOutput,\n PushToHubMixin,\n deprecate,\n is_accelerate_available,\n is_accelerate_version,\n is_torch_npu_available,\n is_torch_version,\n logging,\n numpy_to_pil,\n)\nfrom ..utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom ..utils.torch_utils import is_compiled_module\n\n\nif is_torch_npu_available():\n import torch_npu # noqa: F401\n\n\nfrom .pipeline_loading_utils import (\n ALL_IMPORTABLE_CLASSES,\n CONNECTED_PIPES_KEYS,\n CUSTOM_PIPELINE_FILE_NAME,\n LOADABLE_CLASSES,\n _fetch_class_library_tuple,\n _get_custom_pipeline_class,\n _get_final_device_map,\n _get_pipeline_class,\n _unwrap_model,\n is_safetensors_compatible,\n load_sub_model,\n maybe_raise_or_warn,\n variant_compatible_siblings,\n warn_deprecated_model_variant,\n)\n\n\nif is_accelerate_available():\n import accelerate\n\n\nLIBRARIES = []\nfor library in LOADABLE_CLASSES:\n LIBRARIES.append(library)\n\nSUPPORTED_DEVICE_MAP = [\"balanced\"]\n\nlogger = logging.get_logger(__name__)\n\n\n@dataclass\nclass ImagePipelineOutput(BaseOutput):\n \"\"\"\n Output class for image pipelines.\n\n Args:\n images (`List[PIL.Image.Image]` or `np.ndarray`)\n List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,\n num_channels)`.\n \"\"\"\n\n images: Union[List[PIL.Image.Image], np.ndarray]\n\n\n@dataclass\nclass AudioPipelineOutput(BaseOutput):\n \"\"\"\n Output class for audio pipelines.\n\n Args:\n audios (`np.ndarray`)\n List of denoised audio samples of a NumPy array of shape `(batch_size, num_channels, sample_rate)`.\n \"\"\"\n\n audios: np.ndarray\n\n\nclass DiffusionPipeline(ConfigMixin, PushToHubMixin):\n r\"\"\"\n Base class for all pipelines.\n\n [`DiffusionPipeline`] stores all components (models, schedulers, and processors) for diffusion pipelines and\n provides methods for loading, downloading and saving models. It also includes methods to:\n\n - move all PyTorch modules to the device of your choice\n - enable/disable the progress bar for the denoising iteration\n\n Class attributes:\n\n - **config_name** (`str`) -- The configuration filename that stores the class and module names of all the\n diffusion pipeline's components.\n - **_optional_components** (`List[str]`) -- List of all optional components that don't have to be passed to the\n pipeline to function (should be overridden by subclasses).\n \"\"\"\n\n config_name = \"model_index.json\"\n model_cpu_offload_seq = None\n hf_device_map = None\n _optional_components = []\n _exclude_from_cpu_offload = []\n _load_connected_pipes = False\n _is_onnx = False\n\n def register_modules(self, **kwargs):\n for name, module in kwargs.items():\n # retrieve library\n if module is None or isinstance(module, (tuple, list)) and module[0] is None:\n register_dict = {name: (None, None)}\n else:\n library, class_name = _fetch_class_library_tuple(module)\n register_dict = {name: (library, class_name)}\n\n # save model index config\n self.register_to_config(**register_dict)\n\n # set models\n setattr(self, name, module)\n\n def __setattr__(self, name: str, value: Any):\n if name in self.__dict__ and hasattr(self.config, name):\n # We need to overwrite the config if name exists in config\n if isinstance(getattr(self.config, name), (tuple, list)):\n if value is not None and self.config[name][0] is not None:\n class_library_tuple = _fetch_class_library_tuple(value)\n else:\n class_library_tuple = (None, None)\n\n self.register_to_config(**{name: class_library_tuple})\n else:\n self.register_to_config(**{name: value})\n\n super().__setattr__(name, value)\n\n def save_pretrained(\n self,\n save_directory: Union[str, os.PathLike],\n safe_serialization: bool = True,\n variant: Optional[str] = None,\n push_to_hub: bool = False,\n **kwargs,\n ):\n \"\"\"\n Save all saveable variables of the pipeline to a directory. A pipeline variable can be saved and loaded if its\n class implements both a save and loading method. The pipeline is easily reloaded using the\n [`~DiffusionPipeline.from_pretrained`] class method.\n\n Arguments:\n save_directory (`str` or `os.PathLike`):\n Directory to save a pipeline to. Will be created if it doesn't exist.\n safe_serialization (`bool`, *optional*, defaults to `True`):\n Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.\n variant (`str`, *optional*):\n If specified, weights are saved in the format `pytorch_model..bin`.\n push_to_hub (`bool`, *optional*, defaults to `False`):\n Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the\n repository you want to push to with `repo_id` (will default to the name of `save_directory` in your\n namespace).\n kwargs (`Dict[str, Any]`, *optional*):\n Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.\n \"\"\"\n model_index_dict = dict(self.config)\n model_index_dict.pop(\"_class_name\", None)\n model_index_dict.pop(\"_diffusers_version\", None)\n model_index_dict.pop(\"_module\", None)\n model_index_dict.pop(\"_name_or_path\", None)\n\n if push_to_hub:\n commit_message = kwargs.pop(\"commit_message\", None)\n private = kwargs.pop(\"private\", False)\n create_pr = kwargs.pop(\"create_pr\", False)\n token = kwargs.pop(\"token\", None)\n repo_id = kwargs.pop(\"repo_id\", save_directory.split(os.path.sep)[-1])\n repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id\n\n expected_modules, optional_kwargs = self._get_signature_keys(self)\n\n def is_saveable_module(name, value):\n if name not in expected_modules:\n return False\n if name in self._optional_components and value[0] is None:\n return False\n return True\n\n model_index_dict = {k: v for k, v in model_index_dict.items() if is_saveable_module(k, v)}\n for pipeline_component_name in model_index_dict.keys():\n sub_model = getattr(self, pipeline_component_name)\n model_cls = sub_model.__class__\n\n # Dynamo wraps the original model in a private class.\n # I didn't find a public API to get the original class.\n if is_compiled_module(sub_model):\n sub_model = _unwrap_model(sub_model)\n model_cls = sub_model.__class__\n\n save_method_name = None\n # search for the model's base class in LOADABLE_CLASSES\n for library_name, library_classes in LOADABLE_CLASSES.items():\n if library_name in sys.modules:\n library = importlib.import_module(library_name)\n else:\n logger.info(\n f\"{library_name} is not installed. Cannot save {pipeline_component_name} as {library_classes} from {library_name}\"\n )\n\n for base_class, save_load_methods in library_classes.items():\n class_candidate = getattr(library, base_class, None)\n if class_candidate is not None and issubclass(model_cls, class_candidate):\n # if we found a suitable base class in LOADABLE_CLASSES then grab its save method\n save_method_name = save_load_methods[0]\n break\n if save_method_name is not None:\n break\n\n if save_method_name is None:\n logger.warning(\n f\"self.{pipeline_component_name}={sub_model} of type {type(sub_model)} cannot be saved.\"\n )\n # make sure that unsaveable components are not tried to be loaded afterward\n self.register_to_config(**{pipeline_component_name: (None, None)})\n continue\n\n save_method = getattr(sub_model, save_method_name)\n\n # Call the save method with the argument safe_serialization only if it's supported\n save_method_signature = inspect.signature(save_method)\n save_method_accept_safe = \"safe_serialization\" in save_method_signature.parameters\n save_method_accept_variant = \"variant\" in save_method_signature.parameters\n\n save_kwargs = {}\n if save_method_accept_safe:\n save_kwargs[\"safe_serialization\"] = safe_serialization\n if save_method_accept_variant:\n save_kwargs[\"variant\"] = variant\n\n save_method(os.path.join(save_directory, pipeline_component_name), **save_kwargs)\n\n # finally save the config\n self.save_config(save_directory)\n\n if push_to_hub:\n # Create a new empty model card and eventually tag it\n model_card = load_or_create_model_card(repo_id, token=token, is_pipeline=True)\n model_card = populate_model_card(model_card)\n model_card.save(os.path.join(save_directory, \"README.md\"))\n\n self._upload_folder(\n save_directory,\n repo_id,\n token=token,\n commit_message=commit_message,\n create_pr=create_pr,\n )\n\n def to(self, *args, **kwargs):\n r\"\"\"\n Performs Pipeline dtype and/or device conversion. A torch.dtype and torch.device are inferred from the\n arguments of `self.to(*args, **kwargs).`\n\n \n\n If the pipeline already has the correct torch.dtype and torch.device, then it is returned as is. Otherwise,\n the returned pipeline is a copy of self with the desired torch.dtype and torch.device.\n\n \n\n\n Here are the ways to call `to`:\n\n - `to(dtype, silence_dtype_warnings=False) → DiffusionPipeline` to return a pipeline with the specified\n [`dtype`](https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype)\n - `to(device, silence_dtype_warnings=False) → DiffusionPipeline` to return a pipeline with the specified\n [`device`](https://pytorch.org/docs/stable/tensor_attributes.html#torch.device)\n - `to(device=None, dtype=None, silence_dtype_warnings=False) → DiffusionPipeline` to return a pipeline with the\n specified [`device`](https://pytorch.org/docs/stable/tensor_attributes.html#torch.device) and\n [`dtype`](https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype)\n\n Arguments:\n dtype (`torch.dtype`, *optional*):\n Returns a pipeline with the specified\n [`dtype`](https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype)\n device (`torch.Device`, *optional*):\n Returns a pipeline with the specified\n [`device`](https://pytorch.org/docs/stable/tensor_attributes.html#torch.device)\n silence_dtype_warnings (`str`, *optional*, defaults to `False`):\n Whether to omit warnings if the target `dtype` is not compatible with the target `device`.\n\n Returns:\n [`DiffusionPipeline`]: The pipeline converted to specified `dtype` and/or `dtype`.\n \"\"\"\n dtype = kwargs.pop(\"dtype\", None)\n device = kwargs.pop(\"device\", None)\n silence_dtype_warnings = kwargs.pop(\"silence_dtype_warnings\", False)\n\n dtype_arg = None\n device_arg = None\n if len(args) == 1:\n if isinstance(args[0], torch.dtype):\n dtype_arg = args[0]\n else:\n device_arg = torch.device(args[0]) if args[0] is not None else None\n elif len(args) == 2:\n if isinstance(args[0], torch.dtype):\n raise ValueError(\n \"When passing two arguments, make sure the first corresponds to `device` and the second to `dtype`.\"\n )\n device_arg = torch.device(args[0]) if args[0] is not None else None\n dtype_arg = args[1]\n elif len(args) > 2:\n raise ValueError(\"Please make sure to pass at most two arguments (`device` and `dtype`) `.to(...)`\")\n\n if dtype is not None and dtype_arg is not None:\n raise ValueError(\n \"You have passed `dtype` both as an argument and as a keyword argument. Please only pass one of the two.\"\n )\n\n dtype = dtype or dtype_arg\n\n if device is not None and device_arg is not None:\n raise ValueError(\n \"You have passed `device` both as an argument and as a keyword argument. Please only pass one of the two.\"\n )\n\n device = device or device_arg\n\n # throw warning if pipeline is in \"offloaded\"-mode but user tries to manually set to GPU.\n def module_is_sequentially_offloaded(module):\n if not is_accelerate_available() or is_accelerate_version(\"<\", \"0.14.0\"):\n return False\n\n return hasattr(module, \"_hf_hook\") and (\n isinstance(module._hf_hook, accelerate.hooks.AlignDevicesHook)\n or hasattr(module._hf_hook, \"hooks\")\n and isinstance(module._hf_hook.hooks[0], accelerate.hooks.AlignDevicesHook)\n )\n\n def module_is_offloaded(module):\n if not is_accelerate_available() or is_accelerate_version(\"<\", \"0.17.0.dev0\"):\n return False\n\n return hasattr(module, \"_hf_hook\") and isinstance(module._hf_hook, accelerate.hooks.CpuOffload)\n\n # .to(\"cuda\") would raise an error if the pipeline is sequentially offloaded, so we raise our own to make it clearer\n pipeline_is_sequentially_offloaded = any(\n module_is_sequentially_offloaded(module) for _, module in self.components.items()\n )\n if pipeline_is_sequentially_offloaded and device and torch.device(device).type == \"cuda\":\n raise ValueError(\n \"It seems like you have activated sequential model offloading by calling `enable_sequential_cpu_offload`, but are now attempting to move the pipeline to GPU. This is not compatible with offloading. Please, move your pipeline `.to('cpu')` or consider removing the move altogether if you use sequential offloading.\"\n )\n\n is_pipeline_device_mapped = self.hf_device_map is not None and len(self.hf_device_map) > 1\n if is_pipeline_device_mapped:\n raise ValueError(\n \"It seems like you have activated a device mapping strategy on the pipeline which doesn't allow explicit device placement using `to()`. You can call `reset_device_map()` first and then call `to()`.\"\n )\n\n # Display a warning in this case (the operation succeeds but the benefits are lost)\n pipeline_is_offloaded = any(module_is_offloaded(module) for _, module in self.components.items())\n if pipeline_is_offloaded and device and torch.device(device).type == \"cuda\":\n logger.warning(\n f\"It seems like you have activated model offloading by calling `enable_model_cpu_offload`, but are now manually moving the pipeline to GPU. It is strongly recommended against doing so as memory gains from offloading are likely to be lost. Offloading automatically takes care of moving the individual components {', '.join(self.components.keys())} to GPU when needed. To make sure offloading works as expected, you should consider moving the pipeline back to CPU: `pipeline.to('cpu')` or removing the move altogether if you use offloading.\"\n )\n\n module_names, _ = self._get_signature_keys(self)\n modules = [getattr(self, n, None) for n in module_names]\n modules = [m for m in modules if isinstance(m, torch.nn.Module)]\n\n is_offloaded = pipeline_is_offloaded or pipeline_is_sequentially_offloaded\n for module in modules:\n is_loaded_in_8bit = hasattr(module, \"is_loaded_in_8bit\") and module.is_loaded_in_8bit\n\n if is_loaded_in_8bit and dtype is not None:\n logger.warning(\n f\"The module '{module.__class__.__name__}' has been loaded in 8bit and conversion to {dtype} is not yet supported. Module is still in 8bit precision.\"\n )\n\n if is_loaded_in_8bit and device is not None:\n logger.warning(\n f\"The module '{module.__class__.__name__}' has been loaded in 8bit and moving it to {dtype} via `.to()` is not yet supported. Module is still on {module.device}.\"\n )\n else:\n module.to(device, dtype)\n\n if (\n module.dtype == torch.float16\n and str(device) in [\"cpu\"]\n and not silence_dtype_warnings\n and not is_offloaded\n ):\n logger.warning(\n \"Pipelines loaded with `dtype=torch.float16` cannot run with `cpu` device. It\"\n \" is not recommended to move them to `cpu` as running them will fail. Please make\"\n \" sure to use an accelerator to run the pipeline in inference, due to the lack of\"\n \" support for`float16` operations on this device in PyTorch. Please, remove the\"\n \" `torch_dtype=torch.float16` argument, or use another device for inference.\"\n )\n return self\n\n @property\n def device(self) -> torch.device:\n r\"\"\"\n Returns:\n `torch.device`: The torch device on which the pipeline is located.\n \"\"\"\n module_names, _ = self._get_signature_keys(self)\n modules = [getattr(self, n, None) for n in module_names]\n modules = [m for m in modules if isinstance(m, torch.nn.Module)]\n\n for module in modules:\n return module.device\n\n return torch.device(\"cpu\")\n\n @property\n def dtype(self) -> torch.dtype:\n r\"\"\"\n Returns:\n `torch.dtype`: The torch dtype on which the pipeline is located.\n \"\"\"\n module_names, _ = self._get_signature_keys(self)\n modules = [getattr(self, n, None) for n in module_names]\n modules = [m for m in modules if isinstance(m, torch.nn.Module)]\n\n for module in modules:\n return module.dtype\n\n return torch.float32\n\n @classmethod\n @validate_hf_hub_args\n def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):\n r\"\"\"\n Instantiate a PyTorch diffusion pipeline from pretrained pipeline weights.\n\n The pipeline is set in evaluation mode (`model.eval()`) by default.\n\n If you get the error message below, you need to finetune the weights for your downstream task:\n\n ```\n Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:\n - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated\n You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n ```\n\n Parameters:\n pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):\n Can be either:\n\n - A string, the *repo id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline\n hosted on the Hub.\n - A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights\n saved using\n [`~DiffusionPipeline.save_pretrained`].\n torch_dtype (`str` or `torch.dtype`, *optional*):\n Override the default `torch.dtype` and load the model with another dtype. If \"auto\" is passed, the\n dtype is automatically derived from the model's weights.\n custom_pipeline (`str`, *optional*):\n\n \n\n 🧪 This is an experimental feature and may change in the future.\n\n \n\n Can be either:\n\n - A string, the *repo id* (for example `hf-internal-testing/diffusers-dummy-pipeline`) of a custom\n pipeline hosted on the Hub. The repository must contain a file called pipeline.py that defines\n the custom pipeline.\n - A string, the *file name* of a community pipeline hosted on GitHub under\n [Community](https://github.com/huggingface/diffusers/tree/main/examples/community). Valid file\n names must match the file name and not the pipeline script (`clip_guided_stable_diffusion`\n instead of `clip_guided_stable_diffusion.py`). Community pipelines are always loaded from the\n current main branch of GitHub.\n - A path to a directory (`./my_pipeline_directory/`) containing a custom pipeline. The directory\n must contain a file called `pipeline.py` that defines the custom pipeline.\n\n For more information on how to load and create custom pipelines, please have a look at [Loading and\n Adding Custom\n Pipelines](https://huggingface.co/docs/diffusers/using-diffusers/custom_pipeline_overview)\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\n cached versions if they exist.\n cache_dir (`Union[str, os.PathLike]`, *optional*):\n Path to a directory where a downloaded pretrained model configuration is cached if the standard cache\n is not used.\n resume_download:\n Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1\n of Diffusers.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\n output_loading_info(`bool`, *optional*, defaults to `False`):\n Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.\n local_files_only (`bool`, *optional*, defaults to `False`):\n Whether to only load local model weights and configuration files or not. If set to `True`, the model\n won't be downloaded from the Hub.\n token (`str` or *bool*, *optional*):\n The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from\n `diffusers-cli login` (stored in `~/.huggingface`) is used.\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier\n allowed by Git.\n custom_revision (`str`, *optional*):\n The specific model version to use. It can be a branch name, a tag name, or a commit id similar to\n `revision` when loading a custom pipeline from the Hub. Defaults to the latest stable 🤗 Diffusers\n version.\n mirror (`str`, *optional*):\n Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not\n guarantee the timeliness or safety of the source, and you should refer to the mirror site for more\n information.\n device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):\n A map that specifies where each submodule should go. It doesn’t need to be defined for each\n parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the\n same device.\n\n Set `device_map=\"auto\"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For\n more information about each option see [designing a device\n map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).\n max_memory (`Dict`, *optional*):\n A dictionary device identifier for the maximum memory. Will default to the maximum memory available for\n each GPU and the available CPU RAM if unset.\n offload_folder (`str` or `os.PathLike`, *optional*):\n The path to offload weights if device_map contains the value `\"disk\"`.\n offload_state_dict (`bool`, *optional*):\n If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if\n the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`\n when there is some disk offload.\n low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):\n Speed up model loading only loading the pretrained weights and not initializing the weights. This also\n tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.\n Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this\n argument to `True` will raise an error.\n use_safetensors (`bool`, *optional*, defaults to `None`):\n If set to `None`, the safetensors weights are downloaded if they're available **and** if the\n safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors\n weights. If set to `False`, safetensors weights are not loaded.\n use_onnx (`bool`, *optional*, defaults to `None`):\n If set to `True`, ONNX weights will always be downloaded if present. If set to `False`, ONNX weights\n will never be downloaded. By default `use_onnx` defaults to the `_is_onnx` class attribute which is\n `False` for non-ONNX pipelines and `True` for ONNX pipelines. ONNX weights include both files ending\n with `.onnx` and `.pb`.\n kwargs (remaining dictionary of keyword arguments, *optional*):\n Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline\n class). The overwritten components are passed directly to the pipelines `__init__` method. See example\n below for more information.\n variant (`str`, *optional*):\n Load weights from a specified variant filename such as `\"fp16\"` or `\"ema\"`. This is ignored when\n loading `from_flax`.\n\n \n\n To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with\n `huggingface-cli login`.\n\n \n\n Examples:\n\n ```py\n >>> from diffusers import DiffusionPipeline\n\n >>> # Download pipeline from huggingface.co and cache.\n >>> pipeline = DiffusionPipeline.from_pretrained(\"CompVis/ldm-text2im-large-256\")\n\n >>> # Download pipeline that requires an authorization token\n >>> # For more information on access tokens, please refer to this section\n >>> # of the documentation](https://huggingface.co/docs/hub/security-tokens)\n >>> pipeline = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n\n >>> # Use a different scheduler\n >>> from diffusers import LMSDiscreteScheduler\n\n >>> scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config)\n >>> pipeline.scheduler = scheduler\n ```\n \"\"\"\n cache_dir = kwargs.pop(\"cache_dir\", None)\n resume_download = kwargs.pop(\"resume_download\", None)\n force_download = kwargs.pop(\"force_download\", False)\n proxies = kwargs.pop(\"proxies\", None)\n local_files_only = kwargs.pop(\"local_files_only\", None)\n token = kwargs.pop(\"token\", None)\n revision = kwargs.pop(\"revision\", None)\n from_flax = kwargs.pop(\"from_flax\", False)\n torch_dtype = kwargs.pop(\"torch_dtype\", None)\n custom_pipeline = kwargs.pop(\"custom_pipeline\", None)\n custom_revision = kwargs.pop(\"custom_revision\", None)\n provider = kwargs.pop(\"provider\", None)\n sess_options = kwargs.pop(\"sess_options\", None)\n device_map = kwargs.pop(\"device_map\", None)\n max_memory = kwargs.pop(\"max_memory\", None)\n offload_folder = kwargs.pop(\"offload_folder\", None)\n offload_state_dict = kwargs.pop(\"offload_state_dict\", False)\n low_cpu_mem_usage = kwargs.pop(\"low_cpu_mem_usage\", _LOW_CPU_MEM_USAGE_DEFAULT)\n variant = kwargs.pop(\"variant\", None)\n use_safetensors = kwargs.pop(\"use_safetensors\", None)\n use_onnx = kwargs.pop(\"use_onnx\", None)\n load_connected_pipeline = kwargs.pop(\"load_connected_pipeline\", False)\n\n if low_cpu_mem_usage and not is_accelerate_available():\n low_cpu_mem_usage = False\n logger.warning(\n \"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the\"\n \" environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install\"\n \" `accelerate` for faster and less memory-intense model loading. You can do so with: \\n```\\npip\"\n \" install accelerate\\n```\\n.\"\n )\n\n if low_cpu_mem_usage is True and not is_torch_version(\">=\", \"1.9.0\"):\n raise NotImplementedError(\n \"Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set\"\n \" `low_cpu_mem_usage=False`.\"\n )\n\n if device_map is not None and not is_torch_version(\">=\", \"1.9.0\"):\n raise NotImplementedError(\n \"Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set\"\n \" `device_map=None`.\"\n )\n\n if device_map is not None and not is_accelerate_available():\n raise NotImplementedError(\n \"Using `device_map` requires the `accelerate` library. Please install it using: `pip install accelerate`.\"\n )\n\n if device_map is not None and not isinstance(device_map, str):\n raise ValueError(\"`device_map` must be a string.\")\n\n if device_map is not None and device_map not in SUPPORTED_DEVICE_MAP:\n raise NotImplementedError(\n f\"{device_map} not supported. Supported strategies are: {', '.join(SUPPORTED_DEVICE_MAP)}\"\n )\n\n if device_map is not None and device_map in SUPPORTED_DEVICE_MAP:\n if is_accelerate_version(\"<\", \"0.28.0\"):\n raise NotImplementedError(\"Device placement requires `accelerate` version `0.28.0` or later.\")\n\n if low_cpu_mem_usage is False and device_map is not None:\n raise ValueError(\n f\"You cannot set `low_cpu_mem_usage` to False while using device_map={device_map} for loading and\"\n \" dispatching. Please make sure to set `low_cpu_mem_usage=True`.\"\n )\n\n # 1. Download the checkpoints and configs\n # use snapshot download here to get it working from from_pretrained\n if not os.path.isdir(pretrained_model_name_or_path):\n if pretrained_model_name_or_path.count(\"/\") > 1:\n raise ValueError(\n f'The provided pretrained_model_name_or_path \"{pretrained_model_name_or_path}\"'\n \" is neither a valid local path nor a valid repo id. Please check the parameter.\"\n )\n cached_folder = cls.download(\n pretrained_model_name_or_path,\n cache_dir=cache_dir,\n resume_download=resume_download,\n force_download=force_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n from_flax=from_flax,\n use_safetensors=use_safetensors,\n use_onnx=use_onnx,\n custom_pipeline=custom_pipeline,\n custom_revision=custom_revision,\n variant=variant,\n load_connected_pipeline=load_connected_pipeline,\n **kwargs,\n )\n else:\n cached_folder = pretrained_model_name_or_path\n\n config_dict = cls.load_config(cached_folder)\n\n # pop out \"_ignore_files\" as it is only needed for download\n config_dict.pop(\"_ignore_files\", None)\n\n # 2. Define which model components should load variants\n # We retrieve the information by matching whether variant\n # model checkpoints exist in the subfolders\n model_variants = {}\n if variant is not None:\n for folder in os.listdir(cached_folder):\n folder_path = os.path.join(cached_folder, folder)\n is_folder = os.path.isdir(folder_path) and folder in config_dict\n variant_exists = is_folder and any(\n p.split(\".\")[1].startswith(variant) for p in os.listdir(folder_path)\n )\n if variant_exists:\n model_variants[folder] = variant\n\n # 3. Load the pipeline class, if using custom module then load it from the hub\n # if we load from explicit class, let's use it\n custom_class_name = None\n if os.path.isfile(os.path.join(cached_folder, f\"{custom_pipeline}.py\")):\n custom_pipeline = os.path.join(cached_folder, f\"{custom_pipeline}.py\")\n elif isinstance(config_dict[\"_class_name\"], (list, tuple)) and os.path.isfile(\n os.path.join(cached_folder, f\"{config_dict['_class_name'][0]}.py\")\n ):\n custom_pipeline = os.path.join(cached_folder, f\"{config_dict['_class_name'][0]}.py\")\n custom_class_name = config_dict[\"_class_name\"][1]\n\n pipeline_class = _get_pipeline_class(\n cls,\n config_dict,\n load_connected_pipeline=load_connected_pipeline,\n custom_pipeline=custom_pipeline,\n class_name=custom_class_name,\n cache_dir=cache_dir,\n revision=custom_revision,\n )\n\n if device_map is not None and pipeline_class._load_connected_pipes:\n raise NotImplementedError(\"`device_map` is not yet supported for connected pipelines.\")\n\n # DEPRECATED: To be removed in 1.0.0\n if pipeline_class.__name__ == \"StableDiffusionInpaintPipeline\" and version.parse(\n version.parse(config_dict[\"_diffusers_version\"]).base_version\n ) <= version.parse(\"0.5.1\"):\n from diffusers import StableDiffusionInpaintPipeline, StableDiffusionInpaintPipelineLegacy\n\n pipeline_class = StableDiffusionInpaintPipelineLegacy\n\n deprecation_message = (\n \"You are using a legacy checkpoint for inpainting with Stable Diffusion, therefore we are loading the\"\n f\" {StableDiffusionInpaintPipelineLegacy} class instead of {StableDiffusionInpaintPipeline}. For\"\n \" better inpainting results, we strongly suggest using Stable Diffusion's official inpainting\"\n \" checkpoint: https://huggingface.co/runwayml/stable-diffusion-inpainting instead or adapting your\"\n f\" checkpoint {pretrained_model_name_or_path} to the format of\"\n \" https://huggingface.co/runwayml/stable-diffusion-inpainting. Note that we do not actively maintain\"\n \" the {StableDiffusionInpaintPipelineLegacy} class and will likely remove it in version 1.0.0.\"\n )\n deprecate(\"StableDiffusionInpaintPipelineLegacy\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n # 4. Define expected modules given pipeline signature\n # and define non-None initialized modules (=`init_kwargs`)\n\n # some modules can be passed directly to the init\n # in this case they are already instantiated in `kwargs`\n # extract them here\n expected_modules, optional_kwargs = cls._get_signature_keys(pipeline_class)\n passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}\n passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}\n\n init_dict, unused_kwargs, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)\n\n # define init kwargs and make sure that optional component modules are filtered out\n init_kwargs = {\n k: init_dict.pop(k)\n for k in optional_kwargs\n if k in init_dict and k not in pipeline_class._optional_components\n }\n init_kwargs = {**init_kwargs, **passed_pipe_kwargs}\n\n # remove `null` components\n def load_module(name, value):\n if value[0] is None:\n return False\n if name in passed_class_obj and passed_class_obj[name] is None:\n return False\n return True\n\n init_dict = {k: v for k, v in init_dict.items() if load_module(k, v)}\n\n # Special case: safety_checker must be loaded separately when using `from_flax`\n if from_flax and \"safety_checker\" in init_dict and \"safety_checker\" not in passed_class_obj:\n raise NotImplementedError(\n \"The safety checker cannot be automatically loaded when loading weights `from_flax`.\"\n \" Please, pass `safety_checker=None` to `from_pretrained`, and load the safety checker\"\n \" separately if you need it.\"\n )\n\n # 5. Throw nice warnings / errors for fast accelerate loading\n if len(unused_kwargs) > 0:\n logger.warning(\n f\"Keyword arguments {unused_kwargs} are not expected by {pipeline_class.__name__} and will be ignored.\"\n )\n\n # import it here to avoid circular import\n from diffusers import pipelines\n\n # 6. device map delegation\n final_device_map = None\n if device_map is not None:\n final_device_map = _get_final_device_map(\n device_map=device_map,\n pipeline_class=pipeline_class,\n passed_class_obj=passed_class_obj,\n init_dict=init_dict,\n library=library,\n max_memory=max_memory,\n torch_dtype=torch_dtype,\n cached_folder=cached_folder,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n )\n\n # 7. Load each module in the pipeline\n current_device_map = None\n for name, (library_name, class_name) in logging.tqdm(init_dict.items(), desc=\"Loading pipeline components...\"):\n if final_device_map is not None and len(final_device_map) > 0:\n component_device = final_device_map.get(name, None)\n if component_device is not None:\n current_device_map = {\"\": component_device}\n else:\n current_device_map = None\n\n # 7.1 - now that JAX/Flax is an official framework of the library, we might load from Flax names\n class_name = class_name[4:] if class_name.startswith(\"Flax\") else class_name\n\n # 7.2 Define all importable classes\n is_pipeline_module = hasattr(pipelines, library_name)\n importable_classes = ALL_IMPORTABLE_CLASSES\n loaded_sub_model = None\n\n # 7.3 Use passed sub model or load class_name from library_name\n if name in passed_class_obj:\n # if the model is in a pipeline module, then we load it from the pipeline\n # check that passed_class_obj has correct parent class\n maybe_raise_or_warn(\n library_name, library, class_name, importable_classes, passed_class_obj, name, is_pipeline_module\n )\n\n loaded_sub_model = passed_class_obj[name]\n else:\n # load sub model\n loaded_sub_model = load_sub_model(\n library_name=library_name,\n class_name=class_name,\n importable_classes=importable_classes,\n pipelines=pipelines,\n is_pipeline_module=is_pipeline_module,\n pipeline_class=pipeline_class,\n torch_dtype=torch_dtype,\n provider=provider,\n sess_options=sess_options,\n device_map=current_device_map,\n max_memory=max_memory,\n offload_folder=offload_folder,\n offload_state_dict=offload_state_dict,\n model_variants=model_variants,\n name=name,\n from_flax=from_flax,\n variant=variant,\n low_cpu_mem_usage=low_cpu_mem_usage,\n cached_folder=cached_folder,\n )\n logger.info(\n f\"Loaded {name} as {class_name} from `{name}` subfolder of {pretrained_model_name_or_path}.\"\n )\n\n init_kwargs[name] = loaded_sub_model # UNet(...), # DiffusionSchedule(...)\n\n if pipeline_class._load_connected_pipes and os.path.isfile(os.path.join(cached_folder, \"README.md\")):\n modelcard = ModelCard.load(os.path.join(cached_folder, \"README.md\"))\n connected_pipes = {prefix: getattr(modelcard.data, prefix, [None])[0] for prefix in CONNECTED_PIPES_KEYS}\n load_kwargs = {\n \"cache_dir\": cache_dir,\n \"resume_download\": resume_download,\n \"force_download\": force_download,\n \"proxies\": proxies,\n \"local_files_only\": local_files_only,\n \"token\": token,\n \"revision\": revision,\n \"torch_dtype\": torch_dtype,\n \"custom_pipeline\": custom_pipeline,\n \"custom_revision\": custom_revision,\n \"provider\": provider,\n \"sess_options\": sess_options,\n \"device_map\": device_map,\n \"max_memory\": max_memory,\n \"offload_folder\": offload_folder,\n \"offload_state_dict\": offload_state_dict,\n \"low_cpu_mem_usage\": low_cpu_mem_usage,\n \"variant\": variant,\n \"use_safetensors\": use_safetensors,\n }\n\n def get_connected_passed_kwargs(prefix):\n connected_passed_class_obj = {\n k.replace(f\"{prefix}_\", \"\"): w for k, w in passed_class_obj.items() if k.split(\"_\")[0] == prefix\n }\n connected_passed_pipe_kwargs = {\n k.replace(f\"{prefix}_\", \"\"): w for k, w in passed_pipe_kwargs.items() if k.split(\"_\")[0] == prefix\n }\n\n connected_passed_kwargs = {**connected_passed_class_obj, **connected_passed_pipe_kwargs}\n return connected_passed_kwargs\n\n connected_pipes = {\n prefix: DiffusionPipeline.from_pretrained(\n repo_id, **load_kwargs.copy(), **get_connected_passed_kwargs(prefix)\n )\n for prefix, repo_id in connected_pipes.items()\n if repo_id is not None\n }\n\n for prefix, connected_pipe in connected_pipes.items():\n # add connected pipes to `init_kwargs` with _, e.g. \"prior_text_encoder\"\n init_kwargs.update(\n {\"_\".join([prefix, name]): component for name, component in connected_pipe.components.items()}\n )\n\n # 8. Potentially add passed objects if expected\n missing_modules = set(expected_modules) - set(init_kwargs.keys())\n passed_modules = list(passed_class_obj.keys())\n optional_modules = pipeline_class._optional_components\n if len(missing_modules) > 0 and missing_modules <= set(passed_modules + optional_modules):\n for module in missing_modules:\n init_kwargs[module] = passed_class_obj.get(module, None)\n elif len(missing_modules) > 0:\n passed_modules = set(list(init_kwargs.keys()) + list(passed_class_obj.keys())) - optional_kwargs\n raise ValueError(\n f\"Pipeline {pipeline_class} expected {expected_modules}, but only {passed_modules} were passed.\"\n )\n\n # 10. Instantiate the pipeline\n model = pipeline_class(**init_kwargs)\n\n # 11. Save where the model was instantiated from\n model.register_to_config(_name_or_path=pretrained_model_name_or_path)\n if device_map is not None:\n setattr(model, \"hf_device_map\", final_device_map)\n return model\n\n @property\n def name_or_path(self) -> str:\n return getattr(self.config, \"_name_or_path\", None)\n\n @property\n def _execution_device(self):\n r\"\"\"\n Returns the device on which the pipeline's models will be executed. After calling\n [`~DiffusionPipeline.enable_sequential_cpu_offload`] the execution device can only be inferred from\n Accelerate's module hooks.\n \"\"\"\n for name, model in self.components.items():\n if not isinstance(model, torch.nn.Module) or name in self._exclude_from_cpu_offload:\n continue\n\n if not hasattr(model, \"_hf_hook\"):\n return self.device\n for module in model.modules():\n if (\n hasattr(module, \"_hf_hook\")\n and hasattr(module._hf_hook, \"execution_device\")\n and module._hf_hook.execution_device is not None\n ):\n return torch.device(module._hf_hook.execution_device)\n return self.device\n\n def remove_all_hooks(self):\n r\"\"\"\n Removes all hooks that were added when using `enable_sequential_cpu_offload` or `enable_model_cpu_offload`.\n \"\"\"\n for _, model in self.components.items():\n if isinstance(model, torch.nn.Module) and hasattr(model, \"_hf_hook\"):\n accelerate.hooks.remove_hook_from_module(model, recurse=True)\n self._all_hooks = []\n\n def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = \"cuda\"):\n r\"\"\"\n Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared\n to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`\n method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with\n `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.\n\n Arguments:\n gpu_id (`int`, *optional*):\n The ID of the accelerator that shall be used in inference. If not specified, it will default to 0.\n device (`torch.Device` or `str`, *optional*, defaults to \"cuda\"):\n The PyTorch device type of the accelerator that shall be used in inference. If not specified, it will\n default to \"cuda\".\n \"\"\"\n is_pipeline_device_mapped = self.hf_device_map is not None and len(self.hf_device_map) > 1\n if is_pipeline_device_mapped:\n raise ValueError(\n \"It seems like you have activated a device mapping strategy on the pipeline so calling `enable_model_cpu_offload() isn't allowed. You can call `reset_device_map()` first and then call `enable_model_cpu_offload()`.\"\n )\n\n if self.model_cpu_offload_seq is None:\n raise ValueError(\n \"Model CPU offload cannot be enabled because no `model_cpu_offload_seq` class attribute is set.\"\n )\n\n if is_accelerate_available() and is_accelerate_version(\">=\", \"0.17.0.dev0\"):\n from accelerate import cpu_offload_with_hook\n else:\n raise ImportError(\"`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.\")\n\n self.remove_all_hooks()\n\n torch_device = torch.device(device)\n device_index = torch_device.index\n\n if gpu_id is not None and device_index is not None:\n raise ValueError(\n f\"You have passed both `gpu_id`={gpu_id} and an index as part of the passed device `device`={device}\"\n f\"Cannot pass both. Please make sure to either not define `gpu_id` or not pass the index as part of the device: `device`={torch_device.type}\"\n )\n\n # _offload_gpu_id should be set to passed gpu_id (or id in passed `device`) or default to previously set id or default to 0\n self._offload_gpu_id = gpu_id or torch_device.index or getattr(self, \"_offload_gpu_id\", 0)\n\n device_type = torch_device.type\n device = torch.device(f\"{device_type}:{self._offload_gpu_id}\")\n self._offload_device = device\n\n self.to(\"cpu\", silence_dtype_warnings=True)\n device_mod = getattr(torch, device.type, None)\n if hasattr(device_mod, \"empty_cache\") and device_mod.is_available():\n device_mod.empty_cache() # otherwise we don't see the memory savings (but they probably exist)\n\n all_model_components = {k: v for k, v in self.components.items() if isinstance(v, torch.nn.Module)}\n\n self._all_hooks = []\n hook = None\n for model_str in self.model_cpu_offload_seq.split(\"->\"):\n model = all_model_components.pop(model_str, None)\n if not isinstance(model, torch.nn.Module):\n continue\n\n _, hook = cpu_offload_with_hook(model, device, prev_module_hook=hook)\n self._all_hooks.append(hook)\n\n # CPU offload models that are not in the seq chain unless they are explicitly excluded\n # these models will stay on CPU until maybe_free_model_hooks is called\n # some models cannot be in the seq chain because they are iteratively called, such as controlnet\n for name, model in all_model_components.items():\n if not isinstance(model, torch.nn.Module):\n continue\n\n if name in self._exclude_from_cpu_offload:\n model.to(device)\n else:\n _, hook = cpu_offload_with_hook(model, device)\n self._all_hooks.append(hook)\n\n def maybe_free_model_hooks(self):\n r\"\"\"\n Function that offloads all components, removes all model hooks that were added when using\n `enable_model_cpu_offload` and then applies them again. In case the model has not been offloaded this function\n is a no-op. Make sure to add this function to the end of the `__call__` function of your pipeline so that it\n functions correctly when applying enable_model_cpu_offload.\n \"\"\"\n if not hasattr(self, \"_all_hooks\") or len(self._all_hooks) == 0:\n # `enable_model_cpu_offload` has not be called, so silently do nothing\n return\n\n # make sure the model is in the same state as before calling it\n self.enable_model_cpu_offload(device=getattr(self, \"_offload_device\", \"cuda\"))\n\n def enable_sequential_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = \"cuda\"):\n r\"\"\"\n Offloads all models to CPU using 🤗 Accelerate, significantly reducing memory usage. When called, the state\n dicts of all `torch.nn.Module` components (except those in `self._exclude_from_cpu_offload`) are saved to CPU\n and then moved to `torch.device('meta')` and loaded to GPU only when their specific submodule has its `forward`\n method called. Offloading happens on a submodule basis. Memory savings are higher than with\n `enable_model_cpu_offload`, but performance is lower.\n\n Arguments:\n gpu_id (`int`, *optional*):\n The ID of the accelerator that shall be used in inference. If not specified, it will default to 0.\n device (`torch.Device` or `str`, *optional*, defaults to \"cuda\"):\n The PyTorch device type of the accelerator that shall be used in inference. If not specified, it will\n default to \"cuda\".\n \"\"\"\n if is_accelerate_available() and is_accelerate_version(\">=\", \"0.14.0\"):\n from accelerate import cpu_offload\n else:\n raise ImportError(\"`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher\")\n self.remove_all_hooks()\n\n is_pipeline_device_mapped = self.hf_device_map is not None and len(self.hf_device_map) > 1\n if is_pipeline_device_mapped:\n raise ValueError(\n \"It seems like you have activated a device mapping strategy on the pipeline so calling `enable_sequential_cpu_offload() isn't allowed. You can call `reset_device_map()` first and then call `enable_sequential_cpu_offload()`.\"\n )\n\n torch_device = torch.device(device)\n device_index = torch_device.index\n\n if gpu_id is not None and device_index is not None:\n raise ValueError(\n f\"You have passed both `gpu_id`={gpu_id} and an index as part of the passed device `device`={device}\"\n f\"Cannot pass both. Please make sure to either not define `gpu_id` or not pass the index as part of the device: `device`={torch_device.type}\"\n )\n\n # _offload_gpu_id should be set to passed gpu_id (or id in passed `device`) or default to previously set id or default to 0\n self._offload_gpu_id = gpu_id or torch_device.index or getattr(self, \"_offload_gpu_id\", 0)\n\n device_type = torch_device.type\n device = torch.device(f\"{device_type}:{self._offload_gpu_id}\")\n self._offload_device = device\n\n if self.device.type != \"cpu\":\n self.to(\"cpu\", silence_dtype_warnings=True)\n device_mod = getattr(torch, self.device.type, None)\n if hasattr(device_mod, \"empty_cache\") and device_mod.is_available():\n device_mod.empty_cache() # otherwise we don't see the memory savings (but they probably exist)\n\n for name, model in self.components.items():\n if not isinstance(model, torch.nn.Module):\n continue\n\n if name in self._exclude_from_cpu_offload:\n model.to(device)\n else:\n # make sure to offload buffers if not all high level weights\n # are of type nn.Module\n offload_buffers = len(model._parameters) > 0\n cpu_offload(model, device, offload_buffers=offload_buffers)\n\n def reset_device_map(self):\n r\"\"\"\n Resets the device maps (if any) to None.\n \"\"\"\n if self.hf_device_map is None:\n return\n else:\n self.remove_all_hooks()\n for name, component in self.components.items():\n if isinstance(component, torch.nn.Module):\n component.to(\"cpu\")\n self.hf_device_map = None\n\n @classmethod\n @validate_hf_hub_args\n def download(cls, pretrained_model_name, **kwargs) -> Union[str, os.PathLike]:\n r\"\"\"\n Download and cache a PyTorch diffusion pipeline from pretrained pipeline weights.\n\n Parameters:\n pretrained_model_name (`str` or `os.PathLike`, *optional*):\n A string, the *repository id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline\n hosted on the Hub.\n custom_pipeline (`str`, *optional*):\n Can be either:\n\n - A string, the *repository id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained\n pipeline hosted on the Hub. The repository must contain a file called `pipeline.py` that defines\n the custom pipeline.\n\n - A string, the *file name* of a community pipeline hosted on GitHub under\n [Community](https://github.com/huggingface/diffusers/tree/main/examples/community). Valid file\n names must match the file name and not the pipeline script (`clip_guided_stable_diffusion`\n instead of `clip_guided_stable_diffusion.py`). Community pipelines are always loaded from the\n current `main` branch of GitHub.\n\n - A path to a *directory* (`./my_pipeline_directory/`) containing a custom pipeline. The directory\n must contain a file called `pipeline.py` that defines the custom pipeline.\n\n \n\n 🧪 This is an experimental feature and may change in the future.\n\n \n\n For more information on how to load and create custom pipelines, take a look at [How to contribute a\n community pipeline](https://huggingface.co/docs/diffusers/main/en/using-diffusers/contribute_pipeline).\n\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\n cached versions if they exist.\n resume_download:\n Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1\n of Diffusers.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\n output_loading_info(`bool`, *optional*, defaults to `False`):\n Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.\n local_files_only (`bool`, *optional*, defaults to `False`):\n Whether to only load local model weights and configuration files or not. If set to `True`, the model\n won't be downloaded from the Hub.\n token (`str` or *bool*, *optional*):\n The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from\n `diffusers-cli login` (stored in `~/.huggingface`) is used.\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier\n allowed by Git.\n custom_revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, or a commit id similar to\n `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a\n custom pipeline from GitHub, otherwise it defaults to `\"main\"` when loading from the Hub.\n mirror (`str`, *optional*):\n Mirror source to resolve accessibility issues if you're downloading a model in China. We do not\n guarantee the timeliness or safety of the source, and you should refer to the mirror site for more\n information.\n variant (`str`, *optional*):\n Load weights from a specified variant filename such as `\"fp16\"` or `\"ema\"`. This is ignored when\n loading `from_flax`.\n use_safetensors (`bool`, *optional*, defaults to `None`):\n If set to `None`, the safetensors weights are downloaded if they're available **and** if the\n safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors\n weights. If set to `False`, safetensors weights are not loaded.\n use_onnx (`bool`, *optional*, defaults to `False`):\n If set to `True`, ONNX weights will always be downloaded if present. If set to `False`, ONNX weights\n will never be downloaded. By default `use_onnx` defaults to the `_is_onnx` class attribute which is\n `False` for non-ONNX pipelines and `True` for ONNX pipelines. ONNX weights include both files ending\n with `.onnx` and `.pb`.\n trust_remote_code (`bool`, *optional*, defaults to `False`):\n Whether or not to allow for custom pipelines and components defined on the Hub in their own files. This\n option should only be set to `True` for repositories you trust and in which you have read the code, as\n it will execute code present on the Hub on your local machine.\n\n Returns:\n `os.PathLike`:\n A path to the downloaded pipeline.\n\n \n\n To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with\n `huggingface-cli login`.\n\n \n\n \"\"\"\n cache_dir = kwargs.pop(\"cache_dir\", None)\n resume_download = kwargs.pop(\"resume_download\", None)\n force_download = kwargs.pop(\"force_download\", False)\n proxies = kwargs.pop(\"proxies\", None)\n local_files_only = kwargs.pop(\"local_files_only\", None)\n token = kwargs.pop(\"token\", None)\n revision = kwargs.pop(\"revision\", None)\n from_flax = kwargs.pop(\"from_flax\", False)\n custom_pipeline = kwargs.pop(\"custom_pipeline\", None)\n custom_revision = kwargs.pop(\"custom_revision\", None)\n variant = kwargs.pop(\"variant\", None)\n use_safetensors = kwargs.pop(\"use_safetensors\", None)\n use_onnx = kwargs.pop(\"use_onnx\", None)\n load_connected_pipeline = kwargs.pop(\"load_connected_pipeline\", False)\n trust_remote_code = kwargs.pop(\"trust_remote_code\", False)\n\n allow_pickle = False\n if use_safetensors is None:\n use_safetensors = True\n allow_pickle = True\n\n allow_patterns = None\n ignore_patterns = None\n\n model_info_call_error: Optional[Exception] = None\n if not local_files_only:\n try:\n info = model_info(pretrained_model_name, token=token, revision=revision)\n except (HTTPError, OfflineModeIsEnabled, requests.ConnectionError) as e:\n logger.warning(f\"Couldn't connect to the Hub: {e}.\\nWill try to load from local cache.\")\n local_files_only = True\n model_info_call_error = e # save error to reraise it if model is not cached locally\n\n if not local_files_only:\n config_file = hf_hub_download(\n pretrained_model_name,\n cls.config_name,\n cache_dir=cache_dir,\n revision=revision,\n proxies=proxies,\n force_download=force_download,\n resume_download=resume_download,\n token=token,\n )\n\n config_dict = cls._dict_from_json_file(config_file)\n ignore_filenames = config_dict.pop(\"_ignore_files\", [])\n\n # retrieve all folder_names that contain relevant files\n folder_names = [k for k, v in config_dict.items() if isinstance(v, list) and k != \"_class_name\"]\n\n filenames = {sibling.rfilename for sibling in info.siblings}\n model_filenames, variant_filenames = variant_compatible_siblings(filenames, variant=variant)\n\n diffusers_module = importlib.import_module(__name__.split(\".\")[0])\n pipelines = getattr(diffusers_module, \"pipelines\")\n\n # optionally create a custom component <> custom file mapping\n custom_components = {}\n for component in folder_names:\n module_candidate = config_dict[component][0]\n\n if module_candidate is None or not isinstance(module_candidate, str):\n continue\n\n # We compute candidate file path on the Hub. Do not use `os.path.join`.\n candidate_file = f\"{component}/{module_candidate}.py\"\n\n if candidate_file in filenames:\n custom_components[component] = module_candidate\n elif module_candidate not in LOADABLE_CLASSES and not hasattr(pipelines, module_candidate):\n raise ValueError(\n f\"{candidate_file} as defined in `model_index.json` does not exist in {pretrained_model_name} and is not a module in 'diffusers/pipelines'.\"\n )\n\n if len(variant_filenames) == 0 and variant is not None:\n deprecation_message = (\n f\"You are trying to load the model files of the `variant={variant}`, but no such modeling files are available.\"\n f\"The default model files: {model_filenames} will be loaded instead. Make sure to not load from `variant={variant}`\"\n \"if such variant modeling files are not available. Doing so will lead to an error in v0.24.0 as defaulting to non-variant\"\n \"modeling files is deprecated.\"\n )\n deprecate(\"no variant default\", \"0.24.0\", deprecation_message, standard_warn=False)\n\n # remove ignored filenames\n model_filenames = set(model_filenames) - set(ignore_filenames)\n variant_filenames = set(variant_filenames) - set(ignore_filenames)\n\n # if the whole pipeline is cached we don't have to ping the Hub\n if revision in DEPRECATED_REVISION_ARGS and version.parse(\n version.parse(__version__).base_version\n ) >= version.parse(\"0.22.0\"):\n warn_deprecated_model_variant(pretrained_model_name, token, variant, revision, model_filenames)\n\n model_folder_names = {os.path.split(f)[0] for f in model_filenames if os.path.split(f)[0] in folder_names}\n\n custom_class_name = None\n if custom_pipeline is None and isinstance(config_dict[\"_class_name\"], (list, tuple)):\n custom_pipeline = config_dict[\"_class_name\"][0]\n custom_class_name = config_dict[\"_class_name\"][1]\n\n # all filenames compatible with variant will be added\n allow_patterns = list(model_filenames)\n\n # allow all patterns from non-model folders\n # this enables downloading schedulers, tokenizers, ...\n allow_patterns += [f\"{k}/*\" for k in folder_names if k not in model_folder_names]\n # add custom component files\n allow_patterns += [f\"{k}/{f}.py\" for k, f in custom_components.items()]\n # add custom pipeline file\n allow_patterns += [f\"{custom_pipeline}.py\"] if f\"{custom_pipeline}.py\" in filenames else []\n # also allow downloading config.json files with the model\n allow_patterns += [os.path.join(k, \"config.json\") for k in model_folder_names]\n\n allow_patterns += [\n SCHEDULER_CONFIG_NAME,\n CONFIG_NAME,\n cls.config_name,\n CUSTOM_PIPELINE_FILE_NAME,\n ]\n\n load_pipe_from_hub = custom_pipeline is not None and f\"{custom_pipeline}.py\" in filenames\n load_components_from_hub = len(custom_components) > 0\n\n if load_pipe_from_hub and not trust_remote_code:\n raise ValueError(\n f\"The repository for {pretrained_model_name} contains custom code in {custom_pipeline}.py which must be executed to correctly \"\n f\"load the model. You can inspect the repository content at https://hf.co/{pretrained_model_name}/blob/main/{custom_pipeline}.py.\\n\"\n f\"Please pass the argument `trust_remote_code=True` to allow custom code to be run.\"\n )\n\n if load_components_from_hub and not trust_remote_code:\n raise ValueError(\n f\"The repository for {pretrained_model_name} contains custom code in {'.py, '.join([os.path.join(k, v) for k,v in custom_components.items()])} which must be executed to correctly \"\n f\"load the model. You can inspect the repository content at {', '.join([f'https://hf.co/{pretrained_model_name}/{k}/{v}.py' for k,v in custom_components.items()])}.\\n\"\n f\"Please pass the argument `trust_remote_code=True` to allow custom code to be run.\"\n )\n\n # retrieve passed components that should not be downloaded\n pipeline_class = _get_pipeline_class(\n cls,\n config_dict,\n load_connected_pipeline=load_connected_pipeline,\n custom_pipeline=custom_pipeline,\n repo_id=pretrained_model_name if load_pipe_from_hub else None,\n hub_revision=revision,\n class_name=custom_class_name,\n cache_dir=cache_dir,\n revision=custom_revision,\n )\n expected_components, _ = cls._get_signature_keys(pipeline_class)\n passed_components = [k for k in expected_components if k in kwargs]\n\n if (\n use_safetensors\n and not allow_pickle\n and not is_safetensors_compatible(\n model_filenames, variant=variant, passed_components=passed_components\n )\n ):\n raise EnvironmentError(\n f\"Could not find the necessary `safetensors` weights in {model_filenames} (variant={variant})\"\n )\n if from_flax:\n ignore_patterns = [\"*.bin\", \"*.safetensors\", \"*.onnx\", \"*.pb\"]\n elif use_safetensors and is_safetensors_compatible(\n model_filenames, variant=variant, passed_components=passed_components\n ):\n ignore_patterns = [\"*.bin\", \"*.msgpack\"]\n\n use_onnx = use_onnx if use_onnx is not None else pipeline_class._is_onnx\n if not use_onnx:\n ignore_patterns += [\"*.onnx\", \"*.pb\"]\n\n safetensors_variant_filenames = {f for f in variant_filenames if f.endswith(\".safetensors\")}\n safetensors_model_filenames = {f for f in model_filenames if f.endswith(\".safetensors\")}\n if (\n len(safetensors_variant_filenames) > 0\n and safetensors_model_filenames != safetensors_variant_filenames\n ):\n logger.warning(\n f\"\\nA mixture of {variant} and non-{variant} filenames will be loaded.\\nLoaded {variant} filenames:\\n[{', '.join(safetensors_variant_filenames)}]\\nLoaded non-{variant} filenames:\\n[{', '.join(safetensors_model_filenames - safetensors_variant_filenames)}\\nIf this behavior is not expected, please check your folder structure.\"\n )\n else:\n ignore_patterns = [\"*.safetensors\", \"*.msgpack\"]\n\n use_onnx = use_onnx if use_onnx is not None else pipeline_class._is_onnx\n if not use_onnx:\n ignore_patterns += [\"*.onnx\", \"*.pb\"]\n\n bin_variant_filenames = {f for f in variant_filenames if f.endswith(\".bin\")}\n bin_model_filenames = {f for f in model_filenames if f.endswith(\".bin\")}\n if len(bin_variant_filenames) > 0 and bin_model_filenames != bin_variant_filenames:\n logger.warning(\n f\"\\nA mixture of {variant} and non-{variant} filenames will be loaded.\\nLoaded {variant} filenames:\\n[{', '.join(bin_variant_filenames)}]\\nLoaded non-{variant} filenames:\\n[{', '.join(bin_model_filenames - bin_variant_filenames)}\\nIf this behavior is not expected, please check your folder structure.\"\n )\n\n # Don't download any objects that are passed\n allow_patterns = [\n p for p in allow_patterns if not (len(p.split(\"/\")) == 2 and p.split(\"/\")[0] in passed_components)\n ]\n\n if pipeline_class._load_connected_pipes:\n allow_patterns.append(\"README.md\")\n\n # Don't download index files of forbidden patterns either\n ignore_patterns = ignore_patterns + [f\"{i}.index.*json\" for i in ignore_patterns]\n re_ignore_pattern = [re.compile(fnmatch.translate(p)) for p in ignore_patterns]\n re_allow_pattern = [re.compile(fnmatch.translate(p)) for p in allow_patterns]\n\n expected_files = [f for f in filenames if not any(p.match(f) for p in re_ignore_pattern)]\n expected_files = [f for f in expected_files if any(p.match(f) for p in re_allow_pattern)]\n\n snapshot_folder = Path(config_file).parent\n pipeline_is_cached = all((snapshot_folder / f).is_file() for f in expected_files)\n\n if pipeline_is_cached and not force_download:\n # if the pipeline is cached, we can directly return it\n # else call snapshot_download\n return snapshot_folder\n\n user_agent = {\"pipeline_class\": cls.__name__}\n if custom_pipeline is not None and not custom_pipeline.endswith(\".py\"):\n user_agent[\"custom_pipeline\"] = custom_pipeline\n\n # download all allow_patterns - ignore_patterns\n try:\n cached_folder = snapshot_download(\n pretrained_model_name,\n cache_dir=cache_dir,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n allow_patterns=allow_patterns,\n ignore_patterns=ignore_patterns,\n user_agent=user_agent,\n )\n\n # retrieve pipeline class from local file\n cls_name = cls.load_config(os.path.join(cached_folder, \"model_index.json\")).get(\"_class_name\", None)\n cls_name = cls_name[4:] if isinstance(cls_name, str) and cls_name.startswith(\"Flax\") else cls_name\n\n diffusers_module = importlib.import_module(__name__.split(\".\")[0])\n pipeline_class = getattr(diffusers_module, cls_name, None) if isinstance(cls_name, str) else None\n\n if pipeline_class is not None and pipeline_class._load_connected_pipes:\n modelcard = ModelCard.load(os.path.join(cached_folder, \"README.md\"))\n connected_pipes = sum([getattr(modelcard.data, k, []) for k in CONNECTED_PIPES_KEYS], [])\n for connected_pipe_repo_id in connected_pipes:\n download_kwargs = {\n \"cache_dir\": cache_dir,\n \"resume_download\": resume_download,\n \"force_download\": force_download,\n \"proxies\": proxies,\n \"local_files_only\": local_files_only,\n \"token\": token,\n \"variant\": variant,\n \"use_safetensors\": use_safetensors,\n }\n DiffusionPipeline.download(connected_pipe_repo_id, **download_kwargs)\n\n return cached_folder\n\n except FileNotFoundError:\n # Means we tried to load pipeline with `local_files_only=True` but the files have not been found in local cache.\n # This can happen in two cases:\n # 1. If the user passed `local_files_only=True` => we raise the error directly\n # 2. If we forced `local_files_only=True` when `model_info` failed => we raise the initial error\n if model_info_call_error is None:\n # 1. user passed `local_files_only=True`\n raise\n else:\n # 2. we forced `local_files_only=True` when `model_info` failed\n raise EnvironmentError(\n f\"Cannot load model {pretrained_model_name}: model is not cached locally and an error occurred\"\n \" while trying to fetch metadata from the Hub. Please check out the root cause in the stacktrace\"\n \" above.\"\n ) from model_info_call_error\n\n @classmethod\n def _get_signature_keys(cls, obj):\n parameters = inspect.signature(obj.__init__).parameters\n required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty}\n optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty})\n expected_modules = set(required_parameters.keys()) - {\"self\"}\n\n optional_names = list(optional_parameters)\n for name in optional_names:\n if name in cls._optional_components:\n expected_modules.add(name)\n optional_parameters.remove(name)\n\n return expected_modules, optional_parameters\n\n @classmethod\n def _get_signature_types(cls):\n signature_types = {}\n for k, v in inspect.signature(cls.__init__).parameters.items():\n if inspect.isclass(v.annotation):\n signature_types[k] = (v.annotation,)\n elif get_origin(v.annotation) == Union:\n signature_types[k] = get_args(v.annotation)\n else:\n logger.warning(f\"cannot get type annotation for Parameter {k} of {cls}.\")\n return signature_types\n\n @property\n def components(self) -> Dict[str, Any]:\n r\"\"\"\n The `self.components` property can be useful to run different pipelines with the same weights and\n configurations without reallocating additional memory.\n\n Returns (`dict`):\n A dictionary containing all the modules needed to initialize the pipeline.\n\n Examples:\n\n ```py\n >>> from diffusers import (\n ... StableDiffusionPipeline,\n ... StableDiffusionImg2ImgPipeline,\n ... StableDiffusionInpaintPipeline,\n ... )\n\n >>> text2img = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n >>> img2img = StableDiffusionImg2ImgPipeline(**text2img.components)\n >>> inpaint = StableDiffusionInpaintPipeline(**text2img.components)\n ```\n \"\"\"\n expected_modules, optional_parameters = self._get_signature_keys(self)\n components = {\n k: getattr(self, k) for k in self.config.keys() if not k.startswith(\"_\") and k not in optional_parameters\n }\n\n if set(components.keys()) != expected_modules:\n raise ValueError(\n f\"{self} has been incorrectly initialized or {self.__class__} is incorrectly implemented. Expected\"\n f\" {expected_modules} to be defined, but {components.keys()} are defined.\"\n )\n\n return components\n\n @staticmethod\n def numpy_to_pil(images):\n \"\"\"\n Convert a NumPy image or a batch of images to a PIL image.\n \"\"\"\n return numpy_to_pil(images)\n\n def progress_bar(self, iterable=None, total=None):\n if not hasattr(self, \"_progress_bar_config\"):\n self._progress_bar_config = {}\n elif not isinstance(self._progress_bar_config, dict):\n raise ValueError(\n f\"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}.\"\n )\n\n if iterable is not None:\n return tqdm(iterable, **self._progress_bar_config)\n elif total is not None:\n return tqdm(total=total, **self._progress_bar_config)\n else:\n raise ValueError(\"Either `total` or `iterable` has to be defined.\")\n\n def set_progress_bar_config(self, **kwargs):\n self._progress_bar_config = kwargs\n\n def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):\n r\"\"\"\n Enable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/). When this\n option is enabled, you should observe lower GPU memory usage and a potential speed up during inference. Speed\n up during training is not guaranteed.\n\n \n\n ⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes\n precedent.\n\n \n\n Parameters:\n attention_op (`Callable`, *optional*):\n Override the default `None` operator for use as `op` argument to the\n [`memory_efficient_attention()`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention)\n function of xFormers.\n\n Examples:\n\n ```py\n >>> import torch\n >>> from diffusers import DiffusionPipeline\n >>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp\n\n >>> pipe = DiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-1\", torch_dtype=torch.float16)\n >>> pipe = pipe.to(\"cuda\")\n >>> pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)\n >>> # Workaround for not accepting attention shape using VAE for Flash Attention\n >>> pipe.vae.enable_xformers_memory_efficient_attention(attention_op=None)\n ```\n \"\"\"\n self.set_use_memory_efficient_attention_xformers(True, attention_op)\n\n def disable_xformers_memory_efficient_attention(self):\n r\"\"\"\n Disable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).\n \"\"\"\n self.set_use_memory_efficient_attention_xformers(False)\n\n def set_use_memory_efficient_attention_xformers(\n self, valid: bool, attention_op: Optional[Callable] = None\n ) -> None:\n # Recursively walk through all the children.\n # Any children which exposes the set_use_memory_efficient_attention_xformers method\n # gets the message\n def fn_recursive_set_mem_eff(module: torch.nn.Module):\n if hasattr(module, \"set_use_memory_efficient_attention_xformers\"):\n module.set_use_memory_efficient_attention_xformers(valid, attention_op)\n\n for child in module.children():\n fn_recursive_set_mem_eff(child)\n\n module_names, _ = self._get_signature_keys(self)\n modules = [getattr(self, n, None) for n in module_names]\n modules = [m for m in modules if isinstance(m, torch.nn.Module)]\n\n for module in modules:\n fn_recursive_set_mem_eff(module)\n\n def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = \"auto\"):\n r\"\"\"\n Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor\n in slices to compute attention in several steps. For more than one attention head, the computation is performed\n sequentially over each head. This is useful to save some memory in exchange for a small speed decrease.\n\n \n\n ⚠️ Don't enable attention slicing if you're already using `scaled_dot_product_attention` (SDPA) from PyTorch\n 2.0 or xFormers. These attention computations are already very memory efficient so you won't need to enable\n this function. If you enable attention slicing with SDPA or xFormers, it can lead to serious slow downs!\n\n \n\n Args:\n slice_size (`str` or `int`, *optional*, defaults to `\"auto\"`):\n When `\"auto\"`, halves the input to the attention heads, so attention will be computed in two steps. If\n `\"max\"`, maximum amount of memory will be saved by running only one slice at a time. If a number is\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\n must be a multiple of `slice_size`.\n\n Examples:\n\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusionPipeline\n\n >>> pipe = StableDiffusionPipeline.from_pretrained(\n ... \"runwayml/stable-diffusion-v1-5\",\n ... torch_dtype=torch.float16,\n ... use_safetensors=True,\n ... )\n\n >>> prompt = \"a photo of an astronaut riding a horse on mars\"\n >>> pipe.enable_attention_slicing()\n >>> image = pipe(prompt).images[0]\n ```\n \"\"\"\n self.set_attention_slice(slice_size)\n\n def disable_attention_slicing(self):\n r\"\"\"\n Disable sliced attention computation. If `enable_attention_slicing` was previously called, attention is\n computed in one step.\n \"\"\"\n # set slice_size = `None` to disable `attention slicing`\n self.enable_attention_slicing(None)\n\n def set_attention_slice(self, slice_size: Optional[int]):\n module_names, _ = self._get_signature_keys(self)\n modules = [getattr(self, n, None) for n in module_names]\n modules = [m for m in modules if isinstance(m, torch.nn.Module) and hasattr(m, \"set_attention_slice\")]\n\n for module in modules:\n module.set_attention_slice(slice_size)\n\n @classmethod\n def from_pipe(cls, pipeline, **kwargs):\n r\"\"\"\n Create a new pipeline from a given pipeline. This method is useful to create a new pipeline from the existing\n pipeline components without reallocating additional memory.\n\n Arguments:\n pipeline (`DiffusionPipeline`):\n The pipeline from which to create a new pipeline.\n\n Returns:\n `DiffusionPipeline`:\n A new pipeline with the same weights and configurations as `pipeline`.\n\n Examples:\n\n ```py\n >>> from diffusers import StableDiffusionPipeline, StableDiffusionSAGPipeline\n\n >>> pipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n >>> new_pipe = StableDiffusionSAGPipeline.from_pipe(pipe)\n ```\n \"\"\"\n\n original_config = dict(pipeline.config)\n torch_dtype = kwargs.pop(\"torch_dtype\", None)\n\n # derive the pipeline class to instantiate\n custom_pipeline = kwargs.pop(\"custom_pipeline\", None)\n custom_revision = kwargs.pop(\"custom_revision\", None)\n\n if custom_pipeline is not None:\n pipeline_class = _get_custom_pipeline_class(custom_pipeline, revision=custom_revision)\n else:\n pipeline_class = cls\n\n expected_modules, optional_kwargs = cls._get_signature_keys(pipeline_class)\n # true_optional_modules are optional components with default value in signature so it is ok not to pass them to `__init__`\n # e.g. `image_encoder` for StableDiffusionPipeline\n parameters = inspect.signature(cls.__init__).parameters\n true_optional_modules = set(\n {k for k, v in parameters.items() if v.default != inspect._empty and k in expected_modules}\n )\n\n # get the class of each component based on its type hint\n # e.g. {\"unet\": UNet2DConditionModel, \"text_encoder\": CLIPTextMode}\n component_types = pipeline_class._get_signature_types()\n\n pretrained_model_name_or_path = original_config.pop(\"_name_or_path\", None)\n # allow users pass modules in `kwargs` to override the original pipeline's components\n passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}\n\n original_class_obj = {}\n for name, component in pipeline.components.items():\n if name in expected_modules and name not in passed_class_obj:\n # for model components, we will not switch over if the class does not matches the type hint in the new pipeline's signature\n if (\n not isinstance(component, ModelMixin)\n or type(component) in component_types[name]\n or (component is None and name in cls._optional_components)\n ):\n original_class_obj[name] = component\n else:\n logger.warning(\n f\"component {name} is not switched over to new pipeline because type does not match the expected.\"\n f\" {name} is {type(component)} while the new pipeline expect {component_types[name]}.\"\n f\" please pass the component of the correct type to the new pipeline. `from_pipe(..., {name}={name})`\"\n )\n\n # allow users pass optional kwargs to override the original pipelines config attribute\n passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}\n original_pipe_kwargs = {\n k: original_config[k]\n for k in original_config.keys()\n if k in optional_kwargs and k not in passed_pipe_kwargs\n }\n\n # config attribute that were not expected by pipeline is stored as its private attribute\n # (i.e. when the original pipeline was also instantiated with `from_pipe` from another pipeline that has this config)\n # in this case, we will pass them as optional arguments if they can be accepted by the new pipeline\n additional_pipe_kwargs = [\n k[1:]\n for k in original_config.keys()\n if k.startswith(\"_\") and k[1:] in optional_kwargs and k[1:] not in passed_pipe_kwargs\n ]\n for k in additional_pipe_kwargs:\n original_pipe_kwargs[k] = original_config.pop(f\"_{k}\")\n\n pipeline_kwargs = {\n **passed_class_obj,\n **original_class_obj,\n **passed_pipe_kwargs,\n **original_pipe_kwargs,\n **kwargs,\n }\n\n # store unused config as private attribute in the new pipeline\n unused_original_config = {\n f\"{'' if k.startswith('_') else '_'}{k}\": v for k, v in original_config.items() if k not in pipeline_kwargs\n }\n\n missing_modules = (\n set(expected_modules)\n - set(pipeline._optional_components)\n - set(pipeline_kwargs.keys())\n - set(true_optional_modules)\n )\n\n if len(missing_modules) > 0:\n raise ValueError(\n f\"Pipeline {pipeline_class} expected {expected_modules}, but only {set(list(passed_class_obj.keys()) + list(original_class_obj.keys()))} were passed\"\n )\n\n new_pipeline = pipeline_class(**pipeline_kwargs)\n if pretrained_model_name_or_path is not None:\n new_pipeline.register_to_config(_name_or_path=pretrained_model_name_or_path)\n new_pipeline.register_to_config(**unused_original_config)\n\n if torch_dtype is not None:\n new_pipeline.to(dtype=torch_dtype)\n\n return new_pipeline\n\n\nclass StableDiffusionMixin:\n r\"\"\"\n Helper for DiffusionPipeline with vae and unet.(mainly for LDM such as stable diffusion)\n \"\"\"\n\n def enable_vae_slicing(self):\n r\"\"\"\n Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to\n compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.\n \"\"\"\n self.vae.enable_slicing()\n\n def disable_vae_slicing(self):\n r\"\"\"\n Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to\n computing decoding in one step.\n \"\"\"\n self.vae.disable_slicing()\n\n def enable_vae_tiling(self):\n r\"\"\"\n Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to\n compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow\n processing larger images.\n \"\"\"\n self.vae.enable_tiling()\n\n def disable_vae_tiling(self):\n r\"\"\"\n Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to\n computing decoding in one step.\n \"\"\"\n self.vae.disable_tiling()\n\n def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):\n r\"\"\"Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.\n\n The suffixes after the scaling factors represent the stages where they are being applied.\n\n Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values\n that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.\n\n Args:\n s1 (`float`):\n Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to\n mitigate \"oversmoothing effect\" in the enhanced denoising process.\n s2 (`float`):\n Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to\n mitigate \"oversmoothing effect\" in the enhanced denoising process.\n b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.\n b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.\n \"\"\"\n if not hasattr(self, \"unet\"):\n raise ValueError(\"The pipeline must have `unet` for using FreeU.\")\n self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2)\n\n def disable_freeu(self):\n \"\"\"Disables the FreeU mechanism if enabled.\"\"\"\n self.unet.disable_freeu()\n\n def fuse_qkv_projections(self, unet: bool = True, vae: bool = True):\n \"\"\"\n Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)\n are fused. For cross-attention modules, key and value projection matrices are fused.\n\n \n\n This API is 🧪 experimental.\n\n \n\n Args:\n unet (`bool`, defaults to `True`): To apply fusion on the UNet.\n vae (`bool`, defaults to `True`): To apply fusion on the VAE.\n \"\"\"\n self.fusing_unet = False\n self.fusing_vae = False\n\n if unet:\n self.fusing_unet = True\n self.unet.fuse_qkv_projections()\n self.unet.set_attn_processor(FusedAttnProcessor2_0())\n\n if vae:\n if not isinstance(self.vae, AutoencoderKL):\n raise ValueError(\"`fuse_qkv_projections()` is only supported for the VAE of type `AutoencoderKL`.\")\n\n self.fusing_vae = True\n self.vae.fuse_qkv_projections()\n self.vae.set_attn_processor(FusedAttnProcessor2_0())\n\n def unfuse_qkv_projections(self, unet: bool = True, vae: bool = True):\n \"\"\"Disable QKV projection fusion if enabled.\n\n \n\n This API is 🧪 experimental.\n\n \n\n Args:\n unet (`bool`, defaults to `True`): To apply fusion on the UNet.\n vae (`bool`, defaults to `True`): To apply fusion on the VAE.\n\n \"\"\"\n if unet:\n if not self.fusing_unet:\n logger.warning(\"The UNet was not initially fused for QKV projections. Doing nothing.\")\n else:\n self.unet.unfuse_qkv_projections()\n self.fusing_unet = False\n\n if vae:\n if not self.fusing_vae:\n logger.warning(\"The VAE was not initially fused for QKV projections. Doing nothing.\")\n else:\n self.vae.unfuse_qkv_projections()\n self.fusing_vae = False\n" }, { "path": "diffusers/src/diffusers/pipelines/pixart_alpha/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_pixart_alpha\"] = [\"PixArtAlphaPipeline\"]\n _import_structure[\"pipeline_pixart_sigma\"] = [\"PixArtSigmaPipeline\"]\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n else:\n from .pipeline_pixart_alpha import (\n ASPECT_RATIO_256_BIN,\n ASPECT_RATIO_512_BIN,\n ASPECT_RATIO_1024_BIN,\n PixArtAlphaPipeline,\n )\n from .pipeline_pixart_sigma import ASPECT_RATIO_2048_BIN, PixArtSigmaPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py", "content": "# Copyright 2024 PixArt-Alpha Authors and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport html\nimport inspect\nimport re\nimport urllib.parse as ul\nfrom typing import Callable, List, Optional, Tuple, Union\n\nimport torch\nfrom transformers import T5EncoderModel, T5Tokenizer\n\nfrom ...image_processor import PixArtImageProcessor\nfrom ...models import AutoencoderKL, PixArtTransformer2DModel\nfrom ...schedulers import DPMSolverMultistepScheduler\nfrom ...utils import (\n BACKENDS_MAPPING,\n deprecate,\n is_bs4_available,\n is_ftfy_available,\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nif is_bs4_available():\n from bs4 import BeautifulSoup\n\nif is_ftfy_available():\n import ftfy\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import PixArtAlphaPipeline\n\n >>> # You can replace the checkpoint id with \"PixArt-alpha/PixArt-XL-2-512x512\" too.\n >>> pipe = PixArtAlphaPipeline.from_pretrained(\"PixArt-alpha/PixArt-XL-2-1024-MS\", torch_dtype=torch.float16)\n >>> # Enable memory optimizations.\n >>> pipe.enable_model_cpu_offload()\n\n >>> prompt = \"A small cactus with a happy face in the Sahara desert.\"\n >>> image = pipe(prompt).images[0]\n ```\n\"\"\"\n\nASPECT_RATIO_1024_BIN = {\n \"0.25\": [512.0, 2048.0],\n \"0.28\": [512.0, 1856.0],\n \"0.32\": [576.0, 1792.0],\n \"0.33\": [576.0, 1728.0],\n \"0.35\": [576.0, 1664.0],\n \"0.4\": [640.0, 1600.0],\n \"0.42\": [640.0, 1536.0],\n \"0.48\": [704.0, 1472.0],\n \"0.5\": [704.0, 1408.0],\n \"0.52\": [704.0, 1344.0],\n \"0.57\": [768.0, 1344.0],\n \"0.6\": [768.0, 1280.0],\n \"0.68\": [832.0, 1216.0],\n \"0.72\": [832.0, 1152.0],\n \"0.78\": [896.0, 1152.0],\n \"0.82\": [896.0, 1088.0],\n \"0.88\": [960.0, 1088.0],\n \"0.94\": [960.0, 1024.0],\n \"1.0\": [1024.0, 1024.0],\n \"1.07\": [1024.0, 960.0],\n \"1.13\": [1088.0, 960.0],\n \"1.21\": [1088.0, 896.0],\n \"1.29\": [1152.0, 896.0],\n \"1.38\": [1152.0, 832.0],\n \"1.46\": [1216.0, 832.0],\n \"1.67\": [1280.0, 768.0],\n \"1.75\": [1344.0, 768.0],\n \"2.0\": [1408.0, 704.0],\n \"2.09\": [1472.0, 704.0],\n \"2.4\": [1536.0, 640.0],\n \"2.5\": [1600.0, 640.0],\n \"3.0\": [1728.0, 576.0],\n \"4.0\": [2048.0, 512.0],\n}\n\nASPECT_RATIO_512_BIN = {\n \"0.25\": [256.0, 1024.0],\n \"0.28\": [256.0, 928.0],\n \"0.32\": [288.0, 896.0],\n \"0.33\": [288.0, 864.0],\n \"0.35\": [288.0, 832.0],\n \"0.4\": [320.0, 800.0],\n \"0.42\": [320.0, 768.0],\n \"0.48\": [352.0, 736.0],\n \"0.5\": [352.0, 704.0],\n \"0.52\": [352.0, 672.0],\n \"0.57\": [384.0, 672.0],\n \"0.6\": [384.0, 640.0],\n \"0.68\": [416.0, 608.0],\n \"0.72\": [416.0, 576.0],\n \"0.78\": [448.0, 576.0],\n \"0.82\": [448.0, 544.0],\n \"0.88\": [480.0, 544.0],\n \"0.94\": [480.0, 512.0],\n \"1.0\": [512.0, 512.0],\n \"1.07\": [512.0, 480.0],\n \"1.13\": [544.0, 480.0],\n \"1.21\": [544.0, 448.0],\n \"1.29\": [576.0, 448.0],\n \"1.38\": [576.0, 416.0],\n \"1.46\": [608.0, 416.0],\n \"1.67\": [640.0, 384.0],\n \"1.75\": [672.0, 384.0],\n \"2.0\": [704.0, 352.0],\n \"2.09\": [736.0, 352.0],\n \"2.4\": [768.0, 320.0],\n \"2.5\": [800.0, 320.0],\n \"3.0\": [864.0, 288.0],\n \"4.0\": [1024.0, 256.0],\n}\n\nASPECT_RATIO_256_BIN = {\n \"0.25\": [128.0, 512.0],\n \"0.28\": [128.0, 464.0],\n \"0.32\": [144.0, 448.0],\n \"0.33\": [144.0, 432.0],\n \"0.35\": [144.0, 416.0],\n \"0.4\": [160.0, 400.0],\n \"0.42\": [160.0, 384.0],\n \"0.48\": [176.0, 368.0],\n \"0.5\": [176.0, 352.0],\n \"0.52\": [176.0, 336.0],\n \"0.57\": [192.0, 336.0],\n \"0.6\": [192.0, 320.0],\n \"0.68\": [208.0, 304.0],\n \"0.72\": [208.0, 288.0],\n \"0.78\": [224.0, 288.0],\n \"0.82\": [224.0, 272.0],\n \"0.88\": [240.0, 272.0],\n \"0.94\": [240.0, 256.0],\n \"1.0\": [256.0, 256.0],\n \"1.07\": [256.0, 240.0],\n \"1.13\": [272.0, 240.0],\n \"1.21\": [272.0, 224.0],\n \"1.29\": [288.0, 224.0],\n \"1.38\": [288.0, 208.0],\n \"1.46\": [304.0, 208.0],\n \"1.67\": [320.0, 192.0],\n \"1.75\": [336.0, 192.0],\n \"2.0\": [352.0, 176.0],\n \"2.09\": [368.0, 176.0],\n \"2.4\": [384.0, 160.0],\n \"2.5\": [400.0, 160.0],\n \"3.0\": [432.0, 144.0],\n \"4.0\": [512.0, 128.0],\n}\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass PixArtAlphaPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for text-to-image generation using PixArt-Alpha.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`T5EncoderModel`]):\n Frozen text-encoder. PixArt-Alpha uses\n [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the\n [t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant.\n tokenizer (`T5Tokenizer`):\n Tokenizer of class\n [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).\n transformer ([`PixArtTransformer2DModel`]):\n A text conditioned `PixArtTransformer2DModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `transformer` to denoise the encoded image latents.\n \"\"\"\n\n bad_punct_regex = re.compile(\n r\"[\"\n + \"#®•©™&@·º½¾¿¡§~\"\n + r\"\\)\"\n + r\"\\(\"\n + r\"\\]\"\n + r\"\\[\"\n + r\"\\}\"\n + r\"\\{\"\n + r\"\\|\"\n + \"\\\\\"\n + r\"\\/\"\n + r\"\\*\"\n + r\"]{1,}\"\n ) # noqa\n\n _optional_components = [\"tokenizer\", \"text_encoder\"]\n model_cpu_offload_seq = \"text_encoder->transformer->vae\"\n\n def __init__(\n self,\n tokenizer: T5Tokenizer,\n text_encoder: T5EncoderModel,\n vae: AutoencoderKL,\n transformer: PixArtTransformer2DModel,\n scheduler: DPMSolverMultistepScheduler,\n ):\n super().__init__()\n\n self.register_modules(\n tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler\n )\n\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = PixArtImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n # Adapted from diffusers.pipelines.deepfloyd_if.pipeline_if.encode_prompt\n def encode_prompt(\n self,\n prompt: Union[str, List[str]],\n do_classifier_free_guidance: bool = True,\n negative_prompt: str = \"\",\n num_images_per_prompt: int = 1,\n device: Optional[torch.device] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n prompt_attention_mask: Optional[torch.Tensor] = None,\n negative_prompt_attention_mask: Optional[torch.Tensor] = None,\n clean_caption: bool = False,\n max_sequence_length: int = 120,\n **kwargs,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`\n instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). For\n PixArt-Alpha, this should be \"\".\n do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):\n whether to use classifier free guidance or not\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n number of images that should be generated per prompt\n device: (`torch.device`, *optional*):\n torch device to place the resulting embeddings on\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. For PixArt-Alpha, it's should be the embeddings of the \"\"\n string.\n clean_caption (`bool`, defaults to `False`):\n If `True`, the function will preprocess and clean the provided caption before encoding.\n max_sequence_length (`int`, defaults to 120): Maximum sequence length to use for the prompt.\n \"\"\"\n\n if \"mask_feature\" in kwargs:\n deprecation_message = \"The use of `mask_feature` is deprecated. It is no longer used in any computation and that doesn't affect the end results. It will be removed in a future version.\"\n deprecate(\"mask_feature\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n if device is None:\n device = self._execution_device\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # See Section 3.1. of the paper.\n max_length = max_sequence_length\n\n if prompt_embeds is None:\n prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because T5 can only handle sequences up to\"\n f\" {max_length} tokens: {removed_text}\"\n )\n\n prompt_attention_mask = text_inputs.attention_mask\n prompt_attention_mask = prompt_attention_mask.to(device)\n\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=prompt_attention_mask)\n prompt_embeds = prompt_embeds[0]\n\n if self.text_encoder is not None:\n dtype = self.text_encoder.dtype\n elif self.transformer is not None:\n dtype = self.transformer.dtype\n else:\n dtype = None\n\n prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n prompt_attention_mask = prompt_attention_mask.view(bs_embed, -1)\n prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens = [negative_prompt] * batch_size if isinstance(negative_prompt, str) else negative_prompt\n uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_attention_mask=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n negative_prompt_attention_mask = uncond_input.attention_mask\n negative_prompt_attention_mask = negative_prompt_attention_mask.to(device)\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device), attention_mask=negative_prompt_attention_mask\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n negative_prompt_attention_mask = negative_prompt_attention_mask.view(bs_embed, -1)\n negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1)\n else:\n negative_prompt_embeds = None\n negative_prompt_attention_mask = None\n\n return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n height,\n width,\n negative_prompt,\n callback_steps,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n prompt_attention_mask=None,\n negative_prompt_attention_mask=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and prompt_attention_mask is None:\n raise ValueError(\"Must provide `prompt_attention_mask` when specifying `prompt_embeds`.\")\n\n if negative_prompt_embeds is not None and negative_prompt_attention_mask is None:\n raise ValueError(\"Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.\")\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n if prompt_attention_mask.shape != negative_prompt_attention_mask.shape:\n raise ValueError(\n \"`prompt_attention_mask` and `negative_prompt_attention_mask` must have the same shape when passed directly, but\"\n f\" got: `prompt_attention_mask` {prompt_attention_mask.shape} != `negative_prompt_attention_mask`\"\n f\" {negative_prompt_attention_mask.shape}.\"\n )\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing\n def _text_preprocessing(self, text, clean_caption=False):\n if clean_caption and not is_bs4_available():\n logger.warning(BACKENDS_MAPPING[\"bs4\"][-1].format(\"Setting `clean_caption=True`\"))\n logger.warning(\"Setting `clean_caption` to False...\")\n clean_caption = False\n\n if clean_caption and not is_ftfy_available():\n logger.warning(BACKENDS_MAPPING[\"ftfy\"][-1].format(\"Setting `clean_caption=True`\"))\n logger.warning(\"Setting `clean_caption` to False...\")\n clean_caption = False\n\n if not isinstance(text, (tuple, list)):\n text = [text]\n\n def process(text: str):\n if clean_caption:\n text = self._clean_caption(text)\n text = self._clean_caption(text)\n else:\n text = text.lower().strip()\n return text\n\n return [process(t) for t in text]\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption\n def _clean_caption(self, caption):\n caption = str(caption)\n caption = ul.unquote_plus(caption)\n caption = caption.strip().lower()\n caption = re.sub(\"\", \"person\", caption)\n # urls:\n caption = re.sub(\n r\"\\b((?:https?:(?:\\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\\w/-]*\\b\\/?(?!@)))\", # noqa\n \"\",\n caption,\n ) # regex for urls\n caption = re.sub(\n r\"\\b((?:www:(?:\\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\\w/-]*\\b\\/?(?!@)))\", # noqa\n \"\",\n caption,\n ) # regex for urls\n # html:\n caption = BeautifulSoup(caption, features=\"html.parser\").text\n\n # @\n caption = re.sub(r\"@[\\w\\d]+\\b\", \"\", caption)\n\n # 31C0—31EF CJK Strokes\n # 31F0—31FF Katakana Phonetic Extensions\n # 3200—32FF Enclosed CJK Letters and Months\n # 3300—33FF CJK Compatibility\n # 3400—4DBF CJK Unified Ideographs Extension A\n # 4DC0—4DFF Yijing Hexagram Symbols\n # 4E00—9FFF CJK Unified Ideographs\n caption = re.sub(r\"[\\u31c0-\\u31ef]+\", \"\", caption)\n caption = re.sub(r\"[\\u31f0-\\u31ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3200-\\u32ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3300-\\u33ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3400-\\u4dbf]+\", \"\", caption)\n caption = re.sub(r\"[\\u4dc0-\\u4dff]+\", \"\", caption)\n caption = re.sub(r\"[\\u4e00-\\u9fff]+\", \"\", caption)\n #######################################################\n\n # все виды тире / all types of dash --> \"-\"\n caption = re.sub(\n r\"[\\u002D\\u058A\\u05BE\\u1400\\u1806\\u2010-\\u2015\\u2E17\\u2E1A\\u2E3A\\u2E3B\\u2E40\\u301C\\u3030\\u30A0\\uFE31\\uFE32\\uFE58\\uFE63\\uFF0D]+\", # noqa\n \"-\",\n caption,\n )\n\n # кавычки к одному стандарту\n caption = re.sub(r\"[`´«»“”¨]\", '\"', caption)\n caption = re.sub(r\"[‘’]\", \"'\", caption)\n\n # "\n caption = re.sub(r\""?\", \"\", caption)\n # &\n caption = re.sub(r\"&\", \"\", caption)\n\n # ip adresses:\n caption = re.sub(r\"\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\", \" \", caption)\n\n # article ids:\n caption = re.sub(r\"\\d:\\d\\d\\s+$\", \"\", caption)\n\n # \\n\n caption = re.sub(r\"\\\\n\", \" \", caption)\n\n # \"#123\"\n caption = re.sub(r\"#\\d{1,3}\\b\", \"\", caption)\n # \"#12345..\"\n caption = re.sub(r\"#\\d{5,}\\b\", \"\", caption)\n # \"123456..\"\n caption = re.sub(r\"\\b\\d{6,}\\b\", \"\", caption)\n # filenames:\n caption = re.sub(r\"[\\S]+\\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)\", \"\", caption)\n\n #\n caption = re.sub(r\"[\\\"\\']{2,}\", r'\"', caption) # \"\"\"AUSVERKAUFT\"\"\"\n caption = re.sub(r\"[\\.]{2,}\", r\" \", caption) # \"\"\"AUSVERKAUFT\"\"\"\n\n caption = re.sub(self.bad_punct_regex, r\" \", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT\n caption = re.sub(r\"\\s+\\.\\s+\", r\" \", caption) # \" . \"\n\n # this-is-my-cute-cat / this_is_my_cute_cat\n regex2 = re.compile(r\"(?:\\-|\\_)\")\n if len(re.findall(regex2, caption)) > 3:\n caption = re.sub(regex2, \" \", caption)\n\n caption = ftfy.fix_text(caption)\n caption = html.unescape(html.unescape(caption))\n\n caption = re.sub(r\"\\b[a-zA-Z]{1,3}\\d{3,15}\\b\", \"\", caption) # jc6640\n caption = re.sub(r\"\\b[a-zA-Z]+\\d+[a-zA-Z]+\\b\", \"\", caption) # jc6640vc\n caption = re.sub(r\"\\b\\d+[a-zA-Z]+\\d+\\b\", \"\", caption) # 6640vc231\n\n caption = re.sub(r\"(worldwide\\s+)?(free\\s+)?shipping\", \"\", caption)\n caption = re.sub(r\"(free\\s)?download(\\sfree)?\", \"\", caption)\n caption = re.sub(r\"\\bclick\\b\\s(?:for|on)\\s\\w+\", \"\", caption)\n caption = re.sub(r\"\\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\\simage[s]?)?\", \"\", caption)\n caption = re.sub(r\"\\bpage\\s+\\d+\\b\", \"\", caption)\n\n caption = re.sub(r\"\\b\\d*[a-zA-Z]+\\d+[a-zA-Z]+\\d+[a-zA-Z\\d]*\\b\", r\" \", caption) # j2d1a2a...\n\n caption = re.sub(r\"\\b\\d+\\.?\\d*[xх×]\\d+\\.?\\d*\\b\", \"\", caption)\n\n caption = re.sub(r\"\\b\\s+\\:\\s+\", r\": \", caption)\n caption = re.sub(r\"(\\D[,\\./])\\b\", r\"\\1 \", caption)\n caption = re.sub(r\"\\s+\", \" \", caption)\n\n caption.strip()\n\n caption = re.sub(r\"^[\\\"\\']([\\w\\W]+)[\\\"\\']$\", r\"\\1\", caption)\n caption = re.sub(r\"^[\\'\\_,\\-\\:;]\", r\"\", caption)\n caption = re.sub(r\"[\\'\\_,\\-\\:\\-\\+]$\", r\"\", caption)\n caption = re.sub(r\"^\\.\\S+$\", \"\", caption)\n\n return caption.strip()\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n negative_prompt: str = \"\",\n num_inference_steps: int = 20,\n timesteps: List[int] = None,\n sigmas: List[float] = None,\n guidance_scale: float = 4.5,\n num_images_per_prompt: Optional[int] = 1,\n height: Optional[int] = None,\n width: Optional[int] = None,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n prompt_attention_mask: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_attention_mask: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n clean_caption: bool = True,\n use_resolution_binning: bool = True,\n max_sequence_length: int = 120,\n **kwargs,\n ) -> Union[ImagePipelineOutput, Tuple]:\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n sigmas (`List[float]`, *optional*):\n Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in\n their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed\n will be used.\n guidance_scale (`float`, *optional*, defaults to 4.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n height (`int`, *optional*, defaults to self.unet.config.sample_size):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size):\n The width in pixels of the generated image.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n prompt_attention_mask (`torch.Tensor`, *optional*): Pre-generated attention mask for text embeddings.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. For PixArt-Alpha this negative prompt should be \"\". If not\n provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.\n negative_prompt_attention_mask (`torch.Tensor`, *optional*):\n Pre-generated attention mask for negative text embeddings.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n clean_caption (`bool`, *optional*, defaults to `True`):\n Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to\n be installed. If the dependencies are not installed, the embeddings will be created from the raw\n prompt.\n use_resolution_binning (`bool` defaults to `True`):\n If set to `True`, the requested height and width are first mapped to the closest resolutions using\n `ASPECT_RATIO_1024_BIN`. After the produced latents are decoded into images, they are resized back to\n the requested resolution. Useful for generating non-square images.\n max_sequence_length (`int` defaults to 120): Maximum sequence length to use with the `prompt`.\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is\n returned where the first element is a list with the generated images\n \"\"\"\n if \"mask_feature\" in kwargs:\n deprecation_message = \"The use of `mask_feature` is deprecated. It is no longer used in any computation and that doesn't affect the end results. It will be removed in a future version.\"\n deprecate(\"mask_feature\", \"1.0.0\", deprecation_message, standard_warn=False)\n # 1. Check inputs. Raise error if not correct\n height = height or self.transformer.config.sample_size * self.vae_scale_factor\n width = width or self.transformer.config.sample_size * self.vae_scale_factor\n if use_resolution_binning:\n if self.transformer.config.sample_size == 128:\n aspect_ratio_bin = ASPECT_RATIO_1024_BIN\n elif self.transformer.config.sample_size == 64:\n aspect_ratio_bin = ASPECT_RATIO_512_BIN\n elif self.transformer.config.sample_size == 32:\n aspect_ratio_bin = ASPECT_RATIO_256_BIN\n else:\n raise ValueError(\"Invalid sample size\")\n orig_height, orig_width = height, width\n height, width = self.image_processor.classify_height_width_bin(height, width, ratios=aspect_ratio_bin)\n\n self.check_inputs(\n prompt,\n height,\n width,\n negative_prompt,\n callback_steps,\n prompt_embeds,\n negative_prompt_embeds,\n prompt_attention_mask,\n negative_prompt_attention_mask,\n )\n\n # 2. Default height and width to transformer\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n (\n prompt_embeds,\n prompt_attention_mask,\n negative_prompt_embeds,\n negative_prompt_attention_mask,\n ) = self.encode_prompt(\n prompt,\n do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n prompt_attention_mask=prompt_attention_mask,\n negative_prompt_attention_mask=negative_prompt_attention_mask,\n clean_caption=clean_caption,\n max_sequence_length=max_sequence_length,\n )\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0)\n\n # 4. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(\n self.scheduler, num_inference_steps, device, timesteps, sigmas\n )\n\n # 5. Prepare latents.\n latent_channels = self.transformer.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n latent_channels,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 6.1 Prepare micro-conditions.\n added_cond_kwargs = {\"resolution\": None, \"aspect_ratio\": None}\n if self.transformer.config.sample_size == 128:\n resolution = torch.tensor([height, width]).repeat(batch_size * num_images_per_prompt, 1)\n aspect_ratio = torch.tensor([float(height / width)]).repeat(batch_size * num_images_per_prompt, 1)\n resolution = resolution.to(dtype=prompt_embeds.dtype, device=device)\n aspect_ratio = aspect_ratio.to(dtype=prompt_embeds.dtype, device=device)\n\n if do_classifier_free_guidance:\n resolution = torch.cat([resolution, resolution], dim=0)\n aspect_ratio = torch.cat([aspect_ratio, aspect_ratio], dim=0)\n\n added_cond_kwargs = {\"resolution\": resolution, \"aspect_ratio\": aspect_ratio}\n\n # 7. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n current_timestep = t\n if not torch.is_tensor(current_timestep):\n # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can\n # This would be a good case for the `match` statement (Python 3.10+)\n is_mps = latent_model_input.device.type == \"mps\"\n if isinstance(current_timestep, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device)\n elif len(current_timestep.shape) == 0:\n current_timestep = current_timestep[None].to(latent_model_input.device)\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n current_timestep = current_timestep.expand(latent_model_input.shape[0])\n\n # predict noise model_output\n noise_pred = self.transformer(\n latent_model_input,\n encoder_hidden_states=prompt_embeds,\n encoder_attention_mask=prompt_attention_mask,\n timestep=current_timestep,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # learned sigma\n if self.transformer.config.out_channels // 2 == latent_channels:\n noise_pred = noise_pred.chunk(2, dim=1)[0]\n else:\n noise_pred = noise_pred\n\n # compute previous image: x_t -> x_t-1\n if num_inference_steps == 1:\n # For DMD one step sampling: https://arxiv.org/abs/2311.18828\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).pred_original_sample\n else:\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n if use_resolution_binning:\n image = self.image_processor.resize_and_crop_tensor(image, orig_width, orig_height)\n else:\n image = latents\n\n if not output_type == \"latent\":\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py", "content": "# Copyright 2024 PixArt-Sigma Authors and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport html\nimport inspect\nimport re\nimport urllib.parse as ul\nfrom typing import Callable, List, Optional, Tuple, Union\n\nimport torch\nfrom transformers import T5EncoderModel, T5Tokenizer\n\nfrom ...image_processor import PixArtImageProcessor\nfrom ...models import AutoencoderKL, PixArtTransformer2DModel\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n BACKENDS_MAPPING,\n deprecate,\n is_bs4_available,\n is_ftfy_available,\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\nfrom .pipeline_pixart_alpha import (\n ASPECT_RATIO_256_BIN,\n ASPECT_RATIO_512_BIN,\n ASPECT_RATIO_1024_BIN,\n)\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nif is_bs4_available():\n from bs4 import BeautifulSoup\n\nif is_ftfy_available():\n import ftfy\n\n\nASPECT_RATIO_2048_BIN = {\n \"0.25\": [1024.0, 4096.0],\n \"0.26\": [1024.0, 3968.0],\n \"0.27\": [1024.0, 3840.0],\n \"0.28\": [1024.0, 3712.0],\n \"0.32\": [1152.0, 3584.0],\n \"0.33\": [1152.0, 3456.0],\n \"0.35\": [1152.0, 3328.0],\n \"0.4\": [1280.0, 3200.0],\n \"0.42\": [1280.0, 3072.0],\n \"0.48\": [1408.0, 2944.0],\n \"0.5\": [1408.0, 2816.0],\n \"0.52\": [1408.0, 2688.0],\n \"0.57\": [1536.0, 2688.0],\n \"0.6\": [1536.0, 2560.0],\n \"0.68\": [1664.0, 2432.0],\n \"0.72\": [1664.0, 2304.0],\n \"0.78\": [1792.0, 2304.0],\n \"0.82\": [1792.0, 2176.0],\n \"0.88\": [1920.0, 2176.0],\n \"0.94\": [1920.0, 2048.0],\n \"1.0\": [2048.0, 2048.0],\n \"1.07\": [2048.0, 1920.0],\n \"1.13\": [2176.0, 1920.0],\n \"1.21\": [2176.0, 1792.0],\n \"1.29\": [2304.0, 1792.0],\n \"1.38\": [2304.0, 1664.0],\n \"1.46\": [2432.0, 1664.0],\n \"1.67\": [2560.0, 1536.0],\n \"1.75\": [2688.0, 1536.0],\n \"2.0\": [2816.0, 1408.0],\n \"2.09\": [2944.0, 1408.0],\n \"2.4\": [3072.0, 1280.0],\n \"2.5\": [3200.0, 1280.0],\n \"2.89\": [3328.0, 1152.0],\n \"3.0\": [3456.0, 1152.0],\n \"3.11\": [3584.0, 1152.0],\n \"3.62\": [3712.0, 1024.0],\n \"3.75\": [3840.0, 1024.0],\n \"3.88\": [3968.0, 1024.0],\n \"4.0\": [4096.0, 1024.0],\n}\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import PixArtSigmaPipeline\n\n >>> # You can replace the checkpoint id with \"PixArt-alpha/PixArt-Sigma-XL-2-512-MS\" too.\n >>> pipe = PixArtSigmaPipeline.from_pretrained(\n ... \"PixArt-alpha/PixArt-Sigma-XL-2-1024-MS\", torch_dtype=torch.float16\n ... )\n >>> # Enable memory optimizations.\n >>> # pipe.enable_model_cpu_offload()\n\n >>> prompt = \"A small cactus with a happy face in the Sahara desert.\"\n >>> image = pipe(prompt).images[0]\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass PixArtSigmaPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for text-to-image generation using PixArt-Sigma.\n \"\"\"\n\n bad_punct_regex = re.compile(\n r\"[\"\n + \"#®•©™&@·º½¾¿¡§~\"\n + r\"\\)\"\n + r\"\\(\"\n + r\"\\]\"\n + r\"\\[\"\n + r\"\\}\"\n + r\"\\{\"\n + r\"\\|\"\n + \"\\\\\"\n + r\"\\/\"\n + r\"\\*\"\n + r\"]{1,}\"\n ) # noqa\n\n _optional_components = [\"tokenizer\", \"text_encoder\"]\n model_cpu_offload_seq = \"text_encoder->transformer->vae\"\n\n def __init__(\n self,\n tokenizer: T5Tokenizer,\n text_encoder: T5EncoderModel,\n vae: AutoencoderKL,\n transformer: PixArtTransformer2DModel,\n scheduler: KarrasDiffusionSchedulers,\n ):\n super().__init__()\n\n self.register_modules(\n tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler\n )\n\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = PixArtImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n # Copied from diffusers.pipelines.pixart_alpha.pipeline_pixart_alpha.PixArtAlphaPipeline.encode_prompt with 120->300\n def encode_prompt(\n self,\n prompt: Union[str, List[str]],\n do_classifier_free_guidance: bool = True,\n negative_prompt: str = \"\",\n num_images_per_prompt: int = 1,\n device: Optional[torch.device] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n prompt_attention_mask: Optional[torch.Tensor] = None,\n negative_prompt_attention_mask: Optional[torch.Tensor] = None,\n clean_caption: bool = False,\n max_sequence_length: int = 300,\n **kwargs,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`\n instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). For\n PixArt-Alpha, this should be \"\".\n do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):\n whether to use classifier free guidance or not\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n number of images that should be generated per prompt\n device: (`torch.device`, *optional*):\n torch device to place the resulting embeddings on\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. For PixArt-Alpha, it's should be the embeddings of the \"\"\n string.\n clean_caption (`bool`, defaults to `False`):\n If `True`, the function will preprocess and clean the provided caption before encoding.\n max_sequence_length (`int`, defaults to 300): Maximum sequence length to use for the prompt.\n \"\"\"\n\n if \"mask_feature\" in kwargs:\n deprecation_message = \"The use of `mask_feature` is deprecated. It is no longer used in any computation and that doesn't affect the end results. It will be removed in a future version.\"\n deprecate(\"mask_feature\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n if device is None:\n device = self._execution_device\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # See Section 3.1. of the paper.\n max_length = max_sequence_length\n\n if prompt_embeds is None:\n prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because T5 can only handle sequences up to\"\n f\" {max_length} tokens: {removed_text}\"\n )\n\n prompt_attention_mask = text_inputs.attention_mask\n prompt_attention_mask = prompt_attention_mask.to(device)\n\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=prompt_attention_mask)\n prompt_embeds = prompt_embeds[0]\n\n if self.text_encoder is not None:\n dtype = self.text_encoder.dtype\n elif self.transformer is not None:\n dtype = self.transformer.dtype\n else:\n dtype = None\n\n prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n prompt_attention_mask = prompt_attention_mask.view(bs_embed, -1)\n prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens = [negative_prompt] * batch_size if isinstance(negative_prompt, str) else negative_prompt\n uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_attention_mask=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n negative_prompt_attention_mask = uncond_input.attention_mask\n negative_prompt_attention_mask = negative_prompt_attention_mask.to(device)\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device), attention_mask=negative_prompt_attention_mask\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n negative_prompt_attention_mask = negative_prompt_attention_mask.view(bs_embed, -1)\n negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1)\n else:\n negative_prompt_embeds = None\n negative_prompt_attention_mask = None\n\n return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.pixart_alpha.pipeline_pixart_alpha.PixArtAlphaPipeline.check_inputs\n def check_inputs(\n self,\n prompt,\n height,\n width,\n negative_prompt,\n callback_steps,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n prompt_attention_mask=None,\n negative_prompt_attention_mask=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and prompt_attention_mask is None:\n raise ValueError(\"Must provide `prompt_attention_mask` when specifying `prompt_embeds`.\")\n\n if negative_prompt_embeds is not None and negative_prompt_attention_mask is None:\n raise ValueError(\"Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.\")\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n if prompt_attention_mask.shape != negative_prompt_attention_mask.shape:\n raise ValueError(\n \"`prompt_attention_mask` and `negative_prompt_attention_mask` must have the same shape when passed directly, but\"\n f\" got: `prompt_attention_mask` {prompt_attention_mask.shape} != `negative_prompt_attention_mask`\"\n f\" {negative_prompt_attention_mask.shape}.\"\n )\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing\n def _text_preprocessing(self, text, clean_caption=False):\n if clean_caption and not is_bs4_available():\n logger.warning(BACKENDS_MAPPING[\"bs4\"][-1].format(\"Setting `clean_caption=True`\"))\n logger.warning(\"Setting `clean_caption` to False...\")\n clean_caption = False\n\n if clean_caption and not is_ftfy_available():\n logger.warning(BACKENDS_MAPPING[\"ftfy\"][-1].format(\"Setting `clean_caption=True`\"))\n logger.warning(\"Setting `clean_caption` to False...\")\n clean_caption = False\n\n if not isinstance(text, (tuple, list)):\n text = [text]\n\n def process(text: str):\n if clean_caption:\n text = self._clean_caption(text)\n text = self._clean_caption(text)\n else:\n text = text.lower().strip()\n return text\n\n return [process(t) for t in text]\n\n # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption\n def _clean_caption(self, caption):\n caption = str(caption)\n caption = ul.unquote_plus(caption)\n caption = caption.strip().lower()\n caption = re.sub(\"\", \"person\", caption)\n # urls:\n caption = re.sub(\n r\"\\b((?:https?:(?:\\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\\w/-]*\\b\\/?(?!@)))\", # noqa\n \"\",\n caption,\n ) # regex for urls\n caption = re.sub(\n r\"\\b((?:www:(?:\\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\\w/-]*\\b\\/?(?!@)))\", # noqa\n \"\",\n caption,\n ) # regex for urls\n # html:\n caption = BeautifulSoup(caption, features=\"html.parser\").text\n\n # @\n caption = re.sub(r\"@[\\w\\d]+\\b\", \"\", caption)\n\n # 31C0—31EF CJK Strokes\n # 31F0—31FF Katakana Phonetic Extensions\n # 3200—32FF Enclosed CJK Letters and Months\n # 3300—33FF CJK Compatibility\n # 3400—4DBF CJK Unified Ideographs Extension A\n # 4DC0—4DFF Yijing Hexagram Symbols\n # 4E00—9FFF CJK Unified Ideographs\n caption = re.sub(r\"[\\u31c0-\\u31ef]+\", \"\", caption)\n caption = re.sub(r\"[\\u31f0-\\u31ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3200-\\u32ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3300-\\u33ff]+\", \"\", caption)\n caption = re.sub(r\"[\\u3400-\\u4dbf]+\", \"\", caption)\n caption = re.sub(r\"[\\u4dc0-\\u4dff]+\", \"\", caption)\n caption = re.sub(r\"[\\u4e00-\\u9fff]+\", \"\", caption)\n #######################################################\n\n # все виды тире / all types of dash --> \"-\"\n caption = re.sub(\n r\"[\\u002D\\u058A\\u05BE\\u1400\\u1806\\u2010-\\u2015\\u2E17\\u2E1A\\u2E3A\\u2E3B\\u2E40\\u301C\\u3030\\u30A0\\uFE31\\uFE32\\uFE58\\uFE63\\uFF0D]+\", # noqa\n \"-\",\n caption,\n )\n\n # кавычки к одному стандарту\n caption = re.sub(r\"[`´«»“”¨]\", '\"', caption)\n caption = re.sub(r\"[‘’]\", \"'\", caption)\n\n # "\n caption = re.sub(r\""?\", \"\", caption)\n # &\n caption = re.sub(r\"&\", \"\", caption)\n\n # ip adresses:\n caption = re.sub(r\"\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\", \" \", caption)\n\n # article ids:\n caption = re.sub(r\"\\d:\\d\\d\\s+$\", \"\", caption)\n\n # \\n\n caption = re.sub(r\"\\\\n\", \" \", caption)\n\n # \"#123\"\n caption = re.sub(r\"#\\d{1,3}\\b\", \"\", caption)\n # \"#12345..\"\n caption = re.sub(r\"#\\d{5,}\\b\", \"\", caption)\n # \"123456..\"\n caption = re.sub(r\"\\b\\d{6,}\\b\", \"\", caption)\n # filenames:\n caption = re.sub(r\"[\\S]+\\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)\", \"\", caption)\n\n #\n caption = re.sub(r\"[\\\"\\']{2,}\", r'\"', caption) # \"\"\"AUSVERKAUFT\"\"\"\n caption = re.sub(r\"[\\.]{2,}\", r\" \", caption) # \"\"\"AUSVERKAUFT\"\"\"\n\n caption = re.sub(self.bad_punct_regex, r\" \", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT\n caption = re.sub(r\"\\s+\\.\\s+\", r\" \", caption) # \" . \"\n\n # this-is-my-cute-cat / this_is_my_cute_cat\n regex2 = re.compile(r\"(?:\\-|\\_)\")\n if len(re.findall(regex2, caption)) > 3:\n caption = re.sub(regex2, \" \", caption)\n\n caption = ftfy.fix_text(caption)\n caption = html.unescape(html.unescape(caption))\n\n caption = re.sub(r\"\\b[a-zA-Z]{1,3}\\d{3,15}\\b\", \"\", caption) # jc6640\n caption = re.sub(r\"\\b[a-zA-Z]+\\d+[a-zA-Z]+\\b\", \"\", caption) # jc6640vc\n caption = re.sub(r\"\\b\\d+[a-zA-Z]+\\d+\\b\", \"\", caption) # 6640vc231\n\n caption = re.sub(r\"(worldwide\\s+)?(free\\s+)?shipping\", \"\", caption)\n caption = re.sub(r\"(free\\s)?download(\\sfree)?\", \"\", caption)\n caption = re.sub(r\"\\bclick\\b\\s(?:for|on)\\s\\w+\", \"\", caption)\n caption = re.sub(r\"\\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\\simage[s]?)?\", \"\", caption)\n caption = re.sub(r\"\\bpage\\s+\\d+\\b\", \"\", caption)\n\n caption = re.sub(r\"\\b\\d*[a-zA-Z]+\\d+[a-zA-Z]+\\d+[a-zA-Z\\d]*\\b\", r\" \", caption) # j2d1a2a...\n\n caption = re.sub(r\"\\b\\d+\\.?\\d*[xх×]\\d+\\.?\\d*\\b\", \"\", caption)\n\n caption = re.sub(r\"\\b\\s+\\:\\s+\", r\": \", caption)\n caption = re.sub(r\"(\\D[,\\./])\\b\", r\"\\1 \", caption)\n caption = re.sub(r\"\\s+\", \" \", caption)\n\n caption.strip()\n\n caption = re.sub(r\"^[\\\"\\']([\\w\\W]+)[\\\"\\']$\", r\"\\1\", caption)\n caption = re.sub(r\"^[\\'\\_,\\-\\:;]\", r\"\", caption)\n caption = re.sub(r\"[\\'\\_,\\-\\:\\-\\+]$\", r\"\", caption)\n caption = re.sub(r\"^\\.\\S+$\", \"\", caption)\n\n return caption.strip()\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n negative_prompt: str = \"\",\n num_inference_steps: int = 20,\n timesteps: List[int] = None,\n sigmas: List[float] = None,\n guidance_scale: float = 4.5,\n num_images_per_prompt: Optional[int] = 1,\n height: Optional[int] = None,\n width: Optional[int] = None,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n prompt_attention_mask: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_attention_mask: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n clean_caption: bool = True,\n use_resolution_binning: bool = True,\n max_sequence_length: int = 300,\n **kwargs,\n ) -> Union[ImagePipelineOutput, Tuple]:\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n sigmas (`List[float]`, *optional*):\n Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in\n their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed\n will be used.\n guidance_scale (`float`, *optional*, defaults to 4.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n height (`int`, *optional*, defaults to self.unet.config.sample_size):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size):\n The width in pixels of the generated image.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n prompt_attention_mask (`torch.Tensor`, *optional*): Pre-generated attention mask for text embeddings.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. For PixArt-Sigma this negative prompt should be \"\". If not\n provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.\n negative_prompt_attention_mask (`torch.Tensor`, *optional*):\n Pre-generated attention mask for negative text embeddings.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n clean_caption (`bool`, *optional*, defaults to `True`):\n Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to\n be installed. If the dependencies are not installed, the embeddings will be created from the raw\n prompt.\n use_resolution_binning (`bool` defaults to `True`):\n If set to `True`, the requested height and width are first mapped to the closest resolutions using\n `ASPECT_RATIO_1024_BIN`. After the produced latents are decoded into images, they are resized back to\n the requested resolution. Useful for generating non-square images.\n max_sequence_length (`int` defaults to 300): Maximum sequence length to use with the `prompt`.\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is\n returned where the first element is a list with the generated images\n \"\"\"\n # 1. Check inputs. Raise error if not correct\n height = height or self.transformer.config.sample_size * self.vae_scale_factor\n width = width or self.transformer.config.sample_size * self.vae_scale_factor\n if use_resolution_binning:\n if self.transformer.config.sample_size == 256:\n aspect_ratio_bin = ASPECT_RATIO_2048_BIN\n elif self.transformer.config.sample_size == 128:\n aspect_ratio_bin = ASPECT_RATIO_1024_BIN\n elif self.transformer.config.sample_size == 64:\n aspect_ratio_bin = ASPECT_RATIO_512_BIN\n elif self.transformer.config.sample_size == 32:\n aspect_ratio_bin = ASPECT_RATIO_256_BIN\n else:\n raise ValueError(\"Invalid sample size\")\n orig_height, orig_width = height, width\n height, width = self.image_processor.classify_height_width_bin(height, width, ratios=aspect_ratio_bin)\n\n self.check_inputs(\n prompt,\n height,\n width,\n negative_prompt,\n callback_steps,\n prompt_embeds,\n negative_prompt_embeds,\n prompt_attention_mask,\n negative_prompt_attention_mask,\n )\n\n # 2. Default height and width to transformer\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n (\n prompt_embeds,\n prompt_attention_mask,\n negative_prompt_embeds,\n negative_prompt_attention_mask,\n ) = self.encode_prompt(\n prompt,\n do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n prompt_attention_mask=prompt_attention_mask,\n negative_prompt_attention_mask=negative_prompt_attention_mask,\n clean_caption=clean_caption,\n max_sequence_length=max_sequence_length,\n )\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0)\n\n # 4. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(\n self.scheduler, num_inference_steps, device, timesteps, sigmas\n )\n\n # 5. Prepare latents.\n latent_channels = self.transformer.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n latent_channels,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 6.1 Prepare micro-conditions.\n added_cond_kwargs = {\"resolution\": None, \"aspect_ratio\": None}\n\n # 7. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n current_timestep = t\n if not torch.is_tensor(current_timestep):\n # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can\n # This would be a good case for the `match` statement (Python 3.10+)\n is_mps = latent_model_input.device.type == \"mps\"\n if isinstance(current_timestep, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device)\n elif len(current_timestep.shape) == 0:\n current_timestep = current_timestep[None].to(latent_model_input.device)\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n current_timestep = current_timestep.expand(latent_model_input.shape[0])\n\n # predict noise model_output\n noise_pred = self.transformer(\n latent_model_input,\n encoder_hidden_states=prompt_embeds,\n encoder_attention_mask=prompt_attention_mask,\n timestep=current_timestep,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # learned sigma\n if self.transformer.config.out_channels // 2 == latent_channels:\n noise_pred = noise_pred.chunk(2, dim=1)[0]\n else:\n noise_pred = noise_pred\n\n # compute previous image: x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n if use_resolution_binning:\n image = self.image_processor.resize_and_crop_tensor(image, orig_width, orig_height)\n else:\n image = latents\n\n if not output_type == \"latent\":\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/semantic_stable_diffusion/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_output\"] = [\"SemanticStableDiffusionPipelineOutput\"]\n _import_structure[\"pipeline_semantic_stable_diffusion\"] = [\"SemanticStableDiffusionPipeline\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n else:\n from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_output.py", "content": "from dataclasses import dataclass\nfrom typing import List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\n\nfrom ...utils import BaseOutput\n\n\n@dataclass\nclass SemanticStableDiffusionPipelineOutput(BaseOutput):\n \"\"\"\n Output class for Stable Diffusion pipelines.\n\n Args:\n images (`List[PIL.Image.Image]` or `np.ndarray`)\n List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,\n num_channels)`.\n nsfw_content_detected (`List[bool]`)\n List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content or\n `None` if safety checking could not be performed.\n \"\"\"\n\n images: Union[List[PIL.Image.Image], np.ndarray]\n nsfw_content_detected: Optional[List[bool]]\n" }, { "path": "diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py", "content": "import inspect\nfrom itertools import repeat\nfrom typing import Callable, List, Optional, Union\n\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom ...image_processor import VaeImageProcessor\nfrom ...models import AutoencoderKL, UNet2DConditionModel\nfrom ...pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import deprecate, logging\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom .pipeline_output import SemanticStableDiffusionPipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass SemanticStableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion with latent editing.\n\n This model inherits from [`DiffusionPipeline`] and builds on the [`StableDiffusionPipeline`]. Check the superclass\n documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular\n device, etc.).\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`Q16SafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion_k_diffusion.pipeline_stable_diffusion_k_diffusion.StableDiffusionKDiffusionPipeline.check_inputs\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: int = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n editing_prompt: Optional[Union[str, List[str]]] = None,\n editing_prompt_embeddings: Optional[torch.Tensor] = None,\n reverse_editing_direction: Optional[Union[bool, List[bool]]] = False,\n edit_guidance_scale: Optional[Union[float, List[float]]] = 5,\n edit_warmup_steps: Optional[Union[int, List[int]]] = 10,\n edit_cooldown_steps: Optional[Union[int, List[int]]] = None,\n edit_threshold: Optional[Union[float, List[float]]] = 0.9,\n edit_momentum_scale: Optional[float] = 0.1,\n edit_mom_beta: Optional[float] = 0.4,\n edit_weights: Optional[List[float]] = None,\n sem_guidance: Optional[List[torch.Tensor]] = None,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide image generation.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n editing_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to use for semantic guidance. Semantic guidance is disabled by setting\n `editing_prompt = None`. Guidance direction of prompt should be specified via\n `reverse_editing_direction`.\n editing_prompt_embeddings (`torch.Tensor`, *optional*):\n Pre-computed embeddings to use for semantic guidance. Guidance direction of embedding should be\n specified via `reverse_editing_direction`.\n reverse_editing_direction (`bool` or `List[bool]`, *optional*, defaults to `False`):\n Whether the corresponding prompt in `editing_prompt` should be increased or decreased.\n edit_guidance_scale (`float` or `List[float]`, *optional*, defaults to 5):\n Guidance scale for semantic guidance. If provided as a list, values should correspond to\n `editing_prompt`.\n edit_warmup_steps (`float` or `List[float]`, *optional*, defaults to 10):\n Number of diffusion steps (for each prompt) for which semantic guidance is not applied. Momentum is\n calculated for those steps and applied once all warmup periods are over.\n edit_cooldown_steps (`float` or `List[float]`, *optional*, defaults to `None`):\n Number of diffusion steps (for each prompt) after which semantic guidance is longer applied.\n edit_threshold (`float` or `List[float]`, *optional*, defaults to 0.9):\n Threshold of semantic guidance.\n edit_momentum_scale (`float`, *optional*, defaults to 0.1):\n Scale of the momentum to be added to the semantic guidance at each diffusion step. If set to 0.0,\n momentum is disabled. Momentum is already built up during warmup (for diffusion steps smaller than\n `sld_warmup_steps`). Momentum is only added to latent guidance once all warmup periods are finished.\n edit_mom_beta (`float`, *optional*, defaults to 0.4):\n Defines how semantic guidance momentum builds up. `edit_mom_beta` indicates how much of the previous\n momentum is kept. Momentum is already built up during warmup (for diffusion steps smaller than\n `edit_warmup_steps`).\n edit_weights (`List[float]`, *optional*, defaults to `None`):\n Indicates how much each individual concept should influence the overall guidance. If no weights are\n provided all concepts are applied equally.\n sem_guidance (`List[torch.Tensor]`, *optional*):\n List of pre-generated guidance vectors to be applied at generation. Length of the list has to\n correspond to `num_inference_steps`.\n\n Examples:\n\n ```py\n >>> import torch\n >>> from diffusers import SemanticStableDiffusionPipeline\n\n >>> pipe = SemanticStableDiffusionPipeline.from_pretrained(\n ... \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> out = pipe(\n ... prompt=\"a photo of the face of a woman\",\n ... num_images_per_prompt=1,\n ... guidance_scale=7,\n ... editing_prompt=[\n ... \"smiling, smile\", # Concepts to apply\n ... \"glasses, wearing glasses\",\n ... \"curls, wavy hair, curly hair\",\n ... \"beard, full beard, mustache\",\n ... ],\n ... reverse_editing_direction=[\n ... False,\n ... False,\n ... False,\n ... False,\n ... ], # Direction of guidance i.e. increase all concepts\n ... edit_warmup_steps=[10, 10, 10, 10], # Warmup period for each concept\n ... edit_guidance_scale=[4, 5, 5, 5.4], # Guidance scale for each concept\n ... edit_threshold=[\n ... 0.99,\n ... 0.975,\n ... 0.925,\n ... 0.96,\n ... ], # Threshold for each concept. Threshold equals the percentile of the latent space that will be discarded. I.e. threshold=0.99 uses 1% of the latent dimensions\n ... edit_momentum_scale=0.3, # Momentum scale that will be added to the latent guidance\n ... edit_mom_beta=0.6, # Momentum beta\n ... edit_weights=[1, 1, 1, 1, 1], # Weights of the individual concepts against each other\n ... )\n >>> image = out.images[0]\n ```\n\n Returns:\n [`~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`,\n [`~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput`] is returned, otherwise a\n `tuple` is returned where the first element is a list with the generated images and the second element\n is a list of `bool`s indicating whether the corresponding generated image contains \"not-safe-for-work\"\n (nsfw) content.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(prompt, height, width, callback_steps)\n\n # 2. Define call parameters\n batch_size = 1 if isinstance(prompt, str) else len(prompt)\n device = self._execution_device\n\n if editing_prompt:\n enable_edit_guidance = True\n if isinstance(editing_prompt, str):\n editing_prompt = [editing_prompt]\n enabled_editing_prompts = len(editing_prompt)\n elif editing_prompt_embeddings is not None:\n enable_edit_guidance = True\n enabled_editing_prompts = editing_prompt_embeddings.shape[0]\n else:\n enabled_editing_prompts = 0\n enable_edit_guidance = False\n\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n\n if text_input_ids.shape[-1] > self.tokenizer.model_max_length:\n removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]\n text_embeddings = self.text_encoder(text_input_ids.to(device))[0]\n\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = text_embeddings.shape\n text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)\n text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if enable_edit_guidance:\n # get safety text embeddings\n if editing_prompt_embeddings is None:\n edit_concepts_input = self.tokenizer(\n [x for item in editing_prompt for x in repeat(item, batch_size)],\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n )\n\n edit_concepts_input_ids = edit_concepts_input.input_ids\n\n if edit_concepts_input_ids.shape[-1] > self.tokenizer.model_max_length:\n removed_text = self.tokenizer.batch_decode(\n edit_concepts_input_ids[:, self.tokenizer.model_max_length :]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n edit_concepts_input_ids = edit_concepts_input_ids[:, : self.tokenizer.model_max_length]\n edit_concepts = self.text_encoder(edit_concepts_input_ids.to(device))[0]\n else:\n edit_concepts = editing_prompt_embeddings.to(device).repeat(batch_size, 1, 1)\n\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n bs_embed_edit, seq_len_edit, _ = edit_concepts.shape\n edit_concepts = edit_concepts.repeat(1, num_images_per_prompt, 1)\n edit_concepts = edit_concepts.view(bs_embed_edit * num_images_per_prompt, seq_len_edit, -1)\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n # get unconditional embeddings for classifier free guidance\n\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = text_input_ids.shape[-1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(device))[0]\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = uncond_embeddings.shape[1]\n uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)\n uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if enable_edit_guidance:\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings, edit_concepts])\n else:\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n # get the initial random noise unless the user supplied it\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n text_embeddings.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs.\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # Initialize edit_momentum to None\n edit_momentum = None\n\n self.uncond_estimates = None\n self.text_estimates = None\n self.edit_estimates = None\n self.sem_guidance = None\n\n for i, t in enumerate(self.progress_bar(timesteps)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = (\n torch.cat([latents] * (2 + enabled_editing_prompts)) if do_classifier_free_guidance else latents\n )\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_out = noise_pred.chunk(2 + enabled_editing_prompts) # [b,4, 64, 64]\n noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1]\n noise_pred_edit_concepts = noise_pred_out[2:]\n\n # default text guidance\n noise_guidance = guidance_scale * (noise_pred_text - noise_pred_uncond)\n # noise_guidance = (noise_pred_text - noise_pred_edit_concepts[0])\n\n if self.uncond_estimates is None:\n self.uncond_estimates = torch.zeros((num_inference_steps + 1, *noise_pred_uncond.shape))\n self.uncond_estimates[i] = noise_pred_uncond.detach().cpu()\n\n if self.text_estimates is None:\n self.text_estimates = torch.zeros((num_inference_steps + 1, *noise_pred_text.shape))\n self.text_estimates[i] = noise_pred_text.detach().cpu()\n\n if self.edit_estimates is None and enable_edit_guidance:\n self.edit_estimates = torch.zeros(\n (num_inference_steps + 1, len(noise_pred_edit_concepts), *noise_pred_edit_concepts[0].shape)\n )\n\n if self.sem_guidance is None:\n self.sem_guidance = torch.zeros((num_inference_steps + 1, *noise_pred_text.shape))\n\n if edit_momentum is None:\n edit_momentum = torch.zeros_like(noise_guidance)\n\n if enable_edit_guidance:\n concept_weights = torch.zeros(\n (len(noise_pred_edit_concepts), noise_guidance.shape[0]),\n device=device,\n dtype=noise_guidance.dtype,\n )\n noise_guidance_edit = torch.zeros(\n (len(noise_pred_edit_concepts), *noise_guidance.shape),\n device=device,\n dtype=noise_guidance.dtype,\n )\n # noise_guidance_edit = torch.zeros_like(noise_guidance)\n warmup_inds = []\n for c, noise_pred_edit_concept in enumerate(noise_pred_edit_concepts):\n self.edit_estimates[i, c] = noise_pred_edit_concept\n if isinstance(edit_guidance_scale, list):\n edit_guidance_scale_c = edit_guidance_scale[c]\n else:\n edit_guidance_scale_c = edit_guidance_scale\n\n if isinstance(edit_threshold, list):\n edit_threshold_c = edit_threshold[c]\n else:\n edit_threshold_c = edit_threshold\n if isinstance(reverse_editing_direction, list):\n reverse_editing_direction_c = reverse_editing_direction[c]\n else:\n reverse_editing_direction_c = reverse_editing_direction\n if edit_weights:\n edit_weight_c = edit_weights[c]\n else:\n edit_weight_c = 1.0\n if isinstance(edit_warmup_steps, list):\n edit_warmup_steps_c = edit_warmup_steps[c]\n else:\n edit_warmup_steps_c = edit_warmup_steps\n\n if isinstance(edit_cooldown_steps, list):\n edit_cooldown_steps_c = edit_cooldown_steps[c]\n elif edit_cooldown_steps is None:\n edit_cooldown_steps_c = i + 1\n else:\n edit_cooldown_steps_c = edit_cooldown_steps\n if i >= edit_warmup_steps_c:\n warmup_inds.append(c)\n if i >= edit_cooldown_steps_c:\n noise_guidance_edit[c, :, :, :, :] = torch.zeros_like(noise_pred_edit_concept)\n continue\n\n noise_guidance_edit_tmp = noise_pred_edit_concept - noise_pred_uncond\n # tmp_weights = (noise_pred_text - noise_pred_edit_concept).sum(dim=(1, 2, 3))\n tmp_weights = (noise_guidance - noise_pred_edit_concept).sum(dim=(1, 2, 3))\n\n tmp_weights = torch.full_like(tmp_weights, edit_weight_c) # * (1 / enabled_editing_prompts)\n if reverse_editing_direction_c:\n noise_guidance_edit_tmp = noise_guidance_edit_tmp * -1\n concept_weights[c, :] = tmp_weights\n\n noise_guidance_edit_tmp = noise_guidance_edit_tmp * edit_guidance_scale_c\n\n # torch.quantile function expects float32\n if noise_guidance_edit_tmp.dtype == torch.float32:\n tmp = torch.quantile(\n torch.abs(noise_guidance_edit_tmp).flatten(start_dim=2),\n edit_threshold_c,\n dim=2,\n keepdim=False,\n )\n else:\n tmp = torch.quantile(\n torch.abs(noise_guidance_edit_tmp).flatten(start_dim=2).to(torch.float32),\n edit_threshold_c,\n dim=2,\n keepdim=False,\n ).to(noise_guidance_edit_tmp.dtype)\n\n noise_guidance_edit_tmp = torch.where(\n torch.abs(noise_guidance_edit_tmp) >= tmp[:, :, None, None],\n noise_guidance_edit_tmp,\n torch.zeros_like(noise_guidance_edit_tmp),\n )\n noise_guidance_edit[c, :, :, :, :] = noise_guidance_edit_tmp\n\n # noise_guidance_edit = noise_guidance_edit + noise_guidance_edit_tmp\n\n warmup_inds = torch.tensor(warmup_inds).to(device)\n if len(noise_pred_edit_concepts) > warmup_inds.shape[0] > 0:\n concept_weights = concept_weights.to(\"cpu\") # Offload to cpu\n noise_guidance_edit = noise_guidance_edit.to(\"cpu\")\n\n concept_weights_tmp = torch.index_select(concept_weights.to(device), 0, warmup_inds)\n concept_weights_tmp = torch.where(\n concept_weights_tmp < 0, torch.zeros_like(concept_weights_tmp), concept_weights_tmp\n )\n concept_weights_tmp = concept_weights_tmp / concept_weights_tmp.sum(dim=0)\n # concept_weights_tmp = torch.nan_to_num(concept_weights_tmp)\n\n noise_guidance_edit_tmp = torch.index_select(noise_guidance_edit.to(device), 0, warmup_inds)\n noise_guidance_edit_tmp = torch.einsum(\n \"cb,cbijk->bijk\", concept_weights_tmp, noise_guidance_edit_tmp\n )\n noise_guidance_edit_tmp = noise_guidance_edit_tmp\n noise_guidance = noise_guidance + noise_guidance_edit_tmp\n\n self.sem_guidance[i] = noise_guidance_edit_tmp.detach().cpu()\n\n del noise_guidance_edit_tmp\n del concept_weights_tmp\n concept_weights = concept_weights.to(device)\n noise_guidance_edit = noise_guidance_edit.to(device)\n\n concept_weights = torch.where(\n concept_weights < 0, torch.zeros_like(concept_weights), concept_weights\n )\n\n concept_weights = torch.nan_to_num(concept_weights)\n\n noise_guidance_edit = torch.einsum(\"cb,cbijk->bijk\", concept_weights, noise_guidance_edit)\n noise_guidance_edit = noise_guidance_edit.to(edit_momentum.device)\n\n noise_guidance_edit = noise_guidance_edit + edit_momentum_scale * edit_momentum\n\n edit_momentum = edit_mom_beta * edit_momentum + (1 - edit_mom_beta) * noise_guidance_edit\n\n if warmup_inds.shape[0] == len(noise_pred_edit_concepts):\n noise_guidance = noise_guidance + noise_guidance_edit\n self.sem_guidance[i] = noise_guidance_edit.detach().cpu()\n\n if sem_guidance is not None:\n edit_guidance = sem_guidance[i].to(device)\n noise_guidance = noise_guidance + edit_guidance\n\n noise_pred = noise_pred_uncond + noise_guidance\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # 8. Post-processing\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return SemanticStableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/shap_e/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"camera\"] = [\"create_pan_cameras\"]\n _import_structure[\"pipeline_shap_e\"] = [\"ShapEPipeline\"]\n _import_structure[\"pipeline_shap_e_img2img\"] = [\"ShapEImg2ImgPipeline\"]\n _import_structure[\"renderer\"] = [\n \"BoundingBoxVolume\",\n \"ImportanceRaySampler\",\n \"MLPNeRFModelOutput\",\n \"MLPNeRSTFModel\",\n \"ShapEParamsProjModel\",\n \"ShapERenderer\",\n \"StratifiedRaySampler\",\n \"VoidNeRFModel\",\n ]\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n else:\n from .camera import create_pan_cameras\n from .pipeline_shap_e import ShapEPipeline\n from .pipeline_shap_e_img2img import ShapEImg2ImgPipeline\n from .renderer import (\n BoundingBoxVolume,\n ImportanceRaySampler,\n MLPNeRFModelOutput,\n MLPNeRSTFModel,\n ShapEParamsProjModel,\n ShapERenderer,\n StratifiedRaySampler,\n VoidNeRFModel,\n )\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/shap_e/camera.py", "content": "# Copyright 2024 Open AI and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom dataclasses import dataclass\nfrom typing import Tuple\n\nimport numpy as np\nimport torch\n\n\n@dataclass\nclass DifferentiableProjectiveCamera:\n \"\"\"\n Implements a batch, differentiable, standard pinhole camera\n \"\"\"\n\n origin: torch.Tensor # [batch_size x 3]\n x: torch.Tensor # [batch_size x 3]\n y: torch.Tensor # [batch_size x 3]\n z: torch.Tensor # [batch_size x 3]\n width: int\n height: int\n x_fov: float\n y_fov: float\n shape: Tuple[int]\n\n def __post_init__(self):\n assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]\n assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3\n assert len(self.x.shape) == len(self.y.shape) == len(self.z.shape) == len(self.origin.shape) == 2\n\n def resolution(self):\n return torch.from_numpy(np.array([self.width, self.height], dtype=np.float32))\n\n def fov(self):\n return torch.from_numpy(np.array([self.x_fov, self.y_fov], dtype=np.float32))\n\n def get_image_coords(self) -> torch.Tensor:\n \"\"\"\n :return: coords of shape (width * height, 2)\n \"\"\"\n pixel_indices = torch.arange(self.height * self.width)\n coords = torch.stack(\n [\n pixel_indices % self.width,\n torch.div(pixel_indices, self.width, rounding_mode=\"trunc\"),\n ],\n axis=1,\n )\n return coords\n\n @property\n def camera_rays(self):\n batch_size, *inner_shape = self.shape\n inner_batch_size = int(np.prod(inner_shape))\n\n coords = self.get_image_coords()\n coords = torch.broadcast_to(coords.unsqueeze(0), [batch_size * inner_batch_size, *coords.shape])\n rays = self.get_camera_rays(coords)\n\n rays = rays.view(batch_size, inner_batch_size * self.height * self.width, 2, 3)\n\n return rays\n\n def get_camera_rays(self, coords: torch.Tensor) -> torch.Tensor:\n batch_size, *shape, n_coords = coords.shape\n assert n_coords == 2\n assert batch_size == self.origin.shape[0]\n\n flat = coords.view(batch_size, -1, 2)\n\n res = self.resolution()\n fov = self.fov()\n\n fracs = (flat.float() / (res - 1)) * 2 - 1\n fracs = fracs * torch.tan(fov / 2)\n\n fracs = fracs.view(batch_size, -1, 2)\n directions = (\n self.z.view(batch_size, 1, 3)\n + self.x.view(batch_size, 1, 3) * fracs[:, :, :1]\n + self.y.view(batch_size, 1, 3) * fracs[:, :, 1:]\n )\n directions = directions / directions.norm(dim=-1, keepdim=True)\n rays = torch.stack(\n [\n torch.broadcast_to(self.origin.view(batch_size, 1, 3), [batch_size, directions.shape[1], 3]),\n directions,\n ],\n dim=2,\n )\n return rays.view(batch_size, *shape, 2, 3)\n\n def resize_image(self, width: int, height: int) -> \"DifferentiableProjectiveCamera\":\n \"\"\"\n Creates a new camera for the resized view assuming the aspect ratio does not change.\n \"\"\"\n assert width * self.height == height * self.width, \"The aspect ratio should not change.\"\n return DifferentiableProjectiveCamera(\n origin=self.origin,\n x=self.x,\n y=self.y,\n z=self.z,\n width=width,\n height=height,\n x_fov=self.x_fov,\n y_fov=self.y_fov,\n )\n\n\ndef create_pan_cameras(size: int) -> DifferentiableProjectiveCamera:\n origins = []\n xs = []\n ys = []\n zs = []\n for theta in np.linspace(0, 2 * np.pi, num=20):\n z = np.array([np.sin(theta), np.cos(theta), -0.5])\n z /= np.sqrt(np.sum(z**2))\n origin = -z * 4\n x = np.array([np.cos(theta), -np.sin(theta), 0.0])\n y = np.cross(z, x)\n origins.append(origin)\n xs.append(x)\n ys.append(y)\n zs.append(z)\n return DifferentiableProjectiveCamera(\n origin=torch.from_numpy(np.stack(origins, axis=0)).float(),\n x=torch.from_numpy(np.stack(xs, axis=0)).float(),\n y=torch.from_numpy(np.stack(ys, axis=0)).float(),\n z=torch.from_numpy(np.stack(zs, axis=0)).float(),\n width=size,\n height=size,\n x_fov=0.7,\n y_fov=0.7,\n shape=(1, len(xs)),\n )\n" }, { "path": "diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e.py", "content": "# Copyright 2024 Open AI and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport math\nfrom dataclasses import dataclass\nfrom typing import List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom transformers import CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom ...models import PriorTransformer\nfrom ...schedulers import HeunDiscreteScheduler\nfrom ...utils import (\n BaseOutput,\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline\nfrom .renderer import ShapERenderer\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import DiffusionPipeline\n >>> from diffusers.utils import export_to_gif\n\n >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n >>> repo = \"openai/shap-e\"\n >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n >>> pipe = pipe.to(device)\n\n >>> guidance_scale = 15.0\n >>> prompt = \"a shark\"\n\n >>> images = pipe(\n ... prompt,\n ... guidance_scale=guidance_scale,\n ... num_inference_steps=64,\n ... frame_size=256,\n ... ).images\n\n >>> gif_path = export_to_gif(images[0], \"shark_3d.gif\")\n ```\n\"\"\"\n\n\n@dataclass\nclass ShapEPipelineOutput(BaseOutput):\n \"\"\"\n Output class for [`ShapEPipeline`] and [`ShapEImg2ImgPipeline`].\n\n Args:\n images (`torch.Tensor`)\n A list of images for 3D rendering.\n \"\"\"\n\n images: Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]\n\n\nclass ShapEPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for generating latent representation of a 3D asset and rendering with the NeRF method.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Args:\n prior ([`PriorTransformer`]):\n The canonical unCLIP prior to approximate the image embedding from the text embedding.\n text_encoder ([`~transformers.CLIPTextModelWithProjection`]):\n Frozen text-encoder.\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n scheduler ([`HeunDiscreteScheduler`]):\n A scheduler to be used in combination with the `prior` model to generate image embedding.\n shap_e_renderer ([`ShapERenderer`]):\n Shap-E renderer projects the generated latents into parameters of a MLP to create 3D objects with the NeRF\n rendering method.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->prior\"\n _exclude_from_cpu_offload = [\"shap_e_renderer\"]\n\n def __init__(\n self,\n prior: PriorTransformer,\n text_encoder: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n scheduler: HeunDiscreteScheduler,\n shap_e_renderer: ShapERenderer,\n ):\n super().__init__()\n\n self.register_modules(\n prior=prior,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n scheduler=scheduler,\n shap_e_renderer=shap_e_renderer,\n )\n\n # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents\n def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n latents = latents * scheduler.init_noise_sigma\n return latents\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n ):\n len(prompt) if isinstance(prompt, list) else 1\n\n # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file\n self.tokenizer.pad_token_id = 0\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n text_encoder_output = self.text_encoder(text_input_ids.to(device))\n prompt_embeds = text_encoder_output.text_embeds\n\n prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n # in Shap-E it normalize the prompt_embeds and then later rescale it\n prompt_embeds = prompt_embeds / torch.linalg.norm(prompt_embeds, dim=-1, keepdim=True)\n\n if do_classifier_free_guidance:\n negative_prompt_embeds = torch.zeros_like(prompt_embeds)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # Rescale the features to have unit variance\n prompt_embeds = math.sqrt(prompt_embeds.shape[1]) * prompt_embeds\n\n return prompt_embeds\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: str,\n num_images_per_prompt: int = 1,\n num_inference_steps: int = 25,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n guidance_scale: float = 4.0,\n frame_size: int = 64,\n output_type: Optional[str] = \"pil\", # pil, np, latent, mesh\n return_dict: bool = True,\n ):\n \"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n num_inference_steps (`int`, *optional*, defaults to 25):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n guidance_scale (`float`, *optional*, defaults to 4.0):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n frame_size (`int`, *optional*, default to 64):\n The width and height of each image frame of the generated 3D output.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `\"pil\"` (`PIL.Image.Image`), `\"np\"`\n (`np.array`), `\"latent\"` (`torch.Tensor`), or mesh ([`MeshDecoderOutput`]).\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.shap_e.pipeline_shap_e.ShapEPipelineOutput`] instead of a plain\n tuple.\n\n Examples:\n\n Returns:\n [`~pipelines.shap_e.pipeline_shap_e.ShapEPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.shap_e.pipeline_shap_e.ShapEPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images.\n \"\"\"\n\n if isinstance(prompt, str):\n batch_size = 1\n elif isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n device = self._execution_device\n\n batch_size = batch_size * num_images_per_prompt\n\n do_classifier_free_guidance = guidance_scale > 1.0\n prompt_embeds = self._encode_prompt(prompt, device, num_images_per_prompt, do_classifier_free_guidance)\n\n # prior\n\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n num_embeddings = self.prior.config.num_embeddings\n embedding_dim = self.prior.config.embedding_dim\n\n latents = self.prepare_latents(\n (batch_size, num_embeddings * embedding_dim),\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n self.scheduler,\n )\n\n # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim\n latents = latents.reshape(latents.shape[0], num_embeddings, embedding_dim)\n\n for i, t in enumerate(self.progress_bar(timesteps)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n scaled_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n noise_pred = self.prior(\n scaled_model_input,\n timestep=t,\n proj_embedding=prompt_embeds,\n ).predicted_image_embedding\n\n # remove the variance\n noise_pred, _ = noise_pred.split(\n scaled_model_input.shape[2], dim=2\n ) # batch_size, num_embeddings, embedding_dim\n\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)\n\n latents = self.scheduler.step(\n noise_pred,\n timestep=t,\n sample=latents,\n ).prev_sample\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if output_type not in [\"np\", \"pil\", \"latent\", \"mesh\"]:\n raise ValueError(\n f\"Only the output types `pil`, `np`, `latent` and `mesh` are supported not output_type={output_type}\"\n )\n\n if output_type == \"latent\":\n return ShapEPipelineOutput(images=latents)\n\n images = []\n if output_type == \"mesh\":\n for i, latent in enumerate(latents):\n mesh = self.shap_e_renderer.decode_to_mesh(\n latent[None, :],\n device,\n )\n images.append(mesh)\n\n else:\n # np, pil\n for i, latent in enumerate(latents):\n image = self.shap_e_renderer.decode_to_image(\n latent[None, :],\n device,\n size=frame_size,\n )\n images.append(image)\n\n images = torch.stack(images)\n\n images = images.cpu().numpy()\n\n if output_type == \"pil\":\n images = [self.numpy_to_pil(image) for image in images]\n\n if not return_dict:\n return (images,)\n\n return ShapEPipelineOutput(images=images)\n" }, { "path": "diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e_img2img.py", "content": "# Copyright 2024 Open AI and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom dataclasses import dataclass\nfrom typing import List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPVisionModel\n\nfrom ...models import PriorTransformer\nfrom ...schedulers import HeunDiscreteScheduler\nfrom ...utils import (\n BaseOutput,\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline\nfrom .renderer import ShapERenderer\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from PIL import Image\n >>> import torch\n >>> from diffusers import DiffusionPipeline\n >>> from diffusers.utils import export_to_gif, load_image\n\n >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n >>> repo = \"openai/shap-e-img2img\"\n >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n >>> pipe = pipe.to(device)\n\n >>> guidance_scale = 3.0\n >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"\n >>> image = load_image(image_url).convert(\"RGB\")\n\n >>> images = pipe(\n ... image,\n ... guidance_scale=guidance_scale,\n ... num_inference_steps=64,\n ... frame_size=256,\n ... ).images\n\n >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")\n ```\n\"\"\"\n\n\n@dataclass\nclass ShapEPipelineOutput(BaseOutput):\n \"\"\"\n Output class for [`ShapEPipeline`] and [`ShapEImg2ImgPipeline`].\n\n Args:\n images (`torch.Tensor`)\n A list of images for 3D rendering.\n \"\"\"\n\n images: Union[PIL.Image.Image, np.ndarray]\n\n\nclass ShapEImg2ImgPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for generating latent representation of a 3D asset and rendering with the NeRF method from an image.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Args:\n prior ([`PriorTransformer`]):\n The canonical unCLIP prior to approximate the image embedding from the text embedding.\n image_encoder ([`~transformers.CLIPVisionModel`]):\n Frozen image-encoder.\n image_processor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to process images.\n scheduler ([`HeunDiscreteScheduler`]):\n A scheduler to be used in combination with the `prior` model to generate image embedding.\n shap_e_renderer ([`ShapERenderer`]):\n Shap-E renderer projects the generated latents into parameters of a MLP to create 3D objects with the NeRF\n rendering method.\n \"\"\"\n\n model_cpu_offload_seq = \"image_encoder->prior\"\n _exclude_from_cpu_offload = [\"shap_e_renderer\"]\n\n def __init__(\n self,\n prior: PriorTransformer,\n image_encoder: CLIPVisionModel,\n image_processor: CLIPImageProcessor,\n scheduler: HeunDiscreteScheduler,\n shap_e_renderer: ShapERenderer,\n ):\n super().__init__()\n\n self.register_modules(\n prior=prior,\n image_encoder=image_encoder,\n image_processor=image_processor,\n scheduler=scheduler,\n shap_e_renderer=shap_e_renderer,\n )\n\n # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents\n def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n latents = latents * scheduler.init_noise_sigma\n return latents\n\n def _encode_image(\n self,\n image,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n ):\n if isinstance(image, List) and isinstance(image[0], torch.Tensor):\n image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0)\n\n if not isinstance(image, torch.Tensor):\n image = self.image_processor(image, return_tensors=\"pt\").pixel_values[0].unsqueeze(0)\n\n image = image.to(dtype=self.image_encoder.dtype, device=device)\n\n image_embeds = self.image_encoder(image)[\"last_hidden_state\"]\n image_embeds = image_embeds[:, 1:, :].contiguous() # batch_size, dim, 256\n\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n\n if do_classifier_free_guidance:\n negative_image_embeds = torch.zeros_like(image_embeds)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n image_embeds = torch.cat([negative_image_embeds, image_embeds])\n\n return image_embeds\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n image: Union[PIL.Image.Image, List[PIL.Image.Image]],\n num_images_per_prompt: int = 1,\n num_inference_steps: int = 25,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n guidance_scale: float = 4.0,\n frame_size: int = 64,\n output_type: Optional[str] = \"pil\", # pil, np, latent, mesh\n return_dict: bool = True,\n ):\n \"\"\"\n The call function to the pipeline for generation.\n\n Args:\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image` or tensor representing an image batch to be used as the starting point. Can also accept image\n latents as image, but if passing latents directly it is not encoded again.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n num_inference_steps (`int`, *optional*, defaults to 25):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n guidance_scale (`float`, *optional*, defaults to 4.0):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n frame_size (`int`, *optional*, default to 64):\n The width and height of each image frame of the generated 3D output.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `\"pil\"` (`PIL.Image.Image`), `\"np\"`\n (`np.array`), `\"latent\"` (`torch.Tensor`), or mesh ([`MeshDecoderOutput`]).\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.shap_e.pipeline_shap_e.ShapEPipelineOutput`] instead of a plain\n tuple.\n\n Examples:\n\n Returns:\n [`~pipelines.shap_e.pipeline_shap_e.ShapEPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.shap_e.pipeline_shap_e.ShapEPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images.\n \"\"\"\n\n if isinstance(image, PIL.Image.Image):\n batch_size = 1\n elif isinstance(image, torch.Tensor):\n batch_size = image.shape[0]\n elif isinstance(image, list) and isinstance(image[0], (torch.Tensor, PIL.Image.Image)):\n batch_size = len(image)\n else:\n raise ValueError(\n f\"`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(image)}\"\n )\n\n device = self._execution_device\n\n batch_size = batch_size * num_images_per_prompt\n\n do_classifier_free_guidance = guidance_scale > 1.0\n image_embeds = self._encode_image(image, device, num_images_per_prompt, do_classifier_free_guidance)\n\n # prior\n\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n num_embeddings = self.prior.config.num_embeddings\n embedding_dim = self.prior.config.embedding_dim\n if latents is None:\n latents = self.prepare_latents(\n (batch_size, num_embeddings * embedding_dim),\n image_embeds.dtype,\n device,\n generator,\n latents,\n self.scheduler,\n )\n\n # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim\n latents = latents.reshape(latents.shape[0], num_embeddings, embedding_dim)\n\n for i, t in enumerate(self.progress_bar(timesteps)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n scaled_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n noise_pred = self.prior(\n scaled_model_input,\n timestep=t,\n proj_embedding=image_embeds,\n ).predicted_image_embedding\n\n # remove the variance\n noise_pred, _ = noise_pred.split(\n scaled_model_input.shape[2], dim=2\n ) # batch_size, num_embeddings, embedding_dim\n\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)\n\n latents = self.scheduler.step(\n noise_pred,\n timestep=t,\n sample=latents,\n ).prev_sample\n\n if output_type not in [\"np\", \"pil\", \"latent\", \"mesh\"]:\n raise ValueError(\n f\"Only the output types `pil`, `np`, `latent` and `mesh` are supported not output_type={output_type}\"\n )\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if output_type == \"latent\":\n return ShapEPipelineOutput(images=latents)\n\n images = []\n if output_type == \"mesh\":\n for i, latent in enumerate(latents):\n mesh = self.shap_e_renderer.decode_to_mesh(\n latent[None, :],\n device,\n )\n images.append(mesh)\n\n else:\n # np, pil\n for i, latent in enumerate(latents):\n image = self.shap_e_renderer.decode_to_image(\n latent[None, :],\n device,\n size=frame_size,\n )\n images.append(image)\n\n images = torch.stack(images)\n\n images = images.cpu().numpy()\n\n if output_type == \"pil\":\n images = [self.numpy_to_pil(image) for image in images]\n\n if not return_dict:\n return (images,)\n\n return ShapEPipelineOutput(images=images)\n" }, { "path": "diffusers/src/diffusers/pipelines/shap_e/renderer.py", "content": "# Copyright 2024 Open AI and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport math\nfrom dataclasses import dataclass\nfrom typing import Dict, Optional, Tuple\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...models import ModelMixin\nfrom ...utils import BaseOutput\nfrom .camera import create_pan_cameras\n\n\ndef sample_pmf(pmf: torch.Tensor, n_samples: int) -> torch.Tensor:\n r\"\"\"\n Sample from the given discrete probability distribution with replacement.\n\n The i-th bin is assumed to have mass pmf[i].\n\n Args:\n pmf: [batch_size, *shape, n_samples, 1] where (pmf.sum(dim=-2) == 1).all()\n n_samples: number of samples\n\n Return:\n indices sampled with replacement\n \"\"\"\n\n *shape, support_size, last_dim = pmf.shape\n assert last_dim == 1\n\n cdf = torch.cumsum(pmf.view(-1, support_size), dim=1)\n inds = torch.searchsorted(cdf, torch.rand(cdf.shape[0], n_samples, device=cdf.device))\n\n return inds.view(*shape, n_samples, 1).clamp(0, support_size - 1)\n\n\ndef posenc_nerf(x: torch.Tensor, min_deg: int = 0, max_deg: int = 15) -> torch.Tensor:\n \"\"\"\n Concatenate x and its positional encodings, following NeRF.\n\n Reference: https://arxiv.org/pdf/2210.04628.pdf\n \"\"\"\n if min_deg == max_deg:\n return x\n\n scales = 2.0 ** torch.arange(min_deg, max_deg, dtype=x.dtype, device=x.device)\n *shape, dim = x.shape\n xb = (x.reshape(-1, 1, dim) * scales.view(1, -1, 1)).reshape(*shape, -1)\n assert xb.shape[-1] == dim * (max_deg - min_deg)\n emb = torch.cat([xb, xb + math.pi / 2.0], axis=-1).sin()\n return torch.cat([x, emb], dim=-1)\n\n\ndef encode_position(position):\n return posenc_nerf(position, min_deg=0, max_deg=15)\n\n\ndef encode_direction(position, direction=None):\n if direction is None:\n return torch.zeros_like(posenc_nerf(position, min_deg=0, max_deg=8))\n else:\n return posenc_nerf(direction, min_deg=0, max_deg=8)\n\n\ndef _sanitize_name(x: str) -> str:\n return x.replace(\".\", \"__\")\n\n\ndef integrate_samples(volume_range, ts, density, channels):\n r\"\"\"\n Function integrating the model output.\n\n Args:\n volume_range: Specifies the integral range [t0, t1]\n ts: timesteps\n density: torch.Tensor [batch_size, *shape, n_samples, 1]\n channels: torch.Tensor [batch_size, *shape, n_samples, n_channels]\n returns:\n channels: integrated rgb output weights: torch.Tensor [batch_size, *shape, n_samples, 1] (density\n *transmittance)[i] weight for each rgb output at [..., i, :]. transmittance: transmittance of this volume\n )\n \"\"\"\n\n # 1. Calculate the weights\n _, _, dt = volume_range.partition(ts)\n ddensity = density * dt\n\n mass = torch.cumsum(ddensity, dim=-2)\n transmittance = torch.exp(-mass[..., -1, :])\n\n alphas = 1.0 - torch.exp(-ddensity)\n Ts = torch.exp(torch.cat([torch.zeros_like(mass[..., :1, :]), -mass[..., :-1, :]], dim=-2))\n # This is the probability of light hitting and reflecting off of\n # something at depth [..., i, :].\n weights = alphas * Ts\n\n # 2. Integrate channels\n channels = torch.sum(channels * weights, dim=-2)\n\n return channels, weights, transmittance\n\n\ndef volume_query_points(volume, grid_size):\n indices = torch.arange(grid_size**3, device=volume.bbox_min.device)\n zs = indices % grid_size\n ys = torch.div(indices, grid_size, rounding_mode=\"trunc\") % grid_size\n xs = torch.div(indices, grid_size**2, rounding_mode=\"trunc\") % grid_size\n combined = torch.stack([xs, ys, zs], dim=1)\n return (combined.float() / (grid_size - 1)) * (volume.bbox_max - volume.bbox_min) + volume.bbox_min\n\n\ndef _convert_srgb_to_linear(u: torch.Tensor):\n return torch.where(u <= 0.04045, u / 12.92, ((u + 0.055) / 1.055) ** 2.4)\n\n\ndef _create_flat_edge_indices(\n flat_cube_indices: torch.Tensor,\n grid_size: Tuple[int, int, int],\n):\n num_xs = (grid_size[0] - 1) * grid_size[1] * grid_size[2]\n y_offset = num_xs\n num_ys = grid_size[0] * (grid_size[1] - 1) * grid_size[2]\n z_offset = num_xs + num_ys\n return torch.stack(\n [\n # Edges spanning x-axis.\n flat_cube_indices[:, 0] * grid_size[1] * grid_size[2]\n + flat_cube_indices[:, 1] * grid_size[2]\n + flat_cube_indices[:, 2],\n flat_cube_indices[:, 0] * grid_size[1] * grid_size[2]\n + (flat_cube_indices[:, 1] + 1) * grid_size[2]\n + flat_cube_indices[:, 2],\n flat_cube_indices[:, 0] * grid_size[1] * grid_size[2]\n + flat_cube_indices[:, 1] * grid_size[2]\n + flat_cube_indices[:, 2]\n + 1,\n flat_cube_indices[:, 0] * grid_size[1] * grid_size[2]\n + (flat_cube_indices[:, 1] + 1) * grid_size[2]\n + flat_cube_indices[:, 2]\n + 1,\n # Edges spanning y-axis.\n (\n y_offset\n + flat_cube_indices[:, 0] * (grid_size[1] - 1) * grid_size[2]\n + flat_cube_indices[:, 1] * grid_size[2]\n + flat_cube_indices[:, 2]\n ),\n (\n y_offset\n + (flat_cube_indices[:, 0] + 1) * (grid_size[1] - 1) * grid_size[2]\n + flat_cube_indices[:, 1] * grid_size[2]\n + flat_cube_indices[:, 2]\n ),\n (\n y_offset\n + flat_cube_indices[:, 0] * (grid_size[1] - 1) * grid_size[2]\n + flat_cube_indices[:, 1] * grid_size[2]\n + flat_cube_indices[:, 2]\n + 1\n ),\n (\n y_offset\n + (flat_cube_indices[:, 0] + 1) * (grid_size[1] - 1) * grid_size[2]\n + flat_cube_indices[:, 1] * grid_size[2]\n + flat_cube_indices[:, 2]\n + 1\n ),\n # Edges spanning z-axis.\n (\n z_offset\n + flat_cube_indices[:, 0] * grid_size[1] * (grid_size[2] - 1)\n + flat_cube_indices[:, 1] * (grid_size[2] - 1)\n + flat_cube_indices[:, 2]\n ),\n (\n z_offset\n + (flat_cube_indices[:, 0] + 1) * grid_size[1] * (grid_size[2] - 1)\n + flat_cube_indices[:, 1] * (grid_size[2] - 1)\n + flat_cube_indices[:, 2]\n ),\n (\n z_offset\n + flat_cube_indices[:, 0] * grid_size[1] * (grid_size[2] - 1)\n + (flat_cube_indices[:, 1] + 1) * (grid_size[2] - 1)\n + flat_cube_indices[:, 2]\n ),\n (\n z_offset\n + (flat_cube_indices[:, 0] + 1) * grid_size[1] * (grid_size[2] - 1)\n + (flat_cube_indices[:, 1] + 1) * (grid_size[2] - 1)\n + flat_cube_indices[:, 2]\n ),\n ],\n dim=-1,\n )\n\n\nclass VoidNeRFModel(nn.Module):\n \"\"\"\n Implements the default empty space model where all queries are rendered as background.\n \"\"\"\n\n def __init__(self, background, channel_scale=255.0):\n super().__init__()\n background = nn.Parameter(torch.from_numpy(np.array(background)).to(dtype=torch.float32) / channel_scale)\n\n self.register_buffer(\"background\", background)\n\n def forward(self, position):\n background = self.background[None].to(position.device)\n\n shape = position.shape[:-1]\n ones = [1] * (len(shape) - 1)\n n_channels = background.shape[-1]\n background = torch.broadcast_to(background.view(background.shape[0], *ones, n_channels), [*shape, n_channels])\n\n return background\n\n\n@dataclass\nclass VolumeRange:\n t0: torch.Tensor\n t1: torch.Tensor\n intersected: torch.Tensor\n\n def __post_init__(self):\n assert self.t0.shape == self.t1.shape == self.intersected.shape\n\n def partition(self, ts):\n \"\"\"\n Partitions t0 and t1 into n_samples intervals.\n\n Args:\n ts: [batch_size, *shape, n_samples, 1]\n\n Return:\n\n lower: [batch_size, *shape, n_samples, 1] upper: [batch_size, *shape, n_samples, 1] delta: [batch_size,\n *shape, n_samples, 1]\n\n where\n ts \\\\in [lower, upper] deltas = upper - lower\n \"\"\"\n\n mids = (ts[..., 1:, :] + ts[..., :-1, :]) * 0.5\n lower = torch.cat([self.t0[..., None, :], mids], dim=-2)\n upper = torch.cat([mids, self.t1[..., None, :]], dim=-2)\n delta = upper - lower\n assert lower.shape == upper.shape == delta.shape == ts.shape\n return lower, upper, delta\n\n\nclass BoundingBoxVolume(nn.Module):\n \"\"\"\n Axis-aligned bounding box defined by the two opposite corners.\n \"\"\"\n\n def __init__(\n self,\n *,\n bbox_min,\n bbox_max,\n min_dist: float = 0.0,\n min_t_range: float = 1e-3,\n ):\n \"\"\"\n Args:\n bbox_min: the left/bottommost corner of the bounding box\n bbox_max: the other corner of the bounding box\n min_dist: all rays should start at least this distance away from the origin.\n \"\"\"\n super().__init__()\n\n self.min_dist = min_dist\n self.min_t_range = min_t_range\n\n self.bbox_min = torch.tensor(bbox_min)\n self.bbox_max = torch.tensor(bbox_max)\n self.bbox = torch.stack([self.bbox_min, self.bbox_max])\n assert self.bbox.shape == (2, 3)\n assert min_dist >= 0.0\n assert min_t_range > 0.0\n\n def intersect(\n self,\n origin: torch.Tensor,\n direction: torch.Tensor,\n t0_lower: Optional[torch.Tensor] = None,\n epsilon=1e-6,\n ):\n \"\"\"\n Args:\n origin: [batch_size, *shape, 3]\n direction: [batch_size, *shape, 3]\n t0_lower: Optional [batch_size, *shape, 1] lower bound of t0 when intersecting this volume.\n params: Optional meta parameters in case Volume is parametric\n epsilon: to stabilize calculations\n\n Return:\n A tuple of (t0, t1, intersected) where each has a shape [batch_size, *shape, 1]. If a ray intersects with\n the volume, `o + td` is in the volume for all t in [t0, t1]. If the volume is bounded, t1 is guaranteed to\n be on the boundary of the volume.\n \"\"\"\n\n batch_size, *shape, _ = origin.shape\n ones = [1] * len(shape)\n bbox = self.bbox.view(1, *ones, 2, 3).to(origin.device)\n\n def _safe_divide(a, b, epsilon=1e-6):\n return a / torch.where(b < 0, b - epsilon, b + epsilon)\n\n ts = _safe_divide(bbox - origin[..., None, :], direction[..., None, :], epsilon=epsilon)\n\n # Cases to think about:\n #\n # 1. t1 <= t0: the ray does not pass through the AABB.\n # 2. t0 < t1 <= 0: the ray intersects but the BB is behind the origin.\n # 3. t0 <= 0 <= t1: the ray starts from inside the BB\n # 4. 0 <= t0 < t1: the ray is not inside and intersects with the BB twice.\n #\n # 1 and 4 are clearly handled from t0 < t1 below.\n # Making t0 at least min_dist (>= 0) takes care of 2 and 3.\n t0 = ts.min(dim=-2).values.max(dim=-1, keepdim=True).values.clamp(self.min_dist)\n t1 = ts.max(dim=-2).values.min(dim=-1, keepdim=True).values\n assert t0.shape == t1.shape == (batch_size, *shape, 1)\n if t0_lower is not None:\n assert t0.shape == t0_lower.shape\n t0 = torch.maximum(t0, t0_lower)\n\n intersected = t0 + self.min_t_range < t1\n t0 = torch.where(intersected, t0, torch.zeros_like(t0))\n t1 = torch.where(intersected, t1, torch.ones_like(t1))\n\n return VolumeRange(t0=t0, t1=t1, intersected=intersected)\n\n\nclass StratifiedRaySampler(nn.Module):\n \"\"\"\n Instead of fixed intervals, a sample is drawn uniformly at random from each interval.\n \"\"\"\n\n def __init__(self, depth_mode: str = \"linear\"):\n \"\"\"\n :param depth_mode: linear samples ts linearly in depth. harmonic ensures\n closer points are sampled more densely.\n \"\"\"\n self.depth_mode = depth_mode\n assert self.depth_mode in (\"linear\", \"geometric\", \"harmonic\")\n\n def sample(\n self,\n t0: torch.Tensor,\n t1: torch.Tensor,\n n_samples: int,\n epsilon: float = 1e-3,\n ) -> torch.Tensor:\n \"\"\"\n Args:\n t0: start time has shape [batch_size, *shape, 1]\n t1: finish time has shape [batch_size, *shape, 1]\n n_samples: number of ts to sample\n Return:\n sampled ts of shape [batch_size, *shape, n_samples, 1]\n \"\"\"\n ones = [1] * (len(t0.shape) - 1)\n ts = torch.linspace(0, 1, n_samples).view(*ones, n_samples).to(t0.dtype).to(t0.device)\n\n if self.depth_mode == \"linear\":\n ts = t0 * (1.0 - ts) + t1 * ts\n elif self.depth_mode == \"geometric\":\n ts = (t0.clamp(epsilon).log() * (1.0 - ts) + t1.clamp(epsilon).log() * ts).exp()\n elif self.depth_mode == \"harmonic\":\n # The original NeRF recommends this interpolation scheme for\n # spherical scenes, but there could be some weird edge cases when\n # the observer crosses from the inner to outer volume.\n ts = 1.0 / (1.0 / t0.clamp(epsilon) * (1.0 - ts) + 1.0 / t1.clamp(epsilon) * ts)\n\n mids = 0.5 * (ts[..., 1:] + ts[..., :-1])\n upper = torch.cat([mids, t1], dim=-1)\n lower = torch.cat([t0, mids], dim=-1)\n # yiyi notes: add a random seed here for testing, don't forget to remove\n torch.manual_seed(0)\n t_rand = torch.rand_like(ts)\n\n ts = lower + (upper - lower) * t_rand\n return ts.unsqueeze(-1)\n\n\nclass ImportanceRaySampler(nn.Module):\n \"\"\"\n Given the initial estimate of densities, this samples more from regions/bins expected to have objects.\n \"\"\"\n\n def __init__(\n self,\n volume_range: VolumeRange,\n ts: torch.Tensor,\n weights: torch.Tensor,\n blur_pool: bool = False,\n alpha: float = 1e-5,\n ):\n \"\"\"\n Args:\n volume_range: the range in which a ray intersects the given volume.\n ts: earlier samples from the coarse rendering step\n weights: discretized version of density * transmittance\n blur_pool: if true, use 2-tap max + 2-tap blur filter from mip-NeRF.\n alpha: small value to add to weights.\n \"\"\"\n self.volume_range = volume_range\n self.ts = ts.clone().detach()\n self.weights = weights.clone().detach()\n self.blur_pool = blur_pool\n self.alpha = alpha\n\n @torch.no_grad()\n def sample(self, t0: torch.Tensor, t1: torch.Tensor, n_samples: int) -> torch.Tensor:\n \"\"\"\n Args:\n t0: start time has shape [batch_size, *shape, 1]\n t1: finish time has shape [batch_size, *shape, 1]\n n_samples: number of ts to sample\n Return:\n sampled ts of shape [batch_size, *shape, n_samples, 1]\n \"\"\"\n lower, upper, _ = self.volume_range.partition(self.ts)\n\n batch_size, *shape, n_coarse_samples, _ = self.ts.shape\n\n weights = self.weights\n if self.blur_pool:\n padded = torch.cat([weights[..., :1, :], weights, weights[..., -1:, :]], dim=-2)\n maxes = torch.maximum(padded[..., :-1, :], padded[..., 1:, :])\n weights = 0.5 * (maxes[..., :-1, :] + maxes[..., 1:, :])\n weights = weights + self.alpha\n pmf = weights / weights.sum(dim=-2, keepdim=True)\n inds = sample_pmf(pmf, n_samples)\n assert inds.shape == (batch_size, *shape, n_samples, 1)\n assert (inds >= 0).all() and (inds < n_coarse_samples).all()\n\n t_rand = torch.rand(inds.shape, device=inds.device)\n lower_ = torch.gather(lower, -2, inds)\n upper_ = torch.gather(upper, -2, inds)\n\n ts = lower_ + (upper_ - lower_) * t_rand\n ts = torch.sort(ts, dim=-2).values\n return ts\n\n\n@dataclass\nclass MeshDecoderOutput(BaseOutput):\n \"\"\"\n A 3D triangle mesh with optional data at the vertices and faces.\n\n Args:\n verts (`torch.Tensor` of shape `(N, 3)`):\n array of vertext coordinates\n faces (`torch.Tensor` of shape `(N, 3)`):\n array of triangles, pointing to indices in verts.\n vertext_channels (Dict):\n vertext coordinates for each color channel\n \"\"\"\n\n verts: torch.Tensor\n faces: torch.Tensor\n vertex_channels: Dict[str, torch.Tensor]\n\n\nclass MeshDecoder(nn.Module):\n \"\"\"\n Construct meshes from Signed distance functions (SDFs) using marching cubes method\n \"\"\"\n\n def __init__(self):\n super().__init__()\n cases = torch.zeros(256, 5, 3, dtype=torch.long)\n masks = torch.zeros(256, 5, dtype=torch.bool)\n\n self.register_buffer(\"cases\", cases)\n self.register_buffer(\"masks\", masks)\n\n def forward(self, field: torch.Tensor, min_point: torch.Tensor, size: torch.Tensor):\n \"\"\"\n For a signed distance field, produce a mesh using marching cubes.\n\n :param field: a 3D tensor of field values, where negative values correspond\n to the outside of the shape. The dimensions correspond to the x, y, and z directions, respectively.\n :param min_point: a tensor of shape [3] containing the point corresponding\n to (0, 0, 0) in the field.\n :param size: a tensor of shape [3] containing the per-axis distance from the\n (0, 0, 0) field corner and the (-1, -1, -1) field corner.\n \"\"\"\n assert len(field.shape) == 3, \"input must be a 3D scalar field\"\n dev = field.device\n\n cases = self.cases.to(dev)\n masks = self.masks.to(dev)\n\n min_point = min_point.to(dev)\n size = size.to(dev)\n\n grid_size = field.shape\n grid_size_tensor = torch.tensor(grid_size).to(size)\n\n # Create bitmasks between 0 and 255 (inclusive) indicating the state\n # of the eight corners of each cube.\n bitmasks = (field > 0).to(torch.uint8)\n bitmasks = bitmasks[:-1, :, :] | (bitmasks[1:, :, :] << 1)\n bitmasks = bitmasks[:, :-1, :] | (bitmasks[:, 1:, :] << 2)\n bitmasks = bitmasks[:, :, :-1] | (bitmasks[:, :, 1:] << 4)\n\n # Compute corner coordinates across the entire grid.\n corner_coords = torch.empty(*grid_size, 3, device=dev, dtype=field.dtype)\n corner_coords[range(grid_size[0]), :, :, 0] = torch.arange(grid_size[0], device=dev, dtype=field.dtype)[\n :, None, None\n ]\n corner_coords[:, range(grid_size[1]), :, 1] = torch.arange(grid_size[1], device=dev, dtype=field.dtype)[\n :, None\n ]\n corner_coords[:, :, range(grid_size[2]), 2] = torch.arange(grid_size[2], device=dev, dtype=field.dtype)\n\n # Compute all vertices across all edges in the grid, even though we will\n # throw some out later. We have (X-1)*Y*Z + X*(Y-1)*Z + X*Y*(Z-1) vertices.\n # These are all midpoints, and don't account for interpolation (which is\n # done later based on the used edge midpoints).\n edge_midpoints = torch.cat(\n [\n ((corner_coords[:-1] + corner_coords[1:]) / 2).reshape(-1, 3),\n ((corner_coords[:, :-1] + corner_coords[:, 1:]) / 2).reshape(-1, 3),\n ((corner_coords[:, :, :-1] + corner_coords[:, :, 1:]) / 2).reshape(-1, 3),\n ],\n dim=0,\n )\n\n # Create a flat array of [X, Y, Z] indices for each cube.\n cube_indices = torch.zeros(\n grid_size[0] - 1, grid_size[1] - 1, grid_size[2] - 1, 3, device=dev, dtype=torch.long\n )\n cube_indices[range(grid_size[0] - 1), :, :, 0] = torch.arange(grid_size[0] - 1, device=dev)[:, None, None]\n cube_indices[:, range(grid_size[1] - 1), :, 1] = torch.arange(grid_size[1] - 1, device=dev)[:, None]\n cube_indices[:, :, range(grid_size[2] - 1), 2] = torch.arange(grid_size[2] - 1, device=dev)\n flat_cube_indices = cube_indices.reshape(-1, 3)\n\n # Create a flat array mapping each cube to 12 global edge indices.\n edge_indices = _create_flat_edge_indices(flat_cube_indices, grid_size)\n\n # Apply the LUT to figure out the triangles.\n flat_bitmasks = bitmasks.reshape(-1).long() # must cast to long for indexing to believe this not a mask\n local_tris = cases[flat_bitmasks]\n local_masks = masks[flat_bitmasks]\n # Compute the global edge indices for the triangles.\n global_tris = torch.gather(edge_indices, 1, local_tris.reshape(local_tris.shape[0], -1)).reshape(\n local_tris.shape\n )\n # Select the used triangles for each cube.\n selected_tris = global_tris.reshape(-1, 3)[local_masks.reshape(-1)]\n\n # Now we have a bunch of indices into the full list of possible vertices,\n # but we want to reduce this list to only the used vertices.\n used_vertex_indices = torch.unique(selected_tris.view(-1))\n used_edge_midpoints = edge_midpoints[used_vertex_indices]\n old_index_to_new_index = torch.zeros(len(edge_midpoints), device=dev, dtype=torch.long)\n old_index_to_new_index[used_vertex_indices] = torch.arange(\n len(used_vertex_indices), device=dev, dtype=torch.long\n )\n\n # Rewrite the triangles to use the new indices\n faces = torch.gather(old_index_to_new_index, 0, selected_tris.view(-1)).reshape(selected_tris.shape)\n\n # Compute the actual interpolated coordinates corresponding to edge midpoints.\n v1 = torch.floor(used_edge_midpoints).to(torch.long)\n v2 = torch.ceil(used_edge_midpoints).to(torch.long)\n s1 = field[v1[:, 0], v1[:, 1], v1[:, 2]]\n s2 = field[v2[:, 0], v2[:, 1], v2[:, 2]]\n p1 = (v1.float() / (grid_size_tensor - 1)) * size + min_point\n p2 = (v2.float() / (grid_size_tensor - 1)) * size + min_point\n # The signs of s1 and s2 should be different. We want to find\n # t such that t*s2 + (1-t)*s1 = 0.\n t = (s1 / (s1 - s2))[:, None]\n verts = t * p2 + (1 - t) * p1\n\n return MeshDecoderOutput(verts=verts, faces=faces, vertex_channels=None)\n\n\n@dataclass\nclass MLPNeRFModelOutput(BaseOutput):\n density: torch.Tensor\n signed_distance: torch.Tensor\n channels: torch.Tensor\n ts: torch.Tensor\n\n\nclass MLPNeRSTFModel(ModelMixin, ConfigMixin):\n @register_to_config\n def __init__(\n self,\n d_hidden: int = 256,\n n_output: int = 12,\n n_hidden_layers: int = 6,\n act_fn: str = \"swish\",\n insert_direction_at: int = 4,\n ):\n super().__init__()\n\n # Instantiate the MLP\n\n # Find out the dimension of encoded position and direction\n dummy = torch.eye(1, 3)\n d_posenc_pos = encode_position(position=dummy).shape[-1]\n d_posenc_dir = encode_direction(position=dummy).shape[-1]\n\n mlp_widths = [d_hidden] * n_hidden_layers\n input_widths = [d_posenc_pos] + mlp_widths\n output_widths = mlp_widths + [n_output]\n\n if insert_direction_at is not None:\n input_widths[insert_direction_at] += d_posenc_dir\n\n self.mlp = nn.ModuleList([nn.Linear(d_in, d_out) for d_in, d_out in zip(input_widths, output_widths)])\n\n if act_fn == \"swish\":\n # self.activation = swish\n # yiyi testing:\n self.activation = lambda x: F.silu(x)\n else:\n raise ValueError(f\"Unsupported activation function {act_fn}\")\n\n self.sdf_activation = torch.tanh\n self.density_activation = torch.nn.functional.relu\n self.channel_activation = torch.sigmoid\n\n def map_indices_to_keys(self, output):\n h_map = {\n \"sdf\": (0, 1),\n \"density_coarse\": (1, 2),\n \"density_fine\": (2, 3),\n \"stf\": (3, 6),\n \"nerf_coarse\": (6, 9),\n \"nerf_fine\": (9, 12),\n }\n\n mapped_output = {k: output[..., start:end] for k, (start, end) in h_map.items()}\n\n return mapped_output\n\n def forward(self, *, position, direction, ts, nerf_level=\"coarse\", rendering_mode=\"nerf\"):\n h = encode_position(position)\n\n h_preact = h\n h_directionless = None\n for i, layer in enumerate(self.mlp):\n if i == self.config.insert_direction_at: # 4 in the config\n h_directionless = h_preact\n h_direction = encode_direction(position, direction=direction)\n h = torch.cat([h, h_direction], dim=-1)\n\n h = layer(h)\n\n h_preact = h\n\n if i < len(self.mlp) - 1:\n h = self.activation(h)\n\n h_final = h\n if h_directionless is None:\n h_directionless = h_preact\n\n activation = self.map_indices_to_keys(h_final)\n\n if nerf_level == \"coarse\":\n h_density = activation[\"density_coarse\"]\n else:\n h_density = activation[\"density_fine\"]\n\n if rendering_mode == \"nerf\":\n if nerf_level == \"coarse\":\n h_channels = activation[\"nerf_coarse\"]\n else:\n h_channels = activation[\"nerf_fine\"]\n\n elif rendering_mode == \"stf\":\n h_channels = activation[\"stf\"]\n\n density = self.density_activation(h_density)\n signed_distance = self.sdf_activation(activation[\"sdf\"])\n channels = self.channel_activation(h_channels)\n\n # yiyi notes: I think signed_distance is not used\n return MLPNeRFModelOutput(density=density, signed_distance=signed_distance, channels=channels, ts=ts)\n\n\nclass ChannelsProj(nn.Module):\n def __init__(\n self,\n *,\n vectors: int,\n channels: int,\n d_latent: int,\n ):\n super().__init__()\n self.proj = nn.Linear(d_latent, vectors * channels)\n self.norm = nn.LayerNorm(channels)\n self.d_latent = d_latent\n self.vectors = vectors\n self.channels = channels\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n x_bvd = x\n w_vcd = self.proj.weight.view(self.vectors, self.channels, self.d_latent)\n b_vc = self.proj.bias.view(1, self.vectors, self.channels)\n h = torch.einsum(\"bvd,vcd->bvc\", x_bvd, w_vcd)\n h = self.norm(h)\n\n h = h + b_vc\n return h\n\n\nclass ShapEParamsProjModel(ModelMixin, ConfigMixin):\n \"\"\"\n project the latent representation of a 3D asset to obtain weights of a multi-layer perceptron (MLP).\n\n For more details, see the original paper:\n \"\"\"\n\n @register_to_config\n def __init__(\n self,\n *,\n param_names: Tuple[str] = (\n \"nerstf.mlp.0.weight\",\n \"nerstf.mlp.1.weight\",\n \"nerstf.mlp.2.weight\",\n \"nerstf.mlp.3.weight\",\n ),\n param_shapes: Tuple[Tuple[int]] = (\n (256, 93),\n (256, 256),\n (256, 256),\n (256, 256),\n ),\n d_latent: int = 1024,\n ):\n super().__init__()\n\n # check inputs\n if len(param_names) != len(param_shapes):\n raise ValueError(\"Must provide same number of `param_names` as `param_shapes`\")\n self.projections = nn.ModuleDict({})\n for k, (vectors, channels) in zip(param_names, param_shapes):\n self.projections[_sanitize_name(k)] = ChannelsProj(\n vectors=vectors,\n channels=channels,\n d_latent=d_latent,\n )\n\n def forward(self, x: torch.Tensor):\n out = {}\n start = 0\n for k, shape in zip(self.config.param_names, self.config.param_shapes):\n vectors, _ = shape\n end = start + vectors\n x_bvd = x[:, start:end]\n out[k] = self.projections[_sanitize_name(k)](x_bvd).reshape(len(x), *shape)\n start = end\n return out\n\n\nclass ShapERenderer(ModelMixin, ConfigMixin):\n @register_to_config\n def __init__(\n self,\n *,\n param_names: Tuple[str] = (\n \"nerstf.mlp.0.weight\",\n \"nerstf.mlp.1.weight\",\n \"nerstf.mlp.2.weight\",\n \"nerstf.mlp.3.weight\",\n ),\n param_shapes: Tuple[Tuple[int]] = (\n (256, 93),\n (256, 256),\n (256, 256),\n (256, 256),\n ),\n d_latent: int = 1024,\n d_hidden: int = 256,\n n_output: int = 12,\n n_hidden_layers: int = 6,\n act_fn: str = \"swish\",\n insert_direction_at: int = 4,\n background: Tuple[float] = (\n 255.0,\n 255.0,\n 255.0,\n ),\n ):\n super().__init__()\n\n self.params_proj = ShapEParamsProjModel(\n param_names=param_names,\n param_shapes=param_shapes,\n d_latent=d_latent,\n )\n self.mlp = MLPNeRSTFModel(d_hidden, n_output, n_hidden_layers, act_fn, insert_direction_at)\n self.void = VoidNeRFModel(background=background, channel_scale=255.0)\n self.volume = BoundingBoxVolume(bbox_max=[1.0, 1.0, 1.0], bbox_min=[-1.0, -1.0, -1.0])\n self.mesh_decoder = MeshDecoder()\n\n @torch.no_grad()\n def render_rays(self, rays, sampler, n_samples, prev_model_out=None, render_with_direction=False):\n \"\"\"\n Perform volumetric rendering over a partition of possible t's in the union of rendering volumes (written below\n with some abuse of notations)\n\n C(r) := sum(\n transmittance(t[i]) * integrate(\n lambda t: density(t) * channels(t) * transmittance(t), [t[i], t[i + 1]],\n ) for i in range(len(parts))\n ) + transmittance(t[-1]) * void_model(t[-1]).channels\n\n where\n\n 1) transmittance(s) := exp(-integrate(density, [t[0], s])) calculates the probability of light passing through\n the volume specified by [t[0], s]. (transmittance of 1 means light can pass freely) 2) density and channels are\n obtained by evaluating the appropriate part.model at time t. 3) [t[i], t[i + 1]] is defined as the range of t\n where the ray intersects (parts[i].volume \\\\ union(part.volume for part in parts[:i])) at the surface of the\n shell (if bounded). If the ray does not intersect, the integral over this segment is evaluated as 0 and\n transmittance(t[i + 1]) := transmittance(t[i]). 4) The last term is integration to infinity (e.g. [t[-1],\n math.inf]) that is evaluated by the void_model (i.e. we consider this space to be empty).\n\n Args:\n rays: [batch_size x ... x 2 x 3] origin and direction. sampler: disjoint volume integrals. n_samples:\n number of ts to sample. prev_model_outputs: model outputs from the previous rendering step, including\n\n :return: A tuple of\n - `channels`\n - A importance samplers for additional fine-grained rendering\n - raw model output\n \"\"\"\n origin, direction = rays[..., 0, :], rays[..., 1, :]\n\n # Integrate over [t[i], t[i + 1]]\n\n # 1 Intersect the rays with the current volume and sample ts to integrate along.\n vrange = self.volume.intersect(origin, direction, t0_lower=None)\n ts = sampler.sample(vrange.t0, vrange.t1, n_samples)\n ts = ts.to(rays.dtype)\n\n if prev_model_out is not None:\n # Append the previous ts now before fprop because previous\n # rendering used a different model and we can't reuse the output.\n ts = torch.sort(torch.cat([ts, prev_model_out.ts], dim=-2), dim=-2).values\n\n batch_size, *_shape, _t0_dim = vrange.t0.shape\n _, *ts_shape, _ts_dim = ts.shape\n\n # 2. Get the points along the ray and query the model\n directions = torch.broadcast_to(direction.unsqueeze(-2), [batch_size, *ts_shape, 3])\n positions = origin.unsqueeze(-2) + ts * directions\n\n directions = directions.to(self.mlp.dtype)\n positions = positions.to(self.mlp.dtype)\n\n optional_directions = directions if render_with_direction else None\n\n model_out = self.mlp(\n position=positions,\n direction=optional_directions,\n ts=ts,\n nerf_level=\"coarse\" if prev_model_out is None else \"fine\",\n )\n\n # 3. Integrate the model results\n channels, weights, transmittance = integrate_samples(\n vrange, model_out.ts, model_out.density, model_out.channels\n )\n\n # 4. Clean up results that do not intersect with the volume.\n transmittance = torch.where(vrange.intersected, transmittance, torch.ones_like(transmittance))\n channels = torch.where(vrange.intersected, channels, torch.zeros_like(channels))\n # 5. integration to infinity (e.g. [t[-1], math.inf]) that is evaluated by the void_model (i.e. we consider this space to be empty).\n channels = channels + transmittance * self.void(origin)\n\n weighted_sampler = ImportanceRaySampler(vrange, ts=model_out.ts, weights=weights)\n\n return channels, weighted_sampler, model_out\n\n @torch.no_grad()\n def decode_to_image(\n self,\n latents,\n device,\n size: int = 64,\n ray_batch_size: int = 4096,\n n_coarse_samples=64,\n n_fine_samples=128,\n ):\n # project the parameters from the generated latents\n projected_params = self.params_proj(latents)\n\n # update the mlp layers of the renderer\n for name, param in self.mlp.state_dict().items():\n if f\"nerstf.{name}\" in projected_params.keys():\n param.copy_(projected_params[f\"nerstf.{name}\"].squeeze(0))\n\n # create cameras object\n camera = create_pan_cameras(size)\n rays = camera.camera_rays\n rays = rays.to(device)\n n_batches = rays.shape[1] // ray_batch_size\n\n coarse_sampler = StratifiedRaySampler()\n\n images = []\n\n for idx in range(n_batches):\n rays_batch = rays[:, idx * ray_batch_size : (idx + 1) * ray_batch_size]\n\n # render rays with coarse, stratified samples.\n _, fine_sampler, coarse_model_out = self.render_rays(rays_batch, coarse_sampler, n_coarse_samples)\n # Then, render with additional importance-weighted ray samples.\n channels, _, _ = self.render_rays(\n rays_batch, fine_sampler, n_fine_samples, prev_model_out=coarse_model_out\n )\n\n images.append(channels)\n\n images = torch.cat(images, dim=1)\n images = images.view(*camera.shape, camera.height, camera.width, -1).squeeze(0)\n\n return images\n\n @torch.no_grad()\n def decode_to_mesh(\n self,\n latents,\n device,\n grid_size: int = 128,\n query_batch_size: int = 4096,\n texture_channels: Tuple = (\"R\", \"G\", \"B\"),\n ):\n # 1. project the parameters from the generated latents\n projected_params = self.params_proj(latents)\n\n # 2. update the mlp layers of the renderer\n for name, param in self.mlp.state_dict().items():\n if f\"nerstf.{name}\" in projected_params.keys():\n param.copy_(projected_params[f\"nerstf.{name}\"].squeeze(0))\n\n # 3. decoding with STF rendering\n # 3.1 query the SDF values at vertices along a regular 128**3 grid\n\n query_points = volume_query_points(self.volume, grid_size)\n query_positions = query_points[None].repeat(1, 1, 1).to(device=device, dtype=self.mlp.dtype)\n\n fields = []\n\n for idx in range(0, query_positions.shape[1], query_batch_size):\n query_batch = query_positions[:, idx : idx + query_batch_size]\n\n model_out = self.mlp(\n position=query_batch, direction=None, ts=None, nerf_level=\"fine\", rendering_mode=\"stf\"\n )\n fields.append(model_out.signed_distance)\n\n # predicted SDF values\n fields = torch.cat(fields, dim=1)\n fields = fields.float()\n\n assert (\n len(fields.shape) == 3 and fields.shape[-1] == 1\n ), f\"expected [meta_batch x inner_batch] SDF results, but got {fields.shape}\"\n\n fields = fields.reshape(1, *([grid_size] * 3))\n\n # create grid 128 x 128 x 128\n # - force a negative border around the SDFs to close off all the models.\n full_grid = torch.zeros(\n 1,\n grid_size + 2,\n grid_size + 2,\n grid_size + 2,\n device=fields.device,\n dtype=fields.dtype,\n )\n full_grid.fill_(-1.0)\n full_grid[:, 1:-1, 1:-1, 1:-1] = fields\n fields = full_grid\n\n # apply a differentiable implementation of Marching Cubes to construct meshs\n raw_meshes = []\n mesh_mask = []\n\n for field in fields:\n raw_mesh = self.mesh_decoder(field, self.volume.bbox_min, self.volume.bbox_max - self.volume.bbox_min)\n mesh_mask.append(True)\n raw_meshes.append(raw_mesh)\n\n mesh_mask = torch.tensor(mesh_mask, device=fields.device)\n max_vertices = max(len(m.verts) for m in raw_meshes)\n\n # 3.2. query the texture color head at each vertex of the resulting mesh.\n texture_query_positions = torch.stack(\n [m.verts[torch.arange(0, max_vertices) % len(m.verts)] for m in raw_meshes],\n dim=0,\n )\n texture_query_positions = texture_query_positions.to(device=device, dtype=self.mlp.dtype)\n\n textures = []\n\n for idx in range(0, texture_query_positions.shape[1], query_batch_size):\n query_batch = texture_query_positions[:, idx : idx + query_batch_size]\n\n texture_model_out = self.mlp(\n position=query_batch, direction=None, ts=None, nerf_level=\"fine\", rendering_mode=\"stf\"\n )\n textures.append(texture_model_out.channels)\n\n # predict texture color\n textures = torch.cat(textures, dim=1)\n\n textures = _convert_srgb_to_linear(textures)\n textures = textures.float()\n\n # 3.3 augument the mesh with texture data\n assert len(textures.shape) == 3 and textures.shape[-1] == len(\n texture_channels\n ), f\"expected [meta_batch x inner_batch x texture_channels] field results, but got {textures.shape}\"\n\n for m, texture in zip(raw_meshes, textures):\n texture = texture[: len(m.verts)]\n m.vertex_channels = dict(zip(texture_channels, texture.unbind(-1)))\n\n return raw_meshes[0]\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_cascade/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_stable_cascade\"] = [\"StableCascadeDecoderPipeline\"]\n _import_structure[\"pipeline_stable_cascade_combined\"] = [\"StableCascadeCombinedPipeline\"]\n _import_structure[\"pipeline_stable_cascade_prior\"] = [\"StableCascadePriorPipeline\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import * # noqa F403\n else:\n from .pipeline_stable_cascade import StableCascadeDecoderPipeline\n from .pipeline_stable_cascade_combined import StableCascadeCombinedPipeline\n from .pipeline_stable_cascade_prior import StableCascadePriorPipeline\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Callable, Dict, List, Optional, Union\n\nimport torch\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nfrom ...models import StableCascadeUNet\nfrom ...schedulers import DDPMWuerstchenScheduler\nfrom ...utils import is_torch_version, logging, replace_example_docstring\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\nfrom ..wuerstchen.modeling_paella_vq_model import PaellaVQModel\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableCascadePriorPipeline, StableCascadeDecoderPipeline\n\n >>> prior_pipe = StableCascadePriorPipeline.from_pretrained(\n ... \"stabilityai/stable-cascade-prior\", torch_dtype=torch.bfloat16\n ... ).to(\"cuda\")\n >>> gen_pipe = StableCascadeDecoderPipeline.from_pretrain(\n ... \"stabilityai/stable-cascade\", torch_dtype=torch.float16\n ... ).to(\"cuda\")\n\n >>> prompt = \"an image of a shiba inu, donning a spacesuit and helmet\"\n >>> prior_output = pipe(prompt)\n >>> images = gen_pipe(prior_output.image_embeddings, prompt=prompt)\n ```\n\"\"\"\n\n\nclass StableCascadeDecoderPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for generating images from the Stable Cascade model.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n tokenizer (`CLIPTokenizer`):\n The CLIP tokenizer.\n text_encoder (`CLIPTextModel`):\n The CLIP text encoder.\n decoder ([`StableCascadeUNet`]):\n The Stable Cascade decoder unet.\n vqgan ([`PaellaVQModel`]):\n The VQGAN model.\n scheduler ([`DDPMWuerstchenScheduler`]):\n A scheduler to be used in combination with `prior` to generate image embedding.\n latent_dim_scale (float, `optional`, defaults to 10.67):\n Multiplier to determine the VQ latent space size from the image embeddings. If the image embeddings are\n height=24 and width=24, the VQ latent shape needs to be height=int(24*10.67)=256 and\n width=int(24*10.67)=256 in order to match the training conditions.\n \"\"\"\n\n unet_name = \"decoder\"\n text_encoder_name = \"text_encoder\"\n model_cpu_offload_seq = \"text_encoder->decoder->vqgan\"\n _callback_tensor_inputs = [\n \"latents\",\n \"prompt_embeds_pooled\",\n \"negative_prompt_embeds\",\n \"image_embeddings\",\n ]\n\n def __init__(\n self,\n decoder: StableCascadeUNet,\n tokenizer: CLIPTokenizer,\n text_encoder: CLIPTextModel,\n scheduler: DDPMWuerstchenScheduler,\n vqgan: PaellaVQModel,\n latent_dim_scale: float = 10.67,\n ) -> None:\n super().__init__()\n self.register_modules(\n decoder=decoder,\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n scheduler=scheduler,\n vqgan=vqgan,\n )\n self.register_to_config(latent_dim_scale=latent_dim_scale)\n\n def prepare_latents(\n self, batch_size, image_embeddings, num_images_per_prompt, dtype, device, generator, latents, scheduler\n ):\n _, channels, height, width = image_embeddings.shape\n latents_shape = (\n batch_size * num_images_per_prompt,\n 4,\n int(height * self.config.latent_dim_scale),\n int(width * self.config.latent_dim_scale),\n )\n\n if latents is None:\n latents = randn_tensor(latents_shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != latents_shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {latents_shape}\")\n latents = latents.to(device)\n\n latents = latents * scheduler.init_noise_sigma\n return latents\n\n def encode_prompt(\n self,\n device,\n batch_size,\n num_images_per_prompt,\n do_classifier_free_guidance,\n prompt=None,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n prompt_embeds_pooled: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds_pooled: Optional[torch.Tensor] = None,\n ):\n if prompt_embeds is None:\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n attention_mask = text_inputs.attention_mask\n\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]\n attention_mask = attention_mask[:, : self.tokenizer.model_max_length]\n\n text_encoder_output = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask.to(device), output_hidden_states=True\n )\n prompt_embeds = text_encoder_output.hidden_states[-1]\n if prompt_embeds_pooled is None:\n prompt_embeds_pooled = text_encoder_output.text_embeds.unsqueeze(1)\n\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n prompt_embeds_pooled = prompt_embeds_pooled.to(dtype=self.text_encoder.dtype, device=device)\n prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n prompt_embeds_pooled = prompt_embeds_pooled.repeat_interleave(num_images_per_prompt, dim=0)\n\n if negative_prompt_embeds is None and do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n negative_prompt_embeds_text_encoder_output = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=uncond_input.attention_mask.to(device),\n output_hidden_states=True,\n )\n\n negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.hidden_states[-1]\n negative_prompt_embeds_pooled = negative_prompt_embeds_text_encoder_output.text_embeds.unsqueeze(1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n seq_len = negative_prompt_embeds_pooled.shape[1]\n negative_prompt_embeds_pooled = negative_prompt_embeds_pooled.to(\n dtype=self.text_encoder.dtype, device=device\n )\n negative_prompt_embeds_pooled = negative_prompt_embeds_pooled.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds_pooled = negative_prompt_embeds_pooled.view(\n batch_size * num_images_per_prompt, seq_len, -1\n )\n # done duplicates\n\n return prompt_embeds, prompt_embeds_pooled, negative_prompt_embeds, negative_prompt_embeds_pooled\n\n def check_inputs(\n self,\n prompt,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n image_embeddings: Union[torch.Tensor, List[torch.Tensor]],\n prompt: Union[str, List[str]] = None,\n num_inference_steps: int = 10,\n guidance_scale: float = 0.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n prompt_embeds_pooled: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds_pooled: Optional[torch.Tensor] = None,\n num_images_per_prompt: int = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n image_embedding (`torch.Tensor` or `List[torch.Tensor]`):\n Image Embeddings either extracted from an image or generated by a Prior Model.\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n num_inference_steps (`int`, *optional*, defaults to 12):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 0.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `decoder_guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting\n `decoder_guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely\n linked to the text `prompt`, usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `decoder_guidance_scale` is less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n prompt_embeds_pooled (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n negative_prompt_embeds_pooled (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds_pooled will be generated from `negative_prompt`\n input argument.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between: `\"pil\"` (`PIL.Image.Image`), `\"np\"`\n (`np.array`) or `\"pt\"` (`torch.Tensor`).\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple` [`~pipelines.ImagePipelineOutput`] if `return_dict` is True,\n otherwise a `tuple`. When returning a tuple, the first element is a list with the generated image\n embeddings.\n \"\"\"\n\n # 0. Define commonly used variables\n device = self._execution_device\n dtype = self.decoder.dtype\n self._guidance_scale = guidance_scale\n if is_torch_version(\"<\", \"2.2.0\") and dtype == torch.bfloat16:\n raise ValueError(\"`StableCascadeDecoderPipeline` requires torch>=2.2.0 when using `torch.bfloat16` dtype.\")\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,\n )\n if isinstance(image_embeddings, list):\n image_embeddings = torch.cat(image_embeddings, dim=0)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # Compute the effective number of images per prompt\n # We must account for the fact that the image embeddings from the prior can be generated with num_images_per_prompt > 1\n # This results in a case where a single prompt is associated with multiple image embeddings\n # Divide the number of image embeddings by the batch size to determine if this is the case.\n num_images_per_prompt = num_images_per_prompt * (image_embeddings.shape[0] // batch_size)\n\n # 2. Encode caption\n if prompt_embeds is None and negative_prompt_embeds is None:\n _, prompt_embeds_pooled, _, negative_prompt_embeds_pooled = self.encode_prompt(\n prompt=prompt,\n device=device,\n batch_size=batch_size,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n prompt_embeds_pooled=prompt_embeds_pooled,\n negative_prompt_embeds=negative_prompt_embeds,\n negative_prompt_embeds_pooled=negative_prompt_embeds_pooled,\n )\n\n # The pooled embeds from the prior are pooled again before being passed to the decoder\n prompt_embeds_pooled = (\n torch.cat([prompt_embeds_pooled, negative_prompt_embeds_pooled])\n if self.do_classifier_free_guidance\n else prompt_embeds_pooled\n )\n effnet = (\n torch.cat([image_embeddings, torch.zeros_like(image_embeddings)])\n if self.do_classifier_free_guidance\n else image_embeddings\n )\n\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latents\n latents = self.prepare_latents(\n batch_size, image_embeddings, num_images_per_prompt, dtype, device, generator, latents, self.scheduler\n )\n\n # 6. Run denoising loop\n self._num_timesteps = len(timesteps[:-1])\n for i, t in enumerate(self.progress_bar(timesteps[:-1])):\n timestep_ratio = t.expand(latents.size(0)).to(dtype)\n\n # 7. Denoise latents\n predicted_latents = self.decoder(\n sample=torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents,\n timestep_ratio=torch.cat([timestep_ratio] * 2) if self.do_classifier_free_guidance else timestep_ratio,\n clip_text_pooled=prompt_embeds_pooled,\n effnet=effnet,\n return_dict=False,\n )[0]\n\n # 8. Check for classifier free guidance and apply it\n if self.do_classifier_free_guidance:\n predicted_latents_text, predicted_latents_uncond = predicted_latents.chunk(2)\n predicted_latents = torch.lerp(predicted_latents_uncond, predicted_latents_text, self.guidance_scale)\n\n # 9. Renoise latents to next timestep\n latents = self.scheduler.step(\n model_output=predicted_latents,\n timestep=timestep_ratio,\n sample=latents,\n generator=generator,\n ).prev_sample\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n if output_type not in [\"pt\", \"np\", \"pil\", \"latent\"]:\n raise ValueError(\n f\"Only the output types `pt`, `np`, `pil` and `latent` are supported not output_type={output_type}\"\n )\n\n if not output_type == \"latent\":\n # 10. Scale and decode the image latents with vq-vae\n latents = self.vqgan.config.scale_factor * latents\n images = self.vqgan.decode(latents).sample.clamp(0, 1)\n if output_type == \"np\":\n images = images.permute(0, 2, 3, 1).cpu().float().numpy() # float() as bfloat16-> numpy doesnt work\n elif output_type == \"pil\":\n images = images.permute(0, 2, 3, 1).cpu().float().numpy() # float() as bfloat16-> numpy doesnt work\n images = self.numpy_to_pil(images)\n else:\n images = latents\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return images\n return ImagePipelineOutput(images)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_combined.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Callable, Dict, List, Optional, Union\n\nimport PIL\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...models import StableCascadeUNet\nfrom ...schedulers import DDPMWuerstchenScheduler\nfrom ...utils import is_torch_version, replace_example_docstring\nfrom ..pipeline_utils import DiffusionPipeline\nfrom ..wuerstchen.modeling_paella_vq_model import PaellaVQModel\nfrom .pipeline_stable_cascade import StableCascadeDecoderPipeline\nfrom .pipeline_stable_cascade_prior import StableCascadePriorPipeline\n\n\nTEXT2IMAGE_EXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableCascadeCombinedPipeline\n\n >>> pipe = StableCascadeCombinedPipeline.from_pretrained(\n ... \"stabilityai/stable-cascade\", variant=\"bf16\", torch_dtype=torch.bfloat16\n ... )\n >>> pipe.enable_model_cpu_offload()\n >>> prompt = \"an image of a shiba inu, donning a spacesuit and helmet\"\n >>> images = pipe(prompt=prompt)\n ```\n\"\"\"\n\n\nclass StableCascadeCombinedPipeline(DiffusionPipeline):\n \"\"\"\n Combined Pipeline for text-to-image generation using Stable Cascade.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n tokenizer (`CLIPTokenizer`):\n The decoder tokenizer to be used for text inputs.\n text_encoder (`CLIPTextModel`):\n The decoder text encoder to be used for text inputs.\n decoder (`StableCascadeUNet`):\n The decoder model to be used for decoder image generation pipeline.\n scheduler (`DDPMWuerstchenScheduler`):\n The scheduler to be used for decoder image generation pipeline.\n vqgan (`PaellaVQModel`):\n The VQGAN model to be used for decoder image generation pipeline.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `image_encoder`.\n image_encoder ([`CLIPVisionModelWithProjection`]):\n Frozen CLIP image-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n prior_prior (`StableCascadeUNet`):\n The prior model to be used for prior pipeline.\n prior_scheduler (`DDPMWuerstchenScheduler`):\n The scheduler to be used for prior pipeline.\n \"\"\"\n\n _load_connected_pipes = True\n _optional_components = [\"prior_feature_extractor\", \"prior_image_encoder\"]\n\n def __init__(\n self,\n tokenizer: CLIPTokenizer,\n text_encoder: CLIPTextModel,\n decoder: StableCascadeUNet,\n scheduler: DDPMWuerstchenScheduler,\n vqgan: PaellaVQModel,\n prior_prior: StableCascadeUNet,\n prior_text_encoder: CLIPTextModel,\n prior_tokenizer: CLIPTokenizer,\n prior_scheduler: DDPMWuerstchenScheduler,\n prior_feature_extractor: Optional[CLIPImageProcessor] = None,\n prior_image_encoder: Optional[CLIPVisionModelWithProjection] = None,\n ):\n super().__init__()\n\n self.register_modules(\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n decoder=decoder,\n scheduler=scheduler,\n vqgan=vqgan,\n prior_text_encoder=prior_text_encoder,\n prior_tokenizer=prior_tokenizer,\n prior_prior=prior_prior,\n prior_scheduler=prior_scheduler,\n prior_feature_extractor=prior_feature_extractor,\n prior_image_encoder=prior_image_encoder,\n )\n self.prior_pipe = StableCascadePriorPipeline(\n prior=prior_prior,\n text_encoder=prior_text_encoder,\n tokenizer=prior_tokenizer,\n scheduler=prior_scheduler,\n image_encoder=prior_image_encoder,\n feature_extractor=prior_feature_extractor,\n )\n self.decoder_pipe = StableCascadeDecoderPipeline(\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n decoder=decoder,\n scheduler=scheduler,\n vqgan=vqgan,\n )\n\n def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):\n self.decoder_pipe.enable_xformers_memory_efficient_attention(attention_op)\n\n def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = \"cuda\"):\n r\"\"\"\n Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared\n to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`\n method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with\n `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.\n \"\"\"\n self.prior_pipe.enable_model_cpu_offload(gpu_id=gpu_id, device=device)\n self.decoder_pipe.enable_model_cpu_offload(gpu_id=gpu_id, device=device)\n\n def enable_sequential_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = \"cuda\"):\n r\"\"\"\n Offloads all models (`unet`, `text_encoder`, `vae`, and `safety checker` state dicts) to CPU using 🤗\n Accelerate, significantly reducing memory usage. Models are moved to a `torch.device('meta')` and loaded on a\n GPU only when their specific submodule's `forward` method is called. Offloading happens on a submodule basis.\n Memory savings are higher than using `enable_model_cpu_offload`, but performance is lower.\n \"\"\"\n self.prior_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id, device=device)\n self.decoder_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id, device=device)\n\n def progress_bar(self, iterable=None, total=None):\n self.prior_pipe.progress_bar(iterable=iterable, total=total)\n self.decoder_pipe.progress_bar(iterable=iterable, total=total)\n\n def set_progress_bar_config(self, **kwargs):\n self.prior_pipe.set_progress_bar_config(**kwargs)\n self.decoder_pipe.set_progress_bar_config(**kwargs)\n\n @torch.no_grad()\n @replace_example_docstring(TEXT2IMAGE_EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Optional[Union[str, List[str]]] = None,\n images: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]] = None,\n height: int = 512,\n width: int = 512,\n prior_num_inference_steps: int = 60,\n prior_guidance_scale: float = 4.0,\n num_inference_steps: int = 12,\n decoder_guidance_scale: float = 0.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n prompt_embeds_pooled: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds_pooled: Optional[torch.Tensor] = None,\n num_images_per_prompt: int = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n prior_callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n prior_callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation for the prior and decoder.\n images (`torch.Tensor`, `PIL.Image.Image`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, *optional*):\n The images to guide the image generation for the prior.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings for the prior. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, text embeddings will be generated from `prompt` input argument.\n prompt_embeds_pooled (`torch.Tensor`, *optional*):\n Pre-generated text embeddings for the prior. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings for the prior. Can be used to easily tweak text inputs, *e.g.*\n prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n negative_prompt_embeds_pooled (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings for the prior. Can be used to easily tweak text inputs, *e.g.*\n prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n prior_guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `prior_guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting\n `prior_guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked\n to the text `prompt`, usually at the expense of lower image quality.\n prior_num_inference_steps (`Union[int, Dict[float, int]]`, *optional*, defaults to 60):\n The number of prior denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference. For more specific timestep spacing, you can pass customized\n `prior_timesteps`\n num_inference_steps (`int`, *optional*, defaults to 12):\n The number of decoder denoising steps. More denoising steps usually lead to a higher quality image at\n the expense of slower inference. For more specific timestep spacing, you can pass customized\n `timesteps`\n decoder_guidance_scale (`float`, *optional*, defaults to 0.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between: `\"pil\"` (`PIL.Image.Image`), `\"np\"`\n (`np.array`) or `\"pt\"` (`torch.Tensor`).\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n prior_callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `prior_callback_on_step_end(self: DiffusionPipeline, step: int, timestep:\n int, callback_kwargs: Dict)`.\n prior_callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `prior_callback_on_step_end` function. The tensors specified in the\n list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in\n the `._callback_tensor_inputs` attribute of your pipeline class.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple` [`~pipelines.ImagePipelineOutput`] if `return_dict` is True,\n otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images.\n \"\"\"\n dtype = self.decoder_pipe.decoder.dtype\n if is_torch_version(\"<\", \"2.2.0\") and dtype == torch.bfloat16:\n raise ValueError(\n \"`StableCascadeCombinedPipeline` requires torch>=2.2.0 when using `torch.bfloat16` dtype.\"\n )\n\n prior_outputs = self.prior_pipe(\n prompt=prompt if prompt_embeds is None else None,\n images=images,\n height=height,\n width=width,\n num_inference_steps=prior_num_inference_steps,\n guidance_scale=prior_guidance_scale,\n negative_prompt=negative_prompt if negative_prompt_embeds is None else None,\n prompt_embeds=prompt_embeds,\n prompt_embeds_pooled=prompt_embeds_pooled,\n negative_prompt_embeds=negative_prompt_embeds,\n negative_prompt_embeds_pooled=negative_prompt_embeds_pooled,\n num_images_per_prompt=num_images_per_prompt,\n generator=generator,\n latents=latents,\n output_type=\"pt\",\n return_dict=True,\n callback_on_step_end=prior_callback_on_step_end,\n callback_on_step_end_tensor_inputs=prior_callback_on_step_end_tensor_inputs,\n )\n image_embeddings = prior_outputs.image_embeddings\n prompt_embeds = prior_outputs.get(\"prompt_embeds\", None)\n prompt_embeds_pooled = prior_outputs.get(\"prompt_embeds_pooled\", None)\n negative_prompt_embeds = prior_outputs.get(\"negative_prompt_embeds\", None)\n negative_prompt_embeds_pooled = prior_outputs.get(\"negative_prompt_embeds_pooled\", None)\n\n outputs = self.decoder_pipe(\n image_embeddings=image_embeddings,\n prompt=prompt if prompt_embeds is None else None,\n num_inference_steps=num_inference_steps,\n guidance_scale=decoder_guidance_scale,\n negative_prompt=negative_prompt if negative_prompt_embeds is None else None,\n prompt_embeds=prompt_embeds,\n prompt_embeds_pooled=prompt_embeds_pooled,\n negative_prompt_embeds=negative_prompt_embeds,\n negative_prompt_embeds_pooled=negative_prompt_embeds_pooled,\n generator=generator,\n output_type=output_type,\n return_dict=return_dict,\n callback_on_step_end=callback_on_step_end,\n callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,\n )\n\n return outputs\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom dataclasses import dataclass\nfrom math import ceil\nfrom typing import Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...models import StableCascadeUNet\nfrom ...schedulers import DDPMWuerstchenScheduler\nfrom ...utils import BaseOutput, logging, replace_example_docstring\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nDEFAULT_STAGE_C_TIMESTEPS = list(np.linspace(1.0, 2 / 3, 20)) + list(np.linspace(2 / 3, 0.0, 11))[1:]\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableCascadePriorPipeline\n\n >>> prior_pipe = StableCascadePriorPipeline.from_pretrained(\n ... \"stabilityai/stable-cascade-prior\", torch_dtype=torch.bfloat16\n ... ).to(\"cuda\")\n\n >>> prompt = \"an image of a shiba inu, donning a spacesuit and helmet\"\n >>> prior_output = pipe(prompt)\n ```\n\"\"\"\n\n\n@dataclass\nclass StableCascadePriorPipelineOutput(BaseOutput):\n \"\"\"\n Output class for WuerstchenPriorPipeline.\n\n Args:\n image_embeddings (`torch.Tensor` or `np.ndarray`)\n Prior image embeddings for text prompt\n prompt_embeds (`torch.Tensor`):\n Text embeddings for the prompt.\n negative_prompt_embeds (`torch.Tensor`):\n Text embeddings for the negative prompt.\n \"\"\"\n\n image_embeddings: Union[torch.Tensor, np.ndarray]\n prompt_embeds: Union[torch.Tensor, np.ndarray]\n prompt_embeds_pooled: Union[torch.Tensor, np.ndarray]\n negative_prompt_embeds: Union[torch.Tensor, np.ndarray]\n negative_prompt_embeds_pooled: Union[torch.Tensor, np.ndarray]\n\n\nclass StableCascadePriorPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for generating image prior for Stable Cascade.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n prior ([`StableCascadeUNet`]):\n The Stable Cascade prior to approximate the image embedding from the text and/or image embedding.\n text_encoder ([`CLIPTextModelWithProjection`]):\n Frozen text-encoder\n ([laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)).\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `image_encoder`.\n image_encoder ([`CLIPVisionModelWithProjection`]):\n Frozen CLIP image-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n scheduler ([`DDPMWuerstchenScheduler`]):\n A scheduler to be used in combination with `prior` to generate image embedding.\n resolution_multiple ('float', *optional*, defaults to 42.67):\n Default resolution for multiple images generated.\n \"\"\"\n\n unet_name = \"prior\"\n text_encoder_name = \"text_encoder\"\n model_cpu_offload_seq = \"image_encoder->text_encoder->prior\"\n _optional_components = [\"image_encoder\", \"feature_extractor\"]\n _callback_tensor_inputs = [\"latents\", \"text_encoder_hidden_states\", \"negative_prompt_embeds\"]\n\n def __init__(\n self,\n tokenizer: CLIPTokenizer,\n text_encoder: CLIPTextModelWithProjection,\n prior: StableCascadeUNet,\n scheduler: DDPMWuerstchenScheduler,\n resolution_multiple: float = 42.67,\n feature_extractor: Optional[CLIPImageProcessor] = None,\n image_encoder: Optional[CLIPVisionModelWithProjection] = None,\n ) -> None:\n super().__init__()\n self.register_modules(\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n image_encoder=image_encoder,\n feature_extractor=feature_extractor,\n prior=prior,\n scheduler=scheduler,\n )\n self.register_to_config(resolution_multiple=resolution_multiple)\n\n def prepare_latents(\n self, batch_size, height, width, num_images_per_prompt, dtype, device, generator, latents, scheduler\n ):\n latent_shape = (\n num_images_per_prompt * batch_size,\n self.prior.config.in_channels,\n ceil(height / self.config.resolution_multiple),\n ceil(width / self.config.resolution_multiple),\n )\n\n if latents is None:\n latents = randn_tensor(latent_shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != latent_shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {latent_shape}\")\n latents = latents.to(device)\n\n latents = latents * scheduler.init_noise_sigma\n return latents\n\n def encode_prompt(\n self,\n device,\n batch_size,\n num_images_per_prompt,\n do_classifier_free_guidance,\n prompt=None,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n prompt_embeds_pooled: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds_pooled: Optional[torch.Tensor] = None,\n ):\n if prompt_embeds is None:\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n attention_mask = text_inputs.attention_mask\n\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]\n attention_mask = attention_mask[:, : self.tokenizer.model_max_length]\n\n text_encoder_output = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask.to(device), output_hidden_states=True\n )\n prompt_embeds = text_encoder_output.hidden_states[-1]\n if prompt_embeds_pooled is None:\n prompt_embeds_pooled = text_encoder_output.text_embeds.unsqueeze(1)\n\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n prompt_embeds_pooled = prompt_embeds_pooled.to(dtype=self.text_encoder.dtype, device=device)\n prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n prompt_embeds_pooled = prompt_embeds_pooled.repeat_interleave(num_images_per_prompt, dim=0)\n\n if negative_prompt_embeds is None and do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n negative_prompt_embeds_text_encoder_output = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=uncond_input.attention_mask.to(device),\n output_hidden_states=True,\n )\n\n negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.hidden_states[-1]\n negative_prompt_embeds_pooled = negative_prompt_embeds_text_encoder_output.text_embeds.unsqueeze(1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n seq_len = negative_prompt_embeds_pooled.shape[1]\n negative_prompt_embeds_pooled = negative_prompt_embeds_pooled.to(\n dtype=self.text_encoder.dtype, device=device\n )\n negative_prompt_embeds_pooled = negative_prompt_embeds_pooled.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds_pooled = negative_prompt_embeds_pooled.view(\n batch_size * num_images_per_prompt, seq_len, -1\n )\n # done duplicates\n\n return prompt_embeds, prompt_embeds_pooled, negative_prompt_embeds, negative_prompt_embeds_pooled\n\n def encode_image(self, images, device, dtype, batch_size, num_images_per_prompt):\n image_embeds = []\n for image in images:\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n image = image.to(device=device, dtype=dtype)\n image_embed = self.image_encoder(image).image_embeds.unsqueeze(1)\n image_embeds.append(image_embed)\n image_embeds = torch.cat(image_embeds, dim=1)\n\n image_embeds = image_embeds.repeat(batch_size * num_images_per_prompt, 1, 1)\n negative_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, negative_image_embeds\n\n def check_inputs(\n self,\n prompt,\n images=None,\n image_embeds=None,\n negative_prompt=None,\n prompt_embeds=None,\n prompt_embeds_pooled=None,\n negative_prompt_embeds=None,\n negative_prompt_embeds_pooled=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if prompt_embeds is not None and prompt_embeds_pooled is None:\n raise ValueError(\n \"If `prompt_embeds` are provided, `prompt_embeds_pooled` must also be provided. Make sure to generate `prompt_embeds_pooled` from the same text encoder that was used to generate `prompt_embeds`\"\n )\n\n if negative_prompt_embeds is not None and negative_prompt_embeds_pooled is None:\n raise ValueError(\n \"If `negative_prompt_embeds` are provided, `negative_prompt_embeds_pooled` must also be provided. Make sure to generate `prompt_embeds_pooled` from the same text encoder that was used to generate `prompt_embeds`\"\n )\n\n if prompt_embeds_pooled is not None and negative_prompt_embeds_pooled is not None:\n if prompt_embeds_pooled.shape != negative_prompt_embeds_pooled.shape:\n raise ValueError(\n \"`prompt_embeds_pooled` and `negative_prompt_embeds_pooled` must have the same shape when passed\"\n f\"directly, but got: `prompt_embeds_pooled` {prompt_embeds_pooled.shape} !=\"\n f\"`negative_prompt_embeds_pooled` {negative_prompt_embeds_pooled.shape}.\"\n )\n\n if image_embeds is not None and images is not None:\n raise ValueError(\n f\"Cannot forward both `images`: {images} and `image_embeds`: {image_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n\n if images:\n for i, image in enumerate(images):\n if not isinstance(image, torch.Tensor) and not isinstance(image, PIL.Image.Image):\n raise TypeError(\n f\"'images' must contain images of type 'torch.Tensor' or 'PIL.Image.Image, but got\"\n f\"{type(image)} for image number {i}.\"\n )\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n def get_timestep_ratio_conditioning(self, t, alphas_cumprod):\n s = torch.tensor([0.003])\n clamp_range = [0, 1]\n min_var = torch.cos(s / (1 + s) * torch.pi * 0.5) ** 2\n var = alphas_cumprod[t]\n var = var.clamp(*clamp_range)\n s, min_var = s.to(var.device), min_var.to(var.device)\n ratio = (((var * min_var) ** 0.5).acos() / (torch.pi * 0.5)) * (1 + s) - s\n return ratio\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Optional[Union[str, List[str]]] = None,\n images: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]] = None,\n height: int = 1024,\n width: int = 1024,\n num_inference_steps: int = 20,\n timesteps: List[float] = None,\n guidance_scale: float = 4.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n prompt_embeds_pooled: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds_pooled: Optional[torch.Tensor] = None,\n image_embeds: Optional[torch.Tensor] = None,\n num_images_per_prompt: Optional[int] = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pt\",\n return_dict: bool = True,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n height (`int`, *optional*, defaults to 1024):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 1024):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 60):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 8.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `decoder_guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting\n `decoder_guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely\n linked to the text `prompt`, usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `decoder_guidance_scale` is less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n prompt_embeds_pooled (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n negative_prompt_embeds_pooled (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds_pooled will be generated from `negative_prompt`\n input argument.\n image_embeds (`torch.Tensor`, *optional*):\n Pre-generated image embeddings. Can be used to easily tweak image inputs, *e.g.* prompt weighting. If\n not provided, image embeddings will be generated from `image` input argument if existing.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between: `\"pil\"` (`PIL.Image.Image`), `\"np\"`\n (`np.array`) or `\"pt\"` (`torch.Tensor`).\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`StableCascadePriorPipelineOutput`] or `tuple` [`StableCascadePriorPipelineOutput`] if `return_dict` is\n True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated image\n embeddings.\n \"\"\"\n\n # 0. Define commonly used variables\n device = self._execution_device\n dtype = next(self.prior.parameters()).dtype\n self._guidance_scale = guidance_scale\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n images=images,\n image_embeds=image_embeds,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n prompt_embeds_pooled=prompt_embeds_pooled,\n negative_prompt_embeds=negative_prompt_embeds,\n negative_prompt_embeds_pooled=negative_prompt_embeds_pooled,\n callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,\n )\n\n # 2. Encode caption + images\n (\n prompt_embeds,\n prompt_embeds_pooled,\n negative_prompt_embeds,\n negative_prompt_embeds_pooled,\n ) = self.encode_prompt(\n prompt=prompt,\n device=device,\n batch_size=batch_size,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n prompt_embeds_pooled=prompt_embeds_pooled,\n negative_prompt_embeds=negative_prompt_embeds,\n negative_prompt_embeds_pooled=negative_prompt_embeds_pooled,\n )\n\n if images is not None:\n image_embeds_pooled, uncond_image_embeds_pooled = self.encode_image(\n images=images,\n device=device,\n dtype=dtype,\n batch_size=batch_size,\n num_images_per_prompt=num_images_per_prompt,\n )\n elif image_embeds is not None:\n image_embeds_pooled = image_embeds.repeat(batch_size * num_images_per_prompt, 1, 1)\n uncond_image_embeds_pooled = torch.zeros_like(image_embeds_pooled)\n else:\n image_embeds_pooled = torch.zeros(\n batch_size * num_images_per_prompt,\n 1,\n self.prior.config.clip_image_in_channels,\n device=device,\n dtype=dtype,\n )\n uncond_image_embeds_pooled = torch.zeros(\n batch_size * num_images_per_prompt,\n 1,\n self.prior.config.clip_image_in_channels,\n device=device,\n dtype=dtype,\n )\n\n if self.do_classifier_free_guidance:\n image_embeds = torch.cat([image_embeds_pooled, uncond_image_embeds_pooled], dim=0)\n else:\n image_embeds = image_embeds_pooled\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n text_encoder_hidden_states = (\n torch.cat([prompt_embeds, negative_prompt_embeds]) if negative_prompt_embeds is not None else prompt_embeds\n )\n text_encoder_pooled = (\n torch.cat([prompt_embeds_pooled, negative_prompt_embeds_pooled])\n if negative_prompt_embeds is not None\n else prompt_embeds_pooled\n )\n\n # 4. Prepare and set timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latents\n latents = self.prepare_latents(\n batch_size, height, width, num_images_per_prompt, dtype, device, generator, latents, self.scheduler\n )\n\n if isinstance(self.scheduler, DDPMWuerstchenScheduler):\n timesteps = timesteps[:-1]\n else:\n if self.scheduler.config.clip_sample:\n self.scheduler.config.clip_sample = False # disample sample clipping\n logger.warning(\" set `clip_sample` to be False\")\n # 6. Run denoising loop\n if hasattr(self.scheduler, \"betas\"):\n alphas = 1.0 - self.scheduler.betas\n alphas_cumprod = torch.cumprod(alphas, dim=0)\n else:\n alphas_cumprod = []\n\n self._num_timesteps = len(timesteps)\n for i, t in enumerate(self.progress_bar(timesteps)):\n if not isinstance(self.scheduler, DDPMWuerstchenScheduler):\n if len(alphas_cumprod) > 0:\n timestep_ratio = self.get_timestep_ratio_conditioning(t.long().cpu(), alphas_cumprod)\n timestep_ratio = timestep_ratio.expand(latents.size(0)).to(dtype).to(device)\n else:\n timestep_ratio = t.float().div(self.scheduler.timesteps[-1]).expand(latents.size(0)).to(dtype)\n else:\n timestep_ratio = t.expand(latents.size(0)).to(dtype)\n # 7. Denoise image embeddings\n predicted_image_embedding = self.prior(\n sample=torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents,\n timestep_ratio=torch.cat([timestep_ratio] * 2) if self.do_classifier_free_guidance else timestep_ratio,\n clip_text_pooled=text_encoder_pooled,\n clip_text=text_encoder_hidden_states,\n clip_img=image_embeds,\n return_dict=False,\n )[0]\n\n # 8. Check for classifier free guidance and apply it\n if self.do_classifier_free_guidance:\n predicted_image_embedding_text, predicted_image_embedding_uncond = predicted_image_embedding.chunk(2)\n predicted_image_embedding = torch.lerp(\n predicted_image_embedding_uncond, predicted_image_embedding_text, self.guidance_scale\n )\n\n # 9. Renoise latents to next timestep\n if not isinstance(self.scheduler, DDPMWuerstchenScheduler):\n timestep_ratio = t\n latents = self.scheduler.step(\n model_output=predicted_image_embedding, timestep=timestep_ratio, sample=latents, generator=generator\n ).prev_sample\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if output_type == \"np\":\n latents = latents.cpu().float().numpy() # float() as bfloat16-> numpy doesnt work\n prompt_embeds = prompt_embeds.cpu().float().numpy() # float() as bfloat16-> numpy doesnt work\n negative_prompt_embeds = (\n negative_prompt_embeds.cpu().float().numpy() if negative_prompt_embeds is not None else None\n ) # float() as bfloat16-> numpy doesnt work\n\n if not return_dict:\n return (\n latents,\n prompt_embeds,\n prompt_embeds_pooled,\n negative_prompt_embeds,\n negative_prompt_embeds_pooled,\n )\n\n return StableCascadePriorPipelineOutput(\n image_embeddings=latents,\n prompt_embeds=prompt_embeds,\n prompt_embeds_pooled=prompt_embeds_pooled,\n negative_prompt_embeds=negative_prompt_embeds,\n negative_prompt_embeds_pooled=negative_prompt_embeds_pooled,\n )\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/README.md", "content": "# Stable Diffusion\n\n## Overview\n\nStable Diffusion was proposed in [Stable Diffusion Announcement](https://stability.ai/blog/stable-diffusion-announcement) by Patrick Esser and Robin Rombach and the Stability AI team.\n\nThe summary of the model is the following:\n\n*Stable Diffusion is a text-to-image model that will empower billions of people to create stunning art within seconds. It is a breakthrough in speed and quality meaning that it can run on consumer GPUs. You can see some of the amazing output that has been created by this model without pre or post-processing on this page. The model itself builds upon the work of the team at CompVis and Runway in their widely used latent diffusion model combined with insights from the conditional diffusion models by our lead generative AI developer Katherine Crowson, Dall-E 2 by Open AI, Imagen by Google Brain and many others. We are delighted that AI media generation is a cooperative field and hope it can continue this way to bring the gift of creativity to all.*\n\n## Tips:\n\n- Stable Diffusion has the same architecture as [Latent Diffusion](https://arxiv.org/abs/2112.10752) but uses a frozen CLIP Text Encoder instead of training the text encoder jointly with the diffusion model.\n- An in-detail explanation of the Stable Diffusion model can be found under [Stable Diffusion with 🧨 Diffusers](https://huggingface.co/blog/stable_diffusion).\n- If you don't want to rely on the Hugging Face Hub and having to pass a authentication token, you can\ndownload the weights with `git lfs install; git clone https://huggingface.co/runwayml/stable-diffusion-v1-5` and instead pass the local path to the cloned folder to `from_pretrained` as shown below.\n- Stable Diffusion can work with a variety of different samplers as is shown below.\n\n## Available Pipelines:\n\n| Pipeline | Tasks | Colab\n|---|---|:---:|\n| [pipeline_stable_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py) | *Text-to-Image Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)\n| [pipeline_stable_diffusion_img2img](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py) | *Image-to-Image Text-Guided Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb)\n| [pipeline_stable_diffusion_inpaint](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py) | *Text-Guided Image Inpainting* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb)\n\n## Examples:\n\n### Using Stable Diffusion without being logged into the Hub.\n\nIf you want to download the model weights using a single Python line, you need to be logged in via `huggingface-cli login`.\n\n```python\nfrom diffusers import DiffusionPipeline\n\npipeline = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n```\n\nThis however can make it difficult to build applications on top of `diffusers` as you will always have to pass the token around. A potential way to solve this issue is by downloading the weights to a local path `\"./stable-diffusion-v1-5\"`:\n\n```\ngit lfs install\ngit clone https://huggingface.co/runwayml/stable-diffusion-v1-5\n```\n\nand simply passing the local path to `from_pretrained`:\n\n```python\nfrom diffusers import StableDiffusionPipeline\n\npipe = StableDiffusionPipeline.from_pretrained(\"./stable-diffusion-v1-5\")\n```\n\n### Text-to-Image with default PLMS scheduler\n\n```python\n# make sure you're logged in with `huggingface-cli login`\nfrom diffusers import StableDiffusionPipeline\n\npipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\npipe = pipe.to(\"cuda\")\n\nprompt = \"a photo of an astronaut riding a horse on mars\"\nimage = pipe(prompt).images[0]\n\nimage.save(\"astronaut_rides_horse.png\")\n```\n\n### Text-to-Image with DDIM scheduler\n\n```python\n# make sure you're logged in with `huggingface-cli login`\nfrom diffusers import StableDiffusionPipeline, DDIMScheduler\n\nscheduler = DDIMScheduler.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"scheduler\")\n\npipe = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n scheduler=scheduler,\n).to(\"cuda\")\n\nprompt = \"a photo of an astronaut riding a horse on mars\"\nimage = pipe(prompt).images[0]\n\nimage.save(\"astronaut_rides_horse.png\")\n```\n\n### Text-to-Image with K-LMS scheduler\n\n```python\n# make sure you're logged in with `huggingface-cli login`\nfrom diffusers import StableDiffusionPipeline, LMSDiscreteScheduler\n\nlms = LMSDiscreteScheduler.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"scheduler\")\n\npipe = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n scheduler=lms,\n).to(\"cuda\")\n\nprompt = \"a photo of an astronaut riding a horse on mars\"\nimage = pipe(prompt).images[0]\n\nimage.save(\"astronaut_rides_horse.png\")\n```\n\n### CycleDiffusion using Stable Diffusion and DDIM scheduler\n\n```python\nimport requests\nimport torch\nfrom PIL import Image\nfrom io import BytesIO\n\nfrom diffusers import CycleDiffusionPipeline, DDIMScheduler\n\n\n# load the scheduler. CycleDiffusion only supports stochastic schedulers.\n\n# load the pipeline\n# make sure you're logged in with `huggingface-cli login`\nmodel_id_or_path = \"CompVis/stable-diffusion-v1-4\"\nscheduler = DDIMScheduler.from_pretrained(model_id_or_path, subfolder=\"scheduler\")\npipe = CycleDiffusionPipeline.from_pretrained(model_id_or_path, scheduler=scheduler).to(\"cuda\")\n\n# let's download an initial image\nurl = \"https://raw.githubusercontent.com/ChenWu98/cycle-diffusion/main/data/dalle2/An%20astronaut%20riding%20a%20horse.png\"\nresponse = requests.get(url)\ninit_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\ninit_image = init_image.resize((512, 512))\ninit_image.save(\"horse.png\")\n\n# let's specify a prompt\nsource_prompt = \"An astronaut riding a horse\"\nprompt = \"An astronaut riding an elephant\"\n\n# call the pipeline\nimage = pipe(\n prompt=prompt,\n source_prompt=source_prompt,\n image=init_image,\n num_inference_steps=100,\n eta=0.1,\n strength=0.8,\n guidance_scale=2,\n source_guidance_scale=1,\n).images[0]\n\nimage.save(\"horse_to_elephant.png\")\n\n# let's try another example\n# See more samples at the original repo: https://github.com/ChenWu98/cycle-diffusion\nurl = \"https://raw.githubusercontent.com/ChenWu98/cycle-diffusion/main/data/dalle2/A%20black%20colored%20car.png\"\nresponse = requests.get(url)\ninit_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\ninit_image = init_image.resize((512, 512))\ninit_image.save(\"black.png\")\n\nsource_prompt = \"A black colored car\"\nprompt = \"A blue colored car\"\n\n# call the pipeline\ntorch.manual_seed(0)\nimage = pipe(\n prompt=prompt,\n source_prompt=source_prompt,\n image=init_image,\n num_inference_steps=100,\n eta=0.1,\n strength=0.85,\n guidance_scale=3,\n source_guidance_scale=1,\n).images[0]\n\nimage.save(\"black_to_blue.png\")\n```\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_flax_available,\n is_k_diffusion_available,\n is_k_diffusion_version,\n is_onnx_available,\n is_torch_available,\n is_transformers_available,\n is_transformers_version,\n)\n\n\n_dummy_objects = {}\n_additional_imports = {}\n_import_structure = {\"pipeline_output\": [\"StableDiffusionPipelineOutput\"]}\n\nif is_transformers_available() and is_flax_available():\n _import_structure[\"pipeline_output\"].extend([\"FlaxStableDiffusionPipelineOutput\"])\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"clip_image_project_model\"] = [\"CLIPImageProjection\"]\n _import_structure[\"pipeline_cycle_diffusion\"] = [\"CycleDiffusionPipeline\"]\n _import_structure[\"pipeline_stable_diffusion\"] = [\"StableDiffusionPipeline\"]\n _import_structure[\"pipeline_stable_diffusion_attend_and_excite\"] = [\"StableDiffusionAttendAndExcitePipeline\"]\n _import_structure[\"pipeline_stable_diffusion_gligen\"] = [\"StableDiffusionGLIGENPipeline\"]\n _import_structure[\"pipeline_stable_diffusion_gligen_text_image\"] = [\"StableDiffusionGLIGENTextImagePipeline\"]\n _import_structure[\"pipeline_stable_diffusion_img2img\"] = [\"StableDiffusionImg2ImgPipeline\"]\n _import_structure[\"pipeline_stable_diffusion_inpaint\"] = [\"StableDiffusionInpaintPipeline\"]\n _import_structure[\"pipeline_stable_diffusion_inpaint_legacy\"] = [\"StableDiffusionInpaintPipelineLegacy\"]\n _import_structure[\"pipeline_stable_diffusion_instruct_pix2pix\"] = [\"StableDiffusionInstructPix2PixPipeline\"]\n _import_structure[\"pipeline_stable_diffusion_latent_upscale\"] = [\"StableDiffusionLatentUpscalePipeline\"]\n _import_structure[\"pipeline_stable_diffusion_model_editing\"] = [\"StableDiffusionModelEditingPipeline\"]\n _import_structure[\"pipeline_stable_diffusion_paradigms\"] = [\"StableDiffusionParadigmsPipeline\"]\n _import_structure[\"pipeline_stable_diffusion_upscale\"] = [\"StableDiffusionUpscalePipeline\"]\n _import_structure[\"pipeline_stable_unclip\"] = [\"StableUnCLIPPipeline\"]\n _import_structure[\"pipeline_stable_unclip_img2img\"] = [\"StableUnCLIPImg2ImgPipeline\"]\n _import_structure[\"safety_checker\"] = [\"StableDiffusionSafetyChecker\"]\n _import_structure[\"stable_unclip_image_normalizer\"] = [\"StableUnCLIPImageNormalizer\"]\ntry:\n if not (is_transformers_available() and is_torch_available() and is_transformers_version(\">=\", \"4.25.0\")):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import (\n StableDiffusionImageVariationPipeline,\n )\n\n _dummy_objects.update({\"StableDiffusionImageVariationPipeline\": StableDiffusionImageVariationPipeline})\nelse:\n _import_structure[\"pipeline_stable_diffusion_image_variation\"] = [\"StableDiffusionImageVariationPipeline\"]\ntry:\n if not (is_transformers_available() and is_torch_available() and is_transformers_version(\">=\", \"4.26.0\")):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import (\n StableDiffusionDepth2ImgPipeline,\n )\n\n _dummy_objects.update(\n {\n \"StableDiffusionDepth2ImgPipeline\": StableDiffusionDepth2ImgPipeline,\n }\n )\nelse:\n _import_structure[\"pipeline_stable_diffusion_depth2img\"] = [\"StableDiffusionDepth2ImgPipeline\"]\n\ntry:\n if not (is_transformers_available() and is_onnx_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_onnx_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_onnx_objects))\nelse:\n _import_structure[\"pipeline_onnx_stable_diffusion\"] = [\n \"OnnxStableDiffusionPipeline\",\n \"StableDiffusionOnnxPipeline\",\n ]\n _import_structure[\"pipeline_onnx_stable_diffusion_img2img\"] = [\"OnnxStableDiffusionImg2ImgPipeline\"]\n _import_structure[\"pipeline_onnx_stable_diffusion_inpaint\"] = [\"OnnxStableDiffusionInpaintPipeline\"]\n _import_structure[\"pipeline_onnx_stable_diffusion_inpaint_legacy\"] = [\"OnnxStableDiffusionInpaintPipelineLegacy\"]\n _import_structure[\"pipeline_onnx_stable_diffusion_upscale\"] = [\"OnnxStableDiffusionUpscalePipeline\"]\n\nif is_transformers_available() and is_flax_available():\n from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState\n\n _additional_imports.update({\"PNDMSchedulerState\": PNDMSchedulerState})\n _import_structure[\"pipeline_flax_stable_diffusion\"] = [\"FlaxStableDiffusionPipeline\"]\n _import_structure[\"pipeline_flax_stable_diffusion_img2img\"] = [\"FlaxStableDiffusionImg2ImgPipeline\"]\n _import_structure[\"pipeline_flax_stable_diffusion_inpaint\"] = [\"FlaxStableDiffusionInpaintPipeline\"]\n _import_structure[\"safety_checker_flax\"] = [\"FlaxStableDiffusionSafetyChecker\"]\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n\n else:\n from .clip_image_project_model import CLIPImageProjection\n from .pipeline_stable_diffusion import (\n StableDiffusionPipeline,\n StableDiffusionPipelineOutput,\n )\n from .pipeline_stable_diffusion_img2img import StableDiffusionImg2ImgPipeline\n from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline\n from .pipeline_stable_diffusion_instruct_pix2pix import (\n StableDiffusionInstructPix2PixPipeline,\n )\n from .pipeline_stable_diffusion_latent_upscale import (\n StableDiffusionLatentUpscalePipeline,\n )\n from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline\n from .pipeline_stable_unclip import StableUnCLIPPipeline\n from .pipeline_stable_unclip_img2img import StableUnCLIPImg2ImgPipeline\n from .safety_checker import StableDiffusionSafetyChecker\n from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer\n\n try:\n if not (is_transformers_available() and is_torch_available() and is_transformers_version(\">=\", \"4.25.0\")):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import (\n StableDiffusionImageVariationPipeline,\n )\n else:\n from .pipeline_stable_diffusion_image_variation import (\n StableDiffusionImageVariationPipeline,\n )\n\n try:\n if not (is_transformers_available() and is_torch_available() and is_transformers_version(\">=\", \"4.26.0\")):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import StableDiffusionDepth2ImgPipeline\n else:\n from .pipeline_stable_diffusion_depth2img import (\n StableDiffusionDepth2ImgPipeline,\n )\n\n try:\n if not (is_transformers_available() and is_onnx_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_onnx_objects import *\n else:\n from .pipeline_onnx_stable_diffusion import (\n OnnxStableDiffusionPipeline,\n StableDiffusionOnnxPipeline,\n )\n from .pipeline_onnx_stable_diffusion_img2img import (\n OnnxStableDiffusionImg2ImgPipeline,\n )\n from .pipeline_onnx_stable_diffusion_inpaint import (\n OnnxStableDiffusionInpaintPipeline,\n )\n from .pipeline_onnx_stable_diffusion_upscale import (\n OnnxStableDiffusionUpscalePipeline,\n )\n\n try:\n if not (is_transformers_available() and is_flax_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_flax_objects import *\n else:\n from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline\n from .pipeline_flax_stable_diffusion_img2img import (\n FlaxStableDiffusionImg2ImgPipeline,\n )\n from .pipeline_flax_stable_diffusion_inpaint import (\n FlaxStableDiffusionInpaintPipeline,\n )\n from .pipeline_output import FlaxStableDiffusionPipelineOutput\n from .safety_checker_flax import FlaxStableDiffusionSafetyChecker\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n for name, value in _additional_imports.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/clip_image_project_model.py", "content": "# Copyright 2024 The GLIGEN Authors and HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom torch import nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...models.modeling_utils import ModelMixin\n\n\nclass CLIPImageProjection(ModelMixin, ConfigMixin):\n @register_to_config\n def __init__(self, hidden_size: int = 768):\n super().__init__()\n self.hidden_size = hidden_size\n self.project = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n\n def forward(self, x):\n return self.project(x)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Conversion script for the Stable Diffusion checkpoints.\"\"\"\n\nimport re\nfrom contextlib import nullcontext\nfrom io import BytesIO\nfrom typing import Dict, Optional, Union\n\nimport requests\nimport torch\nimport yaml\nfrom transformers import (\n AutoFeatureExtractor,\n BertTokenizerFast,\n CLIPImageProcessor,\n CLIPTextConfig,\n CLIPTextModel,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionConfig,\n CLIPVisionModelWithProjection,\n)\n\nfrom ...models import (\n AutoencoderKL,\n ControlNetModel,\n PriorTransformer,\n UNet2DConditionModel,\n)\nfrom ...schedulers import (\n DDIMScheduler,\n DDPMScheduler,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n HeunDiscreteScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n UnCLIPScheduler,\n)\nfrom ...utils import is_accelerate_available, logging\nfrom ..latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel\nfrom ..paint_by_example import PaintByExampleImageEncoder\nfrom ..pipeline_utils import DiffusionPipeline\nfrom .safety_checker import StableDiffusionSafetyChecker\nfrom .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer\n\n\nif is_accelerate_available():\n from accelerate import init_empty_weights\n from accelerate.utils import set_module_tensor_to_device\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef shave_segments(path, n_shave_prefix_segments=1):\n \"\"\"\n Removes segments. Positive values shave the first segments, negative shave the last segments.\n \"\"\"\n if n_shave_prefix_segments >= 0:\n return \".\".join(path.split(\".\")[n_shave_prefix_segments:])\n else:\n return \".\".join(path.split(\".\")[:n_shave_prefix_segments])\n\n\ndef renew_resnet_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside resnets to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item.replace(\"in_layers.0\", \"norm1\")\n new_item = new_item.replace(\"in_layers.2\", \"conv1\")\n\n new_item = new_item.replace(\"out_layers.0\", \"norm2\")\n new_item = new_item.replace(\"out_layers.3\", \"conv2\")\n\n new_item = new_item.replace(\"emb_layers.1\", \"time_emb_proj\")\n new_item = new_item.replace(\"skip_connection\", \"conv_shortcut\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside resnets to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n new_item = new_item.replace(\"nin_shortcut\", \"conv_shortcut\")\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef renew_attention_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside attentions to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n # new_item = new_item.replace('norm.weight', 'group_norm.weight')\n # new_item = new_item.replace('norm.bias', 'group_norm.bias')\n\n # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')\n # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')\n\n # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):\n \"\"\"\n Updates paths inside attentions to the new naming scheme (local renaming)\n \"\"\"\n mapping = []\n for old_item in old_list:\n new_item = old_item\n\n new_item = new_item.replace(\"norm.weight\", \"group_norm.weight\")\n new_item = new_item.replace(\"norm.bias\", \"group_norm.bias\")\n\n new_item = new_item.replace(\"q.weight\", \"to_q.weight\")\n new_item = new_item.replace(\"q.bias\", \"to_q.bias\")\n\n new_item = new_item.replace(\"k.weight\", \"to_k.weight\")\n new_item = new_item.replace(\"k.bias\", \"to_k.bias\")\n\n new_item = new_item.replace(\"v.weight\", \"to_v.weight\")\n new_item = new_item.replace(\"v.bias\", \"to_v.bias\")\n\n new_item = new_item.replace(\"proj_out.weight\", \"to_out.0.weight\")\n new_item = new_item.replace(\"proj_out.bias\", \"to_out.0.bias\")\n\n new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)\n\n mapping.append({\"old\": old_item, \"new\": new_item})\n\n return mapping\n\n\ndef assign_to_checkpoint(\n paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None\n):\n \"\"\"\n This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits\n attention layers, and takes into account additional replacements that may arise.\n\n Assigns the weights to the new checkpoint.\n \"\"\"\n assert isinstance(paths, list), \"Paths should be a list of dicts containing 'old' and 'new' keys.\"\n\n # Splits the attention layers into three variables.\n if attention_paths_to_split is not None:\n for path, path_map in attention_paths_to_split.items():\n old_tensor = old_checkpoint[path]\n channels = old_tensor.shape[0] // 3\n\n target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)\n\n num_heads = old_tensor.shape[0] // config[\"num_head_channels\"] // 3\n\n old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])\n query, key, value = old_tensor.split(channels // num_heads, dim=1)\n\n checkpoint[path_map[\"query\"]] = query.reshape(target_shape)\n checkpoint[path_map[\"key\"]] = key.reshape(target_shape)\n checkpoint[path_map[\"value\"]] = value.reshape(target_shape)\n\n for path in paths:\n new_path = path[\"new\"]\n\n # These have already been assigned\n if attention_paths_to_split is not None and new_path in attention_paths_to_split:\n continue\n\n # Global renaming happens here\n new_path = new_path.replace(\"middle_block.0\", \"mid_block.resnets.0\")\n new_path = new_path.replace(\"middle_block.1\", \"mid_block.attentions.0\")\n new_path = new_path.replace(\"middle_block.2\", \"mid_block.resnets.1\")\n\n if additional_replacements is not None:\n for replacement in additional_replacements:\n new_path = new_path.replace(replacement[\"old\"], replacement[\"new\"])\n\n # proj_attn.weight has to be converted from conv 1D to linear\n is_attn_weight = \"proj_attn.weight\" in new_path or (\"attentions\" in new_path and \"to_\" in new_path)\n shape = old_checkpoint[path[\"old\"]].shape\n if is_attn_weight and len(shape) == 3:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]][:, :, 0]\n elif is_attn_weight and len(shape) == 4:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]][:, :, 0, 0]\n else:\n checkpoint[new_path] = old_checkpoint[path[\"old\"]]\n\n\ndef conv_attn_to_linear(checkpoint):\n keys = list(checkpoint.keys())\n attn_keys = [\"query.weight\", \"key.weight\", \"value.weight\"]\n for key in keys:\n if \".\".join(key.split(\".\")[-2:]) in attn_keys:\n if checkpoint[key].ndim > 2:\n checkpoint[key] = checkpoint[key][:, :, 0, 0]\n elif \"proj_attn.weight\" in key:\n if checkpoint[key].ndim > 2:\n checkpoint[key] = checkpoint[key][:, :, 0]\n\n\ndef create_unet_diffusers_config(original_config, image_size: int, controlnet=False):\n \"\"\"\n Creates a config for the diffusers based on the config of the LDM model.\n \"\"\"\n if controlnet:\n unet_params = original_config[\"model\"][\"params\"][\"control_stage_config\"][\"params\"]\n else:\n if (\n \"unet_config\" in original_config[\"model\"][\"params\"]\n and original_config[\"model\"][\"params\"][\"unet_config\"] is not None\n ):\n unet_params = original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"]\n else:\n unet_params = original_config[\"model\"][\"params\"][\"network_config\"][\"params\"]\n\n vae_params = original_config[\"model\"][\"params\"][\"first_stage_config\"][\"params\"][\"ddconfig\"]\n\n block_out_channels = [unet_params[\"model_channels\"] * mult for mult in unet_params[\"channel_mult\"]]\n\n down_block_types = []\n resolution = 1\n for i in range(len(block_out_channels)):\n block_type = \"CrossAttnDownBlock2D\" if resolution in unet_params[\"attention_resolutions\"] else \"DownBlock2D\"\n down_block_types.append(block_type)\n if i != len(block_out_channels) - 1:\n resolution *= 2\n\n up_block_types = []\n for i in range(len(block_out_channels)):\n block_type = \"CrossAttnUpBlock2D\" if resolution in unet_params[\"attention_resolutions\"] else \"UpBlock2D\"\n up_block_types.append(block_type)\n resolution //= 2\n\n if unet_params[\"transformer_depth\"] is not None:\n transformer_layers_per_block = (\n unet_params[\"transformer_depth\"]\n if isinstance(unet_params[\"transformer_depth\"], int)\n else list(unet_params[\"transformer_depth\"])\n )\n else:\n transformer_layers_per_block = 1\n\n vae_scale_factor = 2 ** (len(vae_params[\"ch_mult\"]) - 1)\n\n head_dim = unet_params[\"num_heads\"] if \"num_heads\" in unet_params else None\n use_linear_projection = (\n unet_params[\"use_linear_in_transformer\"] if \"use_linear_in_transformer\" in unet_params else False\n )\n if use_linear_projection:\n # stable diffusion 2-base-512 and 2-768\n if head_dim is None:\n head_dim_mult = unet_params[\"model_channels\"] // unet_params[\"num_head_channels\"]\n head_dim = [head_dim_mult * c for c in list(unet_params[\"channel_mult\"])]\n\n class_embed_type = None\n addition_embed_type = None\n addition_time_embed_dim = None\n projection_class_embeddings_input_dim = None\n context_dim = None\n\n if unet_params[\"context_dim\"] is not None:\n context_dim = (\n unet_params[\"context_dim\"]\n if isinstance(unet_params[\"context_dim\"], int)\n else unet_params[\"context_dim\"][0]\n )\n\n if \"num_classes\" in unet_params:\n if unet_params[\"num_classes\"] == \"sequential\":\n if context_dim in [2048, 1280]:\n # SDXL\n addition_embed_type = \"text_time\"\n addition_time_embed_dim = 256\n else:\n class_embed_type = \"projection\"\n assert \"adm_in_channels\" in unet_params\n projection_class_embeddings_input_dim = unet_params[\"adm_in_channels\"]\n\n config = {\n \"sample_size\": image_size // vae_scale_factor,\n \"in_channels\": unet_params[\"in_channels\"],\n \"down_block_types\": tuple(down_block_types),\n \"block_out_channels\": tuple(block_out_channels),\n \"layers_per_block\": unet_params[\"num_res_blocks\"],\n \"cross_attention_dim\": context_dim,\n \"attention_head_dim\": head_dim,\n \"use_linear_projection\": use_linear_projection,\n \"class_embed_type\": class_embed_type,\n \"addition_embed_type\": addition_embed_type,\n \"addition_time_embed_dim\": addition_time_embed_dim,\n \"projection_class_embeddings_input_dim\": projection_class_embeddings_input_dim,\n \"transformer_layers_per_block\": transformer_layers_per_block,\n }\n\n if \"disable_self_attentions\" in unet_params:\n config[\"only_cross_attention\"] = unet_params[\"disable_self_attentions\"]\n\n if \"num_classes\" in unet_params and isinstance(unet_params[\"num_classes\"], int):\n config[\"num_class_embeds\"] = unet_params[\"num_classes\"]\n\n if controlnet:\n config[\"conditioning_channels\"] = unet_params[\"hint_channels\"]\n else:\n config[\"out_channels\"] = unet_params[\"out_channels\"]\n config[\"up_block_types\"] = tuple(up_block_types)\n\n return config\n\n\ndef create_vae_diffusers_config(original_config, image_size: int):\n \"\"\"\n Creates a config for the diffusers based on the config of the LDM model.\n \"\"\"\n vae_params = original_config[\"model\"][\"params\"][\"first_stage_config\"][\"params\"][\"ddconfig\"]\n _ = original_config[\"model\"][\"params\"][\"first_stage_config\"][\"params\"][\"embed_dim\"]\n\n block_out_channels = [vae_params[\"ch\"] * mult for mult in vae_params[\"ch_mult\"]]\n down_block_types = [\"DownEncoderBlock2D\"] * len(block_out_channels)\n up_block_types = [\"UpDecoderBlock2D\"] * len(block_out_channels)\n\n config = {\n \"sample_size\": image_size,\n \"in_channels\": vae_params[\"in_channels\"],\n \"out_channels\": vae_params[\"out_ch\"],\n \"down_block_types\": tuple(down_block_types),\n \"up_block_types\": tuple(up_block_types),\n \"block_out_channels\": tuple(block_out_channels),\n \"latent_channels\": vae_params[\"z_channels\"],\n \"layers_per_block\": vae_params[\"num_res_blocks\"],\n }\n return config\n\n\ndef create_diffusers_schedular(original_config):\n schedular = DDIMScheduler(\n num_train_timesteps=original_config[\"model\"][\"params\"][\"timesteps\"],\n beta_start=original_config[\"model\"][\"params\"][\"linear_start\"],\n beta_end=original_config[\"model\"][\"params\"][\"linear_end\"],\n beta_schedule=\"scaled_linear\",\n )\n return schedular\n\n\ndef create_ldm_bert_config(original_config):\n bert_params = original_config[\"model\"][\"params\"][\"cond_stage_config\"][\"params\"]\n config = LDMBertConfig(\n d_model=bert_params.n_embed,\n encoder_layers=bert_params.n_layer,\n encoder_ffn_dim=bert_params.n_embed * 4,\n )\n return config\n\n\ndef convert_ldm_unet_checkpoint(\n checkpoint, config, path=None, extract_ema=False, controlnet=False, skip_extract_state_dict=False\n):\n \"\"\"\n Takes a state dict and a config, and returns a converted checkpoint.\n \"\"\"\n\n if skip_extract_state_dict:\n unet_state_dict = checkpoint\n else:\n # extract state_dict for UNet\n unet_state_dict = {}\n keys = list(checkpoint.keys())\n\n if controlnet:\n unet_key = \"control_model.\"\n else:\n unet_key = \"model.diffusion_model.\"\n\n # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA\n if sum(k.startswith(\"model_ema\") for k in keys) > 100 and extract_ema:\n logger.warning(f\"Checkpoint {path} has both EMA and non-EMA weights.\")\n logger.warning(\n \"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA\"\n \" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.\"\n )\n for key in keys:\n if key.startswith(\"model.diffusion_model\"):\n flat_ema_key = \"model_ema.\" + \"\".join(key.split(\".\")[1:])\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint.pop(flat_ema_key)\n else:\n if sum(k.startswith(\"model_ema\") for k in keys) > 100:\n logger.warning(\n \"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA\"\n \" weights (usually better for inference), please make sure to add the `--extract_ema` flag.\"\n )\n\n for key in keys:\n if key.startswith(unet_key):\n unet_state_dict[key.replace(unet_key, \"\")] = checkpoint.pop(key)\n\n new_checkpoint = {}\n\n new_checkpoint[\"time_embedding.linear_1.weight\"] = unet_state_dict[\"time_embed.0.weight\"]\n new_checkpoint[\"time_embedding.linear_1.bias\"] = unet_state_dict[\"time_embed.0.bias\"]\n new_checkpoint[\"time_embedding.linear_2.weight\"] = unet_state_dict[\"time_embed.2.weight\"]\n new_checkpoint[\"time_embedding.linear_2.bias\"] = unet_state_dict[\"time_embed.2.bias\"]\n\n if config[\"class_embed_type\"] is None:\n # No parameters to port\n ...\n elif config[\"class_embed_type\"] == \"timestep\" or config[\"class_embed_type\"] == \"projection\":\n new_checkpoint[\"class_embedding.linear_1.weight\"] = unet_state_dict[\"label_emb.0.0.weight\"]\n new_checkpoint[\"class_embedding.linear_1.bias\"] = unet_state_dict[\"label_emb.0.0.bias\"]\n new_checkpoint[\"class_embedding.linear_2.weight\"] = unet_state_dict[\"label_emb.0.2.weight\"]\n new_checkpoint[\"class_embedding.linear_2.bias\"] = unet_state_dict[\"label_emb.0.2.bias\"]\n else:\n raise NotImplementedError(f\"Not implemented `class_embed_type`: {config['class_embed_type']}\")\n\n if config[\"addition_embed_type\"] == \"text_time\":\n new_checkpoint[\"add_embedding.linear_1.weight\"] = unet_state_dict[\"label_emb.0.0.weight\"]\n new_checkpoint[\"add_embedding.linear_1.bias\"] = unet_state_dict[\"label_emb.0.0.bias\"]\n new_checkpoint[\"add_embedding.linear_2.weight\"] = unet_state_dict[\"label_emb.0.2.weight\"]\n new_checkpoint[\"add_embedding.linear_2.bias\"] = unet_state_dict[\"label_emb.0.2.bias\"]\n\n # Relevant to StableDiffusionUpscalePipeline\n if \"num_class_embeds\" in config:\n if (config[\"num_class_embeds\"] is not None) and (\"label_emb.weight\" in unet_state_dict):\n new_checkpoint[\"class_embedding.weight\"] = unet_state_dict[\"label_emb.weight\"]\n\n new_checkpoint[\"conv_in.weight\"] = unet_state_dict[\"input_blocks.0.0.weight\"]\n new_checkpoint[\"conv_in.bias\"] = unet_state_dict[\"input_blocks.0.0.bias\"]\n\n if not controlnet:\n new_checkpoint[\"conv_norm_out.weight\"] = unet_state_dict[\"out.0.weight\"]\n new_checkpoint[\"conv_norm_out.bias\"] = unet_state_dict[\"out.0.bias\"]\n new_checkpoint[\"conv_out.weight\"] = unet_state_dict[\"out.2.weight\"]\n new_checkpoint[\"conv_out.bias\"] = unet_state_dict[\"out.2.bias\"]\n\n # Retrieves the keys for the input blocks only\n num_input_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"input_blocks\" in layer})\n input_blocks = {\n layer_id: [key for key in unet_state_dict if f\"input_blocks.{layer_id}\" in key]\n for layer_id in range(num_input_blocks)\n }\n\n # Retrieves the keys for the middle blocks only\n num_middle_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"middle_block\" in layer})\n middle_blocks = {\n layer_id: [key for key in unet_state_dict if f\"middle_block.{layer_id}\" in key]\n for layer_id in range(num_middle_blocks)\n }\n\n # Retrieves the keys for the output blocks only\n num_output_blocks = len({\".\".join(layer.split(\".\")[:2]) for layer in unet_state_dict if \"output_blocks\" in layer})\n output_blocks = {\n layer_id: [key for key in unet_state_dict if f\"output_blocks.{layer_id}\" in key]\n for layer_id in range(num_output_blocks)\n }\n\n for i in range(1, num_input_blocks):\n block_id = (i - 1) // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = (i - 1) % (config[\"layers_per_block\"] + 1)\n\n resnets = [\n key for key in input_blocks[i] if f\"input_blocks.{i}.0\" in key and f\"input_blocks.{i}.0.op\" not in key\n ]\n attentions = [key for key in input_blocks[i] if f\"input_blocks.{i}.1\" in key]\n\n if f\"input_blocks.{i}.0.op.weight\" in unet_state_dict:\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.weight\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.op.weight\"\n )\n new_checkpoint[f\"down_blocks.{block_id}.downsamplers.0.conv.bias\"] = unet_state_dict.pop(\n f\"input_blocks.{i}.0.op.bias\"\n )\n\n paths = renew_resnet_paths(resnets)\n meta_path = {\"old\": f\"input_blocks.{i}.0\", \"new\": f\"down_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n\n meta_path = {\"old\": f\"input_blocks.{i}.1\", \"new\": f\"down_blocks.{block_id}.attentions.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n resnet_0 = middle_blocks[0]\n attentions = middle_blocks[1]\n resnet_1 = middle_blocks[2]\n\n resnet_0_paths = renew_resnet_paths(resnet_0)\n assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)\n\n resnet_1_paths = renew_resnet_paths(resnet_1)\n assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)\n\n attentions_paths = renew_attention_paths(attentions)\n meta_path = {\"old\": \"middle_block.1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(\n attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n for i in range(num_output_blocks):\n block_id = i // (config[\"layers_per_block\"] + 1)\n layer_in_block_id = i % (config[\"layers_per_block\"] + 1)\n output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]\n output_block_list = {}\n\n for layer in output_block_layers:\n layer_id, layer_name = layer.split(\".\")[0], shave_segments(layer, 1)\n if layer_id in output_block_list:\n output_block_list[layer_id].append(layer_name)\n else:\n output_block_list[layer_id] = [layer_name]\n\n if len(output_block_list) > 1:\n resnets = [key for key in output_blocks[i] if f\"output_blocks.{i}.0\" in key]\n attentions = [key for key in output_blocks[i] if f\"output_blocks.{i}.1\" in key]\n\n resnet_0_paths = renew_resnet_paths(resnets)\n paths = renew_resnet_paths(resnets)\n\n meta_path = {\"old\": f\"output_blocks.{i}.0\", \"new\": f\"up_blocks.{block_id}.resnets.{layer_in_block_id}\"}\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n\n output_block_list = {k: sorted(v) for k, v in sorted(output_block_list.items())}\n if [\"conv.bias\", \"conv.weight\"] in output_block_list.values():\n index = list(output_block_list.values()).index([\"conv.bias\", \"conv.weight\"])\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.weight\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.weight\"\n ]\n new_checkpoint[f\"up_blocks.{block_id}.upsamplers.0.conv.bias\"] = unet_state_dict[\n f\"output_blocks.{i}.{index}.conv.bias\"\n ]\n\n # Clear attentions as they have been attributed above.\n if len(attentions) == 2:\n attentions = []\n\n if len(attentions):\n paths = renew_attention_paths(attentions)\n meta_path = {\n \"old\": f\"output_blocks.{i}.1\",\n \"new\": f\"up_blocks.{block_id}.attentions.{layer_in_block_id}\",\n }\n assign_to_checkpoint(\n paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config\n )\n else:\n resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)\n for path in resnet_0_paths:\n old_path = \".\".join([\"output_blocks\", str(i), path[\"old\"]])\n new_path = \".\".join([\"up_blocks\", str(block_id), \"resnets\", str(layer_in_block_id), path[\"new\"]])\n\n new_checkpoint[new_path] = unet_state_dict[old_path]\n\n if controlnet:\n # conditioning embedding\n\n orig_index = 0\n\n new_checkpoint[\"controlnet_cond_embedding.conv_in.weight\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.weight\"\n )\n new_checkpoint[\"controlnet_cond_embedding.conv_in.bias\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.bias\"\n )\n\n orig_index += 2\n\n diffusers_index = 0\n\n while diffusers_index < 6:\n new_checkpoint[f\"controlnet_cond_embedding.blocks.{diffusers_index}.weight\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.weight\"\n )\n new_checkpoint[f\"controlnet_cond_embedding.blocks.{diffusers_index}.bias\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.bias\"\n )\n diffusers_index += 1\n orig_index += 2\n\n new_checkpoint[\"controlnet_cond_embedding.conv_out.weight\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.weight\"\n )\n new_checkpoint[\"controlnet_cond_embedding.conv_out.bias\"] = unet_state_dict.pop(\n f\"input_hint_block.{orig_index}.bias\"\n )\n\n # down blocks\n for i in range(num_input_blocks):\n new_checkpoint[f\"controlnet_down_blocks.{i}.weight\"] = unet_state_dict.pop(f\"zero_convs.{i}.0.weight\")\n new_checkpoint[f\"controlnet_down_blocks.{i}.bias\"] = unet_state_dict.pop(f\"zero_convs.{i}.0.bias\")\n\n # mid block\n new_checkpoint[\"controlnet_mid_block.weight\"] = unet_state_dict.pop(\"middle_block_out.0.weight\")\n new_checkpoint[\"controlnet_mid_block.bias\"] = unet_state_dict.pop(\"middle_block_out.0.bias\")\n\n return new_checkpoint\n\n\ndef convert_ldm_vae_checkpoint(checkpoint, config):\n # extract state dict for VAE\n vae_state_dict = {}\n keys = list(checkpoint.keys())\n vae_key = \"first_stage_model.\" if any(k.startswith(\"first_stage_model.\") for k in keys) else \"\"\n for key in keys:\n if key.startswith(vae_key):\n vae_state_dict[key.replace(vae_key, \"\")] = checkpoint.get(key)\n\n new_checkpoint = {}\n\n new_checkpoint[\"encoder.conv_in.weight\"] = vae_state_dict[\"encoder.conv_in.weight\"]\n new_checkpoint[\"encoder.conv_in.bias\"] = vae_state_dict[\"encoder.conv_in.bias\"]\n new_checkpoint[\"encoder.conv_out.weight\"] = vae_state_dict[\"encoder.conv_out.weight\"]\n new_checkpoint[\"encoder.conv_out.bias\"] = vae_state_dict[\"encoder.conv_out.bias\"]\n new_checkpoint[\"encoder.conv_norm_out.weight\"] = vae_state_dict[\"encoder.norm_out.weight\"]\n new_checkpoint[\"encoder.conv_norm_out.bias\"] = vae_state_dict[\"encoder.norm_out.bias\"]\n\n new_checkpoint[\"decoder.conv_in.weight\"] = vae_state_dict[\"decoder.conv_in.weight\"]\n new_checkpoint[\"decoder.conv_in.bias\"] = vae_state_dict[\"decoder.conv_in.bias\"]\n new_checkpoint[\"decoder.conv_out.weight\"] = vae_state_dict[\"decoder.conv_out.weight\"]\n new_checkpoint[\"decoder.conv_out.bias\"] = vae_state_dict[\"decoder.conv_out.bias\"]\n new_checkpoint[\"decoder.conv_norm_out.weight\"] = vae_state_dict[\"decoder.norm_out.weight\"]\n new_checkpoint[\"decoder.conv_norm_out.bias\"] = vae_state_dict[\"decoder.norm_out.bias\"]\n\n new_checkpoint[\"quant_conv.weight\"] = vae_state_dict[\"quant_conv.weight\"]\n new_checkpoint[\"quant_conv.bias\"] = vae_state_dict[\"quant_conv.bias\"]\n new_checkpoint[\"post_quant_conv.weight\"] = vae_state_dict[\"post_quant_conv.weight\"]\n new_checkpoint[\"post_quant_conv.bias\"] = vae_state_dict[\"post_quant_conv.bias\"]\n\n # Retrieves the keys for the encoder down blocks only\n num_down_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in vae_state_dict if \"encoder.down\" in layer})\n down_blocks = {\n layer_id: [key for key in vae_state_dict if f\"down.{layer_id}\" in key] for layer_id in range(num_down_blocks)\n }\n\n # Retrieves the keys for the decoder up blocks only\n num_up_blocks = len({\".\".join(layer.split(\".\")[:3]) for layer in vae_state_dict if \"decoder.up\" in layer})\n up_blocks = {\n layer_id: [key for key in vae_state_dict if f\"up.{layer_id}\" in key] for layer_id in range(num_up_blocks)\n }\n\n for i in range(num_down_blocks):\n resnets = [key for key in down_blocks[i] if f\"down.{i}\" in key and f\"down.{i}.downsample\" not in key]\n\n if f\"encoder.down.{i}.downsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.weight\"] = vae_state_dict.pop(\n f\"encoder.down.{i}.downsample.conv.weight\"\n )\n new_checkpoint[f\"encoder.down_blocks.{i}.downsamplers.0.conv.bias\"] = vae_state_dict.pop(\n f\"encoder.down.{i}.downsample.conv.bias\"\n )\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"down.{i}.block\", \"new\": f\"down_blocks.{i}.resnets\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_resnets = [key for key in vae_state_dict if \"encoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"encoder.mid.block_{i}\" in key]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_attentions = [key for key in vae_state_dict if \"encoder.mid.attn\" in key]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n conv_attn_to_linear(new_checkpoint)\n\n for i in range(num_up_blocks):\n block_id = num_up_blocks - 1 - i\n resnets = [\n key for key in up_blocks[block_id] if f\"up.{block_id}\" in key and f\"up.{block_id}.upsample\" not in key\n ]\n\n if f\"decoder.up.{block_id}.upsample.conv.weight\" in vae_state_dict:\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.weight\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.weight\"\n ]\n new_checkpoint[f\"decoder.up_blocks.{i}.upsamplers.0.conv.bias\"] = vae_state_dict[\n f\"decoder.up.{block_id}.upsample.conv.bias\"\n ]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"up.{block_id}.block\", \"new\": f\"up_blocks.{i}.resnets\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_resnets = [key for key in vae_state_dict if \"decoder.mid.block\" in key]\n num_mid_res_blocks = 2\n for i in range(1, num_mid_res_blocks + 1):\n resnets = [key for key in mid_resnets if f\"decoder.mid.block_{i}\" in key]\n\n paths = renew_vae_resnet_paths(resnets)\n meta_path = {\"old\": f\"mid.block_{i}\", \"new\": f\"mid_block.resnets.{i - 1}\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n\n mid_attentions = [key for key in vae_state_dict if \"decoder.mid.attn\" in key]\n paths = renew_vae_attention_paths(mid_attentions)\n meta_path = {\"old\": \"mid.attn_1\", \"new\": \"mid_block.attentions.0\"}\n assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)\n conv_attn_to_linear(new_checkpoint)\n return new_checkpoint\n\n\ndef convert_ldm_bert_checkpoint(checkpoint, config):\n def _copy_attn_layer(hf_attn_layer, pt_attn_layer):\n hf_attn_layer.q_proj.weight.data = pt_attn_layer.to_q.weight\n hf_attn_layer.k_proj.weight.data = pt_attn_layer.to_k.weight\n hf_attn_layer.v_proj.weight.data = pt_attn_layer.to_v.weight\n\n hf_attn_layer.out_proj.weight = pt_attn_layer.to_out.weight\n hf_attn_layer.out_proj.bias = pt_attn_layer.to_out.bias\n\n def _copy_linear(hf_linear, pt_linear):\n hf_linear.weight = pt_linear.weight\n hf_linear.bias = pt_linear.bias\n\n def _copy_layer(hf_layer, pt_layer):\n # copy layer norms\n _copy_linear(hf_layer.self_attn_layer_norm, pt_layer[0][0])\n _copy_linear(hf_layer.final_layer_norm, pt_layer[1][0])\n\n # copy attn\n _copy_attn_layer(hf_layer.self_attn, pt_layer[0][1])\n\n # copy MLP\n pt_mlp = pt_layer[1][1]\n _copy_linear(hf_layer.fc1, pt_mlp.net[0][0])\n _copy_linear(hf_layer.fc2, pt_mlp.net[2])\n\n def _copy_layers(hf_layers, pt_layers):\n for i, hf_layer in enumerate(hf_layers):\n if i != 0:\n i += i\n pt_layer = pt_layers[i : i + 2]\n _copy_layer(hf_layer, pt_layer)\n\n hf_model = LDMBertModel(config).eval()\n\n # copy embeds\n hf_model.model.embed_tokens.weight = checkpoint.transformer.token_emb.weight\n hf_model.model.embed_positions.weight.data = checkpoint.transformer.pos_emb.emb.weight\n\n # copy layer norm\n _copy_linear(hf_model.model.layer_norm, checkpoint.transformer.norm)\n\n # copy hidden layers\n _copy_layers(hf_model.model.layers, checkpoint.transformer.attn_layers.layers)\n\n _copy_linear(hf_model.to_logits, checkpoint.transformer.to_logits)\n\n return hf_model\n\n\ndef convert_ldm_clip_checkpoint(checkpoint, local_files_only=False, text_encoder=None):\n if text_encoder is None:\n config_name = \"openai/clip-vit-large-patch14\"\n try:\n config = CLIPTextConfig.from_pretrained(config_name, local_files_only=local_files_only)\n except Exception:\n raise ValueError(\n f\"With local_files_only set to {local_files_only}, you must first locally save the configuration in the following path: 'openai/clip-vit-large-patch14'.\"\n )\n\n ctx = init_empty_weights if is_accelerate_available() else nullcontext\n with ctx():\n text_model = CLIPTextModel(config)\n else:\n text_model = text_encoder\n\n keys = list(checkpoint.keys())\n\n text_model_dict = {}\n\n remove_prefixes = [\"cond_stage_model.transformer\", \"conditioner.embedders.0.transformer\"]\n\n for key in keys:\n for prefix in remove_prefixes:\n if key.startswith(prefix):\n text_model_dict[key[len(prefix + \".\") :]] = checkpoint[key]\n\n if is_accelerate_available():\n for param_name, param in text_model_dict.items():\n set_module_tensor_to_device(text_model, param_name, \"cpu\", value=param)\n else:\n if not (hasattr(text_model, \"embeddings\") and hasattr(text_model.embeddings.position_ids)):\n text_model_dict.pop(\"text_model.embeddings.position_ids\", None)\n\n text_model.load_state_dict(text_model_dict)\n\n return text_model\n\n\ntextenc_conversion_lst = [\n (\"positional_embedding\", \"text_model.embeddings.position_embedding.weight\"),\n (\"token_embedding.weight\", \"text_model.embeddings.token_embedding.weight\"),\n (\"ln_final.weight\", \"text_model.final_layer_norm.weight\"),\n (\"ln_final.bias\", \"text_model.final_layer_norm.bias\"),\n (\"text_projection\", \"text_projection.weight\"),\n]\ntextenc_conversion_map = {x[0]: x[1] for x in textenc_conversion_lst}\n\ntextenc_transformer_conversion_lst = [\n # (stable-diffusion, HF Diffusers)\n (\"resblocks.\", \"text_model.encoder.layers.\"),\n (\"ln_1\", \"layer_norm1\"),\n (\"ln_2\", \"layer_norm2\"),\n (\".c_fc.\", \".fc1.\"),\n (\".c_proj.\", \".fc2.\"),\n (\".attn\", \".self_attn\"),\n (\"ln_final.\", \"transformer.text_model.final_layer_norm.\"),\n (\"token_embedding.weight\", \"transformer.text_model.embeddings.token_embedding.weight\"),\n (\"positional_embedding\", \"transformer.text_model.embeddings.position_embedding.weight\"),\n]\nprotected = {re.escape(x[0]): x[1] for x in textenc_transformer_conversion_lst}\ntextenc_pattern = re.compile(\"|\".join(protected.keys()))\n\n\ndef convert_paint_by_example_checkpoint(checkpoint, local_files_only=False):\n config = CLIPVisionConfig.from_pretrained(\"openai/clip-vit-large-patch14\", local_files_only=local_files_only)\n model = PaintByExampleImageEncoder(config)\n\n keys = list(checkpoint.keys())\n\n text_model_dict = {}\n\n for key in keys:\n if key.startswith(\"cond_stage_model.transformer\"):\n text_model_dict[key[len(\"cond_stage_model.transformer.\") :]] = checkpoint[key]\n\n # load clip vision\n model.model.load_state_dict(text_model_dict)\n\n # load mapper\n keys_mapper = {\n k[len(\"cond_stage_model.mapper.res\") :]: v\n for k, v in checkpoint.items()\n if k.startswith(\"cond_stage_model.mapper\")\n }\n\n MAPPING = {\n \"attn.c_qkv\": [\"attn1.to_q\", \"attn1.to_k\", \"attn1.to_v\"],\n \"attn.c_proj\": [\"attn1.to_out.0\"],\n \"ln_1\": [\"norm1\"],\n \"ln_2\": [\"norm3\"],\n \"mlp.c_fc\": [\"ff.net.0.proj\"],\n \"mlp.c_proj\": [\"ff.net.2\"],\n }\n\n mapped_weights = {}\n for key, value in keys_mapper.items():\n prefix = key[: len(\"blocks.i\")]\n suffix = key.split(prefix)[-1].split(\".\")[-1]\n name = key.split(prefix)[-1].split(suffix)[0][1:-1]\n mapped_names = MAPPING[name]\n\n num_splits = len(mapped_names)\n for i, mapped_name in enumerate(mapped_names):\n new_name = \".\".join([prefix, mapped_name, suffix])\n shape = value.shape[0] // num_splits\n mapped_weights[new_name] = value[i * shape : (i + 1) * shape]\n\n model.mapper.load_state_dict(mapped_weights)\n\n # load final layer norm\n model.final_layer_norm.load_state_dict(\n {\n \"bias\": checkpoint[\"cond_stage_model.final_ln.bias\"],\n \"weight\": checkpoint[\"cond_stage_model.final_ln.weight\"],\n }\n )\n\n # load final proj\n model.proj_out.load_state_dict(\n {\n \"bias\": checkpoint[\"proj_out.bias\"],\n \"weight\": checkpoint[\"proj_out.weight\"],\n }\n )\n\n # load uncond vector\n model.uncond_vector.data = torch.nn.Parameter(checkpoint[\"learnable_vector\"])\n return model\n\n\ndef convert_open_clip_checkpoint(\n checkpoint,\n config_name,\n prefix=\"cond_stage_model.model.\",\n has_projection=False,\n local_files_only=False,\n **config_kwargs,\n):\n # text_model = CLIPTextModel.from_pretrained(\"stabilityai/stable-diffusion-2\", subfolder=\"text_encoder\")\n # text_model = CLIPTextModelWithProjection.from_pretrained(\n # \"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\", projection_dim=1280\n # )\n try:\n config = CLIPTextConfig.from_pretrained(config_name, **config_kwargs, local_files_only=local_files_only)\n except Exception:\n raise ValueError(\n f\"With local_files_only set to {local_files_only}, you must first locally save the configuration in the following path: '{config_name}'.\"\n )\n\n ctx = init_empty_weights if is_accelerate_available() else nullcontext\n with ctx():\n text_model = CLIPTextModelWithProjection(config) if has_projection else CLIPTextModel(config)\n\n keys = list(checkpoint.keys())\n\n keys_to_ignore = []\n if config_name == \"stabilityai/stable-diffusion-2\" and config.num_hidden_layers == 23:\n # make sure to remove all keys > 22\n keys_to_ignore += [k for k in keys if k.startswith(\"cond_stage_model.model.transformer.resblocks.23\")]\n keys_to_ignore += [\"cond_stage_model.model.text_projection\"]\n\n text_model_dict = {}\n\n if prefix + \"text_projection\" in checkpoint:\n d_model = int(checkpoint[prefix + \"text_projection\"].shape[0])\n else:\n d_model = 1024\n\n text_model_dict[\"text_model.embeddings.position_ids\"] = text_model.text_model.embeddings.get_buffer(\"position_ids\")\n\n for key in keys:\n if key in keys_to_ignore:\n continue\n if key[len(prefix) :] in textenc_conversion_map:\n if key.endswith(\"text_projection\"):\n value = checkpoint[key].T.contiguous()\n else:\n value = checkpoint[key]\n\n text_model_dict[textenc_conversion_map[key[len(prefix) :]]] = value\n\n if key.startswith(prefix + \"transformer.\"):\n new_key = key[len(prefix + \"transformer.\") :]\n if new_key.endswith(\".in_proj_weight\"):\n new_key = new_key[: -len(\".in_proj_weight\")]\n new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)\n text_model_dict[new_key + \".q_proj.weight\"] = checkpoint[key][:d_model, :]\n text_model_dict[new_key + \".k_proj.weight\"] = checkpoint[key][d_model : d_model * 2, :]\n text_model_dict[new_key + \".v_proj.weight\"] = checkpoint[key][d_model * 2 :, :]\n elif new_key.endswith(\".in_proj_bias\"):\n new_key = new_key[: -len(\".in_proj_bias\")]\n new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)\n text_model_dict[new_key + \".q_proj.bias\"] = checkpoint[key][:d_model]\n text_model_dict[new_key + \".k_proj.bias\"] = checkpoint[key][d_model : d_model * 2]\n text_model_dict[new_key + \".v_proj.bias\"] = checkpoint[key][d_model * 2 :]\n else:\n new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)\n\n text_model_dict[new_key] = checkpoint[key]\n\n if is_accelerate_available():\n for param_name, param in text_model_dict.items():\n set_module_tensor_to_device(text_model, param_name, \"cpu\", value=param)\n else:\n if not (hasattr(text_model, \"embeddings\") and hasattr(text_model.embeddings.position_ids)):\n text_model_dict.pop(\"text_model.embeddings.position_ids\", None)\n\n text_model.load_state_dict(text_model_dict)\n\n return text_model\n\n\ndef stable_unclip_image_encoder(original_config, local_files_only=False):\n \"\"\"\n Returns the image processor and clip image encoder for the img2img unclip pipeline.\n\n We currently know of two types of stable unclip models which separately use the clip and the openclip image\n encoders.\n \"\"\"\n\n image_embedder_config = original_config[\"model\"][\"params\"][\"embedder_config\"]\n\n sd_clip_image_embedder_class = image_embedder_config[\"target\"]\n sd_clip_image_embedder_class = sd_clip_image_embedder_class.split(\".\")[-1]\n\n if sd_clip_image_embedder_class == \"ClipImageEmbedder\":\n clip_model_name = image_embedder_config.params.model\n\n if clip_model_name == \"ViT-L/14\":\n feature_extractor = CLIPImageProcessor()\n image_encoder = CLIPVisionModelWithProjection.from_pretrained(\n \"openai/clip-vit-large-patch14\", local_files_only=local_files_only\n )\n else:\n raise NotImplementedError(f\"Unknown CLIP checkpoint name in stable diffusion checkpoint {clip_model_name}\")\n\n elif sd_clip_image_embedder_class == \"FrozenOpenCLIPImageEmbedder\":\n feature_extractor = CLIPImageProcessor()\n image_encoder = CLIPVisionModelWithProjection.from_pretrained(\n \"laion/CLIP-ViT-H-14-laion2B-s32B-b79K\", local_files_only=local_files_only\n )\n else:\n raise NotImplementedError(\n f\"Unknown CLIP image embedder class in stable diffusion checkpoint {sd_clip_image_embedder_class}\"\n )\n\n return feature_extractor, image_encoder\n\n\ndef stable_unclip_image_noising_components(\n original_config, clip_stats_path: Optional[str] = None, device: Optional[str] = None\n):\n \"\"\"\n Returns the noising components for the img2img and txt2img unclip pipelines.\n\n Converts the stability noise augmentor into\n 1. a `StableUnCLIPImageNormalizer` for holding the CLIP stats\n 2. a `DDPMScheduler` for holding the noise schedule\n\n If the noise augmentor config specifies a clip stats path, the `clip_stats_path` must be provided.\n \"\"\"\n noise_aug_config = original_config[\"model\"][\"params\"][\"noise_aug_config\"]\n noise_aug_class = noise_aug_config[\"target\"]\n noise_aug_class = noise_aug_class.split(\".\")[-1]\n\n if noise_aug_class == \"CLIPEmbeddingNoiseAugmentation\":\n noise_aug_config = noise_aug_config.params\n embedding_dim = noise_aug_config.timestep_dim\n max_noise_level = noise_aug_config.noise_schedule_config.timesteps\n beta_schedule = noise_aug_config.noise_schedule_config.beta_schedule\n\n image_normalizer = StableUnCLIPImageNormalizer(embedding_dim=embedding_dim)\n image_noising_scheduler = DDPMScheduler(num_train_timesteps=max_noise_level, beta_schedule=beta_schedule)\n\n if \"clip_stats_path\" in noise_aug_config:\n if clip_stats_path is None:\n raise ValueError(\"This stable unclip config requires a `clip_stats_path`\")\n\n clip_mean, clip_std = torch.load(clip_stats_path, map_location=device)\n clip_mean = clip_mean[None, :]\n clip_std = clip_std[None, :]\n\n clip_stats_state_dict = {\n \"mean\": clip_mean,\n \"std\": clip_std,\n }\n\n image_normalizer.load_state_dict(clip_stats_state_dict)\n else:\n raise NotImplementedError(f\"Unknown noise augmentor class: {noise_aug_class}\")\n\n return image_normalizer, image_noising_scheduler\n\n\ndef convert_controlnet_checkpoint(\n checkpoint,\n original_config,\n checkpoint_path,\n image_size,\n upcast_attention,\n extract_ema,\n use_linear_projection=None,\n cross_attention_dim=None,\n):\n ctrlnet_config = create_unet_diffusers_config(original_config, image_size=image_size, controlnet=True)\n ctrlnet_config[\"upcast_attention\"] = upcast_attention\n\n ctrlnet_config.pop(\"sample_size\")\n\n if use_linear_projection is not None:\n ctrlnet_config[\"use_linear_projection\"] = use_linear_projection\n\n if cross_attention_dim is not None:\n ctrlnet_config[\"cross_attention_dim\"] = cross_attention_dim\n\n ctx = init_empty_weights if is_accelerate_available() else nullcontext\n with ctx():\n controlnet = ControlNetModel(**ctrlnet_config)\n\n # Some controlnet ckpt files are distributed independently from the rest of the\n # model components i.e. https://huggingface.co/thibaud/controlnet-sd21/\n if \"time_embed.0.weight\" in checkpoint:\n skip_extract_state_dict = True\n else:\n skip_extract_state_dict = False\n\n converted_ctrl_checkpoint = convert_ldm_unet_checkpoint(\n checkpoint,\n ctrlnet_config,\n path=checkpoint_path,\n extract_ema=extract_ema,\n controlnet=True,\n skip_extract_state_dict=skip_extract_state_dict,\n )\n\n if is_accelerate_available():\n for param_name, param in converted_ctrl_checkpoint.items():\n set_module_tensor_to_device(controlnet, param_name, \"cpu\", value=param)\n else:\n controlnet.load_state_dict(converted_ctrl_checkpoint)\n\n return controlnet\n\n\ndef download_from_original_stable_diffusion_ckpt(\n checkpoint_path_or_dict: Union[str, Dict[str, torch.Tensor]],\n original_config_file: str = None,\n image_size: Optional[int] = None,\n prediction_type: str = None,\n model_type: str = None,\n extract_ema: bool = False,\n scheduler_type: str = \"pndm\",\n num_in_channels: Optional[int] = None,\n upcast_attention: Optional[bool] = None,\n device: str = None,\n from_safetensors: bool = False,\n stable_unclip: Optional[str] = None,\n stable_unclip_prior: Optional[str] = None,\n clip_stats_path: Optional[str] = None,\n controlnet: Optional[bool] = None,\n adapter: Optional[bool] = None,\n load_safety_checker: bool = True,\n safety_checker: Optional[StableDiffusionSafetyChecker] = None,\n feature_extractor: Optional[AutoFeatureExtractor] = None,\n pipeline_class: DiffusionPipeline = None,\n local_files_only=False,\n vae_path=None,\n vae=None,\n text_encoder=None,\n text_encoder_2=None,\n tokenizer=None,\n tokenizer_2=None,\n config_files=None,\n) -> DiffusionPipeline:\n \"\"\"\n Load a Stable Diffusion pipeline object from a CompVis-style `.ckpt`/`.safetensors` file and (ideally) a `.yaml`\n config file.\n\n Although many of the arguments can be automatically inferred, some of these rely on brittle checks against the\n global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is\n recommended that you override the default values and/or supply an `original_config_file` wherever possible.\n\n Args:\n checkpoint_path_or_dict (`str` or `dict`): Path to `.ckpt` file, or the state dict.\n original_config_file (`str`):\n Path to `.yaml` config file corresponding to the original architecture. If `None`, will be automatically\n inferred by looking for a key that only exists in SD2.0 models.\n image_size (`int`, *optional*, defaults to 512):\n The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Diffusion v2\n Base. Use 768 for Stable Diffusion v2.\n prediction_type (`str`, *optional*):\n The prediction type that the model was trained on. Use `'epsilon'` for Stable Diffusion v1.X and Stable\n Diffusion v2 Base. Use `'v_prediction'` for Stable Diffusion v2.\n num_in_channels (`int`, *optional*, defaults to None):\n The number of input channels. If `None`, it will be automatically inferred.\n scheduler_type (`str`, *optional*, defaults to 'pndm'):\n Type of scheduler to use. Should be one of `[\"pndm\", \"lms\", \"heun\", \"euler\", \"euler-ancestral\", \"dpm\",\n \"ddim\"]`.\n model_type (`str`, *optional*, defaults to `None`):\n The pipeline type. `None` to automatically infer, or one of `[\"FrozenOpenCLIPEmbedder\",\n \"FrozenCLIPEmbedder\", \"PaintByExample\"]`.\n is_img2img (`bool`, *optional*, defaults to `False`):\n Whether the model should be loaded as an img2img pipeline.\n extract_ema (`bool`, *optional*, defaults to `False`): Only relevant for\n checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults to\n `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher quality images for\n inference. Non-EMA weights are usually better to continue fine-tuning.\n upcast_attention (`bool`, *optional*, defaults to `None`):\n Whether the attention computation should always be upcasted. This is necessary when running stable\n diffusion 2.1.\n device (`str`, *optional*, defaults to `None`):\n The device to use. Pass `None` to determine automatically.\n from_safetensors (`str`, *optional*, defaults to `False`):\n If `checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.\n load_safety_checker (`bool`, *optional*, defaults to `True`):\n Whether to load the safety checker or not. Defaults to `True`.\n safety_checker (`StableDiffusionSafetyChecker`, *optional*, defaults to `None`):\n Safety checker to use. If this parameter is `None`, the function will load a new instance of\n [StableDiffusionSafetyChecker] by itself, if needed.\n feature_extractor (`AutoFeatureExtractor`, *optional*, defaults to `None`):\n Feature extractor to use. If this parameter is `None`, the function will load a new instance of\n [AutoFeatureExtractor] by itself, if needed.\n pipeline_class (`str`, *optional*, defaults to `None`):\n The pipeline class to use. Pass `None` to determine automatically.\n local_files_only (`bool`, *optional*, defaults to `False`):\n Whether or not to only look at local files (i.e., do not try to download the model).\n vae (`AutoencoderKL`, *optional*, defaults to `None`):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. If\n this parameter is `None`, the function will load a new instance of [CLIP] by itself, if needed.\n text_encoder (`CLIPTextModel`, *optional*, defaults to `None`):\n An instance of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel)\n to use, specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)\n variant. If this parameter is `None`, the function will load a new instance of [CLIP] by itself, if needed.\n tokenizer (`CLIPTokenizer`, *optional*, defaults to `None`):\n An instance of\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer)\n to use. If this parameter is `None`, the function will load a new instance of [CLIPTokenizer] by itself, if\n needed.\n config_files (`Dict[str, str]`, *optional*, defaults to `None`):\n A dictionary mapping from config file names to their contents. If this parameter is `None`, the function\n will load the config files by itself, if needed. Valid keys are:\n - `v1`: Config file for Stable Diffusion v1\n - `v2`: Config file for Stable Diffusion v2\n - `xl`: Config file for Stable Diffusion XL\n - `xl_refiner`: Config file for Stable Diffusion XL Refiner\n return: A StableDiffusionPipeline object representing the passed-in `.ckpt`/`.safetensors` file.\n \"\"\"\n\n # import pipelines here to avoid circular import error when using from_single_file method\n from diffusers import (\n LDMTextToImagePipeline,\n PaintByExamplePipeline,\n StableDiffusionControlNetPipeline,\n StableDiffusionInpaintPipeline,\n StableDiffusionPipeline,\n StableDiffusionUpscalePipeline,\n StableDiffusionXLControlNetInpaintPipeline,\n StableDiffusionXLImg2ImgPipeline,\n StableDiffusionXLInpaintPipeline,\n StableDiffusionXLPipeline,\n StableUnCLIPImg2ImgPipeline,\n StableUnCLIPPipeline,\n )\n\n if prediction_type == \"v-prediction\":\n prediction_type = \"v_prediction\"\n\n if isinstance(checkpoint_path_or_dict, str):\n if from_safetensors:\n from safetensors.torch import load_file as safe_load\n\n checkpoint = safe_load(checkpoint_path_or_dict, device=\"cpu\")\n else:\n if device is None:\n device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n checkpoint = torch.load(checkpoint_path_or_dict, map_location=device)\n else:\n checkpoint = torch.load(checkpoint_path_or_dict, map_location=device)\n elif isinstance(checkpoint_path_or_dict, dict):\n checkpoint = checkpoint_path_or_dict\n\n # Sometimes models don't have the global_step item\n if \"global_step\" in checkpoint:\n global_step = checkpoint[\"global_step\"]\n else:\n logger.debug(\"global_step key not found in model\")\n global_step = None\n\n # NOTE: this while loop isn't great but this controlnet checkpoint has one additional\n # \"state_dict\" key https://huggingface.co/thibaud/controlnet-canny-sd21\n while \"state_dict\" in checkpoint:\n checkpoint = checkpoint[\"state_dict\"]\n\n if original_config_file is None:\n key_name_v2_1 = \"model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight\"\n key_name_sd_xl_base = \"conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.bias\"\n key_name_sd_xl_refiner = \"conditioner.embedders.0.model.transformer.resblocks.9.mlp.c_proj.bias\"\n is_upscale = pipeline_class == StableDiffusionUpscalePipeline\n\n config_url = None\n\n # model_type = \"v1\"\n if config_files is not None and \"v1\" in config_files:\n original_config_file = config_files[\"v1\"]\n else:\n config_url = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml\"\n\n if key_name_v2_1 in checkpoint and checkpoint[key_name_v2_1].shape[-1] == 1024:\n # model_type = \"v2\"\n if config_files is not None and \"v2\" in config_files:\n original_config_file = config_files[\"v2\"]\n else:\n config_url = \"https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml\"\n if global_step == 110000:\n # v2.1 needs to upcast attention\n upcast_attention = True\n elif key_name_sd_xl_base in checkpoint:\n # only base xl has two text embedders\n if config_files is not None and \"xl\" in config_files:\n original_config_file = config_files[\"xl\"]\n else:\n config_url = \"https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml\"\n elif key_name_sd_xl_refiner in checkpoint:\n # only refiner xl has embedder and one text embedders\n if config_files is not None and \"xl_refiner\" in config_files:\n original_config_file = config_files[\"xl_refiner\"]\n else:\n config_url = \"https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_refiner.yaml\"\n\n if is_upscale:\n config_url = \"https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/x4-upscaling.yaml\"\n\n if config_url is not None:\n original_config_file = BytesIO(requests.get(config_url).content)\n else:\n with open(original_config_file, \"r\") as f:\n original_config_file = f.read()\n else:\n with open(original_config_file, \"r\") as f:\n original_config_file = f.read()\n\n original_config = yaml.safe_load(original_config_file)\n\n # Convert the text model.\n if (\n model_type is None\n and \"cond_stage_config\" in original_config[\"model\"][\"params\"]\n and original_config[\"model\"][\"params\"][\"cond_stage_config\"] is not None\n ):\n model_type = original_config[\"model\"][\"params\"][\"cond_stage_config\"][\"target\"].split(\".\")[-1]\n logger.debug(f\"no `model_type` given, `model_type` inferred as: {model_type}\")\n elif model_type is None and original_config[\"model\"][\"params\"][\"network_config\"] is not None:\n if original_config[\"model\"][\"params\"][\"network_config\"][\"params\"][\"context_dim\"] == 2048:\n model_type = \"SDXL\"\n else:\n model_type = \"SDXL-Refiner\"\n if image_size is None:\n image_size = 1024\n\n if pipeline_class is None:\n # Check if we have a SDXL or SD model and initialize default pipeline\n if model_type not in [\"SDXL\", \"SDXL-Refiner\"]:\n pipeline_class = StableDiffusionPipeline if not controlnet else StableDiffusionControlNetPipeline\n else:\n pipeline_class = StableDiffusionXLPipeline if model_type == \"SDXL\" else StableDiffusionXLImg2ImgPipeline\n\n if num_in_channels is None and pipeline_class in [\n StableDiffusionInpaintPipeline,\n StableDiffusionXLInpaintPipeline,\n StableDiffusionXLControlNetInpaintPipeline,\n ]:\n num_in_channels = 9\n if num_in_channels is None and pipeline_class == StableDiffusionUpscalePipeline:\n num_in_channels = 7\n elif num_in_channels is None:\n num_in_channels = 4\n\n if \"unet_config\" in original_config[\"model\"][\"params\"]:\n original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"][\"in_channels\"] = num_in_channels\n\n if (\n \"parameterization\" in original_config[\"model\"][\"params\"]\n and original_config[\"model\"][\"params\"][\"parameterization\"] == \"v\"\n ):\n if prediction_type is None:\n # NOTE: For stable diffusion 2 base it is recommended to pass `prediction_type==\"epsilon\"`\n # as it relies on a brittle global step parameter here\n prediction_type = \"epsilon\" if global_step == 875000 else \"v_prediction\"\n if image_size is None:\n # NOTE: For stable diffusion 2 base one has to pass `image_size==512`\n # as it relies on a brittle global step parameter here\n image_size = 512 if global_step == 875000 else 768\n else:\n if prediction_type is None:\n prediction_type = \"epsilon\"\n if image_size is None:\n image_size = 512\n\n if controlnet is None and \"control_stage_config\" in original_config[\"model\"][\"params\"]:\n path = checkpoint_path_or_dict if isinstance(checkpoint_path_or_dict, str) else \"\"\n controlnet = convert_controlnet_checkpoint(\n checkpoint, original_config, path, image_size, upcast_attention, extract_ema\n )\n\n if \"timesteps\" in original_config[\"model\"][\"params\"]:\n num_train_timesteps = original_config[\"model\"][\"params\"][\"timesteps\"]\n else:\n num_train_timesteps = 1000\n\n if model_type in [\"SDXL\", \"SDXL-Refiner\"]:\n scheduler_dict = {\n \"beta_schedule\": \"scaled_linear\",\n \"beta_start\": 0.00085,\n \"beta_end\": 0.012,\n \"interpolation_type\": \"linear\",\n \"num_train_timesteps\": num_train_timesteps,\n \"prediction_type\": \"epsilon\",\n \"sample_max_value\": 1.0,\n \"set_alpha_to_one\": False,\n \"skip_prk_steps\": True,\n \"steps_offset\": 1,\n \"timestep_spacing\": \"leading\",\n }\n scheduler = EulerDiscreteScheduler.from_config(scheduler_dict)\n scheduler_type = \"euler\"\n else:\n if \"linear_start\" in original_config[\"model\"][\"params\"]:\n beta_start = original_config[\"model\"][\"params\"][\"linear_start\"]\n else:\n beta_start = 0.02\n\n if \"linear_end\" in original_config[\"model\"][\"params\"]:\n beta_end = original_config[\"model\"][\"params\"][\"linear_end\"]\n else:\n beta_end = 0.085\n scheduler = DDIMScheduler(\n beta_end=beta_end,\n beta_schedule=\"scaled_linear\",\n beta_start=beta_start,\n num_train_timesteps=num_train_timesteps,\n steps_offset=1,\n clip_sample=False,\n set_alpha_to_one=False,\n prediction_type=prediction_type,\n )\n # make sure scheduler works correctly with DDIM\n scheduler.register_to_config(clip_sample=False)\n\n if scheduler_type == \"pndm\":\n config = dict(scheduler.config)\n config[\"skip_prk_steps\"] = True\n scheduler = PNDMScheduler.from_config(config)\n elif scheduler_type == \"lms\":\n scheduler = LMSDiscreteScheduler.from_config(scheduler.config)\n elif scheduler_type == \"heun\":\n scheduler = HeunDiscreteScheduler.from_config(scheduler.config)\n elif scheduler_type == \"euler\":\n scheduler = EulerDiscreteScheduler.from_config(scheduler.config)\n elif scheduler_type == \"euler-ancestral\":\n scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config)\n elif scheduler_type == \"dpm\":\n scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)\n elif scheduler_type == \"ddim\":\n scheduler = scheduler\n else:\n raise ValueError(f\"Scheduler of type {scheduler_type} doesn't exist!\")\n\n if pipeline_class == StableDiffusionUpscalePipeline:\n image_size = original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"][\"image_size\"]\n\n # Convert the UNet2DConditionModel model.\n unet_config = create_unet_diffusers_config(original_config, image_size=image_size)\n unet_config[\"upcast_attention\"] = upcast_attention\n\n path = checkpoint_path_or_dict if isinstance(checkpoint_path_or_dict, str) else \"\"\n converted_unet_checkpoint = convert_ldm_unet_checkpoint(\n checkpoint, unet_config, path=path, extract_ema=extract_ema\n )\n\n ctx = init_empty_weights if is_accelerate_available() else nullcontext\n with ctx():\n unet = UNet2DConditionModel(**unet_config)\n\n if is_accelerate_available():\n if model_type not in [\"SDXL\", \"SDXL-Refiner\"]: # SBM Delay this.\n for param_name, param in converted_unet_checkpoint.items():\n set_module_tensor_to_device(unet, param_name, \"cpu\", value=param)\n else:\n unet.load_state_dict(converted_unet_checkpoint)\n\n # Convert the VAE model.\n if vae_path is None and vae is None:\n vae_config = create_vae_diffusers_config(original_config, image_size=image_size)\n converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)\n\n if (\n \"model\" in original_config\n and \"params\" in original_config[\"model\"]\n and \"scale_factor\" in original_config[\"model\"][\"params\"]\n ):\n vae_scaling_factor = original_config[\"model\"][\"params\"][\"scale_factor\"]\n else:\n vae_scaling_factor = 0.18215 # default SD scaling factor\n\n vae_config[\"scaling_factor\"] = vae_scaling_factor\n\n ctx = init_empty_weights if is_accelerate_available() else nullcontext\n with ctx():\n vae = AutoencoderKL(**vae_config)\n\n if is_accelerate_available():\n for param_name, param in converted_vae_checkpoint.items():\n set_module_tensor_to_device(vae, param_name, \"cpu\", value=param)\n else:\n vae.load_state_dict(converted_vae_checkpoint)\n elif vae is None:\n vae = AutoencoderKL.from_pretrained(vae_path, local_files_only=local_files_only)\n\n if model_type == \"FrozenOpenCLIPEmbedder\":\n config_name = \"stabilityai/stable-diffusion-2\"\n config_kwargs = {\"subfolder\": \"text_encoder\"}\n\n if text_encoder is None:\n text_model = convert_open_clip_checkpoint(\n checkpoint, config_name, local_files_only=local_files_only, **config_kwargs\n )\n else:\n text_model = text_encoder\n\n try:\n tokenizer = CLIPTokenizer.from_pretrained(\n \"stabilityai/stable-diffusion-2\", subfolder=\"tokenizer\", local_files_only=local_files_only\n )\n except Exception:\n raise ValueError(\n f\"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'stabilityai/stable-diffusion-2'.\"\n )\n\n if stable_unclip is None:\n if controlnet:\n pipe = pipeline_class(\n vae=vae,\n text_encoder=text_model,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n controlnet=controlnet,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n if hasattr(pipe, \"requires_safety_checker\"):\n pipe.requires_safety_checker = False\n\n elif pipeline_class == StableDiffusionUpscalePipeline:\n scheduler = DDIMScheduler.from_pretrained(\n \"stabilityai/stable-diffusion-x4-upscaler\", subfolder=\"scheduler\"\n )\n low_res_scheduler = DDPMScheduler.from_pretrained(\n \"stabilityai/stable-diffusion-x4-upscaler\", subfolder=\"low_res_scheduler\"\n )\n\n pipe = pipeline_class(\n vae=vae,\n text_encoder=text_model,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n low_res_scheduler=low_res_scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n\n else:\n pipe = pipeline_class(\n vae=vae,\n text_encoder=text_model,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n if hasattr(pipe, \"requires_safety_checker\"):\n pipe.requires_safety_checker = False\n\n else:\n image_normalizer, image_noising_scheduler = stable_unclip_image_noising_components(\n original_config, clip_stats_path=clip_stats_path, device=device\n )\n\n if stable_unclip == \"img2img\":\n feature_extractor, image_encoder = stable_unclip_image_encoder(original_config)\n\n pipe = StableUnCLIPImg2ImgPipeline(\n # image encoding components\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n # image noising components\n image_normalizer=image_normalizer,\n image_noising_scheduler=image_noising_scheduler,\n # regular denoising components\n tokenizer=tokenizer,\n text_encoder=text_model,\n unet=unet,\n scheduler=scheduler,\n # vae\n vae=vae,\n )\n elif stable_unclip == \"txt2img\":\n if stable_unclip_prior is None or stable_unclip_prior == \"karlo\":\n karlo_model = \"kakaobrain/karlo-v1-alpha\"\n prior = PriorTransformer.from_pretrained(\n karlo_model, subfolder=\"prior\", local_files_only=local_files_only\n )\n\n try:\n prior_tokenizer = CLIPTokenizer.from_pretrained(\n \"openai/clip-vit-large-patch14\", local_files_only=local_files_only\n )\n except Exception:\n raise ValueError(\n f\"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'openai/clip-vit-large-patch14'.\"\n )\n prior_text_model = CLIPTextModelWithProjection.from_pretrained(\n \"openai/clip-vit-large-patch14\", local_files_only=local_files_only\n )\n\n prior_scheduler = UnCLIPScheduler.from_pretrained(\n karlo_model, subfolder=\"prior_scheduler\", local_files_only=local_files_only\n )\n prior_scheduler = DDPMScheduler.from_config(prior_scheduler.config)\n else:\n raise NotImplementedError(f\"unknown prior for stable unclip model: {stable_unclip_prior}\")\n\n pipe = StableUnCLIPPipeline(\n # prior components\n prior_tokenizer=prior_tokenizer,\n prior_text_encoder=prior_text_model,\n prior=prior,\n prior_scheduler=prior_scheduler,\n # image noising components\n image_normalizer=image_normalizer,\n image_noising_scheduler=image_noising_scheduler,\n # regular denoising components\n tokenizer=tokenizer,\n text_encoder=text_model,\n unet=unet,\n scheduler=scheduler,\n # vae\n vae=vae,\n )\n else:\n raise NotImplementedError(f\"unknown `stable_unclip` type: {stable_unclip}\")\n elif model_type == \"PaintByExample\":\n vision_model = convert_paint_by_example_checkpoint(checkpoint)\n try:\n tokenizer = CLIPTokenizer.from_pretrained(\n \"openai/clip-vit-large-patch14\", local_files_only=local_files_only\n )\n except Exception:\n raise ValueError(\n f\"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'openai/clip-vit-large-patch14'.\"\n )\n try:\n feature_extractor = AutoFeatureExtractor.from_pretrained(\n \"CompVis/stable-diffusion-safety-checker\", local_files_only=local_files_only\n )\n except Exception:\n raise ValueError(\n f\"With local_files_only set to {local_files_only}, you must first locally save the feature_extractor in the following path: 'CompVis/stable-diffusion-safety-checker'.\"\n )\n pipe = PaintByExamplePipeline(\n vae=vae,\n image_encoder=vision_model,\n unet=unet,\n scheduler=scheduler,\n safety_checker=None,\n feature_extractor=feature_extractor,\n )\n elif model_type == \"FrozenCLIPEmbedder\":\n text_model = convert_ldm_clip_checkpoint(\n checkpoint, local_files_only=local_files_only, text_encoder=text_encoder\n )\n try:\n tokenizer = (\n CLIPTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", local_files_only=local_files_only)\n if tokenizer is None\n else tokenizer\n )\n except Exception:\n raise ValueError(\n f\"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'openai/clip-vit-large-patch14'.\"\n )\n\n if load_safety_checker:\n safety_checker = StableDiffusionSafetyChecker.from_pretrained(\n \"CompVis/stable-diffusion-safety-checker\", local_files_only=local_files_only\n )\n feature_extractor = AutoFeatureExtractor.from_pretrained(\n \"CompVis/stable-diffusion-safety-checker\", local_files_only=local_files_only\n )\n\n if controlnet:\n pipe = pipeline_class(\n vae=vae,\n text_encoder=text_model,\n tokenizer=tokenizer,\n unet=unet,\n controlnet=controlnet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n else:\n pipe = pipeline_class(\n vae=vae,\n text_encoder=text_model,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n elif model_type in [\"SDXL\", \"SDXL-Refiner\"]:\n is_refiner = model_type == \"SDXL-Refiner\"\n\n if (is_refiner is False) and (tokenizer is None):\n try:\n tokenizer = CLIPTokenizer.from_pretrained(\n \"openai/clip-vit-large-patch14\", local_files_only=local_files_only\n )\n except Exception:\n raise ValueError(\n f\"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'openai/clip-vit-large-patch14'.\"\n )\n\n if (is_refiner is False) and (text_encoder is None):\n text_encoder = convert_ldm_clip_checkpoint(checkpoint, local_files_only=local_files_only)\n\n if tokenizer_2 is None:\n try:\n tokenizer_2 = CLIPTokenizer.from_pretrained(\n \"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\", pad_token=\"!\", local_files_only=local_files_only\n )\n except Exception:\n raise ValueError(\n f\"With local_files_only set to {local_files_only}, you must first locally save the tokenizer in the following path: 'laion/CLIP-ViT-bigG-14-laion2B-39B-b160k' with `pad_token` set to '!'.\"\n )\n\n if text_encoder_2 is None:\n config_name = \"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\"\n config_kwargs = {\"projection_dim\": 1280}\n prefix = \"conditioner.embedders.0.model.\" if is_refiner else \"conditioner.embedders.1.model.\"\n\n text_encoder_2 = convert_open_clip_checkpoint(\n checkpoint,\n config_name,\n prefix=prefix,\n has_projection=True,\n local_files_only=local_files_only,\n **config_kwargs,\n )\n\n if is_accelerate_available(): # SBM Now move model to cpu.\n for param_name, param in converted_unet_checkpoint.items():\n set_module_tensor_to_device(unet, param_name, \"cpu\", value=param)\n\n if controlnet:\n pipe = pipeline_class(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n text_encoder_2=text_encoder_2,\n tokenizer_2=tokenizer_2,\n unet=unet,\n controlnet=controlnet,\n scheduler=scheduler,\n force_zeros_for_empty_prompt=True,\n )\n elif adapter:\n pipe = pipeline_class(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n text_encoder_2=text_encoder_2,\n tokenizer_2=tokenizer_2,\n unet=unet,\n adapter=adapter,\n scheduler=scheduler,\n force_zeros_for_empty_prompt=True,\n )\n\n else:\n pipeline_kwargs = {\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"text_encoder_2\": text_encoder_2,\n \"tokenizer_2\": tokenizer_2,\n \"unet\": unet,\n \"scheduler\": scheduler,\n }\n\n if (pipeline_class == StableDiffusionXLImg2ImgPipeline) or (\n pipeline_class == StableDiffusionXLInpaintPipeline\n ):\n pipeline_kwargs.update({\"requires_aesthetics_score\": is_refiner})\n\n if is_refiner:\n pipeline_kwargs.update({\"force_zeros_for_empty_prompt\": False})\n\n pipe = pipeline_class(**pipeline_kwargs)\n else:\n text_config = create_ldm_bert_config(original_config)\n text_model = convert_ldm_bert_checkpoint(checkpoint, text_config)\n tokenizer = BertTokenizerFast.from_pretrained(\"bert-base-uncased\", local_files_only=local_files_only)\n pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler)\n\n return pipe\n\n\ndef download_controlnet_from_original_ckpt(\n checkpoint_path: str,\n original_config_file: str,\n image_size: int = 512,\n extract_ema: bool = False,\n num_in_channels: Optional[int] = None,\n upcast_attention: Optional[bool] = None,\n device: str = None,\n from_safetensors: bool = False,\n use_linear_projection: Optional[bool] = None,\n cross_attention_dim: Optional[bool] = None,\n) -> DiffusionPipeline:\n if from_safetensors:\n from safetensors import safe_open\n\n checkpoint = {}\n with safe_open(checkpoint_path, framework=\"pt\", device=\"cpu\") as f:\n for key in f.keys():\n checkpoint[key] = f.get_tensor(key)\n else:\n if device is None:\n device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n checkpoint = torch.load(checkpoint_path, map_location=device)\n else:\n checkpoint = torch.load(checkpoint_path, map_location=device)\n\n # NOTE: this while loop isn't great but this controlnet checkpoint has one additional\n # \"state_dict\" key https://huggingface.co/thibaud/controlnet-canny-sd21\n while \"state_dict\" in checkpoint:\n checkpoint = checkpoint[\"state_dict\"]\n\n with open(original_config_file, \"r\") as f:\n original_config_file = f.read()\n original_config = yaml.safe_load(original_config_file)\n\n if num_in_channels is not None:\n original_config[\"model\"][\"params\"][\"unet_config\"][\"params\"][\"in_channels\"] = num_in_channels\n\n if \"control_stage_config\" not in original_config[\"model\"][\"params\"]:\n raise ValueError(\"`control_stage_config` not present in original config\")\n\n controlnet = convert_controlnet_checkpoint(\n checkpoint,\n original_config,\n checkpoint_path,\n image_size,\n upcast_attention,\n extract_ema,\n use_linear_projection=use_linear_projection,\n cross_attention_dim=cross_attention_dim,\n )\n\n return controlnet\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport warnings\nfrom functools import partial\nfrom typing import Dict, List, Optional, Union\n\nimport jax\nimport jax.numpy as jnp\nimport numpy as np\nfrom flax.core.frozen_dict import FrozenDict\nfrom flax.jax_utils import unreplicate\nfrom flax.training.common_utils import shard\nfrom packaging import version\nfrom PIL import Image\nfrom transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel\n\nfrom ...models import FlaxAutoencoderKL, FlaxUNet2DConditionModel\nfrom ...schedulers import (\n FlaxDDIMScheduler,\n FlaxDPMSolverMultistepScheduler,\n FlaxLMSDiscreteScheduler,\n FlaxPNDMScheduler,\n)\nfrom ...utils import deprecate, logging, replace_example_docstring\nfrom ..pipeline_flax_utils import FlaxDiffusionPipeline\nfrom .pipeline_output import FlaxStableDiffusionPipelineOutput\nfrom .safety_checker_flax import FlaxStableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n# Set to True to use python for loop instead of jax.fori_loop for easier debugging\nDEBUG = False\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import jax\n >>> import numpy as np\n >>> from flax.jax_utils import replicate\n >>> from flax.training.common_utils import shard\n\n >>> from diffusers import FlaxStableDiffusionPipeline\n\n >>> pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(\n ... \"runwayml/stable-diffusion-v1-5\", revision=\"bf16\", dtype=jax.numpy.bfloat16\n ... )\n\n >>> prompt = \"a photo of an astronaut riding a horse on mars\"\n\n >>> prng_seed = jax.random.PRNGKey(0)\n >>> num_inference_steps = 50\n\n >>> num_samples = jax.device_count()\n >>> prompt = num_samples * [prompt]\n >>> prompt_ids = pipeline.prepare_inputs(prompt)\n # shard inputs and rng\n\n >>> params = replicate(params)\n >>> prng_seed = jax.random.split(prng_seed, jax.device_count())\n >>> prompt_ids = shard(prompt_ids)\n\n >>> images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images\n >>> images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))\n ```\n\"\"\"\n\n\nclass FlaxStableDiffusionPipeline(FlaxDiffusionPipeline):\n r\"\"\"\n Flax-based pipeline for text-to-image generation using Stable Diffusion.\n\n This model inherits from [`FlaxDiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Args:\n vae ([`FlaxAutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.FlaxCLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`FlaxUNet2DConditionModel`]):\n A `FlaxUNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`FlaxDDIMScheduler`], [`FlaxLMSDiscreteScheduler`], [`FlaxPNDMScheduler`], or\n [`FlaxDPMSolverMultistepScheduler`].\n safety_checker ([`FlaxStableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n def __init__(\n self,\n vae: FlaxAutoencoderKL,\n text_encoder: FlaxCLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: FlaxUNet2DConditionModel,\n scheduler: Union[\n FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler\n ],\n safety_checker: FlaxStableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n dtype: jnp.dtype = jnp.float32,\n ):\n super().__init__()\n self.dtype = dtype\n\n if safety_checker is None:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n\n def prepare_inputs(self, prompt: Union[str, List[str]]):\n if not isinstance(prompt, (str, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n text_input = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"np\",\n )\n return text_input.input_ids\n\n def _get_has_nsfw_concepts(self, features, params):\n has_nsfw_concepts = self.safety_checker(features, params)\n return has_nsfw_concepts\n\n def _run_safety_checker(self, images, safety_model_params, jit=False):\n # safety_model_params should already be replicated when jit is True\n pil_images = [Image.fromarray(image) for image in images]\n features = self.feature_extractor(pil_images, return_tensors=\"np\").pixel_values\n\n if jit:\n features = shard(features)\n has_nsfw_concepts = _p_get_has_nsfw_concepts(self, features, safety_model_params)\n has_nsfw_concepts = unshard(has_nsfw_concepts)\n safety_model_params = unreplicate(safety_model_params)\n else:\n has_nsfw_concepts = self._get_has_nsfw_concepts(features, safety_model_params)\n\n images_was_copied = False\n for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):\n if has_nsfw_concept:\n if not images_was_copied:\n images_was_copied = True\n images = images.copy()\n\n images[idx] = np.zeros(images[idx].shape, dtype=np.uint8) # black image\n\n if any(has_nsfw_concepts):\n warnings.warn(\n \"Potential NSFW content was detected in one or more images. A black image will be returned\"\n \" instead. Try again with a different prompt and/or seed.\"\n )\n\n return images, has_nsfw_concepts\n\n def _generate(\n self,\n prompt_ids: jnp.array,\n params: Union[Dict, FrozenDict],\n prng_seed: jax.Array,\n num_inference_steps: int,\n height: int,\n width: int,\n guidance_scale: float,\n latents: Optional[jnp.ndarray] = None,\n neg_prompt_ids: Optional[jnp.ndarray] = None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n # get prompt text embeddings\n prompt_embeds = self.text_encoder(prompt_ids, params=params[\"text_encoder\"])[0]\n\n # TODO: currently it is assumed `do_classifier_free_guidance = guidance_scale > 1.0`\n # implement this conditional `do_classifier_free_guidance = guidance_scale > 1.0`\n batch_size = prompt_ids.shape[0]\n\n max_length = prompt_ids.shape[-1]\n\n if neg_prompt_ids is None:\n uncond_input = self.tokenizer(\n [\"\"] * batch_size, padding=\"max_length\", max_length=max_length, return_tensors=\"np\"\n ).input_ids\n else:\n uncond_input = neg_prompt_ids\n negative_prompt_embeds = self.text_encoder(uncond_input, params=params[\"text_encoder\"])[0]\n context = jnp.concatenate([negative_prompt_embeds, prompt_embeds])\n\n # Ensure model output will be `float32` before going into the scheduler\n guidance_scale = jnp.array([guidance_scale], dtype=jnp.float32)\n\n latents_shape = (\n batch_size,\n self.unet.config.in_channels,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n )\n if latents is None:\n latents = jax.random.normal(prng_seed, shape=latents_shape, dtype=jnp.float32)\n else:\n if latents.shape != latents_shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {latents_shape}\")\n\n def loop_body(step, args):\n latents, scheduler_state = args\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n latents_input = jnp.concatenate([latents] * 2)\n\n t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step]\n timestep = jnp.broadcast_to(t, latents_input.shape[0])\n\n latents_input = self.scheduler.scale_model_input(scheduler_state, latents_input, t)\n\n # predict the noise residual\n noise_pred = self.unet.apply(\n {\"params\": params[\"unet\"]},\n jnp.array(latents_input),\n jnp.array(timestep, dtype=jnp.int32),\n encoder_hidden_states=context,\n ).sample\n # perform guidance\n noise_pred_uncond, noise_prediction_text = jnp.split(noise_pred, 2, axis=0)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents, scheduler_state = self.scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple()\n return latents, scheduler_state\n\n scheduler_state = self.scheduler.set_timesteps(\n params[\"scheduler\"], num_inference_steps=num_inference_steps, shape=latents.shape\n )\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * params[\"scheduler\"].init_noise_sigma\n\n if DEBUG:\n # run with python for loop\n for i in range(num_inference_steps):\n latents, scheduler_state = loop_body(i, (latents, scheduler_state))\n else:\n latents, _ = jax.lax.fori_loop(0, num_inference_steps, loop_body, (latents, scheduler_state))\n\n # scale and decode the image latents with vae\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.apply({\"params\": params[\"vae\"]}, latents, method=self.vae.decode).sample\n\n image = (image / 2 + 0.5).clip(0, 1).transpose(0, 2, 3, 1)\n return image\n\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt_ids: jnp.array,\n params: Union[Dict, FrozenDict],\n prng_seed: jax.Array,\n num_inference_steps: int = 50,\n height: Optional[int] = None,\n width: Optional[int] = None,\n guidance_scale: Union[float, jnp.ndarray] = 7.5,\n latents: jnp.ndarray = None,\n neg_prompt_ids: jnp.ndarray = None,\n return_dict: bool = True,\n jit: bool = False,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n latents (`jnp.ndarray`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n array is generated by sampling using the supplied random `generator`.\n jit (`bool`, defaults to `False`):\n Whether to run `pmap` versions of the generation and safety scoring functions.\n\n \n\n This argument exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a\n future release.\n\n \n\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of\n a plain tuple.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] is\n returned, otherwise a `tuple` is returned where the first element is a list with the generated images\n and the second element is a list of `bool`s indicating whether the corresponding generated image\n contains \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n if isinstance(guidance_scale, float):\n # Convert to a tensor so each device gets a copy. Follow the prompt_ids for\n # shape information, as they may be sharded (when `jit` is `True`), or not.\n guidance_scale = jnp.array([guidance_scale] * prompt_ids.shape[0])\n if len(prompt_ids.shape) > 2:\n # Assume sharded\n guidance_scale = guidance_scale[:, None]\n\n if jit:\n images = _p_generate(\n self,\n prompt_ids,\n params,\n prng_seed,\n num_inference_steps,\n height,\n width,\n guidance_scale,\n latents,\n neg_prompt_ids,\n )\n else:\n images = self._generate(\n prompt_ids,\n params,\n prng_seed,\n num_inference_steps,\n height,\n width,\n guidance_scale,\n latents,\n neg_prompt_ids,\n )\n\n if self.safety_checker is not None:\n safety_params = params[\"safety_checker\"]\n images_uint8_casted = (images * 255).round().astype(\"uint8\")\n num_devices, batch_size = images.shape[:2]\n\n images_uint8_casted = np.asarray(images_uint8_casted).reshape(num_devices * batch_size, height, width, 3)\n images_uint8_casted, has_nsfw_concept = self._run_safety_checker(images_uint8_casted, safety_params, jit)\n images = np.asarray(images).copy()\n\n # block images\n if any(has_nsfw_concept):\n for i, is_nsfw in enumerate(has_nsfw_concept):\n if is_nsfw:\n images[i, 0] = np.asarray(images_uint8_casted[i])\n\n images = images.reshape(num_devices, batch_size, height, width, 3)\n else:\n images = np.asarray(images)\n has_nsfw_concept = False\n\n if not return_dict:\n return (images, has_nsfw_concept)\n\n return FlaxStableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept)\n\n\n# Static argnums are pipe, num_inference_steps, height, width. A change would trigger recompilation.\n# Non-static args are (sharded) input tensors mapped over their first dimension (hence, `0`).\n@partial(\n jax.pmap,\n in_axes=(None, 0, 0, 0, None, None, None, 0, 0, 0),\n static_broadcasted_argnums=(0, 4, 5, 6),\n)\ndef _p_generate(\n pipe,\n prompt_ids,\n params,\n prng_seed,\n num_inference_steps,\n height,\n width,\n guidance_scale,\n latents,\n neg_prompt_ids,\n):\n return pipe._generate(\n prompt_ids,\n params,\n prng_seed,\n num_inference_steps,\n height,\n width,\n guidance_scale,\n latents,\n neg_prompt_ids,\n )\n\n\n@partial(jax.pmap, static_broadcasted_argnums=(0,))\ndef _p_get_has_nsfw_concepts(pipe, features, params):\n return pipe._get_has_nsfw_concepts(features, params)\n\n\ndef unshard(x: jnp.ndarray):\n # einops.rearrange(x, 'd b ... -> (d b) ...')\n num_devices, batch_size = x.shape[:2]\n rest = x.shape[2:]\n return x.reshape(num_devices * batch_size, *rest)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport warnings\nfrom functools import partial\nfrom typing import Dict, List, Optional, Union\n\nimport jax\nimport jax.numpy as jnp\nimport numpy as np\nfrom flax.core.frozen_dict import FrozenDict\nfrom flax.jax_utils import unreplicate\nfrom flax.training.common_utils import shard\nfrom PIL import Image\nfrom transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel\n\nfrom ...models import FlaxAutoencoderKL, FlaxUNet2DConditionModel\nfrom ...schedulers import (\n FlaxDDIMScheduler,\n FlaxDPMSolverMultistepScheduler,\n FlaxLMSDiscreteScheduler,\n FlaxPNDMScheduler,\n)\nfrom ...utils import PIL_INTERPOLATION, logging, replace_example_docstring\nfrom ..pipeline_flax_utils import FlaxDiffusionPipeline\nfrom .pipeline_output import FlaxStableDiffusionPipelineOutput\nfrom .safety_checker_flax import FlaxStableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n# Set to True to use python for loop instead of jax.fori_loop for easier debugging\nDEBUG = False\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import jax\n >>> import numpy as np\n >>> import jax.numpy as jnp\n >>> from flax.jax_utils import replicate\n >>> from flax.training.common_utils import shard\n >>> import requests\n >>> from io import BytesIO\n >>> from PIL import Image\n >>> from diffusers import FlaxStableDiffusionImg2ImgPipeline\n\n\n >>> def create_key(seed=0):\n ... return jax.random.PRNGKey(seed)\n\n\n >>> rng = create_key(0)\n\n >>> url = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n >>> response = requests.get(url)\n >>> init_img = Image.open(BytesIO(response.content)).convert(\"RGB\")\n >>> init_img = init_img.resize((768, 512))\n\n >>> prompts = \"A fantasy landscape, trending on artstation\"\n\n >>> pipeline, params = FlaxStableDiffusionImg2ImgPipeline.from_pretrained(\n ... \"CompVis/stable-diffusion-v1-4\",\n ... revision=\"flax\",\n ... dtype=jnp.bfloat16,\n ... )\n\n >>> num_samples = jax.device_count()\n >>> rng = jax.random.split(rng, jax.device_count())\n >>> prompt_ids, processed_image = pipeline.prepare_inputs(\n ... prompt=[prompts] * num_samples, image=[init_img] * num_samples\n ... )\n >>> p_params = replicate(params)\n >>> prompt_ids = shard(prompt_ids)\n >>> processed_image = shard(processed_image)\n\n >>> output = pipeline(\n ... prompt_ids=prompt_ids,\n ... image=processed_image,\n ... params=p_params,\n ... prng_seed=rng,\n ... strength=0.75,\n ... num_inference_steps=50,\n ... jit=True,\n ... height=512,\n ... width=768,\n ... ).images\n\n >>> output_images = pipeline.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:])))\n ```\n\"\"\"\n\n\nclass FlaxStableDiffusionImg2ImgPipeline(FlaxDiffusionPipeline):\n r\"\"\"\n Flax-based pipeline for text-guided image-to-image generation using Stable Diffusion.\n\n This model inherits from [`FlaxDiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Args:\n vae ([`FlaxAutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.FlaxCLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`FlaxUNet2DConditionModel`]):\n A `FlaxUNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`FlaxDDIMScheduler`], [`FlaxLMSDiscreteScheduler`], [`FlaxPNDMScheduler`], or\n [`FlaxDPMSolverMultistepScheduler`].\n safety_checker ([`FlaxStableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n def __init__(\n self,\n vae: FlaxAutoencoderKL,\n text_encoder: FlaxCLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: FlaxUNet2DConditionModel,\n scheduler: Union[\n FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler\n ],\n safety_checker: FlaxStableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n dtype: jnp.dtype = jnp.float32,\n ):\n super().__init__()\n self.dtype = dtype\n\n if safety_checker is None:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n\n def prepare_inputs(self, prompt: Union[str, List[str]], image: Union[Image.Image, List[Image.Image]]):\n if not isinstance(prompt, (str, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if not isinstance(image, (Image.Image, list)):\n raise ValueError(f\"image has to be of type `PIL.Image.Image` or list but is {type(image)}\")\n\n if isinstance(image, Image.Image):\n image = [image]\n\n processed_images = jnp.concatenate([preprocess(img, jnp.float32) for img in image])\n\n text_input = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"np\",\n )\n return text_input.input_ids, processed_images\n\n def _get_has_nsfw_concepts(self, features, params):\n has_nsfw_concepts = self.safety_checker(features, params)\n return has_nsfw_concepts\n\n def _run_safety_checker(self, images, safety_model_params, jit=False):\n # safety_model_params should already be replicated when jit is True\n pil_images = [Image.fromarray(image) for image in images]\n features = self.feature_extractor(pil_images, return_tensors=\"np\").pixel_values\n\n if jit:\n features = shard(features)\n has_nsfw_concepts = _p_get_has_nsfw_concepts(self, features, safety_model_params)\n has_nsfw_concepts = unshard(has_nsfw_concepts)\n safety_model_params = unreplicate(safety_model_params)\n else:\n has_nsfw_concepts = self._get_has_nsfw_concepts(features, safety_model_params)\n\n images_was_copied = False\n for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):\n if has_nsfw_concept:\n if not images_was_copied:\n images_was_copied = True\n images = images.copy()\n\n images[idx] = np.zeros(images[idx].shape, dtype=np.uint8) # black image\n\n if any(has_nsfw_concepts):\n warnings.warn(\n \"Potential NSFW content was detected in one or more images. A black image will be returned\"\n \" instead. Try again with a different prompt and/or seed.\"\n )\n\n return images, has_nsfw_concepts\n\n def get_timestep_start(self, num_inference_steps, strength):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n\n return t_start\n\n def _generate(\n self,\n prompt_ids: jnp.ndarray,\n image: jnp.ndarray,\n params: Union[Dict, FrozenDict],\n prng_seed: jax.Array,\n start_timestep: int,\n num_inference_steps: int,\n height: int,\n width: int,\n guidance_scale: float,\n noise: Optional[jnp.ndarray] = None,\n neg_prompt_ids: Optional[jnp.ndarray] = None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n # get prompt text embeddings\n prompt_embeds = self.text_encoder(prompt_ids, params=params[\"text_encoder\"])[0]\n\n # TODO: currently it is assumed `do_classifier_free_guidance = guidance_scale > 1.0`\n # implement this conditional `do_classifier_free_guidance = guidance_scale > 1.0`\n batch_size = prompt_ids.shape[0]\n\n max_length = prompt_ids.shape[-1]\n\n if neg_prompt_ids is None:\n uncond_input = self.tokenizer(\n [\"\"] * batch_size, padding=\"max_length\", max_length=max_length, return_tensors=\"np\"\n ).input_ids\n else:\n uncond_input = neg_prompt_ids\n negative_prompt_embeds = self.text_encoder(uncond_input, params=params[\"text_encoder\"])[0]\n context = jnp.concatenate([negative_prompt_embeds, prompt_embeds])\n\n latents_shape = (\n batch_size,\n self.unet.config.in_channels,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n )\n if noise is None:\n noise = jax.random.normal(prng_seed, shape=latents_shape, dtype=jnp.float32)\n else:\n if noise.shape != latents_shape:\n raise ValueError(f\"Unexpected latents shape, got {noise.shape}, expected {latents_shape}\")\n\n # Create init_latents\n init_latent_dist = self.vae.apply({\"params\": params[\"vae\"]}, image, method=self.vae.encode).latent_dist\n init_latents = init_latent_dist.sample(key=prng_seed).transpose((0, 3, 1, 2))\n init_latents = self.vae.config.scaling_factor * init_latents\n\n def loop_body(step, args):\n latents, scheduler_state = args\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n latents_input = jnp.concatenate([latents] * 2)\n\n t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step]\n timestep = jnp.broadcast_to(t, latents_input.shape[0])\n\n latents_input = self.scheduler.scale_model_input(scheduler_state, latents_input, t)\n\n # predict the noise residual\n noise_pred = self.unet.apply(\n {\"params\": params[\"unet\"]},\n jnp.array(latents_input),\n jnp.array(timestep, dtype=jnp.int32),\n encoder_hidden_states=context,\n ).sample\n # perform guidance\n noise_pred_uncond, noise_prediction_text = jnp.split(noise_pred, 2, axis=0)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents, scheduler_state = self.scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple()\n return latents, scheduler_state\n\n scheduler_state = self.scheduler.set_timesteps(\n params[\"scheduler\"], num_inference_steps=num_inference_steps, shape=latents_shape\n )\n\n latent_timestep = scheduler_state.timesteps[start_timestep : start_timestep + 1].repeat(batch_size)\n\n latents = self.scheduler.add_noise(params[\"scheduler\"], init_latents, noise, latent_timestep)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * params[\"scheduler\"].init_noise_sigma\n\n if DEBUG:\n # run with python for loop\n for i in range(start_timestep, num_inference_steps):\n latents, scheduler_state = loop_body(i, (latents, scheduler_state))\n else:\n latents, _ = jax.lax.fori_loop(start_timestep, num_inference_steps, loop_body, (latents, scheduler_state))\n\n # scale and decode the image latents with vae\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.apply({\"params\": params[\"vae\"]}, latents, method=self.vae.decode).sample\n\n image = (image / 2 + 0.5).clip(0, 1).transpose(0, 2, 3, 1)\n return image\n\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt_ids: jnp.ndarray,\n image: jnp.ndarray,\n params: Union[Dict, FrozenDict],\n prng_seed: jax.Array,\n strength: float = 0.8,\n num_inference_steps: int = 50,\n height: Optional[int] = None,\n width: Optional[int] = None,\n guidance_scale: Union[float, jnp.ndarray] = 7.5,\n noise: jnp.ndarray = None,\n neg_prompt_ids: jnp.ndarray = None,\n return_dict: bool = True,\n jit: bool = False,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt_ids (`jnp.ndarray`):\n The prompt or prompts to guide image generation.\n image (`jnp.ndarray`):\n Array representing an image batch to be used as the starting point.\n params (`Dict` or `FrozenDict`):\n Dictionary containing the model parameters/weights.\n prng_seed (`jax.Array` or `jax.Array`):\n Array containing random number generator key.\n strength (`float`, *optional*, defaults to 0.8):\n Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a\n starting point and more noise is added the higher the `strength`. The number of denoising steps depends\n on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising\n process runs for the full number of iterations specified in `num_inference_steps`. A value of 1\n essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference. This parameter is modulated by `strength`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n noise (`jnp.ndarray`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. The array is generated by\n sampling using the supplied random `generator`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of\n a plain tuple.\n jit (`bool`, defaults to `False`):\n Whether to run `pmap` versions of the generation and safety scoring functions.\n\n \n\n This argument exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a\n future release.\n\n \n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] is\n returned, otherwise a `tuple` is returned where the first element is a list with the generated images\n and the second element is a list of `bool`s indicating whether the corresponding generated image\n contains \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n if isinstance(guidance_scale, float):\n # Convert to a tensor so each device gets a copy. Follow the prompt_ids for\n # shape information, as they may be sharded (when `jit` is `True`), or not.\n guidance_scale = jnp.array([guidance_scale] * prompt_ids.shape[0])\n if len(prompt_ids.shape) > 2:\n # Assume sharded\n guidance_scale = guidance_scale[:, None]\n\n start_timestep = self.get_timestep_start(num_inference_steps, strength)\n\n if jit:\n images = _p_generate(\n self,\n prompt_ids,\n image,\n params,\n prng_seed,\n start_timestep,\n num_inference_steps,\n height,\n width,\n guidance_scale,\n noise,\n neg_prompt_ids,\n )\n else:\n images = self._generate(\n prompt_ids,\n image,\n params,\n prng_seed,\n start_timestep,\n num_inference_steps,\n height,\n width,\n guidance_scale,\n noise,\n neg_prompt_ids,\n )\n\n if self.safety_checker is not None:\n safety_params = params[\"safety_checker\"]\n images_uint8_casted = (images * 255).round().astype(\"uint8\")\n num_devices, batch_size = images.shape[:2]\n\n images_uint8_casted = np.asarray(images_uint8_casted).reshape(num_devices * batch_size, height, width, 3)\n images_uint8_casted, has_nsfw_concept = self._run_safety_checker(images_uint8_casted, safety_params, jit)\n images = np.asarray(images)\n\n # block images\n if any(has_nsfw_concept):\n for i, is_nsfw in enumerate(has_nsfw_concept):\n if is_nsfw:\n images[i] = np.asarray(images_uint8_casted[i])\n\n images = images.reshape(num_devices, batch_size, height, width, 3)\n else:\n images = np.asarray(images)\n has_nsfw_concept = False\n\n if not return_dict:\n return (images, has_nsfw_concept)\n\n return FlaxStableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept)\n\n\n# Static argnums are pipe, start_timestep, num_inference_steps, height, width. A change would trigger recompilation.\n# Non-static args are (sharded) input tensors mapped over their first dimension (hence, `0`).\n@partial(\n jax.pmap,\n in_axes=(None, 0, 0, 0, 0, None, None, None, None, 0, 0, 0),\n static_broadcasted_argnums=(0, 5, 6, 7, 8),\n)\ndef _p_generate(\n pipe,\n prompt_ids,\n image,\n params,\n prng_seed,\n start_timestep,\n num_inference_steps,\n height,\n width,\n guidance_scale,\n noise,\n neg_prompt_ids,\n):\n return pipe._generate(\n prompt_ids,\n image,\n params,\n prng_seed,\n start_timestep,\n num_inference_steps,\n height,\n width,\n guidance_scale,\n noise,\n neg_prompt_ids,\n )\n\n\n@partial(jax.pmap, static_broadcasted_argnums=(0,))\ndef _p_get_has_nsfw_concepts(pipe, features, params):\n return pipe._get_has_nsfw_concepts(features, params)\n\n\ndef unshard(x: jnp.ndarray):\n # einops.rearrange(x, 'd b ... -> (d b) ...')\n num_devices, batch_size = x.shape[:2]\n rest = x.shape[2:]\n return x.reshape(num_devices * batch_size, *rest)\n\n\ndef preprocess(image, dtype):\n w, h = image.size\n w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32\n image = image.resize((w, h), resample=PIL_INTERPOLATION[\"lanczos\"])\n image = jnp.array(image).astype(dtype) / 255.0\n image = image[None].transpose(0, 3, 1, 2)\n return 2.0 * image - 1.0\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_inpaint.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport warnings\nfrom functools import partial\nfrom typing import Dict, List, Optional, Union\n\nimport jax\nimport jax.numpy as jnp\nimport numpy as np\nfrom flax.core.frozen_dict import FrozenDict\nfrom flax.jax_utils import unreplicate\nfrom flax.training.common_utils import shard\nfrom packaging import version\nfrom PIL import Image\nfrom transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel\n\nfrom ...models import FlaxAutoencoderKL, FlaxUNet2DConditionModel\nfrom ...schedulers import (\n FlaxDDIMScheduler,\n FlaxDPMSolverMultistepScheduler,\n FlaxLMSDiscreteScheduler,\n FlaxPNDMScheduler,\n)\nfrom ...utils import PIL_INTERPOLATION, deprecate, logging, replace_example_docstring\nfrom ..pipeline_flax_utils import FlaxDiffusionPipeline\nfrom .pipeline_output import FlaxStableDiffusionPipelineOutput\nfrom .safety_checker_flax import FlaxStableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n# Set to True to use python for loop instead of jax.fori_loop for easier debugging\nDEBUG = False\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import jax\n >>> import numpy as np\n >>> from flax.jax_utils import replicate\n >>> from flax.training.common_utils import shard\n >>> import PIL\n >>> import requests\n >>> from io import BytesIO\n >>> from diffusers import FlaxStableDiffusionInpaintPipeline\n\n\n >>> def download_image(url):\n ... response = requests.get(url)\n ... return PIL.Image.open(BytesIO(response.content)).convert(\"RGB\")\n\n\n >>> img_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\n >>> mask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\n\n >>> init_image = download_image(img_url).resize((512, 512))\n >>> mask_image = download_image(mask_url).resize((512, 512))\n\n >>> pipeline, params = FlaxStableDiffusionInpaintPipeline.from_pretrained(\n ... \"xvjiarui/stable-diffusion-2-inpainting\"\n ... )\n\n >>> prompt = \"Face of a yellow cat, high resolution, sitting on a park bench\"\n >>> prng_seed = jax.random.PRNGKey(0)\n >>> num_inference_steps = 50\n\n >>> num_samples = jax.device_count()\n >>> prompt = num_samples * [prompt]\n >>> init_image = num_samples * [init_image]\n >>> mask_image = num_samples * [mask_image]\n >>> prompt_ids, processed_masked_images, processed_masks = pipeline.prepare_inputs(\n ... prompt, init_image, mask_image\n ... )\n # shard inputs and rng\n\n >>> params = replicate(params)\n >>> prng_seed = jax.random.split(prng_seed, jax.device_count())\n >>> prompt_ids = shard(prompt_ids)\n >>> processed_masked_images = shard(processed_masked_images)\n >>> processed_masks = shard(processed_masks)\n\n >>> images = pipeline(\n ... prompt_ids, processed_masks, processed_masked_images, params, prng_seed, num_inference_steps, jit=True\n ... ).images\n >>> images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))\n ```\n\"\"\"\n\n\nclass FlaxStableDiffusionInpaintPipeline(FlaxDiffusionPipeline):\n r\"\"\"\n Flax-based pipeline for text-guided image inpainting using Stable Diffusion.\n\n \n\n 🧪 This is an experimental feature!\n\n \n\n This model inherits from [`FlaxDiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Args:\n vae ([`FlaxAutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.FlaxCLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`FlaxUNet2DConditionModel`]):\n A `FlaxUNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`FlaxDDIMScheduler`], [`FlaxLMSDiscreteScheduler`], [`FlaxPNDMScheduler`], or\n [`FlaxDPMSolverMultistepScheduler`].\n safety_checker ([`FlaxStableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n def __init__(\n self,\n vae: FlaxAutoencoderKL,\n text_encoder: FlaxCLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: FlaxUNet2DConditionModel,\n scheduler: Union[\n FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler\n ],\n safety_checker: FlaxStableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n dtype: jnp.dtype = jnp.float32,\n ):\n super().__init__()\n self.dtype = dtype\n\n if safety_checker is None:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n\n def prepare_inputs(\n self,\n prompt: Union[str, List[str]],\n image: Union[Image.Image, List[Image.Image]],\n mask: Union[Image.Image, List[Image.Image]],\n ):\n if not isinstance(prompt, (str, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if not isinstance(image, (Image.Image, list)):\n raise ValueError(f\"image has to be of type `PIL.Image.Image` or list but is {type(image)}\")\n\n if isinstance(image, Image.Image):\n image = [image]\n\n if not isinstance(mask, (Image.Image, list)):\n raise ValueError(f\"image has to be of type `PIL.Image.Image` or list but is {type(image)}\")\n\n if isinstance(mask, Image.Image):\n mask = [mask]\n\n processed_images = jnp.concatenate([preprocess_image(img, jnp.float32) for img in image])\n processed_masks = jnp.concatenate([preprocess_mask(m, jnp.float32) for m in mask])\n # processed_masks[processed_masks < 0.5] = 0\n processed_masks = processed_masks.at[processed_masks < 0.5].set(0)\n # processed_masks[processed_masks >= 0.5] = 1\n processed_masks = processed_masks.at[processed_masks >= 0.5].set(1)\n\n processed_masked_images = processed_images * (processed_masks < 0.5)\n\n text_input = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"np\",\n )\n return text_input.input_ids, processed_masked_images, processed_masks\n\n def _get_has_nsfw_concepts(self, features, params):\n has_nsfw_concepts = self.safety_checker(features, params)\n return has_nsfw_concepts\n\n def _run_safety_checker(self, images, safety_model_params, jit=False):\n # safety_model_params should already be replicated when jit is True\n pil_images = [Image.fromarray(image) for image in images]\n features = self.feature_extractor(pil_images, return_tensors=\"np\").pixel_values\n\n if jit:\n features = shard(features)\n has_nsfw_concepts = _p_get_has_nsfw_concepts(self, features, safety_model_params)\n has_nsfw_concepts = unshard(has_nsfw_concepts)\n safety_model_params = unreplicate(safety_model_params)\n else:\n has_nsfw_concepts = self._get_has_nsfw_concepts(features, safety_model_params)\n\n images_was_copied = False\n for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):\n if has_nsfw_concept:\n if not images_was_copied:\n images_was_copied = True\n images = images.copy()\n\n images[idx] = np.zeros(images[idx].shape, dtype=np.uint8) # black image\n\n if any(has_nsfw_concepts):\n warnings.warn(\n \"Potential NSFW content was detected in one or more images. A black image will be returned\"\n \" instead. Try again with a different prompt and/or seed.\"\n )\n\n return images, has_nsfw_concepts\n\n def _generate(\n self,\n prompt_ids: jnp.ndarray,\n mask: jnp.ndarray,\n masked_image: jnp.ndarray,\n params: Union[Dict, FrozenDict],\n prng_seed: jax.Array,\n num_inference_steps: int,\n height: int,\n width: int,\n guidance_scale: float,\n latents: Optional[jnp.ndarray] = None,\n neg_prompt_ids: Optional[jnp.ndarray] = None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n # get prompt text embeddings\n prompt_embeds = self.text_encoder(prompt_ids, params=params[\"text_encoder\"])[0]\n\n # TODO: currently it is assumed `do_classifier_free_guidance = guidance_scale > 1.0`\n # implement this conditional `do_classifier_free_guidance = guidance_scale > 1.0`\n batch_size = prompt_ids.shape[0]\n\n max_length = prompt_ids.shape[-1]\n\n if neg_prompt_ids is None:\n uncond_input = self.tokenizer(\n [\"\"] * batch_size, padding=\"max_length\", max_length=max_length, return_tensors=\"np\"\n ).input_ids\n else:\n uncond_input = neg_prompt_ids\n negative_prompt_embeds = self.text_encoder(uncond_input, params=params[\"text_encoder\"])[0]\n context = jnp.concatenate([negative_prompt_embeds, prompt_embeds])\n\n latents_shape = (\n batch_size,\n self.vae.config.latent_channels,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n )\n if latents is None:\n latents = jax.random.normal(prng_seed, shape=latents_shape, dtype=self.dtype)\n else:\n if latents.shape != latents_shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {latents_shape}\")\n\n prng_seed, mask_prng_seed = jax.random.split(prng_seed)\n\n masked_image_latent_dist = self.vae.apply(\n {\"params\": params[\"vae\"]}, masked_image, method=self.vae.encode\n ).latent_dist\n masked_image_latents = masked_image_latent_dist.sample(key=mask_prng_seed).transpose((0, 3, 1, 2))\n masked_image_latents = self.vae.config.scaling_factor * masked_image_latents\n del mask_prng_seed\n\n mask = jax.image.resize(mask, (*mask.shape[:-2], *masked_image_latents.shape[-2:]), method=\"nearest\")\n\n # 8. Check that sizes of mask, masked image and latents match\n num_channels_latents = self.vae.config.latent_channels\n num_channels_mask = mask.shape[1]\n num_channels_masked_image = masked_image_latents.shape[1]\n if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:\n raise ValueError(\n f\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\"\n f\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\"\n f\" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}\"\n f\" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of\"\n \" `pipeline.unet` or your `mask_image` or `image` input.\"\n )\n\n def loop_body(step, args):\n latents, mask, masked_image_latents, scheduler_state = args\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n latents_input = jnp.concatenate([latents] * 2)\n mask_input = jnp.concatenate([mask] * 2)\n masked_image_latents_input = jnp.concatenate([masked_image_latents] * 2)\n\n t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step]\n timestep = jnp.broadcast_to(t, latents_input.shape[0])\n\n latents_input = self.scheduler.scale_model_input(scheduler_state, latents_input, t)\n # concat latents, mask, masked_image_latents in the channel dimension\n latents_input = jnp.concatenate([latents_input, mask_input, masked_image_latents_input], axis=1)\n\n # predict the noise residual\n noise_pred = self.unet.apply(\n {\"params\": params[\"unet\"]},\n jnp.array(latents_input),\n jnp.array(timestep, dtype=jnp.int32),\n encoder_hidden_states=context,\n ).sample\n # perform guidance\n noise_pred_uncond, noise_prediction_text = jnp.split(noise_pred, 2, axis=0)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents, scheduler_state = self.scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple()\n return latents, mask, masked_image_latents, scheduler_state\n\n scheduler_state = self.scheduler.set_timesteps(\n params[\"scheduler\"], num_inference_steps=num_inference_steps, shape=latents.shape\n )\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * params[\"scheduler\"].init_noise_sigma\n\n if DEBUG:\n # run with python for loop\n for i in range(num_inference_steps):\n latents, mask, masked_image_latents, scheduler_state = loop_body(\n i, (latents, mask, masked_image_latents, scheduler_state)\n )\n else:\n latents, _, _, _ = jax.lax.fori_loop(\n 0, num_inference_steps, loop_body, (latents, mask, masked_image_latents, scheduler_state)\n )\n\n # scale and decode the image latents with vae\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.apply({\"params\": params[\"vae\"]}, latents, method=self.vae.decode).sample\n\n image = (image / 2 + 0.5).clip(0, 1).transpose(0, 2, 3, 1)\n return image\n\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt_ids: jnp.ndarray,\n mask: jnp.ndarray,\n masked_image: jnp.ndarray,\n params: Union[Dict, FrozenDict],\n prng_seed: jax.Array,\n num_inference_steps: int = 50,\n height: Optional[int] = None,\n width: Optional[int] = None,\n guidance_scale: Union[float, jnp.ndarray] = 7.5,\n latents: jnp.ndarray = None,\n neg_prompt_ids: jnp.ndarray = None,\n return_dict: bool = True,\n jit: bool = False,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide image generation.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference. This parameter is modulated by `strength`.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n latents (`jnp.ndarray`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n array is generated by sampling using the supplied random `generator`.\n jit (`bool`, defaults to `False`):\n Whether to run `pmap` versions of the generation and safety scoring functions.\n\n \n\n This argument exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a\n future release.\n\n \n\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of\n a plain tuple.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] is\n returned, otherwise a `tuple` is returned where the first element is a list with the generated images\n and the second element is a list of `bool`s indicating whether the corresponding generated image\n contains \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n masked_image = jax.image.resize(masked_image, (*masked_image.shape[:-2], height, width), method=\"bicubic\")\n mask = jax.image.resize(mask, (*mask.shape[:-2], height, width), method=\"nearest\")\n\n if isinstance(guidance_scale, float):\n # Convert to a tensor so each device gets a copy. Follow the prompt_ids for\n # shape information, as they may be sharded (when `jit` is `True`), or not.\n guidance_scale = jnp.array([guidance_scale] * prompt_ids.shape[0])\n if len(prompt_ids.shape) > 2:\n # Assume sharded\n guidance_scale = guidance_scale[:, None]\n\n if jit:\n images = _p_generate(\n self,\n prompt_ids,\n mask,\n masked_image,\n params,\n prng_seed,\n num_inference_steps,\n height,\n width,\n guidance_scale,\n latents,\n neg_prompt_ids,\n )\n else:\n images = self._generate(\n prompt_ids,\n mask,\n masked_image,\n params,\n prng_seed,\n num_inference_steps,\n height,\n width,\n guidance_scale,\n latents,\n neg_prompt_ids,\n )\n\n if self.safety_checker is not None:\n safety_params = params[\"safety_checker\"]\n images_uint8_casted = (images * 255).round().astype(\"uint8\")\n num_devices, batch_size = images.shape[:2]\n\n images_uint8_casted = np.asarray(images_uint8_casted).reshape(num_devices * batch_size, height, width, 3)\n images_uint8_casted, has_nsfw_concept = self._run_safety_checker(images_uint8_casted, safety_params, jit)\n images = np.asarray(images)\n\n # block images\n if any(has_nsfw_concept):\n for i, is_nsfw in enumerate(has_nsfw_concept):\n if is_nsfw:\n images[i] = np.asarray(images_uint8_casted[i])\n\n images = images.reshape(num_devices, batch_size, height, width, 3)\n else:\n images = np.asarray(images)\n has_nsfw_concept = False\n\n if not return_dict:\n return (images, has_nsfw_concept)\n\n return FlaxStableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept)\n\n\n# Static argnums are pipe, num_inference_steps, height, width. A change would trigger recompilation.\n# Non-static args are (sharded) input tensors mapped over their first dimension (hence, `0`).\n@partial(\n jax.pmap,\n in_axes=(None, 0, 0, 0, 0, 0, None, None, None, 0, 0, 0),\n static_broadcasted_argnums=(0, 6, 7, 8),\n)\ndef _p_generate(\n pipe,\n prompt_ids,\n mask,\n masked_image,\n params,\n prng_seed,\n num_inference_steps,\n height,\n width,\n guidance_scale,\n latents,\n neg_prompt_ids,\n):\n return pipe._generate(\n prompt_ids,\n mask,\n masked_image,\n params,\n prng_seed,\n num_inference_steps,\n height,\n width,\n guidance_scale,\n latents,\n neg_prompt_ids,\n )\n\n\n@partial(jax.pmap, static_broadcasted_argnums=(0,))\ndef _p_get_has_nsfw_concepts(pipe, features, params):\n return pipe._get_has_nsfw_concepts(features, params)\n\n\ndef unshard(x: jnp.ndarray):\n # einops.rearrange(x, 'd b ... -> (d b) ...')\n num_devices, batch_size = x.shape[:2]\n rest = x.shape[2:]\n return x.reshape(num_devices * batch_size, *rest)\n\n\ndef preprocess_image(image, dtype):\n w, h = image.size\n w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32\n image = image.resize((w, h), resample=PIL_INTERPOLATION[\"lanczos\"])\n image = jnp.array(image).astype(dtype) / 255.0\n image = image[None].transpose(0, 3, 1, 2)\n return 2.0 * image - 1.0\n\n\ndef preprocess_mask(mask, dtype):\n w, h = mask.size\n w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32\n mask = mask.resize((w, h))\n mask = jnp.array(mask.convert(\"L\")).astype(dtype) / 255.0\n mask = jnp.expand_dims(mask, axis=(0, 1))\n\n return mask\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Callable, List, Optional, Union\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTokenizer\n\nfrom ...configuration_utils import FrozenDict\nfrom ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler\nfrom ...utils import deprecate, logging\nfrom ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel\nfrom ..pipeline_utils import DiffusionPipeline\nfrom . import StableDiffusionPipelineOutput\n\n\nlogger = logging.get_logger(__name__)\n\n\nclass OnnxStableDiffusionPipeline(DiffusionPipeline):\n vae_encoder: OnnxRuntimeModel\n vae_decoder: OnnxRuntimeModel\n text_encoder: OnnxRuntimeModel\n tokenizer: CLIPTokenizer\n unet: OnnxRuntimeModel\n scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]\n safety_checker: OnnxRuntimeModel\n feature_extractor: CLIPImageProcessor\n\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n _is_onnx = True\n\n def __init__(\n self,\n vae_encoder: OnnxRuntimeModel,\n vae_decoder: OnnxRuntimeModel,\n text_encoder: OnnxRuntimeModel,\n tokenizer: CLIPTokenizer,\n unet: OnnxRuntimeModel,\n scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],\n safety_checker: OnnxRuntimeModel,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"clip_sample\"] = False\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.register_modules(\n vae_encoder=vae_encoder,\n vae_decoder=vae_decoder,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n def _encode_prompt(\n self,\n prompt: Union[str, List[str]],\n num_images_per_prompt: Optional[int],\n do_classifier_free_guidance: bool,\n negative_prompt: Optional[str],\n prompt_embeds: Optional[np.ndarray] = None,\n negative_prompt_embeds: Optional[np.ndarray] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`):\n prompt to be encoded\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n \"\"\"\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"np\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"max_length\", return_tensors=\"np\").input_ids\n\n if not np.array_equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]\n\n prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt] * batch_size\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"np\",\n )\n negative_prompt_embeds = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0]\n\n if do_classifier_free_guidance:\n negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n def check_inputs(\n self,\n prompt: Union[str, List[str]],\n height: Optional[int],\n width: Optional[int],\n callback_steps: int,\n negative_prompt: Optional[str] = None,\n prompt_embeds: Optional[np.ndarray] = None,\n negative_prompt_embeds: Optional[np.ndarray] = None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = 512,\n width: Optional[int] = 512,\n num_inference_steps: Optional[int] = 50,\n guidance_scale: Optional[float] = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: Optional[float] = 0.0,\n generator: Optional[np.random.RandomState] = None,\n latents: Optional[np.ndarray] = None,\n prompt_embeds: Optional[np.ndarray] = None,\n negative_prompt_embeds: Optional[np.ndarray] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, np.ndarray], None]] = None,\n callback_steps: int = 1,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n image (`PIL.Image.Image` or List[`PIL.Image.Image`] or `torch.Tensor`):\n `Image`, or tensor representing an image batch which will be upscaled. *\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale`\n is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`np.random.RandomState`, *optional*):\n One or a list of [numpy generator(s)](TODO) to make generation deterministic.\n latents (`np.ndarray`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n\n # check inputs. Raise error if not correct\n self.check_inputs(\n prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds\n )\n\n # define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if generator is None:\n generator = np.random\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n prompt_embeds = self._encode_prompt(\n prompt,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n\n # get the initial random noise unless the user supplied it\n latents_dtype = prompt_embeds.dtype\n latents_shape = (batch_size * num_images_per_prompt, 4, height // 8, width // 8)\n if latents is None:\n latents = generator.randn(*latents_shape).astype(latents_dtype)\n elif latents.shape != latents_shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {latents_shape}\")\n\n # set timesteps\n self.scheduler.set_timesteps(num_inference_steps)\n\n latents = latents * np.float64(self.scheduler.init_noise_sigma)\n\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n timestep_dtype = next(\n (input.type for input in self.unet.model.get_inputs() if input.name == \"timestep\"), \"tensor(float)\"\n )\n timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]\n\n for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)\n latent_model_input = latent_model_input.cpu().numpy()\n\n # predict the noise residual\n timestep = np.array([t], dtype=timestep_dtype)\n noise_pred = self.unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds)\n noise_pred = noise_pred[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n scheduler_output = self.scheduler.step(\n torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs\n )\n latents = scheduler_output.prev_sample.numpy()\n\n # call the callback, if provided\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n latents = 1 / 0.18215 * latents\n # image = self.vae_decoder(latent_sample=latents)[0]\n # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1\n image = np.concatenate(\n [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]\n )\n\n image = np.clip(image / 2 + 0.5, 0, 1)\n image = image.transpose((0, 2, 3, 1))\n\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(\n self.numpy_to_pil(image), return_tensors=\"np\"\n ).pixel_values.astype(image.dtype)\n\n images, has_nsfw_concept = [], []\n for i in range(image.shape[0]):\n image_i, has_nsfw_concept_i = self.safety_checker(\n clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]\n )\n images.append(image_i)\n has_nsfw_concept.append(has_nsfw_concept_i[0])\n image = np.concatenate(images)\n else:\n has_nsfw_concept = None\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n\n\nclass StableDiffusionOnnxPipeline(OnnxStableDiffusionPipeline):\n def __init__(\n self,\n vae_encoder: OnnxRuntimeModel,\n vae_decoder: OnnxRuntimeModel,\n text_encoder: OnnxRuntimeModel,\n tokenizer: CLIPTokenizer,\n unet: OnnxRuntimeModel,\n scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],\n safety_checker: OnnxRuntimeModel,\n feature_extractor: CLIPImageProcessor,\n ):\n deprecation_message = \"Please use `OnnxStableDiffusionPipeline` instead of `StableDiffusionOnnxPipeline`.\"\n deprecate(\"StableDiffusionOnnxPipeline\", \"1.0.0\", deprecation_message)\n super().__init__(\n vae_encoder=vae_encoder,\n vae_decoder=vae_decoder,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Callable, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTokenizer\n\nfrom ...configuration_utils import FrozenDict\nfrom ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler\nfrom ...utils import PIL_INTERPOLATION, deprecate, logging\nfrom ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel\nfrom ..pipeline_utils import DiffusionPipeline\nfrom . import StableDiffusionPipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess with 8->64\ndef preprocess(image):\n deprecation_message = \"The preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead\"\n deprecate(\"preprocess\", \"1.0.0\", deprecation_message, standard_warn=False)\n if isinstance(image, torch.Tensor):\n return image\n elif isinstance(image, PIL.Image.Image):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n w, h = image[0].size\n w, h = (x - x % 64 for x in (w, h)) # resize to integer multiple of 64\n\n image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION[\"lanczos\"]))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n image = np.array(image).astype(np.float32) / 255.0\n image = image.transpose(0, 3, 1, 2)\n image = 2.0 * image - 1.0\n image = torch.from_numpy(image)\n elif isinstance(image[0], torch.Tensor):\n image = torch.cat(image, dim=0)\n return image\n\n\nclass OnnxStableDiffusionImg2ImgPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for text-guided image to image generation using Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n vae_encoder: OnnxRuntimeModel\n vae_decoder: OnnxRuntimeModel\n text_encoder: OnnxRuntimeModel\n tokenizer: CLIPTokenizer\n unet: OnnxRuntimeModel\n scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]\n safety_checker: OnnxRuntimeModel\n feature_extractor: CLIPImageProcessor\n\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n _is_onnx = True\n\n def __init__(\n self,\n vae_encoder: OnnxRuntimeModel,\n vae_decoder: OnnxRuntimeModel,\n text_encoder: OnnxRuntimeModel,\n tokenizer: CLIPTokenizer,\n unet: OnnxRuntimeModel,\n scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],\n safety_checker: OnnxRuntimeModel,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"clip_sample\"] = False\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.register_modules(\n vae_encoder=vae_encoder,\n vae_decoder=vae_decoder,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion.OnnxStableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt: Union[str, List[str]],\n num_images_per_prompt: Optional[int],\n do_classifier_free_guidance: bool,\n negative_prompt: Optional[str],\n prompt_embeds: Optional[np.ndarray] = None,\n negative_prompt_embeds: Optional[np.ndarray] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`):\n prompt to be encoded\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n \"\"\"\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"np\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"max_length\", return_tensors=\"np\").input_ids\n\n if not np.array_equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]\n\n prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt] * batch_size\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"np\",\n )\n negative_prompt_embeds = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0]\n\n if do_classifier_free_guidance:\n negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n def check_inputs(\n self,\n prompt: Union[str, List[str]],\n callback_steps: int,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n prompt_embeds: Optional[np.ndarray] = None,\n negative_prompt_embeds: Optional[np.ndarray] = None,\n ):\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n def __call__(\n self,\n prompt: Union[str, List[str]],\n image: Union[np.ndarray, PIL.Image.Image] = None,\n strength: float = 0.8,\n num_inference_steps: Optional[int] = 50,\n guidance_scale: Optional[float] = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: Optional[float] = 0.0,\n generator: Optional[np.random.RandomState] = None,\n prompt_embeds: Optional[np.ndarray] = None,\n negative_prompt_embeds: Optional[np.ndarray] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, np.ndarray], None]] = None,\n callback_steps: int = 1,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n image (`np.ndarray` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process.\n strength (`float`, *optional*, defaults to 0.8):\n Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`\n will be used as a starting point, adding more noise to it the larger the `strength`. The number of\n denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will\n be maximum and the denoising process will run for the full number of iterations specified in\n `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference. This parameter will be modulated by `strength`.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`np.random.RandomState`, *optional*):\n A np.random.RandomState to make generation deterministic.\n prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n\n # check inputs. Raise error if not correct\n self.check_inputs(prompt, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)\n\n # define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n\n if generator is None:\n generator = np.random\n\n # set timesteps\n self.scheduler.set_timesteps(num_inference_steps)\n\n image = preprocess(image).cpu().numpy()\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n prompt_embeds = self._encode_prompt(\n prompt,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n\n latents_dtype = prompt_embeds.dtype\n image = image.astype(latents_dtype)\n # encode the init image into latents and scale the latents\n init_latents = self.vae_encoder(sample=image)[0]\n init_latents = 0.18215 * init_latents\n\n if isinstance(prompt, str):\n prompt = [prompt]\n if len(prompt) > init_latents.shape[0] and len(prompt) % init_latents.shape[0] == 0:\n # expand init_latents for batch_size\n deprecation_message = (\n f\"You have passed {len(prompt)} text prompts (`prompt`), but only {init_latents.shape[0]} initial\"\n \" images (`image`). Initial images are now duplicating to match the number of text prompts. Note\"\n \" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update\"\n \" your script to pass as many initial images as text prompts to suppress this warning.\"\n )\n deprecate(\"len(prompt) != len(image)\", \"1.0.0\", deprecation_message, standard_warn=False)\n additional_image_per_prompt = len(prompt) // init_latents.shape[0]\n init_latents = np.concatenate([init_latents] * additional_image_per_prompt * num_images_per_prompt, axis=0)\n elif len(prompt) > init_latents.shape[0] and len(prompt) % init_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {len(prompt)} text prompts.\"\n )\n else:\n init_latents = np.concatenate([init_latents] * num_images_per_prompt, axis=0)\n\n # get the original timestep using init_timestep\n offset = self.scheduler.config.get(\"steps_offset\", 0)\n init_timestep = int(num_inference_steps * strength) + offset\n init_timestep = min(init_timestep, num_inference_steps)\n\n timesteps = self.scheduler.timesteps.numpy()[-init_timestep]\n timesteps = np.array([timesteps] * batch_size * num_images_per_prompt)\n\n # add noise to latents using the timesteps\n noise = generator.randn(*init_latents.shape).astype(latents_dtype)\n init_latents = self.scheduler.add_noise(\n torch.from_numpy(init_latents), torch.from_numpy(noise), torch.from_numpy(timesteps)\n )\n init_latents = init_latents.numpy()\n\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n latents = init_latents\n\n t_start = max(num_inference_steps - init_timestep + offset, 0)\n timesteps = self.scheduler.timesteps[t_start:].numpy()\n\n timestep_dtype = next(\n (input.type for input in self.unet.model.get_inputs() if input.name == \"timestep\"), \"tensor(float)\"\n )\n timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]\n\n for i, t in enumerate(self.progress_bar(timesteps)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)\n latent_model_input = latent_model_input.cpu().numpy()\n\n # predict the noise residual\n timestep = np.array([t], dtype=timestep_dtype)\n noise_pred = self.unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds)[\n 0\n ]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n scheduler_output = self.scheduler.step(\n torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs\n )\n latents = scheduler_output.prev_sample.numpy()\n\n # call the callback, if provided\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n latents = 1 / 0.18215 * latents\n # image = self.vae_decoder(latent_sample=latents)[0]\n # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1\n image = np.concatenate(\n [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]\n )\n\n image = np.clip(image / 2 + 0.5, 0, 1)\n image = image.transpose((0, 2, 3, 1))\n\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(\n self.numpy_to_pil(image), return_tensors=\"np\"\n ).pixel_values.astype(image.dtype)\n # safety_checker does not support batched inputs yet\n images, has_nsfw_concept = [], []\n for i in range(image.shape[0]):\n image_i, has_nsfw_concept_i = self.safety_checker(\n clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]\n )\n images.append(image_i)\n has_nsfw_concept.append(has_nsfw_concept_i[0])\n image = np.concatenate(images)\n else:\n has_nsfw_concept = None\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Callable, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTokenizer\n\nfrom ...configuration_utils import FrozenDict\nfrom ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler\nfrom ...utils import PIL_INTERPOLATION, deprecate, logging\nfrom ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel\nfrom ..pipeline_utils import DiffusionPipeline\nfrom . import StableDiffusionPipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nNUM_UNET_INPUT_CHANNELS = 9\nNUM_LATENT_CHANNELS = 4\n\n\ndef prepare_mask_and_masked_image(image, mask, latents_shape):\n image = np.array(image.convert(\"RGB\").resize((latents_shape[1] * 8, latents_shape[0] * 8)))\n image = image[None].transpose(0, 3, 1, 2)\n image = image.astype(np.float32) / 127.5 - 1.0\n\n image_mask = np.array(mask.convert(\"L\").resize((latents_shape[1] * 8, latents_shape[0] * 8)))\n masked_image = image * (image_mask < 127.5)\n\n mask = mask.resize((latents_shape[1], latents_shape[0]), PIL_INTERPOLATION[\"nearest\"])\n mask = np.array(mask.convert(\"L\"))\n mask = mask.astype(np.float32) / 255.0\n mask = mask[None, None]\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n\n return mask, masked_image\n\n\nclass OnnxStableDiffusionInpaintPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n vae_encoder: OnnxRuntimeModel\n vae_decoder: OnnxRuntimeModel\n text_encoder: OnnxRuntimeModel\n tokenizer: CLIPTokenizer\n unet: OnnxRuntimeModel\n scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]\n safety_checker: OnnxRuntimeModel\n feature_extractor: CLIPImageProcessor\n\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n _is_onnx = True\n\n def __init__(\n self,\n vae_encoder: OnnxRuntimeModel,\n vae_decoder: OnnxRuntimeModel,\n text_encoder: OnnxRuntimeModel,\n tokenizer: CLIPTokenizer,\n unet: OnnxRuntimeModel,\n scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],\n safety_checker: OnnxRuntimeModel,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n logger.info(\"`OnnxStableDiffusionInpaintPipeline` is experimental and will very likely change in the future.\")\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"clip_sample\"] = False\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.register_modules(\n vae_encoder=vae_encoder,\n vae_decoder=vae_decoder,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion.OnnxStableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt: Union[str, List[str]],\n num_images_per_prompt: Optional[int],\n do_classifier_free_guidance: bool,\n negative_prompt: Optional[str],\n prompt_embeds: Optional[np.ndarray] = None,\n negative_prompt_embeds: Optional[np.ndarray] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`):\n prompt to be encoded\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n \"\"\"\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"np\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"max_length\", return_tensors=\"np\").input_ids\n\n if not np.array_equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]\n\n prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt] * batch_size\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"np\",\n )\n negative_prompt_embeds = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0]\n\n if do_classifier_free_guidance:\n negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion.OnnxStableDiffusionPipeline.check_inputs\n def check_inputs(\n self,\n prompt: Union[str, List[str]],\n height: Optional[int],\n width: Optional[int],\n callback_steps: int,\n negative_prompt: Optional[str] = None,\n prompt_embeds: Optional[np.ndarray] = None,\n negative_prompt_embeds: Optional[np.ndarray] = None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n image: PIL.Image.Image,\n mask_image: PIL.Image.Image,\n height: Optional[int] = 512,\n width: Optional[int] = 512,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[np.random.RandomState] = None,\n latents: Optional[np.ndarray] = None,\n prompt_embeds: Optional[np.ndarray] = None,\n negative_prompt_embeds: Optional[np.ndarray] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, np.ndarray], None]] = None,\n callback_steps: int = 1,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n image (`PIL.Image.Image`):\n `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will\n be masked out with `mask_image` and repainted according to `prompt`.\n mask_image (`PIL.Image.Image`):\n `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be\n repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted\n to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)\n instead of 3, so the expected shape would be `(B, H, W, 1)`.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`np.random.RandomState`, *optional*):\n A np.random.RandomState to make generation deterministic.\n latents (`np.ndarray`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n\n # check inputs. Raise error if not correct\n self.check_inputs(\n prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds\n )\n\n # define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if generator is None:\n generator = np.random\n\n # set timesteps\n self.scheduler.set_timesteps(num_inference_steps)\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n prompt_embeds = self._encode_prompt(\n prompt,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n\n num_channels_latents = NUM_LATENT_CHANNELS\n latents_shape = (batch_size * num_images_per_prompt, num_channels_latents, height // 8, width // 8)\n latents_dtype = prompt_embeds.dtype\n if latents is None:\n latents = generator.randn(*latents_shape).astype(latents_dtype)\n else:\n if latents.shape != latents_shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {latents_shape}\")\n\n # prepare mask and masked_image\n mask, masked_image = prepare_mask_and_masked_image(image, mask_image, latents_shape[-2:])\n mask = mask.astype(latents.dtype)\n masked_image = masked_image.astype(latents.dtype)\n\n masked_image_latents = self.vae_encoder(sample=masked_image)[0]\n masked_image_latents = 0.18215 * masked_image_latents\n\n # duplicate mask and masked_image_latents for each generation per prompt\n mask = mask.repeat(batch_size * num_images_per_prompt, 0)\n masked_image_latents = masked_image_latents.repeat(batch_size * num_images_per_prompt, 0)\n\n mask = np.concatenate([mask] * 2) if do_classifier_free_guidance else mask\n masked_image_latents = (\n np.concatenate([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents\n )\n\n num_channels_mask = mask.shape[1]\n num_channels_masked_image = masked_image_latents.shape[1]\n\n unet_input_channels = NUM_UNET_INPUT_CHANNELS\n if num_channels_latents + num_channels_mask + num_channels_masked_image != unet_input_channels:\n raise ValueError(\n \"Incorrect configuration settings! The config of `pipeline.unet` expects\"\n f\" {unet_input_channels} but received `num_channels_latents`: {num_channels_latents} +\"\n f\" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}\"\n f\" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of\"\n \" `pipeline.unet` or your `mask_image` or `image` input.\"\n )\n\n # set timesteps\n self.scheduler.set_timesteps(num_inference_steps)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * np.float64(self.scheduler.init_noise_sigma)\n\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n timestep_dtype = next(\n (input.type for input in self.unet.model.get_inputs() if input.name == \"timestep\"), \"tensor(float)\"\n )\n timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]\n\n for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents\n # concat latents, mask, masked_image_latnets in the channel dimension\n latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)\n latent_model_input = latent_model_input.cpu().numpy()\n latent_model_input = np.concatenate([latent_model_input, mask, masked_image_latents], axis=1)\n\n # predict the noise residual\n timestep = np.array([t], dtype=timestep_dtype)\n noise_pred = self.unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds)[\n 0\n ]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n scheduler_output = self.scheduler.step(\n torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs\n )\n latents = scheduler_output.prev_sample.numpy()\n\n # call the callback, if provided\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n latents = 1 / 0.18215 * latents\n # image = self.vae_decoder(latent_sample=latents)[0]\n # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1\n image = np.concatenate(\n [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]\n )\n\n image = np.clip(image / 2 + 0.5, 0, 1)\n image = image.transpose((0, 2, 3, 1))\n\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(\n self.numpy_to_pil(image), return_tensors=\"np\"\n ).pixel_values.astype(image.dtype)\n # safety_checker does not support batched inputs yet\n images, has_nsfw_concept = [], []\n for i in range(image.shape[0]):\n image_i, has_nsfw_concept_i = self.safety_checker(\n clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]\n )\n images.append(image_i)\n has_nsfw_concept.append(has_nsfw_concept_i[0])\n image = np.concatenate(images)\n else:\n has_nsfw_concept = None\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTokenizer\n\nfrom ...configuration_utils import FrozenDict\nfrom ...schedulers import DDPMScheduler, KarrasDiffusionSchedulers\nfrom ...utils import deprecate, logging\nfrom ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel\nfrom ..pipeline_utils import DiffusionPipeline\nfrom . import StableDiffusionPipelineOutput\n\n\nlogger = logging.get_logger(__name__)\n\n\ndef preprocess(image):\n if isinstance(image, torch.Tensor):\n return image\n elif isinstance(image, PIL.Image.Image):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n w, h = image[0].size\n w, h = (x - x % 64 for x in (w, h)) # resize to integer multiple of 32\n\n image = [np.array(i.resize((w, h)))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n image = np.array(image).astype(np.float32) / 255.0\n image = image.transpose(0, 3, 1, 2)\n image = 2.0 * image - 1.0\n image = torch.from_numpy(image)\n elif isinstance(image[0], torch.Tensor):\n image = torch.cat(image, dim=0)\n\n return image\n\n\nclass OnnxStableDiffusionUpscalePipeline(DiffusionPipeline):\n vae: OnnxRuntimeModel\n text_encoder: OnnxRuntimeModel\n tokenizer: CLIPTokenizer\n unet: OnnxRuntimeModel\n low_res_scheduler: DDPMScheduler\n scheduler: KarrasDiffusionSchedulers\n safety_checker: OnnxRuntimeModel\n feature_extractor: CLIPImageProcessor\n\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n _is_onnx = True\n\n def __init__(\n self,\n vae: OnnxRuntimeModel,\n text_encoder: OnnxRuntimeModel,\n tokenizer: Any,\n unet: OnnxRuntimeModel,\n low_res_scheduler: DDPMScheduler,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: Optional[OnnxRuntimeModel] = None,\n feature_extractor: Optional[CLIPImageProcessor] = None,\n max_noise_level: int = 350,\n num_latent_channels=4,\n num_unet_input_channels=7,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"clip_sample\"] = False\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n low_res_scheduler=low_res_scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.register_to_config(\n max_noise_level=max_noise_level,\n num_latent_channels=num_latent_channels,\n num_unet_input_channels=num_unet_input_channels,\n )\n\n def check_inputs(\n self,\n prompt: Union[str, List[str]],\n image,\n noise_level,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if (\n not isinstance(image, torch.Tensor)\n and not isinstance(image, PIL.Image.Image)\n and not isinstance(image, np.ndarray)\n and not isinstance(image, list)\n ):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `np.ndarray`, `PIL.Image.Image` or `list` but is {type(image)}\"\n )\n\n # verify batch size of prompt and image are same if image is a list or tensor or numpy array\n if isinstance(image, (list, np.ndarray)):\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if isinstance(image, list):\n image_batch_size = len(image)\n else:\n image_batch_size = image.shape[0]\n if batch_size != image_batch_size:\n raise ValueError(\n f\"`prompt` has batch size {batch_size} and `image` has batch size {image_batch_size}.\"\n \" Please make sure that passed `prompt` matches the batch size of `image`.\"\n )\n\n # check noise level\n if noise_level > self.config.max_noise_level:\n raise ValueError(f\"`noise_level` has to be <= {self.config.max_noise_level} but is {noise_level}\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, generator, latents=None):\n shape = (batch_size, num_channels_latents, height, width)\n if latents is None:\n latents = generator.randn(*shape).astype(dtype)\n elif latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n\n return latents\n\n def decode_latents(self, latents):\n latents = 1 / 0.08333 * latents\n image = self.vae(latent_sample=latents)[0]\n image = np.clip(image / 2 + 0.5, 0, 1)\n image = image.transpose((0, 2, 3, 1))\n return image\n\n def _encode_prompt(\n self,\n prompt: Union[str, List[str]],\n num_images_per_prompt: Optional[int],\n do_classifier_free_guidance: bool,\n negative_prompt: Optional[str],\n prompt_embeds: Optional[np.ndarray] = None,\n negative_prompt_embeds: Optional[np.ndarray] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`):\n prompt to be encoded\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n \"\"\"\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"np\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"max_length\", return_tensors=\"np\").input_ids\n\n if not np.array_equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]\n\n prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt] * batch_size\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"np\",\n )\n negative_prompt_embeds = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0]\n\n if do_classifier_free_guidance:\n negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n def __call__(\n self,\n prompt: Union[str, List[str]],\n image: Union[np.ndarray, PIL.Image.Image, List[PIL.Image.Image]],\n num_inference_steps: int = 75,\n guidance_scale: float = 9.0,\n noise_level: int = 20,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[np.random.RandomState, List[np.random.RandomState]]] = None,\n latents: Optional[np.ndarray] = None,\n prompt_embeds: Optional[np.ndarray] = None,\n negative_prompt_embeds: Optional[np.ndarray] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, np.ndarray], None]] = None,\n callback_steps: Optional[int] = 1,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n image (`np.ndarray` or `PIL.Image.Image`):\n `Image`, or tensor representing an image batch, that will be used as the starting point for the\n process.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference. This parameter will be modulated by `strength`.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n noise_level (`float`, defaults to 0.2):\n Deteremines the amount of noise to add to the initial image before performing upscaling.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`np.random.RandomState`, *optional*):\n A np.random.RandomState to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`np.ndarray`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n\n # 1. Check inputs\n self.check_inputs(\n prompt,\n image,\n noise_level,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if generator is None:\n generator = np.random\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n prompt_embeds = self._encode_prompt(\n prompt,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n\n latents_dtype = prompt_embeds.dtype\n image = preprocess(image).cpu().numpy()\n height, width = image.shape[2:]\n\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n self.config.num_latent_channels,\n height,\n width,\n latents_dtype,\n generator,\n )\n image = image.astype(latents_dtype)\n\n self.scheduler.set_timesteps(num_inference_steps)\n timesteps = self.scheduler.timesteps\n\n # Scale the initial noise by the standard deviation required by the scheduler\n latents = latents * np.float64(self.scheduler.init_noise_sigma)\n\n # 5. Add noise to image\n noise_level = np.array([noise_level]).astype(np.int64)\n noise = generator.randn(*image.shape).astype(latents_dtype)\n\n image = self.low_res_scheduler.add_noise(\n torch.from_numpy(image), torch.from_numpy(noise), torch.from_numpy(noise_level)\n )\n image = image.numpy()\n\n batch_multiplier = 2 if do_classifier_free_guidance else 1\n image = np.concatenate([image] * batch_multiplier * num_images_per_prompt)\n noise_level = np.concatenate([noise_level] * image.shape[0])\n\n # 7. Check that sizes of image and latents match\n num_channels_image = image.shape[1]\n if self.config.num_latent_channels + num_channels_image != self.config.num_unet_input_channels:\n raise ValueError(\n \"Incorrect configuration settings! The config of `pipeline.unet` expects\"\n f\" {self.config.num_unet_input_channels} but received `num_channels_latents`: {self.config.num_latent_channels} +\"\n f\" `num_channels_image`: {num_channels_image} \"\n f\" = {self.config.num_latent_channels + num_channels_image}. Please verify the config of\"\n \" `pipeline.unet` or your `image` input.\"\n )\n\n # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n timestep_dtype = next(\n (input.type for input in self.unet.model.get_inputs() if input.name == \"timestep\"), \"tensor(float)\"\n )\n timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]\n\n # 9. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents\n\n # concat latents, mask, masked_image_latents in the channel dimension\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n latent_model_input = np.concatenate([latent_model_input, image], axis=1)\n\n # timestep to tensor\n timestep = np.array([t], dtype=timestep_dtype)\n\n # predict the noise residual\n noise_pred = self.unet(\n sample=latent_model_input,\n timestep=timestep,\n encoder_hidden_states=prompt_embeds,\n class_labels=noise_level,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(\n torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs\n ).prev_sample\n latents = latents.numpy()\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # 10. Post-processing\n image = self.decode_latents(latents)\n\n if self.safety_checker is not None:\n safety_checker_input = self.feature_extractor(\n self.numpy_to_pil(image), return_tensors=\"np\"\n ).pixel_values.astype(image.dtype)\n\n images, has_nsfw_concept = [], []\n for i in range(image.shape[0]):\n image_i, has_nsfw_concept_i = self.safety_checker(\n clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]\n )\n images.append(image_i)\n has_nsfw_concept.append(has_nsfw_concept_i[0])\n image = np.concatenate(images)\n else:\n has_nsfw_concept = None\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_output.py", "content": "from dataclasses import dataclass\nfrom typing import List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\n\nfrom ...utils import BaseOutput, is_flax_available\n\n\n@dataclass\nclass StableDiffusionPipelineOutput(BaseOutput):\n \"\"\"\n Output class for Stable Diffusion pipelines.\n\n Args:\n images (`List[PIL.Image.Image]` or `np.ndarray`)\n List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,\n num_channels)`.\n nsfw_content_detected (`List[bool]`)\n List indicating whether the corresponding generated image contains \"not-safe-for-work\" (nsfw) content or\n `None` if safety checking could not be performed.\n \"\"\"\n\n images: Union[List[PIL.Image.Image], np.ndarray]\n nsfw_content_detected: Optional[List[bool]]\n\n\nif is_flax_available():\n import flax\n\n @flax.struct.dataclass\n class FlaxStableDiffusionPipelineOutput(BaseOutput):\n \"\"\"\n Output class for Flax-based Stable Diffusion pipelines.\n\n Args:\n images (`np.ndarray`):\n Denoised images of array shape of `(batch_size, height, width, num_channels)`.\n nsfw_content_detected (`List[bool]`):\n List indicating whether the corresponding generated image contains \"not-safe-for-work\" (nsfw) content\n or `None` if safety checking could not be performed.\n \"\"\"\n\n images: np.ndarray\n nsfw_content_detected: List[bool]\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport torch\nfrom packaging import version\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...callbacks import MultiPipelineCallbacks, PipelineCallback\nfrom ...configuration_utils import FrozenDict\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom .pipeline_output import StableDiffusionPipelineOutput\nfrom .safety_checker import StableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusionPipeline\n\n >>> pipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16)\n >>> pipe = pipe.to(\"cuda\")\n\n >>> prompt = \"a photo of an astronaut riding a horse on mars\"\n >>> image = pipe(prompt).images[0]\n ```\n\"\"\"\n\n\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass StableDiffusionPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n TextualInversionLoaderMixin,\n LoraLoaderMixin,\n IPAdapterMixin,\n FromSingleFileMixin,\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->image_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"image_encoder\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder: CLIPVisionModelWithProjection = None,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"clip_sample\"] = False\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n if ip_adapter_image_embeds is not None:\n if not isinstance(ip_adapter_image_embeds, list):\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}\"\n )\n elif ip_adapter_image_embeds[0].ndim not in [3, 4]:\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D\"\n )\n\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(\n self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32\n ) -> torch.Tensor:\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n w (`torch.Tensor`):\n Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.\n embedding_dim (`int`, *optional*, defaults to 512):\n Dimension of the embeddings to generate.\n dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):\n Data type of the generated embeddings.\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def guidance_rescale(self):\n return self._guidance_rescale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @property\n def interrupt(self):\n return self._interrupt\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n sigmas: List[float] = None,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[\n Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]\n ] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n sigmas (`List[float]`, *optional*):\n Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in\n their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed\n will be used.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of\n IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should\n contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not\n provided, embeddings are computed from the `ip_adapter_image` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when\n using zero terminal SNR.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):\n A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of\n each denoising step during the inference. with the following arguments: `callback_on_step_end(self:\n DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a\n list of all tensors as specified by `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n\n if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):\n callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n # to deal with lora scaling and other possible forward hooks\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n ip_adapter_image,\n ip_adapter_image_embeds,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._guidance_rescale = guidance_rescale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._interrupt = False\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_images_per_prompt,\n self.do_classifier_free_guidance,\n )\n\n # 4. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(\n self.scheduler, num_inference_steps, device, timesteps, sigmas\n )\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 6.1 Add image embeds for IP-Adapter\n added_cond_kwargs = (\n {\"image_embeds\": image_embeds}\n if (ip_adapter_image is not None or ip_adapter_image_embeds is not None)\n else None\n )\n\n # 6.2 Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n if self.interrupt:\n continue\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=self.cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[\n 0\n ]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport contextlib\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom packaging import version\nfrom transformers import CLIPTextModel, CLIPTokenizer, DPTFeatureExtractor, DPTForDepthEstimation\n\nfrom ...configuration_utils import FrozenDict\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, UNet2DConditionModel\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import PIL_INTERPOLATION, USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess\ndef preprocess(image):\n deprecation_message = \"The preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead\"\n deprecate(\"preprocess\", \"1.0.0\", deprecation_message, standard_warn=False)\n if isinstance(image, torch.Tensor):\n return image\n elif isinstance(image, PIL.Image.Image):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n w, h = image[0].size\n w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8\n\n image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION[\"lanczos\"]))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n image = np.array(image).astype(np.float32) / 255.0\n image = image.transpose(0, 3, 1, 2)\n image = 2.0 * image - 1.0\n image = torch.from_numpy(image)\n elif isinstance(image[0], torch.Tensor):\n image = torch.cat(image, dim=0)\n return image\n\n\nclass StableDiffusionDepth2ImgPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):\n r\"\"\"\n Pipeline for text-guided depth-based image-to-image generation using Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\", \"depth_mask\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n depth_estimator: DPTForDepthEstimation,\n feature_extractor: DPTFeatureExtractor,\n ):\n super().__init__()\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n depth_estimator=depth_estimator,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n strength,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n if hasattr(self.scheduler, \"set_begin_index\"):\n self.scheduler.set_begin_index(t_start * self.scheduler.order)\n\n return timesteps, num_inference_steps - t_start\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.prepare_latents\n def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n\n if image.shape[1] == 4:\n init_latents = image\n\n else:\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n elif isinstance(generator, list):\n init_latents = [\n retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])\n for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = retrieve_latents(self.vae.encode(image), generator=generator)\n\n init_latents = self.vae.config.scaling_factor * init_latents\n\n if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:\n # expand init_latents for batch_size\n deprecation_message = (\n f\"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial\"\n \" images (`image`). Initial images are now duplicating to match the number of text prompts. Note\"\n \" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update\"\n \" your script to pass as many initial images as text prompts to suppress this warning.\"\n )\n deprecate(\"len(prompt) != len(image)\", \"1.0.0\", deprecation_message, standard_warn=False)\n additional_image_per_prompt = batch_size // init_latents.shape[0]\n init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)\n elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n init_latents = torch.cat([init_latents], dim=0)\n\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n latents = init_latents\n\n return latents\n\n def prepare_depth_map(self, image, depth_map, batch_size, do_classifier_free_guidance, dtype, device):\n if isinstance(image, PIL.Image.Image):\n image = [image]\n else:\n image = list(image)\n\n if isinstance(image[0], PIL.Image.Image):\n width, height = image[0].size\n elif isinstance(image[0], np.ndarray):\n width, height = image[0].shape[:-1]\n else:\n height, width = image[0].shape[-2:]\n\n if depth_map is None:\n pixel_values = self.feature_extractor(images=image, return_tensors=\"pt\").pixel_values\n pixel_values = pixel_values.to(device=device, dtype=dtype)\n # The DPT-Hybrid model uses batch-norm layers which are not compatible with fp16.\n # So we use `torch.autocast` here for half precision inference.\n if torch.backends.mps.is_available():\n autocast_ctx = contextlib.nullcontext()\n logger.warning(\n \"The DPT-Hybrid model uses batch-norm layers which are not compatible with fp16, but autocast is not yet supported on MPS.\"\n )\n else:\n autocast_ctx = torch.autocast(device.type, dtype=dtype)\n\n with autocast_ctx:\n depth_map = self.depth_estimator(pixel_values).predicted_depth\n else:\n depth_map = depth_map.to(device=device, dtype=dtype)\n\n depth_map = torch.nn.functional.interpolate(\n depth_map.unsqueeze(1),\n size=(height // self.vae_scale_factor, width // self.vae_scale_factor),\n mode=\"bicubic\",\n align_corners=False,\n )\n\n depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)\n depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)\n depth_map = 2.0 * (depth_map - depth_min) / (depth_max - depth_min) - 1.0\n depth_map = depth_map.to(dtype)\n\n # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method\n if depth_map.shape[0] < batch_size:\n repeat_by = batch_size // depth_map.shape[0]\n depth_map = depth_map.repeat(repeat_by, 1, 1, 1)\n\n depth_map = torch.cat([depth_map] * 2) if do_classifier_free_guidance else depth_map\n return depth_map\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: PipelineImageInput = None,\n depth_map: Optional[torch.Tensor] = None,\n strength: float = 0.8,\n num_inference_steps: Optional[int] = 50,\n guidance_scale: Optional[float] = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: Optional[float] = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image` or tensor representing an image batch to be used as the starting point. Can accept image\n latents as `image` only if `depth_map` is not `None`.\n depth_map (`torch.Tensor`, *optional*):\n Depth prediction to be used as additional conditioning for the image generation process. If not\n defined, it automatically predicts the depth with `self.depth_estimator`.\n strength (`float`, *optional*, defaults to 0.8):\n Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a\n starting point and more noise is added the higher the `strength`. The number of denoising steps depends\n on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising\n process runs for the full number of iterations specified in `num_inference_steps`. A value of 1\n essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference. This parameter is modulated by `strength`.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n Examples:\n\n ```py\n >>> import torch\n >>> import requests\n >>> from PIL import Image\n\n >>> from diffusers import StableDiffusionDepth2ImgPipeline\n\n >>> pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(\n ... \"stabilityai/stable-diffusion-2-depth\",\n ... torch_dtype=torch.float16,\n ... )\n >>> pipe.to(\"cuda\")\n\n\n >>> url = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\n >>> init_image = Image.open(requests.get(url, stream=True).raw)\n >>> prompt = \"two tigers\"\n >>> n_prompt = \"bad, deformed, ugly, bad anotomy\"\n >>> image = pipe(prompt=prompt, image=init_image, negative_prompt=n_prompt, strength=0.7).images[0]\n ```\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n\n # 1. Check inputs\n self.check_inputs(\n prompt,\n strength,\n callback_steps,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n\n if image is None:\n raise ValueError(\"`image` input cannot be undefined.\")\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=self.clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Prepare depth mask\n depth_mask = self.prepare_depth_map(\n image,\n depth_map,\n batch_size * num_images_per_prompt,\n self.do_classifier_free_guidance,\n prompt_embeds.dtype,\n device,\n )\n\n # 5. Preprocess image\n image = self.image_processor.preprocess(image)\n\n # 6. Set timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n\n # 7. Prepare latent variables\n latents = self.prepare_latents(\n image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator\n )\n\n # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 9. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n latent_model_input = torch.cat([latent_model_input, depth_mask], dim=1)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=self.cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n depth_mask = callback_outputs.pop(\"depth_mask\", depth_mask)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n else:\n image = latents\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Callable, List, Optional, Union\n\nimport PIL.Image\nimport torch\nfrom packaging import version\nfrom transformers import CLIPImageProcessor, CLIPVisionModelWithProjection\n\nfrom ...configuration_utils import FrozenDict\nfrom ...image_processor import VaeImageProcessor\nfrom ...models import AutoencoderKL, UNet2DConditionModel\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import deprecate, logging\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom . import StableDiffusionPipelineOutput\nfrom .safety_checker import StableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass StableDiffusionImageVariationPipeline(DiffusionPipeline, StableDiffusionMixin):\n r\"\"\"\n Pipeline to generate image variations from an input image using Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n image_encoder ([`~transformers.CLIPVisionModelWithProjection`]):\n Frozen CLIP image-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n # TODO: feature_extractor is required to encode images (if they are in PIL format),\n # we should give a descriptive message if the pipeline doesn't have one.\n _optional_components = [\"safety_checker\"]\n model_cpu_offload_seq = \"image_encoder->unet->vae\"\n _exclude_from_cpu_offload = [\"safety_checker\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n image_encoder: CLIPVisionModelWithProjection,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n image_encoder=image_encoder,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n def _encode_image(self, image, device, num_images_per_prompt, do_classifier_free_guidance):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(images=image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n image_embeddings = self.image_encoder(image).image_embeds\n image_embeddings = image_embeddings.unsqueeze(1)\n\n # duplicate image embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = image_embeddings.shape\n image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)\n image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n negative_prompt_embeds = torch.zeros_like(image_embeddings)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings])\n\n return image_embeddings\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(self, image, height, width, callback_steps):\n if (\n not isinstance(image, torch.Tensor)\n and not isinstance(image, PIL.Image.Image)\n and not isinstance(image, list)\n ):\n raise ValueError(\n \"`image` has to be of type `torch.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is\"\n f\" {type(image)}\"\n )\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @torch.no_grad()\n def __call__(\n self,\n image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.Tensor],\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.Tensor`):\n Image or images to guide image generation. If you provide a tensor, it needs to be compatible with\n [`CLIPImageProcessor`](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json).\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference. This parameter is modulated by `strength`.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n\n Examples:\n\n ```py\n from diffusers import StableDiffusionImageVariationPipeline\n from PIL import Image\n from io import BytesIO\n import requests\n\n pipe = StableDiffusionImageVariationPipeline.from_pretrained(\n \"lambdalabs/sd-image-variations-diffusers\", revision=\"v2.0\"\n )\n pipe = pipe.to(\"cuda\")\n\n url = \"https://lh3.googleusercontent.com/y-iFOHfLTwkuQSUegpwDdgKmOjRSTvPxat63dQLB25xkTs4lhIbRUFeNBWZzYf370g=s1200\"\n\n response = requests.get(url)\n image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n\n out = pipe(image, num_images_per_prompt=3, guidance_scale=15)\n out[\"images\"][0].save(\"result.jpg\")\n ```\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(image, height, width, callback_steps)\n\n # 2. Define call parameters\n if isinstance(image, PIL.Image.Image):\n batch_size = 1\n elif isinstance(image, list):\n batch_size = len(image)\n else:\n batch_size = image.shape[0]\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input image\n image_embeddings = self._encode_image(image, device, num_images_per_prompt, do_classifier_free_guidance)\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n image_embeddings.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=image_embeddings).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n self.maybe_free_model_hooks()\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, image_embeddings.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom packaging import version\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...callbacks import MultiPipelineCallbacks, PipelineCallback\nfrom ...configuration_utils import FrozenDict\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n PIL_INTERPOLATION,\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom . import StableDiffusionPipelineOutput\nfrom .safety_checker import StableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import requests\n >>> import torch\n >>> from PIL import Image\n >>> from io import BytesIO\n\n >>> from diffusers import StableDiffusionImg2ImgPipeline\n\n >>> device = \"cuda\"\n >>> model_id_or_path = \"runwayml/stable-diffusion-v1-5\"\n >>> pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n >>> pipe = pipe.to(device)\n\n >>> url = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n\n >>> response = requests.get(url)\n >>> init_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n >>> init_image = init_image.resize((768, 512))\n\n >>> prompt = \"A fantasy landscape, trending on artstation\"\n\n >>> images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images\n >>> images[0].save(\"fantasy_landscape.png\")\n ```\n\"\"\"\n\n\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n\ndef preprocess(image):\n deprecation_message = \"The preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead\"\n deprecate(\"preprocess\", \"1.0.0\", deprecation_message, standard_warn=False)\n if isinstance(image, torch.Tensor):\n return image\n elif isinstance(image, PIL.Image.Image):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n w, h = image[0].size\n w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8\n\n image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION[\"lanczos\"]))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n image = np.array(image).astype(np.float32) / 255.0\n image = image.transpose(0, 3, 1, 2)\n image = 2.0 * image - 1.0\n image = torch.from_numpy(image)\n elif isinstance(image[0], torch.Tensor):\n image = torch.cat(image, dim=0)\n return image\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass StableDiffusionImg2ImgPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n TextualInversionLoaderMixin,\n IPAdapterMixin,\n LoraLoaderMixin,\n FromSingleFileMixin,\n):\n r\"\"\"\n Pipeline for text-guided image-to-image generation using Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->image_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"image_encoder\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder: CLIPVisionModelWithProjection = None,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"clip_sample\"] = False\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n strength,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n if ip_adapter_image_embeds is not None:\n if not isinstance(ip_adapter_image_embeds, list):\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}\"\n )\n elif ip_adapter_image_embeds[0].ndim not in [3, 4]:\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D\"\n )\n\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n if hasattr(self.scheduler, \"set_begin_index\"):\n self.scheduler.set_begin_index(t_start * self.scheduler.order)\n\n return timesteps, num_inference_steps - t_start\n\n def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n\n if image.shape[1] == 4:\n init_latents = image\n\n else:\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n elif isinstance(generator, list):\n init_latents = [\n retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])\n for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = retrieve_latents(self.vae.encode(image), generator=generator)\n\n init_latents = self.vae.config.scaling_factor * init_latents\n\n if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:\n # expand init_latents for batch_size\n deprecation_message = (\n f\"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial\"\n \" images (`image`). Initial images are now duplicating to match the number of text prompts. Note\"\n \" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update\"\n \" your script to pass as many initial images as text prompts to suppress this warning.\"\n )\n deprecate(\"len(prompt) != len(image)\", \"1.0.0\", deprecation_message, standard_warn=False)\n additional_image_per_prompt = batch_size // init_latents.shape[0]\n init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)\n elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n init_latents = torch.cat([init_latents], dim=0)\n\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n latents = init_latents\n\n return latents\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(\n self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32\n ) -> torch.Tensor:\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n w (`torch.Tensor`):\n Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.\n embedding_dim (`int`, *optional*, defaults to 512):\n Dimension of the embeddings to generate.\n dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):\n Data type of the generated embeddings.\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @property\n def interrupt(self):\n return self._interrupt\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: PipelineImageInput = None,\n strength: float = 0.8,\n num_inference_steps: Optional[int] = 50,\n timesteps: List[int] = None,\n sigmas: List[float] = None,\n guidance_scale: Optional[float] = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: Optional[float] = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: int = None,\n callback_on_step_end: Optional[\n Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]\n ] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both\n numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list\n or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a\n list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image\n latents as `image`, but if passing latents directly it is not encoded again.\n strength (`float`, *optional*, defaults to 0.8):\n Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a\n starting point and more noise is added the higher the `strength`. The number of denoising steps depends\n on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising\n process runs for the full number of iterations specified in `num_inference_steps`. A value of 1\n essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference. This parameter is modulated by `strength`.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n sigmas (`List[float]`, *optional*):\n Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in\n their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed\n will be used.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of\n IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should\n contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not\n provided, embeddings are computed from the `ip_adapter_image` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):\n A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of\n each denoising step during the inference. with the following arguments: `callback_on_step_end(self:\n DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a\n list of all tensors as specified by `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n\n if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):\n callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n strength,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n ip_adapter_image,\n ip_adapter_image_embeds,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._interrupt = False\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=self.clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_images_per_prompt,\n self.do_classifier_free_guidance,\n )\n\n # 4. Preprocess image\n image = self.image_processor.preprocess(image)\n\n # 5. set timesteps\n timesteps, num_inference_steps = retrieve_timesteps(\n self.scheduler, num_inference_steps, device, timesteps, sigmas\n )\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n\n # 6. Prepare latent variables\n latents = self.prepare_latents(\n image,\n latent_timestep,\n batch_size,\n num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n generator,\n )\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7.1 Add image embeds for IP-Adapter\n added_cond_kwargs = (\n {\"image_embeds\": image_embeds}\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None\n else None\n )\n\n # 7.2 Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n if self.interrupt:\n continue\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=self.cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[\n 0\n ]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport PIL.Image\nimport torch\nfrom packaging import version\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...callbacks import MultiPipelineCallbacks, PipelineCallback\nfrom ...configuration_utils import FrozenDict\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AsymmetricAutoencoderKL, AutoencoderKL, ImageProjection, UNet2DConditionModel\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom . import StableDiffusionPipelineOutput\nfrom .safety_checker import StableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass StableDiffusionInpaintPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n TextualInversionLoaderMixin,\n IPAdapterMixin,\n LoraLoaderMixin,\n FromSingleFileMixin,\n):\n r\"\"\"\n Pipeline for text-guided image inpainting using Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n\n Args:\n vae ([`AutoencoderKL`, `AsymmetricAutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->image_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"image_encoder\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\", \"mask\", \"masked_image_latents\"]\n\n def __init__(\n self,\n vae: Union[AutoencoderKL, AsymmetricAutoencoderKL],\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder: CLIPVisionModelWithProjection = None,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"skip_prk_steps\") and scheduler.config.skip_prk_steps is False:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration\"\n \" `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make\"\n \" sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to\"\n \" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face\"\n \" Hub, it would be very nice if you could open a Pull request for the\"\n \" `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"skip_prk_steps not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"skip_prk_steps\"] = True\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n # Check shapes, assume num_channels_latents == 4, num_channels_mask == 1, num_channels_masked == 4\n if unet.config.in_channels != 9:\n logger.info(f\"You have loaded a UNet with {unet.config.in_channels} input channels which.\")\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.mask_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True\n )\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n image,\n mask_image,\n height,\n width,\n strength,\n callback_steps,\n output_type,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n padding_mask_crop=None,\n ):\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n\n if height % self.vae_scale_factor != 0 or width % self.vae_scale_factor != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n if padding_mask_crop is not None:\n if not isinstance(image, PIL.Image.Image):\n raise ValueError(\n f\"The image should be a PIL image when inpainting mask crop, but is of type\" f\" {type(image)}.\"\n )\n if not isinstance(mask_image, PIL.Image.Image):\n raise ValueError(\n f\"The mask image should be a PIL image when inpainting mask crop, but is of type\"\n f\" {type(mask_image)}.\"\n )\n if output_type != \"pil\":\n raise ValueError(f\"The output type should be PIL when inpainting mask crop, but is\" f\" {output_type}.\")\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n if ip_adapter_image_embeds is not None:\n if not isinstance(ip_adapter_image_embeds, list):\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}\"\n )\n elif ip_adapter_image_embeds[0].ndim not in [3, 4]:\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D\"\n )\n\n def prepare_latents(\n self,\n batch_size,\n num_channels_latents,\n height,\n width,\n dtype,\n device,\n generator,\n latents=None,\n image=None,\n timestep=None,\n is_strength_max=True,\n return_noise=False,\n return_image_latents=False,\n ):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if (image is None or timestep is None) and not is_strength_max:\n raise ValueError(\n \"Since strength < 1. initial latents are to be initialised as a combination of Image + Noise.\"\n \"However, either the image or the noise timestep has not been provided.\"\n )\n\n if return_image_latents or (latents is None and not is_strength_max):\n image = image.to(device=device, dtype=dtype)\n\n if image.shape[1] == 4:\n image_latents = image\n else:\n image_latents = self._encode_vae_image(image=image, generator=generator)\n image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)\n\n if latents is None:\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n # if strength is 1. then initialise the latents to noise, else initial to image + noise\n latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)\n # if pure noise then scale the initial latents by the Scheduler's init sigma\n latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents\n else:\n noise = latents.to(device)\n latents = noise * self.scheduler.init_noise_sigma\n\n outputs = (latents,)\n\n if return_noise:\n outputs += (noise,)\n\n if return_image_latents:\n outputs += (image_latents,)\n\n return outputs\n\n def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):\n if isinstance(generator, list):\n image_latents = [\n retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])\n for i in range(image.shape[0])\n ]\n image_latents = torch.cat(image_latents, dim=0)\n else:\n image_latents = retrieve_latents(self.vae.encode(image), generator=generator)\n\n image_latents = self.vae.config.scaling_factor * image_latents\n\n return image_latents\n\n def prepare_mask_latents(\n self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance\n ):\n # resize the mask to latents shape as we concatenate the mask to the latents\n # we do that before converting to dtype to avoid breaking in case we're using cpu_offload\n # and half precision\n mask = torch.nn.functional.interpolate(\n mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)\n )\n mask = mask.to(device=device, dtype=dtype)\n\n masked_image = masked_image.to(device=device, dtype=dtype)\n\n if masked_image.shape[1] == 4:\n masked_image_latents = masked_image\n else:\n masked_image_latents = self._encode_vae_image(masked_image, generator=generator)\n\n # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method\n if mask.shape[0] < batch_size:\n if not batch_size % mask.shape[0] == 0:\n raise ValueError(\n \"The passed mask and the required batch size don't match. Masks are supposed to be duplicated to\"\n f\" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number\"\n \" of masks that you pass is divisible by the total requested batch size.\"\n )\n mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)\n if masked_image_latents.shape[0] < batch_size:\n if not batch_size % masked_image_latents.shape[0] == 0:\n raise ValueError(\n \"The passed images and the required batch size don't match. Images are supposed to be duplicated\"\n f\" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed.\"\n \" Make sure the number of images that you pass is divisible by the total requested batch size.\"\n )\n masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)\n\n mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask\n masked_image_latents = (\n torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents\n )\n\n # aligning device to prevent device errors when concating it with the latent model input\n masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)\n return mask, masked_image_latents\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n if hasattr(self.scheduler, \"set_begin_index\"):\n self.scheduler.set_begin_index(t_start * self.scheduler.order)\n\n return timesteps, num_inference_steps - t_start\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(\n self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32\n ) -> torch.Tensor:\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n w (`torch.Tensor`):\n Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.\n embedding_dim (`int`, *optional*, defaults to 512):\n Dimension of the embeddings to generate.\n dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):\n Data type of the generated embeddings.\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @property\n def interrupt(self):\n return self._interrupt\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: PipelineImageInput = None,\n mask_image: PipelineImageInput = None,\n masked_image_latents: torch.Tensor = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n padding_mask_crop: Optional[int] = None,\n strength: float = 1.0,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n sigmas: List[float] = None,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: int = None,\n callback_on_step_end: Optional[\n Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]\n ] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image`, numpy array or tensor representing an image batch to be inpainted (which parts of the image to\n be masked out with `mask_image` and repainted according to `prompt`). For both numpy array and pytorch\n tensor, the expected value range is between `[0, 1]` If it's a tensor or a list or tensors, the\n expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a list of arrays, the\n expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image latents as `image`, but\n if passing latents directly it is not encoded again.\n mask_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image`, numpy array or tensor representing an image batch to mask `image`. White pixels in the mask\n are repainted while black pixels are preserved. If `mask_image` is a PIL image, it is converted to a\n single channel (luminance) before use. If it's a numpy array or pytorch tensor, it should contain one\n color channel (L) instead of 3, so the expected shape for pytorch tensor would be `(B, 1, H, W)`, `(B,\n H, W)`, `(1, H, W)`, `(H, W)`. And for numpy array would be for `(B, H, W, 1)`, `(B, H, W)`, `(H, W,\n 1)`, or `(H, W)`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n padding_mask_crop (`int`, *optional*, defaults to `None`):\n The size of margin in the crop to be applied to the image and masking. If `None`, no crop is applied to\n image and mask_image. If `padding_mask_crop` is not `None`, it will first find a rectangular region\n with the same aspect ration of the image and contains all masked area, and then expand that area based\n on `padding_mask_crop`. The image and mask_image will then be cropped based on the expanded area before\n resizing to the original image size for inpainting. This is useful when the masked area is small while\n the image is large and contain information irrelevant for inpainting, such as background.\n strength (`float`, *optional*, defaults to 1.0):\n Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a\n starting point and more noise is added the higher the `strength`. The number of denoising steps depends\n on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising\n process runs for the full number of iterations specified in `num_inference_steps`. A value of 1\n essentially ignores `image`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference. This parameter is modulated by `strength`.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n sigmas (`List[float]`, *optional*):\n Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in\n their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed\n will be used.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of\n IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should\n contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not\n provided, embeddings are computed from the `ip_adapter_image` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):\n A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of\n each denoising step during the inference. with the following arguments: `callback_on_step_end(self:\n DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a\n list of all tensors as specified by `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n Examples:\n\n ```py\n >>> import PIL\n >>> import requests\n >>> import torch\n >>> from io import BytesIO\n\n >>> from diffusers import StableDiffusionInpaintPipeline\n\n\n >>> def download_image(url):\n ... response = requests.get(url)\n ... return PIL.Image.open(BytesIO(response.content)).convert(\"RGB\")\n\n\n >>> img_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\n >>> mask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\n\n >>> init_image = download_image(img_url).resize((512, 512))\n >>> mask_image = download_image(mask_url).resize((512, 512))\n\n >>> pipe = StableDiffusionInpaintPipeline.from_pretrained(\n ... \"runwayml/stable-diffusion-inpainting\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> prompt = \"Face of a yellow cat, high resolution, sitting on a park bench\"\n >>> image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]\n ```\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n\n if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):\n callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs\n self.check_inputs(\n prompt,\n image,\n mask_image,\n height,\n width,\n strength,\n callback_steps,\n output_type,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n ip_adapter_image,\n ip_adapter_image_embeds,\n callback_on_step_end_tensor_inputs,\n padding_mask_crop,\n )\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._interrupt = False\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=self.clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_images_per_prompt,\n self.do_classifier_free_guidance,\n )\n\n # 4. set timesteps\n timesteps, num_inference_steps = retrieve_timesteps(\n self.scheduler, num_inference_steps, device, timesteps, sigmas\n )\n timesteps, num_inference_steps = self.get_timesteps(\n num_inference_steps=num_inference_steps, strength=strength, device=device\n )\n # check that number of inference steps is not < 1 - as this doesn't make sense\n if num_inference_steps < 1:\n raise ValueError(\n f\"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline\"\n f\"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline.\"\n )\n # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise\n is_strength_max = strength == 1.0\n\n # 5. Preprocess mask and image\n\n if padding_mask_crop is not None:\n crops_coords = self.mask_processor.get_crop_region(mask_image, width, height, pad=padding_mask_crop)\n resize_mode = \"fill\"\n else:\n crops_coords = None\n resize_mode = \"default\"\n\n original_image = image\n init_image = self.image_processor.preprocess(\n image, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode\n )\n init_image = init_image.to(dtype=torch.float32)\n\n # 6. Prepare latent variables\n num_channels_latents = self.vae.config.latent_channels\n num_channels_unet = self.unet.config.in_channels\n return_image_latents = num_channels_unet == 4\n\n latents_outputs = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n image=init_image,\n timestep=latent_timestep,\n is_strength_max=is_strength_max,\n return_noise=True,\n return_image_latents=return_image_latents,\n )\n\n if return_image_latents:\n latents, noise, image_latents = latents_outputs\n else:\n latents, noise = latents_outputs\n\n # 7. Prepare mask latent variables\n mask_condition = self.mask_processor.preprocess(\n mask_image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords\n )\n\n if masked_image_latents is None:\n masked_image = init_image * (mask_condition < 0.5)\n else:\n masked_image = masked_image_latents\n\n mask, masked_image_latents = self.prepare_mask_latents(\n mask_condition,\n masked_image,\n batch_size * num_images_per_prompt,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n self.do_classifier_free_guidance,\n )\n\n # 8. Check that sizes of mask, masked image and latents match\n if num_channels_unet == 9:\n # default case for runwayml/stable-diffusion-inpainting\n num_channels_mask = mask.shape[1]\n num_channels_masked_image = masked_image_latents.shape[1]\n if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:\n raise ValueError(\n f\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\"\n f\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\"\n f\" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}\"\n f\" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of\"\n \" `pipeline.unet` or your `mask_image` or `image` input.\"\n )\n elif num_channels_unet != 4:\n raise ValueError(\n f\"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}.\"\n )\n\n # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 9.1 Add image embeds for IP-Adapter\n added_cond_kwargs = (\n {\"image_embeds\": image_embeds}\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None\n else None\n )\n\n # 9.2 Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n # 10. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n if self.interrupt:\n continue\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n\n # concat latents, mask, masked_image_latents in the channel dimension\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n if num_channels_unet == 9:\n latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=self.cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n if num_channels_unet == 4:\n init_latents_proper = image_latents\n if self.do_classifier_free_guidance:\n init_mask, _ = mask.chunk(2)\n else:\n init_mask = mask\n\n if i < len(timesteps) - 1:\n noise_timestep = timesteps[i + 1]\n init_latents_proper = self.scheduler.add_noise(\n init_latents_proper, noise, torch.tensor([noise_timestep])\n )\n\n latents = (1 - init_mask) * init_latents_proper + init_mask * latents\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n mask = callback_outputs.pop(\"mask\", mask)\n masked_image_latents = callback_outputs.pop(\"masked_image_latents\", masked_image_latents)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n condition_kwargs = {}\n if isinstance(self.vae, AsymmetricAutoencoderKL):\n init_image = init_image.to(device=device, dtype=masked_image_latents.dtype)\n init_image_condition = init_image.clone()\n init_image = self._encode_vae_image(init_image, generator=generator)\n mask_condition = mask_condition.to(device=device, dtype=masked_image_latents.dtype)\n condition_kwargs = {\"image\": init_image_condition, \"mask\": mask_condition}\n image = self.vae.decode(\n latents / self.vae.config.scaling_factor, return_dict=False, generator=generator, **condition_kwargs\n )[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n if padding_mask_crop is not None:\n image = [self.image_processor.apply_overlay(mask_image, original_image, i, crops_coords) for i in image]\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py", "content": "# Copyright 2024 The InstructPix2Pix Authors and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...callbacks import MultiPipelineCallbacks, PipelineCallback\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import PIL_INTERPOLATION, deprecate, logging\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom . import StableDiffusionPipelineOutput\nfrom .safety_checker import StableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess\ndef preprocess(image):\n deprecation_message = \"The preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead\"\n deprecate(\"preprocess\", \"1.0.0\", deprecation_message, standard_warn=False)\n if isinstance(image, torch.Tensor):\n return image\n elif isinstance(image, PIL.Image.Image):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n w, h = image[0].size\n w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8\n\n image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION[\"lanczos\"]))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n image = np.array(image).astype(np.float32) / 255.0\n image = image.transpose(0, 3, 1, 2)\n image = 2.0 * image - 1.0\n image = torch.from_numpy(image)\n elif isinstance(image[0], torch.Tensor):\n image = torch.cat(image, dim=0)\n return image\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n\nclass StableDiffusionInstructPix2PixPipeline(\n DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, LoraLoaderMixin, IPAdapterMixin\n):\n r\"\"\"\n Pipeline for pixel-level image editing by following text instructions (based on Stable Diffusion).\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"image_encoder\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"image_latents\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder: Optional[CLIPVisionModelWithProjection] = None,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: PipelineImageInput = None,\n num_inference_steps: int = 100,\n guidance_scale: float = 7.5,\n image_guidance_scale: float = 1.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback_on_step_end: Optional[\n Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]\n ] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n mask_img: Optional[PipelineImageInput] = None,\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n image (`torch.Tensor` `np.ndarray`, `PIL.Image.Image`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image` or tensor representing an image batch to be repainted according to `prompt`. Can also accept\n image latents as `image`, but if passing latents directly it is not encoded again.\n num_inference_steps (`int`, *optional*, defaults to 100):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n image_guidance_scale (`float`, *optional*, defaults to 1.5):\n Push the generated image towards the initial `image`. Image guidance scale is enabled by setting\n `image_guidance_scale > 1`. Higher image guidance scale encourages generated images that are closely\n linked to the source `image`, usually at the expense of lower image quality. This pipeline requires a\n value of at least `1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*):\n Optional image input to work with IP Adapters.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):\n A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of\n each denoising step during the inference. with the following arguments: `callback_on_step_end(self:\n DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a\n list of all tensors as specified by `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n\n Examples:\n\n ```py\n >>> import PIL\n >>> import requests\n >>> import torch\n >>> from io import BytesIO\n\n >>> from diffusers import StableDiffusionInstructPix2PixPipeline\n\n\n >>> def download_image(url):\n ... response = requests.get(url)\n ... return PIL.Image.open(BytesIO(response.content)).convert(\"RGB\")\n\n\n >>> img_url = \"https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png\"\n\n >>> image = download_image(img_url).resize((512, 512))\n\n >>> pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(\n ... \"timbrooks/instruct-pix2pix\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> prompt = \"make the mountains snowy\"\n >>> image = pipe(prompt=prompt, image=image).images[0]\n ```\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n\n if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):\n callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs\n\n # 0. Check inputs\n self.check_inputs(\n prompt,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n ip_adapter_image,\n ip_adapter_image_embeds,\n callback_on_step_end_tensor_inputs,\n )\n self._guidance_scale = guidance_scale\n self._image_guidance_scale = image_guidance_scale\n\n device = self._execution_device\n\n if image is None:\n raise ValueError(\"`image` input cannot be undefined.\")\n\n # 1. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 2. Encode input prompt\n prompt_embeds = self._encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_images_per_prompt,\n self.do_classifier_free_guidance,\n )\n # 3. Preprocess image\n image = self.image_processor.preprocess(image)\n\n # 4. set timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare Image latents\n image_latents = self.prepare_image_latents(\n image,\n batch_size,\n num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n self.do_classifier_free_guidance,\n )\n\n height, width = image_latents.shape[-2:]\n height = height * self.vae_scale_factor\n width = width * self.vae_scale_factor\n\n # 6. Prepare latent variables\n num_channels_latents = self.vae.config.latent_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 7. Check that shapes of latents and image match the UNet channels\n num_channels_image = image_latents.shape[1]\n if mask_img is not None:\n mask_img = self.image_processor.preprocess(mask_img)\n mask_image_latents = self.prepare_image_latents(\n mask_img,\n batch_size,\n num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n self.do_classifier_free_guidance,\n )\n\n num_channels_image += mask_image_latents.shape[1]\n if num_channels_latents + num_channels_image != self.unet.config.in_channels:\n raise ValueError(\n f\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\"\n f\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\"\n f\" `num_channels_image`: {num_channels_image} \"\n f\" = {num_channels_latents+num_channels_image}. Please verify the config of\"\n \" `pipeline.unet` or your `image` input.\"\n )\n\n # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 8.1 Add image embeds for IP-Adapter\n added_cond_kwargs = {\"image_embeds\": image_embeds} if ip_adapter_image is not None else None\n\n # 9. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # Expand the latents if we are doing classifier free guidance.\n # The latents are expanded 3 times because for pix2pix the guidance\\\n # is applied for both the text and the input image.\n latent_model_input = torch.cat([latents] * 3) if self.do_classifier_free_guidance else latents\n\n # concat latents, image_latents in the channel dimension\n scaled_latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n scaled_latent_model_input = torch.cat([scaled_latent_model_input, image_latents], dim=1)\n if mask_img is not None:\n scaled_latent_model_input = torch.cat([scaled_latent_model_input, mask_image_latents], dim=1)\n # predict the noise residual\n noise_pred = self.unet(\n scaled_latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n added_cond_kwargs=added_cond_kwargs,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_text, noise_pred_image, noise_pred_uncond = noise_pred.chunk(3)\n noise_pred = (\n noise_pred_uncond\n + self.guidance_scale * (noise_pred_text - noise_pred_image)\n + self.image_guidance_scale * (noise_pred_image - noise_pred_uncond)\n )\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n image_latents = callback_outputs.pop(\"image_latents\", image_latents)\n if mask_img is not None:\n mask_image_latents = callback_outputs.pop(\"mask_image_latents\", mask_image_latents)\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_ prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n \"\"\"\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n else:\n prompt_embeds_dtype = self.unet.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n # pix2pix has two negative embeddings, and unlike in other pipelines latents are ordered [prompt_embeds, negative_prompt_embeds, negative_prompt_embeds]\n prompt_embeds = torch.cat([prompt_embeds, negative_prompt_embeds, negative_prompt_embeds])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat(\n [single_image_embeds, single_negative_image_embeds, single_negative_image_embeds]\n )\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n (\n single_image_embeds,\n single_negative_image_embeds,\n single_negative_image_embeds,\n ) = single_image_embeds.chunk(3)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat(\n [single_image_embeds, single_negative_image_embeds, single_negative_image_embeds]\n )\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n def check_inputs(\n self,\n prompt,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n if ip_adapter_image_embeds is not None:\n if not isinstance(ip_adapter_image_embeds, list):\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}\"\n )\n elif ip_adapter_image_embeds[0].ndim not in [3, 4]:\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def prepare_image_latents(\n self, image, batch_size, num_images_per_prompt, dtype, device, do_classifier_free_guidance, generator=None\n ):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n\n if image.shape[1] == 4:\n image_latents = image\n else:\n image_latents = retrieve_latents(self.vae.encode(image), sample_mode=\"argmax\")\n\n if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:\n # expand image_latents for batch_size\n deprecation_message = (\n f\"You have passed {batch_size} text prompts (`prompt`), but only {image_latents.shape[0]} initial\"\n \" images (`image`). Initial images are now duplicating to match the number of text prompts. Note\"\n \" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update\"\n \" your script to pass as many initial images as text prompts to suppress this warning.\"\n )\n deprecate(\"len(prompt) != len(image)\", \"1.0.0\", deprecation_message, standard_warn=False)\n additional_image_per_prompt = batch_size // image_latents.shape[0]\n image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)\n elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n image_latents = torch.cat([image_latents], dim=0)\n\n if do_classifier_free_guidance:\n uncond_image_latents = torch.zeros_like(image_latents)\n image_latents = torch.cat([image_latents, image_latents, uncond_image_latents], dim=0)\n\n return image_latents\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def image_guidance_scale(self):\n return self._image_guidance_scale\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self.guidance_scale > 1.0 and self.image_guidance_scale >= 1.0\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport warnings\nfrom typing import Callable, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import FromSingleFileMixin\nfrom ...models import AutoencoderKL, UNet2DConditionModel\nfrom ...schedulers import EulerDiscreteScheduler\nfrom ...utils import deprecate, logging\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput, StableDiffusionMixin\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.preprocess\ndef preprocess(image):\n warnings.warn(\n \"The preprocess method is deprecated and will be removed in a future version. Please\"\n \" use VaeImageProcessor.preprocess instead\",\n FutureWarning,\n )\n if isinstance(image, torch.Tensor):\n return image\n elif isinstance(image, PIL.Image.Image):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n w, h = image[0].size\n w, h = (x - x % 64 for x in (w, h)) # resize to integer multiple of 64\n\n image = [np.array(i.resize((w, h)))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n image = np.array(image).astype(np.float32) / 255.0\n image = image.transpose(0, 3, 1, 2)\n image = 2.0 * image - 1.0\n image = torch.from_numpy(image)\n elif isinstance(image[0], torch.Tensor):\n image = torch.cat(image, dim=0)\n return image\n\n\nclass StableDiffusionLatentUpscalePipeline(DiffusionPipeline, StableDiffusionMixin, FromSingleFileMixin):\n r\"\"\"\n Pipeline for upscaling Stable Diffusion output image resolution by a factor of 2.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A [`EulerDiscreteScheduler`] to be used in combination with `unet` to denoise the encoded image latents.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: EulerDiscreteScheduler,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, resample=\"bicubic\")\n\n def _encode_prompt(self, prompt, device, do_classifier_free_guidance, negative_prompt):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `list(int)`):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n \"\"\"\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_length=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n text_encoder_out = self.text_encoder(\n text_input_ids.to(device),\n output_hidden_states=True,\n )\n text_embeddings = text_encoder_out.hidden_states[-1]\n text_pooler_out = text_encoder_out.pooler_output\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = text_input_ids.shape[-1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_length=True,\n return_tensors=\"pt\",\n )\n\n uncond_encoder_out = self.text_encoder(\n uncond_input.input_ids.to(device),\n output_hidden_states=True,\n )\n\n uncond_embeddings = uncond_encoder_out.hidden_states[-1]\n uncond_pooler_out = uncond_encoder_out.pooler_output\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n text_pooler_out = torch.cat([uncond_pooler_out, text_pooler_out])\n\n return text_embeddings, text_pooler_out\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n def check_inputs(self, prompt, image, callback_steps):\n if not isinstance(prompt, str) and not isinstance(prompt, list):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if (\n not isinstance(image, torch.Tensor)\n and not isinstance(image, PIL.Image.Image)\n and not isinstance(image, list)\n ):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or `list` but is {type(image)}\"\n )\n\n # verify batch size of prompt and image are same if image is a list or tensor\n if isinstance(image, (list, torch.Tensor)):\n if isinstance(prompt, str):\n batch_size = 1\n else:\n batch_size = len(prompt)\n if isinstance(image, list):\n image_batch_size = len(image)\n else:\n image_batch_size = image.shape[0] if image.ndim == 4 else 1\n if batch_size != image_batch_size:\n raise ValueError(\n f\"`prompt` has batch size {batch_size} and `image` has batch size {image_batch_size}.\"\n \" Please make sure that passed `prompt` matches the batch size of `image`.\"\n )\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (batch_size, num_channels_latents, height, width)\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n image: PipelineImageInput = None,\n num_inference_steps: int = 75,\n guidance_scale: float = 9.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide image upscaling.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image` or tensor representing an image batch to be upscaled. If it's a tensor, it can be either a\n latent output from a Stable Diffusion model or an image tensor in the range `[-1, 1]`. It is considered\n a `latent` if `image.shape[1]` is `4`; otherwise, it is considered to be an image representation and\n encoded using this pipeline's `vae` encoder.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n\n Examples:\n ```py\n >>> from diffusers import StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline\n >>> import torch\n\n\n >>> pipeline = StableDiffusionPipeline.from_pretrained(\n ... \"CompVis/stable-diffusion-v1-4\", torch_dtype=torch.float16\n ... )\n >>> pipeline.to(\"cuda\")\n\n >>> model_id = \"stabilityai/sd-x2-latent-upscaler\"\n >>> upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)\n >>> upscaler.to(\"cuda\")\n\n >>> prompt = \"a photo of an astronaut high resolution, unreal engine, ultra realistic\"\n >>> generator = torch.manual_seed(33)\n\n >>> low_res_latents = pipeline(prompt, generator=generator, output_type=\"latent\").images\n\n >>> with torch.no_grad():\n ... image = pipeline.decode_latents(low_res_latents)\n >>> image = pipeline.numpy_to_pil(image)[0]\n\n >>> image.save(\"../images/a1.png\")\n\n >>> upscaled_image = upscaler(\n ... prompt=prompt,\n ... image=low_res_latents,\n ... num_inference_steps=20,\n ... guidance_scale=0,\n ... generator=generator,\n ... ).images[0]\n\n >>> upscaled_image.save(\"../images/a2.png\")\n ```\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images.\n \"\"\"\n\n # 1. Check inputs\n self.check_inputs(prompt, image, callback_steps)\n\n # 2. Define call parameters\n batch_size = 1 if isinstance(prompt, str) else len(prompt)\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n if guidance_scale == 0:\n prompt = [\"\"] * batch_size\n\n # 3. Encode input prompt\n text_embeddings, text_pooler_out = self._encode_prompt(\n prompt, device, do_classifier_free_guidance, negative_prompt\n )\n\n # 4. Preprocess image\n image = self.image_processor.preprocess(image)\n image = image.to(dtype=text_embeddings.dtype, device=device)\n if image.shape[1] == 3:\n # encode image if not in latent-space yet\n image = self.vae.encode(image).latent_dist.sample() * self.vae.config.scaling_factor\n\n # 5. set timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n batch_multiplier = 2 if do_classifier_free_guidance else 1\n image = image[None, :] if image.ndim == 3 else image\n image = torch.cat([image] * batch_multiplier)\n\n # 5. Add noise to image (set to be 0):\n # (see below notes from the author):\n # \"the This step theoretically can make the model work better on out-of-distribution inputs, but mostly just seems to make it match the input less, so it's turned off by default.\"\n noise_level = torch.tensor([0.0], dtype=torch.float32, device=device)\n noise_level = torch.cat([noise_level] * image.shape[0])\n inv_noise_level = (noise_level**2 + 1) ** (-0.5)\n\n image_cond = F.interpolate(image, scale_factor=2, mode=\"nearest\") * inv_noise_level[:, None, None, None]\n image_cond = image_cond.to(text_embeddings.dtype)\n\n noise_level_embed = torch.cat(\n [\n torch.ones(text_pooler_out.shape[0], 64, dtype=text_pooler_out.dtype, device=device),\n torch.zeros(text_pooler_out.shape[0], 64, dtype=text_pooler_out.dtype, device=device),\n ],\n dim=1,\n )\n\n timestep_condition = torch.cat([noise_level_embed, text_pooler_out], dim=1)\n\n # 6. Prepare latent variables\n height, width = image.shape[2:]\n num_channels_latents = self.vae.config.latent_channels\n latents = self.prepare_latents(\n batch_size,\n num_channels_latents,\n height * 2, # 2x upscale\n width * 2,\n text_embeddings.dtype,\n device,\n generator,\n latents,\n )\n\n # 7. Check that sizes of image and latents match\n num_channels_image = image.shape[1]\n if num_channels_latents + num_channels_image != self.unet.config.in_channels:\n raise ValueError(\n f\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\"\n f\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\"\n f\" `num_channels_image`: {num_channels_image} \"\n f\" = {num_channels_latents+num_channels_image}. Please verify the config of\"\n \" `pipeline.unet` or your `image` input.\"\n )\n\n # 9. Denoising loop\n num_warmup_steps = 0\n\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n sigma = self.scheduler.sigmas[i]\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n scaled_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n scaled_model_input = torch.cat([scaled_model_input, image_cond], dim=1)\n # preconditioning parameter based on Karras et al. (2022) (table 1)\n timestep = torch.log(sigma) * 0.25\n\n noise_pred = self.unet(\n scaled_model_input,\n timestep,\n encoder_hidden_states=text_embeddings,\n timestep_cond=timestep_condition,\n ).sample\n\n # in original repo, the output contains a variance channel that's not used\n noise_pred = noise_pred[:, :-1]\n\n # apply preconditioning, based on table 1 in Karras et al. (2022)\n inv_sigma = 1 / (sigma**2 + 1)\n noise_pred = inv_sigma * latent_model_input + self.scheduler.scale_model_input(sigma, t) * noise_pred\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n else:\n image = latents\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nimport warnings\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, UNet2DConditionModel\nfrom ...models.attention_processor import (\n AttnProcessor2_0,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n XFormersAttnProcessor,\n)\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import DDPMScheduler, KarrasDiffusionSchedulers\nfrom ...utils import USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom . import StableDiffusionPipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef preprocess(image):\n warnings.warn(\n \"The preprocess method is deprecated and will be removed in a future version. Please\"\n \" use VaeImageProcessor.preprocess instead\",\n FutureWarning,\n )\n if isinstance(image, torch.Tensor):\n return image\n elif isinstance(image, PIL.Image.Image):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n w, h = image[0].size\n w, h = (x - x % 64 for x in (w, h)) # resize to integer multiple of 64\n\n image = [np.array(i.resize((w, h)))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n image = np.array(image).astype(np.float32) / 255.0\n image = image.transpose(0, 3, 1, 2)\n image = 2.0 * image - 1.0\n image = torch.from_numpy(image)\n elif isinstance(image[0], torch.Tensor):\n image = torch.cat(image, dim=0)\n return image\n\n\nclass StableDiffusionUpscalePipeline(\n DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin\n):\n r\"\"\"\n Pipeline for text-guided image super-resolution using Stable Diffusion 2.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n low_res_scheduler ([`SchedulerMixin`]):\n A scheduler used to add initial noise to the low resolution conditioning image. It must be an instance of\n [`DDPMScheduler`].\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\"watermarker\", \"safety_checker\", \"feature_extractor\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n low_res_scheduler: DDPMScheduler,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: Optional[Any] = None,\n feature_extractor: Optional[CLIPImageProcessor] = None,\n watermarker: Optional[Any] = None,\n max_noise_level: int = 350,\n ):\n super().__init__()\n\n if hasattr(\n vae, \"config\"\n ): # check if vae has a config attribute `scaling_factor` and if it is set to 0.08333, else set it to 0.08333 and deprecate\n is_vae_scaling_factor_set_to_0_08333 = (\n hasattr(vae.config, \"scaling_factor\") and vae.config.scaling_factor == 0.08333\n )\n if not is_vae_scaling_factor_set_to_0_08333:\n deprecation_message = (\n \"The configuration file of the vae does not contain `scaling_factor` or it is set to\"\n f\" {vae.config.scaling_factor}, which seems highly unlikely. If your checkpoint is a fine-tuned\"\n \" version of `stabilityai/stable-diffusion-x4-upscaler` you should change 'scaling_factor' to\"\n \" 0.08333 Please make sure to update the config accordingly, as not doing so might lead to\"\n \" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging\"\n \" Face Hub, it would be very nice if you could open a Pull Request for the `vae/config.json` file\"\n )\n deprecate(\"wrong scaling_factor\", \"1.0.0\", deprecation_message, standard_warn=False)\n vae.register_to_config(scaling_factor=0.08333)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n low_res_scheduler=low_res_scheduler,\n scheduler=scheduler,\n safety_checker=safety_checker,\n watermarker=watermarker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, resample=\"bicubic\")\n self.register_to_config(max_noise_level=max_noise_level)\n\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is not None:\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, nsfw_detected, watermark_detected = self.safety_checker(\n images=image,\n clip_input=safety_checker_input.pixel_values.to(dtype=dtype),\n )\n else:\n nsfw_detected = None\n watermark_detected = None\n\n if hasattr(self, \"unet_offload_hook\") and self.unet_offload_hook is not None:\n self.unet_offload_hook.offload()\n\n return image, nsfw_detected, watermark_detected\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n def check_inputs(\n self,\n prompt,\n image,\n noise_level,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if (\n not isinstance(image, torch.Tensor)\n and not isinstance(image, PIL.Image.Image)\n and not isinstance(image, np.ndarray)\n and not isinstance(image, list)\n ):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `np.ndarray`, `PIL.Image.Image` or `list` but is {type(image)}\"\n )\n\n # verify batch size of prompt and image are same if image is a list or tensor or numpy array\n if isinstance(image, (list, np.ndarray, torch.Tensor)):\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if isinstance(image, list):\n image_batch_size = len(image)\n else:\n image_batch_size = image.shape[0]\n if batch_size != image_batch_size:\n raise ValueError(\n f\"`prompt` has batch size {batch_size} and `image` has batch size {image_batch_size}.\"\n \" Please make sure that passed `prompt` matches the batch size of `image`.\"\n )\n\n # check noise level\n if noise_level > self.config.max_noise_level:\n raise ValueError(f\"`noise_level` has to be <= {self.config.max_noise_level} but is {noise_level}\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (batch_size, num_channels_latents, height, width)\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def upcast_vae(self):\n dtype = self.vae.dtype\n self.vae.to(dtype=torch.float32)\n use_torch_2_0_or_xformers = isinstance(\n self.vae.decoder.mid_block.attentions[0].processor,\n (\n AttnProcessor2_0,\n XFormersAttnProcessor,\n LoRAXFormersAttnProcessor,\n LoRAAttnProcessor2_0,\n ),\n )\n # if xformers or torch_2_0 is used attention block does not need\n # to be in float32 which can save lots of memory\n if use_torch_2_0_or_xformers:\n self.vae.post_quant_conv.to(dtype)\n self.vae.decoder.conv_in.to(dtype)\n self.vae.decoder.mid_block.to(dtype)\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: PipelineImageInput = None,\n num_inference_steps: int = 75,\n guidance_scale: float = 9.0,\n noise_level: int = 20,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: int = None,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):\n `Image` or tensor representing an image batch to be upscaled.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n Examples:\n ```py\n >>> import requests\n >>> from PIL import Image\n >>> from io import BytesIO\n >>> from diffusers import StableDiffusionUpscalePipeline\n >>> import torch\n\n >>> # load model and scheduler\n >>> model_id = \"stabilityai/stable-diffusion-x4-upscaler\"\n >>> pipeline = StableDiffusionUpscalePipeline.from_pretrained(\n ... model_id, revision=\"fp16\", torch_dtype=torch.float16\n ... )\n >>> pipeline = pipeline.to(\"cuda\")\n\n >>> # let's download an image\n >>> url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png\"\n >>> response = requests.get(url)\n >>> low_res_img = Image.open(BytesIO(response.content)).convert(\"RGB\")\n >>> low_res_img = low_res_img.resize((128, 128))\n >>> prompt = \"a white cat\"\n\n >>> upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]\n >>> upscaled_image.save(\"upsampled_cat.png\")\n ```\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n\n # 1. Check inputs\n self.check_inputs(\n prompt,\n image,\n noise_level,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n )\n\n if image is None:\n raise ValueError(\"`image` input cannot be undefined.\")\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Preprocess image\n image = self.image_processor.preprocess(image)\n image = image.to(dtype=prompt_embeds.dtype, device=device)\n\n # 5. set timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Add noise to image\n noise_level = torch.tensor([noise_level], dtype=torch.long, device=device)\n noise = randn_tensor(image.shape, generator=generator, device=device, dtype=prompt_embeds.dtype)\n image = self.low_res_scheduler.add_noise(image, noise, noise_level)\n\n batch_multiplier = 2 if do_classifier_free_guidance else 1\n image = torch.cat([image] * batch_multiplier * num_images_per_prompt)\n noise_level = torch.cat([noise_level] * image.shape[0])\n\n # 6. Prepare latent variables\n height, width = image.shape[2:]\n num_channels_latents = self.vae.config.latent_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 7. Check that sizes of image and latents match\n num_channels_image = image.shape[1]\n if num_channels_latents + num_channels_image != self.unet.config.in_channels:\n raise ValueError(\n f\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\"\n f\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\"\n f\" `num_channels_image`: {num_channels_image} \"\n f\" = {num_channels_latents+num_channels_image}. Please verify the config of\"\n \" `pipeline.unet` or your `image` input.\"\n )\n\n # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 9. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n\n # concat latents, mask, masked_image_latents in the channel dimension\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n latent_model_input = torch.cat([latent_model_input, image], dim=1)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n class_labels=noise_level,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n\n # Ensure latents are always the same type as the VAE\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n\n image, has_nsfw_concept, _ = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # 11. Apply watermark\n if output_type == \"pil\" and self.watermarker is not None:\n image = self.watermarker.apply_watermark(image)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport torch\nfrom transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer\nfrom transformers.models.clip.modeling_clip import CLIPTextModelOutput\n\nfrom ...image_processor import VaeImageProcessor\nfrom ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, PriorTransformer, UNet2DConditionModel\nfrom ...models.embeddings import get_timestep_embedding\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput, StableDiffusionMixin\nfrom .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableUnCLIPPipeline\n\n >>> pipe = StableUnCLIPPipeline.from_pretrained(\n ... \"fusing/stable-unclip-2-1-l\", torch_dtype=torch.float16\n ... ) # TODO update model path\n >>> pipe = pipe.to(\"cuda\")\n\n >>> prompt = \"a photo of an astronaut riding a horse on mars\"\n >>> images = pipe(prompt).images\n >>> images[0].save(\"astronaut_horse.png\")\n ```\n\"\"\"\n\n\nclass StableUnCLIPPipeline(DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, LoraLoaderMixin):\n \"\"\"\n Pipeline for text-to-image generation using stable unCLIP.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n\n Args:\n prior_tokenizer ([`CLIPTokenizer`]):\n A [`CLIPTokenizer`].\n prior_text_encoder ([`CLIPTextModelWithProjection`]):\n Frozen [`CLIPTextModelWithProjection`] text-encoder.\n prior ([`PriorTransformer`]):\n The canonical unCLIP prior to approximate the image embedding from the text embedding.\n prior_scheduler ([`KarrasDiffusionSchedulers`]):\n Scheduler used in the prior denoising process.\n image_normalizer ([`StableUnCLIPImageNormalizer`]):\n Used to normalize the predicted image embeddings before the noise is applied and un-normalize the image\n embeddings after the noise has been applied.\n image_noising_scheduler ([`KarrasDiffusionSchedulers`]):\n Noise schedule for adding noise to the predicted image embeddings. The amount of noise to add is determined\n by the `noise_level`.\n tokenizer ([`CLIPTokenizer`]):\n A [`CLIPTokenizer`].\n text_encoder ([`CLIPTextModel`]):\n Frozen [`CLIPTextModel`] text-encoder.\n unet ([`UNet2DConditionModel`]):\n A [`UNet2DConditionModel`] to denoise the encoded image latents.\n scheduler ([`KarrasDiffusionSchedulers`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents.\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n \"\"\"\n\n _exclude_from_cpu_offload = [\"prior\", \"image_normalizer\"]\n model_cpu_offload_seq = \"text_encoder->prior_text_encoder->unet->vae\"\n\n # prior components\n prior_tokenizer: CLIPTokenizer\n prior_text_encoder: CLIPTextModelWithProjection\n prior: PriorTransformer\n prior_scheduler: KarrasDiffusionSchedulers\n\n # image noising components\n image_normalizer: StableUnCLIPImageNormalizer\n image_noising_scheduler: KarrasDiffusionSchedulers\n\n # regular denoising components\n tokenizer: CLIPTokenizer\n text_encoder: CLIPTextModel\n unet: UNet2DConditionModel\n scheduler: KarrasDiffusionSchedulers\n\n vae: AutoencoderKL\n\n def __init__(\n self,\n # prior components\n prior_tokenizer: CLIPTokenizer,\n prior_text_encoder: CLIPTextModelWithProjection,\n prior: PriorTransformer,\n prior_scheduler: KarrasDiffusionSchedulers,\n # image noising components\n image_normalizer: StableUnCLIPImageNormalizer,\n image_noising_scheduler: KarrasDiffusionSchedulers,\n # regular denoising components\n tokenizer: CLIPTokenizer,\n text_encoder: CLIPTextModelWithProjection,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n # vae\n vae: AutoencoderKL,\n ):\n super().__init__()\n\n self.register_modules(\n prior_tokenizer=prior_tokenizer,\n prior_text_encoder=prior_text_encoder,\n prior=prior,\n prior_scheduler=prior_scheduler,\n image_normalizer=image_normalizer,\n image_noising_scheduler=image_noising_scheduler,\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n unet=unet,\n scheduler=scheduler,\n vae=vae,\n )\n\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline._encode_prompt with _encode_prompt->_encode_prior_prompt, tokenizer->prior_tokenizer, text_encoder->prior_text_encoder\n def _encode_prior_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,\n text_attention_mask: Optional[torch.Tensor] = None,\n ):\n if text_model_output is None:\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n # get prompt text embeddings\n text_inputs = self.prior_tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.prior_tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n text_mask = text_inputs.attention_mask.bool().to(device)\n\n untruncated_ids = self.prior_tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.prior_tokenizer.batch_decode(\n untruncated_ids[:, self.prior_tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.prior_tokenizer.model_max_length} tokens: {removed_text}\"\n )\n text_input_ids = text_input_ids[:, : self.prior_tokenizer.model_max_length]\n\n prior_text_encoder_output = self.prior_text_encoder(text_input_ids.to(device))\n\n prompt_embeds = prior_text_encoder_output.text_embeds\n text_enc_hid_states = prior_text_encoder_output.last_hidden_state\n\n else:\n batch_size = text_model_output[0].shape[0]\n prompt_embeds, text_enc_hid_states = text_model_output[0], text_model_output[1]\n text_mask = text_attention_mask\n\n prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n text_enc_hid_states = text_enc_hid_states.repeat_interleave(num_images_per_prompt, dim=0)\n text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n if do_classifier_free_guidance:\n uncond_tokens = [\"\"] * batch_size\n\n uncond_input = self.prior_tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=self.prior_tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n uncond_text_mask = uncond_input.attention_mask.bool().to(device)\n negative_prompt_embeds_prior_text_encoder_output = self.prior_text_encoder(\n uncond_input.input_ids.to(device)\n )\n\n negative_prompt_embeds = negative_prompt_embeds_prior_text_encoder_output.text_embeds\n uncond_text_enc_hid_states = negative_prompt_embeds_prior_text_encoder_output.last_hidden_state\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)\n\n seq_len = uncond_text_enc_hid_states.shape[1]\n uncond_text_enc_hid_states = uncond_text_enc_hid_states.repeat(1, num_images_per_prompt, 1)\n uncond_text_enc_hid_states = uncond_text_enc_hid_states.view(\n batch_size * num_images_per_prompt, seq_len, -1\n )\n uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n # done duplicates\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n text_enc_hid_states = torch.cat([uncond_text_enc_hid_states, text_enc_hid_states])\n\n text_mask = torch.cat([uncond_text_mask, text_mask])\n\n return prompt_embeds, text_enc_hid_states, text_mask\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs with prepare_extra_step_kwargs->prepare_prior_extra_step_kwargs, scheduler->prior_scheduler\n def prepare_prior_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the prior_scheduler step, since not all prior_schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other prior_schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.prior_scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the prior_scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.prior_scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n noise_level,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Please make sure to define only one of the two.\"\n )\n\n if prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n\n if prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n \"Provide either `negative_prompt` or `negative_prompt_embeds`. Cannot leave both `negative_prompt` and `negative_prompt_embeds` undefined.\"\n )\n\n if prompt is not None and negative_prompt is not None:\n if type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if noise_level < 0 or noise_level >= self.image_noising_scheduler.config.num_train_timesteps:\n raise ValueError(\n f\"`noise_level` must be between 0 and {self.image_noising_scheduler.config.num_train_timesteps - 1}, inclusive.\"\n )\n\n # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents\n def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n latents = latents * scheduler.init_noise_sigma\n return latents\n\n def noise_image_embeddings(\n self,\n image_embeds: torch.Tensor,\n noise_level: int,\n noise: Optional[torch.Tensor] = None,\n generator: Optional[torch.Generator] = None,\n ):\n \"\"\"\n Add noise to the image embeddings. The amount of noise is controlled by a `noise_level` input. A higher\n `noise_level` increases the variance in the final un-noised images.\n\n The noise is applied in two ways:\n 1. A noise schedule is applied directly to the embeddings.\n 2. A vector of sinusoidal time embeddings are appended to the output.\n\n In both cases, the amount of noise is controlled by the same `noise_level`.\n\n The embeddings are normalized before the noise is applied and un-normalized after the noise is applied.\n \"\"\"\n if noise is None:\n noise = randn_tensor(\n image_embeds.shape, generator=generator, device=image_embeds.device, dtype=image_embeds.dtype\n )\n\n noise_level = torch.tensor([noise_level] * image_embeds.shape[0], device=image_embeds.device)\n\n self.image_normalizer.to(image_embeds.device)\n image_embeds = self.image_normalizer.scale(image_embeds)\n\n image_embeds = self.image_noising_scheduler.add_noise(image_embeds, timesteps=noise_level, noise=noise)\n\n image_embeds = self.image_normalizer.unscale(image_embeds)\n\n noise_level = get_timestep_embedding(\n timesteps=noise_level, embedding_dim=image_embeds.shape[-1], flip_sin_to_cos=True, downscale_freq_shift=0\n )\n\n # `get_timestep_embeddings` does not contain any weights and will always return f32 tensors,\n # but we might actually be running in fp16. so we need to cast here.\n # there might be better ways to encapsulate this.\n noise_level = noise_level.to(image_embeds.dtype)\n\n image_embeds = torch.cat((image_embeds, noise_level), 1)\n\n return image_embeds\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n # regular denoising process args\n prompt: Optional[Union[str, List[str]]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 20,\n guidance_scale: float = 10.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n noise_level: int = 0,\n # prior args\n prior_num_inference_steps: int = 25,\n prior_guidance_scale: float = 4.0,\n prior_latents: Optional[torch.Tensor] = None,\n clip_skip: Optional[int] = None,\n ):\n \"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 20):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 10.0):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n noise_level (`int`, *optional*, defaults to `0`):\n The amount of noise to add to the image embeddings. A higher `noise_level` increases the variance in\n the final un-noised images. See [`StableUnCLIPPipeline.noise_image_embeddings`] for more details.\n prior_num_inference_steps (`int`, *optional*, defaults to 25):\n The number of denoising steps in the prior denoising process. More denoising steps usually lead to a\n higher quality image at the expense of slower inference.\n prior_guidance_scale (`float`, *optional*, defaults to 4.0):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n prior_latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n embedding generation in the prior denoising process. Can be used to tweak the same generation with\n different prompts. If not provided, a latents tensor is generated by sampling using the supplied random\n `generator`.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`:\n [`~ pipeline_utils.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning\n a tuple, the first element is a list with the generated images.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt=prompt,\n height=height,\n width=width,\n callback_steps=callback_steps,\n noise_level=noise_level,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n batch_size = batch_size * num_images_per_prompt\n\n device = self._execution_device\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n prior_do_classifier_free_guidance = prior_guidance_scale > 1.0\n\n # 3. Encode input prompt\n prior_prompt_embeds, prior_text_encoder_hidden_states, prior_text_mask = self._encode_prior_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=prior_do_classifier_free_guidance,\n )\n\n # 4. Prepare prior timesteps\n self.prior_scheduler.set_timesteps(prior_num_inference_steps, device=device)\n prior_timesteps_tensor = self.prior_scheduler.timesteps\n\n # 5. Prepare prior latent variables\n embedding_dim = self.prior.config.embedding_dim\n prior_latents = self.prepare_latents(\n (batch_size, embedding_dim),\n prior_prompt_embeds.dtype,\n device,\n generator,\n prior_latents,\n self.prior_scheduler,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n prior_extra_step_kwargs = self.prepare_prior_extra_step_kwargs(generator, eta)\n\n # 7. Prior denoising loop\n for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([prior_latents] * 2) if prior_do_classifier_free_guidance else prior_latents\n latent_model_input = self.prior_scheduler.scale_model_input(latent_model_input, t)\n\n predicted_image_embedding = self.prior(\n latent_model_input,\n timestep=t,\n proj_embedding=prior_prompt_embeds,\n encoder_hidden_states=prior_text_encoder_hidden_states,\n attention_mask=prior_text_mask,\n ).predicted_image_embedding\n\n if prior_do_classifier_free_guidance:\n predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)\n predicted_image_embedding = predicted_image_embedding_uncond + prior_guidance_scale * (\n predicted_image_embedding_text - predicted_image_embedding_uncond\n )\n\n prior_latents = self.prior_scheduler.step(\n predicted_image_embedding,\n timestep=t,\n sample=prior_latents,\n **prior_extra_step_kwargs,\n return_dict=False,\n )[0]\n\n if callback is not None and i % callback_steps == 0:\n callback(i, t, prior_latents)\n\n prior_latents = self.prior.post_process_latents(prior_latents)\n\n image_embeds = prior_latents\n\n # done prior\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 8. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 9. Prepare image embeddings\n image_embeds = self.noise_image_embeddings(\n image_embeds=image_embeds,\n noise_level=noise_level,\n generator=generator,\n )\n\n if do_classifier_free_guidance:\n negative_prompt_embeds = torch.zeros_like(image_embeds)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n image_embeds = torch.cat([negative_prompt_embeds, image_embeds])\n\n # 10. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 11. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n latents = self.prepare_latents(\n shape=shape,\n dtype=prompt_embeds.dtype,\n device=device,\n generator=generator,\n latents=latents,\n scheduler=self.scheduler,\n )\n\n # 12. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 13. Denoising loop\n for i, t in enumerate(self.progress_bar(timesteps)):\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n class_labels=image_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n else:\n image = latents\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport PIL.Image\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...image_processor import VaeImageProcessor\nfrom ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, UNet2DConditionModel\nfrom ...models.embeddings import get_timestep_embedding\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput, StableDiffusionMixin\nfrom .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import requests\n >>> import torch\n >>> from PIL import Image\n >>> from io import BytesIO\n\n >>> from diffusers import StableUnCLIPImg2ImgPipeline\n\n >>> pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(\n ... \"stabilityai/stable-diffusion-2-1-unclip-small\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> url = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n\n >>> response = requests.get(url)\n >>> init_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n >>> init_image = init_image.resize((768, 512))\n\n >>> prompt = \"A fantasy landscape, trending on artstation\"\n\n >>> images = pipe(init_image, prompt).images\n >>> images[0].save(\"fantasy_landscape.png\")\n ```\n\"\"\"\n\n\nclass StableUnCLIPImg2ImgPipeline(\n DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, LoraLoaderMixin\n):\n \"\"\"\n Pipeline for text-guided image-to-image generation using stable unCLIP.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n\n Args:\n feature_extractor ([`CLIPImageProcessor`]):\n Feature extractor for image pre-processing before being encoded.\n image_encoder ([`CLIPVisionModelWithProjection`]):\n CLIP vision model for encoding images.\n image_normalizer ([`StableUnCLIPImageNormalizer`]):\n Used to normalize the predicted image embeddings before the noise is applied and un-normalize the image\n embeddings after the noise has been applied.\n image_noising_scheduler ([`KarrasDiffusionSchedulers`]):\n Noise schedule for adding noise to the predicted image embeddings. The amount of noise to add is determined\n by the `noise_level`.\n tokenizer (`~transformers.CLIPTokenizer`):\n A [`~transformers.CLIPTokenizer`)].\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen [`~transformers.CLIPTextModel`] text-encoder.\n unet ([`UNet2DConditionModel`]):\n A [`UNet2DConditionModel`] to denoise the encoded image latents.\n scheduler ([`KarrasDiffusionSchedulers`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents.\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->image_encoder->unet->vae\"\n _exclude_from_cpu_offload = [\"image_normalizer\"]\n\n # image encoding components\n feature_extractor: CLIPImageProcessor\n image_encoder: CLIPVisionModelWithProjection\n\n # image noising components\n image_normalizer: StableUnCLIPImageNormalizer\n image_noising_scheduler: KarrasDiffusionSchedulers\n\n # regular denoising components\n tokenizer: CLIPTokenizer\n text_encoder: CLIPTextModel\n unet: UNet2DConditionModel\n scheduler: KarrasDiffusionSchedulers\n\n vae: AutoencoderKL\n\n def __init__(\n self,\n # image encoding components\n feature_extractor: CLIPImageProcessor,\n image_encoder: CLIPVisionModelWithProjection,\n # image noising components\n image_normalizer: StableUnCLIPImageNormalizer,\n image_noising_scheduler: KarrasDiffusionSchedulers,\n # regular denoising components\n tokenizer: CLIPTokenizer,\n text_encoder: CLIPTextModel,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n # vae\n vae: AutoencoderKL,\n ):\n super().__init__()\n\n self.register_modules(\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n image_normalizer=image_normalizer,\n image_noising_scheduler=image_noising_scheduler,\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n unet=unet,\n scheduler=scheduler,\n vae=vae,\n )\n\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n def _encode_image(\n self,\n image,\n device,\n batch_size,\n num_images_per_prompt,\n do_classifier_free_guidance,\n noise_level,\n generator,\n image_embeds,\n ):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if isinstance(image, PIL.Image.Image):\n # the image embedding should repeated so it matches the total batch size of the prompt\n repeat_by = batch_size\n else:\n # assume the image input is already properly batched and just needs to be repeated so\n # it matches the num_images_per_prompt.\n #\n # NOTE(will) this is probably missing a few number of side cases. I.e. batched/non-batched\n # `image_embeds`. If those happen to be common use cases, let's think harder about\n # what the expected dimensions of inputs should be and how we handle the encoding.\n repeat_by = num_images_per_prompt\n\n if image_embeds is None:\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(images=image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n image_embeds = self.image_encoder(image).image_embeds\n\n image_embeds = self.noise_image_embeddings(\n image_embeds=image_embeds,\n noise_level=noise_level,\n generator=generator,\n )\n\n # duplicate image embeddings for each generation per prompt, using mps friendly method\n image_embeds = image_embeds.unsqueeze(1)\n bs_embed, seq_len, _ = image_embeds.shape\n image_embeds = image_embeds.repeat(1, repeat_by, 1)\n image_embeds = image_embeds.view(bs_embed * repeat_by, seq_len, -1)\n image_embeds = image_embeds.squeeze(1)\n\n if do_classifier_free_guidance:\n negative_prompt_embeds = torch.zeros_like(image_embeds)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n image_embeds = torch.cat([negative_prompt_embeds, image_embeds])\n\n return image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n image,\n height,\n width,\n callback_steps,\n noise_level,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n image_embeds=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Please make sure to define only one of the two.\"\n )\n\n if prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n\n if prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n \"Provide either `negative_prompt` or `negative_prompt_embeds`. Cannot leave both `negative_prompt` and `negative_prompt_embeds` undefined.\"\n )\n\n if prompt is not None and negative_prompt is not None:\n if type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if noise_level < 0 or noise_level >= self.image_noising_scheduler.config.num_train_timesteps:\n raise ValueError(\n f\"`noise_level` must be between 0 and {self.image_noising_scheduler.config.num_train_timesteps - 1}, inclusive.\"\n )\n\n if image is not None and image_embeds is not None:\n raise ValueError(\n \"Provide either `image` or `image_embeds`. Please make sure to define only one of the two.\"\n )\n\n if image is None and image_embeds is None:\n raise ValueError(\n \"Provide either `image` or `image_embeds`. Cannot leave both `image` and `image_embeds` undefined.\"\n )\n\n if image is not None:\n if (\n not isinstance(image, torch.Tensor)\n and not isinstance(image, PIL.Image.Image)\n and not isinstance(image, list)\n ):\n raise ValueError(\n \"`image` has to be of type `torch.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is\"\n f\" {type(image)}\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_unclip.StableUnCLIPPipeline.noise_image_embeddings\n def noise_image_embeddings(\n self,\n image_embeds: torch.Tensor,\n noise_level: int,\n noise: Optional[torch.Tensor] = None,\n generator: Optional[torch.Generator] = None,\n ):\n \"\"\"\n Add noise to the image embeddings. The amount of noise is controlled by a `noise_level` input. A higher\n `noise_level` increases the variance in the final un-noised images.\n\n The noise is applied in two ways:\n 1. A noise schedule is applied directly to the embeddings.\n 2. A vector of sinusoidal time embeddings are appended to the output.\n\n In both cases, the amount of noise is controlled by the same `noise_level`.\n\n The embeddings are normalized before the noise is applied and un-normalized after the noise is applied.\n \"\"\"\n if noise is None:\n noise = randn_tensor(\n image_embeds.shape, generator=generator, device=image_embeds.device, dtype=image_embeds.dtype\n )\n\n noise_level = torch.tensor([noise_level] * image_embeds.shape[0], device=image_embeds.device)\n\n self.image_normalizer.to(image_embeds.device)\n image_embeds = self.image_normalizer.scale(image_embeds)\n\n image_embeds = self.image_noising_scheduler.add_noise(image_embeds, timesteps=noise_level, noise=noise)\n\n image_embeds = self.image_normalizer.unscale(image_embeds)\n\n noise_level = get_timestep_embedding(\n timesteps=noise_level, embedding_dim=image_embeds.shape[-1], flip_sin_to_cos=True, downscale_freq_shift=0\n )\n\n # `get_timestep_embeddings` does not contain any weights and will always return f32 tensors,\n # but we might actually be running in fp16. so we need to cast here.\n # there might be better ways to encapsulate this.\n noise_level = noise_level.to(image_embeds.dtype)\n\n image_embeds = torch.cat((image_embeds, noise_level), 1)\n\n return image_embeds\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n image: Union[torch.Tensor, PIL.Image.Image] = None,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 20,\n guidance_scale: float = 10,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n noise_level: int = 0,\n image_embeds: Optional[torch.Tensor] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, either `prompt_embeds` will be\n used or prompt is initialized to `\"\"`.\n image (`torch.Tensor` or `PIL.Image.Image`):\n `Image` or tensor representing an image batch. The image is encoded to its CLIP embedding which the\n `unet` is conditioned on. The image is _not_ encoded by the `vae` and then used as the latents in the\n denoising process like it is in the standard Stable Diffusion text-guided image variation process.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 20):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 10.0):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n noise_level (`int`, *optional*, defaults to `0`):\n The amount of noise to add to the image embeddings. A higher `noise_level` increases the variance in\n the final un-noised images. See [`StableUnCLIPPipeline.noise_image_embeddings`] for more details.\n image_embeds (`torch.Tensor`, *optional*):\n Pre-generated CLIP embeddings to condition the `unet` on. These latents are not used in the denoising\n process. If you want to provide pre-generated latents, pass them to `__call__` as `latents`.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`:\n [`~ pipeline_utils.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning\n a tuple, the first element is a list with the generated images.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n if prompt is None and prompt_embeds is None:\n prompt = len(image) * [\"\"] if isinstance(image, list) else \"\"\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt=prompt,\n image=image,\n height=height,\n width=width,\n callback_steps=callback_steps,\n noise_level=noise_level,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n image_embeds=image_embeds,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n batch_size = batch_size * num_images_per_prompt\n\n device = self._execution_device\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Encoder input image\n noise_level = torch.tensor([noise_level], device=device)\n image_embeds = self._encode_image(\n image=image,\n device=device,\n batch_size=batch_size,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n noise_level=noise_level,\n generator=generator,\n image_embeds=image_embeds,\n )\n\n # 5. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 6. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n if latents is None:\n latents = self.prepare_latents(\n batch_size=batch_size,\n num_channels_latents=num_channels_latents,\n height=height,\n width=width,\n dtype=prompt_embeds.dtype,\n device=device,\n generator=generator,\n latents=latents,\n )\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 8. Denoising loop\n for i, t in enumerate(self.progress_bar(timesteps)):\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n class_labels=image_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # 9. Post-processing\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n else:\n image = latents\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/safety_checker.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel\n\nfrom ...utils import logging\n\n\nlogger = logging.get_logger(__name__)\n\n\ndef cosine_distance(image_embeds, text_embeds):\n normalized_image_embeds = nn.functional.normalize(image_embeds)\n normalized_text_embeds = nn.functional.normalize(text_embeds)\n return torch.mm(normalized_image_embeds, normalized_text_embeds.t())\n\n\nclass StableDiffusionSafetyChecker(PreTrainedModel):\n config_class = CLIPConfig\n main_input_name = \"clip_input\"\n\n _no_split_modules = [\"CLIPEncoderLayer\"]\n\n def __init__(self, config: CLIPConfig):\n super().__init__(config)\n\n self.vision_model = CLIPVisionModel(config.vision_config)\n self.visual_projection = nn.Linear(config.vision_config.hidden_size, config.projection_dim, bias=False)\n\n self.concept_embeds = nn.Parameter(torch.ones(17, config.projection_dim), requires_grad=False)\n self.special_care_embeds = nn.Parameter(torch.ones(3, config.projection_dim), requires_grad=False)\n\n self.concept_embeds_weights = nn.Parameter(torch.ones(17), requires_grad=False)\n self.special_care_embeds_weights = nn.Parameter(torch.ones(3), requires_grad=False)\n\n @torch.no_grad()\n def forward(self, clip_input, images):\n pooled_output = self.vision_model(clip_input)[1] # pooled_output\n image_embeds = self.visual_projection(pooled_output)\n\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds).cpu().float().numpy()\n cos_dist = cosine_distance(image_embeds, self.concept_embeds).cpu().float().numpy()\n\n result = []\n batch_size = image_embeds.shape[0]\n for i in range(batch_size):\n result_img = {\"special_scores\": {}, \"special_care\": [], \"concept_scores\": {}, \"bad_concepts\": []}\n\n # increase this value to create a stronger `nfsw` filter\n # at the cost of increasing the possibility of filtering benign images\n adjustment = 0.0\n\n for concept_idx in range(len(special_cos_dist[0])):\n concept_cos = special_cos_dist[i][concept_idx]\n concept_threshold = self.special_care_embeds_weights[concept_idx].item()\n result_img[\"special_scores\"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)\n if result_img[\"special_scores\"][concept_idx] > 0:\n result_img[\"special_care\"].append({concept_idx, result_img[\"special_scores\"][concept_idx]})\n adjustment = 0.01\n\n for concept_idx in range(len(cos_dist[0])):\n concept_cos = cos_dist[i][concept_idx]\n concept_threshold = self.concept_embeds_weights[concept_idx].item()\n result_img[\"concept_scores\"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)\n if result_img[\"concept_scores\"][concept_idx] > 0:\n result_img[\"bad_concepts\"].append(concept_idx)\n\n result.append(result_img)\n\n has_nsfw_concepts = [len(res[\"bad_concepts\"]) > 0 for res in result]\n\n for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):\n if has_nsfw_concept:\n if torch.is_tensor(images) or torch.is_tensor(images[0]):\n images[idx] = torch.zeros_like(images[idx]) # black image\n else:\n images[idx] = np.zeros(images[idx].shape) # black image\n\n if any(has_nsfw_concepts):\n logger.warning(\n \"Potential NSFW content was detected in one or more images. A black image will be returned instead.\"\n \" Try again with a different prompt and/or seed.\"\n )\n\n return images, has_nsfw_concepts\n\n @torch.no_grad()\n def forward_onnx(self, clip_input: torch.Tensor, images: torch.Tensor):\n pooled_output = self.vision_model(clip_input)[1] # pooled_output\n image_embeds = self.visual_projection(pooled_output)\n\n special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds)\n cos_dist = cosine_distance(image_embeds, self.concept_embeds)\n\n # increase this value to create a stronger `nsfw` filter\n # at the cost of increasing the possibility of filtering benign images\n adjustment = 0.0\n\n special_scores = special_cos_dist - self.special_care_embeds_weights + adjustment\n # special_scores = special_scores.round(decimals=3)\n special_care = torch.any(special_scores > 0, dim=1)\n special_adjustment = special_care * 0.01\n special_adjustment = special_adjustment.unsqueeze(1).expand(-1, cos_dist.shape[1])\n\n concept_scores = (cos_dist - self.concept_embeds_weights) + special_adjustment\n # concept_scores = concept_scores.round(decimals=3)\n has_nsfw_concepts = torch.any(concept_scores > 0, dim=1)\n\n images[has_nsfw_concepts] = 0.0 # black image\n\n return images, has_nsfw_concepts\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/safety_checker_flax.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Optional, Tuple\n\nimport jax\nimport jax.numpy as jnp\nfrom flax import linen as nn\nfrom flax.core.frozen_dict import FrozenDict\nfrom transformers import CLIPConfig, FlaxPreTrainedModel\nfrom transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule\n\n\ndef jax_cosine_distance(emb_1, emb_2, eps=1e-12):\n norm_emb_1 = jnp.divide(emb_1.T, jnp.clip(jnp.linalg.norm(emb_1, axis=1), a_min=eps)).T\n norm_emb_2 = jnp.divide(emb_2.T, jnp.clip(jnp.linalg.norm(emb_2, axis=1), a_min=eps)).T\n return jnp.matmul(norm_emb_1, norm_emb_2.T)\n\n\nclass FlaxStableDiffusionSafetyCheckerModule(nn.Module):\n config: CLIPConfig\n dtype: jnp.dtype = jnp.float32\n\n def setup(self):\n self.vision_model = FlaxCLIPVisionModule(self.config.vision_config)\n self.visual_projection = nn.Dense(self.config.projection_dim, use_bias=False, dtype=self.dtype)\n\n self.concept_embeds = self.param(\"concept_embeds\", jax.nn.initializers.ones, (17, self.config.projection_dim))\n self.special_care_embeds = self.param(\n \"special_care_embeds\", jax.nn.initializers.ones, (3, self.config.projection_dim)\n )\n\n self.concept_embeds_weights = self.param(\"concept_embeds_weights\", jax.nn.initializers.ones, (17,))\n self.special_care_embeds_weights = self.param(\"special_care_embeds_weights\", jax.nn.initializers.ones, (3,))\n\n def __call__(self, clip_input):\n pooled_output = self.vision_model(clip_input)[1]\n image_embeds = self.visual_projection(pooled_output)\n\n special_cos_dist = jax_cosine_distance(image_embeds, self.special_care_embeds)\n cos_dist = jax_cosine_distance(image_embeds, self.concept_embeds)\n\n # increase this value to create a stronger `nfsw` filter\n # at the cost of increasing the possibility of filtering benign image inputs\n adjustment = 0.0\n\n special_scores = special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment\n special_scores = jnp.round(special_scores, 3)\n is_special_care = jnp.any(special_scores > 0, axis=1, keepdims=True)\n # Use a lower threshold if an image has any special care concept\n special_adjustment = is_special_care * 0.01\n\n concept_scores = cos_dist - self.concept_embeds_weights[None, :] + special_adjustment\n concept_scores = jnp.round(concept_scores, 3)\n has_nsfw_concepts = jnp.any(concept_scores > 0, axis=1)\n\n return has_nsfw_concepts\n\n\nclass FlaxStableDiffusionSafetyChecker(FlaxPreTrainedModel):\n config_class = CLIPConfig\n main_input_name = \"clip_input\"\n module_class = FlaxStableDiffusionSafetyCheckerModule\n\n def __init__(\n self,\n config: CLIPConfig,\n input_shape: Optional[Tuple] = None,\n seed: int = 0,\n dtype: jnp.dtype = jnp.float32,\n _do_init: bool = True,\n **kwargs,\n ):\n if input_shape is None:\n input_shape = (1, 224, 224, 3)\n module = self.module_class(config=config, dtype=dtype, **kwargs)\n super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)\n\n def init_weights(self, rng: jax.Array, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:\n # init input tensor\n clip_input = jax.random.normal(rng, input_shape)\n\n params_rng, dropout_rng = jax.random.split(rng)\n rngs = {\"params\": params_rng, \"dropout\": dropout_rng}\n\n random_params = self.module.init(rngs, clip_input)[\"params\"]\n\n return random_params\n\n def __call__(\n self,\n clip_input,\n params: dict = None,\n ):\n clip_input = jnp.transpose(clip_input, (0, 2, 3, 1))\n\n return self.module.apply(\n {\"params\": params or self.params},\n jnp.array(clip_input, dtype=jnp.float32),\n rngs={},\n )\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion/stable_unclip_image_normalizer.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Optional, Union\n\nimport torch\nfrom torch import nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...models.modeling_utils import ModelMixin\n\n\nclass StableUnCLIPImageNormalizer(ModelMixin, ConfigMixin):\n \"\"\"\n This class is used to hold the mean and standard deviation of the CLIP embedder used in stable unCLIP.\n\n It is used to normalize the image embeddings before the noise is applied and un-normalize the noised image\n embeddings.\n \"\"\"\n\n @register_to_config\n def __init__(\n self,\n embedding_dim: int = 768,\n ):\n super().__init__()\n\n self.mean = nn.Parameter(torch.zeros(1, embedding_dim))\n self.std = nn.Parameter(torch.ones(1, embedding_dim))\n\n def to(\n self,\n torch_device: Optional[Union[str, torch.device]] = None,\n torch_dtype: Optional[torch.dtype] = None,\n ):\n self.mean = nn.Parameter(self.mean.to(torch_device).to(torch_dtype))\n self.std = nn.Parameter(self.std.to(torch_device).to(torch_dtype))\n return self\n\n def scale(self, embeds):\n embeds = (embeds - self.mean) * 1.0 / self.std\n return embeds\n\n def unscale(self, embeds):\n embeds = (embeds * self.std) + self.mean\n return embeds\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_3/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_flax_available,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_additional_imports = {}\n_import_structure = {\"pipeline_output\": [\"StableDiffusion3PipelineOutput\"]}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_stable_diffusion_3\"] = [\"StableDiffusion3Pipeline\"]\n _import_structure[\"pipeline_stable_diffusion_3_img2img\"] = [\"StableDiffusion3Img2ImgPipeline\"]\n _import_structure[\"pipeline_stable_diffusion_3_instructpix2pix\"] = [\"StableDiffusion3InstructPix2PixPipeline\"]\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import * # noqa F403\n else:\n from .pipeline_stable_diffusion_3 import StableDiffusion3Pipeline\n from .pipeline_stable_diffusion_3_img2img import StableDiffusion3Img2ImgPipeline\n from .pipeline_stable_diffusion_3_instructpix2pix import StableDiffusion3InstructPix2PixPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n for name, value in _additional_imports.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_output.py", "content": "from dataclasses import dataclass\nfrom typing import List, Union\n\nimport numpy as np\nimport PIL.Image\n\nfrom ...utils import BaseOutput\n\n\n@dataclass\nclass StableDiffusion3PipelineOutput(BaseOutput):\n \"\"\"\n Output class for Stable Diffusion pipelines.\n\n Args:\n images (`List[PIL.Image.Image]` or `np.ndarray`)\n List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,\n num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.\n \"\"\"\n\n images: Union[List[PIL.Image.Image], np.ndarray]\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3.py", "content": "# Copyright 2024 Stability AI and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport torch\nfrom transformers import (\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n T5EncoderModel,\n T5TokenizerFast,\n)\n\nfrom ...image_processor import VaeImageProcessor\nfrom ...loaders import FromSingleFileMixin, SD3LoraLoaderMixin\nfrom ...models.autoencoders import AutoencoderKL\nfrom ...models.transformers import SD3Transformer2DModel\nfrom ...schedulers import FlowMatchEulerDiscreteScheduler\nfrom ...utils import (\n is_torch_xla_available,\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline\nfrom .pipeline_output import StableDiffusion3PipelineOutput\n\n\nif is_torch_xla_available():\n import torch_xla.core.xla_model as xm\n\n XLA_AVAILABLE = True\nelse:\n XLA_AVAILABLE = False\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusion3Pipeline\n\n >>> pipe = StableDiffusion3Pipeline.from_pretrained(\n ... \"stabilityai/stable-diffusion-3-medium-diffusers\", torch_dtype=torch.float16\n ... )\n >>> pipe.to(\"cuda\")\n >>> prompt = \"A cat holding a sign that says hello world\"\n >>> image = pipe(prompt).images[0]\n >>> image.save(\"sd3.png\")\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass StableDiffusion3Pipeline(DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin):\n r\"\"\"\n Args:\n transformer ([`SD3Transformer2DModel`]):\n Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.\n scheduler ([`FlowMatchEulerDiscreteScheduler`]):\n A scheduler to be used in combination with `transformer` to denoise the encoded image latents.\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModelWithProjection`]):\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),\n specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant,\n with an additional added projection layer that is initialized with a diagonal matrix with the `hidden_size`\n as its dimension.\n text_encoder_2 ([`CLIPTextModelWithProjection`]):\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),\n specifically the\n [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)\n variant.\n text_encoder_3 ([`T5EncoderModel`]):\n Frozen text-encoder. Stable Diffusion 3 uses\n [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the\n [t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n tokenizer_2 (`CLIPTokenizer`):\n Second Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n tokenizer_3 (`T5TokenizerFast`):\n Tokenizer of class\n [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->text_encoder_2->text_encoder_3->transformer->vae\"\n _optional_components = []\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\", \"negative_pooled_prompt_embeds\"]\n\n def __init__(\n self,\n transformer: SD3Transformer2DModel,\n scheduler: FlowMatchEulerDiscreteScheduler,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n text_encoder_2: CLIPTextModelWithProjection,\n tokenizer_2: CLIPTokenizer,\n text_encoder_3: T5EncoderModel,\n tokenizer_3: T5TokenizerFast,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n text_encoder_3=text_encoder_3,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n tokenizer_3=tokenizer_3,\n transformer=transformer,\n scheduler=scheduler,\n )\n self.vae_scale_factor = (\n 2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, \"vae\") and self.vae is not None else 8\n )\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.tokenizer_max_length = (\n self.tokenizer.model_max_length if hasattr(self, \"tokenizer\") and self.tokenizer is not None else 77\n )\n self.default_sample_size = (\n self.transformer.config.sample_size\n if hasattr(self, \"transformer\") and self.transformer is not None\n else 128\n )\n\n def _get_t5_prompt_embeds(\n self,\n prompt: Union[str, List[str]] = None,\n num_images_per_prompt: int = 1,\n device: Optional[torch.device] = None,\n dtype: Optional[torch.dtype] = None,\n ):\n device = device or self._execution_device\n dtype = dtype or self.text_encoder.dtype\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n batch_size = len(prompt)\n\n if self.text_encoder_3 is None:\n return torch.zeros(\n (batch_size, self.tokenizer_max_length, self.transformer.config.joint_attention_dim),\n device=device,\n dtype=dtype,\n )\n\n text_inputs = self.tokenizer_3(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer_max_length,\n truncation=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer_3(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer_3.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = self.text_encoder_3(text_input_ids.to(device))[0]\n\n dtype = self.text_encoder_3.dtype\n prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)\n\n _, seq_len, _ = prompt_embeds.shape\n\n # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n return prompt_embeds\n\n def _get_clip_prompt_embeds(\n self,\n prompt: Union[str, List[str]],\n num_images_per_prompt: int = 1,\n device: Optional[torch.device] = None,\n clip_skip: Optional[int] = None,\n clip_model_index: int = 0,\n ):\n device = device or self._execution_device\n\n clip_tokenizers = [self.tokenizer, self.tokenizer_2]\n clip_text_encoders = [self.text_encoder, self.text_encoder_2]\n\n tokenizer = clip_tokenizers[clip_model_index]\n text_encoder = clip_text_encoders[clip_model_index]\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n batch_size = len(prompt)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer_max_length} tokens: {removed_text}\"\n )\n prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)\n pooled_prompt_embeds = prompt_embeds[0]\n\n if clip_skip is None:\n prompt_embeds = prompt_embeds.hidden_states[-2]\n else:\n prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]\n\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n\n _, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(batch_size * num_images_per_prompt, -1)\n\n return prompt_embeds, pooled_prompt_embeds\n\n def encode_prompt(\n self,\n prompt: Union[str, List[str]],\n prompt_2: Union[str, List[str]],\n prompt_3: Union[str, List[str]],\n device: Optional[torch.device] = None,\n num_images_per_prompt: int = 1,\n do_classifier_free_guidance: bool = True,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n negative_prompt_3: Optional[Union[str, List[str]]] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in all text-encoders\n prompt_3 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_3` and `text_encoder_3`. If not defined, `prompt` is\n used in all text-encoders\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders.\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and\n `text_encoder_3`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n device = device or self._execution_device\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n if prompt is not None:\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n prompt_2 = prompt_2 or prompt\n prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2\n\n prompt_3 = prompt_3 or prompt\n prompt_3 = [prompt_3] if isinstance(prompt_3, str) else prompt_3\n\n prompt_embed, pooled_prompt_embed = self._get_clip_prompt_embeds(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n clip_skip=clip_skip,\n clip_model_index=0,\n )\n prompt_2_embed, pooled_prompt_2_embed = self._get_clip_prompt_embeds(\n prompt=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n clip_skip=clip_skip,\n clip_model_index=1,\n )\n clip_prompt_embeds = torch.cat([prompt_embed, prompt_2_embed], dim=-1)\n\n t5_prompt_embed = self._get_t5_prompt_embeds(\n prompt=prompt_3,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n )\n\n clip_prompt_embeds = torch.nn.functional.pad(\n clip_prompt_embeds, (0, t5_prompt_embed.shape[-1] - clip_prompt_embeds.shape[-1])\n )\n\n prompt_embeds = torch.cat([clip_prompt_embeds, t5_prompt_embed], dim=-2)\n pooled_prompt_embeds = torch.cat([pooled_prompt_embed, pooled_prompt_2_embed], dim=-1)\n\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n negative_prompt_3 = negative_prompt_3 or negative_prompt\n\n # normalize str to list\n negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt\n negative_prompt_2 = (\n batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2\n )\n negative_prompt_3 = (\n batch_size * [negative_prompt_3] if isinstance(negative_prompt_3, str) else negative_prompt_3\n )\n\n if prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n\n negative_prompt_embed, negative_pooled_prompt_embed = self._get_clip_prompt_embeds(\n negative_prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n clip_skip=None,\n clip_model_index=0,\n )\n negative_prompt_2_embed, negative_pooled_prompt_2_embed = self._get_clip_prompt_embeds(\n negative_prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n clip_skip=None,\n clip_model_index=1,\n )\n negative_clip_prompt_embeds = torch.cat([negative_prompt_embed, negative_prompt_2_embed], dim=-1)\n\n t5_negative_prompt_embed = self._get_t5_prompt_embeds(\n prompt=negative_prompt_3, num_images_per_prompt=num_images_per_prompt, device=device\n )\n\n negative_clip_prompt_embeds = torch.nn.functional.pad(\n negative_clip_prompt_embeds,\n (0, t5_negative_prompt_embed.shape[-1] - negative_clip_prompt_embeds.shape[-1]),\n )\n\n negative_prompt_embeds = torch.cat([negative_clip_prompt_embeds, t5_negative_prompt_embed], dim=-2)\n negative_pooled_prompt_embeds = torch.cat(\n [negative_pooled_prompt_embed, negative_pooled_prompt_2_embed], dim=-1\n )\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n def check_inputs(\n self,\n prompt,\n prompt_2,\n prompt_3,\n height,\n width,\n negative_prompt=None,\n negative_prompt_2=None,\n negative_prompt_3=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n pooled_prompt_embeds=None,\n negative_pooled_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_3 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_3`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n elif prompt_3 is not None and (not isinstance(prompt_3, str) and not isinstance(prompt_3, list)):\n raise ValueError(f\"`prompt_3` has to be of type `str` or `list` but is {type(prompt_3)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_3 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_3`: {negative_prompt_3} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if prompt_embeds is not None and pooled_prompt_embeds is None:\n raise ValueError(\n \"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.\"\n )\n\n if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:\n raise ValueError(\n \"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.\"\n )\n\n def prepare_latents(\n self,\n batch_size,\n num_channels_latents,\n height,\n width,\n dtype,\n device,\n generator,\n latents=None,\n ):\n if latents is not None:\n return latents.to(device=device, dtype=dtype)\n\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n return latents\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n @property\n def joint_attention_kwargs(self):\n return self._joint_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @property\n def interrupt(self):\n return self._interrupt\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n prompt_3: Optional[Union[str, List[str]]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 28,\n timesteps: List[int] = None,\n guidance_scale: float = 7.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n negative_prompt_3: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n joint_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n will be used instead\n prompt_3 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to `tokenizer_3` and `text_encoder_3`. If not defined, `prompt` is\n will be used instead\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image. This is set to 1024 by default for the best results.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image. This is set to 1024 by default for the best results.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n guidance_scale (`float`, *optional*, defaults to 5.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used instead\n negative_prompt_3 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and\n `text_encoder_3`. If not defined, `negative_prompt` is used instead\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.FloatTensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead\n of a plain tuple.\n joint_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a\n `tuple`. When returning a tuple, the first element is a list with the generated images.\n \"\"\"\n\n height = height or self.default_sample_size * self.vae_scale_factor\n width = width or self.default_sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n prompt_3,\n height,\n width,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n negative_prompt_3=negative_prompt_3,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._joint_attention_kwargs = joint_attention_kwargs\n self._interrupt = False\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n prompt_3=prompt_3,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n negative_prompt_3=negative_prompt_3,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n device=device,\n clip_skip=self.clip_skip,\n num_images_per_prompt=num_images_per_prompt,\n )\n\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)\n\n # 4. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n self._num_timesteps = len(timesteps)\n\n # 5. Prepare latent variables\n num_channels_latents = self.transformer.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Denoising loop\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n if self.interrupt:\n continue\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timestep = t.expand(latent_model_input.shape[0])\n\n noise_pred = self.transformer(\n hidden_states=latent_model_input,\n timestep=timestep,\n encoder_hidden_states=prompt_embeds,\n pooled_projections=pooled_prompt_embeds,\n joint_attention_kwargs=self.joint_attention_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents_dtype = latents.dtype\n latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]\n\n if latents.dtype != latents_dtype:\n if torch.backends.mps.is_available():\n # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272\n latents = latents.to(latents_dtype)\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n negative_pooled_prompt_embeds = callback_outputs.pop(\n \"negative_pooled_prompt_embeds\", negative_pooled_prompt_embeds\n )\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n\n if XLA_AVAILABLE:\n xm.mark_step()\n\n if output_type == \"latent\":\n image = latents\n\n else:\n latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor\n\n image = self.vae.decode(latents, return_dict=False)[0]\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusion3PipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.py", "content": "# Copyright 2024 Stability AI and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Callable, Dict, List, Optional, Union\n\nimport PIL.Image\nimport torch\nfrom transformers import (\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n T5EncoderModel,\n T5TokenizerFast,\n)\n\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...models.autoencoders import AutoencoderKL\nfrom ...models.transformers import SD3Transformer2DModel\nfrom ...schedulers import FlowMatchEulerDiscreteScheduler\nfrom ...utils import (\n is_torch_xla_available,\n logging,\n replace_example_docstring,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline\nfrom .pipeline_output import StableDiffusion3PipelineOutput\n\n\nif is_torch_xla_available():\n import torch_xla.core.xla_model as xm\n\n XLA_AVAILABLE = True\nelse:\n XLA_AVAILABLE = False\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n\n >>> from diffusers import AutoPipelineForImage2Image\n >>> from diffusers.utils import load_image\n\n >>> device = \"cuda\"\n >>> model_id_or_path = \"stabilityai/stable-diffusion-3-medium-diffusers\"\n >>> pipe = AutoPipelineForImage2Image.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n >>> pipe = pipe.to(device)\n\n >>> url = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n >>> init_image = load_image(url).resize((512, 512))\n\n >>> prompt = \"cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k\"\n\n >>> images = pipe(prompt=prompt, image=init_image, strength=0.95, guidance_scale=7.5).images[0]\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass StableDiffusion3Img2ImgPipeline(DiffusionPipeline):\n r\"\"\"\n Args:\n transformer ([`SD3Transformer2DModel`]):\n Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.\n scheduler ([`FlowMatchEulerDiscreteScheduler`]):\n A scheduler to be used in combination with `transformer` to denoise the encoded image latents.\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModelWithProjection`]):\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),\n specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant,\n with an additional added projection layer that is initialized with a diagonal matrix with the `hidden_size`\n as its dimension.\n text_encoder_2 ([`CLIPTextModelWithProjection`]):\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),\n specifically the\n [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)\n variant.\n text_encoder_3 ([`T5EncoderModel`]):\n Frozen text-encoder. Stable Diffusion 3 uses\n [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the\n [t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n tokenizer_2 (`CLIPTokenizer`):\n Second Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n tokenizer_3 (`T5TokenizerFast`):\n Tokenizer of class\n [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->text_encoder_2->text_encoder_3->transformer->vae\"\n _optional_components = []\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\", \"negative_pooled_prompt_embeds\"]\n\n def __init__(\n self,\n transformer: SD3Transformer2DModel,\n scheduler: FlowMatchEulerDiscreteScheduler,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n text_encoder_2: CLIPTextModelWithProjection,\n tokenizer_2: CLIPTokenizer,\n text_encoder_3: T5EncoderModel,\n tokenizer_3: T5TokenizerFast,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n text_encoder_3=text_encoder_3,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n tokenizer_3=tokenizer_3,\n transformer=transformer,\n scheduler=scheduler,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor, vae_latent_channels=self.vae.config.latent_channels\n )\n self.tokenizer_max_length = self.tokenizer.model_max_length\n self.default_sample_size = self.transformer.config.sample_size\n\n # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline._get_t5_prompt_embeds\n def _get_t5_prompt_embeds(\n self,\n prompt: Union[str, List[str]] = None,\n num_images_per_prompt: int = 1,\n device: Optional[torch.device] = None,\n dtype: Optional[torch.dtype] = None,\n ):\n device = device or self._execution_device\n dtype = dtype or self.text_encoder.dtype\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n batch_size = len(prompt)\n\n if self.text_encoder_3 is None:\n return torch.zeros(\n (batch_size, self.tokenizer_max_length, self.transformer.config.joint_attention_dim),\n device=device,\n dtype=dtype,\n )\n\n text_inputs = self.tokenizer_3(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer_max_length,\n truncation=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer_3(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer_3.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = self.text_encoder_3(text_input_ids.to(device))[0]\n\n dtype = self.text_encoder_3.dtype\n prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)\n\n _, seq_len, _ = prompt_embeds.shape\n\n # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline._get_clip_prompt_embeds\n def _get_clip_prompt_embeds(\n self,\n prompt: Union[str, List[str]],\n num_images_per_prompt: int = 1,\n device: Optional[torch.device] = None,\n clip_skip: Optional[int] = None,\n clip_model_index: int = 0,\n ):\n device = device or self._execution_device\n\n clip_tokenizers = [self.tokenizer, self.tokenizer_2]\n clip_text_encoders = [self.text_encoder, self.text_encoder_2]\n\n tokenizer = clip_tokenizers[clip_model_index]\n text_encoder = clip_text_encoders[clip_model_index]\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n batch_size = len(prompt)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer_max_length} tokens: {removed_text}\"\n )\n prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)\n pooled_prompt_embeds = prompt_embeds[0]\n\n if clip_skip is None:\n prompt_embeds = prompt_embeds.hidden_states[-2]\n else:\n prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]\n\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n\n _, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(batch_size * num_images_per_prompt, -1)\n\n return prompt_embeds, pooled_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline.encode_prompt\n def encode_prompt(\n self,\n prompt: Union[str, List[str]],\n prompt_2: Union[str, List[str]],\n prompt_3: Union[str, List[str]],\n device: Optional[torch.device] = None,\n num_images_per_prompt: int = 1,\n do_classifier_free_guidance: bool = True,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n negative_prompt_3: Optional[Union[str, List[str]]] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in all text-encoders\n prompt_3 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_3` and `text_encoder_3`. If not defined, `prompt` is\n used in all text-encoders\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders.\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and\n `text_encoder_3`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n device = device or self._execution_device\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n if prompt is not None:\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n prompt_2 = prompt_2 or prompt\n prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2\n\n prompt_3 = prompt_3 or prompt\n prompt_3 = [prompt_3] if isinstance(prompt_3, str) else prompt_3\n\n prompt_embed, pooled_prompt_embed = self._get_clip_prompt_embeds(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n clip_skip=clip_skip,\n clip_model_index=0,\n )\n prompt_2_embed, pooled_prompt_2_embed = self._get_clip_prompt_embeds(\n prompt=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n clip_skip=clip_skip,\n clip_model_index=1,\n )\n clip_prompt_embeds = torch.cat([prompt_embed, prompt_2_embed], dim=-1)\n\n t5_prompt_embed = self._get_t5_prompt_embeds(\n prompt=prompt_3,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n )\n\n clip_prompt_embeds = torch.nn.functional.pad(\n clip_prompt_embeds, (0, t5_prompt_embed.shape[-1] - clip_prompt_embeds.shape[-1])\n )\n\n prompt_embeds = torch.cat([clip_prompt_embeds, t5_prompt_embed], dim=-2)\n pooled_prompt_embeds = torch.cat([pooled_prompt_embed, pooled_prompt_2_embed], dim=-1)\n\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n negative_prompt_3 = negative_prompt_3 or negative_prompt\n\n # normalize str to list\n negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt\n negative_prompt_2 = (\n batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2\n )\n negative_prompt_3 = (\n batch_size * [negative_prompt_3] if isinstance(negative_prompt_3, str) else negative_prompt_3\n )\n\n if prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n\n negative_prompt_embed, negative_pooled_prompt_embed = self._get_clip_prompt_embeds(\n negative_prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n clip_skip=None,\n clip_model_index=0,\n )\n negative_prompt_2_embed, negative_pooled_prompt_2_embed = self._get_clip_prompt_embeds(\n negative_prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n clip_skip=None,\n clip_model_index=1,\n )\n negative_clip_prompt_embeds = torch.cat([negative_prompt_embed, negative_prompt_2_embed], dim=-1)\n\n t5_negative_prompt_embed = self._get_t5_prompt_embeds(\n prompt=negative_prompt_3, num_images_per_prompt=num_images_per_prompt, device=device\n )\n\n negative_clip_prompt_embeds = torch.nn.functional.pad(\n negative_clip_prompt_embeds,\n (0, t5_negative_prompt_embed.shape[-1] - negative_clip_prompt_embeds.shape[-1]),\n )\n\n negative_prompt_embeds = torch.cat([negative_clip_prompt_embeds, t5_negative_prompt_embed], dim=-2)\n negative_pooled_prompt_embeds = torch.cat(\n [negative_pooled_prompt_embed, negative_pooled_prompt_2_embed], dim=-1\n )\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n def check_inputs(\n self,\n prompt,\n prompt_2,\n prompt_3,\n strength,\n negative_prompt=None,\n negative_prompt_2=None,\n negative_prompt_3=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n pooled_prompt_embeds=None,\n negative_pooled_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_3 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_3`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n elif prompt_3 is not None and (not isinstance(prompt_3, str) and not isinstance(prompt_3, list)):\n raise ValueError(f\"`prompt_3` has to be of type `str` or `list` but is {type(prompt_3)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_3 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_3`: {negative_prompt_3} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if prompt_embeds is not None and pooled_prompt_embeds is None:\n raise ValueError(\n \"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.\"\n )\n\n if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:\n raise ValueError(\n \"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.\"\n )\n\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(num_inference_steps * strength, num_inference_steps)\n\n t_start = int(max(num_inference_steps - init_timestep, 0))\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n if hasattr(self.scheduler, \"set_begin_index\"):\n self.scheduler.set_begin_index(t_start * self.scheduler.order)\n\n return timesteps, num_inference_steps - t_start\n\n def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n if image.shape[1] == self.vae.config.latent_channels:\n init_latents = image\n\n else:\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n elif isinstance(generator, list):\n init_latents = [\n retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])\n for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = retrieve_latents(self.vae.encode(image), generator=generator)\n\n init_latents = (init_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor\n\n if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:\n # expand init_latents for batch_size\n additional_image_per_prompt = batch_size // init_latents.shape[0]\n init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)\n elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n init_latents = torch.cat([init_latents], dim=0)\n\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n # get latents\n init_latents = self.scheduler.scale_noise(init_latents, timestep, noise)\n latents = init_latents.to(device=device, dtype=dtype)\n\n return latents\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @property\n def interrupt(self):\n return self._interrupt\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n prompt_3: Optional[Union[str, List[str]]] = None,\n image: PipelineImageInput = None,\n strength: float = 0.6,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n guidance_scale: float = 7.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n negative_prompt_3: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n will be used instead\n prompt_3 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to `tokenizer_3` and `text_encoder_3`. If not defined, `prompt` is\n will be used instead\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image. This is set to 1024 by default for the best results.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image. This is set to 1024 by default for the best results.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n guidance_scale (`float`, *optional*, defaults to 5.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used instead\n negative_prompt_3 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and\n `text_encoder_3`. If not defined, `negative_prompt` is used instead\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.FloatTensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead\n of a plain tuple.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a\n `tuple`. When returning a tuple, the first element is a list with the generated images.\n \"\"\"\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n prompt_3,\n strength,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n negative_prompt_3=negative_prompt_3,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n self._interrupt = False\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n prompt_3=prompt_3,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n negative_prompt_3=negative_prompt_3,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n device=device,\n clip_skip=self.clip_skip,\n num_images_per_prompt=num_images_per_prompt,\n )\n\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)\n\n # 3. Preprocess image\n image = self.image_processor.preprocess(image)\n\n # 4. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n latent_timestep = timesteps[:1].repeat(batch_size * num_inference_steps)\n\n # 5. Prepare latent variables\n if latents is None:\n latents = self.prepare_latents(\n image,\n latent_timestep,\n batch_size,\n num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n generator,\n )\n\n # 6. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n if self.interrupt:\n continue\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timestep = t.expand(latent_model_input.shape[0])\n\n noise_pred = self.transformer(\n hidden_states=latent_model_input,\n timestep=timestep,\n encoder_hidden_states=prompt_embeds,\n pooled_projections=pooled_prompt_embeds,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents_dtype = latents.dtype\n latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]\n\n if latents.dtype != latents_dtype:\n if torch.backends.mps.is_available():\n # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272\n latents = latents.to(latents_dtype)\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n negative_pooled_prompt_embeds = callback_outputs.pop(\n \"negative_pooled_prompt_embeds\", negative_pooled_prompt_embeds\n )\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n\n if XLA_AVAILABLE:\n xm.mark_step()\n\n if output_type == \"latent\":\n image = latents\n\n else:\n latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor\n\n image = self.vae.decode(latents, return_dict=False)[0]\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusion3PipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_instructpix2pix.py", "content": "# Copyright 2024 Stability AI and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\nimport PIL.Image\nimport torch\nfrom transformers import (\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n T5EncoderModel,\n T5TokenizerFast,\n)\n\nfrom ...image_processor import VaeImageProcessor, PipelineImageInput\nfrom ...loaders import FromSingleFileMixin, SD3LoraLoaderMixin\nfrom ...models.autoencoders import AutoencoderKL\nfrom ...models.transformers import SD3Transformer2DModel\nfrom ...schedulers import FlowMatchEulerDiscreteScheduler\nfrom ...utils import (\n is_torch_xla_available,\n logging,\n replace_example_docstring,\n)\nfrom ...utils import PIL_INTERPOLATION, deprecate, logging\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline\nfrom .pipeline_output import StableDiffusion3PipelineOutput\n\n\nif is_torch_xla_available():\n import torch_xla.core.xla_model as xm\n\n XLA_AVAILABLE = True\nelse:\n XLA_AVAILABLE = False\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusion3Pipeline\n\n >>> pipe = StableDiffusion3Pipeline.from_pretrained(\n ... \"stabilityai/stable-diffusion-3-medium-diffusers\", torch_dtype=torch.float16\n ... )\n >>> pipe.to(\"cuda\")\n >>> prompt = \"A cat holding a sign that says hello world\"\n >>> image = pipe(prompt).images[0]\n >>> image.save(\"sd3.png\")\n ```\n\"\"\"\n\n\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\nclass StableDiffusion3InstructPix2PixPipeline(DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin):\n r\"\"\"\n Args:\n transformer ([`SD3Transformer2DModel`]):\n Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.\n scheduler ([`FlowMatchEulerDiscreteScheduler`]):\n A scheduler to be used in combination with `transformer` to denoise the encoded image latents.\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModelWithProjection`]):\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),\n specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant,\n with an additional added projection layer that is initialized with a diagonal matrix with the `hidden_size`\n as its dimension.\n text_encoder_2 ([`CLIPTextModelWithProjection`]):\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),\n specifically the\n [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)\n variant.\n text_encoder_3 ([`T5EncoderModel`]):\n Frozen text-encoder. Stable Diffusion 3 uses\n [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the\n [t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n tokenizer_2 (`CLIPTokenizer`):\n Second Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n tokenizer_3 (`T5TokenizerFast`):\n Tokenizer of class\n [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->text_encoder_2->text_encoder_3->transformer->vae\"\n _optional_components = []\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\", \"negative_pooled_prompt_embeds\"]\n\n def __init__(\n self,\n transformer: SD3Transformer2DModel,\n scheduler: FlowMatchEulerDiscreteScheduler,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n text_encoder_2: CLIPTextModelWithProjection,\n tokenizer_2: CLIPTokenizer,\n text_encoder_3: T5EncoderModel,\n tokenizer_3: T5TokenizerFast,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n text_encoder_3=text_encoder_3,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n tokenizer_3=tokenizer_3,\n transformer=transformer,\n scheduler=scheduler,\n )\n self.vae_scale_factor = (\n 2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, \"vae\") and self.vae is not None else 8\n )\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.tokenizer_max_length = (\n self.tokenizer.model_max_length if hasattr(self, \"tokenizer\") and self.tokenizer is not None else 77\n )\n self.default_sample_size = (\n self.transformer.config.sample_size\n if hasattr(self, \"transformer\") and self.transformer is not None\n else 128\n )\n\n def _get_t5_prompt_embeds(\n self,\n prompt: Union[str, List[str]] = None,\n num_images_per_prompt: int = 1,\n device: Optional[torch.device] = None,\n dtype: Optional[torch.dtype] = None,\n ):\n device = device or self._execution_device\n dtype = dtype or self.text_encoder.dtype\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n batch_size = len(prompt)\n\n if self.text_encoder_3 is None:\n return torch.zeros(\n (batch_size, self.tokenizer_max_length, self.transformer.config.joint_attention_dim),\n device=device,\n dtype=dtype,\n )\n\n text_inputs = self.tokenizer_3(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer_max_length,\n truncation=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer_3(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer_3.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = self.text_encoder_3(text_input_ids.to(device))[0]\n\n dtype = self.text_encoder_3.dtype\n prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)\n\n _, seq_len, _ = prompt_embeds.shape\n\n # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n return prompt_embeds\n\n def _get_clip_prompt_embeds(\n self,\n prompt: Union[str, List[str]],\n num_images_per_prompt: int = 1,\n device: Optional[torch.device] = None,\n clip_skip: Optional[int] = None,\n clip_model_index: int = 0,\n ):\n device = device or self._execution_device\n\n clip_tokenizers = [self.tokenizer, self.tokenizer_2]\n clip_text_encoders = [self.text_encoder, self.text_encoder_2]\n\n tokenizer = clip_tokenizers[clip_model_index]\n text_encoder = clip_text_encoders[clip_model_index]\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n batch_size = len(prompt)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer_max_length} tokens: {removed_text}\"\n )\n prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)\n pooled_prompt_embeds = prompt_embeds[0]\n\n if clip_skip is None:\n prompt_embeds = prompt_embeds.hidden_states[-2]\n else:\n prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]\n\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n\n _, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(batch_size * num_images_per_prompt, -1)\n\n return prompt_embeds, pooled_prompt_embeds\n\n def encode_prompt(\n self,\n prompt: Union[str, List[str]],\n prompt_2: Union[str, List[str]],\n prompt_3: Union[str, List[str]],\n device: Optional[torch.device] = None,\n num_images_per_prompt: int = 1,\n do_classifier_free_guidance: bool = True,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n negative_prompt_3: Optional[Union[str, List[str]]] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in all text-encoders\n prompt_3 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_3` and `text_encoder_3`. If not defined, `prompt` is\n used in all text-encoders\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders.\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and\n `text_encoder_3`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n device = device or self._execution_device\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n if prompt is not None:\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n prompt_2 = prompt_2 or prompt\n prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2\n\n prompt_3 = prompt_3 or prompt\n prompt_3 = [prompt_3] if isinstance(prompt_3, str) else prompt_3\n\n prompt_embed, pooled_prompt_embed = self._get_clip_prompt_embeds(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n clip_skip=clip_skip,\n clip_model_index=0,\n )\n prompt_2_embed, pooled_prompt_2_embed = self._get_clip_prompt_embeds(\n prompt=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n clip_skip=clip_skip,\n clip_model_index=1,\n )\n clip_prompt_embeds = torch.cat([prompt_embed, prompt_2_embed], dim=-1)\n\n t5_prompt_embed = self._get_t5_prompt_embeds(\n prompt=prompt_3,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n )\n\n clip_prompt_embeds = torch.nn.functional.pad(\n clip_prompt_embeds, (0, t5_prompt_embed.shape[-1] - clip_prompt_embeds.shape[-1])\n )\n\n prompt_embeds = torch.cat([clip_prompt_embeds, t5_prompt_embed], dim=-2)\n pooled_prompt_embeds = torch.cat([pooled_prompt_embed, pooled_prompt_2_embed], dim=-1)\n\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n negative_prompt_3 = negative_prompt_3 or negative_prompt\n\n # normalize str to list\n negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt\n negative_prompt_2 = (\n batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2\n )\n negative_prompt_3 = (\n batch_size * [negative_prompt_3] if isinstance(negative_prompt_3, str) else negative_prompt_3\n )\n\n if prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n\n negative_prompt_embed, negative_pooled_prompt_embed = self._get_clip_prompt_embeds(\n negative_prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n clip_skip=None,\n clip_model_index=0,\n )\n negative_prompt_2_embed, negative_pooled_prompt_2_embed = self._get_clip_prompt_embeds(\n negative_prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n clip_skip=None,\n clip_model_index=1,\n )\n negative_clip_prompt_embeds = torch.cat([negative_prompt_embed, negative_prompt_2_embed], dim=-1)\n\n t5_negative_prompt_embed = self._get_t5_prompt_embeds(\n prompt=negative_prompt_3, num_images_per_prompt=num_images_per_prompt, device=device\n )\n\n negative_clip_prompt_embeds = torch.nn.functional.pad(\n negative_clip_prompt_embeds,\n (0, t5_negative_prompt_embed.shape[-1] - negative_clip_prompt_embeds.shape[-1]),\n )\n\n negative_prompt_embeds = torch.cat([negative_clip_prompt_embeds, t5_negative_prompt_embed], dim=-2)\n negative_pooled_prompt_embeds = torch.cat(\n [negative_pooled_prompt_embed, negative_pooled_prompt_2_embed], dim=-1\n )\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n def check_inputs(\n self,\n prompt,\n prompt_2,\n prompt_3,\n # height,\n # width,\n negative_prompt=None,\n negative_prompt_2=None,\n negative_prompt_3=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n pooled_prompt_embeds=None,\n negative_pooled_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n # if height % 8 != 0 or width % 8 != 0:\n # raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_3 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_3`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n elif prompt_3 is not None and (not isinstance(prompt_3, str) and not isinstance(prompt_3, list)):\n raise ValueError(f\"`prompt_3` has to be of type `str` or `list` but is {type(prompt_3)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_3 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_3`: {negative_prompt_3} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if prompt_embeds is not None and pooled_prompt_embeds is None:\n raise ValueError(\n \"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.\"\n )\n\n if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:\n raise ValueError(\n \"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.\"\n )\n\n def prepare_latents(\n self,\n batch_size,\n num_channels_latents,\n height,\n width,\n dtype,\n device,\n generator,\n latents=None,\n ):\n if latents is not None:\n return latents.to(device=device, dtype=dtype)\n\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n return latents\n \n def prepare_image_latents(\n self, image, batch_size, num_images_per_prompt, dtype, device, do_classifier_free_guidance, generator=None\n ):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n\n if image.shape[1] == self.vae.config.latent_channels:\n image_latents = image\n else:\n image_latents = retrieve_latents(self.vae.encode(image), sample_mode=\"argmax\")\n # ? normalize image latents\n # image_latents = (image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor\n\n if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:\n # expand image_latents for batch_size\n deprecation_message = (\n f\"You have passed {batch_size} text prompts (`prompt`), but only {image_latents.shape[0]} initial\"\n \" images (`image`). Initial images are now duplicating to match the number of text prompts. Note\"\n \" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update\"\n \" your script to pass as many initial images as text prompts to suppress this warning.\"\n )\n deprecate(\"len(prompt) != len(image)\", \"1.0.0\", deprecation_message, standard_warn=False)\n additional_image_per_prompt = batch_size // image_latents.shape[0]\n image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)\n elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n image_latents = torch.cat([image_latents], dim=0)\n\n if do_classifier_free_guidance:\n uncond_image_latents = torch.zeros_like(image_latents)\n image_latents = torch.cat([image_latents, image_latents, uncond_image_latents], dim=0)\n\n return image_latents\n\n\n\n\n\n\n\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n @property\n def image_guidance_scale(self):\n return self._image_guidance_scale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self.guidance_scale > 1.0 and self.image_guidance_scale >= 1.0\n\n @property\n def joint_attention_kwargs(self):\n return self._joint_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @property\n def interrupt(self):\n return self._interrupt\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n prompt_3: Optional[Union[str, List[str]]] = None,\n image: PipelineImageInput = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 28,\n timesteps: List[int] = None,\n guidance_scale: float = 7.0,\n image_guidance_scale: float = 1.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n negative_prompt_3: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n joint_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n mask_img: Optional[PipelineImageInput] = None,\n **kwargs\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n will be used instead\n prompt_3 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to `tokenizer_3` and `text_encoder_3`. If not defined, `prompt` is\n will be used instead\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image. This is set to 1024 by default for the best results.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image. This is set to 1024 by default for the best results.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n guidance_scale (`float`, *optional*, defaults to 5.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used instead\n negative_prompt_3 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and\n `text_encoder_3`. If not defined, `negative_prompt` is used instead\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.FloatTensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead\n of a plain tuple.\n joint_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a\n `tuple`. When returning a tuple, the first element is a list with the generated images.\n \"\"\"\n\n # height = height or self.default_sample_size * self.vae_scale_factor\n # width = width or self.default_sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n prompt_3,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n negative_prompt_3=negative_prompt_3,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._image_guidance_scale = image_guidance_scale\n self._clip_skip = clip_skip\n self._joint_attention_kwargs = joint_attention_kwargs\n self._interrupt = False\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n prompt_3=prompt_3,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n negative_prompt_3=negative_prompt_3,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n device=device,\n clip_skip=self.clip_skip,\n num_images_per_prompt=num_images_per_prompt,\n )\n \n if self.do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = torch.cat([prompt_embeds, negative_prompt_embeds, negative_prompt_embeds], dim=0)\n \n # Similiarly\n pooled_prompt_embeds = torch.cat([pooled_prompt_embeds, negative_pooled_prompt_embeds, negative_pooled_prompt_embeds], dim=0) \n \n # if self.do_classifier_free_guidance:\n # prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n # pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)\n\n # 3. Preprocess image\n image = self.image_processor.preprocess(image)\n \n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n \n # timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n self._num_timesteps = len(timesteps)\n\n # 5. Prepare Image latent\n \n image_latents = self.prepare_image_latents(\n image,\n batch_size,\n num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n self.do_classifier_free_guidance,\n )\n\n height, width = image_latents.shape[-2:]\n height = height * self.vae_scale_factor\n width = width * self.vae_scale_factor\n # 6. Prepare latent variables\n num_channels_latents = self.vae.config.latent_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 7. Check that shapes of latents and image match the DIT in_channels\n num_channels_image = image_latents.shape[1]\n if mask_img is not None:\n mask_img = self.image_processor.preprocess(mask_img)\n mask_image_latents = self.prepare_image_latents(\n mask_img,\n batch_size,\n num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n self.do_classifier_free_guidance,\n )\n num_channels_image += mask_image_latents.shape[1]\n\n if num_channels_latents + num_channels_image != self.transformer.config.in_channels:\n raise ValueError(\n f\"Incorrect configuration settings! The config of `pipeline.transformer`: {self.transformer.config} expects\"\n f\" {self.transformer.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\"\n f\" `num_channels_image`: {num_channels_image} \"\n f\" = {num_channels_latents+num_channels_image}. Please verify the config of\"\n \" `pipeline.transformer` or your `image` input.\"\n )\n \n # 8. Denoising loop\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n if self.interrupt:\n continue\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 3) if self.do_classifier_free_guidance else latents\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timestep = t.expand(latent_model_input.shape[0])\n \n scaled_latent_model_input = torch.cat([latent_model_input, image_latents], dim=1)\n if mask_img is not None:\n scaled_latent_model_input = torch.cat([scaled_latent_model_input, mask_image_latents], dim=1)\n # if \"mask_index\" in kwargs and kwargs['mask_index'] is not None:\n # mask_index = kwargs['mask_index']\n # else:\n # mask_index = None\n noise_pred = self.transformer(\n hidden_states=scaled_latent_model_input,\n timestep=timestep,\n encoder_hidden_states=prompt_embeds,\n pooled_projections=pooled_prompt_embeds,\n joint_attention_kwargs=self.joint_attention_kwargs,\n return_dict=False,\n # mask_index= mask_index,\n )[0]\n\n # perform guidance \n if self.do_classifier_free_guidance:\n noise_pred_text, noise_pred_image, noise_pred_uncond = noise_pred.chunk(3)\n noise_pred = (\n noise_pred_uncond\n + self.guidance_scale * (noise_pred_text - noise_pred_image)\n + self.image_guidance_scale * (noise_pred_image - noise_pred_uncond)\n )\n # noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) # neg, prompt\n # noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents_dtype = latents.dtype\n latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]\n\n if latents.dtype != latents_dtype:\n if torch.backends.mps.is_available():\n # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272\n latents = latents.to(latents_dtype)\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n negative_pooled_prompt_embeds = callback_outputs.pop(\n \"negative_pooled_prompt_embeds\", negative_pooled_prompt_embeds\n )\n image_latents = callback_outputs.pop(\"image_latents\", image_latents)\n if mask_img is not None:\n mask_image_latents = callback_outputs.pop(\"mask_image_latents\", mask_image_latents)\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n\n if XLA_AVAILABLE:\n xm.mark_step()\n\n if output_type == \"latent\":\n image = latents\n\n else:\n # latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor\n latents = latents / self.vae.config.scaling_factor\n image = self.vae.decode(latents, return_dict=False)[0]\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusion3PipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_stable_diffusion_attend_and_excite\"] = [\"StableDiffusionAttendAndExcitePipeline\"]\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n else:\n from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nimport math\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nfrom torch.nn import functional as F\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom ...image_processor import VaeImageProcessor\nfrom ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, UNet2DConditionModel\nfrom ...models.attention_processor import Attention\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ..stable_diffusion import StableDiffusionPipelineOutput\nfrom ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__)\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusionAttendAndExcitePipeline\n\n >>> pipe = StableDiffusionAttendAndExcitePipeline.from_pretrained(\n ... \"CompVis/stable-diffusion-v1-4\", torch_dtype=torch.float16\n ... ).to(\"cuda\")\n\n\n >>> prompt = \"a cat and a frog\"\n\n >>> # use get_indices function to find out indices of the tokens you want to alter\n >>> pipe.get_indices(prompt)\n {0: '<|startoftext|>', 1: 'a', 2: 'cat', 3: 'and', 4: 'a', 5: 'frog', 6: '<|endoftext|>'}\n\n >>> token_indices = [2, 5]\n >>> seed = 6141\n >>> generator = torch.Generator(\"cuda\").manual_seed(seed)\n\n >>> images = pipe(\n ... prompt=prompt,\n ... token_indices=token_indices,\n ... guidance_scale=7.5,\n ... generator=generator,\n ... num_inference_steps=50,\n ... max_iter_to_alter=25,\n ... ).images\n\n >>> image = images[0]\n >>> image.save(f\"../images/{prompt}_{seed}.png\")\n ```\n\"\"\"\n\n\nclass AttentionStore:\n @staticmethod\n def get_empty_store():\n return {\"down\": [], \"mid\": [], \"up\": []}\n\n def __call__(self, attn, is_cross: bool, place_in_unet: str):\n if self.cur_att_layer >= 0 and is_cross:\n if attn.shape[1] == np.prod(self.attn_res):\n self.step_store[place_in_unet].append(attn)\n\n self.cur_att_layer += 1\n if self.cur_att_layer == self.num_att_layers:\n self.cur_att_layer = 0\n self.between_steps()\n\n def between_steps(self):\n self.attention_store = self.step_store\n self.step_store = self.get_empty_store()\n\n def get_average_attention(self):\n average_attention = self.attention_store\n return average_attention\n\n def aggregate_attention(self, from_where: List[str]) -> torch.Tensor:\n \"\"\"Aggregates the attention across the different layers and heads at the specified resolution.\"\"\"\n out = []\n attention_maps = self.get_average_attention()\n for location in from_where:\n for item in attention_maps[location]:\n cross_maps = item.reshape(-1, self.attn_res[0], self.attn_res[1], item.shape[-1])\n out.append(cross_maps)\n out = torch.cat(out, dim=0)\n out = out.sum(0) / out.shape[0]\n return out\n\n def reset(self):\n self.cur_att_layer = 0\n self.step_store = self.get_empty_store()\n self.attention_store = {}\n\n def __init__(self, attn_res):\n \"\"\"\n Initialize an empty AttentionStore :param step_index: used to visualize only a specific step in the diffusion\n process\n \"\"\"\n self.num_att_layers = -1\n self.cur_att_layer = 0\n self.step_store = self.get_empty_store()\n self.attention_store = {}\n self.curr_step_index = 0\n self.attn_res = attn_res\n\n\nclass AttendExciteAttnProcessor:\n def __init__(self, attnstore, place_in_unet):\n super().__init__()\n self.attnstore = attnstore\n self.place_in_unet = place_in_unet\n\n def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):\n batch_size, sequence_length, _ = hidden_states.shape\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n\n query = attn.to_q(hidden_states)\n\n is_cross = encoder_hidden_states is not None\n encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n attention_probs = attn.get_attention_scores(query, key, attention_mask)\n\n # only need to store attention maps during the Attend and Excite process\n if attention_probs.requires_grad:\n self.attnstore(attention_probs, is_cross, self.place_in_unet)\n\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n return hidden_states\n\n\nclass StableDiffusionAttendAndExcitePipeline(DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion and Attend-and-Excite.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n indices,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n indices_is_list_ints = isinstance(indices, list) and isinstance(indices[0], int)\n indices_is_list_list_ints = (\n isinstance(indices, list) and isinstance(indices[0], list) and isinstance(indices[0][0], int)\n )\n\n if not indices_is_list_ints and not indices_is_list_list_ints:\n raise TypeError(\"`indices` must be a list of ints or a list of a list of ints\")\n\n if indices_is_list_ints:\n indices_batch_size = 1\n elif indices_is_list_list_ints:\n indices_batch_size = len(indices)\n\n if prompt is not None and isinstance(prompt, str):\n prompt_batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n prompt_batch_size = len(prompt)\n elif prompt_embeds is not None:\n prompt_batch_size = prompt_embeds.shape[0]\n\n if indices_batch_size != prompt_batch_size:\n raise ValueError(\n f\"indices batch size must be same as prompt batch size. indices batch size: {indices_batch_size}, prompt batch size: {prompt_batch_size}\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @staticmethod\n def _compute_max_attention_per_index(\n attention_maps: torch.Tensor,\n indices: List[int],\n ) -> List[torch.Tensor]:\n \"\"\"Computes the maximum attention value for each of the tokens we wish to alter.\"\"\"\n attention_for_text = attention_maps[:, :, 1:-1]\n attention_for_text *= 100\n attention_for_text = torch.nn.functional.softmax(attention_for_text, dim=-1)\n\n # Shift indices since we removed the first token\n indices = [index - 1 for index in indices]\n\n # Extract the maximum values\n max_indices_list = []\n for i in indices:\n image = attention_for_text[:, :, i]\n smoothing = GaussianSmoothing().to(attention_maps.device)\n input = F.pad(image.unsqueeze(0).unsqueeze(0), (1, 1, 1, 1), mode=\"reflect\")\n image = smoothing(input).squeeze(0).squeeze(0)\n max_indices_list.append(image.max())\n return max_indices_list\n\n def _aggregate_and_get_max_attention_per_token(\n self,\n indices: List[int],\n ):\n \"\"\"Aggregates the attention for each token and computes the max activation value for each token to alter.\"\"\"\n attention_maps = self.attention_store.aggregate_attention(\n from_where=(\"up\", \"down\", \"mid\"),\n )\n max_attention_per_index = self._compute_max_attention_per_index(\n attention_maps=attention_maps,\n indices=indices,\n )\n return max_attention_per_index\n\n @staticmethod\n def _compute_loss(max_attention_per_index: List[torch.Tensor]) -> torch.Tensor:\n \"\"\"Computes the attend-and-excite loss using the maximum attention value for each token.\"\"\"\n losses = [max(0, 1.0 - curr_max) for curr_max in max_attention_per_index]\n loss = max(losses)\n return loss\n\n @staticmethod\n def _update_latent(latents: torch.Tensor, loss: torch.Tensor, step_size: float) -> torch.Tensor:\n \"\"\"Update the latent according to the computed loss.\"\"\"\n grad_cond = torch.autograd.grad(loss.requires_grad_(True), [latents], retain_graph=True)[0]\n latents = latents - step_size * grad_cond\n return latents\n\n def _perform_iterative_refinement_step(\n self,\n latents: torch.Tensor,\n indices: List[int],\n loss: torch.Tensor,\n threshold: float,\n text_embeddings: torch.Tensor,\n step_size: float,\n t: int,\n max_refinement_steps: int = 20,\n ):\n \"\"\"\n Performs the iterative latent refinement introduced in the paper. Here, we continuously update the latent code\n according to our loss objective until the given threshold is reached for all tokens.\n \"\"\"\n iteration = 0\n target_loss = max(0, 1.0 - threshold)\n while loss > target_loss:\n iteration += 1\n\n latents = latents.clone().detach().requires_grad_(True)\n self.unet(latents, t, encoder_hidden_states=text_embeddings).sample\n self.unet.zero_grad()\n\n # Get max activation value for each subject token\n max_attention_per_index = self._aggregate_and_get_max_attention_per_token(\n indices=indices,\n )\n\n loss = self._compute_loss(max_attention_per_index)\n\n if loss != 0:\n latents = self._update_latent(latents, loss, step_size)\n\n logger.info(f\"\\t Try {iteration}. loss: {loss}\")\n\n if iteration >= max_refinement_steps:\n logger.info(f\"\\t Exceeded max number of iterations ({max_refinement_steps})! \")\n break\n\n # Run one more time but don't compute gradients and update the latents.\n # We just need to compute the new loss - the grad update will occur below\n latents = latents.clone().detach().requires_grad_(True)\n _ = self.unet(latents, t, encoder_hidden_states=text_embeddings).sample\n self.unet.zero_grad()\n\n # Get max activation value for each subject token\n max_attention_per_index = self._aggregate_and_get_max_attention_per_token(\n indices=indices,\n )\n loss = self._compute_loss(max_attention_per_index)\n logger.info(f\"\\t Finished with loss of: {loss}\")\n return loss, latents, max_attention_per_index\n\n def register_attention_control(self):\n attn_procs = {}\n cross_att_count = 0\n for name in self.unet.attn_processors.keys():\n if name.startswith(\"mid_block\"):\n place_in_unet = \"mid\"\n elif name.startswith(\"up_blocks\"):\n place_in_unet = \"up\"\n elif name.startswith(\"down_blocks\"):\n place_in_unet = \"down\"\n else:\n continue\n\n cross_att_count += 1\n attn_procs[name] = AttendExciteAttnProcessor(attnstore=self.attention_store, place_in_unet=place_in_unet)\n\n self.unet.set_attn_processor(attn_procs)\n self.attention_store.num_att_layers = cross_att_count\n\n def get_indices(self, prompt: str) -> Dict[str, int]:\n \"\"\"Utility function to list the indices of the tokens you wish to alte\"\"\"\n ids = self.tokenizer(prompt).input_ids\n indices = {i: tok for tok, i in zip(self.tokenizer.convert_ids_to_tokens(ids), range(len(ids)))}\n return indices\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]],\n token_indices: Union[List[int], List[List[int]]],\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: int = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n max_iter_to_alter: int = 25,\n thresholds: dict = {0: 0.05, 10: 0.5, 20: 0.8},\n scale_factor: int = 20,\n attn_res: Optional[Tuple[int]] = (16, 16),\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n token_indices (`List[int]`):\n The token indices to alter with attend-and-excite.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n max_iter_to_alter (`int`, *optional*, defaults to `25`):\n Number of denoising steps to apply attend-and-excite. The `max_iter_to_alter` denoising steps are when\n attend-and-excite is applied. For example, if `max_iter_to_alter` is `25` and there are a total of `30`\n denoising steps, the first `25` denoising steps applies attend-and-excite and the last `5` will not.\n thresholds (`dict`, *optional*, defaults to `{0: 0.05, 10: 0.5, 20: 0.8}`):\n Dictionary defining the iterations and desired thresholds to apply iterative latent refinement in.\n scale_factor (`int`, *optional*, default to 20):\n Scale factor to control the step size of each attend-and-excite update.\n attn_res (`tuple`, *optional*, default computed from width and height):\n The 2D resolution of the semantic attention map.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n token_indices,\n height,\n width,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n clip_skip=clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n if attn_res is None:\n attn_res = int(np.ceil(width / 32)), int(np.ceil(height / 32))\n self.attention_store = AttentionStore(attn_res)\n original_attn_proc = self.unet.attn_processors\n self.register_attention_control()\n\n # default config for step size from original repo\n scale_range = np.linspace(1.0, 0.5, len(self.scheduler.timesteps))\n step_size = scale_factor * np.sqrt(scale_range)\n\n text_embeddings = (\n prompt_embeds[batch_size * num_images_per_prompt :] if do_classifier_free_guidance else prompt_embeds\n )\n\n if isinstance(token_indices[0], int):\n token_indices = [token_indices]\n\n indices = []\n\n for ind in token_indices:\n indices = indices + [ind] * num_images_per_prompt\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # Attend and excite process\n with torch.enable_grad():\n latents = latents.clone().detach().requires_grad_(True)\n updated_latents = []\n for latent, index, text_embedding in zip(latents, indices, text_embeddings):\n # Forward pass of denoising with text conditioning\n latent = latent.unsqueeze(0)\n text_embedding = text_embedding.unsqueeze(0)\n\n self.unet(\n latent,\n t,\n encoder_hidden_states=text_embedding,\n cross_attention_kwargs=cross_attention_kwargs,\n ).sample\n self.unet.zero_grad()\n\n # Get max activation value for each subject token\n max_attention_per_index = self._aggregate_and_get_max_attention_per_token(\n indices=index,\n )\n\n loss = self._compute_loss(max_attention_per_index=max_attention_per_index)\n\n # If this is an iterative refinement step, verify we have reached the desired threshold for all\n if i in thresholds.keys() and loss > 1.0 - thresholds[i]:\n loss, latent, max_attention_per_index = self._perform_iterative_refinement_step(\n latents=latent,\n indices=index,\n loss=loss,\n threshold=thresholds[i],\n text_embeddings=text_embedding,\n step_size=step_size[i],\n t=t,\n )\n\n # Perform gradient update\n if i < max_iter_to_alter:\n if loss != 0:\n latent = self._update_latent(\n latents=latent,\n loss=loss,\n step_size=step_size[i],\n )\n logger.info(f\"Iteration {i} | Loss: {loss:0.4f}\")\n\n updated_latents.append(latent)\n\n latents = torch.cat(updated_latents, dim=0)\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # 8. Post-processing\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n self.maybe_free_model_hooks()\n # make sure to set the original attention processors back\n self.unet.set_attn_processor(original_attn_proc)\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n\n\nclass GaussianSmoothing(torch.nn.Module):\n \"\"\"\n Arguments:\n Apply gaussian smoothing on a 1d, 2d or 3d tensor. Filtering is performed seperately for each channel in the input\n using a depthwise convolution.\n channels (int, sequence): Number of channels of the input tensors. Output will\n have this number of channels as well.\n kernel_size (int, sequence): Size of the gaussian kernel. sigma (float, sequence): Standard deviation of the\n gaussian kernel. dim (int, optional): The number of dimensions of the data.\n Default value is 2 (spatial).\n \"\"\"\n\n # channels=1, kernel_size=kernel_size, sigma=sigma, dim=2\n def __init__(\n self,\n channels: int = 1,\n kernel_size: int = 3,\n sigma: float = 0.5,\n dim: int = 2,\n ):\n super().__init__()\n\n if isinstance(kernel_size, int):\n kernel_size = [kernel_size] * dim\n if isinstance(sigma, float):\n sigma = [sigma] * dim\n\n # The gaussian kernel is the product of the\n # gaussian function of each dimension.\n kernel = 1\n meshgrids = torch.meshgrid([torch.arange(size, dtype=torch.float32) for size in kernel_size])\n for size, std, mgrid in zip(kernel_size, sigma, meshgrids):\n mean = (size - 1) / 2\n kernel *= 1 / (std * math.sqrt(2 * math.pi)) * torch.exp(-(((mgrid - mean) / (2 * std)) ** 2))\n\n # Make sure sum of values in gaussian kernel equals 1.\n kernel = kernel / torch.sum(kernel)\n\n # Reshape to depthwise convolutional weight\n kernel = kernel.view(1, 1, *kernel.size())\n kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1))\n\n self.register_buffer(\"weight\", kernel)\n self.groups = channels\n\n if dim == 1:\n self.conv = F.conv1d\n elif dim == 2:\n self.conv = F.conv2d\n elif dim == 3:\n self.conv = F.conv3d\n else:\n raise RuntimeError(\"Only 1, 2 and 3 dimensions are supported. Received {}.\".format(dim))\n\n def forward(self, input):\n \"\"\"\n Arguments:\n Apply gaussian filter to input.\n input (torch.Tensor): Input to apply gaussian filter on.\n Returns:\n filtered (torch.Tensor): Filtered output.\n \"\"\"\n return self.conv(input, weight=self.weight.to(input.dtype), groups=self.groups)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_stable_diffusion_diffedit\"] = [\"StableDiffusionDiffEditPipeline\"]\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n else:\n from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py", "content": "# Copyright 2024 DiffEdit Authors and Pix2Pix Zero Authors and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom dataclasses import dataclass\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom packaging import version\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom ...configuration_utils import FrozenDict\nfrom ...image_processor import VaeImageProcessor\nfrom ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, UNet2DConditionModel\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import DDIMInverseScheduler, KarrasDiffusionSchedulers\nfrom ...utils import (\n PIL_INTERPOLATION,\n USE_PEFT_BACKEND,\n BaseOutput,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ..stable_diffusion import StableDiffusionPipelineOutput\nfrom ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n@dataclass\nclass DiffEditInversionPipelineOutput(BaseOutput):\n \"\"\"\n Output class for Stable Diffusion pipelines.\n\n Args:\n latents (`torch.Tensor`)\n inverted latents tensor\n images (`List[PIL.Image.Image]` or `np.ndarray`)\n List of denoised PIL images of length `num_timesteps * batch_size` or numpy array of shape `(num_timesteps,\n batch_size, height, width, num_channels)`. PIL images or numpy array present the denoised images of the\n diffusion pipeline.\n \"\"\"\n\n latents: torch.Tensor\n images: Union[List[PIL.Image.Image], np.ndarray]\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n\n ```py\n >>> import PIL\n >>> import requests\n >>> import torch\n >>> from io import BytesIO\n\n >>> from diffusers import StableDiffusionDiffEditPipeline\n\n\n >>> def download_image(url):\n ... response = requests.get(url)\n ... return PIL.Image.open(BytesIO(response.content)).convert(\"RGB\")\n\n\n >>> img_url = \"https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png\"\n\n >>> init_image = download_image(img_url).resize((768, 768))\n\n >>> pipeline = StableDiffusionDiffEditPipeline.from_pretrained(\n ... \"stabilityai/stable-diffusion-2-1\", torch_dtype=torch.float16\n ... )\n\n >>> pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)\n >>> pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)\n >>> pipeline.enable_model_cpu_offload()\n\n >>> mask_prompt = \"A bowl of fruits\"\n >>> prompt = \"A bowl of pears\"\n\n >>> mask_image = pipeline.generate_mask(image=init_image, source_prompt=prompt, target_prompt=mask_prompt)\n >>> image_latents = pipeline.invert(image=init_image, prompt=mask_prompt).latents\n >>> image = pipeline(prompt=prompt, mask_image=mask_image, image_latents=image_latents).images[0]\n ```\n\"\"\"\n\nEXAMPLE_INVERT_DOC_STRING = \"\"\"\n ```py\n >>> import PIL\n >>> import requests\n >>> import torch\n >>> from io import BytesIO\n\n >>> from diffusers import StableDiffusionDiffEditPipeline\n\n\n >>> def download_image(url):\n ... response = requests.get(url)\n ... return PIL.Image.open(BytesIO(response.content)).convert(\"RGB\")\n\n\n >>> img_url = \"https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png\"\n\n >>> init_image = download_image(img_url).resize((768, 768))\n\n >>> pipeline = StableDiffusionDiffEditPipeline.from_pretrained(\n ... \"stabilityai/stable-diffusion-2-1\", torch_dtype=torch.float16\n ... )\n\n >>> pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)\n >>> pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)\n >>> pipeline.enable_model_cpu_offload()\n\n >>> prompt = \"A bowl of fruits\"\n\n >>> inverted_latents = pipeline.invert(image=init_image, prompt=prompt).latents\n ```\n\"\"\"\n\n\ndef auto_corr_loss(hidden_states, generator=None):\n reg_loss = 0.0\n for i in range(hidden_states.shape[0]):\n for j in range(hidden_states.shape[1]):\n noise = hidden_states[i : i + 1, j : j + 1, :, :]\n while True:\n roll_amount = torch.randint(noise.shape[2] // 2, (1,), generator=generator).item()\n reg_loss += (noise * torch.roll(noise, shifts=roll_amount, dims=2)).mean() ** 2\n reg_loss += (noise * torch.roll(noise, shifts=roll_amount, dims=3)).mean() ** 2\n\n if noise.shape[2] <= 8:\n break\n noise = torch.nn.functional.avg_pool2d(noise, kernel_size=2)\n return reg_loss\n\n\ndef kl_divergence(hidden_states):\n return hidden_states.var() + hidden_states.mean() ** 2 - 1 - torch.log(hidden_states.var() + 1e-7)\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess\ndef preprocess(image):\n deprecation_message = \"The preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead\"\n deprecate(\"preprocess\", \"1.0.0\", deprecation_message, standard_warn=False)\n if isinstance(image, torch.Tensor):\n return image\n elif isinstance(image, PIL.Image.Image):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n w, h = image[0].size\n w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8\n\n image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION[\"lanczos\"]))[None, :] for i in image]\n image = np.concatenate(image, axis=0)\n image = np.array(image).astype(np.float32) / 255.0\n image = image.transpose(0, 3, 1, 2)\n image = 2.0 * image - 1.0\n image = torch.from_numpy(image)\n elif isinstance(image[0], torch.Tensor):\n image = torch.cat(image, dim=0)\n return image\n\n\ndef preprocess_mask(mask, batch_size: int = 1):\n if not isinstance(mask, torch.Tensor):\n # preprocess mask\n if isinstance(mask, (PIL.Image.Image, np.ndarray)):\n mask = [mask]\n\n if isinstance(mask, list):\n if isinstance(mask[0], PIL.Image.Image):\n mask = [np.array(m.convert(\"L\")).astype(np.float32) / 255.0 for m in mask]\n if isinstance(mask[0], np.ndarray):\n mask = np.stack(mask, axis=0) if mask[0].ndim < 3 else np.concatenate(mask, axis=0)\n mask = torch.from_numpy(mask)\n elif isinstance(mask[0], torch.Tensor):\n mask = torch.stack(mask, dim=0) if mask[0].ndim < 3 else torch.cat(mask, dim=0)\n\n # Batch and add channel dim for single mask\n if mask.ndim == 2:\n mask = mask.unsqueeze(0).unsqueeze(0)\n\n # Batch single mask or add channel dim\n if mask.ndim == 3:\n # Single batched mask, no channel dim or single mask not batched but channel dim\n if mask.shape[0] == 1:\n mask = mask.unsqueeze(0)\n\n # Batched masks no channel dim\n else:\n mask = mask.unsqueeze(1)\n\n # Check mask shape\n if batch_size > 1:\n if mask.shape[0] == 1:\n mask = torch.cat([mask] * batch_size)\n elif mask.shape[0] > 1 and mask.shape[0] != batch_size:\n raise ValueError(\n f\"`mask_image` with batch size {mask.shape[0]} cannot be broadcasted to batch size {batch_size} \"\n f\"inferred by prompt inputs\"\n )\n\n if mask.shape[1] != 1:\n raise ValueError(f\"`mask_image` must have 1 channel, but has {mask.shape[1]} channels\")\n\n # Check mask is in [0, 1]\n if mask.min() < 0 or mask.max() > 1:\n raise ValueError(\"`mask_image` should be in [0, 1] range\")\n\n # Binarize mask\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n\n return mask\n\n\nclass StableDiffusionDiffEditPipeline(\n DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, LoraLoaderMixin\n):\n r\"\"\"\n \n\n This is an experimental feature!\n\n \n\n Pipeline for text-guided image inpainting using Stable Diffusion and DiffEdit.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading and saving methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents.\n inverse_scheduler ([`DDIMInverseScheduler`]):\n A scheduler to be used in combination with `unet` to fill in the unmasked part of the input latents.\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"inverse_scheduler\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n inverse_scheduler: DDIMInverseScheduler,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"skip_prk_steps\") and scheduler.config.skip_prk_steps is False:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration\"\n \" `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make\"\n \" sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to\"\n \" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face\"\n \" Hub, it would be very nice if you could open a Pull request for the\"\n \" `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"skip_prk_steps not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"skip_prk_steps\"] = True\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n inverse_scheduler=inverse_scheduler,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n def check_inputs(\n self,\n prompt,\n strength,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n if (strength is None) or (strength is not None and (strength < 0 or strength > 1)):\n raise ValueError(\n f\"The value of `strength` should in [0.0, 1.0] but is, but is {strength} of type {type(strength)}.\"\n )\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n def check_source_inputs(\n self,\n source_prompt=None,\n source_negative_prompt=None,\n source_prompt_embeds=None,\n source_negative_prompt_embeds=None,\n ):\n if source_prompt is not None and source_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `source_prompt`: {source_prompt} and `source_prompt_embeds`: {source_prompt_embeds}.\"\n \" Please make sure to only forward one of the two.\"\n )\n elif source_prompt is None and source_prompt_embeds is None:\n raise ValueError(\n \"Provide either `source_image` or `source_prompt_embeds`. Cannot leave all both of the arguments undefined.\"\n )\n elif source_prompt is not None and (\n not isinstance(source_prompt, str) and not isinstance(source_prompt, list)\n ):\n raise ValueError(f\"`source_prompt` has to be of type `str` or `list` but is {type(source_prompt)}\")\n\n if source_negative_prompt is not None and source_negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `source_negative_prompt`: {source_negative_prompt} and `source_negative_prompt_embeds`:\"\n f\" {source_negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if source_prompt_embeds is not None and source_negative_prompt_embeds is not None:\n if source_prompt_embeds.shape != source_negative_prompt_embeds.shape:\n raise ValueError(\n \"`source_prompt_embeds` and `source_negative_prompt_embeds` must have the same shape when passed\"\n f\" directly, but got: `source_prompt_embeds` {source_prompt_embeds.shape} !=\"\n f\" `source_negative_prompt_embeds` {source_negative_prompt_embeds.shape}.\"\n )\n\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n\n return timesteps, num_inference_steps - t_start\n\n def get_inverse_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n\n # safety for t_start overflow to prevent empty timsteps slice\n if t_start == 0:\n return self.inverse_scheduler.timesteps, num_inference_steps\n timesteps = self.inverse_scheduler.timesteps[:-t_start]\n\n return timesteps, num_inference_steps - t_start\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def prepare_image_latents(self, image, batch_size, dtype, device, generator=None):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n image = image.to(device=device, dtype=dtype)\n\n if image.shape[1] == 4:\n latents = image\n\n else:\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if isinstance(generator, list):\n latents = [\n self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)\n ]\n latents = torch.cat(latents, dim=0)\n else:\n latents = self.vae.encode(image).latent_dist.sample(generator)\n\n latents = self.vae.config.scaling_factor * latents\n\n if batch_size != latents.shape[0]:\n if batch_size % latents.shape[0] == 0:\n # expand image_latents for batch_size\n deprecation_message = (\n f\"You have passed {batch_size} text prompts (`prompt`), but only {latents.shape[0]} initial\"\n \" images (`image`). Initial images are now duplicating to match the number of text prompts. Note\"\n \" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update\"\n \" your script to pass as many initial images as text prompts to suppress this warning.\"\n )\n deprecate(\"len(prompt) != len(image)\", \"1.0.0\", deprecation_message, standard_warn=False)\n additional_latents_per_image = batch_size // latents.shape[0]\n latents = torch.cat([latents] * additional_latents_per_image, dim=0)\n else:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n latents = torch.cat([latents], dim=0)\n\n return latents\n\n def get_epsilon(self, model_output: torch.Tensor, sample: torch.Tensor, timestep: int):\n pred_type = self.inverse_scheduler.config.prediction_type\n alpha_prod_t = self.inverse_scheduler.alphas_cumprod[timestep]\n\n beta_prod_t = 1 - alpha_prod_t\n\n if pred_type == \"epsilon\":\n return model_output\n elif pred_type == \"sample\":\n return (sample - alpha_prod_t ** (0.5) * model_output) / beta_prod_t ** (0.5)\n elif pred_type == \"v_prediction\":\n return (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample\n else:\n raise ValueError(\n f\"prediction_type given as {pred_type} must be one of `epsilon`, `sample`, or `v_prediction`\"\n )\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def generate_mask(\n self,\n image: Union[torch.Tensor, PIL.Image.Image] = None,\n target_prompt: Optional[Union[str, List[str]]] = None,\n target_negative_prompt: Optional[Union[str, List[str]]] = None,\n target_prompt_embeds: Optional[torch.Tensor] = None,\n target_negative_prompt_embeds: Optional[torch.Tensor] = None,\n source_prompt: Optional[Union[str, List[str]]] = None,\n source_negative_prompt: Optional[Union[str, List[str]]] = None,\n source_prompt_embeds: Optional[torch.Tensor] = None,\n source_negative_prompt_embeds: Optional[torch.Tensor] = None,\n num_maps_per_mask: Optional[int] = 10,\n mask_encode_strength: Optional[float] = 0.5,\n mask_thresholding_ratio: Optional[float] = 3.0,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n output_type: Optional[str] = \"np\",\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n ):\n r\"\"\"\n Generate a latent mask given a mask prompt, a target prompt, and an image.\n\n Args:\n image (`PIL.Image.Image`):\n `Image` or tensor representing an image batch to be used for computing the mask.\n target_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide semantic mask generation. If not defined, you need to pass\n `prompt_embeds`.\n target_negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n target_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n target_negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n source_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide semantic mask generation using DiffEdit. If not defined, you need to\n pass `source_prompt_embeds` or `source_image` instead.\n source_negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide semantic mask generation away from using DiffEdit. If not defined, you\n need to pass `source_negative_prompt_embeds` or `source_image` instead.\n source_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings to guide the semantic mask generation. Can be used to easily tweak text\n inputs (prompt weighting). If not provided, text embeddings are generated from `source_prompt` input\n argument.\n source_negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings to negatively guide the semantic mask generation. Can be used to easily\n tweak text inputs (prompt weighting). If not provided, text embeddings are generated from\n `source_negative_prompt` input argument.\n num_maps_per_mask (`int`, *optional*, defaults to 10):\n The number of noise maps sampled to generate the semantic mask using DiffEdit.\n mask_encode_strength (`float`, *optional*, defaults to 0.5):\n The strength of the noise maps sampled to generate the semantic mask using DiffEdit. Must be between 0\n and 1.\n mask_thresholding_ratio (`float`, *optional*, defaults to 3.0):\n The maximum multiple of the mean absolute difference used to clamp the semantic guidance map before\n mask binarization.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the\n [`~models.attention_processor.AttnProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n\n Examples:\n\n Returns:\n `List[PIL.Image.Image]` or `np.array`:\n When returning a `List[PIL.Image.Image]`, the list consists of a batch of single-channel binary images\n with dimensions `(height // self.vae_scale_factor, width // self.vae_scale_factor)`. If it's\n `np.array`, the shape is `(batch_size, height // self.vae_scale_factor, width //\n self.vae_scale_factor)`.\n \"\"\"\n\n # 1. Check inputs (Provide dummy argument for callback_steps)\n self.check_inputs(\n target_prompt,\n mask_encode_strength,\n 1,\n target_negative_prompt,\n target_prompt_embeds,\n target_negative_prompt_embeds,\n )\n\n self.check_source_inputs(\n source_prompt,\n source_negative_prompt,\n source_prompt_embeds,\n source_negative_prompt_embeds,\n )\n\n if (num_maps_per_mask is None) or (\n num_maps_per_mask is not None and (not isinstance(num_maps_per_mask, int) or num_maps_per_mask <= 0)\n ):\n raise ValueError(\n f\"`num_maps_per_mask` has to be a positive integer but is {num_maps_per_mask} of type\"\n f\" {type(num_maps_per_mask)}.\"\n )\n\n if mask_thresholding_ratio is None or mask_thresholding_ratio <= 0:\n raise ValueError(\n f\"`mask_thresholding_ratio` has to be positive but is {mask_thresholding_ratio} of type\"\n f\" {type(mask_thresholding_ratio)}.\"\n )\n\n # 2. Define call parameters\n if target_prompt is not None and isinstance(target_prompt, str):\n batch_size = 1\n elif target_prompt is not None and isinstance(target_prompt, list):\n batch_size = len(target_prompt)\n else:\n batch_size = target_prompt_embeds.shape[0]\n if cross_attention_kwargs is None:\n cross_attention_kwargs = {}\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompts\n (cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None)\n target_negative_prompt_embeds, target_prompt_embeds = self.encode_prompt(\n target_prompt,\n device,\n num_maps_per_mask,\n do_classifier_free_guidance,\n target_negative_prompt,\n prompt_embeds=target_prompt_embeds,\n negative_prompt_embeds=target_negative_prompt_embeds,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n target_prompt_embeds = torch.cat([target_negative_prompt_embeds, target_prompt_embeds])\n\n source_negative_prompt_embeds, source_prompt_embeds = self.encode_prompt(\n source_prompt,\n device,\n num_maps_per_mask,\n do_classifier_free_guidance,\n source_negative_prompt,\n prompt_embeds=source_prompt_embeds,\n negative_prompt_embeds=source_negative_prompt_embeds,\n )\n if do_classifier_free_guidance:\n source_prompt_embeds = torch.cat([source_negative_prompt_embeds, source_prompt_embeds])\n\n # 4. Preprocess image\n image = self.image_processor.preprocess(image).repeat_interleave(num_maps_per_mask, dim=0)\n\n # 5. Set timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, _ = self.get_timesteps(num_inference_steps, mask_encode_strength, device)\n encode_timestep = timesteps[0]\n\n # 6. Prepare image latents and add noise with specified strength\n image_latents = self.prepare_image_latents(\n image, batch_size * num_maps_per_mask, self.vae.dtype, device, generator\n )\n noise = randn_tensor(image_latents.shape, generator=generator, device=device, dtype=self.vae.dtype)\n image_latents = self.scheduler.add_noise(image_latents, noise, encode_timestep)\n\n latent_model_input = torch.cat([image_latents] * (4 if do_classifier_free_guidance else 2))\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, encode_timestep)\n\n # 7. Predict the noise residual\n prompt_embeds = torch.cat([source_prompt_embeds, target_prompt_embeds])\n noise_pred = self.unet(\n latent_model_input,\n encode_timestep,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n ).sample\n\n if do_classifier_free_guidance:\n noise_pred_neg_src, noise_pred_source, noise_pred_uncond, noise_pred_target = noise_pred.chunk(4)\n noise_pred_source = noise_pred_neg_src + guidance_scale * (noise_pred_source - noise_pred_neg_src)\n noise_pred_target = noise_pred_uncond + guidance_scale * (noise_pred_target - noise_pred_uncond)\n else:\n noise_pred_source, noise_pred_target = noise_pred.chunk(2)\n\n # 8. Compute the mask from the absolute difference of predicted noise residuals\n # TODO: Consider smoothing mask guidance map\n mask_guidance_map = (\n torch.abs(noise_pred_target - noise_pred_source)\n .reshape(batch_size, num_maps_per_mask, *noise_pred_target.shape[-3:])\n .mean([1, 2])\n )\n clamp_magnitude = mask_guidance_map.mean() * mask_thresholding_ratio\n semantic_mask_image = mask_guidance_map.clamp(0, clamp_magnitude) / clamp_magnitude\n semantic_mask_image = torch.where(semantic_mask_image <= 0.5, 0, 1)\n mask_image = semantic_mask_image.cpu().numpy()\n\n # 9. Convert to Numpy array or PIL.\n if output_type == \"pil\":\n mask_image = self.image_processor.numpy_to_pil(mask_image)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n return mask_image\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_INVERT_DOC_STRING)\n def invert(\n self,\n prompt: Optional[Union[str, List[str]]] = None,\n image: Union[torch.Tensor, PIL.Image.Image] = None,\n num_inference_steps: int = 50,\n inpaint_strength: float = 0.8,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n decode_latents: bool = False,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: Optional[int] = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n lambda_auto_corr: float = 20.0,\n lambda_kl: float = 20.0,\n num_reg_steps: int = 0,\n num_auto_corr_rolls: int = 5,\n ):\n r\"\"\"\n Generate inverted latents given a prompt and image.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n image (`PIL.Image.Image`):\n `Image` or tensor representing an image batch to produce the inverted latents guided by `prompt`.\n inpaint_strength (`float`, *optional*, defaults to 0.8):\n Indicates extent of the noising process to run latent inversion. Must be between 0 and 1. When\n `inpaint_strength` is 1, the inversion process is run for the full number of iterations specified in\n `num_inference_steps`. `image` is used as a reference for the inversion process, and adding more noise\n increases `inpaint_strength`. If `inpaint_strength` is 0, no inpainting occurs.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n decode_latents (`bool`, *optional*, defaults to `False`):\n Whether or not to decode the inverted latents into a generated image. Setting this argument to `True`\n decodes all inverted latents for each timestep into a list of generated images.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.DiffEditInversionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the\n [`~models.attention_processor.AttnProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n lambda_auto_corr (`float`, *optional*, defaults to 20.0):\n Lambda parameter to control auto correction.\n lambda_kl (`float`, *optional*, defaults to 20.0):\n Lambda parameter to control Kullback-Leibler divergence output.\n num_reg_steps (`int`, *optional*, defaults to 0):\n Number of regularization loss steps.\n num_auto_corr_rolls (`int`, *optional*, defaults to 5):\n Number of auto correction roll steps.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.pipeline_stable_diffusion_diffedit.DiffEditInversionPipelineOutput`] or\n `tuple`:\n If `return_dict` is `True`,\n [`~pipelines.stable_diffusion.pipeline_stable_diffusion_diffedit.DiffEditInversionPipelineOutput`] is\n returned, otherwise a `tuple` is returned where the first element is the inverted latents tensors\n ordered by increasing noise, and the second is the corresponding decoded images if `decode_latents` is\n `True`, otherwise `None`.\n \"\"\"\n\n # 1. Check inputs\n self.check_inputs(\n prompt,\n inpaint_strength,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n )\n\n if image is None:\n raise ValueError(\"`image` input cannot be undefined.\")\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n if cross_attention_kwargs is None:\n cross_attention_kwargs = {}\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Preprocess image\n image = self.image_processor.preprocess(image)\n\n # 4. Prepare latent variables\n num_images_per_prompt = 1\n latents = self.prepare_image_latents(\n image, batch_size * num_images_per_prompt, self.vae.dtype, device, generator\n )\n\n # 5. Encode input prompt\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 6. Prepare timesteps\n self.inverse_scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_inverse_timesteps(num_inference_steps, inpaint_strength, device)\n\n # 7. Noising loop where we obtain the intermediate noised latent image for each timestep.\n num_warmup_steps = len(timesteps) - num_inference_steps * self.inverse_scheduler.order\n inverted_latents = []\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.inverse_scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # regularization of the noise prediction (not in original code or paper but borrowed from Pix2PixZero)\n if num_reg_steps > 0:\n with torch.enable_grad():\n for _ in range(num_reg_steps):\n if lambda_auto_corr > 0:\n for _ in range(num_auto_corr_rolls):\n var = torch.autograd.Variable(noise_pred.detach().clone(), requires_grad=True)\n\n # Derive epsilon from model output before regularizing to IID standard normal\n var_epsilon = self.get_epsilon(var, latent_model_input.detach(), t)\n\n l_ac = auto_corr_loss(var_epsilon, generator=generator)\n l_ac.backward()\n\n grad = var.grad.detach() / num_auto_corr_rolls\n noise_pred = noise_pred - lambda_auto_corr * grad\n\n if lambda_kl > 0:\n var = torch.autograd.Variable(noise_pred.detach().clone(), requires_grad=True)\n\n # Derive epsilon from model output before regularizing to IID standard normal\n var_epsilon = self.get_epsilon(var, latent_model_input.detach(), t)\n\n l_kld = kl_divergence(var_epsilon)\n l_kld.backward()\n\n grad = var.grad.detach()\n noise_pred = noise_pred - lambda_kl * grad\n\n noise_pred = noise_pred.detach()\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.inverse_scheduler.step(noise_pred, t, latents).prev_sample\n inverted_latents.append(latents.detach().clone())\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or (\n (i + 1) > num_warmup_steps and (i + 1) % self.inverse_scheduler.order == 0\n ):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n assert len(inverted_latents) == len(timesteps)\n latents = torch.stack(list(reversed(inverted_latents)), 1)\n\n # 8. Post-processing\n image = None\n if decode_latents:\n image = self.decode_latents(latents.flatten(0, 1))\n\n # 9. Convert to PIL.\n if decode_latents and output_type == \"pil\":\n image = self.image_processor.numpy_to_pil(image)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (latents, image)\n\n return DiffEditInversionPipelineOutput(latents=latents, images=image)\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Optional[Union[str, List[str]]] = None,\n mask_image: Union[torch.Tensor, PIL.Image.Image] = None,\n image_latents: Union[torch.Tensor, PIL.Image.Image] = None,\n inpaint_strength: Optional[float] = 0.8,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: int = None,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n mask_image (`PIL.Image.Image`):\n `Image` or tensor representing an image batch to mask the generated image. White pixels in the mask are\n repainted, while black pixels are preserved. If `mask_image` is a PIL image, it is converted to a\n single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)\n instead of 3, so the expected shape would be `(B, 1, H, W)`.\n image_latents (`PIL.Image.Image` or `torch.Tensor`):\n Partially noised image latents from the inversion process to be used as inputs for image generation.\n inpaint_strength (`float`, *optional*, defaults to 0.8):\n Indicates extent to inpaint the masked area. Must be between 0 and 1. When `inpaint_strength` is 1, the\n denoising process is run on the masked area for the full number of iterations specified in\n `num_inference_steps`. `image_latents` is used as a reference for the masked area, and adding more\n noise to a region increases `inpaint_strength`. If `inpaint_strength` is 0, no inpainting occurs.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n\n # 1. Check inputs\n self.check_inputs(\n prompt,\n inpaint_strength,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n )\n\n if mask_image is None:\n raise ValueError(\n \"`mask_image` input cannot be undefined. Use `generate_mask()` to compute `mask_image` from text prompts.\"\n )\n if image_latents is None:\n raise ValueError(\n \"`image_latents` input cannot be undefined. Use `invert()` to compute `image_latents` from input images.\"\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n if cross_attention_kwargs is None:\n cross_attention_kwargs = {}\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Preprocess mask\n mask_image = preprocess_mask(mask_image, batch_size)\n latent_height, latent_width = mask_image.shape[-2:]\n mask_image = torch.cat([mask_image] * num_images_per_prompt)\n mask_image = mask_image.to(device=device, dtype=prompt_embeds.dtype)\n\n # 5. Set timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, inpaint_strength, device)\n\n # 6. Preprocess image latents\n if isinstance(image_latents, list) and any(isinstance(l, torch.Tensor) and l.ndim == 5 for l in image_latents):\n image_latents = torch.cat(image_latents).detach()\n elif isinstance(image_latents, torch.Tensor) and image_latents.ndim == 5:\n image_latents = image_latents.detach()\n else:\n image_latents = self.image_processor.preprocess(image_latents).detach()\n\n latent_shape = (self.vae.config.latent_channels, latent_height, latent_width)\n if image_latents.shape[-3:] != latent_shape:\n raise ValueError(\n f\"Each latent image in `image_latents` must have shape {latent_shape}, \"\n f\"but has shape {image_latents.shape[-3:]}\"\n )\n if image_latents.ndim == 4:\n image_latents = image_latents.reshape(batch_size, len(timesteps), *latent_shape)\n if image_latents.shape[:2] != (batch_size, len(timesteps)):\n raise ValueError(\n f\"`image_latents` must have batch size {batch_size} with latent images from {len(timesteps)}\"\n f\" timesteps, but has batch size {image_latents.shape[0]} with latent images from\"\n f\" {image_latents.shape[1]} timesteps.\"\n )\n image_latents = image_latents.transpose(0, 1).repeat_interleave(num_images_per_prompt, dim=1)\n image_latents = image_latents.to(device=device, dtype=prompt_embeds.dtype)\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 8. Denoising loop\n latents = image_latents[0].clone()\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # mask with inverted latents from appropriate timestep - use original image latent for last step\n latents = latents * mask_image + image_latents[i] * (1 - mask_image)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_gligen/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_stable_diffusion_gligen\"] = [\"StableDiffusionGLIGENPipeline\"]\n _import_structure[\"pipeline_stable_diffusion_gligen_text_image\"] = [\"StableDiffusionGLIGENTextImagePipeline\"]\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n else:\n from .pipeline_stable_diffusion_gligen import StableDiffusionGLIGENPipeline\n from .pipeline_stable_diffusion_gligen_text_image import StableDiffusionGLIGENTextImagePipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py", "content": "# Copyright 2024 The GLIGEN Authors and HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nimport warnings\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport PIL.Image\nimport torch\nfrom transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer\n\nfrom ...image_processor import VaeImageProcessor\nfrom ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, UNet2DConditionModel\nfrom ...models.attention import GatedSelfAttentionDense\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ..stable_diffusion import StableDiffusionPipelineOutput\nfrom ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusionGLIGENPipeline\n >>> from diffusers.utils import load_image\n\n >>> # Insert objects described by text at the region defined by bounding boxes\n >>> pipe = StableDiffusionGLIGENPipeline.from_pretrained(\n ... \"masterful/gligen-1-4-inpainting-text-box\", variant=\"fp16\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> input_image = load_image(\n ... \"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/gligen/livingroom_modern.png\"\n ... )\n >>> prompt = \"a birthday cake\"\n >>> boxes = [[0.2676, 0.6088, 0.4773, 0.7183]]\n >>> phrases = [\"a birthday cake\"]\n\n >>> images = pipe(\n ... prompt=prompt,\n ... gligen_phrases=phrases,\n ... gligen_inpaint_image=input_image,\n ... gligen_boxes=boxes,\n ... gligen_scheduled_sampling_beta=1,\n ... output_type=\"pil\",\n ... num_inference_steps=50,\n ... ).images\n\n >>> images[0].save(\"./gligen-1-4-inpainting-text-box.jpg\")\n\n >>> # Generate an image described by the prompt and\n >>> # insert objects described by text at the region defined by bounding boxes\n >>> pipe = StableDiffusionGLIGENPipeline.from_pretrained(\n ... \"masterful/gligen-1-4-generation-text-box\", variant=\"fp16\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> prompt = \"a waterfall and a modern high speed train running through the tunnel in a beautiful forest with fall foliage\"\n >>> boxes = [[0.1387, 0.2051, 0.4277, 0.7090], [0.4980, 0.4355, 0.8516, 0.7266]]\n >>> phrases = [\"a waterfall\", \"a modern high speed train running through the tunnel\"]\n\n >>> images = pipe(\n ... prompt=prompt,\n ... gligen_phrases=phrases,\n ... gligen_boxes=boxes,\n ... gligen_scheduled_sampling_beta=1,\n ... output_type=\"pil\",\n ... num_inference_steps=50,\n ... ).images\n\n >>> images[0].save(\"./gligen-1-4-generation-text-box.jpg\")\n ```\n\"\"\"\n\n\nclass StableDiffusionGLIGENPipeline(DiffusionPipeline, StableDiffusionMixin):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion with Grounded-Language-to-Image Generation (GLIGEN).\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.).\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _exclude_from_cpu_offload = [\"safety_checker\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPFeatureExtractor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n gligen_phrases,\n gligen_boxes,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if len(gligen_phrases) != len(gligen_boxes):\n raise ValueError(\n \"length of `gligen_phrases` and `gligen_boxes` has to be same, but\"\n f\" got: `gligen_phrases` {len(gligen_phrases)} != `gligen_boxes` {len(gligen_boxes)}\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def enable_fuser(self, enabled=True):\n for module in self.unet.modules():\n if type(module) is GatedSelfAttentionDense:\n module.enabled = enabled\n\n def draw_inpaint_mask_from_boxes(self, boxes, size):\n inpaint_mask = torch.ones(size[0], size[1])\n for box in boxes:\n x0, x1 = box[0] * size[0], box[2] * size[0]\n y0, y1 = box[1] * size[1], box[3] * size[1]\n inpaint_mask[int(y0) : int(y1), int(x0) : int(x1)] = 0\n return inpaint_mask\n\n def crop(self, im, new_width, new_height):\n width, height = im.size\n left = (width - new_width) / 2\n top = (height - new_height) / 2\n right = (width + new_width) / 2\n bottom = (height + new_height) / 2\n return im.crop((left, top, right, bottom))\n\n def target_size_center_crop(self, im, new_hw):\n width, height = im.size\n if width != height:\n im = self.crop(im, min(height, width), min(height, width))\n return im.resize((new_hw, new_hw), PIL.Image.LANCZOS)\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n gligen_scheduled_sampling_beta: float = 0.3,\n gligen_phrases: List[str] = None,\n gligen_boxes: List[List[float]] = None,\n gligen_inpaint_image: Optional[PIL.Image.Image] = None,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n gligen_phrases (`List[str]`):\n The phrases to guide what to include in each of the regions defined by the corresponding\n `gligen_boxes`. There should only be one phrase per bounding box.\n gligen_boxes (`List[List[float]]`):\n The bounding boxes that identify rectangular regions of the image that are going to be filled with the\n content described by the corresponding `gligen_phrases`. Each rectangular box is defined as a\n `List[float]` of 4 elements `[xmin, ymin, xmax, ymax]` where each value is between [0,1].\n gligen_inpaint_image (`PIL.Image.Image`, *optional*):\n The input image, if provided, is inpainted with objects described by the `gligen_boxes` and\n `gligen_phrases`. Otherwise, it is treated as a generation task on a blank input image.\n gligen_scheduled_sampling_beta (`float`, defaults to 0.3):\n Scheduled Sampling factor from [GLIGEN: Open-Set Grounded Text-to-Image\n Generation](https://arxiv.org/pdf/2301.07093.pdf). Scheduled Sampling factor is only varied for\n scheduled sampling during inference for improved quality and controllability.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when\n using zero terminal SNR.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n height,\n width,\n callback_steps,\n gligen_phrases,\n gligen_boxes,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n clip_skip=clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 5.1 Prepare GLIGEN variables\n max_objs = 30\n if len(gligen_boxes) > max_objs:\n warnings.warn(\n f\"More that {max_objs} objects found. Only first {max_objs} objects will be processed.\",\n FutureWarning,\n )\n gligen_phrases = gligen_phrases[:max_objs]\n gligen_boxes = gligen_boxes[:max_objs]\n # prepare batched input to the GLIGENTextBoundingboxProjection (boxes, phrases, mask)\n # Get tokens for phrases from pre-trained CLIPTokenizer\n tokenizer_inputs = self.tokenizer(gligen_phrases, padding=True, return_tensors=\"pt\").to(device)\n # For the token, we use the same pre-trained text encoder\n # to obtain its text feature\n _text_embeddings = self.text_encoder(**tokenizer_inputs).pooler_output\n n_objs = len(gligen_boxes)\n # For each entity, described in phrases, is denoted with a bounding box,\n # we represent the location information as (xmin,ymin,xmax,ymax)\n boxes = torch.zeros(max_objs, 4, device=device, dtype=self.text_encoder.dtype)\n boxes[:n_objs] = torch.tensor(gligen_boxes)\n text_embeddings = torch.zeros(\n max_objs, self.unet.config.cross_attention_dim, device=device, dtype=self.text_encoder.dtype\n )\n text_embeddings[:n_objs] = _text_embeddings\n # Generate a mask for each object that is entity described by phrases\n masks = torch.zeros(max_objs, device=device, dtype=self.text_encoder.dtype)\n masks[:n_objs] = 1\n\n repeat_batch = batch_size * num_images_per_prompt\n boxes = boxes.unsqueeze(0).expand(repeat_batch, -1, -1).clone()\n text_embeddings = text_embeddings.unsqueeze(0).expand(repeat_batch, -1, -1).clone()\n masks = masks.unsqueeze(0).expand(repeat_batch, -1).clone()\n if do_classifier_free_guidance:\n repeat_batch = repeat_batch * 2\n boxes = torch.cat([boxes] * 2)\n text_embeddings = torch.cat([text_embeddings] * 2)\n masks = torch.cat([masks] * 2)\n masks[: repeat_batch // 2] = 0\n if cross_attention_kwargs is None:\n cross_attention_kwargs = {}\n cross_attention_kwargs[\"gligen\"] = {\"boxes\": boxes, \"positive_embeddings\": text_embeddings, \"masks\": masks}\n\n # Prepare latent variables for GLIGEN inpainting\n if gligen_inpaint_image is not None:\n # if the given input image is not of the same size as expected by VAE\n # center crop and resize the input image to expected shape\n if gligen_inpaint_image.size != (self.vae.sample_size, self.vae.sample_size):\n gligen_inpaint_image = self.target_size_center_crop(gligen_inpaint_image, self.vae.sample_size)\n # Convert a single image into a batch of images with a batch size of 1\n # The resulting shape becomes (1, C, H, W), where C is the number of channels,\n # and H and W are the height and width of the image.\n # scales the pixel values to a range [-1, 1]\n gligen_inpaint_image = self.image_processor.preprocess(gligen_inpaint_image)\n gligen_inpaint_image = gligen_inpaint_image.to(dtype=self.vae.dtype, device=self.vae.device)\n # Run AutoEncoder to get corresponding latents\n gligen_inpaint_latent = self.vae.encode(gligen_inpaint_image).latent_dist.sample()\n gligen_inpaint_latent = self.vae.config.scaling_factor * gligen_inpaint_latent\n # Generate an inpainting mask\n # pixel value = 0, where the object is present (defined by bounding boxes above)\n # 1, everywhere else\n gligen_inpaint_mask = self.draw_inpaint_mask_from_boxes(gligen_boxes, gligen_inpaint_latent.shape[2:])\n gligen_inpaint_mask = gligen_inpaint_mask.to(\n dtype=gligen_inpaint_latent.dtype, device=gligen_inpaint_latent.device\n )\n gligen_inpaint_mask = gligen_inpaint_mask[None, None]\n gligen_inpaint_mask_addition = torch.cat(\n (gligen_inpaint_latent * gligen_inpaint_mask, gligen_inpaint_mask), dim=1\n )\n # Convert a single mask into a batch of masks with a batch size of 1\n gligen_inpaint_mask_addition = gligen_inpaint_mask_addition.expand(repeat_batch, -1, -1, -1).clone()\n\n num_grounding_steps = int(gligen_scheduled_sampling_beta * len(timesteps))\n self.enable_fuser(True)\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # Scheduled sampling\n if i == num_grounding_steps:\n self.enable_fuser(False)\n\n if latents.shape[1] != 4:\n latents = torch.randn_like(latents[:, :4])\n\n if gligen_inpaint_image is not None:\n gligen_inpaint_latent_with_noise = (\n self.scheduler.add_noise(\n gligen_inpaint_latent, torch.randn_like(gligen_inpaint_latent), torch.tensor([t])\n )\n .expand(latents.shape[0], -1, -1, -1)\n .clone()\n )\n latents = gligen_inpaint_latent_with_noise * gligen_inpaint_mask + latents * (\n 1 - gligen_inpaint_mask\n )\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n if gligen_inpaint_image is not None:\n latent_model_input = torch.cat((latent_model_input, gligen_inpaint_mask_addition), dim=1)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py", "content": "# Copyright 2024 The GLIGEN Authors and HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nimport warnings\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport PIL.Image\nimport torch\nfrom transformers import (\n CLIPFeatureExtractor,\n CLIPProcessor,\n CLIPTextModel,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n)\n\nfrom ...image_processor import VaeImageProcessor\nfrom ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, UNet2DConditionModel\nfrom ...models.attention import GatedSelfAttentionDense\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import USE_PEFT_BACKEND, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ..stable_diffusion import StableDiffusionPipelineOutput\nfrom ..stable_diffusion.clip_image_project_model import CLIPImageProjection\nfrom ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusionGLIGENTextImagePipeline\n >>> from diffusers.utils import load_image\n\n >>> # Insert objects described by image at the region defined by bounding boxes\n >>> pipe = StableDiffusionGLIGENTextImagePipeline.from_pretrained(\n ... \"anhnct/Gligen_Inpainting_Text_Image\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> input_image = load_image(\n ... \"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/gligen/livingroom_modern.png\"\n ... )\n >>> prompt = \"a backpack\"\n >>> boxes = [[0.2676, 0.4088, 0.4773, 0.7183]]\n >>> phrases = None\n >>> gligen_image = load_image(\n ... \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/gligen/backpack.jpeg\"\n ... )\n\n >>> images = pipe(\n ... prompt=prompt,\n ... gligen_phrases=phrases,\n ... gligen_inpaint_image=input_image,\n ... gligen_boxes=boxes,\n ... gligen_images=[gligen_image],\n ... gligen_scheduled_sampling_beta=1,\n ... output_type=\"pil\",\n ... num_inference_steps=50,\n ... ).images\n\n >>> images[0].save(\"./gligen-inpainting-text-image-box.jpg\")\n\n >>> # Generate an image described by the prompt and\n >>> # insert objects described by text and image at the region defined by bounding boxes\n >>> pipe = StableDiffusionGLIGENTextImagePipeline.from_pretrained(\n ... \"anhnct/Gligen_Text_Image\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> prompt = \"a flower sitting on the beach\"\n >>> boxes = [[0.0, 0.09, 0.53, 0.76]]\n >>> phrases = [\"flower\"]\n >>> gligen_image = load_image(\n ... \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/gligen/pexels-pixabay-60597.jpg\"\n ... )\n\n >>> images = pipe(\n ... prompt=prompt,\n ... gligen_phrases=phrases,\n ... gligen_images=[gligen_image],\n ... gligen_boxes=boxes,\n ... gligen_scheduled_sampling_beta=1,\n ... output_type=\"pil\",\n ... num_inference_steps=50,\n ... ).images\n\n >>> images[0].save(\"./gligen-generation-text-image-box.jpg\")\n\n >>> # Generate an image described by the prompt and\n >>> # transfer style described by image at the region defined by bounding boxes\n >>> pipe = StableDiffusionGLIGENTextImagePipeline.from_pretrained(\n ... \"anhnct/Gligen_Text_Image\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> prompt = \"a dragon flying on the sky\"\n >>> boxes = [[0.4, 0.2, 1.0, 0.8], [0.0, 1.0, 0.0, 1.0]] # Set `[0.0, 1.0, 0.0, 1.0]` for the style\n\n >>> gligen_image = load_image(\n ... \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/landscape.png\"\n ... )\n\n >>> gligen_placeholder = load_image(\n ... \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/landscape.png\"\n ... )\n\n >>> images = pipe(\n ... prompt=prompt,\n ... gligen_phrases=[\n ... \"dragon\",\n ... \"placeholder\",\n ... ], # Can use any text instead of `placeholder` token, because we will use mask here\n ... gligen_images=[\n ... gligen_placeholder,\n ... gligen_image,\n ... ], # Can use any image in gligen_placeholder, because we will use mask here\n ... input_phrases_mask=[1, 0], # Set 0 for the placeholder token\n ... input_images_mask=[0, 1], # Set 0 for the placeholder image\n ... gligen_boxes=boxes,\n ... gligen_scheduled_sampling_beta=1,\n ... output_type=\"pil\",\n ... num_inference_steps=50,\n ... ).images\n\n >>> images[0].save(\"./gligen-generation-text-image-box-style-transfer.jpg\")\n ```\n\"\"\"\n\n\nclass StableDiffusionGLIGENTextImagePipeline(DiffusionPipeline, StableDiffusionMixin):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion with Grounded-Language-to-Image Generation (GLIGEN).\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.).\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n processor ([`~transformers.CLIPProcessor`]):\n A `CLIPProcessor` to procces reference image.\n image_encoder ([`~transformers.CLIPVisionModelWithProjection`]):\n Frozen image-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n image_project ([`CLIPImageProjection`]):\n A `CLIPImageProjection` to project image embedding into phrases embedding space.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n processor: CLIPProcessor,\n image_encoder: CLIPVisionModelWithProjection,\n image_project: CLIPImageProjection,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPFeatureExtractor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n image_encoder=image_encoder,\n processor=processor,\n image_project=image_project,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion_k_diffusion.pipeline_stable_diffusion_k_diffusion.StableDiffusionKDiffusionPipeline.check_inputs\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def enable_fuser(self, enabled=True):\n for module in self.unet.modules():\n if type(module) is GatedSelfAttentionDense:\n module.enabled = enabled\n\n def draw_inpaint_mask_from_boxes(self, boxes, size):\n \"\"\"\n Create an inpainting mask based on given boxes. This function generates an inpainting mask using the provided\n boxes to mark regions that need to be inpainted.\n \"\"\"\n inpaint_mask = torch.ones(size[0], size[1])\n for box in boxes:\n x0, x1 = box[0] * size[0], box[2] * size[0]\n y0, y1 = box[1] * size[1], box[3] * size[1]\n inpaint_mask[int(y0) : int(y1), int(x0) : int(x1)] = 0\n return inpaint_mask\n\n def crop(self, im, new_width, new_height):\n \"\"\"\n Crop the input image to the specified dimensions.\n \"\"\"\n width, height = im.size\n left = (width - new_width) / 2\n top = (height - new_height) / 2\n right = (width + new_width) / 2\n bottom = (height + new_height) / 2\n return im.crop((left, top, right, bottom))\n\n def target_size_center_crop(self, im, new_hw):\n \"\"\"\n Crop and resize the image to the target size while keeping the center.\n \"\"\"\n width, height = im.size\n if width != height:\n im = self.crop(im, min(height, width), min(height, width))\n return im.resize((new_hw, new_hw), PIL.Image.LANCZOS)\n\n def complete_mask(self, has_mask, max_objs, device):\n \"\"\"\n Based on the input mask corresponding value `0 or 1` for each phrases and image, mask the features\n corresponding to phrases and images.\n \"\"\"\n mask = torch.ones(1, max_objs).type(self.text_encoder.dtype).to(device)\n if has_mask is None:\n return mask\n\n if isinstance(has_mask, int):\n return mask * has_mask\n else:\n for idx, value in enumerate(has_mask):\n mask[0, idx] = value\n return mask\n\n def get_clip_feature(self, input, normalize_constant, device, is_image=False):\n \"\"\"\n Get image and phrases embedding by using CLIP pretrain model. The image embedding is transformed into the\n phrases embedding space through a projection.\n \"\"\"\n if is_image:\n if input is None:\n return None\n inputs = self.processor(images=[input], return_tensors=\"pt\").to(device)\n inputs[\"pixel_values\"] = inputs[\"pixel_values\"].to(self.image_encoder.dtype)\n\n outputs = self.image_encoder(**inputs)\n feature = outputs.image_embeds\n feature = self.image_project(feature).squeeze(0)\n feature = (feature / feature.norm()) * normalize_constant\n feature = feature.unsqueeze(0)\n else:\n if input is None:\n return None\n inputs = self.tokenizer(input, return_tensors=\"pt\", padding=True).to(device)\n outputs = self.text_encoder(**inputs)\n feature = outputs.pooler_output\n return feature\n\n def get_cross_attention_kwargs_with_grounded(\n self,\n hidden_size,\n gligen_phrases,\n gligen_images,\n gligen_boxes,\n input_phrases_mask,\n input_images_mask,\n repeat_batch,\n normalize_constant,\n max_objs,\n device,\n ):\n \"\"\"\n Prepare the cross-attention kwargs containing information about the grounded input (boxes, mask, image\n embedding, phrases embedding).\n \"\"\"\n phrases, images = gligen_phrases, gligen_images\n images = [None] * len(phrases) if images is None else images\n phrases = [None] * len(images) if phrases is None else phrases\n\n boxes = torch.zeros(max_objs, 4, device=device, dtype=self.text_encoder.dtype)\n masks = torch.zeros(max_objs, device=device, dtype=self.text_encoder.dtype)\n phrases_masks = torch.zeros(max_objs, device=device, dtype=self.text_encoder.dtype)\n image_masks = torch.zeros(max_objs, device=device, dtype=self.text_encoder.dtype)\n phrases_embeddings = torch.zeros(max_objs, hidden_size, device=device, dtype=self.text_encoder.dtype)\n image_embeddings = torch.zeros(max_objs, hidden_size, device=device, dtype=self.text_encoder.dtype)\n\n text_features = []\n image_features = []\n for phrase, image in zip(phrases, images):\n text_features.append(self.get_clip_feature(phrase, normalize_constant, device, is_image=False))\n image_features.append(self.get_clip_feature(image, normalize_constant, device, is_image=True))\n\n for idx, (box, text_feature, image_feature) in enumerate(zip(gligen_boxes, text_features, image_features)):\n boxes[idx] = torch.tensor(box)\n masks[idx] = 1\n if text_feature is not None:\n phrases_embeddings[idx] = text_feature\n phrases_masks[idx] = 1\n if image_feature is not None:\n image_embeddings[idx] = image_feature\n image_masks[idx] = 1\n\n input_phrases_mask = self.complete_mask(input_phrases_mask, max_objs, device)\n phrases_masks = phrases_masks.unsqueeze(0).repeat(repeat_batch, 1) * input_phrases_mask\n input_images_mask = self.complete_mask(input_images_mask, max_objs, device)\n image_masks = image_masks.unsqueeze(0).repeat(repeat_batch, 1) * input_images_mask\n boxes = boxes.unsqueeze(0).repeat(repeat_batch, 1, 1)\n masks = masks.unsqueeze(0).repeat(repeat_batch, 1)\n phrases_embeddings = phrases_embeddings.unsqueeze(0).repeat(repeat_batch, 1, 1)\n image_embeddings = image_embeddings.unsqueeze(0).repeat(repeat_batch, 1, 1)\n\n out = {\n \"boxes\": boxes,\n \"masks\": masks,\n \"phrases_masks\": phrases_masks,\n \"image_masks\": image_masks,\n \"phrases_embeddings\": phrases_embeddings,\n \"image_embeddings\": image_embeddings,\n }\n\n return out\n\n def get_cross_attention_kwargs_without_grounded(self, hidden_size, repeat_batch, max_objs, device):\n \"\"\"\n Prepare the cross-attention kwargs without information about the grounded input (boxes, mask, image embedding,\n phrases embedding) (All are zero tensor).\n \"\"\"\n boxes = torch.zeros(max_objs, 4, device=device, dtype=self.text_encoder.dtype)\n masks = torch.zeros(max_objs, device=device, dtype=self.text_encoder.dtype)\n phrases_masks = torch.zeros(max_objs, device=device, dtype=self.text_encoder.dtype)\n image_masks = torch.zeros(max_objs, device=device, dtype=self.text_encoder.dtype)\n phrases_embeddings = torch.zeros(max_objs, hidden_size, device=device, dtype=self.text_encoder.dtype)\n image_embeddings = torch.zeros(max_objs, hidden_size, device=device, dtype=self.text_encoder.dtype)\n\n out = {\n \"boxes\": boxes.unsqueeze(0).repeat(repeat_batch, 1, 1),\n \"masks\": masks.unsqueeze(0).repeat(repeat_batch, 1),\n \"phrases_masks\": phrases_masks.unsqueeze(0).repeat(repeat_batch, 1),\n \"image_masks\": image_masks.unsqueeze(0).repeat(repeat_batch, 1),\n \"phrases_embeddings\": phrases_embeddings.unsqueeze(0).repeat(repeat_batch, 1, 1),\n \"image_embeddings\": image_embeddings.unsqueeze(0).repeat(repeat_batch, 1, 1),\n }\n\n return out\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n gligen_scheduled_sampling_beta: float = 0.3,\n gligen_phrases: List[str] = None,\n gligen_images: List[PIL.Image.Image] = None,\n input_phrases_mask: Union[int, List[int]] = None,\n input_images_mask: Union[int, List[int]] = None,\n gligen_boxes: List[List[float]] = None,\n gligen_inpaint_image: Optional[PIL.Image.Image] = None,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n gligen_normalize_constant: float = 28.7,\n clip_skip: int = None,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n gligen_phrases (`List[str]`):\n The phrases to guide what to include in each of the regions defined by the corresponding\n `gligen_boxes`. There should only be one phrase per bounding box.\n gligen_images (`List[PIL.Image.Image]`):\n The images to guide what to include in each of the regions defined by the corresponding `gligen_boxes`.\n There should only be one image per bounding box\n input_phrases_mask (`int` or `List[int]`):\n pre phrases mask input defined by the correspongding `input_phrases_mask`\n input_images_mask (`int` or `List[int]`):\n pre images mask input defined by the correspongding `input_images_mask`\n gligen_boxes (`List[List[float]]`):\n The bounding boxes that identify rectangular regions of the image that are going to be filled with the\n content described by the corresponding `gligen_phrases`. Each rectangular box is defined as a\n `List[float]` of 4 elements `[xmin, ymin, xmax, ymax]` where each value is between [0,1].\n gligen_inpaint_image (`PIL.Image.Image`, *optional*):\n The input image, if provided, is inpainted with objects described by the `gligen_boxes` and\n `gligen_phrases`. Otherwise, it is treated as a generation task on a blank input image.\n gligen_scheduled_sampling_beta (`float`, defaults to 0.3):\n Scheduled Sampling factor from [GLIGEN: Open-Set Grounded Text-to-Image\n Generation](https://arxiv.org/pdf/2301.07093.pdf). Scheduled Sampling factor is only varied for\n scheduled sampling during inference for improved quality and controllability.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n gligen_normalize_constant (`float`, *optional*, defaults to 28.7):\n The normalize value of the image embedding.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n clip_skip=clip_skip,\n )\n\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 5.1 Prepare GLIGEN variables\n max_objs = 30\n if len(gligen_boxes) > max_objs:\n warnings.warn(\n f\"More that {max_objs} objects found. Only first {max_objs} objects will be processed.\",\n FutureWarning,\n )\n gligen_phrases = gligen_phrases[:max_objs]\n gligen_boxes = gligen_boxes[:max_objs]\n gligen_images = gligen_images[:max_objs]\n\n repeat_batch = batch_size * num_images_per_prompt\n\n if do_classifier_free_guidance:\n repeat_batch = repeat_batch * 2\n\n if cross_attention_kwargs is None:\n cross_attention_kwargs = {}\n\n hidden_size = prompt_embeds.shape[2]\n\n cross_attention_kwargs[\"gligen\"] = self.get_cross_attention_kwargs_with_grounded(\n hidden_size=hidden_size,\n gligen_phrases=gligen_phrases,\n gligen_images=gligen_images,\n gligen_boxes=gligen_boxes,\n input_phrases_mask=input_phrases_mask,\n input_images_mask=input_images_mask,\n repeat_batch=repeat_batch,\n normalize_constant=gligen_normalize_constant,\n max_objs=max_objs,\n device=device,\n )\n\n cross_attention_kwargs_without_grounded = {}\n cross_attention_kwargs_without_grounded[\"gligen\"] = self.get_cross_attention_kwargs_without_grounded(\n hidden_size=hidden_size, repeat_batch=repeat_batch, max_objs=max_objs, device=device\n )\n\n # Prepare latent variables for GLIGEN inpainting\n if gligen_inpaint_image is not None:\n # if the given input image is not of the same size as expected by VAE\n # center crop and resize the input image to expected shape\n if gligen_inpaint_image.size != (self.vae.sample_size, self.vae.sample_size):\n gligen_inpaint_image = self.target_size_center_crop(gligen_inpaint_image, self.vae.sample_size)\n # Convert a single image into a batch of images with a batch size of 1\n # The resulting shape becomes (1, C, H, W), where C is the number of channels,\n # and H and W are the height and width of the image.\n # scales the pixel values to a range [-1, 1]\n gligen_inpaint_image = self.image_processor.preprocess(gligen_inpaint_image)\n gligen_inpaint_image = gligen_inpaint_image.to(dtype=self.vae.dtype, device=self.vae.device)\n # Run AutoEncoder to get corresponding latents\n gligen_inpaint_latent = self.vae.encode(gligen_inpaint_image).latent_dist.sample()\n gligen_inpaint_latent = self.vae.config.scaling_factor * gligen_inpaint_latent\n # Generate an inpainting mask\n # pixel value = 0, where the object is present (defined by bounding boxes above)\n # 1, everywhere else\n gligen_inpaint_mask = self.draw_inpaint_mask_from_boxes(gligen_boxes, gligen_inpaint_latent.shape[2:])\n gligen_inpaint_mask = gligen_inpaint_mask.to(\n dtype=gligen_inpaint_latent.dtype, device=gligen_inpaint_latent.device\n )\n gligen_inpaint_mask = gligen_inpaint_mask[None, None]\n gligen_inpaint_mask_addition = torch.cat(\n (gligen_inpaint_latent * gligen_inpaint_mask, gligen_inpaint_mask), dim=1\n )\n # Convert a single mask into a batch of masks with a batch size of 1\n gligen_inpaint_mask_addition = gligen_inpaint_mask_addition.expand(repeat_batch, -1, -1, -1).clone()\n\n int(gligen_scheduled_sampling_beta * len(timesteps))\n self.enable_fuser(True)\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n if latents.shape[1] != 4:\n latents = torch.randn_like(latents[:, :4])\n\n if gligen_inpaint_image is not None:\n gligen_inpaint_latent_with_noise = (\n self.scheduler.add_noise(\n gligen_inpaint_latent, torch.randn_like(gligen_inpaint_latent), torch.tensor([t])\n )\n .expand(latents.shape[0], -1, -1, -1)\n .clone()\n )\n latents = gligen_inpaint_latent_with_noise * gligen_inpaint_mask + latents * (\n 1 - gligen_inpaint_mask\n )\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n if gligen_inpaint_image is not None:\n latent_model_input = torch.cat((latent_model_input, gligen_inpaint_mask_addition), dim=1)\n\n # predict the noise residual with grounded information\n noise_pred_with_grounding = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n ).sample\n\n # predict the noise residual without grounded information\n noise_pred_without_grounding = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs_without_grounded,\n ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n # Using noise_pred_text from noise residual with grounded information and noise_pred_uncond from noise residual without grounded information\n _, noise_pred_text = noise_pred_with_grounding.chunk(2)\n noise_pred_uncond, _ = noise_pred_without_grounding.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n else:\n noise_pred = noise_pred_with_grounding\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_k_diffusion_available,\n is_k_diffusion_version,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\n\ntry:\n if not (\n is_transformers_available()\n and is_torch_available()\n and is_k_diffusion_available()\n and is_k_diffusion_version(\">=\", \"0.0.12\")\n ):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_and_k_diffusion_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_and_k_diffusion_objects))\nelse:\n _import_structure[\"pipeline_stable_diffusion_k_diffusion\"] = [\"StableDiffusionKDiffusionPipeline\"]\n _import_structure[\"pipeline_stable_diffusion_xl_k_diffusion\"] = [\"StableDiffusionXLKDiffusionPipeline\"]\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (\n is_transformers_available()\n and is_torch_available()\n and is_k_diffusion_available()\n and is_k_diffusion_version(\">=\", \"0.0.12\")\n ):\n raise OptionalDependencyNotAvailable()\n\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *\n else:\n from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline\n from .pipeline_stable_diffusion_xl_k_diffusion import StableDiffusionXLKDiffusionPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport importlib\nimport inspect\nfrom typing import Callable, List, Optional, Union\n\nimport torch\nfrom k_diffusion.external import CompVisDenoiser, CompVisVDenoiser\nfrom k_diffusion.sampling import BrownianTreeNoiseSampler, get_sigmas_karras\n\nfrom ...image_processor import VaeImageProcessor\nfrom ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import LMSDiscreteScheduler\nfrom ...utils import USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ..stable_diffusion import StableDiffusionPipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass ModelWrapper:\n def __init__(self, model, alphas_cumprod):\n self.model = model\n self.alphas_cumprod = alphas_cumprod\n\n def apply_model(self, *args, **kwargs):\n if len(args) == 3:\n encoder_hidden_states = args[-1]\n args = args[:2]\n if kwargs.get(\"cond\", None) is not None:\n encoder_hidden_states = kwargs.pop(\"cond\")\n return self.model(*args, encoder_hidden_states=encoder_hidden_states, **kwargs).sample\n\n\nclass StableDiffusionKDiffusionPipeline(\n DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, LoraLoaderMixin\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n\n \n\n This is an experimental pipeline and is likely to change in the future.\n\n \n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n\n def __init__(\n self,\n vae,\n text_encoder,\n tokenizer,\n unet,\n scheduler,\n safety_checker,\n feature_extractor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n logger.info(\n f\"{self.__class__} is an experimntal pipeline and is likely to change in the future. We recommend to use\"\n \" this pipeline for fast experimentation / iteration if needed, but advice to rely on existing pipelines\"\n \" as defined in https://huggingface.co/docs/diffusers/api/schedulers#implemented-schedulers for\"\n \" production settings.\"\n )\n\n # get correct sigmas from LMS\n scheduler = LMSDiscreteScheduler.from_config(scheduler.config)\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n model = ModelWrapper(unet, scheduler.alphas_cumprod)\n if scheduler.config.prediction_type == \"v_prediction\":\n self.k_diffusion_model = CompVisVDenoiser(model)\n else:\n self.k_diffusion_model = CompVisDenoiser(model)\n\n def set_scheduler(self, scheduler_type: str):\n library = importlib.import_module(\"k_diffusion\")\n sampling = getattr(library, \"sampling\")\n try:\n self.sampler = getattr(sampling, scheduler_type)\n except Exception:\n valid_samplers = []\n for s in dir(sampling):\n if \"sample_\" in s:\n valid_samplers.append(s)\n\n raise ValueError(f\"Invalid scheduler type {scheduler_type}. Please choose one of {valid_samplers}.\")\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n return latents\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n use_karras_sigmas: Optional[bool] = False,\n noise_sampler_seed: Optional[int] = None,\n clip_skip: int = None,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale`\n is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n use_karras_sigmas (`bool`, *optional*, defaults to `False`):\n Use karras sigmas. For example, specifying `sample_dpmpp_2m` to `set_scheduler` will be equivalent to\n `DPM++2M` in stable-diffusion-webui. On top of that, setting this option to True will make it `DPM++2M\n Karras`.\n noise_sampler_seed (`int`, *optional*, defaults to `None`):\n The random seed to use for the noise sampler. If `None`, a random seed will be generated.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated images, and the second element is a\n list of `bool`s denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, according to the `safety_checker`.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = True\n if guidance_scale <= 1.0:\n raise ValueError(\"has to use guidance_scale\")\n\n # 3. Encode input prompt\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n clip_skip=clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=prompt_embeds.device)\n\n # 5. Prepare sigmas\n if use_karras_sigmas:\n sigma_min: float = self.k_diffusion_model.sigmas[0].item()\n sigma_max: float = self.k_diffusion_model.sigmas[-1].item()\n sigmas = get_sigmas_karras(n=num_inference_steps, sigma_min=sigma_min, sigma_max=sigma_max)\n sigmas = sigmas.to(device)\n else:\n sigmas = self.scheduler.sigmas\n sigmas = sigmas.to(prompt_embeds.dtype)\n\n # 6. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n latents = latents * sigmas[0]\n self.k_diffusion_model.sigmas = self.k_diffusion_model.sigmas.to(latents.device)\n self.k_diffusion_model.log_sigmas = self.k_diffusion_model.log_sigmas.to(latents.device)\n\n # 7. Define model function\n def model_fn(x, t):\n latent_model_input = torch.cat([x] * 2)\n t = torch.cat([t] * 2)\n\n noise_pred = self.k_diffusion_model(latent_model_input, t, cond=prompt_embeds)\n\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n return noise_pred\n\n # 8. Run k-diffusion solver\n sampler_kwargs = {}\n\n if \"noise_sampler\" in inspect.signature(self.sampler).parameters:\n min_sigma, max_sigma = sigmas[sigmas > 0].min(), sigmas.max()\n noise_sampler = BrownianTreeNoiseSampler(latents, min_sigma, max_sigma, noise_sampler_seed)\n sampler_kwargs[\"noise_sampler\"] = noise_sampler\n\n if \"generator\" in inspect.signature(self.sampler).parameters:\n sampler_kwargs[\"generator\"] = generator\n\n latents = self.sampler(model_fn, latents, sigmas, **sampler_kwargs)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport importlib\nimport inspect\nfrom typing import List, Optional, Tuple, Union\n\nimport torch\nfrom k_diffusion.external import CompVisDenoiser, CompVisVDenoiser\nfrom k_diffusion.sampling import BrownianTreeNoiseSampler, get_sigmas_karras\nfrom transformers import (\n CLIPTextModel,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n)\n\nfrom ...image_processor import VaeImageProcessor\nfrom ...loaders import (\n FromSingleFileMixin,\n IPAdapterMixin,\n StableDiffusionXLLoraLoaderMixin,\n TextualInversionLoaderMixin,\n)\nfrom ...models import AutoencoderKL, UNet2DConditionModel\nfrom ...models.attention_processor import (\n AttnProcessor2_0,\n FusedAttnProcessor2_0,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n XFormersAttnProcessor,\n)\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers, LMSDiscreteScheduler\nfrom ...utils import (\n USE_PEFT_BACKEND,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ..stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusionXLKDiffusionPipeline\n\n >>> pipe = StableDiffusionXLKDiffusionPipeline.from_pretrained(\n ... \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n >>> pipe.set_scheduler(\"sample_dpmpp_2m_sde\")\n\n >>> prompt = \"a photo of an astronaut riding a horse on mars\"\n >>> image = pipe(prompt).images[0]\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion_k_diffusion.pipeline_stable_diffusion_k_diffusion.ModelWrapper\nclass ModelWrapper:\n def __init__(self, model, alphas_cumprod):\n self.model = model\n self.alphas_cumprod = alphas_cumprod\n\n def apply_model(self, *args, **kwargs):\n if len(args) == 3:\n encoder_hidden_states = args[-1]\n args = args[:2]\n if kwargs.get(\"cond\", None) is not None:\n encoder_hidden_states = kwargs.pop(\"cond\")\n return self.model(*args, encoder_hidden_states=encoder_hidden_states, **kwargs).sample\n\n\nclass StableDiffusionXLKDiffusionPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n FromSingleFileMixin,\n StableDiffusionXLLoraLoaderMixin,\n TextualInversionLoaderMixin,\n IPAdapterMixin,\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion XL and k-diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion XL uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n text_encoder_2 ([` CLIPTextModelWithProjection`]):\n Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),\n specifically the\n [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)\n variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n tokenizer_2 (`CLIPTokenizer`):\n Second Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `\"True\"`):\n Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of\n `stabilityai/stable-diffusion-xl-base-1-0`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->text_encoder_2->unet->vae\"\n _optional_components = [\n \"tokenizer\",\n \"tokenizer_2\",\n \"text_encoder\",\n \"text_encoder_2\",\n \"feature_extractor\",\n ]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n text_encoder_2: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n tokenizer_2: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n force_zeros_for_empty_prompt: bool = True,\n ):\n super().__init__()\n\n # get correct sigmas from LMS\n scheduler = LMSDiscreteScheduler.from_config(scheduler.config)\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n unet=unet,\n scheduler=scheduler,\n )\n self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n self.default_sample_size = self.unet.config.sample_size\n\n model = ModelWrapper(unet, scheduler.alphas_cumprod)\n if scheduler.config.prediction_type == \"v_prediction\":\n self.k_diffusion_model = CompVisVDenoiser(model)\n else:\n self.k_diffusion_model = CompVisDenoiser(model)\n\n # Copied from diffusers.pipelines.stable_diffusion_k_diffusion.pipeline_stable_diffusion_k_diffusion.StableDiffusionKDiffusionPipeline.set_scheduler\n def set_scheduler(self, scheduler_type: str):\n library = importlib.import_module(\"k_diffusion\")\n sampling = getattr(library, \"sampling\")\n try:\n self.sampler = getattr(sampling, scheduler_type)\n except Exception:\n valid_samplers = []\n for s in dir(sampling):\n if \"sample_\" in s:\n valid_samplers.append(s)\n\n raise ValueError(f\"Invalid scheduler type {scheduler_type}. Please choose one of {valid_samplers}.\")\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt: str,\n prompt_2: Optional[str] = None,\n device: Optional[torch.device] = None,\n num_images_per_prompt: int = 1,\n do_classifier_free_guidance: bool = True,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n lora_scale (`float`, *optional*):\n A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n device = device or self._execution_device\n\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if self.text_encoder is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)\n else:\n scale_lora_layers(self.text_encoder_2, lora_scale)\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n\n if prompt is not None:\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # Define tokenizers and text encoders\n tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]\n text_encoders = (\n [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]\n )\n\n if prompt_embeds is None:\n prompt_2 = prompt_2 or prompt\n prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2\n\n # textual inversion: process multi-vector tokens if necessary\n prompt_embeds_list = []\n prompts = [prompt, prompt_2]\n for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, tokenizer)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n if clip_skip is None:\n prompt_embeds = prompt_embeds.hidden_states[-2]\n else:\n # \"2\" because SDXL always indexes from the penultimate layer.\n prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]\n\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n\n # get unconditional embeddings for classifier free guidance\n zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt\n if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:\n negative_prompt_embeds = torch.zeros_like(prompt_embeds)\n negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)\n elif do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n\n # normalize str to list\n negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt\n negative_prompt_2 = (\n batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2\n )\n\n uncond_tokens: List[str]\n if prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = [negative_prompt, negative_prompt_2]\n\n negative_prompt_embeds_list = []\n for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n negative_prompt_embeds = text_encoder(\n uncond_input.input_ids.to(device),\n output_hidden_states=True,\n )\n # We are only ALWAYS interested in the pooled output of the final text encoder\n negative_pooled_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]\n\n negative_prompt_embeds_list.append(negative_prompt_embeds)\n\n negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)\n\n if self.text_encoder_2 is not None:\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n if self.text_encoder_2 is not None:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n if do_classifier_free_guidance:\n negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n\n if self.text_encoder is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder_2, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n def check_inputs(\n self,\n prompt,\n prompt_2,\n height,\n width,\n negative_prompt=None,\n negative_prompt_2=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n pooled_prompt_embeds=None,\n negative_pooled_prompt_embeds=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if prompt_embeds is not None and pooled_prompt_embeds is None:\n raise ValueError(\n \"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.\"\n )\n\n if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:\n raise ValueError(\n \"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.\"\n )\n\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n return latents\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline._get_add_time_ids\n def _get_add_time_ids(\n self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None\n ):\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n\n passed_add_embed_dim = (\n self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim\n )\n expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features\n\n if expected_add_embed_dim != passed_add_embed_dim:\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.\"\n )\n\n add_time_ids = torch.tensor([add_time_ids], dtype=dtype)\n return add_time_ids\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.upcast_vae\n def upcast_vae(self):\n dtype = self.vae.dtype\n self.vae.to(dtype=torch.float32)\n use_torch_2_0_or_xformers = isinstance(\n self.vae.decoder.mid_block.attentions[0].processor,\n (\n AttnProcessor2_0,\n XFormersAttnProcessor,\n LoRAXFormersAttnProcessor,\n LoRAAttnProcessor2_0,\n FusedAttnProcessor2_0,\n ),\n )\n # if xformers or torch_2_0 is used attention block does not need\n # to be in float32 which can save lots of memory\n if use_torch_2_0_or_xformers:\n self.vae.post_quant_conv.to(dtype)\n self.vae.decoder.conv_in.to(dtype)\n self.vae.decoder.mid_block.to(dtype)\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n original_size: Optional[Tuple[int, int]] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Optional[Tuple[int, int]] = None,\n negative_original_size: Optional[Tuple[int, int]] = None,\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\n negative_target_size: Optional[Tuple[int, int]] = None,\n use_karras_sigmas: Optional[bool] = False,\n noise_sampler_seed: Optional[int] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image. This is set to 1024 by default for the best results.\n Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image. This is set to 1024 by default for the best results.\n Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 5.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead\n of a plain tuple.\n original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.\n `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as\n explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n `crops_coords_top_left` can be used to generate an image that appears to be \"cropped\" from the position\n `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n For most cases, `target_size` should be set to the desired height and width of the generated image. If\n not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a specific image resolution. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a target image resolution. It should be as same\n as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a\n `tuple`. When returning a tuple, the first element is a list with the generated images.\n \"\"\"\n\n # 0. Default height and width to unet\n height = height or self.default_sample_size * self.vae_scale_factor\n width = width or self.default_sample_size * self.vae_scale_factor\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n height,\n width,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n )\n\n if guidance_scale <= 1.0:\n raise ValueError(\"has to use guidance_scale\")\n\n self._guidance_scale = guidance_scale\n self._clip_skip = clip_skip\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n lora_scale = None\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=prompt_embeds.device)\n\n # 5. Prepare sigmas\n if use_karras_sigmas:\n sigma_min: float = self.k_diffusion_model.sigmas[0].item()\n sigma_max: float = self.k_diffusion_model.sigmas[-1].item()\n sigmas = get_sigmas_karras(n=num_inference_steps, sigma_min=sigma_min, sigma_max=sigma_max)\n else:\n sigmas = self.scheduler.sigmas\n sigmas = sigmas.to(dtype=prompt_embeds.dtype, device=device)\n\n # 6. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n latents = latents * sigmas[0]\n\n self.k_diffusion_model.sigmas = self.k_diffusion_model.sigmas.to(latents.device)\n self.k_diffusion_model.log_sigmas = self.k_diffusion_model.log_sigmas.to(latents.device)\n\n # 7. Prepare added time ids & embeddings\n add_text_embeds = pooled_prompt_embeds\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n add_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n if negative_original_size is not None and negative_target_size is not None:\n negative_add_time_ids = self._get_add_time_ids(\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n else:\n negative_add_time_ids = add_time_ids\n\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)\n\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n\n # 8. Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n # 9. Define model function\n def model_fn(x, t):\n latent_model_input = torch.cat([x] * 2)\n t = torch.cat([t] * 2)\n\n noise_pred = self.k_diffusion_model(\n latent_model_input,\n t,\n cond=prompt_embeds,\n timestep_cond=timestep_cond,\n added_cond_kwargs=added_cond_kwargs,\n )\n\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n return noise_pred\n\n # 10. Run k-diffusion solver\n sampler_kwargs = {}\n\n if \"noise_sampler\" in inspect.signature(self.sampler).parameters:\n min_sigma, max_sigma = sigmas[sigmas > 0].min(), sigmas.max()\n noise_sampler = BrownianTreeNoiseSampler(latents, min_sigma, max_sigma, noise_sampler_seed)\n sampler_kwargs[\"noise_sampler\"] = noise_sampler\n\n if \"generator\" in inspect.signature(self.sampler).parameters:\n sampler_kwargs[\"generator\"] = generator\n\n latents = self.sampler(model_fn, latents, sigmas, **sampler_kwargs)\n\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n image = latents\n\n if not output_type == \"latent\":\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_ldm3d/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_stable_diffusion_ldm3d\"] = [\"StableDiffusionLDM3DPipeline\"]\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n else:\n from .pipeline_stable_diffusion_ldm3d import StableDiffusionLDM3DPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_ldm3d/pipeline_stable_diffusion_ldm3d.py", "content": "# Copyright 2024 The Intel Labs Team Authors and the HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom dataclasses import dataclass\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...image_processor import PipelineImageInput, VaeImageProcessorLDM3D\nfrom ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n USE_PEFT_BACKEND,\n BaseOutput,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```python\n >>> from diffusers import StableDiffusionLDM3DPipeline\n\n >>> pipe = StableDiffusionLDM3DPipeline.from_pretrained(\"Intel/ldm3d-4c\")\n >>> pipe = pipe.to(\"cuda\")\n\n >>> prompt = \"a photo of an astronaut riding a horse on mars\"\n >>> output = pipe(prompt)\n >>> rgb_image, depth_image = output.rgb, output.depth\n >>> rgb_image[0].save(\"astronaut_ldm3d_rgb.jpg\")\n >>> depth_image[0].save(\"astronaut_ldm3d_depth.png\")\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\n@dataclass\nclass LDM3DPipelineOutput(BaseOutput):\n \"\"\"\n Output class for Stable Diffusion pipelines.\n\n Args:\n rgb (`List[PIL.Image.Image]` or `np.ndarray`)\n List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,\n num_channels)`.\n depth (`List[PIL.Image.Image]` or `np.ndarray`)\n List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,\n num_channels)`.\n nsfw_content_detected (`List[bool]`)\n List indicating whether the corresponding generated image contains \"not-safe-for-work\" (nsfw) content or\n `None` if safety checking could not be performed.\n \"\"\"\n\n rgb: Union[List[PIL.Image.Image], np.ndarray]\n depth: Union[List[PIL.Image.Image], np.ndarray]\n nsfw_content_detected: Optional[List[bool]]\n\n\nclass StableDiffusionLDM3DPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n TextualInversionLoaderMixin,\n IPAdapterMixin,\n LoraLoaderMixin,\n FromSingleFileMixin,\n):\n r\"\"\"\n Pipeline for text-to-image and 3D generation using LDM3D.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"image_encoder\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder: Optional[CLIPVisionModelWithProjection],\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessorLDM3D(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n rgb_feature_extractor_input = feature_extractor_input[0]\n safety_checker_input = self.feature_extractor(rgb_feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n if ip_adapter_image_embeds is not None:\n if not isinstance(ip_adapter_image_embeds, list):\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}\"\n )\n elif ip_adapter_image_embeds[0].ndim not in [3, 4]:\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D\"\n )\n\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(\n self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32\n ) -> torch.Tensor:\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n w (`torch.Tensor`):\n Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.\n embedding_dim (`int`, *optional*, defaults to 512):\n Dimension of the embeddings to generate.\n dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):\n Data type of the generated embeddings.\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def guidance_rescale(self):\n return self._guidance_rescale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @property\n def interrupt(self):\n return self._interrupt\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 49,\n timesteps: List[int] = None,\n sigmas: List[float] = None,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n sigmas (`List[float]`, *optional*):\n Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in\n their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed\n will be used.\n guidance_scale (`float`, *optional*, defaults to 5.0):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*):\n Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of\n IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should\n contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not\n provided, embeddings are computed from the `ip_adapter_image` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n ip_adapter_image,\n ip_adapter_image_embeds,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._guidance_rescale = guidance_rescale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._interrupt = False\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_images_per_prompt,\n self.do_classifier_free_guidance,\n )\n\n # 3. Encode input prompt\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n clip_skip=clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(\n self.scheduler, num_inference_steps, device, timesteps, sigmas\n )\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 6.1 Add image embeds for IP-Adapter\n added_cond_kwargs = {\"image_embeds\": image_embeds} if ip_adapter_image is not None else None\n\n # 6.2 Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n if self.interrupt:\n continue\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n rgb, depth = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return ((rgb, depth), has_nsfw_concept)\n\n return LDM3DPipelineOutput(rgb=rgb, depth=depth, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_panorama/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_stable_diffusion_panorama\"] = [\"StableDiffusionPanoramaPipeline\"]\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n else:\n from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py", "content": "# Copyright 2024 MultiDiffusion Authors and The HuggingFace Team. All rights reserved.\"\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport copy\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import DDIMScheduler\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ..stable_diffusion import StableDiffusionPipelineOutput\nfrom ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusionPanoramaPipeline, DDIMScheduler\n\n >>> model_ckpt = \"stabilityai/stable-diffusion-2-base\"\n >>> scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder=\"scheduler\")\n >>> pipe = StableDiffusionPanoramaPipeline.from_pretrained(\n ... model_ckpt, scheduler=scheduler, torch_dtype=torch.float16\n ... )\n\n >>> pipe = pipe.to(\"cuda\")\n\n >>> prompt = \"a photo of the dolomites\"\n >>> image = pipe(prompt).images[0]\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass StableDiffusionPanoramaPipeline(\n DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, LoraLoaderMixin, IPAdapterMixin\n):\n r\"\"\"\n Pipeline for text-to-image generation using MultiDiffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"image_encoder\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n _callback_tensor_inputs = [\"latents\", \"prompt_embeds\", \"negative_prompt_embeds\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: DDIMScheduler,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder: Optional[CLIPVisionModelWithProjection] = None,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n def decode_latents_with_padding(self, latents: torch.Tensor, padding: int = 8) -> torch.Tensor:\n \"\"\"\n Decode the given latents with padding for circular inference.\n\n Args:\n latents (torch.Tensor): The input latents to decode.\n padding (int, optional): The number of latents to add on each side for padding. Defaults to 8.\n\n Returns:\n torch.Tensor: The decoded image with padding removed.\n\n Notes:\n - The padding is added to remove boundary artifacts and improve the output quality.\n - This would slightly increase the memory usage.\n - The padding pixels are then removed from the decoded image.\n\n \"\"\"\n latents = 1 / self.vae.config.scaling_factor * latents\n latents_left = latents[..., :padding]\n latents_right = latents[..., -padding:]\n latents = torch.cat((latents_right, latents, latents_left), axis=-1)\n image = self.vae.decode(latents, return_dict=False)[0]\n padding_pix = self.vae_scale_factor * padding\n image = image[..., padding_pix:-padding_pix]\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n if ip_adapter_image_embeds is not None:\n if not isinstance(ip_adapter_image_embeds, list):\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}\"\n )\n elif ip_adapter_image_embeds[0].ndim not in [3, 4]:\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(\n self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32\n ) -> torch.Tensor:\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n w (`torch.Tensor`):\n Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.\n embedding_dim (`int`, *optional*, defaults to 512):\n Dimension of the embeddings to generate.\n dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):\n Data type of the generated embeddings.\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n def get_views(\n self,\n panorama_height: int,\n panorama_width: int,\n window_size: int = 64,\n stride: int = 8,\n circular_padding: bool = False,\n ) -> List[Tuple[int, int, int, int]]:\n \"\"\"\n Generates a list of views based on the given parameters. Here, we define the mappings F_i (see Eq. 7 in the\n MultiDiffusion paper https://arxiv.org/abs/2302.08113). If panorama's height/width < window_size, num_blocks of\n height/width should return 1.\n\n Args:\n panorama_height (int): The height of the panorama.\n panorama_width (int): The width of the panorama.\n window_size (int, optional): The size of the window. Defaults to 64.\n stride (int, optional): The stride value. Defaults to 8.\n circular_padding (bool, optional): Whether to apply circular padding. Defaults to False.\n\n Returns:\n List[Tuple[int, int, int, int]]: A list of tuples representing the views. Each tuple contains four integers\n representing the start and end coordinates of the window in the panorama.\n\n \"\"\"\n panorama_height /= 8\n panorama_width /= 8\n num_blocks_height = (panorama_height - window_size) // stride + 1 if panorama_height > window_size else 1\n if circular_padding:\n num_blocks_width = panorama_width // stride if panorama_width > window_size else 1\n else:\n num_blocks_width = (panorama_width - window_size) // stride + 1 if panorama_width > window_size else 1\n total_num_blocks = int(num_blocks_height * num_blocks_width)\n views = []\n for i in range(total_num_blocks):\n h_start = int((i // num_blocks_width) * stride)\n h_end = h_start + window_size\n w_start = int((i % num_blocks_width) * stride)\n w_end = w_start + window_size\n views.append((h_start, h_end, w_start, w_end))\n return views\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def guidance_rescale(self):\n return self._guidance_rescale\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n @property\n def do_classifier_free_guidance(self):\n return False\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @property\n def interrupt(self):\n return self._interrupt\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = 512,\n width: Optional[int] = 2048,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n guidance_scale: float = 7.5,\n view_batch_size: int = 1,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n circular_padding: bool = False,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs: Any,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 2048):\n The width in pixels of the generated image. The width is kept high because the pipeline is supposed\n generate panorama-like images.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n The timesteps at which to generate the images. If not specified, then the default timestep spacing\n strategy of the scheduler is used.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n view_batch_size (`int`, *optional*, defaults to 1):\n The batch size to denoise split views. For some GPUs with high performance, higher view batch size can\n speedup the generation and increase the VRAM usage.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*):\n Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of\n IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should\n contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not\n provided, embeddings are computed from the `ip_adapter_image` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n A rescaling factor for the guidance embeddings. A value of 0.0 means no rescaling is applied.\n circular_padding (`bool`, *optional*, defaults to `False`):\n If set to `True`, circular padding is applied to ensure there are no stitching artifacts. Circular\n padding allows the model to seamlessly generate a transition from the rightmost part of the image to\n the leftmost part, maintaining consistency in a 360-degree sense.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List[str]`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`\",\n )\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n ip_adapter_image,\n ip_adapter_image_embeds,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._guidance_rescale = guidance_rescale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._interrupt = False\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_images_per_prompt,\n self.do_classifier_free_guidance,\n )\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Define panorama grid and initialize views for synthesis.\n # prepare batch grid\n views = self.get_views(height, width, circular_padding=circular_padding)\n views_batch = [views[i : i + view_batch_size] for i in range(0, len(views), view_batch_size)]\n views_scheduler_status = [copy.deepcopy(self.scheduler.__dict__)] * len(views_batch)\n count = torch.zeros_like(latents)\n value = torch.zeros_like(latents)\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7.1 Add image embeds for IP-Adapter\n added_cond_kwargs = (\n {\"image_embeds\": image_embeds}\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None\n else None\n )\n\n # 7.2 Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n # 8. Denoising loop\n # Each denoising step also includes refinement of the latents with respect to the\n # views.\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n if self.interrupt:\n continue\n count.zero_()\n value.zero_()\n\n # generate views\n # Here, we iterate through different spatial crops of the latents and denoise them. These\n # denoised (latent) crops are then averaged to produce the final latent\n # for the current timestep via MultiDiffusion. Please see Sec. 4.1 in the\n # MultiDiffusion paper for more details: https://arxiv.org/abs/2302.08113\n # Batch views denoise\n for j, batch_view in enumerate(views_batch):\n vb_size = len(batch_view)\n # get the latents corresponding to the current view coordinates\n if circular_padding:\n latents_for_view = []\n for h_start, h_end, w_start, w_end in batch_view:\n if w_end > latents.shape[3]:\n # Add circular horizontal padding\n latent_view = torch.cat(\n (\n latents[:, :, h_start:h_end, w_start:],\n latents[:, :, h_start:h_end, : w_end - latents.shape[3]],\n ),\n axis=-1,\n )\n else:\n latent_view = latents[:, :, h_start:h_end, w_start:w_end]\n latents_for_view.append(latent_view)\n latents_for_view = torch.cat(latents_for_view)\n else:\n latents_for_view = torch.cat(\n [\n latents[:, :, h_start:h_end, w_start:w_end]\n for h_start, h_end, w_start, w_end in batch_view\n ]\n )\n\n # rematch block's scheduler status\n self.scheduler.__dict__.update(views_scheduler_status[j])\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = (\n latents_for_view.repeat_interleave(2, dim=0)\n if do_classifier_free_guidance\n else latents_for_view\n )\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # repeat prompt_embeds for batch\n prompt_embeds_input = torch.cat([prompt_embeds] * vb_size)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds_input,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred[::2], noise_pred[1::2]\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(\n noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale\n )\n\n # compute the previous noisy sample x_t -> x_t-1\n latents_denoised_batch = self.scheduler.step(\n noise_pred, t, latents_for_view, **extra_step_kwargs\n ).prev_sample\n\n # save views scheduler status after sample\n views_scheduler_status[j] = copy.deepcopy(self.scheduler.__dict__)\n\n # extract value from batch\n for latents_view_denoised, (h_start, h_end, w_start, w_end) in zip(\n latents_denoised_batch.chunk(vb_size), batch_view\n ):\n if circular_padding and w_end > latents.shape[3]:\n # Case for circular padding\n value[:, :, h_start:h_end, w_start:] += latents_view_denoised[\n :, :, h_start:h_end, : latents.shape[3] - w_start\n ]\n value[:, :, h_start:h_end, : w_end - latents.shape[3]] += latents_view_denoised[\n :, :, h_start:h_end, latents.shape[3] - w_start :\n ]\n count[:, :, h_start:h_end, w_start:] += 1\n count[:, :, h_start:h_end, : w_end - latents.shape[3]] += 1\n else:\n value[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised\n count[:, :, h_start:h_end, w_start:w_end] += 1\n\n # take the MultiDiffusion step. Eq. 5 in MultiDiffusion paper: https://arxiv.org/abs/2302.08113\n latents = torch.where(count > 0, value / count, value)\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if output_type != \"latent\":\n if circular_padding:\n image = self.decode_latents_with_padding(latents)\n else:\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_safe/__init__.py", "content": "from dataclasses import dataclass\nfrom enum import Enum\nfrom typing import TYPE_CHECKING, List, Optional, Union\n\nimport numpy as np\nimport PIL\nfrom PIL import Image\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n BaseOutput,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n@dataclass\nclass SafetyConfig(object):\n WEAK = {\n \"sld_warmup_steps\": 15,\n \"sld_guidance_scale\": 20,\n \"sld_threshold\": 0.0,\n \"sld_momentum_scale\": 0.0,\n \"sld_mom_beta\": 0.0,\n }\n MEDIUM = {\n \"sld_warmup_steps\": 10,\n \"sld_guidance_scale\": 1000,\n \"sld_threshold\": 0.01,\n \"sld_momentum_scale\": 0.3,\n \"sld_mom_beta\": 0.4,\n }\n STRONG = {\n \"sld_warmup_steps\": 7,\n \"sld_guidance_scale\": 2000,\n \"sld_threshold\": 0.025,\n \"sld_momentum_scale\": 0.5,\n \"sld_mom_beta\": 0.7,\n }\n MAX = {\n \"sld_warmup_steps\": 0,\n \"sld_guidance_scale\": 5000,\n \"sld_threshold\": 1.0,\n \"sld_momentum_scale\": 0.5,\n \"sld_mom_beta\": 0.7,\n }\n\n\n_dummy_objects = {}\n_additional_imports = {}\n_import_structure = {}\n\n_additional_imports.update({\"SafetyConfig\": SafetyConfig})\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure.update(\n {\n \"pipeline_output\": [\"StableDiffusionSafePipelineOutput\"],\n \"pipeline_stable_diffusion_safe\": [\"StableDiffusionPipelineSafe\"],\n \"safety_checker\": [\"StableDiffusionSafetyChecker\"],\n }\n )\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n else:\n from .pipeline_output import StableDiffusionSafePipelineOutput\n from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe\n from .safety_checker import SafeStableDiffusionSafetyChecker\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n for name, value in _additional_imports.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_output.py", "content": "from dataclasses import dataclass\nfrom typing import List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\n\nfrom ...utils import (\n BaseOutput,\n)\n\n\n@dataclass\nclass StableDiffusionSafePipelineOutput(BaseOutput):\n \"\"\"\n Output class for Safe Stable Diffusion pipelines.\n\n Args:\n images (`List[PIL.Image.Image]` or `np.ndarray`)\n List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,\n num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.\n nsfw_content_detected (`List[bool]`)\n List of flags denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, or `None` if safety checking could not be performed.\n images (`List[PIL.Image.Image]` or `np.ndarray`)\n List of denoised PIL images that were flagged by the safety checker any may contain \"not-safe-for-work\"\n (nsfw) content, or `None` if no safety check was performed or no images were flagged.\n applied_safety_concept (`str`)\n The safety concept that was applied for safety guidance, or `None` if safety guidance was disabled\n \"\"\"\n\n images: Union[List[PIL.Image.Image], np.ndarray]\n nsfw_content_detected: Optional[List[bool]]\n unsafe_images: Optional[Union[List[PIL.Image.Image], np.ndarray]]\n applied_safety_concept: Optional[str]\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py", "content": "import inspect\nimport warnings\nfrom typing import Callable, List, Optional, Union\n\nimport numpy as np\nimport torch\nfrom packaging import version\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...configuration_utils import FrozenDict\nfrom ...image_processor import PipelineImageInput\nfrom ...loaders import IPAdapterMixin\nfrom ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import deprecate, logging\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom . import StableDiffusionSafePipelineOutput\nfrom .safety_checker import SafeStableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass StableDiffusionPipelineSafe(DiffusionPipeline, StableDiffusionMixin, IPAdapterMixin):\n r\"\"\"\n Pipeline based on the [`StableDiffusionPipeline`] for text-to-image generation using Safe Latent Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"image_encoder\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: SafeStableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder: Optional[CLIPVisionModelWithProjection] = None,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n safety_concept: Optional[str] = (\n \"an image showing hate, harassment, violence, suffering, humiliation, harm, suicide, sexual, nudity,\"\n \" bodily fluids, blood, obscene gestures, illegal activity, drug use, theft, vandalism, weapons, child\"\n \" abuse, brutality, cruelty\"\n )\n\n if hasattr(scheduler.config, \"steps_offset\") and scheduler.config.steps_offset != 1:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`\"\n f\" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure \"\n \"to update the config accordingly as leaving `steps_offset` might led to incorrect results\"\n \" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,\"\n \" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`\"\n \" file\"\n )\n deprecate(\"steps_offset!=1\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"steps_offset\"] = 1\n scheduler._internal_dict = FrozenDict(new_config)\n\n if hasattr(scheduler.config, \"clip_sample\") and scheduler.config.clip_sample is True:\n deprecation_message = (\n f\"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`.\"\n \" `clip_sample` should be set to False in the configuration file. Please make sure to update the\"\n \" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in\"\n \" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very\"\n \" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file\"\n )\n deprecate(\"clip_sample not set\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(scheduler.config)\n new_config[\"clip_sample\"] = False\n scheduler._internal_dict = FrozenDict(new_config)\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n is_unet_version_less_0_9_0 = hasattr(unet.config, \"_diffusers_version\") and version.parse(\n version.parse(unet.config._diffusers_version).base_version\n ) < version.parse(\"0.9.0.dev0\")\n is_unet_sample_size_less_64 = hasattr(unet.config, \"sample_size\") and unet.config.sample_size < 64\n if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:\n deprecation_message = (\n \"The configuration file of the unet has set the default `sample_size` to smaller than\"\n \" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the\"\n \" following: \\n- CompVis/stable-diffusion-v1-4 \\n- CompVis/stable-diffusion-v1-3 \\n-\"\n \" CompVis/stable-diffusion-v1-2 \\n- CompVis/stable-diffusion-v1-1 \\n- runwayml/stable-diffusion-v1-5\"\n \" \\n- runwayml/stable-diffusion-inpainting \\n you should change 'sample_size' to 64 in the\"\n \" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`\"\n \" in the config might lead to incorrect results in future versions. If you have downloaded this\"\n \" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for\"\n \" the `unet/config.json` file\"\n )\n deprecate(\"sample_size<64\", \"1.0.0\", deprecation_message, standard_warn=False)\n new_config = dict(unet.config)\n new_config[\"sample_size\"] = 64\n unet._internal_dict = FrozenDict(new_config)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self._safety_text_concept = safety_concept\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n @property\n def safety_concept(self):\n r\"\"\"\n Getter method for the safety concept used with SLD\n\n Returns:\n `str`: The text describing the safety concept\n \"\"\"\n return self._safety_text_concept\n\n @safety_concept.setter\n def safety_concept(self, concept):\n r\"\"\"\n Setter method for the safety concept used with SLD\n\n Args:\n concept (`str`):\n The text of the new safety concept\n \"\"\"\n self._safety_text_concept = concept\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n enable_safety_guidance,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n \"\"\"\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"max_length\", return_tensors=\"pt\").input_ids\n\n if not torch.equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device),\n attention_mask=attention_mask,\n )\n prompt_embeds = prompt_embeds[0]\n\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = text_input_ids.shape[-1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # Encode the safety concept text\n if enable_safety_guidance:\n safety_concept_input = self.tokenizer(\n [self._safety_text_concept],\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n safety_embeddings = self.text_encoder(safety_concept_input.input_ids.to(self.device))[0]\n\n # duplicate safety embeddings for each generation per prompt, using mps friendly method\n seq_len = safety_embeddings.shape[1]\n safety_embeddings = safety_embeddings.repeat(batch_size, num_images_per_prompt, 1)\n safety_embeddings = safety_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n # For classifier free guidance + sld, we need to do three forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing three forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds, safety_embeddings])\n\n else:\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n return prompt_embeds\n\n def run_safety_checker(self, image, device, dtype, enable_safety_guidance):\n if self.safety_checker is not None:\n images = image.copy()\n safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n flagged_images = np.zeros((2, *image.shape[1:]))\n if any(has_nsfw_concept):\n logger.warning(\n \"Potential NSFW content was detected in one or more images. A black image will be returned\"\n \" instead.\"\n f\"{'You may look at this images in the `unsafe_images` variable of the output at your own discretion.' if enable_safety_guidance else 'Try again with a different prompt and/or seed.'}\"\n )\n for idx, has_nsfw_concept in enumerate(has_nsfw_concept):\n if has_nsfw_concept:\n flagged_images[idx] = images[idx]\n image[idx] = np.zeros(image[idx].shape) # black image\n else:\n has_nsfw_concept = None\n flagged_images = None\n return image, has_nsfw_concept, flagged_images\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion_k_diffusion.pipeline_stable_diffusion_k_diffusion.StableDiffusionKDiffusionPipeline.check_inputs\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def perform_safety_guidance(\n self,\n enable_safety_guidance,\n safety_momentum,\n noise_guidance,\n noise_pred_out,\n i,\n sld_guidance_scale,\n sld_warmup_steps,\n sld_threshold,\n sld_momentum_scale,\n sld_mom_beta,\n ):\n # Perform SLD guidance\n if enable_safety_guidance:\n if safety_momentum is None:\n safety_momentum = torch.zeros_like(noise_guidance)\n noise_pred_text, noise_pred_uncond = noise_pred_out[0], noise_pred_out[1]\n noise_pred_safety_concept = noise_pred_out[2]\n\n # Equation 6\n scale = torch.clamp(torch.abs((noise_pred_text - noise_pred_safety_concept)) * sld_guidance_scale, max=1.0)\n\n # Equation 6\n safety_concept_scale = torch.where(\n (noise_pred_text - noise_pred_safety_concept) >= sld_threshold, torch.zeros_like(scale), scale\n )\n\n # Equation 4\n noise_guidance_safety = torch.mul((noise_pred_safety_concept - noise_pred_uncond), safety_concept_scale)\n\n # Equation 7\n noise_guidance_safety = noise_guidance_safety + sld_momentum_scale * safety_momentum\n\n # Equation 8\n safety_momentum = sld_mom_beta * safety_momentum + (1 - sld_mom_beta) * noise_guidance_safety\n\n if i >= sld_warmup_steps: # Warmup\n # Equation 3\n noise_guidance = noise_guidance - noise_guidance_safety\n return noise_guidance, safety_momentum\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n sld_guidance_scale: Optional[float] = 1000,\n sld_warmup_steps: Optional[int] = 10,\n sld_threshold: Optional[float] = 0.01,\n sld_momentum_scale: Optional[float] = 0.3,\n sld_mom_beta: Optional[float] = 0.4,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n ip_adapter_image: (`PipelineImageInput`, *optional*):\n Optional image input to work with IP Adapters.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n sld_guidance_scale (`float`, *optional*, defaults to 1000):\n If `sld_guidance_scale < 1`, safety guidance is disabled.\n sld_warmup_steps (`int`, *optional*, defaults to 10):\n Number of warmup steps for safety guidance. SLD is only be applied for diffusion steps greater than\n `sld_warmup_steps`.\n sld_threshold (`float`, *optional*, defaults to 0.01):\n Threshold that separates the hyperplane between appropriate and inappropriate images.\n sld_momentum_scale (`float`, *optional*, defaults to 0.3):\n Scale of the SLD momentum to be added to the safety guidance at each diffusion step. If set to 0.0,\n momentum is disabled. Momentum is built up during warmup for diffusion steps smaller than\n `sld_warmup_steps`.\n sld_mom_beta (`float`, *optional*, defaults to 0.4):\n Defines how safety guidance momentum builds up. `sld_mom_beta` indicates how much of the previous\n momentum is kept. Momentum is built up during warmup for diffusion steps smaller than\n `sld_warmup_steps`.\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n\n Examples:\n\n ```py\n import torch\n from diffusers import StableDiffusionPipelineSafe\n from diffusers.pipelines.stable_diffusion_safe import SafetyConfig\n\n pipeline = StableDiffusionPipelineSafe.from_pretrained(\n \"AIML-TUDA/stable-diffusion-safe\", torch_dtype=torch.float16\n ).to(\"cuda\")\n prompt = \"the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c. leyendecker\"\n image = pipeline(prompt=prompt, **SafetyConfig.MEDIUM).images[0]\n ```\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(prompt, height, width, callback_steps)\n\n # 2. Define call parameters\n batch_size = 1 if isinstance(prompt, str) else len(prompt)\n device = self._execution_device\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n enable_safety_guidance = sld_guidance_scale > 1.0 and do_classifier_free_guidance\n if not enable_safety_guidance:\n warnings.warn(\"Safety checker disabled!\")\n\n if ip_adapter_image is not None:\n output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True\n image_embeds, negative_image_embeds = self.encode_image(\n ip_adapter_image, device, num_images_per_prompt, output_hidden_state\n )\n if do_classifier_free_guidance:\n if enable_safety_guidance:\n image_embeds = torch.cat([negative_image_embeds, image_embeds, image_embeds])\n else:\n image_embeds = torch.cat([negative_image_embeds, image_embeds])\n\n # 3. Encode input prompt\n prompt_embeds = self._encode_prompt(\n prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, enable_safety_guidance\n )\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs.\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 6.1 Add image embeds for IP-Adapter\n added_cond_kwargs = {\"image_embeds\": image_embeds} if ip_adapter_image is not None else None\n\n safety_momentum = None\n\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = (\n torch.cat([latents] * (3 if enable_safety_guidance else 2))\n if do_classifier_free_guidance\n else latents\n )\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input, t, encoder_hidden_states=prompt_embeds, added_cond_kwargs=added_cond_kwargs\n ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_out = noise_pred.chunk((3 if enable_safety_guidance else 2))\n noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1]\n\n # default classifier free guidance\n noise_guidance = noise_pred_text - noise_pred_uncond\n\n # Perform SLD guidance\n if enable_safety_guidance:\n if safety_momentum is None:\n safety_momentum = torch.zeros_like(noise_guidance)\n noise_pred_safety_concept = noise_pred_out[2]\n\n # Equation 6\n scale = torch.clamp(\n torch.abs((noise_pred_text - noise_pred_safety_concept)) * sld_guidance_scale, max=1.0\n )\n\n # Equation 6\n safety_concept_scale = torch.where(\n (noise_pred_text - noise_pred_safety_concept) >= sld_threshold,\n torch.zeros_like(scale),\n scale,\n )\n\n # Equation 4\n noise_guidance_safety = torch.mul(\n (noise_pred_safety_concept - noise_pred_uncond), safety_concept_scale\n )\n\n # Equation 7\n noise_guidance_safety = noise_guidance_safety + sld_momentum_scale * safety_momentum\n\n # Equation 8\n safety_momentum = sld_mom_beta * safety_momentum + (1 - sld_mom_beta) * noise_guidance_safety\n\n if i >= sld_warmup_steps: # Warmup\n # Equation 3\n noise_guidance = noise_guidance - noise_guidance_safety\n\n noise_pred = noise_pred_uncond + guidance_scale * noise_guidance\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # 8. Post-processing\n image = self.decode_latents(latents)\n\n # 9. Run safety checker\n image, has_nsfw_concept, flagged_images = self.run_safety_checker(\n image, device, prompt_embeds.dtype, enable_safety_guidance\n )\n\n # 10. Convert to PIL\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n if flagged_images is not None:\n flagged_images = self.numpy_to_pil(flagged_images)\n\n if not return_dict:\n return (\n image,\n has_nsfw_concept,\n self._safety_text_concept if enable_safety_guidance else None,\n flagged_images,\n )\n\n return StableDiffusionSafePipelineOutput(\n images=image,\n nsfw_content_detected=has_nsfw_concept,\n applied_safety_concept=self._safety_text_concept if enable_safety_guidance else None,\n unsafe_images=flagged_images,\n )\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_safe/safety_checker.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport torch\nimport torch.nn as nn\nfrom transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel\n\nfrom ...utils import logging\n\n\nlogger = logging.get_logger(__name__)\n\n\ndef cosine_distance(image_embeds, text_embeds):\n normalized_image_embeds = nn.functional.normalize(image_embeds)\n normalized_text_embeds = nn.functional.normalize(text_embeds)\n return torch.mm(normalized_image_embeds, normalized_text_embeds.t())\n\n\nclass SafeStableDiffusionSafetyChecker(PreTrainedModel):\n config_class = CLIPConfig\n\n _no_split_modules = [\"CLIPEncoderLayer\"]\n\n def __init__(self, config: CLIPConfig):\n super().__init__(config)\n\n self.vision_model = CLIPVisionModel(config.vision_config)\n self.visual_projection = nn.Linear(config.vision_config.hidden_size, config.projection_dim, bias=False)\n\n self.concept_embeds = nn.Parameter(torch.ones(17, config.projection_dim), requires_grad=False)\n self.special_care_embeds = nn.Parameter(torch.ones(3, config.projection_dim), requires_grad=False)\n\n self.concept_embeds_weights = nn.Parameter(torch.ones(17), requires_grad=False)\n self.special_care_embeds_weights = nn.Parameter(torch.ones(3), requires_grad=False)\n\n @torch.no_grad()\n def forward(self, clip_input, images):\n pooled_output = self.vision_model(clip_input)[1] # pooled_output\n image_embeds = self.visual_projection(pooled_output)\n\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds).cpu().float().numpy()\n cos_dist = cosine_distance(image_embeds, self.concept_embeds).cpu().float().numpy()\n\n result = []\n batch_size = image_embeds.shape[0]\n for i in range(batch_size):\n result_img = {\"special_scores\": {}, \"special_care\": [], \"concept_scores\": {}, \"bad_concepts\": []}\n\n # increase this value to create a stronger `nfsw` filter\n # at the cost of increasing the possibility of filtering benign images\n adjustment = 0.0\n\n for concept_idx in range(len(special_cos_dist[0])):\n concept_cos = special_cos_dist[i][concept_idx]\n concept_threshold = self.special_care_embeds_weights[concept_idx].item()\n result_img[\"special_scores\"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)\n if result_img[\"special_scores\"][concept_idx] > 0:\n result_img[\"special_care\"].append({concept_idx, result_img[\"special_scores\"][concept_idx]})\n adjustment = 0.01\n\n for concept_idx in range(len(cos_dist[0])):\n concept_cos = cos_dist[i][concept_idx]\n concept_threshold = self.concept_embeds_weights[concept_idx].item()\n result_img[\"concept_scores\"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)\n if result_img[\"concept_scores\"][concept_idx] > 0:\n result_img[\"bad_concepts\"].append(concept_idx)\n\n result.append(result_img)\n\n has_nsfw_concepts = [len(res[\"bad_concepts\"]) > 0 for res in result]\n\n return images, has_nsfw_concepts\n\n @torch.no_grad()\n def forward_onnx(self, clip_input: torch.Tensor, images: torch.Tensor):\n pooled_output = self.vision_model(clip_input)[1] # pooled_output\n image_embeds = self.visual_projection(pooled_output)\n\n special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds)\n cos_dist = cosine_distance(image_embeds, self.concept_embeds)\n\n # increase this value to create a stronger `nsfw` filter\n # at the cost of increasing the possibility of filtering benign images\n adjustment = 0.0\n\n special_scores = special_cos_dist - self.special_care_embeds_weights + adjustment\n # special_scores = special_scores.round(decimals=3)\n special_care = torch.any(special_scores > 0, dim=1)\n special_adjustment = special_care * 0.01\n special_adjustment = special_adjustment.unsqueeze(1).expand(-1, cos_dist.shape[1])\n\n concept_scores = (cos_dist - self.concept_embeds_weights) + special_adjustment\n # concept_scores = concept_scores.round(decimals=3)\n has_nsfw_concepts = torch.any(concept_scores > 0, dim=1)\n\n return images, has_nsfw_concepts\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_sag/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_stable_diffusion_sag\"] = [\"StableDiffusionSAGPipeline\"]\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n else:\n from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py", "content": "# Copyright 2024 Susung Hong and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport torch\nimport torch.nn.functional as F\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection\n\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ..stable_diffusion import StableDiffusionPipelineOutput\nfrom ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusionSAGPipeline\n\n >>> pipe = StableDiffusionSAGPipeline.from_pretrained(\n ... \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> prompt = \"a photo of an astronaut riding a horse on mars\"\n >>> image = pipe(prompt, sag_scale=0.75).images[0]\n ```\n\"\"\"\n\n\n# processes and stores attention probabilities\nclass CrossAttnStoreProcessor:\n def __init__(self):\n self.attention_probs = None\n\n def __call__(\n self,\n attn,\n hidden_states,\n encoder_hidden_states=None,\n attention_mask=None,\n ):\n batch_size, sequence_length, _ = hidden_states.shape\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n query = attn.to_q(hidden_states)\n\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n self.attention_probs = attn.get_attention_scores(query, key, attention_mask)\n hidden_states = torch.bmm(self.attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n return hidden_states\n\n\n# Modified to get self-attention guidance scale in this paper (https://arxiv.org/pdf/2210.00939.pdf) as an input\nclass StableDiffusionSAGPipeline(DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, IPAdapterMixin):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n text_encoder ([`~transformers.CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A `UNet2DConditionModel` to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\", \"image_encoder\"]\n _exclude_from_cpu_offload = [\"safety_checker\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n image_encoder: Optional[CLIPVisionModelWithProjection] = None,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n image_embeds = ip_adapter_image_embeds\n return image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion_k_diffusion.pipeline_stable_diffusion_k_diffusion.StableDiffusionKDiffusionPipeline.check_inputs\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n sag_scale: float = 0.75,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: Optional[int] = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n sag_scale (`float`, *optional*, defaults to 0.75):\n Chosen between [0, 1.0] for better quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*):\n Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. If not provided, embeddings are computed from the\n `ip_adapter_image` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,\n otherwise a `tuple` is returned where the first element is a list with the generated images and the\n second element is a list of `bool`s indicating whether the corresponding generated image contains\n \"not-safe-for-work\" (nsfw) content.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n # and `sag_scale` is` `s` of equation (16)\n # of the self-attention guidance paper: https://arxiv.org/pdf/2210.00939.pdf\n # `sag_scale = 0` means no self-attention guidance\n do_self_attention_guidance = sag_scale > 0.0\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n ip_adapter_image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_images_per_prompt,\n do_classifier_free_guidance,\n )\n\n if do_classifier_free_guidance:\n image_embeds = []\n negative_image_embeds = []\n for tmp_image_embeds in ip_adapter_image_embeds:\n single_negative_image_embeds, single_image_embeds = tmp_image_embeds.chunk(2)\n image_embeds.append(single_image_embeds)\n negative_image_embeds.append(single_negative_image_embeds)\n else:\n image_embeds = ip_adapter_image_embeds\n\n # 3. Encode input prompt\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n clip_skip=clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n if timesteps.dtype not in [torch.int16, torch.int32, torch.int64]:\n raise ValueError(\n f\"{self.__class__.__name__} does not support using a scheduler of type {self.scheduler.__class__.__name__}. Please make sure to use one of 'DDIMScheduler, PNDMScheduler, DDPMScheduler, DEISMultistepScheduler, UniPCMultistepScheduler, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler'.\"\n )\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 6.1 Add image embeds for IP-Adapter\n added_cond_kwargs = (\n {\"image_embeds\": image_embeds}\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None\n else None\n )\n\n if do_classifier_free_guidance:\n added_uncond_kwargs = (\n {\"image_embeds\": negative_image_embeds}\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None\n else None\n )\n\n # 7. Denoising loop\n original_attn_proc = self.unet.attn_processors\n store_processor = CrossAttnStoreProcessor()\n self.unet.mid_block.attentions[0].transformer_blocks[0].attn1.processor = store_processor\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n\n map_size = None\n\n def get_map_size(module, input, output):\n nonlocal map_size\n map_size = output[0].shape[-2:]\n\n with self.unet.mid_block.attentions[0].register_forward_hook(get_map_size):\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # perform self-attention guidance with the stored self-attention map\n if do_self_attention_guidance:\n # classifier-free guidance produces two chunks of attention map\n # and we only use unconditional one according to equation (25)\n # in https://arxiv.org/pdf/2210.00939.pdf\n if do_classifier_free_guidance:\n # DDIM-like prediction of x0\n pred_x0 = self.pred_x0(latents, noise_pred_uncond, t)\n # get the stored attention maps\n uncond_attn, cond_attn = store_processor.attention_probs.chunk(2)\n # self-attention-based degrading of latents\n degraded_latents = self.sag_masking(\n pred_x0, uncond_attn, map_size, t, self.pred_epsilon(latents, noise_pred_uncond, t)\n )\n uncond_emb, _ = prompt_embeds.chunk(2)\n # forward and give guidance\n degraded_pred = self.unet(\n degraded_latents,\n t,\n encoder_hidden_states=uncond_emb,\n added_cond_kwargs=added_uncond_kwargs,\n ).sample\n noise_pred += sag_scale * (noise_pred_uncond - degraded_pred)\n else:\n # DDIM-like prediction of x0\n pred_x0 = self.pred_x0(latents, noise_pred, t)\n # get the stored attention maps\n cond_attn = store_processor.attention_probs\n # self-attention-based degrading of latents\n degraded_latents = self.sag_masking(\n pred_x0, cond_attn, map_size, t, self.pred_epsilon(latents, noise_pred, t)\n )\n # forward and give guidance\n degraded_pred = self.unet(\n degraded_latents,\n t,\n encoder_hidden_states=prompt_embeds,\n added_cond_kwargs=added_cond_kwargs,\n ).sample\n noise_pred += sag_scale * (noise_pred - degraded_pred)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n self.maybe_free_model_hooks()\n # make sure to set the original attention processors back\n self.unet.set_attn_processor(original_attn_proc)\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n\n def sag_masking(self, original_latents, attn_map, map_size, t, eps):\n # Same masking process as in SAG paper: https://arxiv.org/pdf/2210.00939.pdf\n bh, hw1, hw2 = attn_map.shape\n b, latent_channel, latent_h, latent_w = original_latents.shape\n h = self.unet.config.attention_head_dim\n if isinstance(h, list):\n h = h[-1]\n\n # Produce attention mask\n attn_map = attn_map.reshape(b, h, hw1, hw2)\n attn_mask = attn_map.mean(1, keepdim=False).sum(1, keepdim=False) > 1.0\n attn_mask = (\n attn_mask.reshape(b, map_size[0], map_size[1])\n .unsqueeze(1)\n .repeat(1, latent_channel, 1, 1)\n .type(attn_map.dtype)\n )\n attn_mask = F.interpolate(attn_mask, (latent_h, latent_w))\n\n # Blur according to the self-attention mask\n degraded_latents = gaussian_blur_2d(original_latents, kernel_size=9, sigma=1.0)\n degraded_latents = degraded_latents * attn_mask + original_latents * (1 - attn_mask)\n\n # Noise it again to match the noise level\n degraded_latents = self.scheduler.add_noise(degraded_latents, noise=eps, timesteps=t[None])\n\n return degraded_latents\n\n # Modified from diffusers.schedulers.scheduling_ddim.DDIMScheduler.step\n # Note: there are some schedulers that clip or do not return x_0 (PNDMScheduler, DDIMScheduler, etc.)\n def pred_x0(self, sample, model_output, timestep):\n alpha_prod_t = self.scheduler.alphas_cumprod[timestep].to(sample.device)\n\n beta_prod_t = 1 - alpha_prod_t\n if self.scheduler.config.prediction_type == \"epsilon\":\n pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)\n elif self.scheduler.config.prediction_type == \"sample\":\n pred_original_sample = model_output\n elif self.scheduler.config.prediction_type == \"v_prediction\":\n pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output\n # predict V\n model_output = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample\n else:\n raise ValueError(\n f\"prediction_type given as {self.scheduler.config.prediction_type} must be one of `epsilon`, `sample`,\"\n \" or `v_prediction`\"\n )\n\n return pred_original_sample\n\n def pred_epsilon(self, sample, model_output, timestep):\n alpha_prod_t = self.scheduler.alphas_cumprod[timestep]\n\n beta_prod_t = 1 - alpha_prod_t\n if self.scheduler.config.prediction_type == \"epsilon\":\n pred_eps = model_output\n elif self.scheduler.config.prediction_type == \"sample\":\n pred_eps = (sample - (alpha_prod_t**0.5) * model_output) / (beta_prod_t**0.5)\n elif self.scheduler.config.prediction_type == \"v_prediction\":\n pred_eps = (beta_prod_t**0.5) * sample + (alpha_prod_t**0.5) * model_output\n else:\n raise ValueError(\n f\"prediction_type given as {self.scheduler.config.prediction_type} must be one of `epsilon`, `sample`,\"\n \" or `v_prediction`\"\n )\n\n return pred_eps\n\n\n# Gaussian blur\ndef gaussian_blur_2d(img, kernel_size, sigma):\n ksize_half = (kernel_size - 1) * 0.5\n\n x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)\n\n pdf = torch.exp(-0.5 * (x / sigma).pow(2))\n\n x_kernel = pdf / pdf.sum()\n x_kernel = x_kernel.to(device=img.device, dtype=img.dtype)\n\n kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :])\n kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1])\n\n padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2]\n\n img = F.pad(img, padding, mode=\"reflect\")\n img = F.conv2d(img, kernel2d, groups=img.shape[-3])\n\n return img\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_xl/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_flax_available,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_additional_imports = {}\n_import_structure = {\"pipeline_output\": [\"StableDiffusionXLPipelineOutput\"]}\n\nif is_transformers_available() and is_flax_available():\n _import_structure[\"pipeline_output\"].extend([\"FlaxStableDiffusionXLPipelineOutput\"])\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_stable_diffusion_xl\"] = [\"StableDiffusionXLPipeline\"]\n _import_structure[\"pipeline_stable_diffusion_xl_img2img\"] = [\"StableDiffusionXLImg2ImgPipeline\"]\n _import_structure[\"pipeline_stable_diffusion_xl_inpaint\"] = [\"StableDiffusionXLInpaintPipeline\"]\n _import_structure[\"pipeline_stable_diffusion_xl_instruct_pix2pix\"] = [\"StableDiffusionXLInstructPix2PixPipeline\"]\n\nif is_transformers_available() and is_flax_available():\n from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState\n\n _additional_imports.update({\"PNDMSchedulerState\": PNDMSchedulerState})\n _import_structure[\"pipeline_flax_stable_diffusion_xl\"] = [\"FlaxStableDiffusionXLPipeline\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import * # noqa F403\n else:\n from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline\n from .pipeline_stable_diffusion_xl_img2img import StableDiffusionXLImg2ImgPipeline\n from .pipeline_stable_diffusion_xl_inpaint import StableDiffusionXLInpaintPipeline\n from .pipeline_stable_diffusion_xl_instruct_pix2pix import StableDiffusionXLInstructPix2PixPipeline\n\n try:\n if not (is_transformers_available() and is_flax_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_flax_objects import *\n else:\n from .pipeline_flax_stable_diffusion_xl import (\n FlaxStableDiffusionXLPipeline,\n )\n from .pipeline_output import FlaxStableDiffusionXLPipelineOutput\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n for name, value in _additional_imports.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_flax_stable_diffusion_xl.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom functools import partial\nfrom typing import Dict, List, Optional, Union\n\nimport jax\nimport jax.numpy as jnp\nfrom flax.core.frozen_dict import FrozenDict\nfrom transformers import CLIPTokenizer, FlaxCLIPTextModel\n\nfrom diffusers.utils import logging\n\nfrom ...models import FlaxAutoencoderKL, FlaxUNet2DConditionModel\nfrom ...schedulers import (\n FlaxDDIMScheduler,\n FlaxDPMSolverMultistepScheduler,\n FlaxLMSDiscreteScheduler,\n FlaxPNDMScheduler,\n)\nfrom ..pipeline_flax_utils import FlaxDiffusionPipeline\nfrom .pipeline_output import FlaxStableDiffusionXLPipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n# Set to True to use python for loop instead of jax.fori_loop for easier debugging\nDEBUG = False\n\n\nclass FlaxStableDiffusionXLPipeline(FlaxDiffusionPipeline):\n def __init__(\n self,\n text_encoder: FlaxCLIPTextModel,\n text_encoder_2: FlaxCLIPTextModel,\n vae: FlaxAutoencoderKL,\n tokenizer: CLIPTokenizer,\n tokenizer_2: CLIPTokenizer,\n unet: FlaxUNet2DConditionModel,\n scheduler: Union[\n FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler\n ],\n dtype: jnp.dtype = jnp.float32,\n ):\n super().__init__()\n self.dtype = dtype\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n unet=unet,\n scheduler=scheduler,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n\n def prepare_inputs(self, prompt: Union[str, List[str]]):\n if not isinstance(prompt, (str, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n # Assume we have the two encoders\n inputs = []\n for tokenizer in [self.tokenizer, self.tokenizer_2]:\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"np\",\n )\n inputs.append(text_inputs.input_ids)\n inputs = jnp.stack(inputs, axis=1)\n return inputs\n\n def __call__(\n self,\n prompt_ids: jax.Array,\n params: Union[Dict, FrozenDict],\n prng_seed: jax.Array,\n num_inference_steps: int = 50,\n guidance_scale: Union[float, jax.Array] = 7.5,\n height: Optional[int] = None,\n width: Optional[int] = None,\n latents: jnp.array = None,\n neg_prompt_ids: jnp.array = None,\n return_dict: bool = True,\n output_type: str = None,\n jit: bool = False,\n ):\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n if isinstance(guidance_scale, float) and jit:\n # Convert to a tensor so each device gets a copy.\n guidance_scale = jnp.array([guidance_scale] * prompt_ids.shape[0])\n guidance_scale = guidance_scale[:, None]\n\n return_latents = output_type == \"latent\"\n\n if jit:\n images = _p_generate(\n self,\n prompt_ids,\n params,\n prng_seed,\n num_inference_steps,\n height,\n width,\n guidance_scale,\n latents,\n neg_prompt_ids,\n return_latents,\n )\n else:\n images = self._generate(\n prompt_ids,\n params,\n prng_seed,\n num_inference_steps,\n height,\n width,\n guidance_scale,\n latents,\n neg_prompt_ids,\n return_latents,\n )\n\n if not return_dict:\n return (images,)\n\n return FlaxStableDiffusionXLPipelineOutput(images=images)\n\n def get_embeddings(self, prompt_ids: jnp.array, params):\n # We assume we have the two encoders\n\n # bs, encoder_input, seq_length\n te_1_inputs = prompt_ids[:, 0, :]\n te_2_inputs = prompt_ids[:, 1, :]\n\n prompt_embeds = self.text_encoder(te_1_inputs, params=params[\"text_encoder\"], output_hidden_states=True)\n prompt_embeds = prompt_embeds[\"hidden_states\"][-2]\n prompt_embeds_2_out = self.text_encoder_2(\n te_2_inputs, params=params[\"text_encoder_2\"], output_hidden_states=True\n )\n prompt_embeds_2 = prompt_embeds_2_out[\"hidden_states\"][-2]\n text_embeds = prompt_embeds_2_out[\"text_embeds\"]\n prompt_embeds = jnp.concatenate([prompt_embeds, prompt_embeds_2], axis=-1)\n return prompt_embeds, text_embeds\n\n def _get_add_time_ids(self, original_size, crops_coords_top_left, target_size, bs, dtype):\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n add_time_ids = jnp.array([add_time_ids] * bs, dtype=dtype)\n return add_time_ids\n\n def _generate(\n self,\n prompt_ids: jnp.array,\n params: Union[Dict, FrozenDict],\n prng_seed: jax.Array,\n num_inference_steps: int,\n height: int,\n width: int,\n guidance_scale: float,\n latents: Optional[jnp.array] = None,\n neg_prompt_ids: Optional[jnp.array] = None,\n return_latents=False,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n # Encode input prompt\n prompt_embeds, pooled_embeds = self.get_embeddings(prompt_ids, params)\n\n # Get unconditional embeddings\n batch_size = prompt_embeds.shape[0]\n if neg_prompt_ids is None:\n neg_prompt_embeds = jnp.zeros_like(prompt_embeds)\n negative_pooled_embeds = jnp.zeros_like(pooled_embeds)\n else:\n neg_prompt_embeds, negative_pooled_embeds = self.get_embeddings(neg_prompt_ids, params)\n\n add_time_ids = self._get_add_time_ids(\n (height, width), (0, 0), (height, width), prompt_embeds.shape[0], dtype=prompt_embeds.dtype\n )\n\n prompt_embeds = jnp.concatenate([neg_prompt_embeds, prompt_embeds], axis=0) # (2, 77, 2048)\n add_text_embeds = jnp.concatenate([negative_pooled_embeds, pooled_embeds], axis=0)\n add_time_ids = jnp.concatenate([add_time_ids, add_time_ids], axis=0)\n\n # Ensure model output will be `float32` before going into the scheduler\n guidance_scale = jnp.array([guidance_scale], dtype=jnp.float32)\n\n # Create random latents\n latents_shape = (\n batch_size,\n self.unet.config.in_channels,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n )\n if latents is None:\n latents = jax.random.normal(prng_seed, shape=latents_shape, dtype=jnp.float32)\n else:\n if latents.shape != latents_shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {latents_shape}\")\n\n # Prepare scheduler state\n scheduler_state = self.scheduler.set_timesteps(\n params[\"scheduler\"], num_inference_steps=num_inference_steps, shape=latents.shape\n )\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * scheduler_state.init_noise_sigma\n\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n\n # Denoising loop\n def loop_body(step, args):\n latents, scheduler_state = args\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n latents_input = jnp.concatenate([latents] * 2)\n\n t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step]\n timestep = jnp.broadcast_to(t, latents_input.shape[0])\n\n latents_input = self.scheduler.scale_model_input(scheduler_state, latents_input, t)\n\n # predict the noise residual\n noise_pred = self.unet.apply(\n {\"params\": params[\"unet\"]},\n jnp.array(latents_input),\n jnp.array(timestep, dtype=jnp.int32),\n encoder_hidden_states=prompt_embeds,\n added_cond_kwargs=added_cond_kwargs,\n ).sample\n # perform guidance\n noise_pred_uncond, noise_prediction_text = jnp.split(noise_pred, 2, axis=0)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents, scheduler_state = self.scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple()\n return latents, scheduler_state\n\n if DEBUG:\n # run with python for loop\n for i in range(num_inference_steps):\n latents, scheduler_state = loop_body(i, (latents, scheduler_state))\n else:\n latents, _ = jax.lax.fori_loop(0, num_inference_steps, loop_body, (latents, scheduler_state))\n\n if return_latents:\n return latents\n\n # Decode latents\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.apply({\"params\": params[\"vae\"]}, latents, method=self.vae.decode).sample\n\n image = (image / 2 + 0.5).clip(0, 1).transpose(0, 2, 3, 1)\n return image\n\n\n# Static argnums are pipe, num_inference_steps, height, width, return_latents. A change would trigger recompilation.\n# Non-static args are (sharded) input tensors mapped over their first dimension (hence, `0`).\n@partial(\n jax.pmap,\n in_axes=(None, 0, 0, 0, None, None, None, 0, 0, 0, None),\n static_broadcasted_argnums=(0, 4, 5, 6, 10),\n)\ndef _p_generate(\n pipe,\n prompt_ids,\n params,\n prng_seed,\n num_inference_steps,\n height,\n width,\n guidance_scale,\n latents,\n neg_prompt_ids,\n return_latents,\n):\n return pipe._generate(\n prompt_ids,\n params,\n prng_seed,\n num_inference_steps,\n height,\n width,\n guidance_scale,\n latents,\n neg_prompt_ids,\n return_latents,\n )\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_output.py", "content": "from dataclasses import dataclass\nfrom typing import List, Union\n\nimport numpy as np\nimport PIL.Image\n\nfrom ...utils import BaseOutput, is_flax_available\n\n\n@dataclass\nclass StableDiffusionXLPipelineOutput(BaseOutput):\n \"\"\"\n Output class for Stable Diffusion pipelines.\n\n Args:\n images (`List[PIL.Image.Image]` or `np.ndarray`)\n List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,\n num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.\n \"\"\"\n\n images: Union[List[PIL.Image.Image], np.ndarray]\n\n\nif is_flax_available():\n import flax\n\n @flax.struct.dataclass\n class FlaxStableDiffusionXLPipelineOutput(BaseOutput):\n \"\"\"\n Output class for Flax Stable Diffusion XL pipelines.\n\n Args:\n images (`np.ndarray`)\n Array of shape `(batch_size, height, width, num_channels)` with images from the diffusion pipeline.\n \"\"\"\n\n images: np.ndarray\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport torch\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModel,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n)\n\nfrom ...callbacks import MultiPipelineCallbacks, PipelineCallback\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import (\n FromSingleFileMixin,\n IPAdapterMixin,\n StableDiffusionXLLoraLoaderMixin,\n TextualInversionLoaderMixin,\n)\nfrom ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel\nfrom ...models.attention_processor import (\n AttnProcessor2_0,\n FusedAttnProcessor2_0,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n XFormersAttnProcessor,\n)\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n is_invisible_watermark_available,\n is_torch_xla_available,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom .pipeline_output import StableDiffusionXLPipelineOutput\n\n\nif is_invisible_watermark_available():\n from .watermark import StableDiffusionXLWatermarker\n\nif is_torch_xla_available():\n import torch_xla.core.xla_model as xm\n\n XLA_AVAILABLE = True\nelse:\n XLA_AVAILABLE = False\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusionXLPipeline\n\n >>> pipe = StableDiffusionXLPipeline.from_pretrained(\n ... \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n >>> prompt = \"a photo of an astronaut riding a horse on mars\"\n >>> image = pipe(prompt).images[0]\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass StableDiffusionXLPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n FromSingleFileMixin,\n StableDiffusionXLLoraLoaderMixin,\n TextualInversionLoaderMixin,\n IPAdapterMixin,\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion XL.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion XL uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n text_encoder_2 ([` CLIPTextModelWithProjection`]):\n Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),\n specifically the\n [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)\n variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n tokenizer_2 (`CLIPTokenizer`):\n Second Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `\"True\"`):\n Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of\n `stabilityai/stable-diffusion-xl-base-1-0`.\n add_watermarker (`bool`, *optional*):\n Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to\n watermark output images. If not defined, it will default to True if the package is installed, otherwise no\n watermarker will be used.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->text_encoder_2->image_encoder->unet->vae\"\n _optional_components = [\n \"tokenizer\",\n \"tokenizer_2\",\n \"text_encoder\",\n \"text_encoder_2\",\n \"image_encoder\",\n \"feature_extractor\",\n ]\n _callback_tensor_inputs = [\n \"latents\",\n \"prompt_embeds\",\n \"negative_prompt_embeds\",\n \"add_text_embeds\",\n \"add_time_ids\",\n \"negative_pooled_prompt_embeds\",\n \"negative_add_time_ids\",\n ]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n text_encoder_2: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n tokenizer_2: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n image_encoder: CLIPVisionModelWithProjection = None,\n feature_extractor: CLIPImageProcessor = None,\n force_zeros_for_empty_prompt: bool = True,\n add_watermarker: Optional[bool] = None,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n unet=unet,\n scheduler=scheduler,\n image_encoder=image_encoder,\n feature_extractor=feature_extractor,\n )\n self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n self.default_sample_size = self.unet.config.sample_size\n\n add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()\n\n if add_watermarker:\n self.watermark = StableDiffusionXLWatermarker()\n else:\n self.watermark = None\n\n def encode_prompt(\n self,\n prompt: str,\n prompt_2: Optional[str] = None,\n device: Optional[torch.device] = None,\n num_images_per_prompt: int = 1,\n do_classifier_free_guidance: bool = True,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n lora_scale (`float`, *optional*):\n A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n device = device or self._execution_device\n\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if self.text_encoder is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)\n else:\n scale_lora_layers(self.text_encoder_2, lora_scale)\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n\n if prompt is not None:\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # Define tokenizers and text encoders\n tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]\n text_encoders = (\n [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]\n )\n\n if prompt_embeds is None:\n prompt_2 = prompt_2 or prompt\n prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2\n\n # textual inversion: process multi-vector tokens if necessary\n prompt_embeds_list = []\n prompts = [prompt, prompt_2]\n for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, tokenizer)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n if clip_skip is None:\n prompt_embeds = prompt_embeds.hidden_states[-2]\n else:\n # \"2\" because SDXL always indexes from the penultimate layer.\n prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]\n\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n\n # get unconditional embeddings for classifier free guidance\n zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt\n if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:\n negative_prompt_embeds = torch.zeros_like(prompt_embeds)\n negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)\n elif do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n\n # normalize str to list\n negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt\n negative_prompt_2 = (\n batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2\n )\n\n uncond_tokens: List[str]\n if prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = [negative_prompt, negative_prompt_2]\n\n negative_prompt_embeds_list = []\n for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n negative_prompt_embeds = text_encoder(\n uncond_input.input_ids.to(device),\n output_hidden_states=True,\n )\n # We are only ALWAYS interested in the pooled output of the final text encoder\n negative_pooled_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]\n\n negative_prompt_embeds_list.append(negative_prompt_embeds)\n\n negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)\n\n if self.text_encoder_2 is not None:\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n if self.text_encoder_2 is not None:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n if do_classifier_free_guidance:\n negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n\n if self.text_encoder is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder_2, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n prompt_2,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n negative_prompt_2=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n pooled_prompt_embeds=None,\n negative_pooled_prompt_embeds=None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if prompt_embeds is not None and pooled_prompt_embeds is None:\n raise ValueError(\n \"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.\"\n )\n\n if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:\n raise ValueError(\n \"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.\"\n )\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n if ip_adapter_image_embeds is not None:\n if not isinstance(ip_adapter_image_embeds, list):\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}\"\n )\n elif ip_adapter_image_embeds[0].ndim not in [3, 4]:\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def _get_add_time_ids(\n self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None\n ):\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n\n passed_add_embed_dim = (\n self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim\n )\n expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features\n\n if expected_add_embed_dim != passed_add_embed_dim:\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.\"\n )\n\n add_time_ids = torch.tensor([add_time_ids], dtype=dtype)\n return add_time_ids\n\n def upcast_vae(self):\n dtype = self.vae.dtype\n self.vae.to(dtype=torch.float32)\n use_torch_2_0_or_xformers = isinstance(\n self.vae.decoder.mid_block.attentions[0].processor,\n (\n AttnProcessor2_0,\n XFormersAttnProcessor,\n LoRAXFormersAttnProcessor,\n LoRAAttnProcessor2_0,\n FusedAttnProcessor2_0,\n ),\n )\n # if xformers or torch_2_0 is used attention block does not need\n # to be in float32 which can save lots of memory\n if use_torch_2_0_or_xformers:\n self.vae.post_quant_conv.to(dtype)\n self.vae.decoder.conv_in.to(dtype)\n self.vae.decoder.mid_block.to(dtype)\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(\n self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32\n ) -> torch.Tensor:\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n w (`torch.Tensor`):\n Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.\n embedding_dim (`int`, *optional*, defaults to 512):\n Dimension of the embeddings to generate.\n dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):\n Data type of the generated embeddings.\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def guidance_rescale(self):\n return self._guidance_rescale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def denoising_end(self):\n return self._denoising_end\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @property\n def interrupt(self):\n return self._interrupt\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n sigmas: List[float] = None,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Optional[Tuple[int, int]] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Optional[Tuple[int, int]] = None,\n negative_original_size: Optional[Tuple[int, int]] = None,\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\n negative_target_size: Optional[Tuple[int, int]] = None,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[\n Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]\n ] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image. This is set to 1024 by default for the best results.\n Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image. This is set to 1024 by default for the best results.\n Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n sigmas (`List[float]`, *optional*):\n Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in\n their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed\n will be used.\n denoising_end (`float`, *optional*):\n When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be\n completed before it is intentionally prematurely terminated. As a result, the returned sample will\n still retain a substantial amount of noise as determined by the discrete timesteps selected by the\n scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a\n \"Mixture of Denoisers\" multi-pipeline setup, as elaborated in [**Refining the Image\n Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)\n guidance_scale (`float`, *optional*, defaults to 5.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of\n IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should\n contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not\n provided, embeddings are computed from the `ip_adapter_image` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead\n of a plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of\n [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).\n Guidance rescale factor should fix overexposure when using zero terminal SNR.\n original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.\n `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as\n explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n `crops_coords_top_left` can be used to generate an image that appears to be \"cropped\" from the position\n `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n For most cases, `target_size` should be set to the desired height and width of the generated image. If\n not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a specific image resolution. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a target image resolution. It should be as same\n as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):\n A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of\n each denoising step during the inference. with the following arguments: `callback_on_step_end(self:\n DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a\n list of all tensors as specified by `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a\n `tuple`. When returning a tuple, the first element is a list with the generated images.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n\n if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):\n callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs\n\n # 0. Default height and width to unet\n height = height or self.default_sample_size * self.vae_scale_factor\n width = width or self.default_sample_size * self.vae_scale_factor\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n height,\n width,\n callback_steps,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ip_adapter_image,\n ip_adapter_image_embeds,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._guidance_rescale = guidance_rescale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._denoising_end = denoising_end\n self._interrupt = False\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # 4. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(\n self.scheduler, num_inference_steps, device, timesteps, sigmas\n )\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Prepare added time ids & embeddings\n add_text_embeds = pooled_prompt_embeds\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n add_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n if negative_original_size is not None and negative_target_size is not None:\n negative_add_time_ids = self._get_add_time_ids(\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n else:\n negative_add_time_ids = add_time_ids\n\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_images_per_prompt,\n self.do_classifier_free_guidance,\n )\n\n # 8. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n\n # 8.1 Apply denoising_end\n if (\n self.denoising_end is not None\n and isinstance(self.denoising_end, float)\n and self.denoising_end > 0\n and self.denoising_end < 1\n ):\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (self.denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n # 9. Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n if self.interrupt:\n continue\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n added_cond_kwargs[\"image_embeds\"] = image_embeds\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=self.cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents_dtype = latents.dtype\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n if latents.dtype != latents_dtype:\n if torch.backends.mps.is_available():\n # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272\n latents = latents.to(latents_dtype)\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n add_text_embeds = callback_outputs.pop(\"add_text_embeds\", add_text_embeds)\n negative_pooled_prompt_embeds = callback_outputs.pop(\n \"negative_pooled_prompt_embeds\", negative_pooled_prompt_embeds\n )\n add_time_ids = callback_outputs.pop(\"add_time_ids\", add_time_ids)\n negative_add_time_ids = callback_outputs.pop(\"negative_add_time_ids\", negative_add_time_ids)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if XLA_AVAILABLE:\n xm.mark_step()\n\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n elif latents.dtype != self.vae.dtype:\n if torch.backends.mps.is_available():\n # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272\n self.vae = self.vae.to(latents.dtype)\n\n # unscale/denormalize the latents\n # denormalize with the mean and std if available and not None\n has_latents_mean = hasattr(self.vae.config, \"latents_mean\") and self.vae.config.latents_mean is not None\n has_latents_std = hasattr(self.vae.config, \"latents_std\") and self.vae.config.latents_std is not None\n if has_latents_mean and has_latents_std:\n latents_mean = (\n torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)\n )\n latents_std = (\n torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)\n )\n latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean\n else:\n latents = latents / self.vae.config.scaling_factor\n\n image = self.vae.decode(latents, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n image = latents\n\n if not output_type == \"latent\":\n # apply watermark if available\n if self.watermark is not None:\n image = self.watermark.apply_watermark(image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport PIL.Image\nimport torch\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModel,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n)\n\nfrom ...callbacks import MultiPipelineCallbacks, PipelineCallback\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import (\n FromSingleFileMixin,\n IPAdapterMixin,\n StableDiffusionXLLoraLoaderMixin,\n TextualInversionLoaderMixin,\n)\nfrom ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel\nfrom ...models.attention_processor import (\n AttnProcessor2_0,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n XFormersAttnProcessor,\n)\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n is_invisible_watermark_available,\n is_torch_xla_available,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom .pipeline_output import StableDiffusionXLPipelineOutput\n\n\nif is_invisible_watermark_available():\n from .watermark import StableDiffusionXLWatermarker\n\nif is_torch_xla_available():\n import torch_xla.core.xla_model as xm\n\n XLA_AVAILABLE = True\nelse:\n XLA_AVAILABLE = False\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusionXLImg2ImgPipeline\n >>> from diffusers.utils import load_image\n\n >>> pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(\n ... \"stabilityai/stable-diffusion-xl-refiner-1.0\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n >>> url = \"https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/aa_xl/000000009.png\"\n\n >>> init_image = load_image(url).convert(\"RGB\")\n >>> prompt = \"a photo of an astronaut riding a horse on mars\"\n >>> image = pipe(prompt, image=init_image).images[0]\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass StableDiffusionXLImg2ImgPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n TextualInversionLoaderMixin,\n FromSingleFileMixin,\n StableDiffusionXLLoraLoaderMixin,\n IPAdapterMixin,\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion XL.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion XL uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n text_encoder_2 ([` CLIPTextModelWithProjection`]):\n Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),\n specifically the\n [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)\n variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n tokenizer_2 (`CLIPTokenizer`):\n Second Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n requires_aesthetics_score (`bool`, *optional*, defaults to `\"False\"`):\n Whether the `unet` requires an `aesthetic_score` condition to be passed during inference. Also see the\n config of `stabilityai/stable-diffusion-xl-refiner-1-0`.\n force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `\"True\"`):\n Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of\n `stabilityai/stable-diffusion-xl-base-1-0`.\n add_watermarker (`bool`, *optional*):\n Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to\n watermark output images. If not defined, it will default to True if the package is installed, otherwise no\n watermarker will be used.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->text_encoder_2->image_encoder->unet->vae\"\n _optional_components = [\n \"tokenizer\",\n \"tokenizer_2\",\n \"text_encoder\",\n \"text_encoder_2\",\n \"image_encoder\",\n \"feature_extractor\",\n ]\n _callback_tensor_inputs = [\n \"latents\",\n \"prompt_embeds\",\n \"negative_prompt_embeds\",\n \"add_text_embeds\",\n \"add_time_ids\",\n \"negative_pooled_prompt_embeds\",\n \"add_neg_time_ids\",\n ]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n text_encoder_2: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n tokenizer_2: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n image_encoder: CLIPVisionModelWithProjection = None,\n feature_extractor: CLIPImageProcessor = None,\n requires_aesthetics_score: bool = False,\n force_zeros_for_empty_prompt: bool = True,\n add_watermarker: Optional[bool] = None,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n unet=unet,\n image_encoder=image_encoder,\n feature_extractor=feature_extractor,\n scheduler=scheduler,\n )\n self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)\n self.register_to_config(requires_aesthetics_score=requires_aesthetics_score)\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()\n\n if add_watermarker:\n self.watermark = StableDiffusionXLWatermarker()\n else:\n self.watermark = None\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt: str,\n prompt_2: Optional[str] = None,\n device: Optional[torch.device] = None,\n num_images_per_prompt: int = 1,\n do_classifier_free_guidance: bool = True,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n lora_scale (`float`, *optional*):\n A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n device = device or self._execution_device\n\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if self.text_encoder is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)\n else:\n scale_lora_layers(self.text_encoder_2, lora_scale)\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n\n if prompt is not None:\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # Define tokenizers and text encoders\n tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]\n text_encoders = (\n [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]\n )\n\n if prompt_embeds is None:\n prompt_2 = prompt_2 or prompt\n prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2\n\n # textual inversion: process multi-vector tokens if necessary\n prompt_embeds_list = []\n prompts = [prompt, prompt_2]\n for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, tokenizer)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n if clip_skip is None:\n prompt_embeds = prompt_embeds.hidden_states[-2]\n else:\n # \"2\" because SDXL always indexes from the penultimate layer.\n prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]\n\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n\n # get unconditional embeddings for classifier free guidance\n zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt\n if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:\n negative_prompt_embeds = torch.zeros_like(prompt_embeds)\n negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)\n elif do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n\n # normalize str to list\n negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt\n negative_prompt_2 = (\n batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2\n )\n\n uncond_tokens: List[str]\n if prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = [negative_prompt, negative_prompt_2]\n\n negative_prompt_embeds_list = []\n for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n negative_prompt_embeds = text_encoder(\n uncond_input.input_ids.to(device),\n output_hidden_states=True,\n )\n # We are only ALWAYS interested in the pooled output of the final text encoder\n negative_pooled_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]\n\n negative_prompt_embeds_list.append(negative_prompt_embeds)\n\n negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)\n\n if self.text_encoder_2 is not None:\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n if self.text_encoder_2 is not None:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n if do_classifier_free_guidance:\n negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n\n if self.text_encoder is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder_2, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n prompt_2,\n strength,\n num_inference_steps,\n callback_steps,\n negative_prompt=None,\n negative_prompt_2=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n if num_inference_steps is None:\n raise ValueError(\"`num_inference_steps` cannot be None.\")\n elif not isinstance(num_inference_steps, int) or num_inference_steps <= 0:\n raise ValueError(\n f\"`num_inference_steps` has to be a positive integer but is {num_inference_steps} of type\"\n f\" {type(num_inference_steps)}.\"\n )\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n if ip_adapter_image_embeds is not None:\n if not isinstance(ip_adapter_image_embeds, list):\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}\"\n )\n elif ip_adapter_image_embeds[0].ndim not in [3, 4]:\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D\"\n )\n\n def get_timesteps(self, num_inference_steps, strength, device, denoising_start=None):\n # get the original timestep using init_timestep\n if denoising_start is None:\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n t_start = max(num_inference_steps - init_timestep, 0)\n else:\n t_start = 0\n\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n\n # Strength is irrelevant if we directly request a timestep to start at;\n # that is, strength is determined by the denoising_start instead.\n if denoising_start is not None:\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (denoising_start * self.scheduler.config.num_train_timesteps)\n )\n )\n\n num_inference_steps = (timesteps < discrete_timestep_cutoff).sum().item()\n if self.scheduler.order == 2 and num_inference_steps % 2 == 0:\n # if the scheduler is a 2nd order scheduler we might have to do +1\n # because `num_inference_steps` might be even given that every timestep\n # (except the highest one) is duplicated. If `num_inference_steps` is even it would\n # mean that we cut the timesteps in the middle of the denoising step\n # (between 1st and 2nd derivative) which leads to incorrect results. By adding 1\n # we ensure that the denoising process always ends after the 2nd derivate step of the scheduler\n num_inference_steps = num_inference_steps + 1\n\n # because t_n+1 >= t_n, we slice the timesteps starting from the end\n timesteps = timesteps[-num_inference_steps:]\n return timesteps, num_inference_steps\n\n return timesteps, num_inference_steps - t_start\n\n def prepare_latents(\n self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None, add_noise=True\n ):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n latents_mean = latents_std = None\n if hasattr(self.vae.config, \"latents_mean\") and self.vae.config.latents_mean is not None:\n latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1)\n if hasattr(self.vae.config, \"latents_std\") and self.vae.config.latents_std is not None:\n latents_std = torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1)\n\n # Offload text encoder if `enable_model_cpu_offload` was enabled\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.text_encoder_2.to(\"cpu\")\n torch.cuda.empty_cache()\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n\n if image.shape[1] == 4:\n init_latents = image\n\n else:\n # make sure the VAE is in float32 mode, as it overflows in float16\n if self.vae.config.force_upcast:\n image = image.float()\n self.vae.to(dtype=torch.float32)\n\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n elif isinstance(generator, list):\n init_latents = [\n retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])\n for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = retrieve_latents(self.vae.encode(image), generator=generator)\n\n if self.vae.config.force_upcast:\n self.vae.to(dtype)\n\n init_latents = init_latents.to(dtype)\n if latents_mean is not None and latents_std is not None:\n latents_mean = latents_mean.to(device=self.device, dtype=dtype)\n latents_std = latents_std.to(device=self.device, dtype=dtype)\n init_latents = (init_latents - latents_mean) * self.vae.config.scaling_factor / latents_std\n else:\n init_latents = self.vae.config.scaling_factor * init_latents\n\n if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:\n # expand init_latents for batch_size\n additional_image_per_prompt = batch_size // init_latents.shape[0]\n init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)\n elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n init_latents = torch.cat([init_latents], dim=0)\n\n if add_noise:\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n\n latents = init_latents\n\n return latents\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n def _get_add_time_ids(\n self,\n original_size,\n crops_coords_top_left,\n target_size,\n aesthetic_score,\n negative_aesthetic_score,\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype,\n text_encoder_projection_dim=None,\n ):\n if self.config.requires_aesthetics_score:\n add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,))\n add_neg_time_ids = list(\n negative_original_size + negative_crops_coords_top_left + (negative_aesthetic_score,)\n )\n else:\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n add_neg_time_ids = list(negative_original_size + crops_coords_top_left + negative_target_size)\n\n passed_add_embed_dim = (\n self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim\n )\n expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features\n\n if (\n expected_add_embed_dim > passed_add_embed_dim\n and (expected_add_embed_dim - passed_add_embed_dim) == self.unet.config.addition_time_embed_dim\n ):\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to enable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=True)` to make sure `aesthetic_score` {aesthetic_score} and `negative_aesthetic_score` {negative_aesthetic_score} is correctly used by the model.\"\n )\n elif (\n expected_add_embed_dim < passed_add_embed_dim\n and (passed_add_embed_dim - expected_add_embed_dim) == self.unet.config.addition_time_embed_dim\n ):\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the model.\"\n )\n elif expected_add_embed_dim != passed_add_embed_dim:\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.\"\n )\n\n add_time_ids = torch.tensor([add_time_ids], dtype=dtype)\n add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype)\n\n return add_time_ids, add_neg_time_ids\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae\n def upcast_vae(self):\n dtype = self.vae.dtype\n self.vae.to(dtype=torch.float32)\n use_torch_2_0_or_xformers = isinstance(\n self.vae.decoder.mid_block.attentions[0].processor,\n (\n AttnProcessor2_0,\n XFormersAttnProcessor,\n LoRAXFormersAttnProcessor,\n LoRAAttnProcessor2_0,\n ),\n )\n # if xformers or torch_2_0 is used attention block does not need\n # to be in float32 which can save lots of memory\n if use_torch_2_0_or_xformers:\n self.vae.post_quant_conv.to(dtype)\n self.vae.decoder.conv_in.to(dtype)\n self.vae.decoder.mid_block.to(dtype)\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(\n self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32\n ) -> torch.Tensor:\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n w (`torch.Tensor`):\n Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.\n embedding_dim (`int`, *optional*, defaults to 512):\n Dimension of the embeddings to generate.\n dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):\n Data type of the generated embeddings.\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def guidance_rescale(self):\n return self._guidance_rescale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def denoising_end(self):\n return self._denoising_end\n\n @property\n def denoising_start(self):\n return self._denoising_start\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @property\n def interrupt(self):\n return self._interrupt\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n image: PipelineImageInput = None,\n strength: float = 0.3,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n sigmas: List[float] = None,\n denoising_start: Optional[float] = None,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Tuple[int, int] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Tuple[int, int] = None,\n negative_original_size: Optional[Tuple[int, int]] = None,\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\n negative_target_size: Optional[Tuple[int, int]] = None,\n aesthetic_score: float = 6.0,\n negative_aesthetic_score: float = 2.5,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[\n Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]\n ] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n image (`torch.Tensor` or `PIL.Image.Image` or `np.ndarray` or `List[torch.Tensor]` or `List[PIL.Image.Image]` or `List[np.ndarray]`):\n The image(s) to modify with the pipeline.\n strength (`float`, *optional*, defaults to 0.3):\n Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`\n will be used as a starting point, adding more noise to it the larger the `strength`. The number of\n denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will\n be maximum and the denoising process will run for the full number of iterations specified in\n `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. Note that in the case of\n `denoising_start` being declared as an integer, the value of `strength` will be ignored.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n sigmas (`List[float]`, *optional*):\n Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in\n their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed\n will be used.\n denoising_start (`float`, *optional*):\n When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be\n bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and\n it is assumed that the passed `image` is a partly denoised image. Note that when this is specified,\n strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline\n is integrated into a \"Mixture of Denoisers\" multi-pipeline setup, as detailed in [**Refine Image\n Quality**](https://huggingface.co/docs/diffusers/using-diffusers/sdxl#refine-image-quality).\n denoising_end (`float`, *optional*):\n When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be\n completed before it is intentionally prematurely terminated. As a result, the returned sample will\n still retain a substantial amount of noise (ca. final 20% of timesteps still needed) and should be\n denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the\n final 20% of the scheduler. The denoising_end parameter should ideally be utilized when this pipeline\n forms a part of a \"Mixture of Denoisers\" multi-pipeline setup, as elaborated in [**Refine Image\n Quality**](https://huggingface.co/docs/diffusers/using-diffusers/sdxl#refine-image-quality).\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of\n IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should\n contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not\n provided, embeddings are computed from the `ip_adapter_image` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of\n [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).\n Guidance rescale factor should fix overexposure when using zero terminal SNR.\n original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.\n `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as\n explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n `crops_coords_top_left` can be used to generate an image that appears to be \"cropped\" from the position\n `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n For most cases, `target_size` should be set to the desired height and width of the generated image. If\n not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a specific image resolution. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a target image resolution. It should be as same\n as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n aesthetic_score (`float`, *optional*, defaults to 6.0):\n Used to simulate an aesthetic score of the generated image by influencing the positive text condition.\n Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n negative_aesthetic_score (`float`, *optional*, defaults to 2.5):\n Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). Can be used to\n simulate an aesthetic score of the generated image by influencing the negative text condition.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):\n A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of\n each denoising step during the inference. with the following arguments: `callback_on_step_end(self:\n DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a\n list of all tensors as specified by `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a\n `tuple. When returning a tuple, the first element is a list with the generated images.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n\n if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):\n callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n strength,\n num_inference_steps,\n callback_steps,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n ip_adapter_image,\n ip_adapter_image_embeds,\n callback_on_step_end_tensor_inputs,\n )\n\n self._guidance_scale = guidance_scale\n self._guidance_rescale = guidance_rescale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._denoising_end = denoising_end\n self._denoising_start = denoising_start\n self._interrupt = False\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # 4. Preprocess image\n image = self.image_processor.preprocess(image)\n\n # 5. Prepare timesteps\n def denoising_value_valid(dnv):\n return isinstance(dnv, float) and 0 < dnv < 1\n\n timesteps, num_inference_steps = retrieve_timesteps(\n self.scheduler, num_inference_steps, device, timesteps, sigmas\n )\n timesteps, num_inference_steps = self.get_timesteps(\n num_inference_steps,\n strength,\n device,\n denoising_start=self.denoising_start if denoising_value_valid(self.denoising_start) else None,\n )\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n\n add_noise = True if self.denoising_start is None else False\n\n # 6. Prepare latent variables\n if latents is None:\n latents = self.prepare_latents(\n image,\n latent_timestep,\n batch_size,\n num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n generator,\n add_noise,\n )\n # 7. Prepare extra step kwargs.\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n height, width = latents.shape[-2:]\n height = height * self.vae_scale_factor\n width = width * self.vae_scale_factor\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n # 8. Prepare added time ids & embeddings\n if negative_original_size is None:\n negative_original_size = original_size\n if negative_target_size is None:\n negative_target_size = target_size\n\n add_text_embeds = pooled_prompt_embeds\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n add_time_ids, add_neg_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n aesthetic_score,\n negative_aesthetic_score,\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)\n\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1)\n add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device)\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_images_per_prompt,\n self.do_classifier_free_guidance,\n )\n\n # 9. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n\n # 9.1 Apply denoising_end\n if (\n self.denoising_end is not None\n and self.denoising_start is not None\n and denoising_value_valid(self.denoising_end)\n and denoising_value_valid(self.denoising_start)\n and self.denoising_start >= self.denoising_end\n ):\n raise ValueError(\n f\"`denoising_start`: {self.denoising_start} cannot be larger than or equal to `denoising_end`: \"\n + f\" {self.denoising_end} when using type float.\"\n )\n elif self.denoising_end is not None and denoising_value_valid(self.denoising_end):\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (self.denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n # 9.2 Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n if self.interrupt:\n continue\n\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n added_cond_kwargs[\"image_embeds\"] = image_embeds\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=self.cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents_dtype = latents.dtype\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n if latents.dtype != latents_dtype:\n if torch.backends.mps.is_available():\n # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272\n latents = latents.to(latents_dtype)\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n add_text_embeds = callback_outputs.pop(\"add_text_embeds\", add_text_embeds)\n negative_pooled_prompt_embeds = callback_outputs.pop(\n \"negative_pooled_prompt_embeds\", negative_pooled_prompt_embeds\n )\n add_time_ids = callback_outputs.pop(\"add_time_ids\", add_time_ids)\n add_neg_time_ids = callback_outputs.pop(\"add_neg_time_ids\", add_neg_time_ids)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if XLA_AVAILABLE:\n xm.mark_step()\n\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n elif latents.dtype != self.vae.dtype:\n if torch.backends.mps.is_available():\n # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272\n self.vae = self.vae.to(latents.dtype)\n\n # unscale/denormalize the latents\n # denormalize with the mean and std if available and not None\n has_latents_mean = hasattr(self.vae.config, \"latents_mean\") and self.vae.config.latents_mean is not None\n has_latents_std = hasattr(self.vae.config, \"latents_std\") and self.vae.config.latents_std is not None\n if has_latents_mean and has_latents_std:\n latents_mean = (\n torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)\n )\n latents_std = (\n torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)\n )\n latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean\n else:\n latents = latents / self.vae.config.scaling_factor\n\n image = self.vae.decode(latents, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n image = latents\n\n # apply watermark if available\n if self.watermark is not None:\n image = self.watermark.apply_watermark(image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModel,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n)\n\nfrom ...callbacks import MultiPipelineCallbacks, PipelineCallback\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import (\n FromSingleFileMixin,\n IPAdapterMixin,\n StableDiffusionXLLoraLoaderMixin,\n TextualInversionLoaderMixin,\n)\nfrom ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel\nfrom ...models.attention_processor import (\n AttnProcessor2_0,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n XFormersAttnProcessor,\n)\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n is_invisible_watermark_available,\n is_torch_xla_available,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom .pipeline_output import StableDiffusionXLPipelineOutput\n\n\nif is_invisible_watermark_available():\n from .watermark import StableDiffusionXLWatermarker\n\nif is_torch_xla_available():\n import torch_xla.core.xla_model as xm\n\n XLA_AVAILABLE = True\nelse:\n XLA_AVAILABLE = False\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusionXLInpaintPipeline\n >>> from diffusers.utils import load_image\n\n >>> pipe = StableDiffusionXLInpaintPipeline.from_pretrained(\n ... \"stabilityai/stable-diffusion-xl-base-1.0\",\n ... torch_dtype=torch.float16,\n ... variant=\"fp16\",\n ... use_safetensors=True,\n ... )\n >>> pipe.to(\"cuda\")\n\n >>> img_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\n >>> mask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\n\n >>> init_image = load_image(img_url).convert(\"RGB\")\n >>> mask_image = load_image(mask_url).convert(\"RGB\")\n\n >>> prompt = \"A majestic tiger sitting on a bench\"\n >>> image = pipe(\n ... prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=50, strength=0.80\n ... ).images[0]\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\ndef mask_pil_to_torch(mask, height, width):\n # preprocess mask\n if isinstance(mask, (PIL.Image.Image, np.ndarray)):\n mask = [mask]\n\n if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):\n mask = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in mask]\n mask = np.concatenate([np.array(m.convert(\"L\"))[None, None, :] for m in mask], axis=0)\n mask = mask.astype(np.float32) / 255.0\n elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):\n mask = np.concatenate([m[None, None, :] for m in mask], axis=0)\n\n mask = torch.from_numpy(mask)\n return mask\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass StableDiffusionXLInpaintPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n TextualInversionLoaderMixin,\n StableDiffusionXLLoraLoaderMixin,\n FromSingleFileMixin,\n IPAdapterMixin,\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion XL.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion XL uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n text_encoder_2 ([` CLIPTextModelWithProjection`]):\n Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),\n specifically the\n [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)\n variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n tokenizer_2 (`CLIPTokenizer`):\n Second Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n requires_aesthetics_score (`bool`, *optional*, defaults to `\"False\"`):\n Whether the `unet` requires a aesthetic_score condition to be passed during inference. Also see the config\n of `stabilityai/stable-diffusion-xl-refiner-1-0`.\n force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `\"True\"`):\n Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of\n `stabilityai/stable-diffusion-xl-base-1-0`.\n add_watermarker (`bool`, *optional*):\n Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to\n watermark output images. If not defined, it will default to True if the package is installed, otherwise no\n watermarker will be used.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->text_encoder_2->image_encoder->unet->vae\"\n\n _optional_components = [\n \"tokenizer\",\n \"tokenizer_2\",\n \"text_encoder\",\n \"text_encoder_2\",\n \"image_encoder\",\n \"feature_extractor\",\n ]\n _callback_tensor_inputs = [\n \"latents\",\n \"prompt_embeds\",\n \"negative_prompt_embeds\",\n \"add_text_embeds\",\n \"add_time_ids\",\n \"negative_pooled_prompt_embeds\",\n \"add_neg_time_ids\",\n \"mask\",\n \"masked_image_latents\",\n ]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n text_encoder_2: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n tokenizer_2: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n image_encoder: CLIPVisionModelWithProjection = None,\n feature_extractor: CLIPImageProcessor = None,\n requires_aesthetics_score: bool = False,\n force_zeros_for_empty_prompt: bool = True,\n add_watermarker: Optional[bool] = None,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n unet=unet,\n image_encoder=image_encoder,\n feature_extractor=feature_extractor,\n scheduler=scheduler,\n )\n self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)\n self.register_to_config(requires_aesthetics_score=requires_aesthetics_score)\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.mask_processor = VaeImageProcessor(\n vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True\n )\n\n add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()\n\n if add_watermarker:\n self.watermark = StableDiffusionXLWatermarker()\n else:\n self.watermark = None\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt: str,\n prompt_2: Optional[str] = None,\n device: Optional[torch.device] = None,\n num_images_per_prompt: int = 1,\n do_classifier_free_guidance: bool = True,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n lora_scale (`float`, *optional*):\n A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n device = device or self._execution_device\n\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if self.text_encoder is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)\n else:\n scale_lora_layers(self.text_encoder_2, lora_scale)\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n\n if prompt is not None:\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # Define tokenizers and text encoders\n tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]\n text_encoders = (\n [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]\n )\n\n if prompt_embeds is None:\n prompt_2 = prompt_2 or prompt\n prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2\n\n # textual inversion: process multi-vector tokens if necessary\n prompt_embeds_list = []\n prompts = [prompt, prompt_2]\n for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, tokenizer)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n if clip_skip is None:\n prompt_embeds = prompt_embeds.hidden_states[-2]\n else:\n # \"2\" because SDXL always indexes from the penultimate layer.\n prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]\n\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n\n # get unconditional embeddings for classifier free guidance\n zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt\n if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:\n negative_prompt_embeds = torch.zeros_like(prompt_embeds)\n negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)\n elif do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n\n # normalize str to list\n negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt\n negative_prompt_2 = (\n batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2\n )\n\n uncond_tokens: List[str]\n if prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = [negative_prompt, negative_prompt_2]\n\n negative_prompt_embeds_list = []\n for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n negative_prompt_embeds = text_encoder(\n uncond_input.input_ids.to(device),\n output_hidden_states=True,\n )\n # We are only ALWAYS interested in the pooled output of the final text encoder\n negative_pooled_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]\n\n negative_prompt_embeds_list.append(negative_prompt_embeds)\n\n negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)\n\n if self.text_encoder_2 is not None:\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n if self.text_encoder_2 is not None:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n if do_classifier_free_guidance:\n negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n\n if self.text_encoder is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder_2, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n prompt_2,\n image,\n mask_image,\n height,\n width,\n strength,\n callback_steps,\n output_type,\n negative_prompt=None,\n negative_prompt_2=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n padding_mask_crop=None,\n ):\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n if padding_mask_crop is not None:\n if not isinstance(image, PIL.Image.Image):\n raise ValueError(\n f\"The image should be a PIL image when inpainting mask crop, but is of type\" f\" {type(image)}.\"\n )\n if not isinstance(mask_image, PIL.Image.Image):\n raise ValueError(\n f\"The mask image should be a PIL image when inpainting mask crop, but is of type\"\n f\" {type(mask_image)}.\"\n )\n if output_type != \"pil\":\n raise ValueError(f\"The output type should be PIL when inpainting mask crop, but is\" f\" {output_type}.\")\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n if ip_adapter_image_embeds is not None:\n if not isinstance(ip_adapter_image_embeds, list):\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}\"\n )\n elif ip_adapter_image_embeds[0].ndim not in [3, 4]:\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D\"\n )\n\n def prepare_latents(\n self,\n batch_size,\n num_channels_latents,\n height,\n width,\n dtype,\n device,\n generator,\n latents=None,\n image=None,\n timestep=None,\n is_strength_max=True,\n add_noise=True,\n return_noise=False,\n return_image_latents=False,\n ):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if (image is None or timestep is None) and not is_strength_max:\n raise ValueError(\n \"Since strength < 1. initial latents are to be initialised as a combination of Image + Noise.\"\n \"However, either the image or the noise timestep has not been provided.\"\n )\n\n if image.shape[1] == 4:\n image_latents = image.to(device=device, dtype=dtype)\n image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)\n elif return_image_latents or (latents is None and not is_strength_max):\n image = image.to(device=device, dtype=dtype)\n image_latents = self._encode_vae_image(image=image, generator=generator)\n image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)\n\n if latents is None and add_noise:\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n # if strength is 1. then initialise the latents to noise, else initial to image + noise\n latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)\n # if pure noise then scale the initial latents by the Scheduler's init sigma\n latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents\n elif add_noise:\n noise = latents.to(device)\n latents = noise * self.scheduler.init_noise_sigma\n else:\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n latents = image_latents.to(device)\n\n outputs = (latents,)\n\n if return_noise:\n outputs += (noise,)\n\n if return_image_latents:\n outputs += (image_latents,)\n\n return outputs\n\n def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):\n dtype = image.dtype\n if self.vae.config.force_upcast:\n image = image.float()\n self.vae.to(dtype=torch.float32)\n\n if isinstance(generator, list):\n image_latents = [\n retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])\n for i in range(image.shape[0])\n ]\n image_latents = torch.cat(image_latents, dim=0)\n else:\n image_latents = retrieve_latents(self.vae.encode(image), generator=generator)\n\n if self.vae.config.force_upcast:\n self.vae.to(dtype)\n\n image_latents = image_latents.to(dtype)\n image_latents = self.vae.config.scaling_factor * image_latents\n\n return image_latents\n\n def prepare_mask_latents(\n self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance\n ):\n # resize the mask to latents shape as we concatenate the mask to the latents\n # we do that before converting to dtype to avoid breaking in case we're using cpu_offload\n # and half precision\n mask = torch.nn.functional.interpolate(\n mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)\n )\n mask = mask.to(device=device, dtype=dtype)\n\n # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method\n if mask.shape[0] < batch_size:\n if not batch_size % mask.shape[0] == 0:\n raise ValueError(\n \"The passed mask and the required batch size don't match. Masks are supposed to be duplicated to\"\n f\" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number\"\n \" of masks that you pass is divisible by the total requested batch size.\"\n )\n mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)\n\n mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask\n\n if masked_image is not None and masked_image.shape[1] == 4:\n masked_image_latents = masked_image\n else:\n masked_image_latents = None\n\n if masked_image is not None:\n if masked_image_latents is None:\n masked_image = masked_image.to(device=device, dtype=dtype)\n masked_image_latents = self._encode_vae_image(masked_image, generator=generator)\n\n if masked_image_latents.shape[0] < batch_size:\n if not batch_size % masked_image_latents.shape[0] == 0:\n raise ValueError(\n \"The passed images and the required batch size don't match. Images are supposed to be duplicated\"\n f\" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed.\"\n \" Make sure the number of images that you pass is divisible by the total requested batch size.\"\n )\n masked_image_latents = masked_image_latents.repeat(\n batch_size // masked_image_latents.shape[0], 1, 1, 1\n )\n\n masked_image_latents = (\n torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents\n )\n\n # aligning device to prevent device errors when concating it with the latent model input\n masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)\n\n return mask, masked_image_latents\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength, device, denoising_start=None):\n # get the original timestep using init_timestep\n if denoising_start is None:\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n t_start = max(num_inference_steps - init_timestep, 0)\n else:\n t_start = 0\n\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n\n # Strength is irrelevant if we directly request a timestep to start at;\n # that is, strength is determined by the denoising_start instead.\n if denoising_start is not None:\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (denoising_start * self.scheduler.config.num_train_timesteps)\n )\n )\n\n num_inference_steps = (timesteps < discrete_timestep_cutoff).sum().item()\n if self.scheduler.order == 2 and num_inference_steps % 2 == 0:\n # if the scheduler is a 2nd order scheduler we might have to do +1\n # because `num_inference_steps` might be even given that every timestep\n # (except the highest one) is duplicated. If `num_inference_steps` is even it would\n # mean that we cut the timesteps in the middle of the denoising step\n # (between 1st and 2nd derivative) which leads to incorrect results. By adding 1\n # we ensure that the denoising process always ends after the 2nd derivate step of the scheduler\n num_inference_steps = num_inference_steps + 1\n\n # because t_n+1 >= t_n, we slice the timesteps starting from the end\n timesteps = timesteps[-num_inference_steps:]\n return timesteps, num_inference_steps\n\n return timesteps, num_inference_steps - t_start\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline._get_add_time_ids\n def _get_add_time_ids(\n self,\n original_size,\n crops_coords_top_left,\n target_size,\n aesthetic_score,\n negative_aesthetic_score,\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype,\n text_encoder_projection_dim=None,\n ):\n if self.config.requires_aesthetics_score:\n add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,))\n add_neg_time_ids = list(\n negative_original_size + negative_crops_coords_top_left + (negative_aesthetic_score,)\n )\n else:\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n add_neg_time_ids = list(negative_original_size + crops_coords_top_left + negative_target_size)\n\n passed_add_embed_dim = (\n self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim\n )\n expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features\n\n if (\n expected_add_embed_dim > passed_add_embed_dim\n and (expected_add_embed_dim - passed_add_embed_dim) == self.unet.config.addition_time_embed_dim\n ):\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to enable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=True)` to make sure `aesthetic_score` {aesthetic_score} and `negative_aesthetic_score` {negative_aesthetic_score} is correctly used by the model.\"\n )\n elif (\n expected_add_embed_dim < passed_add_embed_dim\n and (passed_add_embed_dim - expected_add_embed_dim) == self.unet.config.addition_time_embed_dim\n ):\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the model.\"\n )\n elif expected_add_embed_dim != passed_add_embed_dim:\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.\"\n )\n\n add_time_ids = torch.tensor([add_time_ids], dtype=dtype)\n add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype)\n\n return add_time_ids, add_neg_time_ids\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae\n def upcast_vae(self):\n dtype = self.vae.dtype\n self.vae.to(dtype=torch.float32)\n use_torch_2_0_or_xformers = isinstance(\n self.vae.decoder.mid_block.attentions[0].processor,\n (\n AttnProcessor2_0,\n XFormersAttnProcessor,\n LoRAXFormersAttnProcessor,\n LoRAAttnProcessor2_0,\n ),\n )\n # if xformers or torch_2_0 is used attention block does not need\n # to be in float32 which can save lots of memory\n if use_torch_2_0_or_xformers:\n self.vae.post_quant_conv.to(dtype)\n self.vae.decoder.conv_in.to(dtype)\n self.vae.decoder.mid_block.to(dtype)\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(\n self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32\n ) -> torch.Tensor:\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n w (`torch.Tensor`):\n Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.\n embedding_dim (`int`, *optional*, defaults to 512):\n Dimension of the embeddings to generate.\n dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):\n Data type of the generated embeddings.\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def guidance_rescale(self):\n return self._guidance_rescale\n\n @property\n def clip_skip(self):\n return self._clip_skip\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @property\n def cross_attention_kwargs(self):\n return self._cross_attention_kwargs\n\n @property\n def denoising_end(self):\n return self._denoising_end\n\n @property\n def denoising_start(self):\n return self._denoising_start\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @property\n def interrupt(self):\n return self._interrupt\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n image: PipelineImageInput = None,\n mask_image: PipelineImageInput = None,\n masked_image_latents: torch.Tensor = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n padding_mask_crop: Optional[int] = None,\n strength: float = 0.9999,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n sigmas: List[float] = None,\n denoising_start: Optional[float] = None,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Tuple[int, int] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Tuple[int, int] = None,\n negative_original_size: Optional[Tuple[int, int]] = None,\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\n negative_target_size: Optional[Tuple[int, int]] = None,\n aesthetic_score: float = 6.0,\n negative_aesthetic_score: float = 2.5,\n clip_skip: Optional[int] = None,\n callback_on_step_end: Optional[\n Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]\n ] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n image (`PIL.Image.Image`):\n `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will\n be masked out with `mask_image` and repainted according to `prompt`.\n mask_image (`PIL.Image.Image`):\n `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be\n repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted\n to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)\n instead of 3, so the expected shape would be `(B, H, W, 1)`.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image. This is set to 1024 by default for the best results.\n Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image. This is set to 1024 by default for the best results.\n Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n padding_mask_crop (`int`, *optional*, defaults to `None`):\n The size of margin in the crop to be applied to the image and masking. If `None`, no crop is applied to\n image and mask_image. If `padding_mask_crop` is not `None`, it will first find a rectangular region\n with the same aspect ration of the image and contains all masked area, and then expand that area based\n on `padding_mask_crop`. The image and mask_image will then be cropped based on the expanded area before\n resizing to the original image size for inpainting. This is useful when the masked area is small while\n the image is large and contain information irrelevant for inpainting, such as background.\n strength (`float`, *optional*, defaults to 0.9999):\n Conceptually, indicates how much to transform the masked portion of the reference `image`. Must be\n between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the\n `strength`. The number of denoising steps depends on the amount of noise initially added. When\n `strength` is 1, added noise will be maximum and the denoising process will run for the full number of\n iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores the masked\n portion of the reference `image`. Note that in the case of `denoising_start` being declared as an\n integer, the value of `strength` will be ignored.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n sigmas (`List[float]`, *optional*):\n Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in\n their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed\n will be used.\n denoising_start (`float`, *optional*):\n When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be\n bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and\n it is assumed that the passed `image` is a partly denoised image. Note that when this is specified,\n strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline\n is integrated into a \"Mixture of Denoisers\" multi-pipeline setup, as detailed in [**Refining the Image\n Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).\n denoising_end (`float`, *optional*):\n When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be\n completed before it is intentionally prematurely terminated. As a result, the returned sample will\n still retain a substantial amount of noise (ca. final 20% of timesteps still needed) and should be\n denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the\n final 20% of the scheduler. The denoising_end parameter should ideally be utilized when this pipeline\n forms a part of a \"Mixture of Denoisers\" multi-pipeline setup, as elaborated in [**Refining the Image\n Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of\n IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should\n contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not\n provided, embeddings are computed from the `ip_adapter_image` input argument.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.\n `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as\n explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n `crops_coords_top_left` can be used to generate an image that appears to be \"cropped\" from the position\n `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n For most cases, `target_size` should be set to the desired height and width of the generated image. If\n not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a specific image resolution. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a target image resolution. It should be as same\n as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n aesthetic_score (`float`, *optional*, defaults to 6.0):\n Used to simulate an aesthetic score of the generated image by influencing the positive text condition.\n Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n negative_aesthetic_score (`float`, *optional*, defaults to 2.5):\n Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). Can be used to\n simulate an aesthetic score of the generated image by influencing the negative text condition.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):\n A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of\n each denoising step during the inference. with the following arguments: `callback_on_step_end(self:\n DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a\n list of all tensors as specified by `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a\n `tuple. `tuple. When returning a tuple, the first element is a list with the generated images.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n\n if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):\n callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs\n self.check_inputs(\n prompt,\n prompt_2,\n image,\n mask_image,\n height,\n width,\n strength,\n callback_steps,\n output_type,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n ip_adapter_image,\n ip_adapter_image_embeds,\n callback_on_step_end_tensor_inputs,\n padding_mask_crop,\n )\n\n self._guidance_scale = guidance_scale\n self._guidance_rescale = guidance_rescale\n self._clip_skip = clip_skip\n self._cross_attention_kwargs = cross_attention_kwargs\n self._denoising_end = denoising_end\n self._denoising_start = denoising_start\n self._interrupt = False\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n self.cross_attention_kwargs.get(\"scale\", None) if self.cross_attention_kwargs is not None else None\n )\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=self.clip_skip,\n )\n\n # 4. set timesteps\n def denoising_value_valid(dnv):\n return isinstance(dnv, float) and 0 < dnv < 1\n\n timesteps, num_inference_steps = retrieve_timesteps(\n self.scheduler, num_inference_steps, device, timesteps, sigmas\n )\n timesteps, num_inference_steps = self.get_timesteps(\n num_inference_steps,\n strength,\n device,\n denoising_start=self.denoising_start if denoising_value_valid(self.denoising_start) else None,\n )\n # check that number of inference steps is not < 1 - as this doesn't make sense\n if num_inference_steps < 1:\n raise ValueError(\n f\"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline\"\n f\"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline.\"\n )\n # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise\n is_strength_max = strength == 1.0\n\n # 5. Preprocess mask and image\n if padding_mask_crop is not None:\n crops_coords = self.mask_processor.get_crop_region(mask_image, width, height, pad=padding_mask_crop)\n resize_mode = \"fill\"\n else:\n crops_coords = None\n resize_mode = \"default\"\n\n original_image = image\n init_image = self.image_processor.preprocess(\n image, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode\n )\n init_image = init_image.to(dtype=torch.float32)\n\n mask = self.mask_processor.preprocess(\n mask_image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords\n )\n\n if masked_image_latents is not None:\n masked_image = masked_image_latents\n elif init_image.shape[1] == 4:\n # if images are in latent space, we can't mask it\n masked_image = None\n else:\n masked_image = init_image * (mask < 0.5)\n\n # 6. Prepare latent variables\n num_channels_latents = self.vae.config.latent_channels\n num_channels_unet = self.unet.config.in_channels\n return_image_latents = num_channels_unet == 4\n\n add_noise = True if self.denoising_start is None else False\n latents_outputs = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n image=init_image,\n timestep=latent_timestep,\n is_strength_max=is_strength_max,\n add_noise=add_noise,\n return_noise=True,\n return_image_latents=return_image_latents,\n )\n\n if return_image_latents:\n latents, noise, image_latents = latents_outputs\n else:\n latents, noise = latents_outputs\n\n # 7. Prepare mask latent variables\n mask, masked_image_latents = self.prepare_mask_latents(\n mask,\n masked_image,\n batch_size * num_images_per_prompt,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n self.do_classifier_free_guidance,\n )\n\n # 8. Check that sizes of mask, masked image and latents match\n if num_channels_unet == 9:\n # default case for runwayml/stable-diffusion-inpainting\n num_channels_mask = mask.shape[1]\n num_channels_masked_image = masked_image_latents.shape[1]\n if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:\n raise ValueError(\n f\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\"\n f\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\"\n f\" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}\"\n f\" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of\"\n \" `pipeline.unet` or your `mask_image` or `image` input.\"\n )\n elif num_channels_unet != 4:\n raise ValueError(\n f\"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}.\"\n )\n # 8.1 Prepare extra step kwargs.\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n height, width = latents.shape[-2:]\n height = height * self.vae_scale_factor\n width = width * self.vae_scale_factor\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n # 10. Prepare added time ids & embeddings\n if negative_original_size is None:\n negative_original_size = original_size\n if negative_target_size is None:\n negative_target_size = target_size\n\n add_text_embeds = pooled_prompt_embeds\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n add_time_ids, add_neg_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n aesthetic_score,\n negative_aesthetic_score,\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)\n\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1)\n add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device)\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_images_per_prompt,\n self.do_classifier_free_guidance,\n )\n\n # 11. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n\n if (\n self.denoising_end is not None\n and self.denoising_start is not None\n and denoising_value_valid(self.denoising_end)\n and denoising_value_valid(self.denoising_start)\n and self.denoising_start >= self.denoising_end\n ):\n raise ValueError(\n f\"`denoising_start`: {self.denoising_start} cannot be larger than or equal to `denoising_end`: \"\n + f\" {self.denoising_end} when using type float.\"\n )\n elif self.denoising_end is not None and denoising_value_valid(self.denoising_end):\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (self.denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n # 11.1 Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n if self.interrupt:\n continue\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n\n # concat latents, mask, masked_image_latents in the channel dimension\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n if num_channels_unet == 9:\n latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)\n\n # predict the noise residual\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n added_cond_kwargs[\"image_embeds\"] = image_embeds\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=self.cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents_dtype = latents.dtype\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n if latents.dtype != latents_dtype:\n if torch.backends.mps.is_available():\n # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272\n latents = latents.to(latents_dtype)\n\n if num_channels_unet == 4:\n init_latents_proper = image_latents\n if self.do_classifier_free_guidance:\n init_mask, _ = mask.chunk(2)\n else:\n init_mask = mask\n\n if i < len(timesteps) - 1:\n noise_timestep = timesteps[i + 1]\n init_latents_proper = self.scheduler.add_noise(\n init_latents_proper, noise, torch.tensor([noise_timestep])\n )\n\n latents = (1 - init_mask) * init_latents_proper + init_mask * latents\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n add_text_embeds = callback_outputs.pop(\"add_text_embeds\", add_text_embeds)\n negative_pooled_prompt_embeds = callback_outputs.pop(\n \"negative_pooled_prompt_embeds\", negative_pooled_prompt_embeds\n )\n add_time_ids = callback_outputs.pop(\"add_time_ids\", add_time_ids)\n add_neg_time_ids = callback_outputs.pop(\"add_neg_time_ids\", add_neg_time_ids)\n mask = callback_outputs.pop(\"mask\", mask)\n masked_image_latents = callback_outputs.pop(\"masked_image_latents\", masked_image_latents)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if XLA_AVAILABLE:\n xm.mark_step()\n\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n elif latents.dtype != self.vae.dtype:\n if torch.backends.mps.is_available():\n # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272\n self.vae = self.vae.to(latents.dtype)\n\n # unscale/denormalize the latents\n # denormalize with the mean and std if available and not None\n has_latents_mean = hasattr(self.vae.config, \"latents_mean\") and self.vae.config.latents_mean is not None\n has_latents_std = hasattr(self.vae.config, \"latents_std\") and self.vae.config.latents_std is not None\n if has_latents_mean and has_latents_std:\n latents_mean = (\n torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)\n )\n latents_std = (\n torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)\n )\n latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean\n else:\n latents = latents / self.vae.config.scaling_factor\n\n image = self.vae.decode(latents, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n return StableDiffusionXLPipelineOutput(images=latents)\n\n # apply watermark if available\n if self.watermark is not None:\n image = self.watermark.apply_watermark(image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n if padding_mask_crop is not None:\n image = [self.image_processor.apply_overlay(mask_image, original_image, i, crops_coords) for i in image]\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_instruct_pix2pix.py", "content": "# Copyright 2024 Harutatsu Akiyama and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport PIL.Image\nimport torch\nfrom transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, UNet2DConditionModel\nfrom ...models.attention_processor import (\n AttnProcessor2_0,\n FusedAttnProcessor2_0,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n XFormersAttnProcessor,\n)\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n is_invisible_watermark_available,\n is_torch_xla_available,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom .pipeline_output import StableDiffusionXLPipelineOutput\n\n\nif is_invisible_watermark_available():\n from .watermark import StableDiffusionXLWatermarker\n\nif is_torch_xla_available():\n import torch_xla.core.xla_model as xm\n\n XLA_AVAILABLE = True\nelse:\n XLA_AVAILABLE = False\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import StableDiffusionXLInstructPix2PixPipeline\n >>> from diffusers.utils import load_image\n\n >>> resolution = 768\n >>> image = load_image(\n ... \"https://hf.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png\"\n ... ).resize((resolution, resolution))\n >>> edit_instruction = \"Turn sky into a cloudy one\"\n\n >>> pipe = StableDiffusionXLInstructPix2PixPipeline.from_pretrained(\n ... \"diffusers/sdxl-instructpix2pix-768\", torch_dtype=torch.float16\n ... ).to(\"cuda\")\n\n >>> edited_image = pipe(\n ... prompt=edit_instruction,\n ... image=image,\n ... height=resolution,\n ... width=resolution,\n ... guidance_scale=3.0,\n ... image_guidance_scale=1.5,\n ... num_inference_steps=30,\n ... ).images[0]\n >>> edited_image\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\nclass StableDiffusionXLInstructPix2PixPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n TextualInversionLoaderMixin,\n FromSingleFileMixin,\n StableDiffusionXLLoraLoaderMixin,\n):\n r\"\"\"\n Pipeline for pixel-level image editing by following text instructions. Based on Stable Diffusion XL.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion XL uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n text_encoder_2 ([` CLIPTextModelWithProjection`]):\n Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),\n specifically the\n [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)\n variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n tokenizer_2 (`CLIPTokenizer`):\n Second Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n requires_aesthetics_score (`bool`, *optional*, defaults to `\"False\"`):\n Whether the `unet` requires a aesthetic_score condition to be passed during inference. Also see the config\n of `stabilityai/stable-diffusion-xl-refiner-1-0`.\n force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `\"True\"`):\n Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of\n `stabilityai/stable-diffusion-xl-base-1-0`.\n add_watermarker (`bool`, *optional*):\n Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to\n watermark output images. If not defined, it will default to True if the package is installed, otherwise no\n watermarker will be used.\n is_cosxl_edit (`bool`, *optional*):\n When set the image latents are scaled.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->text_encoder_2->unet->vae\"\n _optional_components = [\"tokenizer\", \"tokenizer_2\", \"text_encoder\", \"text_encoder_2\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n text_encoder_2: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n tokenizer_2: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n force_zeros_for_empty_prompt: bool = True,\n add_watermarker: Optional[bool] = None,\n is_cosxl_edit: Optional[bool] = False,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n unet=unet,\n scheduler=scheduler,\n )\n self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.default_sample_size = self.unet.config.sample_size\n self.is_cosxl_edit = is_cosxl_edit\n\n add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()\n\n if add_watermarker:\n self.watermark = StableDiffusionXLWatermarker()\n else:\n self.watermark = None\n\n def encode_prompt(\n self,\n prompt: str,\n prompt_2: Optional[str] = None,\n device: Optional[torch.device] = None,\n num_images_per_prompt: int = 1,\n do_classifier_free_guidance: bool = True,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n lora_scale (`float`, *optional*):\n A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n \"\"\"\n device = device or self._execution_device\n\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if self.text_encoder is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)\n else:\n scale_lora_layers(self.text_encoder_2, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # Define tokenizers and text encoders\n tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]\n text_encoders = (\n [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]\n )\n\n if prompt_embeds is None:\n prompt_2 = prompt_2 or prompt\n # textual inversion: process multi-vector tokens if necessary\n prompt_embeds_list = []\n prompts = [prompt, prompt_2]\n for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, tokenizer)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = text_encoder(\n text_input_ids.to(device),\n output_hidden_states=True,\n )\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds.hidden_states[-2]\n\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n\n # get unconditional embeddings for classifier free guidance\n zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt\n if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:\n negative_prompt_embeds = torch.zeros_like(prompt_embeds)\n negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)\n elif do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n\n uncond_tokens: List[str]\n if prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt, negative_prompt_2]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = [negative_prompt, negative_prompt_2]\n\n negative_prompt_embeds_list = []\n for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n negative_prompt_embeds = text_encoder(\n uncond_input.input_ids.to(device),\n output_hidden_states=True,\n )\n # We are only ALWAYS interested in the pooled output of the final text encoder\n negative_pooled_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]\n\n negative_prompt_embeds_list.append(negative_prompt_embeds)\n\n negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)\n\n prompt_embeds_dtype = self.text_encoder_2.dtype if self.text_encoder_2 is not None else self.unet.dtype\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n if do_classifier_free_guidance:\n negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def prepare_image_latents(\n self, image, batch_size, num_images_per_prompt, dtype, device, do_classifier_free_guidance, generator=None\n ):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_images_per_prompt\n\n if image.shape[1] == 4:\n image_latents = image\n else:\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n if needs_upcasting:\n image = image.float()\n self.upcast_vae()\n\n image_latents = retrieve_latents(self.vae.encode(image.to(self.vae.dtype)), sample_mode=\"argmax\")\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n\n if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:\n # expand image_latents for batch_size\n deprecation_message = (\n f\"You have passed {batch_size} text prompts (`prompt`), but only {image_latents.shape[0]} initial\"\n \" images (`image`). Initial images are now duplicating to match the number of text prompts. Note\"\n \" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update\"\n \" your script to pass as many initial images as text prompts to suppress this warning.\"\n )\n deprecate(\"len(prompt) != len(image)\", \"1.0.0\", deprecation_message, standard_warn=False)\n additional_image_per_prompt = batch_size // image_latents.shape[0]\n image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)\n elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n image_latents = torch.cat([image_latents], dim=0)\n\n if do_classifier_free_guidance:\n uncond_image_latents = torch.zeros_like(image_latents)\n image_latents = torch.cat([image_latents, image_latents, uncond_image_latents], dim=0)\n\n if image_latents.dtype != self.vae.dtype:\n image_latents = image_latents.to(dtype=self.vae.dtype)\n\n if self.is_cosxl_edit:\n image_latents = image_latents * self.vae.config.scaling_factor\n\n return image_latents\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline._get_add_time_ids\n def _get_add_time_ids(\n self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None\n ):\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n\n passed_add_embed_dim = (\n self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim\n )\n expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features\n\n if expected_add_embed_dim != passed_add_embed_dim:\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.\"\n )\n\n add_time_ids = torch.tensor([add_time_ids], dtype=dtype)\n return add_time_ids\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.upcast_vae\n def upcast_vae(self):\n dtype = self.vae.dtype\n self.vae.to(dtype=torch.float32)\n use_torch_2_0_or_xformers = isinstance(\n self.vae.decoder.mid_block.attentions[0].processor,\n (\n AttnProcessor2_0,\n XFormersAttnProcessor,\n LoRAXFormersAttnProcessor,\n LoRAAttnProcessor2_0,\n FusedAttnProcessor2_0,\n ),\n )\n # if xformers or torch_2_0 is used attention block does not need\n # to be in float32 which can save lots of memory\n if use_torch_2_0_or_xformers:\n self.vae.post_quant_conv.to(dtype)\n self.vae.decoder.conv_in.to(dtype)\n self.vae.decoder.mid_block.to(dtype)\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n image: PipelineImageInput = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 100,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 5.0,\n image_guidance_scale: float = 1.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Tuple[int, int] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Tuple[int, int] = None,\n mask_img: Optional[PipelineImageInput] = None,\n **kwargs\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n image (`torch.Tensor` or `PIL.Image.Image` or `np.ndarray` or `List[torch.Tensor]` or `List[PIL.Image.Image]` or `List[np.ndarray]`):\n The image(s) to modify with the pipeline.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n denoising_end (`float`, *optional*):\n When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be\n completed before it is intentionally prematurely terminated. As a result, the returned sample will\n still retain a substantial amount of noise as determined by the discrete timesteps selected by the\n scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a\n \"Mixture of Denoisers\" multi-pipeline setup, as elaborated in [**Refining the Image\n Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)\n guidance_scale (`float`, *optional*, defaults to 5.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n image_guidance_scale (`float`, *optional*, defaults to 1.5):\n Image guidance scale is to push the generated image towards the initial image `image`. Image guidance\n scale is enabled by setting `image_guidance_scale > 1`. Higher image guidance scale encourages to\n generate images that are closely linked to the source image `image`, usually at the expense of lower\n image quality. This pipeline requires a value of at least `1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of\n [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).\n Guidance rescale factor should fix overexposure when using zero terminal SNR.\n original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.\n `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as\n explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n `crops_coords_top_left` can be used to generate an image that appears to be \"cropped\" from the position\n `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n For most cases, `target_size` should be set to the desired height and width of the generated image. If\n not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n aesthetic_score (`float`, *optional*, defaults to 6.0):\n Used to simulate an aesthetic score of the generated image by influencing the positive text condition.\n Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n negative_aesthetic_score (`float`, *optional*, defaults to 2.5):\n Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). Can be used to\n simulate an aesthetic score of the generated image by influencing the negative text condition.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a\n `tuple`. When returning a tuple, the first element is a list with the generated images.\n \"\"\"\n\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(prompt, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)\n\n if image is None:\n raise ValueError(\"`image` input cannot be undefined.\")\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0 and image_guidance_scale >= 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n\n # 4. Preprocess image\n image = self.image_processor.preprocess(image, height=height, width=width).to(device)\n\n # 5. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 6. Prepare Image latents\n image_latents = self.prepare_image_latents(\n image,\n batch_size,\n num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n do_classifier_free_guidance,\n )\n if height is None or width is None:\n height, width = image_latents.shape[-2:]\n height = height * self.vae_scale_factor\n width = width * self.vae_scale_factor\n # height = height or self.default_sample_size * self.vae_scale_factor\n # width = width or self.default_sample_size * self.vae_scale_factor\n # 0. Default height and width to unet\n\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n # 7. Prepare latent variables\n num_channels_latents = self.vae.config.latent_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 8. Check that shapes of latents and image match the UNet channels\n num_channels_image = image_latents.shape[1]\n if mask_img is not None:\n mask_img = self.image_processor.preprocess(mask_img)\n mask_image_latents = self.prepare_image_latents(\n mask_img,\n batch_size,\n num_images_per_prompt,\n prompt_embeds.dtype,\n device,\n self.do_classifier_free_guidance,\n )\n\n num_channels_image += mask_image_latents.shape[1]\n\n if num_channels_latents + num_channels_image != self.unet.config.in_channels:\n raise ValueError(\n f\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\"\n f\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\"\n f\" `num_channels_image`: {num_channels_image} \"\n f\" = {num_channels_latents + num_channels_image}. Please verify the config of\"\n \" `pipeline.unet` or your `image` input.\"\n )\n\n # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 10. Prepare added time ids & embeddings\n add_text_embeds = pooled_prompt_embeds\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n add_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n\n if do_classifier_free_guidance:\n # The extra concat similar to how it's done in SD InstructPix2Pix.\n prompt_embeds = torch.cat([prompt_embeds, negative_prompt_embeds, negative_prompt_embeds], dim=0)\n add_text_embeds = torch.cat(\n [add_text_embeds, negative_pooled_prompt_embeds, negative_pooled_prompt_embeds], dim=0\n )\n add_time_ids = torch.cat([add_time_ids, add_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)\n\n # 11. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1:\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # Expand the latents if we are doing classifier free guidance.\n # The latents are expanded 3 times because for pix2pix the guidance\n # is applied for both the text and the input image.\n latent_model_input = torch.cat([latents] * 3) if do_classifier_free_guidance else latents\n\n # concat latents, image_latents in the channel dimension\n scaled_latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n scaled_latent_model_input = torch.cat([scaled_latent_model_input, image_latents], dim=1)\n if mask_img is not None:\n scaled_latent_model_input = torch.cat([scaled_latent_model_input, mask_image_latents], dim=1)\n\n # predict the noise residual\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n scaled_latent_model_input = scaled_latent_model_input.to(self.unet.dtype)\n noise_pred = self.unet(\n scaled_latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_text, noise_pred_image, noise_pred_uncond = noise_pred.chunk(3)\n noise_pred = (\n noise_pred_uncond\n + guidance_scale * (noise_pred_text - noise_pred_image)\n + image_guidance_scale * (noise_pred_image - noise_pred_uncond)\n )\n\n if do_classifier_free_guidance and guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents_dtype = latents.dtype\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n if latents.dtype != latents_dtype:\n if torch.backends.mps.is_available():\n # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272\n latents = latents.to(latents_dtype)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if XLA_AVAILABLE:\n xm.mark_step()\n\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n elif latents.dtype != self.vae.dtype:\n if torch.backends.mps.is_available():\n # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272\n self.vae = self.vae.to(latents.dtype)\n\n # unscale/denormalize the latents\n # denormalize with the mean and std if available and not None\n has_latents_mean = hasattr(self.vae.config, \"latents_mean\") and self.vae.config.latents_mean is not None\n has_latents_std = hasattr(self.vae.config, \"latents_std\") and self.vae.config.latents_std is not None\n if has_latents_mean and has_latents_std:\n latents_mean = (\n torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)\n )\n latents_std = (\n torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)\n )\n latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean\n else:\n latents = latents / self.vae.config.scaling_factor\n\n image = self.vae.decode(latents, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n return StableDiffusionXLPipelineOutput(images=latents)\n\n # apply watermark if available\n if self.watermark is not None:\n image = self.watermark.apply_watermark(image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_diffusion_xl/watermark.py", "content": "import numpy as np\nimport torch\n\nfrom ...utils import is_invisible_watermark_available\n\n\nif is_invisible_watermark_available():\n from imwatermark import WatermarkEncoder\n\n\n# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66\nWATERMARK_MESSAGE = 0b101100111110110010010000011110111011000110011110\n# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1\nWATERMARK_BITS = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]\n\n\nclass StableDiffusionXLWatermarker:\n def __init__(self):\n self.watermark = WATERMARK_BITS\n self.encoder = WatermarkEncoder()\n\n self.encoder.set_watermark(\"bits\", self.watermark)\n\n def apply_watermark(self, images: torch.Tensor):\n # can't encode images that are smaller than 256\n if images.shape[-1] < 256:\n return images\n\n images = (255 * (images / 2 + 0.5)).cpu().permute(0, 2, 3, 1).float().numpy()\n\n # Convert RGB to BGR, which is the channel order expected by the watermark encoder.\n images = images[:, :, :, ::-1]\n\n # Add watermark and convert BGR back to RGB\n images = [self.encoder.encode(image, \"dwtDct\")[:, :, ::-1] for image in images]\n\n images = np.array(images)\n\n images = torch.from_numpy(images).permute(0, 3, 1, 2)\n\n images = torch.clamp(2 * (images / 255 - 0.5), min=-1.0, max=1.0)\n return images\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_video_diffusion/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n BaseOutput,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure.update(\n {\n \"pipeline_stable_video_diffusion\": [\n \"StableVideoDiffusionPipeline\",\n \"StableVideoDiffusionPipelineOutput\",\n ],\n }\n )\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import *\n else:\n from .pipeline_stable_video_diffusion import (\n StableVideoDiffusionPipeline,\n StableVideoDiffusionPipelineOutput,\n )\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom dataclasses import dataclass\nfrom typing import Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPVisionModelWithProjection\n\nfrom ...image_processor import PipelineImageInput\nfrom ...models import AutoencoderKLTemporalDecoder, UNetSpatioTemporalConditionModel\nfrom ...schedulers import EulerDiscreteScheduler\nfrom ...utils import BaseOutput, logging, replace_example_docstring\nfrom ...utils.torch_utils import is_compiled_module, randn_tensor\nfrom ...video_processor import VideoProcessor\nfrom ..pipeline_utils import DiffusionPipeline\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusers import StableVideoDiffusionPipeline\n >>> from diffusers.utils import load_image, export_to_video\n\n >>> pipe = StableVideoDiffusionPipeline.from_pretrained(\n ... \"stabilityai/stable-video-diffusion-img2vid-xt\", torch_dtype=torch.float16, variant=\"fp16\"\n ... )\n >>> pipe.to(\"cuda\")\n\n >>> image = load_image(\n ... \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd-docstring-example.jpeg\"\n ... )\n >>> image = image.resize((1024, 576))\n\n >>> frames = pipe(image, num_frames=25, decode_chunk_size=8).frames[0]\n >>> export_to_video(frames, \"generated.mp4\", fps=7)\n ```\n\"\"\"\n\n\ndef _append_dims(x, target_dims):\n \"\"\"Appends dimensions to the end of a tensor until it has target_dims dimensions.\"\"\"\n dims_to_append = target_dims - x.ndim\n if dims_to_append < 0:\n raise ValueError(f\"input has {x.ndim} dims but target_dims is {target_dims}, which is less\")\n return x[(...,) + (None,) * dims_to_append]\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\n@dataclass\nclass StableVideoDiffusionPipelineOutput(BaseOutput):\n r\"\"\"\n Output class for Stable Video Diffusion pipeline.\n\n Args:\n frames (`[List[List[PIL.Image.Image]]`, `np.ndarray`, `torch.Tensor`]):\n List of denoised PIL images of length `batch_size` or numpy array or torch tensor of shape `(batch_size,\n num_frames, height, width, num_channels)`.\n \"\"\"\n\n frames: Union[List[List[PIL.Image.Image]], np.ndarray, torch.Tensor]\n\n\nclass StableVideoDiffusionPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline to generate video from an input image using Stable Video Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Args:\n vae ([`AutoencoderKLTemporalDecoder`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.\n image_encoder ([`~transformers.CLIPVisionModelWithProjection`]):\n Frozen CLIP image-encoder\n ([laion/CLIP-ViT-H-14-laion2B-s32B-b79K](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K)).\n unet ([`UNetSpatioTemporalConditionModel`]):\n A `UNetSpatioTemporalConditionModel` to denoise the encoded image latents.\n scheduler ([`EulerDiscreteScheduler`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n A `CLIPImageProcessor` to extract features from generated images.\n \"\"\"\n\n model_cpu_offload_seq = \"image_encoder->unet->vae\"\n _callback_tensor_inputs = [\"latents\"]\n\n def __init__(\n self,\n vae: AutoencoderKLTemporalDecoder,\n image_encoder: CLIPVisionModelWithProjection,\n unet: UNetSpatioTemporalConditionModel,\n scheduler: EulerDiscreteScheduler,\n feature_extractor: CLIPImageProcessor,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n image_encoder=image_encoder,\n unet=unet,\n scheduler=scheduler,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.video_processor = VideoProcessor(do_resize=True, vae_scale_factor=self.vae_scale_factor)\n\n def _encode_image(\n self,\n image: PipelineImageInput,\n device: Union[str, torch.device],\n num_videos_per_prompt: int,\n do_classifier_free_guidance: bool,\n ) -> torch.Tensor:\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.video_processor.pil_to_numpy(image)\n image = self.video_processor.numpy_to_pt(image)\n\n # We normalize the image before resizing to match with the original implementation.\n # Then we unnormalize it after resizing.\n image = image * 2.0 - 1.0\n image = _resize_with_antialiasing(image, (224, 224))\n image = (image + 1.0) / 2.0\n\n # Normalize the image with for CLIP input\n image = self.feature_extractor(\n images=image,\n do_normalize=True,\n do_center_crop=False,\n do_resize=False,\n do_rescale=False,\n return_tensors=\"pt\",\n ).pixel_values\n\n image = image.to(device=device, dtype=dtype)\n image_embeddings = self.image_encoder(image).image_embeds\n image_embeddings = image_embeddings.unsqueeze(1)\n\n # duplicate image embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = image_embeddings.shape\n image_embeddings = image_embeddings.repeat(1, num_videos_per_prompt, 1)\n image_embeddings = image_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n negative_image_embeddings = torch.zeros_like(image_embeddings)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n image_embeddings = torch.cat([negative_image_embeddings, image_embeddings])\n\n return image_embeddings\n\n def _encode_vae_image(\n self,\n image: torch.Tensor,\n device: Union[str, torch.device],\n num_videos_per_prompt: int,\n do_classifier_free_guidance: bool,\n ):\n image = image.to(device=device)\n image_latents = self.vae.encode(image).latent_dist.mode()\n\n # duplicate image_latents for each generation per prompt, using mps friendly method\n image_latents = image_latents.repeat(num_videos_per_prompt, 1, 1, 1)\n\n if do_classifier_free_guidance:\n negative_image_latents = torch.zeros_like(image_latents)\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n image_latents = torch.cat([negative_image_latents, image_latents])\n\n return image_latents\n\n def _get_add_time_ids(\n self,\n fps: int,\n motion_bucket_id: int,\n noise_aug_strength: float,\n dtype: torch.dtype,\n batch_size: int,\n num_videos_per_prompt: int,\n do_classifier_free_guidance: bool,\n ):\n add_time_ids = [fps, motion_bucket_id, noise_aug_strength]\n\n passed_add_embed_dim = self.unet.config.addition_time_embed_dim * len(add_time_ids)\n expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features\n\n if expected_add_embed_dim != passed_add_embed_dim:\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.\"\n )\n\n add_time_ids = torch.tensor([add_time_ids], dtype=dtype)\n add_time_ids = add_time_ids.repeat(batch_size * num_videos_per_prompt, 1)\n\n if do_classifier_free_guidance:\n add_time_ids = torch.cat([add_time_ids, add_time_ids])\n\n return add_time_ids\n\n def decode_latents(self, latents: torch.Tensor, num_frames: int, decode_chunk_size: int = 14):\n # [batch, frames, channels, height, width] -> [batch*frames, channels, height, width]\n latents = latents.flatten(0, 1)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n\n forward_vae_fn = self.vae._orig_mod.forward if is_compiled_module(self.vae) else self.vae.forward\n accepts_num_frames = \"num_frames\" in set(inspect.signature(forward_vae_fn).parameters.keys())\n\n # decode decode_chunk_size frames at a time to avoid OOM\n frames = []\n for i in range(0, latents.shape[0], decode_chunk_size):\n num_frames_in = latents[i : i + decode_chunk_size].shape[0]\n decode_kwargs = {}\n if accepts_num_frames:\n # we only pass num_frames_in if it's expected\n decode_kwargs[\"num_frames\"] = num_frames_in\n\n frame = self.vae.decode(latents[i : i + decode_chunk_size], **decode_kwargs).sample\n frames.append(frame)\n frames = torch.cat(frames, dim=0)\n\n # [batch*frames, channels, height, width] -> [batch, channels, frames, height, width]\n frames = frames.reshape(-1, num_frames, *frames.shape[1:]).permute(0, 2, 1, 3, 4)\n\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n frames = frames.float()\n return frames\n\n def check_inputs(self, image, height, width):\n if (\n not isinstance(image, torch.Tensor)\n and not isinstance(image, PIL.Image.Image)\n and not isinstance(image, list)\n ):\n raise ValueError(\n \"`image` has to be of type `torch.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is\"\n f\" {type(image)}\"\n )\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n def prepare_latents(\n self,\n batch_size: int,\n num_frames: int,\n num_channels_latents: int,\n height: int,\n width: int,\n dtype: torch.dtype,\n device: Union[str, torch.device],\n generator: torch.Generator,\n latents: Optional[torch.Tensor] = None,\n ):\n shape = (\n batch_size,\n num_frames,\n num_channels_latents // 2,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n if isinstance(self.guidance_scale, (int, float)):\n return self.guidance_scale > 1\n return self.guidance_scale.max() > 1\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.Tensor],\n height: int = 576,\n width: int = 1024,\n num_frames: Optional[int] = None,\n num_inference_steps: int = 25,\n sigmas: Optional[List[float]] = None,\n min_guidance_scale: float = 1.0,\n max_guidance_scale: float = 3.0,\n fps: int = 7,\n motion_bucket_id: int = 127,\n noise_aug_strength: float = 0.02,\n decode_chunk_size: Optional[int] = None,\n num_videos_per_prompt: Optional[int] = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n return_dict: bool = True,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.Tensor`):\n Image(s) to guide image generation. If you provide a tensor, the expected value range is between `[0,\n 1]`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_frames (`int`, *optional*):\n The number of video frames to generate. Defaults to `self.unet.config.num_frames` (14 for\n `stable-video-diffusion-img2vid` and to 25 for `stable-video-diffusion-img2vid-xt`).\n num_inference_steps (`int`, *optional*, defaults to 25):\n The number of denoising steps. More denoising steps usually lead to a higher quality video at the\n expense of slower inference. This parameter is modulated by `strength`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in\n their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed\n will be used.\n min_guidance_scale (`float`, *optional*, defaults to 1.0):\n The minimum guidance scale. Used for the classifier free guidance with first frame.\n max_guidance_scale (`float`, *optional*, defaults to 3.0):\n The maximum guidance scale. Used for the classifier free guidance with last frame.\n fps (`int`, *optional*, defaults to 7):\n Frames per second. The rate at which the generated images shall be exported to a video after\n generation. Note that Stable Diffusion Video's UNet was micro-conditioned on fps-1 during training.\n motion_bucket_id (`int`, *optional*, defaults to 127):\n Used for conditioning the amount of motion for the generation. The higher the number the more motion\n will be in the video.\n noise_aug_strength (`float`, *optional*, defaults to 0.02):\n The amount of noise added to the init image, the higher it is the less the video will look like the\n init image. Increase it for more motion.\n decode_chunk_size (`int`, *optional*):\n The number of frames to decode at a time. Higher chunk size leads to better temporal consistency at the\n expense of more memory usage. By default, the decoder decodes all frames at once for maximal quality.\n For lower memory usage, reduce `decode_chunk_size`.\n num_videos_per_prompt (`int`, *optional*, defaults to 1):\n The number of videos to generate per prompt.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `pil`, `np` or `pt`.\n callback_on_step_end (`Callable`, *optional*):\n A function that is called at the end of each denoising step during inference. The function is called\n with the following arguments:\n `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`.\n `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a\n plain tuple.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] is\n returned, otherwise a `tuple` of (`List[List[PIL.Image.Image]]` or `np.ndarray` or `torch.Tensor`) is\n returned.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n num_frames = num_frames if num_frames is not None else self.unet.config.num_frames\n decode_chunk_size = decode_chunk_size if decode_chunk_size is not None else num_frames\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(image, height, width)\n\n # 2. Define call parameters\n if isinstance(image, PIL.Image.Image):\n batch_size = 1\n elif isinstance(image, list):\n batch_size = len(image)\n else:\n batch_size = image.shape[0]\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n self._guidance_scale = max_guidance_scale\n\n # 3. Encode input image\n image_embeddings = self._encode_image(image, device, num_videos_per_prompt, self.do_classifier_free_guidance)\n\n # NOTE: Stable Video Diffusion was conditioned on fps - 1, which is why it is reduced here.\n # See: https://github.com/Stability-AI/generative-models/blob/ed0997173f98eaf8f4edf7ba5fe8f15c6b877fd3/scripts/sampling/simple_video_sample.py#L188\n fps = fps - 1\n\n # 4. Encode input image using VAE\n image = self.video_processor.preprocess(image, height=height, width=width).to(device)\n noise = randn_tensor(image.shape, generator=generator, device=device, dtype=image.dtype)\n image = image + noise_aug_strength * noise\n\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n if needs_upcasting:\n self.vae.to(dtype=torch.float32)\n\n image_latents = self._encode_vae_image(\n image,\n device=device,\n num_videos_per_prompt=num_videos_per_prompt,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n )\n image_latents = image_latents.to(image_embeddings.dtype)\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n\n # Repeat the image latents for each frame so we can concatenate them with the noise\n # image_latents [batch, channels, height, width] ->[batch, num_frames, channels, height, width]\n image_latents = image_latents.unsqueeze(1).repeat(1, num_frames, 1, 1, 1)\n\n # 5. Get Added Time IDs\n added_time_ids = self._get_add_time_ids(\n fps,\n motion_bucket_id,\n noise_aug_strength,\n image_embeddings.dtype,\n batch_size,\n num_videos_per_prompt,\n self.do_classifier_free_guidance,\n )\n added_time_ids = added_time_ids.to(device)\n\n # 6. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, None, sigmas)\n\n # 7. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_videos_per_prompt,\n num_frames,\n num_channels_latents,\n height,\n width,\n image_embeddings.dtype,\n device,\n generator,\n latents,\n )\n\n # 8. Prepare guidance scale\n guidance_scale = torch.linspace(min_guidance_scale, max_guidance_scale, num_frames).unsqueeze(0)\n guidance_scale = guidance_scale.to(device, latents.dtype)\n guidance_scale = guidance_scale.repeat(batch_size * num_videos_per_prompt, 1)\n guidance_scale = _append_dims(guidance_scale, latents.ndim)\n\n self._guidance_scale = guidance_scale\n\n # 9. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n self._num_timesteps = len(timesteps)\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # Concatenate image_latents over channels dimension\n latent_model_input = torch.cat([latent_model_input, image_latents], dim=2)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=image_embeddings,\n added_time_ids=added_time_ids,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_cond - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents).prev_sample\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n\n if not output_type == \"latent\":\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n frames = self.decode_latents(latents, num_frames, decode_chunk_size)\n frames = self.video_processor.postprocess_video(video=frames, output_type=output_type)\n else:\n frames = latents\n\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return frames\n\n return StableVideoDiffusionPipelineOutput(frames=frames)\n\n\n# resizing utils\n# TODO: clean up later\ndef _resize_with_antialiasing(input, size, interpolation=\"bicubic\", align_corners=True):\n h, w = input.shape[-2:]\n factors = (h / size[0], w / size[1])\n\n # First, we have to determine sigma\n # Taken from skimage: https://github.com/scikit-image/scikit-image/blob/v0.19.2/skimage/transform/_warps.py#L171\n sigmas = (\n max((factors[0] - 1.0) / 2.0, 0.001),\n max((factors[1] - 1.0) / 2.0, 0.001),\n )\n\n # Now kernel size. Good results are for 3 sigma, but that is kind of slow. Pillow uses 1 sigma\n # https://github.com/python-pillow/Pillow/blob/master/src/libImaging/Resample.c#L206\n # But they do it in the 2 passes, which gives better results. Let's try 2 sigmas for now\n ks = int(max(2.0 * 2 * sigmas[0], 3)), int(max(2.0 * 2 * sigmas[1], 3))\n\n # Make sure it is odd\n if (ks[0] % 2) == 0:\n ks = ks[0] + 1, ks[1]\n\n if (ks[1] % 2) == 0:\n ks = ks[0], ks[1] + 1\n\n input = _gaussian_blur2d(input, ks, sigmas)\n\n output = torch.nn.functional.interpolate(input, size=size, mode=interpolation, align_corners=align_corners)\n return output\n\n\ndef _compute_padding(kernel_size):\n \"\"\"Compute padding tuple.\"\"\"\n # 4 or 6 ints: (padding_left, padding_right,padding_top,padding_bottom)\n # https://pytorch.org/docs/stable/nn.html#torch.nn.functional.pad\n if len(kernel_size) < 2:\n raise AssertionError(kernel_size)\n computed = [k - 1 for k in kernel_size]\n\n # for even kernels we need to do asymmetric padding :(\n out_padding = 2 * len(kernel_size) * [0]\n\n for i in range(len(kernel_size)):\n computed_tmp = computed[-(i + 1)]\n\n pad_front = computed_tmp // 2\n pad_rear = computed_tmp - pad_front\n\n out_padding[2 * i + 0] = pad_front\n out_padding[2 * i + 1] = pad_rear\n\n return out_padding\n\n\ndef _filter2d(input, kernel):\n # prepare kernel\n b, c, h, w = input.shape\n tmp_kernel = kernel[:, None, ...].to(device=input.device, dtype=input.dtype)\n\n tmp_kernel = tmp_kernel.expand(-1, c, -1, -1)\n\n height, width = tmp_kernel.shape[-2:]\n\n padding_shape: List[int] = _compute_padding([height, width])\n input = torch.nn.functional.pad(input, padding_shape, mode=\"reflect\")\n\n # kernel and input tensor reshape to align element-wise or batch-wise params\n tmp_kernel = tmp_kernel.reshape(-1, 1, height, width)\n input = input.view(-1, tmp_kernel.size(0), input.size(-2), input.size(-1))\n\n # convolve the tensor with the kernel.\n output = torch.nn.functional.conv2d(input, tmp_kernel, groups=tmp_kernel.size(0), padding=0, stride=1)\n\n out = output.view(b, c, h, w)\n return out\n\n\ndef _gaussian(window_size: int, sigma):\n if isinstance(sigma, float):\n sigma = torch.tensor([[sigma]])\n\n batch_size = sigma.shape[0]\n\n x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) - window_size // 2).expand(batch_size, -1)\n\n if window_size % 2 == 0:\n x = x + 0.5\n\n gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0)))\n\n return gauss / gauss.sum(-1, keepdim=True)\n\n\ndef _gaussian_blur2d(input, kernel_size, sigma):\n if isinstance(sigma, tuple):\n sigma = torch.tensor([sigma], dtype=input.dtype)\n else:\n sigma = sigma.to(dtype=input.dtype)\n\n ky, kx = int(kernel_size[0]), int(kernel_size[1])\n bs = sigma.shape[0]\n kernel_x = _gaussian(kx, sigma[:, 1].view(bs, 1))\n kernel_y = _gaussian(ky, sigma[:, 0].view(bs, 1))\n out_x = _filter2d(input, kernel_x[..., None, :])\n out = _filter2d(out_x, kernel_y[..., None])\n\n return out\n" }, { "path": "diffusers/src/diffusers/pipelines/t2i_adapter/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_stable_diffusion_adapter\"] = [\"StableDiffusionAdapterPipeline\"]\n _import_structure[\"pipeline_stable_diffusion_xl_adapter\"] = [\"StableDiffusionXLAdapterPipeline\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import * # noqa F403\n else:\n from .pipeline_stable_diffusion_adapter import StableDiffusionAdapterPipeline\n from .pipeline_stable_diffusion_xl_adapter import StableDiffusionXLAdapterPipeline\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py", "content": "# Copyright 2024 TencentARC and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom dataclasses import dataclass\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer\n\nfrom ...image_processor import VaeImageProcessor\nfrom ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, MultiAdapter, T2IAdapter, UNet2DConditionModel\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n PIL_INTERPOLATION,\n USE_PEFT_BACKEND,\n BaseOutput,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker\n\n\n@dataclass\nclass StableDiffusionAdapterPipelineOutput(BaseOutput):\n \"\"\"\n Args:\n images (`List[PIL.Image.Image]` or `np.ndarray`)\n List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,\n num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.\n nsfw_content_detected (`List[bool]`)\n List of flags denoting whether the corresponding generated image likely represents \"not-safe-for-work\"\n (nsfw) content, or `None` if safety checking could not be performed.\n \"\"\"\n\n images: Union[List[PIL.Image.Image], np.ndarray]\n nsfw_content_detected: Optional[List[bool]]\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from PIL import Image\n >>> from diffusers.utils import load_image\n >>> import torch\n >>> from diffusers import StableDiffusionAdapterPipeline, T2IAdapter\n\n >>> image = load_image(\n ... \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/color_ref.png\"\n ... )\n\n >>> color_palette = image.resize((8, 8))\n >>> color_palette = color_palette.resize((512, 512), resample=Image.Resampling.NEAREST)\n\n >>> adapter = T2IAdapter.from_pretrained(\"TencentARC/t2iadapter_color_sd14v1\", torch_dtype=torch.float16)\n >>> pipe = StableDiffusionAdapterPipeline.from_pretrained(\n ... \"CompVis/stable-diffusion-v1-4\",\n ... adapter=adapter,\n ... torch_dtype=torch.float16,\n ... )\n\n >>> pipe.to(\"cuda\")\n\n >>> out_image = pipe(\n ... \"At night, glowing cubes in front of the beach\",\n ... image=color_palette,\n ... ).images[0]\n ```\n\"\"\"\n\n\ndef _preprocess_adapter_image(image, height, width):\n if isinstance(image, torch.Tensor):\n return image\n elif isinstance(image, PIL.Image.Image):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n image = [np.array(i.resize((width, height), resample=PIL_INTERPOLATION[\"lanczos\"])) for i in image]\n image = [\n i[None, ..., None] if i.ndim == 2 else i[None, ...] for i in image\n ] # expand [h, w] or [h, w, c] to [b, h, w, c]\n image = np.concatenate(image, axis=0)\n image = np.array(image).astype(np.float32) / 255.0\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image)\n elif isinstance(image[0], torch.Tensor):\n if image[0].ndim == 3:\n image = torch.stack(image, dim=0)\n elif image[0].ndim == 4:\n image = torch.cat(image, dim=0)\n else:\n raise ValueError(\n f\"Invalid image tensor! Expecting image tensor with 3 or 4 dimension, but recive: {image[0].ndim}\"\n )\n return image\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass StableDiffusionAdapterPipeline(DiffusionPipeline, StableDiffusionMixin):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion augmented with T2I-Adapter\n https://arxiv.org/abs/2302.08453\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n adapter ([`T2IAdapter`] or [`MultiAdapter`] or `List[T2IAdapter]`):\n Provides additional conditioning to the unet during the denoising process. If you set multiple Adapter as a\n list, the outputs from each Adapter are added together to create one combined additional conditioning.\n adapter_weights (`List[float]`, *optional*, defaults to None):\n List of floats representing the weight which will be multiply to each adapter's output before adding them\n together.\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPFeatureExtractor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->adapter->unet->vae\"\n _optional_components = [\"safety_checker\", \"feature_extractor\"]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n adapter: Union[T2IAdapter, MultiAdapter, List[T2IAdapter]],\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPFeatureExtractor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n\n if safety_checker is not None and feature_extractor is None:\n raise ValueError(\n \"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety\"\n \" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead.\"\n )\n\n if isinstance(adapter, (list, tuple)):\n adapter = MultiAdapter(adapter)\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n adapter=adapter,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.register_to_config(requires_safety_checker=requires_safety_checker)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents\n def decode_latents(self, latents):\n deprecation_message = \"The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead\"\n deprecate(\"decode_latents\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n image,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if isinstance(self.adapter, MultiAdapter):\n if not isinstance(image, list):\n raise ValueError(\n \"MultiAdapter is enabled, but `image` is not a list. Please pass a list of images to `image`.\"\n )\n\n if len(image) != len(self.adapter.adapters):\n raise ValueError(\n f\"MultiAdapter requires passing the same number of images as adapters. Given {len(image)} images and {len(self.adapter.adapters)} adapters.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def _default_height_width(self, height, width, image):\n # NOTE: It is possible that a list of images have different\n # dimensions for each image, so just checking the first image\n # is not _exactly_ correct, but it is simple.\n while isinstance(image, list):\n image = image[0]\n\n if height is None:\n if isinstance(image, PIL.Image.Image):\n height = image.height\n elif isinstance(image, torch.Tensor):\n height = image.shape[-2]\n\n # round down to nearest multiple of `self.adapter.downscale_factor`\n height = (height // self.adapter.downscale_factor) * self.adapter.downscale_factor\n\n if width is None:\n if isinstance(image, PIL.Image.Image):\n width = image.width\n elif isinstance(image, torch.Tensor):\n width = image.shape[-1]\n\n # round down to nearest multiple of `self.adapter.downscale_factor`\n width = (width // self.adapter.downscale_factor) * self.adapter.downscale_factor\n\n return height, width\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(\n self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32\n ) -> torch.Tensor:\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n w (`torch.Tensor`):\n Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.\n embedding_dim (`int`, *optional*, defaults to 512):\n Dimension of the embeddings to generate.\n dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):\n Data type of the generated embeddings.\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: Union[torch.Tensor, PIL.Image.Image, List[PIL.Image.Image]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n sigmas: List[float] = None,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n adapter_conditioning_scale: Union[float, List[float]] = 1.0,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n image (`torch.Tensor`, `PIL.Image.Image`, `List[torch.Tensor]` or `List[PIL.Image.Image]` or `List[List[PIL.Image.Image]]`):\n The Adapter input condition. Adapter uses this input condition to generate guidance to Unet. If the\n type is specified as `torch.Tensor`, it is passed to Adapter as is. PIL.Image.Image` can also be\n accepted as an image. The control image is automatically resized to fit the output image.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n sigmas (`List[float]`, *optional*):\n Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in\n their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed\n will be used.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.\n Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionAdapterPipelineOutput`] instead\n of a plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttnProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n adapter_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):\n The outputs of the adapter are multiplied by `adapter_conditioning_scale` before they are added to the\n residual in the original unet. If multiple adapters are specified in init, you can set the\n corresponding scale as a list.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionAdapterPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionAdapterPipelineOutput`] if `return_dict` is True, otherwise a\n `tuple. When returning a tuple, the first element is a list with the generated images, and the second\n element is a list of `bool`s denoting whether the corresponding generated image likely represents\n \"not-safe-for-work\" (nsfw) content, according to the `safety_checker`.\n \"\"\"\n # 0. Default height and width to unet\n height, width = self._default_height_width(height, width, image)\n device = self._execution_device\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt, height, width, callback_steps, image, negative_prompt, prompt_embeds, negative_prompt_embeds\n )\n\n self._guidance_scale = guidance_scale\n\n if isinstance(self.adapter, MultiAdapter):\n adapter_input = []\n\n for one_image in image:\n one_image = _preprocess_adapter_image(one_image, height, width)\n one_image = one_image.to(device=device, dtype=self.adapter.dtype)\n adapter_input.append(one_image)\n else:\n adapter_input = _preprocess_adapter_image(image, height, width)\n adapter_input = adapter_input.to(device=device, dtype=self.adapter.dtype)\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # 3. Encode input prompt\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n clip_skip=clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(\n self.scheduler, num_inference_steps, device, timesteps, sigmas\n )\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 6.5 Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n # 7. Denoising loop\n if isinstance(self.adapter, MultiAdapter):\n adapter_state = self.adapter(adapter_input, adapter_conditioning_scale)\n for k, v in enumerate(adapter_state):\n adapter_state[k] = v\n else:\n adapter_state = self.adapter(adapter_input)\n for k, v in enumerate(adapter_state):\n adapter_state[k] = v * adapter_conditioning_scale\n if num_images_per_prompt > 1:\n for k, v in enumerate(adapter_state):\n adapter_state[k] = v.repeat(num_images_per_prompt, 1, 1, 1)\n if self.do_classifier_free_guidance:\n for k, v in enumerate(adapter_state):\n adapter_state[k] = torch.cat([v] * 2, dim=0)\n\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=cross_attention_kwargs,\n down_intrablock_additional_residuals=[state.clone() for state in adapter_state],\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if output_type == \"latent\":\n image = latents\n has_nsfw_concept = None\n elif output_type == \"pil\":\n # 8. Post-processing\n image = self.decode_latents(latents)\n\n # 9. Run safety checker\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # 10. Convert to PIL\n image = self.numpy_to_pil(image)\n else:\n # 8. Post-processing\n image = self.decode_latents(latents)\n\n # 9. Run safety checker\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionAdapterPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n" }, { "path": "diffusers/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py", "content": "# Copyright 2024 TencentARC and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModel,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n)\n\nfrom ...image_processor import PipelineImageInput, VaeImageProcessor\nfrom ...loaders import (\n FromSingleFileMixin,\n IPAdapterMixin,\n StableDiffusionXLLoraLoaderMixin,\n TextualInversionLoaderMixin,\n)\nfrom ...models import AutoencoderKL, ImageProjection, MultiAdapter, T2IAdapter, UNet2DConditionModel\nfrom ...models.attention_processor import (\n AttnProcessor2_0,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n XFormersAttnProcessor,\n)\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n PIL_INTERPOLATION,\n USE_PEFT_BACKEND,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ..stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import T2IAdapter, StableDiffusionXLAdapterPipeline, DDPMScheduler\n >>> from diffusers.utils import load_image\n\n >>> sketch_image = load_image(\"https://huggingface.co/Adapter/t2iadapter/resolve/main/sketch.png\").convert(\"L\")\n\n >>> model_id = \"stabilityai/stable-diffusion-xl-base-1.0\"\n\n >>> adapter = T2IAdapter.from_pretrained(\n ... \"Adapter/t2iadapter\",\n ... subfolder=\"sketch_sdxl_1.0\",\n ... torch_dtype=torch.float16,\n ... adapter_type=\"full_adapter_xl\",\n ... )\n >>> scheduler = DDPMScheduler.from_pretrained(model_id, subfolder=\"scheduler\")\n\n >>> pipe = StableDiffusionXLAdapterPipeline.from_pretrained(\n ... model_id, adapter=adapter, torch_dtype=torch.float16, variant=\"fp16\", scheduler=scheduler\n ... ).to(\"cuda\")\n\n >>> generator = torch.manual_seed(42)\n >>> sketch_image_out = pipe(\n ... prompt=\"a photo of a dog in real world, high quality\",\n ... negative_prompt=\"extra digit, fewer digits, cropped, worst quality, low quality\",\n ... image=sketch_image,\n ... generator=generator,\n ... guidance_scale=7.5,\n ... ).images[0]\n ```\n\"\"\"\n\n\ndef _preprocess_adapter_image(image, height, width):\n if isinstance(image, torch.Tensor):\n return image\n elif isinstance(image, PIL.Image.Image):\n image = [image]\n\n if isinstance(image[0], PIL.Image.Image):\n image = [np.array(i.resize((width, height), resample=PIL_INTERPOLATION[\"lanczos\"])) for i in image]\n image = [\n i[None, ..., None] if i.ndim == 2 else i[None, ...] for i in image\n ] # expand [h, w] or [h, w, c] to [b, h, w, c]\n image = np.concatenate(image, axis=0)\n image = np.array(image).astype(np.float32) / 255.0\n image = image.transpose(0, 3, 1, 2)\n image = torch.from_numpy(image)\n elif isinstance(image[0], torch.Tensor):\n if image[0].ndim == 3:\n image = torch.stack(image, dim=0)\n elif image[0].ndim == 4:\n image = torch.cat(image, dim=0)\n else:\n raise ValueError(\n f\"Invalid image tensor! Expecting image tensor with 3 or 4 dimension, but recive: {image[0].ndim}\"\n )\n return image\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n\nclass StableDiffusionXLAdapterPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n TextualInversionLoaderMixin,\n StableDiffusionXLLoraLoaderMixin,\n IPAdapterMixin,\n FromSingleFileMixin,\n):\n r\"\"\"\n Pipeline for text-to-image generation using Stable Diffusion augmented with T2I-Adapter\n https://arxiv.org/abs/2302.08453\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files\n - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters\n\n Args:\n adapter ([`T2IAdapter`] or [`MultiAdapter`] or `List[T2IAdapter]`):\n Provides additional conditioning to the unet during the denoising process. If you set multiple Adapter as a\n list, the outputs from each Adapter are added together to create one combined additional conditioning.\n adapter_weights (`List[float]`, *optional*, defaults to None):\n List of floats representing the weight which will be multiply to each adapter's output before adding them\n together.\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.\n feature_extractor ([`CLIPFeatureExtractor`]):\n Model that extracts features from generated images to be used as inputs for the `safety_checker`.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->text_encoder_2->image_encoder->unet->vae\"\n _optional_components = [\n \"tokenizer\",\n \"tokenizer_2\",\n \"text_encoder\",\n \"text_encoder_2\",\n \"feature_extractor\",\n \"image_encoder\",\n ]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n text_encoder_2: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n tokenizer_2: CLIPTokenizer,\n unet: UNet2DConditionModel,\n adapter: Union[T2IAdapter, MultiAdapter, List[T2IAdapter]],\n scheduler: KarrasDiffusionSchedulers,\n force_zeros_for_empty_prompt: bool = True,\n feature_extractor: CLIPImageProcessor = None,\n image_encoder: CLIPVisionModelWithProjection = None,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n unet=unet,\n adapter=adapter,\n scheduler=scheduler,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n )\n self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n self.default_sample_size = self.unet.config.sample_size\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt: str,\n prompt_2: Optional[str] = None,\n device: Optional[torch.device] = None,\n num_images_per_prompt: int = 1,\n do_classifier_free_guidance: bool = True,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n lora_scale (`float`, *optional*):\n A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n device = device or self._execution_device\n\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if self.text_encoder is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)\n else:\n scale_lora_layers(self.text_encoder_2, lora_scale)\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n\n if prompt is not None:\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # Define tokenizers and text encoders\n tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]\n text_encoders = (\n [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]\n )\n\n if prompt_embeds is None:\n prompt_2 = prompt_2 or prompt\n prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2\n\n # textual inversion: process multi-vector tokens if necessary\n prompt_embeds_list = []\n prompts = [prompt, prompt_2]\n for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, tokenizer)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n if clip_skip is None:\n prompt_embeds = prompt_embeds.hidden_states[-2]\n else:\n # \"2\" because SDXL always indexes from the penultimate layer.\n prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]\n\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n\n # get unconditional embeddings for classifier free guidance\n zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt\n if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:\n negative_prompt_embeds = torch.zeros_like(prompt_embeds)\n negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)\n elif do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n\n # normalize str to list\n negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt\n negative_prompt_2 = (\n batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2\n )\n\n uncond_tokens: List[str]\n if prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = [negative_prompt, negative_prompt_2]\n\n negative_prompt_embeds_list = []\n for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n negative_prompt_embeds = text_encoder(\n uncond_input.input_ids.to(device),\n output_hidden_states=True,\n )\n # We are only ALWAYS interested in the pooled output of the final text encoder\n negative_pooled_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]\n\n negative_prompt_embeds_list.append(negative_prompt_embeds)\n\n negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)\n\n if self.text_encoder_2 is not None:\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n if self.text_encoder_2 is not None:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n if do_classifier_free_guidance:\n negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n\n if self.text_encoder is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder_2, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image\n def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n if output_hidden_states:\n image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]\n image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_enc_hidden_states = self.image_encoder(\n torch.zeros_like(image), output_hidden_states=True\n ).hidden_states[-2]\n uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(\n num_images_per_prompt, dim=0\n )\n return image_enc_hidden_states, uncond_image_enc_hidden_states\n else:\n image_embeds = self.image_encoder(image).image_embeds\n image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n uncond_image_embeds = torch.zeros_like(image_embeds)\n\n return image_embeds, uncond_image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds\n def prepare_ip_adapter_image_embeds(\n self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance\n ):\n if ip_adapter_image_embeds is None:\n if not isinstance(ip_adapter_image, list):\n ip_adapter_image = [ip_adapter_image]\n\n if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):\n raise ValueError(\n f\"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters.\"\n )\n\n image_embeds = []\n for single_ip_adapter_image, image_proj_layer in zip(\n ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers\n ):\n output_hidden_state = not isinstance(image_proj_layer, ImageProjection)\n single_image_embeds, single_negative_image_embeds = self.encode_image(\n single_ip_adapter_image, device, 1, output_hidden_state\n )\n single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)\n single_negative_image_embeds = torch.stack(\n [single_negative_image_embeds] * num_images_per_prompt, dim=0\n )\n\n if do_classifier_free_guidance:\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n single_image_embeds = single_image_embeds.to(device)\n\n image_embeds.append(single_image_embeds)\n else:\n repeat_dims = [1]\n image_embeds = []\n for single_image_embeds in ip_adapter_image_embeds:\n if do_classifier_free_guidance:\n single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n single_negative_image_embeds = single_negative_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))\n )\n single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])\n else:\n single_image_embeds = single_image_embeds.repeat(\n num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))\n )\n image_embeds.append(single_image_embeds)\n\n return image_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.check_inputs\n def check_inputs(\n self,\n prompt,\n prompt_2,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n negative_prompt_2=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n pooled_prompt_embeds=None,\n negative_pooled_prompt_embeds=None,\n ip_adapter_image=None,\n ip_adapter_image_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if prompt_embeds is not None and pooled_prompt_embeds is None:\n raise ValueError(\n \"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.\"\n )\n\n if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:\n raise ValueError(\n \"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.\"\n )\n\n if ip_adapter_image is not None and ip_adapter_image_embeds is not None:\n raise ValueError(\n \"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined.\"\n )\n\n if ip_adapter_image_embeds is not None:\n if not isinstance(ip_adapter_image_embeds, list):\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}\"\n )\n elif ip_adapter_image_embeds[0].ndim not in [3, 4]:\n raise ValueError(\n f\"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline._get_add_time_ids\n def _get_add_time_ids(\n self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None\n ):\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n\n passed_add_embed_dim = (\n self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim\n )\n expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features\n\n if expected_add_embed_dim != passed_add_embed_dim:\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.\"\n )\n\n add_time_ids = torch.tensor([add_time_ids], dtype=dtype)\n return add_time_ids\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae\n def upcast_vae(self):\n dtype = self.vae.dtype\n self.vae.to(dtype=torch.float32)\n use_torch_2_0_or_xformers = isinstance(\n self.vae.decoder.mid_block.attentions[0].processor,\n (\n AttnProcessor2_0,\n XFormersAttnProcessor,\n LoRAXFormersAttnProcessor,\n LoRAAttnProcessor2_0,\n ),\n )\n # if xformers or torch_2_0 is used attention block does not need\n # to be in float32 which can save lots of memory\n if use_torch_2_0_or_xformers:\n self.vae.post_quant_conv.to(dtype)\n self.vae.decoder.conv_in.to(dtype)\n self.vae.decoder.mid_block.to(dtype)\n\n # Copied from diffusers.pipelines.t2i_adapter.pipeline_stable_diffusion_adapter.StableDiffusionAdapterPipeline._default_height_width\n def _default_height_width(self, height, width, image):\n # NOTE: It is possible that a list of images have different\n # dimensions for each image, so just checking the first image\n # is not _exactly_ correct, but it is simple.\n while isinstance(image, list):\n image = image[0]\n\n if height is None:\n if isinstance(image, PIL.Image.Image):\n height = image.height\n elif isinstance(image, torch.Tensor):\n height = image.shape[-2]\n\n # round down to nearest multiple of `self.adapter.downscale_factor`\n height = (height // self.adapter.downscale_factor) * self.adapter.downscale_factor\n\n if width is None:\n if isinstance(image, PIL.Image.Image):\n width = image.width\n elif isinstance(image, torch.Tensor):\n width = image.shape[-1]\n\n # round down to nearest multiple of `self.adapter.downscale_factor`\n width = (width // self.adapter.downscale_factor) * self.adapter.downscale_factor\n\n return height, width\n\n # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding\n def get_guidance_scale_embedding(\n self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32\n ) -> torch.Tensor:\n \"\"\"\n See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298\n\n Args:\n w (`torch.Tensor`):\n Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.\n embedding_dim (`int`, *optional*, defaults to 512):\n Dimension of the embeddings to generate.\n dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):\n Data type of the generated embeddings.\n\n Returns:\n `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.\n \"\"\"\n assert len(w.shape) == 1\n w = w * 1000.0\n\n half_dim = embedding_dim // 2\n emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)\n emb = w.to(dtype)[:, None] * emb[None, :]\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)\n if embedding_dim % 2 == 1: # zero pad\n emb = torch.nn.functional.pad(emb, (0, 1))\n assert emb.shape == (w.shape[0], embedding_dim)\n return emb\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n image: PipelineImageInput = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n timesteps: List[int] = None,\n sigmas: List[float] = None,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n ip_adapter_image: Optional[PipelineImageInput] = None,\n ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Optional[Tuple[int, int]] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Optional[Tuple[int, int]] = None,\n negative_original_size: Optional[Tuple[int, int]] = None,\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\n negative_target_size: Optional[Tuple[int, int]] = None,\n adapter_conditioning_scale: Union[float, List[float]] = 1.0,\n adapter_conditioning_factor: float = 1.0,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n image (`torch.Tensor`, `PIL.Image.Image`, `List[torch.Tensor]` or `List[PIL.Image.Image]` or `List[List[PIL.Image.Image]]`):\n The Adapter input condition. Adapter uses this input condition to generate guidance to Unet. If the\n type is specified as `torch.Tensor`, it is passed to Adapter as is. PIL.Image.Image` can also be\n accepted as an image. The control image is automatically resized to fit the output image.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image. Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image. Anything below 512 pixels won't work well for\n [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)\n and checkpoints that are not specifically fine-tuned on low resolutions.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n sigmas (`List[float]`, *optional*):\n Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in\n their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed\n will be used.\n denoising_end (`float`, *optional*):\n When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be\n completed before it is intentionally prematurely terminated. As a result, the returned sample will\n still retain a substantial amount of noise as determined by the discrete timesteps selected by the\n scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a\n \"Mixture of Denoisers\" multi-pipeline setup, as elaborated in [**Refining the Image\n Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)\n guidance_scale (`float`, *optional*, defaults to 5.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.\n ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):\n Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of\n IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should\n contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not\n provided, embeddings are computed from the `ip_adapter_image` input argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionAdapterPipelineOutput`]\n instead of a plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n guidance_rescale (`float`, *optional*, defaults to 0.0):\n Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of\n [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).\n Guidance rescale factor should fix overexposure when using zero terminal SNR.\n original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.\n `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as\n explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n `crops_coords_top_left` can be used to generate an image that appears to be \"cropped\" from the position\n `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n For most cases, `target_size` should be set to the desired height and width of the generated image. If\n not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a specific image resolution. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n To negatively condition the generation process based on a target image resolution. It should be as same\n as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more\n information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.\n adapter_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):\n The outputs of the adapter are multiplied by `adapter_conditioning_scale` before they are added to the\n residual in the original unet. If multiple adapters are specified in init, you can set the\n corresponding scale as a list.\n adapter_conditioning_factor (`float`, *optional*, defaults to 1.0):\n The fraction of timesteps for which adapter should be applied. If `adapter_conditioning_factor` is\n `0.0`, adapter is not applied at all. If `adapter_conditioning_factor` is `1.0`, adapter is applied for\n all timesteps. If `adapter_conditioning_factor` is `0.5`, adapter is applied for half of the timesteps.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.StableDiffusionAdapterPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.StableDiffusionAdapterPipelineOutput`] if `return_dict` is True, otherwise a\n `tuple`. When returning a tuple, the first element is a list with the generated images.\n \"\"\"\n # 0. Default height and width to unet\n\n height, width = self._default_height_width(height, width, image)\n device = self._execution_device\n\n if isinstance(self.adapter, MultiAdapter):\n adapter_input = []\n\n for one_image in image:\n one_image = _preprocess_adapter_image(one_image, height, width)\n one_image = one_image.to(device=device, dtype=self.adapter.dtype)\n adapter_input.append(one_image)\n else:\n adapter_input = _preprocess_adapter_image(image, height, width)\n adapter_input = adapter_input.to(device=device, dtype=self.adapter.dtype)\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n height,\n width,\n callback_steps,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ip_adapter_image,\n ip_adapter_image_embeds,\n )\n\n self._guidance_scale = guidance_scale\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # 3.1 Encode input prompt\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n clip_skip=clip_skip,\n )\n\n # 3.2 Encode ip_adapter_image\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n image_embeds = self.prepare_ip_adapter_image_embeds(\n ip_adapter_image,\n ip_adapter_image_embeds,\n device,\n batch_size * num_images_per_prompt,\n self.do_classifier_free_guidance,\n )\n\n # 4. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(\n self.scheduler, num_inference_steps, device, timesteps, sigmas\n )\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6.1 Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 6.2 Optionally get Guidance Scale Embedding\n timestep_cond = None\n if self.unet.config.time_cond_proj_dim is not None:\n guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)\n timestep_cond = self.get_guidance_scale_embedding(\n guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim\n ).to(device=device, dtype=latents.dtype)\n\n # 7. Prepare added time ids & embeddings & adapter features\n if isinstance(self.adapter, MultiAdapter):\n adapter_state = self.adapter(adapter_input, adapter_conditioning_scale)\n for k, v in enumerate(adapter_state):\n adapter_state[k] = v\n else:\n adapter_state = self.adapter(adapter_input)\n for k, v in enumerate(adapter_state):\n adapter_state[k] = v * adapter_conditioning_scale\n if num_images_per_prompt > 1:\n for k, v in enumerate(adapter_state):\n adapter_state[k] = v.repeat(num_images_per_prompt, 1, 1, 1)\n if self.do_classifier_free_guidance:\n for k, v in enumerate(adapter_state):\n adapter_state[k] = torch.cat([v] * 2, dim=0)\n\n add_text_embeds = pooled_prompt_embeds\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n add_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n if negative_original_size is not None and negative_target_size is not None:\n negative_add_time_ids = self._get_add_time_ids(\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n else:\n negative_add_time_ids = add_time_ids\n\n if self.do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)\n\n # 8. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n # Apply denoising_end\n if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1:\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents\n\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n\n if ip_adapter_image is not None or ip_adapter_image_embeds is not None:\n added_cond_kwargs[\"image_embeds\"] = image_embeds\n\n # predict the noise residual\n if i < int(num_inference_steps * adapter_conditioning_factor):\n down_intrablock_additional_residuals = [state.clone() for state in adapter_state]\n else:\n down_intrablock_additional_residuals = None\n\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n timestep_cond=timestep_cond,\n cross_attention_kwargs=cross_attention_kwargs,\n down_intrablock_additional_residuals=down_intrablock_additional_residuals,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if self.do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if self.do_classifier_free_guidance and guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n image = latents\n return StableDiffusionXLPipelineOutput(images=image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/text_to_video_synthesis/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"pipeline_output\"] = [\"TextToVideoSDPipelineOutput\"]\n _import_structure[\"pipeline_text_to_video_synth\"] = [\"TextToVideoSDPipeline\"]\n _import_structure[\"pipeline_text_to_video_synth_img2img\"] = [\"VideoToVideoSDPipeline\"]\n _import_structure[\"pipeline_text_to_video_zero\"] = [\"TextToVideoZeroPipeline\"]\n _import_structure[\"pipeline_text_to_video_zero_sdxl\"] = [\"TextToVideoZeroSDXLPipeline\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import * # noqa F403\n else:\n from .pipeline_output import TextToVideoSDPipelineOutput\n from .pipeline_text_to_video_synth import TextToVideoSDPipeline\n from .pipeline_text_to_video_synth_img2img import VideoToVideoSDPipeline\n from .pipeline_text_to_video_zero import TextToVideoZeroPipeline\n from .pipeline_text_to_video_zero_sdxl import TextToVideoZeroSDXLPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_output.py", "content": "from dataclasses import dataclass\nfrom typing import List, Union\n\nimport numpy as np\nimport PIL\nimport torch\n\nfrom ...utils import (\n BaseOutput,\n)\n\n\n@dataclass\nclass TextToVideoSDPipelineOutput(BaseOutput):\n \"\"\"\n Output class for text-to-video pipelines.\n\n Args:\n frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):\n List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing\n denoised\n PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape\n `(batch_size, num_frames, channels, height, width)`\n \"\"\"\n\n frames: Union[torch.Tensor, np.ndarray, List[List[PIL.Image.Image]]]\n" }, { "path": "diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport torch\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nfrom ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, UNet3DConditionModel\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ...video_processor import VideoProcessor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom . import TextToVideoSDPipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import TextToVideoSDPipeline\n >>> from diffusers.utils import export_to_video\n\n >>> pipe = TextToVideoSDPipeline.from_pretrained(\n ... \"damo-vilab/text-to-video-ms-1.7b\", torch_dtype=torch.float16, variant=\"fp16\"\n ... )\n >>> pipe.enable_model_cpu_offload()\n\n >>> prompt = \"Spiderman is surfing\"\n >>> video_frames = pipe(prompt).frames[0]\n >>> video_path = export_to_video(video_frames)\n >>> video_path\n ```\n\"\"\"\n\n\nclass TextToVideoSDPipeline(DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, LoraLoaderMixin):\n r\"\"\"\n Pipeline for text-to-video generation.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer (`CLIPTokenizer`):\n A [`~transformers.CLIPTokenizer`] to tokenize text.\n unet ([`UNet3DConditionModel`]):\n A [`UNet3DConditionModel`] to denoise the encoded video latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet3DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.video_processor = VideoProcessor(do_resize=False, vae_scale_factor=self.vae_scale_factor)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n def decode_latents(self, latents):\n latents = 1 / self.vae.config.scaling_factor * latents\n\n batch_size, channels, num_frames, height, width = latents.shape\n latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)\n\n image = self.vae.decode(latents).sample\n video = image[None, :].reshape((batch_size, num_frames, -1) + image.shape[2:]).permute(0, 2, 1, 3, 4)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n video = video.float()\n return video\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion_k_diffusion.pipeline_stable_diffusion_k_diffusion.StableDiffusionKDiffusionPipeline.check_inputs\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n def prepare_latents(\n self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None\n ):\n shape = (\n batch_size,\n num_channels_latents,\n num_frames,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_frames: int = 16,\n num_inference_steps: int = 50,\n guidance_scale: float = 9.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"np\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated video.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated video.\n num_frames (`int`, *optional*, defaults to 16):\n The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds\n amounts to 2 seconds of video.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality videos at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`. Latents should be of shape\n `(batch_size, num_channel, num_frames, height, width)`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n output_type (`str`, *optional*, defaults to `\"np\"`):\n The output format of the generated video. Choose between `torch.Tensor` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] instead\n of a plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n Examples:\n\n Returns:\n [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] is\n returned, otherwise a `tuple` is returned where the first element is a list with the generated frames.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n num_images_per_prompt = 1\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n num_frames,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # reshape latents\n bsz, channel, frames, width, height = latents.shape\n latents = latents.permute(0, 2, 1, 3, 4).reshape(bsz * frames, channel, width, height)\n noise_pred = noise_pred.permute(0, 2, 1, 3, 4).reshape(bsz * frames, channel, width, height)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # reshape latents back\n latents = latents[None, :].reshape(bsz, frames, channel, width, height).permute(0, 2, 1, 3, 4)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # 8. Post processing\n if output_type == \"latent\":\n video = latents\n else:\n video_tensor = self.decode_latents(latents)\n video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)\n\n # 9. Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (video,)\n\n return TextToVideoSDPipelineOutput(frames=video)\n" }, { "path": "diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import Any, Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nfrom ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, UNet3DConditionModel\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n USE_PEFT_BACKEND,\n deprecate,\n logging,\n replace_example_docstring,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ...video_processor import VideoProcessor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom . import TextToVideoSDPipelineOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler\n >>> from diffusers.utils import export_to_video\n\n >>> pipe = DiffusionPipeline.from_pretrained(\"cerspense/zeroscope_v2_576w\", torch_dtype=torch.float16)\n >>> pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n >>> pipe.to(\"cuda\")\n\n >>> prompt = \"spiderman running in the desert\"\n >>> video_frames = pipe(prompt, num_inference_steps=40, height=320, width=576, num_frames=24).frames[0]\n >>> # safe low-res video\n >>> video_path = export_to_video(video_frames, output_video_path=\"./video_576_spiderman.mp4\")\n\n >>> # let's offload the text-to-image model\n >>> pipe.to(\"cpu\")\n\n >>> # and load the image-to-image model\n >>> pipe = DiffusionPipeline.from_pretrained(\n ... \"cerspense/zeroscope_v2_XL\", torch_dtype=torch.float16, revision=\"refs/pr/15\"\n ... )\n >>> pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n >>> pipe.enable_model_cpu_offload()\n\n >>> # The VAE consumes A LOT of memory, let's make sure we run it in sliced mode\n >>> pipe.vae.enable_slicing()\n\n >>> # now let's upscale it\n >>> video = [Image.fromarray(frame).resize((1024, 576)) for frame in video_frames]\n\n >>> # and denoise it\n >>> video_frames = pipe(prompt, video=video, strength=0.6).frames[0]\n >>> video_path = export_to_video(video_frames, output_video_path=\"./video_1024_spiderman.mp4\")\n >>> video_path\n ```\n\"\"\"\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\n\nclass VideoToVideoSDPipeline(DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, LoraLoaderMixin):\n r\"\"\"\n Pipeline for text-guided video-to-video generation.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer (`CLIPTokenizer`):\n A [`~transformers.CLIPTokenizer`] to tokenize text.\n unet ([`UNet3DConditionModel`]):\n A [`UNet3DConditionModel`] to denoise the encoded video latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->unet->vae\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet3DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n ):\n super().__init__()\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.video_processor = VideoProcessor(do_resize=False, vae_scale_factor=self.vae_scale_factor)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents\n def decode_latents(self, latents):\n latents = 1 / self.vae.config.scaling_factor * latents\n\n batch_size, channels, num_frames, height, width = latents.shape\n latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)\n\n image = self.vae.decode(latents).sample\n video = image[None, :].reshape((batch_size, num_frames, -1) + image.shape[2:]).permute(0, 2, 1, 3, 4)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n video = video.float()\n return video\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n prompt,\n strength,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if strength < 0 or strength > 1:\n raise ValueError(f\"The value of strength should in [0.0, 1.0] but is {strength}\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]\n if hasattr(self.scheduler, \"set_begin_index\"):\n self.scheduler.set_begin_index(t_start * self.scheduler.order)\n\n return timesteps, num_inference_steps - t_start\n\n def prepare_latents(self, video, timestep, batch_size, dtype, device, generator=None):\n video = video.to(device=device, dtype=dtype)\n\n # change from (b, c, f, h, w) -> (b * f, c, w, h)\n bsz, channel, frames, width, height = video.shape\n video = video.permute(0, 2, 1, 3, 4).reshape(bsz * frames, channel, width, height)\n\n if video.shape[1] == 4:\n init_latents = video\n else:\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n elif isinstance(generator, list):\n init_latents = [\n retrieve_latents(self.vae.encode(video[i : i + 1]), generator=generator[i])\n for i in range(batch_size)\n ]\n init_latents = torch.cat(init_latents, dim=0)\n else:\n init_latents = retrieve_latents(self.vae.encode(video), generator=generator)\n\n init_latents = self.vae.config.scaling_factor * init_latents\n\n if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `video` of batch size {init_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n init_latents = torch.cat([init_latents], dim=0)\n\n shape = init_latents.shape\n noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n\n # get latents\n init_latents = self.scheduler.add_noise(init_latents, noise, timestep)\n latents = init_latents\n\n latents = latents[None, :].reshape((bsz, frames, latents.shape[1]) + latents.shape[2:]).permute(0, 2, 1, 3, 4)\n\n return latents\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n video: Union[List[np.ndarray], torch.Tensor] = None,\n strength: float = 0.6,\n num_inference_steps: int = 50,\n guidance_scale: float = 15.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"np\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n video (`List[np.ndarray]` or `torch.Tensor`):\n `video` frames or tensor representing a video batch to be used as the starting point for the process.\n Can also accept video latents as `image`, if passing latents directly, it will not be encoded again.\n strength (`float`, *optional*, defaults to 0.8):\n Indicates extent to transform the reference `video`. Must be between 0 and 1. `video` is used as a\n starting point, adding more noise to it the larger the `strength`. The number of denoising steps\n depends on the amount of noise initially added. When `strength` is 1, added noise is maximum and the\n denoising process runs for the full number of iterations specified in `num_inference_steps`. A value of\n 1 essentially ignores `video`.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality videos at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in video generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`. Latents should be of shape\n `(batch_size, num_channel, num_frames, height, width)`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.\n output_type (`str`, *optional*, defaults to `\"np\"`):\n The output format of the generated video. Choose between `torch.Tensor` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] instead\n of a plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n Examples:\n\n Returns:\n [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] is\n returned, otherwise a `tuple` is returned where the first element is a list with the generated frames.\n \"\"\"\n # 0. Default height and width to unet\n num_images_per_prompt = 1\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(prompt, strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n clip_skip=clip_skip,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Preprocess video\n video = self.video_processor.preprocess_video(video)\n\n # 5. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n\n # 6. Prepare latent variables\n latents = self.prepare_latents(video, latent_timestep, batch_size, prompt_embeds.dtype, device, generator)\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # reshape latents\n bsz, channel, frames, width, height = latents.shape\n latents = latents.permute(0, 2, 1, 3, 4).reshape(bsz * frames, channel, width, height)\n noise_pred = noise_pred.permute(0, 2, 1, 3, 4).reshape(bsz * frames, channel, width, height)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # reshape latents back\n latents = latents[None, :].reshape(bsz, frames, channel, width, height).permute(0, 2, 1, 3, 4)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # manually for max memory savings\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.unet.to(\"cpu\")\n\n # 9. Post processing\n if output_type == \"latent\":\n video = latents\n else:\n video_tensor = self.decode_latents(latents)\n video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)\n\n # 10. Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (video,)\n\n return TextToVideoSDPipelineOutput(frames=video)\n" }, { "path": "diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py", "content": "import copy\nimport inspect\nfrom dataclasses import dataclass\nfrom typing import Callable, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nimport torch.nn.functional as F\nfrom torch.nn.functional import grid_sample\nfrom transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n\nfrom ...image_processor import VaeImageProcessor\nfrom ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, UNet2DConditionModel\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import USE_PEFT_BACKEND, BaseOutput, logging, scale_lora_layers, unscale_lora_layers\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\nfrom ..stable_diffusion import StableDiffusionSafetyChecker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef rearrange_0(tensor, f):\n F, C, H, W = tensor.size()\n tensor = torch.permute(torch.reshape(tensor, (F // f, f, C, H, W)), (0, 2, 1, 3, 4))\n return tensor\n\n\ndef rearrange_1(tensor):\n B, C, F, H, W = tensor.size()\n return torch.reshape(torch.permute(tensor, (0, 2, 1, 3, 4)), (B * F, C, H, W))\n\n\ndef rearrange_3(tensor, f):\n F, D, C = tensor.size()\n return torch.reshape(tensor, (F // f, f, D, C))\n\n\ndef rearrange_4(tensor):\n B, F, D, C = tensor.size()\n return torch.reshape(tensor, (B * F, D, C))\n\n\nclass CrossFrameAttnProcessor:\n \"\"\"\n Cross frame attention processor. Each frame attends the first frame.\n\n Args:\n batch_size: The number that represents actual batch size, other than the frames.\n For example, calling unet with a single prompt and num_images_per_prompt=1, batch_size should be equal to\n 2, due to classifier-free guidance.\n \"\"\"\n\n def __init__(self, batch_size=2):\n self.batch_size = batch_size\n\n def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None):\n batch_size, sequence_length, _ = hidden_states.shape\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n query = attn.to_q(hidden_states)\n\n is_cross_attention = encoder_hidden_states is not None\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n # Cross Frame Attention\n if not is_cross_attention:\n video_length = key.size()[0] // self.batch_size\n first_frame_index = [0] * video_length\n\n # rearrange keys to have batch and frames in the 1st and 2nd dims respectively\n key = rearrange_3(key, video_length)\n key = key[:, first_frame_index]\n # rearrange values to have batch and frames in the 1st and 2nd dims respectively\n value = rearrange_3(value, video_length)\n value = value[:, first_frame_index]\n\n # rearrange back to original shape\n key = rearrange_4(key)\n value = rearrange_4(value)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n attention_probs = attn.get_attention_scores(query, key, attention_mask)\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n return hidden_states\n\n\nclass CrossFrameAttnProcessor2_0:\n \"\"\"\n Cross frame attention processor with scaled_dot_product attention of Pytorch 2.0.\n\n Args:\n batch_size: The number that represents actual batch size, other than the frames.\n For example, calling unet with a single prompt and num_images_per_prompt=1, batch_size should be equal to\n 2, due to classifier-free guidance.\n \"\"\"\n\n def __init__(self, batch_size=2):\n if not hasattr(F, \"scaled_dot_product_attention\"):\n raise ImportError(\"AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.\")\n self.batch_size = batch_size\n\n def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None):\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n inner_dim = hidden_states.shape[-1]\n\n if attention_mask is not None:\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n # scaled_dot_product_attention expects attention_mask shape to be\n # (batch, heads, source_length, target_length)\n attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])\n\n query = attn.to_q(hidden_states)\n\n is_cross_attention = encoder_hidden_states is not None\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n # Cross Frame Attention\n if not is_cross_attention:\n video_length = max(1, key.size()[0] // self.batch_size)\n first_frame_index = [0] * video_length\n\n # rearrange keys to have batch and frames in the 1st and 2nd dims respectively\n key = rearrange_3(key, video_length)\n key = key[:, first_frame_index]\n # rearrange values to have batch and frames in the 1st and 2nd dims respectively\n value = rearrange_3(value, video_length)\n value = value[:, first_frame_index]\n\n # rearrange back to original shape\n key = rearrange_4(key)\n value = rearrange_4(value)\n\n head_dim = inner_dim // attn.heads\n query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n # the output of sdp = (batch, num_heads, seq_len, head_dim)\n # TODO: add support for attn.scale when we move to Torch 2.1\n hidden_states = F.scaled_dot_product_attention(\n query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False\n )\n\n hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)\n hidden_states = hidden_states.to(query.dtype)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n return hidden_states\n\n\n@dataclass\nclass TextToVideoPipelineOutput(BaseOutput):\n r\"\"\"\n Output class for zero-shot text-to-video pipeline.\n\n Args:\n images (`[List[PIL.Image.Image]`, `np.ndarray`]):\n List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,\n num_channels)`.\n nsfw_content_detected (`[List[bool]]`):\n List indicating whether the corresponding generated image contains \"not-safe-for-work\" (nsfw) content or\n `None` if safety checking could not be performed.\n \"\"\"\n\n images: Union[List[PIL.Image.Image], np.ndarray]\n nsfw_content_detected: Optional[List[bool]]\n\n\ndef coords_grid(batch, ht, wd, device):\n # Adapted from https://github.com/princeton-vl/RAFT/blob/master/core/utils/utils.py\n coords = torch.meshgrid(torch.arange(ht, device=device), torch.arange(wd, device=device))\n coords = torch.stack(coords[::-1], dim=0).float()\n return coords[None].repeat(batch, 1, 1, 1)\n\n\ndef warp_single_latent(latent, reference_flow):\n \"\"\"\n Warp latent of a single frame with given flow\n\n Args:\n latent: latent code of a single frame\n reference_flow: flow which to warp the latent with\n\n Returns:\n warped: warped latent\n \"\"\"\n _, _, H, W = reference_flow.size()\n _, _, h, w = latent.size()\n coords0 = coords_grid(1, H, W, device=latent.device).to(latent.dtype)\n\n coords_t0 = coords0 + reference_flow\n coords_t0[:, 0] /= W\n coords_t0[:, 1] /= H\n\n coords_t0 = coords_t0 * 2.0 - 1.0\n coords_t0 = F.interpolate(coords_t0, size=(h, w), mode=\"bilinear\")\n coords_t0 = torch.permute(coords_t0, (0, 2, 3, 1))\n\n warped = grid_sample(latent, coords_t0, mode=\"nearest\", padding_mode=\"reflection\")\n return warped\n\n\ndef create_motion_field(motion_field_strength_x, motion_field_strength_y, frame_ids, device, dtype):\n \"\"\"\n Create translation motion field\n\n Args:\n motion_field_strength_x: motion strength along x-axis\n motion_field_strength_y: motion strength along y-axis\n frame_ids: indexes of the frames the latents of which are being processed.\n This is needed when we perform chunk-by-chunk inference\n device: device\n dtype: dtype\n\n Returns:\n\n \"\"\"\n seq_length = len(frame_ids)\n reference_flow = torch.zeros((seq_length, 2, 512, 512), device=device, dtype=dtype)\n for fr_idx in range(seq_length):\n reference_flow[fr_idx, 0, :, :] = motion_field_strength_x * (frame_ids[fr_idx])\n reference_flow[fr_idx, 1, :, :] = motion_field_strength_y * (frame_ids[fr_idx])\n return reference_flow\n\n\ndef create_motion_field_and_warp_latents(motion_field_strength_x, motion_field_strength_y, frame_ids, latents):\n \"\"\"\n Creates translation motion and warps the latents accordingly\n\n Args:\n motion_field_strength_x: motion strength along x-axis\n motion_field_strength_y: motion strength along y-axis\n frame_ids: indexes of the frames the latents of which are being processed.\n This is needed when we perform chunk-by-chunk inference\n latents: latent codes of frames\n\n Returns:\n warped_latents: warped latents\n \"\"\"\n motion_field = create_motion_field(\n motion_field_strength_x=motion_field_strength_x,\n motion_field_strength_y=motion_field_strength_y,\n frame_ids=frame_ids,\n device=latents.device,\n dtype=latents.dtype,\n )\n warped_latents = latents.clone().detach()\n for i in range(len(warped_latents)):\n warped_latents[i] = warp_single_latent(latents[i][None], motion_field[i][None])\n return warped_latents\n\n\nclass TextToVideoZeroPipeline(DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, LoraLoaderMixin):\n r\"\"\"\n Pipeline for zero-shot text-to-video generation using Stable Diffusion.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n tokenizer (`CLIPTokenizer`):\n A [`~transformers.CLIPTokenizer`] to tokenize text.\n unet ([`UNet2DConditionModel`]):\n A [`UNet3DConditionModel`] to denoise the encoded video latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n safety_checker ([`StableDiffusionSafetyChecker`]):\n Classification module that estimates whether generated images could be considered offensive or harmful.\n Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details\n about a model's potential harms.\n feature_extractor ([`CLIPImageProcessor`]):\n A [`CLIPImageProcessor`] to extract features from generated images; used as inputs to the `safety_checker`.\n \"\"\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: StableDiffusionSafetyChecker,\n feature_extractor: CLIPImageProcessor,\n requires_safety_checker: bool = True,\n ):\n super().__init__()\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n )\n if safety_checker is None and requires_safety_checker:\n logger.warning(\n f\"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure\"\n \" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered\"\n \" results in services or applications open to the public. Both the diffusers team and Hugging Face\"\n \" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling\"\n \" it only for use-cases that involve analyzing network behavior or auditing its results. For more\"\n \" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\"\n )\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n def forward_loop(self, x_t0, t0, t1, generator):\n \"\"\"\n Perform DDPM forward process from time t0 to t1. This is the same as adding noise with corresponding variance.\n\n Args:\n x_t0:\n Latent code at time t0.\n t0:\n Timestep at t0.\n t1:\n Timestamp at t1.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n\n Returns:\n x_t1:\n Forward process applied to x_t0 from time t0 to t1.\n \"\"\"\n eps = randn_tensor(x_t0.size(), generator=generator, dtype=x_t0.dtype, device=x_t0.device)\n alpha_vec = torch.prod(self.scheduler.alphas[t0:t1])\n x_t1 = torch.sqrt(alpha_vec) * x_t0 + torch.sqrt(1 - alpha_vec) * eps\n return x_t1\n\n def backward_loop(\n self,\n latents,\n timesteps,\n prompt_embeds,\n guidance_scale,\n callback,\n callback_steps,\n num_warmup_steps,\n extra_step_kwargs,\n cross_attention_kwargs=None,\n ):\n \"\"\"\n Perform backward process given list of time steps.\n\n Args:\n latents:\n Latents at time timesteps[0].\n timesteps:\n Time steps along which to perform backward process.\n prompt_embeds:\n Pre-generated text embeddings.\n guidance_scale:\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n extra_step_kwargs:\n Extra_step_kwargs.\n cross_attention_kwargs:\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n num_warmup_steps:\n number of warmup steps.\n\n Returns:\n latents:\n Latents of backward process output at time timesteps[-1].\n \"\"\"\n do_classifier_free_guidance = guidance_scale > 1.0\n num_steps = (len(timesteps) - num_warmup_steps) // self.scheduler.order\n with self.progress_bar(total=num_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n ).sample\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n return latents.clone().detach()\n\n # Copied from diffusers.pipelines.stable_diffusion_k_diffusion.pipeline_stable_diffusion_k_diffusion.StableDiffusionKDiffusionPipeline.check_inputs\n def check_inputs(\n self,\n prompt,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n video_length: Optional[int] = 8,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_videos_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n motion_field_strength_x: float = 12,\n motion_field_strength_y: float = 12,\n output_type: Optional[str] = \"tensor\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: Optional[int] = 1,\n t0: int = 44,\n t1: int = 47,\n frame_ids: Optional[List[int]] = None,\n ):\n \"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n video_length (`int`, *optional*, defaults to 8):\n The number of generated video frames.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in video generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).\n num_videos_per_prompt (`int`, *optional*, defaults to 1):\n The number of videos to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"np\"`):\n The output format of the generated video. Choose between `\"latent\"` and `\"np\"`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a\n [`~pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoPipelineOutput`] instead of\n a plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n motion_field_strength_x (`float`, *optional*, defaults to 12):\n Strength of motion in generated video along x-axis. See the [paper](https://arxiv.org/abs/2303.13439),\n Sect. 3.3.1.\n motion_field_strength_y (`float`, *optional*, defaults to 12):\n Strength of motion in generated video along y-axis. See the [paper](https://arxiv.org/abs/2303.13439),\n Sect. 3.3.1.\n t0 (`int`, *optional*, defaults to 44):\n Timestep t0. Should be in the range [0, num_inference_steps - 1]. See the\n [paper](https://arxiv.org/abs/2303.13439), Sect. 3.3.1.\n t1 (`int`, *optional*, defaults to 47):\n Timestep t0. Should be in the range [t0 + 1, num_inference_steps - 1]. See the\n [paper](https://arxiv.org/abs/2303.13439), Sect. 3.3.1.\n frame_ids (`List[int]`, *optional*):\n Indexes of the frames that are being generated. This is used when generating longer videos\n chunk-by-chunk.\n\n Returns:\n [`~pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoPipelineOutput`]:\n The output contains a `ndarray` of the generated video, when `output_type` != `\"latent\"`, otherwise a\n latent code of generated videos and a list of `bool`s indicating whether the corresponding generated\n video contains \"not-safe-for-work\" (nsfw) content..\n \"\"\"\n assert video_length > 0\n if frame_ids is None:\n frame_ids = list(range(video_length))\n assert len(frame_ids) == video_length\n\n assert num_videos_per_prompt == 1\n\n # set the processor\n original_attn_proc = self.unet.attn_processors\n processor = (\n CrossFrameAttnProcessor2_0(batch_size=2)\n if hasattr(F, \"scaled_dot_product_attention\")\n else CrossFrameAttnProcessor(batch_size=2)\n )\n self.unet.set_attn_processor(processor)\n\n if isinstance(prompt, str):\n prompt = [prompt]\n if isinstance(negative_prompt, str):\n negative_prompt = [negative_prompt]\n\n # Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # Check inputs. Raise error if not correct\n self.check_inputs(prompt, height, width, callback_steps)\n\n # Define call parameters\n batch_size = 1 if isinstance(prompt, str) else len(prompt)\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # Encode input prompt\n prompt_embeds_tuple = self.encode_prompt(\n prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt\n )\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n # Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n latents = self.prepare_latents(\n batch_size * num_videos_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n # Prepare extra step kwargs.\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n\n # Perform the first backward process up to time T_1\n x_1_t1 = self.backward_loop(\n timesteps=timesteps[: -t1 - 1],\n prompt_embeds=prompt_embeds,\n latents=latents,\n guidance_scale=guidance_scale,\n callback=callback,\n callback_steps=callback_steps,\n extra_step_kwargs=extra_step_kwargs,\n num_warmup_steps=num_warmup_steps,\n )\n scheduler_copy = copy.deepcopy(self.scheduler)\n\n # Perform the second backward process up to time T_0\n x_1_t0 = self.backward_loop(\n timesteps=timesteps[-t1 - 1 : -t0 - 1],\n prompt_embeds=prompt_embeds,\n latents=x_1_t1,\n guidance_scale=guidance_scale,\n callback=callback,\n callback_steps=callback_steps,\n extra_step_kwargs=extra_step_kwargs,\n num_warmup_steps=0,\n )\n\n # Propagate first frame latents at time T_0 to remaining frames\n x_2k_t0 = x_1_t0.repeat(video_length - 1, 1, 1, 1)\n\n # Add motion in latents at time T_0\n x_2k_t0 = create_motion_field_and_warp_latents(\n motion_field_strength_x=motion_field_strength_x,\n motion_field_strength_y=motion_field_strength_y,\n latents=x_2k_t0,\n frame_ids=frame_ids[1:],\n )\n\n # Perform forward process up to time T_1\n x_2k_t1 = self.forward_loop(\n x_t0=x_2k_t0,\n t0=timesteps[-t0 - 1].item(),\n t1=timesteps[-t1 - 1].item(),\n generator=generator,\n )\n\n # Perform backward process from time T_1 to 0\n x_1k_t1 = torch.cat([x_1_t1, x_2k_t1])\n b, l, d = prompt_embeds.size()\n prompt_embeds = prompt_embeds[:, None].repeat(1, video_length, 1, 1).reshape(b * video_length, l, d)\n\n self.scheduler = scheduler_copy\n x_1k_0 = self.backward_loop(\n timesteps=timesteps[-t1 - 1 :],\n prompt_embeds=prompt_embeds,\n latents=x_1k_t1,\n guidance_scale=guidance_scale,\n callback=callback,\n callback_steps=callback_steps,\n extra_step_kwargs=extra_step_kwargs,\n num_warmup_steps=0,\n )\n latents = x_1k_0\n\n # manually for max memory savings\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.unet.to(\"cpu\")\n torch.cuda.empty_cache()\n\n if output_type == \"latent\":\n image = latents\n has_nsfw_concept = None\n else:\n image = self.decode_latents(latents)\n # Run safety checker\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n\n # Offload all models\n self.maybe_free_model_hooks()\n # make sure to set the original attention processors back\n self.unet.set_attn_processor(original_attn_proc)\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return TextToVideoPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker\n def run_safety_checker(self, image, device, dtype):\n if self.safety_checker is None:\n has_nsfw_concept = None\n else:\n if torch.is_tensor(image):\n feature_extractor_input = self.image_processor.postprocess(image, output_type=\"pil\")\n else:\n feature_extractor_input = self.image_processor.numpy_to_pil(image)\n safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors=\"pt\").to(device)\n image, has_nsfw_concept = self.safety_checker(\n images=image, clip_input=safety_checker_input.pixel_values.to(dtype)\n )\n return image, has_nsfw_concept\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.tokenizer)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n def decode_latents(self, latents):\n latents = 1 / self.vae.config.scaling_factor * latents\n image = self.vae.decode(latents, return_dict=False)[0]\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n return image\n" }, { "path": "diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py", "content": "import copy\nimport inspect\nfrom dataclasses import dataclass\nfrom typing import Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport PIL\nimport torch\nimport torch.nn.functional as F\nfrom torch.nn.functional import grid_sample\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModel,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n)\n\nfrom ...image_processor import VaeImageProcessor\nfrom ...loaders import StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL, UNet2DConditionModel\nfrom ...models.attention_processor import (\n AttnProcessor2_0,\n FusedAttnProcessor2_0,\n LoRAAttnProcessor2_0,\n LoRAXFormersAttnProcessor,\n XFormersAttnProcessor,\n)\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import (\n USE_PEFT_BACKEND,\n BaseOutput,\n is_invisible_watermark_available,\n logging,\n scale_lora_layers,\n unscale_lora_layers,\n)\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin\n\n\nif is_invisible_watermark_available():\n from ..stable_diffusion_xl.watermark import StableDiffusionXLWatermarker\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.rearrange_0\ndef rearrange_0(tensor, f):\n F, C, H, W = tensor.size()\n tensor = torch.permute(torch.reshape(tensor, (F // f, f, C, H, W)), (0, 2, 1, 3, 4))\n return tensor\n\n\n# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.rearrange_1\ndef rearrange_1(tensor):\n B, C, F, H, W = tensor.size()\n return torch.reshape(torch.permute(tensor, (0, 2, 1, 3, 4)), (B * F, C, H, W))\n\n\n# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.rearrange_3\ndef rearrange_3(tensor, f):\n F, D, C = tensor.size()\n return torch.reshape(tensor, (F // f, f, D, C))\n\n\n# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.rearrange_4\ndef rearrange_4(tensor):\n B, F, D, C = tensor.size()\n return torch.reshape(tensor, (B * F, D, C))\n\n\n# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.CrossFrameAttnProcessor\nclass CrossFrameAttnProcessor:\n \"\"\"\n Cross frame attention processor. Each frame attends the first frame.\n\n Args:\n batch_size: The number that represents actual batch size, other than the frames.\n For example, calling unet with a single prompt and num_images_per_prompt=1, batch_size should be equal to\n 2, due to classifier-free guidance.\n \"\"\"\n\n def __init__(self, batch_size=2):\n self.batch_size = batch_size\n\n def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None):\n batch_size, sequence_length, _ = hidden_states.shape\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n query = attn.to_q(hidden_states)\n\n is_cross_attention = encoder_hidden_states is not None\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n # Cross Frame Attention\n if not is_cross_attention:\n video_length = key.size()[0] // self.batch_size\n first_frame_index = [0] * video_length\n\n # rearrange keys to have batch and frames in the 1st and 2nd dims respectively\n key = rearrange_3(key, video_length)\n key = key[:, first_frame_index]\n # rearrange values to have batch and frames in the 1st and 2nd dims respectively\n value = rearrange_3(value, video_length)\n value = value[:, first_frame_index]\n\n # rearrange back to original shape\n key = rearrange_4(key)\n value = rearrange_4(value)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n attention_probs = attn.get_attention_scores(query, key, attention_mask)\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n return hidden_states\n\n\n# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.CrossFrameAttnProcessor2_0\nclass CrossFrameAttnProcessor2_0:\n \"\"\"\n Cross frame attention processor with scaled_dot_product attention of Pytorch 2.0.\n\n Args:\n batch_size: The number that represents actual batch size, other than the frames.\n For example, calling unet with a single prompt and num_images_per_prompt=1, batch_size should be equal to\n 2, due to classifier-free guidance.\n \"\"\"\n\n def __init__(self, batch_size=2):\n if not hasattr(F, \"scaled_dot_product_attention\"):\n raise ImportError(\"AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.\")\n self.batch_size = batch_size\n\n def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None):\n batch_size, sequence_length, _ = (\n hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape\n )\n inner_dim = hidden_states.shape[-1]\n\n if attention_mask is not None:\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n # scaled_dot_product_attention expects attention_mask shape to be\n # (batch, heads, source_length, target_length)\n attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])\n\n query = attn.to_q(hidden_states)\n\n is_cross_attention = encoder_hidden_states is not None\n if encoder_hidden_states is None:\n encoder_hidden_states = hidden_states\n elif attn.norm_cross:\n encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)\n\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n # Cross Frame Attention\n if not is_cross_attention:\n video_length = max(1, key.size()[0] // self.batch_size)\n first_frame_index = [0] * video_length\n\n # rearrange keys to have batch and frames in the 1st and 2nd dims respectively\n key = rearrange_3(key, video_length)\n key = key[:, first_frame_index]\n # rearrange values to have batch and frames in the 1st and 2nd dims respectively\n value = rearrange_3(value, video_length)\n value = value[:, first_frame_index]\n\n # rearrange back to original shape\n key = rearrange_4(key)\n value = rearrange_4(value)\n\n head_dim = inner_dim // attn.heads\n query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)\n\n # the output of sdp = (batch, num_heads, seq_len, head_dim)\n # TODO: add support for attn.scale when we move to Torch 2.1\n hidden_states = F.scaled_dot_product_attention(\n query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False\n )\n\n hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)\n hidden_states = hidden_states.to(query.dtype)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n return hidden_states\n\n\n@dataclass\nclass TextToVideoSDXLPipelineOutput(BaseOutput):\n \"\"\"\n Output class for zero-shot text-to-video pipeline.\n\n Args:\n images (`List[PIL.Image.Image]` or `np.ndarray`)\n List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,\n num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.\n \"\"\"\n\n images: Union[List[PIL.Image.Image], np.ndarray]\n\n\n# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.coords_grid\ndef coords_grid(batch, ht, wd, device):\n # Adapted from https://github.com/princeton-vl/RAFT/blob/master/core/utils/utils.py\n coords = torch.meshgrid(torch.arange(ht, device=device), torch.arange(wd, device=device))\n coords = torch.stack(coords[::-1], dim=0).float()\n return coords[None].repeat(batch, 1, 1, 1)\n\n\n# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.warp_single_latent\ndef warp_single_latent(latent, reference_flow):\n \"\"\"\n Warp latent of a single frame with given flow\n\n Args:\n latent: latent code of a single frame\n reference_flow: flow which to warp the latent with\n\n Returns:\n warped: warped latent\n \"\"\"\n _, _, H, W = reference_flow.size()\n _, _, h, w = latent.size()\n coords0 = coords_grid(1, H, W, device=latent.device).to(latent.dtype)\n\n coords_t0 = coords0 + reference_flow\n coords_t0[:, 0] /= W\n coords_t0[:, 1] /= H\n\n coords_t0 = coords_t0 * 2.0 - 1.0\n coords_t0 = F.interpolate(coords_t0, size=(h, w), mode=\"bilinear\")\n coords_t0 = torch.permute(coords_t0, (0, 2, 3, 1))\n\n warped = grid_sample(latent, coords_t0, mode=\"nearest\", padding_mode=\"reflection\")\n return warped\n\n\n# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.create_motion_field\ndef create_motion_field(motion_field_strength_x, motion_field_strength_y, frame_ids, device, dtype):\n \"\"\"\n Create translation motion field\n\n Args:\n motion_field_strength_x: motion strength along x-axis\n motion_field_strength_y: motion strength along y-axis\n frame_ids: indexes of the frames the latents of which are being processed.\n This is needed when we perform chunk-by-chunk inference\n device: device\n dtype: dtype\n\n Returns:\n\n \"\"\"\n seq_length = len(frame_ids)\n reference_flow = torch.zeros((seq_length, 2, 512, 512), device=device, dtype=dtype)\n for fr_idx in range(seq_length):\n reference_flow[fr_idx, 0, :, :] = motion_field_strength_x * (frame_ids[fr_idx])\n reference_flow[fr_idx, 1, :, :] = motion_field_strength_y * (frame_ids[fr_idx])\n return reference_flow\n\n\n# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.create_motion_field_and_warp_latents\ndef create_motion_field_and_warp_latents(motion_field_strength_x, motion_field_strength_y, frame_ids, latents):\n \"\"\"\n Creates translation motion and warps the latents accordingly\n\n Args:\n motion_field_strength_x: motion strength along x-axis\n motion_field_strength_y: motion strength along y-axis\n frame_ids: indexes of the frames the latents of which are being processed.\n This is needed when we perform chunk-by-chunk inference\n latents: latent codes of frames\n\n Returns:\n warped_latents: warped latents\n \"\"\"\n motion_field = create_motion_field(\n motion_field_strength_x=motion_field_strength_x,\n motion_field_strength_y=motion_field_strength_y,\n frame_ids=frame_ids,\n device=latents.device,\n dtype=latents.dtype,\n )\n warped_latents = latents.clone().detach()\n for i in range(len(warped_latents)):\n warped_latents[i] = warp_single_latent(latents[i][None], motion_field[i][None])\n return warped_latents\n\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg\ndef rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):\n \"\"\"\n Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4\n \"\"\"\n std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)\n std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)\n # rescale the results from guidance (fixes overexposure)\n noise_pred_rescaled = noise_cfg * (std_text / std_cfg)\n # mix with the original results from guidance by factor guidance_rescale to avoid \"plain looking\" images\n noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg\n return noise_cfg\n\n\nclass TextToVideoZeroSDXLPipeline(\n DiffusionPipeline,\n StableDiffusionMixin,\n StableDiffusionXLLoraLoaderMixin,\n TextualInversionLoaderMixin,\n):\n r\"\"\"\n Pipeline for zero-shot text-to-video generation using Stable Diffusion XL.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder. Stable Diffusion XL uses the text portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically\n the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.\n text_encoder_2 ([` CLIPTextModelWithProjection`]):\n Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of\n [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),\n specifically the\n [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)\n variant.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n tokenizer_2 (`CLIPTokenizer`):\n Second Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of\n [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->text_encoder_2->unet->vae\"\n _optional_components = [\n \"tokenizer\",\n \"tokenizer_2\",\n \"text_encoder\",\n \"text_encoder_2\",\n \"image_encoder\",\n \"feature_extractor\",\n ]\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n text_encoder_2: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n tokenizer_2: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n image_encoder: CLIPVisionModelWithProjection = None,\n feature_extractor: CLIPImageProcessor = None,\n force_zeros_for_empty_prompt: bool = True,\n add_watermarker: Optional[bool] = None,\n ):\n super().__init__()\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer,\n tokenizer_2=tokenizer_2,\n unet=unet,\n scheduler=scheduler,\n image_encoder=image_encoder,\n feature_extractor=feature_extractor,\n )\n self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n self.default_sample_size = self.unet.config.sample_size\n\n add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()\n\n if add_watermarker:\n self.watermark = StableDiffusionXLWatermarker()\n else:\n self.watermark = None\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.upcast_vae\n def upcast_vae(self):\n dtype = self.vae.dtype\n self.vae.to(dtype=torch.float32)\n use_torch_2_0_or_xformers = isinstance(\n self.vae.decoder.mid_block.attentions[0].processor,\n (\n AttnProcessor2_0,\n XFormersAttnProcessor,\n LoRAXFormersAttnProcessor,\n LoRAAttnProcessor2_0,\n FusedAttnProcessor2_0,\n ),\n )\n # if xformers or torch_2_0 is used attention block does not need\n # to be in float32 which can save lots of memory\n if use_torch_2_0_or_xformers:\n self.vae.post_quant_conv.to(dtype)\n self.vae.decoder.conv_in.to(dtype)\n self.vae.decoder.mid_block.to(dtype)\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline._get_add_time_ids\n def _get_add_time_ids(\n self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None\n ):\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n\n passed_add_embed_dim = (\n self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim\n )\n expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features\n\n if expected_add_embed_dim != passed_add_embed_dim:\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.\"\n )\n\n add_time_ids = torch.tensor([add_time_ids], dtype=dtype)\n return add_time_ids\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):\n shape = (\n batch_size,\n num_channels_latents,\n int(height) // self.vae_scale_factor,\n int(width) // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def check_inputs(\n self,\n prompt,\n prompt_2,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n negative_prompt_2=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n pooled_prompt_embeds=None,\n negative_pooled_prompt_embeds=None,\n callback_on_step_end_tensor_inputs=None,\n ):\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt_2 is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):\n raise ValueError(f\"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n elif negative_prompt_2 is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if prompt_embeds is not None and pooled_prompt_embeds is None:\n raise ValueError(\n \"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.\"\n )\n\n if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:\n raise ValueError(\n \"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.\"\n )\n\n # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt: str,\n prompt_2: Optional[str] = None,\n device: Optional[torch.device] = None,\n num_images_per_prompt: int = 1,\n do_classifier_free_guidance: bool = True,\n negative_prompt: Optional[str] = None,\n negative_prompt_2: Optional[str] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n lora_scale (`float`, *optional*):\n A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n device = device or self._execution_device\n\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if self.text_encoder is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)\n else:\n scale_lora_layers(self.text_encoder_2, lora_scale)\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n\n if prompt is not None:\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # Define tokenizers and text encoders\n tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]\n text_encoders = (\n [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]\n )\n\n if prompt_embeds is None:\n prompt_2 = prompt_2 or prompt\n prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2\n\n # textual inversion: process multi-vector tokens if necessary\n prompt_embeds_list = []\n prompts = [prompt, prompt_2]\n for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, tokenizer)\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n if clip_skip is None:\n prompt_embeds = prompt_embeds.hidden_states[-2]\n else:\n # \"2\" because SDXL always indexes from the penultimate layer.\n prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]\n\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n\n # get unconditional embeddings for classifier free guidance\n zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt\n if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:\n negative_prompt_embeds = torch.zeros_like(prompt_embeds)\n negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)\n elif do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt_2 = negative_prompt_2 or negative_prompt\n\n # normalize str to list\n negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt\n negative_prompt_2 = (\n batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2\n )\n\n uncond_tokens: List[str]\n if prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = [negative_prompt, negative_prompt_2]\n\n negative_prompt_embeds_list = []\n for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):\n if isinstance(self, TextualInversionLoaderMixin):\n negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n negative_prompt_embeds = text_encoder(\n uncond_input.input_ids.to(device),\n output_hidden_states=True,\n )\n # We are only ALWAYS interested in the pooled output of the final text encoder\n negative_pooled_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]\n\n negative_prompt_embeds_list.append(negative_prompt_embeds)\n\n negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)\n\n if self.text_encoder_2 is not None:\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n if self.text_encoder_2 is not None:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)\n else:\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n if do_classifier_free_guidance:\n negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n\n if self.text_encoder is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n if self.text_encoder_2 is not None:\n if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder_2, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n # Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoZeroPipeline.forward_loop\n def forward_loop(self, x_t0, t0, t1, generator):\n \"\"\"\n Perform DDPM forward process from time t0 to t1. This is the same as adding noise with corresponding variance.\n\n Args:\n x_t0:\n Latent code at time t0.\n t0:\n Timestep at t0.\n t1:\n Timestamp at t1.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n\n Returns:\n x_t1:\n Forward process applied to x_t0 from time t0 to t1.\n \"\"\"\n eps = randn_tensor(x_t0.size(), generator=generator, dtype=x_t0.dtype, device=x_t0.device)\n alpha_vec = torch.prod(self.scheduler.alphas[t0:t1])\n x_t1 = torch.sqrt(alpha_vec) * x_t0 + torch.sqrt(1 - alpha_vec) * eps\n return x_t1\n\n def backward_loop(\n self,\n latents,\n timesteps,\n prompt_embeds,\n guidance_scale,\n callback,\n callback_steps,\n num_warmup_steps,\n extra_step_kwargs,\n add_text_embeds,\n add_time_ids,\n cross_attention_kwargs=None,\n guidance_rescale: float = 0.0,\n ):\n \"\"\"\n Perform backward process given list of time steps\n\n Args:\n latents:\n Latents at time timesteps[0].\n timesteps:\n Time steps along which to perform backward process.\n prompt_embeds:\n Pre-generated text embeddings.\n guidance_scale:\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n extra_step_kwargs:\n Extra_step_kwargs.\n cross_attention_kwargs:\n A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in\n [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).\n num_warmup_steps:\n number of warmup steps.\n\n Returns:\n latents: latents of backward process output at time timesteps[-1]\n \"\"\"\n\n do_classifier_free_guidance = guidance_scale > 1.0\n num_steps = (len(timesteps) - num_warmup_steps) // self.scheduler.order\n\n with self.progress_bar(total=num_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # predict the noise residual\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if do_classifier_free_guidance and guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, latents)\n return latents.clone().detach()\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]],\n prompt_2: Optional[Union[str, List[str]]] = None,\n video_length: Optional[int] = 8,\n height: Optional[int] = None,\n width: Optional[int] = None,\n num_inference_steps: int = 50,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_videos_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n frame_ids: Optional[List[int]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n pooled_prompt_embeds: Optional[torch.Tensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,\n latents: Optional[torch.Tensor] = None,\n motion_field_strength_x: float = 12,\n motion_field_strength_y: float = 12,\n output_type: Optional[str] = \"tensor\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Optional[Tuple[int, int]] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Optional[Tuple[int, int]] = None,\n t0: int = 44,\n t1: int = 47,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is\n used in both text-encoders\n video_length (`int`, *optional*, defaults to 8):\n The number of generated video frames.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n denoising_end (`float`, *optional*):\n When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be\n completed before it is intentionally prematurely terminated. As a result, the returned sample will\n still retain a substantial amount of noise as determined by the discrete timesteps selected by the\n scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a\n \"Mixture of Denoisers\" multi-pipeline setup, as elaborated in [**Refining the Image\n Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n negative_prompt_2 (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and\n `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders\n num_videos_per_prompt (`int`, *optional*, defaults to 1):\n The number of videos to generate per prompt.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n frame_ids (`List[int]`, *optional*):\n Indexes of the frames that are being generated. This is used when generating longer videos\n chunk-by-chunk.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.\n If not provided, pooled text embeddings will be generated from `prompt` input argument.\n negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n motion_field_strength_x (`float`, *optional*, defaults to 12):\n Strength of motion in generated video along x-axis. See the [paper](https://arxiv.org/abs/2303.13439),\n Sect. 3.3.1.\n motion_field_strength_y (`float`, *optional*, defaults to 12):\n Strength of motion in generated video along y-axis. See the [paper](https://arxiv.org/abs/2303.13439),\n Sect. 3.3.1.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead\n of a plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).\n guidance_rescale (`float`, *optional*, defaults to 0.7):\n Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are\n Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of\n [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).\n Guidance rescale factor should fix overexposure when using zero terminal SNR.\n original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.\n `original_size` defaults to `(width, height)` if not specified. Part of SDXL's micro-conditioning as\n explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):\n `crops_coords_top_left` can be used to generate an image that appears to be \"cropped\" from the position\n `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting\n `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):\n For most cases, `target_size` should be set to the desired height and width of the generated image. If\n not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in\n section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).\n t0 (`int`, *optional*, defaults to 44):\n Timestep t0. Should be in the range [0, num_inference_steps - 1]. See the\n [paper](https://arxiv.org/abs/2303.13439), Sect. 3.3.1.\n t1 (`int`, *optional*, defaults to 47):\n Timestep t0. Should be in the range [t0 + 1, num_inference_steps - 1]. See the\n [paper](https://arxiv.org/abs/2303.13439), Sect. 3.3.1.\n\n Returns:\n [`~pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoSDXLPipelineOutput`] or\n `tuple`: [`~pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoSDXLPipelineOutput`]\n if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the\n generated images.\n \"\"\"\n assert video_length > 0\n if frame_ids is None:\n frame_ids = list(range(video_length))\n assert len(frame_ids) == video_length\n\n assert num_videos_per_prompt == 1\n\n # set the processor\n original_attn_proc = self.unet.attn_processors\n processor = (\n CrossFrameAttnProcessor2_0(batch_size=2)\n if hasattr(F, \"scaled_dot_product_attention\")\n else CrossFrameAttnProcessor(batch_size=2)\n )\n self.unet.set_attn_processor(processor)\n\n if isinstance(prompt, str):\n prompt = [prompt]\n if isinstance(negative_prompt, str):\n negative_prompt = [negative_prompt]\n\n # 0. Default height and width to unet\n height = height or self.default_sample_size * self.vae_scale_factor\n width = width or self.default_sample_size * self.vae_scale_factor\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n prompt_2,\n height,\n width,\n callback_steps,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n )\n\n # 2. Define call parameters\n batch_size = (\n 1 if isinstance(prompt, str) else len(prompt) if isinstance(prompt, list) else prompt_embeds.shape[0]\n )\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_images_per_prompt=num_videos_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latent variables\n num_channels_latents = self.unet.config.in_channels\n\n latents = self.prepare_latents(\n batch_size * num_videos_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Prepare added time ids & embeddings\n add_text_embeds = pooled_prompt_embeds\n if self.text_encoder_2 is None:\n text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])\n else:\n text_encoder_projection_dim = self.text_encoder_2.config.projection_dim\n\n add_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n dtype=prompt_embeds.dtype,\n text_encoder_projection_dim=text_encoder_projection_dim,\n )\n\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device).repeat(batch_size * num_videos_per_prompt, 1)\n\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n\n # Perform the first backward process up to time T_1\n x_1_t1 = self.backward_loop(\n timesteps=timesteps[: -t1 - 1],\n prompt_embeds=prompt_embeds,\n latents=latents,\n guidance_scale=guidance_scale,\n callback=callback,\n callback_steps=callback_steps,\n extra_step_kwargs=extra_step_kwargs,\n num_warmup_steps=num_warmup_steps,\n add_text_embeds=add_text_embeds,\n add_time_ids=add_time_ids,\n )\n\n scheduler_copy = copy.deepcopy(self.scheduler)\n\n # Perform the second backward process up to time T_0\n x_1_t0 = self.backward_loop(\n timesteps=timesteps[-t1 - 1 : -t0 - 1],\n prompt_embeds=prompt_embeds,\n latents=x_1_t1,\n guidance_scale=guidance_scale,\n callback=callback,\n callback_steps=callback_steps,\n extra_step_kwargs=extra_step_kwargs,\n num_warmup_steps=0,\n add_text_embeds=add_text_embeds,\n add_time_ids=add_time_ids,\n )\n\n # Propagate first frame latents at time T_0 to remaining frames\n x_2k_t0 = x_1_t0.repeat(video_length - 1, 1, 1, 1)\n\n # Add motion in latents at time T_0\n x_2k_t0 = create_motion_field_and_warp_latents(\n motion_field_strength_x=motion_field_strength_x,\n motion_field_strength_y=motion_field_strength_y,\n latents=x_2k_t0,\n frame_ids=frame_ids[1:],\n )\n\n # Perform forward process up to time T_1\n x_2k_t1 = self.forward_loop(\n x_t0=x_2k_t0,\n t0=timesteps[-t0 - 1].to(torch.long),\n t1=timesteps[-t1 - 1].to(torch.long),\n generator=generator,\n )\n\n # Perform backward process from time T_1 to 0\n latents = torch.cat([x_1_t1, x_2k_t1])\n\n self.scheduler = scheduler_copy\n timesteps = timesteps[-t1 - 1 :]\n\n b, l, d = prompt_embeds.size()\n prompt_embeds = prompt_embeds[:, None].repeat(1, video_length, 1, 1).reshape(b * video_length, l, d)\n\n b, k = add_text_embeds.size()\n add_text_embeds = add_text_embeds[:, None].repeat(1, video_length, 1).reshape(b * video_length, k)\n\n b, k = add_time_ids.size()\n add_time_ids = add_time_ids[:, None].repeat(1, video_length, 1).reshape(b * video_length, k)\n\n # 7.1 Apply denoising_end\n if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1:\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n x_1k_0 = self.backward_loop(\n timesteps=timesteps,\n prompt_embeds=prompt_embeds,\n latents=latents,\n guidance_scale=guidance_scale,\n callback=callback,\n callback_steps=callback_steps,\n extra_step_kwargs=extra_step_kwargs,\n num_warmup_steps=0,\n add_text_embeds=add_text_embeds,\n add_time_ids=add_time_ids,\n )\n\n latents = x_1k_0\n\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n image = latents\n return TextToVideoSDXLPipelineOutput(images=image)\n\n # apply watermark if available\n if self.watermark is not None:\n image = self.watermark.apply_watermark(image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n self.maybe_free_model_hooks()\n # make sure to set the original attention processors back\n self.unet.set_attn_processor(original_attn_proc)\n\n if not return_dict:\n return (image,)\n\n return TextToVideoSDXLPipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/unclip/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n is_torch_available,\n is_transformers_available,\n is_transformers_version,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available() and is_transformers_version(\">=\", \"4.25.0\")):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline\n\n _dummy_objects.update(\n {\"UnCLIPImageVariationPipeline\": UnCLIPImageVariationPipeline, \"UnCLIPPipeline\": UnCLIPPipeline}\n )\nelse:\n _import_structure[\"pipeline_unclip\"] = [\"UnCLIPPipeline\"]\n _import_structure[\"pipeline_unclip_image_variation\"] = [\"UnCLIPImageVariationPipeline\"]\n _import_structure[\"text_proj\"] = [\"UnCLIPTextProjModel\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available() and is_transformers_version(\">=\", \"4.25.0\")):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import * # noqa F403\n else:\n from .pipeline_unclip import UnCLIPPipeline\n from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline\n from .text_proj import UnCLIPTextProjModel\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/unclip/pipeline_unclip.py", "content": "# Copyright 2024 Kakao Brain and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import List, Optional, Tuple, Union\n\nimport torch\nfrom torch.nn import functional as F\nfrom transformers import CLIPTextModelWithProjection, CLIPTokenizer\nfrom transformers.models.clip.modeling_clip import CLIPTextModelOutput\n\nfrom ...models import PriorTransformer, UNet2DConditionModel, UNet2DModel\nfrom ...schedulers import UnCLIPScheduler\nfrom ...utils import logging\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\nfrom .text_proj import UnCLIPTextProjModel\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass UnCLIPPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for text-to-image generation using unCLIP.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Args:\n text_encoder ([`~transformers.CLIPTextModelWithProjection`]):\n Frozen text-encoder.\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n prior ([`PriorTransformer`]):\n The canonical unCLIP prior to approximate the image embedding from the text embedding.\n text_proj ([`UnCLIPTextProjModel`]):\n Utility class to prepare and combine the embeddings before they are passed to the decoder.\n decoder ([`UNet2DConditionModel`]):\n The decoder to invert the image embedding into an image.\n super_res_first ([`UNet2DModel`]):\n Super resolution UNet. Used in all but the last step of the super resolution diffusion process.\n super_res_last ([`UNet2DModel`]):\n Super resolution UNet. Used in the last step of the super resolution diffusion process.\n prior_scheduler ([`UnCLIPScheduler`]):\n Scheduler used in the prior denoising process (a modified [`DDPMScheduler`]).\n decoder_scheduler ([`UnCLIPScheduler`]):\n Scheduler used in the decoder denoising process (a modified [`DDPMScheduler`]).\n super_res_scheduler ([`UnCLIPScheduler`]):\n Scheduler used in the super resolution denoising process (a modified [`DDPMScheduler`]).\n\n \"\"\"\n\n _exclude_from_cpu_offload = [\"prior\"]\n\n prior: PriorTransformer\n decoder: UNet2DConditionModel\n text_proj: UnCLIPTextProjModel\n text_encoder: CLIPTextModelWithProjection\n tokenizer: CLIPTokenizer\n super_res_first: UNet2DModel\n super_res_last: UNet2DModel\n\n prior_scheduler: UnCLIPScheduler\n decoder_scheduler: UnCLIPScheduler\n super_res_scheduler: UnCLIPScheduler\n\n model_cpu_offload_seq = \"text_encoder->text_proj->decoder->super_res_first->super_res_last\"\n\n def __init__(\n self,\n prior: PriorTransformer,\n decoder: UNet2DConditionModel,\n text_encoder: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n text_proj: UnCLIPTextProjModel,\n super_res_first: UNet2DModel,\n super_res_last: UNet2DModel,\n prior_scheduler: UnCLIPScheduler,\n decoder_scheduler: UnCLIPScheduler,\n super_res_scheduler: UnCLIPScheduler,\n ):\n super().__init__()\n\n self.register_modules(\n prior=prior,\n decoder=decoder,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n text_proj=text_proj,\n super_res_first=super_res_first,\n super_res_last=super_res_last,\n prior_scheduler=prior_scheduler,\n decoder_scheduler=decoder_scheduler,\n super_res_scheduler=super_res_scheduler,\n )\n\n def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n latents = latents * scheduler.init_noise_sigma\n return latents\n\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,\n text_attention_mask: Optional[torch.Tensor] = None,\n ):\n if text_model_output is None:\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n text_mask = text_inputs.attention_mask.bool().to(device)\n\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]\n\n text_encoder_output = self.text_encoder(text_input_ids.to(device))\n\n prompt_embeds = text_encoder_output.text_embeds\n text_enc_hid_states = text_encoder_output.last_hidden_state\n\n else:\n batch_size = text_model_output[0].shape[0]\n prompt_embeds, text_enc_hid_states = text_model_output[0], text_model_output[1]\n text_mask = text_attention_mask\n\n prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n text_enc_hid_states = text_enc_hid_states.repeat_interleave(num_images_per_prompt, dim=0)\n text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n if do_classifier_free_guidance:\n uncond_tokens = [\"\"] * batch_size\n\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n uncond_text_mask = uncond_input.attention_mask.bool().to(device)\n negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))\n\n negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds\n uncond_text_enc_hid_states = negative_prompt_embeds_text_encoder_output.last_hidden_state\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)\n\n seq_len = uncond_text_enc_hid_states.shape[1]\n uncond_text_enc_hid_states = uncond_text_enc_hid_states.repeat(1, num_images_per_prompt, 1)\n uncond_text_enc_hid_states = uncond_text_enc_hid_states.view(\n batch_size * num_images_per_prompt, seq_len, -1\n )\n uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n # done duplicates\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n text_enc_hid_states = torch.cat([uncond_text_enc_hid_states, text_enc_hid_states])\n\n text_mask = torch.cat([uncond_text_mask, text_mask])\n\n return prompt_embeds, text_enc_hid_states, text_mask\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: int = 1,\n prior_num_inference_steps: int = 25,\n decoder_num_inference_steps: int = 25,\n super_res_num_inference_steps: int = 7,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n prior_latents: Optional[torch.Tensor] = None,\n decoder_latents: Optional[torch.Tensor] = None,\n super_res_latents: Optional[torch.Tensor] = None,\n text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,\n text_attention_mask: Optional[torch.Tensor] = None,\n prior_guidance_scale: float = 4.0,\n decoder_guidance_scale: float = 8.0,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n ):\n \"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide image generation. This can only be left undefined if `text_model_output`\n and `text_attention_mask` is passed.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n prior_num_inference_steps (`int`, *optional*, defaults to 25):\n The number of denoising steps for the prior. More denoising steps usually lead to a higher quality\n image at the expense of slower inference.\n decoder_num_inference_steps (`int`, *optional*, defaults to 25):\n The number of denoising steps for the decoder. More denoising steps usually lead to a higher quality\n image at the expense of slower inference.\n super_res_num_inference_steps (`int`, *optional*, defaults to 7):\n The number of denoising steps for super resolution. More denoising steps usually lead to a higher\n quality image at the expense of slower inference.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n prior_latents (`torch.Tensor` of shape (batch size, embeddings dimension), *optional*):\n Pre-generated noisy latents to be used as inputs for the prior.\n decoder_latents (`torch.Tensor` of shape (batch size, channels, height, width), *optional*):\n Pre-generated noisy latents to be used as inputs for the decoder.\n super_res_latents (`torch.Tensor` of shape (batch size, channels, super res height, super res width), *optional*):\n Pre-generated noisy latents to be used as inputs for the decoder.\n prior_guidance_scale (`float`, *optional*, defaults to 4.0):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n decoder_guidance_scale (`float`, *optional*, defaults to 4.0):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n text_model_output (`CLIPTextModelOutput`, *optional*):\n Pre-defined [`CLIPTextModel`] outputs that can be derived from the text encoder. Pre-defined text\n outputs can be passed for tasks like text embedding interpolations. Make sure to also pass\n `text_attention_mask` in this case. `prompt` can the be left `None`.\n text_attention_mask (`torch.Tensor`, *optional*):\n Pre-defined CLIP text attention mask that can be derived from the tokenizer. Pre-defined text attention\n masks are necessary when passing `text_model_output`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is\n returned where the first element is a list with the generated images.\n \"\"\"\n if prompt is not None:\n if isinstance(prompt, str):\n batch_size = 1\n elif isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n else:\n batch_size = text_model_output[0].shape[0]\n\n device = self._execution_device\n\n batch_size = batch_size * num_images_per_prompt\n\n do_classifier_free_guidance = prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0\n\n prompt_embeds, text_enc_hid_states, text_mask = self._encode_prompt(\n prompt, device, num_images_per_prompt, do_classifier_free_guidance, text_model_output, text_attention_mask\n )\n\n # prior\n\n self.prior_scheduler.set_timesteps(prior_num_inference_steps, device=device)\n prior_timesteps_tensor = self.prior_scheduler.timesteps\n\n embedding_dim = self.prior.config.embedding_dim\n\n prior_latents = self.prepare_latents(\n (batch_size, embedding_dim),\n prompt_embeds.dtype,\n device,\n generator,\n prior_latents,\n self.prior_scheduler,\n )\n\n for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([prior_latents] * 2) if do_classifier_free_guidance else prior_latents\n\n predicted_image_embedding = self.prior(\n latent_model_input,\n timestep=t,\n proj_embedding=prompt_embeds,\n encoder_hidden_states=text_enc_hid_states,\n attention_mask=text_mask,\n ).predicted_image_embedding\n\n if do_classifier_free_guidance:\n predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)\n predicted_image_embedding = predicted_image_embedding_uncond + prior_guidance_scale * (\n predicted_image_embedding_text - predicted_image_embedding_uncond\n )\n\n if i + 1 == prior_timesteps_tensor.shape[0]:\n prev_timestep = None\n else:\n prev_timestep = prior_timesteps_tensor[i + 1]\n\n prior_latents = self.prior_scheduler.step(\n predicted_image_embedding,\n timestep=t,\n sample=prior_latents,\n generator=generator,\n prev_timestep=prev_timestep,\n ).prev_sample\n\n prior_latents = self.prior.post_process_latents(prior_latents)\n\n image_embeddings = prior_latents\n\n # done prior\n\n # decoder\n\n text_enc_hid_states, additive_clip_time_embeddings = self.text_proj(\n image_embeddings=image_embeddings,\n prompt_embeds=prompt_embeds,\n text_encoder_hidden_states=text_enc_hid_states,\n do_classifier_free_guidance=do_classifier_free_guidance,\n )\n\n if device.type == \"mps\":\n # HACK: MPS: There is a panic when padding bool tensors,\n # so cast to int tensor for the pad and back to bool afterwards\n text_mask = text_mask.type(torch.int)\n decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=1)\n decoder_text_mask = decoder_text_mask.type(torch.bool)\n else:\n decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=True)\n\n self.decoder_scheduler.set_timesteps(decoder_num_inference_steps, device=device)\n decoder_timesteps_tensor = self.decoder_scheduler.timesteps\n\n num_channels_latents = self.decoder.config.in_channels\n height = self.decoder.config.sample_size\n width = self.decoder.config.sample_size\n\n decoder_latents = self.prepare_latents(\n (batch_size, num_channels_latents, height, width),\n text_enc_hid_states.dtype,\n device,\n generator,\n decoder_latents,\n self.decoder_scheduler,\n )\n\n for i, t in enumerate(self.progress_bar(decoder_timesteps_tensor)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([decoder_latents] * 2) if do_classifier_free_guidance else decoder_latents\n\n noise_pred = self.decoder(\n sample=latent_model_input,\n timestep=t,\n encoder_hidden_states=text_enc_hid_states,\n class_labels=additive_clip_time_embeddings,\n attention_mask=decoder_text_mask,\n ).sample\n\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred_uncond, _ = noise_pred_uncond.split(latent_model_input.shape[1], dim=1)\n noise_pred_text, predicted_variance = noise_pred_text.split(latent_model_input.shape[1], dim=1)\n noise_pred = noise_pred_uncond + decoder_guidance_scale * (noise_pred_text - noise_pred_uncond)\n noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)\n\n if i + 1 == decoder_timesteps_tensor.shape[0]:\n prev_timestep = None\n else:\n prev_timestep = decoder_timesteps_tensor[i + 1]\n\n # compute the previous noisy sample x_t -> x_t-1\n decoder_latents = self.decoder_scheduler.step(\n noise_pred, t, decoder_latents, prev_timestep=prev_timestep, generator=generator\n ).prev_sample\n\n decoder_latents = decoder_latents.clamp(-1, 1)\n\n image_small = decoder_latents\n\n # done decoder\n\n # super res\n\n self.super_res_scheduler.set_timesteps(super_res_num_inference_steps, device=device)\n super_res_timesteps_tensor = self.super_res_scheduler.timesteps\n\n channels = self.super_res_first.config.in_channels // 2\n height = self.super_res_first.config.sample_size\n width = self.super_res_first.config.sample_size\n\n super_res_latents = self.prepare_latents(\n (batch_size, channels, height, width),\n image_small.dtype,\n device,\n generator,\n super_res_latents,\n self.super_res_scheduler,\n )\n\n if device.type == \"mps\":\n # MPS does not support many interpolations\n image_upscaled = F.interpolate(image_small, size=[height, width])\n else:\n interpolate_antialias = {}\n if \"antialias\" in inspect.signature(F.interpolate).parameters:\n interpolate_antialias[\"antialias\"] = True\n\n image_upscaled = F.interpolate(\n image_small, size=[height, width], mode=\"bicubic\", align_corners=False, **interpolate_antialias\n )\n\n for i, t in enumerate(self.progress_bar(super_res_timesteps_tensor)):\n # no classifier free guidance\n\n if i == super_res_timesteps_tensor.shape[0] - 1:\n unet = self.super_res_last\n else:\n unet = self.super_res_first\n\n latent_model_input = torch.cat([super_res_latents, image_upscaled], dim=1)\n\n noise_pred = unet(\n sample=latent_model_input,\n timestep=t,\n ).sample\n\n if i + 1 == super_res_timesteps_tensor.shape[0]:\n prev_timestep = None\n else:\n prev_timestep = super_res_timesteps_tensor[i + 1]\n\n # compute the previous noisy sample x_t -> x_t-1\n super_res_latents = self.super_res_scheduler.step(\n noise_pred, t, super_res_latents, prev_timestep=prev_timestep, generator=generator\n ).prev_sample\n\n image = super_res_latents\n # done super res\n\n self.maybe_free_model_hooks()\n\n # post processing\n image = image * 0.5 + 0.5\n image = image.clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/unclip/pipeline_unclip_image_variation.py", "content": "# Copyright 2024 Kakao Brain and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom typing import List, Optional, Union\n\nimport PIL.Image\nimport torch\nfrom torch.nn import functional as F\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n)\n\nfrom ...models import UNet2DConditionModel, UNet2DModel\nfrom ...schedulers import UnCLIPScheduler\nfrom ...utils import logging\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\nfrom .text_proj import UnCLIPTextProjModel\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass UnCLIPImageVariationPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline to generate image variations from an input image using UnCLIP.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Args:\n text_encoder ([`~transformers.CLIPTextModelWithProjection`]):\n Frozen text-encoder.\n tokenizer ([`~transformers.CLIPTokenizer`]):\n A `CLIPTokenizer` to tokenize text.\n feature_extractor ([`~transformers.CLIPImageProcessor`]):\n Model that extracts features from generated images to be used as inputs for the `image_encoder`.\n image_encoder ([`~transformers.CLIPVisionModelWithProjection`]):\n Frozen CLIP image-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n text_proj ([`UnCLIPTextProjModel`]):\n Utility class to prepare and combine the embeddings before they are passed to the decoder.\n decoder ([`UNet2DConditionModel`]):\n The decoder to invert the image embedding into an image.\n super_res_first ([`UNet2DModel`]):\n Super resolution UNet. Used in all but the last step of the super resolution diffusion process.\n super_res_last ([`UNet2DModel`]):\n Super resolution UNet. Used in the last step of the super resolution diffusion process.\n decoder_scheduler ([`UnCLIPScheduler`]):\n Scheduler used in the decoder denoising process (a modified [`DDPMScheduler`]).\n super_res_scheduler ([`UnCLIPScheduler`]):\n Scheduler used in the super resolution denoising process (a modified [`DDPMScheduler`]).\n \"\"\"\n\n decoder: UNet2DConditionModel\n text_proj: UnCLIPTextProjModel\n text_encoder: CLIPTextModelWithProjection\n tokenizer: CLIPTokenizer\n feature_extractor: CLIPImageProcessor\n image_encoder: CLIPVisionModelWithProjection\n super_res_first: UNet2DModel\n super_res_last: UNet2DModel\n\n decoder_scheduler: UnCLIPScheduler\n super_res_scheduler: UnCLIPScheduler\n model_cpu_offload_seq = \"text_encoder->image_encoder->text_proj->decoder->super_res_first->super_res_last\"\n\n def __init__(\n self,\n decoder: UNet2DConditionModel,\n text_encoder: CLIPTextModelWithProjection,\n tokenizer: CLIPTokenizer,\n text_proj: UnCLIPTextProjModel,\n feature_extractor: CLIPImageProcessor,\n image_encoder: CLIPVisionModelWithProjection,\n super_res_first: UNet2DModel,\n super_res_last: UNet2DModel,\n decoder_scheduler: UnCLIPScheduler,\n super_res_scheduler: UnCLIPScheduler,\n ):\n super().__init__()\n\n self.register_modules(\n decoder=decoder,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n text_proj=text_proj,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n super_res_first=super_res_first,\n super_res_last=super_res_last,\n decoder_scheduler=decoder_scheduler,\n super_res_scheduler=super_res_scheduler,\n )\n\n # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents\n def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n latents = latents * scheduler.init_noise_sigma\n return latents\n\n def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance):\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n text_mask = text_inputs.attention_mask.bool().to(device)\n text_encoder_output = self.text_encoder(text_input_ids.to(device))\n\n prompt_embeds = text_encoder_output.text_embeds\n text_encoder_hidden_states = text_encoder_output.last_hidden_state\n\n prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n if do_classifier_free_guidance:\n uncond_tokens = [\"\"] * batch_size\n\n max_length = text_input_ids.shape[-1]\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n uncond_text_mask = uncond_input.attention_mask.bool().to(device)\n negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))\n\n negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds\n uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)\n\n seq_len = uncond_text_encoder_hidden_states.shape[1]\n uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)\n uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(\n batch_size * num_images_per_prompt, seq_len, -1\n )\n uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)\n\n # done duplicates\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])\n\n text_mask = torch.cat([uncond_text_mask, text_mask])\n\n return prompt_embeds, text_encoder_hidden_states, text_mask\n\n def _encode_image(self, image, device, num_images_per_prompt, image_embeddings: Optional[torch.Tensor] = None):\n dtype = next(self.image_encoder.parameters()).dtype\n\n if image_embeddings is None:\n if not isinstance(image, torch.Tensor):\n image = self.feature_extractor(images=image, return_tensors=\"pt\").pixel_values\n\n image = image.to(device=device, dtype=dtype)\n image_embeddings = self.image_encoder(image).image_embeds\n\n image_embeddings = image_embeddings.repeat_interleave(num_images_per_prompt, dim=0)\n\n return image_embeddings\n\n @torch.no_grad()\n def __call__(\n self,\n image: Optional[Union[PIL.Image.Image, List[PIL.Image.Image], torch.Tensor]] = None,\n num_images_per_prompt: int = 1,\n decoder_num_inference_steps: int = 25,\n super_res_num_inference_steps: int = 7,\n generator: Optional[torch.Generator] = None,\n decoder_latents: Optional[torch.Tensor] = None,\n super_res_latents: Optional[torch.Tensor] = None,\n image_embeddings: Optional[torch.Tensor] = None,\n decoder_guidance_scale: float = 8.0,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n ):\n \"\"\"\n The call function to the pipeline for generation.\n\n Args:\n image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.Tensor`):\n `Image` or tensor representing an image batch to be used as the starting point. If you provide a\n tensor, it needs to be compatible with the [`CLIPImageProcessor`]\n [configuration](https://huggingface.co/fusing/karlo-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json).\n Can be left as `None` only when `image_embeddings` are passed.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n decoder_num_inference_steps (`int`, *optional*, defaults to 25):\n The number of denoising steps for the decoder. More denoising steps usually lead to a higher quality\n image at the expense of slower inference.\n super_res_num_inference_steps (`int`, *optional*, defaults to 7):\n The number of denoising steps for super resolution. More denoising steps usually lead to a higher\n quality image at the expense of slower inference.\n generator (`torch.Generator`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n decoder_latents (`torch.Tensor` of shape (batch size, channels, height, width), *optional*):\n Pre-generated noisy latents to be used as inputs for the decoder.\n super_res_latents (`torch.Tensor` of shape (batch size, channels, super res height, super res width), *optional*):\n Pre-generated noisy latents to be used as inputs for the decoder.\n decoder_guidance_scale (`float`, *optional*, defaults to 4.0):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n image_embeddings (`torch.Tensor`, *optional*):\n Pre-defined image embeddings that can be derived from the image encoder. Pre-defined image embeddings\n can be passed for tasks like image interpolations. `image` can be left as `None`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is\n returned where the first element is a list with the generated images.\n \"\"\"\n if image is not None:\n if isinstance(image, PIL.Image.Image):\n batch_size = 1\n elif isinstance(image, list):\n batch_size = len(image)\n else:\n batch_size = image.shape[0]\n else:\n batch_size = image_embeddings.shape[0]\n\n prompt = [\"\"] * batch_size\n\n device = self._execution_device\n\n batch_size = batch_size * num_images_per_prompt\n\n do_classifier_free_guidance = decoder_guidance_scale > 1.0\n\n prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt(\n prompt, device, num_images_per_prompt, do_classifier_free_guidance\n )\n\n image_embeddings = self._encode_image(image, device, num_images_per_prompt, image_embeddings)\n\n # decoder\n text_encoder_hidden_states, additive_clip_time_embeddings = self.text_proj(\n image_embeddings=image_embeddings,\n prompt_embeds=prompt_embeds,\n text_encoder_hidden_states=text_encoder_hidden_states,\n do_classifier_free_guidance=do_classifier_free_guidance,\n )\n\n if device.type == \"mps\":\n # HACK: MPS: There is a panic when padding bool tensors,\n # so cast to int tensor for the pad and back to bool afterwards\n text_mask = text_mask.type(torch.int)\n decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=1)\n decoder_text_mask = decoder_text_mask.type(torch.bool)\n else:\n decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=True)\n\n self.decoder_scheduler.set_timesteps(decoder_num_inference_steps, device=device)\n decoder_timesteps_tensor = self.decoder_scheduler.timesteps\n\n num_channels_latents = self.decoder.config.in_channels\n height = self.decoder.config.sample_size\n width = self.decoder.config.sample_size\n\n if decoder_latents is None:\n decoder_latents = self.prepare_latents(\n (batch_size, num_channels_latents, height, width),\n text_encoder_hidden_states.dtype,\n device,\n generator,\n decoder_latents,\n self.decoder_scheduler,\n )\n\n for i, t in enumerate(self.progress_bar(decoder_timesteps_tensor)):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([decoder_latents] * 2) if do_classifier_free_guidance else decoder_latents\n\n noise_pred = self.decoder(\n sample=latent_model_input,\n timestep=t,\n encoder_hidden_states=text_encoder_hidden_states,\n class_labels=additive_clip_time_embeddings,\n attention_mask=decoder_text_mask,\n ).sample\n\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred_uncond, _ = noise_pred_uncond.split(latent_model_input.shape[1], dim=1)\n noise_pred_text, predicted_variance = noise_pred_text.split(latent_model_input.shape[1], dim=1)\n noise_pred = noise_pred_uncond + decoder_guidance_scale * (noise_pred_text - noise_pred_uncond)\n noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)\n\n if i + 1 == decoder_timesteps_tensor.shape[0]:\n prev_timestep = None\n else:\n prev_timestep = decoder_timesteps_tensor[i + 1]\n\n # compute the previous noisy sample x_t -> x_t-1\n decoder_latents = self.decoder_scheduler.step(\n noise_pred, t, decoder_latents, prev_timestep=prev_timestep, generator=generator\n ).prev_sample\n\n decoder_latents = decoder_latents.clamp(-1, 1)\n\n image_small = decoder_latents\n\n # done decoder\n\n # super res\n\n self.super_res_scheduler.set_timesteps(super_res_num_inference_steps, device=device)\n super_res_timesteps_tensor = self.super_res_scheduler.timesteps\n\n channels = self.super_res_first.config.in_channels // 2\n height = self.super_res_first.config.sample_size\n width = self.super_res_first.config.sample_size\n\n if super_res_latents is None:\n super_res_latents = self.prepare_latents(\n (batch_size, channels, height, width),\n image_small.dtype,\n device,\n generator,\n super_res_latents,\n self.super_res_scheduler,\n )\n\n if device.type == \"mps\":\n # MPS does not support many interpolations\n image_upscaled = F.interpolate(image_small, size=[height, width])\n else:\n interpolate_antialias = {}\n if \"antialias\" in inspect.signature(F.interpolate).parameters:\n interpolate_antialias[\"antialias\"] = True\n\n image_upscaled = F.interpolate(\n image_small, size=[height, width], mode=\"bicubic\", align_corners=False, **interpolate_antialias\n )\n\n for i, t in enumerate(self.progress_bar(super_res_timesteps_tensor)):\n # no classifier free guidance\n\n if i == super_res_timesteps_tensor.shape[0] - 1:\n unet = self.super_res_last\n else:\n unet = self.super_res_first\n\n latent_model_input = torch.cat([super_res_latents, image_upscaled], dim=1)\n\n noise_pred = unet(\n sample=latent_model_input,\n timestep=t,\n ).sample\n\n if i + 1 == super_res_timesteps_tensor.shape[0]:\n prev_timestep = None\n else:\n prev_timestep = super_res_timesteps_tensor[i + 1]\n\n # compute the previous noisy sample x_t -> x_t-1\n super_res_latents = self.super_res_scheduler.step(\n noise_pred, t, super_res_latents, prev_timestep=prev_timestep, generator=generator\n ).prev_sample\n\n image = super_res_latents\n\n # done super res\n self.maybe_free_model_hooks()\n\n # post processing\n\n image = image * 0.5 + 0.5\n image = image.clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image,)\n\n return ImagePipelineOutput(images=image)\n" }, { "path": "diffusers/src/diffusers/pipelines/unclip/text_proj.py", "content": "# Copyright 2024 Kakao Brain and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport torch\nfrom torch import nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...models import ModelMixin\n\n\nclass UnCLIPTextProjModel(ModelMixin, ConfigMixin):\n \"\"\"\n Utility class for CLIP embeddings. Used to combine the image and text embeddings into a format usable by the\n decoder.\n\n For more details, see the original paper: https://arxiv.org/abs/2204.06125 section 2.1\n \"\"\"\n\n @register_to_config\n def __init__(\n self,\n *,\n clip_extra_context_tokens: int = 4,\n clip_embeddings_dim: int = 768,\n time_embed_dim: int,\n cross_attention_dim,\n ):\n super().__init__()\n\n self.learned_classifier_free_guidance_embeddings = nn.Parameter(torch.zeros(clip_embeddings_dim))\n\n # parameters for additional clip time embeddings\n self.embedding_proj = nn.Linear(clip_embeddings_dim, time_embed_dim)\n self.clip_image_embeddings_project_to_time_embeddings = nn.Linear(clip_embeddings_dim, time_embed_dim)\n\n # parameters for encoder hidden states\n self.clip_extra_context_tokens = clip_extra_context_tokens\n self.clip_extra_context_tokens_proj = nn.Linear(\n clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim\n )\n self.encoder_hidden_states_proj = nn.Linear(clip_embeddings_dim, cross_attention_dim)\n self.text_encoder_hidden_states_norm = nn.LayerNorm(cross_attention_dim)\n\n def forward(self, *, image_embeddings, prompt_embeds, text_encoder_hidden_states, do_classifier_free_guidance):\n if do_classifier_free_guidance:\n # Add the classifier free guidance embeddings to the image embeddings\n image_embeddings_batch_size = image_embeddings.shape[0]\n classifier_free_guidance_embeddings = self.learned_classifier_free_guidance_embeddings.unsqueeze(0)\n classifier_free_guidance_embeddings = classifier_free_guidance_embeddings.expand(\n image_embeddings_batch_size, -1\n )\n image_embeddings = torch.cat([classifier_free_guidance_embeddings, image_embeddings], dim=0)\n\n # The image embeddings batch size and the text embeddings batch size are equal\n assert image_embeddings.shape[0] == prompt_embeds.shape[0]\n\n batch_size = prompt_embeds.shape[0]\n\n # \"Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and\n # adding CLIP embeddings to the existing timestep embedding, ...\n time_projected_prompt_embeds = self.embedding_proj(prompt_embeds)\n time_projected_image_embeddings = self.clip_image_embeddings_project_to_time_embeddings(image_embeddings)\n additive_clip_time_embeddings = time_projected_image_embeddings + time_projected_prompt_embeds\n\n # ... and by projecting CLIP embeddings into four\n # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder\"\n clip_extra_context_tokens = self.clip_extra_context_tokens_proj(image_embeddings)\n clip_extra_context_tokens = clip_extra_context_tokens.reshape(batch_size, -1, self.clip_extra_context_tokens)\n clip_extra_context_tokens = clip_extra_context_tokens.permute(0, 2, 1)\n\n text_encoder_hidden_states = self.encoder_hidden_states_proj(text_encoder_hidden_states)\n text_encoder_hidden_states = self.text_encoder_hidden_states_norm(text_encoder_hidden_states)\n text_encoder_hidden_states = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states], dim=1)\n\n return text_encoder_hidden_states, additive_clip_time_embeddings\n" }, { "path": "diffusers/src/diffusers/pipelines/unidiffuser/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import (\n ImageTextPipelineOutput,\n UniDiffuserPipeline,\n )\n\n _dummy_objects.update(\n {\"ImageTextPipelineOutput\": ImageTextPipelineOutput, \"UniDiffuserPipeline\": UniDiffuserPipeline}\n )\nelse:\n _import_structure[\"modeling_text_decoder\"] = [\"UniDiffuserTextDecoder\"]\n _import_structure[\"modeling_uvit\"] = [\"UniDiffuserModel\", \"UTransformer2DModel\"]\n _import_structure[\"pipeline_unidiffuser\"] = [\"ImageTextPipelineOutput\", \"UniDiffuserPipeline\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import (\n ImageTextPipelineOutput,\n UniDiffuserPipeline,\n )\n else:\n from .modeling_text_decoder import UniDiffuserTextDecoder\n from .modeling_uvit import UniDiffuserModel, UTransformer2DModel\n from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/unidiffuser/modeling_text_decoder.py", "content": "from typing import Optional\n\nimport numpy as np\nimport torch\nfrom torch import nn\nfrom transformers import GPT2Config, GPT2LMHeadModel\nfrom transformers.modeling_utils import ModuleUtilsMixin\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...models import ModelMixin\n\n\n# Modified from ClipCaptionModel in https://github.com/thu-ml/unidiffuser/blob/main/libs/caption_decoder.py\nclass UniDiffuserTextDecoder(ModelMixin, ConfigMixin, ModuleUtilsMixin):\n \"\"\"\n Text decoder model for a image-text [UniDiffuser](https://arxiv.org/pdf/2303.06555.pdf) model. This is used to\n generate text from the UniDiffuser image-text embedding.\n\n Parameters:\n prefix_length (`int`):\n Max number of prefix tokens that will be supplied to the model.\n prefix_inner_dim (`int`):\n The hidden size of the incoming prefix embeddings. For UniDiffuser, this would be the hidden dim of the\n CLIP text encoder.\n prefix_hidden_dim (`int`, *optional*):\n Hidden dim of the MLP if we encode the prefix.\n vocab_size (`int`, *optional*, defaults to 50257):\n Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the\n `inputs_ids` passed when calling [`GPT2Model`] or [`TFGPT2Model`].\n n_positions (`int`, *optional*, defaults to 1024):\n The maximum sequence length that this model might ever be used with. Typically set this to something large\n just in case (e.g., 512 or 1024 or 2048).\n n_embd (`int`, *optional*, defaults to 768):\n Dimensionality of the embeddings and hidden states.\n n_layer (`int`, *optional*, defaults to 12):\n Number of hidden layers in the Transformer encoder.\n n_head (`int`, *optional*, defaults to 12):\n Number of attention heads for each attention layer in the Transformer encoder.\n n_inner (`int`, *optional*, defaults to None):\n Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd\n activation_function (`str`, *optional*, defaults to `\"gelu\"`):\n Activation function, to be selected in the list `[\"relu\", \"silu\", \"gelu\", \"tanh\", \"gelu_new\"]`.\n resid_pdrop (`float`, *optional*, defaults to 0.1):\n The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.\n embd_pdrop (`float`, *optional*, defaults to 0.1):\n The dropout ratio for the embeddings.\n attn_pdrop (`float`, *optional*, defaults to 0.1):\n The dropout ratio for the attention.\n layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):\n The epsilon to use in the layer normalization layers.\n initializer_range (`float`, *optional*, defaults to 0.02):\n The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\n scale_attn_weights (`bool`, *optional*, defaults to `True`):\n Scale attention weights by dividing by sqrt(hidden_size)..\n use_cache (`bool`, *optional*, defaults to `True`):\n Whether or not the model should return the last key/values attentions (not used by all models).\n scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`):\n Whether to additionally scale attention weights by `1 / layer_idx + 1`.\n reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):\n Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention\n dot-product/softmax to float() when training with mixed precision.\n \"\"\"\n\n _keys_to_ignore_on_load_unexpected = [r\"h\\.\\d+\\.attn\\.bias\", r\"h\\.\\d+\\.attn\\.masked_bias\"]\n\n @register_to_config\n def __init__(\n self,\n prefix_length: int,\n prefix_inner_dim: int,\n prefix_hidden_dim: Optional[int] = None,\n vocab_size: int = 50257, # Start of GPT2 config args\n n_positions: int = 1024,\n n_embd: int = 768,\n n_layer: int = 12,\n n_head: int = 12,\n n_inner: Optional[int] = None,\n activation_function: str = \"gelu_new\",\n resid_pdrop: float = 0.1,\n embd_pdrop: float = 0.1,\n attn_pdrop: float = 0.1,\n layer_norm_epsilon: float = 1e-5,\n initializer_range: float = 0.02,\n scale_attn_weights: bool = True,\n use_cache: bool = True,\n scale_attn_by_inverse_layer_idx: bool = False,\n reorder_and_upcast_attn: bool = False,\n ):\n super().__init__()\n\n self.prefix_length = prefix_length\n\n if prefix_inner_dim != n_embd and prefix_hidden_dim is None:\n raise ValueError(\n f\"`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and\"\n f\" `n_embd`: {n_embd} are not equal.\"\n )\n\n self.prefix_inner_dim = prefix_inner_dim\n self.prefix_hidden_dim = prefix_hidden_dim\n\n self.encode_prefix = (\n nn.Linear(self.prefix_inner_dim, self.prefix_hidden_dim)\n if self.prefix_hidden_dim is not None\n else nn.Identity()\n )\n self.decode_prefix = (\n nn.Linear(self.prefix_hidden_dim, n_embd) if self.prefix_hidden_dim is not None else nn.Identity()\n )\n\n gpt_config = GPT2Config(\n vocab_size=vocab_size,\n n_positions=n_positions,\n n_embd=n_embd,\n n_layer=n_layer,\n n_head=n_head,\n n_inner=n_inner,\n activation_function=activation_function,\n resid_pdrop=resid_pdrop,\n embd_pdrop=embd_pdrop,\n attn_pdrop=attn_pdrop,\n layer_norm_epsilon=layer_norm_epsilon,\n initializer_range=initializer_range,\n scale_attn_weights=scale_attn_weights,\n use_cache=use_cache,\n scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,\n reorder_and_upcast_attn=reorder_and_upcast_attn,\n )\n self.transformer = GPT2LMHeadModel(gpt_config)\n\n def forward(\n self,\n input_ids: torch.Tensor,\n prefix_embeds: torch.Tensor,\n attention_mask: Optional[torch.Tensor] = None,\n labels: Optional[torch.Tensor] = None,\n ):\n \"\"\"\n Args:\n input_ids (`torch.Tensor` of shape `(N, max_seq_len)`):\n Text tokens to use for inference.\n prefix_embeds (`torch.Tensor` of shape `(N, prefix_length, 768)`):\n Prefix embedding to preprend to the embedded tokens.\n attention_mask (`torch.Tensor` of shape `(N, prefix_length + max_seq_len, 768)`, *optional*):\n Attention mask for the prefix embedding.\n labels (`torch.Tensor`, *optional*):\n Labels to use for language modeling.\n \"\"\"\n embedding_text = self.transformer.transformer.wte(input_ids)\n hidden = self.encode_prefix(prefix_embeds)\n prefix_embeds = self.decode_prefix(hidden)\n embedding_cat = torch.cat((prefix_embeds, embedding_text), dim=1)\n\n if labels is not None:\n dummy_token = self.get_dummy_token(input_ids.shape[0], input_ids.device)\n labels = torch.cat((dummy_token, input_ids), dim=1)\n out = self.transformer(inputs_embeds=embedding_cat, labels=labels, attention_mask=attention_mask)\n if self.prefix_hidden_dim is not None:\n return out, hidden\n else:\n return out\n\n def get_dummy_token(self, batch_size: int, device: torch.device) -> torch.Tensor:\n return torch.zeros(batch_size, self.prefix_length, dtype=torch.int64, device=device)\n\n def encode(self, prefix):\n return self.encode_prefix(prefix)\n\n @torch.no_grad()\n def generate_captions(self, features, eos_token_id, device):\n \"\"\"\n Generate captions given text embedding features. Returns list[L].\n\n Args:\n features (`torch.Tensor` of shape `(B, L, D)`):\n Text embedding features to generate captions from.\n eos_token_id (`int`):\n The token ID of the EOS token for the text decoder model.\n device:\n Device to perform text generation on.\n\n Returns:\n `List[str]`: A list of strings generated from the decoder model.\n \"\"\"\n\n features = torch.split(features, 1, dim=0)\n generated_tokens = []\n generated_seq_lengths = []\n for feature in features:\n feature = self.decode_prefix(feature.to(device)) # back to the clip feature\n # Only support beam search for now\n output_tokens, seq_lengths = self.generate_beam(\n input_embeds=feature, device=device, eos_token_id=eos_token_id\n )\n generated_tokens.append(output_tokens[0])\n generated_seq_lengths.append(seq_lengths[0])\n generated_tokens = torch.stack(generated_tokens)\n generated_seq_lengths = torch.stack(generated_seq_lengths)\n return generated_tokens, generated_seq_lengths\n\n @torch.no_grad()\n def generate_beam(\n self,\n input_ids=None,\n input_embeds=None,\n device=None,\n beam_size: int = 5,\n entry_length: int = 67,\n temperature: float = 1.0,\n eos_token_id: Optional[int] = None,\n ):\n \"\"\"\n Generates text using the given tokenizer and text prompt or token embedding via beam search. This\n implementation is based on the beam search implementation from the [original UniDiffuser\n code](https://github.com/thu-ml/unidiffuser/blob/main/libs/caption_decoder.py#L89).\n\n Args:\n eos_token_id (`int`, *optional*):\n The token ID of the EOS token for the text decoder model.\n input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`, *optional*):\n Tokenizer indices of input sequence tokens in the vocabulary. One of `input_ids` and `input_embeds`\n must be supplied.\n input_embeds (`torch.Tensor` of shape `(batch_size, seq_len, hidden_size)`, *optional*):\n An embedded representation to directly pass to the transformer as a prefix for beam search. One of\n `input_ids` and `input_embeds` must be supplied.\n device:\n The device to perform beam search on.\n beam_size (`int`, *optional*, defaults to `5`):\n The number of best states to store during beam search.\n entry_length (`int`, *optional*, defaults to `67`):\n The number of iterations to run beam search.\n temperature (`float`, *optional*, defaults to 1.0):\n The temperature to use when performing the softmax over logits from the decoding model.\n\n Returns:\n `Tuple(torch.Tensor, torch.Tensor)`: A tuple of tensors where the first element is a tensor of generated\n token sequences sorted by score in descending order, and the second element is the sequence lengths\n corresponding to those sequences.\n \"\"\"\n # Generates text until stop_token is reached using beam search with the desired beam size.\n stop_token_index = eos_token_id\n tokens = None\n scores = None\n seq_lengths = torch.ones(beam_size, device=device, dtype=torch.int)\n is_stopped = torch.zeros(beam_size, device=device, dtype=torch.bool)\n\n if input_embeds is not None:\n generated = input_embeds\n else:\n generated = self.transformer.transformer.wte(input_ids)\n\n for i in range(entry_length):\n outputs = self.transformer(inputs_embeds=generated)\n logits = outputs.logits\n logits = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)\n logits = logits.softmax(-1).log()\n\n if scores is None:\n scores, next_tokens = logits.topk(beam_size, -1)\n generated = generated.expand(beam_size, *generated.shape[1:])\n next_tokens, scores = next_tokens.permute(1, 0), scores.squeeze(0)\n if tokens is None:\n tokens = next_tokens\n else:\n tokens = tokens.expand(beam_size, *tokens.shape[1:])\n tokens = torch.cat((tokens, next_tokens), dim=1)\n else:\n logits[is_stopped] = -float(np.inf)\n logits[is_stopped, 0] = 0\n scores_sum = scores[:, None] + logits\n seq_lengths[~is_stopped] += 1\n scores_sum_average = scores_sum / seq_lengths[:, None]\n scores_sum_average, next_tokens = scores_sum_average.view(-1).topk(beam_size, -1)\n next_tokens_source = next_tokens // scores_sum.shape[1]\n seq_lengths = seq_lengths[next_tokens_source]\n next_tokens = next_tokens % scores_sum.shape[1]\n next_tokens = next_tokens.unsqueeze(1)\n tokens = tokens[next_tokens_source]\n tokens = torch.cat((tokens, next_tokens), dim=1)\n generated = generated[next_tokens_source]\n scores = scores_sum_average * seq_lengths\n is_stopped = is_stopped[next_tokens_source]\n\n next_token_embed = self.transformer.transformer.wte(next_tokens.squeeze()).view(generated.shape[0], 1, -1)\n generated = torch.cat((generated, next_token_embed), dim=1)\n is_stopped = is_stopped + next_tokens.eq(stop_token_index).squeeze()\n if is_stopped.all():\n break\n\n scores = scores / seq_lengths\n order = scores.argsort(descending=True)\n # tokens tensors are already padded to max_seq_length\n output_texts = [tokens[i] for i in order]\n output_texts = torch.stack(output_texts, dim=0)\n seq_lengths = torch.tensor([seq_lengths[i] for i in order], dtype=seq_lengths.dtype)\n return output_texts, seq_lengths\n" }, { "path": "diffusers/src/diffusers/pipelines/unidiffuser/modeling_uvit.py", "content": "import math\nfrom typing import Optional, Union\n\nimport torch\nfrom torch import nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...models import ModelMixin\nfrom ...models.attention import FeedForward\nfrom ...models.attention_processor import Attention\nfrom ...models.embeddings import TimestepEmbedding, Timesteps, get_2d_sincos_pos_embed\nfrom ...models.modeling_outputs import Transformer2DModelOutput\nfrom ...models.normalization import AdaLayerNorm\nfrom ...utils import logging\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef _no_grad_trunc_normal_(tensor, mean, std, a, b):\n # Cut & paste from PyTorch official master until it's in a few official releases - RW\n # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf\n def norm_cdf(x):\n # Computes standard normal cumulative distribution function\n return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0\n\n if (mean < a - 2 * std) or (mean > b + 2 * std):\n logger.warning(\n \"mean is more than 2 std from [a, b] in nn.init.trunc_normal_. \"\n \"The distribution of values may be incorrect.\"\n )\n\n with torch.no_grad():\n # Values are generated by using a truncated uniform distribution and\n # then using the inverse CDF for the normal distribution.\n # Get upper and lower cdf values\n l = norm_cdf((a - mean) / std)\n u = norm_cdf((b - mean) / std)\n\n # Uniformly fill tensor with values from [l, u], then translate to\n # [2l-1, 2u-1].\n tensor.uniform_(2 * l - 1, 2 * u - 1)\n\n # Use inverse cdf transform for normal distribution to get truncated\n # standard normal\n tensor.erfinv_()\n\n # Transform to proper mean, std\n tensor.mul_(std * math.sqrt(2.0))\n tensor.add_(mean)\n\n # Clamp to ensure it's in the proper range\n tensor.clamp_(min=a, max=b)\n return tensor\n\n\ndef trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):\n # type: (torch.Tensor, float, float, float, float) -> torch.Tensor\n r\"\"\"Fills the input Tensor with values drawn from a truncated\n normal distribution. The values are effectively drawn from the normal distribution :math:`\\mathcal{N}(\\text{mean},\n \\text{std}^2)` with values outside :math:`[a, b]` redrawn until they are within the bounds. The method used for\n generating the random values works best when :math:`a \\leq \\text{mean} \\leq b`.\n\n Args:\n tensor: an n-dimensional `torch.Tensor`\n mean: the mean of the normal distribution\n std: the standard deviation of the normal distribution\n a: the minimum cutoff value\n b: the maximum cutoff value\n Examples:\n >>> w = torch.empty(3, 5) >>> nn.init.trunc_normal_(w)\n \"\"\"\n return _no_grad_trunc_normal_(tensor, mean, std, a, b)\n\n\nclass PatchEmbed(nn.Module):\n \"\"\"2D Image to Patch Embedding\"\"\"\n\n def __init__(\n self,\n height=224,\n width=224,\n patch_size=16,\n in_channels=3,\n embed_dim=768,\n layer_norm=False,\n flatten=True,\n bias=True,\n use_pos_embed=True,\n ):\n super().__init__()\n\n num_patches = (height // patch_size) * (width // patch_size)\n self.flatten = flatten\n self.layer_norm = layer_norm\n\n self.proj = nn.Conv2d(\n in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias\n )\n if layer_norm:\n self.norm = nn.LayerNorm(embed_dim, elementwise_affine=False, eps=1e-6)\n else:\n self.norm = None\n\n self.use_pos_embed = use_pos_embed\n if self.use_pos_embed:\n pos_embed = get_2d_sincos_pos_embed(embed_dim, int(num_patches**0.5))\n self.register_buffer(\"pos_embed\", torch.from_numpy(pos_embed).float().unsqueeze(0), persistent=False)\n\n def forward(self, latent):\n latent = self.proj(latent)\n if self.flatten:\n latent = latent.flatten(2).transpose(1, 2) # BCHW -> BNC\n if self.layer_norm:\n latent = self.norm(latent)\n if self.use_pos_embed:\n return latent + self.pos_embed\n else:\n return latent\n\n\nclass SkipBlock(nn.Module):\n def __init__(self, dim: int):\n super().__init__()\n\n self.skip_linear = nn.Linear(2 * dim, dim)\n\n # Use torch.nn.LayerNorm for now, following the original code\n self.norm = nn.LayerNorm(dim)\n\n def forward(self, x, skip):\n x = self.skip_linear(torch.cat([x, skip], dim=-1))\n x = self.norm(x)\n\n return x\n\n\n# Modified to support both pre-LayerNorm and post-LayerNorm configurations\n# Don't support AdaLayerNormZero for now\n# Modified from diffusers.models.attention.BasicTransformerBlock\nclass UTransformerBlock(nn.Module):\n r\"\"\"\n A modification of BasicTransformerBlock which supports pre-LayerNorm and post-LayerNorm configurations.\n\n Parameters:\n dim (`int`): The number of channels in the input and output.\n num_attention_heads (`int`): The number of heads to use for multi-head attention.\n attention_head_dim (`int`): The number of channels in each head.\n dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.\n cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.\n activation_fn (`str`, *optional*, defaults to `\"geglu\"`):\n Activation function to be used in feed-forward.\n num_embeds_ada_norm (:obj: `int`, *optional*):\n The number of diffusion steps used during training. See `Transformer2DModel`.\n attention_bias (:obj: `bool`, *optional*, defaults to `False`):\n Configure if the attentions should contain a bias parameter.\n only_cross_attention (`bool`, *optional*):\n Whether to use only cross-attention layers. In this case two cross attention layers are used.\n double_self_attention (`bool`, *optional*):\n Whether to use two self-attention layers. In this case no cross attention layers are used.\n upcast_attention (`bool`, *optional*):\n Whether to upcast the query and key to float32 when performing the attention calculation.\n norm_elementwise_affine (`bool`, *optional*):\n Whether to use learnable per-element affine parameters during layer normalization.\n norm_type (`str`, defaults to `\"layer_norm\"`):\n The layer norm implementation to use.\n pre_layer_norm (`bool`, *optional*):\n Whether to perform layer normalization before the attention and feedforward operations (\"pre-LayerNorm\"),\n as opposed to after (\"post-LayerNorm\"). Note that `BasicTransformerBlock` uses pre-LayerNorm, e.g.\n `pre_layer_norm = True`.\n final_dropout (`bool`, *optional*):\n Whether to use a final Dropout layer after the feedforward network.\n \"\"\"\n\n def __init__(\n self,\n dim: int,\n num_attention_heads: int,\n attention_head_dim: int,\n dropout=0.0,\n cross_attention_dim: Optional[int] = None,\n activation_fn: str = \"geglu\",\n num_embeds_ada_norm: Optional[int] = None,\n attention_bias: bool = False,\n only_cross_attention: bool = False,\n double_self_attention: bool = False,\n upcast_attention: bool = False,\n norm_elementwise_affine: bool = True,\n norm_type: str = \"layer_norm\",\n pre_layer_norm: bool = True,\n final_dropout: bool = False,\n ):\n super().__init__()\n self.only_cross_attention = only_cross_attention\n\n self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == \"ada_norm\"\n\n self.pre_layer_norm = pre_layer_norm\n\n if norm_type in (\"ada_norm\", \"ada_norm_zero\") and num_embeds_ada_norm is None:\n raise ValueError(\n f\"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to\"\n f\" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.\"\n )\n\n # 1. Self-Attn\n self.attn1 = Attention(\n query_dim=dim,\n heads=num_attention_heads,\n dim_head=attention_head_dim,\n dropout=dropout,\n bias=attention_bias,\n cross_attention_dim=cross_attention_dim if only_cross_attention else None,\n upcast_attention=upcast_attention,\n )\n\n # 2. Cross-Attn\n if cross_attention_dim is not None or double_self_attention:\n self.attn2 = Attention(\n query_dim=dim,\n cross_attention_dim=cross_attention_dim if not double_self_attention else None,\n heads=num_attention_heads,\n dim_head=attention_head_dim,\n dropout=dropout,\n bias=attention_bias,\n upcast_attention=upcast_attention,\n ) # is self-attn if encoder_hidden_states is none\n else:\n self.attn2 = None\n\n if self.use_ada_layer_norm:\n self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)\n else:\n self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)\n\n if cross_attention_dim is not None or double_self_attention:\n # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.\n # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during\n # the second cross attention block.\n self.norm2 = (\n AdaLayerNorm(dim, num_embeds_ada_norm)\n if self.use_ada_layer_norm\n else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)\n )\n else:\n self.norm2 = None\n\n # 3. Feed-forward\n self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)\n self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout)\n\n def forward(\n self,\n hidden_states,\n attention_mask=None,\n encoder_hidden_states=None,\n encoder_attention_mask=None,\n timestep=None,\n cross_attention_kwargs=None,\n class_labels=None,\n ):\n # Pre-LayerNorm\n if self.pre_layer_norm:\n if self.use_ada_layer_norm:\n norm_hidden_states = self.norm1(hidden_states, timestep)\n else:\n norm_hidden_states = self.norm1(hidden_states)\n else:\n norm_hidden_states = hidden_states\n\n # 1. Self-Attention\n cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n\n # Post-LayerNorm\n if not self.pre_layer_norm:\n if self.use_ada_layer_norm:\n attn_output = self.norm1(attn_output, timestep)\n else:\n attn_output = self.norm1(attn_output)\n\n hidden_states = attn_output + hidden_states\n\n if self.attn2 is not None:\n # Pre-LayerNorm\n if self.pre_layer_norm:\n norm_hidden_states = (\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\n )\n else:\n norm_hidden_states = hidden_states\n # TODO (Birch-San): Here we should prepare the encoder_attention mask correctly\n # prepare attention mask here\n\n # 2. Cross-Attention\n attn_output = self.attn2(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=encoder_attention_mask,\n **cross_attention_kwargs,\n )\n\n # Post-LayerNorm\n if not self.pre_layer_norm:\n attn_output = self.norm2(attn_output, timestep) if self.use_ada_layer_norm else self.norm2(attn_output)\n\n hidden_states = attn_output + hidden_states\n\n # 3. Feed-forward\n # Pre-LayerNorm\n if self.pre_layer_norm:\n norm_hidden_states = self.norm3(hidden_states)\n else:\n norm_hidden_states = hidden_states\n\n ff_output = self.ff(norm_hidden_states)\n\n # Post-LayerNorm\n if not self.pre_layer_norm:\n ff_output = self.norm3(ff_output)\n\n hidden_states = ff_output + hidden_states\n\n return hidden_states\n\n\n# Like UTransformerBlock except with LayerNorms on the residual backbone of the block\n# Modified from diffusers.models.attention.BasicTransformerBlock\nclass UniDiffuserBlock(nn.Module):\n r\"\"\"\n A modification of BasicTransformerBlock which supports pre-LayerNorm and post-LayerNorm configurations and puts the\n LayerNorms on the residual backbone of the block. This matches the transformer block in the [original UniDiffuser\n implementation](https://github.com/thu-ml/unidiffuser/blob/main/libs/uvit_multi_post_ln_v1.py#L104).\n\n Parameters:\n dim (`int`): The number of channels in the input and output.\n num_attention_heads (`int`): The number of heads to use for multi-head attention.\n attention_head_dim (`int`): The number of channels in each head.\n dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.\n cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.\n activation_fn (`str`, *optional*, defaults to `\"geglu\"`):\n Activation function to be used in feed-forward.\n num_embeds_ada_norm (:obj: `int`, *optional*):\n The number of diffusion steps used during training. See `Transformer2DModel`.\n attention_bias (:obj: `bool`, *optional*, defaults to `False`):\n Configure if the attentions should contain a bias parameter.\n only_cross_attention (`bool`, *optional*):\n Whether to use only cross-attention layers. In this case two cross attention layers are used.\n double_self_attention (`bool`, *optional*):\n Whether to use two self-attention layers. In this case no cross attention layers are used.\n upcast_attention (`bool`, *optional*):\n Whether to upcast the query and key to float() when performing the attention calculation.\n norm_elementwise_affine (`bool`, *optional*):\n Whether to use learnable per-element affine parameters during layer normalization.\n norm_type (`str`, defaults to `\"layer_norm\"`):\n The layer norm implementation to use.\n pre_layer_norm (`bool`, *optional*):\n Whether to perform layer normalization before the attention and feedforward operations (\"pre-LayerNorm\"),\n as opposed to after (\"post-LayerNorm\"). The original UniDiffuser implementation is post-LayerNorm\n (`pre_layer_norm = False`).\n final_dropout (`bool`, *optional*):\n Whether to use a final Dropout layer after the feedforward network.\n \"\"\"\n\n def __init__(\n self,\n dim: int,\n num_attention_heads: int,\n attention_head_dim: int,\n dropout=0.0,\n cross_attention_dim: Optional[int] = None,\n activation_fn: str = \"geglu\",\n num_embeds_ada_norm: Optional[int] = None,\n attention_bias: bool = False,\n only_cross_attention: bool = False,\n double_self_attention: bool = False,\n upcast_attention: bool = False,\n norm_elementwise_affine: bool = True,\n norm_type: str = \"layer_norm\",\n pre_layer_norm: bool = False,\n final_dropout: bool = True,\n ):\n super().__init__()\n self.only_cross_attention = only_cross_attention\n\n self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == \"ada_norm\"\n\n self.pre_layer_norm = pre_layer_norm\n\n if norm_type in (\"ada_norm\", \"ada_norm_zero\") and num_embeds_ada_norm is None:\n raise ValueError(\n f\"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to\"\n f\" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.\"\n )\n\n # 1. Self-Attn\n self.attn1 = Attention(\n query_dim=dim,\n heads=num_attention_heads,\n dim_head=attention_head_dim,\n dropout=dropout,\n bias=attention_bias,\n cross_attention_dim=cross_attention_dim if only_cross_attention else None,\n upcast_attention=upcast_attention,\n )\n\n # 2. Cross-Attn\n if cross_attention_dim is not None or double_self_attention:\n self.attn2 = Attention(\n query_dim=dim,\n cross_attention_dim=cross_attention_dim if not double_self_attention else None,\n heads=num_attention_heads,\n dim_head=attention_head_dim,\n dropout=dropout,\n bias=attention_bias,\n upcast_attention=upcast_attention,\n ) # is self-attn if encoder_hidden_states is none\n else:\n self.attn2 = None\n\n if self.use_ada_layer_norm:\n self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)\n else:\n self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)\n\n if cross_attention_dim is not None or double_self_attention:\n # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.\n # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during\n # the second cross attention block.\n self.norm2 = (\n AdaLayerNorm(dim, num_embeds_ada_norm)\n if self.use_ada_layer_norm\n else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)\n )\n else:\n self.norm2 = None\n\n # 3. Feed-forward\n self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)\n self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout)\n\n def forward(\n self,\n hidden_states,\n attention_mask=None,\n encoder_hidden_states=None,\n encoder_attention_mask=None,\n timestep=None,\n cross_attention_kwargs=None,\n class_labels=None,\n ):\n # Following the diffusers transformer block implementation, put the LayerNorm on the\n # residual backbone\n # Pre-LayerNorm\n if self.pre_layer_norm:\n if self.use_ada_layer_norm:\n hidden_states = self.norm1(hidden_states, timestep)\n else:\n hidden_states = self.norm1(hidden_states)\n\n # 1. Self-Attention\n cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}\n attn_output = self.attn1(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n\n hidden_states = attn_output + hidden_states\n\n # Following the diffusers transformer block implementation, put the LayerNorm on the\n # residual backbone\n # Post-LayerNorm\n if not self.pre_layer_norm:\n if self.use_ada_layer_norm:\n hidden_states = self.norm1(hidden_states, timestep)\n else:\n hidden_states = self.norm1(hidden_states)\n\n if self.attn2 is not None:\n # Pre-LayerNorm\n if self.pre_layer_norm:\n hidden_states = (\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\n )\n # TODO (Birch-San): Here we should prepare the encoder_attention mask correctly\n # prepare attention mask here\n\n # 2. Cross-Attention\n attn_output = self.attn2(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=encoder_attention_mask,\n **cross_attention_kwargs,\n )\n\n hidden_states = attn_output + hidden_states\n\n # Post-LayerNorm\n if not self.pre_layer_norm:\n hidden_states = (\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\n )\n\n # 3. Feed-forward\n # Pre-LayerNorm\n if self.pre_layer_norm:\n hidden_states = self.norm3(hidden_states)\n\n ff_output = self.ff(hidden_states)\n\n hidden_states = ff_output + hidden_states\n\n # Post-LayerNorm\n if not self.pre_layer_norm:\n hidden_states = self.norm3(hidden_states)\n\n return hidden_states\n\n\n# Modified from diffusers.models.transformer_2d.Transformer2DModel\n# Modify the transformer block structure to be U-Net like following U-ViT\n# Only supports patch-style input and torch.nn.LayerNorm currently\n# https://github.com/baofff/U-ViT\nclass UTransformer2DModel(ModelMixin, ConfigMixin):\n \"\"\"\n Transformer model based on the [U-ViT](https://github.com/baofff/U-ViT) architecture for image-like data. Compared\n to [`Transformer2DModel`], this model has skip connections between transformer blocks in a \"U\"-shaped fashion,\n similar to a U-Net. Supports only continuous (actual embeddings) inputs, which are embedded via a [`PatchEmbed`]\n layer and then reshaped to (b, t, d).\n\n Parameters:\n num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.\n attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.\n in_channels (`int`, *optional*):\n Pass if the input is continuous. The number of channels in the input.\n out_channels (`int`, *optional*):\n The number of output channels; if `None`, defaults to `in_channels`.\n num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.\n dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.\n norm_num_groups (`int`, *optional*, defaults to `32`):\n The number of groups to use when performing Group Normalization.\n cross_attention_dim (`int`, *optional*): The number of encoder_hidden_states dimensions to use.\n attention_bias (`bool`, *optional*):\n Configure if the TransformerBlocks' attention should contain a bias parameter.\n sample_size (`int`, *optional*): Pass if the input is discrete. The width of the latent images.\n Note that this is fixed at training time as it is used for learning a number of position embeddings. See\n `ImagePositionalEmbeddings`.\n num_vector_embeds (`int`, *optional*):\n Pass if the input is discrete. The number of classes of the vector embeddings of the latent pixels.\n Includes the class for the masked latent pixel.\n patch_size (`int`, *optional*, defaults to 2):\n The patch size to use in the patch embedding.\n activation_fn (`str`, *optional*, defaults to `\"geglu\"`): Activation function to be used in feed-forward.\n num_embeds_ada_norm ( `int`, *optional*): Pass if at least one of the norm_layers is `AdaLayerNorm`.\n The number of diffusion steps used during training. Note that this is fixed at training time as it is used\n to learn a number of embeddings that are added to the hidden states. During inference, you can denoise for\n up to but not more than steps than `num_embeds_ada_norm`.\n use_linear_projection (int, *optional*): TODO: Not used\n only_cross_attention (`bool`, *optional*):\n Whether to use only cross-attention layers. In this case two cross attention layers are used in each\n transformer block.\n upcast_attention (`bool`, *optional*):\n Whether to upcast the query and key to float() when performing the attention calculation.\n norm_type (`str`, *optional*, defaults to `\"layer_norm\"`):\n The Layer Normalization implementation to use. Defaults to `torch.nn.LayerNorm`.\n block_type (`str`, *optional*, defaults to `\"unidiffuser\"`):\n The transformer block implementation to use. If `\"unidiffuser\"`, has the LayerNorms on the residual\n backbone of each transformer block; otherwise has them in the attention/feedforward branches (the standard\n behavior in `diffusers`.)\n pre_layer_norm (`bool`, *optional*):\n Whether to perform layer normalization before the attention and feedforward operations (\"pre-LayerNorm\"),\n as opposed to after (\"post-LayerNorm\"). The original UniDiffuser implementation is post-LayerNorm\n (`pre_layer_norm = False`).\n norm_elementwise_affine (`bool`, *optional*):\n Whether to use learnable per-element affine parameters during layer normalization.\n use_patch_pos_embed (`bool`, *optional*):\n Whether to use position embeddings inside the patch embedding layer (`PatchEmbed`).\n final_dropout (`bool`, *optional*):\n Whether to use a final Dropout layer after the feedforward network.\n \"\"\"\n\n @register_to_config\n def __init__(\n self,\n num_attention_heads: int = 16,\n attention_head_dim: int = 88,\n in_channels: Optional[int] = None,\n out_channels: Optional[int] = None,\n num_layers: int = 1,\n dropout: float = 0.0,\n norm_num_groups: int = 32,\n cross_attention_dim: Optional[int] = None,\n attention_bias: bool = False,\n sample_size: Optional[int] = None,\n num_vector_embeds: Optional[int] = None,\n patch_size: Optional[int] = 2,\n activation_fn: str = \"geglu\",\n num_embeds_ada_norm: Optional[int] = None,\n use_linear_projection: bool = False,\n only_cross_attention: bool = False,\n upcast_attention: bool = False,\n norm_type: str = \"layer_norm\",\n block_type: str = \"unidiffuser\",\n pre_layer_norm: bool = False,\n norm_elementwise_affine: bool = True,\n use_patch_pos_embed=False,\n ff_final_dropout: bool = False,\n ):\n super().__init__()\n self.use_linear_projection = use_linear_projection\n self.num_attention_heads = num_attention_heads\n self.attention_head_dim = attention_head_dim\n inner_dim = num_attention_heads * attention_head_dim\n\n # 1. Input\n # Only support patch input of shape (batch_size, num_channels, height, width) for now\n assert in_channels is not None and patch_size is not None, \"Patch input requires in_channels and patch_size.\"\n\n assert sample_size is not None, \"UTransformer2DModel over patched input must provide sample_size\"\n\n # 2. Define input layers\n self.height = sample_size\n self.width = sample_size\n\n self.patch_size = patch_size\n self.pos_embed = PatchEmbed(\n height=sample_size,\n width=sample_size,\n patch_size=patch_size,\n in_channels=in_channels,\n embed_dim=inner_dim,\n use_pos_embed=use_patch_pos_embed,\n )\n\n # 3. Define transformers blocks\n # Modify this to have in_blocks (\"downsample\" blocks, even though we don't actually downsample), a mid_block,\n # and out_blocks (\"upsample\" blocks). Like a U-Net, there are skip connections from in_blocks to out_blocks in\n # a \"U\"-shaped fashion (e.g. first in_block to last out_block, etc.).\n # Quick hack to make the transformer block type configurable\n if block_type == \"unidiffuser\":\n block_cls = UniDiffuserBlock\n else:\n block_cls = UTransformerBlock\n self.transformer_in_blocks = nn.ModuleList(\n [\n block_cls(\n inner_dim,\n num_attention_heads,\n attention_head_dim,\n dropout=dropout,\n cross_attention_dim=cross_attention_dim,\n activation_fn=activation_fn,\n num_embeds_ada_norm=num_embeds_ada_norm,\n attention_bias=attention_bias,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n norm_type=norm_type,\n pre_layer_norm=pre_layer_norm,\n norm_elementwise_affine=norm_elementwise_affine,\n final_dropout=ff_final_dropout,\n )\n for d in range(num_layers // 2)\n ]\n )\n\n self.transformer_mid_block = block_cls(\n inner_dim,\n num_attention_heads,\n attention_head_dim,\n dropout=dropout,\n cross_attention_dim=cross_attention_dim,\n activation_fn=activation_fn,\n num_embeds_ada_norm=num_embeds_ada_norm,\n attention_bias=attention_bias,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n norm_type=norm_type,\n pre_layer_norm=pre_layer_norm,\n norm_elementwise_affine=norm_elementwise_affine,\n final_dropout=ff_final_dropout,\n )\n\n # For each skip connection, we use a SkipBlock (concatenation + Linear + LayerNorm) to process the inputs\n # before each transformer out_block.\n self.transformer_out_blocks = nn.ModuleList(\n [\n nn.ModuleDict(\n {\n \"skip\": SkipBlock(\n inner_dim,\n ),\n \"block\": block_cls(\n inner_dim,\n num_attention_heads,\n attention_head_dim,\n dropout=dropout,\n cross_attention_dim=cross_attention_dim,\n activation_fn=activation_fn,\n num_embeds_ada_norm=num_embeds_ada_norm,\n attention_bias=attention_bias,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n norm_type=norm_type,\n pre_layer_norm=pre_layer_norm,\n norm_elementwise_affine=norm_elementwise_affine,\n final_dropout=ff_final_dropout,\n ),\n }\n )\n for d in range(num_layers // 2)\n ]\n )\n\n # 4. Define output layers\n self.out_channels = in_channels if out_channels is None else out_channels\n\n # Following the UniDiffuser U-ViT implementation, we process the transformer output with\n # a LayerNorm layer with per-element affine params\n self.norm_out = nn.LayerNorm(inner_dim)\n\n def forward(\n self,\n hidden_states,\n encoder_hidden_states=None,\n timestep=None,\n class_labels=None,\n cross_attention_kwargs=None,\n return_dict: bool = True,\n hidden_states_is_embedding: bool = False,\n unpatchify: bool = True,\n ):\n \"\"\"\n Args:\n hidden_states ( When discrete, `torch.LongTensor` of shape `(batch size, num latent pixels)`.\n When continuous, `torch.Tensor` of shape `(batch size, channel, height, width)`): Input hidden_states\n encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):\n Conditional embeddings for cross attention layer. If not given, cross-attention defaults to\n self-attention.\n timestep ( `torch.long`, *optional*):\n Optional timestep to be applied as an embedding in AdaLayerNorm's. Used to indicate denoising step.\n class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):\n Optional class labels to be applied as an embedding in AdaLayerZeroNorm. Used to indicate class labels\n conditioning.\n cross_attention_kwargs (*optional*):\n Keyword arguments to supply to the cross attention layers, if used.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain\n tuple.\n hidden_states_is_embedding (`bool`, *optional*, defaults to `False`):\n Whether or not hidden_states is an embedding directly usable by the transformer. In this case we will\n ignore input handling (e.g. continuous, vectorized, etc.) and directly feed hidden_states into the\n transformer blocks.\n unpatchify (`bool`, *optional*, defaults to `True`):\n Whether to unpatchify the transformer output.\n\n Returns:\n [`~models.transformer_2d.Transformer2DModelOutput`] or `tuple`:\n [`~models.transformer_2d.Transformer2DModelOutput`] if `return_dict` is True, otherwise a `tuple`. When\n returning a tuple, the first element is the sample tensor.\n \"\"\"\n # 0. Check inputs\n\n if not unpatchify and return_dict:\n raise ValueError(\n f\"Cannot both define `unpatchify`: {unpatchify} and `return_dict`: {return_dict} since when\"\n f\" `unpatchify` is {unpatchify} the returned output is of shape (batch_size, seq_len, hidden_dim)\"\n \" rather than (batch_size, num_channels, height, width).\"\n )\n\n # 1. Input\n if not hidden_states_is_embedding:\n hidden_states = self.pos_embed(hidden_states)\n\n # 2. Blocks\n\n # In (\"downsample\") blocks\n skips = []\n for in_block in self.transformer_in_blocks:\n hidden_states = in_block(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n timestep=timestep,\n cross_attention_kwargs=cross_attention_kwargs,\n class_labels=class_labels,\n )\n skips.append(hidden_states)\n\n # Mid block\n hidden_states = self.transformer_mid_block(hidden_states)\n\n # Out (\"upsample\") blocks\n for out_block in self.transformer_out_blocks:\n hidden_states = out_block[\"skip\"](hidden_states, skips.pop())\n hidden_states = out_block[\"block\"](\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n timestep=timestep,\n cross_attention_kwargs=cross_attention_kwargs,\n class_labels=class_labels,\n )\n\n # 3. Output\n # Don't support AdaLayerNorm for now, so no conditioning/scale/shift logic\n hidden_states = self.norm_out(hidden_states)\n # hidden_states = self.proj_out(hidden_states)\n\n if unpatchify:\n # unpatchify\n height = width = int(hidden_states.shape[1] ** 0.5)\n hidden_states = hidden_states.reshape(\n shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)\n )\n hidden_states = torch.einsum(\"nhwpqc->nchpwq\", hidden_states)\n output = hidden_states.reshape(\n shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)\n )\n else:\n output = hidden_states\n\n if not return_dict:\n return (output,)\n\n return Transformer2DModelOutput(sample=output)\n\n\nclass UniDiffuserModel(ModelMixin, ConfigMixin):\n \"\"\"\n Transformer model for a image-text [UniDiffuser](https://arxiv.org/pdf/2303.06555.pdf) model. This is a\n modification of [`UTransformer2DModel`] with input and output heads for the VAE-embedded latent image, the\n CLIP-embedded image, and the CLIP-embedded prompt (see paper for more details).\n\n Parameters:\n text_dim (`int`): The hidden dimension of the CLIP text model used to embed images.\n clip_img_dim (`int`): The hidden dimension of the CLIP vision model used to embed prompts.\n num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.\n attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.\n in_channels (`int`, *optional*):\n Pass if the input is continuous. The number of channels in the input.\n out_channels (`int`, *optional*):\n The number of output channels; if `None`, defaults to `in_channels`.\n num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.\n dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.\n norm_num_groups (`int`, *optional*, defaults to `32`):\n The number of groups to use when performing Group Normalization.\n cross_attention_dim (`int`, *optional*): The number of encoder_hidden_states dimensions to use.\n attention_bias (`bool`, *optional*):\n Configure if the TransformerBlocks' attention should contain a bias parameter.\n sample_size (`int`, *optional*): Pass if the input is discrete. The width of the latent images.\n Note that this is fixed at training time as it is used for learning a number of position embeddings. See\n `ImagePositionalEmbeddings`.\n num_vector_embeds (`int`, *optional*):\n Pass if the input is discrete. The number of classes of the vector embeddings of the latent pixels.\n Includes the class for the masked latent pixel.\n patch_size (`int`, *optional*, defaults to 2):\n The patch size to use in the patch embedding.\n activation_fn (`str`, *optional*, defaults to `\"geglu\"`): Activation function to be used in feed-forward.\n num_embeds_ada_norm ( `int`, *optional*): Pass if at least one of the norm_layers is `AdaLayerNorm`.\n The number of diffusion steps used during training. Note that this is fixed at training time as it is used\n to learn a number of embeddings that are added to the hidden states. During inference, you can denoise for\n up to but not more than steps than `num_embeds_ada_norm`.\n use_linear_projection (int, *optional*): TODO: Not used\n only_cross_attention (`bool`, *optional*):\n Whether to use only cross-attention layers. In this case two cross attention layers are used in each\n transformer block.\n upcast_attention (`bool`, *optional*):\n Whether to upcast the query and key to float32 when performing the attention calculation.\n norm_type (`str`, *optional*, defaults to `\"layer_norm\"`):\n The Layer Normalization implementation to use. Defaults to `torch.nn.LayerNorm`.\n block_type (`str`, *optional*, defaults to `\"unidiffuser\"`):\n The transformer block implementation to use. If `\"unidiffuser\"`, has the LayerNorms on the residual\n backbone of each transformer block; otherwise has them in the attention/feedforward branches (the standard\n behavior in `diffusers`.)\n pre_layer_norm (`bool`, *optional*):\n Whether to perform layer normalization before the attention and feedforward operations (\"pre-LayerNorm\"),\n as opposed to after (\"post-LayerNorm\"). The original UniDiffuser implementation is post-LayerNorm\n (`pre_layer_norm = False`).\n norm_elementwise_affine (`bool`, *optional*):\n Whether to use learnable per-element affine parameters during layer normalization.\n use_patch_pos_embed (`bool`, *optional*):\n Whether to use position embeddings inside the patch embedding layer (`PatchEmbed`).\n ff_final_dropout (`bool`, *optional*):\n Whether to use a final Dropout layer after the feedforward network.\n use_data_type_embedding (`bool`, *optional*):\n Whether to use a data type embedding. This is only relevant for UniDiffuser-v1 style models; UniDiffuser-v1\n is continue-trained from UniDiffuser-v0 on non-publically-available data and accepts a `data_type`\n argument, which can either be `1` to use the weights trained on non-publically-available data or `0`\n otherwise. This argument is subsequently embedded by the data type embedding, if used.\n \"\"\"\n\n @register_to_config\n def __init__(\n self,\n text_dim: int = 768,\n clip_img_dim: int = 512,\n num_text_tokens: int = 77,\n num_attention_heads: int = 16,\n attention_head_dim: int = 88,\n in_channels: Optional[int] = None,\n out_channels: Optional[int] = None,\n num_layers: int = 1,\n dropout: float = 0.0,\n norm_num_groups: int = 32,\n cross_attention_dim: Optional[int] = None,\n attention_bias: bool = False,\n sample_size: Optional[int] = None,\n num_vector_embeds: Optional[int] = None,\n patch_size: Optional[int] = None,\n activation_fn: str = \"geglu\",\n num_embeds_ada_norm: Optional[int] = None,\n use_linear_projection: bool = False,\n only_cross_attention: bool = False,\n upcast_attention: bool = False,\n norm_type: str = \"layer_norm\",\n block_type: str = \"unidiffuser\",\n pre_layer_norm: bool = False,\n use_timestep_embedding=False,\n norm_elementwise_affine: bool = True,\n use_patch_pos_embed=False,\n ff_final_dropout: bool = True,\n use_data_type_embedding: bool = False,\n ):\n super().__init__()\n\n # 0. Handle dimensions\n self.inner_dim = num_attention_heads * attention_head_dim\n\n assert sample_size is not None, \"UniDiffuserModel over patched input must provide sample_size\"\n self.sample_size = sample_size\n self.in_channels = in_channels\n self.out_channels = in_channels if out_channels is None else out_channels\n\n self.patch_size = patch_size\n # Assume image is square...\n self.num_patches = (self.sample_size // patch_size) * (self.sample_size // patch_size)\n\n # 1. Define input layers\n # 1.1 Input layers for text and image input\n # For now, only support patch input for VAE latent image input\n self.vae_img_in = PatchEmbed(\n height=sample_size,\n width=sample_size,\n patch_size=patch_size,\n in_channels=in_channels,\n embed_dim=self.inner_dim,\n use_pos_embed=use_patch_pos_embed,\n )\n self.clip_img_in = nn.Linear(clip_img_dim, self.inner_dim)\n self.text_in = nn.Linear(text_dim, self.inner_dim)\n\n # 1.2. Timestep embeddings for t_img, t_text\n self.timestep_img_proj = Timesteps(\n self.inner_dim,\n flip_sin_to_cos=True,\n downscale_freq_shift=0,\n )\n self.timestep_img_embed = (\n TimestepEmbedding(\n self.inner_dim,\n 4 * self.inner_dim,\n out_dim=self.inner_dim,\n )\n if use_timestep_embedding\n else nn.Identity()\n )\n\n self.timestep_text_proj = Timesteps(\n self.inner_dim,\n flip_sin_to_cos=True,\n downscale_freq_shift=0,\n )\n self.timestep_text_embed = (\n TimestepEmbedding(\n self.inner_dim,\n 4 * self.inner_dim,\n out_dim=self.inner_dim,\n )\n if use_timestep_embedding\n else nn.Identity()\n )\n\n # 1.3. Positional embedding\n self.num_text_tokens = num_text_tokens\n self.num_tokens = 1 + 1 + num_text_tokens + 1 + self.num_patches\n self.pos_embed = nn.Parameter(torch.zeros(1, self.num_tokens, self.inner_dim))\n self.pos_embed_drop = nn.Dropout(p=dropout)\n trunc_normal_(self.pos_embed, std=0.02)\n\n # 1.4. Handle data type token embeddings for UniDiffuser-V1, if necessary\n self.use_data_type_embedding = use_data_type_embedding\n if self.use_data_type_embedding:\n self.data_type_token_embedding = nn.Embedding(2, self.inner_dim)\n self.data_type_pos_embed_token = nn.Parameter(torch.zeros(1, 1, self.inner_dim))\n\n # 2. Define transformer blocks\n self.transformer = UTransformer2DModel(\n num_attention_heads=num_attention_heads,\n attention_head_dim=attention_head_dim,\n in_channels=in_channels,\n out_channels=out_channels,\n num_layers=num_layers,\n dropout=dropout,\n norm_num_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n attention_bias=attention_bias,\n sample_size=sample_size,\n num_vector_embeds=num_vector_embeds,\n patch_size=patch_size,\n activation_fn=activation_fn,\n num_embeds_ada_norm=num_embeds_ada_norm,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention,\n upcast_attention=upcast_attention,\n norm_type=norm_type,\n block_type=block_type,\n pre_layer_norm=pre_layer_norm,\n norm_elementwise_affine=norm_elementwise_affine,\n use_patch_pos_embed=use_patch_pos_embed,\n ff_final_dropout=ff_final_dropout,\n )\n\n # 3. Define output layers\n patch_dim = (patch_size**2) * out_channels\n self.vae_img_out = nn.Linear(self.inner_dim, patch_dim)\n self.clip_img_out = nn.Linear(self.inner_dim, clip_img_dim)\n self.text_out = nn.Linear(self.inner_dim, text_dim)\n\n @torch.jit.ignore\n def no_weight_decay(self):\n return {\"pos_embed\"}\n\n def forward(\n self,\n latent_image_embeds: torch.Tensor,\n image_embeds: torch.Tensor,\n prompt_embeds: torch.Tensor,\n timestep_img: Union[torch.Tensor, float, int],\n timestep_text: Union[torch.Tensor, float, int],\n data_type: Optional[Union[torch.Tensor, float, int]] = 1,\n encoder_hidden_states=None,\n cross_attention_kwargs=None,\n ):\n \"\"\"\n Args:\n latent_image_embeds (`torch.Tensor` of shape `(batch size, latent channels, height, width)`):\n Latent image representation from the VAE encoder.\n image_embeds (`torch.Tensor` of shape `(batch size, 1, clip_img_dim)`):\n CLIP-embedded image representation (unsqueezed in the first dimension).\n prompt_embeds (`torch.Tensor` of shape `(batch size, seq_len, text_dim)`):\n CLIP-embedded text representation.\n timestep_img (`torch.long` or `float` or `int`):\n Current denoising step for the image.\n timestep_text (`torch.long` or `float` or `int`):\n Current denoising step for the text.\n data_type: (`torch.int` or `float` or `int`, *optional*, defaults to `1`):\n Only used in UniDiffuser-v1-style models. Can be either `1`, to use weights trained on nonpublic data,\n or `0` otherwise.\n encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):\n Conditional embeddings for cross attention layer. If not given, cross-attention defaults to\n self-attention.\n cross_attention_kwargs (*optional*):\n Keyword arguments to supply to the cross attention layers, if used.\n\n\n Returns:\n `tuple`: Returns relevant parts of the model's noise prediction: the first element of the tuple is tbe VAE\n image embedding, the second element is the CLIP image embedding, and the third element is the CLIP text\n embedding.\n \"\"\"\n batch_size = latent_image_embeds.shape[0]\n\n # 1. Input\n # 1.1. Map inputs to shape (B, N, inner_dim)\n vae_hidden_states = self.vae_img_in(latent_image_embeds)\n clip_hidden_states = self.clip_img_in(image_embeds)\n text_hidden_states = self.text_in(prompt_embeds)\n\n num_text_tokens, num_img_tokens = text_hidden_states.size(1), vae_hidden_states.size(1)\n\n # 1.2. Encode image timesteps to single token (B, 1, inner_dim)\n if not torch.is_tensor(timestep_img):\n timestep_img = torch.tensor([timestep_img], dtype=torch.long, device=vae_hidden_states.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timestep_img = timestep_img * torch.ones(batch_size, dtype=timestep_img.dtype, device=timestep_img.device)\n\n timestep_img_token = self.timestep_img_proj(timestep_img)\n # t_img_token does not contain any weights and will always return f32 tensors\n # but time_embedding might be fp16, so we need to cast here.\n timestep_img_token = timestep_img_token.to(dtype=self.dtype)\n timestep_img_token = self.timestep_img_embed(timestep_img_token)\n timestep_img_token = timestep_img_token.unsqueeze(dim=1)\n\n # 1.3. Encode text timesteps to single token (B, 1, inner_dim)\n if not torch.is_tensor(timestep_text):\n timestep_text = torch.tensor([timestep_text], dtype=torch.long, device=vae_hidden_states.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timestep_text = timestep_text * torch.ones(batch_size, dtype=timestep_text.dtype, device=timestep_text.device)\n\n timestep_text_token = self.timestep_text_proj(timestep_text)\n # t_text_token does not contain any weights and will always return f32 tensors\n # but time_embedding might be fp16, so we need to cast here.\n timestep_text_token = timestep_text_token.to(dtype=self.dtype)\n timestep_text_token = self.timestep_text_embed(timestep_text_token)\n timestep_text_token = timestep_text_token.unsqueeze(dim=1)\n\n # 1.4. Concatenate all of the embeddings together.\n if self.use_data_type_embedding:\n assert data_type is not None, \"data_type must be supplied if the model uses a data type embedding\"\n if not torch.is_tensor(data_type):\n data_type = torch.tensor([data_type], dtype=torch.int, device=vae_hidden_states.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n data_type = data_type * torch.ones(batch_size, dtype=data_type.dtype, device=data_type.device)\n\n data_type_token = self.data_type_token_embedding(data_type).unsqueeze(dim=1)\n hidden_states = torch.cat(\n [\n timestep_img_token,\n timestep_text_token,\n data_type_token,\n text_hidden_states,\n clip_hidden_states,\n vae_hidden_states,\n ],\n dim=1,\n )\n else:\n hidden_states = torch.cat(\n [timestep_img_token, timestep_text_token, text_hidden_states, clip_hidden_states, vae_hidden_states],\n dim=1,\n )\n\n # 1.5. Prepare the positional embeddings and add to hidden states\n # Note: I think img_vae should always have the proper shape, so there's no need to interpolate\n # the position embeddings.\n if self.use_data_type_embedding:\n pos_embed = torch.cat(\n [self.pos_embed[:, : 1 + 1, :], self.data_type_pos_embed_token, self.pos_embed[:, 1 + 1 :, :]], dim=1\n )\n else:\n pos_embed = self.pos_embed\n hidden_states = hidden_states + pos_embed\n hidden_states = self.pos_embed_drop(hidden_states)\n\n # 2. Blocks\n hidden_states = self.transformer(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n timestep=None,\n class_labels=None,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n hidden_states_is_embedding=True,\n unpatchify=False,\n )[0]\n\n # 3. Output\n # Split out the predicted noise representation.\n if self.use_data_type_embedding:\n (\n t_img_token_out,\n t_text_token_out,\n data_type_token_out,\n text_out,\n img_clip_out,\n img_vae_out,\n ) = hidden_states.split((1, 1, 1, num_text_tokens, 1, num_img_tokens), dim=1)\n else:\n t_img_token_out, t_text_token_out, text_out, img_clip_out, img_vae_out = hidden_states.split(\n (1, 1, num_text_tokens, 1, num_img_tokens), dim=1\n )\n\n img_vae_out = self.vae_img_out(img_vae_out)\n\n # unpatchify\n height = width = int(img_vae_out.shape[1] ** 0.5)\n img_vae_out = img_vae_out.reshape(\n shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)\n )\n img_vae_out = torch.einsum(\"nhwpqc->nchpwq\", img_vae_out)\n img_vae_out = img_vae_out.reshape(\n shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)\n )\n\n img_clip_out = self.clip_img_out(img_clip_out)\n\n text_out = self.text_out(text_out)\n\n return img_vae_out, img_clip_out, text_out\n" }, { "path": "diffusers/src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py", "content": "import inspect\nfrom dataclasses import dataclass\nfrom typing import Callable, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModel,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n GPT2Tokenizer,\n)\n\nfrom ...image_processor import VaeImageProcessor\nfrom ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin\nfrom ...models import AutoencoderKL\nfrom ...models.lora import adjust_lora_scale_text_encoder\nfrom ...schedulers import KarrasDiffusionSchedulers\nfrom ...utils import USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers\nfrom ...utils.outputs import BaseOutput\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline\nfrom .modeling_text_decoder import UniDiffuserTextDecoder\nfrom .modeling_uvit import UniDiffuserModel\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n# New BaseOutput child class for joint image-text output\n@dataclass\nclass ImageTextPipelineOutput(BaseOutput):\n \"\"\"\n Output class for joint image-text pipelines.\n\n Args:\n images (`List[PIL.Image.Image]` or `np.ndarray`)\n List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,\n num_channels)`.\n text (`List[str]` or `List[List[str]]`)\n List of generated text strings of length `batch_size` or a list of list of strings whose outer list has\n length `batch_size`.\n \"\"\"\n\n images: Optional[Union[List[PIL.Image.Image], np.ndarray]]\n text: Optional[Union[List[str], List[List[str]]]]\n\n\nclass UniDiffuserPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for a bimodal image-text model which supports unconditional text and image generation, text-conditioned\n image generation, image-conditioned text generation, and joint image-text generation.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n Args:\n vae ([`AutoencoderKL`]):\n Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. This\n is part of the UniDiffuser image representation along with the CLIP vision encoding.\n text_encoder ([`CLIPTextModel`]):\n Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).\n image_encoder ([`CLIPVisionModel`]):\n A [`~transformers.CLIPVisionModel`] to encode images as part of its image representation along with the VAE\n latent representation.\n image_processor ([`CLIPImageProcessor`]):\n [`~transformers.CLIPImageProcessor`] to preprocess an image before CLIP encoding it with `image_encoder`.\n clip_tokenizer ([`CLIPTokenizer`]):\n A [`~transformers.CLIPTokenizer`] to tokenize the prompt before encoding it with `text_encoder`.\n text_decoder ([`UniDiffuserTextDecoder`]):\n Frozen text decoder. This is a GPT-style model which is used to generate text from the UniDiffuser\n embedding.\n text_tokenizer ([`GPT2Tokenizer`]):\n A [`~transformers.GPT2Tokenizer`] to decode text for text generation; used along with the `text_decoder`.\n unet ([`UniDiffuserModel`]):\n A [U-ViT](https://github.com/baofff/U-ViT) model with UNNet-style skip connections between transformer\n layers to denoise the encoded image latents.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image and/or text latents. The\n original UniDiffuser paper uses the [`DPMSolverMultistepScheduler`] scheduler.\n \"\"\"\n\n # TODO: support for moving submodules for components with enable_model_cpu_offload\n model_cpu_offload_seq = \"text_encoder->image_encoder->unet->vae->text_decoder\"\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n image_encoder: CLIPVisionModelWithProjection,\n clip_image_processor: CLIPImageProcessor,\n clip_tokenizer: CLIPTokenizer,\n text_decoder: UniDiffuserTextDecoder,\n text_tokenizer: GPT2Tokenizer,\n unet: UniDiffuserModel,\n scheduler: KarrasDiffusionSchedulers,\n ):\n super().__init__()\n\n if text_encoder.config.hidden_size != text_decoder.prefix_inner_dim:\n raise ValueError(\n f\"The text encoder hidden size and text decoder prefix inner dim must be the same, but\"\n f\" `text_encoder.config.hidden_size`: {text_encoder.config.hidden_size} and `text_decoder.prefix_inner_dim`: {text_decoder.prefix_inner_dim}\"\n )\n\n self.register_modules(\n vae=vae,\n text_encoder=text_encoder,\n image_encoder=image_encoder,\n clip_image_processor=clip_image_processor,\n clip_tokenizer=clip_tokenizer,\n text_decoder=text_decoder,\n text_tokenizer=text_tokenizer,\n unet=unet,\n scheduler=scheduler,\n )\n\n self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)\n self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)\n\n self.num_channels_latents = vae.config.latent_channels\n self.text_encoder_seq_len = text_encoder.config.max_position_embeddings\n self.text_encoder_hidden_size = text_encoder.config.hidden_size\n self.image_encoder_projection_dim = image_encoder.config.projection_dim\n self.unet_resolution = unet.config.sample_size\n\n self.text_intermediate_dim = self.text_encoder_hidden_size\n if self.text_decoder.prefix_hidden_dim is not None:\n self.text_intermediate_dim = self.text_decoder.prefix_hidden_dim\n\n self.mode = None\n\n # TODO: handle safety checking?\n self.safety_checker = None\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def _infer_mode(self, prompt, prompt_embeds, image, latents, prompt_latents, vae_latents, clip_latents):\n r\"\"\"\n Infer the generation task ('mode') from the inputs to `__call__`. If the mode has been manually set, the set\n mode will be used.\n \"\"\"\n prompt_available = (prompt is not None) or (prompt_embeds is not None)\n image_available = image is not None\n input_available = prompt_available or image_available\n\n prompt_latents_available = prompt_latents is not None\n vae_latents_available = vae_latents is not None\n clip_latents_available = clip_latents is not None\n full_latents_available = latents is not None\n image_latents_available = vae_latents_available and clip_latents_available\n all_indv_latents_available = prompt_latents_available and image_latents_available\n\n if self.mode is not None:\n # Preferentially use the mode set by the user\n mode = self.mode\n elif prompt_available:\n mode = \"text2img\"\n elif image_available:\n mode = \"img2text\"\n else:\n # Neither prompt nor image supplied, infer based on availability of latents\n if full_latents_available or all_indv_latents_available:\n mode = \"joint\"\n elif prompt_latents_available:\n mode = \"text\"\n elif image_latents_available:\n mode = \"img\"\n else:\n # No inputs or latents available\n mode = \"joint\"\n\n # Give warnings for ambiguous cases\n if self.mode is None and prompt_available and image_available:\n logger.warning(\n f\"You have supplied both a text prompt and image to the pipeline and mode has not been set manually,\"\n f\" defaulting to mode '{mode}'.\"\n )\n\n if self.mode is None and not input_available:\n if vae_latents_available != clip_latents_available:\n # Exactly one of vae_latents and clip_latents is supplied\n logger.warning(\n f\"You have supplied exactly one of `vae_latents` and `clip_latents`, whereas either both or none\"\n f\" are expected to be supplied. Defaulting to mode '{mode}'.\"\n )\n elif not prompt_latents_available and not vae_latents_available and not clip_latents_available:\n # No inputs or latents supplied\n logger.warning(\n f\"No inputs or latents have been supplied, and mode has not been manually set,\"\n f\" defaulting to mode '{mode}'.\"\n )\n\n return mode\n\n # Copied from diffusers.pipelines.pipeline_utils.StableDiffusionMixin.enable_vae_slicing\n def enable_vae_slicing(self):\n r\"\"\"\n Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to\n compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.\n \"\"\"\n self.vae.enable_slicing()\n\n # Copied from diffusers.pipelines.pipeline_utils.StableDiffusionMixin.disable_vae_slicing\n def disable_vae_slicing(self):\n r\"\"\"\n Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to\n computing decoding in one step.\n \"\"\"\n self.vae.disable_slicing()\n\n # Copied from diffusers.pipelines.pipeline_utils.StableDiffusionMixin.enable_vae_tiling\n def enable_vae_tiling(self):\n r\"\"\"\n Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to\n compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow\n processing larger images.\n \"\"\"\n self.vae.enable_tiling()\n\n # Copied from diffusers.pipelines.pipeline_utils.StableDiffusionMixin.disable_vae_tiling\n def disable_vae_tiling(self):\n r\"\"\"\n Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to\n computing decoding in one step.\n \"\"\"\n self.vae.disable_tiling()\n\n # Functions to manually set the mode\n def set_text_mode(self):\n r\"\"\"Manually set the generation mode to unconditional (\"marginal\") text generation.\"\"\"\n self.mode = \"text\"\n\n def set_image_mode(self):\n r\"\"\"Manually set the generation mode to unconditional (\"marginal\") image generation.\"\"\"\n self.mode = \"img\"\n\n def set_text_to_image_mode(self):\n r\"\"\"Manually set the generation mode to text-conditioned image generation.\"\"\"\n self.mode = \"text2img\"\n\n def set_image_to_text_mode(self):\n r\"\"\"Manually set the generation mode to image-conditioned text generation.\"\"\"\n self.mode = \"img2text\"\n\n def set_joint_mode(self):\n r\"\"\"Manually set the generation mode to unconditional joint image-text generation.\"\"\"\n self.mode = \"joint\"\n\n def reset_mode(self):\n r\"\"\"Removes a manually set mode; after calling this, the pipeline will infer the mode from inputs.\"\"\"\n self.mode = None\n\n def _infer_batch_size(\n self,\n mode,\n prompt,\n prompt_embeds,\n image,\n num_images_per_prompt,\n num_prompts_per_image,\n latents,\n prompt_latents,\n vae_latents,\n clip_latents,\n ):\n r\"\"\"Infers the batch size and multiplier depending on mode and supplied arguments to `__call__`.\"\"\"\n if num_images_per_prompt is None:\n num_images_per_prompt = 1\n if num_prompts_per_image is None:\n num_prompts_per_image = 1\n\n assert num_images_per_prompt > 0, \"num_images_per_prompt must be a positive integer\"\n assert num_prompts_per_image > 0, \"num_prompts_per_image must be a positive integer\"\n\n if mode in [\"text2img\"]:\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n # Either prompt or prompt_embeds must be present for text2img.\n batch_size = prompt_embeds.shape[0]\n multiplier = num_images_per_prompt\n elif mode in [\"img2text\"]:\n if isinstance(image, PIL.Image.Image):\n batch_size = 1\n else:\n # Image must be available and type either PIL.Image.Image or torch.Tensor.\n # Not currently supporting something like image_embeds.\n batch_size = image.shape[0]\n multiplier = num_prompts_per_image\n elif mode in [\"img\"]:\n if vae_latents is not None:\n batch_size = vae_latents.shape[0]\n elif clip_latents is not None:\n batch_size = clip_latents.shape[0]\n else:\n batch_size = 1\n multiplier = num_images_per_prompt\n elif mode in [\"text\"]:\n if prompt_latents is not None:\n batch_size = prompt_latents.shape[0]\n else:\n batch_size = 1\n multiplier = num_prompts_per_image\n elif mode in [\"joint\"]:\n if latents is not None:\n batch_size = latents.shape[0]\n elif prompt_latents is not None:\n batch_size = prompt_latents.shape[0]\n elif vae_latents is not None:\n batch_size = vae_latents.shape[0]\n elif clip_latents is not None:\n batch_size = clip_latents.shape[0]\n else:\n batch_size = 1\n\n if num_images_per_prompt == num_prompts_per_image:\n multiplier = num_images_per_prompt\n else:\n multiplier = min(num_images_per_prompt, num_prompts_per_image)\n logger.warning(\n f\"You are using mode `{mode}` and `num_images_per_prompt`: {num_images_per_prompt} and\"\n f\" num_prompts_per_image: {num_prompts_per_image} are not equal. Using batch size equal to\"\n f\" `min(num_images_per_prompt, num_prompts_per_image) = {batch_size}.\"\n )\n return batch_size, multiplier\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt\n def _encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n **kwargs,\n ):\n deprecation_message = \"`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple.\"\n deprecate(\"_encode_prompt()\", \"1.0.0\", deprecation_message, standard_warn=False)\n\n prompt_embeds_tuple = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=lora_scale,\n **kwargs,\n )\n\n # concatenate for backwards comp\n prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with self.tokenizer->self.clip_tokenizer\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n lora_scale: Optional[float] = None,\n clip_skip: Optional[int] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n device: (`torch.device`):\n torch device\n num_images_per_prompt (`int`):\n number of images that should be generated per prompt\n do_classifier_free_guidance (`bool`):\n whether to use classifier free guidance or not\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n lora_scale (`float`, *optional*):\n A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.\n clip_skip (`int`, *optional*):\n Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that\n the output of the pre-final layer will be used for computing the prompt embeddings.\n \"\"\"\n # set lora scale so that monkey patched LoRA\n # function of text encoder can correctly access it\n if lora_scale is not None and isinstance(self, LoraLoaderMixin):\n self._lora_scale = lora_scale\n\n # dynamically adjust the LoRA scale\n if not USE_PEFT_BACKEND:\n adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)\n else:\n scale_lora_layers(self.text_encoder, lora_scale)\n\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n prompt = self.maybe_convert_prompt(prompt, self.clip_tokenizer)\n\n text_inputs = self.clip_tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.clip_tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.clip_tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.clip_tokenizer.batch_decode(\n untruncated_ids[:, self.clip_tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.clip_tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = text_inputs.attention_mask.to(device)\n else:\n attention_mask = None\n\n if clip_skip is None:\n prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)\n prompt_embeds = prompt_embeds[0]\n else:\n prompt_embeds = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True\n )\n # Access the `hidden_states` first, that contains a tuple of\n # all the hidden states from the encoder layers. Then index into\n # the tuple to access the hidden states from the desired layer.\n prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]\n # We also need to apply the final LayerNorm here to not mess with the\n # representations. The `last_hidden_states` that we typically use for\n # obtaining the final prompt representations passes through the LayerNorm\n # layer.\n prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)\n\n if self.text_encoder is not None:\n prompt_embeds_dtype = self.text_encoder.dtype\n elif self.unet is not None:\n prompt_embeds_dtype = self.unet.dtype\n else:\n prompt_embeds_dtype = prompt_embeds.dtype\n\n prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n # textual inversion: process multi-vector tokens if necessary\n if isinstance(self, TextualInversionLoaderMixin):\n uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.clip_tokenizer)\n\n max_length = prompt_embeds.shape[1]\n uncond_input = self.clip_tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n if hasattr(self.text_encoder.config, \"use_attention_mask\") and self.text_encoder.config.use_attention_mask:\n attention_mask = uncond_input.attention_mask.to(device)\n else:\n attention_mask = None\n\n negative_prompt_embeds = self.text_encoder(\n uncond_input.input_ids.to(device),\n attention_mask=attention_mask,\n )\n negative_prompt_embeds = negative_prompt_embeds[0]\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n if self.text_encoder is not None:\n if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:\n # Retrieve the original scale by scaling back the LoRA layers\n unscale_lora_layers(self.text_encoder, lora_scale)\n\n return prompt_embeds, negative_prompt_embeds\n\n # Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_instruct_pix2pix.StableDiffusionInstructPix2PixPipeline.prepare_image_latents\n # Add num_prompts_per_image argument, sample from autoencoder moment distribution\n def encode_image_vae_latents(\n self,\n image,\n batch_size,\n num_prompts_per_image,\n dtype,\n device,\n do_classifier_free_guidance,\n generator=None,\n ):\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n image = image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_prompts_per_image\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if isinstance(generator, list):\n image_latents = [\n self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i])\n * self.vae.config.scaling_factor\n for i in range(batch_size)\n ]\n image_latents = torch.cat(image_latents, dim=0)\n else:\n image_latents = self.vae.encode(image).latent_dist.sample(generator=generator)\n # Scale image_latents by the VAE's scaling factor\n image_latents = image_latents * self.vae.config.scaling_factor\n\n if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:\n # expand image_latents for batch_size\n deprecation_message = (\n f\"You have passed {batch_size} text prompts (`prompt`), but only {image_latents.shape[0]} initial\"\n \" images (`image`). Initial images are now duplicating to match the number of text prompts. Note\"\n \" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update\"\n \" your script to pass as many initial images as text prompts to suppress this warning.\"\n )\n deprecate(\"len(prompt) != len(image)\", \"1.0.0\", deprecation_message, standard_warn=False)\n additional_image_per_prompt = batch_size // image_latents.shape[0]\n image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)\n elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n image_latents = torch.cat([image_latents], dim=0)\n\n if do_classifier_free_guidance:\n uncond_image_latents = torch.zeros_like(image_latents)\n image_latents = torch.cat([image_latents, image_latents, uncond_image_latents], dim=0)\n\n return image_latents\n\n def encode_image_clip_latents(\n self,\n image,\n batch_size,\n num_prompts_per_image,\n dtype,\n device,\n generator=None,\n ):\n # Map image to CLIP embedding.\n if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):\n raise ValueError(\n f\"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}\"\n )\n\n preprocessed_image = self.clip_image_processor.preprocess(\n image,\n return_tensors=\"pt\",\n )\n preprocessed_image = preprocessed_image.to(device=device, dtype=dtype)\n\n batch_size = batch_size * num_prompts_per_image\n if isinstance(generator, list):\n image_latents = [\n self.image_encoder(**preprocessed_image[i : i + 1]).image_embeds for i in range(batch_size)\n ]\n image_latents = torch.cat(image_latents, dim=0)\n else:\n image_latents = self.image_encoder(**preprocessed_image).image_embeds\n\n if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:\n # expand image_latents for batch_size\n deprecation_message = (\n f\"You have passed {batch_size} text prompts (`prompt`), but only {image_latents.shape[0]} initial\"\n \" images (`image`). Initial images are now duplicating to match the number of text prompts. Note\"\n \" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update\"\n \" your script to pass as many initial images as text prompts to suppress this warning.\"\n )\n deprecate(\"len(prompt) != len(image)\", \"1.0.0\", deprecation_message, standard_warn=False)\n additional_image_per_prompt = batch_size // image_latents.shape[0]\n image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)\n elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:\n raise ValueError(\n f\"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts.\"\n )\n else:\n image_latents = torch.cat([image_latents], dim=0)\n\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n return image_latents\n\n def prepare_text_latents(\n self, batch_size, num_images_per_prompt, seq_len, hidden_size, dtype, device, generator, latents=None\n ):\n # Prepare latents for the CLIP embedded prompt.\n shape = (batch_size * num_images_per_prompt, seq_len, hidden_size)\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n # latents is assumed to have shace (B, L, D)\n latents = latents.repeat(num_images_per_prompt, 1, 1)\n latents = latents.to(device=device, dtype=dtype)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n # Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents\n # Rename prepare_latents -> prepare_image_vae_latents and add num_prompts_per_image argument.\n def prepare_image_vae_latents(\n self,\n batch_size,\n num_prompts_per_image,\n num_channels_latents,\n height,\n width,\n dtype,\n device,\n generator,\n latents=None,\n ):\n shape = (\n batch_size * num_prompts_per_image,\n num_channels_latents,\n height // self.vae_scale_factor,\n width // self.vae_scale_factor,\n )\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n # latents is assumed to have shape (B, C, H, W)\n latents = latents.repeat(num_prompts_per_image, 1, 1, 1)\n latents = latents.to(device=device, dtype=dtype)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def prepare_image_clip_latents(\n self, batch_size, num_prompts_per_image, clip_img_dim, dtype, device, generator, latents=None\n ):\n # Prepare latents for the CLIP embedded image.\n shape = (batch_size * num_prompts_per_image, 1, clip_img_dim)\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n # latents is assumed to have shape (B, L, D)\n latents = latents.repeat(num_prompts_per_image, 1, 1)\n latents = latents.to(device=device, dtype=dtype)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents\n\n def decode_text_latents(self, text_latents, device):\n output_token_list, seq_lengths = self.text_decoder.generate_captions(\n text_latents, self.text_tokenizer.eos_token_id, device=device\n )\n output_list = output_token_list.cpu().numpy()\n generated_text = [\n self.text_tokenizer.decode(output[: int(length)], skip_special_tokens=True)\n for output, length in zip(output_list, seq_lengths)\n ]\n return generated_text\n\n def _split(self, x, height, width):\n r\"\"\"\n Splits a flattened embedding x of shape (B, C * H * W + clip_img_dim) into two tensors of shape (B, C, H, W)\n and (B, 1, clip_img_dim)\n \"\"\"\n batch_size = x.shape[0]\n latent_height = height // self.vae_scale_factor\n latent_width = width // self.vae_scale_factor\n img_vae_dim = self.num_channels_latents * latent_height * latent_width\n\n img_vae, img_clip = x.split([img_vae_dim, self.image_encoder_projection_dim], dim=1)\n\n img_vae = torch.reshape(img_vae, (batch_size, self.num_channels_latents, latent_height, latent_width))\n img_clip = torch.reshape(img_clip, (batch_size, 1, self.image_encoder_projection_dim))\n return img_vae, img_clip\n\n def _combine(self, img_vae, img_clip):\n r\"\"\"\n Combines a latent iamge img_vae of shape (B, C, H, W) and a CLIP-embedded image img_clip of shape (B, 1,\n clip_img_dim) into a single tensor of shape (B, C * H * W + clip_img_dim).\n \"\"\"\n img_vae = torch.reshape(img_vae, (img_vae.shape[0], -1))\n img_clip = torch.reshape(img_clip, (img_clip.shape[0], -1))\n return torch.concat([img_vae, img_clip], dim=-1)\n\n def _split_joint(self, x, height, width):\n r\"\"\"\n Splits a flattened embedding x of shape (B, C * H * W + clip_img_dim + text_seq_len * text_dim] into (img_vae,\n img_clip, text) where img_vae is of shape (B, C, H, W), img_clip is of shape (B, 1, clip_img_dim), and text is\n of shape (B, text_seq_len, text_dim).\n \"\"\"\n batch_size = x.shape[0]\n latent_height = height // self.vae_scale_factor\n latent_width = width // self.vae_scale_factor\n img_vae_dim = self.num_channels_latents * latent_height * latent_width\n text_dim = self.text_encoder_seq_len * self.text_intermediate_dim\n\n img_vae, img_clip, text = x.split([img_vae_dim, self.image_encoder_projection_dim, text_dim], dim=1)\n\n img_vae = torch.reshape(img_vae, (batch_size, self.num_channels_latents, latent_height, latent_width))\n img_clip = torch.reshape(img_clip, (batch_size, 1, self.image_encoder_projection_dim))\n text = torch.reshape(text, (batch_size, self.text_encoder_seq_len, self.text_intermediate_dim))\n return img_vae, img_clip, text\n\n def _combine_joint(self, img_vae, img_clip, text):\n r\"\"\"\n Combines a latent image img_vae of shape (B, C, H, W), a CLIP-embedded image img_clip of shape (B, L_img,\n clip_img_dim), and a text embedding text of shape (B, L_text, text_dim) into a single embedding x of shape (B,\n C * H * W + L_img * clip_img_dim + L_text * text_dim).\n \"\"\"\n img_vae = torch.reshape(img_vae, (img_vae.shape[0], -1))\n img_clip = torch.reshape(img_clip, (img_clip.shape[0], -1))\n text = torch.reshape(text, (text.shape[0], -1))\n return torch.concat([img_vae, img_clip, text], dim=-1)\n\n def _get_noise_pred(\n self,\n mode,\n latents,\n t,\n prompt_embeds,\n img_vae,\n img_clip,\n max_timestep,\n data_type,\n guidance_scale,\n generator,\n device,\n height,\n width,\n ):\n r\"\"\"\n Gets the noise prediction using the `unet` and performs classifier-free guidance, if necessary.\n \"\"\"\n if mode == \"joint\":\n # Joint text-image generation\n img_vae_latents, img_clip_latents, text_latents = self._split_joint(latents, height, width)\n\n img_vae_out, img_clip_out, text_out = self.unet(\n img_vae_latents, img_clip_latents, text_latents, timestep_img=t, timestep_text=t, data_type=data_type\n )\n\n x_out = self._combine_joint(img_vae_out, img_clip_out, text_out)\n\n if guidance_scale <= 1.0:\n return x_out\n\n # Classifier-free guidance\n img_vae_T = randn_tensor(img_vae.shape, generator=generator, device=device, dtype=img_vae.dtype)\n img_clip_T = randn_tensor(img_clip.shape, generator=generator, device=device, dtype=img_clip.dtype)\n text_T = randn_tensor(prompt_embeds.shape, generator=generator, device=device, dtype=prompt_embeds.dtype)\n\n _, _, text_out_uncond = self.unet(\n img_vae_T, img_clip_T, text_latents, timestep_img=max_timestep, timestep_text=t, data_type=data_type\n )\n\n img_vae_out_uncond, img_clip_out_uncond, _ = self.unet(\n img_vae_latents,\n img_clip_latents,\n text_T,\n timestep_img=t,\n timestep_text=max_timestep,\n data_type=data_type,\n )\n\n x_out_uncond = self._combine_joint(img_vae_out_uncond, img_clip_out_uncond, text_out_uncond)\n\n return guidance_scale * x_out + (1.0 - guidance_scale) * x_out_uncond\n elif mode == \"text2img\":\n # Text-conditioned image generation\n img_vae_latents, img_clip_latents = self._split(latents, height, width)\n\n img_vae_out, img_clip_out, text_out = self.unet(\n img_vae_latents, img_clip_latents, prompt_embeds, timestep_img=t, timestep_text=0, data_type=data_type\n )\n\n img_out = self._combine(img_vae_out, img_clip_out)\n\n if guidance_scale <= 1.0:\n return img_out\n\n # Classifier-free guidance\n text_T = randn_tensor(prompt_embeds.shape, generator=generator, device=device, dtype=prompt_embeds.dtype)\n\n img_vae_out_uncond, img_clip_out_uncond, text_out_uncond = self.unet(\n img_vae_latents,\n img_clip_latents,\n text_T,\n timestep_img=t,\n timestep_text=max_timestep,\n data_type=data_type,\n )\n\n img_out_uncond = self._combine(img_vae_out_uncond, img_clip_out_uncond)\n\n return guidance_scale * img_out + (1.0 - guidance_scale) * img_out_uncond\n elif mode == \"img2text\":\n # Image-conditioned text generation\n img_vae_out, img_clip_out, text_out = self.unet(\n img_vae, img_clip, latents, timestep_img=0, timestep_text=t, data_type=data_type\n )\n\n if guidance_scale <= 1.0:\n return text_out\n\n # Classifier-free guidance\n img_vae_T = randn_tensor(img_vae.shape, generator=generator, device=device, dtype=img_vae.dtype)\n img_clip_T = randn_tensor(img_clip.shape, generator=generator, device=device, dtype=img_clip.dtype)\n\n img_vae_out_uncond, img_clip_out_uncond, text_out_uncond = self.unet(\n img_vae_T, img_clip_T, latents, timestep_img=max_timestep, timestep_text=t, data_type=data_type\n )\n\n return guidance_scale * text_out + (1.0 - guidance_scale) * text_out_uncond\n elif mode == \"text\":\n # Unconditional (\"marginal\") text generation (no CFG)\n img_vae_out, img_clip_out, text_out = self.unet(\n img_vae, img_clip, latents, timestep_img=max_timestep, timestep_text=t, data_type=data_type\n )\n\n return text_out\n elif mode == \"img\":\n # Unconditional (\"marginal\") image generation (no CFG)\n img_vae_latents, img_clip_latents = self._split(latents, height, width)\n\n img_vae_out, img_clip_out, text_out = self.unet(\n img_vae_latents,\n img_clip_latents,\n prompt_embeds,\n timestep_img=t,\n timestep_text=max_timestep,\n data_type=data_type,\n )\n\n img_out = self._combine(img_vae_out, img_clip_out)\n return img_out\n\n def check_latents_shape(self, latents_name, latents, expected_shape):\n latents_shape = latents.shape\n expected_num_dims = len(expected_shape) + 1 # expected dimensions plus the batch dimension\n expected_shape_str = \", \".join(str(dim) for dim in expected_shape)\n if len(latents_shape) != expected_num_dims:\n raise ValueError(\n f\"`{latents_name}` should have shape (batch_size, {expected_shape_str}), but the current shape\"\n f\" {latents_shape} has {len(latents_shape)} dimensions.\"\n )\n for i in range(1, expected_num_dims):\n if latents_shape[i] != expected_shape[i - 1]:\n raise ValueError(\n f\"`{latents_name}` should have shape (batch_size, {expected_shape_str}), but the current shape\"\n f\" {latents_shape} has {latents_shape[i]} != {expected_shape[i - 1]} at dimension {i}.\"\n )\n\n def check_inputs(\n self,\n mode,\n prompt,\n image,\n height,\n width,\n callback_steps,\n negative_prompt=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n latents=None,\n prompt_latents=None,\n vae_latents=None,\n clip_latents=None,\n ):\n # Check inputs before running the generative process.\n if height % self.vae_scale_factor != 0 or width % self.vae_scale_factor != 0:\n raise ValueError(\n f\"`height` and `width` have to be divisible by {self.vae_scale_factor} but are {height} and {width}.\"\n )\n\n if (callback_steps is None) or (\n callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)\n ):\n raise ValueError(\n f\"`callback_steps` has to be a positive integer but is {callback_steps} of type\"\n f\" {type(callback_steps)}.\"\n )\n\n if mode == \"text2img\":\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if mode == \"img2text\":\n if image is None:\n raise ValueError(\"`img2text` mode requires an image to be provided.\")\n\n # Check provided latents\n latent_height = height // self.vae_scale_factor\n latent_width = width // self.vae_scale_factor\n full_latents_available = latents is not None\n prompt_latents_available = prompt_latents is not None\n vae_latents_available = vae_latents is not None\n clip_latents_available = clip_latents is not None\n\n if full_latents_available:\n individual_latents_available = (\n prompt_latents is not None or vae_latents is not None or clip_latents is not None\n )\n if individual_latents_available:\n logger.warning(\n \"You have supplied both `latents` and at least one of `prompt_latents`, `vae_latents`, and\"\n \" `clip_latents`. The value of `latents` will override the value of any individually supplied latents.\"\n )\n # Check shape of full latents\n img_vae_dim = self.num_channels_latents * latent_height * latent_width\n text_dim = self.text_encoder_seq_len * self.text_encoder_hidden_size\n latents_dim = img_vae_dim + self.image_encoder_projection_dim + text_dim\n latents_expected_shape = (latents_dim,)\n self.check_latents_shape(\"latents\", latents, latents_expected_shape)\n\n # Check individual latent shapes, if present\n if prompt_latents_available:\n prompt_latents_expected_shape = (self.text_encoder_seq_len, self.text_encoder_hidden_size)\n self.check_latents_shape(\"prompt_latents\", prompt_latents, prompt_latents_expected_shape)\n\n if vae_latents_available:\n vae_latents_expected_shape = (self.num_channels_latents, latent_height, latent_width)\n self.check_latents_shape(\"vae_latents\", vae_latents, vae_latents_expected_shape)\n\n if clip_latents_available:\n clip_latents_expected_shape = (1, self.image_encoder_projection_dim)\n self.check_latents_shape(\"clip_latents\", clip_latents, clip_latents_expected_shape)\n\n if mode in [\"text2img\", \"img\"] and vae_latents_available and clip_latents_available:\n if vae_latents.shape[0] != clip_latents.shape[0]:\n raise ValueError(\n f\"Both `vae_latents` and `clip_latents` are supplied, but their batch dimensions are not equal:\"\n f\" {vae_latents.shape[0]} != {clip_latents.shape[0]}.\"\n )\n\n if mode == \"joint\" and prompt_latents_available and vae_latents_available and clip_latents_available:\n if prompt_latents.shape[0] != vae_latents.shape[0] or prompt_latents.shape[0] != clip_latents.shape[0]:\n raise ValueError(\n f\"All of `prompt_latents`, `vae_latents`, and `clip_latents` are supplied, but their batch\"\n f\" dimensions are not equal: {prompt_latents.shape[0]} != {vae_latents.shape[0]}\"\n f\" != {clip_latents.shape[0]}.\"\n )\n\n @torch.no_grad()\n def __call__(\n self,\n prompt: Optional[Union[str, List[str]]] = None,\n image: Optional[Union[torch.Tensor, PIL.Image.Image]] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n data_type: Optional[int] = 1,\n num_inference_steps: int = 50,\n guidance_scale: float = 8.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n num_prompts_per_image: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n prompt_latents: Optional[torch.Tensor] = None,\n vae_latents: Optional[torch.Tensor] = None,\n clip_latents: Optional[torch.Tensor] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,\n callback_steps: int = 1,\n ):\n r\"\"\"\n The call function to the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.\n Required for text-conditioned image generation (`text2img`) mode.\n image (`torch.Tensor` or `PIL.Image.Image`, *optional*):\n `Image` or tensor representing an image batch. Required for image-conditioned text generation\n (`img2text`) mode.\n height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):\n The width in pixels of the generated image.\n data_type (`int`, *optional*, defaults to 1):\n The data type (either 0 or 1). Only used if you are loading a checkpoint which supports a data type\n embedding; this is added for compatibility with the\n [UniDiffuser-v1](https://huggingface.co/thu-ml/unidiffuser-v1) checkpoint.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 8.0):\n A higher guidance scale value encourages the model to generate images closely linked to the text\n `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide what to not include in image generation. If not defined, you need to\n pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`). Used in\n text-conditioned image generation (`text2img`) mode.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt. Used in `text2img` (text-conditioned image generation) and\n `img` mode. If the mode is joint and both `num_images_per_prompt` and `num_prompts_per_image` are\n supplied, `min(num_images_per_prompt, num_prompts_per_image)` samples are generated.\n num_prompts_per_image (`int`, *optional*, defaults to 1):\n The number of prompts to generate per image. Used in `img2text` (image-conditioned text generation) and\n `text` mode. If the mode is joint and both `num_images_per_prompt` and `num_prompts_per_image` are\n supplied, `min(num_images_per_prompt, num_prompts_per_image)` samples are generated.\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies\n to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make\n generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for joint\n image-text generation. Can be used to tweak the same generation with different prompts. If not\n provided, a latents tensor is generated by sampling using the supplied random `generator`. This assumes\n a full set of VAE, CLIP, and text latents, if supplied, overrides the value of `prompt_latents`,\n `vae_latents`, and `clip_latents`.\n prompt_latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for text\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n vae_latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n clip_latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor is generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not\n provided, text embeddings are generated from the `prompt` input argument. Used in text-conditioned\n image generation (`text2img`) mode.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If\n not provided, `negative_prompt_embeds` are be generated from the `negative_prompt` input argument. Used\n in text-conditioned image generation (`text2img`) mode.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generated image. Choose between `PIL.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImageTextPipelineOutput`] instead of a plain tuple.\n callback (`Callable`, *optional*):\n A function that calls every `callback_steps` steps during inference. The function is called with the\n following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function is called. If not specified, the callback is called at\n every step.\n\n Returns:\n [`~pipelines.unidiffuser.ImageTextPipelineOutput`] or `tuple`:\n If `return_dict` is `True`, [`~pipelines.unidiffuser.ImageTextPipelineOutput`] is returned, otherwise a\n `tuple` is returned where the first element is a list with the generated images and the second element\n is a list of generated texts.\n \"\"\"\n\n # 0. Default height and width to unet\n height = height or self.unet_resolution * self.vae_scale_factor\n width = width or self.unet_resolution * self.vae_scale_factor\n\n # 1. Check inputs\n # Recalculate mode for each call to the pipeline.\n mode = self._infer_mode(prompt, prompt_embeds, image, latents, prompt_latents, vae_latents, clip_latents)\n self.check_inputs(\n mode,\n prompt,\n image,\n height,\n width,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n latents,\n prompt_latents,\n vae_latents,\n clip_latents,\n )\n\n # 2. Define call parameters\n batch_size, multiplier = self._infer_batch_size(\n mode,\n prompt,\n prompt_embeds,\n image,\n num_images_per_prompt,\n num_prompts_per_image,\n latents,\n prompt_latents,\n vae_latents,\n clip_latents,\n )\n device = self._execution_device\n reduce_text_emb_dim = self.text_intermediate_dim < self.text_encoder_hidden_size or self.mode != \"text2img\"\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n # Note that this differs from the formulation in the unidiffusers paper!\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # check if scheduler is in sigmas space\n # scheduler_is_in_sigma_space = hasattr(self.scheduler, \"sigmas\")\n\n # 3. Encode input prompt, if available; otherwise prepare text latents\n if latents is not None:\n # Overwrite individual latents\n vae_latents, clip_latents, prompt_latents = self._split_joint(latents, height, width)\n\n if mode in [\"text2img\"]:\n # 3.1. Encode input prompt, if available\n assert prompt is not None or prompt_embeds is not None\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=multiplier,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n\n # if do_classifier_free_guidance:\n # prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n else:\n # 3.2. Prepare text latent variables, if input not available\n prompt_embeds = self.prepare_text_latents(\n batch_size=batch_size,\n num_images_per_prompt=multiplier,\n seq_len=self.text_encoder_seq_len,\n hidden_size=self.text_encoder_hidden_size,\n dtype=self.text_encoder.dtype, # Should work with both full precision and mixed precision\n device=device,\n generator=generator,\n latents=prompt_latents,\n )\n\n if reduce_text_emb_dim:\n prompt_embeds = self.text_decoder.encode(prompt_embeds)\n\n # 4. Encode image, if available; otherwise prepare image latents\n if mode in [\"img2text\"]:\n # 4.1. Encode images, if available\n assert image is not None, \"`img2text` requires a conditioning image\"\n # Encode image using VAE\n image_vae = self.image_processor.preprocess(image)\n height, width = image_vae.shape[-2:]\n image_vae_latents = self.encode_image_vae_latents(\n image=image_vae,\n batch_size=batch_size,\n num_prompts_per_image=multiplier,\n dtype=prompt_embeds.dtype,\n device=device,\n do_classifier_free_guidance=False, # Copied from InstructPix2Pix, don't use their version of CFG\n generator=generator,\n )\n\n # Encode image using CLIP\n image_clip_latents = self.encode_image_clip_latents(\n image=image,\n batch_size=batch_size,\n num_prompts_per_image=multiplier,\n dtype=prompt_embeds.dtype,\n device=device,\n generator=generator,\n )\n # (batch_size, clip_hidden_size) => (batch_size, 1, clip_hidden_size)\n image_clip_latents = image_clip_latents.unsqueeze(1)\n else:\n # 4.2. Prepare image latent variables, if input not available\n # Prepare image VAE latents in latent space\n image_vae_latents = self.prepare_image_vae_latents(\n batch_size=batch_size,\n num_prompts_per_image=multiplier,\n num_channels_latents=self.num_channels_latents,\n height=height,\n width=width,\n dtype=prompt_embeds.dtype,\n device=device,\n generator=generator,\n latents=vae_latents,\n )\n\n # Prepare image CLIP latents\n image_clip_latents = self.prepare_image_clip_latents(\n batch_size=batch_size,\n num_prompts_per_image=multiplier,\n clip_img_dim=self.image_encoder_projection_dim,\n dtype=prompt_embeds.dtype,\n device=device,\n generator=generator,\n latents=clip_latents,\n )\n\n # 5. Set timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n # max_timestep = timesteps[0]\n max_timestep = self.scheduler.config.num_train_timesteps\n\n # 6. Prepare latent variables\n if mode == \"joint\":\n latents = self._combine_joint(image_vae_latents, image_clip_latents, prompt_embeds)\n elif mode in [\"text2img\", \"img\"]:\n latents = self._combine(image_vae_latents, image_clip_latents)\n elif mode in [\"img2text\", \"text\"]:\n latents = prompt_embeds\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n logger.debug(f\"Scheduler extra step kwargs: {extra_step_kwargs}\")\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # predict the noise residual\n # Also applies classifier-free guidance as described in the UniDiffuser paper\n noise_pred = self._get_noise_pred(\n mode,\n latents,\n t,\n prompt_embeds,\n image_vae_latents,\n image_clip_latents,\n max_timestep,\n data_type,\n guidance_scale,\n generator,\n device,\n height,\n width,\n )\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # 9. Post-processing\n image = None\n text = None\n if mode == \"joint\":\n image_vae_latents, image_clip_latents, text_latents = self._split_joint(latents, height, width)\n\n if not output_type == \"latent\":\n # Map latent VAE image back to pixel space\n image = self.vae.decode(image_vae_latents / self.vae.config.scaling_factor, return_dict=False)[0]\n else:\n image = image_vae_latents\n\n text = self.decode_text_latents(text_latents, device)\n elif mode in [\"text2img\", \"img\"]:\n image_vae_latents, image_clip_latents = self._split(latents, height, width)\n\n if not output_type == \"latent\":\n # Map latent VAE image back to pixel space\n image = self.vae.decode(image_vae_latents / self.vae.config.scaling_factor, return_dict=False)[0]\n else:\n image = image_vae_latents\n elif mode in [\"img2text\", \"text\"]:\n text_latents = latents\n text = self.decode_text_latents(text_latents, device)\n\n self.maybe_free_model_hooks()\n\n # 10. Postprocess the image, if necessary\n if image is not None:\n do_denormalize = [True] * image.shape[0]\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload last model to CPU\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return (image, text)\n\n return ImageTextPipelineOutput(images=image, text=text)\n" }, { "path": "diffusers/src/diffusers/pipelines/wuerstchen/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_torch_and_transformers_objects\n\n _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))\nelse:\n _import_structure[\"modeling_paella_vq_model\"] = [\"PaellaVQModel\"]\n _import_structure[\"modeling_wuerstchen_diffnext\"] = [\"WuerstchenDiffNeXt\"]\n _import_structure[\"modeling_wuerstchen_prior\"] = [\"WuerstchenPrior\"]\n _import_structure[\"pipeline_wuerstchen\"] = [\"WuerstchenDecoderPipeline\"]\n _import_structure[\"pipeline_wuerstchen_combined\"] = [\"WuerstchenCombinedPipeline\"]\n _import_structure[\"pipeline_wuerstchen_prior\"] = [\"DEFAULT_STAGE_C_TIMESTEPS\", \"WuerstchenPriorPipeline\"]\n\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_torch_and_transformers_objects import * # noqa F403\n else:\n from .modeling_paella_vq_model import PaellaVQModel\n from .modeling_wuerstchen_diffnext import WuerstchenDiffNeXt\n from .modeling_wuerstchen_prior import WuerstchenPrior\n from .pipeline_wuerstchen import WuerstchenDecoderPipeline\n from .pipeline_wuerstchen_combined import WuerstchenCombinedPipeline\n from .pipeline_wuerstchen_prior import DEFAULT_STAGE_C_TIMESTEPS, WuerstchenPriorPipeline\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/pipelines/wuerstchen/modeling_paella_vq_model.py", "content": "# Copyright (c) 2022 Dominic Rampas MIT License\n# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Union\n\nimport torch\nimport torch.nn as nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...models.autoencoders.vae import DecoderOutput, VectorQuantizer\nfrom ...models.modeling_utils import ModelMixin\nfrom ...models.vq_model import VQEncoderOutput\nfrom ...utils.accelerate_utils import apply_forward_hook\n\n\nclass MixingResidualBlock(nn.Module):\n \"\"\"\n Residual block with mixing used by Paella's VQ-VAE.\n \"\"\"\n\n def __init__(self, inp_channels, embed_dim):\n super().__init__()\n # depthwise\n self.norm1 = nn.LayerNorm(inp_channels, elementwise_affine=False, eps=1e-6)\n self.depthwise = nn.Sequential(\n nn.ReplicationPad2d(1), nn.Conv2d(inp_channels, inp_channels, kernel_size=3, groups=inp_channels)\n )\n\n # channelwise\n self.norm2 = nn.LayerNorm(inp_channels, elementwise_affine=False, eps=1e-6)\n self.channelwise = nn.Sequential(\n nn.Linear(inp_channels, embed_dim), nn.GELU(), nn.Linear(embed_dim, inp_channels)\n )\n\n self.gammas = nn.Parameter(torch.zeros(6), requires_grad=True)\n\n def forward(self, x):\n mods = self.gammas\n x_temp = self.norm1(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) * (1 + mods[0]) + mods[1]\n x = x + self.depthwise(x_temp) * mods[2]\n x_temp = self.norm2(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) * (1 + mods[3]) + mods[4]\n x = x + self.channelwise(x_temp.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) * mods[5]\n return x\n\n\nclass PaellaVQModel(ModelMixin, ConfigMixin):\n r\"\"\"VQ-VAE model from Paella model.\n\n This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library\n implements for all the model (such as downloading or saving, etc.)\n\n Parameters:\n in_channels (int, *optional*, defaults to 3): Number of channels in the input image.\n out_channels (int, *optional*, defaults to 3): Number of channels in the output.\n up_down_scale_factor (int, *optional*, defaults to 2): Up and Downscale factor of the input image.\n levels (int, *optional*, defaults to 2): Number of levels in the model.\n bottleneck_blocks (int, *optional*, defaults to 12): Number of bottleneck blocks in the model.\n embed_dim (int, *optional*, defaults to 384): Number of hidden channels in the model.\n latent_channels (int, *optional*, defaults to 4): Number of latent channels in the VQ-VAE model.\n num_vq_embeddings (int, *optional*, defaults to 8192): Number of codebook vectors in the VQ-VAE.\n scale_factor (float, *optional*, defaults to 0.3764): Scaling factor of the latent space.\n \"\"\"\n\n @register_to_config\n def __init__(\n self,\n in_channels: int = 3,\n out_channels: int = 3,\n up_down_scale_factor: int = 2,\n levels: int = 2,\n bottleneck_blocks: int = 12,\n embed_dim: int = 384,\n latent_channels: int = 4,\n num_vq_embeddings: int = 8192,\n scale_factor: float = 0.3764,\n ):\n super().__init__()\n\n c_levels = [embed_dim // (2**i) for i in reversed(range(levels))]\n # Encoder blocks\n self.in_block = nn.Sequential(\n nn.PixelUnshuffle(up_down_scale_factor),\n nn.Conv2d(in_channels * up_down_scale_factor**2, c_levels[0], kernel_size=1),\n )\n down_blocks = []\n for i in range(levels):\n if i > 0:\n down_blocks.append(nn.Conv2d(c_levels[i - 1], c_levels[i], kernel_size=4, stride=2, padding=1))\n block = MixingResidualBlock(c_levels[i], c_levels[i] * 4)\n down_blocks.append(block)\n down_blocks.append(\n nn.Sequential(\n nn.Conv2d(c_levels[-1], latent_channels, kernel_size=1, bias=False),\n nn.BatchNorm2d(latent_channels), # then normalize them to have mean 0 and std 1\n )\n )\n self.down_blocks = nn.Sequential(*down_blocks)\n\n # Vector Quantizer\n self.vquantizer = VectorQuantizer(num_vq_embeddings, vq_embed_dim=latent_channels, legacy=False, beta=0.25)\n\n # Decoder blocks\n up_blocks = [nn.Sequential(nn.Conv2d(latent_channels, c_levels[-1], kernel_size=1))]\n for i in range(levels):\n for j in range(bottleneck_blocks if i == 0 else 1):\n block = MixingResidualBlock(c_levels[levels - 1 - i], c_levels[levels - 1 - i] * 4)\n up_blocks.append(block)\n if i < levels - 1:\n up_blocks.append(\n nn.ConvTranspose2d(\n c_levels[levels - 1 - i], c_levels[levels - 2 - i], kernel_size=4, stride=2, padding=1\n )\n )\n self.up_blocks = nn.Sequential(*up_blocks)\n self.out_block = nn.Sequential(\n nn.Conv2d(c_levels[0], out_channels * up_down_scale_factor**2, kernel_size=1),\n nn.PixelShuffle(up_down_scale_factor),\n )\n\n @apply_forward_hook\n def encode(self, x: torch.Tensor, return_dict: bool = True) -> VQEncoderOutput:\n h = self.in_block(x)\n h = self.down_blocks(h)\n\n if not return_dict:\n return (h,)\n\n return VQEncoderOutput(latents=h)\n\n @apply_forward_hook\n def decode(\n self, h: torch.Tensor, force_not_quantize: bool = True, return_dict: bool = True\n ) -> Union[DecoderOutput, torch.Tensor]:\n if not force_not_quantize:\n quant, _, _ = self.vquantizer(h)\n else:\n quant = h\n\n x = self.up_blocks(quant)\n dec = self.out_block(x)\n if not return_dict:\n return (dec,)\n\n return DecoderOutput(sample=dec)\n\n def forward(self, sample: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:\n r\"\"\"\n Args:\n sample (`torch.Tensor`): Input sample.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`DecoderOutput`] instead of a plain tuple.\n \"\"\"\n x = sample\n h = self.encode(x).latents\n dec = self.decode(h).sample\n\n if not return_dict:\n return (dec,)\n\n return DecoderOutput(sample=dec)\n" }, { "path": "diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_common.py", "content": "import torch\nimport torch.nn as nn\n\nfrom ...models.attention_processor import Attention\n\n\nclass WuerstchenLayerNorm(nn.LayerNorm):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n def forward(self, x):\n x = x.permute(0, 2, 3, 1)\n x = super().forward(x)\n return x.permute(0, 3, 1, 2)\n\n\nclass TimestepBlock(nn.Module):\n def __init__(self, c, c_timestep):\n super().__init__()\n\n self.mapper = nn.Linear(c_timestep, c * 2)\n\n def forward(self, x, t):\n a, b = self.mapper(t)[:, :, None, None].chunk(2, dim=1)\n return x * (1 + a) + b\n\n\nclass ResBlock(nn.Module):\n def __init__(self, c, c_skip=0, kernel_size=3, dropout=0.0):\n super().__init__()\n\n self.depthwise = nn.Conv2d(c + c_skip, c, kernel_size=kernel_size, padding=kernel_size // 2, groups=c)\n self.norm = WuerstchenLayerNorm(c, elementwise_affine=False, eps=1e-6)\n self.channelwise = nn.Sequential(\n nn.Linear(c, c * 4), nn.GELU(), GlobalResponseNorm(c * 4), nn.Dropout(dropout), nn.Linear(c * 4, c)\n )\n\n def forward(self, x, x_skip=None):\n x_res = x\n if x_skip is not None:\n x = torch.cat([x, x_skip], dim=1)\n x = self.norm(self.depthwise(x)).permute(0, 2, 3, 1)\n x = self.channelwise(x).permute(0, 3, 1, 2)\n return x + x_res\n\n\n# from https://github.com/facebookresearch/ConvNeXt-V2/blob/3608f67cc1dae164790c5d0aead7bf2d73d9719b/models/utils.py#L105\nclass GlobalResponseNorm(nn.Module):\n def __init__(self, dim):\n super().__init__()\n self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim))\n self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim))\n\n def forward(self, x):\n agg_norm = torch.norm(x, p=2, dim=(1, 2), keepdim=True)\n stand_div_norm = agg_norm / (agg_norm.mean(dim=-1, keepdim=True) + 1e-6)\n return self.gamma * (x * stand_div_norm) + self.beta + x\n\n\nclass AttnBlock(nn.Module):\n def __init__(self, c, c_cond, nhead, self_attn=True, dropout=0.0):\n super().__init__()\n\n self.self_attn = self_attn\n self.norm = WuerstchenLayerNorm(c, elementwise_affine=False, eps=1e-6)\n self.attention = Attention(query_dim=c, heads=nhead, dim_head=c // nhead, dropout=dropout, bias=True)\n self.kv_mapper = nn.Sequential(nn.SiLU(), nn.Linear(c_cond, c))\n\n def forward(self, x, kv):\n kv = self.kv_mapper(kv)\n norm_x = self.norm(x)\n if self.self_attn:\n batch_size, channel, _, _ = x.shape\n kv = torch.cat([norm_x.view(batch_size, channel, -1).transpose(1, 2), kv], dim=1)\n x = x + self.attention(norm_x, encoder_hidden_states=kv)\n return x\n" }, { "path": "diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_diffnext.py", "content": "# Copyright (c) 2023 Dominic Rampas MIT License\n# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport math\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...models.modeling_utils import ModelMixin\nfrom .modeling_wuerstchen_common import AttnBlock, GlobalResponseNorm, TimestepBlock, WuerstchenLayerNorm\n\n\nclass WuerstchenDiffNeXt(ModelMixin, ConfigMixin):\n @register_to_config\n def __init__(\n self,\n c_in=4,\n c_out=4,\n c_r=64,\n patch_size=2,\n c_cond=1024,\n c_hidden=[320, 640, 1280, 1280],\n nhead=[-1, 10, 20, 20],\n blocks=[4, 4, 14, 4],\n level_config=[\"CT\", \"CTA\", \"CTA\", \"CTA\"],\n inject_effnet=[False, True, True, True],\n effnet_embd=16,\n clip_embd=1024,\n kernel_size=3,\n dropout=0.1,\n ):\n super().__init__()\n self.c_r = c_r\n self.c_cond = c_cond\n if not isinstance(dropout, list):\n dropout = [dropout] * len(c_hidden)\n\n # CONDITIONING\n self.clip_mapper = nn.Linear(clip_embd, c_cond)\n self.effnet_mappers = nn.ModuleList(\n [\n nn.Conv2d(effnet_embd, c_cond, kernel_size=1) if inject else None\n for inject in inject_effnet + list(reversed(inject_effnet))\n ]\n )\n self.seq_norm = nn.LayerNorm(c_cond, elementwise_affine=False, eps=1e-6)\n\n self.embedding = nn.Sequential(\n nn.PixelUnshuffle(patch_size),\n nn.Conv2d(c_in * (patch_size**2), c_hidden[0], kernel_size=1),\n WuerstchenLayerNorm(c_hidden[0], elementwise_affine=False, eps=1e-6),\n )\n\n def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0):\n if block_type == \"C\":\n return ResBlockStageB(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout)\n elif block_type == \"A\":\n return AttnBlock(c_hidden, c_cond, nhead, self_attn=True, dropout=dropout)\n elif block_type == \"T\":\n return TimestepBlock(c_hidden, c_r)\n else:\n raise ValueError(f\"Block type {block_type} not supported\")\n\n # BLOCKS\n # -- down blocks\n self.down_blocks = nn.ModuleList()\n for i in range(len(c_hidden)):\n down_block = nn.ModuleList()\n if i > 0:\n down_block.append(\n nn.Sequential(\n WuerstchenLayerNorm(c_hidden[i - 1], elementwise_affine=False, eps=1e-6),\n nn.Conv2d(c_hidden[i - 1], c_hidden[i], kernel_size=2, stride=2),\n )\n )\n for _ in range(blocks[i]):\n for block_type in level_config[i]:\n c_skip = c_cond if inject_effnet[i] else 0\n down_block.append(get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i]))\n self.down_blocks.append(down_block)\n\n # -- up blocks\n self.up_blocks = nn.ModuleList()\n for i in reversed(range(len(c_hidden))):\n up_block = nn.ModuleList()\n for j in range(blocks[i]):\n for k, block_type in enumerate(level_config[i]):\n c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j == k == 0 else 0\n c_skip += c_cond if inject_effnet[i] else 0\n up_block.append(get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i]))\n if i > 0:\n up_block.append(\n nn.Sequential(\n WuerstchenLayerNorm(c_hidden[i], elementwise_affine=False, eps=1e-6),\n nn.ConvTranspose2d(c_hidden[i], c_hidden[i - 1], kernel_size=2, stride=2),\n )\n )\n self.up_blocks.append(up_block)\n\n # OUTPUT\n self.clf = nn.Sequential(\n WuerstchenLayerNorm(c_hidden[0], elementwise_affine=False, eps=1e-6),\n nn.Conv2d(c_hidden[0], 2 * c_out * (patch_size**2), kernel_size=1),\n nn.PixelShuffle(patch_size),\n )\n\n # --- WEIGHT INIT ---\n self.apply(self._init_weights)\n\n def _init_weights(self, m):\n # General init\n if isinstance(m, (nn.Conv2d, nn.Linear)):\n nn.init.xavier_uniform_(m.weight)\n if m.bias is not None:\n nn.init.constant_(m.bias, 0)\n\n for mapper in self.effnet_mappers:\n if mapper is not None:\n nn.init.normal_(mapper.weight, std=0.02) # conditionings\n nn.init.normal_(self.clip_mapper.weight, std=0.02) # conditionings\n nn.init.xavier_uniform_(self.embedding[1].weight, 0.02) # inputs\n nn.init.constant_(self.clf[1].weight, 0) # outputs\n\n # blocks\n for level_block in self.down_blocks + self.up_blocks:\n for block in level_block:\n if isinstance(block, ResBlockStageB):\n block.channelwise[-1].weight.data *= np.sqrt(1 / sum(self.config.blocks))\n elif isinstance(block, TimestepBlock):\n nn.init.constant_(block.mapper.weight, 0)\n\n def gen_r_embedding(self, r, max_positions=10000):\n r = r * max_positions\n half_dim = self.c_r // 2\n emb = math.log(max_positions) / (half_dim - 1)\n emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp()\n emb = r[:, None] * emb[None, :]\n emb = torch.cat([emb.sin(), emb.cos()], dim=1)\n if self.c_r % 2 == 1: # zero pad\n emb = nn.functional.pad(emb, (0, 1), mode=\"constant\")\n return emb.to(dtype=r.dtype)\n\n def gen_c_embeddings(self, clip):\n clip = self.clip_mapper(clip)\n clip = self.seq_norm(clip)\n return clip\n\n def _down_encode(self, x, r_embed, effnet, clip=None):\n level_outputs = []\n for i, down_block in enumerate(self.down_blocks):\n effnet_c = None\n for block in down_block:\n if isinstance(block, ResBlockStageB):\n if effnet_c is None and self.effnet_mappers[i] is not None:\n dtype = effnet.dtype\n effnet_c = self.effnet_mappers[i](\n nn.functional.interpolate(\n effnet.float(), size=x.shape[-2:], mode=\"bicubic\", antialias=True, align_corners=True\n ).to(dtype)\n )\n skip = effnet_c if self.effnet_mappers[i] is not None else None\n x = block(x, skip)\n elif isinstance(block, AttnBlock):\n x = block(x, clip)\n elif isinstance(block, TimestepBlock):\n x = block(x, r_embed)\n else:\n x = block(x)\n level_outputs.insert(0, x)\n return level_outputs\n\n def _up_decode(self, level_outputs, r_embed, effnet, clip=None):\n x = level_outputs[0]\n for i, up_block in enumerate(self.up_blocks):\n effnet_c = None\n for j, block in enumerate(up_block):\n if isinstance(block, ResBlockStageB):\n if effnet_c is None and self.effnet_mappers[len(self.down_blocks) + i] is not None:\n dtype = effnet.dtype\n effnet_c = self.effnet_mappers[len(self.down_blocks) + i](\n nn.functional.interpolate(\n effnet.float(), size=x.shape[-2:], mode=\"bicubic\", antialias=True, align_corners=True\n ).to(dtype)\n )\n skip = level_outputs[i] if j == 0 and i > 0 else None\n if effnet_c is not None:\n if skip is not None:\n skip = torch.cat([skip, effnet_c], dim=1)\n else:\n skip = effnet_c\n x = block(x, skip)\n elif isinstance(block, AttnBlock):\n x = block(x, clip)\n elif isinstance(block, TimestepBlock):\n x = block(x, r_embed)\n else:\n x = block(x)\n return x\n\n def forward(self, x, r, effnet, clip=None, x_cat=None, eps=1e-3, return_noise=True):\n if x_cat is not None:\n x = torch.cat([x, x_cat], dim=1)\n # Process the conditioning embeddings\n r_embed = self.gen_r_embedding(r)\n if clip is not None:\n clip = self.gen_c_embeddings(clip)\n\n # Model Blocks\n x_in = x\n x = self.embedding(x)\n level_outputs = self._down_encode(x, r_embed, effnet, clip)\n x = self._up_decode(level_outputs, r_embed, effnet, clip)\n a, b = self.clf(x).chunk(2, dim=1)\n b = b.sigmoid() * (1 - eps * 2) + eps\n if return_noise:\n return (x_in - a) / b\n else:\n return a, b\n\n\nclass ResBlockStageB(nn.Module):\n def __init__(self, c, c_skip=0, kernel_size=3, dropout=0.0):\n super().__init__()\n self.depthwise = nn.Conv2d(c, c, kernel_size=kernel_size, padding=kernel_size // 2, groups=c)\n self.norm = WuerstchenLayerNorm(c, elementwise_affine=False, eps=1e-6)\n self.channelwise = nn.Sequential(\n nn.Linear(c + c_skip, c * 4),\n nn.GELU(),\n GlobalResponseNorm(c * 4),\n nn.Dropout(dropout),\n nn.Linear(c * 4, c),\n )\n\n def forward(self, x, x_skip=None):\n x_res = x\n x = self.norm(self.depthwise(x))\n if x_skip is not None:\n x = torch.cat([x, x_skip], dim=1)\n x = self.channelwise(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)\n return x + x_res\n" }, { "path": "diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_prior.py", "content": "# Copyright (c) 2023 Dominic Rampas MIT License\n# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport math\nfrom typing import Dict, Union\n\nimport torch\nimport torch.nn as nn\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...loaders import PeftAdapterMixin, UNet2DConditionLoadersMixin\nfrom ...models.attention_processor import (\n ADDED_KV_ATTENTION_PROCESSORS,\n CROSS_ATTENTION_PROCESSORS,\n AttentionProcessor,\n AttnAddedKVProcessor,\n AttnProcessor,\n)\nfrom ...models.modeling_utils import ModelMixin\nfrom ...utils import is_torch_version\nfrom .modeling_wuerstchen_common import AttnBlock, ResBlock, TimestepBlock, WuerstchenLayerNorm\n\n\nclass WuerstchenPrior(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin):\n unet_name = \"prior\"\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(self, c_in=16, c=1280, c_cond=1024, c_r=64, depth=16, nhead=16, dropout=0.1):\n super().__init__()\n\n self.c_r = c_r\n self.projection = nn.Conv2d(c_in, c, kernel_size=1)\n self.cond_mapper = nn.Sequential(\n nn.Linear(c_cond, c),\n nn.LeakyReLU(0.2),\n nn.Linear(c, c),\n )\n\n self.blocks = nn.ModuleList()\n for _ in range(depth):\n self.blocks.append(ResBlock(c, dropout=dropout))\n self.blocks.append(TimestepBlock(c, c_r))\n self.blocks.append(AttnBlock(c, c, nhead, self_attn=True, dropout=dropout))\n self.out = nn.Sequential(\n WuerstchenLayerNorm(c, elementwise_affine=False, eps=1e-6),\n nn.Conv2d(c, c_in * 2, kernel_size=1),\n )\n\n self.gradient_checkpointing = False\n self.set_default_attn_processor()\n\n @property\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors\n def attn_processors(self) -> Dict[str, AttentionProcessor]:\n r\"\"\"\n Returns:\n `dict` of attention processors: A dictionary containing all attention processors used in the model with\n indexed by its weight name.\n \"\"\"\n # set recursively\n processors = {}\n\n def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\n if hasattr(module, \"get_processor\"):\n processors[f\"{name}.processor\"] = module.get_processor(return_deprecated_lora=True)\n\n for sub_name, child in module.named_children():\n fn_recursive_add_processors(f\"{name}.{sub_name}\", child, processors)\n\n return processors\n\n for name, module in self.named_children():\n fn_recursive_add_processors(name, module, processors)\n\n return processors\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor\n def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\n r\"\"\"\n Sets the attention processor to use to compute attention.\n\n Parameters:\n processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):\n The instantiated processor class or a dictionary of processor classes that will be set as the processor\n for **all** `Attention` layers.\n\n If `processor` is a dict, the key needs to define the path to the corresponding cross attention\n processor. This is strongly recommended when setting trainable attention processors.\n\n \"\"\"\n count = len(self.attn_processors.keys())\n\n if isinstance(processor, dict) and len(processor) != count:\n raise ValueError(\n f\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\"\n f\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\"\n )\n\n def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\n if hasattr(module, \"set_processor\"):\n if not isinstance(processor, dict):\n module.set_processor(processor)\n else:\n module.set_processor(processor.pop(f\"{name}.processor\"))\n\n for sub_name, child in module.named_children():\n fn_recursive_attn_processor(f\"{name}.{sub_name}\", child, processor)\n\n for name, module in self.named_children():\n fn_recursive_attn_processor(name, module, processor)\n\n # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor\n def set_default_attn_processor(self):\n \"\"\"\n Disables custom attention processors and sets the default attention implementation.\n \"\"\"\n if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnAddedKVProcessor()\n elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):\n processor = AttnProcessor()\n else:\n raise ValueError(\n f\"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}\"\n )\n\n self.set_attn_processor(processor)\n\n def _set_gradient_checkpointing(self, module, value=False):\n self.gradient_checkpointing = value\n\n def gen_r_embedding(self, r, max_positions=10000):\n r = r * max_positions\n half_dim = self.c_r // 2\n emb = math.log(max_positions) / (half_dim - 1)\n emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp()\n emb = r[:, None] * emb[None, :]\n emb = torch.cat([emb.sin(), emb.cos()], dim=1)\n if self.c_r % 2 == 1: # zero pad\n emb = nn.functional.pad(emb, (0, 1), mode=\"constant\")\n return emb.to(dtype=r.dtype)\n\n def forward(self, x, r, c):\n x_in = x\n x = self.projection(x)\n c_embed = self.cond_mapper(c)\n r_embed = self.gen_r_embedding(r)\n\n if self.training and self.gradient_checkpointing:\n\n def create_custom_forward(module):\n def custom_forward(*inputs):\n return module(*inputs)\n\n return custom_forward\n\n if is_torch_version(\">=\", \"1.11.0\"):\n for block in self.blocks:\n if isinstance(block, AttnBlock):\n x = torch.utils.checkpoint.checkpoint(\n create_custom_forward(block), x, c_embed, use_reentrant=False\n )\n elif isinstance(block, TimestepBlock):\n x = torch.utils.checkpoint.checkpoint(\n create_custom_forward(block), x, r_embed, use_reentrant=False\n )\n else:\n x = torch.utils.checkpoint.checkpoint(create_custom_forward(block), x, use_reentrant=False)\n else:\n for block in self.blocks:\n if isinstance(block, AttnBlock):\n x = torch.utils.checkpoint.checkpoint(create_custom_forward(block), x, c_embed)\n elif isinstance(block, TimestepBlock):\n x = torch.utils.checkpoint.checkpoint(create_custom_forward(block), x, r_embed)\n else:\n x = torch.utils.checkpoint.checkpoint(create_custom_forward(block), x)\n else:\n for block in self.blocks:\n if isinstance(block, AttnBlock):\n x = block(x, c_embed)\n elif isinstance(block, TimestepBlock):\n x = block(x, r_embed)\n else:\n x = block(x)\n a, b = self.out(x).chunk(2, dim=1)\n return (x_in - a) / ((1 - b).abs() + 1e-5)\n" }, { "path": "diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nfrom ...schedulers import DDPMWuerstchenScheduler\nfrom ...utils import deprecate, logging, replace_example_docstring\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput\nfrom .modeling_paella_vq_model import PaellaVQModel\nfrom .modeling_wuerstchen_diffnext import WuerstchenDiffNeXt\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import WuerstchenPriorPipeline, WuerstchenDecoderPipeline\n\n >>> prior_pipe = WuerstchenPriorPipeline.from_pretrained(\n ... \"warp-ai/wuerstchen-prior\", torch_dtype=torch.float16\n ... ).to(\"cuda\")\n >>> gen_pipe = WuerstchenDecoderPipeline.from_pretrain(\"warp-ai/wuerstchen\", torch_dtype=torch.float16).to(\n ... \"cuda\"\n ... )\n\n >>> prompt = \"an image of a shiba inu, donning a spacesuit and helmet\"\n >>> prior_output = pipe(prompt)\n >>> images = gen_pipe(prior_output.image_embeddings, prompt=prompt)\n ```\n\"\"\"\n\n\nclass WuerstchenDecoderPipeline(DiffusionPipeline):\n \"\"\"\n Pipeline for generating images from the Wuerstchen model.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n tokenizer (`CLIPTokenizer`):\n The CLIP tokenizer.\n text_encoder (`CLIPTextModel`):\n The CLIP text encoder.\n decoder ([`WuerstchenDiffNeXt`]):\n The WuerstchenDiffNeXt unet decoder.\n vqgan ([`PaellaVQModel`]):\n The VQGAN model.\n scheduler ([`DDPMWuerstchenScheduler`]):\n A scheduler to be used in combination with `prior` to generate image embedding.\n latent_dim_scale (float, `optional`, defaults to 10.67):\n Multiplier to determine the VQ latent space size from the image embeddings. If the image embeddings are\n height=24 and width=24, the VQ latent shape needs to be height=int(24*10.67)=256 and\n width=int(24*10.67)=256 in order to match the training conditions.\n \"\"\"\n\n model_cpu_offload_seq = \"text_encoder->decoder->vqgan\"\n _callback_tensor_inputs = [\n \"latents\",\n \"text_encoder_hidden_states\",\n \"negative_prompt_embeds\",\n \"image_embeddings\",\n ]\n\n def __init__(\n self,\n tokenizer: CLIPTokenizer,\n text_encoder: CLIPTextModel,\n decoder: WuerstchenDiffNeXt,\n scheduler: DDPMWuerstchenScheduler,\n vqgan: PaellaVQModel,\n latent_dim_scale: float = 10.67,\n ) -> None:\n super().__init__()\n self.register_modules(\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n decoder=decoder,\n scheduler=scheduler,\n vqgan=vqgan,\n )\n self.register_to_config(latent_dim_scale=latent_dim_scale)\n\n # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents\n def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n latents = latents * scheduler.init_noise_sigma\n return latents\n\n def encode_prompt(\n self,\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt=None,\n ):\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n attention_mask = text_inputs.attention_mask\n\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]\n attention_mask = attention_mask[:, : self.tokenizer.model_max_length]\n\n text_encoder_output = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask.to(device))\n text_encoder_hidden_states = text_encoder_output.last_hidden_state\n text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)\n\n uncond_text_encoder_hidden_states = None\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n negative_prompt_embeds_text_encoder_output = self.text_encoder(\n uncond_input.input_ids.to(device), attention_mask=uncond_input.attention_mask.to(device)\n )\n\n uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = uncond_text_encoder_hidden_states.shape[1]\n uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)\n uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(\n batch_size * num_images_per_prompt, seq_len, -1\n )\n # done duplicates\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n return text_encoder_hidden_states, uncond_text_encoder_hidden_states\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n image_embeddings: Union[torch.Tensor, List[torch.Tensor]],\n prompt: Union[str, List[str]] = None,\n num_inference_steps: int = 12,\n timesteps: Optional[List[float]] = None,\n guidance_scale: float = 0.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: int = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n image_embedding (`torch.Tensor` or `List[torch.Tensor]`):\n Image Embeddings either extracted from an image or generated by a Prior Model.\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n num_inference_steps (`int`, *optional*, defaults to 12):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`\n timesteps are used. Must be in descending order.\n guidance_scale (`float`, *optional*, defaults to 0.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `decoder_guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting\n `decoder_guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely\n linked to the text `prompt`, usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `decoder_guidance_scale` is less than `1`).\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between: `\"pil\"` (`PIL.Image.Image`), `\"np\"`\n (`np.array`) or `\"pt\"` (`torch.Tensor`).\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple` [`~pipelines.ImagePipelineOutput`] if `return_dict` is True,\n otherwise a `tuple`. When returning a tuple, the first element is a list with the generated image\n embeddings.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n # 0. Define commonly used variables\n device = self._execution_device\n dtype = self.decoder.dtype\n self._guidance_scale = guidance_scale\n\n # 1. Check inputs. Raise error if not correct\n if not isinstance(prompt, list):\n if isinstance(prompt, str):\n prompt = [prompt]\n else:\n raise TypeError(f\"'prompt' must be of type 'list' or 'str', but got {type(prompt)}.\")\n\n if self.do_classifier_free_guidance:\n if negative_prompt is not None and not isinstance(negative_prompt, list):\n if isinstance(negative_prompt, str):\n negative_prompt = [negative_prompt]\n else:\n raise TypeError(\n f\"'negative_prompt' must be of type 'list' or 'str', but got {type(negative_prompt)}.\"\n )\n\n if isinstance(image_embeddings, list):\n image_embeddings = torch.cat(image_embeddings, dim=0)\n if isinstance(image_embeddings, np.ndarray):\n image_embeddings = torch.Tensor(image_embeddings, device=device).to(dtype=dtype)\n if not isinstance(image_embeddings, torch.Tensor):\n raise TypeError(\n f\"'image_embeddings' must be of type 'torch.Tensor' or 'np.array', but got {type(image_embeddings)}.\"\n )\n\n if not isinstance(num_inference_steps, int):\n raise TypeError(\n f\"'num_inference_steps' must be of type 'int', but got {type(num_inference_steps)}\\\n In Case you want to provide explicit timesteps, please use the 'timesteps' argument.\"\n )\n\n # 2. Encode caption\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n image_embeddings.size(0) * num_images_per_prompt,\n self.do_classifier_free_guidance,\n negative_prompt,\n )\n text_encoder_hidden_states = (\n torch.cat([prompt_embeds, negative_prompt_embeds]) if negative_prompt_embeds is not None else prompt_embeds\n )\n effnet = (\n torch.cat([image_embeddings, torch.zeros_like(image_embeddings)])\n if self.do_classifier_free_guidance\n else image_embeddings\n )\n\n # 3. Determine latent shape of latents\n latent_height = int(image_embeddings.size(2) * self.config.latent_dim_scale)\n latent_width = int(image_embeddings.size(3) * self.config.latent_dim_scale)\n latent_features_shape = (image_embeddings.size(0) * num_images_per_prompt, 4, latent_height, latent_width)\n\n # 4. Prepare and set timesteps\n if timesteps is not None:\n self.scheduler.set_timesteps(timesteps=timesteps, device=device)\n timesteps = self.scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latents\n latents = self.prepare_latents(latent_features_shape, dtype, device, generator, latents, self.scheduler)\n\n # 6. Run denoising loop\n self._num_timesteps = len(timesteps[:-1])\n for i, t in enumerate(self.progress_bar(timesteps[:-1])):\n ratio = t.expand(latents.size(0)).to(dtype)\n # 7. Denoise latents\n predicted_latents = self.decoder(\n torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents,\n r=torch.cat([ratio] * 2) if self.do_classifier_free_guidance else ratio,\n effnet=effnet,\n clip=text_encoder_hidden_states,\n )\n\n # 8. Check for classifier free guidance and apply it\n if self.do_classifier_free_guidance:\n predicted_latents_text, predicted_latents_uncond = predicted_latents.chunk(2)\n predicted_latents = torch.lerp(predicted_latents_uncond, predicted_latents_text, self.guidance_scale)\n\n # 9. Renoise latents to next timestep\n latents = self.scheduler.step(\n model_output=predicted_latents,\n timestep=ratio,\n sample=latents,\n generator=generator,\n ).prev_sample\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n image_embeddings = callback_outputs.pop(\"image_embeddings\", image_embeddings)\n text_encoder_hidden_states = callback_outputs.pop(\n \"text_encoder_hidden_states\", text_encoder_hidden_states\n )\n\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n if output_type not in [\"pt\", \"np\", \"pil\", \"latent\"]:\n raise ValueError(\n f\"Only the output types `pt`, `np`, `pil` and `latent` are supported not output_type={output_type}\"\n )\n\n if not output_type == \"latent\":\n # 10. Scale and decode the image latents with vq-vae\n latents = self.vqgan.config.scale_factor * latents\n images = self.vqgan.decode(latents).sample.clamp(0, 1)\n if output_type == \"np\":\n images = images.permute(0, 2, 3, 1).cpu().float().numpy()\n elif output_type == \"pil\":\n images = images.permute(0, 2, 3, 1).cpu().float().numpy()\n images = self.numpy_to_pil(images)\n else:\n images = latents\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return images\n return ImagePipelineOutput(images)\n" }, { "path": "diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Callable, Dict, List, Optional, Union\n\nimport torch\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nfrom ...schedulers import DDPMWuerstchenScheduler\nfrom ...utils import deprecate, replace_example_docstring\nfrom ..pipeline_utils import DiffusionPipeline\nfrom .modeling_paella_vq_model import PaellaVQModel\nfrom .modeling_wuerstchen_diffnext import WuerstchenDiffNeXt\nfrom .modeling_wuerstchen_prior import WuerstchenPrior\nfrom .pipeline_wuerstchen import WuerstchenDecoderPipeline\nfrom .pipeline_wuerstchen_prior import WuerstchenPriorPipeline\n\n\nTEXT2IMAGE_EXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> from diffusions import WuerstchenCombinedPipeline\n\n >>> pipe = WuerstchenCombinedPipeline.from_pretrained(\"warp-ai/Wuerstchen\", torch_dtype=torch.float16).to(\n ... \"cuda\"\n ... )\n >>> prompt = \"an image of a shiba inu, donning a spacesuit and helmet\"\n >>> images = pipe(prompt=prompt)\n ```\n\"\"\"\n\n\nclass WuerstchenCombinedPipeline(DiffusionPipeline):\n \"\"\"\n Combined Pipeline for text-to-image generation using Wuerstchen\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Args:\n tokenizer (`CLIPTokenizer`):\n The decoder tokenizer to be used for text inputs.\n text_encoder (`CLIPTextModel`):\n The decoder text encoder to be used for text inputs.\n decoder (`WuerstchenDiffNeXt`):\n The decoder model to be used for decoder image generation pipeline.\n scheduler (`DDPMWuerstchenScheduler`):\n The scheduler to be used for decoder image generation pipeline.\n vqgan (`PaellaVQModel`):\n The VQGAN model to be used for decoder image generation pipeline.\n prior_tokenizer (`CLIPTokenizer`):\n The prior tokenizer to be used for text inputs.\n prior_text_encoder (`CLIPTextModel`):\n The prior text encoder to be used for text inputs.\n prior_prior (`WuerstchenPrior`):\n The prior model to be used for prior pipeline.\n prior_scheduler (`DDPMWuerstchenScheduler`):\n The scheduler to be used for prior pipeline.\n \"\"\"\n\n _load_connected_pipes = True\n\n def __init__(\n self,\n tokenizer: CLIPTokenizer,\n text_encoder: CLIPTextModel,\n decoder: WuerstchenDiffNeXt,\n scheduler: DDPMWuerstchenScheduler,\n vqgan: PaellaVQModel,\n prior_tokenizer: CLIPTokenizer,\n prior_text_encoder: CLIPTextModel,\n prior_prior: WuerstchenPrior,\n prior_scheduler: DDPMWuerstchenScheduler,\n ):\n super().__init__()\n\n self.register_modules(\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n decoder=decoder,\n scheduler=scheduler,\n vqgan=vqgan,\n prior_prior=prior_prior,\n prior_text_encoder=prior_text_encoder,\n prior_tokenizer=prior_tokenizer,\n prior_scheduler=prior_scheduler,\n )\n self.prior_pipe = WuerstchenPriorPipeline(\n prior=prior_prior,\n text_encoder=prior_text_encoder,\n tokenizer=prior_tokenizer,\n scheduler=prior_scheduler,\n )\n self.decoder_pipe = WuerstchenDecoderPipeline(\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n decoder=decoder,\n scheduler=scheduler,\n vqgan=vqgan,\n )\n\n def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):\n self.decoder_pipe.enable_xformers_memory_efficient_attention(attention_op)\n\n def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = \"cuda\"):\n r\"\"\"\n Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared\n to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`\n method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with\n `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.\n \"\"\"\n self.prior_pipe.enable_model_cpu_offload(gpu_id=gpu_id, device=device)\n self.decoder_pipe.enable_model_cpu_offload(gpu_id=gpu_id, device=device)\n\n def enable_sequential_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = \"cuda\"):\n r\"\"\"\n Offloads all models (`unet`, `text_encoder`, `vae`, and `safety checker` state dicts) to CPU using 🤗\n Accelerate, significantly reducing memory usage. Models are moved to a `torch.device('meta')` and loaded on a\n GPU only when their specific submodule's `forward` method is called. Offloading happens on a submodule basis.\n Memory savings are higher than using `enable_model_cpu_offload`, but performance is lower.\n \"\"\"\n self.prior_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id, device=device)\n self.decoder_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id, device=device)\n\n def progress_bar(self, iterable=None, total=None):\n self.prior_pipe.progress_bar(iterable=iterable, total=total)\n self.decoder_pipe.progress_bar(iterable=iterable, total=total)\n\n def set_progress_bar_config(self, **kwargs):\n self.prior_pipe.set_progress_bar_config(**kwargs)\n self.decoder_pipe.set_progress_bar_config(**kwargs)\n\n @torch.no_grad()\n @replace_example_docstring(TEXT2IMAGE_EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Optional[Union[str, List[str]]] = None,\n height: int = 512,\n width: int = 512,\n prior_num_inference_steps: int = 60,\n prior_timesteps: Optional[List[float]] = None,\n prior_guidance_scale: float = 4.0,\n num_inference_steps: int = 12,\n decoder_timesteps: Optional[List[float]] = None,\n decoder_guidance_scale: float = 0.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n num_images_per_prompt: int = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n prior_callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n prior_callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation for the prior and decoder.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `guidance_scale` is less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings for the prior. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings for the prior. Can be used to easily tweak text inputs, *e.g.*\n prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt`\n input argument.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n height (`int`, *optional*, defaults to 512):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 512):\n The width in pixels of the generated image.\n prior_guidance_scale (`float`, *optional*, defaults to 4.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `prior_guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting\n `prior_guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked\n to the text `prompt`, usually at the expense of lower image quality.\n prior_num_inference_steps (`Union[int, Dict[float, int]]`, *optional*, defaults to 60):\n The number of prior denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference. For more specific timestep spacing, you can pass customized\n `prior_timesteps`\n num_inference_steps (`int`, *optional*, defaults to 12):\n The number of decoder denoising steps. More denoising steps usually lead to a higher quality image at\n the expense of slower inference. For more specific timestep spacing, you can pass customized\n `timesteps`\n prior_timesteps (`List[float]`, *optional*):\n Custom timesteps to use for the denoising process for the prior. If not defined, equal spaced\n `prior_num_inference_steps` timesteps are used. Must be in descending order.\n decoder_timesteps (`List[float]`, *optional*):\n Custom timesteps to use for the denoising process for the decoder. If not defined, equal spaced\n `num_inference_steps` timesteps are used. Must be in descending order.\n decoder_guidance_scale (`float`, *optional*, defaults to 0.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between: `\"pil\"` (`PIL.Image.Image`), `\"np\"`\n (`np.array`) or `\"pt\"` (`torch.Tensor`).\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n prior_callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `prior_callback_on_step_end(self: DiffusionPipeline, step: int, timestep:\n int, callback_kwargs: Dict)`.\n prior_callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `prior_callback_on_step_end` function. The tensors specified in the\n list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in\n the `._callback_tensor_inputs` attribute of your pipeline class.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple` [`~pipelines.ImagePipelineOutput`] if `return_dict` is True,\n otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images.\n \"\"\"\n prior_kwargs = {}\n if kwargs.get(\"prior_callback\", None) is not None:\n prior_kwargs[\"callback\"] = kwargs.pop(\"prior_callback\")\n deprecate(\n \"prior_callback\",\n \"1.0.0\",\n \"Passing `prior_callback` as an input argument to `__call__` is deprecated, consider use `prior_callback_on_step_end`\",\n )\n if kwargs.get(\"prior_callback_steps\", None) is not None:\n deprecate(\n \"prior_callback_steps\",\n \"1.0.0\",\n \"Passing `prior_callback_steps` as an input argument to `__call__` is deprecated, consider use `prior_callback_on_step_end`\",\n )\n prior_kwargs[\"callback_steps\"] = kwargs.pop(\"prior_callback_steps\")\n\n prior_outputs = self.prior_pipe(\n prompt=prompt if prompt_embeds is None else None,\n height=height,\n width=width,\n num_inference_steps=prior_num_inference_steps,\n timesteps=prior_timesteps,\n guidance_scale=prior_guidance_scale,\n negative_prompt=negative_prompt if negative_prompt_embeds is None else None,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n num_images_per_prompt=num_images_per_prompt,\n generator=generator,\n latents=latents,\n output_type=\"pt\",\n return_dict=False,\n callback_on_step_end=prior_callback_on_step_end,\n callback_on_step_end_tensor_inputs=prior_callback_on_step_end_tensor_inputs,\n **prior_kwargs,\n )\n image_embeddings = prior_outputs[0]\n\n outputs = self.decoder_pipe(\n image_embeddings=image_embeddings,\n prompt=prompt if prompt is not None else \"\",\n num_inference_steps=num_inference_steps,\n timesteps=decoder_timesteps,\n guidance_scale=decoder_guidance_scale,\n negative_prompt=negative_prompt,\n generator=generator,\n output_type=output_type,\n return_dict=return_dict,\n callback_on_step_end=callback_on_step_end,\n callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,\n **kwargs,\n )\n\n return outputs\n" }, { "path": "diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom dataclasses import dataclass\nfrom math import ceil\nfrom typing import Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextModel, CLIPTokenizer\n\nfrom ...loaders import LoraLoaderMixin\nfrom ...schedulers import DDPMWuerstchenScheduler\nfrom ...utils import BaseOutput, deprecate, logging, replace_example_docstring\nfrom ...utils.torch_utils import randn_tensor\nfrom ..pipeline_utils import DiffusionPipeline\nfrom .modeling_wuerstchen_prior import WuerstchenPrior\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nDEFAULT_STAGE_C_TIMESTEPS = list(np.linspace(1.0, 2 / 3, 20)) + list(np.linspace(2 / 3, 0.0, 11))[1:]\n\nEXAMPLE_DOC_STRING = \"\"\"\n Examples:\n ```py\n >>> import torch\n >>> from diffusers import WuerstchenPriorPipeline\n\n >>> prior_pipe = WuerstchenPriorPipeline.from_pretrained(\n ... \"warp-ai/wuerstchen-prior\", torch_dtype=torch.float16\n ... ).to(\"cuda\")\n\n >>> prompt = \"an image of a shiba inu, donning a spacesuit and helmet\"\n >>> prior_output = pipe(prompt)\n ```\n\"\"\"\n\n\n@dataclass\nclass WuerstchenPriorPipelineOutput(BaseOutput):\n \"\"\"\n Output class for WuerstchenPriorPipeline.\n\n Args:\n image_embeddings (`torch.Tensor` or `np.ndarray`)\n Prior image embeddings for text prompt\n\n \"\"\"\n\n image_embeddings: Union[torch.Tensor, np.ndarray]\n\n\nclass WuerstchenPriorPipeline(DiffusionPipeline, LoraLoaderMixin):\n \"\"\"\n Pipeline for generating image prior for Wuerstchen.\n\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n The pipeline also inherits the following loading methods:\n - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights\n - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights\n\n Args:\n prior ([`Prior`]):\n The canonical unCLIP prior to approximate the image embedding from the text embedding.\n text_encoder ([`CLIPTextModelWithProjection`]):\n Frozen text-encoder.\n tokenizer (`CLIPTokenizer`):\n Tokenizer of class\n [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).\n scheduler ([`DDPMWuerstchenScheduler`]):\n A scheduler to be used in combination with `prior` to generate image embedding.\n latent_mean ('float', *optional*, defaults to 42.0):\n Mean value for latent diffusers.\n latent_std ('float', *optional*, defaults to 1.0):\n Standard value for latent diffusers.\n resolution_multiple ('float', *optional*, defaults to 42.67):\n Default resolution for multiple images generated.\n \"\"\"\n\n unet_name = \"prior\"\n text_encoder_name = \"text_encoder\"\n model_cpu_offload_seq = \"text_encoder->prior\"\n _callback_tensor_inputs = [\"latents\", \"text_encoder_hidden_states\", \"negative_prompt_embeds\"]\n\n def __init__(\n self,\n tokenizer: CLIPTokenizer,\n text_encoder: CLIPTextModel,\n prior: WuerstchenPrior,\n scheduler: DDPMWuerstchenScheduler,\n latent_mean: float = 42.0,\n latent_std: float = 1.0,\n resolution_multiple: float = 42.67,\n ) -> None:\n super().__init__()\n self.register_modules(\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n prior=prior,\n scheduler=scheduler,\n )\n self.register_to_config(\n latent_mean=latent_mean, latent_std=latent_std, resolution_multiple=resolution_multiple\n )\n\n # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents\n def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n if latents.shape != shape:\n raise ValueError(f\"Unexpected latents shape, got {latents.shape}, expected {shape}\")\n latents = latents.to(device)\n\n latents = latents * scheduler.init_noise_sigma\n return latents\n\n def encode_prompt(\n self,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n prompt=None,\n negative_prompt=None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n ):\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n # get prompt text embeddings\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n attention_mask = text_inputs.attention_mask\n\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = self.tokenizer.batch_decode(\n untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]\n )\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {self.tokenizer.model_max_length} tokens: {removed_text}\"\n )\n text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]\n attention_mask = attention_mask[:, : self.tokenizer.model_max_length]\n\n text_encoder_output = self.text_encoder(\n text_input_ids.to(device), attention_mask=attention_mask.to(device)\n )\n prompt_embeds = text_encoder_output.last_hidden_state\n\n prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)\n\n if negative_prompt_embeds is None and do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n uncond_input = self.tokenizer(\n uncond_tokens,\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n negative_prompt_embeds_text_encoder_output = self.text_encoder(\n uncond_input.input_ids.to(device), attention_mask=uncond_input.attention_mask.to(device)\n )\n\n negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.last_hidden_state\n\n if do_classifier_free_guidance:\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n # done duplicates\n\n return prompt_embeds, negative_prompt_embeds\n\n def check_inputs(\n self,\n prompt,\n negative_prompt,\n num_inference_steps,\n do_classifier_free_guidance,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if not isinstance(num_inference_steps, int):\n raise TypeError(\n f\"'num_inference_steps' must be of type 'int', but got {type(num_inference_steps)}\\\n In Case you want to provide explicit timesteps, please use the 'timesteps' argument.\"\n )\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def do_classifier_free_guidance(self):\n return self._guidance_scale > 1\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @torch.no_grad()\n @replace_example_docstring(EXAMPLE_DOC_STRING)\n def __call__(\n self,\n prompt: Optional[Union[str, List[str]]] = None,\n height: int = 1024,\n width: int = 1024,\n num_inference_steps: int = 60,\n timesteps: List[float] = None,\n guidance_scale: float = 8.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n num_images_per_prompt: Optional[int] = 1,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.Tensor] = None,\n output_type: Optional[str] = \"pt\",\n return_dict: bool = True,\n callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n **kwargs,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`):\n The prompt or prompts to guide the image generation.\n height (`int`, *optional*, defaults to 1024):\n The height in pixels of the generated image.\n width (`int`, *optional*, defaults to 1024):\n The width in pixels of the generated image.\n num_inference_steps (`int`, *optional*, defaults to 60):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`\n timesteps are used. Must be in descending order.\n guidance_scale (`float`, *optional*, defaults to 8.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `decoder_guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting\n `decoder_guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely\n linked to the text `prompt`, usually at the expense of lower image quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored\n if `decoder_guidance_scale` is less than `1`).\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n num_images_per_prompt (`int`, *optional*, defaults to 1):\n The number of images to generate per prompt.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.Tensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between: `\"pil\"` (`PIL.Image.Image`), `\"np\"`\n (`np.array`) or `\"pt\"` (`torch.Tensor`).\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n\n Examples:\n\n Returns:\n [`~pipelines.WuerstchenPriorPipelineOutput`] or `tuple` [`~pipelines.WuerstchenPriorPipelineOutput`] if\n `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the\n generated image embeddings.\n \"\"\"\n\n callback = kwargs.pop(\"callback\", None)\n callback_steps = kwargs.pop(\"callback_steps\", None)\n\n if callback is not None:\n deprecate(\n \"callback\",\n \"1.0.0\",\n \"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n if callback_steps is not None:\n deprecate(\n \"callback_steps\",\n \"1.0.0\",\n \"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`\",\n )\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n\n # 0. Define commonly used variables\n device = self._execution_device\n self._guidance_scale = guidance_scale\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n # 1. Check inputs. Raise error if not correct\n if prompt is not None and not isinstance(prompt, list):\n if isinstance(prompt, str):\n prompt = [prompt]\n else:\n raise TypeError(f\"'prompt' must be of type 'list' or 'str', but got {type(prompt)}.\")\n\n if self.do_classifier_free_guidance:\n if negative_prompt is not None and not isinstance(negative_prompt, list):\n if isinstance(negative_prompt, str):\n negative_prompt = [negative_prompt]\n else:\n raise TypeError(\n f\"'negative_prompt' must be of type 'list' or 'str', but got {type(negative_prompt)}.\"\n )\n\n self.check_inputs(\n prompt,\n negative_prompt,\n num_inference_steps,\n self.do_classifier_free_guidance,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n\n # 2. Encode caption\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt=prompt,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=self.do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n )\n\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n text_encoder_hidden_states = (\n torch.cat([prompt_embeds, negative_prompt_embeds]) if negative_prompt_embeds is not None else prompt_embeds\n )\n\n # 3. Determine latent shape of image embeddings\n dtype = text_encoder_hidden_states.dtype\n latent_height = ceil(height / self.config.resolution_multiple)\n latent_width = ceil(width / self.config.resolution_multiple)\n num_channels = self.prior.config.c_in\n effnet_features_shape = (num_images_per_prompt * batch_size, num_channels, latent_height, latent_width)\n\n # 4. Prepare and set timesteps\n if timesteps is not None:\n self.scheduler.set_timesteps(timesteps=timesteps, device=device)\n timesteps = self.scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.scheduler.timesteps\n\n # 5. Prepare latents\n latents = self.prepare_latents(effnet_features_shape, dtype, device, generator, latents, self.scheduler)\n\n # 6. Run denoising loop\n self._num_timesteps = len(timesteps[:-1])\n for i, t in enumerate(self.progress_bar(timesteps[:-1])):\n ratio = t.expand(latents.size(0)).to(dtype)\n\n # 7. Denoise image embeddings\n predicted_image_embedding = self.prior(\n torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents,\n r=torch.cat([ratio] * 2) if self.do_classifier_free_guidance else ratio,\n c=text_encoder_hidden_states,\n )\n\n # 8. Check for classifier free guidance and apply it\n if self.do_classifier_free_guidance:\n predicted_image_embedding_text, predicted_image_embedding_uncond = predicted_image_embedding.chunk(2)\n predicted_image_embedding = torch.lerp(\n predicted_image_embedding_uncond, predicted_image_embedding_text, self.guidance_scale\n )\n\n # 9. Renoise latents to next timestep\n latents = self.scheduler.step(\n model_output=predicted_image_embedding,\n timestep=ratio,\n sample=latents,\n generator=generator,\n ).prev_sample\n\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n text_encoder_hidden_states = callback_outputs.pop(\n \"text_encoder_hidden_states\", text_encoder_hidden_states\n )\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n if callback is not None and i % callback_steps == 0:\n step_idx = i // getattr(self.scheduler, \"order\", 1)\n callback(step_idx, t, latents)\n\n # 10. Denormalize the latents\n latents = latents * self.config.latent_mean - self.config.latent_std\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if output_type == \"np\":\n latents = latents.cpu().float().numpy()\n\n if not return_dict:\n return (latents,)\n\n return WuerstchenPriorPipelineOutput(latents)\n" }, { "path": "diffusers/src/diffusers/py.typed", "content": "" }, { "path": "diffusers/src/diffusers/schedulers/README.md", "content": "# Schedulers\n\nFor more information on the schedulers, please refer to the [docs](https://huggingface.co/docs/diffusers/api/schedulers/overview)." }, { "path": "diffusers/src/diffusers/schedulers/__init__.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import TYPE_CHECKING\n\nfrom ..utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_flax_available,\n is_scipy_available,\n is_torch_available,\n is_torchsde_available,\n)\n\n\n_dummy_modules = {}\n_import_structure = {}\n\ntry:\n if not is_torch_available():\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ..utils import dummy_pt_objects # noqa F403\n\n _dummy_modules.update(get_objects_from_module(dummy_pt_objects))\n\nelse:\n _import_structure[\"deprecated\"] = [\"KarrasVeScheduler\", \"ScoreSdeVpScheduler\"]\n _import_structure[\"scheduling_amused\"] = [\"AmusedScheduler\"]\n _import_structure[\"scheduling_consistency_decoder\"] = [\"ConsistencyDecoderScheduler\"]\n _import_structure[\"scheduling_consistency_models\"] = [\"CMStochasticIterativeScheduler\"]\n _import_structure[\"scheduling_ddim\"] = [\"DDIMScheduler\"]\n _import_structure[\"scheduling_ddim_inverse\"] = [\"DDIMInverseScheduler\"]\n _import_structure[\"scheduling_ddim_parallel\"] = [\"DDIMParallelScheduler\"]\n _import_structure[\"scheduling_ddpm\"] = [\"DDPMScheduler\"]\n _import_structure[\"scheduling_ddpm_parallel\"] = [\"DDPMParallelScheduler\"]\n _import_structure[\"scheduling_ddpm_wuerstchen\"] = [\"DDPMWuerstchenScheduler\"]\n _import_structure[\"scheduling_deis_multistep\"] = [\"DEISMultistepScheduler\"]\n _import_structure[\"scheduling_dpmsolver_multistep\"] = [\"DPMSolverMultistepScheduler\"]\n _import_structure[\"scheduling_dpmsolver_multistep_inverse\"] = [\"DPMSolverMultistepInverseScheduler\"]\n _import_structure[\"scheduling_dpmsolver_singlestep\"] = [\"DPMSolverSinglestepScheduler\"]\n _import_structure[\"scheduling_edm_dpmsolver_multistep\"] = [\"EDMDPMSolverMultistepScheduler\"]\n _import_structure[\"scheduling_edm_euler\"] = [\"EDMEulerScheduler\"]\n _import_structure[\"scheduling_euler_ancestral_discrete\"] = [\"EulerAncestralDiscreteScheduler\"]\n _import_structure[\"scheduling_euler_discrete\"] = [\"EulerDiscreteScheduler\"]\n _import_structure[\"scheduling_flow_match_euler_discrete\"] = [\"FlowMatchEulerDiscreteScheduler\"]\n _import_structure[\"scheduling_heun_discrete\"] = [\"HeunDiscreteScheduler\"]\n _import_structure[\"scheduling_ipndm\"] = [\"IPNDMScheduler\"]\n _import_structure[\"scheduling_k_dpm_2_ancestral_discrete\"] = [\"KDPM2AncestralDiscreteScheduler\"]\n _import_structure[\"scheduling_k_dpm_2_discrete\"] = [\"KDPM2DiscreteScheduler\"]\n _import_structure[\"scheduling_lcm\"] = [\"LCMScheduler\"]\n _import_structure[\"scheduling_pndm\"] = [\"PNDMScheduler\"]\n _import_structure[\"scheduling_repaint\"] = [\"RePaintScheduler\"]\n _import_structure[\"scheduling_sasolver\"] = [\"SASolverScheduler\"]\n _import_structure[\"scheduling_sde_ve\"] = [\"ScoreSdeVeScheduler\"]\n _import_structure[\"scheduling_tcd\"] = [\"TCDScheduler\"]\n _import_structure[\"scheduling_unclip\"] = [\"UnCLIPScheduler\"]\n _import_structure[\"scheduling_unipc_multistep\"] = [\"UniPCMultistepScheduler\"]\n _import_structure[\"scheduling_utils\"] = [\"AysSchedules\", \"KarrasDiffusionSchedulers\", \"SchedulerMixin\"]\n _import_structure[\"scheduling_vq_diffusion\"] = [\"VQDiffusionScheduler\"]\n\ntry:\n if not is_flax_available():\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ..utils import dummy_flax_objects # noqa F403\n\n _dummy_modules.update(get_objects_from_module(dummy_flax_objects))\n\nelse:\n _import_structure[\"scheduling_ddim_flax\"] = [\"FlaxDDIMScheduler\"]\n _import_structure[\"scheduling_ddpm_flax\"] = [\"FlaxDDPMScheduler\"]\n _import_structure[\"scheduling_dpmsolver_multistep_flax\"] = [\"FlaxDPMSolverMultistepScheduler\"]\n _import_structure[\"scheduling_euler_discrete_flax\"] = [\"FlaxEulerDiscreteScheduler\"]\n _import_structure[\"scheduling_karras_ve_flax\"] = [\"FlaxKarrasVeScheduler\"]\n _import_structure[\"scheduling_lms_discrete_flax\"] = [\"FlaxLMSDiscreteScheduler\"]\n _import_structure[\"scheduling_pndm_flax\"] = [\"FlaxPNDMScheduler\"]\n _import_structure[\"scheduling_sde_ve_flax\"] = [\"FlaxScoreSdeVeScheduler\"]\n _import_structure[\"scheduling_utils_flax\"] = [\n \"FlaxKarrasDiffusionSchedulers\",\n \"FlaxSchedulerMixin\",\n \"FlaxSchedulerOutput\",\n \"broadcast_to_shape_from_left\",\n ]\n\n\ntry:\n if not (is_torch_available() and is_scipy_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ..utils import dummy_torch_and_scipy_objects # noqa F403\n\n _dummy_modules.update(get_objects_from_module(dummy_torch_and_scipy_objects))\n\nelse:\n _import_structure[\"scheduling_lms_discrete\"] = [\"LMSDiscreteScheduler\"]\n\ntry:\n if not (is_torch_available() and is_torchsde_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ..utils import dummy_torch_and_torchsde_objects # noqa F403\n\n _dummy_modules.update(get_objects_from_module(dummy_torch_and_torchsde_objects))\n\nelse:\n _import_structure[\"scheduling_dpmsolver_sde\"] = [\"DPMSolverSDEScheduler\"]\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n from ..utils import (\n OptionalDependencyNotAvailable,\n is_flax_available,\n is_scipy_available,\n is_torch_available,\n is_torchsde_available,\n )\n\n try:\n if not is_torch_available():\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ..utils.dummy_pt_objects import * # noqa F403\n else:\n from .deprecated import KarrasVeScheduler, ScoreSdeVpScheduler\n from .scheduling_amused import AmusedScheduler\n from .scheduling_consistency_decoder import ConsistencyDecoderScheduler\n from .scheduling_consistency_models import CMStochasticIterativeScheduler\n from .scheduling_ddim import DDIMScheduler\n from .scheduling_ddim_inverse import DDIMInverseScheduler\n from .scheduling_ddim_parallel import DDIMParallelScheduler\n from .scheduling_ddpm import DDPMScheduler\n from .scheduling_ddpm_parallel import DDPMParallelScheduler\n from .scheduling_ddpm_wuerstchen import DDPMWuerstchenScheduler\n from .scheduling_deis_multistep import DEISMultistepScheduler\n from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler\n from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler\n from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler\n from .scheduling_edm_dpmsolver_multistep import EDMDPMSolverMultistepScheduler\n from .scheduling_edm_euler import EDMEulerScheduler\n from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler\n from .scheduling_euler_discrete import EulerDiscreteScheduler\n from .scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler\n from .scheduling_heun_discrete import HeunDiscreteScheduler\n from .scheduling_ipndm import IPNDMScheduler\n from .scheduling_k_dpm_2_ancestral_discrete import KDPM2AncestralDiscreteScheduler\n from .scheduling_k_dpm_2_discrete import KDPM2DiscreteScheduler\n from .scheduling_lcm import LCMScheduler\n from .scheduling_pndm import PNDMScheduler\n from .scheduling_repaint import RePaintScheduler\n from .scheduling_sasolver import SASolverScheduler\n from .scheduling_sde_ve import ScoreSdeVeScheduler\n from .scheduling_tcd import TCDScheduler\n from .scheduling_unclip import UnCLIPScheduler\n from .scheduling_unipc_multistep import UniPCMultistepScheduler\n from .scheduling_utils import AysSchedules, KarrasDiffusionSchedulers, SchedulerMixin\n from .scheduling_vq_diffusion import VQDiffusionScheduler\n\n try:\n if not is_flax_available():\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ..utils.dummy_flax_objects import * # noqa F403\n else:\n from .scheduling_ddim_flax import FlaxDDIMScheduler\n from .scheduling_ddpm_flax import FlaxDDPMScheduler\n from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler\n from .scheduling_euler_discrete_flax import FlaxEulerDiscreteScheduler\n from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler\n from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler\n from .scheduling_pndm_flax import FlaxPNDMScheduler\n from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler\n from .scheduling_utils_flax import (\n FlaxKarrasDiffusionSchedulers,\n FlaxSchedulerMixin,\n FlaxSchedulerOutput,\n broadcast_to_shape_from_left,\n )\n\n try:\n if not (is_torch_available() and is_scipy_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ..utils.dummy_torch_and_scipy_objects import * # noqa F403\n else:\n from .scheduling_lms_discrete import LMSDiscreteScheduler\n\n try:\n if not (is_torch_available() and is_torchsde_available()):\n raise OptionalDependencyNotAvailable()\n except OptionalDependencyNotAvailable:\n from ..utils.dummy_torch_and_torchsde_objects import * # noqa F403\n else:\n from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(__name__, globals()[\"__file__\"], _import_structure, module_spec=__spec__)\n for name, value in _dummy_modules.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/schedulers/deprecated/__init__.py", "content": "from typing import TYPE_CHECKING\n\nfrom ...utils import (\n DIFFUSERS_SLOW_IMPORT,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_torch_available,\n is_transformers_available,\n)\n\n\n_dummy_objects = {}\n_import_structure = {}\n\ntry:\n if not (is_transformers_available() and is_torch_available()):\n raise OptionalDependencyNotAvailable()\nexcept OptionalDependencyNotAvailable:\n from ...utils import dummy_pt_objects # noqa F403\n\n _dummy_objects.update(get_objects_from_module(dummy_pt_objects))\nelse:\n _import_structure[\"scheduling_karras_ve\"] = [\"KarrasVeScheduler\"]\n _import_structure[\"scheduling_sde_vp\"] = [\"ScoreSdeVpScheduler\"]\n\nif TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:\n try:\n if not is_torch_available():\n raise OptionalDependencyNotAvailable()\n\n except OptionalDependencyNotAvailable:\n from ...utils.dummy_pt_objects import * # noqa F403\n else:\n from .scheduling_karras_ve import KarrasVeScheduler\n from .scheduling_sde_vp import ScoreSdeVpScheduler\n\n\nelse:\n import sys\n\n sys.modules[__name__] = _LazyModule(\n __name__,\n globals()[\"__file__\"],\n _import_structure,\n module_spec=__spec__,\n )\n\n for name, value in _dummy_objects.items():\n setattr(sys.modules[__name__], name, value)\n" }, { "path": "diffusers/src/diffusers/schedulers/deprecated/scheduling_karras_ve.py", "content": "# Copyright 2024 NVIDIA and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...utils import BaseOutput\nfrom ...utils.torch_utils import randn_tensor\nfrom ..scheduling_utils import SchedulerMixin\n\n\n@dataclass\nclass KarrasVeOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's step function output.\n\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n derivative (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Derivative of predicted original image sample (x_0).\n pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.Tensor\n derivative: torch.Tensor\n pred_original_sample: Optional[torch.Tensor] = None\n\n\nclass KarrasVeScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n A stochastic scheduler tailored to variance-expanding models.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n \n\n For more details on the parameters, see [Appendix E](https://arxiv.org/abs/2206.00364). The grid search values used\n to find the optimal `{s_noise, s_churn, s_min, s_max}` for a specific model are described in Table 5 of the paper.\n\n \n\n Args:\n sigma_min (`float`, defaults to 0.02):\n The minimum noise magnitude.\n sigma_max (`float`, defaults to 100):\n The maximum noise magnitude.\n s_noise (`float`, defaults to 1.007):\n The amount of additional noise to counteract loss of detail during sampling. A reasonable range is [1.000,\n 1.011].\n s_churn (`float`, defaults to 80):\n The parameter controlling the overall amount of stochasticity. A reasonable range is [0, 100].\n s_min (`float`, defaults to 0.05):\n The start value of the sigma range to add noise (enable stochasticity). A reasonable range is [0, 10].\n s_max (`float`, defaults to 50):\n The end value of the sigma range to add noise. A reasonable range is [0.2, 80].\n \"\"\"\n\n order = 2\n\n @register_to_config\n def __init__(\n self,\n sigma_min: float = 0.02,\n sigma_max: float = 100,\n s_noise: float = 1.007,\n s_churn: float = 80,\n s_min: float = 0.05,\n s_max: float = 50,\n ):\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = sigma_max\n\n # setable values\n self.num_inference_steps: int = None\n self.timesteps: np.IntTensor = None\n self.schedule: torch.Tensor = None # sigma(t_i)\n\n def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`int`, *optional*):\n The current timestep in the diffusion chain.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n return sample\n\n def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n \"\"\"\n self.num_inference_steps = num_inference_steps\n timesteps = np.arange(0, self.num_inference_steps)[::-1].copy()\n self.timesteps = torch.from_numpy(timesteps).to(device)\n schedule = [\n (\n self.config.sigma_max**2\n * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))\n )\n for i in self.timesteps\n ]\n self.schedule = torch.tensor(schedule, dtype=torch.float32, device=device)\n\n def add_noise_to_input(\n self, sample: torch.Tensor, sigma: float, generator: Optional[torch.Generator] = None\n ) -> Tuple[torch.Tensor, float]:\n \"\"\"\n Explicit Langevin-like \"churn\" step of adding noise to the sample according to a `gamma_i ≥ 0` to reach a\n higher noise level `sigma_hat = sigma_i + gamma_i*sigma_i`.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n sigma (`float`):\n generator (`torch.Generator`, *optional*):\n A random number generator.\n \"\"\"\n if self.config.s_min <= sigma <= self.config.s_max:\n gamma = min(self.config.s_churn / self.num_inference_steps, 2**0.5 - 1)\n else:\n gamma = 0\n\n # sample eps ~ N(0, S_noise^2 * I)\n eps = self.config.s_noise * randn_tensor(sample.shape, generator=generator).to(sample.device)\n sigma_hat = sigma + gamma * sigma\n sample_hat = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)\n\n return sample_hat, sigma_hat\n\n def step(\n self,\n model_output: torch.Tensor,\n sigma_hat: float,\n sigma_prev: float,\n sample_hat: torch.Tensor,\n return_dict: bool = True,\n ) -> Union[KarrasVeOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n sigma_hat (`float`):\n sigma_prev (`float`):\n sample_hat (`torch.Tensor`):\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~schedulers.scheduling_karras_ve.KarrasVESchedulerOutput`] or `tuple`.\n\n Returns:\n [`~schedulers.scheduling_karras_ve.KarrasVESchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_karras_ve.KarrasVESchedulerOutput`] is returned,\n otherwise a tuple is returned where the first element is the sample tensor.\n\n \"\"\"\n\n pred_original_sample = sample_hat + sigma_hat * model_output\n derivative = (sample_hat - pred_original_sample) / sigma_hat\n sample_prev = sample_hat + (sigma_prev - sigma_hat) * derivative\n\n if not return_dict:\n return (sample_prev, derivative)\n\n return KarrasVeOutput(\n prev_sample=sample_prev, derivative=derivative, pred_original_sample=pred_original_sample\n )\n\n def step_correct(\n self,\n model_output: torch.Tensor,\n sigma_hat: float,\n sigma_prev: float,\n sample_hat: torch.Tensor,\n sample_prev: torch.Tensor,\n derivative: torch.Tensor,\n return_dict: bool = True,\n ) -> Union[KarrasVeOutput, Tuple]:\n \"\"\"\n Corrects the predicted sample based on the `model_output` of the network.\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n sigma_hat (`float`): TODO\n sigma_prev (`float`): TODO\n sample_hat (`torch.Tensor`): TODO\n sample_prev (`torch.Tensor`): TODO\n derivative (`torch.Tensor`): TODO\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~schedulers.scheduling_ddpm.DDPMSchedulerOutput`] or `tuple`.\n\n Returns:\n prev_sample (TODO): updated sample in the diffusion chain. derivative (TODO): TODO\n\n \"\"\"\n pred_original_sample = sample_prev + sigma_prev * model_output\n derivative_corr = (sample_prev - pred_original_sample) / sigma_prev\n sample_prev = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)\n\n if not return_dict:\n return (sample_prev, derivative)\n\n return KarrasVeOutput(\n prev_sample=sample_prev, derivative=derivative, pred_original_sample=pred_original_sample\n )\n\n def add_noise(self, original_samples, noise, timesteps):\n raise NotImplementedError()\n" }, { "path": "diffusers/src/diffusers/schedulers/deprecated/scheduling_sde_vp.py", "content": "# Copyright 2024 Google Brain and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch\n\nimport math\nfrom typing import Union\n\nimport torch\n\nfrom ...configuration_utils import ConfigMixin, register_to_config\nfrom ...utils.torch_utils import randn_tensor\nfrom ..scheduling_utils import SchedulerMixin\n\n\nclass ScoreSdeVpScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n `ScoreSdeVpScheduler` is a variance preserving stochastic differential equation (SDE) scheduler.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 2000):\n The number of diffusion steps to train the model.\n beta_min (`int`, defaults to 0.1):\n beta_max (`int`, defaults to 20):\n sampling_eps (`int`, defaults to 1e-3):\n The end value of sampling where timesteps decrease progressively from 1 to epsilon.\n \"\"\"\n\n order = 1\n\n @register_to_config\n def __init__(self, num_train_timesteps=2000, beta_min=0.1, beta_max=20, sampling_eps=1e-3):\n self.sigmas = None\n self.discrete_sigmas = None\n self.timesteps = None\n\n def set_timesteps(self, num_inference_steps, device: Union[str, torch.device] = None):\n \"\"\"\n Sets the continuous timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n \"\"\"\n self.timesteps = torch.linspace(1, self.config.sampling_eps, num_inference_steps, device=device)\n\n def step_pred(self, score, x, t, generator=None):\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n score ():\n x ():\n t ():\n generator (`torch.Generator`, *optional*):\n A random number generator.\n \"\"\"\n if self.timesteps is None:\n raise ValueError(\n \"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n # TODO(Patrick) better comments + non-PyTorch\n # postprocess model score\n log_mean_coeff = -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min\n std = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff))\n std = std.flatten()\n while len(std.shape) < len(score.shape):\n std = std.unsqueeze(-1)\n score = -score / std\n\n # compute\n dt = -1.0 / len(self.timesteps)\n\n beta_t = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)\n beta_t = beta_t.flatten()\n while len(beta_t.shape) < len(x.shape):\n beta_t = beta_t.unsqueeze(-1)\n drift = -0.5 * beta_t * x\n\n diffusion = torch.sqrt(beta_t)\n drift = drift - diffusion**2 * score\n x_mean = x + drift * dt\n\n # add noise\n noise = randn_tensor(x.shape, layout=x.layout, generator=generator, device=x.device, dtype=x.dtype)\n x = x_mean + diffusion * math.sqrt(-dt) * noise\n\n return x, x_mean\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_amused.py", "content": "import math\nfrom dataclasses import dataclass\nfrom typing import List, Optional, Tuple, Union\n\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import BaseOutput\nfrom .scheduling_utils import SchedulerMixin\n\n\ndef gumbel_noise(t, generator=None):\n device = generator.device if generator is not None else t.device\n noise = torch.zeros_like(t, device=device).uniform_(0, 1, generator=generator).to(t.device)\n return -torch.log((-torch.log(noise.clamp(1e-20))).clamp(1e-20))\n\n\ndef mask_by_random_topk(mask_len, probs, temperature=1.0, generator=None):\n confidence = torch.log(probs.clamp(1e-20)) + temperature * gumbel_noise(probs, generator=generator)\n sorted_confidence = torch.sort(confidence, dim=-1).values\n cut_off = torch.gather(sorted_confidence, 1, mask_len.long())\n masking = confidence < cut_off\n return masking\n\n\n@dataclass\nclass AmusedSchedulerOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's `step` function output.\n\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample `(x_{0})` based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.Tensor\n pred_original_sample: torch.Tensor = None\n\n\nclass AmusedScheduler(SchedulerMixin, ConfigMixin):\n order = 1\n\n temperatures: torch.Tensor\n\n @register_to_config\n def __init__(\n self,\n mask_token_id: int,\n masking_schedule: str = \"cosine\",\n ):\n self.temperatures = None\n self.timesteps = None\n\n def set_timesteps(\n self,\n num_inference_steps: int,\n temperature: Union[int, Tuple[int, int], List[int]] = (2, 0),\n device: Union[str, torch.device] = None,\n ):\n self.timesteps = torch.arange(num_inference_steps, device=device).flip(0)\n\n if isinstance(temperature, (tuple, list)):\n self.temperatures = torch.linspace(temperature[0], temperature[1], num_inference_steps, device=device)\n else:\n self.temperatures = torch.linspace(temperature, 0.01, num_inference_steps, device=device)\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: torch.long,\n sample: torch.LongTensor,\n starting_mask_ratio: int = 1,\n generator: Optional[torch.Generator] = None,\n return_dict: bool = True,\n ) -> Union[AmusedSchedulerOutput, Tuple]:\n two_dim_input = sample.ndim == 3 and model_output.ndim == 4\n\n if two_dim_input:\n batch_size, codebook_size, height, width = model_output.shape\n sample = sample.reshape(batch_size, height * width)\n model_output = model_output.reshape(batch_size, codebook_size, height * width).permute(0, 2, 1)\n\n unknown_map = sample == self.config.mask_token_id\n\n probs = model_output.softmax(dim=-1)\n\n device = probs.device\n probs_ = probs.to(generator.device) if generator is not None else probs # handles when generator is on CPU\n if probs_.device.type == \"cpu\" and probs_.dtype != torch.float32:\n probs_ = probs_.float() # multinomial is not implemented for cpu half precision\n probs_ = probs_.reshape(-1, probs.size(-1))\n pred_original_sample = torch.multinomial(probs_, 1, generator=generator).to(device=device)\n pred_original_sample = pred_original_sample[:, 0].view(*probs.shape[:-1])\n pred_original_sample = torch.where(unknown_map, pred_original_sample, sample)\n\n if timestep == 0:\n prev_sample = pred_original_sample\n else:\n seq_len = sample.shape[1]\n step_idx = (self.timesteps == timestep).nonzero()\n ratio = (step_idx + 1) / len(self.timesteps)\n\n if self.config.masking_schedule == \"cosine\":\n mask_ratio = torch.cos(ratio * math.pi / 2)\n elif self.config.masking_schedule == \"linear\":\n mask_ratio = 1 - ratio\n else:\n raise ValueError(f\"unknown masking schedule {self.config.masking_schedule}\")\n\n mask_ratio = starting_mask_ratio * mask_ratio\n\n mask_len = (seq_len * mask_ratio).floor()\n # do not mask more than amount previously masked\n mask_len = torch.min(unknown_map.sum(dim=-1, keepdim=True) - 1, mask_len)\n # mask at least one\n mask_len = torch.max(torch.tensor([1], device=model_output.device), mask_len)\n\n selected_probs = torch.gather(probs, -1, pred_original_sample[:, :, None])[:, :, 0]\n # Ignores the tokens given in the input by overwriting their confidence.\n selected_probs = torch.where(unknown_map, selected_probs, torch.finfo(selected_probs.dtype).max)\n\n masking = mask_by_random_topk(mask_len, selected_probs, self.temperatures[step_idx], generator)\n\n # Masks tokens with lower confidence.\n prev_sample = torch.where(masking, self.config.mask_token_id, pred_original_sample)\n\n if two_dim_input:\n prev_sample = prev_sample.reshape(batch_size, height, width)\n pred_original_sample = pred_original_sample.reshape(batch_size, height, width)\n\n if not return_dict:\n return (prev_sample, pred_original_sample)\n\n return AmusedSchedulerOutput(prev_sample, pred_original_sample)\n\n def add_noise(self, sample, timesteps, generator=None):\n step_idx = (self.timesteps == timesteps).nonzero()\n ratio = (step_idx + 1) / len(self.timesteps)\n\n if self.config.masking_schedule == \"cosine\":\n mask_ratio = torch.cos(ratio * math.pi / 2)\n elif self.config.masking_schedule == \"linear\":\n mask_ratio = 1 - ratio\n else:\n raise ValueError(f\"unknown masking schedule {self.config.masking_schedule}\")\n\n mask_indices = (\n torch.rand(\n sample.shape, device=generator.device if generator is not None else sample.device, generator=generator\n ).to(sample.device)\n < mask_ratio\n )\n\n masked_sample = sample.clone()\n\n masked_sample[mask_indices] = self.config.mask_token_id\n\n return masked_sample\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_consistency_decoder.py", "content": "import math\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple, Union\n\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import BaseOutput\nfrom ..utils.torch_utils import randn_tensor\nfrom .scheduling_utils import SchedulerMixin\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\n@dataclass\nclass ConsistencyDecoderSchedulerOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's `step` function.\n\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n \"\"\"\n\n prev_sample: torch.Tensor\n\n\nclass ConsistencyDecoderScheduler(SchedulerMixin, ConfigMixin):\n order = 1\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1024,\n sigma_data: float = 0.5,\n ):\n betas = betas_for_alpha_bar(num_train_timesteps)\n\n alphas = 1.0 - betas\n alphas_cumprod = torch.cumprod(alphas, dim=0)\n\n self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod)\n self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1.0 - alphas_cumprod)\n\n sigmas = torch.sqrt(1.0 / alphas_cumprod - 1)\n\n sqrt_recip_alphas_cumprod = torch.sqrt(1.0 / alphas_cumprod)\n\n self.c_skip = sqrt_recip_alphas_cumprod * sigma_data**2 / (sigmas**2 + sigma_data**2)\n self.c_out = sigmas * sigma_data / (sigmas**2 + sigma_data**2) ** 0.5\n self.c_in = sqrt_recip_alphas_cumprod / (sigmas**2 + sigma_data**2) ** 0.5\n\n def set_timesteps(\n self,\n num_inference_steps: Optional[int] = None,\n device: Union[str, torch.device] = None,\n ):\n if num_inference_steps != 2:\n raise ValueError(\"Currently more than 2 inference steps are not supported.\")\n\n self.timesteps = torch.tensor([1008, 512], dtype=torch.long, device=device)\n self.sqrt_alphas_cumprod = self.sqrt_alphas_cumprod.to(device)\n self.sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod.to(device)\n self.c_skip = self.c_skip.to(device)\n self.c_out = self.c_out.to(device)\n self.c_in = self.c_in.to(device)\n\n @property\n def init_noise_sigma(self):\n return self.sqrt_one_minus_alphas_cumprod[self.timesteps[0]]\n\n def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`int`, *optional*):\n The current timestep in the diffusion chain.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n return sample * self.c_in[timestep]\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: Union[float, torch.Tensor],\n sample: torch.Tensor,\n generator: Optional[torch.Generator] = None,\n return_dict: bool = True,\n ) -> Union[ConsistencyDecoderSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from the learned diffusion model.\n timestep (`float`):\n The current timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n generator (`torch.Generator`, *optional*):\n A random number generator.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a\n [`~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput`] or `tuple`.\n\n Returns:\n [`~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput`] or `tuple`:\n If return_dict is `True`,\n [`~schedulers.scheduling_consistency_models.ConsistencyDecoderSchedulerOutput`] is returned, otherwise\n a tuple is returned where the first element is the sample tensor.\n \"\"\"\n x_0 = self.c_out[timestep] * model_output + self.c_skip[timestep] * sample\n\n timestep_idx = torch.where(self.timesteps == timestep)[0]\n\n if timestep_idx == len(self.timesteps) - 1:\n prev_sample = x_0\n else:\n noise = randn_tensor(x_0.shape, generator=generator, dtype=x_0.dtype, device=x_0.device)\n prev_sample = (\n self.sqrt_alphas_cumprod[self.timesteps[timestep_idx + 1]].to(x_0.dtype) * x_0\n + self.sqrt_one_minus_alphas_cumprod[self.timesteps[timestep_idx + 1]].to(x_0.dtype) * noise\n )\n\n if not return_dict:\n return (prev_sample,)\n\n return ConsistencyDecoderSchedulerOutput(prev_sample=prev_sample)\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_consistency_models.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom dataclasses import dataclass\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import BaseOutput, logging\nfrom ..utils.torch_utils import randn_tensor\nfrom .scheduling_utils import SchedulerMixin\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n@dataclass\nclass CMStochasticIterativeSchedulerOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's `step` function.\n\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n \"\"\"\n\n prev_sample: torch.Tensor\n\n\nclass CMStochasticIterativeScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n Multistep and onestep sampling for consistency models.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 40):\n The number of diffusion steps to train the model.\n sigma_min (`float`, defaults to 0.002):\n Minimum noise magnitude in the sigma schedule. Defaults to 0.002 from the original implementation.\n sigma_max (`float`, defaults to 80.0):\n Maximum noise magnitude in the sigma schedule. Defaults to 80.0 from the original implementation.\n sigma_data (`float`, defaults to 0.5):\n The standard deviation of the data distribution from the EDM\n [paper](https://huggingface.co/papers/2206.00364). Defaults to 0.5 from the original implementation.\n s_noise (`float`, defaults to 1.0):\n The amount of additional noise to counteract loss of detail during sampling. A reasonable range is [1.000,\n 1.011]. Defaults to 1.0 from the original implementation.\n rho (`float`, defaults to 7.0):\n The parameter for calculating the Karras sigma schedule from the EDM\n [paper](https://huggingface.co/papers/2206.00364). Defaults to 7.0 from the original implementation.\n clip_denoised (`bool`, defaults to `True`):\n Whether to clip the denoised outputs to `(-1, 1)`.\n timesteps (`List` or `np.ndarray` or `torch.Tensor`, *optional*):\n An explicit timestep schedule that can be optionally specified. The timesteps are expected to be in\n increasing order.\n \"\"\"\n\n order = 1\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 40,\n sigma_min: float = 0.002,\n sigma_max: float = 80.0,\n sigma_data: float = 0.5,\n s_noise: float = 1.0,\n rho: float = 7.0,\n clip_denoised: bool = True,\n ):\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = sigma_max\n\n ramp = np.linspace(0, 1, num_train_timesteps)\n sigmas = self._convert_to_karras(ramp)\n timesteps = self.sigma_to_t(sigmas)\n\n # setable values\n self.num_inference_steps = None\n self.sigmas = torch.from_numpy(sigmas)\n self.timesteps = torch.from_numpy(timesteps)\n self.custom_timesteps = False\n self.is_scale_input_called = False\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n @property\n def step_index(self):\n \"\"\"\n The index counter for current timestep. It will increase 1 after each scheduler step.\n \"\"\"\n return self._step_index\n\n @property\n def begin_index(self):\n \"\"\"\n The index for the first timestep. It should be set from pipeline with `set_begin_index` method.\n \"\"\"\n return self._begin_index\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index\n def set_begin_index(self, begin_index: int = 0):\n \"\"\"\n Sets the begin index for the scheduler. This function should be run from pipeline before the inference.\n\n Args:\n begin_index (`int`):\n The begin index for the scheduler.\n \"\"\"\n self._begin_index = begin_index\n\n def scale_model_input(self, sample: torch.Tensor, timestep: Union[float, torch.Tensor]) -> torch.Tensor:\n \"\"\"\n Scales the consistency model input by `(sigma**2 + sigma_data**2) ** 0.5`.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`float` or `torch.Tensor`):\n The current timestep in the diffusion chain.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n # Get sigma corresponding to timestep\n if self.step_index is None:\n self._init_step_index(timestep)\n\n sigma = self.sigmas[self.step_index]\n\n sample = sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)\n\n self.is_scale_input_called = True\n return sample\n\n def sigma_to_t(self, sigmas: Union[float, np.ndarray]):\n \"\"\"\n Gets scaled timesteps from the Karras sigmas for input to the consistency model.\n\n Args:\n sigmas (`float` or `np.ndarray`):\n A single Karras sigma or an array of Karras sigmas.\n\n Returns:\n `float` or `np.ndarray`:\n A scaled input timestep or scaled input timestep array.\n \"\"\"\n if not isinstance(sigmas, np.ndarray):\n sigmas = np.array(sigmas, dtype=np.float64)\n\n timesteps = 1000 * 0.25 * np.log(sigmas + 1e-44)\n\n return timesteps\n\n def set_timesteps(\n self,\n num_inference_steps: Optional[int] = None,\n device: Union[str, torch.device] = None,\n timesteps: Optional[List[int]] = None,\n ):\n \"\"\"\n Sets the timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default\n timestep spacing strategy of equal spacing between timesteps is used. If `timesteps` is passed,\n `num_inference_steps` must be `None`.\n \"\"\"\n if num_inference_steps is None and timesteps is None:\n raise ValueError(\"Exactly one of `num_inference_steps` or `timesteps` must be supplied.\")\n\n if num_inference_steps is not None and timesteps is not None:\n raise ValueError(\"Can only pass one of `num_inference_steps` or `timesteps`.\")\n\n # Follow DDPMScheduler custom timesteps logic\n if timesteps is not None:\n for i in range(1, len(timesteps)):\n if timesteps[i] >= timesteps[i - 1]:\n raise ValueError(\"`timesteps` must be in descending order.\")\n\n if timesteps[0] >= self.config.num_train_timesteps:\n raise ValueError(\n f\"`timesteps` must start before `self.config.train_timesteps`:\"\n f\" {self.config.num_train_timesteps}.\"\n )\n\n timesteps = np.array(timesteps, dtype=np.int64)\n self.custom_timesteps = True\n else:\n if num_inference_steps > self.config.num_train_timesteps:\n raise ValueError(\n f\"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:\"\n f\" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle\"\n f\" maximal {self.config.num_train_timesteps} timesteps.\"\n )\n\n self.num_inference_steps = num_inference_steps\n\n step_ratio = self.config.num_train_timesteps // self.num_inference_steps\n timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64)\n self.custom_timesteps = False\n\n # Map timesteps to Karras sigmas directly for multistep sampling\n # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675\n num_train_timesteps = self.config.num_train_timesteps\n ramp = timesteps[::-1].copy()\n ramp = ramp / (num_train_timesteps - 1)\n sigmas = self._convert_to_karras(ramp)\n timesteps = self.sigma_to_t(sigmas)\n\n sigmas = np.concatenate([sigmas, [self.config.sigma_min]]).astype(np.float32)\n self.sigmas = torch.from_numpy(sigmas).to(device=device)\n\n if str(device).startswith(\"mps\"):\n # mps does not support float64\n self.timesteps = torch.from_numpy(timesteps).to(device, dtype=torch.float32)\n else:\n self.timesteps = torch.from_numpy(timesteps).to(device=device)\n\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n # Modified _convert_to_karras implementation that takes in ramp as argument\n def _convert_to_karras(self, ramp):\n \"\"\"Constructs the noise schedule of Karras et al. (2022).\"\"\"\n\n sigma_min: float = self.config.sigma_min\n sigma_max: float = self.config.sigma_max\n\n rho = self.config.rho\n min_inv_rho = sigma_min ** (1 / rho)\n max_inv_rho = sigma_max ** (1 / rho)\n sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho\n return sigmas\n\n def get_scalings(self, sigma):\n sigma_data = self.config.sigma_data\n\n c_skip = sigma_data**2 / (sigma**2 + sigma_data**2)\n c_out = sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5\n return c_skip, c_out\n\n def get_scalings_for_boundary_condition(self, sigma):\n \"\"\"\n Gets the scalings used in the consistency model parameterization (from Appendix C of the\n [paper](https://huggingface.co/papers/2303.01469)) to enforce boundary condition.\n\n \n\n `epsilon` in the equations for `c_skip` and `c_out` is set to `sigma_min`.\n\n \n\n Args:\n sigma (`torch.Tensor`):\n The current sigma in the Karras sigma schedule.\n\n Returns:\n `tuple`:\n A two-element tuple where `c_skip` (which weights the current sample) is the first element and `c_out`\n (which weights the consistency model output) is the second element.\n \"\"\"\n sigma_min = self.config.sigma_min\n sigma_data = self.config.sigma_data\n\n c_skip = sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)\n c_out = (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5\n return c_skip, c_out\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.index_for_timestep\n def index_for_timestep(self, timestep, schedule_timesteps=None):\n if schedule_timesteps is None:\n schedule_timesteps = self.timesteps\n\n indices = (schedule_timesteps == timestep).nonzero()\n\n # The sigma index that is taken for the **very** first `step`\n # is always the second index (or the last index if there is only 1)\n # This way we can ensure we don't accidentally skip a sigma in\n # case we start in the middle of the denoising schedule (e.g. for image-to-image)\n pos = 1 if len(indices) > 1 else 0\n\n return indices[pos].item()\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index\n def _init_step_index(self, timestep):\n if self.begin_index is None:\n if isinstance(timestep, torch.Tensor):\n timestep = timestep.to(self.timesteps.device)\n self._step_index = self.index_for_timestep(timestep)\n else:\n self._step_index = self._begin_index\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: Union[float, torch.Tensor],\n sample: torch.Tensor,\n generator: Optional[torch.Generator] = None,\n return_dict: bool = True,\n ) -> Union[CMStochasticIterativeSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from the learned diffusion model.\n timestep (`float`):\n The current timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n generator (`torch.Generator`, *optional*):\n A random number generator.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a\n [`~schedulers.scheduling_consistency_models.CMStochasticIterativeSchedulerOutput`] or `tuple`.\n\n Returns:\n [`~schedulers.scheduling_consistency_models.CMStochasticIterativeSchedulerOutput`] or `tuple`:\n If return_dict is `True`,\n [`~schedulers.scheduling_consistency_models.CMStochasticIterativeSchedulerOutput`] is returned,\n otherwise a tuple is returned where the first element is the sample tensor.\n \"\"\"\n\n if isinstance(timestep, (int, torch.IntTensor, torch.LongTensor)):\n raise ValueError(\n (\n \"Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to\"\n f\" `{self.__class__}.step()` is not supported. Make sure to pass\"\n \" one of the `scheduler.timesteps` as a timestep.\"\n ),\n )\n\n if not self.is_scale_input_called:\n logger.warning(\n \"The `scale_model_input` function should be called before `step` to ensure correct denoising. \"\n \"See `StableDiffusionPipeline` for a usage example.\"\n )\n\n sigma_min = self.config.sigma_min\n sigma_max = self.config.sigma_max\n\n if self.step_index is None:\n self._init_step_index(timestep)\n\n # sigma_next corresponds to next_t in original implementation\n sigma = self.sigmas[self.step_index]\n if self.step_index + 1 < self.config.num_train_timesteps:\n sigma_next = self.sigmas[self.step_index + 1]\n else:\n # Set sigma_next to sigma_min\n sigma_next = self.sigmas[-1]\n\n # Get scalings for boundary conditions\n c_skip, c_out = self.get_scalings_for_boundary_condition(sigma)\n\n # 1. Denoise model output using boundary conditions\n denoised = c_out * model_output + c_skip * sample\n if self.config.clip_denoised:\n denoised = denoised.clamp(-1, 1)\n\n # 2. Sample z ~ N(0, s_noise^2 * I)\n # Noise is not used for onestep sampling.\n if len(self.timesteps) > 1:\n noise = randn_tensor(\n model_output.shape, dtype=model_output.dtype, device=model_output.device, generator=generator\n )\n else:\n noise = torch.zeros_like(model_output)\n z = noise * self.config.s_noise\n\n sigma_hat = sigma_next.clamp(min=sigma_min, max=sigma_max)\n\n # 3. Return noisy sample\n # tau = sigma_hat, eps = sigma_min\n prev_sample = denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5\n\n # upon completion increase step index by one\n self._step_index += 1\n\n if not return_dict:\n return (prev_sample,)\n\n return CMStochasticIterativeSchedulerOutput(prev_sample=prev_sample)\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.add_noise\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.Tensor,\n ) -> torch.Tensor:\n # Make sure sigmas and timesteps have the same device and dtype as original_samples\n sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)\n if original_samples.device.type == \"mps\" and torch.is_floating_point(timesteps):\n # mps does not support float64\n schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)\n timesteps = timesteps.to(original_samples.device, dtype=torch.float32)\n else:\n schedule_timesteps = self.timesteps.to(original_samples.device)\n timesteps = timesteps.to(original_samples.device)\n\n # self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index\n if self.begin_index is None:\n step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps]\n elif self.step_index is not None:\n # add_noise is called after first denoising step (for inpainting)\n step_indices = [self.step_index] * timesteps.shape[0]\n else:\n # add noise is called before first denoising step to create initial latent(img2img)\n step_indices = [self.begin_index] * timesteps.shape[0]\n\n sigma = sigmas[step_indices].flatten()\n while len(sigma.shape) < len(original_samples.shape):\n sigma = sigma.unsqueeze(-1)\n\n noisy_samples = original_samples + noise * sigma\n return noisy_samples\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_ddim.py", "content": "# Copyright 2024 Stanford University Team and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion\n# and https://github.com/hojonathanho/diffusion\n\nimport math\nfrom dataclasses import dataclass\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import BaseOutput\nfrom ..utils.torch_utils import randn_tensor\nfrom .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin\n\n\n@dataclass\n# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM\nclass DDIMSchedulerOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's `step` function output.\n\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample `(x_{0})` based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.Tensor\n pred_original_sample: Optional[torch.Tensor] = None\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\ndef rescale_zero_terminal_snr(betas):\n \"\"\"\n Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)\n\n\n Args:\n betas (`torch.Tensor`):\n the betas that the scheduler is being initialized with.\n\n Returns:\n `torch.Tensor`: rescaled betas with zero terminal SNR\n \"\"\"\n # Convert betas to alphas_bar_sqrt\n alphas = 1.0 - betas\n alphas_cumprod = torch.cumprod(alphas, dim=0)\n alphas_bar_sqrt = alphas_cumprod.sqrt()\n\n # Store old values.\n alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()\n alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()\n\n # Shift so the last timestep is zero.\n alphas_bar_sqrt -= alphas_bar_sqrt_T\n\n # Scale so the first timestep is back to the old value.\n alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)\n\n # Convert alphas_bar_sqrt to betas\n alphas_bar = alphas_bar_sqrt**2 # Revert sqrt\n alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod\n alphas = torch.cat([alphas_bar[0:1], alphas])\n betas = 1 - alphas\n\n return betas\n\n\nclass DDIMScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n `DDIMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with\n non-Markovian guidance.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n beta_start (`float`, defaults to 0.0001):\n The starting `beta` value of inference.\n beta_end (`float`, defaults to 0.02):\n The final `beta` value.\n beta_schedule (`str`, defaults to `\"linear\"`):\n The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear`, `scaled_linear`, or `squaredcos_cap_v2`.\n trained_betas (`np.ndarray`, *optional*):\n Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.\n clip_sample (`bool`, defaults to `True`):\n Clip the predicted sample for numerical stability.\n clip_sample_range (`float`, defaults to 1.0):\n The maximum magnitude for sample clipping. Valid only when `clip_sample=True`.\n set_alpha_to_one (`bool`, defaults to `True`):\n Each diffusion step uses the alphas product value at that step and at the previous one. For the final step\n there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,\n otherwise it uses the alpha value at step 0.\n steps_offset (`int`, defaults to 0):\n An offset added to the inference steps, as required by some model families.\n prediction_type (`str`, defaults to `epsilon`, *optional*):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),\n `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen\n Video](https://imagen.research.google/video/paper.pdf) paper).\n thresholding (`bool`, defaults to `False`):\n Whether to use the \"dynamic thresholding\" method. This is unsuitable for latent-space diffusion models such\n as Stable Diffusion.\n dynamic_thresholding_ratio (`float`, defaults to 0.995):\n The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.\n sample_max_value (`float`, defaults to 1.0):\n The threshold value for dynamic thresholding. Valid only when `thresholding=True`.\n timestep_spacing (`str`, defaults to `\"leading\"`):\n The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.\n rescale_betas_zero_snr (`bool`, defaults to `False`):\n Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and\n dark samples instead of limiting it to samples with medium brightness. Loosely related to\n [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).\n \"\"\"\n\n _compatibles = [e.name for e in KarrasDiffusionSchedulers]\n order = 1\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[Union[np.ndarray, List[float]]] = None,\n clip_sample: bool = True,\n set_alpha_to_one: bool = True,\n steps_offset: int = 0,\n prediction_type: str = \"epsilon\",\n thresholding: bool = False,\n dynamic_thresholding_ratio: float = 0.995,\n clip_sample_range: float = 1.0,\n sample_max_value: float = 1.0,\n timestep_spacing: str = \"leading\",\n rescale_betas_zero_snr: bool = False,\n ):\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n # Rescale for zero SNR\n if rescale_betas_zero_snr:\n self.betas = rescale_zero_terminal_snr(self.betas)\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n\n # At every step in ddim, we are looking into the previous alphas_cumprod\n # For the final step, there is no previous alphas_cumprod because we are already at 0\n # `set_alpha_to_one` decides whether we set this parameter simply to one or\n # whether we use the final alpha of the \"non-previous\" one.\n self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]\n\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = 1.0\n\n # setable values\n self.num_inference_steps = None\n self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64))\n\n def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`int`, *optional*):\n The current timestep in the diffusion chain.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n return sample\n\n def _get_variance(self, timestep, prev_timestep):\n alpha_prod_t = self.alphas_cumprod[timestep]\n alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n\n variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)\n\n return variance\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample\n def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:\n \"\"\"\n \"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the\n prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by\n s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing\n pixels from saturation at each step. We find that dynamic thresholding results in significantly better\n photorealism as well as better image-text alignment, especially when using very large guidance weights.\"\n\n https://arxiv.org/abs/2205.11487\n \"\"\"\n dtype = sample.dtype\n batch_size, channels, *remaining_dims = sample.shape\n\n if dtype not in (torch.float32, torch.float64):\n sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half\n\n # Flatten sample for doing quantile calculation along each image\n sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))\n\n abs_sample = sample.abs() # \"a certain percentile absolute pixel value\"\n\n s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)\n s = torch.clamp(\n s, min=1, max=self.config.sample_max_value\n ) # When clamped to min=1, equivalent to standard clipping to [-1, 1]\n s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0\n sample = torch.clamp(sample, -s, s) / s # \"we threshold xt0 to the range [-s, s] and then divide by s\"\n\n sample = sample.reshape(batch_size, channels, *remaining_dims)\n sample = sample.to(dtype)\n\n return sample\n\n def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n \"\"\"\n\n if num_inference_steps > self.config.num_train_timesteps:\n raise ValueError(\n f\"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:\"\n f\" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle\"\n f\" maximal {self.config.num_train_timesteps} timesteps.\"\n )\n\n self.num_inference_steps = num_inference_steps\n\n # \"linspace\", \"leading\", \"trailing\" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891\n if self.config.timestep_spacing == \"linspace\":\n timesteps = (\n np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps)\n .round()[::-1]\n .copy()\n .astype(np.int64)\n )\n elif self.config.timestep_spacing == \"leading\":\n step_ratio = self.config.num_train_timesteps // self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64)\n timesteps += self.config.steps_offset\n elif self.config.timestep_spacing == \"trailing\":\n step_ratio = self.config.num_train_timesteps / self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = np.round(np.arange(self.config.num_train_timesteps, 0, -step_ratio)).astype(np.int64)\n timesteps -= 1\n else:\n raise ValueError(\n f\"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'leading' or 'trailing'.\"\n )\n\n self.timesteps = torch.from_numpy(timesteps).to(device)\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n eta: float = 0.0,\n use_clipped_model_output: bool = False,\n generator=None,\n variance_noise: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ) -> Union[DDIMSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`float`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n eta (`float`):\n The weight of noise for added noise in diffusion step.\n use_clipped_model_output (`bool`, defaults to `False`):\n If `True`, computes \"corrected\" `model_output` from the clipped predicted original sample. Necessary\n because predicted original sample is clipped to [-1, 1] when `self.config.clip_sample` is `True`. If no\n clipping has happened, \"corrected\" `model_output` would coincide with the one provided as input and\n `use_clipped_model_output` has no effect.\n generator (`torch.Generator`, *optional*):\n A random number generator.\n variance_noise (`torch.Tensor`):\n Alternative to generating noise with `generator` by directly providing the noise for the variance\n itself. Useful for methods such as [`CycleDiffusion`].\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~schedulers.scheduling_ddim.DDIMSchedulerOutput`] or `tuple`.\n\n Returns:\n [`~schedulers.scheduling_utils.DDIMSchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_ddim.DDIMSchedulerOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n\n \"\"\"\n if self.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf\n # Ideally, read DDIM paper in-detail understanding\n\n # Notation ( -> \n # - pred_noise_t -> e_theta(x_t, t)\n # - pred_original_sample -> f_theta(x_t, t) or x_0\n # - std_dev_t -> sigma_t\n # - eta -> η\n # - pred_sample_direction -> \"direction pointing to x_t\"\n # - pred_prev_sample -> \"x_t-1\"\n\n # 1. get previous step value (=t-1)\n prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps\n\n # 2. compute alphas, betas\n alpha_prod_t = self.alphas_cumprod[timestep]\n alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod\n\n beta_prod_t = 1 - alpha_prod_t\n\n # 3. compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n if self.config.prediction_type == \"epsilon\":\n pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)\n pred_epsilon = model_output\n elif self.config.prediction_type == \"sample\":\n pred_original_sample = model_output\n pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)\n elif self.config.prediction_type == \"v_prediction\":\n pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output\n pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or\"\n \" `v_prediction`\"\n )\n\n # 4. Clip or threshold \"predicted x_0\"\n if self.config.thresholding:\n pred_original_sample = self._threshold_sample(pred_original_sample)\n elif self.config.clip_sample:\n pred_original_sample = pred_original_sample.clamp(\n -self.config.clip_sample_range, self.config.clip_sample_range\n )\n\n # 5. compute variance: \"sigma_t(η)\" -> see formula (16)\n # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)\n variance = self._get_variance(timestep, prev_timestep)\n std_dev_t = eta * variance ** (0.5)\n\n if use_clipped_model_output:\n # the pred_epsilon is always re-derived from the clipped x_0 in Glide\n pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)\n\n # 6. compute \"direction pointing to x_t\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * pred_epsilon\n\n # 7. compute x_t without \"random noise\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction\n\n if eta > 0:\n if variance_noise is not None and generator is not None:\n raise ValueError(\n \"Cannot pass both generator and variance_noise. Please make sure that either `generator` or\"\n \" `variance_noise` stays `None`.\"\n )\n\n if variance_noise is None:\n variance_noise = randn_tensor(\n model_output.shape, generator=generator, device=model_output.device, dtype=model_output.dtype\n )\n variance = std_dev_t * variance_noise\n\n prev_sample = prev_sample + variance\n\n if not return_dict:\n return (prev_sample,)\n\n return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.IntTensor,\n ) -> torch.Tensor:\n # Make sure alphas_cumprod and timestep have same device and dtype as original_samples\n # Move the self.alphas_cumprod to device to avoid redundant CPU to GPU data movement\n # for the subsequent add_noise calls\n self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device)\n alphas_cumprod = self.alphas_cumprod.to(dtype=original_samples.dtype)\n timesteps = timesteps.to(original_samples.device)\n\n sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n while len(sqrt_alpha_prod.shape) < len(original_samples.shape):\n sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n\n noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise\n return noisy_samples\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity\n def get_velocity(self, sample: torch.Tensor, noise: torch.Tensor, timesteps: torch.IntTensor) -> torch.Tensor:\n # Make sure alphas_cumprod and timestep have same device and dtype as sample\n self.alphas_cumprod = self.alphas_cumprod.to(device=sample.device)\n alphas_cumprod = self.alphas_cumprod.to(dtype=sample.dtype)\n timesteps = timesteps.to(sample.device)\n\n sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n while len(sqrt_alpha_prod.shape) < len(sample.shape):\n sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n\n velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample\n return velocity\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_ddim_flax.py", "content": "# Copyright 2024 Stanford University Team and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion\n# and https://github.com/hojonathanho/diffusion\n\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple, Union\n\nimport flax\nimport jax.numpy as jnp\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom .scheduling_utils_flax import (\n CommonSchedulerState,\n FlaxKarrasDiffusionSchedulers,\n FlaxSchedulerMixin,\n FlaxSchedulerOutput,\n add_noise_common,\n get_velocity_common,\n)\n\n\n@flax.struct.dataclass\nclass DDIMSchedulerState:\n common: CommonSchedulerState\n final_alpha_cumprod: jnp.ndarray\n\n # setable values\n init_noise_sigma: jnp.ndarray\n timesteps: jnp.ndarray\n num_inference_steps: Optional[int] = None\n\n @classmethod\n def create(\n cls,\n common: CommonSchedulerState,\n final_alpha_cumprod: jnp.ndarray,\n init_noise_sigma: jnp.ndarray,\n timesteps: jnp.ndarray,\n ):\n return cls(\n common=common,\n final_alpha_cumprod=final_alpha_cumprod,\n init_noise_sigma=init_noise_sigma,\n timesteps=timesteps,\n )\n\n\n@dataclass\nclass FlaxDDIMSchedulerOutput(FlaxSchedulerOutput):\n state: DDIMSchedulerState\n\n\nclass FlaxDDIMScheduler(FlaxSchedulerMixin, ConfigMixin):\n \"\"\"\n Denoising diffusion implicit models is a scheduler that extends the denoising procedure introduced in denoising\n diffusion probabilistic models (DDPMs) with non-Markovian guidance.\n\n [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`\n function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.\n [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and\n [`~SchedulerMixin.from_pretrained`] functions.\n\n For more details, see the original paper: https://arxiv.org/abs/2010.02502\n\n Args:\n num_train_timesteps (`int`): number of diffusion steps used to train the model.\n beta_start (`float`): the starting `beta` value of inference.\n beta_end (`float`): the final `beta` value.\n beta_schedule (`str`):\n the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear`, `scaled_linear`, or `squaredcos_cap_v2`.\n trained_betas (`jnp.ndarray`, optional):\n option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.\n clip_sample (`bool`, default `True`):\n option to clip predicted sample between for numerical stability. The clip range is determined by\n `clip_sample_range`.\n clip_sample_range (`float`, default `1.0`):\n the maximum magnitude for sample clipping. Valid only when `clip_sample=True`.\n set_alpha_to_one (`bool`, default `True`):\n each diffusion step uses the value of alphas product at that step and at the previous one. For the final\n step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,\n otherwise it uses the value of alpha at step 0.\n steps_offset (`int`, default `0`):\n An offset added to the inference steps, as required by some model families.\n prediction_type (`str`, default `epsilon`):\n indicates whether the model predicts the noise (epsilon), or the samples. One of `epsilon`, `sample`.\n `v-prediction` is not supported for this scheduler.\n dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):\n the `dtype` used for params and computation.\n \"\"\"\n\n _compatibles = [e.name for e in FlaxKarrasDiffusionSchedulers]\n\n dtype: jnp.dtype\n\n @property\n def has_state(self):\n return True\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[jnp.ndarray] = None,\n clip_sample: bool = True,\n clip_sample_range: float = 1.0,\n set_alpha_to_one: bool = True,\n steps_offset: int = 0,\n prediction_type: str = \"epsilon\",\n dtype: jnp.dtype = jnp.float32,\n ):\n self.dtype = dtype\n\n def create_state(self, common: Optional[CommonSchedulerState] = None) -> DDIMSchedulerState:\n if common is None:\n common = CommonSchedulerState.create(self)\n\n # At every step in ddim, we are looking into the previous alphas_cumprod\n # For the final step, there is no previous alphas_cumprod because we are already at 0\n # `set_alpha_to_one` decides whether we set this parameter simply to one or\n # whether we use the final alpha of the \"non-previous\" one.\n final_alpha_cumprod = (\n jnp.array(1.0, dtype=self.dtype) if self.config.set_alpha_to_one else common.alphas_cumprod[0]\n )\n\n # standard deviation of the initial noise distribution\n init_noise_sigma = jnp.array(1.0, dtype=self.dtype)\n\n timesteps = jnp.arange(0, self.config.num_train_timesteps).round()[::-1]\n\n return DDIMSchedulerState.create(\n common=common,\n final_alpha_cumprod=final_alpha_cumprod,\n init_noise_sigma=init_noise_sigma,\n timesteps=timesteps,\n )\n\n def scale_model_input(\n self, state: DDIMSchedulerState, sample: jnp.ndarray, timestep: Optional[int] = None\n ) -> jnp.ndarray:\n \"\"\"\n Args:\n state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance.\n sample (`jnp.ndarray`): input sample\n timestep (`int`, optional): current timestep\n\n Returns:\n `jnp.ndarray`: scaled input sample\n \"\"\"\n return sample\n\n def set_timesteps(\n self, state: DDIMSchedulerState, num_inference_steps: int, shape: Tuple = ()\n ) -> DDIMSchedulerState:\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.\n\n Args:\n state (`DDIMSchedulerState`):\n the `FlaxDDIMScheduler` state data class instance.\n num_inference_steps (`int`):\n the number of diffusion steps used when generating samples with a pre-trained model.\n \"\"\"\n step_ratio = self.config.num_train_timesteps // num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # rounding to avoid issues when num_inference_step is power of 3\n timesteps = (jnp.arange(0, num_inference_steps) * step_ratio).round()[::-1] + self.config.steps_offset\n\n return state.replace(\n num_inference_steps=num_inference_steps,\n timesteps=timesteps,\n )\n\n def _get_variance(self, state: DDIMSchedulerState, timestep, prev_timestep):\n alpha_prod_t = state.common.alphas_cumprod[timestep]\n alpha_prod_t_prev = jnp.where(\n prev_timestep >= 0, state.common.alphas_cumprod[prev_timestep], state.final_alpha_cumprod\n )\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n\n variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)\n\n return variance\n\n def step(\n self,\n state: DDIMSchedulerState,\n model_output: jnp.ndarray,\n timestep: int,\n sample: jnp.ndarray,\n eta: float = 0.0,\n return_dict: bool = True,\n ) -> Union[FlaxDDIMSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n state (`DDIMSchedulerState`): the `FlaxDDIMScheduler` state data class instance.\n model_output (`jnp.ndarray`): direct output from learned diffusion model.\n timestep (`int`): current discrete timestep in the diffusion chain.\n sample (`jnp.ndarray`):\n current instance of sample being created by diffusion process.\n return_dict (`bool`): option for returning tuple rather than FlaxDDIMSchedulerOutput class\n\n Returns:\n [`FlaxDDIMSchedulerOutput`] or `tuple`: [`FlaxDDIMSchedulerOutput`] if `return_dict` is True, otherwise a\n `tuple`. When returning a tuple, the first element is the sample tensor.\n\n \"\"\"\n if state.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf\n # Ideally, read DDIM paper in-detail understanding\n\n # Notation ( -> \n # - pred_noise_t -> e_theta(x_t, t)\n # - pred_original_sample -> f_theta(x_t, t) or x_0\n # - std_dev_t -> sigma_t\n # - eta -> η\n # - pred_sample_direction -> \"direction pointing to x_t\"\n # - pred_prev_sample -> \"x_t-1\"\n\n # 1. get previous step value (=t-1)\n prev_timestep = timestep - self.config.num_train_timesteps // state.num_inference_steps\n\n alphas_cumprod = state.common.alphas_cumprod\n final_alpha_cumprod = state.final_alpha_cumprod\n\n # 2. compute alphas, betas\n alpha_prod_t = alphas_cumprod[timestep]\n alpha_prod_t_prev = jnp.where(prev_timestep >= 0, alphas_cumprod[prev_timestep], final_alpha_cumprod)\n\n beta_prod_t = 1 - alpha_prod_t\n\n # 3. compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n if self.config.prediction_type == \"epsilon\":\n pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)\n pred_epsilon = model_output\n elif self.config.prediction_type == \"sample\":\n pred_original_sample = model_output\n pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)\n elif self.config.prediction_type == \"v_prediction\":\n pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output\n pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or\"\n \" `v_prediction`\"\n )\n\n # 4. Clip or threshold \"predicted x_0\"\n if self.config.clip_sample:\n pred_original_sample = pred_original_sample.clip(\n -self.config.clip_sample_range, self.config.clip_sample_range\n )\n\n # 4. compute variance: \"sigma_t(η)\" -> see formula (16)\n # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)\n variance = self._get_variance(state, timestep, prev_timestep)\n std_dev_t = eta * variance ** (0.5)\n\n # 5. compute \"direction pointing to x_t\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * pred_epsilon\n\n # 6. compute x_t without \"random noise\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction\n\n if not return_dict:\n return (prev_sample, state)\n\n return FlaxDDIMSchedulerOutput(prev_sample=prev_sample, state=state)\n\n def add_noise(\n self,\n state: DDIMSchedulerState,\n original_samples: jnp.ndarray,\n noise: jnp.ndarray,\n timesteps: jnp.ndarray,\n ) -> jnp.ndarray:\n return add_noise_common(state.common, original_samples, noise, timesteps)\n\n def get_velocity(\n self,\n state: DDIMSchedulerState,\n sample: jnp.ndarray,\n noise: jnp.ndarray,\n timesteps: jnp.ndarray,\n ) -> jnp.ndarray:\n return get_velocity_common(state.common, sample, noise, timesteps)\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_ddim_inverse.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion\n# and https://github.com/hojonathanho/diffusion\nimport math\nfrom dataclasses import dataclass\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom diffusers.configuration_utils import ConfigMixin, register_to_config\nfrom diffusers.schedulers.scheduling_utils import SchedulerMixin\nfrom diffusers.utils import BaseOutput, deprecate\n\n\n@dataclass\n# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM\nclass DDIMSchedulerOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's `step` function output.\n\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample `(x_{0})` based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.Tensor\n pred_original_sample: Optional[torch.Tensor] = None\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\n# Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr\ndef rescale_zero_terminal_snr(betas):\n \"\"\"\n Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)\n\n\n Args:\n betas (`torch.Tensor`):\n the betas that the scheduler is being initialized with.\n\n Returns:\n `torch.Tensor`: rescaled betas with zero terminal SNR\n \"\"\"\n # Convert betas to alphas_bar_sqrt\n alphas = 1.0 - betas\n alphas_cumprod = torch.cumprod(alphas, dim=0)\n alphas_bar_sqrt = alphas_cumprod.sqrt()\n\n # Store old values.\n alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()\n alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()\n\n # Shift so the last timestep is zero.\n alphas_bar_sqrt -= alphas_bar_sqrt_T\n\n # Scale so the first timestep is back to the old value.\n alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)\n\n # Convert alphas_bar_sqrt to betas\n alphas_bar = alphas_bar_sqrt**2 # Revert sqrt\n alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod\n alphas = torch.cat([alphas_bar[0:1], alphas])\n betas = 1 - alphas\n\n return betas\n\n\nclass DDIMInverseScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n `DDIMInverseScheduler` is the reverse scheduler of [`DDIMScheduler`].\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n beta_start (`float`, defaults to 0.0001):\n The starting `beta` value of inference.\n beta_end (`float`, defaults to 0.02):\n The final `beta` value.\n beta_schedule (`str`, defaults to `\"linear\"`):\n The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear`, `scaled_linear`, or `squaredcos_cap_v2`.\n trained_betas (`np.ndarray`, *optional*):\n Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.\n clip_sample (`bool`, defaults to `True`):\n Clip the predicted sample for numerical stability.\n clip_sample_range (`float`, defaults to 1.0):\n The maximum magnitude for sample clipping. Valid only when `clip_sample=True`.\n set_alpha_to_one (`bool`, defaults to `True`):\n Each diffusion step uses the alphas product value at that step and at the previous one. For the final step\n there is no previous alpha. When this option is `True` the previous alpha product is fixed to 0, otherwise\n it uses the alpha value at step `num_train_timesteps - 1`.\n steps_offset (`int`, defaults to 0):\n An offset added to the inference steps, as required by some model families.\n prediction_type (`str`, defaults to `epsilon`, *optional*):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),\n `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen\n Video](https://imagen.research.google/video/paper.pdf) paper).\n timestep_spacing (`str`, defaults to `\"leading\"`):\n The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.\n rescale_betas_zero_snr (`bool`, defaults to `False`):\n Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and\n dark samples instead of limiting it to samples with medium brightness. Loosely related to\n [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).\n \"\"\"\n\n order = 1\n ignore_for_config = [\"kwargs\"]\n _deprecated_kwargs = [\"set_alpha_to_zero\"]\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[Union[np.ndarray, List[float]]] = None,\n clip_sample: bool = True,\n set_alpha_to_one: bool = True,\n steps_offset: int = 0,\n prediction_type: str = \"epsilon\",\n clip_sample_range: float = 1.0,\n timestep_spacing: str = \"leading\",\n rescale_betas_zero_snr: bool = False,\n **kwargs,\n ):\n if kwargs.get(\"set_alpha_to_zero\", None) is not None:\n deprecation_message = (\n \"The `set_alpha_to_zero` argument is deprecated. Please use `set_alpha_to_one` instead.\"\n )\n deprecate(\"set_alpha_to_zero\", \"1.0.0\", deprecation_message, standard_warn=False)\n set_alpha_to_one = kwargs[\"set_alpha_to_zero\"]\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n # Rescale for zero SNR\n if rescale_betas_zero_snr:\n self.betas = rescale_zero_terminal_snr(self.betas)\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n\n # At every step in inverted ddim, we are looking into the next alphas_cumprod\n # For the initial step, there is no current alphas_cumprod, and the index is out of bounds\n # `set_alpha_to_one` decides whether we set this parameter simply to one\n # in this case, self.step() just output the predicted noise\n # or whether we use the initial alpha used in training the diffusion model.\n self.initial_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]\n\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = 1.0\n\n # setable values\n self.num_inference_steps = None\n self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps).copy().astype(np.int64))\n\n # Copied from diffusers.schedulers.scheduling_ddim.DDIMScheduler.scale_model_input\n def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`int`, *optional*):\n The current timestep in the diffusion chain.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n return sample\n\n def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n \"\"\"\n\n if num_inference_steps > self.config.num_train_timesteps:\n raise ValueError(\n f\"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:\"\n f\" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle\"\n f\" maximal {self.config.num_train_timesteps} timesteps.\"\n )\n\n self.num_inference_steps = num_inference_steps\n\n # \"leading\" and \"trailing\" corresponds to annotation of Table 1. of https://arxiv.org/abs/2305.08891\n if self.config.timestep_spacing == \"leading\":\n step_ratio = self.config.num_train_timesteps // self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (np.arange(0, num_inference_steps) * step_ratio).round().copy().astype(np.int64)\n timesteps += self.config.steps_offset\n elif self.config.timestep_spacing == \"trailing\":\n step_ratio = self.config.num_train_timesteps / self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = np.round(np.arange(self.config.num_train_timesteps, 0, -step_ratio)[::-1]).astype(np.int64)\n timesteps -= 1\n else:\n raise ValueError(\n f\"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'leading' or 'trailing'.\"\n )\n\n self.timesteps = torch.from_numpy(timesteps).to(device)\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n return_dict: bool = True,\n ) -> Union[DDIMSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`float`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n eta (`float`):\n The weight of noise for added noise in diffusion step.\n use_clipped_model_output (`bool`, defaults to `False`):\n If `True`, computes \"corrected\" `model_output` from the clipped predicted original sample. Necessary\n because predicted original sample is clipped to [-1, 1] when `self.config.clip_sample` is `True`. If no\n clipping has happened, \"corrected\" `model_output` would coincide with the one provided as input and\n `use_clipped_model_output` has no effect.\n variance_noise (`torch.Tensor`):\n Alternative to generating noise with `generator` by directly providing the noise for the variance\n itself. Useful for methods such as [`CycleDiffusion`].\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~schedulers.scheduling_ddim_inverse.DDIMInverseSchedulerOutput`] or\n `tuple`.\n\n Returns:\n [`~schedulers.scheduling_ddim_inverse.DDIMInverseSchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_ddim_inverse.DDIMInverseSchedulerOutput`] is\n returned, otherwise a tuple is returned where the first element is the sample tensor.\n\n \"\"\"\n # 1. get previous step value (=t+1)\n prev_timestep = timestep\n timestep = min(\n timestep - self.config.num_train_timesteps // self.num_inference_steps, self.config.num_train_timesteps - 1\n )\n\n # 2. compute alphas, betas\n # change original implementation to exactly match noise levels for analogous forward process\n alpha_prod_t = self.alphas_cumprod[timestep] if timestep >= 0 else self.initial_alpha_cumprod\n alpha_prod_t_prev = self.alphas_cumprod[prev_timestep]\n\n beta_prod_t = 1 - alpha_prod_t\n\n # 3. compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n if self.config.prediction_type == \"epsilon\":\n pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)\n pred_epsilon = model_output\n elif self.config.prediction_type == \"sample\":\n pred_original_sample = model_output\n pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)\n elif self.config.prediction_type == \"v_prediction\":\n pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output\n pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or\"\n \" `v_prediction`\"\n )\n\n # 4. Clip or threshold \"predicted x_0\"\n if self.config.clip_sample:\n pred_original_sample = pred_original_sample.clamp(\n -self.config.clip_sample_range, self.config.clip_sample_range\n )\n\n # 5. compute \"direction pointing to x_t\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n pred_sample_direction = (1 - alpha_prod_t_prev) ** (0.5) * pred_epsilon\n\n # 6. compute x_t without \"random noise\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction\n\n if not return_dict:\n return (prev_sample, pred_original_sample)\n return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_ddim_parallel.py", "content": "# Copyright 2024 ParaDiGMS authors and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion\n# and https://github.com/hojonathanho/diffusion\n\nimport math\nfrom dataclasses import dataclass\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import BaseOutput\nfrom ..utils.torch_utils import randn_tensor\nfrom .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin\n\n\n@dataclass\n# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput\nclass DDIMParallelSchedulerOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's `step` function output.\n\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample `(x_{0})` based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.Tensor\n pred_original_sample: Optional[torch.Tensor] = None\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\n# Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr\ndef rescale_zero_terminal_snr(betas):\n \"\"\"\n Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)\n\n\n Args:\n betas (`torch.Tensor`):\n the betas that the scheduler is being initialized with.\n\n Returns:\n `torch.Tensor`: rescaled betas with zero terminal SNR\n \"\"\"\n # Convert betas to alphas_bar_sqrt\n alphas = 1.0 - betas\n alphas_cumprod = torch.cumprod(alphas, dim=0)\n alphas_bar_sqrt = alphas_cumprod.sqrt()\n\n # Store old values.\n alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()\n alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()\n\n # Shift so the last timestep is zero.\n alphas_bar_sqrt -= alphas_bar_sqrt_T\n\n # Scale so the first timestep is back to the old value.\n alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)\n\n # Convert alphas_bar_sqrt to betas\n alphas_bar = alphas_bar_sqrt**2 # Revert sqrt\n alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod\n alphas = torch.cat([alphas_bar[0:1], alphas])\n betas = 1 - alphas\n\n return betas\n\n\nclass DDIMParallelScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n Denoising diffusion implicit models is a scheduler that extends the denoising procedure introduced in denoising\n diffusion probabilistic models (DDPMs) with non-Markovian guidance.\n\n [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`\n function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.\n [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and\n [`~SchedulerMixin.from_pretrained`] functions.\n\n For more details, see the original paper: https://arxiv.org/abs/2010.02502\n\n Args:\n num_train_timesteps (`int`): number of diffusion steps used to train the model.\n beta_start (`float`): the starting `beta` value of inference.\n beta_end (`float`): the final `beta` value.\n beta_schedule (`str`):\n the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear`, `scaled_linear`, or `squaredcos_cap_v2`.\n trained_betas (`np.ndarray`, optional):\n option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.\n clip_sample (`bool`, default `True`):\n option to clip predicted sample for numerical stability.\n clip_sample_range (`float`, default `1.0`):\n the maximum magnitude for sample clipping. Valid only when `clip_sample=True`.\n set_alpha_to_one (`bool`, default `True`):\n each diffusion step uses the value of alphas product at that step and at the previous one. For the final\n step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,\n otherwise it uses the value of alpha at step 0.\n steps_offset (`int`, default `0`):\n An offset added to the inference steps, as required by some model families.\n prediction_type (`str`, default `epsilon`, optional):\n prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion\n process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4\n https://imagen.research.google/video/paper.pdf)\n thresholding (`bool`, default `False`):\n whether to use the \"dynamic thresholding\" method (introduced by Imagen, https://arxiv.org/abs/2205.11487).\n Note that the thresholding method is unsuitable for latent-space diffusion models (such as\n stable-diffusion).\n dynamic_thresholding_ratio (`float`, default `0.995`):\n the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen\n (https://arxiv.org/abs/2205.11487). Valid only when `thresholding=True`.\n sample_max_value (`float`, default `1.0`):\n the threshold value for dynamic thresholding. Valid only when `thresholding=True`.\n timestep_spacing (`str`, default `\"leading\"`):\n The way the timesteps should be scaled. Refer to Table 2. of [Common Diffusion Noise Schedules and Sample\n Steps are Flawed](https://arxiv.org/abs/2305.08891) for more information.\n rescale_betas_zero_snr (`bool`, default `False`):\n whether to rescale the betas to have zero terminal SNR (proposed by https://arxiv.org/pdf/2305.08891.pdf).\n This can enable the model to generate very bright and dark samples instead of limiting it to samples with\n medium brightness. Loosely related to\n [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).\n \"\"\"\n\n _compatibles = [e.name for e in KarrasDiffusionSchedulers]\n order = 1\n _is_ode_scheduler = True\n\n @register_to_config\n # Copied from diffusers.schedulers.scheduling_ddim.DDIMScheduler.__init__\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[Union[np.ndarray, List[float]]] = None,\n clip_sample: bool = True,\n set_alpha_to_one: bool = True,\n steps_offset: int = 0,\n prediction_type: str = \"epsilon\",\n thresholding: bool = False,\n dynamic_thresholding_ratio: float = 0.995,\n clip_sample_range: float = 1.0,\n sample_max_value: float = 1.0,\n timestep_spacing: str = \"leading\",\n rescale_betas_zero_snr: bool = False,\n ):\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n # Rescale for zero SNR\n if rescale_betas_zero_snr:\n self.betas = rescale_zero_terminal_snr(self.betas)\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n\n # At every step in ddim, we are looking into the previous alphas_cumprod\n # For the final step, there is no previous alphas_cumprod because we are already at 0\n # `set_alpha_to_one` decides whether we set this parameter simply to one or\n # whether we use the final alpha of the \"non-previous\" one.\n self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]\n\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = 1.0\n\n # setable values\n self.num_inference_steps = None\n self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64))\n\n # Copied from diffusers.schedulers.scheduling_ddim.DDIMScheduler.scale_model_input\n def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`int`, *optional*):\n The current timestep in the diffusion chain.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n return sample\n\n def _get_variance(self, timestep, prev_timestep=None):\n if prev_timestep is None:\n prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps\n\n alpha_prod_t = self.alphas_cumprod[timestep]\n alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n\n variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)\n\n return variance\n\n def _batch_get_variance(self, t, prev_t):\n alpha_prod_t = self.alphas_cumprod[t]\n alpha_prod_t_prev = self.alphas_cumprod[torch.clip(prev_t, min=0)]\n alpha_prod_t_prev[prev_t < 0] = torch.tensor(1.0)\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n\n variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)\n\n return variance\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample\n def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:\n \"\"\"\n \"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the\n prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by\n s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing\n pixels from saturation at each step. We find that dynamic thresholding results in significantly better\n photorealism as well as better image-text alignment, especially when using very large guidance weights.\"\n\n https://arxiv.org/abs/2205.11487\n \"\"\"\n dtype = sample.dtype\n batch_size, channels, *remaining_dims = sample.shape\n\n if dtype not in (torch.float32, torch.float64):\n sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half\n\n # Flatten sample for doing quantile calculation along each image\n sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))\n\n abs_sample = sample.abs() # \"a certain percentile absolute pixel value\"\n\n s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)\n s = torch.clamp(\n s, min=1, max=self.config.sample_max_value\n ) # When clamped to min=1, equivalent to standard clipping to [-1, 1]\n s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0\n sample = torch.clamp(sample, -s, s) / s # \"we threshold xt0 to the range [-s, s] and then divide by s\"\n\n sample = sample.reshape(batch_size, channels, *remaining_dims)\n sample = sample.to(dtype)\n\n return sample\n\n # Copied from diffusers.schedulers.scheduling_ddim.DDIMScheduler.set_timesteps\n def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n \"\"\"\n\n if num_inference_steps > self.config.num_train_timesteps:\n raise ValueError(\n f\"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:\"\n f\" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle\"\n f\" maximal {self.config.num_train_timesteps} timesteps.\"\n )\n\n self.num_inference_steps = num_inference_steps\n\n # \"linspace\", \"leading\", \"trailing\" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891\n if self.config.timestep_spacing == \"linspace\":\n timesteps = (\n np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps)\n .round()[::-1]\n .copy()\n .astype(np.int64)\n )\n elif self.config.timestep_spacing == \"leading\":\n step_ratio = self.config.num_train_timesteps // self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64)\n timesteps += self.config.steps_offset\n elif self.config.timestep_spacing == \"trailing\":\n step_ratio = self.config.num_train_timesteps / self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = np.round(np.arange(self.config.num_train_timesteps, 0, -step_ratio)).astype(np.int64)\n timesteps -= 1\n else:\n raise ValueError(\n f\"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'leading' or 'trailing'.\"\n )\n\n self.timesteps = torch.from_numpy(timesteps).to(device)\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n eta: float = 0.0,\n use_clipped_model_output: bool = False,\n generator=None,\n variance_noise: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ) -> Union[DDIMParallelSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`): direct output from learned diffusion model.\n timestep (`int`): current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n current instance of sample being created by diffusion process.\n eta (`float`): weight of noise for added noise in diffusion step.\n use_clipped_model_output (`bool`): if `True`, compute \"corrected\" `model_output` from the clipped\n predicted original sample. Necessary because predicted original sample is clipped to [-1, 1] when\n `self.config.clip_sample` is `True`. If no clipping has happened, \"corrected\" `model_output` would\n coincide with the one provided as input and `use_clipped_model_output` will have not effect.\n generator: random number generator.\n variance_noise (`torch.Tensor`): instead of generating noise for the variance using `generator`, we\n can directly provide the noise for the variance itself. This is useful for methods such as\n CycleDiffusion. (https://arxiv.org/abs/2210.05559)\n return_dict (`bool`): option for returning tuple rather than DDIMParallelSchedulerOutput class\n\n Returns:\n [`~schedulers.scheduling_utils.DDIMParallelSchedulerOutput`] or `tuple`:\n [`~schedulers.scheduling_utils.DDIMParallelSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`.\n When returning a tuple, the first element is the sample tensor.\n\n \"\"\"\n if self.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf\n # Ideally, read DDIM paper in-detail understanding\n\n # Notation ( -> \n # - pred_noise_t -> e_theta(x_t, t)\n # - pred_original_sample -> f_theta(x_t, t) or x_0\n # - std_dev_t -> sigma_t\n # - eta -> η\n # - pred_sample_direction -> \"direction pointing to x_t\"\n # - pred_prev_sample -> \"x_t-1\"\n\n # 1. get previous step value (=t-1)\n prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps\n\n # 2. compute alphas, betas\n alpha_prod_t = self.alphas_cumprod[timestep]\n alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod\n\n beta_prod_t = 1 - alpha_prod_t\n\n # 3. compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n if self.config.prediction_type == \"epsilon\":\n pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)\n pred_epsilon = model_output\n elif self.config.prediction_type == \"sample\":\n pred_original_sample = model_output\n pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)\n elif self.config.prediction_type == \"v_prediction\":\n pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output\n pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or\"\n \" `v_prediction`\"\n )\n\n # 4. Clip or threshold \"predicted x_0\"\n if self.config.thresholding:\n pred_original_sample = self._threshold_sample(pred_original_sample)\n elif self.config.clip_sample:\n pred_original_sample = pred_original_sample.clamp(\n -self.config.clip_sample_range, self.config.clip_sample_range\n )\n\n # 5. compute variance: \"sigma_t(η)\" -> see formula (16)\n # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)\n variance = self._get_variance(timestep, prev_timestep)\n std_dev_t = eta * variance ** (0.5)\n\n if use_clipped_model_output:\n # the pred_epsilon is always re-derived from the clipped x_0 in Glide\n pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)\n\n # 6. compute \"direction pointing to x_t\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * pred_epsilon\n\n # 7. compute x_t without \"random noise\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction\n\n if eta > 0:\n if variance_noise is not None and generator is not None:\n raise ValueError(\n \"Cannot pass both generator and variance_noise. Please make sure that either `generator` or\"\n \" `variance_noise` stays `None`.\"\n )\n\n if variance_noise is None:\n variance_noise = randn_tensor(\n model_output.shape, generator=generator, device=model_output.device, dtype=model_output.dtype\n )\n variance = std_dev_t * variance_noise\n\n prev_sample = prev_sample + variance\n\n if not return_dict:\n return (prev_sample,)\n\n return DDIMParallelSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)\n\n def batch_step_no_noise(\n self,\n model_output: torch.Tensor,\n timesteps: List[int],\n sample: torch.Tensor,\n eta: float = 0.0,\n use_clipped_model_output: bool = False,\n ) -> torch.Tensor:\n \"\"\"\n Batched version of the `step` function, to be able to reverse the SDE for multiple samples/timesteps at once.\n Also, does not add any noise to the predicted sample, which is necessary for parallel sampling where the noise\n is pre-sampled by the pipeline.\n\n Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`): direct output from learned diffusion model.\n timesteps (`List[int]`):\n current discrete timesteps in the diffusion chain. This is now a list of integers.\n sample (`torch.Tensor`):\n current instance of sample being created by diffusion process.\n eta (`float`): weight of noise for added noise in diffusion step.\n use_clipped_model_output (`bool`): if `True`, compute \"corrected\" `model_output` from the clipped\n predicted original sample. Necessary because predicted original sample is clipped to [-1, 1] when\n `self.config.clip_sample` is `True`. If no clipping has happened, \"corrected\" `model_output` would\n coincide with the one provided as input and `use_clipped_model_output` will have not effect.\n\n Returns:\n `torch.Tensor`: sample tensor at previous timestep.\n\n \"\"\"\n if self.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n assert eta == 0.0\n\n # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf\n # Ideally, read DDIM paper in-detail understanding\n\n # Notation ( -> \n # - pred_noise_t -> e_theta(x_t, t)\n # - pred_original_sample -> f_theta(x_t, t) or x_0\n # - std_dev_t -> sigma_t\n # - eta -> η\n # - pred_sample_direction -> \"direction pointing to x_t\"\n # - pred_prev_sample -> \"x_t-1\"\n\n # 1. get previous step value (=t-1)\n t = timesteps\n prev_t = t - self.config.num_train_timesteps // self.num_inference_steps\n\n t = t.view(-1, *([1] * (model_output.ndim - 1)))\n prev_t = prev_t.view(-1, *([1] * (model_output.ndim - 1)))\n\n # 1. compute alphas, betas\n self.alphas_cumprod = self.alphas_cumprod.to(model_output.device)\n self.final_alpha_cumprod = self.final_alpha_cumprod.to(model_output.device)\n alpha_prod_t = self.alphas_cumprod[t]\n alpha_prod_t_prev = self.alphas_cumprod[torch.clip(prev_t, min=0)]\n alpha_prod_t_prev[prev_t < 0] = torch.tensor(1.0)\n\n beta_prod_t = 1 - alpha_prod_t\n\n # 3. compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n if self.config.prediction_type == \"epsilon\":\n pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)\n pred_epsilon = model_output\n elif self.config.prediction_type == \"sample\":\n pred_original_sample = model_output\n pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)\n elif self.config.prediction_type == \"v_prediction\":\n pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output\n pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or\"\n \" `v_prediction`\"\n )\n\n # 4. Clip or threshold \"predicted x_0\"\n if self.config.thresholding:\n pred_original_sample = self._threshold_sample(pred_original_sample)\n elif self.config.clip_sample:\n pred_original_sample = pred_original_sample.clamp(\n -self.config.clip_sample_range, self.config.clip_sample_range\n )\n\n # 5. compute variance: \"sigma_t(η)\" -> see formula (16)\n # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)\n variance = self._batch_get_variance(t, prev_t).to(model_output.device).view(*alpha_prod_t_prev.shape)\n std_dev_t = eta * variance ** (0.5)\n\n if use_clipped_model_output:\n # the pred_epsilon is always re-derived from the clipped x_0 in Glide\n pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)\n\n # 6. compute \"direction pointing to x_t\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * pred_epsilon\n\n # 7. compute x_t without \"random noise\" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction\n\n return prev_sample\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.IntTensor,\n ) -> torch.Tensor:\n # Make sure alphas_cumprod and timestep have same device and dtype as original_samples\n # Move the self.alphas_cumprod to device to avoid redundant CPU to GPU data movement\n # for the subsequent add_noise calls\n self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device)\n alphas_cumprod = self.alphas_cumprod.to(dtype=original_samples.dtype)\n timesteps = timesteps.to(original_samples.device)\n\n sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n while len(sqrt_alpha_prod.shape) < len(original_samples.shape):\n sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n\n noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise\n return noisy_samples\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity\n def get_velocity(self, sample: torch.Tensor, noise: torch.Tensor, timesteps: torch.IntTensor) -> torch.Tensor:\n # Make sure alphas_cumprod and timestep have same device and dtype as sample\n self.alphas_cumprod = self.alphas_cumprod.to(device=sample.device)\n alphas_cumprod = self.alphas_cumprod.to(dtype=sample.dtype)\n timesteps = timesteps.to(sample.device)\n\n sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n while len(sqrt_alpha_prod.shape) < len(sample.shape):\n sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n\n velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample\n return velocity\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_ddpm.py", "content": "# Copyright 2024 UC Berkeley Team and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim\n\nimport math\nfrom dataclasses import dataclass\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import BaseOutput\nfrom ..utils.torch_utils import randn_tensor\nfrom .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin\n\n\n@dataclass\nclass DDPMSchedulerOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's `step` function output.\n\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample `(x_{0})` based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.Tensor\n pred_original_sample: Optional[torch.Tensor] = None\n\n\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\n# Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr\ndef rescale_zero_terminal_snr(betas):\n \"\"\"\n Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)\n\n\n Args:\n betas (`torch.Tensor`):\n the betas that the scheduler is being initialized with.\n\n Returns:\n `torch.Tensor`: rescaled betas with zero terminal SNR\n \"\"\"\n # Convert betas to alphas_bar_sqrt\n alphas = 1.0 - betas\n alphas_cumprod = torch.cumprod(alphas, dim=0)\n alphas_bar_sqrt = alphas_cumprod.sqrt()\n\n # Store old values.\n alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()\n alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()\n\n # Shift so the last timestep is zero.\n alphas_bar_sqrt -= alphas_bar_sqrt_T\n\n # Scale so the first timestep is back to the old value.\n alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)\n\n # Convert alphas_bar_sqrt to betas\n alphas_bar = alphas_bar_sqrt**2 # Revert sqrt\n alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod\n alphas = torch.cat([alphas_bar[0:1], alphas])\n betas = 1 - alphas\n\n return betas\n\n\nclass DDPMScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n `DDPMScheduler` explores the connections between denoising score matching and Langevin dynamics sampling.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n beta_start (`float`, defaults to 0.0001):\n The starting `beta` value of inference.\n beta_end (`float`, defaults to 0.02):\n The final `beta` value.\n beta_schedule (`str`, defaults to `\"linear\"`):\n The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear`, `scaled_linear`, or `squaredcos_cap_v2`.\n trained_betas (`np.ndarray`, *optional*):\n An array of betas to pass directly to the constructor without using `beta_start` and `beta_end`.\n variance_type (`str`, defaults to `\"fixed_small\"`):\n Clip the variance when adding noise to the denoised sample. Choose from `fixed_small`, `fixed_small_log`,\n `fixed_large`, `fixed_large_log`, `learned` or `learned_range`.\n clip_sample (`bool`, defaults to `True`):\n Clip the predicted sample for numerical stability.\n clip_sample_range (`float`, defaults to 1.0):\n The maximum magnitude for sample clipping. Valid only when `clip_sample=True`.\n prediction_type (`str`, defaults to `epsilon`, *optional*):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),\n `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen\n Video](https://imagen.research.google/video/paper.pdf) paper).\n thresholding (`bool`, defaults to `False`):\n Whether to use the \"dynamic thresholding\" method. This is unsuitable for latent-space diffusion models such\n as Stable Diffusion.\n dynamic_thresholding_ratio (`float`, defaults to 0.995):\n The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.\n sample_max_value (`float`, defaults to 1.0):\n The threshold value for dynamic thresholding. Valid only when `thresholding=True`.\n timestep_spacing (`str`, defaults to `\"leading\"`):\n The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.\n steps_offset (`int`, defaults to 0):\n An offset added to the inference steps, as required by some model families.\n rescale_betas_zero_snr (`bool`, defaults to `False`):\n Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and\n dark samples instead of limiting it to samples with medium brightness. Loosely related to\n [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).\n \"\"\"\n\n _compatibles = [e.name for e in KarrasDiffusionSchedulers]\n order = 1\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[Union[np.ndarray, List[float]]] = None,\n variance_type: str = \"fixed_small\",\n clip_sample: bool = True,\n prediction_type: str = \"epsilon\",\n thresholding: bool = False,\n dynamic_thresholding_ratio: float = 0.995,\n clip_sample_range: float = 1.0,\n sample_max_value: float = 1.0,\n timestep_spacing: str = \"leading\",\n steps_offset: int = 0,\n rescale_betas_zero_snr: int = False,\n ):\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n elif beta_schedule == \"sigmoid\":\n # GeoDiff sigmoid schedule\n betas = torch.linspace(-6, 6, num_train_timesteps)\n self.betas = torch.sigmoid(betas) * (beta_end - beta_start) + beta_start\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n # Rescale for zero SNR\n if rescale_betas_zero_snr:\n self.betas = rescale_zero_terminal_snr(self.betas)\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n self.one = torch.tensor(1.0)\n\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = 1.0\n\n # setable values\n self.custom_timesteps = False\n self.num_inference_steps = None\n self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())\n\n self.variance_type = variance_type\n\n def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`int`, *optional*):\n The current timestep in the diffusion chain.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n return sample\n\n def set_timesteps(\n self,\n num_inference_steps: Optional[int] = None,\n device: Union[str, torch.device] = None,\n timesteps: Optional[List[int]] = None,\n ):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used,\n `timesteps` must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default\n timestep spacing strategy of equal spacing between timesteps is used. If `timesteps` is passed,\n `num_inference_steps` must be `None`.\n\n \"\"\"\n if num_inference_steps is not None and timesteps is not None:\n raise ValueError(\"Can only pass one of `num_inference_steps` or `custom_timesteps`.\")\n\n if timesteps is not None:\n for i in range(1, len(timesteps)):\n if timesteps[i] >= timesteps[i - 1]:\n raise ValueError(\"`custom_timesteps` must be in descending order.\")\n\n if timesteps[0] >= self.config.num_train_timesteps:\n raise ValueError(\n f\"`timesteps` must start before `self.config.train_timesteps`:\"\n f\" {self.config.num_train_timesteps}.\"\n )\n\n timesteps = np.array(timesteps, dtype=np.int64)\n self.custom_timesteps = True\n else:\n if num_inference_steps > self.config.num_train_timesteps:\n raise ValueError(\n f\"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:\"\n f\" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle\"\n f\" maximal {self.config.num_train_timesteps} timesteps.\"\n )\n\n self.num_inference_steps = num_inference_steps\n self.custom_timesteps = False\n\n # \"linspace\", \"leading\", \"trailing\" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891\n if self.config.timestep_spacing == \"linspace\":\n timesteps = (\n np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps)\n .round()[::-1]\n .copy()\n .astype(np.int64)\n )\n elif self.config.timestep_spacing == \"leading\":\n step_ratio = self.config.num_train_timesteps // self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64)\n timesteps += self.config.steps_offset\n elif self.config.timestep_spacing == \"trailing\":\n step_ratio = self.config.num_train_timesteps / self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = np.round(np.arange(self.config.num_train_timesteps, 0, -step_ratio)).astype(np.int64)\n timesteps -= 1\n else:\n raise ValueError(\n f\"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.\"\n )\n\n self.timesteps = torch.from_numpy(timesteps).to(device)\n\n def _get_variance(self, t, predicted_variance=None, variance_type=None):\n prev_t = self.previous_timestep(t)\n\n alpha_prod_t = self.alphas_cumprod[t]\n alpha_prod_t_prev = self.alphas_cumprod[prev_t] if prev_t >= 0 else self.one\n current_beta_t = 1 - alpha_prod_t / alpha_prod_t_prev\n\n # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)\n # and sample from it to get previous sample\n # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample\n variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * current_beta_t\n\n # we always take the log of variance, so clamp it to ensure it's not 0\n variance = torch.clamp(variance, min=1e-20)\n\n if variance_type is None:\n variance_type = self.config.variance_type\n\n # hacks - were probably added for training stability\n if variance_type == \"fixed_small\":\n variance = variance\n # for rl-diffuser https://arxiv.org/abs/2205.09991\n elif variance_type == \"fixed_small_log\":\n variance = torch.log(variance)\n variance = torch.exp(0.5 * variance)\n elif variance_type == \"fixed_large\":\n variance = current_beta_t\n elif variance_type == \"fixed_large_log\":\n # Glide max_log\n variance = torch.log(current_beta_t)\n elif variance_type == \"learned\":\n return predicted_variance\n elif variance_type == \"learned_range\":\n min_log = torch.log(variance)\n max_log = torch.log(current_beta_t)\n frac = (predicted_variance + 1) / 2\n variance = frac * max_log + (1 - frac) * min_log\n\n return variance\n\n def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:\n \"\"\"\n \"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the\n prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by\n s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing\n pixels from saturation at each step. We find that dynamic thresholding results in significantly better\n photorealism as well as better image-text alignment, especially when using very large guidance weights.\"\n\n https://arxiv.org/abs/2205.11487\n \"\"\"\n dtype = sample.dtype\n batch_size, channels, *remaining_dims = sample.shape\n\n if dtype not in (torch.float32, torch.float64):\n sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half\n\n # Flatten sample for doing quantile calculation along each image\n sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))\n\n abs_sample = sample.abs() # \"a certain percentile absolute pixel value\"\n\n s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)\n s = torch.clamp(\n s, min=1, max=self.config.sample_max_value\n ) # When clamped to min=1, equivalent to standard clipping to [-1, 1]\n s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0\n sample = torch.clamp(sample, -s, s) / s # \"we threshold xt0 to the range [-s, s] and then divide by s\"\n\n sample = sample.reshape(batch_size, channels, *remaining_dims)\n sample = sample.to(dtype)\n\n return sample\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n generator=None,\n return_dict: bool = True,\n ) -> Union[DDPMSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`float`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n generator (`torch.Generator`, *optional*):\n A random number generator.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~schedulers.scheduling_ddpm.DDPMSchedulerOutput`] or `tuple`.\n\n Returns:\n [`~schedulers.scheduling_ddpm.DDPMSchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_ddpm.DDPMSchedulerOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n\n \"\"\"\n t = timestep\n\n prev_t = self.previous_timestep(t)\n\n if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in [\"learned\", \"learned_range\"]:\n model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1)\n else:\n predicted_variance = None\n\n # 1. compute alphas, betas\n alpha_prod_t = self.alphas_cumprod[t]\n alpha_prod_t_prev = self.alphas_cumprod[prev_t] if prev_t >= 0 else self.one\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n current_alpha_t = alpha_prod_t / alpha_prod_t_prev\n current_beta_t = 1 - current_alpha_t\n\n # 2. compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf\n if self.config.prediction_type == \"epsilon\":\n pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)\n elif self.config.prediction_type == \"sample\":\n pred_original_sample = model_output\n elif self.config.prediction_type == \"v_prediction\":\n pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` or\"\n \" `v_prediction` for the DDPMScheduler.\"\n )\n\n # 3. Clip or threshold \"predicted x_0\"\n if self.config.thresholding:\n pred_original_sample = self._threshold_sample(pred_original_sample)\n elif self.config.clip_sample:\n pred_original_sample = pred_original_sample.clamp(\n -self.config.clip_sample_range, self.config.clip_sample_range\n )\n\n # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t\n # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf\n pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * current_beta_t) / beta_prod_t\n current_sample_coeff = current_alpha_t ** (0.5) * beta_prod_t_prev / beta_prod_t\n\n # 5. Compute predicted previous sample µ_t\n # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf\n pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample\n\n # 6. Add noise\n variance = 0\n if t > 0:\n device = model_output.device\n variance_noise = randn_tensor(\n model_output.shape, generator=generator, device=device, dtype=model_output.dtype\n )\n if self.variance_type == \"fixed_small_log\":\n variance = self._get_variance(t, predicted_variance=predicted_variance) * variance_noise\n elif self.variance_type == \"learned_range\":\n variance = self._get_variance(t, predicted_variance=predicted_variance)\n variance = torch.exp(0.5 * variance) * variance_noise\n else:\n variance = (self._get_variance(t, predicted_variance=predicted_variance) ** 0.5) * variance_noise\n\n pred_prev_sample = pred_prev_sample + variance\n\n if not return_dict:\n return (pred_prev_sample,)\n\n return DDPMSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample)\n\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.IntTensor,\n ) -> torch.Tensor:\n # Make sure alphas_cumprod and timestep have same device and dtype as original_samples\n # Move the self.alphas_cumprod to device to avoid redundant CPU to GPU data movement\n # for the subsequent add_noise calls\n self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device)\n alphas_cumprod = self.alphas_cumprod.to(dtype=original_samples.dtype)\n timesteps = timesteps.to(original_samples.device)\n\n sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n while len(sqrt_alpha_prod.shape) < len(original_samples.shape):\n sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n\n noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise\n return noisy_samples\n\n def get_velocity(self, sample: torch.Tensor, noise: torch.Tensor, timesteps: torch.IntTensor) -> torch.Tensor:\n # Make sure alphas_cumprod and timestep have same device and dtype as sample\n self.alphas_cumprod = self.alphas_cumprod.to(device=sample.device)\n alphas_cumprod = self.alphas_cumprod.to(dtype=sample.dtype)\n timesteps = timesteps.to(sample.device)\n\n sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n while len(sqrt_alpha_prod.shape) < len(sample.shape):\n sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n\n velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample\n return velocity\n\n def __len__(self):\n return self.config.num_train_timesteps\n\n def previous_timestep(self, timestep):\n if self.custom_timesteps:\n index = (self.timesteps == timestep).nonzero(as_tuple=True)[0][0]\n if index == self.timesteps.shape[0] - 1:\n prev_t = torch.tensor(-1)\n else:\n prev_t = self.timesteps[index + 1]\n else:\n num_inference_steps = (\n self.num_inference_steps if self.num_inference_steps else self.config.num_train_timesteps\n )\n prev_t = timestep - self.config.num_train_timesteps // num_inference_steps\n\n return prev_t\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_ddpm_flax.py", "content": "# Copyright 2024 UC Berkeley Team and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim\n\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple, Union\n\nimport flax\nimport jax\nimport jax.numpy as jnp\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom .scheduling_utils_flax import (\n CommonSchedulerState,\n FlaxKarrasDiffusionSchedulers,\n FlaxSchedulerMixin,\n FlaxSchedulerOutput,\n add_noise_common,\n get_velocity_common,\n)\n\n\n@flax.struct.dataclass\nclass DDPMSchedulerState:\n common: CommonSchedulerState\n\n # setable values\n init_noise_sigma: jnp.ndarray\n timesteps: jnp.ndarray\n num_inference_steps: Optional[int] = None\n\n @classmethod\n def create(cls, common: CommonSchedulerState, init_noise_sigma: jnp.ndarray, timesteps: jnp.ndarray):\n return cls(common=common, init_noise_sigma=init_noise_sigma, timesteps=timesteps)\n\n\n@dataclass\nclass FlaxDDPMSchedulerOutput(FlaxSchedulerOutput):\n state: DDPMSchedulerState\n\n\nclass FlaxDDPMScheduler(FlaxSchedulerMixin, ConfigMixin):\n \"\"\"\n Denoising diffusion probabilistic models (DDPMs) explores the connections between denoising score matching and\n Langevin dynamics sampling.\n\n [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`\n function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.\n [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and\n [`~SchedulerMixin.from_pretrained`] functions.\n\n For more details, see the original paper: https://arxiv.org/abs/2006.11239\n\n Args:\n num_train_timesteps (`int`): number of diffusion steps used to train the model.\n beta_start (`float`): the starting `beta` value of inference.\n beta_end (`float`): the final `beta` value.\n beta_schedule (`str`):\n the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear`, `scaled_linear`, or `squaredcos_cap_v2`.\n trained_betas (`np.ndarray`, optional):\n option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.\n variance_type (`str`):\n options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small`,\n `fixed_small_log`, `fixed_large`, `fixed_large_log`, `learned` or `learned_range`.\n clip_sample (`bool`, default `True`):\n option to clip predicted sample between -1 and 1 for numerical stability.\n prediction_type (`str`, default `epsilon`):\n indicates whether the model predicts the noise (epsilon), or the samples. One of `epsilon`, `sample`.\n `v-prediction` is not supported for this scheduler.\n dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):\n the `dtype` used for params and computation.\n \"\"\"\n\n _compatibles = [e.name for e in FlaxKarrasDiffusionSchedulers]\n\n dtype: jnp.dtype\n\n @property\n def has_state(self):\n return True\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[jnp.ndarray] = None,\n variance_type: str = \"fixed_small\",\n clip_sample: bool = True,\n prediction_type: str = \"epsilon\",\n dtype: jnp.dtype = jnp.float32,\n ):\n self.dtype = dtype\n\n def create_state(self, common: Optional[CommonSchedulerState] = None) -> DDPMSchedulerState:\n if common is None:\n common = CommonSchedulerState.create(self)\n\n # standard deviation of the initial noise distribution\n init_noise_sigma = jnp.array(1.0, dtype=self.dtype)\n\n timesteps = jnp.arange(0, self.config.num_train_timesteps).round()[::-1]\n\n return DDPMSchedulerState.create(\n common=common,\n init_noise_sigma=init_noise_sigma,\n timesteps=timesteps,\n )\n\n def scale_model_input(\n self, state: DDPMSchedulerState, sample: jnp.ndarray, timestep: Optional[int] = None\n ) -> jnp.ndarray:\n \"\"\"\n Args:\n state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance.\n sample (`jnp.ndarray`): input sample\n timestep (`int`, optional): current timestep\n\n Returns:\n `jnp.ndarray`: scaled input sample\n \"\"\"\n return sample\n\n def set_timesteps(\n self, state: DDPMSchedulerState, num_inference_steps: int, shape: Tuple = ()\n ) -> DDPMSchedulerState:\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.\n\n Args:\n state (`DDIMSchedulerState`):\n the `FlaxDDPMScheduler` state data class instance.\n num_inference_steps (`int`):\n the number of diffusion steps used when generating samples with a pre-trained model.\n \"\"\"\n\n step_ratio = self.config.num_train_timesteps // num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # rounding to avoid issues when num_inference_step is power of 3\n timesteps = (jnp.arange(0, num_inference_steps) * step_ratio).round()[::-1]\n\n return state.replace(\n num_inference_steps=num_inference_steps,\n timesteps=timesteps,\n )\n\n def _get_variance(self, state: DDPMSchedulerState, t, predicted_variance=None, variance_type=None):\n alpha_prod_t = state.common.alphas_cumprod[t]\n alpha_prod_t_prev = jnp.where(t > 0, state.common.alphas_cumprod[t - 1], jnp.array(1.0, dtype=self.dtype))\n\n # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)\n # and sample from it to get previous sample\n # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample\n variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]\n\n if variance_type is None:\n variance_type = self.config.variance_type\n\n # hacks - were probably added for training stability\n if variance_type == \"fixed_small\":\n variance = jnp.clip(variance, a_min=1e-20)\n # for rl-diffuser https://arxiv.org/abs/2205.09991\n elif variance_type == \"fixed_small_log\":\n variance = jnp.log(jnp.clip(variance, a_min=1e-20))\n elif variance_type == \"fixed_large\":\n variance = state.common.betas[t]\n elif variance_type == \"fixed_large_log\":\n # Glide max_log\n variance = jnp.log(state.common.betas[t])\n elif variance_type == \"learned\":\n return predicted_variance\n elif variance_type == \"learned_range\":\n min_log = variance\n max_log = state.common.betas[t]\n frac = (predicted_variance + 1) / 2\n variance = frac * max_log + (1 - frac) * min_log\n\n return variance\n\n def step(\n self,\n state: DDPMSchedulerState,\n model_output: jnp.ndarray,\n timestep: int,\n sample: jnp.ndarray,\n key: Optional[jax.Array] = None,\n return_dict: bool = True,\n ) -> Union[FlaxDDPMSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n state (`DDPMSchedulerState`): the `FlaxDDPMScheduler` state data class instance.\n model_output (`jnp.ndarray`): direct output from learned diffusion model.\n timestep (`int`): current discrete timestep in the diffusion chain.\n sample (`jnp.ndarray`):\n current instance of sample being created by diffusion process.\n key (`jax.Array`): a PRNG key.\n return_dict (`bool`): option for returning tuple rather than FlaxDDPMSchedulerOutput class\n\n Returns:\n [`FlaxDDPMSchedulerOutput`] or `tuple`: [`FlaxDDPMSchedulerOutput`] if `return_dict` is True, otherwise a\n `tuple`. When returning a tuple, the first element is the sample tensor.\n\n \"\"\"\n t = timestep\n\n if key is None:\n key = jax.random.key(0)\n\n if (\n len(model_output.shape) > 1\n and model_output.shape[1] == sample.shape[1] * 2\n and self.config.variance_type in [\"learned\", \"learned_range\"]\n ):\n model_output, predicted_variance = jnp.split(model_output, sample.shape[1], axis=1)\n else:\n predicted_variance = None\n\n # 1. compute alphas, betas\n alpha_prod_t = state.common.alphas_cumprod[t]\n alpha_prod_t_prev = jnp.where(t > 0, state.common.alphas_cumprod[t - 1], jnp.array(1.0, dtype=self.dtype))\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n\n # 2. compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf\n if self.config.prediction_type == \"epsilon\":\n pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)\n elif self.config.prediction_type == \"sample\":\n pred_original_sample = model_output\n elif self.config.prediction_type == \"v_prediction\":\n pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` \"\n \" for the FlaxDDPMScheduler.\"\n )\n\n # 3. Clip \"predicted x_0\"\n if self.config.clip_sample:\n pred_original_sample = jnp.clip(pred_original_sample, -1, 1)\n\n # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t\n # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf\n pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * state.common.betas[t]) / beta_prod_t\n current_sample_coeff = state.common.alphas[t] ** (0.5) * beta_prod_t_prev / beta_prod_t\n\n # 5. Compute predicted previous sample µ_t\n # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf\n pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample\n\n # 6. Add noise\n def random_variance():\n split_key = jax.random.split(key, num=1)[0]\n noise = jax.random.normal(split_key, shape=model_output.shape, dtype=self.dtype)\n return (self._get_variance(state, t, predicted_variance=predicted_variance) ** 0.5) * noise\n\n variance = jnp.where(t > 0, random_variance(), jnp.zeros(model_output.shape, dtype=self.dtype))\n\n pred_prev_sample = pred_prev_sample + variance\n\n if not return_dict:\n return (pred_prev_sample, state)\n\n return FlaxDDPMSchedulerOutput(prev_sample=pred_prev_sample, state=state)\n\n def add_noise(\n self,\n state: DDPMSchedulerState,\n original_samples: jnp.ndarray,\n noise: jnp.ndarray,\n timesteps: jnp.ndarray,\n ) -> jnp.ndarray:\n return add_noise_common(state.common, original_samples, noise, timesteps)\n\n def get_velocity(\n self,\n state: DDPMSchedulerState,\n sample: jnp.ndarray,\n noise: jnp.ndarray,\n timesteps: jnp.ndarray,\n ) -> jnp.ndarray:\n return get_velocity_common(state.common, sample, noise, timesteps)\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_ddpm_parallel.py", "content": "# Copyright 2024 ParaDiGMS authors and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim\n\nimport math\nfrom dataclasses import dataclass\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import BaseOutput\nfrom ..utils.torch_utils import randn_tensor\nfrom .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin\n\n\n@dataclass\n# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput\nclass DDPMParallelSchedulerOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's `step` function output.\n\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample `(x_{0})` based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.Tensor\n pred_original_sample: Optional[torch.Tensor] = None\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\n# Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr\ndef rescale_zero_terminal_snr(betas):\n \"\"\"\n Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)\n\n\n Args:\n betas (`torch.Tensor`):\n the betas that the scheduler is being initialized with.\n\n Returns:\n `torch.Tensor`: rescaled betas with zero terminal SNR\n \"\"\"\n # Convert betas to alphas_bar_sqrt\n alphas = 1.0 - betas\n alphas_cumprod = torch.cumprod(alphas, dim=0)\n alphas_bar_sqrt = alphas_cumprod.sqrt()\n\n # Store old values.\n alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()\n alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()\n\n # Shift so the last timestep is zero.\n alphas_bar_sqrt -= alphas_bar_sqrt_T\n\n # Scale so the first timestep is back to the old value.\n alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)\n\n # Convert alphas_bar_sqrt to betas\n alphas_bar = alphas_bar_sqrt**2 # Revert sqrt\n alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod\n alphas = torch.cat([alphas_bar[0:1], alphas])\n betas = 1 - alphas\n\n return betas\n\n\nclass DDPMParallelScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n Denoising diffusion probabilistic models (DDPMs) explores the connections between denoising score matching and\n Langevin dynamics sampling.\n\n [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`\n function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.\n [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and\n [`~SchedulerMixin.from_pretrained`] functions.\n\n For more details, see the original paper: https://arxiv.org/abs/2006.11239\n\n Args:\n num_train_timesteps (`int`): number of diffusion steps used to train the model.\n beta_start (`float`): the starting `beta` value of inference.\n beta_end (`float`): the final `beta` value.\n beta_schedule (`str`):\n the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear`, `scaled_linear`, `squaredcos_cap_v2` or `sigmoid`.\n trained_betas (`np.ndarray`, optional):\n option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.\n variance_type (`str`):\n options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small`,\n `fixed_small_log`, `fixed_large`, `fixed_large_log`, `learned` or `learned_range`.\n clip_sample (`bool`, default `True`):\n option to clip predicted sample for numerical stability.\n clip_sample_range (`float`, default `1.0`):\n the maximum magnitude for sample clipping. Valid only when `clip_sample=True`.\n prediction_type (`str`, default `epsilon`, optional):\n prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion\n process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4\n https://imagen.research.google/video/paper.pdf)\n thresholding (`bool`, default `False`):\n whether to use the \"dynamic thresholding\" method (introduced by Imagen, https://arxiv.org/abs/2205.11487).\n Note that the thresholding method is unsuitable for latent-space diffusion models (such as\n stable-diffusion).\n dynamic_thresholding_ratio (`float`, default `0.995`):\n the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen\n (https://arxiv.org/abs/2205.11487). Valid only when `thresholding=True`.\n sample_max_value (`float`, default `1.0`):\n the threshold value for dynamic thresholding. Valid only when `thresholding=True`.\n timestep_spacing (`str`, default `\"leading\"`):\n The way the timesteps should be scaled. Refer to Table 2. of [Common Diffusion Noise Schedules and Sample\n Steps are Flawed](https://arxiv.org/abs/2305.08891) for more information.\n steps_offset (`int`, default `0`):\n An offset added to the inference steps, as required by some model families.\n rescale_betas_zero_snr (`bool`, defaults to `False`):\n Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and\n dark samples instead of limiting it to samples with medium brightness. Loosely related to\n [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).\n \"\"\"\n\n _compatibles = [e.name for e in KarrasDiffusionSchedulers]\n order = 1\n _is_ode_scheduler = False\n\n @register_to_config\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.__init__\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[Union[np.ndarray, List[float]]] = None,\n variance_type: str = \"fixed_small\",\n clip_sample: bool = True,\n prediction_type: str = \"epsilon\",\n thresholding: bool = False,\n dynamic_thresholding_ratio: float = 0.995,\n clip_sample_range: float = 1.0,\n sample_max_value: float = 1.0,\n timestep_spacing: str = \"leading\",\n steps_offset: int = 0,\n rescale_betas_zero_snr: int = False,\n ):\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n elif beta_schedule == \"sigmoid\":\n # GeoDiff sigmoid schedule\n betas = torch.linspace(-6, 6, num_train_timesteps)\n self.betas = torch.sigmoid(betas) * (beta_end - beta_start) + beta_start\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n # Rescale for zero SNR\n if rescale_betas_zero_snr:\n self.betas = rescale_zero_terminal_snr(self.betas)\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n self.one = torch.tensor(1.0)\n\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = 1.0\n\n # setable values\n self.custom_timesteps = False\n self.num_inference_steps = None\n self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())\n\n self.variance_type = variance_type\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.scale_model_input\n def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`int`, *optional*):\n The current timestep in the diffusion chain.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n return sample\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.set_timesteps\n def set_timesteps(\n self,\n num_inference_steps: Optional[int] = None,\n device: Union[str, torch.device] = None,\n timesteps: Optional[List[int]] = None,\n ):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used,\n `timesteps` must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default\n timestep spacing strategy of equal spacing between timesteps is used. If `timesteps` is passed,\n `num_inference_steps` must be `None`.\n\n \"\"\"\n if num_inference_steps is not None and timesteps is not None:\n raise ValueError(\"Can only pass one of `num_inference_steps` or `custom_timesteps`.\")\n\n if timesteps is not None:\n for i in range(1, len(timesteps)):\n if timesteps[i] >= timesteps[i - 1]:\n raise ValueError(\"`custom_timesteps` must be in descending order.\")\n\n if timesteps[0] >= self.config.num_train_timesteps:\n raise ValueError(\n f\"`timesteps` must start before `self.config.train_timesteps`:\"\n f\" {self.config.num_train_timesteps}.\"\n )\n\n timesteps = np.array(timesteps, dtype=np.int64)\n self.custom_timesteps = True\n else:\n if num_inference_steps > self.config.num_train_timesteps:\n raise ValueError(\n f\"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:\"\n f\" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle\"\n f\" maximal {self.config.num_train_timesteps} timesteps.\"\n )\n\n self.num_inference_steps = num_inference_steps\n self.custom_timesteps = False\n\n # \"linspace\", \"leading\", \"trailing\" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891\n if self.config.timestep_spacing == \"linspace\":\n timesteps = (\n np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps)\n .round()[::-1]\n .copy()\n .astype(np.int64)\n )\n elif self.config.timestep_spacing == \"leading\":\n step_ratio = self.config.num_train_timesteps // self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64)\n timesteps += self.config.steps_offset\n elif self.config.timestep_spacing == \"trailing\":\n step_ratio = self.config.num_train_timesteps / self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = np.round(np.arange(self.config.num_train_timesteps, 0, -step_ratio)).astype(np.int64)\n timesteps -= 1\n else:\n raise ValueError(\n f\"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.\"\n )\n\n self.timesteps = torch.from_numpy(timesteps).to(device)\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._get_variance\n def _get_variance(self, t, predicted_variance=None, variance_type=None):\n prev_t = self.previous_timestep(t)\n\n alpha_prod_t = self.alphas_cumprod[t]\n alpha_prod_t_prev = self.alphas_cumprod[prev_t] if prev_t >= 0 else self.one\n current_beta_t = 1 - alpha_prod_t / alpha_prod_t_prev\n\n # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)\n # and sample from it to get previous sample\n # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample\n variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * current_beta_t\n\n # we always take the log of variance, so clamp it to ensure it's not 0\n variance = torch.clamp(variance, min=1e-20)\n\n if variance_type is None:\n variance_type = self.config.variance_type\n\n # hacks - were probably added for training stability\n if variance_type == \"fixed_small\":\n variance = variance\n # for rl-diffuser https://arxiv.org/abs/2205.09991\n elif variance_type == \"fixed_small_log\":\n variance = torch.log(variance)\n variance = torch.exp(0.5 * variance)\n elif variance_type == \"fixed_large\":\n variance = current_beta_t\n elif variance_type == \"fixed_large_log\":\n # Glide max_log\n variance = torch.log(current_beta_t)\n elif variance_type == \"learned\":\n return predicted_variance\n elif variance_type == \"learned_range\":\n min_log = torch.log(variance)\n max_log = torch.log(current_beta_t)\n frac = (predicted_variance + 1) / 2\n variance = frac * max_log + (1 - frac) * min_log\n\n return variance\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample\n def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:\n \"\"\"\n \"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the\n prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by\n s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing\n pixels from saturation at each step. We find that dynamic thresholding results in significantly better\n photorealism as well as better image-text alignment, especially when using very large guidance weights.\"\n\n https://arxiv.org/abs/2205.11487\n \"\"\"\n dtype = sample.dtype\n batch_size, channels, *remaining_dims = sample.shape\n\n if dtype not in (torch.float32, torch.float64):\n sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half\n\n # Flatten sample for doing quantile calculation along each image\n sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))\n\n abs_sample = sample.abs() # \"a certain percentile absolute pixel value\"\n\n s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)\n s = torch.clamp(\n s, min=1, max=self.config.sample_max_value\n ) # When clamped to min=1, equivalent to standard clipping to [-1, 1]\n s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0\n sample = torch.clamp(sample, -s, s) / s # \"we threshold xt0 to the range [-s, s] and then divide by s\"\n\n sample = sample.reshape(batch_size, channels, *remaining_dims)\n sample = sample.to(dtype)\n\n return sample\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n generator=None,\n return_dict: bool = True,\n ) -> Union[DDPMParallelSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`): direct output from learned diffusion model.\n timestep (`int`): current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n current instance of sample being created by diffusion process.\n generator: random number generator.\n return_dict (`bool`): option for returning tuple rather than DDPMParallelSchedulerOutput class\n\n Returns:\n [`~schedulers.scheduling_utils.DDPMParallelSchedulerOutput`] or `tuple`:\n [`~schedulers.scheduling_utils.DDPMParallelSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`.\n When returning a tuple, the first element is the sample tensor.\n\n \"\"\"\n t = timestep\n\n prev_t = self.previous_timestep(t)\n\n if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in [\"learned\", \"learned_range\"]:\n model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1)\n else:\n predicted_variance = None\n\n # 1. compute alphas, betas\n alpha_prod_t = self.alphas_cumprod[t]\n alpha_prod_t_prev = self.alphas_cumprod[prev_t] if prev_t >= 0 else self.one\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n current_alpha_t = alpha_prod_t / alpha_prod_t_prev\n current_beta_t = 1 - current_alpha_t\n\n # 2. compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf\n if self.config.prediction_type == \"epsilon\":\n pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)\n elif self.config.prediction_type == \"sample\":\n pred_original_sample = model_output\n elif self.config.prediction_type == \"v_prediction\":\n pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` or\"\n \" `v_prediction` for the DDPMScheduler.\"\n )\n\n # 3. Clip or threshold \"predicted x_0\"\n if self.config.thresholding:\n pred_original_sample = self._threshold_sample(pred_original_sample)\n elif self.config.clip_sample:\n pred_original_sample = pred_original_sample.clamp(\n -self.config.clip_sample_range, self.config.clip_sample_range\n )\n\n # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t\n # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf\n pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * current_beta_t) / beta_prod_t\n current_sample_coeff = current_alpha_t ** (0.5) * beta_prod_t_prev / beta_prod_t\n\n # 5. Compute predicted previous sample µ_t\n # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf\n pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample\n\n # 6. Add noise\n variance = 0\n if t > 0:\n device = model_output.device\n variance_noise = randn_tensor(\n model_output.shape, generator=generator, device=device, dtype=model_output.dtype\n )\n if self.variance_type == \"fixed_small_log\":\n variance = self._get_variance(t, predicted_variance=predicted_variance) * variance_noise\n elif self.variance_type == \"learned_range\":\n variance = self._get_variance(t, predicted_variance=predicted_variance)\n variance = torch.exp(0.5 * variance) * variance_noise\n else:\n variance = (self._get_variance(t, predicted_variance=predicted_variance) ** 0.5) * variance_noise\n\n pred_prev_sample = pred_prev_sample + variance\n\n if not return_dict:\n return (pred_prev_sample,)\n\n return DDPMParallelSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample)\n\n def batch_step_no_noise(\n self,\n model_output: torch.Tensor,\n timesteps: List[int],\n sample: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"\n Batched version of the `step` function, to be able to reverse the SDE for multiple samples/timesteps at once.\n Also, does not add any noise to the predicted sample, which is necessary for parallel sampling where the noise\n is pre-sampled by the pipeline.\n\n Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`): direct output from learned diffusion model.\n timesteps (`List[int]`):\n current discrete timesteps in the diffusion chain. This is now a list of integers.\n sample (`torch.Tensor`):\n current instance of sample being created by diffusion process.\n\n Returns:\n `torch.Tensor`: sample tensor at previous timestep.\n \"\"\"\n t = timesteps\n num_inference_steps = self.num_inference_steps if self.num_inference_steps else self.config.num_train_timesteps\n prev_t = t - self.config.num_train_timesteps // num_inference_steps\n\n t = t.view(-1, *([1] * (model_output.ndim - 1)))\n prev_t = prev_t.view(-1, *([1] * (model_output.ndim - 1)))\n\n if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in [\"learned\", \"learned_range\"]:\n model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1)\n else:\n pass\n\n # 1. compute alphas, betas\n self.alphas_cumprod = self.alphas_cumprod.to(model_output.device)\n alpha_prod_t = self.alphas_cumprod[t]\n alpha_prod_t_prev = self.alphas_cumprod[torch.clip(prev_t, min=0)]\n alpha_prod_t_prev[prev_t < 0] = torch.tensor(1.0)\n\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n current_alpha_t = alpha_prod_t / alpha_prod_t_prev\n current_beta_t = 1 - current_alpha_t\n\n # 2. compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf\n if self.config.prediction_type == \"epsilon\":\n pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)\n elif self.config.prediction_type == \"sample\":\n pred_original_sample = model_output\n elif self.config.prediction_type == \"v_prediction\":\n pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` or\"\n \" `v_prediction` for the DDPMParallelScheduler.\"\n )\n\n # 3. Clip or threshold \"predicted x_0\"\n if self.config.thresholding:\n pred_original_sample = self._threshold_sample(pred_original_sample)\n elif self.config.clip_sample:\n pred_original_sample = pred_original_sample.clamp(\n -self.config.clip_sample_range, self.config.clip_sample_range\n )\n\n # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t\n # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf\n pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * current_beta_t) / beta_prod_t\n current_sample_coeff = current_alpha_t ** (0.5) * beta_prod_t_prev / beta_prod_t\n\n # 5. Compute predicted previous sample µ_t\n # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf\n pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample\n\n return pred_prev_sample\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.IntTensor,\n ) -> torch.Tensor:\n # Make sure alphas_cumprod and timestep have same device and dtype as original_samples\n # Move the self.alphas_cumprod to device to avoid redundant CPU to GPU data movement\n # for the subsequent add_noise calls\n self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device)\n alphas_cumprod = self.alphas_cumprod.to(dtype=original_samples.dtype)\n timesteps = timesteps.to(original_samples.device)\n\n sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n while len(sqrt_alpha_prod.shape) < len(original_samples.shape):\n sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n\n noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise\n return noisy_samples\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity\n def get_velocity(self, sample: torch.Tensor, noise: torch.Tensor, timesteps: torch.IntTensor) -> torch.Tensor:\n # Make sure alphas_cumprod and timestep have same device and dtype as sample\n self.alphas_cumprod = self.alphas_cumprod.to(device=sample.device)\n alphas_cumprod = self.alphas_cumprod.to(dtype=sample.dtype)\n timesteps = timesteps.to(sample.device)\n\n sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n while len(sqrt_alpha_prod.shape) < len(sample.shape):\n sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n\n velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample\n return velocity\n\n def __len__(self):\n return self.config.num_train_timesteps\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.previous_timestep\n def previous_timestep(self, timestep):\n if self.custom_timesteps:\n index = (self.timesteps == timestep).nonzero(as_tuple=True)[0][0]\n if index == self.timesteps.shape[0] - 1:\n prev_t = torch.tensor(-1)\n else:\n prev_t = self.timesteps[index + 1]\n else:\n num_inference_steps = (\n self.num_inference_steps if self.num_inference_steps else self.config.num_train_timesteps\n )\n prev_t = timestep - self.config.num_train_timesteps // num_inference_steps\n\n return prev_t\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_ddpm_wuerstchen.py", "content": "# Copyright (c) 2022 Pablo Pernías MIT License\n# Copyright 2024 UC Berkeley Team and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim\n\nimport math\nfrom dataclasses import dataclass\nfrom typing import List, Optional, Tuple, Union\n\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import BaseOutput\nfrom ..utils.torch_utils import randn_tensor\nfrom .scheduling_utils import SchedulerMixin\n\n\n@dataclass\nclass DDPMWuerstchenSchedulerOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's step function output.\n\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n \"\"\"\n\n prev_sample: torch.Tensor\n\n\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\nclass DDPMWuerstchenScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n Denoising diffusion probabilistic models (DDPMs) explores the connections between denoising score matching and\n Langevin dynamics sampling.\n\n [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`\n function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.\n [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and\n [`~SchedulerMixin.from_pretrained`] functions.\n\n For more details, see the original paper: https://arxiv.org/abs/2006.11239\n\n Args:\n scaler (`float`): ....\n s (`float`): ....\n \"\"\"\n\n @register_to_config\n def __init__(\n self,\n scaler: float = 1.0,\n s: float = 0.008,\n ):\n self.scaler = scaler\n self.s = torch.tensor([s])\n self._init_alpha_cumprod = torch.cos(self.s / (1 + self.s) * torch.pi * 0.5) ** 2\n\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = 1.0\n\n def _alpha_cumprod(self, t, device):\n if self.scaler > 1:\n t = 1 - (1 - t) ** self.scaler\n elif self.scaler < 1:\n t = t**self.scaler\n alpha_cumprod = torch.cos(\n (t + self.s.to(device)) / (1 + self.s.to(device)) * torch.pi * 0.5\n ) ** 2 / self._init_alpha_cumprod.to(device)\n return alpha_cumprod.clamp(0.0001, 0.9999)\n\n def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`): input sample\n timestep (`int`, optional): current timestep\n\n Returns:\n `torch.Tensor`: scaled input sample\n \"\"\"\n return sample\n\n def set_timesteps(\n self,\n num_inference_steps: int = None,\n timesteps: Optional[List[int]] = None,\n device: Union[str, torch.device] = None,\n ):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.\n\n Args:\n num_inference_steps (`Dict[float, int]`):\n the number of diffusion steps used when generating samples with a pre-trained model. If passed, then\n `timesteps` must be `None`.\n device (`str` or `torch.device`, optional):\n the device to which the timesteps are moved to. {2 / 3: 20, 0.0: 10}\n \"\"\"\n if timesteps is None:\n timesteps = torch.linspace(1.0, 0.0, num_inference_steps + 1, device=device)\n if not isinstance(timesteps, torch.Tensor):\n timesteps = torch.Tensor(timesteps).to(device)\n self.timesteps = timesteps\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n generator=None,\n return_dict: bool = True,\n ) -> Union[DDPMWuerstchenSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`): direct output from learned diffusion model.\n timestep (`int`): current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n current instance of sample being created by diffusion process.\n generator: random number generator.\n return_dict (`bool`): option for returning tuple rather than DDPMWuerstchenSchedulerOutput class\n\n Returns:\n [`DDPMWuerstchenSchedulerOutput`] or `tuple`: [`DDPMWuerstchenSchedulerOutput`] if `return_dict` is True,\n otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.\n\n \"\"\"\n dtype = model_output.dtype\n device = model_output.device\n t = timestep\n\n prev_t = self.previous_timestep(t)\n\n alpha_cumprod = self._alpha_cumprod(t, device).view(t.size(0), *[1 for _ in sample.shape[1:]])\n alpha_cumprod_prev = self._alpha_cumprod(prev_t, device).view(prev_t.size(0), *[1 for _ in sample.shape[1:]])\n alpha = alpha_cumprod / alpha_cumprod_prev\n\n mu = (1.0 / alpha).sqrt() * (sample - (1 - alpha) * model_output / (1 - alpha_cumprod).sqrt())\n\n std_noise = randn_tensor(mu.shape, generator=generator, device=model_output.device, dtype=model_output.dtype)\n std = ((1 - alpha) * (1.0 - alpha_cumprod_prev) / (1.0 - alpha_cumprod)).sqrt() * std_noise\n pred = mu + std * (prev_t != 0).float().view(prev_t.size(0), *[1 for _ in sample.shape[1:]])\n\n if not return_dict:\n return (pred.to(dtype),)\n\n return DDPMWuerstchenSchedulerOutput(prev_sample=pred.to(dtype))\n\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.Tensor,\n ) -> torch.Tensor:\n device = original_samples.device\n dtype = original_samples.dtype\n alpha_cumprod = self._alpha_cumprod(timesteps, device=device).view(\n timesteps.size(0), *[1 for _ in original_samples.shape[1:]]\n )\n noisy_samples = alpha_cumprod.sqrt() * original_samples + (1 - alpha_cumprod).sqrt() * noise\n return noisy_samples.to(dtype=dtype)\n\n def __len__(self):\n return self.config.num_train_timesteps\n\n def previous_timestep(self, timestep):\n index = (self.timesteps - timestep[0]).abs().argmin().item()\n prev_t = self.timesteps[index + 1][None].expand(timestep.shape[0])\n return prev_t\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py", "content": "# Copyright 2024 FLAIR Lab and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: check https://arxiv.org/abs/2204.13902 and https://github.com/qsh-zh/deis for more info\n# The codebase is modified based on https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py\n\nimport math\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import deprecate\nfrom .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\nclass DEISMultistepScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n `DEISMultistepScheduler` is a fast high order solver for diffusion ordinary differential equations (ODEs).\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n beta_start (`float`, defaults to 0.0001):\n The starting `beta` value of inference.\n beta_end (`float`, defaults to 0.02):\n The final `beta` value.\n beta_schedule (`str`, defaults to `\"linear\"`):\n The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear`, `scaled_linear`, or `squaredcos_cap_v2`.\n trained_betas (`np.ndarray`, *optional*):\n Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.\n solver_order (`int`, defaults to 2):\n The DEIS order which can be `1` or `2` or `3`. It is recommended to use `solver_order=2` for guided\n sampling, and `solver_order=3` for unconditional sampling.\n prediction_type (`str`, defaults to `epsilon`):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),\n `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen\n Video](https://imagen.research.google/video/paper.pdf) paper).\n thresholding (`bool`, defaults to `False`):\n Whether to use the \"dynamic thresholding\" method. This is unsuitable for latent-space diffusion models such\n as Stable Diffusion.\n dynamic_thresholding_ratio (`float`, defaults to 0.995):\n The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.\n sample_max_value (`float`, defaults to 1.0):\n The threshold value for dynamic thresholding. Valid only when `thresholding=True`.\n algorithm_type (`str`, defaults to `deis`):\n The algorithm type for the solver.\n lower_order_final (`bool`, defaults to `True`):\n Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps.\n use_karras_sigmas (`bool`, *optional*, defaults to `False`):\n Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`,\n the sigmas are determined according to a sequence of noise levels {σi}.\n timestep_spacing (`str`, defaults to `\"linspace\"`):\n The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.\n steps_offset (`int`, defaults to 0):\n An offset added to the inference steps, as required by some model families.\n \"\"\"\n\n _compatibles = [e.name for e in KarrasDiffusionSchedulers]\n order = 1\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[np.ndarray] = None,\n solver_order: int = 2,\n prediction_type: str = \"epsilon\",\n thresholding: bool = False,\n dynamic_thresholding_ratio: float = 0.995,\n sample_max_value: float = 1.0,\n algorithm_type: str = \"deis\",\n solver_type: str = \"logrho\",\n lower_order_final: bool = True,\n use_karras_sigmas: Optional[bool] = False,\n timestep_spacing: str = \"linspace\",\n steps_offset: int = 0,\n ):\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n # Currently we only support VP-type noise schedule\n self.alpha_t = torch.sqrt(self.alphas_cumprod)\n self.sigma_t = torch.sqrt(1 - self.alphas_cumprod)\n self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t)\n self.sigmas = ((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5\n\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = 1.0\n\n # settings for DEIS\n if algorithm_type not in [\"deis\"]:\n if algorithm_type in [\"dpmsolver\", \"dpmsolver++\"]:\n self.register_to_config(algorithm_type=\"deis\")\n else:\n raise NotImplementedError(f\"{algorithm_type} is not implemented for {self.__class__}\")\n\n if solver_type not in [\"logrho\"]:\n if solver_type in [\"midpoint\", \"heun\", \"bh1\", \"bh2\"]:\n self.register_to_config(solver_type=\"logrho\")\n else:\n raise NotImplementedError(f\"solver type {solver_type} is not implemented for {self.__class__}\")\n\n # setable values\n self.num_inference_steps = None\n timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=np.float32)[::-1].copy()\n self.timesteps = torch.from_numpy(timesteps)\n self.model_outputs = [None] * solver_order\n self.lower_order_nums = 0\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n @property\n def step_index(self):\n \"\"\"\n The index counter for current timestep. It will increase 1 after each scheduler step.\n \"\"\"\n return self._step_index\n\n @property\n def begin_index(self):\n \"\"\"\n The index for the first timestep. It should be set from pipeline with `set_begin_index` method.\n \"\"\"\n return self._begin_index\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index\n def set_begin_index(self, begin_index: int = 0):\n \"\"\"\n Sets the begin index for the scheduler. This function should be run from pipeline before the inference.\n\n Args:\n begin_index (`int`):\n The begin index for the scheduler.\n \"\"\"\n self._begin_index = begin_index\n\n def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n \"\"\"\n # \"linspace\", \"leading\", \"trailing\" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891\n if self.config.timestep_spacing == \"linspace\":\n timesteps = (\n np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps + 1)\n .round()[::-1][:-1]\n .copy()\n .astype(np.int64)\n )\n elif self.config.timestep_spacing == \"leading\":\n step_ratio = self.config.num_train_timesteps // (num_inference_steps + 1)\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (np.arange(0, num_inference_steps + 1) * step_ratio).round()[::-1][:-1].copy().astype(np.int64)\n timesteps += self.config.steps_offset\n elif self.config.timestep_spacing == \"trailing\":\n step_ratio = self.config.num_train_timesteps / num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = np.arange(self.config.num_train_timesteps, 0, -step_ratio).round().copy().astype(np.int64)\n timesteps -= 1\n else:\n raise ValueError(\n f\"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.\"\n )\n\n sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)\n if self.config.use_karras_sigmas:\n log_sigmas = np.log(sigmas)\n sigmas = np.flip(sigmas).copy()\n sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)\n timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]).round()\n sigmas = np.concatenate([sigmas, sigmas[-1:]]).astype(np.float32)\n else:\n sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)\n sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0]) ** 0.5\n sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)\n\n self.sigmas = torch.from_numpy(sigmas)\n self.timesteps = torch.from_numpy(timesteps).to(device=device, dtype=torch.int64)\n\n self.num_inference_steps = len(timesteps)\n\n self.model_outputs = [\n None,\n ] * self.config.solver_order\n self.lower_order_nums = 0\n\n # add an index counter for schedulers that allow duplicated timesteps\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample\n def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:\n \"\"\"\n \"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the\n prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by\n s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing\n pixels from saturation at each step. We find that dynamic thresholding results in significantly better\n photorealism as well as better image-text alignment, especially when using very large guidance weights.\"\n\n https://arxiv.org/abs/2205.11487\n \"\"\"\n dtype = sample.dtype\n batch_size, channels, *remaining_dims = sample.shape\n\n if dtype not in (torch.float32, torch.float64):\n sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half\n\n # Flatten sample for doing quantile calculation along each image\n sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))\n\n abs_sample = sample.abs() # \"a certain percentile absolute pixel value\"\n\n s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)\n s = torch.clamp(\n s, min=1, max=self.config.sample_max_value\n ) # When clamped to min=1, equivalent to standard clipping to [-1, 1]\n s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0\n sample = torch.clamp(sample, -s, s) / s # \"we threshold xt0 to the range [-s, s] and then divide by s\"\n\n sample = sample.reshape(batch_size, channels, *remaining_dims)\n sample = sample.to(dtype)\n\n return sample\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t\n def _sigma_to_t(self, sigma, log_sigmas):\n # get log sigma\n log_sigma = np.log(np.maximum(sigma, 1e-10))\n\n # get distribution\n dists = log_sigma - log_sigmas[:, np.newaxis]\n\n # get sigmas range\n low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)\n high_idx = low_idx + 1\n\n low = log_sigmas[low_idx]\n high = log_sigmas[high_idx]\n\n # interpolate sigmas\n w = (low - log_sigma) / (low - high)\n w = np.clip(w, 0, 1)\n\n # transform interpolation to time range\n t = (1 - w) * low_idx + w * high_idx\n t = t.reshape(sigma.shape)\n return t\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._sigma_to_alpha_sigma_t\n def _sigma_to_alpha_sigma_t(self, sigma):\n alpha_t = 1 / ((sigma**2 + 1) ** 0.5)\n sigma_t = sigma * alpha_t\n\n return alpha_t, sigma_t\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras\n def _convert_to_karras(self, in_sigmas: torch.Tensor, num_inference_steps) -> torch.Tensor:\n \"\"\"Constructs the noise schedule of Karras et al. (2022).\"\"\"\n\n # Hack to make sure that other schedulers which copy this function don't break\n # TODO: Add this logic to the other schedulers\n if hasattr(self.config, \"sigma_min\"):\n sigma_min = self.config.sigma_min\n else:\n sigma_min = None\n\n if hasattr(self.config, \"sigma_max\"):\n sigma_max = self.config.sigma_max\n else:\n sigma_max = None\n\n sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item()\n sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item()\n\n rho = 7.0 # 7.0 is the value used in the paper\n ramp = np.linspace(0, 1, num_inference_steps)\n min_inv_rho = sigma_min ** (1 / rho)\n max_inv_rho = sigma_max ** (1 / rho)\n sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho\n return sigmas\n\n def convert_model_output(\n self,\n model_output: torch.Tensor,\n *args,\n sample: torch.Tensor = None,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n Convert the model output to the corresponding type the DEIS algorithm needs.\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from the learned diffusion model.\n timestep (`int`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n\n Returns:\n `torch.Tensor`:\n The converted model output.\n \"\"\"\n timestep = args[0] if len(args) > 0 else kwargs.pop(\"timestep\", None)\n if sample is None:\n if len(args) > 1:\n sample = args[1]\n else:\n raise ValueError(\"missing `sample` as a required keyward argument\")\n if timestep is not None:\n deprecate(\n \"timesteps\",\n \"1.0.0\",\n \"Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n sigma = self.sigmas[self.step_index]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)\n if self.config.prediction_type == \"epsilon\":\n x0_pred = (sample - sigma_t * model_output) / alpha_t\n elif self.config.prediction_type == \"sample\":\n x0_pred = model_output\n elif self.config.prediction_type == \"v_prediction\":\n x0_pred = alpha_t * sample - sigma_t * model_output\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or\"\n \" `v_prediction` for the DEISMultistepScheduler.\"\n )\n\n if self.config.thresholding:\n x0_pred = self._threshold_sample(x0_pred)\n\n if self.config.algorithm_type == \"deis\":\n return (sample - alpha_t * x0_pred) / sigma_t\n else:\n raise NotImplementedError(\"only support log-rho multistep deis now\")\n\n def deis_first_order_update(\n self,\n model_output: torch.Tensor,\n *args,\n sample: torch.Tensor = None,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n One step for the first-order DEIS (equivalent to DDIM).\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from the learned diffusion model.\n timestep (`int`):\n The current discrete timestep in the diffusion chain.\n prev_timestep (`int`):\n The previous discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n\n Returns:\n `torch.Tensor`:\n The sample tensor at the previous timestep.\n \"\"\"\n timestep = args[0] if len(args) > 0 else kwargs.pop(\"timestep\", None)\n prev_timestep = args[1] if len(args) > 1 else kwargs.pop(\"prev_timestep\", None)\n if sample is None:\n if len(args) > 2:\n sample = args[2]\n else:\n raise ValueError(\" missing `sample` as a required keyward argument\")\n if timestep is not None:\n deprecate(\n \"timesteps\",\n \"1.0.0\",\n \"Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n if prev_timestep is not None:\n deprecate(\n \"prev_timestep\",\n \"1.0.0\",\n \"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n sigma_t, sigma_s = self.sigmas[self.step_index + 1], self.sigmas[self.step_index]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)\n alpha_s, sigma_s = self._sigma_to_alpha_sigma_t(sigma_s)\n lambda_t = torch.log(alpha_t) - torch.log(sigma_t)\n lambda_s = torch.log(alpha_s) - torch.log(sigma_s)\n\n h = lambda_t - lambda_s\n if self.config.algorithm_type == \"deis\":\n x_t = (alpha_t / alpha_s) * sample - (sigma_t * (torch.exp(h) - 1.0)) * model_output\n else:\n raise NotImplementedError(\"only support log-rho multistep deis now\")\n return x_t\n\n def multistep_deis_second_order_update(\n self,\n model_output_list: List[torch.Tensor],\n *args,\n sample: torch.Tensor = None,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n One step for the second-order multistep DEIS.\n\n Args:\n model_output_list (`List[torch.Tensor]`):\n The direct outputs from learned diffusion model at current and latter timesteps.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n\n Returns:\n `torch.Tensor`:\n The sample tensor at the previous timestep.\n \"\"\"\n timestep_list = args[0] if len(args) > 0 else kwargs.pop(\"timestep_list\", None)\n prev_timestep = args[1] if len(args) > 1 else kwargs.pop(\"prev_timestep\", None)\n if sample is None:\n if len(args) > 2:\n sample = args[2]\n else:\n raise ValueError(\" missing `sample` as a required keyward argument\")\n if timestep_list is not None:\n deprecate(\n \"timestep_list\",\n \"1.0.0\",\n \"Passing `timestep_list` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n if prev_timestep is not None:\n deprecate(\n \"prev_timestep\",\n \"1.0.0\",\n \"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n sigma_t, sigma_s0, sigma_s1 = (\n self.sigmas[self.step_index + 1],\n self.sigmas[self.step_index],\n self.sigmas[self.step_index - 1],\n )\n\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)\n alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)\n alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1)\n\n m0, m1 = model_output_list[-1], model_output_list[-2]\n\n rho_t, rho_s0, rho_s1 = sigma_t / alpha_t, sigma_s0 / alpha_s0, sigma_s1 / alpha_s1\n\n if self.config.algorithm_type == \"deis\":\n\n def ind_fn(t, b, c):\n # Integrate[(log(t) - log(c)) / (log(b) - log(c)), {t}]\n return t * (-np.log(c) + np.log(t) - 1) / (np.log(b) - np.log(c))\n\n coef1 = ind_fn(rho_t, rho_s0, rho_s1) - ind_fn(rho_s0, rho_s0, rho_s1)\n coef2 = ind_fn(rho_t, rho_s1, rho_s0) - ind_fn(rho_s0, rho_s1, rho_s0)\n\n x_t = alpha_t * (sample / alpha_s0 + coef1 * m0 + coef2 * m1)\n return x_t\n else:\n raise NotImplementedError(\"only support log-rho multistep deis now\")\n\n def multistep_deis_third_order_update(\n self,\n model_output_list: List[torch.Tensor],\n *args,\n sample: torch.Tensor = None,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n One step for the third-order multistep DEIS.\n\n Args:\n model_output_list (`List[torch.Tensor]`):\n The direct outputs from learned diffusion model at current and latter timesteps.\n sample (`torch.Tensor`):\n A current instance of a sample created by diffusion process.\n\n Returns:\n `torch.Tensor`:\n The sample tensor at the previous timestep.\n \"\"\"\n\n timestep_list = args[0] if len(args) > 0 else kwargs.pop(\"timestep_list\", None)\n prev_timestep = args[1] if len(args) > 1 else kwargs.pop(\"prev_timestep\", None)\n if sample is None:\n if len(args) > 2:\n sample = args[2]\n else:\n raise ValueError(\" missing`sample` as a required keyward argument\")\n if timestep_list is not None:\n deprecate(\n \"timestep_list\",\n \"1.0.0\",\n \"Passing `timestep_list` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n if prev_timestep is not None:\n deprecate(\n \"prev_timestep\",\n \"1.0.0\",\n \"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n sigma_t, sigma_s0, sigma_s1, sigma_s2 = (\n self.sigmas[self.step_index + 1],\n self.sigmas[self.step_index],\n self.sigmas[self.step_index - 1],\n self.sigmas[self.step_index - 2],\n )\n\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)\n alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)\n alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1)\n alpha_s2, sigma_s2 = self._sigma_to_alpha_sigma_t(sigma_s2)\n\n m0, m1, m2 = model_output_list[-1], model_output_list[-2], model_output_list[-3]\n\n rho_t, rho_s0, rho_s1, rho_s2 = (\n sigma_t / alpha_t,\n sigma_s0 / alpha_s0,\n sigma_s1 / alpha_s1,\n sigma_s2 / alpha_s2,\n )\n\n if self.config.algorithm_type == \"deis\":\n\n def ind_fn(t, b, c, d):\n # Integrate[(log(t) - log(c))(log(t) - log(d)) / (log(b) - log(c))(log(b) - log(d)), {t}]\n numerator = t * (\n np.log(c) * (np.log(d) - np.log(t) + 1)\n - np.log(d) * np.log(t)\n + np.log(d)\n + np.log(t) ** 2\n - 2 * np.log(t)\n + 2\n )\n denominator = (np.log(b) - np.log(c)) * (np.log(b) - np.log(d))\n return numerator / denominator\n\n coef1 = ind_fn(rho_t, rho_s0, rho_s1, rho_s2) - ind_fn(rho_s0, rho_s0, rho_s1, rho_s2)\n coef2 = ind_fn(rho_t, rho_s1, rho_s2, rho_s0) - ind_fn(rho_s0, rho_s1, rho_s2, rho_s0)\n coef3 = ind_fn(rho_t, rho_s2, rho_s0, rho_s1) - ind_fn(rho_s0, rho_s2, rho_s0, rho_s1)\n\n x_t = alpha_t * (sample / alpha_s0 + coef1 * m0 + coef2 * m1 + coef3 * m2)\n\n return x_t\n else:\n raise NotImplementedError(\"only support log-rho multistep deis now\")\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.index_for_timestep\n def index_for_timestep(self, timestep, schedule_timesteps=None):\n if schedule_timesteps is None:\n schedule_timesteps = self.timesteps\n\n index_candidates = (schedule_timesteps == timestep).nonzero()\n\n if len(index_candidates) == 0:\n step_index = len(self.timesteps) - 1\n # The sigma index that is taken for the **very** first `step`\n # is always the second index (or the last index if there is only 1)\n # This way we can ensure we don't accidentally skip a sigma in\n # case we start in the middle of the denoising schedule (e.g. for image-to-image)\n elif len(index_candidates) > 1:\n step_index = index_candidates[1].item()\n else:\n step_index = index_candidates[0].item()\n\n return step_index\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._init_step_index\n def _init_step_index(self, timestep):\n \"\"\"\n Initialize the step_index counter for the scheduler.\n \"\"\"\n\n if self.begin_index is None:\n if isinstance(timestep, torch.Tensor):\n timestep = timestep.to(self.timesteps.device)\n self._step_index = self.index_for_timestep(timestep)\n else:\n self._step_index = self._begin_index\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n return_dict: bool = True,\n ) -> Union[SchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with\n the multistep DEIS.\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`float`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n return_dict (`bool`):\n Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`.\n\n Returns:\n [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n\n \"\"\"\n if self.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n if self.step_index is None:\n self._init_step_index(timestep)\n\n lower_order_final = (\n (self.step_index == len(self.timesteps) - 1) and self.config.lower_order_final and len(self.timesteps) < 15\n )\n lower_order_second = (\n (self.step_index == len(self.timesteps) - 2) and self.config.lower_order_final and len(self.timesteps) < 15\n )\n\n model_output = self.convert_model_output(model_output, sample=sample)\n for i in range(self.config.solver_order - 1):\n self.model_outputs[i] = self.model_outputs[i + 1]\n self.model_outputs[-1] = model_output\n\n if self.config.solver_order == 1 or self.lower_order_nums < 1 or lower_order_final:\n prev_sample = self.deis_first_order_update(model_output, sample=sample)\n elif self.config.solver_order == 2 or self.lower_order_nums < 2 or lower_order_second:\n prev_sample = self.multistep_deis_second_order_update(self.model_outputs, sample=sample)\n else:\n prev_sample = self.multistep_deis_third_order_update(self.model_outputs, sample=sample)\n\n if self.lower_order_nums < self.config.solver_order:\n self.lower_order_nums += 1\n\n # upon completion increase step index by one\n self._step_index += 1\n\n if not return_dict:\n return (prev_sample,)\n\n return SchedulerOutput(prev_sample=prev_sample)\n\n def scale_model_input(self, sample: torch.Tensor, *args, **kwargs) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n return sample\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.add_noise\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.IntTensor,\n ) -> torch.Tensor:\n # Make sure sigmas and timesteps have the same device and dtype as original_samples\n sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)\n if original_samples.device.type == \"mps\" and torch.is_floating_point(timesteps):\n # mps does not support float64\n schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)\n timesteps = timesteps.to(original_samples.device, dtype=torch.float32)\n else:\n schedule_timesteps = self.timesteps.to(original_samples.device)\n timesteps = timesteps.to(original_samples.device)\n\n # begin_index is None when the scheduler is used for training or pipeline does not implement set_begin_index\n if self.begin_index is None:\n step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps]\n elif self.step_index is not None:\n # add_noise is called after first denoising step (for inpainting)\n step_indices = [self.step_index] * timesteps.shape[0]\n else:\n # add noise is called before first denoising step to create initial latent(img2img)\n step_indices = [self.begin_index] * timesteps.shape[0]\n\n sigma = sigmas[step_indices].flatten()\n while len(sigma.shape) < len(original_samples.shape):\n sigma = sigma.unsqueeze(-1)\n\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)\n noisy_samples = alpha_t * original_samples + sigma_t * noise\n return noisy_samples\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py", "content": "# Copyright 2024 TSAIL Team and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This file is strongly influenced by https://github.com/LuChengTHU/dpm-solver\n\nimport math\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import deprecate\nfrom ..utils.torch_utils import randn_tensor\nfrom .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\n# Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr\ndef rescale_zero_terminal_snr(betas):\n \"\"\"\n Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)\n\n\n Args:\n betas (`torch.Tensor`):\n the betas that the scheduler is being initialized with.\n\n Returns:\n `torch.Tensor`: rescaled betas with zero terminal SNR\n \"\"\"\n # Convert betas to alphas_bar_sqrt\n alphas = 1.0 - betas\n alphas_cumprod = torch.cumprod(alphas, dim=0)\n alphas_bar_sqrt = alphas_cumprod.sqrt()\n\n # Store old values.\n alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()\n alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()\n\n # Shift so the last timestep is zero.\n alphas_bar_sqrt -= alphas_bar_sqrt_T\n\n # Scale so the first timestep is back to the old value.\n alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)\n\n # Convert alphas_bar_sqrt to betas\n alphas_bar = alphas_bar_sqrt**2 # Revert sqrt\n alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod\n alphas = torch.cat([alphas_bar[0:1], alphas])\n betas = 1 - alphas\n\n return betas\n\n\nclass DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n `DPMSolverMultistepScheduler` is a fast dedicated high-order solver for diffusion ODEs.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n beta_start (`float`, defaults to 0.0001):\n The starting `beta` value of inference.\n beta_end (`float`, defaults to 0.02):\n The final `beta` value.\n beta_schedule (`str`, defaults to `\"linear\"`):\n The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear`, `scaled_linear`, or `squaredcos_cap_v2`.\n trained_betas (`np.ndarray`, *optional*):\n Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.\n solver_order (`int`, defaults to 2):\n The DPMSolver order which can be `1` or `2` or `3`. It is recommended to use `solver_order=2` for guided\n sampling, and `solver_order=3` for unconditional sampling.\n prediction_type (`str`, defaults to `epsilon`, *optional*):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),\n `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen\n Video](https://imagen.research.google/video/paper.pdf) paper).\n thresholding (`bool`, defaults to `False`):\n Whether to use the \"dynamic thresholding\" method. This is unsuitable for latent-space diffusion models such\n as Stable Diffusion.\n dynamic_thresholding_ratio (`float`, defaults to 0.995):\n The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.\n sample_max_value (`float`, defaults to 1.0):\n The threshold value for dynamic thresholding. Valid only when `thresholding=True` and\n `algorithm_type=\"dpmsolver++\"`.\n algorithm_type (`str`, defaults to `dpmsolver++`):\n Algorithm type for the solver; can be `dpmsolver`, `dpmsolver++`, `sde-dpmsolver` or `sde-dpmsolver++`. The\n `dpmsolver` type implements the algorithms in the [DPMSolver](https://huggingface.co/papers/2206.00927)\n paper, and the `dpmsolver++` type implements the algorithms in the\n [DPMSolver++](https://huggingface.co/papers/2211.01095) paper. It is recommended to use `dpmsolver++` or\n `sde-dpmsolver++` with `solver_order=2` for guided sampling like in Stable Diffusion.\n solver_type (`str`, defaults to `midpoint`):\n Solver type for the second-order solver; can be `midpoint` or `heun`. The solver type slightly affects the\n sample quality, especially for a small number of steps. It is recommended to use `midpoint` solvers.\n lower_order_final (`bool`, defaults to `True`):\n Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can\n stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10.\n euler_at_final (`bool`, defaults to `False`):\n Whether to use Euler's method in the final step. It is a trade-off between numerical stability and detail\n richness. This can stabilize the sampling of the SDE variant of DPMSolver for small number of inference\n steps, but sometimes may result in blurring.\n use_karras_sigmas (`bool`, *optional*, defaults to `False`):\n Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`,\n the sigmas are determined according to a sequence of noise levels {σi}.\n use_lu_lambdas (`bool`, *optional*, defaults to `False`):\n Whether to use the uniform-logSNR for step sizes proposed by Lu's DPM-Solver in the noise schedule during\n the sampling process. If `True`, the sigmas and time steps are determined according to a sequence of\n `lambda(t)`.\n final_sigmas_type (`str`, defaults to `\"zero\"`):\n The final `sigma` value for the noise schedule during the sampling process. If `\"sigma_min\"`, the final\n sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0.\n lambda_min_clipped (`float`, defaults to `-inf`):\n Clipping threshold for the minimum value of `lambda(t)` for numerical stability. This is critical for the\n cosine (`squaredcos_cap_v2`) noise schedule.\n variance_type (`str`, *optional*):\n Set to \"learned\" or \"learned_range\" for diffusion models that predict variance. If set, the model's output\n contains the predicted Gaussian variance.\n timestep_spacing (`str`, defaults to `\"linspace\"`):\n The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.\n steps_offset (`int`, defaults to 0):\n An offset added to the inference steps, as required by some model families.\n rescale_betas_zero_snr (`bool`, defaults to `False`):\n Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and\n dark samples instead of limiting it to samples with medium brightness. Loosely related to\n [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).\n \"\"\"\n\n _compatibles = [e.name for e in KarrasDiffusionSchedulers]\n order = 1\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[Union[np.ndarray, List[float]]] = None,\n solver_order: int = 2,\n prediction_type: str = \"epsilon\",\n thresholding: bool = False,\n dynamic_thresholding_ratio: float = 0.995,\n sample_max_value: float = 1.0,\n algorithm_type: str = \"dpmsolver++\",\n solver_type: str = \"midpoint\",\n lower_order_final: bool = True,\n euler_at_final: bool = False,\n use_karras_sigmas: Optional[bool] = False,\n use_lu_lambdas: Optional[bool] = False,\n final_sigmas_type: Optional[str] = \"zero\", # \"zero\", \"sigma_min\"\n lambda_min_clipped: float = -float(\"inf\"),\n variance_type: Optional[str] = None,\n timestep_spacing: str = \"linspace\",\n steps_offset: int = 0,\n rescale_betas_zero_snr: bool = False,\n ):\n if algorithm_type in [\"dpmsolver\", \"sde-dpmsolver\"]:\n deprecation_message = f\"algorithm_type {algorithm_type} is deprecated and will be removed in a future version. Choose from `dpmsolver++` or `sde-dpmsolver++` instead\"\n deprecate(\"algorithm_types dpmsolver and sde-dpmsolver\", \"1.0.0\", deprecation_message)\n\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n if rescale_betas_zero_snr:\n self.betas = rescale_zero_terminal_snr(self.betas)\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n\n if rescale_betas_zero_snr:\n # Close to 0 without being 0 so first sigma is not inf\n # FP16 smallest positive subnormal works well here\n self.alphas_cumprod[-1] = 2**-24\n\n # Currently we only support VP-type noise schedule\n self.alpha_t = torch.sqrt(self.alphas_cumprod)\n self.sigma_t = torch.sqrt(1 - self.alphas_cumprod)\n self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t)\n self.sigmas = ((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5\n\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = 1.0\n\n # settings for DPM-Solver\n if algorithm_type not in [\"dpmsolver\", \"dpmsolver++\", \"sde-dpmsolver\", \"sde-dpmsolver++\"]:\n if algorithm_type == \"deis\":\n self.register_to_config(algorithm_type=\"dpmsolver++\")\n else:\n raise NotImplementedError(f\"{algorithm_type} is not implemented for {self.__class__}\")\n\n if solver_type not in [\"midpoint\", \"heun\"]:\n if solver_type in [\"logrho\", \"bh1\", \"bh2\"]:\n self.register_to_config(solver_type=\"midpoint\")\n else:\n raise NotImplementedError(f\"{solver_type} is not implemented for {self.__class__}\")\n\n if algorithm_type not in [\"dpmsolver++\", \"sde-dpmsolver++\"] and final_sigmas_type == \"zero\":\n raise ValueError(\n f\"`final_sigmas_type` {final_sigmas_type} is not supported for `algorithm_type` {algorithm_type}. Please choose `sigma_min` instead.\"\n )\n\n # setable values\n self.num_inference_steps = None\n timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=np.float32)[::-1].copy()\n self.timesteps = torch.from_numpy(timesteps)\n self.model_outputs = [None] * solver_order\n self.lower_order_nums = 0\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n @property\n def step_index(self):\n \"\"\"\n The index counter for current timestep. It will increase 1 after each scheduler step.\n \"\"\"\n return self._step_index\n\n @property\n def begin_index(self):\n \"\"\"\n The index for the first timestep. It should be set from pipeline with `set_begin_index` method.\n \"\"\"\n return self._begin_index\n\n def set_begin_index(self, begin_index: int = 0):\n \"\"\"\n Sets the begin index for the scheduler. This function should be run from pipeline before the inference.\n\n Args:\n begin_index (`int`):\n The begin index for the scheduler.\n \"\"\"\n self._begin_index = begin_index\n\n def set_timesteps(\n self,\n num_inference_steps: int = None,\n device: Union[str, torch.device] = None,\n timesteps: Optional[List[int]] = None,\n ):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to support arbitrary timesteps schedule. If `None`, timesteps will be generated\n based on the `timestep_spacing` attribute. If `timesteps` is passed, `num_inference_steps` and `sigmas`\n must be `None`, and `timestep_spacing` attribute will be ignored.\n \"\"\"\n if num_inference_steps is None and timesteps is None:\n raise ValueError(\"Must pass exactly one of `num_inference_steps` or `timesteps`.\")\n if num_inference_steps is not None and timesteps is not None:\n raise ValueError(\"Can only pass one of `num_inference_steps` or `custom_timesteps`.\")\n if timesteps is not None and self.config.use_karras_sigmas:\n raise ValueError(\"Cannot use `timesteps` with `config.use_karras_sigmas = True`\")\n if timesteps is not None and self.config.use_lu_lambdas:\n raise ValueError(\"Cannot use `timesteps` with `config.use_lu_lambdas = True`\")\n\n if timesteps is not None:\n timesteps = np.array(timesteps).astype(np.int64)\n else:\n # Clipping the minimum of all lambda(t) for numerical stability.\n # This is critical for cosine (squaredcos_cap_v2) noise schedule.\n clipped_idx = torch.searchsorted(torch.flip(self.lambda_t, [0]), self.config.lambda_min_clipped)\n last_timestep = ((self.config.num_train_timesteps - clipped_idx).numpy()).item()\n\n # \"linspace\", \"leading\", \"trailing\" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891\n if self.config.timestep_spacing == \"linspace\":\n timesteps = (\n np.linspace(0, last_timestep - 1, num_inference_steps + 1)\n .round()[::-1][:-1]\n .copy()\n .astype(np.int64)\n )\n elif self.config.timestep_spacing == \"leading\":\n step_ratio = last_timestep // (num_inference_steps + 1)\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (\n (np.arange(0, num_inference_steps + 1) * step_ratio).round()[::-1][:-1].copy().astype(np.int64)\n )\n timesteps += self.config.steps_offset\n elif self.config.timestep_spacing == \"trailing\":\n step_ratio = self.config.num_train_timesteps / num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = np.arange(last_timestep, 0, -step_ratio).round().copy().astype(np.int64)\n timesteps -= 1\n else:\n raise ValueError(\n f\"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.\"\n )\n\n sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)\n log_sigmas = np.log(sigmas)\n\n if self.config.use_karras_sigmas:\n sigmas = np.flip(sigmas).copy()\n sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)\n timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]).round()\n elif self.config.use_lu_lambdas:\n lambdas = np.flip(log_sigmas.copy())\n lambdas = self._convert_to_lu(in_lambdas=lambdas, num_inference_steps=num_inference_steps)\n sigmas = np.exp(lambdas)\n timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]).round()\n else:\n sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)\n\n if self.config.final_sigmas_type == \"sigma_min\":\n sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0]) ** 0.5\n elif self.config.final_sigmas_type == \"zero\":\n sigma_last = 0\n else:\n raise ValueError(\n f\"`final_sigmas_type` must be one of 'zero', or 'sigma_min', but got {self.config.final_sigmas_type}\"\n )\n\n sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)\n\n self.sigmas = torch.from_numpy(sigmas)\n self.timesteps = torch.from_numpy(timesteps).to(device=device, dtype=torch.int64)\n\n self.num_inference_steps = len(timesteps)\n\n self.model_outputs = [\n None,\n ] * self.config.solver_order\n self.lower_order_nums = 0\n\n # add an index counter for schedulers that allow duplicated timesteps\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample\n def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:\n \"\"\"\n \"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the\n prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by\n s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing\n pixels from saturation at each step. We find that dynamic thresholding results in significantly better\n photorealism as well as better image-text alignment, especially when using very large guidance weights.\"\n\n https://arxiv.org/abs/2205.11487\n \"\"\"\n dtype = sample.dtype\n batch_size, channels, *remaining_dims = sample.shape\n\n if dtype not in (torch.float32, torch.float64):\n sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half\n\n # Flatten sample for doing quantile calculation along each image\n sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))\n\n abs_sample = sample.abs() # \"a certain percentile absolute pixel value\"\n\n s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)\n s = torch.clamp(\n s, min=1, max=self.config.sample_max_value\n ) # When clamped to min=1, equivalent to standard clipping to [-1, 1]\n s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0\n sample = torch.clamp(sample, -s, s) / s # \"we threshold xt0 to the range [-s, s] and then divide by s\"\n\n sample = sample.reshape(batch_size, channels, *remaining_dims)\n sample = sample.to(dtype)\n\n return sample\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t\n def _sigma_to_t(self, sigma, log_sigmas):\n # get log sigma\n log_sigma = np.log(np.maximum(sigma, 1e-10))\n\n # get distribution\n dists = log_sigma - log_sigmas[:, np.newaxis]\n\n # get sigmas range\n low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)\n high_idx = low_idx + 1\n\n low = log_sigmas[low_idx]\n high = log_sigmas[high_idx]\n\n # interpolate sigmas\n w = (low - log_sigma) / (low - high)\n w = np.clip(w, 0, 1)\n\n # transform interpolation to time range\n t = (1 - w) * low_idx + w * high_idx\n t = t.reshape(sigma.shape)\n return t\n\n def _sigma_to_alpha_sigma_t(self, sigma):\n alpha_t = 1 / ((sigma**2 + 1) ** 0.5)\n sigma_t = sigma * alpha_t\n\n return alpha_t, sigma_t\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras\n def _convert_to_karras(self, in_sigmas: torch.Tensor, num_inference_steps) -> torch.Tensor:\n \"\"\"Constructs the noise schedule of Karras et al. (2022).\"\"\"\n\n # Hack to make sure that other schedulers which copy this function don't break\n # TODO: Add this logic to the other schedulers\n if hasattr(self.config, \"sigma_min\"):\n sigma_min = self.config.sigma_min\n else:\n sigma_min = None\n\n if hasattr(self.config, \"sigma_max\"):\n sigma_max = self.config.sigma_max\n else:\n sigma_max = None\n\n sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item()\n sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item()\n\n rho = 7.0 # 7.0 is the value used in the paper\n ramp = np.linspace(0, 1, num_inference_steps)\n min_inv_rho = sigma_min ** (1 / rho)\n max_inv_rho = sigma_max ** (1 / rho)\n sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho\n return sigmas\n\n def _convert_to_lu(self, in_lambdas: torch.Tensor, num_inference_steps) -> torch.Tensor:\n \"\"\"Constructs the noise schedule of Lu et al. (2022).\"\"\"\n\n lambda_min: float = in_lambdas[-1].item()\n lambda_max: float = in_lambdas[0].item()\n\n rho = 1.0 # 1.0 is the value used in the paper\n ramp = np.linspace(0, 1, num_inference_steps)\n min_inv_rho = lambda_min ** (1 / rho)\n max_inv_rho = lambda_max ** (1 / rho)\n lambdas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho\n return lambdas\n\n def convert_model_output(\n self,\n model_output: torch.Tensor,\n *args,\n sample: torch.Tensor = None,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is\n designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to discretize an\n integral of the data prediction model.\n\n \n\n The algorithm and model type are decoupled. You can use either DPMSolver or DPMSolver++ for both noise\n prediction and data prediction models.\n\n \n\n Args:\n model_output (`torch.Tensor`):\n The direct output from the learned diffusion model.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n\n Returns:\n `torch.Tensor`:\n The converted model output.\n \"\"\"\n timestep = args[0] if len(args) > 0 else kwargs.pop(\"timestep\", None)\n if sample is None:\n if len(args) > 1:\n sample = args[1]\n else:\n raise ValueError(\"missing `sample` as a required keyward argument\")\n if timestep is not None:\n deprecate(\n \"timesteps\",\n \"1.0.0\",\n \"Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n # DPM-Solver++ needs to solve an integral of the data prediction model.\n if self.config.algorithm_type in [\"dpmsolver++\", \"sde-dpmsolver++\"]:\n if self.config.prediction_type == \"epsilon\":\n # DPM-Solver and DPM-Solver++ only need the \"mean\" output.\n if self.config.variance_type in [\"learned\", \"learned_range\"]:\n model_output = model_output[:, :3]\n sigma = self.sigmas[self.step_index]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)\n x0_pred = (sample - sigma_t * model_output) / alpha_t\n elif self.config.prediction_type == \"sample\":\n x0_pred = model_output\n elif self.config.prediction_type == \"v_prediction\":\n sigma = self.sigmas[self.step_index]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)\n x0_pred = alpha_t * sample - sigma_t * model_output\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or\"\n \" `v_prediction` for the DPMSolverMultistepScheduler.\"\n )\n\n if self.config.thresholding:\n x0_pred = self._threshold_sample(x0_pred)\n\n return x0_pred\n\n # DPM-Solver needs to solve an integral of the noise prediction model.\n elif self.config.algorithm_type in [\"dpmsolver\", \"sde-dpmsolver\"]:\n if self.config.prediction_type == \"epsilon\":\n # DPM-Solver and DPM-Solver++ only need the \"mean\" output.\n if self.config.variance_type in [\"learned\", \"learned_range\"]:\n epsilon = model_output[:, :3]\n else:\n epsilon = model_output\n elif self.config.prediction_type == \"sample\":\n sigma = self.sigmas[self.step_index]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)\n epsilon = (sample - alpha_t * model_output) / sigma_t\n elif self.config.prediction_type == \"v_prediction\":\n sigma = self.sigmas[self.step_index]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)\n epsilon = alpha_t * model_output + sigma_t * sample\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or\"\n \" `v_prediction` for the DPMSolverMultistepScheduler.\"\n )\n\n if self.config.thresholding:\n sigma = self.sigmas[self.step_index]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)\n x0_pred = (sample - sigma_t * epsilon) / alpha_t\n x0_pred = self._threshold_sample(x0_pred)\n epsilon = (sample - alpha_t * x0_pred) / sigma_t\n\n return epsilon\n\n def dpm_solver_first_order_update(\n self,\n model_output: torch.Tensor,\n *args,\n sample: torch.Tensor = None,\n noise: Optional[torch.Tensor] = None,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n One step for the first-order DPMSolver (equivalent to DDIM).\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from the learned diffusion model.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n\n Returns:\n `torch.Tensor`:\n The sample tensor at the previous timestep.\n \"\"\"\n timestep = args[0] if len(args) > 0 else kwargs.pop(\"timestep\", None)\n prev_timestep = args[1] if len(args) > 1 else kwargs.pop(\"prev_timestep\", None)\n if sample is None:\n if len(args) > 2:\n sample = args[2]\n else:\n raise ValueError(\" missing `sample` as a required keyward argument\")\n if timestep is not None:\n deprecate(\n \"timesteps\",\n \"1.0.0\",\n \"Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n if prev_timestep is not None:\n deprecate(\n \"prev_timestep\",\n \"1.0.0\",\n \"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n sigma_t, sigma_s = self.sigmas[self.step_index + 1], self.sigmas[self.step_index]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)\n alpha_s, sigma_s = self._sigma_to_alpha_sigma_t(sigma_s)\n lambda_t = torch.log(alpha_t) - torch.log(sigma_t)\n lambda_s = torch.log(alpha_s) - torch.log(sigma_s)\n\n h = lambda_t - lambda_s\n if self.config.algorithm_type == \"dpmsolver++\":\n x_t = (sigma_t / sigma_s) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * model_output\n elif self.config.algorithm_type == \"dpmsolver\":\n x_t = (alpha_t / alpha_s) * sample - (sigma_t * (torch.exp(h) - 1.0)) * model_output\n elif self.config.algorithm_type == \"sde-dpmsolver++\":\n assert noise is not None\n x_t = (\n (sigma_t / sigma_s * torch.exp(-h)) * sample\n + (alpha_t * (1 - torch.exp(-2.0 * h))) * model_output\n + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise\n )\n elif self.config.algorithm_type == \"sde-dpmsolver\":\n assert noise is not None\n x_t = (\n (alpha_t / alpha_s) * sample\n - 2.0 * (sigma_t * (torch.exp(h) - 1.0)) * model_output\n + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise\n )\n return x_t\n\n def multistep_dpm_solver_second_order_update(\n self,\n model_output_list: List[torch.Tensor],\n *args,\n sample: torch.Tensor = None,\n noise: Optional[torch.Tensor] = None,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n One step for the second-order multistep DPMSolver.\n\n Args:\n model_output_list (`List[torch.Tensor]`):\n The direct outputs from learned diffusion model at current and latter timesteps.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n\n Returns:\n `torch.Tensor`:\n The sample tensor at the previous timestep.\n \"\"\"\n timestep_list = args[0] if len(args) > 0 else kwargs.pop(\"timestep_list\", None)\n prev_timestep = args[1] if len(args) > 1 else kwargs.pop(\"prev_timestep\", None)\n if sample is None:\n if len(args) > 2:\n sample = args[2]\n else:\n raise ValueError(\" missing `sample` as a required keyward argument\")\n if timestep_list is not None:\n deprecate(\n \"timestep_list\",\n \"1.0.0\",\n \"Passing `timestep_list` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n if prev_timestep is not None:\n deprecate(\n \"prev_timestep\",\n \"1.0.0\",\n \"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n sigma_t, sigma_s0, sigma_s1 = (\n self.sigmas[self.step_index + 1],\n self.sigmas[self.step_index],\n self.sigmas[self.step_index - 1],\n )\n\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)\n alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)\n alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1)\n\n lambda_t = torch.log(alpha_t) - torch.log(sigma_t)\n lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)\n lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1)\n\n m0, m1 = model_output_list[-1], model_output_list[-2]\n\n h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1\n r0 = h_0 / h\n D0, D1 = m0, (1.0 / r0) * (m0 - m1)\n if self.config.algorithm_type == \"dpmsolver++\":\n # See https://arxiv.org/abs/2211.01095 for detailed derivations\n if self.config.solver_type == \"midpoint\":\n x_t = (\n (sigma_t / sigma_s0) * sample\n - (alpha_t * (torch.exp(-h) - 1.0)) * D0\n - 0.5 * (alpha_t * (torch.exp(-h) - 1.0)) * D1\n )\n elif self.config.solver_type == \"heun\":\n x_t = (\n (sigma_t / sigma_s0) * sample\n - (alpha_t * (torch.exp(-h) - 1.0)) * D0\n + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1\n )\n elif self.config.algorithm_type == \"dpmsolver\":\n # See https://arxiv.org/abs/2206.00927 for detailed derivations\n if self.config.solver_type == \"midpoint\":\n x_t = (\n (alpha_t / alpha_s0) * sample\n - (sigma_t * (torch.exp(h) - 1.0)) * D0\n - 0.5 * (sigma_t * (torch.exp(h) - 1.0)) * D1\n )\n elif self.config.solver_type == \"heun\":\n x_t = (\n (alpha_t / alpha_s0) * sample\n - (sigma_t * (torch.exp(h) - 1.0)) * D0\n - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1\n )\n elif self.config.algorithm_type == \"sde-dpmsolver++\":\n assert noise is not None\n if self.config.solver_type == \"midpoint\":\n x_t = (\n (sigma_t / sigma_s0 * torch.exp(-h)) * sample\n + (alpha_t * (1 - torch.exp(-2.0 * h))) * D0\n + 0.5 * (alpha_t * (1 - torch.exp(-2.0 * h))) * D1\n + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise\n )\n elif self.config.solver_type == \"heun\":\n x_t = (\n (sigma_t / sigma_s0 * torch.exp(-h)) * sample\n + (alpha_t * (1 - torch.exp(-2.0 * h))) * D0\n + (alpha_t * ((1.0 - torch.exp(-2.0 * h)) / (-2.0 * h) + 1.0)) * D1\n + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise\n )\n elif self.config.algorithm_type == \"sde-dpmsolver\":\n assert noise is not None\n if self.config.solver_type == \"midpoint\":\n x_t = (\n (alpha_t / alpha_s0) * sample\n - 2.0 * (sigma_t * (torch.exp(h) - 1.0)) * D0\n - (sigma_t * (torch.exp(h) - 1.0)) * D1\n + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise\n )\n elif self.config.solver_type == \"heun\":\n x_t = (\n (alpha_t / alpha_s0) * sample\n - 2.0 * (sigma_t * (torch.exp(h) - 1.0)) * D0\n - 2.0 * (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1\n + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise\n )\n return x_t\n\n def multistep_dpm_solver_third_order_update(\n self,\n model_output_list: List[torch.Tensor],\n *args,\n sample: torch.Tensor = None,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n One step for the third-order multistep DPMSolver.\n\n Args:\n model_output_list (`List[torch.Tensor]`):\n The direct outputs from learned diffusion model at current and latter timesteps.\n sample (`torch.Tensor`):\n A current instance of a sample created by diffusion process.\n\n Returns:\n `torch.Tensor`:\n The sample tensor at the previous timestep.\n \"\"\"\n\n timestep_list = args[0] if len(args) > 0 else kwargs.pop(\"timestep_list\", None)\n prev_timestep = args[1] if len(args) > 1 else kwargs.pop(\"prev_timestep\", None)\n if sample is None:\n if len(args) > 2:\n sample = args[2]\n else:\n raise ValueError(\" missing`sample` as a required keyward argument\")\n if timestep_list is not None:\n deprecate(\n \"timestep_list\",\n \"1.0.0\",\n \"Passing `timestep_list` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n if prev_timestep is not None:\n deprecate(\n \"prev_timestep\",\n \"1.0.0\",\n \"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n sigma_t, sigma_s0, sigma_s1, sigma_s2 = (\n self.sigmas[self.step_index + 1],\n self.sigmas[self.step_index],\n self.sigmas[self.step_index - 1],\n self.sigmas[self.step_index - 2],\n )\n\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)\n alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)\n alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1)\n alpha_s2, sigma_s2 = self._sigma_to_alpha_sigma_t(sigma_s2)\n\n lambda_t = torch.log(alpha_t) - torch.log(sigma_t)\n lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)\n lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1)\n lambda_s2 = torch.log(alpha_s2) - torch.log(sigma_s2)\n\n m0, m1, m2 = model_output_list[-1], model_output_list[-2], model_output_list[-3]\n\n h, h_0, h_1 = lambda_t - lambda_s0, lambda_s0 - lambda_s1, lambda_s1 - lambda_s2\n r0, r1 = h_0 / h, h_1 / h\n D0 = m0\n D1_0, D1_1 = (1.0 / r0) * (m0 - m1), (1.0 / r1) * (m1 - m2)\n D1 = D1_0 + (r0 / (r0 + r1)) * (D1_0 - D1_1)\n D2 = (1.0 / (r0 + r1)) * (D1_0 - D1_1)\n if self.config.algorithm_type == \"dpmsolver++\":\n # See https://arxiv.org/abs/2206.00927 for detailed derivations\n x_t = (\n (sigma_t / sigma_s0) * sample\n - (alpha_t * (torch.exp(-h) - 1.0)) * D0\n + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1\n - (alpha_t * ((torch.exp(-h) - 1.0 + h) / h**2 - 0.5)) * D2\n )\n elif self.config.algorithm_type == \"dpmsolver\":\n # See https://arxiv.org/abs/2206.00927 for detailed derivations\n x_t = (\n (alpha_t / alpha_s0) * sample\n - (sigma_t * (torch.exp(h) - 1.0)) * D0\n - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1\n - (sigma_t * ((torch.exp(h) - 1.0 - h) / h**2 - 0.5)) * D2\n )\n return x_t\n\n def index_for_timestep(self, timestep, schedule_timesteps=None):\n if schedule_timesteps is None:\n schedule_timesteps = self.timesteps\n\n index_candidates = (schedule_timesteps == timestep).nonzero()\n\n if len(index_candidates) == 0:\n step_index = len(self.timesteps) - 1\n # The sigma index that is taken for the **very** first `step`\n # is always the second index (or the last index if there is only 1)\n # This way we can ensure we don't accidentally skip a sigma in\n # case we start in the middle of the denoising schedule (e.g. for image-to-image)\n elif len(index_candidates) > 1:\n step_index = index_candidates[1].item()\n else:\n step_index = index_candidates[0].item()\n\n return step_index\n\n def _init_step_index(self, timestep):\n \"\"\"\n Initialize the step_index counter for the scheduler.\n \"\"\"\n\n if self.begin_index is None:\n if isinstance(timestep, torch.Tensor):\n timestep = timestep.to(self.timesteps.device)\n self._step_index = self.index_for_timestep(timestep)\n else:\n self._step_index = self._begin_index\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n generator=None,\n variance_noise: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ) -> Union[SchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with\n the multistep DPMSolver.\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`int`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n generator (`torch.Generator`, *optional*):\n A random number generator.\n variance_noise (`torch.Tensor`):\n Alternative to generating noise with `generator` by directly providing the noise for the variance\n itself. Useful for methods such as [`LEdits++`].\n return_dict (`bool`):\n Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`.\n\n Returns:\n [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n\n \"\"\"\n if self.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n if self.step_index is None:\n self._init_step_index(timestep)\n\n # Improve numerical stability for small number of steps\n lower_order_final = (self.step_index == len(self.timesteps) - 1) and (\n self.config.euler_at_final\n or (self.config.lower_order_final and len(self.timesteps) < 15)\n or self.config.final_sigmas_type == \"zero\"\n )\n lower_order_second = (\n (self.step_index == len(self.timesteps) - 2) and self.config.lower_order_final and len(self.timesteps) < 15\n )\n\n model_output = self.convert_model_output(model_output, sample=sample)\n for i in range(self.config.solver_order - 1):\n self.model_outputs[i] = self.model_outputs[i + 1]\n self.model_outputs[-1] = model_output\n\n # Upcast to avoid precision issues when computing prev_sample\n sample = sample.to(torch.float32)\n if self.config.algorithm_type in [\"sde-dpmsolver\", \"sde-dpmsolver++\"] and variance_noise is None:\n noise = randn_tensor(\n model_output.shape, generator=generator, device=model_output.device, dtype=torch.float32\n )\n elif self.config.algorithm_type in [\"sde-dpmsolver\", \"sde-dpmsolver++\"]:\n noise = variance_noise.to(device=model_output.device, dtype=torch.float32)\n else:\n noise = None\n\n if self.config.solver_order == 1 or self.lower_order_nums < 1 or lower_order_final:\n prev_sample = self.dpm_solver_first_order_update(model_output, sample=sample, noise=noise)\n elif self.config.solver_order == 2 or self.lower_order_nums < 2 or lower_order_second:\n prev_sample = self.multistep_dpm_solver_second_order_update(self.model_outputs, sample=sample, noise=noise)\n else:\n prev_sample = self.multistep_dpm_solver_third_order_update(self.model_outputs, sample=sample)\n\n if self.lower_order_nums < self.config.solver_order:\n self.lower_order_nums += 1\n\n # Cast sample back to expected dtype\n prev_sample = prev_sample.to(model_output.dtype)\n\n # upon completion increase step index by one\n self._step_index += 1\n\n if not return_dict:\n return (prev_sample,)\n\n return SchedulerOutput(prev_sample=prev_sample)\n\n def scale_model_input(self, sample: torch.Tensor, *args, **kwargs) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n return sample\n\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.IntTensor,\n ) -> torch.Tensor:\n # Make sure sigmas and timesteps have the same device and dtype as original_samples\n sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)\n if original_samples.device.type == \"mps\" and torch.is_floating_point(timesteps):\n # mps does not support float64\n schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)\n timesteps = timesteps.to(original_samples.device, dtype=torch.float32)\n else:\n schedule_timesteps = self.timesteps.to(original_samples.device)\n timesteps = timesteps.to(original_samples.device)\n\n # begin_index is None when the scheduler is used for training or pipeline does not implement set_begin_index\n if self.begin_index is None:\n step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps]\n elif self.step_index is not None:\n # add_noise is called after first denoising step (for inpainting)\n step_indices = [self.step_index] * timesteps.shape[0]\n else:\n # add noise is called before first denoising step to create initial latent(img2img)\n step_indices = [self.begin_index] * timesteps.shape[0]\n\n sigma = sigmas[step_indices].flatten()\n while len(sigma.shape) < len(original_samples.shape):\n sigma = sigma.unsqueeze(-1)\n\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)\n noisy_samples = alpha_t * original_samples + sigma_t * noise\n return noisy_samples\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_flax.py", "content": "# Copyright 2024 TSAIL Team and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This file is strongly influenced by https://github.com/LuChengTHU/dpm-solver\n\nfrom dataclasses import dataclass\nfrom typing import List, Optional, Tuple, Union\n\nimport flax\nimport jax\nimport jax.numpy as jnp\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom .scheduling_utils_flax import (\n CommonSchedulerState,\n FlaxKarrasDiffusionSchedulers,\n FlaxSchedulerMixin,\n FlaxSchedulerOutput,\n add_noise_common,\n)\n\n\n@flax.struct.dataclass\nclass DPMSolverMultistepSchedulerState:\n common: CommonSchedulerState\n alpha_t: jnp.ndarray\n sigma_t: jnp.ndarray\n lambda_t: jnp.ndarray\n\n # setable values\n init_noise_sigma: jnp.ndarray\n timesteps: jnp.ndarray\n num_inference_steps: Optional[int] = None\n\n # running values\n model_outputs: Optional[jnp.ndarray] = None\n lower_order_nums: Optional[jnp.int32] = None\n prev_timestep: Optional[jnp.int32] = None\n cur_sample: Optional[jnp.ndarray] = None\n\n @classmethod\n def create(\n cls,\n common: CommonSchedulerState,\n alpha_t: jnp.ndarray,\n sigma_t: jnp.ndarray,\n lambda_t: jnp.ndarray,\n init_noise_sigma: jnp.ndarray,\n timesteps: jnp.ndarray,\n ):\n return cls(\n common=common,\n alpha_t=alpha_t,\n sigma_t=sigma_t,\n lambda_t=lambda_t,\n init_noise_sigma=init_noise_sigma,\n timesteps=timesteps,\n )\n\n\n@dataclass\nclass FlaxDPMSolverMultistepSchedulerOutput(FlaxSchedulerOutput):\n state: DPMSolverMultistepSchedulerState\n\n\nclass FlaxDPMSolverMultistepScheduler(FlaxSchedulerMixin, ConfigMixin):\n \"\"\"\n DPM-Solver (and the improved version DPM-Solver++) is a fast dedicated high-order solver for diffusion ODEs with\n the convergence order guarantee. Empirically, sampling by DPM-Solver with only 20 steps can generate high-quality\n samples, and it can generate quite good samples even in only 10 steps.\n\n For more details, see the original paper: https://arxiv.org/abs/2206.00927 and https://arxiv.org/abs/2211.01095\n\n Currently, we support the multistep DPM-Solver for both noise prediction models and data prediction models. We\n recommend to use `solver_order=2` for guided sampling, and `solver_order=3` for unconditional sampling.\n\n We also support the \"dynamic thresholding\" method in Imagen (https://arxiv.org/abs/2205.11487). For pixel-space\n diffusion models, you can set both `algorithm_type=\"dpmsolver++\"` and `thresholding=True` to use the dynamic\n thresholding. Note that the thresholding method is unsuitable for latent-space diffusion models (such as\n stable-diffusion).\n\n [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`\n function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.\n [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and\n [`~SchedulerMixin.from_pretrained`] functions.\n\n For more details, see the original paper: https://arxiv.org/abs/2206.00927 and https://arxiv.org/abs/2211.01095\n\n Args:\n num_train_timesteps (`int`): number of diffusion steps used to train the model.\n beta_start (`float`): the starting `beta` value of inference.\n beta_end (`float`): the final `beta` value.\n beta_schedule (`str`):\n the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear`, `scaled_linear`, or `squaredcos_cap_v2`.\n trained_betas (`np.ndarray`, optional):\n option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.\n solver_order (`int`, default `2`):\n the order of DPM-Solver; can be `1` or `2` or `3`. We recommend to use `solver_order=2` for guided\n sampling, and `solver_order=3` for unconditional sampling.\n prediction_type (`str`, default `epsilon`):\n indicates whether the model predicts the noise (epsilon), or the data / `x0`. One of `epsilon`, `sample`,\n or `v-prediction`.\n thresholding (`bool`, default `False`):\n whether to use the \"dynamic thresholding\" method (introduced by Imagen, https://arxiv.org/abs/2205.11487).\n For pixel-space diffusion models, you can set both `algorithm_type=dpmsolver++` and `thresholding=True` to\n use the dynamic thresholding. Note that the thresholding method is unsuitable for latent-space diffusion\n models (such as stable-diffusion).\n dynamic_thresholding_ratio (`float`, default `0.995`):\n the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen\n (https://arxiv.org/abs/2205.11487).\n sample_max_value (`float`, default `1.0`):\n the threshold value for dynamic thresholding. Valid only when `thresholding=True` and\n `algorithm_type=\"dpmsolver++`.\n algorithm_type (`str`, default `dpmsolver++`):\n the algorithm type for the solver. Either `dpmsolver` or `dpmsolver++`. The `dpmsolver` type implements the\n algorithms in https://arxiv.org/abs/2206.00927, and the `dpmsolver++` type implements the algorithms in\n https://arxiv.org/abs/2211.01095. We recommend to use `dpmsolver++` with `solver_order=2` for guided\n sampling (e.g. stable-diffusion).\n solver_type (`str`, default `midpoint`):\n the solver type for the second-order solver. Either `midpoint` or `heun`. The solver type slightly affects\n the sample quality, especially for small number of steps. We empirically find that `midpoint` solvers are\n slightly better, so we recommend to use the `midpoint` type.\n lower_order_final (`bool`, default `True`):\n whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. We empirically\n find this trick can stabilize the sampling of DPM-Solver for steps < 15, especially for steps <= 10.\n timestep_spacing (`str`, defaults to `\"linspace\"`):\n The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.\n dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):\n the `dtype` used for params and computation.\n \"\"\"\n\n _compatibles = [e.name for e in FlaxKarrasDiffusionSchedulers]\n\n dtype: jnp.dtype\n\n @property\n def has_state(self):\n return True\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[jnp.ndarray] = None,\n solver_order: int = 2,\n prediction_type: str = \"epsilon\",\n thresholding: bool = False,\n dynamic_thresholding_ratio: float = 0.995,\n sample_max_value: float = 1.0,\n algorithm_type: str = \"dpmsolver++\",\n solver_type: str = \"midpoint\",\n lower_order_final: bool = True,\n timestep_spacing: str = \"linspace\",\n dtype: jnp.dtype = jnp.float32,\n ):\n self.dtype = dtype\n\n def create_state(self, common: Optional[CommonSchedulerState] = None) -> DPMSolverMultistepSchedulerState:\n if common is None:\n common = CommonSchedulerState.create(self)\n\n # Currently we only support VP-type noise schedule\n alpha_t = jnp.sqrt(common.alphas_cumprod)\n sigma_t = jnp.sqrt(1 - common.alphas_cumprod)\n lambda_t = jnp.log(alpha_t) - jnp.log(sigma_t)\n\n # settings for DPM-Solver\n if self.config.algorithm_type not in [\"dpmsolver\", \"dpmsolver++\"]:\n raise NotImplementedError(f\"{self.config.algorithm_type} is not implemented for {self.__class__}\")\n if self.config.solver_type not in [\"midpoint\", \"heun\"]:\n raise NotImplementedError(f\"{self.config.solver_type} is not implemented for {self.__class__}\")\n\n # standard deviation of the initial noise distribution\n init_noise_sigma = jnp.array(1.0, dtype=self.dtype)\n\n timesteps = jnp.arange(0, self.config.num_train_timesteps).round()[::-1]\n\n return DPMSolverMultistepSchedulerState.create(\n common=common,\n alpha_t=alpha_t,\n sigma_t=sigma_t,\n lambda_t=lambda_t,\n init_noise_sigma=init_noise_sigma,\n timesteps=timesteps,\n )\n\n def set_timesteps(\n self, state: DPMSolverMultistepSchedulerState, num_inference_steps: int, shape: Tuple\n ) -> DPMSolverMultistepSchedulerState:\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.\n\n Args:\n state (`DPMSolverMultistepSchedulerState`):\n the `FlaxDPMSolverMultistepScheduler` state data class instance.\n num_inference_steps (`int`):\n the number of diffusion steps used when generating samples with a pre-trained model.\n shape (`Tuple`):\n the shape of the samples to be generated.\n \"\"\"\n last_timestep = self.config.num_train_timesteps\n if self.config.timestep_spacing == \"linspace\":\n timesteps = (\n jnp.linspace(0, last_timestep - 1, num_inference_steps + 1).round()[::-1][:-1].astype(jnp.int32)\n )\n elif self.config.timestep_spacing == \"leading\":\n step_ratio = last_timestep // (num_inference_steps + 1)\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (\n (jnp.arange(0, num_inference_steps + 1) * step_ratio).round()[::-1][:-1].copy().astype(jnp.int32)\n )\n timesteps += self.config.steps_offset\n elif self.config.timestep_spacing == \"trailing\":\n step_ratio = self.config.num_train_timesteps / num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = jnp.arange(last_timestep, 0, -step_ratio).round().copy().astype(jnp.int32)\n timesteps -= 1\n else:\n raise ValueError(\n f\"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.\"\n )\n\n # initial running values\n\n model_outputs = jnp.zeros((self.config.solver_order,) + shape, dtype=self.dtype)\n lower_order_nums = jnp.int32(0)\n prev_timestep = jnp.int32(-1)\n cur_sample = jnp.zeros(shape, dtype=self.dtype)\n\n return state.replace(\n num_inference_steps=num_inference_steps,\n timesteps=timesteps,\n model_outputs=model_outputs,\n lower_order_nums=lower_order_nums,\n prev_timestep=prev_timestep,\n cur_sample=cur_sample,\n )\n\n def convert_model_output(\n self,\n state: DPMSolverMultistepSchedulerState,\n model_output: jnp.ndarray,\n timestep: int,\n sample: jnp.ndarray,\n ) -> jnp.ndarray:\n \"\"\"\n Convert the model output to the corresponding type that the algorithm (DPM-Solver / DPM-Solver++) needs.\n\n DPM-Solver is designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to\n discretize an integral of the data prediction model. So we need to first convert the model output to the\n corresponding type to match the algorithm.\n\n Note that the algorithm type and the model type is decoupled. That is to say, we can use either DPM-Solver or\n DPM-Solver++ for both noise prediction model and data prediction model.\n\n Args:\n model_output (`jnp.ndarray`): direct output from learned diffusion model.\n timestep (`int`): current discrete timestep in the diffusion chain.\n sample (`jnp.ndarray`):\n current instance of sample being created by diffusion process.\n\n Returns:\n `jnp.ndarray`: the converted model output.\n \"\"\"\n # DPM-Solver++ needs to solve an integral of the data prediction model.\n if self.config.algorithm_type == \"dpmsolver++\":\n if self.config.prediction_type == \"epsilon\":\n alpha_t, sigma_t = state.alpha_t[timestep], state.sigma_t[timestep]\n x0_pred = (sample - sigma_t * model_output) / alpha_t\n elif self.config.prediction_type == \"sample\":\n x0_pred = model_output\n elif self.config.prediction_type == \"v_prediction\":\n alpha_t, sigma_t = state.alpha_t[timestep], state.sigma_t[timestep]\n x0_pred = alpha_t * sample - sigma_t * model_output\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, \"\n \" or `v_prediction` for the FlaxDPMSolverMultistepScheduler.\"\n )\n\n if self.config.thresholding:\n # Dynamic thresholding in https://arxiv.org/abs/2205.11487\n dynamic_max_val = jnp.percentile(\n jnp.abs(x0_pred), self.config.dynamic_thresholding_ratio, axis=tuple(range(1, x0_pred.ndim))\n )\n dynamic_max_val = jnp.maximum(\n dynamic_max_val, self.config.sample_max_value * jnp.ones_like(dynamic_max_val)\n )\n x0_pred = jnp.clip(x0_pred, -dynamic_max_val, dynamic_max_val) / dynamic_max_val\n return x0_pred\n # DPM-Solver needs to solve an integral of the noise prediction model.\n elif self.config.algorithm_type == \"dpmsolver\":\n if self.config.prediction_type == \"epsilon\":\n return model_output\n elif self.config.prediction_type == \"sample\":\n alpha_t, sigma_t = state.alpha_t[timestep], state.sigma_t[timestep]\n epsilon = (sample - alpha_t * model_output) / sigma_t\n return epsilon\n elif self.config.prediction_type == \"v_prediction\":\n alpha_t, sigma_t = state.alpha_t[timestep], state.sigma_t[timestep]\n epsilon = alpha_t * model_output + sigma_t * sample\n return epsilon\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, \"\n \" or `v_prediction` for the FlaxDPMSolverMultistepScheduler.\"\n )\n\n def dpm_solver_first_order_update(\n self,\n state: DPMSolverMultistepSchedulerState,\n model_output: jnp.ndarray,\n timestep: int,\n prev_timestep: int,\n sample: jnp.ndarray,\n ) -> jnp.ndarray:\n \"\"\"\n One step for the first-order DPM-Solver (equivalent to DDIM).\n\n See https://arxiv.org/abs/2206.00927 for the detailed derivation.\n\n Args:\n model_output (`jnp.ndarray`): direct output from learned diffusion model.\n timestep (`int`): current discrete timestep in the diffusion chain.\n prev_timestep (`int`): previous discrete timestep in the diffusion chain.\n sample (`jnp.ndarray`):\n current instance of sample being created by diffusion process.\n\n Returns:\n `jnp.ndarray`: the sample tensor at the previous timestep.\n \"\"\"\n t, s0 = prev_timestep, timestep\n m0 = model_output\n lambda_t, lambda_s = state.lambda_t[t], state.lambda_t[s0]\n alpha_t, alpha_s = state.alpha_t[t], state.alpha_t[s0]\n sigma_t, sigma_s = state.sigma_t[t], state.sigma_t[s0]\n h = lambda_t - lambda_s\n if self.config.algorithm_type == \"dpmsolver++\":\n x_t = (sigma_t / sigma_s) * sample - (alpha_t * (jnp.exp(-h) - 1.0)) * m0\n elif self.config.algorithm_type == \"dpmsolver\":\n x_t = (alpha_t / alpha_s) * sample - (sigma_t * (jnp.exp(h) - 1.0)) * m0\n return x_t\n\n def multistep_dpm_solver_second_order_update(\n self,\n state: DPMSolverMultistepSchedulerState,\n model_output_list: jnp.ndarray,\n timestep_list: List[int],\n prev_timestep: int,\n sample: jnp.ndarray,\n ) -> jnp.ndarray:\n \"\"\"\n One step for the second-order multistep DPM-Solver.\n\n Args:\n model_output_list (`List[jnp.ndarray]`):\n direct outputs from learned diffusion model at current and latter timesteps.\n timestep (`int`): current and latter discrete timestep in the diffusion chain.\n prev_timestep (`int`): previous discrete timestep in the diffusion chain.\n sample (`jnp.ndarray`):\n current instance of sample being created by diffusion process.\n\n Returns:\n `jnp.ndarray`: the sample tensor at the previous timestep.\n \"\"\"\n t, s0, s1 = prev_timestep, timestep_list[-1], timestep_list[-2]\n m0, m1 = model_output_list[-1], model_output_list[-2]\n lambda_t, lambda_s0, lambda_s1 = state.lambda_t[t], state.lambda_t[s0], state.lambda_t[s1]\n alpha_t, alpha_s0 = state.alpha_t[t], state.alpha_t[s0]\n sigma_t, sigma_s0 = state.sigma_t[t], state.sigma_t[s0]\n h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1\n r0 = h_0 / h\n D0, D1 = m0, (1.0 / r0) * (m0 - m1)\n if self.config.algorithm_type == \"dpmsolver++\":\n # See https://arxiv.org/abs/2211.01095 for detailed derivations\n if self.config.solver_type == \"midpoint\":\n x_t = (\n (sigma_t / sigma_s0) * sample\n - (alpha_t * (jnp.exp(-h) - 1.0)) * D0\n - 0.5 * (alpha_t * (jnp.exp(-h) - 1.0)) * D1\n )\n elif self.config.solver_type == \"heun\":\n x_t = (\n (sigma_t / sigma_s0) * sample\n - (alpha_t * (jnp.exp(-h) - 1.0)) * D0\n + (alpha_t * ((jnp.exp(-h) - 1.0) / h + 1.0)) * D1\n )\n elif self.config.algorithm_type == \"dpmsolver\":\n # See https://arxiv.org/abs/2206.00927 for detailed derivations\n if self.config.solver_type == \"midpoint\":\n x_t = (\n (alpha_t / alpha_s0) * sample\n - (sigma_t * (jnp.exp(h) - 1.0)) * D0\n - 0.5 * (sigma_t * (jnp.exp(h) - 1.0)) * D1\n )\n elif self.config.solver_type == \"heun\":\n x_t = (\n (alpha_t / alpha_s0) * sample\n - (sigma_t * (jnp.exp(h) - 1.0)) * D0\n - (sigma_t * ((jnp.exp(h) - 1.0) / h - 1.0)) * D1\n )\n return x_t\n\n def multistep_dpm_solver_third_order_update(\n self,\n state: DPMSolverMultistepSchedulerState,\n model_output_list: jnp.ndarray,\n timestep_list: List[int],\n prev_timestep: int,\n sample: jnp.ndarray,\n ) -> jnp.ndarray:\n \"\"\"\n One step for the third-order multistep DPM-Solver.\n\n Args:\n model_output_list (`List[jnp.ndarray]`):\n direct outputs from learned diffusion model at current and latter timesteps.\n timestep (`int`): current and latter discrete timestep in the diffusion chain.\n prev_timestep (`int`): previous discrete timestep in the diffusion chain.\n sample (`jnp.ndarray`):\n current instance of sample being created by diffusion process.\n\n Returns:\n `jnp.ndarray`: the sample tensor at the previous timestep.\n \"\"\"\n t, s0, s1, s2 = prev_timestep, timestep_list[-1], timestep_list[-2], timestep_list[-3]\n m0, m1, m2 = model_output_list[-1], model_output_list[-2], model_output_list[-3]\n lambda_t, lambda_s0, lambda_s1, lambda_s2 = (\n state.lambda_t[t],\n state.lambda_t[s0],\n state.lambda_t[s1],\n state.lambda_t[s2],\n )\n alpha_t, alpha_s0 = state.alpha_t[t], state.alpha_t[s0]\n sigma_t, sigma_s0 = state.sigma_t[t], state.sigma_t[s0]\n h, h_0, h_1 = lambda_t - lambda_s0, lambda_s0 - lambda_s1, lambda_s1 - lambda_s2\n r0, r1 = h_0 / h, h_1 / h\n D0 = m0\n D1_0, D1_1 = (1.0 / r0) * (m0 - m1), (1.0 / r1) * (m1 - m2)\n D1 = D1_0 + (r0 / (r0 + r1)) * (D1_0 - D1_1)\n D2 = (1.0 / (r0 + r1)) * (D1_0 - D1_1)\n if self.config.algorithm_type == \"dpmsolver++\":\n # See https://arxiv.org/abs/2206.00927 for detailed derivations\n x_t = (\n (sigma_t / sigma_s0) * sample\n - (alpha_t * (jnp.exp(-h) - 1.0)) * D0\n + (alpha_t * ((jnp.exp(-h) - 1.0) / h + 1.0)) * D1\n - (alpha_t * ((jnp.exp(-h) - 1.0 + h) / h**2 - 0.5)) * D2\n )\n elif self.config.algorithm_type == \"dpmsolver\":\n # See https://arxiv.org/abs/2206.00927 for detailed derivations\n x_t = (\n (alpha_t / alpha_s0) * sample\n - (sigma_t * (jnp.exp(h) - 1.0)) * D0\n - (sigma_t * ((jnp.exp(h) - 1.0) / h - 1.0)) * D1\n - (sigma_t * ((jnp.exp(h) - 1.0 - h) / h**2 - 0.5)) * D2\n )\n return x_t\n\n def step(\n self,\n state: DPMSolverMultistepSchedulerState,\n model_output: jnp.ndarray,\n timestep: int,\n sample: jnp.ndarray,\n return_dict: bool = True,\n ) -> Union[FlaxDPMSolverMultistepSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample at the previous timestep by DPM-Solver. Core function to propagate the diffusion process\n from the learned model outputs (most often the predicted noise).\n\n Args:\n state (`DPMSolverMultistepSchedulerState`):\n the `FlaxDPMSolverMultistepScheduler` state data class instance.\n model_output (`jnp.ndarray`): direct output from learned diffusion model.\n timestep (`int`): current discrete timestep in the diffusion chain.\n sample (`jnp.ndarray`):\n current instance of sample being created by diffusion process.\n return_dict (`bool`): option for returning tuple rather than FlaxDPMSolverMultistepSchedulerOutput class\n\n Returns:\n [`FlaxDPMSolverMultistepSchedulerOutput`] or `tuple`: [`FlaxDPMSolverMultistepSchedulerOutput`] if\n `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.\n\n \"\"\"\n if state.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n (step_index,) = jnp.where(state.timesteps == timestep, size=1)\n step_index = step_index[0]\n\n prev_timestep = jax.lax.select(step_index == len(state.timesteps) - 1, 0, state.timesteps[step_index + 1])\n\n model_output = self.convert_model_output(state, model_output, timestep, sample)\n\n model_outputs_new = jnp.roll(state.model_outputs, -1, axis=0)\n model_outputs_new = model_outputs_new.at[-1].set(model_output)\n state = state.replace(\n model_outputs=model_outputs_new,\n prev_timestep=prev_timestep,\n cur_sample=sample,\n )\n\n def step_1(state: DPMSolverMultistepSchedulerState) -> jnp.ndarray:\n return self.dpm_solver_first_order_update(\n state,\n state.model_outputs[-1],\n state.timesteps[step_index],\n state.prev_timestep,\n state.cur_sample,\n )\n\n def step_23(state: DPMSolverMultistepSchedulerState) -> jnp.ndarray:\n def step_2(state: DPMSolverMultistepSchedulerState) -> jnp.ndarray:\n timestep_list = jnp.array([state.timesteps[step_index - 1], state.timesteps[step_index]])\n return self.multistep_dpm_solver_second_order_update(\n state,\n state.model_outputs,\n timestep_list,\n state.prev_timestep,\n state.cur_sample,\n )\n\n def step_3(state: DPMSolverMultistepSchedulerState) -> jnp.ndarray:\n timestep_list = jnp.array(\n [\n state.timesteps[step_index - 2],\n state.timesteps[step_index - 1],\n state.timesteps[step_index],\n ]\n )\n return self.multistep_dpm_solver_third_order_update(\n state,\n state.model_outputs,\n timestep_list,\n state.prev_timestep,\n state.cur_sample,\n )\n\n step_2_output = step_2(state)\n step_3_output = step_3(state)\n\n if self.config.solver_order == 2:\n return step_2_output\n elif self.config.lower_order_final and len(state.timesteps) < 15:\n return jax.lax.select(\n state.lower_order_nums < 2,\n step_2_output,\n jax.lax.select(\n step_index == len(state.timesteps) - 2,\n step_2_output,\n step_3_output,\n ),\n )\n else:\n return jax.lax.select(\n state.lower_order_nums < 2,\n step_2_output,\n step_3_output,\n )\n\n step_1_output = step_1(state)\n step_23_output = step_23(state)\n\n if self.config.solver_order == 1:\n prev_sample = step_1_output\n\n elif self.config.lower_order_final and len(state.timesteps) < 15:\n prev_sample = jax.lax.select(\n state.lower_order_nums < 1,\n step_1_output,\n jax.lax.select(\n step_index == len(state.timesteps) - 1,\n step_1_output,\n step_23_output,\n ),\n )\n\n else:\n prev_sample = jax.lax.select(\n state.lower_order_nums < 1,\n step_1_output,\n step_23_output,\n )\n\n state = state.replace(\n lower_order_nums=jnp.minimum(state.lower_order_nums + 1, self.config.solver_order),\n )\n\n if not return_dict:\n return (prev_sample, state)\n\n return FlaxDPMSolverMultistepSchedulerOutput(prev_sample=prev_sample, state=state)\n\n def scale_model_input(\n self, state: DPMSolverMultistepSchedulerState, sample: jnp.ndarray, timestep: Optional[int] = None\n ) -> jnp.ndarray:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n state (`DPMSolverMultistepSchedulerState`):\n the `FlaxDPMSolverMultistepScheduler` state data class instance.\n sample (`jnp.ndarray`): input sample\n timestep (`int`, optional): current timestep\n\n Returns:\n `jnp.ndarray`: scaled input sample\n \"\"\"\n return sample\n\n def add_noise(\n self,\n state: DPMSolverMultistepSchedulerState,\n original_samples: jnp.ndarray,\n noise: jnp.ndarray,\n timesteps: jnp.ndarray,\n ) -> jnp.ndarray:\n return add_noise_common(state.common, original_samples, noise, timesteps)\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py", "content": "# Copyright 2024 TSAIL Team and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This file is strongly influenced by https://github.com/LuChengTHU/dpm-solver\n\nimport math\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import deprecate\nfrom ..utils.torch_utils import randn_tensor\nfrom .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\nclass DPMSolverMultistepInverseScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n `DPMSolverMultistepInverseScheduler` is the reverse scheduler of [`DPMSolverMultistepScheduler`].\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n beta_start (`float`, defaults to 0.0001):\n The starting `beta` value of inference.\n beta_end (`float`, defaults to 0.02):\n The final `beta` value.\n beta_schedule (`str`, defaults to `\"linear\"`):\n The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear`, `scaled_linear`, or `squaredcos_cap_v2`.\n trained_betas (`np.ndarray`, *optional*):\n Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.\n solver_order (`int`, defaults to 2):\n The DPMSolver order which can be `1` or `2` or `3`. It is recommended to use `solver_order=2` for guided\n sampling, and `solver_order=3` for unconditional sampling.\n prediction_type (`str`, defaults to `epsilon`, *optional*):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),\n `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen\n Video](https://imagen.research.google/video/paper.pdf) paper).\n thresholding (`bool`, defaults to `False`):\n Whether to use the \"dynamic thresholding\" method. This is unsuitable for latent-space diffusion models such\n as Stable Diffusion.\n dynamic_thresholding_ratio (`float`, defaults to 0.995):\n The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.\n sample_max_value (`float`, defaults to 1.0):\n The threshold value for dynamic thresholding. Valid only when `thresholding=True` and\n `algorithm_type=\"dpmsolver++\"`.\n algorithm_type (`str`, defaults to `dpmsolver++`):\n Algorithm type for the solver; can be `dpmsolver`, `dpmsolver++`, `sde-dpmsolver` or `sde-dpmsolver++`. The\n `dpmsolver` type implements the algorithms in the [DPMSolver](https://huggingface.co/papers/2206.00927)\n paper, and the `dpmsolver++` type implements the algorithms in the\n [DPMSolver++](https://huggingface.co/papers/2211.01095) paper. It is recommended to use `dpmsolver++` or\n `sde-dpmsolver++` with `solver_order=2` for guided sampling like in Stable Diffusion.\n solver_type (`str`, defaults to `midpoint`):\n Solver type for the second-order solver; can be `midpoint` or `heun`. The solver type slightly affects the\n sample quality, especially for a small number of steps. It is recommended to use `midpoint` solvers.\n lower_order_final (`bool`, defaults to `True`):\n Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can\n stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10.\n euler_at_final (`bool`, defaults to `False`):\n Whether to use Euler's method in the final step. It is a trade-off between numerical stability and detail\n richness. This can stabilize the sampling of the SDE variant of DPMSolver for small number of inference\n steps, but sometimes may result in blurring.\n use_karras_sigmas (`bool`, *optional*, defaults to `False`):\n Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`,\n the sigmas are determined according to a sequence of noise levels {σi}.\n lambda_min_clipped (`float`, defaults to `-inf`):\n Clipping threshold for the minimum value of `lambda(t)` for numerical stability. This is critical for the\n cosine (`squaredcos_cap_v2`) noise schedule.\n variance_type (`str`, *optional*):\n Set to \"learned\" or \"learned_range\" for diffusion models that predict variance. If set, the model's output\n contains the predicted Gaussian variance.\n timestep_spacing (`str`, defaults to `\"linspace\"`):\n The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.\n steps_offset (`int`, defaults to 0):\n An offset added to the inference steps, as required by some model families.\n \"\"\"\n\n _compatibles = [e.name for e in KarrasDiffusionSchedulers]\n order = 1\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[Union[np.ndarray, List[float]]] = None,\n solver_order: int = 2,\n prediction_type: str = \"epsilon\",\n thresholding: bool = False,\n dynamic_thresholding_ratio: float = 0.995,\n sample_max_value: float = 1.0,\n algorithm_type: str = \"dpmsolver++\",\n solver_type: str = \"midpoint\",\n lower_order_final: bool = True,\n euler_at_final: bool = False,\n use_karras_sigmas: Optional[bool] = False,\n lambda_min_clipped: float = -float(\"inf\"),\n variance_type: Optional[str] = None,\n timestep_spacing: str = \"linspace\",\n steps_offset: int = 0,\n ):\n if algorithm_type in [\"dpmsolver\", \"sde-dpmsolver\"]:\n deprecation_message = f\"algorithm_type {algorithm_type} is deprecated and will be removed in a future version. Choose from `dpmsolver++` or `sde-dpmsolver++` instead\"\n deprecate(\"algorithm_types dpmsolver and sde-dpmsolver\", \"1.0.0\", deprecation_message)\n\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n # Currently we only support VP-type noise schedule\n self.alpha_t = torch.sqrt(self.alphas_cumprod)\n self.sigma_t = torch.sqrt(1 - self.alphas_cumprod)\n self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t)\n self.sigmas = ((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5\n\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = 1.0\n\n # settings for DPM-Solver\n if algorithm_type not in [\"dpmsolver\", \"dpmsolver++\", \"sde-dpmsolver\", \"sde-dpmsolver++\"]:\n if algorithm_type == \"deis\":\n self.register_to_config(algorithm_type=\"dpmsolver++\")\n else:\n raise NotImplementedError(f\"{algorithm_type} is not implemented for {self.__class__}\")\n\n if solver_type not in [\"midpoint\", \"heun\"]:\n if solver_type in [\"logrho\", \"bh1\", \"bh2\"]:\n self.register_to_config(solver_type=\"midpoint\")\n else:\n raise NotImplementedError(f\"{solver_type} is not implemented for {self.__class__}\")\n\n # setable values\n self.num_inference_steps = None\n timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=np.float32).copy()\n self.timesteps = torch.from_numpy(timesteps)\n self.model_outputs = [None] * solver_order\n self.lower_order_nums = 0\n self._step_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n self.use_karras_sigmas = use_karras_sigmas\n\n @property\n def step_index(self):\n \"\"\"\n The index counter for current timestep. It will increase 1 after each scheduler step.\n \"\"\"\n return self._step_index\n\n def set_timesteps(self, num_inference_steps: int = None, device: Union[str, torch.device] = None):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n \"\"\"\n # Clipping the minimum of all lambda(t) for numerical stability.\n # This is critical for cosine (squaredcos_cap_v2) noise schedule.\n clipped_idx = torch.searchsorted(torch.flip(self.lambda_t, [0]), self.config.lambda_min_clipped).item()\n self.noisiest_timestep = self.config.num_train_timesteps - 1 - clipped_idx\n\n # \"linspace\", \"leading\", \"trailing\" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891\n if self.config.timestep_spacing == \"linspace\":\n timesteps = (\n np.linspace(0, self.noisiest_timestep, num_inference_steps + 1).round()[:-1].copy().astype(np.int64)\n )\n elif self.config.timestep_spacing == \"leading\":\n step_ratio = (self.noisiest_timestep + 1) // (num_inference_steps + 1)\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (np.arange(0, num_inference_steps + 1) * step_ratio).round()[:-1].copy().astype(np.int64)\n timesteps += self.config.steps_offset\n elif self.config.timestep_spacing == \"trailing\":\n step_ratio = self.config.num_train_timesteps / num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = np.arange(self.noisiest_timestep + 1, 0, -step_ratio).round()[::-1].copy().astype(np.int64)\n timesteps -= 1\n else:\n raise ValueError(\n f\"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', \"\n \"'leading' or 'trailing'.\"\n )\n\n sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)\n log_sigmas = np.log(sigmas)\n\n if self.config.use_karras_sigmas:\n sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)\n timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]).round()\n timesteps = timesteps.copy().astype(np.int64)\n sigmas = np.concatenate([sigmas, sigmas[-1:]]).astype(np.float32)\n else:\n sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)\n sigma_max = (\n (1 - self.alphas_cumprod[self.noisiest_timestep]) / self.alphas_cumprod[self.noisiest_timestep]\n ) ** 0.5\n sigmas = np.concatenate([sigmas, [sigma_max]]).astype(np.float32)\n\n self.sigmas = torch.from_numpy(sigmas)\n\n # when num_inference_steps == num_train_timesteps, we can end up with\n # duplicates in timesteps.\n _, unique_indices = np.unique(timesteps, return_index=True)\n timesteps = timesteps[np.sort(unique_indices)]\n\n self.timesteps = torch.from_numpy(timesteps).to(device=device, dtype=torch.int64)\n\n self.num_inference_steps = len(timesteps)\n\n self.model_outputs = [\n None,\n ] * self.config.solver_order\n self.lower_order_nums = 0\n\n # add an index counter for schedulers that allow duplicated timesteps\n self._step_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample\n def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:\n \"\"\"\n \"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the\n prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by\n s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing\n pixels from saturation at each step. We find that dynamic thresholding results in significantly better\n photorealism as well as better image-text alignment, especially when using very large guidance weights.\"\n\n https://arxiv.org/abs/2205.11487\n \"\"\"\n dtype = sample.dtype\n batch_size, channels, *remaining_dims = sample.shape\n\n if dtype not in (torch.float32, torch.float64):\n sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half\n\n # Flatten sample for doing quantile calculation along each image\n sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))\n\n abs_sample = sample.abs() # \"a certain percentile absolute pixel value\"\n\n s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)\n s = torch.clamp(\n s, min=1, max=self.config.sample_max_value\n ) # When clamped to min=1, equivalent to standard clipping to [-1, 1]\n s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0\n sample = torch.clamp(sample, -s, s) / s # \"we threshold xt0 to the range [-s, s] and then divide by s\"\n\n sample = sample.reshape(batch_size, channels, *remaining_dims)\n sample = sample.to(dtype)\n\n return sample\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t\n def _sigma_to_t(self, sigma, log_sigmas):\n # get log sigma\n log_sigma = np.log(np.maximum(sigma, 1e-10))\n\n # get distribution\n dists = log_sigma - log_sigmas[:, np.newaxis]\n\n # get sigmas range\n low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)\n high_idx = low_idx + 1\n\n low = log_sigmas[low_idx]\n high = log_sigmas[high_idx]\n\n # interpolate sigmas\n w = (low - log_sigma) / (low - high)\n w = np.clip(w, 0, 1)\n\n # transform interpolation to time range\n t = (1 - w) * low_idx + w * high_idx\n t = t.reshape(sigma.shape)\n return t\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._sigma_to_alpha_sigma_t\n def _sigma_to_alpha_sigma_t(self, sigma):\n alpha_t = 1 / ((sigma**2 + 1) ** 0.5)\n sigma_t = sigma * alpha_t\n\n return alpha_t, sigma_t\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras\n def _convert_to_karras(self, in_sigmas: torch.Tensor, num_inference_steps) -> torch.Tensor:\n \"\"\"Constructs the noise schedule of Karras et al. (2022).\"\"\"\n\n # Hack to make sure that other schedulers which copy this function don't break\n # TODO: Add this logic to the other schedulers\n if hasattr(self.config, \"sigma_min\"):\n sigma_min = self.config.sigma_min\n else:\n sigma_min = None\n\n if hasattr(self.config, \"sigma_max\"):\n sigma_max = self.config.sigma_max\n else:\n sigma_max = None\n\n sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item()\n sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item()\n\n rho = 7.0 # 7.0 is the value used in the paper\n ramp = np.linspace(0, 1, num_inference_steps)\n min_inv_rho = sigma_min ** (1 / rho)\n max_inv_rho = sigma_max ** (1 / rho)\n sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho\n return sigmas\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.convert_model_output\n def convert_model_output(\n self,\n model_output: torch.Tensor,\n *args,\n sample: torch.Tensor = None,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is\n designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to discretize an\n integral of the data prediction model.\n\n \n\n The algorithm and model type are decoupled. You can use either DPMSolver or DPMSolver++ for both noise\n prediction and data prediction models.\n\n \n\n Args:\n model_output (`torch.Tensor`):\n The direct output from the learned diffusion model.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n\n Returns:\n `torch.Tensor`:\n The converted model output.\n \"\"\"\n timestep = args[0] if len(args) > 0 else kwargs.pop(\"timestep\", None)\n if sample is None:\n if len(args) > 1:\n sample = args[1]\n else:\n raise ValueError(\"missing `sample` as a required keyward argument\")\n if timestep is not None:\n deprecate(\n \"timesteps\",\n \"1.0.0\",\n \"Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n # DPM-Solver++ needs to solve an integral of the data prediction model.\n if self.config.algorithm_type in [\"dpmsolver++\", \"sde-dpmsolver++\"]:\n if self.config.prediction_type == \"epsilon\":\n # DPM-Solver and DPM-Solver++ only need the \"mean\" output.\n if self.config.variance_type in [\"learned\", \"learned_range\"]:\n model_output = model_output[:, :3]\n sigma = self.sigmas[self.step_index]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)\n x0_pred = (sample - sigma_t * model_output) / alpha_t\n elif self.config.prediction_type == \"sample\":\n x0_pred = model_output\n elif self.config.prediction_type == \"v_prediction\":\n sigma = self.sigmas[self.step_index]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)\n x0_pred = alpha_t * sample - sigma_t * model_output\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or\"\n \" `v_prediction` for the DPMSolverMultistepScheduler.\"\n )\n\n if self.config.thresholding:\n x0_pred = self._threshold_sample(x0_pred)\n\n return x0_pred\n\n # DPM-Solver needs to solve an integral of the noise prediction model.\n elif self.config.algorithm_type in [\"dpmsolver\", \"sde-dpmsolver\"]:\n if self.config.prediction_type == \"epsilon\":\n # DPM-Solver and DPM-Solver++ only need the \"mean\" output.\n if self.config.variance_type in [\"learned\", \"learned_range\"]:\n epsilon = model_output[:, :3]\n else:\n epsilon = model_output\n elif self.config.prediction_type == \"sample\":\n sigma = self.sigmas[self.step_index]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)\n epsilon = (sample - alpha_t * model_output) / sigma_t\n elif self.config.prediction_type == \"v_prediction\":\n sigma = self.sigmas[self.step_index]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)\n epsilon = alpha_t * model_output + sigma_t * sample\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or\"\n \" `v_prediction` for the DPMSolverMultistepScheduler.\"\n )\n\n if self.config.thresholding:\n sigma = self.sigmas[self.step_index]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)\n x0_pred = (sample - sigma_t * epsilon) / alpha_t\n x0_pred = self._threshold_sample(x0_pred)\n epsilon = (sample - alpha_t * x0_pred) / sigma_t\n\n return epsilon\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.dpm_solver_first_order_update\n def dpm_solver_first_order_update(\n self,\n model_output: torch.Tensor,\n *args,\n sample: torch.Tensor = None,\n noise: Optional[torch.Tensor] = None,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n One step for the first-order DPMSolver (equivalent to DDIM).\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from the learned diffusion model.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n\n Returns:\n `torch.Tensor`:\n The sample tensor at the previous timestep.\n \"\"\"\n timestep = args[0] if len(args) > 0 else kwargs.pop(\"timestep\", None)\n prev_timestep = args[1] if len(args) > 1 else kwargs.pop(\"prev_timestep\", None)\n if sample is None:\n if len(args) > 2:\n sample = args[2]\n else:\n raise ValueError(\" missing `sample` as a required keyward argument\")\n if timestep is not None:\n deprecate(\n \"timesteps\",\n \"1.0.0\",\n \"Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n if prev_timestep is not None:\n deprecate(\n \"prev_timestep\",\n \"1.0.0\",\n \"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n sigma_t, sigma_s = self.sigmas[self.step_index + 1], self.sigmas[self.step_index]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)\n alpha_s, sigma_s = self._sigma_to_alpha_sigma_t(sigma_s)\n lambda_t = torch.log(alpha_t) - torch.log(sigma_t)\n lambda_s = torch.log(alpha_s) - torch.log(sigma_s)\n\n h = lambda_t - lambda_s\n if self.config.algorithm_type == \"dpmsolver++\":\n x_t = (sigma_t / sigma_s) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * model_output\n elif self.config.algorithm_type == \"dpmsolver\":\n x_t = (alpha_t / alpha_s) * sample - (sigma_t * (torch.exp(h) - 1.0)) * model_output\n elif self.config.algorithm_type == \"sde-dpmsolver++\":\n assert noise is not None\n x_t = (\n (sigma_t / sigma_s * torch.exp(-h)) * sample\n + (alpha_t * (1 - torch.exp(-2.0 * h))) * model_output\n + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise\n )\n elif self.config.algorithm_type == \"sde-dpmsolver\":\n assert noise is not None\n x_t = (\n (alpha_t / alpha_s) * sample\n - 2.0 * (sigma_t * (torch.exp(h) - 1.0)) * model_output\n + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise\n )\n return x_t\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.multistep_dpm_solver_second_order_update\n def multistep_dpm_solver_second_order_update(\n self,\n model_output_list: List[torch.Tensor],\n *args,\n sample: torch.Tensor = None,\n noise: Optional[torch.Tensor] = None,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n One step for the second-order multistep DPMSolver.\n\n Args:\n model_output_list (`List[torch.Tensor]`):\n The direct outputs from learned diffusion model at current and latter timesteps.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n\n Returns:\n `torch.Tensor`:\n The sample tensor at the previous timestep.\n \"\"\"\n timestep_list = args[0] if len(args) > 0 else kwargs.pop(\"timestep_list\", None)\n prev_timestep = args[1] if len(args) > 1 else kwargs.pop(\"prev_timestep\", None)\n if sample is None:\n if len(args) > 2:\n sample = args[2]\n else:\n raise ValueError(\" missing `sample` as a required keyward argument\")\n if timestep_list is not None:\n deprecate(\n \"timestep_list\",\n \"1.0.0\",\n \"Passing `timestep_list` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n if prev_timestep is not None:\n deprecate(\n \"prev_timestep\",\n \"1.0.0\",\n \"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n sigma_t, sigma_s0, sigma_s1 = (\n self.sigmas[self.step_index + 1],\n self.sigmas[self.step_index],\n self.sigmas[self.step_index - 1],\n )\n\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)\n alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)\n alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1)\n\n lambda_t = torch.log(alpha_t) - torch.log(sigma_t)\n lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)\n lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1)\n\n m0, m1 = model_output_list[-1], model_output_list[-2]\n\n h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1\n r0 = h_0 / h\n D0, D1 = m0, (1.0 / r0) * (m0 - m1)\n if self.config.algorithm_type == \"dpmsolver++\":\n # See https://arxiv.org/abs/2211.01095 for detailed derivations\n if self.config.solver_type == \"midpoint\":\n x_t = (\n (sigma_t / sigma_s0) * sample\n - (alpha_t * (torch.exp(-h) - 1.0)) * D0\n - 0.5 * (alpha_t * (torch.exp(-h) - 1.0)) * D1\n )\n elif self.config.solver_type == \"heun\":\n x_t = (\n (sigma_t / sigma_s0) * sample\n - (alpha_t * (torch.exp(-h) - 1.0)) * D0\n + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1\n )\n elif self.config.algorithm_type == \"dpmsolver\":\n # See https://arxiv.org/abs/2206.00927 for detailed derivations\n if self.config.solver_type == \"midpoint\":\n x_t = (\n (alpha_t / alpha_s0) * sample\n - (sigma_t * (torch.exp(h) - 1.0)) * D0\n - 0.5 * (sigma_t * (torch.exp(h) - 1.0)) * D1\n )\n elif self.config.solver_type == \"heun\":\n x_t = (\n (alpha_t / alpha_s0) * sample\n - (sigma_t * (torch.exp(h) - 1.0)) * D0\n - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1\n )\n elif self.config.algorithm_type == \"sde-dpmsolver++\":\n assert noise is not None\n if self.config.solver_type == \"midpoint\":\n x_t = (\n (sigma_t / sigma_s0 * torch.exp(-h)) * sample\n + (alpha_t * (1 - torch.exp(-2.0 * h))) * D0\n + 0.5 * (alpha_t * (1 - torch.exp(-2.0 * h))) * D1\n + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise\n )\n elif self.config.solver_type == \"heun\":\n x_t = (\n (sigma_t / sigma_s0 * torch.exp(-h)) * sample\n + (alpha_t * (1 - torch.exp(-2.0 * h))) * D0\n + (alpha_t * ((1.0 - torch.exp(-2.0 * h)) / (-2.0 * h) + 1.0)) * D1\n + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise\n )\n elif self.config.algorithm_type == \"sde-dpmsolver\":\n assert noise is not None\n if self.config.solver_type == \"midpoint\":\n x_t = (\n (alpha_t / alpha_s0) * sample\n - 2.0 * (sigma_t * (torch.exp(h) - 1.0)) * D0\n - (sigma_t * (torch.exp(h) - 1.0)) * D1\n + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise\n )\n elif self.config.solver_type == \"heun\":\n x_t = (\n (alpha_t / alpha_s0) * sample\n - 2.0 * (sigma_t * (torch.exp(h) - 1.0)) * D0\n - 2.0 * (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1\n + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise\n )\n return x_t\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.multistep_dpm_solver_third_order_update\n def multistep_dpm_solver_third_order_update(\n self,\n model_output_list: List[torch.Tensor],\n *args,\n sample: torch.Tensor = None,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n One step for the third-order multistep DPMSolver.\n\n Args:\n model_output_list (`List[torch.Tensor]`):\n The direct outputs from learned diffusion model at current and latter timesteps.\n sample (`torch.Tensor`):\n A current instance of a sample created by diffusion process.\n\n Returns:\n `torch.Tensor`:\n The sample tensor at the previous timestep.\n \"\"\"\n\n timestep_list = args[0] if len(args) > 0 else kwargs.pop(\"timestep_list\", None)\n prev_timestep = args[1] if len(args) > 1 else kwargs.pop(\"prev_timestep\", None)\n if sample is None:\n if len(args) > 2:\n sample = args[2]\n else:\n raise ValueError(\" missing`sample` as a required keyward argument\")\n if timestep_list is not None:\n deprecate(\n \"timestep_list\",\n \"1.0.0\",\n \"Passing `timestep_list` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n if prev_timestep is not None:\n deprecate(\n \"prev_timestep\",\n \"1.0.0\",\n \"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n sigma_t, sigma_s0, sigma_s1, sigma_s2 = (\n self.sigmas[self.step_index + 1],\n self.sigmas[self.step_index],\n self.sigmas[self.step_index - 1],\n self.sigmas[self.step_index - 2],\n )\n\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)\n alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)\n alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1)\n alpha_s2, sigma_s2 = self._sigma_to_alpha_sigma_t(sigma_s2)\n\n lambda_t = torch.log(alpha_t) - torch.log(sigma_t)\n lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)\n lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1)\n lambda_s2 = torch.log(alpha_s2) - torch.log(sigma_s2)\n\n m0, m1, m2 = model_output_list[-1], model_output_list[-2], model_output_list[-3]\n\n h, h_0, h_1 = lambda_t - lambda_s0, lambda_s0 - lambda_s1, lambda_s1 - lambda_s2\n r0, r1 = h_0 / h, h_1 / h\n D0 = m0\n D1_0, D1_1 = (1.0 / r0) * (m0 - m1), (1.0 / r1) * (m1 - m2)\n D1 = D1_0 + (r0 / (r0 + r1)) * (D1_0 - D1_1)\n D2 = (1.0 / (r0 + r1)) * (D1_0 - D1_1)\n if self.config.algorithm_type == \"dpmsolver++\":\n # See https://arxiv.org/abs/2206.00927 for detailed derivations\n x_t = (\n (sigma_t / sigma_s0) * sample\n - (alpha_t * (torch.exp(-h) - 1.0)) * D0\n + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1\n - (alpha_t * ((torch.exp(-h) - 1.0 + h) / h**2 - 0.5)) * D2\n )\n elif self.config.algorithm_type == \"dpmsolver\":\n # See https://arxiv.org/abs/2206.00927 for detailed derivations\n x_t = (\n (alpha_t / alpha_s0) * sample\n - (sigma_t * (torch.exp(h) - 1.0)) * D0\n - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1\n - (sigma_t * ((torch.exp(h) - 1.0 - h) / h**2 - 0.5)) * D2\n )\n return x_t\n\n def _init_step_index(self, timestep):\n if isinstance(timestep, torch.Tensor):\n timestep = timestep.to(self.timesteps.device)\n\n index_candidates = (self.timesteps == timestep).nonzero()\n\n if len(index_candidates) == 0:\n step_index = len(self.timesteps) - 1\n # The sigma index that is taken for the **very** first `step`\n # is always the second index (or the last index if there is only 1)\n # This way we can ensure we don't accidentally skip a sigma in\n # case we start in the middle of the denoising schedule (e.g. for image-to-image)\n elif len(index_candidates) > 1:\n step_index = index_candidates[1].item()\n else:\n step_index = index_candidates[0].item()\n\n self._step_index = step_index\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n generator=None,\n variance_noise: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ) -> Union[SchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with\n the multistep DPMSolver.\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`int`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n generator (`torch.Generator`, *optional*):\n A random number generator.\n variance_noise (`torch.Tensor`):\n Alternative to generating noise with `generator` by directly providing the noise for the variance\n itself. Useful for methods such as [`CycleDiffusion`].\n return_dict (`bool`):\n Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`.\n\n Returns:\n [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n\n \"\"\"\n if self.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n if self.step_index is None:\n self._init_step_index(timestep)\n\n # Improve numerical stability for small number of steps\n lower_order_final = (self.step_index == len(self.timesteps) - 1) and (\n self.config.euler_at_final or (self.config.lower_order_final and len(self.timesteps) < 15)\n )\n lower_order_second = (\n (self.step_index == len(self.timesteps) - 2) and self.config.lower_order_final and len(self.timesteps) < 15\n )\n\n model_output = self.convert_model_output(model_output, sample=sample)\n for i in range(self.config.solver_order - 1):\n self.model_outputs[i] = self.model_outputs[i + 1]\n self.model_outputs[-1] = model_output\n\n if self.config.algorithm_type in [\"sde-dpmsolver\", \"sde-dpmsolver++\"] and variance_noise is None:\n noise = randn_tensor(\n model_output.shape, generator=generator, device=model_output.device, dtype=model_output.dtype\n )\n elif self.config.algorithm_type in [\"sde-dpmsolver\", \"sde-dpmsolver++\"]:\n noise = variance_noise\n else:\n noise = None\n\n if self.config.solver_order == 1 or self.lower_order_nums < 1 or lower_order_final:\n prev_sample = self.dpm_solver_first_order_update(model_output, sample=sample, noise=noise)\n elif self.config.solver_order == 2 or self.lower_order_nums < 2 or lower_order_second:\n prev_sample = self.multistep_dpm_solver_second_order_update(self.model_outputs, sample=sample, noise=noise)\n else:\n prev_sample = self.multistep_dpm_solver_third_order_update(self.model_outputs, sample=sample)\n\n if self.lower_order_nums < self.config.solver_order:\n self.lower_order_nums += 1\n\n # upon completion increase step index by one\n self._step_index += 1\n\n if not return_dict:\n return (prev_sample,)\n\n return SchedulerOutput(prev_sample=prev_sample)\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.scale_model_input\n def scale_model_input(self, sample: torch.Tensor, *args, **kwargs) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n return sample\n\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.IntTensor,\n ) -> torch.Tensor:\n # Make sure sigmas and timesteps have the same device and dtype as original_samples\n sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)\n if original_samples.device.type == \"mps\" and torch.is_floating_point(timesteps):\n # mps does not support float64\n schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)\n timesteps = timesteps.to(original_samples.device, dtype=torch.float32)\n else:\n schedule_timesteps = self.timesteps.to(original_samples.device)\n timesteps = timesteps.to(original_samples.device)\n\n step_indices = []\n for timestep in timesteps:\n index_candidates = (schedule_timesteps == timestep).nonzero()\n if len(index_candidates) == 0:\n step_index = len(schedule_timesteps) - 1\n elif len(index_candidates) > 1:\n step_index = index_candidates[1].item()\n else:\n step_index = index_candidates[0].item()\n step_indices.append(step_index)\n\n sigma = sigmas[step_indices].flatten()\n while len(sigma.shape) < len(original_samples.shape):\n sigma = sigma.unsqueeze(-1)\n\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)\n noisy_samples = alpha_t * original_samples + sigma_t * noise\n return noisy_samples\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_dpmsolver_sde.py", "content": "# Copyright 2024 Katherine Crowson, The HuggingFace Team and hlky. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport math\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torchsde\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput\n\n\nclass BatchedBrownianTree:\n \"\"\"A wrapper around torchsde.BrownianTree that enables batches of entropy.\"\"\"\n\n def __init__(self, x, t0, t1, seed=None, **kwargs):\n t0, t1, self.sign = self.sort(t0, t1)\n w0 = kwargs.get(\"w0\", torch.zeros_like(x))\n if seed is None:\n seed = torch.randint(0, 2**63 - 1, []).item()\n self.batched = True\n try:\n assert len(seed) == x.shape[0]\n w0 = w0[0]\n except TypeError:\n seed = [seed]\n self.batched = False\n self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed]\n\n @staticmethod\n def sort(a, b):\n return (a, b, 1) if a < b else (b, a, -1)\n\n def __call__(self, t0, t1):\n t0, t1, sign = self.sort(t0, t1)\n w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)\n return w if self.batched else w[0]\n\n\nclass BrownianTreeNoiseSampler:\n \"\"\"A noise sampler backed by a torchsde.BrownianTree.\n\n Args:\n x (Tensor): The tensor whose shape, device and dtype to use to generate\n random samples.\n sigma_min (float): The low end of the valid interval.\n sigma_max (float): The high end of the valid interval.\n seed (int or List[int]): The random seed. If a list of seeds is\n supplied instead of a single integer, then the noise sampler will use one BrownianTree per batch item, each\n with its own seed.\n transform (callable): A function that maps sigma to the sampler's\n internal timestep.\n \"\"\"\n\n def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x):\n self.transform = transform\n t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max))\n self.tree = BatchedBrownianTree(x, t0, t1, seed)\n\n def __call__(self, sigma, sigma_next):\n t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next))\n return self.tree(t0, t1) / (t1 - t0).abs().sqrt()\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\nclass DPMSolverSDEScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n DPMSolverSDEScheduler implements the stochastic sampler from the [Elucidating the Design Space of Diffusion-Based\n Generative Models](https://huggingface.co/papers/2206.00364) paper.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n beta_start (`float`, defaults to 0.00085):\n The starting `beta` value of inference.\n beta_end (`float`, defaults to 0.012):\n The final `beta` value.\n beta_schedule (`str`, defaults to `\"linear\"`):\n The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear` or `scaled_linear`.\n trained_betas (`np.ndarray`, *optional*):\n Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.\n prediction_type (`str`, defaults to `epsilon`, *optional*):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),\n `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen\n Video](https://imagen.research.google/video/paper.pdf) paper).\n use_karras_sigmas (`bool`, *optional*, defaults to `False`):\n Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`,\n the sigmas are determined according to a sequence of noise levels {σi}.\n noise_sampler_seed (`int`, *optional*, defaults to `None`):\n The random seed to use for the noise sampler. If `None`, a random seed is generated.\n timestep_spacing (`str`, defaults to `\"linspace\"`):\n The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.\n steps_offset (`int`, defaults to 0):\n An offset added to the inference steps, as required by some model families.\n \"\"\"\n\n _compatibles = [e.name for e in KarrasDiffusionSchedulers]\n order = 2\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.00085, # sensible defaults\n beta_end: float = 0.012,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[Union[np.ndarray, List[float]]] = None,\n prediction_type: str = \"epsilon\",\n use_karras_sigmas: Optional[bool] = False,\n noise_sampler_seed: Optional[int] = None,\n timestep_spacing: str = \"linspace\",\n steps_offset: int = 0,\n ):\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n\n # set all values\n self.set_timesteps(num_train_timesteps, None, num_train_timesteps)\n self.use_karras_sigmas = use_karras_sigmas\n self.noise_sampler = None\n self.noise_sampler_seed = noise_sampler_seed\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.index_for_timestep\n def index_for_timestep(self, timestep, schedule_timesteps=None):\n if schedule_timesteps is None:\n schedule_timesteps = self.timesteps\n\n indices = (schedule_timesteps == timestep).nonzero()\n\n # The sigma index that is taken for the **very** first `step`\n # is always the second index (or the last index if there is only 1)\n # This way we can ensure we don't accidentally skip a sigma in\n # case we start in the middle of the denoising schedule (e.g. for image-to-image)\n pos = 1 if len(indices) > 1 else 0\n\n return indices[pos].item()\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index\n def _init_step_index(self, timestep):\n if self.begin_index is None:\n if isinstance(timestep, torch.Tensor):\n timestep = timestep.to(self.timesteps.device)\n self._step_index = self.index_for_timestep(timestep)\n else:\n self._step_index = self._begin_index\n\n @property\n def init_noise_sigma(self):\n # standard deviation of the initial noise distribution\n if self.config.timestep_spacing in [\"linspace\", \"trailing\"]:\n return self.sigmas.max()\n\n return (self.sigmas.max() ** 2 + 1) ** 0.5\n\n @property\n def step_index(self):\n \"\"\"\n The index counter for current timestep. It will increase 1 after each scheduler step.\n \"\"\"\n return self._step_index\n\n @property\n def begin_index(self):\n \"\"\"\n The index for the first timestep. It should be set from pipeline with `set_begin_index` method.\n \"\"\"\n return self._begin_index\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index\n def set_begin_index(self, begin_index: int = 0):\n \"\"\"\n Sets the begin index for the scheduler. This function should be run from pipeline before the inference.\n\n Args:\n begin_index (`int`):\n The begin index for the scheduler.\n \"\"\"\n self._begin_index = begin_index\n\n def scale_model_input(\n self,\n sample: torch.Tensor,\n timestep: Union[float, torch.Tensor],\n ) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`int`, *optional*):\n The current timestep in the diffusion chain.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n if self.step_index is None:\n self._init_step_index(timestep)\n\n sigma = self.sigmas[self.step_index]\n sigma_input = sigma if self.state_in_first_order else self.mid_point_sigma\n sample = sample / ((sigma_input**2 + 1) ** 0.5)\n return sample\n\n def set_timesteps(\n self,\n num_inference_steps: int,\n device: Union[str, torch.device] = None,\n num_train_timesteps: Optional[int] = None,\n ):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n \"\"\"\n self.num_inference_steps = num_inference_steps\n\n num_train_timesteps = num_train_timesteps or self.config.num_train_timesteps\n\n # \"linspace\", \"leading\", \"trailing\" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891\n if self.config.timestep_spacing == \"linspace\":\n timesteps = np.linspace(0, num_train_timesteps - 1, num_inference_steps, dtype=float)[::-1].copy()\n elif self.config.timestep_spacing == \"leading\":\n step_ratio = num_train_timesteps // self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(float)\n timesteps += self.config.steps_offset\n elif self.config.timestep_spacing == \"trailing\":\n step_ratio = num_train_timesteps / self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (np.arange(num_train_timesteps, 0, -step_ratio)).round().copy().astype(float)\n timesteps -= 1\n else:\n raise ValueError(\n f\"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.\"\n )\n\n sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)\n log_sigmas = np.log(sigmas)\n sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)\n\n if self.config.use_karras_sigmas:\n sigmas = self._convert_to_karras(in_sigmas=sigmas)\n timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas])\n\n second_order_timesteps = self._second_order_timesteps(sigmas, log_sigmas)\n\n sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32)\n sigmas = torch.from_numpy(sigmas).to(device=device)\n self.sigmas = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2), sigmas[-1:]])\n\n timesteps = torch.from_numpy(timesteps)\n second_order_timesteps = torch.from_numpy(second_order_timesteps)\n timesteps = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2)])\n timesteps[1::2] = second_order_timesteps\n\n if str(device).startswith(\"mps\"):\n # mps does not support float64\n self.timesteps = timesteps.to(device, dtype=torch.float32)\n else:\n self.timesteps = timesteps.to(device=device)\n\n # empty first order variables\n self.sample = None\n self.mid_point_sigma = None\n\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n self.noise_sampler = None\n\n def _second_order_timesteps(self, sigmas, log_sigmas):\n def sigma_fn(_t):\n return np.exp(-_t)\n\n def t_fn(_sigma):\n return -np.log(_sigma)\n\n midpoint_ratio = 0.5\n t = t_fn(sigmas)\n delta_time = np.diff(t)\n t_proposed = t[:-1] + delta_time * midpoint_ratio\n sig_proposed = sigma_fn(t_proposed)\n timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sig_proposed])\n return timesteps\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t\n def _sigma_to_t(self, sigma, log_sigmas):\n # get log sigma\n log_sigma = np.log(np.maximum(sigma, 1e-10))\n\n # get distribution\n dists = log_sigma - log_sigmas[:, np.newaxis]\n\n # get sigmas range\n low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)\n high_idx = low_idx + 1\n\n low = log_sigmas[low_idx]\n high = log_sigmas[high_idx]\n\n # interpolate sigmas\n w = (low - log_sigma) / (low - high)\n w = np.clip(w, 0, 1)\n\n # transform interpolation to time range\n t = (1 - w) * low_idx + w * high_idx\n t = t.reshape(sigma.shape)\n return t\n\n # copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras\n def _convert_to_karras(self, in_sigmas: torch.Tensor) -> torch.Tensor:\n \"\"\"Constructs the noise schedule of Karras et al. (2022).\"\"\"\n\n sigma_min: float = in_sigmas[-1].item()\n sigma_max: float = in_sigmas[0].item()\n\n rho = 7.0 # 7.0 is the value used in the paper\n ramp = np.linspace(0, 1, self.num_inference_steps)\n min_inv_rho = sigma_min ** (1 / rho)\n max_inv_rho = sigma_max ** (1 / rho)\n sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho\n return sigmas\n\n @property\n def state_in_first_order(self):\n return self.sample is None\n\n def step(\n self,\n model_output: Union[torch.Tensor, np.ndarray],\n timestep: Union[float, torch.Tensor],\n sample: Union[torch.Tensor, np.ndarray],\n return_dict: bool = True,\n s_noise: float = 1.0,\n ) -> Union[SchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor` or `np.ndarray`):\n The direct output from learned diffusion model.\n timestep (`float` or `torch.Tensor`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor` or `np.ndarray`):\n A current instance of a sample created by the diffusion process.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or tuple.\n s_noise (`float`, *optional*, defaults to 1.0):\n Scaling factor for noise added to the sample.\n\n Returns:\n [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n \"\"\"\n if self.step_index is None:\n self._init_step_index(timestep)\n\n # Create a noise sampler if it hasn't been created yet\n if self.noise_sampler is None:\n min_sigma, max_sigma = self.sigmas[self.sigmas > 0].min(), self.sigmas.max()\n self.noise_sampler = BrownianTreeNoiseSampler(sample, min_sigma, max_sigma, self.noise_sampler_seed)\n\n # Define functions to compute sigma and t from each other\n def sigma_fn(_t: torch.Tensor) -> torch.Tensor:\n return _t.neg().exp()\n\n def t_fn(_sigma: torch.Tensor) -> torch.Tensor:\n return _sigma.log().neg()\n\n if self.state_in_first_order:\n sigma = self.sigmas[self.step_index]\n sigma_next = self.sigmas[self.step_index + 1]\n else:\n # 2nd order\n sigma = self.sigmas[self.step_index - 1]\n sigma_next = self.sigmas[self.step_index]\n\n # Set the midpoint and step size for the current step\n midpoint_ratio = 0.5\n t, t_next = t_fn(sigma), t_fn(sigma_next)\n delta_time = t_next - t\n t_proposed = t + delta_time * midpoint_ratio\n\n # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise\n if self.config.prediction_type == \"epsilon\":\n sigma_input = sigma if self.state_in_first_order else sigma_fn(t_proposed)\n pred_original_sample = sample - sigma_input * model_output\n elif self.config.prediction_type == \"v_prediction\":\n sigma_input = sigma if self.state_in_first_order else sigma_fn(t_proposed)\n pred_original_sample = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (\n sample / (sigma_input**2 + 1)\n )\n elif self.config.prediction_type == \"sample\":\n raise NotImplementedError(\"prediction_type not implemented yet: sample\")\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`\"\n )\n\n if sigma_next == 0:\n derivative = (sample - pred_original_sample) / sigma\n dt = sigma_next - sigma\n prev_sample = sample + derivative * dt\n else:\n if self.state_in_first_order:\n t_next = t_proposed\n else:\n sample = self.sample\n\n sigma_from = sigma_fn(t)\n sigma_to = sigma_fn(t_next)\n sigma_up = min(sigma_to, (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5)\n sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5\n ancestral_t = t_fn(sigma_down)\n prev_sample = (sigma_fn(ancestral_t) / sigma_fn(t)) * sample - (\n t - ancestral_t\n ).expm1() * pred_original_sample\n prev_sample = prev_sample + self.noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * sigma_up\n\n if self.state_in_first_order:\n # store for 2nd order step\n self.sample = sample\n self.mid_point_sigma = sigma_fn(t_next)\n else:\n # free for \"first order mode\"\n self.sample = None\n self.mid_point_sigma = None\n\n # upon completion increase step index by one\n self._step_index += 1\n\n if not return_dict:\n return (prev_sample,)\n\n return SchedulerOutput(prev_sample=prev_sample)\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.add_noise\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.Tensor,\n ) -> torch.Tensor:\n # Make sure sigmas and timesteps have the same device and dtype as original_samples\n sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)\n if original_samples.device.type == \"mps\" and torch.is_floating_point(timesteps):\n # mps does not support float64\n schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)\n timesteps = timesteps.to(original_samples.device, dtype=torch.float32)\n else:\n schedule_timesteps = self.timesteps.to(original_samples.device)\n timesteps = timesteps.to(original_samples.device)\n\n # self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index\n if self.begin_index is None:\n step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps]\n elif self.step_index is not None:\n # add_noise is called after first denoising step (for inpainting)\n step_indices = [self.step_index] * timesteps.shape[0]\n else:\n # add noise is called before first denoising step to create initial latent(img2img)\n step_indices = [self.begin_index] * timesteps.shape[0]\n\n sigma = sigmas[step_indices].flatten()\n while len(sigma.shape) < len(original_samples.shape):\n sigma = sigma.unsqueeze(-1)\n\n noisy_samples = original_samples + noise * sigma\n return noisy_samples\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py", "content": "# Copyright 2024 TSAIL Team and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This file is strongly influenced by https://github.com/LuChengTHU/dpm-solver\n\nimport math\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import deprecate, logging\nfrom .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\nclass DPMSolverSinglestepScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n `DPMSolverSinglestepScheduler` is a fast dedicated high-order solver for diffusion ODEs.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n beta_start (`float`, defaults to 0.0001):\n The starting `beta` value of inference.\n beta_end (`float`, defaults to 0.02):\n The final `beta` value.\n beta_schedule (`str`, defaults to `\"linear\"`):\n The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear`, `scaled_linear`, or `squaredcos_cap_v2`.\n trained_betas (`np.ndarray`, *optional*):\n Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.\n solver_order (`int`, defaults to 2):\n The DPMSolver order which can be `1` or `2` or `3`. It is recommended to use `solver_order=2` for guided\n sampling, and `solver_order=3` for unconditional sampling.\n prediction_type (`str`, defaults to `epsilon`, *optional*):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),\n `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen\n Video](https://imagen.research.google/video/paper.pdf) paper).\n thresholding (`bool`, defaults to `False`):\n Whether to use the \"dynamic thresholding\" method. This is unsuitable for latent-space diffusion models such\n as Stable Diffusion.\n dynamic_thresholding_ratio (`float`, defaults to 0.995):\n The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.\n sample_max_value (`float`, defaults to 1.0):\n The threshold value for dynamic thresholding. Valid only when `thresholding=True` and\n `algorithm_type=\"dpmsolver++\"`.\n algorithm_type (`str`, defaults to `dpmsolver++`):\n Algorithm type for the solver; can be `dpmsolver` or `dpmsolver++`. The `dpmsolver` type implements the\n algorithms in the [DPMSolver](https://huggingface.co/papers/2206.00927) paper, and the `dpmsolver++` type\n implements the algorithms in the [DPMSolver++](https://huggingface.co/papers/2211.01095) paper. It is\n recommended to use `dpmsolver++` or `sde-dpmsolver++` with `solver_order=2` for guided sampling like in\n Stable Diffusion.\n solver_type (`str`, defaults to `midpoint`):\n Solver type for the second-order solver; can be `midpoint` or `heun`. The solver type slightly affects the\n sample quality, especially for a small number of steps. It is recommended to use `midpoint` solvers.\n lower_order_final (`bool`, defaults to `True`):\n Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can\n stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10.\n use_karras_sigmas (`bool`, *optional*, defaults to `False`):\n Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`,\n the sigmas are determined according to a sequence of noise levels {σi}.\n final_sigmas_type (`str`, *optional*, defaults to `\"zero\"`):\n The final `sigma` value for the noise schedule during the sampling process. If `\"sigma_min\"`, the final\n sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0.\n lambda_min_clipped (`float`, defaults to `-inf`):\n Clipping threshold for the minimum value of `lambda(t)` for numerical stability. This is critical for the\n cosine (`squaredcos_cap_v2`) noise schedule.\n variance_type (`str`, *optional*):\n Set to \"learned\" or \"learned_range\" for diffusion models that predict variance. If set, the model's output\n contains the predicted Gaussian variance.\n \"\"\"\n\n _compatibles = [e.name for e in KarrasDiffusionSchedulers]\n order = 1\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[np.ndarray] = None,\n solver_order: int = 2,\n prediction_type: str = \"epsilon\",\n thresholding: bool = False,\n dynamic_thresholding_ratio: float = 0.995,\n sample_max_value: float = 1.0,\n algorithm_type: str = \"dpmsolver++\",\n solver_type: str = \"midpoint\",\n lower_order_final: bool = False,\n use_karras_sigmas: Optional[bool] = False,\n final_sigmas_type: Optional[str] = \"zero\", # \"zero\", \"sigma_min\"\n lambda_min_clipped: float = -float(\"inf\"),\n variance_type: Optional[str] = None,\n ):\n if algorithm_type == \"dpmsolver\":\n deprecation_message = \"algorithm_type `dpmsolver` is deprecated and will be removed in a future version. Choose from `dpmsolver++` or `sde-dpmsolver++` instead\"\n deprecate(\"algorithm_types=dpmsolver\", \"1.0.0\", deprecation_message)\n\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n # Currently we only support VP-type noise schedule\n self.alpha_t = torch.sqrt(self.alphas_cumprod)\n self.sigma_t = torch.sqrt(1 - self.alphas_cumprod)\n self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t)\n self.sigmas = ((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5\n\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = 1.0\n\n # settings for DPM-Solver\n if algorithm_type not in [\"dpmsolver\", \"dpmsolver++\"]:\n if algorithm_type == \"deis\":\n self.register_to_config(algorithm_type=\"dpmsolver++\")\n else:\n raise NotImplementedError(f\"{algorithm_type} is not implemented for {self.__class__}\")\n if solver_type not in [\"midpoint\", \"heun\"]:\n if solver_type in [\"logrho\", \"bh1\", \"bh2\"]:\n self.register_to_config(solver_type=\"midpoint\")\n else:\n raise NotImplementedError(f\"{solver_type} is not implemented for {self.__class__}\")\n\n if algorithm_type != \"dpmsolver++\" and final_sigmas_type == \"zero\":\n raise ValueError(\n f\"`final_sigmas_type` {final_sigmas_type} is not supported for `algorithm_type` {algorithm_type}. Please chooose `sigma_min` instead.\"\n )\n\n # setable values\n self.num_inference_steps = None\n timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=np.float32)[::-1].copy()\n self.timesteps = torch.from_numpy(timesteps)\n self.model_outputs = [None] * solver_order\n self.sample = None\n self.order_list = self.get_order_list(num_train_timesteps)\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n def get_order_list(self, num_inference_steps: int) -> List[int]:\n \"\"\"\n Computes the solver order at each time step.\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n \"\"\"\n steps = num_inference_steps\n order = self.config.solver_order\n if order > 3:\n raise ValueError(\"Order > 3 is not supported by this scheduler\")\n if self.config.lower_order_final:\n if order == 3:\n if steps % 3 == 0:\n orders = [1, 2, 3] * (steps // 3 - 1) + [1, 2] + [1]\n elif steps % 3 == 1:\n orders = [1, 2, 3] * (steps // 3) + [1]\n else:\n orders = [1, 2, 3] * (steps // 3) + [1, 2]\n elif order == 2:\n if steps % 2 == 0:\n orders = [1, 2] * (steps // 2 - 1) + [1, 1]\n else:\n orders = [1, 2] * (steps // 2) + [1]\n elif order == 1:\n orders = [1] * steps\n else:\n if order == 3:\n orders = [1, 2, 3] * (steps // 3)\n elif order == 2:\n orders = [1, 2] * (steps // 2)\n elif order == 1:\n orders = [1] * steps\n return orders\n\n @property\n def step_index(self):\n \"\"\"\n The index counter for current timestep. It will increase 1 after each scheduler step.\n \"\"\"\n return self._step_index\n\n @property\n def begin_index(self):\n \"\"\"\n The index for the first timestep. It should be set from pipeline with `set_begin_index` method.\n \"\"\"\n return self._begin_index\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index\n def set_begin_index(self, begin_index: int = 0):\n \"\"\"\n Sets the begin index for the scheduler. This function should be run from pipeline before the inference.\n\n Args:\n begin_index (`int`):\n The begin index for the scheduler.\n \"\"\"\n self._begin_index = begin_index\n\n def set_timesteps(\n self,\n num_inference_steps: int = None,\n device: Union[str, torch.device] = None,\n timesteps: Optional[List[int]] = None,\n ):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default\n timestep spacing strategy of equal spacing between timesteps schedule is used. If `timesteps` is\n passed, `num_inference_steps` must be `None`.\n \"\"\"\n if num_inference_steps is None and timesteps is None:\n raise ValueError(\"Must pass exactly one of `num_inference_steps` or `timesteps`.\")\n if num_inference_steps is not None and timesteps is not None:\n raise ValueError(\"Must pass exactly one of `num_inference_steps` or `timesteps`.\")\n if timesteps is not None and self.config.use_karras_sigmas:\n raise ValueError(\"Cannot use `timesteps` when `config.use_karras_sigmas=True`.\")\n\n num_inference_steps = num_inference_steps or len(timesteps)\n self.num_inference_steps = num_inference_steps\n\n if timesteps is not None:\n timesteps = np.array(timesteps).astype(np.int64)\n else:\n # Clipping the minimum of all lambda(t) for numerical stability.\n # This is critical for cosine (squaredcos_cap_v2) noise schedule.\n clipped_idx = torch.searchsorted(torch.flip(self.lambda_t, [0]), self.config.lambda_min_clipped)\n timesteps = (\n np.linspace(0, self.config.num_train_timesteps - 1 - clipped_idx, num_inference_steps + 1)\n .round()[::-1][:-1]\n .copy()\n .astype(np.int64)\n )\n\n sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)\n if self.config.use_karras_sigmas:\n log_sigmas = np.log(sigmas)\n sigmas = np.flip(sigmas).copy()\n sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)\n timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]).round()\n else:\n sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)\n\n if self.config.final_sigmas_type == \"sigma_min\":\n sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0]) ** 0.5\n elif self.config.final_sigmas_type == \"zero\":\n sigma_last = 0\n else:\n raise ValueError(\n f\" `final_sigmas_type` must be one of `sigma_min` or `zero`, but got {self.config.final_sigmas_type}\"\n )\n sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)\n\n self.sigmas = torch.from_numpy(sigmas).to(device=device)\n\n self.timesteps = torch.from_numpy(timesteps).to(device=device, dtype=torch.int64)\n self.model_outputs = [None] * self.config.solver_order\n self.sample = None\n\n if not self.config.lower_order_final and num_inference_steps % self.config.solver_order != 0:\n logger.warning(\n \"Changing scheduler {self.config} to have `lower_order_final` set to True to handle uneven amount of inference steps. Please make sure to always use an even number of `num_inference steps when using `lower_order_final=False`.\"\n )\n self.register_to_config(lower_order_final=True)\n\n if not self.config.lower_order_final and self.config.final_sigmas_type == \"zero\":\n logger.warning(\n \" `last_sigmas_type='zero'` is not supported for `lower_order_final=False`. Changing scheduler {self.config} to have `lower_order_final` set to True.\"\n )\n self.register_to_config(lower_order_final=True)\n\n self.order_list = self.get_order_list(num_inference_steps)\n\n # add an index counter for schedulers that allow duplicated timesteps\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample\n def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:\n \"\"\"\n \"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the\n prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by\n s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing\n pixels from saturation at each step. We find that dynamic thresholding results in significantly better\n photorealism as well as better image-text alignment, especially when using very large guidance weights.\"\n\n https://arxiv.org/abs/2205.11487\n \"\"\"\n dtype = sample.dtype\n batch_size, channels, *remaining_dims = sample.shape\n\n if dtype not in (torch.float32, torch.float64):\n sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half\n\n # Flatten sample for doing quantile calculation along each image\n sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))\n\n abs_sample = sample.abs() # \"a certain percentile absolute pixel value\"\n\n s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)\n s = torch.clamp(\n s, min=1, max=self.config.sample_max_value\n ) # When clamped to min=1, equivalent to standard clipping to [-1, 1]\n s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0\n sample = torch.clamp(sample, -s, s) / s # \"we threshold xt0 to the range [-s, s] and then divide by s\"\n\n sample = sample.reshape(batch_size, channels, *remaining_dims)\n sample = sample.to(dtype)\n\n return sample\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t\n def _sigma_to_t(self, sigma, log_sigmas):\n # get log sigma\n log_sigma = np.log(np.maximum(sigma, 1e-10))\n\n # get distribution\n dists = log_sigma - log_sigmas[:, np.newaxis]\n\n # get sigmas range\n low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)\n high_idx = low_idx + 1\n\n low = log_sigmas[low_idx]\n high = log_sigmas[high_idx]\n\n # interpolate sigmas\n w = (low - log_sigma) / (low - high)\n w = np.clip(w, 0, 1)\n\n # transform interpolation to time range\n t = (1 - w) * low_idx + w * high_idx\n t = t.reshape(sigma.shape)\n return t\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._sigma_to_alpha_sigma_t\n def _sigma_to_alpha_sigma_t(self, sigma):\n alpha_t = 1 / ((sigma**2 + 1) ** 0.5)\n sigma_t = sigma * alpha_t\n\n return alpha_t, sigma_t\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras\n def _convert_to_karras(self, in_sigmas: torch.Tensor, num_inference_steps) -> torch.Tensor:\n \"\"\"Constructs the noise schedule of Karras et al. (2022).\"\"\"\n\n # Hack to make sure that other schedulers which copy this function don't break\n # TODO: Add this logic to the other schedulers\n if hasattr(self.config, \"sigma_min\"):\n sigma_min = self.config.sigma_min\n else:\n sigma_min = None\n\n if hasattr(self.config, \"sigma_max\"):\n sigma_max = self.config.sigma_max\n else:\n sigma_max = None\n\n sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item()\n sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item()\n\n rho = 7.0 # 7.0 is the value used in the paper\n ramp = np.linspace(0, 1, num_inference_steps)\n min_inv_rho = sigma_min ** (1 / rho)\n max_inv_rho = sigma_max ** (1 / rho)\n sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho\n return sigmas\n\n def convert_model_output(\n self,\n model_output: torch.Tensor,\n *args,\n sample: torch.Tensor = None,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is\n designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to discretize an\n integral of the data prediction model.\n\n \n\n The algorithm and model type are decoupled. You can use either DPMSolver or DPMSolver++ for both noise\n prediction and data prediction models.\n\n \n\n Args:\n model_output (`torch.Tensor`):\n The direct output from the learned diffusion model.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n\n Returns:\n `torch.Tensor`:\n The converted model output.\n \"\"\"\n timestep = args[0] if len(args) > 0 else kwargs.pop(\"timestep\", None)\n if sample is None:\n if len(args) > 1:\n sample = args[1]\n else:\n raise ValueError(\"missing `sample` as a required keyward argument\")\n if timestep is not None:\n deprecate(\n \"timesteps\",\n \"1.0.0\",\n \"Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n # DPM-Solver++ needs to solve an integral of the data prediction model.\n if self.config.algorithm_type == \"dpmsolver++\":\n if self.config.prediction_type == \"epsilon\":\n # DPM-Solver and DPM-Solver++ only need the \"mean\" output.\n if self.config.variance_type in [\"learned_range\"]:\n model_output = model_output[:, :3]\n sigma = self.sigmas[self.step_index]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)\n x0_pred = (sample - sigma_t * model_output) / alpha_t\n elif self.config.prediction_type == \"sample\":\n x0_pred = model_output\n elif self.config.prediction_type == \"v_prediction\":\n sigma = self.sigmas[self.step_index]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)\n x0_pred = alpha_t * sample - sigma_t * model_output\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or\"\n \" `v_prediction` for the DPMSolverSinglestepScheduler.\"\n )\n\n if self.config.thresholding:\n x0_pred = self._threshold_sample(x0_pred)\n\n return x0_pred\n # DPM-Solver needs to solve an integral of the noise prediction model.\n elif self.config.algorithm_type == \"dpmsolver\":\n if self.config.prediction_type == \"epsilon\":\n # DPM-Solver and DPM-Solver++ only need the \"mean\" output.\n if self.config.variance_type in [\"learned_range\"]:\n model_output = model_output[:, :3]\n return model_output\n elif self.config.prediction_type == \"sample\":\n sigma = self.sigmas[self.step_index]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)\n epsilon = (sample - alpha_t * model_output) / sigma_t\n return epsilon\n elif self.config.prediction_type == \"v_prediction\":\n sigma = self.sigmas[self.step_index]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)\n epsilon = alpha_t * model_output + sigma_t * sample\n return epsilon\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or\"\n \" `v_prediction` for the DPMSolverSinglestepScheduler.\"\n )\n\n def dpm_solver_first_order_update(\n self,\n model_output: torch.Tensor,\n *args,\n sample: torch.Tensor = None,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n One step for the first-order DPMSolver (equivalent to DDIM).\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from the learned diffusion model.\n timestep (`int`):\n The current discrete timestep in the diffusion chain.\n prev_timestep (`int`):\n The previous discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n\n Returns:\n `torch.Tensor`:\n The sample tensor at the previous timestep.\n \"\"\"\n timestep = args[0] if len(args) > 0 else kwargs.pop(\"timestep\", None)\n prev_timestep = args[1] if len(args) > 1 else kwargs.pop(\"prev_timestep\", None)\n if sample is None:\n if len(args) > 2:\n sample = args[2]\n else:\n raise ValueError(\" missing `sample` as a required keyward argument\")\n if timestep is not None:\n deprecate(\n \"timesteps\",\n \"1.0.0\",\n \"Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n if prev_timestep is not None:\n deprecate(\n \"prev_timestep\",\n \"1.0.0\",\n \"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n sigma_t, sigma_s = self.sigmas[self.step_index + 1], self.sigmas[self.step_index]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)\n alpha_s, sigma_s = self._sigma_to_alpha_sigma_t(sigma_s)\n lambda_t = torch.log(alpha_t) - torch.log(sigma_t)\n lambda_s = torch.log(alpha_s) - torch.log(sigma_s)\n h = lambda_t - lambda_s\n if self.config.algorithm_type == \"dpmsolver++\":\n x_t = (sigma_t / sigma_s) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * model_output\n elif self.config.algorithm_type == \"dpmsolver\":\n x_t = (alpha_t / alpha_s) * sample - (sigma_t * (torch.exp(h) - 1.0)) * model_output\n return x_t\n\n def singlestep_dpm_solver_second_order_update(\n self,\n model_output_list: List[torch.Tensor],\n *args,\n sample: torch.Tensor = None,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n One step for the second-order singlestep DPMSolver that computes the solution at time `prev_timestep` from the\n time `timestep_list[-2]`.\n\n Args:\n model_output_list (`List[torch.Tensor]`):\n The direct outputs from learned diffusion model at current and latter timesteps.\n timestep (`int`):\n The current and latter discrete timestep in the diffusion chain.\n prev_timestep (`int`):\n The previous discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n\n Returns:\n `torch.Tensor`:\n The sample tensor at the previous timestep.\n \"\"\"\n timestep_list = args[0] if len(args) > 0 else kwargs.pop(\"timestep_list\", None)\n prev_timestep = args[1] if len(args) > 1 else kwargs.pop(\"prev_timestep\", None)\n if sample is None:\n if len(args) > 2:\n sample = args[2]\n else:\n raise ValueError(\" missing `sample` as a required keyward argument\")\n if timestep_list is not None:\n deprecate(\n \"timestep_list\",\n \"1.0.0\",\n \"Passing `timestep_list` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n if prev_timestep is not None:\n deprecate(\n \"prev_timestep\",\n \"1.0.0\",\n \"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n sigma_t, sigma_s0, sigma_s1 = (\n self.sigmas[self.step_index + 1],\n self.sigmas[self.step_index],\n self.sigmas[self.step_index - 1],\n )\n\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)\n alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)\n alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1)\n\n lambda_t = torch.log(alpha_t) - torch.log(sigma_t)\n lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)\n lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1)\n\n m0, m1 = model_output_list[-1], model_output_list[-2]\n\n h, h_0 = lambda_t - lambda_s1, lambda_s0 - lambda_s1\n r0 = h_0 / h\n D0, D1 = m1, (1.0 / r0) * (m0 - m1)\n if self.config.algorithm_type == \"dpmsolver++\":\n # See https://arxiv.org/abs/2211.01095 for detailed derivations\n if self.config.solver_type == \"midpoint\":\n x_t = (\n (sigma_t / sigma_s1) * sample\n - (alpha_t * (torch.exp(-h) - 1.0)) * D0\n - 0.5 * (alpha_t * (torch.exp(-h) - 1.0)) * D1\n )\n elif self.config.solver_type == \"heun\":\n x_t = (\n (sigma_t / sigma_s1) * sample\n - (alpha_t * (torch.exp(-h) - 1.0)) * D0\n + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1\n )\n elif self.config.algorithm_type == \"dpmsolver\":\n # See https://arxiv.org/abs/2206.00927 for detailed derivations\n if self.config.solver_type == \"midpoint\":\n x_t = (\n (alpha_t / alpha_s1) * sample\n - (sigma_t * (torch.exp(h) - 1.0)) * D0\n - 0.5 * (sigma_t * (torch.exp(h) - 1.0)) * D1\n )\n elif self.config.solver_type == \"heun\":\n x_t = (\n (alpha_t / alpha_s1) * sample\n - (sigma_t * (torch.exp(h) - 1.0)) * D0\n - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1\n )\n return x_t\n\n def singlestep_dpm_solver_third_order_update(\n self,\n model_output_list: List[torch.Tensor],\n *args,\n sample: torch.Tensor = None,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n One step for the third-order singlestep DPMSolver that computes the solution at time `prev_timestep` from the\n time `timestep_list[-3]`.\n\n Args:\n model_output_list (`List[torch.Tensor]`):\n The direct outputs from learned diffusion model at current and latter timesteps.\n timestep (`int`):\n The current and latter discrete timestep in the diffusion chain.\n prev_timestep (`int`):\n The previous discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by diffusion process.\n\n Returns:\n `torch.Tensor`:\n The sample tensor at the previous timestep.\n \"\"\"\n\n timestep_list = args[0] if len(args) > 0 else kwargs.pop(\"timestep_list\", None)\n prev_timestep = args[1] if len(args) > 1 else kwargs.pop(\"prev_timestep\", None)\n if sample is None:\n if len(args) > 2:\n sample = args[2]\n else:\n raise ValueError(\" missing`sample` as a required keyward argument\")\n if timestep_list is not None:\n deprecate(\n \"timestep_list\",\n \"1.0.0\",\n \"Passing `timestep_list` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n if prev_timestep is not None:\n deprecate(\n \"prev_timestep\",\n \"1.0.0\",\n \"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n sigma_t, sigma_s0, sigma_s1, sigma_s2 = (\n self.sigmas[self.step_index + 1],\n self.sigmas[self.step_index],\n self.sigmas[self.step_index - 1],\n self.sigmas[self.step_index - 2],\n )\n\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)\n alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)\n alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1)\n alpha_s2, sigma_s2 = self._sigma_to_alpha_sigma_t(sigma_s2)\n\n lambda_t = torch.log(alpha_t) - torch.log(sigma_t)\n lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)\n lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1)\n lambda_s2 = torch.log(alpha_s2) - torch.log(sigma_s2)\n\n m0, m1, m2 = model_output_list[-1], model_output_list[-2], model_output_list[-3]\n\n h, h_0, h_1 = lambda_t - lambda_s2, lambda_s0 - lambda_s2, lambda_s1 - lambda_s2\n r0, r1 = h_0 / h, h_1 / h\n D0 = m2\n D1_0, D1_1 = (1.0 / r1) * (m1 - m2), (1.0 / r0) * (m0 - m2)\n D1 = (r0 * D1_0 - r1 * D1_1) / (r0 - r1)\n D2 = 2.0 * (D1_1 - D1_0) / (r0 - r1)\n if self.config.algorithm_type == \"dpmsolver++\":\n # See https://arxiv.org/abs/2206.00927 for detailed derivations\n if self.config.solver_type == \"midpoint\":\n x_t = (\n (sigma_t / sigma_s2) * sample\n - (alpha_t * (torch.exp(-h) - 1.0)) * D0\n + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1_1\n )\n elif self.config.solver_type == \"heun\":\n x_t = (\n (sigma_t / sigma_s2) * sample\n - (alpha_t * (torch.exp(-h) - 1.0)) * D0\n + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1\n - (alpha_t * ((torch.exp(-h) - 1.0 + h) / h**2 - 0.5)) * D2\n )\n elif self.config.algorithm_type == \"dpmsolver\":\n # See https://arxiv.org/abs/2206.00927 for detailed derivations\n if self.config.solver_type == \"midpoint\":\n x_t = (\n (alpha_t / alpha_s2) * sample\n - (sigma_t * (torch.exp(h) - 1.0)) * D0\n - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1_1\n )\n elif self.config.solver_type == \"heun\":\n x_t = (\n (alpha_t / alpha_s2) * sample\n - (sigma_t * (torch.exp(h) - 1.0)) * D0\n - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1\n - (sigma_t * ((torch.exp(h) - 1.0 - h) / h**2 - 0.5)) * D2\n )\n return x_t\n\n def singlestep_dpm_solver_update(\n self,\n model_output_list: List[torch.Tensor],\n *args,\n sample: torch.Tensor = None,\n order: int = None,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n One step for the singlestep DPMSolver.\n\n Args:\n model_output_list (`List[torch.Tensor]`):\n The direct outputs from learned diffusion model at current and latter timesteps.\n timestep (`int`):\n The current and latter discrete timestep in the diffusion chain.\n prev_timestep (`int`):\n The previous discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by diffusion process.\n order (`int`):\n The solver order at this step.\n\n Returns:\n `torch.Tensor`:\n The sample tensor at the previous timestep.\n \"\"\"\n timestep_list = args[0] if len(args) > 0 else kwargs.pop(\"timestep_list\", None)\n prev_timestep = args[1] if len(args) > 1 else kwargs.pop(\"prev_timestep\", None)\n if sample is None:\n if len(args) > 2:\n sample = args[2]\n else:\n raise ValueError(\" missing`sample` as a required keyward argument\")\n if order is None:\n if len(args) > 3:\n order = args[3]\n else:\n raise ValueError(\" missing `order` as a required keyward argument\")\n if timestep_list is not None:\n deprecate(\n \"timestep_list\",\n \"1.0.0\",\n \"Passing `timestep_list` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n if prev_timestep is not None:\n deprecate(\n \"prev_timestep\",\n \"1.0.0\",\n \"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n if order == 1:\n return self.dpm_solver_first_order_update(model_output_list[-1], sample=sample)\n elif order == 2:\n return self.singlestep_dpm_solver_second_order_update(model_output_list, sample=sample)\n elif order == 3:\n return self.singlestep_dpm_solver_third_order_update(model_output_list, sample=sample)\n else:\n raise ValueError(f\"Order must be 1, 2, 3, got {order}\")\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.index_for_timestep\n def index_for_timestep(self, timestep, schedule_timesteps=None):\n if schedule_timesteps is None:\n schedule_timesteps = self.timesteps\n\n index_candidates = (schedule_timesteps == timestep).nonzero()\n\n if len(index_candidates) == 0:\n step_index = len(self.timesteps) - 1\n # The sigma index that is taken for the **very** first `step`\n # is always the second index (or the last index if there is only 1)\n # This way we can ensure we don't accidentally skip a sigma in\n # case we start in the middle of the denoising schedule (e.g. for image-to-image)\n elif len(index_candidates) > 1:\n step_index = index_candidates[1].item()\n else:\n step_index = index_candidates[0].item()\n\n return step_index\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._init_step_index\n def _init_step_index(self, timestep):\n \"\"\"\n Initialize the step_index counter for the scheduler.\n \"\"\"\n\n if self.begin_index is None:\n if isinstance(timestep, torch.Tensor):\n timestep = timestep.to(self.timesteps.device)\n self._step_index = self.index_for_timestep(timestep)\n else:\n self._step_index = self._begin_index\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n return_dict: bool = True,\n ) -> Union[SchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with\n the singlestep DPMSolver.\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`int`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n return_dict (`bool`):\n Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`.\n\n Returns:\n [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n\n \"\"\"\n if self.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n if self.step_index is None:\n self._init_step_index(timestep)\n\n model_output = self.convert_model_output(model_output, sample=sample)\n for i in range(self.config.solver_order - 1):\n self.model_outputs[i] = self.model_outputs[i + 1]\n self.model_outputs[-1] = model_output\n\n order = self.order_list[self.step_index]\n\n # For img2img denoising might start with order>1 which is not possible\n # In this case make sure that the first two steps are both order=1\n while self.model_outputs[-order] is None:\n order -= 1\n\n # For single-step solvers, we use the initial value at each time with order = 1.\n if order == 1:\n self.sample = sample\n\n prev_sample = self.singlestep_dpm_solver_update(self.model_outputs, sample=self.sample, order=order)\n\n # upon completion increase step index by one\n self._step_index += 1\n\n if not return_dict:\n return (prev_sample,)\n\n return SchedulerOutput(prev_sample=prev_sample)\n\n def scale_model_input(self, sample: torch.Tensor, *args, **kwargs) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n return sample\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.add_noise\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.IntTensor,\n ) -> torch.Tensor:\n # Make sure sigmas and timesteps have the same device and dtype as original_samples\n sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)\n if original_samples.device.type == \"mps\" and torch.is_floating_point(timesteps):\n # mps does not support float64\n schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)\n timesteps = timesteps.to(original_samples.device, dtype=torch.float32)\n else:\n schedule_timesteps = self.timesteps.to(original_samples.device)\n timesteps = timesteps.to(original_samples.device)\n\n # begin_index is None when the scheduler is used for training or pipeline does not implement set_begin_index\n if self.begin_index is None:\n step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps]\n elif self.step_index is not None:\n # add_noise is called after first denoising step (for inpainting)\n step_indices = [self.step_index] * timesteps.shape[0]\n else:\n # add noise is called before first denoising step to create initial latent(img2img)\n step_indices = [self.begin_index] * timesteps.shape[0]\n\n sigma = sigmas[step_indices].flatten()\n while len(sigma.shape) < len(original_samples.shape):\n sigma = sigma.unsqueeze(-1)\n\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)\n noisy_samples = alpha_t * original_samples + sigma_t * noise\n return noisy_samples\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py", "content": "# Copyright 2024 TSAIL Team and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This file is strongly influenced by https://github.com/LuChengTHU/dpm-solver and https://github.com/NVlabs/edm\n\nimport math\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils.torch_utils import randn_tensor\nfrom .scheduling_utils import SchedulerMixin, SchedulerOutput\n\n\nclass EDMDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n Implements DPMSolverMultistepScheduler in EDM formulation as presented in Karras et al. 2022 [1].\n `EDMDPMSolverMultistepScheduler` is a fast dedicated high-order solver for diffusion ODEs.\n\n [1] Karras, Tero, et al. \"Elucidating the Design Space of Diffusion-Based Generative Models.\"\n https://arxiv.org/abs/2206.00364\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n sigma_min (`float`, *optional*, defaults to 0.002):\n Minimum noise magnitude in the sigma schedule. This was set to 0.002 in the EDM paper [1]; a reasonable\n range is [0, 10].\n sigma_max (`float`, *optional*, defaults to 80.0):\n Maximum noise magnitude in the sigma schedule. This was set to 80.0 in the EDM paper [1]; a reasonable\n range is [0.2, 80.0].\n sigma_data (`float`, *optional*, defaults to 0.5):\n The standard deviation of the data distribution. This is set to 0.5 in the EDM paper [1].\n sigma_schedule (`str`, *optional*, defaults to `karras`):\n Sigma schedule to compute the `sigmas`. By default, we the schedule introduced in the EDM paper\n (https://arxiv.org/abs/2206.00364). Other acceptable value is \"exponential\". The exponential schedule was\n incorporated in this model: https://huggingface.co/stabilityai/cosxl.\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n solver_order (`int`, defaults to 2):\n The DPMSolver order which can be `1` or `2` or `3`. It is recommended to use `solver_order=2` for guided\n sampling, and `solver_order=3` for unconditional sampling.\n prediction_type (`str`, defaults to `epsilon`, *optional*):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),\n `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen\n Video](https://imagen.research.google/video/paper.pdf) paper).\n thresholding (`bool`, defaults to `False`):\n Whether to use the \"dynamic thresholding\" method. This is unsuitable for latent-space diffusion models such\n as Stable Diffusion.\n dynamic_thresholding_ratio (`float`, defaults to 0.995):\n The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.\n sample_max_value (`float`, defaults to 1.0):\n The threshold value for dynamic thresholding. Valid only when `thresholding=True` and\n `algorithm_type=\"dpmsolver++\"`.\n algorithm_type (`str`, defaults to `dpmsolver++`):\n Algorithm type for the solver; can be `dpmsolver++` or `sde-dpmsolver++`. The `dpmsolver++` type implements\n the algorithms in the [DPMSolver++](https://huggingface.co/papers/2211.01095) paper. It is recommended to\n use `dpmsolver++` or `sde-dpmsolver++` with `solver_order=2` for guided sampling like in Stable Diffusion.\n solver_type (`str`, defaults to `midpoint`):\n Solver type for the second-order solver; can be `midpoint` or `heun`. The solver type slightly affects the\n sample quality, especially for a small number of steps. It is recommended to use `midpoint` solvers.\n lower_order_final (`bool`, defaults to `True`):\n Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can\n stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10.\n euler_at_final (`bool`, defaults to `False`):\n Whether to use Euler's method in the final step. It is a trade-off between numerical stability and detail\n richness. This can stabilize the sampling of the SDE variant of DPMSolver for small number of inference\n steps, but sometimes may result in blurring.\n final_sigmas_type (`str`, defaults to `\"zero\"`):\n The final `sigma` value for the noise schedule during the sampling process. If `\"sigma_min\"`, the final\n sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0.\n \"\"\"\n\n _compatibles = []\n order = 1\n\n @register_to_config\n def __init__(\n self,\n sigma_min: float = 0.002,\n sigma_max: float = 80.0,\n sigma_data: float = 0.5,\n sigma_schedule: str = \"karras\",\n num_train_timesteps: int = 1000,\n prediction_type: str = \"epsilon\",\n rho: float = 7.0,\n solver_order: int = 2,\n thresholding: bool = False,\n dynamic_thresholding_ratio: float = 0.995,\n sample_max_value: float = 1.0,\n algorithm_type: str = \"dpmsolver++\",\n solver_type: str = \"midpoint\",\n lower_order_final: bool = True,\n euler_at_final: bool = False,\n final_sigmas_type: Optional[str] = \"zero\", # \"zero\", \"sigma_min\"\n ):\n # settings for DPM-Solver\n if algorithm_type not in [\"dpmsolver++\", \"sde-dpmsolver++\"]:\n if algorithm_type == \"deis\":\n self.register_to_config(algorithm_type=\"dpmsolver++\")\n else:\n raise NotImplementedError(f\"{algorithm_type} is not implemented for {self.__class__}\")\n\n if solver_type not in [\"midpoint\", \"heun\"]:\n if solver_type in [\"logrho\", \"bh1\", \"bh2\"]:\n self.register_to_config(solver_type=\"midpoint\")\n else:\n raise NotImplementedError(f\"{solver_type} is not implemented for {self.__class__}\")\n\n if algorithm_type not in [\"dpmsolver++\", \"sde-dpmsolver++\"] and final_sigmas_type == \"zero\":\n raise ValueError(\n f\"`final_sigmas_type` {final_sigmas_type} is not supported for `algorithm_type` {algorithm_type}. Please choose `sigma_min` instead.\"\n )\n\n ramp = torch.linspace(0, 1, num_train_timesteps)\n if sigma_schedule == \"karras\":\n sigmas = self._compute_karras_sigmas(ramp)\n elif sigma_schedule == \"exponential\":\n sigmas = self._compute_exponential_sigmas(ramp)\n\n self.timesteps = self.precondition_noise(sigmas)\n\n self.sigmas = self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])\n\n # setable values\n self.num_inference_steps = None\n self.model_outputs = [None] * solver_order\n self.lower_order_nums = 0\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n @property\n def init_noise_sigma(self):\n # standard deviation of the initial noise distribution\n return (self.config.sigma_max**2 + 1) ** 0.5\n\n @property\n def step_index(self):\n \"\"\"\n The index counter for current timestep. It will increase 1 after each scheduler step.\n \"\"\"\n return self._step_index\n\n @property\n def begin_index(self):\n \"\"\"\n The index for the first timestep. It should be set from pipeline with `set_begin_index` method.\n \"\"\"\n return self._begin_index\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index\n def set_begin_index(self, begin_index: int = 0):\n \"\"\"\n Sets the begin index for the scheduler. This function should be run from pipeline before the inference.\n\n Args:\n begin_index (`int`):\n The begin index for the scheduler.\n \"\"\"\n self._begin_index = begin_index\n\n # Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler.precondition_inputs\n def precondition_inputs(self, sample, sigma):\n c_in = 1 / ((sigma**2 + self.config.sigma_data**2) ** 0.5)\n scaled_sample = sample * c_in\n return scaled_sample\n\n # Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler.precondition_noise\n def precondition_noise(self, sigma):\n if not isinstance(sigma, torch.Tensor):\n sigma = torch.tensor([sigma])\n\n c_noise = 0.25 * torch.log(sigma)\n\n return c_noise\n\n # Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler.precondition_outputs\n def precondition_outputs(self, sample, model_output, sigma):\n sigma_data = self.config.sigma_data\n c_skip = sigma_data**2 / (sigma**2 + sigma_data**2)\n\n if self.config.prediction_type == \"epsilon\":\n c_out = sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5\n elif self.config.prediction_type == \"v_prediction\":\n c_out = -sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5\n else:\n raise ValueError(f\"Prediction type {self.config.prediction_type} is not supported.\")\n\n denoised = c_skip * sample + c_out * model_output\n\n return denoised\n\n # Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler.scale_model_input\n def scale_model_input(self, sample: torch.Tensor, timestep: Union[float, torch.Tensor]) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep. Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`int`, *optional*):\n The current timestep in the diffusion chain.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n if self.step_index is None:\n self._init_step_index(timestep)\n\n sigma = self.sigmas[self.step_index]\n sample = self.precondition_inputs(sample, sigma)\n\n self.is_scale_input_called = True\n return sample\n\n def set_timesteps(self, num_inference_steps: int = None, device: Union[str, torch.device] = None):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n \"\"\"\n\n self.num_inference_steps = num_inference_steps\n\n ramp = torch.linspace(0, 1, self.num_inference_steps)\n if self.config.sigma_schedule == \"karras\":\n sigmas = self._compute_karras_sigmas(ramp)\n elif self.config.sigma_schedule == \"exponential\":\n sigmas = self._compute_exponential_sigmas(ramp)\n\n sigmas = sigmas.to(dtype=torch.float32, device=device)\n self.timesteps = self.precondition_noise(sigmas)\n\n if self.config.final_sigmas_type == \"sigma_min\":\n sigma_last = self.config.sigma_min\n elif self.config.final_sigmas_type == \"zero\":\n sigma_last = 0\n else:\n raise ValueError(\n f\"`final_sigmas_type` must be one of 'zero', or 'sigma_min', but got {self.config.final_sigmas_type}\"\n )\n\n self.sigmas = torch.cat([sigmas, torch.tensor([sigma_last], dtype=torch.float32, device=device)])\n\n self.model_outputs = [\n None,\n ] * self.config.solver_order\n self.lower_order_nums = 0\n\n # add an index counter for schedulers that allow duplicated timesteps\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n # Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler._compute_karras_sigmas\n def _compute_karras_sigmas(self, ramp, sigma_min=None, sigma_max=None) -> torch.Tensor:\n \"\"\"Constructs the noise schedule of Karras et al. (2022).\"\"\"\n sigma_min = sigma_min or self.config.sigma_min\n sigma_max = sigma_max or self.config.sigma_max\n\n rho = self.config.rho\n min_inv_rho = sigma_min ** (1 / rho)\n max_inv_rho = sigma_max ** (1 / rho)\n sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho\n return sigmas\n\n # Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler._compute_exponential_sigmas\n def _compute_exponential_sigmas(self, ramp, sigma_min=None, sigma_max=None) -> torch.Tensor:\n \"\"\"Implementation closely follows k-diffusion.\n\n https://github.com/crowsonkb/k-diffusion/blob/6ab5146d4a5ef63901326489f31f1d8e7dd36b48/k_diffusion/sampling.py#L26\n \"\"\"\n sigma_min = sigma_min or self.config.sigma_min\n sigma_max = sigma_max or self.config.sigma_max\n sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), len(ramp)).exp().flip(0)\n return sigmas\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample\n def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:\n \"\"\"\n \"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the\n prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by\n s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing\n pixels from saturation at each step. We find that dynamic thresholding results in significantly better\n photorealism as well as better image-text alignment, especially when using very large guidance weights.\"\n\n https://arxiv.org/abs/2205.11487\n \"\"\"\n dtype = sample.dtype\n batch_size, channels, *remaining_dims = sample.shape\n\n if dtype not in (torch.float32, torch.float64):\n sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half\n\n # Flatten sample for doing quantile calculation along each image\n sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))\n\n abs_sample = sample.abs() # \"a certain percentile absolute pixel value\"\n\n s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)\n s = torch.clamp(\n s, min=1, max=self.config.sample_max_value\n ) # When clamped to min=1, equivalent to standard clipping to [-1, 1]\n s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0\n sample = torch.clamp(sample, -s, s) / s # \"we threshold xt0 to the range [-s, s] and then divide by s\"\n\n sample = sample.reshape(batch_size, channels, *remaining_dims)\n sample = sample.to(dtype)\n\n return sample\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t\n def _sigma_to_t(self, sigma, log_sigmas):\n # get log sigma\n log_sigma = np.log(np.maximum(sigma, 1e-10))\n\n # get distribution\n dists = log_sigma - log_sigmas[:, np.newaxis]\n\n # get sigmas range\n low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)\n high_idx = low_idx + 1\n\n low = log_sigmas[low_idx]\n high = log_sigmas[high_idx]\n\n # interpolate sigmas\n w = (low - log_sigma) / (low - high)\n w = np.clip(w, 0, 1)\n\n # transform interpolation to time range\n t = (1 - w) * low_idx + w * high_idx\n t = t.reshape(sigma.shape)\n return t\n\n def _sigma_to_alpha_sigma_t(self, sigma):\n alpha_t = torch.tensor(1) # Inputs are pre-scaled before going into unet, so alpha_t = 1\n sigma_t = sigma\n\n return alpha_t, sigma_t\n\n def convert_model_output(\n self,\n model_output: torch.Tensor,\n sample: torch.Tensor = None,\n ) -> torch.Tensor:\n \"\"\"\n Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is\n designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to discretize an\n integral of the data prediction model.\n\n \n\n The algorithm and model type are decoupled. You can use either DPMSolver or DPMSolver++ for both noise\n prediction and data prediction models.\n\n \n\n Args:\n model_output (`torch.Tensor`):\n The direct output from the learned diffusion model.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n\n Returns:\n `torch.Tensor`:\n The converted model output.\n \"\"\"\n sigma = self.sigmas[self.step_index]\n x0_pred = self.precondition_outputs(sample, model_output, sigma)\n\n if self.config.thresholding:\n x0_pred = self._threshold_sample(x0_pred)\n\n return x0_pred\n\n def dpm_solver_first_order_update(\n self,\n model_output: torch.Tensor,\n sample: torch.Tensor = None,\n noise: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"\n One step for the first-order DPMSolver (equivalent to DDIM).\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from the learned diffusion model.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n\n Returns:\n `torch.Tensor`:\n The sample tensor at the previous timestep.\n \"\"\"\n sigma_t, sigma_s = self.sigmas[self.step_index + 1], self.sigmas[self.step_index]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)\n alpha_s, sigma_s = self._sigma_to_alpha_sigma_t(sigma_s)\n lambda_t = torch.log(alpha_t) - torch.log(sigma_t)\n lambda_s = torch.log(alpha_s) - torch.log(sigma_s)\n\n h = lambda_t - lambda_s\n if self.config.algorithm_type == \"dpmsolver++\":\n x_t = (sigma_t / sigma_s) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * model_output\n elif self.config.algorithm_type == \"sde-dpmsolver++\":\n assert noise is not None\n x_t = (\n (sigma_t / sigma_s * torch.exp(-h)) * sample\n + (alpha_t * (1 - torch.exp(-2.0 * h))) * model_output\n + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise\n )\n\n return x_t\n\n def multistep_dpm_solver_second_order_update(\n self,\n model_output_list: List[torch.Tensor],\n sample: torch.Tensor = None,\n noise: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"\n One step for the second-order multistep DPMSolver.\n\n Args:\n model_output_list (`List[torch.Tensor]`):\n The direct outputs from learned diffusion model at current and latter timesteps.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n\n Returns:\n `torch.Tensor`:\n The sample tensor at the previous timestep.\n \"\"\"\n sigma_t, sigma_s0, sigma_s1 = (\n self.sigmas[self.step_index + 1],\n self.sigmas[self.step_index],\n self.sigmas[self.step_index - 1],\n )\n\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)\n alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)\n alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1)\n\n lambda_t = torch.log(alpha_t) - torch.log(sigma_t)\n lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)\n lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1)\n\n m0, m1 = model_output_list[-1], model_output_list[-2]\n\n h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1\n r0 = h_0 / h\n D0, D1 = m0, (1.0 / r0) * (m0 - m1)\n if self.config.algorithm_type == \"dpmsolver++\":\n # See https://arxiv.org/abs/2211.01095 for detailed derivations\n if self.config.solver_type == \"midpoint\":\n x_t = (\n (sigma_t / sigma_s0) * sample\n - (alpha_t * (torch.exp(-h) - 1.0)) * D0\n - 0.5 * (alpha_t * (torch.exp(-h) - 1.0)) * D1\n )\n elif self.config.solver_type == \"heun\":\n x_t = (\n (sigma_t / sigma_s0) * sample\n - (alpha_t * (torch.exp(-h) - 1.0)) * D0\n + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1\n )\n elif self.config.algorithm_type == \"sde-dpmsolver++\":\n assert noise is not None\n if self.config.solver_type == \"midpoint\":\n x_t = (\n (sigma_t / sigma_s0 * torch.exp(-h)) * sample\n + (alpha_t * (1 - torch.exp(-2.0 * h))) * D0\n + 0.5 * (alpha_t * (1 - torch.exp(-2.0 * h))) * D1\n + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise\n )\n elif self.config.solver_type == \"heun\":\n x_t = (\n (sigma_t / sigma_s0 * torch.exp(-h)) * sample\n + (alpha_t * (1 - torch.exp(-2.0 * h))) * D0\n + (alpha_t * ((1.0 - torch.exp(-2.0 * h)) / (-2.0 * h) + 1.0)) * D1\n + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise\n )\n\n return x_t\n\n def multistep_dpm_solver_third_order_update(\n self,\n model_output_list: List[torch.Tensor],\n sample: torch.Tensor = None,\n ) -> torch.Tensor:\n \"\"\"\n One step for the third-order multistep DPMSolver.\n\n Args:\n model_output_list (`List[torch.Tensor]`):\n The direct outputs from learned diffusion model at current and latter timesteps.\n sample (`torch.Tensor`):\n A current instance of a sample created by diffusion process.\n\n Returns:\n `torch.Tensor`:\n The sample tensor at the previous timestep.\n \"\"\"\n sigma_t, sigma_s0, sigma_s1, sigma_s2 = (\n self.sigmas[self.step_index + 1],\n self.sigmas[self.step_index],\n self.sigmas[self.step_index - 1],\n self.sigmas[self.step_index - 2],\n )\n\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)\n alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)\n alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1)\n alpha_s2, sigma_s2 = self._sigma_to_alpha_sigma_t(sigma_s2)\n\n lambda_t = torch.log(alpha_t) - torch.log(sigma_t)\n lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)\n lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1)\n lambda_s2 = torch.log(alpha_s2) - torch.log(sigma_s2)\n\n m0, m1, m2 = model_output_list[-1], model_output_list[-2], model_output_list[-3]\n\n h, h_0, h_1 = lambda_t - lambda_s0, lambda_s0 - lambda_s1, lambda_s1 - lambda_s2\n r0, r1 = h_0 / h, h_1 / h\n D0 = m0\n D1_0, D1_1 = (1.0 / r0) * (m0 - m1), (1.0 / r1) * (m1 - m2)\n D1 = D1_0 + (r0 / (r0 + r1)) * (D1_0 - D1_1)\n D2 = (1.0 / (r0 + r1)) * (D1_0 - D1_1)\n if self.config.algorithm_type == \"dpmsolver++\":\n # See https://arxiv.org/abs/2206.00927 for detailed derivations\n x_t = (\n (sigma_t / sigma_s0) * sample\n - (alpha_t * (torch.exp(-h) - 1.0)) * D0\n + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1\n - (alpha_t * ((torch.exp(-h) - 1.0 + h) / h**2 - 0.5)) * D2\n )\n\n return x_t\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.index_for_timestep\n def index_for_timestep(self, timestep, schedule_timesteps=None):\n if schedule_timesteps is None:\n schedule_timesteps = self.timesteps\n\n index_candidates = (schedule_timesteps == timestep).nonzero()\n\n if len(index_candidates) == 0:\n step_index = len(self.timesteps) - 1\n # The sigma index that is taken for the **very** first `step`\n # is always the second index (or the last index if there is only 1)\n # This way we can ensure we don't accidentally skip a sigma in\n # case we start in the middle of the denoising schedule (e.g. for image-to-image)\n elif len(index_candidates) > 1:\n step_index = index_candidates[1].item()\n else:\n step_index = index_candidates[0].item()\n\n return step_index\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._init_step_index\n def _init_step_index(self, timestep):\n \"\"\"\n Initialize the step_index counter for the scheduler.\n \"\"\"\n\n if self.begin_index is None:\n if isinstance(timestep, torch.Tensor):\n timestep = timestep.to(self.timesteps.device)\n self._step_index = self.index_for_timestep(timestep)\n else:\n self._step_index = self._begin_index\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n generator=None,\n return_dict: bool = True,\n ) -> Union[SchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with\n the multistep DPMSolver.\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`int`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n generator (`torch.Generator`, *optional*):\n A random number generator.\n return_dict (`bool`):\n Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`.\n\n Returns:\n [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n\n \"\"\"\n if self.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n if self.step_index is None:\n self._init_step_index(timestep)\n\n # Improve numerical stability for small number of steps\n lower_order_final = (self.step_index == len(self.timesteps) - 1) and (\n self.config.euler_at_final\n or (self.config.lower_order_final and len(self.timesteps) < 15)\n or self.config.final_sigmas_type == \"zero\"\n )\n lower_order_second = (\n (self.step_index == len(self.timesteps) - 2) and self.config.lower_order_final and len(self.timesteps) < 15\n )\n\n model_output = self.convert_model_output(model_output, sample=sample)\n for i in range(self.config.solver_order - 1):\n self.model_outputs[i] = self.model_outputs[i + 1]\n self.model_outputs[-1] = model_output\n\n if self.config.algorithm_type == \"sde-dpmsolver++\":\n noise = randn_tensor(\n model_output.shape, generator=generator, device=model_output.device, dtype=model_output.dtype\n )\n else:\n noise = None\n\n if self.config.solver_order == 1 or self.lower_order_nums < 1 or lower_order_final:\n prev_sample = self.dpm_solver_first_order_update(model_output, sample=sample, noise=noise)\n elif self.config.solver_order == 2 or self.lower_order_nums < 2 or lower_order_second:\n prev_sample = self.multistep_dpm_solver_second_order_update(self.model_outputs, sample=sample, noise=noise)\n else:\n prev_sample = self.multistep_dpm_solver_third_order_update(self.model_outputs, sample=sample)\n\n if self.lower_order_nums < self.config.solver_order:\n self.lower_order_nums += 1\n\n # upon completion increase step index by one\n self._step_index += 1\n\n if not return_dict:\n return (prev_sample,)\n\n return SchedulerOutput(prev_sample=prev_sample)\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.add_noise\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.Tensor,\n ) -> torch.Tensor:\n # Make sure sigmas and timesteps have the same device and dtype as original_samples\n sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)\n if original_samples.device.type == \"mps\" and torch.is_floating_point(timesteps):\n # mps does not support float64\n schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)\n timesteps = timesteps.to(original_samples.device, dtype=torch.float32)\n else:\n schedule_timesteps = self.timesteps.to(original_samples.device)\n timesteps = timesteps.to(original_samples.device)\n\n # self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index\n if self.begin_index is None:\n step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps]\n elif self.step_index is not None:\n # add_noise is called after first denoising step (for inpainting)\n step_indices = [self.step_index] * timesteps.shape[0]\n else:\n # add noise is called before first denoising step to create initial latent(img2img)\n step_indices = [self.begin_index] * timesteps.shape[0]\n\n sigma = sigmas[step_indices].flatten()\n while len(sigma.shape) < len(original_samples.shape):\n sigma = sigma.unsqueeze(-1)\n\n noisy_samples = original_samples + noise * sigma\n return noisy_samples\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_edm_euler.py", "content": "# Copyright 2024 Katherine Crowson and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport math\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple, Union\n\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import BaseOutput, logging\nfrom ..utils.torch_utils import randn_tensor\nfrom .scheduling_utils import SchedulerMixin\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n@dataclass\n# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete\nclass EDMEulerSchedulerOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's `step` function output.\n\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample `(x_{0})` based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.Tensor\n pred_original_sample: Optional[torch.Tensor] = None\n\n\nclass EDMEulerScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n Implements the Euler scheduler in EDM formulation as presented in Karras et al. 2022 [1].\n\n [1] Karras, Tero, et al. \"Elucidating the Design Space of Diffusion-Based Generative Models.\"\n https://arxiv.org/abs/2206.00364\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n sigma_min (`float`, *optional*, defaults to 0.002):\n Minimum noise magnitude in the sigma schedule. This was set to 0.002 in the EDM paper [1]; a reasonable\n range is [0, 10].\n sigma_max (`float`, *optional*, defaults to 80.0):\n Maximum noise magnitude in the sigma schedule. This was set to 80.0 in the EDM paper [1]; a reasonable\n range is [0.2, 80.0].\n sigma_data (`float`, *optional*, defaults to 0.5):\n The standard deviation of the data distribution. This is set to 0.5 in the EDM paper [1].\n sigma_schedule (`str`, *optional*, defaults to `karras`):\n Sigma schedule to compute the `sigmas`. By default, we the schedule introduced in the EDM paper\n (https://arxiv.org/abs/2206.00364). Other acceptable value is \"exponential\". The exponential schedule was\n incorporated in this model: https://huggingface.co/stabilityai/cosxl.\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n prediction_type (`str`, defaults to `epsilon`, *optional*):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),\n `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen\n Video](https://imagen.research.google/video/paper.pdf) paper).\n rho (`float`, *optional*, defaults to 7.0):\n The rho parameter used for calculating the Karras sigma schedule, which is set to 7.0 in the EDM paper [1].\n \"\"\"\n\n _compatibles = []\n order = 1\n\n @register_to_config\n def __init__(\n self,\n sigma_min: float = 0.002,\n sigma_max: float = 80.0,\n sigma_data: float = 0.5,\n sigma_schedule: str = \"karras\",\n num_train_timesteps: int = 1000,\n prediction_type: str = \"epsilon\",\n rho: float = 7.0,\n ):\n if sigma_schedule not in [\"karras\", \"exponential\"]:\n raise ValueError(f\"Wrong value for provided for `{sigma_schedule=}`.`\")\n\n # setable values\n self.num_inference_steps = None\n\n ramp = torch.linspace(0, 1, num_train_timesteps)\n if sigma_schedule == \"karras\":\n sigmas = self._compute_karras_sigmas(ramp)\n elif sigma_schedule == \"exponential\":\n sigmas = self._compute_exponential_sigmas(ramp)\n\n self.timesteps = self.precondition_noise(sigmas)\n\n self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])\n\n self.is_scale_input_called = False\n\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n @property\n def init_noise_sigma(self):\n # standard deviation of the initial noise distribution\n return (self.config.sigma_max**2 + 1) ** 0.5\n\n @property\n def step_index(self):\n \"\"\"\n The index counter for current timestep. It will increase 1 after each scheduler step.\n \"\"\"\n return self._step_index\n\n @property\n def begin_index(self):\n \"\"\"\n The index for the first timestep. It should be set from pipeline with `set_begin_index` method.\n \"\"\"\n return self._begin_index\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index\n def set_begin_index(self, begin_index: int = 0):\n \"\"\"\n Sets the begin index for the scheduler. This function should be run from pipeline before the inference.\n\n Args:\n begin_index (`int`):\n The begin index for the scheduler.\n \"\"\"\n self._begin_index = begin_index\n\n def precondition_inputs(self, sample, sigma):\n c_in = 1 / ((sigma**2 + self.config.sigma_data**2) ** 0.5)\n scaled_sample = sample * c_in\n return scaled_sample\n\n def precondition_noise(self, sigma):\n if not isinstance(sigma, torch.Tensor):\n sigma = torch.tensor([sigma])\n\n c_noise = 0.25 * torch.log(sigma)\n\n return c_noise\n\n def precondition_outputs(self, sample, model_output, sigma):\n sigma_data = self.config.sigma_data\n c_skip = sigma_data**2 / (sigma**2 + sigma_data**2)\n\n if self.config.prediction_type == \"epsilon\":\n c_out = sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5\n elif self.config.prediction_type == \"v_prediction\":\n c_out = -sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5\n else:\n raise ValueError(f\"Prediction type {self.config.prediction_type} is not supported.\")\n\n denoised = c_skip * sample + c_out * model_output\n\n return denoised\n\n def scale_model_input(self, sample: torch.Tensor, timestep: Union[float, torch.Tensor]) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep. Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`int`, *optional*):\n The current timestep in the diffusion chain.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n if self.step_index is None:\n self._init_step_index(timestep)\n\n sigma = self.sigmas[self.step_index]\n sample = self.precondition_inputs(sample, sigma)\n\n self.is_scale_input_called = True\n return sample\n\n def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n \"\"\"\n self.num_inference_steps = num_inference_steps\n\n ramp = torch.linspace(0, 1, self.num_inference_steps)\n if self.config.sigma_schedule == \"karras\":\n sigmas = self._compute_karras_sigmas(ramp)\n elif self.config.sigma_schedule == \"exponential\":\n sigmas = self._compute_exponential_sigmas(ramp)\n\n sigmas = sigmas.to(dtype=torch.float32, device=device)\n self.timesteps = self.precondition_noise(sigmas)\n\n self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n # Taken from https://github.com/crowsonkb/k-diffusion/blob/686dbad0f39640ea25c8a8c6a6e56bb40eacefa2/k_diffusion/sampling.py#L17\n def _compute_karras_sigmas(self, ramp, sigma_min=None, sigma_max=None) -> torch.Tensor:\n \"\"\"Constructs the noise schedule of Karras et al. (2022).\"\"\"\n sigma_min = sigma_min or self.config.sigma_min\n sigma_max = sigma_max or self.config.sigma_max\n\n rho = self.config.rho\n min_inv_rho = sigma_min ** (1 / rho)\n max_inv_rho = sigma_max ** (1 / rho)\n sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho\n return sigmas\n\n def _compute_exponential_sigmas(self, ramp, sigma_min=None, sigma_max=None) -> torch.Tensor:\n \"\"\"Implementation closely follows k-diffusion.\n\n https://github.com/crowsonkb/k-diffusion/blob/6ab5146d4a5ef63901326489f31f1d8e7dd36b48/k_diffusion/sampling.py#L26\n \"\"\"\n sigma_min = sigma_min or self.config.sigma_min\n sigma_max = sigma_max or self.config.sigma_max\n sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), len(ramp)).exp().flip(0)\n return sigmas\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.index_for_timestep\n def index_for_timestep(self, timestep, schedule_timesteps=None):\n if schedule_timesteps is None:\n schedule_timesteps = self.timesteps\n\n indices = (schedule_timesteps == timestep).nonzero()\n\n # The sigma index that is taken for the **very** first `step`\n # is always the second index (or the last index if there is only 1)\n # This way we can ensure we don't accidentally skip a sigma in\n # case we start in the middle of the denoising schedule (e.g. for image-to-image)\n pos = 1 if len(indices) > 1 else 0\n\n return indices[pos].item()\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index\n def _init_step_index(self, timestep):\n if self.begin_index is None:\n if isinstance(timestep, torch.Tensor):\n timestep = timestep.to(self.timesteps.device)\n self._step_index = self.index_for_timestep(timestep)\n else:\n self._step_index = self._begin_index\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: Union[float, torch.Tensor],\n sample: torch.Tensor,\n s_churn: float = 0.0,\n s_tmin: float = 0.0,\n s_tmax: float = float(\"inf\"),\n s_noise: float = 1.0,\n generator: Optional[torch.Generator] = None,\n return_dict: bool = True,\n ) -> Union[EDMEulerSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`float`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n s_churn (`float`):\n s_tmin (`float`):\n s_tmax (`float`):\n s_noise (`float`, defaults to 1.0):\n Scaling factor for noise added to the sample.\n generator (`torch.Generator`, *optional*):\n A random number generator.\n return_dict (`bool`):\n Whether or not to return a [`~schedulers.scheduling_euler_discrete.EDMEulerSchedulerOutput`] or tuple.\n\n Returns:\n [`~schedulers.scheduling_euler_discrete.EDMEulerSchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_euler_discrete.EDMEulerSchedulerOutput`] is\n returned, otherwise a tuple is returned where the first element is the sample tensor.\n \"\"\"\n\n if isinstance(timestep, (int, torch.IntTensor, torch.LongTensor)):\n raise ValueError(\n (\n \"Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to\"\n \" `EDMEulerScheduler.step()` is not supported. Make sure to pass\"\n \" one of the `scheduler.timesteps` as a timestep.\"\n ),\n )\n\n if not self.is_scale_input_called:\n logger.warning(\n \"The `scale_model_input` function should be called before `step` to ensure correct denoising. \"\n \"See `StableDiffusionPipeline` for a usage example.\"\n )\n\n if self.step_index is None:\n self._init_step_index(timestep)\n\n # Upcast to avoid precision issues when computing prev_sample\n sample = sample.to(torch.float32)\n\n sigma = self.sigmas[self.step_index]\n\n gamma = min(s_churn / (len(self.sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0.0\n\n noise = randn_tensor(\n model_output.shape, dtype=model_output.dtype, device=model_output.device, generator=generator\n )\n\n eps = noise * s_noise\n sigma_hat = sigma * (gamma + 1)\n\n if gamma > 0:\n sample = sample + eps * (sigma_hat**2 - sigma**2) ** 0.5\n\n # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise\n pred_original_sample = self.precondition_outputs(sample, model_output, sigma_hat)\n\n # 2. Convert to an ODE derivative\n derivative = (sample - pred_original_sample) / sigma_hat\n\n dt = self.sigmas[self.step_index + 1] - sigma_hat\n\n prev_sample = sample + derivative * dt\n\n # Cast sample back to model compatible dtype\n prev_sample = prev_sample.to(model_output.dtype)\n\n # upon completion increase step index by one\n self._step_index += 1\n\n if not return_dict:\n return (prev_sample,)\n\n return EDMEulerSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.add_noise\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.Tensor,\n ) -> torch.Tensor:\n # Make sure sigmas and timesteps have the same device and dtype as original_samples\n sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)\n if original_samples.device.type == \"mps\" and torch.is_floating_point(timesteps):\n # mps does not support float64\n schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)\n timesteps = timesteps.to(original_samples.device, dtype=torch.float32)\n else:\n schedule_timesteps = self.timesteps.to(original_samples.device)\n timesteps = timesteps.to(original_samples.device)\n\n # self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index\n if self.begin_index is None:\n step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps]\n elif self.step_index is not None:\n # add_noise is called after first denoising step (for inpainting)\n step_indices = [self.step_index] * timesteps.shape[0]\n else:\n # add noise is called before first denoising step to create initial latent(img2img)\n step_indices = [self.begin_index] * timesteps.shape[0]\n\n sigma = sigmas[step_indices].flatten()\n while len(sigma.shape) < len(original_samples.shape):\n sigma = sigma.unsqueeze(-1)\n\n noisy_samples = original_samples + noise * sigma\n return noisy_samples\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py", "content": "# Copyright 2024 Katherine Crowson and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport math\nfrom dataclasses import dataclass\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import BaseOutput, logging\nfrom ..utils.torch_utils import randn_tensor\nfrom .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n@dataclass\n# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete\nclass EulerAncestralDiscreteSchedulerOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's `step` function output.\n\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample `(x_{0})` based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.Tensor\n pred_original_sample: Optional[torch.Tensor] = None\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\n# Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr\ndef rescale_zero_terminal_snr(betas):\n \"\"\"\n Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)\n\n\n Args:\n betas (`torch.Tensor`):\n the betas that the scheduler is being initialized with.\n\n Returns:\n `torch.Tensor`: rescaled betas with zero terminal SNR\n \"\"\"\n # Convert betas to alphas_bar_sqrt\n alphas = 1.0 - betas\n alphas_cumprod = torch.cumprod(alphas, dim=0)\n alphas_bar_sqrt = alphas_cumprod.sqrt()\n\n # Store old values.\n alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()\n alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()\n\n # Shift so the last timestep is zero.\n alphas_bar_sqrt -= alphas_bar_sqrt_T\n\n # Scale so the first timestep is back to the old value.\n alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)\n\n # Convert alphas_bar_sqrt to betas\n alphas_bar = alphas_bar_sqrt**2 # Revert sqrt\n alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod\n alphas = torch.cat([alphas_bar[0:1], alphas])\n betas = 1 - alphas\n\n return betas\n\n\nclass EulerAncestralDiscreteScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n Ancestral sampling with Euler method steps.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n beta_start (`float`, defaults to 0.0001):\n The starting `beta` value of inference.\n beta_end (`float`, defaults to 0.02):\n The final `beta` value.\n beta_schedule (`str`, defaults to `\"linear\"`):\n The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear` or `scaled_linear`.\n trained_betas (`np.ndarray`, *optional*):\n Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.\n prediction_type (`str`, defaults to `epsilon`, *optional*):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),\n `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen\n Video](https://imagen.research.google/video/paper.pdf) paper).\n timestep_spacing (`str`, defaults to `\"linspace\"`):\n The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.\n steps_offset (`int`, defaults to 0):\n An offset added to the inference steps, as required by some model families.\n rescale_betas_zero_snr (`bool`, defaults to `False`):\n Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and\n dark samples instead of limiting it to samples with medium brightness. Loosely related to\n [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).\n \"\"\"\n\n _compatibles = [e.name for e in KarrasDiffusionSchedulers]\n order = 1\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[Union[np.ndarray, List[float]]] = None,\n prediction_type: str = \"epsilon\",\n timestep_spacing: str = \"linspace\",\n steps_offset: int = 0,\n rescale_betas_zero_snr: bool = False,\n ):\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n if rescale_betas_zero_snr:\n self.betas = rescale_zero_terminal_snr(self.betas)\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n\n if rescale_betas_zero_snr:\n # Close to 0 without being 0 so first sigma is not inf\n # FP16 smallest positive subnormal works well here\n self.alphas_cumprod[-1] = 2**-24\n\n sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)\n sigmas = np.concatenate([sigmas[::-1], [0.0]]).astype(np.float32)\n self.sigmas = torch.from_numpy(sigmas)\n\n # setable values\n self.num_inference_steps = None\n timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=float)[::-1].copy()\n self.timesteps = torch.from_numpy(timesteps)\n self.is_scale_input_called = False\n\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n @property\n def init_noise_sigma(self):\n # standard deviation of the initial noise distribution\n if self.config.timestep_spacing in [\"linspace\", \"trailing\"]:\n return self.sigmas.max()\n\n return (self.sigmas.max() ** 2 + 1) ** 0.5\n\n @property\n def step_index(self):\n \"\"\"\n The index counter for current timestep. It will increase 1 after each scheduler step.\n \"\"\"\n return self._step_index\n\n @property\n def begin_index(self):\n \"\"\"\n The index for the first timestep. It should be set from pipeline with `set_begin_index` method.\n \"\"\"\n return self._begin_index\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index\n def set_begin_index(self, begin_index: int = 0):\n \"\"\"\n Sets the begin index for the scheduler. This function should be run from pipeline before the inference.\n\n Args:\n begin_index (`int`):\n The begin index for the scheduler.\n \"\"\"\n self._begin_index = begin_index\n\n def scale_model_input(self, sample: torch.Tensor, timestep: Union[float, torch.Tensor]) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep. Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`int`, *optional*):\n The current timestep in the diffusion chain.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n\n if self.step_index is None:\n self._init_step_index(timestep)\n\n sigma = self.sigmas[self.step_index]\n sample = sample / ((sigma**2 + 1) ** 0.5)\n self.is_scale_input_called = True\n return sample\n\n def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n \"\"\"\n self.num_inference_steps = num_inference_steps\n\n # \"linspace\", \"leading\", \"trailing\" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891\n if self.config.timestep_spacing == \"linspace\":\n timesteps = np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps, dtype=np.float32)[\n ::-1\n ].copy()\n elif self.config.timestep_spacing == \"leading\":\n step_ratio = self.config.num_train_timesteps // self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.float32)\n timesteps += self.config.steps_offset\n elif self.config.timestep_spacing == \"trailing\":\n step_ratio = self.config.num_train_timesteps / self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (np.arange(self.config.num_train_timesteps, 0, -step_ratio)).round().copy().astype(np.float32)\n timesteps -= 1\n else:\n raise ValueError(\n f\"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.\"\n )\n\n sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)\n sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)\n sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32)\n self.sigmas = torch.from_numpy(sigmas).to(device=device)\n\n self.timesteps = torch.from_numpy(timesteps).to(device=device)\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.index_for_timestep\n def index_for_timestep(self, timestep, schedule_timesteps=None):\n if schedule_timesteps is None:\n schedule_timesteps = self.timesteps\n\n indices = (schedule_timesteps == timestep).nonzero()\n\n # The sigma index that is taken for the **very** first `step`\n # is always the second index (or the last index if there is only 1)\n # This way we can ensure we don't accidentally skip a sigma in\n # case we start in the middle of the denoising schedule (e.g. for image-to-image)\n pos = 1 if len(indices) > 1 else 0\n\n return indices[pos].item()\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index\n def _init_step_index(self, timestep):\n if self.begin_index is None:\n if isinstance(timestep, torch.Tensor):\n timestep = timestep.to(self.timesteps.device)\n self._step_index = self.index_for_timestep(timestep)\n else:\n self._step_index = self._begin_index\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: Union[float, torch.Tensor],\n sample: torch.Tensor,\n generator: Optional[torch.Generator] = None,\n return_dict: bool = True,\n ) -> Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`float`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n generator (`torch.Generator`, *optional*):\n A random number generator.\n return_dict (`bool`):\n Whether or not to return a\n [`~schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteSchedulerOutput`] or tuple.\n\n Returns:\n [`~schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteSchedulerOutput`] or `tuple`:\n If return_dict is `True`,\n [`~schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteSchedulerOutput`] is returned,\n otherwise a tuple is returned where the first element is the sample tensor.\n\n \"\"\"\n\n if isinstance(timestep, (int, torch.IntTensor, torch.LongTensor)):\n raise ValueError(\n (\n \"Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to\"\n \" `EulerDiscreteScheduler.step()` is not supported. Make sure to pass\"\n \" one of the `scheduler.timesteps` as a timestep.\"\n ),\n )\n\n if not self.is_scale_input_called:\n logger.warning(\n \"The `scale_model_input` function should be called before `step` to ensure correct denoising. \"\n \"See `StableDiffusionPipeline` for a usage example.\"\n )\n\n if self.step_index is None:\n self._init_step_index(timestep)\n\n sigma = self.sigmas[self.step_index]\n\n # Upcast to avoid precision issues when computing prev_sample\n sample = sample.to(torch.float32)\n\n # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise\n if self.config.prediction_type == \"epsilon\":\n pred_original_sample = sample - sigma * model_output\n elif self.config.prediction_type == \"v_prediction\":\n # * c_out + input * c_skip\n pred_original_sample = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))\n elif self.config.prediction_type == \"sample\":\n raise NotImplementedError(\"prediction_type not implemented yet: sample\")\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`\"\n )\n\n sigma_from = self.sigmas[self.step_index]\n sigma_to = self.sigmas[self.step_index + 1]\n sigma_up = (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5\n sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5\n\n # 2. Convert to an ODE derivative\n derivative = (sample - pred_original_sample) / sigma\n\n dt = sigma_down - sigma\n\n prev_sample = sample + derivative * dt\n\n device = model_output.device\n noise = randn_tensor(model_output.shape, dtype=model_output.dtype, device=device, generator=generator)\n\n prev_sample = prev_sample + noise * sigma_up\n\n # Cast sample back to model compatible dtype\n prev_sample = prev_sample.to(model_output.dtype)\n\n # upon completion increase step index by one\n self._step_index += 1\n\n if not return_dict:\n return (prev_sample,)\n\n return EulerAncestralDiscreteSchedulerOutput(\n prev_sample=prev_sample, pred_original_sample=pred_original_sample\n )\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.add_noise\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.Tensor,\n ) -> torch.Tensor:\n # Make sure sigmas and timesteps have the same device and dtype as original_samples\n sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)\n if original_samples.device.type == \"mps\" and torch.is_floating_point(timesteps):\n # mps does not support float64\n schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)\n timesteps = timesteps.to(original_samples.device, dtype=torch.float32)\n else:\n schedule_timesteps = self.timesteps.to(original_samples.device)\n timesteps = timesteps.to(original_samples.device)\n\n # self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index\n if self.begin_index is None:\n step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps]\n elif self.step_index is not None:\n # add_noise is called after first denoising step (for inpainting)\n step_indices = [self.step_index] * timesteps.shape[0]\n else:\n # add noise is called before first denoising step to create initial latent(img2img)\n step_indices = [self.begin_index] * timesteps.shape[0]\n\n sigma = sigmas[step_indices].flatten()\n while len(sigma.shape) < len(original_samples.shape):\n sigma = sigma.unsqueeze(-1)\n\n noisy_samples = original_samples + noise * sigma\n return noisy_samples\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py", "content": "# Copyright 2024 Katherine Crowson and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport math\nfrom dataclasses import dataclass\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import BaseOutput, logging\nfrom ..utils.torch_utils import randn_tensor\nfrom .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n@dataclass\n# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete\nclass EulerDiscreteSchedulerOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's `step` function output.\n\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample `(x_{0})` based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.Tensor\n pred_original_sample: Optional[torch.Tensor] = None\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\n# Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr\ndef rescale_zero_terminal_snr(betas):\n \"\"\"\n Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)\n\n\n Args:\n betas (`torch.Tensor`):\n the betas that the scheduler is being initialized with.\n\n Returns:\n `torch.Tensor`: rescaled betas with zero terminal SNR\n \"\"\"\n # Convert betas to alphas_bar_sqrt\n alphas = 1.0 - betas\n alphas_cumprod = torch.cumprod(alphas, dim=0)\n alphas_bar_sqrt = alphas_cumprod.sqrt()\n\n # Store old values.\n alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()\n alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()\n\n # Shift so the last timestep is zero.\n alphas_bar_sqrt -= alphas_bar_sqrt_T\n\n # Scale so the first timestep is back to the old value.\n alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)\n\n # Convert alphas_bar_sqrt to betas\n alphas_bar = alphas_bar_sqrt**2 # Revert sqrt\n alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod\n alphas = torch.cat([alphas_bar[0:1], alphas])\n betas = 1 - alphas\n\n return betas\n\n\nclass EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n Euler scheduler.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n beta_start (`float`, defaults to 0.0001):\n The starting `beta` value of inference.\n beta_end (`float`, defaults to 0.02):\n The final `beta` value.\n beta_schedule (`str`, defaults to `\"linear\"`):\n The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear` or `scaled_linear`.\n trained_betas (`np.ndarray`, *optional*):\n Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.\n prediction_type (`str`, defaults to `epsilon`, *optional*):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),\n `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen\n Video](https://imagen.research.google/video/paper.pdf) paper).\n interpolation_type(`str`, defaults to `\"linear\"`, *optional*):\n The interpolation type to compute intermediate sigmas for the scheduler denoising steps. Should be on of\n `\"linear\"` or `\"log_linear\"`.\n use_karras_sigmas (`bool`, *optional*, defaults to `False`):\n Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`,\n the sigmas are determined according to a sequence of noise levels {σi}.\n timestep_spacing (`str`, defaults to `\"linspace\"`):\n The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.\n steps_offset (`int`, defaults to 0):\n An offset added to the inference steps, as required by some model families.\n rescale_betas_zero_snr (`bool`, defaults to `False`):\n Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and\n dark samples instead of limiting it to samples with medium brightness. Loosely related to\n [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).\n final_sigmas_type (`str`, defaults to `\"zero\"`):\n The final `sigma` value for the noise schedule during the sampling process. If `\"sigma_min\"`, the final\n sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0.\n \"\"\"\n\n _compatibles = [e.name for e in KarrasDiffusionSchedulers]\n order = 1\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[Union[np.ndarray, List[float]]] = None,\n prediction_type: str = \"epsilon\",\n interpolation_type: str = \"linear\",\n use_karras_sigmas: Optional[bool] = False,\n sigma_min: Optional[float] = None,\n sigma_max: Optional[float] = None,\n timestep_spacing: str = \"linspace\",\n timestep_type: str = \"discrete\", # can be \"discrete\" or \"continuous\"\n steps_offset: int = 0,\n rescale_betas_zero_snr: bool = False,\n final_sigmas_type: str = \"zero\", # can be \"zero\" or \"sigma_min\"\n ):\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n if rescale_betas_zero_snr:\n self.betas = rescale_zero_terminal_snr(self.betas)\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n\n if rescale_betas_zero_snr:\n # Close to 0 without being 0 so first sigma is not inf\n # FP16 smallest positive subnormal works well here\n self.alphas_cumprod[-1] = 2**-24\n\n sigmas = (((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5).flip(0)\n timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=float)[::-1].copy()\n timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32)\n\n # setable values\n self.num_inference_steps = None\n\n # TODO: Support the full EDM scalings for all prediction types and timestep types\n if timestep_type == \"continuous\" and prediction_type == \"v_prediction\":\n self.timesteps = torch.Tensor([0.25 * sigma.log() for sigma in sigmas])\n else:\n self.timesteps = timesteps\n\n self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])\n\n self.is_scale_input_called = False\n self.use_karras_sigmas = use_karras_sigmas\n\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n @property\n def init_noise_sigma(self):\n # standard deviation of the initial noise distribution\n max_sigma = max(self.sigmas) if isinstance(self.sigmas, list) else self.sigmas.max()\n if self.config.timestep_spacing in [\"linspace\", \"trailing\"]:\n return max_sigma\n\n return (max_sigma**2 + 1) ** 0.5\n\n @property\n def step_index(self):\n \"\"\"\n The index counter for current timestep. It will increase 1 after each scheduler step.\n \"\"\"\n return self._step_index\n\n @property\n def begin_index(self):\n \"\"\"\n The index for the first timestep. It should be set from pipeline with `set_begin_index` method.\n \"\"\"\n return self._begin_index\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index\n def set_begin_index(self, begin_index: int = 0):\n \"\"\"\n Sets the begin index for the scheduler. This function should be run from pipeline before the inference.\n\n Args:\n begin_index (`int`):\n The begin index for the scheduler.\n \"\"\"\n self._begin_index = begin_index\n\n def scale_model_input(self, sample: torch.Tensor, timestep: Union[float, torch.Tensor]) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep. Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`int`, *optional*):\n The current timestep in the diffusion chain.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n if self.step_index is None:\n self._init_step_index(timestep)\n\n sigma = self.sigmas[self.step_index]\n sample = sample / ((sigma**2 + 1) ** 0.5)\n\n self.is_scale_input_called = True\n return sample\n\n def set_timesteps(\n self,\n num_inference_steps: int = None,\n device: Union[str, torch.device] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n ):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to support arbitrary timesteps schedule. If `None`, timesteps will be generated\n based on the `timestep_spacing` attribute. If `timesteps` is passed, `num_inference_steps` and `sigmas`\n must be `None`, and `timestep_spacing` attribute will be ignored.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to support arbitrary timesteps schedule schedule. If `None`, timesteps and sigmas\n will be generated based on the relevant scheduler attributes. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`, and the timesteps will be generated based on the\n custom sigmas schedule.\n \"\"\"\n\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` should be set.\")\n if num_inference_steps is None and timesteps is None and sigmas is None:\n raise ValueError(\"Must pass exactly one of `num_inference_steps` or `timesteps` or `sigmas.\")\n if num_inference_steps is not None and (timesteps is not None or sigmas is not None):\n raise ValueError(\"Can only pass one of `num_inference_steps` or `timesteps` or `sigmas`.\")\n if timesteps is not None and self.config.use_karras_sigmas:\n raise ValueError(\"Cannot set `timesteps` with `config.use_karras_sigmas = True`.\")\n if (\n timesteps is not None\n and self.config.timestep_type == \"continuous\"\n and self.config.prediction_type == \"v_prediction\"\n ):\n raise ValueError(\n \"Cannot set `timesteps` with `config.timestep_type = 'continuous'` and `config.prediction_type = 'v_prediction'`.\"\n )\n\n if num_inference_steps is None:\n num_inference_steps = len(timesteps) if timesteps is not None else len(sigmas) - 1\n self.num_inference_steps = num_inference_steps\n\n if sigmas is not None:\n log_sigmas = np.log(np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5))\n sigmas = np.array(sigmas).astype(np.float32)\n timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas[:-1]])\n\n else:\n if timesteps is not None:\n timesteps = np.array(timesteps).astype(np.float32)\n else:\n # \"linspace\", \"leading\", \"trailing\" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891\n if self.config.timestep_spacing == \"linspace\":\n timesteps = np.linspace(\n 0, self.config.num_train_timesteps - 1, num_inference_steps, dtype=np.float32\n )[::-1].copy()\n elif self.config.timestep_spacing == \"leading\":\n step_ratio = self.config.num_train_timesteps // self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (\n (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.float32)\n )\n timesteps += self.config.steps_offset\n elif self.config.timestep_spacing == \"trailing\":\n step_ratio = self.config.num_train_timesteps / self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (\n (np.arange(self.config.num_train_timesteps, 0, -step_ratio)).round().copy().astype(np.float32)\n )\n timesteps -= 1\n else:\n raise ValueError(\n f\"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.\"\n )\n\n sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)\n log_sigmas = np.log(sigmas)\n if self.config.interpolation_type == \"linear\":\n sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)\n elif self.config.interpolation_type == \"log_linear\":\n sigmas = torch.linspace(np.log(sigmas[-1]), np.log(sigmas[0]), num_inference_steps + 1).exp().numpy()\n else:\n raise ValueError(\n f\"{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either\"\n \" 'linear' or 'log_linear'\"\n )\n\n if self.config.use_karras_sigmas:\n sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=self.num_inference_steps)\n timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas])\n\n if self.config.final_sigmas_type == \"sigma_min\":\n sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0]) ** 0.5\n elif self.config.final_sigmas_type == \"zero\":\n sigma_last = 0\n else:\n raise ValueError(\n f\"`final_sigmas_type` must be one of 'zero', or 'sigma_min', but got {self.config.final_sigmas_type}\"\n )\n\n sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)\n\n sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device)\n\n # TODO: Support the full EDM scalings for all prediction types and timestep types\n if self.config.timestep_type == \"continuous\" and self.config.prediction_type == \"v_prediction\":\n self.timesteps = torch.Tensor([0.25 * sigma.log() for sigma in sigmas[:-1]]).to(device=device)\n else:\n self.timesteps = torch.from_numpy(timesteps.astype(np.float32)).to(device=device)\n\n self._step_index = None\n self._begin_index = None\n self.sigmas = sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n def _sigma_to_t(self, sigma, log_sigmas):\n # get log sigma\n log_sigma = np.log(np.maximum(sigma, 1e-10))\n\n # get distribution\n dists = log_sigma - log_sigmas[:, np.newaxis]\n\n # get sigmas range\n low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)\n high_idx = low_idx + 1\n\n low = log_sigmas[low_idx]\n high = log_sigmas[high_idx]\n\n # interpolate sigmas\n w = (low - log_sigma) / (low - high)\n w = np.clip(w, 0, 1)\n\n # transform interpolation to time range\n t = (1 - w) * low_idx + w * high_idx\n t = t.reshape(sigma.shape)\n return t\n\n # Copied from https://github.com/crowsonkb/k-diffusion/blob/686dbad0f39640ea25c8a8c6a6e56bb40eacefa2/k_diffusion/sampling.py#L17\n def _convert_to_karras(self, in_sigmas: torch.Tensor, num_inference_steps) -> torch.Tensor:\n \"\"\"Constructs the noise schedule of Karras et al. (2022).\"\"\"\n\n # Hack to make sure that other schedulers which copy this function don't break\n # TODO: Add this logic to the other schedulers\n if hasattr(self.config, \"sigma_min\"):\n sigma_min = self.config.sigma_min\n else:\n sigma_min = None\n\n if hasattr(self.config, \"sigma_max\"):\n sigma_max = self.config.sigma_max\n else:\n sigma_max = None\n\n sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item()\n sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item()\n\n rho = 7.0 # 7.0 is the value used in the paper\n ramp = np.linspace(0, 1, num_inference_steps)\n min_inv_rho = sigma_min ** (1 / rho)\n max_inv_rho = sigma_max ** (1 / rho)\n sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho\n return sigmas\n\n def index_for_timestep(self, timestep, schedule_timesteps=None):\n if schedule_timesteps is None:\n schedule_timesteps = self.timesteps\n\n indices = (schedule_timesteps == timestep).nonzero()\n\n # The sigma index that is taken for the **very** first `step`\n # is always the second index (or the last index if there is only 1)\n # This way we can ensure we don't accidentally skip a sigma in\n # case we start in the middle of the denoising schedule (e.g. for image-to-image)\n pos = 1 if len(indices) > 1 else 0\n\n return indices[pos].item()\n\n def _init_step_index(self, timestep):\n if self.begin_index is None:\n if isinstance(timestep, torch.Tensor):\n timestep = timestep.to(self.timesteps.device)\n self._step_index = self.index_for_timestep(timestep)\n else:\n self._step_index = self._begin_index\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: Union[float, torch.Tensor],\n sample: torch.Tensor,\n s_churn: float = 0.0,\n s_tmin: float = 0.0,\n s_tmax: float = float(\"inf\"),\n s_noise: float = 1.0,\n generator: Optional[torch.Generator] = None,\n return_dict: bool = True,\n ) -> Union[EulerDiscreteSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`float`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n s_churn (`float`):\n s_tmin (`float`):\n s_tmax (`float`):\n s_noise (`float`, defaults to 1.0):\n Scaling factor for noise added to the sample.\n generator (`torch.Generator`, *optional*):\n A random number generator.\n return_dict (`bool`):\n Whether or not to return a [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or\n tuple.\n\n Returns:\n [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] is\n returned, otherwise a tuple is returned where the first element is the sample tensor.\n \"\"\"\n\n if isinstance(timestep, (int, torch.IntTensor, torch.LongTensor)):\n raise ValueError(\n (\n \"Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to\"\n \" `EulerDiscreteScheduler.step()` is not supported. Make sure to pass\"\n \" one of the `scheduler.timesteps` as a timestep.\"\n ),\n )\n\n if not self.is_scale_input_called:\n logger.warning(\n \"The `scale_model_input` function should be called before `step` to ensure correct denoising. \"\n \"See `StableDiffusionPipeline` for a usage example.\"\n )\n\n if self.step_index is None:\n self._init_step_index(timestep)\n\n # Upcast to avoid precision issues when computing prev_sample\n sample = sample.to(torch.float32)\n\n sigma = self.sigmas[self.step_index]\n\n gamma = min(s_churn / (len(self.sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0.0\n\n noise = randn_tensor(\n model_output.shape, dtype=model_output.dtype, device=model_output.device, generator=generator\n )\n\n eps = noise * s_noise\n sigma_hat = sigma * (gamma + 1)\n\n if gamma > 0:\n sample = sample + eps * (sigma_hat**2 - sigma**2) ** 0.5\n\n # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise\n # NOTE: \"original_sample\" should not be an expected prediction_type but is left in for\n # backwards compatibility\n if self.config.prediction_type == \"original_sample\" or self.config.prediction_type == \"sample\":\n pred_original_sample = model_output\n elif self.config.prediction_type == \"epsilon\":\n pred_original_sample = sample - sigma_hat * model_output\n elif self.config.prediction_type == \"v_prediction\":\n # denoised = model_output * c_out + input * c_skip\n pred_original_sample = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`\"\n )\n\n # 2. Convert to an ODE derivative\n derivative = (sample - pred_original_sample) / sigma_hat\n\n dt = self.sigmas[self.step_index + 1] - sigma_hat\n\n prev_sample = sample + derivative * dt\n\n # Cast sample back to model compatible dtype\n prev_sample = prev_sample.to(model_output.dtype)\n\n # upon completion increase step index by one\n self._step_index += 1\n\n if not return_dict:\n return (prev_sample,)\n\n return EulerDiscreteSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)\n\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.Tensor,\n ) -> torch.Tensor:\n # Make sure sigmas and timesteps have the same device and dtype as original_samples\n sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)\n if original_samples.device.type == \"mps\" and torch.is_floating_point(timesteps):\n # mps does not support float64\n schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)\n timesteps = timesteps.to(original_samples.device, dtype=torch.float32)\n else:\n schedule_timesteps = self.timesteps.to(original_samples.device)\n timesteps = timesteps.to(original_samples.device)\n\n # self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index\n if self.begin_index is None:\n step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps]\n elif self.step_index is not None:\n # add_noise is called after first denoising step (for inpainting)\n step_indices = [self.step_index] * timesteps.shape[0]\n else:\n # add noise is called before first denoising step to create initial latent(img2img)\n step_indices = [self.begin_index] * timesteps.shape[0]\n\n sigma = sigmas[step_indices].flatten()\n while len(sigma.shape) < len(original_samples.shape):\n sigma = sigma.unsqueeze(-1)\n\n noisy_samples = original_samples + noise * sigma\n return noisy_samples\n\n def get_velocity(self, sample: torch.Tensor, noise: torch.Tensor, timesteps: torch.Tensor) -> torch.Tensor:\n if (\n isinstance(timesteps, int)\n or isinstance(timesteps, torch.IntTensor)\n or isinstance(timesteps, torch.LongTensor)\n ):\n raise ValueError(\n (\n \"Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to\"\n \" `EulerDiscreteScheduler.get_velocity()` is not supported. Make sure to pass\"\n \" one of the `scheduler.timesteps` as a timestep.\"\n ),\n )\n\n if sample.device.type == \"mps\" and torch.is_floating_point(timesteps):\n # mps does not support float64\n schedule_timesteps = self.timesteps.to(sample.device, dtype=torch.float32)\n timesteps = timesteps.to(sample.device, dtype=torch.float32)\n else:\n schedule_timesteps = self.timesteps.to(sample.device)\n timesteps = timesteps.to(sample.device)\n\n step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps]\n alphas_cumprod = self.alphas_cumprod.to(sample)\n sqrt_alpha_prod = alphas_cumprod[step_indices] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n while len(sqrt_alpha_prod.shape) < len(sample.shape):\n sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[step_indices]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n\n velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample\n return velocity\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_euler_discrete_flax.py", "content": "# Copyright 2024 Katherine Crowson and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple, Union\n\nimport flax\nimport jax.numpy as jnp\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom .scheduling_utils_flax import (\n CommonSchedulerState,\n FlaxKarrasDiffusionSchedulers,\n FlaxSchedulerMixin,\n FlaxSchedulerOutput,\n broadcast_to_shape_from_left,\n)\n\n\n@flax.struct.dataclass\nclass EulerDiscreteSchedulerState:\n common: CommonSchedulerState\n\n # setable values\n init_noise_sigma: jnp.ndarray\n timesteps: jnp.ndarray\n sigmas: jnp.ndarray\n num_inference_steps: Optional[int] = None\n\n @classmethod\n def create(\n cls, common: CommonSchedulerState, init_noise_sigma: jnp.ndarray, timesteps: jnp.ndarray, sigmas: jnp.ndarray\n ):\n return cls(common=common, init_noise_sigma=init_noise_sigma, timesteps=timesteps, sigmas=sigmas)\n\n\n@dataclass\nclass FlaxEulerDiscreteSchedulerOutput(FlaxSchedulerOutput):\n state: EulerDiscreteSchedulerState\n\n\nclass FlaxEulerDiscreteScheduler(FlaxSchedulerMixin, ConfigMixin):\n \"\"\"\n Euler scheduler (Algorithm 2) from Karras et al. (2022) https://arxiv.org/abs/2206.00364. . Based on the original\n k-diffusion implementation by Katherine Crowson:\n https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L51\n\n\n [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`\n function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.\n [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and\n [`~SchedulerMixin.from_pretrained`] functions.\n\n Args:\n num_train_timesteps (`int`): number of diffusion steps used to train the model.\n beta_start (`float`): the starting `beta` value of inference.\n beta_end (`float`): the final `beta` value.\n beta_schedule (`str`):\n the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear` or `scaled_linear`.\n trained_betas (`jnp.ndarray`, optional):\n option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.\n prediction_type (`str`, default `epsilon`, optional):\n prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion\n process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4\n https://imagen.research.google/video/paper.pdf)\n dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):\n the `dtype` used for params and computation.\n \"\"\"\n\n _compatibles = [e.name for e in FlaxKarrasDiffusionSchedulers]\n\n dtype: jnp.dtype\n\n @property\n def has_state(self):\n return True\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[jnp.ndarray] = None,\n prediction_type: str = \"epsilon\",\n timestep_spacing: str = \"linspace\",\n dtype: jnp.dtype = jnp.float32,\n ):\n self.dtype = dtype\n\n def create_state(self, common: Optional[CommonSchedulerState] = None) -> EulerDiscreteSchedulerState:\n if common is None:\n common = CommonSchedulerState.create(self)\n\n timesteps = jnp.arange(0, self.config.num_train_timesteps).round()[::-1]\n sigmas = ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5\n sigmas = jnp.interp(timesteps, jnp.arange(0, len(sigmas)), sigmas)\n sigmas = jnp.concatenate([sigmas, jnp.array([0.0], dtype=self.dtype)])\n\n # standard deviation of the initial noise distribution\n if self.config.timestep_spacing in [\"linspace\", \"trailing\"]:\n init_noise_sigma = sigmas.max()\n else:\n init_noise_sigma = (sigmas.max() ** 2 + 1) ** 0.5\n\n return EulerDiscreteSchedulerState.create(\n common=common,\n init_noise_sigma=init_noise_sigma,\n timesteps=timesteps,\n sigmas=sigmas,\n )\n\n def scale_model_input(self, state: EulerDiscreteSchedulerState, sample: jnp.ndarray, timestep: int) -> jnp.ndarray:\n \"\"\"\n Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm.\n\n Args:\n state (`EulerDiscreteSchedulerState`):\n the `FlaxEulerDiscreteScheduler` state data class instance.\n sample (`jnp.ndarray`):\n current instance of sample being created by diffusion process.\n timestep (`int`):\n current discrete timestep in the diffusion chain.\n\n Returns:\n `jnp.ndarray`: scaled input sample\n \"\"\"\n (step_index,) = jnp.where(state.timesteps == timestep, size=1)\n step_index = step_index[0]\n\n sigma = state.sigmas[step_index]\n sample = sample / ((sigma**2 + 1) ** 0.5)\n return sample\n\n def set_timesteps(\n self, state: EulerDiscreteSchedulerState, num_inference_steps: int, shape: Tuple = ()\n ) -> EulerDiscreteSchedulerState:\n \"\"\"\n Sets the timesteps used for the diffusion chain. Supporting function to be run before inference.\n\n Args:\n state (`EulerDiscreteSchedulerState`):\n the `FlaxEulerDiscreteScheduler` state data class instance.\n num_inference_steps (`int`):\n the number of diffusion steps used when generating samples with a pre-trained model.\n \"\"\"\n\n if self.config.timestep_spacing == \"linspace\":\n timesteps = jnp.linspace(self.config.num_train_timesteps - 1, 0, num_inference_steps, dtype=self.dtype)\n elif self.config.timestep_spacing == \"leading\":\n step_ratio = self.config.num_train_timesteps // num_inference_steps\n timesteps = (jnp.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(float)\n timesteps += 1\n else:\n raise ValueError(\n f\"timestep_spacing must be one of ['linspace', 'leading'], got {self.config.timestep_spacing}\"\n )\n\n sigmas = ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5\n sigmas = jnp.interp(timesteps, jnp.arange(0, len(sigmas)), sigmas)\n sigmas = jnp.concatenate([sigmas, jnp.array([0.0], dtype=self.dtype)])\n\n # standard deviation of the initial noise distribution\n if self.config.timestep_spacing in [\"linspace\", \"trailing\"]:\n init_noise_sigma = sigmas.max()\n else:\n init_noise_sigma = (sigmas.max() ** 2 + 1) ** 0.5\n\n return state.replace(\n timesteps=timesteps,\n sigmas=sigmas,\n num_inference_steps=num_inference_steps,\n init_noise_sigma=init_noise_sigma,\n )\n\n def step(\n self,\n state: EulerDiscreteSchedulerState,\n model_output: jnp.ndarray,\n timestep: int,\n sample: jnp.ndarray,\n return_dict: bool = True,\n ) -> Union[FlaxEulerDiscreteSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n state (`EulerDiscreteSchedulerState`):\n the `FlaxEulerDiscreteScheduler` state data class instance.\n model_output (`jnp.ndarray`): direct output from learned diffusion model.\n timestep (`int`): current discrete timestep in the diffusion chain.\n sample (`jnp.ndarray`):\n current instance of sample being created by diffusion process.\n order: coefficient for multi-step inference.\n return_dict (`bool`): option for returning tuple rather than FlaxEulerDiscreteScheduler class\n\n Returns:\n [`FlaxEulerDiscreteScheduler`] or `tuple`: [`FlaxEulerDiscreteScheduler`] if `return_dict` is True,\n otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.\n\n \"\"\"\n if state.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n (step_index,) = jnp.where(state.timesteps == timestep, size=1)\n step_index = step_index[0]\n\n sigma = state.sigmas[step_index]\n\n # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise\n if self.config.prediction_type == \"epsilon\":\n pred_original_sample = sample - sigma * model_output\n elif self.config.prediction_type == \"v_prediction\":\n # * c_out + input * c_skip\n pred_original_sample = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`\"\n )\n\n # 2. Convert to an ODE derivative\n derivative = (sample - pred_original_sample) / sigma\n\n # dt = sigma_down - sigma\n dt = state.sigmas[step_index + 1] - sigma\n\n prev_sample = sample + derivative * dt\n\n if not return_dict:\n return (prev_sample, state)\n\n return FlaxEulerDiscreteSchedulerOutput(prev_sample=prev_sample, state=state)\n\n def add_noise(\n self,\n state: EulerDiscreteSchedulerState,\n original_samples: jnp.ndarray,\n noise: jnp.ndarray,\n timesteps: jnp.ndarray,\n ) -> jnp.ndarray:\n sigma = state.sigmas[timesteps].flatten()\n sigma = broadcast_to_shape_from_left(sigma, noise.shape)\n\n noisy_samples = original_samples + noise * sigma\n\n return noisy_samples\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py", "content": "# Copyright 2024 Stability AI, Katherine Crowson and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import BaseOutput, logging\nfrom ..utils.torch_utils import randn_tensor\nfrom .scheduling_utils import SchedulerMixin\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n@dataclass\nclass FlowMatchEulerDiscreteSchedulerOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's `step` function output.\n\n Args:\n prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n \"\"\"\n\n prev_sample: torch.FloatTensor\n\n\nclass FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n Euler scheduler.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n timestep_spacing (`str`, defaults to `\"linspace\"`):\n The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.\n shift (`float`, defaults to 1.0):\n The shift value for the timestep schedule.\n \"\"\"\n\n _compatibles = []\n order = 1\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n shift: float = 1.0,\n ):\n timesteps = np.linspace(1, num_train_timesteps, num_train_timesteps, dtype=np.float32)[::-1].copy()\n timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32)\n\n sigmas = timesteps / num_train_timesteps\n sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)\n\n self.timesteps = sigmas * num_train_timesteps\n\n self._step_index = None\n self._begin_index = None\n\n self.sigmas = sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n self.sigma_min = self.sigmas[-1].item()\n self.sigma_max = self.sigmas[0].item()\n\n @property\n def step_index(self):\n \"\"\"\n The index counter for current timestep. It will increase 1 after each scheduler step.\n \"\"\"\n return self._step_index\n\n @property\n def begin_index(self):\n \"\"\"\n The index for the first timestep. It should be set from pipeline with `set_begin_index` method.\n \"\"\"\n return self._begin_index\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index\n def set_begin_index(self, begin_index: int = 0):\n \"\"\"\n Sets the begin index for the scheduler. This function should be run from pipeline before the inference.\n\n Args:\n begin_index (`int`):\n The begin index for the scheduler.\n \"\"\"\n self._begin_index = begin_index\n\n def scale_noise(\n self,\n sample: torch.FloatTensor,\n timestep: Union[float, torch.FloatTensor],\n noise: Optional[torch.FloatTensor] = None,\n ) -> torch.FloatTensor:\n \"\"\"\n Forward process in flow-matching\n\n Args:\n sample (`torch.FloatTensor`):\n The input sample.\n timestep (`int`, *optional*):\n The current timestep in the diffusion chain.\n\n Returns:\n `torch.FloatTensor`:\n A scaled input sample.\n \"\"\"\n if self.step_index is None:\n self._init_step_index(timestep)\n\n sigma = self.sigmas[self.step_index]\n sample = sigma * noise + (1.0 - sigma) * sample\n\n return sample\n\n def _sigma_to_t(self, sigma):\n return sigma * self.config.num_train_timesteps\n\n def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n \"\"\"\n self.num_inference_steps = num_inference_steps\n\n timesteps = np.linspace(\n self._sigma_to_t(self.sigma_max), self._sigma_to_t(self.sigma_min), num_inference_steps\n )\n\n sigmas = timesteps / self.config.num_train_timesteps\n sigmas = self.config.shift * sigmas / (1 + (self.config.shift - 1) * sigmas)\n sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device)\n\n timesteps = sigmas * self.config.num_train_timesteps\n self.timesteps = timesteps.to(device=device)\n self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])\n\n self._step_index = None\n self._begin_index = None\n\n def index_for_timestep(self, timestep, schedule_timesteps=None):\n if schedule_timesteps is None:\n schedule_timesteps = self.timesteps\n\n indices = (schedule_timesteps == timestep).nonzero()\n\n # The sigma index that is taken for the **very** first `step`\n # is always the second index (or the last index if there is only 1)\n # This way we can ensure we don't accidentally skip a sigma in\n # case we start in the middle of the denoising schedule (e.g. for image-to-image)\n pos = 1 if len(indices) > 1 else 0\n\n return indices[pos].item()\n\n def _init_step_index(self, timestep):\n if self.begin_index is None:\n if isinstance(timestep, torch.Tensor):\n timestep = timestep.to(self.timesteps.device)\n self._step_index = self.index_for_timestep(timestep)\n else:\n self._step_index = self._begin_index\n\n def step(\n self,\n model_output: torch.FloatTensor,\n timestep: Union[float, torch.FloatTensor],\n sample: torch.FloatTensor,\n s_churn: float = 0.0,\n s_tmin: float = 0.0,\n s_tmax: float = float(\"inf\"),\n s_noise: float = 1.0,\n generator: Optional[torch.Generator] = None,\n return_dict: bool = True,\n ) -> Union[FlowMatchEulerDiscreteSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.FloatTensor`):\n The direct output from learned diffusion model.\n timestep (`float`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.FloatTensor`):\n A current instance of a sample created by the diffusion process.\n s_churn (`float`):\n s_tmin (`float`):\n s_tmax (`float`):\n s_noise (`float`, defaults to 1.0):\n Scaling factor for noise added to the sample.\n generator (`torch.Generator`, *optional*):\n A random number generator.\n return_dict (`bool`):\n Whether or not to return a [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or\n tuple.\n\n Returns:\n [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] is\n returned, otherwise a tuple is returned where the first element is the sample tensor.\n \"\"\"\n\n if (\n isinstance(timestep, int)\n or isinstance(timestep, torch.IntTensor)\n or isinstance(timestep, torch.LongTensor)\n ):\n raise ValueError(\n (\n \"Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to\"\n \" `EulerDiscreteScheduler.step()` is not supported. Make sure to pass\"\n \" one of the `scheduler.timesteps` as a timestep.\"\n ),\n )\n\n if self.step_index is None:\n self._init_step_index(timestep)\n\n # Upcast to avoid precision issues when computing prev_sample\n sample = sample.to(torch.float32)\n\n sigma = self.sigmas[self.step_index]\n\n gamma = min(s_churn / (len(self.sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0.0\n\n noise = randn_tensor(\n model_output.shape, dtype=model_output.dtype, device=model_output.device, generator=generator\n )\n\n eps = noise * s_noise\n sigma_hat = sigma * (gamma + 1)\n\n if gamma > 0:\n sample = sample + eps * (sigma_hat**2 - sigma**2) ** 0.5\n\n # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise\n # NOTE: \"original_sample\" should not be an expected prediction_type but is left in for\n # backwards compatibility\n\n # if self.config.prediction_type == \"vector_field\":\n\n denoised = sample - model_output * sigma\n # 2. Convert to an ODE derivative\n derivative = (sample - denoised) / sigma_hat\n\n dt = self.sigmas[self.step_index + 1] - sigma_hat\n\n prev_sample = sample + derivative * dt\n # Cast sample back to model compatible dtype\n prev_sample = prev_sample.to(model_output.dtype)\n\n # upon completion increase step index by one\n self._step_index += 1\n\n if not return_dict:\n return (prev_sample,)\n\n return FlowMatchEulerDiscreteSchedulerOutput(prev_sample=prev_sample)\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_heun_discrete.py", "content": "# Copyright 2024 Katherine Crowson, The HuggingFace Team and hlky. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport math\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\nclass HeunDiscreteScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n Scheduler with Heun steps for discrete beta schedules.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n beta_start (`float`, defaults to 0.0001):\n The starting `beta` value of inference.\n beta_end (`float`, defaults to 0.02):\n The final `beta` value.\n beta_schedule (`str`, defaults to `\"linear\"`):\n The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear` or `scaled_linear`.\n trained_betas (`np.ndarray`, *optional*):\n Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.\n prediction_type (`str`, defaults to `epsilon`, *optional*):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),\n `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen\n Video](https://imagen.research.google/video/paper.pdf) paper).\n clip_sample (`bool`, defaults to `True`):\n Clip the predicted sample for numerical stability.\n clip_sample_range (`float`, defaults to 1.0):\n The maximum magnitude for sample clipping. Valid only when `clip_sample=True`.\n use_karras_sigmas (`bool`, *optional*, defaults to `False`):\n Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`,\n the sigmas are determined according to a sequence of noise levels {σi}.\n timestep_spacing (`str`, defaults to `\"linspace\"`):\n The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.\n steps_offset (`int`, defaults to 0):\n An offset added to the inference steps, as required by some model families.\n \"\"\"\n\n _compatibles = [e.name for e in KarrasDiffusionSchedulers]\n order = 2\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.00085, # sensible defaults\n beta_end: float = 0.012,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[Union[np.ndarray, List[float]]] = None,\n prediction_type: str = \"epsilon\",\n use_karras_sigmas: Optional[bool] = False,\n clip_sample: Optional[bool] = False,\n clip_sample_range: float = 1.0,\n timestep_spacing: str = \"linspace\",\n steps_offset: int = 0,\n ):\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps, alpha_transform_type=\"cosine\")\n elif beta_schedule == \"exp\":\n self.betas = betas_for_alpha_bar(num_train_timesteps, alpha_transform_type=\"exp\")\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n\n # set all values\n self.set_timesteps(num_train_timesteps, None, num_train_timesteps)\n self.use_karras_sigmas = use_karras_sigmas\n\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.index_for_timestep\n def index_for_timestep(self, timestep, schedule_timesteps=None):\n if schedule_timesteps is None:\n schedule_timesteps = self.timesteps\n\n indices = (schedule_timesteps == timestep).nonzero()\n\n # The sigma index that is taken for the **very** first `step`\n # is always the second index (or the last index if there is only 1)\n # This way we can ensure we don't accidentally skip a sigma in\n # case we start in the middle of the denoising schedule (e.g. for image-to-image)\n pos = 1 if len(indices) > 1 else 0\n\n return indices[pos].item()\n\n @property\n def init_noise_sigma(self):\n # standard deviation of the initial noise distribution\n if self.config.timestep_spacing in [\"linspace\", \"trailing\"]:\n return self.sigmas.max()\n\n return (self.sigmas.max() ** 2 + 1) ** 0.5\n\n @property\n def step_index(self):\n \"\"\"\n The index counter for current timestep. It will increase 1 after each scheduler step.\n \"\"\"\n return self._step_index\n\n @property\n def begin_index(self):\n \"\"\"\n The index for the first timestep. It should be set from pipeline with `set_begin_index` method.\n \"\"\"\n return self._begin_index\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index\n def set_begin_index(self, begin_index: int = 0):\n \"\"\"\n Sets the begin index for the scheduler. This function should be run from pipeline before the inference.\n\n Args:\n begin_index (`int`):\n The begin index for the scheduler.\n \"\"\"\n self._begin_index = begin_index\n\n def scale_model_input(\n self,\n sample: torch.Tensor,\n timestep: Union[float, torch.Tensor],\n ) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`int`, *optional*):\n The current timestep in the diffusion chain.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n if self.step_index is None:\n self._init_step_index(timestep)\n\n sigma = self.sigmas[self.step_index]\n sample = sample / ((sigma**2 + 1) ** 0.5)\n return sample\n\n def set_timesteps(\n self,\n num_inference_steps: Optional[int] = None,\n device: Union[str, torch.device] = None,\n num_train_timesteps: Optional[int] = None,\n timesteps: Optional[List[int]] = None,\n ):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n num_train_timesteps (`int`, *optional*):\n The number of diffusion steps used when training the model. If `None`, the default\n `num_train_timesteps` attribute is used.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to support arbitrary spacing between timesteps. If `None`, timesteps will be\n generated based on the `timestep_spacing` attribute. If `timesteps` is passed, `num_inference_steps`\n must be `None`, and `timestep_spacing` attribute will be ignored.\n \"\"\"\n if num_inference_steps is None and timesteps is None:\n raise ValueError(\"Must pass exactly one of `num_inference_steps` or `custom_timesteps`.\")\n if num_inference_steps is not None and timesteps is not None:\n raise ValueError(\"Can only pass one of `num_inference_steps` or `custom_timesteps`.\")\n if timesteps is not None and self.config.use_karras_sigmas:\n raise ValueError(\"Cannot use `timesteps` with `config.use_karras_sigmas = True`\")\n\n num_inference_steps = num_inference_steps or len(timesteps)\n self.num_inference_steps = num_inference_steps\n num_train_timesteps = num_train_timesteps or self.config.num_train_timesteps\n\n if timesteps is not None:\n timesteps = np.array(timesteps, dtype=np.float32)\n else:\n # \"linspace\", \"leading\", \"trailing\" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891\n if self.config.timestep_spacing == \"linspace\":\n timesteps = np.linspace(0, num_train_timesteps - 1, num_inference_steps, dtype=np.float32)[::-1].copy()\n elif self.config.timestep_spacing == \"leading\":\n step_ratio = num_train_timesteps // self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.float32)\n timesteps += self.config.steps_offset\n elif self.config.timestep_spacing == \"trailing\":\n step_ratio = num_train_timesteps / self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (np.arange(num_train_timesteps, 0, -step_ratio)).round().copy().astype(np.float32)\n timesteps -= 1\n else:\n raise ValueError(\n f\"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.\"\n )\n\n sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)\n log_sigmas = np.log(sigmas)\n sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)\n\n if self.config.use_karras_sigmas:\n sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=self.num_inference_steps)\n timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas])\n\n sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32)\n sigmas = torch.from_numpy(sigmas).to(device=device)\n self.sigmas = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2), sigmas[-1:]])\n\n timesteps = torch.from_numpy(timesteps)\n timesteps = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2)])\n\n self.timesteps = timesteps.to(device=device)\n\n # empty dt and derivative\n self.prev_derivative = None\n self.dt = None\n\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t\n def _sigma_to_t(self, sigma, log_sigmas):\n # get log sigma\n log_sigma = np.log(np.maximum(sigma, 1e-10))\n\n # get distribution\n dists = log_sigma - log_sigmas[:, np.newaxis]\n\n # get sigmas range\n low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)\n high_idx = low_idx + 1\n\n low = log_sigmas[low_idx]\n high = log_sigmas[high_idx]\n\n # interpolate sigmas\n w = (low - log_sigma) / (low - high)\n w = np.clip(w, 0, 1)\n\n # transform interpolation to time range\n t = (1 - w) * low_idx + w * high_idx\n t = t.reshape(sigma.shape)\n return t\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras\n def _convert_to_karras(self, in_sigmas: torch.Tensor, num_inference_steps) -> torch.Tensor:\n \"\"\"Constructs the noise schedule of Karras et al. (2022).\"\"\"\n\n # Hack to make sure that other schedulers which copy this function don't break\n # TODO: Add this logic to the other schedulers\n if hasattr(self.config, \"sigma_min\"):\n sigma_min = self.config.sigma_min\n else:\n sigma_min = None\n\n if hasattr(self.config, \"sigma_max\"):\n sigma_max = self.config.sigma_max\n else:\n sigma_max = None\n\n sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item()\n sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item()\n\n rho = 7.0 # 7.0 is the value used in the paper\n ramp = np.linspace(0, 1, num_inference_steps)\n min_inv_rho = sigma_min ** (1 / rho)\n max_inv_rho = sigma_max ** (1 / rho)\n sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho\n return sigmas\n\n @property\n def state_in_first_order(self):\n return self.dt is None\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index\n def _init_step_index(self, timestep):\n if self.begin_index is None:\n if isinstance(timestep, torch.Tensor):\n timestep = timestep.to(self.timesteps.device)\n self._step_index = self.index_for_timestep(timestep)\n else:\n self._step_index = self._begin_index\n\n def step(\n self,\n model_output: Union[torch.Tensor, np.ndarray],\n timestep: Union[float, torch.Tensor],\n sample: Union[torch.Tensor, np.ndarray],\n return_dict: bool = True,\n ) -> Union[SchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`float`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n return_dict (`bool`):\n Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or tuple.\n\n Returns:\n [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n \"\"\"\n if self.step_index is None:\n self._init_step_index(timestep)\n\n if self.state_in_first_order:\n sigma = self.sigmas[self.step_index]\n sigma_next = self.sigmas[self.step_index + 1]\n else:\n # 2nd order / Heun's method\n sigma = self.sigmas[self.step_index - 1]\n sigma_next = self.sigmas[self.step_index]\n\n # currently only gamma=0 is supported. This usually works best anyways.\n # We can support gamma in the future but then need to scale the timestep before\n # passing it to the model which requires a change in API\n gamma = 0\n sigma_hat = sigma * (gamma + 1) # Note: sigma_hat == sigma for now\n\n # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise\n if self.config.prediction_type == \"epsilon\":\n sigma_input = sigma_hat if self.state_in_first_order else sigma_next\n pred_original_sample = sample - sigma_input * model_output\n elif self.config.prediction_type == \"v_prediction\":\n sigma_input = sigma_hat if self.state_in_first_order else sigma_next\n pred_original_sample = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (\n sample / (sigma_input**2 + 1)\n )\n elif self.config.prediction_type == \"sample\":\n pred_original_sample = model_output\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`\"\n )\n\n if self.config.clip_sample:\n pred_original_sample = pred_original_sample.clamp(\n -self.config.clip_sample_range, self.config.clip_sample_range\n )\n\n if self.state_in_first_order:\n # 2. Convert to an ODE derivative for 1st order\n derivative = (sample - pred_original_sample) / sigma_hat\n # 3. delta timestep\n dt = sigma_next - sigma_hat\n\n # store for 2nd order step\n self.prev_derivative = derivative\n self.dt = dt\n self.sample = sample\n else:\n # 2. 2nd order / Heun's method\n derivative = (sample - pred_original_sample) / sigma_next\n derivative = (self.prev_derivative + derivative) / 2\n\n # 3. take prev timestep & sample\n dt = self.dt\n sample = self.sample\n\n # free dt and derivative\n # Note, this puts the scheduler in \"first order mode\"\n self.prev_derivative = None\n self.dt = None\n self.sample = None\n\n prev_sample = sample + derivative * dt\n\n # upon completion increase step index by one\n self._step_index += 1\n\n if not return_dict:\n return (prev_sample,)\n\n return SchedulerOutput(prev_sample=prev_sample)\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.add_noise\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.Tensor,\n ) -> torch.Tensor:\n # Make sure sigmas and timesteps have the same device and dtype as original_samples\n sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)\n if original_samples.device.type == \"mps\" and torch.is_floating_point(timesteps):\n # mps does not support float64\n schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)\n timesteps = timesteps.to(original_samples.device, dtype=torch.float32)\n else:\n schedule_timesteps = self.timesteps.to(original_samples.device)\n timesteps = timesteps.to(original_samples.device)\n\n # self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index\n if self.begin_index is None:\n step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps]\n elif self.step_index is not None:\n # add_noise is called after first denoising step (for inpainting)\n step_indices = [self.step_index] * timesteps.shape[0]\n else:\n # add noise is called before first denoising step to create initial latent(img2img)\n step_indices = [self.begin_index] * timesteps.shape[0]\n\n sigma = sigmas[step_indices].flatten()\n while len(sigma.shape) < len(original_samples.shape):\n sigma = sigma.unsqueeze(-1)\n\n noisy_samples = original_samples + noise * sigma\n return noisy_samples\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_ipndm.py", "content": "# Copyright 2024 Zhejiang University Team and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport math\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom .scheduling_utils import SchedulerMixin, SchedulerOutput\n\n\nclass IPNDMScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n A fourth-order Improved Pseudo Linear Multistep scheduler.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n trained_betas (`np.ndarray`, *optional*):\n Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.\n \"\"\"\n\n order = 1\n\n @register_to_config\n def __init__(\n self, num_train_timesteps: int = 1000, trained_betas: Optional[Union[np.ndarray, List[float]]] = None\n ):\n # set `betas`, `alphas`, `timesteps`\n self.set_timesteps(num_train_timesteps)\n\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = 1.0\n\n # For now we only support F-PNDM, i.e. the runge-kutta method\n # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf\n # mainly at formula (9), (12), (13) and the Algorithm 2.\n self.pndm_order = 4\n\n # running values\n self.ets = []\n self._step_index = None\n self._begin_index = None\n\n @property\n def step_index(self):\n \"\"\"\n The index counter for current timestep. It will increase 1 after each scheduler step.\n \"\"\"\n return self._step_index\n\n @property\n def begin_index(self):\n \"\"\"\n The index for the first timestep. It should be set from pipeline with `set_begin_index` method.\n \"\"\"\n return self._begin_index\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index\n def set_begin_index(self, begin_index: int = 0):\n \"\"\"\n Sets the begin index for the scheduler. This function should be run from pipeline before the inference.\n\n Args:\n begin_index (`int`):\n The begin index for the scheduler.\n \"\"\"\n self._begin_index = begin_index\n\n def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n \"\"\"\n self.num_inference_steps = num_inference_steps\n steps = torch.linspace(1, 0, num_inference_steps + 1)[:-1]\n steps = torch.cat([steps, torch.tensor([0.0])])\n\n if self.config.trained_betas is not None:\n self.betas = torch.tensor(self.config.trained_betas, dtype=torch.float32)\n else:\n self.betas = torch.sin(steps * math.pi / 2) ** 2\n\n self.alphas = (1.0 - self.betas**2) ** 0.5\n\n timesteps = (torch.atan2(self.betas, self.alphas) / math.pi * 2)[:-1]\n self.timesteps = timesteps.to(device)\n\n self.ets = []\n self._step_index = None\n self._begin_index = None\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.index_for_timestep\n def index_for_timestep(self, timestep, schedule_timesteps=None):\n if schedule_timesteps is None:\n schedule_timesteps = self.timesteps\n\n indices = (schedule_timesteps == timestep).nonzero()\n\n # The sigma index that is taken for the **very** first `step`\n # is always the second index (or the last index if there is only 1)\n # This way we can ensure we don't accidentally skip a sigma in\n # case we start in the middle of the denoising schedule (e.g. for image-to-image)\n pos = 1 if len(indices) > 1 else 0\n\n return indices[pos].item()\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index\n def _init_step_index(self, timestep):\n if self.begin_index is None:\n if isinstance(timestep, torch.Tensor):\n timestep = timestep.to(self.timesteps.device)\n self._step_index = self.index_for_timestep(timestep)\n else:\n self._step_index = self._begin_index\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n return_dict: bool = True,\n ) -> Union[SchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with\n the linear multistep method. It performs one forward pass multiple times to approximate the solution.\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`int`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n return_dict (`bool`):\n Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or tuple.\n\n Returns:\n [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n \"\"\"\n if self.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n if self.step_index is None:\n self._init_step_index(timestep)\n\n timestep_index = self.step_index\n prev_timestep_index = self.step_index + 1\n\n ets = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]\n self.ets.append(ets)\n\n if len(self.ets) == 1:\n ets = self.ets[-1]\n elif len(self.ets) == 2:\n ets = (3 * self.ets[-1] - self.ets[-2]) / 2\n elif len(self.ets) == 3:\n ets = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12\n else:\n ets = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])\n\n prev_sample = self._get_prev_sample(sample, timestep_index, prev_timestep_index, ets)\n\n # upon completion increase step index by one\n self._step_index += 1\n\n if not return_dict:\n return (prev_sample,)\n\n return SchedulerOutput(prev_sample=prev_sample)\n\n def scale_model_input(self, sample: torch.Tensor, *args, **kwargs) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n return sample\n\n def _get_prev_sample(self, sample, timestep_index, prev_timestep_index, ets):\n alpha = self.alphas[timestep_index]\n sigma = self.betas[timestep_index]\n\n next_alpha = self.alphas[prev_timestep_index]\n next_sigma = self.betas[prev_timestep_index]\n\n pred = (sample - sigma * ets) / max(alpha, 1e-8)\n prev_sample = next_alpha * pred + ets * next_sigma\n\n return prev_sample\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py", "content": "# Copyright 2024 Katherine Crowson, The HuggingFace Team and hlky. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport math\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils.torch_utils import randn_tensor\nfrom .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\nclass KDPM2AncestralDiscreteScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n KDPM2DiscreteScheduler with ancestral sampling is inspired by the DPMSolver2 and Algorithm 2 from the [Elucidating\n the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n beta_start (`float`, defaults to 0.00085):\n The starting `beta` value of inference.\n beta_end (`float`, defaults to 0.012):\n The final `beta` value.\n beta_schedule (`str`, defaults to `\"linear\"`):\n The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear` or `scaled_linear`.\n trained_betas (`np.ndarray`, *optional*):\n Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.\n use_karras_sigmas (`bool`, *optional*, defaults to `False`):\n Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`,\n the sigmas are determined according to a sequence of noise levels {σi}.\n prediction_type (`str`, defaults to `epsilon`, *optional*):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),\n `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen\n Video](https://imagen.research.google/video/paper.pdf) paper).\n timestep_spacing (`str`, defaults to `\"linspace\"`):\n The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.\n steps_offset (`int`, defaults to 0):\n An offset added to the inference steps, as required by some model families.\n \"\"\"\n\n _compatibles = [e.name for e in KarrasDiffusionSchedulers]\n order = 2\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.00085, # sensible defaults\n beta_end: float = 0.012,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[Union[np.ndarray, List[float]]] = None,\n use_karras_sigmas: Optional[bool] = False,\n prediction_type: str = \"epsilon\",\n timestep_spacing: str = \"linspace\",\n steps_offset: int = 0,\n ):\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n\n # set all values\n self.set_timesteps(num_train_timesteps, None, num_train_timesteps)\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n @property\n def init_noise_sigma(self):\n # standard deviation of the initial noise distribution\n if self.config.timestep_spacing in [\"linspace\", \"trailing\"]:\n return self.sigmas.max()\n\n return (self.sigmas.max() ** 2 + 1) ** 0.5\n\n @property\n def step_index(self):\n \"\"\"\n The index counter for current timestep. It will increase 1 after each scheduler step.\n \"\"\"\n return self._step_index\n\n @property\n def begin_index(self):\n \"\"\"\n The index for the first timestep. It should be set from pipeline with `set_begin_index` method.\n \"\"\"\n return self._begin_index\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index\n def set_begin_index(self, begin_index: int = 0):\n \"\"\"\n Sets the begin index for the scheduler. This function should be run from pipeline before the inference.\n\n Args:\n begin_index (`int`):\n The begin index for the scheduler.\n \"\"\"\n self._begin_index = begin_index\n\n def scale_model_input(\n self,\n sample: torch.Tensor,\n timestep: Union[float, torch.Tensor],\n ) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`int`, *optional*):\n The current timestep in the diffusion chain.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n if self.step_index is None:\n self._init_step_index(timestep)\n\n if self.state_in_first_order:\n sigma = self.sigmas[self.step_index]\n else:\n sigma = self.sigmas_interpol[self.step_index - 1]\n\n sample = sample / ((sigma**2 + 1) ** 0.5)\n return sample\n\n def set_timesteps(\n self,\n num_inference_steps: int,\n device: Union[str, torch.device] = None,\n num_train_timesteps: Optional[int] = None,\n ):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n \"\"\"\n self.num_inference_steps = num_inference_steps\n\n num_train_timesteps = num_train_timesteps or self.config.num_train_timesteps\n\n # \"linspace\", \"leading\", \"trailing\" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891\n if self.config.timestep_spacing == \"linspace\":\n timesteps = np.linspace(0, num_train_timesteps - 1, num_inference_steps, dtype=np.float32)[::-1].copy()\n elif self.config.timestep_spacing == \"leading\":\n step_ratio = num_train_timesteps // self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.float32)\n timesteps += self.config.steps_offset\n elif self.config.timestep_spacing == \"trailing\":\n step_ratio = num_train_timesteps / self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (np.arange(num_train_timesteps, 0, -step_ratio)).round().copy().astype(np.float32)\n timesteps -= 1\n else:\n raise ValueError(\n f\"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.\"\n )\n\n sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)\n log_sigmas = np.log(sigmas)\n\n sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)\n\n if self.config.use_karras_sigmas:\n sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)\n timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]).round()\n\n self.log_sigmas = torch.from_numpy(log_sigmas).to(device)\n sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32)\n sigmas = torch.from_numpy(sigmas).to(device=device)\n\n # compute up and down sigmas\n sigmas_next = sigmas.roll(-1)\n sigmas_next[-1] = 0.0\n sigmas_up = (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5\n sigmas_down = (sigmas_next**2 - sigmas_up**2) ** 0.5\n sigmas_down[-1] = 0.0\n\n # compute interpolated sigmas\n sigmas_interpol = sigmas.log().lerp(sigmas_down.log(), 0.5).exp()\n sigmas_interpol[-2:] = 0.0\n\n # set sigmas\n self.sigmas = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]])\n self.sigmas_interpol = torch.cat(\n [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]]\n )\n self.sigmas_up = torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2), sigmas_up[-1:]])\n self.sigmas_down = torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2), sigmas_down[-1:]])\n\n if str(device).startswith(\"mps\"):\n timesteps = torch.from_numpy(timesteps).to(device, dtype=torch.float32)\n else:\n timesteps = torch.from_numpy(timesteps).to(device)\n\n sigmas_interpol = sigmas_interpol.cpu()\n log_sigmas = self.log_sigmas.cpu()\n timesteps_interpol = np.array(\n [self._sigma_to_t(sigma_interpol, log_sigmas) for sigma_interpol in sigmas_interpol]\n )\n\n timesteps_interpol = torch.from_numpy(timesteps_interpol).to(device, dtype=timesteps.dtype)\n interleaved_timesteps = torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None]), dim=-1).flatten()\n\n self.timesteps = torch.cat([timesteps[:1], interleaved_timesteps])\n\n self.sample = None\n\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t\n def _sigma_to_t(self, sigma, log_sigmas):\n # get log sigma\n log_sigma = np.log(np.maximum(sigma, 1e-10))\n\n # get distribution\n dists = log_sigma - log_sigmas[:, np.newaxis]\n\n # get sigmas range\n low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)\n high_idx = low_idx + 1\n\n low = log_sigmas[low_idx]\n high = log_sigmas[high_idx]\n\n # interpolate sigmas\n w = (low - log_sigma) / (low - high)\n w = np.clip(w, 0, 1)\n\n # transform interpolation to time range\n t = (1 - w) * low_idx + w * high_idx\n t = t.reshape(sigma.shape)\n return t\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras\n def _convert_to_karras(self, in_sigmas: torch.Tensor, num_inference_steps) -> torch.Tensor:\n \"\"\"Constructs the noise schedule of Karras et al. (2022).\"\"\"\n\n # Hack to make sure that other schedulers which copy this function don't break\n # TODO: Add this logic to the other schedulers\n if hasattr(self.config, \"sigma_min\"):\n sigma_min = self.config.sigma_min\n else:\n sigma_min = None\n\n if hasattr(self.config, \"sigma_max\"):\n sigma_max = self.config.sigma_max\n else:\n sigma_max = None\n\n sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item()\n sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item()\n\n rho = 7.0 # 7.0 is the value used in the paper\n ramp = np.linspace(0, 1, num_inference_steps)\n min_inv_rho = sigma_min ** (1 / rho)\n max_inv_rho = sigma_max ** (1 / rho)\n sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho\n return sigmas\n\n @property\n def state_in_first_order(self):\n return self.sample is None\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.index_for_timestep\n def index_for_timestep(self, timestep, schedule_timesteps=None):\n if schedule_timesteps is None:\n schedule_timesteps = self.timesteps\n\n indices = (schedule_timesteps == timestep).nonzero()\n\n # The sigma index that is taken for the **very** first `step`\n # is always the second index (or the last index if there is only 1)\n # This way we can ensure we don't accidentally skip a sigma in\n # case we start in the middle of the denoising schedule (e.g. for image-to-image)\n pos = 1 if len(indices) > 1 else 0\n\n return indices[pos].item()\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index\n def _init_step_index(self, timestep):\n if self.begin_index is None:\n if isinstance(timestep, torch.Tensor):\n timestep = timestep.to(self.timesteps.device)\n self._step_index = self.index_for_timestep(timestep)\n else:\n self._step_index = self._begin_index\n\n def step(\n self,\n model_output: Union[torch.Tensor, np.ndarray],\n timestep: Union[float, torch.Tensor],\n sample: Union[torch.Tensor, np.ndarray],\n generator: Optional[torch.Generator] = None,\n return_dict: bool = True,\n ) -> Union[SchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`float`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n generator (`torch.Generator`, *optional*):\n A random number generator.\n return_dict (`bool`):\n Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or tuple.\n\n Returns:\n [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_ddim.SchedulerOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n \"\"\"\n if self.step_index is None:\n self._init_step_index(timestep)\n\n if self.state_in_first_order:\n sigma = self.sigmas[self.step_index]\n sigma_interpol = self.sigmas_interpol[self.step_index]\n sigma_up = self.sigmas_up[self.step_index]\n sigma_down = self.sigmas_down[self.step_index - 1]\n else:\n # 2nd order / KPDM2's method\n sigma = self.sigmas[self.step_index - 1]\n sigma_interpol = self.sigmas_interpol[self.step_index - 1]\n sigma_up = self.sigmas_up[self.step_index - 1]\n sigma_down = self.sigmas_down[self.step_index - 1]\n\n # currently only gamma=0 is supported. This usually works best anyways.\n # We can support gamma in the future but then need to scale the timestep before\n # passing it to the model which requires a change in API\n gamma = 0\n sigma_hat = sigma * (gamma + 1) # Note: sigma_hat == sigma for now\n\n device = model_output.device\n noise = randn_tensor(model_output.shape, dtype=model_output.dtype, device=device, generator=generator)\n\n # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise\n if self.config.prediction_type == \"epsilon\":\n sigma_input = sigma_hat if self.state_in_first_order else sigma_interpol\n pred_original_sample = sample - sigma_input * model_output\n elif self.config.prediction_type == \"v_prediction\":\n sigma_input = sigma_hat if self.state_in_first_order else sigma_interpol\n pred_original_sample = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (\n sample / (sigma_input**2 + 1)\n )\n elif self.config.prediction_type == \"sample\":\n raise NotImplementedError(\"prediction_type not implemented yet: sample\")\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`\"\n )\n\n if self.state_in_first_order:\n # 2. Convert to an ODE derivative for 1st order\n derivative = (sample - pred_original_sample) / sigma_hat\n # 3. delta timestep\n dt = sigma_interpol - sigma_hat\n\n # store for 2nd order step\n self.sample = sample\n self.dt = dt\n prev_sample = sample + derivative * dt\n else:\n # DPM-Solver-2\n # 2. Convert to an ODE derivative for 2nd order\n derivative = (sample - pred_original_sample) / sigma_interpol\n # 3. delta timestep\n dt = sigma_down - sigma_hat\n\n sample = self.sample\n self.sample = None\n\n prev_sample = sample + derivative * dt\n prev_sample = prev_sample + noise * sigma_up\n\n # upon completion increase step index by one\n self._step_index += 1\n\n if not return_dict:\n return (prev_sample,)\n\n return SchedulerOutput(prev_sample=prev_sample)\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.add_noise\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.Tensor,\n ) -> torch.Tensor:\n # Make sure sigmas and timesteps have the same device and dtype as original_samples\n sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)\n if original_samples.device.type == \"mps\" and torch.is_floating_point(timesteps):\n # mps does not support float64\n schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)\n timesteps = timesteps.to(original_samples.device, dtype=torch.float32)\n else:\n schedule_timesteps = self.timesteps.to(original_samples.device)\n timesteps = timesteps.to(original_samples.device)\n\n # self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index\n if self.begin_index is None:\n step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps]\n elif self.step_index is not None:\n # add_noise is called after first denoising step (for inpainting)\n step_indices = [self.step_index] * timesteps.shape[0]\n else:\n # add noise is called before first denoising step to create initial latent(img2img)\n step_indices = [self.begin_index] * timesteps.shape[0]\n\n sigma = sigmas[step_indices].flatten()\n while len(sigma.shape) < len(original_samples.shape):\n sigma = sigma.unsqueeze(-1)\n\n noisy_samples = original_samples + noise * sigma\n return noisy_samples\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py", "content": "# Copyright 2024 Katherine Crowson, The HuggingFace Team and hlky. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport math\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\nclass KDPM2DiscreteScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n KDPM2DiscreteScheduler is inspired by the DPMSolver2 and Algorithm 2 from the [Elucidating the Design Space of\n Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n beta_start (`float`, defaults to 0.00085):\n The starting `beta` value of inference.\n beta_end (`float`, defaults to 0.012):\n The final `beta` value.\n beta_schedule (`str`, defaults to `\"linear\"`):\n The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear` or `scaled_linear`.\n trained_betas (`np.ndarray`, *optional*):\n Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.\n use_karras_sigmas (`bool`, *optional*, defaults to `False`):\n Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`,\n the sigmas are determined according to a sequence of noise levels {σi}.\n prediction_type (`str`, defaults to `epsilon`, *optional*):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),\n `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen\n Video](https://imagen.research.google/video/paper.pdf) paper).\n timestep_spacing (`str`, defaults to `\"linspace\"`):\n The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.\n steps_offset (`int`, defaults to 0):\n An offset added to the inference steps, as required by some model families.\n \"\"\"\n\n _compatibles = [e.name for e in KarrasDiffusionSchedulers]\n order = 2\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.00085, # sensible defaults\n beta_end: float = 0.012,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[Union[np.ndarray, List[float]]] = None,\n use_karras_sigmas: Optional[bool] = False,\n prediction_type: str = \"epsilon\",\n timestep_spacing: str = \"linspace\",\n steps_offset: int = 0,\n ):\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n\n # set all values\n self.set_timesteps(num_train_timesteps, None, num_train_timesteps)\n\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n @property\n def init_noise_sigma(self):\n # standard deviation of the initial noise distribution\n if self.config.timestep_spacing in [\"linspace\", \"trailing\"]:\n return self.sigmas.max()\n\n return (self.sigmas.max() ** 2 + 1) ** 0.5\n\n @property\n def step_index(self):\n \"\"\"\n The index counter for current timestep. It will increase 1 after each scheduler step.\n \"\"\"\n return self._step_index\n\n @property\n def begin_index(self):\n \"\"\"\n The index for the first timestep. It should be set from pipeline with `set_begin_index` method.\n \"\"\"\n return self._begin_index\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index\n def set_begin_index(self, begin_index: int = 0):\n \"\"\"\n Sets the begin index for the scheduler. This function should be run from pipeline before the inference.\n\n Args:\n begin_index (`int`):\n The begin index for the scheduler.\n \"\"\"\n self._begin_index = begin_index\n\n def scale_model_input(\n self,\n sample: torch.Tensor,\n timestep: Union[float, torch.Tensor],\n ) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`int`, *optional*):\n The current timestep in the diffusion chain.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n if self.step_index is None:\n self._init_step_index(timestep)\n\n if self.state_in_first_order:\n sigma = self.sigmas[self.step_index]\n else:\n sigma = self.sigmas_interpol[self.step_index]\n\n sample = sample / ((sigma**2 + 1) ** 0.5)\n return sample\n\n def set_timesteps(\n self,\n num_inference_steps: int,\n device: Union[str, torch.device] = None,\n num_train_timesteps: Optional[int] = None,\n ):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n \"\"\"\n self.num_inference_steps = num_inference_steps\n\n num_train_timesteps = num_train_timesteps or self.config.num_train_timesteps\n\n # \"linspace\", \"leading\", \"trailing\" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891\n if self.config.timestep_spacing == \"linspace\":\n timesteps = np.linspace(0, num_train_timesteps - 1, num_inference_steps, dtype=np.float32)[::-1].copy()\n elif self.config.timestep_spacing == \"leading\":\n step_ratio = num_train_timesteps // self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.float32)\n timesteps += self.config.steps_offset\n elif self.config.timestep_spacing == \"trailing\":\n step_ratio = num_train_timesteps / self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (np.arange(num_train_timesteps, 0, -step_ratio)).round().copy().astype(np.float32)\n timesteps -= 1\n else:\n raise ValueError(\n f\"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.\"\n )\n\n sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)\n log_sigmas = np.log(sigmas)\n sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)\n\n if self.config.use_karras_sigmas:\n sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)\n timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]).round()\n\n self.log_sigmas = torch.from_numpy(log_sigmas).to(device=device)\n sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32)\n sigmas = torch.from_numpy(sigmas).to(device=device)\n\n # interpolate sigmas\n sigmas_interpol = sigmas.log().lerp(sigmas.roll(1).log(), 0.5).exp()\n\n self.sigmas = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]])\n self.sigmas_interpol = torch.cat(\n [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]]\n )\n\n timesteps = torch.from_numpy(timesteps).to(device)\n\n # interpolate timesteps\n sigmas_interpol = sigmas_interpol.cpu()\n log_sigmas = self.log_sigmas.cpu()\n timesteps_interpol = np.array(\n [self._sigma_to_t(sigma_interpol, log_sigmas) for sigma_interpol in sigmas_interpol]\n )\n timesteps_interpol = torch.from_numpy(timesteps_interpol).to(device, dtype=timesteps.dtype)\n interleaved_timesteps = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]), dim=-1).flatten()\n\n self.timesteps = torch.cat([timesteps[:1], interleaved_timesteps])\n\n self.sample = None\n\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n @property\n def state_in_first_order(self):\n return self.sample is None\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.index_for_timestep\n def index_for_timestep(self, timestep, schedule_timesteps=None):\n if schedule_timesteps is None:\n schedule_timesteps = self.timesteps\n\n indices = (schedule_timesteps == timestep).nonzero()\n\n # The sigma index that is taken for the **very** first `step`\n # is always the second index (or the last index if there is only 1)\n # This way we can ensure we don't accidentally skip a sigma in\n # case we start in the middle of the denoising schedule (e.g. for image-to-image)\n pos = 1 if len(indices) > 1 else 0\n\n return indices[pos].item()\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index\n def _init_step_index(self, timestep):\n if self.begin_index is None:\n if isinstance(timestep, torch.Tensor):\n timestep = timestep.to(self.timesteps.device)\n self._step_index = self.index_for_timestep(timestep)\n else:\n self._step_index = self._begin_index\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t\n def _sigma_to_t(self, sigma, log_sigmas):\n # get log sigma\n log_sigma = np.log(np.maximum(sigma, 1e-10))\n\n # get distribution\n dists = log_sigma - log_sigmas[:, np.newaxis]\n\n # get sigmas range\n low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)\n high_idx = low_idx + 1\n\n low = log_sigmas[low_idx]\n high = log_sigmas[high_idx]\n\n # interpolate sigmas\n w = (low - log_sigma) / (low - high)\n w = np.clip(w, 0, 1)\n\n # transform interpolation to time range\n t = (1 - w) * low_idx + w * high_idx\n t = t.reshape(sigma.shape)\n return t\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras\n def _convert_to_karras(self, in_sigmas: torch.Tensor, num_inference_steps) -> torch.Tensor:\n \"\"\"Constructs the noise schedule of Karras et al. (2022).\"\"\"\n\n # Hack to make sure that other schedulers which copy this function don't break\n # TODO: Add this logic to the other schedulers\n if hasattr(self.config, \"sigma_min\"):\n sigma_min = self.config.sigma_min\n else:\n sigma_min = None\n\n if hasattr(self.config, \"sigma_max\"):\n sigma_max = self.config.sigma_max\n else:\n sigma_max = None\n\n sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item()\n sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item()\n\n rho = 7.0 # 7.0 is the value used in the paper\n ramp = np.linspace(0, 1, num_inference_steps)\n min_inv_rho = sigma_min ** (1 / rho)\n max_inv_rho = sigma_max ** (1 / rho)\n sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho\n return sigmas\n\n def step(\n self,\n model_output: Union[torch.Tensor, np.ndarray],\n timestep: Union[float, torch.Tensor],\n sample: Union[torch.Tensor, np.ndarray],\n return_dict: bool = True,\n ) -> Union[SchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`float`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n return_dict (`bool`):\n Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or tuple.\n\n Returns:\n [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n \"\"\"\n if self.step_index is None:\n self._init_step_index(timestep)\n\n if self.state_in_first_order:\n sigma = self.sigmas[self.step_index]\n sigma_interpol = self.sigmas_interpol[self.step_index + 1]\n sigma_next = self.sigmas[self.step_index + 1]\n else:\n # 2nd order / KDPM2's method\n sigma = self.sigmas[self.step_index - 1]\n sigma_interpol = self.sigmas_interpol[self.step_index]\n sigma_next = self.sigmas[self.step_index]\n\n # currently only gamma=0 is supported. This usually works best anyways.\n # We can support gamma in the future but then need to scale the timestep before\n # passing it to the model which requires a change in API\n gamma = 0\n sigma_hat = sigma * (gamma + 1) # Note: sigma_hat == sigma for now\n\n # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise\n if self.config.prediction_type == \"epsilon\":\n sigma_input = sigma_hat if self.state_in_first_order else sigma_interpol\n pred_original_sample = sample - sigma_input * model_output\n elif self.config.prediction_type == \"v_prediction\":\n sigma_input = sigma_hat if self.state_in_first_order else sigma_interpol\n pred_original_sample = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (\n sample / (sigma_input**2 + 1)\n )\n elif self.config.prediction_type == \"sample\":\n raise NotImplementedError(\"prediction_type not implemented yet: sample\")\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`\"\n )\n\n if self.state_in_first_order:\n # 2. Convert to an ODE derivative for 1st order\n derivative = (sample - pred_original_sample) / sigma_hat\n # 3. delta timestep\n dt = sigma_interpol - sigma_hat\n\n # store for 2nd order step\n self.sample = sample\n else:\n # DPM-Solver-2\n # 2. Convert to an ODE derivative for 2nd order\n derivative = (sample - pred_original_sample) / sigma_interpol\n\n # 3. delta timestep\n dt = sigma_next - sigma_hat\n\n sample = self.sample\n self.sample = None\n\n # upon completion increase step index by one\n self._step_index += 1\n\n prev_sample = sample + derivative * dt\n\n if not return_dict:\n return (prev_sample,)\n\n return SchedulerOutput(prev_sample=prev_sample)\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.add_noise\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.Tensor,\n ) -> torch.Tensor:\n # Make sure sigmas and timesteps have the same device and dtype as original_samples\n sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)\n if original_samples.device.type == \"mps\" and torch.is_floating_point(timesteps):\n # mps does not support float64\n schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)\n timesteps = timesteps.to(original_samples.device, dtype=torch.float32)\n else:\n schedule_timesteps = self.timesteps.to(original_samples.device)\n timesteps = timesteps.to(original_samples.device)\n\n # self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index\n if self.begin_index is None:\n step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps]\n elif self.step_index is not None:\n # add_noise is called after first denoising step (for inpainting)\n step_indices = [self.step_index] * timesteps.shape[0]\n else:\n # add noise is called before first denoising step to create initial latent(img2img)\n step_indices = [self.begin_index] * timesteps.shape[0]\n\n sigma = sigmas[step_indices].flatten()\n while len(sigma.shape) < len(original_samples.shape):\n sigma = sigma.unsqueeze(-1)\n\n noisy_samples = original_samples + noise * sigma\n return noisy_samples\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_karras_ve_flax.py", "content": "# Copyright 2024 NVIDIA and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple, Union\n\nimport flax\nimport jax\nimport jax.numpy as jnp\nfrom jax import random\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import BaseOutput\nfrom .scheduling_utils_flax import FlaxSchedulerMixin\n\n\n@flax.struct.dataclass\nclass KarrasVeSchedulerState:\n # setable values\n num_inference_steps: Optional[int] = None\n timesteps: Optional[jnp.ndarray] = None\n schedule: Optional[jnp.ndarray] = None # sigma(t_i)\n\n @classmethod\n def create(cls):\n return cls()\n\n\n@dataclass\nclass FlaxKarrasVeOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's step function output.\n\n Args:\n prev_sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n derivative (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images):\n Derivative of predicted original image sample (x_0).\n state (`KarrasVeSchedulerState`): the `FlaxKarrasVeScheduler` state data class.\n \"\"\"\n\n prev_sample: jnp.ndarray\n derivative: jnp.ndarray\n state: KarrasVeSchedulerState\n\n\nclass FlaxKarrasVeScheduler(FlaxSchedulerMixin, ConfigMixin):\n \"\"\"\n Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2]. Use Algorithm 2 and\n the VE column of Table 1 from [1] for reference.\n\n [1] Karras, Tero, et al. \"Elucidating the Design Space of Diffusion-Based Generative Models.\"\n https://arxiv.org/abs/2206.00364 [2] Song, Yang, et al. \"Score-based generative modeling through stochastic\n differential equations.\" https://arxiv.org/abs/2011.13456\n\n [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`\n function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.\n [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and\n [`~SchedulerMixin.from_pretrained`] functions.\n\n For more details on the parameters, see the original paper's Appendix E.: \"Elucidating the Design Space of\n Diffusion-Based Generative Models.\" https://arxiv.org/abs/2206.00364. The grid search values used to find the\n optimal {s_noise, s_churn, s_min, s_max} for a specific model are described in Table 5 of the paper.\n\n Args:\n sigma_min (`float`): minimum noise magnitude\n sigma_max (`float`): maximum noise magnitude\n s_noise (`float`): the amount of additional noise to counteract loss of detail during sampling.\n A reasonable range is [1.000, 1.011].\n s_churn (`float`): the parameter controlling the overall amount of stochasticity.\n A reasonable range is [0, 100].\n s_min (`float`): the start value of the sigma range where we add noise (enable stochasticity).\n A reasonable range is [0, 10].\n s_max (`float`): the end value of the sigma range where we add noise.\n A reasonable range is [0.2, 80].\n \"\"\"\n\n @property\n def has_state(self):\n return True\n\n @register_to_config\n def __init__(\n self,\n sigma_min: float = 0.02,\n sigma_max: float = 100,\n s_noise: float = 1.007,\n s_churn: float = 80,\n s_min: float = 0.05,\n s_max: float = 50,\n ):\n pass\n\n def create_state(self):\n return KarrasVeSchedulerState.create()\n\n def set_timesteps(\n self, state: KarrasVeSchedulerState, num_inference_steps: int, shape: Tuple = ()\n ) -> KarrasVeSchedulerState:\n \"\"\"\n Sets the continuous timesteps used for the diffusion chain. Supporting function to be run before inference.\n\n Args:\n state (`KarrasVeSchedulerState`):\n the `FlaxKarrasVeScheduler` state data class.\n num_inference_steps (`int`):\n the number of diffusion steps used when generating samples with a pre-trained model.\n\n \"\"\"\n timesteps = jnp.arange(0, num_inference_steps)[::-1].copy()\n schedule = [\n (\n self.config.sigma_max**2\n * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))\n )\n for i in timesteps\n ]\n\n return state.replace(\n num_inference_steps=num_inference_steps,\n schedule=jnp.array(schedule, dtype=jnp.float32),\n timesteps=timesteps,\n )\n\n def add_noise_to_input(\n self,\n state: KarrasVeSchedulerState,\n sample: jnp.ndarray,\n sigma: float,\n key: jax.Array,\n ) -> Tuple[jnp.ndarray, float]:\n \"\"\"\n Explicit Langevin-like \"churn\" step of adding noise to the sample according to a factor gamma_i ≥ 0 to reach a\n higher noise level sigma_hat = sigma_i + gamma_i*sigma_i.\n\n TODO Args:\n \"\"\"\n if self.config.s_min <= sigma <= self.config.s_max:\n gamma = min(self.config.s_churn / state.num_inference_steps, 2**0.5 - 1)\n else:\n gamma = 0\n\n # sample eps ~ N(0, S_noise^2 * I)\n key = random.split(key, num=1)\n eps = self.config.s_noise * random.normal(key=key, shape=sample.shape)\n sigma_hat = sigma + gamma * sigma\n sample_hat = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)\n\n return sample_hat, sigma_hat\n\n def step(\n self,\n state: KarrasVeSchedulerState,\n model_output: jnp.ndarray,\n sigma_hat: float,\n sigma_prev: float,\n sample_hat: jnp.ndarray,\n return_dict: bool = True,\n ) -> Union[FlaxKarrasVeOutput, Tuple]:\n \"\"\"\n Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n state (`KarrasVeSchedulerState`): the `FlaxKarrasVeScheduler` state data class.\n model_output (`torch.Tensor` or `np.ndarray`): direct output from learned diffusion model.\n sigma_hat (`float`): TODO\n sigma_prev (`float`): TODO\n sample_hat (`torch.Tensor` or `np.ndarray`): TODO\n return_dict (`bool`): option for returning tuple rather than FlaxKarrasVeOutput class\n\n Returns:\n [`~schedulers.scheduling_karras_ve_flax.FlaxKarrasVeOutput`] or `tuple`: Updated sample in the diffusion\n chain and derivative. [`~schedulers.scheduling_karras_ve_flax.FlaxKarrasVeOutput`] if `return_dict` is\n True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.\n \"\"\"\n\n pred_original_sample = sample_hat + sigma_hat * model_output\n derivative = (sample_hat - pred_original_sample) / sigma_hat\n sample_prev = sample_hat + (sigma_prev - sigma_hat) * derivative\n\n if not return_dict:\n return (sample_prev, derivative, state)\n\n return FlaxKarrasVeOutput(prev_sample=sample_prev, derivative=derivative, state=state)\n\n def step_correct(\n self,\n state: KarrasVeSchedulerState,\n model_output: jnp.ndarray,\n sigma_hat: float,\n sigma_prev: float,\n sample_hat: jnp.ndarray,\n sample_prev: jnp.ndarray,\n derivative: jnp.ndarray,\n return_dict: bool = True,\n ) -> Union[FlaxKarrasVeOutput, Tuple]:\n \"\"\"\n Correct the predicted sample based on the output model_output of the network. TODO complete description\n\n Args:\n state (`KarrasVeSchedulerState`): the `FlaxKarrasVeScheduler` state data class.\n model_output (`torch.Tensor` or `np.ndarray`): direct output from learned diffusion model.\n sigma_hat (`float`): TODO\n sigma_prev (`float`): TODO\n sample_hat (`torch.Tensor` or `np.ndarray`): TODO\n sample_prev (`torch.Tensor` or `np.ndarray`): TODO\n derivative (`torch.Tensor` or `np.ndarray`): TODO\n return_dict (`bool`): option for returning tuple rather than FlaxKarrasVeOutput class\n\n Returns:\n prev_sample (TODO): updated sample in the diffusion chain. derivative (TODO): TODO\n\n \"\"\"\n pred_original_sample = sample_prev + sigma_prev * model_output\n derivative_corr = (sample_prev - pred_original_sample) / sigma_prev\n sample_prev = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)\n\n if not return_dict:\n return (sample_prev, derivative, state)\n\n return FlaxKarrasVeOutput(prev_sample=sample_prev, derivative=derivative, state=state)\n\n def add_noise(self, state: KarrasVeSchedulerState, original_samples, noise, timesteps):\n raise NotImplementedError()\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_lcm.py", "content": "# Copyright 2024 Stanford University Team and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion\n# and https://github.com/hojonathanho/diffusion\n\nimport math\nfrom dataclasses import dataclass\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import BaseOutput, logging\nfrom ..utils.torch_utils import randn_tensor\nfrom .scheduling_utils import SchedulerMixin\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n@dataclass\nclass LCMSchedulerOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's `step` function output.\n\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample `(x_{0})` based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.Tensor\n denoised: Optional[torch.Tensor] = None\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\n# Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr\ndef rescale_zero_terminal_snr(betas: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)\n\n\n Args:\n betas (`torch.Tensor`):\n the betas that the scheduler is being initialized with.\n\n Returns:\n `torch.Tensor`: rescaled betas with zero terminal SNR\n \"\"\"\n # Convert betas to alphas_bar_sqrt\n alphas = 1.0 - betas\n alphas_cumprod = torch.cumprod(alphas, dim=0)\n alphas_bar_sqrt = alphas_cumprod.sqrt()\n\n # Store old values.\n alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()\n alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()\n\n # Shift so the last timestep is zero.\n alphas_bar_sqrt -= alphas_bar_sqrt_T\n\n # Scale so the first timestep is back to the old value.\n alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)\n\n # Convert alphas_bar_sqrt to betas\n alphas_bar = alphas_bar_sqrt**2 # Revert sqrt\n alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod\n alphas = torch.cat([alphas_bar[0:1], alphas])\n betas = 1 - alphas\n\n return betas\n\n\nclass LCMScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n `LCMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with\n non-Markovian guidance.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. [`~ConfigMixin`] takes care of storing all config\n attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be\n accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving\n functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n beta_start (`float`, defaults to 0.0001):\n The starting `beta` value of inference.\n beta_end (`float`, defaults to 0.02):\n The final `beta` value.\n beta_schedule (`str`, defaults to `\"linear\"`):\n The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear`, `scaled_linear`, or `squaredcos_cap_v2`.\n trained_betas (`np.ndarray`, *optional*):\n Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.\n original_inference_steps (`int`, *optional*, defaults to 50):\n The default number of inference steps used to generate a linearly-spaced timestep schedule, from which we\n will ultimately take `num_inference_steps` evenly spaced timesteps to form the final timestep schedule.\n clip_sample (`bool`, defaults to `True`):\n Clip the predicted sample for numerical stability.\n clip_sample_range (`float`, defaults to 1.0):\n The maximum magnitude for sample clipping. Valid only when `clip_sample=True`.\n set_alpha_to_one (`bool`, defaults to `True`):\n Each diffusion step uses the alphas product value at that step and at the previous one. For the final step\n there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,\n otherwise it uses the alpha value at step 0.\n steps_offset (`int`, defaults to 0):\n An offset added to the inference steps, as required by some model families.\n prediction_type (`str`, defaults to `epsilon`, *optional*):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),\n `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen\n Video](https://imagen.research.google/video/paper.pdf) paper).\n thresholding (`bool`, defaults to `False`):\n Whether to use the \"dynamic thresholding\" method. This is unsuitable for latent-space diffusion models such\n as Stable Diffusion.\n dynamic_thresholding_ratio (`float`, defaults to 0.995):\n The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.\n sample_max_value (`float`, defaults to 1.0):\n The threshold value for dynamic thresholding. Valid only when `thresholding=True`.\n timestep_spacing (`str`, defaults to `\"leading\"`):\n The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.\n timestep_scaling (`float`, defaults to 10.0):\n The factor the timesteps will be multiplied by when calculating the consistency model boundary conditions\n `c_skip` and `c_out`. Increasing this will decrease the approximation error (although the approximation\n error at the default of `10.0` is already pretty small).\n rescale_betas_zero_snr (`bool`, defaults to `False`):\n Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and\n dark samples instead of limiting it to samples with medium brightness. Loosely related to\n [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).\n \"\"\"\n\n order = 1\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.00085,\n beta_end: float = 0.012,\n beta_schedule: str = \"scaled_linear\",\n trained_betas: Optional[Union[np.ndarray, List[float]]] = None,\n original_inference_steps: int = 50,\n clip_sample: bool = False,\n clip_sample_range: float = 1.0,\n set_alpha_to_one: bool = True,\n steps_offset: int = 0,\n prediction_type: str = \"epsilon\",\n thresholding: bool = False,\n dynamic_thresholding_ratio: float = 0.995,\n sample_max_value: float = 1.0,\n timestep_spacing: str = \"leading\",\n timestep_scaling: float = 10.0,\n rescale_betas_zero_snr: bool = False,\n ):\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n # Rescale for zero SNR\n if rescale_betas_zero_snr:\n self.betas = rescale_zero_terminal_snr(self.betas)\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n\n # At every step in ddim, we are looking into the previous alphas_cumprod\n # For the final step, there is no previous alphas_cumprod because we are already at 0\n # `set_alpha_to_one` decides whether we set this parameter simply to one or\n # whether we use the final alpha of the \"non-previous\" one.\n self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]\n\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = 1.0\n\n # setable values\n self.num_inference_steps = None\n self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64))\n self.custom_timesteps = False\n\n self._step_index = None\n self._begin_index = None\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.index_for_timestep\n def index_for_timestep(self, timestep, schedule_timesteps=None):\n if schedule_timesteps is None:\n schedule_timesteps = self.timesteps\n\n indices = (schedule_timesteps == timestep).nonzero()\n\n # The sigma index that is taken for the **very** first `step`\n # is always the second index (or the last index if there is only 1)\n # This way we can ensure we don't accidentally skip a sigma in\n # case we start in the middle of the denoising schedule (e.g. for image-to-image)\n pos = 1 if len(indices) > 1 else 0\n\n return indices[pos].item()\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index\n def _init_step_index(self, timestep):\n if self.begin_index is None:\n if isinstance(timestep, torch.Tensor):\n timestep = timestep.to(self.timesteps.device)\n self._step_index = self.index_for_timestep(timestep)\n else:\n self._step_index = self._begin_index\n\n @property\n def step_index(self):\n return self._step_index\n\n @property\n def begin_index(self):\n \"\"\"\n The index for the first timestep. It should be set from pipeline with `set_begin_index` method.\n \"\"\"\n return self._begin_index\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index\n def set_begin_index(self, begin_index: int = 0):\n \"\"\"\n Sets the begin index for the scheduler. This function should be run from pipeline before the inference.\n\n Args:\n begin_index (`int`):\n The begin index for the scheduler.\n \"\"\"\n self._begin_index = begin_index\n\n def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`int`, *optional*):\n The current timestep in the diffusion chain.\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n return sample\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample\n def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:\n \"\"\"\n \"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the\n prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by\n s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing\n pixels from saturation at each step. We find that dynamic thresholding results in significantly better\n photorealism as well as better image-text alignment, especially when using very large guidance weights.\"\n\n https://arxiv.org/abs/2205.11487\n \"\"\"\n dtype = sample.dtype\n batch_size, channels, *remaining_dims = sample.shape\n\n if dtype not in (torch.float32, torch.float64):\n sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half\n\n # Flatten sample for doing quantile calculation along each image\n sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))\n\n abs_sample = sample.abs() # \"a certain percentile absolute pixel value\"\n\n s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)\n s = torch.clamp(\n s, min=1, max=self.config.sample_max_value\n ) # When clamped to min=1, equivalent to standard clipping to [-1, 1]\n s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0\n sample = torch.clamp(sample, -s, s) / s # \"we threshold xt0 to the range [-s, s] and then divide by s\"\n\n sample = sample.reshape(batch_size, channels, *remaining_dims)\n sample = sample.to(dtype)\n\n return sample\n\n def set_timesteps(\n self,\n num_inference_steps: Optional[int] = None,\n device: Union[str, torch.device] = None,\n original_inference_steps: Optional[int] = None,\n timesteps: Optional[List[int]] = None,\n strength: int = 1.0,\n ):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`, *optional*):\n The number of diffusion steps used when generating samples with a pre-trained model. If used,\n `timesteps` must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n original_inference_steps (`int`, *optional*):\n The original number of inference steps, which will be used to generate a linearly-spaced timestep\n schedule (which is different from the standard `diffusers` implementation). We will then take\n `num_inference_steps` timesteps from this schedule, evenly spaced in terms of indices, and use that as\n our final timestep schedule. If not set, this will default to the `original_inference_steps` attribute.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default\n timestep spacing strategy of equal spacing between timesteps on the training/distillation timestep\n schedule is used. If `timesteps` is passed, `num_inference_steps` must be `None`.\n \"\"\"\n # 0. Check inputs\n if num_inference_steps is None and timesteps is None:\n raise ValueError(\"Must pass exactly one of `num_inference_steps` or `custom_timesteps`.\")\n\n if num_inference_steps is not None and timesteps is not None:\n raise ValueError(\"Can only pass one of `num_inference_steps` or `custom_timesteps`.\")\n\n # 1. Calculate the LCM original training/distillation timestep schedule.\n original_steps = (\n original_inference_steps if original_inference_steps is not None else self.config.original_inference_steps\n )\n\n if original_steps > self.config.num_train_timesteps:\n raise ValueError(\n f\"`original_steps`: {original_steps} cannot be larger than `self.config.train_timesteps`:\"\n f\" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle\"\n f\" maximal {self.config.num_train_timesteps} timesteps.\"\n )\n\n # LCM Timesteps Setting\n # The skipping step parameter k from the paper.\n k = self.config.num_train_timesteps // original_steps\n # LCM Training/Distillation Steps Schedule\n # Currently, only a linearly-spaced schedule is supported (same as in the LCM distillation scripts).\n lcm_origin_timesteps = np.asarray(list(range(1, int(original_steps * strength) + 1))) * k - 1\n\n # 2. Calculate the LCM inference timestep schedule.\n if timesteps is not None:\n # 2.1 Handle custom timestep schedules.\n train_timesteps = set(lcm_origin_timesteps)\n non_train_timesteps = []\n for i in range(1, len(timesteps)):\n if timesteps[i] >= timesteps[i - 1]:\n raise ValueError(\"`custom_timesteps` must be in descending order.\")\n\n if timesteps[i] not in train_timesteps:\n non_train_timesteps.append(timesteps[i])\n\n if timesteps[0] >= self.config.num_train_timesteps:\n raise ValueError(\n f\"`timesteps` must start before `self.config.train_timesteps`:\"\n f\" {self.config.num_train_timesteps}.\"\n )\n\n # Raise warning if timestep schedule does not start with self.config.num_train_timesteps - 1\n if strength == 1.0 and timesteps[0] != self.config.num_train_timesteps - 1:\n logger.warning(\n f\"The first timestep on the custom timestep schedule is {timesteps[0]}, not\"\n f\" `self.config.num_train_timesteps - 1`: {self.config.num_train_timesteps - 1}. You may get\"\n f\" unexpected results when using this timestep schedule.\"\n )\n\n # Raise warning if custom timestep schedule contains timesteps not on original timestep schedule\n if non_train_timesteps:\n logger.warning(\n f\"The custom timestep schedule contains the following timesteps which are not on the original\"\n f\" training/distillation timestep schedule: {non_train_timesteps}. You may get unexpected results\"\n f\" when using this timestep schedule.\"\n )\n\n # Raise warning if custom timestep schedule is longer than original_steps\n if len(timesteps) > original_steps:\n logger.warning(\n f\"The number of timesteps in the custom timestep schedule is {len(timesteps)}, which exceeds the\"\n f\" the length of the timestep schedule used for training: {original_steps}. You may get some\"\n f\" unexpected results when using this timestep schedule.\"\n )\n\n timesteps = np.array(timesteps, dtype=np.int64)\n self.num_inference_steps = len(timesteps)\n self.custom_timesteps = True\n\n # Apply strength (e.g. for img2img pipelines) (see StableDiffusionImg2ImgPipeline.get_timesteps)\n init_timestep = min(int(self.num_inference_steps * strength), self.num_inference_steps)\n t_start = max(self.num_inference_steps - init_timestep, 0)\n timesteps = timesteps[t_start * self.order :]\n # TODO: also reset self.num_inference_steps?\n else:\n # 2.2 Create the \"standard\" LCM inference timestep schedule.\n if num_inference_steps > self.config.num_train_timesteps:\n raise ValueError(\n f\"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:\"\n f\" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle\"\n f\" maximal {self.config.num_train_timesteps} timesteps.\"\n )\n\n skipping_step = len(lcm_origin_timesteps) // num_inference_steps\n\n if skipping_step < 1:\n raise ValueError(\n f\"The combination of `original_steps x strength`: {original_steps} x {strength} is smaller than `num_inference_steps`: {num_inference_steps}. Make sure to either reduce `num_inference_steps` to a value smaller than {int(original_steps * strength)} or increase `strength` to a value higher than {float(num_inference_steps / original_steps)}.\"\n )\n\n self.num_inference_steps = num_inference_steps\n\n if num_inference_steps > original_steps:\n raise ValueError(\n f\"`num_inference_steps`: {num_inference_steps} cannot be larger than `original_inference_steps`:\"\n f\" {original_steps} because the final timestep schedule will be a subset of the\"\n f\" `original_inference_steps`-sized initial timestep schedule.\"\n )\n\n # LCM Inference Steps Schedule\n lcm_origin_timesteps = lcm_origin_timesteps[::-1].copy()\n # Select (approximately) evenly spaced indices from lcm_origin_timesteps.\n inference_indices = np.linspace(0, len(lcm_origin_timesteps), num=num_inference_steps, endpoint=False)\n inference_indices = np.floor(inference_indices).astype(np.int64)\n timesteps = lcm_origin_timesteps[inference_indices]\n\n self.timesteps = torch.from_numpy(timesteps).to(device=device, dtype=torch.long)\n\n self._step_index = None\n self._begin_index = None\n\n def get_scalings_for_boundary_condition_discrete(self, timestep):\n self.sigma_data = 0.5 # Default: 0.5\n scaled_timestep = timestep * self.config.timestep_scaling\n\n c_skip = self.sigma_data**2 / (scaled_timestep**2 + self.sigma_data**2)\n c_out = scaled_timestep / (scaled_timestep**2 + self.sigma_data**2) ** 0.5\n return c_skip, c_out\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n generator: Optional[torch.Generator] = None,\n return_dict: bool = True,\n ) -> Union[LCMSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`float`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n generator (`torch.Generator`, *optional*):\n A random number generator.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] or `tuple`.\n Returns:\n [`~schedulers.scheduling_utils.LCMSchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n \"\"\"\n if self.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n if self.step_index is None:\n self._init_step_index(timestep)\n\n # 1. get previous step value\n prev_step_index = self.step_index + 1\n if prev_step_index < len(self.timesteps):\n prev_timestep = self.timesteps[prev_step_index]\n else:\n prev_timestep = timestep\n\n # 2. compute alphas, betas\n alpha_prod_t = self.alphas_cumprod[timestep]\n alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod\n\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n\n # 3. Get scalings for boundary conditions\n c_skip, c_out = self.get_scalings_for_boundary_condition_discrete(timestep)\n\n # 4. Compute the predicted original sample x_0 based on the model parameterization\n if self.config.prediction_type == \"epsilon\": # noise-prediction\n predicted_original_sample = (sample - beta_prod_t.sqrt() * model_output) / alpha_prod_t.sqrt()\n elif self.config.prediction_type == \"sample\": # x-prediction\n predicted_original_sample = model_output\n elif self.config.prediction_type == \"v_prediction\": # v-prediction\n predicted_original_sample = alpha_prod_t.sqrt() * sample - beta_prod_t.sqrt() * model_output\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` or\"\n \" `v_prediction` for `LCMScheduler`.\"\n )\n\n # 5. Clip or threshold \"predicted x_0\"\n if self.config.thresholding:\n predicted_original_sample = self._threshold_sample(predicted_original_sample)\n elif self.config.clip_sample:\n predicted_original_sample = predicted_original_sample.clamp(\n -self.config.clip_sample_range, self.config.clip_sample_range\n )\n\n # 6. Denoise model output using boundary conditions\n denoised = c_out * predicted_original_sample + c_skip * sample\n\n # 7. Sample and inject noise z ~ N(0, I) for MultiStep Inference\n # Noise is not used on the final timestep of the timestep schedule.\n # This also means that noise is not used for one-step sampling.\n if self.step_index != self.num_inference_steps - 1:\n noise = randn_tensor(\n model_output.shape, generator=generator, device=model_output.device, dtype=denoised.dtype\n )\n prev_sample = alpha_prod_t_prev.sqrt() * denoised + beta_prod_t_prev.sqrt() * noise\n else:\n prev_sample = denoised\n\n # upon completion increase step index by one\n self._step_index += 1\n\n if not return_dict:\n return (prev_sample, denoised)\n\n return LCMSchedulerOutput(prev_sample=prev_sample, denoised=denoised)\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.IntTensor,\n ) -> torch.Tensor:\n # Make sure alphas_cumprod and timestep have same device and dtype as original_samples\n # Move the self.alphas_cumprod to device to avoid redundant CPU to GPU data movement\n # for the subsequent add_noise calls\n self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device)\n alphas_cumprod = self.alphas_cumprod.to(dtype=original_samples.dtype)\n timesteps = timesteps.to(original_samples.device)\n\n sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n while len(sqrt_alpha_prod.shape) < len(original_samples.shape):\n sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n\n noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise\n return noisy_samples\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity\n def get_velocity(self, sample: torch.Tensor, noise: torch.Tensor, timesteps: torch.IntTensor) -> torch.Tensor:\n # Make sure alphas_cumprod and timestep have same device and dtype as sample\n self.alphas_cumprod = self.alphas_cumprod.to(device=sample.device)\n alphas_cumprod = self.alphas_cumprod.to(dtype=sample.dtype)\n timesteps = timesteps.to(sample.device)\n\n sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n while len(sqrt_alpha_prod.shape) < len(sample.shape):\n sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n\n velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample\n return velocity\n\n def __len__(self):\n return self.config.num_train_timesteps\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.previous_timestep\n def previous_timestep(self, timestep):\n if self.custom_timesteps:\n index = (self.timesteps == timestep).nonzero(as_tuple=True)[0][0]\n if index == self.timesteps.shape[0] - 1:\n prev_t = torch.tensor(-1)\n else:\n prev_t = self.timesteps[index + 1]\n else:\n num_inference_steps = (\n self.num_inference_steps if self.num_inference_steps else self.config.num_train_timesteps\n )\n prev_t = timestep - self.config.num_train_timesteps // num_inference_steps\n\n return prev_t\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_lms_discrete.py", "content": "# Copyright 2024 Katherine Crowson and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport math\nimport warnings\nfrom dataclasses import dataclass\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nfrom scipy import integrate\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import BaseOutput\nfrom .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin\n\n\n@dataclass\n# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete\nclass LMSDiscreteSchedulerOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's `step` function output.\n\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample `(x_{0})` based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.Tensor\n pred_original_sample: Optional[torch.Tensor] = None\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\nclass LMSDiscreteScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n A linear multistep scheduler for discrete beta schedules.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n beta_start (`float`, defaults to 0.0001):\n The starting `beta` value of inference.\n beta_end (`float`, defaults to 0.02):\n The final `beta` value.\n beta_schedule (`str`, defaults to `\"linear\"`):\n The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear` or `scaled_linear`.\n trained_betas (`np.ndarray`, *optional*):\n Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.\n use_karras_sigmas (`bool`, *optional*, defaults to `False`):\n Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`,\n the sigmas are determined according to a sequence of noise levels {σi}.\n prediction_type (`str`, defaults to `epsilon`, *optional*):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),\n `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen\n Video](https://imagen.research.google/video/paper.pdf) paper).\n timestep_spacing (`str`, defaults to `\"linspace\"`):\n The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.\n steps_offset (`int`, defaults to 0):\n An offset added to the inference steps, as required by some model families.\n \"\"\"\n\n _compatibles = [e.name for e in KarrasDiffusionSchedulers]\n order = 1\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[Union[np.ndarray, List[float]]] = None,\n use_karras_sigmas: Optional[bool] = False,\n prediction_type: str = \"epsilon\",\n timestep_spacing: str = \"linspace\",\n steps_offset: int = 0,\n ):\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n\n sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)\n sigmas = np.concatenate([sigmas[::-1], [0.0]]).astype(np.float32)\n self.sigmas = torch.from_numpy(sigmas)\n\n # setable values\n self.num_inference_steps = None\n self.use_karras_sigmas = use_karras_sigmas\n self.set_timesteps(num_train_timesteps, None)\n self.derivatives = []\n self.is_scale_input_called = False\n\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n @property\n def init_noise_sigma(self):\n # standard deviation of the initial noise distribution\n if self.config.timestep_spacing in [\"linspace\", \"trailing\"]:\n return self.sigmas.max()\n\n return (self.sigmas.max() ** 2 + 1) ** 0.5\n\n @property\n def step_index(self):\n \"\"\"\n The index counter for current timestep. It will increase 1 after each scheduler step.\n \"\"\"\n return self._step_index\n\n @property\n def begin_index(self):\n \"\"\"\n The index for the first timestep. It should be set from pipeline with `set_begin_index` method.\n \"\"\"\n return self._begin_index\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index\n def set_begin_index(self, begin_index: int = 0):\n \"\"\"\n Sets the begin index for the scheduler. This function should be run from pipeline before the inference.\n\n Args:\n begin_index (`int`):\n The begin index for the scheduler.\n \"\"\"\n self._begin_index = begin_index\n\n def scale_model_input(self, sample: torch.Tensor, timestep: Union[float, torch.Tensor]) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`float` or `torch.Tensor`):\n The current timestep in the diffusion chain.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n\n if self.step_index is None:\n self._init_step_index(timestep)\n\n sigma = self.sigmas[self.step_index]\n sample = sample / ((sigma**2 + 1) ** 0.5)\n self.is_scale_input_called = True\n return sample\n\n def get_lms_coefficient(self, order, t, current_order):\n \"\"\"\n Compute the linear multistep coefficient.\n\n Args:\n order ():\n t ():\n current_order ():\n \"\"\"\n\n def lms_derivative(tau):\n prod = 1.0\n for k in range(order):\n if current_order == k:\n continue\n prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])\n return prod\n\n integrated_coeff = integrate.quad(lms_derivative, self.sigmas[t], self.sigmas[t + 1], epsrel=1e-4)[0]\n\n return integrated_coeff\n\n def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n \"\"\"\n self.num_inference_steps = num_inference_steps\n\n # \"linspace\", \"leading\", \"trailing\" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891\n if self.config.timestep_spacing == \"linspace\":\n timesteps = np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps, dtype=np.float32)[\n ::-1\n ].copy()\n elif self.config.timestep_spacing == \"leading\":\n step_ratio = self.config.num_train_timesteps // self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.float32)\n timesteps += self.config.steps_offset\n elif self.config.timestep_spacing == \"trailing\":\n step_ratio = self.config.num_train_timesteps / self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (np.arange(self.config.num_train_timesteps, 0, -step_ratio)).round().copy().astype(np.float32)\n timesteps -= 1\n else:\n raise ValueError(\n f\"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.\"\n )\n\n sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)\n log_sigmas = np.log(sigmas)\n sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)\n\n if self.config.use_karras_sigmas:\n sigmas = self._convert_to_karras(in_sigmas=sigmas)\n timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas])\n\n sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32)\n\n self.sigmas = torch.from_numpy(sigmas).to(device=device)\n self.timesteps = torch.from_numpy(timesteps).to(device=device)\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n self.derivatives = []\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.index_for_timestep\n def index_for_timestep(self, timestep, schedule_timesteps=None):\n if schedule_timesteps is None:\n schedule_timesteps = self.timesteps\n\n indices = (schedule_timesteps == timestep).nonzero()\n\n # The sigma index that is taken for the **very** first `step`\n # is always the second index (or the last index if there is only 1)\n # This way we can ensure we don't accidentally skip a sigma in\n # case we start in the middle of the denoising schedule (e.g. for image-to-image)\n pos = 1 if len(indices) > 1 else 0\n\n return indices[pos].item()\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index\n def _init_step_index(self, timestep):\n if self.begin_index is None:\n if isinstance(timestep, torch.Tensor):\n timestep = timestep.to(self.timesteps.device)\n self._step_index = self.index_for_timestep(timestep)\n else:\n self._step_index = self._begin_index\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t\n def _sigma_to_t(self, sigma, log_sigmas):\n # get log sigma\n log_sigma = np.log(np.maximum(sigma, 1e-10))\n\n # get distribution\n dists = log_sigma - log_sigmas[:, np.newaxis]\n\n # get sigmas range\n low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)\n high_idx = low_idx + 1\n\n low = log_sigmas[low_idx]\n high = log_sigmas[high_idx]\n\n # interpolate sigmas\n w = (low - log_sigma) / (low - high)\n w = np.clip(w, 0, 1)\n\n # transform interpolation to time range\n t = (1 - w) * low_idx + w * high_idx\n t = t.reshape(sigma.shape)\n return t\n\n # copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras\n def _convert_to_karras(self, in_sigmas: torch.Tensor) -> torch.Tensor:\n \"\"\"Constructs the noise schedule of Karras et al. (2022).\"\"\"\n\n sigma_min: float = in_sigmas[-1].item()\n sigma_max: float = in_sigmas[0].item()\n\n rho = 7.0 # 7.0 is the value used in the paper\n ramp = np.linspace(0, 1, self.num_inference_steps)\n min_inv_rho = sigma_min ** (1 / rho)\n max_inv_rho = sigma_max ** (1 / rho)\n sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho\n return sigmas\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: Union[float, torch.Tensor],\n sample: torch.Tensor,\n order: int = 4,\n return_dict: bool = True,\n ) -> Union[LMSDiscreteSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`float` or `torch.Tensor`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n order (`int`, defaults to 4):\n The order of the linear multistep method.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or tuple.\n\n Returns:\n [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n\n \"\"\"\n if not self.is_scale_input_called:\n warnings.warn(\n \"The `scale_model_input` function should be called before `step` to ensure correct denoising. \"\n \"See `StableDiffusionPipeline` for a usage example.\"\n )\n\n if self.step_index is None:\n self._init_step_index(timestep)\n\n sigma = self.sigmas[self.step_index]\n\n # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise\n if self.config.prediction_type == \"epsilon\":\n pred_original_sample = sample - sigma * model_output\n elif self.config.prediction_type == \"v_prediction\":\n # * c_out + input * c_skip\n pred_original_sample = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))\n elif self.config.prediction_type == \"sample\":\n pred_original_sample = model_output\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`\"\n )\n\n # 2. Convert to an ODE derivative\n derivative = (sample - pred_original_sample) / sigma\n self.derivatives.append(derivative)\n if len(self.derivatives) > order:\n self.derivatives.pop(0)\n\n # 3. Compute linear multistep coefficients\n order = min(self.step_index + 1, order)\n lms_coeffs = [self.get_lms_coefficient(order, self.step_index, curr_order) for curr_order in range(order)]\n\n # 4. Compute previous sample based on the derivatives path\n prev_sample = sample + sum(\n coeff * derivative for coeff, derivative in zip(lms_coeffs, reversed(self.derivatives))\n )\n\n # upon completion increase step index by one\n self._step_index += 1\n\n if not return_dict:\n return (prev_sample,)\n\n return LMSDiscreteSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.add_noise\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.Tensor,\n ) -> torch.Tensor:\n # Make sure sigmas and timesteps have the same device and dtype as original_samples\n sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)\n if original_samples.device.type == \"mps\" and torch.is_floating_point(timesteps):\n # mps does not support float64\n schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)\n timesteps = timesteps.to(original_samples.device, dtype=torch.float32)\n else:\n schedule_timesteps = self.timesteps.to(original_samples.device)\n timesteps = timesteps.to(original_samples.device)\n\n # self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index\n if self.begin_index is None:\n step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps]\n elif self.step_index is not None:\n # add_noise is called after first denoising step (for inpainting)\n step_indices = [self.step_index] * timesteps.shape[0]\n else:\n # add noise is called before first denoising step to create initial latent(img2img)\n step_indices = [self.begin_index] * timesteps.shape[0]\n\n sigma = sigmas[step_indices].flatten()\n while len(sigma.shape) < len(original_samples.shape):\n sigma = sigma.unsqueeze(-1)\n\n noisy_samples = original_samples + noise * sigma\n return noisy_samples\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_lms_discrete_flax.py", "content": "# Copyright 2024 Katherine Crowson and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple, Union\n\nimport flax\nimport jax.numpy as jnp\nfrom scipy import integrate\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom .scheduling_utils_flax import (\n CommonSchedulerState,\n FlaxKarrasDiffusionSchedulers,\n FlaxSchedulerMixin,\n FlaxSchedulerOutput,\n broadcast_to_shape_from_left,\n)\n\n\n@flax.struct.dataclass\nclass LMSDiscreteSchedulerState:\n common: CommonSchedulerState\n\n # setable values\n init_noise_sigma: jnp.ndarray\n timesteps: jnp.ndarray\n sigmas: jnp.ndarray\n num_inference_steps: Optional[int] = None\n\n # running values\n derivatives: Optional[jnp.ndarray] = None\n\n @classmethod\n def create(\n cls, common: CommonSchedulerState, init_noise_sigma: jnp.ndarray, timesteps: jnp.ndarray, sigmas: jnp.ndarray\n ):\n return cls(common=common, init_noise_sigma=init_noise_sigma, timesteps=timesteps, sigmas=sigmas)\n\n\n@dataclass\nclass FlaxLMSSchedulerOutput(FlaxSchedulerOutput):\n state: LMSDiscreteSchedulerState\n\n\nclass FlaxLMSDiscreteScheduler(FlaxSchedulerMixin, ConfigMixin):\n \"\"\"\n Linear Multistep Scheduler for discrete beta schedules. Based on the original k-diffusion implementation by\n Katherine Crowson:\n https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181\n\n [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`\n function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.\n [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and\n [`~SchedulerMixin.from_pretrained`] functions.\n\n Args:\n num_train_timesteps (`int`): number of diffusion steps used to train the model.\n beta_start (`float`): the starting `beta` value of inference.\n beta_end (`float`): the final `beta` value.\n beta_schedule (`str`):\n the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear` or `scaled_linear`.\n trained_betas (`jnp.ndarray`, optional):\n option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.\n prediction_type (`str`, default `epsilon`, optional):\n prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion\n process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4\n https://imagen.research.google/video/paper.pdf)\n dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):\n the `dtype` used for params and computation.\n \"\"\"\n\n _compatibles = [e.name for e in FlaxKarrasDiffusionSchedulers]\n\n dtype: jnp.dtype\n\n @property\n def has_state(self):\n return True\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[jnp.ndarray] = None,\n prediction_type: str = \"epsilon\",\n dtype: jnp.dtype = jnp.float32,\n ):\n self.dtype = dtype\n\n def create_state(self, common: Optional[CommonSchedulerState] = None) -> LMSDiscreteSchedulerState:\n if common is None:\n common = CommonSchedulerState.create(self)\n\n timesteps = jnp.arange(0, self.config.num_train_timesteps).round()[::-1]\n sigmas = ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5\n\n # standard deviation of the initial noise distribution\n init_noise_sigma = sigmas.max()\n\n return LMSDiscreteSchedulerState.create(\n common=common,\n init_noise_sigma=init_noise_sigma,\n timesteps=timesteps,\n sigmas=sigmas,\n )\n\n def scale_model_input(self, state: LMSDiscreteSchedulerState, sample: jnp.ndarray, timestep: int) -> jnp.ndarray:\n \"\"\"\n Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the K-LMS algorithm.\n\n Args:\n state (`LMSDiscreteSchedulerState`):\n the `FlaxLMSDiscreteScheduler` state data class instance.\n sample (`jnp.ndarray`):\n current instance of sample being created by diffusion process.\n timestep (`int`):\n current discrete timestep in the diffusion chain.\n\n Returns:\n `jnp.ndarray`: scaled input sample\n \"\"\"\n (step_index,) = jnp.where(state.timesteps == timestep, size=1)\n step_index = step_index[0]\n\n sigma = state.sigmas[step_index]\n sample = sample / ((sigma**2 + 1) ** 0.5)\n return sample\n\n def get_lms_coefficient(self, state: LMSDiscreteSchedulerState, order, t, current_order):\n \"\"\"\n Compute a linear multistep coefficient.\n\n Args:\n order (TODO):\n t (TODO):\n current_order (TODO):\n \"\"\"\n\n def lms_derivative(tau):\n prod = 1.0\n for k in range(order):\n if current_order == k:\n continue\n prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])\n return prod\n\n integrated_coeff = integrate.quad(lms_derivative, state.sigmas[t], state.sigmas[t + 1], epsrel=1e-4)[0]\n\n return integrated_coeff\n\n def set_timesteps(\n self, state: LMSDiscreteSchedulerState, num_inference_steps: int, shape: Tuple = ()\n ) -> LMSDiscreteSchedulerState:\n \"\"\"\n Sets the timesteps used for the diffusion chain. Supporting function to be run before inference.\n\n Args:\n state (`LMSDiscreteSchedulerState`):\n the `FlaxLMSDiscreteScheduler` state data class instance.\n num_inference_steps (`int`):\n the number of diffusion steps used when generating samples with a pre-trained model.\n \"\"\"\n\n timesteps = jnp.linspace(self.config.num_train_timesteps - 1, 0, num_inference_steps, dtype=self.dtype)\n\n low_idx = jnp.floor(timesteps).astype(jnp.int32)\n high_idx = jnp.ceil(timesteps).astype(jnp.int32)\n\n frac = jnp.mod(timesteps, 1.0)\n\n sigmas = ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5\n sigmas = (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]\n sigmas = jnp.concatenate([sigmas, jnp.array([0.0], dtype=self.dtype)])\n\n timesteps = timesteps.astype(jnp.int32)\n\n # initial running values\n derivatives = jnp.zeros((0,) + shape, dtype=self.dtype)\n\n return state.replace(\n timesteps=timesteps,\n sigmas=sigmas,\n num_inference_steps=num_inference_steps,\n derivatives=derivatives,\n )\n\n def step(\n self,\n state: LMSDiscreteSchedulerState,\n model_output: jnp.ndarray,\n timestep: int,\n sample: jnp.ndarray,\n order: int = 4,\n return_dict: bool = True,\n ) -> Union[FlaxLMSSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n state (`LMSDiscreteSchedulerState`): the `FlaxLMSDiscreteScheduler` state data class instance.\n model_output (`jnp.ndarray`): direct output from learned diffusion model.\n timestep (`int`): current discrete timestep in the diffusion chain.\n sample (`jnp.ndarray`):\n current instance of sample being created by diffusion process.\n order: coefficient for multi-step inference.\n return_dict (`bool`): option for returning tuple rather than FlaxLMSSchedulerOutput class\n\n Returns:\n [`FlaxLMSSchedulerOutput`] or `tuple`: [`FlaxLMSSchedulerOutput`] if `return_dict` is True, otherwise a\n `tuple`. When returning a tuple, the first element is the sample tensor.\n\n \"\"\"\n if state.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n sigma = state.sigmas[timestep]\n\n # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise\n if self.config.prediction_type == \"epsilon\":\n pred_original_sample = sample - sigma * model_output\n elif self.config.prediction_type == \"v_prediction\":\n # * c_out + input * c_skip\n pred_original_sample = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`\"\n )\n\n # 2. Convert to an ODE derivative\n derivative = (sample - pred_original_sample) / sigma\n state = state.replace(derivatives=jnp.append(state.derivatives, derivative))\n if len(state.derivatives) > order:\n state = state.replace(derivatives=jnp.delete(state.derivatives, 0))\n\n # 3. Compute linear multistep coefficients\n order = min(timestep + 1, order)\n lms_coeffs = [self.get_lms_coefficient(state, order, timestep, curr_order) for curr_order in range(order)]\n\n # 4. Compute previous sample based on the derivatives path\n prev_sample = sample + sum(\n coeff * derivative for coeff, derivative in zip(lms_coeffs, reversed(state.derivatives))\n )\n\n if not return_dict:\n return (prev_sample, state)\n\n return FlaxLMSSchedulerOutput(prev_sample=prev_sample, state=state)\n\n def add_noise(\n self,\n state: LMSDiscreteSchedulerState,\n original_samples: jnp.ndarray,\n noise: jnp.ndarray,\n timesteps: jnp.ndarray,\n ) -> jnp.ndarray:\n sigma = state.sigmas[timesteps].flatten()\n sigma = broadcast_to_shape_from_left(sigma, noise.shape)\n\n noisy_samples = original_samples + noise * sigma\n\n return noisy_samples\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_pndm.py", "content": "# Copyright 2024 Zhejiang University Team and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim\n\nimport math\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\nclass PNDMScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n `PNDMScheduler` uses pseudo numerical methods for diffusion models such as the Runge-Kutta and linear multi-step\n method.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n beta_start (`float`, defaults to 0.0001):\n The starting `beta` value of inference.\n beta_end (`float`, defaults to 0.02):\n The final `beta` value.\n beta_schedule (`str`, defaults to `\"linear\"`):\n The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear`, `scaled_linear`, or `squaredcos_cap_v2`.\n trained_betas (`np.ndarray`, *optional*):\n Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.\n skip_prk_steps (`bool`, defaults to `False`):\n Allows the scheduler to skip the Runge-Kutta steps defined in the original paper as being required before\n PLMS steps.\n set_alpha_to_one (`bool`, defaults to `False`):\n Each diffusion step uses the alphas product value at that step and at the previous one. For the final step\n there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,\n otherwise it uses the alpha value at step 0.\n prediction_type (`str`, defaults to `epsilon`, *optional*):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process)\n or `v_prediction` (see section 2.4 of [Imagen Video](https://imagen.research.google/video/paper.pdf)\n paper).\n timestep_spacing (`str`, defaults to `\"leading\"`):\n The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.\n steps_offset (`int`, defaults to 0):\n An offset added to the inference steps, as required by some model families.\n \"\"\"\n\n _compatibles = [e.name for e in KarrasDiffusionSchedulers]\n order = 1\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[Union[np.ndarray, List[float]]] = None,\n skip_prk_steps: bool = False,\n set_alpha_to_one: bool = False,\n prediction_type: str = \"epsilon\",\n timestep_spacing: str = \"leading\",\n steps_offset: int = 0,\n ):\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n\n self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]\n\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = 1.0\n\n # For now we only support F-PNDM, i.e. the runge-kutta method\n # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf\n # mainly at formula (9), (12), (13) and the Algorithm 2.\n self.pndm_order = 4\n\n # running values\n self.cur_model_output = 0\n self.counter = 0\n self.cur_sample = None\n self.ets = []\n\n # setable values\n self.num_inference_steps = None\n self._timesteps = np.arange(0, num_train_timesteps)[::-1].copy()\n self.prk_timesteps = None\n self.plms_timesteps = None\n self.timesteps = None\n\n def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n \"\"\"\n\n self.num_inference_steps = num_inference_steps\n # \"linspace\", \"leading\", \"trailing\" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891\n if self.config.timestep_spacing == \"linspace\":\n self._timesteps = (\n np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps).round().astype(np.int64)\n )\n elif self.config.timestep_spacing == \"leading\":\n step_ratio = self.config.num_train_timesteps // self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n self._timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()\n self._timesteps += self.config.steps_offset\n elif self.config.timestep_spacing == \"trailing\":\n step_ratio = self.config.num_train_timesteps / self.num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n self._timesteps = np.round(np.arange(self.config.num_train_timesteps, 0, -step_ratio))[::-1].astype(\n np.int64\n )\n self._timesteps -= 1\n else:\n raise ValueError(\n f\"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.\"\n )\n\n if self.config.skip_prk_steps:\n # for some models like stable diffusion the prk steps can/should be skipped to\n # produce better results. When using PNDM with `self.config.skip_prk_steps` the implementation\n # is based on crowsonkb's PLMS sampler implementation: https://github.com/CompVis/latent-diffusion/pull/51\n self.prk_timesteps = np.array([])\n self.plms_timesteps = np.concatenate([self._timesteps[:-1], self._timesteps[-2:-1], self._timesteps[-1:]])[\n ::-1\n ].copy()\n else:\n prk_timesteps = np.array(self._timesteps[-self.pndm_order :]).repeat(2) + np.tile(\n np.array([0, self.config.num_train_timesteps // num_inference_steps // 2]), self.pndm_order\n )\n self.prk_timesteps = (prk_timesteps[:-1].repeat(2)[1:-1])[::-1].copy()\n self.plms_timesteps = self._timesteps[:-3][\n ::-1\n ].copy() # we copy to avoid having negative strides which are not supported by torch.from_numpy\n\n timesteps = np.concatenate([self.prk_timesteps, self.plms_timesteps]).astype(np.int64)\n self.timesteps = torch.from_numpy(timesteps).to(device)\n\n self.ets = []\n self.counter = 0\n self.cur_model_output = 0\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n return_dict: bool = True,\n ) -> Union[SchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise), and calls [`~PNDMScheduler.step_prk`]\n or [`~PNDMScheduler.step_plms`] depending on the internal variable `counter`.\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`int`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n return_dict (`bool`):\n Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`.\n\n Returns:\n [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n\n \"\"\"\n if self.counter < len(self.prk_timesteps) and not self.config.skip_prk_steps:\n return self.step_prk(model_output=model_output, timestep=timestep, sample=sample, return_dict=return_dict)\n else:\n return self.step_plms(model_output=model_output, timestep=timestep, sample=sample, return_dict=return_dict)\n\n def step_prk(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n return_dict: bool = True,\n ) -> Union[SchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with\n the Runge-Kutta method. It performs four forward passes to approximate the solution to the differential\n equation.\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`int`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n return_dict (`bool`):\n Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or tuple.\n\n Returns:\n [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n\n \"\"\"\n if self.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n diff_to_prev = 0 if self.counter % 2 else self.config.num_train_timesteps // self.num_inference_steps // 2\n prev_timestep = timestep - diff_to_prev\n timestep = self.prk_timesteps[self.counter // 4 * 4]\n\n if self.counter % 4 == 0:\n self.cur_model_output += 1 / 6 * model_output\n self.ets.append(model_output)\n self.cur_sample = sample\n elif (self.counter - 1) % 4 == 0:\n self.cur_model_output += 1 / 3 * model_output\n elif (self.counter - 2) % 4 == 0:\n self.cur_model_output += 1 / 3 * model_output\n elif (self.counter - 3) % 4 == 0:\n model_output = self.cur_model_output + 1 / 6 * model_output\n self.cur_model_output = 0\n\n # cur_sample should not be `None`\n cur_sample = self.cur_sample if self.cur_sample is not None else sample\n\n prev_sample = self._get_prev_sample(cur_sample, timestep, prev_timestep, model_output)\n self.counter += 1\n\n if not return_dict:\n return (prev_sample,)\n\n return SchedulerOutput(prev_sample=prev_sample)\n\n def step_plms(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n return_dict: bool = True,\n ) -> Union[SchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with\n the linear multistep method. It performs one forward pass multiple times to approximate the solution.\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`int`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n return_dict (`bool`):\n Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or tuple.\n\n Returns:\n [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n\n \"\"\"\n if self.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n if not self.config.skip_prk_steps and len(self.ets) < 3:\n raise ValueError(\n f\"{self.__class__} can only be run AFTER scheduler has been run \"\n \"in 'prk' mode for at least 12 iterations \"\n \"See: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_pndm.py \"\n \"for more information.\"\n )\n\n prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps\n\n if self.counter != 1:\n self.ets = self.ets[-3:]\n self.ets.append(model_output)\n else:\n prev_timestep = timestep\n timestep = timestep + self.config.num_train_timesteps // self.num_inference_steps\n\n if len(self.ets) == 1 and self.counter == 0:\n model_output = model_output\n self.cur_sample = sample\n elif len(self.ets) == 1 and self.counter == 1:\n model_output = (model_output + self.ets[-1]) / 2\n sample = self.cur_sample\n self.cur_sample = None\n elif len(self.ets) == 2:\n model_output = (3 * self.ets[-1] - self.ets[-2]) / 2\n elif len(self.ets) == 3:\n model_output = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12\n else:\n model_output = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])\n\n prev_sample = self._get_prev_sample(sample, timestep, prev_timestep, model_output)\n self.counter += 1\n\n if not return_dict:\n return (prev_sample,)\n\n return SchedulerOutput(prev_sample=prev_sample)\n\n def scale_model_input(self, sample: torch.Tensor, *args, **kwargs) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n return sample\n\n def _get_prev_sample(self, sample, timestep, prev_timestep, model_output):\n # See formula (9) of PNDM paper https://arxiv.org/pdf/2202.09778.pdf\n # this function computes x_(t−δ) using the formula of (9)\n # Note that x_t needs to be added to both sides of the equation\n\n # Notation ( -> \n # alpha_prod_t -> α_t\n # alpha_prod_t_prev -> α_(t−δ)\n # beta_prod_t -> (1 - α_t)\n # beta_prod_t_prev -> (1 - α_(t−δ))\n # sample -> x_t\n # model_output -> e_θ(x_t, t)\n # prev_sample -> x_(t−δ)\n alpha_prod_t = self.alphas_cumprod[timestep]\n alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n\n if self.config.prediction_type == \"v_prediction\":\n model_output = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample\n elif self.config.prediction_type != \"epsilon\":\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `v_prediction`\"\n )\n\n # corresponds to (α_(t−δ) - α_t) divided by\n # denominator of x_t in formula (9) and plus 1\n # Note: (α_(t−δ) - α_t) / (sqrt(α_t) * (sqrt(α_(t−δ)) + sqr(α_t))) =\n # sqrt(α_(t−δ)) / sqrt(α_t))\n sample_coeff = (alpha_prod_t_prev / alpha_prod_t) ** (0.5)\n\n # corresponds to denominator of e_θ(x_t, t) in formula (9)\n model_output_denom_coeff = alpha_prod_t * beta_prod_t_prev ** (0.5) + (\n alpha_prod_t * beta_prod_t * alpha_prod_t_prev\n ) ** (0.5)\n\n # full formula (9)\n prev_sample = (\n sample_coeff * sample - (alpha_prod_t_prev - alpha_prod_t) * model_output / model_output_denom_coeff\n )\n\n return prev_sample\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.IntTensor,\n ) -> torch.Tensor:\n # Make sure alphas_cumprod and timestep have same device and dtype as original_samples\n # Move the self.alphas_cumprod to device to avoid redundant CPU to GPU data movement\n # for the subsequent add_noise calls\n self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device)\n alphas_cumprod = self.alphas_cumprod.to(dtype=original_samples.dtype)\n timesteps = timesteps.to(original_samples.device)\n\n sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n while len(sqrt_alpha_prod.shape) < len(original_samples.shape):\n sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n\n noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise\n return noisy_samples\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py", "content": "# Copyright 2024 Zhejiang University Team and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim\n\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple, Union\n\nimport flax\nimport jax\nimport jax.numpy as jnp\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom .scheduling_utils_flax import (\n CommonSchedulerState,\n FlaxKarrasDiffusionSchedulers,\n FlaxSchedulerMixin,\n FlaxSchedulerOutput,\n add_noise_common,\n)\n\n\n@flax.struct.dataclass\nclass PNDMSchedulerState:\n common: CommonSchedulerState\n final_alpha_cumprod: jnp.ndarray\n\n # setable values\n init_noise_sigma: jnp.ndarray\n timesteps: jnp.ndarray\n num_inference_steps: Optional[int] = None\n prk_timesteps: Optional[jnp.ndarray] = None\n plms_timesteps: Optional[jnp.ndarray] = None\n\n # running values\n cur_model_output: Optional[jnp.ndarray] = None\n counter: Optional[jnp.int32] = None\n cur_sample: Optional[jnp.ndarray] = None\n ets: Optional[jnp.ndarray] = None\n\n @classmethod\n def create(\n cls,\n common: CommonSchedulerState,\n final_alpha_cumprod: jnp.ndarray,\n init_noise_sigma: jnp.ndarray,\n timesteps: jnp.ndarray,\n ):\n return cls(\n common=common,\n final_alpha_cumprod=final_alpha_cumprod,\n init_noise_sigma=init_noise_sigma,\n timesteps=timesteps,\n )\n\n\n@dataclass\nclass FlaxPNDMSchedulerOutput(FlaxSchedulerOutput):\n state: PNDMSchedulerState\n\n\nclass FlaxPNDMScheduler(FlaxSchedulerMixin, ConfigMixin):\n \"\"\"\n Pseudo numerical methods for diffusion models (PNDM) proposes using more advanced ODE integration techniques,\n namely Runge-Kutta method and a linear multi-step method.\n\n [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`\n function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.\n [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and\n [`~SchedulerMixin.from_pretrained`] functions.\n\n For more details, see the original paper: https://arxiv.org/abs/2202.09778\n\n Args:\n num_train_timesteps (`int`): number of diffusion steps used to train the model.\n beta_start (`float`): the starting `beta` value of inference.\n beta_end (`float`): the final `beta` value.\n beta_schedule (`str`):\n the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear`, `scaled_linear`, or `squaredcos_cap_v2`.\n trained_betas (`jnp.ndarray`, optional):\n option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.\n skip_prk_steps (`bool`):\n allows the scheduler to skip the Runge-Kutta steps that are defined in the original paper as being required\n before plms steps; defaults to `False`.\n set_alpha_to_one (`bool`, default `False`):\n each diffusion step uses the value of alphas product at that step and at the previous one. For the final\n step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,\n otherwise it uses the value of alpha at step 0.\n steps_offset (`int`, default `0`):\n An offset added to the inference steps, as required by some model families.\n prediction_type (`str`, default `epsilon`, optional):\n prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion\n process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4\n https://imagen.research.google/video/paper.pdf)\n dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):\n the `dtype` used for params and computation.\n \"\"\"\n\n _compatibles = [e.name for e in FlaxKarrasDiffusionSchedulers]\n\n dtype: jnp.dtype\n pndm_order: int\n\n @property\n def has_state(self):\n return True\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[jnp.ndarray] = None,\n skip_prk_steps: bool = False,\n set_alpha_to_one: bool = False,\n steps_offset: int = 0,\n prediction_type: str = \"epsilon\",\n dtype: jnp.dtype = jnp.float32,\n ):\n self.dtype = dtype\n\n # For now we only support F-PNDM, i.e. the runge-kutta method\n # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf\n # mainly at formula (9), (12), (13) and the Algorithm 2.\n self.pndm_order = 4\n\n def create_state(self, common: Optional[CommonSchedulerState] = None) -> PNDMSchedulerState:\n if common is None:\n common = CommonSchedulerState.create(self)\n\n # At every step in ddim, we are looking into the previous alphas_cumprod\n # For the final step, there is no previous alphas_cumprod because we are already at 0\n # `set_alpha_to_one` decides whether we set this parameter simply to one or\n # whether we use the final alpha of the \"non-previous\" one.\n final_alpha_cumprod = (\n jnp.array(1.0, dtype=self.dtype) if self.config.set_alpha_to_one else common.alphas_cumprod[0]\n )\n\n # standard deviation of the initial noise distribution\n init_noise_sigma = jnp.array(1.0, dtype=self.dtype)\n\n timesteps = jnp.arange(0, self.config.num_train_timesteps).round()[::-1]\n\n return PNDMSchedulerState.create(\n common=common,\n final_alpha_cumprod=final_alpha_cumprod,\n init_noise_sigma=init_noise_sigma,\n timesteps=timesteps,\n )\n\n def set_timesteps(self, state: PNDMSchedulerState, num_inference_steps: int, shape: Tuple) -> PNDMSchedulerState:\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.\n\n Args:\n state (`PNDMSchedulerState`):\n the `FlaxPNDMScheduler` state data class instance.\n num_inference_steps (`int`):\n the number of diffusion steps used when generating samples with a pre-trained model.\n shape (`Tuple`):\n the shape of the samples to be generated.\n \"\"\"\n\n step_ratio = self.config.num_train_timesteps // num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # rounding to avoid issues when num_inference_step is power of 3\n _timesteps = (jnp.arange(0, num_inference_steps) * step_ratio).round() + self.config.steps_offset\n\n if self.config.skip_prk_steps:\n # for some models like stable diffusion the prk steps can/should be skipped to\n # produce better results. When using PNDM with `self.config.skip_prk_steps` the implementation\n # is based on crowsonkb's PLMS sampler implementation: https://github.com/CompVis/latent-diffusion/pull/51\n\n prk_timesteps = jnp.array([], dtype=jnp.int32)\n plms_timesteps = jnp.concatenate([_timesteps[:-1], _timesteps[-2:-1], _timesteps[-1:]])[::-1]\n\n else:\n prk_timesteps = _timesteps[-self.pndm_order :].repeat(2) + jnp.tile(\n jnp.array([0, self.config.num_train_timesteps // num_inference_steps // 2], dtype=jnp.int32),\n self.pndm_order,\n )\n\n prk_timesteps = (prk_timesteps[:-1].repeat(2)[1:-1])[::-1]\n plms_timesteps = _timesteps[:-3][::-1]\n\n timesteps = jnp.concatenate([prk_timesteps, plms_timesteps])\n\n # initial running values\n\n cur_model_output = jnp.zeros(shape, dtype=self.dtype)\n counter = jnp.int32(0)\n cur_sample = jnp.zeros(shape, dtype=self.dtype)\n ets = jnp.zeros((4,) + shape, dtype=self.dtype)\n\n return state.replace(\n timesteps=timesteps,\n num_inference_steps=num_inference_steps,\n prk_timesteps=prk_timesteps,\n plms_timesteps=plms_timesteps,\n cur_model_output=cur_model_output,\n counter=counter,\n cur_sample=cur_sample,\n ets=ets,\n )\n\n def scale_model_input(\n self, state: PNDMSchedulerState, sample: jnp.ndarray, timestep: Optional[int] = None\n ) -> jnp.ndarray:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance.\n sample (`jnp.ndarray`): input sample\n timestep (`int`, optional): current timestep\n\n Returns:\n `jnp.ndarray`: scaled input sample\n \"\"\"\n return sample\n\n def step(\n self,\n state: PNDMSchedulerState,\n model_output: jnp.ndarray,\n timestep: int,\n sample: jnp.ndarray,\n return_dict: bool = True,\n ) -> Union[FlaxPNDMSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n This function calls `step_prk()` or `step_plms()` depending on the internal variable `counter`.\n\n Args:\n state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance.\n model_output (`jnp.ndarray`): direct output from learned diffusion model.\n timestep (`int`): current discrete timestep in the diffusion chain.\n sample (`jnp.ndarray`):\n current instance of sample being created by diffusion process.\n return_dict (`bool`): option for returning tuple rather than FlaxPNDMSchedulerOutput class\n\n Returns:\n [`FlaxPNDMSchedulerOutput`] or `tuple`: [`FlaxPNDMSchedulerOutput`] if `return_dict` is True, otherwise a\n `tuple`. When returning a tuple, the first element is the sample tensor.\n\n \"\"\"\n\n if state.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n if self.config.skip_prk_steps:\n prev_sample, state = self.step_plms(state, model_output, timestep, sample)\n else:\n prk_prev_sample, prk_state = self.step_prk(state, model_output, timestep, sample)\n plms_prev_sample, plms_state = self.step_plms(state, model_output, timestep, sample)\n\n cond = state.counter < len(state.prk_timesteps)\n\n prev_sample = jax.lax.select(cond, prk_prev_sample, plms_prev_sample)\n\n state = state.replace(\n cur_model_output=jax.lax.select(cond, prk_state.cur_model_output, plms_state.cur_model_output),\n ets=jax.lax.select(cond, prk_state.ets, plms_state.ets),\n cur_sample=jax.lax.select(cond, prk_state.cur_sample, plms_state.cur_sample),\n counter=jax.lax.select(cond, prk_state.counter, plms_state.counter),\n )\n\n if not return_dict:\n return (prev_sample, state)\n\n return FlaxPNDMSchedulerOutput(prev_sample=prev_sample, state=state)\n\n def step_prk(\n self,\n state: PNDMSchedulerState,\n model_output: jnp.ndarray,\n timestep: int,\n sample: jnp.ndarray,\n ) -> Union[FlaxPNDMSchedulerOutput, Tuple]:\n \"\"\"\n Step function propagating the sample with the Runge-Kutta method. RK takes 4 forward passes to approximate the\n solution to the differential equation.\n\n Args:\n state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance.\n model_output (`jnp.ndarray`): direct output from learned diffusion model.\n timestep (`int`): current discrete timestep in the diffusion chain.\n sample (`jnp.ndarray`):\n current instance of sample being created by diffusion process.\n return_dict (`bool`): option for returning tuple rather than FlaxPNDMSchedulerOutput class\n\n Returns:\n [`FlaxPNDMSchedulerOutput`] or `tuple`: [`FlaxPNDMSchedulerOutput`] if `return_dict` is True, otherwise a\n `tuple`. When returning a tuple, the first element is the sample tensor.\n\n \"\"\"\n\n if state.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n diff_to_prev = jnp.where(\n state.counter % 2, 0, self.config.num_train_timesteps // state.num_inference_steps // 2\n )\n prev_timestep = timestep - diff_to_prev\n timestep = state.prk_timesteps[state.counter // 4 * 4]\n\n model_output = jax.lax.select(\n (state.counter % 4) != 3,\n model_output, # remainder 0, 1, 2\n state.cur_model_output + 1 / 6 * model_output, # remainder 3\n )\n\n state = state.replace(\n cur_model_output=jax.lax.select_n(\n state.counter % 4,\n state.cur_model_output + 1 / 6 * model_output, # remainder 0\n state.cur_model_output + 1 / 3 * model_output, # remainder 1\n state.cur_model_output + 1 / 3 * model_output, # remainder 2\n jnp.zeros_like(state.cur_model_output), # remainder 3\n ),\n ets=jax.lax.select(\n (state.counter % 4) == 0,\n state.ets.at[0:3].set(state.ets[1:4]).at[3].set(model_output), # remainder 0\n state.ets, # remainder 1, 2, 3\n ),\n cur_sample=jax.lax.select(\n (state.counter % 4) == 0,\n sample, # remainder 0\n state.cur_sample, # remainder 1, 2, 3\n ),\n )\n\n cur_sample = state.cur_sample\n prev_sample = self._get_prev_sample(state, cur_sample, timestep, prev_timestep, model_output)\n state = state.replace(counter=state.counter + 1)\n\n return (prev_sample, state)\n\n def step_plms(\n self,\n state: PNDMSchedulerState,\n model_output: jnp.ndarray,\n timestep: int,\n sample: jnp.ndarray,\n ) -> Union[FlaxPNDMSchedulerOutput, Tuple]:\n \"\"\"\n Step function propagating the sample with the linear multi-step method. This has one forward pass with multiple\n times to approximate the solution.\n\n Args:\n state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance.\n model_output (`jnp.ndarray`): direct output from learned diffusion model.\n timestep (`int`): current discrete timestep in the diffusion chain.\n sample (`jnp.ndarray`):\n current instance of sample being created by diffusion process.\n return_dict (`bool`): option for returning tuple rather than FlaxPNDMSchedulerOutput class\n\n Returns:\n [`FlaxPNDMSchedulerOutput`] or `tuple`: [`FlaxPNDMSchedulerOutput`] if `return_dict` is True, otherwise a\n `tuple`. When returning a tuple, the first element is the sample tensor.\n\n \"\"\"\n\n if state.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n # NOTE: There is no way to check in the jitted runtime if the prk mode was ran before\n\n prev_timestep = timestep - self.config.num_train_timesteps // state.num_inference_steps\n prev_timestep = jnp.where(prev_timestep > 0, prev_timestep, 0)\n\n # Reference:\n # if state.counter != 1:\n # state.ets.append(model_output)\n # else:\n # prev_timestep = timestep\n # timestep = timestep + self.config.num_train_timesteps // state.num_inference_steps\n\n prev_timestep = jnp.where(state.counter == 1, timestep, prev_timestep)\n timestep = jnp.where(\n state.counter == 1, timestep + self.config.num_train_timesteps // state.num_inference_steps, timestep\n )\n\n # Reference:\n # if len(state.ets) == 1 and state.counter == 0:\n # model_output = model_output\n # state.cur_sample = sample\n # elif len(state.ets) == 1 and state.counter == 1:\n # model_output = (model_output + state.ets[-1]) / 2\n # sample = state.cur_sample\n # state.cur_sample = None\n # elif len(state.ets) == 2:\n # model_output = (3 * state.ets[-1] - state.ets[-2]) / 2\n # elif len(state.ets) == 3:\n # model_output = (23 * state.ets[-1] - 16 * state.ets[-2] + 5 * state.ets[-3]) / 12\n # else:\n # model_output = (1 / 24) * (55 * state.ets[-1] - 59 * state.ets[-2] + 37 * state.ets[-3] - 9 * state.ets[-4])\n\n state = state.replace(\n ets=jax.lax.select(\n state.counter != 1,\n state.ets.at[0:3].set(state.ets[1:4]).at[3].set(model_output), # counter != 1\n state.ets, # counter 1\n ),\n cur_sample=jax.lax.select(\n state.counter != 1,\n sample, # counter != 1\n state.cur_sample, # counter 1\n ),\n )\n\n state = state.replace(\n cur_model_output=jax.lax.select_n(\n jnp.clip(state.counter, 0, 4),\n model_output, # counter 0\n (model_output + state.ets[-1]) / 2, # counter 1\n (3 * state.ets[-1] - state.ets[-2]) / 2, # counter 2\n (23 * state.ets[-1] - 16 * state.ets[-2] + 5 * state.ets[-3]) / 12, # counter 3\n (1 / 24)\n * (55 * state.ets[-1] - 59 * state.ets[-2] + 37 * state.ets[-3] - 9 * state.ets[-4]), # counter >= 4\n ),\n )\n\n sample = state.cur_sample\n model_output = state.cur_model_output\n prev_sample = self._get_prev_sample(state, sample, timestep, prev_timestep, model_output)\n state = state.replace(counter=state.counter + 1)\n\n return (prev_sample, state)\n\n def _get_prev_sample(self, state: PNDMSchedulerState, sample, timestep, prev_timestep, model_output):\n # See formula (9) of PNDM paper https://arxiv.org/pdf/2202.09778.pdf\n # this function computes x_(t−δ) using the formula of (9)\n # Note that x_t needs to be added to both sides of the equation\n\n # Notation ( -> \n # alpha_prod_t -> α_t\n # alpha_prod_t_prev -> α_(t−δ)\n # beta_prod_t -> (1 - α_t)\n # beta_prod_t_prev -> (1 - α_(t−δ))\n # sample -> x_t\n # model_output -> e_θ(x_t, t)\n # prev_sample -> x_(t−δ)\n alpha_prod_t = state.common.alphas_cumprod[timestep]\n alpha_prod_t_prev = jnp.where(\n prev_timestep >= 0, state.common.alphas_cumprod[prev_timestep], state.final_alpha_cumprod\n )\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n\n if self.config.prediction_type == \"v_prediction\":\n model_output = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample\n elif self.config.prediction_type != \"epsilon\":\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `v_prediction`\"\n )\n\n # corresponds to (α_(t−δ) - α_t) divided by\n # denominator of x_t in formula (9) and plus 1\n # Note: (α_(t−δ) - α_t) / (sqrt(α_t) * (sqrt(α_(t−δ)) + sqr(α_t))) =\n # sqrt(α_(t−δ)) / sqrt(α_t))\n sample_coeff = (alpha_prod_t_prev / alpha_prod_t) ** (0.5)\n\n # corresponds to denominator of e_θ(x_t, t) in formula (9)\n model_output_denom_coeff = alpha_prod_t * beta_prod_t_prev ** (0.5) + (\n alpha_prod_t * beta_prod_t * alpha_prod_t_prev\n ) ** (0.5)\n\n # full formula (9)\n prev_sample = (\n sample_coeff * sample - (alpha_prod_t_prev - alpha_prod_t) * model_output / model_output_denom_coeff\n )\n\n return prev_sample\n\n def add_noise(\n self,\n state: PNDMSchedulerState,\n original_samples: jnp.ndarray,\n noise: jnp.ndarray,\n timesteps: jnp.ndarray,\n ) -> jnp.ndarray:\n return add_noise_common(state.common, original_samples, noise, timesteps)\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_repaint.py", "content": "# Copyright 2024 ETH Zurich Computer Vision Lab and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport math\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import BaseOutput\nfrom ..utils.torch_utils import randn_tensor\nfrom .scheduling_utils import SchedulerMixin\n\n\n@dataclass\nclass RePaintSchedulerOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's step function output.\n\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample (x_{0}) based on the model output from\n the current timestep. `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.Tensor\n pred_original_sample: torch.Tensor\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\nclass RePaintScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n `RePaintScheduler` is a scheduler for DDPM inpainting inside a given mask.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n beta_start (`float`, defaults to 0.0001):\n The starting `beta` value of inference.\n beta_end (`float`, defaults to 0.02):\n The final `beta` value.\n beta_schedule (`str`, defaults to `\"linear\"`):\n The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear`, `scaled_linear`, `squaredcos_cap_v2`, or `sigmoid`.\n eta (`float`):\n The weight of noise for added noise in diffusion step. If its value is between 0.0 and 1.0 it corresponds\n to the DDIM scheduler, and if its value is between -0.0 and 1.0 it corresponds to the DDPM scheduler.\n trained_betas (`np.ndarray`, *optional*):\n Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.\n clip_sample (`bool`, defaults to `True`):\n Clip the predicted sample between -1 and 1 for numerical stability.\n\n \"\"\"\n\n order = 1\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n eta: float = 0.0,\n trained_betas: Optional[np.ndarray] = None,\n clip_sample: bool = True,\n ):\n if trained_betas is not None:\n self.betas = torch.from_numpy(trained_betas)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n elif beta_schedule == \"sigmoid\":\n # GeoDiff sigmoid schedule\n betas = torch.linspace(-6, 6, num_train_timesteps)\n self.betas = torch.sigmoid(betas) * (beta_end - beta_start) + beta_start\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n self.one = torch.tensor(1.0)\n\n self.final_alpha_cumprod = torch.tensor(1.0)\n\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = 1.0\n\n # setable values\n self.num_inference_steps = None\n self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())\n\n self.eta = eta\n\n def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`int`, *optional*):\n The current timestep in the diffusion chain.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n return sample\n\n def set_timesteps(\n self,\n num_inference_steps: int,\n jump_length: int = 10,\n jump_n_sample: int = 10,\n device: Union[str, torch.device] = None,\n ):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used,\n `timesteps` must be `None`.\n jump_length (`int`, defaults to 10):\n The number of steps taken forward in time before going backward in time for a single jump (“j” in\n RePaint paper). Take a look at Figure 9 and 10 in the paper.\n jump_n_sample (`int`, defaults to 10):\n The number of times to make a forward time jump for a given chosen time sample. Take a look at Figure 9\n and 10 in the paper.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n\n \"\"\"\n num_inference_steps = min(self.config.num_train_timesteps, num_inference_steps)\n self.num_inference_steps = num_inference_steps\n\n timesteps = []\n\n jumps = {}\n for j in range(0, num_inference_steps - jump_length, jump_length):\n jumps[j] = jump_n_sample - 1\n\n t = num_inference_steps\n while t >= 1:\n t = t - 1\n timesteps.append(t)\n\n if jumps.get(t, 0) > 0:\n jumps[t] = jumps[t] - 1\n for _ in range(jump_length):\n t = t + 1\n timesteps.append(t)\n\n timesteps = np.array(timesteps) * (self.config.num_train_timesteps // self.num_inference_steps)\n self.timesteps = torch.from_numpy(timesteps).to(device)\n\n def _get_variance(self, t):\n prev_timestep = t - self.config.num_train_timesteps // self.num_inference_steps\n\n alpha_prod_t = self.alphas_cumprod[t]\n alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n\n # For t > 0, compute predicted variance βt (see formula (6) and (7) from\n # https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get\n # previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add\n # variance to pred_sample\n # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf\n # without eta.\n # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]\n variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)\n\n return variance\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n original_image: torch.Tensor,\n mask: torch.Tensor,\n generator: Optional[torch.Generator] = None,\n return_dict: bool = True,\n ) -> Union[RePaintSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`int`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n original_image (`torch.Tensor`):\n The original image to inpaint on.\n mask (`torch.Tensor`):\n The mask where a value of 0.0 indicates which part of the original image to inpaint.\n generator (`torch.Generator`, *optional*):\n A random number generator.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~schedulers.scheduling_repaint.RePaintSchedulerOutput`] or `tuple`.\n\n Returns:\n [`~schedulers.scheduling_repaint.RePaintSchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_repaint.RePaintSchedulerOutput`] is returned,\n otherwise a tuple is returned where the first element is the sample tensor.\n\n \"\"\"\n t = timestep\n prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps\n\n # 1. compute alphas, betas\n alpha_prod_t = self.alphas_cumprod[t]\n alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod\n beta_prod_t = 1 - alpha_prod_t\n\n # 2. compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf\n pred_original_sample = (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5\n\n # 3. Clip \"predicted x_0\"\n if self.config.clip_sample:\n pred_original_sample = torch.clamp(pred_original_sample, -1, 1)\n\n # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we\n # substitute formula (7) in the algorithm coming from DDPM paper\n # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.\n # DDIM schedule gives the same results as DDPM with eta = 1.0\n # Noise is being reused in 7. and 8., but no impact on quality has\n # been observed.\n\n # 5. Add noise\n device = model_output.device\n noise = randn_tensor(model_output.shape, generator=generator, device=device, dtype=model_output.dtype)\n std_dev_t = self.eta * self._get_variance(timestep) ** 0.5\n\n variance = 0\n if t > 0 and self.eta > 0:\n variance = std_dev_t * noise\n\n # 6. compute \"direction pointing to x_t\" of formula (12)\n # from https://arxiv.org/pdf/2010.02502.pdf\n pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output\n\n # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf\n prev_unknown_part = alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance\n\n # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf\n prev_known_part = (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise\n\n # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf\n pred_prev_sample = mask * prev_known_part + (1.0 - mask) * prev_unknown_part\n\n if not return_dict:\n return (\n pred_prev_sample,\n pred_original_sample,\n )\n\n return RePaintSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample)\n\n def undo_step(self, sample, timestep, generator=None):\n n = self.config.num_train_timesteps // self.num_inference_steps\n\n for i in range(n):\n beta = self.betas[timestep + i]\n if sample.device.type == \"mps\":\n # randn does not work reproducibly on mps\n noise = randn_tensor(sample.shape, dtype=sample.dtype, generator=generator)\n noise = noise.to(sample.device)\n else:\n noise = randn_tensor(sample.shape, generator=generator, device=sample.device, dtype=sample.dtype)\n\n # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf\n sample = (1 - beta) ** 0.5 * sample + beta**0.5 * noise\n\n return sample\n\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.IntTensor,\n ) -> torch.Tensor:\n raise NotImplementedError(\"Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.\")\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_sasolver.py", "content": "# Copyright 2024 Shuchen Xue, etc. in University of Chinese Academy of Sciences Team and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: check https://arxiv.org/abs/2309.05019\n# The codebase is modified based on https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py\n\nimport math\nfrom typing import Callable, List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import deprecate\nfrom ..utils.torch_utils import randn_tensor\nfrom .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\nclass SASolverScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n `SASolverScheduler` is a fast dedicated high-order solver for diffusion SDEs.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n beta_start (`float`, defaults to 0.0001):\n The starting `beta` value of inference.\n beta_end (`float`, defaults to 0.02):\n The final `beta` value.\n beta_schedule (`str`, defaults to `\"linear\"`):\n The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear`, `scaled_linear`, or `squaredcos_cap_v2`.\n trained_betas (`np.ndarray`, *optional*):\n Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.\n predictor_order (`int`, defaults to 2):\n The predictor order which can be `1` or `2` or `3` or '4'. It is recommended to use `predictor_order=2` for\n guided sampling, and `predictor_order=3` for unconditional sampling.\n corrector_order (`int`, defaults to 2):\n The corrector order which can be `1` or `2` or `3` or '4'. It is recommended to use `corrector_order=2` for\n guided sampling, and `corrector_order=3` for unconditional sampling.\n prediction_type (`str`, defaults to `epsilon`, *optional*):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),\n `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen\n Video](https://imagen.research.google/video/paper.pdf) paper).\n tau_func (`Callable`, *optional*):\n Stochasticity during the sampling. Default in init is `lambda t: 1 if t >= 200 and t <= 800 else 0`.\n SA-Solver will sample from vanilla diffusion ODE if tau_func is set to `lambda t: 0`. SA-Solver will sample\n from vanilla diffusion SDE if tau_func is set to `lambda t: 1`. For more details, please check\n https://arxiv.org/abs/2309.05019\n thresholding (`bool`, defaults to `False`):\n Whether to use the \"dynamic thresholding\" method. This is unsuitable for latent-space diffusion models such\n as Stable Diffusion.\n dynamic_thresholding_ratio (`float`, defaults to 0.995):\n The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.\n sample_max_value (`float`, defaults to 1.0):\n The threshold value for dynamic thresholding. Valid only when `thresholding=True` and\n `algorithm_type=\"dpmsolver++\"`.\n algorithm_type (`str`, defaults to `data_prediction`):\n Algorithm type for the solver; can be `data_prediction` or `noise_prediction`. It is recommended to use\n `data_prediction` with `solver_order=2` for guided sampling like in Stable Diffusion.\n lower_order_final (`bool`, defaults to `True`):\n Whether to use lower-order solvers in the final steps. Default = True.\n use_karras_sigmas (`bool`, *optional*, defaults to `False`):\n Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`,\n the sigmas are determined according to a sequence of noise levels {σi}.\n lambda_min_clipped (`float`, defaults to `-inf`):\n Clipping threshold for the minimum value of `lambda(t)` for numerical stability. This is critical for the\n cosine (`squaredcos_cap_v2`) noise schedule.\n variance_type (`str`, *optional*):\n Set to \"learned\" or \"learned_range\" for diffusion models that predict variance. If set, the model's output\n contains the predicted Gaussian variance.\n timestep_spacing (`str`, defaults to `\"linspace\"`):\n The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.\n steps_offset (`int`, defaults to 0):\n An offset added to the inference steps, as required by some model families.\n \"\"\"\n\n _compatibles = [e.name for e in KarrasDiffusionSchedulers]\n order = 1\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[Union[np.ndarray, List[float]]] = None,\n predictor_order: int = 2,\n corrector_order: int = 2,\n prediction_type: str = \"epsilon\",\n tau_func: Optional[Callable] = None,\n thresholding: bool = False,\n dynamic_thresholding_ratio: float = 0.995,\n sample_max_value: float = 1.0,\n algorithm_type: str = \"data_prediction\",\n lower_order_final: bool = True,\n use_karras_sigmas: Optional[bool] = False,\n lambda_min_clipped: float = -float(\"inf\"),\n variance_type: Optional[str] = None,\n timestep_spacing: str = \"linspace\",\n steps_offset: int = 0,\n ):\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = (\n torch.linspace(\n beta_start**0.5,\n beta_end**0.5,\n num_train_timesteps,\n dtype=torch.float32,\n )\n ** 2\n )\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n # Currently we only support VP-type noise schedule\n self.alpha_t = torch.sqrt(self.alphas_cumprod)\n self.sigma_t = torch.sqrt(1 - self.alphas_cumprod)\n self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t)\n self.sigmas = ((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5\n\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = 1.0\n\n if algorithm_type not in [\"data_prediction\", \"noise_prediction\"]:\n raise NotImplementedError(f\"{algorithm_type} is not implemented for {self.__class__}\")\n\n # setable values\n self.num_inference_steps = None\n timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=np.float32)[::-1].copy()\n self.timesteps = torch.from_numpy(timesteps)\n self.timestep_list = [None] * max(predictor_order, corrector_order - 1)\n self.model_outputs = [None] * max(predictor_order, corrector_order - 1)\n\n if tau_func is None:\n self.tau_func = lambda t: 1 if t >= 200 and t <= 800 else 0\n else:\n self.tau_func = tau_func\n self.predict_x0 = algorithm_type == \"data_prediction\"\n self.lower_order_nums = 0\n self.last_sample = None\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n @property\n def step_index(self):\n \"\"\"\n The index counter for current timestep. It will increase 1 after each scheduler step.\n \"\"\"\n return self._step_index\n\n @property\n def begin_index(self):\n \"\"\"\n The index for the first timestep. It should be set from pipeline with `set_begin_index` method.\n \"\"\"\n return self._begin_index\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index\n def set_begin_index(self, begin_index: int = 0):\n \"\"\"\n Sets the begin index for the scheduler. This function should be run from pipeline before the inference.\n\n Args:\n begin_index (`int`):\n The begin index for the scheduler.\n \"\"\"\n self._begin_index = begin_index\n\n def set_timesteps(self, num_inference_steps: int = None, device: Union[str, torch.device] = None):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n \"\"\"\n # Clipping the minimum of all lambda(t) for numerical stability.\n # This is critical for cosine (squaredcos_cap_v2) noise schedule.\n clipped_idx = torch.searchsorted(torch.flip(self.lambda_t, [0]), self.config.lambda_min_clipped)\n last_timestep = ((self.config.num_train_timesteps - clipped_idx).numpy()).item()\n\n # \"linspace\", \"leading\", \"trailing\" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891\n if self.config.timestep_spacing == \"linspace\":\n timesteps = (\n np.linspace(0, last_timestep - 1, num_inference_steps + 1).round()[::-1][:-1].copy().astype(np.int64)\n )\n\n elif self.config.timestep_spacing == \"leading\":\n step_ratio = last_timestep // (num_inference_steps + 1)\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (np.arange(0, num_inference_steps + 1) * step_ratio).round()[::-1][:-1].copy().astype(np.int64)\n timesteps += self.config.steps_offset\n elif self.config.timestep_spacing == \"trailing\":\n step_ratio = self.config.num_train_timesteps / num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = np.arange(last_timestep, 0, -step_ratio).round().copy().astype(np.int64)\n timesteps -= 1\n else:\n raise ValueError(\n f\"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.\"\n )\n\n sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)\n if self.config.use_karras_sigmas:\n log_sigmas = np.log(sigmas)\n sigmas = np.flip(sigmas).copy()\n sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)\n timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]).round()\n sigmas = np.concatenate([sigmas, sigmas[-1:]]).astype(np.float32)\n else:\n sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)\n sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0]) ** 0.5\n sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)\n\n self.sigmas = torch.from_numpy(sigmas)\n self.timesteps = torch.from_numpy(timesteps).to(device=device, dtype=torch.int64)\n\n self.num_inference_steps = len(timesteps)\n self.model_outputs = [\n None,\n ] * max(self.config.predictor_order, self.config.corrector_order - 1)\n self.lower_order_nums = 0\n self.last_sample = None\n\n # add an index counter for schedulers that allow duplicated timesteps\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample\n def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:\n \"\"\"\n \"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the\n prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by\n s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing\n pixels from saturation at each step. We find that dynamic thresholding results in significantly better\n photorealism as well as better image-text alignment, especially when using very large guidance weights.\"\n\n https://arxiv.org/abs/2205.11487\n \"\"\"\n dtype = sample.dtype\n batch_size, channels, *remaining_dims = sample.shape\n\n if dtype not in (torch.float32, torch.float64):\n sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half\n\n # Flatten sample for doing quantile calculation along each image\n sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))\n\n abs_sample = sample.abs() # \"a certain percentile absolute pixel value\"\n\n s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)\n s = torch.clamp(\n s, min=1, max=self.config.sample_max_value\n ) # When clamped to min=1, equivalent to standard clipping to [-1, 1]\n s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0\n sample = torch.clamp(sample, -s, s) / s # \"we threshold xt0 to the range [-s, s] and then divide by s\"\n\n sample = sample.reshape(batch_size, channels, *remaining_dims)\n sample = sample.to(dtype)\n\n return sample\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t\n def _sigma_to_t(self, sigma, log_sigmas):\n # get log sigma\n log_sigma = np.log(np.maximum(sigma, 1e-10))\n\n # get distribution\n dists = log_sigma - log_sigmas[:, np.newaxis]\n\n # get sigmas range\n low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)\n high_idx = low_idx + 1\n\n low = log_sigmas[low_idx]\n high = log_sigmas[high_idx]\n\n # interpolate sigmas\n w = (low - log_sigma) / (low - high)\n w = np.clip(w, 0, 1)\n\n # transform interpolation to time range\n t = (1 - w) * low_idx + w * high_idx\n t = t.reshape(sigma.shape)\n return t\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._sigma_to_alpha_sigma_t\n def _sigma_to_alpha_sigma_t(self, sigma):\n alpha_t = 1 / ((sigma**2 + 1) ** 0.5)\n sigma_t = sigma * alpha_t\n\n return alpha_t, sigma_t\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras\n def _convert_to_karras(self, in_sigmas: torch.Tensor, num_inference_steps) -> torch.Tensor:\n \"\"\"Constructs the noise schedule of Karras et al. (2022).\"\"\"\n\n # Hack to make sure that other schedulers which copy this function don't break\n # TODO: Add this logic to the other schedulers\n if hasattr(self.config, \"sigma_min\"):\n sigma_min = self.config.sigma_min\n else:\n sigma_min = None\n\n if hasattr(self.config, \"sigma_max\"):\n sigma_max = self.config.sigma_max\n else:\n sigma_max = None\n\n sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item()\n sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item()\n\n rho = 7.0 # 7.0 is the value used in the paper\n ramp = np.linspace(0, 1, num_inference_steps)\n min_inv_rho = sigma_min ** (1 / rho)\n max_inv_rho = sigma_max ** (1 / rho)\n sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho\n return sigmas\n\n def convert_model_output(\n self,\n model_output: torch.Tensor,\n *args,\n sample: torch.Tensor = None,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n Convert the model output to the corresponding type the data_prediction/noise_prediction algorithm needs.\n Noise_prediction is designed to discretize an integral of the noise prediction model, and data_prediction is\n designed to discretize an integral of the data prediction model.\n\n \n\n The algorithm and model type are decoupled. You can use either data_prediction or noise_prediction for both\n noise prediction and data prediction models.\n\n \n\n Args:\n model_output (`torch.Tensor`):\n The direct output from the learned diffusion model.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n\n Returns:\n `torch.Tensor`:\n The converted model output.\n \"\"\"\n timestep = args[0] if len(args) > 0 else kwargs.pop(\"timestep\", None)\n if sample is None:\n if len(args) > 1:\n sample = args[1]\n else:\n raise ValueError(\"missing `sample` as a required keyward argument\")\n if timestep is not None:\n deprecate(\n \"timesteps\",\n \"1.0.0\",\n \"Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n sigma = self.sigmas[self.step_index]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)\n # SA-Solver_data_prediction needs to solve an integral of the data prediction model.\n if self.config.algorithm_type in [\"data_prediction\"]:\n if self.config.prediction_type == \"epsilon\":\n # SA-Solver only needs the \"mean\" output.\n if self.config.variance_type in [\"learned\", \"learned_range\"]:\n model_output = model_output[:, :3]\n x0_pred = (sample - sigma_t * model_output) / alpha_t\n elif self.config.prediction_type == \"sample\":\n x0_pred = model_output\n elif self.config.prediction_type == \"v_prediction\":\n x0_pred = alpha_t * sample - sigma_t * model_output\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or\"\n \" `v_prediction` for the SASolverScheduler.\"\n )\n\n if self.config.thresholding:\n x0_pred = self._threshold_sample(x0_pred)\n\n return x0_pred\n\n # SA-Solver_noise_prediction needs to solve an integral of the noise prediction model.\n elif self.config.algorithm_type in [\"noise_prediction\"]:\n if self.config.prediction_type == \"epsilon\":\n # SA-Solver only needs the \"mean\" output.\n if self.config.variance_type in [\"learned\", \"learned_range\"]:\n epsilon = model_output[:, :3]\n else:\n epsilon = model_output\n elif self.config.prediction_type == \"sample\":\n epsilon = (sample - alpha_t * model_output) / sigma_t\n elif self.config.prediction_type == \"v_prediction\":\n epsilon = alpha_t * model_output + sigma_t * sample\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or\"\n \" `v_prediction` for the SASolverScheduler.\"\n )\n\n if self.config.thresholding:\n alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep]\n x0_pred = (sample - sigma_t * epsilon) / alpha_t\n x0_pred = self._threshold_sample(x0_pred)\n epsilon = (sample - alpha_t * x0_pred) / sigma_t\n\n return epsilon\n\n def get_coefficients_exponential_negative(self, order, interval_start, interval_end):\n \"\"\"\n Calculate the integral of exp(-x) * x^order dx from interval_start to interval_end\n \"\"\"\n assert order in [0, 1, 2, 3], \"order is only supported for 0, 1, 2 and 3\"\n\n if order == 0:\n return torch.exp(-interval_end) * (torch.exp(interval_end - interval_start) - 1)\n elif order == 1:\n return torch.exp(-interval_end) * (\n (interval_start + 1) * torch.exp(interval_end - interval_start) - (interval_end + 1)\n )\n elif order == 2:\n return torch.exp(-interval_end) * (\n (interval_start**2 + 2 * interval_start + 2) * torch.exp(interval_end - interval_start)\n - (interval_end**2 + 2 * interval_end + 2)\n )\n elif order == 3:\n return torch.exp(-interval_end) * (\n (interval_start**3 + 3 * interval_start**2 + 6 * interval_start + 6)\n * torch.exp(interval_end - interval_start)\n - (interval_end**3 + 3 * interval_end**2 + 6 * interval_end + 6)\n )\n\n def get_coefficients_exponential_positive(self, order, interval_start, interval_end, tau):\n \"\"\"\n Calculate the integral of exp(x(1+tau^2)) * x^order dx from interval_start to interval_end\n \"\"\"\n assert order in [0, 1, 2, 3], \"order is only supported for 0, 1, 2 and 3\"\n\n # after change of variable(cov)\n interval_end_cov = (1 + tau**2) * interval_end\n interval_start_cov = (1 + tau**2) * interval_start\n\n if order == 0:\n return (\n torch.exp(interval_end_cov) * (1 - torch.exp(-(interval_end_cov - interval_start_cov))) / (1 + tau**2)\n )\n elif order == 1:\n return (\n torch.exp(interval_end_cov)\n * (\n (interval_end_cov - 1)\n - (interval_start_cov - 1) * torch.exp(-(interval_end_cov - interval_start_cov))\n )\n / ((1 + tau**2) ** 2)\n )\n elif order == 2:\n return (\n torch.exp(interval_end_cov)\n * (\n (interval_end_cov**2 - 2 * interval_end_cov + 2)\n - (interval_start_cov**2 - 2 * interval_start_cov + 2)\n * torch.exp(-(interval_end_cov - interval_start_cov))\n )\n / ((1 + tau**2) ** 3)\n )\n elif order == 3:\n return (\n torch.exp(interval_end_cov)\n * (\n (interval_end_cov**3 - 3 * interval_end_cov**2 + 6 * interval_end_cov - 6)\n - (interval_start_cov**3 - 3 * interval_start_cov**2 + 6 * interval_start_cov - 6)\n * torch.exp(-(interval_end_cov - interval_start_cov))\n )\n / ((1 + tau**2) ** 4)\n )\n\n def lagrange_polynomial_coefficient(self, order, lambda_list):\n \"\"\"\n Calculate the coefficient of lagrange polynomial\n \"\"\"\n\n assert order in [0, 1, 2, 3]\n assert order == len(lambda_list) - 1\n if order == 0:\n return [[1]]\n elif order == 1:\n return [\n [\n 1 / (lambda_list[0] - lambda_list[1]),\n -lambda_list[1] / (lambda_list[0] - lambda_list[1]),\n ],\n [\n 1 / (lambda_list[1] - lambda_list[0]),\n -lambda_list[0] / (lambda_list[1] - lambda_list[0]),\n ],\n ]\n elif order == 2:\n denominator1 = (lambda_list[0] - lambda_list[1]) * (lambda_list[0] - lambda_list[2])\n denominator2 = (lambda_list[1] - lambda_list[0]) * (lambda_list[1] - lambda_list[2])\n denominator3 = (lambda_list[2] - lambda_list[0]) * (lambda_list[2] - lambda_list[1])\n return [\n [\n 1 / denominator1,\n (-lambda_list[1] - lambda_list[2]) / denominator1,\n lambda_list[1] * lambda_list[2] / denominator1,\n ],\n [\n 1 / denominator2,\n (-lambda_list[0] - lambda_list[2]) / denominator2,\n lambda_list[0] * lambda_list[2] / denominator2,\n ],\n [\n 1 / denominator3,\n (-lambda_list[0] - lambda_list[1]) / denominator3,\n lambda_list[0] * lambda_list[1] / denominator3,\n ],\n ]\n elif order == 3:\n denominator1 = (\n (lambda_list[0] - lambda_list[1])\n * (lambda_list[0] - lambda_list[2])\n * (lambda_list[0] - lambda_list[3])\n )\n denominator2 = (\n (lambda_list[1] - lambda_list[0])\n * (lambda_list[1] - lambda_list[2])\n * (lambda_list[1] - lambda_list[3])\n )\n denominator3 = (\n (lambda_list[2] - lambda_list[0])\n * (lambda_list[2] - lambda_list[1])\n * (lambda_list[2] - lambda_list[3])\n )\n denominator4 = (\n (lambda_list[3] - lambda_list[0])\n * (lambda_list[3] - lambda_list[1])\n * (lambda_list[3] - lambda_list[2])\n )\n return [\n [\n 1 / denominator1,\n (-lambda_list[1] - lambda_list[2] - lambda_list[3]) / denominator1,\n (\n lambda_list[1] * lambda_list[2]\n + lambda_list[1] * lambda_list[3]\n + lambda_list[2] * lambda_list[3]\n )\n / denominator1,\n (-lambda_list[1] * lambda_list[2] * lambda_list[3]) / denominator1,\n ],\n [\n 1 / denominator2,\n (-lambda_list[0] - lambda_list[2] - lambda_list[3]) / denominator2,\n (\n lambda_list[0] * lambda_list[2]\n + lambda_list[0] * lambda_list[3]\n + lambda_list[2] * lambda_list[3]\n )\n / denominator2,\n (-lambda_list[0] * lambda_list[2] * lambda_list[3]) / denominator2,\n ],\n [\n 1 / denominator3,\n (-lambda_list[0] - lambda_list[1] - lambda_list[3]) / denominator3,\n (\n lambda_list[0] * lambda_list[1]\n + lambda_list[0] * lambda_list[3]\n + lambda_list[1] * lambda_list[3]\n )\n / denominator3,\n (-lambda_list[0] * lambda_list[1] * lambda_list[3]) / denominator3,\n ],\n [\n 1 / denominator4,\n (-lambda_list[0] - lambda_list[1] - lambda_list[2]) / denominator4,\n (\n lambda_list[0] * lambda_list[1]\n + lambda_list[0] * lambda_list[2]\n + lambda_list[1] * lambda_list[2]\n )\n / denominator4,\n (-lambda_list[0] * lambda_list[1] * lambda_list[2]) / denominator4,\n ],\n ]\n\n def get_coefficients_fn(self, order, interval_start, interval_end, lambda_list, tau):\n assert order in [1, 2, 3, 4]\n assert order == len(lambda_list), \"the length of lambda list must be equal to the order\"\n coefficients = []\n lagrange_coefficient = self.lagrange_polynomial_coefficient(order - 1, lambda_list)\n for i in range(order):\n coefficient = 0\n for j in range(order):\n if self.predict_x0:\n coefficient += lagrange_coefficient[i][j] * self.get_coefficients_exponential_positive(\n order - 1 - j, interval_start, interval_end, tau\n )\n else:\n coefficient += lagrange_coefficient[i][j] * self.get_coefficients_exponential_negative(\n order - 1 - j, interval_start, interval_end\n )\n coefficients.append(coefficient)\n assert len(coefficients) == order, \"the length of coefficients does not match the order\"\n return coefficients\n\n def stochastic_adams_bashforth_update(\n self,\n model_output: torch.Tensor,\n *args,\n sample: torch.Tensor,\n noise: torch.Tensor,\n order: int,\n tau: torch.Tensor,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n One step for the SA-Predictor.\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from the learned diffusion model at the current timestep.\n prev_timestep (`int`):\n The previous discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n order (`int`):\n The order of SA-Predictor at this timestep.\n\n Returns:\n `torch.Tensor`:\n The sample tensor at the previous timestep.\n \"\"\"\n prev_timestep = args[0] if len(args) > 0 else kwargs.pop(\"prev_timestep\", None)\n if sample is None:\n if len(args) > 1:\n sample = args[1]\n else:\n raise ValueError(\" missing `sample` as a required keyward argument\")\n if noise is None:\n if len(args) > 2:\n noise = args[2]\n else:\n raise ValueError(\" missing `noise` as a required keyward argument\")\n if order is None:\n if len(args) > 3:\n order = args[3]\n else:\n raise ValueError(\" missing `order` as a required keyward argument\")\n if tau is None:\n if len(args) > 4:\n tau = args[4]\n else:\n raise ValueError(\" missing `tau` as a required keyward argument\")\n if prev_timestep is not None:\n deprecate(\n \"prev_timestep\",\n \"1.0.0\",\n \"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n model_output_list = self.model_outputs\n sigma_t, sigma_s0 = (\n self.sigmas[self.step_index + 1],\n self.sigmas[self.step_index],\n )\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)\n alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)\n lambda_t = torch.log(alpha_t) - torch.log(sigma_t)\n lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)\n\n gradient_part = torch.zeros_like(sample)\n h = lambda_t - lambda_s0\n lambda_list = []\n\n for i in range(order):\n si = self.step_index - i\n alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si])\n lambda_si = torch.log(alpha_si) - torch.log(sigma_si)\n lambda_list.append(lambda_si)\n\n gradient_coefficients = self.get_coefficients_fn(order, lambda_s0, lambda_t, lambda_list, tau)\n\n x = sample\n\n if self.predict_x0:\n if (\n order == 2\n ): ## if order = 2 we do a modification that does not influence the convergence order similar to unipc. Note: This is used only for few steps sampling.\n # The added term is O(h^3). Empirically we find it will slightly improve the image quality.\n # ODE case\n # gradient_coefficients[0] += 1.0 * torch.exp(lambda_t) * (h ** 2 / 2 - (h - 1 + torch.exp(-h))) / (ns.marginal_lambda(t_prev_list[-1]) - ns.marginal_lambda(t_prev_list[-2]))\n # gradient_coefficients[1] -= 1.0 * torch.exp(lambda_t) * (h ** 2 / 2 - (h - 1 + torch.exp(-h))) / (ns.marginal_lambda(t_prev_list[-1]) - ns.marginal_lambda(t_prev_list[-2]))\n temp_sigma = self.sigmas[self.step_index - 1]\n temp_alpha_s, temp_sigma_s = self._sigma_to_alpha_sigma_t(temp_sigma)\n temp_lambda_s = torch.log(temp_alpha_s) - torch.log(temp_sigma_s)\n gradient_coefficients[0] += (\n 1.0\n * torch.exp((1 + tau**2) * lambda_t)\n * (h**2 / 2 - (h * (1 + tau**2) - 1 + torch.exp((1 + tau**2) * (-h))) / ((1 + tau**2) ** 2))\n / (lambda_s0 - temp_lambda_s)\n )\n gradient_coefficients[1] -= (\n 1.0\n * torch.exp((1 + tau**2) * lambda_t)\n * (h**2 / 2 - (h * (1 + tau**2) - 1 + torch.exp((1 + tau**2) * (-h))) / ((1 + tau**2) ** 2))\n / (lambda_s0 - temp_lambda_s)\n )\n\n for i in range(order):\n if self.predict_x0:\n gradient_part += (\n (1 + tau**2)\n * sigma_t\n * torch.exp(-(tau**2) * lambda_t)\n * gradient_coefficients[i]\n * model_output_list[-(i + 1)]\n )\n else:\n gradient_part += -(1 + tau**2) * alpha_t * gradient_coefficients[i] * model_output_list[-(i + 1)]\n\n if self.predict_x0:\n noise_part = sigma_t * torch.sqrt(1 - torch.exp(-2 * tau**2 * h)) * noise\n else:\n noise_part = tau * sigma_t * torch.sqrt(torch.exp(2 * h) - 1) * noise\n\n if self.predict_x0:\n x_t = torch.exp(-(tau**2) * h) * (sigma_t / sigma_s0) * x + gradient_part + noise_part\n else:\n x_t = (alpha_t / alpha_s0) * x + gradient_part + noise_part\n\n x_t = x_t.to(x.dtype)\n return x_t\n\n def stochastic_adams_moulton_update(\n self,\n this_model_output: torch.Tensor,\n *args,\n last_sample: torch.Tensor,\n last_noise: torch.Tensor,\n this_sample: torch.Tensor,\n order: int,\n tau: torch.Tensor,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n One step for the SA-Corrector.\n\n Args:\n this_model_output (`torch.Tensor`):\n The model outputs at `x_t`.\n this_timestep (`int`):\n The current timestep `t`.\n last_sample (`torch.Tensor`):\n The generated sample before the last predictor `x_{t-1}`.\n this_sample (`torch.Tensor`):\n The generated sample after the last predictor `x_{t}`.\n order (`int`):\n The order of SA-Corrector at this step.\n\n Returns:\n `torch.Tensor`:\n The corrected sample tensor at the current timestep.\n \"\"\"\n\n this_timestep = args[0] if len(args) > 0 else kwargs.pop(\"this_timestep\", None)\n if last_sample is None:\n if len(args) > 1:\n last_sample = args[1]\n else:\n raise ValueError(\" missing`last_sample` as a required keyward argument\")\n if last_noise is None:\n if len(args) > 2:\n last_noise = args[2]\n else:\n raise ValueError(\" missing`last_noise` as a required keyward argument\")\n if this_sample is None:\n if len(args) > 3:\n this_sample = args[3]\n else:\n raise ValueError(\" missing`this_sample` as a required keyward argument\")\n if order is None:\n if len(args) > 4:\n order = args[4]\n else:\n raise ValueError(\" missing`order` as a required keyward argument\")\n if tau is None:\n if len(args) > 5:\n tau = args[5]\n else:\n raise ValueError(\" missing`tau` as a required keyward argument\")\n if this_timestep is not None:\n deprecate(\n \"this_timestep\",\n \"1.0.0\",\n \"Passing `this_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n model_output_list = self.model_outputs\n sigma_t, sigma_s0 = (\n self.sigmas[self.step_index],\n self.sigmas[self.step_index - 1],\n )\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)\n alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)\n\n lambda_t = torch.log(alpha_t) - torch.log(sigma_t)\n lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)\n gradient_part = torch.zeros_like(this_sample)\n h = lambda_t - lambda_s0\n lambda_list = []\n for i in range(order):\n si = self.step_index - i\n alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si])\n lambda_si = torch.log(alpha_si) - torch.log(sigma_si)\n lambda_list.append(lambda_si)\n\n model_prev_list = model_output_list + [this_model_output]\n\n gradient_coefficients = self.get_coefficients_fn(order, lambda_s0, lambda_t, lambda_list, tau)\n\n x = last_sample\n\n if self.predict_x0:\n if (\n order == 2\n ): ## if order = 2 we do a modification that does not influence the convergence order similar to UniPC. Note: This is used only for few steps sampling.\n # The added term is O(h^3). Empirically we find it will slightly improve the image quality.\n # ODE case\n # gradient_coefficients[0] += 1.0 * torch.exp(lambda_t) * (h / 2 - (h - 1 + torch.exp(-h)) / h)\n # gradient_coefficients[1] -= 1.0 * torch.exp(lambda_t) * (h / 2 - (h - 1 + torch.exp(-h)) / h)\n gradient_coefficients[0] += (\n 1.0\n * torch.exp((1 + tau**2) * lambda_t)\n * (h / 2 - (h * (1 + tau**2) - 1 + torch.exp((1 + tau**2) * (-h))) / ((1 + tau**2) ** 2 * h))\n )\n gradient_coefficients[1] -= (\n 1.0\n * torch.exp((1 + tau**2) * lambda_t)\n * (h / 2 - (h * (1 + tau**2) - 1 + torch.exp((1 + tau**2) * (-h))) / ((1 + tau**2) ** 2 * h))\n )\n\n for i in range(order):\n if self.predict_x0:\n gradient_part += (\n (1 + tau**2)\n * sigma_t\n * torch.exp(-(tau**2) * lambda_t)\n * gradient_coefficients[i]\n * model_prev_list[-(i + 1)]\n )\n else:\n gradient_part += -(1 + tau**2) * alpha_t * gradient_coefficients[i] * model_prev_list[-(i + 1)]\n\n if self.predict_x0:\n noise_part = sigma_t * torch.sqrt(1 - torch.exp(-2 * tau**2 * h)) * last_noise\n else:\n noise_part = tau * sigma_t * torch.sqrt(torch.exp(2 * h) - 1) * last_noise\n\n if self.predict_x0:\n x_t = torch.exp(-(tau**2) * h) * (sigma_t / sigma_s0) * x + gradient_part + noise_part\n else:\n x_t = (alpha_t / alpha_s0) * x + gradient_part + noise_part\n\n x_t = x_t.to(x.dtype)\n return x_t\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.index_for_timestep\n def index_for_timestep(self, timestep, schedule_timesteps=None):\n if schedule_timesteps is None:\n schedule_timesteps = self.timesteps\n\n index_candidates = (schedule_timesteps == timestep).nonzero()\n\n if len(index_candidates) == 0:\n step_index = len(self.timesteps) - 1\n # The sigma index that is taken for the **very** first `step`\n # is always the second index (or the last index if there is only 1)\n # This way we can ensure we don't accidentally skip a sigma in\n # case we start in the middle of the denoising schedule (e.g. for image-to-image)\n elif len(index_candidates) > 1:\n step_index = index_candidates[1].item()\n else:\n step_index = index_candidates[0].item()\n\n return step_index\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._init_step_index\n def _init_step_index(self, timestep):\n \"\"\"\n Initialize the step_index counter for the scheduler.\n \"\"\"\n\n if self.begin_index is None:\n if isinstance(timestep, torch.Tensor):\n timestep = timestep.to(self.timesteps.device)\n self._step_index = self.index_for_timestep(timestep)\n else:\n self._step_index = self._begin_index\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n generator=None,\n return_dict: bool = True,\n ) -> Union[SchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with\n the SA-Solver.\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`int`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n generator (`torch.Generator`, *optional*):\n A random number generator.\n return_dict (`bool`):\n Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`.\n\n Returns:\n [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n\n \"\"\"\n if self.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n if self.step_index is None:\n self._init_step_index(timestep)\n\n use_corrector = self.step_index > 0 and self.last_sample is not None\n\n model_output_convert = self.convert_model_output(model_output, sample=sample)\n\n if use_corrector:\n current_tau = self.tau_func(self.timestep_list[-1])\n sample = self.stochastic_adams_moulton_update(\n this_model_output=model_output_convert,\n last_sample=self.last_sample,\n last_noise=self.last_noise,\n this_sample=sample,\n order=self.this_corrector_order,\n tau=current_tau,\n )\n\n for i in range(max(self.config.predictor_order, self.config.corrector_order - 1) - 1):\n self.model_outputs[i] = self.model_outputs[i + 1]\n self.timestep_list[i] = self.timestep_list[i + 1]\n\n self.model_outputs[-1] = model_output_convert\n self.timestep_list[-1] = timestep\n\n noise = randn_tensor(\n model_output.shape,\n generator=generator,\n device=model_output.device,\n dtype=model_output.dtype,\n )\n\n if self.config.lower_order_final:\n this_predictor_order = min(self.config.predictor_order, len(self.timesteps) - self.step_index)\n this_corrector_order = min(self.config.corrector_order, len(self.timesteps) - self.step_index + 1)\n else:\n this_predictor_order = self.config.predictor_order\n this_corrector_order = self.config.corrector_order\n\n self.this_predictor_order = min(this_predictor_order, self.lower_order_nums + 1) # warmup for multistep\n self.this_corrector_order = min(this_corrector_order, self.lower_order_nums + 2) # warmup for multistep\n assert self.this_predictor_order > 0\n assert self.this_corrector_order > 0\n\n self.last_sample = sample\n self.last_noise = noise\n\n current_tau = self.tau_func(self.timestep_list[-1])\n prev_sample = self.stochastic_adams_bashforth_update(\n model_output=model_output_convert,\n sample=sample,\n noise=noise,\n order=self.this_predictor_order,\n tau=current_tau,\n )\n\n if self.lower_order_nums < max(self.config.predictor_order, self.config.corrector_order - 1):\n self.lower_order_nums += 1\n\n # upon completion increase step index by one\n self._step_index += 1\n\n if not return_dict:\n return (prev_sample,)\n\n return SchedulerOutput(prev_sample=prev_sample)\n\n def scale_model_input(self, sample: torch.Tensor, *args, **kwargs) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n return sample\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.IntTensor,\n ) -> torch.Tensor:\n # Make sure alphas_cumprod and timestep have same device and dtype as original_samples\n # Move the self.alphas_cumprod to device to avoid redundant CPU to GPU data movement\n # for the subsequent add_noise calls\n self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device)\n alphas_cumprod = self.alphas_cumprod.to(dtype=original_samples.dtype)\n timesteps = timesteps.to(original_samples.device)\n\n sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n while len(sqrt_alpha_prod.shape) < len(original_samples.shape):\n sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n\n noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise\n return noisy_samples\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_sde_ve.py", "content": "# Copyright 2024 Google Brain and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch\n\nimport math\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple, Union\n\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import BaseOutput\nfrom ..utils.torch_utils import randn_tensor\nfrom .scheduling_utils import SchedulerMixin, SchedulerOutput\n\n\n@dataclass\nclass SdeVeOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's `step` function output.\n\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n prev_sample_mean (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Mean averaged `prev_sample` over previous timesteps.\n \"\"\"\n\n prev_sample: torch.Tensor\n prev_sample_mean: torch.Tensor\n\n\nclass ScoreSdeVeScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n `ScoreSdeVeScheduler` is a variance exploding stochastic differential equation (SDE) scheduler.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n snr (`float`, defaults to 0.15):\n A coefficient weighting the step from the `model_output` sample (from the network) to the random noise.\n sigma_min (`float`, defaults to 0.01):\n The initial noise scale for the sigma sequence in the sampling procedure. The minimum sigma should mirror\n the distribution of the data.\n sigma_max (`float`, defaults to 1348.0):\n The maximum value used for the range of continuous timesteps passed into the model.\n sampling_eps (`float`, defaults to 1e-5):\n The end value of sampling where timesteps decrease progressively from 1 to epsilon.\n correct_steps (`int`, defaults to 1):\n The number of correction steps performed on a produced sample.\n \"\"\"\n\n order = 1\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 2000,\n snr: float = 0.15,\n sigma_min: float = 0.01,\n sigma_max: float = 1348.0,\n sampling_eps: float = 1e-5,\n correct_steps: int = 1,\n ):\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = sigma_max\n\n # setable values\n self.timesteps = None\n\n self.set_sigmas(num_train_timesteps, sigma_min, sigma_max, sampling_eps)\n\n def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`int`, *optional*):\n The current timestep in the diffusion chain.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n return sample\n\n def set_timesteps(\n self, num_inference_steps: int, sampling_eps: float = None, device: Union[str, torch.device] = None\n ):\n \"\"\"\n Sets the continuous timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n sampling_eps (`float`, *optional*):\n The final timestep value (overrides value given during scheduler instantiation).\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n\n \"\"\"\n sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps\n\n self.timesteps = torch.linspace(1, sampling_eps, num_inference_steps, device=device)\n\n def set_sigmas(\n self, num_inference_steps: int, sigma_min: float = None, sigma_max: float = None, sampling_eps: float = None\n ):\n \"\"\"\n Sets the noise scales used for the diffusion chain (to be run before inference). The sigmas control the weight\n of the `drift` and `diffusion` components of the sample update.\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n sigma_min (`float`, optional):\n The initial noise scale value (overrides value given during scheduler instantiation).\n sigma_max (`float`, optional):\n The final noise scale value (overrides value given during scheduler instantiation).\n sampling_eps (`float`, optional):\n The final timestep value (overrides value given during scheduler instantiation).\n\n \"\"\"\n sigma_min = sigma_min if sigma_min is not None else self.config.sigma_min\n sigma_max = sigma_max if sigma_max is not None else self.config.sigma_max\n sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps\n if self.timesteps is None:\n self.set_timesteps(num_inference_steps, sampling_eps)\n\n self.sigmas = sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)\n self.discrete_sigmas = torch.exp(torch.linspace(math.log(sigma_min), math.log(sigma_max), num_inference_steps))\n self.sigmas = torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps])\n\n def get_adjacent_sigma(self, timesteps, t):\n return torch.where(\n timesteps == 0,\n torch.zeros_like(t.to(timesteps.device)),\n self.discrete_sigmas[timesteps - 1].to(timesteps.device),\n )\n\n def step_pred(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n generator: Optional[torch.Generator] = None,\n return_dict: bool = True,\n ) -> Union[SdeVeOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`int`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n generator (`torch.Generator`, *optional*):\n A random number generator.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`.\n\n Returns:\n [`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_sde_ve.SdeVeOutput`] is returned, otherwise a tuple\n is returned where the first element is the sample tensor.\n\n \"\"\"\n if self.timesteps is None:\n raise ValueError(\n \"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n timestep = timestep * torch.ones(\n sample.shape[0], device=sample.device\n ) # torch.repeat_interleave(timestep, sample.shape[0])\n timesteps = (timestep * (len(self.timesteps) - 1)).long()\n\n # mps requires indices to be in the same device, so we use cpu as is the default with cuda\n timesteps = timesteps.to(self.discrete_sigmas.device)\n\n sigma = self.discrete_sigmas[timesteps].to(sample.device)\n adjacent_sigma = self.get_adjacent_sigma(timesteps, timestep).to(sample.device)\n drift = torch.zeros_like(sample)\n diffusion = (sigma**2 - adjacent_sigma**2) ** 0.5\n\n # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)\n # also equation 47 shows the analog from SDE models to ancestral sampling methods\n diffusion = diffusion.flatten()\n while len(diffusion.shape) < len(sample.shape):\n diffusion = diffusion.unsqueeze(-1)\n drift = drift - diffusion**2 * model_output\n\n # equation 6: sample noise for the diffusion term of\n noise = randn_tensor(\n sample.shape, layout=sample.layout, generator=generator, device=sample.device, dtype=sample.dtype\n )\n prev_sample_mean = sample - drift # subtract because `dt` is a small negative timestep\n # TODO is the variable diffusion the correct scaling term for the noise?\n prev_sample = prev_sample_mean + diffusion * noise # add impact of diffusion field g\n\n if not return_dict:\n return (prev_sample, prev_sample_mean)\n\n return SdeVeOutput(prev_sample=prev_sample, prev_sample_mean=prev_sample_mean)\n\n def step_correct(\n self,\n model_output: torch.Tensor,\n sample: torch.Tensor,\n generator: Optional[torch.Generator] = None,\n return_dict: bool = True,\n ) -> Union[SchedulerOutput, Tuple]:\n \"\"\"\n Correct the predicted sample based on the `model_output` of the network. This is often run repeatedly after\n making the prediction for the previous timestep.\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n generator (`torch.Generator`, *optional*):\n A random number generator.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`.\n\n Returns:\n [`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_sde_ve.SdeVeOutput`] is returned, otherwise a tuple\n is returned where the first element is the sample tensor.\n\n \"\"\"\n if self.timesteps is None:\n raise ValueError(\n \"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n # For small batch sizes, the paper \"suggest replacing norm(z) with sqrt(d), where d is the dim. of z\"\n # sample noise for correction\n noise = randn_tensor(sample.shape, layout=sample.layout, generator=generator).to(sample.device)\n\n # compute step size from the model_output, the noise, and the snr\n grad_norm = torch.norm(model_output.reshape(model_output.shape[0], -1), dim=-1).mean()\n noise_norm = torch.norm(noise.reshape(noise.shape[0], -1), dim=-1).mean()\n step_size = (self.config.snr * noise_norm / grad_norm) ** 2 * 2\n step_size = step_size * torch.ones(sample.shape[0]).to(sample.device)\n # self.repeat_scalar(step_size, sample.shape[0])\n\n # compute corrected sample: model_output term and noise term\n step_size = step_size.flatten()\n while len(step_size.shape) < len(sample.shape):\n step_size = step_size.unsqueeze(-1)\n prev_sample_mean = sample + step_size * model_output\n prev_sample = prev_sample_mean + ((step_size * 2) ** 0.5) * noise\n\n if not return_dict:\n return (prev_sample,)\n\n return SchedulerOutput(prev_sample=prev_sample)\n\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.Tensor,\n ) -> torch.Tensor:\n # Make sure sigmas and timesteps have the same device and dtype as original_samples\n timesteps = timesteps.to(original_samples.device)\n sigmas = self.discrete_sigmas.to(original_samples.device)[timesteps]\n noise = (\n noise * sigmas[:, None, None, None]\n if noise is not None\n else torch.randn_like(original_samples) * sigmas[:, None, None, None]\n )\n noisy_samples = noise + original_samples\n return noisy_samples\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_sde_ve_flax.py", "content": "# Copyright 2024 Google Brain and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch\n\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple, Union\n\nimport flax\nimport jax\nimport jax.numpy as jnp\nfrom jax import random\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left\n\n\n@flax.struct.dataclass\nclass ScoreSdeVeSchedulerState:\n # setable values\n timesteps: Optional[jnp.ndarray] = None\n discrete_sigmas: Optional[jnp.ndarray] = None\n sigmas: Optional[jnp.ndarray] = None\n\n @classmethod\n def create(cls):\n return cls()\n\n\n@dataclass\nclass FlaxSdeVeOutput(FlaxSchedulerOutput):\n \"\"\"\n Output class for the ScoreSdeVeScheduler's step function output.\n\n Args:\n state (`ScoreSdeVeSchedulerState`):\n prev_sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n prev_sample_mean (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images):\n Mean averaged `prev_sample`. Same as `prev_sample`, only mean-averaged over previous timesteps.\n \"\"\"\n\n state: ScoreSdeVeSchedulerState\n prev_sample: jnp.ndarray\n prev_sample_mean: Optional[jnp.ndarray] = None\n\n\nclass FlaxScoreSdeVeScheduler(FlaxSchedulerMixin, ConfigMixin):\n \"\"\"\n The variance exploding stochastic differential equation (SDE) scheduler.\n\n For more information, see the original paper: https://arxiv.org/abs/2011.13456\n\n [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`\n function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.\n [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and\n [`~SchedulerMixin.from_pretrained`] functions.\n\n Args:\n num_train_timesteps (`int`): number of diffusion steps used to train the model.\n snr (`float`):\n coefficient weighting the step from the model_output sample (from the network) to the random noise.\n sigma_min (`float`):\n initial noise scale for sigma sequence in sampling procedure. The minimum sigma should mirror the\n distribution of the data.\n sigma_max (`float`): maximum value used for the range of continuous timesteps passed into the model.\n sampling_eps (`float`): the end value of sampling, where timesteps decrease progressively from 1 to\n epsilon.\n correct_steps (`int`): number of correction steps performed on a produced sample.\n \"\"\"\n\n @property\n def has_state(self):\n return True\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 2000,\n snr: float = 0.15,\n sigma_min: float = 0.01,\n sigma_max: float = 1348.0,\n sampling_eps: float = 1e-5,\n correct_steps: int = 1,\n ):\n pass\n\n def create_state(self):\n state = ScoreSdeVeSchedulerState.create()\n return self.set_sigmas(\n state,\n self.config.num_train_timesteps,\n self.config.sigma_min,\n self.config.sigma_max,\n self.config.sampling_eps,\n )\n\n def set_timesteps(\n self, state: ScoreSdeVeSchedulerState, num_inference_steps: int, shape: Tuple = (), sampling_eps: float = None\n ) -> ScoreSdeVeSchedulerState:\n \"\"\"\n Sets the continuous timesteps used for the diffusion chain. Supporting function to be run before inference.\n\n Args:\n state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance.\n num_inference_steps (`int`):\n the number of diffusion steps used when generating samples with a pre-trained model.\n sampling_eps (`float`, optional):\n final timestep value (overrides value given at Scheduler instantiation).\n\n \"\"\"\n sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps\n\n timesteps = jnp.linspace(1, sampling_eps, num_inference_steps)\n return state.replace(timesteps=timesteps)\n\n def set_sigmas(\n self,\n state: ScoreSdeVeSchedulerState,\n num_inference_steps: int,\n sigma_min: float = None,\n sigma_max: float = None,\n sampling_eps: float = None,\n ) -> ScoreSdeVeSchedulerState:\n \"\"\"\n Sets the noise scales used for the diffusion chain. Supporting function to be run before inference.\n\n The sigmas control the weight of the `drift` and `diffusion` components of sample update.\n\n Args:\n state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance.\n num_inference_steps (`int`):\n the number of diffusion steps used when generating samples with a pre-trained model.\n sigma_min (`float`, optional):\n initial noise scale value (overrides value given at Scheduler instantiation).\n sigma_max (`float`, optional):\n final noise scale value (overrides value given at Scheduler instantiation).\n sampling_eps (`float`, optional):\n final timestep value (overrides value given at Scheduler instantiation).\n \"\"\"\n sigma_min = sigma_min if sigma_min is not None else self.config.sigma_min\n sigma_max = sigma_max if sigma_max is not None else self.config.sigma_max\n sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps\n if state.timesteps is None:\n state = self.set_timesteps(state, num_inference_steps, sampling_eps)\n\n discrete_sigmas = jnp.exp(jnp.linspace(jnp.log(sigma_min), jnp.log(sigma_max), num_inference_steps))\n sigmas = jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps])\n\n return state.replace(discrete_sigmas=discrete_sigmas, sigmas=sigmas)\n\n def get_adjacent_sigma(self, state, timesteps, t):\n return jnp.where(timesteps == 0, jnp.zeros_like(t), state.discrete_sigmas[timesteps - 1])\n\n def step_pred(\n self,\n state: ScoreSdeVeSchedulerState,\n model_output: jnp.ndarray,\n timestep: int,\n sample: jnp.ndarray,\n key: jax.Array,\n return_dict: bool = True,\n ) -> Union[FlaxSdeVeOutput, Tuple]:\n \"\"\"\n Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance.\n model_output (`jnp.ndarray`): direct output from learned diffusion model.\n timestep (`int`): current discrete timestep in the diffusion chain.\n sample (`jnp.ndarray`):\n current instance of sample being created by diffusion process.\n generator: random number generator.\n return_dict (`bool`): option for returning tuple rather than FlaxSdeVeOutput class\n\n Returns:\n [`FlaxSdeVeOutput`] or `tuple`: [`FlaxSdeVeOutput`] if `return_dict` is True, otherwise a `tuple`. When\n returning a tuple, the first element is the sample tensor.\n\n \"\"\"\n if state.timesteps is None:\n raise ValueError(\n \"`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n timestep = timestep * jnp.ones(\n sample.shape[0],\n )\n timesteps = (timestep * (len(state.timesteps) - 1)).long()\n\n sigma = state.discrete_sigmas[timesteps]\n adjacent_sigma = self.get_adjacent_sigma(state, timesteps, timestep)\n drift = jnp.zeros_like(sample)\n diffusion = (sigma**2 - adjacent_sigma**2) ** 0.5\n\n # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)\n # also equation 47 shows the analog from SDE models to ancestral sampling methods\n diffusion = diffusion.flatten()\n diffusion = broadcast_to_shape_from_left(diffusion, sample.shape)\n drift = drift - diffusion**2 * model_output\n\n # equation 6: sample noise for the diffusion term of\n key = random.split(key, num=1)\n noise = random.normal(key=key, shape=sample.shape)\n prev_sample_mean = sample - drift # subtract because `dt` is a small negative timestep\n # TODO is the variable diffusion the correct scaling term for the noise?\n prev_sample = prev_sample_mean + diffusion * noise # add impact of diffusion field g\n\n if not return_dict:\n return (prev_sample, prev_sample_mean, state)\n\n return FlaxSdeVeOutput(prev_sample=prev_sample, prev_sample_mean=prev_sample_mean, state=state)\n\n def step_correct(\n self,\n state: ScoreSdeVeSchedulerState,\n model_output: jnp.ndarray,\n sample: jnp.ndarray,\n key: jax.Array,\n return_dict: bool = True,\n ) -> Union[FlaxSdeVeOutput, Tuple]:\n \"\"\"\n Correct the predicted sample based on the output model_output of the network. This is often run repeatedly\n after making the prediction for the previous timestep.\n\n Args:\n state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance.\n model_output (`jnp.ndarray`): direct output from learned diffusion model.\n sample (`jnp.ndarray`):\n current instance of sample being created by diffusion process.\n generator: random number generator.\n return_dict (`bool`): option for returning tuple rather than FlaxSdeVeOutput class\n\n Returns:\n [`FlaxSdeVeOutput`] or `tuple`: [`FlaxSdeVeOutput`] if `return_dict` is True, otherwise a `tuple`. When\n returning a tuple, the first element is the sample tensor.\n\n \"\"\"\n if state.timesteps is None:\n raise ValueError(\n \"`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n # For small batch sizes, the paper \"suggest replacing norm(z) with sqrt(d), where d is the dim. of z\"\n # sample noise for correction\n key = random.split(key, num=1)\n noise = random.normal(key=key, shape=sample.shape)\n\n # compute step size from the model_output, the noise, and the snr\n grad_norm = jnp.linalg.norm(model_output)\n noise_norm = jnp.linalg.norm(noise)\n step_size = (self.config.snr * noise_norm / grad_norm) ** 2 * 2\n step_size = step_size * jnp.ones(sample.shape[0])\n\n # compute corrected sample: model_output term and noise term\n step_size = step_size.flatten()\n step_size = broadcast_to_shape_from_left(step_size, sample.shape)\n prev_sample_mean = sample + step_size * model_output\n prev_sample = prev_sample_mean + ((step_size * 2) ** 0.5) * noise\n\n if not return_dict:\n return (prev_sample, state)\n\n return FlaxSdeVeOutput(prev_sample=prev_sample, state=state)\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_tcd.py", "content": "# Copyright 2024 Stanford University Team and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion\n# and https://github.com/hojonathanho/diffusion\n\nimport math\nfrom dataclasses import dataclass\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..schedulers.scheduling_utils import SchedulerMixin\nfrom ..utils import BaseOutput, logging\nfrom ..utils.torch_utils import randn_tensor\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n@dataclass\nclass TCDSchedulerOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's `step` function output.\n\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_noised_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted noised sample `(x_{s})` based on the model output from the current timestep.\n \"\"\"\n\n prev_sample: torch.Tensor\n pred_noised_sample: Optional[torch.Tensor] = None\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\n# Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr\ndef rescale_zero_terminal_snr(betas: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)\n\n\n Args:\n betas (`torch.Tensor`):\n the betas that the scheduler is being initialized with.\n\n Returns:\n `torch.Tensor`: rescaled betas with zero terminal SNR\n \"\"\"\n # Convert betas to alphas_bar_sqrt\n alphas = 1.0 - betas\n alphas_cumprod = torch.cumprod(alphas, dim=0)\n alphas_bar_sqrt = alphas_cumprod.sqrt()\n\n # Store old values.\n alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()\n alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()\n\n # Shift so the last timestep is zero.\n alphas_bar_sqrt -= alphas_bar_sqrt_T\n\n # Scale so the first timestep is back to the old value.\n alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)\n\n # Convert alphas_bar_sqrt to betas\n alphas_bar = alphas_bar_sqrt**2 # Revert sqrt\n alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod\n alphas = torch.cat([alphas_bar[0:1], alphas])\n betas = 1 - alphas\n\n return betas\n\n\nclass TCDScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n `TCDScheduler` incorporates the `Strategic Stochastic Sampling` introduced by the paper `Trajectory Consistency\n Distillation`, extending the original Multistep Consistency Sampling to enable unrestricted trajectory traversal.\n\n This code is based on the official repo of TCD(https://github.com/jabir-zheng/TCD).\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. [`~ConfigMixin`] takes care of storing all config\n attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be\n accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving\n functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n beta_start (`float`, defaults to 0.0001):\n The starting `beta` value of inference.\n beta_end (`float`, defaults to 0.02):\n The final `beta` value.\n beta_schedule (`str`, defaults to `\"linear\"`):\n The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear`, `scaled_linear`, or `squaredcos_cap_v2`.\n trained_betas (`np.ndarray`, *optional*):\n Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.\n original_inference_steps (`int`, *optional*, defaults to 50):\n The default number of inference steps used to generate a linearly-spaced timestep schedule, from which we\n will ultimately take `num_inference_steps` evenly spaced timesteps to form the final timestep schedule.\n clip_sample (`bool`, defaults to `True`):\n Clip the predicted sample for numerical stability.\n clip_sample_range (`float`, defaults to 1.0):\n The maximum magnitude for sample clipping. Valid only when `clip_sample=True`.\n set_alpha_to_one (`bool`, defaults to `True`):\n Each diffusion step uses the alphas product value at that step and at the previous one. For the final step\n there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,\n otherwise it uses the alpha value at step 0.\n steps_offset (`int`, defaults to 0):\n An offset added to the inference steps, as required by some model families.\n prediction_type (`str`, defaults to `epsilon`, *optional*):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),\n `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen\n Video](https://imagen.research.google/video/paper.pdf) paper).\n thresholding (`bool`, defaults to `False`):\n Whether to use the \"dynamic thresholding\" method. This is unsuitable for latent-space diffusion models such\n as Stable Diffusion.\n dynamic_thresholding_ratio (`float`, defaults to 0.995):\n The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.\n sample_max_value (`float`, defaults to 1.0):\n The threshold value for dynamic thresholding. Valid only when `thresholding=True`.\n timestep_spacing (`str`, defaults to `\"leading\"`):\n The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.\n timestep_scaling (`float`, defaults to 10.0):\n The factor the timesteps will be multiplied by when calculating the consistency model boundary conditions\n `c_skip` and `c_out`. Increasing this will decrease the approximation error (although the approximation\n error at the default of `10.0` is already pretty small).\n rescale_betas_zero_snr (`bool`, defaults to `False`):\n Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and\n dark samples instead of limiting it to samples with medium brightness. Loosely related to\n [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).\n \"\"\"\n\n order = 1\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.00085,\n beta_end: float = 0.012,\n beta_schedule: str = \"scaled_linear\",\n trained_betas: Optional[Union[np.ndarray, List[float]]] = None,\n original_inference_steps: int = 50,\n clip_sample: bool = False,\n clip_sample_range: float = 1.0,\n set_alpha_to_one: bool = True,\n steps_offset: int = 0,\n prediction_type: str = \"epsilon\",\n thresholding: bool = False,\n dynamic_thresholding_ratio: float = 0.995,\n sample_max_value: float = 1.0,\n timestep_spacing: str = \"leading\",\n timestep_scaling: float = 10.0,\n rescale_betas_zero_snr: bool = False,\n ):\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n # Rescale for zero SNR\n if rescale_betas_zero_snr:\n self.betas = rescale_zero_terminal_snr(self.betas)\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n\n # At every step in ddim, we are looking into the previous alphas_cumprod\n # For the final step, there is no previous alphas_cumprod because we are already at 0\n # `set_alpha_to_one` decides whether we set this parameter simply to one or\n # whether we use the final alpha of the \"non-previous\" one.\n self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]\n\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = 1.0\n\n # setable values\n self.num_inference_steps = None\n self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64))\n self.custom_timesteps = False\n\n self._step_index = None\n self._begin_index = None\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.index_for_timestep\n def index_for_timestep(self, timestep, schedule_timesteps=None):\n if schedule_timesteps is None:\n schedule_timesteps = self.timesteps\n\n indices = (schedule_timesteps == timestep).nonzero()\n\n # The sigma index that is taken for the **very** first `step`\n # is always the second index (or the last index if there is only 1)\n # This way we can ensure we don't accidentally skip a sigma in\n # case we start in the middle of the denoising schedule (e.g. for image-to-image)\n pos = 1 if len(indices) > 1 else 0\n\n return indices[pos].item()\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index\n def _init_step_index(self, timestep):\n if self.begin_index is None:\n if isinstance(timestep, torch.Tensor):\n timestep = timestep.to(self.timesteps.device)\n self._step_index = self.index_for_timestep(timestep)\n else:\n self._step_index = self._begin_index\n\n @property\n def step_index(self):\n return self._step_index\n\n @property\n def begin_index(self):\n \"\"\"\n The index for the first timestep. It should be set from pipeline with `set_begin_index` method.\n \"\"\"\n return self._begin_index\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index\n def set_begin_index(self, begin_index: int = 0):\n \"\"\"\n Sets the begin index for the scheduler. This function should be run from pipeline before the inference.\n\n Args:\n begin_index (`int`):\n The begin index for the scheduler.\n \"\"\"\n self._begin_index = begin_index\n\n def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n timestep (`int`, *optional*):\n The current timestep in the diffusion chain.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n return sample\n\n # Copied from diffusers.schedulers.scheduling_ddim.DDIMScheduler._get_variance\n def _get_variance(self, timestep, prev_timestep):\n alpha_prod_t = self.alphas_cumprod[timestep]\n alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n\n variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)\n\n return variance\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample\n def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:\n \"\"\"\n \"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the\n prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by\n s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing\n pixels from saturation at each step. We find that dynamic thresholding results in significantly better\n photorealism as well as better image-text alignment, especially when using very large guidance weights.\"\n\n https://arxiv.org/abs/2205.11487\n \"\"\"\n dtype = sample.dtype\n batch_size, channels, *remaining_dims = sample.shape\n\n if dtype not in (torch.float32, torch.float64):\n sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half\n\n # Flatten sample for doing quantile calculation along each image\n sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))\n\n abs_sample = sample.abs() # \"a certain percentile absolute pixel value\"\n\n s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)\n s = torch.clamp(\n s, min=1, max=self.config.sample_max_value\n ) # When clamped to min=1, equivalent to standard clipping to [-1, 1]\n s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0\n sample = torch.clamp(sample, -s, s) / s # \"we threshold xt0 to the range [-s, s] and then divide by s\"\n\n sample = sample.reshape(batch_size, channels, *remaining_dims)\n sample = sample.to(dtype)\n\n return sample\n\n def set_timesteps(\n self,\n num_inference_steps: Optional[int] = None,\n device: Union[str, torch.device] = None,\n original_inference_steps: Optional[int] = None,\n timesteps: Optional[List[int]] = None,\n strength: float = 1.0,\n ):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`, *optional*):\n The number of diffusion steps used when generating samples with a pre-trained model. If used,\n `timesteps` must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n original_inference_steps (`int`, *optional*):\n The original number of inference steps, which will be used to generate a linearly-spaced timestep\n schedule (which is different from the standard `diffusers` implementation). We will then take\n `num_inference_steps` timesteps from this schedule, evenly spaced in terms of indices, and use that as\n our final timestep schedule. If not set, this will default to the `original_inference_steps` attribute.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default\n timestep spacing strategy of equal spacing between timesteps on the training/distillation timestep\n schedule is used. If `timesteps` is passed, `num_inference_steps` must be `None`.\n strength (`float`, *optional*, defaults to 1.0):\n Used to determine the number of timesteps used for inference when using img2img, inpaint, etc.\n \"\"\"\n # 0. Check inputs\n if num_inference_steps is None and timesteps is None:\n raise ValueError(\"Must pass exactly one of `num_inference_steps` or `custom_timesteps`.\")\n\n if num_inference_steps is not None and timesteps is not None:\n raise ValueError(\"Can only pass one of `num_inference_steps` or `custom_timesteps`.\")\n\n # 1. Calculate the TCD original training/distillation timestep schedule.\n original_steps = (\n original_inference_steps if original_inference_steps is not None else self.config.original_inference_steps\n )\n\n if original_inference_steps is None:\n # default option, timesteps align with discrete inference steps\n if original_steps > self.config.num_train_timesteps:\n raise ValueError(\n f\"`original_steps`: {original_steps} cannot be larger than `self.config.train_timesteps`:\"\n f\" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle\"\n f\" maximal {self.config.num_train_timesteps} timesteps.\"\n )\n # TCD Timesteps Setting\n # The skipping step parameter k from the paper.\n k = self.config.num_train_timesteps // original_steps\n # TCD Training/Distillation Steps Schedule\n tcd_origin_timesteps = np.asarray(list(range(1, int(original_steps * strength) + 1))) * k - 1\n else:\n # customised option, sampled timesteps can be any arbitrary value\n tcd_origin_timesteps = np.asarray(list(range(0, int(self.config.num_train_timesteps * strength))))\n\n # 2. Calculate the TCD inference timestep schedule.\n if timesteps is not None:\n # 2.1 Handle custom timestep schedules.\n train_timesteps = set(tcd_origin_timesteps)\n non_train_timesteps = []\n for i in range(1, len(timesteps)):\n if timesteps[i] >= timesteps[i - 1]:\n raise ValueError(\"`custom_timesteps` must be in descending order.\")\n\n if timesteps[i] not in train_timesteps:\n non_train_timesteps.append(timesteps[i])\n\n if timesteps[0] >= self.config.num_train_timesteps:\n raise ValueError(\n f\"`timesteps` must start before `self.config.train_timesteps`:\"\n f\" {self.config.num_train_timesteps}.\"\n )\n\n # Raise warning if timestep schedule does not start with self.config.num_train_timesteps - 1\n if strength == 1.0 and timesteps[0] != self.config.num_train_timesteps - 1:\n logger.warning(\n f\"The first timestep on the custom timestep schedule is {timesteps[0]}, not\"\n f\" `self.config.num_train_timesteps - 1`: {self.config.num_train_timesteps - 1}. You may get\"\n f\" unexpected results when using this timestep schedule.\"\n )\n\n # Raise warning if custom timestep schedule contains timesteps not on original timestep schedule\n if non_train_timesteps:\n logger.warning(\n f\"The custom timestep schedule contains the following timesteps which are not on the original\"\n f\" training/distillation timestep schedule: {non_train_timesteps}. You may get unexpected results\"\n f\" when using this timestep schedule.\"\n )\n\n # Raise warning if custom timestep schedule is longer than original_steps\n if original_steps is not None:\n if len(timesteps) > original_steps:\n logger.warning(\n f\"The number of timesteps in the custom timestep schedule is {len(timesteps)}, which exceeds the\"\n f\" the length of the timestep schedule used for training: {original_steps}. You may get some\"\n f\" unexpected results when using this timestep schedule.\"\n )\n else:\n if len(timesteps) > self.config.num_train_timesteps:\n logger.warning(\n f\"The number of timesteps in the custom timestep schedule is {len(timesteps)}, which exceeds the\"\n f\" the length of the timestep schedule used for training: {self.config.num_train_timesteps}. You may get some\"\n f\" unexpected results when using this timestep schedule.\"\n )\n\n timesteps = np.array(timesteps, dtype=np.int64)\n self.num_inference_steps = len(timesteps)\n self.custom_timesteps = True\n\n # Apply strength (e.g. for img2img pipelines) (see StableDiffusionImg2ImgPipeline.get_timesteps)\n init_timestep = min(int(self.num_inference_steps * strength), self.num_inference_steps)\n t_start = max(self.num_inference_steps - init_timestep, 0)\n timesteps = timesteps[t_start * self.order :]\n # TODO: also reset self.num_inference_steps?\n else:\n # 2.2 Create the \"standard\" TCD inference timestep schedule.\n if num_inference_steps > self.config.num_train_timesteps:\n raise ValueError(\n f\"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:\"\n f\" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle\"\n f\" maximal {self.config.num_train_timesteps} timesteps.\"\n )\n\n if original_steps is not None:\n skipping_step = len(tcd_origin_timesteps) // num_inference_steps\n\n if skipping_step < 1:\n raise ValueError(\n f\"The combination of `original_steps x strength`: {original_steps} x {strength} is smaller than `num_inference_steps`: {num_inference_steps}. Make sure to either reduce `num_inference_steps` to a value smaller than {int(original_steps * strength)} or increase `strength` to a value higher than {float(num_inference_steps / original_steps)}.\"\n )\n\n self.num_inference_steps = num_inference_steps\n\n if original_steps is not None:\n if num_inference_steps > original_steps:\n raise ValueError(\n f\"`num_inference_steps`: {num_inference_steps} cannot be larger than `original_inference_steps`:\"\n f\" {original_steps} because the final timestep schedule will be a subset of the\"\n f\" `original_inference_steps`-sized initial timestep schedule.\"\n )\n else:\n if num_inference_steps > self.config.num_train_timesteps:\n raise ValueError(\n f\"`num_inference_steps`: {num_inference_steps} cannot be larger than `num_train_timesteps`:\"\n f\" {self.config.num_train_timesteps} because the final timestep schedule will be a subset of the\"\n f\" `num_train_timesteps`-sized initial timestep schedule.\"\n )\n\n # TCD Inference Steps Schedule\n tcd_origin_timesteps = tcd_origin_timesteps[::-1].copy()\n # Select (approximately) evenly spaced indices from tcd_origin_timesteps.\n inference_indices = np.linspace(0, len(tcd_origin_timesteps), num=num_inference_steps, endpoint=False)\n inference_indices = np.floor(inference_indices).astype(np.int64)\n timesteps = tcd_origin_timesteps[inference_indices]\n\n self.timesteps = torch.from_numpy(timesteps).to(device=device, dtype=torch.long)\n\n self._step_index = None\n self._begin_index = None\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n eta: float = 0.3,\n generator: Optional[torch.Generator] = None,\n return_dict: bool = True,\n ) -> Union[TCDSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`int`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n eta (`float`):\n A stochastic parameter (referred to as `gamma` in the paper) used to control the stochasticity in every\n step. When eta = 0, it represents deterministic sampling, whereas eta = 1 indicates full stochastic\n sampling.\n generator (`torch.Generator`, *optional*):\n A random number generator.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~schedulers.scheduling_tcd.TCDSchedulerOutput`] or `tuple`.\n Returns:\n [`~schedulers.scheduling_utils.TCDSchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_tcd.TCDSchedulerOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n \"\"\"\n if self.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n if self.step_index is None:\n self._init_step_index(timestep)\n\n assert 0 <= eta <= 1.0, \"gamma must be less than or equal to 1.0\"\n\n # 1. get previous step value\n prev_step_index = self.step_index + 1\n if prev_step_index < len(self.timesteps):\n prev_timestep = self.timesteps[prev_step_index]\n else:\n prev_timestep = torch.tensor(0)\n\n timestep_s = torch.floor((1 - eta) * prev_timestep).to(dtype=torch.long)\n\n # 2. compute alphas, betas\n alpha_prod_t = self.alphas_cumprod[timestep]\n beta_prod_t = 1 - alpha_prod_t\n\n alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod\n\n alpha_prod_s = self.alphas_cumprod[timestep_s]\n beta_prod_s = 1 - alpha_prod_s\n\n # 3. Compute the predicted noised sample x_s based on the model parameterization\n if self.config.prediction_type == \"epsilon\": # noise-prediction\n pred_original_sample = (sample - beta_prod_t.sqrt() * model_output) / alpha_prod_t.sqrt()\n pred_epsilon = model_output\n pred_noised_sample = alpha_prod_s.sqrt() * pred_original_sample + beta_prod_s.sqrt() * pred_epsilon\n elif self.config.prediction_type == \"sample\": # x-prediction\n pred_original_sample = model_output\n pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)\n pred_noised_sample = alpha_prod_s.sqrt() * pred_original_sample + beta_prod_s.sqrt() * pred_epsilon\n elif self.config.prediction_type == \"v_prediction\": # v-prediction\n pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output\n pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample\n pred_noised_sample = alpha_prod_s.sqrt() * pred_original_sample + beta_prod_s.sqrt() * pred_epsilon\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` or\"\n \" `v_prediction` for `TCDScheduler`.\"\n )\n\n # 4. Sample and inject noise z ~ N(0, I) for MultiStep Inference\n # Noise is not used on the final timestep of the timestep schedule.\n # This also means that noise is not used for one-step sampling.\n # Eta (referred to as \"gamma\" in the paper) was introduced to control the stochasticity in every step.\n # When eta = 0, it represents deterministic sampling, whereas eta = 1 indicates full stochastic sampling.\n if eta > 0:\n if self.step_index != self.num_inference_steps - 1:\n noise = randn_tensor(\n model_output.shape, generator=generator, device=model_output.device, dtype=pred_noised_sample.dtype\n )\n prev_sample = (alpha_prod_t_prev / alpha_prod_s).sqrt() * pred_noised_sample + (\n 1 - alpha_prod_t_prev / alpha_prod_s\n ).sqrt() * noise\n else:\n prev_sample = pred_noised_sample\n else:\n prev_sample = pred_noised_sample\n\n # upon completion increase step index by one\n self._step_index += 1\n\n if not return_dict:\n return (prev_sample, pred_noised_sample)\n\n return TCDSchedulerOutput(prev_sample=prev_sample, pred_noised_sample=pred_noised_sample)\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.IntTensor,\n ) -> torch.Tensor:\n # Make sure alphas_cumprod and timestep have same device and dtype as original_samples\n # Move the self.alphas_cumprod to device to avoid redundant CPU to GPU data movement\n # for the subsequent add_noise calls\n self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device)\n alphas_cumprod = self.alphas_cumprod.to(dtype=original_samples.dtype)\n timesteps = timesteps.to(original_samples.device)\n\n sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n while len(sqrt_alpha_prod.shape) < len(original_samples.shape):\n sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n\n noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise\n return noisy_samples\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity\n def get_velocity(self, sample: torch.Tensor, noise: torch.Tensor, timesteps: torch.IntTensor) -> torch.Tensor:\n # Make sure alphas_cumprod and timestep have same device and dtype as sample\n self.alphas_cumprod = self.alphas_cumprod.to(device=sample.device)\n alphas_cumprod = self.alphas_cumprod.to(dtype=sample.dtype)\n timesteps = timesteps.to(sample.device)\n\n sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n while len(sqrt_alpha_prod.shape) < len(sample.shape):\n sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n\n velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample\n return velocity\n\n def __len__(self):\n return self.config.num_train_timesteps\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.previous_timestep\n def previous_timestep(self, timestep):\n if self.custom_timesteps:\n index = (self.timesteps == timestep).nonzero(as_tuple=True)[0][0]\n if index == self.timesteps.shape[0] - 1:\n prev_t = torch.tensor(-1)\n else:\n prev_t = self.timesteps[index + 1]\n else:\n num_inference_steps = (\n self.num_inference_steps if self.num_inference_steps else self.config.num_train_timesteps\n )\n prev_t = timestep - self.config.num_train_timesteps // num_inference_steps\n\n return prev_t\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_unclip.py", "content": "# Copyright 2024 Kakao Brain and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport math\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import BaseOutput\nfrom ..utils.torch_utils import randn_tensor\nfrom .scheduling_utils import SchedulerMixin\n\n\n@dataclass\n# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP\nclass UnCLIPSchedulerOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's `step` function output.\n\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample `(x_{0})` based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.Tensor\n pred_original_sample: Optional[torch.Tensor] = None\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\nclass UnCLIPScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n NOTE: do not use this scheduler. The DDPM scheduler has been updated to support the changes made here. This\n scheduler will be removed and replaced with DDPM.\n\n This is a modified DDPM Scheduler specifically for the karlo unCLIP model.\n\n This scheduler has some minor variations in how it calculates the learned range variance and dynamically\n re-calculates betas based off the timesteps it is skipping.\n\n The scheduler also uses a slightly different step ratio when computing timesteps to use for inference.\n\n See [`~DDPMScheduler`] for more information on DDPM scheduling\n\n Args:\n num_train_timesteps (`int`): number of diffusion steps used to train the model.\n variance_type (`str`):\n options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small_log`\n or `learned_range`.\n clip_sample (`bool`, default `True`):\n option to clip predicted sample between `-clip_sample_range` and `clip_sample_range` for numerical\n stability.\n clip_sample_range (`float`, default `1.0`):\n The range to clip the sample between. See `clip_sample`.\n prediction_type (`str`, default `epsilon`, optional):\n prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process)\n or `sample` (directly predicting the noisy sample`)\n \"\"\"\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n variance_type: str = \"fixed_small_log\",\n clip_sample: bool = True,\n clip_sample_range: Optional[float] = 1.0,\n prediction_type: str = \"epsilon\",\n beta_schedule: str = \"squaredcos_cap_v2\",\n ):\n if beta_schedule != \"squaredcos_cap_v2\":\n raise ValueError(\"UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'\")\n\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n self.one = torch.tensor(1.0)\n\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = 1.0\n\n # setable values\n self.num_inference_steps = None\n self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())\n\n self.variance_type = variance_type\n\n def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`): input sample\n timestep (`int`, optional): current timestep\n\n Returns:\n `torch.Tensor`: scaled input sample\n \"\"\"\n return sample\n\n def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.\n\n Note that this scheduler uses a slightly different step ratio than the other diffusers schedulers. The\n different step ratio is to mimic the original karlo implementation and does not affect the quality or accuracy\n of the results.\n\n Args:\n num_inference_steps (`int`):\n the number of diffusion steps used when generating samples with a pre-trained model.\n \"\"\"\n self.num_inference_steps = num_inference_steps\n step_ratio = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)\n timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64)\n self.timesteps = torch.from_numpy(timesteps).to(device)\n\n def _get_variance(self, t, prev_timestep=None, predicted_variance=None, variance_type=None):\n if prev_timestep is None:\n prev_timestep = t - 1\n\n alpha_prod_t = self.alphas_cumprod[t]\n alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n\n if prev_timestep == t - 1:\n beta = self.betas[t]\n else:\n beta = 1 - alpha_prod_t / alpha_prod_t_prev\n\n # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)\n # and sample from it to get previous sample\n # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample\n variance = beta_prod_t_prev / beta_prod_t * beta\n\n if variance_type is None:\n variance_type = self.config.variance_type\n\n # hacks - were probably added for training stability\n if variance_type == \"fixed_small_log\":\n variance = torch.log(torch.clamp(variance, min=1e-20))\n variance = torch.exp(0.5 * variance)\n elif variance_type == \"learned_range\":\n # NOTE difference with DDPM scheduler\n min_log = variance.log()\n max_log = beta.log()\n\n frac = (predicted_variance + 1) / 2\n variance = frac * max_log + (1 - frac) * min_log\n\n return variance\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n prev_timestep: Optional[int] = None,\n generator=None,\n return_dict: bool = True,\n ) -> Union[UnCLIPSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion\n process from the learned model outputs (most often the predicted noise).\n\n Args:\n model_output (`torch.Tensor`): direct output from learned diffusion model.\n timestep (`int`): current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n current instance of sample being created by diffusion process.\n prev_timestep (`int`, *optional*): The previous timestep to predict the previous sample at.\n Used to dynamically compute beta. If not given, `t-1` is used and the pre-computed beta is used.\n generator: random number generator.\n return_dict (`bool`): option for returning tuple rather than UnCLIPSchedulerOutput class\n\n Returns:\n [`~schedulers.scheduling_utils.UnCLIPSchedulerOutput`] or `tuple`:\n [`~schedulers.scheduling_utils.UnCLIPSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When\n returning a tuple, the first element is the sample tensor.\n\n \"\"\"\n t = timestep\n\n if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == \"learned_range\":\n model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1)\n else:\n predicted_variance = None\n\n # 1. compute alphas, betas\n if prev_timestep is None:\n prev_timestep = t - 1\n\n alpha_prod_t = self.alphas_cumprod[t]\n alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one\n beta_prod_t = 1 - alpha_prod_t\n beta_prod_t_prev = 1 - alpha_prod_t_prev\n\n if prev_timestep == t - 1:\n beta = self.betas[t]\n alpha = self.alphas[t]\n else:\n beta = 1 - alpha_prod_t / alpha_prod_t_prev\n alpha = 1 - beta\n\n # 2. compute predicted original sample from predicted noise also called\n # \"predicted x_0\" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf\n if self.config.prediction_type == \"epsilon\":\n pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)\n elif self.config.prediction_type == \"sample\":\n pred_original_sample = model_output\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`\"\n \" for the UnCLIPScheduler.\"\n )\n\n # 3. Clip \"predicted x_0\"\n if self.config.clip_sample:\n pred_original_sample = torch.clamp(\n pred_original_sample, -self.config.clip_sample_range, self.config.clip_sample_range\n )\n\n # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t\n # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf\n pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * beta) / beta_prod_t\n current_sample_coeff = alpha ** (0.5) * beta_prod_t_prev / beta_prod_t\n\n # 5. Compute predicted previous sample µ_t\n # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf\n pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample\n\n # 6. Add noise\n variance = 0\n if t > 0:\n variance_noise = randn_tensor(\n model_output.shape, dtype=model_output.dtype, generator=generator, device=model_output.device\n )\n\n variance = self._get_variance(\n t,\n predicted_variance=predicted_variance,\n prev_timestep=prev_timestep,\n )\n\n if self.variance_type == \"fixed_small_log\":\n variance = variance\n elif self.variance_type == \"learned_range\":\n variance = (0.5 * variance).exp()\n else:\n raise ValueError(\n f\"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`\"\n \" for the UnCLIPScheduler.\"\n )\n\n variance = variance * variance_noise\n\n pred_prev_sample = pred_prev_sample + variance\n\n if not return_dict:\n return (pred_prev_sample,)\n\n return UnCLIPSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample)\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.IntTensor,\n ) -> torch.Tensor:\n # Make sure alphas_cumprod and timestep have same device and dtype as original_samples\n # Move the self.alphas_cumprod to device to avoid redundant CPU to GPU data movement\n # for the subsequent add_noise calls\n self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device)\n alphas_cumprod = self.alphas_cumprod.to(dtype=original_samples.dtype)\n timesteps = timesteps.to(original_samples.device)\n\n sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n while len(sqrt_alpha_prod.shape) < len(original_samples.shape):\n sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n\n noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise\n return noisy_samples\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_unipc_multistep.py", "content": "# Copyright 2024 TSAIL Team and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# DISCLAIMER: check https://arxiv.org/abs/2302.04867 and https://github.com/wl-zhao/UniPC for more info\n# The codebase is modified based on https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py\n\nimport math\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import deprecate\nfrom .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput\n\n\n# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar\ndef betas_for_alpha_bar(\n num_diffusion_timesteps,\n max_beta=0.999,\n alpha_transform_type=\"cosine\",\n):\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.\n Choose from `cosine` or `exp`\n\n Returns:\n betas (`np.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n if alpha_transform_type == \"cosine\":\n\n def alpha_bar_fn(t):\n return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2\n\n elif alpha_transform_type == \"exp\":\n\n def alpha_bar_fn(t):\n return math.exp(t * -12.0)\n\n else:\n raise ValueError(f\"Unsupported alpha_transform_type: {alpha_transform_type}\")\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))\n return torch.tensor(betas, dtype=torch.float32)\n\n\n# Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr\ndef rescale_zero_terminal_snr(betas):\n \"\"\"\n Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)\n\n\n Args:\n betas (`torch.Tensor`):\n the betas that the scheduler is being initialized with.\n\n Returns:\n `torch.Tensor`: rescaled betas with zero terminal SNR\n \"\"\"\n # Convert betas to alphas_bar_sqrt\n alphas = 1.0 - betas\n alphas_cumprod = torch.cumprod(alphas, dim=0)\n alphas_bar_sqrt = alphas_cumprod.sqrt()\n\n # Store old values.\n alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()\n alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()\n\n # Shift so the last timestep is zero.\n alphas_bar_sqrt -= alphas_bar_sqrt_T\n\n # Scale so the first timestep is back to the old value.\n alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)\n\n # Convert alphas_bar_sqrt to betas\n alphas_bar = alphas_bar_sqrt**2 # Revert sqrt\n alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod\n alphas = torch.cat([alphas_bar[0:1], alphas])\n betas = 1 - alphas\n\n return betas\n\n\nclass UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n `UniPCMultistepScheduler` is a training-free framework designed for the fast sampling of diffusion models.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_train_timesteps (`int`, defaults to 1000):\n The number of diffusion steps to train the model.\n beta_start (`float`, defaults to 0.0001):\n The starting `beta` value of inference.\n beta_end (`float`, defaults to 0.02):\n The final `beta` value.\n beta_schedule (`str`, defaults to `\"linear\"`):\n The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from\n `linear`, `scaled_linear`, or `squaredcos_cap_v2`.\n trained_betas (`np.ndarray`, *optional*):\n Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.\n solver_order (`int`, default `2`):\n The UniPC order which can be any positive integer. The effective order of accuracy is `solver_order + 1`\n due to the UniC. It is recommended to use `solver_order=2` for guided sampling, and `solver_order=3` for\n unconditional sampling.\n prediction_type (`str`, defaults to `epsilon`, *optional*):\n Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),\n `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen\n Video](https://imagen.research.google/video/paper.pdf) paper).\n thresholding (`bool`, defaults to `False`):\n Whether to use the \"dynamic thresholding\" method. This is unsuitable for latent-space diffusion models such\n as Stable Diffusion.\n dynamic_thresholding_ratio (`float`, defaults to 0.995):\n The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.\n sample_max_value (`float`, defaults to 1.0):\n The threshold value for dynamic thresholding. Valid only when `thresholding=True` and `predict_x0=True`.\n predict_x0 (`bool`, defaults to `True`):\n Whether to use the updating algorithm on the predicted x0.\n solver_type (`str`, default `bh2`):\n Solver type for UniPC. It is recommended to use `bh1` for unconditional sampling when steps < 10, and `bh2`\n otherwise.\n lower_order_final (`bool`, default `True`):\n Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can\n stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10.\n disable_corrector (`list`, default `[]`):\n Decides which step to disable the corrector to mitigate the misalignment between `epsilon_theta(x_t, c)`\n and `epsilon_theta(x_t^c, c)` which can influence convergence for a large guidance scale. Corrector is\n usually disabled during the first few steps.\n solver_p (`SchedulerMixin`, default `None`):\n Any other scheduler that if specified, the algorithm becomes `solver_p + UniC`.\n use_karras_sigmas (`bool`, *optional*, defaults to `False`):\n Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`,\n the sigmas are determined according to a sequence of noise levels {σi}.\n timestep_spacing (`str`, defaults to `\"linspace\"`):\n The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and\n Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.\n steps_offset (`int`, defaults to 0):\n An offset added to the inference steps, as required by some model families.\n final_sigmas_type (`str`, defaults to `\"zero\"`):\n The final `sigma` value for the noise schedule during the sampling process. If `\"sigma_min\"`, the final\n sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0.\n rescale_betas_zero_snr (`bool`, defaults to `False`):\n Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and\n dark samples instead of limiting it to samples with medium brightness. Loosely related to\n [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).\n \"\"\"\n\n _compatibles = [e.name for e in KarrasDiffusionSchedulers]\n order = 1\n\n @register_to_config\n def __init__(\n self,\n num_train_timesteps: int = 1000,\n beta_start: float = 0.0001,\n beta_end: float = 0.02,\n beta_schedule: str = \"linear\",\n trained_betas: Optional[Union[np.ndarray, List[float]]] = None,\n solver_order: int = 2,\n prediction_type: str = \"epsilon\",\n thresholding: bool = False,\n dynamic_thresholding_ratio: float = 0.995,\n sample_max_value: float = 1.0,\n predict_x0: bool = True,\n solver_type: str = \"bh2\",\n lower_order_final: bool = True,\n disable_corrector: List[int] = [],\n solver_p: SchedulerMixin = None,\n use_karras_sigmas: Optional[bool] = False,\n timestep_spacing: str = \"linspace\",\n steps_offset: int = 0,\n final_sigmas_type: Optional[str] = \"zero\", # \"zero\", \"sigma_min\"\n rescale_betas_zero_snr: bool = False,\n ):\n if trained_betas is not None:\n self.betas = torch.tensor(trained_betas, dtype=torch.float32)\n elif beta_schedule == \"linear\":\n self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)\n elif beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2\n elif beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n self.betas = betas_for_alpha_bar(num_train_timesteps)\n else:\n raise NotImplementedError(f\"{beta_schedule} is not implemented for {self.__class__}\")\n\n if rescale_betas_zero_snr:\n self.betas = rescale_zero_terminal_snr(self.betas)\n\n self.alphas = 1.0 - self.betas\n self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)\n\n if rescale_betas_zero_snr:\n # Close to 0 without being 0 so first sigma is not inf\n # FP16 smallest positive subnormal works well here\n self.alphas_cumprod[-1] = 2**-24\n\n # Currently we only support VP-type noise schedule\n self.alpha_t = torch.sqrt(self.alphas_cumprod)\n self.sigma_t = torch.sqrt(1 - self.alphas_cumprod)\n self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t)\n self.sigmas = ((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5\n\n # standard deviation of the initial noise distribution\n self.init_noise_sigma = 1.0\n\n if solver_type not in [\"bh1\", \"bh2\"]:\n if solver_type in [\"midpoint\", \"heun\", \"logrho\"]:\n self.register_to_config(solver_type=\"bh2\")\n else:\n raise NotImplementedError(f\"{solver_type} is not implemented for {self.__class__}\")\n\n self.predict_x0 = predict_x0\n # setable values\n self.num_inference_steps = None\n timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=np.float32)[::-1].copy()\n self.timesteps = torch.from_numpy(timesteps)\n self.model_outputs = [None] * solver_order\n self.timestep_list = [None] * solver_order\n self.lower_order_nums = 0\n self.disable_corrector = disable_corrector\n self.solver_p = solver_p\n self.last_sample = None\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n @property\n def step_index(self):\n \"\"\"\n The index counter for current timestep. It will increase 1 after each scheduler step.\n \"\"\"\n return self._step_index\n\n @property\n def begin_index(self):\n \"\"\"\n The index for the first timestep. It should be set from pipeline with `set_begin_index` method.\n \"\"\"\n return self._begin_index\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index\n def set_begin_index(self, begin_index: int = 0):\n \"\"\"\n Sets the begin index for the scheduler. This function should be run from pipeline before the inference.\n\n Args:\n begin_index (`int`):\n The begin index for the scheduler.\n \"\"\"\n self._begin_index = begin_index\n\n def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n \"\"\"\n # \"linspace\", \"leading\", \"trailing\" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891\n if self.config.timestep_spacing == \"linspace\":\n timesteps = (\n np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps + 1)\n .round()[::-1][:-1]\n .copy()\n .astype(np.int64)\n )\n elif self.config.timestep_spacing == \"leading\":\n step_ratio = self.config.num_train_timesteps // (num_inference_steps + 1)\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = (np.arange(0, num_inference_steps + 1) * step_ratio).round()[::-1][:-1].copy().astype(np.int64)\n timesteps += self.config.steps_offset\n elif self.config.timestep_spacing == \"trailing\":\n step_ratio = self.config.num_train_timesteps / num_inference_steps\n # creates integer timesteps by multiplying by ratio\n # casting to int to avoid issues when num_inference_step is power of 3\n timesteps = np.arange(self.config.num_train_timesteps, 0, -step_ratio).round().copy().astype(np.int64)\n timesteps -= 1\n else:\n raise ValueError(\n f\"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.\"\n )\n\n sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)\n if self.config.use_karras_sigmas:\n log_sigmas = np.log(sigmas)\n sigmas = np.flip(sigmas).copy()\n sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)\n timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]).round()\n if self.config.final_sigmas_type == \"sigma_min\":\n sigma_last = sigmas[-1]\n elif self.config.final_sigmas_type == \"zero\":\n sigma_last = 0\n else:\n raise ValueError(\n f\"`final_sigmas_type` must be one of 'zero', or 'sigma_min', but got {self.config.final_sigmas_type}\"\n )\n sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)\n else:\n sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)\n if self.config.final_sigmas_type == \"sigma_min\":\n sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0]) ** 0.5\n elif self.config.final_sigmas_type == \"zero\":\n sigma_last = 0\n else:\n raise ValueError(\n f\"`final_sigmas_type` must be one of 'zero', or 'sigma_min', but got {self.config.final_sigmas_type}\"\n )\n sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)\n\n self.sigmas = torch.from_numpy(sigmas)\n self.timesteps = torch.from_numpy(timesteps).to(device=device, dtype=torch.int64)\n\n self.num_inference_steps = len(timesteps)\n\n self.model_outputs = [\n None,\n ] * self.config.solver_order\n self.lower_order_nums = 0\n self.last_sample = None\n if self.solver_p:\n self.solver_p.set_timesteps(self.num_inference_steps, device=device)\n\n # add an index counter for schedulers that allow duplicated timesteps\n self._step_index = None\n self._begin_index = None\n self.sigmas = self.sigmas.to(\"cpu\") # to avoid too much CPU/GPU communication\n\n # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample\n def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:\n \"\"\"\n \"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the\n prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by\n s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing\n pixels from saturation at each step. We find that dynamic thresholding results in significantly better\n photorealism as well as better image-text alignment, especially when using very large guidance weights.\"\n\n https://arxiv.org/abs/2205.11487\n \"\"\"\n dtype = sample.dtype\n batch_size, channels, *remaining_dims = sample.shape\n\n if dtype not in (torch.float32, torch.float64):\n sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half\n\n # Flatten sample for doing quantile calculation along each image\n sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))\n\n abs_sample = sample.abs() # \"a certain percentile absolute pixel value\"\n\n s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)\n s = torch.clamp(\n s, min=1, max=self.config.sample_max_value\n ) # When clamped to min=1, equivalent to standard clipping to [-1, 1]\n s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0\n sample = torch.clamp(sample, -s, s) / s # \"we threshold xt0 to the range [-s, s] and then divide by s\"\n\n sample = sample.reshape(batch_size, channels, *remaining_dims)\n sample = sample.to(dtype)\n\n return sample\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t\n def _sigma_to_t(self, sigma, log_sigmas):\n # get log sigma\n log_sigma = np.log(np.maximum(sigma, 1e-10))\n\n # get distribution\n dists = log_sigma - log_sigmas[:, np.newaxis]\n\n # get sigmas range\n low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)\n high_idx = low_idx + 1\n\n low = log_sigmas[low_idx]\n high = log_sigmas[high_idx]\n\n # interpolate sigmas\n w = (low - log_sigma) / (low - high)\n w = np.clip(w, 0, 1)\n\n # transform interpolation to time range\n t = (1 - w) * low_idx + w * high_idx\n t = t.reshape(sigma.shape)\n return t\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._sigma_to_alpha_sigma_t\n def _sigma_to_alpha_sigma_t(self, sigma):\n alpha_t = 1 / ((sigma**2 + 1) ** 0.5)\n sigma_t = sigma * alpha_t\n\n return alpha_t, sigma_t\n\n # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras\n def _convert_to_karras(self, in_sigmas: torch.Tensor, num_inference_steps) -> torch.Tensor:\n \"\"\"Constructs the noise schedule of Karras et al. (2022).\"\"\"\n\n # Hack to make sure that other schedulers which copy this function don't break\n # TODO: Add this logic to the other schedulers\n if hasattr(self.config, \"sigma_min\"):\n sigma_min = self.config.sigma_min\n else:\n sigma_min = None\n\n if hasattr(self.config, \"sigma_max\"):\n sigma_max = self.config.sigma_max\n else:\n sigma_max = None\n\n sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item()\n sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item()\n\n rho = 7.0 # 7.0 is the value used in the paper\n ramp = np.linspace(0, 1, num_inference_steps)\n min_inv_rho = sigma_min ** (1 / rho)\n max_inv_rho = sigma_max ** (1 / rho)\n sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho\n return sigmas\n\n def convert_model_output(\n self,\n model_output: torch.Tensor,\n *args,\n sample: torch.Tensor = None,\n **kwargs,\n ) -> torch.Tensor:\n r\"\"\"\n Convert the model output to the corresponding type the UniPC algorithm needs.\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from the learned diffusion model.\n timestep (`int`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n\n Returns:\n `torch.Tensor`:\n The converted model output.\n \"\"\"\n timestep = args[0] if len(args) > 0 else kwargs.pop(\"timestep\", None)\n if sample is None:\n if len(args) > 1:\n sample = args[1]\n else:\n raise ValueError(\"missing `sample` as a required keyward argument\")\n if timestep is not None:\n deprecate(\n \"timesteps\",\n \"1.0.0\",\n \"Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n sigma = self.sigmas[self.step_index]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)\n\n if self.predict_x0:\n if self.config.prediction_type == \"epsilon\":\n x0_pred = (sample - sigma_t * model_output) / alpha_t\n elif self.config.prediction_type == \"sample\":\n x0_pred = model_output\n elif self.config.prediction_type == \"v_prediction\":\n x0_pred = alpha_t * sample - sigma_t * model_output\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or\"\n \" `v_prediction` for the UniPCMultistepScheduler.\"\n )\n\n if self.config.thresholding:\n x0_pred = self._threshold_sample(x0_pred)\n\n return x0_pred\n else:\n if self.config.prediction_type == \"epsilon\":\n return model_output\n elif self.config.prediction_type == \"sample\":\n epsilon = (sample - alpha_t * model_output) / sigma_t\n return epsilon\n elif self.config.prediction_type == \"v_prediction\":\n epsilon = alpha_t * model_output + sigma_t * sample\n return epsilon\n else:\n raise ValueError(\n f\"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or\"\n \" `v_prediction` for the UniPCMultistepScheduler.\"\n )\n\n def multistep_uni_p_bh_update(\n self,\n model_output: torch.Tensor,\n *args,\n sample: torch.Tensor = None,\n order: int = None,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n One step for the UniP (B(h) version). Alternatively, `self.solver_p` is used if is specified.\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from the learned diffusion model at the current timestep.\n prev_timestep (`int`):\n The previous discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n order (`int`):\n The order of UniP at this timestep (corresponds to the *p* in UniPC-p).\n\n Returns:\n `torch.Tensor`:\n The sample tensor at the previous timestep.\n \"\"\"\n prev_timestep = args[0] if len(args) > 0 else kwargs.pop(\"prev_timestep\", None)\n if sample is None:\n if len(args) > 1:\n sample = args[1]\n else:\n raise ValueError(\" missing `sample` as a required keyward argument\")\n if order is None:\n if len(args) > 2:\n order = args[2]\n else:\n raise ValueError(\" missing `order` as a required keyward argument\")\n if prev_timestep is not None:\n deprecate(\n \"prev_timestep\",\n \"1.0.0\",\n \"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n model_output_list = self.model_outputs\n\n s0 = self.timestep_list[-1]\n m0 = model_output_list[-1]\n x = sample\n\n if self.solver_p:\n x_t = self.solver_p.step(model_output, s0, x).prev_sample\n return x_t\n\n sigma_t, sigma_s0 = self.sigmas[self.step_index + 1], self.sigmas[self.step_index]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)\n alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)\n\n lambda_t = torch.log(alpha_t) - torch.log(sigma_t)\n lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)\n\n h = lambda_t - lambda_s0\n device = sample.device\n\n rks = []\n D1s = []\n for i in range(1, order):\n si = self.step_index - i\n mi = model_output_list[-(i + 1)]\n alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si])\n lambda_si = torch.log(alpha_si) - torch.log(sigma_si)\n rk = (lambda_si - lambda_s0) / h\n rks.append(rk)\n D1s.append((mi - m0) / rk)\n\n rks.append(1.0)\n rks = torch.tensor(rks, device=device)\n\n R = []\n b = []\n\n hh = -h if self.predict_x0 else h\n h_phi_1 = torch.expm1(hh) # h\\phi_1(h) = e^h - 1\n h_phi_k = h_phi_1 / hh - 1\n\n factorial_i = 1\n\n if self.config.solver_type == \"bh1\":\n B_h = hh\n elif self.config.solver_type == \"bh2\":\n B_h = torch.expm1(hh)\n else:\n raise NotImplementedError()\n\n for i in range(1, order + 1):\n R.append(torch.pow(rks, i - 1))\n b.append(h_phi_k * factorial_i / B_h)\n factorial_i *= i + 1\n h_phi_k = h_phi_k / hh - 1 / factorial_i\n\n R = torch.stack(R)\n b = torch.tensor(b, device=device)\n\n if len(D1s) > 0:\n D1s = torch.stack(D1s, dim=1) # (B, K)\n # for order 2, we use a simplified version\n if order == 2:\n rhos_p = torch.tensor([0.5], dtype=x.dtype, device=device)\n else:\n rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1]).to(device).to(x.dtype)\n else:\n D1s = None\n\n if self.predict_x0:\n x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0\n if D1s is not None:\n pred_res = torch.einsum(\"k,bkc...->bc...\", rhos_p, D1s)\n else:\n pred_res = 0\n x_t = x_t_ - alpha_t * B_h * pred_res\n else:\n x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0\n if D1s is not None:\n pred_res = torch.einsum(\"k,bkc...->bc...\", rhos_p, D1s)\n else:\n pred_res = 0\n x_t = x_t_ - sigma_t * B_h * pred_res\n\n x_t = x_t.to(x.dtype)\n return x_t\n\n def multistep_uni_c_bh_update(\n self,\n this_model_output: torch.Tensor,\n *args,\n last_sample: torch.Tensor = None,\n this_sample: torch.Tensor = None,\n order: int = None,\n **kwargs,\n ) -> torch.Tensor:\n \"\"\"\n One step for the UniC (B(h) version).\n\n Args:\n this_model_output (`torch.Tensor`):\n The model outputs at `x_t`.\n this_timestep (`int`):\n The current timestep `t`.\n last_sample (`torch.Tensor`):\n The generated sample before the last predictor `x_{t-1}`.\n this_sample (`torch.Tensor`):\n The generated sample after the last predictor `x_{t}`.\n order (`int`):\n The `p` of UniC-p at this step. The effective order of accuracy should be `order + 1`.\n\n Returns:\n `torch.Tensor`:\n The corrected sample tensor at the current timestep.\n \"\"\"\n this_timestep = args[0] if len(args) > 0 else kwargs.pop(\"this_timestep\", None)\n if last_sample is None:\n if len(args) > 1:\n last_sample = args[1]\n else:\n raise ValueError(\" missing`last_sample` as a required keyward argument\")\n if this_sample is None:\n if len(args) > 2:\n this_sample = args[2]\n else:\n raise ValueError(\" missing`this_sample` as a required keyward argument\")\n if order is None:\n if len(args) > 3:\n order = args[3]\n else:\n raise ValueError(\" missing`order` as a required keyward argument\")\n if this_timestep is not None:\n deprecate(\n \"this_timestep\",\n \"1.0.0\",\n \"Passing `this_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`\",\n )\n\n model_output_list = self.model_outputs\n\n m0 = model_output_list[-1]\n x = last_sample\n x_t = this_sample\n model_t = this_model_output\n\n sigma_t, sigma_s0 = self.sigmas[self.step_index], self.sigmas[self.step_index - 1]\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)\n alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)\n\n lambda_t = torch.log(alpha_t) - torch.log(sigma_t)\n lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)\n\n h = lambda_t - lambda_s0\n device = this_sample.device\n\n rks = []\n D1s = []\n for i in range(1, order):\n si = self.step_index - (i + 1)\n mi = model_output_list[-(i + 1)]\n alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si])\n lambda_si = torch.log(alpha_si) - torch.log(sigma_si)\n rk = (lambda_si - lambda_s0) / h\n rks.append(rk)\n D1s.append((mi - m0) / rk)\n\n rks.append(1.0)\n rks = torch.tensor(rks, device=device)\n\n R = []\n b = []\n\n hh = -h if self.predict_x0 else h\n h_phi_1 = torch.expm1(hh) # h\\phi_1(h) = e^h - 1\n h_phi_k = h_phi_1 / hh - 1\n\n factorial_i = 1\n\n if self.config.solver_type == \"bh1\":\n B_h = hh\n elif self.config.solver_type == \"bh2\":\n B_h = torch.expm1(hh)\n else:\n raise NotImplementedError()\n\n for i in range(1, order + 1):\n R.append(torch.pow(rks, i - 1))\n b.append(h_phi_k * factorial_i / B_h)\n factorial_i *= i + 1\n h_phi_k = h_phi_k / hh - 1 / factorial_i\n\n R = torch.stack(R)\n b = torch.tensor(b, device=device)\n\n if len(D1s) > 0:\n D1s = torch.stack(D1s, dim=1)\n else:\n D1s = None\n\n # for order 1, we use a simplified version\n if order == 1:\n rhos_c = torch.tensor([0.5], dtype=x.dtype, device=device)\n else:\n rhos_c = torch.linalg.solve(R, b).to(device).to(x.dtype)\n\n if self.predict_x0:\n x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0\n if D1s is not None:\n corr_res = torch.einsum(\"k,bkc...->bc...\", rhos_c[:-1], D1s)\n else:\n corr_res = 0\n D1_t = model_t - m0\n x_t = x_t_ - alpha_t * B_h * (corr_res + rhos_c[-1] * D1_t)\n else:\n x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0\n if D1s is not None:\n corr_res = torch.einsum(\"k,bkc...->bc...\", rhos_c[:-1], D1s)\n else:\n corr_res = 0\n D1_t = model_t - m0\n x_t = x_t_ - sigma_t * B_h * (corr_res + rhos_c[-1] * D1_t)\n x_t = x_t.to(x.dtype)\n return x_t\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.index_for_timestep\n def index_for_timestep(self, timestep, schedule_timesteps=None):\n if schedule_timesteps is None:\n schedule_timesteps = self.timesteps\n\n index_candidates = (schedule_timesteps == timestep).nonzero()\n\n if len(index_candidates) == 0:\n step_index = len(self.timesteps) - 1\n # The sigma index that is taken for the **very** first `step`\n # is always the second index (or the last index if there is only 1)\n # This way we can ensure we don't accidentally skip a sigma in\n # case we start in the middle of the denoising schedule (e.g. for image-to-image)\n elif len(index_candidates) > 1:\n step_index = index_candidates[1].item()\n else:\n step_index = index_candidates[0].item()\n\n return step_index\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._init_step_index\n def _init_step_index(self, timestep):\n \"\"\"\n Initialize the step_index counter for the scheduler.\n \"\"\"\n\n if self.begin_index is None:\n if isinstance(timestep, torch.Tensor):\n timestep = timestep.to(self.timesteps.device)\n self._step_index = self.index_for_timestep(timestep)\n else:\n self._step_index = self._begin_index\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: int,\n sample: torch.Tensor,\n return_dict: bool = True,\n ) -> Union[SchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with\n the multistep UniPC.\n\n Args:\n model_output (`torch.Tensor`):\n The direct output from learned diffusion model.\n timestep (`int`):\n The current discrete timestep in the diffusion chain.\n sample (`torch.Tensor`):\n A current instance of a sample created by the diffusion process.\n return_dict (`bool`):\n Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`.\n\n Returns:\n [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a\n tuple is returned where the first element is the sample tensor.\n\n \"\"\"\n if self.num_inference_steps is None:\n raise ValueError(\n \"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler\"\n )\n\n if self.step_index is None:\n self._init_step_index(timestep)\n\n use_corrector = (\n self.step_index > 0 and self.step_index - 1 not in self.disable_corrector and self.last_sample is not None\n )\n\n model_output_convert = self.convert_model_output(model_output, sample=sample)\n if use_corrector:\n sample = self.multistep_uni_c_bh_update(\n this_model_output=model_output_convert,\n last_sample=self.last_sample,\n this_sample=sample,\n order=self.this_order,\n )\n\n for i in range(self.config.solver_order - 1):\n self.model_outputs[i] = self.model_outputs[i + 1]\n self.timestep_list[i] = self.timestep_list[i + 1]\n\n self.model_outputs[-1] = model_output_convert\n self.timestep_list[-1] = timestep\n\n if self.config.lower_order_final:\n this_order = min(self.config.solver_order, len(self.timesteps) - self.step_index)\n else:\n this_order = self.config.solver_order\n\n self.this_order = min(this_order, self.lower_order_nums + 1) # warmup for multistep\n assert self.this_order > 0\n\n self.last_sample = sample\n prev_sample = self.multistep_uni_p_bh_update(\n model_output=model_output, # pass the original non-converted model output, in case solver-p is used\n sample=sample,\n order=self.this_order,\n )\n\n if self.lower_order_nums < self.config.solver_order:\n self.lower_order_nums += 1\n\n # upon completion increase step index by one\n self._step_index += 1\n\n if not return_dict:\n return (prev_sample,)\n\n return SchedulerOutput(prev_sample=prev_sample)\n\n def scale_model_input(self, sample: torch.Tensor, *args, **kwargs) -> torch.Tensor:\n \"\"\"\n Ensures interchangeability with schedulers that need to scale the denoising model input depending on the\n current timestep.\n\n Args:\n sample (`torch.Tensor`):\n The input sample.\n\n Returns:\n `torch.Tensor`:\n A scaled input sample.\n \"\"\"\n return sample\n\n # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.add_noise\n def add_noise(\n self,\n original_samples: torch.Tensor,\n noise: torch.Tensor,\n timesteps: torch.IntTensor,\n ) -> torch.Tensor:\n # Make sure sigmas and timesteps have the same device and dtype as original_samples\n sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)\n if original_samples.device.type == \"mps\" and torch.is_floating_point(timesteps):\n # mps does not support float64\n schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)\n timesteps = timesteps.to(original_samples.device, dtype=torch.float32)\n else:\n schedule_timesteps = self.timesteps.to(original_samples.device)\n timesteps = timesteps.to(original_samples.device)\n\n # begin_index is None when the scheduler is used for training or pipeline does not implement set_begin_index\n if self.begin_index is None:\n step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps]\n elif self.step_index is not None:\n # add_noise is called after first denoising step (for inpainting)\n step_indices = [self.step_index] * timesteps.shape[0]\n else:\n # add noise is called before first denoising step to create initial latent(img2img)\n step_indices = [self.begin_index] * timesteps.shape[0]\n\n sigma = sigmas[step_indices].flatten()\n while len(sigma.shape) < len(original_samples.shape):\n sigma = sigma.unsqueeze(-1)\n\n alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)\n noisy_samples = alpha_t * original_samples + sigma_t * noise\n return noisy_samples\n\n def __len__(self):\n return self.config.num_train_timesteps\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_utils.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport importlib\nimport os\nfrom dataclasses import dataclass\nfrom enum import Enum\nfrom typing import Optional, Union\n\nimport torch\nfrom huggingface_hub.utils import validate_hf_hub_args\n\nfrom ..utils import BaseOutput, PushToHubMixin\n\n\nSCHEDULER_CONFIG_NAME = \"scheduler_config.json\"\n\n\n# NOTE: We make this type an enum because it simplifies usage in docs and prevents\n# circular imports when used for `_compatibles` within the schedulers module.\n# When it's used as a type in pipelines, it really is a Union because the actual\n# scheduler instance is passed in.\nclass KarrasDiffusionSchedulers(Enum):\n DDIMScheduler = 1\n DDPMScheduler = 2\n PNDMScheduler = 3\n LMSDiscreteScheduler = 4\n EulerDiscreteScheduler = 5\n HeunDiscreteScheduler = 6\n EulerAncestralDiscreteScheduler = 7\n DPMSolverMultistepScheduler = 8\n DPMSolverSinglestepScheduler = 9\n KDPM2DiscreteScheduler = 10\n KDPM2AncestralDiscreteScheduler = 11\n DEISMultistepScheduler = 12\n UniPCMultistepScheduler = 13\n DPMSolverSDEScheduler = 14\n EDMEulerScheduler = 15\n\n\nAysSchedules = {\n \"StableDiffusionTimesteps\": [999, 850, 736, 645, 545, 455, 343, 233, 124, 24],\n \"StableDiffusionSigmas\": [14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.0],\n \"StableDiffusionXLTimesteps\": [999, 845, 730, 587, 443, 310, 193, 116, 53, 13],\n \"StableDiffusionXLSigmas\": [14.615, 6.315, 3.771, 2.181, 1.342, 0.862, 0.555, 0.380, 0.234, 0.113, 0.0],\n \"StableDiffusionVideoSigmas\": [700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.0],\n}\n\n\n@dataclass\nclass SchedulerOutput(BaseOutput):\n \"\"\"\n Base class for the output of a scheduler's `step` function.\n\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n \"\"\"\n\n prev_sample: torch.Tensor\n\n\nclass SchedulerMixin(PushToHubMixin):\n \"\"\"\n Base class for all schedulers.\n\n [`SchedulerMixin`] contains common functions shared by all schedulers such as general loading and saving\n functionalities.\n\n [`ConfigMixin`] takes care of storing the configuration attributes (like `num_train_timesteps`) that are passed to\n the scheduler's `__init__` function, and the attributes can be accessed by `scheduler.config.num_train_timesteps`.\n\n Class attributes:\n - **_compatibles** (`List[str]`) -- A list of scheduler classes that are compatible with the parent scheduler\n class. Use [`~ConfigMixin.from_config`] to load a different compatible scheduler class (should be overridden\n by parent class).\n \"\"\"\n\n config_name = SCHEDULER_CONFIG_NAME\n _compatibles = []\n has_compatibles = True\n\n @classmethod\n @validate_hf_hub_args\n def from_pretrained(\n cls,\n pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None,\n subfolder: Optional[str] = None,\n return_unused_kwargs=False,\n **kwargs,\n ):\n r\"\"\"\n Instantiate a scheduler from a pre-defined JSON configuration file in a local directory or Hub repository.\n\n Parameters:\n pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):\n Can be either:\n\n - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on\n the Hub.\n - A path to a *directory* (for example `./my_model_directory`) containing the scheduler\n configuration saved with [`~SchedulerMixin.save_pretrained`].\n subfolder (`str`, *optional*):\n The subfolder location of a model file within a larger model repository on the Hub or locally.\n return_unused_kwargs (`bool`, *optional*, defaults to `False`):\n Whether kwargs that are not consumed by the Python class should be returned or not.\n cache_dir (`Union[str, os.PathLike]`, *optional*):\n Path to a directory where a downloaded pretrained model configuration is cached if the standard cache\n is not used.\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\n cached versions if they exist.\n resume_download:\n Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1\n of Diffusers.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\n output_loading_info(`bool`, *optional*, defaults to `False`):\n Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.\n local_files_only(`bool`, *optional*, defaults to `False`):\n Whether to only load local model weights and configuration files or not. If set to `True`, the model\n won't be downloaded from the Hub.\n token (`str` or *bool*, *optional*):\n The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from\n `diffusers-cli login` (stored in `~/.huggingface`) is used.\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier\n allowed by Git.\n\n \n\n To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with\n `huggingface-cli login`. You can also activate the special\n [\"offline-mode\"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a\n firewalled environment.\n\n \n\n \"\"\"\n config, kwargs, commit_hash = cls.load_config(\n pretrained_model_name_or_path=pretrained_model_name_or_path,\n subfolder=subfolder,\n return_unused_kwargs=True,\n return_commit_hash=True,\n **kwargs,\n )\n return cls.from_config(config, return_unused_kwargs=return_unused_kwargs, **kwargs)\n\n def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):\n \"\"\"\n Save a scheduler configuration object to a directory so that it can be reloaded using the\n [`~SchedulerMixin.from_pretrained`] class method.\n\n Args:\n save_directory (`str` or `os.PathLike`):\n Directory where the configuration JSON file will be saved (will be created if it does not exist).\n push_to_hub (`bool`, *optional*, defaults to `False`):\n Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the\n repository you want to push to with `repo_id` (will default to the name of `save_directory` in your\n namespace).\n kwargs (`Dict[str, Any]`, *optional*):\n Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.\n \"\"\"\n self.save_config(save_directory=save_directory, push_to_hub=push_to_hub, **kwargs)\n\n @property\n def compatibles(self):\n \"\"\"\n Returns all schedulers that are compatible with this scheduler\n\n Returns:\n `List[SchedulerMixin]`: List of compatible schedulers\n \"\"\"\n return self._get_compatibles()\n\n @classmethod\n def _get_compatibles(cls):\n compatible_classes_str = list(set([cls.__name__] + cls._compatibles))\n diffusers_library = importlib.import_module(__name__.split(\".\")[0])\n compatible_classes = [\n getattr(diffusers_library, c) for c in compatible_classes_str if hasattr(diffusers_library, c)\n ]\n return compatible_classes\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_utils_flax.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport importlib\nimport math\nimport os\nfrom dataclasses import dataclass\nfrom enum import Enum\nfrom typing import Optional, Tuple, Union\n\nimport flax\nimport jax.numpy as jnp\nfrom huggingface_hub.utils import validate_hf_hub_args\n\nfrom ..utils import BaseOutput, PushToHubMixin\n\n\nSCHEDULER_CONFIG_NAME = \"scheduler_config.json\"\n\n\n# NOTE: We make this type an enum because it simplifies usage in docs and prevents\n# circular imports when used for `_compatibles` within the schedulers module.\n# When it's used as a type in pipelines, it really is a Union because the actual\n# scheduler instance is passed in.\nclass FlaxKarrasDiffusionSchedulers(Enum):\n FlaxDDIMScheduler = 1\n FlaxDDPMScheduler = 2\n FlaxPNDMScheduler = 3\n FlaxLMSDiscreteScheduler = 4\n FlaxDPMSolverMultistepScheduler = 5\n FlaxEulerDiscreteScheduler = 6\n\n\n@dataclass\nclass FlaxSchedulerOutput(BaseOutput):\n \"\"\"\n Base class for the scheduler's step function output.\n\n Args:\n prev_sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n \"\"\"\n\n prev_sample: jnp.ndarray\n\n\nclass FlaxSchedulerMixin(PushToHubMixin):\n \"\"\"\n Mixin containing common functions for the schedulers.\n\n Class attributes:\n - **_compatibles** (`List[str]`) -- A list of classes that are compatible with the parent class, so that\n `from_config` can be used from a class different than the one used to save the config (should be overridden\n by parent class).\n \"\"\"\n\n config_name = SCHEDULER_CONFIG_NAME\n ignore_for_config = [\"dtype\"]\n _compatibles = []\n has_compatibles = True\n\n @classmethod\n @validate_hf_hub_args\n def from_pretrained(\n cls,\n pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None,\n subfolder: Optional[str] = None,\n return_unused_kwargs=False,\n **kwargs,\n ):\n r\"\"\"\n Instantiate a Scheduler class from a pre-defined JSON-file.\n\n Parameters:\n pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):\n Can be either:\n\n - A string, the *model id* of a model repo on huggingface.co. Valid model ids should have an\n organization name, like `google/ddpm-celebahq-256`.\n - A path to a *directory* containing model weights saved using [`~SchedulerMixin.save_pretrained`],\n e.g., `./my_model_directory/`.\n subfolder (`str`, *optional*):\n In case the relevant files are located inside a subfolder of the model repo (either remote in\n huggingface.co or downloaded locally), you can specify the folder name here.\n return_unused_kwargs (`bool`, *optional*, defaults to `False`):\n Whether kwargs that are not consumed by the Python class should be returned or not.\n\n cache_dir (`Union[str, os.PathLike]`, *optional*):\n Path to a directory in which a downloaded pretrained model configuration should be cached if the\n standard cache should not be used.\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force the (re-)download of the model weights and configuration files, overriding the\n cached versions if they exist.\n resume_download:\n Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1\n of Diffusers.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.\n output_loading_info(`bool`, *optional*, defaults to `False`):\n Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.\n local_files_only(`bool`, *optional*, defaults to `False`):\n Whether or not to only look at local files (i.e., do not try to download the model).\n token (`str` or *bool*, *optional*):\n The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated\n when running `transformers-cli login` (stored in `~/.huggingface`).\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a\n git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any\n identifier allowed by git.\n\n \n\n It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated\n models](https://huggingface.co/docs/hub/models-gated#gated-models).\n\n \n\n \n\n Activate the special [\"offline-mode\"](https://huggingface.co/transformers/installation.html#offline-mode) to\n use this method in a firewalled environment.\n\n \n\n \"\"\"\n config, kwargs = cls.load_config(\n pretrained_model_name_or_path=pretrained_model_name_or_path,\n subfolder=subfolder,\n return_unused_kwargs=True,\n **kwargs,\n )\n scheduler, unused_kwargs = cls.from_config(config, return_unused_kwargs=True, **kwargs)\n\n if hasattr(scheduler, \"create_state\") and getattr(scheduler, \"has_state\", False):\n state = scheduler.create_state()\n\n if return_unused_kwargs:\n return scheduler, state, unused_kwargs\n\n return scheduler, state\n\n def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):\n \"\"\"\n Save a scheduler configuration object to the directory `save_directory`, so that it can be re-loaded using the\n [`~FlaxSchedulerMixin.from_pretrained`] class method.\n\n Args:\n save_directory (`str` or `os.PathLike`):\n Directory where the configuration JSON file will be saved (will be created if it does not exist).\n push_to_hub (`bool`, *optional*, defaults to `False`):\n Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the\n repository you want to push to with `repo_id` (will default to the name of `save_directory` in your\n namespace).\n kwargs (`Dict[str, Any]`, *optional*):\n Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.\n \"\"\"\n self.save_config(save_directory=save_directory, push_to_hub=push_to_hub, **kwargs)\n\n @property\n def compatibles(self):\n \"\"\"\n Returns all schedulers that are compatible with this scheduler\n\n Returns:\n `List[SchedulerMixin]`: List of compatible schedulers\n \"\"\"\n return self._get_compatibles()\n\n @classmethod\n def _get_compatibles(cls):\n compatible_classes_str = list(set([cls.__name__] + cls._compatibles))\n diffusers_library = importlib.import_module(__name__.split(\".\")[0])\n compatible_classes = [\n getattr(diffusers_library, c) for c in compatible_classes_str if hasattr(diffusers_library, c)\n ]\n return compatible_classes\n\n\ndef broadcast_to_shape_from_left(x: jnp.ndarray, shape: Tuple[int]) -> jnp.ndarray:\n assert len(shape) >= x.ndim\n return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(shape) - x.ndim)), shape)\n\n\ndef betas_for_alpha_bar(num_diffusion_timesteps: int, max_beta=0.999, dtype=jnp.float32) -> jnp.ndarray:\n \"\"\"\n Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of\n (1-beta) over time from t = [0,1].\n\n Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up\n to that part of the diffusion process.\n\n\n Args:\n num_diffusion_timesteps (`int`): the number of betas to produce.\n max_beta (`float`): the maximum beta to use; use values lower than 1 to\n prevent singularities.\n\n Returns:\n betas (`jnp.ndarray`): the betas used by the scheduler to step the model outputs\n \"\"\"\n\n def alpha_bar(time_step):\n return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2\n\n betas = []\n for i in range(num_diffusion_timesteps):\n t1 = i / num_diffusion_timesteps\n t2 = (i + 1) / num_diffusion_timesteps\n betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))\n return jnp.array(betas, dtype=dtype)\n\n\n@flax.struct.dataclass\nclass CommonSchedulerState:\n alphas: jnp.ndarray\n betas: jnp.ndarray\n alphas_cumprod: jnp.ndarray\n\n @classmethod\n def create(cls, scheduler):\n config = scheduler.config\n\n if config.trained_betas is not None:\n betas = jnp.asarray(config.trained_betas, dtype=scheduler.dtype)\n elif config.beta_schedule == \"linear\":\n betas = jnp.linspace(config.beta_start, config.beta_end, config.num_train_timesteps, dtype=scheduler.dtype)\n elif config.beta_schedule == \"scaled_linear\":\n # this schedule is very specific to the latent diffusion model.\n betas = (\n jnp.linspace(\n config.beta_start**0.5, config.beta_end**0.5, config.num_train_timesteps, dtype=scheduler.dtype\n )\n ** 2\n )\n elif config.beta_schedule == \"squaredcos_cap_v2\":\n # Glide cosine schedule\n betas = betas_for_alpha_bar(config.num_train_timesteps, dtype=scheduler.dtype)\n else:\n raise NotImplementedError(\n f\"beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}\"\n )\n\n alphas = 1.0 - betas\n\n alphas_cumprod = jnp.cumprod(alphas, axis=0)\n\n return cls(\n alphas=alphas,\n betas=betas,\n alphas_cumprod=alphas_cumprod,\n )\n\n\ndef get_sqrt_alpha_prod(\n state: CommonSchedulerState, original_samples: jnp.ndarray, noise: jnp.ndarray, timesteps: jnp.ndarray\n):\n alphas_cumprod = state.alphas_cumprod\n\n sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5\n sqrt_alpha_prod = sqrt_alpha_prod.flatten()\n sqrt_alpha_prod = broadcast_to_shape_from_left(sqrt_alpha_prod, original_samples.shape)\n\n sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n sqrt_one_minus_alpha_prod = broadcast_to_shape_from_left(sqrt_one_minus_alpha_prod, original_samples.shape)\n\n return sqrt_alpha_prod, sqrt_one_minus_alpha_prod\n\n\ndef add_noise_common(\n state: CommonSchedulerState, original_samples: jnp.ndarray, noise: jnp.ndarray, timesteps: jnp.ndarray\n):\n sqrt_alpha_prod, sqrt_one_minus_alpha_prod = get_sqrt_alpha_prod(state, original_samples, noise, timesteps)\n noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise\n return noisy_samples\n\n\ndef get_velocity_common(state: CommonSchedulerState, sample: jnp.ndarray, noise: jnp.ndarray, timesteps: jnp.ndarray):\n sqrt_alpha_prod, sqrt_one_minus_alpha_prod = get_sqrt_alpha_prod(state, sample, noise, timesteps)\n velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample\n return velocity\n" }, { "path": "diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py", "content": "# Copyright 2024 Microsoft and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom dataclasses import dataclass\nfrom typing import Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\n\nfrom ..configuration_utils import ConfigMixin, register_to_config\nfrom ..utils import BaseOutput\nfrom .scheduling_utils import SchedulerMixin\n\n\n@dataclass\nclass VQDiffusionSchedulerOutput(BaseOutput):\n \"\"\"\n Output class for the scheduler's step function output.\n\n Args:\n prev_sample (`torch.LongTensor` of shape `(batch size, num latent pixels)`):\n Computed sample x_{t-1} of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n \"\"\"\n\n prev_sample: torch.LongTensor\n\n\ndef index_to_log_onehot(x: torch.LongTensor, num_classes: int) -> torch.Tensor:\n \"\"\"\n Convert batch of vector of class indices into batch of log onehot vectors\n\n Args:\n x (`torch.LongTensor` of shape `(batch size, vector length)`):\n Batch of class indices\n\n num_classes (`int`):\n number of classes to be used for the onehot vectors\n\n Returns:\n `torch.Tensor` of shape `(batch size, num classes, vector length)`:\n Log onehot vectors\n \"\"\"\n x_onehot = F.one_hot(x, num_classes)\n x_onehot = x_onehot.permute(0, 2, 1)\n log_x = torch.log(x_onehot.float().clamp(min=1e-30))\n return log_x\n\n\ndef gumbel_noised(logits: torch.Tensor, generator: Optional[torch.Generator]) -> torch.Tensor:\n \"\"\"\n Apply gumbel noise to `logits`\n \"\"\"\n uniform = torch.rand(logits.shape, device=logits.device, generator=generator)\n gumbel_noise = -torch.log(-torch.log(uniform + 1e-30) + 1e-30)\n noised = gumbel_noise + logits\n return noised\n\n\ndef alpha_schedules(num_diffusion_timesteps: int, alpha_cum_start=0.99999, alpha_cum_end=0.000009):\n \"\"\"\n Cumulative and non-cumulative alpha schedules.\n\n See section 4.1.\n \"\"\"\n att = (\n np.arange(0, num_diffusion_timesteps) / (num_diffusion_timesteps - 1) * (alpha_cum_end - alpha_cum_start)\n + alpha_cum_start\n )\n att = np.concatenate(([1], att))\n at = att[1:] / att[:-1]\n att = np.concatenate((att[1:], [1]))\n return at, att\n\n\ndef gamma_schedules(num_diffusion_timesteps: int, gamma_cum_start=0.000009, gamma_cum_end=0.99999):\n \"\"\"\n Cumulative and non-cumulative gamma schedules.\n\n See section 4.1.\n \"\"\"\n ctt = (\n np.arange(0, num_diffusion_timesteps) / (num_diffusion_timesteps - 1) * (gamma_cum_end - gamma_cum_start)\n + gamma_cum_start\n )\n ctt = np.concatenate(([0], ctt))\n one_minus_ctt = 1 - ctt\n one_minus_ct = one_minus_ctt[1:] / one_minus_ctt[:-1]\n ct = 1 - one_minus_ct\n ctt = np.concatenate((ctt[1:], [0]))\n return ct, ctt\n\n\nclass VQDiffusionScheduler(SchedulerMixin, ConfigMixin):\n \"\"\"\n A scheduler for vector quantized diffusion.\n\n This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic\n methods the library implements for all schedulers such as loading and saving.\n\n Args:\n num_vec_classes (`int`):\n The number of classes of the vector embeddings of the latent pixels. Includes the class for the masked\n latent pixel.\n num_train_timesteps (`int`, defaults to 100):\n The number of diffusion steps to train the model.\n alpha_cum_start (`float`, defaults to 0.99999):\n The starting cumulative alpha value.\n alpha_cum_end (`float`, defaults to 0.00009):\n The ending cumulative alpha value.\n gamma_cum_start (`float`, defaults to 0.00009):\n The starting cumulative gamma value.\n gamma_cum_end (`float`, defaults to 0.99999):\n The ending cumulative gamma value.\n \"\"\"\n\n order = 1\n\n @register_to_config\n def __init__(\n self,\n num_vec_classes: int,\n num_train_timesteps: int = 100,\n alpha_cum_start: float = 0.99999,\n alpha_cum_end: float = 0.000009,\n gamma_cum_start: float = 0.000009,\n gamma_cum_end: float = 0.99999,\n ):\n self.num_embed = num_vec_classes\n\n # By convention, the index for the mask class is the last class index\n self.mask_class = self.num_embed - 1\n\n at, att = alpha_schedules(num_train_timesteps, alpha_cum_start=alpha_cum_start, alpha_cum_end=alpha_cum_end)\n ct, ctt = gamma_schedules(num_train_timesteps, gamma_cum_start=gamma_cum_start, gamma_cum_end=gamma_cum_end)\n\n num_non_mask_classes = self.num_embed - 1\n bt = (1 - at - ct) / num_non_mask_classes\n btt = (1 - att - ctt) / num_non_mask_classes\n\n at = torch.tensor(at.astype(\"float64\"))\n bt = torch.tensor(bt.astype(\"float64\"))\n ct = torch.tensor(ct.astype(\"float64\"))\n log_at = torch.log(at)\n log_bt = torch.log(bt)\n log_ct = torch.log(ct)\n\n att = torch.tensor(att.astype(\"float64\"))\n btt = torch.tensor(btt.astype(\"float64\"))\n ctt = torch.tensor(ctt.astype(\"float64\"))\n log_cumprod_at = torch.log(att)\n log_cumprod_bt = torch.log(btt)\n log_cumprod_ct = torch.log(ctt)\n\n self.log_at = log_at.float()\n self.log_bt = log_bt.float()\n self.log_ct = log_ct.float()\n self.log_cumprod_at = log_cumprod_at.float()\n self.log_cumprod_bt = log_cumprod_bt.float()\n self.log_cumprod_ct = log_cumprod_ct.float()\n\n # setable values\n self.num_inference_steps = None\n self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())\n\n def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):\n \"\"\"\n Sets the discrete timesteps used for the diffusion chain (to be run before inference).\n\n Args:\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps and diffusion process parameters (alpha, beta, gamma) should be moved\n to.\n \"\"\"\n self.num_inference_steps = num_inference_steps\n timesteps = np.arange(0, self.num_inference_steps)[::-1].copy()\n self.timesteps = torch.from_numpy(timesteps).to(device)\n\n self.log_at = self.log_at.to(device)\n self.log_bt = self.log_bt.to(device)\n self.log_ct = self.log_ct.to(device)\n self.log_cumprod_at = self.log_cumprod_at.to(device)\n self.log_cumprod_bt = self.log_cumprod_bt.to(device)\n self.log_cumprod_ct = self.log_cumprod_ct.to(device)\n\n def step(\n self,\n model_output: torch.Tensor,\n timestep: torch.long,\n sample: torch.LongTensor,\n generator: Optional[torch.Generator] = None,\n return_dict: bool = True,\n ) -> Union[VQDiffusionSchedulerOutput, Tuple]:\n \"\"\"\n Predict the sample from the previous timestep by the reverse transition distribution. See\n [`~VQDiffusionScheduler.q_posterior`] for more details about how the distribution is computer.\n\n Args:\n log_p_x_0: (`torch.Tensor` of shape `(batch size, num classes - 1, num latent pixels)`):\n The log probabilities for the predicted classes of the initial latent pixels. Does not include a\n prediction for the masked class as the initial unnoised image cannot be masked.\n t (`torch.long`):\n The timestep that determines which transition matrices are used.\n x_t (`torch.LongTensor` of shape `(batch size, num latent pixels)`):\n The classes of each latent pixel at time `t`.\n generator (`torch.Generator`, or `None`):\n A random number generator for the noise applied to `p(x_{t-1} | x_t)` before it is sampled from.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~schedulers.scheduling_vq_diffusion.VQDiffusionSchedulerOutput`] or\n `tuple`.\n\n Returns:\n [`~schedulers.scheduling_vq_diffusion.VQDiffusionSchedulerOutput`] or `tuple`:\n If return_dict is `True`, [`~schedulers.scheduling_vq_diffusion.VQDiffusionSchedulerOutput`] is\n returned, otherwise a tuple is returned where the first element is the sample tensor.\n \"\"\"\n if timestep == 0:\n log_p_x_t_min_1 = model_output\n else:\n log_p_x_t_min_1 = self.q_posterior(model_output, sample, timestep)\n\n log_p_x_t_min_1 = gumbel_noised(log_p_x_t_min_1, generator)\n\n x_t_min_1 = log_p_x_t_min_1.argmax(dim=1)\n\n if not return_dict:\n return (x_t_min_1,)\n\n return VQDiffusionSchedulerOutput(prev_sample=x_t_min_1)\n\n def q_posterior(self, log_p_x_0, x_t, t):\n \"\"\"\n Calculates the log probabilities for the predicted classes of the image at timestep `t-1`:\n\n ```\n p(x_{t-1} | x_t) = sum( q(x_t | x_{t-1}) * q(x_{t-1} | x_0) * p(x_0) / q(x_t | x_0) )\n ```\n\n Args:\n log_p_x_0 (`torch.Tensor` of shape `(batch size, num classes - 1, num latent pixels)`):\n The log probabilities for the predicted classes of the initial latent pixels. Does not include a\n prediction for the masked class as the initial unnoised image cannot be masked.\n x_t (`torch.LongTensor` of shape `(batch size, num latent pixels)`):\n The classes of each latent pixel at time `t`.\n t (`torch.Long`):\n The timestep that determines which transition matrix is used.\n\n Returns:\n `torch.Tensor` of shape `(batch size, num classes, num latent pixels)`:\n The log probabilities for the predicted classes of the image at timestep `t-1`.\n \"\"\"\n log_onehot_x_t = index_to_log_onehot(x_t, self.num_embed)\n\n log_q_x_t_given_x_0 = self.log_Q_t_transitioning_to_known_class(\n t=t, x_t=x_t, log_onehot_x_t=log_onehot_x_t, cumulative=True\n )\n\n log_q_t_given_x_t_min_1 = self.log_Q_t_transitioning_to_known_class(\n t=t, x_t=x_t, log_onehot_x_t=log_onehot_x_t, cumulative=False\n )\n\n # p_0(x_0=C_0 | x_t) / q(x_t | x_0=C_0) ... p_n(x_0=C_0 | x_t) / q(x_t | x_0=C_0)\n # . . .\n # . . .\n # . . .\n # p_0(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) ... p_n(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1})\n q = log_p_x_0 - log_q_x_t_given_x_0\n\n # sum_0 = p_0(x_0=C_0 | x_t) / q(x_t | x_0=C_0) + ... + p_0(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}), ... ,\n # sum_n = p_n(x_0=C_0 | x_t) / q(x_t | x_0=C_0) + ... + p_n(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1})\n q_log_sum_exp = torch.logsumexp(q, dim=1, keepdim=True)\n\n # p_0(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_0 ... p_n(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_n\n # . . .\n # . . .\n # . . .\n # p_0(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) / sum_0 ... p_n(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) / sum_n\n q = q - q_log_sum_exp\n\n # (p_0(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_0) * a_cumulative_{t-1} + b_cumulative_{t-1} ... (p_n(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_n) * a_cumulative_{t-1} + b_cumulative_{t-1}\n # . . .\n # . . .\n # . . .\n # (p_0(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) / sum_0) * a_cumulative_{t-1} + b_cumulative_{t-1} ... (p_n(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) / sum_n) * a_cumulative_{t-1} + b_cumulative_{t-1}\n # c_cumulative_{t-1} ... c_cumulative_{t-1}\n q = self.apply_cumulative_transitions(q, t - 1)\n\n # ((p_0(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_0) * a_cumulative_{t-1} + b_cumulative_{t-1}) * q(x_t | x_{t-1}=C_0) * sum_0 ... ((p_n(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_n) * a_cumulative_{t-1} + b_cumulative_{t-1}) * q(x_t | x_{t-1}=C_0) * sum_n\n # . . .\n # . . .\n # . . .\n # ((p_0(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) / sum_0) * a_cumulative_{t-1} + b_cumulative_{t-1}) * q(x_t | x_{t-1}=C_{k-1}) * sum_0 ... ((p_n(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) / sum_n) * a_cumulative_{t-1} + b_cumulative_{t-1}) * q(x_t | x_{t-1}=C_{k-1}) * sum_n\n # c_cumulative_{t-1} * q(x_t | x_{t-1}=C_k) * sum_0 ... c_cumulative_{t-1} * q(x_t | x_{t-1}=C_k) * sum_0\n log_p_x_t_min_1 = q + log_q_t_given_x_t_min_1 + q_log_sum_exp\n\n # For each column, there are two possible cases.\n #\n # Where:\n # - sum(p_n(x_0))) is summing over all classes for x_0\n # - C_i is the class transitioning from (not to be confused with c_t and c_cumulative_t being used for gamma's)\n # - C_j is the class transitioning to\n #\n # 1. x_t is masked i.e. x_t = c_k\n #\n # Simplifying the expression, the column vector is:\n # .\n # .\n # .\n # (c_t / c_cumulative_t) * (a_cumulative_{t-1} * p_n(x_0 = C_i | x_t) + b_cumulative_{t-1} * sum(p_n(x_0)))\n # .\n # .\n # .\n # (c_cumulative_{t-1} / c_cumulative_t) * sum(p_n(x_0))\n #\n # From equation (11) stated in terms of forward probabilities, the last row is trivially verified.\n #\n # For the other rows, we can state the equation as ...\n #\n # (c_t / c_cumulative_t) * [b_cumulative_{t-1} * p(x_0=c_0) + ... + (a_cumulative_{t-1} + b_cumulative_{t-1}) * p(x_0=C_i) + ... + b_cumulative_{k-1} * p(x_0=c_{k-1})]\n #\n # This verifies the other rows.\n #\n # 2. x_t is not masked\n #\n # Simplifying the expression, there are two cases for the rows of the column vector, where C_j = C_i and where C_j != C_i:\n # .\n # .\n # .\n # C_j != C_i: b_t * ((b_cumulative_{t-1} / b_cumulative_t) * p_n(x_0 = c_0) + ... + ((a_cumulative_{t-1} + b_cumulative_{t-1}) / b_cumulative_t) * p_n(x_0 = C_i) + ... + (b_cumulative_{t-1} / (a_cumulative_t + b_cumulative_t)) * p_n(c_0=C_j) + ... + (b_cumulative_{t-1} / b_cumulative_t) * p_n(x_0 = c_{k-1}))\n # .\n # .\n # .\n # C_j = C_i: (a_t + b_t) * ((b_cumulative_{t-1} / b_cumulative_t) * p_n(x_0 = c_0) + ... + ((a_cumulative_{t-1} + b_cumulative_{t-1}) / (a_cumulative_t + b_cumulative_t)) * p_n(x_0 = C_i = C_j) + ... + (b_cumulative_{t-1} / b_cumulative_t) * p_n(x_0 = c_{k-1}))\n # .\n # .\n # .\n # 0\n #\n # The last row is trivially verified. The other rows can be verified by directly expanding equation (11) stated in terms of forward probabilities.\n return log_p_x_t_min_1\n\n def log_Q_t_transitioning_to_known_class(\n self, *, t: torch.int, x_t: torch.LongTensor, log_onehot_x_t: torch.Tensor, cumulative: bool\n ):\n \"\"\"\n Calculates the log probabilities of the rows from the (cumulative or non-cumulative) transition matrix for each\n latent pixel in `x_t`.\n\n Args:\n t (`torch.Long`):\n The timestep that determines which transition matrix is used.\n x_t (`torch.LongTensor` of shape `(batch size, num latent pixels)`):\n The classes of each latent pixel at time `t`.\n log_onehot_x_t (`torch.Tensor` of shape `(batch size, num classes, num latent pixels)`):\n The log one-hot vectors of `x_t`.\n cumulative (`bool`):\n If cumulative is `False`, the single step transition matrix `t-1`->`t` is used. If cumulative is\n `True`, the cumulative transition matrix `0`->`t` is used.\n\n Returns:\n `torch.Tensor` of shape `(batch size, num classes - 1, num latent pixels)`:\n Each _column_ of the returned matrix is a _row_ of log probabilities of the complete probability\n transition matrix.\n\n When non cumulative, returns `self.num_classes - 1` rows because the initial latent pixel cannot be\n masked.\n\n Where:\n - `q_n` is the probability distribution for the forward process of the `n`th latent pixel.\n - C_0 is a class of a latent pixel embedding\n - C_k is the class of the masked latent pixel\n\n non-cumulative result (omitting logarithms):\n ```\n q_0(x_t | x_{t-1} = C_0) ... q_n(x_t | x_{t-1} = C_0)\n . . .\n . . .\n . . .\n q_0(x_t | x_{t-1} = C_k) ... q_n(x_t | x_{t-1} = C_k)\n ```\n\n cumulative result (omitting logarithms):\n ```\n q_0_cumulative(x_t | x_0 = C_0) ... q_n_cumulative(x_t | x_0 = C_0)\n . . .\n . . .\n . . .\n q_0_cumulative(x_t | x_0 = C_{k-1}) ... q_n_cumulative(x_t | x_0 = C_{k-1})\n ```\n \"\"\"\n if cumulative:\n a = self.log_cumprod_at[t]\n b = self.log_cumprod_bt[t]\n c = self.log_cumprod_ct[t]\n else:\n a = self.log_at[t]\n b = self.log_bt[t]\n c = self.log_ct[t]\n\n if not cumulative:\n # The values in the onehot vector can also be used as the logprobs for transitioning\n # from masked latent pixels. If we are not calculating the cumulative transitions,\n # we need to save these vectors to be re-appended to the final matrix so the values\n # aren't overwritten.\n #\n # `P(x_t!=mask|x_{t-1=mask}) = 0` and 0 will be the value of the last row of the onehot vector\n # if x_t is not masked\n #\n # `P(x_t=mask|x_{t-1=mask}) = 1` and 1 will be the value of the last row of the onehot vector\n # if x_t is masked\n log_onehot_x_t_transitioning_from_masked = log_onehot_x_t[:, -1, :].unsqueeze(1)\n\n # `index_to_log_onehot` will add onehot vectors for masked pixels,\n # so the default one hot matrix has one too many rows. See the doc string\n # for an explanation of the dimensionality of the returned matrix.\n log_onehot_x_t = log_onehot_x_t[:, :-1, :]\n\n # this is a cheeky trick to produce the transition probabilities using log one-hot vectors.\n #\n # Don't worry about what values this sets in the columns that mark transitions\n # to masked latent pixels. They are overwrote later with the `mask_class_mask`.\n #\n # Looking at the below logspace formula in non-logspace, each value will evaluate to either\n # `1 * a + b = a + b` where `log_Q_t` has the one hot value in the column\n # or\n # `0 * a + b = b` where `log_Q_t` has the 0 values in the column.\n #\n # See equation 7 for more details.\n log_Q_t = (log_onehot_x_t + a).logaddexp(b)\n\n # The whole column of each masked pixel is `c`\n mask_class_mask = x_t == self.mask_class\n mask_class_mask = mask_class_mask.unsqueeze(1).expand(-1, self.num_embed - 1, -1)\n log_Q_t[mask_class_mask] = c\n\n if not cumulative:\n log_Q_t = torch.cat((log_Q_t, log_onehot_x_t_transitioning_from_masked), dim=1)\n\n return log_Q_t\n\n def apply_cumulative_transitions(self, q, t):\n bsz = q.shape[0]\n a = self.log_cumprod_at[t]\n b = self.log_cumprod_bt[t]\n c = self.log_cumprod_ct[t]\n\n num_latent_pixels = q.shape[2]\n c = c.expand(bsz, 1, num_latent_pixels)\n\n q = (q + a).logaddexp(b)\n q = torch.cat((q, c), dim=1)\n\n return q\n" }, { "path": "diffusers/src/diffusers/training_utils.py", "content": "import contextlib\nimport copy\nimport random\nfrom typing import Any, Dict, Iterable, List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\n\nfrom .models import UNet2DConditionModel\nfrom .schedulers import SchedulerMixin\nfrom .utils import (\n convert_state_dict_to_diffusers,\n convert_state_dict_to_peft,\n deprecate,\n is_peft_available,\n is_torch_npu_available,\n is_torchvision_available,\n is_transformers_available,\n)\n\n\nif is_transformers_available():\n import transformers\n\nif is_peft_available():\n from peft import set_peft_model_state_dict\n\nif is_torchvision_available():\n from torchvision import transforms\n\nif is_torch_npu_available():\n import torch_npu # noqa: F401\n\n\ndef set_seed(seed: int):\n \"\"\"\n Args:\n Helper function for reproducible behavior to set the seed in `random`, `numpy`, `torch`.\n seed (`int`): The seed to set.\n \"\"\"\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n if is_torch_npu_available():\n torch.npu.manual_seed_all(seed)\n else:\n torch.cuda.manual_seed_all(seed)\n # ^^ safe to call this function even if cuda is not available\n\n\ndef compute_snr(noise_scheduler, timesteps):\n \"\"\"\n Computes SNR as per\n https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849\n \"\"\"\n alphas_cumprod = noise_scheduler.alphas_cumprod\n sqrt_alphas_cumprod = alphas_cumprod**0.5\n sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5\n\n # Expand the tensors.\n # Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026\n sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[timesteps].float()\n while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):\n sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]\n alpha = sqrt_alphas_cumprod.expand(timesteps.shape)\n\n sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(device=timesteps.device)[timesteps].float()\n while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):\n sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]\n sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)\n\n # Compute SNR.\n snr = (alpha / sigma) ** 2\n return snr\n\n\ndef resolve_interpolation_mode(interpolation_type: str):\n \"\"\"\n Maps a string describing an interpolation function to the corresponding torchvision `InterpolationMode` enum. The\n full list of supported enums is documented at\n https://pytorch.org/vision/0.9/transforms.html#torchvision.transforms.functional.InterpolationMode.\n\n Args:\n interpolation_type (`str`):\n A string describing an interpolation method. Currently, `bilinear`, `bicubic`, `box`, `nearest`,\n `nearest_exact`, `hamming`, and `lanczos` are supported, corresponding to the supported interpolation modes\n in torchvision.\n\n Returns:\n `torchvision.transforms.InterpolationMode`: an `InterpolationMode` enum used by torchvision's `resize`\n transform.\n \"\"\"\n if not is_torchvision_available():\n raise ImportError(\n \"Please make sure to install `torchvision` to be able to use the `resolve_interpolation_mode()` function.\"\n )\n\n if interpolation_type == \"bilinear\":\n interpolation_mode = transforms.InterpolationMode.BILINEAR\n elif interpolation_type == \"bicubic\":\n interpolation_mode = transforms.InterpolationMode.BICUBIC\n elif interpolation_type == \"box\":\n interpolation_mode = transforms.InterpolationMode.BOX\n elif interpolation_type == \"nearest\":\n interpolation_mode = transforms.InterpolationMode.NEAREST\n elif interpolation_type == \"nearest_exact\":\n interpolation_mode = transforms.InterpolationMode.NEAREST_EXACT\n elif interpolation_type == \"hamming\":\n interpolation_mode = transforms.InterpolationMode.HAMMING\n elif interpolation_type == \"lanczos\":\n interpolation_mode = transforms.InterpolationMode.LANCZOS\n else:\n raise ValueError(\n f\"The given interpolation mode {interpolation_type} is not supported. Currently supported interpolation\"\n f\" modes are `bilinear`, `bicubic`, `box`, `nearest`, `nearest_exact`, `hamming`, and `lanczos`.\"\n )\n\n return interpolation_mode\n\n\ndef compute_dream_and_update_latents(\n unet: UNet2DConditionModel,\n noise_scheduler: SchedulerMixin,\n timesteps: torch.Tensor,\n noise: torch.Tensor,\n noisy_latents: torch.Tensor,\n target: torch.Tensor,\n encoder_hidden_states: torch.Tensor,\n dream_detail_preservation: float = 1.0,\n) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]:\n \"\"\"\n Implements \"DREAM (Diffusion Rectification and Estimation-Adaptive Models)\" from http://arxiv.org/abs/2312.00210.\n DREAM helps align training with sampling to help training be more efficient and accurate at the cost of an extra\n forward step without gradients.\n\n Args:\n `unet`: The state unet to use to make a prediction.\n `noise_scheduler`: The noise scheduler used to add noise for the given timestep.\n `timesteps`: The timesteps for the noise_scheduler to user.\n `noise`: A tensor of noise in the shape of noisy_latents.\n `noisy_latents`: Previously noise latents from the training loop.\n `target`: The ground-truth tensor to predict after eps is removed.\n `encoder_hidden_states`: Text embeddings from the text model.\n `dream_detail_preservation`: A float value that indicates detail preservation level.\n See reference.\n\n Returns:\n `tuple[torch.Tensor, torch.Tensor]`: Adjusted noisy_latents and target.\n \"\"\"\n alphas_cumprod = noise_scheduler.alphas_cumprod.to(timesteps.device)[timesteps, None, None, None]\n sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5\n\n # The paper uses lambda = sqrt(1 - alpha) ** p, with p = 1 in their experiments.\n dream_lambda = sqrt_one_minus_alphas_cumprod**dream_detail_preservation\n\n pred = None\n with torch.no_grad():\n pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample\n\n _noisy_latents, _target = (None, None)\n if noise_scheduler.config.prediction_type == \"epsilon\":\n predicted_noise = pred\n delta_noise = (noise - predicted_noise).detach()\n delta_noise.mul_(dream_lambda)\n _noisy_latents = noisy_latents.add(sqrt_one_minus_alphas_cumprod * delta_noise)\n _target = target.add(delta_noise)\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n raise NotImplementedError(\"DREAM has not been implemented for v-prediction\")\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n return _noisy_latents, _target\n\n\ndef unet_lora_state_dict(unet: UNet2DConditionModel) -> Dict[str, torch.Tensor]:\n r\"\"\"\n Returns:\n A state dict containing just the LoRA parameters.\n \"\"\"\n lora_state_dict = {}\n\n for name, module in unet.named_modules():\n if hasattr(module, \"set_lora_layer\"):\n lora_layer = getattr(module, \"lora_layer\")\n if lora_layer is not None:\n current_lora_layer_sd = lora_layer.state_dict()\n for lora_layer_matrix_name, lora_param in current_lora_layer_sd.items():\n # The matrix name can either be \"down\" or \"up\".\n lora_state_dict[f\"{name}.lora.{lora_layer_matrix_name}\"] = lora_param\n\n return lora_state_dict\n\n\ndef cast_training_params(model: Union[torch.nn.Module, List[torch.nn.Module]], dtype=torch.float32):\n if not isinstance(model, list):\n model = [model]\n for m in model:\n for param in m.parameters():\n # only upcast trainable parameters into fp32\n if param.requires_grad:\n param.data = param.to(dtype)\n\n\ndef _set_state_dict_into_text_encoder(\n lora_state_dict: Dict[str, torch.Tensor], prefix: str, text_encoder: torch.nn.Module\n):\n \"\"\"\n Sets the `lora_state_dict` into `text_encoder` coming from `transformers`.\n\n Args:\n lora_state_dict: The state dictionary to be set.\n prefix: String identifier to retrieve the portion of the state dict that belongs to `text_encoder`.\n text_encoder: Where the `lora_state_dict` is to be set.\n \"\"\"\n\n text_encoder_state_dict = {\n f'{k.replace(prefix, \"\")}': v for k, v in lora_state_dict.items() if k.startswith(prefix)\n }\n text_encoder_state_dict = convert_state_dict_to_peft(convert_state_dict_to_diffusers(text_encoder_state_dict))\n set_peft_model_state_dict(text_encoder, text_encoder_state_dict, adapter_name=\"default\")\n\n\n# Adapted from torch-ema https://github.com/fadel/pytorch_ema/blob/master/torch_ema/ema.py#L14\nclass EMAModel:\n \"\"\"\n Exponential Moving Average of models weights\n \"\"\"\n\n def __init__(\n self,\n parameters: Iterable[torch.nn.Parameter],\n decay: float = 0.9999,\n min_decay: float = 0.0,\n update_after_step: int = 0,\n use_ema_warmup: bool = False,\n inv_gamma: Union[float, int] = 1.0,\n power: Union[float, int] = 2 / 3,\n model_cls: Optional[Any] = None,\n model_config: Dict[str, Any] = None,\n **kwargs,\n ):\n \"\"\"\n Args:\n parameters (Iterable[torch.nn.Parameter]): The parameters to track.\n decay (float): The decay factor for the exponential moving average.\n min_decay (float): The minimum decay factor for the exponential moving average.\n update_after_step (int): The number of steps to wait before starting to update the EMA weights.\n use_ema_warmup (bool): Whether to use EMA warmup.\n inv_gamma (float):\n Inverse multiplicative factor of EMA warmup. Default: 1. Only used if `use_ema_warmup` is True.\n power (float): Exponential factor of EMA warmup. Default: 2/3. Only used if `use_ema_warmup` is True.\n device (Optional[Union[str, torch.device]]): The device to store the EMA weights on. If None, the EMA\n weights will be stored on CPU.\n\n @crowsonkb's notes on EMA Warmup:\n If gamma=1 and power=1, implements a simple average. gamma=1, power=2/3 are good values for models you plan\n to train for a million or more steps (reaches decay factor 0.999 at 31.6K steps, 0.9999 at 1M steps),\n gamma=1, power=3/4 for models you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999\n at 215.4k steps).\n \"\"\"\n\n if isinstance(parameters, torch.nn.Module):\n deprecation_message = (\n \"Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. \"\n \"Please pass the parameters of the module instead.\"\n )\n deprecate(\n \"passing a `torch.nn.Module` to `ExponentialMovingAverage`\",\n \"1.0.0\",\n deprecation_message,\n standard_warn=False,\n )\n parameters = parameters.parameters()\n\n # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility\n use_ema_warmup = True\n\n if kwargs.get(\"max_value\", None) is not None:\n deprecation_message = \"The `max_value` argument is deprecated. Please use `decay` instead.\"\n deprecate(\"max_value\", \"1.0.0\", deprecation_message, standard_warn=False)\n decay = kwargs[\"max_value\"]\n\n if kwargs.get(\"min_value\", None) is not None:\n deprecation_message = \"The `min_value` argument is deprecated. Please use `min_decay` instead.\"\n deprecate(\"min_value\", \"1.0.0\", deprecation_message, standard_warn=False)\n min_decay = kwargs[\"min_value\"]\n\n parameters = list(parameters)\n self.shadow_params = [p.clone().detach() for p in parameters]\n\n if kwargs.get(\"device\", None) is not None:\n deprecation_message = \"The `device` argument is deprecated. Please use `to` instead.\"\n deprecate(\"device\", \"1.0.0\", deprecation_message, standard_warn=False)\n self.to(device=kwargs[\"device\"])\n\n self.temp_stored_params = None\n\n self.decay = decay\n self.min_decay = min_decay\n self.update_after_step = update_after_step\n self.use_ema_warmup = use_ema_warmup\n self.inv_gamma = inv_gamma\n self.power = power\n self.optimization_step = 0\n self.cur_decay_value = None # set in `step()`\n\n self.model_cls = model_cls\n self.model_config = model_config\n\n @classmethod\n def from_pretrained(cls, path, model_cls) -> \"EMAModel\":\n _, ema_kwargs = model_cls.load_config(path, return_unused_kwargs=True)\n model = model_cls.from_pretrained(path)\n\n ema_model = cls(model.parameters(), model_cls=model_cls, model_config=model.config)\n\n ema_model.load_state_dict(ema_kwargs)\n return ema_model\n\n def save_pretrained(self, path, **kwargs):\n if self.model_cls is None:\n raise ValueError(\"`save_pretrained` can only be used if `model_cls` was defined at __init__.\")\n\n if self.model_config is None:\n raise ValueError(\"`save_pretrained` can only be used if `model_config` was defined at __init__.\")\n\n model = self.model_cls.from_config(self.model_config)\n state_dict = self.state_dict()\n state_dict.pop(\"shadow_params\", None)\n\n model.register_to_config(**state_dict)\n self.copy_to(model.parameters())\n model.save_pretrained(path,**kwargs)\n\n def get_decay(self, optimization_step: int) -> float:\n \"\"\"\n Compute the decay factor for the exponential moving average.\n \"\"\"\n step = max(0, optimization_step - self.update_after_step - 1)\n\n if step <= 0:\n return 0.0\n\n if self.use_ema_warmup:\n cur_decay_value = 1 - (1 + step / self.inv_gamma) ** -self.power\n else:\n cur_decay_value = (1 + step) / (10 + step)\n\n cur_decay_value = min(cur_decay_value, self.decay)\n # make sure decay is not smaller than min_decay\n cur_decay_value = max(cur_decay_value, self.min_decay)\n return cur_decay_value\n\n @torch.no_grad()\n def step(self, parameters: Iterable[torch.nn.Parameter]):\n if isinstance(parameters, torch.nn.Module):\n deprecation_message = (\n \"Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. \"\n \"Please pass the parameters of the module instead.\"\n )\n deprecate(\n \"passing a `torch.nn.Module` to `ExponentialMovingAverage.step`\",\n \"1.0.0\",\n deprecation_message,\n standard_warn=False,\n )\n parameters = parameters.parameters()\n\n parameters = list(parameters)\n\n self.optimization_step += 1\n\n # Compute the decay factor for the exponential moving average.\n decay = self.get_decay(self.optimization_step)\n self.cur_decay_value = decay\n one_minus_decay = 1 - decay\n\n context_manager = contextlib.nullcontext\n if is_transformers_available() and transformers.deepspeed.is_deepspeed_zero3_enabled():\n import deepspeed\n\n for s_param, param in zip(self.shadow_params, parameters):\n if is_transformers_available() and transformers.deepspeed.is_deepspeed_zero3_enabled():\n context_manager = deepspeed.zero.GatheredParameters(param, modifier_rank=None)\n\n with context_manager():\n if param.requires_grad:\n s_param.sub_(one_minus_decay * (s_param - param))\n else:\n s_param.copy_(param)\n\n def copy_to(self, parameters: Iterable[torch.nn.Parameter]) -> None:\n \"\"\"\n Copy current averaged parameters into given collection of parameters.\n\n Args:\n parameters: Iterable of `torch.nn.Parameter`; the parameters to be\n updated with the stored moving averages. If `None`, the parameters with which this\n `ExponentialMovingAverage` was initialized will be used.\n \"\"\"\n parameters = list(parameters)\n for s_param, param in zip(self.shadow_params, parameters):\n param.data.copy_(s_param.to(param.device).data)\n\n def to(self, device=None, dtype=None) -> None:\n r\"\"\"Move internal buffers of the ExponentialMovingAverage to `device`.\n\n Args:\n device: like `device` argument to `torch.Tensor.to`\n \"\"\"\n # .to() on the tensors handles None correctly\n self.shadow_params = [\n p.to(device=device, dtype=dtype) if p.is_floating_point() else p.to(device=device)\n for p in self.shadow_params\n ]\n\n def state_dict(self) -> dict:\n r\"\"\"\n Returns the state of the ExponentialMovingAverage as a dict. This method is used by accelerate during\n checkpointing to save the ema state dict.\n \"\"\"\n # Following PyTorch conventions, references to tensors are returned:\n # \"returns a reference to the state and not its copy!\" -\n # https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict\n return {\n \"decay\": self.decay,\n \"min_decay\": self.min_decay,\n \"optimization_step\": self.optimization_step,\n \"update_after_step\": self.update_after_step,\n \"use_ema_warmup\": self.use_ema_warmup,\n \"inv_gamma\": self.inv_gamma,\n \"power\": self.power,\n \"shadow_params\": self.shadow_params,\n }\n\n def store(self, parameters: Iterable[torch.nn.Parameter]) -> None:\n r\"\"\"\n Args:\n Save the current parameters for restoring later.\n parameters: Iterable of `torch.nn.Parameter`; the parameters to be\n temporarily stored.\n \"\"\"\n self.temp_stored_params = [param.detach().cpu().clone() for param in parameters]\n\n def restore(self, parameters: Iterable[torch.nn.Parameter]) -> None:\n r\"\"\"\n Args:\n Restore the parameters stored with the `store` method. Useful to validate the model with EMA parameters without:\n affecting the original optimization process. Store the parameters before the `copy_to()` method. After\n validation (or model saving), use this to restore the former parameters.\n parameters: Iterable of `torch.nn.Parameter`; the parameters to be\n updated with the stored parameters. If `None`, the parameters with which this\n `ExponentialMovingAverage` was initialized will be used.\n \"\"\"\n if self.temp_stored_params is None:\n raise RuntimeError(\"This ExponentialMovingAverage has no `store()`ed weights \" \"to `restore()`\")\n for c_param, param in zip(self.temp_stored_params, parameters):\n param.data.copy_(c_param.data)\n\n # Better memory-wise.\n self.temp_stored_params = None\n\n def load_state_dict(self, state_dict: dict) -> None:\n r\"\"\"\n Args:\n Loads the ExponentialMovingAverage state. This method is used by accelerate during checkpointing to save the\n ema state dict.\n state_dict (dict): EMA state. Should be an object returned\n from a call to :meth:`state_dict`.\n \"\"\"\n # deepcopy, to be consistent with module API\n state_dict = copy.deepcopy(state_dict)\n\n self.decay = state_dict.get(\"decay\", self.decay)\n if self.decay < 0.0 or self.decay > 1.0:\n raise ValueError(\"Decay must be between 0 and 1\")\n\n self.min_decay = state_dict.get(\"min_decay\", self.min_decay)\n if not isinstance(self.min_decay, float):\n raise ValueError(\"Invalid min_decay\")\n\n self.optimization_step = state_dict.get(\"optimization_step\", self.optimization_step)\n if not isinstance(self.optimization_step, int):\n raise ValueError(\"Invalid optimization_step\")\n\n self.update_after_step = state_dict.get(\"update_after_step\", self.update_after_step)\n if not isinstance(self.update_after_step, int):\n raise ValueError(\"Invalid update_after_step\")\n\n self.use_ema_warmup = state_dict.get(\"use_ema_warmup\", self.use_ema_warmup)\n if not isinstance(self.use_ema_warmup, bool):\n raise ValueError(\"Invalid use_ema_warmup\")\n\n self.inv_gamma = state_dict.get(\"inv_gamma\", self.inv_gamma)\n if not isinstance(self.inv_gamma, (float, int)):\n raise ValueError(\"Invalid inv_gamma\")\n\n self.power = state_dict.get(\"power\", self.power)\n if not isinstance(self.power, (float, int)):\n raise ValueError(\"Invalid power\")\n\n shadow_params = state_dict.get(\"shadow_params\", None)\n if shadow_params is not None:\n self.shadow_params = shadow_params\n if not isinstance(self.shadow_params, list):\n raise ValueError(\"shadow_params must be a list\")\n if not all(isinstance(p, torch.Tensor) for p in self.shadow_params):\n raise ValueError(\"shadow_params must all be Tensors\")\n" }, { "path": "diffusers/src/diffusers/utils/__init__.py", "content": "# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport os\n\nfrom packaging import version\n\nfrom .. import __version__\nfrom .constants import (\n CONFIG_NAME,\n DEPRECATED_REVISION_ARGS,\n DIFFUSERS_DYNAMIC_MODULE_NAME,\n FLAX_WEIGHTS_NAME,\n HF_MODULES_CACHE,\n HUGGINGFACE_CO_RESOLVE_ENDPOINT,\n MIN_PEFT_VERSION,\n ONNX_EXTERNAL_WEIGHTS_NAME,\n ONNX_WEIGHTS_NAME,\n SAFE_WEIGHTS_INDEX_NAME,\n SAFETENSORS_FILE_EXTENSION,\n SAFETENSORS_WEIGHTS_NAME,\n USE_PEFT_BACKEND,\n WEIGHTS_INDEX_NAME,\n WEIGHTS_NAME,\n)\nfrom .deprecation_utils import deprecate\nfrom .doc_utils import replace_example_docstring\nfrom .dynamic_modules_utils import get_class_from_dynamic_module\nfrom .export_utils import export_to_gif, export_to_obj, export_to_ply, export_to_video\nfrom .hub_utils import (\n PushToHubMixin,\n _add_variant,\n _get_checkpoint_shard_files,\n _get_model_file,\n extract_commit_hash,\n http_user_agent,\n)\nfrom .import_utils import (\n BACKENDS_MAPPING,\n DIFFUSERS_SLOW_IMPORT,\n ENV_VARS_TRUE_AND_AUTO_VALUES,\n ENV_VARS_TRUE_VALUES,\n USE_JAX,\n USE_TF,\n USE_TORCH,\n DummyObject,\n OptionalDependencyNotAvailable,\n _LazyModule,\n get_objects_from_module,\n is_accelerate_available,\n is_accelerate_version,\n is_bitsandbytes_available,\n is_bs4_available,\n is_flax_available,\n is_ftfy_available,\n is_google_colab,\n is_inflect_available,\n is_invisible_watermark_available,\n is_k_diffusion_available,\n is_k_diffusion_version,\n is_librosa_available,\n is_matplotlib_available,\n is_note_seq_available,\n is_notebook,\n is_onnx_available,\n is_peft_available,\n is_peft_version,\n is_safetensors_available,\n is_scipy_available,\n is_tensorboard_available,\n is_timm_available,\n is_torch_available,\n is_torch_npu_available,\n is_torch_version,\n is_torch_xla_available,\n is_torchsde_available,\n is_torchvision_available,\n is_transformers_available,\n is_transformers_version,\n is_unidecode_available,\n is_wandb_available,\n is_xformers_available,\n requires_backends,\n)\nfrom .loading_utils import load_image\nfrom .logging import get_logger\nfrom .outputs import BaseOutput\nfrom .peft_utils import (\n check_peft_version,\n delete_adapter_layers,\n get_adapter_name,\n get_peft_kwargs,\n recurse_remove_peft_layers,\n scale_lora_layers,\n set_adapter_layers,\n set_weights_and_activate_adapters,\n unscale_lora_layers,\n)\nfrom .pil_utils import PIL_INTERPOLATION, make_image_grid, numpy_to_pil, pt_to_pil\nfrom .state_dict_utils import (\n convert_all_state_dict_to_peft,\n convert_state_dict_to_diffusers,\n convert_state_dict_to_kohya,\n convert_state_dict_to_peft,\n convert_unet_state_dict_to_peft,\n)\n\n\nlogger = get_logger(__name__)\n\n\ndef check_min_version(min_version):\n if version.parse(__version__) < version.parse(min_version):\n if \"dev\" in min_version:\n error_message = (\n \"This example requires a source install from HuggingFace diffusers (see \"\n \"`https://huggingface.co/docs/diffusers/installation#install-from-source`),\"\n )\n else:\n error_message = f\"This example requires a minimum version of {min_version},\"\n error_message += f\" but the version found is {__version__}.\\n\"\n raise ImportError(error_message)\n" }, { "path": "diffusers/src/diffusers/utils/accelerate_utils.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nAccelerate utilities: Utilities related to accelerate\n\"\"\"\n\nfrom packaging import version\n\nfrom .import_utils import is_accelerate_available\n\n\nif is_accelerate_available():\n import accelerate\n\n\ndef apply_forward_hook(method):\n \"\"\"\n Decorator that applies a registered CpuOffload hook to an arbitrary function rather than `forward`. This is useful\n for cases where a PyTorch module provides functions other than `forward` that should trigger a move to the\n appropriate acceleration device. This is the case for `encode` and `decode` in [`AutoencoderKL`].\n\n This decorator looks inside the internal `_hf_hook` property to find a registered offload hook.\n\n :param method: The method to decorate. This method should be a method of a PyTorch module.\n \"\"\"\n if not is_accelerate_available():\n return method\n accelerate_version = version.parse(accelerate.__version__).base_version\n if version.parse(accelerate_version) < version.parse(\"0.17.0\"):\n return method\n\n def wrapper(self, *args, **kwargs):\n if hasattr(self, \"_hf_hook\") and hasattr(self._hf_hook, \"pre_forward\"):\n self._hf_hook.pre_forward(self)\n return method(self, *args, **kwargs)\n\n return wrapper\n" }, { "path": "diffusers/src/diffusers/utils/constants.py", "content": "# Copyright 2024 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport importlib\nimport os\n\nfrom huggingface_hub.constants import HF_HOME\nfrom packaging import version\n\nfrom ..dependency_versions_check import dep_version_check\nfrom .import_utils import ENV_VARS_TRUE_VALUES, is_peft_available, is_transformers_available\n\n\nMIN_PEFT_VERSION = \"0.6.0\"\nMIN_TRANSFORMERS_VERSION = \"4.34.0\"\n_CHECK_PEFT = os.environ.get(\"_CHECK_PEFT\", \"1\") in ENV_VARS_TRUE_VALUES\n\n\nCONFIG_NAME = \"config.json\"\nWEIGHTS_NAME = \"diffusion_pytorch_model.bin\"\nWEIGHTS_INDEX_NAME = \"diffusion_pytorch_model.bin.index.json\"\nFLAX_WEIGHTS_NAME = \"diffusion_flax_model.msgpack\"\nONNX_WEIGHTS_NAME = \"model.onnx\"\nSAFETENSORS_WEIGHTS_NAME = \"diffusion_pytorch_model.safetensors\"\nSAFE_WEIGHTS_INDEX_NAME = \"diffusion_pytorch_model.safetensors.index.json\"\nSAFETENSORS_FILE_EXTENSION = \"safetensors\"\nONNX_EXTERNAL_WEIGHTS_NAME = \"weights.pb\"\nHUGGINGFACE_CO_RESOLVE_ENDPOINT = os.environ.get(\"HF_ENDPOINT\", \"https://huggingface.co\")\nDIFFUSERS_DYNAMIC_MODULE_NAME = \"diffusers_modules\"\nHF_MODULES_CACHE = os.getenv(\"HF_MODULES_CACHE\", os.path.join(HF_HOME, \"modules\"))\nDEPRECATED_REVISION_ARGS = [\"fp16\", \"non-ema\"]\n\n# Below should be `True` if the current version of `peft` and `transformers` are compatible with\n# PEFT backend. Will automatically fall back to PEFT backend if the correct versions of the libraries are\n# available.\n# For PEFT it is has to be greater than or equal to 0.6.0 and for transformers it has to be greater than or equal to 4.34.0.\n_required_peft_version = is_peft_available() and version.parse(\n version.parse(importlib.metadata.version(\"peft\")).base_version\n) >= version.parse(MIN_PEFT_VERSION)\n_required_transformers_version = is_transformers_available() and version.parse(\n version.parse(importlib.metadata.version(\"transformers\")).base_version\n) >= version.parse(MIN_TRANSFORMERS_VERSION)\n\nUSE_PEFT_BACKEND = _required_peft_version and _required_transformers_version\n\nif USE_PEFT_BACKEND and _CHECK_PEFT:\n dep_version_check(\"peft\")\n" }, { "path": "diffusers/src/diffusers/utils/deprecation_utils.py", "content": "import inspect\nimport warnings\nfrom typing import Any, Dict, Optional, Union\n\nfrom packaging import version\n\n\ndef deprecate(*args, take_from: Optional[Union[Dict, Any]] = None, standard_warn=True, stacklevel=2):\n from .. import __version__\n\n deprecated_kwargs = take_from\n values = ()\n if not isinstance(args[0], tuple):\n args = (args,)\n\n for attribute, version_name, message in args:\n if version.parse(version.parse(__version__).base_version) >= version.parse(version_name):\n raise ValueError(\n f\"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'\"\n f\" version {__version__} is >= {version_name}\"\n )\n\n warning = None\n if isinstance(deprecated_kwargs, dict) and attribute in deprecated_kwargs:\n values += (deprecated_kwargs.pop(attribute),)\n warning = f\"The `{attribute}` argument is deprecated and will be removed in version {version_name}.\"\n elif hasattr(deprecated_kwargs, attribute):\n values += (getattr(deprecated_kwargs, attribute),)\n warning = f\"The `{attribute}` attribute is deprecated and will be removed in version {version_name}.\"\n elif deprecated_kwargs is None:\n warning = f\"`{attribute}` is deprecated and will be removed in version {version_name}.\"\n\n if warning is not None:\n warning = warning + \" \" if standard_warn else \"\"\n warnings.warn(warning + message, FutureWarning, stacklevel=stacklevel)\n\n if isinstance(deprecated_kwargs, dict) and len(deprecated_kwargs) > 0:\n call_frame = inspect.getouterframes(inspect.currentframe())[1]\n filename = call_frame.filename\n line_number = call_frame.lineno\n function = call_frame.function\n key, value = next(iter(deprecated_kwargs.items()))\n raise TypeError(f\"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`\")\n\n if len(values) == 0:\n return\n elif len(values) == 1:\n return values[0]\n return values\n" }, { "path": "diffusers/src/diffusers/utils/doc_utils.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nDoc utilities: Utilities related to documentation\n\"\"\"\n\nimport re\n\n\ndef replace_example_docstring(example_docstring):\n def docstring_decorator(fn):\n func_doc = fn.__doc__\n lines = func_doc.split(\"\\n\")\n i = 0\n while i < len(lines) and re.search(r\"^\\s*Examples?:\\s*$\", lines[i]) is None:\n i += 1\n if i < len(lines):\n lines[i] = example_docstring\n func_doc = \"\\n\".join(lines)\n else:\n raise ValueError(\n f\"The function {fn} should have an empty 'Examples:' in its docstring as placeholder, \"\n f\"current docstring is:\\n{func_doc}\"\n )\n fn.__doc__ = func_doc\n return fn\n\n return docstring_decorator\n" }, { "path": "diffusers/src/diffusers/utils/dummy_flax_and_transformers_objects.py", "content": "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nclass FlaxStableDiffusionControlNetPipeline(metaclass=DummyObject):\n _backends = [\"flax\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"flax\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\", \"transformers\"])\n\n\nclass FlaxStableDiffusionImg2ImgPipeline(metaclass=DummyObject):\n _backends = [\"flax\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"flax\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\", \"transformers\"])\n\n\nclass FlaxStableDiffusionInpaintPipeline(metaclass=DummyObject):\n _backends = [\"flax\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"flax\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\", \"transformers\"])\n\n\nclass FlaxStableDiffusionPipeline(metaclass=DummyObject):\n _backends = [\"flax\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"flax\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\", \"transformers\"])\n\n\nclass FlaxStableDiffusionXLPipeline(metaclass=DummyObject):\n _backends = [\"flax\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"flax\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\", \"transformers\"])\n" }, { "path": "diffusers/src/diffusers/utils/dummy_flax_objects.py", "content": "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nclass FlaxControlNetModel(metaclass=DummyObject):\n _backends = [\"flax\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"flax\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n\nclass FlaxModelMixin(metaclass=DummyObject):\n _backends = [\"flax\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"flax\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n\nclass FlaxUNet2DConditionModel(metaclass=DummyObject):\n _backends = [\"flax\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"flax\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n\nclass FlaxAutoencoderKL(metaclass=DummyObject):\n _backends = [\"flax\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"flax\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n\nclass FlaxDiffusionPipeline(metaclass=DummyObject):\n _backends = [\"flax\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"flax\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n\nclass FlaxDDIMScheduler(metaclass=DummyObject):\n _backends = [\"flax\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"flax\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n\nclass FlaxDDPMScheduler(metaclass=DummyObject):\n _backends = [\"flax\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"flax\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n\nclass FlaxDPMSolverMultistepScheduler(metaclass=DummyObject):\n _backends = [\"flax\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"flax\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n\nclass FlaxEulerDiscreteScheduler(metaclass=DummyObject):\n _backends = [\"flax\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"flax\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n\nclass FlaxKarrasVeScheduler(metaclass=DummyObject):\n _backends = [\"flax\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"flax\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n\nclass FlaxLMSDiscreteScheduler(metaclass=DummyObject):\n _backends = [\"flax\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"flax\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n\nclass FlaxPNDMScheduler(metaclass=DummyObject):\n _backends = [\"flax\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"flax\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n\nclass FlaxSchedulerMixin(metaclass=DummyObject):\n _backends = [\"flax\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"flax\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n\nclass FlaxScoreSdeVeScheduler(metaclass=DummyObject):\n _backends = [\"flax\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"flax\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"flax\"])\n" }, { "path": "diffusers/src/diffusers/utils/dummy_note_seq_objects.py", "content": "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nclass MidiProcessor(metaclass=DummyObject):\n _backends = [\"note_seq\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"note_seq\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"note_seq\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"note_seq\"])\n" }, { "path": "diffusers/src/diffusers/utils/dummy_onnx_objects.py", "content": "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nclass OnnxRuntimeModel(metaclass=DummyObject):\n _backends = [\"onnx\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"onnx\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"onnx\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"onnx\"])\n" }, { "path": "diffusers/src/diffusers/utils/dummy_pt_objects.py", "content": "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nclass AsymmetricAutoencoderKL(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass AutoencoderKL(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass AutoencoderKLTemporalDecoder(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass AutoencoderTiny(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass ConsistencyDecoderVAE(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass ControlNetModel(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass ControlNetXSAdapter(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass DiTTransformer2DModel(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass HunyuanDiT2DModel(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass I2VGenXLUNet(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass Kandinsky3UNet(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass ModelMixin(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass MotionAdapter(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass MultiAdapter(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass PixArtTransformer2DModel(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass PriorTransformer(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass SD3Transformer2DModel(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass T2IAdapter(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass T5FilmDecoder(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass Transformer2DModel(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass UNet1DModel(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass UNet2DConditionModel(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass UNet2DModel(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass UNet3DConditionModel(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass UNetControlNetXSModel(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass UNetMotionModel(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass UNetSpatioTemporalConditionModel(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass UVit2DModel(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass VQModel(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\ndef get_constant_schedule(*args, **kwargs):\n requires_backends(get_constant_schedule, [\"torch\"])\n\n\ndef get_constant_schedule_with_warmup(*args, **kwargs):\n requires_backends(get_constant_schedule_with_warmup, [\"torch\"])\n\n\ndef get_cosine_schedule_with_warmup(*args, **kwargs):\n requires_backends(get_cosine_schedule_with_warmup, [\"torch\"])\n\n\ndef get_cosine_with_hard_restarts_schedule_with_warmup(*args, **kwargs):\n requires_backends(get_cosine_with_hard_restarts_schedule_with_warmup, [\"torch\"])\n\n\ndef get_linear_schedule_with_warmup(*args, **kwargs):\n requires_backends(get_linear_schedule_with_warmup, [\"torch\"])\n\n\ndef get_polynomial_decay_schedule_with_warmup(*args, **kwargs):\n requires_backends(get_polynomial_decay_schedule_with_warmup, [\"torch\"])\n\n\ndef get_scheduler(*args, **kwargs):\n requires_backends(get_scheduler, [\"torch\"])\n\n\nclass AudioPipelineOutput(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass AutoPipelineForImage2Image(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass AutoPipelineForInpainting(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass AutoPipelineForText2Image(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass BlipDiffusionControlNetPipeline(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass BlipDiffusionPipeline(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass CLIPImageProjection(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass ConsistencyModelPipeline(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass DanceDiffusionPipeline(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass DDIMPipeline(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass DDPMPipeline(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass DiffusionPipeline(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass DiTPipeline(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass ImagePipelineOutput(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass KarrasVePipeline(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass LDMPipeline(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass LDMSuperResolutionPipeline(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass PNDMPipeline(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass RePaintPipeline(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass ScoreSdeVePipeline(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass StableDiffusionMixin(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass AmusedScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass CMStochasticIterativeScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass DDIMInverseScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass DDIMParallelScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass DDIMScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass DDPMParallelScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass DDPMScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass DDPMWuerstchenScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass DEISMultistepScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass DPMSolverMultistepInverseScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass DPMSolverMultistepScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass DPMSolverSinglestepScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass EDMDPMSolverMultistepScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass EDMEulerScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass EulerAncestralDiscreteScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass EulerDiscreteScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass FlowMatchEulerDiscreteScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass HeunDiscreteScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass IPNDMScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass KarrasVeScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass KDPM2AncestralDiscreteScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass KDPM2DiscreteScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass LCMScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass PNDMScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass RePaintScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass SASolverScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass SchedulerMixin(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass ScoreSdeVeScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass TCDScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass UnCLIPScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass UniPCMultistepScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass VQDiffusionScheduler(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n\nclass EMAModel(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n" }, { "path": "diffusers/src/diffusers/utils/dummy_torch_and_librosa_objects.py", "content": "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nclass AudioDiffusionPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"librosa\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"librosa\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"librosa\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"librosa\"])\n\n\nclass Mel(metaclass=DummyObject):\n _backends = [\"torch\", \"librosa\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"librosa\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"librosa\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"librosa\"])\n" }, { "path": "diffusers/src/diffusers/utils/dummy_torch_and_scipy_objects.py", "content": "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nclass LMSDiscreteScheduler(metaclass=DummyObject):\n _backends = [\"torch\", \"scipy\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"scipy\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"scipy\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"scipy\"])\n" }, { "path": "diffusers/src/diffusers/utils/dummy_torch_and_torchsde_objects.py", "content": "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nclass DPMSolverSDEScheduler(metaclass=DummyObject):\n _backends = [\"torch\", \"torchsde\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"torchsde\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"torchsde\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"torchsde\"])\n" }, { "path": "diffusers/src/diffusers/utils/dummy_torch_and_transformers_and_k_diffusion_objects.py", "content": "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nclass StableDiffusionKDiffusionPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\", \"k_diffusion\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\", \"k_diffusion\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\", \"k_diffusion\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\", \"k_diffusion\"])\n\n\nclass StableDiffusionXLKDiffusionPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\", \"k_diffusion\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\", \"k_diffusion\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\", \"k_diffusion\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\", \"k_diffusion\"])\n" }, { "path": "diffusers/src/diffusers/utils/dummy_torch_and_transformers_and_onnx_objects.py", "content": "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nclass OnnxStableDiffusionImg2ImgPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\", \"onnx\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\", \"onnx\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\", \"onnx\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\", \"onnx\"])\n\n\nclass OnnxStableDiffusionInpaintPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\", \"onnx\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\", \"onnx\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\", \"onnx\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\", \"onnx\"])\n\n\nclass OnnxStableDiffusionInpaintPipelineLegacy(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\", \"onnx\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\", \"onnx\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\", \"onnx\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\", \"onnx\"])\n\n\nclass OnnxStableDiffusionPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\", \"onnx\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\", \"onnx\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\", \"onnx\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\", \"onnx\"])\n\n\nclass OnnxStableDiffusionUpscalePipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\", \"onnx\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\", \"onnx\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\", \"onnx\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\", \"onnx\"])\n\n\nclass StableDiffusionOnnxPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\", \"onnx\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\", \"onnx\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\", \"onnx\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\", \"onnx\"])\n" }, { "path": "diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py", "content": "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nclass AltDiffusionImg2ImgPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass AltDiffusionPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass AmusedImg2ImgPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass AmusedInpaintPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass AmusedPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass AnimateDiffPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass AnimateDiffSDXLPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass AnimateDiffVideoToVideoPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass AudioLDM2Pipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass AudioLDM2ProjectionModel(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass AudioLDM2UNet2DConditionModel(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass AudioLDMPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass CLIPImageProjection(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass CycleDiffusionPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass HunyuanDiTPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass I2VGenXLPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass IFImg2ImgPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass IFImg2ImgSuperResolutionPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass IFInpaintingPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass IFInpaintingSuperResolutionPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass IFPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass IFSuperResolutionPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass ImageTextPipelineOutput(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass Kandinsky3Img2ImgPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass Kandinsky3Pipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass KandinskyCombinedPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass KandinskyImg2ImgCombinedPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass KandinskyImg2ImgPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass KandinskyInpaintCombinedPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass KandinskyInpaintPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass KandinskyPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass KandinskyPriorPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass KandinskyV22CombinedPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass KandinskyV22ControlnetImg2ImgPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass KandinskyV22ControlnetPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass KandinskyV22Img2ImgCombinedPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass KandinskyV22Img2ImgPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass KandinskyV22InpaintCombinedPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass KandinskyV22InpaintPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass KandinskyV22Pipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass KandinskyV22PriorEmb2EmbPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass KandinskyV22PriorPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass LatentConsistencyModelImg2ImgPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass LatentConsistencyModelPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass LDMTextToImagePipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass LEditsPPPipelineStableDiffusion(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass LEditsPPPipelineStableDiffusionXL(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass MarigoldDepthPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass MarigoldNormalsPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass MusicLDMPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass PaintByExamplePipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass PIAPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass PixArtAlphaPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass PixArtSigmaPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass SemanticStableDiffusionPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass ShapEImg2ImgPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass ShapEPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableCascadeCombinedPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableCascadeDecoderPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableCascadePriorPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusion3Img2ImgPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusion3Pipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionAdapterPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionAttendAndExcitePipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionControlNetImg2ImgPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionControlNetInpaintPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionControlNetPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionControlNetXSPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionDepth2ImgPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionDiffEditPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionGLIGENPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionGLIGENTextImagePipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionImageVariationPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionImg2ImgPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionInpaintPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionInpaintPipelineLegacy(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionInstructPix2PixPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionLatentUpscalePipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionLDM3DPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionModelEditingPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionPanoramaPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionParadigmsPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionPipelineSafe(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionPix2PixZeroPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionSAGPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionUpscalePipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionXLAdapterPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionXLControlNetImg2ImgPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionXLControlNetInpaintPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionXLControlNetPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionXLControlNetXSPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionXLImg2ImgPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionXLInpaintPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionXLInstructPix2PixPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableDiffusionXLPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableUnCLIPImg2ImgPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableUnCLIPPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass StableVideoDiffusionPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass TextToVideoSDPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass TextToVideoZeroPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass TextToVideoZeroSDXLPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass UnCLIPImageVariationPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass UnCLIPPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass UniDiffuserModel(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass UniDiffuserPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass UniDiffuserTextDecoder(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass VersatileDiffusionDualGuidedPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass VersatileDiffusionImageVariationPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass VersatileDiffusionPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass VersatileDiffusionTextToImagePipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass VideoToVideoSDPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass VQDiffusionPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass WuerstchenCombinedPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass WuerstchenDecoderPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n\nclass WuerstchenPriorPipeline(metaclass=DummyObject):\n _backends = [\"torch\", \"transformers\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\", \"transformers\"])\n" }, { "path": "diffusers/src/diffusers/utils/dummy_transformers_and_torch_and_note_seq_objects.py", "content": "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nclass SpectrogramDiffusionPipeline(metaclass=DummyObject):\n _backends = [\"transformers\", \"torch\", \"note_seq\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"transformers\", \"torch\", \"note_seq\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"transformers\", \"torch\", \"note_seq\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"transformers\", \"torch\", \"note_seq\"])\n" }, { "path": "diffusers/src/diffusers/utils/dynamic_modules_utils.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Utilities to dynamically load objects from the Hub.\"\"\"\n\nimport importlib\nimport inspect\nimport json\nimport os\nimport re\nimport shutil\nimport sys\nfrom pathlib import Path\nfrom typing import Dict, Optional, Union\nfrom urllib import request\n\nfrom huggingface_hub import hf_hub_download, model_info\nfrom huggingface_hub.utils import RevisionNotFoundError, validate_hf_hub_args\nfrom packaging import version\n\nfrom .. import __version__\nfrom . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n# See https://huggingface.co/datasets/diffusers/community-pipelines-mirror\nCOMMUNITY_PIPELINES_MIRROR_ID = \"diffusers/community-pipelines-mirror\"\n\n\ndef get_diffusers_versions():\n url = \"https://pypi.org/pypi/diffusers/json\"\n releases = json.loads(request.urlopen(url).read())[\"releases\"].keys()\n return sorted(releases, key=lambda x: version.Version(x))\n\n\ndef init_hf_modules():\n \"\"\"\n Creates the cache directory for modules with an init, and adds it to the Python path.\n \"\"\"\n # This function has already been executed if HF_MODULES_CACHE already is in the Python path.\n if HF_MODULES_CACHE in sys.path:\n return\n\n sys.path.append(HF_MODULES_CACHE)\n os.makedirs(HF_MODULES_CACHE, exist_ok=True)\n init_path = Path(HF_MODULES_CACHE) / \"__init__.py\"\n if not init_path.exists():\n init_path.touch()\n\n\ndef create_dynamic_module(name: Union[str, os.PathLike]):\n \"\"\"\n Creates a dynamic module in the cache directory for modules.\n \"\"\"\n init_hf_modules()\n dynamic_module_path = Path(HF_MODULES_CACHE) / name\n # If the parent module does not exist yet, recursively create it.\n if not dynamic_module_path.parent.exists():\n create_dynamic_module(dynamic_module_path.parent)\n os.makedirs(dynamic_module_path, exist_ok=True)\n init_path = dynamic_module_path / \"__init__.py\"\n if not init_path.exists():\n init_path.touch()\n\n\ndef get_relative_imports(module_file):\n \"\"\"\n Get the list of modules that are relatively imported in a module file.\n\n Args:\n module_file (`str` or `os.PathLike`): The module file to inspect.\n \"\"\"\n with open(module_file, \"r\", encoding=\"utf-8\") as f:\n content = f.read()\n\n # Imports of the form `import .xxx`\n relative_imports = re.findall(r\"^\\s*import\\s+\\.(\\S+)\\s*$\", content, flags=re.MULTILINE)\n # Imports of the form `from .xxx import yyy`\n relative_imports += re.findall(r\"^\\s*from\\s+\\.(\\S+)\\s+import\", content, flags=re.MULTILINE)\n # Unique-ify\n return list(set(relative_imports))\n\n\ndef get_relative_import_files(module_file):\n \"\"\"\n Get the list of all files that are needed for a given module. Note that this function recurses through the relative\n imports (if a imports b and b imports c, it will return module files for b and c).\n\n Args:\n module_file (`str` or `os.PathLike`): The module file to inspect.\n \"\"\"\n no_change = False\n files_to_check = [module_file]\n all_relative_imports = []\n\n # Let's recurse through all relative imports\n while not no_change:\n new_imports = []\n for f in files_to_check:\n new_imports.extend(get_relative_imports(f))\n\n module_path = Path(module_file).parent\n new_import_files = [str(module_path / m) for m in new_imports]\n new_import_files = [f for f in new_import_files if f not in all_relative_imports]\n files_to_check = [f\"{f}.py\" for f in new_import_files]\n\n no_change = len(new_import_files) == 0\n all_relative_imports.extend(files_to_check)\n\n return all_relative_imports\n\n\ndef check_imports(filename):\n \"\"\"\n Check if the current Python environment contains all the libraries that are imported in a file.\n \"\"\"\n with open(filename, \"r\", encoding=\"utf-8\") as f:\n content = f.read()\n\n # Imports of the form `import xxx`\n imports = re.findall(r\"^\\s*import\\s+(\\S+)\\s*$\", content, flags=re.MULTILINE)\n # Imports of the form `from xxx import yyy`\n imports += re.findall(r\"^\\s*from\\s+(\\S+)\\s+import\", content, flags=re.MULTILINE)\n # Only keep the top-level module\n imports = [imp.split(\".\")[0] for imp in imports if not imp.startswith(\".\")]\n\n # Unique-ify and test we got them all\n imports = list(set(imports))\n missing_packages = []\n for imp in imports:\n try:\n importlib.import_module(imp)\n except ImportError:\n missing_packages.append(imp)\n\n if len(missing_packages) > 0:\n raise ImportError(\n \"This modeling file requires the following packages that were not found in your environment: \"\n f\"{', '.join(missing_packages)}. Run `pip install {' '.join(missing_packages)}`\"\n )\n\n return get_relative_imports(filename)\n\n\ndef get_class_in_module(class_name, module_path):\n \"\"\"\n Import a module on the cache directory for modules and extract a class from it.\n \"\"\"\n module_path = module_path.replace(os.path.sep, \".\")\n module = importlib.import_module(module_path)\n\n if class_name is None:\n return find_pipeline_class(module)\n return getattr(module, class_name)\n\n\ndef find_pipeline_class(loaded_module):\n \"\"\"\n Retrieve pipeline class that inherits from `DiffusionPipeline`. Note that there has to be exactly one class\n inheriting from `DiffusionPipeline`.\n \"\"\"\n from ..pipelines import DiffusionPipeline\n\n cls_members = dict(inspect.getmembers(loaded_module, inspect.isclass))\n\n pipeline_class = None\n for cls_name, cls in cls_members.items():\n if (\n cls_name != DiffusionPipeline.__name__\n and issubclass(cls, DiffusionPipeline)\n and cls.__module__.split(\".\")[0] != \"diffusers\"\n ):\n if pipeline_class is not None:\n raise ValueError(\n f\"Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:\"\n f\" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in\"\n f\" {loaded_module}.\"\n )\n pipeline_class = cls\n\n return pipeline_class\n\n\n@validate_hf_hub_args\ndef get_cached_module_file(\n pretrained_model_name_or_path: Union[str, os.PathLike],\n module_file: str,\n cache_dir: Optional[Union[str, os.PathLike]] = None,\n force_download: bool = False,\n resume_download: Optional[bool] = None,\n proxies: Optional[Dict[str, str]] = None,\n token: Optional[Union[bool, str]] = None,\n revision: Optional[str] = None,\n local_files_only: bool = False,\n):\n \"\"\"\n Prepares Downloads a module from a local folder or a distant repo and returns its path inside the cached\n Transformers module.\n\n Args:\n pretrained_model_name_or_path (`str` or `os.PathLike`):\n This can be either:\n\n - a string, the *model id* of a pretrained model configuration hosted inside a model repo on\n huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced\n under a user or organization name, like `dbmdz/bert-base-german-cased`.\n - a path to a *directory* containing a configuration file saved using the\n [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.\n\n module_file (`str`):\n The name of the module file containing the class to look for.\n cache_dir (`str` or `os.PathLike`, *optional*):\n Path to a directory in which a downloaded pretrained model configuration should be cached if the standard\n cache should not be used.\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force to (re-)download the configuration files and override the cached versions if they\n exist. resume_download:\n Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1\n of Diffusers.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.\n token (`str` or *bool*, *optional*):\n The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated\n when running `transformers-cli login` (stored in `~/.huggingface`).\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a\n git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any\n identifier allowed by git.\n local_files_only (`bool`, *optional*, defaults to `False`):\n If `True`, will only try to load the tokenizer configuration from local files.\n\n \n\n You may pass a token in `token` if you are not logged in (`huggingface-cli login`) and want to use private or\n [gated models](https://huggingface.co/docs/hub/models-gated#gated-models).\n\n \n\n Returns:\n `str`: The path to the module inside the cache.\n \"\"\"\n # Download and cache module_file from the repo `pretrained_model_name_or_path` of grab it if it's a local file.\n pretrained_model_name_or_path = str(pretrained_model_name_or_path)\n\n module_file_or_url = os.path.join(pretrained_model_name_or_path, module_file)\n\n if os.path.isfile(module_file_or_url):\n resolved_module_file = module_file_or_url\n submodule = \"local\"\n elif pretrained_model_name_or_path.count(\"/\") == 0:\n available_versions = get_diffusers_versions()\n # cut \".dev0\"\n latest_version = \"v\" + \".\".join(__version__.split(\".\")[:3])\n\n # retrieve github version that matches\n if revision is None:\n revision = latest_version if latest_version[1:] in available_versions else \"main\"\n logger.info(f\"Defaulting to latest_version: {revision}.\")\n elif revision in available_versions:\n revision = f\"v{revision}\"\n elif revision == \"main\":\n revision = revision\n else:\n raise ValueError(\n f\"`custom_revision`: {revision} does not exist. Please make sure to choose one of\"\n f\" {', '.join(available_versions + ['main'])}.\"\n )\n\n try:\n resolved_module_file = hf_hub_download(\n repo_id=COMMUNITY_PIPELINES_MIRROR_ID,\n repo_type=\"dataset\",\n filename=f\"{revision}/{pretrained_model_name_or_path}.py\",\n cache_dir=cache_dir,\n force_download=force_download,\n proxies=proxies,\n local_files_only=local_files_only,\n )\n submodule = \"git\"\n module_file = pretrained_model_name_or_path + \".py\"\n except RevisionNotFoundError as e:\n raise EnvironmentError(\n f\"Revision '{revision}' not found in the community pipelines mirror. Check available revisions on\"\n \" https://huggingface.co/datasets/diffusers/community-pipelines-mirror/tree/main.\"\n \" If you don't find the revision you are looking for, please open an issue on https://github.com/huggingface/diffusers/issues.\"\n ) from e\n except EnvironmentError:\n logger.error(f\"Could not locate the {module_file} inside {pretrained_model_name_or_path}.\")\n raise\n else:\n try:\n # Load from URL or cache if already cached\n resolved_module_file = hf_hub_download(\n pretrained_model_name_or_path,\n module_file,\n cache_dir=cache_dir,\n force_download=force_download,\n proxies=proxies,\n resume_download=resume_download,\n local_files_only=local_files_only,\n token=token,\n )\n submodule = os.path.join(\"local\", \"--\".join(pretrained_model_name_or_path.split(\"/\")))\n except EnvironmentError:\n logger.error(f\"Could not locate the {module_file} inside {pretrained_model_name_or_path}.\")\n raise\n\n # Check we have all the requirements in our environment\n modules_needed = check_imports(resolved_module_file)\n\n # Now we move the module inside our cached dynamic modules.\n full_submodule = DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule\n create_dynamic_module(full_submodule)\n submodule_path = Path(HF_MODULES_CACHE) / full_submodule\n if submodule == \"local\" or submodule == \"git\":\n # We always copy local files (we could hash the file to see if there was a change, and give them the name of\n # that hash, to only copy when there is a modification but it seems overkill for now).\n # The only reason we do the copy is to avoid putting too many folders in sys.path.\n shutil.copy(resolved_module_file, submodule_path / module_file)\n for module_needed in modules_needed:\n if len(module_needed.split(\".\")) == 2:\n module_needed = \"/\".join(module_needed.split(\".\"))\n module_folder = module_needed.split(\"/\")[0]\n if not os.path.exists(submodule_path / module_folder):\n os.makedirs(submodule_path / module_folder)\n module_needed = f\"{module_needed}.py\"\n shutil.copy(os.path.join(pretrained_model_name_or_path, module_needed), submodule_path / module_needed)\n else:\n # Get the commit hash\n # TODO: we will get this info in the etag soon, so retrieve it from there and not here.\n commit_hash = model_info(pretrained_model_name_or_path, revision=revision, token=token).sha\n\n # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the\n # benefit of versioning.\n submodule_path = submodule_path / commit_hash\n full_submodule = full_submodule + os.path.sep + commit_hash\n create_dynamic_module(full_submodule)\n\n if not (submodule_path / module_file).exists():\n if len(module_file.split(\"/\")) == 2:\n module_folder = module_file.split(\"/\")[0]\n if not os.path.exists(submodule_path / module_folder):\n os.makedirs(submodule_path / module_folder)\n shutil.copy(resolved_module_file, submodule_path / module_file)\n\n # Make sure we also have every file with relative\n for module_needed in modules_needed:\n if len(module_needed.split(\".\")) == 2:\n module_needed = \"/\".join(module_needed.split(\".\"))\n if not (submodule_path / module_needed).exists():\n get_cached_module_file(\n pretrained_model_name_or_path,\n f\"{module_needed}.py\",\n cache_dir=cache_dir,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n token=token,\n revision=revision,\n local_files_only=local_files_only,\n )\n return os.path.join(full_submodule, module_file)\n\n\n@validate_hf_hub_args\ndef get_class_from_dynamic_module(\n pretrained_model_name_or_path: Union[str, os.PathLike],\n module_file: str,\n class_name: Optional[str] = None,\n cache_dir: Optional[Union[str, os.PathLike]] = None,\n force_download: bool = False,\n resume_download: Optional[bool] = None,\n proxies: Optional[Dict[str, str]] = None,\n token: Optional[Union[bool, str]] = None,\n revision: Optional[str] = None,\n local_files_only: bool = False,\n **kwargs,\n):\n \"\"\"\n Extracts a class from a module file, present in the local folder or repository of a model.\n\n \n\n Calling this function will execute the code in the module file found locally or downloaded from the Hub. It should\n therefore only be called on trusted repos.\n\n \n\n Args:\n pretrained_model_name_or_path (`str` or `os.PathLike`):\n This can be either:\n\n - a string, the *model id* of a pretrained model configuration hosted inside a model repo on\n huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced\n under a user or organization name, like `dbmdz/bert-base-german-cased`.\n - a path to a *directory* containing a configuration file saved using the\n [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.\n\n module_file (`str`):\n The name of the module file containing the class to look for.\n class_name (`str`):\n The name of the class to import in the module.\n cache_dir (`str` or `os.PathLike`, *optional*):\n Path to a directory in which a downloaded pretrained model configuration should be cached if the standard\n cache should not be used.\n force_download (`bool`, *optional*, defaults to `False`):\n Whether or not to force to (re-)download the configuration files and override the cached versions if they\n exist.\n resume_download:\n Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1 of\n Diffusers.\n proxies (`Dict[str, str]`, *optional*):\n A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',\n 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.\n token (`str` or `bool`, *optional*):\n The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated\n when running `transformers-cli login` (stored in `~/.huggingface`).\n revision (`str`, *optional*, defaults to `\"main\"`):\n The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a\n git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any\n identifier allowed by git.\n local_files_only (`bool`, *optional*, defaults to `False`):\n If `True`, will only try to load the tokenizer configuration from local files.\n\n \n\n You may pass a token in `token` if you are not logged in (`huggingface-cli login`) and want to use private or\n [gated models](https://huggingface.co/docs/hub/models-gated#gated-models).\n\n \n\n Returns:\n `type`: The class, dynamically imported from the module.\n\n Examples:\n\n ```python\n # Download module `modeling.py` from huggingface.co and cache then extract the class `MyBertModel` from this\n # module.\n cls = get_class_from_dynamic_module(\"sgugger/my-bert-model\", \"modeling.py\", \"MyBertModel\")\n ```\"\"\"\n # And lastly we get the class inside our newly created module\n final_module = get_cached_module_file(\n pretrained_model_name_or_path,\n module_file,\n cache_dir=cache_dir,\n force_download=force_download,\n resume_download=resume_download,\n proxies=proxies,\n token=token,\n revision=revision,\n local_files_only=local_files_only,\n )\n return get_class_in_module(class_name, final_module.replace(\".py\", \"\"))\n" }, { "path": "diffusers/src/diffusers/utils/export_utils.py", "content": "import io\nimport random\nimport struct\nimport tempfile\nfrom contextlib import contextmanager\nfrom typing import List, Union\n\nimport numpy as np\nimport PIL.Image\nimport PIL.ImageOps\n\nfrom .import_utils import (\n BACKENDS_MAPPING,\n is_opencv_available,\n)\nfrom .logging import get_logger\n\n\nglobal_rng = random.Random()\n\nlogger = get_logger(__name__)\n\n\n@contextmanager\ndef buffered_writer(raw_f):\n f = io.BufferedWriter(raw_f)\n yield f\n f.flush()\n\n\ndef export_to_gif(image: List[PIL.Image.Image], output_gif_path: str = None, fps: int = 10) -> str:\n if output_gif_path is None:\n output_gif_path = tempfile.NamedTemporaryFile(suffix=\".gif\").name\n\n image[0].save(\n output_gif_path,\n save_all=True,\n append_images=image[1:],\n optimize=False,\n duration=1000 // fps,\n loop=0,\n )\n return output_gif_path\n\n\ndef export_to_ply(mesh, output_ply_path: str = None):\n \"\"\"\n Write a PLY file for a mesh.\n \"\"\"\n if output_ply_path is None:\n output_ply_path = tempfile.NamedTemporaryFile(suffix=\".ply\").name\n\n coords = mesh.verts.detach().cpu().numpy()\n faces = mesh.faces.cpu().numpy()\n rgb = np.stack([mesh.vertex_channels[x].detach().cpu().numpy() for x in \"RGB\"], axis=1)\n\n with buffered_writer(open(output_ply_path, \"wb\")) as f:\n f.write(b\"ply\\n\")\n f.write(b\"format binary_little_endian 1.0\\n\")\n f.write(bytes(f\"element vertex {len(coords)}\\n\", \"ascii\"))\n f.write(b\"property float x\\n\")\n f.write(b\"property float y\\n\")\n f.write(b\"property float z\\n\")\n if rgb is not None:\n f.write(b\"property uchar red\\n\")\n f.write(b\"property uchar green\\n\")\n f.write(b\"property uchar blue\\n\")\n if faces is not None:\n f.write(bytes(f\"element face {len(faces)}\\n\", \"ascii\"))\n f.write(b\"property list uchar int vertex_index\\n\")\n f.write(b\"end_header\\n\")\n\n if rgb is not None:\n rgb = (rgb * 255.499).round().astype(int)\n vertices = [\n (*coord, *rgb)\n for coord, rgb in zip(\n coords.tolist(),\n rgb.tolist(),\n )\n ]\n format = struct.Struct(\"<3f3B\")\n for item in vertices:\n f.write(format.pack(*item))\n else:\n format = struct.Struct(\"<3f\")\n for vertex in coords.tolist():\n f.write(format.pack(*vertex))\n\n if faces is not None:\n format = struct.Struct(\" str:\n if is_opencv_available():\n import cv2\n else:\n raise ImportError(BACKENDS_MAPPING[\"opencv\"][1].format(\"export_to_video\"))\n if output_video_path is None:\n output_video_path = tempfile.NamedTemporaryFile(suffix=\".mp4\").name\n\n if isinstance(video_frames[0], np.ndarray):\n video_frames = [(frame * 255).astype(np.uint8) for frame in video_frames]\n\n elif isinstance(video_frames[0], PIL.Image.Image):\n video_frames = [np.array(frame) for frame in video_frames]\n\n fourcc = cv2.VideoWriter_fourcc(*\"mp4v\")\n h, w, c = video_frames[0].shape\n video_writer = cv2.VideoWriter(output_video_path, fourcc, fps=fps, frameSize=(w, h))\n for i in range(len(video_frames)):\n img = cv2.cvtColor(video_frames[i], cv2.COLOR_RGB2BGR)\n video_writer.write(img)\n return output_video_path\n" }, { "path": "diffusers/src/diffusers/utils/hub_utils.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport json\nimport os\nimport re\nimport sys\nimport tempfile\nimport traceback\nimport warnings\nfrom pathlib import Path\nfrom typing import Dict, List, Optional, Union\nfrom uuid import uuid4\n\nfrom huggingface_hub import (\n ModelCard,\n ModelCardData,\n create_repo,\n hf_hub_download,\n model_info,\n snapshot_download,\n upload_folder,\n)\nfrom huggingface_hub.constants import HF_HUB_CACHE, HF_HUB_DISABLE_TELEMETRY, HF_HUB_OFFLINE\nfrom huggingface_hub.file_download import REGEX_COMMIT_HASH\nfrom huggingface_hub.utils import (\n EntryNotFoundError,\n RepositoryNotFoundError,\n RevisionNotFoundError,\n is_jinja_available,\n validate_hf_hub_args,\n)\nfrom packaging import version\nfrom requests import HTTPError\n\nfrom .. import __version__\nfrom .constants import (\n DEPRECATED_REVISION_ARGS,\n HUGGINGFACE_CO_RESOLVE_ENDPOINT,\n SAFETENSORS_WEIGHTS_NAME,\n WEIGHTS_NAME,\n)\nfrom .import_utils import (\n ENV_VARS_TRUE_VALUES,\n _flax_version,\n _jax_version,\n _onnxruntime_version,\n _torch_version,\n is_flax_available,\n is_onnx_available,\n is_torch_available,\n)\nfrom .logging import get_logger\n\n\nlogger = get_logger(__name__)\n\nMODEL_CARD_TEMPLATE_PATH = Path(__file__).parent / \"model_card_template.md\"\nSESSION_ID = uuid4().hex\n\n\ndef http_user_agent(user_agent: Union[Dict, str, None] = None) -> str:\n \"\"\"\n Formats a user-agent string with basic info about a request.\n \"\"\"\n ua = f\"diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}\"\n if HF_HUB_DISABLE_TELEMETRY or HF_HUB_OFFLINE:\n return ua + \"; telemetry/off\"\n if is_torch_available():\n ua += f\"; torch/{_torch_version}\"\n if is_flax_available():\n ua += f\"; jax/{_jax_version}\"\n ua += f\"; flax/{_flax_version}\"\n if is_onnx_available():\n ua += f\"; onnxruntime/{_onnxruntime_version}\"\n # CI will set this value to True\n if os.environ.get(\"DIFFUSERS_IS_CI\", \"\").upper() in ENV_VARS_TRUE_VALUES:\n ua += \"; is_ci/true\"\n if isinstance(user_agent, dict):\n ua += \"; \" + \"; \".join(f\"{k}/{v}\" for k, v in user_agent.items())\n elif isinstance(user_agent, str):\n ua += \"; \" + user_agent\n return ua\n\n\ndef load_or_create_model_card(\n repo_id_or_path: str = None,\n token: Optional[str] = None,\n is_pipeline: bool = False,\n from_training: bool = False,\n model_description: Optional[str] = None,\n base_model: str = None,\n prompt: Optional[str] = None,\n license: Optional[str] = None,\n widget: Optional[List[dict]] = None,\n inference: Optional[bool] = None,\n) -> ModelCard:\n \"\"\"\n Loads or creates a model card.\n\n Args:\n repo_id_or_path (`str`):\n The repo id (e.g., \"runwayml/stable-diffusion-v1-5\") or local path where to look for the model card.\n token (`str`, *optional*):\n Authentication token. Will default to the stored token. See https://huggingface.co/settings/token for more\n details.\n is_pipeline (`bool`):\n Boolean to indicate if we're adding tag to a [`DiffusionPipeline`].\n from_training: (`bool`): Boolean flag to denote if the model card is being created from a training script.\n model_description (`str`, *optional*): Model description to add to the model card. Helpful when using\n `load_or_create_model_card` from a training script.\n base_model (`str`): Base model identifier (e.g., \"stabilityai/stable-diffusion-xl-base-1.0\"). Useful\n for DreamBooth-like training.\n prompt (`str`, *optional*): Prompt used for training. Useful for DreamBooth-like training.\n license: (`str`, *optional*): License of the output artifact. Helpful when using\n `load_or_create_model_card` from a training script.\n widget (`List[dict]`, *optional*): Widget to accompany a gallery template.\n inference: (`bool`, optional): Whether to turn on inference widget. Helpful when using\n `load_or_create_model_card` from a training script.\n \"\"\"\n if not is_jinja_available():\n raise ValueError(\n \"Modelcard rendering is based on Jinja templates.\"\n \" Please make sure to have `jinja` installed before using `load_or_create_model_card`.\"\n \" To install it, please run `pip install Jinja2`.\"\n )\n\n try:\n # Check if the model card is present on the remote repo\n model_card = ModelCard.load(repo_id_or_path, token=token)\n except (EntryNotFoundError, RepositoryNotFoundError):\n # Otherwise create a model card from template\n if from_training:\n model_card = ModelCard.from_template(\n card_data=ModelCardData( # Card metadata object that will be converted to YAML block\n license=license,\n library_name=\"diffusers\",\n inference=inference,\n base_model=base_model,\n instance_prompt=prompt,\n widget=widget,\n ),\n template_path=MODEL_CARD_TEMPLATE_PATH,\n model_description=model_description,\n )\n else:\n card_data = ModelCardData()\n component = \"pipeline\" if is_pipeline else \"model\"\n if model_description is None:\n model_description = f\"This is the model card of a 🧨 diffusers {component} that has been pushed on the Hub. This model card has been automatically generated.\"\n model_card = ModelCard.from_template(card_data, model_description=model_description)\n\n return model_card\n\n\ndef populate_model_card(model_card: ModelCard, tags: Union[str, List[str]] = None) -> ModelCard:\n \"\"\"Populates the `model_card` with library name and optional tags.\"\"\"\n if model_card.data.library_name is None:\n model_card.data.library_name = \"diffusers\"\n\n if tags is not None:\n if isinstance(tags, str):\n tags = [tags]\n if model_card.data.tags is None:\n model_card.data.tags = []\n for tag in tags:\n model_card.data.tags.append(tag)\n\n return model_card\n\n\ndef extract_commit_hash(resolved_file: Optional[str], commit_hash: Optional[str] = None):\n \"\"\"\n Extracts the commit hash from a resolved filename toward a cache file.\n \"\"\"\n if resolved_file is None or commit_hash is not None:\n return commit_hash\n resolved_file = str(Path(resolved_file).as_posix())\n search = re.search(r\"snapshots/([^/]+)/\", resolved_file)\n if search is None:\n return None\n commit_hash = search.groups()[0]\n return commit_hash if REGEX_COMMIT_HASH.match(commit_hash) else None\n\n\n# Old default cache path, potentially to be migrated.\n# This logic was more or less taken from `transformers`, with the following differences:\n# - Diffusers doesn't use custom environment variables to specify the cache path.\n# - There is no need to migrate the cache format, just move the files to the new location.\nhf_cache_home = os.path.expanduser(\n os.getenv(\"HF_HOME\", os.path.join(os.getenv(\"XDG_CACHE_HOME\", \"~/.cache\"), \"huggingface\"))\n)\nold_diffusers_cache = os.path.join(hf_cache_home, \"diffusers\")\n\n\ndef move_cache(old_cache_dir: Optional[str] = None, new_cache_dir: Optional[str] = None) -> None:\n if new_cache_dir is None:\n new_cache_dir = HF_HUB_CACHE\n if old_cache_dir is None:\n old_cache_dir = old_diffusers_cache\n\n old_cache_dir = Path(old_cache_dir).expanduser()\n new_cache_dir = Path(new_cache_dir).expanduser()\n for old_blob_path in old_cache_dir.glob(\"**/blobs/*\"):\n if old_blob_path.is_file() and not old_blob_path.is_symlink():\n new_blob_path = new_cache_dir / old_blob_path.relative_to(old_cache_dir)\n new_blob_path.parent.mkdir(parents=True, exist_ok=True)\n os.replace(old_blob_path, new_blob_path)\n try:\n os.symlink(new_blob_path, old_blob_path)\n except OSError:\n logger.warning(\n \"Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.\"\n )\n # At this point, old_cache_dir contains symlinks to the new cache (it can still be used).\n\n\ncache_version_file = os.path.join(HF_HUB_CACHE, \"version_diffusers_cache.txt\")\nif not os.path.isfile(cache_version_file):\n cache_version = 0\nelse:\n with open(cache_version_file) as f:\n try:\n cache_version = int(f.read())\n except ValueError:\n cache_version = 0\n\nif cache_version < 1:\n old_cache_is_not_empty = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0\n if old_cache_is_not_empty:\n logger.warning(\n \"The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your \"\n \"existing cached models. This is a one-time operation, you can interrupt it or run it \"\n \"later by calling `diffusers.utils.hub_utils.move_cache()`.\"\n )\n try:\n move_cache()\n except Exception as e:\n trace = \"\\n\".join(traceback.format_tb(e.__traceback__))\n logger.error(\n f\"There was a problem when trying to move your cache:\\n\\n{trace}\\n{e.__class__.__name__}: {e}\\n\\nPlease \"\n \"file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole \"\n \"message and we will do our best to help.\"\n )\n\nif cache_version < 1:\n try:\n os.makedirs(HF_HUB_CACHE, exist_ok=True)\n with open(cache_version_file, \"w\") as f:\n f.write(\"1\")\n except Exception:\n logger.warning(\n f\"There was a problem when trying to write in your cache folder ({HF_HUB_CACHE}). Please, ensure \"\n \"the directory exists and can be written to.\"\n )\n\n\ndef _add_variant(weights_name: str, variant: Optional[str] = None) -> str:\n if variant is not None:\n splits = weights_name.split(\".\")\n splits = splits[:-1] + [variant] + splits[-1:]\n weights_name = \".\".join(splits)\n\n return weights_name\n\n\n@validate_hf_hub_args\ndef _get_model_file(\n pretrained_model_name_or_path: Union[str, Path],\n *,\n weights_name: str,\n subfolder: Optional[str] = None,\n cache_dir: Optional[str] = None,\n force_download: bool = False,\n proxies: Optional[Dict] = None,\n resume_download: Optional[bool] = None,\n local_files_only: bool = False,\n token: Optional[str] = None,\n user_agent: Optional[Union[Dict, str]] = None,\n revision: Optional[str] = None,\n commit_hash: Optional[str] = None,\n):\n pretrained_model_name_or_path = str(pretrained_model_name_or_path)\n if os.path.isfile(pretrained_model_name_or_path):\n return pretrained_model_name_or_path\n elif os.path.isdir(pretrained_model_name_or_path):\n if os.path.isfile(os.path.join(pretrained_model_name_or_path, weights_name)):\n # Load from a PyTorch checkpoint\n model_file = os.path.join(pretrained_model_name_or_path, weights_name)\n return model_file\n elif subfolder is not None and os.path.isfile(\n os.path.join(pretrained_model_name_or_path, subfolder, weights_name)\n ):\n model_file = os.path.join(pretrained_model_name_or_path, subfolder, weights_name)\n return model_file\n else:\n raise EnvironmentError(\n f\"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.\"\n )\n else:\n # 1. First check if deprecated way of loading from branches is used\n if (\n revision in DEPRECATED_REVISION_ARGS\n and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)\n and version.parse(version.parse(__version__).base_version) >= version.parse(\"0.22.0\")\n ):\n try:\n model_file = hf_hub_download(\n pretrained_model_name_or_path,\n filename=_add_variant(weights_name, revision),\n cache_dir=cache_dir,\n force_download=force_download,\n proxies=proxies,\n resume_download=resume_download,\n local_files_only=local_files_only,\n token=token,\n user_agent=user_agent,\n subfolder=subfolder,\n revision=revision or commit_hash,\n )\n warnings.warn(\n f\"Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead.\",\n FutureWarning,\n )\n return model_file\n except: # noqa: E722\n warnings.warn(\n f\"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(weights_name, revision)} file in the 'main' branch of {pretrained_model_name_or_path}. \\n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(weights_name, revision)}' so that the correct variant file can be added.\",\n FutureWarning,\n )\n try:\n # 2. Load model file as usual\n model_file = hf_hub_download(\n pretrained_model_name_or_path,\n filename=weights_name,\n cache_dir=cache_dir,\n force_download=force_download,\n proxies=proxies,\n resume_download=resume_download,\n local_files_only=local_files_only,\n token=token,\n user_agent=user_agent,\n subfolder=subfolder,\n revision=revision or commit_hash,\n )\n return model_file\n\n except RepositoryNotFoundError:\n raise EnvironmentError(\n f\"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier \"\n \"listed on 'https://huggingface.co/models'\\nIf this is a private repository, make sure to pass a \"\n \"token having permission to this repo with `token` or log in with `huggingface-cli \"\n \"login`.\"\n )\n except RevisionNotFoundError:\n raise EnvironmentError(\n f\"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for \"\n \"this model name. Check the model page at \"\n f\"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions.\"\n )\n except EntryNotFoundError:\n raise EnvironmentError(\n f\"{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.\"\n )\n except HTTPError as err:\n raise EnvironmentError(\n f\"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\\n{err}\"\n )\n except ValueError:\n raise EnvironmentError(\n f\"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it\"\n f\" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a\"\n f\" directory containing a file named {weights_name} or\"\n \" \\nCheckout your internet connection or see how to run the library in\"\n \" offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'.\"\n )\n except EnvironmentError:\n raise EnvironmentError(\n f\"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from \"\n \"'https://huggingface.co/models', make sure you don't have a local directory with the same name. \"\n f\"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory \"\n f\"containing a file named {weights_name}\"\n )\n\n\n# Adapted from\n# https://github.com/huggingface/transformers/blob/1360801a69c0b169e3efdbb0cd05d9a0e72bfb70/src/transformers/utils/hub.py#L976\n# Differences are in parallelization of shard downloads and checking if shards are present.\n\n\ndef _check_if_shards_exist_locally(local_dir, subfolder, original_shard_filenames):\n shards_path = os.path.join(local_dir, subfolder)\n shard_filenames = [os.path.join(shards_path, f) for f in original_shard_filenames]\n for shard_file in shard_filenames:\n if not os.path.exists(shard_file):\n raise ValueError(\n f\"{shards_path} does not appear to have a file named {shard_file} which is \"\n \"required according to the checkpoint index.\"\n )\n\n\ndef _get_checkpoint_shard_files(\n pretrained_model_name_or_path,\n index_filename,\n cache_dir=None,\n proxies=None,\n resume_download=False,\n local_files_only=False,\n token=None,\n user_agent=None,\n revision=None,\n subfolder=\"\",\n):\n \"\"\"\n For a given model:\n\n - download and cache all the shards of a sharded checkpoint if `pretrained_model_name_or_path` is a model ID on the\n Hub\n - returns the list of paths to all the shards, as well as some metadata.\n\n For the description of each arg, see [`PreTrainedModel.from_pretrained`]. `index_filename` is the full path to the\n index (downloaded and cached if `pretrained_model_name_or_path` is a model ID on the Hub).\n \"\"\"\n if not os.path.isfile(index_filename):\n raise ValueError(f\"Can't find a checkpoint index ({index_filename}) in {pretrained_model_name_or_path}.\")\n\n with open(index_filename, \"r\") as f:\n index = json.loads(f.read())\n\n original_shard_filenames = sorted(set(index[\"weight_map\"].values()))\n sharded_metadata = index[\"metadata\"]\n sharded_metadata[\"all_checkpoint_keys\"] = list(index[\"weight_map\"].keys())\n sharded_metadata[\"weight_map\"] = index[\"weight_map\"].copy()\n shards_path = os.path.join(pretrained_model_name_or_path, subfolder)\n\n # First, let's deal with local folder.\n if os.path.isdir(pretrained_model_name_or_path):\n _check_if_shards_exist_locally(\n pretrained_model_name_or_path, subfolder=subfolder, original_shard_filenames=original_shard_filenames\n )\n return pretrained_model_name_or_path, sharded_metadata\n\n # At this stage pretrained_model_name_or_path is a model identifier on the Hub\n allow_patterns = original_shard_filenames\n ignore_patterns = [\"*.json\", \"*.md\"]\n if not local_files_only:\n # `model_info` call must guarded with the above condition.\n model_files_info = model_info(pretrained_model_name_or_path)\n for shard_file in original_shard_filenames:\n shard_file_present = any(shard_file in k.rfilename for k in model_files_info.siblings)\n if not shard_file_present:\n raise EnvironmentError(\n f\"{shards_path} does not appear to have a file named {shard_file} which is \"\n \"required according to the checkpoint index.\"\n )\n\n try:\n # Load from URL\n cached_folder = snapshot_download(\n pretrained_model_name_or_path,\n cache_dir=cache_dir,\n resume_download=resume_download,\n proxies=proxies,\n local_files_only=local_files_only,\n token=token,\n revision=revision,\n allow_patterns=allow_patterns,\n ignore_patterns=ignore_patterns,\n user_agent=user_agent,\n )\n\n # We have already dealt with RepositoryNotFoundError and RevisionNotFoundError when getting the index, so\n # we don't have to catch them here. We have also dealt with EntryNotFoundError.\n except HTTPError as e:\n raise EnvironmentError(\n f\"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load {pretrained_model_name_or_path}. You should try\"\n \" again after checking your internet connection.\"\n ) from e\n\n # If `local_files_only=True`, `cached_folder` may not contain all the shard files.\n if local_files_only:\n _check_if_shards_exist_locally(\n local_dir=cache_dir, subfolder=subfolder, original_shard_filenames=original_shard_filenames\n )\n\n return cached_folder, sharded_metadata\n\n\nclass PushToHubMixin:\n \"\"\"\n A Mixin to push a model, scheduler, or pipeline to the Hugging Face Hub.\n \"\"\"\n\n def _upload_folder(\n self,\n working_dir: Union[str, os.PathLike],\n repo_id: str,\n token: Optional[str] = None,\n commit_message: Optional[str] = None,\n create_pr: bool = False,\n ):\n \"\"\"\n Uploads all files in `working_dir` to `repo_id`.\n \"\"\"\n if commit_message is None:\n if \"Model\" in self.__class__.__name__:\n commit_message = \"Upload model\"\n elif \"Scheduler\" in self.__class__.__name__:\n commit_message = \"Upload scheduler\"\n else:\n commit_message = f\"Upload {self.__class__.__name__}\"\n\n logger.info(f\"Uploading the files of {working_dir} to {repo_id}.\")\n return upload_folder(\n repo_id=repo_id, folder_path=working_dir, token=token, commit_message=commit_message, create_pr=create_pr\n )\n\n def push_to_hub(\n self,\n repo_id: str,\n commit_message: Optional[str] = None,\n private: Optional[bool] = None,\n token: Optional[str] = None,\n create_pr: bool = False,\n safe_serialization: bool = True,\n variant: Optional[str] = None,\n ) -> str:\n \"\"\"\n Upload model, scheduler, or pipeline files to the 🤗 Hugging Face Hub.\n\n Parameters:\n repo_id (`str`):\n The name of the repository you want to push your model, scheduler, or pipeline files to. It should\n contain your organization name when pushing to an organization. `repo_id` can also be a path to a local\n directory.\n commit_message (`str`, *optional*):\n Message to commit while pushing. Default to `\"Upload {object}\"`.\n private (`bool`, *optional*):\n Whether or not the repository created should be private.\n token (`str`, *optional*):\n The token to use as HTTP bearer authorization for remote files. The token generated when running\n `huggingface-cli login` (stored in `~/.huggingface`).\n create_pr (`bool`, *optional*, defaults to `False`):\n Whether or not to create a PR with the uploaded files or directly commit.\n safe_serialization (`bool`, *optional*, defaults to `True`):\n Whether or not to convert the model weights to the `safetensors` format.\n variant (`str`, *optional*):\n If specified, weights are saved in the format `pytorch_model..bin`.\n\n Examples:\n\n ```python\n from diffusers import UNet2DConditionModel\n\n unet = UNet2DConditionModel.from_pretrained(\"stabilityai/stable-diffusion-2\", subfolder=\"unet\")\n\n # Push the `unet` to your namespace with the name \"my-finetuned-unet\".\n unet.push_to_hub(\"my-finetuned-unet\")\n\n # Push the `unet` to an organization with the name \"my-finetuned-unet\".\n unet.push_to_hub(\"your-org/my-finetuned-unet\")\n ```\n \"\"\"\n repo_id = create_repo(repo_id, private=private, token=token, exist_ok=True).repo_id\n\n # Create a new empty model card and eventually tag it\n model_card = load_or_create_model_card(repo_id, token=token)\n model_card = populate_model_card(model_card)\n\n # Save all files.\n save_kwargs = {\"safe_serialization\": safe_serialization}\n if \"Scheduler\" not in self.__class__.__name__:\n save_kwargs.update({\"variant\": variant})\n\n with tempfile.TemporaryDirectory() as tmpdir:\n self.save_pretrained(tmpdir, **save_kwargs)\n\n # Update model card if needed:\n model_card.save(os.path.join(tmpdir, \"README.md\"))\n\n return self._upload_folder(\n tmpdir,\n repo_id,\n token=token,\n commit_message=commit_message,\n create_pr=create_pr,\n )\n" }, { "path": "diffusers/src/diffusers/utils/import_utils.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nImport utilities: Utilities related to imports and our lazy inits.\n\"\"\"\n\nimport importlib.util\nimport operator as op\nimport os\nimport sys\nfrom collections import OrderedDict\nfrom itertools import chain\nfrom types import ModuleType\nfrom typing import Any, Union\n\nfrom huggingface_hub.utils import is_jinja_available # noqa: F401\nfrom packaging import version\nfrom packaging.version import Version, parse\n\nfrom . import logging\n\n\n# The package importlib_metadata is in a different place, depending on the python version.\nif sys.version_info < (3, 8):\n import importlib_metadata\nelse:\n import importlib.metadata as importlib_metadata\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\nENV_VARS_TRUE_VALUES = {\"1\", \"ON\", \"YES\", \"TRUE\"}\nENV_VARS_TRUE_AND_AUTO_VALUES = ENV_VARS_TRUE_VALUES.union({\"AUTO\"})\n\nUSE_TF = os.environ.get(\"USE_TF\", \"AUTO\").upper()\nUSE_TORCH = os.environ.get(\"USE_TORCH\", \"AUTO\").upper()\nUSE_JAX = os.environ.get(\"USE_FLAX\", \"AUTO\").upper()\nUSE_SAFETENSORS = os.environ.get(\"USE_SAFETENSORS\", \"AUTO\").upper()\nDIFFUSERS_SLOW_IMPORT = os.environ.get(\"DIFFUSERS_SLOW_IMPORT\", \"FALSE\").upper()\nDIFFUSERS_SLOW_IMPORT = DIFFUSERS_SLOW_IMPORT in ENV_VARS_TRUE_VALUES\n\nSTR_OPERATION_TO_FUNC = {\">\": op.gt, \">=\": op.ge, \"==\": op.eq, \"!=\": op.ne, \"<=\": op.le, \"<\": op.lt}\n\n_torch_version = \"N/A\"\nif USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:\n _torch_available = importlib.util.find_spec(\"torch\") is not None\n if _torch_available:\n try:\n _torch_version = importlib_metadata.version(\"torch\")\n logger.info(f\"PyTorch version {_torch_version} available.\")\n except importlib_metadata.PackageNotFoundError:\n _torch_available = False\nelse:\n logger.info(\"Disabling PyTorch because USE_TORCH is set\")\n _torch_available = False\n\n_torch_xla_available = importlib.util.find_spec(\"torch_xla\") is not None\nif _torch_xla_available:\n try:\n _torch_xla_version = importlib_metadata.version(\"torch_xla\")\n logger.info(f\"PyTorch XLA version {_torch_xla_version} available.\")\n except ImportError:\n _torch_xla_available = False\n\n# check whether torch_npu is available\n_torch_npu_available = importlib.util.find_spec(\"torch_npu\") is not None\nif _torch_npu_available:\n try:\n _torch_npu_version = importlib_metadata.version(\"torch_npu\")\n logger.info(f\"torch_npu version {_torch_npu_version} available.\")\n except ImportError:\n _torch_npu_available = False\n\n_jax_version = \"N/A\"\n_flax_version = \"N/A\"\nif USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:\n _flax_available = importlib.util.find_spec(\"jax\") is not None and importlib.util.find_spec(\"flax\") is not None\n if _flax_available:\n try:\n _jax_version = importlib_metadata.version(\"jax\")\n _flax_version = importlib_metadata.version(\"flax\")\n logger.info(f\"JAX version {_jax_version}, Flax version {_flax_version} available.\")\n except importlib_metadata.PackageNotFoundError:\n _flax_available = False\nelse:\n _flax_available = False\n\nif USE_SAFETENSORS in ENV_VARS_TRUE_AND_AUTO_VALUES:\n _safetensors_available = importlib.util.find_spec(\"safetensors\") is not None\n if _safetensors_available:\n try:\n _safetensors_version = importlib_metadata.version(\"safetensors\")\n logger.info(f\"Safetensors version {_safetensors_version} available.\")\n except importlib_metadata.PackageNotFoundError:\n _safetensors_available = False\nelse:\n logger.info(\"Disabling Safetensors because USE_TF is set\")\n _safetensors_available = False\n\n_transformers_available = importlib.util.find_spec(\"transformers\") is not None\ntry:\n _transformers_version = importlib_metadata.version(\"transformers\")\n logger.debug(f\"Successfully imported transformers version {_transformers_version}\")\nexcept importlib_metadata.PackageNotFoundError:\n _transformers_available = False\n\n\n_inflect_available = importlib.util.find_spec(\"inflect\") is not None\ntry:\n _inflect_version = importlib_metadata.version(\"inflect\")\n logger.debug(f\"Successfully imported inflect version {_inflect_version}\")\nexcept importlib_metadata.PackageNotFoundError:\n _inflect_available = False\n\n\n_unidecode_available = importlib.util.find_spec(\"unidecode\") is not None\ntry:\n _unidecode_version = importlib_metadata.version(\"unidecode\")\n logger.debug(f\"Successfully imported unidecode version {_unidecode_version}\")\nexcept importlib_metadata.PackageNotFoundError:\n _unidecode_available = False\n\n_onnxruntime_version = \"N/A\"\n_onnx_available = importlib.util.find_spec(\"onnxruntime\") is not None\nif _onnx_available:\n candidates = (\n \"onnxruntime\",\n \"onnxruntime-gpu\",\n \"ort_nightly_gpu\",\n \"onnxruntime-directml\",\n \"onnxruntime-openvino\",\n \"ort_nightly_directml\",\n \"onnxruntime-rocm\",\n \"onnxruntime-training\",\n )\n _onnxruntime_version = None\n # For the metadata, we have to look for both onnxruntime and onnxruntime-gpu\n for pkg in candidates:\n try:\n _onnxruntime_version = importlib_metadata.version(pkg)\n break\n except importlib_metadata.PackageNotFoundError:\n pass\n _onnx_available = _onnxruntime_version is not None\n if _onnx_available:\n logger.debug(f\"Successfully imported onnxruntime version {_onnxruntime_version}\")\n\n# (sayakpaul): importlib.util.find_spec(\"opencv-python\") returns None even when it's installed.\n# _opencv_available = importlib.util.find_spec(\"opencv-python\") is not None\ntry:\n candidates = (\n \"opencv-python\",\n \"opencv-contrib-python\",\n \"opencv-python-headless\",\n \"opencv-contrib-python-headless\",\n )\n _opencv_version = None\n for pkg in candidates:\n try:\n _opencv_version = importlib_metadata.version(pkg)\n break\n except importlib_metadata.PackageNotFoundError:\n pass\n _opencv_available = _opencv_version is not None\n if _opencv_available:\n logger.debug(f\"Successfully imported cv2 version {_opencv_version}\")\nexcept importlib_metadata.PackageNotFoundError:\n _opencv_available = False\n\n_scipy_available = importlib.util.find_spec(\"scipy\") is not None\ntry:\n _scipy_version = importlib_metadata.version(\"scipy\")\n logger.debug(f\"Successfully imported scipy version {_scipy_version}\")\nexcept importlib_metadata.PackageNotFoundError:\n _scipy_available = False\n\n_librosa_available = importlib.util.find_spec(\"librosa\") is not None\ntry:\n _librosa_version = importlib_metadata.version(\"librosa\")\n logger.debug(f\"Successfully imported librosa version {_librosa_version}\")\nexcept importlib_metadata.PackageNotFoundError:\n _librosa_available = False\n\n_accelerate_available = importlib.util.find_spec(\"accelerate\") is not None\ntry:\n _accelerate_version = importlib_metadata.version(\"accelerate\")\n logger.debug(f\"Successfully imported accelerate version {_accelerate_version}\")\nexcept importlib_metadata.PackageNotFoundError:\n _accelerate_available = False\n\n_xformers_available = importlib.util.find_spec(\"xformers\") is not None\ntry:\n _xformers_version = importlib_metadata.version(\"xformers\")\n if _torch_available:\n _torch_version = importlib_metadata.version(\"torch\")\n if version.Version(_torch_version) < version.Version(\"1.12\"):\n raise ValueError(\"xformers is installed in your environment and requires PyTorch >= 1.12\")\n\n logger.debug(f\"Successfully imported xformers version {_xformers_version}\")\nexcept importlib_metadata.PackageNotFoundError:\n _xformers_available = False\n\n_k_diffusion_available = importlib.util.find_spec(\"k_diffusion\") is not None\ntry:\n _k_diffusion_version = importlib_metadata.version(\"k_diffusion\")\n logger.debug(f\"Successfully imported k-diffusion version {_k_diffusion_version}\")\nexcept importlib_metadata.PackageNotFoundError:\n _k_diffusion_available = False\n\n_note_seq_available = importlib.util.find_spec(\"note_seq\") is not None\ntry:\n _note_seq_version = importlib_metadata.version(\"note_seq\")\n logger.debug(f\"Successfully imported note-seq version {_note_seq_version}\")\nexcept importlib_metadata.PackageNotFoundError:\n _note_seq_available = False\n\n_wandb_available = importlib.util.find_spec(\"wandb\") is not None\ntry:\n _wandb_version = importlib_metadata.version(\"wandb\")\n logger.debug(f\"Successfully imported wandb version {_wandb_version }\")\nexcept importlib_metadata.PackageNotFoundError:\n _wandb_available = False\n\n\n_tensorboard_available = importlib.util.find_spec(\"tensorboard\")\ntry:\n _tensorboard_version = importlib_metadata.version(\"tensorboard\")\n logger.debug(f\"Successfully imported tensorboard version {_tensorboard_version}\")\nexcept importlib_metadata.PackageNotFoundError:\n _tensorboard_available = False\n\n\n_compel_available = importlib.util.find_spec(\"compel\")\ntry:\n _compel_version = importlib_metadata.version(\"compel\")\n logger.debug(f\"Successfully imported compel version {_compel_version}\")\nexcept importlib_metadata.PackageNotFoundError:\n _compel_available = False\n\n\n_ftfy_available = importlib.util.find_spec(\"ftfy\") is not None\ntry:\n _ftfy_version = importlib_metadata.version(\"ftfy\")\n logger.debug(f\"Successfully imported ftfy version {_ftfy_version}\")\nexcept importlib_metadata.PackageNotFoundError:\n _ftfy_available = False\n\n\n_bs4_available = importlib.util.find_spec(\"bs4\") is not None\ntry:\n # importlib metadata under different name\n _bs4_version = importlib_metadata.version(\"beautifulsoup4\")\n logger.debug(f\"Successfully imported ftfy version {_bs4_version}\")\nexcept importlib_metadata.PackageNotFoundError:\n _bs4_available = False\n\n_torchsde_available = importlib.util.find_spec(\"torchsde\") is not None\ntry:\n _torchsde_version = importlib_metadata.version(\"torchsde\")\n logger.debug(f\"Successfully imported torchsde version {_torchsde_version}\")\nexcept importlib_metadata.PackageNotFoundError:\n _torchsde_available = False\n\n_invisible_watermark_available = importlib.util.find_spec(\"imwatermark\") is not None\ntry:\n _invisible_watermark_version = importlib_metadata.version(\"invisible-watermark\")\n logger.debug(f\"Successfully imported invisible-watermark version {_invisible_watermark_version}\")\nexcept importlib_metadata.PackageNotFoundError:\n _invisible_watermark_available = False\n\n\n_peft_available = importlib.util.find_spec(\"peft\") is not None\ntry:\n _peft_version = importlib_metadata.version(\"peft\")\n logger.debug(f\"Successfully imported peft version {_peft_version}\")\nexcept importlib_metadata.PackageNotFoundError:\n _peft_available = False\n\n_torchvision_available = importlib.util.find_spec(\"torchvision\") is not None\ntry:\n _torchvision_version = importlib_metadata.version(\"torchvision\")\n logger.debug(f\"Successfully imported torchvision version {_torchvision_version}\")\nexcept importlib_metadata.PackageNotFoundError:\n _torchvision_available = False\n\n_matplotlib_available = importlib.util.find_spec(\"matplotlib\") is not None\ntry:\n _matplotlib_version = importlib_metadata.version(\"matplotlib\")\n logger.debug(f\"Successfully imported matplotlib version {_matplotlib_version}\")\nexcept importlib_metadata.PackageNotFoundError:\n _matplotlib_available = False\n\n_timm_available = importlib.util.find_spec(\"timm\") is not None\nif _timm_available:\n try:\n _timm_version = importlib_metadata.version(\"timm\")\n logger.info(f\"Timm version {_timm_version} available.\")\n except importlib_metadata.PackageNotFoundError:\n _timm_available = False\n\n\ndef is_timm_available():\n return _timm_available\n\n\n_bitsandbytes_available = importlib.util.find_spec(\"bitsandbytes\") is not None\ntry:\n _bitsandbytes_version = importlib_metadata.version(\"bitsandbytes\")\n logger.debug(f\"Successfully imported bitsandbytes version {_bitsandbytes_version}\")\nexcept importlib_metadata.PackageNotFoundError:\n _bitsandbytes_available = False\n\n# Taken from `huggingface_hub`.\n_is_notebook = False\ntry:\n shell_class = get_ipython().__class__ # type: ignore # noqa: F821\n for parent_class in shell_class.__mro__: # e.g. \"is subclass of\"\n if parent_class.__name__ == \"ZMQInteractiveShell\":\n _is_notebook = True # Jupyter notebook, Google colab or qtconsole\n break\nexcept NameError:\n pass # Probably standard Python interpreter\n\n_is_google_colab = \"google.colab\" in sys.modules\n\n\ndef is_torch_available():\n return _torch_available\n\n\ndef is_torch_xla_available():\n return _torch_xla_available\n\n\ndef is_torch_npu_available():\n return _torch_npu_available\n\n\ndef is_flax_available():\n return _flax_available\n\n\ndef is_transformers_available():\n return _transformers_available\n\n\ndef is_inflect_available():\n return _inflect_available\n\n\ndef is_unidecode_available():\n return _unidecode_available\n\n\ndef is_onnx_available():\n return _onnx_available\n\n\ndef is_opencv_available():\n return _opencv_available\n\n\ndef is_scipy_available():\n return _scipy_available\n\n\ndef is_librosa_available():\n return _librosa_available\n\n\ndef is_xformers_available():\n return _xformers_available\n\n\ndef is_accelerate_available():\n return _accelerate_available\n\n\ndef is_k_diffusion_available():\n return _k_diffusion_available\n\n\ndef is_note_seq_available():\n return _note_seq_available\n\n\ndef is_wandb_available():\n return _wandb_available\n\n\ndef is_tensorboard_available():\n return _tensorboard_available\n\n\ndef is_compel_available():\n return _compel_available\n\n\ndef is_ftfy_available():\n return _ftfy_available\n\n\ndef is_bs4_available():\n return _bs4_available\n\n\ndef is_torchsde_available():\n return _torchsde_available\n\n\ndef is_invisible_watermark_available():\n return _invisible_watermark_available\n\n\ndef is_peft_available():\n return _peft_available\n\n\ndef is_torchvision_available():\n return _torchvision_available\n\n\ndef is_matplotlib_available():\n return _matplotlib_available\n\n\ndef is_safetensors_available():\n return _safetensors_available\n\n\ndef is_bitsandbytes_available():\n return _bitsandbytes_available\n\n\ndef is_notebook():\n return _is_notebook\n\n\ndef is_google_colab():\n return _is_google_colab\n\n\n# docstyle-ignore\nFLAX_IMPORT_ERROR = \"\"\"\n{0} requires the FLAX library but it was not found in your environment. Checkout the instructions on the\ninstallation page: https://github.com/google/flax and follow the ones that match your environment.\n\"\"\"\n\n# docstyle-ignore\nINFLECT_IMPORT_ERROR = \"\"\"\n{0} requires the inflect library but it was not found in your environment. You can install it with pip: `pip install\ninflect`\n\"\"\"\n\n# docstyle-ignore\nPYTORCH_IMPORT_ERROR = \"\"\"\n{0} requires the PyTorch library but it was not found in your environment. Checkout the instructions on the\ninstallation page: https://pytorch.org/get-started/locally/ and follow the ones that match your environment.\n\"\"\"\n\n# docstyle-ignore\nONNX_IMPORT_ERROR = \"\"\"\n{0} requires the onnxruntime library but it was not found in your environment. You can install it with pip: `pip\ninstall onnxruntime`\n\"\"\"\n\n# docstyle-ignore\nOPENCV_IMPORT_ERROR = \"\"\"\n{0} requires the OpenCV library but it was not found in your environment. You can install it with pip: `pip\ninstall opencv-python`\n\"\"\"\n\n# docstyle-ignore\nSCIPY_IMPORT_ERROR = \"\"\"\n{0} requires the scipy library but it was not found in your environment. You can install it with pip: `pip install\nscipy`\n\"\"\"\n\n# docstyle-ignore\nLIBROSA_IMPORT_ERROR = \"\"\"\n{0} requires the librosa library but it was not found in your environment. Checkout the instructions on the\ninstallation page: https://librosa.org/doc/latest/install.html and follow the ones that match your environment.\n\"\"\"\n\n# docstyle-ignore\nTRANSFORMERS_IMPORT_ERROR = \"\"\"\n{0} requires the transformers library but it was not found in your environment. You can install it with pip: `pip\ninstall transformers`\n\"\"\"\n\n# docstyle-ignore\nUNIDECODE_IMPORT_ERROR = \"\"\"\n{0} requires the unidecode library but it was not found in your environment. You can install it with pip: `pip install\nUnidecode`\n\"\"\"\n\n# docstyle-ignore\nK_DIFFUSION_IMPORT_ERROR = \"\"\"\n{0} requires the k-diffusion library but it was not found in your environment. You can install it with pip: `pip\ninstall k-diffusion`\n\"\"\"\n\n# docstyle-ignore\nNOTE_SEQ_IMPORT_ERROR = \"\"\"\n{0} requires the note-seq library but it was not found in your environment. You can install it with pip: `pip\ninstall note-seq`\n\"\"\"\n\n# docstyle-ignore\nWANDB_IMPORT_ERROR = \"\"\"\n{0} requires the wandb library but it was not found in your environment. You can install it with pip: `pip\ninstall wandb`\n\"\"\"\n\n# docstyle-ignore\nTENSORBOARD_IMPORT_ERROR = \"\"\"\n{0} requires the tensorboard library but it was not found in your environment. You can install it with pip: `pip\ninstall tensorboard`\n\"\"\"\n\n\n# docstyle-ignore\nCOMPEL_IMPORT_ERROR = \"\"\"\n{0} requires the compel library but it was not found in your environment. You can install it with pip: `pip install compel`\n\"\"\"\n\n# docstyle-ignore\nBS4_IMPORT_ERROR = \"\"\"\n{0} requires the Beautiful Soup library but it was not found in your environment. You can install it with pip:\n`pip install beautifulsoup4`. Please note that you may need to restart your runtime after installation.\n\"\"\"\n\n# docstyle-ignore\nFTFY_IMPORT_ERROR = \"\"\"\n{0} requires the ftfy library but it was not found in your environment. Checkout the instructions on the\ninstallation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones\nthat match your environment. Please note that you may need to restart your runtime after installation.\n\"\"\"\n\n# docstyle-ignore\nTORCHSDE_IMPORT_ERROR = \"\"\"\n{0} requires the torchsde library but it was not found in your environment. You can install it with pip: `pip install torchsde`\n\"\"\"\n\n# docstyle-ignore\nINVISIBLE_WATERMARK_IMPORT_ERROR = \"\"\"\n{0} requires the invisible-watermark library but it was not found in your environment. You can install it with pip: `pip install invisible-watermark>=0.2.0`\n\"\"\"\n\n# docstyle-ignore\nPEFT_IMPORT_ERROR = \"\"\"\n{0} requires the peft library but it was not found in your environment. You can install it with pip: `pip install peft`\n\"\"\"\n\n# docstyle-ignore\nSAFETENSORS_IMPORT_ERROR = \"\"\"\n{0} requires the safetensors library but it was not found in your environment. You can install it with pip: `pip install safetensors`\n\"\"\"\n\n# docstyle-ignore\nBITSANDBYTES_IMPORT_ERROR = \"\"\"\n{0} requires the bitsandbytes library but it was not found in your environment. You can install it with pip: `pip install bitsandbytes`\n\"\"\"\n\nBACKENDS_MAPPING = OrderedDict(\n [\n (\"bs4\", (is_bs4_available, BS4_IMPORT_ERROR)),\n (\"flax\", (is_flax_available, FLAX_IMPORT_ERROR)),\n (\"inflect\", (is_inflect_available, INFLECT_IMPORT_ERROR)),\n (\"onnx\", (is_onnx_available, ONNX_IMPORT_ERROR)),\n (\"opencv\", (is_opencv_available, OPENCV_IMPORT_ERROR)),\n (\"scipy\", (is_scipy_available, SCIPY_IMPORT_ERROR)),\n (\"torch\", (is_torch_available, PYTORCH_IMPORT_ERROR)),\n (\"transformers\", (is_transformers_available, TRANSFORMERS_IMPORT_ERROR)),\n (\"unidecode\", (is_unidecode_available, UNIDECODE_IMPORT_ERROR)),\n (\"librosa\", (is_librosa_available, LIBROSA_IMPORT_ERROR)),\n (\"k_diffusion\", (is_k_diffusion_available, K_DIFFUSION_IMPORT_ERROR)),\n (\"note_seq\", (is_note_seq_available, NOTE_SEQ_IMPORT_ERROR)),\n (\"wandb\", (is_wandb_available, WANDB_IMPORT_ERROR)),\n (\"tensorboard\", (is_tensorboard_available, TENSORBOARD_IMPORT_ERROR)),\n (\"compel\", (is_compel_available, COMPEL_IMPORT_ERROR)),\n (\"ftfy\", (is_ftfy_available, FTFY_IMPORT_ERROR)),\n (\"torchsde\", (is_torchsde_available, TORCHSDE_IMPORT_ERROR)),\n (\"invisible_watermark\", (is_invisible_watermark_available, INVISIBLE_WATERMARK_IMPORT_ERROR)),\n (\"peft\", (is_peft_available, PEFT_IMPORT_ERROR)),\n (\"safetensors\", (is_safetensors_available, SAFETENSORS_IMPORT_ERROR)),\n (\"bitsandbytes\", (is_bitsandbytes_available, BITSANDBYTES_IMPORT_ERROR)),\n ]\n)\n\n\ndef requires_backends(obj, backends):\n if not isinstance(backends, (list, tuple)):\n backends = [backends]\n\n name = obj.__name__ if hasattr(obj, \"__name__\") else obj.__class__.__name__\n checks = (BACKENDS_MAPPING[backend] for backend in backends)\n failed = [msg.format(name) for available, msg in checks if not available()]\n if failed:\n raise ImportError(\"\".join(failed))\n\n if name in [\n \"VersatileDiffusionTextToImagePipeline\",\n \"VersatileDiffusionPipeline\",\n \"VersatileDiffusionDualGuidedPipeline\",\n \"StableDiffusionImageVariationPipeline\",\n \"UnCLIPPipeline\",\n ] and is_transformers_version(\"<\", \"4.25.0\"):\n raise ImportError(\n f\"You need to install `transformers>=4.25` in order to use {name}: \\n```\\n pip install\"\n \" --upgrade transformers \\n```\"\n )\n\n if name in [\"StableDiffusionDepth2ImgPipeline\", \"StableDiffusionPix2PixZeroPipeline\"] and is_transformers_version(\n \"<\", \"4.26.0\"\n ):\n raise ImportError(\n f\"You need to install `transformers>=4.26` in order to use {name}: \\n```\\n pip install\"\n \" --upgrade transformers \\n```\"\n )\n\n\nclass DummyObject(type):\n \"\"\"\n Metaclass for the dummy objects. Any class inheriting from it will return the ImportError generated by\n `requires_backend` each time a user tries to access any method of that class.\n \"\"\"\n\n def __getattr__(cls, key):\n if key.startswith(\"_\") and key not in [\"_load_connected_pipes\", \"_is_onnx\"]:\n return super().__getattr__(cls, key)\n requires_backends(cls, cls._backends)\n\n\n# This function was copied from: https://github.com/huggingface/accelerate/blob/874c4967d94badd24f893064cc3bef45f57cadf7/src/accelerate/utils/versions.py#L319\ndef compare_versions(library_or_version: Union[str, Version], operation: str, requirement_version: str):\n \"\"\"\n Args:\n Compares a library version to some requirement using a given operation.\n library_or_version (`str` or `packaging.version.Version`):\n A library name or a version to check.\n operation (`str`):\n A string representation of an operator, such as `\">\"` or `\"<=\"`.\n requirement_version (`str`):\n The version to compare the library version against\n \"\"\"\n if operation not in STR_OPERATION_TO_FUNC.keys():\n raise ValueError(f\"`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys())}, received {operation}\")\n operation = STR_OPERATION_TO_FUNC[operation]\n if isinstance(library_or_version, str):\n library_or_version = parse(importlib_metadata.version(library_or_version))\n return operation(library_or_version, parse(requirement_version))\n\n\n# This function was copied from: https://github.com/huggingface/accelerate/blob/874c4967d94badd24f893064cc3bef45f57cadf7/src/accelerate/utils/versions.py#L338\ndef is_torch_version(operation: str, version: str):\n \"\"\"\n Args:\n Compares the current PyTorch version to a given reference with an operation.\n operation (`str`):\n A string representation of an operator, such as `\">\"` or `\"<=\"`\n version (`str`):\n A string version of PyTorch\n \"\"\"\n return compare_versions(parse(_torch_version), operation, version)\n\n\ndef is_transformers_version(operation: str, version: str):\n \"\"\"\n Args:\n Compares the current Transformers version to a given reference with an operation.\n operation (`str`):\n A string representation of an operator, such as `\">\"` or `\"<=\"`\n version (`str`):\n A version string\n \"\"\"\n if not _transformers_available:\n return False\n return compare_versions(parse(_transformers_version), operation, version)\n\n\ndef is_accelerate_version(operation: str, version: str):\n \"\"\"\n Args:\n Compares the current Accelerate version to a given reference with an operation.\n operation (`str`):\n A string representation of an operator, such as `\">\"` or `\"<=\"`\n version (`str`):\n A version string\n \"\"\"\n if not _accelerate_available:\n return False\n return compare_versions(parse(_accelerate_version), operation, version)\n\n\ndef is_peft_version(operation: str, version: str):\n \"\"\"\n Args:\n Compares the current PEFT version to a given reference with an operation.\n operation (`str`):\n A string representation of an operator, such as `\">\"` or `\"<=\"`\n version (`str`):\n A version string\n \"\"\"\n if not _peft_version:\n return False\n return compare_versions(parse(_peft_version), operation, version)\n\n\ndef is_k_diffusion_version(operation: str, version: str):\n \"\"\"\n Args:\n Compares the current k-diffusion version to a given reference with an operation.\n operation (`str`):\n A string representation of an operator, such as `\">\"` or `\"<=\"`\n version (`str`):\n A version string\n \"\"\"\n if not _k_diffusion_available:\n return False\n return compare_versions(parse(_k_diffusion_version), operation, version)\n\n\ndef get_objects_from_module(module):\n \"\"\"\n Args:\n Returns a dict of object names and values in a module, while skipping private/internal objects\n module (ModuleType):\n Module to extract the objects from.\n\n Returns:\n dict: Dictionary of object names and corresponding values\n \"\"\"\n\n objects = {}\n for name in dir(module):\n if name.startswith(\"_\"):\n continue\n objects[name] = getattr(module, name)\n\n return objects\n\n\nclass OptionalDependencyNotAvailable(BaseException):\n \"\"\"An error indicating that an optional dependency of Diffusers was not found in the environment.\"\"\"\n\n\nclass _LazyModule(ModuleType):\n \"\"\"\n Module class that surfaces all objects but only performs associated imports when the objects are requested.\n \"\"\"\n\n # Very heavily inspired by optuna.integration._IntegrationModule\n # https://github.com/optuna/optuna/blob/master/optuna/integration/__init__.py\n def __init__(self, name, module_file, import_structure, module_spec=None, extra_objects=None):\n super().__init__(name)\n self._modules = set(import_structure.keys())\n self._class_to_module = {}\n for key, values in import_structure.items():\n for value in values:\n self._class_to_module[value] = key\n # Needed for autocompletion in an IDE\n self.__all__ = list(import_structure.keys()) + list(chain(*import_structure.values()))\n self.__file__ = module_file\n self.__spec__ = module_spec\n self.__path__ = [os.path.dirname(module_file)]\n self._objects = {} if extra_objects is None else extra_objects\n self._name = name\n self._import_structure = import_structure\n\n # Needed for autocompletion in an IDE\n def __dir__(self):\n result = super().__dir__()\n # The elements of self.__all__ that are submodules may or may not be in the dir already, depending on whether\n # they have been accessed or not. So we only add the elements of self.__all__ that are not already in the dir.\n for attr in self.__all__:\n if attr not in result:\n result.append(attr)\n return result\n\n def __getattr__(self, name: str) -> Any:\n if name in self._objects:\n return self._objects[name]\n if name in self._modules:\n value = self._get_module(name)\n elif name in self._class_to_module.keys():\n module = self._get_module(self._class_to_module[name])\n value = getattr(module, name)\n else:\n raise AttributeError(f\"module {self.__name__} has no attribute {name}\")\n\n setattr(self, name, value)\n return value\n\n def _get_module(self, module_name: str):\n try:\n return importlib.import_module(\".\" + module_name, self.__name__)\n except Exception as e:\n raise RuntimeError(\n f\"Failed to import {self.__name__}.{module_name} because of the following error (look up to see its\"\n f\" traceback):\\n{e}\"\n ) from e\n\n def __reduce__(self):\n return (self.__class__, (self._name, self.__file__, self._import_structure))\n" }, { "path": "diffusers/src/diffusers/utils/loading_utils.py", "content": "import os\nfrom typing import Callable, Union\n\nimport PIL.Image\nimport PIL.ImageOps\nimport requests\n\n\ndef load_image(\n image: Union[str, PIL.Image.Image], convert_method: Callable[[PIL.Image.Image], PIL.Image.Image] = None\n) -> PIL.Image.Image:\n \"\"\"\n Loads `image` to a PIL Image.\n\n Args:\n image (`str` or `PIL.Image.Image`):\n The image to convert to the PIL Image format.\n convert_method (Callable[[PIL.Image.Image], PIL.Image.Image], optional):\n A conversion method to apply to the image after loading it. When set to `None` the image will be converted\n \"RGB\".\n\n Returns:\n `PIL.Image.Image`:\n A PIL Image.\n \"\"\"\n if isinstance(image, str):\n if image.startswith(\"http://\") or image.startswith(\"https://\"):\n image = PIL.Image.open(requests.get(image, stream=True).raw)\n elif os.path.isfile(image):\n image = PIL.Image.open(image)\n else:\n raise ValueError(\n f\"Incorrect path or URL. URLs must start with `http://` or `https://`, and {image} is not a valid path.\"\n )\n elif isinstance(image, PIL.Image.Image):\n image = image\n else:\n raise ValueError(\n \"Incorrect format used for the image. Should be a URL linking to an image, a local path, or a PIL image.\"\n )\n\n image = PIL.ImageOps.exif_transpose(image)\n\n if convert_method is not None:\n image = convert_method(image)\n else:\n image = image.convert(\"RGB\")\n\n return image\n" }, { "path": "diffusers/src/diffusers/utils/logging.py", "content": "# coding=utf-8\n# Copyright 2024 Optuna, Hugging Face\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Logging utilities.\"\"\"\n\nimport logging\nimport os\nimport sys\nimport threading\nfrom logging import (\n CRITICAL, # NOQA\n DEBUG, # NOQA\n ERROR, # NOQA\n FATAL, # NOQA\n INFO, # NOQA\n NOTSET, # NOQA\n WARN, # NOQA\n WARNING, # NOQA\n)\nfrom typing import Dict, Optional\n\nfrom tqdm import auto as tqdm_lib\n\n\n_lock = threading.Lock()\n_default_handler: Optional[logging.Handler] = None\n\nlog_levels = {\n \"debug\": logging.DEBUG,\n \"info\": logging.INFO,\n \"warning\": logging.WARNING,\n \"error\": logging.ERROR,\n \"critical\": logging.CRITICAL,\n}\n\n_default_log_level = logging.WARNING\n\n_tqdm_active = True\n\n\ndef _get_default_logging_level() -> int:\n \"\"\"\n If DIFFUSERS_VERBOSITY env var is set to one of the valid choices return that as the new default level. If it is\n not - fall back to `_default_log_level`\n \"\"\"\n env_level_str = os.getenv(\"DIFFUSERS_VERBOSITY\", None)\n if env_level_str:\n if env_level_str in log_levels:\n return log_levels[env_level_str]\n else:\n logging.getLogger().warning(\n f\"Unknown option DIFFUSERS_VERBOSITY={env_level_str}, \"\n f\"has to be one of: { ', '.join(log_levels.keys()) }\"\n )\n return _default_log_level\n\n\ndef _get_library_name() -> str:\n return __name__.split(\".\")[0]\n\n\ndef _get_library_root_logger() -> logging.Logger:\n return logging.getLogger(_get_library_name())\n\n\ndef _configure_library_root_logger() -> None:\n global _default_handler\n\n with _lock:\n if _default_handler:\n # This library has already configured the library root logger.\n return\n _default_handler = logging.StreamHandler() # Set sys.stderr as stream.\n\n if sys.stderr: # only if sys.stderr exists, e.g. when not using pythonw in windows\n _default_handler.flush = sys.stderr.flush\n\n # Apply our default configuration to the library root logger.\n library_root_logger = _get_library_root_logger()\n library_root_logger.addHandler(_default_handler)\n library_root_logger.setLevel(_get_default_logging_level())\n library_root_logger.propagate = False\n\n\ndef _reset_library_root_logger() -> None:\n global _default_handler\n\n with _lock:\n if not _default_handler:\n return\n\n library_root_logger = _get_library_root_logger()\n library_root_logger.removeHandler(_default_handler)\n library_root_logger.setLevel(logging.NOTSET)\n _default_handler = None\n\n\ndef get_log_levels_dict() -> Dict[str, int]:\n return log_levels\n\n\ndef get_logger(name: Optional[str] = None) -> logging.Logger:\n \"\"\"\n Return a logger with the specified name.\n\n This function is not supposed to be directly accessed unless you are writing a custom diffusers module.\n \"\"\"\n\n if name is None:\n name = _get_library_name()\n\n _configure_library_root_logger()\n return logging.getLogger(name)\n\n\ndef get_verbosity() -> int:\n \"\"\"\n Return the current level for the 🤗 Diffusers' root logger as an `int`.\n\n Returns:\n `int`:\n Logging level integers which can be one of:\n\n - `50`: `diffusers.logging.CRITICAL` or `diffusers.logging.FATAL`\n - `40`: `diffusers.logging.ERROR`\n - `30`: `diffusers.logging.WARNING` or `diffusers.logging.WARN`\n - `20`: `diffusers.logging.INFO`\n - `10`: `diffusers.logging.DEBUG`\n\n \"\"\"\n\n _configure_library_root_logger()\n return _get_library_root_logger().getEffectiveLevel()\n\n\ndef set_verbosity(verbosity: int) -> None:\n \"\"\"\n Set the verbosity level for the 🤗 Diffusers' root logger.\n\n Args:\n verbosity (`int`):\n Logging level which can be one of:\n\n - `diffusers.logging.CRITICAL` or `diffusers.logging.FATAL`\n - `diffusers.logging.ERROR`\n - `diffusers.logging.WARNING` or `diffusers.logging.WARN`\n - `diffusers.logging.INFO`\n - `diffusers.logging.DEBUG`\n \"\"\"\n\n _configure_library_root_logger()\n _get_library_root_logger().setLevel(verbosity)\n\n\ndef set_verbosity_info() -> None:\n \"\"\"Set the verbosity to the `INFO` level.\"\"\"\n return set_verbosity(INFO)\n\n\ndef set_verbosity_warning() -> None:\n \"\"\"Set the verbosity to the `WARNING` level.\"\"\"\n return set_verbosity(WARNING)\n\n\ndef set_verbosity_debug() -> None:\n \"\"\"Set the verbosity to the `DEBUG` level.\"\"\"\n return set_verbosity(DEBUG)\n\n\ndef set_verbosity_error() -> None:\n \"\"\"Set the verbosity to the `ERROR` level.\"\"\"\n return set_verbosity(ERROR)\n\n\ndef disable_default_handler() -> None:\n \"\"\"Disable the default handler of the 🤗 Diffusers' root logger.\"\"\"\n\n _configure_library_root_logger()\n\n assert _default_handler is not None\n _get_library_root_logger().removeHandler(_default_handler)\n\n\ndef enable_default_handler() -> None:\n \"\"\"Enable the default handler of the 🤗 Diffusers' root logger.\"\"\"\n\n _configure_library_root_logger()\n\n assert _default_handler is not None\n _get_library_root_logger().addHandler(_default_handler)\n\n\ndef add_handler(handler: logging.Handler) -> None:\n \"\"\"adds a handler to the HuggingFace Diffusers' root logger.\"\"\"\n\n _configure_library_root_logger()\n\n assert handler is not None\n _get_library_root_logger().addHandler(handler)\n\n\ndef remove_handler(handler: logging.Handler) -> None:\n \"\"\"removes given handler from the HuggingFace Diffusers' root logger.\"\"\"\n\n _configure_library_root_logger()\n\n assert handler is not None and handler in _get_library_root_logger().handlers\n _get_library_root_logger().removeHandler(handler)\n\n\ndef disable_propagation() -> None:\n \"\"\"\n Disable propagation of the library log outputs. Note that log propagation is disabled by default.\n \"\"\"\n\n _configure_library_root_logger()\n _get_library_root_logger().propagate = False\n\n\ndef enable_propagation() -> None:\n \"\"\"\n Enable propagation of the library log outputs. Please disable the HuggingFace Diffusers' default handler to prevent\n double logging if the root logger has been configured.\n \"\"\"\n\n _configure_library_root_logger()\n _get_library_root_logger().propagate = True\n\n\ndef enable_explicit_format() -> None:\n \"\"\"\n Enable explicit formatting for every 🤗 Diffusers' logger. The explicit formatter is as follows:\n ```\n [LEVELNAME|FILENAME|LINE NUMBER] TIME >> MESSAGE\n ```\n All handlers currently bound to the root logger are affected by this method.\n \"\"\"\n handlers = _get_library_root_logger().handlers\n\n for handler in handlers:\n formatter = logging.Formatter(\"[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s\")\n handler.setFormatter(formatter)\n\n\ndef reset_format() -> None:\n \"\"\"\n Resets the formatting for 🤗 Diffusers' loggers.\n\n All handlers currently bound to the root logger are affected by this method.\n \"\"\"\n handlers = _get_library_root_logger().handlers\n\n for handler in handlers:\n handler.setFormatter(None)\n\n\ndef warning_advice(self, *args, **kwargs) -> None:\n \"\"\"\n This method is identical to `logger.warning()`, but if env var DIFFUSERS_NO_ADVISORY_WARNINGS=1 is set, this\n warning will not be printed\n \"\"\"\n no_advisory_warnings = os.getenv(\"DIFFUSERS_NO_ADVISORY_WARNINGS\", False)\n if no_advisory_warnings:\n return\n self.warning(*args, **kwargs)\n\n\nlogging.Logger.warning_advice = warning_advice\n\n\nclass EmptyTqdm:\n \"\"\"Dummy tqdm which doesn't do anything.\"\"\"\n\n def __init__(self, *args, **kwargs): # pylint: disable=unused-argument\n self._iterator = args[0] if args else None\n\n def __iter__(self):\n return iter(self._iterator)\n\n def __getattr__(self, _):\n \"\"\"Return empty function.\"\"\"\n\n def empty_fn(*args, **kwargs): # pylint: disable=unused-argument\n return\n\n return empty_fn\n\n def __enter__(self):\n return self\n\n def __exit__(self, type_, value, traceback):\n return\n\n\nclass _tqdm_cls:\n def __call__(self, *args, **kwargs):\n if _tqdm_active:\n return tqdm_lib.tqdm(*args, **kwargs)\n else:\n return EmptyTqdm(*args, **kwargs)\n\n def set_lock(self, *args, **kwargs):\n self._lock = None\n if _tqdm_active:\n return tqdm_lib.tqdm.set_lock(*args, **kwargs)\n\n def get_lock(self):\n if _tqdm_active:\n return tqdm_lib.tqdm.get_lock()\n\n\ntqdm = _tqdm_cls()\n\n\ndef is_progress_bar_enabled() -> bool:\n \"\"\"Return a boolean indicating whether tqdm progress bars are enabled.\"\"\"\n global _tqdm_active\n return bool(_tqdm_active)\n\n\ndef enable_progress_bar() -> None:\n \"\"\"Enable tqdm progress bar.\"\"\"\n global _tqdm_active\n _tqdm_active = True\n\n\ndef disable_progress_bar() -> None:\n \"\"\"Disable tqdm progress bar.\"\"\"\n global _tqdm_active\n _tqdm_active = False\n" }, { "path": "diffusers/src/diffusers/utils/model_card_template.md", "content": "---\n{{ card_data }}\n---\n\n\n\n{{ model_description }}\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\n# TODO: add an example code snippet for running this diffusion pipeline\n```\n\n#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]\n\n## Training details\n\n[TODO: describe the data used to train the model]\n" }, { "path": "diffusers/src/diffusers/utils/outputs.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nGeneric utilities\n\"\"\"\n\nfrom collections import OrderedDict\nfrom dataclasses import fields, is_dataclass\nfrom typing import Any, Tuple\n\nimport numpy as np\n\nfrom .import_utils import is_torch_available, is_torch_version\n\n\ndef is_tensor(x) -> bool:\n \"\"\"\n Tests if `x` is a `torch.Tensor` or `np.ndarray`.\n \"\"\"\n if is_torch_available():\n import torch\n\n if isinstance(x, torch.Tensor):\n return True\n\n return isinstance(x, np.ndarray)\n\n\nclass BaseOutput(OrderedDict):\n \"\"\"\n Base class for all model outputs as dataclass. Has a `__getitem__` that allows indexing by integer or slice (like a\n tuple) or strings (like a dictionary) that will ignore the `None` attributes. Otherwise behaves like a regular\n Python dictionary.\n\n \n\n You can't unpack a [`BaseOutput`] directly. Use the [`~utils.BaseOutput.to_tuple`] method to convert it to a tuple\n first.\n\n \n \"\"\"\n\n def __init_subclass__(cls) -> None:\n \"\"\"Register subclasses as pytree nodes.\n\n This is necessary to synchronize gradients when using `torch.nn.parallel.DistributedDataParallel` with\n `static_graph=True` with modules that output `ModelOutput` subclasses.\n \"\"\"\n if is_torch_available():\n import torch.utils._pytree\n\n if is_torch_version(\"<\", \"2.2\"):\n torch.utils._pytree._register_pytree_node(\n cls,\n torch.utils._pytree._dict_flatten,\n lambda values, context: cls(**torch.utils._pytree._dict_unflatten(values, context)),\n )\n else:\n torch.utils._pytree.register_pytree_node(\n cls,\n torch.utils._pytree._dict_flatten,\n lambda values, context: cls(**torch.utils._pytree._dict_unflatten(values, context)),\n )\n\n def __post_init__(self) -> None:\n class_fields = fields(self)\n\n # Safety and consistency checks\n if not len(class_fields):\n raise ValueError(f\"{self.__class__.__name__} has no fields.\")\n\n first_field = getattr(self, class_fields[0].name)\n other_fields_are_none = all(getattr(self, field.name) is None for field in class_fields[1:])\n\n if other_fields_are_none and isinstance(first_field, dict):\n for key, value in first_field.items():\n self[key] = value\n else:\n for field in class_fields:\n v = getattr(self, field.name)\n if v is not None:\n self[field.name] = v\n\n def __delitem__(self, *args, **kwargs):\n raise Exception(f\"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.\")\n\n def setdefault(self, *args, **kwargs):\n raise Exception(f\"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.\")\n\n def pop(self, *args, **kwargs):\n raise Exception(f\"You cannot use ``pop`` on a {self.__class__.__name__} instance.\")\n\n def update(self, *args, **kwargs):\n raise Exception(f\"You cannot use ``update`` on a {self.__class__.__name__} instance.\")\n\n def __getitem__(self, k: Any) -> Any:\n if isinstance(k, str):\n inner_dict = dict(self.items())\n return inner_dict[k]\n else:\n return self.to_tuple()[k]\n\n def __setattr__(self, name: Any, value: Any) -> None:\n if name in self.keys() and value is not None:\n # Don't call self.__setitem__ to avoid recursion errors\n super().__setitem__(name, value)\n super().__setattr__(name, value)\n\n def __setitem__(self, key, value):\n # Will raise a KeyException if needed\n super().__setitem__(key, value)\n # Don't call self.__setattr__ to avoid recursion errors\n super().__setattr__(key, value)\n\n def __reduce__(self):\n if not is_dataclass(self):\n return super().__reduce__()\n callable, _args, *remaining = super().__reduce__()\n args = tuple(getattr(self, field.name) for field in fields(self))\n return callable, args, *remaining\n\n def to_tuple(self) -> Tuple[Any, ...]:\n \"\"\"\n Convert self to a tuple containing all the attributes/keys that are not `None`.\n \"\"\"\n return tuple(self[k] for k in self.keys())\n" }, { "path": "diffusers/src/diffusers/utils/peft_utils.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nPEFT utilities: Utilities related to peft library\n\"\"\"\n\nimport collections\nimport importlib\nfrom typing import Optional\n\nfrom packaging import version\n\nfrom .import_utils import is_peft_available, is_torch_available\n\n\nif is_torch_available():\n import torch\n\n\ndef recurse_remove_peft_layers(model):\n r\"\"\"\n Recursively replace all instances of `LoraLayer` with corresponding new layers in `model`.\n \"\"\"\n from peft.tuners.tuners_utils import BaseTunerLayer\n\n has_base_layer_pattern = False\n for module in model.modules():\n if isinstance(module, BaseTunerLayer):\n has_base_layer_pattern = hasattr(module, \"base_layer\")\n break\n\n if has_base_layer_pattern:\n from peft.utils import _get_submodules\n\n key_list = [key for key, _ in model.named_modules() if \"lora\" not in key]\n for key in key_list:\n try:\n parent, target, target_name = _get_submodules(model, key)\n except AttributeError:\n continue\n if hasattr(target, \"base_layer\"):\n setattr(parent, target_name, target.get_base_layer())\n else:\n # This is for backwards compatibility with PEFT <= 0.6.2.\n # TODO can be removed once that PEFT version is no longer supported.\n from peft.tuners.lora import LoraLayer\n\n for name, module in model.named_children():\n if len(list(module.children())) > 0:\n ## compound module, go inside it\n recurse_remove_peft_layers(module)\n\n module_replaced = False\n\n if isinstance(module, LoraLayer) and isinstance(module, torch.nn.Linear):\n new_module = torch.nn.Linear(\n module.in_features,\n module.out_features,\n bias=module.bias is not None,\n ).to(module.weight.device)\n new_module.weight = module.weight\n if module.bias is not None:\n new_module.bias = module.bias\n\n module_replaced = True\n elif isinstance(module, LoraLayer) and isinstance(module, torch.nn.Conv2d):\n new_module = torch.nn.Conv2d(\n module.in_channels,\n module.out_channels,\n module.kernel_size,\n module.stride,\n module.padding,\n module.dilation,\n module.groups,\n ).to(module.weight.device)\n\n new_module.weight = module.weight\n if module.bias is not None:\n new_module.bias = module.bias\n\n module_replaced = True\n\n if module_replaced:\n setattr(model, name, new_module)\n del module\n\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n return model\n\n\ndef scale_lora_layers(model, weight):\n \"\"\"\n Adjust the weightage given to the LoRA layers of the model.\n\n Args:\n model (`torch.nn.Module`):\n The model to scale.\n weight (`float`):\n The weight to be given to the LoRA layers.\n \"\"\"\n from peft.tuners.tuners_utils import BaseTunerLayer\n\n if weight == 1.0:\n return\n\n for module in model.modules():\n if isinstance(module, BaseTunerLayer):\n module.scale_layer(weight)\n\n\ndef unscale_lora_layers(model, weight: Optional[float] = None):\n \"\"\"\n Removes the previously passed weight given to the LoRA layers of the model.\n\n Args:\n model (`torch.nn.Module`):\n The model to scale.\n weight (`float`, *optional*):\n The weight to be given to the LoRA layers. If no scale is passed the scale of the lora layer will be\n re-initialized to the correct value. If 0.0 is passed, we will re-initialize the scale with the correct\n value.\n \"\"\"\n from peft.tuners.tuners_utils import BaseTunerLayer\n\n if weight == 1.0:\n return\n\n for module in model.modules():\n if isinstance(module, BaseTunerLayer):\n if weight is not None and weight != 0:\n module.unscale_layer(weight)\n elif weight is not None and weight == 0:\n for adapter_name in module.active_adapters:\n # if weight == 0 unscale should re-set the scale to the original value.\n module.set_scale(adapter_name, 1.0)\n\n\ndef get_peft_kwargs(rank_dict, network_alpha_dict, peft_state_dict, is_unet=True):\n rank_pattern = {}\n alpha_pattern = {}\n r = lora_alpha = list(rank_dict.values())[0]\n\n if len(set(rank_dict.values())) > 1:\n # get the rank occuring the most number of times\n r = collections.Counter(rank_dict.values()).most_common()[0][0]\n\n # for modules with rank different from the most occuring rank, add it to the `rank_pattern`\n rank_pattern = dict(filter(lambda x: x[1] != r, rank_dict.items()))\n rank_pattern = {k.split(\".lora_B.\")[0]: v for k, v in rank_pattern.items()}\n\n if network_alpha_dict is not None and len(network_alpha_dict) > 0:\n if len(set(network_alpha_dict.values())) > 1:\n # get the alpha occuring the most number of times\n lora_alpha = collections.Counter(network_alpha_dict.values()).most_common()[0][0]\n\n # for modules with alpha different from the most occuring alpha, add it to the `alpha_pattern`\n alpha_pattern = dict(filter(lambda x: x[1] != lora_alpha, network_alpha_dict.items()))\n if is_unet:\n alpha_pattern = {\n \".\".join(k.split(\".lora_A.\")[0].split(\".\")).replace(\".alpha\", \"\"): v\n for k, v in alpha_pattern.items()\n }\n else:\n alpha_pattern = {\".\".join(k.split(\".down.\")[0].split(\".\")[:-1]): v for k, v in alpha_pattern.items()}\n else:\n lora_alpha = set(network_alpha_dict.values()).pop()\n\n # layer names without the Diffusers specific\n target_modules = list({name.split(\".lora\")[0] for name in peft_state_dict.keys()})\n use_dora = any(\"lora_magnitude_vector\" in k for k in peft_state_dict)\n\n lora_config_kwargs = {\n \"r\": r,\n \"lora_alpha\": lora_alpha,\n \"rank_pattern\": rank_pattern,\n \"alpha_pattern\": alpha_pattern,\n \"target_modules\": target_modules,\n \"use_dora\": use_dora,\n }\n return lora_config_kwargs\n\n\ndef get_adapter_name(model):\n from peft.tuners.tuners_utils import BaseTunerLayer\n\n for module in model.modules():\n if isinstance(module, BaseTunerLayer):\n return f\"default_{len(module.r)}\"\n return \"default_0\"\n\n\ndef set_adapter_layers(model, enabled=True):\n from peft.tuners.tuners_utils import BaseTunerLayer\n\n for module in model.modules():\n if isinstance(module, BaseTunerLayer):\n # The recent version of PEFT needs to call `enable_adapters` instead\n if hasattr(module, \"enable_adapters\"):\n module.enable_adapters(enabled=enabled)\n else:\n module.disable_adapters = not enabled\n\n\ndef delete_adapter_layers(model, adapter_name):\n from peft.tuners.tuners_utils import BaseTunerLayer\n\n for module in model.modules():\n if isinstance(module, BaseTunerLayer):\n if hasattr(module, \"delete_adapter\"):\n module.delete_adapter(adapter_name)\n else:\n raise ValueError(\n \"The version of PEFT you are using is not compatible, please use a version that is greater than 0.6.1\"\n )\n\n # For transformers integration - we need to pop the adapter from the config\n if getattr(model, \"_hf_peft_config_loaded\", False) and hasattr(model, \"peft_config\"):\n model.peft_config.pop(adapter_name, None)\n # In case all adapters are deleted, we need to delete the config\n # and make sure to set the flag to False\n if len(model.peft_config) == 0:\n del model.peft_config\n model._hf_peft_config_loaded = None\n\n\ndef set_weights_and_activate_adapters(model, adapter_names, weights):\n from peft.tuners.tuners_utils import BaseTunerLayer\n\n def get_module_weight(weight_for_adapter, module_name):\n if not isinstance(weight_for_adapter, dict):\n # If weight_for_adapter is a single number, always return it.\n return weight_for_adapter\n\n for layer_name, weight_ in weight_for_adapter.items():\n if layer_name in module_name:\n return weight_\n\n parts = module_name.split(\".\")\n # e.g. key = \"down_blocks.1.attentions.0\"\n key = f\"{parts[0]}.{parts[1]}.attentions.{parts[3]}\"\n block_weight = weight_for_adapter.get(key, 1.0)\n\n return block_weight\n\n # iterate over each adapter, make it active and set the corresponding scaling weight\n for adapter_name, weight in zip(adapter_names, weights):\n for module_name, module in model.named_modules():\n if isinstance(module, BaseTunerLayer):\n # For backward compatbility with previous PEFT versions\n if hasattr(module, \"set_adapter\"):\n module.set_adapter(adapter_name)\n else:\n module.active_adapter = adapter_name\n module.set_scale(adapter_name, get_module_weight(weight, module_name))\n\n # set multiple active adapters\n for module in model.modules():\n if isinstance(module, BaseTunerLayer):\n # For backward compatbility with previous PEFT versions\n if hasattr(module, \"set_adapter\"):\n module.set_adapter(adapter_names)\n else:\n module.active_adapter = adapter_names\n\n\ndef check_peft_version(min_version: str) -> None:\n r\"\"\"\n Checks if the version of PEFT is compatible.\n\n Args:\n version (`str`):\n The version of PEFT to check against.\n \"\"\"\n if not is_peft_available():\n raise ValueError(\"PEFT is not installed. Please install it with `pip install peft`\")\n\n is_peft_version_compatible = version.parse(importlib.metadata.version(\"peft\")) > version.parse(min_version)\n\n if not is_peft_version_compatible:\n raise ValueError(\n f\"The version of PEFT you are using is not compatible, please use a version that is greater\"\n f\" than {min_version}\"\n )\n" }, { "path": "diffusers/src/diffusers/utils/pil_utils.py", "content": "from typing import List\n\nimport PIL.Image\nimport PIL.ImageOps\nfrom packaging import version\nfrom PIL import Image\n\n\nif version.parse(version.parse(PIL.__version__).base_version) >= version.parse(\"9.1.0\"):\n PIL_INTERPOLATION = {\n \"linear\": PIL.Image.Resampling.BILINEAR,\n \"bilinear\": PIL.Image.Resampling.BILINEAR,\n \"bicubic\": PIL.Image.Resampling.BICUBIC,\n \"lanczos\": PIL.Image.Resampling.LANCZOS,\n \"nearest\": PIL.Image.Resampling.NEAREST,\n }\nelse:\n PIL_INTERPOLATION = {\n \"linear\": PIL.Image.LINEAR,\n \"bilinear\": PIL.Image.BILINEAR,\n \"bicubic\": PIL.Image.BICUBIC,\n \"lanczos\": PIL.Image.LANCZOS,\n \"nearest\": PIL.Image.NEAREST,\n }\n\n\ndef pt_to_pil(images):\n \"\"\"\n Convert a torch image to a PIL image.\n \"\"\"\n images = (images / 2 + 0.5).clamp(0, 1)\n images = images.cpu().permute(0, 2, 3, 1).float().numpy()\n images = numpy_to_pil(images)\n return images\n\n\ndef numpy_to_pil(images):\n \"\"\"\n Convert a numpy image or a batch of images to a PIL image.\n \"\"\"\n if images.ndim == 3:\n images = images[None, ...]\n images = (images * 255).round().astype(\"uint8\")\n if images.shape[-1] == 1:\n # special case for grayscale (single channel) images\n pil_images = [Image.fromarray(image.squeeze(), mode=\"L\") for image in images]\n else:\n pil_images = [Image.fromarray(image) for image in images]\n\n return pil_images\n\n\ndef make_image_grid(images: List[PIL.Image.Image], rows: int, cols: int, resize: int = None) -> PIL.Image.Image:\n \"\"\"\n Prepares a single grid of images. Useful for visualization purposes.\n \"\"\"\n assert len(images) == rows * cols\n\n if resize is not None:\n images = [img.resize((resize, resize)) for img in images]\n\n w, h = images[0].size\n grid = Image.new(\"RGB\", size=(cols * w, rows * h))\n\n for i, img in enumerate(images):\n grid.paste(img, box=(i % cols * w, i // cols * h))\n return grid\n" }, { "path": "diffusers/src/diffusers/utils/state_dict_utils.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nState dict utilities: utility methods for converting state dicts easily\n\"\"\"\n\nimport enum\n\nfrom .logging import get_logger\n\n\nlogger = get_logger(__name__)\n\n\nclass StateDictType(enum.Enum):\n \"\"\"\n The mode to use when converting state dicts.\n \"\"\"\n\n DIFFUSERS_OLD = \"diffusers_old\"\n KOHYA_SS = \"kohya_ss\"\n PEFT = \"peft\"\n DIFFUSERS = \"diffusers\"\n\n\n# We need to define a proper mapping for Unet since it uses different output keys than text encoder\n# e.g. to_q_lora -> q_proj / to_q\nUNET_TO_DIFFUSERS = {\n \".to_out_lora.up\": \".to_out.0.lora_B\",\n \".to_out_lora.down\": \".to_out.0.lora_A\",\n \".to_q_lora.down\": \".to_q.lora_A\",\n \".to_q_lora.up\": \".to_q.lora_B\",\n \".to_k_lora.down\": \".to_k.lora_A\",\n \".to_k_lora.up\": \".to_k.lora_B\",\n \".to_v_lora.down\": \".to_v.lora_A\",\n \".to_v_lora.up\": \".to_v.lora_B\",\n \".lora.up\": \".lora_B\",\n \".lora.down\": \".lora_A\",\n \".to_out.lora_magnitude_vector\": \".to_out.0.lora_magnitude_vector\",\n}\n\n\nDIFFUSERS_TO_PEFT = {\n \".q_proj.lora_linear_layer.up\": \".q_proj.lora_B\",\n \".q_proj.lora_linear_layer.down\": \".q_proj.lora_A\",\n \".k_proj.lora_linear_layer.up\": \".k_proj.lora_B\",\n \".k_proj.lora_linear_layer.down\": \".k_proj.lora_A\",\n \".v_proj.lora_linear_layer.up\": \".v_proj.lora_B\",\n \".v_proj.lora_linear_layer.down\": \".v_proj.lora_A\",\n \".out_proj.lora_linear_layer.up\": \".out_proj.lora_B\",\n \".out_proj.lora_linear_layer.down\": \".out_proj.lora_A\",\n \".lora_linear_layer.up\": \".lora_B\",\n \".lora_linear_layer.down\": \".lora_A\",\n \"text_projection.lora.down.weight\": \"text_projection.lora_A.weight\",\n \"text_projection.lora.up.weight\": \"text_projection.lora_B.weight\",\n}\n\nDIFFUSERS_OLD_TO_PEFT = {\n \".to_q_lora.up\": \".q_proj.lora_B\",\n \".to_q_lora.down\": \".q_proj.lora_A\",\n \".to_k_lora.up\": \".k_proj.lora_B\",\n \".to_k_lora.down\": \".k_proj.lora_A\",\n \".to_v_lora.up\": \".v_proj.lora_B\",\n \".to_v_lora.down\": \".v_proj.lora_A\",\n \".to_out_lora.up\": \".out_proj.lora_B\",\n \".to_out_lora.down\": \".out_proj.lora_A\",\n \".lora_linear_layer.up\": \".lora_B\",\n \".lora_linear_layer.down\": \".lora_A\",\n}\n\nPEFT_TO_DIFFUSERS = {\n \".q_proj.lora_B\": \".q_proj.lora_linear_layer.up\",\n \".q_proj.lora_A\": \".q_proj.lora_linear_layer.down\",\n \".k_proj.lora_B\": \".k_proj.lora_linear_layer.up\",\n \".k_proj.lora_A\": \".k_proj.lora_linear_layer.down\",\n \".v_proj.lora_B\": \".v_proj.lora_linear_layer.up\",\n \".v_proj.lora_A\": \".v_proj.lora_linear_layer.down\",\n \".out_proj.lora_B\": \".out_proj.lora_linear_layer.up\",\n \".out_proj.lora_A\": \".out_proj.lora_linear_layer.down\",\n \"to_k.lora_A\": \"to_k.lora.down\",\n \"to_k.lora_B\": \"to_k.lora.up\",\n \"to_q.lora_A\": \"to_q.lora.down\",\n \"to_q.lora_B\": \"to_q.lora.up\",\n \"to_v.lora_A\": \"to_v.lora.down\",\n \"to_v.lora_B\": \"to_v.lora.up\",\n \"to_out.0.lora_A\": \"to_out.0.lora.down\",\n \"to_out.0.lora_B\": \"to_out.0.lora.up\",\n}\n\nDIFFUSERS_OLD_TO_DIFFUSERS = {\n \".to_q_lora.up\": \".q_proj.lora_linear_layer.up\",\n \".to_q_lora.down\": \".q_proj.lora_linear_layer.down\",\n \".to_k_lora.up\": \".k_proj.lora_linear_layer.up\",\n \".to_k_lora.down\": \".k_proj.lora_linear_layer.down\",\n \".to_v_lora.up\": \".v_proj.lora_linear_layer.up\",\n \".to_v_lora.down\": \".v_proj.lora_linear_layer.down\",\n \".to_out_lora.up\": \".out_proj.lora_linear_layer.up\",\n \".to_out_lora.down\": \".out_proj.lora_linear_layer.down\",\n \".to_k.lora_magnitude_vector\": \".k_proj.lora_magnitude_vector\",\n \".to_v.lora_magnitude_vector\": \".v_proj.lora_magnitude_vector\",\n \".to_q.lora_magnitude_vector\": \".q_proj.lora_magnitude_vector\",\n \".to_out.lora_magnitude_vector\": \".out_proj.lora_magnitude_vector\",\n}\n\nPEFT_TO_KOHYA_SS = {\n \"lora_A\": \"lora_down\",\n \"lora_B\": \"lora_up\",\n # This is not a comprehensive dict as kohya format requires replacing `.` with `_` in keys,\n # adding prefixes and adding alpha values\n # Check `convert_state_dict_to_kohya` for more\n}\n\nPEFT_STATE_DICT_MAPPINGS = {\n StateDictType.DIFFUSERS_OLD: DIFFUSERS_OLD_TO_PEFT,\n StateDictType.DIFFUSERS: DIFFUSERS_TO_PEFT,\n}\n\nDIFFUSERS_STATE_DICT_MAPPINGS = {\n StateDictType.DIFFUSERS_OLD: DIFFUSERS_OLD_TO_DIFFUSERS,\n StateDictType.PEFT: PEFT_TO_DIFFUSERS,\n}\n\nKOHYA_STATE_DICT_MAPPINGS = {StateDictType.PEFT: PEFT_TO_KOHYA_SS}\n\nKEYS_TO_ALWAYS_REPLACE = {\n \".processor.\": \".\",\n}\n\n\ndef convert_state_dict(state_dict, mapping):\n r\"\"\"\n Simply iterates over the state dict and replaces the patterns in `mapping` with the corresponding values.\n\n Args:\n state_dict (`dict[str, torch.Tensor]`):\n The state dict to convert.\n mapping (`dict[str, str]`):\n The mapping to use for conversion, the mapping should be a dictionary with the following structure:\n - key: the pattern to replace\n - value: the pattern to replace with\n\n Returns:\n converted_state_dict (`dict`)\n The converted state dict.\n \"\"\"\n converted_state_dict = {}\n for k, v in state_dict.items():\n # First, filter out the keys that we always want to replace\n for pattern in KEYS_TO_ALWAYS_REPLACE.keys():\n if pattern in k:\n new_pattern = KEYS_TO_ALWAYS_REPLACE[pattern]\n k = k.replace(pattern, new_pattern)\n\n for pattern in mapping.keys():\n if pattern in k:\n new_pattern = mapping[pattern]\n k = k.replace(pattern, new_pattern)\n break\n converted_state_dict[k] = v\n return converted_state_dict\n\n\ndef convert_state_dict_to_peft(state_dict, original_type=None, **kwargs):\n r\"\"\"\n Converts a state dict to the PEFT format The state dict can be from previous diffusers format (`OLD_DIFFUSERS`), or\n new diffusers format (`DIFFUSERS`). The method only supports the conversion from diffusers old/new to PEFT for now.\n\n Args:\n state_dict (`dict[str, torch.Tensor]`):\n The state dict to convert.\n original_type (`StateDictType`, *optional*):\n The original type of the state dict, if not provided, the method will try to infer it automatically.\n \"\"\"\n if original_type is None:\n # Old diffusers to PEFT\n if any(\"to_out_lora\" in k for k in state_dict.keys()):\n original_type = StateDictType.DIFFUSERS_OLD\n elif any(\"lora_linear_layer\" in k for k in state_dict.keys()):\n original_type = StateDictType.DIFFUSERS\n else:\n raise ValueError(\"Could not automatically infer state dict type\")\n\n if original_type not in PEFT_STATE_DICT_MAPPINGS.keys():\n raise ValueError(f\"Original type {original_type} is not supported\")\n\n mapping = PEFT_STATE_DICT_MAPPINGS[original_type]\n return convert_state_dict(state_dict, mapping)\n\n\ndef convert_state_dict_to_diffusers(state_dict, original_type=None, **kwargs):\n r\"\"\"\n Converts a state dict to new diffusers format. The state dict can be from previous diffusers format\n (`OLD_DIFFUSERS`), or PEFT format (`PEFT`) or new diffusers format (`DIFFUSERS`). In the last case the method will\n return the state dict as is.\n\n The method only supports the conversion from diffusers old, PEFT to diffusers new for now.\n\n Args:\n state_dict (`dict[str, torch.Tensor]`):\n The state dict to convert.\n original_type (`StateDictType`, *optional*):\n The original type of the state dict, if not provided, the method will try to infer it automatically.\n kwargs (`dict`, *args*):\n Additional arguments to pass to the method.\n\n - **adapter_name**: For example, in case of PEFT, some keys will be pre-pended\n with the adapter name, therefore needs a special handling. By default PEFT also takes care of that in\n `get_peft_model_state_dict` method:\n https://github.com/huggingface/peft/blob/ba0477f2985b1ba311b83459d29895c809404e99/src/peft/utils/save_and_load.py#L92\n but we add it here in case we don't want to rely on that method.\n \"\"\"\n peft_adapter_name = kwargs.pop(\"adapter_name\", None)\n if peft_adapter_name is not None:\n peft_adapter_name = \".\" + peft_adapter_name\n else:\n peft_adapter_name = \"\"\n\n if original_type is None:\n # Old diffusers to PEFT\n if any(\"to_out_lora\" in k for k in state_dict.keys()):\n original_type = StateDictType.DIFFUSERS_OLD\n elif any(f\".lora_A{peft_adapter_name}.weight\" in k for k in state_dict.keys()):\n original_type = StateDictType.PEFT\n elif any(\"lora_linear_layer\" in k for k in state_dict.keys()):\n # nothing to do\n return state_dict\n else:\n raise ValueError(\"Could not automatically infer state dict type\")\n\n if original_type not in DIFFUSERS_STATE_DICT_MAPPINGS.keys():\n raise ValueError(f\"Original type {original_type} is not supported\")\n\n mapping = DIFFUSERS_STATE_DICT_MAPPINGS[original_type]\n return convert_state_dict(state_dict, mapping)\n\n\ndef convert_unet_state_dict_to_peft(state_dict):\n r\"\"\"\n Converts a state dict from UNet format to diffusers format - i.e. by removing some keys\n \"\"\"\n mapping = UNET_TO_DIFFUSERS\n return convert_state_dict(state_dict, mapping)\n\n\ndef convert_all_state_dict_to_peft(state_dict):\n r\"\"\"\n Attempts to first `convert_state_dict_to_peft`, and if it doesn't detect `lora_linear_layer` for a valid\n `DIFFUSERS` LoRA for example, attempts to exclusively convert the Unet `convert_unet_state_dict_to_peft`\n \"\"\"\n try:\n peft_dict = convert_state_dict_to_peft(state_dict)\n except Exception as e:\n if str(e) == \"Could not automatically infer state dict type\":\n peft_dict = convert_unet_state_dict_to_peft(state_dict)\n else:\n raise\n\n if not any(\"lora_A\" in key or \"lora_B\" in key for key in peft_dict.keys()):\n raise ValueError(\"Your LoRA was not converted to PEFT\")\n\n return peft_dict\n\n\ndef convert_state_dict_to_kohya(state_dict, original_type=None, **kwargs):\n r\"\"\"\n Converts a `PEFT` state dict to `Kohya` format that can be used in AUTOMATIC1111, ComfyUI, SD.Next, InvokeAI, etc.\n The method only supports the conversion from PEFT to Kohya for now.\n\n Args:\n state_dict (`dict[str, torch.Tensor]`):\n The state dict to convert.\n original_type (`StateDictType`, *optional*):\n The original type of the state dict, if not provided, the method will try to infer it automatically.\n kwargs (`dict`, *args*):\n Additional arguments to pass to the method.\n\n - **adapter_name**: For example, in case of PEFT, some keys will be pre-pended\n with the adapter name, therefore needs a special handling. By default PEFT also takes care of that in\n `get_peft_model_state_dict` method:\n https://github.com/huggingface/peft/blob/ba0477f2985b1ba311b83459d29895c809404e99/src/peft/utils/save_and_load.py#L92\n but we add it here in case we don't want to rely on that method.\n \"\"\"\n try:\n import torch\n except ImportError:\n logger.error(\"Converting PEFT state dicts to Kohya requires torch to be installed.\")\n raise\n\n peft_adapter_name = kwargs.pop(\"adapter_name\", None)\n if peft_adapter_name is not None:\n peft_adapter_name = \".\" + peft_adapter_name\n else:\n peft_adapter_name = \"\"\n\n if original_type is None:\n if any(f\".lora_A{peft_adapter_name}.weight\" in k for k in state_dict.keys()):\n original_type = StateDictType.PEFT\n\n if original_type not in KOHYA_STATE_DICT_MAPPINGS.keys():\n raise ValueError(f\"Original type {original_type} is not supported\")\n\n # Use the convert_state_dict function with the appropriate mapping\n kohya_ss_partial_state_dict = convert_state_dict(state_dict, KOHYA_STATE_DICT_MAPPINGS[StateDictType.PEFT])\n kohya_ss_state_dict = {}\n\n # Additional logic for replacing header, alpha parameters `.` with `_` in all keys\n for kohya_key, weight in kohya_ss_partial_state_dict.items():\n if \"text_encoder_2.\" in kohya_key:\n kohya_key = kohya_key.replace(\"text_encoder_2.\", \"lora_te2.\")\n elif \"text_encoder.\" in kohya_key:\n kohya_key = kohya_key.replace(\"text_encoder.\", \"lora_te1.\")\n elif \"unet\" in kohya_key:\n kohya_key = kohya_key.replace(\"unet\", \"lora_unet\")\n elif \"lora_magnitude_vector\" in kohya_key:\n kohya_key = kohya_key.replace(\"lora_magnitude_vector\", \"dora_scale\")\n\n kohya_key = kohya_key.replace(\".\", \"_\", kohya_key.count(\".\") - 2)\n kohya_key = kohya_key.replace(peft_adapter_name, \"\") # Kohya doesn't take names\n kohya_ss_state_dict[kohya_key] = weight\n if \"lora_down\" in kohya_key:\n alpha_key = f'{kohya_key.split(\".\")[0]}.alpha'\n kohya_ss_state_dict[alpha_key] = torch.tensor(len(weight))\n\n return kohya_ss_state_dict\n" }, { "path": "diffusers/src/diffusers/utils/testing_utils.py", "content": "import functools\nimport importlib\nimport inspect\nimport io\nimport logging\nimport multiprocessing\nimport os\nimport random\nimport re\nimport struct\nimport sys\nimport tempfile\nimport time\nimport unittest\nimport urllib.parse\nfrom contextlib import contextmanager\nfrom io import BytesIO, StringIO\nfrom pathlib import Path\nfrom typing import Callable, Dict, List, Optional, Union\n\nimport numpy as np\nimport PIL.Image\nimport PIL.ImageOps\nimport requests\nfrom numpy.linalg import norm\nfrom packaging import version\n\nfrom .import_utils import (\n BACKENDS_MAPPING,\n is_compel_available,\n is_flax_available,\n is_note_seq_available,\n is_onnx_available,\n is_opencv_available,\n is_peft_available,\n is_timm_available,\n is_torch_available,\n is_torch_version,\n is_torchsde_available,\n is_transformers_available,\n)\nfrom .logging import get_logger\n\n\nglobal_rng = random.Random()\n\nlogger = get_logger(__name__)\n\n_required_peft_version = is_peft_available() and version.parse(\n version.parse(importlib.metadata.version(\"peft\")).base_version\n) > version.parse(\"0.5\")\n_required_transformers_version = is_transformers_available() and version.parse(\n version.parse(importlib.metadata.version(\"transformers\")).base_version\n) > version.parse(\"4.33\")\n\nUSE_PEFT_BACKEND = _required_peft_version and _required_transformers_version\n\nif is_torch_available():\n import torch\n\n # Set a backend environment variable for any extra module import required for a custom accelerator\n if \"DIFFUSERS_TEST_BACKEND\" in os.environ:\n backend = os.environ[\"DIFFUSERS_TEST_BACKEND\"]\n try:\n _ = importlib.import_module(backend)\n except ModuleNotFoundError as e:\n raise ModuleNotFoundError(\n f\"Failed to import `DIFFUSERS_TEST_BACKEND` '{backend}'! This should be the name of an installed module \\\n to enable a specified backend.):\\n{e}\"\n ) from e\n\n if \"DIFFUSERS_TEST_DEVICE\" in os.environ:\n torch_device = os.environ[\"DIFFUSERS_TEST_DEVICE\"]\n try:\n # try creating device to see if provided device is valid\n _ = torch.device(torch_device)\n except RuntimeError as e:\n raise RuntimeError(\n f\"Unknown testing device specified by environment variable `DIFFUSERS_TEST_DEVICE`: {torch_device}\"\n ) from e\n logger.info(f\"torch_device overrode to {torch_device}\")\n else:\n torch_device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n is_torch_higher_equal_than_1_12 = version.parse(\n version.parse(torch.__version__).base_version\n ) >= version.parse(\"1.12\")\n\n if is_torch_higher_equal_than_1_12:\n # Some builds of torch 1.12 don't have the mps backend registered. See #892 for more details\n mps_backend_registered = hasattr(torch.backends, \"mps\")\n torch_device = \"mps\" if (mps_backend_registered and torch.backends.mps.is_available()) else torch_device\n\n\ndef torch_all_close(a, b, *args, **kwargs):\n if not is_torch_available():\n raise ValueError(\"PyTorch needs to be installed to use this function.\")\n if not torch.allclose(a, b, *args, **kwargs):\n assert False, f\"Max diff is absolute {(a - b).abs().max()}. Diff tensor is {(a - b).abs()}.\"\n return True\n\n\ndef numpy_cosine_similarity_distance(a, b):\n similarity = np.dot(a, b) / (norm(a) * norm(b))\n distance = 1.0 - similarity.mean()\n\n return distance\n\n\ndef print_tensor_test(\n tensor,\n limit_to_slices=None,\n max_torch_print=None,\n filename=\"test_corrections.txt\",\n expected_tensor_name=\"expected_slice\",\n):\n if max_torch_print:\n torch.set_printoptions(threshold=10_000)\n\n test_name = os.environ.get(\"PYTEST_CURRENT_TEST\")\n if not torch.is_tensor(tensor):\n tensor = torch.from_numpy(tensor)\n if limit_to_slices:\n tensor = tensor[0, -3:, -3:, -1]\n\n tensor_str = str(tensor.detach().cpu().flatten().to(torch.float32)).replace(\"\\n\", \"\")\n # format is usually:\n # expected_slice = np.array([-0.5713, -0.3018, -0.9814, 0.04663, -0.879, 0.76, -1.734, 0.1044, 1.161])\n output_str = tensor_str.replace(\"tensor\", f\"{expected_tensor_name} = np.array\")\n test_file, test_class, test_fn = test_name.split(\"::\")\n test_fn = test_fn.split()[0]\n with open(filename, \"a\") as f:\n print(\"::\".join([test_file, test_class, test_fn, output_str]), file=f)\n\n\ndef get_tests_dir(append_path=None):\n \"\"\"\n Args:\n append_path: optional path to append to the tests dir path\n Return:\n The full path to the `tests` dir, so that the tests can be invoked from anywhere. Optionally `append_path` is\n joined after the `tests` dir the former is provided.\n \"\"\"\n # this function caller's __file__\n caller__file__ = inspect.stack()[1][1]\n tests_dir = os.path.abspath(os.path.dirname(caller__file__))\n\n while not tests_dir.endswith(\"tests\"):\n tests_dir = os.path.dirname(tests_dir)\n\n if append_path:\n return Path(tests_dir, append_path).as_posix()\n else:\n return tests_dir\n\n\n# Taken from the following PR:\n# https://github.com/huggingface/accelerate/pull/1964\ndef str_to_bool(value) -> int:\n \"\"\"\n Converts a string representation of truth to `True` (1) or `False` (0). True values are `y`, `yes`, `t`, `true`,\n `on`, and `1`; False value are `n`, `no`, `f`, `false`, `off`, and `0`;\n \"\"\"\n value = value.lower()\n if value in (\"y\", \"yes\", \"t\", \"true\", \"on\", \"1\"):\n return 1\n elif value in (\"n\", \"no\", \"f\", \"false\", \"off\", \"0\"):\n return 0\n else:\n raise ValueError(f\"invalid truth value {value}\")\n\n\ndef parse_flag_from_env(key, default=False):\n try:\n value = os.environ[key]\n except KeyError:\n # KEY isn't set, default to `default`.\n _value = default\n else:\n # KEY is set, convert it to True or False.\n try:\n _value = str_to_bool(value)\n except ValueError:\n # More values are supported, but let's keep the message simple.\n raise ValueError(f\"If set, {key} must be yes or no.\")\n return _value\n\n\n_run_slow_tests = parse_flag_from_env(\"RUN_SLOW\", default=False)\n_run_nightly_tests = parse_flag_from_env(\"RUN_NIGHTLY\", default=False)\n\n\ndef floats_tensor(shape, scale=1.0, rng=None, name=None):\n \"\"\"Creates a random float32 tensor\"\"\"\n if rng is None:\n rng = global_rng\n\n total_dims = 1\n for dim in shape:\n total_dims *= dim\n\n values = []\n for _ in range(total_dims):\n values.append(rng.random() * scale)\n\n return torch.tensor(data=values, dtype=torch.float).view(shape).contiguous()\n\n\ndef slow(test_case):\n \"\"\"\n Decorator marking a test as slow.\n\n Slow tests are skipped by default. Set the RUN_SLOW environment variable to a truthy value to run them.\n\n \"\"\"\n return unittest.skipUnless(_run_slow_tests, \"test is slow\")(test_case)\n\n\ndef nightly(test_case):\n \"\"\"\n Decorator marking a test that runs nightly in the diffusers CI.\n\n Slow tests are skipped by default. Set the RUN_NIGHTLY environment variable to a truthy value to run them.\n\n \"\"\"\n return unittest.skipUnless(_run_nightly_tests, \"test is nightly\")(test_case)\n\n\ndef require_torch(test_case):\n \"\"\"\n Decorator marking a test that requires PyTorch. These tests are skipped when PyTorch isn't installed.\n \"\"\"\n return unittest.skipUnless(is_torch_available(), \"test requires PyTorch\")(test_case)\n\n\ndef require_torch_2(test_case):\n \"\"\"\n Decorator marking a test that requires PyTorch 2. These tests are skipped when it isn't installed.\n \"\"\"\n return unittest.skipUnless(is_torch_available() and is_torch_version(\">=\", \"2.0.0\"), \"test requires PyTorch 2\")(\n test_case\n )\n\n\ndef require_torch_gpu(test_case):\n \"\"\"Decorator marking a test that requires CUDA and PyTorch.\"\"\"\n return unittest.skipUnless(is_torch_available() and torch_device == \"cuda\", \"test requires PyTorch+CUDA\")(\n test_case\n )\n\n\n# These decorators are for accelerator-specific behaviours that are not GPU-specific\ndef require_torch_accelerator(test_case):\n \"\"\"Decorator marking a test that requires an accelerator backend and PyTorch.\"\"\"\n return unittest.skipUnless(is_torch_available() and torch_device != \"cpu\", \"test requires accelerator+PyTorch\")(\n test_case\n )\n\n\ndef require_torch_multi_gpu(test_case):\n \"\"\"\n Decorator marking a test that requires a multi-GPU setup (in PyTorch). These tests are skipped on a machine without\n multiple GPUs. To run *only* the multi_gpu tests, assuming all test names contain multi_gpu: $ pytest -sv ./tests\n -k \"multi_gpu\"\n \"\"\"\n if not is_torch_available():\n return unittest.skip(\"test requires PyTorch\")(test_case)\n\n import torch\n\n return unittest.skipUnless(torch.cuda.device_count() > 1, \"test requires multiple GPUs\")(test_case)\n\n\ndef require_torch_accelerator_with_fp16(test_case):\n \"\"\"Decorator marking a test that requires an accelerator with support for the FP16 data type.\"\"\"\n return unittest.skipUnless(_is_torch_fp16_available(torch_device), \"test requires accelerator with fp16 support\")(\n test_case\n )\n\n\ndef require_torch_accelerator_with_fp64(test_case):\n \"\"\"Decorator marking a test that requires an accelerator with support for the FP64 data type.\"\"\"\n return unittest.skipUnless(_is_torch_fp64_available(torch_device), \"test requires accelerator with fp64 support\")(\n test_case\n )\n\n\ndef require_torch_accelerator_with_training(test_case):\n \"\"\"Decorator marking a test that requires an accelerator with support for training.\"\"\"\n return unittest.skipUnless(\n is_torch_available() and backend_supports_training(torch_device),\n \"test requires accelerator with training support\",\n )(test_case)\n\n\ndef skip_mps(test_case):\n \"\"\"Decorator marking a test to skip if torch_device is 'mps'\"\"\"\n return unittest.skipUnless(torch_device != \"mps\", \"test requires non 'mps' device\")(test_case)\n\n\ndef require_flax(test_case):\n \"\"\"\n Decorator marking a test that requires JAX & Flax. These tests are skipped when one / both are not installed\n \"\"\"\n return unittest.skipUnless(is_flax_available(), \"test requires JAX & Flax\")(test_case)\n\n\ndef require_compel(test_case):\n \"\"\"\n Decorator marking a test that requires compel: https://github.com/damian0815/compel. These tests are skipped when\n the library is not installed.\n \"\"\"\n return unittest.skipUnless(is_compel_available(), \"test requires compel\")(test_case)\n\n\ndef require_onnxruntime(test_case):\n \"\"\"\n Decorator marking a test that requires onnxruntime. These tests are skipped when onnxruntime isn't installed.\n \"\"\"\n return unittest.skipUnless(is_onnx_available(), \"test requires onnxruntime\")(test_case)\n\n\ndef require_note_seq(test_case):\n \"\"\"\n Decorator marking a test that requires note_seq. These tests are skipped when note_seq isn't installed.\n \"\"\"\n return unittest.skipUnless(is_note_seq_available(), \"test requires note_seq\")(test_case)\n\n\ndef require_torchsde(test_case):\n \"\"\"\n Decorator marking a test that requires torchsde. These tests are skipped when torchsde isn't installed.\n \"\"\"\n return unittest.skipUnless(is_torchsde_available(), \"test requires torchsde\")(test_case)\n\n\ndef require_peft_backend(test_case):\n \"\"\"\n Decorator marking a test that requires PEFT backend, this would require some specific versions of PEFT and\n transformers.\n \"\"\"\n return unittest.skipUnless(USE_PEFT_BACKEND, \"test requires PEFT backend\")(test_case)\n\n\ndef require_timm(test_case):\n \"\"\"\n Decorator marking a test that requires timm. These tests are skipped when timm isn't installed.\n \"\"\"\n return unittest.skipUnless(is_timm_available(), \"test requires timm\")(test_case)\n\n\ndef require_peft_version_greater(peft_version):\n \"\"\"\n Decorator marking a test that requires PEFT backend with a specific version, this would require some specific\n versions of PEFT and transformers.\n \"\"\"\n\n def decorator(test_case):\n correct_peft_version = is_peft_available() and version.parse(\n version.parse(importlib.metadata.version(\"peft\")).base_version\n ) > version.parse(peft_version)\n return unittest.skipUnless(\n correct_peft_version, f\"test requires PEFT backend with the version greater than {peft_version}\"\n )(test_case)\n\n return decorator\n\n\ndef require_accelerate_version_greater(accelerate_version):\n def decorator(test_case):\n correct_accelerate_version = is_peft_available() and version.parse(\n version.parse(importlib.metadata.version(\"accelerate\")).base_version\n ) > version.parse(accelerate_version)\n return unittest.skipUnless(\n correct_accelerate_version, f\"Test requires accelerate with the version greater than {accelerate_version}.\"\n )(test_case)\n\n return decorator\n\n\ndef deprecate_after_peft_backend(test_case):\n \"\"\"\n Decorator marking a test that will be skipped after PEFT backend\n \"\"\"\n return unittest.skipUnless(not USE_PEFT_BACKEND, \"test skipped in favor of PEFT backend\")(test_case)\n\n\ndef get_python_version():\n sys_info = sys.version_info\n major, minor = sys_info.major, sys_info.minor\n return major, minor\n\n\ndef require_python39_or_higher(test_case):\n def python39_available():\n major, minor = get_python_version()\n return major == 3 and minor >= 9\n\n return unittest.skipUnless(python39_available(), \"test requires Python 3.9 or higher\")(test_case)\n\n\ndef load_numpy(arry: Union[str, np.ndarray], local_path: Optional[str] = None) -> np.ndarray:\n if isinstance(arry, str):\n if local_path is not None:\n # local_path can be passed to correct images of tests\n return Path(local_path, arry.split(\"/\")[-5], arry.split(\"/\")[-2], arry.split(\"/\")[-1]).as_posix()\n elif arry.startswith(\"http://\") or arry.startswith(\"https://\"):\n response = requests.get(arry)\n response.raise_for_status()\n arry = np.load(BytesIO(response.content))\n elif os.path.isfile(arry):\n arry = np.load(arry)\n else:\n raise ValueError(\n f\"Incorrect path or url, URLs must start with `http://` or `https://`, and {arry} is not a valid path\"\n )\n elif isinstance(arry, np.ndarray):\n pass\n else:\n raise ValueError(\n \"Incorrect format used for numpy ndarray. Should be an url linking to an image, a local path, or a\"\n \" ndarray.\"\n )\n\n return arry\n\n\ndef load_pt(url: str):\n response = requests.get(url)\n response.raise_for_status()\n arry = torch.load(BytesIO(response.content))\n return arry\n\n\ndef load_image(image: Union[str, PIL.Image.Image]) -> PIL.Image.Image:\n \"\"\"\n Loads `image` to a PIL Image.\n\n Args:\n image (`str` or `PIL.Image.Image`):\n The image to convert to the PIL Image format.\n Returns:\n `PIL.Image.Image`:\n A PIL Image.\n \"\"\"\n if isinstance(image, str):\n if image.startswith(\"http://\") or image.startswith(\"https://\"):\n image = PIL.Image.open(requests.get(image, stream=True).raw)\n elif os.path.isfile(image):\n image = PIL.Image.open(image)\n else:\n raise ValueError(\n f\"Incorrect path or url, URLs must start with `http://` or `https://`, and {image} is not a valid path\"\n )\n elif isinstance(image, PIL.Image.Image):\n image = image\n else:\n raise ValueError(\n \"Incorrect format used for image. Should be an url linking to an image, a local path, or a PIL image.\"\n )\n image = PIL.ImageOps.exif_transpose(image)\n image = image.convert(\"RGB\")\n return image\n\n\ndef preprocess_image(image: PIL.Image, batch_size: int):\n w, h = image.size\n w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8\n image = image.resize((w, h), resample=PIL.Image.LANCZOS)\n image = np.array(image).astype(np.float32) / 255.0\n image = np.vstack([image[None].transpose(0, 3, 1, 2)] * batch_size)\n image = torch.from_numpy(image)\n return 2.0 * image - 1.0\n\n\ndef export_to_gif(image: List[PIL.Image.Image], output_gif_path: str = None) -> str:\n if output_gif_path is None:\n output_gif_path = tempfile.NamedTemporaryFile(suffix=\".gif\").name\n\n image[0].save(\n output_gif_path,\n save_all=True,\n append_images=image[1:],\n optimize=False,\n duration=100,\n loop=0,\n )\n return output_gif_path\n\n\n@contextmanager\ndef buffered_writer(raw_f):\n f = io.BufferedWriter(raw_f)\n yield f\n f.flush()\n\n\ndef export_to_ply(mesh, output_ply_path: str = None):\n \"\"\"\n Write a PLY file for a mesh.\n \"\"\"\n if output_ply_path is None:\n output_ply_path = tempfile.NamedTemporaryFile(suffix=\".ply\").name\n\n coords = mesh.verts.detach().cpu().numpy()\n faces = mesh.faces.cpu().numpy()\n rgb = np.stack([mesh.vertex_channels[x].detach().cpu().numpy() for x in \"RGB\"], axis=1)\n\n with buffered_writer(open(output_ply_path, \"wb\")) as f:\n f.write(b\"ply\\n\")\n f.write(b\"format binary_little_endian 1.0\\n\")\n f.write(bytes(f\"element vertex {len(coords)}\\n\", \"ascii\"))\n f.write(b\"property float x\\n\")\n f.write(b\"property float y\\n\")\n f.write(b\"property float z\\n\")\n if rgb is not None:\n f.write(b\"property uchar red\\n\")\n f.write(b\"property uchar green\\n\")\n f.write(b\"property uchar blue\\n\")\n if faces is not None:\n f.write(bytes(f\"element face {len(faces)}\\n\", \"ascii\"))\n f.write(b\"property list uchar int vertex_index\\n\")\n f.write(b\"end_header\\n\")\n\n if rgb is not None:\n rgb = (rgb * 255.499).round().astype(int)\n vertices = [\n (*coord, *rgb)\n for coord, rgb in zip(\n coords.tolist(),\n rgb.tolist(),\n )\n ]\n format = struct.Struct(\"<3f3B\")\n for item in vertices:\n f.write(format.pack(*item))\n else:\n format = struct.Struct(\"<3f\")\n for vertex in coords.tolist():\n f.write(format.pack(*vertex))\n\n if faces is not None:\n format = struct.Struct(\" str:\n if is_opencv_available():\n import cv2\n else:\n raise ImportError(BACKENDS_MAPPING[\"opencv\"][1].format(\"export_to_video\"))\n if output_video_path is None:\n output_video_path = tempfile.NamedTemporaryFile(suffix=\".mp4\").name\n\n fourcc = cv2.VideoWriter_fourcc(*\"mp4v\")\n h, w, c = video_frames[0].shape\n video_writer = cv2.VideoWriter(output_video_path, fourcc, fps=8, frameSize=(w, h))\n for i in range(len(video_frames)):\n img = cv2.cvtColor(video_frames[i], cv2.COLOR_RGB2BGR)\n video_writer.write(img)\n return output_video_path\n\n\ndef load_hf_numpy(path) -> np.ndarray:\n base_url = \"https://huggingface.co/datasets/fusing/diffusers-testing/resolve/main\"\n\n if not path.startswith(\"http://\") and not path.startswith(\"https://\"):\n path = os.path.join(base_url, urllib.parse.quote(path))\n\n return load_numpy(path)\n\n\n# --- pytest conf functions --- #\n\n# to avoid multiple invocation from tests/conftest.py and examples/conftest.py - make sure it's called only once\npytest_opt_registered = {}\n\n\ndef pytest_addoption_shared(parser):\n \"\"\"\n This function is to be called from `conftest.py` via `pytest_addoption` wrapper that has to be defined there.\n\n It allows loading both `conftest.py` files at once without causing a failure due to adding the same `pytest`\n option.\n\n \"\"\"\n option = \"--make-reports\"\n if option not in pytest_opt_registered:\n parser.addoption(\n option,\n action=\"store\",\n default=False,\n help=\"generate report files. The value of this option is used as a prefix to report names\",\n )\n pytest_opt_registered[option] = 1\n\n\ndef pytest_terminal_summary_main(tr, id):\n \"\"\"\n Generate multiple reports at the end of test suite run - each report goes into a dedicated file in the current\n directory. The report files are prefixed with the test suite name.\n\n This function emulates --duration and -rA pytest arguments.\n\n This function is to be called from `conftest.py` via `pytest_terminal_summary` wrapper that has to be defined\n there.\n\n Args:\n - tr: `terminalreporter` passed from `conftest.py`\n - id: unique id like `tests` or `examples` that will be incorporated into the final reports filenames - this is\n needed as some jobs have multiple runs of pytest, so we can't have them overwrite each other.\n\n NB: this functions taps into a private _pytest API and while unlikely, it could break should\n pytest do internal changes - also it calls default internal methods of terminalreporter which\n can be hijacked by various `pytest-` plugins and interfere.\n\n \"\"\"\n from _pytest.config import create_terminal_writer\n\n if not len(id):\n id = \"tests\"\n\n config = tr.config\n orig_writer = config.get_terminal_writer()\n orig_tbstyle = config.option.tbstyle\n orig_reportchars = tr.reportchars\n\n dir = \"reports\"\n Path(dir).mkdir(parents=True, exist_ok=True)\n report_files = {\n k: f\"{dir}/{id}_{k}.txt\"\n for k in [\n \"durations\",\n \"errors\",\n \"failures_long\",\n \"failures_short\",\n \"failures_line\",\n \"passes\",\n \"stats\",\n \"summary_short\",\n \"warnings\",\n ]\n }\n\n # custom durations report\n # note: there is no need to call pytest --durations=XX to get this separate report\n # adapted from https://github.com/pytest-dev/pytest/blob/897f151e/src/_pytest/runner.py#L66\n dlist = []\n for replist in tr.stats.values():\n for rep in replist:\n if hasattr(rep, \"duration\"):\n dlist.append(rep)\n if dlist:\n dlist.sort(key=lambda x: x.duration, reverse=True)\n with open(report_files[\"durations\"], \"w\") as f:\n durations_min = 0.05 # sec\n f.write(\"slowest durations\\n\")\n for i, rep in enumerate(dlist):\n if rep.duration < durations_min:\n f.write(f\"{len(dlist)-i} durations < {durations_min} secs were omitted\")\n break\n f.write(f\"{rep.duration:02.2f}s {rep.when:<8} {rep.nodeid}\\n\")\n\n def summary_failures_short(tr):\n # expecting that the reports were --tb=long (default) so we chop them off here to the last frame\n reports = tr.getreports(\"failed\")\n if not reports:\n return\n tr.write_sep(\"=\", \"FAILURES SHORT STACK\")\n for rep in reports:\n msg = tr._getfailureheadline(rep)\n tr.write_sep(\"_\", msg, red=True, bold=True)\n # chop off the optional leading extra frames, leaving only the last one\n longrepr = re.sub(r\".*_ _ _ (_ ){10,}_ _ \", \"\", rep.longreprtext, 0, re.M | re.S)\n tr._tw.line(longrepr)\n # note: not printing out any rep.sections to keep the report short\n\n # use ready-made report funcs, we are just hijacking the filehandle to log to a dedicated file each\n # adapted from https://github.com/pytest-dev/pytest/blob/897f151e/src/_pytest/terminal.py#L814\n # note: some pytest plugins may interfere by hijacking the default `terminalreporter` (e.g.\n # pytest-instafail does that)\n\n # report failures with line/short/long styles\n config.option.tbstyle = \"auto\" # full tb\n with open(report_files[\"failures_long\"], \"w\") as f:\n tr._tw = create_terminal_writer(config, f)\n tr.summary_failures()\n\n # config.option.tbstyle = \"short\" # short tb\n with open(report_files[\"failures_short\"], \"w\") as f:\n tr._tw = create_terminal_writer(config, f)\n summary_failures_short(tr)\n\n config.option.tbstyle = \"line\" # one line per error\n with open(report_files[\"failures_line\"], \"w\") as f:\n tr._tw = create_terminal_writer(config, f)\n tr.summary_failures()\n\n with open(report_files[\"errors\"], \"w\") as f:\n tr._tw = create_terminal_writer(config, f)\n tr.summary_errors()\n\n with open(report_files[\"warnings\"], \"w\") as f:\n tr._tw = create_terminal_writer(config, f)\n tr.summary_warnings() # normal warnings\n tr.summary_warnings() # final warnings\n\n tr.reportchars = \"wPpsxXEf\" # emulate -rA (used in summary_passes() and short_test_summary())\n with open(report_files[\"passes\"], \"w\") as f:\n tr._tw = create_terminal_writer(config, f)\n tr.summary_passes()\n\n with open(report_files[\"summary_short\"], \"w\") as f:\n tr._tw = create_terminal_writer(config, f)\n tr.short_test_summary()\n\n with open(report_files[\"stats\"], \"w\") as f:\n tr._tw = create_terminal_writer(config, f)\n tr.summary_stats()\n\n # restore:\n tr._tw = orig_writer\n tr.reportchars = orig_reportchars\n config.option.tbstyle = orig_tbstyle\n\n\n# Copied from https://github.com/huggingface/transformers/blob/000e52aec8850d3fe2f360adc6fd256e5b47fe4c/src/transformers/testing_utils.py#L1905\ndef is_flaky(max_attempts: int = 5, wait_before_retry: Optional[float] = None, description: Optional[str] = None):\n \"\"\"\n To decorate flaky tests. They will be retried on failures.\n\n Args:\n max_attempts (`int`, *optional*, defaults to 5):\n The maximum number of attempts to retry the flaky test.\n wait_before_retry (`float`, *optional*):\n If provided, will wait that number of seconds before retrying the test.\n description (`str`, *optional*):\n A string to describe the situation (what / where / why is flaky, link to GH issue/PR comments, errors,\n etc.)\n \"\"\"\n\n def decorator(test_func_ref):\n @functools.wraps(test_func_ref)\n def wrapper(*args, **kwargs):\n retry_count = 1\n\n while retry_count < max_attempts:\n try:\n return test_func_ref(*args, **kwargs)\n\n except Exception as err:\n print(f\"Test failed with {err} at try {retry_count}/{max_attempts}.\", file=sys.stderr)\n if wait_before_retry is not None:\n time.sleep(wait_before_retry)\n retry_count += 1\n\n return test_func_ref(*args, **kwargs)\n\n return wrapper\n\n return decorator\n\n\n# Taken from: https://github.com/huggingface/transformers/blob/3658488ff77ff8d45101293e749263acf437f4d5/src/transformers/testing_utils.py#L1787\ndef run_test_in_subprocess(test_case, target_func, inputs=None, timeout=None):\n \"\"\"\n To run a test in a subprocess. In particular, this can avoid (GPU) memory issue.\n\n Args:\n test_case (`unittest.TestCase`):\n The test that will run `target_func`.\n target_func (`Callable`):\n The function implementing the actual testing logic.\n inputs (`dict`, *optional*, defaults to `None`):\n The inputs that will be passed to `target_func` through an (input) queue.\n timeout (`int`, *optional*, defaults to `None`):\n The timeout (in seconds) that will be passed to the input and output queues. If not specified, the env.\n variable `PYTEST_TIMEOUT` will be checked. If still `None`, its value will be set to `600`.\n \"\"\"\n if timeout is None:\n timeout = int(os.environ.get(\"PYTEST_TIMEOUT\", 600))\n\n start_methohd = \"spawn\"\n ctx = multiprocessing.get_context(start_methohd)\n\n input_queue = ctx.Queue(1)\n output_queue = ctx.JoinableQueue(1)\n\n # We can't send `unittest.TestCase` to the child, otherwise we get issues regarding pickle.\n input_queue.put(inputs, timeout=timeout)\n\n process = ctx.Process(target=target_func, args=(input_queue, output_queue, timeout))\n process.start()\n # Kill the child process if we can't get outputs from it in time: otherwise, the hanging subprocess prevents\n # the test to exit properly.\n try:\n results = output_queue.get(timeout=timeout)\n output_queue.task_done()\n except Exception as e:\n process.terminate()\n test_case.fail(e)\n process.join(timeout=timeout)\n\n if results[\"error\"] is not None:\n test_case.fail(f'{results[\"error\"]}')\n\n\nclass CaptureLogger:\n \"\"\"\n Args:\n Context manager to capture `logging` streams\n logger: 'logging` logger object\n Returns:\n The captured output is available via `self.out`\n Example:\n ```python\n >>> from diffusers import logging\n >>> from diffusers.testing_utils import CaptureLogger\n\n >>> msg = \"Testing 1, 2, 3\"\n >>> logging.set_verbosity_info()\n >>> logger = logging.get_logger(\"diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.py\")\n >>> with CaptureLogger(logger) as cl:\n ... logger.info(msg)\n >>> assert cl.out, msg + \"\\n\"\n ```\n \"\"\"\n\n def __init__(self, logger):\n self.logger = logger\n self.io = StringIO()\n self.sh = logging.StreamHandler(self.io)\n self.out = \"\"\n\n def __enter__(self):\n self.logger.addHandler(self.sh)\n return self\n\n def __exit__(self, *exc):\n self.logger.removeHandler(self.sh)\n self.out = self.io.getvalue()\n\n def __repr__(self):\n return f\"captured: {self.out}\\n\"\n\n\ndef enable_full_determinism():\n \"\"\"\n Helper function for reproducible behavior during distributed training. See\n - https://pytorch.org/docs/stable/notes/randomness.html for pytorch\n \"\"\"\n # Enable PyTorch deterministic mode. This potentially requires either the environment\n # variable 'CUDA_LAUNCH_BLOCKING' or 'CUBLAS_WORKSPACE_CONFIG' to be set,\n # depending on the CUDA version, so we set them both here\n os.environ[\"CUDA_LAUNCH_BLOCKING\"] = \"1\"\n os.environ[\"CUBLAS_WORKSPACE_CONFIG\"] = \":16:8\"\n torch.use_deterministic_algorithms(True)\n\n # Enable CUDNN deterministic mode\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n torch.backends.cuda.matmul.allow_tf32 = False\n\n\ndef disable_full_determinism():\n os.environ[\"CUDA_LAUNCH_BLOCKING\"] = \"0\"\n os.environ[\"CUBLAS_WORKSPACE_CONFIG\"] = \"\"\n torch.use_deterministic_algorithms(False)\n\n\n# Utils for custom and alternative accelerator devices\ndef _is_torch_fp16_available(device):\n if not is_torch_available():\n return False\n\n import torch\n\n device = torch.device(device)\n\n try:\n x = torch.zeros((2, 2), dtype=torch.float16).to(device)\n _ = torch.mul(x, x)\n return True\n\n except Exception as e:\n if device.type == \"cuda\":\n raise ValueError(\n f\"You have passed a device of type 'cuda' which should work with 'fp16', but 'cuda' does not seem to be correctly installed on your machine: {e}\"\n )\n\n return False\n\n\ndef _is_torch_fp64_available(device):\n if not is_torch_available():\n return False\n\n import torch\n\n device = torch.device(device)\n\n try:\n x = torch.zeros((2, 2), dtype=torch.float64).to(device)\n _ = torch.mul(x, x)\n return True\n\n except Exception as e:\n if device.type == \"cuda\":\n raise ValueError(\n f\"You have passed a device of type 'cuda' which should work with 'fp64', but 'cuda' does not seem to be correctly installed on your machine: {e}\"\n )\n\n return False\n\n\n# Guard these lookups for when Torch is not used - alternative accelerator support is for PyTorch\nif is_torch_available():\n # Behaviour flags\n BACKEND_SUPPORTS_TRAINING = {\"cuda\": True, \"cpu\": True, \"mps\": False, \"default\": True}\n\n # Function definitions\n BACKEND_EMPTY_CACHE = {\"cuda\": torch.cuda.empty_cache, \"cpu\": None, \"mps\": None, \"default\": None}\n BACKEND_DEVICE_COUNT = {\"cuda\": torch.cuda.device_count, \"cpu\": lambda: 0, \"mps\": lambda: 0, \"default\": 0}\n BACKEND_MANUAL_SEED = {\"cuda\": torch.cuda.manual_seed, \"cpu\": torch.manual_seed, \"default\": torch.manual_seed}\n\n\n# This dispatches a defined function according to the accelerator from the function definitions.\ndef _device_agnostic_dispatch(device: str, dispatch_table: Dict[str, Callable], *args, **kwargs):\n if device not in dispatch_table:\n return dispatch_table[\"default\"](*args, **kwargs)\n\n fn = dispatch_table[device]\n\n # Some device agnostic functions return values. Need to guard against 'None' instead at\n # user level\n if fn is None:\n return None\n\n return fn(*args, **kwargs)\n\n\n# These are callables which automatically dispatch the function specific to the accelerator\ndef backend_manual_seed(device: str, seed: int):\n return _device_agnostic_dispatch(device, BACKEND_MANUAL_SEED, seed)\n\n\ndef backend_empty_cache(device: str):\n return _device_agnostic_dispatch(device, BACKEND_EMPTY_CACHE)\n\n\ndef backend_device_count(device: str):\n return _device_agnostic_dispatch(device, BACKEND_DEVICE_COUNT)\n\n\n# These are callables which return boolean behaviour flags and can be used to specify some\n# device agnostic alternative where the feature is unsupported.\ndef backend_supports_training(device: str):\n if not is_torch_available():\n return False\n\n if device not in BACKEND_SUPPORTS_TRAINING:\n device = \"default\"\n\n return BACKEND_SUPPORTS_TRAINING[device]\n\n\n# Guard for when Torch is not available\nif is_torch_available():\n # Update device function dict mapping\n def update_mapping_from_spec(device_fn_dict: Dict[str, Callable], attribute_name: str):\n try:\n # Try to import the function directly\n spec_fn = getattr(device_spec_module, attribute_name)\n device_fn_dict[torch_device] = spec_fn\n except AttributeError as e:\n # If the function doesn't exist, and there is no default, throw an error\n if \"default\" not in device_fn_dict:\n raise AttributeError(\n f\"`{attribute_name}` not found in '{device_spec_path}' and no default fallback function found.\"\n ) from e\n\n if \"DIFFUSERS_TEST_DEVICE_SPEC\" in os.environ:\n device_spec_path = os.environ[\"DIFFUSERS_TEST_DEVICE_SPEC\"]\n if not Path(device_spec_path).is_file():\n raise ValueError(f\"Specified path to device specification file is not found. Received {device_spec_path}\")\n\n try:\n import_name = device_spec_path[: device_spec_path.index(\".py\")]\n except ValueError as e:\n raise ValueError(f\"Provided device spec file is not a Python file! Received {device_spec_path}\") from e\n\n device_spec_module = importlib.import_module(import_name)\n\n try:\n device_name = device_spec_module.DEVICE_NAME\n except AttributeError:\n raise AttributeError(\"Device spec file did not contain `DEVICE_NAME`\")\n\n if \"DIFFUSERS_TEST_DEVICE\" in os.environ and torch_device != device_name:\n msg = f\"Mismatch between environment variable `DIFFUSERS_TEST_DEVICE` '{torch_device}' and device found in spec '{device_name}'\\n\"\n msg += \"Either unset `DIFFUSERS_TEST_DEVICE` or ensure it matches device spec name.\"\n raise ValueError(msg)\n\n torch_device = device_name\n\n # Add one entry here for each `BACKEND_*` dictionary.\n update_mapping_from_spec(BACKEND_MANUAL_SEED, \"MANUAL_SEED_FN\")\n update_mapping_from_spec(BACKEND_EMPTY_CACHE, \"EMPTY_CACHE_FN\")\n update_mapping_from_spec(BACKEND_DEVICE_COUNT, \"DEVICE_COUNT_FN\")\n update_mapping_from_spec(BACKEND_SUPPORTS_TRAINING, \"SUPPORTS_TRAINING\")\n" }, { "path": "diffusers/src/diffusers/utils/torch_utils.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nPyTorch utilities: Utilities related to PyTorch\n\"\"\"\n\nfrom typing import List, Optional, Tuple, Union\n\nfrom . import logging\nfrom .import_utils import is_torch_available, is_torch_version\n\n\nif is_torch_available():\n import torch\n from torch.fft import fftn, fftshift, ifftn, ifftshift\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\ntry:\n from torch._dynamo import allow_in_graph as maybe_allow_in_graph\nexcept (ImportError, ModuleNotFoundError):\n\n def maybe_allow_in_graph(cls):\n return cls\n\n\ndef randn_tensor(\n shape: Union[Tuple, List],\n generator: Optional[Union[List[\"torch.Generator\"], \"torch.Generator\"]] = None,\n device: Optional[\"torch.device\"] = None,\n dtype: Optional[\"torch.dtype\"] = None,\n layout: Optional[\"torch.layout\"] = None,\n):\n \"\"\"A helper function to create random tensors on the desired `device` with the desired `dtype`. When\n passing a list of generators, you can seed each batch size individually. If CPU generators are passed, the tensor\n is always created on the CPU.\n \"\"\"\n # device on which tensor is created defaults to device\n rand_device = device\n batch_size = shape[0]\n\n layout = layout or torch.strided\n device = device or torch.device(\"cpu\")\n\n if generator is not None:\n gen_device_type = generator.device.type if not isinstance(generator, list) else generator[0].device.type\n if gen_device_type != device.type and gen_device_type == \"cpu\":\n rand_device = \"cpu\"\n if device != \"mps\":\n logger.info(\n f\"The passed generator was created on 'cpu' even though a tensor on {device} was expected.\"\n f\" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably\"\n f\" slighly speed up this function by passing a generator that was created on the {device} device.\"\n )\n elif gen_device_type != device.type and gen_device_type == \"cuda\":\n raise ValueError(f\"Cannot generate a {device} tensor from a generator of type {gen_device_type}.\")\n\n # make sure generator list of length 1 is treated like a non-list\n if isinstance(generator, list) and len(generator) == 1:\n generator = generator[0]\n\n if isinstance(generator, list):\n shape = (1,) + shape[1:]\n latents = [\n torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype, layout=layout)\n for i in range(batch_size)\n ]\n latents = torch.cat(latents, dim=0).to(device)\n else:\n latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype, layout=layout).to(device)\n\n return latents\n\n\ndef is_compiled_module(module) -> bool:\n \"\"\"Check whether the module was compiled with torch.compile()\"\"\"\n if is_torch_version(\"<\", \"2.0.0\") or not hasattr(torch, \"_dynamo\"):\n return False\n return isinstance(module, torch._dynamo.eval_frame.OptimizedModule)\n\n\ndef fourier_filter(x_in: \"torch.Tensor\", threshold: int, scale: int) -> \"torch.Tensor\":\n \"\"\"Fourier filter as introduced in FreeU (https://arxiv.org/abs/2309.11497).\n\n This version of the method comes from here:\n https://github.com/huggingface/diffusers/pull/5164#issuecomment-1732638706\n \"\"\"\n x = x_in\n B, C, H, W = x.shape\n\n # Non-power of 2 images must be float32\n if (W & (W - 1)) != 0 or (H & (H - 1)) != 0:\n x = x.to(dtype=torch.float32)\n\n # FFT\n x_freq = fftn(x, dim=(-2, -1))\n x_freq = fftshift(x_freq, dim=(-2, -1))\n\n B, C, H, W = x_freq.shape\n mask = torch.ones((B, C, H, W), device=x.device)\n\n crow, ccol = H // 2, W // 2\n mask[..., crow - threshold : crow + threshold, ccol - threshold : ccol + threshold] = scale\n x_freq = x_freq * mask\n\n # IFFT\n x_freq = ifftshift(x_freq, dim=(-2, -1))\n x_filtered = ifftn(x_freq, dim=(-2, -1)).real\n\n return x_filtered.to(dtype=x_in.dtype)\n\n\ndef apply_freeu(\n resolution_idx: int, hidden_states: \"torch.Tensor\", res_hidden_states: \"torch.Tensor\", **freeu_kwargs\n) -> Tuple[\"torch.Tensor\", \"torch.Tensor\"]:\n \"\"\"Applies the FreeU mechanism as introduced in https:\n //arxiv.org/abs/2309.11497. Adapted from the official code repository: https://github.com/ChenyangSi/FreeU.\n\n Args:\n resolution_idx (`int`): Integer denoting the UNet block where FreeU is being applied.\n hidden_states (`torch.Tensor`): Inputs to the underlying block.\n res_hidden_states (`torch.Tensor`): Features from the skip block corresponding to the underlying block.\n s1 (`float`): Scaling factor for stage 1 to attenuate the contributions of the skip features.\n s2 (`float`): Scaling factor for stage 2 to attenuate the contributions of the skip features.\n b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.\n b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.\n \"\"\"\n if resolution_idx == 0:\n num_half_channels = hidden_states.shape[1] // 2\n hidden_states[:, :num_half_channels] = hidden_states[:, :num_half_channels] * freeu_kwargs[\"b1\"]\n res_hidden_states = fourier_filter(res_hidden_states, threshold=1, scale=freeu_kwargs[\"s1\"])\n if resolution_idx == 1:\n num_half_channels = hidden_states.shape[1] // 2\n hidden_states[:, :num_half_channels] = hidden_states[:, :num_half_channels] * freeu_kwargs[\"b2\"]\n res_hidden_states = fourier_filter(res_hidden_states, threshold=1, scale=freeu_kwargs[\"s2\"])\n\n return hidden_states, res_hidden_states\n" }, { "path": "diffusers/src/diffusers/utils/versions.py", "content": "# Copyright 2020 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nUtilities for working with package versions\n\"\"\"\n\nimport importlib.metadata\nimport operator\nimport re\nimport sys\nfrom typing import Optional\n\nfrom packaging import version\n\n\nops = {\n \"<\": operator.lt,\n \"<=\": operator.le,\n \"==\": operator.eq,\n \"!=\": operator.ne,\n \">=\": operator.ge,\n \">\": operator.gt,\n}\n\n\ndef _compare_versions(op, got_ver, want_ver, requirement, pkg, hint):\n if got_ver is None or want_ver is None:\n raise ValueError(\n f\"Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider\"\n f\" reinstalling {pkg}.\"\n )\n if not ops[op](version.parse(got_ver), version.parse(want_ver)):\n raise ImportError(\n f\"{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}\"\n )\n\n\ndef require_version(requirement: str, hint: Optional[str] = None) -> None:\n \"\"\"\n Perform a runtime check of the dependency versions, using the exact same syntax used by pip.\n\n The installed module version comes from the *site-packages* dir via *importlib.metadata*.\n\n Args:\n requirement (`str`): pip style definition, e.g., \"tokenizers==0.9.4\", \"tqdm>=4.27\", \"numpy\"\n hint (`str`, *optional*): what suggestion to print in case of requirements not being met\n\n Example:\n\n ```python\n require_version(\"pandas>1.1.2\")\n require_version(\"numpy>1.18.5\", \"this is important to have for whatever reason\")\n ```\"\"\"\n\n hint = f\"\\n{hint}\" if hint is not None else \"\"\n\n # non-versioned check\n if re.match(r\"^[\\w_\\-\\d]+$\", requirement):\n pkg, op, want_ver = requirement, None, None\n else:\n match = re.findall(r\"^([^!=<>\\s]+)([\\s!=<>]{1,2}.+)\", requirement)\n if not match:\n raise ValueError(\n \"requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but\"\n f\" got {requirement}\"\n )\n pkg, want_full = match[0]\n want_range = want_full.split(\",\") # there could be multiple requirements\n wanted = {}\n for w in want_range:\n match = re.findall(r\"^([\\s!=<>]{1,2})(.+)\", w)\n if not match:\n raise ValueError(\n \"requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,\"\n f\" but got {requirement}\"\n )\n op, want_ver = match[0]\n wanted[op] = want_ver\n if op not in ops:\n raise ValueError(f\"{requirement}: need one of {list(ops.keys())}, but got {op}\")\n\n # special case\n if pkg == \"python\":\n got_ver = \".\".join([str(x) for x in sys.version_info[:3]])\n for op, want_ver in wanted.items():\n _compare_versions(op, got_ver, want_ver, requirement, pkg, hint)\n return\n\n # check if any version is installed\n try:\n got_ver = importlib.metadata.version(pkg)\n except importlib.metadata.PackageNotFoundError:\n raise importlib.metadata.PackageNotFoundError(\n f\"The '{requirement}' distribution was not found and is required by this application. {hint}\"\n )\n\n # check that the right version is installed if version number or a range was provided\n if want_ver is not None:\n for op, want_ver in wanted.items():\n _compare_versions(op, got_ver, want_ver, requirement, pkg, hint)\n\n\ndef require_version_core(requirement):\n \"\"\"require_version wrapper which emits a core-specific hint on failure\"\"\"\n hint = \"Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main\"\n return require_version(requirement, hint)\n" }, { "path": "diffusers/src/diffusers/video_processor.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport warnings\nfrom typing import List, Optional, Union\n\nimport numpy as np\nimport PIL\nimport torch\n\nfrom .image_processor import VaeImageProcessor, is_valid_image, is_valid_image_imagelist\n\n\nclass VideoProcessor(VaeImageProcessor):\n r\"\"\"Simple video processor.\"\"\"\n\n def preprocess_video(self, video, height: Optional[int] = None, width: Optional[int] = None) -> torch.Tensor:\n r\"\"\"\n Preprocesses input video(s).\n\n Args:\n video (`List[PIL.Image]`, `List[List[PIL.Image]]`, `torch.Tensor`, `np.array`, `List[torch.Tensor]`, `List[np.array]`):\n The input video. It can be one of the following:\n * List of the PIL images.\n * List of list of PIL images.\n * 4D Torch tensors (expected shape for each tensor `(num_frames, num_channels, height, width)`).\n * 4D NumPy arrays (expected shape for each array `(num_frames, height, width, num_channels)`).\n * List of 4D Torch tensors (expected shape for each tensor `(num_frames, num_channels, height,\n width)`).\n * List of 4D NumPy arrays (expected shape for each array `(num_frames, height, width, num_channels)`).\n * 5D NumPy arrays: expected shape for each array `(batch_size, num_frames, height, width,\n num_channels)`.\n * 5D Torch tensors: expected shape for each array `(batch_size, num_frames, num_channels, height,\n width)`.\n height (`int`, *optional*, defaults to `None`):\n The height in preprocessed frames of the video. If `None`, will use the `get_default_height_width()` to\n get default height.\n width (`int`, *optional*`, defaults to `None`):\n The width in preprocessed frames of the video. If `None`, will use get_default_height_width()` to get\n the default width.\n \"\"\"\n if isinstance(video, list) and isinstance(video[0], np.ndarray) and video[0].ndim == 5:\n warnings.warn(\n \"Passing `video` as a list of 5d np.ndarray is deprecated.\"\n \"Please concatenate the list along the batch dimension and pass it as a single 5d np.ndarray\",\n FutureWarning,\n )\n video = np.concatenate(video, axis=0)\n if isinstance(video, list) and isinstance(video[0], torch.Tensor) and video[0].ndim == 5:\n warnings.warn(\n \"Passing `video` as a list of 5d torch.Tensor is deprecated.\"\n \"Please concatenate the list along the batch dimension and pass it as a single 5d torch.Tensor\",\n FutureWarning,\n )\n video = torch.cat(video, axis=0)\n\n # ensure the input is a list of videos:\n # - if it is a batch of videos (5d torch.Tensor or np.ndarray), it is converted to a list of videos (a list of 4d torch.Tensor or np.ndarray)\n # - if it is is a single video, it is convereted to a list of one video.\n if isinstance(video, (np.ndarray, torch.Tensor)) and video.ndim == 5:\n video = list(video)\n elif isinstance(video, list) and is_valid_image(video[0]) or is_valid_image_imagelist(video):\n video = [video]\n elif isinstance(video, list) and is_valid_image_imagelist(video[0]):\n video = video\n else:\n raise ValueError(\n \"Input is in incorrect format. Currently, we only support numpy.ndarray, torch.Tensor, PIL.Image.Image\"\n )\n\n video = torch.stack([self.preprocess(img, height=height, width=width) for img in video], dim=0)\n\n # move the number of channels before the number of frames.\n video = video.permute(0, 2, 1, 3, 4)\n\n return video\n\n def postprocess_video(\n self, video: torch.Tensor, output_type: str = \"np\"\n ) -> Union[np.ndarray, torch.Tensor, List[PIL.Image.Image]]:\n r\"\"\"\n Converts a video tensor to a list of frames for export.\n\n Args:\n video (`torch.Tensor`): The video as a tensor.\n output_type (`str`, defaults to `\"np\"`): Output type of the postprocessed `video` tensor.\n \"\"\"\n batch_size = video.shape[0]\n outputs = []\n for batch_idx in range(batch_size):\n batch_vid = video[batch_idx].permute(1, 0, 2, 3)\n batch_output = self.postprocess(batch_vid, output_type)\n outputs.append(batch_output)\n\n if output_type == \"np\":\n outputs = np.stack(outputs)\n elif output_type == \"pt\":\n outputs = torch.stack(outputs)\n elif not output_type == \"pil\":\n raise ValueError(f\"{output_type} does not exist. Please choose one of ['np', 'pt', 'pil']\")\n\n return outputs\n" }, { "path": "diffusers/subd_112030.err", "content": "slurmstepd: error: *** JOB 112030 ON dgx-hyperplane15 CANCELLED AT 2024-06-21T12:09:50 ***\n" }, { "path": "diffusers/subh_112029.err", "content": "slurmstepd: error: *** JOB 112029 ON hgx-hyperplane08 CANCELLED AT 2024-06-23T23:56:07 ***\n" }, { "path": "diffusers/tests/__init__.py", "content": "" }, { "path": "diffusers/tests/conftest.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# tests directory-specific settings - this file is run automatically\n# by pytest before any tests are run\n\nimport sys\nimport warnings\nfrom os.path import abspath, dirname, join\n\n\n# allow having multiple repository checkouts and not needing to remember to rerun\n# 'pip install -e .[dev]' when switching between checkouts and running tests.\ngit_repo_path = abspath(join(dirname(dirname(__file__)), \"src\"))\nsys.path.insert(1, git_repo_path)\n\n# silence FutureWarning warnings in tests since often we can't act on them until\n# they become normal warnings - i.e. the tests still need to test the current functionality\nwarnings.simplefilter(action=\"ignore\", category=FutureWarning)\n\n\ndef pytest_addoption(parser):\n from diffusers.utils.testing_utils import pytest_addoption_shared\n\n pytest_addoption_shared(parser)\n\n\ndef pytest_terminal_summary(terminalreporter):\n from diffusers.utils.testing_utils import pytest_terminal_summary_main\n\n make_reports = terminalreporter.config.getoption(\"--make-reports\")\n if make_reports:\n pytest_terminal_summary_main(terminalreporter, id=make_reports)\n" }, { "path": "diffusers/tests/fixtures/custom_pipeline/pipeline.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\n# limitations under the License.\n\n\nfrom typing import Optional, Tuple, Union\n\nimport torch\n\nfrom diffusers import DiffusionPipeline, ImagePipelineOutput\n\n\nclass CustomLocalPipeline(DiffusionPipeline):\n r\"\"\"\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Parameters:\n unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of\n [`DDPMScheduler`], or [`DDIMScheduler`].\n \"\"\"\n\n def __init__(self, unet, scheduler):\n super().__init__()\n self.register_modules(unet=unet, scheduler=scheduler)\n\n @torch.no_grad()\n def __call__(\n self,\n batch_size: int = 1,\n generator: Optional[torch.Generator] = None,\n num_inference_steps: int = 50,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n **kwargs,\n ) -> Union[ImagePipelineOutput, Tuple]:\n r\"\"\"\n Args:\n batch_size (`int`, *optional*, defaults to 1):\n The number of images to generate.\n generator (`torch.Generator`, *optional*):\n A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation\n deterministic.\n eta (`float`, *optional*, defaults to 0.0):\n The eta parameter which controls the scale of the variance (0 is DDIM and 1 is one type of DDPM).\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.\n\n Returns:\n [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if\n `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the\n generated images.\n \"\"\"\n\n # Sample gaussian noise to begin loop\n image = torch.randn(\n (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),\n generator=generator,\n )\n image = image.to(self.device)\n\n # set step values\n self.scheduler.set_timesteps(num_inference_steps)\n\n for t in self.progress_bar(self.scheduler.timesteps):\n # 1. predict noise model_output\n model_output = self.unet(image, t).sample\n\n # 2. predict previous mean of image x_t-1 and add variance depending on eta\n # eta corresponds to η in paper and should be between [0, 1]\n # do x_t -> x_t-1\n image = self.scheduler.step(model_output, t, image).prev_sample\n\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).numpy()\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image,), \"This is a local test\"\n\n return ImagePipelineOutput(images=image), \"This is a local test\"\n" }, { "path": "diffusers/tests/fixtures/custom_pipeline/what_ever.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n\n# limitations under the License.\n\n\nfrom typing import Optional, Tuple, Union\n\nimport torch\n\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput\n\n\nclass CustomLocalPipeline(DiffusionPipeline):\n r\"\"\"\n This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the\n library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)\n\n Parameters:\n unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.\n scheduler ([`SchedulerMixin`]):\n A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of\n [`DDPMScheduler`], or [`DDIMScheduler`].\n \"\"\"\n\n def __init__(self, unet, scheduler):\n super().__init__()\n self.register_modules(unet=unet, scheduler=scheduler)\n\n @torch.no_grad()\n def __call__(\n self,\n batch_size: int = 1,\n generator: Optional[torch.Generator] = None,\n num_inference_steps: int = 50,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n **kwargs,\n ) -> Union[ImagePipelineOutput, Tuple]:\n r\"\"\"\n Args:\n batch_size (`int`, *optional*, defaults to 1):\n The number of images to generate.\n generator (`torch.Generator`, *optional*):\n A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation\n deterministic.\n eta (`float`, *optional*, defaults to 0.0):\n The eta parameter which controls the scale of the variance (0 is DDIM and 1 is one type of DDPM).\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.\n\n Returns:\n [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if\n `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the\n generated images.\n \"\"\"\n\n # Sample gaussian noise to begin loop\n image = torch.randn(\n (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),\n generator=generator,\n )\n image = image.to(self.device)\n\n # set step values\n self.scheduler.set_timesteps(num_inference_steps)\n\n for t in self.progress_bar(self.scheduler.timesteps):\n # 1. predict noise model_output\n model_output = self.unet(image, t).sample\n\n # 2. predict previous mean of image x_t-1 and add variance depending on eta\n # eta corresponds to η in paper and should be between [0, 1]\n # do x_t -> x_t-1\n image = self.scheduler.step(model_output, t, image).prev_sample\n\n image = (image / 2 + 0.5).clamp(0, 1)\n image = image.cpu().permute(0, 2, 3, 1).numpy()\n if output_type == \"pil\":\n image = self.numpy_to_pil(image)\n\n if not return_dict:\n return (image,), \"This is a local test\"\n\n return ImagePipelineOutput(images=image), \"This is a local test\"\n" }, { "path": "diffusers/tests/lora/test_lora_layers_sd.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport gc\nimport sys\nimport unittest\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom huggingface_hub import hf_hub_download\nfrom huggingface_hub.repocard import RepoCard\nfrom safetensors.torch import load_file\n\nfrom diffusers import (\n AutoPipelineForImage2Image,\n AutoPipelineForText2Image,\n DDIMScheduler,\n DiffusionPipeline,\n LCMScheduler,\n StableDiffusionPipeline,\n)\nfrom diffusers.utils.import_utils import is_accelerate_available\nfrom diffusers.utils.testing_utils import (\n load_image,\n numpy_cosine_similarity_distance,\n require_peft_backend,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\n\nsys.path.append(\".\")\n\nfrom utils import PeftLoraLoaderMixinTests, check_if_lora_correctly_set # noqa: E402\n\n\nif is_accelerate_available():\n from accelerate.utils import release_memory\n\n\nclass StableDiffusionLoRATests(PeftLoraLoaderMixinTests, unittest.TestCase):\n pipeline_class = StableDiffusionPipeline\n scheduler_cls = DDIMScheduler\n scheduler_kwargs = {\n \"beta_start\": 0.00085,\n \"beta_end\": 0.012,\n \"beta_schedule\": \"scaled_linear\",\n \"clip_sample\": False,\n \"set_alpha_to_one\": False,\n \"steps_offset\": 1,\n }\n unet_kwargs = {\n \"block_out_channels\": (32, 64),\n \"layers_per_block\": 2,\n \"sample_size\": 32,\n \"in_channels\": 4,\n \"out_channels\": 4,\n \"down_block_types\": (\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n \"up_block_types\": (\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n \"cross_attention_dim\": 32,\n }\n vae_kwargs = {\n \"block_out_channels\": [32, 64],\n \"in_channels\": 3,\n \"out_channels\": 3,\n \"down_block_types\": [\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n \"up_block_types\": [\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n \"latent_channels\": 4,\n }\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n # Keeping this test here makes sense because it doesn't look any integration\n # (value assertions on logits).\n @slow\n @require_torch_gpu\n def test_integration_move_lora_cpu(self):\n path = \"runwayml/stable-diffusion-v1-5\"\n lora_id = \"takuma104/lora-test-text-encoder-lora-target\"\n\n pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)\n pipe.load_lora_weights(lora_id, adapter_name=\"adapter-1\")\n pipe.load_lora_weights(lora_id, adapter_name=\"adapter-2\")\n pipe = pipe.to(torch_device)\n\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder),\n \"Lora not correctly set in text encoder\",\n )\n\n self.assertTrue(\n check_if_lora_correctly_set(pipe.unet),\n \"Lora not correctly set in text encoder\",\n )\n\n # We will offload the first adapter in CPU and check if the offloading\n # has been performed correctly\n pipe.set_lora_device([\"adapter-1\"], \"cpu\")\n\n for name, module in pipe.unet.named_modules():\n if \"adapter-1\" in name and not isinstance(module, (nn.Dropout, nn.Identity)):\n self.assertTrue(module.weight.device == torch.device(\"cpu\"))\n elif \"adapter-2\" in name and not isinstance(module, (nn.Dropout, nn.Identity)):\n self.assertTrue(module.weight.device != torch.device(\"cpu\"))\n\n for name, module in pipe.text_encoder.named_modules():\n if \"adapter-1\" in name and not isinstance(module, (nn.Dropout, nn.Identity)):\n self.assertTrue(module.weight.device == torch.device(\"cpu\"))\n elif \"adapter-2\" in name and not isinstance(module, (nn.Dropout, nn.Identity)):\n self.assertTrue(module.weight.device != torch.device(\"cpu\"))\n\n pipe.set_lora_device([\"adapter-1\"], 0)\n\n for n, m in pipe.unet.named_modules():\n if \"adapter-1\" in n and not isinstance(m, (nn.Dropout, nn.Identity)):\n self.assertTrue(m.weight.device != torch.device(\"cpu\"))\n\n for n, m in pipe.text_encoder.named_modules():\n if \"adapter-1\" in n and not isinstance(m, (nn.Dropout, nn.Identity)):\n self.assertTrue(m.weight.device != torch.device(\"cpu\"))\n\n pipe.set_lora_device([\"adapter-1\", \"adapter-2\"], torch_device)\n\n for n, m in pipe.unet.named_modules():\n if (\"adapter-1\" in n or \"adapter-2\" in n) and not isinstance(m, (nn.Dropout, nn.Identity)):\n self.assertTrue(m.weight.device != torch.device(\"cpu\"))\n\n for n, m in pipe.text_encoder.named_modules():\n if (\"adapter-1\" in n or \"adapter-2\" in n) and not isinstance(m, (nn.Dropout, nn.Identity)):\n self.assertTrue(m.weight.device != torch.device(\"cpu\"))\n\n @require_torch_gpu\n def test_integration_move_lora_dora_cpu(self):\n from peft import LoraConfig\n\n path = \"runwayml/stable-diffusion-v1-5\"\n unet_lora_config = LoraConfig(\n init_lora_weights=\"gaussian\",\n target_modules=[\"to_k\", \"to_q\", \"to_v\", \"to_out.0\"],\n use_dora=True,\n )\n text_lora_config = LoraConfig(\n init_lora_weights=\"gaussian\",\n target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"out_proj\"],\n use_dora=True,\n )\n\n pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)\n pipe.unet.add_adapter(unet_lora_config, \"adapter-1\")\n pipe.text_encoder.add_adapter(text_lora_config, \"adapter-1\")\n\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder),\n \"Lora not correctly set in text encoder\",\n )\n\n self.assertTrue(\n check_if_lora_correctly_set(pipe.unet),\n \"Lora not correctly set in text encoder\",\n )\n\n for name, param in pipe.unet.named_parameters():\n if \"lora_\" in name:\n self.assertEqual(param.device, torch.device(\"cpu\"))\n\n for name, param in pipe.text_encoder.named_parameters():\n if \"lora_\" in name:\n self.assertEqual(param.device, torch.device(\"cpu\"))\n\n pipe.set_lora_device([\"adapter-1\"], torch_device)\n\n for name, param in pipe.unet.named_parameters():\n if \"lora_\" in name:\n self.assertNotEqual(param.device, torch.device(\"cpu\"))\n\n for name, param in pipe.text_encoder.named_parameters():\n if \"lora_\" in name:\n self.assertNotEqual(param.device, torch.device(\"cpu\"))\n\n\n@slow\n@require_torch_gpu\n@require_peft_backend\nclass LoraIntegrationTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_integration_logits_with_scale(self):\n path = \"runwayml/stable-diffusion-v1-5\"\n lora_id = \"takuma104/lora-test-text-encoder-lora-target\"\n\n pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float32)\n pipe.load_lora_weights(lora_id)\n pipe = pipe.to(torch_device)\n\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder),\n \"Lora not correctly set in text encoder\",\n )\n\n prompt = \"a red sks dog\"\n\n images = pipe(\n prompt=prompt,\n num_inference_steps=15,\n cross_attention_kwargs={\"scale\": 0.5},\n generator=torch.manual_seed(0),\n output_type=\"np\",\n ).images\n\n expected_slice_scale = np.array([0.307, 0.283, 0.310, 0.310, 0.300, 0.314, 0.336, 0.314, 0.321])\n predicted_slice = images[0, -3:, -3:, -1].flatten()\n\n max_diff = numpy_cosine_similarity_distance(expected_slice_scale, predicted_slice)\n assert max_diff < 1e-3\n\n pipe.unload_lora_weights()\n release_memory(pipe)\n\n def test_integration_logits_no_scale(self):\n path = \"runwayml/stable-diffusion-v1-5\"\n lora_id = \"takuma104/lora-test-text-encoder-lora-target\"\n\n pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float32)\n pipe.load_lora_weights(lora_id)\n pipe = pipe.to(torch_device)\n\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder),\n \"Lora not correctly set in text encoder\",\n )\n\n prompt = \"a red sks dog\"\n\n images = pipe(prompt=prompt, num_inference_steps=30, generator=torch.manual_seed(0), output_type=\"np\").images\n\n expected_slice_scale = np.array([0.074, 0.064, 0.073, 0.0842, 0.069, 0.0641, 0.0794, 0.076, 0.084])\n predicted_slice = images[0, -3:, -3:, -1].flatten()\n\n max_diff = numpy_cosine_similarity_distance(expected_slice_scale, predicted_slice)\n\n assert max_diff < 1e-3\n\n pipe.unload_lora_weights()\n release_memory(pipe)\n\n def test_dreambooth_old_format(self):\n generator = torch.Generator(\"cpu\").manual_seed(0)\n\n lora_model_id = \"hf-internal-testing/lora_dreambooth_dog_example\"\n card = RepoCard.load(lora_model_id)\n base_model_id = card.data.to_dict()[\"base_model\"]\n\n pipe = StableDiffusionPipeline.from_pretrained(base_model_id, safety_checker=None)\n pipe = pipe.to(torch_device)\n pipe.load_lora_weights(lora_model_id)\n\n images = pipe(\n \"A photo of a sks dog floating in the river\", output_type=\"np\", generator=generator, num_inference_steps=2\n ).images\n\n images = images[0, -3:, -3:, -1].flatten()\n expected = np.array([0.7207, 0.6787, 0.6010, 0.7478, 0.6838, 0.6064, 0.6984, 0.6443, 0.5785])\n\n max_diff = numpy_cosine_similarity_distance(expected, images)\n assert max_diff < 1e-4\n\n pipe.unload_lora_weights()\n release_memory(pipe)\n\n def test_dreambooth_text_encoder_new_format(self):\n generator = torch.Generator().manual_seed(0)\n\n lora_model_id = \"hf-internal-testing/lora-trained\"\n card = RepoCard.load(lora_model_id)\n base_model_id = card.data.to_dict()[\"base_model\"]\n\n pipe = StableDiffusionPipeline.from_pretrained(base_model_id, safety_checker=None)\n pipe = pipe.to(torch_device)\n pipe.load_lora_weights(lora_model_id)\n\n images = pipe(\"A photo of a sks dog\", output_type=\"np\", generator=generator, num_inference_steps=2).images\n\n images = images[0, -3:, -3:, -1].flatten()\n\n expected = np.array([0.6628, 0.6138, 0.5390, 0.6625, 0.6130, 0.5463, 0.6166, 0.5788, 0.5359])\n\n max_diff = numpy_cosine_similarity_distance(expected, images)\n assert max_diff < 1e-4\n\n pipe.unload_lora_weights()\n release_memory(pipe)\n\n def test_a1111(self):\n generator = torch.Generator().manual_seed(0)\n\n pipe = StableDiffusionPipeline.from_pretrained(\"hf-internal-testing/Counterfeit-V2.5\", safety_checker=None).to(\n torch_device\n )\n lora_model_id = \"hf-internal-testing/civitai-light-shadow-lora\"\n lora_filename = \"light_and_shadow.safetensors\"\n pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)\n\n images = pipe(\n \"masterpiece, best quality, mountain\", output_type=\"np\", generator=generator, num_inference_steps=2\n ).images\n\n images = images[0, -3:, -3:, -1].flatten()\n expected = np.array([0.3636, 0.3708, 0.3694, 0.3679, 0.3829, 0.3677, 0.3692, 0.3688, 0.3292])\n\n max_diff = numpy_cosine_similarity_distance(expected, images)\n assert max_diff < 1e-3\n\n pipe.unload_lora_weights()\n release_memory(pipe)\n\n def test_lycoris(self):\n generator = torch.Generator().manual_seed(0)\n\n pipe = StableDiffusionPipeline.from_pretrained(\n \"hf-internal-testing/Amixx\", safety_checker=None, use_safetensors=True, variant=\"fp16\"\n ).to(torch_device)\n lora_model_id = \"hf-internal-testing/edgLycorisMugler-light\"\n lora_filename = \"edgLycorisMugler-light.safetensors\"\n pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)\n\n images = pipe(\n \"masterpiece, best quality, mountain\", output_type=\"np\", generator=generator, num_inference_steps=2\n ).images\n\n images = images[0, -3:, -3:, -1].flatten()\n expected = np.array([0.6463, 0.658, 0.599, 0.6542, 0.6512, 0.6213, 0.658, 0.6485, 0.6017])\n\n max_diff = numpy_cosine_similarity_distance(expected, images)\n assert max_diff < 1e-3\n\n pipe.unload_lora_weights()\n release_memory(pipe)\n\n def test_a1111_with_model_cpu_offload(self):\n generator = torch.Generator().manual_seed(0)\n\n pipe = StableDiffusionPipeline.from_pretrained(\"hf-internal-testing/Counterfeit-V2.5\", safety_checker=None)\n pipe.enable_model_cpu_offload()\n lora_model_id = \"hf-internal-testing/civitai-light-shadow-lora\"\n lora_filename = \"light_and_shadow.safetensors\"\n pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)\n\n images = pipe(\n \"masterpiece, best quality, mountain\", output_type=\"np\", generator=generator, num_inference_steps=2\n ).images\n\n images = images[0, -3:, -3:, -1].flatten()\n expected = np.array([0.3636, 0.3708, 0.3694, 0.3679, 0.3829, 0.3677, 0.3692, 0.3688, 0.3292])\n\n max_diff = numpy_cosine_similarity_distance(expected, images)\n assert max_diff < 1e-3\n\n pipe.unload_lora_weights()\n release_memory(pipe)\n\n def test_a1111_with_sequential_cpu_offload(self):\n generator = torch.Generator().manual_seed(0)\n\n pipe = StableDiffusionPipeline.from_pretrained(\"hf-internal-testing/Counterfeit-V2.5\", safety_checker=None)\n pipe.enable_sequential_cpu_offload()\n lora_model_id = \"hf-internal-testing/civitai-light-shadow-lora\"\n lora_filename = \"light_and_shadow.safetensors\"\n pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)\n\n images = pipe(\n \"masterpiece, best quality, mountain\", output_type=\"np\", generator=generator, num_inference_steps=2\n ).images\n\n images = images[0, -3:, -3:, -1].flatten()\n expected = np.array([0.3636, 0.3708, 0.3694, 0.3679, 0.3829, 0.3677, 0.3692, 0.3688, 0.3292])\n\n max_diff = numpy_cosine_similarity_distance(expected, images)\n assert max_diff < 1e-3\n\n pipe.unload_lora_weights()\n release_memory(pipe)\n\n def test_kohya_sd_v15_with_higher_dimensions(self):\n generator = torch.Generator().manual_seed(0)\n\n pipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", safety_checker=None).to(\n torch_device\n )\n lora_model_id = \"hf-internal-testing/urushisato-lora\"\n lora_filename = \"urushisato_v15.safetensors\"\n pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)\n\n images = pipe(\n \"masterpiece, best quality, mountain\", output_type=\"np\", generator=generator, num_inference_steps=2\n ).images\n\n images = images[0, -3:, -3:, -1].flatten()\n expected = np.array([0.7165, 0.6616, 0.5833, 0.7504, 0.6718, 0.587, 0.6871, 0.6361, 0.5694])\n\n max_diff = numpy_cosine_similarity_distance(expected, images)\n assert max_diff < 1e-3\n\n pipe.unload_lora_weights()\n release_memory(pipe)\n\n def test_vanilla_funetuning(self):\n generator = torch.Generator().manual_seed(0)\n\n lora_model_id = \"hf-internal-testing/sd-model-finetuned-lora-t4\"\n card = RepoCard.load(lora_model_id)\n base_model_id = card.data.to_dict()[\"base_model\"]\n\n pipe = StableDiffusionPipeline.from_pretrained(base_model_id, safety_checker=None)\n pipe = pipe.to(torch_device)\n pipe.load_lora_weights(lora_model_id)\n\n images = pipe(\"A pokemon with blue eyes.\", output_type=\"np\", generator=generator, num_inference_steps=2).images\n\n images = images[0, -3:, -3:, -1].flatten()\n\n expected = np.array([0.7406, 0.699, 0.5963, 0.7493, 0.7045, 0.6096, 0.6886, 0.6388, 0.583])\n\n max_diff = numpy_cosine_similarity_distance(expected, images)\n assert max_diff < 1e-4\n\n pipe.unload_lora_weights()\n release_memory(pipe)\n\n def test_unload_kohya_lora(self):\n generator = torch.manual_seed(0)\n prompt = \"masterpiece, best quality, mountain\"\n num_inference_steps = 2\n\n pipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", safety_checker=None).to(\n torch_device\n )\n initial_images = pipe(\n prompt, output_type=\"np\", generator=generator, num_inference_steps=num_inference_steps\n ).images\n initial_images = initial_images[0, -3:, -3:, -1].flatten()\n\n lora_model_id = \"hf-internal-testing/civitai-colored-icons-lora\"\n lora_filename = \"Colored_Icons_by_vizsumit.safetensors\"\n\n pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)\n generator = torch.manual_seed(0)\n lora_images = pipe(\n prompt, output_type=\"np\", generator=generator, num_inference_steps=num_inference_steps\n ).images\n lora_images = lora_images[0, -3:, -3:, -1].flatten()\n\n pipe.unload_lora_weights()\n generator = torch.manual_seed(0)\n unloaded_lora_images = pipe(\n prompt, output_type=\"np\", generator=generator, num_inference_steps=num_inference_steps\n ).images\n unloaded_lora_images = unloaded_lora_images[0, -3:, -3:, -1].flatten()\n\n self.assertFalse(np.allclose(initial_images, lora_images))\n self.assertTrue(np.allclose(initial_images, unloaded_lora_images, atol=1e-3))\n\n release_memory(pipe)\n\n def test_load_unload_load_kohya_lora(self):\n # This test ensures that a Kohya-style LoRA can be safely unloaded and then loaded\n # without introducing any side-effects. Even though the test uses a Kohya-style\n # LoRA, the underlying adapter handling mechanism is format-agnostic.\n generator = torch.manual_seed(0)\n prompt = \"masterpiece, best quality, mountain\"\n num_inference_steps = 2\n\n pipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", safety_checker=None).to(\n torch_device\n )\n initial_images = pipe(\n prompt, output_type=\"np\", generator=generator, num_inference_steps=num_inference_steps\n ).images\n initial_images = initial_images[0, -3:, -3:, -1].flatten()\n\n lora_model_id = \"hf-internal-testing/civitai-colored-icons-lora\"\n lora_filename = \"Colored_Icons_by_vizsumit.safetensors\"\n\n pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)\n generator = torch.manual_seed(0)\n lora_images = pipe(\n prompt, output_type=\"np\", generator=generator, num_inference_steps=num_inference_steps\n ).images\n lora_images = lora_images[0, -3:, -3:, -1].flatten()\n\n pipe.unload_lora_weights()\n generator = torch.manual_seed(0)\n unloaded_lora_images = pipe(\n prompt, output_type=\"np\", generator=generator, num_inference_steps=num_inference_steps\n ).images\n unloaded_lora_images = unloaded_lora_images[0, -3:, -3:, -1].flatten()\n\n self.assertFalse(np.allclose(initial_images, lora_images))\n self.assertTrue(np.allclose(initial_images, unloaded_lora_images, atol=1e-3))\n\n # make sure we can load a LoRA again after unloading and they don't have\n # any undesired effects.\n pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)\n generator = torch.manual_seed(0)\n lora_images_again = pipe(\n prompt, output_type=\"np\", generator=generator, num_inference_steps=num_inference_steps\n ).images\n lora_images_again = lora_images_again[0, -3:, -3:, -1].flatten()\n\n self.assertTrue(np.allclose(lora_images, lora_images_again, atol=1e-3))\n release_memory(pipe)\n\n def test_not_empty_state_dict(self):\n # Makes sure https://github.com/huggingface/diffusers/issues/7054 does not happen again\n pipe = AutoPipelineForText2Image.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16\n ).to(torch_device)\n pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\n\n cached_file = hf_hub_download(\"hf-internal-testing/lcm-lora-test-sd-v1-5\", \"test_lora.safetensors\")\n lcm_lora = load_file(cached_file)\n\n pipe.load_lora_weights(lcm_lora, adapter_name=\"lcm\")\n self.assertTrue(lcm_lora != {})\n release_memory(pipe)\n\n def test_load_unload_load_state_dict(self):\n # Makes sure https://github.com/huggingface/diffusers/issues/7054 does not happen again\n pipe = AutoPipelineForText2Image.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16\n ).to(torch_device)\n pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\n\n cached_file = hf_hub_download(\"hf-internal-testing/lcm-lora-test-sd-v1-5\", \"test_lora.safetensors\")\n lcm_lora = load_file(cached_file)\n previous_state_dict = lcm_lora.copy()\n\n pipe.load_lora_weights(lcm_lora, adapter_name=\"lcm\")\n self.assertDictEqual(lcm_lora, previous_state_dict)\n\n pipe.unload_lora_weights()\n pipe.load_lora_weights(lcm_lora, adapter_name=\"lcm\")\n self.assertDictEqual(lcm_lora, previous_state_dict)\n\n release_memory(pipe)\n\n def test_sdv1_5_lcm_lora(self):\n pipe = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16)\n pipe.to(torch_device)\n pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\n\n generator = torch.Generator(\"cpu\").manual_seed(0)\n\n lora_model_id = \"latent-consistency/lcm-lora-sdv1-5\"\n pipe.load_lora_weights(lora_model_id)\n\n image = pipe(\n \"masterpiece, best quality, mountain\", generator=generator, num_inference_steps=4, guidance_scale=0.5\n ).images[0]\n\n expected_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/lcm_lora/sdv15_lcm_lora.png\"\n )\n\n image_np = pipe.image_processor.pil_to_numpy(image)\n expected_image_np = pipe.image_processor.pil_to_numpy(expected_image)\n\n max_diff = numpy_cosine_similarity_distance(image_np.flatten(), expected_image_np.flatten())\n assert max_diff < 1e-4\n\n pipe.unload_lora_weights()\n\n release_memory(pipe)\n\n def test_sdv1_5_lcm_lora_img2img(self):\n pipe = AutoPipelineForImage2Image.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16)\n pipe.to(torch_device)\n pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\n\n init_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape.png\"\n )\n\n generator = torch.Generator(\"cpu\").manual_seed(0)\n\n lora_model_id = \"latent-consistency/lcm-lora-sdv1-5\"\n pipe.load_lora_weights(lora_model_id)\n\n image = pipe(\n \"snowy mountain\",\n generator=generator,\n image=init_image,\n strength=0.5,\n num_inference_steps=4,\n guidance_scale=0.5,\n ).images[0]\n\n expected_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/lcm_lora/sdv15_lcm_lora_img2img.png\"\n )\n\n image_np = pipe.image_processor.pil_to_numpy(image)\n expected_image_np = pipe.image_processor.pil_to_numpy(expected_image)\n\n max_diff = numpy_cosine_similarity_distance(image_np.flatten(), expected_image_np.flatten())\n assert max_diff < 1e-4\n\n pipe.unload_lora_weights()\n\n release_memory(pipe)\n\n def test_sd_load_civitai_empty_network_alpha(self):\n \"\"\"\n This test simply checks that loading a LoRA with an empty network alpha works fine\n See: https://github.com/huggingface/diffusers/issues/5606\n \"\"\"\n pipeline = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n pipeline.enable_sequential_cpu_offload()\n civitai_path = hf_hub_download(\"ybelkada/test-ahi-civitai\", \"ahi_lora_weights.safetensors\")\n pipeline.load_lora_weights(civitai_path, adapter_name=\"ahri\")\n\n images = pipeline(\n \"ahri, masterpiece, league of legends\",\n output_type=\"np\",\n generator=torch.manual_seed(156),\n num_inference_steps=5,\n ).images\n images = images[0, -3:, -3:, -1].flatten()\n expected = np.array([0.0, 0.0, 0.0, 0.002557, 0.020954, 0.001792, 0.006581, 0.00591, 0.002995])\n\n max_diff = numpy_cosine_similarity_distance(expected, images)\n assert max_diff < 1e-3\n\n pipeline.unload_lora_weights()\n release_memory(pipeline)\n" }, { "path": "diffusers/tests/lora/test_lora_layers_sd3.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport os\nimport sys\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer, T5EncoderModel\n\nfrom diffusers import (\n AutoencoderKL,\n FlowMatchEulerDiscreteScheduler,\n SD3Transformer2DModel,\n StableDiffusion3Pipeline,\n)\nfrom diffusers.utils.testing_utils import is_peft_available, require_peft_backend, torch_device\n\n\nif is_peft_available():\n from peft import LoraConfig\n from peft.utils import get_peft_model_state_dict\n\nsys.path.append(\".\")\n\nfrom utils import check_if_lora_correctly_set # noqa: E402\n\n\n@require_peft_backend\nclass SD3LoRATests(unittest.TestCase):\n pipeline_class = StableDiffusion3Pipeline\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n transformer = SD3Transformer2DModel(\n sample_size=32,\n patch_size=1,\n in_channels=4,\n num_layers=1,\n attention_head_dim=8,\n num_attention_heads=4,\n caption_projection_dim=32,\n joint_attention_dim=32,\n pooled_projection_dim=64,\n out_channels=4,\n )\n clip_text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n hidden_act=\"gelu\",\n projection_dim=32,\n )\n\n torch.manual_seed(0)\n text_encoder = CLIPTextModelWithProjection(clip_text_encoder_config)\n\n torch.manual_seed(0)\n text_encoder_2 = CLIPTextModelWithProjection(clip_text_encoder_config)\n\n text_encoder_3 = T5EncoderModel.from_pretrained(\"hf-internal-testing/tiny-random-t5\")\n\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n tokenizer_2 = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n tokenizer_3 = AutoTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-t5\")\n\n torch.manual_seed(0)\n vae = AutoencoderKL(\n sample_size=32,\n in_channels=3,\n out_channels=3,\n block_out_channels=(4,),\n layers_per_block=1,\n latent_channels=4,\n norm_num_groups=1,\n use_quant_conv=False,\n use_post_quant_conv=False,\n shift_factor=0.0609,\n scaling_factor=1.5035,\n )\n\n scheduler = FlowMatchEulerDiscreteScheduler()\n\n return {\n \"scheduler\": scheduler,\n \"text_encoder\": text_encoder,\n \"text_encoder_2\": text_encoder_2,\n \"text_encoder_3\": text_encoder_3,\n \"tokenizer\": tokenizer,\n \"tokenizer_2\": tokenizer_2,\n \"tokenizer_3\": tokenizer_3,\n \"transformer\": transformer,\n \"vae\": vae,\n }\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=\"cpu\").manual_seed(seed)\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 5.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def get_lora_config_for_transformer(self):\n lora_config = LoraConfig(\n r=4,\n lora_alpha=4,\n target_modules=[\"to_q\", \"to_k\", \"to_v\", \"to_out.0\"],\n init_lora_weights=False,\n use_dora=False,\n )\n return lora_config\n\n def test_simple_inference_with_transformer_lora_save_load(self):\n components = self.get_dummy_components()\n transformer_config = self.get_lora_config_for_transformer()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n inputs = self.get_dummy_inputs(torch_device)\n\n pipe.transformer.add_adapter(transformer_config)\n self.assertTrue(check_if_lora_correctly_set(pipe.transformer), \"Lora not correctly set in transformer\")\n inputs = self.get_dummy_inputs(torch_device)\n images_lora = pipe(**inputs).images\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n transformer_state_dict = get_peft_model_state_dict(pipe.transformer)\n\n self.pipeline_class.save_lora_weights(\n save_directory=tmpdirname,\n transformer_lora_layers=transformer_state_dict,\n )\n\n self.assertTrue(os.path.isfile(os.path.join(tmpdirname, \"pytorch_lora_weights.safetensors\")))\n pipe.unload_lora_weights()\n\n pipe.load_lora_weights(os.path.join(tmpdirname, \"pytorch_lora_weights.safetensors\"))\n\n inputs = self.get_dummy_inputs(torch_device)\n images_lora_from_pretrained = pipe(**inputs).images\n self.assertTrue(check_if_lora_correctly_set(pipe.transformer), \"Lora not correctly set in transformer\")\n\n self.assertTrue(\n np.allclose(images_lora, images_lora_from_pretrained, atol=1e-3, rtol=1e-3),\n \"Loading from saved checkpoints should give same results.\",\n )\n\n def test_simple_inference_with_transformer_lora_and_scale(self):\n components = self.get_dummy_components()\n transformer_lora_config = self.get_lora_config_for_transformer()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n output_no_lora = pipe(**inputs).images\n\n pipe.transformer.add_adapter(transformer_lora_config)\n self.assertTrue(check_if_lora_correctly_set(pipe.transformer), \"Lora not correctly set in transformer\")\n\n inputs = self.get_dummy_inputs(torch_device)\n output_lora = pipe(**inputs).images\n self.assertTrue(\n not np.allclose(output_lora, output_no_lora, atol=1e-3, rtol=1e-3), \"Lora should change the output\"\n )\n\n inputs = self.get_dummy_inputs(torch_device)\n output_lora_scale = pipe(**inputs, joint_attention_kwargs={\"scale\": 0.5}).images\n self.assertTrue(\n not np.allclose(output_lora, output_lora_scale, atol=1e-3, rtol=1e-3),\n \"Lora + scale should change the output\",\n )\n\n inputs = self.get_dummy_inputs(torch_device)\n output_lora_0_scale = pipe(**inputs, joint_attention_kwargs={\"scale\": 0.0}).images\n self.assertTrue(\n np.allclose(output_no_lora, output_lora_0_scale, atol=1e-3, rtol=1e-3),\n \"Lora + 0 scale should lead to same result as no LoRA\",\n )\n" }, { "path": "diffusers/tests/lora/test_lora_layers_sdxl.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport copy\nimport gc\nimport importlib\nimport sys\nimport time\nimport unittest\n\nimport numpy as np\nimport torch\nfrom packaging import version\n\nfrom diffusers import (\n ControlNetModel,\n EulerDiscreteScheduler,\n LCMScheduler,\n StableDiffusionXLAdapterPipeline,\n StableDiffusionXLControlNetPipeline,\n StableDiffusionXLPipeline,\n T2IAdapter,\n)\nfrom diffusers.utils.import_utils import is_accelerate_available\nfrom diffusers.utils.testing_utils import (\n load_image,\n nightly,\n numpy_cosine_similarity_distance,\n require_peft_backend,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\n\nsys.path.append(\".\")\n\nfrom utils import PeftLoraLoaderMixinTests, check_if_lora_correctly_set, state_dicts_almost_equal # noqa: E402\n\n\nif is_accelerate_available():\n from accelerate.utils import release_memory\n\n\nclass StableDiffusionXLLoRATests(PeftLoraLoaderMixinTests, unittest.TestCase):\n has_two_text_encoders = True\n pipeline_class = StableDiffusionXLPipeline\n scheduler_cls = EulerDiscreteScheduler\n scheduler_kwargs = {\n \"beta_start\": 0.00085,\n \"beta_end\": 0.012,\n \"beta_schedule\": \"scaled_linear\",\n \"timestep_spacing\": \"leading\",\n \"steps_offset\": 1,\n }\n unet_kwargs = {\n \"block_out_channels\": (32, 64),\n \"layers_per_block\": 2,\n \"sample_size\": 32,\n \"in_channels\": 4,\n \"out_channels\": 4,\n \"down_block_types\": (\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n \"up_block_types\": (\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n \"attention_head_dim\": (2, 4),\n \"use_linear_projection\": True,\n \"addition_embed_type\": \"text_time\",\n \"addition_time_embed_dim\": 8,\n \"transformer_layers_per_block\": (1, 2),\n \"projection_class_embeddings_input_dim\": 80, # 6 * 8 + 32\n \"cross_attention_dim\": 64,\n }\n vae_kwargs = {\n \"block_out_channels\": [32, 64],\n \"in_channels\": 3,\n \"out_channels\": 3,\n \"down_block_types\": [\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n \"up_block_types\": [\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n \"latent_channels\": 4,\n \"sample_size\": 128,\n }\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n\n@slow\n@require_torch_gpu\n@require_peft_backend\nclass LoraSDXLIntegrationTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_sdxl_0_9_lora_one(self):\n generator = torch.Generator().manual_seed(0)\n\n pipe = StableDiffusionXLPipeline.from_pretrained(\"stabilityai/stable-diffusion-xl-base-0.9\")\n lora_model_id = \"hf-internal-testing/sdxl-0.9-daiton-lora\"\n lora_filename = \"daiton-xl-lora-test.safetensors\"\n pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)\n pipe.enable_model_cpu_offload()\n\n images = pipe(\n \"masterpiece, best quality, mountain\", output_type=\"np\", generator=generator, num_inference_steps=2\n ).images\n\n images = images[0, -3:, -3:, -1].flatten()\n expected = np.array([0.3838, 0.3482, 0.3588, 0.3162, 0.319, 0.3369, 0.338, 0.3366, 0.3213])\n\n max_diff = numpy_cosine_similarity_distance(expected, images)\n assert max_diff < 1e-3\n pipe.unload_lora_weights()\n release_memory(pipe)\n\n def test_sdxl_0_9_lora_two(self):\n generator = torch.Generator().manual_seed(0)\n\n pipe = StableDiffusionXLPipeline.from_pretrained(\"stabilityai/stable-diffusion-xl-base-0.9\")\n lora_model_id = \"hf-internal-testing/sdxl-0.9-costumes-lora\"\n lora_filename = \"saijo.safetensors\"\n pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)\n pipe.enable_model_cpu_offload()\n\n images = pipe(\n \"masterpiece, best quality, mountain\", output_type=\"np\", generator=generator, num_inference_steps=2\n ).images\n\n images = images[0, -3:, -3:, -1].flatten()\n expected = np.array([0.3137, 0.3269, 0.3355, 0.255, 0.2577, 0.2563, 0.2679, 0.2758, 0.2626])\n\n max_diff = numpy_cosine_similarity_distance(expected, images)\n assert max_diff < 1e-3\n\n pipe.unload_lora_weights()\n release_memory(pipe)\n\n def test_sdxl_0_9_lora_three(self):\n generator = torch.Generator().manual_seed(0)\n\n pipe = StableDiffusionXLPipeline.from_pretrained(\"stabilityai/stable-diffusion-xl-base-0.9\")\n lora_model_id = \"hf-internal-testing/sdxl-0.9-kamepan-lora\"\n lora_filename = \"kame_sdxl_v2-000020-16rank.safetensors\"\n pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)\n pipe.enable_model_cpu_offload()\n\n images = pipe(\n \"masterpiece, best quality, mountain\", output_type=\"np\", generator=generator, num_inference_steps=2\n ).images\n\n images = images[0, -3:, -3:, -1].flatten()\n expected = np.array([0.4015, 0.3761, 0.3616, 0.3745, 0.3462, 0.3337, 0.3564, 0.3649, 0.3468])\n\n max_diff = numpy_cosine_similarity_distance(expected, images)\n assert max_diff < 5e-3\n\n pipe.unload_lora_weights()\n release_memory(pipe)\n\n def test_sdxl_1_0_lora(self):\n generator = torch.Generator(\"cpu\").manual_seed(0)\n\n pipe = StableDiffusionXLPipeline.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\")\n pipe.enable_model_cpu_offload()\n lora_model_id = \"hf-internal-testing/sdxl-1.0-lora\"\n lora_filename = \"sd_xl_offset_example-lora_1.0.safetensors\"\n pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)\n\n images = pipe(\n \"masterpiece, best quality, mountain\", output_type=\"np\", generator=generator, num_inference_steps=2\n ).images\n\n images = images[0, -3:, -3:, -1].flatten()\n expected = np.array([0.4468, 0.4061, 0.4134, 0.3637, 0.3202, 0.365, 0.3786, 0.3725, 0.3535])\n\n max_diff = numpy_cosine_similarity_distance(expected, images)\n assert max_diff < 1e-4\n\n pipe.unload_lora_weights()\n release_memory(pipe)\n\n def test_sdxl_1_0_blockwise_lora(self):\n generator = torch.Generator(\"cpu\").manual_seed(0)\n\n pipe = StableDiffusionXLPipeline.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\")\n pipe.enable_model_cpu_offload()\n lora_model_id = \"hf-internal-testing/sdxl-1.0-lora\"\n lora_filename = \"sd_xl_offset_example-lora_1.0.safetensors\"\n pipe.load_lora_weights(lora_model_id, weight_name=lora_filename, adapter_name=\"offset\")\n scales = {\n \"unet\": {\n \"down\": {\"block_1\": [1.0, 1.0], \"block_2\": [1.0, 1.0]},\n \"mid\": 1.0,\n \"up\": {\"block_0\": [1.0, 1.0, 1.0], \"block_1\": [1.0, 1.0, 1.0]},\n },\n }\n pipe.set_adapters([\"offset\"], [scales])\n\n images = pipe(\n \"masterpiece, best quality, mountain\", output_type=\"np\", generator=generator, num_inference_steps=2\n ).images\n\n images = images[0, -3:, -3:, -1].flatten()\n expected = np.array([00.4468, 0.4061, 0.4134, 0.3637, 0.3202, 0.365, 0.3786, 0.3725, 0.3535])\n\n max_diff = numpy_cosine_similarity_distance(expected, images)\n assert max_diff < 1e-4\n\n pipe.unload_lora_weights()\n release_memory(pipe)\n\n def test_sdxl_lcm_lora(self):\n pipe = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16\n )\n pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\n pipe.enable_model_cpu_offload()\n\n generator = torch.Generator(\"cpu\").manual_seed(0)\n\n lora_model_id = \"latent-consistency/lcm-lora-sdxl\"\n\n pipe.load_lora_weights(lora_model_id)\n\n image = pipe(\n \"masterpiece, best quality, mountain\", generator=generator, num_inference_steps=4, guidance_scale=0.5\n ).images[0]\n\n expected_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/lcm_lora/sdxl_lcm_lora.png\"\n )\n\n image_np = pipe.image_processor.pil_to_numpy(image)\n expected_image_np = pipe.image_processor.pil_to_numpy(expected_image)\n\n max_diff = numpy_cosine_similarity_distance(image_np.flatten(), expected_image_np.flatten())\n assert max_diff < 1e-4\n\n pipe.unload_lora_weights()\n release_memory(pipe)\n\n def test_sdxl_1_0_lora_fusion(self):\n generator = torch.Generator().manual_seed(0)\n\n pipe = StableDiffusionXLPipeline.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\")\n lora_model_id = \"hf-internal-testing/sdxl-1.0-lora\"\n lora_filename = \"sd_xl_offset_example-lora_1.0.safetensors\"\n pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)\n\n pipe.fuse_lora()\n # We need to unload the lora weights since in the previous API `fuse_lora` led to lora weights being\n # silently deleted - otherwise this will CPU OOM\n pipe.unload_lora_weights()\n\n pipe.enable_model_cpu_offload()\n\n images = pipe(\n \"masterpiece, best quality, mountain\", output_type=\"np\", generator=generator, num_inference_steps=2\n ).images\n\n images = images[0, -3:, -3:, -1].flatten()\n # This way we also test equivalence between LoRA fusion and the non-fusion behaviour.\n expected = np.array([0.4468, 0.4061, 0.4134, 0.3637, 0.3202, 0.365, 0.3786, 0.3725, 0.3535])\n\n max_diff = numpy_cosine_similarity_distance(expected, images)\n assert max_diff < 1e-4\n\n release_memory(pipe)\n\n def test_sdxl_1_0_lora_unfusion(self):\n generator = torch.Generator(\"cpu\").manual_seed(0)\n\n pipe = StableDiffusionXLPipeline.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\")\n lora_model_id = \"hf-internal-testing/sdxl-1.0-lora\"\n lora_filename = \"sd_xl_offset_example-lora_1.0.safetensors\"\n pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)\n pipe.fuse_lora()\n\n pipe.enable_model_cpu_offload()\n\n images = pipe(\n \"masterpiece, best quality, mountain\", output_type=\"np\", generator=generator, num_inference_steps=3\n ).images\n images_with_fusion = images.flatten()\n\n pipe.unfuse_lora()\n generator = torch.Generator(\"cpu\").manual_seed(0)\n images = pipe(\n \"masterpiece, best quality, mountain\", output_type=\"np\", generator=generator, num_inference_steps=3\n ).images\n images_without_fusion = images.flatten()\n\n max_diff = numpy_cosine_similarity_distance(images_with_fusion, images_without_fusion)\n assert max_diff < 1e-4\n\n release_memory(pipe)\n\n def test_sdxl_1_0_lora_unfusion_effectivity(self):\n pipe = StableDiffusionXLPipeline.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\")\n pipe.enable_model_cpu_offload()\n\n generator = torch.Generator().manual_seed(0)\n images = pipe(\n \"masterpiece, best quality, mountain\", output_type=\"np\", generator=generator, num_inference_steps=2\n ).images\n original_image_slice = images[0, -3:, -3:, -1].flatten()\n\n lora_model_id = \"hf-internal-testing/sdxl-1.0-lora\"\n lora_filename = \"sd_xl_offset_example-lora_1.0.safetensors\"\n pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)\n pipe.fuse_lora()\n\n generator = torch.Generator().manual_seed(0)\n _ = pipe(\n \"masterpiece, best quality, mountain\", output_type=\"np\", generator=generator, num_inference_steps=2\n ).images\n\n pipe.unfuse_lora()\n\n # We need to unload the lora weights - in the old API unfuse led to unloading the adapter weights\n pipe.unload_lora_weights()\n\n generator = torch.Generator().manual_seed(0)\n images = pipe(\n \"masterpiece, best quality, mountain\", output_type=\"np\", generator=generator, num_inference_steps=2\n ).images\n images_without_fusion_slice = images[0, -3:, -3:, -1].flatten()\n\n max_diff = numpy_cosine_similarity_distance(images_without_fusion_slice, original_image_slice)\n assert max_diff < 1e-3\n\n release_memory(pipe)\n\n def test_sdxl_1_0_lora_fusion_efficiency(self):\n generator = torch.Generator().manual_seed(0)\n lora_model_id = \"hf-internal-testing/sdxl-1.0-lora\"\n lora_filename = \"sd_xl_offset_example-lora_1.0.safetensors\"\n\n pipe = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16\n )\n pipe.load_lora_weights(lora_model_id, weight_name=lora_filename, torch_dtype=torch.float16)\n pipe.enable_model_cpu_offload()\n\n start_time = time.time()\n for _ in range(3):\n pipe(\n \"masterpiece, best quality, mountain\", output_type=\"np\", generator=generator, num_inference_steps=2\n ).images\n end_time = time.time()\n elapsed_time_non_fusion = end_time - start_time\n\n del pipe\n\n pipe = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16\n )\n pipe.load_lora_weights(lora_model_id, weight_name=lora_filename, torch_dtype=torch.float16)\n pipe.fuse_lora()\n\n # We need to unload the lora weights since in the previous API `fuse_lora` led to lora weights being\n # silently deleted - otherwise this will CPU OOM\n pipe.unload_lora_weights()\n pipe.enable_model_cpu_offload()\n\n generator = torch.Generator().manual_seed(0)\n start_time = time.time()\n for _ in range(3):\n pipe(\n \"masterpiece, best quality, mountain\", output_type=\"np\", generator=generator, num_inference_steps=2\n ).images\n end_time = time.time()\n elapsed_time_fusion = end_time - start_time\n\n self.assertTrue(elapsed_time_fusion < elapsed_time_non_fusion)\n\n release_memory(pipe)\n\n def test_sdxl_1_0_last_ben(self):\n generator = torch.Generator().manual_seed(0)\n\n pipe = StableDiffusionXLPipeline.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\")\n pipe.enable_model_cpu_offload()\n lora_model_id = \"TheLastBen/Papercut_SDXL\"\n lora_filename = \"papercut.safetensors\"\n pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)\n\n images = pipe(\"papercut.safetensors\", output_type=\"np\", generator=generator, num_inference_steps=2).images\n\n images = images[0, -3:, -3:, -1].flatten()\n expected = np.array([0.5244, 0.4347, 0.4312, 0.4246, 0.4398, 0.4409, 0.4884, 0.4938, 0.4094])\n\n max_diff = numpy_cosine_similarity_distance(expected, images)\n assert max_diff < 1e-3\n\n pipe.unload_lora_weights()\n release_memory(pipe)\n\n def test_sdxl_1_0_fuse_unfuse_all(self):\n pipe = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16\n )\n text_encoder_1_sd = copy.deepcopy(pipe.text_encoder.state_dict())\n text_encoder_2_sd = copy.deepcopy(pipe.text_encoder_2.state_dict())\n unet_sd = copy.deepcopy(pipe.unet.state_dict())\n\n pipe.load_lora_weights(\n \"davizca87/sun-flower\", weight_name=\"snfw3rXL-000004.safetensors\", torch_dtype=torch.float16\n )\n\n fused_te_state_dict = pipe.text_encoder.state_dict()\n fused_te_2_state_dict = pipe.text_encoder_2.state_dict()\n unet_state_dict = pipe.unet.state_dict()\n\n peft_ge_070 = version.parse(importlib.metadata.version(\"peft\")) >= version.parse(\"0.7.0\")\n\n def remap_key(key, sd):\n # some keys have moved around for PEFT >= 0.7.0, but they should still be loaded correctly\n if (key in sd) or (not peft_ge_070):\n return key\n\n # instead of linear.weight, we now have linear.base_layer.weight, etc.\n if key.endswith(\".weight\"):\n key = key[:-7] + \".base_layer.weight\"\n elif key.endswith(\".bias\"):\n key = key[:-5] + \".base_layer.bias\"\n return key\n\n for key, value in text_encoder_1_sd.items():\n key = remap_key(key, fused_te_state_dict)\n self.assertTrue(torch.allclose(fused_te_state_dict[key], value))\n\n for key, value in text_encoder_2_sd.items():\n key = remap_key(key, fused_te_2_state_dict)\n self.assertTrue(torch.allclose(fused_te_2_state_dict[key], value))\n\n for key, value in unet_state_dict.items():\n self.assertTrue(torch.allclose(unet_state_dict[key], value))\n\n pipe.fuse_lora()\n pipe.unload_lora_weights()\n\n assert not state_dicts_almost_equal(text_encoder_1_sd, pipe.text_encoder.state_dict())\n assert not state_dicts_almost_equal(text_encoder_2_sd, pipe.text_encoder_2.state_dict())\n assert not state_dicts_almost_equal(unet_sd, pipe.unet.state_dict())\n\n release_memory(pipe)\n del unet_sd, text_encoder_1_sd, text_encoder_2_sd\n\n def test_sdxl_1_0_lora_with_sequential_cpu_offloading(self):\n generator = torch.Generator().manual_seed(0)\n\n pipe = StableDiffusionXLPipeline.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\")\n pipe.enable_sequential_cpu_offload()\n lora_model_id = \"hf-internal-testing/sdxl-1.0-lora\"\n lora_filename = \"sd_xl_offset_example-lora_1.0.safetensors\"\n\n pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)\n\n images = pipe(\n \"masterpiece, best quality, mountain\", output_type=\"np\", generator=generator, num_inference_steps=2\n ).images\n\n images = images[0, -3:, -3:, -1].flatten()\n expected = np.array([0.4468, 0.4087, 0.4134, 0.366, 0.3202, 0.3505, 0.3786, 0.387, 0.3535])\n\n max_diff = numpy_cosine_similarity_distance(expected, images)\n assert max_diff < 1e-3\n\n pipe.unload_lora_weights()\n release_memory(pipe)\n\n def test_controlnet_canny_lora(self):\n controlnet = ControlNetModel.from_pretrained(\"diffusers/controlnet-canny-sdxl-1.0\")\n\n pipe = StableDiffusionXLControlNetPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", controlnet=controlnet\n )\n pipe.load_lora_weights(\"nerijs/pixel-art-xl\", weight_name=\"pixel-art-xl.safetensors\")\n pipe.enable_sequential_cpu_offload()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n prompt = \"corgi\"\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png\"\n )\n images = pipe(prompt, image=image, generator=generator, output_type=\"np\", num_inference_steps=3).images\n\n assert images[0].shape == (768, 512, 3)\n\n original_image = images[0, -3:, -3:, -1].flatten()\n expected_image = np.array([0.4574, 0.4487, 0.4435, 0.5163, 0.4396, 0.4411, 0.518, 0.4465, 0.4333])\n\n max_diff = numpy_cosine_similarity_distance(expected_image, original_image)\n assert max_diff < 1e-4\n\n pipe.unload_lora_weights()\n release_memory(pipe)\n\n def test_sdxl_t2i_adapter_canny_lora(self):\n adapter = T2IAdapter.from_pretrained(\"TencentARC/t2i-adapter-lineart-sdxl-1.0\", torch_dtype=torch.float16).to(\n \"cpu\"\n )\n pipe = StableDiffusionXLAdapterPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n adapter=adapter,\n torch_dtype=torch.float16,\n variant=\"fp16\",\n )\n pipe.load_lora_weights(\"CiroN2022/toy-face\", weight_name=\"toy_face_sdxl.safetensors\")\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n prompt = \"toy\"\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/toy_canny.png\"\n )\n\n images = pipe(prompt, image=image, generator=generator, output_type=\"np\", num_inference_steps=3).images\n\n assert images[0].shape == (768, 512, 3)\n\n image_slice = images[0, -3:, -3:, -1].flatten()\n expected_slice = np.array([0.4284, 0.4337, 0.4319, 0.4255, 0.4329, 0.4280, 0.4338, 0.4420, 0.4226])\n assert numpy_cosine_similarity_distance(image_slice, expected_slice) < 1e-4\n\n @nightly\n def test_sequential_fuse_unfuse(self):\n pipe = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16\n )\n\n # 1. round\n pipe.load_lora_weights(\"Pclanglais/TintinIA\", torch_dtype=torch.float16)\n pipe.to(torch_device)\n pipe.fuse_lora()\n\n generator = torch.Generator().manual_seed(0)\n images = pipe(\n \"masterpiece, best quality, mountain\", output_type=\"np\", generator=generator, num_inference_steps=2\n ).images\n image_slice = images[0, -3:, -3:, -1].flatten()\n\n pipe.unfuse_lora()\n\n # 2. round\n pipe.load_lora_weights(\"ProomptEngineer/pe-balloon-diffusion-style\", torch_dtype=torch.float16)\n pipe.fuse_lora()\n pipe.unfuse_lora()\n\n # 3. round\n pipe.load_lora_weights(\"ostris/crayon_style_lora_sdxl\", torch_dtype=torch.float16)\n pipe.fuse_lora()\n pipe.unfuse_lora()\n\n # 4. back to 1st round\n pipe.load_lora_weights(\"Pclanglais/TintinIA\", torch_dtype=torch.float16)\n pipe.fuse_lora()\n\n generator = torch.Generator().manual_seed(0)\n images_2 = pipe(\n \"masterpiece, best quality, mountain\", output_type=\"np\", generator=generator, num_inference_steps=2\n ).images\n image_slice_2 = images_2[0, -3:, -3:, -1].flatten()\n\n max_diff = numpy_cosine_similarity_distance(image_slice, image_slice_2)\n assert max_diff < 1e-3\n pipe.unload_lora_weights()\n release_memory(pipe)\n\n @nightly\n def test_integration_logits_multi_adapter(self):\n path = \"stabilityai/stable-diffusion-xl-base-1.0\"\n lora_id = \"CiroN2022/toy-face\"\n\n pipe = StableDiffusionXLPipeline.from_pretrained(path, torch_dtype=torch.float16)\n pipe.load_lora_weights(lora_id, weight_name=\"toy_face_sdxl.safetensors\", adapter_name=\"toy\")\n pipe = pipe.to(torch_device)\n\n self.assertTrue(check_if_lora_correctly_set(pipe.unet), \"Lora not correctly set in Unet\")\n\n prompt = \"toy_face of a hacker with a hoodie\"\n\n lora_scale = 0.9\n\n images = pipe(\n prompt=prompt,\n num_inference_steps=30,\n generator=torch.manual_seed(0),\n cross_attention_kwargs={\"scale\": lora_scale},\n output_type=\"np\",\n ).images\n expected_slice_scale = np.array([0.538, 0.539, 0.540, 0.540, 0.542, 0.539, 0.538, 0.541, 0.539])\n\n predicted_slice = images[0, -3:, -3:, -1].flatten()\n max_diff = numpy_cosine_similarity_distance(expected_slice_scale, predicted_slice)\n assert max_diff < 1e-3\n\n pipe.load_lora_weights(\"nerijs/pixel-art-xl\", weight_name=\"pixel-art-xl.safetensors\", adapter_name=\"pixel\")\n pipe.set_adapters(\"pixel\")\n\n prompt = \"pixel art, a hacker with a hoodie, simple, flat colors\"\n images = pipe(\n prompt,\n num_inference_steps=30,\n guidance_scale=7.5,\n cross_attention_kwargs={\"scale\": lora_scale},\n generator=torch.manual_seed(0),\n output_type=\"np\",\n ).images\n\n predicted_slice = images[0, -3:, -3:, -1].flatten()\n expected_slice_scale = np.array(\n [0.61973065, 0.62018543, 0.62181497, 0.61933696, 0.6208608, 0.620576, 0.6200281, 0.62258327, 0.6259889]\n )\n max_diff = numpy_cosine_similarity_distance(expected_slice_scale, predicted_slice)\n assert max_diff < 1e-3\n\n # multi-adapter inference\n pipe.set_adapters([\"pixel\", \"toy\"], adapter_weights=[0.5, 1.0])\n images = pipe(\n prompt,\n num_inference_steps=30,\n guidance_scale=7.5,\n cross_attention_kwargs={\"scale\": 1.0},\n generator=torch.manual_seed(0),\n output_type=\"np\",\n ).images\n predicted_slice = images[0, -3:, -3:, -1].flatten()\n expected_slice_scale = np.array([0.5888, 0.5897, 0.5946, 0.5888, 0.5935, 0.5946, 0.5857, 0.5891, 0.5909])\n max_diff = numpy_cosine_similarity_distance(expected_slice_scale, predicted_slice)\n assert max_diff < 1e-3\n\n # Lora disabled\n pipe.disable_lora()\n images = pipe(\n prompt,\n num_inference_steps=30,\n guidance_scale=7.5,\n cross_attention_kwargs={\"scale\": lora_scale},\n generator=torch.manual_seed(0),\n output_type=\"np\",\n ).images\n predicted_slice = images[0, -3:, -3:, -1].flatten()\n expected_slice_scale = np.array([0.5456, 0.5466, 0.5487, 0.5458, 0.5469, 0.5454, 0.5446, 0.5479, 0.5487])\n max_diff = numpy_cosine_similarity_distance(expected_slice_scale, predicted_slice)\n assert max_diff < 1e-3\n\n @nightly\n def test_integration_logits_for_dora_lora(self):\n pipeline = StableDiffusionXLPipeline.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\")\n pipeline.load_lora_weights(\"hf-internal-testing/dora-trained-on-kohya\")\n pipeline.enable_model_cpu_offload()\n\n images = pipeline(\n \"photo of ohwx dog\",\n num_inference_steps=10,\n generator=torch.manual_seed(0),\n output_type=\"np\",\n ).images\n\n predicted_slice = images[0, -3:, -3:, -1].flatten()\n expected_slice_scale = np.array([0.3932, 0.3742, 0.4429, 0.3737, 0.3504, 0.433, 0.3948, 0.3769, 0.4516])\n max_diff = numpy_cosine_similarity_distance(expected_slice_scale, predicted_slice)\n assert max_diff < 1e-3\n" }, { "path": "diffusers/tests/lora/utils.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport os\nimport tempfile\nimport unittest\nfrom itertools import product\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n LCMScheduler,\n UNet2DConditionModel,\n)\nfrom diffusers.utils.import_utils import is_peft_available\nfrom diffusers.utils.testing_utils import (\n floats_tensor,\n require_peft_backend,\n require_peft_version_greater,\n skip_mps,\n torch_device,\n)\n\n\nif is_peft_available():\n from peft import LoraConfig\n from peft.tuners.tuners_utils import BaseTunerLayer\n from peft.utils import get_peft_model_state_dict\n\n\ndef state_dicts_almost_equal(sd1, sd2):\n sd1 = dict(sorted(sd1.items()))\n sd2 = dict(sorted(sd2.items()))\n\n models_are_equal = True\n for ten1, ten2 in zip(sd1.values(), sd2.values()):\n if (ten1 - ten2).abs().max() > 1e-3:\n models_are_equal = False\n\n return models_are_equal\n\n\ndef check_if_lora_correctly_set(model) -> bool:\n \"\"\"\n Checks if the LoRA layers are correctly set with peft\n \"\"\"\n for module in model.modules():\n if isinstance(module, BaseTunerLayer):\n return True\n return False\n\n\n@require_peft_backend\nclass PeftLoraLoaderMixinTests:\n pipeline_class = None\n scheduler_cls = None\n scheduler_kwargs = None\n has_two_text_encoders = False\n unet_kwargs = None\n vae_kwargs = None\n\n def get_dummy_components(self, scheduler_cls=None, use_dora=False):\n scheduler_cls = self.scheduler_cls if scheduler_cls is None else scheduler_cls\n rank = 4\n\n torch.manual_seed(0)\n unet = UNet2DConditionModel(**self.unet_kwargs)\n\n scheduler = scheduler_cls(**self.scheduler_kwargs)\n\n torch.manual_seed(0)\n vae = AutoencoderKL(**self.vae_kwargs)\n\n text_encoder = CLIPTextModel.from_pretrained(\"peft-internal-testing/tiny-clip-text-2\")\n tokenizer = CLIPTokenizer.from_pretrained(\"peft-internal-testing/tiny-clip-text-2\")\n\n if self.has_two_text_encoders:\n text_encoder_2 = CLIPTextModelWithProjection.from_pretrained(\"peft-internal-testing/tiny-clip-text-2\")\n tokenizer_2 = CLIPTokenizer.from_pretrained(\"peft-internal-testing/tiny-clip-text-2\")\n\n text_lora_config = LoraConfig(\n r=rank,\n lora_alpha=rank,\n target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"out_proj\"],\n init_lora_weights=False,\n use_dora=use_dora,\n )\n\n unet_lora_config = LoraConfig(\n r=rank,\n lora_alpha=rank,\n target_modules=[\"to_q\", \"to_k\", \"to_v\", \"to_out.0\"],\n init_lora_weights=False,\n use_dora=use_dora,\n )\n\n if self.has_two_text_encoders:\n pipeline_components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"text_encoder_2\": text_encoder_2,\n \"tokenizer_2\": tokenizer_2,\n \"image_encoder\": None,\n \"feature_extractor\": None,\n }\n else:\n pipeline_components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n\n return pipeline_components, text_lora_config, unet_lora_config\n\n def get_dummy_inputs(self, with_generator=True):\n batch_size = 1\n sequence_length = 10\n num_channels = 4\n sizes = (32, 32)\n\n generator = torch.manual_seed(0)\n noise = floats_tensor((batch_size, num_channels) + sizes)\n input_ids = torch.randint(1, sequence_length, size=(batch_size, sequence_length), generator=generator)\n\n pipeline_inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"num_inference_steps\": 5,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n }\n if with_generator:\n pipeline_inputs.update({\"generator\": generator})\n\n return noise, input_ids, pipeline_inputs\n\n # Copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb\n def get_dummy_tokens(self):\n max_seq_length = 77\n\n inputs = torch.randint(2, 56, size=(1, max_seq_length), generator=torch.manual_seed(0))\n\n prepared_inputs = {}\n prepared_inputs[\"input_ids\"] = inputs\n return prepared_inputs\n\n def test_simple_inference(self):\n \"\"\"\n Tests a simple inference and makes sure it works as expected\n \"\"\"\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n _, _, inputs = self.get_dummy_inputs()\n output_no_lora = pipe(**inputs).images\n self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))\n\n def test_simple_inference_with_text_lora(self):\n \"\"\"\n Tests a simple inference with lora attached on the text encoder\n and makes sure it works as expected\n \"\"\"\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n _, _, inputs = self.get_dummy_inputs(with_generator=False)\n\n output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))\n\n pipe.text_encoder.add_adapter(text_lora_config)\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n\n if self.has_two_text_encoders:\n pipe.text_encoder_2.add_adapter(text_lora_config)\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Lora not correctly set in text encoder 2\"\n )\n\n output_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n self.assertTrue(\n not np.allclose(output_lora, output_no_lora, atol=1e-3, rtol=1e-3), \"Lora should change the output\"\n )\n\n def test_simple_inference_with_text_lora_and_scale(self):\n \"\"\"\n Tests a simple inference with lora attached on the text encoder + scale argument\n and makes sure it works as expected\n \"\"\"\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n _, _, inputs = self.get_dummy_inputs(with_generator=False)\n\n output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))\n\n pipe.text_encoder.add_adapter(text_lora_config)\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n\n if self.has_two_text_encoders:\n pipe.text_encoder_2.add_adapter(text_lora_config)\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Lora not correctly set in text encoder 2\"\n )\n\n output_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n self.assertTrue(\n not np.allclose(output_lora, output_no_lora, atol=1e-3, rtol=1e-3), \"Lora should change the output\"\n )\n\n output_lora_scale = pipe(\n **inputs, generator=torch.manual_seed(0), cross_attention_kwargs={\"scale\": 0.5}\n ).images\n self.assertTrue(\n not np.allclose(output_lora, output_lora_scale, atol=1e-3, rtol=1e-3),\n \"Lora + scale should change the output\",\n )\n\n output_lora_0_scale = pipe(\n **inputs, generator=torch.manual_seed(0), cross_attention_kwargs={\"scale\": 0.0}\n ).images\n self.assertTrue(\n np.allclose(output_no_lora, output_lora_0_scale, atol=1e-3, rtol=1e-3),\n \"Lora + 0 scale should lead to same result as no LoRA\",\n )\n\n def test_simple_inference_with_text_lora_fused(self):\n \"\"\"\n Tests a simple inference with lora attached into text encoder + fuses the lora weights into base model\n and makes sure it works as expected\n \"\"\"\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n _, _, inputs = self.get_dummy_inputs(with_generator=False)\n\n output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))\n\n pipe.text_encoder.add_adapter(text_lora_config)\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n\n if self.has_two_text_encoders:\n pipe.text_encoder_2.add_adapter(text_lora_config)\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Lora not correctly set in text encoder 2\"\n )\n\n pipe.fuse_lora()\n # Fusing should still keep the LoRA layers\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n\n if self.has_two_text_encoders:\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Lora not correctly set in text encoder 2\"\n )\n\n ouput_fused = pipe(**inputs, generator=torch.manual_seed(0)).images\n self.assertFalse(\n np.allclose(ouput_fused, output_no_lora, atol=1e-3, rtol=1e-3), \"Fused lora should change the output\"\n )\n\n def test_simple_inference_with_text_lora_unloaded(self):\n \"\"\"\n Tests a simple inference with lora attached to text encoder, then unloads the lora weights\n and makes sure it works as expected\n \"\"\"\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n _, _, inputs = self.get_dummy_inputs(with_generator=False)\n\n output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))\n\n pipe.text_encoder.add_adapter(text_lora_config)\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n\n if self.has_two_text_encoders:\n pipe.text_encoder_2.add_adapter(text_lora_config)\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Lora not correctly set in text encoder 2\"\n )\n\n pipe.unload_lora_weights()\n # unloading should remove the LoRA layers\n self.assertFalse(\n check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly unloaded in text encoder\"\n )\n\n if self.has_two_text_encoders:\n self.assertFalse(\n check_if_lora_correctly_set(pipe.text_encoder_2),\n \"Lora not correctly unloaded in text encoder 2\",\n )\n\n ouput_unloaded = pipe(**inputs, generator=torch.manual_seed(0)).images\n self.assertTrue(\n np.allclose(ouput_unloaded, output_no_lora, atol=1e-3, rtol=1e-3),\n \"Fused lora should change the output\",\n )\n\n def test_simple_inference_with_text_lora_save_load(self):\n \"\"\"\n Tests a simple usecase where users could use saving utilities for LoRA.\n \"\"\"\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n _, _, inputs = self.get_dummy_inputs(with_generator=False)\n\n output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))\n\n pipe.text_encoder.add_adapter(text_lora_config)\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n\n if self.has_two_text_encoders:\n pipe.text_encoder_2.add_adapter(text_lora_config)\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Lora not correctly set in text encoder 2\"\n )\n\n images_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n text_encoder_state_dict = get_peft_model_state_dict(pipe.text_encoder)\n if self.has_two_text_encoders:\n text_encoder_2_state_dict = get_peft_model_state_dict(pipe.text_encoder_2)\n\n self.pipeline_class.save_lora_weights(\n save_directory=tmpdirname,\n text_encoder_lora_layers=text_encoder_state_dict,\n text_encoder_2_lora_layers=text_encoder_2_state_dict,\n safe_serialization=False,\n )\n else:\n self.pipeline_class.save_lora_weights(\n save_directory=tmpdirname,\n text_encoder_lora_layers=text_encoder_state_dict,\n safe_serialization=False,\n )\n\n self.assertTrue(os.path.isfile(os.path.join(tmpdirname, \"pytorch_lora_weights.bin\")))\n pipe.unload_lora_weights()\n\n pipe.load_lora_weights(os.path.join(tmpdirname, \"pytorch_lora_weights.bin\"))\n\n images_lora_from_pretrained = pipe(**inputs, generator=torch.manual_seed(0)).images\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n\n if self.has_two_text_encoders:\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Lora not correctly set in text encoder 2\"\n )\n\n self.assertTrue(\n np.allclose(images_lora, images_lora_from_pretrained, atol=1e-3, rtol=1e-3),\n \"Loading from saved checkpoints should give same results.\",\n )\n\n def test_simple_inference_save_pretrained(self):\n \"\"\"\n Tests a simple usecase where users could use saving utilities for LoRA through save_pretrained\n \"\"\"\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n _, _, inputs = self.get_dummy_inputs(with_generator=False)\n\n output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))\n\n pipe.text_encoder.add_adapter(text_lora_config)\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n\n if self.has_two_text_encoders:\n pipe.text_encoder_2.add_adapter(text_lora_config)\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Lora not correctly set in text encoder 2\"\n )\n\n images_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n pipe.save_pretrained(tmpdirname)\n\n pipe_from_pretrained = self.pipeline_class.from_pretrained(tmpdirname)\n pipe_from_pretrained.to(torch_device)\n\n self.assertTrue(\n check_if_lora_correctly_set(pipe_from_pretrained.text_encoder),\n \"Lora not correctly set in text encoder\",\n )\n\n if self.has_two_text_encoders:\n self.assertTrue(\n check_if_lora_correctly_set(pipe_from_pretrained.text_encoder_2),\n \"Lora not correctly set in text encoder 2\",\n )\n\n images_lora_save_pretrained = pipe_from_pretrained(**inputs, generator=torch.manual_seed(0)).images\n\n self.assertTrue(\n np.allclose(images_lora, images_lora_save_pretrained, atol=1e-3, rtol=1e-3),\n \"Loading from saved checkpoints should give same results.\",\n )\n\n def test_simple_inference_with_text_unet_lora_save_load(self):\n \"\"\"\n Tests a simple usecase where users could use saving utilities for LoRA for Unet + text encoder\n \"\"\"\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n _, _, inputs = self.get_dummy_inputs(with_generator=False)\n\n output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))\n\n pipe.text_encoder.add_adapter(text_lora_config)\n pipe.unet.add_adapter(unet_lora_config)\n\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n self.assertTrue(check_if_lora_correctly_set(pipe.unet), \"Lora not correctly set in Unet\")\n\n if self.has_two_text_encoders:\n pipe.text_encoder_2.add_adapter(text_lora_config)\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Lora not correctly set in text encoder 2\"\n )\n\n images_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n text_encoder_state_dict = get_peft_model_state_dict(pipe.text_encoder)\n unet_state_dict = get_peft_model_state_dict(pipe.unet)\n if self.has_two_text_encoders:\n text_encoder_2_state_dict = get_peft_model_state_dict(pipe.text_encoder_2)\n\n self.pipeline_class.save_lora_weights(\n save_directory=tmpdirname,\n text_encoder_lora_layers=text_encoder_state_dict,\n text_encoder_2_lora_layers=text_encoder_2_state_dict,\n unet_lora_layers=unet_state_dict,\n safe_serialization=False,\n )\n else:\n self.pipeline_class.save_lora_weights(\n save_directory=tmpdirname,\n text_encoder_lora_layers=text_encoder_state_dict,\n unet_lora_layers=unet_state_dict,\n safe_serialization=False,\n )\n\n self.assertTrue(os.path.isfile(os.path.join(tmpdirname, \"pytorch_lora_weights.bin\")))\n pipe.unload_lora_weights()\n\n pipe.load_lora_weights(os.path.join(tmpdirname, \"pytorch_lora_weights.bin\"))\n\n images_lora_from_pretrained = pipe(**inputs, generator=torch.manual_seed(0)).images\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n self.assertTrue(check_if_lora_correctly_set(pipe.unet), \"Lora not correctly set in Unet\")\n\n if self.has_two_text_encoders:\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Lora not correctly set in text encoder 2\"\n )\n\n self.assertTrue(\n np.allclose(images_lora, images_lora_from_pretrained, atol=1e-3, rtol=1e-3),\n \"Loading from saved checkpoints should give same results.\",\n )\n\n def test_simple_inference_with_text_unet_lora_and_scale(self):\n \"\"\"\n Tests a simple inference with lora attached on the text encoder + Unet + scale argument\n and makes sure it works as expected\n \"\"\"\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n _, _, inputs = self.get_dummy_inputs(with_generator=False)\n\n output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))\n\n pipe.text_encoder.add_adapter(text_lora_config)\n pipe.unet.add_adapter(unet_lora_config)\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n self.assertTrue(check_if_lora_correctly_set(pipe.unet), \"Lora not correctly set in Unet\")\n\n if self.has_two_text_encoders:\n pipe.text_encoder_2.add_adapter(text_lora_config)\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Lora not correctly set in text encoder 2\"\n )\n\n output_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n self.assertTrue(\n not np.allclose(output_lora, output_no_lora, atol=1e-3, rtol=1e-3), \"Lora should change the output\"\n )\n\n output_lora_scale = pipe(\n **inputs, generator=torch.manual_seed(0), cross_attention_kwargs={\"scale\": 0.5}\n ).images\n self.assertTrue(\n not np.allclose(output_lora, output_lora_scale, atol=1e-3, rtol=1e-3),\n \"Lora + scale should change the output\",\n )\n\n output_lora_0_scale = pipe(\n **inputs, generator=torch.manual_seed(0), cross_attention_kwargs={\"scale\": 0.0}\n ).images\n self.assertTrue(\n np.allclose(output_no_lora, output_lora_0_scale, atol=1e-3, rtol=1e-3),\n \"Lora + 0 scale should lead to same result as no LoRA\",\n )\n\n self.assertTrue(\n pipe.text_encoder.text_model.encoder.layers[0].self_attn.q_proj.scaling[\"default\"] == 1.0,\n \"The scaling parameter has not been correctly restored!\",\n )\n\n def test_simple_inference_with_text_lora_unet_fused(self):\n \"\"\"\n Tests a simple inference with lora attached into text encoder + fuses the lora weights into base model\n and makes sure it works as expected - with unet\n \"\"\"\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n _, _, inputs = self.get_dummy_inputs(with_generator=False)\n\n output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))\n\n pipe.text_encoder.add_adapter(text_lora_config)\n pipe.unet.add_adapter(unet_lora_config)\n\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n self.assertTrue(check_if_lora_correctly_set(pipe.unet), \"Lora not correctly set in Unet\")\n\n if self.has_two_text_encoders:\n pipe.text_encoder_2.add_adapter(text_lora_config)\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Lora not correctly set in text encoder 2\"\n )\n\n pipe.fuse_lora()\n # Fusing should still keep the LoRA layers\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n self.assertTrue(check_if_lora_correctly_set(pipe.unet), \"Lora not correctly set in unet\")\n\n if self.has_two_text_encoders:\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Lora not correctly set in text encoder 2\"\n )\n\n ouput_fused = pipe(**inputs, generator=torch.manual_seed(0)).images\n self.assertFalse(\n np.allclose(ouput_fused, output_no_lora, atol=1e-3, rtol=1e-3), \"Fused lora should change the output\"\n )\n\n def test_simple_inference_with_text_unet_lora_unloaded(self):\n \"\"\"\n Tests a simple inference with lora attached to text encoder and unet, then unloads the lora weights\n and makes sure it works as expected\n \"\"\"\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n _, _, inputs = self.get_dummy_inputs(with_generator=False)\n\n output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))\n\n pipe.text_encoder.add_adapter(text_lora_config)\n pipe.unet.add_adapter(unet_lora_config)\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n self.assertTrue(check_if_lora_correctly_set(pipe.unet), \"Lora not correctly set in Unet\")\n\n if self.has_two_text_encoders:\n pipe.text_encoder_2.add_adapter(text_lora_config)\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Lora not correctly set in text encoder 2\"\n )\n\n pipe.unload_lora_weights()\n # unloading should remove the LoRA layers\n self.assertFalse(\n check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly unloaded in text encoder\"\n )\n self.assertFalse(check_if_lora_correctly_set(pipe.unet), \"Lora not correctly unloaded in Unet\")\n\n if self.has_two_text_encoders:\n self.assertFalse(\n check_if_lora_correctly_set(pipe.text_encoder_2),\n \"Lora not correctly unloaded in text encoder 2\",\n )\n\n ouput_unloaded = pipe(**inputs, generator=torch.manual_seed(0)).images\n self.assertTrue(\n np.allclose(ouput_unloaded, output_no_lora, atol=1e-3, rtol=1e-3),\n \"Fused lora should change the output\",\n )\n\n def test_simple_inference_with_text_unet_lora_unfused(self):\n \"\"\"\n Tests a simple inference with lora attached to text encoder and unet, then unloads the lora weights\n and makes sure it works as expected\n \"\"\"\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n _, _, inputs = self.get_dummy_inputs(with_generator=False)\n\n pipe.text_encoder.add_adapter(text_lora_config)\n pipe.unet.add_adapter(unet_lora_config)\n\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n self.assertTrue(check_if_lora_correctly_set(pipe.unet), \"Lora not correctly set in Unet\")\n\n if self.has_two_text_encoders:\n pipe.text_encoder_2.add_adapter(text_lora_config)\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Lora not correctly set in text encoder 2\"\n )\n\n pipe.fuse_lora()\n\n output_fused_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n pipe.unfuse_lora()\n\n output_unfused_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n # unloading should remove the LoRA layers\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Unfuse should still keep LoRA layers\")\n self.assertTrue(check_if_lora_correctly_set(pipe.unet), \"Unfuse should still keep LoRA layers\")\n\n if self.has_two_text_encoders:\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Unfuse should still keep LoRA layers\"\n )\n\n # Fuse and unfuse should lead to the same results\n self.assertTrue(\n np.allclose(output_fused_lora, output_unfused_lora, atol=1e-3, rtol=1e-3),\n \"Fused lora should change the output\",\n )\n\n def test_simple_inference_with_text_unet_multi_adapter(self):\n \"\"\"\n Tests a simple inference with lora attached to text encoder and unet, attaches\n multiple adapters and set them\n \"\"\"\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n _, _, inputs = self.get_dummy_inputs(with_generator=False)\n\n output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n pipe.text_encoder.add_adapter(text_lora_config, \"adapter-1\")\n pipe.text_encoder.add_adapter(text_lora_config, \"adapter-2\")\n\n pipe.unet.add_adapter(unet_lora_config, \"adapter-1\")\n pipe.unet.add_adapter(unet_lora_config, \"adapter-2\")\n\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n self.assertTrue(check_if_lora_correctly_set(pipe.unet), \"Lora not correctly set in Unet\")\n\n if self.has_two_text_encoders:\n pipe.text_encoder_2.add_adapter(text_lora_config, \"adapter-1\")\n pipe.text_encoder_2.add_adapter(text_lora_config, \"adapter-2\")\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Lora not correctly set in text encoder 2\"\n )\n\n pipe.set_adapters(\"adapter-1\")\n\n output_adapter_1 = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n pipe.set_adapters(\"adapter-2\")\n output_adapter_2 = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n pipe.set_adapters([\"adapter-1\", \"adapter-2\"])\n\n output_adapter_mixed = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n # Fuse and unfuse should lead to the same results\n self.assertFalse(\n np.allclose(output_adapter_1, output_adapter_2, atol=1e-3, rtol=1e-3),\n \"Adapter 1 and 2 should give different results\",\n )\n\n self.assertFalse(\n np.allclose(output_adapter_1, output_adapter_mixed, atol=1e-3, rtol=1e-3),\n \"Adapter 1 and mixed adapters should give different results\",\n )\n\n self.assertFalse(\n np.allclose(output_adapter_2, output_adapter_mixed, atol=1e-3, rtol=1e-3),\n \"Adapter 2 and mixed adapters should give different results\",\n )\n\n pipe.disable_lora()\n\n output_disabled = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n self.assertTrue(\n np.allclose(output_no_lora, output_disabled, atol=1e-3, rtol=1e-3),\n \"output with no lora and output with lora disabled should give same results\",\n )\n\n def test_simple_inference_with_text_unet_block_scale(self):\n \"\"\"\n Tests a simple inference with lora attached to text encoder and unet, attaches\n one adapter and set differnt weights for different blocks (i.e. block lora)\n \"\"\"\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n _, _, inputs = self.get_dummy_inputs(with_generator=False)\n\n output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n pipe.text_encoder.add_adapter(text_lora_config, \"adapter-1\")\n pipe.unet.add_adapter(unet_lora_config, \"adapter-1\")\n\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n self.assertTrue(check_if_lora_correctly_set(pipe.unet), \"Lora not correctly set in Unet\")\n\n if self.has_two_text_encoders:\n pipe.text_encoder_2.add_adapter(text_lora_config, \"adapter-1\")\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Lora not correctly set in text encoder 2\"\n )\n\n weights_1 = {\"text_encoder\": 2, \"unet\": {\"down\": 5}}\n pipe.set_adapters(\"adapter-1\", weights_1)\n output_weights_1 = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n weights_2 = {\"unet\": {\"up\": 5}}\n pipe.set_adapters(\"adapter-1\", weights_2)\n output_weights_2 = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n self.assertFalse(\n np.allclose(output_weights_1, output_weights_2, atol=1e-3, rtol=1e-3),\n \"LoRA weights 1 and 2 should give different results\",\n )\n self.assertFalse(\n np.allclose(output_no_lora, output_weights_1, atol=1e-3, rtol=1e-3),\n \"No adapter and LoRA weights 1 should give different results\",\n )\n self.assertFalse(\n np.allclose(output_no_lora, output_weights_2, atol=1e-3, rtol=1e-3),\n \"No adapter and LoRA weights 2 should give different results\",\n )\n\n pipe.disable_lora()\n output_disabled = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n self.assertTrue(\n np.allclose(output_no_lora, output_disabled, atol=1e-3, rtol=1e-3),\n \"output with no lora and output with lora disabled should give same results\",\n )\n\n def test_simple_inference_with_text_unet_multi_adapter_block_lora(self):\n \"\"\"\n Tests a simple inference with lora attached to text encoder and unet, attaches\n multiple adapters and set differnt weights for different blocks (i.e. block lora)\n \"\"\"\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n _, _, inputs = self.get_dummy_inputs(with_generator=False)\n\n output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n pipe.text_encoder.add_adapter(text_lora_config, \"adapter-1\")\n pipe.text_encoder.add_adapter(text_lora_config, \"adapter-2\")\n\n pipe.unet.add_adapter(unet_lora_config, \"adapter-1\")\n pipe.unet.add_adapter(unet_lora_config, \"adapter-2\")\n\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n self.assertTrue(check_if_lora_correctly_set(pipe.unet), \"Lora not correctly set in Unet\")\n\n if self.has_two_text_encoders:\n pipe.text_encoder_2.add_adapter(text_lora_config, \"adapter-1\")\n pipe.text_encoder_2.add_adapter(text_lora_config, \"adapter-2\")\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Lora not correctly set in text encoder 2\"\n )\n\n scales_1 = {\"text_encoder\": 2, \"unet\": {\"down\": 5}}\n scales_2 = {\"unet\": {\"down\": 5, \"mid\": 5}}\n pipe.set_adapters(\"adapter-1\", scales_1)\n\n output_adapter_1 = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n pipe.set_adapters(\"adapter-2\", scales_2)\n output_adapter_2 = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n pipe.set_adapters([\"adapter-1\", \"adapter-2\"], [scales_1, scales_2])\n\n output_adapter_mixed = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n # Fuse and unfuse should lead to the same results\n self.assertFalse(\n np.allclose(output_adapter_1, output_adapter_2, atol=1e-3, rtol=1e-3),\n \"Adapter 1 and 2 should give different results\",\n )\n\n self.assertFalse(\n np.allclose(output_adapter_1, output_adapter_mixed, atol=1e-3, rtol=1e-3),\n \"Adapter 1 and mixed adapters should give different results\",\n )\n\n self.assertFalse(\n np.allclose(output_adapter_2, output_adapter_mixed, atol=1e-3, rtol=1e-3),\n \"Adapter 2 and mixed adapters should give different results\",\n )\n\n pipe.disable_lora()\n\n output_disabled = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n self.assertTrue(\n np.allclose(output_no_lora, output_disabled, atol=1e-3, rtol=1e-3),\n \"output with no lora and output with lora disabled should give same results\",\n )\n\n # a mismatching number of adapter_names and adapter_weights should raise an error\n with self.assertRaises(ValueError):\n pipe.set_adapters([\"adapter-1\", \"adapter-2\"], [scales_1])\n\n def test_simple_inference_with_text_unet_block_scale_for_all_dict_options(self):\n \"\"\"Tests that any valid combination of lora block scales can be used in pipe.set_adapter\"\"\"\n\n def updown_options(blocks_with_tf, layers_per_block, value):\n \"\"\"\n Generate every possible combination for how a lora weight dict for the up/down part can be.\n E.g. 2, {\"block_1\": 2}, {\"block_1\": [2,2,2]}, {\"block_1\": 2, \"block_2\": [2,2,2]}, ...\n \"\"\"\n num_val = value\n list_val = [value] * layers_per_block\n\n node_opts = [None, num_val, list_val]\n node_opts_foreach_block = [node_opts] * len(blocks_with_tf)\n\n updown_opts = [num_val]\n for nodes in product(*node_opts_foreach_block):\n if all(n is None for n in nodes):\n continue\n opt = {}\n for b, n in zip(blocks_with_tf, nodes):\n if n is not None:\n opt[\"block_\" + str(b)] = n\n updown_opts.append(opt)\n return updown_opts\n\n def all_possible_dict_opts(unet, value):\n \"\"\"\n Generate every possible combination for how a lora weight dict can be.\n E.g. 2, {\"unet: {\"down\": 2}}, {\"unet: {\"down\": [2,2,2]}}, {\"unet: {\"mid\": 2, \"up\": [2,2,2]}}, ...\n \"\"\"\n\n down_blocks_with_tf = [i for i, d in enumerate(unet.down_blocks) if hasattr(d, \"attentions\")]\n up_blocks_with_tf = [i for i, u in enumerate(unet.up_blocks) if hasattr(u, \"attentions\")]\n\n layers_per_block = unet.config.layers_per_block\n\n text_encoder_opts = [None, value]\n text_encoder_2_opts = [None, value]\n mid_opts = [None, value]\n down_opts = [None] + updown_options(down_blocks_with_tf, layers_per_block, value)\n up_opts = [None] + updown_options(up_blocks_with_tf, layers_per_block + 1, value)\n\n opts = []\n\n for t1, t2, d, m, u in product(text_encoder_opts, text_encoder_2_opts, down_opts, mid_opts, up_opts):\n if all(o is None for o in (t1, t2, d, m, u)):\n continue\n opt = {}\n if t1 is not None:\n opt[\"text_encoder\"] = t1\n if t2 is not None:\n opt[\"text_encoder_2\"] = t2\n if all(o is None for o in (d, m, u)):\n # no unet scaling\n continue\n opt[\"unet\"] = {}\n if d is not None:\n opt[\"unet\"][\"down\"] = d\n if m is not None:\n opt[\"unet\"][\"mid\"] = m\n if u is not None:\n opt[\"unet\"][\"up\"] = u\n opts.append(opt)\n\n return opts\n\n components, text_lora_config, unet_lora_config = self.get_dummy_components(self.scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n _, _, inputs = self.get_dummy_inputs(with_generator=False)\n\n pipe.text_encoder.add_adapter(text_lora_config, \"adapter-1\")\n pipe.unet.add_adapter(unet_lora_config, \"adapter-1\")\n\n if self.has_two_text_encoders:\n pipe.text_encoder_2.add_adapter(text_lora_config, \"adapter-1\")\n\n for scale_dict in all_possible_dict_opts(pipe.unet, value=1234):\n # test if lora block scales can be set with this scale_dict\n if not self.has_two_text_encoders and \"text_encoder_2\" in scale_dict:\n del scale_dict[\"text_encoder_2\"]\n\n pipe.set_adapters(\"adapter-1\", scale_dict) # test will fail if this line throws an error\n\n def test_simple_inference_with_text_unet_multi_adapter_delete_adapter(self):\n \"\"\"\n Tests a simple inference with lora attached to text encoder and unet, attaches\n multiple adapters and set/delete them\n \"\"\"\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n _, _, inputs = self.get_dummy_inputs(with_generator=False)\n\n output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n pipe.text_encoder.add_adapter(text_lora_config, \"adapter-1\")\n pipe.text_encoder.add_adapter(text_lora_config, \"adapter-2\")\n\n pipe.unet.add_adapter(unet_lora_config, \"adapter-1\")\n pipe.unet.add_adapter(unet_lora_config, \"adapter-2\")\n\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n self.assertTrue(check_if_lora_correctly_set(pipe.unet), \"Lora not correctly set in Unet\")\n\n if self.has_two_text_encoders:\n pipe.text_encoder_2.add_adapter(text_lora_config, \"adapter-1\")\n pipe.text_encoder_2.add_adapter(text_lora_config, \"adapter-2\")\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Lora not correctly set in text encoder 2\"\n )\n\n pipe.set_adapters(\"adapter-1\")\n\n output_adapter_1 = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n pipe.set_adapters(\"adapter-2\")\n output_adapter_2 = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n pipe.set_adapters([\"adapter-1\", \"adapter-2\"])\n\n output_adapter_mixed = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n self.assertFalse(\n np.allclose(output_adapter_1, output_adapter_2, atol=1e-3, rtol=1e-3),\n \"Adapter 1 and 2 should give different results\",\n )\n\n self.assertFalse(\n np.allclose(output_adapter_1, output_adapter_mixed, atol=1e-3, rtol=1e-3),\n \"Adapter 1 and mixed adapters should give different results\",\n )\n\n self.assertFalse(\n np.allclose(output_adapter_2, output_adapter_mixed, atol=1e-3, rtol=1e-3),\n \"Adapter 2 and mixed adapters should give different results\",\n )\n\n pipe.delete_adapters(\"adapter-1\")\n output_deleted_adapter_1 = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n self.assertTrue(\n np.allclose(output_deleted_adapter_1, output_adapter_2, atol=1e-3, rtol=1e-3),\n \"Adapter 1 and 2 should give different results\",\n )\n\n pipe.delete_adapters(\"adapter-2\")\n output_deleted_adapters = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n self.assertTrue(\n np.allclose(output_no_lora, output_deleted_adapters, atol=1e-3, rtol=1e-3),\n \"output with no lora and output with lora disabled should give same results\",\n )\n\n pipe.text_encoder.add_adapter(text_lora_config, \"adapter-1\")\n pipe.text_encoder.add_adapter(text_lora_config, \"adapter-2\")\n\n pipe.unet.add_adapter(unet_lora_config, \"adapter-1\")\n pipe.unet.add_adapter(unet_lora_config, \"adapter-2\")\n\n pipe.set_adapters([\"adapter-1\", \"adapter-2\"])\n pipe.delete_adapters([\"adapter-1\", \"adapter-2\"])\n\n output_deleted_adapters = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n self.assertTrue(\n np.allclose(output_no_lora, output_deleted_adapters, atol=1e-3, rtol=1e-3),\n \"output with no lora and output with lora disabled should give same results\",\n )\n\n def test_simple_inference_with_text_unet_multi_adapter_weighted(self):\n \"\"\"\n Tests a simple inference with lora attached to text encoder and unet, attaches\n multiple adapters and set them\n \"\"\"\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n _, _, inputs = self.get_dummy_inputs(with_generator=False)\n\n output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n pipe.text_encoder.add_adapter(text_lora_config, \"adapter-1\")\n pipe.text_encoder.add_adapter(text_lora_config, \"adapter-2\")\n\n pipe.unet.add_adapter(unet_lora_config, \"adapter-1\")\n pipe.unet.add_adapter(unet_lora_config, \"adapter-2\")\n\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n self.assertTrue(check_if_lora_correctly_set(pipe.unet), \"Lora not correctly set in Unet\")\n\n if self.has_two_text_encoders:\n pipe.text_encoder_2.add_adapter(text_lora_config, \"adapter-1\")\n pipe.text_encoder_2.add_adapter(text_lora_config, \"adapter-2\")\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Lora not correctly set in text encoder 2\"\n )\n\n pipe.set_adapters(\"adapter-1\")\n\n output_adapter_1 = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n pipe.set_adapters(\"adapter-2\")\n output_adapter_2 = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n pipe.set_adapters([\"adapter-1\", \"adapter-2\"])\n\n output_adapter_mixed = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n # Fuse and unfuse should lead to the same results\n self.assertFalse(\n np.allclose(output_adapter_1, output_adapter_2, atol=1e-3, rtol=1e-3),\n \"Adapter 1 and 2 should give different results\",\n )\n\n self.assertFalse(\n np.allclose(output_adapter_1, output_adapter_mixed, atol=1e-3, rtol=1e-3),\n \"Adapter 1 and mixed adapters should give different results\",\n )\n\n self.assertFalse(\n np.allclose(output_adapter_2, output_adapter_mixed, atol=1e-3, rtol=1e-3),\n \"Adapter 2 and mixed adapters should give different results\",\n )\n\n pipe.set_adapters([\"adapter-1\", \"adapter-2\"], [0.5, 0.6])\n output_adapter_mixed_weighted = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n self.assertFalse(\n np.allclose(output_adapter_mixed_weighted, output_adapter_mixed, atol=1e-3, rtol=1e-3),\n \"Weighted adapter and mixed adapter should give different results\",\n )\n\n pipe.disable_lora()\n\n output_disabled = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n self.assertTrue(\n np.allclose(output_no_lora, output_disabled, atol=1e-3, rtol=1e-3),\n \"output with no lora and output with lora disabled should give same results\",\n )\n\n @skip_mps\n def test_lora_fuse_nan(self):\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n _, _, inputs = self.get_dummy_inputs(with_generator=False)\n\n pipe.text_encoder.add_adapter(text_lora_config, \"adapter-1\")\n\n pipe.unet.add_adapter(unet_lora_config, \"adapter-1\")\n\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n self.assertTrue(check_if_lora_correctly_set(pipe.unet), \"Lora not correctly set in Unet\")\n\n # corrupt one LoRA weight with `inf` values\n with torch.no_grad():\n pipe.unet.mid_block.attentions[0].transformer_blocks[0].attn1.to_q.lora_A[\"adapter-1\"].weight += float(\n \"inf\"\n )\n\n # with `safe_fusing=True` we should see an Error\n with self.assertRaises(ValueError):\n pipe.fuse_lora(safe_fusing=True)\n\n # without we should not see an error, but every image will be black\n pipe.fuse_lora(safe_fusing=False)\n\n out = pipe(\"test\", num_inference_steps=2, output_type=\"np\").images\n\n self.assertTrue(np.isnan(out).all())\n\n def test_get_adapters(self):\n \"\"\"\n Tests a simple usecase where we attach multiple adapters and check if the results\n are the expected results\n \"\"\"\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n _, _, inputs = self.get_dummy_inputs(with_generator=False)\n\n pipe.text_encoder.add_adapter(text_lora_config, \"adapter-1\")\n pipe.unet.add_adapter(unet_lora_config, \"adapter-1\")\n\n adapter_names = pipe.get_active_adapters()\n self.assertListEqual(adapter_names, [\"adapter-1\"])\n\n pipe.text_encoder.add_adapter(text_lora_config, \"adapter-2\")\n pipe.unet.add_adapter(unet_lora_config, \"adapter-2\")\n\n adapter_names = pipe.get_active_adapters()\n self.assertListEqual(adapter_names, [\"adapter-2\"])\n\n pipe.set_adapters([\"adapter-1\", \"adapter-2\"])\n self.assertListEqual(pipe.get_active_adapters(), [\"adapter-1\", \"adapter-2\"])\n\n def test_get_list_adapters(self):\n \"\"\"\n Tests a simple usecase where we attach multiple adapters and check if the results\n are the expected results\n \"\"\"\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n pipe.text_encoder.add_adapter(text_lora_config, \"adapter-1\")\n pipe.unet.add_adapter(unet_lora_config, \"adapter-1\")\n\n adapter_names = pipe.get_list_adapters()\n self.assertDictEqual(adapter_names, {\"text_encoder\": [\"adapter-1\"], \"unet\": [\"adapter-1\"]})\n\n pipe.text_encoder.add_adapter(text_lora_config, \"adapter-2\")\n pipe.unet.add_adapter(unet_lora_config, \"adapter-2\")\n\n adapter_names = pipe.get_list_adapters()\n self.assertDictEqual(\n adapter_names, {\"text_encoder\": [\"adapter-1\", \"adapter-2\"], \"unet\": [\"adapter-1\", \"adapter-2\"]}\n )\n\n pipe.set_adapters([\"adapter-1\", \"adapter-2\"])\n self.assertDictEqual(\n pipe.get_list_adapters(),\n {\"unet\": [\"adapter-1\", \"adapter-2\"], \"text_encoder\": [\"adapter-1\", \"adapter-2\"]},\n )\n\n pipe.unet.add_adapter(unet_lora_config, \"adapter-3\")\n self.assertDictEqual(\n pipe.get_list_adapters(),\n {\"unet\": [\"adapter-1\", \"adapter-2\", \"adapter-3\"], \"text_encoder\": [\"adapter-1\", \"adapter-2\"]},\n )\n\n @require_peft_version_greater(peft_version=\"0.6.2\")\n def test_simple_inference_with_text_lora_unet_fused_multi(self):\n \"\"\"\n Tests a simple inference with lora attached into text encoder + fuses the lora weights into base model\n and makes sure it works as expected - with unet and multi-adapter case\n \"\"\"\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n _, _, inputs = self.get_dummy_inputs(with_generator=False)\n\n output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))\n\n pipe.text_encoder.add_adapter(text_lora_config, \"adapter-1\")\n pipe.unet.add_adapter(unet_lora_config, \"adapter-1\")\n\n # Attach a second adapter\n pipe.text_encoder.add_adapter(text_lora_config, \"adapter-2\")\n pipe.unet.add_adapter(unet_lora_config, \"adapter-2\")\n\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n self.assertTrue(check_if_lora_correctly_set(pipe.unet), \"Lora not correctly set in Unet\")\n\n if self.has_two_text_encoders:\n pipe.text_encoder_2.add_adapter(text_lora_config, \"adapter-1\")\n pipe.text_encoder_2.add_adapter(text_lora_config, \"adapter-2\")\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Lora not correctly set in text encoder 2\"\n )\n\n # set them to multi-adapter inference mode\n pipe.set_adapters([\"adapter-1\", \"adapter-2\"])\n ouputs_all_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n pipe.set_adapters([\"adapter-1\"])\n ouputs_lora_1 = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n pipe.fuse_lora(adapter_names=[\"adapter-1\"])\n\n # Fusing should still keep the LoRA layers so outpout should remain the same\n outputs_lora_1_fused = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n self.assertTrue(\n np.allclose(ouputs_lora_1, outputs_lora_1_fused, atol=1e-3, rtol=1e-3),\n \"Fused lora should not change the output\",\n )\n\n pipe.unfuse_lora()\n pipe.fuse_lora(adapter_names=[\"adapter-2\", \"adapter-1\"])\n\n # Fusing should still keep the LoRA layers\n output_all_lora_fused = pipe(**inputs, generator=torch.manual_seed(0)).images\n self.assertTrue(\n np.allclose(output_all_lora_fused, ouputs_all_lora, atol=1e-3, rtol=1e-3),\n \"Fused lora should not change the output\",\n )\n\n @require_peft_version_greater(peft_version=\"0.9.0\")\n def test_simple_inference_with_dora(self):\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls, use_dora=True)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n _, _, inputs = self.get_dummy_inputs(with_generator=False)\n\n output_no_dora_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n self.assertTrue(output_no_dora_lora.shape == (1, 64, 64, 3))\n\n pipe.text_encoder.add_adapter(text_lora_config)\n pipe.unet.add_adapter(unet_lora_config)\n\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n self.assertTrue(check_if_lora_correctly_set(pipe.unet), \"Lora not correctly set in Unet\")\n\n if self.has_two_text_encoders:\n pipe.text_encoder_2.add_adapter(text_lora_config)\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Lora not correctly set in text encoder 2\"\n )\n\n output_dora_lora = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n self.assertFalse(\n np.allclose(output_dora_lora, output_no_dora_lora, atol=1e-3, rtol=1e-3),\n \"DoRA lora should change the output\",\n )\n\n @unittest.skip(\"This is failing for now - need to investigate\")\n def test_simple_inference_with_text_unet_lora_unfused_torch_compile(self):\n \"\"\"\n Tests a simple inference with lora attached to text encoder and unet, then unloads the lora weights\n and makes sure it works as expected\n \"\"\"\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n _, _, inputs = self.get_dummy_inputs(with_generator=False)\n\n pipe.text_encoder.add_adapter(text_lora_config)\n pipe.unet.add_adapter(unet_lora_config)\n\n self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), \"Lora not correctly set in text encoder\")\n self.assertTrue(check_if_lora_correctly_set(pipe.unet), \"Lora not correctly set in Unet\")\n\n if self.has_two_text_encoders:\n pipe.text_encoder_2.add_adapter(text_lora_config)\n self.assertTrue(\n check_if_lora_correctly_set(pipe.text_encoder_2), \"Lora not correctly set in text encoder 2\"\n )\n\n pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n pipe.text_encoder = torch.compile(pipe.text_encoder, mode=\"reduce-overhead\", fullgraph=True)\n\n if self.has_two_text_encoders:\n pipe.text_encoder_2 = torch.compile(pipe.text_encoder_2, mode=\"reduce-overhead\", fullgraph=True)\n\n # Just makes sure it works..\n _ = pipe(**inputs, generator=torch.manual_seed(0)).images\n\n def test_modify_padding_mode(self):\n def set_pad_mode(network, mode=\"circular\"):\n for _, module in network.named_modules():\n if isinstance(module, torch.nn.Conv2d):\n module.padding_mode = mode\n\n for scheduler_cls in [DDIMScheduler, LCMScheduler]:\n components, _, _ = self.get_dummy_components(scheduler_cls)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n _pad_mode = \"circular\"\n set_pad_mode(pipe.vae, _pad_mode)\n set_pad_mode(pipe.unet, _pad_mode)\n\n _, _, inputs = self.get_dummy_inputs()\n _ = pipe(**inputs).images\n" }, { "path": "diffusers/tests/models/__init__.py", "content": "" }, { "path": "diffusers/tests/models/autoencoders/__init__.py", "content": "" }, { "path": "diffusers/tests/models/autoencoders/test_models_vae.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\n\nfrom diffusers import (\n AsymmetricAutoencoderKL,\n AutoencoderKL,\n AutoencoderKLTemporalDecoder,\n AutoencoderTiny,\n ConsistencyDecoderVAE,\n StableDiffusionPipeline,\n)\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.loading_utils import load_image\nfrom diffusers.utils.testing_utils import (\n backend_empty_cache,\n enable_full_determinism,\n floats_tensor,\n load_hf_numpy,\n require_torch_accelerator,\n require_torch_accelerator_with_fp16,\n require_torch_accelerator_with_training,\n require_torch_gpu,\n skip_mps,\n slow,\n torch_all_close,\n torch_device,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\nfrom ..test_modeling_common import ModelTesterMixin, UNetTesterMixin\n\n\nenable_full_determinism()\n\n\ndef get_autoencoder_kl_config(block_out_channels=None, norm_num_groups=None):\n block_out_channels = block_out_channels or [2, 4]\n norm_num_groups = norm_num_groups or 2\n init_dict = {\n \"block_out_channels\": block_out_channels,\n \"in_channels\": 3,\n \"out_channels\": 3,\n \"down_block_types\": [\"DownEncoderBlock2D\"] * len(block_out_channels),\n \"up_block_types\": [\"UpDecoderBlock2D\"] * len(block_out_channels),\n \"latent_channels\": 4,\n \"norm_num_groups\": norm_num_groups,\n }\n return init_dict\n\n\ndef get_asym_autoencoder_kl_config(block_out_channels=None, norm_num_groups=None):\n block_out_channels = block_out_channels or [2, 4]\n norm_num_groups = norm_num_groups or 2\n init_dict = {\n \"in_channels\": 3,\n \"out_channels\": 3,\n \"down_block_types\": [\"DownEncoderBlock2D\"] * len(block_out_channels),\n \"down_block_out_channels\": block_out_channels,\n \"layers_per_down_block\": 1,\n \"up_block_types\": [\"UpDecoderBlock2D\"] * len(block_out_channels),\n \"up_block_out_channels\": block_out_channels,\n \"layers_per_up_block\": 1,\n \"act_fn\": \"silu\",\n \"latent_channels\": 4,\n \"norm_num_groups\": norm_num_groups,\n \"sample_size\": 32,\n \"scaling_factor\": 0.18215,\n }\n return init_dict\n\n\ndef get_autoencoder_tiny_config(block_out_channels=None):\n block_out_channels = (len(block_out_channels) * [32]) if block_out_channels is not None else [32, 32]\n init_dict = {\n \"in_channels\": 3,\n \"out_channels\": 3,\n \"encoder_block_out_channels\": block_out_channels,\n \"decoder_block_out_channels\": block_out_channels,\n \"num_encoder_blocks\": [b // min(block_out_channels) for b in block_out_channels],\n \"num_decoder_blocks\": [b // min(block_out_channels) for b in reversed(block_out_channels)],\n }\n return init_dict\n\n\ndef get_consistency_vae_config(block_out_channels=None, norm_num_groups=None):\n block_out_channels = block_out_channels or [2, 4]\n norm_num_groups = norm_num_groups or 2\n return {\n \"encoder_block_out_channels\": block_out_channels,\n \"encoder_in_channels\": 3,\n \"encoder_out_channels\": 4,\n \"encoder_down_block_types\": [\"DownEncoderBlock2D\"] * len(block_out_channels),\n \"decoder_add_attention\": False,\n \"decoder_block_out_channels\": block_out_channels,\n \"decoder_down_block_types\": [\"ResnetDownsampleBlock2D\"] * len(block_out_channels),\n \"decoder_downsample_padding\": 1,\n \"decoder_in_channels\": 7,\n \"decoder_layers_per_block\": 1,\n \"decoder_norm_eps\": 1e-05,\n \"decoder_norm_num_groups\": norm_num_groups,\n \"encoder_norm_num_groups\": norm_num_groups,\n \"decoder_num_train_timesteps\": 1024,\n \"decoder_out_channels\": 6,\n \"decoder_resnet_time_scale_shift\": \"scale_shift\",\n \"decoder_time_embedding_type\": \"learned\",\n \"decoder_up_block_types\": [\"ResnetUpsampleBlock2D\"] * len(block_out_channels),\n \"scaling_factor\": 1,\n \"latent_channels\": 4,\n }\n\n\nclass AutoencoderKLTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):\n model_class = AutoencoderKL\n main_input_name = \"sample\"\n base_precision = 1e-2\n\n @property\n def dummy_input(self):\n batch_size = 4\n num_channels = 3\n sizes = (32, 32)\n\n image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)\n\n return {\"sample\": image}\n\n @property\n def input_shape(self):\n return (3, 32, 32)\n\n @property\n def output_shape(self):\n return (3, 32, 32)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = get_autoencoder_kl_config()\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n def test_forward_signature(self):\n pass\n\n def test_training(self):\n pass\n\n @require_torch_accelerator_with_training\n def test_gradient_checkpointing(self):\n # enable deterministic behavior for gradient checkpointing\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n model.to(torch_device)\n\n assert not model.is_gradient_checkpointing and model.training\n\n out = model(**inputs_dict).sample\n # run the backwards pass on the model. For backwards pass, for simplicity purpose,\n # we won't calculate the loss and rather backprop on out.sum()\n model.zero_grad()\n\n labels = torch.randn_like(out)\n loss = (out - labels).mean()\n loss.backward()\n\n # re-instantiate the model now enabling gradient checkpointing\n model_2 = self.model_class(**init_dict)\n # clone model\n model_2.load_state_dict(model.state_dict())\n model_2.to(torch_device)\n model_2.enable_gradient_checkpointing()\n\n assert model_2.is_gradient_checkpointing and model_2.training\n\n out_2 = model_2(**inputs_dict).sample\n # run the backwards pass on the model. For backwards pass, for simplicity purpose,\n # we won't calculate the loss and rather backprop on out.sum()\n model_2.zero_grad()\n loss_2 = (out_2 - labels).mean()\n loss_2.backward()\n\n # compare the output and parameters gradients\n self.assertTrue((loss - loss_2).abs() < 1e-5)\n named_params = dict(model.named_parameters())\n named_params_2 = dict(model_2.named_parameters())\n for name, param in named_params.items():\n self.assertTrue(torch_all_close(param.grad.data, named_params_2[name].grad.data, atol=5e-5))\n\n def test_from_pretrained_hub(self):\n model, loading_info = AutoencoderKL.from_pretrained(\"fusing/autoencoder-kl-dummy\", output_loading_info=True)\n self.assertIsNotNone(model)\n self.assertEqual(len(loading_info[\"missing_keys\"]), 0)\n\n model.to(torch_device)\n image = model(**self.dummy_input)\n\n assert image is not None, \"Make sure output is not None\"\n\n def test_output_pretrained(self):\n model = AutoencoderKL.from_pretrained(\"fusing/autoencoder-kl-dummy\")\n model = model.to(torch_device)\n model.eval()\n\n # Keep generator on CPU for non-CUDA devices to compare outputs with CPU result tensors\n generator_device = \"cpu\" if not torch_device.startswith(\"cuda\") else \"cuda\"\n if torch_device != \"mps\":\n generator = torch.Generator(device=generator_device).manual_seed(0)\n else:\n generator = torch.manual_seed(0)\n\n image = torch.randn(\n 1,\n model.config.in_channels,\n model.config.sample_size,\n model.config.sample_size,\n generator=torch.manual_seed(0),\n )\n image = image.to(torch_device)\n with torch.no_grad():\n output = model(image, sample_posterior=True, generator=generator).sample\n\n output_slice = output[0, -1, -3:, -3:].flatten().cpu()\n\n # Since the VAE Gaussian prior's generator is seeded on the appropriate device,\n # the expected output slices are not the same for CPU and GPU.\n if torch_device == \"mps\":\n expected_output_slice = torch.tensor(\n [\n -4.0078e-01,\n -3.8323e-04,\n -1.2681e-01,\n -1.1462e-01,\n 2.0095e-01,\n 1.0893e-01,\n -8.8247e-02,\n -3.0361e-01,\n -9.8644e-03,\n ]\n )\n elif generator_device == \"cpu\":\n expected_output_slice = torch.tensor(\n [\n -0.1352,\n 0.0878,\n 0.0419,\n -0.0818,\n -0.1069,\n 0.0688,\n -0.1458,\n -0.4446,\n -0.0026,\n ]\n )\n else:\n expected_output_slice = torch.tensor(\n [\n -0.2421,\n 0.4642,\n 0.2507,\n -0.0438,\n 0.0682,\n 0.3160,\n -0.2018,\n -0.0727,\n 0.2485,\n ]\n )\n\n self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-2))\n\n\nclass AsymmetricAutoencoderKLTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):\n model_class = AsymmetricAutoencoderKL\n main_input_name = \"sample\"\n base_precision = 1e-2\n\n @property\n def dummy_input(self):\n batch_size = 4\n num_channels = 3\n sizes = (32, 32)\n\n image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)\n mask = torch.ones((batch_size, 1) + sizes).to(torch_device)\n\n return {\"sample\": image, \"mask\": mask}\n\n @property\n def input_shape(self):\n return (3, 32, 32)\n\n @property\n def output_shape(self):\n return (3, 32, 32)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = get_asym_autoencoder_kl_config()\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n def test_forward_signature(self):\n pass\n\n def test_forward_with_norm_groups(self):\n pass\n\n\nclass AutoencoderTinyTests(ModelTesterMixin, unittest.TestCase):\n model_class = AutoencoderTiny\n main_input_name = \"sample\"\n base_precision = 1e-2\n\n @property\n def dummy_input(self):\n batch_size = 4\n num_channels = 3\n sizes = (32, 32)\n\n image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)\n\n return {\"sample\": image}\n\n @property\n def input_shape(self):\n return (3, 32, 32)\n\n @property\n def output_shape(self):\n return (3, 32, 32)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = get_autoencoder_tiny_config()\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n def test_outputs_equivalence(self):\n pass\n\n\nclass ConsistencyDecoderVAETests(ModelTesterMixin, unittest.TestCase):\n model_class = ConsistencyDecoderVAE\n main_input_name = \"sample\"\n base_precision = 1e-2\n forward_requires_fresh_args = True\n\n def inputs_dict(self, seed=None):\n generator = torch.Generator(\"cpu\")\n if seed is not None:\n generator.manual_seed(0)\n image = randn_tensor((4, 3, 32, 32), generator=generator, device=torch.device(torch_device))\n\n return {\"sample\": image, \"generator\": generator}\n\n @property\n def input_shape(self):\n return (3, 32, 32)\n\n @property\n def output_shape(self):\n return (3, 32, 32)\n\n @property\n def init_dict(self):\n return get_consistency_vae_config()\n\n def prepare_init_args_and_inputs_for_common(self):\n return self.init_dict, self.inputs_dict()\n\n @unittest.skip\n def test_training(self):\n ...\n\n @unittest.skip\n def test_ema_training(self):\n ...\n\n\nclass AutoencoderKLTemporalDecoderFastTests(ModelTesterMixin, unittest.TestCase):\n model_class = AutoencoderKLTemporalDecoder\n main_input_name = \"sample\"\n base_precision = 1e-2\n\n @property\n def dummy_input(self):\n batch_size = 3\n num_channels = 3\n sizes = (32, 32)\n\n image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)\n num_frames = 3\n\n return {\"sample\": image, \"num_frames\": num_frames}\n\n @property\n def input_shape(self):\n return (3, 32, 32)\n\n @property\n def output_shape(self):\n return (3, 32, 32)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = {\n \"block_out_channels\": [32, 64],\n \"in_channels\": 3,\n \"out_channels\": 3,\n \"down_block_types\": [\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n \"latent_channels\": 4,\n \"layers_per_block\": 2,\n }\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n def test_forward_signature(self):\n pass\n\n def test_training(self):\n pass\n\n @unittest.skipIf(torch_device == \"mps\", \"Gradient checkpointing skipped on MPS\")\n def test_gradient_checkpointing(self):\n # enable deterministic behavior for gradient checkpointing\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n model.to(torch_device)\n\n assert not model.is_gradient_checkpointing and model.training\n\n out = model(**inputs_dict).sample\n # run the backwards pass on the model. For backwards pass, for simplicity purpose,\n # we won't calculate the loss and rather backprop on out.sum()\n model.zero_grad()\n\n labels = torch.randn_like(out)\n loss = (out - labels).mean()\n loss.backward()\n\n # re-instantiate the model now enabling gradient checkpointing\n model_2 = self.model_class(**init_dict)\n # clone model\n model_2.load_state_dict(model.state_dict())\n model_2.to(torch_device)\n model_2.enable_gradient_checkpointing()\n\n assert model_2.is_gradient_checkpointing and model_2.training\n\n out_2 = model_2(**inputs_dict).sample\n # run the backwards pass on the model. For backwards pass, for simplicity purpose,\n # we won't calculate the loss and rather backprop on out.sum()\n model_2.zero_grad()\n loss_2 = (out_2 - labels).mean()\n loss_2.backward()\n\n # compare the output and parameters gradients\n self.assertTrue((loss - loss_2).abs() < 1e-5)\n named_params = dict(model.named_parameters())\n named_params_2 = dict(model_2.named_parameters())\n for name, param in named_params.items():\n if \"post_quant_conv\" in name:\n continue\n\n self.assertTrue(torch_all_close(param.grad.data, named_params_2[name].grad.data, atol=5e-5))\n\n\n@slow\nclass AutoencoderTinyIntegrationTests(unittest.TestCase):\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n backend_empty_cache(torch_device)\n\n def get_file_format(self, seed, shape):\n return f\"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy\"\n\n def get_sd_image(self, seed=0, shape=(4, 3, 512, 512), fp16=False):\n dtype = torch.float16 if fp16 else torch.float32\n image = torch.from_numpy(load_hf_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype)\n return image\n\n def get_sd_vae_model(self, model_id=\"hf-internal-testing/taesd-diffusers\", fp16=False):\n torch_dtype = torch.float16 if fp16 else torch.float32\n\n model = AutoencoderTiny.from_pretrained(model_id, torch_dtype=torch_dtype)\n model.to(torch_device).eval()\n return model\n\n @parameterized.expand(\n [\n [(1, 4, 73, 97), (1, 3, 584, 776)],\n [(1, 4, 97, 73), (1, 3, 776, 584)],\n [(1, 4, 49, 65), (1, 3, 392, 520)],\n [(1, 4, 65, 49), (1, 3, 520, 392)],\n [(1, 4, 49, 49), (1, 3, 392, 392)],\n ]\n )\n def test_tae_tiling(self, in_shape, out_shape):\n model = self.get_sd_vae_model()\n model.enable_tiling()\n with torch.no_grad():\n zeros = torch.zeros(in_shape).to(torch_device)\n dec = model.decode(zeros).sample\n assert dec.shape == out_shape\n\n def test_stable_diffusion(self):\n model = self.get_sd_vae_model()\n image = self.get_sd_image(seed=33)\n\n with torch.no_grad():\n sample = model(image).sample\n\n assert sample.shape == image.shape\n\n output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()\n expected_output_slice = torch.tensor([0.0093, 0.6385, -0.1274, 0.1631, -0.1762, 0.5232, -0.3108, -0.0382])\n\n assert torch_all_close(output_slice, expected_output_slice, atol=3e-3)\n\n @parameterized.expand([(True,), (False,)])\n def test_tae_roundtrip(self, enable_tiling):\n # load the autoencoder\n model = self.get_sd_vae_model()\n if enable_tiling:\n model.enable_tiling()\n\n # make a black image with a white square in the middle,\n # which is large enough to split across multiple tiles\n image = -torch.ones(1, 3, 1024, 1024, device=torch_device)\n image[..., 256:768, 256:768] = 1.0\n\n # round-trip the image through the autoencoder\n with torch.no_grad():\n sample = model(image).sample\n\n # the autoencoder reconstruction should match original image, sorta\n def downscale(x):\n return torch.nn.functional.avg_pool2d(x, model.spatial_scale_factor)\n\n assert torch_all_close(downscale(sample), downscale(image), atol=0.125)\n\n\n@slow\nclass AutoencoderKLIntegrationTests(unittest.TestCase):\n def get_file_format(self, seed, shape):\n return f\"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy\"\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n backend_empty_cache(torch_device)\n\n def get_sd_image(self, seed=0, shape=(4, 3, 512, 512), fp16=False):\n dtype = torch.float16 if fp16 else torch.float32\n image = torch.from_numpy(load_hf_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype)\n return image\n\n def get_sd_vae_model(self, model_id=\"CompVis/stable-diffusion-v1-4\", fp16=False):\n revision = \"fp16\" if fp16 else None\n torch_dtype = torch.float16 if fp16 else torch.float32\n\n model = AutoencoderKL.from_pretrained(\n model_id,\n subfolder=\"vae\",\n torch_dtype=torch_dtype,\n revision=revision,\n )\n model.to(torch_device)\n\n return model\n\n def get_generator(self, seed=0):\n generator_device = \"cpu\" if not torch_device.startswith(\"cuda\") else \"cuda\"\n if torch_device != \"mps\":\n return torch.Generator(device=generator_device).manual_seed(seed)\n return torch.manual_seed(seed)\n\n @parameterized.expand(\n [\n # fmt: off\n [\n 33,\n [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824],\n [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824],\n ],\n [\n 47,\n [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089],\n [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131],\n ],\n # fmt: on\n ]\n )\n def test_stable_diffusion(self, seed, expected_slice, expected_slice_mps):\n model = self.get_sd_vae_model()\n image = self.get_sd_image(seed)\n generator = self.get_generator(seed)\n\n with torch.no_grad():\n sample = model(image, generator=generator, sample_posterior=True).sample\n\n assert sample.shape == image.shape\n\n output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()\n expected_output_slice = torch.tensor(expected_slice_mps if torch_device == \"mps\" else expected_slice)\n\n assert torch_all_close(output_slice, expected_output_slice, atol=3e-3)\n\n @parameterized.expand(\n [\n # fmt: off\n [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],\n [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],\n # fmt: on\n ]\n )\n @require_torch_accelerator_with_fp16\n def test_stable_diffusion_fp16(self, seed, expected_slice):\n model = self.get_sd_vae_model(fp16=True)\n image = self.get_sd_image(seed, fp16=True)\n generator = self.get_generator(seed)\n\n with torch.no_grad():\n sample = model(image, generator=generator, sample_posterior=True).sample\n\n assert sample.shape == image.shape\n\n output_slice = sample[-1, -2:, :2, -2:].flatten().float().cpu()\n expected_output_slice = torch.tensor(expected_slice)\n\n assert torch_all_close(output_slice, expected_output_slice, atol=1e-2)\n\n @parameterized.expand(\n [\n # fmt: off\n [\n 33,\n [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814],\n [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824],\n ],\n [\n 47,\n [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085],\n [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131],\n ],\n # fmt: on\n ]\n )\n def test_stable_diffusion_mode(self, seed, expected_slice, expected_slice_mps):\n model = self.get_sd_vae_model()\n image = self.get_sd_image(seed)\n\n with torch.no_grad():\n sample = model(image).sample\n\n assert sample.shape == image.shape\n\n output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()\n expected_output_slice = torch.tensor(expected_slice_mps if torch_device == \"mps\" else expected_slice)\n\n assert torch_all_close(output_slice, expected_output_slice, atol=3e-3)\n\n @parameterized.expand(\n [\n # fmt: off\n [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],\n [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],\n # fmt: on\n ]\n )\n @require_torch_accelerator\n @skip_mps\n def test_stable_diffusion_decode(self, seed, expected_slice):\n model = self.get_sd_vae_model()\n encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64))\n\n with torch.no_grad():\n sample = model.decode(encoding).sample\n\n assert list(sample.shape) == [3, 3, 512, 512]\n\n output_slice = sample[-1, -2:, :2, -2:].flatten().cpu()\n expected_output_slice = torch.tensor(expected_slice)\n\n assert torch_all_close(output_slice, expected_output_slice, atol=1e-3)\n\n @parameterized.expand(\n [\n # fmt: off\n [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],\n [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],\n # fmt: on\n ]\n )\n @require_torch_accelerator_with_fp16\n def test_stable_diffusion_decode_fp16(self, seed, expected_slice):\n model = self.get_sd_vae_model(fp16=True)\n encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64), fp16=True)\n\n with torch.no_grad():\n sample = model.decode(encoding).sample\n\n assert list(sample.shape) == [3, 3, 512, 512]\n\n output_slice = sample[-1, -2:, :2, -2:].flatten().float().cpu()\n expected_output_slice = torch.tensor(expected_slice)\n\n assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)\n\n @parameterized.expand([(13,), (16,), (27,)])\n @require_torch_gpu\n @unittest.skipIf(\n not is_xformers_available(),\n reason=\"xformers is not required when using PyTorch 2.0.\",\n )\n def test_stable_diffusion_decode_xformers_vs_2_0_fp16(self, seed):\n model = self.get_sd_vae_model(fp16=True)\n encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64), fp16=True)\n\n with torch.no_grad():\n sample = model.decode(encoding).sample\n\n model.enable_xformers_memory_efficient_attention()\n with torch.no_grad():\n sample_2 = model.decode(encoding).sample\n\n assert list(sample.shape) == [3, 3, 512, 512]\n\n assert torch_all_close(sample, sample_2, atol=1e-1)\n\n @parameterized.expand([(13,), (16,), (37,)])\n @require_torch_gpu\n @unittest.skipIf(\n not is_xformers_available(),\n reason=\"xformers is not required when using PyTorch 2.0.\",\n )\n def test_stable_diffusion_decode_xformers_vs_2_0(self, seed):\n model = self.get_sd_vae_model()\n encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64))\n\n with torch.no_grad():\n sample = model.decode(encoding).sample\n\n model.enable_xformers_memory_efficient_attention()\n with torch.no_grad():\n sample_2 = model.decode(encoding).sample\n\n assert list(sample.shape) == [3, 3, 512, 512]\n\n assert torch_all_close(sample, sample_2, atol=1e-2)\n\n @parameterized.expand(\n [\n # fmt: off\n [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],\n [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],\n # fmt: on\n ]\n )\n def test_stable_diffusion_encode_sample(self, seed, expected_slice):\n model = self.get_sd_vae_model()\n image = self.get_sd_image(seed)\n generator = self.get_generator(seed)\n\n with torch.no_grad():\n dist = model.encode(image).latent_dist\n sample = dist.sample(generator=generator)\n\n assert list(sample.shape) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]\n\n output_slice = sample[0, -1, -3:, -3:].flatten().cpu()\n expected_output_slice = torch.tensor(expected_slice)\n\n tolerance = 3e-3 if torch_device != \"mps\" else 1e-2\n assert torch_all_close(output_slice, expected_output_slice, atol=tolerance)\n\n\n@slow\nclass AsymmetricAutoencoderKLIntegrationTests(unittest.TestCase):\n def get_file_format(self, seed, shape):\n return f\"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy\"\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n backend_empty_cache(torch_device)\n\n def get_sd_image(self, seed=0, shape=(4, 3, 512, 512), fp16=False):\n dtype = torch.float16 if fp16 else torch.float32\n image = torch.from_numpy(load_hf_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype)\n return image\n\n def get_sd_vae_model(self, model_id=\"cross-attention/asymmetric-autoencoder-kl-x-1-5\", fp16=False):\n revision = \"main\"\n torch_dtype = torch.float32\n\n model = AsymmetricAutoencoderKL.from_pretrained(\n model_id,\n torch_dtype=torch_dtype,\n revision=revision,\n )\n model.to(torch_device).eval()\n\n return model\n\n def get_generator(self, seed=0):\n generator_device = \"cpu\" if not torch_device.startswith(\"cuda\") else \"cuda\"\n if torch_device != \"mps\":\n return torch.Generator(device=generator_device).manual_seed(seed)\n return torch.manual_seed(seed)\n\n @parameterized.expand(\n [\n # fmt: off\n [\n 33,\n [-0.0344, 0.2912, 0.1687, -0.0137, -0.3462, 0.3552, -0.1337, 0.1078],\n [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824],\n ],\n [\n 47,\n [0.4400, 0.0543, 0.2873, 0.2946, 0.0553, 0.0839, -0.1585, 0.2529],\n [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089],\n ],\n # fmt: on\n ]\n )\n def test_stable_diffusion(self, seed, expected_slice, expected_slice_mps):\n model = self.get_sd_vae_model()\n image = self.get_sd_image(seed)\n generator = self.get_generator(seed)\n\n with torch.no_grad():\n sample = model(image, generator=generator, sample_posterior=True).sample\n\n assert sample.shape == image.shape\n\n output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()\n expected_output_slice = torch.tensor(expected_slice_mps if torch_device == \"mps\" else expected_slice)\n\n assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)\n\n @parameterized.expand(\n [\n # fmt: off\n [\n 33,\n [-0.0340, 0.2870, 0.1698, -0.0105, -0.3448, 0.3529, -0.1321, 0.1097],\n [-0.0344, 0.2912, 0.1687, -0.0137, -0.3462, 0.3552, -0.1337, 0.1078],\n ],\n [\n 47,\n [0.4397, 0.0550, 0.2873, 0.2946, 0.0567, 0.0855, -0.1580, 0.2531],\n [0.4397, 0.0550, 0.2873, 0.2946, 0.0567, 0.0855, -0.1580, 0.2531],\n ],\n # fmt: on\n ]\n )\n def test_stable_diffusion_mode(self, seed, expected_slice, expected_slice_mps):\n model = self.get_sd_vae_model()\n image = self.get_sd_image(seed)\n\n with torch.no_grad():\n sample = model(image).sample\n\n assert sample.shape == image.shape\n\n output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()\n expected_output_slice = torch.tensor(expected_slice_mps if torch_device == \"mps\" else expected_slice)\n\n assert torch_all_close(output_slice, expected_output_slice, atol=3e-3)\n\n @parameterized.expand(\n [\n # fmt: off\n [13, [-0.0521, -0.2939, 0.1540, -0.1855, -0.5936, -0.3138, -0.4579, -0.2275]],\n [37, [-0.1820, -0.4345, -0.0455, -0.2923, -0.8035, -0.5089, -0.4795, -0.3106]],\n # fmt: on\n ]\n )\n @require_torch_accelerator\n @skip_mps\n def test_stable_diffusion_decode(self, seed, expected_slice):\n model = self.get_sd_vae_model()\n encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64))\n\n with torch.no_grad():\n sample = model.decode(encoding).sample\n\n assert list(sample.shape) == [3, 3, 512, 512]\n\n output_slice = sample[-1, -2:, :2, -2:].flatten().cpu()\n expected_output_slice = torch.tensor(expected_slice)\n\n assert torch_all_close(output_slice, expected_output_slice, atol=2e-3)\n\n @parameterized.expand([(13,), (16,), (37,)])\n @require_torch_gpu\n @unittest.skipIf(\n not is_xformers_available(),\n reason=\"xformers is not required when using PyTorch 2.0.\",\n )\n def test_stable_diffusion_decode_xformers_vs_2_0(self, seed):\n model = self.get_sd_vae_model()\n encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64))\n\n with torch.no_grad():\n sample = model.decode(encoding).sample\n\n model.enable_xformers_memory_efficient_attention()\n with torch.no_grad():\n sample_2 = model.decode(encoding).sample\n\n assert list(sample.shape) == [3, 3, 512, 512]\n\n assert torch_all_close(sample, sample_2, atol=5e-2)\n\n @parameterized.expand(\n [\n # fmt: off\n [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],\n [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],\n # fmt: on\n ]\n )\n def test_stable_diffusion_encode_sample(self, seed, expected_slice):\n model = self.get_sd_vae_model()\n image = self.get_sd_image(seed)\n generator = self.get_generator(seed)\n\n with torch.no_grad():\n dist = model.encode(image).latent_dist\n sample = dist.sample(generator=generator)\n\n assert list(sample.shape) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]\n\n output_slice = sample[0, -1, -3:, -3:].flatten().cpu()\n expected_output_slice = torch.tensor(expected_slice)\n\n tolerance = 3e-3 if torch_device != \"mps\" else 1e-2\n assert torch_all_close(output_slice, expected_output_slice, atol=tolerance)\n\n\n@slow\nclass ConsistencyDecoderVAEIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n @torch.no_grad()\n def test_encode_decode(self):\n vae = ConsistencyDecoderVAE.from_pretrained(\"openai/consistency-decoder\") # TODO - update\n vae.to(torch_device)\n\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/img2img/sketch-mountains-input.jpg\"\n ).resize((256, 256))\n image = torch.from_numpy(np.array(image).transpose(2, 0, 1).astype(np.float32) / 127.5 - 1)[\n None, :, :, :\n ].cuda()\n\n latent = vae.encode(image).latent_dist.mean\n\n sample = vae.decode(latent, generator=torch.Generator(\"cpu\").manual_seed(0)).sample\n\n actual_output = sample[0, :2, :2, :2].flatten().cpu()\n expected_output = torch.tensor([-0.0141, -0.0014, 0.0115, 0.0086, 0.1051, 0.1053, 0.1031, 0.1024])\n\n assert torch_all_close(actual_output, expected_output, atol=5e-3)\n\n def test_sd(self):\n vae = ConsistencyDecoderVAE.from_pretrained(\"openai/consistency-decoder\") # TODO - update\n pipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", vae=vae, safety_checker=None)\n pipe.to(torch_device)\n\n out = pipe(\n \"horse\",\n num_inference_steps=2,\n output_type=\"pt\",\n generator=torch.Generator(\"cpu\").manual_seed(0),\n ).images[0]\n\n actual_output = out[:2, :2, :2].flatten().cpu()\n expected_output = torch.tensor([0.7686, 0.8228, 0.6489, 0.7455, 0.8661, 0.8797, 0.8241, 0.8759])\n\n assert torch_all_close(actual_output, expected_output, atol=5e-3)\n\n def test_encode_decode_f16(self):\n vae = ConsistencyDecoderVAE.from_pretrained(\n \"openai/consistency-decoder\", torch_dtype=torch.float16\n ) # TODO - update\n vae.to(torch_device)\n\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/img2img/sketch-mountains-input.jpg\"\n ).resize((256, 256))\n image = (\n torch.from_numpy(np.array(image).transpose(2, 0, 1).astype(np.float32) / 127.5 - 1)[None, :, :, :]\n .half()\n .cuda()\n )\n\n latent = vae.encode(image).latent_dist.mean\n\n sample = vae.decode(latent, generator=torch.Generator(\"cpu\").manual_seed(0)).sample\n\n actual_output = sample[0, :2, :2, :2].flatten().cpu()\n expected_output = torch.tensor(\n [-0.0111, -0.0125, -0.0017, -0.0007, 0.1257, 0.1465, 0.1450, 0.1471],\n dtype=torch.float16,\n )\n\n assert torch_all_close(actual_output, expected_output, atol=5e-3)\n\n def test_sd_f16(self):\n vae = ConsistencyDecoderVAE.from_pretrained(\n \"openai/consistency-decoder\", torch_dtype=torch.float16\n ) # TODO - update\n pipe = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n torch_dtype=torch.float16,\n vae=vae,\n safety_checker=None,\n )\n pipe.to(torch_device)\n\n out = pipe(\n \"horse\",\n num_inference_steps=2,\n output_type=\"pt\",\n generator=torch.Generator(\"cpu\").manual_seed(0),\n ).images[0]\n\n actual_output = out[:2, :2, :2].flatten().cpu()\n expected_output = torch.tensor(\n [0.0000, 0.0249, 0.0000, 0.0000, 0.1709, 0.2773, 0.0471, 0.1035],\n dtype=torch.float16,\n )\n\n assert torch_all_close(actual_output, expected_output, atol=5e-3)\n\n def test_vae_tiling(self):\n vae = ConsistencyDecoderVAE.from_pretrained(\"openai/consistency-decoder\", torch_dtype=torch.float16)\n pipe = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", vae=vae, safety_checker=None, torch_dtype=torch.float16\n )\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n out_1 = pipe(\n \"horse\",\n num_inference_steps=2,\n output_type=\"pt\",\n generator=torch.Generator(\"cpu\").manual_seed(0),\n ).images[0]\n\n # make sure tiled vae decode yields the same result\n pipe.enable_vae_tiling()\n out_2 = pipe(\n \"horse\",\n num_inference_steps=2,\n output_type=\"pt\",\n generator=torch.Generator(\"cpu\").manual_seed(0),\n ).images[0]\n\n assert torch_all_close(out_1, out_2, atol=5e-3)\n\n # test that tiled decode works with various shapes\n shapes = [(1, 4, 73, 97), (1, 4, 97, 73), (1, 4, 49, 65), (1, 4, 65, 49)]\n with torch.no_grad():\n for shape in shapes:\n image = torch.zeros(shape, device=torch_device, dtype=pipe.vae.dtype)\n pipe.vae.decode(image)\n" }, { "path": "diffusers/tests/models/autoencoders/test_models_vae_flax.py", "content": "import unittest\n\nfrom diffusers import FlaxAutoencoderKL\nfrom diffusers.utils import is_flax_available\nfrom diffusers.utils.testing_utils import require_flax\n\nfrom ..test_modeling_common_flax import FlaxModelTesterMixin\n\n\nif is_flax_available():\n import jax\n\n\n@require_flax\nclass FlaxAutoencoderKLTests(FlaxModelTesterMixin, unittest.TestCase):\n model_class = FlaxAutoencoderKL\n\n @property\n def dummy_input(self):\n batch_size = 4\n num_channels = 3\n sizes = (32, 32)\n\n prng_key = jax.random.PRNGKey(0)\n image = jax.random.uniform(prng_key, ((batch_size, num_channels) + sizes))\n\n return {\"sample\": image, \"prng_key\": prng_key}\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = {\n \"block_out_channels\": [32, 64],\n \"in_channels\": 3,\n \"out_channels\": 3,\n \"down_block_types\": [\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n \"up_block_types\": [\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n \"latent_channels\": 4,\n }\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n" }, { "path": "diffusers/tests/models/autoencoders/test_models_vq.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\n\nimport torch\n\nfrom diffusers import VQModel\nfrom diffusers.utils.testing_utils import (\n backend_manual_seed,\n enable_full_determinism,\n floats_tensor,\n torch_device,\n)\n\nfrom ..test_modeling_common import ModelTesterMixin, UNetTesterMixin\n\n\nenable_full_determinism()\n\n\nclass VQModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):\n model_class = VQModel\n main_input_name = \"sample\"\n\n @property\n def dummy_input(self, sizes=(32, 32)):\n batch_size = 4\n num_channels = 3\n\n image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)\n\n return {\"sample\": image}\n\n @property\n def input_shape(self):\n return (3, 32, 32)\n\n @property\n def output_shape(self):\n return (3, 32, 32)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = {\n \"block_out_channels\": [8, 16],\n \"norm_num_groups\": 8,\n \"in_channels\": 3,\n \"out_channels\": 3,\n \"down_block_types\": [\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n \"up_block_types\": [\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n \"latent_channels\": 3,\n }\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n def test_forward_signature(self):\n pass\n\n def test_training(self):\n pass\n\n def test_from_pretrained_hub(self):\n model, loading_info = VQModel.from_pretrained(\"fusing/vqgan-dummy\", output_loading_info=True)\n self.assertIsNotNone(model)\n self.assertEqual(len(loading_info[\"missing_keys\"]), 0)\n\n model.to(torch_device)\n image = model(**self.dummy_input)\n\n assert image is not None, \"Make sure output is not None\"\n\n def test_output_pretrained(self):\n model = VQModel.from_pretrained(\"fusing/vqgan-dummy\")\n model.to(torch_device).eval()\n\n torch.manual_seed(0)\n backend_manual_seed(torch_device, 0)\n\n image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)\n image = image.to(torch_device)\n with torch.no_grad():\n output = model(image).sample\n\n output_slice = output[0, -1, -3:, -3:].flatten().cpu()\n # fmt: off\n expected_output_slice = torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143])\n # fmt: on\n self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))\n\n def test_loss_pretrained(self):\n model = VQModel.from_pretrained(\"fusing/vqgan-dummy\")\n model.to(torch_device).eval()\n\n torch.manual_seed(0)\n backend_manual_seed(torch_device, 0)\n\n image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)\n image = image.to(torch_device)\n with torch.no_grad():\n output = model(image).commit_loss.cpu()\n # fmt: off\n expected_output = torch.tensor([0.1936])\n # fmt: on\n self.assertTrue(torch.allclose(output, expected_output, atol=1e-3))\n" }, { "path": "diffusers/tests/models/test_activations.py", "content": "import unittest\n\nimport torch\nfrom torch import nn\n\nfrom diffusers.models.activations import get_activation\n\n\nclass ActivationsTests(unittest.TestCase):\n def test_swish(self):\n act = get_activation(\"swish\")\n\n self.assertIsInstance(act, nn.SiLU)\n\n self.assertEqual(act(torch.tensor(-100, dtype=torch.float32)).item(), 0)\n self.assertNotEqual(act(torch.tensor(-1, dtype=torch.float32)).item(), 0)\n self.assertEqual(act(torch.tensor(0, dtype=torch.float32)).item(), 0)\n self.assertEqual(act(torch.tensor(20, dtype=torch.float32)).item(), 20)\n\n def test_silu(self):\n act = get_activation(\"silu\")\n\n self.assertIsInstance(act, nn.SiLU)\n\n self.assertEqual(act(torch.tensor(-100, dtype=torch.float32)).item(), 0)\n self.assertNotEqual(act(torch.tensor(-1, dtype=torch.float32)).item(), 0)\n self.assertEqual(act(torch.tensor(0, dtype=torch.float32)).item(), 0)\n self.assertEqual(act(torch.tensor(20, dtype=torch.float32)).item(), 20)\n\n def test_mish(self):\n act = get_activation(\"mish\")\n\n self.assertIsInstance(act, nn.Mish)\n\n self.assertEqual(act(torch.tensor(-200, dtype=torch.float32)).item(), 0)\n self.assertNotEqual(act(torch.tensor(-1, dtype=torch.float32)).item(), 0)\n self.assertEqual(act(torch.tensor(0, dtype=torch.float32)).item(), 0)\n self.assertEqual(act(torch.tensor(20, dtype=torch.float32)).item(), 20)\n\n def test_gelu(self):\n act = get_activation(\"gelu\")\n\n self.assertIsInstance(act, nn.GELU)\n\n self.assertEqual(act(torch.tensor(-100, dtype=torch.float32)).item(), 0)\n self.assertNotEqual(act(torch.tensor(-1, dtype=torch.float32)).item(), 0)\n self.assertEqual(act(torch.tensor(0, dtype=torch.float32)).item(), 0)\n self.assertEqual(act(torch.tensor(20, dtype=torch.float32)).item(), 20)\n" }, { "path": "diffusers/tests/models/test_attention_processor.py", "content": "import tempfile\nimport unittest\n\nimport numpy as np\nimport torch\n\nfrom diffusers import DiffusionPipeline\nfrom diffusers.models.attention_processor import Attention, AttnAddedKVProcessor\n\n\nclass AttnAddedKVProcessorTests(unittest.TestCase):\n def get_constructor_arguments(self, only_cross_attention: bool = False):\n query_dim = 10\n\n if only_cross_attention:\n cross_attention_dim = 12\n else:\n # when only cross attention is not set, the cross attention dim must be the same as the query dim\n cross_attention_dim = query_dim\n\n return {\n \"query_dim\": query_dim,\n \"cross_attention_dim\": cross_attention_dim,\n \"heads\": 2,\n \"dim_head\": 4,\n \"added_kv_proj_dim\": 6,\n \"norm_num_groups\": 1,\n \"only_cross_attention\": only_cross_attention,\n \"processor\": AttnAddedKVProcessor(),\n }\n\n def get_forward_arguments(self, query_dim, added_kv_proj_dim):\n batch_size = 2\n\n hidden_states = torch.rand(batch_size, query_dim, 3, 2)\n encoder_hidden_states = torch.rand(batch_size, 4, added_kv_proj_dim)\n attention_mask = None\n\n return {\n \"hidden_states\": hidden_states,\n \"encoder_hidden_states\": encoder_hidden_states,\n \"attention_mask\": attention_mask,\n }\n\n def test_only_cross_attention(self):\n # self and cross attention\n\n torch.manual_seed(0)\n\n constructor_args = self.get_constructor_arguments(only_cross_attention=False)\n attn = Attention(**constructor_args)\n\n self.assertTrue(attn.to_k is not None)\n self.assertTrue(attn.to_v is not None)\n\n forward_args = self.get_forward_arguments(\n query_dim=constructor_args[\"query_dim\"], added_kv_proj_dim=constructor_args[\"added_kv_proj_dim\"]\n )\n\n self_and_cross_attn_out = attn(**forward_args)\n\n # only self attention\n\n torch.manual_seed(0)\n\n constructor_args = self.get_constructor_arguments(only_cross_attention=True)\n attn = Attention(**constructor_args)\n\n self.assertTrue(attn.to_k is None)\n self.assertTrue(attn.to_v is None)\n\n forward_args = self.get_forward_arguments(\n query_dim=constructor_args[\"query_dim\"], added_kv_proj_dim=constructor_args[\"added_kv_proj_dim\"]\n )\n\n only_cross_attn_out = attn(**forward_args)\n\n self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all())\n\n\nclass DeprecatedAttentionBlockTests(unittest.TestCase):\n def test_conversion_when_using_device_map(self):\n pipe = DiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\", safety_checker=None\n )\n\n pre_conversion = pipe(\n \"foo\",\n num_inference_steps=2,\n generator=torch.Generator(\"cpu\").manual_seed(0),\n output_type=\"np\",\n ).images\n\n # the initial conversion succeeds\n pipe = DiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\", device_map=\"balanced\", safety_checker=None\n )\n\n conversion = pipe(\n \"foo\",\n num_inference_steps=2,\n generator=torch.Generator(\"cpu\").manual_seed(0),\n output_type=\"np\",\n ).images\n\n with tempfile.TemporaryDirectory() as tmpdir:\n # save the converted model\n pipe.save_pretrained(tmpdir)\n\n # can also load the converted weights\n pipe = DiffusionPipeline.from_pretrained(tmpdir, device_map=\"balanced\", safety_checker=None)\n after_conversion = pipe(\n \"foo\",\n num_inference_steps=2,\n generator=torch.Generator(\"cpu\").manual_seed(0),\n output_type=\"np\",\n ).images\n\n self.assertTrue(np.allclose(pre_conversion, conversion, atol=1e-3))\n self.assertTrue(np.allclose(conversion, after_conversion, atol=1e-3))\n" }, { "path": "diffusers/tests/models/test_layers_utils.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport unittest\n\nimport numpy as np\nimport torch\nfrom torch import nn\n\nfrom diffusers.models.attention import GEGLU, AdaLayerNorm, ApproximateGELU\nfrom diffusers.models.embeddings import get_timestep_embedding\nfrom diffusers.models.resnet import Downsample2D, ResnetBlock2D, Upsample2D\nfrom diffusers.models.transformers.transformer_2d import Transformer2DModel\nfrom diffusers.utils.testing_utils import (\n backend_manual_seed,\n require_torch_accelerator_with_fp64,\n torch_device,\n)\n\n\nclass EmbeddingsTests(unittest.TestCase):\n def test_timestep_embeddings(self):\n embedding_dim = 256\n timesteps = torch.arange(16)\n\n t1 = get_timestep_embedding(timesteps, embedding_dim)\n\n # first vector should always be composed only of 0's and 1's\n assert (t1[0, : embedding_dim // 2] - 0).abs().sum() < 1e-5\n assert (t1[0, embedding_dim // 2 :] - 1).abs().sum() < 1e-5\n\n # last element of each vector should be one\n assert (t1[:, -1] - 1).abs().sum() < 1e-5\n\n # For large embeddings (e.g. 128) the frequency of every vector is higher\n # than the previous one which means that the gradients of later vectors are\n # ALWAYS higher than the previous ones\n grad_mean = np.abs(np.gradient(t1, axis=-1)).mean(axis=1)\n\n prev_grad = 0.0\n for grad in grad_mean:\n assert grad > prev_grad\n prev_grad = grad\n\n def test_timestep_defaults(self):\n embedding_dim = 16\n timesteps = torch.arange(10)\n\n t1 = get_timestep_embedding(timesteps, embedding_dim)\n t2 = get_timestep_embedding(\n timesteps, embedding_dim, flip_sin_to_cos=False, downscale_freq_shift=1, max_period=10_000\n )\n\n assert torch.allclose(t1.cpu(), t2.cpu(), 1e-3)\n\n def test_timestep_flip_sin_cos(self):\n embedding_dim = 16\n timesteps = torch.arange(10)\n\n t1 = get_timestep_embedding(timesteps, embedding_dim, flip_sin_to_cos=True)\n t1 = torch.cat([t1[:, embedding_dim // 2 :], t1[:, : embedding_dim // 2]], dim=-1)\n\n t2 = get_timestep_embedding(timesteps, embedding_dim, flip_sin_to_cos=False)\n\n assert torch.allclose(t1.cpu(), t2.cpu(), 1e-3)\n\n def test_timestep_downscale_freq_shift(self):\n embedding_dim = 16\n timesteps = torch.arange(10)\n\n t1 = get_timestep_embedding(timesteps, embedding_dim, downscale_freq_shift=0)\n t2 = get_timestep_embedding(timesteps, embedding_dim, downscale_freq_shift=1)\n\n # get cosine half (vectors that are wrapped into cosine)\n cosine_half = (t1 - t2)[:, embedding_dim // 2 :]\n\n # cosine needs to be negative\n assert (np.abs((cosine_half <= 0).numpy()) - 1).sum() < 1e-5\n\n def test_sinoid_embeddings_hardcoded(self):\n embedding_dim = 64\n timesteps = torch.arange(128)\n\n # standard unet, score_vde\n t1 = get_timestep_embedding(timesteps, embedding_dim, downscale_freq_shift=1, flip_sin_to_cos=False)\n # glide, ldm\n t2 = get_timestep_embedding(timesteps, embedding_dim, downscale_freq_shift=0, flip_sin_to_cos=True)\n # grad-tts\n t3 = get_timestep_embedding(timesteps, embedding_dim, scale=1000)\n\n assert torch.allclose(\n t1[23:26, 47:50].flatten().cpu(),\n torch.tensor([0.9646, 0.9804, 0.9892, 0.9615, 0.9787, 0.9882, 0.9582, 0.9769, 0.9872]),\n 1e-3,\n )\n assert torch.allclose(\n t2[23:26, 47:50].flatten().cpu(),\n torch.tensor([0.3019, 0.2280, 0.1716, 0.3146, 0.2377, 0.1790, 0.3272, 0.2474, 0.1864]),\n 1e-3,\n )\n assert torch.allclose(\n t3[23:26, 47:50].flatten().cpu(),\n torch.tensor([-0.9801, -0.9464, -0.9349, -0.3952, 0.8887, -0.9709, 0.5299, -0.2853, -0.9927]),\n 1e-3,\n )\n\n\nclass Upsample2DBlockTests(unittest.TestCase):\n def test_upsample_default(self):\n torch.manual_seed(0)\n sample = torch.randn(1, 32, 32, 32)\n upsample = Upsample2D(channels=32, use_conv=False)\n with torch.no_grad():\n upsampled = upsample(sample)\n\n assert upsampled.shape == (1, 32, 64, 64)\n output_slice = upsampled[0, -1, -3:, -3:]\n expected_slice = torch.tensor([-0.2173, -1.2079, -1.2079, 0.2952, 1.1254, 1.1254, 0.2952, 1.1254, 1.1254])\n assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)\n\n def test_upsample_with_conv(self):\n torch.manual_seed(0)\n sample = torch.randn(1, 32, 32, 32)\n upsample = Upsample2D(channels=32, use_conv=True)\n with torch.no_grad():\n upsampled = upsample(sample)\n\n assert upsampled.shape == (1, 32, 64, 64)\n output_slice = upsampled[0, -1, -3:, -3:]\n expected_slice = torch.tensor([0.7145, 1.3773, 0.3492, 0.8448, 1.0839, -0.3341, 0.5956, 0.1250, -0.4841])\n assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)\n\n def test_upsample_with_conv_out_dim(self):\n torch.manual_seed(0)\n sample = torch.randn(1, 32, 32, 32)\n upsample = Upsample2D(channels=32, use_conv=True, out_channels=64)\n with torch.no_grad():\n upsampled = upsample(sample)\n\n assert upsampled.shape == (1, 64, 64, 64)\n output_slice = upsampled[0, -1, -3:, -3:]\n expected_slice = torch.tensor([0.2703, 0.1656, -0.2538, -0.0553, -0.2984, 0.1044, 0.1155, 0.2579, 0.7755])\n assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)\n\n def test_upsample_with_transpose(self):\n torch.manual_seed(0)\n sample = torch.randn(1, 32, 32, 32)\n upsample = Upsample2D(channels=32, use_conv=False, use_conv_transpose=True)\n with torch.no_grad():\n upsampled = upsample(sample)\n\n assert upsampled.shape == (1, 32, 64, 64)\n output_slice = upsampled[0, -1, -3:, -3:]\n expected_slice = torch.tensor([-0.3028, -0.1582, 0.0071, 0.0350, -0.4799, -0.1139, 0.1056, -0.1153, -0.1046])\n assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)\n\n\nclass Downsample2DBlockTests(unittest.TestCase):\n def test_downsample_default(self):\n torch.manual_seed(0)\n sample = torch.randn(1, 32, 64, 64)\n downsample = Downsample2D(channels=32, use_conv=False)\n with torch.no_grad():\n downsampled = downsample(sample)\n\n assert downsampled.shape == (1, 32, 32, 32)\n output_slice = downsampled[0, -1, -3:, -3:]\n expected_slice = torch.tensor([-0.0513, -0.3889, 0.0640, 0.0836, -0.5460, -0.0341, -0.0169, -0.6967, 0.1179])\n max_diff = (output_slice.flatten() - expected_slice).abs().sum().item()\n assert max_diff <= 1e-3\n # assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-1)\n\n def test_downsample_with_conv(self):\n torch.manual_seed(0)\n sample = torch.randn(1, 32, 64, 64)\n downsample = Downsample2D(channels=32, use_conv=True)\n with torch.no_grad():\n downsampled = downsample(sample)\n\n assert downsampled.shape == (1, 32, 32, 32)\n output_slice = downsampled[0, -1, -3:, -3:]\n\n expected_slice = torch.tensor(\n [0.9267, 0.5878, 0.3337, 1.2321, -0.1191, -0.3984, -0.7532, -0.0715, -0.3913],\n )\n assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)\n\n def test_downsample_with_conv_pad1(self):\n torch.manual_seed(0)\n sample = torch.randn(1, 32, 64, 64)\n downsample = Downsample2D(channels=32, use_conv=True, padding=1)\n with torch.no_grad():\n downsampled = downsample(sample)\n\n assert downsampled.shape == (1, 32, 32, 32)\n output_slice = downsampled[0, -1, -3:, -3:]\n expected_slice = torch.tensor([0.9267, 0.5878, 0.3337, 1.2321, -0.1191, -0.3984, -0.7532, -0.0715, -0.3913])\n assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)\n\n def test_downsample_with_conv_out_dim(self):\n torch.manual_seed(0)\n sample = torch.randn(1, 32, 64, 64)\n downsample = Downsample2D(channels=32, use_conv=True, out_channels=16)\n with torch.no_grad():\n downsampled = downsample(sample)\n\n assert downsampled.shape == (1, 16, 32, 32)\n output_slice = downsampled[0, -1, -3:, -3:]\n expected_slice = torch.tensor([-0.6586, 0.5985, 0.0721, 0.1256, -0.1492, 0.4436, -0.2544, 0.5021, 1.1522])\n assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)\n\n\nclass ResnetBlock2DTests(unittest.TestCase):\n def test_resnet_default(self):\n torch.manual_seed(0)\n sample = torch.randn(1, 32, 64, 64).to(torch_device)\n temb = torch.randn(1, 128).to(torch_device)\n resnet_block = ResnetBlock2D(in_channels=32, temb_channels=128).to(torch_device)\n with torch.no_grad():\n output_tensor = resnet_block(sample, temb)\n\n assert output_tensor.shape == (1, 32, 64, 64)\n output_slice = output_tensor[0, -1, -3:, -3:]\n expected_slice = torch.tensor(\n [-1.9010, -0.2974, -0.8245, -1.3533, 0.8742, -0.9645, -2.0584, 1.3387, -0.4746], device=torch_device\n )\n assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)\n\n def test_restnet_with_use_in_shortcut(self):\n torch.manual_seed(0)\n sample = torch.randn(1, 32, 64, 64).to(torch_device)\n temb = torch.randn(1, 128).to(torch_device)\n resnet_block = ResnetBlock2D(in_channels=32, temb_channels=128, use_in_shortcut=True).to(torch_device)\n with torch.no_grad():\n output_tensor = resnet_block(sample, temb)\n\n assert output_tensor.shape == (1, 32, 64, 64)\n output_slice = output_tensor[0, -1, -3:, -3:]\n expected_slice = torch.tensor(\n [0.2226, -1.0791, -0.1629, 0.3659, -0.2889, -1.2376, 0.0582, 0.9206, 0.0044], device=torch_device\n )\n assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)\n\n def test_resnet_up(self):\n torch.manual_seed(0)\n sample = torch.randn(1, 32, 64, 64).to(torch_device)\n temb = torch.randn(1, 128).to(torch_device)\n resnet_block = ResnetBlock2D(in_channels=32, temb_channels=128, up=True).to(torch_device)\n with torch.no_grad():\n output_tensor = resnet_block(sample, temb)\n\n assert output_tensor.shape == (1, 32, 128, 128)\n output_slice = output_tensor[0, -1, -3:, -3:]\n expected_slice = torch.tensor(\n [1.2130, -0.8753, -0.9027, 1.5783, -0.5362, -0.5001, 1.0726, -0.7732, -0.4182], device=torch_device\n )\n assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)\n\n def test_resnet_down(self):\n torch.manual_seed(0)\n sample = torch.randn(1, 32, 64, 64).to(torch_device)\n temb = torch.randn(1, 128).to(torch_device)\n resnet_block = ResnetBlock2D(in_channels=32, temb_channels=128, down=True).to(torch_device)\n with torch.no_grad():\n output_tensor = resnet_block(sample, temb)\n\n assert output_tensor.shape == (1, 32, 32, 32)\n output_slice = output_tensor[0, -1, -3:, -3:]\n expected_slice = torch.tensor(\n [-0.3002, -0.7135, 0.1359, 0.0561, -0.7935, 0.0113, -0.1766, -0.6714, -0.0436], device=torch_device\n )\n assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)\n\n def test_restnet_with_kernel_fir(self):\n torch.manual_seed(0)\n sample = torch.randn(1, 32, 64, 64).to(torch_device)\n temb = torch.randn(1, 128).to(torch_device)\n resnet_block = ResnetBlock2D(in_channels=32, temb_channels=128, kernel=\"fir\", down=True).to(torch_device)\n with torch.no_grad():\n output_tensor = resnet_block(sample, temb)\n\n assert output_tensor.shape == (1, 32, 32, 32)\n output_slice = output_tensor[0, -1, -3:, -3:]\n expected_slice = torch.tensor(\n [-0.0934, -0.5729, 0.0909, -0.2710, -0.5044, 0.0243, -0.0665, -0.5267, -0.3136], device=torch_device\n )\n assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)\n\n def test_restnet_with_kernel_sde_vp(self):\n torch.manual_seed(0)\n sample = torch.randn(1, 32, 64, 64).to(torch_device)\n temb = torch.randn(1, 128).to(torch_device)\n resnet_block = ResnetBlock2D(in_channels=32, temb_channels=128, kernel=\"sde_vp\", down=True).to(torch_device)\n with torch.no_grad():\n output_tensor = resnet_block(sample, temb)\n\n assert output_tensor.shape == (1, 32, 32, 32)\n output_slice = output_tensor[0, -1, -3:, -3:]\n expected_slice = torch.tensor(\n [-0.3002, -0.7135, 0.1359, 0.0561, -0.7935, 0.0113, -0.1766, -0.6714, -0.0436], device=torch_device\n )\n assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)\n\n\nclass Transformer2DModelTests(unittest.TestCase):\n def test_spatial_transformer_default(self):\n torch.manual_seed(0)\n backend_manual_seed(torch_device, 0)\n\n sample = torch.randn(1, 32, 64, 64).to(torch_device)\n spatial_transformer_block = Transformer2DModel(\n in_channels=32,\n num_attention_heads=1,\n attention_head_dim=32,\n dropout=0.0,\n cross_attention_dim=None,\n ).to(torch_device)\n with torch.no_grad():\n attention_scores = spatial_transformer_block(sample).sample\n\n assert attention_scores.shape == (1, 32, 64, 64)\n output_slice = attention_scores[0, -1, -3:, -3:]\n\n expected_slice = torch.tensor(\n [-1.9455, -0.0066, -1.3933, -1.5878, 0.5325, -0.6486, -1.8648, 0.7515, -0.9689], device=torch_device\n )\n assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)\n\n def test_spatial_transformer_cross_attention_dim(self):\n torch.manual_seed(0)\n backend_manual_seed(torch_device, 0)\n\n sample = torch.randn(1, 64, 64, 64).to(torch_device)\n spatial_transformer_block = Transformer2DModel(\n in_channels=64,\n num_attention_heads=2,\n attention_head_dim=32,\n dropout=0.0,\n cross_attention_dim=64,\n ).to(torch_device)\n with torch.no_grad():\n context = torch.randn(1, 4, 64).to(torch_device)\n attention_scores = spatial_transformer_block(sample, context).sample\n\n assert attention_scores.shape == (1, 64, 64, 64)\n output_slice = attention_scores[0, -1, -3:, -3:]\n expected_slice = torch.tensor(\n [0.0143, -0.6909, -2.1547, -1.8893, 1.4097, 0.1359, -0.2521, -1.3359, 0.2598], device=torch_device\n )\n assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)\n\n def test_spatial_transformer_timestep(self):\n torch.manual_seed(0)\n backend_manual_seed(torch_device, 0)\n\n num_embeds_ada_norm = 5\n\n sample = torch.randn(1, 64, 64, 64).to(torch_device)\n spatial_transformer_block = Transformer2DModel(\n in_channels=64,\n num_attention_heads=2,\n attention_head_dim=32,\n dropout=0.0,\n cross_attention_dim=64,\n num_embeds_ada_norm=num_embeds_ada_norm,\n ).to(torch_device)\n with torch.no_grad():\n timestep_1 = torch.tensor(1, dtype=torch.long).to(torch_device)\n timestep_2 = torch.tensor(2, dtype=torch.long).to(torch_device)\n attention_scores_1 = spatial_transformer_block(sample, timestep=timestep_1).sample\n attention_scores_2 = spatial_transformer_block(sample, timestep=timestep_2).sample\n\n assert attention_scores_1.shape == (1, 64, 64, 64)\n assert attention_scores_2.shape == (1, 64, 64, 64)\n\n output_slice_1 = attention_scores_1[0, -1, -3:, -3:]\n output_slice_2 = attention_scores_2[0, -1, -3:, -3:]\n\n expected_slice = torch.tensor(\n [-0.3923, -1.0923, -1.7144, -1.5570, 1.4154, 0.1738, -0.1157, -1.2998, -0.1703], device=torch_device\n )\n expected_slice_2 = torch.tensor(\n [-0.4311, -1.1376, -1.7732, -1.5997, 1.3450, 0.0964, -0.1569, -1.3590, -0.2348], device=torch_device\n )\n\n assert torch.allclose(output_slice_1.flatten(), expected_slice, atol=1e-3)\n assert torch.allclose(output_slice_2.flatten(), expected_slice_2, atol=1e-3)\n\n def test_spatial_transformer_dropout(self):\n torch.manual_seed(0)\n backend_manual_seed(torch_device, 0)\n\n sample = torch.randn(1, 32, 64, 64).to(torch_device)\n spatial_transformer_block = (\n Transformer2DModel(\n in_channels=32,\n num_attention_heads=2,\n attention_head_dim=16,\n dropout=0.3,\n cross_attention_dim=None,\n )\n .to(torch_device)\n .eval()\n )\n with torch.no_grad():\n attention_scores = spatial_transformer_block(sample).sample\n\n assert attention_scores.shape == (1, 32, 64, 64)\n output_slice = attention_scores[0, -1, -3:, -3:]\n\n expected_slice = torch.tensor(\n [-1.9380, -0.0083, -1.3771, -1.5819, 0.5209, -0.6441, -1.8545, 0.7563, -0.9615], device=torch_device\n )\n assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)\n\n @require_torch_accelerator_with_fp64\n def test_spatial_transformer_discrete(self):\n torch.manual_seed(0)\n backend_manual_seed(torch_device, 0)\n\n num_embed = 5\n\n sample = torch.randint(0, num_embed, (1, 32)).to(torch_device)\n spatial_transformer_block = (\n Transformer2DModel(\n num_attention_heads=1,\n attention_head_dim=32,\n num_vector_embeds=num_embed,\n sample_size=16,\n )\n .to(torch_device)\n .eval()\n )\n\n with torch.no_grad():\n attention_scores = spatial_transformer_block(sample).sample\n\n assert attention_scores.shape == (1, num_embed - 1, 32)\n\n output_slice = attention_scores[0, -2:, -3:]\n\n expected_slice = torch.tensor([-1.7648, -1.0241, -2.0985, -1.8035, -1.6404, -1.2098], device=torch_device)\n assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)\n\n def test_spatial_transformer_default_norm_layers(self):\n spatial_transformer_block = Transformer2DModel(num_attention_heads=1, attention_head_dim=32, in_channels=32)\n\n assert spatial_transformer_block.transformer_blocks[0].norm1.__class__ == nn.LayerNorm\n assert spatial_transformer_block.transformer_blocks[0].norm3.__class__ == nn.LayerNorm\n\n def test_spatial_transformer_ada_norm_layers(self):\n spatial_transformer_block = Transformer2DModel(\n num_attention_heads=1,\n attention_head_dim=32,\n in_channels=32,\n num_embeds_ada_norm=5,\n )\n\n assert spatial_transformer_block.transformer_blocks[0].norm1.__class__ == AdaLayerNorm\n assert spatial_transformer_block.transformer_blocks[0].norm3.__class__ == nn.LayerNorm\n\n def test_spatial_transformer_default_ff_layers(self):\n spatial_transformer_block = Transformer2DModel(\n num_attention_heads=1,\n attention_head_dim=32,\n in_channels=32,\n )\n\n assert spatial_transformer_block.transformer_blocks[0].ff.net[0].__class__ == GEGLU\n assert spatial_transformer_block.transformer_blocks[0].ff.net[1].__class__ == nn.Dropout\n assert spatial_transformer_block.transformer_blocks[0].ff.net[2].__class__ == nn.Linear\n\n dim = 32\n inner_dim = 128\n\n # First dimension change\n assert spatial_transformer_block.transformer_blocks[0].ff.net[0].proj.in_features == dim\n # NOTE: inner_dim * 2 because GEGLU\n assert spatial_transformer_block.transformer_blocks[0].ff.net[0].proj.out_features == inner_dim * 2\n\n # Second dimension change\n assert spatial_transformer_block.transformer_blocks[0].ff.net[2].in_features == inner_dim\n assert spatial_transformer_block.transformer_blocks[0].ff.net[2].out_features == dim\n\n def test_spatial_transformer_geglu_approx_ff_layers(self):\n spatial_transformer_block = Transformer2DModel(\n num_attention_heads=1,\n attention_head_dim=32,\n in_channels=32,\n activation_fn=\"geglu-approximate\",\n )\n\n assert spatial_transformer_block.transformer_blocks[0].ff.net[0].__class__ == ApproximateGELU\n assert spatial_transformer_block.transformer_blocks[0].ff.net[1].__class__ == nn.Dropout\n assert spatial_transformer_block.transformer_blocks[0].ff.net[2].__class__ == nn.Linear\n\n dim = 32\n inner_dim = 128\n\n # First dimension change\n assert spatial_transformer_block.transformer_blocks[0].ff.net[0].proj.in_features == dim\n assert spatial_transformer_block.transformer_blocks[0].ff.net[0].proj.out_features == inner_dim\n\n # Second dimension change\n assert spatial_transformer_block.transformer_blocks[0].ff.net[2].in_features == inner_dim\n assert spatial_transformer_block.transformer_blocks[0].ff.net[2].out_features == dim\n\n def test_spatial_transformer_attention_bias(self):\n spatial_transformer_block = Transformer2DModel(\n num_attention_heads=1, attention_head_dim=32, in_channels=32, attention_bias=True\n )\n\n assert spatial_transformer_block.transformer_blocks[0].attn1.to_q.bias is not None\n assert spatial_transformer_block.transformer_blocks[0].attn1.to_k.bias is not None\n assert spatial_transformer_block.transformer_blocks[0].attn1.to_v.bias is not None\n" }, { "path": "diffusers/tests/models/test_modeling_common.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nimport json\nimport os\nimport tempfile\nimport traceback\nimport unittest\nimport unittest.mock as mock\nimport uuid\nfrom typing import Dict, List, Tuple\n\nimport numpy as np\nimport requests_mock\nimport torch\nfrom accelerate.utils import compute_module_sizes\nfrom huggingface_hub import ModelCard, delete_repo\nfrom huggingface_hub.utils import is_jinja_available\nfrom requests.exceptions import HTTPError\n\nfrom diffusers.models import UNet2DConditionModel\nfrom diffusers.models.attention_processor import (\n AttnProcessor,\n AttnProcessor2_0,\n AttnProcessorNPU,\n XFormersAttnProcessor,\n)\nfrom diffusers.training_utils import EMAModel\nfrom diffusers.utils import SAFE_WEIGHTS_INDEX_NAME, is_torch_npu_available, is_xformers_available, logging\nfrom diffusers.utils.testing_utils import (\n CaptureLogger,\n get_python_version,\n require_python39_or_higher,\n require_torch_2,\n require_torch_accelerator_with_training,\n require_torch_gpu,\n require_torch_multi_gpu,\n run_test_in_subprocess,\n torch_device,\n)\n\nfrom ..others.test_utils import TOKEN, USER, is_staging_test\n\n\n# Will be run via run_test_in_subprocess\ndef _test_from_save_pretrained_dynamo(in_queue, out_queue, timeout):\n error = None\n try:\n init_dict, model_class = in_queue.get(timeout=timeout)\n\n model = model_class(**init_dict)\n model.to(torch_device)\n model = torch.compile(model)\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n model.save_pretrained(tmpdirname, safe_serialization=False)\n new_model = model_class.from_pretrained(tmpdirname)\n new_model.to(torch_device)\n\n assert new_model.__class__ == model_class\n except Exception:\n error = f\"{traceback.format_exc()}\"\n\n results = {\"error\": error}\n out_queue.put(results, timeout=timeout)\n out_queue.join()\n\n\nclass ModelUtilsTest(unittest.TestCase):\n def tearDown(self):\n super().tearDown()\n\n def test_accelerate_loading_error_message(self):\n with self.assertRaises(ValueError) as error_context:\n UNet2DConditionModel.from_pretrained(\"hf-internal-testing/stable-diffusion-broken\", subfolder=\"unet\")\n\n # make sure that error message states what keys are missing\n assert \"conv_out.bias\" in str(error_context.exception)\n\n def test_cached_files_are_used_when_no_internet(self):\n # A mock response for an HTTP head request to emulate server down\n response_mock = mock.Mock()\n response_mock.status_code = 500\n response_mock.headers = {}\n response_mock.raise_for_status.side_effect = HTTPError\n response_mock.json.return_value = {}\n\n # Download this model to make sure it's in the cache.\n orig_model = UNet2DConditionModel.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\", subfolder=\"unet\"\n )\n\n # Under the mock environment we get a 500 error when trying to reach the model.\n with mock.patch(\"requests.request\", return_value=response_mock):\n # Download this model to make sure it's in the cache.\n model = UNet2DConditionModel.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\", subfolder=\"unet\", local_files_only=True\n )\n\n for p1, p2 in zip(orig_model.parameters(), model.parameters()):\n if p1.data.ne(p2.data).sum() > 0:\n assert False, \"Parameters not the same!\"\n\n def test_one_request_upon_cached(self):\n # TODO: For some reason this test fails on MPS where no HEAD call is made.\n if torch_device == \"mps\":\n return\n\n use_safetensors = False\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n with requests_mock.mock(real_http=True) as m:\n UNet2DConditionModel.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\",\n subfolder=\"unet\",\n cache_dir=tmpdirname,\n use_safetensors=use_safetensors,\n )\n\n download_requests = [r.method for r in m.request_history]\n assert (\n download_requests.count(\"HEAD\") == 3\n ), \"3 HEAD requests one for config, one for model, and one for shard index file.\"\n assert download_requests.count(\"GET\") == 2, \"2 GET requests one for config, one for model\"\n\n with requests_mock.mock(real_http=True) as m:\n UNet2DConditionModel.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\",\n subfolder=\"unet\",\n cache_dir=tmpdirname,\n use_safetensors=use_safetensors,\n )\n\n cache_requests = [r.method for r in m.request_history]\n assert (\n \"HEAD\" == cache_requests[0] and len(cache_requests) == 2\n ), \"We should call only `model_info` to check for commit hash and knowing if shard index is present.\"\n\n def test_weight_overwrite(self):\n with tempfile.TemporaryDirectory() as tmpdirname, self.assertRaises(ValueError) as error_context:\n UNet2DConditionModel.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\",\n subfolder=\"unet\",\n cache_dir=tmpdirname,\n in_channels=9,\n )\n\n # make sure that error message states what keys are missing\n assert \"Cannot load\" in str(error_context.exception)\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n model = UNet2DConditionModel.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\",\n subfolder=\"unet\",\n cache_dir=tmpdirname,\n in_channels=9,\n low_cpu_mem_usage=False,\n ignore_mismatched_sizes=True,\n )\n\n assert model.config.in_channels == 9\n\n\nclass UNetTesterMixin:\n def test_forward_signature(self):\n init_dict, _ = self.prepare_init_args_and_inputs_for_common()\n\n model = self.model_class(**init_dict)\n signature = inspect.signature(model.forward)\n # signature.parameters is an OrderedDict => so arg_names order is deterministic\n arg_names = [*signature.parameters.keys()]\n\n expected_arg_names = [\"sample\", \"timestep\"]\n self.assertListEqual(arg_names[:2], expected_arg_names)\n\n def test_forward_with_norm_groups(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n init_dict[\"norm_num_groups\"] = 16\n init_dict[\"block_out_channels\"] = (16, 32)\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.eval()\n\n with torch.no_grad():\n output = model(**inputs_dict)\n\n if isinstance(output, dict):\n output = output.to_tuple()[0]\n\n self.assertIsNotNone(output)\n expected_shape = inputs_dict[\"sample\"].shape\n self.assertEqual(output.shape, expected_shape, \"Input and output shapes do not match\")\n\n\nclass ModelTesterMixin:\n main_input_name = None # overwrite in model specific tester class\n base_precision = 1e-3\n forward_requires_fresh_args = False\n model_split_percents = [0.5, 0.7, 0.9]\n\n def check_device_map_is_respected(self, model, device_map):\n for param_name, param in model.named_parameters():\n # Find device in device_map\n while len(param_name) > 0 and param_name not in device_map:\n param_name = \".\".join(param_name.split(\".\")[:-1])\n if param_name not in device_map:\n raise ValueError(\"device map is incomplete, it does not contain any device for `param_name`.\")\n\n param_device = device_map[param_name]\n if param_device in [\"cpu\", \"disk\"]:\n self.assertEqual(param.device, torch.device(\"meta\"))\n else:\n self.assertEqual(param.device, torch.device(param_device))\n\n def test_from_save_pretrained(self, expected_max_diff=5e-5):\n if self.forward_requires_fresh_args:\n model = self.model_class(**self.init_dict)\n else:\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n\n if hasattr(model, \"set_default_attn_processor\"):\n model.set_default_attn_processor()\n model.to(torch_device)\n model.eval()\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n model.save_pretrained(tmpdirname, safe_serialization=False)\n new_model = self.model_class.from_pretrained(tmpdirname)\n if hasattr(new_model, \"set_default_attn_processor\"):\n new_model.set_default_attn_processor()\n new_model.to(torch_device)\n\n with torch.no_grad():\n if self.forward_requires_fresh_args:\n image = model(**self.inputs_dict(0))\n else:\n image = model(**inputs_dict)\n\n if isinstance(image, dict):\n image = image.to_tuple()[0]\n\n if self.forward_requires_fresh_args:\n new_image = new_model(**self.inputs_dict(0))\n else:\n new_image = new_model(**inputs_dict)\n\n if isinstance(new_image, dict):\n new_image = new_image.to_tuple()[0]\n\n max_diff = (image - new_image).abs().max().item()\n self.assertLessEqual(max_diff, expected_max_diff, \"Models give different forward passes\")\n\n def test_getattr_is_correct(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n\n # save some things to test\n model.dummy_attribute = 5\n model.register_to_config(test_attribute=5)\n\n logger = logging.get_logger(\"diffusers.models.modeling_utils\")\n # 30 for warning\n logger.setLevel(30)\n with CaptureLogger(logger) as cap_logger:\n assert hasattr(model, \"dummy_attribute\")\n assert getattr(model, \"dummy_attribute\") == 5\n assert model.dummy_attribute == 5\n\n # no warning should be thrown\n assert cap_logger.out == \"\"\n\n logger = logging.get_logger(\"diffusers.models.modeling_utils\")\n # 30 for warning\n logger.setLevel(30)\n with CaptureLogger(logger) as cap_logger:\n assert hasattr(model, \"save_pretrained\")\n fn = model.save_pretrained\n fn_1 = getattr(model, \"save_pretrained\")\n\n assert fn == fn_1\n # no warning should be thrown\n assert cap_logger.out == \"\"\n\n # warning should be thrown\n with self.assertWarns(FutureWarning):\n assert model.test_attribute == 5\n\n with self.assertWarns(FutureWarning):\n assert getattr(model, \"test_attribute\") == 5\n\n with self.assertRaises(AttributeError) as error:\n model.does_not_exist\n\n assert str(error.exception) == f\"'{type(model).__name__}' object has no attribute 'does_not_exist'\"\n\n @unittest.skipIf(\n torch_device != \"npu\" or not is_torch_npu_available(),\n reason=\"torch npu flash attention is only available with NPU and `torch_npu` installed\",\n )\n def test_set_torch_npu_flash_attn_processor_determinism(self):\n torch.use_deterministic_algorithms(False)\n if self.forward_requires_fresh_args:\n model = self.model_class(**self.init_dict)\n else:\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n model.to(torch_device)\n\n if not hasattr(model, \"set_attn_processor\"):\n # If not has `set_attn_processor`, skip test\n return\n\n model.set_default_attn_processor()\n assert all(type(proc) == AttnProcessorNPU for proc in model.attn_processors.values())\n with torch.no_grad():\n if self.forward_requires_fresh_args:\n output = model(**self.inputs_dict(0))[0]\n else:\n output = model(**inputs_dict)[0]\n\n model.enable_npu_flash_attention()\n assert all(type(proc) == AttnProcessorNPU for proc in model.attn_processors.values())\n with torch.no_grad():\n if self.forward_requires_fresh_args:\n output_2 = model(**self.inputs_dict(0))[0]\n else:\n output_2 = model(**inputs_dict)[0]\n\n model.set_attn_processor(AttnProcessorNPU())\n assert all(type(proc) == AttnProcessorNPU for proc in model.attn_processors.values())\n with torch.no_grad():\n if self.forward_requires_fresh_args:\n output_3 = model(**self.inputs_dict(0))[0]\n else:\n output_3 = model(**inputs_dict)[0]\n\n torch.use_deterministic_algorithms(True)\n\n assert torch.allclose(output, output_2, atol=self.base_precision)\n assert torch.allclose(output, output_3, atol=self.base_precision)\n assert torch.allclose(output_2, output_3, atol=self.base_precision)\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_set_xformers_attn_processor_for_determinism(self):\n torch.use_deterministic_algorithms(False)\n if self.forward_requires_fresh_args:\n model = self.model_class(**self.init_dict)\n else:\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n model.to(torch_device)\n\n if not hasattr(model, \"set_attn_processor\"):\n # If not has `set_attn_processor`, skip test\n return\n\n model.set_default_attn_processor()\n assert all(type(proc) == AttnProcessor for proc in model.attn_processors.values())\n with torch.no_grad():\n if self.forward_requires_fresh_args:\n output = model(**self.inputs_dict(0))[0]\n else:\n output = model(**inputs_dict)[0]\n\n model.enable_xformers_memory_efficient_attention()\n assert all(type(proc) == XFormersAttnProcessor for proc in model.attn_processors.values())\n with torch.no_grad():\n if self.forward_requires_fresh_args:\n output_2 = model(**self.inputs_dict(0))[0]\n else:\n output_2 = model(**inputs_dict)[0]\n\n model.set_attn_processor(XFormersAttnProcessor())\n assert all(type(proc) == XFormersAttnProcessor for proc in model.attn_processors.values())\n with torch.no_grad():\n if self.forward_requires_fresh_args:\n output_3 = model(**self.inputs_dict(0))[0]\n else:\n output_3 = model(**inputs_dict)[0]\n\n torch.use_deterministic_algorithms(True)\n\n assert torch.allclose(output, output_2, atol=self.base_precision)\n assert torch.allclose(output, output_3, atol=self.base_precision)\n assert torch.allclose(output_2, output_3, atol=self.base_precision)\n\n @require_torch_gpu\n def test_set_attn_processor_for_determinism(self):\n torch.use_deterministic_algorithms(False)\n if self.forward_requires_fresh_args:\n model = self.model_class(**self.init_dict)\n else:\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n\n model.to(torch_device)\n\n if not hasattr(model, \"set_attn_processor\"):\n # If not has `set_attn_processor`, skip test\n return\n\n assert all(type(proc) == AttnProcessor2_0 for proc in model.attn_processors.values())\n with torch.no_grad():\n if self.forward_requires_fresh_args:\n output_1 = model(**self.inputs_dict(0))[0]\n else:\n output_1 = model(**inputs_dict)[0]\n\n model.set_default_attn_processor()\n assert all(type(proc) == AttnProcessor for proc in model.attn_processors.values())\n with torch.no_grad():\n if self.forward_requires_fresh_args:\n output_2 = model(**self.inputs_dict(0))[0]\n else:\n output_2 = model(**inputs_dict)[0]\n\n model.set_attn_processor(AttnProcessor2_0())\n assert all(type(proc) == AttnProcessor2_0 for proc in model.attn_processors.values())\n with torch.no_grad():\n if self.forward_requires_fresh_args:\n output_4 = model(**self.inputs_dict(0))[0]\n else:\n output_4 = model(**inputs_dict)[0]\n\n model.set_attn_processor(AttnProcessor())\n assert all(type(proc) == AttnProcessor for proc in model.attn_processors.values())\n with torch.no_grad():\n if self.forward_requires_fresh_args:\n output_5 = model(**self.inputs_dict(0))[0]\n else:\n output_5 = model(**inputs_dict)[0]\n\n torch.use_deterministic_algorithms(True)\n\n # make sure that outputs match\n assert torch.allclose(output_2, output_1, atol=self.base_precision)\n assert torch.allclose(output_2, output_4, atol=self.base_precision)\n assert torch.allclose(output_2, output_5, atol=self.base_precision)\n\n def test_from_save_pretrained_variant(self, expected_max_diff=5e-5):\n if self.forward_requires_fresh_args:\n model = self.model_class(**self.init_dict)\n else:\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n\n if hasattr(model, \"set_default_attn_processor\"):\n model.set_default_attn_processor()\n\n model.to(torch_device)\n model.eval()\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n model.save_pretrained(tmpdirname, variant=\"fp16\", safe_serialization=False)\n new_model = self.model_class.from_pretrained(tmpdirname, variant=\"fp16\")\n if hasattr(new_model, \"set_default_attn_processor\"):\n new_model.set_default_attn_processor()\n\n # non-variant cannot be loaded\n with self.assertRaises(OSError) as error_context:\n self.model_class.from_pretrained(tmpdirname)\n\n # make sure that error message states what keys are missing\n assert \"Error no file named diffusion_pytorch_model.bin found in directory\" in str(error_context.exception)\n\n new_model.to(torch_device)\n\n with torch.no_grad():\n if self.forward_requires_fresh_args:\n image = model(**self.inputs_dict(0))\n else:\n image = model(**inputs_dict)\n if isinstance(image, dict):\n image = image.to_tuple()[0]\n\n if self.forward_requires_fresh_args:\n new_image = new_model(**self.inputs_dict(0))\n else:\n new_image = new_model(**inputs_dict)\n\n if isinstance(new_image, dict):\n new_image = new_image.to_tuple()[0]\n\n max_diff = (image - new_image).abs().max().item()\n self.assertLessEqual(max_diff, expected_max_diff, \"Models give different forward passes\")\n\n @require_python39_or_higher\n @require_torch_2\n @unittest.skipIf(\n get_python_version == (3, 12),\n reason=\"Torch Dynamo isn't yet supported for Python 3.12.\",\n )\n def test_from_save_pretrained_dynamo(self):\n init_dict, _ = self.prepare_init_args_and_inputs_for_common()\n inputs = [init_dict, self.model_class]\n run_test_in_subprocess(test_case=self, target_func=_test_from_save_pretrained_dynamo, inputs=inputs)\n\n def test_from_save_pretrained_dtype(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.eval()\n\n for dtype in [torch.float32, torch.float16, torch.bfloat16]:\n if torch_device == \"mps\" and dtype == torch.bfloat16:\n continue\n with tempfile.TemporaryDirectory() as tmpdirname:\n model.to(dtype)\n model.save_pretrained(tmpdirname, safe_serialization=False)\n new_model = self.model_class.from_pretrained(tmpdirname, low_cpu_mem_usage=True, torch_dtype=dtype)\n assert new_model.dtype == dtype\n new_model = self.model_class.from_pretrained(tmpdirname, low_cpu_mem_usage=False, torch_dtype=dtype)\n assert new_model.dtype == dtype\n\n def test_determinism(self, expected_max_diff=1e-5):\n if self.forward_requires_fresh_args:\n model = self.model_class(**self.init_dict)\n else:\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.eval()\n\n with torch.no_grad():\n if self.forward_requires_fresh_args:\n first = model(**self.inputs_dict(0))\n else:\n first = model(**inputs_dict)\n if isinstance(first, dict):\n first = first.to_tuple()[0]\n\n if self.forward_requires_fresh_args:\n second = model(**self.inputs_dict(0))\n else:\n second = model(**inputs_dict)\n if isinstance(second, dict):\n second = second.to_tuple()[0]\n\n out_1 = first.cpu().numpy()\n out_2 = second.cpu().numpy()\n out_1 = out_1[~np.isnan(out_1)]\n out_2 = out_2[~np.isnan(out_2)]\n max_diff = np.amax(np.abs(out_1 - out_2))\n self.assertLessEqual(max_diff, expected_max_diff)\n\n def test_output(self, expected_output_shape=None):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.eval()\n\n with torch.no_grad():\n output = model(**inputs_dict)\n\n if isinstance(output, dict):\n output = output.to_tuple()[0]\n\n self.assertIsNotNone(output)\n\n # input & output have to have the same shape\n input_tensor = inputs_dict[self.main_input_name]\n\n if expected_output_shape is None:\n expected_shape = input_tensor.shape\n self.assertEqual(output.shape, expected_shape, \"Input and output shapes do not match\")\n else:\n self.assertEqual(output.shape, expected_output_shape, \"Input and output shapes do not match\")\n\n def test_model_from_pretrained(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.eval()\n\n # test if the model can be loaded from the config\n # and has all the expected shape\n with tempfile.TemporaryDirectory() as tmpdirname:\n model.save_pretrained(tmpdirname, safe_serialization=False)\n new_model = self.model_class.from_pretrained(tmpdirname)\n new_model.to(torch_device)\n new_model.eval()\n\n # check if all parameters shape are the same\n for param_name in model.state_dict().keys():\n param_1 = model.state_dict()[param_name]\n param_2 = new_model.state_dict()[param_name]\n self.assertEqual(param_1.shape, param_2.shape)\n\n with torch.no_grad():\n output_1 = model(**inputs_dict)\n\n if isinstance(output_1, dict):\n output_1 = output_1.to_tuple()[0]\n\n output_2 = new_model(**inputs_dict)\n\n if isinstance(output_2, dict):\n output_2 = output_2.to_tuple()[0]\n\n self.assertEqual(output_1.shape, output_2.shape)\n\n @require_torch_accelerator_with_training\n def test_training(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.train()\n output = model(**inputs_dict)\n\n if isinstance(output, dict):\n output = output.to_tuple()[0]\n\n input_tensor = inputs_dict[self.main_input_name]\n noise = torch.randn((input_tensor.shape[0],) + self.output_shape).to(torch_device)\n loss = torch.nn.functional.mse_loss(output, noise)\n loss.backward()\n\n @require_torch_accelerator_with_training\n def test_ema_training(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.train()\n ema_model = EMAModel(model.parameters())\n\n output = model(**inputs_dict)\n\n if isinstance(output, dict):\n output = output.to_tuple()[0]\n\n input_tensor = inputs_dict[self.main_input_name]\n noise = torch.randn((input_tensor.shape[0],) + self.output_shape).to(torch_device)\n loss = torch.nn.functional.mse_loss(output, noise)\n loss.backward()\n ema_model.step(model.parameters())\n\n def test_outputs_equivalence(self):\n def set_nan_tensor_to_zero(t):\n # Temporary fallback until `aten::_index_put_impl_` is implemented in mps\n # Track progress in https://github.com/pytorch/pytorch/issues/77764\n device = t.device\n if device.type == \"mps\":\n t = t.to(\"cpu\")\n t[t != t] = 0\n return t.to(device)\n\n def recursive_check(tuple_object, dict_object):\n if isinstance(tuple_object, (List, Tuple)):\n for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object.values()):\n recursive_check(tuple_iterable_value, dict_iterable_value)\n elif isinstance(tuple_object, Dict):\n for tuple_iterable_value, dict_iterable_value in zip(tuple_object.values(), dict_object.values()):\n recursive_check(tuple_iterable_value, dict_iterable_value)\n elif tuple_object is None:\n return\n else:\n self.assertTrue(\n torch.allclose(\n set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5\n ),\n msg=(\n \"Tuple and dict output are not equal. Difference:\"\n f\" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:\"\n f\" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has\"\n f\" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}.\"\n ),\n )\n\n if self.forward_requires_fresh_args:\n model = self.model_class(**self.init_dict)\n else:\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n\n model.to(torch_device)\n model.eval()\n\n with torch.no_grad():\n if self.forward_requires_fresh_args:\n outputs_dict = model(**self.inputs_dict(0))\n outputs_tuple = model(**self.inputs_dict(0), return_dict=False)\n else:\n outputs_dict = model(**inputs_dict)\n outputs_tuple = model(**inputs_dict, return_dict=False)\n\n recursive_check(outputs_tuple, outputs_dict)\n\n @require_torch_accelerator_with_training\n def test_enable_disable_gradient_checkpointing(self):\n if not self.model_class._supports_gradient_checkpointing:\n return # Skip test if model does not support gradient checkpointing\n\n init_dict, _ = self.prepare_init_args_and_inputs_for_common()\n\n # at init model should have gradient checkpointing disabled\n model = self.model_class(**init_dict)\n self.assertFalse(model.is_gradient_checkpointing)\n\n # check enable works\n model.enable_gradient_checkpointing()\n self.assertTrue(model.is_gradient_checkpointing)\n\n # check disable works\n model.disable_gradient_checkpointing()\n self.assertFalse(model.is_gradient_checkpointing)\n\n def test_deprecated_kwargs(self):\n has_kwarg_in_model_class = \"kwargs\" in inspect.signature(self.model_class.__init__).parameters\n has_deprecated_kwarg = len(self.model_class._deprecated_kwargs) > 0\n\n if has_kwarg_in_model_class and not has_deprecated_kwarg:\n raise ValueError(\n f\"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs\"\n \" under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are\"\n \" no deprecated arguments or add the deprecated argument with `_deprecated_kwargs =\"\n \" []`\"\n )\n\n if not has_kwarg_in_model_class and has_deprecated_kwarg:\n raise ValueError(\n f\"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs\"\n \" under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to\"\n f\" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument\"\n \" from `_deprecated_kwargs = []`\"\n )\n\n @require_torch_gpu\n def test_cpu_offload(self):\n config, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**config).eval()\n if model._no_split_modules is None:\n return\n\n model = model.to(torch_device)\n\n torch.manual_seed(0)\n base_output = model(**inputs_dict)\n\n model_size = compute_module_sizes(model)[\"\"]\n # We test several splits of sizes to make sure it works.\n max_gpu_sizes = [int(p * model_size) for p in self.model_split_percents[1:]]\n with tempfile.TemporaryDirectory() as tmp_dir:\n model.cpu().save_pretrained(tmp_dir)\n\n for max_size in max_gpu_sizes:\n max_memory = {0: max_size, \"cpu\": model_size * 2}\n new_model = self.model_class.from_pretrained(tmp_dir, device_map=\"auto\", max_memory=max_memory)\n # Making sure part of the model will actually end up offloaded\n self.assertSetEqual(set(new_model.hf_device_map.values()), {0, \"cpu\"})\n\n self.check_device_map_is_respected(new_model, new_model.hf_device_map)\n torch.manual_seed(0)\n new_output = new_model(**inputs_dict)\n\n self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))\n\n @require_torch_gpu\n def test_disk_offload_without_safetensors(self):\n config, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**config).eval()\n if model._no_split_modules is None:\n return\n\n model = model.to(torch_device)\n\n torch.manual_seed(0)\n base_output = model(**inputs_dict)\n\n model_size = compute_module_sizes(model)[\"\"]\n with tempfile.TemporaryDirectory() as tmp_dir:\n model.cpu().save_pretrained(tmp_dir, safe_serialization=False)\n\n with self.assertRaises(ValueError):\n max_size = int(self.model_split_percents[0] * model_size)\n max_memory = {0: max_size, \"cpu\": max_size}\n # This errors out because it's missing an offload folder\n new_model = self.model_class.from_pretrained(tmp_dir, device_map=\"auto\", max_memory=max_memory)\n\n max_size = int(self.model_split_percents[0] * model_size)\n max_memory = {0: max_size, \"cpu\": max_size}\n new_model = self.model_class.from_pretrained(\n tmp_dir, device_map=\"auto\", max_memory=max_memory, offload_folder=tmp_dir\n )\n\n self.check_device_map_is_respected(new_model, new_model.hf_device_map)\n torch.manual_seed(0)\n new_output = new_model(**inputs_dict)\n\n self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))\n\n @require_torch_gpu\n def test_disk_offload_with_safetensors(self):\n config, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**config).eval()\n if model._no_split_modules is None:\n return\n\n model = model.to(torch_device)\n\n torch.manual_seed(0)\n base_output = model(**inputs_dict)\n\n model_size = compute_module_sizes(model)[\"\"]\n with tempfile.TemporaryDirectory() as tmp_dir:\n model.cpu().save_pretrained(tmp_dir)\n\n max_size = int(self.model_split_percents[0] * model_size)\n max_memory = {0: max_size, \"cpu\": max_size}\n new_model = self.model_class.from_pretrained(\n tmp_dir, device_map=\"auto\", offload_folder=tmp_dir, max_memory=max_memory\n )\n\n self.check_device_map_is_respected(new_model, new_model.hf_device_map)\n torch.manual_seed(0)\n new_output = new_model(**inputs_dict)\n\n self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))\n\n @require_torch_multi_gpu\n def test_model_parallelism(self):\n config, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**config).eval()\n if model._no_split_modules is None:\n return\n\n model = model.to(torch_device)\n\n torch.manual_seed(0)\n base_output = model(**inputs_dict)\n\n model_size = compute_module_sizes(model)[\"\"]\n # We test several splits of sizes to make sure it works.\n max_gpu_sizes = [int(p * model_size) for p in self.model_split_percents[1:]]\n with tempfile.TemporaryDirectory() as tmp_dir:\n model.cpu().save_pretrained(tmp_dir)\n\n for max_size in max_gpu_sizes:\n max_memory = {0: max_size, 1: model_size * 2, \"cpu\": model_size * 2}\n new_model = self.model_class.from_pretrained(tmp_dir, device_map=\"auto\", max_memory=max_memory)\n # Making sure part of the model will actually end up offloaded\n self.assertSetEqual(set(new_model.hf_device_map.values()), {0, 1})\n\n self.check_device_map_is_respected(new_model, new_model.hf_device_map)\n\n torch.manual_seed(0)\n new_output = new_model(**inputs_dict)\n\n self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))\n\n @require_torch_gpu\n def test_sharded_checkpoints(self):\n config, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**config).eval()\n if model._no_split_modules is None:\n return\n model = model.to(torch_device)\n\n torch.manual_seed(0)\n base_output = model(**inputs_dict)\n\n model_size = compute_module_sizes(model)[\"\"]\n max_shard_size = int((model_size * 0.75) / (2**10)) # Convert to KB as these test models are small.\n with tempfile.TemporaryDirectory() as tmp_dir:\n model.cpu().save_pretrained(tmp_dir, max_shard_size=f\"{max_shard_size}KB\")\n self.assertTrue(os.path.exists(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME)))\n\n # Now check if the right number of shards exists. First, let's get the number of shards.\n # Since this number can be dependent on the model being tested, it's important that we calculate it\n # instead of hardcoding it.\n with open(os.path.join(tmp_dir, SAFE_WEIGHTS_INDEX_NAME)) as f:\n weight_map_dict = json.load(f)[\"weight_map\"]\n first_key = list(weight_map_dict.keys())[0]\n weight_loc = weight_map_dict[first_key] # e.g., diffusion_pytorch_model-00001-of-00002.safetensors\n expected_num_shards = int(weight_loc.split(\"-\")[-1].split(\".\")[0])\n\n actual_num_shards = len([file for file in os.listdir(tmp_dir) if file.endswith(\".safetensors\")])\n self.assertTrue(actual_num_shards == expected_num_shards)\n\n new_model = self.model_class.from_pretrained(tmp_dir, device_map=\"auto\")\n\n torch.manual_seed(0)\n new_output = new_model(**inputs_dict)\n self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))\n\n\n@is_staging_test\nclass ModelPushToHubTester(unittest.TestCase):\n identifier = uuid.uuid4()\n repo_id = f\"test-model-{identifier}\"\n org_repo_id = f\"valid_org/{repo_id}-org\"\n\n def test_push_to_hub(self):\n model = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n )\n model.push_to_hub(self.repo_id, token=TOKEN)\n\n new_model = UNet2DConditionModel.from_pretrained(f\"{USER}/{self.repo_id}\")\n for p1, p2 in zip(model.parameters(), new_model.parameters()):\n self.assertTrue(torch.equal(p1, p2))\n\n # Reset repo\n delete_repo(token=TOKEN, repo_id=self.repo_id)\n\n # Push to hub via save_pretrained\n with tempfile.TemporaryDirectory() as tmp_dir:\n model.save_pretrained(tmp_dir, repo_id=self.repo_id, push_to_hub=True, token=TOKEN)\n\n new_model = UNet2DConditionModel.from_pretrained(f\"{USER}/{self.repo_id}\")\n for p1, p2 in zip(model.parameters(), new_model.parameters()):\n self.assertTrue(torch.equal(p1, p2))\n\n # Reset repo\n delete_repo(self.repo_id, token=TOKEN)\n\n def test_push_to_hub_in_organization(self):\n model = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n )\n model.push_to_hub(self.org_repo_id, token=TOKEN)\n\n new_model = UNet2DConditionModel.from_pretrained(self.org_repo_id)\n for p1, p2 in zip(model.parameters(), new_model.parameters()):\n self.assertTrue(torch.equal(p1, p2))\n\n # Reset repo\n delete_repo(token=TOKEN, repo_id=self.org_repo_id)\n\n # Push to hub via save_pretrained\n with tempfile.TemporaryDirectory() as tmp_dir:\n model.save_pretrained(tmp_dir, push_to_hub=True, token=TOKEN, repo_id=self.org_repo_id)\n\n new_model = UNet2DConditionModel.from_pretrained(self.org_repo_id)\n for p1, p2 in zip(model.parameters(), new_model.parameters()):\n self.assertTrue(torch.equal(p1, p2))\n\n # Reset repo\n delete_repo(self.org_repo_id, token=TOKEN)\n\n @unittest.skipIf(\n not is_jinja_available(),\n reason=\"Model card tests cannot be performed without Jinja installed.\",\n )\n def test_push_to_hub_library_name(self):\n model = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n )\n model.push_to_hub(self.repo_id, token=TOKEN)\n\n model_card = ModelCard.load(f\"{USER}/{self.repo_id}\", token=TOKEN).data\n assert model_card.library_name == \"diffusers\"\n\n # Reset repo\n delete_repo(self.repo_id, token=TOKEN)\n" }, { "path": "diffusers/tests/models/test_modeling_common_flax.py", "content": "import inspect\n\nfrom diffusers.utils import is_flax_available\nfrom diffusers.utils.testing_utils import require_flax\n\n\nif is_flax_available():\n import jax\n\n\n@require_flax\nclass FlaxModelTesterMixin:\n def test_output(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n model = self.model_class(**init_dict)\n variables = model.init(inputs_dict[\"prng_key\"], inputs_dict[\"sample\"])\n jax.lax.stop_gradient(variables)\n\n output = model.apply(variables, inputs_dict[\"sample\"])\n\n if isinstance(output, dict):\n output = output.sample\n\n self.assertIsNotNone(output)\n expected_shape = inputs_dict[\"sample\"].shape\n self.assertEqual(output.shape, expected_shape, \"Input and output shapes do not match\")\n\n def test_forward_with_norm_groups(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n init_dict[\"norm_num_groups\"] = 16\n init_dict[\"block_out_channels\"] = (16, 32)\n\n model = self.model_class(**init_dict)\n variables = model.init(inputs_dict[\"prng_key\"], inputs_dict[\"sample\"])\n jax.lax.stop_gradient(variables)\n\n output = model.apply(variables, inputs_dict[\"sample\"])\n\n if isinstance(output, dict):\n output = output.sample\n\n self.assertIsNotNone(output)\n expected_shape = inputs_dict[\"sample\"].shape\n self.assertEqual(output.shape, expected_shape, \"Input and output shapes do not match\")\n\n def test_deprecated_kwargs(self):\n has_kwarg_in_model_class = \"kwargs\" in inspect.signature(self.model_class.__init__).parameters\n has_deprecated_kwarg = len(self.model_class._deprecated_kwargs) > 0\n\n if has_kwarg_in_model_class and not has_deprecated_kwarg:\n raise ValueError(\n f\"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs\"\n \" under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are\"\n \" no deprecated arguments or add the deprecated argument with `_deprecated_kwargs =\"\n \" []`\"\n )\n\n if not has_kwarg_in_model_class and has_deprecated_kwarg:\n raise ValueError(\n f\"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs\"\n \" under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to\"\n f\" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument\"\n \" from `_deprecated_kwargs = []`\"\n )\n" }, { "path": "diffusers/tests/models/transformers/__init__.py", "content": "" }, { "path": "diffusers/tests/models/transformers/test_models_dit_transformer2d.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\n\nimport torch\n\nfrom diffusers import DiTTransformer2DModel, Transformer2DModel\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n slow,\n torch_device,\n)\n\nfrom ..test_modeling_common import ModelTesterMixin\n\n\nenable_full_determinism()\n\n\nclass DiTTransformer2DModelTests(ModelTesterMixin, unittest.TestCase):\n model_class = DiTTransformer2DModel\n main_input_name = \"hidden_states\"\n\n @property\n def dummy_input(self):\n batch_size = 4\n in_channels = 4\n sample_size = 8\n scheduler_num_train_steps = 1000\n num_class_labels = 4\n\n hidden_states = floats_tensor((batch_size, in_channels, sample_size, sample_size)).to(torch_device)\n timesteps = torch.randint(0, scheduler_num_train_steps, size=(batch_size,)).to(torch_device)\n class_label_ids = torch.randint(0, num_class_labels, size=(batch_size,)).to(torch_device)\n\n return {\"hidden_states\": hidden_states, \"timestep\": timesteps, \"class_labels\": class_label_ids}\n\n @property\n def input_shape(self):\n return (4, 8, 8)\n\n @property\n def output_shape(self):\n return (8, 8, 8)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = {\n \"in_channels\": 4,\n \"out_channels\": 8,\n \"activation_fn\": \"gelu-approximate\",\n \"num_attention_heads\": 2,\n \"attention_head_dim\": 4,\n \"attention_bias\": True,\n \"num_layers\": 1,\n \"norm_type\": \"ada_norm_zero\",\n \"num_embeds_ada_norm\": 8,\n \"patch_size\": 2,\n \"sample_size\": 8,\n }\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n def test_output(self):\n super().test_output(\n expected_output_shape=(self.dummy_input[self.main_input_name].shape[0],) + self.output_shape\n )\n\n def test_correct_class_remapping_from_dict_config(self):\n init_dict, _ = self.prepare_init_args_and_inputs_for_common()\n model = Transformer2DModel.from_config(init_dict)\n assert isinstance(model, DiTTransformer2DModel)\n\n def test_correct_class_remapping_from_pretrained_config(self):\n config = DiTTransformer2DModel.load_config(\"facebook/DiT-XL-2-256\", subfolder=\"transformer\")\n model = Transformer2DModel.from_config(config)\n assert isinstance(model, DiTTransformer2DModel)\n\n @slow\n def test_correct_class_remapping(self):\n model = Transformer2DModel.from_pretrained(\"facebook/DiT-XL-2-256\", subfolder=\"transformer\")\n assert isinstance(model, DiTTransformer2DModel)\n" }, { "path": "diffusers/tests/models/transformers/test_models_pixart_transformer2d.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\n\nimport torch\n\nfrom diffusers import PixArtTransformer2DModel, Transformer2DModel\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n slow,\n torch_device,\n)\n\nfrom ..test_modeling_common import ModelTesterMixin\n\n\nenable_full_determinism()\n\n\nclass PixArtTransformer2DModelTests(ModelTesterMixin, unittest.TestCase):\n model_class = PixArtTransformer2DModel\n main_input_name = \"hidden_states\"\n # We override the items here because the transformer under consideration is small.\n model_split_percents = [0.7, 0.6, 0.6]\n\n @property\n def dummy_input(self):\n batch_size = 4\n in_channels = 4\n sample_size = 8\n scheduler_num_train_steps = 1000\n cross_attention_dim = 8\n seq_len = 8\n\n hidden_states = floats_tensor((batch_size, in_channels, sample_size, sample_size)).to(torch_device)\n timesteps = torch.randint(0, scheduler_num_train_steps, size=(batch_size,)).to(torch_device)\n encoder_hidden_states = floats_tensor((batch_size, seq_len, cross_attention_dim)).to(torch_device)\n\n return {\n \"hidden_states\": hidden_states,\n \"timestep\": timesteps,\n \"encoder_hidden_states\": encoder_hidden_states,\n \"added_cond_kwargs\": {\"aspect_ratio\": None, \"resolution\": None},\n }\n\n @property\n def input_shape(self):\n return (4, 8, 8)\n\n @property\n def output_shape(self):\n return (8, 8, 8)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = {\n \"sample_size\": 8,\n \"num_layers\": 1,\n \"patch_size\": 2,\n \"attention_head_dim\": 2,\n \"num_attention_heads\": 2,\n \"in_channels\": 4,\n \"cross_attention_dim\": 8,\n \"out_channels\": 8,\n \"attention_bias\": True,\n \"activation_fn\": \"gelu-approximate\",\n \"num_embeds_ada_norm\": 8,\n \"norm_type\": \"ada_norm_single\",\n \"norm_elementwise_affine\": False,\n \"norm_eps\": 1e-6,\n \"use_additional_conditions\": False,\n \"caption_channels\": None,\n }\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n def test_output(self):\n super().test_output(\n expected_output_shape=(self.dummy_input[self.main_input_name].shape[0],) + self.output_shape\n )\n\n def test_correct_class_remapping_from_dict_config(self):\n init_dict, _ = self.prepare_init_args_and_inputs_for_common()\n model = Transformer2DModel.from_config(init_dict)\n assert isinstance(model, PixArtTransformer2DModel)\n\n def test_correct_class_remapping_from_pretrained_config(self):\n config = PixArtTransformer2DModel.load_config(\"PixArt-alpha/PixArt-XL-2-1024-MS\", subfolder=\"transformer\")\n model = Transformer2DModel.from_config(config)\n assert isinstance(model, PixArtTransformer2DModel)\n\n @slow\n def test_correct_class_remapping(self):\n model = Transformer2DModel.from_pretrained(\"PixArt-alpha/PixArt-XL-2-1024-MS\", subfolder=\"transformer\")\n assert isinstance(model, PixArtTransformer2DModel)\n" }, { "path": "diffusers/tests/models/transformers/test_models_prior.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport inspect\nimport unittest\n\nimport torch\nfrom parameterized import parameterized\n\nfrom diffusers import PriorTransformer\nfrom diffusers.utils.testing_utils import (\n backend_empty_cache,\n enable_full_determinism,\n floats_tensor,\n slow,\n torch_all_close,\n torch_device,\n)\n\nfrom ..test_modeling_common import ModelTesterMixin\n\n\nenable_full_determinism()\n\n\nclass PriorTransformerTests(ModelTesterMixin, unittest.TestCase):\n model_class = PriorTransformer\n main_input_name = \"hidden_states\"\n\n @property\n def dummy_input(self):\n batch_size = 4\n embedding_dim = 8\n num_embeddings = 7\n\n hidden_states = floats_tensor((batch_size, embedding_dim)).to(torch_device)\n\n proj_embedding = floats_tensor((batch_size, embedding_dim)).to(torch_device)\n encoder_hidden_states = floats_tensor((batch_size, num_embeddings, embedding_dim)).to(torch_device)\n\n return {\n \"hidden_states\": hidden_states,\n \"timestep\": 2,\n \"proj_embedding\": proj_embedding,\n \"encoder_hidden_states\": encoder_hidden_states,\n }\n\n def get_dummy_seed_input(self, seed=0):\n torch.manual_seed(seed)\n batch_size = 4\n embedding_dim = 8\n num_embeddings = 7\n\n hidden_states = torch.randn((batch_size, embedding_dim)).to(torch_device)\n\n proj_embedding = torch.randn((batch_size, embedding_dim)).to(torch_device)\n encoder_hidden_states = torch.randn((batch_size, num_embeddings, embedding_dim)).to(torch_device)\n\n return {\n \"hidden_states\": hidden_states,\n \"timestep\": 2,\n \"proj_embedding\": proj_embedding,\n \"encoder_hidden_states\": encoder_hidden_states,\n }\n\n @property\n def input_shape(self):\n return (4, 8)\n\n @property\n def output_shape(self):\n return (4, 8)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = {\n \"num_attention_heads\": 2,\n \"attention_head_dim\": 4,\n \"num_layers\": 2,\n \"embedding_dim\": 8,\n \"num_embeddings\": 7,\n \"additional_embeddings\": 4,\n }\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n def test_from_pretrained_hub(self):\n model, loading_info = PriorTransformer.from_pretrained(\n \"hf-internal-testing/prior-dummy\", output_loading_info=True\n )\n self.assertIsNotNone(model)\n self.assertEqual(len(loading_info[\"missing_keys\"]), 0)\n\n model.to(torch_device)\n hidden_states = model(**self.dummy_input)[0]\n\n assert hidden_states is not None, \"Make sure output is not None\"\n\n def test_forward_signature(self):\n init_dict, _ = self.prepare_init_args_and_inputs_for_common()\n\n model = self.model_class(**init_dict)\n signature = inspect.signature(model.forward)\n # signature.parameters is an OrderedDict => so arg_names order is deterministic\n arg_names = [*signature.parameters.keys()]\n\n expected_arg_names = [\"hidden_states\", \"timestep\"]\n self.assertListEqual(arg_names[:2], expected_arg_names)\n\n def test_output_pretrained(self):\n model = PriorTransformer.from_pretrained(\"hf-internal-testing/prior-dummy\")\n model = model.to(torch_device)\n\n if hasattr(model, \"set_default_attn_processor\"):\n model.set_default_attn_processor()\n\n input = self.get_dummy_seed_input()\n\n with torch.no_grad():\n output = model(**input)[0]\n\n output_slice = output[0, :5].flatten().cpu()\n print(output_slice)\n\n # Since the VAE Gaussian prior's generator is seeded on the appropriate device,\n # the expected output slices are not the same for CPU and GPU.\n expected_output_slice = torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239])\n self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-2))\n\n\n@slow\nclass PriorTransformerIntegrationTests(unittest.TestCase):\n def get_dummy_seed_input(self, batch_size=1, embedding_dim=768, num_embeddings=77, seed=0):\n torch.manual_seed(seed)\n batch_size = batch_size\n embedding_dim = embedding_dim\n num_embeddings = num_embeddings\n\n hidden_states = torch.randn((batch_size, embedding_dim)).to(torch_device)\n\n proj_embedding = torch.randn((batch_size, embedding_dim)).to(torch_device)\n encoder_hidden_states = torch.randn((batch_size, num_embeddings, embedding_dim)).to(torch_device)\n\n return {\n \"hidden_states\": hidden_states,\n \"timestep\": 2,\n \"proj_embedding\": proj_embedding,\n \"encoder_hidden_states\": encoder_hidden_states,\n }\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n backend_empty_cache(torch_device)\n\n @parameterized.expand(\n [\n # fmt: off\n [13, [-0.5861, 0.1283, -0.0931, 0.0882, 0.4476, 0.1329, -0.0498, 0.0640]],\n [37, [-0.4913, 0.0110, -0.0483, 0.0541, 0.4954, -0.0170, 0.0354, 0.1651]],\n # fmt: on\n ]\n )\n def test_kandinsky_prior(self, seed, expected_slice):\n model = PriorTransformer.from_pretrained(\"kandinsky-community/kandinsky-2-1-prior\", subfolder=\"prior\")\n model.to(torch_device)\n input = self.get_dummy_seed_input(seed=seed)\n\n with torch.no_grad():\n sample = model(**input)[0]\n\n assert list(sample.shape) == [1, 768]\n\n output_slice = sample[0, :8].flatten().cpu()\n print(output_slice)\n expected_output_slice = torch.tensor(expected_slice)\n\n assert torch_all_close(output_slice, expected_output_slice, atol=1e-3)\n" }, { "path": "diffusers/tests/models/transformers/test_models_transformer_sd3.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\n\nimport torch\n\nfrom diffusers import SD3Transformer2DModel\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n torch_device,\n)\n\nfrom ..test_modeling_common import ModelTesterMixin\n\n\nenable_full_determinism()\n\n\nclass SD3TransformerTests(ModelTesterMixin, unittest.TestCase):\n model_class = SD3Transformer2DModel\n main_input_name = \"hidden_states\"\n\n @property\n def dummy_input(self):\n batch_size = 2\n num_channels = 4\n height = width = embedding_dim = 32\n pooled_embedding_dim = embedding_dim * 2\n sequence_length = 154\n\n hidden_states = torch.randn((batch_size, num_channels, height, width)).to(torch_device)\n encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)\n pooled_prompt_embeds = torch.randn((batch_size, pooled_embedding_dim)).to(torch_device)\n timestep = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)\n\n return {\n \"hidden_states\": hidden_states,\n \"encoder_hidden_states\": encoder_hidden_states,\n \"pooled_projections\": pooled_prompt_embeds,\n \"timestep\": timestep,\n }\n\n @property\n def input_shape(self):\n return (4, 32, 32)\n\n @property\n def output_shape(self):\n return (4, 32, 32)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = {\n \"sample_size\": 32,\n \"patch_size\": 1,\n \"in_channels\": 4,\n \"num_layers\": 1,\n \"attention_head_dim\": 8,\n \"num_attention_heads\": 4,\n \"caption_projection_dim\": 32,\n \"joint_attention_dim\": 32,\n \"pooled_projection_dim\": 64,\n \"out_channels\": 4,\n }\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n" }, { "path": "diffusers/tests/models/unets/__init__.py", "content": "" }, { "path": "diffusers/tests/models/unets/test_models_unet_1d.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\n\nimport torch\n\nfrom diffusers import UNet1DModel\nfrom diffusers.utils.testing_utils import (\n backend_manual_seed,\n floats_tensor,\n slow,\n torch_device,\n)\n\nfrom ..test_modeling_common import ModelTesterMixin, UNetTesterMixin\n\n\nclass UNet1DModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):\n model_class = UNet1DModel\n main_input_name = \"sample\"\n\n @property\n def dummy_input(self):\n batch_size = 4\n num_features = 14\n seq_len = 16\n\n noise = floats_tensor((batch_size, num_features, seq_len)).to(torch_device)\n time_step = torch.tensor([10] * batch_size).to(torch_device)\n\n return {\"sample\": noise, \"timestep\": time_step}\n\n @property\n def input_shape(self):\n return (4, 14, 16)\n\n @property\n def output_shape(self):\n return (4, 14, 16)\n\n def test_ema_training(self):\n pass\n\n def test_training(self):\n pass\n\n def test_determinism(self):\n super().test_determinism()\n\n def test_outputs_equivalence(self):\n super().test_outputs_equivalence()\n\n def test_from_save_pretrained(self):\n super().test_from_save_pretrained()\n\n def test_from_save_pretrained_variant(self):\n super().test_from_save_pretrained_variant()\n\n def test_model_from_pretrained(self):\n super().test_model_from_pretrained()\n\n def test_output(self):\n super().test_output()\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = {\n \"block_out_channels\": (8, 8, 16, 16),\n \"in_channels\": 14,\n \"out_channels\": 14,\n \"time_embedding_type\": \"positional\",\n \"use_timestep_embedding\": True,\n \"flip_sin_to_cos\": False,\n \"freq_shift\": 1.0,\n \"out_block_type\": \"OutConv1DBlock\",\n \"mid_block_type\": \"MidResTemporalBlock1D\",\n \"down_block_types\": (\"DownResnetBlock1D\", \"DownResnetBlock1D\", \"DownResnetBlock1D\", \"DownResnetBlock1D\"),\n \"up_block_types\": (\"UpResnetBlock1D\", \"UpResnetBlock1D\", \"UpResnetBlock1D\"),\n \"act_fn\": \"swish\",\n }\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n def test_from_pretrained_hub(self):\n model, loading_info = UNet1DModel.from_pretrained(\n \"bglick13/hopper-medium-v2-value-function-hor32\", output_loading_info=True, subfolder=\"unet\"\n )\n self.assertIsNotNone(model)\n self.assertEqual(len(loading_info[\"missing_keys\"]), 0)\n\n model.to(torch_device)\n image = model(**self.dummy_input)\n\n assert image is not None, \"Make sure output is not None\"\n\n def test_output_pretrained(self):\n model = UNet1DModel.from_pretrained(\"bglick13/hopper-medium-v2-value-function-hor32\", subfolder=\"unet\")\n torch.manual_seed(0)\n backend_manual_seed(torch_device, 0)\n\n num_features = model.config.in_channels\n seq_len = 16\n noise = torch.randn((1, seq_len, num_features)).permute(\n 0, 2, 1\n ) # match original, we can update values and remove\n time_step = torch.full((num_features,), 0)\n\n with torch.no_grad():\n output = model(noise, time_step).sample.permute(0, 2, 1)\n\n output_slice = output[0, -3:, -3:].flatten()\n # fmt: off\n expected_output_slice = torch.tensor([-2.137172, 1.1426016, 0.3688687, -0.766922, 0.7303146, 0.11038864, -0.4760633, 0.13270172, 0.02591348])\n # fmt: on\n self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3))\n\n def test_forward_with_norm_groups(self):\n # Not implemented yet for this UNet\n pass\n\n @slow\n def test_unet_1d_maestro(self):\n model_id = \"harmonai/maestro-150k\"\n model = UNet1DModel.from_pretrained(model_id, subfolder=\"unet\")\n model.to(torch_device)\n\n sample_size = 65536\n noise = torch.sin(torch.arange(sample_size)[None, None, :].repeat(1, 2, 1)).to(torch_device)\n timestep = torch.tensor([1]).to(torch_device)\n\n with torch.no_grad():\n output = model(noise, timestep).sample\n\n output_sum = output.abs().sum()\n output_max = output.abs().max()\n\n assert (output_sum - 224.0896).abs() < 0.5\n assert (output_max - 0.0607).abs() < 4e-4\n\n\nclass UNetRLModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):\n model_class = UNet1DModel\n main_input_name = \"sample\"\n\n @property\n def dummy_input(self):\n batch_size = 4\n num_features = 14\n seq_len = 16\n\n noise = floats_tensor((batch_size, num_features, seq_len)).to(torch_device)\n time_step = torch.tensor([10] * batch_size).to(torch_device)\n\n return {\"sample\": noise, \"timestep\": time_step}\n\n @property\n def input_shape(self):\n return (4, 14, 16)\n\n @property\n def output_shape(self):\n return (4, 14, 1)\n\n def test_determinism(self):\n super().test_determinism()\n\n def test_outputs_equivalence(self):\n super().test_outputs_equivalence()\n\n def test_from_save_pretrained(self):\n super().test_from_save_pretrained()\n\n def test_from_save_pretrained_variant(self):\n super().test_from_save_pretrained_variant()\n\n def test_model_from_pretrained(self):\n super().test_model_from_pretrained()\n\n def test_output(self):\n # UNetRL is a value-function is different output shape\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.eval()\n\n with torch.no_grad():\n output = model(**inputs_dict)\n\n if isinstance(output, dict):\n output = output.sample\n\n self.assertIsNotNone(output)\n expected_shape = torch.Size((inputs_dict[\"sample\"].shape[0], 1))\n self.assertEqual(output.shape, expected_shape, \"Input and output shapes do not match\")\n\n def test_ema_training(self):\n pass\n\n def test_training(self):\n pass\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = {\n \"in_channels\": 14,\n \"out_channels\": 14,\n \"down_block_types\": [\"DownResnetBlock1D\", \"DownResnetBlock1D\", \"DownResnetBlock1D\", \"DownResnetBlock1D\"],\n \"up_block_types\": [],\n \"out_block_type\": \"ValueFunction\",\n \"mid_block_type\": \"ValueFunctionMidBlock1D\",\n \"block_out_channels\": [32, 64, 128, 256],\n \"layers_per_block\": 1,\n \"downsample_each_block\": True,\n \"use_timestep_embedding\": True,\n \"freq_shift\": 1.0,\n \"flip_sin_to_cos\": False,\n \"time_embedding_type\": \"positional\",\n \"act_fn\": \"mish\",\n }\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n def test_from_pretrained_hub(self):\n value_function, vf_loading_info = UNet1DModel.from_pretrained(\n \"bglick13/hopper-medium-v2-value-function-hor32\", output_loading_info=True, subfolder=\"value_function\"\n )\n self.assertIsNotNone(value_function)\n self.assertEqual(len(vf_loading_info[\"missing_keys\"]), 0)\n\n value_function.to(torch_device)\n image = value_function(**self.dummy_input)\n\n assert image is not None, \"Make sure output is not None\"\n\n def test_output_pretrained(self):\n value_function, vf_loading_info = UNet1DModel.from_pretrained(\n \"bglick13/hopper-medium-v2-value-function-hor32\", output_loading_info=True, subfolder=\"value_function\"\n )\n torch.manual_seed(0)\n backend_manual_seed(torch_device, 0)\n\n num_features = value_function.config.in_channels\n seq_len = 14\n noise = torch.randn((1, seq_len, num_features)).permute(\n 0, 2, 1\n ) # match original, we can update values and remove\n time_step = torch.full((num_features,), 0)\n\n with torch.no_grad():\n output = value_function(noise, time_step).sample\n\n # fmt: off\n expected_output_slice = torch.tensor([165.25] * seq_len)\n # fmt: on\n self.assertTrue(torch.allclose(output, expected_output_slice, rtol=1e-3))\n\n def test_forward_with_norm_groups(self):\n # Not implemented yet for this UNet\n pass\n" }, { "path": "diffusers/tests/models/unets/test_models_unet_2d.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport math\nimport unittest\n\nimport torch\n\nfrom diffusers import UNet2DModel\nfrom diffusers.utils import logging\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n require_torch_accelerator,\n slow,\n torch_all_close,\n torch_device,\n)\n\nfrom ..test_modeling_common import ModelTesterMixin, UNetTesterMixin\n\n\nlogger = logging.get_logger(__name__)\n\nenable_full_determinism()\n\n\nclass Unet2DModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):\n model_class = UNet2DModel\n main_input_name = \"sample\"\n\n @property\n def dummy_input(self):\n batch_size = 4\n num_channels = 3\n sizes = (32, 32)\n\n noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)\n time_step = torch.tensor([10]).to(torch_device)\n\n return {\"sample\": noise, \"timestep\": time_step}\n\n @property\n def input_shape(self):\n return (3, 32, 32)\n\n @property\n def output_shape(self):\n return (3, 32, 32)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = {\n \"block_out_channels\": (4, 8),\n \"norm_num_groups\": 2,\n \"down_block_types\": (\"DownBlock2D\", \"AttnDownBlock2D\"),\n \"up_block_types\": (\"AttnUpBlock2D\", \"UpBlock2D\"),\n \"attention_head_dim\": 3,\n \"out_channels\": 3,\n \"in_channels\": 3,\n \"layers_per_block\": 2,\n \"sample_size\": 32,\n }\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n def test_mid_block_attn_groups(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n init_dict[\"add_attention\"] = True\n init_dict[\"attn_norm_num_groups\"] = 4\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.eval()\n\n self.assertIsNotNone(\n model.mid_block.attentions[0].group_norm, \"Mid block Attention group norm should exist but does not.\"\n )\n self.assertEqual(\n model.mid_block.attentions[0].group_norm.num_groups,\n init_dict[\"attn_norm_num_groups\"],\n \"Mid block Attention group norm does not have the expected number of groups.\",\n )\n\n with torch.no_grad():\n output = model(**inputs_dict)\n\n if isinstance(output, dict):\n output = output.to_tuple()[0]\n\n self.assertIsNotNone(output)\n expected_shape = inputs_dict[\"sample\"].shape\n self.assertEqual(output.shape, expected_shape, \"Input and output shapes do not match\")\n\n\nclass UNetLDMModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):\n model_class = UNet2DModel\n main_input_name = \"sample\"\n\n @property\n def dummy_input(self):\n batch_size = 4\n num_channels = 4\n sizes = (32, 32)\n\n noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)\n time_step = torch.tensor([10]).to(torch_device)\n\n return {\"sample\": noise, \"timestep\": time_step}\n\n @property\n def input_shape(self):\n return (4, 32, 32)\n\n @property\n def output_shape(self):\n return (4, 32, 32)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = {\n \"sample_size\": 32,\n \"in_channels\": 4,\n \"out_channels\": 4,\n \"layers_per_block\": 2,\n \"block_out_channels\": (32, 64),\n \"attention_head_dim\": 32,\n \"down_block_types\": (\"DownBlock2D\", \"DownBlock2D\"),\n \"up_block_types\": (\"UpBlock2D\", \"UpBlock2D\"),\n }\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n def test_from_pretrained_hub(self):\n model, loading_info = UNet2DModel.from_pretrained(\"fusing/unet-ldm-dummy-update\", output_loading_info=True)\n\n self.assertIsNotNone(model)\n self.assertEqual(len(loading_info[\"missing_keys\"]), 0)\n\n model.to(torch_device)\n image = model(**self.dummy_input).sample\n\n assert image is not None, \"Make sure output is not None\"\n\n @require_torch_accelerator\n def test_from_pretrained_accelerate(self):\n model, _ = UNet2DModel.from_pretrained(\"fusing/unet-ldm-dummy-update\", output_loading_info=True)\n model.to(torch_device)\n image = model(**self.dummy_input).sample\n\n assert image is not None, \"Make sure output is not None\"\n\n @require_torch_accelerator\n def test_from_pretrained_accelerate_wont_change_results(self):\n # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`\n model_accelerate, _ = UNet2DModel.from_pretrained(\"fusing/unet-ldm-dummy-update\", output_loading_info=True)\n model_accelerate.to(torch_device)\n model_accelerate.eval()\n\n noise = torch.randn(\n 1,\n model_accelerate.config.in_channels,\n model_accelerate.config.sample_size,\n model_accelerate.config.sample_size,\n generator=torch.manual_seed(0),\n )\n noise = noise.to(torch_device)\n time_step = torch.tensor([10] * noise.shape[0]).to(torch_device)\n\n arr_accelerate = model_accelerate(noise, time_step)[\"sample\"]\n\n # two models don't need to stay in the device at the same time\n del model_accelerate\n torch.cuda.empty_cache()\n gc.collect()\n\n model_normal_load, _ = UNet2DModel.from_pretrained(\n \"fusing/unet-ldm-dummy-update\", output_loading_info=True, low_cpu_mem_usage=False\n )\n model_normal_load.to(torch_device)\n model_normal_load.eval()\n arr_normal_load = model_normal_load(noise, time_step)[\"sample\"]\n\n assert torch_all_close(arr_accelerate, arr_normal_load, rtol=1e-3)\n\n def test_output_pretrained(self):\n model = UNet2DModel.from_pretrained(\"fusing/unet-ldm-dummy-update\")\n model.eval()\n model.to(torch_device)\n\n noise = torch.randn(\n 1,\n model.config.in_channels,\n model.config.sample_size,\n model.config.sample_size,\n generator=torch.manual_seed(0),\n )\n noise = noise.to(torch_device)\n time_step = torch.tensor([10] * noise.shape[0]).to(torch_device)\n\n with torch.no_grad():\n output = model(noise, time_step).sample\n\n output_slice = output[0, -1, -3:, -3:].flatten().cpu()\n # fmt: off\n expected_output_slice = torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800])\n # fmt: on\n\n self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-3))\n\n\nclass NCSNppModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):\n model_class = UNet2DModel\n main_input_name = \"sample\"\n\n @property\n def dummy_input(self, sizes=(32, 32)):\n batch_size = 4\n num_channels = 3\n\n noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)\n time_step = torch.tensor(batch_size * [10]).to(dtype=torch.int32, device=torch_device)\n\n return {\"sample\": noise, \"timestep\": time_step}\n\n @property\n def input_shape(self):\n return (3, 32, 32)\n\n @property\n def output_shape(self):\n return (3, 32, 32)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = {\n \"block_out_channels\": [32, 64, 64, 64],\n \"in_channels\": 3,\n \"layers_per_block\": 1,\n \"out_channels\": 3,\n \"time_embedding_type\": \"fourier\",\n \"norm_eps\": 1e-6,\n \"mid_block_scale_factor\": math.sqrt(2.0),\n \"norm_num_groups\": None,\n \"down_block_types\": [\n \"SkipDownBlock2D\",\n \"AttnSkipDownBlock2D\",\n \"SkipDownBlock2D\",\n \"SkipDownBlock2D\",\n ],\n \"up_block_types\": [\n \"SkipUpBlock2D\",\n \"SkipUpBlock2D\",\n \"AttnSkipUpBlock2D\",\n \"SkipUpBlock2D\",\n ],\n }\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n @slow\n def test_from_pretrained_hub(self):\n model, loading_info = UNet2DModel.from_pretrained(\"google/ncsnpp-celebahq-256\", output_loading_info=True)\n self.assertIsNotNone(model)\n self.assertEqual(len(loading_info[\"missing_keys\"]), 0)\n\n model.to(torch_device)\n inputs = self.dummy_input\n noise = floats_tensor((4, 3) + (256, 256)).to(torch_device)\n inputs[\"sample\"] = noise\n image = model(**inputs)\n\n assert image is not None, \"Make sure output is not None\"\n\n @slow\n def test_output_pretrained_ve_mid(self):\n model = UNet2DModel.from_pretrained(\"google/ncsnpp-celebahq-256\")\n model.to(torch_device)\n\n batch_size = 4\n num_channels = 3\n sizes = (256, 256)\n\n noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device)\n time_step = torch.tensor(batch_size * [1e-4]).to(torch_device)\n\n with torch.no_grad():\n output = model(noise, time_step).sample\n\n output_slice = output[0, -3:, -3:, -1].flatten().cpu()\n # fmt: off\n expected_output_slice = torch.tensor([-4836.2178, -6487.1470, -3816.8196, -7964.9302, -10966.3037, -20043.5957, 8137.0513, 2340.3328, 544.6056])\n # fmt: on\n\n self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-2))\n\n def test_output_pretrained_ve_large(self):\n model = UNet2DModel.from_pretrained(\"fusing/ncsnpp-ffhq-ve-dummy-update\")\n model.to(torch_device)\n\n batch_size = 4\n num_channels = 3\n sizes = (32, 32)\n\n noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device)\n time_step = torch.tensor(batch_size * [1e-4]).to(torch_device)\n\n with torch.no_grad():\n output = model(noise, time_step).sample\n\n output_slice = output[0, -3:, -3:, -1].flatten().cpu()\n # fmt: off\n expected_output_slice = torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256])\n # fmt: on\n\n self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-2))\n\n def test_forward_with_norm_groups(self):\n # not required for this model\n pass\n" }, { "path": "diffusers/tests/models/unets/test_models_unet_2d_condition.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport copy\nimport gc\nimport os\nimport tempfile\nimport unittest\nfrom collections import OrderedDict\n\nimport torch\nfrom huggingface_hub import snapshot_download\nfrom parameterized import parameterized\nfrom pytest import mark\n\nfrom diffusers import UNet2DConditionModel\nfrom diffusers.models.attention_processor import (\n CustomDiffusionAttnProcessor,\n IPAdapterAttnProcessor,\n IPAdapterAttnProcessor2_0,\n)\nfrom diffusers.models.embeddings import ImageProjection, IPAdapterFaceIDImageProjection, IPAdapterPlusImageProjection\nfrom diffusers.utils import logging\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.testing_utils import (\n backend_empty_cache,\n enable_full_determinism,\n floats_tensor,\n is_peft_available,\n load_hf_numpy,\n require_peft_backend,\n require_torch_accelerator,\n require_torch_accelerator_with_fp16,\n require_torch_accelerator_with_training,\n require_torch_gpu,\n skip_mps,\n slow,\n torch_all_close,\n torch_device,\n)\n\nfrom ..test_modeling_common import ModelTesterMixin, UNetTesterMixin\n\n\nif is_peft_available():\n from peft import LoraConfig\n from peft.tuners.tuners_utils import BaseTunerLayer\n\n\nlogger = logging.get_logger(__name__)\n\nenable_full_determinism()\n\n\ndef get_unet_lora_config():\n rank = 4\n unet_lora_config = LoraConfig(\n r=rank,\n lora_alpha=rank,\n target_modules=[\"to_q\", \"to_k\", \"to_v\", \"to_out.0\"],\n init_lora_weights=False,\n use_dora=False,\n )\n return unet_lora_config\n\n\ndef check_if_lora_correctly_set(model) -> bool:\n \"\"\"\n Checks if the LoRA layers are correctly set with peft\n \"\"\"\n for module in model.modules():\n if isinstance(module, BaseTunerLayer):\n return True\n return False\n\n\ndef create_ip_adapter_state_dict(model):\n # \"ip_adapter\" (cross-attention weights)\n ip_cross_attn_state_dict = {}\n key_id = 1\n\n for name in model.attn_processors.keys():\n cross_attention_dim = (\n None if name.endswith(\"attn1.processor\") or \"motion_module\" in name else model.config.cross_attention_dim\n )\n\n if name.startswith(\"mid_block\"):\n hidden_size = model.config.block_out_channels[-1]\n elif name.startswith(\"up_blocks\"):\n block_id = int(name[len(\"up_blocks.\")])\n hidden_size = list(reversed(model.config.block_out_channels))[block_id]\n elif name.startswith(\"down_blocks\"):\n block_id = int(name[len(\"down_blocks.\")])\n hidden_size = model.config.block_out_channels[block_id]\n\n if cross_attention_dim is not None:\n sd = IPAdapterAttnProcessor(\n hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, scale=1.0\n ).state_dict()\n ip_cross_attn_state_dict.update(\n {\n f\"{key_id}.to_k_ip.weight\": sd[\"to_k_ip.0.weight\"],\n f\"{key_id}.to_v_ip.weight\": sd[\"to_v_ip.0.weight\"],\n }\n )\n\n key_id += 2\n\n # \"image_proj\" (ImageProjection layer weights)\n cross_attention_dim = model.config[\"cross_attention_dim\"]\n image_projection = ImageProjection(\n cross_attention_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, num_image_text_embeds=4\n )\n\n ip_image_projection_state_dict = {}\n sd = image_projection.state_dict()\n ip_image_projection_state_dict.update(\n {\n \"proj.weight\": sd[\"image_embeds.weight\"],\n \"proj.bias\": sd[\"image_embeds.bias\"],\n \"norm.weight\": sd[\"norm.weight\"],\n \"norm.bias\": sd[\"norm.bias\"],\n }\n )\n\n del sd\n ip_state_dict = {}\n ip_state_dict.update({\"image_proj\": ip_image_projection_state_dict, \"ip_adapter\": ip_cross_attn_state_dict})\n return ip_state_dict\n\n\ndef create_ip_adapter_plus_state_dict(model):\n # \"ip_adapter\" (cross-attention weights)\n ip_cross_attn_state_dict = {}\n key_id = 1\n\n for name in model.attn_processors.keys():\n cross_attention_dim = None if name.endswith(\"attn1.processor\") else model.config.cross_attention_dim\n if name.startswith(\"mid_block\"):\n hidden_size = model.config.block_out_channels[-1]\n elif name.startswith(\"up_blocks\"):\n block_id = int(name[len(\"up_blocks.\")])\n hidden_size = list(reversed(model.config.block_out_channels))[block_id]\n elif name.startswith(\"down_blocks\"):\n block_id = int(name[len(\"down_blocks.\")])\n hidden_size = model.config.block_out_channels[block_id]\n if cross_attention_dim is not None:\n sd = IPAdapterAttnProcessor(\n hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, scale=1.0\n ).state_dict()\n ip_cross_attn_state_dict.update(\n {\n f\"{key_id}.to_k_ip.weight\": sd[\"to_k_ip.0.weight\"],\n f\"{key_id}.to_v_ip.weight\": sd[\"to_v_ip.0.weight\"],\n }\n )\n\n key_id += 2\n\n # \"image_proj\" (ImageProjection layer weights)\n cross_attention_dim = model.config[\"cross_attention_dim\"]\n image_projection = IPAdapterPlusImageProjection(\n embed_dims=cross_attention_dim, output_dims=cross_attention_dim, dim_head=32, heads=2, num_queries=4\n )\n\n ip_image_projection_state_dict = OrderedDict()\n keys = [k for k in image_projection.state_dict() if \"layers.\" in k]\n print(keys)\n for k, v in image_projection.state_dict().items():\n if \"2.to\" in k:\n k = k.replace(\"2.to\", \"0.to\")\n elif \"layers.0.ln0\" in k:\n k = k.replace(\"layers.0.ln0\", \"layers.0.0.norm1\")\n elif \"layers.0.ln1\" in k:\n k = k.replace(\"layers.0.ln1\", \"layers.0.0.norm2\")\n elif \"layers.1.ln0\" in k:\n k = k.replace(\"layers.1.ln0\", \"layers.1.0.norm1\")\n elif \"layers.1.ln1\" in k:\n k = k.replace(\"layers.1.ln1\", \"layers.1.0.norm2\")\n elif \"layers.2.ln0\" in k:\n k = k.replace(\"layers.2.ln0\", \"layers.2.0.norm1\")\n elif \"layers.2.ln1\" in k:\n k = k.replace(\"layers.2.ln1\", \"layers.2.0.norm2\")\n elif \"layers.3.ln0\" in k:\n k = k.replace(\"layers.3.ln0\", \"layers.3.0.norm1\")\n elif \"layers.3.ln1\" in k:\n k = k.replace(\"layers.3.ln1\", \"layers.3.0.norm2\")\n elif \"to_q\" in k:\n parts = k.split(\".\")\n parts[2] = \"attn\"\n k = \".\".join(parts)\n elif \"to_out.0\" in k:\n parts = k.split(\".\")\n parts[2] = \"attn\"\n k = \".\".join(parts)\n k = k.replace(\"to_out.0\", \"to_out\")\n else:\n k = k.replace(\"0.ff.0\", \"0.1.0\")\n k = k.replace(\"0.ff.1.net.0.proj\", \"0.1.1\")\n k = k.replace(\"0.ff.1.net.2\", \"0.1.3\")\n\n k = k.replace(\"1.ff.0\", \"1.1.0\")\n k = k.replace(\"1.ff.1.net.0.proj\", \"1.1.1\")\n k = k.replace(\"1.ff.1.net.2\", \"1.1.3\")\n\n k = k.replace(\"2.ff.0\", \"2.1.0\")\n k = k.replace(\"2.ff.1.net.0.proj\", \"2.1.1\")\n k = k.replace(\"2.ff.1.net.2\", \"2.1.3\")\n\n k = k.replace(\"3.ff.0\", \"3.1.0\")\n k = k.replace(\"3.ff.1.net.0.proj\", \"3.1.1\")\n k = k.replace(\"3.ff.1.net.2\", \"3.1.3\")\n\n # if \"norm_cross\" in k:\n # ip_image_projection_state_dict[k.replace(\"norm_cross\", \"norm1\")] = v\n # elif \"layer_norm\" in k:\n # ip_image_projection_state_dict[k.replace(\"layer_norm\", \"norm2\")] = v\n if \"to_k\" in k:\n parts = k.split(\".\")\n parts[2] = \"attn\"\n k = \".\".join(parts)\n ip_image_projection_state_dict[k.replace(\"to_k\", \"to_kv\")] = torch.cat([v, v], dim=0)\n elif \"to_v\" in k:\n continue\n else:\n ip_image_projection_state_dict[k] = v\n\n ip_state_dict = {}\n ip_state_dict.update({\"image_proj\": ip_image_projection_state_dict, \"ip_adapter\": ip_cross_attn_state_dict})\n return ip_state_dict\n\n\ndef create_ip_adapter_faceid_state_dict(model):\n # \"ip_adapter\" (cross-attention weights)\n # no LoRA weights\n ip_cross_attn_state_dict = {}\n key_id = 1\n\n for name in model.attn_processors.keys():\n cross_attention_dim = (\n None if name.endswith(\"attn1.processor\") or \"motion_module\" in name else model.config.cross_attention_dim\n )\n\n if name.startswith(\"mid_block\"):\n hidden_size = model.config.block_out_channels[-1]\n elif name.startswith(\"up_blocks\"):\n block_id = int(name[len(\"up_blocks.\")])\n hidden_size = list(reversed(model.config.block_out_channels))[block_id]\n elif name.startswith(\"down_blocks\"):\n block_id = int(name[len(\"down_blocks.\")])\n hidden_size = model.config.block_out_channels[block_id]\n\n if cross_attention_dim is not None:\n sd = IPAdapterAttnProcessor(\n hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, scale=1.0\n ).state_dict()\n ip_cross_attn_state_dict.update(\n {\n f\"{key_id}.to_k_ip.weight\": sd[\"to_k_ip.0.weight\"],\n f\"{key_id}.to_v_ip.weight\": sd[\"to_v_ip.0.weight\"],\n }\n )\n\n key_id += 2\n\n # \"image_proj\" (ImageProjection layer weights)\n cross_attention_dim = model.config[\"cross_attention_dim\"]\n image_projection = IPAdapterFaceIDImageProjection(\n cross_attention_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, mult=2, num_tokens=4\n )\n\n ip_image_projection_state_dict = {}\n sd = image_projection.state_dict()\n ip_image_projection_state_dict.update(\n {\n \"proj.0.weight\": sd[\"ff.net.0.proj.weight\"],\n \"proj.0.bias\": sd[\"ff.net.0.proj.bias\"],\n \"proj.2.weight\": sd[\"ff.net.2.weight\"],\n \"proj.2.bias\": sd[\"ff.net.2.bias\"],\n \"norm.weight\": sd[\"norm.weight\"],\n \"norm.bias\": sd[\"norm.bias\"],\n }\n )\n\n del sd\n ip_state_dict = {}\n ip_state_dict.update({\"image_proj\": ip_image_projection_state_dict, \"ip_adapter\": ip_cross_attn_state_dict})\n return ip_state_dict\n\n\ndef create_custom_diffusion_layers(model, mock_weights: bool = True):\n train_kv = True\n train_q_out = True\n custom_diffusion_attn_procs = {}\n\n st = model.state_dict()\n for name, _ in model.attn_processors.items():\n cross_attention_dim = None if name.endswith(\"attn1.processor\") else model.config.cross_attention_dim\n if name.startswith(\"mid_block\"):\n hidden_size = model.config.block_out_channels[-1]\n elif name.startswith(\"up_blocks\"):\n block_id = int(name[len(\"up_blocks.\")])\n hidden_size = list(reversed(model.config.block_out_channels))[block_id]\n elif name.startswith(\"down_blocks\"):\n block_id = int(name[len(\"down_blocks.\")])\n hidden_size = model.config.block_out_channels[block_id]\n layer_name = name.split(\".processor\")[0]\n weights = {\n \"to_k_custom_diffusion.weight\": st[layer_name + \".to_k.weight\"],\n \"to_v_custom_diffusion.weight\": st[layer_name + \".to_v.weight\"],\n }\n if train_q_out:\n weights[\"to_q_custom_diffusion.weight\"] = st[layer_name + \".to_q.weight\"]\n weights[\"to_out_custom_diffusion.0.weight\"] = st[layer_name + \".to_out.0.weight\"]\n weights[\"to_out_custom_diffusion.0.bias\"] = st[layer_name + \".to_out.0.bias\"]\n if cross_attention_dim is not None:\n custom_diffusion_attn_procs[name] = CustomDiffusionAttnProcessor(\n train_kv=train_kv,\n train_q_out=train_q_out,\n hidden_size=hidden_size,\n cross_attention_dim=cross_attention_dim,\n ).to(model.device)\n custom_diffusion_attn_procs[name].load_state_dict(weights)\n if mock_weights:\n # add 1 to weights to mock trained weights\n with torch.no_grad():\n custom_diffusion_attn_procs[name].to_k_custom_diffusion.weight += 1\n custom_diffusion_attn_procs[name].to_v_custom_diffusion.weight += 1\n else:\n custom_diffusion_attn_procs[name] = CustomDiffusionAttnProcessor(\n train_kv=False,\n train_q_out=False,\n hidden_size=hidden_size,\n cross_attention_dim=cross_attention_dim,\n )\n del st\n return custom_diffusion_attn_procs\n\n\nclass UNet2DConditionModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):\n model_class = UNet2DConditionModel\n main_input_name = \"sample\"\n # We override the items here because the unet under consideration is small.\n model_split_percents = [0.5, 0.3, 0.4]\n\n @property\n def dummy_input(self):\n batch_size = 4\n num_channels = 4\n sizes = (16, 16)\n\n noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)\n time_step = torch.tensor([10]).to(torch_device)\n encoder_hidden_states = floats_tensor((batch_size, 4, 8)).to(torch_device)\n\n return {\"sample\": noise, \"timestep\": time_step, \"encoder_hidden_states\": encoder_hidden_states}\n\n @property\n def input_shape(self):\n return (4, 16, 16)\n\n @property\n def output_shape(self):\n return (4, 16, 16)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = {\n \"block_out_channels\": (4, 8),\n \"norm_num_groups\": 4,\n \"down_block_types\": (\"CrossAttnDownBlock2D\", \"DownBlock2D\"),\n \"up_block_types\": (\"UpBlock2D\", \"CrossAttnUpBlock2D\"),\n \"cross_attention_dim\": 8,\n \"attention_head_dim\": 2,\n \"out_channels\": 4,\n \"in_channels\": 4,\n \"layers_per_block\": 1,\n \"sample_size\": 16,\n }\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_enable_works(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n\n model.enable_xformers_memory_efficient_attention()\n\n assert (\n model.mid_block.attentions[0].transformer_blocks[0].attn1.processor.__class__.__name__\n == \"XFormersAttnProcessor\"\n ), \"xformers is not enabled\"\n\n @require_torch_accelerator_with_training\n def test_gradient_checkpointing(self):\n # enable deterministic behavior for gradient checkpointing\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n model.to(torch_device)\n\n assert not model.is_gradient_checkpointing and model.training\n\n out = model(**inputs_dict).sample\n # run the backwards pass on the model. For backwards pass, for simplicity purpose,\n # we won't calculate the loss and rather backprop on out.sum()\n model.zero_grad()\n\n labels = torch.randn_like(out)\n loss = (out - labels).mean()\n loss.backward()\n\n # re-instantiate the model now enabling gradient checkpointing\n model_2 = self.model_class(**init_dict)\n # clone model\n model_2.load_state_dict(model.state_dict())\n model_2.to(torch_device)\n model_2.enable_gradient_checkpointing()\n\n assert model_2.is_gradient_checkpointing and model_2.training\n\n out_2 = model_2(**inputs_dict).sample\n # run the backwards pass on the model. For backwards pass, for simplicity purpose,\n # we won't calculate the loss and rather backprop on out.sum()\n model_2.zero_grad()\n loss_2 = (out_2 - labels).mean()\n loss_2.backward()\n\n # compare the output and parameters gradients\n self.assertTrue((loss - loss_2).abs() < 1e-5)\n named_params = dict(model.named_parameters())\n named_params_2 = dict(model_2.named_parameters())\n for name, param in named_params.items():\n self.assertTrue(torch_all_close(param.grad.data, named_params_2[name].grad.data, atol=5e-5))\n\n def test_model_with_attention_head_dim_tuple(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n init_dict[\"block_out_channels\"] = (16, 32)\n init_dict[\"attention_head_dim\"] = (8, 16)\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.eval()\n\n with torch.no_grad():\n output = model(**inputs_dict)\n\n if isinstance(output, dict):\n output = output.sample\n\n self.assertIsNotNone(output)\n expected_shape = inputs_dict[\"sample\"].shape\n self.assertEqual(output.shape, expected_shape, \"Input and output shapes do not match\")\n\n def test_model_with_use_linear_projection(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n init_dict[\"use_linear_projection\"] = True\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.eval()\n\n with torch.no_grad():\n output = model(**inputs_dict)\n\n if isinstance(output, dict):\n output = output.sample\n\n self.assertIsNotNone(output)\n expected_shape = inputs_dict[\"sample\"].shape\n self.assertEqual(output.shape, expected_shape, \"Input and output shapes do not match\")\n\n def test_model_with_cross_attention_dim_tuple(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n init_dict[\"cross_attention_dim\"] = (8, 8)\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.eval()\n\n with torch.no_grad():\n output = model(**inputs_dict)\n\n if isinstance(output, dict):\n output = output.sample\n\n self.assertIsNotNone(output)\n expected_shape = inputs_dict[\"sample\"].shape\n self.assertEqual(output.shape, expected_shape, \"Input and output shapes do not match\")\n\n def test_model_with_simple_projection(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n batch_size, _, _, sample_size = inputs_dict[\"sample\"].shape\n\n init_dict[\"class_embed_type\"] = \"simple_projection\"\n init_dict[\"projection_class_embeddings_input_dim\"] = sample_size\n\n inputs_dict[\"class_labels\"] = floats_tensor((batch_size, sample_size)).to(torch_device)\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.eval()\n\n with torch.no_grad():\n output = model(**inputs_dict)\n\n if isinstance(output, dict):\n output = output.sample\n\n self.assertIsNotNone(output)\n expected_shape = inputs_dict[\"sample\"].shape\n self.assertEqual(output.shape, expected_shape, \"Input and output shapes do not match\")\n\n def test_model_with_class_embeddings_concat(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n batch_size, _, _, sample_size = inputs_dict[\"sample\"].shape\n\n init_dict[\"class_embed_type\"] = \"simple_projection\"\n init_dict[\"projection_class_embeddings_input_dim\"] = sample_size\n init_dict[\"class_embeddings_concat\"] = True\n\n inputs_dict[\"class_labels\"] = floats_tensor((batch_size, sample_size)).to(torch_device)\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.eval()\n\n with torch.no_grad():\n output = model(**inputs_dict)\n\n if isinstance(output, dict):\n output = output.sample\n\n self.assertIsNotNone(output)\n expected_shape = inputs_dict[\"sample\"].shape\n self.assertEqual(output.shape, expected_shape, \"Input and output shapes do not match\")\n\n def test_model_attention_slicing(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n init_dict[\"block_out_channels\"] = (16, 32)\n init_dict[\"attention_head_dim\"] = (8, 16)\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.eval()\n\n model.set_attention_slice(\"auto\")\n with torch.no_grad():\n output = model(**inputs_dict)\n assert output is not None\n\n model.set_attention_slice(\"max\")\n with torch.no_grad():\n output = model(**inputs_dict)\n assert output is not None\n\n model.set_attention_slice(2)\n with torch.no_grad():\n output = model(**inputs_dict)\n assert output is not None\n\n def test_model_sliceable_head_dim(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n init_dict[\"block_out_channels\"] = (16, 32)\n init_dict[\"attention_head_dim\"] = (8, 16)\n\n model = self.model_class(**init_dict)\n\n def check_sliceable_dim_attr(module: torch.nn.Module):\n if hasattr(module, \"set_attention_slice\"):\n assert isinstance(module.sliceable_head_dim, int)\n\n for child in module.children():\n check_sliceable_dim_attr(child)\n\n # retrieve number of attention layers\n for module in model.children():\n check_sliceable_dim_attr(module)\n\n def test_gradient_checkpointing_is_applied(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n init_dict[\"block_out_channels\"] = (16, 32)\n init_dict[\"attention_head_dim\"] = (8, 16)\n\n model_class_copy = copy.copy(self.model_class)\n\n modules_with_gc_enabled = {}\n\n # now monkey patch the following function:\n # def _set_gradient_checkpointing(self, module, value=False):\n # if hasattr(module, \"gradient_checkpointing\"):\n # module.gradient_checkpointing = value\n\n def _set_gradient_checkpointing_new(self, module, value=False):\n if hasattr(module, \"gradient_checkpointing\"):\n module.gradient_checkpointing = value\n modules_with_gc_enabled[module.__class__.__name__] = True\n\n model_class_copy._set_gradient_checkpointing = _set_gradient_checkpointing_new\n\n model = model_class_copy(**init_dict)\n model.enable_gradient_checkpointing()\n\n EXPECTED_SET = {\n \"CrossAttnUpBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"UNetMidBlock2DCrossAttn\",\n \"UpBlock2D\",\n \"Transformer2DModel\",\n \"DownBlock2D\",\n }\n\n assert set(modules_with_gc_enabled.keys()) == EXPECTED_SET\n assert all(modules_with_gc_enabled.values()), \"All modules should be enabled\"\n\n def test_special_attn_proc(self):\n class AttnEasyProc(torch.nn.Module):\n def __init__(self, num):\n super().__init__()\n self.weight = torch.nn.Parameter(torch.tensor(num))\n self.is_run = False\n self.number = 0\n self.counter = 0\n\n def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None, number=None):\n batch_size, sequence_length, _ = hidden_states.shape\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)\n\n query = attn.to_q(hidden_states)\n\n encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n attention_probs = attn.get_attention_scores(query, key, attention_mask)\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n hidden_states += self.weight\n\n self.is_run = True\n self.counter += 1\n self.number = number\n\n return hidden_states\n\n # enable deterministic behavior for gradient checkpointing\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n init_dict[\"block_out_channels\"] = (16, 32)\n init_dict[\"attention_head_dim\"] = (8, 16)\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n\n processor = AttnEasyProc(5.0)\n\n model.set_attn_processor(processor)\n model(**inputs_dict, cross_attention_kwargs={\"number\": 123}).sample\n\n assert processor.counter == 8\n assert processor.is_run\n assert processor.number == 123\n\n @parameterized.expand(\n [\n # fmt: off\n [torch.bool],\n [torch.long],\n [torch.float],\n # fmt: on\n ]\n )\n def test_model_xattn_mask(self, mask_dtype):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n model = self.model_class(**{**init_dict, \"attention_head_dim\": (8, 16), \"block_out_channels\": (16, 32)})\n model.to(torch_device)\n model.eval()\n\n cond = inputs_dict[\"encoder_hidden_states\"]\n with torch.no_grad():\n full_cond_out = model(**inputs_dict).sample\n assert full_cond_out is not None\n\n keepall_mask = torch.ones(*cond.shape[:-1], device=cond.device, dtype=mask_dtype)\n full_cond_keepallmask_out = model(**{**inputs_dict, \"encoder_attention_mask\": keepall_mask}).sample\n assert full_cond_keepallmask_out.allclose(\n full_cond_out, rtol=1e-05, atol=1e-05\n ), \"a 'keep all' mask should give the same result as no mask\"\n\n trunc_cond = cond[:, :-1, :]\n trunc_cond_out = model(**{**inputs_dict, \"encoder_hidden_states\": trunc_cond}).sample\n assert not trunc_cond_out.allclose(\n full_cond_out, rtol=1e-05, atol=1e-05\n ), \"discarding the last token from our cond should change the result\"\n\n batch, tokens, _ = cond.shape\n mask_last = (torch.arange(tokens) < tokens - 1).expand(batch, -1).to(cond.device, mask_dtype)\n masked_cond_out = model(**{**inputs_dict, \"encoder_attention_mask\": mask_last}).sample\n assert masked_cond_out.allclose(\n trunc_cond_out, rtol=1e-05, atol=1e-05\n ), \"masking the last token from our cond should be equivalent to truncating that token out of the condition\"\n\n # see diffusers.models.attention_processor::Attention#prepare_attention_mask\n # note: we may not need to fix mask padding to work for stable-diffusion cross-attn masks.\n # since the use-case (somebody passes in a too-short cross-attn mask) is pretty esoteric.\n # maybe it's fine that this only works for the unclip use-case.\n @mark.skip(\n reason=\"we currently pad mask by target_length tokens (what unclip needs), whereas stable-diffusion's cross-attn needs to instead pad by remaining_length.\"\n )\n def test_model_xattn_padding(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n model = self.model_class(**{**init_dict, \"attention_head_dim\": (8, 16)})\n model.to(torch_device)\n model.eval()\n\n cond = inputs_dict[\"encoder_hidden_states\"]\n with torch.no_grad():\n full_cond_out = model(**inputs_dict).sample\n assert full_cond_out is not None\n\n batch, tokens, _ = cond.shape\n keeplast_mask = (torch.arange(tokens) == tokens - 1).expand(batch, -1).to(cond.device, torch.bool)\n keeplast_out = model(**{**inputs_dict, \"encoder_attention_mask\": keeplast_mask}).sample\n assert not keeplast_out.allclose(full_cond_out), \"a 'keep last token' mask should change the result\"\n\n trunc_mask = torch.zeros(batch, tokens - 1, device=cond.device, dtype=torch.bool)\n trunc_mask_out = model(**{**inputs_dict, \"encoder_attention_mask\": trunc_mask}).sample\n assert trunc_mask_out.allclose(\n keeplast_out\n ), \"a mask with fewer tokens than condition, will be padded with 'keep' tokens. a 'discard-all' mask missing the final token is thus equivalent to a 'keep last' mask.\"\n\n def test_custom_diffusion_processors(self):\n # enable deterministic behavior for gradient checkpointing\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n init_dict[\"block_out_channels\"] = (16, 32)\n init_dict[\"attention_head_dim\"] = (8, 16)\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n\n with torch.no_grad():\n sample1 = model(**inputs_dict).sample\n\n custom_diffusion_attn_procs = create_custom_diffusion_layers(model, mock_weights=False)\n\n # make sure we can set a list of attention processors\n model.set_attn_processor(custom_diffusion_attn_procs)\n model.to(torch_device)\n\n # test that attn processors can be set to itself\n model.set_attn_processor(model.attn_processors)\n\n with torch.no_grad():\n sample2 = model(**inputs_dict).sample\n\n assert (sample1 - sample2).abs().max() < 3e-3\n\n def test_custom_diffusion_save_load(self):\n # enable deterministic behavior for gradient checkpointing\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n init_dict[\"block_out_channels\"] = (16, 32)\n init_dict[\"attention_head_dim\"] = (8, 16)\n\n torch.manual_seed(0)\n model = self.model_class(**init_dict)\n model.to(torch_device)\n\n with torch.no_grad():\n old_sample = model(**inputs_dict).sample\n\n custom_diffusion_attn_procs = create_custom_diffusion_layers(model, mock_weights=False)\n model.set_attn_processor(custom_diffusion_attn_procs)\n\n with torch.no_grad():\n sample = model(**inputs_dict).sample\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n model.save_attn_procs(tmpdirname, safe_serialization=False)\n self.assertTrue(os.path.isfile(os.path.join(tmpdirname, \"pytorch_custom_diffusion_weights.bin\")))\n torch.manual_seed(0)\n new_model = self.model_class(**init_dict)\n new_model.load_attn_procs(tmpdirname, weight_name=\"pytorch_custom_diffusion_weights.bin\")\n new_model.to(torch_device)\n\n with torch.no_grad():\n new_sample = new_model(**inputs_dict).sample\n\n assert (sample - new_sample).abs().max() < 1e-4\n\n # custom diffusion and no custom diffusion should be the same\n assert (sample - old_sample).abs().max() < 3e-3\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_custom_diffusion_xformers_on_off(self):\n # enable deterministic behavior for gradient checkpointing\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n init_dict[\"block_out_channels\"] = (16, 32)\n init_dict[\"attention_head_dim\"] = (8, 16)\n\n torch.manual_seed(0)\n model = self.model_class(**init_dict)\n model.to(torch_device)\n custom_diffusion_attn_procs = create_custom_diffusion_layers(model, mock_weights=False)\n model.set_attn_processor(custom_diffusion_attn_procs)\n\n # default\n with torch.no_grad():\n sample = model(**inputs_dict).sample\n\n model.enable_xformers_memory_efficient_attention()\n on_sample = model(**inputs_dict).sample\n\n model.disable_xformers_memory_efficient_attention()\n off_sample = model(**inputs_dict).sample\n\n assert (sample - on_sample).abs().max() < 1e-4\n assert (sample - off_sample).abs().max() < 1e-4\n\n def test_pickle(self):\n # enable deterministic behavior for gradient checkpointing\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n init_dict[\"block_out_channels\"] = (16, 32)\n init_dict[\"attention_head_dim\"] = (8, 16)\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n\n with torch.no_grad():\n sample = model(**inputs_dict).sample\n\n sample_copy = copy.copy(sample)\n\n assert (sample - sample_copy).abs().max() < 1e-4\n\n def test_asymmetrical_unet(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n # Add asymmetry to configs\n init_dict[\"transformer_layers_per_block\"] = [[3, 2], 1]\n init_dict[\"reverse_transformer_layers_per_block\"] = [[3, 4], 1]\n\n torch.manual_seed(0)\n model = self.model_class(**init_dict)\n model.to(torch_device)\n\n output = model(**inputs_dict).sample\n expected_shape = inputs_dict[\"sample\"].shape\n\n # Check if input and output shapes are the same\n self.assertEqual(output.shape, expected_shape, \"Input and output shapes do not match\")\n\n def test_ip_adapter(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n init_dict[\"block_out_channels\"] = (16, 32)\n init_dict[\"attention_head_dim\"] = (8, 16)\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n\n # forward pass without ip-adapter\n with torch.no_grad():\n sample1 = model(**inputs_dict).sample\n\n # update inputs_dict for ip-adapter\n batch_size = inputs_dict[\"encoder_hidden_states\"].shape[0]\n # for ip-adapter image_embeds has shape [batch_size, num_image, embed_dim]\n image_embeds = floats_tensor((batch_size, 1, model.config.cross_attention_dim)).to(torch_device)\n inputs_dict[\"added_cond_kwargs\"] = {\"image_embeds\": [image_embeds]}\n\n # make ip_adapter_1 and ip_adapter_2\n ip_adapter_1 = create_ip_adapter_state_dict(model)\n\n image_proj_state_dict_2 = {k: w + 1.0 for k, w in ip_adapter_1[\"image_proj\"].items()}\n cross_attn_state_dict_2 = {k: w + 1.0 for k, w in ip_adapter_1[\"ip_adapter\"].items()}\n ip_adapter_2 = {}\n ip_adapter_2.update({\"image_proj\": image_proj_state_dict_2, \"ip_adapter\": cross_attn_state_dict_2})\n\n # forward pass ip_adapter_1\n model._load_ip_adapter_weights([ip_adapter_1])\n assert model.config.encoder_hid_dim_type == \"ip_image_proj\"\n assert model.encoder_hid_proj is not None\n assert model.down_blocks[0].attentions[0].transformer_blocks[0].attn2.processor.__class__.__name__ in (\n \"IPAdapterAttnProcessor\",\n \"IPAdapterAttnProcessor2_0\",\n )\n with torch.no_grad():\n sample2 = model(**inputs_dict).sample\n\n # forward pass with ip_adapter_2\n model._load_ip_adapter_weights([ip_adapter_2])\n with torch.no_grad():\n sample3 = model(**inputs_dict).sample\n\n # forward pass with ip_adapter_1 again\n model._load_ip_adapter_weights([ip_adapter_1])\n with torch.no_grad():\n sample4 = model(**inputs_dict).sample\n\n # forward pass with multiple ip-adapters and multiple images\n model._load_ip_adapter_weights([ip_adapter_1, ip_adapter_2])\n # set the scale for ip_adapter_2 to 0 so that result should be same as only load ip_adapter_1\n for attn_processor in model.attn_processors.values():\n if isinstance(attn_processor, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0)):\n attn_processor.scale = [1, 0]\n image_embeds_multi = image_embeds.repeat(1, 2, 1)\n inputs_dict[\"added_cond_kwargs\"] = {\"image_embeds\": [image_embeds_multi, image_embeds_multi]}\n with torch.no_grad():\n sample5 = model(**inputs_dict).sample\n\n # forward pass with single ip-adapter & single image when image_embeds is not a list and a 2-d tensor\n image_embeds = image_embeds.squeeze(1)\n inputs_dict[\"added_cond_kwargs\"] = {\"image_embeds\": image_embeds}\n\n model._load_ip_adapter_weights(ip_adapter_1)\n with torch.no_grad():\n sample6 = model(**inputs_dict).sample\n\n assert not sample1.allclose(sample2, atol=1e-4, rtol=1e-4)\n assert not sample2.allclose(sample3, atol=1e-4, rtol=1e-4)\n assert sample2.allclose(sample4, atol=1e-4, rtol=1e-4)\n assert sample2.allclose(sample5, atol=1e-4, rtol=1e-4)\n assert sample2.allclose(sample6, atol=1e-4, rtol=1e-4)\n\n def test_ip_adapter_plus(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n init_dict[\"block_out_channels\"] = (16, 32)\n init_dict[\"attention_head_dim\"] = (8, 16)\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n\n # forward pass without ip-adapter\n with torch.no_grad():\n sample1 = model(**inputs_dict).sample\n\n # update inputs_dict for ip-adapter\n batch_size = inputs_dict[\"encoder_hidden_states\"].shape[0]\n # for ip-adapter-plus image_embeds has shape [batch_size, num_image, sequence_length, embed_dim]\n image_embeds = floats_tensor((batch_size, 1, 1, model.config.cross_attention_dim)).to(torch_device)\n inputs_dict[\"added_cond_kwargs\"] = {\"image_embeds\": [image_embeds]}\n\n # make ip_adapter_1 and ip_adapter_2\n ip_adapter_1 = create_ip_adapter_plus_state_dict(model)\n\n image_proj_state_dict_2 = {k: w + 1.0 for k, w in ip_adapter_1[\"image_proj\"].items()}\n cross_attn_state_dict_2 = {k: w + 1.0 for k, w in ip_adapter_1[\"ip_adapter\"].items()}\n ip_adapter_2 = {}\n ip_adapter_2.update({\"image_proj\": image_proj_state_dict_2, \"ip_adapter\": cross_attn_state_dict_2})\n\n # forward pass ip_adapter_1\n model._load_ip_adapter_weights([ip_adapter_1])\n assert model.config.encoder_hid_dim_type == \"ip_image_proj\"\n assert model.encoder_hid_proj is not None\n assert model.down_blocks[0].attentions[0].transformer_blocks[0].attn2.processor.__class__.__name__ in (\n \"IPAdapterAttnProcessor\",\n \"IPAdapterAttnProcessor2_0\",\n )\n with torch.no_grad():\n sample2 = model(**inputs_dict).sample\n\n # forward pass with ip_adapter_2\n model._load_ip_adapter_weights([ip_adapter_2])\n with torch.no_grad():\n sample3 = model(**inputs_dict).sample\n\n # forward pass with ip_adapter_1 again\n model._load_ip_adapter_weights([ip_adapter_1])\n with torch.no_grad():\n sample4 = model(**inputs_dict).sample\n\n # forward pass with multiple ip-adapters and multiple images\n model._load_ip_adapter_weights([ip_adapter_1, ip_adapter_2])\n # set the scale for ip_adapter_2 to 0 so that result should be same as only load ip_adapter_1\n for attn_processor in model.attn_processors.values():\n if isinstance(attn_processor, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0)):\n attn_processor.scale = [1, 0]\n image_embeds_multi = image_embeds.repeat(1, 2, 1, 1)\n inputs_dict[\"added_cond_kwargs\"] = {\"image_embeds\": [image_embeds_multi, image_embeds_multi]}\n with torch.no_grad():\n sample5 = model(**inputs_dict).sample\n\n # forward pass with single ip-adapter & single image when image_embeds is a 3-d tensor\n image_embeds = image_embeds[:,].squeeze(1)\n inputs_dict[\"added_cond_kwargs\"] = {\"image_embeds\": image_embeds}\n\n model._load_ip_adapter_weights(ip_adapter_1)\n with torch.no_grad():\n sample6 = model(**inputs_dict).sample\n\n assert not sample1.allclose(sample2, atol=1e-4, rtol=1e-4)\n assert not sample2.allclose(sample3, atol=1e-4, rtol=1e-4)\n assert sample2.allclose(sample4, atol=1e-4, rtol=1e-4)\n assert sample2.allclose(sample5, atol=1e-4, rtol=1e-4)\n assert sample2.allclose(sample6, atol=1e-4, rtol=1e-4)\n\n @require_torch_gpu\n def test_load_sharded_checkpoint_from_hub(self):\n _, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n loaded_model = self.model_class.from_pretrained(\"hf-internal-testing/unet2d-sharded-dummy\", device_map=\"auto\")\n new_output = loaded_model(**inputs_dict)\n\n assert loaded_model\n assert new_output.sample.shape == (4, 4, 16, 16)\n\n @require_torch_gpu\n def test_load_sharded_checkpoint_from_hub_local(self):\n _, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n ckpt_path = snapshot_download(\"hf-internal-testing/unet2d-sharded-dummy\")\n loaded_model = self.model_class.from_pretrained(ckpt_path, local_files_only=True, device_map=\"auto\")\n new_output = loaded_model(**inputs_dict)\n\n assert loaded_model\n assert new_output.sample.shape == (4, 4, 16, 16)\n\n @require_peft_backend\n def test_lora(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n model.to(torch_device)\n\n # forward pass without LoRA\n with torch.no_grad():\n non_lora_sample = model(**inputs_dict).sample\n\n unet_lora_config = get_unet_lora_config()\n model.add_adapter(unet_lora_config)\n\n assert check_if_lora_correctly_set(model), \"Lora not correctly set in UNet.\"\n\n # forward pass with LoRA\n with torch.no_grad():\n lora_sample = model(**inputs_dict).sample\n\n assert not torch.allclose(\n non_lora_sample, lora_sample, atol=1e-4, rtol=1e-4\n ), \"LoRA injected UNet should produce different results.\"\n\n @require_peft_backend\n def test_lora_serialization(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n model.to(torch_device)\n\n # forward pass without LoRA\n with torch.no_grad():\n non_lora_sample = model(**inputs_dict).sample\n\n unet_lora_config = get_unet_lora_config()\n model.add_adapter(unet_lora_config)\n\n assert check_if_lora_correctly_set(model), \"Lora not correctly set in UNet.\"\n\n # forward pass with LoRA\n with torch.no_grad():\n lora_sample_1 = model(**inputs_dict).sample\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n model.save_attn_procs(tmpdirname)\n model.unload_lora()\n model.load_attn_procs(os.path.join(tmpdirname, \"pytorch_lora_weights.safetensors\"))\n\n assert check_if_lora_correctly_set(model), \"Lora not correctly set in UNet.\"\n\n with torch.no_grad():\n lora_sample_2 = model(**inputs_dict).sample\n\n assert not torch.allclose(\n non_lora_sample, lora_sample_1, atol=1e-4, rtol=1e-4\n ), \"LoRA injected UNet should produce different results.\"\n assert torch.allclose(\n lora_sample_1, lora_sample_2, atol=1e-4, rtol=1e-4\n ), \"Loading from a saved checkpoint should produce identical results.\"\n\n\n@slow\nclass UNet2DConditionModelIntegrationTests(unittest.TestCase):\n def get_file_format(self, seed, shape):\n return f\"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy\"\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n backend_empty_cache(torch_device)\n\n def get_latents(self, seed=0, shape=(4, 4, 64, 64), fp16=False):\n dtype = torch.float16 if fp16 else torch.float32\n image = torch.from_numpy(load_hf_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype)\n return image\n\n def get_unet_model(self, fp16=False, model_id=\"CompVis/stable-diffusion-v1-4\"):\n revision = \"fp16\" if fp16 else None\n torch_dtype = torch.float16 if fp16 else torch.float32\n\n model = UNet2DConditionModel.from_pretrained(\n model_id, subfolder=\"unet\", torch_dtype=torch_dtype, revision=revision\n )\n model.to(torch_device).eval()\n\n return model\n\n @require_torch_gpu\n def test_set_attention_slice_auto(self):\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n unet = self.get_unet_model()\n unet.set_attention_slice(\"auto\")\n\n latents = self.get_latents(33)\n encoder_hidden_states = self.get_encoder_hidden_states(33)\n timestep = 1\n\n with torch.no_grad():\n _ = unet(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample\n\n mem_bytes = torch.cuda.max_memory_allocated()\n\n assert mem_bytes < 5 * 10**9\n\n @require_torch_gpu\n def test_set_attention_slice_max(self):\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n unet = self.get_unet_model()\n unet.set_attention_slice(\"max\")\n\n latents = self.get_latents(33)\n encoder_hidden_states = self.get_encoder_hidden_states(33)\n timestep = 1\n\n with torch.no_grad():\n _ = unet(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample\n\n mem_bytes = torch.cuda.max_memory_allocated()\n\n assert mem_bytes < 5 * 10**9\n\n @require_torch_gpu\n def test_set_attention_slice_int(self):\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n unet = self.get_unet_model()\n unet.set_attention_slice(2)\n\n latents = self.get_latents(33)\n encoder_hidden_states = self.get_encoder_hidden_states(33)\n timestep = 1\n\n with torch.no_grad():\n _ = unet(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample\n\n mem_bytes = torch.cuda.max_memory_allocated()\n\n assert mem_bytes < 5 * 10**9\n\n @require_torch_gpu\n def test_set_attention_slice_list(self):\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n # there are 32 sliceable layers\n slice_list = 16 * [2, 3]\n unet = self.get_unet_model()\n unet.set_attention_slice(slice_list)\n\n latents = self.get_latents(33)\n encoder_hidden_states = self.get_encoder_hidden_states(33)\n timestep = 1\n\n with torch.no_grad():\n _ = unet(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample\n\n mem_bytes = torch.cuda.max_memory_allocated()\n\n assert mem_bytes < 5 * 10**9\n\n def get_encoder_hidden_states(self, seed=0, shape=(4, 77, 768), fp16=False):\n dtype = torch.float16 if fp16 else torch.float32\n hidden_states = torch.from_numpy(load_hf_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype)\n return hidden_states\n\n @parameterized.expand(\n [\n # fmt: off\n [33, 4, [-0.4424, 0.1510, -0.1937, 0.2118, 0.3746, -0.3957, 0.0160, -0.0435]],\n [47, 0.55, [-0.1508, 0.0379, -0.3075, 0.2540, 0.3633, -0.0821, 0.1719, -0.0207]],\n [21, 0.89, [-0.6479, 0.6364, -0.3464, 0.8697, 0.4443, -0.6289, -0.0091, 0.1778]],\n [9, 1000, [0.8888, -0.5659, 0.5834, -0.7469, 1.1912, -0.3923, 1.1241, -0.4424]],\n # fmt: on\n ]\n )\n @require_torch_accelerator_with_fp16\n def test_compvis_sd_v1_4(self, seed, timestep, expected_slice):\n model = self.get_unet_model(model_id=\"CompVis/stable-diffusion-v1-4\")\n latents = self.get_latents(seed)\n encoder_hidden_states = self.get_encoder_hidden_states(seed)\n\n timestep = torch.tensor([timestep], dtype=torch.long, device=torch_device)\n\n with torch.no_grad():\n sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample\n\n assert sample.shape == latents.shape\n\n output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()\n expected_output_slice = torch.tensor(expected_slice)\n\n assert torch_all_close(output_slice, expected_output_slice, atol=1e-3)\n\n @parameterized.expand(\n [\n # fmt: off\n [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],\n [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],\n [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],\n [3, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],\n # fmt: on\n ]\n )\n @require_torch_accelerator_with_fp16\n def test_compvis_sd_v1_4_fp16(self, seed, timestep, expected_slice):\n model = self.get_unet_model(model_id=\"CompVis/stable-diffusion-v1-4\", fp16=True)\n latents = self.get_latents(seed, fp16=True)\n encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)\n\n timestep = torch.tensor([timestep], dtype=torch.long, device=torch_device)\n\n with torch.no_grad():\n sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample\n\n assert sample.shape == latents.shape\n\n output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()\n expected_output_slice = torch.tensor(expected_slice)\n\n assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)\n\n @parameterized.expand(\n [\n # fmt: off\n [33, 4, [-0.4430, 0.1570, -0.1867, 0.2376, 0.3205, -0.3681, 0.0525, -0.0722]],\n [47, 0.55, [-0.1415, 0.0129, -0.3136, 0.2257, 0.3430, -0.0536, 0.2114, -0.0436]],\n [21, 0.89, [-0.7091, 0.6664, -0.3643, 0.9032, 0.4499, -0.6541, 0.0139, 0.1750]],\n [9, 1000, [0.8878, -0.5659, 0.5844, -0.7442, 1.1883, -0.3927, 1.1192, -0.4423]],\n # fmt: on\n ]\n )\n @require_torch_accelerator\n @skip_mps\n def test_compvis_sd_v1_5(self, seed, timestep, expected_slice):\n model = self.get_unet_model(model_id=\"runwayml/stable-diffusion-v1-5\")\n latents = self.get_latents(seed)\n encoder_hidden_states = self.get_encoder_hidden_states(seed)\n\n timestep = torch.tensor([timestep], dtype=torch.long, device=torch_device)\n\n with torch.no_grad():\n sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample\n\n assert sample.shape == latents.shape\n\n output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()\n expected_output_slice = torch.tensor(expected_slice)\n\n assert torch_all_close(output_slice, expected_output_slice, atol=1e-3)\n\n @parameterized.expand(\n [\n # fmt: off\n [83, 4, [-0.2695, -0.1669, 0.0073, -0.3181, -0.1187, -0.1676, -0.1395, -0.5972]],\n [17, 0.55, [-0.1290, -0.2588, 0.0551, -0.0916, 0.3286, 0.0238, -0.3669, 0.0322]],\n [8, 0.89, [-0.5283, 0.1198, 0.0870, -0.1141, 0.9189, -0.0150, 0.5474, 0.4319]],\n [3, 1000, [-0.5601, 0.2411, -0.5435, 0.1268, 1.1338, -0.2427, -0.0280, -1.0020]],\n # fmt: on\n ]\n )\n @require_torch_accelerator_with_fp16\n def test_compvis_sd_v1_5_fp16(self, seed, timestep, expected_slice):\n model = self.get_unet_model(model_id=\"runwayml/stable-diffusion-v1-5\", fp16=True)\n latents = self.get_latents(seed, fp16=True)\n encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)\n\n timestep = torch.tensor([timestep], dtype=torch.long, device=torch_device)\n\n with torch.no_grad():\n sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample\n\n assert sample.shape == latents.shape\n\n output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()\n expected_output_slice = torch.tensor(expected_slice)\n\n assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)\n\n @parameterized.expand(\n [\n # fmt: off\n [33, 4, [-0.7639, 0.0106, -0.1615, -0.3487, -0.0423, -0.7972, 0.0085, -0.4858]],\n [47, 0.55, [-0.6564, 0.0795, -1.9026, -0.6258, 1.8235, 1.2056, 1.2169, 0.9073]],\n [21, 0.89, [0.0327, 0.4399, -0.6358, 0.3417, 0.4120, -0.5621, -0.0397, -1.0430]],\n [9, 1000, [0.1600, 0.7303, -1.0556, -0.3515, -0.7440, -1.2037, -1.8149, -1.8931]],\n # fmt: on\n ]\n )\n @require_torch_accelerator\n @skip_mps\n def test_compvis_sd_inpaint(self, seed, timestep, expected_slice):\n model = self.get_unet_model(model_id=\"runwayml/stable-diffusion-inpainting\")\n latents = self.get_latents(seed, shape=(4, 9, 64, 64))\n encoder_hidden_states = self.get_encoder_hidden_states(seed)\n\n timestep = torch.tensor([timestep], dtype=torch.long, device=torch_device)\n\n with torch.no_grad():\n sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample\n\n assert sample.shape == (4, 4, 64, 64)\n\n output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()\n expected_output_slice = torch.tensor(expected_slice)\n\n assert torch_all_close(output_slice, expected_output_slice, atol=3e-3)\n\n @parameterized.expand(\n [\n # fmt: off\n [83, 4, [-0.1047, -1.7227, 0.1067, 0.0164, -0.5698, -0.4172, -0.1388, 1.1387]],\n [17, 0.55, [0.0975, -0.2856, -0.3508, -0.4600, 0.3376, 0.2930, -0.2747, -0.7026]],\n [8, 0.89, [-0.0952, 0.0183, -0.5825, -0.1981, 0.1131, 0.4668, -0.0395, -0.3486]],\n [3, 1000, [0.4790, 0.4949, -1.0732, -0.7158, 0.7959, -0.9478, 0.1105, -0.9741]],\n # fmt: on\n ]\n )\n @require_torch_accelerator_with_fp16\n def test_compvis_sd_inpaint_fp16(self, seed, timestep, expected_slice):\n model = self.get_unet_model(model_id=\"runwayml/stable-diffusion-inpainting\", fp16=True)\n latents = self.get_latents(seed, shape=(4, 9, 64, 64), fp16=True)\n encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)\n\n timestep = torch.tensor([timestep], dtype=torch.long, device=torch_device)\n\n with torch.no_grad():\n sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample\n\n assert sample.shape == (4, 4, 64, 64)\n\n output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()\n expected_output_slice = torch.tensor(expected_slice)\n\n assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)\n\n @parameterized.expand(\n [\n # fmt: off\n [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],\n [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],\n [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],\n [3, 1000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],\n # fmt: on\n ]\n )\n @require_torch_accelerator_with_fp16\n def test_stabilityai_sd_v2_fp16(self, seed, timestep, expected_slice):\n model = self.get_unet_model(model_id=\"stabilityai/stable-diffusion-2\", fp16=True)\n latents = self.get_latents(seed, shape=(4, 4, 96, 96), fp16=True)\n encoder_hidden_states = self.get_encoder_hidden_states(seed, shape=(4, 77, 1024), fp16=True)\n\n timestep = torch.tensor([timestep], dtype=torch.long, device=torch_device)\n\n with torch.no_grad():\n sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample\n\n assert sample.shape == latents.shape\n\n output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()\n expected_output_slice = torch.tensor(expected_slice)\n\n assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)\n" }, { "path": "diffusers/tests/models/unets/test_models_unet_2d_flax.py", "content": "import gc\nimport unittest\n\nfrom parameterized import parameterized\n\nfrom diffusers import FlaxUNet2DConditionModel\nfrom diffusers.utils import is_flax_available\nfrom diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow\n\n\nif is_flax_available():\n import jax\n import jax.numpy as jnp\n\n\n@slow\n@require_flax\nclass FlaxUNet2DConditionModelIntegrationTests(unittest.TestCase):\n def get_file_format(self, seed, shape):\n return f\"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy\"\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n\n def get_latents(self, seed=0, shape=(4, 4, 64, 64), fp16=False):\n dtype = jnp.bfloat16 if fp16 else jnp.float32\n image = jnp.array(load_hf_numpy(self.get_file_format(seed, shape)), dtype=dtype)\n return image\n\n def get_unet_model(self, fp16=False, model_id=\"CompVis/stable-diffusion-v1-4\"):\n dtype = jnp.bfloat16 if fp16 else jnp.float32\n revision = \"bf16\" if fp16 else None\n\n model, params = FlaxUNet2DConditionModel.from_pretrained(\n model_id, subfolder=\"unet\", dtype=dtype, revision=revision\n )\n return model, params\n\n def get_encoder_hidden_states(self, seed=0, shape=(4, 77, 768), fp16=False):\n dtype = jnp.bfloat16 if fp16 else jnp.float32\n hidden_states = jnp.array(load_hf_numpy(self.get_file_format(seed, shape)), dtype=dtype)\n return hidden_states\n\n @parameterized.expand(\n [\n # fmt: off\n [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],\n [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],\n [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],\n [3, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],\n # fmt: on\n ]\n )\n def test_compvis_sd_v1_4_flax_vs_torch_fp16(self, seed, timestep, expected_slice):\n model, params = self.get_unet_model(model_id=\"CompVis/stable-diffusion-v1-4\", fp16=True)\n latents = self.get_latents(seed, fp16=True)\n encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)\n\n sample = model.apply(\n {\"params\": params},\n latents,\n jnp.array(timestep, dtype=jnp.int32),\n encoder_hidden_states=encoder_hidden_states,\n ).sample\n\n assert sample.shape == latents.shape\n\n output_slice = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())), dtype=jnp.float32)\n expected_output_slice = jnp.array(expected_slice, dtype=jnp.float32)\n\n # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware\n assert jnp.allclose(output_slice, expected_output_slice, atol=1e-2)\n\n @parameterized.expand(\n [\n # fmt: off\n [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],\n [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],\n [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],\n [3, 1000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],\n # fmt: on\n ]\n )\n def test_stabilityai_sd_v2_flax_vs_torch_fp16(self, seed, timestep, expected_slice):\n model, params = self.get_unet_model(model_id=\"stabilityai/stable-diffusion-2\", fp16=True)\n latents = self.get_latents(seed, shape=(4, 4, 96, 96), fp16=True)\n encoder_hidden_states = self.get_encoder_hidden_states(seed, shape=(4, 77, 1024), fp16=True)\n\n sample = model.apply(\n {\"params\": params},\n latents,\n jnp.array(timestep, dtype=jnp.int32),\n encoder_hidden_states=encoder_hidden_states,\n ).sample\n\n assert sample.shape == latents.shape\n\n output_slice = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())), dtype=jnp.float32)\n expected_output_slice = jnp.array(expected_slice, dtype=jnp.float32)\n\n # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware\n assert jnp.allclose(output_slice, expected_output_slice, atol=1e-2)\n" }, { "path": "diffusers/tests/models/unets/test_models_unet_3d_condition.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\n\nimport numpy as np\nimport torch\n\nfrom diffusers.models import ModelMixin, UNet3DConditionModel\nfrom diffusers.utils import logging\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, skip_mps, torch_device\n\nfrom ..test_modeling_common import ModelTesterMixin, UNetTesterMixin\n\n\nenable_full_determinism()\n\nlogger = logging.get_logger(__name__)\n\n\n@skip_mps\nclass UNet3DConditionModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):\n model_class = UNet3DConditionModel\n main_input_name = \"sample\"\n\n @property\n def dummy_input(self):\n batch_size = 4\n num_channels = 4\n num_frames = 4\n sizes = (16, 16)\n\n noise = floats_tensor((batch_size, num_channels, num_frames) + sizes).to(torch_device)\n time_step = torch.tensor([10]).to(torch_device)\n encoder_hidden_states = floats_tensor((batch_size, 4, 8)).to(torch_device)\n\n return {\"sample\": noise, \"timestep\": time_step, \"encoder_hidden_states\": encoder_hidden_states}\n\n @property\n def input_shape(self):\n return (4, 4, 16, 16)\n\n @property\n def output_shape(self):\n return (4, 4, 16, 16)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = {\n \"block_out_channels\": (4, 8),\n \"norm_num_groups\": 4,\n \"down_block_types\": (\n \"CrossAttnDownBlock3D\",\n \"DownBlock3D\",\n ),\n \"up_block_types\": (\"UpBlock3D\", \"CrossAttnUpBlock3D\"),\n \"cross_attention_dim\": 8,\n \"attention_head_dim\": 2,\n \"out_channels\": 4,\n \"in_channels\": 4,\n \"layers_per_block\": 1,\n \"sample_size\": 16,\n }\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_enable_works(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n\n model.enable_xformers_memory_efficient_attention()\n\n assert (\n model.mid_block.attentions[0].transformer_blocks[0].attn1.processor.__class__.__name__\n == \"XFormersAttnProcessor\"\n ), \"xformers is not enabled\"\n\n # Overriding to set `norm_num_groups` needs to be different for this model.\n def test_forward_with_norm_groups(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n init_dict[\"block_out_channels\"] = (32, 64)\n init_dict[\"norm_num_groups\"] = 32\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.eval()\n\n with torch.no_grad():\n output = model(**inputs_dict)\n\n if isinstance(output, dict):\n output = output.sample\n\n self.assertIsNotNone(output)\n expected_shape = inputs_dict[\"sample\"].shape\n self.assertEqual(output.shape, expected_shape, \"Input and output shapes do not match\")\n\n # Overriding since the UNet3D outputs a different structure.\n def test_determinism(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.eval()\n\n with torch.no_grad():\n # Warmup pass when using mps (see #372)\n if torch_device == \"mps\" and isinstance(model, ModelMixin):\n model(**self.dummy_input)\n\n first = model(**inputs_dict)\n if isinstance(first, dict):\n first = first.sample\n\n second = model(**inputs_dict)\n if isinstance(second, dict):\n second = second.sample\n\n out_1 = first.cpu().numpy()\n out_2 = second.cpu().numpy()\n out_1 = out_1[~np.isnan(out_1)]\n out_2 = out_2[~np.isnan(out_2)]\n max_diff = np.amax(np.abs(out_1 - out_2))\n self.assertLessEqual(max_diff, 1e-5)\n\n def test_model_attention_slicing(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n init_dict[\"block_out_channels\"] = (16, 32)\n init_dict[\"attention_head_dim\"] = 8\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.eval()\n\n model.set_attention_slice(\"auto\")\n with torch.no_grad():\n output = model(**inputs_dict)\n assert output is not None\n\n model.set_attention_slice(\"max\")\n with torch.no_grad():\n output = model(**inputs_dict)\n assert output is not None\n\n model.set_attention_slice(2)\n with torch.no_grad():\n output = model(**inputs_dict)\n assert output is not None\n\n def test_feed_forward_chunking(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n init_dict[\"block_out_channels\"] = (32, 64)\n init_dict[\"norm_num_groups\"] = 32\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.eval()\n\n with torch.no_grad():\n output = model(**inputs_dict)[0]\n\n model.enable_forward_chunking()\n with torch.no_grad():\n output_2 = model(**inputs_dict)[0]\n\n self.assertEqual(output.shape, output_2.shape, \"Shape doesn't match\")\n assert np.abs(output.cpu() - output_2.cpu()).max() < 1e-2\n" }, { "path": "diffusers/tests/models/unets/test_models_unet_controlnetxs.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport copy\nimport unittest\n\nimport numpy as np\nimport torch\nfrom torch import nn\n\nfrom diffusers import ControlNetXSAdapter, UNet2DConditionModel, UNetControlNetXSModel\nfrom diffusers.utils import logging\nfrom diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, is_flaky, torch_device\n\nfrom ..test_modeling_common import ModelTesterMixin, UNetTesterMixin\n\n\nlogger = logging.get_logger(__name__)\n\nenable_full_determinism()\n\n\nclass UNetControlNetXSModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):\n model_class = UNetControlNetXSModel\n main_input_name = \"sample\"\n\n @property\n def dummy_input(self):\n batch_size = 4\n num_channels = 4\n sizes = (16, 16)\n conditioning_image_size = (3, 32, 32) # size of additional, unprocessed image for control-conditioning\n\n noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)\n time_step = torch.tensor([10]).to(torch_device)\n encoder_hidden_states = floats_tensor((batch_size, 4, 8)).to(torch_device)\n controlnet_cond = floats_tensor((batch_size, *conditioning_image_size)).to(torch_device)\n conditioning_scale = 1\n\n return {\n \"sample\": noise,\n \"timestep\": time_step,\n \"encoder_hidden_states\": encoder_hidden_states,\n \"controlnet_cond\": controlnet_cond,\n \"conditioning_scale\": conditioning_scale,\n }\n\n @property\n def input_shape(self):\n return (4, 16, 16)\n\n @property\n def output_shape(self):\n return (4, 16, 16)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = {\n \"sample_size\": 16,\n \"down_block_types\": (\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n \"up_block_types\": (\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n \"block_out_channels\": (4, 8),\n \"cross_attention_dim\": 8,\n \"transformer_layers_per_block\": 1,\n \"num_attention_heads\": 2,\n \"norm_num_groups\": 4,\n \"upcast_attention\": False,\n \"ctrl_block_out_channels\": [2, 4],\n \"ctrl_num_attention_heads\": 4,\n \"ctrl_max_norm_num_groups\": 2,\n \"ctrl_conditioning_embedding_out_channels\": (2, 2),\n }\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n def get_dummy_unet(self):\n \"\"\"For some tests we also need the underlying UNet. For these, we'll build the UNetControlNetXSModel from the UNet and ControlNetXS-Adapter\"\"\"\n return UNet2DConditionModel(\n block_out_channels=(4, 8),\n layers_per_block=2,\n sample_size=16,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=8,\n norm_num_groups=4,\n use_linear_projection=True,\n )\n\n def get_dummy_controlnet_from_unet(self, unet, **kwargs):\n \"\"\"For some tests we also need the underlying ControlNetXS-Adapter. For these, we'll build the UNetControlNetXSModel from the UNet and ControlNetXS-Adapter\"\"\"\n # size_ratio and conditioning_embedding_out_channels chosen to keep model small\n return ControlNetXSAdapter.from_unet(unet, size_ratio=1, conditioning_embedding_out_channels=(2, 2), **kwargs)\n\n def test_from_unet(self):\n unet = self.get_dummy_unet()\n controlnet = self.get_dummy_controlnet_from_unet(unet)\n\n model = UNetControlNetXSModel.from_unet(unet, controlnet)\n model_state_dict = model.state_dict()\n\n def assert_equal_weights(module, weight_dict_prefix):\n for param_name, param_value in module.named_parameters():\n assert torch.equal(model_state_dict[weight_dict_prefix + \".\" + param_name], param_value)\n\n # # check unet\n # everything expect down,mid,up blocks\n modules_from_unet = [\n \"time_embedding\",\n \"conv_in\",\n \"conv_norm_out\",\n \"conv_out\",\n ]\n for p in modules_from_unet:\n assert_equal_weights(getattr(unet, p), \"base_\" + p)\n optional_modules_from_unet = [\n \"class_embedding\",\n \"add_time_proj\",\n \"add_embedding\",\n ]\n for p in optional_modules_from_unet:\n if hasattr(unet, p) and getattr(unet, p) is not None:\n assert_equal_weights(getattr(unet, p), \"base_\" + p)\n # down blocks\n assert len(unet.down_blocks) == len(model.down_blocks)\n for i, d in enumerate(unet.down_blocks):\n assert_equal_weights(d.resnets, f\"down_blocks.{i}.base_resnets\")\n if hasattr(d, \"attentions\"):\n assert_equal_weights(d.attentions, f\"down_blocks.{i}.base_attentions\")\n if hasattr(d, \"downsamplers\") and getattr(d, \"downsamplers\") is not None:\n assert_equal_weights(d.downsamplers[0], f\"down_blocks.{i}.base_downsamplers\")\n # mid block\n assert_equal_weights(unet.mid_block, \"mid_block.base_midblock\")\n # up blocks\n assert len(unet.up_blocks) == len(model.up_blocks)\n for i, u in enumerate(unet.up_blocks):\n assert_equal_weights(u.resnets, f\"up_blocks.{i}.resnets\")\n if hasattr(u, \"attentions\"):\n assert_equal_weights(u.attentions, f\"up_blocks.{i}.attentions\")\n if hasattr(u, \"upsamplers\") and getattr(u, \"upsamplers\") is not None:\n assert_equal_weights(u.upsamplers[0], f\"up_blocks.{i}.upsamplers\")\n\n # # check controlnet\n # everything expect down,mid,up blocks\n modules_from_controlnet = {\n \"controlnet_cond_embedding\": \"controlnet_cond_embedding\",\n \"conv_in\": \"ctrl_conv_in\",\n \"control_to_base_for_conv_in\": \"control_to_base_for_conv_in\",\n }\n optional_modules_from_controlnet = {\"time_embedding\": \"ctrl_time_embedding\"}\n for name_in_controlnet, name_in_unetcnxs in modules_from_controlnet.items():\n assert_equal_weights(getattr(controlnet, name_in_controlnet), name_in_unetcnxs)\n\n for name_in_controlnet, name_in_unetcnxs in optional_modules_from_controlnet.items():\n if hasattr(controlnet, name_in_controlnet) and getattr(controlnet, name_in_controlnet) is not None:\n assert_equal_weights(getattr(controlnet, name_in_controlnet), name_in_unetcnxs)\n # down blocks\n assert len(controlnet.down_blocks) == len(model.down_blocks)\n for i, d in enumerate(controlnet.down_blocks):\n assert_equal_weights(d.resnets, f\"down_blocks.{i}.ctrl_resnets\")\n assert_equal_weights(d.base_to_ctrl, f\"down_blocks.{i}.base_to_ctrl\")\n assert_equal_weights(d.ctrl_to_base, f\"down_blocks.{i}.ctrl_to_base\")\n if d.attentions is not None:\n assert_equal_weights(d.attentions, f\"down_blocks.{i}.ctrl_attentions\")\n if d.downsamplers is not None:\n assert_equal_weights(d.downsamplers, f\"down_blocks.{i}.ctrl_downsamplers\")\n # mid block\n assert_equal_weights(controlnet.mid_block.base_to_ctrl, \"mid_block.base_to_ctrl\")\n assert_equal_weights(controlnet.mid_block.midblock, \"mid_block.ctrl_midblock\")\n assert_equal_weights(controlnet.mid_block.ctrl_to_base, \"mid_block.ctrl_to_base\")\n # up blocks\n assert len(controlnet.up_connections) == len(model.up_blocks)\n for i, u in enumerate(controlnet.up_connections):\n assert_equal_weights(u.ctrl_to_base, f\"up_blocks.{i}.ctrl_to_base\")\n\n def test_freeze_unet(self):\n def assert_frozen(module):\n for p in module.parameters():\n assert not p.requires_grad\n\n def assert_unfrozen(module):\n for p in module.parameters():\n assert p.requires_grad\n\n init_dict, _ = self.prepare_init_args_and_inputs_for_common()\n model = UNetControlNetXSModel(**init_dict)\n model.freeze_unet_params()\n\n # # check unet\n # everything expect down,mid,up blocks\n modules_from_unet = [\n model.base_time_embedding,\n model.base_conv_in,\n model.base_conv_norm_out,\n model.base_conv_out,\n ]\n for m in modules_from_unet:\n assert_frozen(m)\n\n optional_modules_from_unet = [\n model.base_add_time_proj,\n model.base_add_embedding,\n ]\n for m in optional_modules_from_unet:\n if m is not None:\n assert_frozen(m)\n\n # down blocks\n for i, d in enumerate(model.down_blocks):\n assert_frozen(d.base_resnets)\n if isinstance(d.base_attentions, nn.ModuleList): # attentions can be list of Nones\n assert_frozen(d.base_attentions)\n if d.base_downsamplers is not None:\n assert_frozen(d.base_downsamplers)\n\n # mid block\n assert_frozen(model.mid_block.base_midblock)\n\n # up blocks\n for i, u in enumerate(model.up_blocks):\n assert_frozen(u.resnets)\n if isinstance(u.attentions, nn.ModuleList): # attentions can be list of Nones\n assert_frozen(u.attentions)\n if u.upsamplers is not None:\n assert_frozen(u.upsamplers)\n\n # # check controlnet\n # everything expect down,mid,up blocks\n modules_from_controlnet = [\n model.controlnet_cond_embedding,\n model.ctrl_conv_in,\n model.control_to_base_for_conv_in,\n ]\n optional_modules_from_controlnet = [model.ctrl_time_embedding]\n\n for m in modules_from_controlnet:\n assert_unfrozen(m)\n for m in optional_modules_from_controlnet:\n if m is not None:\n assert_unfrozen(m)\n\n # down blocks\n for d in model.down_blocks:\n assert_unfrozen(d.ctrl_resnets)\n assert_unfrozen(d.base_to_ctrl)\n assert_unfrozen(d.ctrl_to_base)\n if isinstance(d.ctrl_attentions, nn.ModuleList): # attentions can be list of Nones\n assert_unfrozen(d.ctrl_attentions)\n if d.ctrl_downsamplers is not None:\n assert_unfrozen(d.ctrl_downsamplers)\n # mid block\n assert_unfrozen(model.mid_block.base_to_ctrl)\n assert_unfrozen(model.mid_block.ctrl_midblock)\n assert_unfrozen(model.mid_block.ctrl_to_base)\n # up blocks\n for u in model.up_blocks:\n assert_unfrozen(u.ctrl_to_base)\n\n def test_gradient_checkpointing_is_applied(self):\n model_class_copy = copy.copy(UNetControlNetXSModel)\n\n modules_with_gc_enabled = {}\n\n # now monkey patch the following function:\n # def _set_gradient_checkpointing(self, module, value=False):\n # if hasattr(module, \"gradient_checkpointing\"):\n # module.gradient_checkpointing = value\n\n def _set_gradient_checkpointing_new(self, module, value=False):\n if hasattr(module, \"gradient_checkpointing\"):\n module.gradient_checkpointing = value\n modules_with_gc_enabled[module.__class__.__name__] = True\n\n model_class_copy._set_gradient_checkpointing = _set_gradient_checkpointing_new\n\n init_dict, _ = self.prepare_init_args_and_inputs_for_common()\n model = model_class_copy(**init_dict)\n\n model.enable_gradient_checkpointing()\n\n EXPECTED_SET = {\n \"Transformer2DModel\",\n \"UNetMidBlock2DCrossAttn\",\n \"ControlNetXSCrossAttnDownBlock2D\",\n \"ControlNetXSCrossAttnMidBlock2D\",\n \"ControlNetXSCrossAttnUpBlock2D\",\n }\n\n assert set(modules_with_gc_enabled.keys()) == EXPECTED_SET\n assert all(modules_with_gc_enabled.values()), \"All modules should be enabled\"\n\n @is_flaky\n def test_forward_no_control(self):\n unet = self.get_dummy_unet()\n controlnet = self.get_dummy_controlnet_from_unet(unet)\n\n model = UNetControlNetXSModel.from_unet(unet, controlnet)\n\n unet = unet.to(torch_device)\n model = model.to(torch_device)\n\n input_ = self.dummy_input\n\n control_specific_input = [\"controlnet_cond\", \"conditioning_scale\"]\n input_for_unet = {k: v for k, v in input_.items() if k not in control_specific_input}\n\n with torch.no_grad():\n unet_output = unet(**input_for_unet).sample.cpu()\n unet_controlnet_output = model(**input_, apply_control=False).sample.cpu()\n\n assert np.abs(unet_output.flatten() - unet_controlnet_output.flatten()).max() < 3e-4\n\n def test_time_embedding_mixing(self):\n unet = self.get_dummy_unet()\n controlnet = self.get_dummy_controlnet_from_unet(unet)\n controlnet_mix_time = self.get_dummy_controlnet_from_unet(\n unet, time_embedding_mix=0.5, learn_time_embedding=True\n )\n\n model = UNetControlNetXSModel.from_unet(unet, controlnet)\n model_mix_time = UNetControlNetXSModel.from_unet(unet, controlnet_mix_time)\n\n unet = unet.to(torch_device)\n model = model.to(torch_device)\n model_mix_time = model_mix_time.to(torch_device)\n\n input_ = self.dummy_input\n\n with torch.no_grad():\n output = model(**input_).sample\n output_mix_time = model_mix_time(**input_).sample\n\n assert output.shape == output_mix_time.shape\n\n def test_forward_with_norm_groups(self):\n # UNetControlNetXSModel currently only supports StableDiffusion and StableDiffusion-XL, both of which have norm_num_groups fixed at 32. So we don't need to test different values for norm_num_groups.\n pass\n" }, { "path": "diffusers/tests/models/unets/test_models_unet_motion.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport copy\nimport os\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\n\nfrom diffusers import MotionAdapter, UNet2DConditionModel, UNetMotionModel\nfrom diffusers.utils import logging\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n torch_device,\n)\n\nfrom ..test_modeling_common import ModelTesterMixin, UNetTesterMixin\n\n\nlogger = logging.get_logger(__name__)\n\nenable_full_determinism()\n\n\nclass UNetMotionModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):\n model_class = UNetMotionModel\n main_input_name = \"sample\"\n\n @property\n def dummy_input(self):\n batch_size = 4\n num_channels = 4\n num_frames = 4\n sizes = (16, 16)\n\n noise = floats_tensor((batch_size, num_channels, num_frames) + sizes).to(torch_device)\n time_step = torch.tensor([10]).to(torch_device)\n encoder_hidden_states = floats_tensor((batch_size, 4, 16)).to(torch_device)\n\n return {\"sample\": noise, \"timestep\": time_step, \"encoder_hidden_states\": encoder_hidden_states}\n\n @property\n def input_shape(self):\n return (4, 4, 16, 16)\n\n @property\n def output_shape(self):\n return (4, 4, 16, 16)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = {\n \"block_out_channels\": (16, 32),\n \"norm_num_groups\": 16,\n \"down_block_types\": (\"CrossAttnDownBlockMotion\", \"DownBlockMotion\"),\n \"up_block_types\": (\"UpBlockMotion\", \"CrossAttnUpBlockMotion\"),\n \"cross_attention_dim\": 16,\n \"num_attention_heads\": 2,\n \"out_channels\": 4,\n \"in_channels\": 4,\n \"layers_per_block\": 1,\n \"sample_size\": 16,\n }\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n def test_from_unet2d(self):\n torch.manual_seed(0)\n unet2d = UNet2DConditionModel()\n\n torch.manual_seed(1)\n model = self.model_class.from_unet2d(unet2d)\n model_state_dict = model.state_dict()\n\n for param_name, param_value in unet2d.named_parameters():\n self.assertTrue(torch.equal(model_state_dict[param_name], param_value))\n\n def test_freeze_unet2d(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n model.freeze_unet2d_params()\n\n for param_name, param_value in model.named_parameters():\n if \"motion_modules\" not in param_name:\n self.assertFalse(param_value.requires_grad)\n\n else:\n self.assertTrue(param_value.requires_grad)\n\n def test_loading_motion_adapter(self):\n model = self.model_class()\n adapter = MotionAdapter()\n model.load_motion_modules(adapter)\n\n for idx, down_block in enumerate(model.down_blocks):\n adapter_state_dict = adapter.down_blocks[idx].motion_modules.state_dict()\n for param_name, param_value in down_block.motion_modules.named_parameters():\n self.assertTrue(torch.equal(adapter_state_dict[param_name], param_value))\n\n for idx, up_block in enumerate(model.up_blocks):\n adapter_state_dict = adapter.up_blocks[idx].motion_modules.state_dict()\n for param_name, param_value in up_block.motion_modules.named_parameters():\n self.assertTrue(torch.equal(adapter_state_dict[param_name], param_value))\n\n mid_block_adapter_state_dict = adapter.mid_block.motion_modules.state_dict()\n for param_name, param_value in model.mid_block.motion_modules.named_parameters():\n self.assertTrue(torch.equal(mid_block_adapter_state_dict[param_name], param_value))\n\n def test_saving_motion_modules(self):\n torch.manual_seed(0)\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n model.to(torch_device)\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n model.save_motion_modules(tmpdirname)\n self.assertTrue(os.path.isfile(os.path.join(tmpdirname, \"diffusion_pytorch_model.safetensors\")))\n\n adapter_loaded = MotionAdapter.from_pretrained(tmpdirname)\n\n torch.manual_seed(0)\n model_loaded = self.model_class(**init_dict)\n model_loaded.load_motion_modules(adapter_loaded)\n model_loaded.to(torch_device)\n\n with torch.no_grad():\n output = model(**inputs_dict)[0]\n output_loaded = model_loaded(**inputs_dict)[0]\n\n max_diff = (output - output_loaded).abs().max().item()\n self.assertLessEqual(max_diff, 1e-4, \"Models give different forward passes\")\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_enable_works(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n\n model.enable_xformers_memory_efficient_attention()\n\n assert (\n model.mid_block.attentions[0].transformer_blocks[0].attn1.processor.__class__.__name__\n == \"XFormersAttnProcessor\"\n ), \"xformers is not enabled\"\n\n def test_gradient_checkpointing_is_applied(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model_class_copy = copy.copy(self.model_class)\n\n modules_with_gc_enabled = {}\n\n # now monkey patch the following function:\n # def _set_gradient_checkpointing(self, module, value=False):\n # if hasattr(module, \"gradient_checkpointing\"):\n # module.gradient_checkpointing = value\n\n def _set_gradient_checkpointing_new(self, module, value=False):\n if hasattr(module, \"gradient_checkpointing\"):\n module.gradient_checkpointing = value\n modules_with_gc_enabled[module.__class__.__name__] = True\n\n model_class_copy._set_gradient_checkpointing = _set_gradient_checkpointing_new\n\n model = model_class_copy(**init_dict)\n model.enable_gradient_checkpointing()\n\n EXPECTED_SET = {\n \"CrossAttnUpBlockMotion\",\n \"CrossAttnDownBlockMotion\",\n \"UNetMidBlockCrossAttnMotion\",\n \"UpBlockMotion\",\n \"Transformer2DModel\",\n \"DownBlockMotion\",\n }\n\n assert set(modules_with_gc_enabled.keys()) == EXPECTED_SET\n assert all(modules_with_gc_enabled.values()), \"All modules should be enabled\"\n\n def test_feed_forward_chunking(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n init_dict[\"block_out_channels\"] = (32, 64)\n init_dict[\"norm_num_groups\"] = 32\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.eval()\n\n with torch.no_grad():\n output = model(**inputs_dict)[0]\n\n model.enable_forward_chunking()\n with torch.no_grad():\n output_2 = model(**inputs_dict)[0]\n\n self.assertEqual(output.shape, output_2.shape, \"Shape doesn't match\")\n assert np.abs(output.cpu() - output_2.cpu()).max() < 1e-2\n\n def test_pickle(self):\n # enable deterministic behavior for gradient checkpointing\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n model.to(torch_device)\n\n with torch.no_grad():\n sample = model(**inputs_dict).sample\n\n sample_copy = copy.copy(sample)\n\n assert (sample - sample_copy).abs().max() < 1e-4\n\n def test_from_save_pretrained(self, expected_max_diff=5e-5):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n torch.manual_seed(0)\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.eval()\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n model.save_pretrained(tmpdirname, safe_serialization=False)\n torch.manual_seed(0)\n new_model = self.model_class.from_pretrained(tmpdirname)\n new_model.to(torch_device)\n\n with torch.no_grad():\n image = model(**inputs_dict)\n if isinstance(image, dict):\n image = image.to_tuple()[0]\n\n new_image = new_model(**inputs_dict)\n\n if isinstance(new_image, dict):\n new_image = new_image.to_tuple()[0]\n\n max_diff = (image - new_image).abs().max().item()\n self.assertLessEqual(max_diff, expected_max_diff, \"Models give different forward passes\")\n\n def test_from_save_pretrained_variant(self, expected_max_diff=5e-5):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n torch.manual_seed(0)\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.eval()\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n model.save_pretrained(tmpdirname, variant=\"fp16\", safe_serialization=False)\n\n torch.manual_seed(0)\n new_model = self.model_class.from_pretrained(tmpdirname, variant=\"fp16\")\n # non-variant cannot be loaded\n with self.assertRaises(OSError) as error_context:\n self.model_class.from_pretrained(tmpdirname)\n\n # make sure that error message states what keys are missing\n assert \"Error no file named diffusion_pytorch_model.bin found in directory\" in str(error_context.exception)\n\n new_model.to(torch_device)\n\n with torch.no_grad():\n image = model(**inputs_dict)\n if isinstance(image, dict):\n image = image.to_tuple()[0]\n\n new_image = new_model(**inputs_dict)\n\n if isinstance(new_image, dict):\n new_image = new_image.to_tuple()[0]\n\n max_diff = (image - new_image).abs().max().item()\n self.assertLessEqual(max_diff, expected_max_diff, \"Models give different forward passes\")\n\n def test_forward_with_norm_groups(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n init_dict[\"norm_num_groups\"] = 16\n init_dict[\"block_out_channels\"] = (16, 32)\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.eval()\n\n with torch.no_grad():\n output = model(**inputs_dict)\n\n if isinstance(output, dict):\n output = output.to_tuple()[0]\n\n self.assertIsNotNone(output)\n expected_shape = inputs_dict[\"sample\"].shape\n self.assertEqual(output.shape, expected_shape, \"Input and output shapes do not match\")\n" }, { "path": "diffusers/tests/models/unets/test_models_unet_spatiotemporal.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport copy\nimport unittest\n\nimport torch\n\nfrom diffusers import UNetSpatioTemporalConditionModel\nfrom diffusers.utils import logging\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n skip_mps,\n torch_all_close,\n torch_device,\n)\n\nfrom ..test_modeling_common import ModelTesterMixin, UNetTesterMixin\n\n\nlogger = logging.get_logger(__name__)\n\nenable_full_determinism()\n\n\n@skip_mps\nclass UNetSpatioTemporalConditionModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):\n model_class = UNetSpatioTemporalConditionModel\n main_input_name = \"sample\"\n\n @property\n def dummy_input(self):\n batch_size = 2\n num_frames = 2\n num_channels = 4\n sizes = (32, 32)\n\n noise = floats_tensor((batch_size, num_frames, num_channels) + sizes).to(torch_device)\n time_step = torch.tensor([10]).to(torch_device)\n encoder_hidden_states = floats_tensor((batch_size, 1, 32)).to(torch_device)\n\n return {\n \"sample\": noise,\n \"timestep\": time_step,\n \"encoder_hidden_states\": encoder_hidden_states,\n \"added_time_ids\": self._get_add_time_ids(),\n }\n\n @property\n def input_shape(self):\n return (2, 2, 4, 32, 32)\n\n @property\n def output_shape(self):\n return (4, 32, 32)\n\n @property\n def fps(self):\n return 6\n\n @property\n def motion_bucket_id(self):\n return 127\n\n @property\n def noise_aug_strength(self):\n return 0.02\n\n @property\n def addition_time_embed_dim(self):\n return 32\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = {\n \"block_out_channels\": (32, 64),\n \"down_block_types\": (\n \"CrossAttnDownBlockSpatioTemporal\",\n \"DownBlockSpatioTemporal\",\n ),\n \"up_block_types\": (\n \"UpBlockSpatioTemporal\",\n \"CrossAttnUpBlockSpatioTemporal\",\n ),\n \"cross_attention_dim\": 32,\n \"num_attention_heads\": 8,\n \"out_channels\": 4,\n \"in_channels\": 4,\n \"layers_per_block\": 2,\n \"sample_size\": 32,\n \"projection_class_embeddings_input_dim\": self.addition_time_embed_dim * 3,\n \"addition_time_embed_dim\": self.addition_time_embed_dim,\n }\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n def _get_add_time_ids(self, do_classifier_free_guidance=True):\n add_time_ids = [self.fps, self.motion_bucket_id, self.noise_aug_strength]\n\n passed_add_embed_dim = self.addition_time_embed_dim * len(add_time_ids)\n expected_add_embed_dim = self.addition_time_embed_dim * 3\n\n if expected_add_embed_dim != passed_add_embed_dim:\n raise ValueError(\n f\"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`.\"\n )\n\n add_time_ids = torch.tensor([add_time_ids], device=torch_device)\n add_time_ids = add_time_ids.repeat(1, 1)\n if do_classifier_free_guidance:\n add_time_ids = torch.cat([add_time_ids, add_time_ids])\n\n return add_time_ids\n\n @unittest.skip(\"Number of Norm Groups is not configurable\")\n def test_forward_with_norm_groups(self):\n pass\n\n @unittest.skip(\"Deprecated functionality\")\n def test_model_attention_slicing(self):\n pass\n\n @unittest.skip(\"Not supported\")\n def test_model_with_use_linear_projection(self):\n pass\n\n @unittest.skip(\"Not supported\")\n def test_model_with_simple_projection(self):\n pass\n\n @unittest.skip(\"Not supported\")\n def test_model_with_class_embeddings_concat(self):\n pass\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_enable_works(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n\n model.enable_xformers_memory_efficient_attention()\n\n assert (\n model.mid_block.attentions[0].transformer_blocks[0].attn1.processor.__class__.__name__\n == \"XFormersAttnProcessor\"\n ), \"xformers is not enabled\"\n\n @unittest.skipIf(torch_device == \"mps\", \"Gradient checkpointing skipped on MPS\")\n def test_gradient_checkpointing(self):\n # enable deterministic behavior for gradient checkpointing\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.model_class(**init_dict)\n model.to(torch_device)\n\n assert not model.is_gradient_checkpointing and model.training\n\n out = model(**inputs_dict).sample\n # run the backwards pass on the model. For backwards pass, for simplicity purpose,\n # we won't calculate the loss and rather backprop on out.sum()\n model.zero_grad()\n\n labels = torch.randn_like(out)\n loss = (out - labels).mean()\n loss.backward()\n\n # re-instantiate the model now enabling gradient checkpointing\n model_2 = self.model_class(**init_dict)\n # clone model\n model_2.load_state_dict(model.state_dict())\n model_2.to(torch_device)\n model_2.enable_gradient_checkpointing()\n\n assert model_2.is_gradient_checkpointing and model_2.training\n\n out_2 = model_2(**inputs_dict).sample\n # run the backwards pass on the model. For backwards pass, for simplicity purpose,\n # we won't calculate the loss and rather backprop on out.sum()\n model_2.zero_grad()\n loss_2 = (out_2 - labels).mean()\n loss_2.backward()\n\n # compare the output and parameters gradients\n self.assertTrue((loss - loss_2).abs() < 1e-5)\n named_params = dict(model.named_parameters())\n named_params_2 = dict(model_2.named_parameters())\n for name, param in named_params.items():\n self.assertTrue(torch_all_close(param.grad.data, named_params_2[name].grad.data, atol=5e-5))\n\n def test_model_with_num_attention_heads_tuple(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n init_dict[\"num_attention_heads\"] = (8, 16)\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.eval()\n\n with torch.no_grad():\n output = model(**inputs_dict)\n\n if isinstance(output, dict):\n output = output.sample\n\n self.assertIsNotNone(output)\n expected_shape = inputs_dict[\"sample\"].shape\n self.assertEqual(output.shape, expected_shape, \"Input and output shapes do not match\")\n\n def test_model_with_cross_attention_dim_tuple(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n init_dict[\"cross_attention_dim\"] = (32, 32)\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n model.eval()\n\n with torch.no_grad():\n output = model(**inputs_dict)\n\n if isinstance(output, dict):\n output = output.sample\n\n self.assertIsNotNone(output)\n expected_shape = inputs_dict[\"sample\"].shape\n self.assertEqual(output.shape, expected_shape, \"Input and output shapes do not match\")\n\n def test_gradient_checkpointing_is_applied(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n init_dict[\"num_attention_heads\"] = (8, 16)\n\n model_class_copy = copy.copy(self.model_class)\n\n modules_with_gc_enabled = {}\n\n # now monkey patch the following function:\n # def _set_gradient_checkpointing(self, module, value=False):\n # if hasattr(module, \"gradient_checkpointing\"):\n # module.gradient_checkpointing = value\n\n def _set_gradient_checkpointing_new(self, module, value=False):\n if hasattr(module, \"gradient_checkpointing\"):\n module.gradient_checkpointing = value\n modules_with_gc_enabled[module.__class__.__name__] = True\n\n model_class_copy._set_gradient_checkpointing = _set_gradient_checkpointing_new\n\n model = model_class_copy(**init_dict)\n model.enable_gradient_checkpointing()\n\n EXPECTED_SET = {\n \"TransformerSpatioTemporalModel\",\n \"CrossAttnDownBlockSpatioTemporal\",\n \"DownBlockSpatioTemporal\",\n \"UpBlockSpatioTemporal\",\n \"CrossAttnUpBlockSpatioTemporal\",\n \"UNetMidBlockSpatioTemporal\",\n }\n\n assert set(modules_with_gc_enabled.keys()) == EXPECTED_SET\n assert all(modules_with_gc_enabled.values()), \"All modules should be enabled\"\n\n def test_pickle(self):\n # enable deterministic behavior for gradient checkpointing\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n\n init_dict[\"num_attention_heads\"] = (8, 16)\n\n model = self.model_class(**init_dict)\n model.to(torch_device)\n\n with torch.no_grad():\n sample = model(**inputs_dict).sample\n\n sample_copy = copy.copy(sample)\n\n assert (sample - sample_copy).abs().max() < 1e-4\n" }, { "path": "diffusers/tests/models/unets/test_unet_2d_blocks.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport unittest\n\nfrom diffusers.models.unets.unet_2d_blocks import * # noqa F403\nfrom diffusers.utils.testing_utils import torch_device\n\nfrom .test_unet_blocks_common import UNetBlockTesterMixin\n\n\nclass DownBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):\n block_class = DownBlock2D # noqa F405\n block_type = \"down\"\n\n def test_output(self):\n expected_slice = [-0.0232, -0.9869, 0.8054, -0.0637, -0.1688, -1.4264, 0.4470, -1.3394, 0.0904]\n super().test_output(expected_slice)\n\n\nclass ResnetDownsampleBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):\n block_class = ResnetDownsampleBlock2D # noqa F405\n block_type = \"down\"\n\n def test_output(self):\n expected_slice = [0.0710, 0.2410, -0.7320, -1.0757, -1.1343, 0.3540, -0.0133, -0.2576, 0.0948]\n super().test_output(expected_slice)\n\n\nclass AttnDownBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):\n block_class = AttnDownBlock2D # noqa F405\n block_type = \"down\"\n\n def test_output(self):\n expected_slice = [0.0636, 0.8964, -0.6234, -1.0131, 0.0844, 0.4935, 0.3437, 0.0911, -0.2957]\n super().test_output(expected_slice)\n\n\nclass CrossAttnDownBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):\n block_class = CrossAttnDownBlock2D # noqa F405\n block_type = \"down\"\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict, inputs_dict = super().prepare_init_args_and_inputs_for_common()\n init_dict[\"cross_attention_dim\"] = 32\n return init_dict, inputs_dict\n\n def test_output(self):\n expected_slice = [0.2238, -0.7396, -0.2255, -0.3829, 0.1925, 1.1665, 0.0603, -0.7295, 0.1983]\n super().test_output(expected_slice)\n\n\nclass SimpleCrossAttnDownBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):\n block_class = SimpleCrossAttnDownBlock2D # noqa F405\n block_type = \"down\"\n\n @property\n def dummy_input(self):\n return super().get_dummy_input(include_encoder_hidden_states=True)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict, inputs_dict = super().prepare_init_args_and_inputs_for_common()\n init_dict[\"cross_attention_dim\"] = 32\n return init_dict, inputs_dict\n\n @unittest.skipIf(torch_device == \"mps\", \"MPS result is not consistent\")\n def test_output(self):\n expected_slice = [0.7921, -0.0992, -0.1962, -0.7695, -0.4242, 0.7804, 0.4737, 0.2765, 0.3338]\n super().test_output(expected_slice)\n\n\nclass SkipDownBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):\n block_class = SkipDownBlock2D # noqa F405\n block_type = \"down\"\n\n @property\n def dummy_input(self):\n return super().get_dummy_input(include_skip_sample=True)\n\n def test_output(self):\n expected_slice = [-0.0845, -0.2087, -0.2465, 0.0971, 0.1900, -0.0484, 0.2664, 0.4179, 0.5069]\n super().test_output(expected_slice)\n\n\nclass AttnSkipDownBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):\n block_class = AttnSkipDownBlock2D # noqa F405\n block_type = \"down\"\n\n @property\n def dummy_input(self):\n return super().get_dummy_input(include_skip_sample=True)\n\n def test_output(self):\n expected_slice = [0.5539, 0.1609, 0.4924, 0.0537, -0.1995, 0.4050, 0.0979, -0.2721, -0.0642]\n super().test_output(expected_slice)\n\n\nclass DownEncoderBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):\n block_class = DownEncoderBlock2D # noqa F405\n block_type = \"down\"\n\n @property\n def dummy_input(self):\n return super().get_dummy_input(include_temb=False)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = {\n \"in_channels\": 32,\n \"out_channels\": 32,\n }\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n def test_output(self):\n expected_slice = [1.1102, 0.5302, 0.4872, -0.0023, -0.8042, 0.0483, -0.3489, -0.5632, 0.7626]\n super().test_output(expected_slice)\n\n\nclass AttnDownEncoderBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):\n block_class = AttnDownEncoderBlock2D # noqa F405\n block_type = \"down\"\n\n @property\n def dummy_input(self):\n return super().get_dummy_input(include_temb=False)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = {\n \"in_channels\": 32,\n \"out_channels\": 32,\n }\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n def test_output(self):\n expected_slice = [0.8966, -0.1486, 0.8568, 0.8141, -0.9046, -0.1342, -0.0972, -0.7417, 0.1538]\n super().test_output(expected_slice)\n\n\nclass UNetMidBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):\n block_class = UNetMidBlock2D # noqa F405\n block_type = \"mid\"\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = {\n \"in_channels\": 32,\n \"temb_channels\": 128,\n }\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n def test_output(self):\n expected_slice = [-0.1062, 1.7248, 0.3494, 1.4569, -0.0910, -1.2421, -0.9984, 0.6736, 1.0028]\n super().test_output(expected_slice)\n\n\nclass UNetMidBlock2DCrossAttnTests(UNetBlockTesterMixin, unittest.TestCase):\n block_class = UNetMidBlock2DCrossAttn # noqa F405\n block_type = \"mid\"\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict, inputs_dict = super().prepare_init_args_and_inputs_for_common()\n init_dict[\"cross_attention_dim\"] = 32\n return init_dict, inputs_dict\n\n def test_output(self):\n expected_slice = [0.0187, 2.4220, 0.4484, 1.1203, -0.6121, -1.5122, -0.8270, 0.7851, 1.8335]\n super().test_output(expected_slice)\n\n\nclass UNetMidBlock2DSimpleCrossAttnTests(UNetBlockTesterMixin, unittest.TestCase):\n block_class = UNetMidBlock2DSimpleCrossAttn # noqa F405\n block_type = \"mid\"\n\n @property\n def dummy_input(self):\n return super().get_dummy_input(include_encoder_hidden_states=True)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict, inputs_dict = super().prepare_init_args_and_inputs_for_common()\n init_dict[\"cross_attention_dim\"] = 32\n return init_dict, inputs_dict\n\n def test_output(self):\n expected_slice = [0.7143, 1.9974, 0.5448, 1.3977, 0.1282, -1.1237, -1.4238, 0.5530, 0.8880]\n super().test_output(expected_slice)\n\n\nclass UpBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):\n block_class = UpBlock2D # noqa F405\n block_type = \"up\"\n\n @property\n def dummy_input(self):\n return super().get_dummy_input(include_res_hidden_states_tuple=True)\n\n def test_output(self):\n expected_slice = [-0.2041, -0.4165, -0.3022, 0.0041, -0.6628, -0.7053, 0.1928, -0.0325, 0.0523]\n super().test_output(expected_slice)\n\n\nclass ResnetUpsampleBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):\n block_class = ResnetUpsampleBlock2D # noqa F405\n block_type = \"up\"\n\n @property\n def dummy_input(self):\n return super().get_dummy_input(include_res_hidden_states_tuple=True)\n\n def test_output(self):\n expected_slice = [0.2287, 0.3549, -0.1346, 0.4797, -0.1715, -0.9649, 0.7305, -0.5864, -0.6244]\n super().test_output(expected_slice)\n\n\nclass CrossAttnUpBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):\n block_class = CrossAttnUpBlock2D # noqa F405\n block_type = \"up\"\n\n @property\n def dummy_input(self):\n return super().get_dummy_input(include_res_hidden_states_tuple=True)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict, inputs_dict = super().prepare_init_args_and_inputs_for_common()\n init_dict[\"cross_attention_dim\"] = 32\n return init_dict, inputs_dict\n\n def test_output(self):\n expected_slice = [-0.1403, -0.3515, -0.0420, -0.1425, 0.3167, 0.5094, -0.2181, 0.5931, 0.5582]\n super().test_output(expected_slice)\n\n\nclass SimpleCrossAttnUpBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):\n block_class = SimpleCrossAttnUpBlock2D # noqa F405\n block_type = \"up\"\n\n @property\n def dummy_input(self):\n return super().get_dummy_input(include_res_hidden_states_tuple=True, include_encoder_hidden_states=True)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict, inputs_dict = super().prepare_init_args_and_inputs_for_common()\n init_dict[\"cross_attention_dim\"] = 32\n return init_dict, inputs_dict\n\n def test_output(self):\n expected_slice = [0.2645, 0.1480, 0.0909, 0.8044, -0.9758, -0.9083, 0.0994, -1.1453, -0.7402]\n super().test_output(expected_slice)\n\n\nclass AttnUpBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):\n block_class = AttnUpBlock2D # noqa F405\n block_type = \"up\"\n\n @property\n def dummy_input(self):\n return super().get_dummy_input(include_res_hidden_states_tuple=True)\n\n @unittest.skipIf(torch_device == \"mps\", \"MPS result is not consistent\")\n def test_output(self):\n expected_slice = [0.0979, 0.1326, 0.0021, 0.0659, 0.2249, 0.0059, 0.1132, 0.5952, 0.1033]\n super().test_output(expected_slice)\n\n\nclass SkipUpBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):\n block_class = SkipUpBlock2D # noqa F405\n block_type = \"up\"\n\n @property\n def dummy_input(self):\n return super().get_dummy_input(include_res_hidden_states_tuple=True)\n\n def test_output(self):\n expected_slice = [-0.0893, -0.1234, -0.1506, -0.0332, 0.0123, -0.0211, 0.0566, 0.0143, 0.0362]\n super().test_output(expected_slice)\n\n\nclass AttnSkipUpBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):\n block_class = AttnSkipUpBlock2D # noqa F405\n block_type = \"up\"\n\n @property\n def dummy_input(self):\n return super().get_dummy_input(include_res_hidden_states_tuple=True)\n\n def test_output(self):\n expected_slice = [0.0361, 0.0617, 0.2787, -0.0350, 0.0342, 0.3421, -0.0843, 0.0913, 0.3015]\n super().test_output(expected_slice)\n\n\nclass UpDecoderBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):\n block_class = UpDecoderBlock2D # noqa F405\n block_type = \"up\"\n\n @property\n def dummy_input(self):\n return super().get_dummy_input(include_temb=False)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = {\"in_channels\": 32, \"out_channels\": 32}\n\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n def test_output(self):\n expected_slice = [0.4404, 0.1998, -0.9886, -0.3320, -0.3128, -0.7034, -0.6955, -0.2338, -0.3137]\n super().test_output(expected_slice)\n\n\nclass AttnUpDecoderBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):\n block_class = AttnUpDecoderBlock2D # noqa F405\n block_type = \"up\"\n\n @property\n def dummy_input(self):\n return super().get_dummy_input(include_temb=False)\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = {\"in_channels\": 32, \"out_channels\": 32}\n\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n def test_output(self):\n expected_slice = [0.6738, 0.4491, 0.1055, 1.0710, 0.7316, 0.3339, 0.3352, 0.1023, 0.3568]\n super().test_output(expected_slice)\n" }, { "path": "diffusers/tests/models/unets/test_unet_blocks_common.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom typing import Tuple\n\nimport torch\n\nfrom diffusers.utils.testing_utils import (\n floats_tensor,\n require_torch,\n require_torch_accelerator_with_training,\n torch_all_close,\n torch_device,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\n\n@require_torch\nclass UNetBlockTesterMixin:\n @property\n def dummy_input(self):\n return self.get_dummy_input()\n\n @property\n def output_shape(self):\n if self.block_type == \"down\":\n return (4, 32, 16, 16)\n elif self.block_type == \"mid\":\n return (4, 32, 32, 32)\n elif self.block_type == \"up\":\n return (4, 32, 64, 64)\n\n raise ValueError(f\"'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'.\")\n\n def get_dummy_input(\n self,\n include_temb=True,\n include_res_hidden_states_tuple=False,\n include_encoder_hidden_states=False,\n include_skip_sample=False,\n ):\n batch_size = 4\n num_channels = 32\n sizes = (32, 32)\n\n generator = torch.manual_seed(0)\n device = torch.device(torch_device)\n shape = (batch_size, num_channels) + sizes\n hidden_states = randn_tensor(shape, generator=generator, device=device)\n dummy_input = {\"hidden_states\": hidden_states}\n\n if include_temb:\n temb_channels = 128\n dummy_input[\"temb\"] = randn_tensor((batch_size, temb_channels), generator=generator, device=device)\n\n if include_res_hidden_states_tuple:\n generator_1 = torch.manual_seed(1)\n dummy_input[\"res_hidden_states_tuple\"] = (randn_tensor(shape, generator=generator_1, device=device),)\n\n if include_encoder_hidden_states:\n dummy_input[\"encoder_hidden_states\"] = floats_tensor((batch_size, 32, 32)).to(torch_device)\n\n if include_skip_sample:\n dummy_input[\"skip_sample\"] = randn_tensor(((batch_size, 3) + sizes), generator=generator, device=device)\n\n return dummy_input\n\n def prepare_init_args_and_inputs_for_common(self):\n init_dict = {\n \"in_channels\": 32,\n \"out_channels\": 32,\n \"temb_channels\": 128,\n }\n if self.block_type == \"up\":\n init_dict[\"prev_output_channel\"] = 32\n\n if self.block_type == \"mid\":\n init_dict.pop(\"out_channels\")\n\n inputs_dict = self.dummy_input\n return init_dict, inputs_dict\n\n def test_output(self, expected_slice):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n unet_block = self.block_class(**init_dict)\n unet_block.to(torch_device)\n unet_block.eval()\n\n with torch.no_grad():\n output = unet_block(**inputs_dict)\n\n if isinstance(output, Tuple):\n output = output[0]\n\n self.assertEqual(output.shape, self.output_shape)\n\n output_slice = output[0, -1, -3:, -3:]\n expected_slice = torch.tensor(expected_slice).to(torch_device)\n assert torch_all_close(output_slice.flatten(), expected_slice, atol=5e-3)\n\n @require_torch_accelerator_with_training\n def test_training(self):\n init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()\n model = self.block_class(**init_dict)\n model.to(torch_device)\n model.train()\n output = model(**inputs_dict)\n\n if isinstance(output, Tuple):\n output = output[0]\n\n device = torch.device(torch_device)\n noise = randn_tensor(output.shape, device=device)\n loss = torch.nn.functional.mse_loss(output, noise)\n loss.backward()\n" }, { "path": "diffusers/tests/others/test_check_copies.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os\nimport re\nimport shutil\nimport sys\nimport tempfile\nimport unittest\n\n\ngit_repo_path = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))\nsys.path.append(os.path.join(git_repo_path, \"utils\"))\n\nimport check_copies # noqa: E402\n\n\n# This is the reference code that will be used in the tests.\n# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.\nREFERENCE_CODE = \"\"\" \\\"\"\"\n Output class for the scheduler's `step` function output.\n\n Args:\n prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample `(x_{0})` based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \\\"\"\"\n\n prev_sample: torch.Tensor\n pred_original_sample: Optional[torch.Tensor] = None\n\"\"\"\n\n\nclass CopyCheckTester(unittest.TestCase):\n def setUp(self):\n self.diffusers_dir = tempfile.mkdtemp()\n os.makedirs(os.path.join(self.diffusers_dir, \"schedulers/\"))\n check_copies.DIFFUSERS_PATH = self.diffusers_dir\n shutil.copy(\n os.path.join(git_repo_path, \"src/diffusers/schedulers/scheduling_ddpm.py\"),\n os.path.join(self.diffusers_dir, \"schedulers/scheduling_ddpm.py\"),\n )\n\n def tearDown(self):\n check_copies.DIFFUSERS_PATH = \"src/diffusers\"\n shutil.rmtree(self.diffusers_dir)\n\n def check_copy_consistency(self, comment, class_name, class_code, overwrite_result=None):\n code = comment + f\"\\nclass {class_name}(nn.Module):\\n\" + class_code\n if overwrite_result is not None:\n expected = comment + f\"\\nclass {class_name}(nn.Module):\\n\" + overwrite_result\n code = check_copies.run_ruff(code)\n fname = os.path.join(self.diffusers_dir, \"new_code.py\")\n with open(fname, \"w\", newline=\"\\n\") as f:\n f.write(code)\n if overwrite_result is None:\n self.assertTrue(len(check_copies.is_copy_consistent(fname)) == 0)\n else:\n check_copies.is_copy_consistent(f.name, overwrite=True)\n with open(fname, \"r\") as f:\n self.assertTrue(f.read(), expected)\n\n def test_find_code_in_diffusers(self):\n code = check_copies.find_code_in_diffusers(\"schedulers.scheduling_ddpm.DDPMSchedulerOutput\")\n self.assertEqual(code, REFERENCE_CODE)\n\n def test_is_copy_consistent(self):\n # Base copy consistency\n self.check_copy_consistency(\n \"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput\",\n \"DDPMSchedulerOutput\",\n REFERENCE_CODE + \"\\n\",\n )\n\n # With no empty line at the end\n self.check_copy_consistency(\n \"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput\",\n \"DDPMSchedulerOutput\",\n REFERENCE_CODE,\n )\n\n # Copy consistency with rename\n self.check_copy_consistency(\n \"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test\",\n \"TestSchedulerOutput\",\n re.sub(\"DDPM\", \"Test\", REFERENCE_CODE),\n )\n\n # Copy consistency with a really long name\n long_class_name = \"TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason\"\n self.check_copy_consistency(\n f\"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}\",\n f\"{long_class_name}SchedulerOutput\",\n re.sub(\"Bert\", long_class_name, REFERENCE_CODE),\n )\n\n # Copy consistency with overwrite\n self.check_copy_consistency(\n \"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test\",\n \"TestSchedulerOutput\",\n REFERENCE_CODE,\n overwrite_result=re.sub(\"DDPM\", \"Test\", REFERENCE_CODE),\n )\n" }, { "path": "diffusers/tests/others/test_check_dummies.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os\nimport sys\nimport unittest\n\n\ngit_repo_path = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))\nsys.path.append(os.path.join(git_repo_path, \"utils\"))\n\nimport check_dummies # noqa: E402\nfrom check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402\n\n\n# Align TRANSFORMERS_PATH in check_dummies with the current path\ncheck_dummies.PATH_TO_DIFFUSERS = os.path.join(git_repo_path, \"src\", \"diffusers\")\n\n\nclass CheckDummiesTester(unittest.TestCase):\n def test_find_backend(self):\n simple_backend = find_backend(\" if not is_torch_available():\")\n self.assertEqual(simple_backend, \"torch\")\n\n # backend_with_underscore = find_backend(\" if not is_tensorflow_text_available():\")\n # self.assertEqual(backend_with_underscore, \"tensorflow_text\")\n\n double_backend = find_backend(\" if not (is_torch_available() and is_transformers_available()):\")\n self.assertEqual(double_backend, \"torch_and_transformers\")\n\n # double_backend_with_underscore = find_backend(\n # \" if not (is_sentencepiece_available() and is_tensorflow_text_available()):\"\n # )\n # self.assertEqual(double_backend_with_underscore, \"sentencepiece_and_tensorflow_text\")\n\n triple_backend = find_backend(\n \" if not (is_torch_available() and is_transformers_available() and is_onnx_available()):\"\n )\n self.assertEqual(triple_backend, \"torch_and_transformers_and_onnx\")\n\n def test_read_init(self):\n objects = read_init()\n # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects\n self.assertIn(\"torch\", objects)\n self.assertIn(\"torch_and_transformers\", objects)\n self.assertIn(\"flax_and_transformers\", objects)\n self.assertIn(\"torch_and_transformers_and_onnx\", objects)\n\n # Likewise, we can't assert on the exact content of a key\n self.assertIn(\"UNet2DModel\", objects[\"torch\"])\n self.assertIn(\"FlaxUNet2DConditionModel\", objects[\"flax\"])\n self.assertIn(\"StableDiffusionPipeline\", objects[\"torch_and_transformers\"])\n self.assertIn(\"FlaxStableDiffusionPipeline\", objects[\"flax_and_transformers\"])\n self.assertIn(\"LMSDiscreteScheduler\", objects[\"torch_and_scipy\"])\n self.assertIn(\"OnnxStableDiffusionPipeline\", objects[\"torch_and_transformers_and_onnx\"])\n\n def test_create_dummy_object(self):\n dummy_constant = create_dummy_object(\"CONSTANT\", \"'torch'\")\n self.assertEqual(dummy_constant, \"\\nCONSTANT = None\\n\")\n\n dummy_function = create_dummy_object(\"function\", \"'torch'\")\n self.assertEqual(\n dummy_function, \"\\ndef function(*args, **kwargs):\\n requires_backends(function, 'torch')\\n\"\n )\n\n expected_dummy_class = \"\"\"\nclass FakeClass(metaclass=DummyObject):\n _backends = 'torch'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, 'torch')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, 'torch')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, 'torch')\n\"\"\"\n dummy_class = create_dummy_object(\"FakeClass\", \"'torch'\")\n self.assertEqual(dummy_class, expected_dummy_class)\n\n def test_create_dummy_files(self):\n expected_dummy_pytorch_file = \"\"\"# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\"\"\"\n dummy_files = create_dummy_files({\"torch\": [\"CONSTANT\", \"function\", \"FakeClass\"]})\n self.assertEqual(dummy_files[\"torch\"], expected_dummy_pytorch_file)\n" }, { "path": "diffusers/tests/others/test_config.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport tempfile\nimport unittest\n\nfrom diffusers import (\n DDIMScheduler,\n DDPMScheduler,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n PNDMScheduler,\n logging,\n)\nfrom diffusers.configuration_utils import ConfigMixin, register_to_config\nfrom diffusers.utils.testing_utils import CaptureLogger\n\n\nclass SampleObject(ConfigMixin):\n config_name = \"config.json\"\n\n @register_to_config\n def __init__(\n self,\n a=2,\n b=5,\n c=(2, 5),\n d=\"for diffusion\",\n e=[1, 3],\n ):\n pass\n\n\nclass SampleObject2(ConfigMixin):\n config_name = \"config.json\"\n\n @register_to_config\n def __init__(\n self,\n a=2,\n b=5,\n c=(2, 5),\n d=\"for diffusion\",\n f=[1, 3],\n ):\n pass\n\n\nclass SampleObject3(ConfigMixin):\n config_name = \"config.json\"\n\n @register_to_config\n def __init__(\n self,\n a=2,\n b=5,\n c=(2, 5),\n d=\"for diffusion\",\n e=[1, 3],\n f=[1, 3],\n ):\n pass\n\n\nclass SampleObject4(ConfigMixin):\n config_name = \"config.json\"\n\n @register_to_config\n def __init__(\n self,\n a=2,\n b=5,\n c=(2, 5),\n d=\"for diffusion\",\n e=[1, 5],\n f=[5, 4],\n ):\n pass\n\n\nclass ConfigTester(unittest.TestCase):\n def test_load_not_from_mixin(self):\n with self.assertRaises(ValueError):\n ConfigMixin.load_config(\"dummy_path\")\n\n def test_register_to_config(self):\n obj = SampleObject()\n config = obj.config\n assert config[\"a\"] == 2\n assert config[\"b\"] == 5\n assert config[\"c\"] == (2, 5)\n assert config[\"d\"] == \"for diffusion\"\n assert config[\"e\"] == [1, 3]\n\n # init ignore private arguments\n obj = SampleObject(_name_or_path=\"lalala\")\n config = obj.config\n assert config[\"a\"] == 2\n assert config[\"b\"] == 5\n assert config[\"c\"] == (2, 5)\n assert config[\"d\"] == \"for diffusion\"\n assert config[\"e\"] == [1, 3]\n\n # can override default\n obj = SampleObject(c=6)\n config = obj.config\n assert config[\"a\"] == 2\n assert config[\"b\"] == 5\n assert config[\"c\"] == 6\n assert config[\"d\"] == \"for diffusion\"\n assert config[\"e\"] == [1, 3]\n\n # can use positional arguments.\n obj = SampleObject(1, c=6)\n config = obj.config\n assert config[\"a\"] == 1\n assert config[\"b\"] == 5\n assert config[\"c\"] == 6\n assert config[\"d\"] == \"for diffusion\"\n assert config[\"e\"] == [1, 3]\n\n def test_save_load(self):\n obj = SampleObject()\n config = obj.config\n\n assert config[\"a\"] == 2\n assert config[\"b\"] == 5\n assert config[\"c\"] == (2, 5)\n assert config[\"d\"] == \"for diffusion\"\n assert config[\"e\"] == [1, 3]\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n obj.save_config(tmpdirname)\n new_obj = SampleObject.from_config(SampleObject.load_config(tmpdirname))\n new_config = new_obj.config\n\n # unfreeze configs\n config = dict(config)\n new_config = dict(new_config)\n\n assert config.pop(\"c\") == (2, 5) # instantiated as tuple\n assert new_config.pop(\"c\") == [2, 5] # saved & loaded as list because of json\n config.pop(\"_use_default_values\")\n assert config == new_config\n\n def test_load_ddim_from_pndm(self):\n logger = logging.get_logger(\"diffusers.configuration_utils\")\n # 30 for warning\n logger.setLevel(30)\n\n with CaptureLogger(logger) as cap_logger:\n ddim = DDIMScheduler.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\", subfolder=\"scheduler\"\n )\n\n assert ddim.__class__ == DDIMScheduler\n # no warning should be thrown\n assert cap_logger.out == \"\"\n\n def test_load_euler_from_pndm(self):\n logger = logging.get_logger(\"diffusers.configuration_utils\")\n # 30 for warning\n logger.setLevel(30)\n\n with CaptureLogger(logger) as cap_logger:\n euler = EulerDiscreteScheduler.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\", subfolder=\"scheduler\"\n )\n\n assert euler.__class__ == EulerDiscreteScheduler\n # no warning should be thrown\n assert cap_logger.out == \"\"\n\n def test_load_euler_ancestral_from_pndm(self):\n logger = logging.get_logger(\"diffusers.configuration_utils\")\n # 30 for warning\n logger.setLevel(30)\n\n with CaptureLogger(logger) as cap_logger:\n euler = EulerAncestralDiscreteScheduler.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\", subfolder=\"scheduler\"\n )\n\n assert euler.__class__ == EulerAncestralDiscreteScheduler\n # no warning should be thrown\n assert cap_logger.out == \"\"\n\n def test_load_pndm(self):\n logger = logging.get_logger(\"diffusers.configuration_utils\")\n # 30 for warning\n logger.setLevel(30)\n\n with CaptureLogger(logger) as cap_logger:\n pndm = PNDMScheduler.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\", subfolder=\"scheduler\"\n )\n\n assert pndm.__class__ == PNDMScheduler\n # no warning should be thrown\n assert cap_logger.out == \"\"\n\n def test_overwrite_config_on_load(self):\n logger = logging.get_logger(\"diffusers.configuration_utils\")\n # 30 for warning\n logger.setLevel(30)\n\n with CaptureLogger(logger) as cap_logger:\n ddpm = DDPMScheduler.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\",\n subfolder=\"scheduler\",\n prediction_type=\"sample\",\n beta_end=8,\n )\n\n with CaptureLogger(logger) as cap_logger_2:\n ddpm_2 = DDPMScheduler.from_pretrained(\"google/ddpm-celebahq-256\", beta_start=88)\n\n assert ddpm.__class__ == DDPMScheduler\n assert ddpm.config.prediction_type == \"sample\"\n assert ddpm.config.beta_end == 8\n assert ddpm_2.config.beta_start == 88\n\n # no warning should be thrown\n assert cap_logger.out == \"\"\n assert cap_logger_2.out == \"\"\n\n def test_load_dpmsolver(self):\n logger = logging.get_logger(\"diffusers.configuration_utils\")\n # 30 for warning\n logger.setLevel(30)\n\n with CaptureLogger(logger) as cap_logger:\n dpm = DPMSolverMultistepScheduler.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\", subfolder=\"scheduler\"\n )\n\n assert dpm.__class__ == DPMSolverMultistepScheduler\n # no warning should be thrown\n assert cap_logger.out == \"\"\n\n def test_use_default_values(self):\n # let's first save a config that should be in the form\n # a=2,\n # b=5,\n # c=(2, 5),\n # d=\"for diffusion\",\n # e=[1, 3],\n\n config = SampleObject()\n\n config_dict = {k: v for k, v in config.config.items() if not k.startswith(\"_\")}\n\n # make sure that default config has all keys in `_use_default_values`\n assert set(config_dict.keys()) == set(config.config._use_default_values)\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n config.save_config(tmpdirname)\n\n # now loading it with SampleObject2 should put f into `_use_default_values`\n config = SampleObject2.from_config(SampleObject2.load_config(tmpdirname))\n\n assert \"f\" in config.config._use_default_values\n assert config.config.f == [1, 3]\n\n # now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value\n # **BECAUSE** it is part of `config.config._use_default_values`\n new_config = SampleObject4.from_config(config.config)\n assert new_config.config.f == [5, 4]\n\n config.config._use_default_values.pop()\n new_config_2 = SampleObject4.from_config(config.config)\n assert new_config_2.config.f == [1, 3]\n\n # Nevertheless \"e\" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]\n assert new_config_2.config.e == [1, 3]\n" }, { "path": "diffusers/tests/others/test_dependencies.py", "content": "# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nimport unittest\nfrom importlib import import_module\n\n\nclass DependencyTester(unittest.TestCase):\n def test_diffusers_import(self):\n try:\n import diffusers # noqa: F401\n except ImportError:\n assert False\n\n def test_backend_registration(self):\n import diffusers\n from diffusers.dependency_versions_table import deps\n\n all_classes = inspect.getmembers(diffusers, inspect.isclass)\n\n for cls_name, cls_module in all_classes:\n if \"dummy_\" in cls_module.__module__:\n for backend in cls_module._backends:\n if backend == \"k_diffusion\":\n backend = \"k-diffusion\"\n elif backend == \"invisible_watermark\":\n backend = \"invisible-watermark\"\n assert backend in deps, f\"{backend} is not in the deps table!\"\n\n def test_pipeline_imports(self):\n import diffusers\n import diffusers.pipelines\n\n all_classes = inspect.getmembers(diffusers, inspect.isclass)\n for cls_name, cls_module in all_classes:\n if hasattr(diffusers.pipelines, cls_name):\n pipeline_folder_module = \".\".join(str(cls_module.__module__).split(\".\")[:3])\n _ = import_module(pipeline_folder_module, str(cls_name))\n" }, { "path": "diffusers/tests/others/test_ema.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport tempfile\nimport unittest\n\nimport torch\n\nfrom diffusers import UNet2DConditionModel\nfrom diffusers.training_utils import EMAModel\nfrom diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device\n\n\nenable_full_determinism()\n\n\nclass EMAModelTests(unittest.TestCase):\n model_id = \"hf-internal-testing/tiny-stable-diffusion-pipe\"\n batch_size = 1\n prompt_length = 77\n text_encoder_hidden_dim = 32\n num_in_channels = 4\n latent_height = latent_width = 64\n generator = torch.manual_seed(0)\n\n def get_models(self, decay=0.9999):\n unet = UNet2DConditionModel.from_pretrained(self.model_id, subfolder=\"unet\")\n unet = unet.to(torch_device)\n ema_unet = EMAModel(unet.parameters(), decay=decay, model_cls=UNet2DConditionModel, model_config=unet.config)\n return unet, ema_unet\n\n def get_dummy_inputs(self):\n noisy_latents = torch.randn(\n self.batch_size, self.num_in_channels, self.latent_height, self.latent_width, generator=self.generator\n ).to(torch_device)\n timesteps = torch.randint(0, 1000, size=(self.batch_size,), generator=self.generator).to(torch_device)\n encoder_hidden_states = torch.randn(\n self.batch_size, self.prompt_length, self.text_encoder_hidden_dim, generator=self.generator\n ).to(torch_device)\n return noisy_latents, timesteps, encoder_hidden_states\n\n def simulate_backprop(self, unet):\n updated_state_dict = {}\n for k, param in unet.state_dict().items():\n updated_param = torch.randn_like(param) + (param * torch.randn_like(param))\n updated_state_dict.update({k: updated_param})\n unet.load_state_dict(updated_state_dict)\n return unet\n\n def test_optimization_steps_updated(self):\n unet, ema_unet = self.get_models()\n # Take the first (hypothetical) EMA step.\n ema_unet.step(unet.parameters())\n assert ema_unet.optimization_step == 1\n\n # Take two more.\n for _ in range(2):\n ema_unet.step(unet.parameters())\n assert ema_unet.optimization_step == 3\n\n def test_shadow_params_not_updated(self):\n unet, ema_unet = self.get_models()\n # Since the `unet` is not being updated (i.e., backprop'd)\n # there won't be any difference between the `params` of `unet`\n # and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.\n ema_unet.step(unet.parameters())\n orig_params = list(unet.parameters())\n for s_param, param in zip(ema_unet.shadow_params, orig_params):\n assert torch.allclose(s_param, param)\n\n # The above holds true even if we call `ema.step()` multiple times since\n # `unet` params are still not being updated.\n for _ in range(4):\n ema_unet.step(unet.parameters())\n for s_param, param in zip(ema_unet.shadow_params, orig_params):\n assert torch.allclose(s_param, param)\n\n def test_shadow_params_updated(self):\n unet, ema_unet = self.get_models()\n # Here we simulate the parameter updates for `unet`. Since there might\n # be some parameters which are initialized to zero we take extra care to\n # initialize their values to something non-zero before the multiplication.\n unet_pseudo_updated_step_one = self.simulate_backprop(unet)\n\n # Take the EMA step.\n ema_unet.step(unet_pseudo_updated_step_one.parameters())\n\n # Now the EMA'd parameters won't be equal to the original model parameters.\n orig_params = list(unet_pseudo_updated_step_one.parameters())\n for s_param, param in zip(ema_unet.shadow_params, orig_params):\n assert ~torch.allclose(s_param, param)\n\n # Ensure this is the case when we take multiple EMA steps.\n for _ in range(4):\n ema_unet.step(unet.parameters())\n for s_param, param in zip(ema_unet.shadow_params, orig_params):\n assert ~torch.allclose(s_param, param)\n\n def test_consecutive_shadow_params_updated(self):\n # If we call EMA step after a backpropagation consecutively for two times,\n # the shadow params from those two steps should be different.\n unet, ema_unet = self.get_models()\n\n # First backprop + EMA\n unet_step_one = self.simulate_backprop(unet)\n ema_unet.step(unet_step_one.parameters())\n step_one_shadow_params = ema_unet.shadow_params\n\n # Second backprop + EMA\n unet_step_two = self.simulate_backprop(unet_step_one)\n ema_unet.step(unet_step_two.parameters())\n step_two_shadow_params = ema_unet.shadow_params\n\n for step_one, step_two in zip(step_one_shadow_params, step_two_shadow_params):\n assert ~torch.allclose(step_one, step_two)\n\n def test_zero_decay(self):\n # If there's no decay even if there are backprops, EMA steps\n # won't take any effect i.e., the shadow params would remain the\n # same.\n unet, ema_unet = self.get_models(decay=0.0)\n unet_step_one = self.simulate_backprop(unet)\n ema_unet.step(unet_step_one.parameters())\n step_one_shadow_params = ema_unet.shadow_params\n\n unet_step_two = self.simulate_backprop(unet_step_one)\n ema_unet.step(unet_step_two.parameters())\n step_two_shadow_params = ema_unet.shadow_params\n\n for step_one, step_two in zip(step_one_shadow_params, step_two_shadow_params):\n assert torch.allclose(step_one, step_two)\n\n @skip_mps\n def test_serialization(self):\n unet, ema_unet = self.get_models()\n noisy_latents, timesteps, encoder_hidden_states = self.get_dummy_inputs()\n\n with tempfile.TemporaryDirectory() as tmpdir:\n ema_unet.save_pretrained(tmpdir)\n loaded_unet = UNet2DConditionModel.from_pretrained(tmpdir, model_cls=UNet2DConditionModel)\n loaded_unet = loaded_unet.to(unet.device)\n\n # Since no EMA step has been performed the outputs should match.\n output = unet(noisy_latents, timesteps, encoder_hidden_states).sample\n output_loaded = loaded_unet(noisy_latents, timesteps, encoder_hidden_states).sample\n\n assert torch.allclose(output, output_loaded, atol=1e-4)\n" }, { "path": "diffusers/tests/others/test_hub_utils.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport unittest\nfrom pathlib import Path\nfrom tempfile import TemporaryDirectory\n\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\n\n\nclass CreateModelCardTest(unittest.TestCase):\n def test_generate_model_card_with_library_name(self):\n with TemporaryDirectory() as tmpdir:\n file_path = Path(tmpdir) / \"README.md\"\n file_path.write_text(\"---\\nlibrary_name: foo\\n---\\nContent\\n\")\n model_card = load_or_create_model_card(file_path)\n populate_model_card(model_card)\n assert model_card.data.library_name == \"foo\"\n" }, { "path": "diffusers/tests/others/test_image_processor.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\n\nimport numpy as np\nimport PIL.Image\nimport torch\n\nfrom diffusers.image_processor import VaeImageProcessor\n\n\nclass ImageProcessorTest(unittest.TestCase):\n @property\n def dummy_sample(self):\n batch_size = 1\n num_channels = 3\n height = 8\n width = 8\n\n sample = torch.rand((batch_size, num_channels, height, width))\n\n return sample\n\n @property\n def dummy_mask(self):\n batch_size = 1\n num_channels = 1\n height = 8\n width = 8\n\n sample = torch.rand((batch_size, num_channels, height, width))\n\n return sample\n\n def to_np(self, image):\n if isinstance(image[0], PIL.Image.Image):\n return np.stack([np.array(i) for i in image], axis=0)\n elif isinstance(image, torch.Tensor):\n return image.cpu().numpy().transpose(0, 2, 3, 1)\n return image\n\n def test_vae_image_processor_pt(self):\n image_processor = VaeImageProcessor(do_resize=False, do_normalize=True)\n\n input_pt = self.dummy_sample\n input_np = self.to_np(input_pt)\n\n for output_type in [\"pt\", \"np\", \"pil\"]:\n out = image_processor.postprocess(\n image_processor.preprocess(input_pt),\n output_type=output_type,\n )\n out_np = self.to_np(out)\n in_np = (input_np * 255).round() if output_type == \"pil\" else input_np\n assert (\n np.abs(in_np - out_np).max() < 1e-6\n ), f\"decoded output does not match input for output_type {output_type}\"\n\n def test_vae_image_processor_np(self):\n image_processor = VaeImageProcessor(do_resize=False, do_normalize=True)\n input_np = self.dummy_sample.cpu().numpy().transpose(0, 2, 3, 1)\n\n for output_type in [\"pt\", \"np\", \"pil\"]:\n out = image_processor.postprocess(image_processor.preprocess(input_np), output_type=output_type)\n\n out_np = self.to_np(out)\n in_np = (input_np * 255).round() if output_type == \"pil\" else input_np\n assert (\n np.abs(in_np - out_np).max() < 1e-6\n ), f\"decoded output does not match input for output_type {output_type}\"\n\n def test_vae_image_processor_pil(self):\n image_processor = VaeImageProcessor(do_resize=False, do_normalize=True)\n\n input_np = self.dummy_sample.cpu().numpy().transpose(0, 2, 3, 1)\n input_pil = image_processor.numpy_to_pil(input_np)\n\n for output_type in [\"pt\", \"np\", \"pil\"]:\n out = image_processor.postprocess(image_processor.preprocess(input_pil), output_type=output_type)\n for i, o in zip(input_pil, out):\n in_np = np.array(i)\n out_np = self.to_np(out) if output_type == \"pil\" else (self.to_np(out) * 255).round()\n assert (\n np.abs(in_np - out_np).max() < 1e-6\n ), f\"decoded output does not match input for output_type {output_type}\"\n\n def test_preprocess_input_3d(self):\n image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)\n\n input_pt_4d = self.dummy_sample\n input_pt_3d = input_pt_4d.squeeze(0)\n\n out_pt_4d = image_processor.postprocess(\n image_processor.preprocess(input_pt_4d),\n output_type=\"np\",\n )\n out_pt_3d = image_processor.postprocess(\n image_processor.preprocess(input_pt_3d),\n output_type=\"np\",\n )\n\n input_np_4d = self.to_np(self.dummy_sample)\n input_np_3d = input_np_4d.squeeze(0)\n\n out_np_4d = image_processor.postprocess(\n image_processor.preprocess(input_np_4d),\n output_type=\"np\",\n )\n out_np_3d = image_processor.postprocess(\n image_processor.preprocess(input_np_3d),\n output_type=\"np\",\n )\n\n assert np.abs(out_pt_4d - out_pt_3d).max() < 1e-6\n assert np.abs(out_np_4d - out_np_3d).max() < 1e-6\n\n def test_preprocess_input_list(self):\n image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)\n\n input_pt_4d = self.dummy_sample\n input_pt_list = list(input_pt_4d)\n\n out_pt_4d = image_processor.postprocess(\n image_processor.preprocess(input_pt_4d),\n output_type=\"np\",\n )\n\n out_pt_list = image_processor.postprocess(\n image_processor.preprocess(input_pt_list),\n output_type=\"np\",\n )\n\n input_np_4d = self.to_np(self.dummy_sample)\n input_np_list = list(input_np_4d)\n\n out_np_4d = image_processor.postprocess(\n image_processor.preprocess(input_np_4d),\n output_type=\"np\",\n )\n\n out_np_list = image_processor.postprocess(\n image_processor.preprocess(input_np_list),\n output_type=\"np\",\n )\n\n assert np.abs(out_pt_4d - out_pt_list).max() < 1e-6\n assert np.abs(out_np_4d - out_np_list).max() < 1e-6\n\n def test_preprocess_input_mask_3d(self):\n image_processor = VaeImageProcessor(\n do_resize=False, do_normalize=False, do_binarize=True, do_convert_grayscale=True\n )\n\n input_pt_4d = self.dummy_mask\n input_pt_3d = input_pt_4d.squeeze(0)\n input_pt_2d = input_pt_3d.squeeze(0)\n\n out_pt_4d = image_processor.postprocess(\n image_processor.preprocess(input_pt_4d),\n output_type=\"np\",\n )\n out_pt_3d = image_processor.postprocess(\n image_processor.preprocess(input_pt_3d),\n output_type=\"np\",\n )\n\n out_pt_2d = image_processor.postprocess(\n image_processor.preprocess(input_pt_2d),\n output_type=\"np\",\n )\n\n input_np_4d = self.to_np(self.dummy_mask)\n input_np_3d = input_np_4d.squeeze(0)\n input_np_3d_1 = input_np_4d.squeeze(-1)\n input_np_2d = input_np_3d.squeeze(-1)\n\n out_np_4d = image_processor.postprocess(\n image_processor.preprocess(input_np_4d),\n output_type=\"np\",\n )\n out_np_3d = image_processor.postprocess(\n image_processor.preprocess(input_np_3d),\n output_type=\"np\",\n )\n\n out_np_3d_1 = image_processor.postprocess(\n image_processor.preprocess(input_np_3d_1),\n output_type=\"np\",\n )\n\n out_np_2d = image_processor.postprocess(\n image_processor.preprocess(input_np_2d),\n output_type=\"np\",\n )\n\n assert np.abs(out_pt_4d - out_pt_3d).max() == 0\n assert np.abs(out_pt_4d - out_pt_2d).max() == 0\n assert np.abs(out_np_4d - out_np_3d).max() == 0\n assert np.abs(out_np_4d - out_np_3d_1).max() == 0\n assert np.abs(out_np_4d - out_np_2d).max() == 0\n\n def test_preprocess_input_mask_list(self):\n image_processor = VaeImageProcessor(do_resize=False, do_normalize=False, do_convert_grayscale=True)\n\n input_pt_4d = self.dummy_mask\n input_pt_3d = input_pt_4d.squeeze(0)\n input_pt_2d = input_pt_3d.squeeze(0)\n\n inputs_pt = [input_pt_4d, input_pt_3d, input_pt_2d]\n inputs_pt_list = [[input_pt] for input_pt in inputs_pt]\n\n for input_pt, input_pt_list in zip(inputs_pt, inputs_pt_list):\n out_pt = image_processor.postprocess(\n image_processor.preprocess(input_pt),\n output_type=\"np\",\n )\n out_pt_list = image_processor.postprocess(\n image_processor.preprocess(input_pt_list),\n output_type=\"np\",\n )\n assert np.abs(out_pt - out_pt_list).max() < 1e-6\n\n input_np_4d = self.to_np(self.dummy_mask)\n input_np_3d = input_np_4d.squeeze(0)\n input_np_2d = input_np_3d.squeeze(-1)\n\n inputs_np = [input_np_4d, input_np_3d, input_np_2d]\n inputs_np_list = [[input_np] for input_np in inputs_np]\n\n for input_np, input_np_list in zip(inputs_np, inputs_np_list):\n out_np = image_processor.postprocess(\n image_processor.preprocess(input_np),\n output_type=\"np\",\n )\n out_np_list = image_processor.postprocess(\n image_processor.preprocess(input_np_list),\n output_type=\"np\",\n )\n assert np.abs(out_np - out_np_list).max() < 1e-6\n\n def test_preprocess_input_mask_3d_batch(self):\n image_processor = VaeImageProcessor(do_resize=False, do_normalize=False, do_convert_grayscale=True)\n\n # create a dummy mask input with batch_size 2\n dummy_mask_batch = torch.cat([self.dummy_mask] * 2, axis=0)\n\n # squeeze out the channel dimension\n input_pt_3d = dummy_mask_batch.squeeze(1)\n input_np_3d = self.to_np(dummy_mask_batch).squeeze(-1)\n\n input_pt_3d_list = list(input_pt_3d)\n input_np_3d_list = list(input_np_3d)\n\n out_pt_3d = image_processor.postprocess(\n image_processor.preprocess(input_pt_3d),\n output_type=\"np\",\n )\n out_pt_3d_list = image_processor.postprocess(\n image_processor.preprocess(input_pt_3d_list),\n output_type=\"np\",\n )\n\n assert np.abs(out_pt_3d - out_pt_3d_list).max() < 1e-6\n\n out_np_3d = image_processor.postprocess(\n image_processor.preprocess(input_np_3d),\n output_type=\"np\",\n )\n out_np_3d_list = image_processor.postprocess(\n image_processor.preprocess(input_np_3d_list),\n output_type=\"np\",\n )\n\n assert np.abs(out_np_3d - out_np_3d_list).max() < 1e-6\n\n def test_vae_image_processor_resize_pt(self):\n image_processor = VaeImageProcessor(do_resize=True, vae_scale_factor=1)\n input_pt = self.dummy_sample\n b, c, h, w = input_pt.shape\n scale = 2\n out_pt = image_processor.resize(image=input_pt, height=h // scale, width=w // scale)\n exp_pt_shape = (b, c, h // scale, w // scale)\n assert (\n out_pt.shape == exp_pt_shape\n ), f\"resized image output shape '{out_pt.shape}' didn't match expected shape '{exp_pt_shape}'.\"\n\n def test_vae_image_processor_resize_np(self):\n image_processor = VaeImageProcessor(do_resize=True, vae_scale_factor=1)\n input_pt = self.dummy_sample\n b, c, h, w = input_pt.shape\n scale = 2\n input_np = self.to_np(input_pt)\n out_np = image_processor.resize(image=input_np, height=h // scale, width=w // scale)\n exp_np_shape = (b, h // scale, w // scale, c)\n assert (\n out_np.shape == exp_np_shape\n ), f\"resized image output shape '{out_np.shape}' didn't match expected shape '{exp_np_shape}'.\"\n" }, { "path": "diffusers/tests/others/test_outputs.py", "content": "import pickle as pkl\nimport unittest\nfrom dataclasses import dataclass\nfrom typing import List, Union\n\nimport numpy as np\nimport PIL.Image\n\nfrom diffusers.utils.outputs import BaseOutput\nfrom diffusers.utils.testing_utils import require_torch\n\n\n@dataclass\nclass CustomOutput(BaseOutput):\n images: Union[List[PIL.Image.Image], np.ndarray]\n\n\nclass ConfigTester(unittest.TestCase):\n def test_outputs_single_attribute(self):\n outputs = CustomOutput(images=np.random.rand(1, 3, 4, 4))\n\n # check every way of getting the attribute\n assert isinstance(outputs.images, np.ndarray)\n assert outputs.images.shape == (1, 3, 4, 4)\n assert isinstance(outputs[\"images\"], np.ndarray)\n assert outputs[\"images\"].shape == (1, 3, 4, 4)\n assert isinstance(outputs[0], np.ndarray)\n assert outputs[0].shape == (1, 3, 4, 4)\n\n # test with a non-tensor attribute\n outputs = CustomOutput(images=[PIL.Image.new(\"RGB\", (4, 4))])\n\n # check every way of getting the attribute\n assert isinstance(outputs.images, list)\n assert isinstance(outputs.images[0], PIL.Image.Image)\n assert isinstance(outputs[\"images\"], list)\n assert isinstance(outputs[\"images\"][0], PIL.Image.Image)\n assert isinstance(outputs[0], list)\n assert isinstance(outputs[0][0], PIL.Image.Image)\n\n def test_outputs_dict_init(self):\n # test output reinitialization with a `dict` for compatibility with `accelerate`\n outputs = CustomOutput({\"images\": np.random.rand(1, 3, 4, 4)})\n\n # check every way of getting the attribute\n assert isinstance(outputs.images, np.ndarray)\n assert outputs.images.shape == (1, 3, 4, 4)\n assert isinstance(outputs[\"images\"], np.ndarray)\n assert outputs[\"images\"].shape == (1, 3, 4, 4)\n assert isinstance(outputs[0], np.ndarray)\n assert outputs[0].shape == (1, 3, 4, 4)\n\n # test with a non-tensor attribute\n outputs = CustomOutput({\"images\": [PIL.Image.new(\"RGB\", (4, 4))]})\n\n # check every way of getting the attribute\n assert isinstance(outputs.images, list)\n assert isinstance(outputs.images[0], PIL.Image.Image)\n assert isinstance(outputs[\"images\"], list)\n assert isinstance(outputs[\"images\"][0], PIL.Image.Image)\n assert isinstance(outputs[0], list)\n assert isinstance(outputs[0][0], PIL.Image.Image)\n\n def test_outputs_serialization(self):\n outputs_orig = CustomOutput(images=[PIL.Image.new(\"RGB\", (4, 4))])\n serialized = pkl.dumps(outputs_orig)\n outputs_copy = pkl.loads(serialized)\n\n # Check original and copy are equal\n assert dir(outputs_orig) == dir(outputs_copy)\n assert dict(outputs_orig) == dict(outputs_copy)\n assert vars(outputs_orig) == vars(outputs_copy)\n\n @require_torch\n def test_torch_pytree(self):\n # ensure torch.utils._pytree treats ModelOutput subclasses as nodes (and not leaves)\n # this is important for DistributedDataParallel gradient synchronization with static_graph=True\n import torch\n import torch.utils._pytree\n\n data = np.random.rand(1, 3, 4, 4)\n x = CustomOutput(images=data)\n self.assertFalse(torch.utils._pytree._is_leaf(x))\n\n expected_flat_outs = [data]\n expected_tree_spec = torch.utils._pytree.TreeSpec(CustomOutput, [\"images\"], [torch.utils._pytree.LeafSpec()])\n\n actual_flat_outs, actual_tree_spec = torch.utils._pytree.tree_flatten(x)\n self.assertEqual(expected_flat_outs, actual_flat_outs)\n self.assertEqual(expected_tree_spec, actual_tree_spec)\n\n unflattened_x = torch.utils._pytree.tree_unflatten(actual_flat_outs, actual_tree_spec)\n self.assertEqual(x, unflattened_x)\n" }, { "path": "diffusers/tests/others/test_training.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\n\nimport torch\n\nfrom diffusers import DDIMScheduler, DDPMScheduler, UNet2DModel\nfrom diffusers.training_utils import set_seed\nfrom diffusers.utils.testing_utils import slow\n\n\ntorch.backends.cuda.matmul.allow_tf32 = False\n\n\nclass TrainingTests(unittest.TestCase):\n def get_model_optimizer(self, resolution=32):\n set_seed(0)\n model = UNet2DModel(sample_size=resolution, in_channels=3, out_channels=3)\n optimizer = torch.optim.SGD(model.parameters(), lr=0.0001)\n return model, optimizer\n\n @slow\n def test_training_step_equality(self):\n device = \"cpu\" # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable\n ddpm_scheduler = DDPMScheduler(\n num_train_timesteps=1000,\n beta_start=0.0001,\n beta_end=0.02,\n beta_schedule=\"linear\",\n clip_sample=True,\n )\n ddim_scheduler = DDIMScheduler(\n num_train_timesteps=1000,\n beta_start=0.0001,\n beta_end=0.02,\n beta_schedule=\"linear\",\n clip_sample=True,\n )\n\n assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps\n\n # shared batches for DDPM and DDIM\n set_seed(0)\n clean_images = [torch.randn((4, 3, 32, 32)).clip(-1, 1).to(device) for _ in range(4)]\n noise = [torch.randn((4, 3, 32, 32)).to(device) for _ in range(4)]\n timesteps = [torch.randint(0, 1000, (4,)).long().to(device) for _ in range(4)]\n\n # train with a DDPM scheduler\n model, optimizer = self.get_model_optimizer(resolution=32)\n model.train().to(device)\n for i in range(4):\n optimizer.zero_grad()\n ddpm_noisy_images = ddpm_scheduler.add_noise(clean_images[i], noise[i], timesteps[i])\n ddpm_noise_pred = model(ddpm_noisy_images, timesteps[i]).sample\n loss = torch.nn.functional.mse_loss(ddpm_noise_pred, noise[i])\n loss.backward()\n optimizer.step()\n del model, optimizer\n\n # recreate the model and optimizer, and retry with DDIM\n model, optimizer = self.get_model_optimizer(resolution=32)\n model.train().to(device)\n for i in range(4):\n optimizer.zero_grad()\n ddim_noisy_images = ddim_scheduler.add_noise(clean_images[i], noise[i], timesteps[i])\n ddim_noise_pred = model(ddim_noisy_images, timesteps[i]).sample\n loss = torch.nn.functional.mse_loss(ddim_noise_pred, noise[i])\n loss.backward()\n optimizer.step()\n del model, optimizer\n\n self.assertTrue(torch.allclose(ddpm_noisy_images, ddim_noisy_images, atol=1e-5))\n self.assertTrue(torch.allclose(ddpm_noise_pred, ddim_noise_pred, atol=1e-5))\n" }, { "path": "diffusers/tests/others/test_utils.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os\nimport unittest\n\nimport pytest\n\nfrom diffusers import __version__\nfrom diffusers.utils import deprecate\nfrom diffusers.utils.testing_utils import str_to_bool\n\n\n# Used to test the hub\nUSER = \"__DUMMY_TRANSFORMERS_USER__\"\nENDPOINT_STAGING = \"https://hub-ci.huggingface.co\"\n\n# Not critical, only usable on the sandboxed CI instance.\nTOKEN = \"hf_94wBhPGp6KrrTH3KDchhKpRxZwd6dmHWLL\"\n\n\nclass DeprecateTester(unittest.TestCase):\n higher_version = \".\".join([str(int(__version__.split(\".\")[0]) + 1)] + __version__.split(\".\")[1:])\n lower_version = \"0.0.1\"\n\n def test_deprecate_function_arg(self):\n kwargs = {\"deprecated_arg\": 4}\n\n with self.assertWarns(FutureWarning) as warning:\n output = deprecate(\"deprecated_arg\", self.higher_version, \"message\", take_from=kwargs)\n\n assert output == 4\n assert (\n str(warning.warning)\n == f\"The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}.\"\n \" message\"\n )\n\n def test_deprecate_function_arg_tuple(self):\n kwargs = {\"deprecated_arg\": 4}\n\n with self.assertWarns(FutureWarning) as warning:\n output = deprecate((\"deprecated_arg\", self.higher_version, \"message\"), take_from=kwargs)\n\n assert output == 4\n assert (\n str(warning.warning)\n == f\"The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}.\"\n \" message\"\n )\n\n def test_deprecate_function_args(self):\n kwargs = {\"deprecated_arg_1\": 4, \"deprecated_arg_2\": 8}\n with self.assertWarns(FutureWarning) as warning:\n output_1, output_2 = deprecate(\n (\"deprecated_arg_1\", self.higher_version, \"Hey\"),\n (\"deprecated_arg_2\", self.higher_version, \"Hey\"),\n take_from=kwargs,\n )\n assert output_1 == 4\n assert output_2 == 8\n assert (\n str(warning.warnings[0].message)\n == \"The `deprecated_arg_1` argument is deprecated and will be removed in version\"\n f\" {self.higher_version}. Hey\"\n )\n assert (\n str(warning.warnings[1].message)\n == \"The `deprecated_arg_2` argument is deprecated and will be removed in version\"\n f\" {self.higher_version}. Hey\"\n )\n\n def test_deprecate_function_incorrect_arg(self):\n kwargs = {\"deprecated_arg\": 4}\n\n with self.assertRaises(TypeError) as error:\n deprecate((\"wrong_arg\", self.higher_version, \"message\"), take_from=kwargs)\n\n assert \"test_deprecate_function_incorrect_arg in\" in str(error.exception)\n assert \"line\" in str(error.exception)\n assert \"got an unexpected keyword argument `deprecated_arg`\" in str(error.exception)\n\n def test_deprecate_arg_no_kwarg(self):\n with self.assertWarns(FutureWarning) as warning:\n deprecate((\"deprecated_arg\", self.higher_version, \"message\"))\n\n assert (\n str(warning.warning)\n == f\"`deprecated_arg` is deprecated and will be removed in version {self.higher_version}. message\"\n )\n\n def test_deprecate_args_no_kwarg(self):\n with self.assertWarns(FutureWarning) as warning:\n deprecate(\n (\"deprecated_arg_1\", self.higher_version, \"Hey\"),\n (\"deprecated_arg_2\", self.higher_version, \"Hey\"),\n )\n assert (\n str(warning.warnings[0].message)\n == f\"`deprecated_arg_1` is deprecated and will be removed in version {self.higher_version}. Hey\"\n )\n assert (\n str(warning.warnings[1].message)\n == f\"`deprecated_arg_2` is deprecated and will be removed in version {self.higher_version}. Hey\"\n )\n\n def test_deprecate_class_obj(self):\n class Args:\n arg = 5\n\n with self.assertWarns(FutureWarning) as warning:\n arg = deprecate((\"arg\", self.higher_version, \"message\"), take_from=Args())\n\n assert arg == 5\n assert (\n str(warning.warning)\n == f\"The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message\"\n )\n\n def test_deprecate_class_objs(self):\n class Args:\n arg = 5\n foo = 7\n\n with self.assertWarns(FutureWarning) as warning:\n arg_1, arg_2 = deprecate(\n (\"arg\", self.higher_version, \"message\"),\n (\"foo\", self.higher_version, \"message\"),\n (\"does not exist\", self.higher_version, \"message\"),\n take_from=Args(),\n )\n\n assert arg_1 == 5\n assert arg_2 == 7\n assert (\n str(warning.warning)\n == f\"The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message\"\n )\n assert (\n str(warning.warnings[0].message)\n == f\"The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message\"\n )\n assert (\n str(warning.warnings[1].message)\n == f\"The `foo` attribute is deprecated and will be removed in version {self.higher_version}. message\"\n )\n\n def test_deprecate_incorrect_version(self):\n kwargs = {\"deprecated_arg\": 4}\n\n with self.assertRaises(ValueError) as error:\n deprecate((\"wrong_arg\", self.lower_version, \"message\"), take_from=kwargs)\n\n assert (\n str(error.exception)\n == \"The deprecation tuple ('wrong_arg', '0.0.1', 'message') should be removed since diffusers' version\"\n f\" {__version__} is >= {self.lower_version}\"\n )\n\n def test_deprecate_incorrect_no_standard_warn(self):\n with self.assertWarns(FutureWarning) as warning:\n deprecate((\"deprecated_arg\", self.higher_version, \"This message is better!!!\"), standard_warn=False)\n\n assert str(warning.warning) == \"This message is better!!!\"\n\n def test_deprecate_stacklevel(self):\n with self.assertWarns(FutureWarning) as warning:\n deprecate((\"deprecated_arg\", self.higher_version, \"This message is better!!!\"), standard_warn=False)\n assert str(warning.warning) == \"This message is better!!!\"\n assert \"diffusers/tests/others/test_utils.py\" in warning.filename\n\n\ndef parse_flag_from_env(key, default=False):\n try:\n value = os.environ[key]\n except KeyError:\n # KEY isn't set, default to `default`.\n _value = default\n else:\n # KEY is set, convert it to True or False.\n try:\n _value = str_to_bool(value)\n except ValueError:\n # More values are supported, but let's keep the message simple.\n raise ValueError(f\"If set, {key} must be yes or no.\")\n return _value\n\n\n_run_staging = parse_flag_from_env(\"HUGGINGFACE_CO_STAGING\", default=False)\n\n\ndef is_staging_test(test_case):\n \"\"\"\n Decorator marking a test as a staging test.\n\n Those tests will run using the staging environment of huggingface.co instead of the real model hub.\n \"\"\"\n if not _run_staging:\n return unittest.skip(\"test is staging test\")(test_case)\n else:\n return pytest.mark.is_staging_test()(test_case)\n" }, { "path": "diffusers/tests/others/test_video_processor.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom parameterized import parameterized\n\nfrom diffusers.video_processor import VideoProcessor\n\n\nnp.random.seed(0)\ntorch.manual_seed(0)\n\n\nclass VideoProcessorTest(unittest.TestCase):\n def get_dummy_sample(self, input_type):\n batch_size = 1\n num_frames = 5\n num_channels = 3\n height = 8\n width = 8\n\n def generate_image():\n return PIL.Image.fromarray(np.random.randint(0, 256, size=(height, width, num_channels)).astype(\"uint8\"))\n\n def generate_4d_array():\n return np.random.rand(num_frames, height, width, num_channels)\n\n def generate_5d_array():\n return np.random.rand(batch_size, num_frames, height, width, num_channels)\n\n def generate_4d_tensor():\n return torch.rand(num_frames, num_channels, height, width)\n\n def generate_5d_tensor():\n return torch.rand(batch_size, num_frames, num_channels, height, width)\n\n if input_type == \"list_images\":\n sample = [generate_image() for _ in range(num_frames)]\n elif input_type == \"list_list_images\":\n sample = [[generate_image() for _ in range(num_frames)] for _ in range(num_frames)]\n elif input_type == \"list_4d_np\":\n sample = [generate_4d_array() for _ in range(num_frames)]\n elif input_type == \"list_list_4d_np\":\n sample = [[generate_4d_array() for _ in range(num_frames)] for _ in range(num_frames)]\n elif input_type == \"list_5d_np\":\n sample = [generate_5d_array() for _ in range(num_frames)]\n elif input_type == \"5d_np\":\n sample = generate_5d_array()\n elif input_type == \"list_4d_pt\":\n sample = [generate_4d_tensor() for _ in range(num_frames)]\n elif input_type == \"list_list_4d_pt\":\n sample = [[generate_4d_tensor() for _ in range(num_frames)] for _ in range(num_frames)]\n elif input_type == \"list_5d_pt\":\n sample = [generate_5d_tensor() for _ in range(num_frames)]\n elif input_type == \"5d_pt\":\n sample = generate_5d_tensor()\n\n return sample\n\n def to_np(self, video):\n # List of images.\n if isinstance(video[0], PIL.Image.Image):\n video = np.stack([np.array(i) for i in video], axis=0)\n\n # List of list of images.\n elif isinstance(video, list) and isinstance(video[0][0], PIL.Image.Image):\n frames = []\n for vid in video:\n all_current_frames = np.stack([np.array(i) for i in vid], axis=0)\n frames.append(all_current_frames)\n video = np.stack([np.array(frame) for frame in frames], axis=0)\n\n # List of 4d/5d {ndarrays, torch tensors}.\n elif isinstance(video, list) and isinstance(video[0], (torch.Tensor, np.ndarray)):\n if isinstance(video[0], np.ndarray):\n video = np.stack(video, axis=0) if video[0].ndim == 4 else np.concatenate(video, axis=0)\n else:\n if video[0].ndim == 4:\n video = np.stack([i.cpu().numpy().transpose(0, 2, 3, 1) for i in video], axis=0)\n elif video[0].ndim == 5:\n video = np.concatenate([i.cpu().numpy().transpose(0, 1, 3, 4, 2) for i in video], axis=0)\n\n # List of list of 4d/5d {ndarrays, torch tensors}.\n elif (\n isinstance(video, list)\n and isinstance(video[0], list)\n and isinstance(video[0][0], (torch.Tensor, np.ndarray))\n ):\n all_frames = []\n for list_of_videos in video:\n temp_frames = []\n for vid in list_of_videos:\n if vid.ndim == 4:\n current_vid_frames = np.stack(\n [i if isinstance(i, np.ndarray) else i.cpu().numpy().transpose(1, 2, 0) for i in vid],\n axis=0,\n )\n elif vid.ndim == 5:\n current_vid_frames = np.concatenate(\n [i if isinstance(i, np.ndarray) else i.cpu().numpy().transpose(0, 2, 3, 1) for i in vid],\n axis=0,\n )\n temp_frames.append(current_vid_frames)\n temp_frames = np.stack(temp_frames, axis=0)\n all_frames.append(temp_frames)\n\n video = np.concatenate(all_frames, axis=0)\n\n # Just 5d {ndarrays, torch tensors}.\n elif isinstance(video, (torch.Tensor, np.ndarray)) and video.ndim == 5:\n video = video if isinstance(video, np.ndarray) else video.cpu().numpy().transpose(0, 1, 3, 4, 2)\n\n return video\n\n @parameterized.expand([\"list_images\", \"list_list_images\"])\n def test_video_processor_pil(self, input_type):\n video_processor = VideoProcessor(do_resize=False, do_normalize=True)\n\n input = self.get_dummy_sample(input_type=input_type)\n\n for output_type in [\"pt\", \"np\", \"pil\"]:\n out = video_processor.postprocess_video(video_processor.preprocess_video(input), output_type=output_type)\n out_np = self.to_np(out)\n input_np = self.to_np(input).astype(\"float32\") / 255.0 if output_type != \"pil\" else self.to_np(input)\n assert np.abs(input_np - out_np).max() < 1e-6, f\"Decoded output does not match input for {output_type=}\"\n\n @parameterized.expand([\"list_4d_np\", \"list_5d_np\", \"5d_np\"])\n def test_video_processor_np(self, input_type):\n video_processor = VideoProcessor(do_resize=False, do_normalize=True)\n\n input = self.get_dummy_sample(input_type=input_type)\n\n for output_type in [\"pt\", \"np\", \"pil\"]:\n out = video_processor.postprocess_video(video_processor.preprocess_video(input), output_type=output_type)\n out_np = self.to_np(out)\n input_np = (\n (self.to_np(input) * 255.0).round().astype(\"uint8\") if output_type == \"pil\" else self.to_np(input)\n )\n assert np.abs(input_np - out_np).max() < 1e-6, f\"Decoded output does not match input for {output_type=}\"\n\n @parameterized.expand([\"list_4d_pt\", \"list_5d_pt\", \"5d_pt\"])\n def test_video_processor_pt(self, input_type):\n video_processor = VideoProcessor(do_resize=False, do_normalize=True)\n\n input = self.get_dummy_sample(input_type=input_type)\n\n for output_type in [\"pt\", \"np\", \"pil\"]:\n out = video_processor.postprocess_video(video_processor.preprocess_video(input), output_type=output_type)\n out_np = self.to_np(out)\n input_np = (\n (self.to_np(input) * 255.0).round().astype(\"uint8\") if output_type == \"pil\" else self.to_np(input)\n )\n assert np.abs(input_np - out_np).max() < 1e-6, f\"Decoded output does not match input for {output_type=}\"\n" }, { "path": "diffusers/tests/pipelines/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/amused/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/amused/test_amused.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom diffusers import AmusedPipeline, AmusedScheduler, UVit2DModel, VQModel\nfrom diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device\n\nfrom ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass AmusedPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = AmusedPipeline\n params = TEXT_TO_IMAGE_PARAMS | {\"encoder_hidden_states\", \"negative_encoder_hidden_states\"}\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n transformer = UVit2DModel(\n hidden_size=8,\n use_bias=False,\n hidden_dropout=0.0,\n cond_embed_dim=8,\n micro_cond_encode_dim=2,\n micro_cond_embed_dim=10,\n encoder_hidden_size=8,\n vocab_size=32,\n codebook_size=8,\n in_channels=8,\n block_out_channels=8,\n num_res_blocks=1,\n downsample=True,\n upsample=True,\n block_num_heads=1,\n num_hidden_layers=1,\n num_attention_heads=1,\n attention_dropout=0.0,\n intermediate_size=8,\n layer_norm_eps=1e-06,\n ln_elementwise_affine=True,\n )\n scheduler = AmusedScheduler(mask_token_id=31)\n torch.manual_seed(0)\n vqvae = VQModel(\n act_fn=\"silu\",\n block_out_channels=[8],\n down_block_types=[\n \"DownEncoderBlock2D\",\n ],\n in_channels=3,\n latent_channels=8,\n layers_per_block=1,\n norm_num_groups=8,\n num_vq_embeddings=8,\n out_channels=3,\n sample_size=8,\n up_block_types=[\n \"UpDecoderBlock2D\",\n ],\n mid_block_add_attention=False,\n lookup_from_codebook=True,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=8,\n intermediate_size=8,\n layer_norm_eps=1e-05,\n num_attention_heads=1,\n num_hidden_layers=1,\n pad_token_id=1,\n vocab_size=1000,\n projection_dim=8,\n )\n text_encoder = CLIPTextModelWithProjection(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"transformer\": transformer,\n \"scheduler\": scheduler,\n \"vqvae\": vqvae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n \"height\": 4,\n \"width\": 4,\n }\n return inputs\n\n def test_inference_batch_consistent(self, batch_sizes=[2]):\n self._test_inference_batch_consistent(batch_sizes=batch_sizes, batch_generator=False)\n\n @unittest.skip(\"aMUSEd does not support lists of generators\")\n def test_inference_batch_single_identical(self):\n ...\n\n\n@slow\n@require_torch_gpu\nclass AmusedPipelineSlowTests(unittest.TestCase):\n def test_amused_256(self):\n pipe = AmusedPipeline.from_pretrained(\"amused/amused-256\")\n pipe.to(torch_device)\n\n image = pipe(\"dog\", generator=torch.Generator().manual_seed(0), num_inference_steps=2, output_type=\"np\").images\n\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 256, 256, 3)\n expected_slice = np.array([0.4011, 0.3992, 0.3790, 0.3856, 0.3772, 0.3711, 0.3919, 0.3850, 0.3625])\n assert np.abs(image_slice - expected_slice).max() < 3e-3\n\n def test_amused_256_fp16(self):\n pipe = AmusedPipeline.from_pretrained(\"amused/amused-256\", variant=\"fp16\", torch_dtype=torch.float16)\n pipe.to(torch_device)\n\n image = pipe(\"dog\", generator=torch.Generator().manual_seed(0), num_inference_steps=2, output_type=\"np\").images\n\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 256, 256, 3)\n expected_slice = np.array([0.0554, 0.05129, 0.0344, 0.0452, 0.0476, 0.0271, 0.0495, 0.0527, 0.0158])\n assert np.abs(image_slice - expected_slice).max() < 7e-3\n\n def test_amused_512(self):\n pipe = AmusedPipeline.from_pretrained(\"amused/amused-512\")\n pipe.to(torch_device)\n\n image = pipe(\"dog\", generator=torch.Generator().manual_seed(0), num_inference_steps=2, output_type=\"np\").images\n\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.9960, 0.9960, 0.9946, 0.9980, 0.9947, 0.9932, 0.9960, 0.9961, 0.9947])\n assert np.abs(image_slice - expected_slice).max() < 3e-3\n\n def test_amused_512_fp16(self):\n pipe = AmusedPipeline.from_pretrained(\"amused/amused-512\", variant=\"fp16\", torch_dtype=torch.float16)\n pipe.to(torch_device)\n\n image = pipe(\"dog\", generator=torch.Generator().manual_seed(0), num_inference_steps=2, output_type=\"np\").images\n\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.9983, 1.0, 1.0, 1.0, 1.0, 0.9989, 0.9994, 0.9976, 0.9977])\n assert np.abs(image_slice - expected_slice).max() < 3e-3\n" }, { "path": "diffusers/tests/pipelines/amused/test_amused_img2img.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom diffusers import AmusedImg2ImgPipeline, AmusedScheduler, UVit2DModel, VQModel\nfrom diffusers.utils import load_image\nfrom diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device\n\nfrom ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass AmusedImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = AmusedImg2ImgPipeline\n params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {\"height\", \"width\", \"latents\"}\n batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS\n required_optional_params = PipelineTesterMixin.required_optional_params - {\n \"latents\",\n }\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n transformer = UVit2DModel(\n hidden_size=8,\n use_bias=False,\n hidden_dropout=0.0,\n cond_embed_dim=8,\n micro_cond_encode_dim=2,\n micro_cond_embed_dim=10,\n encoder_hidden_size=8,\n vocab_size=32,\n codebook_size=8,\n in_channels=8,\n block_out_channels=8,\n num_res_blocks=1,\n downsample=True,\n upsample=True,\n block_num_heads=1,\n num_hidden_layers=1,\n num_attention_heads=1,\n attention_dropout=0.0,\n intermediate_size=8,\n layer_norm_eps=1e-06,\n ln_elementwise_affine=True,\n )\n scheduler = AmusedScheduler(mask_token_id=31)\n torch.manual_seed(0)\n vqvae = VQModel(\n act_fn=\"silu\",\n block_out_channels=[8],\n down_block_types=[\n \"DownEncoderBlock2D\",\n ],\n in_channels=3,\n latent_channels=8,\n layers_per_block=1,\n norm_num_groups=8,\n num_vq_embeddings=32, # reducing this to 16 or 8 -> RuntimeError: \"cdist_cuda\" not implemented for 'Half'\n out_channels=3,\n sample_size=8,\n up_block_types=[\n \"UpDecoderBlock2D\",\n ],\n mid_block_add_attention=False,\n lookup_from_codebook=True,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=8,\n intermediate_size=8,\n layer_norm_eps=1e-05,\n num_attention_heads=1,\n num_hidden_layers=1,\n pad_token_id=1,\n vocab_size=1000,\n projection_dim=8,\n )\n text_encoder = CLIPTextModelWithProjection(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"transformer\": transformer,\n \"scheduler\": scheduler,\n \"vqvae\": vqvae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n image = torch.full((1, 3, 4, 4), 1.0, dtype=torch.float32, device=device)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n \"image\": image,\n }\n return inputs\n\n def test_inference_batch_consistent(self, batch_sizes=[2]):\n self._test_inference_batch_consistent(batch_sizes=batch_sizes, batch_generator=False)\n\n @unittest.skip(\"aMUSEd does not support lists of generators\")\n def test_inference_batch_single_identical(self):\n ...\n\n\n@slow\n@require_torch_gpu\nclass AmusedImg2ImgPipelineSlowTests(unittest.TestCase):\n def test_amused_256(self):\n pipe = AmusedImg2ImgPipeline.from_pretrained(\"amused/amused-256\")\n pipe.to(torch_device)\n\n image = (\n load_image(\"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains.jpg\")\n .resize((256, 256))\n .convert(\"RGB\")\n )\n\n image = pipe(\n \"winter mountains\",\n image,\n generator=torch.Generator().manual_seed(0),\n num_inference_steps=2,\n output_type=\"np\",\n ).images\n\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 256, 256, 3)\n expected_slice = np.array([0.9993, 1.0, 0.9996, 1.0, 0.9995, 0.9925, 0.9990, 0.9954, 1.0])\n\n assert np.abs(image_slice - expected_slice).max() < 1e-2\n\n def test_amused_256_fp16(self):\n pipe = AmusedImg2ImgPipeline.from_pretrained(\"amused/amused-256\", torch_dtype=torch.float16, variant=\"fp16\")\n pipe.to(torch_device)\n\n image = (\n load_image(\"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains.jpg\")\n .resize((256, 256))\n .convert(\"RGB\")\n )\n\n image = pipe(\n \"winter mountains\",\n image,\n generator=torch.Generator().manual_seed(0),\n num_inference_steps=2,\n output_type=\"np\",\n ).images\n\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 256, 256, 3)\n expected_slice = np.array([0.9980, 0.9980, 0.9940, 0.9944, 0.9960, 0.9908, 1.0, 1.0, 0.9986])\n\n assert np.abs(image_slice - expected_slice).max() < 1e-2\n\n def test_amused_512(self):\n pipe = AmusedImg2ImgPipeline.from_pretrained(\"amused/amused-512\")\n pipe.to(torch_device)\n\n image = (\n load_image(\"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains.jpg\")\n .resize((512, 512))\n .convert(\"RGB\")\n )\n\n image = pipe(\n \"winter mountains\",\n image,\n generator=torch.Generator().manual_seed(0),\n num_inference_steps=2,\n output_type=\"np\",\n ).images\n\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.1344, 0.0985, 0.0, 0.1194, 0.1809, 0.0765, 0.0854, 0.1371, 0.0933])\n assert np.abs(image_slice - expected_slice).max() < 0.1\n\n def test_amused_512_fp16(self):\n pipe = AmusedImg2ImgPipeline.from_pretrained(\"amused/amused-512\", variant=\"fp16\", torch_dtype=torch.float16)\n pipe.to(torch_device)\n\n image = (\n load_image(\"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains.jpg\")\n .resize((512, 512))\n .convert(\"RGB\")\n )\n\n image = pipe(\n \"winter mountains\",\n image,\n generator=torch.Generator().manual_seed(0),\n num_inference_steps=2,\n output_type=\"np\",\n ).images\n\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.1536, 0.1767, 0.0227, 0.1079, 0.2400, 0.1427, 0.1511, 0.1564, 0.1542])\n assert np.abs(image_slice - expected_slice).max() < 0.1\n" }, { "path": "diffusers/tests/pipelines/amused/test_amused_inpaint.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom diffusers import AmusedInpaintPipeline, AmusedScheduler, UVit2DModel, VQModel\nfrom diffusers.utils import load_image\nfrom diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device\n\nfrom ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass AmusedInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = AmusedInpaintPipeline\n params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {\"width\", \"height\"}\n batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS\n required_optional_params = PipelineTesterMixin.required_optional_params - {\n \"latents\",\n }\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n transformer = UVit2DModel(\n hidden_size=8,\n use_bias=False,\n hidden_dropout=0.0,\n cond_embed_dim=8,\n micro_cond_encode_dim=2,\n micro_cond_embed_dim=10,\n encoder_hidden_size=8,\n vocab_size=32,\n codebook_size=32, # codebook size needs to be consistent with num_vq_embeddings for inpaint tests\n in_channels=8,\n block_out_channels=8,\n num_res_blocks=1,\n downsample=True,\n upsample=True,\n block_num_heads=1,\n num_hidden_layers=1,\n num_attention_heads=1,\n attention_dropout=0.0,\n intermediate_size=8,\n layer_norm_eps=1e-06,\n ln_elementwise_affine=True,\n )\n scheduler = AmusedScheduler(mask_token_id=31)\n torch.manual_seed(0)\n vqvae = VQModel(\n act_fn=\"silu\",\n block_out_channels=[8],\n down_block_types=[\n \"DownEncoderBlock2D\",\n ],\n in_channels=3,\n latent_channels=8,\n layers_per_block=1,\n norm_num_groups=8,\n num_vq_embeddings=32, # reducing this to 16 or 8 -> RuntimeError: \"cdist_cuda\" not implemented for 'Half'\n out_channels=3,\n sample_size=8,\n up_block_types=[\n \"UpDecoderBlock2D\",\n ],\n mid_block_add_attention=False,\n lookup_from_codebook=True,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=8,\n intermediate_size=8,\n layer_norm_eps=1e-05,\n num_attention_heads=1,\n num_hidden_layers=1,\n pad_token_id=1,\n vocab_size=1000,\n projection_dim=8,\n )\n text_encoder = CLIPTextModelWithProjection(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"transformer\": transformer,\n \"scheduler\": scheduler,\n \"vqvae\": vqvae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n image = torch.full((1, 3, 4, 4), 1.0, dtype=torch.float32, device=device)\n mask_image = torch.full((1, 1, 4, 4), 1.0, dtype=torch.float32, device=device)\n mask_image[0, 0, 0, 0] = 0\n mask_image[0, 0, 0, 1] = 0\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n \"image\": image,\n \"mask_image\": mask_image,\n }\n return inputs\n\n def test_inference_batch_consistent(self, batch_sizes=[2]):\n self._test_inference_batch_consistent(batch_sizes=batch_sizes, batch_generator=False)\n\n @unittest.skip(\"aMUSEd does not support lists of generators\")\n def test_inference_batch_single_identical(self):\n ...\n\n\n@slow\n@require_torch_gpu\nclass AmusedInpaintPipelineSlowTests(unittest.TestCase):\n def test_amused_256(self):\n pipe = AmusedInpaintPipeline.from_pretrained(\"amused/amused-256\")\n pipe.to(torch_device)\n\n image = (\n load_image(\"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1.jpg\")\n .resize((256, 256))\n .convert(\"RGB\")\n )\n\n mask_image = (\n load_image(\n \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1_mask.png\"\n )\n .resize((256, 256))\n .convert(\"L\")\n )\n\n image = pipe(\n \"winter mountains\",\n image,\n mask_image,\n generator=torch.Generator().manual_seed(0),\n num_inference_steps=2,\n output_type=\"np\",\n ).images\n\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 256, 256, 3)\n expected_slice = np.array([0.0699, 0.0716, 0.0608, 0.0715, 0.0797, 0.0638, 0.0802, 0.0924, 0.0634])\n assert np.abs(image_slice - expected_slice).max() < 0.1\n\n def test_amused_256_fp16(self):\n pipe = AmusedInpaintPipeline.from_pretrained(\"amused/amused-256\", variant=\"fp16\", torch_dtype=torch.float16)\n pipe.to(torch_device)\n\n image = (\n load_image(\"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1.jpg\")\n .resize((256, 256))\n .convert(\"RGB\")\n )\n\n mask_image = (\n load_image(\n \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1_mask.png\"\n )\n .resize((256, 256))\n .convert(\"L\")\n )\n\n image = pipe(\n \"winter mountains\",\n image,\n mask_image,\n generator=torch.Generator().manual_seed(0),\n num_inference_steps=2,\n output_type=\"np\",\n ).images\n\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 256, 256, 3)\n expected_slice = np.array([0.0735, 0.0749, 0.0650, 0.0739, 0.0805, 0.0667, 0.0802, 0.0923, 0.0622])\n assert np.abs(image_slice - expected_slice).max() < 0.1\n\n def test_amused_512(self):\n pipe = AmusedInpaintPipeline.from_pretrained(\"amused/amused-512\")\n pipe.to(torch_device)\n\n image = (\n load_image(\"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1.jpg\")\n .resize((512, 512))\n .convert(\"RGB\")\n )\n\n mask_image = (\n load_image(\n \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1_mask.png\"\n )\n .resize((512, 512))\n .convert(\"L\")\n )\n\n image = pipe(\n \"winter mountains\",\n image,\n mask_image,\n generator=torch.Generator().manual_seed(0),\n num_inference_steps=2,\n output_type=\"np\",\n ).images\n\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0005, 0.0])\n assert np.abs(image_slice - expected_slice).max() < 0.05\n\n def test_amused_512_fp16(self):\n pipe = AmusedInpaintPipeline.from_pretrained(\"amused/amused-512\", variant=\"fp16\", torch_dtype=torch.float16)\n pipe.to(torch_device)\n\n image = (\n load_image(\"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1.jpg\")\n .resize((512, 512))\n .convert(\"RGB\")\n )\n\n mask_image = (\n load_image(\n \"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1_mask.png\"\n )\n .resize((512, 512))\n .convert(\"L\")\n )\n\n image = pipe(\n \"winter mountains\",\n image,\n mask_image,\n generator=torch.Generator().manual_seed(0),\n num_inference_steps=2,\n output_type=\"np\",\n ).images\n\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0025, 0.0])\n assert np.abs(image_slice - expected_slice).max() < 3e-3\n" }, { "path": "diffusers/tests/pipelines/animatediff/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/animatediff/test_animatediff.py", "content": "import gc\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nimport diffusers\nfrom diffusers import (\n AnimateDiffPipeline,\n AutoencoderKL,\n DDIMScheduler,\n MotionAdapter,\n UNet2DConditionModel,\n UNetMotionModel,\n)\nfrom diffusers.utils import is_xformers_available, logging\nfrom diffusers.utils.testing_utils import numpy_cosine_similarity_distance, require_torch_gpu, slow, torch_device\n\nfrom ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS\nfrom ..test_pipelines_common import (\n IPAdapterTesterMixin,\n PipelineFromPipeTesterMixin,\n PipelineTesterMixin,\n SDFunctionTesterMixin,\n)\n\n\ndef to_np(tensor):\n if isinstance(tensor, torch.Tensor):\n tensor = tensor.detach().cpu().numpy()\n\n return tensor\n\n\nclass AnimateDiffPipelineFastTests(\n IPAdapterTesterMixin, SDFunctionTesterMixin, PipelineTesterMixin, PipelineFromPipeTesterMixin, unittest.TestCase\n):\n pipeline_class = AnimateDiffPipeline\n params = TEXT_TO_IMAGE_PARAMS\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n required_optional_params = frozenset(\n [\n \"num_inference_steps\",\n \"generator\",\n \"latents\",\n \"return_dict\",\n \"callback_on_step_end\",\n \"callback_on_step_end_tensor_inputs\",\n ]\n )\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"CrossAttnDownBlock2D\", \"DownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n norm_num_groups=2,\n )\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"linear\",\n clip_sample=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n motion_adapter = MotionAdapter(\n block_out_channels=(32, 64),\n motion_layers_per_block=2,\n motion_norm_num_groups=2,\n motion_num_attention_heads=4,\n )\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"motion_adapter\": motion_adapter,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 7.5,\n \"output_type\": \"pt\",\n }\n return inputs\n\n def test_motion_unet_loading(self):\n components = self.get_dummy_components()\n pipe = AnimateDiffPipeline(**components)\n\n assert isinstance(pipe.unet, UNetMotionModel)\n\n @unittest.skip(\"Attention slicing is not enabled in this pipeline\")\n def test_attention_slicing_forward_pass(self):\n pass\n\n def test_ip_adapter_single(self):\n expected_pipe_slice = None\n if torch_device == \"cpu\":\n expected_pipe_slice = np.array(\n [\n 0.5541,\n 0.5802,\n 0.5074,\n 0.4583,\n 0.4729,\n 0.5374,\n 0.4051,\n 0.4495,\n 0.4480,\n 0.5292,\n 0.6322,\n 0.6265,\n 0.5455,\n 0.4771,\n 0.5795,\n 0.5845,\n 0.4172,\n 0.6066,\n 0.6535,\n 0.4113,\n 0.6833,\n 0.5736,\n 0.3589,\n 0.5730,\n 0.4205,\n 0.3786,\n 0.5323,\n ]\n )\n return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)\n\n def test_dict_tuple_outputs_equivalent(self):\n expected_slice = None\n if torch_device == \"cpu\":\n expected_slice = np.array([0.4051, 0.4495, 0.4480, 0.5845, 0.4172, 0.6066, 0.4205, 0.3786, 0.5323])\n return super().test_dict_tuple_outputs_equivalent(expected_slice=expected_slice)\n\n def test_inference_batch_single_identical(\n self,\n batch_size=2,\n expected_max_diff=1e-4,\n additional_params_copy_to_batched_inputs=[\"num_inference_steps\"],\n ):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for components in pipe.components.values():\n if hasattr(components, \"set_default_attn_processor\"):\n components.set_default_attn_processor()\n\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n inputs = self.get_dummy_inputs(torch_device)\n # Reset generator in case it is has been used in self.get_dummy_inputs\n inputs[\"generator\"] = self.get_generator(0)\n\n logger = logging.get_logger(pipe.__module__)\n logger.setLevel(level=diffusers.logging.FATAL)\n\n # batchify inputs\n batched_inputs = {}\n batched_inputs.update(inputs)\n\n for name in self.batch_params:\n if name not in inputs:\n continue\n\n value = inputs[name]\n if name == \"prompt\":\n len_prompt = len(value)\n batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]\n batched_inputs[name][-1] = 100 * \"very long\"\n\n else:\n batched_inputs[name] = batch_size * [value]\n\n if \"generator\" in inputs:\n batched_inputs[\"generator\"] = [self.get_generator(i) for i in range(batch_size)]\n\n if \"batch_size\" in inputs:\n batched_inputs[\"batch_size\"] = batch_size\n\n for arg in additional_params_copy_to_batched_inputs:\n batched_inputs[arg] = inputs[arg]\n\n output = pipe(**inputs)\n output_batch = pipe(**batched_inputs)\n\n assert output_batch[0].shape[0] == batch_size\n\n max_diff = np.abs(to_np(output_batch[0][0]) - to_np(output[0][0])).max()\n assert max_diff < expected_max_diff\n\n @unittest.skipIf(torch_device != \"cuda\", reason=\"CUDA and CPU are required to switch devices\")\n def test_to_device(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n\n pipe.to(\"cpu\")\n # pipeline creates a new motion UNet under the hood. So we need to check the device from pipe.components\n model_devices = [\n component.device.type for component in pipe.components.values() if hasattr(component, \"device\")\n ]\n self.assertTrue(all(device == \"cpu\" for device in model_devices))\n\n output_cpu = pipe(**self.get_dummy_inputs(\"cpu\"))[0]\n self.assertTrue(np.isnan(output_cpu).sum() == 0)\n\n pipe.to(\"cuda\")\n model_devices = [\n component.device.type for component in pipe.components.values() if hasattr(component, \"device\")\n ]\n self.assertTrue(all(device == \"cuda\" for device in model_devices))\n\n output_cuda = pipe(**self.get_dummy_inputs(\"cuda\"))[0]\n self.assertTrue(np.isnan(to_np(output_cuda)).sum() == 0)\n\n def test_to_dtype(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n\n # pipeline creates a new motion UNet under the hood. So we need to check the dtype from pipe.components\n model_dtypes = [component.dtype for component in pipe.components.values() if hasattr(component, \"dtype\")]\n self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes))\n\n pipe.to(dtype=torch.float16)\n model_dtypes = [component.dtype for component in pipe.components.values() if hasattr(component, \"dtype\")]\n self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes))\n\n def test_prompt_embeds(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n pipe.to(torch_device)\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs.pop(\"prompt\")\n inputs[\"prompt_embeds\"] = torch.randn((1, 4, 32), device=torch_device)\n pipe(**inputs)\n\n def test_free_init(self):\n components = self.get_dummy_components()\n pipe: AnimateDiffPipeline = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n pipe.to(torch_device)\n\n inputs_normal = self.get_dummy_inputs(torch_device)\n frames_normal = pipe(**inputs_normal).frames[0]\n\n pipe.enable_free_init(\n num_iters=2,\n use_fast_sampling=True,\n method=\"butterworth\",\n order=4,\n spatial_stop_frequency=0.25,\n temporal_stop_frequency=0.25,\n )\n inputs_enable_free_init = self.get_dummy_inputs(torch_device)\n frames_enable_free_init = pipe(**inputs_enable_free_init).frames[0]\n\n pipe.disable_free_init()\n inputs_disable_free_init = self.get_dummy_inputs(torch_device)\n frames_disable_free_init = pipe(**inputs_disable_free_init).frames[0]\n\n sum_enabled = np.abs(to_np(frames_normal) - to_np(frames_enable_free_init)).sum()\n max_diff_disabled = np.abs(to_np(frames_normal) - to_np(frames_disable_free_init)).max()\n self.assertGreater(\n sum_enabled, 1e1, \"Enabling of FreeInit should lead to results different from the default pipeline results\"\n )\n self.assertLess(\n max_diff_disabled,\n 1e-4,\n \"Disabling of FreeInit should lead to results similar to the default pipeline results\",\n )\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n output_without_offload = pipe(**inputs).frames[0]\n output_without_offload = (\n output_without_offload.cpu() if torch.is_tensor(output_without_offload) else output_without_offload\n )\n\n pipe.enable_xformers_memory_efficient_attention()\n inputs = self.get_dummy_inputs(torch_device)\n output_with_offload = pipe(**inputs).frames[0]\n output_with_offload = (\n output_with_offload.cpu() if torch.is_tensor(output_with_offload) else output_without_offload\n )\n\n max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()\n self.assertLess(max_diff, 1e-4, \"XFormers attention should not affect the inference results\")\n\n def test_vae_slicing(self):\n return super().test_vae_slicing(image_count=2)\n\n\n@slow\n@require_torch_gpu\nclass AnimateDiffPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_animatediff(self):\n adapter = MotionAdapter.from_pretrained(\"guoyww/animatediff-motion-adapter-v1-5-2\")\n pipe = AnimateDiffPipeline.from_pretrained(\"frankjoshua/toonyou_beta6\", motion_adapter=adapter)\n pipe = pipe.to(torch_device)\n pipe.scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"linear\",\n steps_offset=1,\n clip_sample=False,\n )\n pipe.enable_vae_slicing()\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n prompt = \"night, b&w photo of old house, post apocalypse, forest, storm weather, wind, rocks, 8k uhd, dslr, soft lighting, high quality, film grain\"\n negative_prompt = \"bad quality, worse quality\"\n\n generator = torch.Generator(\"cpu\").manual_seed(0)\n output = pipe(\n prompt,\n negative_prompt=negative_prompt,\n num_frames=16,\n generator=generator,\n guidance_scale=7.5,\n num_inference_steps=3,\n output_type=\"np\",\n )\n\n image = output.frames[0]\n assert image.shape == (16, 512, 512, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array(\n [\n 0.11357737,\n 0.11285847,\n 0.11180121,\n 0.11084166,\n 0.11414117,\n 0.09785956,\n 0.10742754,\n 0.10510018,\n 0.08045256,\n ]\n )\n assert numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice.flatten()) < 1e-3\n" }, { "path": "diffusers/tests/pipelines/animatediff/test_animatediff_sdxl.py", "content": "import unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer\n\nimport diffusers\nfrom diffusers import (\n AnimateDiffSDXLPipeline,\n AutoencoderKL,\n DDIMScheduler,\n MotionAdapter,\n UNet2DConditionModel,\n UNetMotionModel,\n)\nfrom diffusers.utils import is_xformers_available, logging\nfrom diffusers.utils.testing_utils import torch_device\n\nfrom ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS, TEXT_TO_IMAGE_PARAMS\nfrom ..test_pipelines_common import (\n IPAdapterTesterMixin,\n PipelineTesterMixin,\n SDFunctionTesterMixin,\n SDXLOptionalComponentsTesterMixin,\n)\n\n\ndef to_np(tensor):\n if isinstance(tensor, torch.Tensor):\n tensor = tensor.detach().cpu().numpy()\n\n return tensor\n\n\nclass AnimateDiffPipelineSDXLFastTests(\n IPAdapterTesterMixin,\n SDFunctionTesterMixin,\n PipelineTesterMixin,\n SDXLOptionalComponentsTesterMixin,\n unittest.TestCase,\n):\n pipeline_class = AnimateDiffSDXLPipeline\n params = TEXT_TO_IMAGE_PARAMS\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n required_optional_params = frozenset(\n [\n \"num_inference_steps\",\n \"generator\",\n \"latents\",\n \"return_dict\",\n \"callback_on_step_end\",\n \"callback_on_step_end_tensor_inputs\",\n ]\n )\n callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union({\"add_text_embeds\", \"add_time_ids\"})\n\n def get_dummy_components(self, time_cond_proj_dim=None):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64, 128),\n layers_per_block=2,\n time_cond_proj_dim=time_cond_proj_dim,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n # SD2-specific config below\n attention_head_dim=(2, 4, 8),\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2, 4),\n projection_class_embeddings_input_dim=80, # 6 * 8 + 32\n cross_attention_dim=64,\n norm_num_groups=1,\n )\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"linear\",\n clip_sample=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n sample_size=128,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n # SD2-specific config below\n hidden_act=\"gelu\",\n projection_dim=32,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)\n tokenizer_2 = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n motion_adapter = MotionAdapter(\n block_out_channels=(32, 64, 128),\n motion_layers_per_block=2,\n motion_norm_num_groups=2,\n motion_num_attention_heads=4,\n use_motion_mid_block=False,\n )\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"motion_adapter\": motion_adapter,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"text_encoder_2\": text_encoder_2,\n \"tokenizer_2\": tokenizer_2,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_motion_unet_loading(self):\n components = self.get_dummy_components()\n pipe = AnimateDiffSDXLPipeline(**components)\n\n assert isinstance(pipe.unet, UNetMotionModel)\n\n @unittest.skip(\"Attention slicing is not enabled in this pipeline\")\n def test_attention_slicing_forward_pass(self):\n pass\n\n def test_inference_batch_single_identical(\n self,\n batch_size=2,\n expected_max_diff=1e-4,\n additional_params_copy_to_batched_inputs=[\"num_inference_steps\"],\n ):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for components in pipe.components.values():\n if hasattr(components, \"set_default_attn_processor\"):\n components.set_default_attn_processor()\n\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n inputs = self.get_dummy_inputs(torch_device)\n # Reset generator in case it is has been used in self.get_dummy_inputs\n inputs[\"generator\"] = self.get_generator(0)\n\n logger = logging.get_logger(pipe.__module__)\n logger.setLevel(level=diffusers.logging.FATAL)\n\n # batchify inputs\n batched_inputs = {}\n batched_inputs.update(inputs)\n\n for name in self.batch_params:\n if name not in inputs:\n continue\n\n value = inputs[name]\n if name == \"prompt\":\n len_prompt = len(value)\n batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]\n batched_inputs[name][-1] = 100 * \"very long\"\n\n else:\n batched_inputs[name] = batch_size * [value]\n\n if \"generator\" in inputs:\n batched_inputs[\"generator\"] = [self.get_generator(i) for i in range(batch_size)]\n\n if \"batch_size\" in inputs:\n batched_inputs[\"batch_size\"] = batch_size\n\n for arg in additional_params_copy_to_batched_inputs:\n batched_inputs[arg] = inputs[arg]\n\n output = pipe(**inputs)\n output_batch = pipe(**batched_inputs)\n\n assert output_batch[0].shape[0] == batch_size\n\n max_diff = np.abs(to_np(output_batch[0][0]) - to_np(output[0][0])).max()\n assert max_diff < expected_max_diff\n\n @unittest.skipIf(torch_device != \"cuda\", reason=\"CUDA and CPU are required to switch devices\")\n def test_to_device(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n\n pipe.to(\"cpu\")\n # pipeline creates a new motion UNet under the hood. So we need to check the device from pipe.components\n model_devices = [\n component.device.type for component in pipe.components.values() if hasattr(component, \"device\")\n ]\n self.assertTrue(all(device == \"cpu\" for device in model_devices))\n\n output_cpu = pipe(**self.get_dummy_inputs(\"cpu\"))[0]\n self.assertTrue(np.isnan(output_cpu).sum() == 0)\n\n pipe.to(\"cuda\")\n model_devices = [\n component.device.type for component in pipe.components.values() if hasattr(component, \"device\")\n ]\n self.assertTrue(all(device == \"cuda\" for device in model_devices))\n\n output_cuda = pipe(**self.get_dummy_inputs(\"cuda\"))[0]\n self.assertTrue(np.isnan(to_np(output_cuda)).sum() == 0)\n\n def test_to_dtype(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n\n # pipeline creates a new motion UNet under the hood. So we need to check the dtype from pipe.components\n model_dtypes = [component.dtype for component in pipe.components.values() if hasattr(component, \"dtype\")]\n self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes))\n\n pipe.to(dtype=torch.float16)\n model_dtypes = [component.dtype for component in pipe.components.values() if hasattr(component, \"dtype\")]\n self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes))\n\n def test_prompt_embeds(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n pipe.to(torch_device)\n\n inputs = self.get_dummy_inputs(torch_device)\n prompt = inputs.pop(\"prompt\")\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = pipe.encode_prompt(prompt)\n\n pipe(\n **inputs,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n )\n\n def test_save_load_optional_components(self):\n self._test_save_load_optional_components()\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n output_without_offload = pipe(**inputs).frames[0]\n output_without_offload = (\n output_without_offload.cpu() if torch.is_tensor(output_without_offload) else output_without_offload\n )\n\n pipe.enable_xformers_memory_efficient_attention()\n inputs = self.get_dummy_inputs(torch_device)\n output_with_offload = pipe(**inputs).frames[0]\n output_with_offload = (\n output_with_offload.cpu() if torch.is_tensor(output_with_offload) else output_without_offload\n )\n\n max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()\n self.assertLess(max_diff, 1e-4, \"XFormers attention should not affect the inference results\")\n" }, { "path": "diffusers/tests/pipelines/animatediff/test_animatediff_video2video.py", "content": "import unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nimport diffusers\nfrom diffusers import (\n AnimateDiffVideoToVideoPipeline,\n AutoencoderKL,\n DDIMScheduler,\n MotionAdapter,\n UNet2DConditionModel,\n UNetMotionModel,\n)\nfrom diffusers.utils import is_xformers_available, logging\nfrom diffusers.utils.testing_utils import torch_device\n\nfrom ..pipeline_params import TEXT_TO_IMAGE_PARAMS, VIDEO_TO_VIDEO_BATCH_PARAMS\nfrom ..test_pipelines_common import IPAdapterTesterMixin, PipelineFromPipeTesterMixin, PipelineTesterMixin\n\n\ndef to_np(tensor):\n if isinstance(tensor, torch.Tensor):\n tensor = tensor.detach().cpu().numpy()\n\n return tensor\n\n\nclass AnimateDiffVideoToVideoPipelineFastTests(\n IPAdapterTesterMixin, PipelineTesterMixin, PipelineFromPipeTesterMixin, unittest.TestCase\n):\n pipeline_class = AnimateDiffVideoToVideoPipeline\n params = TEXT_TO_IMAGE_PARAMS\n batch_params = VIDEO_TO_VIDEO_BATCH_PARAMS\n required_optional_params = frozenset(\n [\n \"num_inference_steps\",\n \"generator\",\n \"latents\",\n \"return_dict\",\n \"callback_on_step_end\",\n \"callback_on_step_end_tensor_inputs\",\n ]\n )\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"CrossAttnDownBlock2D\", \"DownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n norm_num_groups=2,\n )\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"linear\",\n clip_sample=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n motion_adapter = MotionAdapter(\n block_out_channels=(32, 64),\n motion_layers_per_block=2,\n motion_norm_num_groups=2,\n motion_num_attention_heads=4,\n )\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"motion_adapter\": motion_adapter,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n video_height = 32\n video_width = 32\n video_num_frames = 2\n video = [Image.new(\"RGB\", (video_width, video_height))] * video_num_frames\n\n inputs = {\n \"video\": video,\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 7.5,\n \"output_type\": \"pt\",\n }\n return inputs\n\n def test_motion_unet_loading(self):\n components = self.get_dummy_components()\n pipe = AnimateDiffVideoToVideoPipeline(**components)\n\n assert isinstance(pipe.unet, UNetMotionModel)\n\n @unittest.skip(\"Attention slicing is not enabled in this pipeline\")\n def test_attention_slicing_forward_pass(self):\n pass\n\n def test_ip_adapter_single(self):\n expected_pipe_slice = None\n\n if torch_device == \"cpu\":\n expected_pipe_slice = np.array(\n [\n 0.4947,\n 0.4780,\n 0.4340,\n 0.4666,\n 0.4028,\n 0.4645,\n 0.4915,\n 0.4101,\n 0.4308,\n 0.4581,\n 0.3582,\n 0.4953,\n 0.4466,\n 0.5348,\n 0.5863,\n 0.5299,\n 0.5213,\n 0.5017,\n ]\n )\n return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)\n\n def test_inference_batch_single_identical(\n self,\n batch_size=2,\n expected_max_diff=1e-4,\n additional_params_copy_to_batched_inputs=[\"num_inference_steps\"],\n ):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for components in pipe.components.values():\n if hasattr(components, \"set_default_attn_processor\"):\n components.set_default_attn_processor()\n\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n inputs = self.get_dummy_inputs(torch_device)\n # Reset generator in case it is has been used in self.get_dummy_inputs\n inputs[\"generator\"] = self.get_generator(0)\n\n logger = logging.get_logger(pipe.__module__)\n logger.setLevel(level=diffusers.logging.FATAL)\n\n # batchify inputs\n batched_inputs = {}\n batched_inputs.update(inputs)\n\n for name in self.batch_params:\n if name not in inputs:\n continue\n\n value = inputs[name]\n if name == \"prompt\":\n len_prompt = len(value)\n batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]\n batched_inputs[name][-1] = 100 * \"very long\"\n\n else:\n batched_inputs[name] = batch_size * [value]\n\n if \"generator\" in inputs:\n batched_inputs[\"generator\"] = [self.get_generator(i) for i in range(batch_size)]\n\n if \"batch_size\" in inputs:\n batched_inputs[\"batch_size\"] = batch_size\n\n for arg in additional_params_copy_to_batched_inputs:\n batched_inputs[arg] = inputs[arg]\n\n output = pipe(**inputs)\n output_batch = pipe(**batched_inputs)\n\n assert output_batch[0].shape[0] == batch_size\n\n max_diff = np.abs(to_np(output_batch[0][0]) - to_np(output[0][0])).max()\n assert max_diff < expected_max_diff\n\n @unittest.skipIf(torch_device != \"cuda\", reason=\"CUDA and CPU are required to switch devices\")\n def test_to_device(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n\n pipe.to(\"cpu\")\n # pipeline creates a new motion UNet under the hood. So we need to check the device from pipe.components\n model_devices = [\n component.device.type for component in pipe.components.values() if hasattr(component, \"device\")\n ]\n self.assertTrue(all(device == \"cpu\" for device in model_devices))\n\n output_cpu = pipe(**self.get_dummy_inputs(\"cpu\"))[0]\n self.assertTrue(np.isnan(output_cpu).sum() == 0)\n\n pipe.to(\"cuda\")\n model_devices = [\n component.device.type for component in pipe.components.values() if hasattr(component, \"device\")\n ]\n self.assertTrue(all(device == \"cuda\" for device in model_devices))\n\n output_cuda = pipe(**self.get_dummy_inputs(\"cuda\"))[0]\n self.assertTrue(np.isnan(to_np(output_cuda)).sum() == 0)\n\n def test_to_dtype(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n\n # pipeline creates a new motion UNet under the hood. So we need to check the dtype from pipe.components\n model_dtypes = [component.dtype for component in pipe.components.values() if hasattr(component, \"dtype\")]\n self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes))\n\n pipe.to(dtype=torch.float16)\n model_dtypes = [component.dtype for component in pipe.components.values() if hasattr(component, \"dtype\")]\n self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes))\n\n def test_prompt_embeds(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n pipe.to(torch_device)\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs.pop(\"prompt\")\n inputs[\"prompt_embeds\"] = torch.randn((1, 4, 32), device=torch_device)\n pipe(**inputs)\n\n def test_latent_inputs(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n pipe.to(torch_device)\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"latents\"] = torch.randn((1, 4, 1, 32, 32), device=torch_device)\n inputs.pop(\"video\")\n pipe(**inputs)\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n output_without_offload = pipe(**inputs).frames[0]\n output_without_offload = (\n output_without_offload.cpu() if torch.is_tensor(output_without_offload) else output_without_offload\n )\n\n pipe.enable_xformers_memory_efficient_attention()\n inputs = self.get_dummy_inputs(torch_device)\n output_with_offload = pipe(**inputs).frames[0]\n output_with_offload = (\n output_with_offload.cpu() if torch.is_tensor(output_with_offload) else output_without_offload\n )\n\n max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()\n self.assertLess(max_diff, 1e-4, \"XFormers attention should not affect the inference results\")\n\n def test_free_init(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n pipe.to(torch_device)\n\n inputs_normal = self.get_dummy_inputs(torch_device)\n frames_normal = pipe(**inputs_normal).frames[0]\n\n pipe.enable_free_init(\n num_iters=2,\n use_fast_sampling=True,\n method=\"butterworth\",\n order=4,\n spatial_stop_frequency=0.25,\n temporal_stop_frequency=0.25,\n )\n inputs_enable_free_init = self.get_dummy_inputs(torch_device)\n frames_enable_free_init = pipe(**inputs_enable_free_init).frames[0]\n\n pipe.disable_free_init()\n inputs_disable_free_init = self.get_dummy_inputs(torch_device)\n frames_disable_free_init = pipe(**inputs_disable_free_init).frames[0]\n\n sum_enabled = np.abs(to_np(frames_normal) - to_np(frames_enable_free_init)).sum()\n max_diff_disabled = np.abs(to_np(frames_normal) - to_np(frames_disable_free_init)).max()\n self.assertGreater(\n sum_enabled, 1e1, \"Enabling of FreeInit should lead to results different from the default pipeline results\"\n )\n self.assertLess(\n max_diff_disabled,\n 1e-4,\n \"Disabling of FreeInit should lead to results similar to the default pipeline results\",\n )\n" }, { "path": "diffusers/tests/pipelines/audioldm/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/audioldm/test_audioldm.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport gc\nimport unittest\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom transformers import (\n ClapTextConfig,\n ClapTextModelWithProjection,\n RobertaTokenizer,\n SpeechT5HifiGan,\n SpeechT5HifiGanConfig,\n)\n\nfrom diffusers import (\n AudioLDMPipeline,\n AutoencoderKL,\n DDIMScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n UNet2DConditionModel,\n)\nfrom diffusers.utils import is_xformers_available\nfrom diffusers.utils.testing_utils import enable_full_determinism, nightly, torch_device\n\nfrom ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass AudioLDMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = AudioLDMPipeline\n params = TEXT_TO_AUDIO_PARAMS\n batch_params = TEXT_TO_AUDIO_BATCH_PARAMS\n required_optional_params = frozenset(\n [\n \"num_inference_steps\",\n \"num_waveforms_per_prompt\",\n \"generator\",\n \"latents\",\n \"output_type\",\n \"return_dict\",\n \"callback\",\n \"callback_steps\",\n ]\n )\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(8, 16),\n layers_per_block=1,\n norm_num_groups=8,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=(8, 16),\n class_embed_type=\"simple_projection\",\n projection_class_embeddings_input_dim=8,\n class_embeddings_concat=True,\n )\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[8, 16],\n in_channels=1,\n out_channels=1,\n norm_num_groups=8,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = ClapTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=8,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=1,\n num_hidden_layers=1,\n pad_token_id=1,\n vocab_size=1000,\n projection_dim=8,\n )\n text_encoder = ClapTextModelWithProjection(text_encoder_config)\n tokenizer = RobertaTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-roberta\", model_max_length=77)\n\n vocoder_config = SpeechT5HifiGanConfig(\n model_in_dim=8,\n sampling_rate=16000,\n upsample_initial_channel=16,\n upsample_rates=[2, 2],\n upsample_kernel_sizes=[4, 4],\n resblock_kernel_sizes=[3, 7],\n resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]],\n normalize_before=False,\n )\n\n vocoder = SpeechT5HifiGan(vocoder_config)\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"vocoder\": vocoder,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A hammer hitting a wooden surface\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n }\n return inputs\n\n def test_audioldm_ddim(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components()\n audioldm_pipe = AudioLDMPipeline(**components)\n audioldm_pipe = audioldm_pipe.to(torch_device)\n audioldm_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = audioldm_pipe(**inputs)\n audio = output.audios[0]\n\n assert audio.ndim == 1\n assert len(audio) == 256\n\n audio_slice = audio[:10]\n expected_slice = np.array(\n [-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033]\n )\n\n assert np.abs(audio_slice - expected_slice).max() < 1e-2\n\n def test_audioldm_prompt_embeds(self):\n components = self.get_dummy_components()\n audioldm_pipe = AudioLDMPipeline(**components)\n audioldm_pipe = audioldm_pipe.to(torch_device)\n audioldm_pipe = audioldm_pipe.to(torch_device)\n audioldm_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"prompt\"] = 3 * [inputs[\"prompt\"]]\n\n # forward\n output = audioldm_pipe(**inputs)\n audio_1 = output.audios[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n prompt = 3 * [inputs.pop(\"prompt\")]\n\n text_inputs = audioldm_pipe.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=audioldm_pipe.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_inputs = text_inputs[\"input_ids\"].to(torch_device)\n\n prompt_embeds = audioldm_pipe.text_encoder(\n text_inputs,\n )\n prompt_embeds = prompt_embeds.text_embeds\n # additional L_2 normalization over each hidden-state\n prompt_embeds = F.normalize(prompt_embeds, dim=-1)\n\n inputs[\"prompt_embeds\"] = prompt_embeds\n\n # forward\n output = audioldm_pipe(**inputs)\n audio_2 = output.audios[0]\n\n assert np.abs(audio_1 - audio_2).max() < 1e-2\n\n def test_audioldm_negative_prompt_embeds(self):\n components = self.get_dummy_components()\n audioldm_pipe = AudioLDMPipeline(**components)\n audioldm_pipe = audioldm_pipe.to(torch_device)\n audioldm_pipe = audioldm_pipe.to(torch_device)\n audioldm_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n negative_prompt = 3 * [\"this is a negative prompt\"]\n inputs[\"negative_prompt\"] = negative_prompt\n inputs[\"prompt\"] = 3 * [inputs[\"prompt\"]]\n\n # forward\n output = audioldm_pipe(**inputs)\n audio_1 = output.audios[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n prompt = 3 * [inputs.pop(\"prompt\")]\n\n embeds = []\n for p in [prompt, negative_prompt]:\n text_inputs = audioldm_pipe.tokenizer(\n p,\n padding=\"max_length\",\n max_length=audioldm_pipe.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_inputs = text_inputs[\"input_ids\"].to(torch_device)\n\n text_embeds = audioldm_pipe.text_encoder(\n text_inputs,\n )\n text_embeds = text_embeds.text_embeds\n # additional L_2 normalization over each hidden-state\n text_embeds = F.normalize(text_embeds, dim=-1)\n\n embeds.append(text_embeds)\n\n inputs[\"prompt_embeds\"], inputs[\"negative_prompt_embeds\"] = embeds\n\n # forward\n output = audioldm_pipe(**inputs)\n audio_2 = output.audios[0]\n\n assert np.abs(audio_1 - audio_2).max() < 1e-2\n\n def test_audioldm_negative_prompt(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n components[\"scheduler\"] = PNDMScheduler(skip_prk_steps=True)\n audioldm_pipe = AudioLDMPipeline(**components)\n audioldm_pipe = audioldm_pipe.to(device)\n audioldm_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n negative_prompt = \"egg cracking\"\n output = audioldm_pipe(**inputs, negative_prompt=negative_prompt)\n audio = output.audios[0]\n\n assert audio.ndim == 1\n assert len(audio) == 256\n\n audio_slice = audio[:10]\n expected_slice = np.array(\n [-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032]\n )\n\n assert np.abs(audio_slice - expected_slice).max() < 1e-2\n\n def test_audioldm_num_waveforms_per_prompt(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n components[\"scheduler\"] = PNDMScheduler(skip_prk_steps=True)\n audioldm_pipe = AudioLDMPipeline(**components)\n audioldm_pipe = audioldm_pipe.to(device)\n audioldm_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A hammer hitting a wooden surface\"\n\n # test num_waveforms_per_prompt=1 (default)\n audios = audioldm_pipe(prompt, num_inference_steps=2).audios\n\n assert audios.shape == (1, 256)\n\n # test num_waveforms_per_prompt=1 (default) for batch of prompts\n batch_size = 2\n audios = audioldm_pipe([prompt] * batch_size, num_inference_steps=2).audios\n\n assert audios.shape == (batch_size, 256)\n\n # test num_waveforms_per_prompt for single prompt\n num_waveforms_per_prompt = 2\n audios = audioldm_pipe(prompt, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt).audios\n\n assert audios.shape == (num_waveforms_per_prompt, 256)\n\n # test num_waveforms_per_prompt for batch of prompts\n batch_size = 2\n audios = audioldm_pipe(\n [prompt] * batch_size, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt\n ).audios\n\n assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)\n\n def test_audioldm_audio_length_in_s(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n audioldm_pipe = AudioLDMPipeline(**components)\n audioldm_pipe = audioldm_pipe.to(torch_device)\n audioldm_pipe.set_progress_bar_config(disable=None)\n vocoder_sampling_rate = audioldm_pipe.vocoder.config.sampling_rate\n\n inputs = self.get_dummy_inputs(device)\n output = audioldm_pipe(audio_length_in_s=0.016, **inputs)\n audio = output.audios[0]\n\n assert audio.ndim == 1\n assert len(audio) / vocoder_sampling_rate == 0.016\n\n output = audioldm_pipe(audio_length_in_s=0.032, **inputs)\n audio = output.audios[0]\n\n assert audio.ndim == 1\n assert len(audio) / vocoder_sampling_rate == 0.032\n\n def test_audioldm_vocoder_model_in_dim(self):\n components = self.get_dummy_components()\n audioldm_pipe = AudioLDMPipeline(**components)\n audioldm_pipe = audioldm_pipe.to(torch_device)\n audioldm_pipe.set_progress_bar_config(disable=None)\n\n prompt = [\"hey\"]\n\n output = audioldm_pipe(prompt, num_inference_steps=1)\n audio_shape = output.audios.shape\n assert audio_shape == (1, 256)\n\n config = audioldm_pipe.vocoder.config\n config.model_in_dim *= 2\n audioldm_pipe.vocoder = SpeechT5HifiGan(config).to(torch_device)\n output = audioldm_pipe(prompt, num_inference_steps=1)\n audio_shape = output.audios.shape\n # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram\n assert audio_shape == (1, 256)\n\n def test_attention_slicing_forward_pass(self):\n self._test_attention_slicing_forward_pass(test_mean_pixel_difference=False)\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical()\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False)\n\n\n@nightly\nclass AudioLDMPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n latents = np.random.RandomState(seed).standard_normal((1, 8, 128, 16))\n latents = torch.from_numpy(latents).to(device=device, dtype=dtype)\n inputs = {\n \"prompt\": \"A hammer hitting a wooden surface\",\n \"latents\": latents,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"guidance_scale\": 2.5,\n }\n return inputs\n\n def test_audioldm(self):\n audioldm_pipe = AudioLDMPipeline.from_pretrained(\"cvssp/audioldm\")\n audioldm_pipe = audioldm_pipe.to(torch_device)\n audioldm_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n inputs[\"num_inference_steps\"] = 25\n audio = audioldm_pipe(**inputs).audios[0]\n\n assert audio.ndim == 1\n assert len(audio) == 81920\n\n audio_slice = audio[77230:77240]\n expected_slice = np.array(\n [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]\n )\n max_diff = np.abs(expected_slice - audio_slice).max()\n assert max_diff < 1e-2\n\n\n@nightly\nclass AudioLDMPipelineNightlyTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n latents = np.random.RandomState(seed).standard_normal((1, 8, 128, 16))\n latents = torch.from_numpy(latents).to(device=device, dtype=dtype)\n inputs = {\n \"prompt\": \"A hammer hitting a wooden surface\",\n \"latents\": latents,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"guidance_scale\": 2.5,\n }\n return inputs\n\n def test_audioldm_lms(self):\n audioldm_pipe = AudioLDMPipeline.from_pretrained(\"cvssp/audioldm\")\n audioldm_pipe.scheduler = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config)\n audioldm_pipe = audioldm_pipe.to(torch_device)\n audioldm_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n audio = audioldm_pipe(**inputs).audios[0]\n\n assert audio.ndim == 1\n assert len(audio) == 81920\n\n audio_slice = audio[27780:27790]\n expected_slice = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212])\n max_diff = np.abs(expected_slice - audio_slice).max()\n assert max_diff < 3e-2\n" }, { "path": "diffusers/tests/pipelines/audioldm2/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/audioldm2/test_audioldm2.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport gc\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import (\n ClapAudioConfig,\n ClapConfig,\n ClapFeatureExtractor,\n ClapModel,\n ClapTextConfig,\n GPT2Config,\n GPT2Model,\n RobertaTokenizer,\n SpeechT5HifiGan,\n SpeechT5HifiGanConfig,\n T5Config,\n T5EncoderModel,\n T5Tokenizer,\n)\n\nfrom diffusers import (\n AudioLDM2Pipeline,\n AudioLDM2ProjectionModel,\n AudioLDM2UNet2DConditionModel,\n AutoencoderKL,\n DDIMScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n)\nfrom diffusers.utils.testing_utils import enable_full_determinism, nightly, torch_device\n\nfrom ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass AudioLDM2PipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = AudioLDM2Pipeline\n params = TEXT_TO_AUDIO_PARAMS\n batch_params = TEXT_TO_AUDIO_BATCH_PARAMS\n required_optional_params = frozenset(\n [\n \"num_inference_steps\",\n \"num_waveforms_per_prompt\",\n \"generator\",\n \"latents\",\n \"output_type\",\n \"return_dict\",\n \"callback\",\n \"callback_steps\",\n ]\n )\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = AudioLDM2UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=([None, 16, 32], [None, 16, 32]),\n )\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=1,\n out_channels=1,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_branch_config = ClapTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=16,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=2,\n num_hidden_layers=2,\n pad_token_id=1,\n vocab_size=1000,\n projection_dim=16,\n )\n audio_branch_config = ClapAudioConfig(\n spec_size=64,\n window_size=4,\n num_mel_bins=64,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n depths=[2, 2],\n num_attention_heads=[2, 2],\n num_hidden_layers=2,\n hidden_size=192,\n projection_dim=16,\n patch_size=2,\n patch_stride=2,\n patch_embed_input_channels=4,\n )\n text_encoder_config = ClapConfig.from_text_audio_configs(\n text_config=text_branch_config, audio_config=audio_branch_config, projection_dim=16\n )\n text_encoder = ClapModel(text_encoder_config)\n tokenizer = RobertaTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-roberta\", model_max_length=77)\n feature_extractor = ClapFeatureExtractor.from_pretrained(\n \"hf-internal-testing/tiny-random-ClapModel\", hop_length=7900\n )\n\n torch.manual_seed(0)\n text_encoder_2_config = T5Config(\n vocab_size=32100,\n d_model=32,\n d_ff=37,\n d_kv=8,\n num_heads=2,\n num_layers=2,\n )\n text_encoder_2 = T5EncoderModel(text_encoder_2_config)\n tokenizer_2 = T5Tokenizer.from_pretrained(\"hf-internal-testing/tiny-random-T5Model\", model_max_length=77)\n\n torch.manual_seed(0)\n language_model_config = GPT2Config(\n n_embd=16,\n n_head=2,\n n_layer=2,\n vocab_size=1000,\n n_ctx=99,\n n_positions=99,\n )\n language_model = GPT2Model(language_model_config)\n language_model.config.max_new_tokens = 8\n\n torch.manual_seed(0)\n projection_model = AudioLDM2ProjectionModel(text_encoder_dim=16, text_encoder_1_dim=32, langauge_model_dim=16)\n\n vocoder_config = SpeechT5HifiGanConfig(\n model_in_dim=8,\n sampling_rate=16000,\n upsample_initial_channel=16,\n upsample_rates=[2, 2],\n upsample_kernel_sizes=[4, 4],\n resblock_kernel_sizes=[3, 7],\n resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]],\n normalize_before=False,\n )\n\n vocoder = SpeechT5HifiGan(vocoder_config)\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"text_encoder_2\": text_encoder_2,\n \"tokenizer\": tokenizer,\n \"tokenizer_2\": tokenizer_2,\n \"feature_extractor\": feature_extractor,\n \"language_model\": language_model,\n \"projection_model\": projection_model,\n \"vocoder\": vocoder,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A hammer hitting a wooden surface\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n }\n return inputs\n\n def test_audioldm2_ddim(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components()\n audioldm_pipe = AudioLDM2Pipeline(**components)\n audioldm_pipe = audioldm_pipe.to(torch_device)\n audioldm_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = audioldm_pipe(**inputs)\n audio = output.audios[0]\n\n assert audio.ndim == 1\n assert len(audio) == 256\n\n audio_slice = audio[:10]\n expected_slice = np.array(\n [0.0025, 0.0018, 0.0018, -0.0023, -0.0026, -0.0020, -0.0026, -0.0021, -0.0027, -0.0020]\n )\n\n assert np.abs(audio_slice - expected_slice).max() < 1e-4\n\n def test_audioldm2_prompt_embeds(self):\n components = self.get_dummy_components()\n audioldm_pipe = AudioLDM2Pipeline(**components)\n audioldm_pipe = audioldm_pipe.to(torch_device)\n audioldm_pipe = audioldm_pipe.to(torch_device)\n audioldm_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"prompt\"] = 3 * [inputs[\"prompt\"]]\n\n # forward\n output = audioldm_pipe(**inputs)\n audio_1 = output.audios[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n prompt = 3 * [inputs.pop(\"prompt\")]\n\n text_inputs = audioldm_pipe.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=audioldm_pipe.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_inputs = text_inputs[\"input_ids\"].to(torch_device)\n\n clap_prompt_embeds = audioldm_pipe.text_encoder.get_text_features(text_inputs)\n clap_prompt_embeds = clap_prompt_embeds[:, None, :]\n\n text_inputs = audioldm_pipe.tokenizer_2(\n prompt,\n padding=\"max_length\",\n max_length=True,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_inputs = text_inputs[\"input_ids\"].to(torch_device)\n\n t5_prompt_embeds = audioldm_pipe.text_encoder_2(\n text_inputs,\n )\n t5_prompt_embeds = t5_prompt_embeds[0]\n\n projection_embeds = audioldm_pipe.projection_model(clap_prompt_embeds, t5_prompt_embeds)[0]\n generated_prompt_embeds = audioldm_pipe.generate_language_model(projection_embeds, max_new_tokens=8)\n\n inputs[\"prompt_embeds\"] = t5_prompt_embeds\n inputs[\"generated_prompt_embeds\"] = generated_prompt_embeds\n\n # forward\n output = audioldm_pipe(**inputs)\n audio_2 = output.audios[0]\n\n assert np.abs(audio_1 - audio_2).max() < 1e-2\n\n def test_audioldm2_negative_prompt_embeds(self):\n components = self.get_dummy_components()\n audioldm_pipe = AudioLDM2Pipeline(**components)\n audioldm_pipe = audioldm_pipe.to(torch_device)\n audioldm_pipe = audioldm_pipe.to(torch_device)\n audioldm_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n negative_prompt = 3 * [\"this is a negative prompt\"]\n inputs[\"negative_prompt\"] = negative_prompt\n inputs[\"prompt\"] = 3 * [inputs[\"prompt\"]]\n\n # forward\n output = audioldm_pipe(**inputs)\n audio_1 = output.audios[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n prompt = 3 * [inputs.pop(\"prompt\")]\n\n embeds = []\n generated_embeds = []\n for p in [prompt, negative_prompt]:\n text_inputs = audioldm_pipe.tokenizer(\n p,\n padding=\"max_length\",\n max_length=audioldm_pipe.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_inputs = text_inputs[\"input_ids\"].to(torch_device)\n\n clap_prompt_embeds = audioldm_pipe.text_encoder.get_text_features(text_inputs)\n clap_prompt_embeds = clap_prompt_embeds[:, None, :]\n\n text_inputs = audioldm_pipe.tokenizer_2(\n prompt,\n padding=\"max_length\",\n max_length=True if len(embeds) == 0 else embeds[0].shape[1],\n truncation=True,\n return_tensors=\"pt\",\n )\n text_inputs = text_inputs[\"input_ids\"].to(torch_device)\n\n t5_prompt_embeds = audioldm_pipe.text_encoder_2(\n text_inputs,\n )\n t5_prompt_embeds = t5_prompt_embeds[0]\n\n projection_embeds = audioldm_pipe.projection_model(clap_prompt_embeds, t5_prompt_embeds)[0]\n generated_prompt_embeds = audioldm_pipe.generate_language_model(projection_embeds, max_new_tokens=8)\n\n embeds.append(t5_prompt_embeds)\n generated_embeds.append(generated_prompt_embeds)\n\n inputs[\"prompt_embeds\"], inputs[\"negative_prompt_embeds\"] = embeds\n inputs[\"generated_prompt_embeds\"], inputs[\"negative_generated_prompt_embeds\"] = generated_embeds\n\n # forward\n output = audioldm_pipe(**inputs)\n audio_2 = output.audios[0]\n\n assert np.abs(audio_1 - audio_2).max() < 1e-2\n\n def test_audioldm2_negative_prompt(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n components[\"scheduler\"] = PNDMScheduler(skip_prk_steps=True)\n audioldm_pipe = AudioLDM2Pipeline(**components)\n audioldm_pipe = audioldm_pipe.to(device)\n audioldm_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n negative_prompt = \"egg cracking\"\n output = audioldm_pipe(**inputs, negative_prompt=negative_prompt)\n audio = output.audios[0]\n\n assert audio.ndim == 1\n assert len(audio) == 256\n\n audio_slice = audio[:10]\n expected_slice = np.array(\n [0.0025, 0.0018, 0.0018, -0.0023, -0.0026, -0.0020, -0.0026, -0.0021, -0.0027, -0.0020]\n )\n\n assert np.abs(audio_slice - expected_slice).max() < 1e-4\n\n def test_audioldm2_num_waveforms_per_prompt(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n components[\"scheduler\"] = PNDMScheduler(skip_prk_steps=True)\n audioldm_pipe = AudioLDM2Pipeline(**components)\n audioldm_pipe = audioldm_pipe.to(device)\n audioldm_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A hammer hitting a wooden surface\"\n\n # test num_waveforms_per_prompt=1 (default)\n audios = audioldm_pipe(prompt, num_inference_steps=2).audios\n\n assert audios.shape == (1, 256)\n\n # test num_waveforms_per_prompt=1 (default) for batch of prompts\n batch_size = 2\n audios = audioldm_pipe([prompt] * batch_size, num_inference_steps=2).audios\n\n assert audios.shape == (batch_size, 256)\n\n # test num_waveforms_per_prompt for single prompt\n num_waveforms_per_prompt = 2\n audios = audioldm_pipe(prompt, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt).audios\n\n assert audios.shape == (num_waveforms_per_prompt, 256)\n\n # test num_waveforms_per_prompt for batch of prompts\n batch_size = 2\n audios = audioldm_pipe(\n [prompt] * batch_size, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt\n ).audios\n\n assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)\n\n def test_audioldm2_audio_length_in_s(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n audioldm_pipe = AudioLDM2Pipeline(**components)\n audioldm_pipe = audioldm_pipe.to(torch_device)\n audioldm_pipe.set_progress_bar_config(disable=None)\n vocoder_sampling_rate = audioldm_pipe.vocoder.config.sampling_rate\n\n inputs = self.get_dummy_inputs(device)\n output = audioldm_pipe(audio_length_in_s=0.016, **inputs)\n audio = output.audios[0]\n\n assert audio.ndim == 1\n assert len(audio) / vocoder_sampling_rate == 0.016\n\n output = audioldm_pipe(audio_length_in_s=0.032, **inputs)\n audio = output.audios[0]\n\n assert audio.ndim == 1\n assert len(audio) / vocoder_sampling_rate == 0.032\n\n def test_audioldm2_vocoder_model_in_dim(self):\n components = self.get_dummy_components()\n audioldm_pipe = AudioLDM2Pipeline(**components)\n audioldm_pipe = audioldm_pipe.to(torch_device)\n audioldm_pipe.set_progress_bar_config(disable=None)\n\n prompt = [\"hey\"]\n\n output = audioldm_pipe(prompt, num_inference_steps=1)\n audio_shape = output.audios.shape\n assert audio_shape == (1, 256)\n\n config = audioldm_pipe.vocoder.config\n config.model_in_dim *= 2\n audioldm_pipe.vocoder = SpeechT5HifiGan(config).to(torch_device)\n output = audioldm_pipe(prompt, num_inference_steps=1)\n audio_shape = output.audios.shape\n # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram\n assert audio_shape == (1, 256)\n\n def test_attention_slicing_forward_pass(self):\n self._test_attention_slicing_forward_pass(test_mean_pixel_difference=False)\n\n @unittest.skip(\"Raises a not implemented error in AudioLDM2\")\n def test_xformers_attention_forwardGenerator_pass(self):\n pass\n\n def test_dict_tuple_outputs_equivalent(self):\n # increase tolerance from 1e-4 -> 2e-4 to account for large composite model\n super().test_dict_tuple_outputs_equivalent(expected_max_difference=2e-4)\n\n def test_inference_batch_single_identical(self):\n # increase tolerance from 1e-4 -> 2e-4 to account for large composite model\n self._test_inference_batch_single_identical(expected_max_diff=2e-4)\n\n def test_save_load_local(self):\n # increase tolerance from 1e-4 -> 2e-4 to account for large composite model\n super().test_save_load_local(expected_max_difference=2e-4)\n\n def test_save_load_optional_components(self):\n # increase tolerance from 1e-4 -> 2e-4 to account for large composite model\n super().test_save_load_optional_components(expected_max_difference=2e-4)\n\n def test_to_dtype(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n\n # The method component.dtype returns the dtype of the first parameter registered in the model, not the\n # dtype of the entire model. In the case of CLAP, the first parameter is a float64 constant (logit scale)\n model_dtypes = {key: component.dtype for key, component in components.items() if hasattr(component, \"dtype\")}\n\n # Without the logit scale parameters, everything is float32\n model_dtypes.pop(\"text_encoder\")\n self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes.values()))\n\n # the CLAP sub-models are float32\n model_dtypes[\"clap_text_branch\"] = components[\"text_encoder\"].text_model.dtype\n self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes.values()))\n\n # Once we send to fp16, all params are in half-precision, including the logit scale\n pipe.to(dtype=torch.float16)\n model_dtypes = {key: component.dtype for key, component in components.items() if hasattr(component, \"dtype\")}\n self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes.values()))\n\n def test_sequential_cpu_offload_forward_pass(self):\n pass\n\n\n@nightly\nclass AudioLDM2PipelineSlowTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n latents = np.random.RandomState(seed).standard_normal((1, 8, 128, 16))\n latents = torch.from_numpy(latents).to(device=device, dtype=dtype)\n inputs = {\n \"prompt\": \"A hammer hitting a wooden surface\",\n \"latents\": latents,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"guidance_scale\": 2.5,\n }\n return inputs\n\n def get_inputs_tts(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n latents = np.random.RandomState(seed).standard_normal((1, 8, 128, 16))\n latents = torch.from_numpy(latents).to(device=device, dtype=dtype)\n inputs = {\n \"prompt\": \"A men saying\",\n \"transcription\": \"hello my name is John\",\n \"latents\": latents,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"guidance_scale\": 2.5,\n }\n return inputs\n\n def test_audioldm2(self):\n audioldm_pipe = AudioLDM2Pipeline.from_pretrained(\"cvssp/audioldm2\")\n audioldm_pipe = audioldm_pipe.to(torch_device)\n audioldm_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n inputs[\"num_inference_steps\"] = 25\n audio = audioldm_pipe(**inputs).audios[0]\n\n assert audio.ndim == 1\n assert len(audio) == 81952\n\n # check the portion of the generated audio with the largest dynamic range (reduces flakiness)\n audio_slice = audio[17275:17285]\n expected_slice = np.array([0.0791, 0.0666, 0.1158, 0.1227, 0.1171, -0.2880, -0.1940, -0.0283, -0.0126, 0.1127])\n max_diff = np.abs(expected_slice - audio_slice).max()\n assert max_diff < 1e-3\n\n def test_audioldm2_lms(self):\n audioldm_pipe = AudioLDM2Pipeline.from_pretrained(\"cvssp/audioldm2\")\n audioldm_pipe.scheduler = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config)\n audioldm_pipe = audioldm_pipe.to(torch_device)\n audioldm_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n audio = audioldm_pipe(**inputs).audios[0]\n\n assert audio.ndim == 1\n assert len(audio) == 81952\n\n # check the portion of the generated audio with the largest dynamic range (reduces flakiness)\n audio_slice = audio[31390:31400]\n expected_slice = np.array(\n [-0.1318, -0.0577, 0.0446, -0.0573, 0.0659, 0.1074, -0.2600, 0.0080, -0.2190, -0.4301]\n )\n max_diff = np.abs(expected_slice - audio_slice).max()\n assert max_diff < 1e-3\n\n def test_audioldm2_large(self):\n audioldm_pipe = AudioLDM2Pipeline.from_pretrained(\"cvssp/audioldm2-large\")\n audioldm_pipe = audioldm_pipe.to(torch_device)\n audioldm_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n audio = audioldm_pipe(**inputs).audios[0]\n\n assert audio.ndim == 1\n assert len(audio) == 81952\n\n # check the portion of the generated audio with the largest dynamic range (reduces flakiness)\n audio_slice = audio[8825:8835]\n expected_slice = np.array(\n [-0.1829, -0.1461, 0.0759, -0.1493, -0.1396, 0.5783, 0.3001, -0.3038, -0.0639, -0.2244]\n )\n max_diff = np.abs(expected_slice - audio_slice).max()\n assert max_diff < 1e-3\n\n def test_audioldm2_tts(self):\n audioldm_tts_pipe = AudioLDM2Pipeline.from_pretrained(\"anhnct/audioldm2_gigaspeech\")\n audioldm_tts_pipe = audioldm_tts_pipe.to(torch_device)\n audioldm_tts_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs_tts(torch_device)\n audio = audioldm_tts_pipe(**inputs).audios[0]\n\n assert audio.ndim == 1\n assert len(audio) == 81952\n\n # check the portion of the generated audio with the largest dynamic range (reduces flakiness)\n audio_slice = audio[8825:8835]\n expected_slice = np.array(\n [-0.1829, -0.1461, 0.0759, -0.1493, -0.1396, 0.5783, 0.3001, -0.3038, -0.0639, -0.2244]\n )\n max_diff = np.abs(expected_slice - audio_slice).max()\n assert max_diff < 1e-3\n" }, { "path": "diffusers/tests/pipelines/blipdiffusion/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/blipdiffusion/test_blipdiffusion.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom transformers import CLIPTokenizer\nfrom transformers.models.blip_2.configuration_blip_2 import Blip2Config\nfrom transformers.models.clip.configuration_clip import CLIPTextConfig\n\nfrom diffusers import AutoencoderKL, BlipDiffusionPipeline, PNDMScheduler, UNet2DConditionModel\nfrom diffusers.utils.testing_utils import enable_full_determinism\nfrom src.diffusers.pipelines.blip_diffusion.blip_image_processing import BlipImageProcessor\nfrom src.diffusers.pipelines.blip_diffusion.modeling_blip2 import Blip2QFormerModel\nfrom src.diffusers.pipelines.blip_diffusion.modeling_ctx_clip import ContextCLIPTextModel\n\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass BlipDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = BlipDiffusionPipeline\n params = [\n \"prompt\",\n \"reference_image\",\n \"source_subject_category\",\n \"target_subject_category\",\n ]\n batch_params = [\n \"prompt\",\n \"reference_image\",\n \"source_subject_category\",\n \"target_subject_category\",\n ]\n required_optional_params = [\n \"generator\",\n \"height\",\n \"width\",\n \"latents\",\n \"guidance_scale\",\n \"num_inference_steps\",\n \"neg_prompt\",\n \"guidance_scale\",\n \"prompt_strength\",\n \"prompt_reps\",\n ]\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n vocab_size=1000,\n hidden_size=8,\n intermediate_size=8,\n projection_dim=8,\n num_hidden_layers=1,\n num_attention_heads=1,\n max_position_embeddings=77,\n )\n text_encoder = ContextCLIPTextModel(text_encoder_config)\n\n vae = AutoencoderKL(\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownEncoderBlock2D\",),\n up_block_types=(\"UpDecoderBlock2D\",),\n block_out_channels=(8,),\n norm_num_groups=8,\n layers_per_block=1,\n act_fn=\"silu\",\n latent_channels=4,\n sample_size=8,\n )\n\n blip_vision_config = {\n \"hidden_size\": 8,\n \"intermediate_size\": 8,\n \"num_hidden_layers\": 1,\n \"num_attention_heads\": 1,\n \"image_size\": 224,\n \"patch_size\": 14,\n \"hidden_act\": \"quick_gelu\",\n }\n\n blip_qformer_config = {\n \"vocab_size\": 1000,\n \"hidden_size\": 8,\n \"num_hidden_layers\": 1,\n \"num_attention_heads\": 1,\n \"intermediate_size\": 8,\n \"max_position_embeddings\": 512,\n \"cross_attention_frequency\": 1,\n \"encoder_hidden_size\": 8,\n }\n qformer_config = Blip2Config(\n vision_config=blip_vision_config,\n qformer_config=blip_qformer_config,\n num_query_tokens=8,\n tokenizer=\"hf-internal-testing/tiny-random-bert\",\n )\n qformer = Blip2QFormerModel(qformer_config)\n\n unet = UNet2DConditionModel(\n block_out_channels=(8, 16),\n norm_num_groups=8,\n layers_per_block=1,\n sample_size=16,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=8,\n )\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n scheduler = PNDMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n set_alpha_to_one=False,\n skip_prk_steps=True,\n )\n\n vae.eval()\n qformer.eval()\n text_encoder.eval()\n\n image_processor = BlipImageProcessor()\n\n components = {\n \"text_encoder\": text_encoder,\n \"vae\": vae,\n \"qformer\": qformer,\n \"unet\": unet,\n \"tokenizer\": tokenizer,\n \"scheduler\": scheduler,\n \"image_processor\": image_processor,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n np.random.seed(seed)\n reference_image = np.random.rand(32, 32, 3) * 255\n reference_image = Image.fromarray(reference_image.astype(\"uint8\")).convert(\"RGBA\")\n\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"swimming underwater\",\n \"generator\": generator,\n \"reference_image\": reference_image,\n \"source_subject_category\": \"dog\",\n \"target_subject_category\": \"dog\",\n \"height\": 32,\n \"width\": 32,\n \"guidance_scale\": 7.5,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_blipdiffusion(self):\n device = \"cpu\"\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n image = pipe(**self.get_dummy_inputs(device))[0]\n image_slice = image[0, -3:, -3:, 0]\n\n assert image.shape == (1, 16, 16, 4)\n\n expected_slice = np.array(\n [0.5329548, 0.8372512, 0.33269387, 0.82096875, 0.43657133, 0.3783, 0.5953028, 0.51934963, 0.42142007]\n )\n\n assert (\n np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {image_slice.flatten()}, but got {image_slice.flatten()}\"\n" }, { "path": "diffusers/tests/pipelines/consistency_models/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/consistency_models/test_consistency_models.py", "content": "import gc\nimport unittest\n\nimport numpy as np\nimport torch\nfrom torch.backends.cuda import sdp_kernel\n\nfrom diffusers import (\n CMStochasticIterativeScheduler,\n ConsistencyModelPipeline,\n UNet2DModel,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n nightly,\n require_torch_2,\n require_torch_gpu,\n torch_device,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\nfrom ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass ConsistencyModelPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = ConsistencyModelPipeline\n params = UNCONDITIONAL_IMAGE_GENERATION_PARAMS\n batch_params = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS\n\n # Override required_optional_params to remove num_images_per_prompt\n required_optional_params = frozenset(\n [\n \"num_inference_steps\",\n \"generator\",\n \"latents\",\n \"output_type\",\n \"return_dict\",\n \"callback\",\n \"callback_steps\",\n ]\n )\n\n @property\n def dummy_uncond_unet(self):\n unet = UNet2DModel.from_pretrained(\n \"diffusers/consistency-models-test\",\n subfolder=\"test_unet\",\n )\n return unet\n\n @property\n def dummy_cond_unet(self):\n unet = UNet2DModel.from_pretrained(\n \"diffusers/consistency-models-test\",\n subfolder=\"test_unet_class_cond\",\n )\n return unet\n\n def get_dummy_components(self, class_cond=False):\n if class_cond:\n unet = self.dummy_cond_unet\n else:\n unet = self.dummy_uncond_unet\n\n # Default to CM multistep sampler\n scheduler = CMStochasticIterativeScheduler(\n num_train_timesteps=40,\n sigma_min=0.002,\n sigma_max=80.0,\n )\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n inputs = {\n \"batch_size\": 1,\n \"num_inference_steps\": None,\n \"timesteps\": [22, 0],\n \"generator\": generator,\n \"output_type\": \"np\",\n }\n\n return inputs\n\n def test_consistency_model_pipeline_multistep(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n pipe = ConsistencyModelPipeline(**components)\n pipe = pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = pipe(**inputs).images\n assert image.shape == (1, 32, 32, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_consistency_model_pipeline_multistep_class_cond(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components(class_cond=True)\n pipe = ConsistencyModelPipeline(**components)\n pipe = pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"class_labels\"] = 0\n image = pipe(**inputs).images\n assert image.shape == (1, 32, 32, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_consistency_model_pipeline_onestep(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n pipe = ConsistencyModelPipeline(**components)\n pipe = pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"num_inference_steps\"] = 1\n inputs[\"timesteps\"] = None\n image = pipe(**inputs).images\n assert image.shape == (1, 32, 32, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_consistency_model_pipeline_onestep_class_cond(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components(class_cond=True)\n pipe = ConsistencyModelPipeline(**components)\n pipe = pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"num_inference_steps\"] = 1\n inputs[\"timesteps\"] = None\n inputs[\"class_labels\"] = 0\n image = pipe(**inputs).images\n assert image.shape == (1, 32, 32, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n\n@nightly\n@require_torch_gpu\nclass ConsistencyModelPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, seed=0, get_fixed_latents=False, device=\"cpu\", dtype=torch.float32, shape=(1, 3, 64, 64)):\n generator = torch.manual_seed(seed)\n\n inputs = {\n \"num_inference_steps\": None,\n \"timesteps\": [22, 0],\n \"class_labels\": 0,\n \"generator\": generator,\n \"output_type\": \"np\",\n }\n\n if get_fixed_latents:\n latents = self.get_fixed_latents(seed=seed, device=device, dtype=dtype, shape=shape)\n inputs[\"latents\"] = latents\n\n return inputs\n\n def get_fixed_latents(self, seed=0, device=\"cpu\", dtype=torch.float32, shape=(1, 3, 64, 64)):\n if isinstance(device, str):\n device = torch.device(device)\n generator = torch.Generator(device=device).manual_seed(seed)\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n return latents\n\n def test_consistency_model_cd_multistep(self):\n unet = UNet2DModel.from_pretrained(\"diffusers/consistency_models\", subfolder=\"diffusers_cd_imagenet64_l2\")\n scheduler = CMStochasticIterativeScheduler(\n num_train_timesteps=40,\n sigma_min=0.002,\n sigma_max=80.0,\n )\n pipe = ConsistencyModelPipeline(unet=unet, scheduler=scheduler)\n pipe.to(torch_device=torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs()\n image = pipe(**inputs).images\n assert image.shape == (1, 64, 64, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n\n expected_slice = np.array([0.0146, 0.0158, 0.0092, 0.0086, 0.0000, 0.0000, 0.0000, 0.0000, 0.0058])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_consistency_model_cd_onestep(self):\n unet = UNet2DModel.from_pretrained(\"diffusers/consistency_models\", subfolder=\"diffusers_cd_imagenet64_l2\")\n scheduler = CMStochasticIterativeScheduler(\n num_train_timesteps=40,\n sigma_min=0.002,\n sigma_max=80.0,\n )\n pipe = ConsistencyModelPipeline(unet=unet, scheduler=scheduler)\n pipe.to(torch_device=torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs()\n inputs[\"num_inference_steps\"] = 1\n inputs[\"timesteps\"] = None\n image = pipe(**inputs).images\n assert image.shape == (1, 64, 64, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n\n expected_slice = np.array([0.0059, 0.0003, 0.0000, 0.0023, 0.0052, 0.0007, 0.0165, 0.0081, 0.0095])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n @require_torch_2\n def test_consistency_model_cd_multistep_flash_attn(self):\n unet = UNet2DModel.from_pretrained(\"diffusers/consistency_models\", subfolder=\"diffusers_cd_imagenet64_l2\")\n scheduler = CMStochasticIterativeScheduler(\n num_train_timesteps=40,\n sigma_min=0.002,\n sigma_max=80.0,\n )\n pipe = ConsistencyModelPipeline(unet=unet, scheduler=scheduler)\n pipe.to(torch_device=torch_device, torch_dtype=torch.float16)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(get_fixed_latents=True, device=torch_device)\n # Ensure usage of flash attention in torch 2.0\n with sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):\n image = pipe(**inputs).images\n assert image.shape == (1, 64, 64, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n\n expected_slice = np.array([0.1845, 0.1371, 0.1211, 0.2035, 0.1954, 0.1323, 0.1773, 0.1593, 0.1314])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n @require_torch_2\n def test_consistency_model_cd_onestep_flash_attn(self):\n unet = UNet2DModel.from_pretrained(\"diffusers/consistency_models\", subfolder=\"diffusers_cd_imagenet64_l2\")\n scheduler = CMStochasticIterativeScheduler(\n num_train_timesteps=40,\n sigma_min=0.002,\n sigma_max=80.0,\n )\n pipe = ConsistencyModelPipeline(unet=unet, scheduler=scheduler)\n pipe.to(torch_device=torch_device, torch_dtype=torch.float16)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(get_fixed_latents=True, device=torch_device)\n inputs[\"num_inference_steps\"] = 1\n inputs[\"timesteps\"] = None\n # Ensure usage of flash attention in torch 2.0\n with sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):\n image = pipe(**inputs).images\n assert image.shape == (1, 64, 64, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n\n expected_slice = np.array([0.1623, 0.2009, 0.2387, 0.1731, 0.1168, 0.1202, 0.2031, 0.1327, 0.2447])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n" }, { "path": "diffusers/tests/pipelines/controlnet/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/controlnet/test_controlnet.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport tempfile\nimport traceback\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n ControlNetModel,\n DDIMScheduler,\n EulerDiscreteScheduler,\n LCMScheduler,\n StableDiffusionControlNetPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.pipelines.controlnet.pipeline_controlnet import MultiControlNetModel\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n get_python_version,\n load_image,\n load_numpy,\n require_python39_or_higher,\n require_torch_2,\n require_torch_gpu,\n run_test_in_subprocess,\n slow,\n torch_device,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\nfrom ..pipeline_params import (\n IMAGE_TO_IMAGE_IMAGE_PARAMS,\n TEXT_TO_IMAGE_BATCH_PARAMS,\n TEXT_TO_IMAGE_IMAGE_PARAMS,\n TEXT_TO_IMAGE_PARAMS,\n)\nfrom ..test_pipelines_common import (\n IPAdapterTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n)\n\n\nenable_full_determinism()\n\n\n# Will be run via run_test_in_subprocess\ndef _test_stable_diffusion_compile(in_queue, out_queue, timeout):\n error = None\n try:\n _ = in_queue.get(timeout=timeout)\n\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\")\n\n pipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", safety_checker=None, controlnet=controlnet\n )\n pipe.to(\"cuda\")\n pipe.set_progress_bar_config(disable=None)\n\n pipe.unet.to(memory_format=torch.channels_last)\n pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n\n pipe.controlnet.to(memory_format=torch.channels_last)\n pipe.controlnet = torch.compile(pipe.controlnet, mode=\"reduce-overhead\", fullgraph=True)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n prompt = \"bird\"\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png\"\n ).resize((512, 512))\n\n output = pipe(prompt, image, num_inference_steps=10, generator=generator, output_type=\"np\")\n image = output.images[0]\n\n assert image.shape == (512, 512, 3)\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny_out_full.npy\"\n )\n expected_image = np.resize(expected_image, (512, 512, 3))\n\n assert np.abs(expected_image - image).max() < 1.0\n\n except Exception:\n error = f\"{traceback.format_exc()}\"\n\n results = {\"error\": error}\n out_queue.put(results, timeout=timeout)\n out_queue.join()\n\n\nclass ControlNetPipelineFastTests(\n IPAdapterTesterMixin,\n PipelineLatentTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineTesterMixin,\n unittest.TestCase,\n):\n pipeline_class = StableDiffusionControlNetPipeline\n params = TEXT_TO_IMAGE_PARAMS\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n\n def get_dummy_components(self, time_cond_proj_dim=None):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(4, 8),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n norm_num_groups=1,\n time_cond_proj_dim=time_cond_proj_dim,\n )\n torch.manual_seed(0)\n controlnet = ControlNetModel(\n block_out_channels=(4, 8),\n layers_per_block=2,\n in_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n cross_attention_dim=32,\n conditioning_embedding_out_channels=(16, 32),\n norm_num_groups=1,\n )\n torch.manual_seed(0)\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[4, 8],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n norm_num_groups=2,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"controlnet\": controlnet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n controlnet_embedder_scale_factor = 2\n image = randn_tensor(\n (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),\n generator=generator,\n device=torch.device(device),\n )\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n \"image\": image,\n }\n\n return inputs\n\n def test_attention_slicing_forward_pass(self):\n return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)\n\n def test_ip_adapter_single(self):\n expected_pipe_slice = None\n if torch_device == \"cpu\":\n expected_pipe_slice = np.array([0.5234, 0.3333, 0.1745, 0.7605, 0.6224, 0.4637, 0.6989, 0.7526, 0.4665])\n return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=2e-3)\n\n def test_controlnet_lcm(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionControlNetPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = sd_pipe(**inputs)\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array(\n [0.52700454, 0.3930534, 0.25509018, 0.7132304, 0.53696585, 0.46568912, 0.7095368, 0.7059624, 0.4744786]\n )\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_controlnet_lcm_custom_timesteps(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionControlNetPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n del inputs[\"num_inference_steps\"]\n inputs[\"timesteps\"] = [999, 499]\n output = sd_pipe(**inputs)\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array(\n [0.52700454, 0.3930534, 0.25509018, 0.7132304, 0.53696585, 0.46568912, 0.7095368, 0.7059624, 0.4744786]\n )\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n\nclass StableDiffusionMultiControlNetPipelineFastTests(\n IPAdapterTesterMixin, PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase\n):\n pipeline_class = StableDiffusionControlNetPipeline\n params = TEXT_TO_IMAGE_PARAMS\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n image_params = frozenset([]) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(4, 8),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n norm_num_groups=1,\n )\n torch.manual_seed(0)\n\n def init_weights(m):\n if isinstance(m, torch.nn.Conv2d):\n torch.nn.init.normal_(m.weight)\n m.bias.data.fill_(1.0)\n\n controlnet1 = ControlNetModel(\n block_out_channels=(4, 8),\n layers_per_block=2,\n in_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n cross_attention_dim=32,\n conditioning_embedding_out_channels=(16, 32),\n norm_num_groups=1,\n )\n controlnet1.controlnet_down_blocks.apply(init_weights)\n\n torch.manual_seed(0)\n controlnet2 = ControlNetModel(\n block_out_channels=(4, 8),\n layers_per_block=2,\n in_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n cross_attention_dim=32,\n conditioning_embedding_out_channels=(16, 32),\n norm_num_groups=1,\n )\n controlnet2.controlnet_down_blocks.apply(init_weights)\n\n torch.manual_seed(0)\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[4, 8],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n norm_num_groups=2,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n controlnet = MultiControlNetModel([controlnet1, controlnet2])\n\n components = {\n \"unet\": unet,\n \"controlnet\": controlnet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n controlnet_embedder_scale_factor = 2\n\n images = [\n randn_tensor(\n (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),\n generator=generator,\n device=torch.device(device),\n ),\n randn_tensor(\n (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),\n generator=generator,\n device=torch.device(device),\n ),\n ]\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n \"image\": images,\n }\n\n return inputs\n\n def test_control_guidance_switch(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n\n scale = 10.0\n steps = 4\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_1 = pipe(**inputs)[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_2 = pipe(**inputs, control_guidance_start=0.1, control_guidance_end=0.2)[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_3 = pipe(**inputs, control_guidance_start=[0.1, 0.3], control_guidance_end=[0.2, 0.7])[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_4 = pipe(**inputs, control_guidance_start=0.4, control_guidance_end=[0.5, 0.8])[0]\n\n # make sure that all outputs are different\n assert np.sum(np.abs(output_1 - output_2)) > 1e-3\n assert np.sum(np.abs(output_1 - output_3)) > 1e-3\n assert np.sum(np.abs(output_1 - output_4)) > 1e-3\n\n def test_attention_slicing_forward_pass(self):\n return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=2e-3)\n\n def test_ip_adapter_single(self):\n expected_pipe_slice = None\n if torch_device == \"cpu\":\n expected_pipe_slice = np.array([0.2422, 0.3425, 0.4048, 0.5351, 0.3503, 0.2419, 0.4645, 0.4570, 0.3804])\n return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)\n\n def test_save_pretrained_raise_not_implemented_exception(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n with tempfile.TemporaryDirectory() as tmpdir:\n try:\n # save_pretrained is not implemented for Multi-ControlNet\n pipe.save_pretrained(tmpdir)\n except NotImplementedError:\n pass\n\n def test_inference_multiple_prompt_input(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionControlNetPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"prompt\"] = [inputs[\"prompt\"], inputs[\"prompt\"]]\n inputs[\"image\"] = [inputs[\"image\"], inputs[\"image\"]]\n output = sd_pipe(**inputs)\n image = output.images\n\n assert image.shape == (2, 64, 64, 3)\n\n image_1, image_2 = image\n # make sure that the outputs are different\n assert np.sum(np.abs(image_1 - image_2)) > 1e-3\n\n # multiple prompts, single image conditioning\n inputs = self.get_dummy_inputs(device)\n inputs[\"prompt\"] = [inputs[\"prompt\"], inputs[\"prompt\"]]\n output_1 = sd_pipe(**inputs)\n\n assert np.abs(image - output_1.images).max() < 1e-3\n\n # multiple prompts, multiple image conditioning\n inputs = self.get_dummy_inputs(device)\n inputs[\"prompt\"] = [inputs[\"prompt\"], inputs[\"prompt\"], inputs[\"prompt\"], inputs[\"prompt\"]]\n inputs[\"image\"] = [inputs[\"image\"], inputs[\"image\"], inputs[\"image\"], inputs[\"image\"]]\n output_2 = sd_pipe(**inputs)\n image = output_2.images\n\n assert image.shape == (4, 64, 64, 3)\n\n\nclass StableDiffusionMultiControlNetOneModelPipelineFastTests(\n IPAdapterTesterMixin, PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase\n):\n pipeline_class = StableDiffusionControlNetPipeline\n params = TEXT_TO_IMAGE_PARAMS\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n image_params = frozenset([]) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(4, 8),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n norm_num_groups=1,\n )\n torch.manual_seed(0)\n\n def init_weights(m):\n if isinstance(m, torch.nn.Conv2d):\n torch.nn.init.normal_(m.weight)\n m.bias.data.fill_(1.0)\n\n controlnet = ControlNetModel(\n block_out_channels=(4, 8),\n layers_per_block=2,\n in_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n cross_attention_dim=32,\n conditioning_embedding_out_channels=(16, 32),\n norm_num_groups=1,\n )\n controlnet.controlnet_down_blocks.apply(init_weights)\n\n torch.manual_seed(0)\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[4, 8],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n norm_num_groups=2,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n controlnet = MultiControlNetModel([controlnet])\n\n components = {\n \"unet\": unet,\n \"controlnet\": controlnet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n controlnet_embedder_scale_factor = 2\n\n images = [\n randn_tensor(\n (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),\n generator=generator,\n device=torch.device(device),\n ),\n ]\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n \"image\": images,\n }\n\n return inputs\n\n def test_control_guidance_switch(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n\n scale = 10.0\n steps = 4\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_1 = pipe(**inputs)[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_2 = pipe(**inputs, control_guidance_start=0.1, control_guidance_end=0.2)[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_3 = pipe(\n **inputs,\n control_guidance_start=[0.1],\n control_guidance_end=[0.2],\n )[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_4 = pipe(**inputs, control_guidance_start=0.4, control_guidance_end=[0.5])[0]\n\n # make sure that all outputs are different\n assert np.sum(np.abs(output_1 - output_2)) > 1e-3\n assert np.sum(np.abs(output_1 - output_3)) > 1e-3\n assert np.sum(np.abs(output_1 - output_4)) > 1e-3\n\n def test_attention_slicing_forward_pass(self):\n return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=2e-3)\n\n def test_ip_adapter_single(self):\n expected_pipe_slice = None\n if torch_device == \"cpu\":\n expected_pipe_slice = np.array([0.5264, 0.3203, 0.1602, 0.8235, 0.6332, 0.4593, 0.7226, 0.7777, 0.4780])\n return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)\n\n def test_save_pretrained_raise_not_implemented_exception(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n with tempfile.TemporaryDirectory() as tmpdir:\n try:\n # save_pretrained is not implemented for Multi-ControlNet\n pipe.save_pretrained(tmpdir)\n except NotImplementedError:\n pass\n\n\n@slow\n@require_torch_gpu\nclass ControlNetPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_canny(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\")\n\n pipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", safety_checker=None, controlnet=controlnet\n )\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n prompt = \"bird\"\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png\"\n )\n\n output = pipe(prompt, image, generator=generator, output_type=\"np\", num_inference_steps=3)\n\n image = output.images[0]\n\n assert image.shape == (768, 512, 3)\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny_out.npy\"\n )\n\n assert np.abs(expected_image - image).max() < 9e-2\n\n def test_depth(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-depth\")\n\n pipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", safety_checker=None, controlnet=controlnet\n )\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n prompt = \"Stormtrooper's lecture\"\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth.png\"\n )\n\n output = pipe(prompt, image, generator=generator, output_type=\"np\", num_inference_steps=3)\n\n image = output.images[0]\n\n assert image.shape == (512, 512, 3)\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth_out.npy\"\n )\n\n assert np.abs(expected_image - image).max() < 8e-1\n\n def test_hed(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-hed\")\n\n pipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", safety_checker=None, controlnet=controlnet\n )\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n prompt = \"oil painting of handsome old man, masterpiece\"\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/man_hed.png\"\n )\n\n output = pipe(prompt, image, generator=generator, output_type=\"np\", num_inference_steps=3)\n\n image = output.images[0]\n\n assert image.shape == (704, 512, 3)\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/man_hed_out.npy\"\n )\n\n assert np.abs(expected_image - image).max() < 8e-2\n\n def test_mlsd(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-mlsd\")\n\n pipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", safety_checker=None, controlnet=controlnet\n )\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n prompt = \"room\"\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/room_mlsd.png\"\n )\n\n output = pipe(prompt, image, generator=generator, output_type=\"np\", num_inference_steps=3)\n\n image = output.images[0]\n\n assert image.shape == (704, 512, 3)\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/room_mlsd_out.npy\"\n )\n\n assert np.abs(expected_image - image).max() < 5e-2\n\n def test_normal(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-normal\")\n\n pipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", safety_checker=None, controlnet=controlnet\n )\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n prompt = \"cute toy\"\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/cute_toy_normal.png\"\n )\n\n output = pipe(prompt, image, generator=generator, output_type=\"np\", num_inference_steps=3)\n\n image = output.images[0]\n\n assert image.shape == (512, 512, 3)\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/cute_toy_normal_out.npy\"\n )\n\n assert np.abs(expected_image - image).max() < 5e-2\n\n def test_openpose(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-openpose\")\n\n pipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", safety_checker=None, controlnet=controlnet\n )\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n prompt = \"Chef in the kitchen\"\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png\"\n )\n\n output = pipe(prompt, image, generator=generator, output_type=\"np\", num_inference_steps=3)\n\n image = output.images[0]\n\n assert image.shape == (768, 512, 3)\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/chef_pose_out.npy\"\n )\n\n assert np.abs(expected_image - image).max() < 8e-2\n\n def test_scribble(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-scribble\")\n\n pipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", safety_checker=None, controlnet=controlnet\n )\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(5)\n prompt = \"bag\"\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bag_scribble.png\"\n )\n\n output = pipe(prompt, image, generator=generator, output_type=\"np\", num_inference_steps=3)\n\n image = output.images[0]\n\n assert image.shape == (640, 512, 3)\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bag_scribble_out.npy\"\n )\n\n assert np.abs(expected_image - image).max() < 8e-2\n\n def test_seg(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-seg\")\n\n pipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", safety_checker=None, controlnet=controlnet\n )\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(5)\n prompt = \"house\"\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/house_seg.png\"\n )\n\n output = pipe(prompt, image, generator=generator, output_type=\"np\", num_inference_steps=3)\n\n image = output.images[0]\n\n assert image.shape == (512, 512, 3)\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/house_seg_out.npy\"\n )\n\n assert np.abs(expected_image - image).max() < 8e-2\n\n def test_sequential_cpu_offloading(self):\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-seg\")\n\n pipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", safety_checker=None, controlnet=controlnet\n )\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n pipe.enable_sequential_cpu_offload()\n\n prompt = \"house\"\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/house_seg.png\"\n )\n\n _ = pipe(\n prompt,\n image,\n num_inference_steps=2,\n output_type=\"np\",\n )\n\n mem_bytes = torch.cuda.max_memory_allocated()\n # make sure that less than 7 GB is allocated\n assert mem_bytes < 4 * 10**9\n\n def test_canny_guess_mode(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\")\n\n pipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", safety_checker=None, controlnet=controlnet\n )\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n prompt = \"\"\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png\"\n )\n\n output = pipe(\n prompt,\n image,\n generator=generator,\n output_type=\"np\",\n num_inference_steps=3,\n guidance_scale=3.0,\n guess_mode=True,\n )\n\n image = output.images[0]\n assert image.shape == (768, 512, 3)\n\n image_slice = image[-3:, -3:, -1]\n expected_slice = np.array([0.2724, 0.2846, 0.2724, 0.3843, 0.3682, 0.2736, 0.4675, 0.3862, 0.2887])\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_canny_guess_mode_euler(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\")\n\n pipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", safety_checker=None, controlnet=controlnet\n )\n pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n prompt = \"\"\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png\"\n )\n\n output = pipe(\n prompt,\n image,\n generator=generator,\n output_type=\"np\",\n num_inference_steps=3,\n guidance_scale=3.0,\n guess_mode=True,\n )\n\n image = output.images[0]\n assert image.shape == (768, 512, 3)\n\n image_slice = image[-3:, -3:, -1]\n expected_slice = np.array([0.1655, 0.1721, 0.1623, 0.1685, 0.1711, 0.1646, 0.1651, 0.1631, 0.1494])\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n @require_python39_or_higher\n @require_torch_2\n @unittest.skipIf(\n get_python_version == (3, 12),\n reason=\"Torch Dynamo isn't yet supported for Python 3.12.\",\n )\n def test_stable_diffusion_compile(self):\n run_test_in_subprocess(test_case=self, target_func=_test_stable_diffusion_compile, inputs=None)\n\n def test_v11_shuffle_global_pool_conditions(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11e_sd15_shuffle\")\n\n pipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", safety_checker=None, controlnet=controlnet\n )\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n prompt = \"New York\"\n image = load_image(\n \"https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png\"\n )\n\n output = pipe(\n prompt,\n image,\n generator=generator,\n output_type=\"np\",\n num_inference_steps=3,\n guidance_scale=7.0,\n )\n\n image = output.images[0]\n assert image.shape == (512, 640, 3)\n\n image_slice = image[-3:, -3:, -1]\n expected_slice = np.array([0.1338, 0.1597, 0.1202, 0.1687, 0.1377, 0.1017, 0.2070, 0.1574, 0.1348])\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionMultiControlNetPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_pose_and_canny(self):\n controlnet_canny = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\")\n controlnet_pose = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-openpose\")\n\n pipe = StableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", safety_checker=None, controlnet=[controlnet_pose, controlnet_canny]\n )\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n prompt = \"bird and Chef\"\n image_canny = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png\"\n )\n image_pose = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png\"\n )\n\n output = pipe(prompt, [image_pose, image_canny], generator=generator, output_type=\"np\", num_inference_steps=3)\n\n image = output.images[0]\n\n assert image.shape == (768, 512, 3)\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose_canny_out.npy\"\n )\n\n assert np.abs(expected_image - image).max() < 5e-2\n" }, { "path": "diffusers/tests/pipelines/controlnet/test_controlnet_blip_diffusion.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom transformers import CLIPTokenizer\nfrom transformers.models.blip_2.configuration_blip_2 import Blip2Config\nfrom transformers.models.clip.configuration_clip import CLIPTextConfig\n\nfrom diffusers import (\n AutoencoderKL,\n BlipDiffusionControlNetPipeline,\n ControlNetModel,\n PNDMScheduler,\n UNet2DConditionModel,\n)\nfrom diffusers.utils.testing_utils import enable_full_determinism, torch_device\nfrom src.diffusers.pipelines.blip_diffusion.blip_image_processing import BlipImageProcessor\nfrom src.diffusers.pipelines.blip_diffusion.modeling_blip2 import Blip2QFormerModel\nfrom src.diffusers.pipelines.blip_diffusion.modeling_ctx_clip import ContextCLIPTextModel\n\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass BlipDiffusionControlNetPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = BlipDiffusionControlNetPipeline\n params = [\n \"prompt\",\n \"reference_image\",\n \"source_subject_category\",\n \"target_subject_category\",\n \"condtioning_image\",\n ]\n batch_params = [\n \"prompt\",\n \"reference_image\",\n \"source_subject_category\",\n \"target_subject_category\",\n \"condtioning_image\",\n ]\n required_optional_params = [\n \"generator\",\n \"height\",\n \"width\",\n \"latents\",\n \"guidance_scale\",\n \"num_inference_steps\",\n \"neg_prompt\",\n \"guidance_scale\",\n \"prompt_strength\",\n \"prompt_reps\",\n ]\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n vocab_size=1000,\n hidden_size=16,\n intermediate_size=16,\n projection_dim=16,\n num_hidden_layers=1,\n num_attention_heads=1,\n max_position_embeddings=77,\n )\n text_encoder = ContextCLIPTextModel(text_encoder_config)\n\n vae = AutoencoderKL(\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownEncoderBlock2D\",),\n up_block_types=(\"UpDecoderBlock2D\",),\n block_out_channels=(32,),\n layers_per_block=1,\n act_fn=\"silu\",\n latent_channels=4,\n norm_num_groups=16,\n sample_size=16,\n )\n\n blip_vision_config = {\n \"hidden_size\": 16,\n \"intermediate_size\": 16,\n \"num_hidden_layers\": 1,\n \"num_attention_heads\": 1,\n \"image_size\": 224,\n \"patch_size\": 14,\n \"hidden_act\": \"quick_gelu\",\n }\n\n blip_qformer_config = {\n \"vocab_size\": 1000,\n \"hidden_size\": 16,\n \"num_hidden_layers\": 1,\n \"num_attention_heads\": 1,\n \"intermediate_size\": 16,\n \"max_position_embeddings\": 512,\n \"cross_attention_frequency\": 1,\n \"encoder_hidden_size\": 16,\n }\n qformer_config = Blip2Config(\n vision_config=blip_vision_config,\n qformer_config=blip_qformer_config,\n num_query_tokens=16,\n tokenizer=\"hf-internal-testing/tiny-random-bert\",\n )\n qformer = Blip2QFormerModel(qformer_config)\n\n unet = UNet2DConditionModel(\n block_out_channels=(4, 16),\n layers_per_block=1,\n norm_num_groups=4,\n sample_size=16,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=16,\n )\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n scheduler = PNDMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n set_alpha_to_one=False,\n skip_prk_steps=True,\n )\n controlnet = ControlNetModel(\n block_out_channels=(4, 16),\n layers_per_block=1,\n in_channels=4,\n norm_num_groups=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n cross_attention_dim=16,\n conditioning_embedding_out_channels=(8, 16),\n )\n\n vae.eval()\n qformer.eval()\n text_encoder.eval()\n\n image_processor = BlipImageProcessor()\n\n components = {\n \"text_encoder\": text_encoder,\n \"vae\": vae,\n \"qformer\": qformer,\n \"unet\": unet,\n \"tokenizer\": tokenizer,\n \"scheduler\": scheduler,\n \"controlnet\": controlnet,\n \"image_processor\": image_processor,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n np.random.seed(seed)\n reference_image = np.random.rand(32, 32, 3) * 255\n reference_image = Image.fromarray(reference_image.astype(\"uint8\")).convert(\"RGBA\")\n cond_image = np.random.rand(32, 32, 3) * 255\n cond_image = Image.fromarray(cond_image.astype(\"uint8\")).convert(\"RGBA\")\n\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"swimming underwater\",\n \"generator\": generator,\n \"reference_image\": reference_image,\n \"condtioning_image\": cond_image,\n \"source_subject_category\": \"dog\",\n \"target_subject_category\": \"dog\",\n \"height\": 32,\n \"width\": 32,\n \"guidance_scale\": 7.5,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_dict_tuple_outputs_equivalent(self):\n expected_slice = None\n if torch_device == \"cpu\":\n expected_slice = np.array([0.4803, 0.3865, 0.1422, 0.6119, 0.2283, 0.6365, 0.5453, 0.5205, 0.3581])\n super().test_dict_tuple_outputs_equivalent(expected_slice=expected_slice)\n\n def test_blipdiffusion_controlnet(self):\n device = \"cpu\"\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n image = pipe(**self.get_dummy_inputs(device))[0]\n image_slice = image[0, -3:, -3:, 0]\n\n assert image.shape == (1, 16, 16, 4)\n expected_slice = np.array([0.7953, 0.7136, 0.6597, 0.4779, 0.7389, 0.4111, 0.5826, 0.4150, 0.8422])\n\n assert (\n np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_slice.flatten()}\"\n" }, { "path": "diffusers/tests/pipelines/controlnet/test_controlnet_img2img.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/\n\nimport gc\nimport random\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n ControlNetModel,\n DDIMScheduler,\n StableDiffusionControlNetImg2ImgPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.pipelines.controlnet.pipeline_controlnet import MultiControlNetModel\nfrom diffusers.utils import load_image\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_numpy,\n require_torch_gpu,\n slow,\n torch_device,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\nfrom ..pipeline_params import (\n IMAGE_TO_IMAGE_IMAGE_PARAMS,\n TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,\n TEXT_GUIDED_IMAGE_VARIATION_PARAMS,\n)\nfrom ..test_pipelines_common import (\n IPAdapterTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n)\n\n\nenable_full_determinism()\n\n\nclass ControlNetImg2ImgPipelineFastTests(\n IPAdapterTesterMixin,\n PipelineLatentTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineTesterMixin,\n unittest.TestCase,\n):\n pipeline_class = StableDiffusionControlNetImg2ImgPipeline\n params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {\"height\", \"width\"}\n batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS\n image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS.union({\"control_image\"})\n image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(4, 8),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n norm_num_groups=1,\n )\n torch.manual_seed(0)\n controlnet = ControlNetModel(\n block_out_channels=(4, 8),\n layers_per_block=2,\n in_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n cross_attention_dim=32,\n conditioning_embedding_out_channels=(16, 32),\n norm_num_groups=1,\n )\n torch.manual_seed(0)\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[4, 8],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n norm_num_groups=2,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"controlnet\": controlnet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n controlnet_embedder_scale_factor = 2\n control_image = randn_tensor(\n (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),\n generator=generator,\n device=torch.device(device),\n )\n image = floats_tensor(control_image.shape, rng=random.Random(seed)).to(device)\n image = image.cpu().permute(0, 2, 3, 1)[0]\n image = Image.fromarray(np.uint8(image)).convert(\"RGB\").resize((64, 64))\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n \"image\": image,\n \"control_image\": control_image,\n }\n\n return inputs\n\n def test_attention_slicing_forward_pass(self):\n return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)\n\n def test_ip_adapter_single(self):\n expected_pipe_slice = None\n if torch_device == \"cpu\":\n expected_pipe_slice = np.array([0.7096, 0.5149, 0.3571, 0.5897, 0.4715, 0.4052, 0.6098, 0.6886, 0.4213])\n return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=2e-3)\n\n\nclass StableDiffusionMultiControlNetPipelineFastTests(\n IPAdapterTesterMixin, PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase\n):\n pipeline_class = StableDiffusionControlNetImg2ImgPipeline\n params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {\"height\", \"width\"}\n batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS\n image_params = frozenset([]) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(4, 8),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n norm_num_groups=1,\n )\n torch.manual_seed(0)\n\n def init_weights(m):\n if isinstance(m, torch.nn.Conv2d):\n torch.nn.init.normal_(m.weight)\n m.bias.data.fill_(1.0)\n\n controlnet1 = ControlNetModel(\n block_out_channels=(4, 8),\n layers_per_block=2,\n in_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n cross_attention_dim=32,\n conditioning_embedding_out_channels=(16, 32),\n norm_num_groups=1,\n )\n controlnet1.controlnet_down_blocks.apply(init_weights)\n\n torch.manual_seed(0)\n controlnet2 = ControlNetModel(\n block_out_channels=(4, 8),\n layers_per_block=2,\n in_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n cross_attention_dim=32,\n conditioning_embedding_out_channels=(16, 32),\n norm_num_groups=1,\n )\n controlnet2.controlnet_down_blocks.apply(init_weights)\n\n torch.manual_seed(0)\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[4, 8],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n norm_num_groups=2,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n controlnet = MultiControlNetModel([controlnet1, controlnet2])\n\n components = {\n \"unet\": unet,\n \"controlnet\": controlnet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n controlnet_embedder_scale_factor = 2\n\n control_image = [\n randn_tensor(\n (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),\n generator=generator,\n device=torch.device(device),\n ),\n randn_tensor(\n (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),\n generator=generator,\n device=torch.device(device),\n ),\n ]\n\n image = floats_tensor(control_image[0].shape, rng=random.Random(seed)).to(device)\n image = image.cpu().permute(0, 2, 3, 1)[0]\n image = Image.fromarray(np.uint8(image)).convert(\"RGB\").resize((64, 64))\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n \"image\": image,\n \"control_image\": control_image,\n }\n\n return inputs\n\n def test_control_guidance_switch(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n\n scale = 10.0\n steps = 4\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_1 = pipe(**inputs)[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_2 = pipe(**inputs, control_guidance_start=0.1, control_guidance_end=0.2)[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_3 = pipe(**inputs, control_guidance_start=[0.1, 0.3], control_guidance_end=[0.2, 0.7])[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_4 = pipe(**inputs, control_guidance_start=0.4, control_guidance_end=[0.5, 0.8])[0]\n\n # make sure that all outputs are different\n assert np.sum(np.abs(output_1 - output_2)) > 1e-3\n assert np.sum(np.abs(output_1 - output_3)) > 1e-3\n assert np.sum(np.abs(output_1 - output_4)) > 1e-3\n\n def test_attention_slicing_forward_pass(self):\n return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=2e-3)\n\n def test_ip_adapter_single(self):\n expected_pipe_slice = None\n if torch_device == \"cpu\":\n expected_pipe_slice = np.array([0.5293, 0.7339, 0.6642, 0.3950, 0.5212, 0.5175, 0.7002, 0.5907, 0.5182])\n return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)\n\n def test_save_pretrained_raise_not_implemented_exception(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n with tempfile.TemporaryDirectory() as tmpdir:\n try:\n # save_pretrained is not implemented for Multi-ControlNet\n pipe.save_pretrained(tmpdir)\n except NotImplementedError:\n pass\n\n\n@slow\n@require_torch_gpu\nclass ControlNetImg2ImgPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_canny(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\")\n\n pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", safety_checker=None, controlnet=controlnet\n )\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n prompt = \"evil space-punk bird\"\n control_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png\"\n ).resize((512, 512))\n image = load_image(\n \"https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png\"\n ).resize((512, 512))\n\n output = pipe(\n prompt,\n image,\n control_image=control_image,\n generator=generator,\n output_type=\"np\",\n num_inference_steps=50,\n strength=0.6,\n )\n\n image = output.images[0]\n\n assert image.shape == (512, 512, 3)\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy\"\n )\n\n assert np.abs(expected_image - image).max() < 9e-2\n" }, { "path": "diffusers/tests/pipelines/controlnet/test_controlnet_inpaint.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# This model implementation is heavily based on:\n\nimport gc\nimport random\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n ControlNetModel,\n DDIMScheduler,\n StableDiffusionControlNetInpaintPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.pipelines.controlnet.pipeline_controlnet import MultiControlNetModel\nfrom diffusers.utils import load_image\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_numpy,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n slow,\n torch_device,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\nfrom ..pipeline_params import (\n TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,\n TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,\n TEXT_TO_IMAGE_IMAGE_PARAMS,\n)\nfrom ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass ControlNetInpaintPipelineFastTests(\n PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase\n):\n pipeline_class = StableDiffusionControlNetInpaintPipeline\n params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS\n batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS\n image_params = frozenset({\"control_image\"}) # skip `image` and `mask` for now, only test for control_image\n image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=9,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n )\n torch.manual_seed(0)\n controlnet = ControlNetModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n in_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n cross_attention_dim=32,\n conditioning_embedding_out_channels=(16, 32),\n )\n torch.manual_seed(0)\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"controlnet\": controlnet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n controlnet_embedder_scale_factor = 2\n control_image = randn_tensor(\n (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),\n generator=generator,\n device=torch.device(device),\n )\n init_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n init_image = init_image.cpu().permute(0, 2, 3, 1)[0]\n\n image = Image.fromarray(np.uint8(init_image)).convert(\"RGB\").resize((64, 64))\n mask_image = Image.fromarray(np.uint8(init_image + 4)).convert(\"RGB\").resize((64, 64))\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n \"image\": image,\n \"mask_image\": mask_image,\n \"control_image\": control_image,\n }\n\n return inputs\n\n def test_attention_slicing_forward_pass(self):\n return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=2e-3)\n\n\nclass ControlNetSimpleInpaintPipelineFastTests(ControlNetInpaintPipelineFastTests):\n pipeline_class = StableDiffusionControlNetInpaintPipeline\n params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS\n batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS\n image_params = frozenset([])\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n )\n torch.manual_seed(0)\n controlnet = ControlNetModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n in_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n cross_attention_dim=32,\n conditioning_embedding_out_channels=(16, 32),\n )\n torch.manual_seed(0)\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"controlnet\": controlnet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n\nclass MultiControlNetInpaintPipelineFastTests(\n PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase\n):\n pipeline_class = StableDiffusionControlNetInpaintPipeline\n params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS\n batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=9,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n )\n torch.manual_seed(0)\n\n def init_weights(m):\n if isinstance(m, torch.nn.Conv2d):\n torch.nn.init.normal_(m.weight)\n m.bias.data.fill_(1.0)\n\n controlnet1 = ControlNetModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n in_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n cross_attention_dim=32,\n conditioning_embedding_out_channels=(16, 32),\n )\n controlnet1.controlnet_down_blocks.apply(init_weights)\n\n torch.manual_seed(0)\n controlnet2 = ControlNetModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n in_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n cross_attention_dim=32,\n conditioning_embedding_out_channels=(16, 32),\n )\n controlnet2.controlnet_down_blocks.apply(init_weights)\n\n torch.manual_seed(0)\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n controlnet = MultiControlNetModel([controlnet1, controlnet2])\n\n components = {\n \"unet\": unet,\n \"controlnet\": controlnet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n controlnet_embedder_scale_factor = 2\n\n control_image = [\n randn_tensor(\n (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),\n generator=generator,\n device=torch.device(device),\n ),\n randn_tensor(\n (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),\n generator=generator,\n device=torch.device(device),\n ),\n ]\n init_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n init_image = init_image.cpu().permute(0, 2, 3, 1)[0]\n\n image = Image.fromarray(np.uint8(init_image)).convert(\"RGB\").resize((64, 64))\n mask_image = Image.fromarray(np.uint8(init_image + 4)).convert(\"RGB\").resize((64, 64))\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n \"image\": image,\n \"mask_image\": mask_image,\n \"control_image\": control_image,\n }\n\n return inputs\n\n def test_control_guidance_switch(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n\n scale = 10.0\n steps = 4\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_1 = pipe(**inputs)[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_2 = pipe(**inputs, control_guidance_start=0.1, control_guidance_end=0.2)[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_3 = pipe(**inputs, control_guidance_start=[0.1, 0.3], control_guidance_end=[0.2, 0.7])[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_4 = pipe(**inputs, control_guidance_start=0.4, control_guidance_end=[0.5, 0.8])[0]\n\n # make sure that all outputs are different\n assert np.sum(np.abs(output_1 - output_2)) > 1e-3\n assert np.sum(np.abs(output_1 - output_3)) > 1e-3\n assert np.sum(np.abs(output_1 - output_4)) > 1e-3\n\n def test_attention_slicing_forward_pass(self):\n return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=2e-3)\n\n def test_save_pretrained_raise_not_implemented_exception(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n with tempfile.TemporaryDirectory() as tmpdir:\n try:\n # save_pretrained is not implemented for Multi-ControlNet\n pipe.save_pretrained(tmpdir)\n except NotImplementedError:\n pass\n\n\n@slow\n@require_torch_gpu\nclass ControlNetInpaintPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_canny(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/sd-controlnet-canny\")\n\n pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", safety_checker=None, controlnet=controlnet\n )\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n image = load_image(\n \"https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png\"\n ).resize((512, 512))\n\n mask_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_inpaint/input_bench_mask.png\"\n ).resize((512, 512))\n\n prompt = \"pitch black hole\"\n\n control_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png\"\n ).resize((512, 512))\n\n output = pipe(\n prompt,\n image=image,\n mask_image=mask_image,\n control_image=control_image,\n generator=generator,\n output_type=\"np\",\n num_inference_steps=3,\n )\n\n image = output.images[0]\n\n assert image.shape == (512, 512, 3)\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy\"\n )\n\n assert np.abs(expected_image - image).max() < 9e-2\n\n def test_inpaint(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11p_sd15_inpaint\")\n\n pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", safety_checker=None, controlnet=controlnet\n )\n pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(33)\n\n init_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png\"\n )\n init_image = init_image.resize((512, 512))\n\n mask_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png\"\n )\n mask_image = mask_image.resize((512, 512))\n\n prompt = \"a handsome man with ray-ban sunglasses\"\n\n def make_inpaint_condition(image, image_mask):\n image = np.array(image.convert(\"RGB\")).astype(np.float32) / 255.0\n image_mask = np.array(image_mask.convert(\"L\")).astype(np.float32) / 255.0\n\n assert image.shape[0:1] == image_mask.shape[0:1], \"image and image_mask must have the same image size\"\n image[image_mask > 0.5] = -1.0 # set as masked pixel\n image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)\n image = torch.from_numpy(image)\n return image\n\n control_image = make_inpaint_condition(init_image, mask_image)\n\n output = pipe(\n prompt,\n image=init_image,\n mask_image=mask_image,\n control_image=control_image,\n guidance_scale=9.0,\n eta=1.0,\n generator=generator,\n num_inference_steps=20,\n output_type=\"np\",\n )\n image = output.images[0]\n\n assert image.shape == (512, 512, 3)\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy\"\n )\n\n assert numpy_cosine_similarity_distance(expected_image.flatten(), image.flatten()) < 1e-2\n" }, { "path": "diffusers/tests/pipelines/controlnet/test_controlnet_inpaint_sdxl.py", "content": "# coding=utf-8\n# Copyright 2024 Harutatsu Akiyama, Jinbin Bai, and HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextConfig,\n CLIPTextModel,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionConfig,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers import (\n AutoencoderKL,\n ControlNetModel,\n EulerDiscreteScheduler,\n StableDiffusionXLControlNetInpaintPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device\n\nfrom ..pipeline_params import (\n IMAGE_TO_IMAGE_IMAGE_PARAMS,\n TEXT_TO_IMAGE_BATCH_PARAMS,\n TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,\n TEXT_TO_IMAGE_IMAGE_PARAMS,\n TEXT_TO_IMAGE_PARAMS,\n)\nfrom ..test_pipelines_common import (\n PipelineKarrasSchedulerTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n)\n\n\nenable_full_determinism()\n\n\nclass ControlNetPipelineSDXLFastTests(\n PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase\n):\n pipeline_class = StableDiffusionXLControlNetInpaintPipeline\n params = TEXT_TO_IMAGE_PARAMS\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n image_params = frozenset(IMAGE_TO_IMAGE_IMAGE_PARAMS.union({\"mask_image\", \"control_image\"}))\n image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union(\n {\n \"add_text_embeds\",\n \"add_time_ids\",\n \"mask\",\n \"masked_image_latents\",\n }\n )\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2),\n projection_class_embeddings_input_dim=80, # 6 * 8 + 32\n cross_attention_dim=64,\n )\n torch.manual_seed(0)\n controlnet = ControlNetModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n in_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n conditioning_embedding_out_channels=(16, 32),\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2),\n projection_class_embeddings_input_dim=80, # 6 * 8 + 32\n cross_attention_dim=64,\n )\n scheduler = EulerDiscreteScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n steps_offset=1,\n beta_schedule=\"scaled_linear\",\n timestep_spacing=\"leading\",\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n # SD2-specific config below\n hidden_act=\"gelu\",\n projection_dim=32,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n torch.manual_seed(0)\n text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)\n tokenizer_2 = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n image_encoder_config = CLIPVisionConfig(\n hidden_size=32,\n image_size=224,\n projection_dim=32,\n intermediate_size=37,\n num_attention_heads=4,\n num_channels=3,\n num_hidden_layers=5,\n patch_size=14,\n )\n\n image_encoder = CLIPVisionModelWithProjection(image_encoder_config)\n\n feature_extractor = CLIPImageProcessor(\n crop_size=224,\n do_center_crop=True,\n do_normalize=True,\n do_resize=True,\n image_mean=[0.48145466, 0.4578275, 0.40821073],\n image_std=[0.26862954, 0.26130258, 0.27577711],\n resample=3,\n size=224,\n )\n\n components = {\n \"unet\": unet,\n \"controlnet\": controlnet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"text_encoder_2\": text_encoder_2,\n \"tokenizer_2\": tokenizer_2,\n \"image_encoder\": image_encoder,\n \"feature_extractor\": feature_extractor,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0, img_res=64):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n # Get random floats in [0, 1] as image\n image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n image = image.cpu().permute(0, 2, 3, 1)[0]\n mask_image = torch.ones_like(image)\n controlnet_embedder_scale_factor = 2\n control_image = (\n floats_tensor(\n (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),\n rng=random.Random(seed),\n )\n .to(device)\n .cpu()\n )\n control_image = control_image.cpu().permute(0, 2, 3, 1)[0]\n # Convert image and mask_image to [0, 255]\n image = 255 * image\n mask_image = 255 * mask_image\n control_image = 255 * control_image\n # Convert to PIL image\n init_image = Image.fromarray(np.uint8(image)).convert(\"RGB\").resize((img_res, img_res))\n mask_image = Image.fromarray(np.uint8(mask_image)).convert(\"L\").resize((img_res, img_res))\n control_image = Image.fromarray(np.uint8(control_image)).convert(\"RGB\").resize((img_res, img_res))\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n \"image\": init_image,\n \"mask_image\": mask_image,\n \"control_image\": control_image,\n }\n return inputs\n\n def test_attention_slicing_forward_pass(self):\n return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)\n\n def test_dict_tuple_outputs_equivalent(self):\n expected_slice = None\n if torch_device == \"cpu\":\n expected_slice = np.array([0.5490, 0.5053, 0.4676, 0.5816, 0.5364, 0.4830, 0.5937, 0.5719, 0.4318])\n super().test_dict_tuple_outputs_equivalent(expected_slice=expected_slice)\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=2e-3)\n\n @require_torch_gpu\n def test_stable_diffusion_xl_offloads(self):\n pipes = []\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(torch_device)\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_model_cpu_offload()\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_sequential_cpu_offload()\n pipes.append(sd_pipe)\n\n image_slices = []\n for pipe in pipes:\n pipe.unet.set_default_attn_processor()\n\n inputs = self.get_dummy_inputs(torch_device)\n image = pipe(**inputs).images\n\n image_slices.append(image[0, -3:, -3:, -1].flatten())\n\n assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3\n assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3\n\n def test_stable_diffusion_xl_multi_prompts(self):\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(torch_device)\n\n # forward with single prompt\n inputs = self.get_dummy_inputs(torch_device)\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with same prompt duplicated\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"prompt_2\"] = inputs[\"prompt\"]\n output = sd_pipe(**inputs)\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n # forward with different prompt\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"prompt_2\"] = \"different prompt\"\n output = sd_pipe(**inputs)\n image_slice_3 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are not equal\n assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4\n\n # manually set a negative_prompt\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"negative_prompt\"] = \"negative prompt\"\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with same negative_prompt duplicated\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"negative_prompt\"] = \"negative prompt\"\n inputs[\"negative_prompt_2\"] = inputs[\"negative_prompt\"]\n output = sd_pipe(**inputs)\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n # forward with different negative_prompt\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"negative_prompt\"] = \"negative prompt\"\n inputs[\"negative_prompt_2\"] = \"different negative prompt\"\n output = sd_pipe(**inputs)\n image_slice_3 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are not equal\n assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4\n\n def test_controlnet_sdxl_guess(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n sd_pipe = self.pipeline_class(**components)\n sd_pipe = sd_pipe.to(device)\n\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"guess_mode\"] = True\n\n output = sd_pipe(**inputs)\n image_slice = output.images[0, -3:, -3:, -1]\n expected_slice = np.array([0.549, 0.5053, 0.4676, 0.5816, 0.5364, 0.483, 0.5937, 0.5719, 0.4318])\n\n # make sure that it's equal\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-4\n\n # TODO(Patrick, Sayak) - skip for now as this requires more refiner tests\n def test_save_load_optional_components(self):\n pass\n\n def test_float16_inference(self):\n super().test_float16_inference(expected_max_diff=5e-1)\n" }, { "path": "diffusers/tests/pipelines/controlnet/test_controlnet_sdxl.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport copy\nimport gc\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n ControlNetModel,\n EulerDiscreteScheduler,\n HeunDiscreteScheduler,\n LCMScheduler,\n StableDiffusionXLControlNetPipeline,\n StableDiffusionXLImg2ImgPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.models.unets.unet_2d_blocks import UNetMidBlock2D\nfrom diffusers.pipelines.controlnet.pipeline_controlnet import MultiControlNetModel\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n load_image,\n require_torch_gpu,\n slow,\n torch_device,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\nfrom ..pipeline_params import (\n IMAGE_TO_IMAGE_IMAGE_PARAMS,\n TEXT_TO_IMAGE_BATCH_PARAMS,\n TEXT_TO_IMAGE_IMAGE_PARAMS,\n TEXT_TO_IMAGE_PARAMS,\n)\nfrom ..test_pipelines_common import (\n IPAdapterTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n SDXLOptionalComponentsTesterMixin,\n)\n\n\nenable_full_determinism()\n\n\nclass StableDiffusionXLControlNetPipelineFastTests(\n IPAdapterTesterMixin,\n PipelineLatentTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineTesterMixin,\n SDXLOptionalComponentsTesterMixin,\n unittest.TestCase,\n):\n pipeline_class = StableDiffusionXLControlNetPipeline\n params = TEXT_TO_IMAGE_PARAMS\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n\n def get_dummy_components(self, time_cond_proj_dim=None):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2),\n projection_class_embeddings_input_dim=80, # 6 * 8 + 32\n cross_attention_dim=64,\n time_cond_proj_dim=time_cond_proj_dim,\n )\n torch.manual_seed(0)\n controlnet = ControlNetModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n in_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n conditioning_embedding_out_channels=(16, 32),\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2),\n projection_class_embeddings_input_dim=80, # 6 * 8 + 32\n cross_attention_dim=64,\n )\n torch.manual_seed(0)\n scheduler = EulerDiscreteScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n steps_offset=1,\n beta_schedule=\"scaled_linear\",\n timestep_spacing=\"leading\",\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n # SD2-specific config below\n hidden_act=\"gelu\",\n projection_dim=32,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)\n tokenizer_2 = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"controlnet\": controlnet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"text_encoder_2\": text_encoder_2,\n \"tokenizer_2\": tokenizer_2,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n controlnet_embedder_scale_factor = 2\n image = randn_tensor(\n (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),\n generator=generator,\n device=torch.device(device),\n )\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n \"image\": image,\n }\n\n return inputs\n\n def test_attention_slicing_forward_pass(self):\n return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)\n\n def test_ip_adapter_single(self, from_ssd1b=False, expected_pipe_slice=None):\n if not from_ssd1b:\n expected_pipe_slice = None\n if torch_device == \"cpu\":\n expected_pipe_slice = np.array(\n [0.7331, 0.5907, 0.5667, 0.6029, 0.5679, 0.5968, 0.4033, 0.4761, 0.5090]\n )\n return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=2e-3)\n\n def test_save_load_optional_components(self):\n self._test_save_load_optional_components()\n\n @require_torch_gpu\n def test_stable_diffusion_xl_offloads(self):\n pipes = []\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(torch_device)\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_model_cpu_offload()\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_sequential_cpu_offload()\n pipes.append(sd_pipe)\n\n image_slices = []\n for pipe in pipes:\n pipe.unet.set_default_attn_processor()\n\n inputs = self.get_dummy_inputs(torch_device)\n image = pipe(**inputs).images\n\n image_slices.append(image[0, -3:, -3:, -1].flatten())\n\n assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3\n assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3\n\n def test_stable_diffusion_xl_multi_prompts(self):\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(torch_device)\n\n # forward with single prompt\n inputs = self.get_dummy_inputs(torch_device)\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with same prompt duplicated\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"prompt_2\"] = inputs[\"prompt\"]\n output = sd_pipe(**inputs)\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n # forward with different prompt\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"prompt_2\"] = \"different prompt\"\n output = sd_pipe(**inputs)\n image_slice_3 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are not equal\n assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4\n\n # manually set a negative_prompt\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"negative_prompt\"] = \"negative prompt\"\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with same negative_prompt duplicated\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"negative_prompt\"] = \"negative prompt\"\n inputs[\"negative_prompt_2\"] = inputs[\"negative_prompt\"]\n output = sd_pipe(**inputs)\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n # forward with different negative_prompt\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"negative_prompt\"] = \"negative prompt\"\n inputs[\"negative_prompt_2\"] = \"different negative prompt\"\n output = sd_pipe(**inputs)\n image_slice_3 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are not equal\n assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4\n\n # Copied from test_stable_diffusion_xl.py\n def test_stable_diffusion_xl_prompt_embeds(self):\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n # forward without prompt embeds\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"prompt\"] = 2 * [inputs[\"prompt\"]]\n inputs[\"num_images_per_prompt\"] = 2\n\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with prompt embeds\n inputs = self.get_dummy_inputs(torch_device)\n prompt = 2 * [inputs.pop(\"prompt\")]\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = sd_pipe.encode_prompt(prompt)\n\n output = sd_pipe(\n **inputs,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n )\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # make sure that it's equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n def test_controlnet_sdxl_guess(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n sd_pipe = self.pipeline_class(**components)\n sd_pipe = sd_pipe.to(device)\n\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"guess_mode\"] = True\n\n output = sd_pipe(**inputs)\n image_slice = output.images[0, -3:, -3:, -1]\n expected_slice = np.array(\n [0.7330834, 0.590667, 0.5667336, 0.6029023, 0.5679491, 0.5968194, 0.4032986, 0.47612396, 0.5089609]\n )\n\n # make sure that it's equal\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-4\n\n def test_controlnet_sdxl_lcm(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionXLControlNetPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = sd_pipe(**inputs)\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.7799, 0.614, 0.6162, 0.7082, 0.6662, 0.5833, 0.4148, 0.5182, 0.4866])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n # Copied from test_stable_diffusion_xl.py:test_stable_diffusion_two_xl_mixture_of_denoiser_fast\n # with `StableDiffusionXLControlNetPipeline` instead of `StableDiffusionXLPipeline`\n def test_controlnet_sdxl_two_mixture_of_denoiser_fast(self):\n components = self.get_dummy_components()\n pipe_1 = StableDiffusionXLControlNetPipeline(**components).to(torch_device)\n pipe_1.unet.set_default_attn_processor()\n\n components_without_controlnet = {k: v for k, v in components.items() if k != \"controlnet\"}\n pipe_2 = StableDiffusionXLImg2ImgPipeline(**components_without_controlnet).to(torch_device)\n pipe_2.unet.set_default_attn_processor()\n\n def assert_run_mixture(\n num_steps,\n split,\n scheduler_cls_orig,\n expected_tss,\n num_train_timesteps=pipe_1.scheduler.config.num_train_timesteps,\n ):\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = num_steps\n\n class scheduler_cls(scheduler_cls_orig):\n pass\n\n pipe_1.scheduler = scheduler_cls.from_config(pipe_1.scheduler.config)\n pipe_2.scheduler = scheduler_cls.from_config(pipe_2.scheduler.config)\n\n # Let's retrieve the number of timesteps we want to use\n pipe_1.scheduler.set_timesteps(num_steps)\n expected_steps = pipe_1.scheduler.timesteps.tolist()\n\n if pipe_1.scheduler.order == 2:\n expected_steps_1 = list(filter(lambda ts: ts >= split, expected_tss))\n expected_steps_2 = expected_steps_1[-1:] + list(filter(lambda ts: ts < split, expected_tss))\n expected_steps = expected_steps_1 + expected_steps_2\n else:\n expected_steps_1 = list(filter(lambda ts: ts >= split, expected_tss))\n expected_steps_2 = list(filter(lambda ts: ts < split, expected_tss))\n\n # now we monkey patch step `done_steps`\n # list into the step function for testing\n done_steps = []\n old_step = copy.copy(scheduler_cls.step)\n\n def new_step(self, *args, **kwargs):\n done_steps.append(args[1].cpu().item()) # args[1] is always the passed `t`\n return old_step(self, *args, **kwargs)\n\n scheduler_cls.step = new_step\n\n inputs_1 = {\n **inputs,\n **{\n \"denoising_end\": 1.0 - (split / num_train_timesteps),\n \"output_type\": \"latent\",\n },\n }\n latents = pipe_1(**inputs_1).images[0]\n\n assert expected_steps_1 == done_steps, f\"Failure with {scheduler_cls.__name__} and {num_steps} and {split}\"\n\n inputs_2 = {\n **inputs,\n **{\n \"denoising_start\": 1.0 - (split / num_train_timesteps),\n \"image\": latents,\n },\n }\n pipe_2(**inputs_2).images[0]\n\n assert expected_steps_2 == done_steps[len(expected_steps_1) :]\n assert expected_steps == done_steps, f\"Failure with {scheduler_cls.__name__} and {num_steps} and {split}\"\n\n steps = 10\n for split in [300, 700]:\n for scheduler_cls_timesteps in [\n (EulerDiscreteScheduler, [901, 801, 701, 601, 501, 401, 301, 201, 101, 1]),\n (\n HeunDiscreteScheduler,\n [\n 901.0,\n 801.0,\n 801.0,\n 701.0,\n 701.0,\n 601.0,\n 601.0,\n 501.0,\n 501.0,\n 401.0,\n 401.0,\n 301.0,\n 301.0,\n 201.0,\n 201.0,\n 101.0,\n 101.0,\n 1.0,\n 1.0,\n ],\n ),\n ]:\n assert_run_mixture(steps, split, scheduler_cls_timesteps[0], scheduler_cls_timesteps[1])\n\n\nclass StableDiffusionXLMultiControlNetPipelineFastTests(\n PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, SDXLOptionalComponentsTesterMixin, unittest.TestCase\n):\n pipeline_class = StableDiffusionXLControlNetPipeline\n params = TEXT_TO_IMAGE_PARAMS\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n image_params = frozenset([]) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2),\n projection_class_embeddings_input_dim=80, # 6 * 8 + 32\n cross_attention_dim=64,\n )\n torch.manual_seed(0)\n\n def init_weights(m):\n if isinstance(m, torch.nn.Conv2d):\n torch.nn.init.normal_(m.weight)\n m.bias.data.fill_(1.0)\n\n controlnet1 = ControlNetModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n in_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n conditioning_embedding_out_channels=(16, 32),\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2),\n projection_class_embeddings_input_dim=80, # 6 * 8 + 32\n cross_attention_dim=64,\n )\n controlnet1.controlnet_down_blocks.apply(init_weights)\n\n torch.manual_seed(0)\n controlnet2 = ControlNetModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n in_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n conditioning_embedding_out_channels=(16, 32),\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2),\n projection_class_embeddings_input_dim=80, # 6 * 8 + 32\n cross_attention_dim=64,\n )\n controlnet2.controlnet_down_blocks.apply(init_weights)\n\n torch.manual_seed(0)\n scheduler = EulerDiscreteScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n steps_offset=1,\n beta_schedule=\"scaled_linear\",\n timestep_spacing=\"leading\",\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n # SD2-specific config below\n hidden_act=\"gelu\",\n projection_dim=32,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)\n tokenizer_2 = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n controlnet = MultiControlNetModel([controlnet1, controlnet2])\n\n components = {\n \"unet\": unet,\n \"controlnet\": controlnet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"text_encoder_2\": text_encoder_2,\n \"tokenizer_2\": tokenizer_2,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n controlnet_embedder_scale_factor = 2\n\n images = [\n randn_tensor(\n (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),\n generator=generator,\n device=torch.device(device),\n ),\n randn_tensor(\n (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),\n generator=generator,\n device=torch.device(device),\n ),\n ]\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n \"image\": images,\n }\n\n return inputs\n\n def test_control_guidance_switch(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n\n scale = 10.0\n steps = 4\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_1 = pipe(**inputs)[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_2 = pipe(**inputs, control_guidance_start=0.1, control_guidance_end=0.2)[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_3 = pipe(**inputs, control_guidance_start=[0.1, 0.3], control_guidance_end=[0.2, 0.7])[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_4 = pipe(**inputs, control_guidance_start=0.4, control_guidance_end=[0.5, 0.8])[0]\n\n # make sure that all outputs are different\n assert np.sum(np.abs(output_1 - output_2)) > 1e-3\n assert np.sum(np.abs(output_1 - output_3)) > 1e-3\n assert np.sum(np.abs(output_1 - output_4)) > 1e-3\n\n def test_attention_slicing_forward_pass(self):\n return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=2e-3)\n\n def test_save_load_optional_components(self):\n return self._test_save_load_optional_components()\n\n\nclass StableDiffusionXLMultiControlNetOneModelPipelineFastTests(\n PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, SDXLOptionalComponentsTesterMixin, unittest.TestCase\n):\n pipeline_class = StableDiffusionXLControlNetPipeline\n params = TEXT_TO_IMAGE_PARAMS\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n image_params = frozenset([]) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2),\n projection_class_embeddings_input_dim=80, # 6 * 8 + 32\n cross_attention_dim=64,\n )\n torch.manual_seed(0)\n\n def init_weights(m):\n if isinstance(m, torch.nn.Conv2d):\n torch.nn.init.normal_(m.weight)\n m.bias.data.fill_(1.0)\n\n controlnet = ControlNetModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n in_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n conditioning_embedding_out_channels=(16, 32),\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2),\n projection_class_embeddings_input_dim=80, # 6 * 8 + 32\n cross_attention_dim=64,\n )\n controlnet.controlnet_down_blocks.apply(init_weights)\n\n torch.manual_seed(0)\n scheduler = EulerDiscreteScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n steps_offset=1,\n beta_schedule=\"scaled_linear\",\n timestep_spacing=\"leading\",\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n # SD2-specific config below\n hidden_act=\"gelu\",\n projection_dim=32,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)\n tokenizer_2 = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n controlnet = MultiControlNetModel([controlnet])\n\n components = {\n \"unet\": unet,\n \"controlnet\": controlnet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"text_encoder_2\": text_encoder_2,\n \"tokenizer_2\": tokenizer_2,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n controlnet_embedder_scale_factor = 2\n images = [\n randn_tensor(\n (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),\n generator=generator,\n device=torch.device(device),\n ),\n ]\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n \"image\": images,\n }\n\n return inputs\n\n def test_control_guidance_switch(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n\n scale = 10.0\n steps = 4\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_1 = pipe(**inputs)[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_2 = pipe(**inputs, control_guidance_start=0.1, control_guidance_end=0.2)[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_3 = pipe(\n **inputs,\n control_guidance_start=[0.1],\n control_guidance_end=[0.2],\n )[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = steps\n inputs[\"controlnet_conditioning_scale\"] = scale\n output_4 = pipe(**inputs, control_guidance_start=0.4, control_guidance_end=[0.5])[0]\n\n # make sure that all outputs are different\n assert np.sum(np.abs(output_1 - output_2)) > 1e-3\n assert np.sum(np.abs(output_1 - output_3)) > 1e-3\n assert np.sum(np.abs(output_1 - output_4)) > 1e-3\n\n def test_attention_slicing_forward_pass(self):\n return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=2e-3)\n\n def test_save_load_optional_components(self):\n self._test_save_load_optional_components()\n\n def test_negative_conditions(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n\n inputs = self.get_dummy_inputs(torch_device)\n image = pipe(**inputs).images\n image_slice_without_neg_cond = image[0, -3:, -3:, -1]\n\n image = pipe(\n **inputs,\n negative_original_size=(512, 512),\n negative_crops_coords_top_left=(0, 0),\n negative_target_size=(1024, 1024),\n ).images\n image_slice_with_neg_cond = image[0, -3:, -3:, -1]\n\n self.assertTrue(np.abs(image_slice_without_neg_cond - image_slice_with_neg_cond).max() > 1e-2)\n\n\n@slow\n@require_torch_gpu\nclass ControlNetSDXLPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_canny(self):\n controlnet = ControlNetModel.from_pretrained(\"diffusers/controlnet-canny-sdxl-1.0\")\n\n pipe = StableDiffusionXLControlNetPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", controlnet=controlnet\n )\n pipe.enable_sequential_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n prompt = \"bird\"\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png\"\n )\n\n images = pipe(prompt, image=image, generator=generator, output_type=\"np\", num_inference_steps=3).images\n\n assert images[0].shape == (768, 512, 3)\n\n original_image = images[0, -3:, -3:, -1].flatten()\n expected_image = np.array([0.4185, 0.4127, 0.4089, 0.4046, 0.4115, 0.4096, 0.4081, 0.4112, 0.3913])\n assert np.allclose(original_image, expected_image, atol=1e-04)\n\n def test_depth(self):\n controlnet = ControlNetModel.from_pretrained(\"diffusers/controlnet-depth-sdxl-1.0\")\n\n pipe = StableDiffusionXLControlNetPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", controlnet=controlnet\n )\n pipe.enable_sequential_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n prompt = \"Stormtrooper's lecture\"\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth.png\"\n )\n\n images = pipe(prompt, image=image, generator=generator, output_type=\"np\", num_inference_steps=3).images\n\n assert images[0].shape == (512, 512, 3)\n\n original_image = images[0, -3:, -3:, -1].flatten()\n expected_image = np.array([0.4399, 0.5112, 0.5478, 0.4314, 0.472, 0.4823, 0.4647, 0.4957, 0.4853])\n assert np.allclose(original_image, expected_image, atol=1e-04)\n\n\nclass StableDiffusionSSD1BControlNetPipelineFastTests(StableDiffusionXLControlNetPipelineFastTests):\n def test_controlnet_sdxl_guess(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n sd_pipe = self.pipeline_class(**components)\n sd_pipe = sd_pipe.to(device)\n\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"guess_mode\"] = True\n\n output = sd_pipe(**inputs)\n image_slice = output.images[0, -3:, -3:, -1]\n expected_slice = np.array(\n [0.6831671, 0.5702532, 0.5459845, 0.6299793, 0.58563006, 0.6033695, 0.4493941, 0.46132287, 0.5035841]\n )\n\n # make sure that it's equal\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-4\n\n def test_ip_adapter_single(self):\n expected_pipe_slice = None\n if torch_device == \"cpu\":\n expected_pipe_slice = np.array([0.6832, 0.5703, 0.5460, 0.6300, 0.5856, 0.6034, 0.4494, 0.4613, 0.5036])\n return super().test_ip_adapter_single(from_ssd1b=True, expected_pipe_slice=expected_pipe_slice)\n\n def test_controlnet_sdxl_lcm(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionXLControlNetPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = sd_pipe(**inputs)\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.6850, 0.5135, 0.5545, 0.7033, 0.6617, 0.5971, 0.4165, 0.5480, 0.5070])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_conditioning_channels(self):\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n mid_block_type=\"UNetMidBlock2D\",\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2),\n projection_class_embeddings_input_dim=80, # 6 * 8 + 32\n cross_attention_dim=64,\n time_cond_proj_dim=None,\n )\n\n controlnet = ControlNetModel.from_unet(unet, conditioning_channels=4)\n assert type(controlnet.mid_block) == UNetMidBlock2D\n assert controlnet.conditioning_channels == 4\n\n def get_dummy_components(self, time_cond_proj_dim=None):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n mid_block_type=\"UNetMidBlock2D\",\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2),\n projection_class_embeddings_input_dim=80, # 6 * 8 + 32\n cross_attention_dim=64,\n time_cond_proj_dim=time_cond_proj_dim,\n )\n torch.manual_seed(0)\n controlnet = ControlNetModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n in_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n conditioning_embedding_out_channels=(16, 32),\n mid_block_type=\"UNetMidBlock2D\",\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2),\n projection_class_embeddings_input_dim=80, # 6 * 8 + 32\n cross_attention_dim=64,\n )\n torch.manual_seed(0)\n scheduler = EulerDiscreteScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n steps_offset=1,\n beta_schedule=\"scaled_linear\",\n timestep_spacing=\"leading\",\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n # SD2-specific config below\n hidden_act=\"gelu\",\n projection_dim=32,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)\n tokenizer_2 = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"controlnet\": controlnet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"text_encoder_2\": text_encoder_2,\n \"tokenizer_2\": tokenizer_2,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n" }, { "path": "diffusers/tests/pipelines/controlnet/test_controlnet_sdxl_img2img.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n ControlNetModel,\n EulerDiscreteScheduler,\n StableDiffusionXLControlNetImg2ImgPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device\n\nfrom ..pipeline_params import (\n IMAGE_TO_IMAGE_IMAGE_PARAMS,\n TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,\n TEXT_GUIDED_IMAGE_VARIATION_PARAMS,\n TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,\n)\nfrom ..test_pipelines_common import (\n IPAdapterTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n)\n\n\nenable_full_determinism()\n\n\nclass ControlNetPipelineSDXLImg2ImgFastTests(\n IPAdapterTesterMixin,\n PipelineLatentTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineTesterMixin,\n unittest.TestCase,\n):\n pipeline_class = StableDiffusionXLControlNetImg2ImgPipeline\n params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS\n required_optional_params = PipelineTesterMixin.required_optional_params - {\"latents\"}\n batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS\n image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union(\n {\"add_text_embeds\", \"add_time_ids\", \"add_neg_time_ids\"}\n )\n\n def get_dummy_components(self, skip_first_text_encoder=False):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2),\n projection_class_embeddings_input_dim=80, # 6 * 8 + 32\n cross_attention_dim=64 if not skip_first_text_encoder else 32,\n )\n torch.manual_seed(0)\n controlnet = ControlNetModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n in_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n conditioning_embedding_out_channels=(16, 32),\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2),\n projection_class_embeddings_input_dim=80, # 6 * 8 + 32\n cross_attention_dim=64,\n )\n torch.manual_seed(0)\n scheduler = EulerDiscreteScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n steps_offset=1,\n beta_schedule=\"scaled_linear\",\n timestep_spacing=\"leading\",\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n # SD2-specific config below\n hidden_act=\"gelu\",\n projection_dim=32,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)\n tokenizer_2 = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"controlnet\": controlnet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder if not skip_first_text_encoder else None,\n \"tokenizer\": tokenizer if not skip_first_text_encoder else None,\n \"text_encoder_2\": text_encoder_2,\n \"tokenizer_2\": tokenizer_2,\n \"image_encoder\": None,\n \"feature_extractor\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n controlnet_embedder_scale_factor = 2\n image = floats_tensor(\n (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),\n rng=random.Random(seed),\n ).to(device)\n\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n \"image\": image,\n \"control_image\": image,\n }\n\n return inputs\n\n def test_ip_adapter_single(self):\n expected_pipe_slice = None\n if torch_device == \"cpu\":\n expected_pipe_slice = np.array([0.6265, 0.5441, 0.5384, 0.5446, 0.5810, 0.5908, 0.5414, 0.5428, 0.5353])\n return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)\n\n def test_stable_diffusion_xl_controlnet_img2img(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array(\n [0.5557202, 0.46418434, 0.46983826, 0.623529, 0.5557242, 0.49262643, 0.6070508, 0.5702978, 0.43777135]\n )\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_xl_controlnet_img2img_guess(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n sd_pipe = self.pipeline_class(**components)\n sd_pipe = sd_pipe.to(device)\n\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"guess_mode\"] = True\n\n output = sd_pipe(**inputs)\n image_slice = output.images[0, -3:, -3:, -1]\n assert output.images.shape == (1, 64, 64, 3)\n\n expected_slice = np.array(\n [0.5557202, 0.46418434, 0.46983826, 0.623529, 0.5557242, 0.49262643, 0.6070508, 0.5702978, 0.43777135]\n )\n\n # make sure that it's equal\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_attention_slicing_forward_pass(self):\n return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=2e-3)\n\n # TODO(Patrick, Sayak) - skip for now as this requires more refiner tests\n def test_save_load_optional_components(self):\n pass\n\n @require_torch_gpu\n def test_stable_diffusion_xl_offloads(self):\n pipes = []\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(torch_device)\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_model_cpu_offload()\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_sequential_cpu_offload()\n pipes.append(sd_pipe)\n\n image_slices = []\n for pipe in pipes:\n pipe.unet.set_default_attn_processor()\n\n inputs = self.get_dummy_inputs(torch_device)\n image = pipe(**inputs).images\n\n image_slices.append(image[0, -3:, -3:, -1].flatten())\n\n assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3\n assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3\n\n def test_stable_diffusion_xl_multi_prompts(self):\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(torch_device)\n\n # forward with single prompt\n inputs = self.get_dummy_inputs(torch_device)\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with same prompt duplicated\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"prompt_2\"] = inputs[\"prompt\"]\n output = sd_pipe(**inputs)\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n # forward with different prompt\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"prompt_2\"] = \"different prompt\"\n output = sd_pipe(**inputs)\n image_slice_3 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are not equal\n assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4\n\n # manually set a negative_prompt\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"negative_prompt\"] = \"negative prompt\"\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with same negative_prompt duplicated\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"negative_prompt\"] = \"negative prompt\"\n inputs[\"negative_prompt_2\"] = inputs[\"negative_prompt\"]\n output = sd_pipe(**inputs)\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n # forward with different negative_prompt\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"negative_prompt\"] = \"negative prompt\"\n inputs[\"negative_prompt_2\"] = \"different negative prompt\"\n output = sd_pipe(**inputs)\n image_slice_3 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are not equal\n assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4\n\n # Copied from test_stable_diffusion_xl.py\n def test_stable_diffusion_xl_prompt_embeds(self):\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n # forward without prompt embeds\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"prompt\"] = 2 * [inputs[\"prompt\"]]\n inputs[\"num_images_per_prompt\"] = 2\n\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with prompt embeds\n inputs = self.get_dummy_inputs(torch_device)\n prompt = 2 * [inputs.pop(\"prompt\")]\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = sd_pipe.encode_prompt(prompt)\n\n output = sd_pipe(\n **inputs,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n )\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # make sure that it's equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n" }, { "path": "diffusers/tests/pipelines/controlnet/test_flax_controlnet.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nfrom diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline\nfrom diffusers.utils import is_flax_available, load_image\nfrom diffusers.utils.testing_utils import require_flax, slow\n\n\nif is_flax_available():\n import jax\n import jax.numpy as jnp\n from flax.jax_utils import replicate\n from flax.training.common_utils import shard\n\n\n@slow\n@require_flax\nclass FlaxControlNetPipelineIntegrationTests(unittest.TestCase):\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n\n def test_canny(self):\n controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(\n \"lllyasviel/sd-controlnet-canny\", from_pt=True, dtype=jnp.bfloat16\n )\n pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, from_pt=True, dtype=jnp.bfloat16\n )\n params[\"controlnet\"] = controlnet_params\n\n prompts = \"bird\"\n num_samples = jax.device_count()\n prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples)\n\n canny_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png\"\n )\n processed_image = pipe.prepare_image_inputs([canny_image] * num_samples)\n\n rng = jax.random.PRNGKey(0)\n rng = jax.random.split(rng, jax.device_count())\n\n p_params = replicate(params)\n prompt_ids = shard(prompt_ids)\n processed_image = shard(processed_image)\n\n images = pipe(\n prompt_ids=prompt_ids,\n image=processed_image,\n params=p_params,\n prng_seed=rng,\n num_inference_steps=50,\n jit=True,\n ).images\n assert images.shape == (jax.device_count(), 1, 768, 512, 3)\n\n images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])\n image_slice = images[0, 253:256, 253:256, -1]\n\n output_slice = jnp.asarray(jax.device_get(image_slice.flatten()))\n expected_slice = jnp.array(\n [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]\n )\n print(f\"output_slice: {output_slice}\")\n assert jnp.abs(output_slice - expected_slice).max() < 1e-2\n\n def test_pose(self):\n controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(\n \"lllyasviel/sd-controlnet-openpose\", from_pt=True, dtype=jnp.bfloat16\n )\n pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, from_pt=True, dtype=jnp.bfloat16\n )\n params[\"controlnet\"] = controlnet_params\n\n prompts = \"Chef in the kitchen\"\n num_samples = jax.device_count()\n prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples)\n\n pose_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png\"\n )\n processed_image = pipe.prepare_image_inputs([pose_image] * num_samples)\n\n rng = jax.random.PRNGKey(0)\n rng = jax.random.split(rng, jax.device_count())\n\n p_params = replicate(params)\n prompt_ids = shard(prompt_ids)\n processed_image = shard(processed_image)\n\n images = pipe(\n prompt_ids=prompt_ids,\n image=processed_image,\n params=p_params,\n prng_seed=rng,\n num_inference_steps=50,\n jit=True,\n ).images\n assert images.shape == (jax.device_count(), 1, 768, 512, 3)\n\n images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])\n image_slice = images[0, 253:256, 253:256, -1]\n\n output_slice = jnp.asarray(jax.device_get(image_slice.flatten()))\n expected_slice = jnp.array(\n [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]\n )\n print(f\"output_slice: {output_slice}\")\n assert jnp.abs(output_slice - expected_slice).max() < 1e-2\n" }, { "path": "diffusers/tests/pipelines/controlnet_xs/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/controlnet_xs/test_controlnetxs.py", "content": "# coding=utf-8\n# Copyright 2023 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport traceback\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AsymmetricAutoencoderKL,\n AutoencoderKL,\n AutoencoderTiny,\n ConsistencyDecoderVAE,\n ControlNetXSAdapter,\n DDIMScheduler,\n LCMScheduler,\n StableDiffusionControlNetXSPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n load_image,\n load_numpy,\n require_python39_or_higher,\n require_torch_2,\n require_torch_gpu,\n run_test_in_subprocess,\n slow,\n torch_device,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\nfrom ...models.autoencoders.test_models_vae import (\n get_asym_autoencoder_kl_config,\n get_autoencoder_kl_config,\n get_autoencoder_tiny_config,\n get_consistency_vae_config,\n)\nfrom ..pipeline_params import (\n IMAGE_TO_IMAGE_IMAGE_PARAMS,\n TEXT_TO_IMAGE_BATCH_PARAMS,\n TEXT_TO_IMAGE_IMAGE_PARAMS,\n TEXT_TO_IMAGE_PARAMS,\n)\nfrom ..test_pipelines_common import (\n PipelineKarrasSchedulerTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n SDFunctionTesterMixin,\n)\n\n\nenable_full_determinism()\n\n\ndef to_np(tensor):\n if isinstance(tensor, torch.Tensor):\n tensor = tensor.detach().cpu().numpy()\n\n return tensor\n\n\n# Will be run via run_test_in_subprocess\ndef _test_stable_diffusion_compile(in_queue, out_queue, timeout):\n error = None\n try:\n _ = in_queue.get(timeout=timeout)\n\n controlnet = ControlNetXSAdapter.from_pretrained(\n \"UmerHA/Testing-ConrolNetXS-SD2.1-canny\", torch_dtype=torch.float16\n )\n pipe = StableDiffusionControlNetXSPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-1-base\",\n controlnet=controlnet,\n safety_checker=None,\n torch_dtype=torch.float16,\n )\n pipe.to(\"cuda\")\n pipe.set_progress_bar_config(disable=None)\n\n pipe.unet.to(memory_format=torch.channels_last)\n pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n prompt = \"bird\"\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png\"\n ).resize((512, 512))\n\n output = pipe(prompt, image, num_inference_steps=10, generator=generator, output_type=\"np\")\n image = output.images[0]\n\n assert image.shape == (512, 512, 3)\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny_out_full.npy\"\n )\n expected_image = np.resize(expected_image, (512, 512, 3))\n\n assert np.abs(expected_image - image).max() < 1.0\n\n except Exception:\n error = f\"{traceback.format_exc()}\"\n\n results = {\"error\": error}\n out_queue.put(results, timeout=timeout)\n out_queue.join()\n\n\nclass ControlNetXSPipelineFastTests(\n PipelineLatentTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineTesterMixin,\n SDFunctionTesterMixin,\n unittest.TestCase,\n):\n pipeline_class = StableDiffusionControlNetXSPipeline\n params = TEXT_TO_IMAGE_PARAMS\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n\n test_attention_slicing = False\n\n def get_dummy_components(self, time_cond_proj_dim=None):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(4, 8),\n layers_per_block=2,\n sample_size=16,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=8,\n norm_num_groups=4,\n time_cond_proj_dim=time_cond_proj_dim,\n use_linear_projection=True,\n )\n torch.manual_seed(0)\n controlnet = ControlNetXSAdapter.from_unet(\n unet=unet,\n size_ratio=1,\n learn_time_embedding=True,\n conditioning_embedding_out_channels=(2, 2),\n )\n torch.manual_seed(0)\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[4, 8],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n norm_num_groups=2,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=8,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"controlnet\": controlnet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n controlnet_embedder_scale_factor = 2\n image = randn_tensor(\n (1, 3, 8 * controlnet_embedder_scale_factor, 8 * controlnet_embedder_scale_factor),\n generator=generator,\n device=torch.device(device),\n )\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"numpy\",\n \"image\": image,\n }\n\n return inputs\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=2e-3)\n\n def test_controlnet_lcm(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components(time_cond_proj_dim=8)\n sd_pipe = StableDiffusionControlNetXSPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = sd_pipe(**inputs)\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 16, 16, 3)\n expected_slice = np.array([0.745, 0.753, 0.767, 0.543, 0.523, 0.502, 0.314, 0.521, 0.478])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_to_dtype(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n\n # pipeline creates a new UNetControlNetXSModel under the hood. So we need to check the dtype from pipe.components\n model_dtypes = [component.dtype for component in pipe.components.values() if hasattr(component, \"dtype\")]\n self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes))\n\n pipe.to(dtype=torch.float16)\n model_dtypes = [component.dtype for component in pipe.components.values() if hasattr(component, \"dtype\")]\n self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes))\n\n def test_multi_vae(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n block_out_channels = pipe.vae.config.block_out_channels\n norm_num_groups = pipe.vae.config.norm_num_groups\n\n vae_classes = [AutoencoderKL, AsymmetricAutoencoderKL, ConsistencyDecoderVAE, AutoencoderTiny]\n configs = [\n get_autoencoder_kl_config(block_out_channels, norm_num_groups),\n get_asym_autoencoder_kl_config(block_out_channels, norm_num_groups),\n get_consistency_vae_config(block_out_channels, norm_num_groups),\n get_autoencoder_tiny_config(block_out_channels),\n ]\n\n out_np = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type=\"np\"))[0]\n\n for vae_cls, config in zip(vae_classes, configs):\n vae = vae_cls(**config)\n vae = vae.to(torch_device)\n components[\"vae\"] = vae\n vae_pipe = self.pipeline_class(**components)\n\n # pipeline creates a new UNetControlNetXSModel under the hood, which aren't on device.\n # So we need to move the new pipe to device.\n vae_pipe.to(torch_device)\n vae_pipe.set_progress_bar_config(disable=None)\n\n out_vae_np = vae_pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type=\"np\"))[0]\n\n assert out_vae_np.shape == out_np.shape\n\n @unittest.skipIf(torch_device != \"cuda\", reason=\"CUDA and CPU are required to switch devices\")\n def test_to_device(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n\n pipe.to(\"cpu\")\n # pipeline creates a new UNetControlNetXSModel under the hood. So we need to check the device from pipe.components\n model_devices = [\n component.device.type for component in pipe.components.values() if hasattr(component, \"device\")\n ]\n self.assertTrue(all(device == \"cpu\" for device in model_devices))\n\n output_cpu = pipe(**self.get_dummy_inputs(\"cpu\"))[0]\n self.assertTrue(np.isnan(output_cpu).sum() == 0)\n\n pipe.to(\"cuda\")\n model_devices = [\n component.device.type for component in pipe.components.values() if hasattr(component, \"device\")\n ]\n self.assertTrue(all(device == \"cuda\" for device in model_devices))\n\n output_cuda = pipe(**self.get_dummy_inputs(\"cuda\"))[0]\n self.assertTrue(np.isnan(to_np(output_cuda)).sum() == 0)\n\n\n@slow\n@require_torch_gpu\nclass ControlNetXSPipelineSlowTests(unittest.TestCase):\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_canny(self):\n controlnet = ControlNetXSAdapter.from_pretrained(\n \"UmerHA/Testing-ConrolNetXS-SD2.1-canny\", torch_dtype=torch.float16\n )\n pipe = StableDiffusionControlNetXSPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-1-base\", controlnet=controlnet, torch_dtype=torch.float16\n )\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n prompt = \"bird\"\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png\"\n )\n\n output = pipe(prompt, image, generator=generator, output_type=\"np\", num_inference_steps=3)\n\n image = output.images[0]\n\n assert image.shape == (768, 512, 3)\n\n original_image = image[-3:, -3:, -1].flatten()\n expected_image = np.array([0.1963, 0.229, 0.2659, 0.2109, 0.2332, 0.2827, 0.2534, 0.2422, 0.2808])\n assert np.allclose(original_image, expected_image, atol=1e-04)\n\n def test_depth(self):\n controlnet = ControlNetXSAdapter.from_pretrained(\n \"UmerHA/Testing-ConrolNetXS-SD2.1-depth\", torch_dtype=torch.float16\n )\n pipe = StableDiffusionControlNetXSPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-1-base\", controlnet=controlnet, torch_dtype=torch.float16\n )\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n prompt = \"Stormtrooper's lecture\"\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth.png\"\n )\n\n output = pipe(prompt, image, generator=generator, output_type=\"np\", num_inference_steps=3)\n\n image = output.images[0]\n\n assert image.shape == (512, 512, 3)\n\n original_image = image[-3:, -3:, -1].flatten()\n expected_image = np.array([0.4844, 0.4937, 0.4956, 0.4663, 0.5039, 0.5044, 0.4565, 0.4883, 0.4941])\n assert np.allclose(original_image, expected_image, atol=1e-04)\n\n @require_python39_or_higher\n @require_torch_2\n def test_stable_diffusion_compile(self):\n run_test_in_subprocess(test_case=self, target_func=_test_stable_diffusion_compile, inputs=None)\n" }, { "path": "diffusers/tests/pipelines/controlnet_xs/test_controlnetxs_sdxl.py", "content": "# coding=utf-8\n# Copyright 2023 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom diffusers import (\n AsymmetricAutoencoderKL,\n AutoencoderKL,\n AutoencoderTiny,\n ConsistencyDecoderVAE,\n ControlNetXSAdapter,\n EulerDiscreteScheduler,\n StableDiffusionXLControlNetXSPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, slow, torch_device\nfrom diffusers.utils.torch_utils import randn_tensor\n\nfrom ...models.autoencoders.test_models_vae import (\n get_asym_autoencoder_kl_config,\n get_autoencoder_kl_config,\n get_autoencoder_tiny_config,\n get_consistency_vae_config,\n)\nfrom ..pipeline_params import (\n IMAGE_TO_IMAGE_IMAGE_PARAMS,\n TEXT_TO_IMAGE_BATCH_PARAMS,\n TEXT_TO_IMAGE_IMAGE_PARAMS,\n TEXT_TO_IMAGE_PARAMS,\n)\nfrom ..test_pipelines_common import (\n PipelineKarrasSchedulerTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n SDXLOptionalComponentsTesterMixin,\n)\n\n\nenable_full_determinism()\n\n\nclass StableDiffusionXLControlNetXSPipelineFastTests(\n PipelineLatentTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineTesterMixin,\n SDXLOptionalComponentsTesterMixin,\n unittest.TestCase,\n):\n pipeline_class = StableDiffusionXLControlNetXSPipeline\n params = TEXT_TO_IMAGE_PARAMS\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n\n test_attention_slicing = False\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(4, 8),\n layers_per_block=2,\n sample_size=16,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n use_linear_projection=True,\n norm_num_groups=4,\n # SD2-specific config below\n attention_head_dim=(2, 4),\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2),\n projection_class_embeddings_input_dim=56, # 6 * 8 (addition_time_embed_dim) + 8 (cross_attention_dim)\n cross_attention_dim=8,\n )\n torch.manual_seed(0)\n controlnet = ControlNetXSAdapter.from_unet(\n unet=unet,\n size_ratio=0.5,\n learn_time_embedding=True,\n conditioning_embedding_out_channels=(2, 2),\n )\n torch.manual_seed(0)\n scheduler = EulerDiscreteScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n steps_offset=1,\n beta_schedule=\"scaled_linear\",\n timestep_spacing=\"leading\",\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[4, 8],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n norm_num_groups=2,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=4,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n # SD2-specific config below\n hidden_act=\"gelu\",\n projection_dim=8,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)\n tokenizer_2 = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"controlnet\": controlnet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"text_encoder_2\": text_encoder_2,\n \"tokenizer_2\": tokenizer_2,\n \"feature_extractor\": None,\n }\n return components\n\n # Copied from test_controlnet_sdxl.py\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n controlnet_embedder_scale_factor = 2\n image = randn_tensor(\n (1, 3, 8 * controlnet_embedder_scale_factor, 8 * controlnet_embedder_scale_factor),\n generator=generator,\n device=torch.device(device),\n )\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n \"image\": image,\n }\n\n return inputs\n\n # Copied from test_controlnet_sdxl.py\n def test_attention_slicing_forward_pass(self):\n return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n # Copied from test_controlnet_sdxl.py\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)\n\n # Copied from test_controlnet_sdxl.py\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=2e-3)\n\n @require_torch_gpu\n # Copied from test_controlnet_sdxl.py\n def test_stable_diffusion_xl_offloads(self):\n pipes = []\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(torch_device)\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_model_cpu_offload()\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_sequential_cpu_offload()\n pipes.append(sd_pipe)\n\n image_slices = []\n for pipe in pipes:\n pipe.unet.set_default_attn_processor()\n\n inputs = self.get_dummy_inputs(torch_device)\n image = pipe(**inputs).images\n\n image_slices.append(image[0, -3:, -3:, -1].flatten())\n\n assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3\n assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3\n\n # Copied from test_controlnet_sdxl.py\n def test_stable_diffusion_xl_multi_prompts(self):\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(torch_device)\n\n # forward with single prompt\n inputs = self.get_dummy_inputs(torch_device)\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with same prompt duplicated\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"prompt_2\"] = inputs[\"prompt\"]\n output = sd_pipe(**inputs)\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n # forward with different prompt\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"prompt_2\"] = \"different prompt\"\n output = sd_pipe(**inputs)\n image_slice_3 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are not equal\n assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4\n\n # manually set a negative_prompt\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"negative_prompt\"] = \"negative prompt\"\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with same negative_prompt duplicated\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"negative_prompt\"] = \"negative prompt\"\n inputs[\"negative_prompt_2\"] = inputs[\"negative_prompt\"]\n output = sd_pipe(**inputs)\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n # forward with different negative_prompt\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"negative_prompt\"] = \"negative prompt\"\n inputs[\"negative_prompt_2\"] = \"different negative prompt\"\n output = sd_pipe(**inputs)\n image_slice_3 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are not equal\n assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4\n\n # Copied from test_stable_diffusion_xl.py\n def test_stable_diffusion_xl_prompt_embeds(self):\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n # forward without prompt embeds\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"prompt\"] = 2 * [inputs[\"prompt\"]]\n inputs[\"num_images_per_prompt\"] = 2\n\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with prompt embeds\n inputs = self.get_dummy_inputs(torch_device)\n prompt = 2 * [inputs.pop(\"prompt\")]\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = sd_pipe.encode_prompt(prompt)\n\n output = sd_pipe(\n **inputs,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n )\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # make sure that it's equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1.1e-4\n\n # Copied from test_stable_diffusion_xl.py\n def test_save_load_optional_components(self):\n self._test_save_load_optional_components()\n\n # Copied from test_controlnetxs.py\n def test_to_dtype(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n\n # pipeline creates a new UNetControlNetXSModel under the hood. So we need to check the dtype from pipe.components\n model_dtypes = [component.dtype for component in pipe.components.values() if hasattr(component, \"dtype\")]\n self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes))\n\n pipe.to(dtype=torch.float16)\n model_dtypes = [component.dtype for component in pipe.components.values() if hasattr(component, \"dtype\")]\n self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes))\n\n def test_multi_vae(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n block_out_channels = pipe.vae.config.block_out_channels\n norm_num_groups = pipe.vae.config.norm_num_groups\n\n vae_classes = [AutoencoderKL, AsymmetricAutoencoderKL, ConsistencyDecoderVAE, AutoencoderTiny]\n configs = [\n get_autoencoder_kl_config(block_out_channels, norm_num_groups),\n get_asym_autoencoder_kl_config(block_out_channels, norm_num_groups),\n get_consistency_vae_config(block_out_channels, norm_num_groups),\n get_autoencoder_tiny_config(block_out_channels),\n ]\n\n out_np = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type=\"np\"))[0]\n\n for vae_cls, config in zip(vae_classes, configs):\n vae = vae_cls(**config)\n vae = vae.to(torch_device)\n components[\"vae\"] = vae\n vae_pipe = self.pipeline_class(**components)\n\n # pipeline creates a new UNetControlNetXSModel under the hood, which aren't on device.\n # So we need to move the new pipe to device.\n vae_pipe.to(torch_device)\n vae_pipe.set_progress_bar_config(disable=None)\n\n out_vae_np = vae_pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type=\"np\"))[0]\n\n assert out_vae_np.shape == out_np.shape\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionXLControlNetXSPipelineSlowTests(unittest.TestCase):\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_canny(self):\n controlnet = ControlNetXSAdapter.from_pretrained(\n \"UmerHA/Testing-ConrolNetXS-SDXL-canny\", torch_dtype=torch.float16\n )\n pipe = StableDiffusionXLControlNetXSPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", controlnet=controlnet, torch_dtype=torch.float16\n )\n pipe.enable_sequential_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n prompt = \"bird\"\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png\"\n )\n\n images = pipe(prompt, image=image, generator=generator, output_type=\"np\", num_inference_steps=3).images\n\n assert images[0].shape == (768, 512, 3)\n\n original_image = images[0, -3:, -3:, -1].flatten()\n expected_image = np.array([0.3202, 0.3151, 0.3328, 0.3172, 0.337, 0.3381, 0.3378, 0.3389, 0.3224])\n assert np.allclose(original_image, expected_image, atol=1e-04)\n\n def test_depth(self):\n controlnet = ControlNetXSAdapter.from_pretrained(\n \"UmerHA/Testing-ConrolNetXS-SDXL-depth\", torch_dtype=torch.float16\n )\n pipe = StableDiffusionXLControlNetXSPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", controlnet=controlnet, torch_dtype=torch.float16\n )\n pipe.enable_sequential_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n prompt = \"Stormtrooper's lecture\"\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth.png\"\n )\n\n images = pipe(prompt, image=image, generator=generator, output_type=\"np\", num_inference_steps=3).images\n\n assert images[0].shape == (512, 512, 3)\n\n original_image = images[0, -3:, -3:, -1].flatten()\n expected_image = np.array([0.5448, 0.5437, 0.5426, 0.5543, 0.553, 0.5475, 0.5595, 0.5602, 0.5529])\n assert np.allclose(original_image, expected_image, atol=1e-04)\n" }, { "path": "diffusers/tests/pipelines/dance_diffusion/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/dance_diffusion/test_dance_diffusion.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nimport numpy as np\nimport torch\n\nfrom diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNet1DModel\nfrom diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, skip_mps, torch_device\n\nfrom ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass DanceDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = DanceDiffusionPipeline\n params = UNCONDITIONAL_AUDIO_GENERATION_PARAMS\n required_optional_params = PipelineTesterMixin.required_optional_params - {\n \"callback\",\n \"latents\",\n \"callback_steps\",\n \"output_type\",\n \"num_images_per_prompt\",\n }\n batch_params = UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS\n test_attention_slicing = False\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet1DModel(\n block_out_channels=(32, 32, 64),\n extra_in_channels=16,\n sample_size=512,\n sample_rate=16_000,\n in_channels=2,\n out_channels=2,\n flip_sin_to_cos=True,\n use_timestep_embedding=False,\n time_embedding_type=\"fourier\",\n mid_block_type=\"UNetMidBlock1D\",\n down_block_types=(\"DownBlock1DNoSkip\", \"DownBlock1D\", \"AttnDownBlock1D\"),\n up_block_types=(\"AttnUpBlock1D\", \"UpBlock1D\", \"UpBlock1DNoSkip\"),\n )\n scheduler = IPNDMScheduler()\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"batch_size\": 1,\n \"generator\": generator,\n \"num_inference_steps\": 4,\n }\n return inputs\n\n def test_dance_diffusion(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n pipe = DanceDiffusionPipeline(**components)\n pipe = pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = pipe(**inputs)\n audio = output.audios\n\n audio_slice = audio[0, -3:, -3:]\n\n assert audio.shape == (1, 2, components[\"unet\"].sample_size)\n expected_slice = np.array([-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000])\n assert np.abs(audio_slice.flatten() - expected_slice).max() < 1e-2\n\n @skip_mps\n def test_save_load_local(self):\n return super().test_save_load_local()\n\n @skip_mps\n def test_dict_tuple_outputs_equivalent(self):\n return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)\n\n @skip_mps\n def test_save_load_optional_components(self):\n return super().test_save_load_optional_components()\n\n @skip_mps\n def test_attention_slicing_forward_pass(self):\n return super().test_attention_slicing_forward_pass()\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=3e-3)\n\n\n@nightly\n@require_torch_gpu\nclass PipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_dance_diffusion(self):\n device = torch_device\n\n pipe = DanceDiffusionPipeline.from_pretrained(\"harmonai/maestro-150k\")\n pipe = pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.manual_seed(0)\n output = pipe(generator=generator, num_inference_steps=100, audio_length_in_s=4.096)\n audio = output.audios\n\n audio_slice = audio[0, -3:, -3:]\n\n assert audio.shape == (1, 2, pipe.unet.config.sample_size)\n expected_slice = np.array([-0.0192, -0.0231, -0.0318, -0.0059, 0.0002, -0.0020])\n\n assert np.abs(audio_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_dance_diffusion_fp16(self):\n device = torch_device\n\n pipe = DanceDiffusionPipeline.from_pretrained(\"harmonai/maestro-150k\", torch_dtype=torch.float16)\n pipe = pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.manual_seed(0)\n output = pipe(generator=generator, num_inference_steps=100, audio_length_in_s=4.096)\n audio = output.audios\n\n audio_slice = audio[0, -3:, -3:]\n\n assert audio.shape == (1, 2, pipe.unet.config.sample_size)\n expected_slice = np.array([-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341])\n\n assert np.abs(audio_slice.flatten() - expected_slice).max() < 1e-2\n" }, { "path": "diffusers/tests/pipelines/ddim/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/ddim/test_ddim.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\n\nimport numpy as np\nimport torch\n\nfrom diffusers import DDIMPipeline, DDIMScheduler, UNet2DModel\nfrom diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device\n\nfrom ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass DDIMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = DDIMPipeline\n params = UNCONDITIONAL_IMAGE_GENERATION_PARAMS\n required_optional_params = PipelineTesterMixin.required_optional_params - {\n \"num_images_per_prompt\",\n \"latents\",\n \"callback\",\n \"callback_steps\",\n }\n batch_params = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DModel(\n block_out_channels=(4, 8),\n layers_per_block=1,\n norm_num_groups=4,\n sample_size=8,\n in_channels=3,\n out_channels=3,\n down_block_types=(\"DownBlock2D\", \"AttnDownBlock2D\"),\n up_block_types=(\"AttnUpBlock2D\", \"UpBlock2D\"),\n )\n scheduler = DDIMScheduler()\n components = {\"unet\": unet, \"scheduler\": scheduler}\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"batch_size\": 1,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_inference(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n self.assertEqual(image.shape, (1, 8, 8, 3))\n expected_slice = np.array([0.0, 9.979e-01, 0.0, 9.999e-01, 9.986e-01, 9.991e-01, 7.106e-04, 0.0, 0.0])\n max_diff = np.abs(image_slice.flatten() - expected_slice).max()\n self.assertLessEqual(max_diff, 1e-3)\n\n def test_dict_tuple_outputs_equivalent(self):\n super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)\n\n def test_save_load_local(self):\n super().test_save_load_local(expected_max_difference=3e-3)\n\n def test_save_load_optional_components(self):\n super().test_save_load_optional_components(expected_max_difference=3e-3)\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=3e-3)\n\n\n@slow\n@require_torch_gpu\nclass DDIMPipelineIntegrationTests(unittest.TestCase):\n def test_inference_cifar10(self):\n model_id = \"google/ddpm-cifar10-32\"\n\n unet = UNet2DModel.from_pretrained(model_id)\n scheduler = DDIMScheduler()\n\n ddim = DDIMPipeline(unet=unet, scheduler=scheduler)\n ddim.to(torch_device)\n ddim.set_progress_bar_config(disable=None)\n\n generator = torch.manual_seed(0)\n image = ddim(generator=generator, eta=0.0, output_type=\"np\").images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 32, 32, 3)\n expected_slice = np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_inference_ema_bedroom(self):\n model_id = \"google/ddpm-ema-bedroom-256\"\n\n unet = UNet2DModel.from_pretrained(model_id)\n scheduler = DDIMScheduler.from_pretrained(model_id)\n\n ddpm = DDIMPipeline(unet=unet, scheduler=scheduler)\n ddpm.to(torch_device)\n ddpm.set_progress_bar_config(disable=None)\n\n generator = torch.manual_seed(0)\n image = ddpm(generator=generator, output_type=\"np\").images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 256, 256, 3)\n expected_slice = np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n" }, { "path": "diffusers/tests/pipelines/ddpm/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/ddpm/test_ddpm.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\n\nimport numpy as np\nimport torch\n\nfrom diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel\nfrom diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device\n\n\nenable_full_determinism()\n\n\nclass DDPMPipelineFastTests(unittest.TestCase):\n @property\n def dummy_uncond_unet(self):\n torch.manual_seed(0)\n model = UNet2DModel(\n block_out_channels=(4, 8),\n layers_per_block=1,\n norm_num_groups=4,\n sample_size=8,\n in_channels=3,\n out_channels=3,\n down_block_types=(\"DownBlock2D\", \"AttnDownBlock2D\"),\n up_block_types=(\"AttnUpBlock2D\", \"UpBlock2D\"),\n )\n return model\n\n def test_fast_inference(self):\n device = \"cpu\"\n unet = self.dummy_uncond_unet\n scheduler = DDPMScheduler()\n\n ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)\n ddpm.to(device)\n ddpm.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=device).manual_seed(0)\n image = ddpm(generator=generator, num_inference_steps=2, output_type=\"np\").images\n\n generator = torch.Generator(device=device).manual_seed(0)\n image_from_tuple = ddpm(generator=generator, num_inference_steps=2, output_type=\"np\", return_dict=False)[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 8, 8, 3)\n expected_slice = np.array([0.0, 0.9996672, 0.00329116, 1.0, 0.9995991, 1.0, 0.0060907, 0.00115037, 0.0])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_inference_predict_sample(self):\n unet = self.dummy_uncond_unet\n scheduler = DDPMScheduler(prediction_type=\"sample\")\n\n ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)\n ddpm.to(torch_device)\n ddpm.set_progress_bar_config(disable=None)\n\n generator = torch.manual_seed(0)\n image = ddpm(generator=generator, num_inference_steps=2, output_type=\"np\").images\n\n generator = torch.manual_seed(0)\n image_eps = ddpm(generator=generator, num_inference_steps=2, output_type=\"np\")[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_eps_slice = image_eps[0, -3:, -3:, -1]\n\n assert image.shape == (1, 8, 8, 3)\n tolerance = 1e-2 if torch_device != \"mps\" else 3e-2\n assert np.abs(image_slice.flatten() - image_eps_slice.flatten()).max() < tolerance\n\n\n@slow\n@require_torch_gpu\nclass DDPMPipelineIntegrationTests(unittest.TestCase):\n def test_inference_cifar10(self):\n model_id = \"google/ddpm-cifar10-32\"\n\n unet = UNet2DModel.from_pretrained(model_id)\n scheduler = DDPMScheduler.from_pretrained(model_id)\n\n ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)\n ddpm.to(torch_device)\n ddpm.set_progress_bar_config(disable=None)\n\n generator = torch.manual_seed(0)\n image = ddpm(generator=generator, output_type=\"np\").images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 32, 32, 3)\n expected_slice = np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385])\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n" }, { "path": "diffusers/tests/pipelines/deepfloyd_if/__init__.py", "content": "import tempfile\n\nimport numpy as np\nimport torch\nfrom transformers import AutoTokenizer, T5EncoderModel\n\nfrom diffusers import DDPMScheduler, UNet2DConditionModel\nfrom diffusers.models.attention_processor import AttnAddedKVProcessor\nfrom diffusers.pipelines.deepfloyd_if import IFWatermarker\nfrom diffusers.utils.testing_utils import torch_device\n\nfrom ..test_pipelines_common import to_np\n\n\n# WARN: the hf-internal-testing/tiny-random-t5 text encoder has some non-determinism in the `save_load` tests.\n\n\nclass IFPipelineTesterMixin:\n def _get_dummy_components(self):\n torch.manual_seed(0)\n text_encoder = T5EncoderModel.from_pretrained(\"hf-internal-testing/tiny-random-t5\")\n\n torch.manual_seed(0)\n tokenizer = AutoTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-t5\")\n\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n sample_size=32,\n layers_per_block=1,\n block_out_channels=[32, 64],\n down_block_types=[\n \"ResnetDownsampleBlock2D\",\n \"SimpleCrossAttnDownBlock2D\",\n ],\n mid_block_type=\"UNetMidBlock2DSimpleCrossAttn\",\n up_block_types=[\"SimpleCrossAttnUpBlock2D\", \"ResnetUpsampleBlock2D\"],\n in_channels=3,\n out_channels=6,\n cross_attention_dim=32,\n encoder_hid_dim=32,\n attention_head_dim=8,\n addition_embed_type=\"text\",\n addition_embed_type_num_heads=2,\n cross_attention_norm=\"group_norm\",\n resnet_time_scale_shift=\"scale_shift\",\n act_fn=\"gelu\",\n )\n unet.set_attn_processor(AttnAddedKVProcessor()) # For reproducibility tests\n\n torch.manual_seed(0)\n scheduler = DDPMScheduler(\n num_train_timesteps=1000,\n beta_schedule=\"squaredcos_cap_v2\",\n beta_start=0.0001,\n beta_end=0.02,\n thresholding=True,\n dynamic_thresholding_ratio=0.95,\n sample_max_value=1.0,\n prediction_type=\"epsilon\",\n variance_type=\"learned_range\",\n )\n\n torch.manual_seed(0)\n watermarker = IFWatermarker()\n\n return {\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"watermarker\": watermarker,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n }\n\n def _get_superresolution_dummy_components(self):\n torch.manual_seed(0)\n text_encoder = T5EncoderModel.from_pretrained(\"hf-internal-testing/tiny-random-t5\")\n\n torch.manual_seed(0)\n tokenizer = AutoTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-t5\")\n\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n sample_size=32,\n layers_per_block=[1, 2],\n block_out_channels=[32, 64],\n down_block_types=[\n \"ResnetDownsampleBlock2D\",\n \"SimpleCrossAttnDownBlock2D\",\n ],\n mid_block_type=\"UNetMidBlock2DSimpleCrossAttn\",\n up_block_types=[\"SimpleCrossAttnUpBlock2D\", \"ResnetUpsampleBlock2D\"],\n in_channels=6,\n out_channels=6,\n cross_attention_dim=32,\n encoder_hid_dim=32,\n attention_head_dim=8,\n addition_embed_type=\"text\",\n addition_embed_type_num_heads=2,\n cross_attention_norm=\"group_norm\",\n resnet_time_scale_shift=\"scale_shift\",\n act_fn=\"gelu\",\n class_embed_type=\"timestep\",\n mid_block_scale_factor=1.414,\n time_embedding_act_fn=\"gelu\",\n time_embedding_dim=32,\n )\n unet.set_attn_processor(AttnAddedKVProcessor()) # For reproducibility tests\n\n torch.manual_seed(0)\n scheduler = DDPMScheduler(\n num_train_timesteps=1000,\n beta_schedule=\"squaredcos_cap_v2\",\n beta_start=0.0001,\n beta_end=0.02,\n thresholding=True,\n dynamic_thresholding_ratio=0.95,\n sample_max_value=1.0,\n prediction_type=\"epsilon\",\n variance_type=\"learned_range\",\n )\n\n torch.manual_seed(0)\n image_noising_scheduler = DDPMScheduler(\n num_train_timesteps=1000,\n beta_schedule=\"squaredcos_cap_v2\",\n beta_start=0.0001,\n beta_end=0.02,\n )\n\n torch.manual_seed(0)\n watermarker = IFWatermarker()\n\n return {\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"image_noising_scheduler\": image_noising_scheduler,\n \"watermarker\": watermarker,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n }\n\n # this test is modified from the base class because if pipelines set the text encoder\n # as optional with the intention that the user is allowed to encode the prompt once\n # and then pass the embeddings directly to the pipeline. The base class test uses\n # the unmodified arguments from `self.get_dummy_inputs` which will pass the unencoded\n # prompt to the pipeline when the text encoder is set to None, throwing an error.\n # So we make the test reflect the intended usage of setting the text encoder to None.\n def _test_save_load_optional_components(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n\n prompt = inputs[\"prompt\"]\n generator = inputs[\"generator\"]\n num_inference_steps = inputs[\"num_inference_steps\"]\n output_type = inputs[\"output_type\"]\n\n if \"image\" in inputs:\n image = inputs[\"image\"]\n else:\n image = None\n\n if \"mask_image\" in inputs:\n mask_image = inputs[\"mask_image\"]\n else:\n mask_image = None\n\n if \"original_image\" in inputs:\n original_image = inputs[\"original_image\"]\n else:\n original_image = None\n\n prompt_embeds, negative_prompt_embeds = pipe.encode_prompt(prompt)\n\n # inputs with prompt converted to embeddings\n inputs = {\n \"prompt_embeds\": prompt_embeds,\n \"negative_prompt_embeds\": negative_prompt_embeds,\n \"generator\": generator,\n \"num_inference_steps\": num_inference_steps,\n \"output_type\": output_type,\n }\n\n if image is not None:\n inputs[\"image\"] = image\n\n if mask_image is not None:\n inputs[\"mask_image\"] = mask_image\n\n if original_image is not None:\n inputs[\"original_image\"] = original_image\n\n # set all optional components to None\n for optional_component in pipe._optional_components:\n setattr(pipe, optional_component, None)\n\n output = pipe(**inputs)[0]\n\n with tempfile.TemporaryDirectory() as tmpdir:\n pipe.save_pretrained(tmpdir)\n pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)\n pipe_loaded.to(torch_device)\n pipe_loaded.set_progress_bar_config(disable=None)\n\n pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()) # For reproducibility tests\n\n for optional_component in pipe._optional_components:\n self.assertTrue(\n getattr(pipe_loaded, optional_component) is None,\n f\"`{optional_component}` did not stay set to None after loading.\",\n )\n\n inputs = self.get_dummy_inputs(torch_device)\n\n generator = inputs[\"generator\"]\n num_inference_steps = inputs[\"num_inference_steps\"]\n output_type = inputs[\"output_type\"]\n\n # inputs with prompt converted to embeddings\n inputs = {\n \"prompt_embeds\": prompt_embeds,\n \"negative_prompt_embeds\": negative_prompt_embeds,\n \"generator\": generator,\n \"num_inference_steps\": num_inference_steps,\n \"output_type\": output_type,\n }\n\n if image is not None:\n inputs[\"image\"] = image\n\n if mask_image is not None:\n inputs[\"mask_image\"] = mask_image\n\n if original_image is not None:\n inputs[\"original_image\"] = original_image\n\n output_loaded = pipe_loaded(**inputs)[0]\n\n max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()\n self.assertLess(max_diff, 1e-4)\n\n # Modified from `PipelineTesterMixin` to set the attn processor as it's not serialized.\n # This should be handled in the base test and then this method can be removed.\n def _test_save_load_local(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n output = pipe(**inputs)[0]\n\n with tempfile.TemporaryDirectory() as tmpdir:\n pipe.save_pretrained(tmpdir)\n pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)\n pipe_loaded.to(torch_device)\n pipe_loaded.set_progress_bar_config(disable=None)\n\n pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()) # For reproducibility tests\n\n inputs = self.get_dummy_inputs(torch_device)\n output_loaded = pipe_loaded(**inputs)[0]\n\n max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()\n self.assertLess(max_diff, 1e-4)\n" }, { "path": "diffusers/tests/pipelines/deepfloyd_if/test_if.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nimport torch\n\nfrom diffusers import (\n IFPipeline,\n)\nfrom diffusers.models.attention_processor import AttnAddedKVProcessor\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.testing_utils import load_numpy, require_torch_gpu, skip_mps, slow, torch_device\n\nfrom ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS\nfrom ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference\nfrom . import IFPipelineTesterMixin\n\n\n@skip_mps\nclass IFPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMixin, unittest.TestCase):\n pipeline_class = IFPipeline\n params = TEXT_TO_IMAGE_PARAMS - {\"width\", \"height\", \"latents\"}\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n required_optional_params = PipelineTesterMixin.required_optional_params - {\"latents\"}\n\n def get_dummy_components(self):\n return self._get_dummy_components()\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n\n return inputs\n\n def test_save_load_optional_components(self):\n self._test_save_load_optional_components()\n\n @unittest.skipIf(torch_device != \"cuda\", reason=\"float16 requires CUDA\")\n def test_save_load_float16(self):\n # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder\n super().test_save_load_float16(expected_max_diff=1e-1)\n\n def test_attention_slicing_forward_pass(self):\n self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)\n\n def test_save_load_local(self):\n self._test_save_load_local()\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(\n expected_max_diff=1e-2,\n )\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)\n\n\n@slow\n@require_torch_gpu\nclass IFPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_if_text_to_image(self):\n pipe = IFPipeline.from_pretrained(\"DeepFloyd/IF-I-XL-v1.0\", variant=\"fp16\", torch_dtype=torch.float16)\n pipe.unet.set_attn_processor(AttnAddedKVProcessor())\n pipe.enable_model_cpu_offload()\n\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.empty_cache()\n torch.cuda.reset_peak_memory_stats()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n output = pipe(\n prompt=\"anime turtle\",\n num_inference_steps=2,\n generator=generator,\n output_type=\"np\",\n )\n\n image = output.images[0]\n\n mem_bytes = torch.cuda.max_memory_allocated()\n assert mem_bytes < 12 * 10**9\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy\"\n )\n assert_mean_pixel_difference(image, expected_image)\n pipe.remove_all_hooks()\n" }, { "path": "diffusers/tests/pipelines/deepfloyd_if/test_if_img2img.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport torch\n\nfrom diffusers import IFImg2ImgPipeline\nfrom diffusers.models.attention_processor import AttnAddedKVProcessor\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device\n\nfrom ..pipeline_params import (\n TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,\n TEXT_GUIDED_IMAGE_VARIATION_PARAMS,\n)\nfrom ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference\nfrom . import IFPipelineTesterMixin\n\n\n@skip_mps\nclass IFImg2ImgPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMixin, unittest.TestCase):\n pipeline_class = IFImg2ImgPipeline\n params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {\"width\", \"height\"}\n batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS\n required_optional_params = PipelineTesterMixin.required_optional_params - {\"latents\"}\n\n def get_dummy_components(self):\n return self._get_dummy_components()\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"image\": image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n\n return inputs\n\n def test_save_load_optional_components(self):\n self._test_save_load_optional_components()\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)\n\n @unittest.skipIf(torch_device != \"cuda\", reason=\"float16 requires CUDA\")\n def test_save_load_float16(self):\n # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder\n super().test_save_load_float16(expected_max_diff=1e-1)\n\n @unittest.skipIf(torch_device != \"cuda\", reason=\"float16 requires CUDA\")\n def test_float16_inference(self):\n super().test_float16_inference(expected_max_diff=1e-1)\n\n def test_attention_slicing_forward_pass(self):\n self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)\n\n def test_save_load_local(self):\n self._test_save_load_local()\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(\n expected_max_diff=1e-2,\n )\n\n\n@slow\n@require_torch_gpu\nclass IFImg2ImgPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_if_img2img(self):\n pipe = IFImg2ImgPipeline.from_pretrained(\n \"DeepFloyd/IF-I-L-v1.0\",\n variant=\"fp16\",\n torch_dtype=torch.float16,\n )\n pipe.unet.set_attn_processor(AttnAddedKVProcessor())\n pipe.enable_model_cpu_offload()\n\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.empty_cache()\n torch.cuda.reset_peak_memory_stats()\n\n image = floats_tensor((1, 3, 64, 64), rng=random.Random(0)).to(torch_device)\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n output = pipe(\n prompt=\"anime turtle\",\n image=image,\n num_inference_steps=2,\n generator=generator,\n output_type=\"np\",\n )\n image = output.images[0]\n\n mem_bytes = torch.cuda.max_memory_allocated()\n assert mem_bytes < 12 * 10**9\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy\"\n )\n assert_mean_pixel_difference(image, expected_image)\n\n pipe.remove_all_hooks()\n" }, { "path": "diffusers/tests/pipelines/deepfloyd_if/test_if_img2img_superresolution.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport torch\n\nfrom diffusers import IFImg2ImgSuperResolutionPipeline\nfrom diffusers.models.attention_processor import AttnAddedKVProcessor\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device\n\nfrom ..pipeline_params import (\n TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,\n TEXT_GUIDED_IMAGE_VARIATION_PARAMS,\n)\nfrom ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference\nfrom . import IFPipelineTesterMixin\n\n\n@skip_mps\nclass IFImg2ImgSuperResolutionPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMixin, unittest.TestCase):\n pipeline_class = IFImg2ImgSuperResolutionPipeline\n params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {\"width\", \"height\"}\n batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({\"original_image\"})\n required_optional_params = PipelineTesterMixin.required_optional_params - {\"latents\"}\n\n def get_dummy_components(self):\n return self._get_superresolution_dummy_components()\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n original_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n image = floats_tensor((1, 3, 16, 16), rng=random.Random(seed)).to(device)\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"image\": image,\n \"original_image\": original_image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n\n return inputs\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)\n\n def test_save_load_optional_components(self):\n self._test_save_load_optional_components()\n\n @unittest.skipIf(torch_device != \"cuda\", reason=\"float16 requires CUDA\")\n def test_save_load_float16(self):\n # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder\n super().test_save_load_float16(expected_max_diff=1e-1)\n\n def test_attention_slicing_forward_pass(self):\n self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)\n\n def test_save_load_local(self):\n self._test_save_load_local()\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(\n expected_max_diff=1e-2,\n )\n\n\n@slow\n@require_torch_gpu\nclass IFImg2ImgSuperResolutionPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_if_img2img_superresolution(self):\n pipe = IFImg2ImgSuperResolutionPipeline.from_pretrained(\n \"DeepFloyd/IF-II-L-v1.0\",\n variant=\"fp16\",\n torch_dtype=torch.float16,\n )\n pipe.unet.set_attn_processor(AttnAddedKVProcessor())\n pipe.enable_model_cpu_offload()\n\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.empty_cache()\n torch.cuda.reset_peak_memory_stats()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n\n original_image = floats_tensor((1, 3, 256, 256), rng=random.Random(0)).to(torch_device)\n image = floats_tensor((1, 3, 64, 64), rng=random.Random(0)).to(torch_device)\n\n output = pipe(\n prompt=\"anime turtle\",\n image=image,\n original_image=original_image,\n generator=generator,\n num_inference_steps=2,\n output_type=\"np\",\n )\n\n image = output.images[0]\n\n assert image.shape == (256, 256, 3)\n\n mem_bytes = torch.cuda.max_memory_allocated()\n assert mem_bytes < 12 * 10**9\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy\"\n )\n assert_mean_pixel_difference(image, expected_image)\n\n pipe.remove_all_hooks()\n" }, { "path": "diffusers/tests/pipelines/deepfloyd_if/test_if_inpainting.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport torch\n\nfrom diffusers import IFInpaintingPipeline\nfrom diffusers.models.attention_processor import AttnAddedKVProcessor\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device\n\nfrom ..pipeline_params import (\n TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,\n TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,\n)\nfrom ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference\nfrom . import IFPipelineTesterMixin\n\n\n@skip_mps\nclass IFInpaintingPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMixin, unittest.TestCase):\n pipeline_class = IFInpaintingPipeline\n params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {\"width\", \"height\"}\n batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS\n required_optional_params = PipelineTesterMixin.required_optional_params - {\"latents\"}\n\n def get_dummy_components(self):\n return self._get_dummy_components()\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n mask_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"image\": image,\n \"mask_image\": mask_image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n\n return inputs\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)\n\n def test_save_load_optional_components(self):\n self._test_save_load_optional_components()\n\n @unittest.skipIf(torch_device != \"cuda\", reason=\"float16 requires CUDA\")\n def test_save_load_float16(self):\n # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder\n super().test_save_load_float16(expected_max_diff=1e-1)\n\n def test_attention_slicing_forward_pass(self):\n self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)\n\n def test_save_load_local(self):\n self._test_save_load_local()\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(\n expected_max_diff=1e-2,\n )\n\n\n@slow\n@require_torch_gpu\nclass IFInpaintingPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_if_inpainting(self):\n pipe = IFInpaintingPipeline.from_pretrained(\n \"DeepFloyd/IF-I-XL-v1.0\", variant=\"fp16\", torch_dtype=torch.float16\n )\n pipe.unet.set_attn_processor(AttnAddedKVProcessor())\n pipe.enable_model_cpu_offload()\n\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n image = floats_tensor((1, 3, 64, 64), rng=random.Random(0)).to(torch_device)\n mask_image = floats_tensor((1, 3, 64, 64), rng=random.Random(1)).to(torch_device)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n output = pipe(\n prompt=\"anime prompts\",\n image=image,\n mask_image=mask_image,\n num_inference_steps=2,\n generator=generator,\n output_type=\"np\",\n )\n image = output.images[0]\n\n mem_bytes = torch.cuda.max_memory_allocated()\n assert mem_bytes < 12 * 10**9\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy\"\n )\n assert_mean_pixel_difference(image, expected_image)\n pipe.remove_all_hooks()\n" }, { "path": "diffusers/tests/pipelines/deepfloyd_if/test_if_inpainting_superresolution.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport torch\n\nfrom diffusers import IFInpaintingSuperResolutionPipeline\nfrom diffusers.models.attention_processor import AttnAddedKVProcessor\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device\n\nfrom ..pipeline_params import (\n TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,\n TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,\n)\nfrom ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference\nfrom . import IFPipelineTesterMixin\n\n\n@skip_mps\nclass IFInpaintingSuperResolutionPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMixin, unittest.TestCase):\n pipeline_class = IFInpaintingSuperResolutionPipeline\n params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {\"width\", \"height\"}\n batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({\"original_image\"})\n required_optional_params = PipelineTesterMixin.required_optional_params - {\"latents\"}\n\n def get_dummy_components(self):\n return self._get_superresolution_dummy_components()\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n image = floats_tensor((1, 3, 16, 16), rng=random.Random(seed)).to(device)\n original_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n mask_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"image\": image,\n \"original_image\": original_image,\n \"mask_image\": mask_image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n\n return inputs\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)\n\n def test_save_load_optional_components(self):\n self._test_save_load_optional_components()\n\n @unittest.skipIf(torch_device != \"cuda\", reason=\"float16 requires CUDA\")\n def test_save_load_float16(self):\n # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder\n super().test_save_load_float16(expected_max_diff=1e-1)\n\n def test_attention_slicing_forward_pass(self):\n self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)\n\n def test_save_load_local(self):\n self._test_save_load_local()\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(\n expected_max_diff=1e-2,\n )\n\n\n@slow\n@require_torch_gpu\nclass IFInpaintingSuperResolutionPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_if_inpainting_superresolution(self):\n pipe = IFInpaintingSuperResolutionPipeline.from_pretrained(\n \"DeepFloyd/IF-II-L-v1.0\", variant=\"fp16\", torch_dtype=torch.float16\n )\n pipe.unet.set_attn_processor(AttnAddedKVProcessor())\n pipe.enable_model_cpu_offload()\n\n # Super resolution test\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n\n image = floats_tensor((1, 3, 64, 64), rng=random.Random(0)).to(torch_device)\n original_image = floats_tensor((1, 3, 256, 256), rng=random.Random(0)).to(torch_device)\n mask_image = floats_tensor((1, 3, 256, 256), rng=random.Random(1)).to(torch_device)\n\n output = pipe(\n prompt=\"anime turtle\",\n image=image,\n original_image=original_image,\n mask_image=mask_image,\n generator=generator,\n num_inference_steps=2,\n output_type=\"np\",\n )\n\n image = output.images[0]\n\n assert image.shape == (256, 256, 3)\n\n mem_bytes = torch.cuda.max_memory_allocated()\n assert mem_bytes < 12 * 10**9\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy\"\n )\n assert_mean_pixel_difference(image, expected_image)\n\n pipe.remove_all_hooks()\n" }, { "path": "diffusers/tests/pipelines/deepfloyd_if/test_if_superresolution.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport torch\n\nfrom diffusers import IFSuperResolutionPipeline\nfrom diffusers.models.attention_processor import AttnAddedKVProcessor\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device\n\nfrom ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS\nfrom ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference\nfrom . import IFPipelineTesterMixin\n\n\n@skip_mps\nclass IFSuperResolutionPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMixin, unittest.TestCase):\n pipeline_class = IFSuperResolutionPipeline\n params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {\"width\", \"height\"}\n batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS\n required_optional_params = PipelineTesterMixin.required_optional_params - {\"latents\"}\n\n def get_dummy_components(self):\n return self._get_superresolution_dummy_components()\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"image\": image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n\n return inputs\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)\n\n def test_save_load_optional_components(self):\n self._test_save_load_optional_components()\n\n @unittest.skipIf(torch_device != \"cuda\", reason=\"float16 requires CUDA\")\n def test_save_load_float16(self):\n # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder\n super().test_save_load_float16(expected_max_diff=1e-1)\n\n def test_attention_slicing_forward_pass(self):\n self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)\n\n def test_save_load_local(self):\n self._test_save_load_local()\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(\n expected_max_diff=1e-2,\n )\n\n\n@slow\n@require_torch_gpu\nclass IFSuperResolutionPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_if_superresolution(self):\n pipe = IFSuperResolutionPipeline.from_pretrained(\n \"DeepFloyd/IF-II-L-v1.0\", variant=\"fp16\", torch_dtype=torch.float16\n )\n pipe.unet.set_attn_processor(AttnAddedKVProcessor())\n pipe.enable_model_cpu_offload()\n\n # Super resolution test\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n image = floats_tensor((1, 3, 64, 64), rng=random.Random(0)).to(torch_device)\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n output = pipe(\n prompt=\"anime turtle\",\n image=image,\n generator=generator,\n num_inference_steps=2,\n output_type=\"np\",\n )\n\n image = output.images[0]\n\n assert image.shape == (256, 256, 3)\n\n mem_bytes = torch.cuda.max_memory_allocated()\n assert mem_bytes < 12 * 10**9\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy\"\n )\n assert_mean_pixel_difference(image, expected_image)\n\n pipe.remove_all_hooks()\n" }, { "path": "diffusers/tests/pipelines/dit/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/dit/test_dit.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nimport numpy as np\nimport torch\n\nfrom diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DiTTransformer2DModel, DPMSolverMultistepScheduler\nfrom diffusers.utils import is_xformers_available\nfrom diffusers.utils.testing_utils import enable_full_determinism, load_numpy, nightly, require_torch_gpu, torch_device\n\nfrom ..pipeline_params import (\n CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,\n CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,\n)\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass DiTPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = DiTPipeline\n params = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS\n required_optional_params = PipelineTesterMixin.required_optional_params - {\n \"latents\",\n \"num_images_per_prompt\",\n \"callback\",\n \"callback_steps\",\n }\n batch_params = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n transformer = DiTTransformer2DModel(\n sample_size=16,\n num_layers=2,\n patch_size=4,\n attention_head_dim=8,\n num_attention_heads=2,\n in_channels=4,\n out_channels=8,\n attention_bias=True,\n activation_fn=\"gelu-approximate\",\n num_embeds_ada_norm=1000,\n norm_type=\"ada_norm_zero\",\n norm_elementwise_affine=False,\n )\n vae = AutoencoderKL()\n scheduler = DDIMScheduler()\n components = {\"transformer\": transformer.eval(), \"vae\": vae.eval(), \"scheduler\": scheduler}\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"class_labels\": [1],\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_inference(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n self.assertEqual(image.shape, (1, 16, 16, 3))\n expected_slice = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457])\n max_diff = np.abs(image_slice.flatten() - expected_slice).max()\n self.assertLessEqual(max_diff, 1e-3)\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=1e-3)\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)\n\n\n@nightly\n@require_torch_gpu\nclass DiTPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_dit_256(self):\n generator = torch.manual_seed(0)\n\n pipe = DiTPipeline.from_pretrained(\"facebook/DiT-XL-2-256\")\n pipe.to(\"cuda\")\n\n words = [\"vase\", \"umbrella\", \"white shark\", \"white wolf\"]\n ids = pipe.get_label_ids(words)\n\n images = pipe(ids, generator=generator, num_inference_steps=40, output_type=\"np\").images\n\n for word, image in zip(words, images):\n expected_image = load_numpy(\n f\"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy\"\n )\n assert np.abs((expected_image - image).max()) < 1e-2\n\n def test_dit_512(self):\n pipe = DiTPipeline.from_pretrained(\"facebook/DiT-XL-2-512\")\n pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n pipe.to(\"cuda\")\n\n words = [\"vase\", \"umbrella\"]\n ids = pipe.get_label_ids(words)\n\n generator = torch.manual_seed(0)\n images = pipe(ids, generator=generator, num_inference_steps=25, output_type=\"np\").images\n\n for word, image in zip(words, images):\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n f\"/dit/{word}_512.npy\"\n )\n\n assert np.abs((expected_image - image).max()) < 1e-1\n" }, { "path": "diffusers/tests/pipelines/hunyuan_dit/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/hunyuan_dit/test_hunyuan_dit.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import AutoTokenizer, BertModel, T5EncoderModel\n\nfrom diffusers import (\n AutoencoderKL,\n DDPMScheduler,\n HunyuanDiT2DModel,\n HunyuanDiTPipeline,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\nfrom ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS\nfrom ..test_pipelines_common import PipelineTesterMixin, to_np\n\n\nenable_full_determinism()\n\n\nclass HunyuanDiTPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = HunyuanDiTPipeline\n params = TEXT_TO_IMAGE_PARAMS - {\"cross_attention_kwargs\"}\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n image_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n\n required_optional_params = PipelineTesterMixin.required_optional_params\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n transformer = HunyuanDiT2DModel(\n sample_size=16,\n num_layers=2,\n patch_size=2,\n attention_head_dim=8,\n num_attention_heads=3,\n in_channels=4,\n cross_attention_dim=32,\n cross_attention_dim_t5=32,\n pooled_projection_dim=16,\n hidden_size=24,\n activation_fn=\"gelu-approximate\",\n )\n torch.manual_seed(0)\n vae = AutoencoderKL()\n\n scheduler = DDPMScheduler()\n text_encoder = BertModel.from_pretrained(\"hf-internal-testing/tiny-random-BertModel\")\n tokenizer = AutoTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-BertModel\")\n text_encoder_2 = T5EncoderModel.from_pretrained(\"hf-internal-testing/tiny-random-t5\")\n tokenizer_2 = AutoTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-t5\")\n\n components = {\n \"transformer\": transformer.eval(),\n \"vae\": vae.eval(),\n \"scheduler\": scheduler,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"text_encoder_2\": text_encoder_2,\n \"tokenizer_2\": tokenizer_2,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 5.0,\n \"output_type\": \"np\",\n \"use_resolution_binning\": False,\n }\n return inputs\n\n def test_inference(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n self.assertEqual(image.shape, (1, 16, 16, 3))\n expected_slice = np.array(\n [0.56939435, 0.34541583, 0.35915792, 0.46489206, 0.38775963, 0.45004836, 0.5957267, 0.59481275, 0.33287364]\n )\n max_diff = np.abs(image_slice.flatten() - expected_slice).max()\n self.assertLessEqual(max_diff, 1e-3)\n\n def test_sequential_cpu_offload_forward_pass(self):\n # TODO(YiYi) need to fix later\n pass\n\n def test_sequential_offload_forward_pass_twice(self):\n # TODO(YiYi) need to fix later\n pass\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(\n expected_max_diff=1e-3,\n )\n\n def test_save_load_optional_components(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n\n prompt = inputs[\"prompt\"]\n generator = inputs[\"generator\"]\n num_inference_steps = inputs[\"num_inference_steps\"]\n output_type = inputs[\"output_type\"]\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n prompt_attention_mask,\n negative_prompt_attention_mask,\n ) = pipe.encode_prompt(prompt, device=torch_device, dtype=torch.float32, text_encoder_index=0)\n\n (\n prompt_embeds_2,\n negative_prompt_embeds_2,\n prompt_attention_mask_2,\n negative_prompt_attention_mask_2,\n ) = pipe.encode_prompt(\n prompt,\n device=torch_device,\n dtype=torch.float32,\n text_encoder_index=1,\n )\n\n # inputs with prompt converted to embeddings\n inputs = {\n \"prompt_embeds\": prompt_embeds,\n \"prompt_attention_mask\": prompt_attention_mask,\n \"negative_prompt_embeds\": negative_prompt_embeds,\n \"negative_prompt_attention_mask\": negative_prompt_attention_mask,\n \"prompt_embeds_2\": prompt_embeds_2,\n \"prompt_attention_mask_2\": prompt_attention_mask_2,\n \"negative_prompt_embeds_2\": negative_prompt_embeds_2,\n \"negative_prompt_attention_mask_2\": negative_prompt_attention_mask_2,\n \"generator\": generator,\n \"num_inference_steps\": num_inference_steps,\n \"output_type\": output_type,\n \"use_resolution_binning\": False,\n }\n\n # set all optional components to None\n for optional_component in pipe._optional_components:\n setattr(pipe, optional_component, None)\n\n output = pipe(**inputs)[0]\n\n with tempfile.TemporaryDirectory() as tmpdir:\n pipe.save_pretrained(tmpdir)\n pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)\n pipe_loaded.to(torch_device)\n pipe_loaded.set_progress_bar_config(disable=None)\n\n for optional_component in pipe._optional_components:\n self.assertTrue(\n getattr(pipe_loaded, optional_component) is None,\n f\"`{optional_component}` did not stay set to None after loading.\",\n )\n\n inputs = self.get_dummy_inputs(torch_device)\n\n generator = inputs[\"generator\"]\n num_inference_steps = inputs[\"num_inference_steps\"]\n output_type = inputs[\"output_type\"]\n\n # inputs with prompt converted to embeddings\n inputs = {\n \"prompt_embeds\": prompt_embeds,\n \"prompt_attention_mask\": prompt_attention_mask,\n \"negative_prompt_embeds\": negative_prompt_embeds,\n \"negative_prompt_attention_mask\": negative_prompt_attention_mask,\n \"prompt_embeds_2\": prompt_embeds_2,\n \"prompt_attention_mask_2\": prompt_attention_mask_2,\n \"negative_prompt_embeds_2\": negative_prompt_embeds_2,\n \"negative_prompt_attention_mask_2\": negative_prompt_attention_mask_2,\n \"generator\": generator,\n \"num_inference_steps\": num_inference_steps,\n \"output_type\": output_type,\n \"use_resolution_binning\": False,\n }\n\n output_loaded = pipe_loaded(**inputs)[0]\n\n max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()\n self.assertLess(max_diff, 1e-4)\n\n def test_feed_forward_chunking(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = pipe(**inputs).images\n image_slice_no_chunking = image[0, -3:, -3:, -1]\n\n pipe.transformer.enable_forward_chunking(chunk_size=1, dim=0)\n inputs = self.get_dummy_inputs(device)\n image = pipe(**inputs).images\n image_slice_chunking = image[0, -3:, -3:, -1]\n\n max_diff = np.abs(to_np(image_slice_no_chunking) - to_np(image_slice_chunking)).max()\n self.assertLess(max_diff, 1e-4)\n\n def test_fused_qkv_projections(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"return_dict\"] = False\n image = pipe(**inputs)[0]\n original_image_slice = image[0, -3:, -3:, -1]\n\n pipe.transformer.fuse_qkv_projections()\n inputs = self.get_dummy_inputs(device)\n inputs[\"return_dict\"] = False\n image_fused = pipe(**inputs)[0]\n image_slice_fused = image_fused[0, -3:, -3:, -1]\n\n pipe.transformer.unfuse_qkv_projections()\n inputs = self.get_dummy_inputs(device)\n inputs[\"return_dict\"] = False\n image_disabled = pipe(**inputs)[0]\n image_slice_disabled = image_disabled[0, -3:, -3:, -1]\n\n assert np.allclose(\n original_image_slice, image_slice_fused, atol=1e-2, rtol=1e-2\n ), \"Fusion of QKV projections shouldn't affect the outputs.\"\n assert np.allclose(\n image_slice_fused, image_slice_disabled, atol=1e-2, rtol=1e-2\n ), \"Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled.\"\n assert np.allclose(\n original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2\n ), \"Original outputs should match when fused QKV projections are disabled.\"\n\n\n@slow\n@require_torch_gpu\nclass HunyuanDiTPipelineIntegrationTests(unittest.TestCase):\n prompt = \"一个宇航员在骑马\"\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_hunyuan_dit_1024(self):\n generator = torch.Generator(\"cpu\").manual_seed(0)\n\n pipe = HunyuanDiTPipeline.from_pretrained(\n \"XCLiu/HunyuanDiT-0523\", revision=\"refs/pr/2\", torch_dtype=torch.float16\n )\n pipe.enable_model_cpu_offload()\n prompt = self.prompt\n\n image = pipe(\n prompt=prompt, height=1024, width=1024, generator=generator, num_inference_steps=2, output_type=\"np\"\n ).images\n\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array(\n [0.48388672, 0.33789062, 0.30737305, 0.47875977, 0.25097656, 0.30029297, 0.4440918, 0.26953125, 0.30078125]\n )\n\n max_diff = numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice)\n assert max_diff < 1e-3, f\"Max diff is too high. got {image_slice.flatten()}\"\n" }, { "path": "diffusers/tests/pipelines/i2vgen_xl/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/i2vgen_xl/test_i2vgenxl.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextConfig,\n CLIPTextModel,\n CLIPTokenizer,\n CLIPVisionConfig,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n I2VGenXLPipeline,\n)\nfrom diffusers.models.unets import I2VGenXLUNet\nfrom diffusers.utils import is_xformers_available, load_image\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n numpy_cosine_similarity_distance,\n print_tensor_test,\n require_torch_gpu,\n skip_mps,\n slow,\n torch_device,\n)\n\nfrom ..test_pipelines_common import PipelineTesterMixin, SDFunctionTesterMixin\n\n\nenable_full_determinism()\n\n\n@skip_mps\nclass I2VGenXLPipelineFastTests(SDFunctionTesterMixin, PipelineTesterMixin, unittest.TestCase):\n pipeline_class = I2VGenXLPipeline\n params = frozenset([\"prompt\", \"negative_prompt\", \"image\"])\n batch_params = frozenset([\"prompt\", \"negative_prompt\", \"image\", \"generator\"])\n # No `output_type`.\n required_optional_params = frozenset([\"num_inference_steps\", \"generator\", \"latents\", \"return_dict\"])\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n\n torch.manual_seed(0)\n unet = I2VGenXLUNet(\n block_out_channels=(4, 8),\n layers_per_block=1,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"CrossAttnDownBlock3D\", \"DownBlock3D\"),\n up_block_types=(\"UpBlock3D\", \"CrossAttnUpBlock3D\"),\n cross_attention_dim=4,\n attention_head_dim=4,\n num_attention_heads=None,\n norm_num_groups=2,\n )\n\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=(8,),\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\"],\n latent_channels=4,\n sample_size=32,\n norm_num_groups=2,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=4,\n intermediate_size=16,\n layer_norm_eps=1e-05,\n num_attention_heads=2,\n num_hidden_layers=2,\n pad_token_id=1,\n vocab_size=1000,\n hidden_act=\"gelu\",\n projection_dim=32,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n torch.manual_seed(0)\n vision_encoder_config = CLIPVisionConfig(\n hidden_size=4,\n projection_dim=4,\n num_hidden_layers=2,\n num_attention_heads=2,\n image_size=32,\n intermediate_size=16,\n patch_size=1,\n )\n image_encoder = CLIPVisionModelWithProjection(vision_encoder_config)\n\n torch.manual_seed(0)\n feature_extractor = CLIPImageProcessor(crop_size=32, size=32)\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"image_encoder\": image_encoder,\n \"tokenizer\": tokenizer,\n \"feature_extractor\": feature_extractor,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n input_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"image\": input_image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"pt\",\n \"num_frames\": 4,\n \"width\": 32,\n \"height\": 32,\n }\n return inputs\n\n def test_text_to_video_default_case(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"output_type\"] = \"np\"\n frames = pipe(**inputs).frames\n\n image_slice = frames[0][0][-3:, -3:, -1]\n\n assert frames[0][0].shape == (32, 32, 3)\n expected_slice = np.array([0.5146, 0.6525, 0.6032, 0.5204, 0.5675, 0.4125, 0.3016, 0.5172, 0.4095])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_save_load_local(self):\n super().test_save_load_local(expected_max_difference=0.006)\n\n def test_sequential_cpu_offload_forward_pass(self):\n super().test_sequential_cpu_offload_forward_pass(expected_max_diff=0.008)\n\n def test_dict_tuple_outputs_equivalent(self):\n super().test_dict_tuple_outputs_equivalent(expected_max_difference=0.008)\n\n def test_save_load_optional_components(self):\n super().test_save_load_optional_components(expected_max_difference=0.008)\n\n @unittest.skip(\"Deprecated functionality\")\n def test_attention_slicing_forward_pass(self):\n pass\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False, expected_max_diff=1e-2)\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(batch_size=2, expected_max_diff=0.008)\n\n def test_model_cpu_offload_forward_pass(self):\n super().test_model_cpu_offload_forward_pass(expected_max_diff=0.008)\n\n def test_num_videos_per_prompt(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"output_type\"] = \"np\"\n frames = pipe(**inputs, num_videos_per_prompt=2).frames\n\n assert frames.shape == (2, 4, 32, 32, 3)\n assert frames[0][0].shape == (32, 32, 3)\n\n image_slice = frames[0][0][-3:, -3:, -1]\n expected_slice = np.array([0.5146, 0.6525, 0.6032, 0.5204, 0.5675, 0.4125, 0.3016, 0.5172, 0.4095])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n\n@slow\n@require_torch_gpu\nclass I2VGenXLPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_i2vgen_xl(self):\n pipe = I2VGenXLPipeline.from_pretrained(\"ali-vilab/i2vgen-xl\", torch_dtype=torch.float16, variant=\"fp16\")\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png?download=true\"\n )\n\n generator = torch.Generator(\"cpu\").manual_seed(0)\n num_frames = 3\n\n output = pipe(\n image=image,\n prompt=\"my cat\",\n num_frames=num_frames,\n generator=generator,\n num_inference_steps=3,\n output_type=\"np\",\n )\n\n image = output.frames[0]\n assert image.shape == (num_frames, 704, 1280, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n print_tensor_test(image_slice.flatten())\n expected_slice = np.array([0.5482, 0.6244, 0.6274, 0.4584, 0.5935, 0.5937, 0.4579, 0.5767, 0.5892])\n assert numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice.flatten()) < 1e-3\n" }, { "path": "diffusers/tests/pipelines/ip_adapters/test_ip_adapter_stable_diffusion.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import (\n CLIPImageProcessor,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers import (\n StableDiffusionImg2ImgPipeline,\n StableDiffusionInpaintPipeline,\n StableDiffusionPipeline,\n StableDiffusionXLImg2ImgPipeline,\n StableDiffusionXLInpaintPipeline,\n StableDiffusionXLPipeline,\n)\nfrom diffusers.image_processor import IPAdapterMaskProcessor\nfrom diffusers.utils import load_image\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n is_flaky,\n load_pt,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\n\nenable_full_determinism()\n\n\nclass IPAdapterNightlyTestsMixin(unittest.TestCase):\n dtype = torch.float16\n\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_image_encoder(self, repo_id, subfolder):\n image_encoder = CLIPVisionModelWithProjection.from_pretrained(\n repo_id, subfolder=subfolder, torch_dtype=self.dtype\n ).to(torch_device)\n return image_encoder\n\n def get_image_processor(self, repo_id):\n image_processor = CLIPImageProcessor.from_pretrained(repo_id)\n return image_processor\n\n def get_dummy_inputs(\n self, for_image_to_image=False, for_inpainting=False, for_sdxl=False, for_masks=False, for_instant_style=False\n ):\n image = load_image(\n \"https://user-images.githubusercontent.com/24734142/266492875-2d50d223-8475-44f0-a7c6-08b51cb53572.png\"\n )\n if for_sdxl:\n image = image.resize((1024, 1024))\n\n input_kwargs = {\n \"prompt\": \"best quality, high quality\",\n \"negative_prompt\": \"monochrome, lowres, bad anatomy, worst quality, low quality\",\n \"num_inference_steps\": 5,\n \"generator\": torch.Generator(device=\"cpu\").manual_seed(33),\n \"ip_adapter_image\": image,\n \"output_type\": \"np\",\n }\n if for_image_to_image:\n image = load_image(\"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/vermeer.jpg\")\n ip_image = load_image(\"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/river.png\")\n\n if for_sdxl:\n image = image.resize((1024, 1024))\n ip_image = ip_image.resize((1024, 1024))\n\n input_kwargs.update({\"image\": image, \"ip_adapter_image\": ip_image})\n\n elif for_inpainting:\n image = load_image(\"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/inpaint_image.png\")\n mask = load_image(\"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/mask.png\")\n ip_image = load_image(\"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/girl.png\")\n\n if for_sdxl:\n image = image.resize((1024, 1024))\n mask = mask.resize((1024, 1024))\n ip_image = ip_image.resize((1024, 1024))\n\n input_kwargs.update({\"image\": image, \"mask_image\": mask, \"ip_adapter_image\": ip_image})\n\n elif for_masks:\n face_image1 = load_image(\n \"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ip_mask_girl1.png\"\n )\n face_image2 = load_image(\n \"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ip_mask_girl2.png\"\n )\n mask1 = load_image(\"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ip_mask_mask1.png\")\n mask2 = load_image(\"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ip_mask_mask2.png\")\n input_kwargs.update(\n {\n \"ip_adapter_image\": [[face_image1], [face_image2]],\n \"cross_attention_kwargs\": {\"ip_adapter_masks\": [mask1, mask2]},\n }\n )\n\n elif for_instant_style:\n composition_mask = load_image(\n \"https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/1024_whole_mask.png\"\n )\n female_mask = load_image(\n \"https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/ip_adapter_None_20240321125641_mask.png\"\n )\n male_mask = load_image(\n \"https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/ip_adapter_None_20240321125344_mask.png\"\n )\n background_mask = load_image(\n \"https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/ip_adapter_6_20240321130722_mask.png\"\n )\n ip_composition_image = load_image(\n \"https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/ip_adapter__20240321125152.png\"\n )\n ip_female_style = load_image(\n \"https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/ip_adapter__20240321125625.png\"\n )\n ip_male_style = load_image(\n \"https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/ip_adapter__20240321125329.png\"\n )\n ip_background = load_image(\n \"https://huggingface.co/datasets/OzzyGT/testing-resources/resolve/main/ip_adapter__20240321130643.png\"\n )\n input_kwargs.update(\n {\n \"ip_adapter_image\": [ip_composition_image, [ip_female_style, ip_male_style, ip_background]],\n \"cross_attention_kwargs\": {\n \"ip_adapter_masks\": [[composition_mask], [female_mask, male_mask, background_mask]]\n },\n }\n )\n\n return input_kwargs\n\n\n@slow\n@require_torch_gpu\nclass IPAdapterSDIntegrationTests(IPAdapterNightlyTestsMixin):\n def test_text_to_image(self):\n image_encoder = self.get_image_encoder(repo_id=\"h94/IP-Adapter\", subfolder=\"models/image_encoder\")\n pipeline = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype\n )\n pipeline.to(torch_device)\n pipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"models\", weight_name=\"ip-adapter_sd15.bin\")\n\n inputs = self.get_dummy_inputs()\n images = pipeline(**inputs).images\n image_slice = images[0, :3, :3, -1].flatten()\n\n expected_slice = np.array([0.80810547, 0.88183594, 0.9296875, 0.9189453, 0.9848633, 1.0, 0.97021484, 1.0, 1.0])\n\n max_diff = numpy_cosine_similarity_distance(image_slice, expected_slice)\n assert max_diff < 5e-4\n\n pipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"models\", weight_name=\"ip-adapter-plus_sd15.bin\")\n\n inputs = self.get_dummy_inputs()\n images = pipeline(**inputs).images\n image_slice = images[0, :3, :3, -1].flatten()\n\n expected_slice = np.array(\n [0.30444336, 0.26513672, 0.22436523, 0.2758789, 0.25585938, 0.20751953, 0.25390625, 0.24633789, 0.21923828]\n )\n\n max_diff = numpy_cosine_similarity_distance(image_slice, expected_slice)\n assert max_diff < 5e-4\n\n def test_image_to_image(self):\n image_encoder = self.get_image_encoder(repo_id=\"h94/IP-Adapter\", subfolder=\"models/image_encoder\")\n pipeline = StableDiffusionImg2ImgPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype\n )\n pipeline.to(torch_device)\n pipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"models\", weight_name=\"ip-adapter_sd15.bin\")\n\n inputs = self.get_dummy_inputs(for_image_to_image=True)\n images = pipeline(**inputs).images\n image_slice = images[0, :3, :3, -1].flatten()\n\n expected_slice = np.array(\n [0.22167969, 0.21875, 0.21728516, 0.22607422, 0.21948242, 0.23925781, 0.22387695, 0.25268555, 0.2722168]\n )\n\n max_diff = numpy_cosine_similarity_distance(image_slice, expected_slice)\n assert max_diff < 5e-4\n\n pipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"models\", weight_name=\"ip-adapter-plus_sd15.bin\")\n\n inputs = self.get_dummy_inputs(for_image_to_image=True)\n images = pipeline(**inputs).images\n image_slice = images[0, :3, :3, -1].flatten()\n\n expected_slice = np.array(\n [0.35913086, 0.265625, 0.26367188, 0.24658203, 0.19750977, 0.39990234, 0.15258789, 0.20336914, 0.5517578]\n )\n\n max_diff = numpy_cosine_similarity_distance(image_slice, expected_slice)\n assert max_diff < 5e-4\n\n def test_inpainting(self):\n image_encoder = self.get_image_encoder(repo_id=\"h94/IP-Adapter\", subfolder=\"models/image_encoder\")\n pipeline = StableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype\n )\n pipeline.to(torch_device)\n pipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"models\", weight_name=\"ip-adapter_sd15.bin\")\n\n inputs = self.get_dummy_inputs(for_inpainting=True)\n images = pipeline(**inputs).images\n image_slice = images[0, :3, :3, -1].flatten()\n\n expected_slice = np.array(\n [0.27148438, 0.24047852, 0.22167969, 0.23217773, 0.21118164, 0.21142578, 0.21875, 0.20751953, 0.20019531]\n )\n\n max_diff = numpy_cosine_similarity_distance(image_slice, expected_slice)\n assert max_diff < 5e-4\n\n pipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"models\", weight_name=\"ip-adapter-plus_sd15.bin\")\n\n inputs = self.get_dummy_inputs(for_inpainting=True)\n images = pipeline(**inputs).images\n image_slice = images[0, :3, :3, -1].flatten()\n\n max_diff = numpy_cosine_similarity_distance(image_slice, expected_slice)\n assert max_diff < 5e-4\n\n def test_text_to_image_model_cpu_offload(self):\n image_encoder = self.get_image_encoder(repo_id=\"h94/IP-Adapter\", subfolder=\"models/image_encoder\")\n pipeline = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype\n )\n pipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"models\", weight_name=\"ip-adapter_sd15.bin\")\n pipeline.to(torch_device)\n\n inputs = self.get_dummy_inputs()\n output_without_offload = pipeline(**inputs).images\n\n pipeline.enable_model_cpu_offload()\n inputs = self.get_dummy_inputs()\n output_with_offload = pipeline(**inputs).images\n max_diff = np.abs(output_with_offload - output_without_offload).max()\n self.assertLess(max_diff, 1e-3, \"CPU offloading should not affect the inference results\")\n\n offloaded_modules = [\n v\n for k, v in pipeline.components.items()\n if isinstance(v, torch.nn.Module) and k not in pipeline._exclude_from_cpu_offload\n ]\n (\n self.assertTrue(all(v.device.type == \"cpu\" for v in offloaded_modules)),\n f\"Not offloaded: {[v for v in offloaded_modules if v.device.type != 'cpu']}\",\n )\n\n def test_text_to_image_full_face(self):\n image_encoder = self.get_image_encoder(repo_id=\"h94/IP-Adapter\", subfolder=\"models/image_encoder\")\n pipeline = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype\n )\n pipeline.to(torch_device)\n pipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"models\", weight_name=\"ip-adapter-full-face_sd15.bin\")\n pipeline.set_ip_adapter_scale(0.7)\n\n inputs = self.get_dummy_inputs()\n images = pipeline(**inputs).images\n image_slice = images[0, :3, :3, -1].flatten()\n expected_slice = np.array([0.1704, 0.1296, 0.1272, 0.2212, 0.1514, 0.1479, 0.4172, 0.4263, 0.4360])\n\n max_diff = numpy_cosine_similarity_distance(image_slice, expected_slice)\n assert max_diff < 5e-4\n\n def test_unload(self):\n image_encoder = self.get_image_encoder(repo_id=\"h94/IP-Adapter\", subfolder=\"models/image_encoder\")\n pipeline = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype\n )\n before_processors = [attn_proc.__class__ for attn_proc in pipeline.unet.attn_processors.values()]\n pipeline.to(torch_device)\n pipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"models\", weight_name=\"ip-adapter_sd15.bin\")\n pipeline.set_ip_adapter_scale(0.7)\n\n pipeline.unload_ip_adapter()\n\n assert getattr(pipeline, \"image_encoder\") is None\n assert getattr(pipeline, \"feature_extractor\") is not None\n after_processors = [attn_proc.__class__ for attn_proc in pipeline.unet.attn_processors.values()]\n\n assert before_processors == after_processors\n\n @is_flaky\n def test_multi(self):\n image_encoder = self.get_image_encoder(repo_id=\"h94/IP-Adapter\", subfolder=\"models/image_encoder\")\n pipeline = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype\n )\n pipeline.to(torch_device)\n pipeline.load_ip_adapter(\n \"h94/IP-Adapter\", subfolder=\"models\", weight_name=[\"ip-adapter_sd15.bin\", \"ip-adapter-plus_sd15.bin\"]\n )\n pipeline.set_ip_adapter_scale([0.7, 0.3])\n\n inputs = self.get_dummy_inputs()\n ip_adapter_image = inputs[\"ip_adapter_image\"]\n inputs[\"ip_adapter_image\"] = [ip_adapter_image, [ip_adapter_image] * 2]\n images = pipeline(**inputs).images\n image_slice = images[0, :3, :3, -1].flatten()\n expected_slice = np.array([0.5234, 0.5352, 0.5625, 0.5713, 0.5947, 0.6206, 0.5786, 0.6187, 0.6494])\n\n max_diff = numpy_cosine_similarity_distance(image_slice, expected_slice)\n assert max_diff < 5e-4\n\n def test_text_to_image_face_id(self):\n pipeline = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", safety_checker=None, torch_dtype=self.dtype\n )\n pipeline.to(torch_device)\n pipeline.load_ip_adapter(\n \"h94/IP-Adapter-FaceID\",\n subfolder=None,\n weight_name=\"ip-adapter-faceid_sd15.bin\",\n image_encoder_folder=None,\n )\n pipeline.set_ip_adapter_scale(0.7)\n\n inputs = self.get_dummy_inputs()\n id_embeds = load_pt(\"https://huggingface.co/datasets/fabiorigano/testing-images/resolve/main/ai_face2.ipadpt\")[\n 0\n ]\n id_embeds = id_embeds.reshape((2, 1, 1, 512))\n inputs[\"ip_adapter_image_embeds\"] = [id_embeds]\n inputs[\"ip_adapter_image\"] = None\n images = pipeline(**inputs).images\n image_slice = images[0, :3, :3, -1].flatten()\n\n expected_slice = np.array(\n [0.32714844, 0.3239746, 0.3466797, 0.31835938, 0.30004883, 0.3251953, 0.3215332, 0.3552246, 0.3251953]\n )\n max_diff = numpy_cosine_similarity_distance(image_slice, expected_slice)\n assert max_diff < 5e-4\n\n\n@slow\n@require_torch_gpu\nclass IPAdapterSDXLIntegrationTests(IPAdapterNightlyTestsMixin):\n def test_text_to_image_sdxl(self):\n image_encoder = self.get_image_encoder(repo_id=\"h94/IP-Adapter\", subfolder=\"sdxl_models/image_encoder\")\n feature_extractor = self.get_image_processor(\"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\")\n\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n image_encoder=image_encoder,\n feature_extractor=feature_extractor,\n torch_dtype=self.dtype,\n )\n pipeline.enable_model_cpu_offload()\n pipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"sdxl_models\", weight_name=\"ip-adapter_sdxl.bin\")\n\n inputs = self.get_dummy_inputs()\n images = pipeline(**inputs).images\n image_slice = images[0, :3, :3, -1].flatten()\n\n expected_slice = np.array(\n [\n 0.09630299,\n 0.09551358,\n 0.08480701,\n 0.09070173,\n 0.09437338,\n 0.09264627,\n 0.08883232,\n 0.09287417,\n 0.09197289,\n ]\n )\n\n max_diff = numpy_cosine_similarity_distance(image_slice, expected_slice)\n assert max_diff < 5e-4\n\n image_encoder = self.get_image_encoder(repo_id=\"h94/IP-Adapter\", subfolder=\"models/image_encoder\")\n\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n image_encoder=image_encoder,\n feature_extractor=feature_extractor,\n torch_dtype=self.dtype,\n )\n pipeline.to(torch_device)\n pipeline.load_ip_adapter(\n \"h94/IP-Adapter\",\n subfolder=\"sdxl_models\",\n weight_name=\"ip-adapter-plus_sdxl_vit-h.bin\",\n )\n\n inputs = self.get_dummy_inputs()\n images = pipeline(**inputs).images\n image_slice = images[0, :3, :3, -1].flatten()\n\n expected_slice = np.array(\n [0.0576596, 0.05600825, 0.04479006, 0.05288461, 0.05461192, 0.05137569, 0.04867965, 0.05301541, 0.04939842]\n )\n\n max_diff = numpy_cosine_similarity_distance(image_slice, expected_slice)\n assert max_diff < 5e-4\n\n def test_image_to_image_sdxl(self):\n image_encoder = self.get_image_encoder(repo_id=\"h94/IP-Adapter\", subfolder=\"sdxl_models/image_encoder\")\n feature_extractor = self.get_image_processor(\"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\")\n\n pipeline = StableDiffusionXLImg2ImgPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n image_encoder=image_encoder,\n feature_extractor=feature_extractor,\n torch_dtype=self.dtype,\n )\n pipeline.enable_model_cpu_offload()\n pipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"sdxl_models\", weight_name=\"ip-adapter_sdxl.bin\")\n\n inputs = self.get_dummy_inputs(for_image_to_image=True)\n images = pipeline(**inputs).images\n image_slice = images[0, :3, :3, -1].flatten()\n\n expected_slice = np.array(\n [\n 0.06513795,\n 0.07009393,\n 0.07234055,\n 0.07426041,\n 0.07002589,\n 0.06415862,\n 0.07827643,\n 0.07962808,\n 0.07411247,\n ]\n )\n\n assert np.allclose(image_slice, expected_slice, atol=1e-3)\n\n image_encoder = self.get_image_encoder(repo_id=\"h94/IP-Adapter\", subfolder=\"models/image_encoder\")\n feature_extractor = self.get_image_processor(\"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\")\n\n pipeline = StableDiffusionXLImg2ImgPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n image_encoder=image_encoder,\n feature_extractor=feature_extractor,\n torch_dtype=self.dtype,\n )\n pipeline.to(torch_device)\n pipeline.load_ip_adapter(\n \"h94/IP-Adapter\",\n subfolder=\"sdxl_models\",\n weight_name=\"ip-adapter-plus_sdxl_vit-h.bin\",\n )\n\n inputs = self.get_dummy_inputs(for_image_to_image=True)\n images = pipeline(**inputs).images\n image_slice = images[0, :3, :3, -1].flatten()\n\n expected_slice = np.array(\n [\n 0.07126552,\n 0.07025367,\n 0.07348302,\n 0.07580167,\n 0.07467338,\n 0.06918576,\n 0.07480252,\n 0.08279955,\n 0.08547315,\n ]\n )\n\n assert np.allclose(image_slice, expected_slice, atol=1e-3)\n\n def test_inpainting_sdxl(self):\n image_encoder = self.get_image_encoder(repo_id=\"h94/IP-Adapter\", subfolder=\"sdxl_models/image_encoder\")\n feature_extractor = self.get_image_processor(\"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\")\n\n pipeline = StableDiffusionXLInpaintPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n image_encoder=image_encoder,\n feature_extractor=feature_extractor,\n torch_dtype=self.dtype,\n )\n pipeline.enable_model_cpu_offload()\n pipeline.load_ip_adapter(\"h94/IP-Adapter\", subfolder=\"sdxl_models\", weight_name=\"ip-adapter_sdxl.bin\")\n\n inputs = self.get_dummy_inputs(for_inpainting=True)\n images = pipeline(**inputs).images\n image_slice = images[0, :3, :3, -1].flatten()\n image_slice.tolist()\n\n expected_slice = np.array(\n [0.14181179, 0.1493012, 0.14283323, 0.14602411, 0.14915377, 0.15015268, 0.14725655, 0.15009224, 0.15164584]\n )\n\n max_diff = numpy_cosine_similarity_distance(image_slice, expected_slice)\n assert max_diff < 5e-4\n\n image_encoder = self.get_image_encoder(repo_id=\"h94/IP-Adapter\", subfolder=\"models/image_encoder\")\n feature_extractor = self.get_image_processor(\"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\")\n\n pipeline = StableDiffusionXLInpaintPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n image_encoder=image_encoder,\n feature_extractor=feature_extractor,\n torch_dtype=self.dtype,\n )\n pipeline.to(torch_device)\n pipeline.load_ip_adapter(\n \"h94/IP-Adapter\",\n subfolder=\"sdxl_models\",\n weight_name=\"ip-adapter-plus_sdxl_vit-h.bin\",\n )\n\n inputs = self.get_dummy_inputs(for_inpainting=True)\n images = pipeline(**inputs).images\n image_slice = images[0, :3, :3, -1].flatten()\n image_slice.tolist()\n\n expected_slice = np.array([0.1398, 0.1476, 0.1407, 0.1442, 0.1470, 0.1480, 0.1449, 0.1481, 0.1494])\n\n max_diff = numpy_cosine_similarity_distance(image_slice, expected_slice)\n assert max_diff < 5e-4\n\n def test_ip_adapter_single_mask(self):\n image_encoder = self.get_image_encoder(repo_id=\"h94/IP-Adapter\", subfolder=\"models/image_encoder\")\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n image_encoder=image_encoder,\n torch_dtype=self.dtype,\n )\n pipeline.enable_model_cpu_offload()\n pipeline.load_ip_adapter(\n \"h94/IP-Adapter\", subfolder=\"sdxl_models\", weight_name=\"ip-adapter-plus-face_sdxl_vit-h.safetensors\"\n )\n pipeline.set_ip_adapter_scale(0.7)\n\n inputs = self.get_dummy_inputs(for_masks=True)\n mask = inputs[\"cross_attention_kwargs\"][\"ip_adapter_masks\"][0]\n processor = IPAdapterMaskProcessor()\n mask = processor.preprocess(mask)\n inputs[\"cross_attention_kwargs\"][\"ip_adapter_masks\"] = mask\n inputs[\"ip_adapter_image\"] = inputs[\"ip_adapter_image\"][0]\n images = pipeline(**inputs).images\n image_slice = images[0, :3, :3, -1].flatten()\n expected_slice = np.array(\n [0.7307304, 0.73450166, 0.73731124, 0.7377061, 0.7318013, 0.73720926, 0.74746597, 0.7409929, 0.74074936]\n )\n\n max_diff = numpy_cosine_similarity_distance(image_slice, expected_slice)\n assert max_diff < 5e-4\n\n def test_ip_adapter_multiple_masks(self):\n image_encoder = self.get_image_encoder(repo_id=\"h94/IP-Adapter\", subfolder=\"models/image_encoder\")\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n image_encoder=image_encoder,\n torch_dtype=self.dtype,\n )\n pipeline.enable_model_cpu_offload()\n pipeline.load_ip_adapter(\n \"h94/IP-Adapter\", subfolder=\"sdxl_models\", weight_name=[\"ip-adapter-plus-face_sdxl_vit-h.safetensors\"] * 2\n )\n pipeline.set_ip_adapter_scale([0.7] * 2)\n\n inputs = self.get_dummy_inputs(for_masks=True)\n masks = inputs[\"cross_attention_kwargs\"][\"ip_adapter_masks\"]\n processor = IPAdapterMaskProcessor()\n masks = processor.preprocess(masks)\n inputs[\"cross_attention_kwargs\"][\"ip_adapter_masks\"] = masks\n images = pipeline(**inputs).images\n image_slice = images[0, :3, :3, -1].flatten()\n expected_slice = np.array(\n [0.79474676, 0.7977683, 0.8013954, 0.7988008, 0.7970615, 0.8029355, 0.80614823, 0.8050743, 0.80627424]\n )\n\n max_diff = numpy_cosine_similarity_distance(image_slice, expected_slice)\n assert max_diff < 5e-4\n\n def test_instant_style_multiple_masks(self):\n image_encoder = CLIPVisionModelWithProjection.from_pretrained(\n \"h94/IP-Adapter\", subfolder=\"models/image_encoder\", torch_dtype=torch.float16\n )\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n \"RunDiffusion/Juggernaut-XL-v9\", torch_dtype=torch.float16, image_encoder=image_encoder, variant=\"fp16\"\n )\n pipeline.enable_model_cpu_offload()\n\n pipeline.load_ip_adapter(\n [\"ostris/ip-composition-adapter\", \"h94/IP-Adapter\"],\n subfolder=[\"\", \"sdxl_models\"],\n weight_name=[\n \"ip_plus_composition_sdxl.safetensors\",\n \"ip-adapter_sdxl_vit-h.safetensors\",\n ],\n image_encoder_folder=None,\n )\n scale_1 = {\n \"down\": [[0.0, 0.0, 1.0]],\n \"mid\": [[0.0, 0.0, 1.0]],\n \"up\": {\"block_0\": [[0.0, 0.0, 1.0], [1.0, 1.0, 1.0], [0.0, 0.0, 1.0]], \"block_1\": [[0.0, 0.0, 1.0]]},\n }\n pipeline.set_ip_adapter_scale([1.0, scale_1])\n\n inputs = self.get_dummy_inputs(for_instant_style=True)\n processor = IPAdapterMaskProcessor()\n masks1 = inputs[\"cross_attention_kwargs\"][\"ip_adapter_masks\"][0]\n masks2 = inputs[\"cross_attention_kwargs\"][\"ip_adapter_masks\"][1]\n masks1 = processor.preprocess(masks1, height=1024, width=1024)\n masks2 = processor.preprocess(masks2, height=1024, width=1024)\n masks2 = masks2.reshape(1, masks2.shape[0], masks2.shape[2], masks2.shape[3])\n inputs[\"cross_attention_kwargs\"][\"ip_adapter_masks\"] = [masks1, masks2]\n images = pipeline(**inputs).images\n image_slice = images[0, :3, :3, -1].flatten()\n expected_slice = np.array(\n [0.23551631, 0.20476806, 0.14099443, 0.0, 0.07675594, 0.05672678, 0.0, 0.0, 0.02099729]\n )\n\n max_diff = numpy_cosine_similarity_distance(image_slice, expected_slice)\n assert max_diff < 5e-4\n\n def test_ip_adapter_multiple_masks_one_adapter(self):\n image_encoder = self.get_image_encoder(repo_id=\"h94/IP-Adapter\", subfolder=\"models/image_encoder\")\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n image_encoder=image_encoder,\n torch_dtype=self.dtype,\n )\n pipeline.enable_model_cpu_offload()\n pipeline.load_ip_adapter(\n \"h94/IP-Adapter\", subfolder=\"sdxl_models\", weight_name=[\"ip-adapter-plus-face_sdxl_vit-h.safetensors\"]\n )\n pipeline.set_ip_adapter_scale([[0.7, 0.7]])\n\n inputs = self.get_dummy_inputs(for_masks=True)\n masks = inputs[\"cross_attention_kwargs\"][\"ip_adapter_masks\"]\n processor = IPAdapterMaskProcessor()\n masks = processor.preprocess(masks)\n masks = masks.reshape(1, masks.shape[0], masks.shape[2], masks.shape[3])\n inputs[\"cross_attention_kwargs\"][\"ip_adapter_masks\"] = [masks]\n ip_images = inputs[\"ip_adapter_image\"]\n inputs[\"ip_adapter_image\"] = [[image[0] for image in ip_images]]\n images = pipeline(**inputs).images\n image_slice = images[0, :3, :3, -1].flatten()\n expected_slice = np.array(\n [0.79474676, 0.7977683, 0.8013954, 0.7988008, 0.7970615, 0.8029355, 0.80614823, 0.8050743, 0.80627424]\n )\n\n max_diff = numpy_cosine_similarity_distance(image_slice, expected_slice)\n assert max_diff < 5e-4\n" }, { "path": "diffusers/tests/pipelines/kandinsky/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/kandinsky/test_kandinsky.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import XLMRobertaTokenizerFast\n\nfrom diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNet2DConditionModel, VQModel\nfrom diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_numpy,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\nfrom ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference\n\n\nenable_full_determinism()\n\n\nclass Dummies:\n @property\n def text_embedder_hidden_size(self):\n return 32\n\n @property\n def time_input_dim(self):\n return 32\n\n @property\n def block_out_channels_0(self):\n return self.time_input_dim\n\n @property\n def time_embed_dim(self):\n return self.time_input_dim * 4\n\n @property\n def cross_attention_dim(self):\n return 32\n\n @property\n def dummy_tokenizer(self):\n tokenizer = XLMRobertaTokenizerFast.from_pretrained(\"YiYiXu/tiny-random-mclip-base\")\n return tokenizer\n\n @property\n def dummy_text_encoder(self):\n torch.manual_seed(0)\n config = MCLIPConfig(\n numDims=self.cross_attention_dim,\n transformerDimensions=self.text_embedder_hidden_size,\n hidden_size=self.text_embedder_hidden_size,\n intermediate_size=37,\n num_attention_heads=4,\n num_hidden_layers=5,\n vocab_size=1005,\n )\n\n text_encoder = MultilingualCLIP(config)\n text_encoder = text_encoder.eval()\n\n return text_encoder\n\n @property\n def dummy_unet(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"in_channels\": 4,\n # Out channels is double in channels because predicts mean and variance\n \"out_channels\": 8,\n \"addition_embed_type\": \"text_image\",\n \"down_block_types\": (\"ResnetDownsampleBlock2D\", \"SimpleCrossAttnDownBlock2D\"),\n \"up_block_types\": (\"SimpleCrossAttnUpBlock2D\", \"ResnetUpsampleBlock2D\"),\n \"mid_block_type\": \"UNetMidBlock2DSimpleCrossAttn\",\n \"block_out_channels\": (self.block_out_channels_0, self.block_out_channels_0 * 2),\n \"layers_per_block\": 1,\n \"encoder_hid_dim\": self.text_embedder_hidden_size,\n \"encoder_hid_dim_type\": \"text_image_proj\",\n \"cross_attention_dim\": self.cross_attention_dim,\n \"attention_head_dim\": 4,\n \"resnet_time_scale_shift\": \"scale_shift\",\n \"class_embed_type\": None,\n }\n\n model = UNet2DConditionModel(**model_kwargs)\n return model\n\n @property\n def dummy_movq_kwargs(self):\n return {\n \"block_out_channels\": [32, 64],\n \"down_block_types\": [\"DownEncoderBlock2D\", \"AttnDownEncoderBlock2D\"],\n \"in_channels\": 3,\n \"latent_channels\": 4,\n \"layers_per_block\": 1,\n \"norm_num_groups\": 8,\n \"norm_type\": \"spatial\",\n \"num_vq_embeddings\": 12,\n \"out_channels\": 3,\n \"up_block_types\": [\n \"AttnUpDecoderBlock2D\",\n \"UpDecoderBlock2D\",\n ],\n \"vq_embed_dim\": 4,\n }\n\n @property\n def dummy_movq(self):\n torch.manual_seed(0)\n model = VQModel(**self.dummy_movq_kwargs)\n return model\n\n def get_dummy_components(self):\n text_encoder = self.dummy_text_encoder\n tokenizer = self.dummy_tokenizer\n unet = self.dummy_unet\n movq = self.dummy_movq\n\n scheduler = DDIMScheduler(\n num_train_timesteps=1000,\n beta_schedule=\"linear\",\n beta_start=0.00085,\n beta_end=0.012,\n clip_sample=False,\n set_alpha_to_one=False,\n steps_offset=1,\n prediction_type=\"epsilon\",\n thresholding=False,\n )\n\n components = {\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"movq\": movq,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n image_embeds = floats_tensor((1, self.cross_attention_dim), rng=random.Random(seed)).to(device)\n negative_image_embeds = floats_tensor((1, self.cross_attention_dim), rng=random.Random(seed + 1)).to(device)\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"horse\",\n \"image_embeds\": image_embeds,\n \"negative_image_embeds\": negative_image_embeds,\n \"generator\": generator,\n \"height\": 64,\n \"width\": 64,\n \"guidance_scale\": 4.0,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n return inputs\n\n\nclass KandinskyPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = KandinskyPipeline\n params = [\n \"prompt\",\n \"image_embeds\",\n \"negative_image_embeds\",\n ]\n batch_params = [\"prompt\", \"negative_prompt\", \"image_embeds\", \"negative_image_embeds\"]\n required_optional_params = [\n \"generator\",\n \"height\",\n \"width\",\n \"latents\",\n \"guidance_scale\",\n \"negative_prompt\",\n \"num_inference_steps\",\n \"return_dict\",\n \"guidance_scale\",\n \"num_images_per_prompt\",\n \"output_type\",\n \"return_dict\",\n ]\n test_xformers_attention = False\n\n def get_dummy_components(self):\n dummy = Dummies()\n return dummy.get_dummy_components()\n\n def get_dummy_inputs(self, device, seed=0):\n dummy = Dummies()\n return dummy.get_dummy_inputs(device=device, seed=seed)\n\n def test_kandinsky(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.images\n\n image_from_tuple = pipe(\n **self.get_dummy_inputs(device),\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([1.0000, 1.0000, 0.2766, 1.0000, 0.5447, 0.1737, 1.0000, 0.4316, 0.9024])\n\n assert (\n np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_slice.flatten()}\"\n assert (\n np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}\"\n\n @require_torch_gpu\n def test_offloads(self):\n pipes = []\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(torch_device)\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_model_cpu_offload()\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_sequential_cpu_offload()\n pipes.append(sd_pipe)\n\n image_slices = []\n for pipe in pipes:\n inputs = self.get_dummy_inputs(torch_device)\n image = pipe(**inputs).images\n\n image_slices.append(image[0, -3:, -3:, -1].flatten())\n\n assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3\n assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3\n\n\n@slow\n@require_torch_gpu\nclass KandinskyPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_kandinsky_text2img(self):\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/kandinsky/kandinsky_text2img_cat_fp16.npy\"\n )\n\n pipe_prior = KandinskyPriorPipeline.from_pretrained(\n \"kandinsky-community/kandinsky-2-1-prior\", torch_dtype=torch.float16\n )\n pipe_prior.to(torch_device)\n\n pipeline = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\", torch_dtype=torch.float16)\n pipeline.to(torch_device)\n pipeline.set_progress_bar_config(disable=None)\n\n prompt = \"red cat, 4k photo\"\n\n generator = torch.Generator(device=\"cuda\").manual_seed(0)\n image_emb, zero_image_emb = pipe_prior(\n prompt,\n generator=generator,\n num_inference_steps=5,\n negative_prompt=\"\",\n ).to_tuple()\n\n generator = torch.Generator(device=\"cuda\").manual_seed(0)\n output = pipeline(\n prompt,\n image_embeds=image_emb,\n negative_image_embeds=zero_image_emb,\n generator=generator,\n num_inference_steps=100,\n output_type=\"np\",\n )\n\n image = output.images[0]\n\n assert image.shape == (512, 512, 3)\n\n assert_mean_pixel_difference(image, expected_image)\n" }, { "path": "diffusers/tests/pipelines/kandinsky/test_kandinsky_combined.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\n\nimport numpy as np\n\nfrom diffusers import KandinskyCombinedPipeline, KandinskyImg2ImgCombinedPipeline, KandinskyInpaintCombinedPipeline\nfrom diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, torch_device\n\nfrom ..test_pipelines_common import PipelineTesterMixin\nfrom .test_kandinsky import Dummies\nfrom .test_kandinsky_img2img import Dummies as Img2ImgDummies\nfrom .test_kandinsky_inpaint import Dummies as InpaintDummies\nfrom .test_kandinsky_prior import Dummies as PriorDummies\n\n\nenable_full_determinism()\n\n\nclass KandinskyPipelineCombinedFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = KandinskyCombinedPipeline\n params = [\n \"prompt\",\n ]\n batch_params = [\"prompt\", \"negative_prompt\"]\n required_optional_params = [\n \"generator\",\n \"height\",\n \"width\",\n \"latents\",\n \"guidance_scale\",\n \"negative_prompt\",\n \"num_inference_steps\",\n \"return_dict\",\n \"guidance_scale\",\n \"num_images_per_prompt\",\n \"output_type\",\n \"return_dict\",\n ]\n test_xformers_attention = True\n\n def get_dummy_components(self):\n dummy = Dummies()\n prior_dummy = PriorDummies()\n components = dummy.get_dummy_components()\n\n components.update({f\"prior_{k}\": v for k, v in prior_dummy.get_dummy_components().items()})\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n prior_dummy = PriorDummies()\n inputs = prior_dummy.get_dummy_inputs(device=device, seed=seed)\n inputs.update(\n {\n \"height\": 64,\n \"width\": 64,\n }\n )\n return inputs\n\n def test_kandinsky(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.images\n\n image_from_tuple = pipe(\n **self.get_dummy_inputs(device),\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.0000, 0.0000, 0.6777, 0.1363, 0.3624, 0.7868, 0.3869, 0.3395, 0.5068])\n\n assert (\n np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_slice.flatten()}\"\n assert (\n np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}\"\n\n @require_torch_gpu\n def test_offloads(self):\n pipes = []\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(torch_device)\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_model_cpu_offload()\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_sequential_cpu_offload()\n pipes.append(sd_pipe)\n\n image_slices = []\n for pipe in pipes:\n inputs = self.get_dummy_inputs(torch_device)\n image = pipe(**inputs).images\n\n image_slices.append(image[0, -3:, -3:, -1].flatten())\n\n assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3\n assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=1e-2)\n\n def test_float16_inference(self):\n super().test_float16_inference(expected_max_diff=2e-1)\n\n def test_dict_tuple_outputs_equivalent(self):\n super().test_dict_tuple_outputs_equivalent(expected_max_difference=5e-4)\n\n\nclass KandinskyPipelineImg2ImgCombinedFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = KandinskyImg2ImgCombinedPipeline\n params = [\"prompt\", \"image\"]\n batch_params = [\"prompt\", \"negative_prompt\", \"image\"]\n required_optional_params = [\n \"generator\",\n \"height\",\n \"width\",\n \"latents\",\n \"guidance_scale\",\n \"negative_prompt\",\n \"num_inference_steps\",\n \"return_dict\",\n \"guidance_scale\",\n \"num_images_per_prompt\",\n \"output_type\",\n \"return_dict\",\n ]\n test_xformers_attention = False\n\n def get_dummy_components(self):\n dummy = Img2ImgDummies()\n prior_dummy = PriorDummies()\n components = dummy.get_dummy_components()\n\n components.update({f\"prior_{k}\": v for k, v in prior_dummy.get_dummy_components().items()})\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n prior_dummy = PriorDummies()\n dummy = Img2ImgDummies()\n inputs = prior_dummy.get_dummy_inputs(device=device, seed=seed)\n inputs.update(dummy.get_dummy_inputs(device=device, seed=seed))\n inputs.pop(\"image_embeds\")\n inputs.pop(\"negative_image_embeds\")\n return inputs\n\n def test_kandinsky(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.images\n\n image_from_tuple = pipe(\n **self.get_dummy_inputs(device),\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.4260, 0.3596, 0.4571, 0.3890, 0.4087, 0.5137, 0.4819, 0.4116, 0.5053])\n\n assert (\n np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_slice.flatten()}\"\n assert (\n np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}\"\n\n @require_torch_gpu\n def test_offloads(self):\n pipes = []\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(torch_device)\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_model_cpu_offload()\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_sequential_cpu_offload()\n pipes.append(sd_pipe)\n\n image_slices = []\n for pipe in pipes:\n inputs = self.get_dummy_inputs(torch_device)\n image = pipe(**inputs).images\n\n image_slices.append(image[0, -3:, -3:, -1].flatten())\n\n assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3\n assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=1e-2)\n\n def test_float16_inference(self):\n super().test_float16_inference(expected_max_diff=5e-1)\n\n def test_dict_tuple_outputs_equivalent(self):\n super().test_dict_tuple_outputs_equivalent(expected_max_difference=5e-4)\n\n def test_save_load_optional_components(self):\n super().test_save_load_optional_components(expected_max_difference=5e-4)\n\n\nclass KandinskyPipelineInpaintCombinedFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = KandinskyInpaintCombinedPipeline\n params = [\"prompt\", \"image\", \"mask_image\"]\n batch_params = [\"prompt\", \"negative_prompt\", \"image\", \"mask_image\"]\n required_optional_params = [\n \"generator\",\n \"height\",\n \"width\",\n \"latents\",\n \"guidance_scale\",\n \"negative_prompt\",\n \"num_inference_steps\",\n \"return_dict\",\n \"guidance_scale\",\n \"num_images_per_prompt\",\n \"output_type\",\n \"return_dict\",\n ]\n test_xformers_attention = False\n\n def get_dummy_components(self):\n dummy = InpaintDummies()\n prior_dummy = PriorDummies()\n components = dummy.get_dummy_components()\n\n components.update({f\"prior_{k}\": v for k, v in prior_dummy.get_dummy_components().items()})\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n prior_dummy = PriorDummies()\n dummy = InpaintDummies()\n inputs = prior_dummy.get_dummy_inputs(device=device, seed=seed)\n inputs.update(dummy.get_dummy_inputs(device=device, seed=seed))\n inputs.pop(\"image_embeds\")\n inputs.pop(\"negative_image_embeds\")\n return inputs\n\n def test_kandinsky(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.images\n\n image_from_tuple = pipe(\n **self.get_dummy_inputs(device),\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.0477, 0.0808, 0.2972, 0.2705, 0.3620, 0.6247, 0.4464, 0.2870, 0.3530])\n\n assert (\n np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_slice.flatten()}\"\n assert (\n np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}\"\n\n @require_torch_gpu\n def test_offloads(self):\n pipes = []\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(torch_device)\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_model_cpu_offload()\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_sequential_cpu_offload()\n pipes.append(sd_pipe)\n\n image_slices = []\n for pipe in pipes:\n inputs = self.get_dummy_inputs(torch_device)\n image = pipe(**inputs).images\n\n image_slices.append(image[0, -3:, -3:, -1].flatten())\n\n assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3\n assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=1e-2)\n\n def test_float16_inference(self):\n super().test_float16_inference(expected_max_diff=5e-1)\n\n def test_dict_tuple_outputs_equivalent(self):\n super().test_dict_tuple_outputs_equivalent(expected_max_difference=5e-4)\n\n def test_save_load_optional_components(self):\n super().test_save_load_optional_components(expected_max_difference=5e-4)\n\n def test_save_load_local(self):\n super().test_save_load_local(expected_max_difference=5e-3)\n" }, { "path": "diffusers/tests/pipelines/kandinsky/test_kandinsky_img2img.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom transformers import XLMRobertaTokenizerFast\n\nfrom diffusers import (\n DDIMScheduler,\n DDPMScheduler,\n KandinskyImg2ImgPipeline,\n KandinskyPriorPipeline,\n UNet2DConditionModel,\n VQModel,\n)\nfrom diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n load_numpy,\n nightly,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\nfrom ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference\n\n\nenable_full_determinism()\n\n\nclass Dummies:\n @property\n def text_embedder_hidden_size(self):\n return 32\n\n @property\n def time_input_dim(self):\n return 32\n\n @property\n def block_out_channels_0(self):\n return self.time_input_dim\n\n @property\n def time_embed_dim(self):\n return self.time_input_dim * 4\n\n @property\n def cross_attention_dim(self):\n return 32\n\n @property\n def dummy_tokenizer(self):\n tokenizer = XLMRobertaTokenizerFast.from_pretrained(\"YiYiXu/tiny-random-mclip-base\")\n return tokenizer\n\n @property\n def dummy_text_encoder(self):\n torch.manual_seed(0)\n config = MCLIPConfig(\n numDims=self.cross_attention_dim,\n transformerDimensions=self.text_embedder_hidden_size,\n hidden_size=self.text_embedder_hidden_size,\n intermediate_size=37,\n num_attention_heads=4,\n num_hidden_layers=5,\n vocab_size=1005,\n )\n\n text_encoder = MultilingualCLIP(config)\n text_encoder = text_encoder.eval()\n\n return text_encoder\n\n @property\n def dummy_unet(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"in_channels\": 4,\n # Out channels is double in channels because predicts mean and variance\n \"out_channels\": 8,\n \"addition_embed_type\": \"text_image\",\n \"down_block_types\": (\"ResnetDownsampleBlock2D\", \"SimpleCrossAttnDownBlock2D\"),\n \"up_block_types\": (\"SimpleCrossAttnUpBlock2D\", \"ResnetUpsampleBlock2D\"),\n \"mid_block_type\": \"UNetMidBlock2DSimpleCrossAttn\",\n \"block_out_channels\": (self.block_out_channels_0, self.block_out_channels_0 * 2),\n \"layers_per_block\": 1,\n \"encoder_hid_dim\": self.text_embedder_hidden_size,\n \"encoder_hid_dim_type\": \"text_image_proj\",\n \"cross_attention_dim\": self.cross_attention_dim,\n \"attention_head_dim\": 4,\n \"resnet_time_scale_shift\": \"scale_shift\",\n \"class_embed_type\": None,\n }\n\n model = UNet2DConditionModel(**model_kwargs)\n return model\n\n @property\n def dummy_movq_kwargs(self):\n return {\n \"block_out_channels\": [32, 64],\n \"down_block_types\": [\"DownEncoderBlock2D\", \"AttnDownEncoderBlock2D\"],\n \"in_channels\": 3,\n \"latent_channels\": 4,\n \"layers_per_block\": 1,\n \"norm_num_groups\": 8,\n \"norm_type\": \"spatial\",\n \"num_vq_embeddings\": 12,\n \"out_channels\": 3,\n \"up_block_types\": [\n \"AttnUpDecoderBlock2D\",\n \"UpDecoderBlock2D\",\n ],\n \"vq_embed_dim\": 4,\n }\n\n @property\n def dummy_movq(self):\n torch.manual_seed(0)\n model = VQModel(**self.dummy_movq_kwargs)\n return model\n\n def get_dummy_components(self):\n text_encoder = self.dummy_text_encoder\n tokenizer = self.dummy_tokenizer\n unet = self.dummy_unet\n movq = self.dummy_movq\n\n ddim_config = {\n \"num_train_timesteps\": 1000,\n \"beta_schedule\": \"linear\",\n \"beta_start\": 0.00085,\n \"beta_end\": 0.012,\n \"clip_sample\": False,\n \"set_alpha_to_one\": False,\n \"steps_offset\": 0,\n \"prediction_type\": \"epsilon\",\n \"thresholding\": False,\n }\n\n scheduler = DDIMScheduler(**ddim_config)\n\n components = {\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"movq\": movq,\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n image_embeds = floats_tensor((1, self.cross_attention_dim), rng=random.Random(seed)).to(device)\n negative_image_embeds = floats_tensor((1, self.cross_attention_dim), rng=random.Random(seed + 1)).to(device)\n # create init_image\n image = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device)\n image = image.cpu().permute(0, 2, 3, 1)[0]\n init_image = Image.fromarray(np.uint8(image)).convert(\"RGB\").resize((256, 256))\n\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"horse\",\n \"image\": init_image,\n \"image_embeds\": image_embeds,\n \"negative_image_embeds\": negative_image_embeds,\n \"generator\": generator,\n \"height\": 64,\n \"width\": 64,\n \"num_inference_steps\": 10,\n \"guidance_scale\": 7.0,\n \"strength\": 0.2,\n \"output_type\": \"np\",\n }\n return inputs\n\n\nclass KandinskyImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = KandinskyImg2ImgPipeline\n params = [\"prompt\", \"image_embeds\", \"negative_image_embeds\", \"image\"]\n batch_params = [\n \"prompt\",\n \"negative_prompt\",\n \"image_embeds\",\n \"negative_image_embeds\",\n \"image\",\n ]\n required_optional_params = [\n \"generator\",\n \"height\",\n \"width\",\n \"strength\",\n \"guidance_scale\",\n \"negative_prompt\",\n \"num_inference_steps\",\n \"return_dict\",\n \"guidance_scale\",\n \"num_images_per_prompt\",\n \"output_type\",\n \"return_dict\",\n ]\n test_xformers_attention = False\n\n def get_dummy_components(self):\n dummies = Dummies()\n return dummies.get_dummy_components()\n\n def get_dummy_inputs(self, device, seed=0):\n dummies = Dummies()\n return dummies.get_dummy_inputs(device=device, seed=seed)\n\n def test_kandinsky_img2img(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.images\n\n image_from_tuple = pipe(\n **self.get_dummy_inputs(device),\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.5816, 0.5872, 0.4634, 0.5982, 0.4767, 0.4710, 0.4669, 0.4717, 0.4966])\n assert (\n np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_slice.flatten()}\"\n assert (\n np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}\"\n\n @require_torch_gpu\n def test_offloads(self):\n pipes = []\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(torch_device)\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_model_cpu_offload()\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_sequential_cpu_offload()\n pipes.append(sd_pipe)\n\n image_slices = []\n for pipe in pipes:\n inputs = self.get_dummy_inputs(torch_device)\n image = pipe(**inputs).images\n\n image_slices.append(image[0, -3:, -3:, -1].flatten())\n\n assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3\n assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3\n\n def test_dict_tuple_outputs_equivalent(self):\n super().test_dict_tuple_outputs_equivalent(expected_max_difference=5e-4)\n\n\n@slow\n@require_torch_gpu\nclass KandinskyImg2ImgPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_kandinsky_img2img(self):\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/kandinsky/kandinsky_img2img_frog.npy\"\n )\n\n init_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\" \"/kandinsky/cat.png\"\n )\n prompt = \"A red cartoon frog, 4k\"\n\n pipe_prior = KandinskyPriorPipeline.from_pretrained(\n \"kandinsky-community/kandinsky-2-1-prior\", torch_dtype=torch.float16\n )\n pipe_prior.to(torch_device)\n\n pipeline = KandinskyImg2ImgPipeline.from_pretrained(\n \"kandinsky-community/kandinsky-2-1\", torch_dtype=torch.float16\n )\n pipeline = pipeline.to(torch_device)\n\n pipeline.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n image_emb, zero_image_emb = pipe_prior(\n prompt,\n generator=generator,\n num_inference_steps=5,\n negative_prompt=\"\",\n ).to_tuple()\n\n output = pipeline(\n prompt,\n image=init_image,\n image_embeds=image_emb,\n negative_image_embeds=zero_image_emb,\n generator=generator,\n num_inference_steps=100,\n height=768,\n width=768,\n strength=0.2,\n output_type=\"np\",\n )\n\n image = output.images[0]\n\n assert image.shape == (768, 768, 3)\n\n assert_mean_pixel_difference(image, expected_image)\n\n\n@nightly\n@require_torch_gpu\nclass KandinskyImg2ImgPipelineNightlyTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_kandinsky_img2img_ddpm(self):\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/kandinsky/kandinsky_img2img_ddpm_frog.npy\"\n )\n\n init_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\" \"/kandinsky/frog.png\"\n )\n prompt = \"A red cartoon frog, 4k\"\n\n pipe_prior = KandinskyPriorPipeline.from_pretrained(\n \"kandinsky-community/kandinsky-2-1-prior\", torch_dtype=torch.float16\n )\n pipe_prior.to(torch_device)\n\n scheduler = DDPMScheduler.from_pretrained(\"kandinsky-community/kandinsky-2-1\", subfolder=\"ddpm_scheduler\")\n pipeline = KandinskyImg2ImgPipeline.from_pretrained(\n \"kandinsky-community/kandinsky-2-1\", scheduler=scheduler, torch_dtype=torch.float16\n )\n pipeline = pipeline.to(torch_device)\n\n pipeline.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n image_emb, zero_image_emb = pipe_prior(\n prompt,\n generator=generator,\n num_inference_steps=5,\n negative_prompt=\"\",\n ).to_tuple()\n\n output = pipeline(\n prompt,\n image=init_image,\n image_embeds=image_emb,\n negative_image_embeds=zero_image_emb,\n generator=generator,\n num_inference_steps=100,\n height=768,\n width=768,\n strength=0.2,\n output_type=\"np\",\n )\n\n image = output.images[0]\n\n assert image.shape == (768, 768, 3)\n\n assert_mean_pixel_difference(image, expected_image)\n" }, { "path": "diffusers/tests/pipelines/kandinsky/test_kandinsky_inpaint.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom transformers import XLMRobertaTokenizerFast\n\nfrom diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNet2DConditionModel, VQModel\nfrom diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n load_numpy,\n nightly,\n require_torch_gpu,\n torch_device,\n)\n\nfrom ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference\n\n\nenable_full_determinism()\n\n\nclass Dummies:\n @property\n def text_embedder_hidden_size(self):\n return 32\n\n @property\n def time_input_dim(self):\n return 32\n\n @property\n def block_out_channels_0(self):\n return self.time_input_dim\n\n @property\n def time_embed_dim(self):\n return self.time_input_dim * 4\n\n @property\n def cross_attention_dim(self):\n return 32\n\n @property\n def dummy_tokenizer(self):\n tokenizer = XLMRobertaTokenizerFast.from_pretrained(\"YiYiXu/tiny-random-mclip-base\")\n return tokenizer\n\n @property\n def dummy_text_encoder(self):\n torch.manual_seed(0)\n config = MCLIPConfig(\n numDims=self.cross_attention_dim,\n transformerDimensions=self.text_embedder_hidden_size,\n hidden_size=self.text_embedder_hidden_size,\n intermediate_size=37,\n num_attention_heads=4,\n num_hidden_layers=5,\n vocab_size=1005,\n )\n\n text_encoder = MultilingualCLIP(config)\n text_encoder = text_encoder.eval()\n\n return text_encoder\n\n @property\n def dummy_unet(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"in_channels\": 9,\n # Out channels is double in channels because predicts mean and variance\n \"out_channels\": 8,\n \"addition_embed_type\": \"text_image\",\n \"down_block_types\": (\"ResnetDownsampleBlock2D\", \"SimpleCrossAttnDownBlock2D\"),\n \"up_block_types\": (\"SimpleCrossAttnUpBlock2D\", \"ResnetUpsampleBlock2D\"),\n \"mid_block_type\": \"UNetMidBlock2DSimpleCrossAttn\",\n \"block_out_channels\": (self.block_out_channels_0, self.block_out_channels_0 * 2),\n \"layers_per_block\": 1,\n \"encoder_hid_dim\": self.text_embedder_hidden_size,\n \"encoder_hid_dim_type\": \"text_image_proj\",\n \"cross_attention_dim\": self.cross_attention_dim,\n \"attention_head_dim\": 4,\n \"resnet_time_scale_shift\": \"scale_shift\",\n \"class_embed_type\": None,\n }\n\n model = UNet2DConditionModel(**model_kwargs)\n return model\n\n @property\n def dummy_movq_kwargs(self):\n return {\n \"block_out_channels\": [32, 64],\n \"down_block_types\": [\"DownEncoderBlock2D\", \"AttnDownEncoderBlock2D\"],\n \"in_channels\": 3,\n \"latent_channels\": 4,\n \"layers_per_block\": 1,\n \"norm_num_groups\": 8,\n \"norm_type\": \"spatial\",\n \"num_vq_embeddings\": 12,\n \"out_channels\": 3,\n \"up_block_types\": [\n \"AttnUpDecoderBlock2D\",\n \"UpDecoderBlock2D\",\n ],\n \"vq_embed_dim\": 4,\n }\n\n @property\n def dummy_movq(self):\n torch.manual_seed(0)\n model = VQModel(**self.dummy_movq_kwargs)\n return model\n\n def get_dummy_components(self):\n text_encoder = self.dummy_text_encoder\n tokenizer = self.dummy_tokenizer\n unet = self.dummy_unet\n movq = self.dummy_movq\n\n scheduler = DDIMScheduler(\n num_train_timesteps=1000,\n beta_schedule=\"linear\",\n beta_start=0.00085,\n beta_end=0.012,\n clip_sample=False,\n set_alpha_to_one=False,\n steps_offset=1,\n prediction_type=\"epsilon\",\n thresholding=False,\n )\n\n components = {\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"movq\": movq,\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n image_embeds = floats_tensor((1, self.cross_attention_dim), rng=random.Random(seed)).to(device)\n negative_image_embeds = floats_tensor((1, self.cross_attention_dim), rng=random.Random(seed + 1)).to(device)\n # create init_image\n image = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device)\n image = image.cpu().permute(0, 2, 3, 1)[0]\n init_image = Image.fromarray(np.uint8(image)).convert(\"RGB\").resize((256, 256))\n # create mask\n mask = np.zeros((64, 64), dtype=np.float32)\n mask[:32, :32] = 1\n\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"horse\",\n \"image\": init_image,\n \"mask_image\": mask,\n \"image_embeds\": image_embeds,\n \"negative_image_embeds\": negative_image_embeds,\n \"generator\": generator,\n \"height\": 64,\n \"width\": 64,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 4.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n\nclass KandinskyInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = KandinskyInpaintPipeline\n params = [\"prompt\", \"image_embeds\", \"negative_image_embeds\", \"image\", \"mask_image\"]\n batch_params = [\n \"prompt\",\n \"negative_prompt\",\n \"image_embeds\",\n \"negative_image_embeds\",\n \"image\",\n \"mask_image\",\n ]\n required_optional_params = [\n \"generator\",\n \"height\",\n \"width\",\n \"latents\",\n \"guidance_scale\",\n \"negative_prompt\",\n \"num_inference_steps\",\n \"return_dict\",\n \"guidance_scale\",\n \"num_images_per_prompt\",\n \"output_type\",\n \"return_dict\",\n ]\n test_xformers_attention = False\n\n def get_dummy_components(self):\n dummies = Dummies()\n return dummies.get_dummy_components()\n\n def get_dummy_inputs(self, device, seed=0):\n dummies = Dummies()\n return dummies.get_dummy_inputs(device=device, seed=seed)\n\n def test_kandinsky_inpaint(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.images\n\n image_from_tuple = pipe(\n **self.get_dummy_inputs(device),\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.8222, 0.8896, 0.4373, 0.8088, 0.4905, 0.2609, 0.6816, 0.4291, 0.5129])\n\n assert (\n np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_slice.flatten()}\"\n assert (\n np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}\"\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=3e-3)\n\n @require_torch_gpu\n def test_offloads(self):\n pipes = []\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(torch_device)\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_model_cpu_offload()\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_sequential_cpu_offload()\n pipes.append(sd_pipe)\n\n image_slices = []\n for pipe in pipes:\n inputs = self.get_dummy_inputs(torch_device)\n image = pipe(**inputs).images\n\n image_slices.append(image[0, -3:, -3:, -1].flatten())\n\n assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3\n assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3\n\n def test_float16_inference(self):\n super().test_float16_inference(expected_max_diff=5e-1)\n\n\n@nightly\n@require_torch_gpu\nclass KandinskyInpaintPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_kandinsky_inpaint(self):\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy\"\n )\n\n init_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\" \"/kandinsky/cat.png\"\n )\n mask = np.zeros((768, 768), dtype=np.float32)\n mask[:250, 250:-250] = 1\n\n prompt = \"a hat\"\n\n pipe_prior = KandinskyPriorPipeline.from_pretrained(\n \"kandinsky-community/kandinsky-2-1-prior\", torch_dtype=torch.float16\n )\n pipe_prior.to(torch_device)\n\n pipeline = KandinskyInpaintPipeline.from_pretrained(\n \"kandinsky-community/kandinsky-2-1-inpaint\", torch_dtype=torch.float16\n )\n pipeline = pipeline.to(torch_device)\n pipeline.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n image_emb, zero_image_emb = pipe_prior(\n prompt,\n generator=generator,\n num_inference_steps=5,\n negative_prompt=\"\",\n ).to_tuple()\n\n output = pipeline(\n prompt,\n image=init_image,\n mask_image=mask,\n image_embeds=image_emb,\n negative_image_embeds=zero_image_emb,\n generator=generator,\n num_inference_steps=100,\n height=768,\n width=768,\n output_type=\"np\",\n )\n\n image = output.images[0]\n\n assert image.shape == (768, 768, 3)\n\n assert_mean_pixel_difference(image, expected_image)\n" }, { "path": "diffusers/tests/pipelines/kandinsky/test_kandinsky_prior.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\n\nimport numpy as np\nimport torch\nfrom torch import nn\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextConfig,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionConfig,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler\nfrom diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device\n\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass Dummies:\n @property\n def text_embedder_hidden_size(self):\n return 32\n\n @property\n def time_input_dim(self):\n return 32\n\n @property\n def block_out_channels_0(self):\n return self.time_input_dim\n\n @property\n def time_embed_dim(self):\n return self.time_input_dim * 4\n\n @property\n def cross_attention_dim(self):\n return 100\n\n @property\n def dummy_tokenizer(self):\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n return tokenizer\n\n @property\n def dummy_text_encoder(self):\n torch.manual_seed(0)\n config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=self.text_embedder_hidden_size,\n projection_dim=self.text_embedder_hidden_size,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n return CLIPTextModelWithProjection(config)\n\n @property\n def dummy_prior(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"num_attention_heads\": 2,\n \"attention_head_dim\": 12,\n \"embedding_dim\": self.text_embedder_hidden_size,\n \"num_layers\": 1,\n }\n\n model = PriorTransformer(**model_kwargs)\n # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0\n model.clip_std = nn.Parameter(torch.ones(model.clip_std.shape))\n return model\n\n @property\n def dummy_image_encoder(self):\n torch.manual_seed(0)\n config = CLIPVisionConfig(\n hidden_size=self.text_embedder_hidden_size,\n image_size=224,\n projection_dim=self.text_embedder_hidden_size,\n intermediate_size=37,\n num_attention_heads=4,\n num_channels=3,\n num_hidden_layers=5,\n patch_size=14,\n )\n\n model = CLIPVisionModelWithProjection(config)\n return model\n\n @property\n def dummy_image_processor(self):\n image_processor = CLIPImageProcessor(\n crop_size=224,\n do_center_crop=True,\n do_normalize=True,\n do_resize=True,\n image_mean=[0.48145466, 0.4578275, 0.40821073],\n image_std=[0.26862954, 0.26130258, 0.27577711],\n resample=3,\n size=224,\n )\n\n return image_processor\n\n def get_dummy_components(self):\n prior = self.dummy_prior\n image_encoder = self.dummy_image_encoder\n text_encoder = self.dummy_text_encoder\n tokenizer = self.dummy_tokenizer\n image_processor = self.dummy_image_processor\n\n scheduler = UnCLIPScheduler(\n variance_type=\"fixed_small_log\",\n prediction_type=\"sample\",\n num_train_timesteps=1000,\n clip_sample=True,\n clip_sample_range=10.0,\n )\n\n components = {\n \"prior\": prior,\n \"image_encoder\": image_encoder,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"scheduler\": scheduler,\n \"image_processor\": image_processor,\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"horse\",\n \"generator\": generator,\n \"guidance_scale\": 4.0,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n return inputs\n\n\nclass KandinskyPriorPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = KandinskyPriorPipeline\n params = [\"prompt\"]\n batch_params = [\"prompt\", \"negative_prompt\"]\n required_optional_params = [\n \"num_images_per_prompt\",\n \"generator\",\n \"num_inference_steps\",\n \"latents\",\n \"negative_prompt\",\n \"guidance_scale\",\n \"output_type\",\n \"return_dict\",\n ]\n test_xformers_attention = False\n\n def get_dummy_components(self):\n dummy = Dummies()\n return dummy.get_dummy_components()\n\n def get_dummy_inputs(self, device, seed=0):\n dummy = Dummies()\n return dummy.get_dummy_inputs(device=device, seed=seed)\n\n def test_kandinsky_prior(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.image_embeds\n\n image_from_tuple = pipe(\n **self.get_dummy_inputs(device),\n return_dict=False,\n )[0]\n\n image_slice = image[0, -10:]\n image_from_tuple_slice = image_from_tuple[0, -10:]\n\n assert image.shape == (1, 32)\n\n expected_slice = np.array(\n [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]\n )\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n\n @skip_mps\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=1e-2)\n\n @skip_mps\n def test_attention_slicing_forward_pass(self):\n test_max_difference = torch_device == \"cpu\"\n test_mean_pixel_difference = False\n\n self._test_attention_slicing_forward_pass(\n test_max_difference=test_max_difference,\n test_mean_pixel_difference=test_mean_pixel_difference,\n )\n" }, { "path": "diffusers/tests/pipelines/kandinsky2_2/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/kandinsky2_2/test_kandinsky.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\n\nfrom diffusers import DDIMScheduler, KandinskyV22Pipeline, KandinskyV22PriorPipeline, UNet2DConditionModel, VQModel\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_numpy,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n slow,\n)\n\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass Dummies:\n @property\n def text_embedder_hidden_size(self):\n return 32\n\n @property\n def time_input_dim(self):\n return 32\n\n @property\n def block_out_channels_0(self):\n return self.time_input_dim\n\n @property\n def time_embed_dim(self):\n return self.time_input_dim * 4\n\n @property\n def cross_attention_dim(self):\n return 32\n\n @property\n def dummy_unet(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"in_channels\": 4,\n # Out channels is double in channels because predicts mean and variance\n \"out_channels\": 8,\n \"addition_embed_type\": \"image\",\n \"down_block_types\": (\"ResnetDownsampleBlock2D\", \"SimpleCrossAttnDownBlock2D\"),\n \"up_block_types\": (\"SimpleCrossAttnUpBlock2D\", \"ResnetUpsampleBlock2D\"),\n \"mid_block_type\": \"UNetMidBlock2DSimpleCrossAttn\",\n \"block_out_channels\": (self.block_out_channels_0, self.block_out_channels_0 * 2),\n \"layers_per_block\": 1,\n \"encoder_hid_dim\": self.text_embedder_hidden_size,\n \"encoder_hid_dim_type\": \"image_proj\",\n \"cross_attention_dim\": self.cross_attention_dim,\n \"attention_head_dim\": 4,\n \"resnet_time_scale_shift\": \"scale_shift\",\n \"class_embed_type\": None,\n }\n\n model = UNet2DConditionModel(**model_kwargs)\n return model\n\n @property\n def dummy_movq_kwargs(self):\n return {\n \"block_out_channels\": [32, 64],\n \"down_block_types\": [\"DownEncoderBlock2D\", \"AttnDownEncoderBlock2D\"],\n \"in_channels\": 3,\n \"latent_channels\": 4,\n \"layers_per_block\": 1,\n \"norm_num_groups\": 8,\n \"norm_type\": \"spatial\",\n \"num_vq_embeddings\": 12,\n \"out_channels\": 3,\n \"up_block_types\": [\n \"AttnUpDecoderBlock2D\",\n \"UpDecoderBlock2D\",\n ],\n \"vq_embed_dim\": 4,\n }\n\n @property\n def dummy_movq(self):\n torch.manual_seed(0)\n model = VQModel(**self.dummy_movq_kwargs)\n return model\n\n def get_dummy_components(self):\n unet = self.dummy_unet\n movq = self.dummy_movq\n\n scheduler = DDIMScheduler(\n num_train_timesteps=1000,\n beta_schedule=\"linear\",\n beta_start=0.00085,\n beta_end=0.012,\n clip_sample=False,\n set_alpha_to_one=False,\n steps_offset=1,\n prediction_type=\"epsilon\",\n thresholding=False,\n )\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"movq\": movq,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n image_embeds = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed)).to(device)\n negative_image_embeds = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed + 1)).to(\n device\n )\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"image_embeds\": image_embeds,\n \"negative_image_embeds\": negative_image_embeds,\n \"generator\": generator,\n \"height\": 64,\n \"width\": 64,\n \"guidance_scale\": 4.0,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n return inputs\n\n\nclass KandinskyV22PipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = KandinskyV22Pipeline\n params = [\n \"image_embeds\",\n \"negative_image_embeds\",\n ]\n batch_params = [\"image_embeds\", \"negative_image_embeds\"]\n required_optional_params = [\n \"generator\",\n \"height\",\n \"width\",\n \"latents\",\n \"guidance_scale\",\n \"num_inference_steps\",\n \"return_dict\",\n \"guidance_scale\",\n \"num_images_per_prompt\",\n \"output_type\",\n \"return_dict\",\n ]\n callback_cfg_params = [\"image_embds\"]\n test_xformers_attention = False\n\n def get_dummy_inputs(self, device, seed=0):\n dummies = Dummies()\n return dummies.get_dummy_inputs(device=device, seed=seed)\n\n def get_dummy_components(self):\n dummies = Dummies()\n return dummies.get_dummy_components()\n\n def test_kandinsky(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.images\n\n image_from_tuple = pipe(\n **self.get_dummy_inputs(device),\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.3420, 0.9505, 0.3919, 1.0000, 0.5188, 0.3109, 0.6139, 0.5624, 0.6811])\n\n assert (\n np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_slice.flatten()}\"\n\n assert (\n np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}\"\n\n def test_float16_inference(self):\n super().test_float16_inference(expected_max_diff=1e-1)\n\n\n@slow\n@require_torch_gpu\nclass KandinskyV22PipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_kandinsky_text2img(self):\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy\"\n )\n\n pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n )\n pipe_prior.enable_model_cpu_offload()\n\n pipeline = KandinskyV22Pipeline.from_pretrained(\n \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n )\n pipeline.enable_model_cpu_offload()\n pipeline.set_progress_bar_config(disable=None)\n\n prompt = \"red cat, 4k photo\"\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n image_emb, zero_image_emb = pipe_prior(\n prompt,\n generator=generator,\n num_inference_steps=3,\n negative_prompt=\"\",\n ).to_tuple()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n output = pipeline(\n image_embeds=image_emb,\n negative_image_embeds=zero_image_emb,\n generator=generator,\n num_inference_steps=3,\n output_type=\"np\",\n )\n image = output.images[0]\n assert image.shape == (512, 512, 3)\n\n max_diff = numpy_cosine_similarity_distance(expected_image.flatten(), image.flatten())\n assert max_diff < 1e-4\n" }, { "path": "diffusers/tests/pipelines/kandinsky2_2/test_kandinsky_combined.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\n\nimport numpy as np\n\nfrom diffusers import (\n KandinskyV22CombinedPipeline,\n KandinskyV22Img2ImgCombinedPipeline,\n KandinskyV22InpaintCombinedPipeline,\n)\nfrom diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, torch_device\n\nfrom ..test_pipelines_common import PipelineTesterMixin\nfrom .test_kandinsky import Dummies\nfrom .test_kandinsky_img2img import Dummies as Img2ImgDummies\nfrom .test_kandinsky_inpaint import Dummies as InpaintDummies\nfrom .test_kandinsky_prior import Dummies as PriorDummies\n\n\nenable_full_determinism()\n\n\nclass KandinskyV22PipelineCombinedFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = KandinskyV22CombinedPipeline\n params = [\n \"prompt\",\n ]\n batch_params = [\"prompt\", \"negative_prompt\"]\n required_optional_params = [\n \"generator\",\n \"height\",\n \"width\",\n \"latents\",\n \"guidance_scale\",\n \"negative_prompt\",\n \"num_inference_steps\",\n \"return_dict\",\n \"guidance_scale\",\n \"num_images_per_prompt\",\n \"output_type\",\n \"return_dict\",\n ]\n test_xformers_attention = True\n callback_cfg_params = [\"image_embds\"]\n\n def get_dummy_components(self):\n dummy = Dummies()\n prior_dummy = PriorDummies()\n components = dummy.get_dummy_components()\n\n components.update({f\"prior_{k}\": v for k, v in prior_dummy.get_dummy_components().items()})\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n prior_dummy = PriorDummies()\n inputs = prior_dummy.get_dummy_inputs(device=device, seed=seed)\n inputs.update(\n {\n \"height\": 64,\n \"width\": 64,\n }\n )\n return inputs\n\n def test_kandinsky(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.images\n\n image_from_tuple = pipe(\n **self.get_dummy_inputs(device),\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.3013, 0.0471, 0.5176, 0.1817, 0.2566, 0.7076, 0.6712, 0.4421, 0.7503])\n\n assert (\n np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_slice.flatten()}\"\n assert (\n np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}\"\n\n @require_torch_gpu\n def test_offloads(self):\n pipes = []\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(torch_device)\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_model_cpu_offload()\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_sequential_cpu_offload()\n pipes.append(sd_pipe)\n\n image_slices = []\n for pipe in pipes:\n inputs = self.get_dummy_inputs(torch_device)\n image = pipe(**inputs).images\n\n image_slices.append(image[0, -3:, -3:, -1].flatten())\n\n assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3\n assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=1e-2)\n\n def test_float16_inference(self):\n super().test_float16_inference(expected_max_diff=5e-1)\n\n def test_dict_tuple_outputs_equivalent(self):\n super().test_dict_tuple_outputs_equivalent(expected_max_difference=5e-4)\n\n def test_model_cpu_offload_forward_pass(self):\n super().test_model_cpu_offload_forward_pass(expected_max_diff=5e-4)\n\n def test_save_load_local(self):\n super().test_save_load_local(expected_max_difference=5e-3)\n\n def test_save_load_optional_components(self):\n super().test_save_load_optional_components(expected_max_difference=5e-3)\n\n def test_callback_inputs(self):\n pass\n\n def test_callback_cfg(self):\n pass\n\n\nclass KandinskyV22PipelineImg2ImgCombinedFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = KandinskyV22Img2ImgCombinedPipeline\n params = [\"prompt\", \"image\"]\n batch_params = [\"prompt\", \"negative_prompt\", \"image\"]\n required_optional_params = [\n \"generator\",\n \"height\",\n \"width\",\n \"latents\",\n \"guidance_scale\",\n \"negative_prompt\",\n \"num_inference_steps\",\n \"return_dict\",\n \"guidance_scale\",\n \"num_images_per_prompt\",\n \"output_type\",\n \"return_dict\",\n ]\n test_xformers_attention = False\n callback_cfg_params = [\"image_embds\"]\n\n def get_dummy_components(self):\n dummy = Img2ImgDummies()\n prior_dummy = PriorDummies()\n components = dummy.get_dummy_components()\n\n components.update({f\"prior_{k}\": v for k, v in prior_dummy.get_dummy_components().items()})\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n prior_dummy = PriorDummies()\n dummy = Img2ImgDummies()\n inputs = prior_dummy.get_dummy_inputs(device=device, seed=seed)\n inputs.update(dummy.get_dummy_inputs(device=device, seed=seed))\n inputs.pop(\"image_embeds\")\n inputs.pop(\"negative_image_embeds\")\n return inputs\n\n def test_kandinsky(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.images\n\n image_from_tuple = pipe(\n **self.get_dummy_inputs(device),\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.4353, 0.4710, 0.5128, 0.4806, 0.5054, 0.5348, 0.5224, 0.4603, 0.5025])\n\n assert (\n np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_slice.flatten()}\"\n assert (\n np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}\"\n\n @require_torch_gpu\n def test_offloads(self):\n pipes = []\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(torch_device)\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_model_cpu_offload()\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_sequential_cpu_offload()\n pipes.append(sd_pipe)\n\n image_slices = []\n for pipe in pipes:\n inputs = self.get_dummy_inputs(torch_device)\n image = pipe(**inputs).images\n\n image_slices.append(image[0, -3:, -3:, -1].flatten())\n\n assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3\n assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=1e-2)\n\n def test_float16_inference(self):\n super().test_float16_inference(expected_max_diff=2e-1)\n\n def test_dict_tuple_outputs_equivalent(self):\n super().test_dict_tuple_outputs_equivalent(expected_max_difference=5e-4)\n\n def test_model_cpu_offload_forward_pass(self):\n super().test_model_cpu_offload_forward_pass(expected_max_diff=5e-4)\n\n def test_save_load_optional_components(self):\n super().test_save_load_optional_components(expected_max_difference=5e-4)\n\n def save_load_local(self):\n super().test_save_load_local(expected_max_difference=5e-3)\n\n def test_callback_inputs(self):\n pass\n\n def test_callback_cfg(self):\n pass\n\n\nclass KandinskyV22PipelineInpaintCombinedFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = KandinskyV22InpaintCombinedPipeline\n params = [\"prompt\", \"image\", \"mask_image\"]\n batch_params = [\"prompt\", \"negative_prompt\", \"image\", \"mask_image\"]\n required_optional_params = [\n \"generator\",\n \"height\",\n \"width\",\n \"latents\",\n \"guidance_scale\",\n \"negative_prompt\",\n \"num_inference_steps\",\n \"return_dict\",\n \"guidance_scale\",\n \"num_images_per_prompt\",\n \"output_type\",\n \"return_dict\",\n ]\n test_xformers_attention = False\n\n def get_dummy_components(self):\n dummy = InpaintDummies()\n prior_dummy = PriorDummies()\n components = dummy.get_dummy_components()\n\n components.update({f\"prior_{k}\": v for k, v in prior_dummy.get_dummy_components().items()})\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n prior_dummy = PriorDummies()\n dummy = InpaintDummies()\n inputs = prior_dummy.get_dummy_inputs(device=device, seed=seed)\n inputs.update(dummy.get_dummy_inputs(device=device, seed=seed))\n inputs.pop(\"image_embeds\")\n inputs.pop(\"negative_image_embeds\")\n return inputs\n\n def test_kandinsky(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.images\n\n image_from_tuple = pipe(\n **self.get_dummy_inputs(device),\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.5039, 0.4926, 0.4898, 0.4978, 0.4838, 0.4942, 0.4738, 0.4702, 0.4816])\n\n assert (\n np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_slice.flatten()}\"\n assert (\n np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}\"\n\n @require_torch_gpu\n def test_offloads(self):\n pipes = []\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(torch_device)\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_model_cpu_offload()\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_sequential_cpu_offload()\n pipes.append(sd_pipe)\n\n image_slices = []\n for pipe in pipes:\n inputs = self.get_dummy_inputs(torch_device)\n image = pipe(**inputs).images\n\n image_slices.append(image[0, -3:, -3:, -1].flatten())\n\n assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3\n assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=1e-2)\n\n def test_float16_inference(self):\n super().test_float16_inference(expected_max_diff=5e-1)\n\n def test_dict_tuple_outputs_equivalent(self):\n super().test_dict_tuple_outputs_equivalent(expected_max_difference=5e-4)\n\n def test_model_cpu_offload_forward_pass(self):\n super().test_model_cpu_offload_forward_pass(expected_max_diff=5e-4)\n\n def test_save_load_local(self):\n super().test_save_load_local(expected_max_difference=5e-3)\n\n def test_save_load_optional_components(self):\n super().test_save_load_optional_components(expected_max_difference=5e-4)\n\n def test_sequential_cpu_offload_forward_pass(self):\n super().test_sequential_cpu_offload_forward_pass(expected_max_diff=5e-4)\n\n def test_callback_inputs(self):\n pass\n\n def test_callback_cfg(self):\n pass\n" }, { "path": "diffusers/tests/pipelines/kandinsky2_2/test_kandinsky_controlnet.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\n\nfrom diffusers import (\n DDIMScheduler,\n KandinskyV22ControlnetPipeline,\n KandinskyV22PriorPipeline,\n UNet2DConditionModel,\n VQModel,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n load_numpy,\n nightly,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n)\n\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass KandinskyV22ControlnetPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = KandinskyV22ControlnetPipeline\n params = [\"image_embeds\", \"negative_image_embeds\", \"hint\"]\n batch_params = [\"image_embeds\", \"negative_image_embeds\", \"hint\"]\n required_optional_params = [\n \"generator\",\n \"height\",\n \"width\",\n \"latents\",\n \"guidance_scale\",\n \"num_inference_steps\",\n \"return_dict\",\n \"guidance_scale\",\n \"num_images_per_prompt\",\n \"output_type\",\n \"return_dict\",\n ]\n test_xformers_attention = False\n\n @property\n def text_embedder_hidden_size(self):\n return 32\n\n @property\n def time_input_dim(self):\n return 32\n\n @property\n def block_out_channels_0(self):\n return self.time_input_dim\n\n @property\n def time_embed_dim(self):\n return self.time_input_dim * 4\n\n @property\n def cross_attention_dim(self):\n return 100\n\n @property\n def dummy_unet(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"in_channels\": 8,\n # Out channels is double in channels because predicts mean and variance\n \"out_channels\": 8,\n \"addition_embed_type\": \"image_hint\",\n \"down_block_types\": (\"ResnetDownsampleBlock2D\", \"SimpleCrossAttnDownBlock2D\"),\n \"up_block_types\": (\"SimpleCrossAttnUpBlock2D\", \"ResnetUpsampleBlock2D\"),\n \"mid_block_type\": \"UNetMidBlock2DSimpleCrossAttn\",\n \"block_out_channels\": (self.block_out_channels_0, self.block_out_channels_0 * 2),\n \"layers_per_block\": 1,\n \"encoder_hid_dim\": self.text_embedder_hidden_size,\n \"encoder_hid_dim_type\": \"image_proj\",\n \"cross_attention_dim\": self.cross_attention_dim,\n \"attention_head_dim\": 4,\n \"resnet_time_scale_shift\": \"scale_shift\",\n \"class_embed_type\": None,\n }\n\n model = UNet2DConditionModel(**model_kwargs)\n return model\n\n @property\n def dummy_movq_kwargs(self):\n return {\n \"block_out_channels\": [32, 32, 64, 64],\n \"down_block_types\": [\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n \"AttnDownEncoderBlock2D\",\n ],\n \"in_channels\": 3,\n \"latent_channels\": 4,\n \"layers_per_block\": 1,\n \"norm_num_groups\": 8,\n \"norm_type\": \"spatial\",\n \"num_vq_embeddings\": 12,\n \"out_channels\": 3,\n \"up_block_types\": [\"AttnUpDecoderBlock2D\", \"UpDecoderBlock2D\", \"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n \"vq_embed_dim\": 4,\n }\n\n @property\n def dummy_movq(self):\n torch.manual_seed(0)\n model = VQModel(**self.dummy_movq_kwargs)\n return model\n\n def get_dummy_components(self):\n unet = self.dummy_unet\n movq = self.dummy_movq\n\n scheduler = DDIMScheduler(\n num_train_timesteps=1000,\n beta_schedule=\"linear\",\n beta_start=0.00085,\n beta_end=0.012,\n clip_sample=False,\n set_alpha_to_one=False,\n steps_offset=1,\n prediction_type=\"epsilon\",\n thresholding=False,\n )\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"movq\": movq,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n image_embeds = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed)).to(device)\n negative_image_embeds = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed + 1)).to(\n device\n )\n\n # create hint\n hint = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device)\n\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"image_embeds\": image_embeds,\n \"negative_image_embeds\": negative_image_embeds,\n \"hint\": hint,\n \"generator\": generator,\n \"height\": 64,\n \"width\": 64,\n \"guidance_scale\": 4.0,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_kandinsky_controlnet(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.images\n\n image_from_tuple = pipe(\n **self.get_dummy_inputs(device),\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array(\n [0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595]\n )\n\n assert (\n np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_slice.flatten()}\"\n\n assert (\n np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}\"\n\n def test_float16_inference(self):\n super().test_float16_inference(expected_max_diff=1e-1)\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=5e-4)\n\n\n@nightly\n@require_torch_gpu\nclass KandinskyV22ControlnetPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_kandinsky_controlnet(self):\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy\"\n )\n\n hint = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/kandinskyv22/hint_image_cat.png\"\n )\n hint = torch.from_numpy(np.array(hint)).float() / 255.0\n hint = hint.permute(2, 0, 1).unsqueeze(0)\n\n pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n )\n pipe_prior.enable_model_cpu_offload()\n\n pipeline = KandinskyV22ControlnetPipeline.from_pretrained(\n \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n )\n pipeline.enable_model_cpu_offload()\n pipeline.set_progress_bar_config(disable=None)\n\n prompt = \"A robot, 4k photo\"\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n image_emb, zero_image_emb = pipe_prior(\n prompt,\n generator=generator,\n num_inference_steps=2,\n negative_prompt=\"\",\n ).to_tuple()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n output = pipeline(\n image_embeds=image_emb,\n negative_image_embeds=zero_image_emb,\n hint=hint,\n generator=generator,\n num_inference_steps=2,\n output_type=\"np\",\n )\n\n image = output.images[0]\n\n assert image.shape == (512, 512, 3)\n\n max_diff = numpy_cosine_similarity_distance(expected_image.flatten(), image.flatten())\n assert max_diff < 1e-4\n" }, { "path": "diffusers/tests/pipelines/kandinsky2_2/test_kandinsky_controlnet_img2img.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\n\nfrom diffusers import (\n DDIMScheduler,\n KandinskyV22ControlnetImg2ImgPipeline,\n KandinskyV22PriorEmb2EmbPipeline,\n UNet2DConditionModel,\n VQModel,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n load_numpy,\n nightly,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n)\n\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass KandinskyV22ControlnetImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = KandinskyV22ControlnetImg2ImgPipeline\n params = [\"image_embeds\", \"negative_image_embeds\", \"image\", \"hint\"]\n batch_params = [\"image_embeds\", \"negative_image_embeds\", \"image\", \"hint\"]\n required_optional_params = [\n \"generator\",\n \"height\",\n \"width\",\n \"strength\",\n \"guidance_scale\",\n \"num_inference_steps\",\n \"return_dict\",\n \"guidance_scale\",\n \"num_images_per_prompt\",\n \"output_type\",\n \"return_dict\",\n ]\n test_xformers_attention = False\n\n @property\n def text_embedder_hidden_size(self):\n return 32\n\n @property\n def time_input_dim(self):\n return 32\n\n @property\n def block_out_channels_0(self):\n return self.time_input_dim\n\n @property\n def time_embed_dim(self):\n return self.time_input_dim * 4\n\n @property\n def cross_attention_dim(self):\n return 100\n\n @property\n def dummy_unet(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"in_channels\": 8,\n # Out channels is double in channels because predicts mean and variance\n \"out_channels\": 8,\n \"addition_embed_type\": \"image_hint\",\n \"down_block_types\": (\"ResnetDownsampleBlock2D\", \"SimpleCrossAttnDownBlock2D\"),\n \"up_block_types\": (\"SimpleCrossAttnUpBlock2D\", \"ResnetUpsampleBlock2D\"),\n \"mid_block_type\": \"UNetMidBlock2DSimpleCrossAttn\",\n \"block_out_channels\": (self.block_out_channels_0, self.block_out_channels_0 * 2),\n \"layers_per_block\": 1,\n \"encoder_hid_dim\": self.text_embedder_hidden_size,\n \"encoder_hid_dim_type\": \"image_proj\",\n \"cross_attention_dim\": self.cross_attention_dim,\n \"attention_head_dim\": 4,\n \"resnet_time_scale_shift\": \"scale_shift\",\n \"class_embed_type\": None,\n }\n\n model = UNet2DConditionModel(**model_kwargs)\n return model\n\n @property\n def dummy_movq_kwargs(self):\n return {\n \"block_out_channels\": [32, 32, 64, 64],\n \"down_block_types\": [\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n \"AttnDownEncoderBlock2D\",\n ],\n \"in_channels\": 3,\n \"latent_channels\": 4,\n \"layers_per_block\": 1,\n \"norm_num_groups\": 8,\n \"norm_type\": \"spatial\",\n \"num_vq_embeddings\": 12,\n \"out_channels\": 3,\n \"up_block_types\": [\"AttnUpDecoderBlock2D\", \"UpDecoderBlock2D\", \"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n \"vq_embed_dim\": 4,\n }\n\n @property\n def dummy_movq(self):\n torch.manual_seed(0)\n model = VQModel(**self.dummy_movq_kwargs)\n return model\n\n def get_dummy_components(self):\n unet = self.dummy_unet\n movq = self.dummy_movq\n\n ddim_config = {\n \"num_train_timesteps\": 1000,\n \"beta_schedule\": \"linear\",\n \"beta_start\": 0.00085,\n \"beta_end\": 0.012,\n \"clip_sample\": False,\n \"set_alpha_to_one\": False,\n \"steps_offset\": 0,\n \"prediction_type\": \"epsilon\",\n \"thresholding\": False,\n }\n\n scheduler = DDIMScheduler(**ddim_config)\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"movq\": movq,\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n image_embeds = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed)).to(device)\n negative_image_embeds = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed + 1)).to(\n device\n )\n # create init_image\n image = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device)\n image = image.cpu().permute(0, 2, 3, 1)[0]\n init_image = Image.fromarray(np.uint8(image)).convert(\"RGB\").resize((256, 256))\n # create hint\n hint = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device)\n\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"image\": init_image,\n \"image_embeds\": image_embeds,\n \"negative_image_embeds\": negative_image_embeds,\n \"hint\": hint,\n \"generator\": generator,\n \"height\": 64,\n \"width\": 64,\n \"num_inference_steps\": 10,\n \"guidance_scale\": 7.0,\n \"strength\": 0.2,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_kandinsky_controlnet_img2img(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.images\n\n image_from_tuple = pipe(\n **self.get_dummy_inputs(device),\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array(\n [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736]\n )\n assert (\n np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_slice.flatten()}\"\n assert (\n np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}\"\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=1.75e-3)\n\n def test_float16_inference(self):\n super().test_float16_inference(expected_max_diff=2e-1)\n\n\n@nightly\n@require_torch_gpu\nclass KandinskyV22ControlnetImg2ImgPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_kandinsky_controlnet_img2img(self):\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy\"\n )\n\n init_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\" \"/kandinsky/cat.png\"\n )\n init_image = init_image.resize((512, 512))\n\n hint = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/kandinskyv22/hint_image_cat.png\"\n )\n hint = torch.from_numpy(np.array(hint)).float() / 255.0\n hint = hint.permute(2, 0, 1).unsqueeze(0)\n\n prompt = \"A robot, 4k photo\"\n\n pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n )\n pipe_prior.enable_model_cpu_offload()\n\n pipeline = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n )\n pipeline.enable_model_cpu_offload()\n\n pipeline.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n\n image_emb, zero_image_emb = pipe_prior(\n prompt,\n image=init_image,\n strength=0.85,\n generator=generator,\n negative_prompt=\"\",\n num_inference_steps=5,\n ).to_tuple()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n output = pipeline(\n image=init_image,\n image_embeds=image_emb,\n negative_image_embeds=zero_image_emb,\n hint=hint,\n generator=generator,\n num_inference_steps=5,\n height=512,\n width=512,\n strength=0.5,\n output_type=\"np\",\n )\n\n image = output.images[0]\n\n assert image.shape == (512, 512, 3)\n\n max_diff = numpy_cosine_similarity_distance(expected_image.flatten(), image.flatten())\n assert max_diff < 1e-4\n" }, { "path": "diffusers/tests/pipelines/kandinsky2_2/test_kandinsky_img2img.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\n\nfrom diffusers import (\n DDIMScheduler,\n KandinskyV22Img2ImgPipeline,\n KandinskyV22PriorPipeline,\n UNet2DConditionModel,\n VQModel,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n load_numpy,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n slow,\n)\n\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass Dummies:\n @property\n def text_embedder_hidden_size(self):\n return 32\n\n @property\n def time_input_dim(self):\n return 32\n\n @property\n def block_out_channels_0(self):\n return self.time_input_dim\n\n @property\n def time_embed_dim(self):\n return self.time_input_dim * 4\n\n @property\n def cross_attention_dim(self):\n return 32\n\n @property\n def dummy_unet(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"in_channels\": 4,\n # Out channels is double in channels because predicts mean and variance\n \"out_channels\": 8,\n \"addition_embed_type\": \"image\",\n \"down_block_types\": (\"ResnetDownsampleBlock2D\", \"SimpleCrossAttnDownBlock2D\"),\n \"up_block_types\": (\"SimpleCrossAttnUpBlock2D\", \"ResnetUpsampleBlock2D\"),\n \"mid_block_type\": \"UNetMidBlock2DSimpleCrossAttn\",\n \"block_out_channels\": (self.block_out_channels_0, self.block_out_channels_0 * 2),\n \"layers_per_block\": 1,\n \"encoder_hid_dim\": self.text_embedder_hidden_size,\n \"encoder_hid_dim_type\": \"image_proj\",\n \"cross_attention_dim\": self.cross_attention_dim,\n \"attention_head_dim\": 4,\n \"resnet_time_scale_shift\": \"scale_shift\",\n \"class_embed_type\": None,\n }\n\n model = UNet2DConditionModel(**model_kwargs)\n return model\n\n @property\n def dummy_movq_kwargs(self):\n return {\n \"block_out_channels\": [32, 64],\n \"down_block_types\": [\"DownEncoderBlock2D\", \"AttnDownEncoderBlock2D\"],\n \"in_channels\": 3,\n \"latent_channels\": 4,\n \"layers_per_block\": 1,\n \"norm_num_groups\": 8,\n \"norm_type\": \"spatial\",\n \"num_vq_embeddings\": 12,\n \"out_channels\": 3,\n \"up_block_types\": [\n \"AttnUpDecoderBlock2D\",\n \"UpDecoderBlock2D\",\n ],\n \"vq_embed_dim\": 4,\n }\n\n @property\n def dummy_movq(self):\n torch.manual_seed(0)\n model = VQModel(**self.dummy_movq_kwargs)\n return model\n\n def get_dummy_components(self):\n unet = self.dummy_unet\n movq = self.dummy_movq\n\n ddim_config = {\n \"num_train_timesteps\": 1000,\n \"beta_schedule\": \"linear\",\n \"beta_start\": 0.00085,\n \"beta_end\": 0.012,\n \"clip_sample\": False,\n \"set_alpha_to_one\": False,\n \"steps_offset\": 0,\n \"prediction_type\": \"epsilon\",\n \"thresholding\": False,\n }\n\n scheduler = DDIMScheduler(**ddim_config)\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"movq\": movq,\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n image_embeds = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed)).to(device)\n negative_image_embeds = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed + 1)).to(\n device\n )\n # create init_image\n image = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device)\n image = image.cpu().permute(0, 2, 3, 1)[0]\n init_image = Image.fromarray(np.uint8(image)).convert(\"RGB\").resize((256, 256))\n\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"image\": init_image,\n \"image_embeds\": image_embeds,\n \"negative_image_embeds\": negative_image_embeds,\n \"generator\": generator,\n \"height\": 64,\n \"width\": 64,\n \"num_inference_steps\": 10,\n \"guidance_scale\": 7.0,\n \"strength\": 0.2,\n \"output_type\": \"np\",\n }\n return inputs\n\n\nclass KandinskyV22Img2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = KandinskyV22Img2ImgPipeline\n params = [\"image_embeds\", \"negative_image_embeds\", \"image\"]\n batch_params = [\n \"image_embeds\",\n \"negative_image_embeds\",\n \"image\",\n ]\n required_optional_params = [\n \"generator\",\n \"height\",\n \"width\",\n \"strength\",\n \"guidance_scale\",\n \"num_inference_steps\",\n \"return_dict\",\n \"guidance_scale\",\n \"num_images_per_prompt\",\n \"output_type\",\n \"return_dict\",\n ]\n test_xformers_attention = False\n callback_cfg_params = [\"image_embeds\"]\n\n def get_dummy_components(self):\n dummies = Dummies()\n return dummies.get_dummy_components()\n\n def get_dummy_inputs(self, device, seed=0):\n dummies = Dummies()\n return dummies.get_dummy_inputs(device=device, seed=seed)\n\n def test_kandinsky_img2img(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.images\n\n image_from_tuple = pipe(\n **self.get_dummy_inputs(device),\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.5712, 0.5443, 0.4725, 0.6195, 0.5184, 0.4651, 0.4473, 0.4590, 0.5016])\n assert (\n np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_slice.flatten()}\"\n assert (\n np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}\"\n\n def test_float16_inference(self):\n super().test_float16_inference(expected_max_diff=2e-1)\n\n\n@slow\n@require_torch_gpu\nclass KandinskyV22Img2ImgPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_kandinsky_img2img(self):\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/kandinskyv22/kandinskyv22_img2img_frog.npy\"\n )\n\n init_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\" \"/kandinsky/cat.png\"\n )\n prompt = \"A red cartoon frog, 4k\"\n\n pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n )\n pipe_prior.enable_model_cpu_offload()\n\n pipeline = KandinskyV22Img2ImgPipeline.from_pretrained(\n \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n )\n pipeline.enable_model_cpu_offload()\n pipeline.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n image_emb, zero_image_emb = pipe_prior(\n prompt,\n generator=generator,\n num_inference_steps=5,\n negative_prompt=\"\",\n ).to_tuple()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n output = pipeline(\n image=init_image,\n image_embeds=image_emb,\n negative_image_embeds=zero_image_emb,\n generator=generator,\n num_inference_steps=5,\n height=768,\n width=768,\n strength=0.2,\n output_type=\"np\",\n )\n\n image = output.images[0]\n\n assert image.shape == (768, 768, 3)\n\n max_diff = numpy_cosine_similarity_distance(expected_image.flatten(), image.flatten())\n assert max_diff < 1e-4\n" }, { "path": "diffusers/tests/pipelines/kandinsky2_2/test_kandinsky_inpaint.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\n\nfrom diffusers import (\n DDIMScheduler,\n KandinskyV22InpaintPipeline,\n KandinskyV22PriorPipeline,\n UNet2DConditionModel,\n VQModel,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n is_flaky,\n load_image,\n load_numpy,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass Dummies:\n @property\n def text_embedder_hidden_size(self):\n return 32\n\n @property\n def time_input_dim(self):\n return 32\n\n @property\n def block_out_channels_0(self):\n return self.time_input_dim\n\n @property\n def time_embed_dim(self):\n return self.time_input_dim * 4\n\n @property\n def cross_attention_dim(self):\n return 32\n\n @property\n def dummy_unet(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"in_channels\": 9,\n # Out channels is double in channels because predicts mean and variance\n \"out_channels\": 8,\n \"addition_embed_type\": \"image\",\n \"down_block_types\": (\"ResnetDownsampleBlock2D\", \"SimpleCrossAttnDownBlock2D\"),\n \"up_block_types\": (\"SimpleCrossAttnUpBlock2D\", \"ResnetUpsampleBlock2D\"),\n \"mid_block_type\": \"UNetMidBlock2DSimpleCrossAttn\",\n \"block_out_channels\": (self.block_out_channels_0, self.block_out_channels_0 * 2),\n \"layers_per_block\": 1,\n \"encoder_hid_dim\": self.text_embedder_hidden_size,\n \"encoder_hid_dim_type\": \"image_proj\",\n \"cross_attention_dim\": self.cross_attention_dim,\n \"attention_head_dim\": 4,\n \"resnet_time_scale_shift\": \"scale_shift\",\n \"class_embed_type\": None,\n }\n\n model = UNet2DConditionModel(**model_kwargs)\n return model\n\n @property\n def dummy_movq_kwargs(self):\n return {\n \"block_out_channels\": [32, 64],\n \"down_block_types\": [\"DownEncoderBlock2D\", \"AttnDownEncoderBlock2D\"],\n \"in_channels\": 3,\n \"latent_channels\": 4,\n \"layers_per_block\": 1,\n \"norm_num_groups\": 8,\n \"norm_type\": \"spatial\",\n \"num_vq_embeddings\": 12,\n \"out_channels\": 3,\n \"up_block_types\": [\n \"AttnUpDecoderBlock2D\",\n \"UpDecoderBlock2D\",\n ],\n \"vq_embed_dim\": 4,\n }\n\n @property\n def dummy_movq(self):\n torch.manual_seed(0)\n model = VQModel(**self.dummy_movq_kwargs)\n return model\n\n def get_dummy_components(self):\n unet = self.dummy_unet\n movq = self.dummy_movq\n\n scheduler = DDIMScheduler(\n num_train_timesteps=1000,\n beta_schedule=\"linear\",\n beta_start=0.00085,\n beta_end=0.012,\n clip_sample=False,\n set_alpha_to_one=False,\n steps_offset=1,\n prediction_type=\"epsilon\",\n thresholding=False,\n )\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"movq\": movq,\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n image_embeds = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed)).to(device)\n negative_image_embeds = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed + 1)).to(\n device\n )\n # create init_image\n image = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device)\n image = image.cpu().permute(0, 2, 3, 1)[0]\n init_image = Image.fromarray(np.uint8(image)).convert(\"RGB\").resize((256, 256))\n # create mask\n mask = np.zeros((64, 64), dtype=np.float32)\n mask[:32, :32] = 1\n\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"image\": init_image,\n \"mask_image\": mask,\n \"image_embeds\": image_embeds,\n \"negative_image_embeds\": negative_image_embeds,\n \"generator\": generator,\n \"height\": 64,\n \"width\": 64,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 4.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n\nclass KandinskyV22InpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = KandinskyV22InpaintPipeline\n params = [\"image_embeds\", \"negative_image_embeds\", \"image\", \"mask_image\"]\n batch_params = [\n \"image_embeds\",\n \"negative_image_embeds\",\n \"image\",\n \"mask_image\",\n ]\n required_optional_params = [\n \"generator\",\n \"height\",\n \"width\",\n \"latents\",\n \"guidance_scale\",\n \"num_inference_steps\",\n \"return_dict\",\n \"guidance_scale\",\n \"num_images_per_prompt\",\n \"output_type\",\n \"return_dict\",\n ]\n test_xformers_attention = False\n callback_cfg_params = [\"image_embeds\", \"masked_image\", \"mask_image\"]\n\n def get_dummy_components(self):\n dummies = Dummies()\n return dummies.get_dummy_components()\n\n def get_dummy_inputs(self, device, seed=0):\n dummies = Dummies()\n return dummies.get_dummy_inputs(device=device, seed=seed)\n\n def test_kandinsky_inpaint(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.images\n\n image_from_tuple = pipe(\n **self.get_dummy_inputs(device),\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array(\n [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]\n )\n\n assert (\n np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_slice.flatten()}\"\n assert (\n np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}\"\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=3e-3)\n\n def test_float16_inference(self):\n super().test_float16_inference(expected_max_diff=5e-1)\n\n @is_flaky()\n def test_model_cpu_offload_forward_pass(self):\n super().test_inference_batch_single_identical(expected_max_diff=8e-4)\n\n def test_save_load_optional_components(self):\n super().test_save_load_optional_components(expected_max_difference=5e-4)\n\n def test_sequential_cpu_offload_forward_pass(self):\n super().test_sequential_cpu_offload_forward_pass(expected_max_diff=5e-4)\n\n # override default test because we need to zero out mask too in order to make sure final latent is all zero\n def test_callback_inputs(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n self.assertTrue(\n hasattr(pipe, \"_callback_tensor_inputs\"),\n f\" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs\",\n )\n\n def callback_inputs_test(pipe, i, t, callback_kwargs):\n missing_callback_inputs = set()\n for v in pipe._callback_tensor_inputs:\n if v not in callback_kwargs:\n missing_callback_inputs.add(v)\n self.assertTrue(\n len(missing_callback_inputs) == 0, f\"Missing callback tensor inputs: {missing_callback_inputs}\"\n )\n last_i = pipe.num_timesteps - 1\n if i == last_i:\n callback_kwargs[\"latents\"] = torch.zeros_like(callback_kwargs[\"latents\"])\n callback_kwargs[\"mask_image\"] = torch.zeros_like(callback_kwargs[\"mask_image\"])\n return callback_kwargs\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"callback_on_step_end\"] = callback_inputs_test\n inputs[\"callback_on_step_end_tensor_inputs\"] = pipe._callback_tensor_inputs\n inputs[\"output_type\"] = \"latent\"\n\n output = pipe(**inputs)[0]\n assert output.abs().sum() == 0\n\n\n@slow\n@require_torch_gpu\nclass KandinskyV22InpaintPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_kandinsky_inpaint(self):\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy\"\n )\n\n init_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\" \"/kandinsky/cat.png\"\n )\n mask = np.zeros((768, 768), dtype=np.float32)\n mask[:250, 250:-250] = 1\n\n prompt = \"a hat\"\n\n pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n )\n pipe_prior.to(torch_device)\n\n pipeline = KandinskyV22InpaintPipeline.from_pretrained(\n \"kandinsky-community/kandinsky-2-2-decoder-inpaint\", torch_dtype=torch.float16\n )\n pipeline = pipeline.to(torch_device)\n pipeline.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n image_emb, zero_image_emb = pipe_prior(\n prompt,\n generator=generator,\n num_inference_steps=2,\n negative_prompt=\"\",\n ).to_tuple()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n output = pipeline(\n image=init_image,\n mask_image=mask,\n image_embeds=image_emb,\n negative_image_embeds=zero_image_emb,\n generator=generator,\n num_inference_steps=2,\n height=768,\n width=768,\n output_type=\"np\",\n )\n\n image = output.images[0]\n\n assert image.shape == (768, 768, 3)\n\n max_diff = numpy_cosine_similarity_distance(expected_image.flatten(), image.flatten())\n assert max_diff < 1e-4\n" }, { "path": "diffusers/tests/pipelines/kandinsky2_2/test_kandinsky_prior.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nimport unittest\n\nimport numpy as np\nimport torch\nfrom torch import nn\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextConfig,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionConfig,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers import KandinskyV22PriorPipeline, PriorTransformer, UnCLIPScheduler\nfrom diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device\n\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass Dummies:\n @property\n def text_embedder_hidden_size(self):\n return 32\n\n @property\n def time_input_dim(self):\n return 32\n\n @property\n def block_out_channels_0(self):\n return self.time_input_dim\n\n @property\n def time_embed_dim(self):\n return self.time_input_dim * 4\n\n @property\n def cross_attention_dim(self):\n return 100\n\n @property\n def dummy_tokenizer(self):\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n return tokenizer\n\n @property\n def dummy_text_encoder(self):\n torch.manual_seed(0)\n config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=self.text_embedder_hidden_size,\n projection_dim=self.text_embedder_hidden_size,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n return CLIPTextModelWithProjection(config)\n\n @property\n def dummy_prior(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"num_attention_heads\": 2,\n \"attention_head_dim\": 12,\n \"embedding_dim\": self.text_embedder_hidden_size,\n \"num_layers\": 1,\n }\n\n model = PriorTransformer(**model_kwargs)\n # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0\n model.clip_std = nn.Parameter(torch.ones(model.clip_std.shape))\n return model\n\n @property\n def dummy_image_encoder(self):\n torch.manual_seed(0)\n config = CLIPVisionConfig(\n hidden_size=self.text_embedder_hidden_size,\n image_size=224,\n projection_dim=self.text_embedder_hidden_size,\n intermediate_size=37,\n num_attention_heads=4,\n num_channels=3,\n num_hidden_layers=5,\n patch_size=14,\n )\n\n model = CLIPVisionModelWithProjection(config)\n return model\n\n @property\n def dummy_image_processor(self):\n image_processor = CLIPImageProcessor(\n crop_size=224,\n do_center_crop=True,\n do_normalize=True,\n do_resize=True,\n image_mean=[0.48145466, 0.4578275, 0.40821073],\n image_std=[0.26862954, 0.26130258, 0.27577711],\n resample=3,\n size=224,\n )\n\n return image_processor\n\n def get_dummy_components(self):\n prior = self.dummy_prior\n image_encoder = self.dummy_image_encoder\n text_encoder = self.dummy_text_encoder\n tokenizer = self.dummy_tokenizer\n image_processor = self.dummy_image_processor\n\n scheduler = UnCLIPScheduler(\n variance_type=\"fixed_small_log\",\n prediction_type=\"sample\",\n num_train_timesteps=1000,\n clip_sample=True,\n clip_sample_range=10.0,\n )\n\n components = {\n \"prior\": prior,\n \"image_encoder\": image_encoder,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"scheduler\": scheduler,\n \"image_processor\": image_processor,\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"horse\",\n \"generator\": generator,\n \"guidance_scale\": 4.0,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n return inputs\n\n\nclass KandinskyV22PriorPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = KandinskyV22PriorPipeline\n params = [\"prompt\"]\n batch_params = [\"prompt\", \"negative_prompt\"]\n required_optional_params = [\n \"num_images_per_prompt\",\n \"generator\",\n \"num_inference_steps\",\n \"latents\",\n \"negative_prompt\",\n \"guidance_scale\",\n \"output_type\",\n \"return_dict\",\n ]\n callback_cfg_params = [\"prompt_embeds\", \"text_encoder_hidden_states\", \"text_mask\"]\n test_xformers_attention = False\n\n def get_dummy_components(self):\n dummies = Dummies()\n return dummies.get_dummy_components()\n\n def get_dummy_inputs(self, device, seed=0):\n dummies = Dummies()\n return dummies.get_dummy_inputs(device=device, seed=seed)\n\n def test_kandinsky_prior(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.image_embeds\n\n image_from_tuple = pipe(\n **self.get_dummy_inputs(device),\n return_dict=False,\n )[0]\n\n image_slice = image[0, -10:]\n image_from_tuple_slice = image_from_tuple[0, -10:]\n\n assert image.shape == (1, 32)\n\n expected_slice = np.array(\n [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]\n )\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n\n @skip_mps\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=1e-3)\n\n @skip_mps\n def test_attention_slicing_forward_pass(self):\n test_max_difference = torch_device == \"cpu\"\n test_mean_pixel_difference = False\n\n self._test_attention_slicing_forward_pass(\n test_max_difference=test_max_difference,\n test_mean_pixel_difference=test_mean_pixel_difference,\n )\n\n # override default test because no output_type \"latent\", use \"pt\" instead\n def test_callback_inputs(self):\n sig = inspect.signature(self.pipeline_class.__call__)\n\n if not (\"callback_on_step_end_tensor_inputs\" in sig.parameters and \"callback_on_step_end\" in sig.parameters):\n return\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n self.assertTrue(\n hasattr(pipe, \"_callback_tensor_inputs\"),\n f\" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs\",\n )\n\n def callback_inputs_test(pipe, i, t, callback_kwargs):\n missing_callback_inputs = set()\n for v in pipe._callback_tensor_inputs:\n if v not in callback_kwargs:\n missing_callback_inputs.add(v)\n self.assertTrue(\n len(missing_callback_inputs) == 0, f\"Missing callback tensor inputs: {missing_callback_inputs}\"\n )\n last_i = pipe.num_timesteps - 1\n if i == last_i:\n callback_kwargs[\"latents\"] = torch.zeros_like(callback_kwargs[\"latents\"])\n return callback_kwargs\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"callback_on_step_end\"] = callback_inputs_test\n inputs[\"callback_on_step_end_tensor_inputs\"] = pipe._callback_tensor_inputs\n inputs[\"num_inference_steps\"] = 2\n inputs[\"output_type\"] = \"pt\"\n\n output = pipe(**inputs)[0]\n assert output.abs().sum() == 0\n" }, { "path": "diffusers/tests/pipelines/kandinsky2_2/test_kandinsky_prior_emb2emb.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom torch import nn\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextConfig,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionConfig,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers import KandinskyV22PriorEmb2EmbPipeline, PriorTransformer, UnCLIPScheduler\nfrom diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, skip_mps, torch_device\n\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass KandinskyV22PriorEmb2EmbPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = KandinskyV22PriorEmb2EmbPipeline\n params = [\"prompt\", \"image\"]\n batch_params = [\"prompt\", \"image\"]\n required_optional_params = [\n \"num_images_per_prompt\",\n \"strength\",\n \"generator\",\n \"num_inference_steps\",\n \"negative_prompt\",\n \"guidance_scale\",\n \"output_type\",\n \"return_dict\",\n ]\n test_xformers_attention = False\n\n @property\n def text_embedder_hidden_size(self):\n return 32\n\n @property\n def time_input_dim(self):\n return 32\n\n @property\n def block_out_channels_0(self):\n return self.time_input_dim\n\n @property\n def time_embed_dim(self):\n return self.time_input_dim * 4\n\n @property\n def cross_attention_dim(self):\n return 100\n\n @property\n def dummy_tokenizer(self):\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n return tokenizer\n\n @property\n def dummy_text_encoder(self):\n torch.manual_seed(0)\n config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=self.text_embedder_hidden_size,\n projection_dim=self.text_embedder_hidden_size,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n return CLIPTextModelWithProjection(config)\n\n @property\n def dummy_prior(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"num_attention_heads\": 2,\n \"attention_head_dim\": 12,\n \"embedding_dim\": self.text_embedder_hidden_size,\n \"num_layers\": 1,\n }\n\n model = PriorTransformer(**model_kwargs)\n # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0\n model.clip_std = nn.Parameter(torch.ones(model.clip_std.shape))\n return model\n\n @property\n def dummy_image_encoder(self):\n torch.manual_seed(0)\n config = CLIPVisionConfig(\n hidden_size=self.text_embedder_hidden_size,\n image_size=224,\n projection_dim=self.text_embedder_hidden_size,\n intermediate_size=37,\n num_attention_heads=4,\n num_channels=3,\n num_hidden_layers=5,\n patch_size=14,\n )\n\n model = CLIPVisionModelWithProjection(config)\n return model\n\n @property\n def dummy_image_processor(self):\n image_processor = CLIPImageProcessor(\n crop_size=224,\n do_center_crop=True,\n do_normalize=True,\n do_resize=True,\n image_mean=[0.48145466, 0.4578275, 0.40821073],\n image_std=[0.26862954, 0.26130258, 0.27577711],\n resample=3,\n size=224,\n )\n\n return image_processor\n\n def get_dummy_components(self):\n prior = self.dummy_prior\n image_encoder = self.dummy_image_encoder\n text_encoder = self.dummy_text_encoder\n tokenizer = self.dummy_tokenizer\n image_processor = self.dummy_image_processor\n\n scheduler = UnCLIPScheduler(\n variance_type=\"fixed_small_log\",\n prediction_type=\"sample\",\n num_train_timesteps=1000,\n clip_sample=True,\n clip_sample_range=10.0,\n )\n\n components = {\n \"prior\": prior,\n \"image_encoder\": image_encoder,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"scheduler\": scheduler,\n \"image_processor\": image_processor,\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n image = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device)\n image = image.cpu().permute(0, 2, 3, 1)[0]\n init_image = Image.fromarray(np.uint8(image)).convert(\"RGB\").resize((256, 256))\n\n inputs = {\n \"prompt\": \"horse\",\n \"image\": init_image,\n \"strength\": 0.5,\n \"generator\": generator,\n \"guidance_scale\": 4.0,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_kandinsky_prior_emb2emb(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.image_embeds\n\n image_from_tuple = pipe(\n **self.get_dummy_inputs(device),\n return_dict=False,\n )[0]\n\n image_slice = image[0, -10:]\n image_from_tuple_slice = image_from_tuple[0, -10:]\n\n assert image.shape == (1, 32)\n\n expected_slice = np.array(\n [\n 0.1071284,\n 1.3330271,\n 0.61260223,\n -0.6691065,\n -0.3846852,\n -1.0303661,\n 0.22716111,\n 0.03348901,\n 0.30040675,\n -0.24805029,\n ]\n )\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n\n @skip_mps\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=1e-2)\n\n @skip_mps\n def test_attention_slicing_forward_pass(self):\n test_max_difference = torch_device == \"cpu\"\n test_mean_pixel_difference = False\n\n self._test_attention_slicing_forward_pass(\n test_max_difference=test_max_difference,\n test_mean_pixel_difference=test_mean_pixel_difference,\n )\n" }, { "path": "diffusers/tests/pipelines/kandinsky3/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/kandinsky3/test_kandinsky3.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom transformers import AutoTokenizer, T5EncoderModel\n\nfrom diffusers import (\n AutoPipelineForImage2Image,\n AutoPipelineForText2Image,\n Kandinsky3Pipeline,\n Kandinsky3UNet,\n VQModel,\n)\nfrom diffusers.image_processor import VaeImageProcessor\nfrom diffusers.schedulers.scheduling_ddpm import DDPMScheduler\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n load_image,\n require_torch_gpu,\n slow,\n)\n\nfrom ..pipeline_params import (\n TEXT_TO_IMAGE_BATCH_PARAMS,\n TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,\n TEXT_TO_IMAGE_IMAGE_PARAMS,\n TEXT_TO_IMAGE_PARAMS,\n)\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass Kandinsky3PipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = Kandinsky3Pipeline\n params = TEXT_TO_IMAGE_PARAMS - {\"cross_attention_kwargs\"}\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n image_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS\n test_xformers_attention = False\n\n @property\n def dummy_movq_kwargs(self):\n return {\n \"block_out_channels\": [32, 64],\n \"down_block_types\": [\"DownEncoderBlock2D\", \"AttnDownEncoderBlock2D\"],\n \"in_channels\": 3,\n \"latent_channels\": 4,\n \"layers_per_block\": 1,\n \"norm_num_groups\": 8,\n \"norm_type\": \"spatial\",\n \"num_vq_embeddings\": 12,\n \"out_channels\": 3,\n \"up_block_types\": [\n \"AttnUpDecoderBlock2D\",\n \"UpDecoderBlock2D\",\n ],\n \"vq_embed_dim\": 4,\n }\n\n @property\n def dummy_movq(self):\n torch.manual_seed(0)\n model = VQModel(**self.dummy_movq_kwargs)\n return model\n\n def get_dummy_components(self, time_cond_proj_dim=None):\n torch.manual_seed(0)\n unet = Kandinsky3UNet(\n in_channels=4,\n time_embedding_dim=4,\n groups=2,\n attention_head_dim=4,\n layers_per_block=3,\n block_out_channels=(32, 64),\n cross_attention_dim=4,\n encoder_hid_dim=32,\n )\n scheduler = DDPMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n steps_offset=1,\n beta_schedule=\"squaredcos_cap_v2\",\n clip_sample=True,\n thresholding=False,\n )\n torch.manual_seed(0)\n movq = self.dummy_movq\n torch.manual_seed(0)\n text_encoder = T5EncoderModel.from_pretrained(\"hf-internal-testing/tiny-random-t5\")\n\n torch.manual_seed(0)\n tokenizer = AutoTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-t5\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"movq\": movq,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n \"width\": 16,\n \"height\": 16,\n }\n return inputs\n\n def test_kandinsky3(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 16, 16, 3)\n\n expected_slice = np.array([0.3768, 0.4373, 0.4865, 0.4890, 0.4299, 0.5122, 0.4921, 0.4924, 0.5599])\n\n assert (\n np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_slice.flatten()}\"\n\n def test_float16_inference(self):\n super().test_float16_inference(expected_max_diff=1e-1)\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=1e-2)\n\n\n@slow\n@require_torch_gpu\nclass Kandinsky3PipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_kandinskyV3(self):\n pipe = AutoPipelineForText2Image.from_pretrained(\n \"kandinsky-community/kandinsky-3\", variant=\"fp16\", torch_dtype=torch.float16\n )\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A photograph of the inside of a subway train. There are raccoons sitting on the seats. One of them is reading a newspaper. The window shows the city in the background.\"\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n\n image = pipe(prompt, num_inference_steps=5, generator=generator).images[0]\n\n assert image.size == (1024, 1024)\n\n expected_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky3/t2i.png\"\n )\n\n image_processor = VaeImageProcessor()\n\n image_np = image_processor.pil_to_numpy(image)\n expected_image_np = image_processor.pil_to_numpy(expected_image)\n\n self.assertTrue(np.allclose(image_np, expected_image_np, atol=5e-2))\n\n def test_kandinskyV3_img2img(self):\n pipe = AutoPipelineForImage2Image.from_pretrained(\n \"kandinsky-community/kandinsky-3\", variant=\"fp16\", torch_dtype=torch.float16\n )\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky3/t2i.png\"\n )\n w, h = 512, 512\n image = image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)\n prompt = \"A painting of the inside of a subway train with tiny raccoons.\"\n\n image = pipe(prompt, image=image, strength=0.75, num_inference_steps=5, generator=generator).images[0]\n\n assert image.size == (512, 512)\n\n expected_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky3/i2i.png\"\n )\n\n image_processor = VaeImageProcessor()\n\n image_np = image_processor.pil_to_numpy(image)\n expected_image_np = image_processor.pil_to_numpy(expected_image)\n\n self.assertTrue(np.allclose(image_np, expected_image_np, atol=5e-2))\n" }, { "path": "diffusers/tests/pipelines/kandinsky3/test_kandinsky3_img2img.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom transformers import AutoTokenizer, T5EncoderModel\n\nfrom diffusers import (\n AutoPipelineForImage2Image,\n Kandinsky3Img2ImgPipeline,\n Kandinsky3UNet,\n VQModel,\n)\nfrom diffusers.image_processor import VaeImageProcessor\nfrom diffusers.schedulers.scheduling_ddpm import DDPMScheduler\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\nfrom ..pipeline_params import (\n IMAGE_TO_IMAGE_IMAGE_PARAMS,\n TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,\n TEXT_GUIDED_IMAGE_VARIATION_PARAMS,\n TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,\n TEXT_TO_IMAGE_IMAGE_PARAMS,\n)\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass Kandinsky3Img2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = Kandinsky3Img2ImgPipeline\n params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {\"height\", \"width\"}\n batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS\n image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS\n test_xformers_attention = False\n required_optional_params = frozenset(\n [\n \"num_inference_steps\",\n \"num_images_per_prompt\",\n \"generator\",\n \"output_type\",\n \"return_dict\",\n ]\n )\n\n @property\n def dummy_movq_kwargs(self):\n return {\n \"block_out_channels\": [32, 64],\n \"down_block_types\": [\"DownEncoderBlock2D\", \"AttnDownEncoderBlock2D\"],\n \"in_channels\": 3,\n \"latent_channels\": 4,\n \"layers_per_block\": 1,\n \"norm_num_groups\": 8,\n \"norm_type\": \"spatial\",\n \"num_vq_embeddings\": 12,\n \"out_channels\": 3,\n \"up_block_types\": [\n \"AttnUpDecoderBlock2D\",\n \"UpDecoderBlock2D\",\n ],\n \"vq_embed_dim\": 4,\n }\n\n @property\n def dummy_movq(self):\n torch.manual_seed(0)\n model = VQModel(**self.dummy_movq_kwargs)\n return model\n\n def get_dummy_components(self, time_cond_proj_dim=None):\n torch.manual_seed(0)\n unet = Kandinsky3UNet(\n in_channels=4,\n time_embedding_dim=4,\n groups=2,\n attention_head_dim=4,\n layers_per_block=3,\n block_out_channels=(32, 64),\n cross_attention_dim=4,\n encoder_hid_dim=32,\n )\n scheduler = DDPMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n steps_offset=1,\n beta_schedule=\"squaredcos_cap_v2\",\n clip_sample=True,\n thresholding=False,\n )\n torch.manual_seed(0)\n movq = self.dummy_movq\n torch.manual_seed(0)\n text_encoder = T5EncoderModel.from_pretrained(\"hf-internal-testing/tiny-random-t5\")\n\n torch.manual_seed(0)\n tokenizer = AutoTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-t5\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"movq\": movq,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n # create init_image\n image = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device)\n image = image.cpu().permute(0, 2, 3, 1)[0]\n init_image = Image.fromarray(np.uint8(image)).convert(\"RGB\")\n\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"image\": init_image,\n \"generator\": generator,\n \"strength\": 0.75,\n \"num_inference_steps\": 10,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_dict_tuple_outputs_equivalent(self):\n expected_slice = None\n if torch_device == \"cpu\":\n expected_slice = np.array([0.5762, 0.6112, 0.4150, 0.6018, 0.6167, 0.4626, 0.5426, 0.5641, 0.6536])\n super().test_dict_tuple_outputs_equivalent(expected_slice=expected_slice)\n\n def test_kandinsky3_img2img(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array(\n [0.576259, 0.6132097, 0.41703486, 0.603196, 0.62062526, 0.4655338, 0.5434324, 0.5660727, 0.65433365]\n )\n\n assert (\n np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_slice.flatten()}\"\n\n def test_float16_inference(self):\n super().test_float16_inference(expected_max_diff=1e-1)\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=1e-2)\n\n\n@slow\n@require_torch_gpu\nclass Kandinsky3Img2ImgPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_kandinskyV3_img2img(self):\n pipe = AutoPipelineForImage2Image.from_pretrained(\n \"kandinsky-community/kandinsky-3\", variant=\"fp16\", torch_dtype=torch.float16\n )\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky3/t2i.png\"\n )\n w, h = 512, 512\n image = image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)\n prompt = \"A painting of the inside of a subway train with tiny raccoons.\"\n\n image = pipe(prompt, image=image, strength=0.75, num_inference_steps=5, generator=generator).images[0]\n\n assert image.size == (512, 512)\n\n expected_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky3/i2i.png\"\n )\n\n image_processor = VaeImageProcessor()\n\n image_np = image_processor.pil_to_numpy(image)\n expected_image_np = image_processor.pil_to_numpy(expected_image)\n\n self.assertTrue(np.allclose(image_np, expected_image_np, atol=5e-2))\n" }, { "path": "diffusers/tests/pipelines/latent_consistency_models/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/latent_consistency_models/test_latent_consistency_models.py", "content": "import gc\nimport inspect\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n LatentConsistencyModelPipeline,\n LCMScheduler,\n UNet2DConditionModel,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\nfrom ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS\nfrom ..test_pipelines_common import IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass LatentConsistencyModelPipelineFastTests(\n IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase\n):\n pipeline_class = LatentConsistencyModelPipeline\n params = TEXT_TO_IMAGE_PARAMS - {\"negative_prompt\", \"negative_prompt_embeds\"}\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS - {\"negative_prompt\"}\n image_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(4, 8),\n layers_per_block=1,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n norm_num_groups=2,\n time_cond_proj_dim=32,\n )\n scheduler = LCMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[4, 8],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n norm_num_groups=2,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=64,\n layer_norm_eps=1e-05,\n num_attention_heads=8,\n num_hidden_layers=3,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n \"requires_safety_checker\": False,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_ip_adapter_single(self):\n expected_pipe_slice = None\n if torch_device == \"cpu\":\n expected_pipe_slice = np.array([0.1403, 0.5072, 0.5316, 0.1202, 0.3865, 0.4211, 0.5363, 0.3557, 0.3645])\n return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)\n\n def test_lcm_onestep(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components()\n pipe = LatentConsistencyModelPipeline(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"num_inference_steps\"] = 1\n output = pipe(**inputs)\n image = output.images\n assert image.shape == (1, 64, 64, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array([0.1441, 0.5304, 0.5452, 0.1361, 0.4011, 0.4370, 0.5326, 0.3492, 0.3637])\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_lcm_multistep(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components()\n pipe = LatentConsistencyModelPipeline(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = pipe(**inputs)\n image = output.images\n assert image.shape == (1, 64, 64, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array([0.1403, 0.5072, 0.5316, 0.1202, 0.3865, 0.4211, 0.5363, 0.3557, 0.3645])\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_lcm_custom_timesteps(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components()\n pipe = LatentConsistencyModelPipeline(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n del inputs[\"num_inference_steps\"]\n inputs[\"timesteps\"] = [999, 499]\n output = pipe(**inputs)\n image = output.images\n assert image.shape == (1, 64, 64, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array([0.1403, 0.5072, 0.5316, 0.1202, 0.3865, 0.4211, 0.5363, 0.3557, 0.3645])\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=5e-4)\n\n # skip because lcm pipeline apply cfg differently\n def test_callback_cfg(self):\n pass\n\n # override default test because the final latent variable is \"denoised\" instead of \"latents\"\n def test_callback_inputs(self):\n sig = inspect.signature(self.pipeline_class.__call__)\n\n if not (\"callback_on_step_end_tensor_inputs\" in sig.parameters and \"callback_on_step_end\" in sig.parameters):\n return\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n self.assertTrue(\n hasattr(pipe, \"_callback_tensor_inputs\"),\n f\" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs\",\n )\n\n def callback_inputs_test(pipe, i, t, callback_kwargs):\n missing_callback_inputs = set()\n for v in pipe._callback_tensor_inputs:\n if v not in callback_kwargs:\n missing_callback_inputs.add(v)\n self.assertTrue(\n len(missing_callback_inputs) == 0, f\"Missing callback tensor inputs: {missing_callback_inputs}\"\n )\n last_i = pipe.num_timesteps - 1\n if i == last_i:\n callback_kwargs[\"denoised\"] = torch.zeros_like(callback_kwargs[\"denoised\"])\n return callback_kwargs\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"callback_on_step_end\"] = callback_inputs_test\n inputs[\"callback_on_step_end_tensor_inputs\"] = pipe._callback_tensor_inputs\n inputs[\"output_type\"] = \"latent\"\n\n output = pipe(**inputs)[0]\n assert output.abs().sum() == 0\n\n\n@slow\n@require_torch_gpu\nclass LatentConsistencyModelPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))\n latents = torch.from_numpy(latents).to(device=device, dtype=dtype)\n inputs = {\n \"prompt\": \"a photograph of an astronaut riding a horse\",\n \"latents\": latents,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_lcm_onestep(self):\n pipe = LatentConsistencyModelPipeline.from_pretrained(\"SimianLuo/LCM_Dreamshaper_v7\", safety_checker=None)\n pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n inputs[\"num_inference_steps\"] = 1\n image = pipe(**inputs).images\n assert image.shape == (1, 512, 512, 3)\n\n image_slice = image[0, -3:, -3:, -1].flatten()\n expected_slice = np.array([0.1025, 0.0911, 0.0984, 0.0981, 0.0901, 0.0918, 0.1055, 0.0940, 0.0730])\n assert np.abs(image_slice - expected_slice).max() < 1e-3\n\n def test_lcm_multistep(self):\n pipe = LatentConsistencyModelPipeline.from_pretrained(\"SimianLuo/LCM_Dreamshaper_v7\", safety_checker=None)\n pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = pipe(**inputs).images\n assert image.shape == (1, 512, 512, 3)\n\n image_slice = image[0, -3:, -3:, -1].flatten()\n expected_slice = np.array([0.01855, 0.01855, 0.01489, 0.01392, 0.01782, 0.01465, 0.01831, 0.02539, 0.0])\n assert np.abs(image_slice - expected_slice).max() < 1e-3\n" }, { "path": "diffusers/tests/pipelines/latent_consistency_models/test_latent_consistency_models_img2img.py", "content": "import gc\nimport inspect\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n LatentConsistencyModelImg2ImgPipeline,\n LCMScheduler,\n UNet2DConditionModel,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\nfrom ..pipeline_params import (\n IMAGE_TO_IMAGE_IMAGE_PARAMS,\n TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,\n TEXT_GUIDED_IMAGE_VARIATION_PARAMS,\n)\nfrom ..test_pipelines_common import IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass LatentConsistencyModelImg2ImgPipelineFastTests(\n IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase\n):\n pipeline_class = LatentConsistencyModelImg2ImgPipeline\n params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {\"height\", \"width\", \"negative_prompt\", \"negative_prompt_embeds\"}\n required_optional_params = PipelineTesterMixin.required_optional_params - {\"latents\", \"negative_prompt\"}\n batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS\n image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(4, 8),\n layers_per_block=1,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n norm_num_groups=2,\n time_cond_proj_dim=32,\n )\n scheduler = LCMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[4, 8],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n norm_num_groups=2,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=64,\n layer_norm_eps=1e-05,\n num_attention_heads=8,\n num_hidden_layers=3,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n \"requires_safety_checker\": False,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n image = image / 2 + 0.5\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"image\": image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_ip_adapter_single(self):\n expected_pipe_slice = None\n if torch_device == \"cpu\":\n expected_pipe_slice = np.array([0.4003, 0.3718, 0.2863, 0.5500, 0.5587, 0.3772, 0.4617, 0.4961, 0.4417])\n return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)\n\n def test_lcm_onestep(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"num_inference_steps\"] = 1\n output = pipe(**inputs)\n image = output.images\n assert image.shape == (1, 32, 32, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array([0.4388, 0.3717, 0.2202, 0.7213, 0.6370, 0.3664, 0.5815, 0.6080, 0.4977])\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_lcm_multistep(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = pipe(**inputs)\n image = output.images\n assert image.shape == (1, 32, 32, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array([0.4150, 0.3719, 0.2479, 0.6333, 0.6024, 0.3778, 0.5036, 0.5420, 0.4678])\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_lcm_custom_timesteps(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n del inputs[\"num_inference_steps\"]\n inputs[\"timesteps\"] = [999, 499]\n output = pipe(**inputs)\n image = output.images\n assert image.shape == (1, 32, 32, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array([0.3994, 0.3471, 0.2540, 0.7030, 0.6193, 0.3645, 0.5777, 0.5850, 0.4965])\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=5e-4)\n\n # override default test because the final latent variable is \"denoised\" instead of \"latents\"\n def test_callback_inputs(self):\n sig = inspect.signature(self.pipeline_class.__call__)\n\n if not (\"callback_on_step_end_tensor_inputs\" in sig.parameters and \"callback_on_step_end\" in sig.parameters):\n return\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n self.assertTrue(\n hasattr(pipe, \"_callback_tensor_inputs\"),\n f\" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs\",\n )\n\n def callback_inputs_test(pipe, i, t, callback_kwargs):\n missing_callback_inputs = set()\n for v in pipe._callback_tensor_inputs:\n if v not in callback_kwargs:\n missing_callback_inputs.add(v)\n self.assertTrue(\n len(missing_callback_inputs) == 0, f\"Missing callback tensor inputs: {missing_callback_inputs}\"\n )\n last_i = pipe.num_timesteps - 1\n if i == last_i:\n callback_kwargs[\"denoised\"] = torch.zeros_like(callback_kwargs[\"denoised\"])\n return callback_kwargs\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"callback_on_step_end\"] = callback_inputs_test\n inputs[\"callback_on_step_end_tensor_inputs\"] = pipe._callback_tensor_inputs\n inputs[\"output_type\"] = \"latent\"\n\n output = pipe(**inputs)[0]\n assert output.abs().sum() == 0\n\n\n@slow\n@require_torch_gpu\nclass LatentConsistencyModelImg2ImgPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))\n latents = torch.from_numpy(latents).to(device=device, dtype=dtype)\n init_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_img2img/sketch-mountains-input.png\"\n )\n init_image = init_image.resize((512, 512))\n\n inputs = {\n \"prompt\": \"a photograph of an astronaut riding a horse\",\n \"latents\": latents,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n \"image\": init_image,\n }\n return inputs\n\n def test_lcm_onestep(self):\n pipe = LatentConsistencyModelImg2ImgPipeline.from_pretrained(\n \"SimianLuo/LCM_Dreamshaper_v7\", safety_checker=None\n )\n pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n inputs[\"num_inference_steps\"] = 1\n image = pipe(**inputs).images\n assert image.shape == (1, 512, 512, 3)\n\n image_slice = image[0, -3:, -3:, -1].flatten()\n expected_slice = np.array([0.1950, 0.1961, 0.2308, 0.1786, 0.1837, 0.2320, 0.1898, 0.1885, 0.2309])\n assert np.abs(image_slice - expected_slice).max() < 1e-3\n\n def test_lcm_multistep(self):\n pipe = LatentConsistencyModelImg2ImgPipeline.from_pretrained(\n \"SimianLuo/LCM_Dreamshaper_v7\", safety_checker=None\n )\n pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = pipe(**inputs).images\n assert image.shape == (1, 512, 512, 3)\n\n image_slice = image[0, -3:, -3:, -1].flatten()\n expected_slice = np.array([0.3756, 0.3816, 0.3767, 0.3718, 0.3739, 0.3735, 0.3863, 0.3803, 0.3563])\n assert np.abs(image_slice - expected_slice).max() < 1e-3\n" }, { "path": "diffusers/tests/pipelines/latent_diffusion/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/latent_diffusion/test_latent_diffusion.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNet2DConditionModel\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n load_numpy,\n nightly,\n require_torch_gpu,\n torch_device,\n)\n\nfrom ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass LDMTextToImagePipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = LDMTextToImagePipeline\n params = TEXT_TO_IMAGE_PARAMS - {\n \"negative_prompt\",\n \"negative_prompt_embeds\",\n \"cross_attention_kwargs\",\n \"prompt_embeds\",\n }\n required_optional_params = PipelineTesterMixin.required_optional_params - {\n \"num_images_per_prompt\",\n \"callback\",\n \"callback_steps\",\n }\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n )\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=(32, 64),\n in_channels=3,\n out_channels=3,\n down_block_types=(\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"),\n up_block_types=(\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"),\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vqvae\": vae,\n \"bert\": text_encoder,\n \"tokenizer\": tokenizer,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_inference_text2img(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components()\n pipe = LDMTextToImagePipeline(**components)\n pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 16, 16, 3)\n expected_slice = np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n\n@nightly\n@require_torch_gpu\nclass LDMTextToImagePipelineSlowTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, dtype=torch.float32, seed=0):\n generator = torch.manual_seed(seed)\n latents = np.random.RandomState(seed).standard_normal((1, 4, 32, 32))\n latents = torch.from_numpy(latents).to(device=device, dtype=dtype)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"latents\": latents,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_ldm_default_ddim(self):\n pipe = LDMTextToImagePipeline.from_pretrained(\"CompVis/ldm-text2im-large-256\").to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 256, 256, 3)\n expected_slice = np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878])\n max_diff = np.abs(expected_slice - image_slice).max()\n assert max_diff < 1e-3\n\n\n@nightly\n@require_torch_gpu\nclass LDMTextToImagePipelineNightlyTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, dtype=torch.float32, seed=0):\n generator = torch.manual_seed(seed)\n latents = np.random.RandomState(seed).standard_normal((1, 4, 32, 32))\n latents = torch.from_numpy(latents).to(device=device, dtype=dtype)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"latents\": latents,\n \"generator\": generator,\n \"num_inference_steps\": 50,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_ldm_default_ddim(self):\n pipe = LDMTextToImagePipeline.from_pretrained(\"CompVis/ldm-text2im-large-256\").to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n" }, { "path": "diffusers/tests/pipelines/latent_diffusion/test_latent_diffusion_superresolution.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\n\nfrom diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNet2DModel, VQModel\nfrom diffusers.utils import PIL_INTERPOLATION\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n nightly,\n require_torch,\n torch_device,\n)\n\n\nenable_full_determinism()\n\n\nclass LDMSuperResolutionPipelineFastTests(unittest.TestCase):\n @property\n def dummy_image(self):\n batch_size = 1\n num_channels = 3\n sizes = (32, 32)\n\n image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)\n return image\n\n @property\n def dummy_uncond_unet(self):\n torch.manual_seed(0)\n model = UNet2DModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=6,\n out_channels=3,\n down_block_types=(\"DownBlock2D\", \"AttnDownBlock2D\"),\n up_block_types=(\"AttnUpBlock2D\", \"UpBlock2D\"),\n )\n return model\n\n @property\n def dummy_vq_model(self):\n torch.manual_seed(0)\n model = VQModel(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=3,\n )\n return model\n\n def test_inference_superresolution(self):\n device = \"cpu\"\n unet = self.dummy_uncond_unet\n scheduler = DDIMScheduler()\n vqvae = self.dummy_vq_model\n\n ldm = LDMSuperResolutionPipeline(unet=unet, vqvae=vqvae, scheduler=scheduler)\n ldm.to(device)\n ldm.set_progress_bar_config(disable=None)\n\n init_image = self.dummy_image.to(device)\n\n generator = torch.Generator(device=device).manual_seed(0)\n image = ldm(image=init_image, generator=generator, num_inference_steps=2, output_type=\"np\").images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.8678, 0.8245, 0.6381, 0.6830, 0.4385, 0.5599, 0.4641, 0.6201, 0.5150])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n @unittest.skipIf(torch_device != \"cuda\", \"This test requires a GPU\")\n def test_inference_superresolution_fp16(self):\n unet = self.dummy_uncond_unet\n scheduler = DDIMScheduler()\n vqvae = self.dummy_vq_model\n\n # put models in fp16\n unet = unet.half()\n vqvae = vqvae.half()\n\n ldm = LDMSuperResolutionPipeline(unet=unet, vqvae=vqvae, scheduler=scheduler)\n ldm.to(torch_device)\n ldm.set_progress_bar_config(disable=None)\n\n init_image = self.dummy_image.to(torch_device)\n\n image = ldm(init_image, num_inference_steps=2, output_type=\"np\").images\n\n assert image.shape == (1, 64, 64, 3)\n\n\n@nightly\n@require_torch\nclass LDMSuperResolutionPipelineIntegrationTests(unittest.TestCase):\n def test_inference_superresolution(self):\n init_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/vq_diffusion/teddy_bear_pool.png\"\n )\n init_image = init_image.resize((64, 64), resample=PIL_INTERPOLATION[\"lanczos\"])\n\n ldm = LDMSuperResolutionPipeline.from_pretrained(\"duongna/ldm-super-resolution\")\n ldm.set_progress_bar_config(disable=None)\n\n generator = torch.manual_seed(0)\n image = ldm(image=init_image, generator=generator, num_inference_steps=20, output_type=\"np\").images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 256, 256, 3)\n expected_slice = np.array([0.7644, 0.7679, 0.7642, 0.7633, 0.7666, 0.7560, 0.7425, 0.7257, 0.6907])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n" }, { "path": "diffusers/tests/pipelines/ledits_pp/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/ledits_pp/test_ledits_pp_stable_diffusion.py", "content": "# coding=utf-8\n# Copyright 2023 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DPMSolverMultistepScheduler,\n LEditsPPPipelineStableDiffusion,\n UNet2DConditionModel,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n require_torch_gpu,\n skip_mps,\n slow,\n torch_device,\n)\n\n\nenable_full_determinism()\n\n\n@skip_mps\nclass LEditsPPPipelineStableDiffusionFastTests(unittest.TestCase):\n pipeline_class = LEditsPPPipelineStableDiffusion\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n )\n scheduler = DPMSolverMultistepScheduler(algorithm_type=\"sde-dpmsolver++\", solver_order=2)\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"generator\": generator,\n \"editing_prompt\": [\"wearing glasses\", \"sunshine\"],\n \"reverse_editing_direction\": [False, True],\n \"edit_guidance_scale\": [10.0, 5.0],\n }\n return inputs\n\n def get_dummy_inversion_inputs(self, device, seed=0):\n images = floats_tensor((2, 3, 32, 32), rng=random.Random(0)).cpu().permute(0, 2, 3, 1)\n images = 255 * images\n image_1 = Image.fromarray(np.uint8(images[0])).convert(\"RGB\")\n image_2 = Image.fromarray(np.uint8(images[1])).convert(\"RGB\")\n\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n inputs = {\n \"image\": [image_1, image_2],\n \"source_prompt\": \"\",\n \"source_guidance_scale\": 3.5,\n \"num_inversion_steps\": 20,\n \"skip\": 0.15,\n \"generator\": generator,\n }\n return inputs\n\n def test_ledits_pp_inversion(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = LEditsPPPipelineStableDiffusion(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inversion_inputs(device)\n inputs[\"image\"] = inputs[\"image\"][0]\n sd_pipe.invert(**inputs)\n assert sd_pipe.init_latents.shape == (\n 1,\n 4,\n int(32 / sd_pipe.vae_scale_factor),\n int(32 / sd_pipe.vae_scale_factor),\n )\n\n latent_slice = sd_pipe.init_latents[0, -1, -3:, -3:].to(device)\n print(latent_slice.flatten())\n expected_slice = np.array([-0.9084, -0.0367, 0.2940, 0.0839, 0.6890, 0.2651, -0.7104, 2.1090, -0.7822])\n assert np.abs(latent_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_ledits_pp_inversion_batch(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = LEditsPPPipelineStableDiffusion(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inversion_inputs(device)\n sd_pipe.invert(**inputs)\n assert sd_pipe.init_latents.shape == (\n 2,\n 4,\n int(32 / sd_pipe.vae_scale_factor),\n int(32 / sd_pipe.vae_scale_factor),\n )\n\n latent_slice = sd_pipe.init_latents[0, -1, -3:, -3:].to(device)\n print(latent_slice.flatten())\n expected_slice = np.array([0.2528, 0.1458, -0.2166, 0.4565, -0.5657, -1.0286, -0.9961, 0.5933, 1.1173])\n assert np.abs(latent_slice.flatten() - expected_slice).max() < 1e-3\n\n latent_slice = sd_pipe.init_latents[1, -1, -3:, -3:].to(device)\n print(latent_slice.flatten())\n expected_slice = np.array([-0.0796, 2.0583, 0.5501, 0.5358, 0.0282, -0.2803, -1.0470, 0.7023, -0.0072])\n assert np.abs(latent_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_ledits_pp_warmup_steps(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n pipe = LEditsPPPipelineStableDiffusion(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inversion_inputs = self.get_dummy_inversion_inputs(device)\n pipe.invert(**inversion_inputs)\n\n inputs = self.get_dummy_inputs(device)\n\n inputs[\"edit_warmup_steps\"] = [0, 5]\n pipe(**inputs).images\n\n inputs[\"edit_warmup_steps\"] = [5, 0]\n pipe(**inputs).images\n\n inputs[\"edit_warmup_steps\"] = [5, 10]\n pipe(**inputs).images\n\n inputs[\"edit_warmup_steps\"] = [10, 5]\n pipe(**inputs).images\n\n\n@slow\n@require_torch_gpu\nclass LEditsPPPipelineStableDiffusionSlowTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n @classmethod\n def setUpClass(cls):\n raw_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png\"\n )\n raw_image = raw_image.convert(\"RGB\").resize((512, 512))\n cls.raw_image = raw_image\n\n def test_ledits_pp_editing(self):\n pipe = LEditsPPPipelineStableDiffusion.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", safety_checker=None, torch_dtype=torch.float16\n )\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.manual_seed(0)\n _ = pipe.invert(image=self.raw_image, generator=generator)\n generator = torch.manual_seed(0)\n inputs = {\n \"generator\": generator,\n \"editing_prompt\": [\"cat\", \"dog\"],\n \"reverse_editing_direction\": [True, False],\n \"edit_guidance_scale\": [5.0, 5.0],\n \"edit_threshold\": [0.8, 0.8],\n }\n reconstruction = pipe(**inputs, output_type=\"np\").images[0]\n\n output_slice = reconstruction[150:153, 140:143, -1]\n output_slice = output_slice.flatten()\n expected_slice = np.array(\n [0.9453125, 0.93310547, 0.84521484, 0.94628906, 0.9111328, 0.80859375, 0.93847656, 0.9042969, 0.8144531]\n )\n assert np.abs(output_slice - expected_slice).max() < 1e-2\n" }, { "path": "diffusers/tests/pipelines/ledits_pp/test_ledits_pp_stable_diffusion_xl.py", "content": "# coding=utf-8\n# Copyright 2023 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextConfig,\n CLIPTextModel,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionConfig,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers import (\n AutoencoderKL,\n DPMSolverMultistepScheduler,\n LEditsPPPipelineStableDiffusionXL,\n UNet2DConditionModel,\n)\n\n# from diffusers.image_processor import VaeImageProcessor\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n require_torch_gpu,\n skip_mps,\n slow,\n torch_device,\n)\n\n\nenable_full_determinism()\n\n\n@skip_mps\nclass LEditsPPPipelineStableDiffusionXLFastTests(unittest.TestCase):\n pipeline_class = LEditsPPPipelineStableDiffusionXL\n\n def get_dummy_components(self, skip_first_text_encoder=False, time_cond_proj_dim=None):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n time_cond_proj_dim=time_cond_proj_dim,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2),\n projection_class_embeddings_input_dim=80, # 6 * 8 + 32\n cross_attention_dim=64 if not skip_first_text_encoder else 32,\n )\n scheduler = DPMSolverMultistepScheduler(algorithm_type=\"sde-dpmsolver++\", solver_order=2)\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n sample_size=128,\n )\n torch.manual_seed(0)\n image_encoder_config = CLIPVisionConfig(\n hidden_size=32,\n image_size=224,\n projection_dim=32,\n intermediate_size=37,\n num_attention_heads=4,\n num_channels=3,\n num_hidden_layers=5,\n patch_size=14,\n )\n image_encoder = CLIPVisionModelWithProjection(image_encoder_config)\n\n feature_extractor = CLIPImageProcessor(\n crop_size=224,\n do_center_crop=True,\n do_normalize=True,\n do_resize=True,\n image_mean=[0.48145466, 0.4578275, 0.40821073],\n image_std=[0.26862954, 0.26130258, 0.27577711],\n resample=3,\n size=224,\n )\n\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n # SD2-specific config below\n hidden_act=\"gelu\",\n projection_dim=32,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)\n tokenizer_2 = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder if not skip_first_text_encoder else None,\n \"tokenizer\": tokenizer if not skip_first_text_encoder else None,\n \"text_encoder_2\": text_encoder_2,\n \"tokenizer_2\": tokenizer_2,\n \"image_encoder\": image_encoder,\n \"feature_extractor\": feature_extractor,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"generator\": generator,\n \"editing_prompt\": [\"wearing glasses\", \"sunshine\"],\n \"reverse_editing_direction\": [False, True],\n \"edit_guidance_scale\": [10.0, 5.0],\n }\n return inputs\n\n def get_dummy_inversion_inputs(self, device, seed=0):\n images = floats_tensor((2, 3, 32, 32), rng=random.Random(0)).cpu().permute(0, 2, 3, 1)\n images = 255 * images\n image_1 = Image.fromarray(np.uint8(images[0])).convert(\"RGB\")\n image_2 = Image.fromarray(np.uint8(images[1])).convert(\"RGB\")\n\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n inputs = {\n \"image\": [image_1, image_2],\n \"source_prompt\": \"\",\n \"source_guidance_scale\": 3.5,\n \"num_inversion_steps\": 20,\n \"skip\": 0.15,\n \"generator\": generator,\n }\n return inputs\n\n def test_ledits_pp_inversion(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = LEditsPPPipelineStableDiffusionXL(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inversion_inputs(device)\n inputs[\"image\"] = inputs[\"image\"][0]\n sd_pipe.invert(**inputs)\n assert sd_pipe.init_latents.shape == (\n 1,\n 4,\n int(32 / sd_pipe.vae_scale_factor),\n int(32 / sd_pipe.vae_scale_factor),\n )\n\n latent_slice = sd_pipe.init_latents[0, -1, -3:, -3:].to(device)\n expected_slice = np.array([-0.9084, -0.0367, 0.2940, 0.0839, 0.6890, 0.2651, -0.7103, 2.1090, -0.7821])\n assert np.abs(latent_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_ledits_pp_inversion_batch(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = LEditsPPPipelineStableDiffusionXL(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inversion_inputs(device)\n sd_pipe.invert(**inputs)\n assert sd_pipe.init_latents.shape == (\n 2,\n 4,\n int(32 / sd_pipe.vae_scale_factor),\n int(32 / sd_pipe.vae_scale_factor),\n )\n\n latent_slice = sd_pipe.init_latents[0, -1, -3:, -3:].to(device)\n print(latent_slice.flatten())\n expected_slice = np.array([0.2528, 0.1458, -0.2166, 0.4565, -0.5656, -1.0286, -0.9961, 0.5933, 1.1172])\n assert np.abs(latent_slice.flatten() - expected_slice).max() < 1e-3\n\n latent_slice = sd_pipe.init_latents[1, -1, -3:, -3:].to(device)\n print(latent_slice.flatten())\n expected_slice = np.array([-0.0796, 2.0583, 0.5500, 0.5358, 0.0282, -0.2803, -1.0470, 0.7024, -0.0072])\n print(latent_slice.flatten())\n assert np.abs(latent_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_ledits_pp_warmup_steps(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n pipe = LEditsPPPipelineStableDiffusionXL(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inversion_inputs = self.get_dummy_inversion_inputs(device)\n inversion_inputs[\"image\"] = inversion_inputs[\"image\"][0]\n pipe.invert(**inversion_inputs)\n\n inputs = self.get_dummy_inputs(device)\n\n inputs[\"edit_warmup_steps\"] = [0, 5]\n pipe(**inputs).images\n\n inputs[\"edit_warmup_steps\"] = [5, 0]\n pipe(**inputs).images\n\n inputs[\"edit_warmup_steps\"] = [5, 10]\n pipe(**inputs).images\n\n inputs[\"edit_warmup_steps\"] = [10, 5]\n pipe(**inputs).images\n\n\n@slow\n@require_torch_gpu\nclass LEditsPPPipelineStableDiffusionXLSlowTests(unittest.TestCase):\n @classmethod\n def setUpClass(cls):\n raw_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png\"\n )\n raw_image = raw_image.convert(\"RGB\").resize((512, 512))\n cls.raw_image = raw_image\n\n def test_ledits_pp_edit(self):\n pipe = LEditsPPPipelineStableDiffusionXL.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", safety_checker=None, add_watermarker=None\n )\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.manual_seed(0)\n _ = pipe.invert(image=self.raw_image, generator=generator, num_zero_noise_steps=0)\n inputs = {\n \"generator\": generator,\n \"editing_prompt\": [\"cat\", \"dog\"],\n \"reverse_editing_direction\": [True, False],\n \"edit_guidance_scale\": [2.0, 4.0],\n \"edit_threshold\": [0.8, 0.8],\n }\n reconstruction = pipe(**inputs, output_type=\"np\").images[0]\n\n output_slice = reconstruction[150:153, 140:143, -1]\n output_slice = output_slice.flatten()\n expected_slice = np.array(\n [0.56419, 0.44121838, 0.2765603, 0.5708484, 0.42763475, 0.30945742, 0.5387106, 0.4735807, 0.3547244]\n )\n assert np.abs(output_slice - expected_slice).max() < 1e-3\n" }, { "path": "diffusers/tests/pipelines/marigold/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/marigold/test_marigold_depth.py", "content": "# Copyright 2024 Marigold authors, PRS ETH Zurich. All rights reserved.\n# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# --------------------------------------------------------------------------\n# More information and citation instructions are available on the\n# Marigold project website: https://marigoldmonodepth.github.io\n# --------------------------------------------------------------------------\nimport gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n AutoencoderTiny,\n LCMScheduler,\n MarigoldDepthPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n require_torch_gpu,\n slow,\n)\n\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass MarigoldDepthPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = MarigoldDepthPipeline\n params = frozenset([\"image\"])\n batch_params = frozenset([\"image\"])\n image_params = frozenset([\"image\"])\n image_latents_params = frozenset([\"latents\"])\n callback_cfg_params = frozenset([])\n test_xformers_attention = False\n required_optional_params = frozenset(\n [\n \"num_inference_steps\",\n \"generator\",\n \"output_type\",\n ]\n )\n\n def get_dummy_components(self, time_cond_proj_dim=None):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n time_cond_proj_dim=time_cond_proj_dim,\n sample_size=32,\n in_channels=8,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n )\n scheduler = LCMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n prediction_type=\"v_prediction\",\n set_alpha_to_one=False,\n steps_offset=1,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n thresholding=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"prediction_type\": \"depth\",\n \"scale_invariant\": True,\n \"shift_invariant\": True,\n }\n return components\n\n def get_dummy_tiny_autoencoder(self):\n return AutoencoderTiny(in_channels=3, out_channels=3, latent_channels=4)\n\n def get_dummy_inputs(self, device, seed=0):\n image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n image = image / 2 + 0.5\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"image\": image,\n \"num_inference_steps\": 1,\n \"processing_resolution\": 0,\n \"generator\": generator,\n \"output_type\": \"np\",\n }\n return inputs\n\n def _test_marigold_depth(\n self,\n generator_seed: int = 0,\n expected_slice: np.ndarray = None,\n atol: float = 1e-4,\n **pipe_kwargs,\n ):\n device = \"cpu\"\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n pipe_inputs = self.get_dummy_inputs(device, seed=generator_seed)\n pipe_inputs.update(**pipe_kwargs)\n\n prediction = pipe(**pipe_inputs).prediction\n\n prediction_slice = prediction[0, -3:, -3:, -1].flatten()\n\n if pipe_inputs.get(\"match_input_resolution\", True):\n self.assertEqual(prediction.shape, (1, 32, 32, 1), \"Unexpected output resolution\")\n else:\n self.assertTrue(prediction.shape[0] == 1 and prediction.shape[3] == 1, \"Unexpected output dimensions\")\n self.assertEqual(\n max(prediction.shape[1:3]),\n pipe_inputs.get(\"processing_resolution\", 768),\n \"Unexpected output resolution\",\n )\n\n self.assertTrue(np.allclose(prediction_slice, expected_slice, atol=atol))\n\n def test_marigold_depth_dummy_defaults(self):\n self._test_marigold_depth(\n expected_slice=np.array([0.4529, 0.5184, 0.4985, 0.4355, 0.4273, 0.4153, 0.5229, 0.4818, 0.4627]),\n )\n\n def test_marigold_depth_dummy_G0_S1_P32_E1_B1_M1(self):\n self._test_marigold_depth(\n generator_seed=0,\n expected_slice=np.array([0.4529, 0.5184, 0.4985, 0.4355, 0.4273, 0.4153, 0.5229, 0.4818, 0.4627]),\n num_inference_steps=1,\n processing_resolution=32,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_depth_dummy_G0_S1_P16_E1_B1_M1(self):\n self._test_marigold_depth(\n generator_seed=0,\n expected_slice=np.array([0.4511, 0.4531, 0.4542, 0.5024, 0.4987, 0.4969, 0.5281, 0.5215, 0.5182]),\n num_inference_steps=1,\n processing_resolution=16,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_depth_dummy_G2024_S1_P32_E1_B1_M1(self):\n self._test_marigold_depth(\n generator_seed=2024,\n expected_slice=np.array([0.4671, 0.4739, 0.5130, 0.4308, 0.4411, 0.4720, 0.5064, 0.4796, 0.4795]),\n num_inference_steps=1,\n processing_resolution=32,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_depth_dummy_G0_S2_P32_E1_B1_M1(self):\n self._test_marigold_depth(\n generator_seed=0,\n expected_slice=np.array([0.4165, 0.4485, 0.4647, 0.4003, 0.4577, 0.5074, 0.5106, 0.5077, 0.5042]),\n num_inference_steps=2,\n processing_resolution=32,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_depth_dummy_G0_S1_P64_E1_B1_M1(self):\n self._test_marigold_depth(\n generator_seed=0,\n expected_slice=np.array([0.4817, 0.5425, 0.5146, 0.5367, 0.5034, 0.4743, 0.4395, 0.4734, 0.4399]),\n num_inference_steps=1,\n processing_resolution=64,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_depth_dummy_G0_S1_P32_E3_B1_M1(self):\n self._test_marigold_depth(\n generator_seed=0,\n expected_slice=np.array([0.3260, 0.3591, 0.2837, 0.2971, 0.2750, 0.2426, 0.4200, 0.3588, 0.3254]),\n num_inference_steps=1,\n processing_resolution=32,\n ensemble_size=3,\n ensembling_kwargs={\"reduction\": \"mean\"},\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_depth_dummy_G0_S1_P32_E4_B2_M1(self):\n self._test_marigold_depth(\n generator_seed=0,\n expected_slice=np.array([0.3180, 0.4194, 0.3013, 0.2902, 0.3245, 0.2897, 0.4718, 0.4174, 0.3705]),\n num_inference_steps=1,\n processing_resolution=32,\n ensemble_size=4,\n ensembling_kwargs={\"reduction\": \"mean\"},\n batch_size=2,\n match_input_resolution=True,\n )\n\n def test_marigold_depth_dummy_G0_S1_P16_E1_B1_M0(self):\n self._test_marigold_depth(\n generator_seed=0,\n expected_slice=np.array([0.5515, 0.4588, 0.4197, 0.4741, 0.4229, 0.4328, 0.5333, 0.5314, 0.5182]),\n num_inference_steps=1,\n processing_resolution=16,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=False,\n )\n\n def test_marigold_depth_dummy_no_num_inference_steps(self):\n with self.assertRaises(ValueError) as e:\n self._test_marigold_depth(\n num_inference_steps=None,\n expected_slice=np.array([0.0]),\n )\n self.assertIn(\"num_inference_steps\", str(e))\n\n def test_marigold_depth_dummy_no_processing_resolution(self):\n with self.assertRaises(ValueError) as e:\n self._test_marigold_depth(\n processing_resolution=None,\n expected_slice=np.array([0.0]),\n )\n self.assertIn(\"processing_resolution\", str(e))\n\n\n@slow\n@require_torch_gpu\nclass MarigoldDepthPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def _test_marigold_depth(\n self,\n is_fp16: bool = True,\n device: str = \"cuda\",\n generator_seed: int = 0,\n expected_slice: np.ndarray = None,\n model_id: str = \"prs-eth/marigold-lcm-v1-0\",\n image_url: str = \"https://marigoldmonodepth.github.io/images/einstein.jpg\",\n atol: float = 1e-4,\n **pipe_kwargs,\n ):\n from_pretrained_kwargs = {}\n if is_fp16:\n from_pretrained_kwargs[\"variant\"] = \"fp16\"\n from_pretrained_kwargs[\"torch_dtype\"] = torch.float16\n\n pipe = MarigoldDepthPipeline.from_pretrained(model_id, **from_pretrained_kwargs)\n if device == \"cuda\":\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=device).manual_seed(generator_seed)\n\n image = load_image(image_url)\n width, height = image.size\n\n prediction = pipe(image, generator=generator, **pipe_kwargs).prediction\n\n prediction_slice = prediction[0, -3:, -3:, -1].flatten()\n\n if pipe_kwargs.get(\"match_input_resolution\", True):\n self.assertEqual(prediction.shape, (1, height, width, 1), \"Unexpected output resolution\")\n else:\n self.assertTrue(prediction.shape[0] == 1 and prediction.shape[3] == 1, \"Unexpected output dimensions\")\n self.assertEqual(\n max(prediction.shape[1:3]),\n pipe_kwargs.get(\"processing_resolution\", 768),\n \"Unexpected output resolution\",\n )\n\n self.assertTrue(np.allclose(prediction_slice, expected_slice, atol=atol))\n\n def test_marigold_depth_einstein_f32_cpu_G0_S1_P32_E1_B1_M1(self):\n self._test_marigold_depth(\n is_fp16=False,\n device=\"cpu\",\n generator_seed=0,\n expected_slice=np.array([0.4323, 0.4323, 0.4323, 0.4323, 0.4323, 0.4323, 0.4323, 0.4323, 0.4323]),\n num_inference_steps=1,\n processing_resolution=32,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_depth_einstein_f32_cuda_G0_S1_P768_E1_B1_M1(self):\n self._test_marigold_depth(\n is_fp16=False,\n device=\"cuda\",\n generator_seed=0,\n expected_slice=np.array([0.1244, 0.1265, 0.1292, 0.1240, 0.1252, 0.1266, 0.1246, 0.1226, 0.1180]),\n num_inference_steps=1,\n processing_resolution=768,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_depth_einstein_f16_cuda_G0_S1_P768_E1_B1_M1(self):\n self._test_marigold_depth(\n is_fp16=True,\n device=\"cuda\",\n generator_seed=0,\n expected_slice=np.array([0.1241, 0.1262, 0.1290, 0.1238, 0.1250, 0.1265, 0.1244, 0.1225, 0.1179]),\n num_inference_steps=1,\n processing_resolution=768,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_depth_einstein_f16_cuda_G2024_S1_P768_E1_B1_M1(self):\n self._test_marigold_depth(\n is_fp16=True,\n device=\"cuda\",\n generator_seed=2024,\n expected_slice=np.array([0.1710, 0.1725, 0.1738, 0.1700, 0.1700, 0.1696, 0.1698, 0.1663, 0.1592]),\n num_inference_steps=1,\n processing_resolution=768,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_depth_einstein_f16_cuda_G0_S2_P768_E1_B1_M1(self):\n self._test_marigold_depth(\n is_fp16=True,\n device=\"cuda\",\n generator_seed=0,\n expected_slice=np.array([0.1085, 0.1098, 0.1110, 0.1081, 0.1085, 0.1082, 0.1085, 0.1057, 0.0996]),\n num_inference_steps=2,\n processing_resolution=768,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_depth_einstein_f16_cuda_G0_S1_P512_E1_B1_M1(self):\n self._test_marigold_depth(\n is_fp16=True,\n device=\"cuda\",\n generator_seed=0,\n expected_slice=np.array([0.2683, 0.2693, 0.2698, 0.2666, 0.2632, 0.2615, 0.2656, 0.2603, 0.2573]),\n num_inference_steps=1,\n processing_resolution=512,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_depth_einstein_f16_cuda_G0_S1_P768_E3_B1_M1(self):\n self._test_marigold_depth(\n is_fp16=True,\n device=\"cuda\",\n generator_seed=0,\n expected_slice=np.array([0.1200, 0.1215, 0.1237, 0.1193, 0.1197, 0.1202, 0.1196, 0.1166, 0.1109]),\n num_inference_steps=1,\n processing_resolution=768,\n ensemble_size=3,\n ensembling_kwargs={\"reduction\": \"mean\"},\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_depth_einstein_f16_cuda_G0_S1_P768_E4_B2_M1(self):\n self._test_marigold_depth(\n is_fp16=True,\n device=\"cuda\",\n generator_seed=0,\n expected_slice=np.array([0.1121, 0.1135, 0.1155, 0.1111, 0.1115, 0.1118, 0.1111, 0.1079, 0.1019]),\n num_inference_steps=1,\n processing_resolution=768,\n ensemble_size=4,\n ensembling_kwargs={\"reduction\": \"mean\"},\n batch_size=2,\n match_input_resolution=True,\n )\n\n def test_marigold_depth_einstein_f16_cuda_G0_S1_P512_E1_B1_M0(self):\n self._test_marigold_depth(\n is_fp16=True,\n device=\"cuda\",\n generator_seed=0,\n expected_slice=np.array([0.2671, 0.2690, 0.2720, 0.2659, 0.2676, 0.2739, 0.2664, 0.2686, 0.2573]),\n num_inference_steps=1,\n processing_resolution=512,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=False,\n )\n" }, { "path": "diffusers/tests/pipelines/marigold/test_marigold_normals.py", "content": "# Copyright 2024 Marigold authors, PRS ETH Zurich. All rights reserved.\n# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# --------------------------------------------------------------------------\n# More information and citation instructions are available on the\n# Marigold project website: https://marigoldmonodepth.github.io\n# --------------------------------------------------------------------------\nimport gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n AutoencoderTiny,\n LCMScheduler,\n MarigoldNormalsPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n require_torch_gpu,\n slow,\n)\n\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass MarigoldNormalsPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = MarigoldNormalsPipeline\n params = frozenset([\"image\"])\n batch_params = frozenset([\"image\"])\n image_params = frozenset([\"image\"])\n image_latents_params = frozenset([\"latents\"])\n callback_cfg_params = frozenset([])\n test_xformers_attention = False\n required_optional_params = frozenset(\n [\n \"num_inference_steps\",\n \"generator\",\n \"output_type\",\n ]\n )\n\n def get_dummy_components(self, time_cond_proj_dim=None):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n time_cond_proj_dim=time_cond_proj_dim,\n sample_size=32,\n in_channels=8,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n )\n torch.manual_seed(0)\n scheduler = LCMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n prediction_type=\"v_prediction\",\n set_alpha_to_one=False,\n steps_offset=1,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n thresholding=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"prediction_type\": \"normals\",\n \"use_full_z_range\": True,\n }\n return components\n\n def get_dummy_tiny_autoencoder(self):\n return AutoencoderTiny(in_channels=3, out_channels=3, latent_channels=4)\n\n def get_dummy_inputs(self, device, seed=0):\n image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n image = image / 2 + 0.5\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"image\": image,\n \"num_inference_steps\": 1,\n \"processing_resolution\": 0,\n \"generator\": generator,\n \"output_type\": \"np\",\n }\n return inputs\n\n def _test_marigold_normals(\n self,\n generator_seed: int = 0,\n expected_slice: np.ndarray = None,\n atol: float = 1e-4,\n **pipe_kwargs,\n ):\n device = \"cpu\"\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n pipe_inputs = self.get_dummy_inputs(device, seed=generator_seed)\n pipe_inputs.update(**pipe_kwargs)\n\n prediction = pipe(**pipe_inputs).prediction\n\n prediction_slice = prediction[0, -3:, -3:, -1].flatten()\n\n if pipe_inputs.get(\"match_input_resolution\", True):\n self.assertEqual(prediction.shape, (1, 32, 32, 3), \"Unexpected output resolution\")\n else:\n self.assertTrue(prediction.shape[0] == 1 and prediction.shape[3] == 3, \"Unexpected output dimensions\")\n self.assertEqual(\n max(prediction.shape[1:3]),\n pipe_inputs.get(\"processing_resolution\", 768),\n \"Unexpected output resolution\",\n )\n\n self.assertTrue(np.allclose(prediction_slice, expected_slice, atol=atol))\n\n def test_marigold_depth_dummy_defaults(self):\n self._test_marigold_normals(\n expected_slice=np.array([0.0967, 0.5234, 0.1448, -0.3155, -0.2550, -0.5578, 0.6854, 0.5657, -0.1263]),\n )\n\n def test_marigold_depth_dummy_G0_S1_P32_E1_B1_M1(self):\n self._test_marigold_normals(\n generator_seed=0,\n expected_slice=np.array([0.0967, 0.5234, 0.1448, -0.3155, -0.2550, -0.5578, 0.6854, 0.5657, -0.1263]),\n num_inference_steps=1,\n processing_resolution=32,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_depth_dummy_G0_S1_P16_E1_B1_M1(self):\n self._test_marigold_normals(\n generator_seed=0,\n expected_slice=np.array([-0.4128, -0.5918, -0.6540, 0.2446, -0.2687, -0.4607, 0.2935, -0.0483, -0.2086]),\n num_inference_steps=1,\n processing_resolution=16,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_depth_dummy_G2024_S1_P32_E1_B1_M1(self):\n self._test_marigold_normals(\n generator_seed=2024,\n expected_slice=np.array([0.5731, -0.7631, -0.0199, 0.1609, -0.4628, -0.7044, 0.5761, -0.3471, -0.4498]),\n num_inference_steps=1,\n processing_resolution=32,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_depth_dummy_G0_S2_P32_E1_B1_M1(self):\n self._test_marigold_normals(\n generator_seed=0,\n expected_slice=np.array([0.1017, -0.6823, -0.2533, 0.1988, 0.3389, 0.8478, 0.7757, 0.5220, 0.8668]),\n num_inference_steps=2,\n processing_resolution=32,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_depth_dummy_G0_S1_P64_E1_B1_M1(self):\n self._test_marigold_normals(\n generator_seed=0,\n expected_slice=np.array([-0.2391, 0.7969, 0.6224, 0.0698, 0.5669, -0.2167, -0.1362, -0.8945, -0.5501]),\n num_inference_steps=1,\n processing_resolution=64,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_depth_dummy_G0_S1_P32_E3_B1_M1(self):\n self._test_marigold_normals(\n generator_seed=0,\n expected_slice=np.array([0.3826, -0.9634, -0.3835, 0.3514, 0.0691, -0.6182, 0.8709, 0.1590, -0.2181]),\n num_inference_steps=1,\n processing_resolution=32,\n ensemble_size=3,\n ensembling_kwargs={\"reduction\": \"mean\"},\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_depth_dummy_G0_S1_P32_E4_B2_M1(self):\n self._test_marigold_normals(\n generator_seed=0,\n expected_slice=np.array([0.2500, -0.3928, -0.2415, 0.1133, 0.2357, -0.4223, 0.9967, 0.4859, -0.1282]),\n num_inference_steps=1,\n processing_resolution=32,\n ensemble_size=4,\n ensembling_kwargs={\"reduction\": \"mean\"},\n batch_size=2,\n match_input_resolution=True,\n )\n\n def test_marigold_depth_dummy_G0_S1_P16_E1_B1_M0(self):\n self._test_marigold_normals(\n generator_seed=0,\n expected_slice=np.array([0.9588, 0.3326, -0.0825, -0.0994, -0.3534, -0.4302, 0.3562, 0.4421, -0.2086]),\n num_inference_steps=1,\n processing_resolution=16,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=False,\n )\n\n def test_marigold_depth_dummy_no_num_inference_steps(self):\n with self.assertRaises(ValueError) as e:\n self._test_marigold_normals(\n num_inference_steps=None,\n expected_slice=np.array([0.0]),\n )\n self.assertIn(\"num_inference_steps\", str(e))\n\n def test_marigold_depth_dummy_no_processing_resolution(self):\n with self.assertRaises(ValueError) as e:\n self._test_marigold_normals(\n processing_resolution=None,\n expected_slice=np.array([0.0]),\n )\n self.assertIn(\"processing_resolution\", str(e))\n\n\n@slow\n@require_torch_gpu\nclass MarigoldNormalsPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def _test_marigold_normals(\n self,\n is_fp16: bool = True,\n device: str = \"cuda\",\n generator_seed: int = 0,\n expected_slice: np.ndarray = None,\n model_id: str = \"prs-eth/marigold-normals-lcm-v0-1\",\n image_url: str = \"https://marigoldmonodepth.github.io/images/einstein.jpg\",\n atol: float = 1e-4,\n **pipe_kwargs,\n ):\n from_pretrained_kwargs = {}\n if is_fp16:\n from_pretrained_kwargs[\"variant\"] = \"fp16\"\n from_pretrained_kwargs[\"torch_dtype\"] = torch.float16\n\n pipe = MarigoldNormalsPipeline.from_pretrained(model_id, **from_pretrained_kwargs)\n if device == \"cuda\":\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=device).manual_seed(generator_seed)\n\n image = load_image(image_url)\n width, height = image.size\n\n prediction = pipe(image, generator=generator, **pipe_kwargs).prediction\n\n prediction_slice = prediction[0, -3:, -3:, -1].flatten()\n\n if pipe_kwargs.get(\"match_input_resolution\", True):\n self.assertEqual(prediction.shape, (1, height, width, 3), \"Unexpected output resolution\")\n else:\n self.assertTrue(prediction.shape[0] == 1 and prediction.shape[3] == 3, \"Unexpected output dimensions\")\n self.assertEqual(\n max(prediction.shape[1:3]),\n pipe_kwargs.get(\"processing_resolution\", 768),\n \"Unexpected output resolution\",\n )\n\n self.assertTrue(np.allclose(prediction_slice, expected_slice, atol=atol))\n\n def test_marigold_normals_einstein_f32_cpu_G0_S1_P32_E1_B1_M1(self):\n self._test_marigold_normals(\n is_fp16=False,\n device=\"cpu\",\n generator_seed=0,\n expected_slice=np.array([0.8971, 0.8971, 0.8971, 0.8971, 0.8971, 0.8971, 0.8971, 0.8971, 0.8971]),\n num_inference_steps=1,\n processing_resolution=32,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_normals_einstein_f32_cuda_G0_S1_P768_E1_B1_M1(self):\n self._test_marigold_normals(\n is_fp16=False,\n device=\"cuda\",\n generator_seed=0,\n expected_slice=np.array([0.7980, 0.7952, 0.7914, 0.7931, 0.7871, 0.7816, 0.7844, 0.7710, 0.7601]),\n num_inference_steps=1,\n processing_resolution=768,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_normals_einstein_f16_cuda_G0_S1_P768_E1_B1_M1(self):\n self._test_marigold_normals(\n is_fp16=True,\n device=\"cuda\",\n generator_seed=0,\n expected_slice=np.array([0.7979, 0.7949, 0.7915, 0.7930, 0.7871, 0.7817, 0.7842, 0.7710, 0.7603]),\n num_inference_steps=1,\n processing_resolution=768,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_normals_einstein_f16_cuda_G2024_S1_P768_E1_B1_M1(self):\n self._test_marigold_normals(\n is_fp16=True,\n device=\"cuda\",\n generator_seed=2024,\n expected_slice=np.array([0.8428, 0.8428, 0.8433, 0.8369, 0.8325, 0.8315, 0.8271, 0.8135, 0.8057]),\n num_inference_steps=1,\n processing_resolution=768,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_normals_einstein_f16_cuda_G0_S2_P768_E1_B1_M1(self):\n self._test_marigold_normals(\n is_fp16=True,\n device=\"cuda\",\n generator_seed=0,\n expected_slice=np.array([0.7095, 0.7095, 0.7104, 0.7070, 0.7051, 0.7061, 0.7017, 0.6938, 0.6914]),\n num_inference_steps=2,\n processing_resolution=768,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_normals_einstein_f16_cuda_G0_S1_P512_E1_B1_M1(self):\n self._test_marigold_normals(\n is_fp16=True,\n device=\"cuda\",\n generator_seed=0,\n expected_slice=np.array([0.7168, 0.7163, 0.7163, 0.7080, 0.7061, 0.7046, 0.7031, 0.7007, 0.6987]),\n num_inference_steps=1,\n processing_resolution=512,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_normals_einstein_f16_cuda_G0_S1_P768_E3_B1_M1(self):\n self._test_marigold_normals(\n is_fp16=True,\n device=\"cuda\",\n generator_seed=0,\n expected_slice=np.array([0.7114, 0.7124, 0.7144, 0.7085, 0.7070, 0.7080, 0.7051, 0.6958, 0.6924]),\n num_inference_steps=1,\n processing_resolution=768,\n ensemble_size=3,\n ensembling_kwargs={\"reduction\": \"mean\"},\n batch_size=1,\n match_input_resolution=True,\n )\n\n def test_marigold_normals_einstein_f16_cuda_G0_S1_P768_E4_B2_M1(self):\n self._test_marigold_normals(\n is_fp16=True,\n device=\"cuda\",\n generator_seed=0,\n expected_slice=np.array([0.7412, 0.7441, 0.7490, 0.7383, 0.7388, 0.7437, 0.7329, 0.7271, 0.7300]),\n num_inference_steps=1,\n processing_resolution=768,\n ensemble_size=4,\n ensembling_kwargs={\"reduction\": \"mean\"},\n batch_size=2,\n match_input_resolution=True,\n )\n\n def test_marigold_normals_einstein_f16_cuda_G0_S1_P512_E1_B1_M0(self):\n self._test_marigold_normals(\n is_fp16=True,\n device=\"cuda\",\n generator_seed=0,\n expected_slice=np.array([0.7188, 0.7144, 0.7134, 0.7178, 0.7207, 0.7222, 0.7231, 0.7041, 0.6987]),\n num_inference_steps=1,\n processing_resolution=512,\n ensemble_size=1,\n batch_size=1,\n match_input_resolution=False,\n )\n" }, { "path": "diffusers/tests/pipelines/musicldm/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/musicldm/test_musicldm.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport gc\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import (\n ClapAudioConfig,\n ClapConfig,\n ClapFeatureExtractor,\n ClapModel,\n ClapTextConfig,\n RobertaTokenizer,\n SpeechT5HifiGan,\n SpeechT5HifiGanConfig,\n)\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n LMSDiscreteScheduler,\n MusicLDMPipeline,\n PNDMScheduler,\n UNet2DConditionModel,\n)\nfrom diffusers.utils import is_xformers_available\nfrom diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device\n\nfrom ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass MusicLDMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = MusicLDMPipeline\n params = TEXT_TO_AUDIO_PARAMS\n batch_params = TEXT_TO_AUDIO_BATCH_PARAMS\n required_optional_params = frozenset(\n [\n \"num_inference_steps\",\n \"num_waveforms_per_prompt\",\n \"generator\",\n \"latents\",\n \"output_type\",\n \"return_dict\",\n \"callback\",\n \"callback_steps\",\n ]\n )\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=(32, 64),\n class_embed_type=\"simple_projection\",\n projection_class_embeddings_input_dim=32,\n class_embeddings_concat=True,\n )\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=1,\n out_channels=1,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_branch_config = ClapTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=16,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=2,\n num_hidden_layers=2,\n pad_token_id=1,\n vocab_size=1000,\n )\n audio_branch_config = ClapAudioConfig(\n spec_size=64,\n window_size=4,\n num_mel_bins=64,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n depths=[2, 2],\n num_attention_heads=[2, 2],\n num_hidden_layers=2,\n hidden_size=192,\n patch_size=2,\n patch_stride=2,\n patch_embed_input_channels=4,\n )\n text_encoder_config = ClapConfig.from_text_audio_configs(\n text_config=text_branch_config, audio_config=audio_branch_config, projection_dim=32\n )\n text_encoder = ClapModel(text_encoder_config)\n tokenizer = RobertaTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-roberta\", model_max_length=77)\n feature_extractor = ClapFeatureExtractor.from_pretrained(\n \"hf-internal-testing/tiny-random-ClapModel\", hop_length=7900\n )\n\n torch.manual_seed(0)\n vocoder_config = SpeechT5HifiGanConfig(\n model_in_dim=8,\n sampling_rate=16000,\n upsample_initial_channel=16,\n upsample_rates=[2, 2],\n upsample_kernel_sizes=[4, 4],\n resblock_kernel_sizes=[3, 7],\n resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]],\n normalize_before=False,\n )\n\n vocoder = SpeechT5HifiGan(vocoder_config)\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"feature_extractor\": feature_extractor,\n \"vocoder\": vocoder,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A hammer hitting a wooden surface\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n }\n return inputs\n\n def test_musicldm_ddim(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components()\n musicldm_pipe = MusicLDMPipeline(**components)\n musicldm_pipe = musicldm_pipe.to(torch_device)\n musicldm_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = musicldm_pipe(**inputs)\n audio = output.audios[0]\n\n assert audio.ndim == 1\n assert len(audio) == 256\n\n audio_slice = audio[:10]\n expected_slice = np.array(\n [-0.0027, -0.0036, -0.0037, -0.0020, -0.0035, -0.0019, -0.0037, -0.0020, -0.0038, -0.0019]\n )\n\n assert np.abs(audio_slice - expected_slice).max() < 1e-4\n\n def test_musicldm_prompt_embeds(self):\n components = self.get_dummy_components()\n musicldm_pipe = MusicLDMPipeline(**components)\n musicldm_pipe = musicldm_pipe.to(torch_device)\n musicldm_pipe = musicldm_pipe.to(torch_device)\n musicldm_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"prompt\"] = 3 * [inputs[\"prompt\"]]\n\n # forward\n output = musicldm_pipe(**inputs)\n audio_1 = output.audios[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n prompt = 3 * [inputs.pop(\"prompt\")]\n\n text_inputs = musicldm_pipe.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=musicldm_pipe.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_inputs = text_inputs[\"input_ids\"].to(torch_device)\n\n prompt_embeds = musicldm_pipe.text_encoder.get_text_features(text_inputs)\n\n inputs[\"prompt_embeds\"] = prompt_embeds\n\n # forward\n output = musicldm_pipe(**inputs)\n audio_2 = output.audios[0]\n\n assert np.abs(audio_1 - audio_2).max() < 1e-2\n\n def test_musicldm_negative_prompt_embeds(self):\n components = self.get_dummy_components()\n musicldm_pipe = MusicLDMPipeline(**components)\n musicldm_pipe = musicldm_pipe.to(torch_device)\n musicldm_pipe = musicldm_pipe.to(torch_device)\n musicldm_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n negative_prompt = 3 * [\"this is a negative prompt\"]\n inputs[\"negative_prompt\"] = negative_prompt\n inputs[\"prompt\"] = 3 * [inputs[\"prompt\"]]\n\n # forward\n output = musicldm_pipe(**inputs)\n audio_1 = output.audios[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n prompt = 3 * [inputs.pop(\"prompt\")]\n\n embeds = []\n for p in [prompt, negative_prompt]:\n text_inputs = musicldm_pipe.tokenizer(\n p,\n padding=\"max_length\",\n max_length=musicldm_pipe.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_inputs = text_inputs[\"input_ids\"].to(torch_device)\n\n text_embeds = musicldm_pipe.text_encoder.get_text_features(\n text_inputs,\n )\n embeds.append(text_embeds)\n\n inputs[\"prompt_embeds\"], inputs[\"negative_prompt_embeds\"] = embeds\n\n # forward\n output = musicldm_pipe(**inputs)\n audio_2 = output.audios[0]\n\n assert np.abs(audio_1 - audio_2).max() < 1e-2\n\n def test_musicldm_negative_prompt(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n components[\"scheduler\"] = PNDMScheduler(skip_prk_steps=True)\n musicldm_pipe = MusicLDMPipeline(**components)\n musicldm_pipe = musicldm_pipe.to(device)\n musicldm_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n negative_prompt = \"egg cracking\"\n output = musicldm_pipe(**inputs, negative_prompt=negative_prompt)\n audio = output.audios[0]\n\n assert audio.ndim == 1\n assert len(audio) == 256\n\n audio_slice = audio[:10]\n expected_slice = np.array(\n [-0.0027, -0.0036, -0.0037, -0.0019, -0.0035, -0.0018, -0.0037, -0.0021, -0.0038, -0.0018]\n )\n\n assert np.abs(audio_slice - expected_slice).max() < 1e-4\n\n def test_musicldm_num_waveforms_per_prompt(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n components[\"scheduler\"] = PNDMScheduler(skip_prk_steps=True)\n musicldm_pipe = MusicLDMPipeline(**components)\n musicldm_pipe = musicldm_pipe.to(device)\n musicldm_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A hammer hitting a wooden surface\"\n\n # test num_waveforms_per_prompt=1 (default)\n audios = musicldm_pipe(prompt, num_inference_steps=2).audios\n\n assert audios.shape == (1, 256)\n\n # test num_waveforms_per_prompt=1 (default) for batch of prompts\n batch_size = 2\n audios = musicldm_pipe([prompt] * batch_size, num_inference_steps=2).audios\n\n assert audios.shape == (batch_size, 256)\n\n # test num_waveforms_per_prompt for single prompt\n num_waveforms_per_prompt = 2\n audios = musicldm_pipe(prompt, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt).audios\n\n assert audios.shape == (num_waveforms_per_prompt, 256)\n\n # test num_waveforms_per_prompt for batch of prompts\n batch_size = 2\n audios = musicldm_pipe(\n [prompt] * batch_size, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt\n ).audios\n\n assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)\n\n def test_musicldm_audio_length_in_s(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n musicldm_pipe = MusicLDMPipeline(**components)\n musicldm_pipe = musicldm_pipe.to(torch_device)\n musicldm_pipe.set_progress_bar_config(disable=None)\n vocoder_sampling_rate = musicldm_pipe.vocoder.config.sampling_rate\n\n inputs = self.get_dummy_inputs(device)\n output = musicldm_pipe(audio_length_in_s=0.016, **inputs)\n audio = output.audios[0]\n\n assert audio.ndim == 1\n assert len(audio) / vocoder_sampling_rate == 0.016\n\n output = musicldm_pipe(audio_length_in_s=0.032, **inputs)\n audio = output.audios[0]\n\n assert audio.ndim == 1\n assert len(audio) / vocoder_sampling_rate == 0.032\n\n def test_musicldm_vocoder_model_in_dim(self):\n components = self.get_dummy_components()\n musicldm_pipe = MusicLDMPipeline(**components)\n musicldm_pipe = musicldm_pipe.to(torch_device)\n musicldm_pipe.set_progress_bar_config(disable=None)\n\n prompt = [\"hey\"]\n\n output = musicldm_pipe(prompt, num_inference_steps=1)\n audio_shape = output.audios.shape\n assert audio_shape == (1, 256)\n\n config = musicldm_pipe.vocoder.config\n config.model_in_dim *= 2\n musicldm_pipe.vocoder = SpeechT5HifiGan(config).to(torch_device)\n output = musicldm_pipe(prompt, num_inference_steps=1)\n audio_shape = output.audios.shape\n # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram\n assert audio_shape == (1, 256)\n\n def test_attention_slicing_forward_pass(self):\n self._test_attention_slicing_forward_pass(test_mean_pixel_difference=False)\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical()\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False)\n\n def test_to_dtype(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n\n # The method component.dtype returns the dtype of the first parameter registered in the model, not the\n # dtype of the entire model. In the case of CLAP, the first parameter is a float64 constant (logit scale)\n model_dtypes = {key: component.dtype for key, component in components.items() if hasattr(component, \"dtype\")}\n\n # Without the logit scale parameters, everything is float32\n model_dtypes.pop(\"text_encoder\")\n self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes.values()))\n\n # the CLAP sub-models are float32\n model_dtypes[\"clap_text_branch\"] = components[\"text_encoder\"].text_model.dtype\n self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes.values()))\n\n # Once we send to fp16, all params are in half-precision, including the logit scale\n pipe.to(dtype=torch.float16)\n model_dtypes = {key: component.dtype for key, component in components.items() if hasattr(component, \"dtype\")}\n self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes.values()))\n\n\n@nightly\n@require_torch_gpu\nclass MusicLDMPipelineNightlyTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n latents = np.random.RandomState(seed).standard_normal((1, 8, 128, 16))\n latents = torch.from_numpy(latents).to(device=device, dtype=dtype)\n inputs = {\n \"prompt\": \"A hammer hitting a wooden surface\",\n \"latents\": latents,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"guidance_scale\": 2.5,\n }\n return inputs\n\n def test_musicldm(self):\n musicldm_pipe = MusicLDMPipeline.from_pretrained(\"cvssp/musicldm\")\n musicldm_pipe = musicldm_pipe.to(torch_device)\n musicldm_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n inputs[\"num_inference_steps\"] = 25\n audio = musicldm_pipe(**inputs).audios[0]\n\n assert audio.ndim == 1\n assert len(audio) == 81952\n\n # check the portion of the generated audio with the largest dynamic range (reduces flakiness)\n audio_slice = audio[8680:8690]\n expected_slice = np.array(\n [-0.1042, -0.1068, -0.1235, -0.1387, -0.1428, -0.136, -0.1213, -0.1097, -0.0967, -0.0945]\n )\n max_diff = np.abs(expected_slice - audio_slice).max()\n assert max_diff < 1e-3\n\n def test_musicldm_lms(self):\n musicldm_pipe = MusicLDMPipeline.from_pretrained(\"cvssp/musicldm\")\n musicldm_pipe.scheduler = LMSDiscreteScheduler.from_config(musicldm_pipe.scheduler.config)\n musicldm_pipe = musicldm_pipe.to(torch_device)\n musicldm_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n audio = musicldm_pipe(**inputs).audios[0]\n\n assert audio.ndim == 1\n assert len(audio) == 81952\n\n # check the portion of the generated audio with the largest dynamic range (reduces flakiness)\n audio_slice = audio[58020:58030]\n expected_slice = np.array([0.3592, 0.3477, 0.4084, 0.4665, 0.5048, 0.5891, 0.6461, 0.5579, 0.4595, 0.4403])\n max_diff = np.abs(expected_slice - audio_slice).max()\n assert max_diff < 1e-3\n" }, { "path": "diffusers/tests/pipelines/paint_by_example/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/paint_by_example/test_paint_by_example.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom transformers import CLIPImageProcessor, CLIPVisionConfig\n\nfrom diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNet2DConditionModel\nfrom diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n nightly,\n require_torch_gpu,\n torch_device,\n)\n\nfrom ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass PaintByExamplePipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = PaintByExamplePipeline\n params = IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS\n batch_params = IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS\n image_params = frozenset([]) # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=9,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n )\n scheduler = PNDMScheduler(skip_prk_steps=True)\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n config = CLIPVisionConfig(\n hidden_size=32,\n projection_dim=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n image_size=32,\n patch_size=4,\n )\n image_encoder = PaintByExampleImageEncoder(config, proj_size=32)\n feature_extractor = CLIPImageProcessor(crop_size=32, size=32)\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"image_encoder\": image_encoder,\n \"safety_checker\": None,\n \"feature_extractor\": feature_extractor,\n }\n return components\n\n def convert_to_pt(self, image):\n image = np.array(image.convert(\"RGB\"))\n image = image[None].transpose(0, 3, 1, 2)\n image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0\n return image\n\n def get_dummy_inputs(self, device=\"cpu\", seed=0):\n # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched\n image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n image = image.cpu().permute(0, 2, 3, 1)[0]\n init_image = Image.fromarray(np.uint8(image)).convert(\"RGB\").resize((64, 64))\n mask_image = Image.fromarray(np.uint8(image + 4)).convert(\"RGB\").resize((64, 64))\n example_image = Image.fromarray(np.uint8(image)).convert(\"RGB\").resize((32, 32))\n\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"example_image\": example_image,\n \"image\": init_image,\n \"mask_image\": mask_image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_paint_by_example_inpaint(self):\n components = self.get_dummy_components()\n\n # make sure here that pndm scheduler skips prk\n pipe = PaintByExamplePipeline(**components)\n pipe = pipe.to(\"cpu\")\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs()\n output = pipe(**inputs)\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_paint_by_example_image_tensor(self):\n device = \"cpu\"\n inputs = self.get_dummy_inputs()\n inputs.pop(\"mask_image\")\n image = self.convert_to_pt(inputs.pop(\"image\"))\n mask_image = image.clamp(0, 1) / 2\n\n # make sure here that pndm scheduler skips prk\n pipe = PaintByExamplePipeline(**self.get_dummy_components())\n pipe = pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(image=image, mask_image=mask_image[:, 0], **inputs)\n out_1 = output.images\n\n image = image.cpu().permute(0, 2, 3, 1)[0]\n mask_image = mask_image.cpu().permute(0, 2, 3, 1)[0]\n\n image = Image.fromarray(np.uint8(image)).convert(\"RGB\")\n mask_image = Image.fromarray(np.uint8(mask_image)).convert(\"RGB\")\n\n output = pipe(**self.get_dummy_inputs())\n out_2 = output.images\n\n assert out_1.shape == (1, 64, 64, 3)\n assert np.abs(out_1.flatten() - out_2.flatten()).max() < 5e-2\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=3e-3)\n\n\n@nightly\n@require_torch_gpu\nclass PaintByExamplePipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_paint_by_example(self):\n # make sure here that pndm scheduler skips prk\n init_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/paint_by_example/dog_in_bucket.png\"\n )\n mask_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/paint_by_example/mask.png\"\n )\n example_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/paint_by_example/panda.jpg\"\n )\n\n pipe = PaintByExamplePipeline.from_pretrained(\"Fantasy-Studio/Paint-by-Example\")\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.manual_seed(321)\n output = pipe(\n image=init_image,\n mask_image=mask_image,\n example_image=example_image,\n generator=generator,\n guidance_scale=5.0,\n num_inference_steps=50,\n output_type=\"np\",\n )\n\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n" }, { "path": "diffusers/tests/pipelines/pia/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/pia/test_pia.py", "content": "import random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n MotionAdapter,\n PIAPipeline,\n UNet2DConditionModel,\n UNetMotionModel,\n)\nfrom diffusers.utils import is_xformers_available, logging\nfrom diffusers.utils.testing_utils import floats_tensor, torch_device\n\nfrom ..test_pipelines_common import IPAdapterTesterMixin, PipelineFromPipeTesterMixin, PipelineTesterMixin\n\n\ndef to_np(tensor):\n if isinstance(tensor, torch.Tensor):\n tensor = tensor.detach().cpu().numpy()\n\n return tensor\n\n\nclass PIAPipelineFastTests(IPAdapterTesterMixin, PipelineTesterMixin, PipelineFromPipeTesterMixin, unittest.TestCase):\n pipeline_class = PIAPipeline\n params = frozenset(\n [\n \"prompt\",\n \"height\",\n \"width\",\n \"guidance_scale\",\n \"negative_prompt\",\n \"prompt_embeds\",\n \"negative_prompt_embeds\",\n \"cross_attention_kwargs\",\n ]\n )\n batch_params = frozenset([\"prompt\", \"image\", \"generator\"])\n required_optional_params = frozenset(\n [\n \"num_inference_steps\",\n \"generator\",\n \"latents\",\n \"return_dict\",\n \"callback_on_step_end\",\n \"callback_on_step_end_tensor_inputs\",\n ]\n )\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"CrossAttnDownBlock2D\", \"DownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n norm_num_groups=2,\n )\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"linear\",\n clip_sample=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n motion_adapter = MotionAdapter(\n block_out_channels=(32, 64),\n motion_layers_per_block=2,\n motion_norm_num_groups=2,\n motion_num_attention_heads=4,\n conv_in_channels=9,\n )\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"motion_adapter\": motion_adapter,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n inputs = {\n \"image\": image,\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 7.5,\n \"output_type\": \"pt\",\n }\n return inputs\n\n def test_motion_unet_loading(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n\n assert isinstance(pipe.unet, UNetMotionModel)\n\n def test_ip_adapter_single(self):\n expected_pipe_slice = None\n\n if torch_device == \"cpu\":\n expected_pipe_slice = np.array(\n [\n 0.5609,\n 0.5756,\n 0.4830,\n 0.4420,\n 0.4547,\n 0.5129,\n 0.3779,\n 0.4042,\n 0.3772,\n 0.4450,\n 0.5710,\n 0.5536,\n 0.4835,\n 0.4308,\n 0.5578,\n 0.5578,\n 0.4395,\n 0.5440,\n 0.6051,\n 0.4651,\n 0.6258,\n 0.5662,\n 0.3988,\n 0.5108,\n 0.4153,\n 0.3993,\n 0.4803,\n ]\n )\n return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)\n\n def test_dict_tuple_outputs_equivalent(self):\n expected_slice = None\n if torch_device == \"cpu\":\n expected_slice = np.array([0.3740, 0.4284, 0.4038, 0.5417, 0.4405, 0.5521, 0.4273, 0.4124, 0.4997])\n return super().test_dict_tuple_outputs_equivalent(expected_slice=expected_slice)\n\n @unittest.skip(\"Attention slicing is not enabled in this pipeline\")\n def test_attention_slicing_forward_pass(self):\n pass\n\n def test_inference_batch_single_identical(\n self,\n batch_size=2,\n expected_max_diff=1e-4,\n additional_params_copy_to_batched_inputs=[\"num_inference_steps\"],\n ):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for components in pipe.components.values():\n if hasattr(components, \"set_default_attn_processor\"):\n components.set_default_attn_processor()\n\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n inputs = self.get_dummy_inputs(torch_device)\n # Reset generator in case it is has been used in self.get_dummy_inputs\n inputs[\"generator\"] = self.get_generator(0)\n\n logger = logging.get_logger(pipe.__module__)\n logger.setLevel(level=diffusers.logging.FATAL)\n\n # batchify inputs\n batched_inputs = {}\n batched_inputs.update(inputs)\n\n for name in self.batch_params:\n if name not in inputs:\n continue\n\n value = inputs[name]\n if name == \"prompt\":\n len_prompt = len(value)\n batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]\n batched_inputs[name][-1] = 100 * \"very long\"\n\n else:\n batched_inputs[name] = batch_size * [value]\n\n if \"generator\" in inputs:\n batched_inputs[\"generator\"] = [self.get_generator(i) for i in range(batch_size)]\n\n if \"batch_size\" in inputs:\n batched_inputs[\"batch_size\"] = batch_size\n\n for arg in additional_params_copy_to_batched_inputs:\n batched_inputs[arg] = inputs[arg]\n\n output = pipe(**inputs)\n output_batch = pipe(**batched_inputs)\n\n assert output_batch[0].shape[0] == batch_size\n\n max_diff = np.abs(to_np(output_batch[0][0]) - to_np(output[0][0])).max()\n assert max_diff < expected_max_diff\n\n @unittest.skipIf(torch_device != \"cuda\", reason=\"CUDA and CPU are required to switch devices\")\n def test_to_device(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n\n pipe.to(\"cpu\")\n # pipeline creates a new motion UNet under the hood. So we need to check the device from pipe.components\n model_devices = [\n component.device.type for component in pipe.components.values() if hasattr(component, \"device\")\n ]\n self.assertTrue(all(device == \"cpu\" for device in model_devices))\n\n output_cpu = pipe(**self.get_dummy_inputs(\"cpu\"))[0]\n self.assertTrue(np.isnan(output_cpu).sum() == 0)\n\n pipe.to(\"cuda\")\n model_devices = [\n component.device.type for component in pipe.components.values() if hasattr(component, \"device\")\n ]\n self.assertTrue(all(device == \"cuda\" for device in model_devices))\n\n output_cuda = pipe(**self.get_dummy_inputs(\"cuda\"))[0]\n self.assertTrue(np.isnan(to_np(output_cuda)).sum() == 0)\n\n def test_to_dtype(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n\n # pipeline creates a new motion UNet under the hood. So we need to check the dtype from pipe.components\n model_dtypes = [component.dtype for component in pipe.components.values() if hasattr(component, \"dtype\")]\n self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes))\n\n pipe.to(dtype=torch.float16)\n model_dtypes = [component.dtype for component in pipe.components.values() if hasattr(component, \"dtype\")]\n self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes))\n\n def test_prompt_embeds(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n pipe.to(torch_device)\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs.pop(\"prompt\")\n inputs[\"prompt_embeds\"] = torch.randn((1, 4, 32), device=torch_device)\n pipe(**inputs)\n\n def test_free_init(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n pipe.to(torch_device)\n\n inputs_normal = self.get_dummy_inputs(torch_device)\n frames_normal = pipe(**inputs_normal).frames[0]\n\n pipe.enable_free_init(\n num_iters=2,\n use_fast_sampling=True,\n method=\"butterworth\",\n order=4,\n spatial_stop_frequency=0.25,\n temporal_stop_frequency=0.25,\n )\n inputs_enable_free_init = self.get_dummy_inputs(torch_device)\n frames_enable_free_init = pipe(**inputs_enable_free_init).frames[0]\n\n pipe.disable_free_init()\n inputs_disable_free_init = self.get_dummy_inputs(torch_device)\n frames_disable_free_init = pipe(**inputs_disable_free_init).frames[0]\n\n sum_enabled = np.abs(to_np(frames_normal) - to_np(frames_enable_free_init)).sum()\n max_diff_disabled = np.abs(to_np(frames_normal) - to_np(frames_disable_free_init)).max()\n self.assertGreater(\n sum_enabled, 1e1, \"Enabling of FreeInit should lead to results different from the default pipeline results\"\n )\n self.assertLess(\n max_diff_disabled,\n 1e-4,\n \"Disabling of FreeInit should lead to results similar to the default pipeline results\",\n )\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n output_without_offload = pipe(**inputs).frames[0]\n output_without_offload = (\n output_without_offload.cpu() if torch.is_tensor(output_without_offload) else output_without_offload\n )\n\n pipe.enable_xformers_memory_efficient_attention()\n inputs = self.get_dummy_inputs(torch_device)\n output_with_offload = pipe(**inputs).frames[0]\n output_with_offload = (\n output_with_offload.cpu() if torch.is_tensor(output_with_offload) else output_without_offload\n )\n\n max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()\n self.assertLess(max_diff, 1e-4, \"XFormers attention should not affect the inference results\")\n" }, { "path": "diffusers/tests/pipelines/pipeline_params.py", "content": "# These are canonical sets of parameters for different types of pipelines.\n# They are set on subclasses of `PipelineTesterMixin` as `params` and\n# `batch_params`.\n#\n# If a pipeline's set of arguments has minor changes from one of the common sets\n# of arguments, do not make modifications to the existing common sets of arguments.\n# I.e. a text to image pipeline with non-configurable height and width arguments\n# should set its attribute as `params = TEXT_TO_IMAGE_PARAMS - {'height', 'width'}`.\n\nTEXT_TO_IMAGE_PARAMS = frozenset(\n [\n \"prompt\",\n \"height\",\n \"width\",\n \"guidance_scale\",\n \"negative_prompt\",\n \"prompt_embeds\",\n \"negative_prompt_embeds\",\n \"cross_attention_kwargs\",\n ]\n)\n\nTEXT_TO_IMAGE_BATCH_PARAMS = frozenset([\"prompt\", \"negative_prompt\"])\n\nTEXT_TO_IMAGE_IMAGE_PARAMS = frozenset([])\n\nIMAGE_TO_IMAGE_IMAGE_PARAMS = frozenset([\"image\"])\n\nIMAGE_VARIATION_PARAMS = frozenset(\n [\n \"image\",\n \"height\",\n \"width\",\n \"guidance_scale\",\n ]\n)\n\nIMAGE_VARIATION_BATCH_PARAMS = frozenset([\"image\"])\n\nTEXT_GUIDED_IMAGE_VARIATION_PARAMS = frozenset(\n [\n \"prompt\",\n \"image\",\n \"height\",\n \"width\",\n \"guidance_scale\",\n \"negative_prompt\",\n \"prompt_embeds\",\n \"negative_prompt_embeds\",\n ]\n)\n\nTEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS = frozenset([\"prompt\", \"image\", \"negative_prompt\"])\n\nTEXT_GUIDED_IMAGE_INPAINTING_PARAMS = frozenset(\n [\n # Text guided image variation with an image mask\n \"prompt\",\n \"image\",\n \"mask_image\",\n \"height\",\n \"width\",\n \"guidance_scale\",\n \"negative_prompt\",\n \"prompt_embeds\",\n \"negative_prompt_embeds\",\n ]\n)\n\nTEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS = frozenset([\"prompt\", \"image\", \"mask_image\", \"negative_prompt\"])\n\nIMAGE_INPAINTING_PARAMS = frozenset(\n [\n # image variation with an image mask\n \"image\",\n \"mask_image\",\n \"height\",\n \"width\",\n \"guidance_scale\",\n ]\n)\n\nIMAGE_INPAINTING_BATCH_PARAMS = frozenset([\"image\", \"mask_image\"])\n\nIMAGE_GUIDED_IMAGE_INPAINTING_PARAMS = frozenset(\n [\n \"example_image\",\n \"image\",\n \"mask_image\",\n \"height\",\n \"width\",\n \"guidance_scale\",\n ]\n)\n\nIMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS = frozenset([\"example_image\", \"image\", \"mask_image\"])\n\nCLASS_CONDITIONED_IMAGE_GENERATION_PARAMS = frozenset([\"class_labels\"])\n\nCLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS = frozenset([\"class_labels\"])\n\nUNCONDITIONAL_IMAGE_GENERATION_PARAMS = frozenset([\"batch_size\"])\n\nUNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS = frozenset([])\n\nUNCONDITIONAL_AUDIO_GENERATION_PARAMS = frozenset([\"batch_size\"])\n\nUNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS = frozenset([])\n\nTEXT_TO_AUDIO_PARAMS = frozenset(\n [\n \"prompt\",\n \"audio_length_in_s\",\n \"guidance_scale\",\n \"negative_prompt\",\n \"prompt_embeds\",\n \"negative_prompt_embeds\",\n \"cross_attention_kwargs\",\n ]\n)\n\nTEXT_TO_AUDIO_BATCH_PARAMS = frozenset([\"prompt\", \"negative_prompt\"])\nTOKENS_TO_AUDIO_GENERATION_PARAMS = frozenset([\"input_tokens\"])\n\nTOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS = frozenset([\"input_tokens\"])\n\nTEXT_TO_IMAGE_CALLBACK_CFG_PARAMS = frozenset([\"prompt_embeds\"])\n\nVIDEO_TO_VIDEO_BATCH_PARAMS = frozenset([\"prompt\", \"negative_prompt\", \"video\"])\n" }, { "path": "diffusers/tests/pipelines/pixart_alpha/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/pixart_alpha/test_pixart.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import AutoTokenizer, T5EncoderModel\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n PixArtAlphaPipeline,\n PixArtTransformer2DModel,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\nfrom ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS\nfrom ..test_pipelines_common import PipelineTesterMixin, to_np\n\n\nenable_full_determinism()\n\n\nclass PixArtAlphaPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = PixArtAlphaPipeline\n params = TEXT_TO_IMAGE_PARAMS - {\"cross_attention_kwargs\"}\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n image_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n\n required_optional_params = PipelineTesterMixin.required_optional_params\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n transformer = PixArtTransformer2DModel(\n sample_size=8,\n num_layers=2,\n patch_size=2,\n attention_head_dim=8,\n num_attention_heads=3,\n caption_channels=32,\n in_channels=4,\n cross_attention_dim=24,\n out_channels=8,\n attention_bias=True,\n activation_fn=\"gelu-approximate\",\n num_embeds_ada_norm=1000,\n norm_type=\"ada_norm_single\",\n norm_elementwise_affine=False,\n norm_eps=1e-6,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL()\n\n scheduler = DDIMScheduler()\n text_encoder = T5EncoderModel.from_pretrained(\"hf-internal-testing/tiny-random-t5\")\n\n tokenizer = AutoTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-t5\")\n\n components = {\n \"transformer\": transformer.eval(),\n \"vae\": vae.eval(),\n \"scheduler\": scheduler,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 5.0,\n \"use_resolution_binning\": False,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_sequential_cpu_offload_forward_pass(self):\n # TODO(PVP, Sayak) need to fix later\n return\n\n def test_save_load_optional_components(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n\n prompt = inputs[\"prompt\"]\n generator = inputs[\"generator\"]\n num_inference_steps = inputs[\"num_inference_steps\"]\n output_type = inputs[\"output_type\"]\n\n (\n prompt_embeds,\n prompt_attention_mask,\n negative_prompt_embeds,\n negative_prompt_attention_mask,\n ) = pipe.encode_prompt(prompt)\n\n # inputs with prompt converted to embeddings\n inputs = {\n \"prompt_embeds\": prompt_embeds,\n \"prompt_attention_mask\": prompt_attention_mask,\n \"negative_prompt\": None,\n \"negative_prompt_embeds\": negative_prompt_embeds,\n \"negative_prompt_attention_mask\": negative_prompt_attention_mask,\n \"generator\": generator,\n \"num_inference_steps\": num_inference_steps,\n \"output_type\": output_type,\n \"use_resolution_binning\": False,\n }\n\n # set all optional components to None\n for optional_component in pipe._optional_components:\n setattr(pipe, optional_component, None)\n\n output = pipe(**inputs)[0]\n\n with tempfile.TemporaryDirectory() as tmpdir:\n pipe.save_pretrained(tmpdir)\n pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)\n pipe_loaded.to(torch_device)\n pipe_loaded.set_progress_bar_config(disable=None)\n\n for optional_component in pipe._optional_components:\n self.assertTrue(\n getattr(pipe_loaded, optional_component) is None,\n f\"`{optional_component}` did not stay set to None after loading.\",\n )\n\n inputs = self.get_dummy_inputs(torch_device)\n\n generator = inputs[\"generator\"]\n num_inference_steps = inputs[\"num_inference_steps\"]\n output_type = inputs[\"output_type\"]\n\n # inputs with prompt converted to embeddings\n inputs = {\n \"prompt_embeds\": prompt_embeds,\n \"prompt_attention_mask\": prompt_attention_mask,\n \"negative_prompt\": None,\n \"negative_prompt_embeds\": negative_prompt_embeds,\n \"negative_prompt_attention_mask\": negative_prompt_attention_mask,\n \"generator\": generator,\n \"num_inference_steps\": num_inference_steps,\n \"output_type\": output_type,\n \"use_resolution_binning\": False,\n }\n\n output_loaded = pipe_loaded(**inputs)[0]\n\n max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()\n self.assertLess(max_diff, 1e-4)\n\n def test_inference(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n self.assertEqual(image.shape, (1, 8, 8, 3))\n expected_slice = np.array([0.6319, 0.3526, 0.3806, 0.6327, 0.4639, 0.483, 0.2583, 0.5331, 0.4852])\n max_diff = np.abs(image_slice.flatten() - expected_slice).max()\n self.assertLessEqual(max_diff, 1e-3)\n\n def test_inference_non_square_images(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = pipe(**inputs, height=32, width=48).images\n image_slice = image[0, -3:, -3:, -1]\n self.assertEqual(image.shape, (1, 32, 48, 3))\n\n expected_slice = np.array([0.6493, 0.537, 0.4081, 0.4762, 0.3695, 0.4711, 0.3026, 0.5218, 0.5263])\n max_diff = np.abs(image_slice.flatten() - expected_slice).max()\n self.assertLessEqual(max_diff, 1e-3)\n\n def test_inference_with_embeddings_and_multiple_images(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n\n prompt = inputs[\"prompt\"]\n generator = inputs[\"generator\"]\n num_inference_steps = inputs[\"num_inference_steps\"]\n output_type = inputs[\"output_type\"]\n\n prompt_embeds, prompt_attn_mask, negative_prompt_embeds, neg_prompt_attn_mask = pipe.encode_prompt(prompt)\n\n # inputs with prompt converted to embeddings\n inputs = {\n \"prompt_embeds\": prompt_embeds,\n \"prompt_attention_mask\": prompt_attn_mask,\n \"negative_prompt\": None,\n \"negative_prompt_embeds\": negative_prompt_embeds,\n \"negative_prompt_attention_mask\": neg_prompt_attn_mask,\n \"generator\": generator,\n \"num_inference_steps\": num_inference_steps,\n \"output_type\": output_type,\n \"num_images_per_prompt\": 2,\n \"use_resolution_binning\": False,\n }\n\n # set all optional components to None\n for optional_component in pipe._optional_components:\n setattr(pipe, optional_component, None)\n\n output = pipe(**inputs)[0]\n\n with tempfile.TemporaryDirectory() as tmpdir:\n pipe.save_pretrained(tmpdir)\n pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)\n pipe_loaded.to(torch_device)\n pipe_loaded.set_progress_bar_config(disable=None)\n\n for optional_component in pipe._optional_components:\n self.assertTrue(\n getattr(pipe_loaded, optional_component) is None,\n f\"`{optional_component}` did not stay set to None after loading.\",\n )\n\n inputs = self.get_dummy_inputs(torch_device)\n\n generator = inputs[\"generator\"]\n num_inference_steps = inputs[\"num_inference_steps\"]\n output_type = inputs[\"output_type\"]\n\n # inputs with prompt converted to embeddings\n inputs = {\n \"prompt_embeds\": prompt_embeds,\n \"prompt_attention_mask\": prompt_attn_mask,\n \"negative_prompt\": None,\n \"negative_prompt_embeds\": negative_prompt_embeds,\n \"negative_prompt_attention_mask\": neg_prompt_attn_mask,\n \"generator\": generator,\n \"num_inference_steps\": num_inference_steps,\n \"output_type\": output_type,\n \"num_images_per_prompt\": 2,\n \"use_resolution_binning\": False,\n }\n\n output_loaded = pipe_loaded(**inputs)[0]\n\n max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()\n self.assertLess(max_diff, 1e-4)\n\n def test_inference_with_multiple_images_per_prompt(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"num_images_per_prompt\"] = 2\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n self.assertEqual(image.shape, (2, 8, 8, 3))\n expected_slice = np.array([0.6319, 0.3526, 0.3806, 0.6327, 0.4639, 0.483, 0.2583, 0.5331, 0.4852])\n max_diff = np.abs(image_slice.flatten() - expected_slice).max()\n self.assertLessEqual(max_diff, 1e-3)\n\n def test_raises_warning_for_mask_feature(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs.update({\"mask_feature\": True})\n\n with self.assertWarns(FutureWarning) as warning_ctx:\n _ = pipe(**inputs).images\n\n assert \"mask_feature\" in str(warning_ctx.warning)\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=1e-3)\n\n\n@slow\n@require_torch_gpu\nclass PixArtAlphaPipelineIntegrationTests(unittest.TestCase):\n ckpt_id_1024 = \"PixArt-alpha/PixArt-XL-2-1024-MS\"\n ckpt_id_512 = \"PixArt-alpha/PixArt-XL-2-512x512\"\n prompt = \"A small cactus with a happy face in the Sahara desert.\"\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_pixart_1024(self):\n generator = torch.Generator(\"cpu\").manual_seed(0)\n\n pipe = PixArtAlphaPipeline.from_pretrained(self.ckpt_id_1024, torch_dtype=torch.float16)\n pipe.enable_model_cpu_offload()\n prompt = self.prompt\n\n image = pipe(prompt, generator=generator, num_inference_steps=2, output_type=\"np\").images\n\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array([0.0742, 0.0835, 0.2114, 0.0295, 0.0784, 0.2361, 0.1738, 0.2251, 0.3589])\n\n max_diff = numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice)\n self.assertLessEqual(max_diff, 1e-4)\n\n def test_pixart_512(self):\n generator = torch.Generator(\"cpu\").manual_seed(0)\n\n pipe = PixArtAlphaPipeline.from_pretrained(self.ckpt_id_512, torch_dtype=torch.float16)\n pipe.enable_model_cpu_offload()\n\n prompt = self.prompt\n\n image = pipe(prompt, generator=generator, num_inference_steps=2, output_type=\"np\").images\n\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array([0.3477, 0.3882, 0.4541, 0.3413, 0.3821, 0.4463, 0.4001, 0.4409, 0.4958])\n\n max_diff = numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice)\n self.assertLessEqual(max_diff, 1e-4)\n\n def test_pixart_1024_without_resolution_binning(self):\n generator = torch.manual_seed(0)\n\n pipe = PixArtAlphaPipeline.from_pretrained(self.ckpt_id_1024, torch_dtype=torch.float16)\n pipe.enable_model_cpu_offload()\n\n prompt = self.prompt\n height, width = 1024, 768\n num_inference_steps = 2\n\n image = pipe(\n prompt,\n height=height,\n width=width,\n generator=generator,\n num_inference_steps=num_inference_steps,\n output_type=\"np\",\n ).images\n image_slice = image[0, -3:, -3:, -1]\n\n generator = torch.manual_seed(0)\n no_res_bin_image = pipe(\n prompt,\n height=height,\n width=width,\n generator=generator,\n num_inference_steps=num_inference_steps,\n output_type=\"np\",\n use_resolution_binning=False,\n ).images\n no_res_bin_image_slice = no_res_bin_image[0, -3:, -3:, -1]\n\n assert not np.allclose(image_slice, no_res_bin_image_slice, atol=1e-4, rtol=1e-4)\n\n def test_pixart_512_without_resolution_binning(self):\n generator = torch.manual_seed(0)\n\n pipe = PixArtAlphaPipeline.from_pretrained(self.ckpt_id_512, torch_dtype=torch.float16)\n pipe.enable_model_cpu_offload()\n\n prompt = self.prompt\n height, width = 512, 768\n num_inference_steps = 2\n\n image = pipe(\n prompt,\n height=height,\n width=width,\n generator=generator,\n num_inference_steps=num_inference_steps,\n output_type=\"np\",\n ).images\n image_slice = image[0, -3:, -3:, -1]\n\n generator = torch.manual_seed(0)\n no_res_bin_image = pipe(\n prompt,\n height=height,\n width=width,\n generator=generator,\n num_inference_steps=num_inference_steps,\n output_type=\"np\",\n use_resolution_binning=False,\n ).images\n no_res_bin_image_slice = no_res_bin_image[0, -3:, -3:, -1]\n\n assert not np.allclose(image_slice, no_res_bin_image_slice, atol=1e-4, rtol=1e-4)\n" }, { "path": "diffusers/tests/pipelines/pixart_sigma/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/pixart_sigma/test_pixart.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import AutoTokenizer, T5EncoderModel\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n PixArtSigmaPipeline,\n PixArtTransformer2DModel,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\nfrom ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS\nfrom ..test_pipelines_common import PipelineTesterMixin, to_np\n\n\nenable_full_determinism()\n\n\nclass PixArtSigmaPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = PixArtSigmaPipeline\n params = TEXT_TO_IMAGE_PARAMS - {\"cross_attention_kwargs\"}\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n image_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n\n required_optional_params = PipelineTesterMixin.required_optional_params\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n transformer = PixArtTransformer2DModel(\n sample_size=8,\n num_layers=2,\n patch_size=2,\n attention_head_dim=8,\n num_attention_heads=3,\n caption_channels=32,\n in_channels=4,\n cross_attention_dim=24,\n out_channels=8,\n attention_bias=True,\n activation_fn=\"gelu-approximate\",\n num_embeds_ada_norm=1000,\n norm_type=\"ada_norm_single\",\n norm_elementwise_affine=False,\n norm_eps=1e-6,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL()\n\n scheduler = DDIMScheduler()\n text_encoder = T5EncoderModel.from_pretrained(\"hf-internal-testing/tiny-random-t5\")\n\n tokenizer = AutoTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-t5\")\n\n components = {\n \"transformer\": transformer.eval(),\n \"vae\": vae.eval(),\n \"scheduler\": scheduler,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 5.0,\n \"use_resolution_binning\": False,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_sequential_cpu_offload_forward_pass(self):\n # TODO(PVP, Sayak) need to fix later\n return\n\n def test_save_load_optional_components(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n\n prompt = inputs[\"prompt\"]\n generator = inputs[\"generator\"]\n num_inference_steps = inputs[\"num_inference_steps\"]\n output_type = inputs[\"output_type\"]\n\n (\n prompt_embeds,\n prompt_attention_mask,\n negative_prompt_embeds,\n negative_prompt_attention_mask,\n ) = pipe.encode_prompt(prompt)\n\n # inputs with prompt converted to embeddings\n inputs = {\n \"prompt_embeds\": prompt_embeds,\n \"prompt_attention_mask\": prompt_attention_mask,\n \"negative_prompt\": None,\n \"negative_prompt_embeds\": negative_prompt_embeds,\n \"negative_prompt_attention_mask\": negative_prompt_attention_mask,\n \"generator\": generator,\n \"num_inference_steps\": num_inference_steps,\n \"output_type\": output_type,\n \"use_resolution_binning\": False,\n }\n\n # set all optional components to None\n for optional_component in pipe._optional_components:\n setattr(pipe, optional_component, None)\n\n output = pipe(**inputs)[0]\n\n with tempfile.TemporaryDirectory() as tmpdir:\n pipe.save_pretrained(tmpdir)\n pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)\n pipe_loaded.to(torch_device)\n pipe_loaded.set_progress_bar_config(disable=None)\n\n for optional_component in pipe._optional_components:\n self.assertTrue(\n getattr(pipe_loaded, optional_component) is None,\n f\"`{optional_component}` did not stay set to None after loading.\",\n )\n\n inputs = self.get_dummy_inputs(torch_device)\n\n generator = inputs[\"generator\"]\n num_inference_steps = inputs[\"num_inference_steps\"]\n output_type = inputs[\"output_type\"]\n\n # inputs with prompt converted to embeddings\n inputs = {\n \"prompt_embeds\": prompt_embeds,\n \"prompt_attention_mask\": prompt_attention_mask,\n \"negative_prompt\": None,\n \"negative_prompt_embeds\": negative_prompt_embeds,\n \"negative_prompt_attention_mask\": negative_prompt_attention_mask,\n \"generator\": generator,\n \"num_inference_steps\": num_inference_steps,\n \"output_type\": output_type,\n \"use_resolution_binning\": False,\n }\n\n output_loaded = pipe_loaded(**inputs)[0]\n\n max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()\n self.assertLess(max_diff, 1e-4)\n\n def test_inference(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n self.assertEqual(image.shape, (1, 8, 8, 3))\n expected_slice = np.array([0.6319, 0.3526, 0.3806, 0.6327, 0.4639, 0.4830, 0.2583, 0.5331, 0.4852])\n max_diff = np.abs(image_slice.flatten() - expected_slice).max()\n self.assertLessEqual(max_diff, 1e-3)\n\n def test_inference_non_square_images(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = pipe(**inputs, height=32, width=48).images\n image_slice = image[0, -3:, -3:, -1]\n self.assertEqual(image.shape, (1, 32, 48, 3))\n\n expected_slice = np.array([0.6493, 0.5370, 0.4081, 0.4762, 0.3695, 0.4711, 0.3026, 0.5218, 0.5263])\n max_diff = np.abs(image_slice.flatten() - expected_slice).max()\n self.assertLessEqual(max_diff, 1e-3)\n\n def test_inference_with_embeddings_and_multiple_images(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n\n prompt = inputs[\"prompt\"]\n generator = inputs[\"generator\"]\n num_inference_steps = inputs[\"num_inference_steps\"]\n output_type = inputs[\"output_type\"]\n\n prompt_embeds, prompt_attn_mask, negative_prompt_embeds, neg_prompt_attn_mask = pipe.encode_prompt(prompt)\n\n # inputs with prompt converted to embeddings\n inputs = {\n \"prompt_embeds\": prompt_embeds,\n \"prompt_attention_mask\": prompt_attn_mask,\n \"negative_prompt\": None,\n \"negative_prompt_embeds\": negative_prompt_embeds,\n \"negative_prompt_attention_mask\": neg_prompt_attn_mask,\n \"generator\": generator,\n \"num_inference_steps\": num_inference_steps,\n \"output_type\": output_type,\n \"num_images_per_prompt\": 2,\n \"use_resolution_binning\": False,\n }\n\n # set all optional components to None\n for optional_component in pipe._optional_components:\n setattr(pipe, optional_component, None)\n\n output = pipe(**inputs)[0]\n\n with tempfile.TemporaryDirectory() as tmpdir:\n pipe.save_pretrained(tmpdir)\n pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)\n pipe_loaded.to(torch_device)\n pipe_loaded.set_progress_bar_config(disable=None)\n\n for optional_component in pipe._optional_components:\n self.assertTrue(\n getattr(pipe_loaded, optional_component) is None,\n f\"`{optional_component}` did not stay set to None after loading.\",\n )\n\n inputs = self.get_dummy_inputs(torch_device)\n\n generator = inputs[\"generator\"]\n num_inference_steps = inputs[\"num_inference_steps\"]\n output_type = inputs[\"output_type\"]\n\n # inputs with prompt converted to embeddings\n inputs = {\n \"prompt_embeds\": prompt_embeds,\n \"prompt_attention_mask\": prompt_attn_mask,\n \"negative_prompt\": None,\n \"negative_prompt_embeds\": negative_prompt_embeds,\n \"negative_prompt_attention_mask\": neg_prompt_attn_mask,\n \"generator\": generator,\n \"num_inference_steps\": num_inference_steps,\n \"output_type\": output_type,\n \"num_images_per_prompt\": 2,\n \"use_resolution_binning\": False,\n }\n\n output_loaded = pipe_loaded(**inputs)[0]\n\n max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()\n self.assertLess(max_diff, 1e-4)\n\n def test_inference_with_multiple_images_per_prompt(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"num_images_per_prompt\"] = 2\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n self.assertEqual(image.shape, (2, 8, 8, 3))\n expected_slice = np.array([0.6319, 0.3526, 0.3806, 0.6327, 0.4639, 0.4830, 0.2583, 0.5331, 0.4852])\n max_diff = np.abs(image_slice.flatten() - expected_slice).max()\n self.assertLessEqual(max_diff, 1e-3)\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=1e-3)\n\n\n@slow\n@require_torch_gpu\nclass PixArtSigmaPipelineIntegrationTests(unittest.TestCase):\n ckpt_id_1024 = \"PixArt-alpha/PixArt-Sigma-XL-2-1024-MS\"\n ckpt_id_512 = \"PixArt-alpha/PixArt-Sigma-XL-2-512-MS\"\n prompt = \"A small cactus with a happy face in the Sahara desert.\"\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_pixart_1024(self):\n generator = torch.Generator(\"cpu\").manual_seed(0)\n\n pipe = PixArtSigmaPipeline.from_pretrained(self.ckpt_id_1024, torch_dtype=torch.float16)\n pipe.enable_model_cpu_offload()\n prompt = self.prompt\n\n image = pipe(prompt, generator=generator, num_inference_steps=2, output_type=\"np\").images\n\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array([0.4517, 0.4446, 0.4375, 0.449, 0.4399, 0.4365, 0.4583, 0.4629, 0.4473])\n\n max_diff = numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice)\n self.assertLessEqual(max_diff, 1e-4)\n\n def test_pixart_512(self):\n generator = torch.Generator(\"cpu\").manual_seed(0)\n\n transformer = PixArtTransformer2DModel.from_pretrained(\n self.ckpt_id_512, subfolder=\"transformer\", torch_dtype=torch.float16\n )\n pipe = PixArtSigmaPipeline.from_pretrained(\n self.ckpt_id_1024, transformer=transformer, torch_dtype=torch.float16\n )\n pipe.enable_model_cpu_offload()\n\n prompt = self.prompt\n\n image = pipe(prompt, generator=generator, num_inference_steps=2, output_type=\"np\").images\n\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array([0.0479, 0.0378, 0.0217, 0.0942, 0.064, 0.0791, 0.2073, 0.1975, 0.2017])\n\n max_diff = numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice)\n self.assertLessEqual(max_diff, 1e-4)\n\n def test_pixart_1024_without_resolution_binning(self):\n generator = torch.manual_seed(0)\n\n pipe = PixArtSigmaPipeline.from_pretrained(self.ckpt_id_1024, torch_dtype=torch.float16)\n pipe.enable_model_cpu_offload()\n\n prompt = self.prompt\n height, width = 1024, 768\n num_inference_steps = 2\n\n image = pipe(\n prompt,\n height=height,\n width=width,\n generator=generator,\n num_inference_steps=num_inference_steps,\n output_type=\"np\",\n ).images\n image_slice = image[0, -3:, -3:, -1]\n\n generator = torch.manual_seed(0)\n no_res_bin_image = pipe(\n prompt,\n height=height,\n width=width,\n generator=generator,\n num_inference_steps=num_inference_steps,\n output_type=\"np\",\n use_resolution_binning=False,\n ).images\n no_res_bin_image_slice = no_res_bin_image[0, -3:, -3:, -1]\n\n assert not np.allclose(image_slice, no_res_bin_image_slice, atol=1e-4, rtol=1e-4)\n\n def test_pixart_512_without_resolution_binning(self):\n generator = torch.manual_seed(0)\n\n transformer = PixArtTransformer2DModel.from_pretrained(\n self.ckpt_id_512, subfolder=\"transformer\", torch_dtype=torch.float16\n )\n pipe = PixArtSigmaPipeline.from_pretrained(\n self.ckpt_id_1024, transformer=transformer, torch_dtype=torch.float16\n )\n pipe.enable_model_cpu_offload()\n\n prompt = self.prompt\n height, width = 512, 768\n num_inference_steps = 2\n\n image = pipe(\n prompt,\n height=height,\n width=width,\n generator=generator,\n num_inference_steps=num_inference_steps,\n output_type=\"np\",\n ).images\n image_slice = image[0, -3:, -3:, -1]\n\n generator = torch.manual_seed(0)\n no_res_bin_image = pipe(\n prompt,\n height=height,\n width=width,\n generator=generator,\n num_inference_steps=num_inference_steps,\n output_type=\"np\",\n use_resolution_binning=False,\n ).images\n no_res_bin_image_slice = no_res_bin_image[0, -3:, -3:, -1]\n\n assert not np.allclose(image_slice, no_res_bin_image_slice, atol=1e-4, rtol=1e-4)\n" }, { "path": "diffusers/tests/pipelines/pndm/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/pndm/test_pndm.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\n\nimport numpy as np\nimport torch\n\nfrom diffusers import PNDMPipeline, PNDMScheduler, UNet2DModel\nfrom diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch, torch_device\n\n\nenable_full_determinism()\n\n\nclass PNDMPipelineFastTests(unittest.TestCase):\n @property\n def dummy_uncond_unet(self):\n torch.manual_seed(0)\n model = UNet2DModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=3,\n out_channels=3,\n down_block_types=(\"DownBlock2D\", \"AttnDownBlock2D\"),\n up_block_types=(\"AttnUpBlock2D\", \"UpBlock2D\"),\n )\n return model\n\n def test_inference(self):\n unet = self.dummy_uncond_unet\n scheduler = PNDMScheduler()\n\n pndm = PNDMPipeline(unet=unet, scheduler=scheduler)\n pndm.to(torch_device)\n pndm.set_progress_bar_config(disable=None)\n\n generator = torch.manual_seed(0)\n image = pndm(generator=generator, num_inference_steps=20, output_type=\"np\").images\n\n generator = torch.manual_seed(0)\n image_from_tuple = pndm(generator=generator, num_inference_steps=20, output_type=\"np\", return_dict=False)[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 32, 32, 3)\n expected_slice = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n\n\n@nightly\n@require_torch\nclass PNDMPipelineIntegrationTests(unittest.TestCase):\n def test_inference_cifar10(self):\n model_id = \"google/ddpm-cifar10-32\"\n\n unet = UNet2DModel.from_pretrained(model_id)\n scheduler = PNDMScheduler()\n\n pndm = PNDMPipeline(unet=unet, scheduler=scheduler)\n pndm.to(torch_device)\n pndm.set_progress_bar_config(disable=None)\n generator = torch.manual_seed(0)\n image = pndm(generator=generator, output_type=\"np\").images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 32, 32, 3)\n expected_slice = np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n" }, { "path": "diffusers/tests/pipelines/semantic_stable_diffusion/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/semantic_stable_diffusion/test_semantic_diffusion.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNet2DConditionModel\nfrom diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n nightly,\n require_torch_gpu,\n torch_device,\n)\n\n\nenable_full_determinism()\n\n\nclass SafeDiffusionPipelineFastTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n @property\n def dummy_image(self):\n batch_size = 1\n num_channels = 3\n sizes = (32, 32)\n\n image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)\n return image\n\n @property\n def dummy_cond_unet(self):\n torch.manual_seed(0)\n model = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n )\n return model\n\n @property\n def dummy_vae(self):\n torch.manual_seed(0)\n model = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n return model\n\n @property\n def dummy_text_encoder(self):\n torch.manual_seed(0)\n config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n return CLIPTextModel(config)\n\n @property\n def dummy_extractor(self):\n def extract(*args, **kwargs):\n class Out:\n def __init__(self):\n self.pixel_values = torch.ones([0])\n\n def to(self, device):\n self.pixel_values.to(device)\n return self\n\n return Out()\n\n return extract\n\n def test_semantic_diffusion_ddim(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n unet = self.dummy_cond_unet\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n\n vae = self.dummy_vae\n bert = self.dummy_text_encoder\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n # make sure here that pndm scheduler skips prk\n sd_pipe = StableDiffusionPipeline(\n unet=unet,\n scheduler=scheduler,\n vae=vae,\n text_encoder=bert,\n tokenizer=tokenizer,\n safety_checker=None,\n feature_extractor=self.dummy_extractor,\n )\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A painting of a squirrel eating a burger\"\n\n generator = torch.Generator(device=device).manual_seed(0)\n output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type=\"np\")\n image = output.images\n\n generator = torch.Generator(device=device).manual_seed(0)\n image_from_tuple = sd_pipe(\n [prompt],\n generator=generator,\n guidance_scale=6.0,\n num_inference_steps=2,\n output_type=\"np\",\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.5753, 0.6114, 0.5001, 0.5034, 0.5470, 0.4729, 0.4971, 0.4867, 0.4867])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_semantic_diffusion_pndm(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n unet = self.dummy_cond_unet\n scheduler = PNDMScheduler(skip_prk_steps=True)\n vae = self.dummy_vae\n bert = self.dummy_text_encoder\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n # make sure here that pndm scheduler skips prk\n sd_pipe = StableDiffusionPipeline(\n unet=unet,\n scheduler=scheduler,\n vae=vae,\n text_encoder=bert,\n tokenizer=tokenizer,\n safety_checker=None,\n feature_extractor=self.dummy_extractor,\n )\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A painting of a squirrel eating a burger\"\n generator = torch.Generator(device=device).manual_seed(0)\n output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type=\"np\")\n\n image = output.images\n\n generator = torch.Generator(device=device).manual_seed(0)\n image_from_tuple = sd_pipe(\n [prompt],\n generator=generator,\n guidance_scale=6.0,\n num_inference_steps=2,\n output_type=\"np\",\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.5122, 0.5712, 0.4825, 0.5053, 0.5646, 0.4769, 0.5179, 0.4894, 0.4994])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_semantic_diffusion_no_safety_checker(self):\n pipe = StableDiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-lms-pipe\", safety_checker=None\n )\n assert isinstance(pipe, StableDiffusionPipeline)\n assert isinstance(pipe.scheduler, LMSDiscreteScheduler)\n assert pipe.safety_checker is None\n\n image = pipe(\"example prompt\", num_inference_steps=2).images[0]\n assert image is not None\n\n # check that there's no error when saving a pipeline with one of the models being None\n with tempfile.TemporaryDirectory() as tmpdirname:\n pipe.save_pretrained(tmpdirname)\n pipe = StableDiffusionPipeline.from_pretrained(tmpdirname)\n\n # sanity check that the pipeline still works\n assert pipe.safety_checker is None\n image = pipe(\"example prompt\", num_inference_steps=2).images[0]\n assert image is not None\n\n @unittest.skipIf(torch_device != \"cuda\", \"This test requires a GPU\")\n def test_semantic_diffusion_fp16(self):\n \"\"\"Test that stable diffusion works with fp16\"\"\"\n unet = self.dummy_cond_unet\n scheduler = PNDMScheduler(skip_prk_steps=True)\n vae = self.dummy_vae\n bert = self.dummy_text_encoder\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n # put models in fp16\n unet = unet.half()\n vae = vae.half()\n bert = bert.half()\n\n # make sure here that pndm scheduler skips prk\n sd_pipe = StableDiffusionPipeline(\n unet=unet,\n scheduler=scheduler,\n vae=vae,\n text_encoder=bert,\n tokenizer=tokenizer,\n safety_checker=None,\n feature_extractor=self.dummy_extractor,\n )\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A painting of a squirrel eating a burger\"\n image = sd_pipe([prompt], num_inference_steps=2, output_type=\"np\").images\n\n assert image.shape == (1, 64, 64, 3)\n\n\n@nightly\n@require_torch_gpu\nclass SemanticDiffusionPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_positive_guidance(self):\n torch_device = \"cuda\"\n pipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n prompt = \"a photo of a cat\"\n edit = {\n \"editing_prompt\": [\"sunglasses\"],\n \"reverse_editing_direction\": [False],\n \"edit_warmup_steps\": 10,\n \"edit_guidance_scale\": 6,\n \"edit_threshold\": 0.95,\n \"edit_momentum_scale\": 0.5,\n \"edit_mom_beta\": 0.6,\n }\n\n seed = 3\n guidance_scale = 7\n\n # no sega enabled\n generator = torch.Generator(torch_device)\n generator.manual_seed(seed)\n output = pipe(\n [prompt],\n generator=generator,\n guidance_scale=guidance_scale,\n num_inference_steps=50,\n output_type=\"np\",\n width=512,\n height=512,\n )\n\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = [\n 0.34673113,\n 0.38492733,\n 0.37597352,\n 0.34086335,\n 0.35650748,\n 0.35579205,\n 0.3384763,\n 0.34340236,\n 0.3573271,\n ]\n\n assert image.shape == (1, 512, 512, 3)\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n # with sega enabled\n # generator = torch.manual_seed(seed)\n generator.manual_seed(seed)\n output = pipe(\n [prompt],\n generator=generator,\n guidance_scale=guidance_scale,\n num_inference_steps=50,\n output_type=\"np\",\n width=512,\n height=512,\n **edit,\n )\n\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = [\n 0.41887826,\n 0.37728766,\n 0.30138272,\n 0.41416335,\n 0.41664985,\n 0.36283392,\n 0.36191246,\n 0.43364465,\n 0.43001732,\n ]\n\n assert image.shape == (1, 512, 512, 3)\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_negative_guidance(self):\n torch_device = \"cuda\"\n pipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n prompt = \"an image of a crowded boulevard, realistic, 4k\"\n edit = {\n \"editing_prompt\": \"crowd, crowded, people\",\n \"reverse_editing_direction\": True,\n \"edit_warmup_steps\": 10,\n \"edit_guidance_scale\": 8.3,\n \"edit_threshold\": 0.9,\n \"edit_momentum_scale\": 0.5,\n \"edit_mom_beta\": 0.6,\n }\n\n seed = 9\n guidance_scale = 7\n\n # no sega enabled\n generator = torch.Generator(torch_device)\n generator.manual_seed(seed)\n output = pipe(\n [prompt],\n generator=generator,\n guidance_scale=guidance_scale,\n num_inference_steps=50,\n output_type=\"np\",\n width=512,\n height=512,\n )\n\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = [\n 0.43497998,\n 0.91814065,\n 0.7540739,\n 0.55580205,\n 0.8467265,\n 0.5389691,\n 0.62574506,\n 0.58897763,\n 0.50926757,\n ]\n\n assert image.shape == (1, 512, 512, 3)\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n # with sega enabled\n # generator = torch.manual_seed(seed)\n generator.manual_seed(seed)\n output = pipe(\n [prompt],\n generator=generator,\n guidance_scale=guidance_scale,\n num_inference_steps=50,\n output_type=\"np\",\n width=512,\n height=512,\n **edit,\n )\n\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = [\n 0.3089719,\n 0.30500144,\n 0.29016042,\n 0.30630964,\n 0.325687,\n 0.29419225,\n 0.2908091,\n 0.28723598,\n 0.27696294,\n ]\n\n assert image.shape == (1, 512, 512, 3)\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_multi_cond_guidance(self):\n torch_device = \"cuda\"\n pipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n prompt = \"a castle next to a river\"\n edit = {\n \"editing_prompt\": [\"boat on a river, boat\", \"monet, impression, sunrise\"],\n \"reverse_editing_direction\": False,\n \"edit_warmup_steps\": [15, 18],\n \"edit_guidance_scale\": 6,\n \"edit_threshold\": [0.9, 0.8],\n \"edit_momentum_scale\": 0.5,\n \"edit_mom_beta\": 0.6,\n }\n\n seed = 48\n guidance_scale = 7\n\n # no sega enabled\n generator = torch.Generator(torch_device)\n generator.manual_seed(seed)\n output = pipe(\n [prompt],\n generator=generator,\n guidance_scale=guidance_scale,\n num_inference_steps=50,\n output_type=\"np\",\n width=512,\n height=512,\n )\n\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = [\n 0.75163555,\n 0.76037145,\n 0.61785,\n 0.9189673,\n 0.8627701,\n 0.85189694,\n 0.8512813,\n 0.87012076,\n 0.8312857,\n ]\n\n assert image.shape == (1, 512, 512, 3)\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n # with sega enabled\n # generator = torch.manual_seed(seed)\n generator.manual_seed(seed)\n output = pipe(\n [prompt],\n generator=generator,\n guidance_scale=guidance_scale,\n num_inference_steps=50,\n output_type=\"np\",\n width=512,\n height=512,\n **edit,\n )\n\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = [\n 0.73553365,\n 0.7537271,\n 0.74341905,\n 0.66480356,\n 0.6472925,\n 0.63039416,\n 0.64812905,\n 0.6749717,\n 0.6517102,\n ]\n\n assert image.shape == (1, 512, 512, 3)\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_guidance_fp16(self):\n torch_device = \"cuda\"\n pipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n prompt = \"a photo of a cat\"\n edit = {\n \"editing_prompt\": [\"sunglasses\"],\n \"reverse_editing_direction\": [False],\n \"edit_warmup_steps\": 10,\n \"edit_guidance_scale\": 6,\n \"edit_threshold\": 0.95,\n \"edit_momentum_scale\": 0.5,\n \"edit_mom_beta\": 0.6,\n }\n\n seed = 3\n guidance_scale = 7\n\n # no sega enabled\n generator = torch.Generator(torch_device)\n generator.manual_seed(seed)\n output = pipe(\n [prompt],\n generator=generator,\n guidance_scale=guidance_scale,\n num_inference_steps=50,\n output_type=\"np\",\n width=512,\n height=512,\n )\n\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = [\n 0.34887695,\n 0.3876953,\n 0.375,\n 0.34423828,\n 0.3581543,\n 0.35717773,\n 0.3383789,\n 0.34570312,\n 0.359375,\n ]\n\n assert image.shape == (1, 512, 512, 3)\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n # with sega enabled\n # generator = torch.manual_seed(seed)\n generator.manual_seed(seed)\n output = pipe(\n [prompt],\n generator=generator,\n guidance_scale=guidance_scale,\n num_inference_steps=50,\n output_type=\"np\",\n width=512,\n height=512,\n **edit,\n )\n\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = [\n 0.42285156,\n 0.36914062,\n 0.29077148,\n 0.42041016,\n 0.41918945,\n 0.35498047,\n 0.3618164,\n 0.4423828,\n 0.43115234,\n ]\n\n assert image.shape == (1, 512, 512, 3)\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n" }, { "path": "diffusers/tests/pipelines/shap_e/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/shap_e/test_shap_e.py", "content": "# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline\nfrom diffusers.pipelines.shap_e import ShapERenderer\nfrom diffusers.utils.testing_utils import load_numpy, nightly, require_torch_gpu, torch_device\n\nfrom ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference\n\n\nclass ShapEPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = ShapEPipeline\n params = [\"prompt\"]\n batch_params = [\"prompt\"]\n required_optional_params = [\n \"num_images_per_prompt\",\n \"num_inference_steps\",\n \"generator\",\n \"latents\",\n \"guidance_scale\",\n \"frame_size\",\n \"output_type\",\n \"return_dict\",\n ]\n test_xformers_attention = False\n\n @property\n def text_embedder_hidden_size(self):\n return 16\n\n @property\n def time_input_dim(self):\n return 16\n\n @property\n def time_embed_dim(self):\n return self.time_input_dim * 4\n\n @property\n def renderer_dim(self):\n return 8\n\n @property\n def dummy_tokenizer(self):\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n return tokenizer\n\n @property\n def dummy_text_encoder(self):\n torch.manual_seed(0)\n config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=self.text_embedder_hidden_size,\n projection_dim=self.text_embedder_hidden_size,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n return CLIPTextModelWithProjection(config)\n\n @property\n def dummy_prior(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"num_attention_heads\": 2,\n \"attention_head_dim\": 16,\n \"embedding_dim\": self.time_input_dim,\n \"num_embeddings\": 32,\n \"embedding_proj_dim\": self.text_embedder_hidden_size,\n \"time_embed_dim\": self.time_embed_dim,\n \"num_layers\": 1,\n \"clip_embed_dim\": self.time_input_dim * 2,\n \"additional_embeddings\": 0,\n \"time_embed_act_fn\": \"gelu\",\n \"norm_in_type\": \"layer\",\n \"encoder_hid_proj_type\": None,\n \"added_emb_type\": None,\n }\n\n model = PriorTransformer(**model_kwargs)\n return model\n\n @property\n def dummy_renderer(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"param_shapes\": (\n (self.renderer_dim, 93),\n (self.renderer_dim, 8),\n (self.renderer_dim, 8),\n (self.renderer_dim, 8),\n ),\n \"d_latent\": self.time_input_dim,\n \"d_hidden\": self.renderer_dim,\n \"n_output\": 12,\n \"background\": (\n 0.1,\n 0.1,\n 0.1,\n ),\n }\n model = ShapERenderer(**model_kwargs)\n return model\n\n def get_dummy_components(self):\n prior = self.dummy_prior\n text_encoder = self.dummy_text_encoder\n tokenizer = self.dummy_tokenizer\n shap_e_renderer = self.dummy_renderer\n\n scheduler = HeunDiscreteScheduler(\n beta_schedule=\"exp\",\n num_train_timesteps=1024,\n prediction_type=\"sample\",\n use_karras_sigmas=True,\n clip_sample=True,\n clip_sample_range=1.0,\n )\n components = {\n \"prior\": prior,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"shap_e_renderer\": shap_e_renderer,\n \"scheduler\": scheduler,\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"horse\",\n \"generator\": generator,\n \"num_inference_steps\": 1,\n \"frame_size\": 32,\n \"output_type\": \"latent\",\n }\n return inputs\n\n def test_shap_e(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.images[0]\n image = image.cpu().numpy()\n image_slice = image[-3:, -3:]\n\n assert image.shape == (32, 16)\n\n expected_slice = np.array([-1.0000, -0.6241, 1.0000, -0.8978, -0.6866, 0.7876, -0.7473, -0.2874, 0.6103])\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_inference_batch_consistent(self):\n # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches\n self._test_inference_batch_consistent(batch_sizes=[1, 2])\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(batch_size=2, expected_max_diff=6e-3)\n\n def test_num_images_per_prompt(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n batch_size = 1\n num_images_per_prompt = 2\n\n inputs = self.get_dummy_inputs(torch_device)\n\n for key in inputs.keys():\n if key in self.batch_params:\n inputs[key] = batch_size * [inputs[key]]\n\n images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt)[0]\n\n assert images.shape[0] == batch_size * num_images_per_prompt\n\n def test_float16_inference(self):\n super().test_float16_inference(expected_max_diff=5e-1)\n\n def test_save_load_local(self):\n super().test_save_load_local(expected_max_difference=5e-3)\n\n @unittest.skip(\"Key error is raised with accelerate\")\n def test_sequential_cpu_offload_forward_pass(self):\n pass\n\n\n@nightly\n@require_torch_gpu\nclass ShapEPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_shap_e(self):\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/shap_e/test_shap_e_np_out.npy\"\n )\n pipe = ShapEPipeline.from_pretrained(\"openai/shap-e\")\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=torch_device).manual_seed(0)\n\n images = pipe(\n \"a shark\",\n generator=generator,\n guidance_scale=15.0,\n num_inference_steps=64,\n frame_size=64,\n output_type=\"np\",\n ).images[0]\n\n assert images.shape == (20, 64, 64, 3)\n\n assert_mean_pixel_difference(images, expected_image)\n" }, { "path": "diffusers/tests/pipelines/shap_e/test_shap_e_img2img.py", "content": "# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel\n\nfrom diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImg2ImgPipeline\nfrom diffusers.pipelines.shap_e import ShapERenderer\nfrom diffusers.utils.testing_utils import (\n floats_tensor,\n load_image,\n load_numpy,\n nightly,\n require_torch_gpu,\n torch_device,\n)\n\nfrom ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference\n\n\nclass ShapEImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = ShapEImg2ImgPipeline\n params = [\"image\"]\n batch_params = [\"image\"]\n required_optional_params = [\n \"num_images_per_prompt\",\n \"num_inference_steps\",\n \"generator\",\n \"latents\",\n \"guidance_scale\",\n \"frame_size\",\n \"output_type\",\n \"return_dict\",\n ]\n test_xformers_attention = False\n\n @property\n def text_embedder_hidden_size(self):\n return 16\n\n @property\n def time_input_dim(self):\n return 16\n\n @property\n def time_embed_dim(self):\n return self.time_input_dim * 4\n\n @property\n def renderer_dim(self):\n return 8\n\n @property\n def dummy_image_encoder(self):\n torch.manual_seed(0)\n config = CLIPVisionConfig(\n hidden_size=self.text_embedder_hidden_size,\n image_size=32,\n projection_dim=self.text_embedder_hidden_size,\n intermediate_size=24,\n num_attention_heads=2,\n num_channels=3,\n num_hidden_layers=5,\n patch_size=1,\n )\n\n model = CLIPVisionModel(config)\n return model\n\n @property\n def dummy_image_processor(self):\n image_processor = CLIPImageProcessor(\n crop_size=224,\n do_center_crop=True,\n do_normalize=True,\n do_resize=True,\n image_mean=[0.48145466, 0.4578275, 0.40821073],\n image_std=[0.26862954, 0.26130258, 0.27577711],\n resample=3,\n size=224,\n )\n\n return image_processor\n\n @property\n def dummy_prior(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"num_attention_heads\": 2,\n \"attention_head_dim\": 16,\n \"embedding_dim\": self.time_input_dim,\n \"num_embeddings\": 32,\n \"embedding_proj_dim\": self.text_embedder_hidden_size,\n \"time_embed_dim\": self.time_embed_dim,\n \"num_layers\": 1,\n \"clip_embed_dim\": self.time_input_dim * 2,\n \"additional_embeddings\": 0,\n \"time_embed_act_fn\": \"gelu\",\n \"norm_in_type\": \"layer\",\n \"embedding_proj_norm_type\": \"layer\",\n \"encoder_hid_proj_type\": None,\n \"added_emb_type\": None,\n }\n\n model = PriorTransformer(**model_kwargs)\n return model\n\n @property\n def dummy_renderer(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"param_shapes\": (\n (self.renderer_dim, 93),\n (self.renderer_dim, 8),\n (self.renderer_dim, 8),\n (self.renderer_dim, 8),\n ),\n \"d_latent\": self.time_input_dim,\n \"d_hidden\": self.renderer_dim,\n \"n_output\": 12,\n \"background\": (\n 0.1,\n 0.1,\n 0.1,\n ),\n }\n model = ShapERenderer(**model_kwargs)\n return model\n\n def get_dummy_components(self):\n prior = self.dummy_prior\n image_encoder = self.dummy_image_encoder\n image_processor = self.dummy_image_processor\n shap_e_renderer = self.dummy_renderer\n\n scheduler = HeunDiscreteScheduler(\n beta_schedule=\"exp\",\n num_train_timesteps=1024,\n prediction_type=\"sample\",\n use_karras_sigmas=True,\n clip_sample=True,\n clip_sample_range=1.0,\n )\n components = {\n \"prior\": prior,\n \"image_encoder\": image_encoder,\n \"image_processor\": image_processor,\n \"shap_e_renderer\": shap_e_renderer,\n \"scheduler\": scheduler,\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n input_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"image\": input_image,\n \"generator\": generator,\n \"num_inference_steps\": 1,\n \"frame_size\": 32,\n \"output_type\": \"latent\",\n }\n return inputs\n\n def test_shap_e(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.images[0]\n image_slice = image[-3:, -3:].cpu().numpy()\n\n assert image.shape == (32, 16)\n\n expected_slice = np.array(\n [-1.0, 0.40668195, 0.57322013, -0.9469888, 0.4283227, 0.30348337, -0.81094897, 0.74555075, 0.15342723]\n )\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_inference_batch_consistent(self):\n # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches\n self._test_inference_batch_consistent(batch_sizes=[2])\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(\n batch_size=2,\n expected_max_diff=6e-3,\n )\n\n def test_num_images_per_prompt(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n batch_size = 1\n num_images_per_prompt = 2\n\n inputs = self.get_dummy_inputs(torch_device)\n\n for key in inputs.keys():\n if key in self.batch_params:\n inputs[key] = batch_size * [inputs[key]]\n\n images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt)[0]\n\n assert images.shape[0] == batch_size * num_images_per_prompt\n\n def test_float16_inference(self):\n super().test_float16_inference(expected_max_diff=1e-1)\n\n def test_save_load_local(self):\n super().test_save_load_local(expected_max_difference=5e-3)\n\n @unittest.skip(\"Key error is raised with accelerate\")\n def test_sequential_cpu_offload_forward_pass(self):\n pass\n\n\n@nightly\n@require_torch_gpu\nclass ShapEImg2ImgPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_shap_e_img2img(self):\n input_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\" \"/shap_e/corgi.png\"\n )\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/shap_e/test_shap_e_img2img_out.npy\"\n )\n pipe = ShapEImg2ImgPipeline.from_pretrained(\"openai/shap-e-img2img\")\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=torch_device).manual_seed(0)\n\n images = pipe(\n input_image,\n generator=generator,\n guidance_scale=3.0,\n num_inference_steps=64,\n frame_size=64,\n output_type=\"np\",\n ).images[0]\n\n assert images.shape == (20, 64, 64, 3)\n\n assert_mean_pixel_difference(images, expected_image)\n" }, { "path": "diffusers/tests/pipelines/stable_cascade/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/stable_cascade/test_stable_cascade_combined.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom diffusers import DDPMWuerstchenScheduler, StableCascadeCombinedPipeline\nfrom diffusers.models import StableCascadeUNet\nfrom diffusers.pipelines.wuerstchen import PaellaVQModel\nfrom diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, torch_device\n\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass StableCascadeCombinedPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = StableCascadeCombinedPipeline\n params = [\"prompt\"]\n batch_params = [\"prompt\", \"negative_prompt\"]\n required_optional_params = [\n \"generator\",\n \"height\",\n \"width\",\n \"latents\",\n \"prior_guidance_scale\",\n \"decoder_guidance_scale\",\n \"negative_prompt\",\n \"num_inference_steps\",\n \"return_dict\",\n \"prior_num_inference_steps\",\n \"output_type\",\n ]\n test_xformers_attention = True\n\n @property\n def text_embedder_hidden_size(self):\n return 32\n\n @property\n def dummy_prior(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"conditioning_dim\": 128,\n \"block_out_channels\": (128, 128),\n \"num_attention_heads\": (2, 2),\n \"down_num_layers_per_block\": (1, 1),\n \"up_num_layers_per_block\": (1, 1),\n \"clip_image_in_channels\": 768,\n \"switch_level\": (False,),\n \"clip_text_in_channels\": self.text_embedder_hidden_size,\n \"clip_text_pooled_in_channels\": self.text_embedder_hidden_size,\n }\n\n model = StableCascadeUNet(**model_kwargs)\n return model.eval()\n\n @property\n def dummy_tokenizer(self):\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n return tokenizer\n\n @property\n def dummy_text_encoder(self):\n torch.manual_seed(0)\n config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n projection_dim=self.text_embedder_hidden_size,\n hidden_size=self.text_embedder_hidden_size,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n return CLIPTextModelWithProjection(config).eval()\n\n @property\n def dummy_vqgan(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"bottleneck_blocks\": 1,\n \"num_vq_embeddings\": 2,\n }\n model = PaellaVQModel(**model_kwargs)\n return model.eval()\n\n @property\n def dummy_decoder(self):\n torch.manual_seed(0)\n model_kwargs = {\n \"in_channels\": 4,\n \"out_channels\": 4,\n \"conditioning_dim\": 128,\n \"block_out_channels\": (16, 32, 64, 128),\n \"num_attention_heads\": (-1, -1, 1, 2),\n \"down_num_layers_per_block\": (1, 1, 1, 1),\n \"up_num_layers_per_block\": (1, 1, 1, 1),\n \"down_blocks_repeat_mappers\": (1, 1, 1, 1),\n \"up_blocks_repeat_mappers\": (3, 3, 2, 2),\n \"block_types_per_layer\": (\n (\"SDCascadeResBlock\", \"SDCascadeTimestepBlock\"),\n (\"SDCascadeResBlock\", \"SDCascadeTimestepBlock\"),\n (\"SDCascadeResBlock\", \"SDCascadeTimestepBlock\", \"SDCascadeAttnBlock\"),\n (\"SDCascadeResBlock\", \"SDCascadeTimestepBlock\", \"SDCascadeAttnBlock\"),\n ),\n \"switch_level\": None,\n \"clip_text_pooled_in_channels\": 32,\n \"dropout\": (0.1, 0.1, 0.1, 0.1),\n }\n\n model = StableCascadeUNet(**model_kwargs)\n return model.eval()\n\n def get_dummy_components(self):\n prior = self.dummy_prior\n\n scheduler = DDPMWuerstchenScheduler()\n tokenizer = self.dummy_tokenizer\n text_encoder = self.dummy_text_encoder\n decoder = self.dummy_decoder\n vqgan = self.dummy_vqgan\n prior_text_encoder = self.dummy_text_encoder\n prior_tokenizer = self.dummy_tokenizer\n\n components = {\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"decoder\": decoder,\n \"scheduler\": scheduler,\n \"vqgan\": vqgan,\n \"prior_text_encoder\": prior_text_encoder,\n \"prior_tokenizer\": prior_tokenizer,\n \"prior_prior\": prior,\n \"prior_scheduler\": scheduler,\n \"prior_feature_extractor\": None,\n \"prior_image_encoder\": None,\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"horse\",\n \"generator\": generator,\n \"prior_guidance_scale\": 4.0,\n \"decoder_guidance_scale\": 4.0,\n \"num_inference_steps\": 2,\n \"prior_num_inference_steps\": 2,\n \"output_type\": \"np\",\n \"height\": 128,\n \"width\": 128,\n }\n return inputs\n\n def test_stable_cascade(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.images\n\n image_from_tuple = pipe(**self.get_dummy_inputs(device), return_dict=False)[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[-3:, -3:, -1]\n\n assert image.shape == (1, 128, 128, 3)\n\n expected_slice = np.array([0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0])\n assert (\n np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_slice.flatten()}\"\n assert (\n np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}\"\n\n @require_torch_gpu\n def test_offloads(self):\n pipes = []\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(torch_device)\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_sequential_cpu_offload()\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_model_cpu_offload()\n pipes.append(sd_pipe)\n\n image_slices = []\n for pipe in pipes:\n inputs = self.get_dummy_inputs(torch_device)\n image = pipe(**inputs).images\n\n image_slices.append(image[0, -3:, -3:, -1].flatten())\n\n assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3\n assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=2e-2)\n\n @unittest.skip(reason=\"fp16 not supported\")\n def test_float16_inference(self):\n super().test_float16_inference()\n\n @unittest.skip(reason=\"no callback test for combined pipeline\")\n def test_callback_inputs(self):\n super().test_callback_inputs()\n\n def test_stable_cascade_combined_prompt_embeds(self):\n device = \"cpu\"\n components = self.get_dummy_components()\n\n pipe = StableCascadeCombinedPipeline(**components)\n pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A photograph of a shiba inu, wearing a hat\"\n (\n prompt_embeds,\n prompt_embeds_pooled,\n negative_prompt_embeds,\n negative_prompt_embeds_pooled,\n ) = pipe.prior_pipe.encode_prompt(device, 1, 1, False, prompt=prompt)\n generator = torch.Generator(device=device)\n\n output_prompt = pipe(\n prompt=prompt,\n num_inference_steps=1,\n prior_num_inference_steps=1,\n output_type=\"np\",\n generator=generator.manual_seed(0),\n )\n output_prompt_embeds = pipe(\n prompt=None,\n prompt_embeds=prompt_embeds,\n prompt_embeds_pooled=prompt_embeds_pooled,\n negative_prompt_embeds=negative_prompt_embeds,\n negative_prompt_embeds_pooled=negative_prompt_embeds_pooled,\n num_inference_steps=1,\n prior_num_inference_steps=1,\n output_type=\"np\",\n generator=generator.manual_seed(0),\n )\n\n assert np.abs(output_prompt.images - output_prompt_embeds.images).max() < 1e-5\n" }, { "path": "diffusers/tests/pipelines/stable_cascade/test_stable_cascade_decoder.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom diffusers import DDPMWuerstchenScheduler, StableCascadeDecoderPipeline\nfrom diffusers.models import StableCascadeUNet\nfrom diffusers.pipelines.wuerstchen import PaellaVQModel\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n load_numpy,\n load_pt,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n skip_mps,\n slow,\n torch_device,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass StableCascadeDecoderPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = StableCascadeDecoderPipeline\n params = [\"prompt\"]\n batch_params = [\"image_embeddings\", \"prompt\", \"negative_prompt\"]\n required_optional_params = [\n \"num_images_per_prompt\",\n \"num_inference_steps\",\n \"latents\",\n \"negative_prompt\",\n \"guidance_scale\",\n \"output_type\",\n \"return_dict\",\n ]\n test_xformers_attention = False\n callback_cfg_params = [\"image_embeddings\", \"text_encoder_hidden_states\"]\n\n @property\n def text_embedder_hidden_size(self):\n return 32\n\n @property\n def time_input_dim(self):\n return 32\n\n @property\n def block_out_channels_0(self):\n return self.time_input_dim\n\n @property\n def time_embed_dim(self):\n return self.time_input_dim * 4\n\n @property\n def dummy_tokenizer(self):\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n return tokenizer\n\n @property\n def dummy_text_encoder(self):\n torch.manual_seed(0)\n config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n projection_dim=self.text_embedder_hidden_size,\n hidden_size=self.text_embedder_hidden_size,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n return CLIPTextModelWithProjection(config).eval()\n\n @property\n def dummy_vqgan(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"bottleneck_blocks\": 1,\n \"num_vq_embeddings\": 2,\n }\n model = PaellaVQModel(**model_kwargs)\n return model.eval()\n\n @property\n def dummy_decoder(self):\n torch.manual_seed(0)\n model_kwargs = {\n \"in_channels\": 4,\n \"out_channels\": 4,\n \"conditioning_dim\": 128,\n \"block_out_channels\": [16, 32, 64, 128],\n \"num_attention_heads\": [-1, -1, 1, 2],\n \"down_num_layers_per_block\": [1, 1, 1, 1],\n \"up_num_layers_per_block\": [1, 1, 1, 1],\n \"down_blocks_repeat_mappers\": [1, 1, 1, 1],\n \"up_blocks_repeat_mappers\": [3, 3, 2, 2],\n \"block_types_per_layer\": [\n [\"SDCascadeResBlock\", \"SDCascadeTimestepBlock\"],\n [\"SDCascadeResBlock\", \"SDCascadeTimestepBlock\"],\n [\"SDCascadeResBlock\", \"SDCascadeTimestepBlock\", \"SDCascadeAttnBlock\"],\n [\"SDCascadeResBlock\", \"SDCascadeTimestepBlock\", \"SDCascadeAttnBlock\"],\n ],\n \"switch_level\": None,\n \"clip_text_pooled_in_channels\": 32,\n \"dropout\": [0.1, 0.1, 0.1, 0.1],\n }\n model = StableCascadeUNet(**model_kwargs)\n return model.eval()\n\n def get_dummy_components(self):\n decoder = self.dummy_decoder\n text_encoder = self.dummy_text_encoder\n tokenizer = self.dummy_tokenizer\n vqgan = self.dummy_vqgan\n\n scheduler = DDPMWuerstchenScheduler()\n\n components = {\n \"decoder\": decoder,\n \"vqgan\": vqgan,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"scheduler\": scheduler,\n \"latent_dim_scale\": 4.0,\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"image_embeddings\": torch.ones((1, 4, 4, 4), device=device),\n \"prompt\": \"horse\",\n \"generator\": generator,\n \"guidance_scale\": 2.0,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_wuerstchen_decoder(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.images\n\n image_from_tuple = pipe(**self.get_dummy_inputs(device), return_dict=False)\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0])\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n\n @skip_mps\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=1e-2)\n\n @skip_mps\n def test_attention_slicing_forward_pass(self):\n test_max_difference = torch_device == \"cpu\"\n test_mean_pixel_difference = False\n\n self._test_attention_slicing_forward_pass(\n test_max_difference=test_max_difference,\n test_mean_pixel_difference=test_mean_pixel_difference,\n )\n\n @unittest.skip(reason=\"fp16 not supported\")\n def test_float16_inference(self):\n super().test_float16_inference()\n\n def test_stable_cascade_decoder_prompt_embeds(self):\n device = \"cpu\"\n components = self.get_dummy_components()\n\n pipe = StableCascadeDecoderPipeline(**components)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image_embeddings = inputs[\"image_embeddings\"]\n prompt = \"A photograph of a shiba inu, wearing a hat\"\n (\n prompt_embeds,\n prompt_embeds_pooled,\n negative_prompt_embeds,\n negative_prompt_embeds_pooled,\n ) = pipe.encode_prompt(device, 1, 1, False, prompt=prompt)\n generator = torch.Generator(device=device)\n\n decoder_output_prompt = pipe(\n image_embeddings=image_embeddings,\n prompt=prompt,\n num_inference_steps=1,\n output_type=\"np\",\n generator=generator.manual_seed(0),\n )\n decoder_output_prompt_embeds = pipe(\n image_embeddings=image_embeddings,\n prompt=None,\n prompt_embeds=prompt_embeds,\n prompt_embeds_pooled=prompt_embeds_pooled,\n negative_prompt_embeds=negative_prompt_embeds,\n negative_prompt_embeds_pooled=negative_prompt_embeds_pooled,\n num_inference_steps=1,\n output_type=\"np\",\n generator=generator.manual_seed(0),\n )\n\n assert np.abs(decoder_output_prompt.images - decoder_output_prompt_embeds.images).max() < 1e-5\n\n def test_stable_cascade_decoder_single_prompt_multiple_image_embeddings(self):\n device = \"cpu\"\n components = self.get_dummy_components()\n\n pipe = StableCascadeDecoderPipeline(**components)\n pipe.set_progress_bar_config(disable=None)\n\n prior_num_images_per_prompt = 2\n decoder_num_images_per_prompt = 2\n prompt = [\"a cat\"]\n batch_size = len(prompt)\n\n generator = torch.Generator(device)\n image_embeddings = randn_tensor(\n (batch_size * prior_num_images_per_prompt, 4, 4, 4), generator=generator.manual_seed(0)\n )\n decoder_output = pipe(\n image_embeddings=image_embeddings,\n prompt=prompt,\n num_inference_steps=1,\n output_type=\"np\",\n guidance_scale=0.0,\n generator=generator.manual_seed(0),\n num_images_per_prompt=decoder_num_images_per_prompt,\n )\n\n assert decoder_output.images.shape[0] == (\n batch_size * prior_num_images_per_prompt * decoder_num_images_per_prompt\n )\n\n def test_stable_cascade_decoder_single_prompt_multiple_image_embeddings_with_guidance(self):\n device = \"cpu\"\n components = self.get_dummy_components()\n\n pipe = StableCascadeDecoderPipeline(**components)\n pipe.set_progress_bar_config(disable=None)\n\n prior_num_images_per_prompt = 2\n decoder_num_images_per_prompt = 2\n prompt = [\"a cat\"]\n batch_size = len(prompt)\n\n generator = torch.Generator(device)\n image_embeddings = randn_tensor(\n (batch_size * prior_num_images_per_prompt, 4, 4, 4), generator=generator.manual_seed(0)\n )\n decoder_output = pipe(\n image_embeddings=image_embeddings,\n prompt=prompt,\n num_inference_steps=1,\n output_type=\"np\",\n guidance_scale=2.0,\n generator=generator.manual_seed(0),\n num_images_per_prompt=decoder_num_images_per_prompt,\n )\n\n assert decoder_output.images.shape[0] == (\n batch_size * prior_num_images_per_prompt * decoder_num_images_per_prompt\n )\n\n\n@slow\n@require_torch_gpu\nclass StableCascadeDecoderPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_stable_cascade_decoder(self):\n pipe = StableCascadeDecoderPipeline.from_pretrained(\n \"stabilityai/stable-cascade\", variant=\"bf16\", torch_dtype=torch.bfloat16\n )\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A photograph of the inside of a subway train. There are raccoons sitting on the seats. One of them is reading a newspaper. The window shows the city in the background.\"\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n image_embedding = load_pt(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_cascade/image_embedding.pt\"\n )\n\n image = pipe(\n prompt=prompt,\n image_embeddings=image_embedding,\n output_type=\"np\",\n num_inference_steps=2,\n generator=generator,\n ).images[0]\n\n assert image.shape == (1024, 1024, 3)\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_cascade/stable_cascade_decoder_image.npy\"\n )\n max_diff = numpy_cosine_similarity_distance(image.flatten(), expected_image.flatten())\n assert max_diff < 1e-4\n" }, { "path": "diffusers/tests/pipelines/stable_cascade/test_stable_cascade_prior.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom diffusers import DDPMWuerstchenScheduler, StableCascadePriorPipeline\nfrom diffusers.loaders import AttnProcsLayers\nfrom diffusers.models import StableCascadeUNet\nfrom diffusers.models.attention_processor import LoRAAttnProcessor, LoRAAttnProcessor2_0\nfrom diffusers.utils.import_utils import is_peft_available\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n load_numpy,\n numpy_cosine_similarity_distance,\n require_peft_backend,\n require_torch_gpu,\n skip_mps,\n slow,\n torch_device,\n)\n\n\nif is_peft_available():\n from peft import LoraConfig\n from peft.tuners.tuners_utils import BaseTunerLayer\n\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\ndef create_prior_lora_layers(unet: nn.Module):\n lora_attn_procs = {}\n for name in unet.attn_processors.keys():\n lora_attn_processor_class = (\n LoRAAttnProcessor2_0 if hasattr(F, \"scaled_dot_product_attention\") else LoRAAttnProcessor\n )\n lora_attn_procs[name] = lora_attn_processor_class(\n hidden_size=unet.config.c,\n )\n unet_lora_layers = AttnProcsLayers(lora_attn_procs)\n return lora_attn_procs, unet_lora_layers\n\n\nclass StableCascadePriorPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = StableCascadePriorPipeline\n params = [\"prompt\"]\n batch_params = [\"prompt\", \"negative_prompt\"]\n required_optional_params = [\n \"num_images_per_prompt\",\n \"generator\",\n \"num_inference_steps\",\n \"latents\",\n \"negative_prompt\",\n \"guidance_scale\",\n \"output_type\",\n \"return_dict\",\n ]\n test_xformers_attention = False\n callback_cfg_params = [\"text_encoder_hidden_states\"]\n\n @property\n def text_embedder_hidden_size(self):\n return 32\n\n @property\n def time_input_dim(self):\n return 32\n\n @property\n def block_out_channels_0(self):\n return self.time_input_dim\n\n @property\n def time_embed_dim(self):\n return self.time_input_dim * 4\n\n @property\n def dummy_tokenizer(self):\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n return tokenizer\n\n @property\n def dummy_text_encoder(self):\n torch.manual_seed(0)\n config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=self.text_embedder_hidden_size,\n projection_dim=self.text_embedder_hidden_size,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n return CLIPTextModelWithProjection(config).eval()\n\n @property\n def dummy_prior(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"conditioning_dim\": 128,\n \"block_out_channels\": (128, 128),\n \"num_attention_heads\": (2, 2),\n \"down_num_layers_per_block\": (1, 1),\n \"up_num_layers_per_block\": (1, 1),\n \"switch_level\": (False,),\n \"clip_image_in_channels\": 768,\n \"clip_text_in_channels\": self.text_embedder_hidden_size,\n \"clip_text_pooled_in_channels\": self.text_embedder_hidden_size,\n \"dropout\": (0.1, 0.1),\n }\n\n model = StableCascadeUNet(**model_kwargs)\n return model.eval()\n\n def get_dummy_components(self):\n prior = self.dummy_prior\n text_encoder = self.dummy_text_encoder\n tokenizer = self.dummy_tokenizer\n\n scheduler = DDPMWuerstchenScheduler()\n\n components = {\n \"prior\": prior,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"scheduler\": scheduler,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"horse\",\n \"generator\": generator,\n \"guidance_scale\": 4.0,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_wuerstchen_prior(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.image_embeddings\n\n image_from_tuple = pipe(**self.get_dummy_inputs(device), return_dict=False)[0]\n\n image_slice = image[0, 0, 0, -10:]\n image_from_tuple_slice = image_from_tuple[0, 0, 0, -10:]\n assert image.shape == (1, 16, 24, 24)\n\n expected_slice = np.array(\n [\n 96.139565,\n -20.213179,\n -116.40341,\n -191.57129,\n 39.350136,\n 74.80767,\n 39.782352,\n -184.67352,\n -46.426907,\n 168.41783,\n ]\n )\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2\n assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 5e-2\n\n @skip_mps\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=2e-1)\n\n @skip_mps\n def test_attention_slicing_forward_pass(self):\n test_max_difference = torch_device == \"cpu\"\n test_mean_pixel_difference = False\n\n self._test_attention_slicing_forward_pass(\n test_max_difference=test_max_difference,\n test_mean_pixel_difference=test_mean_pixel_difference,\n )\n\n @unittest.skip(reason=\"fp16 not supported\")\n def test_float16_inference(self):\n super().test_float16_inference()\n\n def check_if_lora_correctly_set(self, model) -> bool:\n \"\"\"\n Checks if the LoRA layers are correctly set with peft\n \"\"\"\n for module in model.modules():\n if isinstance(module, BaseTunerLayer):\n return True\n return False\n\n def get_lora_components(self):\n prior = self.dummy_prior\n\n prior_lora_config = LoraConfig(\n r=4, lora_alpha=4, target_modules=[\"to_q\", \"to_k\", \"to_v\", \"to_out.0\"], init_lora_weights=False\n )\n\n prior_lora_attn_procs, prior_lora_layers = create_prior_lora_layers(prior)\n\n lora_components = {\n \"prior_lora_layers\": prior_lora_layers,\n \"prior_lora_attn_procs\": prior_lora_attn_procs,\n }\n\n return prior, prior_lora_config, lora_components\n\n @require_peft_backend\n @unittest.skip(reason=\"no lora support for now\")\n def test_inference_with_prior_lora(self):\n _, prior_lora_config, _ = self.get_lora_components()\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output_no_lora = pipe(**self.get_dummy_inputs(device))\n image_embed = output_no_lora.image_embeddings\n self.assertTrue(image_embed.shape == (1, 16, 24, 24))\n\n pipe.prior.add_adapter(prior_lora_config)\n self.assertTrue(self.check_if_lora_correctly_set(pipe.prior), \"Lora not correctly set in prior\")\n\n output_lora = pipe(**self.get_dummy_inputs(device))\n lora_image_embed = output_lora.image_embeddings\n\n self.assertTrue(image_embed.shape == lora_image_embed.shape)\n\n def test_stable_cascade_decoder_prompt_embeds(self):\n device = \"cpu\"\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A photograph of a shiba inu, wearing a hat\"\n (\n prompt_embeds,\n prompt_embeds_pooled,\n negative_prompt_embeds,\n negative_prompt_embeds_pooled,\n ) = pipe.encode_prompt(device, 1, 1, False, prompt=prompt)\n generator = torch.Generator(device=device)\n\n output_prompt = pipe(\n prompt=prompt,\n num_inference_steps=1,\n output_type=\"np\",\n generator=generator.manual_seed(0),\n )\n output_prompt_embeds = pipe(\n prompt=None,\n prompt_embeds=prompt_embeds,\n prompt_embeds_pooled=prompt_embeds_pooled,\n negative_prompt_embeds=negative_prompt_embeds,\n negative_prompt_embeds_pooled=negative_prompt_embeds_pooled,\n num_inference_steps=1,\n output_type=\"np\",\n generator=generator.manual_seed(0),\n )\n\n assert np.abs(output_prompt.image_embeddings - output_prompt_embeds.image_embeddings).max() < 1e-5\n\n\n@slow\n@require_torch_gpu\nclass StableCascadePriorPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_stable_cascade_prior(self):\n pipe = StableCascadePriorPipeline.from_pretrained(\n \"stabilityai/stable-cascade-prior\", variant=\"bf16\", torch_dtype=torch.bfloat16\n )\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A photograph of the inside of a subway train. There are raccoons sitting on the seats. One of them is reading a newspaper. The window shows the city in the background.\"\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n\n output = pipe(prompt, num_inference_steps=2, output_type=\"np\", generator=generator)\n image_embedding = output.image_embeddings\n expected_image_embedding = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_cascade/stable_cascade_prior_image_embeddings.npy\"\n )\n assert image_embedding.shape == (1, 16, 24, 24)\n\n max_diff = numpy_cosine_similarity_distance(image_embedding.flatten(), expected_image_embedding.flatten())\n assert max_diff < 1e-4\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport tempfile\nimport unittest\n\nimport numpy as np\n\nfrom diffusers import (\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n LMSDiscreteScheduler,\n OnnxStableDiffusionPipeline,\n PNDMScheduler,\n)\nfrom diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu\n\nfrom ..test_pipelines_onnx_common import OnnxPipelineTesterMixin\n\n\nif is_onnx_available():\n import onnxruntime as ort\n\n\nclass OnnxStableDiffusionPipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):\n hub_checkpoint = \"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline\"\n\n def get_dummy_inputs(self, seed=0):\n generator = np.random.RandomState(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_pipeline_default_ddim(self):\n pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider=\"CPUExecutionProvider\")\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 128, 128, 3)\n expected_slice = np.array([0.65072, 0.58492, 0.48219, 0.55521, 0.53180, 0.55939, 0.50697, 0.39800, 0.46455])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_pipeline_pndm(self):\n pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider=\"CPUExecutionProvider\")\n pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config, skip_prk_steps=True)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 128, 128, 3)\n expected_slice = np.array([0.65863, 0.59425, 0.49326, 0.56313, 0.53875, 0.56627, 0.51065, 0.39777, 0.46330])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_pipeline_lms(self):\n pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider=\"CPUExecutionProvider\")\n pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 128, 128, 3)\n expected_slice = np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_pipeline_euler(self):\n pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider=\"CPUExecutionProvider\")\n pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 128, 128, 3)\n expected_slice = np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_pipeline_euler_ancestral(self):\n pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider=\"CPUExecutionProvider\")\n pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 128, 128, 3)\n expected_slice = np.array([0.53817, 0.60812, 0.47384, 0.49530, 0.51894, 0.49814, 0.47984, 0.38958, 0.44271])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_pipeline_dpm_multistep(self):\n pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider=\"CPUExecutionProvider\")\n pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 128, 128, 3)\n expected_slice = np.array([0.53895, 0.60808, 0.47933, 0.49608, 0.51886, 0.49950, 0.48053, 0.38957, 0.44200])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_prompt_embeds(self):\n pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider=\"CPUExecutionProvider\")\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs()\n inputs[\"prompt\"] = 3 * [inputs[\"prompt\"]]\n\n # forward\n output = pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n inputs = self.get_dummy_inputs()\n prompt = 3 * [inputs.pop(\"prompt\")]\n\n text_inputs = pipe.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=pipe.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"np\",\n )\n text_inputs = text_inputs[\"input_ids\"]\n\n prompt_embeds = pipe.text_encoder(input_ids=text_inputs.astype(np.int32))[0]\n\n inputs[\"prompt_embeds\"] = prompt_embeds\n\n # forward\n output = pipe(**inputs)\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n def test_stable_diffusion_negative_prompt_embeds(self):\n pipe = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint, provider=\"CPUExecutionProvider\")\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs()\n negative_prompt = 3 * [\"this is a negative prompt\"]\n inputs[\"negative_prompt\"] = negative_prompt\n inputs[\"prompt\"] = 3 * [inputs[\"prompt\"]]\n\n # forward\n output = pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n inputs = self.get_dummy_inputs()\n prompt = 3 * [inputs.pop(\"prompt\")]\n\n embeds = []\n for p in [prompt, negative_prompt]:\n text_inputs = pipe.tokenizer(\n p,\n padding=\"max_length\",\n max_length=pipe.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"np\",\n )\n text_inputs = text_inputs[\"input_ids\"]\n\n embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.int32))[0])\n\n inputs[\"prompt_embeds\"], inputs[\"negative_prompt_embeds\"] = embeds\n\n # forward\n output = pipe(**inputs)\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n\n@nightly\n@require_onnxruntime\n@require_torch_gpu\nclass OnnxStableDiffusionPipelineIntegrationTests(unittest.TestCase):\n @property\n def gpu_provider(self):\n return (\n \"CUDAExecutionProvider\",\n {\n \"gpu_mem_limit\": \"15000000000\", # 15GB\n \"arena_extend_strategy\": \"kSameAsRequested\",\n },\n )\n\n @property\n def gpu_options(self):\n options = ort.SessionOptions()\n options.enable_mem_pattern = False\n return options\n\n def test_inference_default_pndm(self):\n # using the PNDM scheduler by default\n sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n revision=\"onnx\",\n safety_checker=None,\n feature_extractor=None,\n provider=self.gpu_provider,\n sess_options=self.gpu_options,\n )\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A painting of a squirrel eating a burger\"\n np.random.seed(0)\n output = sd_pipe([prompt], guidance_scale=6.0, num_inference_steps=10, output_type=\"np\")\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_inference_ddim(self):\n ddim_scheduler = DDIMScheduler.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", subfolder=\"scheduler\", revision=\"onnx\"\n )\n sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n revision=\"onnx\",\n scheduler=ddim_scheduler,\n safety_checker=None,\n feature_extractor=None,\n provider=self.gpu_provider,\n sess_options=self.gpu_options,\n )\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"open neural network exchange\"\n generator = np.random.RandomState(0)\n output = sd_pipe([prompt], guidance_scale=7.5, num_inference_steps=10, generator=generator, output_type=\"np\")\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_inference_k_lms(self):\n lms_scheduler = LMSDiscreteScheduler.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", subfolder=\"scheduler\", revision=\"onnx\"\n )\n sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n revision=\"onnx\",\n scheduler=lms_scheduler,\n safety_checker=None,\n feature_extractor=None,\n provider=self.gpu_provider,\n sess_options=self.gpu_options,\n )\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"open neural network exchange\"\n generator = np.random.RandomState(0)\n output = sd_pipe([prompt], guidance_scale=7.5, num_inference_steps=10, generator=generator, output_type=\"np\")\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_intermediate_state(self):\n number_of_steps = 0\n\n def test_callback_fn(step: int, timestep: int, latents: np.ndarray) -> None:\n test_callback_fn.has_been_called = True\n nonlocal number_of_steps\n number_of_steps += 1\n if step == 0:\n assert latents.shape == (1, 4, 64, 64)\n latents_slice = latents[0, -3:, -3:, -1]\n expected_slice = np.array(\n [-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167]\n )\n\n assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3\n elif step == 5:\n assert latents.shape == (1, 4, 64, 64)\n latents_slice = latents[0, -3:, -3:, -1]\n expected_slice = np.array(\n [-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875]\n )\n\n assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3\n\n test_callback_fn.has_been_called = False\n\n pipe = OnnxStableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n revision=\"onnx\",\n safety_checker=None,\n feature_extractor=None,\n provider=self.gpu_provider,\n sess_options=self.gpu_options,\n )\n pipe.set_progress_bar_config(disable=None)\n\n prompt = \"Andromeda galaxy in a bottle\"\n\n generator = np.random.RandomState(0)\n pipe(\n prompt=prompt,\n num_inference_steps=5,\n guidance_scale=7.5,\n generator=generator,\n callback=test_callback_fn,\n callback_steps=1,\n )\n assert test_callback_fn.has_been_called\n assert number_of_steps == 6\n\n def test_stable_diffusion_no_safety_checker(self):\n pipe = OnnxStableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n revision=\"onnx\",\n safety_checker=None,\n feature_extractor=None,\n provider=self.gpu_provider,\n sess_options=self.gpu_options,\n )\n assert isinstance(pipe, OnnxStableDiffusionPipeline)\n assert pipe.safety_checker is None\n\n image = pipe(\"example prompt\", num_inference_steps=2).images[0]\n assert image is not None\n\n # check that there's no error when saving a pipeline with one of the models being None\n with tempfile.TemporaryDirectory() as tmpdirname:\n pipe.save_pretrained(tmpdirname)\n pipe = OnnxStableDiffusionPipeline.from_pretrained(tmpdirname)\n\n # sanity check that the pipeline still works\n assert pipe.safety_checker is None\n image = pipe(\"example prompt\", num_inference_steps=2).images[0]\n assert image is not None\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_img2img.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport random\nimport unittest\n\nimport numpy as np\n\nfrom diffusers import (\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n LMSDiscreteScheduler,\n OnnxStableDiffusionImg2ImgPipeline,\n PNDMScheduler,\n)\nfrom diffusers.utils.testing_utils import (\n floats_tensor,\n is_onnx_available,\n load_image,\n nightly,\n require_onnxruntime,\n require_torch_gpu,\n)\n\nfrom ..test_pipelines_onnx_common import OnnxPipelineTesterMixin\n\n\nif is_onnx_available():\n import onnxruntime as ort\n\n\nclass OnnxStableDiffusionImg2ImgPipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):\n hub_checkpoint = \"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline\"\n\n def get_dummy_inputs(self, seed=0):\n image = floats_tensor((1, 3, 128, 128), rng=random.Random(seed))\n generator = np.random.RandomState(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"image\": image,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"strength\": 0.75,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_pipeline_default_ddim(self):\n pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider=\"CPUExecutionProvider\")\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 128, 128, 3)\n expected_slice = np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087])\n assert np.abs(image_slice - expected_slice).max() < 1e-1\n\n def test_pipeline_pndm(self):\n pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider=\"CPUExecutionProvider\")\n pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config, skip_prk_steps=True)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 128, 128, 3)\n expected_slice = np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1\n\n def test_pipeline_lms(self):\n pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider=\"CPUExecutionProvider\")\n pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)\n pipe.set_progress_bar_config(disable=None)\n\n # warmup pass to apply optimizations\n _ = pipe(**self.get_dummy_inputs())\n\n inputs = self.get_dummy_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 128, 128, 3)\n expected_slice = np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1\n\n def test_pipeline_euler(self):\n pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider=\"CPUExecutionProvider\")\n pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 128, 128, 3)\n expected_slice = np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1\n\n def test_pipeline_euler_ancestral(self):\n pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider=\"CPUExecutionProvider\")\n pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 128, 128, 3)\n expected_slice = np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1\n\n def test_pipeline_dpm_multistep(self):\n pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider=\"CPUExecutionProvider\")\n pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 128, 128, 3)\n expected_slice = np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1\n\n\n@nightly\n@require_onnxruntime\n@require_torch_gpu\nclass OnnxStableDiffusionImg2ImgPipelineIntegrationTests(unittest.TestCase):\n @property\n def gpu_provider(self):\n return (\n \"CUDAExecutionProvider\",\n {\n \"gpu_mem_limit\": \"15000000000\", # 15GB\n \"arena_extend_strategy\": \"kSameAsRequested\",\n },\n )\n\n @property\n def gpu_options(self):\n options = ort.SessionOptions()\n options.enable_mem_pattern = False\n return options\n\n def test_inference_default_pndm(self):\n init_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/img2img/sketch-mountains-input.jpg\"\n )\n init_image = init_image.resize((768, 512))\n # using the PNDM scheduler by default\n pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n revision=\"onnx\",\n safety_checker=None,\n feature_extractor=None,\n provider=self.gpu_provider,\n sess_options=self.gpu_options,\n )\n pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A fantasy landscape, trending on artstation\"\n\n generator = np.random.RandomState(0)\n output = pipe(\n prompt=prompt,\n image=init_image,\n strength=0.75,\n guidance_scale=7.5,\n num_inference_steps=10,\n generator=generator,\n output_type=\"np\",\n )\n images = output.images\n image_slice = images[0, 255:258, 383:386, -1]\n\n assert images.shape == (1, 512, 768, 3)\n expected_slice = np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019])\n # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2\n\n def test_inference_k_lms(self):\n init_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/img2img/sketch-mountains-input.jpg\"\n )\n init_image = init_image.resize((768, 512))\n lms_scheduler = LMSDiscreteScheduler.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", subfolder=\"scheduler\", revision=\"onnx\"\n )\n pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n revision=\"onnx\",\n scheduler=lms_scheduler,\n safety_checker=None,\n feature_extractor=None,\n provider=self.gpu_provider,\n sess_options=self.gpu_options,\n )\n pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A fantasy landscape, trending on artstation\"\n\n generator = np.random.RandomState(0)\n output = pipe(\n prompt=prompt,\n image=init_image,\n strength=0.75,\n guidance_scale=7.5,\n num_inference_steps=20,\n generator=generator,\n output_type=\"np\",\n )\n images = output.images\n image_slice = images[0, 255:258, 383:386, -1]\n\n assert images.shape == (1, 512, 768, 3)\n expected_slice = np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431])\n # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_inpaint.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\n\nimport numpy as np\n\nfrom diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline\nfrom diffusers.utils.testing_utils import (\n is_onnx_available,\n load_image,\n nightly,\n require_onnxruntime,\n require_torch_gpu,\n)\n\nfrom ..test_pipelines_onnx_common import OnnxPipelineTesterMixin\n\n\nif is_onnx_available():\n import onnxruntime as ort\n\n\nclass OnnxStableDiffusionPipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):\n # FIXME: add fast tests\n pass\n\n\n@nightly\n@require_onnxruntime\n@require_torch_gpu\nclass OnnxStableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase):\n @property\n def gpu_provider(self):\n return (\n \"CUDAExecutionProvider\",\n {\n \"gpu_mem_limit\": \"15000000000\", # 15GB\n \"arena_extend_strategy\": \"kSameAsRequested\",\n },\n )\n\n @property\n def gpu_options(self):\n options = ort.SessionOptions()\n options.enable_mem_pattern = False\n return options\n\n def test_inference_default_pndm(self):\n init_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/in_paint/overture-creations-5sI6fQgYIuo.png\"\n )\n mask_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/in_paint/overture-creations-5sI6fQgYIuo_mask.png\"\n )\n pipe = OnnxStableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\",\n revision=\"onnx\",\n safety_checker=None,\n feature_extractor=None,\n provider=self.gpu_provider,\n sess_options=self.gpu_options,\n )\n pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A red cat sitting on a park bench\"\n\n generator = np.random.RandomState(0)\n output = pipe(\n prompt=prompt,\n image=init_image,\n mask_image=mask_image,\n guidance_scale=7.5,\n num_inference_steps=10,\n generator=generator,\n output_type=\"np\",\n )\n images = output.images\n image_slice = images[0, 255:258, 255:258, -1]\n\n assert images.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_inference_k_lms(self):\n init_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/in_paint/overture-creations-5sI6fQgYIuo.png\"\n )\n mask_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/in_paint/overture-creations-5sI6fQgYIuo_mask.png\"\n )\n lms_scheduler = LMSDiscreteScheduler.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", subfolder=\"scheduler\", revision=\"onnx\"\n )\n pipe = OnnxStableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\",\n revision=\"onnx\",\n scheduler=lms_scheduler,\n safety_checker=None,\n feature_extractor=None,\n provider=self.gpu_provider,\n sess_options=self.gpu_options,\n )\n pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A red cat sitting on a park bench\"\n\n generator = np.random.RandomState(0)\n output = pipe(\n prompt=prompt,\n image=init_image,\n mask_image=mask_image,\n guidance_scale=7.5,\n num_inference_steps=20,\n generator=generator,\n output_type=\"np\",\n )\n images = output.images\n image_slice = images[0, 255:258, 255:258, -1]\n\n assert images.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_upscale.py", "content": "# coding=utf-8\n# Copyright 2022 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport random\nimport unittest\n\nimport numpy as np\n\nfrom diffusers import (\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n LMSDiscreteScheduler,\n OnnxStableDiffusionUpscalePipeline,\n PNDMScheduler,\n)\nfrom diffusers.utils.testing_utils import (\n floats_tensor,\n is_onnx_available,\n load_image,\n nightly,\n require_onnxruntime,\n require_torch_gpu,\n)\n\nfrom ..test_pipelines_onnx_common import OnnxPipelineTesterMixin\n\n\nif is_onnx_available():\n import onnxruntime as ort\n\n\nclass OnnxStableDiffusionUpscalePipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):\n # TODO: is there an appropriate internal test set?\n hub_checkpoint = \"ssube/stable-diffusion-x4-upscaler-onnx\"\n\n def get_dummy_inputs(self, seed=0):\n image = floats_tensor((1, 3, 128, 128), rng=random.Random(seed))\n generator = np.random.RandomState(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"image\": image,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_pipeline_default_ddpm(self):\n pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint, provider=\"CPUExecutionProvider\")\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n # started as 128, should now be 512\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.6957, 0.7002, 0.7186, 0.6881, 0.6693, 0.6910, 0.7445, 0.7274, 0.7056])\n assert np.abs(image_slice - expected_slice).max() < 1e-1\n\n def test_pipeline_pndm(self):\n pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint, provider=\"CPUExecutionProvider\")\n pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config, skip_prk_steps=True)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.7349, 0.7347, 0.7034, 0.7696, 0.7876, 0.7597, 0.7916, 0.8085, 0.8036])\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1\n\n def test_pipeline_dpm_multistep(self):\n pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint, provider=\"CPUExecutionProvider\")\n pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array(\n [0.7659278, 0.76437664, 0.75579107, 0.7691116, 0.77666986, 0.7727672, 0.7758664, 0.7812226, 0.76942515]\n )\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1\n\n def test_pipeline_euler(self):\n pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint, provider=\"CPUExecutionProvider\")\n pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array(\n [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223]\n )\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1\n\n def test_pipeline_euler_ancestral(self):\n pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint, provider=\"CPUExecutionProvider\")\n pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array(\n [0.77424496, 0.773601, 0.7645288, 0.7769598, 0.7772739, 0.7738688, 0.78187233, 0.77879584, 0.767043]\n )\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1\n\n\n@nightly\n@require_onnxruntime\n@require_torch_gpu\nclass OnnxStableDiffusionUpscalePipelineIntegrationTests(unittest.TestCase):\n @property\n def gpu_provider(self):\n return (\n \"CUDAExecutionProvider\",\n {\n \"gpu_mem_limit\": \"15000000000\", # 15GB\n \"arena_extend_strategy\": \"kSameAsRequested\",\n },\n )\n\n @property\n def gpu_options(self):\n options = ort.SessionOptions()\n options.enable_mem_pattern = False\n return options\n\n def test_inference_default_ddpm(self):\n init_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/img2img/sketch-mountains-input.jpg\"\n )\n init_image = init_image.resize((128, 128))\n # using the PNDM scheduler by default\n pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(\n \"ssube/stable-diffusion-x4-upscaler-onnx\",\n provider=self.gpu_provider,\n sess_options=self.gpu_options,\n )\n pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A fantasy landscape, trending on artstation\"\n\n generator = np.random.RandomState(0)\n output = pipe(\n prompt=prompt,\n image=init_image,\n guidance_scale=7.5,\n num_inference_steps=10,\n generator=generator,\n output_type=\"np\",\n )\n images = output.images\n image_slice = images[0, 255:258, 383:386, -1]\n\n assert images.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.4883, 0.4947, 0.4980, 0.4975, 0.4982, 0.4980, 0.5000, 0.5006, 0.4972])\n # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2\n\n def test_inference_k_lms(self):\n init_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/img2img/sketch-mountains-input.jpg\"\n )\n init_image = init_image.resize((128, 128))\n lms_scheduler = LMSDiscreteScheduler.from_pretrained(\n \"ssube/stable-diffusion-x4-upscaler-onnx\", subfolder=\"scheduler\"\n )\n pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(\n \"ssube/stable-diffusion-x4-upscaler-onnx\",\n scheduler=lms_scheduler,\n provider=self.gpu_provider,\n sess_options=self.gpu_options,\n )\n pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A fantasy landscape, trending on artstation\"\n\n generator = np.random.RandomState(0)\n output = pipe(\n prompt=prompt,\n image=init_image,\n guidance_scale=7.5,\n num_inference_steps=20,\n generator=generator,\n output_type=\"np\",\n )\n images = output.images\n image_slice = images[0, 255:258, 383:386, -1]\n\n assert images.shape == (1, 512, 512, 3)\n expected_slice = np.array(\n [0.50173753, 0.50223356, 0.502039, 0.50233036, 0.5023725, 0.5022601, 0.5018758, 0.50234085, 0.50241566]\n )\n # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport gc\nimport tempfile\nimport time\nimport traceback\nimport unittest\n\nimport numpy as np\nimport torch\nfrom huggingface_hub import hf_hub_download\nfrom transformers import (\n CLIPTextConfig,\n CLIPTextModel,\n CLIPTokenizer,\n)\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n LCMScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n StableDiffusionPipeline,\n UNet2DConditionModel,\n logging,\n)\nfrom diffusers.utils.testing_utils import (\n CaptureLogger,\n enable_full_determinism,\n load_image,\n load_numpy,\n nightly,\n numpy_cosine_similarity_distance,\n require_accelerate_version_greater,\n require_python39_or_higher,\n require_torch_2,\n require_torch_gpu,\n require_torch_multi_gpu,\n run_test_in_subprocess,\n skip_mps,\n slow,\n torch_device,\n)\n\nfrom ..pipeline_params import (\n TEXT_TO_IMAGE_BATCH_PARAMS,\n TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,\n TEXT_TO_IMAGE_IMAGE_PARAMS,\n TEXT_TO_IMAGE_PARAMS,\n)\nfrom ..test_pipelines_common import (\n IPAdapterTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n)\n\n\nenable_full_determinism()\n\n\n# Will be run via run_test_in_subprocess\ndef _test_stable_diffusion_compile(in_queue, out_queue, timeout):\n error = None\n try:\n inputs = in_queue.get(timeout=timeout)\n torch_device = inputs.pop(\"torch_device\")\n seed = inputs.pop(\"seed\")\n inputs[\"generator\"] = torch.Generator(device=torch_device).manual_seed(seed)\n\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", safety_checker=None)\n sd_pipe.scheduler = DDIMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(torch_device)\n\n sd_pipe.unet.to(memory_format=torch.channels_last)\n sd_pipe.unet = torch.compile(sd_pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n\n sd_pipe.set_progress_bar_config(disable=None)\n\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.38019, 0.28647, 0.27321, 0.40377, 0.38290, 0.35446, 0.39218, 0.38165, 0.42239])\n\n assert np.abs(image_slice - expected_slice).max() < 5e-3\n except Exception:\n error = f\"{traceback.format_exc()}\"\n\n results = {\"error\": error}\n out_queue.put(results, timeout=timeout)\n out_queue.join()\n\n\nclass StableDiffusionPipelineFastTests(\n IPAdapterTesterMixin,\n PipelineLatentTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineTesterMixin,\n unittest.TestCase,\n):\n pipeline_class = StableDiffusionPipeline\n params = TEXT_TO_IMAGE_PARAMS\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n image_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS\n\n def get_dummy_components(self, time_cond_proj_dim=None):\n cross_attention_dim = 8\n\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(4, 8),\n layers_per_block=1,\n sample_size=32,\n time_cond_proj_dim=time_cond_proj_dim,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=cross_attention_dim,\n norm_num_groups=2,\n )\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[4, 8],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n norm_num_groups=2,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=cross_attention_dim,\n intermediate_size=16,\n layer_norm_eps=1e-05,\n num_attention_heads=2,\n num_hidden_layers=2,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_stable_diffusion_ddim(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = sd_pipe(**inputs)\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.1763, 0.4776, 0.4986, 0.2566, 0.3802, 0.4596, 0.5363, 0.3277, 0.3949])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_lcm(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = sd_pipe(**inputs)\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.2368, 0.4900, 0.5019, 0.2723, 0.4473, 0.4578, 0.4551, 0.3532, 0.4133])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_lcm_custom_timesteps(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n del inputs[\"num_inference_steps\"]\n inputs[\"timesteps\"] = [999, 499]\n output = sd_pipe(**inputs)\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.2368, 0.4900, 0.5019, 0.2723, 0.4473, 0.4578, 0.4551, 0.3532, 0.4133])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_ays(self):\n from diffusers.schedulers import AysSchedules\n\n timestep_schedule = AysSchedules[\"StableDiffusionTimesteps\"]\n sigma_schedule = AysSchedules[\"StableDiffusionSigmas\"]\n\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe.scheduler = EulerDiscreteScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"num_inference_steps\"] = 10\n output = sd_pipe(**inputs).images\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"num_inference_steps\"] = None\n inputs[\"timesteps\"] = timestep_schedule\n output_ts = sd_pipe(**inputs).images\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"num_inference_steps\"] = None\n inputs[\"sigmas\"] = sigma_schedule\n output_sigmas = sd_pipe(**inputs).images\n\n assert (\n np.abs(output_sigmas.flatten() - output_ts.flatten()).max() < 1e-3\n ), \"ays timesteps and ays sigmas should have the same outputs\"\n assert (\n np.abs(output.flatten() - output_ts.flatten()).max() > 1e-3\n ), \"use ays timesteps should have different outputs\"\n assert (\n np.abs(output.flatten() - output_sigmas.flatten()).max() > 1e-3\n ), \"use ays sigmas should have different outputs\"\n\n def test_stable_diffusion_prompt_embeds(self):\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"prompt\"] = 3 * [inputs[\"prompt\"]]\n\n # forward\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n inputs = self.get_dummy_inputs(torch_device)\n prompt = 3 * [inputs.pop(\"prompt\")]\n\n text_inputs = sd_pipe.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=sd_pipe.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_inputs = text_inputs[\"input_ids\"].to(torch_device)\n\n prompt_embeds = sd_pipe.text_encoder(text_inputs)[0]\n\n inputs[\"prompt_embeds\"] = prompt_embeds\n\n # forward\n output = sd_pipe(**inputs)\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n def test_stable_diffusion_negative_prompt_embeds(self):\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n negative_prompt = 3 * [\"this is a negative prompt\"]\n inputs[\"negative_prompt\"] = negative_prompt\n inputs[\"prompt\"] = 3 * [inputs[\"prompt\"]]\n\n # forward\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n inputs = self.get_dummy_inputs(torch_device)\n prompt = 3 * [inputs.pop(\"prompt\")]\n\n embeds = []\n for p in [prompt, negative_prompt]:\n text_inputs = sd_pipe.tokenizer(\n p,\n padding=\"max_length\",\n max_length=sd_pipe.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_inputs = text_inputs[\"input_ids\"].to(torch_device)\n\n embeds.append(sd_pipe.text_encoder(text_inputs)[0])\n\n inputs[\"prompt_embeds\"], inputs[\"negative_prompt_embeds\"] = embeds\n\n # forward\n output = sd_pipe(**inputs)\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n def test_stable_diffusion_prompt_embeds_no_text_encoder_or_tokenizer(self):\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"negative_prompt\"] = \"this is a negative prompt\"\n\n # forward\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n inputs = self.get_dummy_inputs(torch_device)\n prompt = inputs.pop(\"prompt\")\n negative_prompt = \"this is a negative prompt\"\n\n prompt_embeds, negative_prompt_embeds = sd_pipe.encode_prompt(\n prompt,\n torch_device,\n 1,\n True,\n negative_prompt=negative_prompt,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n )\n\n inputs[\"prompt_embeds\"] = prompt_embeds\n inputs[\"negative_prompt_embeds\"] = negative_prompt_embeds\n\n sd_pipe.text_encoder = None\n sd_pipe.tokenizer = None\n\n # forward\n output = sd_pipe(**inputs)\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n def test_stable_diffusion_prompt_embeds_with_plain_negative_prompt_list(self):\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n negative_prompt = 3 * [\"this is a negative prompt\"]\n inputs[\"negative_prompt\"] = negative_prompt\n inputs[\"prompt\"] = 3 * [inputs[\"prompt\"]]\n\n # forward\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"negative_prompt\"] = negative_prompt\n prompt = 3 * [inputs.pop(\"prompt\")]\n\n text_inputs = sd_pipe.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=sd_pipe.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_inputs = text_inputs[\"input_ids\"].to(torch_device)\n\n prompt_embeds = sd_pipe.text_encoder(text_inputs)[0]\n\n inputs[\"prompt_embeds\"] = prompt_embeds\n\n # forward\n output = sd_pipe(**inputs)\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n def test_stable_diffusion_ddim_factor_8(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = sd_pipe(**inputs, height=136, width=136)\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 136, 136, 3)\n expected_slice = np.array([0.4720, 0.5426, 0.5160, 0.3961, 0.4696, 0.4296, 0.5738, 0.5888, 0.5481])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_pndm(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe.scheduler = PNDMScheduler(skip_prk_steps=True)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = sd_pipe(**inputs)\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.1941, 0.4748, 0.4880, 0.2222, 0.4221, 0.4545, 0.5604, 0.3488, 0.3902])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_no_safety_checker(self):\n pipe = StableDiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-lms-pipe\", safety_checker=None\n )\n assert isinstance(pipe, StableDiffusionPipeline)\n assert isinstance(pipe.scheduler, LMSDiscreteScheduler)\n assert pipe.safety_checker is None\n\n image = pipe(\"example prompt\", num_inference_steps=2).images[0]\n assert image is not None\n\n # check that there's no error when saving a pipeline with one of the models being None\n with tempfile.TemporaryDirectory() as tmpdirname:\n pipe.save_pretrained(tmpdirname)\n pipe = StableDiffusionPipeline.from_pretrained(tmpdirname)\n\n # sanity check that the pipeline still works\n assert pipe.safety_checker is None\n image = pipe(\"example prompt\", num_inference_steps=2).images[0]\n assert image is not None\n\n def test_stable_diffusion_k_lms(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = sd_pipe(**inputs)\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.2681, 0.4785, 0.4857, 0.2426, 0.4473, 0.4481, 0.5610, 0.3676, 0.3855])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_k_euler_ancestral(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = sd_pipe(**inputs)\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.2682, 0.4782, 0.4855, 0.2424, 0.4472, 0.4479, 0.5612, 0.3676, 0.3854])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_k_euler(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe.scheduler = EulerDiscreteScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = sd_pipe(**inputs)\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.2681, 0.4785, 0.4857, 0.2426, 0.4473, 0.4481, 0.5610, 0.3676, 0.3855])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_vae_slicing(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n components[\"scheduler\"] = LMSDiscreteScheduler.from_config(components[\"scheduler\"].config)\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n image_count = 4\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"prompt\"] = [inputs[\"prompt\"]] * image_count\n output_1 = sd_pipe(**inputs)\n\n # make sure sliced vae decode yields the same result\n sd_pipe.enable_vae_slicing()\n inputs = self.get_dummy_inputs(device)\n inputs[\"prompt\"] = [inputs[\"prompt\"]] * image_count\n output_2 = sd_pipe(**inputs)\n\n # there is a small discrepancy at image borders vs. full batch decode\n assert np.abs(output_2.images.flatten() - output_1.images.flatten()).max() < 3e-3\n\n def test_stable_diffusion_vae_tiling(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n\n # make sure here that pndm scheduler skips prk\n components[\"safety_checker\"] = None\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A painting of a squirrel eating a burger\"\n\n # Test that tiled decode at 512x512 yields the same result as the non-tiled decode\n generator = torch.Generator(device=device).manual_seed(0)\n output_1 = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type=\"np\")\n\n # make sure tiled vae decode yields the same result\n sd_pipe.enable_vae_tiling()\n generator = torch.Generator(device=device).manual_seed(0)\n output_2 = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type=\"np\")\n\n assert np.abs(output_2.images.flatten() - output_1.images.flatten()).max() < 5e-1\n\n # test that tiled decode works with various shapes\n shapes = [(1, 4, 73, 97), (1, 4, 97, 73), (1, 4, 49, 65), (1, 4, 65, 49)]\n for shape in shapes:\n zeros = torch.zeros(shape).to(device)\n sd_pipe.vae.decode(zeros)\n\n def test_stable_diffusion_negative_prompt(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n components[\"scheduler\"] = PNDMScheduler(skip_prk_steps=True)\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n negative_prompt = \"french fries\"\n output = sd_pipe(**inputs, negative_prompt=negative_prompt)\n\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.1907, 0.4709, 0.4858, 0.2224, 0.4223, 0.4539, 0.5606, 0.3489, 0.3900])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_long_prompt(self):\n components = self.get_dummy_components()\n components[\"scheduler\"] = LMSDiscreteScheduler.from_config(components[\"scheduler\"].config)\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n do_classifier_free_guidance = True\n negative_prompt = None\n num_images_per_prompt = 1\n logger = logging.get_logger(\"diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion\")\n logger.setLevel(logging.WARNING)\n\n prompt = 100 * \"@\"\n with CaptureLogger(logger) as cap_logger:\n negative_text_embeddings, text_embeddings = sd_pipe.encode_prompt(\n prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt\n )\n if negative_text_embeddings is not None:\n text_embeddings = torch.cat([negative_text_embeddings, text_embeddings])\n\n # 100 - 77 + 1 (BOS token) + 1 (EOS token) = 25\n assert cap_logger.out.count(\"@\") == 25\n\n negative_prompt = \"Hello\"\n with CaptureLogger(logger) as cap_logger_2:\n negative_text_embeddings_2, text_embeddings_2 = sd_pipe.encode_prompt(\n prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt\n )\n if negative_text_embeddings_2 is not None:\n text_embeddings_2 = torch.cat([negative_text_embeddings_2, text_embeddings_2])\n\n assert cap_logger.out == cap_logger_2.out\n\n prompt = 25 * \"@\"\n with CaptureLogger(logger) as cap_logger_3:\n negative_text_embeddings_3, text_embeddings_3 = sd_pipe.encode_prompt(\n prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt\n )\n if negative_text_embeddings_3 is not None:\n text_embeddings_3 = torch.cat([negative_text_embeddings_3, text_embeddings_3])\n\n assert text_embeddings_3.shape == text_embeddings_2.shape == text_embeddings.shape\n assert text_embeddings.shape[1] == 77\n assert cap_logger_3.out == \"\"\n\n def test_stable_diffusion_height_width_opt(self):\n components = self.get_dummy_components()\n components[\"scheduler\"] = LMSDiscreteScheduler.from_config(components[\"scheduler\"].config)\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"hey\"\n\n output = sd_pipe(prompt, num_inference_steps=1, output_type=\"np\")\n image_shape = output.images[0].shape[:2]\n assert image_shape == (64, 64)\n\n output = sd_pipe(prompt, num_inference_steps=1, height=96, width=96, output_type=\"np\")\n image_shape = output.images[0].shape[:2]\n assert image_shape == (96, 96)\n\n config = dict(sd_pipe.unet.config)\n config[\"sample_size\"] = 96\n sd_pipe.unet = UNet2DConditionModel.from_config(config).to(torch_device)\n output = sd_pipe(prompt, num_inference_steps=1, output_type=\"np\")\n image_shape = output.images[0].shape[:2]\n assert image_shape == (192, 192)\n\n def test_attention_slicing_forward_pass(self):\n super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=3e-3)\n\n # MPS currently doesn't support ComplexFloats, which are required for freeU - see https://github.com/huggingface/diffusers/issues/7569.\n @skip_mps\n def test_freeu_enabled(self):\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"hey\"\n output = sd_pipe(prompt, num_inference_steps=1, output_type=\"np\", generator=torch.manual_seed(0)).images\n\n sd_pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4)\n output_freeu = sd_pipe(prompt, num_inference_steps=1, output_type=\"np\", generator=torch.manual_seed(0)).images\n\n assert not np.allclose(\n output[0, -3:, -3:, -1], output_freeu[0, -3:, -3:, -1]\n ), \"Enabling of FreeU should lead to different results.\"\n\n def test_freeu_disabled(self):\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"hey\"\n output = sd_pipe(prompt, num_inference_steps=1, output_type=\"np\", generator=torch.manual_seed(0)).images\n\n sd_pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4)\n sd_pipe.disable_freeu()\n\n freeu_keys = {\"s1\", \"s2\", \"b1\", \"b2\"}\n for upsample_block in sd_pipe.unet.up_blocks:\n for key in freeu_keys:\n assert getattr(upsample_block, key) is None, f\"Disabling of FreeU should have set {key} to None.\"\n\n output_no_freeu = sd_pipe(\n prompt, num_inference_steps=1, output_type=\"np\", generator=torch.manual_seed(0)\n ).images\n\n assert np.allclose(\n output[0, -3:, -3:, -1], output_no_freeu[0, -3:, -3:, -1]\n ), \"Disabling of FreeU should lead to results similar to the default pipeline results.\"\n\n def test_fused_qkv_projections(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n original_image_slice = image[0, -3:, -3:, -1]\n\n sd_pipe.fuse_qkv_projections()\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice_fused = image[0, -3:, -3:, -1]\n\n sd_pipe.unfuse_qkv_projections()\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice_disabled = image[0, -3:, -3:, -1]\n\n assert np.allclose(\n original_image_slice, image_slice_fused, atol=1e-2, rtol=1e-2\n ), \"Fusion of QKV projections shouldn't affect the outputs.\"\n assert np.allclose(\n image_slice_fused, image_slice_disabled, atol=1e-2, rtol=1e-2\n ), \"Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled.\"\n assert np.allclose(\n original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2\n ), \"Original outputs should match when fused QKV projections are disabled.\"\n\n def test_pipeline_interrupt(self):\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"hey\"\n num_inference_steps = 3\n\n # store intermediate latents from the generation process\n class PipelineState:\n def __init__(self):\n self.state = []\n\n def apply(self, pipe, i, t, callback_kwargs):\n self.state.append(callback_kwargs[\"latents\"])\n return callback_kwargs\n\n pipe_state = PipelineState()\n sd_pipe(\n prompt,\n num_inference_steps=num_inference_steps,\n output_type=\"np\",\n generator=torch.Generator(\"cpu\").manual_seed(0),\n callback_on_step_end=pipe_state.apply,\n ).images\n\n # interrupt generation at step index\n interrupt_step_idx = 1\n\n def callback_on_step_end(pipe, i, t, callback_kwargs):\n if i == interrupt_step_idx:\n pipe._interrupt = True\n\n return callback_kwargs\n\n output_interrupted = sd_pipe(\n prompt,\n num_inference_steps=num_inference_steps,\n output_type=\"latent\",\n generator=torch.Generator(\"cpu\").manual_seed(0),\n callback_on_step_end=callback_on_step_end,\n ).images\n\n # fetch intermediate latents at the interrupted step\n # from the completed generation process\n intermediate_latent = pipe_state.state[interrupt_step_idx]\n\n # compare the intermediate latent to the output of the interrupted process\n # they should be the same\n assert torch.allclose(intermediate_latent, output_interrupted, atol=1e-4)\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))\n latents = torch.from_numpy(latents).to(device=device, dtype=dtype)\n inputs = {\n \"prompt\": \"a photograph of an astronaut riding a horse\",\n \"latents\": latents,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_stable_diffusion_1_1_pndm(self):\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-1\")\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.4363, 0.4355, 0.3667, 0.4066, 0.3970, 0.3866, 0.4394, 0.4356, 0.4059])\n assert np.abs(image_slice - expected_slice).max() < 3e-3\n\n def test_stable_diffusion_v1_4_with_freeu(self):\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\").to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n inputs[\"num_inference_steps\"] = 25\n\n sd_pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4)\n image = sd_pipe(**inputs).images\n image = image[0, -3:, -3:, -1].flatten()\n expected_image = [0.0721, 0.0588, 0.0268, 0.0384, 0.0636, 0.0, 0.0429, 0.0344, 0.0309]\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n\n def test_stable_diffusion_1_4_pndm(self):\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\")\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.5740, 0.4784, 0.3162, 0.6358, 0.5831, 0.5505, 0.5082, 0.5631, 0.5575])\n assert np.abs(image_slice - expected_slice).max() < 3e-3\n\n def test_stable_diffusion_ddim(self):\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", safety_checker=None)\n sd_pipe.scheduler = DDIMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.38019, 0.28647, 0.27321, 0.40377, 0.38290, 0.35446, 0.39218, 0.38165, 0.42239])\n assert np.abs(image_slice - expected_slice).max() < 1e-4\n\n def test_stable_diffusion_lms(self):\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", safety_checker=None)\n sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.10542, 0.09620, 0.07332, 0.09015, 0.09382, 0.07597, 0.08496, 0.07806, 0.06455])\n assert np.abs(image_slice - expected_slice).max() < 3e-3\n\n def test_stable_diffusion_dpm(self):\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", safety_checker=None)\n sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(\n sd_pipe.scheduler.config,\n final_sigmas_type=\"sigma_min\",\n )\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.03503, 0.03494, 0.01087, 0.03128, 0.02552, 0.00803, 0.00742, 0.00372, 0.00000])\n assert np.abs(image_slice - expected_slice).max() < 3e-3\n\n def test_stable_diffusion_attention_slicing(self):\n torch.cuda.reset_peak_memory_stats()\n pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", torch_dtype=torch.float16)\n pipe.unet.set_default_attn_processor()\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n # enable attention slicing\n pipe.enable_attention_slicing()\n inputs = self.get_inputs(torch_device, dtype=torch.float16)\n image_sliced = pipe(**inputs).images\n\n mem_bytes = torch.cuda.max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n # make sure that less than 3.75 GB is allocated\n assert mem_bytes < 3.75 * 10**9\n\n # disable slicing\n pipe.disable_attention_slicing()\n pipe.unet.set_default_attn_processor()\n inputs = self.get_inputs(torch_device, dtype=torch.float16)\n image = pipe(**inputs).images\n\n # make sure that more than 3.75 GB is allocated\n mem_bytes = torch.cuda.max_memory_allocated()\n assert mem_bytes > 3.75 * 10**9\n max_diff = numpy_cosine_similarity_distance(image_sliced.flatten(), image.flatten())\n assert max_diff < 1e-3\n\n def test_stable_diffusion_vae_slicing(self):\n torch.cuda.reset_peak_memory_stats()\n pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", torch_dtype=torch.float16)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n # enable vae slicing\n pipe.enable_vae_slicing()\n inputs = self.get_inputs(torch_device, dtype=torch.float16)\n inputs[\"prompt\"] = [inputs[\"prompt\"]] * 4\n inputs[\"latents\"] = torch.cat([inputs[\"latents\"]] * 4)\n image_sliced = pipe(**inputs).images\n\n mem_bytes = torch.cuda.max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n # make sure that less than 4 GB is allocated\n assert mem_bytes < 4e9\n\n # disable vae slicing\n pipe.disable_vae_slicing()\n inputs = self.get_inputs(torch_device, dtype=torch.float16)\n inputs[\"prompt\"] = [inputs[\"prompt\"]] * 4\n inputs[\"latents\"] = torch.cat([inputs[\"latents\"]] * 4)\n image = pipe(**inputs).images\n\n # make sure that more than 4 GB is allocated\n mem_bytes = torch.cuda.max_memory_allocated()\n assert mem_bytes > 4e9\n # There is a small discrepancy at the image borders vs. a fully batched version.\n max_diff = numpy_cosine_similarity_distance(image_sliced.flatten(), image.flatten())\n assert max_diff < 1e-2\n\n def test_stable_diffusion_vae_tiling(self):\n torch.cuda.reset_peak_memory_stats()\n model_id = \"CompVis/stable-diffusion-v1-4\"\n pipe = StableDiffusionPipeline.from_pretrained(\n model_id, revision=\"fp16\", torch_dtype=torch.float16, safety_checker=None\n )\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n pipe.unet = pipe.unet.to(memory_format=torch.channels_last)\n pipe.vae = pipe.vae.to(memory_format=torch.channels_last)\n\n prompt = \"a photograph of an astronaut riding a horse\"\n\n # enable vae tiling\n pipe.enable_vae_tiling()\n pipe.enable_model_cpu_offload()\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n output_chunked = pipe(\n [prompt],\n width=1024,\n height=1024,\n generator=generator,\n guidance_scale=7.5,\n num_inference_steps=2,\n output_type=\"np\",\n )\n image_chunked = output_chunked.images\n\n mem_bytes = torch.cuda.max_memory_allocated()\n\n # disable vae tiling\n pipe.disable_vae_tiling()\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n output = pipe(\n [prompt],\n width=1024,\n height=1024,\n generator=generator,\n guidance_scale=7.5,\n num_inference_steps=2,\n output_type=\"np\",\n )\n image = output.images\n\n assert mem_bytes < 1e10\n max_diff = numpy_cosine_similarity_distance(image_chunked.flatten(), image.flatten())\n assert max_diff < 1e-2\n\n def test_stable_diffusion_fp16_vs_autocast(self):\n # this test makes sure that the original model with autocast\n # and the new model with fp16 yield the same result\n pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", torch_dtype=torch.float16)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device, dtype=torch.float16)\n image_fp16 = pipe(**inputs).images\n\n with torch.autocast(torch_device):\n inputs = self.get_inputs(torch_device)\n image_autocast = pipe(**inputs).images\n\n # Make sure results are close enough\n diff = np.abs(image_fp16.flatten() - image_autocast.flatten())\n # They ARE different since ops are not run always at the same precision\n # however, they should be extremely close.\n assert diff.mean() < 2e-2\n\n def test_stable_diffusion_intermediate_state(self):\n number_of_steps = 0\n\n def callback_fn(step: int, timestep: int, latents: torch.Tensor) -> None:\n callback_fn.has_been_called = True\n nonlocal number_of_steps\n number_of_steps += 1\n if step == 1:\n latents = latents.detach().cpu().numpy()\n assert latents.shape == (1, 4, 64, 64)\n latents_slice = latents[0, -3:, -3:, -1]\n expected_slice = np.array(\n [-0.5693, -0.3018, -0.9746, 0.0518, -0.8770, 0.7559, -1.7402, 0.1022, 1.1582]\n )\n\n assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2\n elif step == 2:\n latents = latents.detach().cpu().numpy()\n assert latents.shape == (1, 4, 64, 64)\n latents_slice = latents[0, -3:, -3:, -1]\n expected_slice = np.array(\n [-0.1958, -0.2993, -1.0166, -0.5005, -0.4810, 0.6162, -0.9492, 0.6621, 1.4492]\n )\n\n assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2\n\n callback_fn.has_been_called = False\n\n pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", torch_dtype=torch.float16)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(torch_device, dtype=torch.float16)\n pipe(**inputs, callback=callback_fn, callback_steps=1)\n assert callback_fn.has_been_called\n assert number_of_steps == inputs[\"num_inference_steps\"]\n\n def test_stable_diffusion_low_cpu_mem_usage(self):\n pipeline_id = \"CompVis/stable-diffusion-v1-4\"\n\n start_time = time.time()\n pipeline_low_cpu_mem_usage = StableDiffusionPipeline.from_pretrained(pipeline_id, torch_dtype=torch.float16)\n pipeline_low_cpu_mem_usage.to(torch_device)\n low_cpu_mem_usage_time = time.time() - start_time\n\n start_time = time.time()\n _ = StableDiffusionPipeline.from_pretrained(pipeline_id, torch_dtype=torch.float16, low_cpu_mem_usage=False)\n normal_load_time = time.time() - start_time\n\n assert 2 * low_cpu_mem_usage_time < normal_load_time\n\n def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", torch_dtype=torch.float16)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing(1)\n pipe.enable_sequential_cpu_offload()\n\n inputs = self.get_inputs(torch_device, dtype=torch.float16)\n _ = pipe(**inputs)\n\n mem_bytes = torch.cuda.max_memory_allocated()\n # make sure that less than 2.8 GB is allocated\n assert mem_bytes < 2.8 * 10**9\n\n def test_stable_diffusion_pipeline_with_model_offloading(self):\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n inputs = self.get_inputs(torch_device, dtype=torch.float16)\n\n # Normal inference\n\n pipe = StableDiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n torch_dtype=torch.float16,\n )\n pipe.unet.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n outputs = pipe(**inputs)\n mem_bytes = torch.cuda.max_memory_allocated()\n\n # With model offloading\n\n # Reload but don't move to cuda\n pipe = StableDiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n torch_dtype=torch.float16,\n )\n pipe.unet.set_default_attn_processor()\n\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n inputs = self.get_inputs(torch_device, dtype=torch.float16)\n\n outputs_offloaded = pipe(**inputs)\n mem_bytes_offloaded = torch.cuda.max_memory_allocated()\n\n images = outputs.images\n offloaded_images = outputs_offloaded.images\n\n max_diff = numpy_cosine_similarity_distance(images.flatten(), offloaded_images.flatten())\n assert max_diff < 1e-3\n assert mem_bytes_offloaded < mem_bytes\n assert mem_bytes_offloaded < 3.5 * 10**9\n for module in pipe.text_encoder, pipe.unet, pipe.vae:\n assert module.device == torch.device(\"cpu\")\n\n # With attention slicing\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n pipe.enable_attention_slicing()\n _ = pipe(**inputs)\n mem_bytes_slicing = torch.cuda.max_memory_allocated()\n\n assert mem_bytes_slicing < mem_bytes_offloaded\n assert mem_bytes_slicing < 3 * 10**9\n\n def test_stable_diffusion_textual_inversion(self):\n pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\")\n pipe.load_textual_inversion(\"sd-concepts-library/low-poly-hd-logos-icons\")\n\n a111_file = hf_hub_download(\"hf-internal-testing/text_inv_embedding_a1111_format\", \"winter_style.pt\")\n a111_file_neg = hf_hub_download(\n \"hf-internal-testing/text_inv_embedding_a1111_format\", \"winter_style_negative.pt\"\n )\n pipe.load_textual_inversion(a111_file)\n pipe.load_textual_inversion(a111_file_neg)\n pipe.to(\"cuda\")\n\n generator = torch.Generator(device=\"cpu\").manual_seed(1)\n\n prompt = \"An logo of a turtle in strong Style-Winter with \"\n neg_prompt = \"Style-Winter-neg\"\n\n image = pipe(prompt=prompt, negative_prompt=neg_prompt, generator=generator, output_type=\"np\").images[0]\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_inv/winter_logo_style.npy\"\n )\n\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 8e-1\n\n def test_stable_diffusion_textual_inversion_with_model_cpu_offload(self):\n pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\")\n pipe.enable_model_cpu_offload()\n pipe.load_textual_inversion(\"sd-concepts-library/low-poly-hd-logos-icons\")\n\n a111_file = hf_hub_download(\"hf-internal-testing/text_inv_embedding_a1111_format\", \"winter_style.pt\")\n a111_file_neg = hf_hub_download(\n \"hf-internal-testing/text_inv_embedding_a1111_format\", \"winter_style_negative.pt\"\n )\n pipe.load_textual_inversion(a111_file)\n pipe.load_textual_inversion(a111_file_neg)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(1)\n\n prompt = \"An logo of a turtle in strong Style-Winter with \"\n neg_prompt = \"Style-Winter-neg\"\n\n image = pipe(prompt=prompt, negative_prompt=neg_prompt, generator=generator, output_type=\"np\").images[0]\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_inv/winter_logo_style.npy\"\n )\n\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 8e-1\n\n def test_stable_diffusion_textual_inversion_with_sequential_cpu_offload(self):\n pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\")\n pipe.enable_sequential_cpu_offload()\n pipe.load_textual_inversion(\"sd-concepts-library/low-poly-hd-logos-icons\")\n\n a111_file = hf_hub_download(\"hf-internal-testing/text_inv_embedding_a1111_format\", \"winter_style.pt\")\n a111_file_neg = hf_hub_download(\n \"hf-internal-testing/text_inv_embedding_a1111_format\", \"winter_style_negative.pt\"\n )\n pipe.load_textual_inversion(a111_file)\n pipe.load_textual_inversion(a111_file_neg)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(1)\n\n prompt = \"An logo of a turtle in strong Style-Winter with \"\n neg_prompt = \"Style-Winter-neg\"\n\n image = pipe(prompt=prompt, negative_prompt=neg_prompt, generator=generator, output_type=\"np\").images[0]\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_inv/winter_logo_style.npy\"\n )\n\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 8e-1\n\n @require_python39_or_higher\n @require_torch_2\n def test_stable_diffusion_compile(self):\n seed = 0\n inputs = self.get_inputs(torch_device, seed=seed)\n # Can't pickle a Generator object\n del inputs[\"generator\"]\n inputs[\"torch_device\"] = torch_device\n inputs[\"seed\"] = seed\n run_test_in_subprocess(test_case=self, target_func=_test_stable_diffusion_compile, inputs=inputs)\n\n def test_stable_diffusion_lcm(self):\n unet = UNet2DConditionModel.from_pretrained(\"SimianLuo/LCM_Dreamshaper_v7\", subfolder=\"unet\")\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"Lykon/dreamshaper-7\", unet=unet).to(torch_device)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n inputs[\"num_inference_steps\"] = 6\n inputs[\"output_type\"] = \"pil\"\n\n image = sd_pipe(**inputs).images[0]\n\n expected_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/lcm_full/stable_diffusion_lcm.png\"\n )\n\n image = sd_pipe.image_processor.pil_to_numpy(image)\n expected_image = sd_pipe.image_processor.pil_to_numpy(expected_image)\n\n max_diff = numpy_cosine_similarity_distance(image.flatten(), expected_image.flatten())\n\n assert max_diff < 1e-2\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionPipelineCkptTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_download_from_hub(self):\n ckpt_paths = [\n \"https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors\",\n \"https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix/AbyssOrangeMix.safetensors\",\n ]\n\n for ckpt_path in ckpt_paths:\n pipe = StableDiffusionPipeline.from_single_file(ckpt_path, torch_dtype=torch.float16)\n pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\n pipe.to(\"cuda\")\n\n image_out = pipe(\"test\", num_inference_steps=1, output_type=\"np\").images[0]\n\n assert image_out.shape == (512, 512, 3)\n\n def test_download_local(self):\n ckpt_filename = hf_hub_download(\"runwayml/stable-diffusion-v1-5\", filename=\"v1-5-pruned-emaonly.safetensors\")\n config_filename = hf_hub_download(\"runwayml/stable-diffusion-v1-5\", filename=\"v1-inference.yaml\")\n\n pipe = StableDiffusionPipeline.from_single_file(\n ckpt_filename, config_files={\"v1\": config_filename}, torch_dtype=torch.float16\n )\n pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\n pipe.to(\"cuda\")\n\n image_out = pipe(\"test\", num_inference_steps=1, output_type=\"np\").images[0]\n\n assert image_out.shape == (512, 512, 3)\n\n\n@nightly\n@require_torch_gpu\nclass StableDiffusionPipelineNightlyTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))\n latents = torch.from_numpy(latents).to(device=device, dtype=dtype)\n inputs = {\n \"prompt\": \"a photograph of an astronaut riding a horse\",\n \"latents\": latents,\n \"generator\": generator,\n \"num_inference_steps\": 50,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_stable_diffusion_1_4_pndm(self):\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\").to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = sd_pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_text2img/stable_diffusion_1_4_pndm.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n\n def test_stable_diffusion_1_5_pndm(self):\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\").to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = sd_pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_text2img/stable_diffusion_1_5_pndm.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n\n def test_stable_diffusion_ddim(self):\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\").to(torch_device)\n sd_pipe.scheduler = DDIMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = sd_pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_text2img/stable_diffusion_1_4_ddim.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 3e-3\n\n def test_stable_diffusion_lms(self):\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\").to(torch_device)\n sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = sd_pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_text2img/stable_diffusion_1_4_lms.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n\n def test_stable_diffusion_euler(self):\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\").to(torch_device)\n sd_pipe.scheduler = EulerDiscreteScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = sd_pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_text2img/stable_diffusion_1_4_euler.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n\n\n# (sayakpaul): This test suite was run in the DGX with two GPUs (1, 2).\n@slow\n@require_torch_multi_gpu\n@require_accelerate_version_greater(\"0.27.0\")\nclass StableDiffusionPipelineDeviceMapTests(unittest.TestCase):\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, generator_device=\"cpu\", seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n inputs = {\n \"prompt\": \"a photograph of an astronaut riding a horse\",\n \"generator\": generator,\n \"num_inference_steps\": 50,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def get_pipeline_output_without_device_map(self):\n sd_pipe = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16\n ).to(torch_device)\n sd_pipe.set_progress_bar_config(disable=True)\n inputs = self.get_inputs()\n no_device_map_image = sd_pipe(**inputs).images\n\n del sd_pipe\n\n return no_device_map_image\n\n def test_forward_pass_balanced_device_map(self):\n no_device_map_image = self.get_pipeline_output_without_device_map()\n\n sd_pipe_with_device_map = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", device_map=\"balanced\", torch_dtype=torch.float16\n )\n sd_pipe_with_device_map.set_progress_bar_config(disable=True)\n inputs = self.get_inputs()\n device_map_image = sd_pipe_with_device_map(**inputs).images\n\n max_diff = np.abs(device_map_image - no_device_map_image).max()\n assert max_diff < 1e-3\n\n def test_components_put_in_right_devices(self):\n sd_pipe_with_device_map = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", device_map=\"balanced\", torch_dtype=torch.float16\n )\n\n assert len(set(sd_pipe_with_device_map.hf_device_map.values())) >= 2\n\n def test_max_memory(self):\n no_device_map_image = self.get_pipeline_output_without_device_map()\n\n sd_pipe_with_device_map = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\",\n device_map=\"balanced\",\n max_memory={0: \"1GB\", 1: \"1GB\"},\n torch_dtype=torch.float16,\n )\n sd_pipe_with_device_map.set_progress_bar_config(disable=True)\n inputs = self.get_inputs()\n device_map_image = sd_pipe_with_device_map(**inputs).images\n\n max_diff = np.abs(device_map_image - no_device_map_image).max()\n assert max_diff < 1e-3\n\n def test_reset_device_map(self):\n sd_pipe_with_device_map = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", device_map=\"balanced\", torch_dtype=torch.float16\n )\n sd_pipe_with_device_map.reset_device_map()\n\n assert sd_pipe_with_device_map.hf_device_map is None\n\n for name, component in sd_pipe_with_device_map.components.items():\n if isinstance(component, torch.nn.Module):\n assert component.device.type == \"cpu\"\n\n def test_reset_device_map_to(self):\n sd_pipe_with_device_map = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", device_map=\"balanced\", torch_dtype=torch.float16\n )\n sd_pipe_with_device_map.reset_device_map()\n\n assert sd_pipe_with_device_map.hf_device_map is None\n\n # Make sure `to()` can be used and the pipeline can be called.\n pipe = sd_pipe_with_device_map.to(\"cuda\")\n _ = pipe(\"hello\", num_inference_steps=2)\n\n def test_reset_device_map_enable_model_cpu_offload(self):\n sd_pipe_with_device_map = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", device_map=\"balanced\", torch_dtype=torch.float16\n )\n sd_pipe_with_device_map.reset_device_map()\n\n assert sd_pipe_with_device_map.hf_device_map is None\n\n # Make sure `enable_model_cpu_offload()` can be used and the pipeline can be called.\n sd_pipe_with_device_map.enable_model_cpu_offload()\n _ = sd_pipe_with_device_map(\"hello\", num_inference_steps=2)\n\n def test_reset_device_map_enable_sequential_cpu_offload(self):\n sd_pipe_with_device_map = StableDiffusionPipeline.from_pretrained(\n \"runwayml/stable-diffusion-v1-5\", device_map=\"balanced\", torch_dtype=torch.float16\n )\n sd_pipe_with_device_map.reset_device_map()\n\n assert sd_pipe_with_device_map.hf_device_map is None\n\n # Make sure `enable_sequential_cpu_offload()` can be used and the pipeline can be called.\n sd_pipe_with_device_map.enable_sequential_cpu_offload()\n _ = sd_pipe_with_device_map(\"hello\", num_inference_steps=2)\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport traceback\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n AutoencoderTiny,\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n HeunDiscreteScheduler,\n LCMScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n StableDiffusionImg2ImgPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n load_numpy,\n nightly,\n require_python39_or_higher,\n require_torch_2,\n require_torch_gpu,\n run_test_in_subprocess,\n skip_mps,\n slow,\n torch_device,\n)\n\nfrom ..pipeline_params import (\n IMAGE_TO_IMAGE_IMAGE_PARAMS,\n TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,\n TEXT_GUIDED_IMAGE_VARIATION_PARAMS,\n TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,\n)\nfrom ..test_pipelines_common import (\n IPAdapterTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n)\n\n\nenable_full_determinism()\n\n\n# Will be run via run_test_in_subprocess\ndef _test_img2img_compile(in_queue, out_queue, timeout):\n error = None\n try:\n inputs = in_queue.get(timeout=timeout)\n torch_device = inputs.pop(\"torch_device\")\n seed = inputs.pop(\"seed\")\n inputs[\"generator\"] = torch.Generator(device=torch_device).manual_seed(seed)\n\n pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", safety_checker=None)\n pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\n pipe.unet.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.unet.to(memory_format=torch.channels_last)\n pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 768, 3)\n expected_slice = np.array([0.0606, 0.0570, 0.0805, 0.0579, 0.0628, 0.0623, 0.0843, 0.1115, 0.0806])\n\n assert np.abs(expected_slice - image_slice).max() < 1e-3\n except Exception:\n error = f\"{traceback.format_exc()}\"\n\n results = {\"error\": error}\n out_queue.put(results, timeout=timeout)\n out_queue.join()\n\n\nclass StableDiffusionImg2ImgPipelineFastTests(\n IPAdapterTesterMixin,\n PipelineLatentTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineTesterMixin,\n unittest.TestCase,\n):\n pipeline_class = StableDiffusionImg2ImgPipeline\n params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {\"height\", \"width\"}\n required_optional_params = PipelineTesterMixin.required_optional_params - {\"latents\"}\n batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS\n image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS\n\n def get_dummy_components(self, time_cond_proj_dim=None):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n time_cond_proj_dim=time_cond_proj_dim,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n )\n scheduler = PNDMScheduler(skip_prk_steps=True)\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_tiny_autoencoder(self):\n return AutoencoderTiny(in_channels=3, out_channels=3, latent_channels=4)\n\n def get_dummy_inputs(self, device, seed=0):\n image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n image = image / 2 + 0.5\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"image\": image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_stable_diffusion_img2img_default_case(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionImg2ImgPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 32, 32, 3)\n expected_slice = np.array([0.4555, 0.3216, 0.4049, 0.4620, 0.4618, 0.4126, 0.4122, 0.4629, 0.4579])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_stable_diffusion_img2img_default_case_lcm(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionImg2ImgPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 32, 32, 3)\n expected_slice = np.array([0.5709, 0.4614, 0.4587, 0.5978, 0.5298, 0.6910, 0.6240, 0.5212, 0.5454])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_stable_diffusion_img2img_default_case_lcm_custom_timesteps(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionImg2ImgPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n del inputs[\"num_inference_steps\"]\n inputs[\"timesteps\"] = [999, 499]\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 32, 32, 3)\n expected_slice = np.array([0.5709, 0.4614, 0.4587, 0.5978, 0.5298, 0.6910, 0.6240, 0.5212, 0.5454])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_stable_diffusion_img2img_negative_prompt(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionImg2ImgPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n negative_prompt = \"french fries\"\n output = sd_pipe(**inputs, negative_prompt=negative_prompt)\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 32, 32, 3)\n expected_slice = np.array([0.4593, 0.3408, 0.4232, 0.4749, 0.4476, 0.4115, 0.4357, 0.4733, 0.4663])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_ip_adapter_single(self):\n expected_pipe_slice = None\n if torch_device == \"cpu\":\n expected_pipe_slice = np.array([0.4932, 0.5092, 0.5135, 0.5517, 0.5626, 0.6621, 0.6490, 0.5021, 0.5441])\n return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)\n\n def test_stable_diffusion_img2img_multiple_init_images(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionImg2ImgPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"prompt\"] = [inputs[\"prompt\"]] * 2\n inputs[\"image\"] = inputs[\"image\"].repeat(2, 1, 1, 1)\n image = sd_pipe(**inputs).images\n image_slice = image[-1, -3:, -3:, -1]\n\n assert image.shape == (2, 32, 32, 3)\n expected_slice = np.array([0.4241, 0.5576, 0.5711, 0.4792, 0.4311, 0.5952, 0.5827, 0.5138, 0.5109])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_stable_diffusion_img2img_k_lms(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n components[\"scheduler\"] = LMSDiscreteScheduler(\n beta_start=0.00085, beta_end=0.012, beta_schedule=\"scaled_linear\"\n )\n sd_pipe = StableDiffusionImg2ImgPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 32, 32, 3)\n expected_slice = np.array([0.4398, 0.4949, 0.4337, 0.6580, 0.5555, 0.4338, 0.5769, 0.5955, 0.5175])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_stable_diffusion_img2img_tiny_autoencoder(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionImg2ImgPipeline(**components)\n sd_pipe.vae = self.get_dummy_tiny_autoencoder()\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 32, 32, 3)\n expected_slice = np.array([0.00669, 0.00669, 0.0, 0.00693, 0.00858, 0.0, 0.00567, 0.00515, 0.00125])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n @skip_mps\n def test_save_load_local(self):\n return super().test_save_load_local()\n\n @skip_mps\n def test_dict_tuple_outputs_equivalent(self):\n return super().test_dict_tuple_outputs_equivalent()\n\n @skip_mps\n def test_save_load_optional_components(self):\n return super().test_save_load_optional_components()\n\n @skip_mps\n def test_attention_slicing_forward_pass(self):\n return super().test_attention_slicing_forward_pass(expected_max_diff=5e-3)\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=3e-3)\n\n def test_float16_inference(self):\n super().test_float16_inference(expected_max_diff=5e-1)\n\n def test_pipeline_interrupt(self):\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionImg2ImgPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n\n prompt = \"hey\"\n num_inference_steps = 3\n\n # store intermediate latents from the generation process\n class PipelineState:\n def __init__(self):\n self.state = []\n\n def apply(self, pipe, i, t, callback_kwargs):\n self.state.append(callback_kwargs[\"latents\"])\n return callback_kwargs\n\n pipe_state = PipelineState()\n sd_pipe(\n prompt,\n image=inputs[\"image\"],\n num_inference_steps=num_inference_steps,\n output_type=\"np\",\n generator=torch.Generator(\"cpu\").manual_seed(0),\n callback_on_step_end=pipe_state.apply,\n ).images\n\n # interrupt generation at step index\n interrupt_step_idx = 1\n\n def callback_on_step_end(pipe, i, t, callback_kwargs):\n if i == interrupt_step_idx:\n pipe._interrupt = True\n\n return callback_kwargs\n\n output_interrupted = sd_pipe(\n prompt,\n image=inputs[\"image\"],\n num_inference_steps=num_inference_steps,\n output_type=\"latent\",\n generator=torch.Generator(\"cpu\").manual_seed(0),\n callback_on_step_end=callback_on_step_end,\n ).images\n\n # fetch intermediate latents at the interrupted step\n # from the completed generation process\n intermediate_latent = pipe_state.state[interrupt_step_idx]\n\n # compare the intermediate latent to the output of the interrupted process\n # they should be the same\n assert torch.allclose(intermediate_latent, output_interrupted, atol=1e-4)\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionImg2ImgPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n init_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_img2img/sketch-mountains-input.png\"\n )\n inputs = {\n \"prompt\": \"a fantasy landscape, concept art, high resolution\",\n \"image\": init_image,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"strength\": 0.75,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_stable_diffusion_img2img_default(self):\n pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", safety_checker=None)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(torch_device)\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 768, 3)\n expected_slice = np.array([0.4300, 0.4662, 0.4930, 0.3990, 0.4307, 0.4525, 0.3719, 0.4064, 0.3923])\n\n assert np.abs(expected_slice - image_slice).max() < 1e-3\n\n def test_stable_diffusion_img2img_k_lms(self):\n pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", safety_checker=None)\n pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(torch_device)\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 768, 3)\n expected_slice = np.array([0.0389, 0.0346, 0.0415, 0.0290, 0.0218, 0.0210, 0.0408, 0.0567, 0.0271])\n\n assert np.abs(expected_slice - image_slice).max() < 1e-3\n\n def test_stable_diffusion_img2img_ddim(self):\n pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", safety_checker=None)\n pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(torch_device)\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 768, 3)\n expected_slice = np.array([0.0593, 0.0607, 0.0851, 0.0582, 0.0636, 0.0721, 0.0751, 0.0981, 0.0781])\n\n assert np.abs(expected_slice - image_slice).max() < 1e-3\n\n def test_stable_diffusion_img2img_intermediate_state(self):\n number_of_steps = 0\n\n def callback_fn(step: int, timestep: int, latents: torch.Tensor) -> None:\n callback_fn.has_been_called = True\n nonlocal number_of_steps\n number_of_steps += 1\n if step == 1:\n latents = latents.detach().cpu().numpy()\n assert latents.shape == (1, 4, 64, 96)\n latents_slice = latents[0, -3:, -3:, -1]\n expected_slice = np.array([-0.4958, 0.5107, 1.1045, 2.7539, 4.6680, 3.8320, 1.5049, 1.8633, 2.6523])\n\n assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2\n elif step == 2:\n latents = latents.detach().cpu().numpy()\n assert latents.shape == (1, 4, 64, 96)\n latents_slice = latents[0, -3:, -3:, -1]\n expected_slice = np.array([-0.4956, 0.5078, 1.0918, 2.7520, 4.6484, 3.8125, 1.5146, 1.8633, 2.6367])\n\n assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2\n\n callback_fn.has_been_called = False\n\n pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\", safety_checker=None, torch_dtype=torch.float16\n )\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(torch_device, dtype=torch.float16)\n pipe(**inputs, callback=callback_fn, callback_steps=1)\n assert callback_fn.has_been_called\n assert number_of_steps == 2\n\n def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\", safety_checker=None, torch_dtype=torch.float16\n )\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing(1)\n pipe.enable_sequential_cpu_offload()\n\n inputs = self.get_inputs(torch_device, dtype=torch.float16)\n _ = pipe(**inputs)\n\n mem_bytes = torch.cuda.max_memory_allocated()\n # make sure that less than 2.2 GB is allocated\n assert mem_bytes < 2.2 * 10**9\n\n def test_stable_diffusion_pipeline_with_model_offloading(self):\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n inputs = self.get_inputs(torch_device, dtype=torch.float16)\n\n # Normal inference\n\n pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n safety_checker=None,\n torch_dtype=torch.float16,\n )\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe(**inputs)\n mem_bytes = torch.cuda.max_memory_allocated()\n\n # With model offloading\n\n # Reload but don't move to cuda\n pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n safety_checker=None,\n torch_dtype=torch.float16,\n )\n\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n _ = pipe(**inputs)\n mem_bytes_offloaded = torch.cuda.max_memory_allocated()\n\n assert mem_bytes_offloaded < mem_bytes\n for module in pipe.text_encoder, pipe.unet, pipe.vae:\n assert module.device == torch.device(\"cpu\")\n\n def test_img2img_2nd_order(self):\n sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n sd_pipe.scheduler = HeunDiscreteScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n inputs[\"num_inference_steps\"] = 10\n inputs[\"strength\"] = 0.75\n image = sd_pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/img2img_heun.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 5e-2\n\n inputs = self.get_inputs(torch_device)\n inputs[\"num_inference_steps\"] = 11\n inputs[\"strength\"] = 0.75\n image_other = sd_pipe(**inputs).images[0]\n\n mean_diff = np.abs(image - image_other).mean()\n\n # images should be very similar\n assert mean_diff < 5e-2\n\n def test_stable_diffusion_img2img_pipeline_multiple_of_8(self):\n init_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/img2img/sketch-mountains-input.jpg\"\n )\n # resize to resolution that is divisible by 8 but not 16 or 32\n init_image = init_image.resize((760, 504))\n\n model_id = \"CompVis/stable-diffusion-v1-4\"\n pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\n model_id,\n safety_checker=None,\n )\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n prompt = \"A fantasy landscape, trending on artstation\"\n\n generator = torch.manual_seed(0)\n output = pipe(\n prompt=prompt,\n image=init_image,\n strength=0.75,\n guidance_scale=7.5,\n generator=generator,\n output_type=\"np\",\n )\n image = output.images[0]\n\n image_slice = image[255:258, 383:386, -1]\n\n assert image.shape == (504, 760, 3)\n expected_slice = np.array([0.9393, 0.9500, 0.9399, 0.9438, 0.9458, 0.9400, 0.9455, 0.9414, 0.9423])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3\n\n def test_img2img_safety_checker_works(self):\n sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n inputs[\"num_inference_steps\"] = 20\n # make sure the safety checker is activated\n inputs[\"prompt\"] = \"naked, sex, porn\"\n out = sd_pipe(**inputs)\n\n assert out.nsfw_content_detected[0], f\"Safety checker should work for prompt: {inputs['prompt']}\"\n assert np.abs(out.images[0]).sum() < 1e-5 # should be all zeros\n\n @require_python39_or_higher\n @require_torch_2\n def test_img2img_compile(self):\n seed = 0\n inputs = self.get_inputs(torch_device, seed=seed)\n # Can't pickle a Generator object\n del inputs[\"generator\"]\n inputs[\"torch_device\"] = torch_device\n inputs[\"seed\"] = seed\n run_test_in_subprocess(test_case=self, target_func=_test_img2img_compile, inputs=inputs)\n\n\n@nightly\n@require_torch_gpu\nclass StableDiffusionImg2ImgPipelineNightlyTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n init_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_img2img/sketch-mountains-input.png\"\n )\n inputs = {\n \"prompt\": \"a fantasy landscape, concept art, high resolution\",\n \"image\": init_image,\n \"generator\": generator,\n \"num_inference_steps\": 50,\n \"strength\": 0.75,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_img2img_pndm(self):\n sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = sd_pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_img2img/stable_diffusion_1_5_pndm.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n\n def test_img2img_ddim(self):\n sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n sd_pipe.scheduler = DDIMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = sd_pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_img2img/stable_diffusion_1_5_ddim.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n\n def test_img2img_lms(self):\n sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = sd_pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_img2img/stable_diffusion_1_5_lms.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n\n def test_img2img_dpm(self):\n sd_pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n inputs[\"num_inference_steps\"] = 30\n image = sd_pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_img2img/stable_diffusion_1_5_dpm.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport traceback\nimport unittest\n\nimport numpy as np\nimport torch\nfrom huggingface_hub import hf_hub_download\nfrom PIL import Image\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AsymmetricAutoencoderKL,\n AutoencoderKL,\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n LCMScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n StableDiffusionInpaintPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n load_numpy,\n nightly,\n require_python39_or_higher,\n require_torch_2,\n require_torch_gpu,\n run_test_in_subprocess,\n slow,\n torch_device,\n)\n\nfrom ..pipeline_params import (\n TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,\n TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,\n TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,\n)\nfrom ..test_pipelines_common import (\n IPAdapterTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n)\n\n\nenable_full_determinism()\n\n\n# Will be run via run_test_in_subprocess\ndef _test_inpaint_compile(in_queue, out_queue, timeout):\n error = None\n try:\n inputs = in_queue.get(timeout=timeout)\n torch_device = inputs.pop(\"torch_device\")\n seed = inputs.pop(\"seed\")\n inputs[\"generator\"] = torch.Generator(device=torch_device).manual_seed(seed)\n\n pipe = StableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", safety_checker=None\n )\n pipe.unet.set_default_attn_processor()\n pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n pipe.unet.to(memory_format=torch.channels_last)\n pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n\n image = pipe(**inputs).images\n image_slice = image[0, 253:256, 253:256, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.0689, 0.0699, 0.0790, 0.0536, 0.0470, 0.0488, 0.041, 0.0508, 0.04179])\n assert np.abs(expected_slice - image_slice).max() < 3e-3\n except Exception:\n error = f\"{traceback.format_exc()}\"\n\n results = {\"error\": error}\n out_queue.put(results, timeout=timeout)\n out_queue.join()\n\n\nclass StableDiffusionInpaintPipelineFastTests(\n IPAdapterTesterMixin,\n PipelineLatentTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineTesterMixin,\n unittest.TestCase,\n):\n pipeline_class = StableDiffusionInpaintPipeline\n params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS\n batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS\n image_params = frozenset([])\n # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess\n image_latents_params = frozenset([])\n callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union({\"mask\", \"masked_image_latents\"})\n\n def get_dummy_components(self, time_cond_proj_dim=None):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n time_cond_proj_dim=time_cond_proj_dim,\n layers_per_block=2,\n sample_size=32,\n in_channels=9,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n )\n scheduler = PNDMScheduler(skip_prk_steps=True)\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0, img_res=64, output_pil=True):\n # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched\n if output_pil:\n # Get random floats in [0, 1] as image\n image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n image = image.cpu().permute(0, 2, 3, 1)[0]\n mask_image = torch.ones_like(image)\n # Convert image and mask_image to [0, 255]\n image = 255 * image\n mask_image = 255 * mask_image\n # Convert to PIL image\n init_image = Image.fromarray(np.uint8(image)).convert(\"RGB\").resize((img_res, img_res))\n mask_image = Image.fromarray(np.uint8(mask_image)).convert(\"RGB\").resize((img_res, img_res))\n else:\n # Get random floats in [0, 1] as image with spatial size (img_res, img_res)\n image = floats_tensor((1, 3, img_res, img_res), rng=random.Random(seed)).to(device)\n # Convert image to [-1, 1]\n init_image = 2.0 * image - 1.0\n mask_image = torch.ones((1, 1, img_res, img_res), device=device)\n\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"image\": init_image,\n \"mask_image\": mask_image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_stable_diffusion_inpaint(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionInpaintPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.4703, 0.5697, 0.3879, 0.5470, 0.6042, 0.4413, 0.5078, 0.4728, 0.4469])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_inpaint_lcm(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionInpaintPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.4931, 0.5988, 0.4569, 0.5556, 0.6650, 0.5087, 0.5966, 0.5358, 0.5269])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_inpaint_lcm_custom_timesteps(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionInpaintPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n del inputs[\"num_inference_steps\"]\n inputs[\"timesteps\"] = [999, 499]\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.4931, 0.5988, 0.4569, 0.5556, 0.6650, 0.5087, 0.5966, 0.5358, 0.5269])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_inpaint_image_tensor(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionInpaintPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = sd_pipe(**inputs)\n out_pil = output.images\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"image\"] = torch.tensor(np.array(inputs[\"image\"]) / 127.5 - 1).permute(2, 0, 1).unsqueeze(0)\n inputs[\"mask_image\"] = torch.tensor(np.array(inputs[\"mask_image\"]) / 255).permute(2, 0, 1)[:1].unsqueeze(0)\n output = sd_pipe(**inputs)\n out_tensor = output.images\n\n assert out_pil.shape == (1, 64, 64, 3)\n assert np.abs(out_pil.flatten() - out_tensor.flatten()).max() < 5e-2\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=3e-3)\n\n def test_stable_diffusion_inpaint_strength_zero_test(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionInpaintPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n\n # check that the pipeline raises value error when num_inference_steps is < 1\n inputs[\"strength\"] = 0.01\n with self.assertRaises(ValueError):\n sd_pipe(**inputs).images\n\n def test_stable_diffusion_inpaint_mask_latents(self):\n device = \"cpu\"\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n # normal mask + normal image\n ## `image`: pil, `mask_image``: pil, `masked_image_latents``: None\n inputs = self.get_dummy_inputs(device)\n inputs[\"strength\"] = 0.9\n out_0 = sd_pipe(**inputs).images\n\n # image latents + mask latents\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe.image_processor.preprocess(inputs[\"image\"]).to(sd_pipe.device)\n mask = sd_pipe.mask_processor.preprocess(inputs[\"mask_image\"]).to(sd_pipe.device)\n masked_image = image * (mask < 0.5)\n\n generator = torch.Generator(device=device).manual_seed(0)\n image_latents = (\n sd_pipe.vae.encode(image).latent_dist.sample(generator=generator) * sd_pipe.vae.config.scaling_factor\n )\n torch.randn((1, 4, 32, 32), generator=generator)\n mask_latents = (\n sd_pipe.vae.encode(masked_image).latent_dist.sample(generator=generator)\n * sd_pipe.vae.config.scaling_factor\n )\n inputs[\"image\"] = image_latents\n inputs[\"masked_image_latents\"] = mask_latents\n inputs[\"mask_image\"] = mask\n inputs[\"strength\"] = 0.9\n generator = torch.Generator(device=device).manual_seed(0)\n torch.randn((1, 4, 32, 32), generator=generator)\n inputs[\"generator\"] = generator\n out_1 = sd_pipe(**inputs).images\n assert np.abs(out_0 - out_1).max() < 1e-2\n\n def test_pipeline_interrupt(self):\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionInpaintPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n\n prompt = \"hey\"\n num_inference_steps = 3\n\n # store intermediate latents from the generation process\n class PipelineState:\n def __init__(self):\n self.state = []\n\n def apply(self, pipe, i, t, callback_kwargs):\n self.state.append(callback_kwargs[\"latents\"])\n return callback_kwargs\n\n pipe_state = PipelineState()\n sd_pipe(\n prompt,\n image=inputs[\"image\"],\n mask_image=inputs[\"mask_image\"],\n num_inference_steps=num_inference_steps,\n output_type=\"np\",\n generator=torch.Generator(\"cpu\").manual_seed(0),\n callback_on_step_end=pipe_state.apply,\n ).images\n\n # interrupt generation at step index\n interrupt_step_idx = 1\n\n def callback_on_step_end(pipe, i, t, callback_kwargs):\n if i == interrupt_step_idx:\n pipe._interrupt = True\n\n return callback_kwargs\n\n output_interrupted = sd_pipe(\n prompt,\n image=inputs[\"image\"],\n mask_image=inputs[\"mask_image\"],\n num_inference_steps=num_inference_steps,\n output_type=\"latent\",\n generator=torch.Generator(\"cpu\").manual_seed(0),\n callback_on_step_end=callback_on_step_end,\n ).images\n\n # fetch intermediate latents at the interrupted step\n # from the completed generation process\n intermediate_latent = pipe_state.state[interrupt_step_idx]\n\n # compare the intermediate latent to the output of the interrupted process\n # they should be the same\n assert torch.allclose(intermediate_latent, output_interrupted, atol=1e-4)\n\n def test_ip_adapter_single(self, from_simple=False, expected_pipe_slice=None):\n if not from_simple:\n expected_pipe_slice = None\n if torch_device == \"cpu\":\n expected_pipe_slice = np.array(\n [0.4390, 0.5452, 0.3772, 0.5448, 0.6031, 0.4480, 0.5194, 0.4687, 0.4640]\n )\n return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)\n\n\nclass StableDiffusionSimpleInpaintPipelineFastTests(StableDiffusionInpaintPipelineFastTests):\n pipeline_class = StableDiffusionInpaintPipeline\n params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS\n batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS\n image_params = frozenset([])\n # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess\n\n def get_dummy_components(self, time_cond_proj_dim=None):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n time_cond_proj_dim=time_cond_proj_dim,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n )\n scheduler = PNDMScheduler(skip_prk_steps=True)\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs_2images(self, device, seed=0, img_res=64):\n # Get random floats in [0, 1] as image with spatial size (img_res, img_res)\n image1 = floats_tensor((1, 3, img_res, img_res), rng=random.Random(seed)).to(device)\n image2 = floats_tensor((1, 3, img_res, img_res), rng=random.Random(seed + 22)).to(device)\n # Convert images to [-1, 1]\n init_image1 = 2.0 * image1 - 1.0\n init_image2 = 2.0 * image2 - 1.0\n\n # empty mask\n mask_image = torch.zeros((1, 1, img_res, img_res), device=device)\n\n if str(device).startswith(\"mps\"):\n generator1 = torch.manual_seed(seed)\n generator2 = torch.manual_seed(seed)\n else:\n generator1 = torch.Generator(device=device).manual_seed(seed)\n generator2 = torch.Generator(device=device).manual_seed(seed)\n\n inputs = {\n \"prompt\": [\"A painting of a squirrel eating a burger\"] * 2,\n \"image\": [init_image1, init_image2],\n \"mask_image\": [mask_image] * 2,\n \"generator\": [generator1, generator2],\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_ip_adapter_single(self):\n expected_pipe_slice = None\n if torch_device == \"cpu\":\n expected_pipe_slice = np.array([0.6345, 0.5395, 0.5611, 0.5403, 0.5830, 0.5855, 0.5193, 0.5443, 0.5211])\n return super().test_ip_adapter_single(from_simple=True, expected_pipe_slice=expected_pipe_slice)\n\n def test_stable_diffusion_inpaint(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionInpaintPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.6584, 0.5424, 0.5649, 0.5449, 0.5897, 0.6111, 0.5404, 0.5463, 0.5214])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_inpaint_lcm(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionInpaintPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.6240, 0.5355, 0.5649, 0.5378, 0.5374, 0.6242, 0.5132, 0.5347, 0.5396])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_inpaint_lcm_custom_timesteps(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionInpaintPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n del inputs[\"num_inference_steps\"]\n inputs[\"timesteps\"] = [999, 499]\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.6240, 0.5355, 0.5649, 0.5378, 0.5374, 0.6242, 0.5132, 0.5347, 0.5396])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_inpaint_2_images(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n # test to confirm if we pass two same image, we will get same output\n inputs = self.get_dummy_inputs(device)\n gen1 = torch.Generator(device=device).manual_seed(0)\n gen2 = torch.Generator(device=device).manual_seed(0)\n for name in [\"prompt\", \"image\", \"mask_image\"]:\n inputs[name] = [inputs[name]] * 2\n inputs[\"generator\"] = [gen1, gen2]\n images = sd_pipe(**inputs).images\n\n assert images.shape == (2, 64, 64, 3)\n\n image_slice1 = images[0, -3:, -3:, -1]\n image_slice2 = images[1, -3:, -3:, -1]\n assert np.abs(image_slice1.flatten() - image_slice2.flatten()).max() < 1e-4\n\n # test to confirm that if we pass two different images, we will get different output\n inputs = self.get_dummy_inputs_2images(device)\n images = sd_pipe(**inputs).images\n assert images.shape == (2, 64, 64, 3)\n\n image_slice1 = images[0, -3:, -3:, -1]\n image_slice2 = images[1, -3:, -3:, -1]\n assert np.abs(image_slice1.flatten() - image_slice2.flatten()).max() > 1e-2\n\n def test_stable_diffusion_inpaint_euler(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionInpaintPipeline(**components)\n sd_pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device, output_pil=False)\n half_dim = inputs[\"image\"].shape[2] // 2\n inputs[\"mask_image\"][0, 0, :half_dim, :half_dim] = 0\n\n inputs[\"num_inference_steps\"] = 4\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array(\n [[0.6387283, 0.5564158, 0.58631873, 0.5539942, 0.5494673, 0.6461868, 0.5251618, 0.5497595, 0.5508756]]\n )\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-4\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionInpaintPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n init_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_inpaint/input_bench_image.png\"\n )\n mask_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_inpaint/input_bench_mask.png\"\n )\n inputs = {\n \"prompt\": \"Face of a yellow cat, high resolution, sitting on a park bench\",\n \"image\": init_image,\n \"mask_image\": mask_image,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_stable_diffusion_inpaint_ddim(self):\n pipe = StableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", safety_checker=None\n )\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(torch_device)\n image = pipe(**inputs).images\n image_slice = image[0, 253:256, 253:256, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.0427, 0.0460, 0.0483, 0.0460, 0.0584, 0.0521, 0.1549, 0.1695, 0.1794])\n\n assert np.abs(expected_slice - image_slice).max() < 6e-4\n\n def test_stable_diffusion_inpaint_fp16(self):\n pipe = StableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", torch_dtype=torch.float16, safety_checker=None\n )\n pipe.unet.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(torch_device, dtype=torch.float16)\n image = pipe(**inputs).images\n image_slice = image[0, 253:256, 253:256, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.1509, 0.1245, 0.1672, 0.1655, 0.1519, 0.1226, 0.1462, 0.1567, 0.2451])\n assert np.abs(expected_slice - image_slice).max() < 1e-1\n\n def test_stable_diffusion_inpaint_pndm(self):\n pipe = StableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", safety_checker=None\n )\n pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(torch_device)\n image = pipe(**inputs).images\n image_slice = image[0, 253:256, 253:256, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.0425, 0.0273, 0.0344, 0.1694, 0.1727, 0.1812, 0.3256, 0.3311, 0.3272])\n\n assert np.abs(expected_slice - image_slice).max() < 5e-3\n\n def test_stable_diffusion_inpaint_k_lms(self):\n pipe = StableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", safety_checker=None\n )\n pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(torch_device)\n image = pipe(**inputs).images\n image_slice = image[0, 253:256, 253:256, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.9314, 0.7575, 0.9432, 0.8885, 0.9028, 0.7298, 0.9811, 0.9667, 0.7633])\n\n assert np.abs(expected_slice - image_slice).max() < 6e-3\n\n def test_stable_diffusion_inpaint_with_sequential_cpu_offloading(self):\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n pipe = StableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", safety_checker=None, torch_dtype=torch.float16\n )\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing(1)\n pipe.enable_sequential_cpu_offload()\n\n inputs = self.get_inputs(torch_device, dtype=torch.float16)\n _ = pipe(**inputs)\n\n mem_bytes = torch.cuda.max_memory_allocated()\n # make sure that less than 2.2 GB is allocated\n assert mem_bytes < 2.2 * 10**9\n\n @require_python39_or_higher\n @require_torch_2\n def test_inpaint_compile(self):\n seed = 0\n inputs = self.get_inputs(torch_device, seed=seed)\n # Can't pickle a Generator object\n del inputs[\"generator\"]\n inputs[\"torch_device\"] = torch_device\n inputs[\"seed\"] = seed\n run_test_in_subprocess(test_case=self, target_func=_test_inpaint_compile, inputs=inputs)\n\n def test_stable_diffusion_inpaint_pil_input_resolution_test(self):\n pipe = StableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", safety_checker=None\n )\n pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(torch_device)\n # change input image to a random size (one that would cause a tensor mismatch error)\n inputs[\"image\"] = inputs[\"image\"].resize((127, 127))\n inputs[\"mask_image\"] = inputs[\"mask_image\"].resize((127, 127))\n inputs[\"height\"] = 128\n inputs[\"width\"] = 128\n image = pipe(**inputs).images\n # verify that the returned image has the same height and width as the input height and width\n assert image.shape == (1, inputs[\"height\"], inputs[\"width\"], 3)\n\n def test_stable_diffusion_inpaint_strength_test(self):\n pipe = StableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", safety_checker=None\n )\n pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)\n pipe.unet.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(torch_device)\n # change input strength\n inputs[\"strength\"] = 0.75\n image = pipe(**inputs).images\n # verify that the returned image has the same height and width as the input height and width\n assert image.shape == (1, 512, 512, 3)\n\n image_slice = image[0, 253:256, 253:256, -1].flatten()\n expected_slice = np.array([0.2728, 0.2803, 0.2665, 0.2511, 0.2774, 0.2586, 0.2391, 0.2392, 0.2582])\n assert np.abs(expected_slice - image_slice).max() < 1e-3\n\n def test_stable_diffusion_simple_inpaint_ddim(self):\n pipe = StableDiffusionInpaintPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", safety_checker=None)\n pipe.unet.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(torch_device)\n image = pipe(**inputs).images\n\n image_slice = image[0, 253:256, 253:256, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.3757, 0.3875, 0.4445, 0.4353, 0.3780, 0.4513, 0.3965, 0.3984, 0.4362])\n assert np.abs(expected_slice - image_slice).max() < 1e-3\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n init_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_inpaint/input_bench_image.png\"\n )\n mask_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_inpaint/input_bench_mask.png\"\n )\n inputs = {\n \"prompt\": \"Face of a yellow cat, high resolution, sitting on a park bench\",\n \"image\": init_image,\n \"mask_image\": mask_image,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_stable_diffusion_inpaint_ddim(self):\n vae = AsymmetricAutoencoderKL.from_pretrained(\"cross-attention/asymmetric-autoencoder-kl-x-1-5\")\n pipe = StableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", safety_checker=None\n )\n pipe.vae = vae\n pipe.unet.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(torch_device)\n image = pipe(**inputs).images\n image_slice = image[0, 253:256, 253:256, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.0522, 0.0604, 0.0596, 0.0449, 0.0493, 0.0427, 0.1186, 0.1289, 0.1442])\n\n assert np.abs(expected_slice - image_slice).max() < 1e-3\n\n def test_stable_diffusion_inpaint_fp16(self):\n vae = AsymmetricAutoencoderKL.from_pretrained(\n \"cross-attention/asymmetric-autoencoder-kl-x-1-5\", torch_dtype=torch.float16\n )\n pipe = StableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", torch_dtype=torch.float16, safety_checker=None\n )\n pipe.unet.set_default_attn_processor()\n pipe.vae = vae\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(torch_device, dtype=torch.float16)\n image = pipe(**inputs).images\n image_slice = image[0, 253:256, 253:256, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.1343, 0.1406, 0.1440, 0.1504, 0.1729, 0.0989, 0.1807, 0.2822, 0.1179])\n\n assert np.abs(expected_slice - image_slice).max() < 5e-2\n\n def test_stable_diffusion_inpaint_pndm(self):\n vae = AsymmetricAutoencoderKL.from_pretrained(\"cross-attention/asymmetric-autoencoder-kl-x-1-5\")\n pipe = StableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", safety_checker=None\n )\n pipe.unet.set_default_attn_processor()\n pipe.vae = vae\n pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(torch_device)\n image = pipe(**inputs).images\n image_slice = image[0, 253:256, 253:256, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.0966, 0.1083, 0.1148, 0.1422, 0.1318, 0.1197, 0.3702, 0.3537, 0.3288])\n\n assert np.abs(expected_slice - image_slice).max() < 5e-3\n\n def test_stable_diffusion_inpaint_k_lms(self):\n vae = AsymmetricAutoencoderKL.from_pretrained(\"cross-attention/asymmetric-autoencoder-kl-x-1-5\")\n pipe = StableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", safety_checker=None\n )\n pipe.unet.set_default_attn_processor()\n pipe.vae = vae\n pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(torch_device)\n image = pipe(**inputs).images\n image_slice = image[0, 253:256, 253:256, -1].flatten()\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.8931, 0.8683, 0.8965, 0.8501, 0.8592, 0.9118, 0.8734, 0.7463, 0.8990])\n assert np.abs(expected_slice - image_slice).max() < 6e-3\n\n def test_stable_diffusion_inpaint_with_sequential_cpu_offloading(self):\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n vae = AsymmetricAutoencoderKL.from_pretrained(\n \"cross-attention/asymmetric-autoencoder-kl-x-1-5\", torch_dtype=torch.float16\n )\n pipe = StableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", safety_checker=None, torch_dtype=torch.float16\n )\n pipe.vae = vae\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing(1)\n pipe.enable_sequential_cpu_offload()\n\n inputs = self.get_inputs(torch_device, dtype=torch.float16)\n _ = pipe(**inputs)\n\n mem_bytes = torch.cuda.max_memory_allocated()\n # make sure that less than 2.45 GB is allocated\n assert mem_bytes < 2.45 * 10**9\n\n @require_python39_or_higher\n @require_torch_2\n def test_inpaint_compile(self):\n pass\n\n def test_stable_diffusion_inpaint_pil_input_resolution_test(self):\n vae = AsymmetricAutoencoderKL.from_pretrained(\n \"cross-attention/asymmetric-autoencoder-kl-x-1-5\",\n )\n pipe = StableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", safety_checker=None\n )\n pipe.vae = vae\n pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(torch_device)\n # change input image to a random size (one that would cause a tensor mismatch error)\n inputs[\"image\"] = inputs[\"image\"].resize((127, 127))\n inputs[\"mask_image\"] = inputs[\"mask_image\"].resize((127, 127))\n inputs[\"height\"] = 128\n inputs[\"width\"] = 128\n image = pipe(**inputs).images\n # verify that the returned image has the same height and width as the input height and width\n assert image.shape == (1, inputs[\"height\"], inputs[\"width\"], 3)\n\n def test_stable_diffusion_inpaint_strength_test(self):\n vae = AsymmetricAutoencoderKL.from_pretrained(\"cross-attention/asymmetric-autoencoder-kl-x-1-5\")\n pipe = StableDiffusionInpaintPipeline.from_pretrained(\n \"runwayml/stable-diffusion-inpainting\", safety_checker=None\n )\n pipe.unet.set_default_attn_processor()\n pipe.vae = vae\n pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(torch_device)\n # change input strength\n inputs[\"strength\"] = 0.75\n image = pipe(**inputs).images\n # verify that the returned image has the same height and width as the input height and width\n assert image.shape == (1, 512, 512, 3)\n\n image_slice = image[0, 253:256, 253:256, -1].flatten()\n expected_slice = np.array([0.2458, 0.2576, 0.3124, 0.2679, 0.2669, 0.2796, 0.2872, 0.2975, 0.2661])\n assert np.abs(expected_slice - image_slice).max() < 3e-3\n\n def test_stable_diffusion_simple_inpaint_ddim(self):\n vae = AsymmetricAutoencoderKL.from_pretrained(\"cross-attention/asymmetric-autoencoder-kl-x-1-5\")\n pipe = StableDiffusionInpaintPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", safety_checker=None)\n pipe.vae = vae\n pipe.unet.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(torch_device)\n image = pipe(**inputs).images\n\n image_slice = image[0, 253:256, 253:256, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.3296, 0.4041, 0.4097, 0.4145, 0.4342, 0.4152, 0.4927, 0.4931, 0.4430])\n assert np.abs(expected_slice - image_slice).max() < 1e-3\n\n def test_download_local(self):\n vae = AsymmetricAutoencoderKL.from_pretrained(\n \"cross-attention/asymmetric-autoencoder-kl-x-1-5\", torch_dtype=torch.float16\n )\n filename = hf_hub_download(\"runwayml/stable-diffusion-inpainting\", filename=\"sd-v1-5-inpainting.ckpt\")\n\n pipe = StableDiffusionInpaintPipeline.from_single_file(filename, torch_dtype=torch.float16)\n pipe.vae = vae\n pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\n pipe.to(\"cuda\")\n\n inputs = self.get_inputs(torch_device)\n inputs[\"num_inference_steps\"] = 1\n image_out = pipe(**inputs).images[0]\n\n assert image_out.shape == (512, 512, 3)\n\n\n@nightly\n@require_torch_gpu\nclass StableDiffusionInpaintPipelineNightlyTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n init_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_inpaint/input_bench_image.png\"\n )\n mask_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_inpaint/input_bench_mask.png\"\n )\n inputs = {\n \"prompt\": \"Face of a yellow cat, high resolution, sitting on a park bench\",\n \"image\": init_image,\n \"mask_image\": mask_image,\n \"generator\": generator,\n \"num_inference_steps\": 50,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_inpaint_ddim(self):\n sd_pipe = StableDiffusionInpaintPipeline.from_pretrained(\"runwayml/stable-diffusion-inpainting\")\n sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = sd_pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_inpaint/stable_diffusion_inpaint_ddim.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n\n def test_inpaint_pndm(self):\n sd_pipe = StableDiffusionInpaintPipeline.from_pretrained(\"runwayml/stable-diffusion-inpainting\")\n sd_pipe.scheduler = PNDMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = sd_pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_inpaint/stable_diffusion_inpaint_pndm.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n\n def test_inpaint_lms(self):\n sd_pipe = StableDiffusionInpaintPipeline.from_pretrained(\"runwayml/stable-diffusion-inpainting\")\n sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = sd_pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_inpaint/stable_diffusion_inpaint_lms.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n\n def test_inpaint_dpm(self):\n sd_pipe = StableDiffusionInpaintPipeline.from_pretrained(\"runwayml/stable-diffusion-inpainting\")\n sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n inputs[\"num_inference_steps\"] = 30\n image = sd_pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_inpaint/stable_diffusion_inpaint_dpm_multi.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_instruction_pix2pix.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n EulerAncestralDiscreteScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n StableDiffusionInstructPix2PixPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.image_processor import VaeImageProcessor\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\nfrom ..pipeline_params import (\n IMAGE_TO_IMAGE_IMAGE_PARAMS,\n TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,\n TEXT_GUIDED_IMAGE_VARIATION_PARAMS,\n TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,\n)\nfrom ..test_pipelines_common import (\n PipelineKarrasSchedulerTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n)\n\n\nenable_full_determinism()\n\n\nclass StableDiffusionInstructPix2PixPipelineFastTests(\n PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase\n):\n pipeline_class = StableDiffusionInstructPix2PixPipeline\n params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {\"height\", \"width\", \"cross_attention_kwargs\"}\n batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS\n image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union({\"image_latents\"}) - {\"negative_prompt_embeds\"}\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=8,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n )\n scheduler = PNDMScheduler(skip_prk_steps=True)\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n image = image.cpu().permute(0, 2, 3, 1)[0]\n image = Image.fromarray(np.uint8(image)).convert(\"RGB\")\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"image\": image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"image_guidance_scale\": 1,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_stable_diffusion_pix2pix_default_case(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionInstructPix2PixPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n assert image.shape == (1, 32, 32, 3)\n expected_slice = np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_stable_diffusion_pix2pix_negative_prompt(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionInstructPix2PixPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n negative_prompt = \"french fries\"\n output = sd_pipe(**inputs, negative_prompt=negative_prompt)\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 32, 32, 3)\n expected_slice = np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_stable_diffusion_pix2pix_multiple_init_images(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionInstructPix2PixPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"prompt\"] = [inputs[\"prompt\"]] * 2\n\n image = np.array(inputs[\"image\"]).astype(np.float32) / 255.0\n image = torch.from_numpy(image).unsqueeze(0).to(device)\n image = image / 2 + 0.5\n image = image.permute(0, 3, 1, 2)\n inputs[\"image\"] = image.repeat(2, 1, 1, 1)\n\n image = sd_pipe(**inputs).images\n image_slice = image[-1, -3:, -3:, -1]\n\n assert image.shape == (2, 32, 32, 3)\n expected_slice = np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_stable_diffusion_pix2pix_euler(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n components[\"scheduler\"] = EulerAncestralDiscreteScheduler(\n beta_start=0.00085, beta_end=0.012, beta_schedule=\"scaled_linear\"\n )\n sd_pipe = StableDiffusionInstructPix2PixPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n slice = [round(x, 4) for x in image_slice.flatten().tolist()]\n print(\",\".join([str(x) for x in slice]))\n\n assert image.shape == (1, 32, 32, 3)\n expected_slice = np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=3e-3)\n\n # Overwrite the default test_latents_inputs because pix2pix encode the image differently\n def test_latents_input(self):\n components = self.get_dummy_components()\n pipe = StableDiffusionInstructPix2PixPipeline(**components)\n pipe.image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n out = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type=\"pt\"))[0]\n\n vae = components[\"vae\"]\n inputs = self.get_dummy_inputs_by_type(torch_device, input_image_type=\"pt\")\n\n for image_param in self.image_latents_params:\n if image_param in inputs.keys():\n inputs[image_param] = vae.encode(inputs[image_param]).latent_dist.mode()\n\n out_latents_inputs = pipe(**inputs)[0]\n\n max_diff = np.abs(out - out_latents_inputs).max()\n self.assertLess(max_diff, 1e-4, \"passing latents as image input generate different result from passing image\")\n\n # Override the default test_callback_cfg because pix2pix create inputs for cfg differently\n def test_callback_cfg(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n def callback_no_cfg(pipe, i, t, callback_kwargs):\n if i == 1:\n for k, w in callback_kwargs.items():\n if k in self.callback_cfg_params:\n callback_kwargs[k] = callback_kwargs[k].chunk(3)[0]\n pipe._guidance_scale = 1.0\n\n return callback_kwargs\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"guidance_scale\"] = 1.0\n inputs[\"num_inference_steps\"] = 2\n out_no_cfg = pipe(**inputs)[0]\n\n inputs[\"guidance_scale\"] = 7.5\n inputs[\"callback_on_step_end\"] = callback_no_cfg\n inputs[\"callback_on_step_end_tensor_inputs\"] = pipe._callback_tensor_inputs\n out_callback_no_cfg = pipe(**inputs)[0]\n\n assert out_no_cfg.shape == out_callback_no_cfg.shape\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionInstructPix2PixPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, seed=0):\n generator = torch.manual_seed(seed)\n image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg\"\n )\n inputs = {\n \"prompt\": \"turn him into a cyborg\",\n \"image\": image,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"guidance_scale\": 7.5,\n \"image_guidance_scale\": 1.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_stable_diffusion_pix2pix_default(self):\n pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(\n \"timbrooks/instruct-pix2pix\", safety_checker=None\n )\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555])\n\n assert np.abs(expected_slice - image_slice).max() < 1e-3\n\n def test_stable_diffusion_pix2pix_k_lms(self):\n pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(\n \"timbrooks/instruct-pix2pix\", safety_checker=None\n )\n pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301])\n\n assert np.abs(expected_slice - image_slice).max() < 1e-3\n\n def test_stable_diffusion_pix2pix_ddim(self):\n pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(\n \"timbrooks/instruct-pix2pix\", safety_checker=None\n )\n pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753])\n\n assert np.abs(expected_slice - image_slice).max() < 1e-3\n\n def test_stable_diffusion_pix2pix_intermediate_state(self):\n number_of_steps = 0\n\n def callback_fn(step: int, timestep: int, latents: torch.Tensor) -> None:\n callback_fn.has_been_called = True\n nonlocal number_of_steps\n number_of_steps += 1\n if step == 1:\n latents = latents.detach().cpu().numpy()\n assert latents.shape == (1, 4, 64, 64)\n latents_slice = latents[0, -3:, -3:, -1]\n expected_slice = np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983])\n\n assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2\n elif step == 2:\n latents = latents.detach().cpu().numpy()\n assert latents.shape == (1, 4, 64, 64)\n latents_slice = latents[0, -3:, -3:, -1]\n expected_slice = np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115])\n\n assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2\n\n callback_fn.has_been_called = False\n\n pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(\n \"timbrooks/instruct-pix2pix\", safety_checker=None, torch_dtype=torch.float16\n )\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs()\n pipe(**inputs, callback=callback_fn, callback_steps=1)\n assert callback_fn.has_been_called\n assert number_of_steps == 3\n\n def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(\n \"timbrooks/instruct-pix2pix\", safety_checker=None, torch_dtype=torch.float16\n )\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing(1)\n pipe.enable_sequential_cpu_offload()\n\n inputs = self.get_inputs()\n _ = pipe(**inputs)\n\n mem_bytes = torch.cuda.max_memory_allocated()\n # make sure that less than 2.2 GB is allocated\n assert mem_bytes < 2.2 * 10**9\n\n def test_stable_diffusion_pix2pix_pipeline_multiple_of_8(self):\n inputs = self.get_inputs()\n # resize to resolution that is divisible by 8 but not 16 or 32\n inputs[\"image\"] = inputs[\"image\"].resize((504, 504))\n\n model_id = \"timbrooks/instruct-pix2pix\"\n pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(\n model_id,\n safety_checker=None,\n )\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n output = pipe(**inputs)\n image = output.images[0]\n\n image_slice = image[255:258, 383:386, -1]\n\n assert image.shape == (504, 504, 3)\n expected_slice = np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_2/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n StableDiffusionPipeline,\n UNet2DConditionModel,\n logging,\n)\nfrom diffusers.utils.testing_utils import (\n CaptureLogger,\n backend_empty_cache,\n enable_full_determinism,\n load_numpy,\n nightly,\n numpy_cosine_similarity_distance,\n require_torch_accelerator,\n require_torch_gpu,\n skip_mps,\n slow,\n torch_device,\n)\n\nfrom ..pipeline_params import (\n TEXT_TO_IMAGE_BATCH_PARAMS,\n TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,\n TEXT_TO_IMAGE_IMAGE_PARAMS,\n TEXT_TO_IMAGE_PARAMS,\n)\nfrom ..test_pipelines_common import (\n PipelineKarrasSchedulerTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n SDFunctionTesterMixin,\n)\n\n\nenable_full_determinism()\n\n\nclass StableDiffusion2PipelineFastTests(\n SDFunctionTesterMixin,\n PipelineLatentTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineTesterMixin,\n unittest.TestCase,\n):\n pipeline_class = StableDiffusionPipeline\n params = TEXT_TO_IMAGE_PARAMS\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n image_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n )\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n sample_size=128,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n # SD2-specific config below\n hidden_act=\"gelu\",\n projection_dim=512,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n generator_device = \"cpu\" if not device.startswith(\"cuda\") else \"cuda\"\n if not str(device).startswith(\"mps\"):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n else:\n generator = torch.manual_seed(seed)\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_stable_diffusion_ddim(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.5753, 0.6113, 0.5005, 0.5036, 0.5464, 0.4725, 0.4982, 0.4865, 0.4861])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_pndm(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n components[\"scheduler\"] = PNDMScheduler(skip_prk_steps=True)\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.5121, 0.5714, 0.4827, 0.5057, 0.5646, 0.4766, 0.5189, 0.4895, 0.4990])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_k_lms(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n components[\"scheduler\"] = LMSDiscreteScheduler.from_config(components[\"scheduler\"].config)\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.4865, 0.5439, 0.4840, 0.4995, 0.5543, 0.4846, 0.5199, 0.4942, 0.5061])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_k_euler_ancestral(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n components[\"scheduler\"] = EulerAncestralDiscreteScheduler.from_config(components[\"scheduler\"].config)\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.4864, 0.5440, 0.4842, 0.4994, 0.5543, 0.4846, 0.5196, 0.4942, 0.5063])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_k_euler(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n components[\"scheduler\"] = EulerDiscreteScheduler.from_config(components[\"scheduler\"].config)\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.4865, 0.5439, 0.4840, 0.4995, 0.5543, 0.4846, 0.5199, 0.4942, 0.5061])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_unflawed(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n components[\"scheduler\"] = DDIMScheduler.from_config(\n components[\"scheduler\"].config, timestep_spacing=\"trailing\"\n )\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"guidance_rescale\"] = 0.7\n inputs[\"num_inference_steps\"] = 10\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.4736, 0.5405, 0.4705, 0.4955, 0.5675, 0.4812, 0.5310, 0.4967, 0.5064])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_long_prompt(self):\n components = self.get_dummy_components()\n components[\"scheduler\"] = LMSDiscreteScheduler.from_config(components[\"scheduler\"].config)\n sd_pipe = StableDiffusionPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n do_classifier_free_guidance = True\n negative_prompt = None\n num_images_per_prompt = 1\n logger = logging.get_logger(\"diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion\")\n logger.setLevel(logging.WARNING)\n\n prompt = 25 * \"@\"\n with CaptureLogger(logger) as cap_logger_3:\n text_embeddings_3, negeative_text_embeddings_3 = sd_pipe.encode_prompt(\n prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt\n )\n if negeative_text_embeddings_3 is not None:\n text_embeddings_3 = torch.cat([negeative_text_embeddings_3, text_embeddings_3])\n\n prompt = 100 * \"@\"\n with CaptureLogger(logger) as cap_logger:\n text_embeddings, negative_embeddings = sd_pipe.encode_prompt(\n prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt\n )\n if negative_embeddings is not None:\n text_embeddings = torch.cat([negative_embeddings, text_embeddings])\n\n negative_prompt = \"Hello\"\n with CaptureLogger(logger) as cap_logger_2:\n text_embeddings_2, negative_text_embeddings_2 = sd_pipe.encode_prompt(\n prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt\n )\n if negative_text_embeddings_2 is not None:\n text_embeddings_2 = torch.cat([negative_text_embeddings_2, text_embeddings_2])\n\n assert text_embeddings_3.shape == text_embeddings_2.shape == text_embeddings.shape\n assert text_embeddings.shape[1] == 77\n\n assert cap_logger.out == cap_logger_2.out\n # 100 - 77 + 1 (BOS token) + 1 (EOS token) = 25\n assert cap_logger.out.count(\"@\") == 25\n assert cap_logger_3.out == \"\"\n\n def test_attention_slicing_forward_pass(self):\n super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=3e-3)\n\n\n@slow\n@require_torch_accelerator\n@skip_mps\nclass StableDiffusion2PipelineSlowTests(unittest.TestCase):\n def tearDown(self):\n super().tearDown()\n gc.collect()\n backend_empty_cache(torch_device)\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n _generator_device = \"cpu\" if not generator_device.startswith(\"cuda\") else \"cuda\"\n if not str(device).startswith(\"mps\"):\n generator = torch.Generator(device=_generator_device).manual_seed(seed)\n else:\n generator = torch.manual_seed(seed)\n\n latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))\n latents = torch.from_numpy(latents).to(device=device, dtype=dtype)\n inputs = {\n \"prompt\": \"a photograph of an astronaut riding a horse\",\n \"latents\": latents,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_stable_diffusion_default_ddim(self):\n pipe = StableDiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-base\")\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506])\n assert np.abs(image_slice - expected_slice).max() < 7e-3\n\n def test_stable_diffusion_pndm(self):\n pipe = StableDiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-base\")\n pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506])\n assert np.abs(image_slice - expected_slice).max() < 7e-3\n\n def test_stable_diffusion_k_lms(self):\n pipe = StableDiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-base\")\n pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.10440, 0.13115, 0.11100, 0.10141, 0.11440, 0.07215, 0.11332, 0.09693, 0.10006])\n assert np.abs(image_slice - expected_slice).max() < 3e-3\n\n @require_torch_gpu\n def test_stable_diffusion_attention_slicing(self):\n torch.cuda.reset_peak_memory_stats()\n pipe = StableDiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-base\", torch_dtype=torch.float16\n )\n pipe.unet.set_default_attn_processor()\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n # enable attention slicing\n pipe.enable_attention_slicing()\n inputs = self.get_inputs(torch_device, dtype=torch.float16)\n image_sliced = pipe(**inputs).images\n\n mem_bytes = torch.cuda.max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n # make sure that less than 3.3 GB is allocated\n assert mem_bytes < 3.3 * 10**9\n\n # disable slicing\n pipe.disable_attention_slicing()\n pipe.unet.set_default_attn_processor()\n inputs = self.get_inputs(torch_device, dtype=torch.float16)\n image = pipe(**inputs).images\n\n # make sure that more than 3.3 GB is allocated\n mem_bytes = torch.cuda.max_memory_allocated()\n assert mem_bytes > 3.3 * 10**9\n max_diff = numpy_cosine_similarity_distance(image.flatten(), image_sliced.flatten())\n assert max_diff < 5e-3\n\n def test_stable_diffusion_text2img_intermediate_state(self):\n number_of_steps = 0\n\n def callback_fn(step: int, timestep: int, latents: torch.Tensor) -> None:\n callback_fn.has_been_called = True\n nonlocal number_of_steps\n number_of_steps += 1\n if step == 1:\n latents = latents.detach().cpu().numpy()\n assert latents.shape == (1, 4, 64, 64)\n latents_slice = latents[0, -3:, -3:, -1]\n expected_slice = np.array(\n [-0.3862, -0.4507, -1.1729, 0.0686, -1.1045, 0.7124, -1.8301, 0.1903, 1.2773]\n )\n\n assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2\n elif step == 2:\n latents = latents.detach().cpu().numpy()\n assert latents.shape == (1, 4, 64, 64)\n latents_slice = latents[0, -3:, -3:, -1]\n expected_slice = np.array(\n [0.2720, -0.1863, -0.7383, -0.5029, -0.7534, 0.3970, -0.7646, 0.4468, 1.2686]\n )\n\n assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2\n\n callback_fn.has_been_called = False\n\n pipe = StableDiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-base\", torch_dtype=torch.float16\n )\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(torch_device, dtype=torch.float16)\n pipe(**inputs, callback=callback_fn, callback_steps=1)\n assert callback_fn.has_been_called\n assert number_of_steps == inputs[\"num_inference_steps\"]\n\n @require_torch_gpu\n def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n pipe = StableDiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-base\", torch_dtype=torch.float16\n )\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing(1)\n pipe.enable_sequential_cpu_offload()\n\n inputs = self.get_inputs(torch_device, dtype=torch.float16)\n _ = pipe(**inputs)\n\n mem_bytes = torch.cuda.max_memory_allocated()\n # make sure that less than 2.8 GB is allocated\n assert mem_bytes < 2.8 * 10**9\n\n @require_torch_gpu\n def test_stable_diffusion_pipeline_with_model_offloading(self):\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n inputs = self.get_inputs(torch_device, dtype=torch.float16)\n\n # Normal inference\n\n pipe = StableDiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-base\",\n torch_dtype=torch.float16,\n )\n pipe.unet.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n outputs = pipe(**inputs)\n mem_bytes = torch.cuda.max_memory_allocated()\n\n # With model offloading\n\n # Reload but don't move to cuda\n pipe = StableDiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-base\",\n torch_dtype=torch.float16,\n )\n pipe.unet.set_default_attn_processor()\n\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n inputs = self.get_inputs(torch_device, dtype=torch.float16)\n outputs_offloaded = pipe(**inputs)\n mem_bytes_offloaded = torch.cuda.max_memory_allocated()\n\n images = outputs.images\n images_offloaded = outputs_offloaded.images\n max_diff = numpy_cosine_similarity_distance(images.flatten(), images_offloaded.flatten())\n assert max_diff < 1e-3\n assert mem_bytes_offloaded < mem_bytes\n assert mem_bytes_offloaded < 3 * 10**9\n for module in pipe.text_encoder, pipe.unet, pipe.vae:\n assert module.device == torch.device(\"cpu\")\n\n # With attention slicing\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n pipe.enable_attention_slicing()\n _ = pipe(**inputs)\n mem_bytes_slicing = torch.cuda.max_memory_allocated()\n assert mem_bytes_slicing < mem_bytes_offloaded\n\n\n@nightly\n@require_torch_accelerator\n@skip_mps\nclass StableDiffusion2PipelineNightlyTests(unittest.TestCase):\n def tearDown(self):\n super().tearDown()\n gc.collect()\n backend_empty_cache(torch_device)\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n _generator_device = \"cpu\" if not generator_device.startswith(\"cuda\") else \"cuda\"\n if not str(device).startswith(\"mps\"):\n generator = torch.Generator(device=_generator_device).manual_seed(seed)\n else:\n generator = torch.manual_seed(seed)\n\n latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))\n latents = torch.from_numpy(latents).to(device=device, dtype=dtype)\n inputs = {\n \"prompt\": \"a photograph of an astronaut riding a horse\",\n \"latents\": latents,\n \"generator\": generator,\n \"num_inference_steps\": 50,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_stable_diffusion_2_0_default_ddim(self):\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-base\").to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = sd_pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_2_text2img/stable_diffusion_2_0_base_ddim.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n\n def test_stable_diffusion_2_1_default_pndm(self):\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-1-base\").to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = sd_pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_2_text2img/stable_diffusion_2_1_base_pndm.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n\n def test_stable_diffusion_ddim(self):\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-1-base\").to(torch_device)\n sd_pipe.scheduler = DDIMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = sd_pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_2_text2img/stable_diffusion_2_1_base_ddim.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n\n def test_stable_diffusion_lms(self):\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-1-base\").to(torch_device)\n sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = sd_pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_2_text2img/stable_diffusion_2_1_base_lms.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n\n def test_stable_diffusion_euler(self):\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-1-base\").to(torch_device)\n sd_pipe.scheduler = EulerDiscreteScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = sd_pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_2_text2img/stable_diffusion_2_1_base_euler.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n\n def test_stable_diffusion_dpm(self):\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-1-base\").to(torch_device)\n sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(\n sd_pipe.scheduler.config, final_sigmas_type=\"sigma_min\"\n )\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n inputs[\"num_inference_steps\"] = 25\n image = sd_pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_2_text2img/stable_diffusion_2_1_base_dpm_multi.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_attend_and_excite.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n StableDiffusionAttendAndExcitePipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.utils.testing_utils import (\n load_numpy,\n nightly,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n skip_mps,\n torch_device,\n)\n\nfrom ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS\nfrom ..test_pipelines_common import (\n PipelineFromPipeTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n)\n\n\ntorch.backends.cuda.matmul.allow_tf32 = False\n\n\n@skip_mps\nclass StableDiffusionAttendAndExcitePipelineFastTests(\n PipelineLatentTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineTesterMixin,\n PipelineFromPipeTesterMixin,\n unittest.TestCase,\n):\n pipeline_class = StableDiffusionAttendAndExcitePipeline\n test_attention_slicing = False\n params = TEXT_TO_IMAGE_PARAMS\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS.union({\"token_indices\"})\n image_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n\n # Attend and excite requires being able to run a backward pass at\n # inference time. There's no deterministic backward operator for pad\n\n @classmethod\n def setUpClass(cls):\n super().setUpClass()\n torch.use_deterministic_algorithms(False)\n\n @classmethod\n def tearDownClass(cls):\n super().tearDownClass()\n torch.use_deterministic_algorithms(True)\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=1,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n )\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n sample_size=128,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n # SD2-specific config below\n hidden_act=\"gelu\",\n projection_dim=512,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = inputs = {\n \"prompt\": \"a cat and a frog\",\n \"token_indices\": [2, 5],\n \"generator\": generator,\n \"num_inference_steps\": 1,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n \"max_iter_to_alter\": 2,\n \"thresholds\": {0: 0.7},\n }\n return inputs\n\n def test_dict_tuple_outputs_equivalent(self):\n expected_slice = None\n if torch_device == \"cpu\":\n expected_slice = np.array([0.6391, 0.6290, 0.4860, 0.5134, 0.5550, 0.4577, 0.5033, 0.5023, 0.4538])\n super().test_dict_tuple_outputs_equivalent(expected_slice=expected_slice, expected_max_difference=3e-3)\n\n def test_inference(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n self.assertEqual(image.shape, (1, 64, 64, 3))\n expected_slice = np.array(\n [0.63905364, 0.62897307, 0.48599017, 0.5133624, 0.5550048, 0.45769516, 0.50326973, 0.5023139, 0.45384496]\n )\n max_diff = np.abs(image_slice.flatten() - expected_slice).max()\n self.assertLessEqual(max_diff, 1e-3)\n\n def test_sequential_cpu_offload_forward_pass(self):\n super().test_sequential_cpu_offload_forward_pass(expected_max_diff=5e-4)\n\n def test_inference_batch_consistent(self):\n # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches\n self._test_inference_batch_consistent(batch_sizes=[1, 2])\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(batch_size=2, expected_max_diff=7e-4)\n\n def test_pt_np_pil_outputs_equivalent(self):\n super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4)\n\n def test_save_load_local(self):\n super().test_save_load_local(expected_max_difference=5e-4)\n\n def test_save_load_optional_components(self):\n super().test_save_load_optional_components(expected_max_difference=4e-4)\n\n def test_karras_schedulers_shape(self):\n super().test_karras_schedulers_shape(num_inference_steps_for_strength_for_iterations=3)\n\n\n@require_torch_gpu\n@nightly\nclass StableDiffusionAttendAndExcitePipelineIntegrationTests(unittest.TestCase):\n # Attend and excite requires being able to run a backward pass at\n # inference time. There's no deterministic backward operator for pad\n\n @classmethod\n def setUpClass(cls):\n super().setUpClass()\n torch.use_deterministic_algorithms(False)\n\n @classmethod\n def tearDownClass(cls):\n super().tearDownClass()\n torch.use_deterministic_algorithms(True)\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_attend_and_excite_fp16(self):\n generator = torch.manual_seed(51)\n\n pipe = StableDiffusionAttendAndExcitePipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\", safety_checker=None, torch_dtype=torch.float16\n )\n pipe.to(\"cuda\")\n\n prompt = \"a painting of an elephant with glasses\"\n token_indices = [5, 7]\n\n image = pipe(\n prompt=prompt,\n token_indices=token_indices,\n guidance_scale=7.5,\n generator=generator,\n num_inference_steps=5,\n max_iter_to_alter=5,\n output_type=\"np\",\n ).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy\"\n )\n max_diff = numpy_cosine_similarity_distance(image.flatten(), expected_image.flatten())\n assert max_diff < 5e-1\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom transformers import (\n CLIPTextConfig,\n CLIPTextModel,\n CLIPTokenizer,\n DPTConfig,\n DPTFeatureExtractor,\n DPTForDepthEstimation,\n)\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n StableDiffusionDepth2ImgPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.utils import is_accelerate_available, is_accelerate_version\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n load_numpy,\n nightly,\n require_torch_gpu,\n skip_mps,\n slow,\n torch_device,\n)\n\nfrom ..pipeline_params import (\n IMAGE_TO_IMAGE_IMAGE_PARAMS,\n TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,\n TEXT_GUIDED_IMAGE_VARIATION_PARAMS,\n TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,\n TEXT_TO_IMAGE_IMAGE_PARAMS,\n)\nfrom ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\n@skip_mps\nclass StableDiffusionDepth2ImgPipelineFastTests(\n PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase\n):\n pipeline_class = StableDiffusionDepth2ImgPipeline\n test_save_load_optional_components = False\n params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {\"height\", \"width\"}\n required_optional_params = PipelineTesterMixin.required_optional_params - {\"latents\"}\n batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS\n image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union({\"depth_mask\"})\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=5,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n )\n scheduler = PNDMScheduler(skip_prk_steps=True)\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n backbone_config = {\n \"global_padding\": \"same\",\n \"layer_type\": \"bottleneck\",\n \"depths\": [3, 4, 9],\n \"out_features\": [\"stage1\", \"stage2\", \"stage3\"],\n \"embedding_dynamic_padding\": True,\n \"hidden_sizes\": [96, 192, 384, 768],\n \"num_groups\": 2,\n }\n depth_estimator_config = DPTConfig(\n image_size=32,\n patch_size=16,\n num_channels=3,\n hidden_size=32,\n num_hidden_layers=4,\n backbone_out_indices=(0, 1, 2, 3),\n num_attention_heads=4,\n intermediate_size=37,\n hidden_act=\"gelu\",\n hidden_dropout_prob=0.1,\n attention_probs_dropout_prob=0.1,\n is_decoder=False,\n initializer_range=0.02,\n is_hybrid=True,\n backbone_config=backbone_config,\n backbone_featmap_shape=[1, 384, 24, 24],\n )\n depth_estimator = DPTForDepthEstimation(depth_estimator_config).eval()\n feature_extractor = DPTFeatureExtractor.from_pretrained(\n \"hf-internal-testing/tiny-random-DPTForDepthEstimation\"\n )\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"depth_estimator\": depth_estimator,\n \"feature_extractor\": feature_extractor,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed))\n image = image.cpu().permute(0, 2, 3, 1)[0]\n image = Image.fromarray(np.uint8(image)).convert(\"RGB\").resize((32, 32))\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"image\": image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_save_load_local(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n output = pipe(**inputs)[0]\n\n with tempfile.TemporaryDirectory() as tmpdir:\n pipe.save_pretrained(tmpdir)\n pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)\n pipe_loaded.to(torch_device)\n pipe_loaded.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n output_loaded = pipe_loaded(**inputs)[0]\n\n max_diff = np.abs(output - output_loaded).max()\n self.assertLess(max_diff, 1e-4)\n\n @unittest.skipIf(torch_device != \"cuda\", reason=\"float16 requires CUDA\")\n def test_save_load_float16(self):\n components = self.get_dummy_components()\n for name, module in components.items():\n if hasattr(module, \"half\"):\n components[name] = module.to(torch_device).half()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n output = pipe(**inputs)[0]\n\n with tempfile.TemporaryDirectory() as tmpdir:\n pipe.save_pretrained(tmpdir)\n pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, torch_dtype=torch.float16)\n pipe_loaded.to(torch_device)\n pipe_loaded.set_progress_bar_config(disable=None)\n\n for name, component in pipe_loaded.components.items():\n if hasattr(component, \"dtype\"):\n self.assertTrue(\n component.dtype == torch.float16,\n f\"`{name}.dtype` switched from `float16` to {component.dtype} after loading.\",\n )\n\n inputs = self.get_dummy_inputs(torch_device)\n output_loaded = pipe_loaded(**inputs)[0]\n\n max_diff = np.abs(output - output_loaded).max()\n self.assertLess(max_diff, 2e-2, \"The output of the fp16 pipeline changed after saving and loading.\")\n\n @unittest.skipIf(torch_device != \"cuda\", reason=\"float16 requires CUDA\")\n def test_float16_inference(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n for name, module in components.items():\n if hasattr(module, \"half\"):\n components[name] = module.half()\n pipe_fp16 = self.pipeline_class(**components)\n pipe_fp16.to(torch_device)\n pipe_fp16.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(torch_device))[0]\n output_fp16 = pipe_fp16(**self.get_dummy_inputs(torch_device))[0]\n\n max_diff = np.abs(output - output_fp16).max()\n self.assertLess(max_diff, 1.3e-2, \"The outputs of the fp16 and fp32 pipelines are too different.\")\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_accelerate_available() or is_accelerate_version(\"<\", \"0.14.0\"),\n reason=\"CPU offload is only available with CUDA and `accelerate v0.14.0` or higher\",\n )\n def test_cpu_offload_forward_pass(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n output_without_offload = pipe(**inputs)[0]\n\n pipe.enable_sequential_cpu_offload()\n inputs = self.get_dummy_inputs(torch_device)\n output_with_offload = pipe(**inputs)[0]\n\n max_diff = np.abs(output_with_offload - output_without_offload).max()\n self.assertLess(max_diff, 1e-4, \"CPU offloading should not affect the inference results\")\n\n def test_dict_tuple_outputs_equivalent(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(torch_device))[0]\n output_tuple = pipe(**self.get_dummy_inputs(torch_device), return_dict=False)[0]\n\n max_diff = np.abs(output - output_tuple).max()\n self.assertLess(max_diff, 1e-4)\n\n def test_progress_bar(self):\n super().test_progress_bar()\n\n def test_stable_diffusion_depth2img_default_case(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n pipe = StableDiffusionDepth2ImgPipeline(**components)\n pipe = pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 32, 32, 3)\n if torch_device == \"mps\":\n expected_slice = np.array([0.6071, 0.5035, 0.4378, 0.5776, 0.5753, 0.4316, 0.4513, 0.5263, 0.4546])\n else:\n expected_slice = np.array([0.5435, 0.4992, 0.3783, 0.4411, 0.5842, 0.4654, 0.3786, 0.5077, 0.4655])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_stable_diffusion_depth2img_negative_prompt(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n pipe = StableDiffusionDepth2ImgPipeline(**components)\n pipe = pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n negative_prompt = \"french fries\"\n output = pipe(**inputs, negative_prompt=negative_prompt)\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 32, 32, 3)\n if torch_device == \"mps\":\n expected_slice = np.array([0.6296, 0.5125, 0.3890, 0.4456, 0.5955, 0.4621, 0.3810, 0.5310, 0.4626])\n else:\n expected_slice = np.array([0.6012, 0.4507, 0.3769, 0.4121, 0.5566, 0.4585, 0.3803, 0.5045, 0.4631])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_stable_diffusion_depth2img_multiple_init_images(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n pipe = StableDiffusionDepth2ImgPipeline(**components)\n pipe = pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"prompt\"] = [inputs[\"prompt\"]] * 2\n inputs[\"image\"] = 2 * [inputs[\"image\"]]\n image = pipe(**inputs).images\n image_slice = image[-1, -3:, -3:, -1]\n\n assert image.shape == (2, 32, 32, 3)\n\n if torch_device == \"mps\":\n expected_slice = np.array([0.6501, 0.5150, 0.4939, 0.6688, 0.5437, 0.5758, 0.5115, 0.4406, 0.4551])\n else:\n expected_slice = np.array([0.6557, 0.6214, 0.6254, 0.5775, 0.4785, 0.5949, 0.5904, 0.4785, 0.4730])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_stable_diffusion_depth2img_pil(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n pipe = StableDiffusionDepth2ImgPipeline(**components)\n pipe = pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n if torch_device == \"mps\":\n expected_slice = np.array([0.53232, 0.47015, 0.40868, 0.45651, 0.4891, 0.4668, 0.4287, 0.48822, 0.47439])\n else:\n expected_slice = np.array([0.5435, 0.4992, 0.3783, 0.4411, 0.5842, 0.4654, 0.3786, 0.5077, 0.4655])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n @skip_mps\n def test_attention_slicing_forward_pass(self):\n return super().test_attention_slicing_forward_pass()\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=7e-3)\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionDepth2ImgPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=device).manual_seed(seed)\n init_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/depth2img/two_cats.png\"\n )\n inputs = {\n \"prompt\": \"two tigers\",\n \"image\": init_image,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"strength\": 0.75,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_stable_diffusion_depth2img_pipeline_default(self):\n pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-depth\", safety_checker=None\n )\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, 253:256, 253:256, -1].flatten()\n\n assert image.shape == (1, 480, 640, 3)\n expected_slice = np.array([0.5435, 0.4992, 0.3783, 0.4411, 0.5842, 0.4654, 0.3786, 0.5077, 0.4655])\n\n assert np.abs(expected_slice - image_slice).max() < 6e-1\n\n def test_stable_diffusion_depth2img_pipeline_k_lms(self):\n pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-depth\", safety_checker=None\n )\n pipe.unet.set_default_attn_processor()\n pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, 253:256, 253:256, -1].flatten()\n\n assert image.shape == (1, 480, 640, 3)\n expected_slice = np.array([0.6363, 0.6274, 0.6309, 0.6370, 0.6226, 0.6286, 0.6213, 0.6453, 0.6306])\n\n assert np.abs(expected_slice - image_slice).max() < 8e-4\n\n def test_stable_diffusion_depth2img_pipeline_ddim(self):\n pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-depth\", safety_checker=None\n )\n pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, 253:256, 253:256, -1].flatten()\n\n assert image.shape == (1, 480, 640, 3)\n expected_slice = np.array([0.6424, 0.6524, 0.6249, 0.6041, 0.6634, 0.6420, 0.6522, 0.6555, 0.6436])\n\n assert np.abs(expected_slice - image_slice).max() < 5e-4\n\n def test_stable_diffusion_depth2img_intermediate_state(self):\n number_of_steps = 0\n\n def callback_fn(step: int, timestep: int, latents: torch.Tensor) -> None:\n callback_fn.has_been_called = True\n nonlocal number_of_steps\n number_of_steps += 1\n if step == 1:\n latents = latents.detach().cpu().numpy()\n assert latents.shape == (1, 4, 60, 80)\n latents_slice = latents[0, -3:, -3:, -1]\n expected_slice = np.array(\n [-0.7168, -1.5137, -0.1418, -2.9219, -2.7266, -2.4414, -2.1035, -3.0078, -1.7051]\n )\n\n assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2\n elif step == 2:\n latents = latents.detach().cpu().numpy()\n assert latents.shape == (1, 4, 60, 80)\n latents_slice = latents[0, -3:, -3:, -1]\n expected_slice = np.array(\n [-0.7109, -1.5068, -0.1403, -2.9160, -2.7207, -2.4414, -2.1035, -3.0059, -1.7090]\n )\n\n assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2\n\n callback_fn.has_been_called = False\n\n pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-depth\", safety_checker=None, torch_dtype=torch.float16\n )\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(dtype=torch.float16)\n pipe(**inputs, callback=callback_fn, callback_steps=1)\n assert callback_fn.has_been_called\n assert number_of_steps == 2\n\n def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-depth\", safety_checker=None, torch_dtype=torch.float16\n )\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing(1)\n pipe.enable_sequential_cpu_offload()\n\n inputs = self.get_inputs(dtype=torch.float16)\n _ = pipe(**inputs)\n\n mem_bytes = torch.cuda.max_memory_allocated()\n # make sure that less than 2.9 GB is allocated\n assert mem_bytes < 2.9 * 10**9\n\n\n@nightly\n@require_torch_gpu\nclass StableDiffusionImg2ImgPipelineNightlyTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=device).manual_seed(seed)\n init_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/depth2img/two_cats.png\"\n )\n inputs = {\n \"prompt\": \"two tigers\",\n \"image\": init_image,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"strength\": 0.75,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_depth2img_pndm(self):\n pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-depth\")\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs()\n image = pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_depth2img/stable_diffusion_2_0_pndm.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n\n def test_depth2img_ddim(self):\n pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-depth\")\n pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs()\n image = pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_depth2img/stable_diffusion_2_0_ddim.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n\n def test_img2img_lms(self):\n pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-depth\")\n pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs()\n image = pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_depth2img/stable_diffusion_2_0_lms.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n\n def test_img2img_dpm(self):\n pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-depth\")\n pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs()\n inputs[\"num_inference_steps\"] = 30\n image = pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_depth2img/stable_diffusion_2_0_dpm_multi.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_diffedit.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMInverseScheduler,\n DDIMScheduler,\n DPMSolverMultistepInverseScheduler,\n DPMSolverMultistepScheduler,\n StableDiffusionDiffEditPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n nightly,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n torch_device,\n)\n\nfrom ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS\nfrom ..test_pipelines_common import PipelineFromPipeTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass StableDiffusionDiffEditPipelineFastTests(\n PipelineLatentTesterMixin, PipelineTesterMixin, PipelineFromPipeTesterMixin, unittest.TestCase\n):\n pipeline_class = StableDiffusionDiffEditPipeline\n params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {\"height\", \"width\", \"image\"} | {\"image_latents\"}\n batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {\"image\"} | {\"image_latents\"}\n image_params = frozenset(\n []\n ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess\n image_latents_params = frozenset([])\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n )\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n inverse_scheduler = DDIMInverseScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_zero=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n sample_size=128,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n # SD2-specific config below\n hidden_act=\"gelu\",\n projection_dim=512,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"inverse_scheduler\": inverse_scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n mask = floats_tensor((1, 16, 16), rng=random.Random(seed)).to(device)\n latents = floats_tensor((1, 2, 4, 16, 16), rng=random.Random(seed)).to(device)\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"a dog and a newt\",\n \"mask_image\": mask,\n \"image_latents\": latents,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"inpaint_strength\": 1.0,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n }\n\n return inputs\n\n def get_dummy_mask_inputs(self, device, seed=0):\n image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n image = image.cpu().permute(0, 2, 3, 1)[0]\n image = Image.fromarray(np.uint8(image)).convert(\"RGB\")\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"image\": image,\n \"source_prompt\": \"a cat and a frog\",\n \"target_prompt\": \"a dog and a newt\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"num_maps_per_mask\": 2,\n \"mask_encode_strength\": 1.0,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n }\n\n return inputs\n\n def get_dummy_inversion_inputs(self, device, seed=0):\n image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n image = image.cpu().permute(0, 2, 3, 1)[0]\n image = Image.fromarray(np.uint8(image)).convert(\"RGB\")\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"image\": image,\n \"prompt\": \"a cat and a frog\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"inpaint_strength\": 1.0,\n \"guidance_scale\": 6.0,\n \"decode_latents\": True,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_save_load_optional_components(self):\n if not hasattr(self.pipeline_class, \"_optional_components\"):\n return\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n # set all optional components to None and update pipeline config accordingly\n for optional_component in pipe._optional_components:\n setattr(pipe, optional_component, None)\n pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components})\n\n inputs = self.get_dummy_inputs(torch_device)\n output = pipe(**inputs)[0]\n\n with tempfile.TemporaryDirectory() as tmpdir:\n pipe.save_pretrained(tmpdir)\n pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)\n pipe_loaded.to(torch_device)\n pipe_loaded.set_progress_bar_config(disable=None)\n\n for optional_component in pipe._optional_components:\n self.assertTrue(\n getattr(pipe_loaded, optional_component) is None,\n f\"`{optional_component}` did not stay set to None after loading.\",\n )\n\n inputs = self.get_dummy_inputs(torch_device)\n output_loaded = pipe_loaded(**inputs)[0]\n\n max_diff = np.abs(output - output_loaded).max()\n self.assertLess(max_diff, 1e-4)\n\n def test_mask(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_mask_inputs(device)\n mask = pipe.generate_mask(**inputs)\n mask_slice = mask[0, -3:, -3:]\n\n self.assertEqual(mask.shape, (1, 16, 16))\n expected_slice = np.array([0] * 9)\n max_diff = np.abs(mask_slice.flatten() - expected_slice).max()\n self.assertLessEqual(max_diff, 1e-3)\n self.assertEqual(mask[0, -3, -4], 0)\n\n def test_inversion(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inversion_inputs(device)\n image = pipe.invert(**inputs).images\n image_slice = image[0, -1, -3:, -3:]\n\n self.assertEqual(image.shape, (2, 32, 32, 3))\n expected_slice = np.array(\n [0.5160, 0.5115, 0.5060, 0.5456, 0.4704, 0.5060, 0.5019, 0.4405, 0.4726],\n )\n max_diff = np.abs(image_slice.flatten() - expected_slice).max()\n self.assertLessEqual(max_diff, 1e-3)\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=5e-3)\n\n def test_inversion_dpm(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n scheduler_args = {\"beta_start\": 0.00085, \"beta_end\": 0.012, \"beta_schedule\": \"scaled_linear\"}\n components[\"scheduler\"] = DPMSolverMultistepScheduler(**scheduler_args)\n components[\"inverse_scheduler\"] = DPMSolverMultistepInverseScheduler(**scheduler_args)\n\n pipe = self.pipeline_class(**components)\n pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inversion_inputs(device)\n image = pipe.invert(**inputs).images\n image_slice = image[0, -1, -3:, -3:]\n\n self.assertEqual(image.shape, (2, 32, 32, 3))\n expected_slice = np.array(\n [0.5305, 0.4673, 0.5314, 0.5308, 0.4886, 0.5279, 0.5142, 0.4724, 0.4892],\n )\n max_diff = np.abs(image_slice.flatten() - expected_slice).max()\n self.assertLessEqual(max_diff, 1e-3)\n\n\n@require_torch_gpu\n@nightly\nclass StableDiffusionDiffEditPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n @classmethod\n def setUpClass(cls):\n raw_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png\"\n )\n raw_image = raw_image.convert(\"RGB\").resize((256, 256))\n\n cls.raw_image = raw_image\n\n def test_stable_diffusion_diffedit_full(self):\n generator = torch.manual_seed(0)\n\n pipe = StableDiffusionDiffEditPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-1-base\", safety_checker=None, torch_dtype=torch.float16\n )\n pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\n pipe.scheduler.clip_sample = True\n\n pipe.inverse_scheduler = DDIMInverseScheduler.from_config(pipe.scheduler.config)\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n source_prompt = \"a bowl of fruit\"\n target_prompt = \"a bowl of pears\"\n\n mask_image = pipe.generate_mask(\n image=self.raw_image,\n source_prompt=source_prompt,\n target_prompt=target_prompt,\n generator=generator,\n )\n\n inv_latents = pipe.invert(\n prompt=source_prompt,\n image=self.raw_image,\n inpaint_strength=0.7,\n generator=generator,\n num_inference_steps=5,\n ).latents\n\n image = pipe(\n prompt=target_prompt,\n mask_image=mask_image,\n image_latents=inv_latents,\n generator=generator,\n negative_prompt=source_prompt,\n inpaint_strength=0.7,\n num_inference_steps=5,\n output_type=\"np\",\n ).images[0]\n\n expected_image = (\n np.array(\n load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/diffedit/pears.png\"\n ).resize((256, 256))\n )\n / 255\n )\n\n assert numpy_cosine_similarity_distance(expected_image.flatten(), image.flatten()) < 2e-1\n\n\n@nightly\n@require_torch_gpu\nclass StableDiffusionDiffEditPipelineNightlyTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n @classmethod\n def setUpClass(cls):\n raw_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png\"\n )\n\n raw_image = raw_image.convert(\"RGB\").resize((768, 768))\n\n cls.raw_image = raw_image\n\n def test_stable_diffusion_diffedit_dpm(self):\n generator = torch.manual_seed(0)\n\n pipe = StableDiffusionDiffEditPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-1\", safety_checker=None, torch_dtype=torch.float16\n )\n pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n pipe.inverse_scheduler = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config)\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n source_prompt = \"a bowl of fruit\"\n target_prompt = \"a bowl of pears\"\n\n mask_image = pipe.generate_mask(\n image=self.raw_image,\n source_prompt=source_prompt,\n target_prompt=target_prompt,\n generator=generator,\n )\n\n inv_latents = pipe.invert(\n prompt=source_prompt,\n image=self.raw_image,\n inpaint_strength=0.7,\n generator=generator,\n num_inference_steps=25,\n ).latents\n\n image = pipe(\n prompt=target_prompt,\n mask_image=mask_image,\n image_latents=inv_latents,\n generator=generator,\n negative_prompt=source_prompt,\n inpaint_strength=0.7,\n num_inference_steps=25,\n output_type=\"np\",\n ).images[0]\n\n expected_image = (\n np.array(\n load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/diffedit/pears.png\"\n ).resize((768, 768))\n )\n / 255\n )\n assert np.abs((expected_image - image).max()) < 5e-1\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_flax.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nfrom diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline\nfrom diffusers.utils import is_flax_available\nfrom diffusers.utils.testing_utils import nightly, require_flax\n\n\nif is_flax_available():\n import jax\n import jax.numpy as jnp\n from flax.jax_utils import replicate\n from flax.training.common_utils import shard\n\n\n@nightly\n@require_flax\nclass FlaxStableDiffusion2PipelineIntegrationTests(unittest.TestCase):\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n\n def test_stable_diffusion_flax(self):\n sd_pipe, params = FlaxStableDiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2\",\n revision=\"bf16\",\n dtype=jnp.bfloat16,\n )\n\n prompt = \"A painting of a squirrel eating a burger\"\n num_samples = jax.device_count()\n prompt = num_samples * [prompt]\n prompt_ids = sd_pipe.prepare_inputs(prompt)\n\n params = replicate(params)\n prompt_ids = shard(prompt_ids)\n\n prng_seed = jax.random.PRNGKey(0)\n prng_seed = jax.random.split(prng_seed, jax.device_count())\n\n images = sd_pipe(prompt_ids, params, prng_seed, num_inference_steps=25, jit=True)[0]\n assert images.shape == (jax.device_count(), 1, 768, 768, 3)\n\n images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])\n image_slice = images[0, 253:256, 253:256, -1]\n\n output_slice = jnp.asarray(jax.device_get(image_slice.flatten()))\n expected_slice = jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512])\n print(f\"output_slice: {output_slice}\")\n assert jnp.abs(output_slice - expected_slice).max() < 1e-2\n\n\n@nightly\n@require_flax\nclass FlaxStableDiffusion2PipelineNightlyTests(unittest.TestCase):\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n\n def test_stable_diffusion_dpm_flax(self):\n model_id = \"stabilityai/stable-diffusion-2\"\n scheduler, scheduler_params = FlaxDPMSolverMultistepScheduler.from_pretrained(model_id, subfolder=\"scheduler\")\n sd_pipe, params = FlaxStableDiffusionPipeline.from_pretrained(\n model_id,\n scheduler=scheduler,\n revision=\"bf16\",\n dtype=jnp.bfloat16,\n )\n params[\"scheduler\"] = scheduler_params\n\n prompt = \"A painting of a squirrel eating a burger\"\n num_samples = jax.device_count()\n prompt = num_samples * [prompt]\n prompt_ids = sd_pipe.prepare_inputs(prompt)\n\n params = replicate(params)\n prompt_ids = shard(prompt_ids)\n\n prng_seed = jax.random.PRNGKey(0)\n prng_seed = jax.random.split(prng_seed, jax.device_count())\n\n images = sd_pipe(prompt_ids, params, prng_seed, num_inference_steps=25, jit=True)[0]\n assert images.shape == (jax.device_count(), 1, 768, 768, 3)\n\n images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])\n image_slice = images[0, 253:256, 253:256, -1]\n\n output_slice = jnp.asarray(jax.device_get(image_slice.flatten()))\n expected_slice = jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297])\n print(f\"output_slice: {output_slice}\")\n assert jnp.abs(output_slice - expected_slice).max() < 1e-2\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_flax_inpaint.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nfrom diffusers import FlaxStableDiffusionInpaintPipeline\nfrom diffusers.utils import is_flax_available, load_image\nfrom diffusers.utils.testing_utils import require_flax, slow\n\n\nif is_flax_available():\n import jax\n import jax.numpy as jnp\n from flax.jax_utils import replicate\n from flax.training.common_utils import shard\n\n\n@slow\n@require_flax\nclass FlaxStableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase):\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n\n def test_stable_diffusion_inpaint_pipeline(self):\n init_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/sd2-inpaint/init_image.png\"\n )\n mask_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png\"\n )\n\n model_id = \"xvjiarui/stable-diffusion-2-inpainting\"\n pipeline, params = FlaxStableDiffusionInpaintPipeline.from_pretrained(model_id, safety_checker=None)\n\n prompt = \"Face of a yellow cat, high resolution, sitting on a park bench\"\n\n prng_seed = jax.random.PRNGKey(0)\n num_inference_steps = 50\n\n num_samples = jax.device_count()\n prompt = num_samples * [prompt]\n init_image = num_samples * [init_image]\n mask_image = num_samples * [mask_image]\n prompt_ids, processed_masked_images, processed_masks = pipeline.prepare_inputs(prompt, init_image, mask_image)\n\n # shard inputs and rng\n params = replicate(params)\n prng_seed = jax.random.split(prng_seed, jax.device_count())\n prompt_ids = shard(prompt_ids)\n processed_masked_images = shard(processed_masked_images)\n processed_masks = shard(processed_masks)\n\n output = pipeline(\n prompt_ids, processed_masks, processed_masked_images, params, prng_seed, num_inference_steps, jit=True\n )\n\n images = output.images.reshape(num_samples, 512, 512, 3)\n\n image_slice = images[0, 253:256, 253:256, -1]\n\n output_slice = jnp.asarray(jax.device_get(image_slice.flatten()))\n expected_slice = jnp.array(\n [0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084]\n )\n print(f\"output_slice: {output_slice}\")\n assert jnp.abs(output_slice - expected_slice).max() < 1e-2\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_inpaint.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNet2DConditionModel\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n load_numpy,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\nfrom ..pipeline_params import (\n TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,\n TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,\n TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,\n)\nfrom ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass StableDiffusion2InpaintPipelineFastTests(\n PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase\n):\n pipeline_class = StableDiffusionInpaintPipeline\n params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS\n batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS\n image_params = frozenset(\n []\n ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess\n image_latents_params = frozenset([])\n callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union({\"mask\", \"masked_image_latents\"})\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=9,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n )\n scheduler = PNDMScheduler(skip_prk_steps=True)\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n sample_size=128,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n # SD2-specific config below\n hidden_act=\"gelu\",\n projection_dim=512,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched\n image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n image = image.cpu().permute(0, 2, 3, 1)[0]\n init_image = Image.fromarray(np.uint8(image)).convert(\"RGB\").resize((64, 64))\n mask_image = Image.fromarray(np.uint8(image + 4)).convert(\"RGB\").resize((64, 64))\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"image\": init_image,\n \"mask_image\": mask_image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_stable_diffusion_inpaint(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionInpaintPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=3e-3)\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_stable_diffusion_inpaint_pipeline(self):\n init_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/sd2-inpaint/init_image.png\"\n )\n mask_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png\"\n )\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint\"\n \"/yellow_cat_sitting_on_a_park_bench.npy\"\n )\n\n model_id = \"stabilityai/stable-diffusion-2-inpainting\"\n pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, safety_checker=None)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n prompt = \"Face of a yellow cat, high resolution, sitting on a park bench\"\n\n generator = torch.manual_seed(0)\n output = pipe(\n prompt=prompt,\n image=init_image,\n mask_image=mask_image,\n generator=generator,\n output_type=\"np\",\n )\n image = output.images[0]\n\n assert image.shape == (512, 512, 3)\n assert np.abs(expected_image - image).max() < 9e-3\n\n def test_stable_diffusion_inpaint_pipeline_fp16(self):\n init_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/sd2-inpaint/init_image.png\"\n )\n mask_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png\"\n )\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint\"\n \"/yellow_cat_sitting_on_a_park_bench_fp16.npy\"\n )\n\n model_id = \"stabilityai/stable-diffusion-2-inpainting\"\n pipe = StableDiffusionInpaintPipeline.from_pretrained(\n model_id,\n torch_dtype=torch.float16,\n safety_checker=None,\n )\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n prompt = \"Face of a yellow cat, high resolution, sitting on a park bench\"\n\n generator = torch.manual_seed(0)\n output = pipe(\n prompt=prompt,\n image=init_image,\n mask_image=mask_image,\n generator=generator,\n output_type=\"np\",\n )\n image = output.images[0]\n\n assert image.shape == (512, 512, 3)\n assert np.abs(expected_image - image).max() < 5e-1\n\n def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n init_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/sd2-inpaint/init_image.png\"\n )\n mask_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png\"\n )\n\n model_id = \"stabilityai/stable-diffusion-2-inpainting\"\n pndm = PNDMScheduler.from_pretrained(model_id, subfolder=\"scheduler\")\n pipe = StableDiffusionInpaintPipeline.from_pretrained(\n model_id,\n safety_checker=None,\n scheduler=pndm,\n torch_dtype=torch.float16,\n )\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing(1)\n pipe.enable_sequential_cpu_offload()\n\n prompt = \"Face of a yellow cat, high resolution, sitting on a park bench\"\n\n generator = torch.manual_seed(0)\n _ = pipe(\n prompt=prompt,\n image=init_image,\n mask_image=mask_image,\n generator=generator,\n num_inference_steps=2,\n output_type=\"np\",\n )\n\n mem_bytes = torch.cuda.max_memory_allocated()\n # make sure that less than 2.65 GB is allocated\n assert mem_bytes < 2.65 * 10**9\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_latent_upscale.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n EulerDiscreteScheduler,\n StableDiffusionLatentUpscalePipeline,\n StableDiffusionPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n load_numpy,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\nfrom ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS\nfrom ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\ndef check_same_shape(tensor_list):\n shapes = [tensor.shape for tensor in tensor_list]\n return all(shape == shapes[0] for shape in shapes[1:])\n\n\nclass StableDiffusionLatentUpscalePipelineFastTests(\n PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase\n):\n pipeline_class = StableDiffusionLatentUpscalePipeline\n params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {\n \"height\",\n \"width\",\n \"cross_attention_kwargs\",\n \"negative_prompt_embeds\",\n \"prompt_embeds\",\n }\n required_optional_params = PipelineTesterMixin.required_optional_params - {\"num_images_per_prompt\"}\n batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS\n image_params = frozenset(\n []\n ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess\n image_latents_params = frozenset([])\n\n @property\n def dummy_image(self):\n batch_size = 1\n num_channels = 4\n sizes = (16, 16)\n\n image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)\n return image\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n model = UNet2DConditionModel(\n act_fn=\"gelu\",\n attention_head_dim=8,\n norm_num_groups=None,\n block_out_channels=[32, 32, 64, 64],\n time_cond_proj_dim=160,\n conv_in_kernel=1,\n conv_out_kernel=1,\n cross_attention_dim=32,\n down_block_types=(\n \"KDownBlock2D\",\n \"KCrossAttnDownBlock2D\",\n \"KCrossAttnDownBlock2D\",\n \"KCrossAttnDownBlock2D\",\n ),\n in_channels=8,\n mid_block_type=None,\n only_cross_attention=False,\n out_channels=5,\n resnet_time_scale_shift=\"scale_shift\",\n time_embedding_type=\"fourier\",\n timestep_post_act=\"gelu\",\n up_block_types=(\"KCrossAttnUpBlock2D\", \"KCrossAttnUpBlock2D\", \"KCrossAttnUpBlock2D\", \"KUpBlock2D\"),\n )\n vae = AutoencoderKL(\n block_out_channels=[32, 32, 64, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n \"DownEncoderBlock2D\",\n ],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\", \"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n scheduler = EulerDiscreteScheduler(prediction_type=\"sample\")\n text_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n hidden_act=\"quick_gelu\",\n projection_dim=512,\n )\n text_encoder = CLIPTextModel(text_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": model.eval(),\n \"vae\": vae.eval(),\n \"scheduler\": scheduler,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"image\": self.dummy_image.cpu(),\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_inference(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n self.assertEqual(image.shape, (1, 256, 256, 3))\n expected_slice = np.array(\n [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055]\n )\n max_diff = np.abs(image_slice.flatten() - expected_slice).max()\n self.assertLessEqual(max_diff, 1e-3)\n\n def test_attention_slicing_forward_pass(self):\n super().test_attention_slicing_forward_pass(expected_max_diff=7e-3)\n\n def test_sequential_cpu_offload_forward_pass(self):\n super().test_sequential_cpu_offload_forward_pass(expected_max_diff=3e-3)\n\n def test_dict_tuple_outputs_equivalent(self):\n super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=7e-3)\n\n def test_pt_np_pil_outputs_equivalent(self):\n super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3)\n\n def test_save_load_local(self):\n super().test_save_load_local(expected_max_difference=3e-3)\n\n def test_save_load_optional_components(self):\n super().test_save_load_optional_components(expected_max_difference=3e-3)\n\n def test_karras_schedulers_shape(self):\n skip_schedulers = [\n \"DDIMScheduler\",\n \"DDPMScheduler\",\n \"PNDMScheduler\",\n \"HeunDiscreteScheduler\",\n \"EulerAncestralDiscreteScheduler\",\n \"KDPM2DiscreteScheduler\",\n \"KDPM2AncestralDiscreteScheduler\",\n \"DPMSolverSDEScheduler\",\n \"EDMEulerScheduler\",\n ]\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n\n # make sure that PNDM does not need warm-up\n pipe.scheduler.register_to_config(skip_prk_steps=True)\n\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = 2\n\n outputs = []\n for scheduler_enum in KarrasDiffusionSchedulers:\n if scheduler_enum.name in skip_schedulers:\n # no sigma schedulers are not supported\n # no schedulers\n continue\n\n scheduler_cls = getattr(diffusers, scheduler_enum.name)\n pipe.scheduler = scheduler_cls.from_config(pipe.scheduler.config)\n output = pipe(**inputs)[0]\n outputs.append(output)\n\n assert check_same_shape(outputs)\n\n def test_float16_inference(self):\n super().test_float16_inference(expected_max_diff=5e-1)\n\n\n@require_torch_gpu\n@slow\nclass StableDiffusionLatentUpscalePipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_latent_upscaler_fp16(self):\n generator = torch.manual_seed(33)\n\n pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", torch_dtype=torch.float16)\n pipe.to(\"cuda\")\n\n upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(\n \"stabilityai/sd-x2-latent-upscaler\", torch_dtype=torch.float16\n )\n upscaler.to(\"cuda\")\n\n prompt = \"a photo of an astronaut high resolution, unreal engine, ultra realistic\"\n\n low_res_latents = pipe(prompt, generator=generator, output_type=\"latent\").images\n\n image = upscaler(\n prompt=prompt,\n image=low_res_latents,\n num_inference_steps=20,\n guidance_scale=0,\n generator=generator,\n output_type=\"np\",\n ).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy\"\n )\n assert np.abs((expected_image - image).mean()) < 5e-2\n\n def test_latent_upscaler_fp16_image(self):\n generator = torch.manual_seed(33)\n\n upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(\n \"stabilityai/sd-x2-latent-upscaler\", torch_dtype=torch.float16\n )\n upscaler.to(\"cuda\")\n\n prompt = \"the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas\"\n\n low_res_img = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png\"\n )\n\n image = upscaler(\n prompt=prompt,\n image=low_res_img,\n num_inference_steps=20,\n guidance_scale=0,\n generator=generator,\n output_type=\"np\",\n ).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy\"\n )\n assert np.abs((expected_image - image).max()) < 5e-2\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_upscale.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNet2DConditionModel\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n load_numpy,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\n\nenable_full_determinism()\n\n\nclass StableDiffusionUpscalePipelineFastTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n @property\n def dummy_image(self):\n batch_size = 1\n num_channels = 3\n sizes = (32, 32)\n\n image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)\n return image\n\n @property\n def dummy_cond_unet_upscale(self):\n torch.manual_seed(0)\n model = UNet2DConditionModel(\n block_out_channels=(32, 32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=7,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n # SD2-specific config below\n attention_head_dim=8,\n use_linear_projection=True,\n only_cross_attention=(True, True, False),\n num_class_embeds=100,\n )\n return model\n\n @property\n def dummy_vae(self):\n torch.manual_seed(0)\n model = AutoencoderKL(\n block_out_channels=[32, 32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n return model\n\n @property\n def dummy_text_encoder(self):\n torch.manual_seed(0)\n config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n # SD2-specific config below\n hidden_act=\"gelu\",\n projection_dim=512,\n )\n return CLIPTextModel(config)\n\n def test_stable_diffusion_upscale(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n unet = self.dummy_cond_unet_upscale\n low_res_scheduler = DDPMScheduler()\n scheduler = DDIMScheduler(prediction_type=\"v_prediction\")\n vae = self.dummy_vae\n text_encoder = self.dummy_text_encoder\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]\n low_res_image = Image.fromarray(np.uint8(image)).convert(\"RGB\").resize((64, 64))\n\n # make sure here that pndm scheduler skips prk\n sd_pipe = StableDiffusionUpscalePipeline(\n unet=unet,\n low_res_scheduler=low_res_scheduler,\n scheduler=scheduler,\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n max_noise_level=350,\n )\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A painting of a squirrel eating a burger\"\n generator = torch.Generator(device=device).manual_seed(0)\n output = sd_pipe(\n [prompt],\n image=low_res_image,\n generator=generator,\n guidance_scale=6.0,\n noise_level=20,\n num_inference_steps=2,\n output_type=\"np\",\n )\n\n image = output.images\n\n generator = torch.Generator(device=device).manual_seed(0)\n image_from_tuple = sd_pipe(\n [prompt],\n image=low_res_image,\n generator=generator,\n guidance_scale=6.0,\n noise_level=20,\n num_inference_steps=2,\n output_type=\"np\",\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n expected_height_width = low_res_image.size[0] * 4\n assert image.shape == (1, expected_height_width, expected_height_width, 3)\n expected_slice = np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_upscale_batch(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n unet = self.dummy_cond_unet_upscale\n low_res_scheduler = DDPMScheduler()\n scheduler = DDIMScheduler(prediction_type=\"v_prediction\")\n vae = self.dummy_vae\n text_encoder = self.dummy_text_encoder\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]\n low_res_image = Image.fromarray(np.uint8(image)).convert(\"RGB\").resize((64, 64))\n\n # make sure here that pndm scheduler skips prk\n sd_pipe = StableDiffusionUpscalePipeline(\n unet=unet,\n low_res_scheduler=low_res_scheduler,\n scheduler=scheduler,\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n max_noise_level=350,\n )\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A painting of a squirrel eating a burger\"\n output = sd_pipe(\n 2 * [prompt],\n image=2 * [low_res_image],\n guidance_scale=6.0,\n noise_level=20,\n num_inference_steps=2,\n output_type=\"np\",\n )\n image = output.images\n assert image.shape[0] == 2\n\n generator = torch.Generator(device=device).manual_seed(0)\n output = sd_pipe(\n [prompt],\n image=low_res_image,\n generator=generator,\n num_images_per_prompt=2,\n guidance_scale=6.0,\n noise_level=20,\n num_inference_steps=2,\n output_type=\"np\",\n )\n image = output.images\n assert image.shape[0] == 2\n\n def test_stable_diffusion_upscale_prompt_embeds(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n unet = self.dummy_cond_unet_upscale\n low_res_scheduler = DDPMScheduler()\n scheduler = DDIMScheduler(prediction_type=\"v_prediction\")\n vae = self.dummy_vae\n text_encoder = self.dummy_text_encoder\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]\n low_res_image = Image.fromarray(np.uint8(image)).convert(\"RGB\").resize((64, 64))\n\n # make sure here that pndm scheduler skips prk\n sd_pipe = StableDiffusionUpscalePipeline(\n unet=unet,\n low_res_scheduler=low_res_scheduler,\n scheduler=scheduler,\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n max_noise_level=350,\n )\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A painting of a squirrel eating a burger\"\n generator = torch.Generator(device=device).manual_seed(0)\n output = sd_pipe(\n [prompt],\n image=low_res_image,\n generator=generator,\n guidance_scale=6.0,\n noise_level=20,\n num_inference_steps=2,\n output_type=\"np\",\n )\n\n image = output.images\n\n generator = torch.Generator(device=device).manual_seed(0)\n prompt_embeds, negative_prompt_embeds = sd_pipe.encode_prompt(prompt, device, 1, False)\n if negative_prompt_embeds is not None:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n image_from_prompt_embeds = sd_pipe(\n prompt_embeds=prompt_embeds,\n image=[low_res_image],\n generator=generator,\n guidance_scale=6.0,\n noise_level=20,\n num_inference_steps=2,\n output_type=\"np\",\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_prompt_embeds_slice = image_from_prompt_embeds[0, -3:, -3:, -1]\n\n expected_height_width = low_res_image.size[0] * 4\n assert image.shape == (1, expected_height_width, expected_height_width, 3)\n expected_slice = np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n assert np.abs(image_from_prompt_embeds_slice.flatten() - expected_slice).max() < 1e-2\n\n @unittest.skipIf(torch_device != \"cuda\", \"This test requires a GPU\")\n def test_stable_diffusion_upscale_fp16(self):\n \"\"\"Test that stable diffusion upscale works with fp16\"\"\"\n unet = self.dummy_cond_unet_upscale\n low_res_scheduler = DDPMScheduler()\n scheduler = DDIMScheduler(prediction_type=\"v_prediction\")\n vae = self.dummy_vae\n text_encoder = self.dummy_text_encoder\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]\n low_res_image = Image.fromarray(np.uint8(image)).convert(\"RGB\").resize((64, 64))\n\n # put models in fp16, except vae as it overflows in fp16\n unet = unet.half()\n text_encoder = text_encoder.half()\n\n # make sure here that pndm scheduler skips prk\n sd_pipe = StableDiffusionUpscalePipeline(\n unet=unet,\n low_res_scheduler=low_res_scheduler,\n scheduler=scheduler,\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n max_noise_level=350,\n )\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A painting of a squirrel eating a burger\"\n generator = torch.manual_seed(0)\n image = sd_pipe(\n [prompt],\n image=low_res_image,\n generator=generator,\n num_inference_steps=2,\n output_type=\"np\",\n ).images\n\n expected_height_width = low_res_image.size[0] * 4\n assert image.shape == (1, expected_height_width, expected_height_width, 3)\n\n def test_stable_diffusion_upscale_from_save_pretrained(self):\n pipes = []\n\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n low_res_scheduler = DDPMScheduler()\n scheduler = DDIMScheduler(prediction_type=\"v_prediction\")\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n # make sure here that pndm scheduler skips prk\n sd_pipe = StableDiffusionUpscalePipeline(\n unet=self.dummy_cond_unet_upscale,\n low_res_scheduler=low_res_scheduler,\n scheduler=scheduler,\n vae=self.dummy_vae,\n text_encoder=self.dummy_text_encoder,\n tokenizer=tokenizer,\n max_noise_level=350,\n )\n sd_pipe = sd_pipe.to(device)\n pipes.append(sd_pipe)\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n sd_pipe.save_pretrained(tmpdirname)\n sd_pipe = StableDiffusionUpscalePipeline.from_pretrained(tmpdirname).to(device)\n pipes.append(sd_pipe)\n\n prompt = \"A painting of a squirrel eating a burger\"\n image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]\n low_res_image = Image.fromarray(np.uint8(image)).convert(\"RGB\").resize((64, 64))\n\n image_slices = []\n for pipe in pipes:\n generator = torch.Generator(device=device).manual_seed(0)\n image = pipe(\n [prompt],\n image=low_res_image,\n generator=generator,\n guidance_scale=6.0,\n noise_level=20,\n num_inference_steps=2,\n output_type=\"np\",\n ).images\n image_slices.append(image[0, -3:, -3:, -1].flatten())\n\n assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionUpscalePipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_stable_diffusion_upscale_pipeline(self):\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/sd2-upscale/low_res_cat.png\"\n )\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale\"\n \"/upsampled_cat.npy\"\n )\n\n model_id = \"stabilityai/stable-diffusion-x4-upscaler\"\n pipe = StableDiffusionUpscalePipeline.from_pretrained(model_id)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n prompt = \"a cat sitting on a park bench\"\n\n generator = torch.manual_seed(0)\n output = pipe(\n prompt=prompt,\n image=image,\n generator=generator,\n output_type=\"np\",\n )\n image = output.images[0]\n\n assert image.shape == (512, 512, 3)\n assert np.abs(expected_image - image).max() < 1e-3\n\n def test_stable_diffusion_upscale_pipeline_fp16(self):\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/sd2-upscale/low_res_cat.png\"\n )\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale\"\n \"/upsampled_cat_fp16.npy\"\n )\n\n model_id = \"stabilityai/stable-diffusion-x4-upscaler\"\n pipe = StableDiffusionUpscalePipeline.from_pretrained(\n model_id,\n torch_dtype=torch.float16,\n )\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n prompt = \"a cat sitting on a park bench\"\n\n generator = torch.manual_seed(0)\n output = pipe(\n prompt=prompt,\n image=image,\n generator=generator,\n output_type=\"np\",\n )\n image = output.images[0]\n\n assert image.shape == (512, 512, 3)\n assert np.abs(expected_image - image).max() < 5e-1\n\n def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/sd2-upscale/low_res_cat.png\"\n )\n\n model_id = \"stabilityai/stable-diffusion-x4-upscaler\"\n pipe = StableDiffusionUpscalePipeline.from_pretrained(\n model_id,\n torch_dtype=torch.float16,\n )\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing(1)\n pipe.enable_sequential_cpu_offload()\n\n prompt = \"a cat sitting on a park bench\"\n\n generator = torch.manual_seed(0)\n _ = pipe(\n prompt=prompt,\n image=image,\n generator=generator,\n num_inference_steps=5,\n output_type=\"np\",\n )\n\n mem_bytes = torch.cuda.max_memory_allocated()\n # make sure that less than 2.9 GB is allocated\n assert mem_bytes < 2.9 * 10**9\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_v_pred.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport time\nimport unittest\n\nimport numpy as np\nimport torch\nfrom huggingface_hub import hf_hub_download\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EulerDiscreteScheduler,\n StableDiffusionPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n load_numpy,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\n\nenable_full_determinism()\n\n\nclass StableDiffusion2VPredictionPipelineFastTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n @property\n def dummy_cond_unet(self):\n torch.manual_seed(0)\n model = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n )\n return model\n\n @property\n def dummy_vae(self):\n torch.manual_seed(0)\n model = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n sample_size=128,\n )\n return model\n\n @property\n def dummy_text_encoder(self):\n torch.manual_seed(0)\n config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n # SD2-specific config below\n hidden_act=\"gelu\",\n projection_dim=64,\n )\n return CLIPTextModel(config)\n\n def test_stable_diffusion_v_pred_ddim(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n unet = self.dummy_cond_unet\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n prediction_type=\"v_prediction\",\n )\n\n vae = self.dummy_vae\n bert = self.dummy_text_encoder\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n # make sure here that pndm scheduler skips prk\n sd_pipe = StableDiffusionPipeline(\n unet=unet,\n scheduler=scheduler,\n vae=vae,\n text_encoder=bert,\n tokenizer=tokenizer,\n safety_checker=None,\n feature_extractor=None,\n image_encoder=None,\n requires_safety_checker=False,\n )\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A painting of a squirrel eating a burger\"\n\n generator = torch.Generator(device=device).manual_seed(0)\n output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type=\"np\")\n image = output.images\n\n generator = torch.Generator(device=device).manual_seed(0)\n image_from_tuple = sd_pipe(\n [prompt],\n generator=generator,\n guidance_scale=6.0,\n num_inference_steps=2,\n output_type=\"np\",\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.6569, 0.6525, 0.5142, 0.4968, 0.4923, 0.4601, 0.4996, 0.5041, 0.4544])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_v_pred_k_euler(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n unet = self.dummy_cond_unet\n scheduler = EulerDiscreteScheduler(\n beta_start=0.00085, beta_end=0.012, beta_schedule=\"scaled_linear\", prediction_type=\"v_prediction\"\n )\n vae = self.dummy_vae\n bert = self.dummy_text_encoder\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n # make sure here that pndm scheduler skips prk\n sd_pipe = StableDiffusionPipeline(\n unet=unet,\n scheduler=scheduler,\n vae=vae,\n text_encoder=bert,\n tokenizer=tokenizer,\n safety_checker=None,\n feature_extractor=None,\n image_encoder=None,\n requires_safety_checker=False,\n )\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A painting of a squirrel eating a burger\"\n generator = torch.Generator(device=device).manual_seed(0)\n output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type=\"np\")\n\n image = output.images\n\n generator = torch.Generator(device=device).manual_seed(0)\n image_from_tuple = sd_pipe(\n [prompt],\n generator=generator,\n guidance_scale=6.0,\n num_inference_steps=2,\n output_type=\"np\",\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.5644, 0.6514, 0.5190, 0.5663, 0.5287, 0.4953, 0.5430, 0.5243, 0.4778])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n\n @unittest.skipIf(torch_device != \"cuda\", \"This test requires a GPU\")\n def test_stable_diffusion_v_pred_fp16(self):\n \"\"\"Test that stable diffusion v-prediction works with fp16\"\"\"\n unet = self.dummy_cond_unet\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n prediction_type=\"v_prediction\",\n )\n vae = self.dummy_vae\n bert = self.dummy_text_encoder\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n # put models in fp16\n unet = unet.half()\n vae = vae.half()\n bert = bert.half()\n\n # make sure here that pndm scheduler skips prk\n sd_pipe = StableDiffusionPipeline(\n unet=unet,\n scheduler=scheduler,\n vae=vae,\n text_encoder=bert,\n tokenizer=tokenizer,\n safety_checker=None,\n feature_extractor=None,\n image_encoder=None,\n requires_safety_checker=False,\n )\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A painting of a squirrel eating a burger\"\n generator = torch.manual_seed(0)\n image = sd_pipe([prompt], generator=generator, num_inference_steps=2, output_type=\"np\").images\n\n assert image.shape == (1, 64, 64, 3)\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusion2VPredictionPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_stable_diffusion_v_pred_default(self):\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-2\")\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.enable_attention_slicing()\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A painting of a squirrel eating a burger\"\n generator = torch.manual_seed(0)\n output = sd_pipe([prompt], generator=generator, guidance_scale=7.5, num_inference_steps=20, output_type=\"np\")\n\n image = output.images\n image_slice = image[0, 253:256, 253:256, -1]\n\n assert image.shape == (1, 768, 768, 3)\n expected_slice = np.array([0.1868, 0.1922, 0.1527, 0.1921, 0.1908, 0.1624, 0.1779, 0.1652, 0.1734])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_v_pred_upcast_attention(self):\n sd_pipe = StableDiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-1\", torch_dtype=torch.float16\n )\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.enable_attention_slicing()\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A painting of a squirrel eating a burger\"\n generator = torch.manual_seed(0)\n output = sd_pipe([prompt], generator=generator, guidance_scale=7.5, num_inference_steps=20, output_type=\"np\")\n\n image = output.images\n image_slice = image[0, 253:256, 253:256, -1]\n\n assert image.shape == (1, 768, 768, 3)\n expected_slice = np.array([0.4209, 0.4087, 0.4097, 0.4209, 0.3860, 0.4329, 0.4280, 0.4324, 0.4187])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2\n\n def test_stable_diffusion_v_pred_euler(self):\n scheduler = EulerDiscreteScheduler.from_pretrained(\"stabilityai/stable-diffusion-2\", subfolder=\"scheduler\")\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-2\", scheduler=scheduler)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.enable_attention_slicing()\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A painting of a squirrel eating a burger\"\n generator = torch.manual_seed(0)\n\n output = sd_pipe([prompt], generator=generator, num_inference_steps=5, output_type=\"np\")\n image = output.images\n\n image_slice = image[0, 253:256, 253:256, -1]\n\n assert image.shape == (1, 768, 768, 3)\n expected_slice = np.array([0.1781, 0.1695, 0.1661, 0.1705, 0.1588, 0.1699, 0.2005, 0.1589, 0.1677])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_v_pred_dpm(self):\n \"\"\"\n TODO: update this test after making DPM compatible with V-prediction!\n \"\"\"\n scheduler = DPMSolverMultistepScheduler.from_pretrained(\n \"stabilityai/stable-diffusion-2\",\n subfolder=\"scheduler\",\n final_sigmas_type=\"sigma_min\",\n )\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-2\", scheduler=scheduler)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.enable_attention_slicing()\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"a photograph of an astronaut riding a horse\"\n generator = torch.manual_seed(0)\n image = sd_pipe(\n [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=5, output_type=\"np\"\n ).images\n\n image_slice = image[0, 253:256, 253:256, -1]\n assert image.shape == (1, 768, 768, 3)\n expected_slice = np.array([0.3303, 0.3184, 0.3291, 0.3300, 0.3256, 0.3113, 0.2965, 0.3134, 0.3192])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_attention_slicing_v_pred(self):\n torch.cuda.reset_peak_memory_stats()\n model_id = \"stabilityai/stable-diffusion-2\"\n pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n prompt = \"a photograph of an astronaut riding a horse\"\n\n # make attention efficient\n pipe.enable_attention_slicing()\n generator = torch.manual_seed(0)\n output_chunked = pipe(\n [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type=\"np\"\n )\n image_chunked = output_chunked.images\n\n mem_bytes = torch.cuda.max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n # make sure that less than 5.5 GB is allocated\n assert mem_bytes < 5.5 * 10**9\n\n # disable slicing\n pipe.disable_attention_slicing()\n generator = torch.manual_seed(0)\n output = pipe([prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type=\"np\")\n image = output.images\n\n # make sure that more than 3.0 GB is allocated\n mem_bytes = torch.cuda.max_memory_allocated()\n assert mem_bytes > 3 * 10**9\n max_diff = numpy_cosine_similarity_distance(image.flatten(), image_chunked.flatten())\n assert max_diff < 1e-3\n\n def test_stable_diffusion_text2img_pipeline_v_pred_default(self):\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/\"\n \"sd2-text2img/astronaut_riding_a_horse_v_pred.npy\"\n )\n\n pipe = StableDiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-2\")\n pipe.to(torch_device)\n pipe.enable_attention_slicing()\n pipe.set_progress_bar_config(disable=None)\n\n prompt = \"astronaut riding a horse\"\n\n generator = torch.manual_seed(0)\n output = pipe(prompt=prompt, guidance_scale=7.5, generator=generator, output_type=\"np\")\n image = output.images[0]\n\n assert image.shape == (768, 768, 3)\n max_diff = numpy_cosine_similarity_distance(image.flatten(), expected_image.flatten())\n assert max_diff < 1e-3\n\n def test_stable_diffusion_text2img_pipeline_unflawed(self):\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/\"\n \"sd2-text2img/lion_galaxy.npy\"\n )\n\n pipe = StableDiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-1\")\n pipe.scheduler = DDIMScheduler.from_config(\n pipe.scheduler.config, timestep_spacing=\"trailing\", rescale_betas_zero_snr=True\n )\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A lion in galaxies, spirals, nebulae, stars, smoke, iridescent, intricate detail, octane render, 8k\"\n\n generator = torch.Generator(\"cpu\").manual_seed(0)\n output = pipe(\n prompt=prompt,\n guidance_scale=7.5,\n num_inference_steps=10,\n guidance_rescale=0.7,\n generator=generator,\n output_type=\"np\",\n )\n image = output.images[0]\n\n assert image.shape == (768, 768, 3)\n max_diff = numpy_cosine_similarity_distance(image.flatten(), expected_image.flatten())\n assert max_diff < 5e-2\n\n def test_stable_diffusion_text2img_pipeline_v_pred_fp16(self):\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/\"\n \"sd2-text2img/astronaut_riding_a_horse_v_pred_fp16.npy\"\n )\n\n pipe = StableDiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-2\", torch_dtype=torch.float16)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n prompt = \"astronaut riding a horse\"\n\n generator = torch.manual_seed(0)\n output = pipe(prompt=prompt, guidance_scale=7.5, generator=generator, output_type=\"np\")\n image = output.images[0]\n\n assert image.shape == (768, 768, 3)\n max_diff = numpy_cosine_similarity_distance(image.flatten(), expected_image.flatten())\n assert max_diff < 1e-3\n\n def test_download_local(self):\n filename = hf_hub_download(\"stabilityai/stable-diffusion-2-1\", filename=\"v2-1_768-ema-pruned.safetensors\")\n\n pipe = StableDiffusionPipeline.from_single_file(filename, torch_dtype=torch.float16)\n pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\n pipe.enable_model_cpu_offload()\n\n image_out = pipe(\"test\", num_inference_steps=1, output_type=\"np\").images[0]\n\n assert image_out.shape == (768, 768, 3)\n\n def test_stable_diffusion_text2img_intermediate_state_v_pred(self):\n number_of_steps = 0\n\n def test_callback_fn(step: int, timestep: int, latents: torch.Tensor) -> None:\n test_callback_fn.has_been_called = True\n nonlocal number_of_steps\n number_of_steps += 1\n if step == 0:\n latents = latents.detach().cpu().numpy()\n assert latents.shape == (1, 4, 96, 96)\n latents_slice = latents[0, -3:, -3:, -1]\n expected_slice = np.array([0.7749, 0.0325, 0.5088, 0.1619, 0.3372, 0.3667, -0.5186, 0.6860, 1.4326])\n\n assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2\n elif step == 19:\n latents = latents.detach().cpu().numpy()\n assert latents.shape == (1, 4, 96, 96)\n latents_slice = latents[0, -3:, -3:, -1]\n expected_slice = np.array([1.3887, 1.0273, 1.7266, 0.0726, 0.6611, 0.1598, -1.0547, 0.1522, 0.0227])\n\n assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2\n\n test_callback_fn.has_been_called = False\n\n pipe = StableDiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-2\", torch_dtype=torch.float16)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n prompt = \"Andromeda galaxy in a bottle\"\n\n generator = torch.manual_seed(0)\n pipe(\n prompt=prompt,\n num_inference_steps=20,\n guidance_scale=7.5,\n generator=generator,\n callback=test_callback_fn,\n callback_steps=1,\n )\n assert test_callback_fn.has_been_called\n assert number_of_steps == 20\n\n def test_stable_diffusion_low_cpu_mem_usage_v_pred(self):\n pipeline_id = \"stabilityai/stable-diffusion-2\"\n\n start_time = time.time()\n pipeline_low_cpu_mem_usage = StableDiffusionPipeline.from_pretrained(pipeline_id, torch_dtype=torch.float16)\n pipeline_low_cpu_mem_usage.to(torch_device)\n low_cpu_mem_usage_time = time.time() - start_time\n\n start_time = time.time()\n _ = StableDiffusionPipeline.from_pretrained(pipeline_id, torch_dtype=torch.float16, low_cpu_mem_usage=False)\n normal_load_time = time.time() - start_time\n\n assert 2 * low_cpu_mem_usage_time < normal_load_time\n\n def test_stable_diffusion_pipeline_with_sequential_cpu_offloading_v_pred(self):\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n pipeline_id = \"stabilityai/stable-diffusion-2\"\n prompt = \"Andromeda galaxy in a bottle\"\n\n pipeline = StableDiffusionPipeline.from_pretrained(pipeline_id, torch_dtype=torch.float16)\n pipeline.enable_attention_slicing(1)\n pipeline.enable_sequential_cpu_offload()\n\n generator = torch.manual_seed(0)\n _ = pipeline(prompt, generator=generator, num_inference_steps=5)\n\n mem_bytes = torch.cuda.max_memory_allocated()\n # make sure that less than 2.8 GB is allocated\n assert mem_bytes < 2.8 * 10**9\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_3/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/stable_diffusion_3/test_pipeline_stable_diffusion_3.py", "content": "import gc\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer, T5EncoderModel\n\nfrom diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, SD3Transformer2DModel, StableDiffusion3Pipeline\nfrom diffusers.utils.testing_utils import (\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nclass StableDiffusion3PipelineFastTests(unittest.TestCase, PipelineTesterMixin):\n pipeline_class = StableDiffusion3Pipeline\n params = frozenset(\n [\n \"prompt\",\n \"height\",\n \"width\",\n \"guidance_scale\",\n \"negative_prompt\",\n \"prompt_embeds\",\n \"negative_prompt_embeds\",\n ]\n )\n batch_params = frozenset([\"prompt\", \"negative_prompt\"])\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n transformer = SD3Transformer2DModel(\n sample_size=32,\n patch_size=1,\n in_channels=4,\n num_layers=1,\n attention_head_dim=8,\n num_attention_heads=4,\n caption_projection_dim=32,\n joint_attention_dim=32,\n pooled_projection_dim=64,\n out_channels=4,\n )\n clip_text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n hidden_act=\"gelu\",\n projection_dim=32,\n )\n\n torch.manual_seed(0)\n text_encoder = CLIPTextModelWithProjection(clip_text_encoder_config)\n\n torch.manual_seed(0)\n text_encoder_2 = CLIPTextModelWithProjection(clip_text_encoder_config)\n\n text_encoder_3 = T5EncoderModel.from_pretrained(\"hf-internal-testing/tiny-random-t5\")\n\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n tokenizer_2 = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n tokenizer_3 = AutoTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-t5\")\n\n torch.manual_seed(0)\n vae = AutoencoderKL(\n sample_size=32,\n in_channels=3,\n out_channels=3,\n block_out_channels=(4,),\n layers_per_block=1,\n latent_channels=4,\n norm_num_groups=1,\n use_quant_conv=False,\n use_post_quant_conv=False,\n shift_factor=0.0609,\n scaling_factor=1.5035,\n )\n\n scheduler = FlowMatchEulerDiscreteScheduler()\n\n return {\n \"scheduler\": scheduler,\n \"text_encoder\": text_encoder,\n \"text_encoder_2\": text_encoder_2,\n \"text_encoder_3\": text_encoder_3,\n \"tokenizer\": tokenizer,\n \"tokenizer_2\": tokenizer_2,\n \"tokenizer_3\": tokenizer_3,\n \"transformer\": transformer,\n \"vae\": vae,\n }\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=\"cpu\").manual_seed(seed)\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 5.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_stable_diffusion_3_different_prompts(self):\n pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)\n\n inputs = self.get_dummy_inputs(torch_device)\n output_same_prompt = pipe(**inputs).images[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"prompt_2\"] = \"a different prompt\"\n inputs[\"prompt_3\"] = \"another different prompt\"\n output_different_prompts = pipe(**inputs).images[0]\n\n max_diff = np.abs(output_same_prompt - output_different_prompts).max()\n\n # Outputs should be different here\n assert max_diff > 1e-2\n\n def test_stable_diffusion_3_different_negative_prompts(self):\n pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)\n\n inputs = self.get_dummy_inputs(torch_device)\n output_same_prompt = pipe(**inputs).images[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"negative_prompt_2\"] = \"deformed\"\n inputs[\"negative_prompt_3\"] = \"blurry\"\n output_different_prompts = pipe(**inputs).images[0]\n\n max_diff = np.abs(output_same_prompt - output_different_prompts).max()\n\n # Outputs should be different here\n assert max_diff > 1e-2\n\n def test_stable_diffusion_3_prompt_embeds(self):\n pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)\n inputs = self.get_dummy_inputs(torch_device)\n\n output_with_prompt = pipe(**inputs).images[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n prompt = inputs.pop(\"prompt\")\n\n do_classifier_free_guidance = inputs[\"guidance_scale\"] > 1\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = pipe.encode_prompt(\n prompt,\n prompt_2=None,\n prompt_3=None,\n do_classifier_free_guidance=do_classifier_free_guidance,\n device=torch_device,\n )\n output_with_embeds = pipe(\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n **inputs,\n ).images[0]\n\n max_diff = np.abs(output_with_prompt - output_with_embeds).max()\n assert max_diff < 1e-4\n\n def test_fused_qkv_projections(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = pipe(**inputs).images\n original_image_slice = image[0, -3:, -3:, -1]\n\n pipe.transformer.fuse_qkv_projections()\n inputs = self.get_dummy_inputs(device)\n image = pipe(**inputs).images\n image_slice_fused = image[0, -3:, -3:, -1]\n\n pipe.transformer.unfuse_qkv_projections()\n inputs = self.get_dummy_inputs(device)\n image = pipe(**inputs).images\n image_slice_disabled = image[0, -3:, -3:, -1]\n\n assert np.allclose(\n original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3\n ), \"Fusion of QKV projections shouldn't affect the outputs.\"\n assert np.allclose(\n image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3\n ), \"Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled.\"\n assert np.allclose(\n original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2\n ), \"Original outputs should match when fused QKV projections are disabled.\"\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusion3PipelineSlowTests(unittest.TestCase):\n pipeline_class = StableDiffusion3Pipeline\n repo_id = \"stabilityai/stable-diffusion-3-medium-diffusers\"\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=\"cpu\").manual_seed(seed)\n\n return {\n \"prompt\": \"A photo of a cat\",\n \"num_inference_steps\": 2,\n \"guidance_scale\": 5.0,\n \"output_type\": \"np\",\n \"generator\": generator,\n }\n\n def test_sd3_inference(self):\n pipe = self.pipeline_class.from_pretrained(self.repo_id, torch_dtype=torch.float16)\n pipe.enable_model_cpu_offload()\n\n inputs = self.get_inputs(torch_device)\n\n image = pipe(**inputs).images[0]\n image_slice = image[0, :10, :10]\n expected_slice = np.array(\n [\n [0.36132812, 0.30004883, 0.25830078],\n [0.36669922, 0.31103516, 0.23754883],\n [0.34814453, 0.29248047, 0.23583984],\n [0.35791016, 0.30981445, 0.23999023],\n [0.36328125, 0.31274414, 0.2607422],\n [0.37304688, 0.32177734, 0.26171875],\n [0.3671875, 0.31933594, 0.25756836],\n [0.36035156, 0.31103516, 0.2578125],\n [0.3857422, 0.33789062, 0.27563477],\n [0.3701172, 0.31982422, 0.265625],\n ],\n dtype=np.float32,\n )\n\n max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), image_slice.flatten())\n\n assert max_diff < 1e-4\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_3/test_pipeline_stable_diffusion_3_img2img.py", "content": "import gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer, T5EncoderModel\n\nfrom diffusers import (\n AutoencoderKL,\n FlowMatchEulerDiscreteScheduler,\n SD3Transformer2DModel,\n StableDiffusion3Img2ImgPipeline,\n)\nfrom diffusers.utils import load_image\nfrom diffusers.utils.testing_utils import (\n floats_tensor,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\nfrom ..pipeline_params import (\n IMAGE_TO_IMAGE_IMAGE_PARAMS,\n TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,\n TEXT_GUIDED_IMAGE_VARIATION_PARAMS,\n)\nfrom ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin\n\n\nclass StableDiffusion3Img2ImgPipelineFastTests(PipelineLatentTesterMixin, unittest.TestCase, PipelineTesterMixin):\n pipeline_class = StableDiffusion3Img2ImgPipeline\n params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {\"height\", \"width\"}\n required_optional_params = PipelineTesterMixin.required_optional_params\n batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS\n image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n transformer = SD3Transformer2DModel(\n sample_size=32,\n patch_size=1,\n in_channels=4,\n num_layers=1,\n attention_head_dim=8,\n num_attention_heads=4,\n joint_attention_dim=32,\n caption_projection_dim=32,\n pooled_projection_dim=64,\n out_channels=4,\n )\n clip_text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n hidden_act=\"gelu\",\n projection_dim=32,\n )\n\n torch.manual_seed(0)\n text_encoder = CLIPTextModelWithProjection(clip_text_encoder_config)\n\n torch.manual_seed(0)\n text_encoder_2 = CLIPTextModelWithProjection(clip_text_encoder_config)\n\n text_encoder_3 = T5EncoderModel.from_pretrained(\"hf-internal-testing/tiny-random-t5\")\n\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n tokenizer_2 = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n tokenizer_3 = AutoTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-t5\")\n\n torch.manual_seed(0)\n vae = AutoencoderKL(\n sample_size=32,\n in_channels=3,\n out_channels=3,\n block_out_channels=(4,),\n layers_per_block=1,\n latent_channels=4,\n norm_num_groups=1,\n use_quant_conv=False,\n use_post_quant_conv=False,\n shift_factor=0.0609,\n scaling_factor=1.5035,\n )\n\n scheduler = FlowMatchEulerDiscreteScheduler()\n\n return {\n \"scheduler\": scheduler,\n \"text_encoder\": text_encoder,\n \"text_encoder_2\": text_encoder_2,\n \"text_encoder_3\": text_encoder_3,\n \"tokenizer\": tokenizer,\n \"tokenizer_2\": tokenizer_2,\n \"tokenizer_3\": tokenizer_3,\n \"transformer\": transformer,\n \"vae\": vae,\n }\n\n def get_dummy_inputs(self, device, seed=0):\n image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n image = image / 2 + 0.5\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=\"cpu\").manual_seed(seed)\n\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"image\": image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 5.0,\n \"output_type\": \"np\",\n \"strength\": 0.8,\n }\n return inputs\n\n def test_stable_diffusion_3_img2img_different_prompts(self):\n pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)\n\n inputs = self.get_dummy_inputs(torch_device)\n output_same_prompt = pipe(**inputs).images[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"prompt_2\"] = \"a different prompt\"\n inputs[\"prompt_3\"] = \"another different prompt\"\n output_different_prompts = pipe(**inputs).images[0]\n\n max_diff = np.abs(output_same_prompt - output_different_prompts).max()\n\n # Outputs should be different here\n assert max_diff > 1e-2\n\n def test_stable_diffusion_3_img2img_different_negative_prompts(self):\n pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)\n\n inputs = self.get_dummy_inputs(torch_device)\n output_same_prompt = pipe(**inputs).images[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"negative_prompt_2\"] = \"deformed\"\n inputs[\"negative_prompt_3\"] = \"blurry\"\n output_different_prompts = pipe(**inputs).images[0]\n\n max_diff = np.abs(output_same_prompt - output_different_prompts).max()\n\n # Outputs should be different here\n assert max_diff > 1e-2\n\n def test_stable_diffusion_3_img2img_prompt_embeds(self):\n pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)\n inputs = self.get_dummy_inputs(torch_device)\n\n output_with_prompt = pipe(**inputs).images[0]\n\n inputs = self.get_dummy_inputs(torch_device)\n prompt = inputs.pop(\"prompt\")\n\n do_classifier_free_guidance = inputs[\"guidance_scale\"] > 1\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = pipe.encode_prompt(\n prompt,\n prompt_2=None,\n prompt_3=None,\n do_classifier_free_guidance=do_classifier_free_guidance,\n device=torch_device,\n )\n output_with_embeds = pipe(\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n **inputs,\n ).images[0]\n\n max_diff = np.abs(output_with_prompt - output_with_embeds).max()\n assert max_diff < 1e-4\n\n def test_multi_vae(self):\n pass\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusion3Img2ImgPipelineSlowTests(unittest.TestCase):\n pipeline_class = StableDiffusion3Img2ImgPipeline\n repo_id = \"stabilityai/stable-diffusion-3-medium-diffusers\"\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, seed=0):\n init_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_img2img/sketch-mountains-input.png\"\n )\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=\"cpu\").manual_seed(seed)\n\n return {\n \"prompt\": \"A photo of a cat\",\n \"num_inference_steps\": 2,\n \"guidance_scale\": 5.0,\n \"output_type\": \"np\",\n \"generator\": generator,\n \"image\": init_image,\n }\n\n def test_sd3_img2img_inference(self):\n pipe = self.pipeline_class.from_pretrained(self.repo_id, torch_dtype=torch.float16)\n pipe.enable_model_cpu_offload()\n\n inputs = self.get_inputs(torch_device)\n\n image = pipe(**inputs).images[0]\n image_slice = image[0, :10, :10]\n expected_slice = np.array(\n [\n [0.50097656, 0.44726562, 0.40429688],\n [0.5048828, 0.45703125, 0.38110352],\n [0.4987793, 0.45141602, 0.38134766],\n [0.49682617, 0.45336914, 0.38354492],\n [0.49804688, 0.4555664, 0.39379883],\n [0.5083008, 0.4645996, 0.40039062],\n [0.50341797, 0.46240234, 0.39770508],\n [0.49926758, 0.4572754, 0.39575195],\n [0.50634766, 0.46435547, 0.39794922],\n [0.50341797, 0.4572754, 0.39746094],\n ],\n dtype=np.float32,\n )\n\n max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), image_slice.flatten())\n\n assert max_diff < 1e-4, f\"Outputs are not close enough, got {image_slice}\"\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_adapter/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/stable_diffusion_adapter/test_stable_diffusion_adapter.py", "content": "# coding=utf-8\n# Copyright 2022 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n LCMScheduler,\n MultiAdapter,\n PNDMScheduler,\n StableDiffusionAdapterPipeline,\n T2IAdapter,\n UNet2DConditionModel,\n)\nfrom diffusers.utils import logging\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n load_numpy,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\nfrom ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS\nfrom ..test_pipelines_common import PipelineFromPipeTesterMixin, PipelineTesterMixin, assert_mean_pixel_difference\n\n\nenable_full_determinism()\n\n\nclass AdapterTests:\n pipeline_class = StableDiffusionAdapterPipeline\n params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS\n batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS\n\n def get_dummy_components(self, adapter_type, time_cond_proj_dim=None):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"CrossAttnDownBlock2D\", \"DownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n time_cond_proj_dim=time_cond_proj_dim,\n )\n scheduler = PNDMScheduler(skip_prk_steps=True)\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n torch.manual_seed(0)\n\n if adapter_type == \"full_adapter\" or adapter_type == \"light_adapter\":\n adapter = T2IAdapter(\n in_channels=3,\n channels=[32, 64],\n num_res_blocks=2,\n downscale_factor=2,\n adapter_type=adapter_type,\n )\n elif adapter_type == \"multi_adapter\":\n adapter = MultiAdapter(\n [\n T2IAdapter(\n in_channels=3,\n channels=[32, 64],\n num_res_blocks=2,\n downscale_factor=2,\n adapter_type=\"full_adapter\",\n ),\n T2IAdapter(\n in_channels=3,\n channels=[32, 64],\n num_res_blocks=2,\n downscale_factor=2,\n adapter_type=\"full_adapter\",\n ),\n ]\n )\n else:\n raise ValueError(\n f\"Unknown adapter type: {adapter_type}, must be one of 'full_adapter', 'light_adapter', or 'multi_adapter''\"\n )\n\n components = {\n \"adapter\": adapter,\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n }\n return components\n\n def get_dummy_components_with_full_downscaling(self, adapter_type):\n \"\"\"Get dummy components with x8 VAE downscaling and 4 UNet down blocks.\n These dummy components are intended to fully-exercise the T2I-Adapter\n downscaling behavior.\n \"\"\"\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 32, 32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\", \"DownBlock2D\"),\n up_block_types=(\"UpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\"),\n cross_attention_dim=32,\n )\n scheduler = PNDMScheduler(skip_prk_steps=True)\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 32, 32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\", \"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\", \"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n torch.manual_seed(0)\n\n if adapter_type == \"full_adapter\" or adapter_type == \"light_adapter\":\n adapter = T2IAdapter(\n in_channels=3,\n channels=[32, 32, 32, 64],\n num_res_blocks=2,\n downscale_factor=8,\n adapter_type=adapter_type,\n )\n elif adapter_type == \"multi_adapter\":\n adapter = MultiAdapter(\n [\n T2IAdapter(\n in_channels=3,\n channels=[32, 32, 32, 64],\n num_res_blocks=2,\n downscale_factor=8,\n adapter_type=\"full_adapter\",\n ),\n T2IAdapter(\n in_channels=3,\n channels=[32, 32, 32, 64],\n num_res_blocks=2,\n downscale_factor=8,\n adapter_type=\"full_adapter\",\n ),\n ]\n )\n else:\n raise ValueError(\n f\"Unknown adapter type: {adapter_type}, must be one of 'full_adapter', 'light_adapter', or 'multi_adapter''\"\n )\n\n components = {\n \"adapter\": adapter,\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0, height=64, width=64, num_images=1):\n if num_images == 1:\n image = floats_tensor((1, 3, height, width), rng=random.Random(seed)).to(device)\n else:\n image = [\n floats_tensor((1, 3, height, width), rng=random.Random(seed)).to(device) for _ in range(num_images)\n ]\n\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"image\": image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_attention_slicing_forward_pass(self):\n return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)\n\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=2e-3)\n\n @parameterized.expand(\n [\n # (dim=264) The internal feature map will be 33x33 after initial pixel unshuffling (downscaled x8).\n (((4 * 8 + 1) * 8),),\n # (dim=272) The internal feature map will be 17x17 after the first T2I down block (downscaled x16).\n (((4 * 4 + 1) * 16),),\n # (dim=288) The internal feature map will be 9x9 after the second T2I down block (downscaled x32).\n (((4 * 2 + 1) * 32),),\n # (dim=320) The internal feature map will be 5x5 after the third T2I down block (downscaled x64).\n (((4 * 1 + 1) * 64),),\n ]\n )\n def test_multiple_image_dimensions(self, dim):\n \"\"\"Test that the T2I-Adapter pipeline supports any input dimension that\n is divisible by the adapter's `downscale_factor`. This test was added in\n response to an issue where the T2I Adapter's downscaling padding\n behavior did not match the UNet's behavior.\n\n Note that we have selected `dim` values to produce odd resolutions at\n each downscaling level.\n \"\"\"\n components = self.get_dummy_components_with_full_downscaling()\n sd_pipe = StableDiffusionAdapterPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device, height=dim, width=dim)\n image = sd_pipe(**inputs).images\n\n assert image.shape == (1, dim, dim, 3)\n\n def test_adapter_lcm(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionAdapterPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = sd_pipe(**inputs)\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.4535, 0.5493, 0.4359, 0.5452, 0.6086, 0.4441, 0.5544, 0.501, 0.4859])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_adapter_lcm_custom_timesteps(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionAdapterPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n del inputs[\"num_inference_steps\"]\n inputs[\"timesteps\"] = [999, 499]\n output = sd_pipe(**inputs)\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.4535, 0.5493, 0.4359, 0.5452, 0.6086, 0.4441, 0.5544, 0.501, 0.4859])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n\nclass StableDiffusionFullAdapterPipelineFastTests(\n AdapterTests, PipelineTesterMixin, PipelineFromPipeTesterMixin, unittest.TestCase\n):\n def get_dummy_components(self, time_cond_proj_dim=None):\n return super().get_dummy_components(\"full_adapter\", time_cond_proj_dim=time_cond_proj_dim)\n\n def get_dummy_components_with_full_downscaling(self):\n return super().get_dummy_components_with_full_downscaling(\"full_adapter\")\n\n def test_stable_diffusion_adapter_default_case(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionAdapterPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.4858, 0.5500, 0.4278, 0.4669, 0.6184, 0.4322, 0.5010, 0.5033, 0.4746])\n assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3\n\n\nclass StableDiffusionLightAdapterPipelineFastTests(AdapterTests, PipelineTesterMixin, unittest.TestCase):\n def get_dummy_components(self, time_cond_proj_dim=None):\n return super().get_dummy_components(\"light_adapter\", time_cond_proj_dim=time_cond_proj_dim)\n\n def get_dummy_components_with_full_downscaling(self):\n return super().get_dummy_components_with_full_downscaling(\"light_adapter\")\n\n def test_stable_diffusion_adapter_default_case(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionAdapterPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.4965, 0.5548, 0.4330, 0.4771, 0.6226, 0.4382, 0.5037, 0.5071, 0.4782])\n assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3\n\n\nclass StableDiffusionMultiAdapterPipelineFastTests(AdapterTests, PipelineTesterMixin, unittest.TestCase):\n def get_dummy_components(self, time_cond_proj_dim=None):\n return super().get_dummy_components(\"multi_adapter\", time_cond_proj_dim=time_cond_proj_dim)\n\n def get_dummy_components_with_full_downscaling(self):\n return super().get_dummy_components_with_full_downscaling(\"multi_adapter\")\n\n def get_dummy_inputs(self, device, height=64, width=64, seed=0):\n inputs = super().get_dummy_inputs(device, seed, height=height, width=width, num_images=2)\n inputs[\"adapter_conditioning_scale\"] = [0.5, 0.5]\n return inputs\n\n def test_stable_diffusion_adapter_default_case(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionAdapterPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.4902, 0.5539, 0.4317, 0.4682, 0.6190, 0.4351, 0.5018, 0.5046, 0.4772])\n assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3\n\n def test_inference_batch_consistent(\n self, batch_sizes=[2, 4, 13], additional_params_copy_to_batched_inputs=[\"num_inference_steps\"]\n ):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n\n logger = logging.get_logger(pipe.__module__)\n logger.setLevel(level=diffusers.logging.FATAL)\n\n # batchify inputs\n for batch_size in batch_sizes:\n batched_inputs = {}\n for name, value in inputs.items():\n if name in self.batch_params:\n # prompt is string\n if name == \"prompt\":\n len_prompt = len(value)\n # make unequal batch sizes\n batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]\n\n # make last batch super long\n batched_inputs[name][-1] = 100 * \"very long\"\n elif name == \"image\":\n batched_images = []\n\n for image in value:\n batched_images.append(batch_size * [image])\n\n batched_inputs[name] = batched_images\n else:\n batched_inputs[name] = batch_size * [value]\n\n elif name == \"batch_size\":\n batched_inputs[name] = batch_size\n else:\n batched_inputs[name] = value\n\n for arg in additional_params_copy_to_batched_inputs:\n batched_inputs[arg] = inputs[arg]\n\n batched_inputs[\"output_type\"] = \"np\"\n\n if self.pipeline_class.__name__ == \"DanceDiffusionPipeline\":\n batched_inputs.pop(\"output_type\")\n\n output = pipe(**batched_inputs)\n\n assert len(output[0]) == batch_size\n\n batched_inputs[\"output_type\"] = \"np\"\n\n if self.pipeline_class.__name__ == \"DanceDiffusionPipeline\":\n batched_inputs.pop(\"output_type\")\n\n output = pipe(**batched_inputs)[0]\n\n assert output.shape[0] == batch_size\n\n logger.setLevel(level=diffusers.logging.WARNING)\n\n def test_num_images_per_prompt(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n batch_sizes = [1, 2]\n num_images_per_prompts = [1, 2]\n\n for batch_size in batch_sizes:\n for num_images_per_prompt in num_images_per_prompts:\n inputs = self.get_dummy_inputs(torch_device)\n\n for key in inputs.keys():\n if key in self.batch_params:\n if key == \"image\":\n batched_images = []\n\n for image in inputs[key]:\n batched_images.append(batch_size * [image])\n\n inputs[key] = batched_images\n else:\n inputs[key] = batch_size * [inputs[key]]\n\n images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt)[0]\n\n assert images.shape[0] == batch_size * num_images_per_prompt\n\n def test_inference_batch_single_identical(\n self,\n batch_size=3,\n test_max_difference=None,\n test_mean_pixel_difference=None,\n relax_max_difference=False,\n expected_max_diff=2e-3,\n additional_params_copy_to_batched_inputs=[\"num_inference_steps\"],\n ):\n if test_max_difference is None:\n # TODO(Pedro) - not sure why, but not at all reproducible at the moment it seems\n # make sure that batched and non-batched is identical\n test_max_difference = torch_device != \"mps\"\n\n if test_mean_pixel_difference is None:\n # TODO same as above\n test_mean_pixel_difference = torch_device != \"mps\"\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n\n logger = logging.get_logger(pipe.__module__)\n logger.setLevel(level=diffusers.logging.FATAL)\n\n # batchify inputs\n batched_inputs = {}\n batch_size = batch_size\n for name, value in inputs.items():\n if name in self.batch_params:\n # prompt is string\n if name == \"prompt\":\n len_prompt = len(value)\n # make unequal batch sizes\n batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]\n\n # make last batch super long\n batched_inputs[name][-1] = 100 * \"very long\"\n elif name == \"image\":\n batched_images = []\n\n for image in value:\n batched_images.append(batch_size * [image])\n\n batched_inputs[name] = batched_images\n else:\n batched_inputs[name] = batch_size * [value]\n elif name == \"batch_size\":\n batched_inputs[name] = batch_size\n elif name == \"generator\":\n batched_inputs[name] = [self.get_generator(i) for i in range(batch_size)]\n else:\n batched_inputs[name] = value\n\n for arg in additional_params_copy_to_batched_inputs:\n batched_inputs[arg] = inputs[arg]\n\n if self.pipeline_class.__name__ != \"DanceDiffusionPipeline\":\n batched_inputs[\"output_type\"] = \"np\"\n\n output_batch = pipe(**batched_inputs)\n assert output_batch[0].shape[0] == batch_size\n\n inputs[\"generator\"] = self.get_generator(0)\n\n output = pipe(**inputs)\n\n logger.setLevel(level=diffusers.logging.WARNING)\n if test_max_difference:\n if relax_max_difference:\n # Taking the median of the largest differences\n # is resilient to outliers\n diff = np.abs(output_batch[0][0] - output[0][0])\n diff = diff.flatten()\n diff.sort()\n max_diff = np.median(diff[-5:])\n else:\n max_diff = np.abs(output_batch[0][0] - output[0][0]).max()\n assert max_diff < expected_max_diff\n\n if test_mean_pixel_difference:\n assert_mean_pixel_difference(output_batch[0][0], output[0][0])\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionAdapterPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_stable_diffusion_adapter_color(self):\n adapter_model = \"TencentARC/t2iadapter_color_sd14v1\"\n sd_model = \"CompVis/stable-diffusion-v1-4\"\n prompt = \"snail\"\n image_url = (\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/color.png\"\n )\n input_channels = 3\n out_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_color_sd14v1.npy\"\n\n image = load_image(image_url)\n expected_out = load_numpy(out_url)\n if input_channels == 1:\n image = image.convert(\"L\")\n\n adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)\n\n pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type=\"np\").images\n\n max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())\n assert max_diff < 1e-2\n\n def test_stable_diffusion_adapter_depth(self):\n adapter_model = \"TencentARC/t2iadapter_depth_sd14v1\"\n sd_model = \"CompVis/stable-diffusion-v1-4\"\n prompt = \"snail\"\n image_url = (\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/color.png\"\n )\n input_channels = 3\n out_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_color_sd14v1.npy\"\n\n image = load_image(image_url)\n expected_out = load_numpy(out_url)\n if input_channels == 1:\n image = image.convert(\"L\")\n\n adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)\n\n pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type=\"np\").images\n\n max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())\n assert max_diff < 1e-2\n\n def test_stable_diffusion_adapter_depth_sd_v14(self):\n adapter_model = \"TencentARC/t2iadapter_depth_sd14v1\"\n sd_model = \"CompVis/stable-diffusion-v1-4\"\n prompt = \"desk\"\n image_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/desk_depth.png\"\n input_channels = 3\n out_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_depth_sd14v1.npy\"\n\n image = load_image(image_url)\n expected_out = load_numpy(out_url)\n if input_channels == 1:\n image = image.convert(\"L\")\n\n adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)\n\n pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type=\"np\").images\n\n max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())\n assert max_diff < 1e-2\n\n def test_stable_diffusion_adapter_depth_sd_v15(self):\n adapter_model = \"TencentARC/t2iadapter_depth_sd15v2\"\n sd_model = \"runwayml/stable-diffusion-v1-5\"\n prompt = \"desk\"\n image_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/desk_depth.png\"\n input_channels = 3\n out_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_depth_sd15v2.npy\"\n\n image = load_image(image_url)\n expected_out = load_numpy(out_url)\n if input_channels == 1:\n image = image.convert(\"L\")\n\n adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)\n\n pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type=\"np\").images\n\n max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())\n assert max_diff < 1e-2\n\n def test_stable_diffusion_adapter_keypose_sd_v14(self):\n adapter_model = \"TencentARC/t2iadapter_keypose_sd14v1\"\n sd_model = \"CompVis/stable-diffusion-v1-4\"\n prompt = \"person\"\n image_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/person_keypose.png\"\n input_channels = 3\n out_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_keypose_sd14v1.npy\"\n\n image = load_image(image_url)\n expected_out = load_numpy(out_url)\n if input_channels == 1:\n image = image.convert(\"L\")\n\n adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)\n\n pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type=\"np\").images\n\n max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())\n assert max_diff < 1e-2\n\n def test_stable_diffusion_adapter_openpose_sd_v14(self):\n adapter_model = \"TencentARC/t2iadapter_openpose_sd14v1\"\n sd_model = \"CompVis/stable-diffusion-v1-4\"\n prompt = \"person\"\n image_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/iron_man_pose.png\"\n input_channels = 3\n out_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_openpose_sd14v1.npy\"\n\n image = load_image(image_url)\n expected_out = load_numpy(out_url)\n if input_channels == 1:\n image = image.convert(\"L\")\n\n adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)\n\n pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type=\"np\").images\n\n max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())\n assert max_diff < 1e-2\n\n def test_stable_diffusion_adapter_seg_sd_v14(self):\n adapter_model = \"TencentARC/t2iadapter_seg_sd14v1\"\n sd_model = \"CompVis/stable-diffusion-v1-4\"\n prompt = \"motorcycle\"\n image_url = (\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/motor.png\"\n )\n input_channels = 3\n out_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_seg_sd14v1.npy\"\n\n image = load_image(image_url)\n expected_out = load_numpy(out_url)\n if input_channels == 1:\n image = image.convert(\"L\")\n\n adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)\n\n pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type=\"np\").images\n\n max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())\n assert max_diff < 1e-2\n\n def test_stable_diffusion_adapter_zoedepth_sd_v15(self):\n adapter_model = \"TencentARC/t2iadapter_zoedepth_sd15v1\"\n sd_model = \"runwayml/stable-diffusion-v1-5\"\n prompt = \"motorcycle\"\n image_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/motorcycle.png\"\n input_channels = 3\n out_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_zoedepth_sd15v1.npy\"\n\n image = load_image(image_url)\n expected_out = load_numpy(out_url)\n if input_channels == 1:\n image = image.convert(\"L\")\n\n adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)\n\n pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_model_cpu_offload()\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type=\"np\").images\n\n max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())\n assert max_diff < 1e-2\n\n def test_stable_diffusion_adapter_canny_sd_v14(self):\n adapter_model = \"TencentARC/t2iadapter_canny_sd14v1\"\n sd_model = \"CompVis/stable-diffusion-v1-4\"\n prompt = \"toy\"\n image_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/toy_canny.png\"\n input_channels = 1\n out_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_canny_sd14v1.npy\"\n\n image = load_image(image_url)\n expected_out = load_numpy(out_url)\n if input_channels == 1:\n image = image.convert(\"L\")\n\n adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)\n\n pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n\n out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type=\"np\").images\n\n max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())\n assert max_diff < 1e-2\n\n def test_stable_diffusion_adapter_canny_sd_v15(self):\n adapter_model = \"TencentARC/t2iadapter_canny_sd15v2\"\n sd_model = \"runwayml/stable-diffusion-v1-5\"\n prompt = \"toy\"\n image_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/toy_canny.png\"\n input_channels = 1\n out_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_canny_sd15v2.npy\"\n\n image = load_image(image_url)\n expected_out = load_numpy(out_url)\n if input_channels == 1:\n image = image.convert(\"L\")\n\n adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)\n\n pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)\n\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n\n out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type=\"np\").images\n\n max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())\n assert max_diff < 1e-2\n\n def test_stable_diffusion_adapter_sketch_sd14(self):\n adapter_model = \"TencentARC/t2iadapter_sketch_sd14v1\"\n sd_model = \"CompVis/stable-diffusion-v1-4\"\n prompt = \"cat\"\n image_url = (\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/edge.png\"\n )\n input_channels = 1\n out_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_sketch_sd14v1.npy\"\n\n image = load_image(image_url)\n expected_out = load_numpy(out_url)\n if input_channels == 1:\n image = image.convert(\"L\")\n\n adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)\n\n pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)\n\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n\n out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type=\"np\").images\n\n max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())\n assert max_diff < 1e-2\n\n def test_stable_diffusion_adapter_sketch_sd15(self):\n adapter_model = \"TencentARC/t2iadapter_sketch_sd15v2\"\n sd_model = \"runwayml/stable-diffusion-v1-5\"\n prompt = \"cat\"\n image_url = (\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/edge.png\"\n )\n input_channels = 1\n out_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_sketch_sd15v2.npy\"\n\n image = load_image(image_url)\n expected_out = load_numpy(out_url)\n if input_channels == 1:\n image = image.convert(\"L\")\n\n adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)\n\n pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)\n\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n\n out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type=\"np\").images\n\n max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())\n assert max_diff < 1e-2\n\n def test_stable_diffusion_adapter_pipeline_with_sequential_cpu_offloading(self):\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n adapter = T2IAdapter.from_pretrained(\"TencentARC/t2iadapter_seg_sd14v1\")\n pipe = StableDiffusionAdapterPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\", adapter=adapter, safety_checker=None\n )\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing(1)\n pipe.enable_sequential_cpu_offload()\n\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/motor.png\"\n )\n\n pipe(prompt=\"foo\", image=image, num_inference_steps=2)\n\n mem_bytes = torch.cuda.max_memory_allocated()\n assert mem_bytes < 5 * 10**9\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_gligen/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/stable_diffusion_gligen/test_stable_diffusion_gligen.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n EulerAncestralDiscreteScheduler,\n StableDiffusionGLIGENPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.utils.testing_utils import enable_full_determinism\n\nfrom ..pipeline_params import (\n TEXT_TO_IMAGE_BATCH_PARAMS,\n TEXT_TO_IMAGE_IMAGE_PARAMS,\n TEXT_TO_IMAGE_PARAMS,\n)\nfrom ..test_pipelines_common import (\n PipelineFromPipeTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n)\n\n\nenable_full_determinism()\n\n\nclass GligenPipelineFastTests(\n PipelineLatentTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineTesterMixin,\n PipelineFromPipeTesterMixin,\n unittest.TestCase,\n):\n pipeline_class = StableDiffusionGLIGENPipeline\n params = TEXT_TO_IMAGE_PARAMS | {\"gligen_phrases\", \"gligen_boxes\"}\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n image_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n attention_type=\"gated\",\n )\n # unet.position_net = PositionNet(32,32)\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n sample_size=128,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A modern livingroom\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"gligen_phrases\": [\"a birthday cake\"],\n \"gligen_boxes\": [[0.2676, 0.6088, 0.4773, 0.7183]],\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_stable_diffusion_gligen_default_case(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionGLIGENPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.5069, 0.5561, 0.4577, 0.4792, 0.5203, 0.4089, 0.5039, 0.4919, 0.4499])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_gligen_k_euler_ancestral(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionGLIGENPipeline(**components)\n sd_pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = sd_pipe(**inputs)\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.425, 0.494, 0.429, 0.469, 0.525, 0.417, 0.533, 0.5, 0.47])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_attention_slicing_forward_pass(self):\n super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(batch_size=3, expected_max_diff=3e-3)\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_gligen_text_image/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/stable_diffusion_gligen_text_image/test_stable_diffusion_gligen_text_image.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import (\n CLIPProcessor,\n CLIPTextConfig,\n CLIPTextModel,\n CLIPTokenizer,\n CLIPVisionConfig,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n EulerAncestralDiscreteScheduler,\n StableDiffusionGLIGENTextImagePipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.pipelines.stable_diffusion import CLIPImageProjection\nfrom diffusers.utils import load_image\nfrom diffusers.utils.testing_utils import enable_full_determinism, torch_device\n\nfrom ..pipeline_params import (\n TEXT_TO_IMAGE_BATCH_PARAMS,\n TEXT_TO_IMAGE_IMAGE_PARAMS,\n TEXT_TO_IMAGE_PARAMS,\n)\nfrom ..test_pipelines_common import (\n PipelineFromPipeTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n)\n\n\nenable_full_determinism()\n\n\nclass GligenTextImagePipelineFastTests(\n PipelineLatentTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineTesterMixin,\n PipelineFromPipeTesterMixin,\n unittest.TestCase,\n):\n pipeline_class = StableDiffusionGLIGENTextImagePipeline\n params = TEXT_TO_IMAGE_PARAMS | {\"gligen_phrases\", \"gligen_images\", \"gligen_boxes\"}\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n image_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n attention_type=\"gated-text-image\",\n )\n # unet.position_net = PositionNet(32,32)\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n sample_size=128,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n image_encoder_config = CLIPVisionConfig(\n hidden_size=32,\n projection_dim=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n )\n image_encoder = CLIPVisionModelWithProjection(image_encoder_config)\n processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-large-patch14\")\n\n image_project = CLIPImageProjection(hidden_size=32)\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": image_encoder,\n \"image_project\": image_project,\n \"processor\": processor,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n gligen_images = load_image(\n \"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/gligen/livingroom_modern.png\"\n )\n inputs = {\n \"prompt\": \"A modern livingroom\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"gligen_phrases\": [\"a birthday cake\"],\n \"gligen_images\": [gligen_images],\n \"gligen_boxes\": [[0.2676, 0.6088, 0.4773, 0.7183]],\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_dict_tuple_outputs_equivalent(self):\n expected_slice = None\n if torch_device == \"cpu\":\n expected_slice = np.array([0.5052, 0.5546, 0.4567, 0.4770, 0.5195, 0.4085, 0.5026, 0.4909, 0.4495])\n super().test_dict_tuple_outputs_equivalent(expected_slice=expected_slice)\n\n def test_stable_diffusion_gligen_text_image_default_case(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionGLIGENTextImagePipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.5069, 0.5561, 0.4577, 0.4792, 0.5203, 0.4089, 0.5039, 0.4919, 0.4499])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_gligen_k_euler_ancestral(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionGLIGENTextImagePipeline(**components)\n sd_pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.425, 0.494, 0.429, 0.469, 0.525, 0.417, 0.533, 0.5, 0.47])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_attention_slicing_forward_pass(self):\n super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(batch_size=3, expected_max_diff=3e-3)\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_image_variation/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/stable_diffusion_image_variation/test_stable_diffusion_image_variation.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection\n\nfrom diffusers import (\n AutoencoderKL,\n DPMSolverMultistepScheduler,\n PNDMScheduler,\n StableDiffusionImageVariationPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n load_numpy,\n nightly,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\nfrom ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS\nfrom ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass StableDiffusionImageVariationPipelineFastTests(\n PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase\n):\n pipeline_class = StableDiffusionImageVariationPipeline\n params = IMAGE_VARIATION_PARAMS\n batch_params = IMAGE_VARIATION_BATCH_PARAMS\n image_params = frozenset([])\n # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess\n image_latents_params = frozenset([])\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n )\n scheduler = PNDMScheduler(skip_prk_steps=True)\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n image_encoder_config = CLIPVisionConfig(\n hidden_size=32,\n projection_dim=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n image_size=32,\n patch_size=4,\n )\n image_encoder = CLIPVisionModelWithProjection(image_encoder_config)\n feature_extractor = CLIPImageProcessor(crop_size=32, size=32)\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"image_encoder\": image_encoder,\n \"feature_extractor\": feature_extractor,\n \"safety_checker\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed))\n image = image.cpu().permute(0, 2, 3, 1)[0]\n image = Image.fromarray(np.uint8(image)).convert(\"RGB\").resize((32, 32))\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"image\": image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_stable_diffusion_img_variation_default_case(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionImageVariationPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.5239, 0.5723, 0.4796, 0.5049, 0.5550, 0.4685, 0.5329, 0.4891, 0.4921])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_stable_diffusion_img_variation_multiple_images(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionImageVariationPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"image\"] = 2 * [inputs[\"image\"]]\n output = sd_pipe(**inputs)\n\n image = output.images\n\n image_slice = image[-1, -3:, -3:, -1]\n\n assert image.shape == (2, 64, 64, 3)\n expected_slice = np.array([0.6892, 0.5637, 0.5836, 0.5771, 0.6254, 0.6409, 0.5580, 0.5569, 0.5289])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=3e-3)\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionImageVariationPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n init_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_imgvar/input_image_vermeer.png\"\n )\n latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))\n latents = torch.from_numpy(latents).to(device=device, dtype=dtype)\n inputs = {\n \"image\": init_image,\n \"latents\": latents,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_stable_diffusion_img_variation_pipeline_default(self):\n sd_pipe = StableDiffusionImageVariationPipeline.from_pretrained(\n \"lambdalabs/sd-image-variations-diffusers\", safety_checker=None\n )\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n generator_device = \"cpu\"\n inputs = self.get_inputs(generator_device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.8449, 0.9079, 0.7571, 0.7873, 0.8348, 0.7010, 0.6694, 0.6873, 0.6138])\n\n max_diff = numpy_cosine_similarity_distance(image_slice, expected_slice)\n assert max_diff < 1e-4\n\n def test_stable_diffusion_img_variation_intermediate_state(self):\n number_of_steps = 0\n\n def callback_fn(step: int, timestep: int, latents: torch.Tensor) -> None:\n callback_fn.has_been_called = True\n nonlocal number_of_steps\n number_of_steps += 1\n if step == 1:\n latents = latents.detach().cpu().numpy()\n assert latents.shape == (1, 4, 64, 64)\n latents_slice = latents[0, -3:, -3:, -1]\n expected_slice = np.array([-0.7974, -0.4343, -1.087, 0.04785, -1.327, 0.855, -2.148, -0.1725, 1.439])\n max_diff = numpy_cosine_similarity_distance(latents_slice.flatten(), expected_slice)\n\n assert max_diff < 1e-3\n\n elif step == 2:\n latents = latents.detach().cpu().numpy()\n assert latents.shape == (1, 4, 64, 64)\n latents_slice = latents[0, -3:, -3:, -1]\n expected_slice = np.array([0.3232, 0.004883, 0.913, -1.084, 0.6143, -1.6875, -2.463, -0.439, -0.419])\n max_diff = numpy_cosine_similarity_distance(latents_slice.flatten(), expected_slice)\n\n assert max_diff < 1e-3\n\n callback_fn.has_been_called = False\n\n pipe = StableDiffusionImageVariationPipeline.from_pretrained(\n \"lambdalabs/sd-image-variations-diffusers\",\n safety_checker=None,\n torch_dtype=torch.float16,\n )\n\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n\n generator_device = \"cpu\"\n inputs = self.get_inputs(generator_device, dtype=torch.float16)\n pipe(**inputs, callback=callback_fn, callback_steps=1)\n assert callback_fn.has_been_called\n assert number_of_steps == inputs[\"num_inference_steps\"]\n\n def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n pipe = StableDiffusionImageVariationPipeline.from_pretrained(\n \"lambdalabs/sd-image-variations-diffusers\", safety_checker=None, torch_dtype=torch.float16\n )\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing(1)\n pipe.enable_sequential_cpu_offload()\n\n inputs = self.get_inputs(torch_device, dtype=torch.float16)\n _ = pipe(**inputs)\n\n mem_bytes = torch.cuda.max_memory_allocated()\n # make sure that less than 2.6 GB is allocated\n assert mem_bytes < 2.6 * 10**9\n\n\n@nightly\n@require_torch_gpu\nclass StableDiffusionImageVariationPipelineNightlyTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n init_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_imgvar/input_image_vermeer.png\"\n )\n latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))\n latents = torch.from_numpy(latents).to(device=device, dtype=dtype)\n inputs = {\n \"image\": init_image,\n \"latents\": latents,\n \"generator\": generator,\n \"num_inference_steps\": 50,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_img_variation_pndm(self):\n sd_pipe = StableDiffusionImageVariationPipeline.from_pretrained(\"fusing/sd-image-variations-diffusers\")\n sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n image = sd_pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n\n def test_img_variation_dpm(self):\n sd_pipe = StableDiffusionImageVariationPipeline.from_pretrained(\"fusing/sd-image-variations-diffusers\")\n sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n inputs[\"num_inference_steps\"] = 25\n image = sd_pipe(**inputs).images[0]\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy\"\n )\n max_diff = np.abs(expected_image - image).max()\n assert max_diff < 1e-3\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_k_diffusion/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/stable_diffusion_k_diffusion/test_stable_diffusion_k_diffusion.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nimport numpy as np\nimport torch\n\nfrom diffusers import StableDiffusionKDiffusionPipeline\nfrom diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device\n\n\nenable_full_determinism()\n\n\n@nightly\n@require_torch_gpu\nclass StableDiffusionPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_stable_diffusion_1(self):\n sd_pipe = StableDiffusionKDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\")\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n sd_pipe.set_scheduler(\"sample_euler\")\n\n prompt = \"A painting of a squirrel eating a burger\"\n generator = torch.manual_seed(0)\n output = sd_pipe([prompt], generator=generator, guidance_scale=9.0, num_inference_steps=20, output_type=\"np\")\n\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_2(self):\n sd_pipe = StableDiffusionKDiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-1-base\")\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n sd_pipe.set_scheduler(\"sample_euler\")\n\n prompt = \"A painting of a squirrel eating a burger\"\n generator = torch.manual_seed(0)\n output = sd_pipe([prompt], generator=generator, guidance_scale=9.0, num_inference_steps=20, output_type=\"np\")\n\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-1\n\n def test_stable_diffusion_karras_sigmas(self):\n sd_pipe = StableDiffusionKDiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-1-base\")\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n sd_pipe.set_scheduler(\"sample_dpmpp_2m\")\n\n prompt = \"A painting of a squirrel eating a burger\"\n generator = torch.manual_seed(0)\n output = sd_pipe(\n [prompt],\n generator=generator,\n guidance_scale=7.5,\n num_inference_steps=15,\n output_type=\"np\",\n use_karras_sigmas=True,\n )\n\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array(\n [0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048]\n )\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_noise_sampler_seed(self):\n sd_pipe = StableDiffusionKDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\")\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n sd_pipe.set_scheduler(\"sample_dpmpp_sde\")\n\n prompt = \"A painting of a squirrel eating a burger\"\n seed = 0\n images1 = sd_pipe(\n [prompt],\n generator=torch.manual_seed(seed),\n noise_sampler_seed=seed,\n guidance_scale=9.0,\n num_inference_steps=20,\n output_type=\"np\",\n ).images\n images2 = sd_pipe(\n [prompt],\n generator=torch.manual_seed(seed),\n noise_sampler_seed=seed,\n guidance_scale=9.0,\n num_inference_steps=20,\n output_type=\"np\",\n ).images\n\n assert images1.shape == (1, 512, 512, 3)\n assert images2.shape == (1, 512, 512, 3)\n assert np.abs(images1.flatten() - images2.flatten()).max() < 1e-2\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_ldm3d/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/stable_diffusion_ldm3d/test_stable_diffusion_ldm3d.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nimport gc\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n PNDMScheduler,\n StableDiffusionLDM3DPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device\n\nfrom ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS\n\n\nenable_full_determinism()\n\n\nclass StableDiffusionLDM3DPipelineFastTests(unittest.TestCase):\n pipeline_class = StableDiffusionLDM3DPipeline\n params = TEXT_TO_IMAGE_PARAMS\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n image_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n )\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=6,\n out_channels=6,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_stable_diffusion_ddim(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components()\n ldm3d_pipe = StableDiffusionLDM3DPipeline(**components)\n ldm3d_pipe = ldm3d_pipe.to(torch_device)\n ldm3d_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = ldm3d_pipe(**inputs)\n rgb, depth = output.rgb, output.depth\n\n image_slice_rgb = rgb[0, -3:, -3:, -1]\n image_slice_depth = depth[0, -3:, -1]\n\n assert rgb.shape == (1, 64, 64, 3)\n assert depth.shape == (1, 64, 64)\n\n expected_slice_rgb = np.array(\n [0.37338176, 0.70247, 0.74203193, 0.51643604, 0.58256793, 0.60932136, 0.4181095, 0.48355877, 0.46535262]\n )\n expected_slice_depth = np.array([103.46727, 85.812004, 87.849236])\n\n assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb).max() < 1e-2\n assert np.abs(image_slice_depth.flatten() - expected_slice_depth).max() < 1e-2\n\n def test_stable_diffusion_prompt_embeds(self):\n components = self.get_dummy_components()\n ldm3d_pipe = StableDiffusionLDM3DPipeline(**components)\n ldm3d_pipe = ldm3d_pipe.to(torch_device)\n ldm3d_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"prompt\"] = 3 * [inputs[\"prompt\"]]\n\n # forward\n output = ldm3d_pipe(**inputs)\n rgb_slice_1, depth_slice_1 = output.rgb, output.depth\n rgb_slice_1 = rgb_slice_1[0, -3:, -3:, -1]\n depth_slice_1 = depth_slice_1[0, -3:, -1]\n\n inputs = self.get_dummy_inputs(torch_device)\n prompt = 3 * [inputs.pop(\"prompt\")]\n\n text_inputs = ldm3d_pipe.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=ldm3d_pipe.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_inputs = text_inputs[\"input_ids\"].to(torch_device)\n\n prompt_embeds = ldm3d_pipe.text_encoder(text_inputs)[0]\n\n inputs[\"prompt_embeds\"] = prompt_embeds\n\n # forward\n output = ldm3d_pipe(**inputs)\n rgb_slice_2, depth_slice_2 = output.rgb, output.depth\n rgb_slice_2 = rgb_slice_2[0, -3:, -3:, -1]\n depth_slice_2 = depth_slice_2[0, -3:, -1]\n\n assert np.abs(rgb_slice_1.flatten() - rgb_slice_2.flatten()).max() < 1e-4\n assert np.abs(depth_slice_1.flatten() - depth_slice_2.flatten()).max() < 1e-4\n\n def test_stable_diffusion_negative_prompt(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n components[\"scheduler\"] = PNDMScheduler(skip_prk_steps=True)\n ldm3d_pipe = StableDiffusionLDM3DPipeline(**components)\n ldm3d_pipe = ldm3d_pipe.to(device)\n ldm3d_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n negative_prompt = \"french fries\"\n output = ldm3d_pipe(**inputs, negative_prompt=negative_prompt)\n\n rgb, depth = output.rgb, output.depth\n rgb_slice = rgb[0, -3:, -3:, -1]\n depth_slice = depth[0, -3:, -1]\n\n assert rgb.shape == (1, 64, 64, 3)\n assert depth.shape == (1, 64, 64)\n\n expected_slice_rgb = np.array(\n [0.37044, 0.71811503, 0.7223251, 0.48603675, 0.5638391, 0.6364948, 0.42833704, 0.4901315, 0.47926217]\n )\n expected_slice_depth = np.array([107.84738, 84.62802, 89.962135])\n assert np.abs(rgb_slice.flatten() - expected_slice_rgb).max() < 1e-2\n assert np.abs(depth_slice.flatten() - expected_slice_depth).max() < 1e-2\n\n\n@nightly\n@require_torch_gpu\nclass StableDiffusionLDM3DPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))\n latents = torch.from_numpy(latents).to(device=device, dtype=dtype)\n inputs = {\n \"prompt\": \"a photograph of an astronaut riding a horse\",\n \"latents\": latents,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_ldm3d_stable_diffusion(self):\n ldm3d_pipe = StableDiffusionLDM3DPipeline.from_pretrained(\"Intel/ldm3d\")\n ldm3d_pipe = ldm3d_pipe.to(torch_device)\n ldm3d_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n output = ldm3d_pipe(**inputs)\n rgb, depth = output.rgb, output.depth\n rgb_slice = rgb[0, -3:, -3:, -1].flatten()\n depth_slice = rgb[0, -3:, -1].flatten()\n\n assert rgb.shape == (1, 512, 512, 3)\n assert depth.shape == (1, 512, 512)\n\n expected_slice_rgb = np.array(\n [0.53805465, 0.56707305, 0.5486515, 0.57012236, 0.5814511, 0.56253487, 0.54843014, 0.55092263, 0.6459706]\n )\n expected_slice_depth = np.array(\n [0.9263781, 0.6678672, 0.5486515, 0.92202145, 0.67831135, 0.56253487, 0.9241694, 0.7551478, 0.6459706]\n )\n assert np.abs(rgb_slice - expected_slice_rgb).max() < 3e-3\n assert np.abs(depth_slice - expected_slice_depth).max() < 3e-3\n\n\n@nightly\n@require_torch_gpu\nclass StableDiffusionPipelineNightlyTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))\n latents = torch.from_numpy(latents).to(device=device, dtype=dtype)\n inputs = {\n \"prompt\": \"a photograph of an astronaut riding a horse\",\n \"latents\": latents,\n \"generator\": generator,\n \"num_inference_steps\": 50,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_ldm3d(self):\n ldm3d_pipe = StableDiffusionLDM3DPipeline.from_pretrained(\"Intel/ldm3d\").to(torch_device)\n ldm3d_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n output = ldm3d_pipe(**inputs)\n rgb, depth = output.rgb, output.depth\n\n expected_rgb_mean = 0.495586\n expected_rgb_std = 0.33795515\n expected_depth_mean = 112.48518\n expected_depth_std = 98.489746\n assert np.abs(expected_rgb_mean - rgb.mean()) < 1e-3\n assert np.abs(expected_rgb_std - rgb.std()) < 1e-3\n assert np.abs(expected_depth_mean - depth.mean()) < 1e-3\n assert np.abs(expected_depth_std - depth.std()) < 1e-3\n\n def test_ldm3d_v2(self):\n ldm3d_pipe = StableDiffusionLDM3DPipeline.from_pretrained(\"Intel/ldm3d-4c\").to(torch_device)\n ldm3d_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n output = ldm3d_pipe(**inputs)\n rgb, depth = output.rgb, output.depth\n\n expected_rgb_mean = 0.4194127\n expected_rgb_std = 0.35375586\n expected_depth_mean = 0.5638502\n expected_depth_std = 0.34686103\n\n assert rgb.shape == (1, 512, 512, 3)\n assert depth.shape == (1, 512, 512, 1)\n assert np.abs(expected_rgb_mean - rgb.mean()) < 1e-3\n assert np.abs(expected_rgb_std - rgb.std()) < 1e-3\n assert np.abs(expected_depth_mean - depth.mean()) < 1e-3\n assert np.abs(expected_depth_std - depth.std()) < 1e-3\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_panorama/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/stable_diffusion_panorama/test_stable_diffusion_panorama.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n EulerAncestralDiscreteScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n StableDiffusionPanoramaPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, skip_mps, torch_device\n\nfrom ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS\nfrom ..test_pipelines_common import (\n IPAdapterTesterMixin,\n PipelineFromPipeTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n)\n\n\nenable_full_determinism()\n\n\n@skip_mps\nclass StableDiffusionPanoramaPipelineFastTests(\n IPAdapterTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n PipelineFromPipeTesterMixin,\n unittest.TestCase,\n):\n pipeline_class = StableDiffusionPanoramaPipeline\n params = TEXT_TO_IMAGE_PARAMS\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n image_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=1,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n )\n scheduler = DDIMScheduler()\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"a photo of the dolomites\",\n \"generator\": generator,\n # Setting height and width to None to prevent OOMs on CPU.\n \"height\": None,\n \"width\": None,\n \"num_inference_steps\": 1,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_stable_diffusion_panorama_default_case(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionPanoramaPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_panorama_circular_padding_case(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionPanoramaPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs, circular_padding=True).images\n image_slice = image[0, -3:, -3:, -1]\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.6127, 0.6299, 0.4595, 0.4051, 0.4543, 0.3925, 0.5510, 0.5693, 0.5031])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n # override to speed the overall test timing up.\n def test_inference_batch_consistent(self):\n super().test_inference_batch_consistent(batch_sizes=[1, 2])\n\n # override to speed the overall test timing up.\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(batch_size=2, expected_max_diff=5.0e-3)\n\n def test_float16_inference(self):\n super().test_float16_inference(expected_max_diff=1e-1)\n\n def test_stable_diffusion_panorama_negative_prompt(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionPanoramaPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n negative_prompt = \"french fries\"\n output = sd_pipe(**inputs, negative_prompt=negative_prompt)\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_panorama_views_batch(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionPanoramaPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = sd_pipe(**inputs, view_batch_size=2)\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_panorama_views_batch_circular_padding(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionPanoramaPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = sd_pipe(**inputs, circular_padding=True, view_batch_size=2)\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.6127, 0.6299, 0.4595, 0.4051, 0.4543, 0.3925, 0.5510, 0.5693, 0.5031])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_panorama_euler(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n components[\"scheduler\"] = EulerAncestralDiscreteScheduler(\n beta_start=0.00085, beta_end=0.012, beta_schedule=\"scaled_linear\"\n )\n sd_pipe = StableDiffusionPanoramaPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_panorama_pndm(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n components[\"scheduler\"] = PNDMScheduler(\n beta_start=0.00085, beta_end=0.012, beta_schedule=\"scaled_linear\", skip_prk_steps=True\n )\n sd_pipe = StableDiffusionPanoramaPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n\n@nightly\n@require_torch_gpu\nclass StableDiffusionPanoramaNightlyTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, seed=0):\n generator = torch.manual_seed(seed)\n inputs = {\n \"prompt\": \"a photo of the dolomites\",\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_stable_diffusion_panorama_default(self):\n model_ckpt = \"stabilityai/stable-diffusion-2-base\"\n scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder=\"scheduler\")\n pipe = StableDiffusionPanoramaPipeline.from_pretrained(model_ckpt, scheduler=scheduler, safety_checker=None)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 2048, 3)\n\n expected_slice = np.array(\n [\n 0.36968392,\n 0.27025372,\n 0.32446766,\n 0.28379387,\n 0.36363274,\n 0.30733347,\n 0.27100027,\n 0.27054125,\n 0.25536096,\n ]\n )\n\n assert np.abs(expected_slice - image_slice).max() < 1e-2\n\n def test_stable_diffusion_panorama_k_lms(self):\n pipe = StableDiffusionPanoramaPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-2-base\", safety_checker=None\n )\n pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)\n pipe.unet.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n assert image.shape == (1, 512, 2048, 3)\n\n expected_slice = np.array(\n [\n [\n 0.0,\n 0.0,\n 0.0,\n 0.0,\n 0.0,\n 0.0,\n 0.0,\n 0.0,\n 0.0,\n ]\n ]\n )\n\n assert np.abs(expected_slice - image_slice).max() < 1e-2\n\n def test_stable_diffusion_panorama_intermediate_state(self):\n number_of_steps = 0\n\n def callback_fn(step: int, timestep: int, latents: torch.Tensor) -> None:\n callback_fn.has_been_called = True\n nonlocal number_of_steps\n number_of_steps += 1\n if step == 1:\n latents = latents.detach().cpu().numpy()\n assert latents.shape == (1, 4, 64, 256)\n latents_slice = latents[0, -3:, -3:, -1]\n\n expected_slice = np.array(\n [\n 0.18681869,\n 0.33907816,\n 0.5361276,\n 0.14432865,\n -0.02856611,\n -0.73941123,\n 0.23397987,\n 0.47322682,\n -0.37823164,\n ]\n )\n assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2\n elif step == 2:\n latents = latents.detach().cpu().numpy()\n assert latents.shape == (1, 4, 64, 256)\n latents_slice = latents[0, -3:, -3:, -1]\n\n expected_slice = np.array(\n [\n 0.18539645,\n 0.33987248,\n 0.5378559,\n 0.14437142,\n -0.02455261,\n -0.7338317,\n 0.23990755,\n 0.47356272,\n -0.3786505,\n ]\n )\n\n assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2\n\n callback_fn.has_been_called = False\n\n model_ckpt = \"stabilityai/stable-diffusion-2-base\"\n scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder=\"scheduler\")\n pipe = StableDiffusionPanoramaPipeline.from_pretrained(model_ckpt, scheduler=scheduler, safety_checker=None)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs()\n pipe(**inputs, callback=callback_fn, callback_steps=1)\n assert callback_fn.has_been_called\n assert number_of_steps == 3\n\n def test_stable_diffusion_panorama_pipeline_with_sequential_cpu_offloading(self):\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n model_ckpt = \"stabilityai/stable-diffusion-2-base\"\n scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder=\"scheduler\")\n pipe = StableDiffusionPanoramaPipeline.from_pretrained(model_ckpt, scheduler=scheduler, safety_checker=None)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing(1)\n pipe.enable_sequential_cpu_offload()\n\n inputs = self.get_inputs()\n _ = pipe(**inputs)\n\n mem_bytes = torch.cuda.max_memory_allocated()\n # make sure that less than 5.2 GB is allocated\n assert mem_bytes < 5.5 * 10**9\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_safe/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/stable_diffusion_safe/test_safe_diffusion.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNet2DConditionModel\nfrom diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline\nfrom diffusers.utils.testing_utils import floats_tensor, nightly, require_torch_gpu, torch_device\n\n\nclass SafeDiffusionPipelineFastTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n @property\n def dummy_image(self):\n batch_size = 1\n num_channels = 3\n sizes = (32, 32)\n\n image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)\n return image\n\n @property\n def dummy_cond_unet(self):\n torch.manual_seed(0)\n model = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n )\n return model\n\n @property\n def dummy_vae(self):\n torch.manual_seed(0)\n model = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n return model\n\n @property\n def dummy_text_encoder(self):\n torch.manual_seed(0)\n config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n return CLIPTextModel(config)\n\n @property\n def dummy_extractor(self):\n def extract(*args, **kwargs):\n class Out:\n def __init__(self):\n self.pixel_values = torch.ones([0])\n\n def to(self, device):\n self.pixel_values.to(device)\n return self\n\n return Out()\n\n return extract\n\n def test_safe_diffusion_ddim(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n unet = self.dummy_cond_unet\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n\n vae = self.dummy_vae\n bert = self.dummy_text_encoder\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n # make sure here that pndm scheduler skips prk\n sd_pipe = StableDiffusionPipeline(\n unet=unet,\n scheduler=scheduler,\n vae=vae,\n text_encoder=bert,\n tokenizer=tokenizer,\n safety_checker=None,\n feature_extractor=self.dummy_extractor,\n )\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A painting of a squirrel eating a burger\"\n\n generator = torch.Generator(device=device).manual_seed(0)\n output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type=\"np\")\n image = output.images\n\n generator = torch.Generator(device=device).manual_seed(0)\n image_from_tuple = sd_pipe(\n [prompt],\n generator=generator,\n guidance_scale=6.0,\n num_inference_steps=2,\n output_type=\"np\",\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_pndm(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n unet = self.dummy_cond_unet\n scheduler = PNDMScheduler(skip_prk_steps=True)\n vae = self.dummy_vae\n bert = self.dummy_text_encoder\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n # make sure here that pndm scheduler skips prk\n sd_pipe = StableDiffusionPipeline(\n unet=unet,\n scheduler=scheduler,\n vae=vae,\n text_encoder=bert,\n tokenizer=tokenizer,\n safety_checker=None,\n feature_extractor=self.dummy_extractor,\n )\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A painting of a squirrel eating a burger\"\n generator = torch.Generator(device=device).manual_seed(0)\n output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type=\"np\")\n\n image = output.images\n\n generator = torch.Generator(device=device).manual_seed(0)\n image_from_tuple = sd_pipe(\n [prompt],\n generator=generator,\n guidance_scale=6.0,\n num_inference_steps=2,\n output_type=\"np\",\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_no_safety_checker(self):\n pipe = StableDiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-lms-pipe\", safety_checker=None\n )\n assert isinstance(pipe, StableDiffusionPipeline)\n assert isinstance(pipe.scheduler, LMSDiscreteScheduler)\n assert pipe.safety_checker is None\n\n image = pipe(\"example prompt\", num_inference_steps=2).images[0]\n assert image is not None\n\n # check that there's no error when saving a pipeline with one of the models being None\n with tempfile.TemporaryDirectory() as tmpdirname:\n pipe.save_pretrained(tmpdirname)\n pipe = StableDiffusionPipeline.from_pretrained(tmpdirname)\n\n # sanity check that the pipeline still works\n assert pipe.safety_checker is None\n image = pipe(\"example prompt\", num_inference_steps=2).images[0]\n assert image is not None\n\n @unittest.skipIf(torch_device != \"cuda\", \"This test requires a GPU\")\n def test_stable_diffusion_fp16(self):\n \"\"\"Test that stable diffusion works with fp16\"\"\"\n unet = self.dummy_cond_unet\n scheduler = PNDMScheduler(skip_prk_steps=True)\n vae = self.dummy_vae\n bert = self.dummy_text_encoder\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n # put models in fp16\n unet = unet.half()\n vae = vae.half()\n bert = bert.half()\n\n # make sure here that pndm scheduler skips prk\n sd_pipe = StableDiffusionPipeline(\n unet=unet,\n scheduler=scheduler,\n vae=vae,\n text_encoder=bert,\n tokenizer=tokenizer,\n safety_checker=None,\n feature_extractor=self.dummy_extractor,\n )\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"A painting of a squirrel eating a burger\"\n image = sd_pipe([prompt], num_inference_steps=2, output_type=\"np\").images\n\n assert image.shape == (1, 64, 64, 3)\n\n\n@nightly\n@require_torch_gpu\nclass SafeDiffusionPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_harm_safe_stable_diffusion(self):\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", safety_checker=None)\n sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = (\n \"portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle\"\n \" coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with\"\n \" anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and\"\n \" children from bahnhof zoo, detailed \"\n )\n seed = 4003660346\n guidance_scale = 7\n\n # without safety guidance (sld_guidance_scale = 0)\n generator = torch.manual_seed(seed)\n output = sd_pipe(\n [prompt],\n generator=generator,\n guidance_scale=guidance_scale,\n num_inference_steps=50,\n output_type=\"np\",\n width=512,\n height=512,\n sld_guidance_scale=0,\n )\n\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]\n\n assert image.shape == (1, 512, 512, 3)\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n # without safety guidance (strong configuration)\n generator = torch.manual_seed(seed)\n output = sd_pipe(\n [prompt],\n generator=generator,\n guidance_scale=guidance_scale,\n num_inference_steps=50,\n output_type=\"np\",\n width=512,\n height=512,\n sld_guidance_scale=2000,\n sld_warmup_steps=7,\n sld_threshold=0.025,\n sld_momentum_scale=0.5,\n sld_mom_beta=0.7,\n )\n\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]\n\n assert image.shape == (1, 512, 512, 3)\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_nudity_safe_stable_diffusion(self):\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\", safety_checker=None)\n sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"padme amidala taking a bath artwork, safe for work, no nudity\"\n seed = 2734971755\n guidance_scale = 7\n\n generator = torch.manual_seed(seed)\n output = sd_pipe(\n [prompt],\n generator=generator,\n guidance_scale=guidance_scale,\n num_inference_steps=50,\n output_type=\"np\",\n width=512,\n height=512,\n sld_guidance_scale=0,\n )\n\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]\n\n assert image.shape == (1, 512, 512, 3)\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n generator = torch.manual_seed(seed)\n output = sd_pipe(\n [prompt],\n generator=generator,\n guidance_scale=guidance_scale,\n num_inference_steps=50,\n output_type=\"np\",\n width=512,\n height=512,\n sld_guidance_scale=2000,\n sld_warmup_steps=7,\n sld_threshold=0.025,\n sld_momentum_scale=0.5,\n sld_mom_beta=0.7,\n )\n\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]\n\n assert image.shape == (1, 512, 512, 3)\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_nudity_safetychecker_safe_stable_diffusion(self):\n sd_pipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = (\n \"the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.\"\n \" leyendecker\"\n )\n seed = 1044355234\n guidance_scale = 12\n\n generator = torch.manual_seed(seed)\n output = sd_pipe(\n [prompt],\n generator=generator,\n guidance_scale=guidance_scale,\n num_inference_steps=50,\n output_type=\"np\",\n width=512,\n height=512,\n sld_guidance_scale=0,\n )\n\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])\n\n assert image.shape == (1, 512, 512, 3)\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-7\n\n generator = torch.manual_seed(seed)\n output = sd_pipe(\n [prompt],\n generator=generator,\n guidance_scale=guidance_scale,\n num_inference_steps=50,\n output_type=\"np\",\n width=512,\n height=512,\n sld_guidance_scale=2000,\n sld_warmup_steps=7,\n sld_threshold=0.025,\n sld_momentum_scale=0.5,\n sld_mom_beta=0.7,\n )\n\n image = output.images\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561])\n assert image.shape == (1, 512, 512, 3)\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_sag/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/stable_diffusion_sag/test_stable_diffusion_sag.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n DEISMultistepScheduler,\n DPMSolverMultistepScheduler,\n EulerDiscreteScheduler,\n StableDiffusionSAGPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device\n\nfrom ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS\nfrom ..test_pipelines_common import (\n IPAdapterTesterMixin,\n PipelineFromPipeTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n)\n\n\nenable_full_determinism()\n\n\nclass StableDiffusionSAGPipelineFastTests(\n IPAdapterTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n PipelineFromPipeTesterMixin,\n unittest.TestCase,\n):\n pipeline_class = StableDiffusionSAGPipeline\n params = TEXT_TO_IMAGE_PARAMS\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n image_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(4, 8),\n layers_per_block=2,\n sample_size=8,\n norm_num_groups=1,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=8,\n )\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[4, 8],\n norm_num_groups=1,\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=8,\n num_hidden_layers=2,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \".\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 1.0,\n \"sag_scale\": 1.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=3e-3)\n\n @unittest.skip(\"Not necessary to test here.\")\n def test_xformers_attention_forwardGenerator_pass(self):\n pass\n\n def test_pipeline_different_schedulers(self):\n pipeline = self.pipeline_class(**self.get_dummy_components())\n inputs = self.get_dummy_inputs(\"cpu\")\n\n expected_image_size = (16, 16, 3)\n for scheduler_cls in [DDIMScheduler, DEISMultistepScheduler, DPMSolverMultistepScheduler]:\n pipeline.scheduler = scheduler_cls.from_config(pipeline.scheduler.config)\n image = pipeline(**inputs).images[0]\n\n shape = image.shape\n assert shape == expected_image_size\n\n pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)\n\n with self.assertRaises(ValueError):\n # Karras schedulers are not supported\n image = pipeline(**inputs).images[0]\n\n\n@nightly\n@require_torch_gpu\nclass StableDiffusionPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_stable_diffusion_1(self):\n sag_pipe = StableDiffusionSAGPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\")\n sag_pipe = sag_pipe.to(torch_device)\n sag_pipe.set_progress_bar_config(disable=None)\n\n prompt = \".\"\n generator = torch.manual_seed(0)\n output = sag_pipe(\n [prompt], generator=generator, guidance_scale=7.5, sag_scale=1.0, num_inference_steps=20, output_type=\"np\"\n )\n\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2\n\n def test_stable_diffusion_2(self):\n sag_pipe = StableDiffusionSAGPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-1-base\")\n sag_pipe = sag_pipe.to(torch_device)\n sag_pipe.set_progress_bar_config(disable=None)\n\n prompt = \".\"\n generator = torch.manual_seed(0)\n output = sag_pipe(\n [prompt], generator=generator, guidance_scale=7.5, sag_scale=1.0, num_inference_steps=20, output_type=\"np\"\n )\n\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2\n\n def test_stable_diffusion_2_non_square(self):\n sag_pipe = StableDiffusionSAGPipeline.from_pretrained(\"stabilityai/stable-diffusion-2-1-base\")\n sag_pipe = sag_pipe.to(torch_device)\n sag_pipe.set_progress_bar_config(disable=None)\n\n prompt = \".\"\n generator = torch.manual_seed(0)\n output = sag_pipe(\n [prompt],\n width=768,\n height=512,\n generator=generator,\n guidance_scale=7.5,\n sag_scale=1.0,\n num_inference_steps=20,\n output_type=\"np\",\n )\n\n image = output.images\n\n assert image.shape == (1, 512, 768, 3)\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_xl/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport copy\nimport gc\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EulerDiscreteScheduler,\n HeunDiscreteScheduler,\n LCMScheduler,\n StableDiffusionXLImg2ImgPipeline,\n StableDiffusionXLPipeline,\n UNet2DConditionModel,\n UniPCMultistepScheduler,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n load_image,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\nfrom ..pipeline_params import (\n TEXT_TO_IMAGE_BATCH_PARAMS,\n TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,\n TEXT_TO_IMAGE_IMAGE_PARAMS,\n TEXT_TO_IMAGE_PARAMS,\n)\nfrom ..test_pipelines_common import (\n IPAdapterTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n SDFunctionTesterMixin,\n SDXLOptionalComponentsTesterMixin,\n)\n\n\nenable_full_determinism()\n\n\nclass StableDiffusionXLPipelineFastTests(\n SDFunctionTesterMixin,\n IPAdapterTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n SDXLOptionalComponentsTesterMixin,\n unittest.TestCase,\n):\n pipeline_class = StableDiffusionXLPipeline\n params = TEXT_TO_IMAGE_PARAMS\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n image_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union({\"add_text_embeds\", \"add_time_ids\"})\n\n def get_dummy_components(self, time_cond_proj_dim=None):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(2, 4),\n layers_per_block=2,\n time_cond_proj_dim=time_cond_proj_dim,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2),\n projection_class_embeddings_input_dim=80, # 6 * 8 + 32\n cross_attention_dim=64,\n norm_num_groups=1,\n )\n scheduler = EulerDiscreteScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n steps_offset=1,\n beta_schedule=\"scaled_linear\",\n timestep_spacing=\"leading\",\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n sample_size=128,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n # SD2-specific config below\n hidden_act=\"gelu\",\n projection_dim=32,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)\n tokenizer_2 = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"text_encoder_2\": text_encoder_2,\n \"tokenizer_2\": tokenizer_2,\n \"image_encoder\": None,\n \"feature_extractor\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 5.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_stable_diffusion_xl_euler(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.5552, 0.5569, 0.4725, 0.4348, 0.4994, 0.4632, 0.5142, 0.5012, 0.47])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_xl_euler_lcm(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionXLPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.4917, 0.6555, 0.4348, 0.5219, 0.7324, 0.4855, 0.5168, 0.5447, 0.5156])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_xl_euler_lcm_custom_timesteps(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionXLPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n del inputs[\"num_inference_steps\"]\n inputs[\"timesteps\"] = [999, 499]\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.4917, 0.6555, 0.4348, 0.5219, 0.7324, 0.4855, 0.5168, 0.5447, 0.5156])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_ays(self):\n from diffusers.schedulers import AysSchedules\n\n timestep_schedule = AysSchedules[\"StableDiffusionXLTimesteps\"]\n sigma_schedule = AysSchedules[\"StableDiffusionXLSigmas\"]\n\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionXLPipeline(**components)\n sd_pipe.scheduler = EulerDiscreteScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"num_inference_steps\"] = 10\n output = sd_pipe(**inputs).images\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"num_inference_steps\"] = None\n inputs[\"timesteps\"] = timestep_schedule\n output_ts = sd_pipe(**inputs).images\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"num_inference_steps\"] = None\n inputs[\"sigmas\"] = sigma_schedule\n output_sigmas = sd_pipe(**inputs).images\n\n assert (\n np.abs(output_sigmas.flatten() - output_ts.flatten()).max() < 1e-3\n ), \"ays timesteps and ays sigmas should have the same outputs\"\n assert (\n np.abs(output.flatten() - output_ts.flatten()).max() > 1e-3\n ), \"use ays timesteps should have different outputs\"\n assert (\n np.abs(output.flatten() - output_sigmas.flatten()).max() > 1e-3\n ), \"use ays sigmas should have different outputs\"\n\n def test_stable_diffusion_xl_prompt_embeds(self):\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n # forward without prompt embeds\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"prompt\"] = 2 * [inputs[\"prompt\"]]\n inputs[\"num_images_per_prompt\"] = 2\n\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with prompt embeds\n inputs = self.get_dummy_inputs(torch_device)\n prompt = 2 * [inputs.pop(\"prompt\")]\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = sd_pipe.encode_prompt(prompt)\n\n output = sd_pipe(\n **inputs,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n )\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # make sure that it's equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n def test_stable_diffusion_xl_negative_prompt_embeds(self):\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n # forward without prompt embeds\n inputs = self.get_dummy_inputs(torch_device)\n negative_prompt = 3 * [\"this is a negative prompt\"]\n inputs[\"negative_prompt\"] = negative_prompt\n inputs[\"prompt\"] = 3 * [inputs[\"prompt\"]]\n\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with prompt embeds\n inputs = self.get_dummy_inputs(torch_device)\n negative_prompt = 3 * [\"this is a negative prompt\"]\n prompt = 3 * [inputs.pop(\"prompt\")]\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = sd_pipe.encode_prompt(prompt, negative_prompt=negative_prompt)\n\n output = sd_pipe(\n **inputs,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n )\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # make sure that it's equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n def test_ip_adapter_single(self):\n expected_pipe_slice = None\n if torch_device == \"cpu\":\n expected_pipe_slice = np.array([0.5552, 0.5569, 0.4725, 0.4348, 0.4994, 0.4632, 0.5142, 0.5012, 0.4700])\n return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)\n\n def test_attention_slicing_forward_pass(self):\n super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=3e-3)\n\n def test_save_load_optional_components(self):\n self._test_save_load_optional_components()\n\n @require_torch_gpu\n def test_stable_diffusion_xl_offloads(self):\n pipes = []\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLPipeline(**components).to(torch_device)\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLPipeline(**components)\n sd_pipe.enable_model_cpu_offload()\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLPipeline(**components)\n sd_pipe.enable_sequential_cpu_offload()\n pipes.append(sd_pipe)\n\n image_slices = []\n for pipe in pipes:\n pipe.unet.set_default_attn_processor()\n\n inputs = self.get_dummy_inputs(torch_device)\n image = pipe(**inputs).images\n\n image_slices.append(image[0, -3:, -3:, -1].flatten())\n\n assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3\n assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3\n\n def test_stable_diffusion_xl_img2img_prompt_embeds_only(self):\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n # forward without prompt embeds\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n inputs[\"prompt\"] = 3 * [inputs[\"prompt\"]]\n\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with prompt embeds\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n prompt = 3 * [inputs.pop(\"prompt\")]\n\n (\n prompt_embeds,\n _,\n pooled_prompt_embeds,\n _,\n ) = sd_pipe.encode_prompt(prompt)\n\n output = sd_pipe(\n **inputs,\n prompt_embeds=prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n )\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # make sure that it's equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n def test_stable_diffusion_two_xl_mixture_of_denoiser_fast(self):\n components = self.get_dummy_components()\n pipe_1 = StableDiffusionXLPipeline(**components).to(torch_device)\n pipe_1.unet.set_default_attn_processor()\n pipe_2 = StableDiffusionXLImg2ImgPipeline(**components).to(torch_device)\n pipe_2.unet.set_default_attn_processor()\n\n def assert_run_mixture(\n num_steps,\n split,\n scheduler_cls_orig,\n expected_tss,\n num_train_timesteps=pipe_1.scheduler.config.num_train_timesteps,\n ):\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = num_steps\n\n class scheduler_cls(scheduler_cls_orig):\n pass\n\n pipe_1.scheduler = scheduler_cls.from_config(pipe_1.scheduler.config)\n pipe_2.scheduler = scheduler_cls.from_config(pipe_2.scheduler.config)\n\n # Let's retrieve the number of timesteps we want to use\n pipe_1.scheduler.set_timesteps(num_steps)\n expected_steps = pipe_1.scheduler.timesteps.tolist()\n\n if pipe_1.scheduler.order == 2:\n expected_steps_1 = list(filter(lambda ts: ts >= split, expected_tss))\n expected_steps_2 = expected_steps_1[-1:] + list(filter(lambda ts: ts < split, expected_tss))\n expected_steps = expected_steps_1 + expected_steps_2\n else:\n expected_steps_1 = list(filter(lambda ts: ts >= split, expected_tss))\n expected_steps_2 = list(filter(lambda ts: ts < split, expected_tss))\n\n # now we monkey patch step `done_steps`\n # list into the step function for testing\n done_steps = []\n old_step = copy.copy(scheduler_cls.step)\n\n def new_step(self, *args, **kwargs):\n done_steps.append(args[1].cpu().item()) # args[1] is always the passed `t`\n return old_step(self, *args, **kwargs)\n\n scheduler_cls.step = new_step\n\n inputs_1 = {\n **inputs,\n **{\n \"denoising_end\": 1.0 - (split / num_train_timesteps),\n \"output_type\": \"latent\",\n },\n }\n latents = pipe_1(**inputs_1).images[0]\n\n assert expected_steps_1 == done_steps, f\"Failure with {scheduler_cls.__name__} and {num_steps} and {split}\"\n\n inputs_2 = {\n **inputs,\n **{\n \"denoising_start\": 1.0 - (split / num_train_timesteps),\n \"image\": latents,\n },\n }\n pipe_2(**inputs_2).images[0]\n\n assert expected_steps_2 == done_steps[len(expected_steps_1) :]\n assert expected_steps == done_steps, f\"Failure with {scheduler_cls.__name__} and {num_steps} and {split}\"\n\n steps = 10\n for split in [300, 700]:\n for scheduler_cls_timesteps in [\n (EulerDiscreteScheduler, [901, 801, 701, 601, 501, 401, 301, 201, 101, 1]),\n (\n HeunDiscreteScheduler,\n [\n 901.0,\n 801.0,\n 801.0,\n 701.0,\n 701.0,\n 601.0,\n 601.0,\n 501.0,\n 501.0,\n 401.0,\n 401.0,\n 301.0,\n 301.0,\n 201.0,\n 201.0,\n 101.0,\n 101.0,\n 1.0,\n 1.0,\n ],\n ),\n ]:\n assert_run_mixture(steps, split, scheduler_cls_timesteps[0], scheduler_cls_timesteps[1])\n\n @slow\n def test_stable_diffusion_two_xl_mixture_of_denoiser(self):\n components = self.get_dummy_components()\n pipe_1 = StableDiffusionXLPipeline(**components).to(torch_device)\n pipe_1.unet.set_default_attn_processor()\n pipe_2 = StableDiffusionXLImg2ImgPipeline(**components).to(torch_device)\n pipe_2.unet.set_default_attn_processor()\n\n def assert_run_mixture(\n num_steps,\n split,\n scheduler_cls_orig,\n expected_tss,\n num_train_timesteps=pipe_1.scheduler.config.num_train_timesteps,\n ):\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = num_steps\n\n class scheduler_cls(scheduler_cls_orig):\n pass\n\n pipe_1.scheduler = scheduler_cls.from_config(pipe_1.scheduler.config)\n pipe_2.scheduler = scheduler_cls.from_config(pipe_2.scheduler.config)\n\n # Let's retrieve the number of timesteps we want to use\n pipe_1.scheduler.set_timesteps(num_steps)\n expected_steps = pipe_1.scheduler.timesteps.tolist()\n\n if pipe_1.scheduler.order == 2:\n expected_steps_1 = list(filter(lambda ts: ts >= split, expected_tss))\n expected_steps_2 = expected_steps_1[-1:] + list(filter(lambda ts: ts < split, expected_tss))\n expected_steps = expected_steps_1 + expected_steps_2\n else:\n expected_steps_1 = list(filter(lambda ts: ts >= split, expected_tss))\n expected_steps_2 = list(filter(lambda ts: ts < split, expected_tss))\n\n # now we monkey patch step `done_steps`\n # list into the step function for testing\n done_steps = []\n old_step = copy.copy(scheduler_cls.step)\n\n def new_step(self, *args, **kwargs):\n done_steps.append(args[1].cpu().item()) # args[1] is always the passed `t`\n return old_step(self, *args, **kwargs)\n\n scheduler_cls.step = new_step\n\n inputs_1 = {\n **inputs,\n **{\n \"denoising_end\": 1.0 - (split / num_train_timesteps),\n \"output_type\": \"latent\",\n },\n }\n latents = pipe_1(**inputs_1).images[0]\n\n assert expected_steps_1 == done_steps, f\"Failure with {scheduler_cls.__name__} and {num_steps} and {split}\"\n\n inputs_2 = {\n **inputs,\n **{\n \"denoising_start\": 1.0 - (split / num_train_timesteps),\n \"image\": latents,\n },\n }\n pipe_2(**inputs_2).images[0]\n\n assert expected_steps_2 == done_steps[len(expected_steps_1) :]\n assert expected_steps == done_steps, f\"Failure with {scheduler_cls.__name__} and {num_steps} and {split}\"\n\n steps = 10\n for split in [300, 500, 700]:\n for scheduler_cls_timesteps in [\n (DDIMScheduler, [901, 801, 701, 601, 501, 401, 301, 201, 101, 1]),\n (EulerDiscreteScheduler, [901, 801, 701, 601, 501, 401, 301, 201, 101, 1]),\n (DPMSolverMultistepScheduler, [901, 811, 721, 631, 541, 451, 361, 271, 181, 91]),\n (UniPCMultistepScheduler, [901, 811, 721, 631, 541, 451, 361, 271, 181, 91]),\n (\n HeunDiscreteScheduler,\n [\n 901.0,\n 801.0,\n 801.0,\n 701.0,\n 701.0,\n 601.0,\n 601.0,\n 501.0,\n 501.0,\n 401.0,\n 401.0,\n 301.0,\n 301.0,\n 201.0,\n 201.0,\n 101.0,\n 101.0,\n 1.0,\n 1.0,\n ],\n ),\n ]:\n assert_run_mixture(steps, split, scheduler_cls_timesteps[0], scheduler_cls_timesteps[1])\n\n steps = 25\n for split in [300, 500, 700]:\n for scheduler_cls_timesteps in [\n (\n DDIMScheduler,\n [\n 961,\n 921,\n 881,\n 841,\n 801,\n 761,\n 721,\n 681,\n 641,\n 601,\n 561,\n 521,\n 481,\n 441,\n 401,\n 361,\n 321,\n 281,\n 241,\n 201,\n 161,\n 121,\n 81,\n 41,\n 1,\n ],\n ),\n (\n EulerDiscreteScheduler,\n [\n 961.0,\n 921.0,\n 881.0,\n 841.0,\n 801.0,\n 761.0,\n 721.0,\n 681.0,\n 641.0,\n 601.0,\n 561.0,\n 521.0,\n 481.0,\n 441.0,\n 401.0,\n 361.0,\n 321.0,\n 281.0,\n 241.0,\n 201.0,\n 161.0,\n 121.0,\n 81.0,\n 41.0,\n 1.0,\n ],\n ),\n (\n DPMSolverMultistepScheduler,\n [\n 951,\n 913,\n 875,\n 837,\n 799,\n 761,\n 723,\n 685,\n 647,\n 609,\n 571,\n 533,\n 495,\n 457,\n 419,\n 381,\n 343,\n 305,\n 267,\n 229,\n 191,\n 153,\n 115,\n 77,\n 39,\n ],\n ),\n (\n UniPCMultistepScheduler,\n [\n 951,\n 913,\n 875,\n 837,\n 799,\n 761,\n 723,\n 685,\n 647,\n 609,\n 571,\n 533,\n 495,\n 457,\n 419,\n 381,\n 343,\n 305,\n 267,\n 229,\n 191,\n 153,\n 115,\n 77,\n 39,\n ],\n ),\n (\n HeunDiscreteScheduler,\n [\n 961.0,\n 921.0,\n 921.0,\n 881.0,\n 881.0,\n 841.0,\n 841.0,\n 801.0,\n 801.0,\n 761.0,\n 761.0,\n 721.0,\n 721.0,\n 681.0,\n 681.0,\n 641.0,\n 641.0,\n 601.0,\n 601.0,\n 561.0,\n 561.0,\n 521.0,\n 521.0,\n 481.0,\n 481.0,\n 441.0,\n 441.0,\n 401.0,\n 401.0,\n 361.0,\n 361.0,\n 321.0,\n 321.0,\n 281.0,\n 281.0,\n 241.0,\n 241.0,\n 201.0,\n 201.0,\n 161.0,\n 161.0,\n 121.0,\n 121.0,\n 81.0,\n 81.0,\n 41.0,\n 41.0,\n 1.0,\n 1.0,\n ],\n ),\n ]:\n assert_run_mixture(steps, split, scheduler_cls_timesteps[0], scheduler_cls_timesteps[1])\n\n @slow\n def test_stable_diffusion_three_xl_mixture_of_denoiser(self):\n components = self.get_dummy_components()\n pipe_1 = StableDiffusionXLPipeline(**components).to(torch_device)\n pipe_1.unet.set_default_attn_processor()\n pipe_2 = StableDiffusionXLImg2ImgPipeline(**components).to(torch_device)\n pipe_2.unet.set_default_attn_processor()\n pipe_3 = StableDiffusionXLImg2ImgPipeline(**components).to(torch_device)\n pipe_3.unet.set_default_attn_processor()\n\n def assert_run_mixture(\n num_steps,\n split_1,\n split_2,\n scheduler_cls_orig,\n num_train_timesteps=pipe_1.scheduler.config.num_train_timesteps,\n ):\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = num_steps\n\n class scheduler_cls(scheduler_cls_orig):\n pass\n\n pipe_1.scheduler = scheduler_cls.from_config(pipe_1.scheduler.config)\n pipe_2.scheduler = scheduler_cls.from_config(pipe_2.scheduler.config)\n pipe_3.scheduler = scheduler_cls.from_config(pipe_3.scheduler.config)\n\n # Let's retrieve the number of timesteps we want to use\n pipe_1.scheduler.set_timesteps(num_steps)\n expected_steps = pipe_1.scheduler.timesteps.tolist()\n\n split_1_ts = num_train_timesteps - int(round(num_train_timesteps * split_1))\n split_2_ts = num_train_timesteps - int(round(num_train_timesteps * split_2))\n\n if pipe_1.scheduler.order == 2:\n expected_steps_1 = list(filter(lambda ts: ts >= split_1_ts, expected_steps))\n expected_steps_2 = expected_steps_1[-1:] + list(\n filter(lambda ts: ts >= split_2_ts and ts < split_1_ts, expected_steps)\n )\n expected_steps_3 = expected_steps_2[-1:] + list(filter(lambda ts: ts < split_2_ts, expected_steps))\n expected_steps = expected_steps_1 + expected_steps_2 + expected_steps_3\n else:\n expected_steps_1 = list(filter(lambda ts: ts >= split_1_ts, expected_steps))\n expected_steps_2 = list(filter(lambda ts: ts >= split_2_ts and ts < split_1_ts, expected_steps))\n expected_steps_3 = list(filter(lambda ts: ts < split_2_ts, expected_steps))\n\n # now we monkey patch step `done_steps`\n # list into the step function for testing\n done_steps = []\n old_step = copy.copy(scheduler_cls.step)\n\n def new_step(self, *args, **kwargs):\n done_steps.append(args[1].cpu().item()) # args[1] is always the passed `t`\n return old_step(self, *args, **kwargs)\n\n scheduler_cls.step = new_step\n\n inputs_1 = {**inputs, **{\"denoising_end\": split_1, \"output_type\": \"latent\"}}\n latents = pipe_1(**inputs_1).images[0]\n\n assert (\n expected_steps_1 == done_steps\n ), f\"Failure with {scheduler_cls.__name__} and {num_steps} and {split_1} and {split_2}\"\n\n with self.assertRaises(ValueError) as cm:\n inputs_2 = {\n **inputs,\n **{\n \"denoising_start\": split_2,\n \"denoising_end\": split_1,\n \"image\": latents,\n \"output_type\": \"latent\",\n },\n }\n pipe_2(**inputs_2).images[0]\n assert \"cannot be larger than or equal to `denoising_end`\" in str(cm.exception)\n\n inputs_2 = {\n **inputs,\n **{\"denoising_start\": split_1, \"denoising_end\": split_2, \"image\": latents, \"output_type\": \"latent\"},\n }\n pipe_2(**inputs_2).images[0]\n\n assert expected_steps_2 == done_steps[len(expected_steps_1) :]\n\n inputs_3 = {**inputs, **{\"denoising_start\": split_2, \"image\": latents}}\n pipe_3(**inputs_3).images[0]\n\n assert expected_steps_3 == done_steps[len(expected_steps_1) + len(expected_steps_2) :]\n assert (\n expected_steps == done_steps\n ), f\"Failure with {scheduler_cls.__name__} and {num_steps} and {split_1} and {split_2}\"\n\n for steps in [7, 11, 20]:\n for split_1, split_2 in zip([0.19, 0.32], [0.81, 0.68]):\n for scheduler_cls in [\n DDIMScheduler,\n EulerDiscreteScheduler,\n DPMSolverMultistepScheduler,\n UniPCMultistepScheduler,\n HeunDiscreteScheduler,\n ]:\n assert_run_mixture(steps, split_1, split_2, scheduler_cls)\n\n def test_stable_diffusion_xl_multi_prompts(self):\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(torch_device)\n\n # forward with single prompt\n inputs = self.get_dummy_inputs(torch_device)\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with same prompt duplicated\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"prompt_2\"] = inputs[\"prompt\"]\n output = sd_pipe(**inputs)\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n # forward with different prompt\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"prompt_2\"] = \"different prompt\"\n output = sd_pipe(**inputs)\n image_slice_3 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are not equal\n assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4\n\n # manually set a negative_prompt\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"negative_prompt\"] = \"negative prompt\"\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with same negative_prompt duplicated\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"negative_prompt\"] = \"negative prompt\"\n inputs[\"negative_prompt_2\"] = inputs[\"negative_prompt\"]\n output = sd_pipe(**inputs)\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n # forward with different negative_prompt\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"negative_prompt\"] = \"negative prompt\"\n inputs[\"negative_prompt_2\"] = \"different negative prompt\"\n output = sd_pipe(**inputs)\n image_slice_3 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are not equal\n assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4\n\n def test_stable_diffusion_xl_negative_conditions(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice_with_no_neg_cond = image[0, -3:, -3:, -1]\n\n image = sd_pipe(\n **inputs,\n negative_original_size=(512, 512),\n negative_crops_coords_top_left=(0, 0),\n negative_target_size=(1024, 1024),\n ).images\n image_slice_with_neg_cond = image[0, -3:, -3:, -1]\n\n self.assertTrue(np.abs(image_slice_with_no_neg_cond - image_slice_with_neg_cond).max() > 1e-2)\n\n def test_stable_diffusion_xl_save_from_pretrained(self):\n pipes = []\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLPipeline(**components).to(torch_device)\n pipes.append(sd_pipe)\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n sd_pipe.save_pretrained(tmpdirname)\n sd_pipe = StableDiffusionXLPipeline.from_pretrained(tmpdirname).to(torch_device)\n pipes.append(sd_pipe)\n\n image_slices = []\n for pipe in pipes:\n pipe.unet.set_default_attn_processor()\n\n inputs = self.get_dummy_inputs(torch_device)\n image = pipe(**inputs).images\n\n image_slices.append(image[0, -3:, -3:, -1].flatten())\n\n assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3\n\n def test_pipeline_interrupt(self):\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"hey\"\n num_inference_steps = 3\n\n # store intermediate latents from the generation process\n class PipelineState:\n def __init__(self):\n self.state = []\n\n def apply(self, pipe, i, t, callback_kwargs):\n self.state.append(callback_kwargs[\"latents\"])\n return callback_kwargs\n\n pipe_state = PipelineState()\n sd_pipe(\n prompt,\n num_inference_steps=num_inference_steps,\n output_type=\"np\",\n generator=torch.Generator(\"cpu\").manual_seed(0),\n callback_on_step_end=pipe_state.apply,\n ).images\n\n # interrupt generation at step index\n interrupt_step_idx = 1\n\n def callback_on_step_end(pipe, i, t, callback_kwargs):\n if i == interrupt_step_idx:\n pipe._interrupt = True\n\n return callback_kwargs\n\n output_interrupted = sd_pipe(\n prompt,\n num_inference_steps=num_inference_steps,\n output_type=\"latent\",\n generator=torch.Generator(\"cpu\").manual_seed(0),\n callback_on_step_end=callback_on_step_end,\n ).images\n\n # fetch intermediate latents at the interrupted step\n # from the completed generation process\n intermediate_latent = pipe_state.state[interrupt_step_idx]\n\n # compare the intermediate latent to the output of the interrupted process\n # they should be the same\n assert torch.allclose(intermediate_latent, output_interrupted, atol=1e-4)\n\n\n@slow\nclass StableDiffusionXLPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_stable_diffusion_lcm(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel.from_pretrained(\n \"latent-consistency/lcm-ssd-1b\", torch_dtype=torch.float16, variant=\"fp16\"\n )\n sd_pipe = StableDiffusionXLPipeline.from_pretrained(\n \"segmind/SSD-1B\", unet=unet, torch_dtype=torch.float16, variant=\"fp16\"\n ).to(torch_device)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe.set_progress_bar_config(disable=None)\n\n prompt = \"a red car standing on the side of the street\"\n\n image = sd_pipe(prompt, num_inference_steps=4, guidance_scale=8.0).images[0]\n\n expected_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/lcm_full/stable_diffusion_ssd_1b_lcm.png\"\n )\n\n image = sd_pipe.image_processor.pil_to_numpy(image)\n expected_image = sd_pipe.image_processor.pil_to_numpy(expected_image)\n\n max_diff = numpy_cosine_similarity_distance(image.flatten(), expected_image.flatten())\n\n assert max_diff < 1e-2\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_adapter.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom parameterized import parameterized\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n EulerDiscreteScheduler,\n LCMScheduler,\n MultiAdapter,\n StableDiffusionXLAdapterPipeline,\n T2IAdapter,\n UNet2DConditionModel,\n)\nfrom diffusers.utils import logging\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n torch_device,\n)\n\nfrom ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS\nfrom ..test_pipelines_common import (\n IPAdapterTesterMixin,\n PipelineTesterMixin,\n SDXLOptionalComponentsTesterMixin,\n assert_mean_pixel_difference,\n)\n\n\nenable_full_determinism()\n\n\nclass StableDiffusionXLAdapterPipelineFastTests(\n IPAdapterTesterMixin, PipelineTesterMixin, SDXLOptionalComponentsTesterMixin, unittest.TestCase\n):\n pipeline_class = StableDiffusionXLAdapterPipeline\n params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS\n batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS\n\n def get_dummy_components(self, adapter_type=\"full_adapter_xl\", time_cond_proj_dim=None):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2),\n projection_class_embeddings_input_dim=80, # 6 * 8 + 32\n cross_attention_dim=64,\n time_cond_proj_dim=time_cond_proj_dim,\n )\n scheduler = EulerDiscreteScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n steps_offset=1,\n beta_schedule=\"scaled_linear\",\n timestep_spacing=\"leading\",\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n sample_size=128,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n # SD2-specific config below\n hidden_act=\"gelu\",\n projection_dim=32,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)\n tokenizer_2 = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n if adapter_type == \"full_adapter_xl\":\n adapter = T2IAdapter(\n in_channels=3,\n channels=[32, 64],\n num_res_blocks=2,\n downscale_factor=4,\n adapter_type=adapter_type,\n )\n elif adapter_type == \"multi_adapter\":\n adapter = MultiAdapter(\n [\n T2IAdapter(\n in_channels=3,\n channels=[32, 64],\n num_res_blocks=2,\n downscale_factor=4,\n adapter_type=\"full_adapter_xl\",\n ),\n T2IAdapter(\n in_channels=3,\n channels=[32, 64],\n num_res_blocks=2,\n downscale_factor=4,\n adapter_type=\"full_adapter_xl\",\n ),\n ]\n )\n else:\n raise ValueError(\n f\"Unknown adapter type: {adapter_type}, must be one of 'full_adapter_xl', or 'multi_adapter''\"\n )\n\n components = {\n \"adapter\": adapter,\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"text_encoder_2\": text_encoder_2,\n \"tokenizer_2\": tokenizer_2,\n # \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_components_with_full_downscaling(self, adapter_type=\"full_adapter_xl\"):\n \"\"\"Get dummy components with x8 VAE downscaling and 3 UNet down blocks.\n These dummy components are intended to fully-exercise the T2I-Adapter\n downscaling behavior.\n \"\"\"\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n # SD2-specific config below\n attention_head_dim=2,\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=1,\n projection_class_embeddings_input_dim=80, # 6 * 8 + 32\n cross_attention_dim=64,\n )\n scheduler = EulerDiscreteScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n steps_offset=1,\n beta_schedule=\"scaled_linear\",\n timestep_spacing=\"leading\",\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 32, 32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\", \"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\", \"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n sample_size=128,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n # SD2-specific config below\n hidden_act=\"gelu\",\n projection_dim=32,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)\n tokenizer_2 = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n if adapter_type == \"full_adapter_xl\":\n adapter = T2IAdapter(\n in_channels=3,\n channels=[32, 32, 64],\n num_res_blocks=2,\n downscale_factor=16,\n adapter_type=adapter_type,\n )\n elif adapter_type == \"multi_adapter\":\n adapter = MultiAdapter(\n [\n T2IAdapter(\n in_channels=3,\n channels=[32, 32, 64],\n num_res_blocks=2,\n downscale_factor=16,\n adapter_type=\"full_adapter_xl\",\n ),\n T2IAdapter(\n in_channels=3,\n channels=[32, 32, 64],\n num_res_blocks=2,\n downscale_factor=16,\n adapter_type=\"full_adapter_xl\",\n ),\n ]\n )\n else:\n raise ValueError(\n f\"Unknown adapter type: {adapter_type}, must be one of 'full_adapter_xl', or 'multi_adapter''\"\n )\n\n components = {\n \"adapter\": adapter,\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"text_encoder_2\": text_encoder_2,\n \"tokenizer_2\": tokenizer_2,\n # \"safety_checker\": None,\n \"feature_extractor\": None,\n \"image_encoder\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0, height=64, width=64, num_images=1):\n if num_images == 1:\n image = floats_tensor((1, 3, height, width), rng=random.Random(seed)).to(device)\n else:\n image = [\n floats_tensor((1, 3, height, width), rng=random.Random(seed)).to(device) for _ in range(num_images)\n ]\n\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"image\": image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 5.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_ip_adapter_single(self, from_multi=False, expected_pipe_slice=None):\n if not from_multi:\n expected_pipe_slice = None\n if torch_device == \"cpu\":\n expected_pipe_slice = np.array(\n [0.5753, 0.6022, 0.4728, 0.4986, 0.5708, 0.4645, 0.5194, 0.5134, 0.4730]\n )\n return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)\n\n def test_stable_diffusion_adapter_default_case(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLAdapterPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array(\n [0.5752919, 0.6022097, 0.4728038, 0.49861962, 0.57084894, 0.4644975, 0.5193715, 0.5133664, 0.4729858]\n )\n assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3\n\n @parameterized.expand(\n [\n # (dim=144) The internal feature map will be 9x9 after initial pixel unshuffling (downscaled x16).\n (((4 * 2 + 1) * 16),),\n # (dim=160) The internal feature map will be 5x5 after the first T2I down block (downscaled x32).\n (((4 * 1 + 1) * 32),),\n ]\n )\n def test_multiple_image_dimensions(self, dim):\n \"\"\"Test that the T2I-Adapter pipeline supports any input dimension that\n is divisible by the adapter's `downscale_factor`. This test was added in\n response to an issue where the T2I Adapter's downscaling padding\n behavior did not match the UNet's behavior.\n\n Note that we have selected `dim` values to produce odd resolutions at\n each downscaling level.\n \"\"\"\n components = self.get_dummy_components_with_full_downscaling()\n sd_pipe = StableDiffusionXLAdapterPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device, height=dim, width=dim)\n image = sd_pipe(**inputs).images\n\n assert image.shape == (1, dim, dim, 3)\n\n @parameterized.expand([\"full_adapter\", \"full_adapter_xl\", \"light_adapter\"])\n def test_total_downscale_factor(self, adapter_type):\n \"\"\"Test that the T2IAdapter correctly reports its total_downscale_factor.\"\"\"\n batch_size = 1\n in_channels = 3\n out_channels = [320, 640, 1280, 1280]\n in_image_size = 512\n\n adapter = T2IAdapter(\n in_channels=in_channels,\n channels=out_channels,\n num_res_blocks=2,\n downscale_factor=8,\n adapter_type=adapter_type,\n )\n adapter.to(torch_device)\n\n in_image = floats_tensor((batch_size, in_channels, in_image_size, in_image_size)).to(torch_device)\n\n adapter_state = adapter(in_image)\n\n # Assume that the last element in `adapter_state` has been downsampled the most, and check\n # that it matches the `total_downscale_factor`.\n expected_out_image_size = in_image_size // adapter.total_downscale_factor\n assert adapter_state[-1].shape == (\n batch_size,\n out_channels[-1],\n expected_out_image_size,\n expected_out_image_size,\n )\n\n def test_save_load_optional_components(self):\n return self._test_save_load_optional_components()\n\n def test_adapter_sdxl_lcm(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionXLAdapterPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = sd_pipe(**inputs)\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.5425, 0.5385, 0.4964, 0.5045, 0.6149, 0.4974, 0.5469, 0.5332, 0.5426])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_adapter_sdxl_lcm_custom_timesteps(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionXLAdapterPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n del inputs[\"num_inference_steps\"]\n inputs[\"timesteps\"] = [999, 499]\n output = sd_pipe(**inputs)\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.5425, 0.5385, 0.4964, 0.5045, 0.6149, 0.4974, 0.5469, 0.5332, 0.5426])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n\nclass StableDiffusionXLMultiAdapterPipelineFastTests(\n StableDiffusionXLAdapterPipelineFastTests, PipelineTesterMixin, unittest.TestCase\n):\n def get_dummy_components(self, time_cond_proj_dim=None):\n return super().get_dummy_components(\"multi_adapter\", time_cond_proj_dim=time_cond_proj_dim)\n\n def get_dummy_components_with_full_downscaling(self):\n return super().get_dummy_components_with_full_downscaling(\"multi_adapter\")\n\n def get_dummy_inputs(self, device, seed=0, height=64, width=64):\n inputs = super().get_dummy_inputs(device, seed, height, width, num_images=2)\n inputs[\"adapter_conditioning_scale\"] = [0.5, 0.5]\n return inputs\n\n def test_stable_diffusion_adapter_default_case(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLAdapterPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array(\n [0.5813032, 0.60995954, 0.47563356, 0.5056669, 0.57199144, 0.4631841, 0.5176794, 0.51252556, 0.47183886]\n )\n assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3\n\n def test_ip_adapter_single(self):\n expected_pipe_slice = None\n if torch_device == \"cpu\":\n expected_pipe_slice = np.array([0.5813, 0.6100, 0.4756, 0.5057, 0.5720, 0.4632, 0.5177, 0.5125, 0.4718])\n return super().test_ip_adapter_single(from_multi=True, expected_pipe_slice=expected_pipe_slice)\n\n def test_inference_batch_consistent(\n self, batch_sizes=[2, 4, 13], additional_params_copy_to_batched_inputs=[\"num_inference_steps\"]\n ):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n\n logger = logging.get_logger(pipe.__module__)\n logger.setLevel(level=diffusers.logging.FATAL)\n\n # batchify inputs\n for batch_size in batch_sizes:\n batched_inputs = {}\n for name, value in inputs.items():\n if name in self.batch_params:\n # prompt is string\n if name == \"prompt\":\n len_prompt = len(value)\n # make unequal batch sizes\n batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]\n\n # make last batch super long\n batched_inputs[name][-1] = 100 * \"very long\"\n elif name == \"image\":\n batched_images = []\n\n for image in value:\n batched_images.append(batch_size * [image])\n\n batched_inputs[name] = batched_images\n else:\n batched_inputs[name] = batch_size * [value]\n\n elif name == \"batch_size\":\n batched_inputs[name] = batch_size\n else:\n batched_inputs[name] = value\n\n for arg in additional_params_copy_to_batched_inputs:\n batched_inputs[arg] = inputs[arg]\n\n batched_inputs[\"output_type\"] = \"np\"\n\n output = pipe(**batched_inputs)\n\n assert len(output[0]) == batch_size\n\n batched_inputs[\"output_type\"] = \"np\"\n\n output = pipe(**batched_inputs)[0]\n\n assert output.shape[0] == batch_size\n\n logger.setLevel(level=diffusers.logging.WARNING)\n\n def test_num_images_per_prompt(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n batch_sizes = [1, 2]\n num_images_per_prompts = [1, 2]\n\n for batch_size in batch_sizes:\n for num_images_per_prompt in num_images_per_prompts:\n inputs = self.get_dummy_inputs(torch_device)\n\n for key in inputs.keys():\n if key in self.batch_params:\n if key == \"image\":\n batched_images = []\n\n for image in inputs[key]:\n batched_images.append(batch_size * [image])\n\n inputs[key] = batched_images\n else:\n inputs[key] = batch_size * [inputs[key]]\n\n images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt)[0]\n\n assert images.shape[0] == batch_size * num_images_per_prompt\n\n def test_inference_batch_single_identical(\n self,\n batch_size=3,\n test_max_difference=None,\n test_mean_pixel_difference=None,\n relax_max_difference=False,\n expected_max_diff=2e-3,\n additional_params_copy_to_batched_inputs=[\"num_inference_steps\"],\n ):\n if test_max_difference is None:\n # TODO(Pedro) - not sure why, but not at all reproducible at the moment it seems\n # make sure that batched and non-batched is identical\n test_max_difference = torch_device != \"mps\"\n\n if test_mean_pixel_difference is None:\n # TODO same as above\n test_mean_pixel_difference = torch_device != \"mps\"\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n\n logger = logging.get_logger(pipe.__module__)\n logger.setLevel(level=diffusers.logging.FATAL)\n\n # batchify inputs\n batched_inputs = {}\n batch_size = batch_size\n for name, value in inputs.items():\n if name in self.batch_params:\n # prompt is string\n if name == \"prompt\":\n len_prompt = len(value)\n # make unequal batch sizes\n batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]\n\n # make last batch super long\n batched_inputs[name][-1] = 100 * \"very long\"\n elif name == \"image\":\n batched_images = []\n\n for image in value:\n batched_images.append(batch_size * [image])\n\n batched_inputs[name] = batched_images\n else:\n batched_inputs[name] = batch_size * [value]\n elif name == \"batch_size\":\n batched_inputs[name] = batch_size\n elif name == \"generator\":\n batched_inputs[name] = [self.get_generator(i) for i in range(batch_size)]\n else:\n batched_inputs[name] = value\n\n for arg in additional_params_copy_to_batched_inputs:\n batched_inputs[arg] = inputs[arg]\n\n output_batch = pipe(**batched_inputs)\n assert output_batch[0].shape[0] == batch_size\n\n inputs[\"generator\"] = self.get_generator(0)\n\n output = pipe(**inputs)\n\n logger.setLevel(level=diffusers.logging.WARNING)\n if test_max_difference:\n if relax_max_difference:\n # Taking the median of the largest differences\n # is resilient to outliers\n diff = np.abs(output_batch[0][0] - output[0][0])\n diff = diff.flatten()\n diff.sort()\n max_diff = np.median(diff[-5:])\n else:\n max_diff = np.abs(output_batch[0][0] - output[0][0]).max()\n assert max_diff < expected_max_diff\n\n if test_mean_pixel_difference:\n assert_mean_pixel_difference(output_batch[0][0], output[0][0])\n\n def test_adapter_sdxl_lcm(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionXLAdapterPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n output = sd_pipe(**inputs)\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.5313, 0.5375, 0.4942, 0.5021, 0.6142, 0.4968, 0.5434, 0.5311, 0.5448])\n\n debug = [str(round(i, 4)) for i in image_slice.flatten().tolist()]\n print(\",\".join(debug))\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_adapter_sdxl_lcm_custom_timesteps(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionXLAdapterPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n del inputs[\"num_inference_steps\"]\n inputs[\"timesteps\"] = [999, 499]\n output = sd_pipe(**inputs)\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n expected_slice = np.array([0.5313, 0.5375, 0.4942, 0.5021, 0.6142, 0.4968, 0.5434, 0.5311, 0.5448])\n\n debug = [str(round(i, 4)) for i in image_slice.flatten().tolist()]\n print(\",\".join(debug))\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_img2img.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextConfig,\n CLIPTextModel,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionConfig,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers import (\n AutoencoderKL,\n AutoencoderTiny,\n EulerDiscreteScheduler,\n LCMScheduler,\n StableDiffusionXLImg2ImgPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n require_torch_gpu,\n torch_device,\n)\n\nfrom ..pipeline_params import (\n IMAGE_TO_IMAGE_IMAGE_PARAMS,\n TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,\n TEXT_GUIDED_IMAGE_VARIATION_PARAMS,\n TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,\n)\nfrom ..test_pipelines_common import (\n IPAdapterTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n SDXLOptionalComponentsTesterMixin,\n)\n\n\nenable_full_determinism()\n\n\nclass StableDiffusionXLImg2ImgPipelineFastTests(\n IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase\n):\n pipeline_class = StableDiffusionXLImg2ImgPipeline\n params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {\"height\", \"width\"}\n required_optional_params = PipelineTesterMixin.required_optional_params - {\"latents\"}\n batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS\n image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union(\n {\"add_text_embeds\", \"add_time_ids\", \"add_neg_time_ids\"}\n )\n\n def get_dummy_components(self, skip_first_text_encoder=False, time_cond_proj_dim=None):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n time_cond_proj_dim=time_cond_proj_dim,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2),\n projection_class_embeddings_input_dim=72, # 5 * 8 + 32\n cross_attention_dim=64 if not skip_first_text_encoder else 32,\n )\n scheduler = EulerDiscreteScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n steps_offset=1,\n beta_schedule=\"scaled_linear\",\n timestep_spacing=\"leading\",\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n sample_size=128,\n )\n torch.manual_seed(0)\n image_encoder_config = CLIPVisionConfig(\n hidden_size=32,\n image_size=224,\n projection_dim=32,\n intermediate_size=37,\n num_attention_heads=4,\n num_channels=3,\n num_hidden_layers=5,\n patch_size=14,\n )\n\n image_encoder = CLIPVisionModelWithProjection(image_encoder_config)\n\n feature_extractor = CLIPImageProcessor(\n crop_size=224,\n do_center_crop=True,\n do_normalize=True,\n do_resize=True,\n image_mean=[0.48145466, 0.4578275, 0.40821073],\n image_std=[0.26862954, 0.26130258, 0.27577711],\n resample=3,\n size=224,\n )\n\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n # SD2-specific config below\n hidden_act=\"gelu\",\n projection_dim=32,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)\n tokenizer_2 = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder if not skip_first_text_encoder else None,\n \"tokenizer\": tokenizer if not skip_first_text_encoder else None,\n \"text_encoder_2\": text_encoder_2,\n \"tokenizer_2\": tokenizer_2,\n \"requires_aesthetics_score\": True,\n \"image_encoder\": image_encoder,\n \"feature_extractor\": feature_extractor,\n }\n return components\n\n def get_dummy_tiny_autoencoder(self):\n return AutoencoderTiny(in_channels=3, out_channels=3, latent_channels=4)\n\n def test_components_function(self):\n init_components = self.get_dummy_components()\n init_components.pop(\"requires_aesthetics_score\")\n pipe = self.pipeline_class(**init_components)\n\n self.assertTrue(hasattr(pipe, \"components\"))\n self.assertTrue(set(pipe.components.keys()) == set(init_components.keys()))\n\n def get_dummy_inputs(self, device, seed=0):\n image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n image = image / 2 + 0.5\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"image\": image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 5.0,\n \"output_type\": \"np\",\n \"strength\": 0.8,\n }\n return inputs\n\n def test_stable_diffusion_xl_img2img_euler(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 32, 32, 3)\n\n expected_slice = np.array([0.4664, 0.4886, 0.4403, 0.6902, 0.5592, 0.4534, 0.5931, 0.5951, 0.5224])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_xl_img2img_euler_lcm(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.config)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 32, 32, 3)\n\n expected_slice = np.array([0.5604, 0.4352, 0.4717, 0.5844, 0.5101, 0.6704, 0.6290, 0.5460, 0.5286])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_xl_img2img_euler_lcm_custom_timesteps(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.config)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n del inputs[\"num_inference_steps\"]\n inputs[\"timesteps\"] = [999, 499]\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 32, 32, 3)\n\n expected_slice = np.array([0.5604, 0.4352, 0.4717, 0.5844, 0.5101, 0.6704, 0.6290, 0.5460, 0.5286])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_attention_slicing_forward_pass(self):\n super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=3e-3)\n\n # TODO(Patrick, Sayak) - skip for now as this requires more refiner tests\n def test_save_load_optional_components(self):\n pass\n\n def test_stable_diffusion_xl_img2img_negative_prompt_embeds(self):\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n # forward without prompt embeds\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n negative_prompt = 3 * [\"this is a negative prompt\"]\n inputs[\"negative_prompt\"] = negative_prompt\n inputs[\"prompt\"] = 3 * [inputs[\"prompt\"]]\n\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with prompt embeds\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n negative_prompt = 3 * [\"this is a negative prompt\"]\n prompt = 3 * [inputs.pop(\"prompt\")]\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = sd_pipe.encode_prompt(prompt, negative_prompt=negative_prompt)\n\n output = sd_pipe(\n **inputs,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n )\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # make sure that it's equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n def test_ip_adapter_single(self):\n expected_pipe_slice = None\n if torch_device == \"cpu\":\n expected_pipe_slice = np.array([0.5174, 0.4512, 0.5006, 0.6273, 0.5160, 0.6825, 0.6655, 0.5840, 0.5675])\n return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)\n\n def test_stable_diffusion_xl_img2img_tiny_autoencoder(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)\n sd_pipe.vae = self.get_dummy_tiny_autoencoder()\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 32, 32, 3)\n expected_slice = np.array([0.0, 0.0, 0.0106, 0.0, 0.0, 0.0087, 0.0052, 0.0062, 0.0177])\n\n assert np.allclose(image_slice, expected_slice, atol=1e-4, rtol=1e-4)\n\n @require_torch_gpu\n def test_stable_diffusion_xl_offloads(self):\n pipes = []\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLImg2ImgPipeline(**components).to(torch_device)\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)\n sd_pipe.enable_model_cpu_offload()\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)\n sd_pipe.enable_sequential_cpu_offload()\n pipes.append(sd_pipe)\n\n image_slices = []\n for pipe in pipes:\n pipe.unet.set_default_attn_processor()\n\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n image = pipe(**inputs).images\n\n image_slices.append(image[0, -3:, -3:, -1].flatten())\n\n assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3\n assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3\n\n def test_stable_diffusion_xl_multi_prompts(self):\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(torch_device)\n\n # forward with single prompt\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n inputs[\"num_inference_steps\"] = 5\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with same prompt duplicated\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n inputs[\"num_inference_steps\"] = 5\n inputs[\"prompt_2\"] = inputs[\"prompt\"]\n output = sd_pipe(**inputs)\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n # forward with different prompt\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n inputs[\"num_inference_steps\"] = 5\n inputs[\"prompt_2\"] = \"different prompt\"\n output = sd_pipe(**inputs)\n image_slice_3 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are not equal\n assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4\n\n # manually set a negative_prompt\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n inputs[\"num_inference_steps\"] = 5\n inputs[\"negative_prompt\"] = \"negative prompt\"\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with same negative_prompt duplicated\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n inputs[\"num_inference_steps\"] = 5\n inputs[\"negative_prompt\"] = \"negative prompt\"\n inputs[\"negative_prompt_2\"] = inputs[\"negative_prompt\"]\n output = sd_pipe(**inputs)\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n # forward with different negative_prompt\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n inputs[\"num_inference_steps\"] = 5\n inputs[\"negative_prompt\"] = \"negative prompt\"\n inputs[\"negative_prompt_2\"] = \"different negative prompt\"\n output = sd_pipe(**inputs)\n image_slice_3 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are not equal\n assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4\n\n def test_stable_diffusion_xl_img2img_negative_conditions(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n\n sd_pipe = self.pipeline_class(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice_with_no_neg_conditions = image[0, -3:, -3:, -1]\n\n image = sd_pipe(\n **inputs,\n negative_original_size=(512, 512),\n negative_crops_coords_top_left=(\n 0,\n 0,\n ),\n negative_target_size=(1024, 1024),\n ).images\n image_slice_with_neg_conditions = image[0, -3:, -3:, -1]\n\n assert (\n np.abs(image_slice_with_no_neg_conditions.flatten() - image_slice_with_neg_conditions.flatten()).max()\n > 1e-4\n )\n\n def test_pipeline_interrupt(self):\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n\n prompt = \"hey\"\n num_inference_steps = 5\n\n # store intermediate latents from the generation process\n class PipelineState:\n def __init__(self):\n self.state = []\n\n def apply(self, pipe, i, t, callback_kwargs):\n self.state.append(callback_kwargs[\"latents\"])\n return callback_kwargs\n\n pipe_state = PipelineState()\n sd_pipe(\n prompt,\n image=inputs[\"image\"],\n strength=0.8,\n num_inference_steps=num_inference_steps,\n output_type=\"np\",\n generator=torch.Generator(\"cpu\").manual_seed(0),\n callback_on_step_end=pipe_state.apply,\n ).images\n\n # interrupt generation at step index\n interrupt_step_idx = 1\n\n def callback_on_step_end(pipe, i, t, callback_kwargs):\n if i == interrupt_step_idx:\n pipe._interrupt = True\n\n return callback_kwargs\n\n output_interrupted = sd_pipe(\n prompt,\n image=inputs[\"image\"],\n strength=0.8,\n num_inference_steps=num_inference_steps,\n output_type=\"latent\",\n generator=torch.Generator(\"cpu\").manual_seed(0),\n callback_on_step_end=callback_on_step_end,\n ).images\n\n # fetch intermediate latents at the interrupted step\n # from the completed generation process\n intermediate_latent = pipe_state.state[interrupt_step_idx]\n\n # compare the intermediate latent to the output of the interrupted process\n # they should be the same\n assert torch.allclose(intermediate_latent, output_interrupted, atol=1e-4)\n\n\nclass StableDiffusionXLImg2ImgRefinerOnlyPipelineFastTests(\n PipelineLatentTesterMixin, PipelineTesterMixin, SDXLOptionalComponentsTesterMixin, unittest.TestCase\n):\n pipeline_class = StableDiffusionXLImg2ImgPipeline\n params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {\"height\", \"width\"}\n required_optional_params = PipelineTesterMixin.required_optional_params - {\"latents\"}\n batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS\n image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2),\n projection_class_embeddings_input_dim=72, # 5 * 8 + 32\n cross_attention_dim=32,\n )\n scheduler = EulerDiscreteScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n steps_offset=1,\n beta_schedule=\"scaled_linear\",\n timestep_spacing=\"leading\",\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n sample_size=128,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n # SD2-specific config below\n hidden_act=\"gelu\",\n projection_dim=32,\n )\n text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)\n tokenizer_2 = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"tokenizer\": None,\n \"text_encoder\": None,\n \"text_encoder_2\": text_encoder_2,\n \"tokenizer_2\": tokenizer_2,\n \"requires_aesthetics_score\": True,\n \"image_encoder\": None,\n \"feature_extractor\": None,\n }\n return components\n\n def test_components_function(self):\n init_components = self.get_dummy_components()\n init_components.pop(\"requires_aesthetics_score\")\n pipe = self.pipeline_class(**init_components)\n\n self.assertTrue(hasattr(pipe, \"components\"))\n self.assertTrue(set(pipe.components.keys()) == set(init_components.keys()))\n\n def get_dummy_inputs(self, device, seed=0):\n image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n image = image / 2 + 0.5\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"image\": image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 5.0,\n \"output_type\": \"np\",\n \"strength\": 0.8,\n }\n return inputs\n\n def test_stable_diffusion_xl_img2img_euler(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 32, 32, 3)\n\n expected_slice = np.array([0.4745, 0.4924, 0.4338, 0.6468, 0.5547, 0.4419, 0.5646, 0.5897, 0.5146])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n @require_torch_gpu\n def test_stable_diffusion_xl_offloads(self):\n pipes = []\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLImg2ImgPipeline(**components).to(torch_device)\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)\n sd_pipe.enable_model_cpu_offload()\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)\n sd_pipe.enable_sequential_cpu_offload()\n pipes.append(sd_pipe)\n\n image_slices = []\n for pipe in pipes:\n pipe.unet.set_default_attn_processor()\n\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n image = pipe(**inputs).images\n\n image_slices.append(image[0, -3:, -3:, -1].flatten())\n\n assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3\n assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3\n\n def test_stable_diffusion_xl_img2img_negative_conditions(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n\n sd_pipe = self.pipeline_class(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice_with_no_neg_conditions = image[0, -3:, -3:, -1]\n\n image = sd_pipe(\n **inputs,\n negative_original_size=(512, 512),\n negative_crops_coords_top_left=(\n 0,\n 0,\n ),\n negative_target_size=(1024, 1024),\n ).images\n image_slice_with_neg_conditions = image[0, -3:, -3:, -1]\n\n assert (\n np.abs(image_slice_with_no_neg_conditions.flatten() - image_slice_with_neg_conditions.flatten()).max()\n > 1e-4\n )\n\n def test_stable_diffusion_xl_img2img_negative_prompt_embeds(self):\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n # forward without prompt embeds\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n negative_prompt = 3 * [\"this is a negative prompt\"]\n inputs[\"negative_prompt\"] = negative_prompt\n inputs[\"prompt\"] = 3 * [inputs[\"prompt\"]]\n\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with prompt embeds\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n negative_prompt = 3 * [\"this is a negative prompt\"]\n prompt = 3 * [inputs.pop(\"prompt\")]\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = sd_pipe.encode_prompt(prompt, negative_prompt=negative_prompt)\n\n output = sd_pipe(\n **inputs,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n )\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # make sure that it's equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n def test_stable_diffusion_xl_img2img_prompt_embeds_only(self):\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n # forward without prompt embeds\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n inputs[\"prompt\"] = 3 * [inputs[\"prompt\"]]\n\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with prompt embeds\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n prompt = 3 * [inputs.pop(\"prompt\")]\n\n (\n prompt_embeds,\n _,\n pooled_prompt_embeds,\n _,\n ) = sd_pipe.encode_prompt(prompt)\n\n output = sd_pipe(\n **inputs,\n prompt_embeds=prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n )\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # make sure that it's equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n def test_attention_slicing_forward_pass(self):\n super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=3e-3)\n\n def test_save_load_optional_components(self):\n self._test_save_load_optional_components()\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_inpaint.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport copy\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextConfig,\n CLIPTextModel,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionConfig,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EulerDiscreteScheduler,\n HeunDiscreteScheduler,\n LCMScheduler,\n StableDiffusionXLInpaintPipeline,\n UNet2DConditionModel,\n UniPCMultistepScheduler,\n)\nfrom diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, slow, torch_device\n\nfrom ..pipeline_params import (\n TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,\n TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,\n TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,\n)\nfrom ..test_pipelines_common import IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass StableDiffusionXLInpaintPipelineFastTests(\n IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase\n):\n pipeline_class = StableDiffusionXLInpaintPipeline\n params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS\n batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS\n image_params = frozenset([])\n # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess\n image_latents_params = frozenset([])\n callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union(\n {\n \"add_text_embeds\",\n \"add_time_ids\",\n \"mask\",\n \"masked_image_latents\",\n }\n )\n\n def get_dummy_components(self, skip_first_text_encoder=False, time_cond_proj_dim=None):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n time_cond_proj_dim=time_cond_proj_dim,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2),\n projection_class_embeddings_input_dim=72, # 5 * 8 + 32\n cross_attention_dim=64 if not skip_first_text_encoder else 32,\n )\n scheduler = EulerDiscreteScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n steps_offset=1,\n beta_schedule=\"scaled_linear\",\n timestep_spacing=\"leading\",\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n sample_size=128,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n # SD2-specific config below\n hidden_act=\"gelu\",\n projection_dim=32,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)\n tokenizer_2 = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n torch.manual_seed(0)\n image_encoder_config = CLIPVisionConfig(\n hidden_size=32,\n image_size=224,\n projection_dim=32,\n intermediate_size=37,\n num_attention_heads=4,\n num_channels=3,\n num_hidden_layers=5,\n patch_size=14,\n )\n\n image_encoder = CLIPVisionModelWithProjection(image_encoder_config)\n\n feature_extractor = CLIPImageProcessor(\n crop_size=224,\n do_center_crop=True,\n do_normalize=True,\n do_resize=True,\n image_mean=[0.48145466, 0.4578275, 0.40821073],\n image_std=[0.26862954, 0.26130258, 0.27577711],\n resample=3,\n size=224,\n )\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder if not skip_first_text_encoder else None,\n \"tokenizer\": tokenizer if not skip_first_text_encoder else None,\n \"text_encoder_2\": text_encoder_2,\n \"tokenizer_2\": tokenizer_2,\n \"image_encoder\": image_encoder,\n \"feature_extractor\": feature_extractor,\n \"requires_aesthetics_score\": True,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched\n image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n image = image.cpu().permute(0, 2, 3, 1)[0]\n init_image = Image.fromarray(np.uint8(image)).convert(\"RGB\").resize((64, 64))\n # create mask\n image[8:, 8:, :] = 255\n mask_image = Image.fromarray(np.uint8(image)).convert(\"L\").resize((64, 64))\n\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"image\": init_image,\n \"mask_image\": mask_image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"strength\": 1.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def get_dummy_inputs_2images(self, device, seed=0, img_res=64):\n # Get random floats in [0, 1] as image with spatial size (img_res, img_res)\n image1 = floats_tensor((1, 3, img_res, img_res), rng=random.Random(seed)).to(device)\n image2 = floats_tensor((1, 3, img_res, img_res), rng=random.Random(seed + 22)).to(device)\n # Convert images to [-1, 1]\n init_image1 = 2.0 * image1 - 1.0\n init_image2 = 2.0 * image2 - 1.0\n\n # empty mask\n mask_image = torch.zeros((1, 1, img_res, img_res), device=device)\n\n if str(device).startswith(\"mps\"):\n generator1 = torch.manual_seed(seed)\n generator2 = torch.manual_seed(seed)\n else:\n generator1 = torch.Generator(device=device).manual_seed(seed)\n generator2 = torch.Generator(device=device).manual_seed(seed)\n\n inputs = {\n \"prompt\": [\"A painting of a squirrel eating a burger\"] * 2,\n \"image\": [init_image1, init_image2],\n \"mask_image\": [mask_image] * 2,\n \"generator\": [generator1, generator2],\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_ip_adapter_single(self):\n expected_pipe_slice = None\n if torch_device == \"cpu\":\n expected_pipe_slice = np.array([0.7971, 0.5371, 0.5973, 0.5642, 0.6689, 0.6894, 0.5770, 0.6063, 0.5261])\n return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)\n\n def test_components_function(self):\n init_components = self.get_dummy_components()\n init_components.pop(\"requires_aesthetics_score\")\n pipe = self.pipeline_class(**init_components)\n\n self.assertTrue(hasattr(pipe, \"components\"))\n self.assertTrue(set(pipe.components.keys()) == set(init_components.keys()))\n\n def test_stable_diffusion_xl_inpaint_euler(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLInpaintPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.8029, 0.5523, 0.5825, 0.6003, 0.6702, 0.7018, 0.6369, 0.5955, 0.5123])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_xl_inpaint_euler_lcm(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionXLInpaintPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.config)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.6611, 0.5569, 0.5531, 0.5471, 0.5918, 0.6393, 0.5074, 0.5468, 0.5185])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_xl_inpaint_euler_lcm_custom_timesteps(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components(time_cond_proj_dim=256)\n sd_pipe = StableDiffusionXLInpaintPipeline(**components)\n sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.config)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n del inputs[\"num_inference_steps\"]\n inputs[\"timesteps\"] = [999, 499]\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.6611, 0.5569, 0.5531, 0.5471, 0.5918, 0.6393, 0.5074, 0.5468, 0.5185])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_attention_slicing_forward_pass(self):\n super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=3e-3)\n\n # TODO(Patrick, Sayak) - skip for now as this requires more refiner tests\n def test_save_load_optional_components(self):\n pass\n\n def test_stable_diffusion_xl_inpaint_negative_prompt_embeds(self):\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLInpaintPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n # forward without prompt embeds\n inputs = self.get_dummy_inputs(torch_device)\n negative_prompt = 3 * [\"this is a negative prompt\"]\n inputs[\"negative_prompt\"] = negative_prompt\n inputs[\"prompt\"] = 3 * [inputs[\"prompt\"]]\n\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with prompt embeds\n inputs = self.get_dummy_inputs(torch_device)\n negative_prompt = 3 * [\"this is a negative prompt\"]\n prompt = 3 * [inputs.pop(\"prompt\")]\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = sd_pipe.encode_prompt(prompt, negative_prompt=negative_prompt)\n\n output = sd_pipe(\n **inputs,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n )\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # make sure that it's equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n @require_torch_gpu\n def test_stable_diffusion_xl_offloads(self):\n pipes = []\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLInpaintPipeline(**components).to(torch_device)\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLInpaintPipeline(**components)\n sd_pipe.enable_model_cpu_offload()\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLInpaintPipeline(**components)\n sd_pipe.enable_sequential_cpu_offload()\n pipes.append(sd_pipe)\n\n image_slices = []\n for pipe in pipes:\n pipe.unet.set_default_attn_processor()\n\n inputs = self.get_dummy_inputs(torch_device)\n image = pipe(**inputs).images\n\n image_slices.append(image[0, -3:, -3:, -1].flatten())\n\n assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3\n assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3\n\n def test_stable_diffusion_xl_refiner(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components(skip_first_text_encoder=True)\n\n sd_pipe = self.pipeline_class(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.7045, 0.4838, 0.5454, 0.6270, 0.6168, 0.6717, 0.6484, 0.5681, 0.4922])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_two_xl_mixture_of_denoiser_fast(self):\n components = self.get_dummy_components()\n pipe_1 = StableDiffusionXLInpaintPipeline(**components).to(torch_device)\n pipe_1.unet.set_default_attn_processor()\n pipe_2 = StableDiffusionXLInpaintPipeline(**components).to(torch_device)\n pipe_2.unet.set_default_attn_processor()\n\n def assert_run_mixture(\n num_steps, split, scheduler_cls_orig, num_train_timesteps=pipe_1.scheduler.config.num_train_timesteps\n ):\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = num_steps\n\n class scheduler_cls(scheduler_cls_orig):\n pass\n\n pipe_1.scheduler = scheduler_cls.from_config(pipe_1.scheduler.config)\n pipe_2.scheduler = scheduler_cls.from_config(pipe_2.scheduler.config)\n\n # Let's retrieve the number of timesteps we want to use\n pipe_1.scheduler.set_timesteps(num_steps)\n expected_steps = pipe_1.scheduler.timesteps.tolist()\n\n split_ts = num_train_timesteps - int(round(num_train_timesteps * split))\n\n if pipe_1.scheduler.order == 2:\n expected_steps_1 = list(filter(lambda ts: ts >= split_ts, expected_steps))\n expected_steps_2 = expected_steps_1[-1:] + list(filter(lambda ts: ts < split_ts, expected_steps))\n expected_steps = expected_steps_1 + expected_steps_2\n else:\n expected_steps_1 = list(filter(lambda ts: ts >= split_ts, expected_steps))\n expected_steps_2 = list(filter(lambda ts: ts < split_ts, expected_steps))\n\n # now we monkey patch step `done_steps`\n # list into the step function for testing\n done_steps = []\n old_step = copy.copy(scheduler_cls.step)\n\n def new_step(self, *args, **kwargs):\n done_steps.append(args[1].cpu().item()) # args[1] is always the passed `t`\n return old_step(self, *args, **kwargs)\n\n scheduler_cls.step = new_step\n\n inputs_1 = {**inputs, **{\"denoising_end\": split, \"output_type\": \"latent\"}}\n latents = pipe_1(**inputs_1).images[0]\n\n assert expected_steps_1 == done_steps, f\"Failure with {scheduler_cls.__name__} and {num_steps} and {split}\"\n\n inputs_2 = {**inputs, **{\"denoising_start\": split, \"image\": latents}}\n pipe_2(**inputs_2).images[0]\n\n assert expected_steps_2 == done_steps[len(expected_steps_1) :]\n assert expected_steps == done_steps, f\"Failure with {scheduler_cls.__name__} and {num_steps} and {split}\"\n\n for steps in [7, 20]:\n assert_run_mixture(steps, 0.33, EulerDiscreteScheduler)\n assert_run_mixture(steps, 0.33, HeunDiscreteScheduler)\n\n @slow\n def test_stable_diffusion_two_xl_mixture_of_denoiser(self):\n components = self.get_dummy_components()\n pipe_1 = StableDiffusionXLInpaintPipeline(**components).to(torch_device)\n pipe_1.unet.set_default_attn_processor()\n pipe_2 = StableDiffusionXLInpaintPipeline(**components).to(torch_device)\n pipe_2.unet.set_default_attn_processor()\n\n def assert_run_mixture(\n num_steps, split, scheduler_cls_orig, num_train_timesteps=pipe_1.scheduler.config.num_train_timesteps\n ):\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = num_steps\n\n class scheduler_cls(scheduler_cls_orig):\n pass\n\n pipe_1.scheduler = scheduler_cls.from_config(pipe_1.scheduler.config)\n pipe_2.scheduler = scheduler_cls.from_config(pipe_2.scheduler.config)\n\n # Let's retrieve the number of timesteps we want to use\n pipe_1.scheduler.set_timesteps(num_steps)\n expected_steps = pipe_1.scheduler.timesteps.tolist()\n\n split_ts = num_train_timesteps - int(round(num_train_timesteps * split))\n\n if pipe_1.scheduler.order == 2:\n expected_steps_1 = list(filter(lambda ts: ts >= split_ts, expected_steps))\n expected_steps_2 = expected_steps_1[-1:] + list(filter(lambda ts: ts < split_ts, expected_steps))\n expected_steps = expected_steps_1 + expected_steps_2\n else:\n expected_steps_1 = list(filter(lambda ts: ts >= split_ts, expected_steps))\n expected_steps_2 = list(filter(lambda ts: ts < split_ts, expected_steps))\n\n # now we monkey patch step `done_steps`\n # list into the step function for testing\n done_steps = []\n old_step = copy.copy(scheduler_cls.step)\n\n def new_step(self, *args, **kwargs):\n done_steps.append(args[1].cpu().item()) # args[1] is always the passed `t`\n return old_step(self, *args, **kwargs)\n\n scheduler_cls.step = new_step\n\n inputs_1 = {**inputs, **{\"denoising_end\": split, \"output_type\": \"latent\"}}\n latents = pipe_1(**inputs_1).images[0]\n\n assert expected_steps_1 == done_steps, f\"Failure with {scheduler_cls.__name__} and {num_steps} and {split}\"\n\n inputs_2 = {**inputs, **{\"denoising_start\": split, \"image\": latents}}\n pipe_2(**inputs_2).images[0]\n\n assert expected_steps_2 == done_steps[len(expected_steps_1) :]\n assert expected_steps == done_steps, f\"Failure with {scheduler_cls.__name__} and {num_steps} and {split}\"\n\n for steps in [5, 8, 20]:\n for split in [0.33, 0.49, 0.71]:\n for scheduler_cls in [\n DDIMScheduler,\n EulerDiscreteScheduler,\n DPMSolverMultistepScheduler,\n UniPCMultistepScheduler,\n HeunDiscreteScheduler,\n ]:\n assert_run_mixture(steps, split, scheduler_cls)\n\n @slow\n def test_stable_diffusion_three_xl_mixture_of_denoiser(self):\n components = self.get_dummy_components()\n pipe_1 = StableDiffusionXLInpaintPipeline(**components).to(torch_device)\n pipe_1.unet.set_default_attn_processor()\n pipe_2 = StableDiffusionXLInpaintPipeline(**components).to(torch_device)\n pipe_2.unet.set_default_attn_processor()\n pipe_3 = StableDiffusionXLInpaintPipeline(**components).to(torch_device)\n pipe_3.unet.set_default_attn_processor()\n\n def assert_run_mixture(\n num_steps,\n split_1,\n split_2,\n scheduler_cls_orig,\n num_train_timesteps=pipe_1.scheduler.config.num_train_timesteps,\n ):\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = num_steps\n\n class scheduler_cls(scheduler_cls_orig):\n pass\n\n pipe_1.scheduler = scheduler_cls.from_config(pipe_1.scheduler.config)\n pipe_2.scheduler = scheduler_cls.from_config(pipe_2.scheduler.config)\n pipe_3.scheduler = scheduler_cls.from_config(pipe_3.scheduler.config)\n\n # Let's retrieve the number of timesteps we want to use\n pipe_1.scheduler.set_timesteps(num_steps)\n expected_steps = pipe_1.scheduler.timesteps.tolist()\n\n split_1_ts = num_train_timesteps - int(round(num_train_timesteps * split_1))\n split_2_ts = num_train_timesteps - int(round(num_train_timesteps * split_2))\n\n if pipe_1.scheduler.order == 2:\n expected_steps_1 = list(filter(lambda ts: ts >= split_1_ts, expected_steps))\n expected_steps_2 = expected_steps_1[-1:] + list(\n filter(lambda ts: ts >= split_2_ts and ts < split_1_ts, expected_steps)\n )\n expected_steps_3 = expected_steps_2[-1:] + list(filter(lambda ts: ts < split_2_ts, expected_steps))\n expected_steps = expected_steps_1 + expected_steps_2 + expected_steps_3\n else:\n expected_steps_1 = list(filter(lambda ts: ts >= split_1_ts, expected_steps))\n expected_steps_2 = list(filter(lambda ts: ts >= split_2_ts and ts < split_1_ts, expected_steps))\n expected_steps_3 = list(filter(lambda ts: ts < split_2_ts, expected_steps))\n\n # now we monkey patch step `done_steps`\n # list into the step function for testing\n done_steps = []\n old_step = copy.copy(scheduler_cls.step)\n\n def new_step(self, *args, **kwargs):\n done_steps.append(args[1].cpu().item()) # args[1] is always the passed `t`\n return old_step(self, *args, **kwargs)\n\n scheduler_cls.step = new_step\n\n inputs_1 = {**inputs, **{\"denoising_end\": split_1, \"output_type\": \"latent\"}}\n latents = pipe_1(**inputs_1).images[0]\n\n assert (\n expected_steps_1 == done_steps\n ), f\"Failure with {scheduler_cls.__name__} and {num_steps} and {split_1} and {split_2}\"\n\n inputs_2 = {\n **inputs,\n **{\"denoising_start\": split_1, \"denoising_end\": split_2, \"image\": latents, \"output_type\": \"latent\"},\n }\n pipe_2(**inputs_2).images[0]\n\n assert expected_steps_2 == done_steps[len(expected_steps_1) :]\n\n inputs_3 = {**inputs, **{\"denoising_start\": split_2, \"image\": latents}}\n pipe_3(**inputs_3).images[0]\n\n assert expected_steps_3 == done_steps[len(expected_steps_1) + len(expected_steps_2) :]\n assert (\n expected_steps == done_steps\n ), f\"Failure with {scheduler_cls.__name__} and {num_steps} and {split_1} and {split_2}\"\n\n for steps in [7, 11, 20]:\n for split_1, split_2 in zip([0.19, 0.32], [0.81, 0.68]):\n for scheduler_cls in [\n DDIMScheduler,\n EulerDiscreteScheduler,\n DPMSolverMultistepScheduler,\n UniPCMultistepScheduler,\n HeunDiscreteScheduler,\n ]:\n assert_run_mixture(steps, split_1, split_2, scheduler_cls)\n\n def test_stable_diffusion_xl_multi_prompts(self):\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(torch_device)\n\n # forward with single prompt\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = 5\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with same prompt duplicated\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = 5\n inputs[\"prompt_2\"] = inputs[\"prompt\"]\n output = sd_pipe(**inputs)\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n # forward with different prompt\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = 5\n inputs[\"prompt_2\"] = \"different prompt\"\n output = sd_pipe(**inputs)\n image_slice_3 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are not equal\n assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4\n\n # manually set a negative_prompt\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = 5\n inputs[\"negative_prompt\"] = \"negative prompt\"\n output = sd_pipe(**inputs)\n image_slice_1 = output.images[0, -3:, -3:, -1]\n\n # forward with same negative_prompt duplicated\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = 5\n inputs[\"negative_prompt\"] = \"negative prompt\"\n inputs[\"negative_prompt_2\"] = inputs[\"negative_prompt\"]\n output = sd_pipe(**inputs)\n image_slice_2 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are equal\n assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4\n\n # forward with different negative_prompt\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = 5\n inputs[\"negative_prompt\"] = \"negative prompt\"\n inputs[\"negative_prompt_2\"] = \"different negative prompt\"\n output = sd_pipe(**inputs)\n image_slice_3 = output.images[0, -3:, -3:, -1]\n\n # ensure the results are not equal\n assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4\n\n def test_stable_diffusion_xl_img2img_negative_conditions(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe(**inputs).images\n image_slice_with_no_neg_conditions = image[0, -3:, -3:, -1]\n\n image = sd_pipe(\n **inputs,\n negative_original_size=(512, 512),\n negative_crops_coords_top_left=(\n 0,\n 0,\n ),\n negative_target_size=(1024, 1024),\n ).images\n image_slice_with_neg_conditions = image[0, -3:, -3:, -1]\n\n assert (\n np.abs(image_slice_with_no_neg_conditions.flatten() - image_slice_with_neg_conditions.flatten()).max()\n > 1e-4\n )\n\n def test_stable_diffusion_xl_inpaint_mask_latents(self):\n device = \"cpu\"\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n # normal mask + normal image\n ## `image`: pil, `mask_image``: pil, `masked_image_latents``: None\n inputs = self.get_dummy_inputs(device)\n inputs[\"strength\"] = 0.9\n out_0 = sd_pipe(**inputs).images\n\n # image latents + mask latents\n inputs = self.get_dummy_inputs(device)\n image = sd_pipe.image_processor.preprocess(inputs[\"image\"]).to(sd_pipe.device)\n mask = sd_pipe.mask_processor.preprocess(inputs[\"mask_image\"]).to(sd_pipe.device)\n masked_image = image * (mask < 0.5)\n\n generator = torch.Generator(device=device).manual_seed(0)\n image_latents = sd_pipe._encode_vae_image(image, generator=generator)\n torch.randn((1, 4, 32, 32), generator=generator)\n mask_latents = sd_pipe._encode_vae_image(masked_image, generator=generator)\n inputs[\"image\"] = image_latents\n inputs[\"masked_image_latents\"] = mask_latents\n inputs[\"mask_image\"] = mask\n inputs[\"strength\"] = 0.9\n generator = torch.Generator(device=device).manual_seed(0)\n torch.randn((1, 4, 32, 32), generator=generator)\n inputs[\"generator\"] = generator\n out_1 = sd_pipe(**inputs).images\n assert np.abs(out_0 - out_1).max() < 1e-2\n\n def test_stable_diffusion_xl_inpaint_2_images(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n # test to confirm if we pass two same image, we will get same output\n inputs = self.get_dummy_inputs(device)\n gen1 = torch.Generator(device=device).manual_seed(0)\n gen2 = torch.Generator(device=device).manual_seed(0)\n for name in [\"prompt\", \"image\", \"mask_image\"]:\n inputs[name] = [inputs[name]] * 2\n inputs[\"generator\"] = [gen1, gen2]\n images = sd_pipe(**inputs).images\n\n assert images.shape == (2, 64, 64, 3)\n\n image_slice1 = images[0, -3:, -3:, -1]\n image_slice2 = images[1, -3:, -3:, -1]\n assert np.abs(image_slice1.flatten() - image_slice2.flatten()).max() < 1e-4\n\n # test to confirm that if we pass two different images, we will get different output\n inputs = self.get_dummy_inputs_2images(device)\n images = sd_pipe(**inputs).images\n assert images.shape == (2, 64, 64, 3)\n\n image_slice1 = images[0, -3:, -3:, -1]\n image_slice2 = images[1, -3:, -3:, -1]\n assert np.abs(image_slice1.flatten() - image_slice2.flatten()).max() > 1e-2\n\n def test_pipeline_interrupt(self):\n components = self.get_dummy_components()\n sd_pipe = StableDiffusionXLInpaintPipeline(**components)\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n\n prompt = \"hey\"\n num_inference_steps = 5\n\n # store intermediate latents from the generation process\n class PipelineState:\n def __init__(self):\n self.state = []\n\n def apply(self, pipe, i, t, callback_kwargs):\n self.state.append(callback_kwargs[\"latents\"])\n return callback_kwargs\n\n pipe_state = PipelineState()\n sd_pipe(\n prompt,\n image=inputs[\"image\"],\n mask_image=inputs[\"mask_image\"],\n strength=0.8,\n num_inference_steps=num_inference_steps,\n output_type=\"np\",\n generator=torch.Generator(\"cpu\").manual_seed(0),\n callback_on_step_end=pipe_state.apply,\n ).images\n\n # interrupt generation at step index\n interrupt_step_idx = 1\n\n def callback_on_step_end(pipe, i, t, callback_kwargs):\n if i == interrupt_step_idx:\n pipe._interrupt = True\n\n return callback_kwargs\n\n output_interrupted = sd_pipe(\n prompt,\n image=inputs[\"image\"],\n mask_image=inputs[\"mask_image\"],\n strength=0.8,\n num_inference_steps=num_inference_steps,\n output_type=\"latent\",\n generator=torch.Generator(\"cpu\").manual_seed(0),\n callback_on_step_end=callback_on_step_end,\n ).images\n\n # fetch intermediate latents at the interrupted step\n # from the completed generation process\n intermediate_latent = pipe_state.state[interrupt_step_idx]\n\n # compare the intermediate latent to the output of the interrupted process\n # they should be the same\n assert torch.allclose(intermediate_latent, output_interrupted, atol=1e-4)\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_instruction_pix2pix.py", "content": "# coding=utf-8\n# Copyright 2024 Harutatsu Akiyama and HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n EulerDiscreteScheduler,\n UNet2DConditionModel,\n)\nfrom diffusers.image_processor import VaeImageProcessor\nfrom diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_instruct_pix2pix import (\n StableDiffusionXLInstructPix2PixPipeline,\n)\nfrom diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, torch_device\n\nfrom ..pipeline_params import (\n IMAGE_TO_IMAGE_IMAGE_PARAMS,\n TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,\n TEXT_GUIDED_IMAGE_VARIATION_PARAMS,\n)\nfrom ..test_pipelines_common import (\n PipelineKarrasSchedulerTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n SDXLOptionalComponentsTesterMixin,\n)\n\n\nenable_full_determinism()\n\n\nclass StableDiffusionXLInstructPix2PixPipelineFastTests(\n PipelineLatentTesterMixin,\n PipelineKarrasSchedulerTesterMixin,\n PipelineTesterMixin,\n SDXLOptionalComponentsTesterMixin,\n unittest.TestCase,\n):\n pipeline_class = StableDiffusionXLInstructPix2PixPipeline\n params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {\"height\", \"width\", \"cross_attention_kwargs\"}\n batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS\n image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=8,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2),\n projection_class_embeddings_input_dim=80, # 5 * 8 + 32\n cross_attention_dim=64,\n )\n\n scheduler = EulerDiscreteScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n steps_offset=1,\n beta_schedule=\"scaled_linear\",\n timestep_spacing=\"leading\",\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n sample_size=128,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n # SD2-specific config below\n hidden_act=\"gelu\",\n projection_dim=32,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)\n tokenizer_2 = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"text_encoder_2\": text_encoder_2,\n \"tokenizer_2\": tokenizer_2,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n image = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device)\n image = image / 2 + 0.5\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"image\": image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"image_guidance_scale\": 1,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_components_function(self):\n init_components = self.get_dummy_components()\n pipe = self.pipeline_class(**init_components)\n\n self.assertTrue(hasattr(pipe, \"components\"))\n self.assertTrue(set(pipe.components.keys()) == set(init_components.keys()))\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=3e-3)\n\n def test_attention_slicing_forward_pass(self):\n super().test_attention_slicing_forward_pass(expected_max_diff=2e-3)\n\n # Overwrite the default test_latents_inputs because pix2pix encode the image differently\n def test_latents_input(self):\n components = self.get_dummy_components()\n pipe = StableDiffusionXLInstructPix2PixPipeline(**components)\n pipe.image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n out = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type=\"pt\"))[0]\n\n vae = components[\"vae\"]\n inputs = self.get_dummy_inputs_by_type(torch_device, input_image_type=\"pt\")\n\n for image_param in self.image_latents_params:\n if image_param in inputs.keys():\n inputs[image_param] = vae.encode(inputs[image_param]).latent_dist.mode()\n\n out_latents_inputs = pipe(**inputs)[0]\n\n max_diff = np.abs(out - out_latents_inputs).max()\n self.assertLess(max_diff, 1e-4, \"passing latents as image input generate different result from passing image\")\n\n def test_cfg(self):\n pass\n\n def test_save_load_optional_components(self):\n self._test_save_load_optional_components()\n" }, { "path": "diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_k_diffusion.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nimport numpy as np\nimport torch\n\nfrom diffusers import StableDiffusionXLKDiffusionPipeline\nfrom diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device\n\n\nenable_full_determinism()\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionXLKPipelineIntegrationTests(unittest.TestCase):\n dtype = torch.float16\n\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_stable_diffusion_xl(self):\n sd_pipe = StableDiffusionXLKDiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=self.dtype\n )\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n sd_pipe.set_scheduler(\"sample_euler\")\n\n prompt = \"A painting of a squirrel eating a burger\"\n generator = torch.manual_seed(0)\n output = sd_pipe(\n [prompt],\n generator=generator,\n guidance_scale=9.0,\n num_inference_steps=20,\n height=512,\n width=512,\n output_type=\"np\",\n )\n\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array(\n [0.79600024, 0.796546, 0.80682373, 0.79428387, 0.7905743, 0.8008807, 0.786183, 0.7835959, 0.797892]\n )\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_karras_sigmas(self):\n sd_pipe = StableDiffusionXLKDiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=self.dtype\n )\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n sd_pipe.set_scheduler(\"sample_dpmpp_2m\")\n\n prompt = \"A painting of a squirrel eating a burger\"\n generator = torch.manual_seed(0)\n output = sd_pipe(\n [prompt],\n generator=generator,\n guidance_scale=7.5,\n num_inference_steps=15,\n output_type=\"np\",\n use_karras_sigmas=True,\n height=512,\n width=512,\n )\n\n image = output.images\n\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array(\n [0.9506951, 0.9527786, 0.95309967, 0.9511477, 0.952523, 0.9515326, 0.9511933, 0.9480397, 0.94930184]\n )\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_stable_diffusion_noise_sampler_seed(self):\n sd_pipe = StableDiffusionXLKDiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=self.dtype\n )\n sd_pipe = sd_pipe.to(torch_device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n sd_pipe.set_scheduler(\"sample_dpmpp_sde\")\n\n prompt = \"A painting of a squirrel eating a burger\"\n seed = 0\n images1 = sd_pipe(\n [prompt],\n generator=torch.manual_seed(seed),\n noise_sampler_seed=seed,\n guidance_scale=9.0,\n num_inference_steps=20,\n output_type=\"np\",\n height=512,\n width=512,\n ).images\n images2 = sd_pipe(\n [prompt],\n generator=torch.manual_seed(seed),\n noise_sampler_seed=seed,\n guidance_scale=9.0,\n num_inference_steps=20,\n output_type=\"np\",\n height=512,\n width=512,\n ).images\n assert images1.shape == (1, 512, 512, 3)\n assert images2.shape == (1, 512, 512, 3)\n assert np.abs(images1.flatten() - images2.flatten()).max() < 1e-2\n" }, { "path": "diffusers/tests/pipelines/stable_unclip/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/stable_unclip/test_stable_unclip.py", "content": "import gc\nimport unittest\n\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n DDPMScheduler,\n PriorTransformer,\n StableUnCLIPPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer\nfrom diffusers.utils.testing_utils import enable_full_determinism, load_numpy, nightly, require_torch_gpu, torch_device\n\nfrom ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS\nfrom ..test_pipelines_common import (\n PipelineKarrasSchedulerTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n assert_mean_pixel_difference,\n)\n\n\nenable_full_determinism()\n\n\nclass StableUnCLIPPipelineFastTests(\n PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase\n):\n pipeline_class = StableUnCLIPPipeline\n params = TEXT_TO_IMAGE_PARAMS\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n image_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n\n # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false\n test_xformers_attention = False\n\n def get_dummy_components(self):\n embedder_hidden_size = 32\n embedder_projection_dim = embedder_hidden_size\n\n # prior components\n\n torch.manual_seed(0)\n prior_tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n torch.manual_seed(0)\n prior_text_encoder = CLIPTextModelWithProjection(\n CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=embedder_hidden_size,\n projection_dim=embedder_projection_dim,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n )\n\n torch.manual_seed(0)\n prior = PriorTransformer(\n num_attention_heads=2,\n attention_head_dim=12,\n embedding_dim=embedder_projection_dim,\n num_layers=1,\n )\n\n torch.manual_seed(0)\n prior_scheduler = DDPMScheduler(\n variance_type=\"fixed_small_log\",\n prediction_type=\"sample\",\n num_train_timesteps=1000,\n clip_sample=True,\n clip_sample_range=5.0,\n beta_schedule=\"squaredcos_cap_v2\",\n )\n\n # regular denoising components\n\n torch.manual_seed(0)\n image_normalizer = StableUnCLIPImageNormalizer(embedding_dim=embedder_hidden_size)\n image_noising_scheduler = DDPMScheduler(beta_schedule=\"squaredcos_cap_v2\")\n\n torch.manual_seed(0)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n torch.manual_seed(0)\n text_encoder = CLIPTextModel(\n CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=embedder_hidden_size,\n projection_dim=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n )\n\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"CrossAttnDownBlock2D\", \"DownBlock2D\"),\n up_block_types=(\"UpBlock2D\", \"CrossAttnUpBlock2D\"),\n block_out_channels=(32, 64),\n attention_head_dim=(2, 4),\n class_embed_type=\"projection\",\n # The class embeddings are the noise augmented image embeddings.\n # I.e. the image embeddings concated with the noised embeddings of the same dimension\n projection_class_embeddings_input_dim=embedder_projection_dim * 2,\n cross_attention_dim=embedder_hidden_size,\n layers_per_block=1,\n upcast_attention=True,\n use_linear_projection=True,\n )\n\n torch.manual_seed(0)\n scheduler = DDIMScheduler(\n beta_schedule=\"scaled_linear\",\n beta_start=0.00085,\n beta_end=0.012,\n prediction_type=\"v_prediction\",\n set_alpha_to_one=False,\n steps_offset=1,\n )\n\n torch.manual_seed(0)\n vae = AutoencoderKL()\n\n components = {\n # prior components\n \"prior_tokenizer\": prior_tokenizer,\n \"prior_text_encoder\": prior_text_encoder,\n \"prior\": prior,\n \"prior_scheduler\": prior_scheduler,\n # image noising components\n \"image_normalizer\": image_normalizer,\n \"image_noising_scheduler\": image_noising_scheduler,\n # regular denoising components\n \"tokenizer\": tokenizer,\n \"text_encoder\": text_encoder,\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"prior_num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n return inputs\n\n # Overriding PipelineTesterMixin::test_attention_slicing_forward_pass\n # because UnCLIP GPU undeterminism requires a looser check.\n def test_attention_slicing_forward_pass(self):\n test_max_difference = torch_device == \"cpu\"\n\n self._test_attention_slicing_forward_pass(test_max_difference=test_max_difference)\n\n # Overriding PipelineTesterMixin::test_inference_batch_single_identical\n # because UnCLIP undeterminism requires a looser check.\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=1e-3)\n\n\n@nightly\n@require_torch_gpu\nclass StableUnCLIPPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_stable_unclip(self):\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy\"\n )\n\n pipe = StableUnCLIPPipeline.from_pretrained(\"fusing/stable-unclip-2-1-l\", torch_dtype=torch.float16)\n pipe.set_progress_bar_config(disable=None)\n # stable unclip will oom when integration tests are run on a V100,\n # so turn on memory savings\n pipe.enable_attention_slicing()\n pipe.enable_sequential_cpu_offload()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n output = pipe(\"anime turle\", generator=generator, output_type=\"np\")\n\n image = output.images[0]\n\n assert image.shape == (768, 768, 3)\n\n assert_mean_pixel_difference(image, expected_image)\n\n def test_stable_unclip_pipeline_with_sequential_cpu_offloading(self):\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n pipe = StableUnCLIPPipeline.from_pretrained(\"fusing/stable-unclip-2-1-l\", torch_dtype=torch.float16)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n pipe.enable_sequential_cpu_offload()\n\n _ = pipe(\n \"anime turtle\",\n prior_num_inference_steps=2,\n num_inference_steps=2,\n output_type=\"np\",\n )\n\n mem_bytes = torch.cuda.max_memory_allocated()\n # make sure that less than 7 GB is allocated\n assert mem_bytes < 7 * 10**9\n" }, { "path": "diffusers/tests/pipelines/stable_unclip/test_stable_unclip_img2img.py", "content": "import gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextConfig,\n CLIPTextModel,\n CLIPTokenizer,\n CLIPVisionConfig,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImg2ImgPipeline, UNet2DConditionModel\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline\nfrom diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n load_numpy,\n nightly,\n require_torch_gpu,\n skip_mps,\n torch_device,\n)\n\nfrom ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS\nfrom ..test_pipelines_common import (\n PipelineKarrasSchedulerTesterMixin,\n PipelineLatentTesterMixin,\n PipelineTesterMixin,\n assert_mean_pixel_difference,\n)\n\n\nenable_full_determinism()\n\n\nclass StableUnCLIPImg2ImgPipelineFastTests(\n PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase\n):\n pipeline_class = StableUnCLIPImg2ImgPipeline\n params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS\n batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS\n image_params = frozenset(\n []\n ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess\n image_latents_params = frozenset([])\n\n def get_dummy_components(self):\n embedder_hidden_size = 32\n embedder_projection_dim = embedder_hidden_size\n\n # image encoding components\n\n feature_extractor = CLIPImageProcessor(crop_size=32, size=32)\n\n torch.manual_seed(0)\n image_encoder = CLIPVisionModelWithProjection(\n CLIPVisionConfig(\n hidden_size=embedder_hidden_size,\n projection_dim=embedder_projection_dim,\n num_hidden_layers=5,\n num_attention_heads=4,\n image_size=32,\n intermediate_size=37,\n patch_size=1,\n )\n )\n\n # regular denoising components\n\n torch.manual_seed(0)\n image_normalizer = StableUnCLIPImageNormalizer(embedding_dim=embedder_hidden_size)\n image_noising_scheduler = DDPMScheduler(beta_schedule=\"squaredcos_cap_v2\")\n\n torch.manual_seed(0)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n torch.manual_seed(0)\n text_encoder = CLIPTextModel(\n CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=embedder_hidden_size,\n projection_dim=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n )\n\n torch.manual_seed(0)\n unet = UNet2DConditionModel(\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"CrossAttnDownBlock2D\", \"DownBlock2D\"),\n up_block_types=(\"UpBlock2D\", \"CrossAttnUpBlock2D\"),\n block_out_channels=(32, 64),\n attention_head_dim=(2, 4),\n class_embed_type=\"projection\",\n # The class embeddings are the noise augmented image embeddings.\n # I.e. the image embeddings concated with the noised embeddings of the same dimension\n projection_class_embeddings_input_dim=embedder_projection_dim * 2,\n cross_attention_dim=embedder_hidden_size,\n layers_per_block=1,\n upcast_attention=True,\n use_linear_projection=True,\n )\n\n torch.manual_seed(0)\n scheduler = DDIMScheduler(\n beta_schedule=\"scaled_linear\",\n beta_start=0.00085,\n beta_end=0.012,\n prediction_type=\"v_prediction\",\n set_alpha_to_one=False,\n steps_offset=1,\n )\n\n torch.manual_seed(0)\n vae = AutoencoderKL()\n\n components = {\n # image encoding components\n \"feature_extractor\": feature_extractor,\n \"image_encoder\": image_encoder.eval(),\n # image noising components\n \"image_normalizer\": image_normalizer.eval(),\n \"image_noising_scheduler\": image_noising_scheduler,\n # regular denoising components\n \"tokenizer\": tokenizer,\n \"text_encoder\": text_encoder.eval(),\n \"unet\": unet.eval(),\n \"scheduler\": scheduler,\n \"vae\": vae.eval(),\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0, pil_image=True):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n input_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n\n if pil_image:\n input_image = input_image * 0.5 + 0.5\n input_image = input_image.clamp(0, 1)\n input_image = input_image.cpu().permute(0, 2, 3, 1).float().numpy()\n input_image = DiffusionPipeline.numpy_to_pil(input_image)[0]\n\n return {\n \"prompt\": \"An anime racoon running a marathon\",\n \"image\": input_image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n\n @skip_mps\n def test_image_embeds_none(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = StableUnCLIPImg2ImgPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs.update({\"image_embeds\": None})\n image = sd_pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1]\n\n assert image.shape == (1, 32, 32, 3)\n expected_slice = np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n # Overriding PipelineTesterMixin::test_attention_slicing_forward_pass\n # because GPU undeterminism requires a looser check.\n def test_attention_slicing_forward_pass(self):\n test_max_difference = torch_device in [\"cpu\", \"mps\"]\n\n self._test_attention_slicing_forward_pass(test_max_difference=test_max_difference)\n\n # Overriding PipelineTesterMixin::test_inference_batch_single_identical\n # because undeterminism requires a looser check.\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=1e-3)\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(test_max_difference=False)\n\n\n@nightly\n@require_torch_gpu\nclass StableUnCLIPImg2ImgPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_stable_unclip_l_img2img(self):\n input_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png\"\n )\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy\"\n )\n\n pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(\n \"fusing/stable-unclip-2-1-l-img2img\", torch_dtype=torch.float16\n )\n pipe.set_progress_bar_config(disable=None)\n # stable unclip will oom when integration tests are run on a V100,\n # so turn on memory savings\n pipe.enable_attention_slicing()\n pipe.enable_sequential_cpu_offload()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n output = pipe(input_image, \"anime turle\", generator=generator, output_type=\"np\")\n\n image = output.images[0]\n\n assert image.shape == (768, 768, 3)\n\n assert_mean_pixel_difference(image, expected_image)\n\n def test_stable_unclip_h_img2img(self):\n input_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png\"\n )\n\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy\"\n )\n\n pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(\n \"fusing/stable-unclip-2-1-h-img2img\", torch_dtype=torch.float16\n )\n pipe.set_progress_bar_config(disable=None)\n # stable unclip will oom when integration tests are run on a V100,\n # so turn on memory savings\n pipe.enable_attention_slicing()\n pipe.enable_sequential_cpu_offload()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n output = pipe(input_image, \"anime turle\", generator=generator, output_type=\"np\")\n\n image = output.images[0]\n\n assert image.shape == (768, 768, 3)\n\n assert_mean_pixel_difference(image, expected_image)\n\n def test_stable_unclip_img2img_pipeline_with_sequential_cpu_offloading(self):\n input_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png\"\n )\n\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(\n \"fusing/stable-unclip-2-1-h-img2img\", torch_dtype=torch.float16\n )\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n pipe.enable_sequential_cpu_offload()\n\n _ = pipe(\n input_image,\n \"anime turtle\",\n num_inference_steps=2,\n output_type=\"np\",\n )\n\n mem_bytes = torch.cuda.max_memory_allocated()\n # make sure that less than 7 GB is allocated\n assert mem_bytes < 7 * 10**9\n" }, { "path": "diffusers/tests/pipelines/stable_video_diffusion/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/stable_video_diffusion/test_stable_video_diffusion.py", "content": "import gc\nimport random\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import (\n CLIPImageProcessor,\n CLIPVisionConfig,\n CLIPVisionModelWithProjection,\n)\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKLTemporalDecoder,\n EulerDiscreteScheduler,\n StableVideoDiffusionPipeline,\n UNetSpatioTemporalConditionModel,\n)\nfrom diffusers.utils import is_accelerate_available, is_accelerate_version, load_image, logging\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.testing_utils import (\n CaptureLogger,\n enable_full_determinism,\n floats_tensor,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\ndef to_np(tensor):\n if isinstance(tensor, torch.Tensor):\n tensor = tensor.detach().cpu().numpy()\n\n return tensor\n\n\nclass StableVideoDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = StableVideoDiffusionPipeline\n params = frozenset([\"image\"])\n batch_params = frozenset([\"image\", \"generator\"])\n required_optional_params = frozenset(\n [\n \"num_inference_steps\",\n \"generator\",\n \"latents\",\n \"return_dict\",\n ]\n )\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNetSpatioTemporalConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=8,\n out_channels=4,\n down_block_types=(\n \"CrossAttnDownBlockSpatioTemporal\",\n \"DownBlockSpatioTemporal\",\n ),\n up_block_types=(\"UpBlockSpatioTemporal\", \"CrossAttnUpBlockSpatioTemporal\"),\n cross_attention_dim=32,\n num_attention_heads=8,\n projection_class_embeddings_input_dim=96,\n addition_time_embed_dim=32,\n )\n scheduler = EulerDiscreteScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n interpolation_type=\"linear\",\n num_train_timesteps=1000,\n prediction_type=\"v_prediction\",\n sigma_max=700.0,\n sigma_min=0.002,\n steps_offset=1,\n timestep_spacing=\"leading\",\n timestep_type=\"continuous\",\n trained_betas=None,\n use_karras_sigmas=True,\n )\n\n torch.manual_seed(0)\n vae = AutoencoderKLTemporalDecoder(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n latent_channels=4,\n )\n\n torch.manual_seed(0)\n config = CLIPVisionConfig(\n hidden_size=32,\n projection_dim=32,\n num_hidden_layers=5,\n num_attention_heads=4,\n image_size=32,\n intermediate_size=37,\n patch_size=1,\n )\n image_encoder = CLIPVisionModelWithProjection(config)\n\n torch.manual_seed(0)\n feature_extractor = CLIPImageProcessor(crop_size=32, size=32)\n components = {\n \"unet\": unet,\n \"image_encoder\": image_encoder,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"feature_extractor\": feature_extractor,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=\"cpu\").manual_seed(seed)\n\n image = floats_tensor((1, 3, 32, 32), rng=random.Random(0)).to(device)\n inputs = {\n \"generator\": generator,\n \"image\": image,\n \"num_inference_steps\": 2,\n \"output_type\": \"pt\",\n \"min_guidance_scale\": 1.0,\n \"max_guidance_scale\": 2.5,\n \"num_frames\": 2,\n \"height\": 32,\n \"width\": 32,\n }\n return inputs\n\n @unittest.skip(\"Deprecated functionality\")\n def test_attention_slicing_forward_pass(self):\n pass\n\n @unittest.skip(\"Batched inference works and outputs look correct, but the test is failing\")\n def test_inference_batch_single_identical(\n self,\n batch_size=2,\n expected_max_diff=1e-4,\n ):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for components in pipe.components.values():\n if hasattr(components, \"set_default_attn_processor\"):\n components.set_default_attn_processor()\n pipe.to(torch_device)\n\n pipe.set_progress_bar_config(disable=None)\n inputs = self.get_dummy_inputs(torch_device)\n\n # Reset generator in case it is has been used in self.get_dummy_inputs\n inputs[\"generator\"] = torch.Generator(\"cpu\").manual_seed(0)\n\n logger = logging.get_logger(pipe.__module__)\n logger.setLevel(level=diffusers.logging.FATAL)\n\n # batchify inputs\n batched_inputs = {}\n batched_inputs.update(inputs)\n\n batched_inputs[\"generator\"] = [torch.Generator(\"cpu\").manual_seed(0) for i in range(batch_size)]\n batched_inputs[\"image\"] = torch.cat([inputs[\"image\"]] * batch_size, dim=0)\n\n output = pipe(**inputs).frames\n output_batch = pipe(**batched_inputs).frames\n\n assert len(output_batch) == batch_size\n\n max_diff = np.abs(to_np(output_batch[0]) - to_np(output[0])).max()\n assert max_diff < expected_max_diff\n\n @unittest.skip(\"Test is similar to test_inference_batch_single_identical\")\n def test_inference_batch_consistent(self):\n pass\n\n def test_np_output_type(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n inputs[\"output_type\"] = \"np\"\n output = pipe(**inputs).frames\n self.assertTrue(isinstance(output, np.ndarray))\n self.assertEqual(len(output.shape), 5)\n\n def test_dict_tuple_outputs_equivalent(self, expected_max_difference=1e-4):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n generator_device = \"cpu\"\n output = pipe(**self.get_dummy_inputs(generator_device)).frames[0]\n output_tuple = pipe(**self.get_dummy_inputs(generator_device), return_dict=False)[0]\n\n max_diff = np.abs(to_np(output) - to_np(output_tuple)).max()\n self.assertLess(max_diff, expected_max_difference)\n\n @unittest.skip(\"Test is currently failing\")\n def test_float16_inference(self, expected_max_diff=5e-2):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n components = self.get_dummy_components()\n pipe_fp16 = self.pipeline_class(**components)\n for component in pipe_fp16.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n\n pipe_fp16.to(torch_device, torch.float16)\n pipe_fp16.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n output = pipe(**inputs).frames[0]\n\n fp16_inputs = self.get_dummy_inputs(torch_device)\n output_fp16 = pipe_fp16(**fp16_inputs).frames[0]\n\n max_diff = np.abs(to_np(output) - to_np(output_fp16)).max()\n self.assertLess(max_diff, expected_max_diff, \"The outputs of the fp16 and fp32 pipelines are too different.\")\n\n @unittest.skipIf(torch_device != \"cuda\", reason=\"float16 requires CUDA\")\n def test_save_load_float16(self, expected_max_diff=1e-2):\n components = self.get_dummy_components()\n for name, module in components.items():\n if hasattr(module, \"half\"):\n components[name] = module.to(torch_device).half()\n\n pipe = self.pipeline_class(**components)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n output = pipe(**inputs).frames[0]\n\n with tempfile.TemporaryDirectory() as tmpdir:\n pipe.save_pretrained(tmpdir)\n pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, torch_dtype=torch.float16)\n for component in pipe_loaded.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n pipe_loaded.to(torch_device)\n pipe_loaded.set_progress_bar_config(disable=None)\n\n for name, component in pipe_loaded.components.items():\n if hasattr(component, \"dtype\"):\n self.assertTrue(\n component.dtype == torch.float16,\n f\"`{name}.dtype` switched from `float16` to {component.dtype} after loading.\",\n )\n\n inputs = self.get_dummy_inputs(torch_device)\n output_loaded = pipe_loaded(**inputs).frames[0]\n max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()\n self.assertLess(\n max_diff, expected_max_diff, \"The output of the fp16 pipeline changed after saving and loading.\"\n )\n\n def test_save_load_optional_components(self, expected_max_difference=1e-4):\n if not hasattr(self.pipeline_class, \"_optional_components\"):\n return\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n # set all optional components to None\n for optional_component in pipe._optional_components:\n setattr(pipe, optional_component, None)\n\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n output = pipe(**inputs).frames[0]\n\n with tempfile.TemporaryDirectory() as tmpdir:\n pipe.save_pretrained(tmpdir, safe_serialization=False)\n pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)\n for component in pipe_loaded.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n pipe_loaded.to(torch_device)\n pipe_loaded.set_progress_bar_config(disable=None)\n\n for optional_component in pipe._optional_components:\n self.assertTrue(\n getattr(pipe_loaded, optional_component) is None,\n f\"`{optional_component}` did not stay set to None after loading.\",\n )\n\n inputs = self.get_dummy_inputs(generator_device)\n output_loaded = pipe_loaded(**inputs).frames[0]\n\n max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()\n self.assertLess(max_diff, expected_max_difference)\n\n def test_save_load_local(self, expected_max_difference=9e-4):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n output = pipe(**inputs).frames[0]\n\n logger = logging.get_logger(\"diffusers.pipelines.pipeline_utils\")\n logger.setLevel(diffusers.logging.INFO)\n\n with tempfile.TemporaryDirectory() as tmpdir:\n pipe.save_pretrained(tmpdir, safe_serialization=False)\n\n with CaptureLogger(logger) as cap_logger:\n pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)\n\n for name in pipe_loaded.components.keys():\n if name not in pipe_loaded._optional_components:\n assert name in str(cap_logger)\n\n pipe_loaded.to(torch_device)\n pipe_loaded.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n output_loaded = pipe_loaded(**inputs).frames[0]\n\n max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()\n self.assertLess(max_diff, expected_max_difference)\n\n @unittest.skipIf(torch_device != \"cuda\", reason=\"CUDA and CPU are required to switch devices\")\n def test_to_device(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n\n pipe.to(\"cpu\")\n model_devices = [\n component.device.type for component in pipe.components.values() if hasattr(component, \"device\")\n ]\n self.assertTrue(all(device == \"cpu\" for device in model_devices))\n\n output_cpu = pipe(**self.get_dummy_inputs(\"cpu\")).frames[0]\n self.assertTrue(np.isnan(output_cpu).sum() == 0)\n\n pipe.to(\"cuda\")\n model_devices = [\n component.device.type for component in pipe.components.values() if hasattr(component, \"device\")\n ]\n self.assertTrue(all(device == \"cuda\" for device in model_devices))\n\n output_cuda = pipe(**self.get_dummy_inputs(\"cuda\")).frames[0]\n self.assertTrue(np.isnan(to_np(output_cuda)).sum() == 0)\n\n def test_to_dtype(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n\n model_dtypes = [component.dtype for component in pipe.components.values() if hasattr(component, \"dtype\")]\n self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes))\n\n pipe.to(dtype=torch.float16)\n model_dtypes = [component.dtype for component in pipe.components.values() if hasattr(component, \"dtype\")]\n self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes))\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_accelerate_available() or is_accelerate_version(\"<\", \"0.14.0\"),\n reason=\"CPU offload is only available with CUDA and `accelerate v0.14.0` or higher\",\n )\n def test_sequential_cpu_offload_forward_pass(self, expected_max_diff=1e-4):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n output_without_offload = pipe(**inputs).frames[0]\n\n pipe.enable_sequential_cpu_offload()\n\n inputs = self.get_dummy_inputs(generator_device)\n output_with_offload = pipe(**inputs).frames[0]\n\n max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()\n self.assertLess(max_diff, expected_max_diff, \"CPU offloading should not affect the inference results\")\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_accelerate_available() or is_accelerate_version(\"<\", \"0.17.0\"),\n reason=\"CPU offload is only available with CUDA and `accelerate v0.17.0` or higher\",\n )\n def test_model_cpu_offload_forward_pass(self, expected_max_diff=2e-4):\n generator_device = \"cpu\"\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(generator_device)\n output_without_offload = pipe(**inputs).frames[0]\n\n pipe.enable_model_cpu_offload()\n inputs = self.get_dummy_inputs(generator_device)\n output_with_offload = pipe(**inputs).frames[0]\n\n max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()\n self.assertLess(max_diff, expected_max_diff, \"CPU offloading should not affect the inference results\")\n offloaded_modules = [\n v\n for k, v in pipe.components.items()\n if isinstance(v, torch.nn.Module) and k not in pipe._exclude_from_cpu_offload\n ]\n (\n self.assertTrue(all(v.device.type == \"cpu\" for v in offloaded_modules)),\n f\"Not offloaded: {[v for v in offloaded_modules if v.device.type != 'cpu']}\",\n )\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n expected_max_diff = 9e-4\n\n if not self.test_xformers_attention:\n return\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n output_without_offload = pipe(**inputs).frames[0]\n output_without_offload = (\n output_without_offload.cpu() if torch.is_tensor(output_without_offload) else output_without_offload\n )\n\n pipe.enable_xformers_memory_efficient_attention()\n inputs = self.get_dummy_inputs(torch_device)\n output_with_offload = pipe(**inputs).frames[0]\n output_with_offload = (\n output_with_offload.cpu() if torch.is_tensor(output_with_offload) else output_without_offload\n )\n\n max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()\n self.assertLess(max_diff, expected_max_diff, \"XFormers attention should not affect the inference results\")\n\n def test_disable_cfg(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n inputs[\"max_guidance_scale\"] = 1.0\n output = pipe(**inputs).frames\n self.assertEqual(len(output.shape), 5)\n\n\n@slow\n@require_torch_gpu\nclass StableVideoDiffusionPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_sd_video(self):\n pipe = StableVideoDiffusionPipeline.from_pretrained(\n \"stabilityai/stable-video-diffusion-img2vid\",\n variant=\"fp16\",\n torch_dtype=torch.float16,\n )\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png?download=true\"\n )\n\n generator = torch.Generator(\"cpu\").manual_seed(0)\n num_frames = 3\n\n output = pipe(\n image=image,\n num_frames=num_frames,\n generator=generator,\n num_inference_steps=3,\n output_type=\"np\",\n )\n\n image = output.frames[0]\n assert image.shape == (num_frames, 576, 1024, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array([0.8592, 0.8645, 0.8499, 0.8722, 0.8769, 0.8421, 0.8557, 0.8528, 0.8285])\n assert numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice.flatten()) < 1e-3\n" }, { "path": "diffusers/tests/pipelines/test_pipeline_utils.py", "content": "import unittest\n\nfrom diffusers.pipelines.pipeline_utils import is_safetensors_compatible\n\n\nclass IsSafetensorsCompatibleTests(unittest.TestCase):\n def test_all_is_compatible(self):\n filenames = [\n \"safety_checker/pytorch_model.bin\",\n \"safety_checker/model.safetensors\",\n \"vae/diffusion_pytorch_model.bin\",\n \"vae/diffusion_pytorch_model.safetensors\",\n \"text_encoder/pytorch_model.bin\",\n \"text_encoder/model.safetensors\",\n \"unet/diffusion_pytorch_model.bin\",\n \"unet/diffusion_pytorch_model.safetensors\",\n ]\n self.assertTrue(is_safetensors_compatible(filenames))\n\n def test_diffusers_model_is_compatible(self):\n filenames = [\n \"unet/diffusion_pytorch_model.bin\",\n \"unet/diffusion_pytorch_model.safetensors\",\n ]\n self.assertTrue(is_safetensors_compatible(filenames))\n\n def test_diffusers_model_is_not_compatible(self):\n filenames = [\n \"safety_checker/pytorch_model.bin\",\n \"safety_checker/model.safetensors\",\n \"vae/diffusion_pytorch_model.bin\",\n \"vae/diffusion_pytorch_model.safetensors\",\n \"text_encoder/pytorch_model.bin\",\n \"text_encoder/model.safetensors\",\n \"unet/diffusion_pytorch_model.bin\",\n # Removed: 'unet/diffusion_pytorch_model.safetensors',\n ]\n self.assertFalse(is_safetensors_compatible(filenames))\n\n def test_transformer_model_is_compatible(self):\n filenames = [\n \"text_encoder/pytorch_model.bin\",\n \"text_encoder/model.safetensors\",\n ]\n self.assertTrue(is_safetensors_compatible(filenames))\n\n def test_transformer_model_is_not_compatible(self):\n filenames = [\n \"safety_checker/pytorch_model.bin\",\n \"safety_checker/model.safetensors\",\n \"vae/diffusion_pytorch_model.bin\",\n \"vae/diffusion_pytorch_model.safetensors\",\n \"text_encoder/pytorch_model.bin\",\n # Removed: 'text_encoder/model.safetensors',\n \"unet/diffusion_pytorch_model.bin\",\n \"unet/diffusion_pytorch_model.safetensors\",\n ]\n self.assertFalse(is_safetensors_compatible(filenames))\n\n def test_all_is_compatible_variant(self):\n filenames = [\n \"safety_checker/pytorch_model.fp16.bin\",\n \"safety_checker/model.fp16.safetensors\",\n \"vae/diffusion_pytorch_model.fp16.bin\",\n \"vae/diffusion_pytorch_model.fp16.safetensors\",\n \"text_encoder/pytorch_model.fp16.bin\",\n \"text_encoder/model.fp16.safetensors\",\n \"unet/diffusion_pytorch_model.fp16.bin\",\n \"unet/diffusion_pytorch_model.fp16.safetensors\",\n ]\n variant = \"fp16\"\n self.assertTrue(is_safetensors_compatible(filenames, variant=variant))\n\n def test_diffusers_model_is_compatible_variant(self):\n filenames = [\n \"unet/diffusion_pytorch_model.fp16.bin\",\n \"unet/diffusion_pytorch_model.fp16.safetensors\",\n ]\n variant = \"fp16\"\n self.assertTrue(is_safetensors_compatible(filenames, variant=variant))\n\n def test_diffusers_model_is_compatible_variant_partial(self):\n # pass variant but use the non-variant filenames\n filenames = [\n \"unet/diffusion_pytorch_model.bin\",\n \"unet/diffusion_pytorch_model.safetensors\",\n ]\n variant = \"fp16\"\n self.assertTrue(is_safetensors_compatible(filenames, variant=variant))\n\n def test_diffusers_model_is_not_compatible_variant(self):\n filenames = [\n \"safety_checker/pytorch_model.fp16.bin\",\n \"safety_checker/model.fp16.safetensors\",\n \"vae/diffusion_pytorch_model.fp16.bin\",\n \"vae/diffusion_pytorch_model.fp16.safetensors\",\n \"text_encoder/pytorch_model.fp16.bin\",\n \"text_encoder/model.fp16.safetensors\",\n \"unet/diffusion_pytorch_model.fp16.bin\",\n # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',\n ]\n variant = \"fp16\"\n self.assertFalse(is_safetensors_compatible(filenames, variant=variant))\n\n def test_transformer_model_is_compatible_variant(self):\n filenames = [\n \"text_encoder/pytorch_model.fp16.bin\",\n \"text_encoder/model.fp16.safetensors\",\n ]\n variant = \"fp16\"\n self.assertTrue(is_safetensors_compatible(filenames, variant=variant))\n\n def test_transformer_model_is_compatible_variant_partial(self):\n # pass variant but use the non-variant filenames\n filenames = [\n \"text_encoder/pytorch_model.bin\",\n \"text_encoder/model.safetensors\",\n ]\n variant = \"fp16\"\n self.assertTrue(is_safetensors_compatible(filenames, variant=variant))\n\n def test_transformer_model_is_not_compatible_variant(self):\n filenames = [\n \"safety_checker/pytorch_model.fp16.bin\",\n \"safety_checker/model.fp16.safetensors\",\n \"vae/diffusion_pytorch_model.fp16.bin\",\n \"vae/diffusion_pytorch_model.fp16.safetensors\",\n \"text_encoder/pytorch_model.fp16.bin\",\n # 'text_encoder/model.fp16.safetensors',\n \"unet/diffusion_pytorch_model.fp16.bin\",\n \"unet/diffusion_pytorch_model.fp16.safetensors\",\n ]\n variant = \"fp16\"\n self.assertFalse(is_safetensors_compatible(filenames, variant=variant))\n" }, { "path": "diffusers/tests/pipelines/test_pipelines.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport json\nimport os\nimport random\nimport shutil\nimport sys\nimport tempfile\nimport traceback\nimport unittest\nimport unittest.mock as mock\n\nimport numpy as np\nimport PIL.Image\nimport requests_mock\nimport safetensors.torch\nimport torch\nimport torch.nn as nn\nfrom parameterized import parameterized\nfrom PIL import Image\nfrom requests.exceptions import HTTPError\nfrom transformers import CLIPImageProcessor, CLIPModel, CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n ConfigMixin,\n DDIMPipeline,\n DDIMScheduler,\n DDPMPipeline,\n DDPMScheduler,\n DiffusionPipeline,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n LMSDiscreteScheduler,\n ModelMixin,\n PNDMScheduler,\n StableDiffusionImg2ImgPipeline,\n StableDiffusionInpaintPipelineLegacy,\n StableDiffusionPipeline,\n UNet2DConditionModel,\n UNet2DModel,\n UniPCMultistepScheduler,\n logging,\n)\nfrom diffusers.pipelines.pipeline_utils import _get_pipeline_class\nfrom diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME\nfrom diffusers.utils import (\n CONFIG_NAME,\n WEIGHTS_NAME,\n)\nfrom diffusers.utils.testing_utils import (\n CaptureLogger,\n enable_full_determinism,\n floats_tensor,\n get_python_version,\n get_tests_dir,\n load_numpy,\n nightly,\n require_compel,\n require_flax,\n require_onnxruntime,\n require_python39_or_higher,\n require_torch_2,\n require_torch_gpu,\n run_test_in_subprocess,\n slow,\n torch_device,\n)\nfrom diffusers.utils.torch_utils import is_compiled_module\n\n\nenable_full_determinism()\n\n\n# Will be run via run_test_in_subprocess\ndef _test_from_save_pretrained_dynamo(in_queue, out_queue, timeout):\n error = None\n try:\n # 1. Load models\n model = UNet2DModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=3,\n out_channels=3,\n down_block_types=(\"DownBlock2D\", \"AttnDownBlock2D\"),\n up_block_types=(\"AttnUpBlock2D\", \"UpBlock2D\"),\n )\n model = torch.compile(model)\n scheduler = DDPMScheduler(num_train_timesteps=10)\n\n ddpm = DDPMPipeline(model, scheduler)\n\n # previous diffusers versions stripped compilation off\n # compiled modules\n assert is_compiled_module(ddpm.unet)\n\n ddpm.to(torch_device)\n ddpm.set_progress_bar_config(disable=None)\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n ddpm.save_pretrained(tmpdirname)\n new_ddpm = DDPMPipeline.from_pretrained(tmpdirname)\n new_ddpm.to(torch_device)\n\n generator = torch.Generator(device=torch_device).manual_seed(0)\n image = ddpm(generator=generator, num_inference_steps=5, output_type=\"np\").images\n\n generator = torch.Generator(device=torch_device).manual_seed(0)\n new_image = new_ddpm(generator=generator, num_inference_steps=5, output_type=\"np\").images\n\n assert np.abs(image - new_image).max() < 1e-5, \"Models don't give the same forward pass\"\n except Exception:\n error = f\"{traceback.format_exc()}\"\n\n results = {\"error\": error}\n out_queue.put(results, timeout=timeout)\n out_queue.join()\n\n\nclass CustomEncoder(ModelMixin, ConfigMixin):\n def __init__(self):\n super().__init__()\n self.linear = nn.Linear(3, 3)\n\n\nclass CustomPipeline(DiffusionPipeline):\n def __init__(self, encoder: CustomEncoder, scheduler: DDIMScheduler):\n super().__init__()\n self.register_modules(encoder=encoder, scheduler=scheduler)\n\n\nclass DownloadTests(unittest.TestCase):\n def test_one_request_upon_cached(self):\n # TODO: For some reason this test fails on MPS where no HEAD call is made.\n if torch_device == \"mps\":\n return\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n with requests_mock.mock(real_http=True) as m:\n DiffusionPipeline.download(\"hf-internal-testing/tiny-stable-diffusion-pipe\", cache_dir=tmpdirname)\n\n download_requests = [r.method for r in m.request_history]\n assert download_requests.count(\"HEAD\") == 15, \"15 calls to files\"\n assert download_requests.count(\"GET\") == 17, \"15 calls to files + model_info + model_index.json\"\n assert (\n len(download_requests) == 32\n ), \"2 calls per file (15 files) + send_telemetry, model_info and model_index.json\"\n\n with requests_mock.mock(real_http=True) as m:\n DiffusionPipeline.download(\n \"hf-internal-testing/tiny-stable-diffusion-pipe\", safety_checker=None, cache_dir=tmpdirname\n )\n\n cache_requests = [r.method for r in m.request_history]\n assert cache_requests.count(\"HEAD\") == 1, \"model_index.json is only HEAD\"\n assert cache_requests.count(\"GET\") == 1, \"model info is only GET\"\n assert (\n len(cache_requests) == 2\n ), \"We should call only `model_info` to check for _commit hash and `send_telemetry`\"\n\n def test_less_downloads_passed_object(self):\n with tempfile.TemporaryDirectory() as tmpdirname:\n cached_folder = DiffusionPipeline.download(\n \"hf-internal-testing/tiny-stable-diffusion-pipe\", safety_checker=None, cache_dir=tmpdirname\n )\n\n # make sure safety checker is not downloaded\n assert \"safety_checker\" not in os.listdir(cached_folder)\n\n # make sure rest is downloaded\n assert \"unet\" in os.listdir(cached_folder)\n assert \"tokenizer\" in os.listdir(cached_folder)\n assert \"vae\" in os.listdir(cached_folder)\n assert \"model_index.json\" in os.listdir(cached_folder)\n assert \"scheduler\" in os.listdir(cached_folder)\n assert \"feature_extractor\" in os.listdir(cached_folder)\n\n def test_less_downloads_passed_object_calls(self):\n # TODO: For some reason this test fails on MPS where no HEAD call is made.\n if torch_device == \"mps\":\n return\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n with requests_mock.mock(real_http=True) as m:\n DiffusionPipeline.download(\n \"hf-internal-testing/tiny-stable-diffusion-pipe\", safety_checker=None, cache_dir=tmpdirname\n )\n\n download_requests = [r.method for r in m.request_history]\n # 15 - 2 because no call to config or model file for `safety_checker`\n assert download_requests.count(\"HEAD\") == 13, \"13 calls to files\"\n # 17 - 2 because no call to config or model file for `safety_checker`\n assert download_requests.count(\"GET\") == 15, \"13 calls to files + model_info + model_index.json\"\n assert (\n len(download_requests) == 28\n ), \"2 calls per file (13 files) + send_telemetry, model_info and model_index.json\"\n\n with requests_mock.mock(real_http=True) as m:\n DiffusionPipeline.download(\n \"hf-internal-testing/tiny-stable-diffusion-pipe\", safety_checker=None, cache_dir=tmpdirname\n )\n\n cache_requests = [r.method for r in m.request_history]\n assert cache_requests.count(\"HEAD\") == 1, \"model_index.json is only HEAD\"\n assert cache_requests.count(\"GET\") == 1, \"model info is only GET\"\n assert (\n len(cache_requests) == 2\n ), \"We should call only `model_info` to check for _commit hash and `send_telemetry`\"\n\n def test_download_only_pytorch(self):\n with tempfile.TemporaryDirectory() as tmpdirname:\n # pipeline has Flax weights\n tmpdirname = DiffusionPipeline.download(\n \"hf-internal-testing/tiny-stable-diffusion-pipe\", safety_checker=None, cache_dir=tmpdirname\n )\n\n all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))]\n files = [item for sublist in all_root_files for item in sublist]\n\n # None of the downloaded files should be a flax file even if we have some here:\n # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_flax_model.msgpack\n assert not any(f.endswith(\".msgpack\") for f in files)\n # We need to never convert this tiny model to safetensors for this test to pass\n assert not any(f.endswith(\".safetensors\") for f in files)\n\n def test_force_safetensors_error(self):\n with tempfile.TemporaryDirectory() as tmpdirname:\n # pipeline has Flax weights\n with self.assertRaises(EnvironmentError):\n tmpdirname = DiffusionPipeline.download(\n \"hf-internal-testing/tiny-stable-diffusion-pipe-no-safetensors\",\n safety_checker=None,\n cache_dir=tmpdirname,\n use_safetensors=True,\n )\n\n def test_download_safetensors(self):\n with tempfile.TemporaryDirectory() as tmpdirname:\n # pipeline has Flax weights\n tmpdirname = DiffusionPipeline.download(\n \"hf-internal-testing/tiny-stable-diffusion-pipe-safetensors\",\n safety_checker=None,\n cache_dir=tmpdirname,\n )\n\n all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))]\n files = [item for sublist in all_root_files for item in sublist]\n\n # None of the downloaded files should be a pytorch file even if we have some here:\n # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_flax_model.msgpack\n assert not any(f.endswith(\".bin\") for f in files)\n\n def test_download_safetensors_index(self):\n for variant in [\"fp16\", None]:\n with tempfile.TemporaryDirectory() as tmpdirname:\n tmpdirname = DiffusionPipeline.download(\n \"hf-internal-testing/tiny-stable-diffusion-pipe-indexes\",\n cache_dir=tmpdirname,\n use_safetensors=True,\n variant=variant,\n )\n\n all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))]\n files = [item for sublist in all_root_files for item in sublist]\n\n # None of the downloaded files should be a safetensors file even if we have some here:\n # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe-indexes/tree/main/text_encoder\n if variant is None:\n assert not any(\"fp16\" in f for f in files)\n else:\n model_files = [f for f in files if \"safetensors\" in f]\n assert all(\"fp16\" in f for f in model_files)\n\n assert len([f for f in files if \".safetensors\" in f]) == 8\n assert not any(\".bin\" in f for f in files)\n\n def test_download_bin_index(self):\n for variant in [\"fp16\", None]:\n with tempfile.TemporaryDirectory() as tmpdirname:\n tmpdirname = DiffusionPipeline.download(\n \"hf-internal-testing/tiny-stable-diffusion-pipe-indexes\",\n cache_dir=tmpdirname,\n use_safetensors=False,\n variant=variant,\n )\n\n all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))]\n files = [item for sublist in all_root_files for item in sublist]\n\n # None of the downloaded files should be a safetensors file even if we have some here:\n # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe-indexes/tree/main/text_encoder\n if variant is None:\n assert not any(\"fp16\" in f for f in files)\n else:\n model_files = [f for f in files if \"bin\" in f]\n assert all(\"fp16\" in f for f in model_files)\n\n assert len([f for f in files if \".bin\" in f]) == 8\n assert not any(\".safetensors\" in f for f in files)\n\n def test_download_no_openvino_by_default(self):\n with tempfile.TemporaryDirectory() as tmpdirname:\n tmpdirname = DiffusionPipeline.download(\n \"hf-internal-testing/tiny-stable-diffusion-open-vino\",\n cache_dir=tmpdirname,\n )\n\n all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))]\n files = [item for sublist in all_root_files for item in sublist]\n\n # make sure that by default no openvino weights are downloaded\n assert all((f.endswith(\".json\") or f.endswith(\".bin\") or f.endswith(\".txt\")) for f in files)\n assert not any(\"openvino_\" in f for f in files)\n\n def test_download_no_onnx_by_default(self):\n with tempfile.TemporaryDirectory() as tmpdirname:\n tmpdirname = DiffusionPipeline.download(\n \"hf-internal-testing/tiny-stable-diffusion-xl-pipe\",\n cache_dir=tmpdirname,\n use_safetensors=False,\n )\n\n all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))]\n files = [item for sublist in all_root_files for item in sublist]\n\n # make sure that by default no onnx weights are downloaded for non-ONNX pipelines\n assert all((f.endswith(\".json\") or f.endswith(\".bin\") or f.endswith(\".txt\")) for f in files)\n assert not any((f.endswith(\".onnx\") or f.endswith(\".pb\")) for f in files)\n\n @require_onnxruntime\n def test_download_onnx_by_default_for_onnx_pipelines(self):\n with tempfile.TemporaryDirectory() as tmpdirname:\n tmpdirname = DiffusionPipeline.download(\n \"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline\",\n cache_dir=tmpdirname,\n )\n\n all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))]\n files = [item for sublist in all_root_files for item in sublist]\n\n # make sure that by default onnx weights are downloaded for ONNX pipelines\n assert any((f.endswith(\".json\") or f.endswith(\".bin\") or f.endswith(\".txt\")) for f in files)\n assert any((f.endswith(\".onnx\")) for f in files)\n assert any((f.endswith(\".pb\")) for f in files)\n\n def test_download_no_safety_checker(self):\n prompt = \"hello\"\n pipe = StableDiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\", safety_checker=None\n )\n pipe = pipe.to(torch_device)\n generator = torch.manual_seed(0)\n out = pipe(prompt, num_inference_steps=2, generator=generator, output_type=\"np\").images\n\n pipe_2 = StableDiffusionPipeline.from_pretrained(\"hf-internal-testing/tiny-stable-diffusion-torch\")\n pipe_2 = pipe_2.to(torch_device)\n generator = torch.manual_seed(0)\n out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type=\"np\").images\n\n assert np.max(np.abs(out - out_2)) < 1e-3\n\n def test_load_no_safety_checker_explicit_locally(self):\n prompt = \"hello\"\n pipe = StableDiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\", safety_checker=None\n )\n pipe = pipe.to(torch_device)\n generator = torch.manual_seed(0)\n out = pipe(prompt, num_inference_steps=2, generator=generator, output_type=\"np\").images\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n pipe.save_pretrained(tmpdirname)\n pipe_2 = StableDiffusionPipeline.from_pretrained(tmpdirname, safety_checker=None)\n pipe_2 = pipe_2.to(torch_device)\n\n generator = torch.manual_seed(0)\n\n out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type=\"np\").images\n\n assert np.max(np.abs(out - out_2)) < 1e-3\n\n def test_load_no_safety_checker_default_locally(self):\n prompt = \"hello\"\n pipe = StableDiffusionPipeline.from_pretrained(\"hf-internal-testing/tiny-stable-diffusion-torch\")\n pipe = pipe.to(torch_device)\n\n generator = torch.manual_seed(0)\n out = pipe(prompt, num_inference_steps=2, generator=generator, output_type=\"np\").images\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n pipe.save_pretrained(tmpdirname)\n pipe_2 = StableDiffusionPipeline.from_pretrained(tmpdirname)\n pipe_2 = pipe_2.to(torch_device)\n\n generator = torch.manual_seed(0)\n\n out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type=\"np\").images\n\n assert np.max(np.abs(out - out_2)) < 1e-3\n\n def test_cached_files_are_used_when_no_internet(self):\n # A mock response for an HTTP head request to emulate server down\n response_mock = mock.Mock()\n response_mock.status_code = 500\n response_mock.headers = {}\n response_mock.raise_for_status.side_effect = HTTPError\n response_mock.json.return_value = {}\n\n # Download this model to make sure it's in the cache.\n orig_pipe = DiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\", safety_checker=None\n )\n orig_comps = {k: v for k, v in orig_pipe.components.items() if hasattr(v, \"parameters\")}\n\n # Under the mock environment we get a 500 error when trying to reach the model.\n with mock.patch(\"requests.request\", return_value=response_mock):\n # Download this model to make sure it's in the cache.\n pipe = DiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\", safety_checker=None\n )\n comps = {k: v for k, v in pipe.components.items() if hasattr(v, \"parameters\")}\n\n for m1, m2 in zip(orig_comps.values(), comps.values()):\n for p1, p2 in zip(m1.parameters(), m2.parameters()):\n if p1.data.ne(p2.data).sum() > 0:\n assert False, \"Parameters not the same!\"\n\n def test_local_files_only_are_used_when_no_internet(self):\n # A mock response for an HTTP head request to emulate server down\n response_mock = mock.Mock()\n response_mock.status_code = 500\n response_mock.headers = {}\n response_mock.raise_for_status.side_effect = HTTPError\n response_mock.json.return_value = {}\n\n # first check that with local files only the pipeline can only be used if cached\n with self.assertRaises(FileNotFoundError):\n with tempfile.TemporaryDirectory() as tmpdirname:\n orig_pipe = DiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\", local_files_only=True, cache_dir=tmpdirname\n )\n\n # now download\n orig_pipe = DiffusionPipeline.download(\"hf-internal-testing/tiny-stable-diffusion-torch\")\n\n # make sure it can be loaded with local_files_only\n orig_pipe = DiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\", local_files_only=True\n )\n orig_comps = {k: v for k, v in orig_pipe.components.items() if hasattr(v, \"parameters\")}\n\n # Under the mock environment we get a 500 error when trying to connect to the internet.\n # Make sure it works local_files_only only works here!\n with mock.patch(\"requests.request\", return_value=response_mock):\n # Download this model to make sure it's in the cache.\n pipe = DiffusionPipeline.from_pretrained(\"hf-internal-testing/tiny-stable-diffusion-torch\")\n comps = {k: v for k, v in pipe.components.items() if hasattr(v, \"parameters\")}\n\n for m1, m2 in zip(orig_comps.values(), comps.values()):\n for p1, p2 in zip(m1.parameters(), m2.parameters()):\n if p1.data.ne(p2.data).sum() > 0:\n assert False, \"Parameters not the same!\"\n\n def test_download_from_variant_folder(self):\n for use_safetensors in [False, True]:\n other_format = \".bin\" if use_safetensors else \".safetensors\"\n with tempfile.TemporaryDirectory() as tmpdirname:\n tmpdirname = StableDiffusionPipeline.download(\n \"hf-internal-testing/stable-diffusion-all-variants\",\n cache_dir=tmpdirname,\n use_safetensors=use_safetensors,\n )\n all_root_files = [t[-1] for t in os.walk(tmpdirname)]\n files = [item for sublist in all_root_files for item in sublist]\n\n # None of the downloaded files should be a variant file even if we have some here:\n # https://huggingface.co/hf-internal-testing/stable-diffusion-all-variants/tree/main/unet\n assert len(files) == 15, f\"We should only download 15 files, not {len(files)}\"\n assert not any(f.endswith(other_format) for f in files)\n # no variants\n assert not any(len(f.split(\".\")) == 3 for f in files)\n\n def test_download_variant_all(self):\n for use_safetensors in [False, True]:\n other_format = \".bin\" if use_safetensors else \".safetensors\"\n this_format = \".safetensors\" if use_safetensors else \".bin\"\n variant = \"fp16\"\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n tmpdirname = StableDiffusionPipeline.download(\n \"hf-internal-testing/stable-diffusion-all-variants\",\n cache_dir=tmpdirname,\n variant=variant,\n use_safetensors=use_safetensors,\n )\n all_root_files = [t[-1] for t in os.walk(tmpdirname)]\n files = [item for sublist in all_root_files for item in sublist]\n\n # None of the downloaded files should be a non-variant file even if we have some here:\n # https://huggingface.co/hf-internal-testing/stable-diffusion-all-variants/tree/main/unet\n assert len(files) == 15, f\"We should only download 15 files, not {len(files)}\"\n # unet, vae, text_encoder, safety_checker\n assert len([f for f in files if f.endswith(f\"{variant}{this_format}\")]) == 4\n # all checkpoints should have variant ending\n assert not any(f.endswith(this_format) and not f.endswith(f\"{variant}{this_format}\") for f in files)\n assert not any(f.endswith(other_format) for f in files)\n\n def test_download_variant_partly(self):\n for use_safetensors in [False, True]:\n other_format = \".bin\" if use_safetensors else \".safetensors\"\n this_format = \".safetensors\" if use_safetensors else \".bin\"\n variant = \"no_ema\"\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n tmpdirname = StableDiffusionPipeline.download(\n \"hf-internal-testing/stable-diffusion-all-variants\",\n cache_dir=tmpdirname,\n variant=variant,\n use_safetensors=use_safetensors,\n )\n all_root_files = [t[-1] for t in os.walk(tmpdirname)]\n files = [item for sublist in all_root_files for item in sublist]\n\n unet_files = os.listdir(os.path.join(tmpdirname, \"unet\"))\n\n # Some of the downloaded files should be a non-variant file, check:\n # https://huggingface.co/hf-internal-testing/stable-diffusion-all-variants/tree/main/unet\n assert len(files) == 15, f\"We should only download 15 files, not {len(files)}\"\n # only unet has \"no_ema\" variant\n assert f\"diffusion_pytorch_model.{variant}{this_format}\" in unet_files\n assert len([f for f in files if f.endswith(f\"{variant}{this_format}\")]) == 1\n # vae, safety_checker and text_encoder should have no variant\n assert sum(f.endswith(this_format) and not f.endswith(f\"{variant}{this_format}\") for f in files) == 3\n assert not any(f.endswith(other_format) for f in files)\n\n def test_download_broken_variant(self):\n for use_safetensors in [False, True]:\n # text encoder is missing no variant and \"no_ema\" variant weights, so the following can't work\n for variant in [None, \"no_ema\"]:\n with self.assertRaises(OSError) as error_context:\n with tempfile.TemporaryDirectory() as tmpdirname:\n tmpdirname = StableDiffusionPipeline.from_pretrained(\n \"hf-internal-testing/stable-diffusion-broken-variants\",\n cache_dir=tmpdirname,\n variant=variant,\n use_safetensors=use_safetensors,\n )\n\n assert \"Error no file name\" in str(error_context.exception)\n\n # text encoder has fp16 variants so we can load it\n with tempfile.TemporaryDirectory() as tmpdirname:\n tmpdirname = StableDiffusionPipeline.download(\n \"hf-internal-testing/stable-diffusion-broken-variants\",\n use_safetensors=use_safetensors,\n cache_dir=tmpdirname,\n variant=\"fp16\",\n )\n\n all_root_files = [t[-1] for t in os.walk(tmpdirname)]\n files = [item for sublist in all_root_files for item in sublist]\n\n # None of the downloaded files should be a non-variant file even if we have some here:\n # https://huggingface.co/hf-internal-testing/stable-diffusion-broken-variants/tree/main/unet\n assert len(files) == 15, f\"We should only download 15 files, not {len(files)}\"\n # only unet has \"no_ema\" variant\n\n def test_local_save_load_index(self):\n prompt = \"hello\"\n for variant in [None, \"fp16\"]:\n for use_safe in [True, False]:\n pipe = StableDiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-pipe-indexes\",\n variant=variant,\n use_safetensors=use_safe,\n safety_checker=None,\n )\n pipe = pipe.to(torch_device)\n generator = torch.manual_seed(0)\n out = pipe(prompt, num_inference_steps=2, generator=generator, output_type=\"np\").images\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n pipe.save_pretrained(tmpdirname)\n pipe_2 = StableDiffusionPipeline.from_pretrained(\n tmpdirname, safe_serialization=use_safe, variant=variant\n )\n pipe_2 = pipe_2.to(torch_device)\n\n generator = torch.manual_seed(0)\n\n out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type=\"np\").images\n\n assert np.max(np.abs(out - out_2)) < 1e-3\n\n def test_text_inversion_download(self):\n pipe = StableDiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\", safety_checker=None\n )\n pipe = pipe.to(torch_device)\n\n num_tokens = len(pipe.tokenizer)\n\n # single token load local\n with tempfile.TemporaryDirectory() as tmpdirname:\n ten = {\"<*>\": torch.ones((32,))}\n torch.save(ten, os.path.join(tmpdirname, \"learned_embeds.bin\"))\n\n pipe.load_textual_inversion(tmpdirname)\n\n token = pipe.tokenizer.convert_tokens_to_ids(\"<*>\")\n assert token == num_tokens, \"Added token must be at spot `num_tokens`\"\n assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 32\n assert pipe._maybe_convert_prompt(\"<*>\", pipe.tokenizer) == \"<*>\"\n\n prompt = \"hey <*>\"\n out = pipe(prompt, num_inference_steps=1, output_type=\"np\").images\n assert out.shape == (1, 128, 128, 3)\n\n # single token load local with weight name\n with tempfile.TemporaryDirectory() as tmpdirname:\n ten = {\"<**>\": 2 * torch.ones((1, 32))}\n torch.save(ten, os.path.join(tmpdirname, \"learned_embeds.bin\"))\n\n pipe.load_textual_inversion(tmpdirname, weight_name=\"learned_embeds.bin\")\n\n token = pipe.tokenizer.convert_tokens_to_ids(\"<**>\")\n assert token == num_tokens + 1, \"Added token must be at spot `num_tokens`\"\n assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 64\n assert pipe._maybe_convert_prompt(\"<**>\", pipe.tokenizer) == \"<**>\"\n\n prompt = \"hey <**>\"\n out = pipe(prompt, num_inference_steps=1, output_type=\"np\").images\n assert out.shape == (1, 128, 128, 3)\n\n # multi token load\n with tempfile.TemporaryDirectory() as tmpdirname:\n ten = {\"<***>\": torch.cat([3 * torch.ones((1, 32)), 4 * torch.ones((1, 32)), 5 * torch.ones((1, 32))])}\n torch.save(ten, os.path.join(tmpdirname, \"learned_embeds.bin\"))\n\n pipe.load_textual_inversion(tmpdirname)\n\n token = pipe.tokenizer.convert_tokens_to_ids(\"<***>\")\n token_1 = pipe.tokenizer.convert_tokens_to_ids(\"<***>_1\")\n token_2 = pipe.tokenizer.convert_tokens_to_ids(\"<***>_2\")\n\n assert token == num_tokens + 2, \"Added token must be at spot `num_tokens`\"\n assert token_1 == num_tokens + 3, \"Added token must be at spot `num_tokens`\"\n assert token_2 == num_tokens + 4, \"Added token must be at spot `num_tokens`\"\n assert pipe.text_encoder.get_input_embeddings().weight[-3].sum().item() == 96\n assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 128\n assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 160\n assert pipe._maybe_convert_prompt(\"<***>\", pipe.tokenizer) == \"<***> <***>_1 <***>_2\"\n\n prompt = \"hey <***>\"\n out = pipe(prompt, num_inference_steps=1, output_type=\"np\").images\n assert out.shape == (1, 128, 128, 3)\n\n # multi token load a1111\n with tempfile.TemporaryDirectory() as tmpdirname:\n ten = {\n \"string_to_param\": {\n \"*\": torch.cat([3 * torch.ones((1, 32)), 4 * torch.ones((1, 32)), 5 * torch.ones((1, 32))])\n },\n \"name\": \"<****>\",\n }\n torch.save(ten, os.path.join(tmpdirname, \"a1111.bin\"))\n\n pipe.load_textual_inversion(tmpdirname, weight_name=\"a1111.bin\")\n\n token = pipe.tokenizer.convert_tokens_to_ids(\"<****>\")\n token_1 = pipe.tokenizer.convert_tokens_to_ids(\"<****>_1\")\n token_2 = pipe.tokenizer.convert_tokens_to_ids(\"<****>_2\")\n\n assert token == num_tokens + 5, \"Added token must be at spot `num_tokens`\"\n assert token_1 == num_tokens + 6, \"Added token must be at spot `num_tokens`\"\n assert token_2 == num_tokens + 7, \"Added token must be at spot `num_tokens`\"\n assert pipe.text_encoder.get_input_embeddings().weight[-3].sum().item() == 96\n assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 128\n assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 160\n assert pipe._maybe_convert_prompt(\"<****>\", pipe.tokenizer) == \"<****> <****>_1 <****>_2\"\n\n prompt = \"hey <****>\"\n out = pipe(prompt, num_inference_steps=1, output_type=\"np\").images\n assert out.shape == (1, 128, 128, 3)\n\n # multi embedding load\n with tempfile.TemporaryDirectory() as tmpdirname1:\n with tempfile.TemporaryDirectory() as tmpdirname2:\n ten = {\"<*****>\": torch.ones((32,))}\n torch.save(ten, os.path.join(tmpdirname1, \"learned_embeds.bin\"))\n\n ten = {\"<******>\": 2 * torch.ones((1, 32))}\n torch.save(ten, os.path.join(tmpdirname2, \"learned_embeds.bin\"))\n\n pipe.load_textual_inversion([tmpdirname1, tmpdirname2])\n\n token = pipe.tokenizer.convert_tokens_to_ids(\"<*****>\")\n assert token == num_tokens + 8, \"Added token must be at spot `num_tokens`\"\n assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 32\n assert pipe._maybe_convert_prompt(\"<*****>\", pipe.tokenizer) == \"<*****>\"\n\n token = pipe.tokenizer.convert_tokens_to_ids(\"<******>\")\n assert token == num_tokens + 9, \"Added token must be at spot `num_tokens`\"\n assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 64\n assert pipe._maybe_convert_prompt(\"<******>\", pipe.tokenizer) == \"<******>\"\n\n prompt = \"hey <*****> <******>\"\n out = pipe(prompt, num_inference_steps=1, output_type=\"np\").images\n assert out.shape == (1, 128, 128, 3)\n\n # single token state dict load\n ten = {\"\": torch.ones((32,))}\n pipe.load_textual_inversion(ten)\n\n token = pipe.tokenizer.convert_tokens_to_ids(\"\")\n assert token == num_tokens + 10, \"Added token must be at spot `num_tokens`\"\n assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 32\n assert pipe._maybe_convert_prompt(\"\", pipe.tokenizer) == \"\"\n\n prompt = \"hey \"\n out = pipe(prompt, num_inference_steps=1, output_type=\"np\").images\n assert out.shape == (1, 128, 128, 3)\n\n # multi embedding state dict load\n ten1 = {\"\": torch.ones((32,))}\n ten2 = {\"\": 2 * torch.ones((1, 32))}\n\n pipe.load_textual_inversion([ten1, ten2])\n\n token = pipe.tokenizer.convert_tokens_to_ids(\"\")\n assert token == num_tokens + 11, \"Added token must be at spot `num_tokens`\"\n assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 32\n assert pipe._maybe_convert_prompt(\"\", pipe.tokenizer) == \"\"\n\n token = pipe.tokenizer.convert_tokens_to_ids(\"\")\n assert token == num_tokens + 12, \"Added token must be at spot `num_tokens`\"\n assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 64\n assert pipe._maybe_convert_prompt(\"\", pipe.tokenizer) == \"\"\n\n prompt = \"hey \"\n out = pipe(prompt, num_inference_steps=1, output_type=\"np\").images\n assert out.shape == (1, 128, 128, 3)\n\n # auto1111 multi-token state dict load\n ten = {\n \"string_to_param\": {\n \"*\": torch.cat([3 * torch.ones((1, 32)), 4 * torch.ones((1, 32)), 5 * torch.ones((1, 32))])\n },\n \"name\": \"\",\n }\n\n pipe.load_textual_inversion(ten)\n\n token = pipe.tokenizer.convert_tokens_to_ids(\"\")\n token_1 = pipe.tokenizer.convert_tokens_to_ids(\"_1\")\n token_2 = pipe.tokenizer.convert_tokens_to_ids(\"_2\")\n\n assert token == num_tokens + 13, \"Added token must be at spot `num_tokens`\"\n assert token_1 == num_tokens + 14, \"Added token must be at spot `num_tokens`\"\n assert token_2 == num_tokens + 15, \"Added token must be at spot `num_tokens`\"\n assert pipe.text_encoder.get_input_embeddings().weight[-3].sum().item() == 96\n assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 128\n assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 160\n assert pipe._maybe_convert_prompt(\"\", pipe.tokenizer) == \" _1 _2\"\n\n prompt = \"hey \"\n out = pipe(prompt, num_inference_steps=1, output_type=\"np\").images\n assert out.shape == (1, 128, 128, 3)\n\n # multiple references to multi embedding\n ten = {\"\": torch.ones(3, 32)}\n pipe.load_textual_inversion(ten)\n\n assert (\n pipe._maybe_convert_prompt(\" \", pipe.tokenizer) == \" _1 _2 _1 _2\"\n )\n\n prompt = \"hey \"\n out = pipe(prompt, num_inference_steps=1, output_type=\"np\").images\n assert out.shape == (1, 128, 128, 3)\n\n def test_text_inversion_multi_tokens(self):\n pipe1 = StableDiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\", safety_checker=None\n )\n pipe1 = pipe1.to(torch_device)\n\n token1, token2 = \"<*>\", \"<**>\"\n ten1 = torch.ones((32,))\n ten2 = torch.ones((32,)) * 2\n\n num_tokens = len(pipe1.tokenizer)\n\n pipe1.load_textual_inversion(ten1, token=token1)\n pipe1.load_textual_inversion(ten2, token=token2)\n emb1 = pipe1.text_encoder.get_input_embeddings().weight\n\n pipe2 = StableDiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\", safety_checker=None\n )\n pipe2 = pipe2.to(torch_device)\n pipe2.load_textual_inversion([ten1, ten2], token=[token1, token2])\n emb2 = pipe2.text_encoder.get_input_embeddings().weight\n\n pipe3 = StableDiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\", safety_checker=None\n )\n pipe3 = pipe3.to(torch_device)\n pipe3.load_textual_inversion(torch.stack([ten1, ten2], dim=0), token=[token1, token2])\n emb3 = pipe3.text_encoder.get_input_embeddings().weight\n\n assert len(pipe1.tokenizer) == len(pipe2.tokenizer) == len(pipe3.tokenizer) == num_tokens + 2\n assert (\n pipe1.tokenizer.convert_tokens_to_ids(token1)\n == pipe2.tokenizer.convert_tokens_to_ids(token1)\n == pipe3.tokenizer.convert_tokens_to_ids(token1)\n == num_tokens\n )\n assert (\n pipe1.tokenizer.convert_tokens_to_ids(token2)\n == pipe2.tokenizer.convert_tokens_to_ids(token2)\n == pipe3.tokenizer.convert_tokens_to_ids(token2)\n == num_tokens + 1\n )\n assert emb1[num_tokens].sum().item() == emb2[num_tokens].sum().item() == emb3[num_tokens].sum().item()\n assert (\n emb1[num_tokens + 1].sum().item() == emb2[num_tokens + 1].sum().item() == emb3[num_tokens + 1].sum().item()\n )\n\n def test_download_ignore_files(self):\n # Check https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe-ignore-files/blob/72f58636e5508a218c6b3f60550dc96445547817/model_index.json#L4\n with tempfile.TemporaryDirectory() as tmpdirname:\n # pipeline has Flax weights\n tmpdirname = DiffusionPipeline.download(\"hf-internal-testing/tiny-stable-diffusion-pipe-ignore-files\")\n all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))]\n files = [item for sublist in all_root_files for item in sublist]\n\n # None of the downloaded files should be a pytorch file even if we have some here:\n # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_flax_model.msgpack\n assert not any(f in [\"vae/diffusion_pytorch_model.bin\", \"text_encoder/config.json\"] for f in files)\n assert len(files) == 14\n\n def test_get_pipeline_class_from_flax(self):\n flax_config = {\"_class_name\": \"FlaxStableDiffusionPipeline\"}\n config = {\"_class_name\": \"StableDiffusionPipeline\"}\n\n # when loading a PyTorch Pipeline from a FlaxPipeline `model_index.json`, e.g.: https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-lms-pipe/blob/7a9063578b325779f0f1967874a6771caa973cad/model_index.json#L2\n # we need to make sure that we don't load the Flax Pipeline class, but instead the PyTorch pipeline class\n assert _get_pipeline_class(DiffusionPipeline, flax_config) == _get_pipeline_class(DiffusionPipeline, config)\n\n\nclass CustomPipelineTests(unittest.TestCase):\n def test_load_custom_pipeline(self):\n pipeline = DiffusionPipeline.from_pretrained(\n \"google/ddpm-cifar10-32\", custom_pipeline=\"hf-internal-testing/diffusers-dummy-pipeline\"\n )\n pipeline = pipeline.to(torch_device)\n # NOTE that `\"CustomPipeline\"` is not a class that is defined in this library, but solely on the Hub\n # under https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py#L24\n assert pipeline.__class__.__name__ == \"CustomPipeline\"\n\n def test_load_custom_github(self):\n pipeline = DiffusionPipeline.from_pretrained(\n \"google/ddpm-cifar10-32\", custom_pipeline=\"one_step_unet\", custom_revision=\"main\"\n )\n\n # make sure that on \"main\" pipeline gives only ones because of: https://github.com/huggingface/diffusers/pull/1690\n with torch.no_grad():\n output = pipeline()\n\n assert output.numel() == output.sum()\n\n # hack since Python doesn't like overwriting modules: https://stackoverflow.com/questions/3105801/unload-a-module-in-python\n # Could in the future work with hashes instead.\n del sys.modules[\"diffusers_modules.git.one_step_unet\"]\n\n pipeline = DiffusionPipeline.from_pretrained(\n \"google/ddpm-cifar10-32\", custom_pipeline=\"one_step_unet\", custom_revision=\"0.10.2\"\n )\n with torch.no_grad():\n output = pipeline()\n\n assert output.numel() != output.sum()\n\n assert pipeline.__class__.__name__ == \"UnetSchedulerOneForwardPipeline\"\n\n def test_run_custom_pipeline(self):\n pipeline = DiffusionPipeline.from_pretrained(\n \"google/ddpm-cifar10-32\", custom_pipeline=\"hf-internal-testing/diffusers-dummy-pipeline\"\n )\n pipeline = pipeline.to(torch_device)\n images, output_str = pipeline(num_inference_steps=2, output_type=\"np\")\n\n assert images[0].shape == (1, 32, 32, 3)\n\n # compare output to https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py#L102\n assert output_str == \"This is a test\"\n\n def test_remote_components(self):\n # make sure that trust remote code has to be passed\n with self.assertRaises(ValueError):\n pipeline = DiffusionPipeline.from_pretrained(\"hf-internal-testing/tiny-sdxl-custom-components\")\n\n # Check that only loading custom components \"my_unet\", \"my_scheduler\" works\n pipeline = DiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-sdxl-custom-components\", trust_remote_code=True\n )\n\n assert pipeline.config.unet == (\"diffusers_modules.local.my_unet_model\", \"MyUNetModel\")\n assert pipeline.config.scheduler == (\"diffusers_modules.local.my_scheduler\", \"MyScheduler\")\n assert pipeline.__class__.__name__ == \"StableDiffusionXLPipeline\"\n\n pipeline = pipeline.to(torch_device)\n images = pipeline(\"test\", num_inference_steps=2, output_type=\"np\")[0]\n\n assert images.shape == (1, 64, 64, 3)\n\n # Check that only loading custom components \"my_unet\", \"my_scheduler\" and explicit custom pipeline works\n pipeline = DiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-sdxl-custom-components\", custom_pipeline=\"my_pipeline\", trust_remote_code=True\n )\n\n assert pipeline.config.unet == (\"diffusers_modules.local.my_unet_model\", \"MyUNetModel\")\n assert pipeline.config.scheduler == (\"diffusers_modules.local.my_scheduler\", \"MyScheduler\")\n assert pipeline.__class__.__name__ == \"MyPipeline\"\n\n pipeline = pipeline.to(torch_device)\n images = pipeline(\"test\", num_inference_steps=2, output_type=\"np\")[0]\n\n assert images.shape == (1, 64, 64, 3)\n\n def test_remote_auto_custom_pipe(self):\n # make sure that trust remote code has to be passed\n with self.assertRaises(ValueError):\n pipeline = DiffusionPipeline.from_pretrained(\"hf-internal-testing/tiny-sdxl-custom-all\")\n\n # Check that only loading custom components \"my_unet\", \"my_scheduler\" and auto custom pipeline works\n pipeline = DiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-sdxl-custom-all\", trust_remote_code=True\n )\n\n assert pipeline.config.unet == (\"diffusers_modules.local.my_unet_model\", \"MyUNetModel\")\n assert pipeline.config.scheduler == (\"diffusers_modules.local.my_scheduler\", \"MyScheduler\")\n assert pipeline.__class__.__name__ == \"MyPipeline\"\n\n pipeline = pipeline.to(torch_device)\n images = pipeline(\"test\", num_inference_steps=2, output_type=\"np\")[0]\n\n assert images.shape == (1, 64, 64, 3)\n\n def test_local_custom_pipeline_repo(self):\n local_custom_pipeline_path = get_tests_dir(\"fixtures/custom_pipeline\")\n pipeline = DiffusionPipeline.from_pretrained(\n \"google/ddpm-cifar10-32\", custom_pipeline=local_custom_pipeline_path\n )\n pipeline = pipeline.to(torch_device)\n images, output_str = pipeline(num_inference_steps=2, output_type=\"np\")\n\n assert pipeline.__class__.__name__ == \"CustomLocalPipeline\"\n assert images[0].shape == (1, 32, 32, 3)\n # compare to https://github.com/huggingface/diffusers/blob/main/tests/fixtures/custom_pipeline/pipeline.py#L102\n assert output_str == \"This is a local test\"\n\n def test_local_custom_pipeline_file(self):\n local_custom_pipeline_path = get_tests_dir(\"fixtures/custom_pipeline\")\n local_custom_pipeline_path = os.path.join(local_custom_pipeline_path, \"what_ever.py\")\n pipeline = DiffusionPipeline.from_pretrained(\n \"google/ddpm-cifar10-32\", custom_pipeline=local_custom_pipeline_path\n )\n pipeline = pipeline.to(torch_device)\n images, output_str = pipeline(num_inference_steps=2, output_type=\"np\")\n\n assert pipeline.__class__.__name__ == \"CustomLocalPipeline\"\n assert images[0].shape == (1, 32, 32, 3)\n # compare to https://github.com/huggingface/diffusers/blob/main/tests/fixtures/custom_pipeline/pipeline.py#L102\n assert output_str == \"This is a local test\"\n\n def test_custom_model_and_pipeline(self):\n pipe = CustomPipeline(\n encoder=CustomEncoder(),\n scheduler=DDIMScheduler(),\n )\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n pipe.save_pretrained(tmpdirname, safe_serialization=False)\n\n pipe_new = CustomPipeline.from_pretrained(tmpdirname)\n pipe_new.save_pretrained(tmpdirname)\n\n conf_1 = dict(pipe.config)\n conf_2 = dict(pipe_new.config)\n\n del conf_2[\"_name_or_path\"]\n\n assert conf_1 == conf_2\n\n @slow\n @require_torch_gpu\n def test_download_from_git(self):\n # Because adaptive_avg_pool2d_backward_cuda\n # does not have a deterministic implementation.\n clip_model_id = \"laion/CLIP-ViT-B-32-laion2B-s34B-b79K\"\n\n feature_extractor = CLIPImageProcessor.from_pretrained(clip_model_id)\n clip_model = CLIPModel.from_pretrained(clip_model_id, torch_dtype=torch.float16)\n\n pipeline = DiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n custom_pipeline=\"clip_guided_stable_diffusion\",\n clip_model=clip_model,\n feature_extractor=feature_extractor,\n torch_dtype=torch.float16,\n )\n pipeline.enable_attention_slicing()\n pipeline = pipeline.to(torch_device)\n\n # NOTE that `\"CLIPGuidedStableDiffusion\"` is not a class that is defined in the pypi package of th e library, but solely on the community examples folder of GitHub under:\n # https://github.com/huggingface/diffusers/blob/main/examples/community/clip_guided_stable_diffusion.py\n assert pipeline.__class__.__name__ == \"CLIPGuidedStableDiffusion\"\n\n image = pipeline(\"a prompt\", num_inference_steps=2, output_type=\"np\").images[0]\n assert image.shape == (512, 512, 3)\n\n def test_save_pipeline_change_config(self):\n pipe = DiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\", safety_checker=None\n )\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n pipe.save_pretrained(tmpdirname)\n pipe = DiffusionPipeline.from_pretrained(tmpdirname)\n\n assert pipe.scheduler.__class__.__name__ == \"PNDMScheduler\"\n\n # let's make sure that changing the scheduler is correctly reflected\n with tempfile.TemporaryDirectory() as tmpdirname:\n pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n pipe.save_pretrained(tmpdirname)\n pipe = DiffusionPipeline.from_pretrained(tmpdirname)\n\n assert pipe.scheduler.__class__.__name__ == \"DPMSolverMultistepScheduler\"\n\n\nclass PipelineFastTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def dummy_image(self):\n batch_size = 1\n num_channels = 3\n sizes = (32, 32)\n\n image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)\n return image\n\n def dummy_uncond_unet(self, sample_size=32):\n torch.manual_seed(0)\n model = UNet2DModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=sample_size,\n in_channels=3,\n out_channels=3,\n down_block_types=(\"DownBlock2D\", \"AttnDownBlock2D\"),\n up_block_types=(\"AttnUpBlock2D\", \"UpBlock2D\"),\n )\n return model\n\n def dummy_cond_unet(self, sample_size=32):\n torch.manual_seed(0)\n model = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=sample_size,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n )\n return model\n\n @property\n def dummy_vae(self):\n torch.manual_seed(0)\n model = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n return model\n\n @property\n def dummy_text_encoder(self):\n torch.manual_seed(0)\n config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n return CLIPTextModel(config)\n\n @property\n def dummy_extractor(self):\n def extract(*args, **kwargs):\n class Out:\n def __init__(self):\n self.pixel_values = torch.ones([0])\n\n def to(self, device):\n self.pixel_values.to(device)\n return self\n\n return Out()\n\n return extract\n\n @parameterized.expand(\n [\n [DDIMScheduler, DDIMPipeline, 32],\n [DDPMScheduler, DDPMPipeline, 32],\n [DDIMScheduler, DDIMPipeline, (32, 64)],\n [DDPMScheduler, DDPMPipeline, (64, 32)],\n ]\n )\n def test_uncond_unet_components(self, scheduler_fn=DDPMScheduler, pipeline_fn=DDPMPipeline, sample_size=32):\n unet = self.dummy_uncond_unet(sample_size)\n scheduler = scheduler_fn()\n pipeline = pipeline_fn(unet, scheduler).to(torch_device)\n\n generator = torch.manual_seed(0)\n out_image = pipeline(\n generator=generator,\n num_inference_steps=2,\n output_type=\"np\",\n ).images\n sample_size = (sample_size, sample_size) if isinstance(sample_size, int) else sample_size\n assert out_image.shape == (1, *sample_size, 3)\n\n def test_stable_diffusion_components(self):\n \"\"\"Test that components property works correctly\"\"\"\n unet = self.dummy_cond_unet()\n scheduler = PNDMScheduler(skip_prk_steps=True)\n vae = self.dummy_vae\n bert = self.dummy_text_encoder\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n image = self.dummy_image().cpu().permute(0, 2, 3, 1)[0]\n init_image = Image.fromarray(np.uint8(image)).convert(\"RGB\")\n mask_image = Image.fromarray(np.uint8(image + 4)).convert(\"RGB\").resize((32, 32))\n\n # make sure here that pndm scheduler skips prk\n inpaint = StableDiffusionInpaintPipelineLegacy(\n unet=unet,\n scheduler=scheduler,\n vae=vae,\n text_encoder=bert,\n tokenizer=tokenizer,\n safety_checker=None,\n feature_extractor=self.dummy_extractor,\n ).to(torch_device)\n img2img = StableDiffusionImg2ImgPipeline(**inpaint.components, image_encoder=None).to(torch_device)\n text2img = StableDiffusionPipeline(**inpaint.components, image_encoder=None).to(torch_device)\n\n prompt = \"A painting of a squirrel eating a burger\"\n\n generator = torch.manual_seed(0)\n image_inpaint = inpaint(\n [prompt],\n generator=generator,\n num_inference_steps=2,\n output_type=\"np\",\n image=init_image,\n mask_image=mask_image,\n ).images\n image_img2img = img2img(\n [prompt],\n generator=generator,\n num_inference_steps=2,\n output_type=\"np\",\n image=init_image,\n ).images\n image_text2img = text2img(\n [prompt],\n generator=generator,\n num_inference_steps=2,\n output_type=\"np\",\n ).images\n\n assert image_inpaint.shape == (1, 32, 32, 3)\n assert image_img2img.shape == (1, 32, 32, 3)\n assert image_text2img.shape == (1, 64, 64, 3)\n\n @require_torch_gpu\n def test_pipe_false_offload_warn(self):\n unet = self.dummy_cond_unet()\n scheduler = PNDMScheduler(skip_prk_steps=True)\n vae = self.dummy_vae\n bert = self.dummy_text_encoder\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n sd = StableDiffusionPipeline(\n unet=unet,\n scheduler=scheduler,\n vae=vae,\n text_encoder=bert,\n tokenizer=tokenizer,\n safety_checker=None,\n feature_extractor=self.dummy_extractor,\n )\n\n sd.enable_model_cpu_offload()\n\n logger = logging.get_logger(\"diffusers.pipelines.pipeline_utils\")\n with CaptureLogger(logger) as cap_logger:\n sd.to(\"cuda\")\n\n assert \"It is strongly recommended against doing so\" in str(cap_logger)\n\n sd = StableDiffusionPipeline(\n unet=unet,\n scheduler=scheduler,\n vae=vae,\n text_encoder=bert,\n tokenizer=tokenizer,\n safety_checker=None,\n feature_extractor=self.dummy_extractor,\n )\n\n def test_set_scheduler(self):\n unet = self.dummy_cond_unet()\n scheduler = PNDMScheduler(skip_prk_steps=True)\n vae = self.dummy_vae\n bert = self.dummy_text_encoder\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n sd = StableDiffusionPipeline(\n unet=unet,\n scheduler=scheduler,\n vae=vae,\n text_encoder=bert,\n tokenizer=tokenizer,\n safety_checker=None,\n feature_extractor=self.dummy_extractor,\n )\n\n sd.scheduler = DDIMScheduler.from_config(sd.scheduler.config)\n assert isinstance(sd.scheduler, DDIMScheduler)\n sd.scheduler = DDPMScheduler.from_config(sd.scheduler.config)\n assert isinstance(sd.scheduler, DDPMScheduler)\n sd.scheduler = PNDMScheduler.from_config(sd.scheduler.config)\n assert isinstance(sd.scheduler, PNDMScheduler)\n sd.scheduler = LMSDiscreteScheduler.from_config(sd.scheduler.config)\n assert isinstance(sd.scheduler, LMSDiscreteScheduler)\n sd.scheduler = EulerDiscreteScheduler.from_config(sd.scheduler.config)\n assert isinstance(sd.scheduler, EulerDiscreteScheduler)\n sd.scheduler = EulerAncestralDiscreteScheduler.from_config(sd.scheduler.config)\n assert isinstance(sd.scheduler, EulerAncestralDiscreteScheduler)\n sd.scheduler = DPMSolverMultistepScheduler.from_config(sd.scheduler.config)\n assert isinstance(sd.scheduler, DPMSolverMultistepScheduler)\n\n def test_set_component_to_none(self):\n unet = self.dummy_cond_unet()\n scheduler = PNDMScheduler(skip_prk_steps=True)\n vae = self.dummy_vae\n bert = self.dummy_text_encoder\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n pipeline = StableDiffusionPipeline(\n unet=unet,\n scheduler=scheduler,\n vae=vae,\n text_encoder=bert,\n tokenizer=tokenizer,\n safety_checker=None,\n feature_extractor=self.dummy_extractor,\n )\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n\n prompt = \"This is a flower\"\n\n out_image = pipeline(\n prompt=prompt,\n generator=generator,\n num_inference_steps=1,\n output_type=\"np\",\n ).images\n\n pipeline.feature_extractor = None\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n out_image_2 = pipeline(\n prompt=prompt,\n generator=generator,\n num_inference_steps=1,\n output_type=\"np\",\n ).images\n\n assert out_image.shape == (1, 64, 64, 3)\n assert np.abs(out_image - out_image_2).max() < 1e-3\n\n def test_optional_components_is_none(self):\n unet = self.dummy_cond_unet()\n scheduler = PNDMScheduler(skip_prk_steps=True)\n vae = self.dummy_vae\n bert = self.dummy_text_encoder\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n items = {\n \"feature_extractor\": self.dummy_extractor,\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": bert,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n # we don't add an image encoder\n }\n\n pipeline = StableDiffusionPipeline(**items)\n\n assert sorted(pipeline.components.keys()) == sorted([\"image_encoder\"] + list(items.keys()))\n assert pipeline.image_encoder is None\n\n def test_set_scheduler_consistency(self):\n unet = self.dummy_cond_unet()\n pndm = PNDMScheduler.from_config(\"hf-internal-testing/tiny-stable-diffusion-torch\", subfolder=\"scheduler\")\n ddim = DDIMScheduler.from_config(\"hf-internal-testing/tiny-stable-diffusion-torch\", subfolder=\"scheduler\")\n vae = self.dummy_vae\n bert = self.dummy_text_encoder\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n sd = StableDiffusionPipeline(\n unet=unet,\n scheduler=pndm,\n vae=vae,\n text_encoder=bert,\n tokenizer=tokenizer,\n safety_checker=None,\n feature_extractor=self.dummy_extractor,\n )\n\n pndm_config = sd.scheduler.config\n sd.scheduler = DDPMScheduler.from_config(pndm_config)\n sd.scheduler = PNDMScheduler.from_config(sd.scheduler.config)\n pndm_config_2 = sd.scheduler.config\n pndm_config_2 = {k: v for k, v in pndm_config_2.items() if k in pndm_config}\n\n assert dict(pndm_config) == dict(pndm_config_2)\n\n sd = StableDiffusionPipeline(\n unet=unet,\n scheduler=ddim,\n vae=vae,\n text_encoder=bert,\n tokenizer=tokenizer,\n safety_checker=None,\n feature_extractor=self.dummy_extractor,\n )\n\n ddim_config = sd.scheduler.config\n sd.scheduler = LMSDiscreteScheduler.from_config(ddim_config)\n sd.scheduler = DDIMScheduler.from_config(sd.scheduler.config)\n ddim_config_2 = sd.scheduler.config\n ddim_config_2 = {k: v for k, v in ddim_config_2.items() if k in ddim_config}\n\n assert dict(ddim_config) == dict(ddim_config_2)\n\n def test_save_safe_serialization(self):\n pipeline = StableDiffusionPipeline.from_pretrained(\"hf-internal-testing/tiny-stable-diffusion-torch\")\n with tempfile.TemporaryDirectory() as tmpdirname:\n pipeline.save_pretrained(tmpdirname, safe_serialization=True)\n\n # Validate that the VAE safetensor exists and are of the correct format\n vae_path = os.path.join(tmpdirname, \"vae\", \"diffusion_pytorch_model.safetensors\")\n assert os.path.exists(vae_path), f\"Could not find {vae_path}\"\n _ = safetensors.torch.load_file(vae_path)\n\n # Validate that the UNet safetensor exists and are of the correct format\n unet_path = os.path.join(tmpdirname, \"unet\", \"diffusion_pytorch_model.safetensors\")\n assert os.path.exists(unet_path), f\"Could not find {unet_path}\"\n _ = safetensors.torch.load_file(unet_path)\n\n # Validate that the text encoder safetensor exists and are of the correct format\n text_encoder_path = os.path.join(tmpdirname, \"text_encoder\", \"model.safetensors\")\n assert os.path.exists(text_encoder_path), f\"Could not find {text_encoder_path}\"\n _ = safetensors.torch.load_file(text_encoder_path)\n\n pipeline = StableDiffusionPipeline.from_pretrained(tmpdirname)\n assert pipeline.unet is not None\n assert pipeline.vae is not None\n assert pipeline.text_encoder is not None\n assert pipeline.scheduler is not None\n assert pipeline.feature_extractor is not None\n\n def test_no_pytorch_download_when_doing_safetensors(self):\n # by default we don't download\n with tempfile.TemporaryDirectory() as tmpdirname:\n _ = StableDiffusionPipeline.from_pretrained(\n \"hf-internal-testing/diffusers-stable-diffusion-tiny-all\", cache_dir=tmpdirname\n )\n\n path = os.path.join(\n tmpdirname,\n \"models--hf-internal-testing--diffusers-stable-diffusion-tiny-all\",\n \"snapshots\",\n \"07838d72e12f9bcec1375b0482b80c1d399be843\",\n \"unet\",\n )\n # safetensors exists\n assert os.path.exists(os.path.join(path, \"diffusion_pytorch_model.safetensors\"))\n # pytorch does not\n assert not os.path.exists(os.path.join(path, \"diffusion_pytorch_model.bin\"))\n\n def test_no_safetensors_download_when_doing_pytorch(self):\n use_safetensors = False\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n _ = StableDiffusionPipeline.from_pretrained(\n \"hf-internal-testing/diffusers-stable-diffusion-tiny-all\",\n cache_dir=tmpdirname,\n use_safetensors=use_safetensors,\n )\n\n path = os.path.join(\n tmpdirname,\n \"models--hf-internal-testing--diffusers-stable-diffusion-tiny-all\",\n \"snapshots\",\n \"07838d72e12f9bcec1375b0482b80c1d399be843\",\n \"unet\",\n )\n # safetensors does not exists\n assert not os.path.exists(os.path.join(path, \"diffusion_pytorch_model.safetensors\"))\n # pytorch does\n assert os.path.exists(os.path.join(path, \"diffusion_pytorch_model.bin\"))\n\n def test_optional_components(self):\n unet = self.dummy_cond_unet()\n pndm = PNDMScheduler.from_config(\"hf-internal-testing/tiny-stable-diffusion-torch\", subfolder=\"scheduler\")\n vae = self.dummy_vae\n bert = self.dummy_text_encoder\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n orig_sd = StableDiffusionPipeline(\n unet=unet,\n scheduler=pndm,\n vae=vae,\n text_encoder=bert,\n tokenizer=tokenizer,\n safety_checker=unet,\n feature_extractor=self.dummy_extractor,\n )\n sd = orig_sd\n\n assert sd.config.requires_safety_checker is True\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n sd.save_pretrained(tmpdirname)\n\n # Test that passing None works\n sd = StableDiffusionPipeline.from_pretrained(\n tmpdirname, feature_extractor=None, safety_checker=None, requires_safety_checker=False\n )\n\n assert sd.config.requires_safety_checker is False\n assert sd.config.safety_checker == (None, None)\n assert sd.config.feature_extractor == (None, None)\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n sd.save_pretrained(tmpdirname)\n\n # Test that loading previous None works\n sd = StableDiffusionPipeline.from_pretrained(tmpdirname)\n\n assert sd.config.requires_safety_checker is False\n assert sd.config.safety_checker == (None, None)\n assert sd.config.feature_extractor == (None, None)\n\n orig_sd.save_pretrained(tmpdirname)\n\n # Test that loading without any directory works\n shutil.rmtree(os.path.join(tmpdirname, \"safety_checker\"))\n with open(os.path.join(tmpdirname, sd.config_name)) as f:\n config = json.load(f)\n config[\"safety_checker\"] = [None, None]\n with open(os.path.join(tmpdirname, sd.config_name), \"w\") as f:\n json.dump(config, f)\n\n sd = StableDiffusionPipeline.from_pretrained(tmpdirname, requires_safety_checker=False)\n sd.save_pretrained(tmpdirname)\n sd = StableDiffusionPipeline.from_pretrained(tmpdirname)\n\n assert sd.config.requires_safety_checker is False\n assert sd.config.safety_checker == (None, None)\n assert sd.config.feature_extractor == (None, None)\n\n # Test that loading from deleted model index works\n with open(os.path.join(tmpdirname, sd.config_name)) as f:\n config = json.load(f)\n del config[\"safety_checker\"]\n del config[\"feature_extractor\"]\n with open(os.path.join(tmpdirname, sd.config_name), \"w\") as f:\n json.dump(config, f)\n\n sd = StableDiffusionPipeline.from_pretrained(tmpdirname)\n\n assert sd.config.requires_safety_checker is False\n assert sd.config.safety_checker == (None, None)\n assert sd.config.feature_extractor == (None, None)\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n sd.save_pretrained(tmpdirname)\n\n # Test that partially loading works\n sd = StableDiffusionPipeline.from_pretrained(tmpdirname, feature_extractor=self.dummy_extractor)\n\n assert sd.config.requires_safety_checker is False\n assert sd.config.safety_checker == (None, None)\n assert sd.config.feature_extractor != (None, None)\n\n # Test that partially loading works\n sd = StableDiffusionPipeline.from_pretrained(\n tmpdirname,\n feature_extractor=self.dummy_extractor,\n safety_checker=unet,\n requires_safety_checker=[True, True],\n )\n\n assert sd.config.requires_safety_checker == [True, True]\n assert sd.config.safety_checker != (None, None)\n assert sd.config.feature_extractor != (None, None)\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n sd.save_pretrained(tmpdirname)\n sd = StableDiffusionPipeline.from_pretrained(tmpdirname, feature_extractor=self.dummy_extractor)\n\n assert sd.config.requires_safety_checker == [True, True]\n assert sd.config.safety_checker != (None, None)\n assert sd.config.feature_extractor != (None, None)\n\n def test_name_or_path(self):\n model_path = \"hf-internal-testing/tiny-stable-diffusion-torch\"\n sd = DiffusionPipeline.from_pretrained(model_path)\n\n assert sd.name_or_path == model_path\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n sd.save_pretrained(tmpdirname)\n sd = DiffusionPipeline.from_pretrained(tmpdirname)\n\n assert sd.name_or_path == tmpdirname\n\n def test_error_no_variant_available(self):\n variant = \"fp16\"\n with self.assertRaises(ValueError) as error_context:\n _ = StableDiffusionPipeline.download(\n \"hf-internal-testing/diffusers-stable-diffusion-tiny-all\", variant=variant\n )\n\n assert \"but no such modeling files are available\" in str(error_context.exception)\n assert variant in str(error_context.exception)\n\n def test_pipe_to(self):\n unet = self.dummy_cond_unet()\n scheduler = PNDMScheduler(skip_prk_steps=True)\n vae = self.dummy_vae\n bert = self.dummy_text_encoder\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n sd = StableDiffusionPipeline(\n unet=unet,\n scheduler=scheduler,\n vae=vae,\n text_encoder=bert,\n tokenizer=tokenizer,\n safety_checker=None,\n feature_extractor=self.dummy_extractor,\n )\n\n device_type = torch.device(torch_device).type\n\n sd1 = sd.to(device_type)\n sd2 = sd.to(torch.device(device_type))\n sd3 = sd.to(device_type, torch.float32)\n sd4 = sd.to(device=device_type)\n sd5 = sd.to(torch_device=device_type)\n sd6 = sd.to(device_type, dtype=torch.float32)\n sd7 = sd.to(device_type, torch_dtype=torch.float32)\n\n assert sd1.device.type == device_type\n assert sd2.device.type == device_type\n assert sd3.device.type == device_type\n assert sd4.device.type == device_type\n assert sd5.device.type == device_type\n assert sd6.device.type == device_type\n assert sd7.device.type == device_type\n\n sd1 = sd.to(torch.float16)\n sd2 = sd.to(None, torch.float16)\n sd3 = sd.to(dtype=torch.float16)\n sd4 = sd.to(dtype=torch.float16)\n sd5 = sd.to(None, dtype=torch.float16)\n sd6 = sd.to(None, torch_dtype=torch.float16)\n\n assert sd1.dtype == torch.float16\n assert sd2.dtype == torch.float16\n assert sd3.dtype == torch.float16\n assert sd4.dtype == torch.float16\n assert sd5.dtype == torch.float16\n assert sd6.dtype == torch.float16\n\n sd1 = sd.to(device=device_type, dtype=torch.float16)\n sd2 = sd.to(torch_device=device_type, torch_dtype=torch.float16)\n sd3 = sd.to(device_type, torch.float16)\n\n assert sd1.dtype == torch.float16\n assert sd2.dtype == torch.float16\n assert sd3.dtype == torch.float16\n\n assert sd1.device.type == device_type\n assert sd2.device.type == device_type\n assert sd3.device.type == device_type\n\n def test_pipe_same_device_id_offload(self):\n unet = self.dummy_cond_unet()\n scheduler = PNDMScheduler(skip_prk_steps=True)\n vae = self.dummy_vae\n bert = self.dummy_text_encoder\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n sd = StableDiffusionPipeline(\n unet=unet,\n scheduler=scheduler,\n vae=vae,\n text_encoder=bert,\n tokenizer=tokenizer,\n safety_checker=None,\n feature_extractor=self.dummy_extractor,\n )\n\n sd.enable_model_cpu_offload(gpu_id=5)\n assert sd._offload_gpu_id == 5\n sd.maybe_free_model_hooks()\n assert sd._offload_gpu_id == 5\n\n\n@slow\n@require_torch_gpu\nclass PipelineSlowTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_smart_download(self):\n model_id = \"hf-internal-testing/unet-pipeline-dummy\"\n with tempfile.TemporaryDirectory() as tmpdirname:\n _ = DiffusionPipeline.from_pretrained(model_id, cache_dir=tmpdirname, force_download=True)\n local_repo_name = \"--\".join([\"models\"] + model_id.split(\"/\"))\n snapshot_dir = os.path.join(tmpdirname, local_repo_name, \"snapshots\")\n snapshot_dir = os.path.join(snapshot_dir, os.listdir(snapshot_dir)[0])\n\n # inspect all downloaded files to make sure that everything is included\n assert os.path.isfile(os.path.join(snapshot_dir, DiffusionPipeline.config_name))\n assert os.path.isfile(os.path.join(snapshot_dir, CONFIG_NAME))\n assert os.path.isfile(os.path.join(snapshot_dir, SCHEDULER_CONFIG_NAME))\n assert os.path.isfile(os.path.join(snapshot_dir, WEIGHTS_NAME))\n assert os.path.isfile(os.path.join(snapshot_dir, \"scheduler\", SCHEDULER_CONFIG_NAME))\n assert os.path.isfile(os.path.join(snapshot_dir, \"unet\", WEIGHTS_NAME))\n assert os.path.isfile(os.path.join(snapshot_dir, \"unet\", WEIGHTS_NAME))\n # let's make sure the super large numpy file:\n # https://huggingface.co/hf-internal-testing/unet-pipeline-dummy/blob/main/big_array.npy\n # is not downloaded, but all the expected ones\n assert not os.path.isfile(os.path.join(snapshot_dir, \"big_array.npy\"))\n\n def test_warning_unused_kwargs(self):\n model_id = \"hf-internal-testing/unet-pipeline-dummy\"\n logger = logging.get_logger(\"diffusers.pipelines\")\n with tempfile.TemporaryDirectory() as tmpdirname:\n with CaptureLogger(logger) as cap_logger:\n DiffusionPipeline.from_pretrained(\n model_id,\n not_used=True,\n cache_dir=tmpdirname,\n force_download=True,\n )\n\n assert (\n cap_logger.out.strip().split(\"\\n\")[-1]\n == \"Keyword arguments {'not_used': True} are not expected by DDPMPipeline and will be ignored.\"\n )\n\n def test_from_save_pretrained(self):\n # 1. Load models\n model = UNet2DModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=3,\n out_channels=3,\n down_block_types=(\"DownBlock2D\", \"AttnDownBlock2D\"),\n up_block_types=(\"AttnUpBlock2D\", \"UpBlock2D\"),\n )\n scheduler = DDPMScheduler(num_train_timesteps=10)\n\n ddpm = DDPMPipeline(model, scheduler)\n ddpm.to(torch_device)\n ddpm.set_progress_bar_config(disable=None)\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n ddpm.save_pretrained(tmpdirname)\n new_ddpm = DDPMPipeline.from_pretrained(tmpdirname)\n new_ddpm.to(torch_device)\n\n generator = torch.Generator(device=torch_device).manual_seed(0)\n image = ddpm(generator=generator, num_inference_steps=5, output_type=\"np\").images\n\n generator = torch.Generator(device=torch_device).manual_seed(0)\n new_image = new_ddpm(generator=generator, num_inference_steps=5, output_type=\"np\").images\n\n assert np.abs(image - new_image).max() < 1e-5, \"Models don't give the same forward pass\"\n\n @require_python39_or_higher\n @require_torch_2\n @unittest.skipIf(\n get_python_version == (3, 12),\n reason=\"Torch Dynamo isn't yet supported for Python 3.12.\",\n )\n def test_from_save_pretrained_dynamo(self):\n run_test_in_subprocess(test_case=self, target_func=_test_from_save_pretrained_dynamo, inputs=None)\n\n def test_from_pretrained_hub(self):\n model_path = \"google/ddpm-cifar10-32\"\n\n scheduler = DDPMScheduler(num_train_timesteps=10)\n\n ddpm = DDPMPipeline.from_pretrained(model_path, scheduler=scheduler)\n ddpm = ddpm.to(torch_device)\n ddpm.set_progress_bar_config(disable=None)\n\n ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler)\n ddpm_from_hub = ddpm_from_hub.to(torch_device)\n ddpm_from_hub.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=torch_device).manual_seed(0)\n image = ddpm(generator=generator, num_inference_steps=5, output_type=\"np\").images\n\n generator = torch.Generator(device=torch_device).manual_seed(0)\n new_image = ddpm_from_hub(generator=generator, num_inference_steps=5, output_type=\"np\").images\n\n assert np.abs(image - new_image).max() < 1e-5, \"Models don't give the same forward pass\"\n\n def test_from_pretrained_hub_pass_model(self):\n model_path = \"google/ddpm-cifar10-32\"\n\n scheduler = DDPMScheduler(num_train_timesteps=10)\n\n # pass unet into DiffusionPipeline\n unet = UNet2DModel.from_pretrained(model_path)\n ddpm_from_hub_custom_model = DiffusionPipeline.from_pretrained(model_path, unet=unet, scheduler=scheduler)\n ddpm_from_hub_custom_model = ddpm_from_hub_custom_model.to(torch_device)\n ddpm_from_hub_custom_model.set_progress_bar_config(disable=None)\n\n ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler)\n ddpm_from_hub = ddpm_from_hub.to(torch_device)\n ddpm_from_hub_custom_model.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=torch_device).manual_seed(0)\n image = ddpm_from_hub_custom_model(generator=generator, num_inference_steps=5, output_type=\"np\").images\n\n generator = torch.Generator(device=torch_device).manual_seed(0)\n new_image = ddpm_from_hub(generator=generator, num_inference_steps=5, output_type=\"np\").images\n\n assert np.abs(image - new_image).max() < 1e-5, \"Models don't give the same forward pass\"\n\n def test_output_format(self):\n model_path = \"google/ddpm-cifar10-32\"\n\n scheduler = DDIMScheduler.from_pretrained(model_path)\n pipe = DDIMPipeline.from_pretrained(model_path, scheduler=scheduler)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n images = pipe(output_type=\"np\").images\n assert images.shape == (1, 32, 32, 3)\n assert isinstance(images, np.ndarray)\n\n images = pipe(output_type=\"pil\", num_inference_steps=4).images\n assert isinstance(images, list)\n assert len(images) == 1\n assert isinstance(images[0], PIL.Image.Image)\n\n # use PIL by default\n images = pipe(num_inference_steps=4).images\n assert isinstance(images, list)\n assert isinstance(images[0], PIL.Image.Image)\n\n @require_flax\n def test_from_flax_from_pt(self):\n pipe_pt = StableDiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-torch\", safety_checker=None\n )\n pipe_pt.to(torch_device)\n\n from diffusers import FlaxStableDiffusionPipeline\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n pipe_pt.save_pretrained(tmpdirname)\n\n pipe_flax, params = FlaxStableDiffusionPipeline.from_pretrained(\n tmpdirname, safety_checker=None, from_pt=True\n )\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n pipe_flax.save_pretrained(tmpdirname, params=params)\n pipe_pt_2 = StableDiffusionPipeline.from_pretrained(tmpdirname, safety_checker=None, from_flax=True)\n pipe_pt_2.to(torch_device)\n\n prompt = \"Hello\"\n\n generator = torch.manual_seed(0)\n image_0 = pipe_pt(\n [prompt],\n generator=generator,\n num_inference_steps=2,\n output_type=\"np\",\n ).images[0]\n\n generator = torch.manual_seed(0)\n image_1 = pipe_pt_2(\n [prompt],\n generator=generator,\n num_inference_steps=2,\n output_type=\"np\",\n ).images[0]\n\n assert np.abs(image_0 - image_1).sum() < 1e-5, \"Models don't give the same forward pass\"\n\n @require_compel\n def test_weighted_prompts_compel(self):\n from compel import Compel\n\n pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\")\n pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n pipe.enable_model_cpu_offload()\n pipe.enable_attention_slicing()\n\n compel = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder)\n\n prompt = \"a red cat playing with a ball{}\"\n\n prompts = [prompt.format(s) for s in [\"\", \"++\", \"--\"]]\n\n prompt_embeds = compel(prompts)\n\n generator = [torch.Generator(device=\"cpu\").manual_seed(33) for _ in range(prompt_embeds.shape[0])]\n\n images = pipe(\n prompt_embeds=prompt_embeds, generator=generator, num_inference_steps=20, output_type=\"np\"\n ).images\n\n for i, image in enumerate(images):\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n f\"/compel/forest_{i}.npy\"\n )\n\n assert np.abs(image - expected_image).max() < 3e-1\n\n\n@nightly\n@require_torch_gpu\nclass PipelineNightlyTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_ddpm_ddim_equality_batched(self):\n seed = 0\n model_id = \"google/ddpm-cifar10-32\"\n\n unet = UNet2DModel.from_pretrained(model_id)\n ddpm_scheduler = DDPMScheduler()\n ddim_scheduler = DDIMScheduler()\n\n ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler)\n ddpm.to(torch_device)\n ddpm.set_progress_bar_config(disable=None)\n\n ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler)\n ddim.to(torch_device)\n ddim.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=torch_device).manual_seed(seed)\n ddpm_images = ddpm(batch_size=2, generator=generator, output_type=\"np\").images\n\n generator = torch.Generator(device=torch_device).manual_seed(seed)\n ddim_images = ddim(\n batch_size=2,\n generator=generator,\n num_inference_steps=1000,\n eta=1.0,\n output_type=\"np\",\n use_clipped_model_output=True, # Need this to make DDIM match DDPM\n ).images\n\n # the values aren't exactly equal, but the images look the same visually\n assert np.abs(ddpm_images - ddim_images).max() < 1e-1\n" }, { "path": "diffusers/tests/pipelines/test_pipelines_auto.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport os\nimport shutil\nimport unittest\nfrom collections import OrderedDict\nfrom pathlib import Path\n\nimport torch\nfrom transformers import CLIPVisionConfig, CLIPVisionModelWithProjection\n\nfrom diffusers import (\n AutoPipelineForImage2Image,\n AutoPipelineForInpainting,\n AutoPipelineForText2Image,\n ControlNetModel,\n DiffusionPipeline,\n)\nfrom diffusers.pipelines.auto_pipeline import (\n AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,\n AUTO_INPAINT_PIPELINES_MAPPING,\n AUTO_TEXT2IMAGE_PIPELINES_MAPPING,\n)\nfrom diffusers.utils.testing_utils import slow\n\n\nPRETRAINED_MODEL_REPO_MAPPING = OrderedDict(\n [\n (\"stable-diffusion\", \"runwayml/stable-diffusion-v1-5\"),\n (\"if\", \"DeepFloyd/IF-I-XL-v1.0\"),\n (\"kandinsky\", \"kandinsky-community/kandinsky-2-1\"),\n (\"kandinsky22\", \"kandinsky-community/kandinsky-2-2-decoder\"),\n ]\n)\n\n\nclass AutoPipelineFastTest(unittest.TestCase):\n @property\n def dummy_image_encoder(self):\n torch.manual_seed(0)\n config = CLIPVisionConfig(\n hidden_size=1,\n projection_dim=1,\n num_hidden_layers=1,\n num_attention_heads=1,\n image_size=1,\n intermediate_size=1,\n patch_size=1,\n )\n return CLIPVisionModelWithProjection(config)\n\n def test_from_pipe_consistent(self):\n pipe = AutoPipelineForText2Image.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-pipe\", requires_safety_checker=False\n )\n original_config = dict(pipe.config)\n\n pipe = AutoPipelineForImage2Image.from_pipe(pipe)\n assert dict(pipe.config) == original_config\n\n pipe = AutoPipelineForText2Image.from_pipe(pipe)\n assert dict(pipe.config) == original_config\n\n def test_from_pipe_override(self):\n pipe = AutoPipelineForText2Image.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-pipe\", requires_safety_checker=False\n )\n\n pipe = AutoPipelineForImage2Image.from_pipe(pipe, requires_safety_checker=True)\n assert pipe.config.requires_safety_checker is True\n\n pipe = AutoPipelineForText2Image.from_pipe(pipe, requires_safety_checker=True)\n assert pipe.config.requires_safety_checker is True\n\n def test_from_pipe_consistent_sdxl(self):\n pipe = AutoPipelineForImage2Image.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-xl-pipe\",\n requires_aesthetics_score=True,\n force_zeros_for_empty_prompt=False,\n )\n\n original_config = dict(pipe.config)\n\n pipe = AutoPipelineForText2Image.from_pipe(pipe)\n pipe = AutoPipelineForImage2Image.from_pipe(pipe)\n\n assert dict(pipe.config) == original_config\n\n def test_kwargs_local_files_only(self):\n repo = \"hf-internal-testing/tiny-stable-diffusion-torch\"\n tmpdirname = DiffusionPipeline.download(repo)\n tmpdirname = Path(tmpdirname)\n\n # edit commit_id to so that it's not the latest commit\n commit_id = tmpdirname.name\n new_commit_id = commit_id + \"hug\"\n\n ref_dir = tmpdirname.parent.parent / \"refs/main\"\n with open(ref_dir, \"w\") as f:\n f.write(new_commit_id)\n\n new_tmpdirname = tmpdirname.parent / new_commit_id\n os.rename(tmpdirname, new_tmpdirname)\n\n try:\n AutoPipelineForText2Image.from_pretrained(repo, local_files_only=True)\n except OSError:\n assert False, \"not able to load local files\"\n\n shutil.rmtree(tmpdirname.parent.parent)\n\n def test_from_pipe_controlnet_text2img(self):\n pipe = AutoPipelineForText2Image.from_pretrained(\"hf-internal-testing/tiny-stable-diffusion-pipe\")\n controlnet = ControlNetModel.from_pretrained(\"hf-internal-testing/tiny-controlnet\")\n\n pipe = AutoPipelineForText2Image.from_pipe(pipe, controlnet=controlnet)\n assert pipe.__class__.__name__ == \"StableDiffusionControlNetPipeline\"\n assert \"controlnet\" in pipe.components\n\n pipe = AutoPipelineForText2Image.from_pipe(pipe, controlnet=None)\n assert pipe.__class__.__name__ == \"StableDiffusionPipeline\"\n assert \"controlnet\" not in pipe.components\n\n def test_from_pipe_controlnet_img2img(self):\n pipe = AutoPipelineForImage2Image.from_pretrained(\"hf-internal-testing/tiny-stable-diffusion-pipe\")\n controlnet = ControlNetModel.from_pretrained(\"hf-internal-testing/tiny-controlnet\")\n\n pipe = AutoPipelineForImage2Image.from_pipe(pipe, controlnet=controlnet)\n assert pipe.__class__.__name__ == \"StableDiffusionControlNetImg2ImgPipeline\"\n assert \"controlnet\" in pipe.components\n\n pipe = AutoPipelineForImage2Image.from_pipe(pipe, controlnet=None)\n assert pipe.__class__.__name__ == \"StableDiffusionImg2ImgPipeline\"\n assert \"controlnet\" not in pipe.components\n\n def test_from_pipe_controlnet_inpaint(self):\n pipe = AutoPipelineForInpainting.from_pretrained(\"hf-internal-testing/tiny-stable-diffusion-torch\")\n controlnet = ControlNetModel.from_pretrained(\"hf-internal-testing/tiny-controlnet\")\n\n pipe = AutoPipelineForInpainting.from_pipe(pipe, controlnet=controlnet)\n assert pipe.__class__.__name__ == \"StableDiffusionControlNetInpaintPipeline\"\n assert \"controlnet\" in pipe.components\n\n pipe = AutoPipelineForInpainting.from_pipe(pipe, controlnet=None)\n assert pipe.__class__.__name__ == \"StableDiffusionInpaintPipeline\"\n assert \"controlnet\" not in pipe.components\n\n def test_from_pipe_controlnet_new_task(self):\n pipe_text2img = AutoPipelineForText2Image.from_pretrained(\"hf-internal-testing/tiny-stable-diffusion-torch\")\n controlnet = ControlNetModel.from_pretrained(\"hf-internal-testing/tiny-controlnet\")\n\n pipe_control_img2img = AutoPipelineForImage2Image.from_pipe(pipe_text2img, controlnet=controlnet)\n assert pipe_control_img2img.__class__.__name__ == \"StableDiffusionControlNetImg2ImgPipeline\"\n assert \"controlnet\" in pipe_control_img2img.components\n\n pipe_inpaint = AutoPipelineForInpainting.from_pipe(pipe_control_img2img, controlnet=None)\n assert pipe_inpaint.__class__.__name__ == \"StableDiffusionInpaintPipeline\"\n assert \"controlnet\" not in pipe_inpaint.components\n\n # testing `from_pipe` for text2img controlnet\n ## 1. from a different controlnet pipe, without controlnet argument\n pipe_control_text2img = AutoPipelineForText2Image.from_pipe(pipe_control_img2img)\n assert pipe_control_text2img.__class__.__name__ == \"StableDiffusionControlNetPipeline\"\n assert \"controlnet\" in pipe_control_text2img.components\n\n ## 2. from a different controlnet pipe, with controlnet argument\n pipe_control_text2img = AutoPipelineForText2Image.from_pipe(pipe_control_img2img, controlnet=controlnet)\n assert pipe_control_text2img.__class__.__name__ == \"StableDiffusionControlNetPipeline\"\n assert \"controlnet\" in pipe_control_text2img.components\n\n ## 3. from same controlnet pipeline class, with a different controlnet component\n pipe_control_text2img = AutoPipelineForText2Image.from_pipe(pipe_control_text2img, controlnet=controlnet)\n assert pipe_control_text2img.__class__.__name__ == \"StableDiffusionControlNetPipeline\"\n assert \"controlnet\" in pipe_control_text2img.components\n\n # testing from_pipe for inpainting\n ## 1. from a different controlnet pipeline class\n pipe_control_inpaint = AutoPipelineForInpainting.from_pipe(pipe_control_img2img)\n assert pipe_control_inpaint.__class__.__name__ == \"StableDiffusionControlNetInpaintPipeline\"\n assert \"controlnet\" in pipe_control_inpaint.components\n\n ## from a different controlnet pipe, with a different controlnet\n pipe_control_inpaint = AutoPipelineForInpainting.from_pipe(pipe_control_img2img, controlnet=controlnet)\n assert pipe_control_inpaint.__class__.__name__ == \"StableDiffusionControlNetInpaintPipeline\"\n assert \"controlnet\" in pipe_control_inpaint.components\n\n ## from same controlnet pipe, with a different controlnet\n pipe_control_inpaint = AutoPipelineForInpainting.from_pipe(pipe_control_inpaint, controlnet=controlnet)\n assert pipe_control_inpaint.__class__.__name__ == \"StableDiffusionControlNetInpaintPipeline\"\n assert \"controlnet\" in pipe_control_inpaint.components\n\n # testing from_pipe from img2img controlnet\n ## from a different controlnet pipe, without controlnet argument\n pipe_control_img2img = AutoPipelineForImage2Image.from_pipe(pipe_control_text2img)\n assert pipe_control_img2img.__class__.__name__ == \"StableDiffusionControlNetImg2ImgPipeline\"\n assert \"controlnet\" in pipe_control_img2img.components\n\n # from a different controlnet pipe, with a different controlnet component\n pipe_control_img2img = AutoPipelineForImage2Image.from_pipe(pipe_control_text2img, controlnet=controlnet)\n assert pipe_control_img2img.__class__.__name__ == \"StableDiffusionControlNetImg2ImgPipeline\"\n assert \"controlnet\" in pipe_control_img2img.components\n\n # from same controlnet pipeline class, with a different controlnet\n pipe_control_img2img = AutoPipelineForImage2Image.from_pipe(pipe_control_img2img, controlnet=controlnet)\n assert pipe_control_img2img.__class__.__name__ == \"StableDiffusionControlNetImg2ImgPipeline\"\n assert \"controlnet\" in pipe_control_img2img.components\n\n def test_from_pipe_optional_components(self):\n image_encoder = self.dummy_image_encoder\n\n pipe = AutoPipelineForText2Image.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-pipe\",\n image_encoder=image_encoder,\n )\n\n pipe = AutoPipelineForImage2Image.from_pipe(pipe)\n assert pipe.image_encoder is not None\n\n pipe = AutoPipelineForText2Image.from_pipe(pipe, image_encoder=None)\n assert pipe.image_encoder is None\n\n\n@slow\nclass AutoPipelineIntegrationTest(unittest.TestCase):\n def test_pipe_auto(self):\n for model_name, model_repo in PRETRAINED_MODEL_REPO_MAPPING.items():\n # test txt2img\n pipe_txt2img = AutoPipelineForText2Image.from_pretrained(\n model_repo, variant=\"fp16\", torch_dtype=torch.float16\n )\n self.assertIsInstance(pipe_txt2img, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name])\n\n pipe_to = AutoPipelineForText2Image.from_pipe(pipe_txt2img)\n self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name])\n\n pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_txt2img)\n self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name])\n\n if \"kandinsky\" not in model_name:\n pipe_to = AutoPipelineForInpainting.from_pipe(pipe_txt2img)\n self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING[model_name])\n\n del pipe_txt2img, pipe_to\n gc.collect()\n\n # test img2img\n\n pipe_img2img = AutoPipelineForImage2Image.from_pretrained(\n model_repo, variant=\"fp16\", torch_dtype=torch.float16\n )\n self.assertIsInstance(pipe_img2img, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name])\n\n pipe_to = AutoPipelineForText2Image.from_pipe(pipe_img2img)\n self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name])\n\n pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_img2img)\n self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name])\n\n if \"kandinsky\" not in model_name:\n pipe_to = AutoPipelineForInpainting.from_pipe(pipe_img2img)\n self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING[model_name])\n\n del pipe_img2img, pipe_to\n gc.collect()\n\n # test inpaint\n\n if \"kandinsky\" not in model_name:\n pipe_inpaint = AutoPipelineForInpainting.from_pretrained(\n model_repo, variant=\"fp16\", torch_dtype=torch.float16\n )\n self.assertIsInstance(pipe_inpaint, AUTO_INPAINT_PIPELINES_MAPPING[model_name])\n\n pipe_to = AutoPipelineForText2Image.from_pipe(pipe_inpaint)\n self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name])\n\n pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_inpaint)\n self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name])\n\n pipe_to = AutoPipelineForInpainting.from_pipe(pipe_inpaint)\n self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING[model_name])\n\n del pipe_inpaint, pipe_to\n gc.collect()\n\n def test_from_pipe_consistent(self):\n for model_name, model_repo in PRETRAINED_MODEL_REPO_MAPPING.items():\n if model_name in [\"kandinsky\", \"kandinsky22\"]:\n auto_pipes = [AutoPipelineForText2Image, AutoPipelineForImage2Image]\n else:\n auto_pipes = [AutoPipelineForText2Image, AutoPipelineForImage2Image, AutoPipelineForInpainting]\n\n # test from_pretrained\n for pipe_from_class in auto_pipes:\n pipe_from = pipe_from_class.from_pretrained(model_repo, variant=\"fp16\", torch_dtype=torch.float16)\n pipe_from_config = dict(pipe_from.config)\n\n for pipe_to_class in auto_pipes:\n pipe_to = pipe_to_class.from_pipe(pipe_from)\n self.assertEqual(dict(pipe_to.config), pipe_from_config)\n\n del pipe_from, pipe_to\n gc.collect()\n\n def test_controlnet(self):\n # test from_pretrained\n model_repo = \"runwayml/stable-diffusion-v1-5\"\n controlnet_repo = \"lllyasviel/sd-controlnet-canny\"\n\n controlnet = ControlNetModel.from_pretrained(controlnet_repo, torch_dtype=torch.float16)\n\n pipe_txt2img = AutoPipelineForText2Image.from_pretrained(\n model_repo, controlnet=controlnet, torch_dtype=torch.float16\n )\n self.assertIsInstance(pipe_txt2img, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[\"stable-diffusion-controlnet\"])\n\n pipe_img2img = AutoPipelineForImage2Image.from_pretrained(\n model_repo, controlnet=controlnet, torch_dtype=torch.float16\n )\n self.assertIsInstance(pipe_img2img, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[\"stable-diffusion-controlnet\"])\n\n pipe_inpaint = AutoPipelineForInpainting.from_pretrained(\n model_repo, controlnet=controlnet, torch_dtype=torch.float16\n )\n self.assertIsInstance(pipe_inpaint, AUTO_INPAINT_PIPELINES_MAPPING[\"stable-diffusion-controlnet\"])\n\n # test from_pipe\n for pipe_from in [pipe_txt2img, pipe_img2img, pipe_inpaint]:\n pipe_to = AutoPipelineForText2Image.from_pipe(pipe_from)\n self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[\"stable-diffusion-controlnet\"])\n self.assertEqual(dict(pipe_to.config), dict(pipe_txt2img.config))\n\n pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_from)\n self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[\"stable-diffusion-controlnet\"])\n self.assertEqual(dict(pipe_to.config), dict(pipe_img2img.config))\n\n pipe_to = AutoPipelineForInpainting.from_pipe(pipe_from)\n self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING[\"stable-diffusion-controlnet\"])\n self.assertEqual(dict(pipe_to.config), dict(pipe_inpaint.config))\n" }, { "path": "diffusers/tests/pipelines/test_pipelines_combined.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\n\nimport torch\nfrom huggingface_hub import ModelCard\n\nfrom diffusers import (\n DDPMScheduler,\n DiffusionPipeline,\n KandinskyV22CombinedPipeline,\n KandinskyV22Pipeline,\n KandinskyV22PriorPipeline,\n)\nfrom diffusers.pipelines.pipeline_utils import CONNECTED_PIPES_KEYS\n\n\ndef state_dicts_almost_equal(sd1, sd2):\n sd1 = dict(sorted(sd1.items()))\n sd2 = dict(sorted(sd2.items()))\n\n models_are_equal = True\n for ten1, ten2 in zip(sd1.values(), sd2.values()):\n if (ten1 - ten2).abs().sum() > 1e-3:\n models_are_equal = False\n\n return models_are_equal\n\n\nclass CombinedPipelineFastTest(unittest.TestCase):\n def modelcard_has_connected_pipeline(self, model_id):\n modelcard = ModelCard.load(model_id)\n connected_pipes = {prefix: getattr(modelcard.data, prefix, [None])[0] for prefix in CONNECTED_PIPES_KEYS}\n connected_pipes = {k: v for k, v in connected_pipes.items() if v is not None}\n\n return len(connected_pipes) > 0\n\n def test_correct_modelcard_format(self):\n # hf-internal-testing/tiny-random-kandinsky-v22-prior has no metadata\n assert not self.modelcard_has_connected_pipeline(\"hf-internal-testing/tiny-random-kandinsky-v22-prior\")\n\n # see https://huggingface.co/hf-internal-testing/tiny-random-kandinsky-v22-decoder/blob/8baff9897c6be017013e21b5c562e5a381646c7e/README.md?code=true#L2\n assert self.modelcard_has_connected_pipeline(\"hf-internal-testing/tiny-random-kandinsky-v22-decoder\")\n\n def test_load_connected_checkpoint_when_specified(self):\n pipeline_prior = DiffusionPipeline.from_pretrained(\"hf-internal-testing/tiny-random-kandinsky-v22-prior\")\n pipeline_prior_connected = DiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-random-kandinsky-v22-prior\", load_connected_pipeline=True\n )\n\n # Passing `load_connected_pipeline` to prior is a no-op as the pipeline has no connected pipeline\n assert pipeline_prior.__class__ == pipeline_prior_connected.__class__\n\n pipeline = DiffusionPipeline.from_pretrained(\"hf-internal-testing/tiny-random-kandinsky-v22-decoder\")\n pipeline_connected = DiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-random-kandinsky-v22-decoder\", load_connected_pipeline=True\n )\n\n # Passing `load_connected_pipeline` to decoder loads the combined pipeline\n assert pipeline.__class__ != pipeline_connected.__class__\n assert pipeline.__class__ == KandinskyV22Pipeline\n assert pipeline_connected.__class__ == KandinskyV22CombinedPipeline\n\n # check that loaded components match prior and decoder components\n assert set(pipeline_connected.components.keys()) == set(\n [\"prior_\" + k for k in pipeline_prior.components.keys()] + list(pipeline.components.keys())\n )\n\n def test_load_connected_checkpoint_default(self):\n prior = KandinskyV22PriorPipeline.from_pretrained(\"hf-internal-testing/tiny-random-kandinsky-v22-prior\")\n decoder = KandinskyV22Pipeline.from_pretrained(\"hf-internal-testing/tiny-random-kandinsky-v22-decoder\")\n\n # check that combined pipeline loads both prior & decoder because of\n # https://huggingface.co/hf-internal-testing/tiny-random-kandinsky-v22-decoder/blob/8baff9897c6be017013e21b5c562e5a381646c7e/README.md?code=true#L3\n assert (\n KandinskyV22CombinedPipeline._load_connected_pipes\n ) # combined pipelines will download more checkpoints that just the one specified\n pipeline = KandinskyV22CombinedPipeline.from_pretrained(\n \"hf-internal-testing/tiny-random-kandinsky-v22-decoder\"\n )\n\n prior_comps = prior.components\n decoder_comps = decoder.components\n for k, component in pipeline.components.items():\n if k.startswith(\"prior_\"):\n k = k[6:]\n comp = prior_comps[k]\n else:\n comp = decoder_comps[k]\n\n if isinstance(component, torch.nn.Module):\n assert state_dicts_almost_equal(component.state_dict(), comp.state_dict())\n elif hasattr(component, \"config\"):\n assert dict(component.config) == dict(comp.config)\n else:\n assert component.__class__ == comp.__class__\n\n def test_load_connected_checkpoint_with_passed_obj(self):\n pipeline = KandinskyV22CombinedPipeline.from_pretrained(\n \"hf-internal-testing/tiny-random-kandinsky-v22-decoder\"\n )\n prior_scheduler = DDPMScheduler.from_config(pipeline.prior_scheduler.config)\n scheduler = DDPMScheduler.from_config(pipeline.scheduler.config)\n\n # make sure we pass a different scheduler and prior_scheduler\n assert pipeline.prior_scheduler.__class__ != prior_scheduler.__class__\n assert pipeline.scheduler.__class__ != scheduler.__class__\n\n pipeline_new = KandinskyV22CombinedPipeline.from_pretrained(\n \"hf-internal-testing/tiny-random-kandinsky-v22-decoder\",\n prior_scheduler=prior_scheduler,\n scheduler=scheduler,\n )\n assert dict(pipeline_new.prior_scheduler.config) == dict(prior_scheduler.config)\n assert dict(pipeline_new.scheduler.config) == dict(scheduler.config)\n" }, { "path": "diffusers/tests/pipelines/test_pipelines_common.py", "content": "import contextlib\nimport gc\nimport inspect\nimport io\nimport json\nimport os\nimport re\nimport tempfile\nimport unittest\nimport uuid\nfrom typing import Any, Callable, Dict, Union\n\nimport numpy as np\nimport PIL.Image\nimport torch\nfrom huggingface_hub import ModelCard, delete_repo\nfrom huggingface_hub.utils import is_jinja_available\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nimport diffusers\nfrom diffusers import (\n AsymmetricAutoencoderKL,\n AutoencoderKL,\n AutoencoderTiny,\n ConsistencyDecoderVAE,\n DDIMScheduler,\n DiffusionPipeline,\n StableDiffusionPipeline,\n StableDiffusionXLPipeline,\n UNet2DConditionModel,\n)\nfrom diffusers.image_processor import VaeImageProcessor\nfrom diffusers.loaders import IPAdapterMixin\nfrom diffusers.models.attention_processor import AttnProcessor\nfrom diffusers.models.controlnet_xs import UNetControlNetXSModel\nfrom diffusers.models.unets.unet_3d_condition import UNet3DConditionModel\nfrom diffusers.models.unets.unet_i2vgen_xl import I2VGenXLUNet\nfrom diffusers.models.unets.unet_motion_model import UNetMotionModel\nfrom diffusers.pipelines.pipeline_utils import StableDiffusionMixin\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom diffusers.utils import logging\nfrom diffusers.utils.import_utils import is_accelerate_available, is_accelerate_version, is_xformers_available\nfrom diffusers.utils.testing_utils import CaptureLogger, require_torch, skip_mps, torch_device\n\nfrom ..models.autoencoders.test_models_vae import (\n get_asym_autoencoder_kl_config,\n get_autoencoder_kl_config,\n get_autoencoder_tiny_config,\n get_consistency_vae_config,\n)\nfrom ..models.unets.test_models_unet_2d_condition import (\n create_ip_adapter_faceid_state_dict,\n create_ip_adapter_state_dict,\n)\nfrom ..others.test_utils import TOKEN, USER, is_staging_test\n\n\ndef to_np(tensor):\n if isinstance(tensor, torch.Tensor):\n tensor = tensor.detach().cpu().numpy()\n\n return tensor\n\n\ndef check_same_shape(tensor_list):\n shapes = [tensor.shape for tensor in tensor_list]\n return all(shape == shapes[0] for shape in shapes[1:])\n\n\nclass SDFunctionTesterMixin:\n \"\"\"\n This mixin is designed to be used with PipelineTesterMixin and unittest.TestCase classes.\n It provides a set of common tests for PyTorch pipeline that inherit from StableDiffusionMixin, e.g. vae_slicing, vae_tiling, freeu, etc.\n \"\"\"\n\n def test_vae_slicing(self, image_count=4):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n # components[\"scheduler\"] = LMSDiscreteScheduler.from_config(components[\"scheduler\"].config)\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"prompt\"] = [inputs[\"prompt\"]] * image_count\n if \"image\" in inputs: # fix batch size mismatch in I2V_Gen pipeline\n inputs[\"image\"] = [inputs[\"image\"]] * image_count\n output_1 = pipe(**inputs)\n\n # make sure sliced vae decode yields the same result\n pipe.enable_vae_slicing()\n inputs = self.get_dummy_inputs(device)\n inputs[\"prompt\"] = [inputs[\"prompt\"]] * image_count\n if \"image\" in inputs:\n inputs[\"image\"] = [inputs[\"image\"]] * image_count\n inputs[\"return_dict\"] = False\n output_2 = pipe(**inputs)\n\n assert np.abs(output_2[0].flatten() - output_1[0].flatten()).max() < 1e-2\n\n def test_vae_tiling(self):\n components = self.get_dummy_components()\n\n # make sure here that pndm scheduler skips prk\n if \"safety_checker\" in components:\n components[\"safety_checker\"] = None\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"return_dict\"] = False\n\n # Test that tiled decode at 512x512 yields the same result as the non-tiled decode\n output_1 = pipe(**inputs)[0]\n\n # make sure tiled vae decode yields the same result\n pipe.enable_vae_tiling()\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"return_dict\"] = False\n output_2 = pipe(**inputs)[0]\n\n assert np.abs(to_np(output_2) - to_np(output_1)).max() < 5e-1\n\n # test that tiled decode works with various shapes\n shapes = [(1, 4, 73, 97), (1, 4, 97, 73), (1, 4, 49, 65), (1, 4, 65, 49)]\n with torch.no_grad():\n for shape in shapes:\n zeros = torch.zeros(shape).to(torch_device)\n pipe.vae.decode(zeros)\n\n # MPS currently doesn't support ComplexFloats, which are required for freeU - see https://github.com/huggingface/diffusers/issues/7569.\n @skip_mps\n def test_freeu_enabled(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"return_dict\"] = False\n inputs[\"output_type\"] = \"np\"\n\n output = pipe(**inputs)[0]\n\n pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4)\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"return_dict\"] = False\n inputs[\"output_type\"] = \"np\"\n\n output_freeu = pipe(**inputs)[0]\n\n assert not np.allclose(\n output[0, -3:, -3:, -1], output_freeu[0, -3:, -3:, -1]\n ), \"Enabling of FreeU should lead to different results.\"\n\n def test_freeu_disabled(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"return_dict\"] = False\n inputs[\"output_type\"] = \"np\"\n\n output = pipe(**inputs)[0]\n\n pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4)\n pipe.disable_freeu()\n\n freeu_keys = {\"s1\", \"s2\", \"b1\", \"b2\"}\n for upsample_block in pipe.unet.up_blocks:\n for key in freeu_keys:\n assert getattr(upsample_block, key) is None, f\"Disabling of FreeU should have set {key} to None.\"\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"return_dict\"] = False\n inputs[\"output_type\"] = \"np\"\n\n output_no_freeu = pipe(**inputs)[0]\n assert np.allclose(\n output, output_no_freeu, atol=1e-2\n ), f\"Disabling of FreeU should lead to results similar to the default pipeline results but Max Abs Error={np.abs(output_no_freeu - output).max()}.\"\n\n def test_fused_qkv_projections(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"return_dict\"] = False\n image = pipe(**inputs)[0]\n original_image_slice = image[0, -3:, -3:, -1]\n\n pipe.fuse_qkv_projections()\n inputs = self.get_dummy_inputs(device)\n inputs[\"return_dict\"] = False\n image_fused = pipe(**inputs)[0]\n image_slice_fused = image_fused[0, -3:, -3:, -1]\n\n pipe.unfuse_qkv_projections()\n inputs = self.get_dummy_inputs(device)\n inputs[\"return_dict\"] = False\n image_disabled = pipe(**inputs)[0]\n image_slice_disabled = image_disabled[0, -3:, -3:, -1]\n\n assert np.allclose(\n original_image_slice, image_slice_fused, atol=1e-2, rtol=1e-2\n ), \"Fusion of QKV projections shouldn't affect the outputs.\"\n assert np.allclose(\n image_slice_fused, image_slice_disabled, atol=1e-2, rtol=1e-2\n ), \"Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled.\"\n assert np.allclose(\n original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2\n ), \"Original outputs should match when fused QKV projections are disabled.\"\n\n\nclass IPAdapterTesterMixin:\n \"\"\"\n This mixin is designed to be used with PipelineTesterMixin and unittest.TestCase classes.\n It provides a set of common tests for pipelines that support IP Adapters.\n \"\"\"\n\n def test_pipeline_signature(self):\n parameters = inspect.signature(self.pipeline_class.__call__).parameters\n\n assert issubclass(self.pipeline_class, IPAdapterMixin)\n self.assertIn(\n \"ip_adapter_image\",\n parameters,\n \"`ip_adapter_image` argument must be supported by the `__call__` method\",\n )\n self.assertIn(\n \"ip_adapter_image_embeds\",\n parameters,\n \"`ip_adapter_image_embeds` argument must be supported by the `__call__` method\",\n )\n\n def _get_dummy_image_embeds(self, cross_attention_dim: int = 32):\n return torch.randn((2, 1, cross_attention_dim), device=torch_device)\n\n def _get_dummy_faceid_image_embeds(self, cross_attention_dim: int = 32):\n return torch.randn((2, 1, 1, cross_attention_dim), device=torch_device)\n\n def _get_dummy_masks(self, input_size: int = 64):\n _masks = torch.zeros((1, 1, input_size, input_size), device=torch_device)\n _masks[0, :, :, : int(input_size / 2)] = 1\n return _masks\n\n def _modify_inputs_for_ip_adapter_test(self, inputs: Dict[str, Any]):\n parameters = inspect.signature(self.pipeline_class.__call__).parameters\n if \"image\" in parameters.keys() and \"strength\" in parameters.keys():\n inputs[\"num_inference_steps\"] = 4\n\n inputs[\"output_type\"] = \"np\"\n inputs[\"return_dict\"] = False\n return inputs\n\n def test_ip_adapter_single(self, expected_max_diff: float = 1e-4, expected_pipe_slice=None):\n # Raising the tolerance for this test when it's run on a CPU because we\n # compare against static slices and that can be shaky (with a VVVV low probability).\n expected_max_diff = 9e-4 if torch_device == \"cpu\" else expected_max_diff\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components).to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n cross_attention_dim = pipe.unet.config.get(\"cross_attention_dim\", 32)\n\n # forward pass without ip adapter\n inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))\n if expected_pipe_slice is None:\n output_without_adapter = pipe(**inputs)[0]\n else:\n output_without_adapter = expected_pipe_slice\n\n adapter_state_dict = create_ip_adapter_state_dict(pipe.unet)\n pipe.unet._load_ip_adapter_weights(adapter_state_dict)\n\n # forward pass with single ip adapter, but scale=0 which should have no effect\n inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))\n inputs[\"ip_adapter_image_embeds\"] = [self._get_dummy_image_embeds(cross_attention_dim)]\n pipe.set_ip_adapter_scale(0.0)\n output_without_adapter_scale = pipe(**inputs)[0]\n if expected_pipe_slice is not None:\n output_without_adapter_scale = output_without_adapter_scale[0, -3:, -3:, -1].flatten()\n\n # forward pass with single ip adapter, but with scale of adapter weights\n inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))\n inputs[\"ip_adapter_image_embeds\"] = [self._get_dummy_image_embeds(cross_attention_dim)]\n pipe.set_ip_adapter_scale(42.0)\n output_with_adapter_scale = pipe(**inputs)[0]\n if expected_pipe_slice is not None:\n output_with_adapter_scale = output_with_adapter_scale[0, -3:, -3:, -1].flatten()\n\n max_diff_without_adapter_scale = np.abs(output_without_adapter_scale - output_without_adapter).max()\n max_diff_with_adapter_scale = np.abs(output_with_adapter_scale - output_without_adapter).max()\n\n self.assertLess(\n max_diff_without_adapter_scale,\n expected_max_diff,\n \"Output without ip-adapter must be same as normal inference\",\n )\n self.assertGreater(\n max_diff_with_adapter_scale, 1e-2, \"Output with ip-adapter must be different from normal inference\"\n )\n\n def test_ip_adapter_multi(self, expected_max_diff: float = 1e-4):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components).to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n cross_attention_dim = pipe.unet.config.get(\"cross_attention_dim\", 32)\n\n # forward pass without ip adapter\n inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))\n output_without_adapter = pipe(**inputs)[0]\n\n adapter_state_dict_1 = create_ip_adapter_state_dict(pipe.unet)\n adapter_state_dict_2 = create_ip_adapter_state_dict(pipe.unet)\n pipe.unet._load_ip_adapter_weights([adapter_state_dict_1, adapter_state_dict_2])\n\n # forward pass with multi ip adapter, but scale=0 which should have no effect\n inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))\n inputs[\"ip_adapter_image_embeds\"] = [self._get_dummy_image_embeds(cross_attention_dim)] * 2\n pipe.set_ip_adapter_scale([0.0, 0.0])\n output_without_multi_adapter_scale = pipe(**inputs)[0]\n\n # forward pass with multi ip adapter, but with scale of adapter weights\n inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))\n inputs[\"ip_adapter_image_embeds\"] = [self._get_dummy_image_embeds(cross_attention_dim)] * 2\n pipe.set_ip_adapter_scale([42.0, 42.0])\n output_with_multi_adapter_scale = pipe(**inputs)[0]\n\n max_diff_without_multi_adapter_scale = np.abs(\n output_without_multi_adapter_scale - output_without_adapter\n ).max()\n max_diff_with_multi_adapter_scale = np.abs(output_with_multi_adapter_scale - output_without_adapter).max()\n self.assertLess(\n max_diff_without_multi_adapter_scale,\n expected_max_diff,\n \"Output without multi-ip-adapter must be same as normal inference\",\n )\n self.assertGreater(\n max_diff_with_multi_adapter_scale,\n 1e-2,\n \"Output with multi-ip-adapter scale must be different from normal inference\",\n )\n\n def test_ip_adapter_cfg(self, expected_max_diff: float = 1e-4):\n parameters = inspect.signature(self.pipeline_class.__call__).parameters\n\n if \"guidance_scale\" not in parameters:\n return\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components).to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n cross_attention_dim = pipe.unet.config.get(\"cross_attention_dim\", 32)\n\n adapter_state_dict = create_ip_adapter_state_dict(pipe.unet)\n pipe.unet._load_ip_adapter_weights(adapter_state_dict)\n pipe.set_ip_adapter_scale(1.0)\n\n # forward pass with CFG not applied\n inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))\n inputs[\"ip_adapter_image_embeds\"] = [self._get_dummy_image_embeds(cross_attention_dim)[0].unsqueeze(0)]\n inputs[\"guidance_scale\"] = 1.0\n out_no_cfg = pipe(**inputs)[0]\n\n # forward pass with CFG applied\n inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))\n inputs[\"ip_adapter_image_embeds\"] = [self._get_dummy_image_embeds(cross_attention_dim)]\n inputs[\"guidance_scale\"] = 7.5\n out_cfg = pipe(**inputs)[0]\n\n assert out_cfg.shape == out_no_cfg.shape\n\n def test_ip_adapter_masks(self, expected_max_diff: float = 1e-4):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components).to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n cross_attention_dim = pipe.unet.config.get(\"cross_attention_dim\", 32)\n sample_size = pipe.unet.config.get(\"sample_size\", 32)\n block_out_channels = pipe.vae.config.get(\"block_out_channels\", [128, 256, 512, 512])\n input_size = sample_size * (2 ** (len(block_out_channels) - 1))\n\n # forward pass without ip adapter\n inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))\n output_without_adapter = pipe(**inputs)[0]\n output_without_adapter = output_without_adapter[0, -3:, -3:, -1].flatten()\n\n adapter_state_dict = create_ip_adapter_state_dict(pipe.unet)\n pipe.unet._load_ip_adapter_weights(adapter_state_dict)\n\n # forward pass with single ip adapter and masks, but scale=0 which should have no effect\n inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))\n inputs[\"ip_adapter_image_embeds\"] = [self._get_dummy_image_embeds(cross_attention_dim)]\n inputs[\"cross_attention_kwargs\"] = {\"ip_adapter_masks\": [self._get_dummy_masks(input_size)]}\n pipe.set_ip_adapter_scale(0.0)\n output_without_adapter_scale = pipe(**inputs)[0]\n output_without_adapter_scale = output_without_adapter_scale[0, -3:, -3:, -1].flatten()\n\n # forward pass with single ip adapter and masks, but with scale of adapter weights\n inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))\n inputs[\"ip_adapter_image_embeds\"] = [self._get_dummy_image_embeds(cross_attention_dim)]\n inputs[\"cross_attention_kwargs\"] = {\"ip_adapter_masks\": [self._get_dummy_masks(input_size)]}\n pipe.set_ip_adapter_scale(42.0)\n output_with_adapter_scale = pipe(**inputs)[0]\n output_with_adapter_scale = output_with_adapter_scale[0, -3:, -3:, -1].flatten()\n\n max_diff_without_adapter_scale = np.abs(output_without_adapter_scale - output_without_adapter).max()\n max_diff_with_adapter_scale = np.abs(output_with_adapter_scale - output_without_adapter).max()\n\n self.assertLess(\n max_diff_without_adapter_scale,\n expected_max_diff,\n \"Output without ip-adapter must be same as normal inference\",\n )\n self.assertGreater(\n max_diff_with_adapter_scale, 1e-3, \"Output with ip-adapter must be different from normal inference\"\n )\n\n def test_ip_adapter_faceid(self, expected_max_diff: float = 1e-4):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components).to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n cross_attention_dim = pipe.unet.config.get(\"cross_attention_dim\", 32)\n\n # forward pass without ip adapter\n inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))\n output_without_adapter = pipe(**inputs)[0]\n output_without_adapter = output_without_adapter[0, -3:, -3:, -1].flatten()\n\n adapter_state_dict = create_ip_adapter_faceid_state_dict(pipe.unet)\n pipe.unet._load_ip_adapter_weights(adapter_state_dict)\n\n # forward pass with single ip adapter, but scale=0 which should have no effect\n inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))\n inputs[\"ip_adapter_image_embeds\"] = [self._get_dummy_faceid_image_embeds(cross_attention_dim)]\n pipe.set_ip_adapter_scale(0.0)\n output_without_adapter_scale = pipe(**inputs)[0]\n output_without_adapter_scale = output_without_adapter_scale[0, -3:, -3:, -1].flatten()\n\n # forward pass with single ip adapter, but with scale of adapter weights\n inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))\n inputs[\"ip_adapter_image_embeds\"] = [self._get_dummy_faceid_image_embeds(cross_attention_dim)]\n pipe.set_ip_adapter_scale(42.0)\n output_with_adapter_scale = pipe(**inputs)[0]\n output_with_adapter_scale = output_with_adapter_scale[0, -3:, -3:, -1].flatten()\n\n max_diff_without_adapter_scale = np.abs(output_without_adapter_scale - output_without_adapter).max()\n max_diff_with_adapter_scale = np.abs(output_with_adapter_scale - output_without_adapter).max()\n\n self.assertLess(\n max_diff_without_adapter_scale,\n expected_max_diff,\n \"Output without ip-adapter must be same as normal inference\",\n )\n self.assertGreater(\n max_diff_with_adapter_scale, 1e-3, \"Output with ip-adapter must be different from normal inference\"\n )\n\n\nclass PipelineLatentTesterMixin:\n \"\"\"\n This mixin is designed to be used with PipelineTesterMixin and unittest.TestCase classes.\n It provides a set of common tests for PyTorch pipeline that has vae, e.g.\n equivalence of different input and output types, etc.\n \"\"\"\n\n @property\n def image_params(self) -> frozenset:\n raise NotImplementedError(\n \"You need to set the attribute `image_params` in the child test class. \"\n \"`image_params` are tested for if all accepted input image types (i.e. `pt`,`pil`,`np`) are producing same results\"\n )\n\n @property\n def image_latents_params(self) -> frozenset:\n raise NotImplementedError(\n \"You need to set the attribute `image_latents_params` in the child test class. \"\n \"`image_latents_params` are tested for if passing latents directly are producing same results\"\n )\n\n def get_dummy_inputs_by_type(self, device, seed=0, input_image_type=\"pt\", output_type=\"np\"):\n inputs = self.get_dummy_inputs(device, seed)\n\n def convert_to_pt(image):\n if isinstance(image, torch.Tensor):\n input_image = image\n elif isinstance(image, np.ndarray):\n input_image = VaeImageProcessor.numpy_to_pt(image)\n elif isinstance(image, PIL.Image.Image):\n input_image = VaeImageProcessor.pil_to_numpy(image)\n input_image = VaeImageProcessor.numpy_to_pt(input_image)\n else:\n raise ValueError(f\"unsupported input_image_type {type(image)}\")\n return input_image\n\n def convert_pt_to_type(image, input_image_type):\n if input_image_type == \"pt\":\n input_image = image\n elif input_image_type == \"np\":\n input_image = VaeImageProcessor.pt_to_numpy(image)\n elif input_image_type == \"pil\":\n input_image = VaeImageProcessor.pt_to_numpy(image)\n input_image = VaeImageProcessor.numpy_to_pil(input_image)\n else:\n raise ValueError(f\"unsupported input_image_type {input_image_type}.\")\n return input_image\n\n for image_param in self.image_params:\n if image_param in inputs.keys():\n inputs[image_param] = convert_pt_to_type(\n convert_to_pt(inputs[image_param]).to(device), input_image_type\n )\n\n inputs[\"output_type\"] = output_type\n\n return inputs\n\n def test_pt_np_pil_outputs_equivalent(self, expected_max_diff=1e-4):\n self._test_pt_np_pil_outputs_equivalent(expected_max_diff=expected_max_diff)\n\n def _test_pt_np_pil_outputs_equivalent(self, expected_max_diff=1e-4, input_image_type=\"pt\"):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n output_pt = pipe(\n **self.get_dummy_inputs_by_type(torch_device, input_image_type=input_image_type, output_type=\"pt\")\n )[0]\n output_np = pipe(\n **self.get_dummy_inputs_by_type(torch_device, input_image_type=input_image_type, output_type=\"np\")\n )[0]\n output_pil = pipe(\n **self.get_dummy_inputs_by_type(torch_device, input_image_type=input_image_type, output_type=\"pil\")\n )[0]\n\n max_diff = np.abs(output_pt.cpu().numpy().transpose(0, 2, 3, 1) - output_np).max()\n self.assertLess(\n max_diff, expected_max_diff, \"`output_type=='pt'` generate different results from `output_type=='np'`\"\n )\n\n max_diff = np.abs(np.array(output_pil[0]) - (output_np * 255).round()).max()\n self.assertLess(max_diff, 2.0, \"`output_type=='pil'` generate different results from `output_type=='np'`\")\n\n def test_pt_np_pil_inputs_equivalent(self):\n if len(self.image_params) == 0:\n return\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n out_input_pt = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type=\"pt\"))[0]\n out_input_np = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type=\"np\"))[0]\n out_input_pil = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type=\"pil\"))[0]\n\n max_diff = np.abs(out_input_pt - out_input_np).max()\n self.assertLess(max_diff, 1e-4, \"`input_type=='pt'` generate different result from `input_type=='np'`\")\n max_diff = np.abs(out_input_pil - out_input_np).max()\n self.assertLess(max_diff, 1e-2, \"`input_type=='pt'` generate different result from `input_type=='np'`\")\n\n def test_latents_input(self):\n if len(self.image_latents_params) == 0:\n return\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n out = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type=\"pt\"))[0]\n\n vae = components[\"vae\"]\n inputs = self.get_dummy_inputs_by_type(torch_device, input_image_type=\"pt\")\n generator = inputs[\"generator\"]\n for image_param in self.image_latents_params:\n if image_param in inputs.keys():\n inputs[image_param] = (\n vae.encode(inputs[image_param]).latent_dist.sample(generator) * vae.config.scaling_factor\n )\n out_latents_inputs = pipe(**inputs)[0]\n\n max_diff = np.abs(out - out_latents_inputs).max()\n self.assertLess(max_diff, 1e-4, \"passing latents as image input generate different result from passing image\")\n\n def test_multi_vae(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n block_out_channels = pipe.vae.config.block_out_channels\n norm_num_groups = pipe.vae.config.norm_num_groups\n\n vae_classes = [AutoencoderKL, AsymmetricAutoencoderKL, ConsistencyDecoderVAE, AutoencoderTiny]\n configs = [\n get_autoencoder_kl_config(block_out_channels, norm_num_groups),\n get_asym_autoencoder_kl_config(block_out_channels, norm_num_groups),\n get_consistency_vae_config(block_out_channels, norm_num_groups),\n get_autoencoder_tiny_config(block_out_channels),\n ]\n\n out_np = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type=\"np\"))[0]\n\n for vae_cls, config in zip(vae_classes, configs):\n vae = vae_cls(**config)\n vae = vae.to(torch_device)\n components[\"vae\"] = vae\n vae_pipe = self.pipeline_class(**components)\n out_vae_np = vae_pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type=\"np\"))[0]\n\n assert out_vae_np.shape == out_np.shape\n\n\n@require_torch\nclass PipelineFromPipeTesterMixin:\n @property\n def original_pipeline_class(self):\n if \"xl\" in self.pipeline_class.__name__.lower():\n original_pipeline_class = StableDiffusionXLPipeline\n else:\n original_pipeline_class = StableDiffusionPipeline\n\n return original_pipeline_class\n\n def get_dummy_inputs_pipe(self, device, seed=0):\n inputs = self.get_dummy_inputs(device, seed=seed)\n inputs[\"output_type\"] = \"np\"\n inputs[\"return_dict\"] = False\n return inputs\n\n def get_dummy_inputs_for_pipe_original(self, device, seed=0):\n inputs = {}\n for k, v in self.get_dummy_inputs_pipe(device, seed=seed).items():\n if k in set(inspect.signature(self.original_pipeline_class.__call__).parameters.keys()):\n inputs[k] = v\n return inputs\n\n def test_from_pipe_consistent_config(self):\n if self.original_pipeline_class == StableDiffusionPipeline:\n original_repo = \"hf-internal-testing/tiny-stable-diffusion-pipe\"\n original_kwargs = {\"requires_safety_checker\": False}\n elif self.original_pipeline_class == StableDiffusionXLPipeline:\n original_repo = \"hf-internal-testing/tiny-stable-diffusion-xl-pipe\"\n original_kwargs = {\"requires_aesthetics_score\": True, \"force_zeros_for_empty_prompt\": False}\n else:\n raise ValueError(\n \"original_pipeline_class must be either StableDiffusionPipeline or StableDiffusionXLPipeline\"\n )\n\n # create original_pipeline_class(sd/sdxl)\n pipe_original = self.original_pipeline_class.from_pretrained(original_repo, **original_kwargs)\n\n # original_pipeline_class(sd/sdxl) -> pipeline_class\n pipe_components = self.get_dummy_components()\n pipe_additional_components = {}\n for name, component in pipe_components.items():\n if name not in pipe_original.components:\n pipe_additional_components[name] = component\n\n pipe = self.pipeline_class.from_pipe(pipe_original, **pipe_additional_components)\n\n # pipeline_class -> original_pipeline_class(sd/sdxl)\n original_pipe_additional_components = {}\n for name, component in pipe_original.components.items():\n if name not in pipe.components or not isinstance(component, pipe.components[name].__class__):\n original_pipe_additional_components[name] = component\n\n pipe_original_2 = self.original_pipeline_class.from_pipe(pipe, **original_pipe_additional_components)\n\n # compare the config\n original_config = {k: v for k, v in pipe_original.config.items() if not k.startswith(\"_\")}\n original_config_2 = {k: v for k, v in pipe_original_2.config.items() if not k.startswith(\"_\")}\n assert original_config_2 == original_config\n\n def test_from_pipe_consistent_forward_pass(self, expected_max_diff=1e-3):\n components = self.get_dummy_components()\n original_expected_modules, _ = self.original_pipeline_class._get_signature_keys(self.original_pipeline_class)\n\n # pipeline components that are also expected to be in the original pipeline\n original_pipe_components = {}\n # additional components that are not in the pipeline, but expected in the original pipeline\n original_pipe_additional_components = {}\n # additional components that are in the pipeline, but not expected in the original pipeline\n current_pipe_additional_components = {}\n\n for name, component in components.items():\n if name in original_expected_modules:\n original_pipe_components[name] = component\n else:\n current_pipe_additional_components[name] = component\n for name in original_expected_modules:\n if name not in original_pipe_components:\n if name in self.original_pipeline_class._optional_components:\n original_pipe_additional_components[name] = None\n else:\n raise ValueError(f\"missing required module for {self.original_pipeline_class.__class__}: {name}\")\n\n pipe_original = self.original_pipeline_class(**original_pipe_components, **original_pipe_additional_components)\n for component in pipe_original.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n pipe_original.to(torch_device)\n pipe_original.set_progress_bar_config(disable=None)\n inputs = self.get_dummy_inputs_for_pipe_original(torch_device)\n output_original = pipe_original(**inputs)[0]\n\n pipe = self.pipeline_class(**components)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n inputs = self.get_dummy_inputs_pipe(torch_device)\n output = pipe(**inputs)[0]\n\n pipe_from_original = self.pipeline_class.from_pipe(pipe_original, **current_pipe_additional_components)\n pipe_from_original.to(torch_device)\n pipe_from_original.set_progress_bar_config(disable=None)\n inputs = self.get_dummy_inputs_pipe(torch_device)\n output_from_original = pipe_from_original(**inputs)[0]\n\n max_diff = np.abs(to_np(output) - to_np(output_from_original)).max()\n self.assertLess(\n max_diff,\n expected_max_diff,\n \"The outputs of the pipelines created with `from_pipe` and `__init__` are different.\",\n )\n\n inputs = self.get_dummy_inputs_for_pipe_original(torch_device)\n output_original_2 = pipe_original(**inputs)[0]\n\n max_diff = np.abs(to_np(output_original) - to_np(output_original_2)).max()\n self.assertLess(max_diff, expected_max_diff, \"`from_pipe` should not change the output of original pipeline.\")\n\n for component in pipe_original.components.values():\n if hasattr(component, \"attn_processors\"):\n assert all(\n type(proc) == AttnProcessor for proc in component.attn_processors.values()\n ), \"`from_pipe` changed the attention processor in original pipeline.\"\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_accelerate_available() or is_accelerate_version(\"<\", \"0.14.0\"),\n reason=\"CPU offload is only available with CUDA and `accelerate v0.14.0` or higher\",\n )\n def test_from_pipe_consistent_forward_pass_cpu_offload(self, expected_max_diff=1e-3):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n pipe.enable_model_cpu_offload()\n pipe.set_progress_bar_config(disable=None)\n inputs = self.get_dummy_inputs_pipe(torch_device)\n output = pipe(**inputs)[0]\n\n original_expected_modules, _ = self.original_pipeline_class._get_signature_keys(self.original_pipeline_class)\n # pipeline components that are also expected to be in the original pipeline\n original_pipe_components = {}\n # additional components that are not in the pipeline, but expected in the original pipeline\n original_pipe_additional_components = {}\n # additional components that are in the pipeline, but not expected in the original pipeline\n current_pipe_additional_components = {}\n for name, component in components.items():\n if name in original_expected_modules:\n original_pipe_components[name] = component\n else:\n current_pipe_additional_components[name] = component\n for name in original_expected_modules:\n if name not in original_pipe_components:\n if name in self.original_pipeline_class._optional_components:\n original_pipe_additional_components[name] = None\n else:\n raise ValueError(f\"missing required module for {self.original_pipeline_class.__class__}: {name}\")\n\n pipe_original = self.original_pipeline_class(**original_pipe_components, **original_pipe_additional_components)\n for component in pipe_original.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n pipe_original.set_progress_bar_config(disable=None)\n pipe_from_original = self.pipeline_class.from_pipe(pipe_original, **current_pipe_additional_components)\n pipe_from_original.enable_model_cpu_offload()\n pipe_from_original.set_progress_bar_config(disable=None)\n inputs = self.get_dummy_inputs_pipe(torch_device)\n output_from_original = pipe_from_original(**inputs)[0]\n\n max_diff = np.abs(to_np(output) - to_np(output_from_original)).max()\n self.assertLess(\n max_diff,\n expected_max_diff,\n \"The outputs of the pipelines created with `from_pipe` and `__init__` are different.\",\n )\n\n\n@require_torch\nclass PipelineKarrasSchedulerTesterMixin:\n \"\"\"\n This mixin is designed to be used with unittest.TestCase classes.\n It provides a set of common tests for each PyTorch pipeline that makes use of KarrasDiffusionSchedulers\n equivalence of dict and tuple outputs, etc.\n \"\"\"\n\n def test_karras_schedulers_shape(\n self, num_inference_steps_for_strength=4, num_inference_steps_for_strength_for_iterations=5\n ):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n\n # make sure that PNDM does not need warm-up\n pipe.scheduler.register_to_config(skip_prk_steps=True)\n\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"num_inference_steps\"] = 2\n\n if \"strength\" in inputs:\n inputs[\"num_inference_steps\"] = num_inference_steps_for_strength\n inputs[\"strength\"] = 0.5\n\n outputs = []\n for scheduler_enum in KarrasDiffusionSchedulers:\n if \"KDPM2\" in scheduler_enum.name:\n inputs[\"num_inference_steps\"] = num_inference_steps_for_strength_for_iterations\n\n scheduler_cls = getattr(diffusers, scheduler_enum.name)\n pipe.scheduler = scheduler_cls.from_config(pipe.scheduler.config)\n output = pipe(**inputs)[0]\n outputs.append(output)\n\n if \"KDPM2\" in scheduler_enum.name:\n inputs[\"num_inference_steps\"] = 2\n\n assert check_same_shape(outputs)\n\n\n@require_torch\nclass PipelineTesterMixin:\n \"\"\"\n This mixin is designed to be used with unittest.TestCase classes.\n It provides a set of common tests for each PyTorch pipeline, e.g. saving and loading the pipeline,\n equivalence of dict and tuple outputs, etc.\n \"\"\"\n\n # Canonical parameters that are passed to `__call__` regardless\n # of the type of pipeline. They are always optional and have common\n # sense default values.\n required_optional_params = frozenset(\n [\n \"num_inference_steps\",\n \"num_images_per_prompt\",\n \"generator\",\n \"latents\",\n \"output_type\",\n \"return_dict\",\n ]\n )\n\n # set these parameters to False in the child class if the pipeline does not support the corresponding functionality\n test_attention_slicing = True\n\n test_xformers_attention = True\n\n def get_generator(self, seed):\n device = torch_device if torch_device != \"mps\" else \"cpu\"\n generator = torch.Generator(device).manual_seed(seed)\n return generator\n\n @property\n def pipeline_class(self) -> Union[Callable, DiffusionPipeline]:\n raise NotImplementedError(\n \"You need to set the attribute `pipeline_class = ClassNameOfPipeline` in the child test class. \"\n \"See existing pipeline tests for reference.\"\n )\n\n def get_dummy_components(self):\n raise NotImplementedError(\n \"You need to implement `get_dummy_components(self)` in the child test class. \"\n \"See existing pipeline tests for reference.\"\n )\n\n def get_dummy_inputs(self, device, seed=0):\n raise NotImplementedError(\n \"You need to implement `get_dummy_inputs(self, device, seed)` in the child test class. \"\n \"See existing pipeline tests for reference.\"\n )\n\n @property\n def params(self) -> frozenset:\n raise NotImplementedError(\n \"You need to set the attribute `params` in the child test class. \"\n \"`params` are checked for if all values are present in `__call__`'s signature.\"\n \" You can set `params` using one of the common set of parameters defined in `pipeline_params.py`\"\n \" e.g., `TEXT_TO_IMAGE_PARAMS` defines the common parameters used in text to \"\n \"image pipelines, including prompts and prompt embedding overrides.\"\n \"If your pipeline's set of arguments has minor changes from one of the common sets of arguments, \"\n \"do not make modifications to the existing common sets of arguments. I.e. a text to image pipeline \"\n \"with non-configurable height and width arguments should set the attribute as \"\n \"`params = TEXT_TO_IMAGE_PARAMS - {'height', 'width'}`. \"\n \"See existing pipeline tests for reference.\"\n )\n\n @property\n def batch_params(self) -> frozenset:\n raise NotImplementedError(\n \"You need to set the attribute `batch_params` in the child test class. \"\n \"`batch_params` are the parameters required to be batched when passed to the pipeline's \"\n \"`__call__` method. `pipeline_params.py` provides some common sets of parameters such as \"\n \"`TEXT_TO_IMAGE_BATCH_PARAMS`, `IMAGE_VARIATION_BATCH_PARAMS`, etc... If your pipeline's \"\n \"set of batch arguments has minor changes from one of the common sets of batch arguments, \"\n \"do not make modifications to the existing common sets of batch arguments. I.e. a text to \"\n \"image pipeline `negative_prompt` is not batched should set the attribute as \"\n \"`batch_params = TEXT_TO_IMAGE_BATCH_PARAMS - {'negative_prompt'}`. \"\n \"See existing pipeline tests for reference.\"\n )\n\n @property\n def callback_cfg_params(self) -> frozenset:\n raise NotImplementedError(\n \"You need to set the attribute `callback_cfg_params` in the child test class that requires to run test_callback_cfg. \"\n \"`callback_cfg_params` are the parameters that needs to be passed to the pipeline's callback \"\n \"function when dynamically adjusting `guidance_scale`. They are variables that require special\"\n \"treatment when `do_classifier_free_guidance` is `True`. `pipeline_params.py` provides some common\"\n \" sets of parameters such as `TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS`. If your pipeline's \"\n \"set of cfg arguments has minor changes from one of the common sets of cfg arguments, \"\n \"do not make modifications to the existing common sets of cfg arguments. I.e. for inpaint pipeline, you \"\n \" need to adjust batch size of `mask` and `masked_image_latents` so should set the attribute as\"\n \"`callback_cfg_params = TEXT_TO_IMAGE_CFG_PARAMS.union({'mask', 'masked_image_latents'})`\"\n )\n\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test in case of CUDA runtime errors\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_save_load_local(self, expected_max_difference=5e-4):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n output = pipe(**inputs)[0]\n\n logger = logging.get_logger(\"diffusers.pipelines.pipeline_utils\")\n logger.setLevel(diffusers.logging.INFO)\n\n with tempfile.TemporaryDirectory() as tmpdir:\n pipe.save_pretrained(tmpdir, safe_serialization=False)\n\n with CaptureLogger(logger) as cap_logger:\n pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)\n\n for component in pipe_loaded.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n\n for name in pipe_loaded.components.keys():\n if name not in pipe_loaded._optional_components:\n assert name in str(cap_logger)\n\n pipe_loaded.to(torch_device)\n pipe_loaded.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n output_loaded = pipe_loaded(**inputs)[0]\n\n max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()\n self.assertLess(max_diff, expected_max_difference)\n\n def test_pipeline_call_signature(self):\n self.assertTrue(\n hasattr(self.pipeline_class, \"__call__\"), f\"{self.pipeline_class} should have a `__call__` method\"\n )\n\n parameters = inspect.signature(self.pipeline_class.__call__).parameters\n\n optional_parameters = set()\n\n for k, v in parameters.items():\n if v.default != inspect._empty:\n optional_parameters.add(k)\n\n parameters = set(parameters.keys())\n parameters.remove(\"self\")\n parameters.discard(\"kwargs\") # kwargs can be added if arguments of pipeline call function are deprecated\n\n remaining_required_parameters = set()\n\n for param in self.params:\n if param not in parameters:\n remaining_required_parameters.add(param)\n\n self.assertTrue(\n len(remaining_required_parameters) == 0,\n f\"Required parameters not present: {remaining_required_parameters}\",\n )\n\n remaining_required_optional_parameters = set()\n\n for param in self.required_optional_params:\n if param not in optional_parameters:\n remaining_required_optional_parameters.add(param)\n\n self.assertTrue(\n len(remaining_required_optional_parameters) == 0,\n f\"Required optional parameters not present: {remaining_required_optional_parameters}\",\n )\n\n def test_inference_batch_consistent(self, batch_sizes=[2]):\n self._test_inference_batch_consistent(batch_sizes=batch_sizes)\n\n def _test_inference_batch_consistent(\n self, batch_sizes=[2], additional_params_copy_to_batched_inputs=[\"num_inference_steps\"], batch_generator=True\n ):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"generator\"] = self.get_generator(0)\n\n logger = logging.get_logger(pipe.__module__)\n logger.setLevel(level=diffusers.logging.FATAL)\n\n # prepare batched inputs\n batched_inputs = []\n for batch_size in batch_sizes:\n batched_input = {}\n batched_input.update(inputs)\n\n for name in self.batch_params:\n if name not in inputs:\n continue\n\n value = inputs[name]\n if name == \"prompt\":\n len_prompt = len(value)\n # make unequal batch sizes\n batched_input[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]\n\n # make last batch super long\n batched_input[name][-1] = 100 * \"very long\"\n\n else:\n batched_input[name] = batch_size * [value]\n\n if batch_generator and \"generator\" in inputs:\n batched_input[\"generator\"] = [self.get_generator(i) for i in range(batch_size)]\n\n if \"batch_size\" in inputs:\n batched_input[\"batch_size\"] = batch_size\n\n batched_inputs.append(batched_input)\n\n logger.setLevel(level=diffusers.logging.WARNING)\n for batch_size, batched_input in zip(batch_sizes, batched_inputs):\n output = pipe(**batched_input)\n assert len(output[0]) == batch_size\n\n def test_inference_batch_single_identical(self, batch_size=3, expected_max_diff=1e-4):\n self._test_inference_batch_single_identical(batch_size=batch_size, expected_max_diff=expected_max_diff)\n\n def _test_inference_batch_single_identical(\n self,\n batch_size=2,\n expected_max_diff=1e-4,\n additional_params_copy_to_batched_inputs=[\"num_inference_steps\"],\n ):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for components in pipe.components.values():\n if hasattr(components, \"set_default_attn_processor\"):\n components.set_default_attn_processor()\n\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n inputs = self.get_dummy_inputs(torch_device)\n # Reset generator in case it is has been used in self.get_dummy_inputs\n inputs[\"generator\"] = self.get_generator(0)\n\n logger = logging.get_logger(pipe.__module__)\n logger.setLevel(level=diffusers.logging.FATAL)\n\n # batchify inputs\n batched_inputs = {}\n batched_inputs.update(inputs)\n\n for name in self.batch_params:\n if name not in inputs:\n continue\n\n value = inputs[name]\n if name == \"prompt\":\n len_prompt = len(value)\n batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]\n batched_inputs[name][-1] = 100 * \"very long\"\n\n else:\n batched_inputs[name] = batch_size * [value]\n\n if \"generator\" in inputs:\n batched_inputs[\"generator\"] = [self.get_generator(i) for i in range(batch_size)]\n\n if \"batch_size\" in inputs:\n batched_inputs[\"batch_size\"] = batch_size\n\n for arg in additional_params_copy_to_batched_inputs:\n batched_inputs[arg] = inputs[arg]\n\n output = pipe(**inputs)\n output_batch = pipe(**batched_inputs)\n\n assert output_batch[0].shape[0] == batch_size\n\n max_diff = np.abs(to_np(output_batch[0][0]) - to_np(output[0][0])).max()\n assert max_diff < expected_max_diff\n\n def test_dict_tuple_outputs_equivalent(self, expected_slice=None, expected_max_difference=1e-4):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n generator_device = \"cpu\"\n if expected_slice is None:\n output = pipe(**self.get_dummy_inputs(generator_device))[0]\n else:\n output = expected_slice\n\n output_tuple = pipe(**self.get_dummy_inputs(generator_device), return_dict=False)[0]\n\n if expected_slice is None:\n max_diff = np.abs(to_np(output) - to_np(output_tuple)).max()\n else:\n if output_tuple.ndim != 5:\n max_diff = np.abs(to_np(output) - to_np(output_tuple)[0, -3:, -3:, -1].flatten()).max()\n else:\n max_diff = np.abs(to_np(output) - to_np(output_tuple)[0, -3:, -3:, -1, -1].flatten()).max()\n\n self.assertLess(max_diff, expected_max_difference)\n\n def test_components_function(self):\n init_components = self.get_dummy_components()\n init_components = {k: v for k, v in init_components.items() if not isinstance(v, (str, int, float))}\n\n pipe = self.pipeline_class(**init_components)\n\n self.assertTrue(hasattr(pipe, \"components\"))\n self.assertTrue(set(pipe.components.keys()) == set(init_components.keys()))\n\n @unittest.skipIf(torch_device != \"cuda\", reason=\"float16 requires CUDA\")\n def test_float16_inference(self, expected_max_diff=5e-2):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n components = self.get_dummy_components()\n pipe_fp16 = self.pipeline_class(**components)\n for component in pipe_fp16.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n\n pipe_fp16.to(torch_device, torch.float16)\n pipe_fp16.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n # Reset generator in case it is used inside dummy inputs\n if \"generator\" in inputs:\n inputs[\"generator\"] = self.get_generator(0)\n\n output = pipe(**inputs)[0]\n\n fp16_inputs = self.get_dummy_inputs(torch_device)\n # Reset generator in case it is used inside dummy inputs\n if \"generator\" in fp16_inputs:\n fp16_inputs[\"generator\"] = self.get_generator(0)\n\n output_fp16 = pipe_fp16(**fp16_inputs)[0]\n\n max_diff = np.abs(to_np(output) - to_np(output_fp16)).max()\n self.assertLess(max_diff, expected_max_diff, \"The outputs of the fp16 and fp32 pipelines are too different.\")\n\n @unittest.skipIf(torch_device != \"cuda\", reason=\"float16 requires CUDA\")\n def test_save_load_float16(self, expected_max_diff=1e-2):\n components = self.get_dummy_components()\n for name, module in components.items():\n if hasattr(module, \"half\"):\n components[name] = module.to(torch_device).half()\n\n pipe = self.pipeline_class(**components)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n output = pipe(**inputs)[0]\n\n with tempfile.TemporaryDirectory() as tmpdir:\n pipe.save_pretrained(tmpdir)\n pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, torch_dtype=torch.float16)\n for component in pipe_loaded.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n pipe_loaded.to(torch_device)\n pipe_loaded.set_progress_bar_config(disable=None)\n\n for name, component in pipe_loaded.components.items():\n if hasattr(component, \"dtype\"):\n self.assertTrue(\n component.dtype == torch.float16,\n f\"`{name}.dtype` switched from `float16` to {component.dtype} after loading.\",\n )\n\n inputs = self.get_dummy_inputs(torch_device)\n output_loaded = pipe_loaded(**inputs)[0]\n max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()\n self.assertLess(\n max_diff, expected_max_diff, \"The output of the fp16 pipeline changed after saving and loading.\"\n )\n\n def test_save_load_optional_components(self, expected_max_difference=1e-4):\n if not hasattr(self.pipeline_class, \"_optional_components\"):\n return\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n # set all optional components to None\n for optional_component in pipe._optional_components:\n setattr(pipe, optional_component, None)\n\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n output = pipe(**inputs)[0]\n\n with tempfile.TemporaryDirectory() as tmpdir:\n pipe.save_pretrained(tmpdir, safe_serialization=False)\n pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)\n for component in pipe_loaded.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n pipe_loaded.to(torch_device)\n pipe_loaded.set_progress_bar_config(disable=None)\n\n for optional_component in pipe._optional_components:\n self.assertTrue(\n getattr(pipe_loaded, optional_component) is None,\n f\"`{optional_component}` did not stay set to None after loading.\",\n )\n\n inputs = self.get_dummy_inputs(generator_device)\n output_loaded = pipe_loaded(**inputs)[0]\n\n max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()\n self.assertLess(max_diff, expected_max_difference)\n\n @unittest.skipIf(torch_device != \"cuda\", reason=\"CUDA and CPU are required to switch devices\")\n def test_to_device(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n\n pipe.to(\"cpu\")\n model_devices = [component.device.type for component in components.values() if hasattr(component, \"device\")]\n self.assertTrue(all(device == \"cpu\" for device in model_devices))\n\n output_cpu = pipe(**self.get_dummy_inputs(\"cpu\"))[0]\n self.assertTrue(np.isnan(output_cpu).sum() == 0)\n\n pipe.to(\"cuda\")\n model_devices = [component.device.type for component in components.values() if hasattr(component, \"device\")]\n self.assertTrue(all(device == \"cuda\" for device in model_devices))\n\n output_cuda = pipe(**self.get_dummy_inputs(\"cuda\"))[0]\n self.assertTrue(np.isnan(to_np(output_cuda)).sum() == 0)\n\n def test_to_dtype(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n\n model_dtypes = [component.dtype for component in components.values() if hasattr(component, \"dtype\")]\n self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes))\n\n pipe.to(dtype=torch.float16)\n model_dtypes = [component.dtype for component in components.values() if hasattr(component, \"dtype\")]\n self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes))\n\n def test_attention_slicing_forward_pass(self, expected_max_diff=1e-3):\n self._test_attention_slicing_forward_pass(expected_max_diff=expected_max_diff)\n\n def _test_attention_slicing_forward_pass(\n self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3\n ):\n if not self.test_attention_slicing:\n return\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n output_without_slicing = pipe(**inputs)[0]\n\n pipe.enable_attention_slicing(slice_size=1)\n inputs = self.get_dummy_inputs(generator_device)\n output_with_slicing = pipe(**inputs)[0]\n\n if test_max_difference:\n max_diff = np.abs(to_np(output_with_slicing) - to_np(output_without_slicing)).max()\n self.assertLess(max_diff, expected_max_diff, \"Attention slicing should not affect the inference results\")\n\n if test_mean_pixel_difference:\n assert_mean_pixel_difference(to_np(output_with_slicing[0]), to_np(output_without_slicing[0]))\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_accelerate_available() or is_accelerate_version(\"<\", \"0.14.0\"),\n reason=\"CPU offload is only available with CUDA and `accelerate v0.14.0` or higher\",\n )\n def test_sequential_cpu_offload_forward_pass(self, expected_max_diff=1e-4):\n import accelerate\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n output_without_offload = pipe(**inputs)[0]\n\n pipe.enable_sequential_cpu_offload()\n assert pipe._execution_device.type == \"cuda\"\n\n inputs = self.get_dummy_inputs(generator_device)\n output_with_offload = pipe(**inputs)[0]\n\n max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()\n self.assertLess(max_diff, expected_max_diff, \"CPU offloading should not affect the inference results\")\n\n # make sure all `torch.nn.Module` components (except those in `self._exclude_from_cpu_offload`) are offloaded correctly\n offloaded_modules = {\n k: v\n for k, v in pipe.components.items()\n if isinstance(v, torch.nn.Module) and k not in pipe._exclude_from_cpu_offload\n }\n # 1. all offloaded modules should be saved to cpu and moved to meta device\n self.assertTrue(\n all(v.device.type == \"meta\" for v in offloaded_modules.values()),\n f\"Not offloaded: {[k for k, v in offloaded_modules.items() if v.device.type != 'meta']}\",\n )\n # 2. all offloaded modules should have hook installed\n self.assertTrue(\n all(hasattr(v, \"_hf_hook\") for k, v in offloaded_modules.items()),\n f\"No hook attached: {[k for k, v in offloaded_modules.items() if not hasattr(v, '_hf_hook')]}\",\n )\n # 3. all offloaded modules should have correct hooks installed, should be either one of these two\n # - `AlignDevicesHook`\n # - a SequentialHook` that contains `AlignDevicesHook`\n offloaded_modules_with_incorrect_hooks = {}\n for k, v in offloaded_modules.items():\n if hasattr(v, \"_hf_hook\"):\n if isinstance(v._hf_hook, accelerate.hooks.SequentialHook):\n # if it is a `SequentialHook`, we loop through its `hooks` attribute to check if it only contains `AlignDevicesHook`\n for hook in v._hf_hook.hooks:\n if not isinstance(hook, accelerate.hooks.AlignDevicesHook):\n offloaded_modules_with_incorrect_hooks[k] = type(v._hf_hook.hooks[0])\n elif not isinstance(v._hf_hook, accelerate.hooks.AlignDevicesHook):\n offloaded_modules_with_incorrect_hooks[k] = type(v._hf_hook)\n\n self.assertTrue(\n len(offloaded_modules_with_incorrect_hooks) == 0,\n f\"Not installed correct hook: {offloaded_modules_with_incorrect_hooks}\",\n )\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_accelerate_available() or is_accelerate_version(\"<\", \"0.17.0\"),\n reason=\"CPU offload is only available with CUDA and `accelerate v0.17.0` or higher\",\n )\n def test_model_cpu_offload_forward_pass(self, expected_max_diff=2e-4):\n import accelerate\n\n generator_device = \"cpu\"\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(generator_device)\n output_without_offload = pipe(**inputs)[0]\n\n pipe.enable_model_cpu_offload()\n assert pipe._execution_device.type == \"cuda\"\n\n inputs = self.get_dummy_inputs(generator_device)\n output_with_offload = pipe(**inputs)[0]\n\n max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()\n self.assertLess(max_diff, expected_max_diff, \"CPU offloading should not affect the inference results\")\n\n # make sure all `torch.nn.Module` components (except those in `self._exclude_from_cpu_offload`) are offloaded correctly\n offloaded_modules = {\n k: v\n for k, v in pipe.components.items()\n if isinstance(v, torch.nn.Module) and k not in pipe._exclude_from_cpu_offload\n }\n # 1. check if all offloaded modules are saved to cpu\n self.assertTrue(\n all(v.device.type == \"cpu\" for v in offloaded_modules.values()),\n f\"Not offloaded: {[k for k, v in offloaded_modules.items() if v.device.type != 'cpu']}\",\n )\n # 2. check if all offloaded modules have hooks installed\n self.assertTrue(\n all(hasattr(v, \"_hf_hook\") for k, v in offloaded_modules.items()),\n f\"No hook attached: {[k for k, v in offloaded_modules.items() if not hasattr(v, '_hf_hook')]}\",\n )\n # 3. check if all offloaded modules have correct type of hooks installed, should be `CpuOffload`\n offloaded_modules_with_incorrect_hooks = {}\n for k, v in offloaded_modules.items():\n if hasattr(v, \"_hf_hook\") and not isinstance(v._hf_hook, accelerate.hooks.CpuOffload):\n offloaded_modules_with_incorrect_hooks[k] = type(v._hf_hook)\n\n self.assertTrue(\n len(offloaded_modules_with_incorrect_hooks) == 0,\n f\"Not installed correct hook: {offloaded_modules_with_incorrect_hooks}\",\n )\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_accelerate_available() or is_accelerate_version(\"<\", \"0.17.0\"),\n reason=\"CPU offload is only available with CUDA and `accelerate v0.17.0` or higher\",\n )\n def test_cpu_offload_forward_pass_twice(self, expected_max_diff=2e-4):\n import accelerate\n\n generator_device = \"cpu\"\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n\n pipe.set_progress_bar_config(disable=None)\n\n pipe.enable_model_cpu_offload()\n inputs = self.get_dummy_inputs(generator_device)\n output_with_offload = pipe(**inputs)[0]\n\n pipe.enable_model_cpu_offload()\n inputs = self.get_dummy_inputs(generator_device)\n output_with_offload_twice = pipe(**inputs)[0]\n\n max_diff = np.abs(to_np(output_with_offload) - to_np(output_with_offload_twice)).max()\n self.assertLess(\n max_diff, expected_max_diff, \"running CPU offloading 2nd time should not affect the inference results\"\n )\n\n # make sure all `torch.nn.Module` components (except those in `self._exclude_from_cpu_offload`) are offloaded correctly\n offloaded_modules = {\n k: v\n for k, v in pipe.components.items()\n if isinstance(v, torch.nn.Module) and k not in pipe._exclude_from_cpu_offload\n }\n # 1. check if all offloaded modules are saved to cpu\n self.assertTrue(\n all(v.device.type == \"cpu\" for v in offloaded_modules.values()),\n f\"Not offloaded: {[k for k, v in offloaded_modules.items() if v.device.type != 'cpu']}\",\n )\n # 2. check if all offloaded modules have hooks installed\n self.assertTrue(\n all(hasattr(v, \"_hf_hook\") for k, v in offloaded_modules.items()),\n f\"No hook attached: {[k for k, v in offloaded_modules.items() if not hasattr(v, '_hf_hook')]}\",\n )\n # 3. check if all offloaded modules have correct type of hooks installed, should be `CpuOffload`\n offloaded_modules_with_incorrect_hooks = {}\n for k, v in offloaded_modules.items():\n if hasattr(v, \"_hf_hook\") and not isinstance(v._hf_hook, accelerate.hooks.CpuOffload):\n offloaded_modules_with_incorrect_hooks[k] = type(v._hf_hook)\n\n self.assertTrue(\n len(offloaded_modules_with_incorrect_hooks) == 0,\n f\"Not installed correct hook: {offloaded_modules_with_incorrect_hooks}\",\n )\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_accelerate_available() or is_accelerate_version(\"<\", \"0.14.0\"),\n reason=\"CPU offload is only available with CUDA and `accelerate v0.14.0` or higher\",\n )\n def test_sequential_offload_forward_pass_twice(self, expected_max_diff=2e-4):\n import accelerate\n\n generator_device = \"cpu\"\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n\n pipe.set_progress_bar_config(disable=None)\n\n pipe.enable_sequential_cpu_offload()\n inputs = self.get_dummy_inputs(generator_device)\n output_with_offload = pipe(**inputs)[0]\n\n pipe.enable_sequential_cpu_offload()\n inputs = self.get_dummy_inputs(generator_device)\n output_with_offload_twice = pipe(**inputs)[0]\n\n max_diff = np.abs(to_np(output_with_offload) - to_np(output_with_offload_twice)).max()\n self.assertLess(\n max_diff, expected_max_diff, \"running sequential offloading second time should have the inference results\"\n )\n\n # make sure all `torch.nn.Module` components (except those in `self._exclude_from_cpu_offload`) are offloaded correctly\n offloaded_modules = {\n k: v\n for k, v in pipe.components.items()\n if isinstance(v, torch.nn.Module) and k not in pipe._exclude_from_cpu_offload\n }\n # 1. check if all offloaded modules are moved to meta device\n self.assertTrue(\n all(v.device.type == \"meta\" for v in offloaded_modules.values()),\n f\"Not offloaded: {[k for k, v in offloaded_modules.items() if v.device.type != 'meta']}\",\n )\n # 2. check if all offloaded modules have hook installed\n self.assertTrue(\n all(hasattr(v, \"_hf_hook\") for k, v in offloaded_modules.items()),\n f\"No hook attached: {[k for k, v in offloaded_modules.items() if not hasattr(v, '_hf_hook')]}\",\n )\n # 3. check if all offloaded modules have correct hooks installed, should be either one of these two\n # - `AlignDevicesHook`\n # - a SequentialHook` that contains `AlignDevicesHook`\n offloaded_modules_with_incorrect_hooks = {}\n for k, v in offloaded_modules.items():\n if hasattr(v, \"_hf_hook\"):\n if isinstance(v._hf_hook, accelerate.hooks.SequentialHook):\n # if it is a `SequentialHook`, we loop through its `hooks` attribute to check if it only contains `AlignDevicesHook`\n for hook in v._hf_hook.hooks:\n if not isinstance(hook, accelerate.hooks.AlignDevicesHook):\n offloaded_modules_with_incorrect_hooks[k] = type(v._hf_hook.hooks[0])\n elif not isinstance(v._hf_hook, accelerate.hooks.AlignDevicesHook):\n offloaded_modules_with_incorrect_hooks[k] = type(v._hf_hook)\n\n self.assertTrue(\n len(offloaded_modules_with_incorrect_hooks) == 0,\n f\"Not installed correct hook: {offloaded_modules_with_incorrect_hooks}\",\n )\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass()\n\n def _test_xformers_attention_forwardGenerator_pass(\n self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-4\n ):\n if not self.test_xformers_attention:\n return\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n output_without_offload = pipe(**inputs)[0]\n output_without_offload = (\n output_without_offload.cpu() if torch.is_tensor(output_without_offload) else output_without_offload\n )\n\n pipe.enable_xformers_memory_efficient_attention()\n inputs = self.get_dummy_inputs(torch_device)\n output_with_offload = pipe(**inputs)[0]\n output_with_offload = (\n output_with_offload.cpu() if torch.is_tensor(output_with_offload) else output_without_offload\n )\n\n if test_max_difference:\n max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()\n self.assertLess(max_diff, expected_max_diff, \"XFormers attention should not affect the inference results\")\n\n if test_mean_pixel_difference:\n assert_mean_pixel_difference(output_with_offload[0], output_without_offload[0])\n\n def test_progress_bar(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n\n inputs = self.get_dummy_inputs(torch_device)\n with io.StringIO() as stderr, contextlib.redirect_stderr(stderr):\n _ = pipe(**inputs)\n stderr = stderr.getvalue()\n # we can't calculate the number of progress steps beforehand e.g. for strength-dependent img2img,\n # so we just match \"5\" in \"#####| 1/5 [00:01<00:00]\"\n max_steps = re.search(\"/(.*?) \", stderr).group(1)\n self.assertTrue(max_steps is not None and len(max_steps) > 0)\n self.assertTrue(\n f\"{max_steps}/{max_steps}\" in stderr, \"Progress bar should be enabled and stopped at the max step\"\n )\n\n pipe.set_progress_bar_config(disable=True)\n with io.StringIO() as stderr, contextlib.redirect_stderr(stderr):\n _ = pipe(**inputs)\n self.assertTrue(stderr.getvalue() == \"\", \"Progress bar should be disabled\")\n\n def test_num_images_per_prompt(self):\n sig = inspect.signature(self.pipeline_class.__call__)\n\n if \"num_images_per_prompt\" not in sig.parameters:\n return\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n batch_sizes = [1, 2]\n num_images_per_prompts = [1, 2]\n\n for batch_size in batch_sizes:\n for num_images_per_prompt in num_images_per_prompts:\n inputs = self.get_dummy_inputs(torch_device)\n\n for key in inputs.keys():\n if key in self.batch_params:\n inputs[key] = batch_size * [inputs[key]]\n\n images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt)[0]\n\n assert images.shape[0] == batch_size * num_images_per_prompt\n\n def test_cfg(self):\n sig = inspect.signature(self.pipeline_class.__call__)\n\n if \"guidance_scale\" not in sig.parameters:\n return\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(torch_device)\n\n inputs[\"guidance_scale\"] = 1.0\n out_no_cfg = pipe(**inputs)[0]\n\n inputs[\"guidance_scale\"] = 7.5\n out_cfg = pipe(**inputs)[0]\n\n assert out_cfg.shape == out_no_cfg.shape\n\n def test_callback_inputs(self):\n sig = inspect.signature(self.pipeline_class.__call__)\n has_callback_tensor_inputs = \"callback_on_step_end_tensor_inputs\" in sig.parameters\n has_callback_step_end = \"callback_on_step_end\" in sig.parameters\n\n if not (has_callback_tensor_inputs and has_callback_step_end):\n return\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n self.assertTrue(\n hasattr(pipe, \"_callback_tensor_inputs\"),\n f\" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs\",\n )\n\n def callback_inputs_subset(pipe, i, t, callback_kwargs):\n # iterate over callback args\n for tensor_name, tensor_value in callback_kwargs.items():\n # check that we're only passing in allowed tensor inputs\n assert tensor_name in pipe._callback_tensor_inputs\n\n return callback_kwargs\n\n def callback_inputs_all(pipe, i, t, callback_kwargs):\n for tensor_name in pipe._callback_tensor_inputs:\n assert tensor_name in callback_kwargs\n\n # iterate over callback args\n for tensor_name, tensor_value in callback_kwargs.items():\n # check that we're only passing in allowed tensor inputs\n assert tensor_name in pipe._callback_tensor_inputs\n\n return callback_kwargs\n\n inputs = self.get_dummy_inputs(torch_device)\n\n # Test passing in a subset\n inputs[\"callback_on_step_end\"] = callback_inputs_subset\n inputs[\"callback_on_step_end_tensor_inputs\"] = [\"latents\"]\n inputs[\"output_type\"] = \"latent\"\n output = pipe(**inputs)[0]\n\n # Test passing in a everything\n inputs[\"callback_on_step_end\"] = callback_inputs_all\n inputs[\"callback_on_step_end_tensor_inputs\"] = pipe._callback_tensor_inputs\n inputs[\"output_type\"] = \"latent\"\n output = pipe(**inputs)[0]\n\n def callback_inputs_change_tensor(pipe, i, t, callback_kwargs):\n is_last = i == (pipe.num_timesteps - 1)\n if is_last:\n callback_kwargs[\"latents\"] = torch.zeros_like(callback_kwargs[\"latents\"])\n return callback_kwargs\n\n inputs[\"callback_on_step_end\"] = callback_inputs_change_tensor\n inputs[\"callback_on_step_end_tensor_inputs\"] = pipe._callback_tensor_inputs\n inputs[\"output_type\"] = \"latent\"\n output = pipe(**inputs)[0]\n assert output.abs().sum() == 0\n\n def test_callback_cfg(self):\n sig = inspect.signature(self.pipeline_class.__call__)\n has_callback_tensor_inputs = \"callback_on_step_end_tensor_inputs\" in sig.parameters\n has_callback_step_end = \"callback_on_step_end\" in sig.parameters\n\n if not (has_callback_tensor_inputs and has_callback_step_end):\n return\n\n if \"guidance_scale\" not in sig.parameters:\n return\n\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n self.assertTrue(\n hasattr(pipe, \"_callback_tensor_inputs\"),\n f\" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs\",\n )\n\n def callback_increase_guidance(pipe, i, t, callback_kwargs):\n pipe._guidance_scale += 1.0\n\n return callback_kwargs\n\n inputs = self.get_dummy_inputs(torch_device)\n\n # use cfg guidance because some pipelines modify the shape of the latents\n # outside of the denoising loop\n inputs[\"guidance_scale\"] = 2.0\n inputs[\"callback_on_step_end\"] = callback_increase_guidance\n inputs[\"callback_on_step_end_tensor_inputs\"] = pipe._callback_tensor_inputs\n _ = pipe(**inputs)[0]\n\n # we increase the guidance scale by 1.0 at every step\n # check that the guidance scale is increased by the number of scheduler timesteps\n # accounts for models that modify the number of inference steps based on strength\n assert pipe.guidance_scale == (inputs[\"guidance_scale\"] + pipe.num_timesteps)\n\n def test_StableDiffusionMixin_component(self):\n \"\"\"Any pipeline that have LDMFuncMixin should have vae and unet components.\"\"\"\n if not issubclass(self.pipeline_class, StableDiffusionMixin):\n return\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n self.assertTrue(hasattr(pipe, \"vae\") and isinstance(pipe.vae, (AutoencoderKL, AutoencoderTiny)))\n self.assertTrue(\n hasattr(pipe, \"unet\")\n and isinstance(\n pipe.unet,\n (UNet2DConditionModel, UNet3DConditionModel, I2VGenXLUNet, UNetMotionModel, UNetControlNetXSModel),\n )\n )\n\n\n@is_staging_test\nclass PipelinePushToHubTester(unittest.TestCase):\n identifier = uuid.uuid4()\n repo_id = f\"test-pipeline-{identifier}\"\n org_repo_id = f\"valid_org/{repo_id}-org\"\n\n def get_pipeline_components(self):\n unet = UNet2DConditionModel(\n block_out_channels=(32, 64),\n layers_per_block=2,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n cross_attention_dim=32,\n )\n\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n )\n\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n\n with tempfile.TemporaryDirectory() as tmpdir:\n dummy_vocab = {\"<|startoftext|>\": 0, \"<|endoftext|>\": 1, \"!\": 2}\n vocab_path = os.path.join(tmpdir, \"vocab.json\")\n with open(vocab_path, \"w\") as f:\n json.dump(dummy_vocab, f)\n\n merges = \"Ġ t\\nĠt h\"\n merges_path = os.path.join(tmpdir, \"merges.txt\")\n with open(merges_path, \"w\") as f:\n f.writelines(merges)\n tokenizer = CLIPTokenizer(vocab_file=vocab_path, merges_file=merges_path)\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"safety_checker\": None,\n \"feature_extractor\": None,\n }\n return components\n\n def test_push_to_hub(self):\n components = self.get_pipeline_components()\n pipeline = StableDiffusionPipeline(**components)\n pipeline.push_to_hub(self.repo_id, token=TOKEN)\n\n new_model = UNet2DConditionModel.from_pretrained(f\"{USER}/{self.repo_id}\", subfolder=\"unet\")\n unet = components[\"unet\"]\n for p1, p2 in zip(unet.parameters(), new_model.parameters()):\n self.assertTrue(torch.equal(p1, p2))\n\n # Reset repo\n delete_repo(token=TOKEN, repo_id=self.repo_id)\n\n # Push to hub via save_pretrained\n with tempfile.TemporaryDirectory() as tmp_dir:\n pipeline.save_pretrained(tmp_dir, repo_id=self.repo_id, push_to_hub=True, token=TOKEN)\n\n new_model = UNet2DConditionModel.from_pretrained(f\"{USER}/{self.repo_id}\", subfolder=\"unet\")\n for p1, p2 in zip(unet.parameters(), new_model.parameters()):\n self.assertTrue(torch.equal(p1, p2))\n\n # Reset repo\n delete_repo(self.repo_id, token=TOKEN)\n\n def test_push_to_hub_in_organization(self):\n components = self.get_pipeline_components()\n pipeline = StableDiffusionPipeline(**components)\n pipeline.push_to_hub(self.org_repo_id, token=TOKEN)\n\n new_model = UNet2DConditionModel.from_pretrained(self.org_repo_id, subfolder=\"unet\")\n unet = components[\"unet\"]\n for p1, p2 in zip(unet.parameters(), new_model.parameters()):\n self.assertTrue(torch.equal(p1, p2))\n\n # Reset repo\n delete_repo(token=TOKEN, repo_id=self.org_repo_id)\n\n # Push to hub via save_pretrained\n with tempfile.TemporaryDirectory() as tmp_dir:\n pipeline.save_pretrained(tmp_dir, push_to_hub=True, token=TOKEN, repo_id=self.org_repo_id)\n\n new_model = UNet2DConditionModel.from_pretrained(self.org_repo_id, subfolder=\"unet\")\n for p1, p2 in zip(unet.parameters(), new_model.parameters()):\n self.assertTrue(torch.equal(p1, p2))\n\n # Reset repo\n delete_repo(self.org_repo_id, token=TOKEN)\n\n @unittest.skipIf(\n not is_jinja_available(),\n reason=\"Model card tests cannot be performed without Jinja installed.\",\n )\n def test_push_to_hub_library_name(self):\n components = self.get_pipeline_components()\n pipeline = StableDiffusionPipeline(**components)\n pipeline.push_to_hub(self.repo_id, token=TOKEN)\n\n model_card = ModelCard.load(f\"{USER}/{self.repo_id}\", token=TOKEN).data\n assert model_card.library_name == \"diffusers\"\n\n # Reset repo\n delete_repo(self.repo_id, token=TOKEN)\n\n\n# For SDXL and its derivative pipelines (such as ControlNet), we have the text encoders\n# and the tokenizers as optional components. So, we need to override the `test_save_load_optional_components()`\n# test for all such pipelines. This requires us to use a custom `encode_prompt()` function.\nclass SDXLOptionalComponentsTesterMixin:\n def encode_prompt(\n self, tokenizers, text_encoders, prompt: str, num_images_per_prompt: int = 1, negative_prompt: str = None\n ):\n device = text_encoders[0].device\n\n if isinstance(prompt, str):\n prompt = [prompt]\n batch_size = len(prompt)\n\n prompt_embeds_list = []\n for tokenizer, text_encoder in zip(tokenizers, text_encoders):\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n\n prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds.hidden_states[-2]\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n\n if negative_prompt is None:\n negative_prompt_embeds = torch.zeros_like(prompt_embeds)\n negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)\n else:\n negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt\n\n negative_prompt_embeds_list = []\n for tokenizer, text_encoder in zip(tokenizers, text_encoders):\n uncond_input = tokenizer(\n negative_prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n negative_prompt_embeds = text_encoder(uncond_input.input_ids.to(device), output_hidden_states=True)\n negative_pooled_prompt_embeds = negative_prompt_embeds[0]\n negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]\n negative_prompt_embeds_list.append(negative_prompt_embeds)\n\n negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)\n\n bs_embed, seq_len, _ = prompt_embeds.shape\n\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)\n\n # for classifier-free guidance\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = negative_prompt_embeds.shape[1]\n\n negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)\n negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n\n # for classifier-free guidance\n negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(\n bs_embed * num_images_per_prompt, -1\n )\n\n return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds\n\n def _test_save_load_optional_components(self, expected_max_difference=1e-4):\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n for optional_component in pipe._optional_components:\n setattr(pipe, optional_component, None)\n\n for component in pipe.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n generator_device = \"cpu\"\n inputs = self.get_dummy_inputs(generator_device)\n\n tokenizer = components.pop(\"tokenizer\")\n tokenizer_2 = components.pop(\"tokenizer_2\")\n text_encoder = components.pop(\"text_encoder\")\n text_encoder_2 = components.pop(\"text_encoder_2\")\n\n tokenizers = [tokenizer, tokenizer_2] if tokenizer is not None else [tokenizer_2]\n text_encoders = [text_encoder, text_encoder_2] if text_encoder is not None else [text_encoder_2]\n prompt = inputs.pop(\"prompt\")\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(tokenizers, text_encoders, prompt)\n inputs[\"prompt_embeds\"] = prompt_embeds\n inputs[\"negative_prompt_embeds\"] = negative_prompt_embeds\n inputs[\"pooled_prompt_embeds\"] = pooled_prompt_embeds\n inputs[\"negative_pooled_prompt_embeds\"] = negative_pooled_prompt_embeds\n\n output = pipe(**inputs)[0]\n\n with tempfile.TemporaryDirectory() as tmpdir:\n pipe.save_pretrained(tmpdir)\n pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)\n for component in pipe_loaded.components.values():\n if hasattr(component, \"set_default_attn_processor\"):\n component.set_default_attn_processor()\n pipe_loaded.to(torch_device)\n pipe_loaded.set_progress_bar_config(disable=None)\n\n for optional_component in pipe._optional_components:\n self.assertTrue(\n getattr(pipe_loaded, optional_component) is None,\n f\"`{optional_component}` did not stay set to None after loading.\",\n )\n\n inputs = self.get_dummy_inputs(generator_device)\n _ = inputs.pop(\"prompt\")\n inputs[\"prompt_embeds\"] = prompt_embeds\n inputs[\"negative_prompt_embeds\"] = negative_prompt_embeds\n inputs[\"pooled_prompt_embeds\"] = pooled_prompt_embeds\n inputs[\"negative_pooled_prompt_embeds\"] = negative_pooled_prompt_embeds\n\n output_loaded = pipe_loaded(**inputs)[0]\n\n max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()\n self.assertLess(max_diff, expected_max_difference)\n\n\n# Some models (e.g. unCLIP) are extremely likely to significantly deviate depending on which hardware is used.\n# This helper function is used to check that the image doesn't deviate on average more than 10 pixels from a\n# reference image.\ndef assert_mean_pixel_difference(image, expected_image, expected_max_diff=10):\n image = np.asarray(DiffusionPipeline.numpy_to_pil(image)[0], dtype=np.float32)\n expected_image = np.asarray(DiffusionPipeline.numpy_to_pil(expected_image)[0], dtype=np.float32)\n avg_diff = np.abs(image - expected_image).mean()\n assert avg_diff < expected_max_diff, f\"Error image deviates {avg_diff} pixels on average\"\n" }, { "path": "diffusers/tests/pipelines/test_pipelines_flax.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os\nimport tempfile\nimport unittest\n\nimport numpy as np\n\nfrom diffusers.utils import is_flax_available\nfrom diffusers.utils.testing_utils import require_flax, slow\n\n\nif is_flax_available():\n import jax\n import jax.numpy as jnp\n from flax.jax_utils import replicate\n from flax.training.common_utils import shard\n\n from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline\n\n\n@require_flax\nclass DownloadTests(unittest.TestCase):\n def test_download_only_pytorch(self):\n with tempfile.TemporaryDirectory() as tmpdirname:\n # pipeline has Flax weights\n _ = FlaxDiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-pipe\", safety_checker=None, cache_dir=tmpdirname\n )\n\n all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname, os.listdir(tmpdirname)[0], \"snapshots\"))]\n files = [item for sublist in all_root_files for item in sublist]\n\n # None of the downloaded files should be a PyTorch file even if we have some here:\n # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin\n assert not any(f.endswith(\".bin\") for f in files)\n\n\n@slow\n@require_flax\nclass FlaxPipelineTests(unittest.TestCase):\n def test_dummy_all_tpus(self):\n pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-pipe\", safety_checker=None\n )\n\n prompt = (\n \"A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of\"\n \" field, close up, split lighting, cinematic\"\n )\n\n prng_seed = jax.random.PRNGKey(0)\n num_inference_steps = 4\n\n num_samples = jax.device_count()\n prompt = num_samples * [prompt]\n prompt_ids = pipeline.prepare_inputs(prompt)\n\n # shard inputs and rng\n params = replicate(params)\n prng_seed = jax.random.split(prng_seed, num_samples)\n prompt_ids = shard(prompt_ids)\n\n images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images\n\n assert images.shape == (num_samples, 1, 64, 64, 3)\n if jax.device_count() == 8:\n assert np.abs(np.abs(images[0, 0, :2, :2, -2:], dtype=np.float32).sum() - 4.1514745) < 1e-3\n assert np.abs(np.abs(images, dtype=np.float32).sum() - 49947.875) < 5e-1\n\n images_pil = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))\n assert len(images_pil) == num_samples\n\n def test_stable_diffusion_v1_4(self):\n pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\", revision=\"flax\", safety_checker=None\n )\n\n prompt = (\n \"A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of\"\n \" field, close up, split lighting, cinematic\"\n )\n\n prng_seed = jax.random.PRNGKey(0)\n num_inference_steps = 50\n\n num_samples = jax.device_count()\n prompt = num_samples * [prompt]\n prompt_ids = pipeline.prepare_inputs(prompt)\n\n # shard inputs and rng\n params = replicate(params)\n prng_seed = jax.random.split(prng_seed, num_samples)\n prompt_ids = shard(prompt_ids)\n\n images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images\n\n assert images.shape == (num_samples, 1, 512, 512, 3)\n if jax.device_count() == 8:\n assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.float32).sum() - 0.05652401)) < 1e-2\n assert np.abs((np.abs(images, dtype=np.float32).sum() - 2383808.2)) < 5e-1\n\n def test_stable_diffusion_v1_4_bfloat_16(self):\n pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\", revision=\"bf16\", dtype=jnp.bfloat16, safety_checker=None\n )\n\n prompt = (\n \"A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of\"\n \" field, close up, split lighting, cinematic\"\n )\n\n prng_seed = jax.random.PRNGKey(0)\n num_inference_steps = 50\n\n num_samples = jax.device_count()\n prompt = num_samples * [prompt]\n prompt_ids = pipeline.prepare_inputs(prompt)\n\n # shard inputs and rng\n params = replicate(params)\n prng_seed = jax.random.split(prng_seed, num_samples)\n prompt_ids = shard(prompt_ids)\n\n images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images\n\n assert images.shape == (num_samples, 1, 512, 512, 3)\n if jax.device_count() == 8:\n assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.float32).sum() - 0.04003906)) < 5e-2\n assert np.abs((np.abs(images, dtype=np.float32).sum() - 2373516.75)) < 5e-1\n\n def test_stable_diffusion_v1_4_bfloat_16_with_safety(self):\n pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\", revision=\"bf16\", dtype=jnp.bfloat16\n )\n\n prompt = (\n \"A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of\"\n \" field, close up, split lighting, cinematic\"\n )\n\n prng_seed = jax.random.PRNGKey(0)\n num_inference_steps = 50\n\n num_samples = jax.device_count()\n prompt = num_samples * [prompt]\n prompt_ids = pipeline.prepare_inputs(prompt)\n\n # shard inputs and rng\n params = replicate(params)\n prng_seed = jax.random.split(prng_seed, num_samples)\n prompt_ids = shard(prompt_ids)\n\n images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images\n\n assert images.shape == (num_samples, 1, 512, 512, 3)\n if jax.device_count() == 8:\n assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.float32).sum() - 0.04003906)) < 5e-2\n assert np.abs((np.abs(images, dtype=np.float32).sum() - 2373516.75)) < 5e-1\n\n def test_stable_diffusion_v1_4_bfloat_16_ddim(self):\n scheduler = FlaxDDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n set_alpha_to_one=False,\n steps_offset=1,\n )\n\n pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n revision=\"bf16\",\n dtype=jnp.bfloat16,\n scheduler=scheduler,\n safety_checker=None,\n )\n scheduler_state = scheduler.create_state()\n\n params[\"scheduler\"] = scheduler_state\n\n prompt = (\n \"A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of\"\n \" field, close up, split lighting, cinematic\"\n )\n\n prng_seed = jax.random.PRNGKey(0)\n num_inference_steps = 50\n\n num_samples = jax.device_count()\n prompt = num_samples * [prompt]\n prompt_ids = pipeline.prepare_inputs(prompt)\n\n # shard inputs and rng\n params = replicate(params)\n prng_seed = jax.random.split(prng_seed, num_samples)\n prompt_ids = shard(prompt_ids)\n\n images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images\n\n assert images.shape == (num_samples, 1, 512, 512, 3)\n if jax.device_count() == 8:\n assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.float32).sum() - 0.045043945)) < 5e-2\n assert np.abs((np.abs(images, dtype=np.float32).sum() - 2347693.5)) < 5e-1\n\n def test_jax_memory_efficient_attention(self):\n prompt = (\n \"A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of\"\n \" field, close up, split lighting, cinematic\"\n )\n\n num_samples = jax.device_count()\n prompt = num_samples * [prompt]\n prng_seed = jax.random.split(jax.random.PRNGKey(0), num_samples)\n\n pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n revision=\"bf16\",\n dtype=jnp.bfloat16,\n safety_checker=None,\n )\n\n params = replicate(params)\n prompt_ids = pipeline.prepare_inputs(prompt)\n prompt_ids = shard(prompt_ids)\n images = pipeline(prompt_ids, params, prng_seed, jit=True).images\n assert images.shape == (num_samples, 1, 512, 512, 3)\n slice = images[2, 0, 256, 10:17, 1]\n\n # With memory efficient attention\n pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(\n \"CompVis/stable-diffusion-v1-4\",\n revision=\"bf16\",\n dtype=jnp.bfloat16,\n safety_checker=None,\n use_memory_efficient_attention=True,\n )\n\n params = replicate(params)\n prompt_ids = pipeline.prepare_inputs(prompt)\n prompt_ids = shard(prompt_ids)\n images_eff = pipeline(prompt_ids, params, prng_seed, jit=True).images\n assert images_eff.shape == (num_samples, 1, 512, 512, 3)\n slice_eff = images[2, 0, 256, 10:17, 1]\n\n # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`\n # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.\n assert abs(slice_eff - slice).max() < 1e-2\n" }, { "path": "diffusers/tests/pipelines/test_pipelines_onnx_common.py", "content": "from diffusers.utils.testing_utils import require_onnxruntime\n\n\n@require_onnxruntime\nclass OnnxPipelineTesterMixin:\n \"\"\"\n This mixin is designed to be used with unittest.TestCase classes.\n It provides a set of common tests for each ONNXRuntime pipeline, e.g. saving and loading the pipeline,\n equivalence of dict and tuple outputs, etc.\n \"\"\"\n\n pass\n" }, { "path": "diffusers/tests/pipelines/text_to_video_synthesis/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/text_to_video_synthesis/test_text_to_video.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n TextToVideoSDPipeline,\n UNet3DConditionModel,\n)\nfrom diffusers.utils import is_xformers_available\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n load_numpy,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n skip_mps,\n slow,\n torch_device,\n)\n\nfrom ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS\nfrom ..test_pipelines_common import PipelineTesterMixin, SDFunctionTesterMixin\n\n\nenable_full_determinism()\n\n\n@skip_mps\nclass TextToVideoSDPipelineFastTests(PipelineTesterMixin, SDFunctionTesterMixin, unittest.TestCase):\n pipeline_class = TextToVideoSDPipeline\n params = TEXT_TO_IMAGE_PARAMS\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n # No `output_type`.\n required_optional_params = frozenset(\n [\n \"num_inference_steps\",\n \"generator\",\n \"latents\",\n \"return_dict\",\n \"callback\",\n \"callback_steps\",\n ]\n )\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet3DConditionModel(\n block_out_channels=(4, 8),\n layers_per_block=1,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"CrossAttnDownBlock3D\", \"DownBlock3D\"),\n up_block_types=(\"UpBlock3D\", \"CrossAttnUpBlock3D\"),\n cross_attention_dim=4,\n attention_head_dim=4,\n norm_num_groups=2,\n )\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=(8,),\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\"],\n latent_channels=4,\n sample_size=32,\n norm_num_groups=2,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=4,\n intermediate_size=16,\n layer_norm_eps=1e-05,\n num_attention_heads=2,\n num_hidden_layers=2,\n pad_token_id=1,\n vocab_size=1000,\n hidden_act=\"gelu\",\n projection_dim=32,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"pt\",\n }\n return inputs\n\n def test_dict_tuple_outputs_equivalent(self):\n expected_slice = None\n if torch_device == \"cpu\":\n expected_slice = np.array([0.4903, 0.5649, 0.5504, 0.5179, 0.4821, 0.5466, 0.4131, 0.5052, 0.5077])\n return super().test_dict_tuple_outputs_equivalent(expected_slice=expected_slice)\n\n def test_text_to_video_default_case(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = TextToVideoSDPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"output_type\"] = \"np\"\n frames = sd_pipe(**inputs).frames\n\n image_slice = frames[0][0][-3:, -3:, -1]\n\n assert frames[0][0].shape == (32, 32, 3)\n expected_slice = np.array([0.7537, 0.1752, 0.6157, 0.5508, 0.4240, 0.4110, 0.4838, 0.5648, 0.5094])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n @unittest.skipIf(torch_device != \"cuda\", reason=\"Feature isn't heavily used. Test in CUDA environment only.\")\n def test_attention_slicing_forward_pass(self):\n self._test_attention_slicing_forward_pass(test_mean_pixel_difference=False, expected_max_diff=3e-3)\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False, expected_max_diff=1e-2)\n\n # (todo): sayakpaul\n @unittest.skip(reason=\"Batching needs to be properly figured out first for this pipeline.\")\n def test_inference_batch_consistent(self):\n pass\n\n # (todo): sayakpaul\n @unittest.skip(reason=\"Batching needs to be properly figured out first for this pipeline.\")\n def test_inference_batch_single_identical(self):\n pass\n\n @unittest.skip(reason=\"`num_images_per_prompt` argument is not supported for this pipeline.\")\n def test_num_images_per_prompt(self):\n pass\n\n def test_progress_bar(self):\n return super().test_progress_bar()\n\n\n@slow\n@skip_mps\n@require_torch_gpu\nclass TextToVideoSDPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_two_step_model(self):\n expected_video = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/video_2step.npy\"\n )\n\n pipe = TextToVideoSDPipeline.from_pretrained(\"damo-vilab/text-to-video-ms-1.7b\")\n pipe = pipe.to(torch_device)\n\n prompt = \"Spiderman is surfing\"\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n\n video_frames = pipe(prompt, generator=generator, num_inference_steps=2, output_type=\"np\").frames\n assert numpy_cosine_similarity_distance(expected_video.flatten(), video_frames.flatten()) < 1e-4\n\n def test_two_step_model_with_freeu(self):\n expected_video = []\n\n pipe = TextToVideoSDPipeline.from_pretrained(\"damo-vilab/text-to-video-ms-1.7b\")\n pipe = pipe.to(torch_device)\n\n prompt = \"Spiderman is surfing\"\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n\n pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4)\n video_frames = pipe(prompt, generator=generator, num_inference_steps=2, output_type=\"np\").frames\n video = video_frames[0, 0, -3:, -3:, -1].flatten()\n\n expected_video = [0.3643, 0.3455, 0.3831, 0.3923, 0.2978, 0.3247, 0.3278, 0.3201, 0.3475]\n\n assert np.abs(expected_video - video).mean() < 5e-2\n" }, { "path": "diffusers/tests/pipelines/text_to_video_synthesis/test_text_to_video_zero.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nimport torch\n\nfrom diffusers import DDIMScheduler, TextToVideoZeroPipeline\nfrom diffusers.utils.testing_utils import load_pt, nightly, require_torch_gpu\n\nfrom ..test_pipelines_common import assert_mean_pixel_difference\n\n\n@nightly\n@require_torch_gpu\nclass TextToVideoZeroPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_full_model(self):\n model_id = \"runwayml/stable-diffusion-v1-5\"\n pipe = TextToVideoZeroPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(\"cuda\")\n pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\n generator = torch.Generator(device=\"cuda\").manual_seed(0)\n\n prompt = \"A bear is playing a guitar on Times Square\"\n result = pipe(prompt=prompt, generator=generator).images\n\n expected_result = load_pt(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt\"\n )\n\n assert_mean_pixel_difference(result, expected_result)\n" }, { "path": "diffusers/tests/pipelines/text_to_video_synthesis/test_text_to_video_zero_sdxl.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport contextlib\nimport gc\nimport inspect\nimport io\nimport re\nimport tempfile\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom diffusers import AutoencoderKL, DDIMScheduler, TextToVideoZeroSDXLPipeline, UNet2DConditionModel\nfrom diffusers.utils.import_utils import is_accelerate_available, is_accelerate_version\nfrom diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device\n\nfrom ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS\nfrom ..test_pipelines_common import PipelineFromPipeTesterMixin, PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\ndef to_np(tensor):\n if isinstance(tensor, torch.Tensor):\n tensor = tensor.detach().cpu().numpy()\n\n return tensor\n\n\nclass TextToVideoZeroSDXLPipelineFastTests(PipelineTesterMixin, PipelineFromPipeTesterMixin, unittest.TestCase):\n pipeline_class = TextToVideoZeroSDXLPipeline\n params = TEXT_TO_IMAGE_PARAMS\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n image_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS\n generator_device = \"cpu\"\n\n def get_dummy_components(self, seed=0):\n torch.manual_seed(seed)\n unet = UNet2DConditionModel(\n block_out_channels=(2, 4),\n layers_per_block=2,\n sample_size=2,\n norm_num_groups=2,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"DownBlock2D\", \"CrossAttnDownBlock2D\"),\n up_block_types=(\"CrossAttnUpBlock2D\", \"UpBlock2D\"),\n # SD2-specific config below\n attention_head_dim=(2, 4),\n use_linear_projection=True,\n addition_embed_type=\"text_time\",\n addition_time_embed_dim=8,\n transformer_layers_per_block=(1, 2),\n projection_class_embeddings_input_dim=80, # 6 * 8 + 32\n cross_attention_dim=64,\n )\n scheduler = DDIMScheduler(\n num_train_timesteps=1000,\n beta_start=0.0001,\n beta_end=0.02,\n beta_schedule=\"linear\",\n trained_betas=None,\n clip_sample=True,\n set_alpha_to_one=True,\n steps_offset=0,\n prediction_type=\"epsilon\",\n thresholding=False,\n dynamic_thresholding_ratio=0.995,\n clip_sample_range=1.0,\n sample_max_value=1.0,\n timestep_spacing=\"leading\",\n rescale_betas_zero_snr=False,\n )\n torch.manual_seed(seed)\n vae = AutoencoderKL(\n block_out_channels=[32, 64],\n in_channels=3,\n out_channels=3,\n down_block_types=[\"DownEncoderBlock2D\", \"DownEncoderBlock2D\"],\n up_block_types=[\"UpDecoderBlock2D\", \"UpDecoderBlock2D\"],\n latent_channels=4,\n sample_size=128,\n )\n torch.manual_seed(seed)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n # SD2-specific config below\n hidden_act=\"gelu\",\n projection_dim=32,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)\n tokenizer_2 = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"text_encoder_2\": text_encoder_2,\n \"tokenizer_2\": tokenizer_2,\n \"image_encoder\": None,\n \"feature_extractor\": None,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A panda dancing in Antarctica\",\n \"generator\": generator,\n \"num_inference_steps\": 5,\n \"t0\": 1,\n \"t1\": 3,\n \"height\": 64,\n \"width\": 64,\n \"video_length\": 3,\n \"output_type\": \"np\",\n }\n return inputs\n\n def get_generator(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n return generator\n\n def test_text_to_video_zero_sdxl(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n\n inputs = self.get_dummy_inputs(self.generator_device)\n result = pipe(**inputs).images\n\n first_frame_slice = result[0, -3:, -3:, -1]\n last_frame_slice = result[-1, -3:, -3:, 0]\n\n expected_slice1 = np.array([0.48, 0.58, 0.53, 0.59, 0.50, 0.44, 0.60, 0.65, 0.52])\n expected_slice2 = np.array([0.66, 0.49, 0.40, 0.70, 0.47, 0.51, 0.73, 0.65, 0.52])\n\n assert np.abs(first_frame_slice.flatten() - expected_slice1).max() < 1e-2\n assert np.abs(last_frame_slice.flatten() - expected_slice2).max() < 1e-2\n\n @unittest.skip(\n reason=\"Cannot call `set_default_attn_processor` as this pipeline uses a specific attention processor.\"\n )\n def test_attention_slicing_forward_pass(self):\n pass\n\n def test_cfg(self):\n sig = inspect.signature(self.pipeline_class.__call__)\n if \"guidance_scale\" not in sig.parameters:\n return\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(self.generator_device)\n\n inputs[\"guidance_scale\"] = 1.0\n out_no_cfg = pipe(**inputs)[0]\n\n inputs[\"guidance_scale\"] = 7.5\n out_cfg = pipe(**inputs)[0]\n\n assert out_cfg.shape == out_no_cfg.shape\n\n def test_dict_tuple_outputs_equivalent(self, expected_max_difference=1e-4):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(self.generator_device))[0]\n output_tuple = pipe(**self.get_dummy_inputs(self.generator_device), return_dict=False)[0]\n\n max_diff = np.abs(to_np(output) - to_np(output_tuple)).max()\n self.assertLess(max_diff, expected_max_difference)\n\n @unittest.skipIf(torch_device != \"cuda\", reason=\"float16 requires CUDA\")\n def test_float16_inference(self, expected_max_diff=5e-2):\n components = self.get_dummy_components()\n for name, module in components.items():\n if hasattr(module, \"half\"):\n components[name] = module.to(torch_device).half()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n components = self.get_dummy_components()\n pipe_fp16 = self.pipeline_class(**components)\n pipe_fp16.to(torch_device, torch.float16)\n pipe_fp16.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(self.generator_device)\n # # Reset generator in case it is used inside dummy inputs\n if \"generator\" in inputs:\n inputs[\"generator\"] = self.get_generator(self.generator_device)\n\n output = pipe(**inputs)[0]\n\n fp16_inputs = self.get_dummy_inputs(self.generator_device)\n # Reset generator in case it is used inside dummy inputs\n if \"generator\" in fp16_inputs:\n fp16_inputs[\"generator\"] = self.get_generator(self.generator_device)\n\n output_fp16 = pipe_fp16(**fp16_inputs)[0]\n\n max_diff = np.abs(to_np(output) - to_np(output_fp16)).max()\n self.assertLess(max_diff, expected_max_diff, \"The outputs of the fp16 and fp32 pipelines are too different.\")\n\n @unittest.skip(reason=\"Batching needs to be properly figured out first for this pipeline.\")\n def test_inference_batch_consistent(self):\n pass\n\n @unittest.skip(\n reason=\"Cannot call `set_default_attn_processor` as this pipeline uses a specific attention processor.\"\n )\n def test_inference_batch_single_identical(self):\n pass\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_accelerate_available() or is_accelerate_version(\"<\", \"0.17.0\"),\n reason=\"CPU offload is only available with CUDA and `accelerate v0.17.0` or higher\",\n )\n def test_model_cpu_offload_forward_pass(self, expected_max_diff=2e-4):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(self.generator_device)\n output_without_offload = pipe(**inputs)[0]\n\n pipe.enable_model_cpu_offload()\n inputs = self.get_dummy_inputs(self.generator_device)\n output_with_offload = pipe(**inputs)[0]\n\n max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()\n self.assertLess(max_diff, expected_max_diff, \"CPU offloading should not affect the inference results\")\n\n @unittest.skip(reason=\"`num_images_per_prompt` argument is not supported for this pipeline.\")\n def test_pipeline_call_signature(self):\n pass\n\n def test_progress_bar(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n\n inputs = self.get_dummy_inputs(self.generator_device)\n with io.StringIO() as stderr, contextlib.redirect_stderr(stderr):\n _ = pipe(**inputs)\n stderr = stderr.getvalue()\n # we can't calculate the number of progress steps beforehand e.g. for strength-dependent img2img,\n # so we just match \"5\" in \"#####| 1/5 [00:01<00:00]\"\n max_steps = re.search(\"/(.*?) \", stderr).group(1)\n self.assertTrue(max_steps is not None and len(max_steps) > 0)\n self.assertTrue(\n f\"{max_steps}/{max_steps}\" in stderr, \"Progress bar should be enabled and stopped at the max step\"\n )\n\n pipe.set_progress_bar_config(disable=True)\n with io.StringIO() as stderr, contextlib.redirect_stderr(stderr):\n _ = pipe(**inputs)\n self.assertTrue(stderr.getvalue() == \"\", \"Progress bar should be disabled\")\n\n @unittest.skipIf(torch_device != \"cuda\", reason=\"float16 requires CUDA\")\n def test_save_load_float16(self, expected_max_diff=1e-2):\n components = self.get_dummy_components()\n for name, module in components.items():\n if hasattr(module, \"half\"):\n components[name] = module.to(torch_device).half()\n\n pipe = self.pipeline_class(**components)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(self.generator_device)\n output = pipe(**inputs)[0]\n\n with tempfile.TemporaryDirectory() as tmpdir:\n pipe.save_pretrained(tmpdir)\n pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, torch_dtype=torch.float16)\n pipe_loaded.to(torch_device)\n pipe_loaded.set_progress_bar_config(disable=None)\n\n for name, component in pipe_loaded.components.items():\n if hasattr(component, \"dtype\"):\n self.assertTrue(\n component.dtype == torch.float16,\n f\"`{name}.dtype` switched from `float16` to {component.dtype} after loading.\",\n )\n\n inputs = self.get_dummy_inputs(self.generator_device)\n output_loaded = pipe_loaded(**inputs)[0]\n max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()\n self.assertLess(\n max_diff, expected_max_diff, \"The output of the fp16 pipeline changed after saving and loading.\"\n )\n\n @unittest.skip(\n reason=\"Cannot call `set_default_attn_processor` as this pipeline uses a specific attention processor.\"\n )\n def test_save_load_local(self):\n pass\n\n @unittest.skip(\n reason=\"Cannot call `set_default_attn_processor` as this pipeline uses a specific attention processor.\"\n )\n def test_save_load_optional_components(self):\n pass\n\n @unittest.skip(\n reason=\"Cannot call `set_default_attn_processor` as this pipeline uses a specific attention processor.\"\n )\n def test_sequential_cpu_offload_forward_pass(self):\n pass\n\n @unittest.skipIf(torch_device != \"cuda\", reason=\"CUDA and CPU are required to switch devices\")\n def test_to_device(self):\n components = self.get_dummy_components()\n pipe = self.pipeline_class(**components)\n pipe.set_progress_bar_config(disable=None)\n\n pipe.to(\"cpu\")\n model_devices = [component.device.type for component in components.values() if hasattr(component, \"device\")]\n self.assertTrue(all(device == \"cpu\" for device in model_devices))\n\n output_cpu = pipe(**self.get_dummy_inputs(\"cpu\"))[0] # generator set to cpu\n self.assertTrue(np.isnan(output_cpu).sum() == 0)\n\n pipe.to(\"cuda\")\n model_devices = [component.device.type for component in components.values() if hasattr(component, \"device\")]\n self.assertTrue(all(device == \"cuda\" for device in model_devices))\n\n output_cuda = pipe(**self.get_dummy_inputs(\"cpu\"))[0] # generator set to cpu\n self.assertTrue(np.isnan(to_np(output_cuda)).sum() == 0)\n\n @unittest.skip(\n reason=\"Cannot call `set_default_attn_processor` as this pipeline uses a specific attention processor.\"\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n pass\n\n\n@nightly\n@require_torch_gpu\nclass TextToVideoZeroSDXLPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_full_model(self):\n model_id = \"stabilityai/stable-diffusion-xl-base-1.0\"\n pipe = TextToVideoZeroSDXLPipeline.from_pretrained(\n model_id, torch_dtype=torch.float16, variant=\"fp16\", use_safetensors=True\n )\n pipe.enable_model_cpu_offload()\n pipe.enable_vae_slicing()\n\n pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n\n prompt = \"A panda dancing in Antarctica\"\n result = pipe(prompt=prompt, generator=generator).images\n\n first_frame_slice = result[0, -3:, -3:, -1]\n last_frame_slice = result[-1, -3:, -3:, 0]\n\n expected_slice1 = np.array([0.57, 0.57, 0.57, 0.57, 0.57, 0.56, 0.55, 0.56, 0.56])\n expected_slice2 = np.array([0.54, 0.53, 0.53, 0.53, 0.53, 0.52, 0.53, 0.53, 0.53])\n\n assert np.abs(first_frame_slice.flatten() - expected_slice1).max() < 1e-2\n assert np.abs(last_frame_slice.flatten() - expected_slice2).max() < 1e-2\n" }, { "path": "diffusers/tests/pipelines/text_to_video_synthesis/test_video_to_video.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import (\n AutoencoderKL,\n DDIMScheduler,\n UNet3DConditionModel,\n VideoToVideoSDPipeline,\n)\nfrom diffusers.utils import is_xformers_available\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n is_flaky,\n nightly,\n numpy_cosine_similarity_distance,\n skip_mps,\n torch_device,\n)\n\nfrom ..pipeline_params import (\n TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,\n TEXT_GUIDED_IMAGE_VARIATION_PARAMS,\n)\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\n@skip_mps\nclass VideoToVideoSDPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = VideoToVideoSDPipeline\n params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({\"video\"}) - {\"image\", \"width\", \"height\"}\n batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({\"video\"}) - {\"image\"}\n required_optional_params = PipelineTesterMixin.required_optional_params - {\"latents\"}\n test_attention_slicing = False\n\n # No `output_type`.\n required_optional_params = frozenset(\n [\n \"num_inference_steps\",\n \"generator\",\n \"latents\",\n \"return_dict\",\n \"callback\",\n \"callback_steps\",\n ]\n )\n\n def get_dummy_components(self):\n torch.manual_seed(0)\n unet = UNet3DConditionModel(\n block_out_channels=(4, 8),\n layers_per_block=1,\n sample_size=32,\n in_channels=4,\n out_channels=4,\n down_block_types=(\"CrossAttnDownBlock3D\", \"DownBlock3D\"),\n up_block_types=(\"UpBlock3D\", \"CrossAttnUpBlock3D\"),\n cross_attention_dim=32,\n attention_head_dim=4,\n norm_num_groups=2,\n )\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=True,\n set_alpha_to_one=False,\n )\n torch.manual_seed(0)\n vae = AutoencoderKL(\n block_out_channels=[\n 8,\n ],\n in_channels=3,\n out_channels=3,\n down_block_types=[\n \"DownEncoderBlock2D\",\n ],\n up_block_types=[\"UpDecoderBlock2D\"],\n latent_channels=4,\n sample_size=32,\n norm_num_groups=2,\n )\n torch.manual_seed(0)\n text_encoder_config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=32,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n hidden_act=\"gelu\",\n projection_dim=512,\n )\n text_encoder = CLIPTextModel(text_encoder_config)\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n components = {\n \"unet\": unet,\n \"scheduler\": scheduler,\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n }\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n # 3 frames\n video = floats_tensor((1, 3, 3, 32, 32), rng=random.Random(seed)).to(device)\n\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"A painting of a squirrel eating a burger\",\n \"video\": video,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"pt\",\n }\n return inputs\n\n def test_text_to_video_default_case(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n sd_pipe = VideoToVideoSDPipeline(**components)\n sd_pipe = sd_pipe.to(device)\n sd_pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs(device)\n inputs[\"output_type\"] = \"np\"\n frames = sd_pipe(**inputs).frames\n image_slice = frames[0][0][-3:, -3:, -1]\n\n assert frames[0][0].shape == (32, 32, 3)\n expected_slice = np.array([0.6391, 0.5350, 0.5202, 0.5521, 0.5453, 0.5393, 0.6652, 0.5270, 0.5185])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n\n @is_flaky()\n def test_save_load_optional_components(self):\n super().test_save_load_optional_components(expected_max_difference=0.001)\n\n @is_flaky()\n def test_dict_tuple_outputs_equivalent(self):\n super().test_dict_tuple_outputs_equivalent()\n\n @is_flaky()\n def test_save_load_local(self):\n super().test_save_load_local()\n\n @unittest.skipIf(\n torch_device != \"cuda\" or not is_xformers_available(),\n reason=\"XFormers attention is only available with CUDA and `xformers` installed\",\n )\n def test_xformers_attention_forwardGenerator_pass(self):\n self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False, expected_max_diff=5e-3)\n\n # (todo): sayakpaul\n @unittest.skip(reason=\"Batching needs to be properly figured out first for this pipeline.\")\n def test_inference_batch_consistent(self):\n pass\n\n # (todo): sayakpaul\n @unittest.skip(reason=\"Batching needs to be properly figured out first for this pipeline.\")\n def test_inference_batch_single_identical(self):\n pass\n\n @unittest.skip(reason=\"`num_images_per_prompt` argument is not supported for this pipeline.\")\n def test_num_images_per_prompt(self):\n pass\n\n def test_progress_bar(self):\n return super().test_progress_bar()\n\n\n@nightly\n@skip_mps\nclass VideoToVideoSDPipelineSlowTests(unittest.TestCase):\n def test_two_step_model(self):\n pipe = VideoToVideoSDPipeline.from_pretrained(\"cerspense/zeroscope_v2_576w\", torch_dtype=torch.float16)\n pipe.enable_model_cpu_offload()\n\n # 10 frames\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n video = torch.randn((1, 10, 3, 320, 576), generator=generator)\n\n prompt = \"Spiderman is surfing\"\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n video_frames = pipe(prompt, video=video, generator=generator, num_inference_steps=3, output_type=\"np\").frames\n\n expected_array = np.array(\n [0.17114258, 0.13720703, 0.08886719, 0.14819336, 0.1730957, 0.24584961, 0.22021484, 0.35180664, 0.2607422]\n )\n output_array = video_frames[0, 0, :3, :3, 0].flatten()\n assert numpy_cosine_similarity_distance(expected_array, output_array) < 1e-3\n" }, { "path": "diffusers/tests/pipelines/unclip/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/unclip/test_unclip.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer\n\nfrom diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNet2DConditionModel, UNet2DModel\nfrom diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n load_numpy,\n nightly,\n require_torch_gpu,\n skip_mps,\n torch_device,\n)\n\nfrom ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS\nfrom ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference\n\n\nenable_full_determinism()\n\n\nclass UnCLIPPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = UnCLIPPipeline\n params = TEXT_TO_IMAGE_PARAMS - {\n \"negative_prompt\",\n \"height\",\n \"width\",\n \"negative_prompt_embeds\",\n \"guidance_scale\",\n \"prompt_embeds\",\n \"cross_attention_kwargs\",\n }\n batch_params = TEXT_TO_IMAGE_BATCH_PARAMS\n required_optional_params = [\n \"generator\",\n \"return_dict\",\n \"prior_num_inference_steps\",\n \"decoder_num_inference_steps\",\n \"super_res_num_inference_steps\",\n ]\n test_xformers_attention = False\n\n @property\n def text_embedder_hidden_size(self):\n return 32\n\n @property\n def time_input_dim(self):\n return 32\n\n @property\n def block_out_channels_0(self):\n return self.time_input_dim\n\n @property\n def time_embed_dim(self):\n return self.time_input_dim * 4\n\n @property\n def cross_attention_dim(self):\n return 100\n\n @property\n def dummy_tokenizer(self):\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n return tokenizer\n\n @property\n def dummy_text_encoder(self):\n torch.manual_seed(0)\n config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=self.text_embedder_hidden_size,\n projection_dim=self.text_embedder_hidden_size,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n return CLIPTextModelWithProjection(config)\n\n @property\n def dummy_prior(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"num_attention_heads\": 2,\n \"attention_head_dim\": 12,\n \"embedding_dim\": self.text_embedder_hidden_size,\n \"num_layers\": 1,\n }\n\n model = PriorTransformer(**model_kwargs)\n return model\n\n @property\n def dummy_text_proj(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"clip_embeddings_dim\": self.text_embedder_hidden_size,\n \"time_embed_dim\": self.time_embed_dim,\n \"cross_attention_dim\": self.cross_attention_dim,\n }\n\n model = UnCLIPTextProjModel(**model_kwargs)\n return model\n\n @property\n def dummy_decoder(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"sample_size\": 32,\n # RGB in channels\n \"in_channels\": 3,\n # Out channels is double in channels because predicts mean and variance\n \"out_channels\": 6,\n \"down_block_types\": (\"ResnetDownsampleBlock2D\", \"SimpleCrossAttnDownBlock2D\"),\n \"up_block_types\": (\"SimpleCrossAttnUpBlock2D\", \"ResnetUpsampleBlock2D\"),\n \"mid_block_type\": \"UNetMidBlock2DSimpleCrossAttn\",\n \"block_out_channels\": (self.block_out_channels_0, self.block_out_channels_0 * 2),\n \"layers_per_block\": 1,\n \"cross_attention_dim\": self.cross_attention_dim,\n \"attention_head_dim\": 4,\n \"resnet_time_scale_shift\": \"scale_shift\",\n \"class_embed_type\": \"identity\",\n }\n\n model = UNet2DConditionModel(**model_kwargs)\n return model\n\n @property\n def dummy_super_res_kwargs(self):\n return {\n \"sample_size\": 64,\n \"layers_per_block\": 1,\n \"down_block_types\": (\"ResnetDownsampleBlock2D\", \"ResnetDownsampleBlock2D\"),\n \"up_block_types\": (\"ResnetUpsampleBlock2D\", \"ResnetUpsampleBlock2D\"),\n \"block_out_channels\": (self.block_out_channels_0, self.block_out_channels_0 * 2),\n \"in_channels\": 6,\n \"out_channels\": 3,\n }\n\n @property\n def dummy_super_res_first(self):\n torch.manual_seed(0)\n\n model = UNet2DModel(**self.dummy_super_res_kwargs)\n return model\n\n @property\n def dummy_super_res_last(self):\n # seeded differently to get different unet than `self.dummy_super_res_first`\n torch.manual_seed(1)\n\n model = UNet2DModel(**self.dummy_super_res_kwargs)\n return model\n\n def get_dummy_components(self):\n prior = self.dummy_prior\n decoder = self.dummy_decoder\n text_proj = self.dummy_text_proj\n text_encoder = self.dummy_text_encoder\n tokenizer = self.dummy_tokenizer\n super_res_first = self.dummy_super_res_first\n super_res_last = self.dummy_super_res_last\n\n prior_scheduler = UnCLIPScheduler(\n variance_type=\"fixed_small_log\",\n prediction_type=\"sample\",\n num_train_timesteps=1000,\n clip_sample_range=5.0,\n )\n\n decoder_scheduler = UnCLIPScheduler(\n variance_type=\"learned_range\",\n prediction_type=\"epsilon\",\n num_train_timesteps=1000,\n )\n\n super_res_scheduler = UnCLIPScheduler(\n variance_type=\"fixed_small_log\",\n prediction_type=\"epsilon\",\n num_train_timesteps=1000,\n )\n\n components = {\n \"prior\": prior,\n \"decoder\": decoder,\n \"text_proj\": text_proj,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"super_res_first\": super_res_first,\n \"super_res_last\": super_res_last,\n \"prior_scheduler\": prior_scheduler,\n \"decoder_scheduler\": decoder_scheduler,\n \"super_res_scheduler\": super_res_scheduler,\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"horse\",\n \"generator\": generator,\n \"prior_num_inference_steps\": 2,\n \"decoder_num_inference_steps\": 2,\n \"super_res_num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_unclip(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.images\n\n image_from_tuple = pipe(\n **self.get_dummy_inputs(device),\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array(\n [\n 0.9997,\n 0.9988,\n 0.0028,\n 0.9997,\n 0.9984,\n 0.9965,\n 0.0029,\n 0.9986,\n 0.0025,\n ]\n )\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_unclip_passed_text_embed(self):\n device = torch.device(\"cpu\")\n\n class DummyScheduler:\n init_noise_sigma = 1\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n prior = components[\"prior\"]\n decoder = components[\"decoder\"]\n super_res_first = components[\"super_res_first\"]\n tokenizer = components[\"tokenizer\"]\n text_encoder = components[\"text_encoder\"]\n\n generator = torch.Generator(device=device).manual_seed(0)\n dtype = prior.dtype\n batch_size = 1\n\n shape = (batch_size, prior.config.embedding_dim)\n prior_latents = pipe.prepare_latents(\n shape, dtype=dtype, device=device, generator=generator, latents=None, scheduler=DummyScheduler()\n )\n shape = (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)\n decoder_latents = pipe.prepare_latents(\n shape, dtype=dtype, device=device, generator=generator, latents=None, scheduler=DummyScheduler()\n )\n\n shape = (\n batch_size,\n super_res_first.config.in_channels // 2,\n super_res_first.config.sample_size,\n super_res_first.config.sample_size,\n )\n super_res_latents = pipe.prepare_latents(\n shape, dtype=dtype, device=device, generator=generator, latents=None, scheduler=DummyScheduler()\n )\n\n pipe.set_progress_bar_config(disable=None)\n\n prompt = \"this is a prompt example\"\n\n generator = torch.Generator(device=device).manual_seed(0)\n output = pipe(\n [prompt],\n generator=generator,\n prior_num_inference_steps=2,\n decoder_num_inference_steps=2,\n super_res_num_inference_steps=2,\n prior_latents=prior_latents,\n decoder_latents=decoder_latents,\n super_res_latents=super_res_latents,\n output_type=\"np\",\n )\n image = output.images\n\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n return_tensors=\"pt\",\n )\n text_model_output = text_encoder(text_inputs.input_ids)\n text_attention_mask = text_inputs.attention_mask\n\n generator = torch.Generator(device=device).manual_seed(0)\n image_from_text = pipe(\n generator=generator,\n prior_num_inference_steps=2,\n decoder_num_inference_steps=2,\n super_res_num_inference_steps=2,\n prior_latents=prior_latents,\n decoder_latents=decoder_latents,\n super_res_latents=super_res_latents,\n text_model_output=text_model_output,\n text_attention_mask=text_attention_mask,\n output_type=\"np\",\n )[0]\n\n # make sure passing text embeddings manually is identical\n assert np.abs(image - image_from_text).max() < 1e-4\n\n # Overriding PipelineTesterMixin::test_attention_slicing_forward_pass\n # because UnCLIP GPU undeterminism requires a looser check.\n @skip_mps\n def test_attention_slicing_forward_pass(self):\n test_max_difference = torch_device == \"cpu\"\n\n self._test_attention_slicing_forward_pass(test_max_difference=test_max_difference, expected_max_diff=0.01)\n\n # Overriding PipelineTesterMixin::test_inference_batch_single_identical\n # because UnCLIP undeterminism requires a looser check.\n @skip_mps\n def test_inference_batch_single_identical(self):\n additional_params_copy_to_batched_inputs = [\n \"prior_num_inference_steps\",\n \"decoder_num_inference_steps\",\n \"super_res_num_inference_steps\",\n ]\n\n self._test_inference_batch_single_identical(\n additional_params_copy_to_batched_inputs=additional_params_copy_to_batched_inputs, expected_max_diff=5e-3\n )\n\n def test_inference_batch_consistent(self):\n additional_params_copy_to_batched_inputs = [\n \"prior_num_inference_steps\",\n \"decoder_num_inference_steps\",\n \"super_res_num_inference_steps\",\n ]\n\n if torch_device == \"mps\":\n # TODO: MPS errors with larger batch sizes\n batch_sizes = [2, 3]\n self._test_inference_batch_consistent(\n batch_sizes=batch_sizes,\n additional_params_copy_to_batched_inputs=additional_params_copy_to_batched_inputs,\n )\n else:\n self._test_inference_batch_consistent(\n additional_params_copy_to_batched_inputs=additional_params_copy_to_batched_inputs\n )\n\n @skip_mps\n def test_dict_tuple_outputs_equivalent(self):\n return super().test_dict_tuple_outputs_equivalent()\n\n @skip_mps\n def test_save_load_local(self):\n return super().test_save_load_local(expected_max_difference=5e-3)\n\n @skip_mps\n def test_save_load_optional_components(self):\n return super().test_save_load_optional_components()\n\n @unittest.skip(\"UnCLIP produces very large differences in fp16 vs fp32. Test is not useful.\")\n def test_float16_inference(self):\n super().test_float16_inference(expected_max_diff=1.0)\n\n\n@nightly\nclass UnCLIPPipelineCPUIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_unclip_karlo_cpu_fp32(self):\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/unclip/karlo_v1_alpha_horse_cpu.npy\"\n )\n\n pipeline = UnCLIPPipeline.from_pretrained(\"kakaobrain/karlo-v1-alpha\")\n pipeline.set_progress_bar_config(disable=None)\n\n generator = torch.manual_seed(0)\n output = pipeline(\n \"horse\",\n num_images_per_prompt=1,\n generator=generator,\n output_type=\"np\",\n )\n\n image = output.images[0]\n\n assert image.shape == (256, 256, 3)\n assert np.abs(expected_image - image).max() < 1e-1\n\n\n@nightly\n@require_torch_gpu\nclass UnCLIPPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_unclip_karlo(self):\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/unclip/karlo_v1_alpha_horse_fp16.npy\"\n )\n\n pipeline = UnCLIPPipeline.from_pretrained(\"kakaobrain/karlo-v1-alpha\", torch_dtype=torch.float16)\n pipeline = pipeline.to(torch_device)\n pipeline.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n output = pipeline(\n \"horse\",\n generator=generator,\n output_type=\"np\",\n )\n\n image = output.images[0]\n\n assert image.shape == (256, 256, 3)\n\n assert_mean_pixel_difference(image, expected_image)\n\n def test_unclip_pipeline_with_sequential_cpu_offloading(self):\n torch.cuda.empty_cache()\n torch.cuda.reset_max_memory_allocated()\n torch.cuda.reset_peak_memory_stats()\n\n pipe = UnCLIPPipeline.from_pretrained(\"kakaobrain/karlo-v1-alpha\", torch_dtype=torch.float16)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n pipe.enable_sequential_cpu_offload()\n\n _ = pipe(\n \"horse\",\n num_images_per_prompt=1,\n prior_num_inference_steps=2,\n decoder_num_inference_steps=2,\n super_res_num_inference_steps=2,\n output_type=\"np\",\n )\n\n mem_bytes = torch.cuda.max_memory_allocated()\n # make sure that less than 7 GB is allocated\n assert mem_bytes < 7 * 10**9\n" }, { "path": "diffusers/tests/pipelines/unclip/test_unclip_image_variation.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport random\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextConfig,\n CLIPTextModelWithProjection,\n CLIPTokenizer,\n CLIPVisionConfig,\n CLIPVisionModelWithProjection,\n)\n\nfrom diffusers import (\n DiffusionPipeline,\n UnCLIPImageVariationPipeline,\n UnCLIPScheduler,\n UNet2DConditionModel,\n UNet2DModel,\n)\nfrom diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n load_numpy,\n nightly,\n require_torch_gpu,\n skip_mps,\n torch_device,\n)\n\nfrom ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS\nfrom ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference\n\n\nenable_full_determinism()\n\n\nclass UnCLIPImageVariationPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = UnCLIPImageVariationPipeline\n params = IMAGE_VARIATION_PARAMS - {\"height\", \"width\", \"guidance_scale\"}\n batch_params = IMAGE_VARIATION_BATCH_PARAMS\n\n required_optional_params = [\n \"generator\",\n \"return_dict\",\n \"decoder_num_inference_steps\",\n \"super_res_num_inference_steps\",\n ]\n test_xformers_attention = False\n\n @property\n def text_embedder_hidden_size(self):\n return 32\n\n @property\n def time_input_dim(self):\n return 32\n\n @property\n def block_out_channels_0(self):\n return self.time_input_dim\n\n @property\n def time_embed_dim(self):\n return self.time_input_dim * 4\n\n @property\n def cross_attention_dim(self):\n return 100\n\n @property\n def dummy_tokenizer(self):\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n return tokenizer\n\n @property\n def dummy_text_encoder(self):\n torch.manual_seed(0)\n config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=self.text_embedder_hidden_size,\n projection_dim=self.text_embedder_hidden_size,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n return CLIPTextModelWithProjection(config)\n\n @property\n def dummy_image_encoder(self):\n torch.manual_seed(0)\n config = CLIPVisionConfig(\n hidden_size=self.text_embedder_hidden_size,\n projection_dim=self.text_embedder_hidden_size,\n num_hidden_layers=5,\n num_attention_heads=4,\n image_size=32,\n intermediate_size=37,\n patch_size=1,\n )\n return CLIPVisionModelWithProjection(config)\n\n @property\n def dummy_text_proj(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"clip_embeddings_dim\": self.text_embedder_hidden_size,\n \"time_embed_dim\": self.time_embed_dim,\n \"cross_attention_dim\": self.cross_attention_dim,\n }\n\n model = UnCLIPTextProjModel(**model_kwargs)\n return model\n\n @property\n def dummy_decoder(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"sample_size\": 32,\n # RGB in channels\n \"in_channels\": 3,\n # Out channels is double in channels because predicts mean and variance\n \"out_channels\": 6,\n \"down_block_types\": (\"ResnetDownsampleBlock2D\", \"SimpleCrossAttnDownBlock2D\"),\n \"up_block_types\": (\"SimpleCrossAttnUpBlock2D\", \"ResnetUpsampleBlock2D\"),\n \"mid_block_type\": \"UNetMidBlock2DSimpleCrossAttn\",\n \"block_out_channels\": (self.block_out_channels_0, self.block_out_channels_0 * 2),\n \"layers_per_block\": 1,\n \"cross_attention_dim\": self.cross_attention_dim,\n \"attention_head_dim\": 4,\n \"resnet_time_scale_shift\": \"scale_shift\",\n \"class_embed_type\": \"identity\",\n }\n\n model = UNet2DConditionModel(**model_kwargs)\n return model\n\n @property\n def dummy_super_res_kwargs(self):\n return {\n \"sample_size\": 64,\n \"layers_per_block\": 1,\n \"down_block_types\": (\"ResnetDownsampleBlock2D\", \"ResnetDownsampleBlock2D\"),\n \"up_block_types\": (\"ResnetUpsampleBlock2D\", \"ResnetUpsampleBlock2D\"),\n \"block_out_channels\": (self.block_out_channels_0, self.block_out_channels_0 * 2),\n \"in_channels\": 6,\n \"out_channels\": 3,\n }\n\n @property\n def dummy_super_res_first(self):\n torch.manual_seed(0)\n\n model = UNet2DModel(**self.dummy_super_res_kwargs)\n return model\n\n @property\n def dummy_super_res_last(self):\n # seeded differently to get different unet than `self.dummy_super_res_first`\n torch.manual_seed(1)\n\n model = UNet2DModel(**self.dummy_super_res_kwargs)\n return model\n\n def get_dummy_components(self):\n decoder = self.dummy_decoder\n text_proj = self.dummy_text_proj\n text_encoder = self.dummy_text_encoder\n tokenizer = self.dummy_tokenizer\n super_res_first = self.dummy_super_res_first\n super_res_last = self.dummy_super_res_last\n\n decoder_scheduler = UnCLIPScheduler(\n variance_type=\"learned_range\",\n prediction_type=\"epsilon\",\n num_train_timesteps=1000,\n )\n\n super_res_scheduler = UnCLIPScheduler(\n variance_type=\"fixed_small_log\",\n prediction_type=\"epsilon\",\n num_train_timesteps=1000,\n )\n\n feature_extractor = CLIPImageProcessor(crop_size=32, size=32)\n\n image_encoder = self.dummy_image_encoder\n\n return {\n \"decoder\": decoder,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"text_proj\": text_proj,\n \"feature_extractor\": feature_extractor,\n \"image_encoder\": image_encoder,\n \"super_res_first\": super_res_first,\n \"super_res_last\": super_res_last,\n \"decoder_scheduler\": decoder_scheduler,\n \"super_res_scheduler\": super_res_scheduler,\n }\n\n def get_dummy_inputs(self, device, seed=0, pil_image=True):\n input_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n if pil_image:\n input_image = input_image * 0.5 + 0.5\n input_image = input_image.clamp(0, 1)\n input_image = input_image.cpu().permute(0, 2, 3, 1).float().numpy()\n input_image = DiffusionPipeline.numpy_to_pil(input_image)[0]\n\n return {\n \"image\": input_image,\n \"generator\": generator,\n \"decoder_num_inference_steps\": 2,\n \"super_res_num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n\n def test_unclip_image_variation_input_tensor(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n pipeline_inputs = self.get_dummy_inputs(device, pil_image=False)\n\n output = pipe(**pipeline_inputs)\n image = output.images\n\n tuple_pipeline_inputs = self.get_dummy_inputs(device, pil_image=False)\n\n image_from_tuple = pipe(\n **tuple_pipeline_inputs,\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array(\n [\n 0.9997,\n 0.0002,\n 0.9997,\n 0.9997,\n 0.9969,\n 0.0023,\n 0.9997,\n 0.9969,\n 0.9970,\n ]\n )\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_unclip_image_variation_input_image(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n pipeline_inputs = self.get_dummy_inputs(device, pil_image=True)\n\n output = pipe(**pipeline_inputs)\n image = output.images\n\n tuple_pipeline_inputs = self.get_dummy_inputs(device, pil_image=True)\n\n image_from_tuple = pipe(\n **tuple_pipeline_inputs,\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971])\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_unclip_image_variation_input_list_images(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n pipeline_inputs = self.get_dummy_inputs(device, pil_image=True)\n pipeline_inputs[\"image\"] = [\n pipeline_inputs[\"image\"],\n pipeline_inputs[\"image\"],\n ]\n\n output = pipe(**pipeline_inputs)\n image = output.images\n\n tuple_pipeline_inputs = self.get_dummy_inputs(device, pil_image=True)\n tuple_pipeline_inputs[\"image\"] = [\n tuple_pipeline_inputs[\"image\"],\n tuple_pipeline_inputs[\"image\"],\n ]\n\n image_from_tuple = pipe(\n **tuple_pipeline_inputs,\n return_dict=False,\n )[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (2, 64, 64, 3)\n\n expected_slice = np.array(\n [\n 0.9997,\n 0.9989,\n 0.0008,\n 0.0021,\n 0.9960,\n 0.0018,\n 0.0014,\n 0.0002,\n 0.9933,\n ]\n )\n\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n\n def test_unclip_passed_image_embed(self):\n device = torch.device(\"cpu\")\n\n class DummyScheduler:\n init_noise_sigma = 1\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=device).manual_seed(0)\n dtype = pipe.decoder.dtype\n batch_size = 1\n\n shape = (\n batch_size,\n pipe.decoder.config.in_channels,\n pipe.decoder.config.sample_size,\n pipe.decoder.config.sample_size,\n )\n decoder_latents = pipe.prepare_latents(\n shape, dtype=dtype, device=device, generator=generator, latents=None, scheduler=DummyScheduler()\n )\n\n shape = (\n batch_size,\n pipe.super_res_first.config.in_channels // 2,\n pipe.super_res_first.config.sample_size,\n pipe.super_res_first.config.sample_size,\n )\n super_res_latents = pipe.prepare_latents(\n shape, dtype=dtype, device=device, generator=generator, latents=None, scheduler=DummyScheduler()\n )\n\n pipeline_inputs = self.get_dummy_inputs(device, pil_image=False)\n\n img_out_1 = pipe(\n **pipeline_inputs, decoder_latents=decoder_latents, super_res_latents=super_res_latents\n ).images\n\n pipeline_inputs = self.get_dummy_inputs(device, pil_image=False)\n # Don't pass image, instead pass embedding\n image = pipeline_inputs.pop(\"image\")\n image_embeddings = pipe.image_encoder(image).image_embeds\n\n img_out_2 = pipe(\n **pipeline_inputs,\n decoder_latents=decoder_latents,\n super_res_latents=super_res_latents,\n image_embeddings=image_embeddings,\n ).images\n\n # make sure passing text embeddings manually is identical\n assert np.abs(img_out_1 - img_out_2).max() < 1e-4\n\n # Overriding PipelineTesterMixin::test_attention_slicing_forward_pass\n # because UnCLIP GPU undeterminism requires a looser check.\n @skip_mps\n def test_attention_slicing_forward_pass(self):\n test_max_difference = torch_device == \"cpu\"\n\n # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor\n expected_max_diff = 1e-2\n\n self._test_attention_slicing_forward_pass(\n test_max_difference=test_max_difference, expected_max_diff=expected_max_diff\n )\n\n # Overriding PipelineTesterMixin::test_inference_batch_single_identical\n # because UnCLIP undeterminism requires a looser check.\n @unittest.skip(\"UnCLIP produces very large differences. Test is not useful.\")\n @skip_mps\n def test_inference_batch_single_identical(self):\n additional_params_copy_to_batched_inputs = [\n \"decoder_num_inference_steps\",\n \"super_res_num_inference_steps\",\n ]\n self._test_inference_batch_single_identical(\n additional_params_copy_to_batched_inputs=additional_params_copy_to_batched_inputs, expected_max_diff=5e-3\n )\n\n def test_inference_batch_consistent(self):\n additional_params_copy_to_batched_inputs = [\n \"decoder_num_inference_steps\",\n \"super_res_num_inference_steps\",\n ]\n\n if torch_device == \"mps\":\n # TODO: MPS errors with larger batch sizes\n batch_sizes = [2, 3]\n self._test_inference_batch_consistent(\n batch_sizes=batch_sizes,\n additional_params_copy_to_batched_inputs=additional_params_copy_to_batched_inputs,\n )\n else:\n self._test_inference_batch_consistent(\n additional_params_copy_to_batched_inputs=additional_params_copy_to_batched_inputs\n )\n\n @skip_mps\n def test_dict_tuple_outputs_equivalent(self):\n return super().test_dict_tuple_outputs_equivalent()\n\n @unittest.skip(\"UnCLIP produces very large difference. Test is not useful.\")\n @skip_mps\n def test_save_load_local(self):\n return super().test_save_load_local(expected_max_difference=4e-3)\n\n @skip_mps\n def test_save_load_optional_components(self):\n return super().test_save_load_optional_components()\n\n @unittest.skip(\"UnCLIP produces very large difference in fp16 vs fp32. Test is not useful.\")\n def test_float16_inference(self):\n super().test_float16_inference(expected_max_diff=1.0)\n\n\n@nightly\n@require_torch_gpu\nclass UnCLIPImageVariationPipelineIntegrationTests(unittest.TestCase):\n def setUp(self):\n # clean up the VRAM before each test\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n # clean up the VRAM after each test\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_unclip_image_variation_karlo(self):\n input_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png\"\n )\n expected_image = load_numpy(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/unclip/karlo_v1_alpha_cat_variation_fp16.npy\"\n )\n\n pipeline = UnCLIPImageVariationPipeline.from_pretrained(\n \"kakaobrain/karlo-v1-alpha-image-variations\", torch_dtype=torch.float16\n )\n pipeline = pipeline.to(torch_device)\n pipeline.set_progress_bar_config(disable=None)\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n output = pipeline(\n input_image,\n generator=generator,\n output_type=\"np\",\n )\n\n image = output.images[0]\n\n assert image.shape == (256, 256, 3)\n\n assert_mean_pixel_difference(image, expected_image, 15)\n" }, { "path": "diffusers/tests/pipelines/unidiffuser/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/unidiffuser/test_unidiffuser.py", "content": "import gc\nimport random\nimport traceback\nimport unittest\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom transformers import (\n CLIPImageProcessor,\n CLIPTextModel,\n CLIPTokenizer,\n CLIPVisionModelWithProjection,\n GPT2Tokenizer,\n)\n\nfrom diffusers import (\n AutoencoderKL,\n DPMSolverMultistepScheduler,\n UniDiffuserModel,\n UniDiffuserPipeline,\n UniDiffuserTextDecoder,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n floats_tensor,\n load_image,\n nightly,\n require_torch_2,\n require_torch_gpu,\n run_test_in_subprocess,\n torch_device,\n)\nfrom diffusers.utils.torch_utils import randn_tensor\n\nfrom ..pipeline_params import (\n IMAGE_TO_IMAGE_IMAGE_PARAMS,\n TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,\n TEXT_GUIDED_IMAGE_VARIATION_PARAMS,\n)\nfrom ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\n# Will be run via run_test_in_subprocess\ndef _test_unidiffuser_compile(in_queue, out_queue, timeout):\n error = None\n try:\n inputs = in_queue.get(timeout=timeout)\n torch_device = inputs.pop(\"torch_device\")\n seed = inputs.pop(\"seed\")\n inputs[\"generator\"] = torch.Generator(device=torch_device).manual_seed(seed)\n\n pipe = UniDiffuserPipeline.from_pretrained(\"thu-ml/unidiffuser-v1\")\n # pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\n pipe = pipe.to(torch_device)\n\n pipe.unet.to(memory_format=torch.channels_last)\n pipe.unet = torch.compile(pipe.unet, mode=\"reduce-overhead\", fullgraph=True)\n\n pipe.set_progress_bar_config(disable=None)\n\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n\n assert image.shape == (1, 512, 512, 3)\n expected_slice = np.array([0.2402, 0.2375, 0.2285, 0.2378, 0.2407, 0.2263, 0.2354, 0.2307, 0.2520])\n assert np.abs(image_slice - expected_slice).max() < 1e-1\n except Exception:\n error = f\"{traceback.format_exc()}\"\n\n results = {\"error\": error}\n out_queue.put(results, timeout=timeout)\n out_queue.join()\n\n\nclass UniDiffuserPipelineFastTests(\n PipelineTesterMixin, PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase\n):\n pipeline_class = UniDiffuserPipeline\n params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS\n batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS\n image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS\n # vae_latents, not latents, is the argument that corresponds to VAE latent inputs\n image_latents_params = frozenset([\"vae_latents\"])\n\n def get_dummy_components(self):\n unet = UniDiffuserModel.from_pretrained(\n \"hf-internal-testing/unidiffuser-diffusers-test\",\n subfolder=\"unet\",\n )\n\n scheduler = DPMSolverMultistepScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n solver_order=3,\n )\n\n vae = AutoencoderKL.from_pretrained(\n \"hf-internal-testing/unidiffuser-diffusers-test\",\n subfolder=\"vae\",\n )\n\n text_encoder = CLIPTextModel.from_pretrained(\n \"hf-internal-testing/unidiffuser-diffusers-test\",\n subfolder=\"text_encoder\",\n )\n clip_tokenizer = CLIPTokenizer.from_pretrained(\n \"hf-internal-testing/unidiffuser-diffusers-test\",\n subfolder=\"clip_tokenizer\",\n )\n\n image_encoder = CLIPVisionModelWithProjection.from_pretrained(\n \"hf-internal-testing/unidiffuser-diffusers-test\",\n subfolder=\"image_encoder\",\n )\n # From the Stable Diffusion Image Variation pipeline tests\n clip_image_processor = CLIPImageProcessor(crop_size=32, size=32)\n # image_processor = CLIPImageProcessor.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n\n text_tokenizer = GPT2Tokenizer.from_pretrained(\n \"hf-internal-testing/unidiffuser-diffusers-test\",\n subfolder=\"text_tokenizer\",\n )\n text_decoder = UniDiffuserTextDecoder.from_pretrained(\n \"hf-internal-testing/unidiffuser-diffusers-test\",\n subfolder=\"text_decoder\",\n )\n\n components = {\n \"vae\": vae,\n \"text_encoder\": text_encoder,\n \"image_encoder\": image_encoder,\n \"clip_image_processor\": clip_image_processor,\n \"clip_tokenizer\": clip_tokenizer,\n \"text_decoder\": text_decoder,\n \"text_tokenizer\": text_tokenizer,\n \"unet\": unet,\n \"scheduler\": scheduler,\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n image = image.cpu().permute(0, 2, 3, 1)[0]\n image = Image.fromarray(np.uint8(image)).convert(\"RGB\")\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"an elephant under the sea\",\n \"image\": image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n }\n return inputs\n\n def get_fixed_latents(self, device, seed=0):\n if isinstance(device, str):\n device = torch.device(device)\n generator = torch.Generator(device=device).manual_seed(seed)\n # Hardcode the shapes for now.\n prompt_latents = randn_tensor((1, 77, 32), generator=generator, device=device, dtype=torch.float32)\n vae_latents = randn_tensor((1, 4, 16, 16), generator=generator, device=device, dtype=torch.float32)\n clip_latents = randn_tensor((1, 1, 32), generator=generator, device=device, dtype=torch.float32)\n\n latents = {\n \"prompt_latents\": prompt_latents,\n \"vae_latents\": vae_latents,\n \"clip_latents\": clip_latents,\n }\n return latents\n\n def get_dummy_inputs_with_latents(self, device, seed=0):\n # image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)\n # image = image.cpu().permute(0, 2, 3, 1)[0]\n # image = Image.fromarray(np.uint8(image)).convert(\"RGB\")\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unidiffuser/unidiffuser_example_image.jpg\",\n )\n image = image.resize((32, 32))\n latents = self.get_fixed_latents(device, seed=seed)\n\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n\n inputs = {\n \"prompt\": \"an elephant under the sea\",\n \"image\": image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 6.0,\n \"output_type\": \"np\",\n \"prompt_latents\": latents.get(\"prompt_latents\"),\n \"vae_latents\": latents.get(\"vae_latents\"),\n \"clip_latents\": latents.get(\"clip_latents\"),\n }\n return inputs\n\n def test_dict_tuple_outputs_equivalent(self):\n expected_slice = None\n if torch_device == \"cpu\":\n expected_slice = np.array([0.7489, 0.3722, 0.4475, 0.5630, 0.5923, 0.4992, 0.3936, 0.5844, 0.4975])\n super().test_dict_tuple_outputs_equivalent(expected_slice=expected_slice)\n\n def test_unidiffuser_default_joint_v0(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n unidiffuser_pipe = UniDiffuserPipeline(**components)\n unidiffuser_pipe = unidiffuser_pipe.to(device)\n unidiffuser_pipe.set_progress_bar_config(disable=None)\n\n # Set mode to 'joint'\n unidiffuser_pipe.set_joint_mode()\n assert unidiffuser_pipe.mode == \"joint\"\n\n # inputs = self.get_dummy_inputs(device)\n inputs = self.get_dummy_inputs_with_latents(device)\n # Delete prompt and image for joint inference.\n del inputs[\"prompt\"]\n del inputs[\"image\"]\n sample = unidiffuser_pipe(**inputs)\n image = sample.images\n text = sample.text\n assert image.shape == (1, 32, 32, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_img_slice = np.array([0.5760, 0.6270, 0.6571, 0.4965, 0.4638, 0.5663, 0.5254, 0.5068, 0.5716])\n assert np.abs(image_slice.flatten() - expected_img_slice).max() < 1e-3\n\n expected_text_prefix = \" no no no \"\n assert text[0][:10] == expected_text_prefix\n\n def test_unidiffuser_default_joint_no_cfg_v0(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n unidiffuser_pipe = UniDiffuserPipeline(**components)\n unidiffuser_pipe = unidiffuser_pipe.to(device)\n unidiffuser_pipe.set_progress_bar_config(disable=None)\n\n # Set mode to 'joint'\n unidiffuser_pipe.set_joint_mode()\n assert unidiffuser_pipe.mode == \"joint\"\n\n # inputs = self.get_dummy_inputs(device)\n inputs = self.get_dummy_inputs_with_latents(device)\n # Delete prompt and image for joint inference.\n del inputs[\"prompt\"]\n del inputs[\"image\"]\n # Set guidance scale to 1.0 to turn off CFG\n inputs[\"guidance_scale\"] = 1.0\n sample = unidiffuser_pipe(**inputs)\n image = sample.images\n text = sample.text\n assert image.shape == (1, 32, 32, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_img_slice = np.array([0.5760, 0.6270, 0.6571, 0.4965, 0.4638, 0.5663, 0.5254, 0.5068, 0.5716])\n assert np.abs(image_slice.flatten() - expected_img_slice).max() < 1e-3\n\n expected_text_prefix = \" no no no \"\n assert text[0][:10] == expected_text_prefix\n\n def test_unidiffuser_default_text2img_v0(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n unidiffuser_pipe = UniDiffuserPipeline(**components)\n unidiffuser_pipe = unidiffuser_pipe.to(device)\n unidiffuser_pipe.set_progress_bar_config(disable=None)\n\n # Set mode to 'text2img'\n unidiffuser_pipe.set_text_to_image_mode()\n assert unidiffuser_pipe.mode == \"text2img\"\n\n inputs = self.get_dummy_inputs_with_latents(device)\n # Delete image for text-conditioned image generation\n del inputs[\"image\"]\n image = unidiffuser_pipe(**inputs).images\n assert image.shape == (1, 32, 32, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array([0.5758, 0.6269, 0.6570, 0.4967, 0.4639, 0.5664, 0.5257, 0.5067, 0.5715])\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_unidiffuser_default_image_0(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n unidiffuser_pipe = UniDiffuserPipeline(**components)\n unidiffuser_pipe = unidiffuser_pipe.to(device)\n unidiffuser_pipe.set_progress_bar_config(disable=None)\n\n # Set mode to 'img'\n unidiffuser_pipe.set_image_mode()\n assert unidiffuser_pipe.mode == \"img\"\n\n inputs = self.get_dummy_inputs(device)\n # Delete prompt and image for unconditional (\"marginal\") text generation.\n del inputs[\"prompt\"]\n del inputs[\"image\"]\n image = unidiffuser_pipe(**inputs).images\n assert image.shape == (1, 32, 32, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array([0.5760, 0.6270, 0.6571, 0.4966, 0.4638, 0.5663, 0.5254, 0.5068, 0.5715])\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_unidiffuser_default_text_v0(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n unidiffuser_pipe = UniDiffuserPipeline(**components)\n unidiffuser_pipe = unidiffuser_pipe.to(device)\n unidiffuser_pipe.set_progress_bar_config(disable=None)\n\n # Set mode to 'img'\n unidiffuser_pipe.set_text_mode()\n assert unidiffuser_pipe.mode == \"text\"\n\n inputs = self.get_dummy_inputs(device)\n # Delete prompt and image for unconditional (\"marginal\") text generation.\n del inputs[\"prompt\"]\n del inputs[\"image\"]\n text = unidiffuser_pipe(**inputs).text\n\n expected_text_prefix = \" no no no \"\n assert text[0][:10] == expected_text_prefix\n\n def test_unidiffuser_default_img2text_v0(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n unidiffuser_pipe = UniDiffuserPipeline(**components)\n unidiffuser_pipe = unidiffuser_pipe.to(device)\n unidiffuser_pipe.set_progress_bar_config(disable=None)\n\n # Set mode to 'img2text'\n unidiffuser_pipe.set_image_to_text_mode()\n assert unidiffuser_pipe.mode == \"img2text\"\n\n inputs = self.get_dummy_inputs_with_latents(device)\n # Delete text for image-conditioned text generation\n del inputs[\"prompt\"]\n text = unidiffuser_pipe(**inputs).text\n\n expected_text_prefix = \" no no no \"\n assert text[0][:10] == expected_text_prefix\n\n def test_unidiffuser_default_joint_v1(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n unidiffuser_pipe = UniDiffuserPipeline.from_pretrained(\"hf-internal-testing/unidiffuser-test-v1\")\n unidiffuser_pipe = unidiffuser_pipe.to(device)\n unidiffuser_pipe.set_progress_bar_config(disable=None)\n\n # Set mode to 'joint'\n unidiffuser_pipe.set_joint_mode()\n assert unidiffuser_pipe.mode == \"joint\"\n\n # inputs = self.get_dummy_inputs(device)\n inputs = self.get_dummy_inputs_with_latents(device)\n # Delete prompt and image for joint inference.\n del inputs[\"prompt\"]\n del inputs[\"image\"]\n inputs[\"data_type\"] = 1\n sample = unidiffuser_pipe(**inputs)\n image = sample.images\n text = sample.text\n assert image.shape == (1, 32, 32, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_img_slice = np.array([0.5760, 0.6270, 0.6571, 0.4965, 0.4638, 0.5663, 0.5254, 0.5068, 0.5716])\n assert np.abs(image_slice.flatten() - expected_img_slice).max() < 1e-3\n\n expected_text_prefix = \" no no no \"\n assert text[0][:10] == expected_text_prefix\n\n def test_unidiffuser_default_text2img_v1(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n unidiffuser_pipe = UniDiffuserPipeline.from_pretrained(\"hf-internal-testing/unidiffuser-test-v1\")\n unidiffuser_pipe = unidiffuser_pipe.to(device)\n unidiffuser_pipe.set_progress_bar_config(disable=None)\n\n # Set mode to 'text2img'\n unidiffuser_pipe.set_text_to_image_mode()\n assert unidiffuser_pipe.mode == \"text2img\"\n\n inputs = self.get_dummy_inputs_with_latents(device)\n # Delete image for text-conditioned image generation\n del inputs[\"image\"]\n image = unidiffuser_pipe(**inputs).images\n assert image.shape == (1, 32, 32, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array([0.5758, 0.6269, 0.6570, 0.4967, 0.4639, 0.5664, 0.5257, 0.5067, 0.5715])\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3\n\n def test_unidiffuser_default_img2text_v1(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n unidiffuser_pipe = UniDiffuserPipeline.from_pretrained(\"hf-internal-testing/unidiffuser-test-v1\")\n unidiffuser_pipe = unidiffuser_pipe.to(device)\n unidiffuser_pipe.set_progress_bar_config(disable=None)\n\n # Set mode to 'img2text'\n unidiffuser_pipe.set_image_to_text_mode()\n assert unidiffuser_pipe.mode == \"img2text\"\n\n inputs = self.get_dummy_inputs_with_latents(device)\n # Delete text for image-conditioned text generation\n del inputs[\"prompt\"]\n text = unidiffuser_pipe(**inputs).text\n\n expected_text_prefix = \" no no no \"\n assert text[0][:10] == expected_text_prefix\n\n def test_unidiffuser_text2img_multiple_images(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n unidiffuser_pipe = UniDiffuserPipeline(**components)\n unidiffuser_pipe = unidiffuser_pipe.to(device)\n unidiffuser_pipe.set_progress_bar_config(disable=None)\n\n # Set mode to 'text2img'\n unidiffuser_pipe.set_text_to_image_mode()\n assert unidiffuser_pipe.mode == \"text2img\"\n\n inputs = self.get_dummy_inputs(device)\n # Delete image for text-conditioned image generation\n del inputs[\"image\"]\n inputs[\"num_images_per_prompt\"] = 2\n inputs[\"num_prompts_per_image\"] = 3\n image = unidiffuser_pipe(**inputs).images\n assert image.shape == (2, 32, 32, 3)\n\n def test_unidiffuser_img2text_multiple_prompts(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n unidiffuser_pipe = UniDiffuserPipeline(**components)\n unidiffuser_pipe = unidiffuser_pipe.to(device)\n unidiffuser_pipe.set_progress_bar_config(disable=None)\n\n # Set mode to 'img2text'\n unidiffuser_pipe.set_image_to_text_mode()\n assert unidiffuser_pipe.mode == \"img2text\"\n\n inputs = self.get_dummy_inputs(device)\n # Delete text for image-conditioned text generation\n del inputs[\"prompt\"]\n inputs[\"num_images_per_prompt\"] = 2\n inputs[\"num_prompts_per_image\"] = 3\n text = unidiffuser_pipe(**inputs).text\n\n assert len(text) == 3\n\n def test_unidiffuser_text2img_multiple_images_with_latents(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n unidiffuser_pipe = UniDiffuserPipeline(**components)\n unidiffuser_pipe = unidiffuser_pipe.to(device)\n unidiffuser_pipe.set_progress_bar_config(disable=None)\n\n # Set mode to 'text2img'\n unidiffuser_pipe.set_text_to_image_mode()\n assert unidiffuser_pipe.mode == \"text2img\"\n\n inputs = self.get_dummy_inputs_with_latents(device)\n # Delete image for text-conditioned image generation\n del inputs[\"image\"]\n inputs[\"num_images_per_prompt\"] = 2\n inputs[\"num_prompts_per_image\"] = 3\n image = unidiffuser_pipe(**inputs).images\n assert image.shape == (2, 32, 32, 3)\n\n def test_unidiffuser_img2text_multiple_prompts_with_latents(self):\n device = \"cpu\" # ensure determinism for the device-dependent torch.Generator\n components = self.get_dummy_components()\n unidiffuser_pipe = UniDiffuserPipeline(**components)\n unidiffuser_pipe = unidiffuser_pipe.to(device)\n unidiffuser_pipe.set_progress_bar_config(disable=None)\n\n # Set mode to 'img2text'\n unidiffuser_pipe.set_image_to_text_mode()\n assert unidiffuser_pipe.mode == \"img2text\"\n\n inputs = self.get_dummy_inputs_with_latents(device)\n # Delete text for image-conditioned text generation\n del inputs[\"prompt\"]\n inputs[\"num_images_per_prompt\"] = 2\n inputs[\"num_prompts_per_image\"] = 3\n text = unidiffuser_pipe(**inputs).text\n\n assert len(text) == 3\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=2e-4)\n\n @require_torch_gpu\n def test_unidiffuser_default_joint_v1_cuda_fp16(self):\n device = \"cuda\"\n unidiffuser_pipe = UniDiffuserPipeline.from_pretrained(\n \"hf-internal-testing/unidiffuser-test-v1\", torch_dtype=torch.float16\n )\n unidiffuser_pipe = unidiffuser_pipe.to(device)\n unidiffuser_pipe.set_progress_bar_config(disable=None)\n\n # Set mode to 'joint'\n unidiffuser_pipe.set_joint_mode()\n assert unidiffuser_pipe.mode == \"joint\"\n\n inputs = self.get_dummy_inputs_with_latents(device)\n # Delete prompt and image for joint inference.\n del inputs[\"prompt\"]\n del inputs[\"image\"]\n inputs[\"data_type\"] = 1\n sample = unidiffuser_pipe(**inputs)\n image = sample.images\n text = sample.text\n assert image.shape == (1, 32, 32, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_img_slice = np.array([0.5049, 0.5498, 0.5854, 0.3052, 0.4460, 0.6489, 0.5122, 0.4810, 0.6138])\n assert np.abs(image_slice.flatten() - expected_img_slice).max() < 1e-3\n\n expected_text_prefix = '\" This This'\n assert text[0][: len(expected_text_prefix)] == expected_text_prefix\n\n @require_torch_gpu\n def test_unidiffuser_default_text2img_v1_cuda_fp16(self):\n device = \"cuda\"\n unidiffuser_pipe = UniDiffuserPipeline.from_pretrained(\n \"hf-internal-testing/unidiffuser-test-v1\", torch_dtype=torch.float16\n )\n unidiffuser_pipe = unidiffuser_pipe.to(device)\n unidiffuser_pipe.set_progress_bar_config(disable=None)\n\n # Set mode to 'text2img'\n unidiffuser_pipe.set_text_to_image_mode()\n assert unidiffuser_pipe.mode == \"text2img\"\n\n inputs = self.get_dummy_inputs_with_latents(device)\n # Delete prompt and image for joint inference.\n del inputs[\"image\"]\n inputs[\"data_type\"] = 1\n sample = unidiffuser_pipe(**inputs)\n image = sample.images\n assert image.shape == (1, 32, 32, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_img_slice = np.array([0.5054, 0.5498, 0.5854, 0.3052, 0.4458, 0.6489, 0.5122, 0.4810, 0.6138])\n assert np.abs(image_slice.flatten() - expected_img_slice).max() < 1e-3\n\n @require_torch_gpu\n def test_unidiffuser_default_img2text_v1_cuda_fp16(self):\n device = \"cuda\"\n unidiffuser_pipe = UniDiffuserPipeline.from_pretrained(\n \"hf-internal-testing/unidiffuser-test-v1\", torch_dtype=torch.float16\n )\n unidiffuser_pipe = unidiffuser_pipe.to(device)\n unidiffuser_pipe.set_progress_bar_config(disable=None)\n\n # Set mode to 'img2text'\n unidiffuser_pipe.set_image_to_text_mode()\n assert unidiffuser_pipe.mode == \"img2text\"\n\n inputs = self.get_dummy_inputs_with_latents(device)\n # Delete prompt and image for joint inference.\n del inputs[\"prompt\"]\n inputs[\"data_type\"] = 1\n text = unidiffuser_pipe(**inputs).text\n\n expected_text_prefix = '\" This This'\n assert text[0][: len(expected_text_prefix)] == expected_text_prefix\n\n\n@nightly\n@require_torch_gpu\nclass UniDiffuserPipelineSlowTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, seed=0, generate_latents=False):\n generator = torch.manual_seed(seed)\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unidiffuser/unidiffuser_example_image.jpg\"\n )\n inputs = {\n \"prompt\": \"an elephant under the sea\",\n \"image\": image,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"guidance_scale\": 8.0,\n \"output_type\": \"np\",\n }\n if generate_latents:\n latents = self.get_fixed_latents(device, seed=seed)\n for latent_name, latent_tensor in latents.items():\n inputs[latent_name] = latent_tensor\n return inputs\n\n def get_fixed_latents(self, device, seed=0):\n if isinstance(device, str):\n device = torch.device(device)\n latent_device = torch.device(\"cpu\")\n generator = torch.Generator(device=latent_device).manual_seed(seed)\n # Hardcode the shapes for now.\n prompt_latents = randn_tensor((1, 77, 768), generator=generator, device=device, dtype=torch.float32)\n vae_latents = randn_tensor((1, 4, 64, 64), generator=generator, device=device, dtype=torch.float32)\n clip_latents = randn_tensor((1, 1, 512), generator=generator, device=device, dtype=torch.float32)\n\n # Move latents onto desired device.\n prompt_latents = prompt_latents.to(device)\n vae_latents = vae_latents.to(device)\n clip_latents = clip_latents.to(device)\n\n latents = {\n \"prompt_latents\": prompt_latents,\n \"vae_latents\": vae_latents,\n \"clip_latents\": clip_latents,\n }\n return latents\n\n def test_unidiffuser_default_joint_v1(self):\n pipe = UniDiffuserPipeline.from_pretrained(\"thu-ml/unidiffuser-v1\")\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n # inputs = self.get_dummy_inputs(device)\n inputs = self.get_inputs(device=torch_device, generate_latents=True)\n # Delete prompt and image for joint inference.\n del inputs[\"prompt\"]\n del inputs[\"image\"]\n sample = pipe(**inputs)\n image = sample.images\n text = sample.text\n assert image.shape == (1, 512, 512, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_img_slice = np.array([0.2402, 0.2375, 0.2285, 0.2378, 0.2407, 0.2263, 0.2354, 0.2307, 0.2520])\n assert np.abs(image_slice.flatten() - expected_img_slice).max() < 1e-1\n\n expected_text_prefix = \"a living room\"\n assert text[0][: len(expected_text_prefix)] == expected_text_prefix\n\n def test_unidiffuser_default_text2img_v1(self):\n pipe = UniDiffuserPipeline.from_pretrained(\"thu-ml/unidiffuser-v1\")\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(device=torch_device, generate_latents=True)\n del inputs[\"image\"]\n sample = pipe(**inputs)\n image = sample.images\n assert image.shape == (1, 512, 512, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array([0.0242, 0.0103, 0.0022, 0.0129, 0.0000, 0.0090, 0.0376, 0.0508, 0.0005])\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1\n\n def test_unidiffuser_default_img2text_v1(self):\n pipe = UniDiffuserPipeline.from_pretrained(\"thu-ml/unidiffuser-v1\")\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(device=torch_device, generate_latents=True)\n del inputs[\"prompt\"]\n sample = pipe(**inputs)\n text = sample.text\n\n expected_text_prefix = \"An astronaut\"\n assert text[0][: len(expected_text_prefix)] == expected_text_prefix\n\n @unittest.skip(reason=\"Skip torch.compile test to speed up the slow test suite.\")\n @require_torch_2\n def test_unidiffuser_compile(self, seed=0):\n inputs = self.get_inputs(torch_device, seed=seed, generate_latents=True)\n # Delete prompt and image for joint inference.\n del inputs[\"prompt\"]\n del inputs[\"image\"]\n # Can't pickle a Generator object\n del inputs[\"generator\"]\n inputs[\"torch_device\"] = torch_device\n inputs[\"seed\"] = seed\n run_test_in_subprocess(test_case=self, target_func=_test_unidiffuser_compile, inputs=inputs)\n\n\n@nightly\n@require_torch_gpu\nclass UniDiffuserPipelineNightlyTests(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, seed=0, generate_latents=False):\n generator = torch.manual_seed(seed)\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unidiffuser/unidiffuser_example_image.jpg\"\n )\n inputs = {\n \"prompt\": \"an elephant under the sea\",\n \"image\": image,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"guidance_scale\": 8.0,\n \"output_type\": \"np\",\n }\n if generate_latents:\n latents = self.get_fixed_latents(device, seed=seed)\n for latent_name, latent_tensor in latents.items():\n inputs[latent_name] = latent_tensor\n return inputs\n\n def get_fixed_latents(self, device, seed=0):\n if isinstance(device, str):\n device = torch.device(device)\n latent_device = torch.device(\"cpu\")\n generator = torch.Generator(device=latent_device).manual_seed(seed)\n # Hardcode the shapes for now.\n prompt_latents = randn_tensor((1, 77, 768), generator=generator, device=device, dtype=torch.float32)\n vae_latents = randn_tensor((1, 4, 64, 64), generator=generator, device=device, dtype=torch.float32)\n clip_latents = randn_tensor((1, 1, 512), generator=generator, device=device, dtype=torch.float32)\n\n # Move latents onto desired device.\n prompt_latents = prompt_latents.to(device)\n vae_latents = vae_latents.to(device)\n clip_latents = clip_latents.to(device)\n\n latents = {\n \"prompt_latents\": prompt_latents,\n \"vae_latents\": vae_latents,\n \"clip_latents\": clip_latents,\n }\n return latents\n\n def test_unidiffuser_default_joint_v1_fp16(self):\n pipe = UniDiffuserPipeline.from_pretrained(\"thu-ml/unidiffuser-v1\", torch_dtype=torch.float16)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n # inputs = self.get_dummy_inputs(device)\n inputs = self.get_inputs(device=torch_device, generate_latents=True)\n # Delete prompt and image for joint inference.\n del inputs[\"prompt\"]\n del inputs[\"image\"]\n sample = pipe(**inputs)\n image = sample.images\n text = sample.text\n assert image.shape == (1, 512, 512, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_img_slice = np.array([0.2402, 0.2375, 0.2285, 0.2378, 0.2407, 0.2263, 0.2354, 0.2307, 0.2520])\n assert np.abs(image_slice.flatten() - expected_img_slice).max() < 2e-1\n\n expected_text_prefix = \"a living room\"\n assert text[0][: len(expected_text_prefix)] == expected_text_prefix\n\n def test_unidiffuser_default_text2img_v1_fp16(self):\n pipe = UniDiffuserPipeline.from_pretrained(\"thu-ml/unidiffuser-v1\", torch_dtype=torch.float16)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(device=torch_device, generate_latents=True)\n del inputs[\"image\"]\n sample = pipe(**inputs)\n image = sample.images\n assert image.shape == (1, 512, 512, 3)\n\n image_slice = image[0, -3:, -3:, -1]\n expected_slice = np.array([0.0242, 0.0103, 0.0022, 0.0129, 0.0000, 0.0090, 0.0376, 0.0508, 0.0005])\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1\n\n def test_unidiffuser_default_img2text_v1_fp16(self):\n pipe = UniDiffuserPipeline.from_pretrained(\"thu-ml/unidiffuser-v1\", torch_dtype=torch.float16)\n pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n pipe.enable_attention_slicing()\n\n inputs = self.get_inputs(device=torch_device, generate_latents=True)\n del inputs[\"prompt\"]\n sample = pipe(**inputs)\n text = sample.text\n\n expected_text_prefix = \"An astronaut\"\n assert text[0][: len(expected_text_prefix)] == expected_text_prefix\n" }, { "path": "diffusers/tests/pipelines/wuerstchen/__init__.py", "content": "" }, { "path": "diffusers/tests/pipelines/wuerstchen/test_wuerstchen_combined.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import DDPMWuerstchenScheduler, WuerstchenCombinedPipeline\nfrom diffusers.pipelines.wuerstchen import PaellaVQModel, WuerstchenDiffNeXt, WuerstchenPrior\nfrom diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, torch_device\n\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass WuerstchenCombinedPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = WuerstchenCombinedPipeline\n params = [\"prompt\"]\n batch_params = [\"prompt\", \"negative_prompt\"]\n required_optional_params = [\n \"generator\",\n \"height\",\n \"width\",\n \"latents\",\n \"prior_guidance_scale\",\n \"decoder_guidance_scale\",\n \"negative_prompt\",\n \"num_inference_steps\",\n \"return_dict\",\n \"prior_num_inference_steps\",\n \"output_type\",\n ]\n test_xformers_attention = True\n\n @property\n def text_embedder_hidden_size(self):\n return 32\n\n @property\n def dummy_prior(self):\n torch.manual_seed(0)\n\n model_kwargs = {\"c_in\": 2, \"c\": 8, \"depth\": 2, \"c_cond\": 32, \"c_r\": 8, \"nhead\": 2}\n model = WuerstchenPrior(**model_kwargs)\n return model.eval()\n\n @property\n def dummy_tokenizer(self):\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n return tokenizer\n\n @property\n def dummy_prior_text_encoder(self):\n torch.manual_seed(0)\n config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=self.text_embedder_hidden_size,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n return CLIPTextModel(config).eval()\n\n @property\n def dummy_text_encoder(self):\n torch.manual_seed(0)\n config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n projection_dim=self.text_embedder_hidden_size,\n hidden_size=self.text_embedder_hidden_size,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n return CLIPTextModel(config).eval()\n\n @property\n def dummy_vqgan(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"bottleneck_blocks\": 1,\n \"num_vq_embeddings\": 2,\n }\n model = PaellaVQModel(**model_kwargs)\n return model.eval()\n\n @property\n def dummy_decoder(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"c_cond\": self.text_embedder_hidden_size,\n \"c_hidden\": [320],\n \"nhead\": [-1],\n \"blocks\": [4],\n \"level_config\": [\"CT\"],\n \"clip_embd\": self.text_embedder_hidden_size,\n \"inject_effnet\": [False],\n }\n\n model = WuerstchenDiffNeXt(**model_kwargs)\n return model.eval()\n\n def get_dummy_components(self):\n prior = self.dummy_prior\n prior_text_encoder = self.dummy_prior_text_encoder\n\n scheduler = DDPMWuerstchenScheduler()\n tokenizer = self.dummy_tokenizer\n\n text_encoder = self.dummy_text_encoder\n decoder = self.dummy_decoder\n vqgan = self.dummy_vqgan\n\n components = {\n \"tokenizer\": tokenizer,\n \"text_encoder\": text_encoder,\n \"decoder\": decoder,\n \"vqgan\": vqgan,\n \"scheduler\": scheduler,\n \"prior_prior\": prior,\n \"prior_text_encoder\": prior_text_encoder,\n \"prior_tokenizer\": tokenizer,\n \"prior_scheduler\": scheduler,\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"horse\",\n \"generator\": generator,\n \"prior_guidance_scale\": 4.0,\n \"decoder_guidance_scale\": 4.0,\n \"num_inference_steps\": 2,\n \"prior_num_inference_steps\": 2,\n \"output_type\": \"np\",\n \"height\": 128,\n \"width\": 128,\n }\n return inputs\n\n def test_wuerstchen(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.images\n\n image_from_tuple = pipe(**self.get_dummy_inputs(device), return_dict=False)[0]\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[-3:, -3:, -1]\n\n assert image.shape == (1, 128, 128, 3)\n\n expected_slice = np.array([0.7616304, 0.0, 1.0, 0.0, 1.0, 0.0, 0.05925313, 0.0, 0.951898])\n\n assert (\n np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_slice.flatten()}\"\n assert (\n np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n ), f\" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}\"\n\n @require_torch_gpu\n def test_offloads(self):\n pipes = []\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components).to(torch_device)\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_sequential_cpu_offload()\n pipes.append(sd_pipe)\n\n components = self.get_dummy_components()\n sd_pipe = self.pipeline_class(**components)\n sd_pipe.enable_model_cpu_offload()\n pipes.append(sd_pipe)\n\n image_slices = []\n for pipe in pipes:\n inputs = self.get_dummy_inputs(torch_device)\n image = pipe(**inputs).images\n\n image_slices.append(image[0, -3:, -3:, -1].flatten())\n\n assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3\n assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3\n\n def test_inference_batch_single_identical(self):\n super().test_inference_batch_single_identical(expected_max_diff=1e-2)\n\n @unittest.skip(reason=\"flakey and float16 requires CUDA\")\n def test_float16_inference(self):\n super().test_float16_inference()\n\n def test_callback_inputs(self):\n pass\n\n def test_callback_cfg(self):\n pass\n" }, { "path": "diffusers/tests/pipelines/wuerstchen/test_wuerstchen_decoder.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\n\nimport numpy as np\nimport torch\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import DDPMWuerstchenScheduler, WuerstchenDecoderPipeline\nfrom diffusers.pipelines.wuerstchen import PaellaVQModel, WuerstchenDiffNeXt\nfrom diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device\n\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\nclass WuerstchenDecoderPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = WuerstchenDecoderPipeline\n params = [\"prompt\"]\n batch_params = [\"image_embeddings\", \"prompt\", \"negative_prompt\"]\n required_optional_params = [\n \"num_images_per_prompt\",\n \"num_inference_steps\",\n \"latents\",\n \"negative_prompt\",\n \"guidance_scale\",\n \"output_type\",\n \"return_dict\",\n ]\n test_xformers_attention = False\n callback_cfg_params = [\"image_embeddings\", \"text_encoder_hidden_states\"]\n\n @property\n def text_embedder_hidden_size(self):\n return 32\n\n @property\n def time_input_dim(self):\n return 32\n\n @property\n def block_out_channels_0(self):\n return self.time_input_dim\n\n @property\n def time_embed_dim(self):\n return self.time_input_dim * 4\n\n @property\n def dummy_tokenizer(self):\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n return tokenizer\n\n @property\n def dummy_text_encoder(self):\n torch.manual_seed(0)\n config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n projection_dim=self.text_embedder_hidden_size,\n hidden_size=self.text_embedder_hidden_size,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n return CLIPTextModel(config).eval()\n\n @property\n def dummy_vqgan(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"bottleneck_blocks\": 1,\n \"num_vq_embeddings\": 2,\n }\n model = PaellaVQModel(**model_kwargs)\n return model.eval()\n\n @property\n def dummy_decoder(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"c_cond\": self.text_embedder_hidden_size,\n \"c_hidden\": [320],\n \"nhead\": [-1],\n \"blocks\": [4],\n \"level_config\": [\"CT\"],\n \"clip_embd\": self.text_embedder_hidden_size,\n \"inject_effnet\": [False],\n }\n\n model = WuerstchenDiffNeXt(**model_kwargs)\n return model.eval()\n\n def get_dummy_components(self):\n decoder = self.dummy_decoder\n text_encoder = self.dummy_text_encoder\n tokenizer = self.dummy_tokenizer\n vqgan = self.dummy_vqgan\n\n scheduler = DDPMWuerstchenScheduler()\n\n components = {\n \"decoder\": decoder,\n \"vqgan\": vqgan,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"scheduler\": scheduler,\n \"latent_dim_scale\": 4.0,\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"image_embeddings\": torch.ones((1, 4, 4, 4), device=device),\n \"prompt\": \"horse\",\n \"generator\": generator,\n \"guidance_scale\": 1.0,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_wuerstchen_decoder(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.images\n\n image_from_tuple = pipe(**self.get_dummy_inputs(device), return_dict=False)\n\n image_slice = image[0, -3:, -3:, -1]\n image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]\n\n assert image.shape == (1, 64, 64, 3)\n\n expected_slice = np.array([0.0000, 0.0000, 0.0089, 1.0000, 1.0000, 0.3927, 1.0000, 1.0000, 1.0000])\n assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2\n assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2\n\n @skip_mps\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(expected_max_diff=1e-5)\n\n @skip_mps\n def test_attention_slicing_forward_pass(self):\n test_max_difference = torch_device == \"cpu\"\n test_mean_pixel_difference = False\n\n self._test_attention_slicing_forward_pass(\n test_max_difference=test_max_difference,\n test_mean_pixel_difference=test_mean_pixel_difference,\n )\n\n @unittest.skip(reason=\"bf16 not supported and requires CUDA\")\n def test_float16_inference(self):\n super().test_float16_inference()\n" }, { "path": "diffusers/tests/pipelines/wuerstchen/test_wuerstchen_prior.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport unittest\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer\n\nfrom diffusers import DDPMWuerstchenScheduler, WuerstchenPriorPipeline\nfrom diffusers.loaders import AttnProcsLayers\nfrom diffusers.models.attention_processor import (\n LoRAAttnProcessor,\n LoRAAttnProcessor2_0,\n)\nfrom diffusers.pipelines.wuerstchen import WuerstchenPrior\nfrom diffusers.utils.import_utils import is_peft_available\nfrom diffusers.utils.testing_utils import enable_full_determinism, require_peft_backend, skip_mps, torch_device\n\n\nif is_peft_available():\n from peft import LoraConfig\n from peft.tuners.tuners_utils import BaseTunerLayer\n\nfrom ..test_pipelines_common import PipelineTesterMixin\n\n\nenable_full_determinism()\n\n\ndef create_prior_lora_layers(unet: nn.Module):\n lora_attn_procs = {}\n for name in unet.attn_processors.keys():\n lora_attn_processor_class = (\n LoRAAttnProcessor2_0 if hasattr(F, \"scaled_dot_product_attention\") else LoRAAttnProcessor\n )\n lora_attn_procs[name] = lora_attn_processor_class(\n hidden_size=unet.config.c,\n )\n unet_lora_layers = AttnProcsLayers(lora_attn_procs)\n return lora_attn_procs, unet_lora_layers\n\n\nclass WuerstchenPriorPipelineFastTests(PipelineTesterMixin, unittest.TestCase):\n pipeline_class = WuerstchenPriorPipeline\n params = [\"prompt\"]\n batch_params = [\"prompt\", \"negative_prompt\"]\n required_optional_params = [\n \"num_images_per_prompt\",\n \"generator\",\n \"num_inference_steps\",\n \"latents\",\n \"negative_prompt\",\n \"guidance_scale\",\n \"output_type\",\n \"return_dict\",\n ]\n test_xformers_attention = False\n callback_cfg_params = [\"text_encoder_hidden_states\"]\n\n @property\n def text_embedder_hidden_size(self):\n return 32\n\n @property\n def time_input_dim(self):\n return 32\n\n @property\n def block_out_channels_0(self):\n return self.time_input_dim\n\n @property\n def time_embed_dim(self):\n return self.time_input_dim * 4\n\n @property\n def dummy_tokenizer(self):\n tokenizer = CLIPTokenizer.from_pretrained(\"hf-internal-testing/tiny-random-clip\")\n return tokenizer\n\n @property\n def dummy_text_encoder(self):\n torch.manual_seed(0)\n config = CLIPTextConfig(\n bos_token_id=0,\n eos_token_id=2,\n hidden_size=self.text_embedder_hidden_size,\n intermediate_size=37,\n layer_norm_eps=1e-05,\n num_attention_heads=4,\n num_hidden_layers=5,\n pad_token_id=1,\n vocab_size=1000,\n )\n return CLIPTextModel(config).eval()\n\n @property\n def dummy_prior(self):\n torch.manual_seed(0)\n\n model_kwargs = {\n \"c_in\": 2,\n \"c\": 8,\n \"depth\": 2,\n \"c_cond\": 32,\n \"c_r\": 8,\n \"nhead\": 2,\n }\n\n model = WuerstchenPrior(**model_kwargs)\n return model.eval()\n\n def get_dummy_components(self):\n prior = self.dummy_prior\n text_encoder = self.dummy_text_encoder\n tokenizer = self.dummy_tokenizer\n\n scheduler = DDPMWuerstchenScheduler()\n\n components = {\n \"prior\": prior,\n \"text_encoder\": text_encoder,\n \"tokenizer\": tokenizer,\n \"scheduler\": scheduler,\n }\n\n return components\n\n def get_dummy_inputs(self, device, seed=0):\n if str(device).startswith(\"mps\"):\n generator = torch.manual_seed(seed)\n else:\n generator = torch.Generator(device=device).manual_seed(seed)\n inputs = {\n \"prompt\": \"horse\",\n \"generator\": generator,\n \"guidance_scale\": 4.0,\n \"num_inference_steps\": 2,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_wuerstchen_prior(self):\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output = pipe(**self.get_dummy_inputs(device))\n image = output.image_embeddings\n\n image_from_tuple = pipe(**self.get_dummy_inputs(device), return_dict=False)[0]\n\n image_slice = image[0, 0, 0, -10:]\n image_from_tuple_slice = image_from_tuple[0, 0, 0, -10:]\n assert image.shape == (1, 2, 24, 24)\n\n expected_slice = np.array(\n [\n -7172.837,\n -3438.855,\n -1093.312,\n 388.8835,\n -7471.467,\n -7998.1206,\n -5328.259,\n 218.00089,\n -2731.5745,\n -8056.734,\n ]\n )\n assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2\n assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 5e-2\n\n @skip_mps\n def test_inference_batch_single_identical(self):\n self._test_inference_batch_single_identical(\n expected_max_diff=3e-1,\n )\n\n @skip_mps\n def test_attention_slicing_forward_pass(self):\n test_max_difference = torch_device == \"cpu\"\n test_mean_pixel_difference = False\n\n self._test_attention_slicing_forward_pass(\n test_max_difference=test_max_difference,\n test_mean_pixel_difference=test_mean_pixel_difference,\n )\n\n @unittest.skip(reason=\"flaky for now\")\n def test_float16_inference(self):\n super().test_float16_inference()\n\n # override because we need to make sure latent_mean and latent_std to be 0\n def test_callback_inputs(self):\n components = self.get_dummy_components()\n components[\"latent_mean\"] = 0\n components[\"latent_std\"] = 0\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(torch_device)\n pipe.set_progress_bar_config(disable=None)\n\n self.assertTrue(\n hasattr(pipe, \"_callback_tensor_inputs\"),\n f\" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs\",\n )\n\n def callback_inputs_test(pipe, i, t, callback_kwargs):\n missing_callback_inputs = set()\n for v in pipe._callback_tensor_inputs:\n if v not in callback_kwargs:\n missing_callback_inputs.add(v)\n self.assertTrue(\n len(missing_callback_inputs) == 0, f\"Missing callback tensor inputs: {missing_callback_inputs}\"\n )\n last_i = pipe.num_timesteps - 1\n if i == last_i:\n callback_kwargs[\"latents\"] = torch.zeros_like(callback_kwargs[\"latents\"])\n return callback_kwargs\n\n inputs = self.get_dummy_inputs(torch_device)\n inputs[\"callback_on_step_end\"] = callback_inputs_test\n inputs[\"callback_on_step_end_tensor_inputs\"] = pipe._callback_tensor_inputs\n inputs[\"output_type\"] = \"latent\"\n\n output = pipe(**inputs)[0]\n assert output.abs().sum() == 0\n\n def check_if_lora_correctly_set(self, model) -> bool:\n \"\"\"\n Checks if the LoRA layers are correctly set with peft\n \"\"\"\n for module in model.modules():\n if isinstance(module, BaseTunerLayer):\n return True\n return False\n\n def get_lora_components(self):\n prior = self.dummy_prior\n\n prior_lora_config = LoraConfig(\n r=4, lora_alpha=4, target_modules=[\"to_q\", \"to_k\", \"to_v\", \"to_out.0\"], init_lora_weights=False\n )\n\n prior_lora_attn_procs, prior_lora_layers = create_prior_lora_layers(prior)\n\n lora_components = {\n \"prior_lora_layers\": prior_lora_layers,\n \"prior_lora_attn_procs\": prior_lora_attn_procs,\n }\n\n return prior, prior_lora_config, lora_components\n\n @require_peft_backend\n def test_inference_with_prior_lora(self):\n _, prior_lora_config, _ = self.get_lora_components()\n device = \"cpu\"\n\n components = self.get_dummy_components()\n\n pipe = self.pipeline_class(**components)\n pipe = pipe.to(device)\n\n pipe.set_progress_bar_config(disable=None)\n\n output_no_lora = pipe(**self.get_dummy_inputs(device))\n image_embed = output_no_lora.image_embeddings\n self.assertTrue(image_embed.shape == (1, 2, 24, 24))\n\n pipe.prior.add_adapter(prior_lora_config)\n self.assertTrue(self.check_if_lora_correctly_set(pipe.prior), \"Lora not correctly set in prior\")\n\n output_lora = pipe(**self.get_dummy_inputs(device))\n lora_image_embed = output_lora.image_embeddings\n\n self.assertTrue(image_embed.shape == lora_image_embed.shape)\n" }, { "path": "diffusers/tests/schedulers/__init__.py", "content": "" }, { "path": "diffusers/tests/schedulers/test_scheduler_consistency_model.py", "content": "import torch\n\nfrom diffusers import CMStochasticIterativeScheduler\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass CMStochasticIterativeSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (CMStochasticIterativeScheduler,)\n num_inference_steps = 10\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 201,\n \"sigma_min\": 0.002,\n \"sigma_max\": 80.0,\n }\n\n config.update(**kwargs)\n return config\n\n # Override test_step_shape to add CMStochasticIterativeScheduler-specific logic regarding timesteps\n # Problem is that we don't know two timesteps that will always be in the timestep schedule from only the scheduler\n # config; scaled sigma_max is always in the timestep schedule, but sigma_min is in the sigma schedule while scaled\n # sigma_min is not in the timestep schedule\n def test_step_shape(self):\n num_inference_steps = 10\n\n scheduler_config = self.get_scheduler_config()\n scheduler = self.scheduler_classes[0](**scheduler_config)\n\n scheduler.set_timesteps(num_inference_steps)\n\n timestep_0 = scheduler.timesteps[0]\n timestep_1 = scheduler.timesteps[1]\n\n sample = self.dummy_sample\n residual = 0.1 * sample\n\n output_0 = scheduler.step(residual, timestep_0, sample).prev_sample\n output_1 = scheduler.step(residual, timestep_1, sample).prev_sample\n\n self.assertEqual(output_0.shape, sample.shape)\n self.assertEqual(output_0.shape, output_1.shape)\n\n def test_timesteps(self):\n for timesteps in [10, 50, 100, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_clip_denoised(self):\n for clip_denoised in [True, False]:\n self.check_over_configs(clip_denoised=clip_denoised)\n\n def test_full_loop_no_noise_onestep(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 1\n scheduler.set_timesteps(num_inference_steps)\n timesteps = scheduler.timesteps\n\n generator = torch.manual_seed(0)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n\n for i, t in enumerate(timesteps):\n # 1. scale model input\n scaled_sample = scheduler.scale_model_input(sample, t)\n\n # 2. predict noise residual\n residual = model(scaled_sample, t)\n\n # 3. predict previous sample x_t-1\n pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample\n\n sample = pred_prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 192.7614) < 1e-2\n assert abs(result_mean.item() - 0.2510) < 1e-3\n\n def test_full_loop_no_noise_multistep(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n timesteps = [106, 0]\n scheduler.set_timesteps(timesteps=timesteps)\n timesteps = scheduler.timesteps\n\n generator = torch.manual_seed(0)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n\n for t in timesteps:\n # 1. scale model input\n scaled_sample = scheduler.scale_model_input(sample, t)\n\n # 2. predict noise residual\n residual = model(scaled_sample, t)\n\n # 3. predict previous sample x_t-1\n pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample\n\n sample = pred_prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 347.6357) < 1e-2\n assert abs(result_mean.item() - 0.4527) < 1e-3\n\n def test_full_loop_with_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 10\n t_start = 8\n\n scheduler.set_timesteps(num_inference_steps)\n timesteps = scheduler.timesteps\n\n generator = torch.manual_seed(0)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n\n noise = self.dummy_noise_deter\n timesteps = scheduler.timesteps[t_start * scheduler.order :]\n\n sample = scheduler.add_noise(sample, noise, timesteps[:1])\n\n for t in timesteps:\n # 1. scale model input\n scaled_sample = scheduler.scale_model_input(sample, t)\n\n # 2. predict noise residual\n residual = model(scaled_sample, t)\n\n # 3. predict previous sample x_t-1\n pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample\n\n sample = pred_prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 763.9186) < 1e-2, f\" expected result sum 763.9186, but get {result_sum}\"\n assert abs(result_mean.item() - 0.9947) < 1e-3, f\" expected result mean 0.9947, but get {result_mean}\"\n\n def test_custom_timesteps_increasing_order(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n timesteps = [39, 30, 12, 15, 0]\n\n with self.assertRaises(ValueError, msg=\"`timesteps` must be in descending order.\"):\n scheduler.set_timesteps(timesteps=timesteps)\n\n def test_custom_timesteps_passing_both_num_inference_steps_and_timesteps(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n timesteps = [39, 30, 12, 1, 0]\n num_inference_steps = len(timesteps)\n\n with self.assertRaises(ValueError, msg=\"Can only pass one of `num_inference_steps` or `timesteps`.\"):\n scheduler.set_timesteps(num_inference_steps=num_inference_steps, timesteps=timesteps)\n\n def test_custom_timesteps_too_large(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n timesteps = [scheduler.config.num_train_timesteps]\n\n with self.assertRaises(\n ValueError,\n msg=\"`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}\",\n ):\n scheduler.set_timesteps(timesteps=timesteps)\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_ddim.py", "content": "import torch\n\nfrom diffusers import DDIMScheduler\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass DDIMSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (DDIMScheduler,)\n forward_default_kwargs = ((\"eta\", 0.0), (\"num_inference_steps\", 50))\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1000,\n \"beta_start\": 0.0001,\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n \"clip_sample\": True,\n }\n\n config.update(**kwargs)\n return config\n\n def full_loop(self, **config):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps, eta = 10, 0.0\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n\n scheduler.set_timesteps(num_inference_steps)\n\n for t in scheduler.timesteps:\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample, eta).prev_sample\n\n return sample\n\n def test_timesteps(self):\n for timesteps in [100, 500, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_steps_offset(self):\n for steps_offset in [0, 1]:\n self.check_over_configs(steps_offset=steps_offset)\n\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(steps_offset=1)\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(5)\n assert torch.equal(scheduler.timesteps, torch.LongTensor([801, 601, 401, 201, 1]))\n\n def test_betas(self):\n for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]):\n self.check_over_configs(beta_start=beta_start, beta_end=beta_end)\n\n def test_schedules(self):\n for schedule in [\"linear\", \"squaredcos_cap_v2\"]:\n self.check_over_configs(beta_schedule=schedule)\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(prediction_type=prediction_type)\n\n def test_clip_sample(self):\n for clip_sample in [True, False]:\n self.check_over_configs(clip_sample=clip_sample)\n\n def test_timestep_spacing(self):\n for timestep_spacing in [\"trailing\", \"leading\"]:\n self.check_over_configs(timestep_spacing=timestep_spacing)\n\n def test_rescale_betas_zero_snr(self):\n for rescale_betas_zero_snr in [True, False]:\n self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr)\n\n def test_thresholding(self):\n self.check_over_configs(thresholding=False)\n for threshold in [0.5, 1.0, 2.0]:\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(\n thresholding=True,\n prediction_type=prediction_type,\n sample_max_value=threshold,\n )\n\n def test_time_indices(self):\n for t in [1, 10, 49]:\n self.check_over_forward(time_step=t)\n\n def test_inference_steps(self):\n for t, num_inference_steps in zip([1, 10, 50], [10, 50, 500]):\n self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps)\n\n def test_eta(self):\n for t, eta in zip([1, 10, 49], [0.0, 0.5, 1.0]):\n self.check_over_forward(time_step=t, eta=eta)\n\n def test_variance(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n assert torch.sum(torch.abs(scheduler._get_variance(0, 0) - 0.0)) < 1e-5\n assert torch.sum(torch.abs(scheduler._get_variance(420, 400) - 0.14771)) < 1e-5\n assert torch.sum(torch.abs(scheduler._get_variance(980, 960) - 0.32460)) < 1e-5\n assert torch.sum(torch.abs(scheduler._get_variance(0, 0) - 0.0)) < 1e-5\n assert torch.sum(torch.abs(scheduler._get_variance(487, 486) - 0.00979)) < 1e-5\n assert torch.sum(torch.abs(scheduler._get_variance(999, 998) - 0.02)) < 1e-5\n\n def test_full_loop_no_noise(self):\n sample = self.full_loop()\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 172.0067) < 1e-2\n assert abs(result_mean.item() - 0.223967) < 1e-3\n\n def test_full_loop_with_v_prediction(self):\n sample = self.full_loop(prediction_type=\"v_prediction\")\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 52.5302) < 1e-2\n assert abs(result_mean.item() - 0.0684) < 1e-3\n\n def test_full_loop_with_set_alpha_to_one(self):\n # We specify different beta, so that the first alpha is 0.99\n sample = self.full_loop(set_alpha_to_one=True, beta_start=0.01)\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 149.8295) < 1e-2\n assert abs(result_mean.item() - 0.1951) < 1e-3\n\n def test_full_loop_with_no_set_alpha_to_one(self):\n # We specify different beta, so that the first alpha is 0.99\n sample = self.full_loop(set_alpha_to_one=False, beta_start=0.01)\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 149.0784) < 1e-2\n assert abs(result_mean.item() - 0.1941) < 1e-3\n\n def test_full_loop_with_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps, eta = 10, 0.0\n t_start = 8\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n\n scheduler.set_timesteps(num_inference_steps)\n\n # add noise\n noise = self.dummy_noise_deter\n timesteps = scheduler.timesteps[t_start * scheduler.order :]\n sample = scheduler.add_noise(sample, noise, timesteps[:1])\n\n for t in timesteps:\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample, eta).prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 354.5418) < 1e-2, f\" expected result sum 218.4379, but get {result_sum}\"\n assert abs(result_mean.item() - 0.4616) < 1e-3, f\" expected result mean 0.2844, but get {result_mean}\"\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_ddim_inverse.py", "content": "import torch\n\nfrom diffusers import DDIMInverseScheduler\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass DDIMInverseSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (DDIMInverseScheduler,)\n forward_default_kwargs = ((\"num_inference_steps\", 50),)\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1000,\n \"beta_start\": 0.0001,\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n \"clip_sample\": True,\n }\n\n config.update(**kwargs)\n return config\n\n def full_loop(self, **config):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 10\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n\n scheduler.set_timesteps(num_inference_steps)\n\n for t in scheduler.timesteps:\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample).prev_sample\n\n return sample\n\n def test_timesteps(self):\n for timesteps in [100, 500, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_steps_offset(self):\n for steps_offset in [0, 1]:\n self.check_over_configs(steps_offset=steps_offset)\n\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(steps_offset=1)\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(5)\n assert torch.equal(scheduler.timesteps, torch.LongTensor([1, 201, 401, 601, 801]))\n\n def test_betas(self):\n for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]):\n self.check_over_configs(beta_start=beta_start, beta_end=beta_end)\n\n def test_schedules(self):\n for schedule in [\"linear\", \"squaredcos_cap_v2\"]:\n self.check_over_configs(beta_schedule=schedule)\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(prediction_type=prediction_type)\n\n def test_clip_sample(self):\n for clip_sample in [True, False]:\n self.check_over_configs(clip_sample=clip_sample)\n\n def test_timestep_spacing(self):\n for timestep_spacing in [\"trailing\", \"leading\"]:\n self.check_over_configs(timestep_spacing=timestep_spacing)\n\n def test_rescale_betas_zero_snr(self):\n for rescale_betas_zero_snr in [True, False]:\n self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr)\n\n def test_thresholding(self):\n self.check_over_configs(thresholding=False)\n for threshold in [0.5, 1.0, 2.0]:\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(\n thresholding=True,\n prediction_type=prediction_type,\n sample_max_value=threshold,\n )\n\n def test_time_indices(self):\n for t in [1, 10, 49]:\n self.check_over_forward(time_step=t)\n\n def test_inference_steps(self):\n for t, num_inference_steps in zip([1, 10, 50], [10, 50, 500]):\n self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps)\n\n def test_add_noise_device(self):\n pass\n\n def test_full_loop_no_noise(self):\n sample = self.full_loop()\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 671.6816) < 1e-2\n assert abs(result_mean.item() - 0.8746) < 1e-3\n\n def test_full_loop_with_v_prediction(self):\n sample = self.full_loop(prediction_type=\"v_prediction\")\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 1394.2185) < 1e-2\n assert abs(result_mean.item() - 1.8154) < 1e-3\n\n def test_full_loop_with_set_alpha_to_one(self):\n # We specify different beta, so that the first alpha is 0.99\n sample = self.full_loop(set_alpha_to_one=True, beta_start=0.01)\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 539.9622) < 1e-2\n assert abs(result_mean.item() - 0.7031) < 1e-3\n\n def test_full_loop_with_no_set_alpha_to_one(self):\n # We specify different beta, so that the first alpha is 0.99\n sample = self.full_loop(set_alpha_to_one=False, beta_start=0.01)\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 542.6722) < 1e-2\n assert abs(result_mean.item() - 0.7066) < 1e-3\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_ddim_parallel.py", "content": "# Copyright 2024 ParaDiGMS authors and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport torch\n\nfrom diffusers import DDIMParallelScheduler\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass DDIMParallelSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (DDIMParallelScheduler,)\n forward_default_kwargs = ((\"eta\", 0.0), (\"num_inference_steps\", 50))\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1000,\n \"beta_start\": 0.0001,\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n \"clip_sample\": True,\n }\n\n config.update(**kwargs)\n return config\n\n def full_loop(self, **config):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps, eta = 10, 0.0\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n\n scheduler.set_timesteps(num_inference_steps)\n\n for t in scheduler.timesteps:\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample, eta).prev_sample\n\n return sample\n\n def test_timesteps(self):\n for timesteps in [100, 500, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_steps_offset(self):\n for steps_offset in [0, 1]:\n self.check_over_configs(steps_offset=steps_offset)\n\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(steps_offset=1)\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(5)\n assert torch.equal(scheduler.timesteps, torch.LongTensor([801, 601, 401, 201, 1]))\n\n def test_betas(self):\n for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]):\n self.check_over_configs(beta_start=beta_start, beta_end=beta_end)\n\n def test_schedules(self):\n for schedule in [\"linear\", \"squaredcos_cap_v2\"]:\n self.check_over_configs(beta_schedule=schedule)\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(prediction_type=prediction_type)\n\n def test_clip_sample(self):\n for clip_sample in [True, False]:\n self.check_over_configs(clip_sample=clip_sample)\n\n def test_timestep_spacing(self):\n for timestep_spacing in [\"trailing\", \"leading\"]:\n self.check_over_configs(timestep_spacing=timestep_spacing)\n\n def test_rescale_betas_zero_snr(self):\n for rescale_betas_zero_snr in [True, False]:\n self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr)\n\n def test_thresholding(self):\n self.check_over_configs(thresholding=False)\n for threshold in [0.5, 1.0, 2.0]:\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(\n thresholding=True,\n prediction_type=prediction_type,\n sample_max_value=threshold,\n )\n\n def test_time_indices(self):\n for t in [1, 10, 49]:\n self.check_over_forward(time_step=t)\n\n def test_inference_steps(self):\n for t, num_inference_steps in zip([1, 10, 50], [10, 50, 500]):\n self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps)\n\n def test_eta(self):\n for t, eta in zip([1, 10, 49], [0.0, 0.5, 1.0]):\n self.check_over_forward(time_step=t, eta=eta)\n\n def test_variance(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n assert torch.sum(torch.abs(scheduler._get_variance(0, 0) - 0.0)) < 1e-5\n assert torch.sum(torch.abs(scheduler._get_variance(420, 400) - 0.14771)) < 1e-5\n assert torch.sum(torch.abs(scheduler._get_variance(980, 960) - 0.32460)) < 1e-5\n assert torch.sum(torch.abs(scheduler._get_variance(0, 0) - 0.0)) < 1e-5\n assert torch.sum(torch.abs(scheduler._get_variance(487, 486) - 0.00979)) < 1e-5\n assert torch.sum(torch.abs(scheduler._get_variance(999, 998) - 0.02)) < 1e-5\n\n def test_batch_step_no_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps, eta = 10, 0.0\n scheduler.set_timesteps(num_inference_steps)\n\n model = self.dummy_model()\n sample1 = self.dummy_sample_deter\n sample2 = self.dummy_sample_deter + 0.1\n sample3 = self.dummy_sample_deter - 0.1\n\n per_sample_batch = sample1.shape[0]\n samples = torch.stack([sample1, sample2, sample3], dim=0)\n timesteps = torch.arange(num_inference_steps)[0:3, None].repeat(1, per_sample_batch)\n\n residual = model(samples.flatten(0, 1), timesteps.flatten(0, 1))\n pred_prev_sample = scheduler.batch_step_no_noise(residual, timesteps.flatten(0, 1), samples.flatten(0, 1), eta)\n\n result_sum = torch.sum(torch.abs(pred_prev_sample))\n result_mean = torch.mean(torch.abs(pred_prev_sample))\n\n assert abs(result_sum.item() - 1147.7904) < 1e-2\n assert abs(result_mean.item() - 0.4982) < 1e-3\n\n def test_full_loop_no_noise(self):\n sample = self.full_loop()\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 172.0067) < 1e-2\n assert abs(result_mean.item() - 0.223967) < 1e-3\n\n def test_full_loop_with_v_prediction(self):\n sample = self.full_loop(prediction_type=\"v_prediction\")\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 52.5302) < 1e-2\n assert abs(result_mean.item() - 0.0684) < 1e-3\n\n def test_full_loop_with_set_alpha_to_one(self):\n # We specify different beta, so that the first alpha is 0.99\n sample = self.full_loop(set_alpha_to_one=True, beta_start=0.01)\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 149.8295) < 1e-2\n assert abs(result_mean.item() - 0.1951) < 1e-3\n\n def test_full_loop_with_no_set_alpha_to_one(self):\n # We specify different beta, so that the first alpha is 0.99\n sample = self.full_loop(set_alpha_to_one=False, beta_start=0.01)\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 149.0784) < 1e-2\n assert abs(result_mean.item() - 0.1941) < 1e-3\n\n def test_full_loop_with_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps, eta = 10, 0.0\n t_start = 8\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n\n scheduler.set_timesteps(num_inference_steps)\n\n # add noise\n noise = self.dummy_noise_deter\n timesteps = scheduler.timesteps[t_start * scheduler.order :]\n sample = scheduler.add_noise(sample, noise, timesteps[:1])\n\n for t in timesteps:\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample, eta).prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 354.5418) < 1e-2, f\" expected result sum 354.5418, but get {result_sum}\"\n assert abs(result_mean.item() - 0.4616) < 1e-3, f\" expected result mean 0.4616, but get {result_mean}\"\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_ddpm.py", "content": "import torch\n\nfrom diffusers import DDPMScheduler\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass DDPMSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (DDPMScheduler,)\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1000,\n \"beta_start\": 0.0001,\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n \"variance_type\": \"fixed_small\",\n \"clip_sample\": True,\n }\n\n config.update(**kwargs)\n return config\n\n def test_timesteps(self):\n for timesteps in [1, 5, 100, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_betas(self):\n for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]):\n self.check_over_configs(beta_start=beta_start, beta_end=beta_end)\n\n def test_schedules(self):\n for schedule in [\"linear\", \"squaredcos_cap_v2\"]:\n self.check_over_configs(beta_schedule=schedule)\n\n def test_variance_type(self):\n for variance in [\"fixed_small\", \"fixed_large\", \"other\"]:\n self.check_over_configs(variance_type=variance)\n\n def test_clip_sample(self):\n for clip_sample in [True, False]:\n self.check_over_configs(clip_sample=clip_sample)\n\n def test_thresholding(self):\n self.check_over_configs(thresholding=False)\n for threshold in [0.5, 1.0, 2.0]:\n for prediction_type in [\"epsilon\", \"sample\", \"v_prediction\"]:\n self.check_over_configs(\n thresholding=True,\n prediction_type=prediction_type,\n sample_max_value=threshold,\n )\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"sample\", \"v_prediction\"]:\n self.check_over_configs(prediction_type=prediction_type)\n\n def test_time_indices(self):\n for t in [0, 500, 999]:\n self.check_over_forward(time_step=t)\n\n def test_variance(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5\n assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.00979)) < 1e-5\n assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1e-5\n\n def test_rescale_betas_zero_snr(self):\n for rescale_betas_zero_snr in [True, False]:\n self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr)\n\n def test_full_loop_no_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n num_trained_timesteps = len(scheduler)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n generator = torch.manual_seed(0)\n\n for t in reversed(range(num_trained_timesteps)):\n # 1. predict noise residual\n residual = model(sample, t)\n\n # 2. predict previous mean of sample x_t-1\n pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample\n\n # if t > 0:\n # noise = self.dummy_sample_deter\n # variance = scheduler.get_variance(t) ** (0.5) * noise\n #\n # sample = pred_prev_sample + variance\n sample = pred_prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 258.9606) < 1e-2\n assert abs(result_mean.item() - 0.3372) < 1e-3\n\n def test_full_loop_with_v_prediction(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(prediction_type=\"v_prediction\")\n scheduler = scheduler_class(**scheduler_config)\n\n num_trained_timesteps = len(scheduler)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n generator = torch.manual_seed(0)\n\n for t in reversed(range(num_trained_timesteps)):\n # 1. predict noise residual\n residual = model(sample, t)\n\n # 2. predict previous mean of sample x_t-1\n pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample\n\n # if t > 0:\n # noise = self.dummy_sample_deter\n # variance = scheduler.get_variance(t) ** (0.5) * noise\n #\n # sample = pred_prev_sample + variance\n sample = pred_prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 202.0296) < 1e-2\n assert abs(result_mean.item() - 0.2631) < 1e-3\n\n def test_custom_timesteps(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n timesteps = [100, 87, 50, 1, 0]\n\n scheduler.set_timesteps(timesteps=timesteps)\n\n scheduler_timesteps = scheduler.timesteps\n\n for i, timestep in enumerate(scheduler_timesteps):\n if i == len(timesteps) - 1:\n expected_prev_t = -1\n else:\n expected_prev_t = timesteps[i + 1]\n\n prev_t = scheduler.previous_timestep(timestep)\n prev_t = prev_t.item()\n\n self.assertEqual(prev_t, expected_prev_t)\n\n def test_custom_timesteps_increasing_order(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n timesteps = [100, 87, 50, 51, 0]\n\n with self.assertRaises(ValueError, msg=\"`custom_timesteps` must be in descending order.\"):\n scheduler.set_timesteps(timesteps=timesteps)\n\n def test_custom_timesteps_passing_both_num_inference_steps_and_timesteps(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n timesteps = [100, 87, 50, 1, 0]\n num_inference_steps = len(timesteps)\n\n with self.assertRaises(ValueError, msg=\"Can only pass one of `num_inference_steps` or `custom_timesteps`.\"):\n scheduler.set_timesteps(num_inference_steps=num_inference_steps, timesteps=timesteps)\n\n def test_custom_timesteps_too_large(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n timesteps = [scheduler.config.num_train_timesteps]\n\n with self.assertRaises(\n ValueError,\n msg=\"`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}\",\n ):\n scheduler.set_timesteps(timesteps=timesteps)\n\n def test_full_loop_with_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n num_trained_timesteps = len(scheduler)\n t_start = num_trained_timesteps - 2\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n generator = torch.manual_seed(0)\n\n # add noise\n noise = self.dummy_noise_deter\n timesteps = scheduler.timesteps[t_start * scheduler.order :]\n sample = scheduler.add_noise(sample, noise, timesteps[:1])\n\n for t in timesteps:\n # 1. predict noise residual\n residual = model(sample, t)\n\n # 2. predict previous mean of sample x_t-1\n pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample\n sample = pred_prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 387.9466) < 1e-2, f\" expected result sum 387.9466, but get {result_sum}\"\n assert abs(result_mean.item() - 0.5051) < 1e-3, f\" expected result mean 0.5051, but get {result_mean}\"\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_ddpm_parallel.py", "content": "# Copyright 2024 ParaDiGMS authors and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport torch\n\nfrom diffusers import DDPMParallelScheduler\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass DDPMParallelSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (DDPMParallelScheduler,)\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1000,\n \"beta_start\": 0.0001,\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n \"variance_type\": \"fixed_small\",\n \"clip_sample\": True,\n }\n\n config.update(**kwargs)\n return config\n\n def test_timesteps(self):\n for timesteps in [1, 5, 100, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_betas(self):\n for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]):\n self.check_over_configs(beta_start=beta_start, beta_end=beta_end)\n\n def test_schedules(self):\n for schedule in [\"linear\", \"squaredcos_cap_v2\"]:\n self.check_over_configs(beta_schedule=schedule)\n\n def test_variance_type(self):\n for variance in [\"fixed_small\", \"fixed_large\", \"other\"]:\n self.check_over_configs(variance_type=variance)\n\n def test_clip_sample(self):\n for clip_sample in [True, False]:\n self.check_over_configs(clip_sample=clip_sample)\n\n def test_thresholding(self):\n self.check_over_configs(thresholding=False)\n for threshold in [0.5, 1.0, 2.0]:\n for prediction_type in [\"epsilon\", \"sample\", \"v_prediction\"]:\n self.check_over_configs(\n thresholding=True,\n prediction_type=prediction_type,\n sample_max_value=threshold,\n )\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"sample\", \"v_prediction\"]:\n self.check_over_configs(prediction_type=prediction_type)\n\n def test_time_indices(self):\n for t in [0, 500, 999]:\n self.check_over_forward(time_step=t)\n\n def test_variance(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5\n assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.00979)) < 1e-5\n assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1e-5\n\n def test_rescale_betas_zero_snr(self):\n for rescale_betas_zero_snr in [True, False]:\n self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr)\n\n def test_batch_step_no_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n num_trained_timesteps = len(scheduler)\n\n model = self.dummy_model()\n sample1 = self.dummy_sample_deter\n sample2 = self.dummy_sample_deter + 0.1\n sample3 = self.dummy_sample_deter - 0.1\n\n per_sample_batch = sample1.shape[0]\n samples = torch.stack([sample1, sample2, sample3], dim=0)\n timesteps = torch.arange(num_trained_timesteps)[0:3, None].repeat(1, per_sample_batch)\n\n residual = model(samples.flatten(0, 1), timesteps.flatten(0, 1))\n pred_prev_sample = scheduler.batch_step_no_noise(residual, timesteps.flatten(0, 1), samples.flatten(0, 1))\n\n result_sum = torch.sum(torch.abs(pred_prev_sample))\n result_mean = torch.mean(torch.abs(pred_prev_sample))\n\n assert abs(result_sum.item() - 1153.1833) < 1e-2\n assert abs(result_mean.item() - 0.5005) < 1e-3\n\n def test_full_loop_no_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n num_trained_timesteps = len(scheduler)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n generator = torch.manual_seed(0)\n\n for t in reversed(range(num_trained_timesteps)):\n # 1. predict noise residual\n residual = model(sample, t)\n\n # 2. predict previous mean of sample x_t-1\n pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample\n\n sample = pred_prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 258.9606) < 1e-2\n assert abs(result_mean.item() - 0.3372) < 1e-3\n\n def test_full_loop_with_v_prediction(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(prediction_type=\"v_prediction\")\n scheduler = scheduler_class(**scheduler_config)\n\n num_trained_timesteps = len(scheduler)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n generator = torch.manual_seed(0)\n\n for t in reversed(range(num_trained_timesteps)):\n # 1. predict noise residual\n residual = model(sample, t)\n\n # 2. predict previous mean of sample x_t-1\n pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample\n\n sample = pred_prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 202.0296) < 1e-2\n assert abs(result_mean.item() - 0.2631) < 1e-3\n\n def test_custom_timesteps(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n timesteps = [100, 87, 50, 1, 0]\n\n scheduler.set_timesteps(timesteps=timesteps)\n\n scheduler_timesteps = scheduler.timesteps\n\n for i, timestep in enumerate(scheduler_timesteps):\n if i == len(timesteps) - 1:\n expected_prev_t = -1\n else:\n expected_prev_t = timesteps[i + 1]\n\n prev_t = scheduler.previous_timestep(timestep)\n prev_t = prev_t.item()\n\n self.assertEqual(prev_t, expected_prev_t)\n\n def test_custom_timesteps_increasing_order(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n timesteps = [100, 87, 50, 51, 0]\n\n with self.assertRaises(ValueError, msg=\"`custom_timesteps` must be in descending order.\"):\n scheduler.set_timesteps(timesteps=timesteps)\n\n def test_custom_timesteps_passing_both_num_inference_steps_and_timesteps(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n timesteps = [100, 87, 50, 1, 0]\n num_inference_steps = len(timesteps)\n\n with self.assertRaises(ValueError, msg=\"Can only pass one of `num_inference_steps` or `custom_timesteps`.\"):\n scheduler.set_timesteps(num_inference_steps=num_inference_steps, timesteps=timesteps)\n\n def test_custom_timesteps_too_large(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n timesteps = [scheduler.config.num_train_timesteps]\n\n with self.assertRaises(\n ValueError,\n msg=\"`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}\",\n ):\n scheduler.set_timesteps(timesteps=timesteps)\n\n def test_full_loop_with_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n num_trained_timesteps = len(scheduler)\n t_start = num_trained_timesteps - 2\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n generator = torch.manual_seed(0)\n\n # add noise\n noise = self.dummy_noise_deter\n timesteps = scheduler.timesteps[t_start * scheduler.order :]\n sample = scheduler.add_noise(sample, noise, timesteps[:1])\n\n for t in timesteps:\n # 1. predict noise residual\n residual = model(sample, t)\n\n # 2. predict previous mean of sample x_t-1\n pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample\n sample = pred_prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 387.9466) < 1e-2, f\" expected result sum 387.9466, but get {result_sum}\"\n assert abs(result_mean.item() - 0.5051) < 1e-3, f\" expected result mean 0.5051, but get {result_mean}\"\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_deis.py", "content": "import tempfile\n\nimport torch\n\nfrom diffusers import (\n DEISMultistepScheduler,\n DPMSolverMultistepScheduler,\n DPMSolverSinglestepScheduler,\n UniPCMultistepScheduler,\n)\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass DEISMultistepSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (DEISMultistepScheduler,)\n forward_default_kwargs = ((\"num_inference_steps\", 25),)\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1000,\n \"beta_start\": 0.0001,\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n \"solver_order\": 2,\n }\n\n config.update(**kwargs)\n return config\n\n def check_over_configs(self, time_step=0, **config):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n sample = self.dummy_sample\n residual = 0.1 * sample\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(num_inference_steps)\n # copy over dummy past residuals\n scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n new_scheduler.set_timesteps(num_inference_steps)\n # copy over dummy past residuals\n new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order]\n\n output, new_output = sample, sample\n for t in range(time_step, time_step + scheduler.config.solver_order + 1):\n t = scheduler.timesteps[t]\n output = scheduler.step(residual, t, output, **kwargs).prev_sample\n new_output = new_scheduler.step(residual, t, new_output, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def test_from_save_pretrained(self):\n pass\n\n def check_over_forward(self, time_step=0, **forward_kwargs):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n sample = self.dummy_sample\n residual = 0.1 * sample\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(num_inference_steps)\n\n # copy over dummy past residuals (must be after setting timesteps)\n scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n # copy over dummy past residuals\n new_scheduler.set_timesteps(num_inference_steps)\n\n # copy over dummy past residual (must be after setting timesteps)\n new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order]\n\n output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def full_loop(self, scheduler=None, **config):\n if scheduler is None:\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 10\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n scheduler.set_timesteps(num_inference_steps)\n\n for i, t in enumerate(scheduler.timesteps):\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample).prev_sample\n\n return sample\n\n def test_step_shape(self):\n kwargs = dict(self.forward_default_kwargs)\n\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n sample = self.dummy_sample\n residual = 0.1 * sample\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n scheduler.set_timesteps(num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n # copy over dummy past residuals (must be done after set_timesteps)\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]\n scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]\n\n time_step_0 = scheduler.timesteps[5]\n time_step_1 = scheduler.timesteps[6]\n\n output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample\n output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample\n\n self.assertEqual(output_0.shape, sample.shape)\n self.assertEqual(output_0.shape, output_1.shape)\n\n def test_switch(self):\n # make sure that iterating over schedulers with same config names gives same results\n # for defaults\n scheduler = DEISMultistepScheduler(**self.get_scheduler_config())\n sample = self.full_loop(scheduler=scheduler)\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.23916) < 1e-3\n\n scheduler = DPMSolverSinglestepScheduler.from_config(scheduler.config)\n scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)\n scheduler = UniPCMultistepScheduler.from_config(scheduler.config)\n scheduler = DEISMultistepScheduler.from_config(scheduler.config)\n\n sample = self.full_loop(scheduler=scheduler)\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.23916) < 1e-3\n\n def test_timesteps(self):\n for timesteps in [25, 50, 100, 999, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_thresholding(self):\n self.check_over_configs(thresholding=False)\n for order in [1, 2, 3]:\n for solver_type in [\"logrho\"]:\n for threshold in [0.5, 1.0, 2.0]:\n for prediction_type in [\"epsilon\", \"sample\"]:\n self.check_over_configs(\n thresholding=True,\n prediction_type=prediction_type,\n sample_max_value=threshold,\n algorithm_type=\"deis\",\n solver_order=order,\n solver_type=solver_type,\n )\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(prediction_type=prediction_type)\n\n def test_solver_order_and_type(self):\n for algorithm_type in [\"deis\"]:\n for solver_type in [\"logrho\"]:\n for order in [1, 2, 3]:\n for prediction_type in [\"epsilon\", \"sample\"]:\n self.check_over_configs(\n solver_order=order,\n solver_type=solver_type,\n prediction_type=prediction_type,\n algorithm_type=algorithm_type,\n )\n sample = self.full_loop(\n solver_order=order,\n solver_type=solver_type,\n prediction_type=prediction_type,\n algorithm_type=algorithm_type,\n )\n assert not torch.isnan(sample).any(), \"Samples have nan numbers\"\n\n def test_lower_order_final(self):\n self.check_over_configs(lower_order_final=True)\n self.check_over_configs(lower_order_final=False)\n\n def test_inference_steps(self):\n for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:\n self.check_over_forward(num_inference_steps=num_inference_steps, time_step=0)\n\n def test_full_loop_no_noise(self):\n sample = self.full_loop()\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.23916) < 1e-3\n\n def test_full_loop_with_v_prediction(self):\n sample = self.full_loop(prediction_type=\"v_prediction\")\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.091) < 1e-3\n\n def test_fp16_support(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(thresholding=True, dynamic_thresholding_ratio=0)\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 10\n model = self.dummy_model()\n sample = self.dummy_sample_deter.half()\n scheduler.set_timesteps(num_inference_steps)\n\n for i, t in enumerate(scheduler.timesteps):\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample).prev_sample\n\n assert sample.dtype == torch.float16\n\n def test_full_loop_with_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 10\n t_start = 8\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n scheduler.set_timesteps(num_inference_steps)\n\n # add noise\n noise = self.dummy_noise_deter\n timesteps = scheduler.timesteps[t_start * scheduler.order :]\n sample = scheduler.add_noise(sample, noise, timesteps[:1])\n\n for i, t in enumerate(timesteps):\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample).prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 315.3016) < 1e-2, f\" expected result sum 315.3016, but get {result_sum}\"\n assert abs(result_mean.item() - 0.41054) < 1e-3, f\" expected result mean 0.41054, but get {result_mean}\"\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_dpm_multi.py", "content": "import tempfile\n\nimport torch\n\nfrom diffusers import (\n DEISMultistepScheduler,\n DPMSolverMultistepScheduler,\n DPMSolverSinglestepScheduler,\n UniPCMultistepScheduler,\n)\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass DPMSolverMultistepSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (DPMSolverMultistepScheduler,)\n forward_default_kwargs = ((\"num_inference_steps\", 25),)\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1000,\n \"beta_start\": 0.0001,\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n \"solver_order\": 2,\n \"prediction_type\": \"epsilon\",\n \"thresholding\": False,\n \"sample_max_value\": 1.0,\n \"algorithm_type\": \"dpmsolver++\",\n \"solver_type\": \"midpoint\",\n \"lower_order_final\": False,\n \"euler_at_final\": False,\n \"lambda_min_clipped\": -float(\"inf\"),\n \"variance_type\": None,\n \"final_sigmas_type\": \"sigma_min\",\n }\n\n config.update(**kwargs)\n return config\n\n def check_over_configs(self, time_step=0, **config):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n sample = self.dummy_sample\n residual = 0.1 * sample\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(num_inference_steps)\n # copy over dummy past residuals\n scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n new_scheduler.set_timesteps(num_inference_steps)\n # copy over dummy past residuals\n new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order]\n\n output, new_output = sample, sample\n for t in range(time_step, time_step + scheduler.config.solver_order + 1):\n t = new_scheduler.timesteps[t]\n output = scheduler.step(residual, t, output, **kwargs).prev_sample\n new_output = new_scheduler.step(residual, t, new_output, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def test_from_save_pretrained(self):\n pass\n\n def check_over_forward(self, time_step=0, **forward_kwargs):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n sample = self.dummy_sample\n residual = 0.1 * sample\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(num_inference_steps)\n\n # copy over dummy past residuals (must be after setting timesteps)\n scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n # copy over dummy past residuals\n new_scheduler.set_timesteps(num_inference_steps)\n\n # copy over dummy past residual (must be after setting timesteps)\n new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order]\n\n time_step = new_scheduler.timesteps[time_step]\n output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def full_loop(self, scheduler=None, **config):\n if scheduler is None:\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 10\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n scheduler.set_timesteps(num_inference_steps)\n\n generator = torch.manual_seed(0)\n\n for i, t in enumerate(scheduler.timesteps):\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample, generator=generator).prev_sample\n\n return sample\n\n def full_loop_custom_timesteps(self, **config):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 10\n scheduler.set_timesteps(num_inference_steps)\n timesteps = scheduler.timesteps\n # reset the timesteps using `timesteps`\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(num_inference_steps=None, timesteps=timesteps)\n\n generator = torch.manual_seed(0)\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n\n for i, t in enumerate(scheduler.timesteps):\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample, generator=generator).prev_sample\n\n return sample\n\n def test_step_shape(self):\n kwargs = dict(self.forward_default_kwargs)\n\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n sample = self.dummy_sample\n residual = 0.1 * sample\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n scheduler.set_timesteps(num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n # copy over dummy past residuals (must be done after set_timesteps)\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]\n scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]\n\n time_step_0 = scheduler.timesteps[5]\n time_step_1 = scheduler.timesteps[6]\n\n output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample\n output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample\n\n self.assertEqual(output_0.shape, sample.shape)\n self.assertEqual(output_0.shape, output_1.shape)\n\n def test_timesteps(self):\n for timesteps in [25, 50, 100, 999, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_thresholding(self):\n self.check_over_configs(thresholding=False)\n for order in [1, 2, 3]:\n for solver_type in [\"midpoint\", \"heun\"]:\n for threshold in [0.5, 1.0, 2.0]:\n for prediction_type in [\"epsilon\", \"sample\"]:\n self.check_over_configs(\n thresholding=True,\n prediction_type=prediction_type,\n sample_max_value=threshold,\n algorithm_type=\"dpmsolver++\",\n solver_order=order,\n solver_type=solver_type,\n )\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(prediction_type=prediction_type)\n\n def test_solver_order_and_type(self):\n for algorithm_type in [\"dpmsolver\", \"dpmsolver++\", \"sde-dpmsolver\", \"sde-dpmsolver++\"]:\n for solver_type in [\"midpoint\", \"heun\"]:\n for order in [1, 2, 3]:\n for prediction_type in [\"epsilon\", \"sample\"]:\n if algorithm_type in [\"sde-dpmsolver\", \"sde-dpmsolver++\"]:\n if order == 3:\n continue\n else:\n self.check_over_configs(\n solver_order=order,\n solver_type=solver_type,\n prediction_type=prediction_type,\n algorithm_type=algorithm_type,\n )\n sample = self.full_loop(\n solver_order=order,\n solver_type=solver_type,\n prediction_type=prediction_type,\n algorithm_type=algorithm_type,\n )\n assert not torch.isnan(sample).any(), \"Samples have nan numbers\"\n\n def test_lower_order_final(self):\n self.check_over_configs(lower_order_final=True)\n self.check_over_configs(lower_order_final=False)\n\n def test_euler_at_final(self):\n self.check_over_configs(euler_at_final=True)\n self.check_over_configs(euler_at_final=False)\n\n def test_lambda_min_clipped(self):\n self.check_over_configs(lambda_min_clipped=-float(\"inf\"))\n self.check_over_configs(lambda_min_clipped=-5.1)\n\n def test_variance_type(self):\n self.check_over_configs(variance_type=None)\n self.check_over_configs(variance_type=\"learned_range\")\n\n def test_inference_steps(self):\n for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:\n self.check_over_forward(num_inference_steps=num_inference_steps, time_step=0)\n\n def test_rescale_betas_zero_snr(self):\n for rescale_betas_zero_snr in [True, False]:\n self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr)\n\n def test_full_loop_no_noise(self):\n sample = self.full_loop()\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.3301) < 1e-3\n\n def test_full_loop_with_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 10\n t_start = 5\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n scheduler.set_timesteps(num_inference_steps)\n\n # add noise\n noise = self.dummy_noise_deter\n timesteps = scheduler.timesteps[t_start * scheduler.order :]\n sample = scheduler.add_noise(sample, noise, timesteps[:1])\n\n for i, t in enumerate(timesteps):\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample).prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 318.4111) < 1e-2, f\" expected result sum 318.4111, but get {result_sum}\"\n assert abs(result_mean.item() - 0.4146) < 1e-3, f\" expected result mean 0.4146, but get {result_mean}\"\n\n def test_full_loop_no_noise_thres(self):\n sample = self.full_loop(thresholding=True, dynamic_thresholding_ratio=0.87, sample_max_value=0.5)\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 1.1364) < 1e-3\n\n def test_full_loop_with_v_prediction(self):\n sample = self.full_loop(prediction_type=\"v_prediction\")\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.2251) < 1e-3\n\n def test_full_loop_with_karras_and_v_prediction(self):\n sample = self.full_loop(prediction_type=\"v_prediction\", use_karras_sigmas=True)\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.2096) < 1e-3\n\n def test_full_loop_with_lu_and_v_prediction(self):\n sample = self.full_loop(prediction_type=\"v_prediction\", use_lu_lambdas=True)\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.1554) < 1e-3\n\n def test_switch(self):\n # make sure that iterating over schedulers with same config names gives same results\n # for defaults\n scheduler = DPMSolverMultistepScheduler(**self.get_scheduler_config())\n sample = self.full_loop(scheduler=scheduler)\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.3301) < 1e-3\n\n scheduler = DPMSolverSinglestepScheduler.from_config(scheduler.config)\n scheduler = UniPCMultistepScheduler.from_config(scheduler.config)\n scheduler = DEISMultistepScheduler.from_config(scheduler.config)\n scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)\n\n sample = self.full_loop(scheduler=scheduler)\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.3301) < 1e-3\n\n def test_fp16_support(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(thresholding=True, dynamic_thresholding_ratio=0)\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 10\n model = self.dummy_model()\n sample = self.dummy_sample_deter.half()\n scheduler.set_timesteps(num_inference_steps)\n\n for i, t in enumerate(scheduler.timesteps):\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample).prev_sample\n\n assert sample.dtype == torch.float16\n\n def test_duplicated_timesteps(self):\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(scheduler.config.num_train_timesteps)\n assert len(scheduler.timesteps) == scheduler.num_inference_steps\n\n def test_custom_timesteps(self):\n for algorithm_type in [\"dpmsolver++\", \"sde-dpmsolver++\"]:\n for prediction_type in [\"epsilon\", \"sample\", \"v_prediction\"]:\n for final_sigmas_type in [\"sigma_min\", \"zero\"]:\n sample = self.full_loop(\n algorithm_type=algorithm_type,\n prediction_type=prediction_type,\n final_sigmas_type=final_sigmas_type,\n )\n sample_custom_timesteps = self.full_loop_custom_timesteps(\n algorithm_type=algorithm_type,\n prediction_type=prediction_type,\n final_sigmas_type=final_sigmas_type,\n )\n assert (\n torch.sum(torch.abs(sample - sample_custom_timesteps)) < 1e-5\n ), f\"Scheduler outputs are not identical for algorithm_type: {algorithm_type}, prediction_type: {prediction_type} and final_sigmas_type: {final_sigmas_type}\"\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_dpm_multi_inverse.py", "content": "import tempfile\n\nimport torch\n\nfrom diffusers import DPMSolverMultistepInverseScheduler, DPMSolverMultistepScheduler\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass DPMSolverMultistepSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (DPMSolverMultistepInverseScheduler,)\n forward_default_kwargs = ((\"num_inference_steps\", 25),)\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1000,\n \"beta_start\": 0.0001,\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n \"solver_order\": 2,\n \"prediction_type\": \"epsilon\",\n \"thresholding\": False,\n \"sample_max_value\": 1.0,\n \"algorithm_type\": \"dpmsolver++\",\n \"solver_type\": \"midpoint\",\n \"lower_order_final\": False,\n \"lambda_min_clipped\": -float(\"inf\"),\n \"variance_type\": None,\n }\n\n config.update(**kwargs)\n return config\n\n def check_over_configs(self, time_step=0, **config):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n sample = self.dummy_sample\n residual = 0.1 * sample\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(num_inference_steps)\n # copy over dummy past residuals\n scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n new_scheduler.set_timesteps(num_inference_steps)\n # copy over dummy past residuals\n new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order]\n\n output, new_output = sample, sample\n for t in range(time_step, time_step + scheduler.config.solver_order + 1):\n t = scheduler.timesteps[t]\n output = scheduler.step(residual, t, output, **kwargs).prev_sample\n new_output = new_scheduler.step(residual, t, new_output, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def test_from_save_pretrained(self):\n pass\n\n def check_over_forward(self, time_step=0, **forward_kwargs):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n sample = self.dummy_sample\n residual = 0.1 * sample\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(num_inference_steps)\n\n # copy over dummy past residuals (must be after setting timesteps)\n scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n # copy over dummy past residuals\n new_scheduler.set_timesteps(num_inference_steps)\n\n # copy over dummy past residual (must be after setting timesteps)\n new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order]\n\n output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def full_loop(self, scheduler=None, **config):\n if scheduler is None:\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 10\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n scheduler.set_timesteps(num_inference_steps)\n\n for i, t in enumerate(scheduler.timesteps):\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample).prev_sample\n\n return sample\n\n def test_step_shape(self):\n kwargs = dict(self.forward_default_kwargs)\n\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n sample = self.dummy_sample\n residual = 0.1 * sample\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n scheduler.set_timesteps(num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n # copy over dummy past residuals (must be done after set_timesteps)\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]\n scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]\n\n time_step_0 = scheduler.timesteps[5]\n time_step_1 = scheduler.timesteps[6]\n\n output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample\n output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample\n\n self.assertEqual(output_0.shape, sample.shape)\n self.assertEqual(output_0.shape, output_1.shape)\n\n def test_timesteps(self):\n for timesteps in [25, 50, 100, 999, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_thresholding(self):\n self.check_over_configs(thresholding=False)\n for order in [1, 2, 3]:\n for solver_type in [\"midpoint\", \"heun\"]:\n for threshold in [0.5, 1.0, 2.0]:\n for prediction_type in [\"epsilon\", \"sample\"]:\n self.check_over_configs(\n thresholding=True,\n prediction_type=prediction_type,\n sample_max_value=threshold,\n algorithm_type=\"dpmsolver++\",\n solver_order=order,\n solver_type=solver_type,\n )\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(prediction_type=prediction_type)\n\n def test_solver_order_and_type(self):\n for algorithm_type in [\"dpmsolver\", \"dpmsolver++\"]:\n for solver_type in [\"midpoint\", \"heun\"]:\n for order in [1, 2, 3]:\n for prediction_type in [\"epsilon\", \"sample\"]:\n self.check_over_configs(\n solver_order=order,\n solver_type=solver_type,\n prediction_type=prediction_type,\n algorithm_type=algorithm_type,\n )\n sample = self.full_loop(\n solver_order=order,\n solver_type=solver_type,\n prediction_type=prediction_type,\n algorithm_type=algorithm_type,\n )\n assert not torch.isnan(sample).any(), \"Samples have nan numbers\"\n\n def test_lower_order_final(self):\n self.check_over_configs(lower_order_final=True)\n self.check_over_configs(lower_order_final=False)\n\n def test_lambda_min_clipped(self):\n self.check_over_configs(lambda_min_clipped=-float(\"inf\"))\n self.check_over_configs(lambda_min_clipped=-5.1)\n\n def test_variance_type(self):\n self.check_over_configs(variance_type=None)\n self.check_over_configs(variance_type=\"learned_range\")\n\n def test_timestep_spacing(self):\n for timestep_spacing in [\"trailing\", \"leading\"]:\n self.check_over_configs(timestep_spacing=timestep_spacing)\n\n def test_inference_steps(self):\n for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:\n self.check_over_forward(num_inference_steps=num_inference_steps, time_step=0)\n\n def test_full_loop_no_noise(self):\n sample = self.full_loop()\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.7047) < 1e-3\n\n def test_full_loop_no_noise_thres(self):\n sample = self.full_loop(thresholding=True, dynamic_thresholding_ratio=0.87, sample_max_value=0.5)\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 19.8933) < 1e-3\n\n def test_full_loop_with_v_prediction(self):\n sample = self.full_loop(prediction_type=\"v_prediction\")\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 1.5194) < 1e-3\n\n def test_full_loop_with_karras_and_v_prediction(self):\n sample = self.full_loop(prediction_type=\"v_prediction\", use_karras_sigmas=True)\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 1.7833) < 2e-3\n\n def test_switch(self):\n # make sure that iterating over schedulers with same config names gives same results\n # for defaults\n scheduler = DPMSolverMultistepInverseScheduler(**self.get_scheduler_config())\n sample = self.full_loop(scheduler=scheduler)\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.7047) < 1e-3\n\n scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)\n scheduler = DPMSolverMultistepInverseScheduler.from_config(scheduler.config)\n\n sample = self.full_loop(scheduler=scheduler)\n new_result_mean = torch.mean(torch.abs(sample))\n\n assert abs(new_result_mean.item() - result_mean.item()) < 1e-3\n\n def test_fp16_support(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(thresholding=True, dynamic_thresholding_ratio=0)\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 10\n model = self.dummy_model()\n sample = self.dummy_sample_deter.half()\n scheduler.set_timesteps(num_inference_steps)\n\n for i, t in enumerate(scheduler.timesteps):\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample).prev_sample\n\n assert sample.dtype == torch.float16\n\n def test_unique_timesteps(self, **config):\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(scheduler.config.num_train_timesteps)\n assert len(scheduler.timesteps.unique()) == scheduler.num_inference_steps\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_dpm_sde.py", "content": "import torch\n\nfrom diffusers import DPMSolverSDEScheduler\nfrom diffusers.utils.testing_utils import require_torchsde, torch_device\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\n@require_torchsde\nclass DPMSolverSDESchedulerTest(SchedulerCommonTest):\n scheduler_classes = (DPMSolverSDEScheduler,)\n num_inference_steps = 10\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1100,\n \"beta_start\": 0.0001,\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n \"noise_sampler_seed\": 0,\n }\n\n config.update(**kwargs)\n return config\n\n def test_timesteps(self):\n for timesteps in [10, 50, 100, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_betas(self):\n for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):\n self.check_over_configs(beta_start=beta_start, beta_end=beta_end)\n\n def test_schedules(self):\n for schedule in [\"linear\", \"scaled_linear\"]:\n self.check_over_configs(beta_schedule=schedule)\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(prediction_type=prediction_type)\n\n def test_full_loop_no_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n sample = sample.to(torch_device)\n\n for i, t in enumerate(scheduler.timesteps):\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n if torch_device in [\"mps\"]:\n assert abs(result_sum.item() - 167.47821044921875) < 1e-2\n assert abs(result_mean.item() - 0.2178705964565277) < 1e-3\n elif torch_device in [\"cuda\"]:\n assert abs(result_sum.item() - 171.59352111816406) < 1e-2\n assert abs(result_mean.item() - 0.22342906892299652) < 1e-3\n else:\n assert abs(result_sum.item() - 162.52383422851562) < 1e-2\n assert abs(result_mean.item() - 0.211619570851326) < 1e-3\n\n def test_full_loop_with_v_prediction(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(prediction_type=\"v_prediction\")\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n sample = sample.to(torch_device)\n\n for i, t in enumerate(scheduler.timesteps):\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n if torch_device in [\"mps\"]:\n assert abs(result_sum.item() - 124.77149200439453) < 1e-2\n assert abs(result_mean.item() - 0.16226289014816284) < 1e-3\n elif torch_device in [\"cuda\"]:\n assert abs(result_sum.item() - 128.1663360595703) < 1e-2\n assert abs(result_mean.item() - 0.16688326001167297) < 1e-3\n else:\n assert abs(result_sum.item() - 119.8487548828125) < 1e-2\n assert abs(result_mean.item() - 0.1560530662536621) < 1e-3\n\n def test_full_loop_device(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps, device=torch_device)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma\n\n for t in scheduler.timesteps:\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n if torch_device in [\"mps\"]:\n assert abs(result_sum.item() - 167.46957397460938) < 1e-2\n assert abs(result_mean.item() - 0.21805934607982635) < 1e-3\n elif torch_device in [\"cuda\"]:\n assert abs(result_sum.item() - 171.59353637695312) < 1e-2\n assert abs(result_mean.item() - 0.22342908382415771) < 1e-3\n else:\n assert abs(result_sum.item() - 162.52383422851562) < 1e-2\n assert abs(result_mean.item() - 0.211619570851326) < 1e-3\n\n def test_full_loop_device_karras_sigmas(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config, use_karras_sigmas=True)\n\n scheduler.set_timesteps(self.num_inference_steps, device=torch_device)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma\n sample = sample.to(torch_device)\n\n for t in scheduler.timesteps:\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n if torch_device in [\"mps\"]:\n assert abs(result_sum.item() - 176.66974135742188) < 1e-2\n assert abs(result_mean.item() - 0.23003872730981811) < 1e-2\n elif torch_device in [\"cuda\"]:\n assert abs(result_sum.item() - 177.63653564453125) < 1e-2\n assert abs(result_mean.item() - 0.23003872730981811) < 1e-2\n else:\n assert abs(result_sum.item() - 170.3135223388672) < 1e-2\n assert abs(result_mean.item() - 0.23003872730981811) < 1e-2\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_dpm_single.py", "content": "import tempfile\n\nimport torch\n\nfrom diffusers import (\n DEISMultistepScheduler,\n DPMSolverMultistepScheduler,\n DPMSolverSinglestepScheduler,\n UniPCMultistepScheduler,\n)\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass DPMSolverSinglestepSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (DPMSolverSinglestepScheduler,)\n forward_default_kwargs = ((\"num_inference_steps\", 25),)\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1000,\n \"beta_start\": 0.0001,\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n \"solver_order\": 2,\n \"prediction_type\": \"epsilon\",\n \"thresholding\": False,\n \"sample_max_value\": 1.0,\n \"algorithm_type\": \"dpmsolver++\",\n \"solver_type\": \"midpoint\",\n \"lambda_min_clipped\": -float(\"inf\"),\n \"variance_type\": None,\n \"final_sigmas_type\": \"sigma_min\",\n }\n\n config.update(**kwargs)\n return config\n\n def check_over_configs(self, time_step=0, **config):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n sample = self.dummy_sample\n residual = 0.1 * sample\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(num_inference_steps)\n # copy over dummy past residuals\n scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n new_scheduler.set_timesteps(num_inference_steps)\n # copy over dummy past residuals\n new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order]\n\n output, new_output = sample, sample\n for t in range(time_step, time_step + scheduler.config.solver_order + 1):\n t = scheduler.timesteps[t]\n output = scheduler.step(residual, t, output, **kwargs).prev_sample\n new_output = new_scheduler.step(residual, t, new_output, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def test_from_save_pretrained(self):\n pass\n\n def check_over_forward(self, time_step=0, **forward_kwargs):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n sample = self.dummy_sample\n residual = 0.1 * sample\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(num_inference_steps)\n\n # copy over dummy past residuals (must be after setting timesteps)\n scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n # copy over dummy past residuals\n new_scheduler.set_timesteps(num_inference_steps)\n\n # copy over dummy past residual (must be after setting timesteps)\n new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order]\n\n output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def full_loop(self, scheduler=None, **config):\n if scheduler is None:\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 10\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n scheduler.set_timesteps(num_inference_steps)\n\n for i, t in enumerate(scheduler.timesteps):\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample).prev_sample\n\n return sample\n\n def full_loop_custom_timesteps(self, **config):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 10\n scheduler.set_timesteps(num_inference_steps)\n timesteps = scheduler.timesteps\n # reset the timesteps using`timesteps`\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(num_inference_steps=None, timesteps=timesteps)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n\n for i, t in enumerate(scheduler.timesteps):\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample).prev_sample\n\n return sample\n\n def test_full_uneven_loop(self):\n scheduler = DPMSolverSinglestepScheduler(**self.get_scheduler_config())\n num_inference_steps = 50\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n scheduler.set_timesteps(num_inference_steps)\n\n # make sure that the first t is uneven\n for i, t in enumerate(scheduler.timesteps[3:]):\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample).prev_sample\n\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.2574) < 1e-3\n\n def test_timesteps(self):\n for timesteps in [25, 50, 100, 999, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_switch(self):\n # make sure that iterating over schedulers with same config names gives same results\n # for defaults\n scheduler = DPMSolverSinglestepScheduler(**self.get_scheduler_config())\n sample = self.full_loop(scheduler=scheduler)\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.2791) < 1e-3\n\n scheduler = DEISMultistepScheduler.from_config(scheduler.config)\n scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)\n scheduler = UniPCMultistepScheduler.from_config(scheduler.config)\n scheduler = DPMSolverSinglestepScheduler.from_config(scheduler.config)\n\n sample = self.full_loop(scheduler=scheduler)\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.2791) < 1e-3\n\n def test_thresholding(self):\n self.check_over_configs(thresholding=False)\n for order in [1, 2, 3]:\n for solver_type in [\"midpoint\", \"heun\"]:\n for threshold in [0.5, 1.0, 2.0]:\n for prediction_type in [\"epsilon\", \"sample\"]:\n self.check_over_configs(\n thresholding=True,\n prediction_type=prediction_type,\n sample_max_value=threshold,\n algorithm_type=\"dpmsolver++\",\n solver_order=order,\n solver_type=solver_type,\n )\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(prediction_type=prediction_type)\n\n def test_solver_order_and_type(self):\n for algorithm_type in [\"dpmsolver\", \"dpmsolver++\"]:\n for solver_type in [\"midpoint\", \"heun\"]:\n for order in [1, 2, 3]:\n for prediction_type in [\"epsilon\", \"sample\"]:\n self.check_over_configs(\n solver_order=order,\n solver_type=solver_type,\n prediction_type=prediction_type,\n algorithm_type=algorithm_type,\n )\n sample = self.full_loop(\n solver_order=order,\n solver_type=solver_type,\n prediction_type=prediction_type,\n algorithm_type=algorithm_type,\n )\n assert not torch.isnan(sample).any(), \"Samples have nan numbers\"\n\n def test_lower_order_final(self):\n self.check_over_configs(lower_order_final=True)\n self.check_over_configs(lower_order_final=False)\n\n def test_lambda_min_clipped(self):\n self.check_over_configs(lambda_min_clipped=-float(\"inf\"))\n self.check_over_configs(lambda_min_clipped=-5.1)\n\n def test_variance_type(self):\n self.check_over_configs(variance_type=None)\n self.check_over_configs(variance_type=\"learned_range\")\n\n def test_inference_steps(self):\n for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:\n self.check_over_forward(num_inference_steps=num_inference_steps, time_step=0)\n\n def test_full_loop_no_noise(self):\n sample = self.full_loop()\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.2791) < 1e-3\n\n def test_full_loop_with_karras(self):\n sample = self.full_loop(use_karras_sigmas=True)\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.2248) < 1e-3\n\n def test_full_loop_with_v_prediction(self):\n sample = self.full_loop(prediction_type=\"v_prediction\")\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.1453) < 1e-3\n\n def test_full_loop_with_karras_and_v_prediction(self):\n sample = self.full_loop(prediction_type=\"v_prediction\", use_karras_sigmas=True)\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.0649) < 1e-3\n\n def test_fp16_support(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(thresholding=True, dynamic_thresholding_ratio=0)\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 10\n model = self.dummy_model()\n sample = self.dummy_sample_deter.half()\n scheduler.set_timesteps(num_inference_steps)\n\n for i, t in enumerate(scheduler.timesteps):\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample).prev_sample\n\n assert sample.dtype == torch.float16\n\n def test_step_shape(self):\n kwargs = dict(self.forward_default_kwargs)\n\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n sample = self.dummy_sample\n residual = 0.1 * sample\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n scheduler.set_timesteps(num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n # copy over dummy past residuals (must be done after set_timesteps)\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]\n scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]\n\n time_step_0 = scheduler.timesteps[0]\n time_step_1 = scheduler.timesteps[1]\n\n output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample\n output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample\n\n self.assertEqual(output_0.shape, sample.shape)\n self.assertEqual(output_0.shape, output_1.shape)\n\n def test_full_loop_with_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 10\n t_start = 5\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n scheduler.set_timesteps(num_inference_steps)\n\n # add noise\n noise = self.dummy_noise_deter\n timesteps = scheduler.timesteps[t_start * scheduler.order :]\n sample = scheduler.add_noise(sample, noise, timesteps[:1])\n\n for i, t in enumerate(timesteps):\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample).prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 269.2187) < 1e-2, f\" expected result sum 269.2187, but get {result_sum}\"\n assert abs(result_mean.item() - 0.3505) < 1e-3, f\" expected result mean 0.3505, but get {result_mean}\"\n\n def test_custom_timesteps(self):\n for prediction_type in [\"epsilon\", \"sample\", \"v_prediction\"]:\n for lower_order_final in [True, False]:\n for final_sigmas_type in [\"sigma_min\", \"zero\"]:\n sample = self.full_loop(\n prediction_type=prediction_type,\n lower_order_final=lower_order_final,\n final_sigmas_type=final_sigmas_type,\n )\n sample_custom_timesteps = self.full_loop_custom_timesteps(\n prediction_type=prediction_type,\n lower_order_final=lower_order_final,\n final_sigmas_type=final_sigmas_type,\n )\n assert (\n torch.sum(torch.abs(sample - sample_custom_timesteps)) < 1e-5\n ), f\"Scheduler outputs are not identical for prediction_type: {prediction_type}, lower_order_final: {lower_order_final} and final_sigmas_type: {final_sigmas_type}\"\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_edm_dpmsolver_multistep.py", "content": "import tempfile\nimport unittest\n\nimport torch\n\nfrom diffusers import (\n EDMDPMSolverMultistepScheduler,\n)\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass EDMDPMSolverMultistepSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (EDMDPMSolverMultistepScheduler,)\n forward_default_kwargs = ((\"num_inference_steps\", 25),)\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"sigma_min\": 0.002,\n \"sigma_max\": 80.0,\n \"sigma_data\": 0.5,\n \"num_train_timesteps\": 1000,\n \"solver_order\": 2,\n \"prediction_type\": \"epsilon\",\n \"thresholding\": False,\n \"sample_max_value\": 1.0,\n \"algorithm_type\": \"dpmsolver++\",\n \"solver_type\": \"midpoint\",\n \"lower_order_final\": False,\n \"euler_at_final\": False,\n \"final_sigmas_type\": \"sigma_min\",\n }\n\n config.update(**kwargs)\n return config\n\n def check_over_configs(self, time_step=0, **config):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n sample = self.dummy_sample\n residual = 0.1 * sample\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(num_inference_steps)\n # copy over dummy past residuals\n scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n new_scheduler.set_timesteps(num_inference_steps)\n # copy over dummy past residuals\n new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order]\n\n output, new_output = sample, sample\n for t in range(time_step, time_step + scheduler.config.solver_order + 1):\n t = new_scheduler.timesteps[t]\n output = scheduler.step(residual, t, output, **kwargs).prev_sample\n new_output = new_scheduler.step(residual, t, new_output, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def test_from_save_pretrained(self):\n pass\n\n def check_over_forward(self, time_step=0, **forward_kwargs):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n sample = self.dummy_sample\n residual = 0.1 * sample\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(num_inference_steps)\n\n # copy over dummy past residuals (must be after setting timesteps)\n scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n # copy over dummy past residuals\n new_scheduler.set_timesteps(num_inference_steps)\n\n # copy over dummy past residual (must be after setting timesteps)\n new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order]\n\n time_step = new_scheduler.timesteps[time_step]\n output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def full_loop(self, scheduler=None, **config):\n if scheduler is None:\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 10\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n scheduler.set_timesteps(num_inference_steps)\n\n for i, t in enumerate(scheduler.timesteps):\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample).prev_sample\n\n return sample\n\n def test_step_shape(self):\n kwargs = dict(self.forward_default_kwargs)\n\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n sample = self.dummy_sample\n residual = 0.1 * sample\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n scheduler.set_timesteps(num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n # copy over dummy past residuals (must be done after set_timesteps)\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]\n scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]\n\n time_step_0 = scheduler.timesteps[5]\n time_step_1 = scheduler.timesteps[6]\n\n output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample\n output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample\n\n self.assertEqual(output_0.shape, sample.shape)\n self.assertEqual(output_0.shape, output_1.shape)\n\n def test_timesteps(self):\n for timesteps in [25, 50, 100, 999, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_thresholding(self):\n self.check_over_configs(thresholding=False)\n for order in [1, 2, 3]:\n for solver_type in [\"midpoint\", \"heun\"]:\n for threshold in [0.5, 1.0, 2.0]:\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(\n thresholding=True,\n prediction_type=prediction_type,\n sample_max_value=threshold,\n algorithm_type=\"dpmsolver++\",\n solver_order=order,\n solver_type=solver_type,\n )\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(prediction_type=prediction_type)\n\n # TODO (patil-suraj): Fix this test\n @unittest.skip(\"Skip for now, as it failing currently but works with the actual model\")\n def test_solver_order_and_type(self):\n for algorithm_type in [\"dpmsolver++\", \"sde-dpmsolver++\"]:\n for solver_type in [\"midpoint\", \"heun\"]:\n for order in [1, 2, 3]:\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n if algorithm_type == \"sde-dpmsolver++\":\n if order == 3:\n continue\n else:\n self.check_over_configs(\n solver_order=order,\n solver_type=solver_type,\n prediction_type=prediction_type,\n algorithm_type=algorithm_type,\n )\n sample = self.full_loop(\n solver_order=order,\n solver_type=solver_type,\n prediction_type=prediction_type,\n algorithm_type=algorithm_type,\n )\n assert (\n not torch.isnan(sample).any()\n ), f\"Samples have nan numbers, {order}, {solver_type}, {prediction_type}, {algorithm_type}\"\n\n def test_lower_order_final(self):\n self.check_over_configs(lower_order_final=True)\n self.check_over_configs(lower_order_final=False)\n\n def test_euler_at_final(self):\n self.check_over_configs(euler_at_final=True)\n self.check_over_configs(euler_at_final=False)\n\n def test_inference_steps(self):\n for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:\n self.check_over_forward(num_inference_steps=num_inference_steps, time_step=0)\n\n def test_full_loop_no_noise(self):\n sample = self.full_loop()\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.0001) < 1e-3\n\n def test_full_loop_with_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 10\n t_start = 5\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n scheduler.set_timesteps(num_inference_steps)\n\n # add noise\n noise = self.dummy_noise_deter\n timesteps = scheduler.timesteps[t_start * scheduler.order :]\n sample = scheduler.add_noise(sample, noise, timesteps[:1])\n\n for i, t in enumerate(timesteps):\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample).prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 8.1661) < 1e-2, f\" expected result sum 8.1661, but get {result_sum}\"\n assert abs(result_mean.item() - 0.0106) < 1e-3, f\" expected result mean 0.0106, but get {result_mean}\"\n\n def test_full_loop_no_noise_thres(self):\n sample = self.full_loop(thresholding=True, dynamic_thresholding_ratio=0.87, sample_max_value=0.5)\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.0080) < 1e-3\n\n def test_full_loop_with_v_prediction(self):\n sample = self.full_loop(prediction_type=\"v_prediction\")\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.0092) < 1e-3\n\n def test_duplicated_timesteps(self, **config):\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(scheduler.config.num_train_timesteps)\n assert len(scheduler.timesteps) == scheduler.num_inference_steps\n\n def test_trained_betas(self):\n pass\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_edm_euler.py", "content": "import inspect\nimport tempfile\nimport unittest\nfrom typing import Dict, List, Tuple\n\nimport torch\n\nfrom diffusers import EDMEulerScheduler\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass EDMEulerSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (EDMEulerScheduler,)\n forward_default_kwargs = ((\"num_inference_steps\", 10),)\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 256,\n \"sigma_min\": 0.002,\n \"sigma_max\": 80.0,\n }\n\n config.update(**kwargs)\n return config\n\n def test_timesteps(self):\n for timesteps in [10, 50, 100, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(prediction_type=prediction_type)\n\n def test_full_loop_no_noise(self, num_inference_steps=10, seed=0):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(num_inference_steps)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n\n for i, t in enumerate(scheduler.timesteps):\n scaled_sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(scaled_sample, t)\n\n output = scheduler.step(model_output, t, sample)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 34.1855) < 1e-3\n assert abs(result_mean.item() - 0.044) < 1e-3\n\n def test_full_loop_device(self, num_inference_steps=10, seed=0):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(num_inference_steps)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n\n for i, t in enumerate(scheduler.timesteps):\n scaled_sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(scaled_sample, t)\n\n output = scheduler.step(model_output, t, sample)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 34.1855) < 1e-3\n assert abs(result_mean.item() - 0.044) < 1e-3\n\n # Override test_from_save_pretrined to use EDMEulerScheduler-specific logic\n def test_from_save_pretrained(self):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n sample = self.dummy_sample\n residual = 0.1 * sample\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n\n scheduler.set_timesteps(num_inference_steps)\n new_scheduler.set_timesteps(num_inference_steps)\n timestep = scheduler.timesteps[0]\n\n sample = self.dummy_sample\n\n scaled_sample = scheduler.scale_model_input(sample, timestep)\n residual = 0.1 * scaled_sample\n\n new_scaled_sample = new_scheduler.scale_model_input(sample, timestep)\n new_residual = 0.1 * new_scaled_sample\n\n if \"generator\" in set(inspect.signature(scheduler.step).parameters.keys()):\n kwargs[\"generator\"] = torch.manual_seed(0)\n output = scheduler.step(residual, timestep, sample, **kwargs).prev_sample\n\n if \"generator\" in set(inspect.signature(scheduler.step).parameters.keys()):\n kwargs[\"generator\"] = torch.manual_seed(0)\n new_output = new_scheduler.step(new_residual, timestep, sample, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n # Override test_from_save_pretrined to use EDMEulerScheduler-specific logic\n def test_step_shape(self):\n num_inference_steps = 10\n\n scheduler_config = self.get_scheduler_config()\n scheduler = self.scheduler_classes[0](**scheduler_config)\n\n scheduler.set_timesteps(num_inference_steps)\n\n timestep_0 = scheduler.timesteps[0]\n timestep_1 = scheduler.timesteps[1]\n\n sample = self.dummy_sample\n scaled_sample = scheduler.scale_model_input(sample, timestep_0)\n residual = 0.1 * scaled_sample\n\n output_0 = scheduler.step(residual, timestep_0, sample).prev_sample\n output_1 = scheduler.step(residual, timestep_1, sample).prev_sample\n\n self.assertEqual(output_0.shape, sample.shape)\n self.assertEqual(output_0.shape, output_1.shape)\n\n # Override test_from_save_pretrained to use EDMEulerScheduler-specific logic\n def test_scheduler_outputs_equivalence(self):\n def set_nan_tensor_to_zero(t):\n t[t != t] = 0\n return t\n\n def recursive_check(tuple_object, dict_object):\n if isinstance(tuple_object, (List, Tuple)):\n for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object.values()):\n recursive_check(tuple_iterable_value, dict_iterable_value)\n elif isinstance(tuple_object, Dict):\n for tuple_iterable_value, dict_iterable_value in zip(tuple_object.values(), dict_object.values()):\n recursive_check(tuple_iterable_value, dict_iterable_value)\n elif tuple_object is None:\n return\n else:\n self.assertTrue(\n torch.allclose(\n set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5\n ),\n msg=(\n \"Tuple and dict output are not equal. Difference:\"\n f\" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:\"\n f\" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has\"\n f\" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}.\"\n ),\n )\n\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", 50)\n\n timestep = 0\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(num_inference_steps)\n timestep = scheduler.timesteps[0]\n\n sample = self.dummy_sample\n scaled_sample = scheduler.scale_model_input(sample, timestep)\n residual = 0.1 * scaled_sample\n\n # Set the seed before state as some schedulers are stochastic like EulerAncestralDiscreteScheduler, EulerDiscreteScheduler\n if \"generator\" in set(inspect.signature(scheduler.step).parameters.keys()):\n kwargs[\"generator\"] = torch.manual_seed(0)\n outputs_dict = scheduler.step(residual, timestep, sample, **kwargs)\n\n scheduler.set_timesteps(num_inference_steps)\n\n scaled_sample = scheduler.scale_model_input(sample, timestep)\n residual = 0.1 * scaled_sample\n\n # Set the seed before state as some schedulers are stochastic like EulerAncestralDiscreteScheduler, EulerDiscreteScheduler\n if \"generator\" in set(inspect.signature(scheduler.step).parameters.keys()):\n kwargs[\"generator\"] = torch.manual_seed(0)\n outputs_tuple = scheduler.step(residual, timestep, sample, return_dict=False, **kwargs)\n\n recursive_check(outputs_tuple, outputs_dict)\n\n @unittest.skip(reason=\"EDMEulerScheduler does not support beta schedules.\")\n def test_trained_betas(self):\n pass\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_euler.py", "content": "import torch\n\nfrom diffusers import EulerDiscreteScheduler\nfrom diffusers.utils.testing_utils import torch_device\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass EulerDiscreteSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (EulerDiscreteScheduler,)\n num_inference_steps = 10\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1100,\n \"beta_start\": 0.0001,\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n }\n\n config.update(**kwargs)\n return config\n\n def test_timesteps(self):\n for timesteps in [10, 50, 100, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_betas(self):\n for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):\n self.check_over_configs(beta_start=beta_start, beta_end=beta_end)\n\n def test_schedules(self):\n for schedule in [\"linear\", \"scaled_linear\"]:\n self.check_over_configs(beta_schedule=schedule)\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(prediction_type=prediction_type)\n\n def test_timestep_type(self):\n timestep_types = [\"discrete\", \"continuous\"]\n for timestep_type in timestep_types:\n self.check_over_configs(timestep_type=timestep_type)\n\n def test_karras_sigmas(self):\n self.check_over_configs(use_karras_sigmas=True, sigma_min=0.02, sigma_max=700.0)\n\n def test_rescale_betas_zero_snr(self):\n for rescale_betas_zero_snr in [True, False]:\n self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr)\n\n def full_loop(self, **config):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = self.num_inference_steps\n scheduler.set_timesteps(num_inference_steps)\n\n generator = torch.manual_seed(0)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n sample = sample.to(torch_device)\n\n for i, t in enumerate(scheduler.timesteps):\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample, generator=generator)\n sample = output.prev_sample\n return sample\n\n def full_loop_custom_timesteps(self, **config):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = self.num_inference_steps\n scheduler.set_timesteps(num_inference_steps)\n timesteps = scheduler.timesteps\n # reset the timesteps using `timesteps`\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(num_inference_steps=None, timesteps=timesteps)\n\n generator = torch.manual_seed(0)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n sample = sample.to(torch_device)\n\n for i, t in enumerate(scheduler.timesteps):\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample, generator=generator)\n sample = output.prev_sample\n return sample\n\n def full_loop_custom_sigmas(self, **config):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = self.num_inference_steps\n scheduler.set_timesteps(num_inference_steps)\n sigmas = scheduler.sigmas\n # reset the timesteps using `sigmas`\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(num_inference_steps=None, sigmas=sigmas)\n\n generator = torch.manual_seed(0)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n sample = sample.to(torch_device)\n\n for i, t in enumerate(scheduler.timesteps):\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample, generator=generator)\n sample = output.prev_sample\n return sample\n\n def test_full_loop_no_noise(self):\n sample = self.full_loop()\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 10.0807) < 1e-2\n assert abs(result_mean.item() - 0.0131) < 1e-3\n\n def test_full_loop_with_v_prediction(self):\n sample = self.full_loop(prediction_type=\"v_prediction\")\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 0.0002) < 1e-2\n assert abs(result_mean.item() - 2.2676e-06) < 1e-3\n\n def test_full_loop_device(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps, device=torch_device)\n\n generator = torch.manual_seed(0)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()\n sample = sample.to(torch_device)\n\n for t in scheduler.timesteps:\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample, generator=generator)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 10.0807) < 1e-2\n assert abs(result_mean.item() - 0.0131) < 1e-3\n\n def test_full_loop_device_karras_sigmas(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config, use_karras_sigmas=True)\n\n scheduler.set_timesteps(self.num_inference_steps, device=torch_device)\n\n generator = torch.manual_seed(0)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()\n sample = sample.to(torch_device)\n\n for t in scheduler.timesteps:\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample, generator=generator)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 124.52299499511719) < 1e-2\n assert abs(result_mean.item() - 0.16213932633399963) < 1e-3\n\n def test_full_loop_with_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps)\n\n generator = torch.manual_seed(0)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n\n # add noise\n t_start = self.num_inference_steps - 2\n noise = self.dummy_noise_deter\n noise = noise.to(sample.device)\n timesteps = scheduler.timesteps[t_start * scheduler.order :]\n sample = scheduler.add_noise(sample, noise, timesteps[:1])\n\n for i, t in enumerate(timesteps):\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample, generator=generator)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 57062.9297) < 1e-2, f\" expected result sum 57062.9297, but get {result_sum}\"\n assert abs(result_mean.item() - 74.3007) < 1e-3, f\" expected result mean 74.3007, but get {result_mean}\"\n\n def test_custom_timesteps(self):\n for prediction_type in [\"epsilon\", \"sample\", \"v_prediction\"]:\n for interpolation_type in [\"linear\", \"log_linear\"]:\n for final_sigmas_type in [\"sigma_min\", \"zero\"]:\n sample = self.full_loop(\n prediction_type=prediction_type,\n interpolation_type=interpolation_type,\n final_sigmas_type=final_sigmas_type,\n )\n sample_custom_timesteps = self.full_loop_custom_timesteps(\n prediction_type=prediction_type,\n interpolation_type=interpolation_type,\n final_sigmas_type=final_sigmas_type,\n )\n assert (\n torch.sum(torch.abs(sample - sample_custom_timesteps)) < 1e-5\n ), f\"Scheduler outputs are not identical for prediction_type: {prediction_type}, interpolation_type: {interpolation_type} and final_sigmas_type: {final_sigmas_type}\"\n\n def test_custom_sigmas(self):\n for prediction_type in [\"epsilon\", \"sample\", \"v_prediction\"]:\n for final_sigmas_type in [\"sigma_min\", \"zero\"]:\n sample = self.full_loop(\n prediction_type=prediction_type,\n final_sigmas_type=final_sigmas_type,\n )\n sample_custom_timesteps = self.full_loop_custom_sigmas(\n prediction_type=prediction_type,\n final_sigmas_type=final_sigmas_type,\n )\n assert (\n torch.sum(torch.abs(sample - sample_custom_timesteps)) < 1e-5\n ), f\"Scheduler outputs are not identical for prediction_type: {prediction_type} and final_sigmas_type: {final_sigmas_type}\"\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_euler_ancestral.py", "content": "import torch\n\nfrom diffusers import EulerAncestralDiscreteScheduler\nfrom diffusers.utils.testing_utils import torch_device\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass EulerAncestralDiscreteSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (EulerAncestralDiscreteScheduler,)\n num_inference_steps = 10\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1100,\n \"beta_start\": 0.0001,\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n }\n\n config.update(**kwargs)\n return config\n\n def test_timesteps(self):\n for timesteps in [10, 50, 100, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_betas(self):\n for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):\n self.check_over_configs(beta_start=beta_start, beta_end=beta_end)\n\n def test_schedules(self):\n for schedule in [\"linear\", \"scaled_linear\"]:\n self.check_over_configs(beta_schedule=schedule)\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(prediction_type=prediction_type)\n\n def test_rescale_betas_zero_snr(self):\n for rescale_betas_zero_snr in [True, False]:\n self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr)\n\n def test_full_loop_no_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps)\n\n generator = torch.manual_seed(0)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()\n sample = sample.to(torch_device)\n\n for i, t in enumerate(scheduler.timesteps):\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample, generator=generator)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 152.3192) < 1e-2\n assert abs(result_mean.item() - 0.1983) < 1e-3\n\n def test_full_loop_with_v_prediction(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(prediction_type=\"v_prediction\")\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps)\n\n generator = torch.manual_seed(0)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n sample = sample.to(torch_device)\n\n for i, t in enumerate(scheduler.timesteps):\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample, generator=generator)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 108.4439) < 1e-2\n assert abs(result_mean.item() - 0.1412) < 1e-3\n\n def test_full_loop_device(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps, device=torch_device)\n generator = torch.manual_seed(0)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()\n sample = sample.to(torch_device)\n\n for t in scheduler.timesteps:\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample, generator=generator)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 152.3192) < 1e-2\n assert abs(result_mean.item() - 0.1983) < 1e-3\n\n def test_full_loop_with_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n t_start = self.num_inference_steps - 2\n\n scheduler.set_timesteps(self.num_inference_steps)\n\n generator = torch.manual_seed(0)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n\n # add noise\n noise = self.dummy_noise_deter\n noise = noise.to(sample.device)\n timesteps = scheduler.timesteps[t_start * scheduler.order :]\n sample = scheduler.add_noise(sample, noise, timesteps[:1])\n\n for i, t in enumerate(timesteps):\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample, generator=generator)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 56163.0508) < 1e-2, f\" expected result sum 56163.0508, but get {result_sum}\"\n assert abs(result_mean.item() - 73.1290) < 1e-3, f\" expected result mean 73.1290, but get {result_mean}\"\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_flax.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport inspect\nimport tempfile\nimport unittest\nfrom typing import Dict, List, Tuple\n\nfrom diffusers import FlaxDDIMScheduler, FlaxDDPMScheduler, FlaxPNDMScheduler\nfrom diffusers.utils import is_flax_available\nfrom diffusers.utils.testing_utils import require_flax\n\n\nif is_flax_available():\n import jax\n import jax.numpy as jnp\n from jax import random\n\n jax_device = jax.default_backend()\n\n\n@require_flax\nclass FlaxSchedulerCommonTest(unittest.TestCase):\n scheduler_classes = ()\n forward_default_kwargs = ()\n\n @property\n def dummy_sample(self):\n batch_size = 4\n num_channels = 3\n height = 8\n width = 8\n\n key1, key2 = random.split(random.PRNGKey(0))\n sample = random.uniform(key1, (batch_size, num_channels, height, width))\n\n return sample, key2\n\n @property\n def dummy_sample_deter(self):\n batch_size = 4\n num_channels = 3\n height = 8\n width = 8\n\n num_elems = batch_size * num_channels * height * width\n sample = jnp.arange(num_elems)\n sample = sample.reshape(num_channels, height, width, batch_size)\n sample = sample / num_elems\n return jnp.transpose(sample, (3, 0, 1, 2))\n\n def get_scheduler_config(self):\n raise NotImplementedError\n\n def dummy_model(self):\n def model(sample, t, *args):\n return sample * t / (t + 1)\n\n return model\n\n def check_over_configs(self, time_step=0, **config):\n kwargs = dict(self.forward_default_kwargs)\n\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n sample, key = self.dummy_sample\n residual = 0.1 * sample\n\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n state = scheduler.create_state()\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler, new_state = scheduler_class.from_pretrained(tmpdirname)\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n state = scheduler.set_timesteps(state, num_inference_steps)\n new_state = new_scheduler.set_timesteps(new_state, num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n output = scheduler.step(state, residual, time_step, sample, key, **kwargs).prev_sample\n new_output = new_scheduler.step(new_state, residual, time_step, sample, key, **kwargs).prev_sample\n\n assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def check_over_forward(self, time_step=0, **forward_kwargs):\n kwargs = dict(self.forward_default_kwargs)\n kwargs.update(forward_kwargs)\n\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n sample, key = self.dummy_sample\n residual = 0.1 * sample\n\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n state = scheduler.create_state()\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler, new_state = scheduler_class.from_pretrained(tmpdirname)\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n state = scheduler.set_timesteps(state, num_inference_steps)\n new_state = new_scheduler.set_timesteps(new_state, num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n output = scheduler.step(state, residual, time_step, sample, key, **kwargs).prev_sample\n new_output = new_scheduler.step(new_state, residual, time_step, sample, key, **kwargs).prev_sample\n\n assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def test_from_save_pretrained(self):\n kwargs = dict(self.forward_default_kwargs)\n\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n sample, key = self.dummy_sample\n residual = 0.1 * sample\n\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n state = scheduler.create_state()\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler, new_state = scheduler_class.from_pretrained(tmpdirname)\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n state = scheduler.set_timesteps(state, num_inference_steps)\n new_state = new_scheduler.set_timesteps(new_state, num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n output = scheduler.step(state, residual, 1, sample, key, **kwargs).prev_sample\n new_output = new_scheduler.step(new_state, residual, 1, sample, key, **kwargs).prev_sample\n\n assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def test_step_shape(self):\n kwargs = dict(self.forward_default_kwargs)\n\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n state = scheduler.create_state()\n\n sample, key = self.dummy_sample\n residual = 0.1 * sample\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n state = scheduler.set_timesteps(state, num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n output_0 = scheduler.step(state, residual, 0, sample, key, **kwargs).prev_sample\n output_1 = scheduler.step(state, residual, 1, sample, key, **kwargs).prev_sample\n\n self.assertEqual(output_0.shape, sample.shape)\n self.assertEqual(output_0.shape, output_1.shape)\n\n def test_scheduler_outputs_equivalence(self):\n def set_nan_tensor_to_zero(t):\n return t.at[t != t].set(0)\n\n def recursive_check(tuple_object, dict_object):\n if isinstance(tuple_object, (List, Tuple)):\n for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object.values()):\n recursive_check(tuple_iterable_value, dict_iterable_value)\n elif isinstance(tuple_object, Dict):\n for tuple_iterable_value, dict_iterable_value in zip(tuple_object.values(), dict_object.values()):\n recursive_check(tuple_iterable_value, dict_iterable_value)\n elif tuple_object is None:\n return\n else:\n self.assertTrue(\n jnp.allclose(set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5),\n msg=(\n \"Tuple and dict output are not equal. Difference:\"\n f\" {jnp.max(jnp.abs(tuple_object - dict_object))}. Tuple has `nan`:\"\n f\" {jnp.isnan(tuple_object).any()} and `inf`: {jnp.isinf(tuple_object)}. Dict has\"\n f\" `nan`: {jnp.isnan(dict_object).any()} and `inf`: {jnp.isinf(dict_object)}.\"\n ),\n )\n\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n state = scheduler.create_state()\n\n sample, key = self.dummy_sample\n residual = 0.1 * sample\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n state = scheduler.set_timesteps(state, num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n outputs_dict = scheduler.step(state, residual, 0, sample, key, **kwargs)\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n state = scheduler.set_timesteps(state, num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n outputs_tuple = scheduler.step(state, residual, 0, sample, key, return_dict=False, **kwargs)\n\n recursive_check(outputs_tuple[0], outputs_dict.prev_sample)\n\n def test_deprecated_kwargs(self):\n for scheduler_class in self.scheduler_classes:\n has_kwarg_in_model_class = \"kwargs\" in inspect.signature(scheduler_class.__init__).parameters\n has_deprecated_kwarg = len(scheduler_class._deprecated_kwargs) > 0\n\n if has_kwarg_in_model_class and not has_deprecated_kwarg:\n raise ValueError(\n f\"{scheduler_class} has `**kwargs` in its __init__ method but has not defined any deprecated\"\n \" kwargs under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if\"\n \" there are no deprecated arguments or add the deprecated argument with `_deprecated_kwargs =\"\n \" []`\"\n )\n\n if not has_kwarg_in_model_class and has_deprecated_kwarg:\n raise ValueError(\n f\"{scheduler_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated\"\n \" kwargs under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs`\"\n f\" argument to {self.model_class}.__init__ if there are deprecated arguments or remove the\"\n \" deprecated argument from `_deprecated_kwargs = []`\"\n )\n\n\n@require_flax\nclass FlaxDDPMSchedulerTest(FlaxSchedulerCommonTest):\n scheduler_classes = (FlaxDDPMScheduler,)\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1000,\n \"beta_start\": 0.0001,\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n \"variance_type\": \"fixed_small\",\n \"clip_sample\": True,\n }\n\n config.update(**kwargs)\n return config\n\n def test_timesteps(self):\n for timesteps in [1, 5, 100, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_betas(self):\n for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]):\n self.check_over_configs(beta_start=beta_start, beta_end=beta_end)\n\n def test_schedules(self):\n for schedule in [\"linear\", \"squaredcos_cap_v2\"]:\n self.check_over_configs(beta_schedule=schedule)\n\n def test_variance_type(self):\n for variance in [\"fixed_small\", \"fixed_large\", \"other\"]:\n self.check_over_configs(variance_type=variance)\n\n def test_clip_sample(self):\n for clip_sample in [True, False]:\n self.check_over_configs(clip_sample=clip_sample)\n\n def test_time_indices(self):\n for t in [0, 500, 999]:\n self.check_over_forward(time_step=t)\n\n def test_variance(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n state = scheduler.create_state()\n\n assert jnp.sum(jnp.abs(scheduler._get_variance(state, 0) - 0.0)) < 1e-5\n assert jnp.sum(jnp.abs(scheduler._get_variance(state, 487) - 0.00979)) < 1e-5\n assert jnp.sum(jnp.abs(scheduler._get_variance(state, 999) - 0.02)) < 1e-5\n\n def test_full_loop_no_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n state = scheduler.create_state()\n\n num_trained_timesteps = len(scheduler)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n key1, key2 = random.split(random.PRNGKey(0))\n\n for t in reversed(range(num_trained_timesteps)):\n # 1. predict noise residual\n residual = model(sample, t)\n\n # 2. predict previous mean of sample x_t-1\n output = scheduler.step(state, residual, t, sample, key1)\n pred_prev_sample = output.prev_sample\n state = output.state\n key1, key2 = random.split(key2)\n\n # if t > 0:\n # noise = self.dummy_sample_deter\n # variance = scheduler.get_variance(t) ** (0.5) * noise\n #\n # sample = pred_prev_sample + variance\n sample = pred_prev_sample\n\n result_sum = jnp.sum(jnp.abs(sample))\n result_mean = jnp.mean(jnp.abs(sample))\n\n if jax_device == \"tpu\":\n assert abs(result_sum - 255.0714) < 1e-2\n assert abs(result_mean - 0.332124) < 1e-3\n else:\n assert abs(result_sum - 255.1113) < 1e-2\n assert abs(result_mean - 0.332176) < 1e-3\n\n\n@require_flax\nclass FlaxDDIMSchedulerTest(FlaxSchedulerCommonTest):\n scheduler_classes = (FlaxDDIMScheduler,)\n forward_default_kwargs = ((\"num_inference_steps\", 50),)\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1000,\n \"beta_start\": 0.0001,\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n }\n\n config.update(**kwargs)\n return config\n\n def full_loop(self, **config):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n state = scheduler.create_state()\n key1, key2 = random.split(random.PRNGKey(0))\n\n num_inference_steps = 10\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n\n state = scheduler.set_timesteps(state, num_inference_steps)\n\n for t in state.timesteps:\n residual = model(sample, t)\n output = scheduler.step(state, residual, t, sample)\n sample = output.prev_sample\n state = output.state\n key1, key2 = random.split(key2)\n\n return sample\n\n def check_over_configs(self, time_step=0, **config):\n kwargs = dict(self.forward_default_kwargs)\n\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n sample, _ = self.dummy_sample\n residual = 0.1 * sample\n\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n state = scheduler.create_state()\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler, new_state = scheduler_class.from_pretrained(tmpdirname)\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n state = scheduler.set_timesteps(state, num_inference_steps)\n new_state = new_scheduler.set_timesteps(new_state, num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n output = scheduler.step(state, residual, time_step, sample, **kwargs).prev_sample\n new_output = new_scheduler.step(new_state, residual, time_step, sample, **kwargs).prev_sample\n\n assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def test_from_save_pretrained(self):\n kwargs = dict(self.forward_default_kwargs)\n\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n sample, _ = self.dummy_sample\n residual = 0.1 * sample\n\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n state = scheduler.create_state()\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler, new_state = scheduler_class.from_pretrained(tmpdirname)\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n state = scheduler.set_timesteps(state, num_inference_steps)\n new_state = new_scheduler.set_timesteps(new_state, num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n output = scheduler.step(state, residual, 1, sample, **kwargs).prev_sample\n new_output = new_scheduler.step(new_state, residual, 1, sample, **kwargs).prev_sample\n\n assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def check_over_forward(self, time_step=0, **forward_kwargs):\n kwargs = dict(self.forward_default_kwargs)\n kwargs.update(forward_kwargs)\n\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n sample, _ = self.dummy_sample\n residual = 0.1 * sample\n\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n state = scheduler.create_state()\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler, new_state = scheduler_class.from_pretrained(tmpdirname)\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n state = scheduler.set_timesteps(state, num_inference_steps)\n new_state = new_scheduler.set_timesteps(new_state, num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n output = scheduler.step(state, residual, time_step, sample, **kwargs).prev_sample\n new_output = new_scheduler.step(new_state, residual, time_step, sample, **kwargs).prev_sample\n\n assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def test_scheduler_outputs_equivalence(self):\n def set_nan_tensor_to_zero(t):\n return t.at[t != t].set(0)\n\n def recursive_check(tuple_object, dict_object):\n if isinstance(tuple_object, (List, Tuple)):\n for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object.values()):\n recursive_check(tuple_iterable_value, dict_iterable_value)\n elif isinstance(tuple_object, Dict):\n for tuple_iterable_value, dict_iterable_value in zip(tuple_object.values(), dict_object.values()):\n recursive_check(tuple_iterable_value, dict_iterable_value)\n elif tuple_object is None:\n return\n else:\n self.assertTrue(\n jnp.allclose(set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5),\n msg=(\n \"Tuple and dict output are not equal. Difference:\"\n f\" {jnp.max(jnp.abs(tuple_object - dict_object))}. Tuple has `nan`:\"\n f\" {jnp.isnan(tuple_object).any()} and `inf`: {jnp.isinf(tuple_object)}. Dict has\"\n f\" `nan`: {jnp.isnan(dict_object).any()} and `inf`: {jnp.isinf(dict_object)}.\"\n ),\n )\n\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n state = scheduler.create_state()\n\n sample, _ = self.dummy_sample\n residual = 0.1 * sample\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n state = scheduler.set_timesteps(state, num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n outputs_dict = scheduler.step(state, residual, 0, sample, **kwargs)\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n state = scheduler.set_timesteps(state, num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n outputs_tuple = scheduler.step(state, residual, 0, sample, return_dict=False, **kwargs)\n\n recursive_check(outputs_tuple[0], outputs_dict.prev_sample)\n\n def test_step_shape(self):\n kwargs = dict(self.forward_default_kwargs)\n\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n state = scheduler.create_state()\n\n sample, _ = self.dummy_sample\n residual = 0.1 * sample\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n state = scheduler.set_timesteps(state, num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n output_0 = scheduler.step(state, residual, 0, sample, **kwargs).prev_sample\n output_1 = scheduler.step(state, residual, 1, sample, **kwargs).prev_sample\n\n self.assertEqual(output_0.shape, sample.shape)\n self.assertEqual(output_0.shape, output_1.shape)\n\n def test_timesteps(self):\n for timesteps in [100, 500, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_steps_offset(self):\n for steps_offset in [0, 1]:\n self.check_over_configs(steps_offset=steps_offset)\n\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(steps_offset=1)\n scheduler = scheduler_class(**scheduler_config)\n state = scheduler.create_state()\n state = scheduler.set_timesteps(state, 5)\n assert jnp.equal(state.timesteps, jnp.array([801, 601, 401, 201, 1])).all()\n\n def test_betas(self):\n for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]):\n self.check_over_configs(beta_start=beta_start, beta_end=beta_end)\n\n def test_schedules(self):\n for schedule in [\"linear\", \"squaredcos_cap_v2\"]:\n self.check_over_configs(beta_schedule=schedule)\n\n def test_time_indices(self):\n for t in [1, 10, 49]:\n self.check_over_forward(time_step=t)\n\n def test_inference_steps(self):\n for t, num_inference_steps in zip([1, 10, 50], [10, 50, 500]):\n self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps)\n\n def test_variance(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n state = scheduler.create_state()\n\n assert jnp.sum(jnp.abs(scheduler._get_variance(state, 0, 0) - 0.0)) < 1e-5\n assert jnp.sum(jnp.abs(scheduler._get_variance(state, 420, 400) - 0.14771)) < 1e-5\n assert jnp.sum(jnp.abs(scheduler._get_variance(state, 980, 960) - 0.32460)) < 1e-5\n assert jnp.sum(jnp.abs(scheduler._get_variance(state, 0, 0) - 0.0)) < 1e-5\n assert jnp.sum(jnp.abs(scheduler._get_variance(state, 487, 486) - 0.00979)) < 1e-5\n assert jnp.sum(jnp.abs(scheduler._get_variance(state, 999, 998) - 0.02)) < 1e-5\n\n def test_full_loop_no_noise(self):\n sample = self.full_loop()\n\n result_sum = jnp.sum(jnp.abs(sample))\n result_mean = jnp.mean(jnp.abs(sample))\n\n assert abs(result_sum - 172.0067) < 1e-2\n assert abs(result_mean - 0.223967) < 1e-3\n\n def test_full_loop_with_set_alpha_to_one(self):\n # We specify different beta, so that the first alpha is 0.99\n sample = self.full_loop(set_alpha_to_one=True, beta_start=0.01)\n result_sum = jnp.sum(jnp.abs(sample))\n result_mean = jnp.mean(jnp.abs(sample))\n\n if jax_device == \"tpu\":\n assert abs(result_sum - 149.8409) < 1e-2\n assert abs(result_mean - 0.1951) < 1e-3\n else:\n assert abs(result_sum - 149.8295) < 1e-2\n assert abs(result_mean - 0.1951) < 1e-3\n\n def test_full_loop_with_no_set_alpha_to_one(self):\n # We specify different beta, so that the first alpha is 0.99\n sample = self.full_loop(set_alpha_to_one=False, beta_start=0.01)\n result_sum = jnp.sum(jnp.abs(sample))\n result_mean = jnp.mean(jnp.abs(sample))\n\n if jax_device == \"tpu\":\n pass\n # FIXME: both result_sum and result_mean are nan on TPU\n # assert jnp.isnan(result_sum)\n # assert jnp.isnan(result_mean)\n else:\n assert abs(result_sum - 149.0784) < 1e-2\n assert abs(result_mean - 0.1941) < 1e-3\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"sample\", \"v_prediction\"]:\n self.check_over_configs(prediction_type=prediction_type)\n\n\n@require_flax\nclass FlaxPNDMSchedulerTest(FlaxSchedulerCommonTest):\n scheduler_classes = (FlaxPNDMScheduler,)\n forward_default_kwargs = ((\"num_inference_steps\", 50),)\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1000,\n \"beta_start\": 0.0001,\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n }\n\n config.update(**kwargs)\n return config\n\n def check_over_configs(self, time_step=0, **config):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n sample, _ = self.dummy_sample\n residual = 0.1 * sample\n dummy_past_residuals = jnp.array([residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05])\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n state = scheduler.create_state()\n state = scheduler.set_timesteps(state, num_inference_steps, shape=sample.shape)\n # copy over dummy past residuals\n state = state.replace(ets=dummy_past_residuals[:])\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler, new_state = scheduler_class.from_pretrained(tmpdirname)\n new_state = new_scheduler.set_timesteps(new_state, num_inference_steps, shape=sample.shape)\n # copy over dummy past residuals\n new_state = new_state.replace(ets=dummy_past_residuals[:])\n\n (prev_sample, state) = scheduler.step_prk(state, residual, time_step, sample, **kwargs)\n (new_prev_sample, new_state) = new_scheduler.step_prk(new_state, residual, time_step, sample, **kwargs)\n\n assert jnp.sum(jnp.abs(prev_sample - new_prev_sample)) < 1e-5, \"Scheduler outputs are not identical\"\n\n output, _ = scheduler.step_plms(state, residual, time_step, sample, **kwargs)\n new_output, _ = new_scheduler.step_plms(new_state, residual, time_step, sample, **kwargs)\n\n assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def test_from_save_pretrained(self):\n pass\n\n def test_scheduler_outputs_equivalence(self):\n def set_nan_tensor_to_zero(t):\n return t.at[t != t].set(0)\n\n def recursive_check(tuple_object, dict_object):\n if isinstance(tuple_object, (List, Tuple)):\n for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object.values()):\n recursive_check(tuple_iterable_value, dict_iterable_value)\n elif isinstance(tuple_object, Dict):\n for tuple_iterable_value, dict_iterable_value in zip(tuple_object.values(), dict_object.values()):\n recursive_check(tuple_iterable_value, dict_iterable_value)\n elif tuple_object is None:\n return\n else:\n self.assertTrue(\n jnp.allclose(set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5),\n msg=(\n \"Tuple and dict output are not equal. Difference:\"\n f\" {jnp.max(jnp.abs(tuple_object - dict_object))}. Tuple has `nan`:\"\n f\" {jnp.isnan(tuple_object).any()} and `inf`: {jnp.isinf(tuple_object)}. Dict has\"\n f\" `nan`: {jnp.isnan(dict_object).any()} and `inf`: {jnp.isinf(dict_object)}.\"\n ),\n )\n\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n state = scheduler.create_state()\n\n sample, _ = self.dummy_sample\n residual = 0.1 * sample\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n state = scheduler.set_timesteps(state, num_inference_steps, shape=sample.shape)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n outputs_dict = scheduler.step(state, residual, 0, sample, **kwargs)\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n state = scheduler.set_timesteps(state, num_inference_steps, shape=sample.shape)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n outputs_tuple = scheduler.step(state, residual, 0, sample, return_dict=False, **kwargs)\n\n recursive_check(outputs_tuple[0], outputs_dict.prev_sample)\n\n def check_over_forward(self, time_step=0, **forward_kwargs):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n sample, _ = self.dummy_sample\n residual = 0.1 * sample\n dummy_past_residuals = jnp.array([residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05])\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n state = scheduler.create_state()\n state = scheduler.set_timesteps(state, num_inference_steps, shape=sample.shape)\n\n # copy over dummy past residuals (must be after setting timesteps)\n scheduler.ets = dummy_past_residuals[:]\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler, new_state = scheduler_class.from_pretrained(tmpdirname)\n # copy over dummy past residuals\n new_state = new_scheduler.set_timesteps(new_state, num_inference_steps, shape=sample.shape)\n\n # copy over dummy past residual (must be after setting timesteps)\n new_state.replace(ets=dummy_past_residuals[:])\n\n output, state = scheduler.step_prk(state, residual, time_step, sample, **kwargs)\n new_output, new_state = new_scheduler.step_prk(new_state, residual, time_step, sample, **kwargs)\n\n assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n output, _ = scheduler.step_plms(state, residual, time_step, sample, **kwargs)\n new_output, _ = new_scheduler.step_plms(new_state, residual, time_step, sample, **kwargs)\n\n assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def full_loop(self, **config):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n state = scheduler.create_state()\n\n num_inference_steps = 10\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n state = scheduler.set_timesteps(state, num_inference_steps, shape=sample.shape)\n\n for i, t in enumerate(state.prk_timesteps):\n residual = model(sample, t)\n sample, state = scheduler.step_prk(state, residual, t, sample)\n\n for i, t in enumerate(state.plms_timesteps):\n residual = model(sample, t)\n sample, state = scheduler.step_plms(state, residual, t, sample)\n\n return sample\n\n def test_step_shape(self):\n kwargs = dict(self.forward_default_kwargs)\n\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n state = scheduler.create_state()\n\n sample, _ = self.dummy_sample\n residual = 0.1 * sample\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n state = scheduler.set_timesteps(state, num_inference_steps, shape=sample.shape)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n # copy over dummy past residuals (must be done after set_timesteps)\n dummy_past_residuals = jnp.array([residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05])\n state = state.replace(ets=dummy_past_residuals[:])\n\n output_0, state = scheduler.step_prk(state, residual, 0, sample, **kwargs)\n output_1, state = scheduler.step_prk(state, residual, 1, sample, **kwargs)\n\n self.assertEqual(output_0.shape, sample.shape)\n self.assertEqual(output_0.shape, output_1.shape)\n\n output_0, state = scheduler.step_plms(state, residual, 0, sample, **kwargs)\n output_1, state = scheduler.step_plms(state, residual, 1, sample, **kwargs)\n\n self.assertEqual(output_0.shape, sample.shape)\n self.assertEqual(output_0.shape, output_1.shape)\n\n def test_timesteps(self):\n for timesteps in [100, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_steps_offset(self):\n for steps_offset in [0, 1]:\n self.check_over_configs(steps_offset=steps_offset)\n\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(steps_offset=1)\n scheduler = scheduler_class(**scheduler_config)\n state = scheduler.create_state()\n state = scheduler.set_timesteps(state, 10, shape=())\n assert jnp.equal(\n state.timesteps,\n jnp.array([901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]),\n ).all()\n\n def test_betas(self):\n for beta_start, beta_end in zip([0.0001, 0.001], [0.002, 0.02]):\n self.check_over_configs(beta_start=beta_start, beta_end=beta_end)\n\n def test_schedules(self):\n for schedule in [\"linear\", \"squaredcos_cap_v2\"]:\n self.check_over_configs(beta_schedule=schedule)\n\n def test_time_indices(self):\n for t in [1, 5, 10]:\n self.check_over_forward(time_step=t)\n\n def test_inference_steps(self):\n for t, num_inference_steps in zip([1, 5, 10], [10, 50, 100]):\n self.check_over_forward(num_inference_steps=num_inference_steps)\n\n def test_pow_of_3_inference_steps(self):\n # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3\n num_inference_steps = 27\n\n for scheduler_class in self.scheduler_classes:\n sample, _ = self.dummy_sample\n residual = 0.1 * sample\n\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n state = scheduler.create_state()\n\n state = scheduler.set_timesteps(state, num_inference_steps, shape=sample.shape)\n\n # before power of 3 fix, would error on first step, so we only need to do two\n for i, t in enumerate(state.prk_timesteps[:2]):\n sample, state = scheduler.step_prk(state, residual, t, sample)\n\n def test_inference_plms_no_past_residuals(self):\n with self.assertRaises(ValueError):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n state = scheduler.create_state()\n\n scheduler.step_plms(state, self.dummy_sample, 1, self.dummy_sample).prev_sample\n\n def test_full_loop_no_noise(self):\n sample = self.full_loop()\n result_sum = jnp.sum(jnp.abs(sample))\n result_mean = jnp.mean(jnp.abs(sample))\n\n if jax_device == \"tpu\":\n assert abs(result_sum - 198.1275) < 1e-2\n assert abs(result_mean - 0.2580) < 1e-3\n else:\n assert abs(result_sum - 198.1318) < 1e-2\n assert abs(result_mean - 0.2580) < 1e-3\n\n def test_full_loop_with_set_alpha_to_one(self):\n # We specify different beta, so that the first alpha is 0.99\n sample = self.full_loop(set_alpha_to_one=True, beta_start=0.01)\n result_sum = jnp.sum(jnp.abs(sample))\n result_mean = jnp.mean(jnp.abs(sample))\n\n if jax_device == \"tpu\":\n assert abs(result_sum - 186.83226) < 1e-2\n assert abs(result_mean - 0.24327) < 1e-3\n else:\n assert abs(result_sum - 186.9466) < 1e-2\n assert abs(result_mean - 0.24342) < 1e-3\n\n def test_full_loop_with_no_set_alpha_to_one(self):\n # We specify different beta, so that the first alpha is 0.99\n sample = self.full_loop(set_alpha_to_one=False, beta_start=0.01)\n result_sum = jnp.sum(jnp.abs(sample))\n result_mean = jnp.mean(jnp.abs(sample))\n\n if jax_device == \"tpu\":\n assert abs(result_sum - 186.83226) < 1e-2\n assert abs(result_mean - 0.24327) < 1e-3\n else:\n assert abs(result_sum - 186.9482) < 1e-2\n assert abs(result_mean - 0.2434) < 1e-3\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_heun.py", "content": "import torch\n\nfrom diffusers import HeunDiscreteScheduler\nfrom diffusers.utils.testing_utils import torch_device\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass HeunDiscreteSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (HeunDiscreteScheduler,)\n num_inference_steps = 10\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1100,\n \"beta_start\": 0.0001,\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n }\n\n config.update(**kwargs)\n return config\n\n def test_timesteps(self):\n for timesteps in [10, 50, 100, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_betas(self):\n for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):\n self.check_over_configs(beta_start=beta_start, beta_end=beta_end)\n\n def test_schedules(self):\n for schedule in [\"linear\", \"scaled_linear\", \"exp\"]:\n self.check_over_configs(beta_schedule=schedule)\n\n def test_clip_sample(self):\n for clip_sample_range in [1.0, 2.0, 3.0]:\n self.check_over_configs(clip_sample_range=clip_sample_range, clip_sample=True)\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"v_prediction\", \"sample\"]:\n self.check_over_configs(prediction_type=prediction_type)\n\n def full_loop(self, **config):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = self.num_inference_steps\n scheduler.set_timesteps(num_inference_steps)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n sample = sample.to(torch_device)\n\n for i, t in enumerate(scheduler.timesteps):\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample)\n sample = output.prev_sample\n\n return sample\n\n def full_loop_custom_timesteps(self, **config):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = self.num_inference_steps\n scheduler.set_timesteps(num_inference_steps)\n timesteps = scheduler.timesteps\n timesteps = torch.cat([timesteps[:1], timesteps[1::2]])\n # reset the timesteps using `timesteps`\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(num_inference_steps=None, timesteps=timesteps)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n sample = sample.to(torch_device)\n\n for i, t in enumerate(scheduler.timesteps):\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample)\n sample = output.prev_sample\n\n return sample\n\n def test_full_loop_no_noise(self):\n sample = self.full_loop()\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n if torch_device in [\"cpu\", \"mps\"]:\n assert abs(result_sum.item() - 0.1233) < 1e-2\n assert abs(result_mean.item() - 0.0002) < 1e-3\n else:\n # CUDA\n assert abs(result_sum.item() - 0.1233) < 1e-2\n assert abs(result_mean.item() - 0.0002) < 1e-3\n\n def test_full_loop_with_v_prediction(self):\n sample = self.full_loop(prediction_type=\"v_prediction\")\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n if torch_device in [\"cpu\", \"mps\"]:\n assert abs(result_sum.item() - 4.6934e-07) < 1e-2\n assert abs(result_mean.item() - 6.1112e-10) < 1e-3\n else:\n # CUDA\n assert abs(result_sum.item() - 4.693428650170972e-07) < 1e-2\n assert abs(result_mean.item() - 0.0002) < 1e-3\n\n def test_full_loop_device(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps, device=torch_device)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma\n\n for t in scheduler.timesteps:\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n if str(torch_device).startswith(\"cpu\"):\n # The following sum varies between 148 and 156 on mps. Why?\n assert abs(result_sum.item() - 0.1233) < 1e-2\n assert abs(result_mean.item() - 0.0002) < 1e-3\n elif str(torch_device).startswith(\"mps\"):\n # Larger tolerance on mps\n assert abs(result_mean.item() - 0.0002) < 1e-2\n else:\n # CUDA\n assert abs(result_sum.item() - 0.1233) < 1e-2\n assert abs(result_mean.item() - 0.0002) < 1e-3\n\n def test_full_loop_device_karras_sigmas(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config, use_karras_sigmas=True)\n\n scheduler.set_timesteps(self.num_inference_steps, device=torch_device)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma\n sample = sample.to(torch_device)\n\n for t in scheduler.timesteps:\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 0.00015) < 1e-2\n assert abs(result_mean.item() - 1.9869554535034695e-07) < 1e-2\n\n def test_full_loop_with_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n sample = sample.to(torch_device)\n\n t_start = self.num_inference_steps - 2\n noise = self.dummy_noise_deter\n noise = noise.to(torch_device)\n timesteps = scheduler.timesteps[t_start * scheduler.order :]\n sample = scheduler.add_noise(sample, noise, timesteps[:1])\n\n for i, t in enumerate(timesteps):\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 75074.8906) < 1e-2, f\" expected result sum 75074.8906, but get {result_sum}\"\n assert abs(result_mean.item() - 97.7538) < 1e-3, f\" expected result mean 97.7538, but get {result_mean}\"\n\n def test_custom_timesteps(self):\n for prediction_type in [\"epsilon\", \"sample\", \"v_prediction\"]:\n for timestep_spacing in [\"linspace\", \"leading\"]:\n sample = self.full_loop(\n prediction_type=prediction_type,\n timestep_spacing=timestep_spacing,\n )\n sample_custom_timesteps = self.full_loop_custom_timesteps(\n prediction_type=prediction_type,\n timestep_spacing=timestep_spacing,\n )\n assert (\n torch.sum(torch.abs(sample - sample_custom_timesteps)) < 1e-5\n ), f\"Scheduler outputs are not identical for prediction_type: {prediction_type}, timestep_spacing: {timestep_spacing}\"\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_ipndm.py", "content": "import tempfile\n\nimport torch\n\nfrom diffusers import IPNDMScheduler\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass IPNDMSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (IPNDMScheduler,)\n forward_default_kwargs = ((\"num_inference_steps\", 50),)\n\n def get_scheduler_config(self, **kwargs):\n config = {\"num_train_timesteps\": 1000}\n config.update(**kwargs)\n return config\n\n def check_over_configs(self, time_step=0, **config):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n sample = self.dummy_sample\n residual = 0.1 * sample\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(num_inference_steps)\n # copy over dummy past residuals\n scheduler.ets = dummy_past_residuals[:]\n\n if time_step is None:\n time_step = scheduler.timesteps[len(scheduler.timesteps) // 2]\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n new_scheduler.set_timesteps(num_inference_steps)\n # copy over dummy past residuals\n new_scheduler.ets = dummy_past_residuals[:]\n\n output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def test_from_save_pretrained(self):\n pass\n\n def check_over_forward(self, time_step=0, **forward_kwargs):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n sample = self.dummy_sample\n residual = 0.1 * sample\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(num_inference_steps)\n\n # copy over dummy past residuals (must be after setting timesteps)\n scheduler.ets = dummy_past_residuals[:]\n\n if time_step is None:\n time_step = scheduler.timesteps[len(scheduler.timesteps) // 2]\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n # copy over dummy past residuals\n new_scheduler.set_timesteps(num_inference_steps)\n\n # copy over dummy past residual (must be after setting timesteps)\n new_scheduler.ets = dummy_past_residuals[:]\n\n output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def full_loop(self, **config):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 10\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n scheduler.set_timesteps(num_inference_steps)\n\n for i, t in enumerate(scheduler.timesteps):\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample).prev_sample\n\n scheduler._step_index = None\n\n for i, t in enumerate(scheduler.timesteps):\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample).prev_sample\n\n return sample\n\n def test_step_shape(self):\n kwargs = dict(self.forward_default_kwargs)\n\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n sample = self.dummy_sample\n residual = 0.1 * sample\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n scheduler.set_timesteps(num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n # copy over dummy past residuals (must be done after set_timesteps)\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]\n scheduler.ets = dummy_past_residuals[:]\n\n time_step_0 = scheduler.timesteps[5]\n time_step_1 = scheduler.timesteps[6]\n\n output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample\n output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample\n\n self.assertEqual(output_0.shape, sample.shape)\n self.assertEqual(output_0.shape, output_1.shape)\n\n output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample\n output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample\n\n self.assertEqual(output_0.shape, sample.shape)\n self.assertEqual(output_0.shape, output_1.shape)\n\n def test_timesteps(self):\n for timesteps in [100, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps, time_step=None)\n\n def test_inference_steps(self):\n for t, num_inference_steps in zip([1, 5, 10], [10, 50, 100]):\n self.check_over_forward(num_inference_steps=num_inference_steps, time_step=None)\n\n def test_full_loop_no_noise(self):\n sample = self.full_loop()\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 2540529) < 10\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_kdpm2_ancestral.py", "content": "import torch\n\nfrom diffusers import KDPM2AncestralDiscreteScheduler\nfrom diffusers.utils.testing_utils import torch_device\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass KDPM2AncestralDiscreteSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (KDPM2AncestralDiscreteScheduler,)\n num_inference_steps = 10\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1100,\n \"beta_start\": 0.0001,\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n }\n\n config.update(**kwargs)\n return config\n\n def test_timesteps(self):\n for timesteps in [10, 50, 100, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_betas(self):\n for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):\n self.check_over_configs(beta_start=beta_start, beta_end=beta_end)\n\n def test_schedules(self):\n for schedule in [\"linear\", \"scaled_linear\"]:\n self.check_over_configs(beta_schedule=schedule)\n\n def test_full_loop_no_noise(self):\n if torch_device == \"mps\":\n return\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps)\n\n generator = torch.manual_seed(0)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n sample = sample.to(torch_device)\n\n for i, t in enumerate(scheduler.timesteps):\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample, generator=generator)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 13849.3877) < 1e-2\n assert abs(result_mean.item() - 18.0331) < 5e-3\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(prediction_type=prediction_type)\n\n def test_full_loop_with_v_prediction(self):\n if torch_device == \"mps\":\n return\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(prediction_type=\"v_prediction\")\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n sample = sample.to(torch_device)\n\n generator = torch.manual_seed(0)\n\n for i, t in enumerate(scheduler.timesteps):\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample, generator=generator)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 328.9970) < 1e-2\n assert abs(result_mean.item() - 0.4284) < 1e-3\n\n def test_full_loop_device(self):\n if torch_device == \"mps\":\n return\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps, device=torch_device)\n generator = torch.manual_seed(0)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma\n\n for t in scheduler.timesteps:\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample, generator=generator)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 13849.3818) < 1e-1\n assert abs(result_mean.item() - 18.0331) < 1e-3\n\n def test_full_loop_with_noise(self):\n if torch_device == \"mps\":\n return\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps)\n\n generator = torch.manual_seed(0)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n\n # add noise\n t_start = self.num_inference_steps - 2\n noise = self.dummy_noise_deter\n noise = noise.to(sample.device)\n timesteps = scheduler.timesteps[t_start * scheduler.order :]\n sample = scheduler.add_noise(sample, noise, timesteps[:1])\n\n for i, t in enumerate(timesteps):\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample, generator=generator)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 93087.0312) < 1e-2, f\" expected result sum 93087.0312, but get {result_sum}\"\n assert abs(result_mean.item() - 121.2071) < 5e-3, f\" expected result mean 121.2071, but get {result_mean}\"\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_kdpm2_discrete.py", "content": "import torch\n\nfrom diffusers import KDPM2DiscreteScheduler\nfrom diffusers.utils.testing_utils import torch_device\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass KDPM2DiscreteSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (KDPM2DiscreteScheduler,)\n num_inference_steps = 10\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1100,\n \"beta_start\": 0.0001,\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n }\n\n config.update(**kwargs)\n return config\n\n def test_timesteps(self):\n for timesteps in [10, 50, 100, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_betas(self):\n for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):\n self.check_over_configs(beta_start=beta_start, beta_end=beta_end)\n\n def test_schedules(self):\n for schedule in [\"linear\", \"scaled_linear\"]:\n self.check_over_configs(beta_schedule=schedule)\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(prediction_type=prediction_type)\n\n def test_full_loop_with_v_prediction(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(prediction_type=\"v_prediction\")\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n sample = sample.to(torch_device)\n\n for i, t in enumerate(scheduler.timesteps):\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n if torch_device in [\"cpu\", \"mps\"]:\n assert abs(result_sum.item() - 4.6934e-07) < 1e-2\n assert abs(result_mean.item() - 6.1112e-10) < 1e-3\n else:\n # CUDA\n assert abs(result_sum.item() - 4.693428650170972e-07) < 1e-2\n assert abs(result_mean.item() - 0.0002) < 1e-3\n\n def test_full_loop_no_noise(self):\n if torch_device == \"mps\":\n return\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n sample = sample.to(torch_device)\n\n for i, t in enumerate(scheduler.timesteps):\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n if torch_device in [\"cpu\", \"mps\"]:\n assert abs(result_sum.item() - 20.4125) < 1e-2\n assert abs(result_mean.item() - 0.0266) < 1e-3\n else:\n # CUDA\n assert abs(result_sum.item() - 20.4125) < 1e-2\n assert abs(result_mean.item() - 0.0266) < 1e-3\n\n def test_full_loop_device(self):\n if torch_device == \"mps\":\n return\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps, device=torch_device)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma\n\n for t in scheduler.timesteps:\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n if str(torch_device).startswith(\"cpu\"):\n # The following sum varies between 148 and 156 on mps. Why?\n assert abs(result_sum.item() - 20.4125) < 1e-2\n assert abs(result_mean.item() - 0.0266) < 1e-3\n else:\n # CUDA\n assert abs(result_sum.item() - 20.4125) < 1e-2\n assert abs(result_mean.item() - 0.0266) < 1e-3\n\n def test_full_loop_with_noise(self):\n if torch_device == \"mps\":\n return\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n sample = sample.to(torch_device)\n\n # add noise\n t_start = self.num_inference_steps - 2\n noise = self.dummy_noise_deter\n noise = noise.to(sample.device)\n timesteps = scheduler.timesteps[t_start * scheduler.order :]\n sample = scheduler.add_noise(sample, noise, timesteps[:1])\n\n for i, t in enumerate(timesteps):\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 70408.4062) < 1e-2, f\" expected result sum 70408.4062, but get {result_sum}\"\n assert abs(result_mean.item() - 91.6776) < 1e-3, f\" expected result mean 91.6776, but get {result_mean}\"\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_lcm.py", "content": "import tempfile\nfrom typing import Dict, List, Tuple\n\nimport torch\n\nfrom diffusers import LCMScheduler\nfrom diffusers.utils.testing_utils import torch_device\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass LCMSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (LCMScheduler,)\n forward_default_kwargs = ((\"num_inference_steps\", 10),)\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1000,\n \"beta_start\": 0.00085,\n \"beta_end\": 0.0120,\n \"beta_schedule\": \"scaled_linear\",\n \"prediction_type\": \"epsilon\",\n }\n\n config.update(**kwargs)\n return config\n\n @property\n def default_valid_timestep(self):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n scheduler_config = self.get_scheduler_config()\n scheduler = self.scheduler_classes[0](**scheduler_config)\n\n scheduler.set_timesteps(num_inference_steps)\n timestep = scheduler.timesteps[-1]\n return timestep\n\n def test_timesteps(self):\n for timesteps in [100, 500, 1000]:\n # 0 is not guaranteed to be in the timestep schedule, but timesteps - 1 is\n self.check_over_configs(time_step=timesteps - 1, num_train_timesteps=timesteps)\n\n def test_betas(self):\n for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]):\n self.check_over_configs(time_step=self.default_valid_timestep, beta_start=beta_start, beta_end=beta_end)\n\n def test_schedules(self):\n for schedule in [\"linear\", \"scaled_linear\", \"squaredcos_cap_v2\"]:\n self.check_over_configs(time_step=self.default_valid_timestep, beta_schedule=schedule)\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(time_step=self.default_valid_timestep, prediction_type=prediction_type)\n\n def test_clip_sample(self):\n for clip_sample in [True, False]:\n self.check_over_configs(time_step=self.default_valid_timestep, clip_sample=clip_sample)\n\n def test_thresholding(self):\n self.check_over_configs(time_step=self.default_valid_timestep, thresholding=False)\n for threshold in [0.5, 1.0, 2.0]:\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(\n time_step=self.default_valid_timestep,\n thresholding=True,\n prediction_type=prediction_type,\n sample_max_value=threshold,\n )\n\n def test_time_indices(self):\n # Get default timestep schedule.\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n scheduler_config = self.get_scheduler_config()\n scheduler = self.scheduler_classes[0](**scheduler_config)\n\n scheduler.set_timesteps(num_inference_steps)\n timesteps = scheduler.timesteps\n for t in timesteps:\n self.check_over_forward(time_step=t)\n\n def test_inference_steps(self):\n # Hardcoded for now\n for t, num_inference_steps in zip([99, 39, 39, 19], [10, 25, 26, 50]):\n self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps)\n\n # Override test_add_noise_device because the hardcoded num_inference_steps of 100 doesn't work\n # for LCMScheduler under default settings\n def test_add_noise_device(self, num_inference_steps=10):\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(num_inference_steps)\n\n sample = self.dummy_sample.to(torch_device)\n scaled_sample = scheduler.scale_model_input(sample, 0.0)\n self.assertEqual(sample.shape, scaled_sample.shape)\n\n noise = torch.randn_like(scaled_sample).to(torch_device)\n t = scheduler.timesteps[5][None]\n noised = scheduler.add_noise(scaled_sample, noise, t)\n self.assertEqual(noised.shape, scaled_sample.shape)\n\n # Override test_from_save_pretrained because it hardcodes a timestep of 1\n def test_from_save_pretrained(self):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n timestep = self.default_valid_timestep\n\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n sample = self.dummy_sample\n residual = 0.1 * sample\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n\n scheduler.set_timesteps(num_inference_steps)\n new_scheduler.set_timesteps(num_inference_steps)\n\n kwargs[\"generator\"] = torch.manual_seed(0)\n output = scheduler.step(residual, timestep, sample, **kwargs).prev_sample\n\n kwargs[\"generator\"] = torch.manual_seed(0)\n new_output = new_scheduler.step(residual, timestep, sample, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n # Override test_step_shape because uses 0 and 1 as hardcoded timesteps\n def test_step_shape(self):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n sample = self.dummy_sample\n residual = 0.1 * sample\n\n scheduler.set_timesteps(num_inference_steps)\n\n timestep_0 = scheduler.timesteps[-2]\n timestep_1 = scheduler.timesteps[-1]\n\n output_0 = scheduler.step(residual, timestep_0, sample, **kwargs).prev_sample\n output_1 = scheduler.step(residual, timestep_1, sample, **kwargs).prev_sample\n\n self.assertEqual(output_0.shape, sample.shape)\n self.assertEqual(output_0.shape, output_1.shape)\n\n # Override test_set_scheduler_outputs_equivalence since it uses 0 as a hardcoded timestep\n def test_scheduler_outputs_equivalence(self):\n def set_nan_tensor_to_zero(t):\n t[t != t] = 0\n return t\n\n def recursive_check(tuple_object, dict_object):\n if isinstance(tuple_object, (List, Tuple)):\n for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object.values()):\n recursive_check(tuple_iterable_value, dict_iterable_value)\n elif isinstance(tuple_object, Dict):\n for tuple_iterable_value, dict_iterable_value in zip(tuple_object.values(), dict_object.values()):\n recursive_check(tuple_iterable_value, dict_iterable_value)\n elif tuple_object is None:\n return\n else:\n self.assertTrue(\n torch.allclose(\n set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5\n ),\n msg=(\n \"Tuple and dict output are not equal. Difference:\"\n f\" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:\"\n f\" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has\"\n f\" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}.\"\n ),\n )\n\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", 50)\n\n timestep = self.default_valid_timestep\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n sample = self.dummy_sample\n residual = 0.1 * sample\n\n scheduler.set_timesteps(num_inference_steps)\n kwargs[\"generator\"] = torch.manual_seed(0)\n outputs_dict = scheduler.step(residual, timestep, sample, **kwargs)\n\n scheduler.set_timesteps(num_inference_steps)\n kwargs[\"generator\"] = torch.manual_seed(0)\n outputs_tuple = scheduler.step(residual, timestep, sample, return_dict=False, **kwargs)\n\n recursive_check(outputs_tuple, outputs_dict)\n\n def full_loop(self, num_inference_steps=10, seed=0, **config):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n generator = torch.manual_seed(seed)\n\n scheduler.set_timesteps(num_inference_steps)\n\n for t in scheduler.timesteps:\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample, generator).prev_sample\n\n return sample\n\n def test_full_loop_onestep(self):\n sample = self.full_loop(num_inference_steps=1)\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n # TODO: get expected sum and mean\n assert abs(result_sum.item() - 18.7097) < 1e-3\n assert abs(result_mean.item() - 0.0244) < 1e-3\n\n def test_full_loop_multistep(self):\n sample = self.full_loop(num_inference_steps=10)\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n # TODO: get expected sum and mean\n assert abs(result_sum.item() - 197.7616) < 1e-3\n assert abs(result_mean.item() - 0.2575) < 1e-3\n\n def test_custom_timesteps(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n timesteps = [100, 87, 50, 1, 0]\n\n scheduler.set_timesteps(timesteps=timesteps)\n\n scheduler_timesteps = scheduler.timesteps\n\n for i, timestep in enumerate(scheduler_timesteps):\n if i == len(timesteps) - 1:\n expected_prev_t = -1\n else:\n expected_prev_t = timesteps[i + 1]\n\n prev_t = scheduler.previous_timestep(timestep)\n prev_t = prev_t.item()\n\n self.assertEqual(prev_t, expected_prev_t)\n\n def test_custom_timesteps_increasing_order(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n timesteps = [100, 87, 50, 51, 0]\n\n with self.assertRaises(ValueError, msg=\"`custom_timesteps` must be in descending order.\"):\n scheduler.set_timesteps(timesteps=timesteps)\n\n def test_custom_timesteps_passing_both_num_inference_steps_and_timesteps(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n timesteps = [100, 87, 50, 1, 0]\n num_inference_steps = len(timesteps)\n\n with self.assertRaises(ValueError, msg=\"Can only pass one of `num_inference_steps` or `custom_timesteps`.\"):\n scheduler.set_timesteps(num_inference_steps=num_inference_steps, timesteps=timesteps)\n\n def test_custom_timesteps_too_large(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n timesteps = [scheduler.config.num_train_timesteps]\n\n with self.assertRaises(\n ValueError,\n msg=\"`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}\",\n ):\n scheduler.set_timesteps(timesteps=timesteps)\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_lms.py", "content": "import torch\n\nfrom diffusers import LMSDiscreteScheduler\nfrom diffusers.utils.testing_utils import torch_device\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass LMSDiscreteSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (LMSDiscreteScheduler,)\n num_inference_steps = 10\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1100,\n \"beta_start\": 0.0001,\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n }\n\n config.update(**kwargs)\n return config\n\n def test_timesteps(self):\n for timesteps in [10, 50, 100, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_betas(self):\n for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):\n self.check_over_configs(beta_start=beta_start, beta_end=beta_end)\n\n def test_schedules(self):\n for schedule in [\"linear\", \"scaled_linear\"]:\n self.check_over_configs(beta_schedule=schedule)\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(prediction_type=prediction_type)\n\n def test_time_indices(self):\n for t in [0, 500, 800]:\n self.check_over_forward(time_step=t)\n\n def test_full_loop_no_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n\n for i, t in enumerate(scheduler.timesteps):\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 1006.388) < 1e-2\n assert abs(result_mean.item() - 1.31) < 1e-3\n\n def test_full_loop_with_v_prediction(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(prediction_type=\"v_prediction\")\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n\n for i, t in enumerate(scheduler.timesteps):\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 0.0017) < 1e-2\n assert abs(result_mean.item() - 2.2676e-06) < 1e-3\n\n def test_full_loop_device(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps, device=torch_device)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()\n sample = sample.to(torch_device)\n\n for i, t in enumerate(scheduler.timesteps):\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 1006.388) < 1e-2\n assert abs(result_mean.item() - 1.31) < 1e-3\n\n def test_full_loop_device_karras_sigmas(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config, use_karras_sigmas=True)\n\n scheduler.set_timesteps(self.num_inference_steps, device=torch_device)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma\n sample = sample.to(torch_device)\n\n for t in scheduler.timesteps:\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 3812.9927) < 2e-2\n assert abs(result_mean.item() - 4.9648) < 1e-3\n\n def test_full_loop_with_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n\n # add noise\n t_start = self.num_inference_steps - 2\n noise = self.dummy_noise_deter\n timesteps = scheduler.timesteps[t_start * scheduler.order :]\n sample = scheduler.add_noise(sample, noise, timesteps[:1])\n\n for i, t in enumerate(timesteps):\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 27663.6895) < 1e-2\n assert abs(result_mean.item() - 36.0204) < 1e-3\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_pndm.py", "content": "import tempfile\n\nimport torch\n\nfrom diffusers import PNDMScheduler\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass PNDMSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (PNDMScheduler,)\n forward_default_kwargs = ((\"num_inference_steps\", 50),)\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1000,\n \"beta_start\": 0.0001,\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n }\n\n config.update(**kwargs)\n return config\n\n def check_over_configs(self, time_step=0, **config):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n sample = self.dummy_sample\n residual = 0.1 * sample\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(num_inference_steps)\n # copy over dummy past residuals\n scheduler.ets = dummy_past_residuals[:]\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n new_scheduler.set_timesteps(num_inference_steps)\n # copy over dummy past residuals\n new_scheduler.ets = dummy_past_residuals[:]\n\n output = scheduler.step_prk(residual, time_step, sample, **kwargs).prev_sample\n new_output = new_scheduler.step_prk(residual, time_step, sample, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n output = scheduler.step_plms(residual, time_step, sample, **kwargs).prev_sample\n new_output = new_scheduler.step_plms(residual, time_step, sample, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def test_from_save_pretrained(self):\n pass\n\n def check_over_forward(self, time_step=0, **forward_kwargs):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n sample = self.dummy_sample\n residual = 0.1 * sample\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(num_inference_steps)\n\n # copy over dummy past residuals (must be after setting timesteps)\n scheduler.ets = dummy_past_residuals[:]\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n # copy over dummy past residuals\n new_scheduler.set_timesteps(num_inference_steps)\n\n # copy over dummy past residual (must be after setting timesteps)\n new_scheduler.ets = dummy_past_residuals[:]\n\n output = scheduler.step_prk(residual, time_step, sample, **kwargs).prev_sample\n new_output = new_scheduler.step_prk(residual, time_step, sample, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n output = scheduler.step_plms(residual, time_step, sample, **kwargs).prev_sample\n new_output = new_scheduler.step_plms(residual, time_step, sample, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def full_loop(self, **config):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 10\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n scheduler.set_timesteps(num_inference_steps)\n\n for i, t in enumerate(scheduler.prk_timesteps):\n residual = model(sample, t)\n sample = scheduler.step_prk(residual, t, sample).prev_sample\n\n for i, t in enumerate(scheduler.plms_timesteps):\n residual = model(sample, t)\n sample = scheduler.step_plms(residual, t, sample).prev_sample\n\n return sample\n\n def test_step_shape(self):\n kwargs = dict(self.forward_default_kwargs)\n\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n sample = self.dummy_sample\n residual = 0.1 * sample\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n scheduler.set_timesteps(num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n # copy over dummy past residuals (must be done after set_timesteps)\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]\n scheduler.ets = dummy_past_residuals[:]\n\n output_0 = scheduler.step_prk(residual, 0, sample, **kwargs).prev_sample\n output_1 = scheduler.step_prk(residual, 1, sample, **kwargs).prev_sample\n\n self.assertEqual(output_0.shape, sample.shape)\n self.assertEqual(output_0.shape, output_1.shape)\n\n output_0 = scheduler.step_plms(residual, 0, sample, **kwargs).prev_sample\n output_1 = scheduler.step_plms(residual, 1, sample, **kwargs).prev_sample\n\n self.assertEqual(output_0.shape, sample.shape)\n self.assertEqual(output_0.shape, output_1.shape)\n\n def test_timesteps(self):\n for timesteps in [100, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_steps_offset(self):\n for steps_offset in [0, 1]:\n self.check_over_configs(steps_offset=steps_offset)\n\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(steps_offset=1)\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(10)\n assert torch.equal(\n scheduler.timesteps,\n torch.LongTensor(\n [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]\n ),\n )\n\n def test_betas(self):\n for beta_start, beta_end in zip([0.0001, 0.001], [0.002, 0.02]):\n self.check_over_configs(beta_start=beta_start, beta_end=beta_end)\n\n def test_schedules(self):\n for schedule in [\"linear\", \"squaredcos_cap_v2\"]:\n self.check_over_configs(beta_schedule=schedule)\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(prediction_type=prediction_type)\n\n def test_time_indices(self):\n for t in [1, 5, 10]:\n self.check_over_forward(time_step=t)\n\n def test_inference_steps(self):\n for t, num_inference_steps in zip([1, 5, 10], [10, 50, 100]):\n self.check_over_forward(num_inference_steps=num_inference_steps)\n\n def test_pow_of_3_inference_steps(self):\n # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3\n num_inference_steps = 27\n\n for scheduler_class in self.scheduler_classes:\n sample = self.dummy_sample\n residual = 0.1 * sample\n\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(num_inference_steps)\n\n # before power of 3 fix, would error on first step, so we only need to do two\n for i, t in enumerate(scheduler.prk_timesteps[:2]):\n sample = scheduler.step_prk(residual, t, sample).prev_sample\n\n def test_inference_plms_no_past_residuals(self):\n with self.assertRaises(ValueError):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.step_plms(self.dummy_sample, 1, self.dummy_sample).prev_sample\n\n def test_full_loop_no_noise(self):\n sample = self.full_loop()\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 198.1318) < 1e-2\n assert abs(result_mean.item() - 0.2580) < 1e-3\n\n def test_full_loop_with_v_prediction(self):\n sample = self.full_loop(prediction_type=\"v_prediction\")\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 67.3986) < 1e-2\n assert abs(result_mean.item() - 0.0878) < 1e-3\n\n def test_full_loop_with_set_alpha_to_one(self):\n # We specify different beta, so that the first alpha is 0.99\n sample = self.full_loop(set_alpha_to_one=True, beta_start=0.01)\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 230.0399) < 1e-2\n assert abs(result_mean.item() - 0.2995) < 1e-3\n\n def test_full_loop_with_no_set_alpha_to_one(self):\n # We specify different beta, so that the first alpha is 0.99\n sample = self.full_loop(set_alpha_to_one=False, beta_start=0.01)\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 186.9482) < 1e-2\n assert abs(result_mean.item() - 0.2434) < 1e-3\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_sasolver.py", "content": "import torch\n\nfrom diffusers import SASolverScheduler\nfrom diffusers.utils.testing_utils import require_torchsde, torch_device\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\n@require_torchsde\nclass SASolverSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (SASolverScheduler,)\n forward_default_kwargs = ((\"num_inference_steps\", 10),)\n num_inference_steps = 10\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1100,\n \"beta_start\": 0.0001,\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n }\n\n config.update(**kwargs)\n return config\n\n def test_step_shape(self):\n kwargs = dict(self.forward_default_kwargs)\n\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n sample = self.dummy_sample\n residual = 0.1 * sample\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n scheduler.set_timesteps(num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n # copy over dummy past residuals (must be done after set_timesteps)\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]\n scheduler.model_outputs = dummy_past_residuals[\n : max(\n scheduler.config.predictor_order,\n scheduler.config.corrector_order - 1,\n )\n ]\n\n time_step_0 = scheduler.timesteps[5]\n time_step_1 = scheduler.timesteps[6]\n\n output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample\n output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample\n\n self.assertEqual(output_0.shape, sample.shape)\n self.assertEqual(output_0.shape, output_1.shape)\n\n def test_timesteps(self):\n for timesteps in [10, 50, 100, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_betas(self):\n for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):\n self.check_over_configs(beta_start=beta_start, beta_end=beta_end)\n\n def test_schedules(self):\n for schedule in [\"linear\", \"scaled_linear\"]:\n self.check_over_configs(beta_schedule=schedule)\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(prediction_type=prediction_type)\n\n def test_full_loop_no_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n sample = sample.to(torch_device)\n generator = torch.manual_seed(0)\n\n for i, t in enumerate(scheduler.timesteps):\n sample = scheduler.scale_model_input(sample, t, generator=generator)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n if torch_device in [\"cpu\"]:\n assert abs(result_sum.item() - 337.394287109375) < 1e-2\n assert abs(result_mean.item() - 0.43931546807289124) < 1e-3\n elif torch_device in [\"cuda\"]:\n assert abs(result_sum.item() - 329.1999816894531) < 1e-2\n assert abs(result_mean.item() - 0.4286458194255829) < 1e-3\n else:\n print(\"None\")\n\n def test_full_loop_with_v_prediction(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(prediction_type=\"v_prediction\")\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter * scheduler.init_noise_sigma\n sample = sample.to(torch_device)\n generator = torch.manual_seed(0)\n\n for i, t in enumerate(scheduler.timesteps):\n sample = scheduler.scale_model_input(sample, t, generator=generator)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n if torch_device in [\"cpu\"]:\n assert abs(result_sum.item() - 193.1467742919922) < 1e-2\n assert abs(result_mean.item() - 0.2514931857585907) < 1e-3\n elif torch_device in [\"cuda\"]:\n assert abs(result_sum.item() - 193.4154052734375) < 1e-2\n assert abs(result_mean.item() - 0.2518429756164551) < 1e-3\n else:\n print(\"None\")\n\n def test_full_loop_device(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(self.num_inference_steps, device=torch_device)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma\n generator = torch.manual_seed(0)\n\n for t in scheduler.timesteps:\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample, generator=generator)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n if torch_device in [\"cpu\"]:\n assert abs(result_sum.item() - 337.394287109375) < 1e-2\n assert abs(result_mean.item() - 0.43931546807289124) < 1e-3\n elif torch_device in [\"cuda\"]:\n assert abs(result_sum.item() - 337.394287109375) < 1e-2\n assert abs(result_mean.item() - 0.4393154978752136) < 1e-3\n else:\n print(\"None\")\n\n def test_full_loop_device_karras_sigmas(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config, use_karras_sigmas=True)\n\n scheduler.set_timesteps(self.num_inference_steps, device=torch_device)\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma\n sample = sample.to(torch_device)\n generator = torch.manual_seed(0)\n\n for t in scheduler.timesteps:\n sample = scheduler.scale_model_input(sample, t)\n\n model_output = model(sample, t)\n\n output = scheduler.step(model_output, t, sample, generator=generator)\n sample = output.prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n if torch_device in [\"cpu\"]:\n assert abs(result_sum.item() - 837.2554931640625) < 1e-2\n assert abs(result_mean.item() - 1.0901764631271362) < 1e-2\n elif torch_device in [\"cuda\"]:\n assert abs(result_sum.item() - 837.25537109375) < 1e-2\n assert abs(result_mean.item() - 1.0901763439178467) < 1e-2\n else:\n print(\"None\")\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_score_sde_ve.py", "content": "import tempfile\nimport unittest\n\nimport numpy as np\nimport torch\n\nfrom diffusers import ScoreSdeVeScheduler\n\n\nclass ScoreSdeVeSchedulerTest(unittest.TestCase):\n # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)\n scheduler_classes = (ScoreSdeVeScheduler,)\n forward_default_kwargs = ()\n\n @property\n def dummy_sample(self):\n batch_size = 4\n num_channels = 3\n height = 8\n width = 8\n\n sample = torch.rand((batch_size, num_channels, height, width))\n\n return sample\n\n @property\n def dummy_sample_deter(self):\n batch_size = 4\n num_channels = 3\n height = 8\n width = 8\n\n num_elems = batch_size * num_channels * height * width\n sample = torch.arange(num_elems)\n sample = sample.reshape(num_channels, height, width, batch_size)\n sample = sample / num_elems\n sample = sample.permute(3, 0, 1, 2)\n\n return sample\n\n def dummy_model(self):\n def model(sample, t, *args):\n return sample * t / (t + 1)\n\n return model\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 2000,\n \"snr\": 0.15,\n \"sigma_min\": 0.01,\n \"sigma_max\": 1348,\n \"sampling_eps\": 1e-5,\n }\n\n config.update(**kwargs)\n return config\n\n def check_over_configs(self, time_step=0, **config):\n kwargs = dict(self.forward_default_kwargs)\n\n for scheduler_class in self.scheduler_classes:\n sample = self.dummy_sample\n residual = 0.1 * sample\n\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n\n output = scheduler.step_pred(\n residual, time_step, sample, generator=torch.manual_seed(0), **kwargs\n ).prev_sample\n new_output = new_scheduler.step_pred(\n residual, time_step, sample, generator=torch.manual_seed(0), **kwargs\n ).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n output = scheduler.step_correct(residual, sample, generator=torch.manual_seed(0), **kwargs).prev_sample\n new_output = new_scheduler.step_correct(\n residual, sample, generator=torch.manual_seed(0), **kwargs\n ).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler correction are not identical\"\n\n def check_over_forward(self, time_step=0, **forward_kwargs):\n kwargs = dict(self.forward_default_kwargs)\n kwargs.update(forward_kwargs)\n\n for scheduler_class in self.scheduler_classes:\n sample = self.dummy_sample\n residual = 0.1 * sample\n\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n\n output = scheduler.step_pred(\n residual, time_step, sample, generator=torch.manual_seed(0), **kwargs\n ).prev_sample\n new_output = new_scheduler.step_pred(\n residual, time_step, sample, generator=torch.manual_seed(0), **kwargs\n ).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n output = scheduler.step_correct(residual, sample, generator=torch.manual_seed(0), **kwargs).prev_sample\n new_output = new_scheduler.step_correct(\n residual, sample, generator=torch.manual_seed(0), **kwargs\n ).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler correction are not identical\"\n\n def test_timesteps(self):\n for timesteps in [10, 100, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_sigmas(self):\n for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01], [1, 100, 1000]):\n self.check_over_configs(sigma_min=sigma_min, sigma_max=sigma_max)\n\n def test_time_indices(self):\n for t in [0.1, 0.5, 0.75]:\n self.check_over_forward(time_step=t)\n\n def test_full_loop_no_noise(self):\n kwargs = dict(self.forward_default_kwargs)\n\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 3\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n\n scheduler.set_sigmas(num_inference_steps)\n scheduler.set_timesteps(num_inference_steps)\n generator = torch.manual_seed(0)\n\n for i, t in enumerate(scheduler.timesteps):\n sigma_t = scheduler.sigmas[i]\n\n for _ in range(scheduler.config.correct_steps):\n with torch.no_grad():\n model_output = model(sample, sigma_t)\n sample = scheduler.step_correct(model_output, sample, generator=generator, **kwargs).prev_sample\n\n with torch.no_grad():\n model_output = model(sample, sigma_t)\n\n output = scheduler.step_pred(model_output, t, sample, generator=generator, **kwargs)\n sample, _ = output.prev_sample, output.prev_sample_mean\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert np.isclose(result_sum.item(), 14372758528.0)\n assert np.isclose(result_mean.item(), 18714530.0)\n\n def test_step_shape(self):\n kwargs = dict(self.forward_default_kwargs)\n\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n sample = self.dummy_sample\n residual = 0.1 * sample\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n scheduler.set_timesteps(num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n output_0 = scheduler.step_pred(residual, 0, sample, generator=torch.manual_seed(0), **kwargs).prev_sample\n output_1 = scheduler.step_pred(residual, 1, sample, generator=torch.manual_seed(0), **kwargs).prev_sample\n\n self.assertEqual(output_0.shape, sample.shape)\n self.assertEqual(output_0.shape, output_1.shape)\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_tcd.py", "content": "import torch\n\nfrom diffusers import TCDScheduler\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass TCDSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (TCDScheduler,)\n forward_default_kwargs = ((\"num_inference_steps\", 10),)\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1000,\n \"beta_start\": 0.00085,\n \"beta_end\": 0.0120,\n \"beta_schedule\": \"scaled_linear\",\n \"prediction_type\": \"epsilon\",\n }\n\n config.update(**kwargs)\n return config\n\n @property\n def default_num_inference_steps(self):\n return 10\n\n @property\n def default_valid_timestep(self):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n scheduler_config = self.get_scheduler_config()\n scheduler = self.scheduler_classes[0](**scheduler_config)\n\n scheduler.set_timesteps(num_inference_steps)\n timestep = scheduler.timesteps[-1]\n return timestep\n\n def test_timesteps(self):\n for timesteps in [100, 500, 1000]:\n # 0 is not guaranteed to be in the timestep schedule, but timesteps - 1 is\n self.check_over_configs(time_step=timesteps - 1, num_train_timesteps=timesteps)\n\n def test_betas(self):\n for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]):\n self.check_over_configs(time_step=self.default_valid_timestep, beta_start=beta_start, beta_end=beta_end)\n\n def test_schedules(self):\n for schedule in [\"linear\", \"scaled_linear\", \"squaredcos_cap_v2\"]:\n self.check_over_configs(time_step=self.default_valid_timestep, beta_schedule=schedule)\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(time_step=self.default_valid_timestep, prediction_type=prediction_type)\n\n def test_clip_sample(self):\n for clip_sample in [True, False]:\n self.check_over_configs(time_step=self.default_valid_timestep, clip_sample=clip_sample)\n\n def test_thresholding(self):\n self.check_over_configs(time_step=self.default_valid_timestep, thresholding=False)\n for threshold in [0.5, 1.0, 2.0]:\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(\n time_step=self.default_valid_timestep,\n thresholding=True,\n prediction_type=prediction_type,\n sample_max_value=threshold,\n )\n\n def test_time_indices(self):\n # Get default timestep schedule.\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n scheduler_config = self.get_scheduler_config()\n scheduler = self.scheduler_classes[0](**scheduler_config)\n\n scheduler.set_timesteps(num_inference_steps)\n timesteps = scheduler.timesteps\n for t in timesteps:\n self.check_over_forward(time_step=t)\n\n def test_inference_steps(self):\n # Hardcoded for now\n for t, num_inference_steps in zip([99, 39, 39, 19], [10, 25, 26, 50]):\n self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps)\n\n def full_loop(self, num_inference_steps=10, seed=0, **config):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n eta = 0.0 # refer to gamma in the paper\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n generator = torch.manual_seed(seed)\n scheduler.set_timesteps(num_inference_steps)\n\n for t in scheduler.timesteps:\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample, eta, generator).prev_sample\n\n return sample\n\n def test_full_loop_onestep_deter(self):\n sample = self.full_loop(num_inference_steps=1)\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 29.8715) < 1e-3 # 0.0778918\n assert abs(result_mean.item() - 0.0389) < 1e-3\n\n def test_full_loop_multistep_deter(self):\n sample = self.full_loop(num_inference_steps=10)\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 181.2040) < 1e-3\n assert abs(result_mean.item() - 0.2359) < 1e-3\n\n def test_custom_timesteps(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n timesteps = [100, 87, 50, 1, 0]\n\n scheduler.set_timesteps(timesteps=timesteps)\n\n scheduler_timesteps = scheduler.timesteps\n\n for i, timestep in enumerate(scheduler_timesteps):\n if i == len(timesteps) - 1:\n expected_prev_t = -1\n else:\n expected_prev_t = timesteps[i + 1]\n\n prev_t = scheduler.previous_timestep(timestep)\n prev_t = prev_t.item()\n\n self.assertEqual(prev_t, expected_prev_t)\n\n def test_custom_timesteps_increasing_order(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n timesteps = [100, 87, 50, 51, 0]\n\n with self.assertRaises(ValueError, msg=\"`custom_timesteps` must be in descending order.\"):\n scheduler.set_timesteps(timesteps=timesteps)\n\n def test_custom_timesteps_passing_both_num_inference_steps_and_timesteps(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n timesteps = [100, 87, 50, 1, 0]\n num_inference_steps = len(timesteps)\n\n with self.assertRaises(ValueError, msg=\"Can only pass one of `num_inference_steps` or `custom_timesteps`.\"):\n scheduler.set_timesteps(num_inference_steps=num_inference_steps, timesteps=timesteps)\n\n def test_custom_timesteps_too_large(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n timesteps = [scheduler.config.num_train_timesteps]\n\n with self.assertRaises(\n ValueError,\n msg=\"`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}\",\n ):\n scheduler.set_timesteps(timesteps=timesteps)\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_unclip.py", "content": "import torch\n\nfrom diffusers import UnCLIPScheduler\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\n# UnCLIPScheduler is a modified DDPMScheduler with a subset of the configuration.\nclass UnCLIPSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (UnCLIPScheduler,)\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1000,\n \"variance_type\": \"fixed_small_log\",\n \"clip_sample\": True,\n \"clip_sample_range\": 1.0,\n \"prediction_type\": \"epsilon\",\n }\n\n config.update(**kwargs)\n return config\n\n def test_timesteps(self):\n for timesteps in [1, 5, 100, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_variance_type(self):\n for variance in [\"fixed_small_log\", \"learned_range\"]:\n self.check_over_configs(variance_type=variance)\n\n def test_clip_sample(self):\n for clip_sample in [True, False]:\n self.check_over_configs(clip_sample=clip_sample)\n\n def test_clip_sample_range(self):\n for clip_sample_range in [1, 5, 10, 20]:\n self.check_over_configs(clip_sample_range=clip_sample_range)\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"sample\"]:\n self.check_over_configs(prediction_type=prediction_type)\n\n def test_time_indices(self):\n for time_step in [0, 500, 999]:\n for prev_timestep in [None, 5, 100, 250, 500, 750]:\n if prev_timestep is not None and prev_timestep >= time_step:\n continue\n\n self.check_over_forward(time_step=time_step, prev_timestep=prev_timestep)\n\n def test_variance_fixed_small_log(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(variance_type=\"fixed_small_log\")\n scheduler = scheduler_class(**scheduler_config)\n\n assert torch.sum(torch.abs(scheduler._get_variance(0) - 1.0000e-10)) < 1e-5\n assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0549625)) < 1e-5\n assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.9994987)) < 1e-5\n\n def test_variance_learned_range(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(variance_type=\"learned_range\")\n scheduler = scheduler_class(**scheduler_config)\n\n predicted_variance = 0.5\n\n assert scheduler._get_variance(1, predicted_variance=predicted_variance) - -10.1712790 < 1e-5\n assert scheduler._get_variance(487, predicted_variance=predicted_variance) - -5.7998052 < 1e-5\n assert scheduler._get_variance(999, predicted_variance=predicted_variance) - -0.0010011 < 1e-5\n\n def test_full_loop(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n timesteps = scheduler.timesteps\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n generator = torch.manual_seed(0)\n\n for i, t in enumerate(timesteps):\n # 1. predict noise residual\n residual = model(sample, t)\n\n # 2. predict previous mean of sample x_t-1\n pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample\n\n sample = pred_prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 252.2682495) < 1e-2\n assert abs(result_mean.item() - 0.3284743) < 1e-3\n\n def test_full_loop_skip_timesteps(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler.set_timesteps(25)\n\n timesteps = scheduler.timesteps\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n generator = torch.manual_seed(0)\n\n for i, t in enumerate(timesteps):\n # 1. predict noise residual\n residual = model(sample, t)\n\n if i + 1 == timesteps.shape[0]:\n prev_timestep = None\n else:\n prev_timestep = timesteps[i + 1]\n\n # 2. predict previous mean of sample x_t-1\n pred_prev_sample = scheduler.step(\n residual, t, sample, prev_timestep=prev_timestep, generator=generator\n ).prev_sample\n\n sample = pred_prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 258.2044983) < 1e-2\n assert abs(result_mean.item() - 0.3362038) < 1e-3\n\n def test_trained_betas(self):\n pass\n\n def test_add_noise_device(self):\n pass\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_unipc.py", "content": "import tempfile\n\nimport torch\n\nfrom diffusers import (\n DEISMultistepScheduler,\n DPMSolverMultistepScheduler,\n DPMSolverSinglestepScheduler,\n UniPCMultistepScheduler,\n)\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass UniPCMultistepSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (UniPCMultistepScheduler,)\n forward_default_kwargs = ((\"num_inference_steps\", 25),)\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_train_timesteps\": 1000,\n \"beta_start\": 0.0001,\n \"beta_end\": 0.02,\n \"beta_schedule\": \"linear\",\n \"solver_order\": 2,\n \"solver_type\": \"bh2\",\n \"final_sigmas_type\": \"sigma_min\",\n }\n\n config.update(**kwargs)\n return config\n\n def check_over_configs(self, time_step=0, **config):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n sample = self.dummy_sample\n residual = 0.1 * sample\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(num_inference_steps)\n # copy over dummy past residuals\n scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n new_scheduler.set_timesteps(num_inference_steps)\n # copy over dummy past residuals\n new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order]\n\n output, new_output = sample, sample\n for t in range(time_step, time_step + scheduler.config.solver_order + 1):\n t = scheduler.timesteps[t]\n output = scheduler.step(residual, t, output, **kwargs).prev_sample\n new_output = new_scheduler.step(residual, t, new_output, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def check_over_forward(self, time_step=0, **forward_kwargs):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n sample = self.dummy_sample\n residual = 0.1 * sample\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(num_inference_steps)\n\n # copy over dummy past residuals (must be after setting timesteps)\n scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n # copy over dummy past residuals\n new_scheduler.set_timesteps(num_inference_steps)\n\n # copy over dummy past residual (must be after setting timesteps)\n new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order]\n\n output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def full_loop(self, scheduler=None, **config):\n if scheduler is None:\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 10\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n scheduler.set_timesteps(num_inference_steps)\n\n for i, t in enumerate(scheduler.timesteps):\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample).prev_sample\n\n return sample\n\n def test_step_shape(self):\n kwargs = dict(self.forward_default_kwargs)\n\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n sample = self.dummy_sample\n residual = 0.1 * sample\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n scheduler.set_timesteps(num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n # copy over dummy past residuals (must be done after set_timesteps)\n dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]\n scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]\n\n time_step_0 = scheduler.timesteps[5]\n time_step_1 = scheduler.timesteps[6]\n\n output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample\n output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample\n\n self.assertEqual(output_0.shape, sample.shape)\n self.assertEqual(output_0.shape, output_1.shape)\n\n def test_switch(self):\n # make sure that iterating over schedulers with same config names gives same results\n # for defaults\n scheduler = UniPCMultistepScheduler(**self.get_scheduler_config())\n sample = self.full_loop(scheduler=scheduler)\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.2464) < 1e-3\n\n scheduler = DPMSolverSinglestepScheduler.from_config(scheduler.config)\n scheduler = DEISMultistepScheduler.from_config(scheduler.config)\n scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)\n scheduler = UniPCMultistepScheduler.from_config(scheduler.config)\n\n sample = self.full_loop(scheduler=scheduler)\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.2464) < 1e-3\n\n def test_timesteps(self):\n for timesteps in [25, 50, 100, 999, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_thresholding(self):\n self.check_over_configs(thresholding=False)\n for order in [1, 2, 3]:\n for solver_type in [\"bh1\", \"bh2\"]:\n for threshold in [0.5, 1.0, 2.0]:\n for prediction_type in [\"epsilon\", \"sample\"]:\n self.check_over_configs(\n thresholding=True,\n prediction_type=prediction_type,\n sample_max_value=threshold,\n solver_order=order,\n solver_type=solver_type,\n )\n\n def test_prediction_type(self):\n for prediction_type in [\"epsilon\", \"v_prediction\"]:\n self.check_over_configs(prediction_type=prediction_type)\n\n def test_rescale_betas_zero_snr(self):\n for rescale_betas_zero_snr in [True, False]:\n self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr)\n\n def test_solver_order_and_type(self):\n for solver_type in [\"bh1\", \"bh2\"]:\n for order in [1, 2, 3]:\n for prediction_type in [\"epsilon\", \"sample\"]:\n self.check_over_configs(\n solver_order=order,\n solver_type=solver_type,\n prediction_type=prediction_type,\n )\n sample = self.full_loop(\n solver_order=order,\n solver_type=solver_type,\n prediction_type=prediction_type,\n )\n assert not torch.isnan(sample).any(), \"Samples have nan numbers\"\n\n def test_lower_order_final(self):\n self.check_over_configs(lower_order_final=True)\n self.check_over_configs(lower_order_final=False)\n\n def test_inference_steps(self):\n for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:\n self.check_over_forward(num_inference_steps=num_inference_steps, time_step=0)\n\n def test_full_loop_no_noise(self):\n sample = self.full_loop()\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.2464) < 1e-3\n\n def test_full_loop_with_karras(self):\n sample = self.full_loop(use_karras_sigmas=True)\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.2925) < 1e-3\n\n def test_full_loop_with_v_prediction(self):\n sample = self.full_loop(prediction_type=\"v_prediction\")\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.1014) < 1e-3\n\n def test_full_loop_with_karras_and_v_prediction(self):\n sample = self.full_loop(prediction_type=\"v_prediction\", use_karras_sigmas=True)\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.1966) < 1e-3\n\n def test_fp16_support(self):\n for order in [1, 2, 3]:\n for solver_type in [\"bh1\", \"bh2\"]:\n for prediction_type in [\"epsilon\", \"sample\", \"v_prediction\"]:\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config(\n thresholding=True,\n dynamic_thresholding_ratio=0,\n prediction_type=prediction_type,\n solver_order=order,\n solver_type=solver_type,\n )\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 10\n model = self.dummy_model()\n sample = self.dummy_sample_deter.half()\n scheduler.set_timesteps(num_inference_steps)\n\n for i, t in enumerate(scheduler.timesteps):\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample).prev_sample\n\n assert sample.dtype == torch.float16\n\n def test_full_loop_with_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 10\n t_start = 8\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n scheduler.set_timesteps(num_inference_steps)\n\n # add noise\n noise = self.dummy_noise_deter\n timesteps = scheduler.timesteps[t_start * scheduler.order :]\n sample = scheduler.add_noise(sample, noise, timesteps[:1])\n\n for i, t in enumerate(timesteps):\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample).prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 315.5757) < 1e-2, f\" expected result sum 315.5757, but get {result_sum}\"\n assert abs(result_mean.item() - 0.4109) < 1e-3, f\" expected result mean 0.4109, but get {result_mean}\"\n\n\nclass UniPCMultistepScheduler1DTest(UniPCMultistepSchedulerTest):\n @property\n def dummy_sample(self):\n batch_size = 4\n num_channels = 3\n width = 8\n\n sample = torch.rand((batch_size, num_channels, width))\n\n return sample\n\n @property\n def dummy_noise_deter(self):\n batch_size = 4\n num_channels = 3\n width = 8\n\n num_elems = batch_size * num_channels * width\n sample = torch.arange(num_elems).flip(-1)\n sample = sample.reshape(num_channels, width, batch_size)\n sample = sample / num_elems\n sample = sample.permute(2, 0, 1)\n\n return sample\n\n @property\n def dummy_sample_deter(self):\n batch_size = 4\n num_channels = 3\n width = 8\n\n num_elems = batch_size * num_channels * width\n sample = torch.arange(num_elems)\n sample = sample.reshape(num_channels, width, batch_size)\n sample = sample / num_elems\n sample = sample.permute(2, 0, 1)\n\n return sample\n\n def test_switch(self):\n # make sure that iterating over schedulers with same config names gives same results\n # for defaults\n scheduler = UniPCMultistepScheduler(**self.get_scheduler_config())\n sample = self.full_loop(scheduler=scheduler)\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.2441) < 1e-3\n\n scheduler = DPMSolverSinglestepScheduler.from_config(scheduler.config)\n scheduler = DEISMultistepScheduler.from_config(scheduler.config)\n scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)\n scheduler = UniPCMultistepScheduler.from_config(scheduler.config)\n\n sample = self.full_loop(scheduler=scheduler)\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.2441) < 1e-3\n\n def test_full_loop_no_noise(self):\n sample = self.full_loop()\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.2441) < 1e-3\n\n def test_full_loop_with_karras(self):\n sample = self.full_loop(use_karras_sigmas=True)\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.2898) < 1e-3\n\n def test_full_loop_with_v_prediction(self):\n sample = self.full_loop(prediction_type=\"v_prediction\")\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.1014) < 1e-3\n\n def test_full_loop_with_karras_and_v_prediction(self):\n sample = self.full_loop(prediction_type=\"v_prediction\", use_karras_sigmas=True)\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_mean.item() - 0.1944) < 1e-3\n\n def test_full_loop_with_noise(self):\n scheduler_class = self.scheduler_classes[0]\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n num_inference_steps = 10\n t_start = 8\n\n model = self.dummy_model()\n sample = self.dummy_sample_deter\n scheduler.set_timesteps(num_inference_steps)\n\n # add noise\n noise = self.dummy_noise_deter\n timesteps = scheduler.timesteps[t_start * scheduler.order :]\n sample = scheduler.add_noise(sample, noise, timesteps[:1])\n\n for i, t in enumerate(timesteps):\n residual = model(sample, t)\n sample = scheduler.step(residual, t, sample).prev_sample\n\n result_sum = torch.sum(torch.abs(sample))\n result_mean = torch.mean(torch.abs(sample))\n\n assert abs(result_sum.item() - 39.0870) < 1e-2, f\" expected result sum 39.0870, but get {result_sum}\"\n assert abs(result_mean.item() - 0.4072) < 1e-3, f\" expected result mean 0.4072, but get {result_mean}\"\n" }, { "path": "diffusers/tests/schedulers/test_scheduler_vq_diffusion.py", "content": "import torch\nimport torch.nn.functional as F\n\nfrom diffusers import VQDiffusionScheduler\n\nfrom .test_schedulers import SchedulerCommonTest\n\n\nclass VQDiffusionSchedulerTest(SchedulerCommonTest):\n scheduler_classes = (VQDiffusionScheduler,)\n\n def get_scheduler_config(self, **kwargs):\n config = {\n \"num_vec_classes\": 4097,\n \"num_train_timesteps\": 100,\n }\n\n config.update(**kwargs)\n return config\n\n def dummy_sample(self, num_vec_classes):\n batch_size = 4\n height = 8\n width = 8\n\n sample = torch.randint(0, num_vec_classes, (batch_size, height * width))\n\n return sample\n\n @property\n def dummy_sample_deter(self):\n assert False\n\n def dummy_model(self, num_vec_classes):\n def model(sample, t, *args):\n batch_size, num_latent_pixels = sample.shape\n logits = torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels))\n return_value = F.log_softmax(logits.double(), dim=1).float()\n return return_value\n\n return model\n\n def test_timesteps(self):\n for timesteps in [2, 5, 100, 1000]:\n self.check_over_configs(num_train_timesteps=timesteps)\n\n def test_num_vec_classes(self):\n for num_vec_classes in [5, 100, 1000, 4000]:\n self.check_over_configs(num_vec_classes=num_vec_classes)\n\n def test_time_indices(self):\n for t in [0, 50, 99]:\n self.check_over_forward(time_step=t)\n\n def test_add_noise_device(self):\n pass\n" }, { "path": "diffusers/tests/schedulers/test_schedulers.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport inspect\nimport json\nimport os\nimport tempfile\nimport unittest\nimport uuid\nfrom typing import Dict, List, Tuple\n\nimport numpy as np\nimport torch\nfrom huggingface_hub import delete_repo\n\nimport diffusers\nfrom diffusers import (\n CMStochasticIterativeScheduler,\n DDIMScheduler,\n DEISMultistepScheduler,\n DiffusionPipeline,\n EDMEulerScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n IPNDMScheduler,\n LMSDiscreteScheduler,\n UniPCMultistepScheduler,\n VQDiffusionScheduler,\n)\nfrom diffusers.configuration_utils import ConfigMixin, register_to_config\nfrom diffusers.schedulers.scheduling_utils import SchedulerMixin\nfrom diffusers.utils import logging\nfrom diffusers.utils.testing_utils import CaptureLogger, torch_device\n\nfrom ..others.test_utils import TOKEN, USER, is_staging_test\n\n\ntorch.backends.cuda.matmul.allow_tf32 = False\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass SchedulerObject(SchedulerMixin, ConfigMixin):\n config_name = \"config.json\"\n\n @register_to_config\n def __init__(\n self,\n a=2,\n b=5,\n c=(2, 5),\n d=\"for diffusion\",\n e=[1, 3],\n ):\n pass\n\n\nclass SchedulerObject2(SchedulerMixin, ConfigMixin):\n config_name = \"config.json\"\n\n @register_to_config\n def __init__(\n self,\n a=2,\n b=5,\n c=(2, 5),\n d=\"for diffusion\",\n f=[1, 3],\n ):\n pass\n\n\nclass SchedulerObject3(SchedulerMixin, ConfigMixin):\n config_name = \"config.json\"\n\n @register_to_config\n def __init__(\n self,\n a=2,\n b=5,\n c=(2, 5),\n d=\"for diffusion\",\n e=[1, 3],\n f=[1, 3],\n ):\n pass\n\n\nclass SchedulerBaseTests(unittest.TestCase):\n def test_save_load_from_different_config(self):\n obj = SchedulerObject()\n\n # mock add obj class to `diffusers`\n setattr(diffusers, \"SchedulerObject\", SchedulerObject)\n logger = logging.get_logger(\"diffusers.configuration_utils\")\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n obj.save_config(tmpdirname)\n with CaptureLogger(logger) as cap_logger_1:\n config = SchedulerObject2.load_config(tmpdirname)\n new_obj_1 = SchedulerObject2.from_config(config)\n\n # now save a config parameter that is not expected\n with open(os.path.join(tmpdirname, SchedulerObject.config_name), \"r\") as f:\n data = json.load(f)\n data[\"unexpected\"] = True\n\n with open(os.path.join(tmpdirname, SchedulerObject.config_name), \"w\") as f:\n json.dump(data, f)\n\n with CaptureLogger(logger) as cap_logger_2:\n config = SchedulerObject.load_config(tmpdirname)\n new_obj_2 = SchedulerObject.from_config(config)\n\n with CaptureLogger(logger) as cap_logger_3:\n config = SchedulerObject2.load_config(tmpdirname)\n new_obj_3 = SchedulerObject2.from_config(config)\n\n assert new_obj_1.__class__ == SchedulerObject2\n assert new_obj_2.__class__ == SchedulerObject\n assert new_obj_3.__class__ == SchedulerObject2\n\n assert cap_logger_1.out == \"\"\n assert (\n cap_logger_2.out\n == \"The config attributes {'unexpected': True} were passed to SchedulerObject, but are not expected and\"\n \" will\"\n \" be ignored. Please verify your config.json configuration file.\\n\"\n )\n assert cap_logger_2.out.replace(\"SchedulerObject\", \"SchedulerObject2\") == cap_logger_3.out\n\n def test_save_load_compatible_schedulers(self):\n SchedulerObject2._compatibles = [\"SchedulerObject\"]\n SchedulerObject._compatibles = [\"SchedulerObject2\"]\n\n obj = SchedulerObject()\n\n # mock add obj class to `diffusers`\n setattr(diffusers, \"SchedulerObject\", SchedulerObject)\n setattr(diffusers, \"SchedulerObject2\", SchedulerObject2)\n logger = logging.get_logger(\"diffusers.configuration_utils\")\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n obj.save_config(tmpdirname)\n\n # now save a config parameter that is expected by another class, but not origin class\n with open(os.path.join(tmpdirname, SchedulerObject.config_name), \"r\") as f:\n data = json.load(f)\n data[\"f\"] = [0, 0]\n data[\"unexpected\"] = True\n\n with open(os.path.join(tmpdirname, SchedulerObject.config_name), \"w\") as f:\n json.dump(data, f)\n\n with CaptureLogger(logger) as cap_logger:\n config = SchedulerObject.load_config(tmpdirname)\n new_obj = SchedulerObject.from_config(config)\n\n assert new_obj.__class__ == SchedulerObject\n\n assert (\n cap_logger.out\n == \"The config attributes {'unexpected': True} were passed to SchedulerObject, but are not expected and\"\n \" will\"\n \" be ignored. Please verify your config.json configuration file.\\n\"\n )\n\n def test_save_load_from_different_config_comp_schedulers(self):\n SchedulerObject3._compatibles = [\"SchedulerObject\", \"SchedulerObject2\"]\n SchedulerObject2._compatibles = [\"SchedulerObject\", \"SchedulerObject3\"]\n SchedulerObject._compatibles = [\"SchedulerObject2\", \"SchedulerObject3\"]\n\n obj = SchedulerObject()\n\n # mock add obj class to `diffusers`\n setattr(diffusers, \"SchedulerObject\", SchedulerObject)\n setattr(diffusers, \"SchedulerObject2\", SchedulerObject2)\n setattr(diffusers, \"SchedulerObject3\", SchedulerObject3)\n logger = logging.get_logger(\"diffusers.configuration_utils\")\n logger.setLevel(diffusers.logging.INFO)\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n obj.save_config(tmpdirname)\n\n with CaptureLogger(logger) as cap_logger_1:\n config = SchedulerObject.load_config(tmpdirname)\n new_obj_1 = SchedulerObject.from_config(config)\n\n with CaptureLogger(logger) as cap_logger_2:\n config = SchedulerObject2.load_config(tmpdirname)\n new_obj_2 = SchedulerObject2.from_config(config)\n\n with CaptureLogger(logger) as cap_logger_3:\n config = SchedulerObject3.load_config(tmpdirname)\n new_obj_3 = SchedulerObject3.from_config(config)\n\n assert new_obj_1.__class__ == SchedulerObject\n assert new_obj_2.__class__ == SchedulerObject2\n assert new_obj_3.__class__ == SchedulerObject3\n\n assert cap_logger_1.out == \"\"\n assert cap_logger_2.out == \"{'f'} was not found in config. Values will be initialized to default values.\\n\"\n assert cap_logger_3.out == \"{'f'} was not found in config. Values will be initialized to default values.\\n\"\n\n def test_default_arguments_not_in_config(self):\n pipe = DiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-pipe\", torch_dtype=torch.float16\n )\n assert pipe.scheduler.__class__ == DDIMScheduler\n\n # Default for DDIMScheduler\n assert pipe.scheduler.config.timestep_spacing == \"leading\"\n\n # Switch to a different one, verify we use the default for that class\n pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)\n assert pipe.scheduler.config.timestep_spacing == \"linspace\"\n\n # Override with kwargs\n pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing=\"trailing\")\n assert pipe.scheduler.config.timestep_spacing == \"trailing\"\n\n # Verify overridden kwargs stick\n pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)\n assert pipe.scheduler.config.timestep_spacing == \"trailing\"\n\n # And stick\n pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)\n assert pipe.scheduler.config.timestep_spacing == \"trailing\"\n\n def test_default_solver_type_after_switch(self):\n pipe = DiffusionPipeline.from_pretrained(\n \"hf-internal-testing/tiny-stable-diffusion-pipe\", torch_dtype=torch.float16\n )\n assert pipe.scheduler.__class__ == DDIMScheduler\n\n pipe.scheduler = DEISMultistepScheduler.from_config(pipe.scheduler.config)\n assert pipe.scheduler.config.solver_type == \"logrho\"\n\n # Switch to UniPC, verify the solver is the default\n pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\n assert pipe.scheduler.config.solver_type == \"bh2\"\n\n\nclass SchedulerCommonTest(unittest.TestCase):\n scheduler_classes = ()\n forward_default_kwargs = ()\n\n @property\n def default_num_inference_steps(self):\n return 50\n\n @property\n def default_timestep(self):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.get(\"num_inference_steps\", self.default_num_inference_steps)\n\n try:\n scheduler_config = self.get_scheduler_config()\n scheduler = self.scheduler_classes[0](**scheduler_config)\n\n scheduler.set_timesteps(num_inference_steps)\n timestep = scheduler.timesteps[0]\n except NotImplementedError:\n logger.warning(\n f\"The scheduler {self.__class__.__name__} does not implement a `get_scheduler_config` method.\"\n f\" `default_timestep` will be set to the default value of 1.\"\n )\n timestep = 1\n\n return timestep\n\n # NOTE: currently taking the convention that default_timestep > default_timestep_2 (alternatively,\n # default_timestep comes earlier in the timestep schedule than default_timestep_2)\n @property\n def default_timestep_2(self):\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.get(\"num_inference_steps\", self.default_num_inference_steps)\n\n try:\n scheduler_config = self.get_scheduler_config()\n scheduler = self.scheduler_classes[0](**scheduler_config)\n\n scheduler.set_timesteps(num_inference_steps)\n if len(scheduler.timesteps) >= 2:\n timestep_2 = scheduler.timesteps[1]\n else:\n logger.warning(\n f\"Using num_inference_steps from the scheduler testing class's default config leads to a timestep\"\n f\" scheduler of length {len(scheduler.timesteps)} < 2. The default `default_timestep_2` value of 0\"\n f\" will be used.\"\n )\n timestep_2 = 0\n except NotImplementedError:\n logger.warning(\n f\"The scheduler {self.__class__.__name__} does not implement a `get_scheduler_config` method.\"\n f\" `default_timestep_2` will be set to the default value of 0.\"\n )\n timestep_2 = 0\n\n return timestep_2\n\n @property\n def dummy_sample(self):\n batch_size = 4\n num_channels = 3\n height = 8\n width = 8\n\n sample = torch.rand((batch_size, num_channels, height, width))\n\n return sample\n\n @property\n def dummy_noise_deter(self):\n batch_size = 4\n num_channels = 3\n height = 8\n width = 8\n\n num_elems = batch_size * num_channels * height * width\n sample = torch.arange(num_elems).flip(-1)\n sample = sample.reshape(num_channels, height, width, batch_size)\n sample = sample / num_elems\n sample = sample.permute(3, 0, 1, 2)\n\n return sample\n\n @property\n def dummy_sample_deter(self):\n batch_size = 4\n num_channels = 3\n height = 8\n width = 8\n\n num_elems = batch_size * num_channels * height * width\n sample = torch.arange(num_elems)\n sample = sample.reshape(num_channels, height, width, batch_size)\n sample = sample / num_elems\n sample = sample.permute(3, 0, 1, 2)\n\n return sample\n\n def get_scheduler_config(self):\n raise NotImplementedError\n\n def dummy_model(self):\n def model(sample, t, *args):\n # if t is a tensor, match the number of dimensions of sample\n if isinstance(t, torch.Tensor):\n num_dims = len(sample.shape)\n # pad t with 1s to match num_dims\n t = t.reshape(-1, *(1,) * (num_dims - 1)).to(sample.device).to(sample.dtype)\n\n return sample * t / (t + 1)\n\n return model\n\n def check_over_configs(self, time_step=0, **config):\n kwargs = dict(self.forward_default_kwargs)\n\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n time_step = time_step if time_step is not None else self.default_timestep\n\n for scheduler_class in self.scheduler_classes:\n # TODO(Suraj) - delete the following two lines once DDPM, DDIM, and PNDM have timesteps casted to float by default\n if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler):\n time_step = float(time_step)\n\n scheduler_config = self.get_scheduler_config(**config)\n scheduler = scheduler_class(**scheduler_config)\n\n if scheduler_class == CMStochasticIterativeScheduler:\n # Get valid timestep based on sigma_max, which should always be in timestep schedule.\n scaled_sigma_max = scheduler.sigma_to_t(scheduler.config.sigma_max)\n time_step = scaled_sigma_max\n\n if scheduler_class == EDMEulerScheduler:\n time_step = scheduler.timesteps[-1]\n\n if scheduler_class == VQDiffusionScheduler:\n num_vec_classes = scheduler_config[\"num_vec_classes\"]\n sample = self.dummy_sample(num_vec_classes)\n model = self.dummy_model(num_vec_classes)\n residual = model(sample, time_step)\n else:\n sample = self.dummy_sample\n residual = 0.1 * sample\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n scheduler.set_timesteps(num_inference_steps)\n new_scheduler.set_timesteps(num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n # Make sure `scale_model_input` is invoked to prevent a warning\n if scheduler_class == CMStochasticIterativeScheduler:\n # Get valid timestep based on sigma_max, which should always be in timestep schedule.\n _ = scheduler.scale_model_input(sample, scaled_sigma_max)\n _ = new_scheduler.scale_model_input(sample, scaled_sigma_max)\n elif scheduler_class != VQDiffusionScheduler:\n _ = scheduler.scale_model_input(sample, scheduler.timesteps[-1])\n _ = new_scheduler.scale_model_input(sample, scheduler.timesteps[-1])\n\n # Set the seed before step() as some schedulers are stochastic like EulerAncestralDiscreteScheduler, EulerDiscreteScheduler\n if \"generator\" in set(inspect.signature(scheduler.step).parameters.keys()):\n kwargs[\"generator\"] = torch.manual_seed(0)\n output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n\n if \"generator\" in set(inspect.signature(scheduler.step).parameters.keys()):\n kwargs[\"generator\"] = torch.manual_seed(0)\n new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def check_over_forward(self, time_step=0, **forward_kwargs):\n kwargs = dict(self.forward_default_kwargs)\n kwargs.update(forward_kwargs)\n\n num_inference_steps = kwargs.pop(\"num_inference_steps\", None)\n time_step = time_step if time_step is not None else self.default_timestep\n\n for scheduler_class in self.scheduler_classes:\n if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler):\n time_step = float(time_step)\n\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n if scheduler_class == VQDiffusionScheduler:\n num_vec_classes = scheduler_config[\"num_vec_classes\"]\n sample = self.dummy_sample(num_vec_classes)\n model = self.dummy_model(num_vec_classes)\n residual = model(sample, time_step)\n else:\n sample = self.dummy_sample\n residual = 0.1 * sample\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n scheduler.set_timesteps(num_inference_steps)\n new_scheduler.set_timesteps(num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n if \"generator\" in set(inspect.signature(scheduler.step).parameters.keys()):\n kwargs[\"generator\"] = torch.manual_seed(0)\n output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n\n if \"generator\" in set(inspect.signature(scheduler.step).parameters.keys()):\n kwargs[\"generator\"] = torch.manual_seed(0)\n new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def test_from_save_pretrained(self):\n kwargs = dict(self.forward_default_kwargs)\n\n num_inference_steps = kwargs.pop(\"num_inference_steps\", self.default_num_inference_steps)\n\n for scheduler_class in self.scheduler_classes:\n timestep = self.default_timestep\n if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler):\n timestep = float(timestep)\n\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n if scheduler_class == CMStochasticIterativeScheduler:\n # Get valid timestep based on sigma_max, which should always be in timestep schedule.\n timestep = scheduler.sigma_to_t(scheduler.config.sigma_max)\n\n if scheduler_class == VQDiffusionScheduler:\n num_vec_classes = scheduler_config[\"num_vec_classes\"]\n sample = self.dummy_sample(num_vec_classes)\n model = self.dummy_model(num_vec_classes)\n residual = model(sample, timestep)\n else:\n sample = self.dummy_sample\n residual = 0.1 * sample\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_config(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n scheduler.set_timesteps(num_inference_steps)\n new_scheduler.set_timesteps(num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n if \"generator\" in set(inspect.signature(scheduler.step).parameters.keys()):\n kwargs[\"generator\"] = torch.manual_seed(0)\n output = scheduler.step(residual, timestep, sample, **kwargs).prev_sample\n\n if \"generator\" in set(inspect.signature(scheduler.step).parameters.keys()):\n kwargs[\"generator\"] = torch.manual_seed(0)\n new_output = new_scheduler.step(residual, timestep, sample, **kwargs).prev_sample\n\n assert torch.sum(torch.abs(output - new_output)) < 1e-5, \"Scheduler outputs are not identical\"\n\n def test_compatibles(self):\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n\n scheduler = scheduler_class(**scheduler_config)\n\n assert all(c is not None for c in scheduler.compatibles)\n\n for comp_scheduler_cls in scheduler.compatibles:\n comp_scheduler = comp_scheduler_cls.from_config(scheduler.config)\n assert comp_scheduler is not None\n\n new_scheduler = scheduler_class.from_config(comp_scheduler.config)\n\n new_scheduler_config = {k: v for k, v in new_scheduler.config.items() if k in scheduler.config}\n scheduler_diff = {k: v for k, v in new_scheduler.config.items() if k not in scheduler.config}\n\n # make sure that configs are essentially identical\n assert new_scheduler_config == dict(scheduler.config)\n\n # make sure that only differences are for configs that are not in init\n init_keys = inspect.signature(scheduler_class.__init__).parameters.keys()\n assert set(scheduler_diff.keys()).intersection(set(init_keys)) == set()\n\n def test_from_pretrained(self):\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n\n scheduler = scheduler_class(**scheduler_config)\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_pretrained(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n\n # `_use_default_values` should not exist for just saved & loaded scheduler\n scheduler_config = dict(scheduler.config)\n del scheduler_config[\"_use_default_values\"]\n\n assert scheduler_config == new_scheduler.config\n\n def test_step_shape(self):\n kwargs = dict(self.forward_default_kwargs)\n\n num_inference_steps = kwargs.pop(\"num_inference_steps\", self.default_num_inference_steps)\n\n timestep_0 = self.default_timestep\n timestep_1 = self.default_timestep_2\n\n for scheduler_class in self.scheduler_classes:\n if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler):\n timestep_0 = float(timestep_0)\n timestep_1 = float(timestep_1)\n\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n if scheduler_class == VQDiffusionScheduler:\n num_vec_classes = scheduler_config[\"num_vec_classes\"]\n sample = self.dummy_sample(num_vec_classes)\n model = self.dummy_model(num_vec_classes)\n residual = model(sample, timestep_0)\n else:\n sample = self.dummy_sample\n residual = 0.1 * sample\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n scheduler.set_timesteps(num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n output_0 = scheduler.step(residual, timestep_0, sample, **kwargs).prev_sample\n output_1 = scheduler.step(residual, timestep_1, sample, **kwargs).prev_sample\n\n self.assertEqual(output_0.shape, sample.shape)\n self.assertEqual(output_0.shape, output_1.shape)\n\n def test_scheduler_outputs_equivalence(self):\n def set_nan_tensor_to_zero(t):\n t[t != t] = 0\n return t\n\n def recursive_check(tuple_object, dict_object):\n if isinstance(tuple_object, (List, Tuple)):\n for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object.values()):\n recursive_check(tuple_iterable_value, dict_iterable_value)\n elif isinstance(tuple_object, Dict):\n for tuple_iterable_value, dict_iterable_value in zip(tuple_object.values(), dict_object.values()):\n recursive_check(tuple_iterable_value, dict_iterable_value)\n elif tuple_object is None:\n return\n else:\n self.assertTrue(\n torch.allclose(\n set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5\n ),\n msg=(\n \"Tuple and dict output are not equal. Difference:\"\n f\" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:\"\n f\" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has\"\n f\" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}.\"\n ),\n )\n\n kwargs = dict(self.forward_default_kwargs)\n num_inference_steps = kwargs.pop(\"num_inference_steps\", self.default_num_inference_steps)\n\n timestep = self.default_timestep\n if len(self.scheduler_classes) > 0 and self.scheduler_classes[0] == IPNDMScheduler:\n timestep = 1\n\n for scheduler_class in self.scheduler_classes:\n if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler):\n timestep = float(timestep)\n\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n if scheduler_class == CMStochasticIterativeScheduler:\n # Get valid timestep based on sigma_max, which should always be in timestep schedule.\n timestep = scheduler.sigma_to_t(scheduler.config.sigma_max)\n\n if scheduler_class == VQDiffusionScheduler:\n num_vec_classes = scheduler_config[\"num_vec_classes\"]\n sample = self.dummy_sample(num_vec_classes)\n model = self.dummy_model(num_vec_classes)\n residual = model(sample, timestep)\n else:\n sample = self.dummy_sample\n residual = 0.1 * sample\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n scheduler.set_timesteps(num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n # Set the seed before state as some schedulers are stochastic like EulerAncestralDiscreteScheduler, EulerDiscreteScheduler\n if \"generator\" in set(inspect.signature(scheduler.step).parameters.keys()):\n kwargs[\"generator\"] = torch.manual_seed(0)\n outputs_dict = scheduler.step(residual, timestep, sample, **kwargs)\n\n if num_inference_steps is not None and hasattr(scheduler, \"set_timesteps\"):\n scheduler.set_timesteps(num_inference_steps)\n elif num_inference_steps is not None and not hasattr(scheduler, \"set_timesteps\"):\n kwargs[\"num_inference_steps\"] = num_inference_steps\n\n # Set the seed before state as some schedulers are stochastic like EulerAncestralDiscreteScheduler, EulerDiscreteScheduler\n if \"generator\" in set(inspect.signature(scheduler.step).parameters.keys()):\n kwargs[\"generator\"] = torch.manual_seed(0)\n outputs_tuple = scheduler.step(residual, timestep, sample, return_dict=False, **kwargs)\n\n recursive_check(outputs_tuple, outputs_dict)\n\n def test_scheduler_public_api(self):\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n if scheduler_class != VQDiffusionScheduler:\n self.assertTrue(\n hasattr(scheduler, \"init_noise_sigma\"),\n f\"{scheduler_class} does not implement a required attribute `init_noise_sigma`\",\n )\n self.assertTrue(\n hasattr(scheduler, \"scale_model_input\"),\n (\n f\"{scheduler_class} does not implement a required class method `scale_model_input(sample,\"\n \" timestep)`\"\n ),\n )\n self.assertTrue(\n hasattr(scheduler, \"step\"),\n f\"{scheduler_class} does not implement a required class method `step(...)`\",\n )\n\n if scheduler_class != VQDiffusionScheduler:\n sample = self.dummy_sample\n if scheduler_class == CMStochasticIterativeScheduler:\n # Get valid timestep based on sigma_max, which should always be in timestep schedule.\n scaled_sigma_max = scheduler.sigma_to_t(scheduler.config.sigma_max)\n scaled_sample = scheduler.scale_model_input(sample, scaled_sigma_max)\n elif scheduler_class == EDMEulerScheduler:\n scaled_sample = scheduler.scale_model_input(sample, scheduler.timesteps[-1])\n else:\n scaled_sample = scheduler.scale_model_input(sample, 0.0)\n self.assertEqual(sample.shape, scaled_sample.shape)\n\n def test_add_noise_device(self):\n for scheduler_class in self.scheduler_classes:\n if scheduler_class == IPNDMScheduler:\n continue\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n scheduler.set_timesteps(self.default_num_inference_steps)\n\n sample = self.dummy_sample.to(torch_device)\n if scheduler_class == CMStochasticIterativeScheduler:\n # Get valid timestep based on sigma_max, which should always be in timestep schedule.\n scaled_sigma_max = scheduler.sigma_to_t(scheduler.config.sigma_max)\n scaled_sample = scheduler.scale_model_input(sample, scaled_sigma_max)\n elif scheduler_class == EDMEulerScheduler:\n scaled_sample = scheduler.scale_model_input(sample, scheduler.timesteps[-1])\n else:\n scaled_sample = scheduler.scale_model_input(sample, 0.0)\n self.assertEqual(sample.shape, scaled_sample.shape)\n\n noise = torch.randn_like(scaled_sample).to(torch_device)\n t = scheduler.timesteps[5][None]\n noised = scheduler.add_noise(scaled_sample, noise, t)\n self.assertEqual(noised.shape, scaled_sample.shape)\n\n def test_deprecated_kwargs(self):\n for scheduler_class in self.scheduler_classes:\n has_kwarg_in_model_class = \"kwargs\" in inspect.signature(scheduler_class.__init__).parameters\n has_deprecated_kwarg = len(scheduler_class._deprecated_kwargs) > 0\n\n if has_kwarg_in_model_class and not has_deprecated_kwarg:\n raise ValueError(\n f\"{scheduler_class} has `**kwargs` in its __init__ method but has not defined any deprecated\"\n \" kwargs under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if\"\n \" there are no deprecated arguments or add the deprecated argument with `_deprecated_kwargs =\"\n \" []`\"\n )\n\n if not has_kwarg_in_model_class and has_deprecated_kwarg:\n raise ValueError(\n f\"{scheduler_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated\"\n \" kwargs under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs`\"\n f\" argument to {self.model_class}.__init__ if there are deprecated arguments or remove the\"\n \" deprecated argument from `_deprecated_kwargs = []`\"\n )\n\n def test_trained_betas(self):\n for scheduler_class in self.scheduler_classes:\n if scheduler_class in (VQDiffusionScheduler, CMStochasticIterativeScheduler):\n continue\n\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config, trained_betas=np.array([0.1, 0.3]))\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n scheduler.save_pretrained(tmpdirname)\n new_scheduler = scheduler_class.from_pretrained(tmpdirname)\n\n assert scheduler.betas.tolist() == new_scheduler.betas.tolist()\n\n def test_getattr_is_correct(self):\n for scheduler_class in self.scheduler_classes:\n scheduler_config = self.get_scheduler_config()\n scheduler = scheduler_class(**scheduler_config)\n\n # save some things to test\n scheduler.dummy_attribute = 5\n scheduler.register_to_config(test_attribute=5)\n\n logger = logging.get_logger(\"diffusers.configuration_utils\")\n # 30 for warning\n logger.setLevel(30)\n with CaptureLogger(logger) as cap_logger:\n assert hasattr(scheduler, \"dummy_attribute\")\n assert getattr(scheduler, \"dummy_attribute\") == 5\n assert scheduler.dummy_attribute == 5\n\n # no warning should be thrown\n assert cap_logger.out == \"\"\n\n logger = logging.get_logger(\"diffusers.schedulers.scheduling_utils\")\n # 30 for warning\n logger.setLevel(30)\n with CaptureLogger(logger) as cap_logger:\n assert hasattr(scheduler, \"save_pretrained\")\n fn = scheduler.save_pretrained\n fn_1 = getattr(scheduler, \"save_pretrained\")\n\n assert fn == fn_1\n # no warning should be thrown\n assert cap_logger.out == \"\"\n\n # warning should be thrown\n with self.assertWarns(FutureWarning):\n assert scheduler.test_attribute == 5\n\n with self.assertWarns(FutureWarning):\n assert getattr(scheduler, \"test_attribute\") == 5\n\n with self.assertRaises(AttributeError) as error:\n scheduler.does_not_exist\n\n assert str(error.exception) == f\"'{type(scheduler).__name__}' object has no attribute 'does_not_exist'\"\n\n\n@is_staging_test\nclass SchedulerPushToHubTester(unittest.TestCase):\n identifier = uuid.uuid4()\n repo_id = f\"test-scheduler-{identifier}\"\n org_repo_id = f\"valid_org/{repo_id}-org\"\n\n def test_push_to_hub(self):\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n scheduler.push_to_hub(self.repo_id, token=TOKEN)\n scheduler_loaded = DDIMScheduler.from_pretrained(f\"{USER}/{self.repo_id}\")\n\n assert type(scheduler) == type(scheduler_loaded)\n\n # Reset repo\n delete_repo(token=TOKEN, repo_id=self.repo_id)\n\n # Push to hub via save_config\n with tempfile.TemporaryDirectory() as tmp_dir:\n scheduler.save_config(tmp_dir, repo_id=self.repo_id, push_to_hub=True, token=TOKEN)\n\n scheduler_loaded = DDIMScheduler.from_pretrained(f\"{USER}/{self.repo_id}\")\n\n assert type(scheduler) == type(scheduler_loaded)\n\n # Reset repo\n delete_repo(token=TOKEN, repo_id=self.repo_id)\n\n def test_push_to_hub_in_organization(self):\n scheduler = DDIMScheduler(\n beta_start=0.00085,\n beta_end=0.012,\n beta_schedule=\"scaled_linear\",\n clip_sample=False,\n set_alpha_to_one=False,\n )\n scheduler.push_to_hub(self.org_repo_id, token=TOKEN)\n scheduler_loaded = DDIMScheduler.from_pretrained(self.org_repo_id)\n\n assert type(scheduler) == type(scheduler_loaded)\n\n # Reset repo\n delete_repo(token=TOKEN, repo_id=self.org_repo_id)\n\n # Push to hub via save_config\n with tempfile.TemporaryDirectory() as tmp_dir:\n scheduler.save_config(tmp_dir, repo_id=self.org_repo_id, push_to_hub=True, token=TOKEN)\n\n scheduler_loaded = DDIMScheduler.from_pretrained(self.org_repo_id)\n\n assert type(scheduler) == type(scheduler_loaded)\n\n # Reset repo\n delete_repo(token=TOKEN, repo_id=self.org_repo_id)\n" }, { "path": "diffusers/tests/single_file/__init__.py", "content": "" }, { "path": "diffusers/tests/single_file/single_file_testing_utils.py", "content": "import tempfile\nfrom io import BytesIO\n\nimport requests\nimport torch\nfrom huggingface_hub import hf_hub_download, snapshot_download\n\nfrom diffusers.models.attention_processor import AttnProcessor\nfrom diffusers.utils.testing_utils import (\n numpy_cosine_similarity_distance,\n torch_device,\n)\n\n\ndef download_single_file_checkpoint(repo_id, filename, tmpdir):\n path = hf_hub_download(repo_id, filename=filename, local_dir=tmpdir)\n return path\n\n\ndef download_original_config(config_url, tmpdir):\n original_config_file = BytesIO(requests.get(config_url).content)\n path = f\"{tmpdir}/config.yaml\"\n with open(path, \"wb\") as f:\n f.write(original_config_file.read())\n\n return path\n\n\ndef download_diffusers_config(repo_id, tmpdir):\n path = snapshot_download(\n repo_id,\n ignore_patterns=[\n \"**/*.ckpt\",\n \"*.ckpt\",\n \"**/*.bin\",\n \"*.bin\",\n \"**/*.pt\",\n \"*.pt\",\n \"**/*.safetensors\",\n \"*.safetensors\",\n ],\n allow_patterns=[\"**/*.json\", \"*.json\", \"*.txt\", \"**/*.txt\"],\n local_dir=tmpdir,\n )\n return path\n\n\nclass SDSingleFileTesterMixin:\n def _compare_component_configs(self, pipe, single_file_pipe):\n for param_name, param_value in single_file_pipe.text_encoder.config.to_dict().items():\n if param_name in [\"torch_dtype\", \"architectures\", \"_name_or_path\"]:\n continue\n assert pipe.text_encoder.config.to_dict()[param_name] == param_value\n\n PARAMS_TO_IGNORE = [\n \"torch_dtype\",\n \"_name_or_path\",\n \"architectures\",\n \"_use_default_values\",\n \"_diffusers_version\",\n ]\n for component_name, component in single_file_pipe.components.items():\n if component_name in single_file_pipe._optional_components:\n continue\n\n # skip testing transformer based components here\n # skip text encoders / safety checkers since they have already been tested\n if component_name in [\"text_encoder\", \"tokenizer\", \"safety_checker\", \"feature_extractor\"]:\n continue\n\n assert component_name in pipe.components, f\"single file {component_name} not found in pretrained pipeline\"\n assert isinstance(\n component, pipe.components[component_name].__class__\n ), f\"single file {component.__class__.__name__} and pretrained {pipe.components[component_name].__class__.__name__} are not the same\"\n\n for param_name, param_value in component.config.items():\n if param_name in PARAMS_TO_IGNORE:\n continue\n\n # Some pretrained configs will set upcast attention to None\n # In single file loading it defaults to the value in the class __init__ which is False\n if param_name == \"upcast_attention\" and pipe.components[component_name].config[param_name] is None:\n pipe.components[component_name].config[param_name] = param_value\n\n assert (\n pipe.components[component_name].config[param_name] == param_value\n ), f\"single file {param_name}: {param_value} differs from pretrained {pipe.components[component_name].config[param_name]}\"\n\n def test_single_file_components(self, pipe=None, single_file_pipe=None):\n single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file(\n self.ckpt_path, safety_checker=None\n )\n pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None)\n\n self._compare_component_configs(pipe, single_file_pipe)\n\n def test_single_file_components_local_files_only(self, pipe=None, single_file_pipe=None):\n pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None)\n\n with tempfile.TemporaryDirectory() as tmpdir:\n ckpt_filename = self.ckpt_path.split(\"/\")[-1]\n local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)\n\n single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file(\n local_ckpt_path, safety_checker=None, local_files_only=True\n )\n\n self._compare_component_configs(pipe, single_file_pipe)\n\n def test_single_file_components_with_original_config(\n self,\n pipe=None,\n single_file_pipe=None,\n ):\n pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None)\n # Not possible to infer this value when original config is provided\n # we just pass it in here otherwise this test will fail\n upcast_attention = pipe.unet.config.upcast_attention\n\n single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file(\n self.ckpt_path,\n original_config=self.original_config,\n safety_checker=None,\n upcast_attention=upcast_attention,\n )\n\n self._compare_component_configs(pipe, single_file_pipe)\n\n def test_single_file_components_with_original_config_local_files_only(\n self,\n pipe=None,\n single_file_pipe=None,\n ):\n pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None)\n\n # Not possible to infer this value when original config is provided\n # we just pass it in here otherwise this test will fail\n upcast_attention = pipe.unet.config.upcast_attention\n\n with tempfile.TemporaryDirectory() as tmpdir:\n ckpt_filename = self.ckpt_path.split(\"/\")[-1]\n local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)\n local_original_config = download_original_config(self.original_config, tmpdir)\n\n single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file(\n local_ckpt_path,\n original_config=local_original_config,\n safety_checker=None,\n upcast_attention=upcast_attention,\n local_files_only=True,\n )\n\n self._compare_component_configs(pipe, single_file_pipe)\n\n def test_single_file_format_inference_is_same_as_pretrained(self, expected_max_diff=1e-4):\n sf_pipe = self.pipeline_class.from_single_file(self.ckpt_path, safety_checker=None)\n sf_pipe.unet.set_attn_processor(AttnProcessor())\n sf_pipe.enable_model_cpu_offload()\n\n inputs = self.get_inputs(torch_device)\n image_single_file = sf_pipe(**inputs).images[0]\n\n pipe = self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None)\n pipe.unet.set_attn_processor(AttnProcessor())\n pipe.enable_model_cpu_offload()\n\n inputs = self.get_inputs(torch_device)\n image = pipe(**inputs).images[0]\n\n max_diff = numpy_cosine_similarity_distance(image.flatten(), image_single_file.flatten())\n\n assert max_diff < expected_max_diff\n\n def test_single_file_components_with_diffusers_config(\n self,\n pipe=None,\n single_file_pipe=None,\n ):\n single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file(\n self.ckpt_path, config=self.repo_id, safety_checker=None\n )\n pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None)\n\n self._compare_component_configs(pipe, single_file_pipe)\n\n def test_single_file_components_with_diffusers_config_local_files_only(\n self,\n pipe=None,\n single_file_pipe=None,\n ):\n pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None)\n\n with tempfile.TemporaryDirectory() as tmpdir:\n ckpt_filename = self.ckpt_path.split(\"/\")[-1]\n local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)\n local_diffusers_config = download_diffusers_config(self.repo_id, tmpdir)\n\n single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file(\n local_ckpt_path, config=local_diffusers_config, safety_checker=None, local_files_only=True\n )\n\n self._compare_component_configs(pipe, single_file_pipe)\n\n\nclass SDXLSingleFileTesterMixin:\n def _compare_component_configs(self, pipe, single_file_pipe):\n # Skip testing the text_encoder for Refiner Pipelines\n if pipe.text_encoder:\n for param_name, param_value in single_file_pipe.text_encoder.config.to_dict().items():\n if param_name in [\"torch_dtype\", \"architectures\", \"_name_or_path\"]:\n continue\n assert pipe.text_encoder.config.to_dict()[param_name] == param_value\n\n for param_name, param_value in single_file_pipe.text_encoder_2.config.to_dict().items():\n if param_name in [\"torch_dtype\", \"architectures\", \"_name_or_path\"]:\n continue\n assert pipe.text_encoder_2.config.to_dict()[param_name] == param_value\n\n PARAMS_TO_IGNORE = [\n \"torch_dtype\",\n \"_name_or_path\",\n \"architectures\",\n \"_use_default_values\",\n \"_diffusers_version\",\n ]\n for component_name, component in single_file_pipe.components.items():\n if component_name in single_file_pipe._optional_components:\n continue\n\n # skip text encoders since they have already been tested\n if component_name in [\"text_encoder\", \"text_encoder_2\", \"tokenizer\", \"tokenizer_2\"]:\n continue\n\n # skip safety checker if it is not present in the pipeline\n if component_name in [\"safety_checker\", \"feature_extractor\"]:\n continue\n\n assert component_name in pipe.components, f\"single file {component_name} not found in pretrained pipeline\"\n assert isinstance(\n component, pipe.components[component_name].__class__\n ), f\"single file {component.__class__.__name__} and pretrained {pipe.components[component_name].__class__.__name__} are not the same\"\n\n for param_name, param_value in component.config.items():\n if param_name in PARAMS_TO_IGNORE:\n continue\n\n # Some pretrained configs will set upcast attention to None\n # In single file loading it defaults to the value in the class __init__ which is False\n if param_name == \"upcast_attention\" and pipe.components[component_name].config[param_name] is None:\n pipe.components[component_name].config[param_name] = param_value\n\n assert (\n pipe.components[component_name].config[param_name] == param_value\n ), f\"single file {param_name}: {param_value} differs from pretrained {pipe.components[component_name].config[param_name]}\"\n\n def test_single_file_components(self, pipe=None, single_file_pipe=None):\n single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file(\n self.ckpt_path, safety_checker=None\n )\n pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None)\n\n self._compare_component_configs(\n pipe,\n single_file_pipe,\n )\n\n def test_single_file_components_local_files_only(\n self,\n pipe=None,\n single_file_pipe=None,\n ):\n pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None)\n\n with tempfile.TemporaryDirectory() as tmpdir:\n ckpt_filename = self.ckpt_path.split(\"/\")[-1]\n local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)\n\n single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file(\n local_ckpt_path, safety_checker=None, local_files_only=True\n )\n\n self._compare_component_configs(pipe, single_file_pipe)\n\n def test_single_file_components_with_original_config(\n self,\n pipe=None,\n single_file_pipe=None,\n ):\n pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None)\n # Not possible to infer this value when original config is provided\n # we just pass it in here otherwise this test will fail\n upcast_attention = pipe.unet.config.upcast_attention\n single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file(\n self.ckpt_path,\n original_config=self.original_config,\n safety_checker=None,\n upcast_attention=upcast_attention,\n )\n\n self._compare_component_configs(\n pipe,\n single_file_pipe,\n )\n\n def test_single_file_components_with_original_config_local_files_only(\n self,\n pipe=None,\n single_file_pipe=None,\n ):\n pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None)\n # Not possible to infer this value when original config is provided\n # we just pass it in here otherwise this test will fail\n upcast_attention = pipe.unet.config.upcast_attention\n\n with tempfile.TemporaryDirectory() as tmpdir:\n ckpt_filename = self.ckpt_path.split(\"/\")[-1]\n local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)\n local_original_config = download_original_config(self.original_config, tmpdir)\n\n single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file(\n local_ckpt_path,\n original_config=local_original_config,\n upcast_attention=upcast_attention,\n safety_checker=None,\n local_files_only=True,\n )\n\n self._compare_component_configs(\n pipe,\n single_file_pipe,\n )\n\n def test_single_file_components_with_diffusers_config(\n self,\n pipe=None,\n single_file_pipe=None,\n ):\n single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file(\n self.ckpt_path, config=self.repo_id, safety_checker=None\n )\n pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None)\n\n self._compare_component_configs(pipe, single_file_pipe)\n\n def test_single_file_components_with_diffusers_config_local_files_only(\n self,\n pipe=None,\n single_file_pipe=None,\n ):\n pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None)\n\n with tempfile.TemporaryDirectory() as tmpdir:\n ckpt_filename = self.ckpt_path.split(\"/\")[-1]\n local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)\n local_diffusers_config = download_diffusers_config(self.repo_id, tmpdir)\n\n single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file(\n local_ckpt_path, config=local_diffusers_config, safety_checker=None, local_files_only=True\n )\n\n self._compare_component_configs(pipe, single_file_pipe)\n\n def test_single_file_format_inference_is_same_as_pretrained(self, expected_max_diff=1e-4):\n sf_pipe = self.pipeline_class.from_single_file(self.ckpt_path, torch_dtype=torch.float16, safety_checker=None)\n sf_pipe.unet.set_default_attn_processor()\n sf_pipe.enable_model_cpu_offload()\n\n inputs = self.get_inputs(torch_device)\n image_single_file = sf_pipe(**inputs).images[0]\n\n pipe = self.pipeline_class.from_pretrained(self.repo_id, torch_dtype=torch.float16, safety_checker=None)\n pipe.unet.set_default_attn_processor()\n pipe.enable_model_cpu_offload()\n\n inputs = self.get_inputs(torch_device)\n image = pipe(**inputs).images[0]\n\n max_diff = numpy_cosine_similarity_distance(image.flatten(), image_single_file.flatten())\n\n assert max_diff < expected_max_diff\n" }, { "path": "diffusers/tests/single_file/test_model_controlnet_single_file.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nimport torch\n\nfrom diffusers import (\n ControlNetModel,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n require_torch_gpu,\n slow,\n)\n\n\nenable_full_determinism()\n\n\n@slow\n@require_torch_gpu\nclass ControlNetModelSingleFileTests(unittest.TestCase):\n model_class = ControlNetModel\n ckpt_path = \"https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth\"\n repo_id = \"lllyasviel/control_v11p_sd15_canny\"\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_single_file_components(self):\n model = self.model_class.from_pretrained(self.repo_id)\n model_single_file = self.model_class.from_single_file(self.ckpt_path)\n\n PARAMS_TO_IGNORE = [\"torch_dtype\", \"_name_or_path\", \"_use_default_values\", \"_diffusers_version\"]\n for param_name, param_value in model_single_file.config.items():\n if param_name in PARAMS_TO_IGNORE:\n continue\n assert (\n model.config[param_name] == param_value\n ), f\"{param_name} differs between single file loading and pretrained loading\"\n\n def test_single_file_arguments(self):\n model_default = self.model_class.from_single_file(self.ckpt_path)\n\n assert model_default.config.upcast_attention is False\n assert model_default.dtype == torch.float32\n\n torch_dtype = torch.float16\n upcast_attention = True\n\n model = self.model_class.from_single_file(\n self.ckpt_path,\n upcast_attention=upcast_attention,\n torch_dtype=torch_dtype,\n )\n assert model.config.upcast_attention == upcast_attention\n assert model.dtype == torch_dtype\n" }, { "path": "diffusers/tests/single_file/test_model_sd_cascade_unet_single_file.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nimport torch\n\nfrom diffusers import StableCascadeUNet\nfrom diffusers.utils import logging\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n require_torch_gpu,\n slow,\n)\n\n\nlogger = logging.get_logger(__name__)\n\nenable_full_determinism()\n\n\n@slow\n@require_torch_gpu\nclass StableCascadeUNetSingleFileTest(unittest.TestCase):\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_single_file_components_stage_b(self):\n model_single_file = StableCascadeUNet.from_single_file(\n \"https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b_bf16.safetensors\",\n torch_dtype=torch.bfloat16,\n )\n model = StableCascadeUNet.from_pretrained(\n \"stabilityai/stable-cascade\", variant=\"bf16\", subfolder=\"decoder\", use_safetensors=True\n )\n\n PARAMS_TO_IGNORE = [\"torch_dtype\", \"_name_or_path\", \"_use_default_values\", \"_diffusers_version\"]\n for param_name, param_value in model_single_file.config.items():\n if param_name in PARAMS_TO_IGNORE:\n continue\n assert (\n model.config[param_name] == param_value\n ), f\"{param_name} differs between single file loading and pretrained loading\"\n\n def test_single_file_components_stage_b_lite(self):\n model_single_file = StableCascadeUNet.from_single_file(\n \"https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b_lite_bf16.safetensors\",\n torch_dtype=torch.bfloat16,\n )\n model = StableCascadeUNet.from_pretrained(\n \"stabilityai/stable-cascade\", variant=\"bf16\", subfolder=\"decoder_lite\"\n )\n\n PARAMS_TO_IGNORE = [\"torch_dtype\", \"_name_or_path\", \"_use_default_values\", \"_diffusers_version\"]\n for param_name, param_value in model_single_file.config.items():\n if param_name in PARAMS_TO_IGNORE:\n continue\n assert (\n model.config[param_name] == param_value\n ), f\"{param_name} differs between single file loading and pretrained loading\"\n\n def test_single_file_components_stage_c(self):\n model_single_file = StableCascadeUNet.from_single_file(\n \"https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_c_bf16.safetensors\",\n torch_dtype=torch.bfloat16,\n )\n model = StableCascadeUNet.from_pretrained(\n \"stabilityai/stable-cascade-prior\", variant=\"bf16\", subfolder=\"prior\"\n )\n\n PARAMS_TO_IGNORE = [\"torch_dtype\", \"_name_or_path\", \"_use_default_values\", \"_diffusers_version\"]\n for param_name, param_value in model_single_file.config.items():\n if param_name in PARAMS_TO_IGNORE:\n continue\n assert (\n model.config[param_name] == param_value\n ), f\"{param_name} differs between single file loading and pretrained loading\"\n\n def test_single_file_components_stage_c_lite(self):\n model_single_file = StableCascadeUNet.from_single_file(\n \"https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_c_lite_bf16.safetensors\",\n torch_dtype=torch.bfloat16,\n )\n model = StableCascadeUNet.from_pretrained(\n \"stabilityai/stable-cascade-prior\", variant=\"bf16\", subfolder=\"prior_lite\"\n )\n\n PARAMS_TO_IGNORE = [\"torch_dtype\", \"_name_or_path\", \"_use_default_values\", \"_diffusers_version\"]\n for param_name, param_value in model_single_file.config.items():\n if param_name in PARAMS_TO_IGNORE:\n continue\n assert (\n model.config[param_name] == param_value\n ), f\"{param_name} differs between single file loading and pretrained loading\"\n" }, { "path": "diffusers/tests/single_file/test_model_vae_single_file.py", "content": "# coding=utf-8\n# Copyright 2024 HuggingFace Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport gc\nimport unittest\n\nimport torch\n\nfrom diffusers import (\n AutoencoderKL,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n load_hf_numpy,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\n\nenable_full_determinism()\n\n\n@slow\n@require_torch_gpu\nclass AutoencoderKLSingleFileTests(unittest.TestCase):\n model_class = AutoencoderKL\n ckpt_path = (\n \"https://huggingface.co/stabilityai/sd-vae-ft-mse-original/blob/main/vae-ft-mse-840000-ema-pruned.safetensors\"\n )\n repo_id = \"stabilityai/sd-vae-ft-mse\"\n main_input_name = \"sample\"\n base_precision = 1e-2\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_file_format(self, seed, shape):\n return f\"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy\"\n\n def get_sd_image(self, seed=0, shape=(4, 3, 512, 512), fp16=False):\n dtype = torch.float16 if fp16 else torch.float32\n image = torch.from_numpy(load_hf_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype)\n return image\n\n def test_single_file_inference_same_as_pretrained(self):\n model_1 = self.model_class.from_pretrained(self.repo_id).to(torch_device)\n model_2 = self.model_class.from_single_file(self.ckpt_path, config=self.repo_id).to(torch_device)\n\n image = self.get_sd_image(33)\n\n generator = torch.Generator(torch_device)\n\n with torch.no_grad():\n sample_1 = model_1(image, generator=generator.manual_seed(0)).sample\n sample_2 = model_2(image, generator=generator.manual_seed(0)).sample\n\n assert sample_1.shape == sample_2.shape\n\n output_slice_1 = sample_1.flatten().float().cpu()\n output_slice_2 = sample_2.flatten().float().cpu()\n\n assert numpy_cosine_similarity_distance(output_slice_1, output_slice_2) < 1e-4\n\n def test_single_file_components(self):\n model = self.model_class.from_pretrained(self.repo_id)\n model_single_file = self.model_class.from_single_file(self.ckpt_path, config=self.repo_id)\n\n PARAMS_TO_IGNORE = [\"torch_dtype\", \"_name_or_path\", \"_use_default_values\", \"_diffusers_version\"]\n for param_name, param_value in model_single_file.config.items():\n if param_name in PARAMS_TO_IGNORE:\n continue\n assert (\n model.config[param_name] == param_value\n ), f\"{param_name} differs between pretrained loading and single file loading\"\n\n def test_single_file_arguments(self):\n model_default = self.model_class.from_single_file(self.ckpt_path, config=self.repo_id)\n\n assert model_default.config.scaling_factor == 0.18215\n assert model_default.config.sample_size == 256\n assert model_default.dtype == torch.float32\n\n scaling_factor = 2.0\n sample_size = 512\n torch_dtype = torch.float16\n\n model = self.model_class.from_single_file(\n self.ckpt_path,\n config=self.repo_id,\n sample_size=sample_size,\n scaling_factor=scaling_factor,\n torch_dtype=torch_dtype,\n )\n assert model.config.scaling_factor == scaling_factor\n assert model.config.sample_size == sample_size\n assert model.dtype == torch_dtype\n" }, { "path": "diffusers/tests/single_file/test_stable_diffusion_controlnet_img2img_single_file.py", "content": "import gc\nimport tempfile\nimport unittest\n\nimport torch\n\nfrom diffusers import ControlNetModel, StableDiffusionControlNetPipeline\nfrom diffusers.utils import load_image\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\nfrom .single_file_testing_utils import (\n SDSingleFileTesterMixin,\n download_diffusers_config,\n download_original_config,\n download_single_file_checkpoint,\n)\n\n\nenable_full_determinism()\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionControlNetPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):\n pipeline_class = StableDiffusionControlNetPipeline\n ckpt_path = \"https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors\"\n original_config = (\n \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml\"\n )\n repo_id = \"runwayml/stable-diffusion-v1-5\"\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n init_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_img2img/sketch-mountains-input.png\"\n )\n control_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png\"\n ).resize((512, 512))\n prompt = \"bird\"\n\n inputs = {\n \"prompt\": prompt,\n \"image\": init_image,\n \"control_image\": control_image,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"strength\": 0.75,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_single_file_format_inference_is_same_as_pretrained(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11p_sd15_canny\")\n pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet)\n pipe.unet.set_default_attn_processor()\n pipe.enable_model_cpu_offload()\n\n pipe_sf = self.pipeline_class.from_single_file(\n self.ckpt_path,\n controlnet=controlnet,\n )\n pipe_sf.unet.set_default_attn_processor()\n pipe_sf.enable_model_cpu_offload()\n\n inputs = self.get_inputs(torch_device)\n output = pipe(**inputs).images[0]\n\n inputs = self.get_inputs(torch_device)\n output_sf = pipe_sf(**inputs).images[0]\n\n max_diff = numpy_cosine_similarity_distance(output_sf.flatten(), output.flatten())\n assert max_diff < 1e-3\n\n def test_single_file_components(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11p_sd15_canny\")\n pipe = self.pipeline_class.from_pretrained(\n self.repo_id, variant=\"fp16\", safety_checker=None, controlnet=controlnet\n )\n pipe_single_file = self.pipeline_class.from_single_file(\n self.ckpt_path,\n safety_checker=None,\n controlnet=controlnet,\n )\n\n super()._compare_component_configs(pipe, pipe_single_file)\n\n def test_single_file_components_local_files_only(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11p_sd15_canny\")\n pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet)\n\n with tempfile.TemporaryDirectory() as tmpdir:\n ckpt_filename = self.ckpt_path.split(\"/\")[-1]\n local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)\n\n pipe_single_file = self.pipeline_class.from_single_file(\n local_ckpt_path, controlnet=controlnet, safety_checker=None, local_files_only=True\n )\n\n super()._compare_component_configs(pipe, pipe_single_file)\n\n def test_single_file_components_with_original_config(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11p_sd15_canny\", variant=\"fp16\")\n pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet)\n pipe_single_file = self.pipeline_class.from_single_file(\n self.ckpt_path, controlnet=controlnet, safety_checker=None, original_config=self.original_config\n )\n\n super()._compare_component_configs(pipe, pipe_single_file)\n\n def test_single_file_components_with_original_config_local_files_only(self):\n controlnet = ControlNetModel.from_pretrained(\n \"lllyasviel/control_v11p_sd15_canny\", torch_dtype=torch.float16, variant=\"fp16\"\n )\n pipe = self.pipeline_class.from_pretrained(\n self.repo_id,\n controlnet=controlnet,\n )\n\n with tempfile.TemporaryDirectory() as tmpdir:\n ckpt_filename = self.ckpt_path.split(\"/\")[-1]\n local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)\n local_original_config = download_original_config(self.original_config, tmpdir)\n\n pipe_single_file = self.pipeline_class.from_single_file(\n local_ckpt_path,\n original_config=local_original_config,\n controlnet=controlnet,\n safety_checker=None,\n local_files_only=True,\n )\n super()._compare_component_configs(pipe, pipe_single_file)\n\n def test_single_file_components_with_diffusers_config(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11p_sd15_canny\", variant=\"fp16\")\n pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet)\n pipe_single_file = self.pipeline_class.from_single_file(\n self.ckpt_path, controlnet=controlnet, safety_checker=None, original_config=self.original_config\n )\n\n super()._compare_component_configs(pipe, pipe_single_file)\n\n def test_single_file_components_with_diffusers_config_local_files_only(self):\n controlnet = ControlNetModel.from_pretrained(\n \"lllyasviel/control_v11p_sd15_canny\", torch_dtype=torch.float16, variant=\"fp16\"\n )\n pipe = self.pipeline_class.from_pretrained(\n self.repo_id,\n controlnet=controlnet,\n )\n\n with tempfile.TemporaryDirectory() as tmpdir:\n ckpt_filename = self.ckpt_path.split(\"/\")[-1]\n local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)\n local_diffusers_config = download_diffusers_config(self.repo_id, tmpdir)\n\n pipe_single_file = self.pipeline_class.from_single_file(\n local_ckpt_path,\n config=local_diffusers_config,\n safety_checker=None,\n controlnet=controlnet,\n local_files_only=True,\n )\n super()._compare_component_configs(pipe, pipe_single_file)\n" }, { "path": "diffusers/tests/single_file/test_stable_diffusion_controlnet_inpaint_single_file.py", "content": "import gc\nimport tempfile\nimport unittest\n\nimport torch\n\nfrom diffusers import ControlNetModel, StableDiffusionControlNetInpaintPipeline\nfrom diffusers.utils import load_image\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n slow,\n)\n\nfrom .single_file_testing_utils import (\n SDSingleFileTesterMixin,\n download_diffusers_config,\n download_original_config,\n download_single_file_checkpoint,\n)\n\n\nenable_full_determinism()\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionControlNetInpaintPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):\n pipeline_class = StableDiffusionControlNetInpaintPipeline\n ckpt_path = \"https://huggingface.co/runwayml/stable-diffusion-inpainting/blob/main/sd-v1-5-inpainting.ckpt\"\n original_config = \"https://raw.githubusercontent.com/runwayml/stable-diffusion/main/configs/stable-diffusion/v1-inpainting-inference.yaml\"\n repo_id = \"runwayml/stable-diffusion-inpainting\"\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self):\n control_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png\"\n ).resize((512, 512))\n image = load_image(\n \"https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png\"\n ).resize((512, 512))\n mask_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_inpaint/input_bench_mask.png\"\n ).resize((512, 512))\n\n inputs = {\n \"prompt\": \"bird\",\n \"image\": image,\n \"control_image\": control_image,\n \"mask_image\": mask_image,\n \"generator\": torch.Generator(device=\"cpu\").manual_seed(0),\n \"num_inference_steps\": 3,\n \"output_type\": \"np\",\n }\n\n return inputs\n\n def test_single_file_format_inference_is_same_as_pretrained(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11p_sd15_canny\")\n pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet, safety_checker=None)\n pipe.unet.set_default_attn_processor()\n pipe.enable_model_cpu_offload()\n\n pipe_sf = self.pipeline_class.from_single_file(self.ckpt_path, controlnet=controlnet, safety_checker=None)\n pipe_sf.unet.set_default_attn_processor()\n pipe_sf.enable_model_cpu_offload()\n\n inputs = self.get_inputs()\n output = pipe(**inputs).images[0]\n\n inputs = self.get_inputs()\n output_sf = pipe_sf(**inputs).images[0]\n\n max_diff = numpy_cosine_similarity_distance(output_sf.flatten(), output.flatten())\n assert max_diff < 1e-3\n\n def test_single_file_components(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11p_sd15_canny\")\n pipe = self.pipeline_class.from_pretrained(\n self.repo_id, variant=\"fp16\", safety_checker=None, controlnet=controlnet\n )\n pipe_single_file = self.pipeline_class.from_single_file(\n self.ckpt_path,\n safety_checker=None,\n controlnet=controlnet,\n )\n\n super()._compare_component_configs(pipe, pipe_single_file)\n\n def test_single_file_components_local_files_only(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11p_sd15_canny\")\n pipe = self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None, controlnet=controlnet)\n\n with tempfile.TemporaryDirectory() as tmpdir:\n ckpt_filename = self.ckpt_path.split(\"/\")[-1]\n local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)\n\n pipe_single_file = self.pipeline_class.from_single_file(\n local_ckpt_path, controlnet=controlnet, safety_checker=None, local_files_only=True\n )\n\n super()._compare_component_configs(pipe, pipe_single_file)\n\n def test_single_file_components_with_original_config(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11p_sd15_canny\", variant=\"fp16\")\n pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet)\n pipe_single_file = self.pipeline_class.from_single_file(\n self.ckpt_path, controlnet=controlnet, original_config=self.original_config\n )\n\n super()._compare_component_configs(pipe, pipe_single_file)\n\n def test_single_file_components_with_original_config_local_files_only(self):\n controlnet = ControlNetModel.from_pretrained(\n \"lllyasviel/control_v11p_sd15_canny\", torch_dtype=torch.float16, variant=\"fp16\"\n )\n pipe = self.pipeline_class.from_pretrained(\n self.repo_id,\n controlnet=controlnet,\n safety_checker=None,\n )\n\n with tempfile.TemporaryDirectory() as tmpdir:\n ckpt_filename = self.ckpt_path.split(\"/\")[-1]\n local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)\n local_original_config = download_original_config(self.original_config, tmpdir)\n\n pipe_single_file = self.pipeline_class.from_single_file(\n local_ckpt_path,\n original_config=local_original_config,\n controlnet=controlnet,\n safety_checker=None,\n local_files_only=True,\n )\n super()._compare_component_configs(pipe, pipe_single_file)\n\n def test_single_file_components_with_diffusers_config(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11p_sd15_canny\", variant=\"fp16\")\n pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet)\n pipe_single_file = self.pipeline_class.from_single_file(\n self.ckpt_path,\n controlnet=controlnet,\n config=self.repo_id,\n )\n\n super()._compare_component_configs(pipe, pipe_single_file)\n\n def test_single_file_components_with_diffusers_config_local_files_only(self):\n controlnet = ControlNetModel.from_pretrained(\n \"lllyasviel/control_v11p_sd15_canny\",\n torch_dtype=torch.float16,\n variant=\"fp16\",\n )\n pipe = self.pipeline_class.from_pretrained(\n self.repo_id,\n controlnet=controlnet,\n safety_checker=None,\n )\n\n with tempfile.TemporaryDirectory() as tmpdir:\n ckpt_filename = self.ckpt_path.split(\"/\")[-1]\n local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)\n local_diffusers_config = download_diffusers_config(self.repo_id, tmpdir)\n\n pipe_single_file = self.pipeline_class.from_single_file(\n local_ckpt_path,\n config=local_diffusers_config,\n controlnet=controlnet,\n safety_checker=None,\n local_files_only=True,\n )\n super()._compare_component_configs(pipe, pipe_single_file)\n" }, { "path": "diffusers/tests/single_file/test_stable_diffusion_controlnet_single_file.py", "content": "import gc\nimport tempfile\nimport unittest\n\nimport torch\n\nfrom diffusers import ControlNetModel, StableDiffusionControlNetPipeline\nfrom diffusers.utils import load_image\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n slow,\n)\n\nfrom .single_file_testing_utils import (\n SDSingleFileTesterMixin,\n download_diffusers_config,\n download_original_config,\n download_single_file_checkpoint,\n)\n\n\nenable_full_determinism()\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionControlNetPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):\n pipeline_class = StableDiffusionControlNetPipeline\n ckpt_path = \"https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors\"\n original_config = (\n \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml\"\n )\n repo_id = \"runwayml/stable-diffusion-v1-5\"\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self):\n control_image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png\"\n ).resize((512, 512))\n inputs = {\n \"prompt\": \"bird\",\n \"image\": control_image,\n \"generator\": torch.Generator(device=\"cpu\").manual_seed(0),\n \"num_inference_steps\": 3,\n \"output_type\": \"np\",\n }\n\n return inputs\n\n def test_single_file_format_inference_is_same_as_pretrained(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11p_sd15_canny\")\n pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet)\n pipe.unet.set_default_attn_processor()\n pipe.enable_model_cpu_offload()\n\n pipe_sf = self.pipeline_class.from_single_file(\n self.ckpt_path,\n controlnet=controlnet,\n )\n pipe_sf.unet.set_default_attn_processor()\n pipe_sf.enable_model_cpu_offload()\n\n inputs = self.get_inputs()\n output = pipe(**inputs).images[0]\n\n inputs = self.get_inputs()\n output_sf = pipe_sf(**inputs).images[0]\n\n max_diff = numpy_cosine_similarity_distance(output_sf.flatten(), output.flatten())\n assert max_diff < 1e-3\n\n def test_single_file_components(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11p_sd15_canny\")\n pipe = self.pipeline_class.from_pretrained(\n self.repo_id, variant=\"fp16\", safety_checker=None, controlnet=controlnet\n )\n pipe_single_file = self.pipeline_class.from_single_file(\n self.ckpt_path,\n safety_checker=None,\n controlnet=controlnet,\n )\n\n super()._compare_component_configs(pipe, pipe_single_file)\n\n def test_single_file_components_local_files_only(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11p_sd15_canny\")\n pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet)\n\n with tempfile.TemporaryDirectory() as tmpdir:\n ckpt_filename = self.ckpt_path.split(\"/\")[-1]\n local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)\n\n pipe_single_file = self.pipeline_class.from_single_file(\n local_ckpt_path, controlnet=controlnet, local_files_only=True\n )\n\n super()._compare_component_configs(pipe, pipe_single_file)\n\n def test_single_file_components_with_original_config(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11p_sd15_canny\", variant=\"fp16\")\n pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet)\n pipe_single_file = self.pipeline_class.from_single_file(\n self.ckpt_path, controlnet=controlnet, original_config=self.original_config\n )\n\n super()._compare_component_configs(pipe, pipe_single_file)\n\n def test_single_file_components_with_original_config_local_files_only(self):\n controlnet = ControlNetModel.from_pretrained(\n \"lllyasviel/control_v11p_sd15_canny\", torch_dtype=torch.float16, variant=\"fp16\"\n )\n pipe = self.pipeline_class.from_pretrained(\n self.repo_id,\n controlnet=controlnet,\n )\n\n with tempfile.TemporaryDirectory() as tmpdir:\n ckpt_filename = self.ckpt_path.split(\"/\")[-1]\n local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)\n local_original_config = download_original_config(self.original_config, tmpdir)\n\n pipe_single_file = self.pipeline_class.from_single_file(\n local_ckpt_path, original_config=local_original_config, controlnet=controlnet, local_files_only=True\n )\n pipe_single_file.scheduler = pipe.scheduler\n\n super()._compare_component_configs(pipe, pipe_single_file)\n\n def test_single_file_components_with_diffusers_config(self):\n controlnet = ControlNetModel.from_pretrained(\"lllyasviel/control_v11p_sd15_canny\", variant=\"fp16\")\n pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet)\n pipe_single_file = self.pipeline_class.from_single_file(\n self.ckpt_path, controlnet=controlnet, safety_checker=None, config=self.repo_id\n )\n\n super()._compare_component_configs(pipe, pipe_single_file)\n\n def test_single_file_components_with_diffusers_config_local_files_only(self):\n controlnet = ControlNetModel.from_pretrained(\n \"lllyasviel/control_v11p_sd15_canny\", torch_dtype=torch.float16, variant=\"fp16\"\n )\n pipe = self.pipeline_class.from_pretrained(\n self.repo_id,\n controlnet=controlnet,\n safety_checker=None,\n )\n\n with tempfile.TemporaryDirectory() as tmpdir:\n ckpt_filename = self.ckpt_path.split(\"/\")[-1]\n local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)\n local_diffusers_config = download_diffusers_config(self.repo_id, tmpdir)\n\n pipe_single_file = self.pipeline_class.from_single_file(\n local_ckpt_path,\n config=local_diffusers_config,\n controlnet=controlnet,\n safety_checker=None,\n local_files_only=True,\n )\n super()._compare_component_configs(pipe, pipe_single_file)\n" }, { "path": "diffusers/tests/single_file/test_stable_diffusion_img2img_single_file.py", "content": "import gc\nimport unittest\n\nimport torch\n\nfrom diffusers import (\n StableDiffusionImg2ImgPipeline,\n)\nfrom diffusers.utils import load_image\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n require_torch_gpu,\n slow,\n)\n\nfrom .single_file_testing_utils import SDSingleFileTesterMixin\n\n\nenable_full_determinism()\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionImg2ImgPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):\n pipeline_class = StableDiffusionImg2ImgPipeline\n ckpt_path = \"https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors\"\n original_config = (\n \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml\"\n )\n repo_id = \"runwayml/stable-diffusion-v1-5\"\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n init_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_img2img/sketch-mountains-input.png\"\n )\n inputs = {\n \"prompt\": \"a fantasy landscape, concept art, high resolution\",\n \"image\": init_image,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"strength\": 0.75,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_single_file_format_inference_is_same_as_pretrained(self):\n super().test_single_file_format_inference_is_same_as_pretrained(expected_max_diff=1e-3)\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusion21Img2ImgPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):\n pipeline_class = StableDiffusionImg2ImgPipeline\n ckpt_path = \"https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-ema-pruned.safetensors\"\n original_config = \"https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml\"\n repo_id = \"stabilityai/stable-diffusion-2-1\"\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n init_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_img2img/sketch-mountains-input.png\"\n )\n inputs = {\n \"prompt\": \"a fantasy landscape, concept art, high resolution\",\n \"image\": init_image,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"strength\": 0.75,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_single_file_format_inference_is_same_as_pretrained(self):\n super().test_single_file_format_inference_is_same_as_pretrained(expected_max_diff=1e-3)\n" }, { "path": "diffusers/tests/single_file/test_stable_diffusion_inpaint_single_file.py", "content": "import gc\nimport unittest\n\nimport torch\n\nfrom diffusers import (\n StableDiffusionInpaintPipeline,\n)\nfrom diffusers.utils import load_image\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n require_torch_gpu,\n slow,\n)\n\nfrom .single_file_testing_utils import SDSingleFileTesterMixin\n\n\nenable_full_determinism()\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionInpaintPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):\n pipeline_class = StableDiffusionInpaintPipeline\n ckpt_path = \"https://huggingface.co/runwayml/stable-diffusion-inpainting/blob/main/sd-v1-5-inpainting.ckpt\"\n original_config = \"https://raw.githubusercontent.com/runwayml/stable-diffusion/main/configs/stable-diffusion/v1-inpainting-inference.yaml\"\n repo_id = \"runwayml/stable-diffusion-inpainting\"\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n init_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_inpaint/input_bench_image.png\"\n )\n mask_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_inpaint/input_bench_mask.png\"\n )\n inputs = {\n \"prompt\": \"Face of a yellow cat, high resolution, sitting on a park bench\",\n \"image\": init_image,\n \"mask_image\": mask_image,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_single_file_format_inference_is_same_as_pretrained(self):\n super().test_single_file_format_inference_is_same_as_pretrained(expected_max_diff=1e-3)\n\n def test_single_file_loading_4_channel_unet(self):\n # Test loading single file inpaint with a 4 channel UNet\n ckpt_path = \"https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors\"\n pipe = self.pipeline_class.from_single_file(ckpt_path)\n\n assert pipe.unet.config.in_channels == 4\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusion21InpaintPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):\n pipeline_class = StableDiffusionInpaintPipeline\n ckpt_path = (\n \"https://huggingface.co/stabilityai/stable-diffusion-2-inpainting/blob/main/512-inpainting-ema.safetensors\"\n )\n original_config = \"https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inpainting-inference.yaml\"\n repo_id = \"stabilityai/stable-diffusion-2-inpainting\"\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n init_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_inpaint/input_bench_image.png\"\n )\n mask_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_inpaint/input_bench_mask.png\"\n )\n inputs = {\n \"prompt\": \"Face of a yellow cat, high resolution, sitting on a park bench\",\n \"image\": init_image,\n \"mask_image\": mask_image,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_single_file_format_inference_is_same_as_pretrained(self):\n super().test_single_file_format_inference_is_same_as_pretrained(expected_max_diff=1e-3)\n" }, { "path": "diffusers/tests/single_file/test_stable_diffusion_single_file.py", "content": "import gc\nimport tempfile\nimport unittest\n\nimport torch\n\nfrom diffusers import EulerDiscreteScheduler, StableDiffusionPipeline\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n require_torch_gpu,\n slow,\n)\n\nfrom .single_file_testing_utils import (\n SDSingleFileTesterMixin,\n download_original_config,\n download_single_file_checkpoint,\n)\n\n\nenable_full_determinism()\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):\n pipeline_class = StableDiffusionPipeline\n ckpt_path = \"https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors\"\n original_config = (\n \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml\"\n )\n repo_id = \"runwayml/stable-diffusion-v1-5\"\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n inputs = {\n \"prompt\": \"a fantasy landscape, concept art, high resolution\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"strength\": 0.75,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_single_file_format_inference_is_same_as_pretrained(self):\n super().test_single_file_format_inference_is_same_as_pretrained(expected_max_diff=1e-3)\n\n def test_single_file_legacy_scheduler_loading(self):\n with tempfile.TemporaryDirectory() as tmpdir:\n ckpt_filename = self.ckpt_path.split(\"/\")[-1]\n local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)\n local_original_config = download_original_config(self.original_config, tmpdir)\n\n pipe = self.pipeline_class.from_single_file(\n local_ckpt_path,\n original_config=local_original_config,\n cache_dir=tmpdir,\n local_files_only=True,\n scheduler_type=\"euler\",\n )\n\n # Default is PNDM for this checkpoint\n assert isinstance(pipe.scheduler, EulerDiscreteScheduler)\n\n def test_single_file_legacy_scaling_factor(self):\n new_scaling_factor = 10.0\n init_pipe = self.pipeline_class.from_single_file(self.ckpt_path)\n pipe = self.pipeline_class.from_single_file(self.ckpt_path, scaling_factor=new_scaling_factor)\n\n assert init_pipe.vae.config.scaling_factor != new_scaling_factor\n assert pipe.vae.config.scaling_factor == new_scaling_factor\n\n\n@slow\nclass StableDiffusion21PipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):\n pipeline_class = StableDiffusionPipeline\n ckpt_path = \"https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-ema-pruned.safetensors\"\n original_config = \"https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml\"\n repo_id = \"stabilityai/stable-diffusion-2-1\"\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n inputs = {\n \"prompt\": \"a fantasy landscape, concept art, high resolution\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"strength\": 0.75,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_single_file_format_inference_is_same_as_pretrained(self):\n super().test_single_file_format_inference_is_same_as_pretrained(expected_max_diff=1e-3)\n" }, { "path": "diffusers/tests/single_file/test_stable_diffusion_upscale_single_file.py", "content": "import gc\nimport unittest\n\nimport torch\n\nfrom diffusers import (\n StableDiffusionUpscalePipeline,\n)\nfrom diffusers.utils import load_image\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n slow,\n)\n\nfrom .single_file_testing_utils import SDSingleFileTesterMixin\n\n\nenable_full_determinism()\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionUpscalePipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):\n pipeline_class = StableDiffusionUpscalePipeline\n ckpt_path = \"https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler/blob/main/x4-upscaler-ema.safetensors\"\n original_config = \"https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/x4-upscaling.yaml\"\n repo_id = \"stabilityai/stable-diffusion-x4-upscaler\"\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def test_single_file_format_inference_is_same_as_pretrained(self):\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n \"/sd2-upscale/low_res_cat.png\"\n )\n\n prompt = \"a cat sitting on a park bench\"\n pipe = StableDiffusionUpscalePipeline.from_pretrained(self.repo_id)\n pipe.enable_model_cpu_offload()\n\n generator = torch.Generator(\"cpu\").manual_seed(0)\n output = pipe(prompt=prompt, image=image, generator=generator, output_type=\"np\", num_inference_steps=3)\n image_from_pretrained = output.images[0]\n\n pipe_from_single_file = StableDiffusionUpscalePipeline.from_single_file(self.ckpt_path)\n pipe_from_single_file.enable_model_cpu_offload()\n\n generator = torch.Generator(\"cpu\").manual_seed(0)\n output_from_single_file = pipe_from_single_file(\n prompt=prompt, image=image, generator=generator, output_type=\"np\", num_inference_steps=3\n )\n image_from_single_file = output_from_single_file.images[0]\n\n assert image_from_pretrained.shape == (512, 512, 3)\n assert image_from_single_file.shape == (512, 512, 3)\n assert (\n numpy_cosine_similarity_distance(image_from_pretrained.flatten(), image_from_single_file.flatten()) < 1e-3\n )\n" }, { "path": "diffusers/tests/single_file/test_stable_diffusion_xl_adapter_single_file.py", "content": "import gc\nimport tempfile\nimport unittest\n\nimport torch\n\nfrom diffusers import (\n StableDiffusionXLAdapterPipeline,\n T2IAdapter,\n)\nfrom diffusers.utils import load_image\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n slow,\n)\n\nfrom .single_file_testing_utils import (\n SDXLSingleFileTesterMixin,\n download_diffusers_config,\n download_original_config,\n download_single_file_checkpoint,\n)\n\n\nenable_full_determinism()\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionXLAdapterPipelineSingleFileSlowTests(unittest.TestCase, SDXLSingleFileTesterMixin):\n pipeline_class = StableDiffusionXLAdapterPipeline\n ckpt_path = \"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors\"\n repo_id = \"stabilityai/stable-diffusion-xl-base-1.0\"\n original_config = (\n \"https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml\"\n )\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self):\n prompt = \"toy\"\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/toy_canny.png\"\n )\n\n inputs = {\n \"prompt\": prompt,\n \"image\": image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n\n return inputs\n\n def test_single_file_format_inference_is_same_as_pretrained(self):\n adapter = T2IAdapter.from_pretrained(\"TencentARC/t2i-adapter-lineart-sdxl-1.0\", torch_dtype=torch.float16)\n pipe_single_file = StableDiffusionXLAdapterPipeline.from_single_file(\n self.ckpt_path,\n adapter=adapter,\n torch_dtype=torch.float16,\n safety_checker=None,\n )\n pipe_single_file.enable_model_cpu_offload()\n pipe_single_file.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs()\n images_single_file = pipe_single_file(**inputs).images[0]\n\n pipe = StableDiffusionXLAdapterPipeline.from_pretrained(\n self.repo_id,\n adapter=adapter,\n torch_dtype=torch.float16,\n safety_checker=None,\n )\n pipe.enable_model_cpu_offload()\n\n inputs = self.get_inputs()\n images = pipe(**inputs).images[0]\n\n assert images_single_file.shape == (768, 512, 3)\n assert images.shape == (768, 512, 3)\n\n max_diff = numpy_cosine_similarity_distance(images.flatten(), images_single_file.flatten())\n assert max_diff < 5e-3\n\n def test_single_file_components(self):\n adapter = T2IAdapter.from_pretrained(\"TencentARC/t2i-adapter-lineart-sdxl-1.0\", torch_dtype=torch.float16)\n pipe = self.pipeline_class.from_pretrained(\n self.repo_id,\n variant=\"fp16\",\n adapter=adapter,\n torch_dtype=torch.float16,\n )\n\n pipe_single_file = self.pipeline_class.from_single_file(self.ckpt_path, safety_checker=None, adapter=adapter)\n super().test_single_file_components(pipe, pipe_single_file)\n\n def test_single_file_components_local_files_only(self):\n adapter = T2IAdapter.from_pretrained(\"TencentARC/t2i-adapter-lineart-sdxl-1.0\", torch_dtype=torch.float16)\n pipe = self.pipeline_class.from_pretrained(\n self.repo_id,\n variant=\"fp16\",\n adapter=adapter,\n torch_dtype=torch.float16,\n )\n\n with tempfile.TemporaryDirectory() as tmpdir:\n ckpt_filename = self.ckpt_path.split(\"/\")[-1]\n local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)\n\n single_file_pipe = self.pipeline_class.from_single_file(\n local_ckpt_path, adapter=adapter, safety_checker=None, local_files_only=True\n )\n\n self._compare_component_configs(pipe, single_file_pipe)\n\n def test_single_file_components_with_diffusers_config(self):\n adapter = T2IAdapter.from_pretrained(\"TencentARC/t2i-adapter-lineart-sdxl-1.0\", torch_dtype=torch.float16)\n pipe = self.pipeline_class.from_pretrained(\n self.repo_id,\n variant=\"fp16\",\n adapter=adapter,\n torch_dtype=torch.float16,\n safety_checker=None,\n )\n\n pipe_single_file = self.pipeline_class.from_single_file(self.ckpt_path, config=self.repo_id, adapter=adapter)\n self._compare_component_configs(pipe, pipe_single_file)\n\n def test_single_file_components_with_diffusers_config_local_files_only(self):\n adapter = T2IAdapter.from_pretrained(\"TencentARC/t2i-adapter-lineart-sdxl-1.0\", torch_dtype=torch.float16)\n pipe = self.pipeline_class.from_pretrained(\n self.repo_id,\n variant=\"fp16\",\n adapter=adapter,\n torch_dtype=torch.float16,\n )\n\n with tempfile.TemporaryDirectory() as tmpdir:\n ckpt_filename = self.ckpt_path.split(\"/\")[-1]\n local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)\n local_diffusers_config = download_diffusers_config(self.repo_id, tmpdir)\n\n pipe_single_file = self.pipeline_class.from_single_file(\n local_ckpt_path,\n config=local_diffusers_config,\n adapter=adapter,\n safety_checker=None,\n local_files_only=True,\n )\n self._compare_component_configs(pipe, pipe_single_file)\n\n def test_single_file_components_with_original_config(self):\n adapter = T2IAdapter.from_pretrained(\"TencentARC/t2i-adapter-lineart-sdxl-1.0\", torch_dtype=torch.float16)\n pipe = self.pipeline_class.from_pretrained(\n self.repo_id,\n variant=\"fp16\",\n adapter=adapter,\n torch_dtype=torch.float16,\n safety_checker=None,\n )\n\n pipe_single_file = self.pipeline_class.from_single_file(\n self.ckpt_path, original_config=self.original_config, adapter=adapter\n )\n self._compare_component_configs(pipe, pipe_single_file)\n\n def test_single_file_components_with_original_config_local_files_only(self):\n adapter = T2IAdapter.from_pretrained(\"TencentARC/t2i-adapter-lineart-sdxl-1.0\", torch_dtype=torch.float16)\n pipe = self.pipeline_class.from_pretrained(\n self.repo_id,\n variant=\"fp16\",\n adapter=adapter,\n torch_dtype=torch.float16,\n )\n\n with tempfile.TemporaryDirectory() as tmpdir:\n ckpt_filename = self.ckpt_path.split(\"/\")[-1]\n local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)\n local_original_config = download_original_config(self.original_config, tmpdir)\n\n pipe_single_file = self.pipeline_class.from_single_file(\n local_ckpt_path,\n original_config=local_original_config,\n adapter=adapter,\n safety_checker=None,\n local_files_only=True,\n )\n self._compare_component_configs(pipe, pipe_single_file)\n" }, { "path": "diffusers/tests/single_file/test_stable_diffusion_xl_controlnet_single_file.py", "content": "import gc\nimport tempfile\nimport unittest\n\nimport torch\n\nfrom diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline\nfrom diffusers.utils import load_image\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n slow,\n torch_device,\n)\n\nfrom .single_file_testing_utils import (\n SDXLSingleFileTesterMixin,\n download_diffusers_config,\n download_single_file_checkpoint,\n)\n\n\nenable_full_determinism()\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionXLControlNetPipelineSingleFileSlowTests(unittest.TestCase, SDXLSingleFileTesterMixin):\n pipeline_class = StableDiffusionXLControlNetPipeline\n ckpt_path = \"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors\"\n repo_id = \"stabilityai/stable-diffusion-xl-base-1.0\"\n original_config = (\n \"https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml\"\n )\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n image = load_image(\n \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth.png\"\n )\n inputs = {\n \"prompt\": \"Stormtrooper's lecture\",\n \"image\": image,\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"strength\": 0.75,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n\n return inputs\n\n def test_single_file_format_inference_is_same_as_pretrained(self):\n controlnet = ControlNetModel.from_pretrained(\"diffusers/controlnet-depth-sdxl-1.0\", torch_dtype=torch.float16)\n pipe_single_file = self.pipeline_class.from_single_file(\n self.ckpt_path, controlnet=controlnet, torch_dtype=torch.float16\n )\n pipe_single_file.unet.set_default_attn_processor()\n pipe_single_file.enable_model_cpu_offload()\n pipe_single_file.set_progress_bar_config(disable=None)\n\n inputs = self.get_inputs(torch_device)\n single_file_images = pipe_single_file(**inputs).images[0]\n\n pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet, torch_dtype=torch.float16)\n pipe.unet.set_default_attn_processor()\n pipe.enable_model_cpu_offload()\n\n inputs = self.get_inputs(torch_device)\n images = pipe(**inputs).images[0]\n\n assert images.shape == (512, 512, 3)\n assert single_file_images.shape == (512, 512, 3)\n\n max_diff = numpy_cosine_similarity_distance(images[0].flatten(), single_file_images[0].flatten())\n assert max_diff < 5e-2\n\n def test_single_file_components(self):\n controlnet = ControlNetModel.from_pretrained(\n \"diffusers/controlnet-depth-sdxl-1.0\", torch_dtype=torch.float16, variant=\"fp16\"\n )\n pipe = self.pipeline_class.from_pretrained(\n self.repo_id,\n variant=\"fp16\",\n controlnet=controlnet,\n torch_dtype=torch.float16,\n )\n\n pipe_single_file = self.pipeline_class.from_single_file(self.ckpt_path, controlnet=controlnet)\n super().test_single_file_components(pipe, pipe_single_file)\n\n def test_single_file_components_local_files_only(self):\n controlnet = ControlNetModel.from_pretrained(\n \"diffusers/controlnet-depth-sdxl-1.0\", torch_dtype=torch.float16, variant=\"fp16\"\n )\n pipe = self.pipeline_class.from_pretrained(\n self.repo_id,\n variant=\"fp16\",\n controlnet=controlnet,\n torch_dtype=torch.float16,\n )\n\n with tempfile.TemporaryDirectory() as tmpdir:\n ckpt_filename = self.ckpt_path.split(\"/\")[-1]\n local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)\n\n single_file_pipe = self.pipeline_class.from_single_file(\n local_ckpt_path, controlnet=controlnet, safety_checker=None, local_files_only=True\n )\n\n self._compare_component_configs(pipe, single_file_pipe)\n\n def test_single_file_components_with_original_config(self):\n controlnet = ControlNetModel.from_pretrained(\n \"diffusers/controlnet-depth-sdxl-1.0\", torch_dtype=torch.float16, variant=\"fp16\"\n )\n pipe = self.pipeline_class.from_pretrained(\n self.repo_id,\n variant=\"fp16\",\n controlnet=controlnet,\n torch_dtype=torch.float16,\n )\n\n pipe_single_file = self.pipeline_class.from_single_file(\n self.ckpt_path,\n original_config=self.original_config,\n controlnet=controlnet,\n )\n self._compare_component_configs(pipe, pipe_single_file)\n\n def test_single_file_components_with_original_config_local_files_only(self):\n controlnet = ControlNetModel.from_pretrained(\n \"diffusers/controlnet-depth-sdxl-1.0\", torch_dtype=torch.float16, variant=\"fp16\"\n )\n pipe = self.pipeline_class.from_pretrained(\n self.repo_id,\n variant=\"fp16\",\n controlnet=controlnet,\n torch_dtype=torch.float16,\n )\n\n with tempfile.TemporaryDirectory() as tmpdir:\n ckpt_filename = self.ckpt_path.split(\"/\")[-1]\n local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)\n\n pipe_single_file = self.pipeline_class.from_single_file(\n local_ckpt_path,\n safety_checker=None,\n controlnet=controlnet,\n local_files_only=True,\n )\n self._compare_component_configs(pipe, pipe_single_file)\n\n def test_single_file_components_with_diffusers_config(self):\n controlnet = ControlNetModel.from_pretrained(\n \"diffusers/controlnet-depth-sdxl-1.0\", torch_dtype=torch.float16, variant=\"fp16\"\n )\n pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet)\n pipe_single_file = self.pipeline_class.from_single_file(\n self.ckpt_path, controlnet=controlnet, config=self.repo_id\n )\n\n super()._compare_component_configs(pipe, pipe_single_file)\n\n def test_single_file_components_with_diffusers_config_local_files_only(self):\n controlnet = ControlNetModel.from_pretrained(\n \"diffusers/controlnet-depth-sdxl-1.0\", torch_dtype=torch.float16, variant=\"fp16\"\n )\n pipe = self.pipeline_class.from_pretrained(\n self.repo_id,\n controlnet=controlnet,\n )\n\n with tempfile.TemporaryDirectory() as tmpdir:\n ckpt_filename = self.ckpt_path.split(\"/\")[-1]\n local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)\n local_diffusers_config = download_diffusers_config(self.repo_id, tmpdir)\n\n pipe_single_file = self.pipeline_class.from_single_file(\n local_ckpt_path,\n config=local_diffusers_config,\n safety_checker=None,\n controlnet=controlnet,\n local_files_only=True,\n )\n super()._compare_component_configs(pipe, pipe_single_file)\n" }, { "path": "diffusers/tests/single_file/test_stable_diffusion_xl_img2img_single_file.py", "content": "import gc\nimport unittest\n\nimport torch\n\nfrom diffusers import (\n DDIMScheduler,\n StableDiffusionXLImg2ImgPipeline,\n)\nfrom diffusers.utils import load_image\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n numpy_cosine_similarity_distance,\n require_torch_gpu,\n slow,\n)\n\nfrom .single_file_testing_utils import SDXLSingleFileTesterMixin\n\n\nenable_full_determinism()\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionXLImg2ImgPipelineSingleFileSlowTests(unittest.TestCase, SDXLSingleFileTesterMixin):\n pipeline_class = StableDiffusionXLImg2ImgPipeline\n ckpt_path = \"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors\"\n repo_id = \"stabilityai/stable-diffusion-xl-base-1.0\"\n original_config = (\n \"https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml\"\n )\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n init_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_img2img/sketch-mountains-input.png\"\n )\n inputs = {\n \"prompt\": \"a fantasy landscape, concept art, high resolution\",\n \"image\": init_image,\n \"generator\": generator,\n \"num_inference_steps\": 3,\n \"strength\": 0.75,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_single_file_format_inference_is_same_as_pretrained(self):\n super().test_single_file_format_inference_is_same_as_pretrained(expected_max_diff=1e-3)\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionXLImg2ImgRefinerPipelineSingleFileSlowTests(unittest.TestCase):\n pipeline_class = StableDiffusionXLImg2ImgPipeline\n ckpt_path = (\n \"https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/blob/main/sd_xl_refiner_1.0.safetensors\"\n )\n repo_id = \"stabilityai/stable-diffusion-xl-refiner-1.0\"\n original_config = (\n \"https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_refiner.yaml\"\n )\n\n def test_single_file_format_inference_is_same_as_pretrained(self):\n init_image = load_image(\n \"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main\"\n \"/stable_diffusion_img2img/sketch-mountains-input.png\"\n )\n\n pipe = self.pipeline_class.from_pretrained(self.repo_id, torch_dtype=torch.float16)\n pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)\n pipe.unet.set_default_attn_processor()\n pipe.enable_model_cpu_offload()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n image = pipe(\n prompt=\"mountains\", image=init_image, num_inference_steps=5, generator=generator, output_type=\"np\"\n ).images[0]\n\n pipe_single_file = self.pipeline_class.from_single_file(self.ckpt_path, torch_dtype=torch.float16)\n pipe_single_file.scheduler = DDIMScheduler.from_config(pipe_single_file.scheduler.config)\n pipe_single_file.unet.set_default_attn_processor()\n pipe_single_file.enable_model_cpu_offload()\n\n generator = torch.Generator(device=\"cpu\").manual_seed(0)\n image_single_file = pipe_single_file(\n prompt=\"mountains\", image=init_image, num_inference_steps=5, generator=generator, output_type=\"np\"\n ).images[0]\n\n max_diff = numpy_cosine_similarity_distance(image.flatten(), image_single_file.flatten())\n\n assert max_diff < 5e-4\n" }, { "path": "diffusers/tests/single_file/test_stable_diffusion_xl_instruct_pix2pix.py", "content": "import gc\nimport unittest\n\nimport torch\n\nfrom diffusers import StableDiffusionXLInstructPix2PixPipeline\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n require_torch_gpu,\n slow,\n)\n\n\nenable_full_determinism()\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionXLInstructPix2PixPipeline(unittest.TestCase):\n pipeline_class = StableDiffusionXLInstructPix2PixPipeline\n ckpt_path = \"https://huggingface.co/stabilityai/cosxl/blob/main/cosxl_edit.safetensors\"\n original_config = None\n repo_id = \"diffusers/sdxl-instructpix2pix-768\"\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n inputs = {\n \"prompt\": \"a fantasy landscape, concept art, high resolution\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"strength\": 0.75,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_single_file_setting_cosxl_edit(self):\n # Default is PNDM for this checkpoint\n pipe = self.pipeline_class.from_single_file(self.ckpt_path, config=self.repo_id, is_cosxl_edit=True)\n assert pipe.is_cosxl_edit is True\n" }, { "path": "diffusers/tests/single_file/test_stable_diffusion_xl_single_file.py", "content": "import gc\nimport unittest\n\nimport torch\n\nfrom diffusers import (\n StableDiffusionXLPipeline,\n)\nfrom diffusers.utils.testing_utils import (\n enable_full_determinism,\n require_torch_gpu,\n slow,\n)\n\nfrom .single_file_testing_utils import SDXLSingleFileTesterMixin\n\n\nenable_full_determinism()\n\n\n@slow\n@require_torch_gpu\nclass StableDiffusionXLPipelineSingleFileSlowTests(unittest.TestCase, SDXLSingleFileTesterMixin):\n pipeline_class = StableDiffusionXLPipeline\n ckpt_path = \"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors\"\n repo_id = \"stabilityai/stable-diffusion-xl-base-1.0\"\n original_config = (\n \"https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml\"\n )\n\n def setUp(self):\n super().setUp()\n gc.collect()\n torch.cuda.empty_cache()\n\n def tearDown(self):\n super().tearDown()\n gc.collect()\n torch.cuda.empty_cache()\n\n def get_inputs(self, device, generator_device=\"cpu\", dtype=torch.float32, seed=0):\n generator = torch.Generator(device=generator_device).manual_seed(seed)\n inputs = {\n \"prompt\": \"a fantasy landscape, concept art, high resolution\",\n \"generator\": generator,\n \"num_inference_steps\": 2,\n \"strength\": 0.75,\n \"guidance_scale\": 7.5,\n \"output_type\": \"np\",\n }\n return inputs\n\n def test_single_file_format_inference_is_same_as_pretrained(self):\n super().test_single_file_format_inference_is_same_as_pretrained(expected_max_diff=1e-3)\n" }, { "path": "diffusers/utils/check_config_docstrings.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport importlib\nimport inspect\nimport os\nimport re\n\n\n# All paths are set with the intent you should run this script from the root of the repo with the command\n# python utils/check_config_docstrings.py\nPATH_TO_TRANSFORMERS = \"src/transformers\"\n\n\n# This is to make sure the transformers module imported is the one in the repo.\nspec = importlib.util.spec_from_file_location(\n \"transformers\",\n os.path.join(PATH_TO_TRANSFORMERS, \"__init__.py\"),\n submodule_search_locations=[PATH_TO_TRANSFORMERS],\n)\ntransformers = spec.loader.load_module()\n\nCONFIG_MAPPING = transformers.models.auto.configuration_auto.CONFIG_MAPPING\n\n# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.\n# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`\n_re_checkpoint = re.compile(r\"\\[(.+?)\\]\\((https://huggingface\\.co/.+?)\\)\")\n\n\nCONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK = {\n \"CLIPConfigMixin\",\n \"DecisionTransformerConfigMixin\",\n \"EncoderDecoderConfigMixin\",\n \"RagConfigMixin\",\n \"SpeechEncoderDecoderConfigMixin\",\n \"VisionEncoderDecoderConfigMixin\",\n \"VisionTextDualEncoderConfigMixin\",\n}\n\n\ndef check_config_docstrings_have_checkpoints():\n configs_without_checkpoint = []\n\n for config_class in list(CONFIG_MAPPING.values()):\n checkpoint_found = False\n\n # source code of `config_class`\n config_source = inspect.getsource(config_class)\n checkpoints = _re_checkpoint.findall(config_source)\n\n for checkpoint in checkpoints:\n # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.\n # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`\n ckpt_name, ckpt_link = checkpoint\n\n # verify the checkpoint name corresponds to the checkpoint link\n ckpt_link_from_name = f\"https://huggingface.co/{ckpt_name}\"\n if ckpt_link == ckpt_link_from_name:\n checkpoint_found = True\n break\n\n name = config_class.__name__\n if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:\n configs_without_checkpoint.append(name)\n\n if len(configs_without_checkpoint) > 0:\n message = \"\\n\".join(sorted(configs_without_checkpoint))\n raise ValueError(f\"The following configurations don't contain any valid checkpoint:\\n{message}\")\n\n\nif __name__ == \"__main__\":\n check_config_docstrings_have_checkpoints()\n" }, { "path": "diffusers/utils/check_copies.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport argparse\nimport glob\nimport os\nimport re\nimport subprocess\n\n\n# All paths are set with the intent you should run this script from the root of the repo with the command\n# python utils/check_copies.py\nDIFFUSERS_PATH = \"src/diffusers\"\nREPO_PATH = \".\"\n\n\ndef _should_continue(line, indent):\n return line.startswith(indent) or len(line) <= 1 or re.search(r\"^\\s*\\)(\\s*->.*:|:)\\s*$\", line) is not None\n\n\ndef find_code_in_diffusers(object_name):\n \"\"\"Find and return the code source code of `object_name`.\"\"\"\n parts = object_name.split(\".\")\n i = 0\n\n # First let's find the module where our object lives.\n module = parts[i]\n while i < len(parts) and not os.path.isfile(os.path.join(DIFFUSERS_PATH, f\"{module}.py\")):\n i += 1\n if i < len(parts):\n module = os.path.join(module, parts[i])\n if i >= len(parts):\n raise ValueError(f\"`object_name` should begin with the name of a module of diffusers but got {object_name}.\")\n\n with open(\n os.path.join(DIFFUSERS_PATH, f\"{module}.py\"),\n \"r\",\n encoding=\"utf-8\",\n newline=\"\\n\",\n ) as f:\n lines = f.readlines()\n\n # Now let's find the class / func in the code!\n indent = \"\"\n line_index = 0\n for name in parts[i + 1 :]:\n while (\n line_index < len(lines) and re.search(rf\"^{indent}(class|def)\\s+{name}(\\(|\\:)\", lines[line_index]) is None\n ):\n line_index += 1\n indent += \" \"\n line_index += 1\n\n if line_index >= len(lines):\n raise ValueError(f\" {object_name} does not match any function or class in {module}.\")\n\n # We found the beginning of the class / func, now let's find the end (when the indent diminishes).\n start_index = line_index\n while line_index < len(lines) and _should_continue(lines[line_index], indent):\n line_index += 1\n # Clean up empty lines at the end (if any).\n while len(lines[line_index - 1]) <= 1:\n line_index -= 1\n\n code_lines = lines[start_index:line_index]\n return \"\".join(code_lines)\n\n\n_re_copy_warning = re.compile(r\"^(\\s*)#\\s*Copied from\\s+diffusers\\.(\\S+\\.\\S+)\\s*($|\\S.*$)\")\n_re_replace_pattern = re.compile(r\"^\\s*(\\S+)->(\\S+)(\\s+.*|$)\")\n_re_fill_pattern = re.compile(r\"]*>\")\n\n\ndef get_indent(code):\n lines = code.split(\"\\n\")\n idx = 0\n while idx < len(lines) and len(lines[idx]) == 0:\n idx += 1\n if idx < len(lines):\n return re.search(r\"^(\\s*)\\S\", lines[idx]).groups()[0]\n return \"\"\n\n\ndef run_ruff(code):\n command = [\"ruff\", \"format\", \"-\", \"--config\", \"pyproject.toml\", \"--silent\"]\n process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE)\n stdout, _ = process.communicate(input=code.encode())\n return stdout.decode()\n\n\ndef stylify(code: str) -> str:\n \"\"\"\n Applies the ruff part of our `make style` command to some code. This formats the code using `ruff format`.\n As `ruff` does not provide a python api this cannot be done on the fly.\n\n Args:\n code (`str`): The code to format.\n\n Returns:\n `str`: The formatted code.\n \"\"\"\n has_indent = len(get_indent(code)) > 0\n if has_indent:\n code = f\"class Bla:\\n{code}\"\n formatted_code = run_ruff(code)\n return formatted_code[len(\"class Bla:\\n\") :] if has_indent else formatted_code\n\n\ndef is_copy_consistent(filename, overwrite=False):\n \"\"\"\n Check if the code commented as a copy in `filename` matches the original.\n Return the differences or overwrites the content depending on `overwrite`.\n \"\"\"\n with open(filename, \"r\", encoding=\"utf-8\", newline=\"\\n\") as f:\n lines = f.readlines()\n diffs = []\n line_index = 0\n # Not a for loop cause `lines` is going to change (if `overwrite=True`).\n while line_index < len(lines):\n search = _re_copy_warning.search(lines[line_index])\n if search is None:\n line_index += 1\n continue\n\n # There is some copied code here, let's retrieve the original.\n indent, object_name, replace_pattern = search.groups()\n theoretical_code = find_code_in_diffusers(object_name)\n theoretical_indent = get_indent(theoretical_code)\n\n start_index = line_index + 1 if indent == theoretical_indent else line_index + 2\n indent = theoretical_indent\n line_index = start_index\n\n # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.\n should_continue = True\n while line_index < len(lines) and should_continue:\n line_index += 1\n if line_index >= len(lines):\n break\n line = lines[line_index]\n should_continue = _should_continue(line, indent) and re.search(f\"^{indent}# End copy\", line) is None\n # Clean up empty lines at the end (if any).\n while len(lines[line_index - 1]) <= 1:\n line_index -= 1\n\n observed_code_lines = lines[start_index:line_index]\n observed_code = \"\".join(observed_code_lines)\n\n # Remove any nested `Copied from` comments to avoid circular copies\n theoretical_code = [line for line in theoretical_code.split(\"\\n\") if _re_copy_warning.search(line) is None]\n theoretical_code = \"\\n\".join(theoretical_code)\n\n # Before comparing, use the `replace_pattern` on the original code.\n if len(replace_pattern) > 0:\n patterns = replace_pattern.replace(\"with\", \"\").split(\",\")\n patterns = [_re_replace_pattern.search(p) for p in patterns]\n for pattern in patterns:\n if pattern is None:\n continue\n obj1, obj2, option = pattern.groups()\n theoretical_code = re.sub(obj1, obj2, theoretical_code)\n if option.strip() == \"all-casing\":\n theoretical_code = re.sub(obj1.lower(), obj2.lower(), theoretical_code)\n theoretical_code = re.sub(obj1.upper(), obj2.upper(), theoretical_code)\n\n # stylify after replacement. To be able to do that, we need the header (class or function definition)\n # from the previous line\n theoretical_code = stylify(lines[start_index - 1] + theoretical_code)\n theoretical_code = theoretical_code[len(lines[start_index - 1]) :]\n\n # Test for a diff and act accordingly.\n if observed_code != theoretical_code:\n diffs.append([object_name, start_index])\n if overwrite:\n lines = lines[:start_index] + [theoretical_code] + lines[line_index:]\n line_index = start_index + 1\n\n if overwrite and len(diffs) > 0:\n # Warn the user a file has been modified.\n print(f\"Detected changes, rewriting {filename}.\")\n with open(filename, \"w\", encoding=\"utf-8\", newline=\"\\n\") as f:\n f.writelines(lines)\n return diffs\n\n\ndef check_copies(overwrite: bool = False):\n all_files = glob.glob(os.path.join(DIFFUSERS_PATH, \"**/*.py\"), recursive=True)\n diffs = []\n for filename in all_files:\n new_diffs = is_copy_consistent(filename, overwrite)\n diffs += [f\"- {filename}: copy does not match {d[0]} at line {d[1]}\" for d in new_diffs]\n if not overwrite and len(diffs) > 0:\n diff = \"\\n\".join(diffs)\n raise Exception(\n \"Found the following copy inconsistencies:\\n\"\n + diff\n + \"\\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.\"\n )\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--fix_and_overwrite\",\n action=\"store_true\",\n help=\"Whether to fix inconsistencies.\",\n )\n args = parser.parse_args()\n\n check_copies(args.fix_and_overwrite)\n" }, { "path": "diffusers/utils/check_doc_toc.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport argparse\nfrom collections import defaultdict\n\nimport yaml\n\n\nPATH_TO_TOC = \"docs/source/en/_toctree.yml\"\n\n\ndef clean_doc_toc(doc_list):\n \"\"\"\n Cleans the table of content of the model documentation by removing duplicates and sorting models alphabetically.\n \"\"\"\n counts = defaultdict(int)\n overview_doc = []\n new_doc_list = []\n for doc in doc_list:\n if \"local\" in doc:\n counts[doc[\"local\"]] += 1\n\n if doc[\"title\"].lower() == \"overview\":\n overview_doc.append({\"local\": doc[\"local\"], \"title\": doc[\"title\"]})\n else:\n new_doc_list.append(doc)\n\n doc_list = new_doc_list\n duplicates = [key for key, value in counts.items() if value > 1]\n\n new_doc = []\n for duplicate_key in duplicates:\n titles = list({doc[\"title\"] for doc in doc_list if doc[\"local\"] == duplicate_key})\n if len(titles) > 1:\n raise ValueError(\n f\"{duplicate_key} is present several times in the documentation table of content at \"\n \"`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the \"\n \"others.\"\n )\n # Only add this once\n new_doc.append({\"local\": duplicate_key, \"title\": titles[0]})\n\n # Add none duplicate-keys\n new_doc.extend([doc for doc in doc_list if \"local\" not in counts or counts[doc[\"local\"]] == 1])\n new_doc = sorted(new_doc, key=lambda s: s[\"title\"].lower())\n\n # \"overview\" gets special treatment and is always first\n if len(overview_doc) > 1:\n raise ValueError(\"{doc_list} has two 'overview' docs which is not allowed.\")\n\n overview_doc.extend(new_doc)\n\n # Sort\n return overview_doc\n\n\ndef check_scheduler_doc(overwrite=False):\n with open(PATH_TO_TOC, encoding=\"utf-8\") as f:\n content = yaml.safe_load(f.read())\n\n # Get to the API doc\n api_idx = 0\n while content[api_idx][\"title\"] != \"API\":\n api_idx += 1\n api_doc = content[api_idx][\"sections\"]\n\n # Then to the model doc\n scheduler_idx = 0\n while api_doc[scheduler_idx][\"title\"] != \"Schedulers\":\n scheduler_idx += 1\n\n scheduler_doc = api_doc[scheduler_idx][\"sections\"]\n new_scheduler_doc = clean_doc_toc(scheduler_doc)\n\n diff = False\n if new_scheduler_doc != scheduler_doc:\n diff = True\n if overwrite:\n api_doc[scheduler_idx][\"sections\"] = new_scheduler_doc\n\n if diff:\n if overwrite:\n content[api_idx][\"sections\"] = api_doc\n with open(PATH_TO_TOC, \"w\", encoding=\"utf-8\") as f:\n f.write(yaml.dump(content, allow_unicode=True))\n else:\n raise ValueError(\n \"The model doc part of the table of content is not properly sorted, run `make style` to fix this.\"\n )\n\n\ndef check_pipeline_doc(overwrite=False):\n with open(PATH_TO_TOC, encoding=\"utf-8\") as f:\n content = yaml.safe_load(f.read())\n\n # Get to the API doc\n api_idx = 0\n while content[api_idx][\"title\"] != \"API\":\n api_idx += 1\n api_doc = content[api_idx][\"sections\"]\n\n # Then to the model doc\n pipeline_idx = 0\n while api_doc[pipeline_idx][\"title\"] != \"Pipelines\":\n pipeline_idx += 1\n\n diff = False\n pipeline_docs = api_doc[pipeline_idx][\"sections\"]\n new_pipeline_docs = []\n\n # sort sub pipeline docs\n for pipeline_doc in pipeline_docs:\n if \"section\" in pipeline_doc:\n sub_pipeline_doc = pipeline_doc[\"section\"]\n new_sub_pipeline_doc = clean_doc_toc(sub_pipeline_doc)\n if overwrite:\n pipeline_doc[\"section\"] = new_sub_pipeline_doc\n new_pipeline_docs.append(pipeline_doc)\n\n # sort overall pipeline doc\n new_pipeline_docs = clean_doc_toc(new_pipeline_docs)\n\n if new_pipeline_docs != pipeline_docs:\n diff = True\n if overwrite:\n api_doc[pipeline_idx][\"sections\"] = new_pipeline_docs\n\n if diff:\n if overwrite:\n content[api_idx][\"sections\"] = api_doc\n with open(PATH_TO_TOC, \"w\", encoding=\"utf-8\") as f:\n f.write(yaml.dump(content, allow_unicode=True))\n else:\n raise ValueError(\n \"The model doc part of the table of content is not properly sorted, run `make style` to fix this.\"\n )\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--fix_and_overwrite\", action=\"store_true\", help=\"Whether to fix inconsistencies.\")\n args = parser.parse_args()\n\n check_scheduler_doc(args.fix_and_overwrite)\n check_pipeline_doc(args.fix_and_overwrite)\n" }, { "path": "diffusers/utils/check_dummies.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport argparse\nimport os\nimport re\n\n\n# All paths are set with the intent you should run this script from the root of the repo with the command\n# python utils/check_dummies.py\nPATH_TO_DIFFUSERS = \"src/diffusers\"\n\n# Matches is_xxx_available()\n_re_backend = re.compile(r\"is\\_([a-z_]*)_available\\(\\)\")\n# Matches from xxx import bla\n_re_single_line_import = re.compile(r\"\\s+from\\s+\\S*\\s+import\\s+([^\\(\\s].*)\\n\")\n\n\nDUMMY_CONSTANT = \"\"\"\n{0} = None\n\"\"\"\n\nDUMMY_CLASS = \"\"\"\nclass {0}(metaclass=DummyObject):\n _backends = {1}\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, {1})\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, {1})\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, {1})\n\"\"\"\n\n\nDUMMY_FUNCTION = \"\"\"\ndef {0}(*args, **kwargs):\n requires_backends({0}, {1})\n\"\"\"\n\n\ndef find_backend(line):\n \"\"\"Find one (or multiple) backend in a code line of the init.\"\"\"\n backends = _re_backend.findall(line)\n if len(backends) == 0:\n return None\n\n return \"_and_\".join(backends)\n\n\ndef read_init():\n \"\"\"Read the init and extracts PyTorch, TensorFlow, SentencePiece and Tokenizers objects.\"\"\"\n with open(os.path.join(PATH_TO_DIFFUSERS, \"__init__.py\"), \"r\", encoding=\"utf-8\", newline=\"\\n\") as f:\n lines = f.readlines()\n\n # Get to the point we do the actual imports for type checking\n line_index = 0\n while not lines[line_index].startswith(\"if TYPE_CHECKING\"):\n line_index += 1\n\n backend_specific_objects = {}\n # Go through the end of the file\n while line_index < len(lines):\n # If the line contains is_backend_available, we grab all objects associated with the `else` block\n backend = find_backend(lines[line_index])\n if backend is not None:\n while not lines[line_index].startswith(\" else:\"):\n line_index += 1\n line_index += 1\n objects = []\n # Until we unindent, add backend objects to the list\n while len(lines[line_index]) <= 1 or lines[line_index].startswith(\" \" * 8):\n line = lines[line_index]\n single_line_import_search = _re_single_line_import.search(line)\n if single_line_import_search is not None:\n objects.extend(single_line_import_search.groups()[0].split(\", \"))\n elif line.startswith(\" \" * 12):\n objects.append(line[12:-2])\n line_index += 1\n\n if len(objects) > 0:\n backend_specific_objects[backend] = objects\n else:\n line_index += 1\n\n return backend_specific_objects\n\n\ndef create_dummy_object(name, backend_name):\n \"\"\"Create the code for the dummy object corresponding to `name`.\"\"\"\n if name.isupper():\n return DUMMY_CONSTANT.format(name)\n elif name.islower():\n return DUMMY_FUNCTION.format(name, backend_name)\n else:\n return DUMMY_CLASS.format(name, backend_name)\n\n\ndef create_dummy_files(backend_specific_objects=None):\n \"\"\"Create the content of the dummy files.\"\"\"\n if backend_specific_objects is None:\n backend_specific_objects = read_init()\n # For special correspondence backend to module name as used in the function requires_modulename\n dummy_files = {}\n\n for backend, objects in backend_specific_objects.items():\n backend_name = \"[\" + \", \".join(f'\"{b}\"' for b in backend.split(\"_and_\")) + \"]\"\n dummy_file = \"# This file is autogenerated by the command `make fix-copies`, do not edit.\\n\"\n dummy_file += \"from ..utils import DummyObject, requires_backends\\n\\n\"\n dummy_file += \"\\n\".join([create_dummy_object(o, backend_name) for o in objects])\n dummy_files[backend] = dummy_file\n\n return dummy_files\n\n\ndef check_dummies(overwrite=False):\n \"\"\"Check if the dummy files are up to date and maybe `overwrite` with the right content.\"\"\"\n dummy_files = create_dummy_files()\n # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py\n short_names = {\"torch\": \"pt\"}\n\n # Locate actual dummy modules and read their content.\n path = os.path.join(PATH_TO_DIFFUSERS, \"utils\")\n dummy_file_paths = {\n backend: os.path.join(path, f\"dummy_{short_names.get(backend, backend)}_objects.py\")\n for backend in dummy_files.keys()\n }\n\n actual_dummies = {}\n for backend, file_path in dummy_file_paths.items():\n if os.path.isfile(file_path):\n with open(file_path, \"r\", encoding=\"utf-8\", newline=\"\\n\") as f:\n actual_dummies[backend] = f.read()\n else:\n actual_dummies[backend] = \"\"\n\n for backend in dummy_files.keys():\n if dummy_files[backend] != actual_dummies[backend]:\n if overwrite:\n print(\n f\"Updating diffusers.utils.dummy_{short_names.get(backend, backend)}_objects.py as the main \"\n \"__init__ has new objects.\"\n )\n with open(dummy_file_paths[backend], \"w\", encoding=\"utf-8\", newline=\"\\n\") as f:\n f.write(dummy_files[backend])\n else:\n raise ValueError(\n \"The main __init__ has objects that are not present in \"\n f\"diffusers.utils.dummy_{short_names.get(backend, backend)}_objects.py. Run `make fix-copies` \"\n \"to fix this.\"\n )\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--fix_and_overwrite\", action=\"store_true\", help=\"Whether to fix inconsistencies.\")\n args = parser.parse_args()\n\n check_dummies(args.fix_and_overwrite)\n" }, { "path": "diffusers/utils/check_inits.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport collections\nimport importlib.util\nimport os\nimport re\nfrom pathlib import Path\n\n\nPATH_TO_TRANSFORMERS = \"src/transformers\"\n\n\n# Matches is_xxx_available()\n_re_backend = re.compile(r\"is\\_([a-z_]*)_available()\")\n# Catches a one-line _import_struct = {xxx}\n_re_one_line_import_struct = re.compile(r\"^_import_structure\\s+=\\s+\\{([^\\}]+)\\}\")\n# Catches a line with a key-values pattern: \"bla\": [\"foo\", \"bar\"]\n_re_import_struct_key_value = re.compile(r'\\s+\"\\S*\":\\s+\\[([^\\]]*)\\]')\n# Catches a line if not is_foo_available\n_re_test_backend = re.compile(r\"^\\s*if\\s+not\\s+is\\_[a-z_]*\\_available\\(\\)\")\n# Catches a line _import_struct[\"bla\"].append(\"foo\")\n_re_import_struct_add_one = re.compile(r'^\\s*_import_structure\\[\"\\S*\"\\]\\.append\\(\"(\\S*)\"\\)')\n# Catches a line _import_struct[\"bla\"].extend([\"foo\", \"bar\"]) or _import_struct[\"bla\"] = [\"foo\", \"bar\"]\n_re_import_struct_add_many = re.compile(r\"^\\s*_import_structure\\[\\S*\\](?:\\.extend\\(|\\s*=\\s+)\\[([^\\]]*)\\]\")\n# Catches a line with an object between quotes and a comma: \"MyModel\",\n_re_quote_object = re.compile(r'^\\s+\"([^\"]+)\",')\n# Catches a line with objects between brackets only: [\"foo\", \"bar\"],\n_re_between_brackets = re.compile(r\"^\\s+\\[([^\\]]+)\\]\")\n# Catches a line with from foo import bar, bla, boo\n_re_import = re.compile(r\"\\s+from\\s+\\S*\\s+import\\s+([^\\(\\s].*)\\n\")\n# Catches a line with try:\n_re_try = re.compile(r\"^\\s*try:\")\n# Catches a line with else:\n_re_else = re.compile(r\"^\\s*else:\")\n\n\ndef find_backend(line):\n \"\"\"Find one (or multiple) backend in a code line of the init.\"\"\"\n if _re_test_backend.search(line) is None:\n return None\n backends = [b[0] for b in _re_backend.findall(line)]\n backends.sort()\n return \"_and_\".join(backends)\n\n\ndef parse_init(init_file):\n \"\"\"\n Read an init_file and parse (per backend) the _import_structure objects defined and the TYPE_CHECKING objects\n defined\n \"\"\"\n with open(init_file, \"r\", encoding=\"utf-8\", newline=\"\\n\") as f:\n lines = f.readlines()\n\n line_index = 0\n while line_index < len(lines) and not lines[line_index].startswith(\"_import_structure = {\"):\n line_index += 1\n\n # If this is a traditional init, just return.\n if line_index >= len(lines):\n return None\n\n # First grab the objects without a specific backend in _import_structure\n objects = []\n while not lines[line_index].startswith(\"if TYPE_CHECKING\") and find_backend(lines[line_index]) is None:\n line = lines[line_index]\n # If we have everything on a single line, let's deal with it.\n if _re_one_line_import_struct.search(line):\n content = _re_one_line_import_struct.search(line).groups()[0]\n imports = re.findall(r\"\\[([^\\]]+)\\]\", content)\n for imp in imports:\n objects.extend([obj[1:-1] for obj in imp.split(\", \")])\n line_index += 1\n continue\n single_line_import_search = _re_import_struct_key_value.search(line)\n if single_line_import_search is not None:\n imports = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(\", \") if len(obj) > 0]\n objects.extend(imports)\n elif line.startswith(\" \" * 8 + '\"'):\n objects.append(line[9:-3])\n line_index += 1\n\n import_dict_objects = {\"none\": objects}\n # Let's continue with backend-specific objects in _import_structure\n while not lines[line_index].startswith(\"if TYPE_CHECKING\"):\n # If the line is an if not is_backend_available, we grab all objects associated.\n backend = find_backend(lines[line_index])\n # Check if the backend declaration is inside a try block:\n if _re_try.search(lines[line_index - 1]) is None:\n backend = None\n\n if backend is not None:\n line_index += 1\n\n # Scroll until we hit the else block of try-except-else\n while _re_else.search(lines[line_index]) is None:\n line_index += 1\n\n line_index += 1\n\n objects = []\n # Until we unindent, add backend objects to the list\n while len(lines[line_index]) <= 1 or lines[line_index].startswith(\" \" * 4):\n line = lines[line_index]\n if _re_import_struct_add_one.search(line) is not None:\n objects.append(_re_import_struct_add_one.search(line).groups()[0])\n elif _re_import_struct_add_many.search(line) is not None:\n imports = _re_import_struct_add_many.search(line).groups()[0].split(\", \")\n imports = [obj[1:-1] for obj in imports if len(obj) > 0]\n objects.extend(imports)\n elif _re_between_brackets.search(line) is not None:\n imports = _re_between_brackets.search(line).groups()[0].split(\", \")\n imports = [obj[1:-1] for obj in imports if len(obj) > 0]\n objects.extend(imports)\n elif _re_quote_object.search(line) is not None:\n objects.append(_re_quote_object.search(line).groups()[0])\n elif line.startswith(\" \" * 8 + '\"'):\n objects.append(line[9:-3])\n elif line.startswith(\" \" * 12 + '\"'):\n objects.append(line[13:-3])\n line_index += 1\n\n import_dict_objects[backend] = objects\n else:\n line_index += 1\n\n # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend\n objects = []\n while (\n line_index < len(lines)\n and find_backend(lines[line_index]) is None\n and not lines[line_index].startswith(\"else\")\n ):\n line = lines[line_index]\n single_line_import_search = _re_import.search(line)\n if single_line_import_search is not None:\n objects.extend(single_line_import_search.groups()[0].split(\", \"))\n elif line.startswith(\" \" * 8):\n objects.append(line[8:-2])\n line_index += 1\n\n type_hint_objects = {\"none\": objects}\n # Let's continue with backend-specific objects\n while line_index < len(lines):\n # If the line is an if is_backend_available, we grab all objects associated.\n backend = find_backend(lines[line_index])\n # Check if the backend declaration is inside a try block:\n if _re_try.search(lines[line_index - 1]) is None:\n backend = None\n\n if backend is not None:\n line_index += 1\n\n # Scroll until we hit the else block of try-except-else\n while _re_else.search(lines[line_index]) is None:\n line_index += 1\n\n line_index += 1\n\n objects = []\n # Until we unindent, add backend objects to the list\n while len(lines[line_index]) <= 1 or lines[line_index].startswith(\" \" * 8):\n line = lines[line_index]\n single_line_import_search = _re_import.search(line)\n if single_line_import_search is not None:\n objects.extend(single_line_import_search.groups()[0].split(\", \"))\n elif line.startswith(\" \" * 12):\n objects.append(line[12:-2])\n line_index += 1\n\n type_hint_objects[backend] = objects\n else:\n line_index += 1\n\n return import_dict_objects, type_hint_objects\n\n\ndef analyze_results(import_dict_objects, type_hint_objects):\n \"\"\"\n Analyze the differences between _import_structure objects and TYPE_CHECKING objects found in an init.\n \"\"\"\n\n def find_duplicates(seq):\n return [k for k, v in collections.Counter(seq).items() if v > 1]\n\n if list(import_dict_objects.keys()) != list(type_hint_objects.keys()):\n return [\"Both sides of the init do not have the same backends!\"]\n\n errors = []\n for key in import_dict_objects.keys():\n duplicate_imports = find_duplicates(import_dict_objects[key])\n if duplicate_imports:\n errors.append(f\"Duplicate _import_structure definitions for: {duplicate_imports}\")\n duplicate_type_hints = find_duplicates(type_hint_objects[key])\n if duplicate_type_hints:\n errors.append(f\"Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}\")\n\n if sorted(set(import_dict_objects[key])) != sorted(set(type_hint_objects[key])):\n name = \"base imports\" if key == \"none\" else f\"{key} backend\"\n errors.append(f\"Differences for {name}:\")\n for a in type_hint_objects[key]:\n if a not in import_dict_objects[key]:\n errors.append(f\" {a} in TYPE_HINT but not in _import_structure.\")\n for a in import_dict_objects[key]:\n if a not in type_hint_objects[key]:\n errors.append(f\" {a} in _import_structure but not in TYPE_HINT.\")\n return errors\n\n\ndef check_all_inits():\n \"\"\"\n Check all inits in the transformers repo and raise an error if at least one does not define the same objects in\n both halves.\n \"\"\"\n failures = []\n for root, _, files in os.walk(PATH_TO_TRANSFORMERS):\n if \"__init__.py\" in files:\n fname = os.path.join(root, \"__init__.py\")\n objects = parse_init(fname)\n if objects is not None:\n errors = analyze_results(*objects)\n if len(errors) > 0:\n errors[0] = f\"Problem in {fname}, both halves do not define the same objects.\\n{errors[0]}\"\n failures.append(\"\\n\".join(errors))\n if len(failures) > 0:\n raise ValueError(\"\\n\\n\".join(failures))\n\n\ndef get_transformers_submodules():\n \"\"\"\n Returns the list of Transformers submodules.\n \"\"\"\n submodules = []\n for path, directories, files in os.walk(PATH_TO_TRANSFORMERS):\n for folder in directories:\n # Ignore private modules\n if folder.startswith(\"_\"):\n directories.remove(folder)\n continue\n # Ignore leftovers from branches (empty folders apart from pycache)\n if len(list((Path(path) / folder).glob(\"*.py\"))) == 0:\n continue\n short_path = str((Path(path) / folder).relative_to(PATH_TO_TRANSFORMERS))\n submodule = short_path.replace(os.path.sep, \".\")\n submodules.append(submodule)\n for fname in files:\n if fname == \"__init__.py\":\n continue\n short_path = str((Path(path) / fname).relative_to(PATH_TO_TRANSFORMERS))\n submodule = short_path.replace(\".py\", \"\").replace(os.path.sep, \".\")\n if len(submodule.split(\".\")) == 1:\n submodules.append(submodule)\n return submodules\n\n\nIGNORE_SUBMODULES = [\n \"convert_pytorch_checkpoint_to_tf2\",\n \"modeling_flax_pytorch_utils\",\n]\n\n\ndef check_submodules():\n # This is to make sure the transformers module imported is the one in the repo.\n spec = importlib.util.spec_from_file_location(\n \"transformers\",\n os.path.join(PATH_TO_TRANSFORMERS, \"__init__.py\"),\n submodule_search_locations=[PATH_TO_TRANSFORMERS],\n )\n transformers = spec.loader.load_module()\n\n module_not_registered = [\n module\n for module in get_transformers_submodules()\n if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()\n ]\n if len(module_not_registered) > 0:\n list_of_modules = \"\\n\".join(f\"- {module}\" for module in module_not_registered)\n raise ValueError(\n \"The following submodules are not properly registered in the main init of Transformers:\\n\"\n f\"{list_of_modules}\\n\"\n \"Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.\"\n )\n\n\nif __name__ == \"__main__\":\n check_all_inits()\n check_submodules()\n" }, { "path": "diffusers/utils/check_repo.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport importlib\nimport inspect\nimport os\nimport re\nimport warnings\nfrom collections import OrderedDict\nfrom difflib import get_close_matches\nfrom pathlib import Path\n\nfrom diffusers.models.auto import get_values\nfrom diffusers.utils import ENV_VARS_TRUE_VALUES, is_flax_available, is_torch_available\n\n\n# All paths are set with the intent you should run this script from the root of the repo with the command\n# python utils/check_repo.py\nPATH_TO_DIFFUSERS = \"src/diffusers\"\nPATH_TO_TESTS = \"tests\"\nPATH_TO_DOC = \"docs/source/en\"\n\n# Update this list with models that are supposed to be private.\nPRIVATE_MODELS = [\n \"DPRSpanPredictor\",\n \"RealmBertModel\",\n \"T5Stack\",\n \"TFDPRSpanPredictor\",\n]\n\n# Update this list for models that are not tested with a comment explaining the reason it should not be.\n# Being in this list is an exception and should **not** be the rule.\nIGNORE_NON_TESTED = PRIVATE_MODELS.copy() + [\n # models to ignore for not tested\n \"OPTDecoder\", # Building part of bigger (tested) model.\n \"DecisionTransformerGPT2Model\", # Building part of bigger (tested) model.\n \"SegformerDecodeHead\", # Building part of bigger (tested) model.\n \"PLBartEncoder\", # Building part of bigger (tested) model.\n \"PLBartDecoder\", # Building part of bigger (tested) model.\n \"PLBartDecoderWrapper\", # Building part of bigger (tested) model.\n \"BigBirdPegasusEncoder\", # Building part of bigger (tested) model.\n \"BigBirdPegasusDecoder\", # Building part of bigger (tested) model.\n \"BigBirdPegasusDecoderWrapper\", # Building part of bigger (tested) model.\n \"DetrEncoder\", # Building part of bigger (tested) model.\n \"DetrDecoder\", # Building part of bigger (tested) model.\n \"DetrDecoderWrapper\", # Building part of bigger (tested) model.\n \"M2M100Encoder\", # Building part of bigger (tested) model.\n \"M2M100Decoder\", # Building part of bigger (tested) model.\n \"Speech2TextEncoder\", # Building part of bigger (tested) model.\n \"Speech2TextDecoder\", # Building part of bigger (tested) model.\n \"LEDEncoder\", # Building part of bigger (tested) model.\n \"LEDDecoder\", # Building part of bigger (tested) model.\n \"BartDecoderWrapper\", # Building part of bigger (tested) model.\n \"BartEncoder\", # Building part of bigger (tested) model.\n \"BertLMHeadModel\", # Needs to be setup as decoder.\n \"BlenderbotSmallEncoder\", # Building part of bigger (tested) model.\n \"BlenderbotSmallDecoderWrapper\", # Building part of bigger (tested) model.\n \"BlenderbotEncoder\", # Building part of bigger (tested) model.\n \"BlenderbotDecoderWrapper\", # Building part of bigger (tested) model.\n \"MBartEncoder\", # Building part of bigger (tested) model.\n \"MBartDecoderWrapper\", # Building part of bigger (tested) model.\n \"MegatronBertLMHeadModel\", # Building part of bigger (tested) model.\n \"MegatronBertEncoder\", # Building part of bigger (tested) model.\n \"MegatronBertDecoder\", # Building part of bigger (tested) model.\n \"MegatronBertDecoderWrapper\", # Building part of bigger (tested) model.\n \"PegasusEncoder\", # Building part of bigger (tested) model.\n \"PegasusDecoderWrapper\", # Building part of bigger (tested) model.\n \"DPREncoder\", # Building part of bigger (tested) model.\n \"ProphetNetDecoderWrapper\", # Building part of bigger (tested) model.\n \"RealmBertModel\", # Building part of bigger (tested) model.\n \"RealmReader\", # Not regular model.\n \"RealmScorer\", # Not regular model.\n \"RealmForOpenQA\", # Not regular model.\n \"ReformerForMaskedLM\", # Needs to be setup as decoder.\n \"Speech2Text2DecoderWrapper\", # Building part of bigger (tested) model.\n \"TFDPREncoder\", # Building part of bigger (tested) model.\n \"TFElectraMainLayer\", # Building part of bigger (tested) model (should it be a TFModelMixin ?)\n \"TFRobertaForMultipleChoice\", # TODO: fix\n \"TrOCRDecoderWrapper\", # Building part of bigger (tested) model.\n \"SeparableConv1D\", # Building part of bigger (tested) model.\n \"FlaxBartForCausalLM\", # Building part of bigger (tested) model.\n \"FlaxBertForCausalLM\", # Building part of bigger (tested) model. Tested implicitly through FlaxRobertaForCausalLM.\n \"OPTDecoderWrapper\",\n]\n\n# Update this list with test files that don't have a tester with a `all_model_classes` variable and which don't\n# trigger the common tests.\nTEST_FILES_WITH_NO_COMMON_TESTS = [\n \"models/decision_transformer/test_modeling_decision_transformer.py\",\n \"models/camembert/test_modeling_camembert.py\",\n \"models/mt5/test_modeling_flax_mt5.py\",\n \"models/mbart/test_modeling_mbart.py\",\n \"models/mt5/test_modeling_mt5.py\",\n \"models/pegasus/test_modeling_pegasus.py\",\n \"models/camembert/test_modeling_tf_camembert.py\",\n \"models/mt5/test_modeling_tf_mt5.py\",\n \"models/xlm_roberta/test_modeling_tf_xlm_roberta.py\",\n \"models/xlm_roberta/test_modeling_flax_xlm_roberta.py\",\n \"models/xlm_prophetnet/test_modeling_xlm_prophetnet.py\",\n \"models/xlm_roberta/test_modeling_xlm_roberta.py\",\n \"models/vision_text_dual_encoder/test_modeling_vision_text_dual_encoder.py\",\n \"models/vision_text_dual_encoder/test_modeling_flax_vision_text_dual_encoder.py\",\n \"models/decision_transformer/test_modeling_decision_transformer.py\",\n]\n\n# Update this list for models that are not in any of the auto MODEL_XXX_MAPPING. Being in this list is an exception and\n# should **not** be the rule.\nIGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [\n # models to ignore for model xxx mapping\n \"DPTForDepthEstimation\",\n \"DecisionTransformerGPT2Model\",\n \"GLPNForDepthEstimation\",\n \"ViltForQuestionAnswering\",\n \"ViltForImagesAndTextClassification\",\n \"ViltForImageAndTextRetrieval\",\n \"ViltForMaskedLM\",\n \"XGLMEncoder\",\n \"XGLMDecoder\",\n \"XGLMDecoderWrapper\",\n \"PerceiverForMultimodalAutoencoding\",\n \"PerceiverForOpticalFlow\",\n \"SegformerDecodeHead\",\n \"FlaxBeitForMaskedImageModeling\",\n \"PLBartEncoder\",\n \"PLBartDecoder\",\n \"PLBartDecoderWrapper\",\n \"BeitForMaskedImageModeling\",\n \"CLIPTextModel\",\n \"CLIPVisionModel\",\n \"TFCLIPTextModel\",\n \"TFCLIPVisionModel\",\n \"FlaxCLIPTextModel\",\n \"FlaxCLIPVisionModel\",\n \"FlaxWav2Vec2ForCTC\",\n \"DetrForSegmentation\",\n \"DPRReader\",\n \"FlaubertForQuestionAnswering\",\n \"FlavaImageCodebook\",\n \"FlavaTextModel\",\n \"FlavaImageModel\",\n \"FlavaMultimodalModel\",\n \"GPT2DoubleHeadsModel\",\n \"LukeForMaskedLM\",\n \"LukeForEntityClassification\",\n \"LukeForEntityPairClassification\",\n \"LukeForEntitySpanClassification\",\n \"OpenAIGPTDoubleHeadsModel\",\n \"RagModel\",\n \"RagSequenceForGeneration\",\n \"RagTokenForGeneration\",\n \"RealmEmbedder\",\n \"RealmForOpenQA\",\n \"RealmScorer\",\n \"RealmReader\",\n \"TFDPRReader\",\n \"TFGPT2DoubleHeadsModel\",\n \"TFOpenAIGPTDoubleHeadsModel\",\n \"TFRagModel\",\n \"TFRagSequenceForGeneration\",\n \"TFRagTokenForGeneration\",\n \"Wav2Vec2ForCTC\",\n \"HubertForCTC\",\n \"SEWForCTC\",\n \"SEWDForCTC\",\n \"XLMForQuestionAnswering\",\n \"XLNetForQuestionAnswering\",\n \"SeparableConv1D\",\n \"VisualBertForRegionToPhraseAlignment\",\n \"VisualBertForVisualReasoning\",\n \"VisualBertForQuestionAnswering\",\n \"VisualBertForMultipleChoice\",\n \"TFWav2Vec2ForCTC\",\n \"TFHubertForCTC\",\n \"MaskFormerForInstanceSegmentation\",\n]\n\n# Update this list for models that have multiple model types for the same\n# model doc\nMODEL_TYPE_TO_DOC_MAPPING = OrderedDict(\n [\n (\"data2vec-text\", \"data2vec\"),\n (\"data2vec-audio\", \"data2vec\"),\n (\"data2vec-vision\", \"data2vec\"),\n ]\n)\n\n\n# This is to make sure the transformers module imported is the one in the repo.\nspec = importlib.util.spec_from_file_location(\n \"diffusers\",\n os.path.join(PATH_TO_DIFFUSERS, \"__init__.py\"),\n submodule_search_locations=[PATH_TO_DIFFUSERS],\n)\ndiffusers = spec.loader.load_module()\n\n\ndef check_model_list():\n \"\"\"Check the model list inside the transformers library.\"\"\"\n # Get the models from the directory structure of `src/diffusers/models/`\n models_dir = os.path.join(PATH_TO_DIFFUSERS, \"models\")\n _models = []\n for model in os.listdir(models_dir):\n model_dir = os.path.join(models_dir, model)\n if os.path.isdir(model_dir) and \"__init__.py\" in os.listdir(model_dir):\n _models.append(model)\n\n # Get the models from the directory structure of `src/transformers/models/`\n models = [model for model in dir(diffusers.models) if not model.startswith(\"__\")]\n\n missing_models = sorted(set(_models).difference(models))\n if missing_models:\n raise Exception(\n f\"The following models should be included in {models_dir}/__init__.py: {','.join(missing_models)}.\"\n )\n\n\n# If some modeling modules should be ignored for all checks, they should be added in the nested list\n# _ignore_modules of this function.\ndef get_model_modules():\n \"\"\"Get the model modules inside the transformers library.\"\"\"\n _ignore_modules = [\n \"modeling_auto\",\n \"modeling_encoder_decoder\",\n \"modeling_marian\",\n \"modeling_mmbt\",\n \"modeling_outputs\",\n \"modeling_retribert\",\n \"modeling_utils\",\n \"modeling_flax_auto\",\n \"modeling_flax_encoder_decoder\",\n \"modeling_flax_utils\",\n \"modeling_speech_encoder_decoder\",\n \"modeling_flax_speech_encoder_decoder\",\n \"modeling_flax_vision_encoder_decoder\",\n \"modeling_transfo_xl_utilities\",\n \"modeling_tf_auto\",\n \"modeling_tf_encoder_decoder\",\n \"modeling_tf_outputs\",\n \"modeling_tf_pytorch_utils\",\n \"modeling_tf_utils\",\n \"modeling_tf_transfo_xl_utilities\",\n \"modeling_tf_vision_encoder_decoder\",\n \"modeling_vision_encoder_decoder\",\n ]\n modules = []\n for model in dir(diffusers.models):\n # There are some magic dunder attributes in the dir, we ignore them\n if not model.startswith(\"__\"):\n model_module = getattr(diffusers.models, model)\n for submodule in dir(model_module):\n if submodule.startswith(\"modeling\") and submodule not in _ignore_modules:\n modeling_module = getattr(model_module, submodule)\n if inspect.ismodule(modeling_module):\n modules.append(modeling_module)\n return modules\n\n\ndef get_models(module, include_pretrained=False):\n \"\"\"Get the objects in module that are models.\"\"\"\n models = []\n model_classes = (diffusers.ModelMixin, diffusers.TFModelMixin, diffusers.FlaxModelMixin)\n for attr_name in dir(module):\n if not include_pretrained and (\"Pretrained\" in attr_name or \"PreTrained\" in attr_name):\n continue\n attr = getattr(module, attr_name)\n if isinstance(attr, type) and issubclass(attr, model_classes) and attr.__module__ == module.__name__:\n models.append((attr_name, attr))\n return models\n\n\ndef is_a_private_model(model):\n \"\"\"Returns True if the model should not be in the main init.\"\"\"\n if model in PRIVATE_MODELS:\n return True\n\n # Wrapper, Encoder and Decoder are all privates\n if model.endswith(\"Wrapper\"):\n return True\n if model.endswith(\"Encoder\"):\n return True\n if model.endswith(\"Decoder\"):\n return True\n return False\n\n\ndef check_models_are_in_init():\n \"\"\"Checks all models defined in the library are in the main init.\"\"\"\n models_not_in_init = []\n dir_transformers = dir(diffusers)\n for module in get_model_modules():\n models_not_in_init += [\n model[0] for model in get_models(module, include_pretrained=True) if model[0] not in dir_transformers\n ]\n\n # Remove private models\n models_not_in_init = [model for model in models_not_in_init if not is_a_private_model(model)]\n if len(models_not_in_init) > 0:\n raise Exception(f\"The following models should be in the main init: {','.join(models_not_in_init)}.\")\n\n\n# If some test_modeling files should be ignored when checking models are all tested, they should be added in the\n# nested list _ignore_files of this function.\ndef get_model_test_files():\n \"\"\"Get the model test files.\n\n The returned files should NOT contain the `tests` (i.e. `PATH_TO_TESTS` defined in this script). They will be\n considered as paths relative to `tests`. A caller has to use `os.path.join(PATH_TO_TESTS, ...)` to access the files.\n \"\"\"\n\n _ignore_files = [\n \"test_modeling_common\",\n \"test_modeling_encoder_decoder\",\n \"test_modeling_flax_encoder_decoder\",\n \"test_modeling_flax_speech_encoder_decoder\",\n \"test_modeling_marian\",\n \"test_modeling_tf_common\",\n \"test_modeling_tf_encoder_decoder\",\n ]\n test_files = []\n # Check both `PATH_TO_TESTS` and `PATH_TO_TESTS/models`\n model_test_root = os.path.join(PATH_TO_TESTS, \"models\")\n model_test_dirs = []\n for x in os.listdir(model_test_root):\n x = os.path.join(model_test_root, x)\n if os.path.isdir(x):\n model_test_dirs.append(x)\n\n for target_dir in [PATH_TO_TESTS] + model_test_dirs:\n for file_or_dir in os.listdir(target_dir):\n path = os.path.join(target_dir, file_or_dir)\n if os.path.isfile(path):\n filename = os.path.split(path)[-1]\n if \"test_modeling\" in filename and os.path.splitext(filename)[0] not in _ignore_files:\n file = os.path.join(*path.split(os.sep)[1:])\n test_files.append(file)\n\n return test_files\n\n\n# This is a bit hacky but I didn't find a way to import the test_file as a module and read inside the tester class\n# for the all_model_classes variable.\ndef find_tested_models(test_file):\n \"\"\"Parse the content of test_file to detect what's in all_model_classes\"\"\"\n # This is a bit hacky but I didn't find a way to import the test_file as a module and read inside the class\n with open(os.path.join(PATH_TO_TESTS, test_file), \"r\", encoding=\"utf-8\", newline=\"\\n\") as f:\n content = f.read()\n all_models = re.findall(r\"all_model_classes\\s+=\\s+\\(\\s*\\(([^\\)]*)\\)\", content)\n # Check with one less parenthesis as well\n all_models += re.findall(r\"all_model_classes\\s+=\\s+\\(([^\\)]*)\\)\", content)\n if len(all_models) > 0:\n model_tested = []\n for entry in all_models:\n for line in entry.split(\",\"):\n name = line.strip()\n if len(name) > 0:\n model_tested.append(name)\n return model_tested\n\n\ndef check_models_are_tested(module, test_file):\n \"\"\"Check models defined in module are tested in test_file.\"\"\"\n # XxxModelMixin are not tested\n defined_models = get_models(module)\n tested_models = find_tested_models(test_file)\n if tested_models is None:\n if test_file.replace(os.path.sep, \"/\") in TEST_FILES_WITH_NO_COMMON_TESTS:\n return\n return [\n f\"{test_file} should define `all_model_classes` to apply common tests to the models it tests. \"\n + \"If this intentional, add the test filename to `TEST_FILES_WITH_NO_COMMON_TESTS` in the file \"\n + \"`utils/check_repo.py`.\"\n ]\n failures = []\n for model_name, _ in defined_models:\n if model_name not in tested_models and model_name not in IGNORE_NON_TESTED:\n failures.append(\n f\"{model_name} is defined in {module.__name__} but is not tested in \"\n + f\"{os.path.join(PATH_TO_TESTS, test_file)}. Add it to the all_model_classes in that file.\"\n + \"If common tests should not applied to that model, add its name to `IGNORE_NON_TESTED`\"\n + \"in the file `utils/check_repo.py`.\"\n )\n return failures\n\n\ndef check_all_models_are_tested():\n \"\"\"Check all models are properly tested.\"\"\"\n modules = get_model_modules()\n test_files = get_model_test_files()\n failures = []\n for module in modules:\n test_file = [file for file in test_files if f\"test_{module.__name__.split('.')[-1]}.py\" in file]\n if len(test_file) == 0:\n failures.append(f\"{module.__name__} does not have its corresponding test file {test_file}.\")\n elif len(test_file) > 1:\n failures.append(f\"{module.__name__} has several test files: {test_file}.\")\n else:\n test_file = test_file[0]\n new_failures = check_models_are_tested(module, test_file)\n if new_failures is not None:\n failures += new_failures\n if len(failures) > 0:\n raise Exception(f\"There were {len(failures)} failures:\\n\" + \"\\n\".join(failures))\n\n\ndef get_all_auto_configured_models():\n \"\"\"Return the list of all models in at least one auto class.\"\"\"\n result = set() # To avoid duplicates we concatenate all model classes in a set.\n if is_torch_available():\n for attr_name in dir(diffusers.models.auto.modeling_auto):\n if attr_name.startswith(\"MODEL_\") and attr_name.endswith(\"MAPPING_NAMES\"):\n result = result | set(get_values(getattr(diffusers.models.auto.modeling_auto, attr_name)))\n if is_flax_available():\n for attr_name in dir(diffusers.models.auto.modeling_flax_auto):\n if attr_name.startswith(\"FLAX_MODEL_\") and attr_name.endswith(\"MAPPING_NAMES\"):\n result = result | set(get_values(getattr(diffusers.models.auto.modeling_flax_auto, attr_name)))\n return list(result)\n\n\ndef ignore_unautoclassed(model_name):\n \"\"\"Rules to determine if `name` should be in an auto class.\"\"\"\n # Special white list\n if model_name in IGNORE_NON_AUTO_CONFIGURED:\n return True\n # Encoder and Decoder should be ignored\n if \"Encoder\" in model_name or \"Decoder\" in model_name:\n return True\n return False\n\n\ndef check_models_are_auto_configured(module, all_auto_models):\n \"\"\"Check models defined in module are each in an auto class.\"\"\"\n defined_models = get_models(module)\n failures = []\n for model_name, _ in defined_models:\n if model_name not in all_auto_models and not ignore_unautoclassed(model_name):\n failures.append(\n f\"{model_name} is defined in {module.__name__} but is not present in any of the auto mapping. \"\n \"If that is intended behavior, add its name to `IGNORE_NON_AUTO_CONFIGURED` in the file \"\n \"`utils/check_repo.py`.\"\n )\n return failures\n\n\ndef check_all_models_are_auto_configured():\n \"\"\"Check all models are each in an auto class.\"\"\"\n missing_backends = []\n if not is_torch_available():\n missing_backends.append(\"PyTorch\")\n if not is_flax_available():\n missing_backends.append(\"Flax\")\n if len(missing_backends) > 0:\n missing = \", \".join(missing_backends)\n if os.getenv(\"TRANSFORMERS_IS_CI\", \"\").upper() in ENV_VARS_TRUE_VALUES:\n raise Exception(\n \"Full quality checks require all backends to be installed (with `pip install -e .[dev]` in the \"\n f\"Transformers repo, the following are missing: {missing}.\"\n )\n else:\n warnings.warn(\n \"Full quality checks require all backends to be installed (with `pip install -e .[dev]` in the \"\n f\"Transformers repo, the following are missing: {missing}. While it's probably fine as long as you \"\n \"didn't make any change in one of those backends modeling files, you should probably execute the \"\n \"command above to be on the safe side.\"\n )\n modules = get_model_modules()\n all_auto_models = get_all_auto_configured_models()\n failures = []\n for module in modules:\n new_failures = check_models_are_auto_configured(module, all_auto_models)\n if new_failures is not None:\n failures += new_failures\n if len(failures) > 0:\n raise Exception(f\"There were {len(failures)} failures:\\n\" + \"\\n\".join(failures))\n\n\n_re_decorator = re.compile(r\"^\\s*@(\\S+)\\s+$\")\n\n\ndef check_decorator_order(filename):\n \"\"\"Check that in the test file `filename` the slow decorator is always last.\"\"\"\n with open(filename, \"r\", encoding=\"utf-8\", newline=\"\\n\") as f:\n lines = f.readlines()\n decorator_before = None\n errors = []\n for i, line in enumerate(lines):\n search = _re_decorator.search(line)\n if search is not None:\n decorator_name = search.groups()[0]\n if decorator_before is not None and decorator_name.startswith(\"parameterized\"):\n errors.append(i)\n decorator_before = decorator_name\n elif decorator_before is not None:\n decorator_before = None\n return errors\n\n\ndef check_all_decorator_order():\n \"\"\"Check that in all test files, the slow decorator is always last.\"\"\"\n errors = []\n for fname in os.listdir(PATH_TO_TESTS):\n if fname.endswith(\".py\"):\n filename = os.path.join(PATH_TO_TESTS, fname)\n new_errors = check_decorator_order(filename)\n errors += [f\"- {filename}, line {i}\" for i in new_errors]\n if len(errors) > 0:\n msg = \"\\n\".join(errors)\n raise ValueError(\n \"The parameterized decorator (and its variants) should always be first, but this is not the case in the\"\n f\" following files:\\n{msg}\"\n )\n\n\ndef find_all_documented_objects():\n \"\"\"Parse the content of all doc files to detect which classes and functions it documents\"\"\"\n documented_obj = []\n for doc_file in Path(PATH_TO_DOC).glob(\"**/*.rst\"):\n with open(doc_file, \"r\", encoding=\"utf-8\", newline=\"\\n\") as f:\n content = f.read()\n raw_doc_objs = re.findall(r\"(?:autoclass|autofunction):: transformers.(\\S+)\\s+\", content)\n documented_obj += [obj.split(\".\")[-1] for obj in raw_doc_objs]\n for doc_file in Path(PATH_TO_DOC).glob(\"**/*.md\"):\n with open(doc_file, \"r\", encoding=\"utf-8\", newline=\"\\n\") as f:\n content = f.read()\n raw_doc_objs = re.findall(r\"\\[\\[autodoc\\]\\]\\s+(\\S+)\\s+\", content)\n documented_obj += [obj.split(\".\")[-1] for obj in raw_doc_objs]\n return documented_obj\n\n\n# One good reason for not being documented is to be deprecated. Put in this list deprecated objects.\nDEPRECATED_OBJECTS = [\n \"AutoModelWithLMHead\",\n \"BartPretrainedModel\",\n \"DataCollator\",\n \"DataCollatorForSOP\",\n \"GlueDataset\",\n \"GlueDataTrainingArguments\",\n \"LineByLineTextDataset\",\n \"LineByLineWithRefDataset\",\n \"LineByLineWithSOPTextDataset\",\n \"PretrainedBartModel\",\n \"PretrainedFSMTModel\",\n \"SingleSentenceClassificationProcessor\",\n \"SquadDataTrainingArguments\",\n \"SquadDataset\",\n \"SquadExample\",\n \"SquadFeatures\",\n \"SquadV1Processor\",\n \"SquadV2Processor\",\n \"TFAutoModelWithLMHead\",\n \"TFBartPretrainedModel\",\n \"TextDataset\",\n \"TextDatasetForNextSentencePrediction\",\n \"Wav2Vec2ForMaskedLM\",\n \"Wav2Vec2Tokenizer\",\n \"glue_compute_metrics\",\n \"glue_convert_examples_to_features\",\n \"glue_output_modes\",\n \"glue_processors\",\n \"glue_tasks_num_labels\",\n \"squad_convert_examples_to_features\",\n \"xnli_compute_metrics\",\n \"xnli_output_modes\",\n \"xnli_processors\",\n \"xnli_tasks_num_labels\",\n \"TFTrainer\",\n \"TFTrainingArguments\",\n]\n\n# Exceptionally, some objects should not be documented after all rules passed.\n# ONLY PUT SOMETHING IN THIS LIST AS A LAST RESORT!\nUNDOCUMENTED_OBJECTS = [\n \"AddedToken\", # This is a tokenizers class.\n \"BasicTokenizer\", # Internal, should never have been in the main init.\n \"CharacterTokenizer\", # Internal, should never have been in the main init.\n \"DPRPretrainedReader\", # Like an Encoder.\n \"DummyObject\", # Just picked by mistake sometimes.\n \"MecabTokenizer\", # Internal, should never have been in the main init.\n \"ModelCard\", # Internal type.\n \"SqueezeBertModule\", # Internal building block (should have been called SqueezeBertLayer)\n \"TFDPRPretrainedReader\", # Like an Encoder.\n \"TransfoXLCorpus\", # Internal type.\n \"WordpieceTokenizer\", # Internal, should never have been in the main init.\n \"absl\", # External module\n \"add_end_docstrings\", # Internal, should never have been in the main init.\n \"add_start_docstrings\", # Internal, should never have been in the main init.\n \"cached_path\", # Internal used for downloading models.\n \"convert_tf_weight_name_to_pt_weight_name\", # Internal used to convert model weights\n \"logger\", # Internal logger\n \"logging\", # External module\n \"requires_backends\", # Internal function\n]\n\n# This list should be empty. Objects in it should get their own doc page.\nSHOULD_HAVE_THEIR_OWN_PAGE = [\n # Benchmarks\n \"PyTorchBenchmark\",\n \"PyTorchBenchmarkArguments\",\n \"TensorFlowBenchmark\",\n \"TensorFlowBenchmarkArguments\",\n]\n\n\ndef ignore_undocumented(name):\n \"\"\"Rules to determine if `name` should be undocumented.\"\"\"\n # NOT DOCUMENTED ON PURPOSE.\n # Constants uppercase are not documented.\n if name.isupper():\n return True\n # ModelMixins / Encoders / Decoders / Layers / Embeddings / Attention are not documented.\n if (\n name.endswith(\"ModelMixin\")\n or name.endswith(\"Decoder\")\n or name.endswith(\"Encoder\")\n or name.endswith(\"Layer\")\n or name.endswith(\"Embeddings\")\n or name.endswith(\"Attention\")\n ):\n return True\n # Submodules are not documented.\n if os.path.isdir(os.path.join(PATH_TO_DIFFUSERS, name)) or os.path.isfile(\n os.path.join(PATH_TO_DIFFUSERS, f\"{name}.py\")\n ):\n return True\n # All load functions are not documented.\n if name.startswith(\"load_tf\") or name.startswith(\"load_pytorch\"):\n return True\n # is_xxx_available functions are not documented.\n if name.startswith(\"is_\") and name.endswith(\"_available\"):\n return True\n # Deprecated objects are not documented.\n if name in DEPRECATED_OBJECTS or name in UNDOCUMENTED_OBJECTS:\n return True\n # MMBT model does not really work.\n if name.startswith(\"MMBT\"):\n return True\n if name in SHOULD_HAVE_THEIR_OWN_PAGE:\n return True\n return False\n\n\ndef check_all_objects_are_documented():\n \"\"\"Check all models are properly documented.\"\"\"\n documented_objs = find_all_documented_objects()\n modules = diffusers._modules\n objects = [c for c in dir(diffusers) if c not in modules and not c.startswith(\"_\")]\n undocumented_objs = [c for c in objects if c not in documented_objs and not ignore_undocumented(c)]\n if len(undocumented_objs) > 0:\n raise Exception(\n \"The following objects are in the public init so should be documented:\\n - \"\n + \"\\n - \".join(undocumented_objs)\n )\n check_docstrings_are_in_md()\n check_model_type_doc_match()\n\n\ndef check_model_type_doc_match():\n \"\"\"Check all doc pages have a corresponding model type.\"\"\"\n model_doc_folder = Path(PATH_TO_DOC) / \"model_doc\"\n model_docs = [m.stem for m in model_doc_folder.glob(\"*.md\")]\n\n model_types = list(diffusers.models.auto.configuration_auto.MODEL_NAMES_MAPPING.keys())\n model_types = [MODEL_TYPE_TO_DOC_MAPPING[m] if m in MODEL_TYPE_TO_DOC_MAPPING else m for m in model_types]\n\n errors = []\n for m in model_docs:\n if m not in model_types and m != \"auto\":\n close_matches = get_close_matches(m, model_types)\n error_message = f\"{m} is not a proper model identifier.\"\n if len(close_matches) > 0:\n close_matches = \"/\".join(close_matches)\n error_message += f\" Did you mean {close_matches}?\"\n errors.append(error_message)\n\n if len(errors) > 0:\n raise ValueError(\n \"Some model doc pages do not match any existing model type:\\n\"\n + \"\\n\".join(errors)\n + \"\\nYou can add any missing model type to the `MODEL_NAMES_MAPPING` constant in \"\n \"models/auto/configuration_auto.py.\"\n )\n\n\n# Re pattern to catch :obj:`xx`, :class:`xx`, :func:`xx` or :meth:`xx`.\n_re_rst_special_words = re.compile(r\":(?:obj|func|class|meth):`([^`]+)`\")\n# Re pattern to catch things between double backquotes.\n_re_double_backquotes = re.compile(r\"(^|[^`])``([^`]+)``([^`]|$)\")\n# Re pattern to catch example introduction.\n_re_rst_example = re.compile(r\"^\\s*Example.*::\\s*$\", flags=re.MULTILINE)\n\n\ndef is_rst_docstring(docstring):\n \"\"\"\n Returns `True` if `docstring` is written in rst.\n \"\"\"\n if _re_rst_special_words.search(docstring) is not None:\n return True\n if _re_double_backquotes.search(docstring) is not None:\n return True\n if _re_rst_example.search(docstring) is not None:\n return True\n return False\n\n\ndef check_docstrings_are_in_md():\n \"\"\"Check all docstrings are in md\"\"\"\n files_with_rst = []\n for file in Path(PATH_TO_DIFFUSERS).glob(\"**/*.py\"):\n with open(file, \"r\") as f:\n code = f.read()\n docstrings = code.split('\"\"\"')\n\n for idx, docstring in enumerate(docstrings):\n if idx % 2 == 0 or not is_rst_docstring(docstring):\n continue\n files_with_rst.append(file)\n break\n\n if len(files_with_rst) > 0:\n raise ValueError(\n \"The following files have docstrings written in rst:\\n\"\n + \"\\n\".join([f\"- {f}\" for f in files_with_rst])\n + \"\\nTo fix this run `doc-builder convert path_to_py_file` after installing `doc-builder`\\n\"\n \"(`pip install git+https://github.com/huggingface/doc-builder`)\"\n )\n\n\ndef check_repo_quality():\n \"\"\"Check all models are properly tested and documented.\"\"\"\n print(\"Checking all models are included.\")\n check_model_list()\n print(\"Checking all models are public.\")\n check_models_are_in_init()\n print(\"Checking all models are properly tested.\")\n check_all_decorator_order()\n check_all_models_are_tested()\n print(\"Checking all objects are properly documented.\")\n check_all_objects_are_documented()\n print(\"Checking all models are in at least one auto class.\")\n check_all_models_are_auto_configured()\n\n\nif __name__ == \"__main__\":\n check_repo_quality()\n" }, { "path": "diffusers/utils/check_table.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport argparse\nimport collections\nimport importlib.util\nimport os\nimport re\n\n\n# All paths are set with the intent you should run this script from the root of the repo with the command\n# python utils/check_table.py\nTRANSFORMERS_PATH = \"src/diffusers\"\nPATH_TO_DOCS = \"docs/source/en\"\nREPO_PATH = \".\"\n\n\ndef _find_text_in_file(filename, start_prompt, end_prompt):\n \"\"\"\n Find the text in `filename` between a line beginning with `start_prompt` and before `end_prompt`, removing empty\n lines.\n \"\"\"\n with open(filename, \"r\", encoding=\"utf-8\", newline=\"\\n\") as f:\n lines = f.readlines()\n # Find the start prompt.\n start_index = 0\n while not lines[start_index].startswith(start_prompt):\n start_index += 1\n start_index += 1\n\n end_index = start_index\n while not lines[end_index].startswith(end_prompt):\n end_index += 1\n end_index -= 1\n\n while len(lines[start_index]) <= 1:\n start_index += 1\n while len(lines[end_index]) <= 1:\n end_index -= 1\n end_index += 1\n return \"\".join(lines[start_index:end_index]), start_index, end_index, lines\n\n\n# Add here suffixes that are used to identify models, separated by |\nALLOWED_MODEL_SUFFIXES = \"Model|Encoder|Decoder|ForConditionalGeneration\"\n# Regexes that match TF/Flax/PT model names.\n_re_tf_models = re.compile(r\"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)\")\n_re_flax_models = re.compile(r\"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)\")\n# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.\n_re_pt_models = re.compile(r\"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)\")\n\n\n# This is to make sure the diffusers module imported is the one in the repo.\nspec = importlib.util.spec_from_file_location(\n \"diffusers\",\n os.path.join(TRANSFORMERS_PATH, \"__init__.py\"),\n submodule_search_locations=[TRANSFORMERS_PATH],\n)\ndiffusers_module = spec.loader.load_module()\n\n\n# Thanks to https://stackoverflow.com/questions/29916065/how-to-do-camelcase-split-in-python\ndef camel_case_split(identifier):\n \"\"\"Split a camelcased `identifier` into words.\"\"\"\n matches = re.finditer(\".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)\", identifier)\n return [m.group(0) for m in matches]\n\n\ndef _center_text(text, width):\n text_length = 2 if text == \"✅\" or text == \"❌\" else len(text)\n left_indent = (width - text_length) // 2\n right_indent = width - text_length - left_indent\n return \" \" * left_indent + text + \" \" * right_indent\n\n\ndef get_model_table_from_auto_modules():\n \"\"\"Generates an up-to-date model table from the content of the auto modules.\"\"\"\n # Dictionary model names to config.\n config_mapping_names = diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES\n model_name_to_config = {\n name: config_mapping_names[code]\n for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()\n if code in config_mapping_names\n }\n model_name_to_prefix = {name: config.replace(\"ConfigMixin\", \"\") for name, config in model_name_to_config.items()}\n\n # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.\n slow_tokenizers = collections.defaultdict(bool)\n fast_tokenizers = collections.defaultdict(bool)\n pt_models = collections.defaultdict(bool)\n tf_models = collections.defaultdict(bool)\n flax_models = collections.defaultdict(bool)\n\n # Let's lookup through all diffusers object (once).\n for attr_name in dir(diffusers_module):\n lookup_dict = None\n if attr_name.endswith(\"Tokenizer\"):\n lookup_dict = slow_tokenizers\n attr_name = attr_name[:-9]\n elif attr_name.endswith(\"TokenizerFast\"):\n lookup_dict = fast_tokenizers\n attr_name = attr_name[:-13]\n elif _re_tf_models.match(attr_name) is not None:\n lookup_dict = tf_models\n attr_name = _re_tf_models.match(attr_name).groups()[0]\n elif _re_flax_models.match(attr_name) is not None:\n lookup_dict = flax_models\n attr_name = _re_flax_models.match(attr_name).groups()[0]\n elif _re_pt_models.match(attr_name) is not None:\n lookup_dict = pt_models\n attr_name = _re_pt_models.match(attr_name).groups()[0]\n\n if lookup_dict is not None:\n while len(attr_name) > 0:\n if attr_name in model_name_to_prefix.values():\n lookup_dict[attr_name] = True\n break\n # Try again after removing the last word in the name\n attr_name = \"\".join(camel_case_split(attr_name)[:-1])\n\n # Let's build that table!\n model_names = list(model_name_to_config.keys())\n model_names.sort(key=str.lower)\n columns = [\"Model\", \"Tokenizer slow\", \"Tokenizer fast\", \"PyTorch support\", \"TensorFlow support\", \"Flax Support\"]\n # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).\n widths = [len(c) + 2 for c in columns]\n widths[0] = max([len(name) for name in model_names]) + 2\n\n # Build the table per se\n table = \"|\" + \"|\".join([_center_text(c, w) for c, w in zip(columns, widths)]) + \"|\\n\"\n # Use \":-----:\" format to center-aligned table cell texts\n table += \"|\" + \"|\".join([\":\" + \"-\" * (w - 2) + \":\" for w in widths]) + \"|\\n\"\n\n check = {True: \"✅\", False: \"❌\"}\n for name in model_names:\n prefix = model_name_to_prefix[name]\n line = [\n name,\n check[slow_tokenizers[prefix]],\n check[fast_tokenizers[prefix]],\n check[pt_models[prefix]],\n check[tf_models[prefix]],\n check[flax_models[prefix]],\n ]\n table += \"|\" + \"|\".join([_center_text(l, w) for l, w in zip(line, widths)]) + \"|\\n\"\n return table\n\n\ndef check_model_table(overwrite=False):\n \"\"\"Check the model table in the index.rst is consistent with the state of the lib and maybe `overwrite`.\"\"\"\n current_table, start_index, end_index, lines = _find_text_in_file(\n filename=os.path.join(PATH_TO_DOCS, \"index.md\"),\n start_prompt=\"\",\n )\n new_table = get_model_table_from_auto_modules()\n\n if current_table != new_table:\n if overwrite:\n with open(os.path.join(PATH_TO_DOCS, \"index.md\"), \"w\", encoding=\"utf-8\", newline=\"\\n\") as f:\n f.writelines(lines[:start_index] + [new_table] + lines[end_index:])\n else:\n raise ValueError(\n \"The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.\"\n )\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--fix_and_overwrite\", action=\"store_true\", help=\"Whether to fix inconsistencies.\")\n args = parser.parse_args()\n\n check_model_table(args.fix_and_overwrite)\n" }, { "path": "diffusers/utils/custom_init_isort.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nUtility that sorts the imports in the custom inits of Diffusers. Diffusers uses init files that delay the\nimport of an object to when it's actually needed. This is to avoid the main init importing all models, which would\nmake the line `import transformers` very slow when the user has all optional dependencies installed. The inits with\ndelayed imports have two halves: one defining a dictionary `_import_structure` which maps modules to the name of the\nobjects in each module, and one in `TYPE_CHECKING` which looks like a normal init for type-checkers. `isort` or `ruff`\nproperly sort the second half which looks like traditionl imports, the goal of this script is to sort the first half.\n\nUse from the root of the repo with:\n\n```bash\npython utils/custom_init_isort.py\n```\n\nwhich will auto-sort the imports (used in `make style`).\n\nFor a check only (as used in `make quality`) run:\n\n```bash\npython utils/custom_init_isort.py --check_only\n```\n\"\"\"\n\nimport argparse\nimport os\nimport re\nfrom typing import Any, Callable, List, Optional\n\n\n# Path is defined with the intent you should run this script from the root of the repo.\nPATH_TO_TRANSFORMERS = \"src/diffusers\"\n\n# Pattern that looks at the indentation in a line.\n_re_indent = re.compile(r\"^(\\s*)\\S\")\n# Pattern that matches `\"key\":\" and puts `key` in group 0.\n_re_direct_key = re.compile(r'^\\s*\"([^\"]+)\":')\n# Pattern that matches `_import_structure[\"key\"]` and puts `key` in group 0.\n_re_indirect_key = re.compile(r'^\\s*_import_structure\\[\"([^\"]+)\"\\]')\n# Pattern that matches `\"key\",` and puts `key` in group 0.\n_re_strip_line = re.compile(r'^\\s*\"([^\"]+)\",\\s*$')\n# Pattern that matches any `[stuff]` and puts `stuff` in group 0.\n_re_bracket_content = re.compile(r\"\\[([^\\]]+)\\]\")\n\n\ndef get_indent(line: str) -> str:\n \"\"\"Returns the indent in given line (as string).\"\"\"\n search = _re_indent.search(line)\n return \"\" if search is None else search.groups()[0]\n\n\ndef split_code_in_indented_blocks(\n code: str, indent_level: str = \"\", start_prompt: Optional[str] = None, end_prompt: Optional[str] = None\n) -> List[str]:\n \"\"\"\n Split some code into its indented blocks, starting at a given level.\n\n Args:\n code (`str`): The code to split.\n indent_level (`str`): The indent level (as string) to use for identifying the blocks to split.\n start_prompt (`str`, *optional*): If provided, only starts splitting at the line where this text is.\n end_prompt (`str`, *optional*): If provided, stops splitting at a line where this text is.\n\n Warning:\n The text before `start_prompt` or after `end_prompt` (if provided) is not ignored, just not split. The input `code`\n can thus be retrieved by joining the result.\n\n Returns:\n `List[str]`: The list of blocks.\n \"\"\"\n # Let's split the code into lines and move to start_index.\n index = 0\n lines = code.split(\"\\n\")\n if start_prompt is not None:\n while not lines[index].startswith(start_prompt):\n index += 1\n blocks = [\"\\n\".join(lines[:index])]\n else:\n blocks = []\n\n # This variable contains the block treated at a given time.\n current_block = [lines[index]]\n index += 1\n # We split into blocks until we get to the `end_prompt` (or the end of the file).\n while index < len(lines) and (end_prompt is None or not lines[index].startswith(end_prompt)):\n # We have a non-empty line with the proper indent -> start of a new block\n if len(lines[index]) > 0 and get_indent(lines[index]) == indent_level:\n # Store the current block in the result and rest. There are two cases: the line is part of the block (like\n # a closing parenthesis) or not.\n if len(current_block) > 0 and get_indent(current_block[-1]).startswith(indent_level + \" \"):\n # Line is part of the current block\n current_block.append(lines[index])\n blocks.append(\"\\n\".join(current_block))\n if index < len(lines) - 1:\n current_block = [lines[index + 1]]\n index += 1\n else:\n current_block = []\n else:\n # Line is not part of the current block\n blocks.append(\"\\n\".join(current_block))\n current_block = [lines[index]]\n else:\n # Just add the line to the current block\n current_block.append(lines[index])\n index += 1\n\n # Adds current block if it's nonempty.\n if len(current_block) > 0:\n blocks.append(\"\\n\".join(current_block))\n\n # Add final block after end_prompt if provided.\n if end_prompt is not None and index < len(lines):\n blocks.append(\"\\n\".join(lines[index:]))\n\n return blocks\n\n\ndef ignore_underscore_and_lowercase(key: Callable[[Any], str]) -> Callable[[Any], str]:\n \"\"\"\n Wraps a key function (as used in a sort) to lowercase and ignore underscores.\n \"\"\"\n\n def _inner(x):\n return key(x).lower().replace(\"_\", \"\")\n\n return _inner\n\n\ndef sort_objects(objects: List[Any], key: Optional[Callable[[Any], str]] = None) -> List[Any]:\n \"\"\"\n Sort a list of objects following the rules of isort (all uppercased first, camel-cased second and lower-cased\n last).\n\n Args:\n objects (`List[Any]`):\n The list of objects to sort.\n key (`Callable[[Any], str]`, *optional*):\n A function taking an object as input and returning a string, used to sort them by alphabetical order.\n If not provided, will default to noop (so a `key` must be provided if the `objects` are not of type string).\n\n Returns:\n `List[Any]`: The sorted list with the same elements as in the inputs\n \"\"\"\n\n # If no key is provided, we use a noop.\n def noop(x):\n return x\n\n if key is None:\n key = noop\n # Constants are all uppercase, they go first.\n constants = [obj for obj in objects if key(obj).isupper()]\n # Classes are not all uppercase but start with a capital, they go second.\n classes = [obj for obj in objects if key(obj)[0].isupper() and not key(obj).isupper()]\n # Functions begin with a lowercase, they go last.\n functions = [obj for obj in objects if not key(obj)[0].isupper()]\n\n # Then we sort each group.\n key1 = ignore_underscore_and_lowercase(key)\n return sorted(constants, key=key1) + sorted(classes, key=key1) + sorted(functions, key=key1)\n\n\ndef sort_objects_in_import(import_statement: str) -> str:\n \"\"\"\n Sorts the imports in a single import statement.\n\n Args:\n import_statement (`str`): The import statement in which to sort the imports.\n\n Returns:\n `str`: The same as the input, but with objects properly sorted.\n \"\"\"\n\n # This inner function sort imports between [ ].\n def _replace(match):\n imports = match.groups()[0]\n # If there is one import only, nothing to do.\n if \",\" not in imports:\n return f\"[{imports}]\"\n keys = [part.strip().replace('\"', \"\") for part in imports.split(\",\")]\n # We will have a final empty element if the line finished with a comma.\n if len(keys[-1]) == 0:\n keys = keys[:-1]\n return \"[\" + \", \".join([f'\"{k}\"' for k in sort_objects(keys)]) + \"]\"\n\n lines = import_statement.split(\"\\n\")\n if len(lines) > 3:\n # Here we have to sort internal imports that are on several lines (one per name):\n # key: [\n # \"object1\",\n # \"object2\",\n # ...\n # ]\n\n # We may have to ignore one or two lines on each side.\n idx = 2 if lines[1].strip() == \"[\" else 1\n keys_to_sort = [(i, _re_strip_line.search(line).groups()[0]) for i, line in enumerate(lines[idx:-idx])]\n sorted_indices = sort_objects(keys_to_sort, key=lambda x: x[1])\n sorted_lines = [lines[x[0] + idx] for x in sorted_indices]\n return \"\\n\".join(lines[:idx] + sorted_lines + lines[-idx:])\n elif len(lines) == 3:\n # Here we have to sort internal imports that are on one separate line:\n # key: [\n # \"object1\", \"object2\", ...\n # ]\n if _re_bracket_content.search(lines[1]) is not None:\n lines[1] = _re_bracket_content.sub(_replace, lines[1])\n else:\n keys = [part.strip().replace('\"', \"\") for part in lines[1].split(\",\")]\n # We will have a final empty element if the line finished with a comma.\n if len(keys[-1]) == 0:\n keys = keys[:-1]\n lines[1] = get_indent(lines[1]) + \", \".join([f'\"{k}\"' for k in sort_objects(keys)])\n return \"\\n\".join(lines)\n else:\n # Finally we have to deal with imports fitting on one line\n import_statement = _re_bracket_content.sub(_replace, import_statement)\n return import_statement\n\n\ndef sort_imports(file: str, check_only: bool = True):\n \"\"\"\n Sort the imports defined in the `_import_structure` of a given init.\n\n Args:\n file (`str`): The path to the init to check/fix.\n check_only (`bool`, *optional*, defaults to `True`): Whether or not to just check (and not auto-fix) the init.\n \"\"\"\n with open(file, encoding=\"utf-8\") as f:\n code = f.read()\n\n # If the file is not a custom init, there is nothing to do.\n if \"_import_structure\" not in code:\n return\n\n # Blocks of indent level 0\n main_blocks = split_code_in_indented_blocks(\n code, start_prompt=\"_import_structure = {\", end_prompt=\"if TYPE_CHECKING:\"\n )\n\n # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).\n for block_idx in range(1, len(main_blocks) - 1):\n # Check if the block contains some `_import_structure`s thingy to sort.\n block = main_blocks[block_idx]\n block_lines = block.split(\"\\n\")\n\n # Get to the start of the imports.\n line_idx = 0\n while line_idx < len(block_lines) and \"_import_structure\" not in block_lines[line_idx]:\n # Skip dummy import blocks\n if \"import dummy\" in block_lines[line_idx]:\n line_idx = len(block_lines)\n else:\n line_idx += 1\n if line_idx >= len(block_lines):\n continue\n\n # Ignore beginning and last line: they don't contain anything.\n internal_block_code = \"\\n\".join(block_lines[line_idx:-1])\n indent = get_indent(block_lines[1])\n # Slit the internal block into blocks of indent level 1.\n internal_blocks = split_code_in_indented_blocks(internal_block_code, indent_level=indent)\n # We have two categories of import key: list or _import_structure[key].append/extend\n pattern = _re_direct_key if \"_import_structure = {\" in block_lines[0] else _re_indirect_key\n # Grab the keys, but there is a trap: some lines are empty or just comments.\n keys = [(pattern.search(b).groups()[0] if pattern.search(b) is not None else None) for b in internal_blocks]\n # We only sort the lines with a key.\n keys_to_sort = [(i, key) for i, key in enumerate(keys) if key is not None]\n sorted_indices = [x[0] for x in sorted(keys_to_sort, key=lambda x: x[1])]\n\n # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.\n count = 0\n reordered_blocks = []\n for i in range(len(internal_blocks)):\n if keys[i] is None:\n reordered_blocks.append(internal_blocks[i])\n else:\n block = sort_objects_in_import(internal_blocks[sorted_indices[count]])\n reordered_blocks.append(block)\n count += 1\n\n # And we put our main block back together with its first and last line.\n main_blocks[block_idx] = \"\\n\".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]])\n\n if code != \"\\n\".join(main_blocks):\n if check_only:\n return True\n else:\n print(f\"Overwriting {file}.\")\n with open(file, \"w\", encoding=\"utf-8\") as f:\n f.write(\"\\n\".join(main_blocks))\n\n\ndef sort_imports_in_all_inits(check_only=True):\n \"\"\"\n Sort the imports defined in the `_import_structure` of all inits in the repo.\n\n Args:\n check_only (`bool`, *optional*, defaults to `True`): Whether or not to just check (and not auto-fix) the init.\n \"\"\"\n failures = []\n for root, _, files in os.walk(PATH_TO_TRANSFORMERS):\n if \"__init__.py\" in files:\n result = sort_imports(os.path.join(root, \"__init__.py\"), check_only=check_only)\n if result:\n failures = [os.path.join(root, \"__init__.py\")]\n if len(failures) > 0:\n raise ValueError(f\"Would overwrite {len(failures)} files, run `make style`.\")\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--check_only\", action=\"store_true\", help=\"Whether to only check or fix style.\")\n args = parser.parse_args()\n\n sort_imports_in_all_inits(check_only=args.check_only)\n" }, { "path": "diffusers/utils/fetch_latest_release_branch.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport requests\nfrom packaging.version import parse\n\n\n# GitHub repository details\nUSER = \"huggingface\"\nREPO = \"diffusers\"\n\n\ndef fetch_all_branches(user, repo):\n branches = [] # List to store all branches\n page = 1 # Start from first page\n while True:\n # Make a request to the GitHub API for the branches\n response = requests.get(f\"https://api.github.com/repos/{user}/{repo}/branches\", params={\"page\": page})\n\n # Check if the request was successful\n if response.status_code == 200:\n # Add the branches from the current page to the list\n branches.extend([branch[\"name\"] for branch in response.json()])\n\n # Check if there is a 'next' link for pagination\n if \"next\" in response.links:\n page += 1 # Move to the next page\n else:\n break # Exit loop if there is no next page\n else:\n print(\"Failed to retrieve branches:\", response.status_code)\n break\n\n return branches\n\n\ndef main():\n # Fetch all branches\n branches = fetch_all_branches(USER, REPO)\n\n # Filter branches.\n # print(f\"Total branches: {len(branches)}\")\n filtered_branches = []\n for branch in branches:\n if branch.startswith(\"v\") and (\"-release\" in branch or \"-patch\" in branch):\n filtered_branches.append(branch)\n # print(f\"Filtered: {branch}\")\n\n sorted_branches = sorted(filtered_branches, key=lambda x: parse(x.split(\"-\")[0][1:]), reverse=True)\n latest_branch = sorted_branches[0]\n # print(f\"Latest branch: {latest_branch}\")\n return latest_branch\n\n\nif __name__ == \"__main__\":\n print(main())\n" }, { "path": "diffusers/utils/fetch_torch_cuda_pipeline_test_matrix.py", "content": "import json\nimport logging\nimport os\nfrom collections import defaultdict\nfrom pathlib import Path\n\nfrom huggingface_hub import HfApi, ModelFilter\n\nimport diffusers\n\n\nPATH_TO_REPO = Path(__file__).parent.parent.resolve()\nALWAYS_TEST_PIPELINE_MODULES = [\n \"controlnet\",\n \"stable_diffusion\",\n \"stable_diffusion_2\",\n \"stable_diffusion_xl\",\n \"stable_diffusion_adapter\",\n \"deepfloyd_if\",\n \"ip_adapters\",\n \"kandinsky\",\n \"kandinsky2_2\",\n \"text_to_video_synthesis\",\n \"wuerstchen\",\n]\nPIPELINE_USAGE_CUTOFF = int(os.getenv(\"PIPELINE_USAGE_CUTOFF\", 50000))\n\nlogger = logging.getLogger(__name__)\napi = HfApi()\nfilter = ModelFilter(library=\"diffusers\")\n\n\ndef filter_pipelines(usage_dict, usage_cutoff=10000):\n output = []\n for diffusers_object, usage in usage_dict.items():\n if usage < usage_cutoff:\n continue\n\n is_diffusers_pipeline = hasattr(diffusers.pipelines, diffusers_object)\n if not is_diffusers_pipeline:\n continue\n\n output.append(diffusers_object)\n\n return output\n\n\ndef fetch_pipeline_objects():\n models = api.list_models(filter=filter)\n downloads = defaultdict(int)\n\n for model in models:\n is_counted = False\n for tag in model.tags:\n if tag.startswith(\"diffusers:\"):\n is_counted = True\n downloads[tag[len(\"diffusers:\") :]] += model.downloads\n\n if not is_counted:\n downloads[\"other\"] += model.downloads\n\n # Remove 0 downloads\n downloads = {k: v for k, v in downloads.items() if v > 0}\n pipeline_objects = filter_pipelines(downloads, PIPELINE_USAGE_CUTOFF)\n\n return pipeline_objects\n\n\ndef fetch_pipeline_modules_to_test():\n try:\n pipeline_objects = fetch_pipeline_objects()\n except Exception as e:\n logger.error(e)\n raise RuntimeError(\"Unable to fetch model list from HuggingFace Hub.\")\n\n test_modules = []\n for pipeline_name in pipeline_objects:\n module = getattr(diffusers, pipeline_name)\n\n test_module = module.__module__.split(\".\")[-2].strip()\n test_modules.append(test_module)\n\n return test_modules\n\n\ndef main():\n test_modules = fetch_pipeline_modules_to_test()\n test_modules.extend(ALWAYS_TEST_PIPELINE_MODULES)\n\n # Get unique modules\n test_modules = list(set(test_modules))\n print(json.dumps(test_modules))\n\n save_path = f\"{PATH_TO_REPO}/reports\"\n os.makedirs(save_path, exist_ok=True)\n\n with open(f\"{save_path}/test-pipelines.json\", \"w\") as f:\n json.dump({\"pipeline_test_modules\": test_modules}, f)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/utils/get_modified_files.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:\n# python ./utils/get_modified_files.py utils src tests examples\n#\n# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered\n# since the output of this script is fed into Makefile commands it doesn't print a newline after the results\n\nimport re\nimport subprocess\nimport sys\n\n\nfork_point_sha = subprocess.check_output(\"git merge-base main HEAD\".split()).decode(\"utf-8\")\nmodified_files = subprocess.check_output(f\"git diff --name-only {fork_point_sha}\".split()).decode(\"utf-8\").split()\n\njoined_dirs = \"|\".join(sys.argv[1:])\nregex = re.compile(rf\"^({joined_dirs}).*?\\.py$\")\n\nrelevant_modified_files = [x for x in modified_files if regex.match(x)]\nprint(\" \".join(relevant_modified_files), end=\"\")\n" }, { "path": "diffusers/utils/notify_slack_about_release.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os\n\nimport requests\n\n\n# Configuration\nLIBRARY_NAME = \"diffusers\"\nGITHUB_REPO = \"huggingface/diffusers\"\nSLACK_WEBHOOK_URL = os.getenv(\"SLACK_WEBHOOK_URL\")\n\n\ndef check_pypi_for_latest_release(library_name):\n \"\"\"Check PyPI for the latest release of the library.\"\"\"\n response = requests.get(f\"https://pypi.org/pypi/{library_name}/json\")\n if response.status_code == 200:\n data = response.json()\n return data[\"info\"][\"version\"]\n else:\n print(\"Failed to fetch library details from PyPI.\")\n return None\n\n\ndef get_github_release_info(github_repo):\n \"\"\"Fetch the latest release info from GitHub.\"\"\"\n url = f\"https://api.github.com/repos/{github_repo}/releases/latest\"\n response = requests.get(url)\n\n if response.status_code == 200:\n data = response.json()\n return {\"tag_name\": data[\"tag_name\"], \"url\": data[\"html_url\"], \"release_time\": data[\"published_at\"]}\n\n else:\n print(\"Failed to fetch release info from GitHub.\")\n return None\n\n\ndef notify_slack(webhook_url, library_name, version, release_info):\n \"\"\"Send a notification to a Slack channel.\"\"\"\n message = (\n f\"🚀 New release for {library_name} available: version **{version}** 🎉\\n\"\n f\"📜 Release Notes: {release_info['url']}\\n\"\n f\"⏱️ Release time: {release_info['release_time']}\"\n )\n payload = {\"text\": message}\n response = requests.post(webhook_url, json=payload)\n\n if response.status_code == 200:\n print(\"Notification sent to Slack successfully.\")\n else:\n print(\"Failed to send notification to Slack.\")\n\n\ndef main():\n latest_version = check_pypi_for_latest_release(LIBRARY_NAME)\n release_info = get_github_release_info(GITHUB_REPO)\n parsed_version = release_info[\"tag_name\"].replace(\"v\", \"\")\n\n if latest_version and release_info and latest_version == parsed_version:\n notify_slack(SLACK_WEBHOOK_URL, LIBRARY_NAME, latest_version, release_info)\n else:\n raise ValueError(\"There were some problems.\")\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/utils/overwrite_expected_slice.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport argparse\nfrom collections import defaultdict\n\n\ndef overwrite_file(file, class_name, test_name, correct_line, done_test):\n _id = f\"{file}_{class_name}_{test_name}\"\n done_test[_id] += 1\n\n with open(file, \"r\") as f:\n lines = f.readlines()\n\n class_regex = f\"class {class_name}(\"\n test_regex = f\"{4 * ' '}def {test_name}(\"\n line_begin_regex = f\"{8 * ' '}{correct_line.split()[0]}\"\n another_line_begin_regex = f\"{16 * ' '}{correct_line.split()[0]}\"\n in_class = False\n in_func = False\n in_line = False\n insert_line = False\n count = 0\n spaces = 0\n\n new_lines = []\n for line in lines:\n if line.startswith(class_regex):\n in_class = True\n elif in_class and line.startswith(test_regex):\n in_func = True\n elif in_class and in_func and (line.startswith(line_begin_regex) or line.startswith(another_line_begin_regex)):\n spaces = len(line.split(correct_line.split()[0])[0])\n count += 1\n\n if count == done_test[_id]:\n in_line = True\n\n if in_class and in_func and in_line:\n if \")\" not in line:\n continue\n else:\n insert_line = True\n\n if in_class and in_func and in_line and insert_line:\n new_lines.append(f\"{spaces * ' '}{correct_line}\")\n in_class = in_func = in_line = insert_line = False\n else:\n new_lines.append(line)\n\n with open(file, \"w\") as f:\n for line in new_lines:\n f.write(line)\n\n\ndef main(correct, fail=None):\n if fail is not None:\n with open(fail, \"r\") as f:\n test_failures = {l.strip() for l in f.readlines()}\n else:\n test_failures = None\n\n with open(correct, \"r\") as f:\n correct_lines = f.readlines()\n\n done_tests = defaultdict(int)\n for line in correct_lines:\n file, class_name, test_name, correct_line = line.split(\";\")\n if test_failures is None or \"::\".join([file, class_name, test_name]) in test_failures:\n overwrite_file(file, class_name, test_name, correct_line, done_tests)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--correct_filename\", help=\"filename of tests with expected result\")\n parser.add_argument(\"--fail_filename\", help=\"filename of test failures\", type=str, default=None)\n args = parser.parse_args()\n\n main(args.correct_filename, args.fail_filename)\n" }, { "path": "diffusers/utils/print_env.py", "content": "#!/usr/bin/env python3\n\n# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# this script dumps information about the environment\n\nimport os\nimport platform\nimport sys\n\n\nos.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n\nprint(\"Python version:\", sys.version)\n\nprint(\"OS platform:\", platform.platform())\nprint(\"OS architecture:\", platform.machine())\n\ntry:\n import torch\n\n print(\"Torch version:\", torch.__version__)\n print(\"Cuda available:\", torch.cuda.is_available())\n print(\"Cuda version:\", torch.version.cuda)\n print(\"CuDNN version:\", torch.backends.cudnn.version())\n print(\"Number of GPUs available:\", torch.cuda.device_count())\nexcept ImportError:\n print(\"Torch version:\", None)\n\ntry:\n import transformers\n\n print(\"transformers version:\", transformers.__version__)\nexcept ImportError:\n print(\"transformers version:\", None)\n" }, { "path": "diffusers/utils/release.py", "content": "# coding=utf-8\n# Copyright 2021 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport argparse\nimport os\nimport re\n\nimport packaging.version\n\n\nPATH_TO_EXAMPLES = \"examples/\"\nREPLACE_PATTERNS = {\n \"examples\": (re.compile(r'^check_min_version\\(\"[^\"]+\"\\)\\s*$', re.MULTILINE), 'check_min_version(\"VERSION\")\\n'),\n \"init\": (re.compile(r'^__version__\\s+=\\s+\"([^\"]+)\"\\s*$', re.MULTILINE), '__version__ = \"VERSION\"\\n'),\n \"setup\": (re.compile(r'^(\\s*)version\\s*=\\s*\"[^\"]+\",', re.MULTILINE), r'\\1version=\"VERSION\",'),\n \"doc\": (re.compile(r'^(\\s*)release\\s*=\\s*\"[^\"]+\"$', re.MULTILINE), 'release = \"VERSION\"\\n'),\n}\nREPLACE_FILES = {\n \"init\": \"src/diffusers/__init__.py\",\n \"setup\": \"setup.py\",\n}\nREADME_FILE = \"README.md\"\n\n\ndef update_version_in_file(fname, version, pattern):\n \"\"\"Update the version in one file using a specific pattern.\"\"\"\n with open(fname, \"r\", encoding=\"utf-8\", newline=\"\\n\") as f:\n code = f.read()\n re_pattern, replace = REPLACE_PATTERNS[pattern]\n replace = replace.replace(\"VERSION\", version)\n code = re_pattern.sub(replace, code)\n with open(fname, \"w\", encoding=\"utf-8\", newline=\"\\n\") as f:\n f.write(code)\n\n\ndef update_version_in_examples(version):\n \"\"\"Update the version in all examples files.\"\"\"\n for folder, directories, fnames in os.walk(PATH_TO_EXAMPLES):\n # Removing some of the folders with non-actively maintained examples from the walk\n if \"research_projects\" in directories:\n directories.remove(\"research_projects\")\n if \"legacy\" in directories:\n directories.remove(\"legacy\")\n for fname in fnames:\n if fname.endswith(\".py\"):\n update_version_in_file(os.path.join(folder, fname), version, pattern=\"examples\")\n\n\ndef global_version_update(version, patch=False):\n \"\"\"Update the version in all needed files.\"\"\"\n for pattern, fname in REPLACE_FILES.items():\n update_version_in_file(fname, version, pattern)\n if not patch:\n update_version_in_examples(version)\n\n\ndef clean_main_ref_in_model_list():\n \"\"\"Replace the links from main doc tp stable doc in the model list of the README.\"\"\"\n # If the introduction or the conclusion of the list change, the prompts may need to be updated.\n _start_prompt = \"🤗 Transformers currently provides the following architectures\"\n _end_prompt = \"1. Want to contribute a new model?\"\n with open(README_FILE, \"r\", encoding=\"utf-8\", newline=\"\\n\") as f:\n lines = f.readlines()\n\n # Find the start of the list.\n start_index = 0\n while not lines[start_index].startswith(_start_prompt):\n start_index += 1\n start_index += 1\n\n index = start_index\n # Update the lines in the model list.\n while not lines[index].startswith(_end_prompt):\n if lines[index].startswith(\"1.\"):\n lines[index] = lines[index].replace(\n \"https://huggingface.co/docs/diffusers/main/model_doc\",\n \"https://huggingface.co/docs/diffusers/model_doc\",\n )\n index += 1\n\n with open(README_FILE, \"w\", encoding=\"utf-8\", newline=\"\\n\") as f:\n f.writelines(lines)\n\n\ndef get_version():\n \"\"\"Reads the current version in the __init__.\"\"\"\n with open(REPLACE_FILES[\"init\"], \"r\") as f:\n code = f.read()\n default_version = REPLACE_PATTERNS[\"init\"][0].search(code).groups()[0]\n return packaging.version.parse(default_version)\n\n\ndef pre_release_work(patch=False):\n \"\"\"Do all the necessary pre-release steps.\"\"\"\n # First let's get the default version: base version if we are in dev, bump minor otherwise.\n default_version = get_version()\n if patch and default_version.is_devrelease:\n raise ValueError(\"Can't create a patch version from the dev branch, checkout a released version!\")\n if default_version.is_devrelease:\n default_version = default_version.base_version\n elif patch:\n default_version = f\"{default_version.major}.{default_version.minor}.{default_version.micro + 1}\"\n else:\n default_version = f\"{default_version.major}.{default_version.minor + 1}.0\"\n\n # Now let's ask nicely if that's the right one.\n version = input(f\"Which version are you releasing? [{default_version}]\")\n if len(version) == 0:\n version = default_version\n\n print(f\"Updating version to {version}.\")\n global_version_update(version, patch=patch)\n\n\n# if not patch:\n# print(\"Cleaning main README, don't forget to run `make fix-copies`.\")\n# clean_main_ref_in_model_list()\n\n\ndef post_release_work():\n \"\"\"Do all the necessary post-release steps.\"\"\"\n # First let's get the current version\n current_version = get_version()\n dev_version = f\"{current_version.major}.{current_version.minor + 1}.0.dev0\"\n current_version = current_version.base_version\n\n # Check with the user we got that right.\n version = input(f\"Which version are we developing now? [{dev_version}]\")\n if len(version) == 0:\n version = dev_version\n\n print(f\"Updating version to {version}.\")\n global_version_update(version)\n\n\n# print(\"Cleaning main README, don't forget to run `make fix-copies`.\")\n# clean_main_ref_in_model_list()\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--post_release\", action=\"store_true\", help=\"Whether this is pre or post release.\")\n parser.add_argument(\"--patch\", action=\"store_true\", help=\"Whether or not this is a patch release.\")\n args = parser.parse_args()\n if not args.post_release:\n pre_release_work(patch=args.patch)\n elif args.patch:\n print(\"Nothing to do after a patch :-)\")\n else:\n post_release_work()\n" }, { "path": "diffusers/utils/stale.py", "content": "# Copyright 2024 The HuggingFace Team, the AllenNLP library authors. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nScript to close stale issue. Taken in part from the AllenNLP repository.\nhttps://github.com/allenai/allennlp.\n\"\"\"\n\nimport os\nfrom datetime import datetime as dt\nfrom datetime import timezone\n\nfrom github import Github\n\n\nLABELS_TO_EXEMPT = [\n \"good first issue\",\n \"good second issue\",\n \"good difficult issue\",\n \"enhancement\",\n \"new pipeline/model\",\n \"new scheduler\",\n \"wip\",\n]\n\n\ndef main():\n g = Github(os.environ[\"GITHUB_TOKEN\"])\n repo = g.get_repo(\"huggingface/diffusers\")\n open_issues = repo.get_issues(state=\"open\")\n\n for issue in open_issues:\n labels = [label.name.lower() for label in issue.get_labels()]\n if \"stale\" in labels:\n comments = sorted(issue.get_comments(), key=lambda i: i.created_at, reverse=True)\n last_comment = comments[0] if len(comments) > 0 else None\n if last_comment is not None and last_comment.user.login != \"github-actions[bot]\":\n # Opens the issue if someone other than Stalebot commented.\n issue.edit(state=\"open\")\n issue.remove_from_labels(\"stale\")\n elif (\n (dt.now(timezone.utc) - issue.updated_at).days > 23\n and (dt.now(timezone.utc) - issue.created_at).days >= 30\n and not any(label in LABELS_TO_EXEMPT for label in labels)\n ):\n # Post a Stalebot notification after 23 days of inactivity.\n issue.create_comment(\n \"This issue has been automatically marked as stale because it has not had \"\n \"recent activity. If you think this still needs to be addressed \"\n \"please comment on this thread.\\n\\nPlease note that issues that do not follow the \"\n \"[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) \"\n \"are likely to be ignored.\"\n )\n issue.add_to_labels(\"stale\")\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "diffusers/utils/tests_fetcher.py", "content": "# coding=utf-8\n# Copyright 2021 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"\nWelcome to tests_fetcher V2.\n\nThis util is designed to fetch tests to run on a PR so that only the tests impacted by the modifications are run, and\nwhen too many models are being impacted, only run the tests of a subset of core models. It works like this.\n\nStage 1: Identify the modified files. For jobs that run on the main branch, it's just the diff with the last commit.\nOn a PR, this takes all the files from the branching point to the current commit (so all modifications in a PR, not\njust the last commit) but excludes modifications that are on docstrings or comments only.\n\nStage 2: Extract the tests to run. This is done by looking at the imports in each module and test file: if module A\nimports module B, then changing module B impacts module A, so the tests using module A should be run. We thus get the\ndependencies of each model and then recursively builds the 'reverse' map of dependencies to get all modules and tests\nimpacted by a given file. We then only keep the tests (and only the core models tests if there are too many modules).\n\nCaveats:\n - This module only filters tests by files (not individual tests) so it's better to have tests for different things\n in different files.\n - This module assumes inits are just importing things, not really building objects, so it's better to structure\n them this way and move objects building in separate submodules.\n\nUsage:\n\nBase use to fetch the tests in a pull request\n\n```bash\npython utils/tests_fetcher.py\n```\n\nBase use to fetch the tests on a the main branch (with diff from the last commit):\n\n```bash\npython utils/tests_fetcher.py --diff_with_last_commit\n```\n\"\"\"\n\nimport argparse\nimport collections\nimport json\nimport os\nimport re\nfrom contextlib import contextmanager\nfrom pathlib import Path\nfrom typing import Dict, List, Optional, Tuple, Union\n\nfrom git import Repo\n\n\nPATH_TO_REPO = Path(__file__).parent.parent.resolve()\nPATH_TO_EXAMPLES = PATH_TO_REPO / \"examples\"\nPATH_TO_DIFFUSERS = PATH_TO_REPO / \"src/diffusers\"\nPATH_TO_TESTS = PATH_TO_REPO / \"tests\"\n\n# Ignore fixtures in tests folder\n# Ignore lora since they are always tested\nMODULES_TO_IGNORE = [\"fixtures\", \"lora\"]\n\nIMPORTANT_PIPELINES = [\n \"controlnet\",\n \"stable_diffusion\",\n \"stable_diffusion_2\",\n \"stable_diffusion_xl\",\n \"stable_video_diffusion\",\n \"deepfloyd_if\",\n \"kandinsky\",\n \"kandinsky2_2\",\n \"text_to_video_synthesis\",\n \"wuerstchen\",\n]\n\n\n@contextmanager\ndef checkout_commit(repo: Repo, commit_id: str):\n \"\"\"\n Context manager that checks out a given commit when entered, but gets back to the reference it was at on exit.\n\n Args:\n repo (`git.Repo`): A git repository (for instance the Transformers repo).\n commit_id (`str`): The commit reference to checkout inside the context manager.\n \"\"\"\n current_head = repo.head.commit if repo.head.is_detached else repo.head.ref\n\n try:\n repo.git.checkout(commit_id)\n yield\n\n finally:\n repo.git.checkout(current_head)\n\n\ndef clean_code(content: str) -> str:\n \"\"\"\n Remove docstrings, empty line or comments from some code (used to detect if a diff is real or only concern\n comments or docstings).\n\n Args:\n content (`str`): The code to clean\n\n Returns:\n `str`: The cleaned code.\n \"\"\"\n # We need to deactivate autoformatting here to write escaped triple quotes (we cannot use real triple quotes or\n # this would mess up the result if this function applied to this particular file).\n # fmt: off\n # Remove docstrings by splitting on triple \" then triple ':\n splits = content.split('\\\"\\\"\\\"')\n content = \"\".join(splits[::2])\n splits = content.split(\"\\'\\'\\'\")\n # fmt: on\n content = \"\".join(splits[::2])\n\n # Remove empty lines and comments\n lines_to_keep = []\n for line in content.split(\"\\n\"):\n # remove anything that is after a # sign.\n line = re.sub(\"#.*$\", \"\", line)\n # remove white lines\n if len(line) != 0 and not line.isspace():\n lines_to_keep.append(line)\n return \"\\n\".join(lines_to_keep)\n\n\ndef keep_doc_examples_only(content: str) -> str:\n \"\"\"\n Remove everything from the code content except the doc examples (used to determined if a diff should trigger doc\n tests or not).\n\n Args:\n content (`str`): The code to clean\n\n Returns:\n `str`: The cleaned code.\n \"\"\"\n # Keep doc examples only by splitting on triple \"`\"\n splits = content.split(\"```\")\n # Add leading and trailing \"```\" so the navigation is easier when compared to the original input `content`\n content = \"```\" + \"```\".join(splits[1::2]) + \"```\"\n\n # Remove empty lines and comments\n lines_to_keep = []\n for line in content.split(\"\\n\"):\n # remove anything that is after a # sign.\n line = re.sub(\"#.*$\", \"\", line)\n # remove white lines\n if len(line) != 0 and not line.isspace():\n lines_to_keep.append(line)\n return \"\\n\".join(lines_to_keep)\n\n\ndef get_all_tests() -> List[str]:\n \"\"\"\n Walks the `tests` folder to return a list of files/subfolders. This is used to split the tests to run when using\n paralellism. The split is:\n\n - folders under `tests`: (`tokenization`, `pipelines`, etc) except the subfolder `models` is excluded.\n - folders under `tests/models`: `bert`, `gpt2`, etc.\n - test files under `tests`: `test_modeling_common.py`, `test_tokenization_common.py`, etc.\n \"\"\"\n\n # test folders/files directly under `tests` folder\n tests = os.listdir(PATH_TO_TESTS)\n tests = [f\"tests/{f}\" for f in tests if \"__pycache__\" not in f]\n tests = sorted([f for f in tests if (PATH_TO_REPO / f).is_dir() or f.startswith(\"tests/test_\")])\n\n return tests\n\n\ndef diff_is_docstring_only(repo: Repo, branching_point: str, filename: str) -> bool:\n \"\"\"\n Check if the diff is only in docstrings (or comments and whitespace) in a filename.\n\n Args:\n repo (`git.Repo`): A git repository (for instance the Transformers repo).\n branching_point (`str`): The commit reference of where to compare for the diff.\n filename (`str`): The filename where we want to know if the diff isonly in docstrings/comments.\n\n Returns:\n `bool`: Whether the diff is docstring/comments only or not.\n \"\"\"\n folder = Path(repo.working_dir)\n with checkout_commit(repo, branching_point):\n with open(folder / filename, \"r\", encoding=\"utf-8\") as f:\n old_content = f.read()\n\n with open(folder / filename, \"r\", encoding=\"utf-8\") as f:\n new_content = f.read()\n\n old_content_clean = clean_code(old_content)\n new_content_clean = clean_code(new_content)\n\n return old_content_clean == new_content_clean\n\n\ndef diff_contains_doc_examples(repo: Repo, branching_point: str, filename: str) -> bool:\n \"\"\"\n Check if the diff is only in code examples of the doc in a filename.\n\n Args:\n repo (`git.Repo`): A git repository (for instance the Transformers repo).\n branching_point (`str`): The commit reference of where to compare for the diff.\n filename (`str`): The filename where we want to know if the diff is only in codes examples.\n\n Returns:\n `bool`: Whether the diff is only in code examples of the doc or not.\n \"\"\"\n folder = Path(repo.working_dir)\n with checkout_commit(repo, branching_point):\n with open(folder / filename, \"r\", encoding=\"utf-8\") as f:\n old_content = f.read()\n\n with open(folder / filename, \"r\", encoding=\"utf-8\") as f:\n new_content = f.read()\n\n old_content_clean = keep_doc_examples_only(old_content)\n new_content_clean = keep_doc_examples_only(new_content)\n\n return old_content_clean != new_content_clean\n\n\ndef get_diff(repo: Repo, base_commit: str, commits: List[str]) -> List[str]:\n \"\"\"\n Get the diff between a base commit and one or several commits.\n\n Args:\n repo (`git.Repo`):\n A git repository (for instance the Transformers repo).\n base_commit (`str`):\n The commit reference of where to compare for the diff. This is the current commit, not the branching point!\n commits (`List[str]`):\n The list of commits with which to compare the repo at `base_commit` (so the branching point).\n\n Returns:\n `List[str]`: The list of Python files with a diff (files added, renamed or deleted are always returned, files\n modified are returned if the diff in the file is not only in docstrings or comments, see\n `diff_is_docstring_only`).\n \"\"\"\n print(\"\\n### DIFF ###\\n\")\n code_diff = []\n for commit in commits:\n for diff_obj in commit.diff(base_commit):\n # We always add new python files\n if diff_obj.change_type == \"A\" and diff_obj.b_path.endswith(\".py\"):\n code_diff.append(diff_obj.b_path)\n # We check that deleted python files won't break corresponding tests.\n elif diff_obj.change_type == \"D\" and diff_obj.a_path.endswith(\".py\"):\n code_diff.append(diff_obj.a_path)\n # Now for modified files\n elif diff_obj.change_type in [\"M\", \"R\"] and diff_obj.b_path.endswith(\".py\"):\n # In case of renames, we'll look at the tests using both the old and new name.\n if diff_obj.a_path != diff_obj.b_path:\n code_diff.extend([diff_obj.a_path, diff_obj.b_path])\n else:\n # Otherwise, we check modifications are in code and not docstrings.\n if diff_is_docstring_only(repo, commit, diff_obj.b_path):\n print(f\"Ignoring diff in {diff_obj.b_path} as it only concerns docstrings or comments.\")\n else:\n code_diff.append(diff_obj.a_path)\n\n return code_diff\n\n\ndef get_modified_python_files(diff_with_last_commit: bool = False) -> List[str]:\n \"\"\"\n Return a list of python files that have been modified between:\n\n - the current head and the main branch if `diff_with_last_commit=False` (default)\n - the current head and its parent commit otherwise.\n\n Returns:\n `List[str]`: The list of Python files with a diff (files added, renamed or deleted are always returned, files\n modified are returned if the diff in the file is not only in docstrings or comments, see\n `diff_is_docstring_only`).\n \"\"\"\n repo = Repo(PATH_TO_REPO)\n\n if not diff_with_last_commit:\n # Need to fetch refs for main using remotes when running with github actions.\n upstream_main = repo.remotes.origin.refs.main\n\n print(f\"main is at {upstream_main.commit}\")\n print(f\"Current head is at {repo.head.commit}\")\n\n branching_commits = repo.merge_base(upstream_main, repo.head)\n for commit in branching_commits:\n print(f\"Branching commit: {commit}\")\n return get_diff(repo, repo.head.commit, branching_commits)\n else:\n print(f\"main is at {repo.head.commit}\")\n parent_commits = repo.head.commit.parents\n for commit in parent_commits:\n print(f\"Parent commit: {commit}\")\n return get_diff(repo, repo.head.commit, parent_commits)\n\n\ndef get_diff_for_doctesting(repo: Repo, base_commit: str, commits: List[str]) -> List[str]:\n \"\"\"\n Get the diff in doc examples between a base commit and one or several commits.\n\n Args:\n repo (`git.Repo`):\n A git repository (for instance the Transformers repo).\n base_commit (`str`):\n The commit reference of where to compare for the diff. This is the current commit, not the branching point!\n commits (`List[str]`):\n The list of commits with which to compare the repo at `base_commit` (so the branching point).\n\n Returns:\n `List[str]`: The list of Python and Markdown files with a diff (files added or renamed are always returned, files\n modified are returned if the diff in the file is only in doctest examples).\n \"\"\"\n print(\"\\n### DIFF ###\\n\")\n code_diff = []\n for commit in commits:\n for diff_obj in commit.diff(base_commit):\n # We only consider Python files and doc files.\n if not diff_obj.b_path.endswith(\".py\") and not diff_obj.b_path.endswith(\".md\"):\n continue\n # We always add new python/md files\n if diff_obj.change_type in [\"A\"]:\n code_diff.append(diff_obj.b_path)\n # Now for modified files\n elif diff_obj.change_type in [\"M\", \"R\"]:\n # In case of renames, we'll look at the tests using both the old and new name.\n if diff_obj.a_path != diff_obj.b_path:\n code_diff.extend([diff_obj.a_path, diff_obj.b_path])\n else:\n # Otherwise, we check modifications contain some doc example(s).\n if diff_contains_doc_examples(repo, commit, diff_obj.b_path):\n code_diff.append(diff_obj.a_path)\n else:\n print(f\"Ignoring diff in {diff_obj.b_path} as it doesn't contain any doc example.\")\n\n return code_diff\n\n\ndef get_all_doctest_files() -> List[str]:\n \"\"\"\n Return the complete list of python and Markdown files on which we run doctest.\n\n At this moment, we restrict this to only take files from `src/` or `docs/source/en/` that are not in `utils/not_doctested.txt`.\n\n Returns:\n `List[str]`: The complete list of Python and Markdown files on which we run doctest.\n \"\"\"\n py_files = [str(x.relative_to(PATH_TO_REPO)) for x in PATH_TO_REPO.glob(\"**/*.py\")]\n md_files = [str(x.relative_to(PATH_TO_REPO)) for x in PATH_TO_REPO.glob(\"**/*.md\")]\n test_files_to_run = py_files + md_files\n\n # only include files in `src` or `docs/source/en/`\n test_files_to_run = [x for x in test_files_to_run if x.startswith((\"src/\", \"docs/source/en/\"))]\n # not include init files\n test_files_to_run = [x for x in test_files_to_run if not x.endswith((\"__init__.py\",))]\n\n # These are files not doctested yet.\n with open(\"utils/not_doctested.txt\") as fp:\n not_doctested = {x.split(\" \")[0] for x in fp.read().strip().split(\"\\n\")}\n\n # So far we don't have 100% coverage for doctest. This line will be removed once we achieve 100%.\n test_files_to_run = [x for x in test_files_to_run if x not in not_doctested]\n\n return sorted(test_files_to_run)\n\n\ndef get_new_doctest_files(repo, base_commit, branching_commit) -> List[str]:\n \"\"\"\n Get the list of files that were removed from \"utils/not_doctested.txt\", between `base_commit` and\n `branching_commit`.\n\n Returns:\n `List[str]`: List of files that were removed from \"utils/not_doctested.txt\".\n \"\"\"\n for diff_obj in branching_commit.diff(base_commit):\n # Ignores all but the \"utils/not_doctested.txt\" file.\n if diff_obj.a_path != \"utils/not_doctested.txt\":\n continue\n # Loads the two versions\n folder = Path(repo.working_dir)\n with checkout_commit(repo, branching_commit):\n with open(folder / \"utils/not_doctested.txt\", \"r\", encoding=\"utf-8\") as f:\n old_content = f.read()\n with open(folder / \"utils/not_doctested.txt\", \"r\", encoding=\"utf-8\") as f:\n new_content = f.read()\n # Compute the removed lines and return them\n removed_content = {x.split(\" \")[0] for x in old_content.split(\"\\n\")} - {\n x.split(\" \")[0] for x in new_content.split(\"\\n\")\n }\n return sorted(removed_content)\n return []\n\n\ndef get_doctest_files(diff_with_last_commit: bool = False) -> List[str]:\n \"\"\"\n Return a list of python and Markdown files where doc example have been modified between:\n\n - the current head and the main branch if `diff_with_last_commit=False` (default)\n - the current head and its parent commit otherwise.\n\n Returns:\n `List[str]`: The list of Python and Markdown files with a diff (files added or renamed are always returned, files\n modified are returned if the diff in the file is only in doctest examples).\n \"\"\"\n repo = Repo(PATH_TO_REPO)\n\n test_files_to_run = [] # noqa\n if not diff_with_last_commit:\n upstream_main = repo.remotes.origin.refs.main\n print(f\"main is at {upstream_main.commit}\")\n print(f\"Current head is at {repo.head.commit}\")\n\n branching_commits = repo.merge_base(upstream_main, repo.head)\n for commit in branching_commits:\n print(f\"Branching commit: {commit}\")\n test_files_to_run = get_diff_for_doctesting(repo, repo.head.commit, branching_commits)\n else:\n print(f\"main is at {repo.head.commit}\")\n parent_commits = repo.head.commit.parents\n for commit in parent_commits:\n print(f\"Parent commit: {commit}\")\n test_files_to_run = get_diff_for_doctesting(repo, repo.head.commit, parent_commits)\n\n all_test_files_to_run = get_all_doctest_files()\n\n # Add to the test files to run any removed entry from \"utils/not_doctested.txt\".\n new_test_files = get_new_doctest_files(repo, repo.head.commit, upstream_main.commit)\n test_files_to_run = list(set(test_files_to_run + new_test_files))\n\n # Do not run slow doctest tests on CircleCI\n with open(\"utils/slow_documentation_tests.txt\") as fp:\n slow_documentation_tests = set(fp.read().strip().split(\"\\n\"))\n test_files_to_run = [\n x for x in test_files_to_run if x in all_test_files_to_run and x not in slow_documentation_tests\n ]\n\n # Make sure we did not end up with a test file that was removed\n test_files_to_run = [f for f in test_files_to_run if (PATH_TO_REPO / f).exists()]\n\n return sorted(test_files_to_run)\n\n\n# (:?^|\\n) -> Non-catching group for the beginning of the doc or a new line.\n# \\s*from\\s+(\\.+\\S+)\\s+import\\s+([^\\n]+) -> Line only contains from .xxx import yyy and we catch .xxx and yyy\n# (?=\\n) -> Look-ahead to a new line. We can't just put \\n here or using find_all on this re will only catch every\n# other import.\n_re_single_line_relative_imports = re.compile(r\"(?:^|\\n)\\s*from\\s+(\\.+\\S+)\\s+import\\s+([^\\n]+)(?=\\n)\")\n# (:?^|\\n) -> Non-catching group for the beginning of the doc or a new line.\n# \\s*from\\s+(\\.+\\S+)\\s+import\\s+\\(([^\\)]+)\\) -> Line continues with from .xxx import (yyy) and we catch .xxx and yyy\n# yyy will take multiple lines otherwise there wouldn't be parenthesis.\n_re_multi_line_relative_imports = re.compile(r\"(?:^|\\n)\\s*from\\s+(\\.+\\S+)\\s+import\\s+\\(([^\\)]+)\\)\")\n# (:?^|\\n) -> Non-catching group for the beginning of the doc or a new line.\n# \\s*from\\s+transformers(\\S*)\\s+import\\s+([^\\n]+) -> Line only contains from transformers.xxx import yyy and we catch\n# .xxx and yyy\n# (?=\\n) -> Look-ahead to a new line. We can't just put \\n here or using find_all on this re will only catch every\n# other import.\n_re_single_line_direct_imports = re.compile(r\"(?:^|\\n)\\s*from\\s+diffusers(\\S*)\\s+import\\s+([^\\n]+)(?=\\n)\")\n# (:?^|\\n) -> Non-catching group for the beginning of the doc or a new line.\n# \\s*from\\s+transformers(\\S*)\\s+import\\s+\\(([^\\)]+)\\) -> Line continues with from transformers.xxx import (yyy) and we\n# catch .xxx and yyy. yyy will take multiple lines otherwise there wouldn't be parenthesis.\n_re_multi_line_direct_imports = re.compile(r\"(?:^|\\n)\\s*from\\s+diffusers(\\S*)\\s+import\\s+\\(([^\\)]+)\\)\")\n\n\ndef extract_imports(module_fname: str, cache: Dict[str, List[str]] = None) -> List[str]:\n \"\"\"\n Get the imports a given module makes.\n\n Args:\n module_fname (`str`):\n The name of the file of the module where we want to look at the imports (given relative to the root of\n the repo).\n cache (Dictionary `str` to `List[str]`, *optional*):\n To speed up this function if it was previously called on `module_fname`, the cache of all previously\n computed results.\n\n Returns:\n `List[str]`: The list of module filenames imported in the input `module_fname` (a submodule we import from that\n is a subfolder will give its init file).\n \"\"\"\n if cache is not None and module_fname in cache:\n return cache[module_fname]\n\n with open(PATH_TO_REPO / module_fname, \"r\", encoding=\"utf-8\") as f:\n content = f.read()\n\n # Filter out all docstrings to not get imports in code examples. As before we need to deactivate formatting to\n # keep this as escaped quotes and avoid this function failing on this file.\n # fmt: off\n splits = content.split('\\\"\\\"\\\"')\n # fmt: on\n content = \"\".join(splits[::2])\n\n module_parts = str(module_fname).split(os.path.sep)\n imported_modules = []\n\n # Let's start with relative imports\n relative_imports = _re_single_line_relative_imports.findall(content)\n relative_imports = [\n (mod, imp) for mod, imp in relative_imports if \"# tests_ignore\" not in imp and imp.strip() != \"(\"\n ]\n multiline_relative_imports = _re_multi_line_relative_imports.findall(content)\n relative_imports += [(mod, imp) for mod, imp in multiline_relative_imports if \"# tests_ignore\" not in imp]\n\n # We need to remove parts of the module name depending on the depth of the relative imports.\n for module, imports in relative_imports:\n level = 0\n while module.startswith(\".\"):\n module = module[1:]\n level += 1\n\n if len(module) > 0:\n dep_parts = module_parts[: len(module_parts) - level] + module.split(\".\")\n else:\n dep_parts = module_parts[: len(module_parts) - level]\n imported_module = os.path.sep.join(dep_parts)\n imported_modules.append((imported_module, [imp.strip() for imp in imports.split(\",\")]))\n\n # Let's continue with direct imports\n direct_imports = _re_single_line_direct_imports.findall(content)\n direct_imports = [(mod, imp) for mod, imp in direct_imports if \"# tests_ignore\" not in imp and imp.strip() != \"(\"]\n multiline_direct_imports = _re_multi_line_direct_imports.findall(content)\n direct_imports += [(mod, imp) for mod, imp in multiline_direct_imports if \"# tests_ignore\" not in imp]\n\n # We need to find the relative path of those imports.\n for module, imports in direct_imports:\n import_parts = module.split(\".\")[1:] # ignore the name of the repo since we add it below.\n dep_parts = [\"src\", \"diffusers\"] + import_parts\n imported_module = os.path.sep.join(dep_parts)\n imported_modules.append((imported_module, [imp.strip() for imp in imports.split(\",\")]))\n\n result = []\n # Double check we get proper modules (either a python file or a folder with an init).\n for module_file, imports in imported_modules:\n if (PATH_TO_REPO / f\"{module_file}.py\").is_file():\n module_file = f\"{module_file}.py\"\n elif (PATH_TO_REPO / module_file).is_dir() and (PATH_TO_REPO / module_file / \"__init__.py\").is_file():\n module_file = os.path.sep.join([module_file, \"__init__.py\"])\n imports = [imp for imp in imports if len(imp) > 0 and re.match(\"^[A-Za-z0-9_]*$\", imp)]\n if len(imports) > 0:\n result.append((module_file, imports))\n\n if cache is not None:\n cache[module_fname] = result\n\n return result\n\n\ndef get_module_dependencies(module_fname: str, cache: Dict[str, List[str]] = None) -> List[str]:\n \"\"\"\n Refines the result of `extract_imports` to remove subfolders and get a proper list of module filenames: if a file\n as an import `from utils import Foo, Bar`, with `utils` being a subfolder containing many files, this will traverse\n the `utils` init file to check where those dependencies come from: for instance the files utils/foo.py and utils/bar.py.\n\n Warning: This presupposes that all intermediate inits are properly built (with imports from the respective\n submodules) and work better if objects are defined in submodules and not the intermediate init (otherwise the\n intermediate init is added, and inits usually have a lot of dependencies).\n\n Args:\n module_fname (`str`):\n The name of the file of the module where we want to look at the imports (given relative to the root of\n the repo).\n cache (Dictionary `str` to `List[str]`, *optional*):\n To speed up this function if it was previously called on `module_fname`, the cache of all previously\n computed results.\n\n Returns:\n `List[str]`: The list of module filenames imported in the input `module_fname` (with submodule imports refined).\n \"\"\"\n dependencies = []\n imported_modules = extract_imports(module_fname, cache=cache)\n # The while loop is to recursively traverse all inits we may encounter: we will add things as we go.\n while len(imported_modules) > 0:\n new_modules = []\n for module, imports in imported_modules:\n # If we end up in an __init__ we are often not actually importing from this init (except in the case where\n # the object is fully defined in the __init__)\n if module.endswith(\"__init__.py\"):\n # So we get the imports from that init then try to find where our objects come from.\n new_imported_modules = extract_imports(module, cache=cache)\n for new_module, new_imports in new_imported_modules:\n if any(i in new_imports for i in imports):\n if new_module not in dependencies:\n new_modules.append((new_module, [i for i in new_imports if i in imports]))\n imports = [i for i in imports if i not in new_imports]\n if len(imports) > 0:\n # If there are any objects lefts, they may be a submodule\n path_to_module = PATH_TO_REPO / module.replace(\"__init__.py\", \"\")\n dependencies.extend(\n [\n os.path.join(module.replace(\"__init__.py\", \"\"), f\"{i}.py\")\n for i in imports\n if (path_to_module / f\"{i}.py\").is_file()\n ]\n )\n imports = [i for i in imports if not (path_to_module / f\"{i}.py\").is_file()]\n if len(imports) > 0:\n # Then if there are still objects left, they are fully defined in the init, so we keep it as a\n # dependency.\n dependencies.append(module)\n else:\n dependencies.append(module)\n\n imported_modules = new_modules\n\n return dependencies\n\n\ndef create_reverse_dependency_tree() -> List[Tuple[str, str]]:\n \"\"\"\n Create a list of all edges (a, b) which mean that modifying a impacts b with a going over all module and test files.\n \"\"\"\n cache = {}\n all_modules = list(PATH_TO_DIFFUSERS.glob(\"**/*.py\")) + list(PATH_TO_TESTS.glob(\"**/*.py\"))\n all_modules = [str(mod.relative_to(PATH_TO_REPO)) for mod in all_modules]\n edges = [(dep, mod) for mod in all_modules for dep in get_module_dependencies(mod, cache=cache)]\n\n return list(set(edges))\n\n\ndef get_tree_starting_at(module: str, edges: List[Tuple[str, str]]) -> List[Union[str, List[str]]]:\n \"\"\"\n Returns the tree starting at a given module following all edges.\n\n Args:\n module (`str`): The module that will be the root of the subtree we want.\n eges (`List[Tuple[str, str]]`): The list of all edges of the tree.\n\n Returns:\n `List[Union[str, List[str]]]`: The tree to print in the following format: [module, [list of edges\n starting at module], [list of edges starting at the preceding level], ...]\n \"\"\"\n vertices_seen = [module]\n new_edges = [edge for edge in edges if edge[0] == module and edge[1] != module and \"__init__.py\" not in edge[1]]\n tree = [module]\n while len(new_edges) > 0:\n tree.append(new_edges)\n final_vertices = list({edge[1] for edge in new_edges})\n vertices_seen.extend(final_vertices)\n new_edges = [\n edge\n for edge in edges\n if edge[0] in final_vertices and edge[1] not in vertices_seen and \"__init__.py\" not in edge[1]\n ]\n\n return tree\n\n\ndef print_tree_deps_of(module, all_edges=None):\n \"\"\"\n Prints the tree of modules depending on a given module.\n\n Args:\n module (`str`): The module that will be the root of the subtree we want.\n all_eges (`List[Tuple[str, str]]`, *optional*):\n The list of all edges of the tree. Will be set to `create_reverse_dependency_tree()` if not passed.\n \"\"\"\n if all_edges is None:\n all_edges = create_reverse_dependency_tree()\n tree = get_tree_starting_at(module, all_edges)\n\n # The list of lines is a list of tuples (line_to_be_printed, module)\n # Keeping the modules lets us know where to insert each new lines in the list.\n lines = [(tree[0], tree[0])]\n for index in range(1, len(tree)):\n edges = tree[index]\n start_edges = {edge[0] for edge in edges}\n\n for start in start_edges:\n end_edges = {edge[1] for edge in edges if edge[0] == start}\n # We will insert all those edges just after the line showing start.\n pos = 0\n while lines[pos][1] != start:\n pos += 1\n lines = lines[: pos + 1] + [(\" \" * (2 * index) + end, end) for end in end_edges] + lines[pos + 1 :]\n\n for line in lines:\n # We don't print the refs that where just here to help build lines.\n print(line[0])\n\n\ndef init_test_examples_dependencies() -> Tuple[Dict[str, List[str]], List[str]]:\n \"\"\"\n The test examples do not import from the examples (which are just scripts, not modules) so we need some extra\n care initializing the dependency map, which is the goal of this function. It initializes the dependency map for\n example files by linking each example to the example test file for the example framework.\n\n Returns:\n `Tuple[Dict[str, List[str]], List[str]]`: A tuple with two elements: the initialized dependency map which is a\n dict test example file to list of example files potentially tested by that test file, and the list of all\n example files (to avoid recomputing it later).\n \"\"\"\n test_example_deps = {}\n all_examples = []\n for framework in [\"flax\", \"pytorch\", \"tensorflow\"]:\n test_files = list((PATH_TO_EXAMPLES / framework).glob(\"test_*.py\"))\n all_examples.extend(test_files)\n # Remove the files at the root of examples/framework since they are not proper examples (they are eith utils\n # or example test files).\n examples = [\n f for f in (PATH_TO_EXAMPLES / framework).glob(\"**/*.py\") if f.parent != PATH_TO_EXAMPLES / framework\n ]\n all_examples.extend(examples)\n for test_file in test_files:\n with open(test_file, \"r\", encoding=\"utf-8\") as f:\n content = f.read()\n # Map all examples to the test files found in examples/framework.\n test_example_deps[str(test_file.relative_to(PATH_TO_REPO))] = [\n str(e.relative_to(PATH_TO_REPO)) for e in examples if e.name in content\n ]\n # Also map the test files to themselves.\n test_example_deps[str(test_file.relative_to(PATH_TO_REPO))].append(\n str(test_file.relative_to(PATH_TO_REPO))\n )\n return test_example_deps, all_examples\n\n\ndef create_reverse_dependency_map() -> Dict[str, List[str]]:\n \"\"\"\n Create the dependency map from module/test filename to the list of modules/tests that depend on it recursively.\n\n Returns:\n `Dict[str, List[str]]`: The reverse dependency map as a dictionary mapping filenames to all the filenames\n depending on it recursively. This way the tests impacted by a change in file A are the test files in the list\n corresponding to key A in this result.\n \"\"\"\n cache = {}\n # Start from the example deps init.\n example_deps, examples = init_test_examples_dependencies()\n # Add all modules and all tests to all examples\n all_modules = list(PATH_TO_DIFFUSERS.glob(\"**/*.py\")) + list(PATH_TO_TESTS.glob(\"**/*.py\")) + examples\n all_modules = [str(mod.relative_to(PATH_TO_REPO)) for mod in all_modules]\n # Compute the direct dependencies of all modules.\n direct_deps = {m: get_module_dependencies(m, cache=cache) for m in all_modules}\n direct_deps.update(example_deps)\n\n # This recurses the dependencies\n something_changed = True\n while something_changed:\n something_changed = False\n for m in all_modules:\n for d in direct_deps[m]:\n # We stop recursing at an init (cause we always end up in the main init and we don't want to add all\n # files which the main init imports)\n if d.endswith(\"__init__.py\"):\n continue\n if d not in direct_deps:\n raise ValueError(f\"KeyError:{d}. From {m}\")\n new_deps = set(direct_deps[d]) - set(direct_deps[m])\n if len(new_deps) > 0:\n direct_deps[m].extend(list(new_deps))\n something_changed = True\n\n # Finally we can build the reverse map.\n reverse_map = collections.defaultdict(list)\n for m in all_modules:\n for d in direct_deps[m]:\n reverse_map[d].append(m)\n\n # For inits, we don't do the reverse deps but the direct deps: if modifying an init, we want to make sure we test\n # all the modules impacted by that init.\n for m in [f for f in all_modules if f.endswith(\"__init__.py\")]:\n direct_deps = get_module_dependencies(m, cache=cache)\n deps = sum([reverse_map[d] for d in direct_deps if not d.endswith(\"__init__.py\")], direct_deps)\n reverse_map[m] = list(set(deps) - {m})\n\n return reverse_map\n\n\ndef create_module_to_test_map(reverse_map: Dict[str, List[str]] = None) -> Dict[str, List[str]]:\n \"\"\"\n Extract the tests from the reverse_dependency_map and potentially filters the model tests.\n\n Args:\n reverse_map (`Dict[str, List[str]]`, *optional*):\n The reverse dependency map as created by `create_reverse_dependency_map`. Will default to the result of\n that function if not provided.\n filter_pipelines (`bool`, *optional*, defaults to `False`):\n Whether or not to filter pipeline tests to only include core pipelines if a file impacts a lot of models.\n\n Returns:\n `Dict[str, List[str]]`: A dictionary that maps each file to the tests to execute if that file was modified.\n \"\"\"\n if reverse_map is None:\n reverse_map = create_reverse_dependency_map()\n\n # Utility that tells us if a given file is a test (taking test examples into account)\n def is_test(fname):\n if fname.startswith(\"tests\"):\n return True\n if fname.startswith(\"examples\") and fname.split(os.path.sep)[-1].startswith(\"test\"):\n return True\n return False\n\n # Build the test map\n test_map = {module: [f for f in deps if is_test(f)] for module, deps in reverse_map.items()}\n\n return test_map\n\n\ndef check_imports_all_exist():\n \"\"\"\n Isn't used per se by the test fetcher but might be used later as a quality check. Putting this here for now so the\n code is not lost. This checks all imports in a given file do exist.\n \"\"\"\n cache = {}\n all_modules = list(PATH_TO_DIFFUSERS.glob(\"**/*.py\")) + list(PATH_TO_TESTS.glob(\"**/*.py\"))\n all_modules = [str(mod.relative_to(PATH_TO_REPO)) for mod in all_modules]\n direct_deps = {m: get_module_dependencies(m, cache=cache) for m in all_modules}\n\n for module, deps in direct_deps.items():\n for dep in deps:\n if not (PATH_TO_REPO / dep).is_file():\n print(f\"{module} has dependency on {dep} which does not exist.\")\n\n\ndef _print_list(l) -> str:\n \"\"\"\n Pretty print a list of elements with one line per element and a - starting each line.\n \"\"\"\n return \"\\n\".join([f\"- {f}\" for f in l])\n\n\ndef update_test_map_with_core_pipelines(json_output_file: str):\n print(f\"\\n### ADD CORE PIPELINE TESTS ###\\n{_print_list(IMPORTANT_PIPELINES)}\")\n with open(json_output_file, \"rb\") as fp:\n test_map = json.load(fp)\n\n # Add core pipelines as their own test group\n test_map[\"core_pipelines\"] = \" \".join(\n sorted([str(PATH_TO_TESTS / f\"pipelines/{pipe}\") for pipe in IMPORTANT_PIPELINES])\n )\n\n # If there are no existing pipeline tests save the map\n if \"pipelines\" not in test_map:\n with open(json_output_file, \"w\", encoding=\"UTF-8\") as fp:\n json.dump(test_map, fp, ensure_ascii=False)\n\n pipeline_tests = test_map.pop(\"pipelines\")\n pipeline_tests = pipeline_tests.split(\" \")\n\n # Remove core pipeline tests from the fetched pipeline tests\n updated_pipeline_tests = []\n for pipe in pipeline_tests:\n if pipe == \"tests/pipelines\" or Path(pipe).parts[2] in IMPORTANT_PIPELINES:\n continue\n updated_pipeline_tests.append(pipe)\n\n if len(updated_pipeline_tests) > 0:\n test_map[\"pipelines\"] = \" \".join(sorted(updated_pipeline_tests))\n\n with open(json_output_file, \"w\", encoding=\"UTF-8\") as fp:\n json.dump(test_map, fp, ensure_ascii=False)\n\n\ndef create_json_map(test_files_to_run: List[str], json_output_file: Optional[str] = None):\n \"\"\"\n Creates a map from a list of tests to run to easily split them by category, when running parallelism of slow tests.\n\n Args:\n test_files_to_run (`List[str]`): The list of tests to run.\n json_output_file (`str`): The path where to store the built json map.\n \"\"\"\n if json_output_file is None:\n return\n\n test_map = {}\n for test_file in test_files_to_run:\n # `test_file` is a path to a test folder/file, starting with `tests/`. For example,\n # - `tests/models/bert/test_modeling_bert.py` or `tests/models/bert`\n # - `tests/trainer/test_trainer.py` or `tests/trainer`\n # - `tests/test_modeling_common.py`\n names = test_file.split(os.path.sep)\n module = names[1]\n if module in MODULES_TO_IGNORE:\n continue\n\n if len(names) > 2 or not test_file.endswith(\".py\"):\n # test folders under `tests` or python files under them\n # take the part like tokenization, `pipeline`, etc. for other test categories\n key = os.path.sep.join(names[1:2])\n else:\n # common test files directly under `tests/`\n key = \"common\"\n\n if key not in test_map:\n test_map[key] = []\n test_map[key].append(test_file)\n\n # sort the keys & values\n keys = sorted(test_map.keys())\n test_map = {k: \" \".join(sorted(test_map[k])) for k in keys}\n\n with open(json_output_file, \"w\", encoding=\"UTF-8\") as fp:\n json.dump(test_map, fp, ensure_ascii=False)\n\n\ndef infer_tests_to_run(\n output_file: str,\n diff_with_last_commit: bool = False,\n json_output_file: Optional[str] = None,\n):\n \"\"\"\n The main function called by the test fetcher. Determines the tests to run from the diff.\n\n Args:\n output_file (`str`):\n The path where to store the summary of the test fetcher analysis. Other files will be stored in the same\n folder:\n\n - examples_test_list.txt: The list of examples tests to run.\n - test_repo_utils.txt: Will indicate if the repo utils tests should be run or not.\n - doctest_list.txt: The list of doctests to run.\n\n diff_with_last_commit (`bool`, *optional*, defaults to `False`):\n Whether to analyze the diff with the last commit (for use on the main branch after a PR is merged) or with\n the branching point from main (for use on each PR).\n filter_models (`bool`, *optional*, defaults to `True`):\n Whether or not to filter the tests to core models only, when a file modified results in a lot of model\n tests.\n json_output_file (`str`, *optional*):\n The path where to store the json file mapping categories of tests to tests to run (used for parallelism or\n the slow tests).\n \"\"\"\n modified_files = get_modified_python_files(diff_with_last_commit=diff_with_last_commit)\n print(f\"\\n### MODIFIED FILES ###\\n{_print_list(modified_files)}\")\n # Create the map that will give us all impacted modules.\n reverse_map = create_reverse_dependency_map()\n impacted_files = modified_files.copy()\n for f in modified_files:\n if f in reverse_map:\n impacted_files.extend(reverse_map[f])\n\n # Remove duplicates\n impacted_files = sorted(set(impacted_files))\n print(f\"\\n### IMPACTED FILES ###\\n{_print_list(impacted_files)}\")\n\n # Grab the corresponding test files:\n if any(x in modified_files for x in [\"setup.py\"]):\n test_files_to_run = [\"tests\", \"examples\"]\n\n # in order to trigger pipeline tests even if no code change at all\n if \"tests/utils/tiny_model_summary.json\" in modified_files:\n test_files_to_run = [\"tests\"]\n any(f.split(os.path.sep)[0] == \"utils\" for f in modified_files)\n else:\n # All modified tests need to be run.\n test_files_to_run = [\n f for f in modified_files if f.startswith(\"tests\") and f.split(os.path.sep)[-1].startswith(\"test\")\n ]\n # Then we grab the corresponding test files.\n test_map = create_module_to_test_map(reverse_map=reverse_map)\n for f in modified_files:\n if f in test_map:\n test_files_to_run.extend(test_map[f])\n test_files_to_run = sorted(set(test_files_to_run))\n # Make sure we did not end up with a test file that was removed\n test_files_to_run = [f for f in test_files_to_run if (PATH_TO_REPO / f).exists()]\n\n any(f.split(os.path.sep)[0] == \"utils\" for f in modified_files)\n\n examples_tests_to_run = [f for f in test_files_to_run if f.startswith(\"examples\")]\n test_files_to_run = [f for f in test_files_to_run if not f.startswith(\"examples\")]\n print(f\"\\n### TEST TO RUN ###\\n{_print_list(test_files_to_run)}\")\n if len(test_files_to_run) > 0:\n with open(output_file, \"w\", encoding=\"utf-8\") as f:\n f.write(\" \".join(test_files_to_run))\n\n # Create a map that maps test categories to test files, i.e. `models/bert` -> [...test_modeling_bert.py, ...]\n\n # Get all test directories (and some common test files) under `tests` and `tests/models` if `test_files_to_run`\n # contains `tests` (i.e. when `setup.py` is changed).\n if \"tests\" in test_files_to_run:\n test_files_to_run = get_all_tests()\n\n create_json_map(test_files_to_run, json_output_file)\n\n print(f\"\\n### EXAMPLES TEST TO RUN ###\\n{_print_list(examples_tests_to_run)}\")\n if len(examples_tests_to_run) > 0:\n # We use `all` in the case `commit_flags[\"test_all\"]` as well as in `create_circleci_config.py` for processing\n if examples_tests_to_run == [\"examples\"]:\n examples_tests_to_run = [\"all\"]\n example_file = Path(output_file).parent / \"examples_test_list.txt\"\n with open(example_file, \"w\", encoding=\"utf-8\") as f:\n f.write(\" \".join(examples_tests_to_run))\n\n\ndef filter_tests(output_file: str, filters: List[str]):\n \"\"\"\n Reads the content of the output file and filters out all the tests in a list of given folders.\n\n Args:\n output_file (`str` or `os.PathLike`): The path to the output file of the tests fetcher.\n filters (`List[str]`): A list of folders to filter.\n \"\"\"\n if not os.path.isfile(output_file):\n print(\"No test file found.\")\n return\n with open(output_file, \"r\", encoding=\"utf-8\") as f:\n test_files = f.read().split(\" \")\n\n if len(test_files) == 0 or test_files == [\"\"]:\n print(\"No tests to filter.\")\n return\n\n if test_files == [\"tests\"]:\n test_files = [os.path.join(\"tests\", f) for f in os.listdir(\"tests\") if f not in [\"__init__.py\"] + filters]\n else:\n test_files = [f for f in test_files if f.split(os.path.sep)[1] not in filters]\n\n with open(output_file, \"w\", encoding=\"utf-8\") as f:\n f.write(\" \".join(test_files))\n\n\ndef parse_commit_message(commit_message: str) -> Dict[str, bool]:\n \"\"\"\n Parses the commit message to detect if a command is there to skip, force all or part of the CI.\n\n Args:\n commit_message (`str`): The commit message of the current commit.\n\n Returns:\n `Dict[str, bool]`: A dictionary of strings to bools with keys the following keys: `\"skip\"`,\n `\"test_all_models\"` and `\"test_all\"`.\n \"\"\"\n if commit_message is None:\n return {\"skip\": False, \"no_filter\": False, \"test_all\": False}\n\n command_search = re.search(r\"\\[([^\\]]*)\\]\", commit_message)\n if command_search is not None:\n command = command_search.groups()[0]\n command = command.lower().replace(\"-\", \" \").replace(\"_\", \" \")\n skip = command in [\"ci skip\", \"skip ci\", \"circleci skip\", \"skip circleci\"]\n no_filter = set(command.split(\" \")) == {\"no\", \"filter\"}\n test_all = set(command.split(\" \")) == {\"test\", \"all\"}\n return {\"skip\": skip, \"no_filter\": no_filter, \"test_all\": test_all}\n else:\n return {\"skip\": False, \"no_filter\": False, \"test_all\": False}\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--output_file\", type=str, default=\"test_list.txt\", help=\"Where to store the list of tests to run\"\n )\n parser.add_argument(\n \"--json_output_file\",\n type=str,\n default=\"test_map.json\",\n help=\"Where to store the tests to run in a dictionary format mapping test categories to test files\",\n )\n parser.add_argument(\n \"--diff_with_last_commit\",\n action=\"store_true\",\n help=\"To fetch the tests between the current commit and the last commit\",\n )\n parser.add_argument(\n \"--filter_tests\",\n action=\"store_true\",\n help=\"Will filter the pipeline/repo utils tests outside of the generated list of tests.\",\n )\n parser.add_argument(\n \"--print_dependencies_of\",\n type=str,\n help=\"Will only print the tree of modules depending on the file passed.\",\n default=None,\n )\n parser.add_argument(\n \"--commit_message\",\n type=str,\n help=\"The commit message (which could contain a command to force all tests or skip the CI).\",\n default=None,\n )\n args = parser.parse_args()\n if args.print_dependencies_of is not None:\n print_tree_deps_of(args.print_dependencies_of)\n else:\n repo = Repo(PATH_TO_REPO)\n commit_message = repo.head.commit.message\n commit_flags = parse_commit_message(commit_message)\n if commit_flags[\"skip\"]:\n print(\"Force-skipping the CI\")\n quit()\n if commit_flags[\"no_filter\"]:\n print(\"Running all tests fetched without filtering.\")\n if commit_flags[\"test_all\"]:\n print(\"Force-launching all tests\")\n\n diff_with_last_commit = args.diff_with_last_commit\n if not diff_with_last_commit and not repo.head.is_detached and repo.head.ref == repo.refs.main:\n print(\"main branch detected, fetching tests against last commit.\")\n diff_with_last_commit = True\n\n if not commit_flags[\"test_all\"]:\n try:\n infer_tests_to_run(\n args.output_file,\n diff_with_last_commit=diff_with_last_commit,\n json_output_file=args.json_output_file,\n )\n filter_tests(args.output_file, [\"repo_utils\"])\n update_test_map_with_core_pipelines(json_output_file=args.json_output_file)\n\n except Exception as e:\n print(f\"\\nError when trying to grab the relevant tests: {e}\\n\\nRunning all tests.\")\n commit_flags[\"test_all\"] = True\n\n if commit_flags[\"test_all\"]:\n with open(args.output_file, \"w\", encoding=\"utf-8\") as f:\n f.write(\"tests\")\n example_file = Path(args.output_file).parent / \"examples_test_list.txt\"\n with open(example_file, \"w\", encoding=\"utf-8\") as f:\n f.write(\"all\")\n\n test_files_to_run = get_all_tests()\n create_json_map(test_files_to_run, args.json_output_file)\n update_test_map_with_core_pipelines(json_output_file=args.json_output_file)\n" }, { "path": "diffusers/utils/update_metadata.py", "content": "# coding=utf-8\n# Copyright 2024 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nUtility that updates the metadata of the Diffusers library in the repository `huggingface/diffusers-metadata`.\n\nUsage for an update (as used by the GitHub action `update_metadata`):\n\n```bash\npython utils/update_metadata.py\n```\n\nScript modified from:\nhttps://github.com/huggingface/transformers/blob/main/utils/update_metadata.py\n\"\"\"\n\nimport argparse\nimport os\nimport tempfile\n\nimport pandas as pd\nfrom datasets import Dataset\nfrom huggingface_hub import hf_hub_download, upload_folder\n\nfrom diffusers.pipelines.auto_pipeline import (\n AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,\n AUTO_INPAINT_PIPELINES_MAPPING,\n AUTO_TEXT2IMAGE_PIPELINES_MAPPING,\n)\n\n\nPIPELINE_TAG_JSON = \"pipeline_tags.json\"\n\n\ndef get_supported_pipeline_table() -> dict:\n \"\"\"\n Generates a dictionary containing the supported auto classes for each pipeline type,\n using the content of the auto modules.\n \"\"\"\n # All supported pipelines for automatic mapping.\n all_supported_pipeline_classes = [\n (class_name.__name__, \"text-to-image\", \"AutoPipelineForText2Image\")\n for _, class_name in AUTO_TEXT2IMAGE_PIPELINES_MAPPING.items()\n ]\n all_supported_pipeline_classes += [\n (class_name.__name__, \"image-to-image\", \"AutoPipelineForImage2Image\")\n for _, class_name in AUTO_IMAGE2IMAGE_PIPELINES_MAPPING.items()\n ]\n all_supported_pipeline_classes += [\n (class_name.__name__, \"image-to-image\", \"AutoPipelineForInpainting\")\n for _, class_name in AUTO_INPAINT_PIPELINES_MAPPING.items()\n ]\n all_supported_pipeline_classes = list(set(all_supported_pipeline_classes))\n all_supported_pipeline_classes.sort(key=lambda x: x[0])\n\n data = {}\n data[\"pipeline_class\"] = [sample[0] for sample in all_supported_pipeline_classes]\n data[\"pipeline_tag\"] = [sample[1] for sample in all_supported_pipeline_classes]\n data[\"auto_class\"] = [sample[2] for sample in all_supported_pipeline_classes]\n\n return data\n\n\ndef update_metadata(commit_sha: str):\n \"\"\"\n Update the metadata for the Diffusers repo in `huggingface/diffusers-metadata`.\n\n Args:\n commit_sha (`str`): The commit SHA on Diffusers corresponding to this update.\n \"\"\"\n pipelines_table = get_supported_pipeline_table()\n pipelines_table = pd.DataFrame(pipelines_table)\n pipelines_dataset = Dataset.from_pandas(pipelines_table)\n\n hub_pipeline_tags_json = hf_hub_download(\n repo_id=\"huggingface/diffusers-metadata\",\n filename=PIPELINE_TAG_JSON,\n repo_type=\"dataset\",\n )\n with open(hub_pipeline_tags_json) as f:\n hub_pipeline_tags_json = f.read()\n\n with tempfile.TemporaryDirectory() as tmp_dir:\n pipelines_dataset.to_json(os.path.join(tmp_dir, PIPELINE_TAG_JSON))\n\n with open(os.path.join(tmp_dir, PIPELINE_TAG_JSON)) as f:\n pipeline_tags_json = f.read()\n\n hub_pipeline_tags_equal = hub_pipeline_tags_json == pipeline_tags_json\n if hub_pipeline_tags_equal:\n print(\"No updates, not pushing the metadata files.\")\n return\n\n if commit_sha is not None:\n commit_message = (\n f\"Update with commit {commit_sha}\\n\\nSee: \"\n f\"https://github.com/huggingface/diffusers/commit/{commit_sha}\"\n )\n else:\n commit_message = \"Update\"\n\n upload_folder(\n repo_id=\"huggingface/diffusers-metadata\",\n folder_path=tmp_dir,\n repo_type=\"dataset\",\n commit_message=commit_message,\n )\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--commit_sha\", default=None, type=str, help=\"The sha of the commit going with this update.\")\n args = parser.parse_args()\n\n update_metadata(args.commit_sha)\n" }, { "path": "example.py", "content": "# For Editing with SD3\r\nimport torch\r\nfrom diffusers import StableDiffusion3InstructPix2PixPipeline\r\nfrom diffusers.utils import load_image\r\nimport requests\r\nimport PIL.Image\r\nimport PIL.ImageOps\r\n\r\npipe = StableDiffusion3InstructPix2PixPipeline.from_pretrained(\"BleachNick/SD3_UltraEdit_w_mask\", torch_dtype=torch.float16)\r\npipe = pipe.to(\"cuda\")\r\nprompt=\"What if the horse wears a hat?\"\r\nimg = load_image(\"input.png\").resize((512, 512))\r\nmask_img = load_image(\"mask_img.png\").resize(img.size)\r\n# For free form Editing, seed a blank mask\r\n# mask_img = PIL.Image.new(\"RGB\", img.size, (255, 255, 255))\r\nimage = pipe(\r\n prompt,\r\n image=img,\r\n mask_img=mask_img,\r\n negative_prompt=\"\",\r\n num_inference_steps=50,\r\n image_guidance_scale=1.5,\r\n guidance_scale=7.5,\r\n).images[0]\r\nimage.save(\"edited_image.png\")\r\n# display image\r\n\r\n\r\n" }, { "path": "requirements.txt", "content": "accelerate==0.31.0\nclip @ git+https://github.com/openai/CLIP.git@dcba3cb2e2827b402d2701e7e1c7d9fed8a20ef1\ndatasets==2.20.0\nhuggingface-hub==0.23.4\nnumpy==1.26.3\nomegaconf==2.3.0\nopenai-clip==1.0.1\nsentencepiece==0.2.0\ntokenizers==0.19.1\ntorch==2.3.0+cu118\ntransformers==4.41.2\nxformers" }, { "path": "scripts/run_sft_512_sd3_stage1.sh", "content": "#!/bin/bash\nexport PYTHONFAULTHANDLER=1\nexport CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7\npretrained_model_name_or_path=stabilityai/stable-diffusion-3-medium-diffusers\nori_model_name_or_path=stabilityai/stable-diffusion-3-medium-diffusers\ntrain_data_jsonl=Sorted_free_from_editing_final_4M.jsonl # for you own dataset\n#dataset_name=BleachNick/UltraEdit_500k # 500k for example; better performance using the final 4M data\nresolution=512\ntrain_batch_size=8\ngradient_accumulation_steps=4\ncheckpointing_steps=1000\nvalidation_step=500\n\nepoch=5\nlr=5e-05\noutput_dir=\"resolution_${resolution}_model_epoch_${epoch}_sd3_5e5\"\naccelerate launch --mixed_precision=\"fp16\" --multi_gpu --main_process_port=29555 training/train_sd3_pix2pix.py \\\n --pretrained_model_name_or_path ${pretrained_model_name_or_path} \\\n --output_dir ${output_dir} \\\n --resolution ${resolution} \\\n --num_train_epochs ${epoch} \\\n --random_flip \\\n --train_batch_size ${train_batch_size} \\\n --gradient_accumulation_steps ${gradient_accumulation_steps} \\\n --checkpointing_steps ${checkpointing_steps} \\\n --checkpoints_total_limit 2 \\\n --learning_rate ${lr} \\\n --lr_warmup_steps 500 \\\n --conditioning_dropout_prob 0.05 \\\n --mixed_precision fp16 \\\n --seed 3345 \\\n --num_validation_images 4 \\\n --validation_step ${validation_step} \\\n --dataloader_num_workers 64 \\\n --lr_scheduler cosine \\\n --report_to wandb \\\n --val_image_url input.png \\\n --validation_prompt \"What if the horse wears a hat?\" \\\n --ori_model_name_or_path ${ori_model_name_or_path} \\\n --max_sequence_length 256 \\\n --train_data_jsonl ${train_data_jsonl} \\\n# --dataset_name ${dataset_name} \\\n\n\n\n" }, { "path": "scripts/run_sft_512_sdxl_stage1.sh", "content": "#!/bin/bash\nexport PYTHONFAULTHANDLER=1\nexport CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7\npretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0\nori_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0\ntrain_data_jsonl=Sorted_free_from_editing_final_4M.jsonl # for you own dataset\nresolution=512\ntrain_batch_size=18\ngradient_accumulation_steps=2\ncheckpointing_steps=500\nvalidation_step=500\n# max_train_samples=1\nepoch=2\nlr=5e-05\noutput_dir=\"resolution_${resolution}_model_cosine_ep${epoch}_sdxl\"\naccelerate launch --mixed_precision=\"fp16\" --multi_gpu --main_process_port=29555 training/train_sdxl_pix2pix.py \\\n --pretrained_model_name_or_path ${pretrained_model_name_or_path} \\\n --use_ema \\\n --output_dir ${output_dir} \\\n --train_data_jsonl ${train_data_jsonl} \\\n --resolution ${resolution} \\\n --num_train_epochs ${epoch} \\\n --random_flip \\\n --train_batch_size ${train_batch_size} \\\n --gradient_accumulation_steps ${gradient_accumulation_steps} \\\n --checkpointing_steps ${checkpointing_steps} \\\n --checkpoints_total_limit 2 \\\n --learning_rate ${lr} \\\n --lr_warmup_steps 500 \\\n --conditioning_dropout_prob 0.05 \\\n --mixed_precision fp16 \\\n --seed 42 \\\n --gradient_checkpointing \\\n --num_validation_images 4 \\\n --val_image_url input.png \\\n --validation_prompt \"What if the horse wears a hat?\" \\\n --validation_step ${validation_step} \\\n --lr_scheduler cosine \\\n --ori_model_name_or_path ${ori_model_name_or_path} \\\n --enable_xformers_memory_efficient_attention \\\n --dataloader_num_workers 16 \\\n --gradient_checkpointing \\\n\n" }, { "path": "scripts/run_sft_512_sdxl_with_mask_stage2.sh", "content": "#!/bin/bash\nexport PYTHONFAULTHANDLER=1\nexport CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7\npretrained_model_name_or_path=resolution_512_model_cosine_ep2_sdxl\nori_model_name_or_path=resolution_512_model_cosine_ep2_sdxl\ntrain_data_jsonl=mixed_mask_free_1M_6_29.jsonl # mix the redion-basd and free-form image editing data\nresolution=512\ntrain_batch_size=18\ngradient_accumulation_steps=2\ncheckpointing_steps=500\nvalidation_step=500\n# max_train_samples=1\nepoch=2\nlr=1e-05\noutput_dir=\"resolution_${resolution}_model_cosine_ep{epoch}_sdxl\"\naccelerate launch --mixed_precision=\"fp16\" --multi_gpu --main_process_port=29555 training/train_sdxl_pix2pix.py \\\n --pretrained_model_name_or_path ${pretrained_model_name_or_path} \\\n --use_ema \\\n --output_dir ${output_dir} \\\n --train_data_jsonl ${train_data_jsonl} \\\n --resolution ${resolution} \\\n --do_mask \\\n --num_train_epochs ${epoch} \\\n --random_flip \\\n --train_batch_size ${train_batch_size} \\\n --gradient_accumulation_steps ${gradient_accumulation_steps} \\\n --checkpointing_steps ${checkpointing_steps} \\\n --checkpoints_total_limit 2 \\\n --learning_rate ${lr} \\\n --lr_warmup_steps 500 \\\n --conditioning_dropout_prob 0.05 \\\n --mixed_precision fp16 \\\n --seed 42 \\\n --gradient_checkpointing \\\n --num_validation_images 4 \\\n --val_image_url input.png \\\n --validation_prompt \"What if the horse wears a hat?\" \\\n --val_mask_url mask_img.png \\\n --validation_step ${validation_step} \\\n --lr_scheduler cosine \\\n --ori_model_name_or_path ${ori_model_name_or_path} \\\n --enable_xformers_memory_efficient_attention \\\n --dataloader_num_workers 16 \\\n --gradient_checkpointing \\\n\n" }, { "path": "scripts/run_sft_512_with_mask_sd3_stage2.sh", "content": "#!/bin/bash\nexport PYTHONFAULTHANDLER=1\nexport CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7\npretrained_model_name_or_path=resolution_512_model_epoch_5_sd3_5e5\nori_model_name_or_path=resolution_512_model_epoch_5_sd3_5e5\nresolution=512\ntrain_data_jsonl=mixed_mask_free_1M_6_29.jsonl # mix the redion-basd and free-form image editing data\ntrain_batch_size=8\ngradient_accumulation_steps=4\ncheckpointing_steps=400\nvalidation_step=400\n\nepoch=2\nlr=1e-05\noutput_dir=\"resolution_${resolution}_model_epoch_${epoch}_conitnue_1e5_with_mask\"\naccelerate launch --mixed_precision=\"fp16\" --multi_gpu --main_process_port=29555 training/train_sd3_pix2pix.py \\\n --pretrained_model_name_or_path ${pretrained_model_name_or_path} \\\n --output_dir ${output_dir} \\\n --train_data_jsonl ${train_data_jsonl} \\\n --resolution ${resolution} \\\n --do_mask \\\n --num_train_epochs ${epoch} \\\n --random_flip \\\n --train_batch_size ${train_batch_size} \\\n --gradient_accumulation_steps ${gradient_accumulation_steps} \\\n --checkpointing_steps ${checkpointing_steps} \\\n --checkpoints_total_limit 2 \\\n --learning_rate ${lr} \\\n --lr_warmup_steps 500 \\\n --conditioning_dropout_prob 0.05 \\\n --mixed_precision fp16 \\\n --seed 3345 \\\n --num_validation_images 4 \\\n --validation_step ${validation_step} \\\n --dataloader_num_workers 64 \\\n --lr_scheduler cosine \\\n --report_to wandb \\\n --val_image_url input.png \\\n --val_mask_url mask_img.png \\\n --validation_prompt \"What if the horse wears a hat?\" \\\n --ori_model_name_or_path ${ori_model_name_or_path} \\\n --max_sequence_length 256 \\\n\n\n\n\n" }, { "path": "traning/train_sd3_pix2pix.py", "content": "import torch.nn.init as init\nimport argparse\nfrom cgitb import text\nimport copy\nimport gc\nimport itertools\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nfrom tkinter import NO\nimport warnings\nfrom contextlib import nullcontext\nfrom pathlib import Path\nimport PIL.Image\nimport PIL.ImageOps\nimport numpy as np\nfrom sympy import N\nimport torch\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import DistributedDataParallelKwargs, ProjectConfiguration, set_seed\nfrom huggingface_hub import create_repo, upload_folder\nfrom huggingface_hub.utils import insecure_hashlib\nfrom PIL import Image\nfrom PIL.ImageOps import exif_transpose\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom torchvision.transforms.functional import crop\nfrom tqdm.auto import tqdm\nfrom transformers import CLIPTextModelWithProjection, CLIPTokenizer, PretrainedConfig, T5EncoderModel, T5TokenizerFast\n# from transformer_sd3 import SD3Transformer2DModel\n\nimport diffusers\nfrom diffusers import (\n AutoencoderKL,\n FlowMatchEulerDiscreteScheduler,\n StableDiffusion3Pipeline,\n SD3Transformer2DModel,\n StableDiffusion3InstructPix2PixPipeline\n)\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import (\n check_min_version,\n is_wandb_available,\n)\nfrom diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card\nfrom diffusers.utils.torch_utils import is_compiled_module\n\nimport accelerate\nimport datasets\nimport PIL\nimport requests\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom os.path import join\nfrom datasets import load_dataset\nfrom packaging import version\n\n\ndef load_text_encoders(class_one, class_two, class_three):\n text_encoder_one = class_one.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n text_encoder_two = class_two.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder_2\", revision=args.revision, variant=args.variant\n )\n text_encoder_three = class_three.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder_3\", revision=args.revision, variant=args.variant\n )\n return text_encoder_one, text_encoder_two, text_encoder_three\n\n\ndef tokenize_prompt(tokenizer, prompt, max_sequence_length=77):\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=max_sequence_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n return text_input_ids\n\n\ndef _encode_prompt_with_t5(\n text_encoder,\n tokenizer,\n max_sequence_length,\n text_encoder_dtype,\n prompt=None,\n num_images_per_prompt=1,\n device=None,\n text_input_ids=None\n\n):\n prompt = [prompt] if isinstance(prompt, str) else prompt\n batch_size = len(prompt)\n if text_input_ids is None:\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=max_sequence_length,\n truncation=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n prompt_embeds = text_encoder(text_input_ids.to(device))[0]\n\n prompt_embeds = prompt_embeds.to(dtype=text_encoder_dtype, device=device)\n\n _, seq_len, _ = prompt_embeds.shape\n\n # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n return prompt_embeds\n\n\ndef _encode_prompt_with_clip(\n text_encoder,\n tokenizer,\n prompt: str,\n text_encoder_dtype,\n device=None,\n num_images_per_prompt: int = 1,\n text_input_ids=None\n):\n prompt = [prompt] if isinstance(prompt, str) else prompt\n batch_size = len(prompt)\n if text_input_ids is None:\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=77,\n truncation=True,\n return_tensors=\"pt\",\n )\n\n text_input_ids = text_inputs.input_ids\n prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)\n\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds.hidden_states[-2]\n prompt_embeds = prompt_embeds.to(dtype=text_encoder_dtype, device=device)\n\n _, seq_len, _ = prompt_embeds.shape\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)\n\n return prompt_embeds, pooled_prompt_embeds\n\n\ndef encode_prompt(\n text_encoders,\n tokenizers,\n prompt: str,\n max_sequence_length=None,\n text_encoders_dtypes=[torch.float32,torch.float32,torch.float32],\n device=None,\n num_images_per_prompt: int = 1,\n text_input_ids_list=None\n):\n prompt = [prompt] if isinstance(prompt, str) else prompt\n clip_prompt_embeds_list = []\n clip_pooled_prompt_embeds_list = []\n clip_tokenizers = tokenizers[:2]\n clip_text_encoders = text_encoders[:2]\n clip_text_encoders_dtypes = text_encoders_dtypes[:2]\n if text_input_ids_list is not None:\n clip_text_input_ids_list = text_input_ids_list[:2]\n else:\n clip_text_input_ids_list = [None, None]\n zipped_text_encoders = zip(clip_tokenizers, clip_text_encoders, clip_text_encoders_dtypes, clip_text_input_ids_list)\n for tokenizer, text_encoder, clip_text_encoder_dtype, text_input_ids in zipped_text_encoders:\n prompt_embeds, pooled_prompt_embeds = _encode_prompt_with_clip(\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n prompt=prompt,\n text_encoder_dtype=clip_text_encoder_dtype,\n device=device if device is not None else text_encoder.device,\n num_images_per_prompt=num_images_per_prompt,\n text_input_ids=text_input_ids,\n\n )\n clip_prompt_embeds_list.append(prompt_embeds)\n clip_pooled_prompt_embeds_list.append(pooled_prompt_embeds)\n\n clip_prompt_embeds = torch.cat(clip_prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = torch.cat(clip_pooled_prompt_embeds_list, dim=-1)\n\n if text_input_ids_list is not None:\n t5_text_input_ids = text_input_ids_list[-1]\n else:\n t5_text_input_ids = None\n t5_prompt_embed = _encode_prompt_with_t5(\n text_encoders[-1],\n tokenizers[-1],\n max_sequence_length,\n clip_text_encoders_dtypes[-1],\n prompt=prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device if device is not None else text_encoders[-1].device,\n text_input_ids=t5_text_input_ids\n )\n\n clip_prompt_embeds = torch.nn.functional.pad(\n clip_prompt_embeds, (0, t5_prompt_embed.shape[-1] - clip_prompt_embeds.shape[-1])\n )\n prompt_embeds = torch.cat([clip_prompt_embeds, t5_prompt_embed], dim=-2)\n\n return prompt_embeds, pooled_prompt_embeds\n\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\nDATASET_NAME_MAPPING = {\n \"BleachNick/UltraEdit_500k\": (\"source_image\", \"edited_image\", \"edit_prompt\"),\n}\nWANDB_TABLE_COL_NAMES = [\"source_image\", \"edited_image\", \"edit_prompt\"]\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script for InstructPix2Pix.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--ori_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to ori_model_name_or_path.\",\n )\n parser.add_argument(\n \"--weighting_scheme\", type=str, default=\"logit_normal\", choices=[\"sigma_sqrt\", \"logit_normal\", \"mode\", \"cosmap\"]\n )\n parser.add_argument(\n \"--logit_mean\", type=float, default=0.0, help=\"mean to use when using the `'logit_normal'` weighting scheme.\"\n )\n parser.add_argument(\n \"--logit_std\", type=float, default=1.0, help=\"std to use when using the `'logit_normal'` weighting scheme.\"\n )\n parser.add_argument(\n \"--mode_scale\",\n type=float,\n default=1.29,\n help=\"Scale of mode weighting scheme. Only effective when using the `'mode'` as the `weighting_scheme`.\",\n )\n\n parser.add_argument(\n \"--optimizer\",\n type=str,\n default=\"AdamW\",\n help=('The optimizer type to use. Choose between [\"AdamW\", \"prodigy\"]'),\n )\n\n parser.add_argument(\n \"--use_8bit_adam\",\n action=\"store_true\",\n help=\"Whether or not to use 8-bit Adam from bitsandbytes. Ignored if optimizer is not set to AdamW\",\n )\n\n parser.add_argument(\n \"--adam_weight_decay_text_encoder\", type=float, default=1e-03, help=\"Weight decay to use for text_encoder\"\n )\n parser.add_argument(\n \"--prodigy_beta3\",\n type=float,\n default=None,\n help=\"coefficients for computing the Prodidy stepsize using running averages. If set to None, \"\n \"uses the value of square root of beta2. Ignored if optimizer is adamW\",\n )\n\n parser.add_argument(\n \"--prodigy_use_bias_correction\",\n type=bool,\n default=True,\n help=\"Turn on Adam's bias correction. True by default. Ignored if optimizer is adamW\",\n )\n parser.add_argument(\"--prodigy_decouple\", type=bool, default=True, help=\"Use AdamW style decoupled weight decay\")\n parser.add_argument(\n \"--prodigy_safeguard_warmup\",\n type=bool,\n default=True,\n help=\"Remove lr from the denominator of D estimate to avoid issues during warm-up stage. True by default. \"\n \"Ignored if optimizer is adamW\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_jsonl\",\n type=str,\n default=None,\n help=(\n \"A folder containing the training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--original_image_column\",\n type=str,\n default=\"source_image\",\n help=\"The column of the dataset containing the original image on which edits where made.\",\n )\n parser.add_argument(\n \"--config_file\",\n type=str,\n default=None,\n )\n parser.add_argument(\n \"--edited_image_column\",\n type=str,\n default=\"edited_image\",\n help=\"The column of the dataset containing the edited image.\",\n )\n parser.add_argument(\n \"--edit_prompt_column\",\n type=str,\n default=\"edit_prompt\",\n help=\"The column of the dataset containing the edit instruction.\",\n )\n parser.add_argument(\n \"--val_image_url\",\n type=str,\n default=None,\n help=\"URL to the original image that you would like to edit (used during inference for debugging purposes).\",\n )\n parser.add_argument(\n '--val_mask_url',\n type=str,\n default=None,\n help=\"URL to the mask image that you would like to edit (used during inference for debugging purposes).\",\n )\n parser.add_argument(\n \"--validation_prompt\", type=str, default=None, help=\"A prompt that is sampled during training for inference.\"\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_epochs\",\n type=int,\n default=1,\n help=(\n \"Run fine-tuning validation every X epochs. The validation process consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--validation_step\",\n type=int,\n default=5000,\n help=(\n \"Run fine-tuning validation every X steps. The validation process consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--top_training_data_sample\",\n type=int,\n default=None,\n help=\"Number of top samples to use for training, ranked by clip-sim-dit. If None, use the full dataset.\",\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"sd3_edit\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=256,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--eval_resolution\",\n type=int,\n default=512,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this\"\n \" resolution\"\n ),\n )\n parser.add_argument(\n \"--max_sequence_length\",\n type=int,\n default=77,\n help=\"Maximum sequence length to use with with the T5 text encoder\",\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"cosine\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--conditioning_dropout_prob\",\n type=float,\n default=None,\n help=\"Conditioning dropout probability. Drops out the conditionings (image and edit prompt) used in training InstructPix2Pix. See section 3.2.1 in the paper: https://arxiv.org/abs/2211.09800.\",\n )\n\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n\n parser.add_argument(\n \"--text_encoder_lr\",\n type=float,\n default=5e-6,\n help=\"Text encoder learning rate to use.\",\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--lr_num_cycles\",\n type=int,\n default=1,\n help=\"Number of hard resets of the lr in cosine_with_restarts scheduler.\",\n )\n parser.add_argument(\"--lr_power\", type=float, default=1.0, help=\"Power factor of the polynomial scheduler.\")\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--train_text_encoder\",\n action=\"store_true\"\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--do_mask\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--mask_column\",\n type=str,\n default=\"mask_image\",\n help=\"The column of the dataset containing the original image`s mask.\",\n )\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_jsonl is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n\n # default to using the same revision for the non-ema model if not specified\n\n return args\n\n\ndef combine_rgb_and_mask_to_rgba(rgb_image, mask_image):\n # Ensure the input images are the same size\n if rgb_image.size != mask_image.size:\n raise ValueError(\"The RGB image and the mask image must have the same dimensions\")\n\n # Convert the mask image to 'L' mode (grayscale) if it is not\n if mask_image.mode != 'L':\n mask_image = mask_image.convert('L')\n\n # Split the RGB image into its three channels\n r, g, b = rgb_image.split()\n\n # Combine the RGB channels with the mask to form an RGBA image\n rgba_image = Image.merge(\"RGBA\", (r, g, b, mask_image))\n\n return rgba_image\n\n\ndef convert_to_np(image, resolution):\n try:\n if isinstance(image, str):\n if image == \"NONE\":\n image = PIL.Image.new(\"RGB\", (resolution, resolution), (255, 255, 255))\n else:\n image = PIL.Image.open(image)\n elif image is None:\n image = PIL.Image.new(\"RGB\", (resolution, resolution), (255, 255, 255))\n image = image.convert(\"RGB\").resize((resolution, resolution))\n return np.array(image).transpose(2, 0, 1)\n except Exception as e:\n print(\"Load error\", image)\n print(e)\n # New blank image\n image = PIL.Image.new(\"RGB\", (resolution, resolution), (255, 255, 255))\n return np.array(image).transpose(2, 0, 1)\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n if model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n elif model_class == \"T5EncoderModel\":\n from transformers import T5EncoderModel\n\n return T5EncoderModel\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef main():\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n logging_dir = os.path.join(args.output_dir, args.logging_dir)\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n from accelerate import DistributedDataParallelKwargs as DDPK\n kwargs = DDPK(find_unused_parameters=True)\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n kwargs_handlers=[kwargs],\n )\n if torch.backends.mps.is_available():\n accelerator.native_amp = False\n\n def download_image(path_or_url,resolution=512):\n # Check if path_or_url is a local file path\n if path_or_url is None:\n # return a white RBG image image\n return PIL.Image.new(\"RGB\", (resolution, resolution), (255, 255, 255))\n if os.path.exists(path_or_url):\n image = Image.open(path_or_url).convert(\"RGB\").resize((resolution, resolution))\n\n else:\n image = Image.open(requests.get(path_or_url, stream=True).raw).convert(\"RGB\")\n\n image = PIL.ImageOps.exif_transpose(image)\n return image\n\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n if args.report_to == \"wandb\":\n if not is_wandb_available():\n raise ImportError(\"Make sure to install wandb if you want to use it for logging during training.\")\n import wandb\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n # Load scheduler, tokenizer and models.\n\n # Load the tokenizers\n tokenizer_one = CLIPTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n )\n tokenizer_two = CLIPTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer_2\",\n revision=args.revision,\n )\n tokenizer_three = T5TokenizerFast.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer_3\",\n revision=args.revision,\n )\n\n # import correct text encoder classes\n text_encoder_cls_one = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision\n )\n text_encoder_cls_two = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision, subfolder=\"text_encoder_2\"\n )\n text_encoder_cls_three = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision, subfolder=\"text_encoder_3\"\n )\n\n # Load scheduler and models\n noise_scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"scheduler\"\n )\n noise_scheduler_copy = copy.deepcopy(noise_scheduler)\n text_encoder_one, text_encoder_two, text_encoder_three = load_text_encoders(\n text_encoder_cls_one, text_encoder_cls_two, text_encoder_cls_three\n )\n vae = AutoencoderKL.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"vae\",\n revision=args.revision,\n variant=args.variant,\n )\n transformer = SD3Transformer2DModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"transformer\", revision=args.revision, variant=args.variant\n )\n\n # TODO\n logger.info(\"Initializing the new channel of DIT from the pretrained DIT.\")\n in_channels = int(1.5 * transformer.config.in_channels) if args.do_mask else 2 * transformer.config.in_channels # 48 for mask\n out_channels = transformer.pos_embed.proj.out_channels\n\n load_num_channel = transformer.config.in_channels\n print(\"Do mask\",args.do_mask)\n print(\"new in_channels\",in_channels)\n print(\"load_num_channel\",load_num_channel)\n\n transformer.register_to_config(in_channels=in_channels)\n print(\"transformer.pos_embed.proj.weight.shape\", transformer.pos_embed.proj.weight.shape)\n print(\"load_num_channel\", load_num_channel)\n with torch.no_grad():\n\n new_proj = nn.Conv2d(\n in_channels, out_channels, kernel_size=(transformer.config.patch_size, transformer.config.patch_size),\n stride=transformer.config.patch_size, bias=True\n )\n print(\"new_proj\", new_proj)\n\n new_proj.weight.zero_()\n # init.kaiming_normal_(new_proj.weight, mode='fan_out', nonlinearity='relu')\n # if new_proj.bias is not None and transformer.pos_embed.proj.bias is not None:\n # new_proj.bias.copy_(transformer.pos_embed.proj.bias)\n # else:\n # if new_proj.bias is not None:\n # new_proj.bias.zero_()\n new_proj = new_proj.to(transformer.pos_embed.proj.weight.dtype)\n new_proj.weight[:, :load_num_channel, :, :].copy_(transformer.pos_embed.proj.weight)\n new_proj.bias.copy_(transformer.pos_embed.proj.bias)\n print(\"new_proj\", new_proj.weight.shape)\n print(\"transformer.pos_embed.proj\", transformer.pos_embed.proj.weight.shape)\n transformer.pos_embed.proj = new_proj\n\n for param in transformer.parameters():\n param.requires_grad = True\n\n\n transformer.requires_grad_(True)\n vae.requires_grad_(False)\n if args.train_text_encoder:\n text_encoder_one.requires_grad_(True)\n text_encoder_two.requires_grad_(True)\n text_encoder_three.requires_grad_(True)\n else:\n text_encoder_one.requires_grad_(False)\n text_encoder_two.requires_grad_(False)\n text_encoder_three.requires_grad_(False)\n\n # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora transformer) to half-precision\n # as these weights are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n\n if torch.backends.mps.is_available() and weight_dtype == torch.bfloat16:\n # due to pytorch#99272, MPS does not yet support bfloat16.\n raise ValueError(\n \"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead.\"\n )\n\n vae.to(accelerator.device, dtype=torch.float32)\n\n if not args.train_text_encoder:\n text_encoder_one.to(accelerator.device, dtype=weight_dtype)\n text_encoder_two.to(accelerator.device, dtype=weight_dtype)\n text_encoder_three.to(accelerator.device, dtype=weight_dtype)\n\n if args.gradient_checkpointing:\n transformer.enable_gradient_checkpointing()\n if args.train_text_encoder:\n text_encoder_one.gradient_checkpointing_enable()\n text_encoder_two.gradient_checkpointing_enable()\n text_encoder_three.gradient_checkpointing_enable()\n\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n for i, model in enumerate(models):\n if isinstance(unwrap_model(model), SD3Transformer2DModel):\n unwrap_model(model).save_pretrained(os.path.join(output_dir, \"transformer\"))\n elif isinstance(unwrap_model(model), (CLIPTextModelWithProjection, T5EncoderModel)):\n if isinstance(unwrap_model(model), CLIPTextModelWithProjection):\n hidden_size = unwrap_model(model).config.hidden_size\n if hidden_size == 768:\n unwrap_model(model).save_pretrained(os.path.join(output_dir, \"text_encoder\"))\n elif hidden_size == 1280:\n unwrap_model(model).save_pretrained(os.path.join(output_dir, \"text_encoder_2\"))\n else:\n unwrap_model(model).save_pretrained(os.path.join(output_dir, \"text_encoder_3\"))\n else:\n raise ValueError(f\"Wrong model supplied: {type(model)=}.\")\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n\n def load_model_hook(models, input_dir):\n for _ in range(len(models)):\n # pop models so that they are not loaded again\n model = models.pop()\n\n # load diffusers style into model\n if isinstance(unwrap_model(model), SD3Transformer2DModel):\n load_model = SD3Transformer2DModel.from_pretrained(input_dir, subfolder=\"transformer\")\n model.register_to_config(**load_model.config)\n\n model.load_state_dict(load_model.state_dict())\n elif isinstance(unwrap_model(model), (CLIPTextModelWithProjection, T5EncoderModel)):\n try:\n load_model = CLIPTextModelWithProjection.from_pretrained(input_dir, subfolder=\"text_encoder\")\n model(**load_model.config)\n model.load_state_dict(load_model.state_dict())\n except Exception:\n try:\n load_model = CLIPTextModelWithProjection.from_pretrained(input_dir, subfolder=\"text_encoder_2\")\n model(**load_model.config)\n model.load_state_dict(load_model.state_dict())\n except Exception:\n try:\n load_model = T5EncoderModel.from_pretrained(input_dir, subfolder=\"text_encoder_3\")\n model(**load_model.config)\n model.load_state_dict(load_model.state_dict())\n except Exception:\n raise ValueError(f\"Couldn't load the model of type: ({type(model)}).\")\n else:\n raise ValueError(f\"Unsupported model found: {type(model)=}\")\n\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n transformer_parameters_with_lr = {\"params\": transformer.parameters(), \"lr\": args.learning_rate}\n if args.train_text_encoder:\n # different learning rate for text encoder and unet\n text_parameters_one_with_lr = {\n \"params\": text_encoder_one.parameters(),\n \"weight_decay\": args.adam_weight_decay_text_encoder,\n \"lr\": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate,\n }\n text_parameters_two_with_lr = {\n \"params\": text_encoder_two.parameters(),\n \"weight_decay\": args.adam_weight_decay_text_encoder,\n \"lr\": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate,\n }\n text_parameters_three_with_lr = {\n \"params\": text_encoder_three.parameters(),\n \"weight_decay\": args.adam_weight_decay_text_encoder,\n \"lr\": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate,\n }\n params_to_optimize = [\n transformer_parameters_with_lr,\n text_parameters_one_with_lr,\n text_parameters_two_with_lr,\n text_parameters_three_with_lr,\n ]\n else:\n params_to_optimize = [transformer_parameters_with_lr]\n\n if not (args.optimizer.lower() == \"prodigy\" or args.optimizer.lower() == \"adamw\"):\n logger.warning(\n f\"Unsupported choice of optimizer: {args.optimizer}.Supported optimizers include [adamW, prodigy].\"\n \"Defaulting to adamW\"\n )\n args.optimizer = \"adamw\"\n\n # Initialize the optimizer\n if args.use_8bit_adam and not args.optimizer.lower() == \"adamw\":\n logger.warning(\n f\"use_8bit_adam is ignored when optimizer is not set to 'AdamW'. Optimizer was \"\n f\"set to {args.optimizer.lower()}\"\n )\n\n if args.optimizer.lower() == \"adamw\":\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.\"\n )\n\n optimizer_class = bnb.optim.AdamW8bit\n else:\n optimizer_class = torch.optim.AdamW\n\n optimizer = optimizer_class(\n params_to_optimize,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n if args.optimizer.lower() == \"prodigy\":\n try:\n import prodigyopt\n except ImportError:\n raise ImportError(\"To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`\")\n\n optimizer_class = prodigyopt.Prodigy\n\n if args.learning_rate <= 0.1:\n logger.warning(\n \"Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0\"\n )\n if args.train_text_encoder and args.text_encoder_lr:\n logger.warning(\n f\"Learning rates were provided both for the transformer and the text encoder- e.g. text_encoder_lr:\"\n f\" {args.text_encoder_lr} and learning_rate: {args.learning_rate}. \"\n f\"When using prodigy only learning_rate is used as the initial learning rate.\"\n )\n # changes the learning rate of text_encoder_parameters_one and text_encoder_parameters_two to be\n # --learning_rate\n params_to_optimize[1][\"lr\"] = args.learning_rate\n params_to_optimize[2][\"lr\"] = args.learning_rate\n params_to_optimize[3][\"lr\"] = args.learning_rate\n\n optimizer = optimizer_class(\n params_to_optimize,\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n beta3=args.prodigy_beta3,\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n decouple=args.prodigy_decouple,\n use_bias_correction=args.prodigy_use_bias_correction,\n safeguard_warmup=args.prodigy_safeguard_warmup,\n )\n text_encoders_dtypes = [text_encoder_one.dtype, text_encoder_two.dtype, text_encoder_three.dtype]\n if not args.train_text_encoder:\n tokenizers = [tokenizer_one, tokenizer_two, tokenizer_three]\n text_encoders = [text_encoder_one, text_encoder_two, text_encoder_three]\n def compute_text_embeddings(prompt, text_encoders, tokenizers,text_encoders_dtypes):\n with torch.no_grad():\n prompt_embeds, pooled_prompt_embeds = encode_prompt(\n text_encoders, tokenizers, prompt, args.max_sequence_length, text_encoders_dtypes\n )\n prompt_embeds = prompt_embeds.to(accelerator.device)\n pooled_prompt_embeds = pooled_prompt_embeds.to(accelerator.device)\n return prompt_embeds, pooled_prompt_embeds\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n )\n else:\n if args.train_data_jsonl is not None:\n dataset = load_dataset(\n \"json\",\n data_files=args.train_data_jsonl,\n cache_dir=args.cache_dir,\n # split=\"train\"\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/main/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)\n\n if args.original_image_column is None:\n original_image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n else:\n original_image_column = args.original_image_column\n if original_image_column not in column_names:\n raise ValueError(\n f\"--original_image_column' value '{args.original_image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.edit_prompt_column is None:\n edit_prompt_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n else:\n edit_prompt_column = args.edit_prompt_column\n if edit_prompt_column not in column_names:\n raise ValueError(\n f\"--edit_prompt_column' value '{args.edit_prompt_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.edited_image_column is None:\n edited_image_column = dataset_columns[2] if dataset_columns is not None else column_names[2]\n else:\n edited_image_column = args.edited_image_column\n if edited_image_column not in column_names:\n raise ValueError(\n f\"--edited_image_column' value '{args.edited_image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n\n # Preprocessing the datasets.\n # We need to tokenize input captions and transform the images.\n # def tokenize_captions(captions):\n # inputs = tokenizer(\n # captions, max_length=tokenizer.model_max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\"\n # )\n # return inputs.input_ids\n\n # Preprocessing the datasets.\n train_transforms = transforms.Compose(\n [\n transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),\n transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),\n ]\n )\n\n def preprocess_images(examples):\n\n original_images = np.concatenate(\n [convert_to_np(image, args.resolution) for image in examples[original_image_column]]\n )\n edited_images = np.concatenate(\n [convert_to_np(image, args.resolution) for image in examples[edited_image_column]]\n )\n if args.do_mask:\n mask_images = np.concatenate(\n [convert_to_np(image, args.resolution) for image in examples[args.mask_column]]\n )\n # We need to ensure that the original and the edited images undergo the same\n # augmentation transforms.\n images = np.concatenate([original_images, edited_images, mask_images])\n images = torch.tensor(images)\n images = 2 * (images / 255) - 1\n # mask_index = torch.tensor([image == \"NONE\" for image in examples[args.mask_column]],dtype=torch.bool)\n # return train_transforms(images),mask_index\n return train_transforms(images)\n # We need to ensure that the original and the edited images undergo the same\n # augmentation transforms.\n images = np.concatenate([original_images, edited_images])\n images = torch.tensor(images)\n images = 2 * (images / 255) - 1\n return train_transforms(images)\n\n def preprocess_train(examples):\n # Preprocess images.\n # Since the original and edited images were concatenated before\n # applying the transformations, we need to separate them and reshape\n # them accordingly.\n preprocessed_images = preprocess_images(examples)\n if not args.do_mask:\n # preprocessed_images = preprocess_images(examples)\n original_images, edited_images = preprocessed_images.chunk(2)\n else:\n # preprocessed_images = preprocess_images(examples)\n # preprocessed_images,mask_index = preprocess_images(examples)\n original_images, edited_images, mask_images = preprocessed_images.chunk(3)\n mask_images = mask_images.reshape(-1, 3, args.resolution, args.resolution)\n # examples[\"mask_index\"] = mask_index\n examples[\"mask_pixel_values\"] = mask_images\n original_images = original_images.reshape(-1, 3, args.resolution, args.resolution)\n edited_images = edited_images.reshape(-1, 3, args.resolution, args.resolution)\n\n examples[\"original_pixel_values\"] = original_images\n examples[\"edited_pixel_values\"] = edited_images\n\n # Preprocess the captions.\n # captions = list(examples[edit_prompt_column])\n # examples[edit_prompt_column] = captions\n return examples\n\n with accelerator.main_process_first():\n if args.top_training_data_sample is not None:\n dataset[\"train\"] = dataset[\"train\"].select(range(args.top_training_data_sample)).shuffle(seed=args.seed)\n\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n def collate_fn(examples):\n original_pixel_values = torch.stack([example[\"original_pixel_values\"] for example in examples])\n original_pixel_values = original_pixel_values.to(memory_format=torch.contiguous_format).float()\n edited_pixel_values = torch.stack([example[\"edited_pixel_values\"] for example in examples])\n edited_pixel_values = edited_pixel_values.to(memory_format=torch.contiguous_format).float()\n prompts = [example[edit_prompt_column] for example in examples]\n if args.do_mask:\n mask_pixel_values = torch.stack([example[\"mask_pixel_values\"] for example in examples])\n mask_pixel_values = mask_pixel_values.to(memory_format=torch.contiguous_format).float()\n return {\n \"original_pixel_values\": original_pixel_values,\n \"edited_pixel_values\": edited_pixel_values,\n edit_prompt_column: prompts,\n \"mask_pixel_values\": mask_pixel_values,\n }\n else:\n return {\n \"original_pixel_values\": original_pixel_values,\n \"edited_pixel_values\": edited_pixel_values,\n edit_prompt_column: prompts,\n\n }\n\n # DataLoaders creation:\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_cycles=args.lr_num_cycles,\n power=args.lr_power,\n )\n\n # Prepare everything with our `accelerator`.\n if args.train_text_encoder:\n (\n transformer,\n text_encoder_one,\n text_encoder_two,\n text_encoder_three,\n optimizer,\n train_dataloader,\n lr_scheduler,\n ) = accelerator.prepare(\n transformer,\n text_encoder_one,\n text_encoder_two,\n text_encoder_three,\n optimizer,\n train_dataloader,\n lr_scheduler,\n )\n else:\n transformer, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n transformer, optimizer, train_dataloader, lr_scheduler\n )\n\n # For mixed precision training we cast the text_encoder and vae weights to half-precision\n # as these models are only used for inference, keeping weights in full precision is not required.\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n if accelerator.is_main_process:\n pretrained_path = args.pretrained_model_name_or_path\n pipeline = StableDiffusion3InstructPix2PixPipeline.from_pretrained(\n pretrained_path,\n vae=vae,\n text_encoder=accelerator.unwrap_model(text_encoder_one),\n text_encoder_2=accelerator.unwrap_model(text_encoder_two),\n text_encoder_3=accelerator.unwrap_model(text_encoder_three),\n transformer=accelerator.unwrap_model(transformer),\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n generator = torch.Generator(device=accelerator.device).manual_seed(\n args.seed) if args.seed else None\n if args.do_mask:\n original_image = download_image(args.val_image_url, args.eval_resolution)\n mask_image = download_image(args.val_mask_url, args.eval_resolution)\n else:\n original_image = download_image(args.val_image_url, args.eval_resolution)\n mask_image = None\n\n edited_images = []\n with torch.autocast(\n str(accelerator.device).replace(\":0\", \"\"),\n enabled=(accelerator.mixed_precision == \"fp16\") | (\n accelerator.mixed_precision == \"bf16\")\n ):\n for i in range(args.num_validation_images):\n edited_images.append(\n pipeline(\n args.validation_prompt,\n image=original_image,\n mask_img=mask_image,\n num_inference_steps=50,\n image_guidance_scale=1.5,\n guidance_scale=7.5,\n generator=generator,\n ).images[0]\n )\n path = join(args.output_dir, f\"start_test\")\n os.makedirs(path, exist_ok=True)\n original_image.save(join(path, f\"original.jpg\"))\n for idx, edited_image in enumerate(edited_images):\n edited_image.save(join(path, f\"sample_{idx}.jpg\"))\n\n del pipeline\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n gc.collect()\n\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n print('=========num_update_steps_per_epoch==========', num_update_steps_per_epoch)\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"instruct-pix2pix_sd3\", config=vars(args))\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n resume_global_step = global_step * args.gradient_accumulation_steps\n resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)\n else:\n initial_global_step = 0\n # Only show the progress bar once on each machine.\n progress_bar = tqdm(range(0, args.max_train_steps), initial=initial_global_step, desc=\"Steps\",\n disable=not accelerator.is_local_main_process)\n\n def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):\n sigmas = noise_scheduler_copy.sigmas.to(device=accelerator.device, dtype=dtype)\n schedule_timesteps = noise_scheduler_copy.timesteps.to(accelerator.device)\n timesteps = timesteps.to(accelerator.device)\n step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]\n\n sigma = sigmas[step_indices].flatten()\n while len(sigma.shape) < n_dim:\n sigma = sigma.unsqueeze(-1)\n return sigma\n\n # with torch.autograd.set_detect_anomaly(True):\n for epoch in range(first_epoch, args.num_train_epochs):\n transformer.train()\n if args.train_text_encoder:\n text_encoder_one.train()\n text_encoder_two.train()\n text_encoder_three.train()\n\n train_loss = 0.0\n for step, batch in enumerate(train_dataloader):\n # Skip steps until we reach the resumed step\n # if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:\n # if step % args.gradient_accumulation_steps == 0:\n # progress_bar.update(1)\n # continue\n\n models_to_accumulate = [transformer]\n if args.train_text_encoder:\n models_to_accumulate.extend([text_encoder_one, text_encoder_two, text_encoder_three])\n with accelerator.accumulate(models_to_accumulate):\n # We want to learn the denoising process w.r.t the edited images which\n # are conditioned on the original image (which was edited) and the edit instruction.\n # So, first, convert images to latent space.]\n pixel_values = batch[\"edited_pixel_values\"].to(dtype=vae.dtype)\n prompt = batch[edit_prompt_column]\n\n if not args.train_text_encoder:\n prompt_embeds, pooled_prompt_embeds = compute_text_embeddings(\n prompt, text_encoders, tokenizers,text_encoders_dtypes\n )\n else:\n tokens_one = tokenize_prompt(tokenizer_one, prompt)\n tokens_two = tokenize_prompt(tokenizer_two, prompt)\n tokens_three = tokenize_prompt(tokenizer_three, prompt, args.max_sequence_length)\n\n latents = vae.encode(pixel_values).latent_dist.sample()\n latents = latents * vae.config.scaling_factor\n latents = latents.to(dtype=weight_dtype)\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n # for weighting schemes where we sample timesteps non-uniformly\n if args.weighting_scheme == \"logit_normal\":\n # See 3.1 in the SD3 paper ($rf/lognorm(0.00,1.00)$).\n u = torch.normal(mean=args.logit_mean, std=args.logit_std, size=(bsz,), device=\"cpu\")\n u = torch.nn.functional.sigmoid(u)\n elif args.weighting_scheme == \"mode\":\n u = torch.rand(size=(bsz,), device=\"cpu\")\n u = 1 - u - args.mode_scale * (torch.cos(math.pi * u / 2) ** 2 - 1 + u)\n else:\n u = torch.rand(size=(bsz,), device=\"cpu\")\n\n indices = (u * noise_scheduler_copy.config.num_train_timesteps).long()\n timesteps = noise_scheduler_copy.timesteps[indices].to(device=latents.device)\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n sigmas = get_sigmas(timesteps, n_dim=latents.ndim, dtype=latents.dtype)\n noisy_model_input = sigmas * noise + (1.0 - sigmas) * latents\n\n # Get the additional image embedding for conditioning.\n # Instead of getting a diagonal Gaussian here, we simply take the mode.\n original_image_embeds = vae.encode(batch[\"original_pixel_values\"].to(vae.dtype)).latent_dist.mode()\n concatenated_noisy_latents = torch.cat([noisy_model_input, original_image_embeds], dim=1)\n\n if args.do_mask:\n mask_embeds = vae.encode(batch[\"mask_pixel_values\"].to(vae.dtype)).latent_dist.mode()\n concatenated_noisy_latents = torch.cat([concatenated_noisy_latents, mask_embeds], dim=1)\n\n\n # Predict the noise residual\n if not args.train_text_encoder:\n model_pred = transformer(\n hidden_states=concatenated_noisy_latents,\n timestep=timesteps,\n encoder_hidden_states=prompt_embeds,\n pooled_projections=pooled_prompt_embeds,\n return_dict=False,\n # mask_index = mask_index\n )[0]\n else:\n prompt_embeds, pooled_prompt_embeds = encode_prompt(\n text_encoders=[text_encoder_one, text_encoder_two, text_encoder_three],\n tokenizers=[tokenizer_one, tokenizer_two, tokenizer_three],\n prompt=prompt,\n text_input_ids_list=[tokens_one, tokens_two, tokens_three],\n max_sequence_length=args.max_sequence_length,\n text_encoders_dtypes = text_encoders_dtypes\n )\n\n model_pred = transformer(\n hidden_states=concatenated_noisy_latents,\n timestep=timesteps,\n encoder_hidden_states=prompt_embeds,\n pooled_projections=pooled_prompt_embeds,\n return_dict=False,\n mask_index=mask_index\n\n )[0]\n\n model_pred = model_pred * (-sigmas) + noisy_model_input\n # these weighting schemes use a uniform timestep sampling\n # and instead post-weight the loss\n if args.weighting_scheme == \"sigma_sqrt\":\n weighting = (sigmas ** -2.0).float()\n elif args.weighting_scheme == \"cosmap\":\n bot = 1 - 2 * sigmas + 2 * sigmas ** 2\n weighting = 2 / (math.pi * bot)\n else:\n weighting = torch.ones_like(sigmas)\n\n target = latents\n # Conditioning dropout to support classifier-free guidance during inference. For more details\n # check out the section 3.2.1 of the original paper https://arxiv.org/abs/2211.09800.\n\n # Concatenate the `original_image_embeds` with the `noisy_latents`.\n\n # Get the target for loss depending on the prediction type\n loss = torch.mean(\n (weighting.float() * (model_pred.float() - target.float()) ** 2).reshape(target.shape[0], -1),\n 1,\n )\n loss = loss.mean()\n\n avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n train_loss += avg_loss.item() / args.gradient_accumulation_steps\n # Backpropagate\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n params_to_clip = (\n itertools.chain(\n transformer.parameters(),\n text_encoder_one.parameters(),\n text_encoder_two.parameters(),\n text_encoder_three.parameters(),\n )\n if args.train_text_encoder\n else transformer.parameters()\n )\n accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)\n\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n progress_bar.update(1)\n global_step += 1\n accelerator.log({\"train_loss\": train_loss}, step=global_step)\n train_loss = 0.0\n\n if accelerator.is_main_process:\n if global_step % args.checkpointing_steps == 0:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n if accelerator.is_main_process:\n if (\n (args.val_image_url is not None)\n and (args.validation_prompt is not None)\n and (global_step % args.validation_step == 0)\n ):\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n # create pipeline\n # if not args.train_text_encoder:\n # text_encoder_one, text_encoder_two, text_encoder_three = load_text_encoders(\n # text_encoder_cls_one, text_encoder_cls_two, text_encoder_cls_three\n # )\n if args.do_mask:\n pretrained_path = args.ori_model_name_or_path\n else:\n pretrained_path = args.pretrained_model_name_or_path\n pipeline = StableDiffusion3InstructPix2PixPipeline.from_pretrained(\n pretrained_path,\n vae=vae,\n text_encoder=accelerator.unwrap_model(text_encoder_one),\n text_encoder_2=accelerator.unwrap_model(text_encoder_two),\n text_encoder_3=accelerator.unwrap_model(text_encoder_three),\n transformer=accelerator.unwrap_model(transformer),\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n generator = torch.Generator(device=accelerator.device).manual_seed(\n args.seed) if args.seed else None\n # run inference\n if args.do_mask:\n original_image = download_image(args.val_image_url,args.eval_resolution)\n mask_image = download_image(args.val_mask_url,args.eval_resolution)\n else:\n original_image = download_image(args.val_image_url,args.eval_resolution)\n mask_image = None\n\n edited_images = []\n with torch.autocast(\n str(accelerator.device).replace(\":0\", \"\"),\n enabled=(accelerator.mixed_precision == \"fp16\") | (\n accelerator.mixed_precision == \"bf16\")\n ):\n for i in range(args.num_validation_images):\n\n edited_images.append(\n pipeline(\n args.validation_prompt,\n image=original_image,\n mask_img=mask_image,\n num_inference_steps=50,\n image_guidance_scale=1.5,\n guidance_scale=7.5,\n generator=generator,\n ).images[0]\n )\n\n for tracker in accelerator.trackers:\n path = join(args.output_dir, f\"eval_{global_step}\")\n os.makedirs(path, exist_ok=True)\n original_image.save(join(path, f\"original.jpg\"))\n if tracker.name == \"wandb\":\n wandb_table = wandb.Table(columns=WANDB_TABLE_COL_NAMES)\n\n for idx, edited_image in enumerate(edited_images):\n wandb_table.add_data(\n wandb.Image(original_image), wandb.Image(edited_image), args.validation_prompt\n )\n # save in the dir as well\n tracker.log({\"validation\": wandb_table})\n\n for idx, edited_image in enumerate(edited_images):\n edited_image.save(join(path, f\"sample_{idx}.jpg\"))\n\n del pipeline\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n gc.collect()\n\n logs = {\"step_loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n accelerator.log(logs, step=global_step)\n if global_step >= args.max_train_steps:\n break\n\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n transformer = unwrap_model(transformer)\n\n if args.train_text_encoder:\n text_encoder_one = unwrap_model(text_encoder_one)\n text_encoder_two = unwrap_model(text_encoder_two)\n text_encoder_three = unwrap_model(text_encoder_three)\n pipeline = StableDiffusion3InstructPix2PixPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n transformer=transformer,\n text_encoder=text_encoder_one,\n text_encoder_2=text_encoder_two,\n text_encoder_3=text_encoder_three,\n )\n else:\n pipeline = StableDiffusion3InstructPix2PixPipeline.from_pretrained(\n args.pretrained_model_name_or_path, transformer=transformer\n )\n\n pipeline.save_pretrained(args.output_dir)\n\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n\n main()\n\n" }, { "path": "traning/train_sdxl_pix2pix.py", "content": "#!/usr/bin/env python\n# coding=utf-8\n# Copyright 2024 Harutatsu Akiyama and The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport argparse\nimport logging\nimport math\nimport os\nimport shutil\nimport warnings\nfrom pathlib import Path\nfrom urllib.parse import urlparse\nimport accelerate\nimport datasets\nimport numpy as np\nimport PIL\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport transformers\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import ProjectConfiguration, set_seed\nfrom datasets import load_dataset\nfrom huggingface_hub import create_repo, upload_folder\nfrom PIL import Image\nfrom packaging import version\nfrom PIL import Image\nfrom torchvision import transforms\nfrom tqdm.auto import tqdm\nfrom transformers import AutoTokenizer, PretrainedConfig\n\nimport diffusers\nfrom diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_instruct_pix2pix import (\n StableDiffusionXLInstructPix2PixPipeline,\n)\nfrom diffusers.training_utils import EMAModel\nfrom diffusers.utils import check_min_version, deprecate, is_wandb_available, load_image\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.utils.torch_utils import is_compiled_module\nfrom transformers import CLIPTextModel\nfrom transformers import CLIPTextModelWithProjection\n\nif is_wandb_available():\n import wandb\n\n# Will error if the minimal version of diffusers is not installed. Remove at your own risks.\ncheck_min_version(\"0.28.0.dev0\")\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\nDATASET_NAME_MAPPING = {\n \"BleachNick/UltraEdit_500k\": (\"source_image\", \"edited_image\", \"edit_prompt\"),\n}\nWANDB_TABLE_COL_NAMES = [\"source_image\", \"edited_image\", \"edit_prompt\"]\n\nTORCH_DTYPE_MAPPING = {\"fp32\": torch.float32, \"fp16\": torch.float16, \"bf16\": torch.bfloat16}\n\n\ndef log_validation(\n pipeline, args, accelerator, generator, global_step, is_final_validation=False, enable_autocast=True\n):\n logger.info(\n f\"Running validation... \\n Generating {args.num_validation_images} images with prompt:\"\n f\" {args.validation_prompt}.\"\n )\n\n pipeline = pipeline.to(accelerator.device)\n pipeline.set_progress_bar_config(disable=True)\n\n val_save_dir = os.path.join(args.output_dir, \"validation_images\")\n if not os.path.exists(val_save_dir):\n os.makedirs(val_save_dir)\n if args.do_mask:\n original_image = download_image(args.val_image_url)\n mask_image = download_image(args.val_mask_url)\n else:\n original_image = download_image(args.val_image_url)\n mask_image = None\n\n with torch.autocast(accelerator.device.type, enabled=enable_autocast):\n edited_images = []\n # Run inference\n for val_img_idx in range(args.num_validation_images):\n a_val_img = pipeline(\n args.validation_prompt,\n image=original_image,\n num_inference_steps=50,\n image_guidance_scale=1.5,\n guidance_scale=7.5,\n generator=generator,\n mask_img = mask_image\n ).images[0]\n edited_images.append(a_val_img)\n # Save validation images\n # a_val_img.save(os.path.join(val_save_dir, f\"step_{global_step}_val_img_{val_img_idx}.png\"))\n\n for tracker in accelerator.trackers:\n if tracker.name == \"wandb\":\n wandb_table = wandb.Table(columns=WANDB_TABLE_COL_NAMES)\n for edited_image in edited_images:\n wandb_table.add_data(wandb.Image(original_image), wandb.Image(edited_image), args.validation_prompt)\n logger_name = \"test\" if is_final_validation else \"validation\"\n tracker.log({logger_name: wandb_table})\n\n for idx, edited_image in enumerate(edited_images):\n edited_image.save(os.path.join(val_save_dir, f\"step_{global_step}_val_img_{idx}.png\"))\n\n\ndef import_model_class_from_model_name_or_path(\n pretrained_model_name_or_path: str, revision: str, subfolder: str = \"text_encoder\"\n):\n text_encoder_config = PretrainedConfig.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, revision=revision\n )\n model_class = text_encoder_config.architectures[0]\n\n if model_class == \"CLIPTextModel\":\n from transformers import CLIPTextModel\n\n return CLIPTextModel\n elif model_class == \"CLIPTextModelWithProjection\":\n from transformers import CLIPTextModelWithProjection\n\n return CLIPTextModelWithProjection\n else:\n raise ValueError(f\"{model_class} is not supported.\")\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Script to train Stable Diffusion XL for InstructPix2Pix.\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_vae_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to an improved VAE to stabilize training. For more details check out: https://github.com/huggingface/diffusers/pull/4038.\",\n )\n parser.add_argument(\n \"--vae_precision\",\n type=str,\n choices=[\"fp32\", \"fp16\", \"bf16\"],\n default=\"fp32\",\n help=(\n \"The vanilla SDXL 1.0 VAE can cause NaNs due to large activation values. Some custom models might already have a solution\"\n \" to this problem, and this flag allows you to use mixed precision to stabilize training.\"\n ),\n )\n parser.add_argument(\n \"--ori_model_name_or_path\",\n type=str,\n default=None,\n help=\"Path to ori_model_name_or_path.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n )\n parser.add_argument(\n \"--dataset_name\",\n type=str,\n default=None,\n help=(\n \"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,\"\n \" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,\"\n \" or to a folder containing files that 🤗 Datasets can understand.\"\n ),\n )\n parser.add_argument(\n \"--dataset_config_name\",\n type=str,\n default=None,\n help=\"The config of the Dataset, leave as None if there's only one config.\",\n )\n parser.add_argument(\n \"--train_data_jsonl\",\n type=str,\n default=None,\n help=(\n \"jsonl of training data. Folder contents must follow the structure described in\"\n \" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file\"\n \" must exist to provide the captions for the images. Ignored if `dataset_name` is specified.\"\n ),\n )\n parser.add_argument(\n \"--original_image_column\",\n type=str,\n default=\"source_image\",\n help=\"The column of the dataset containing the original image on which edits where made.\",\n )\n parser.add_argument(\n \"--edited_image_column\",\n type=str,\n default=\"edited_image\",\n help=\"The column of the dataset containing the edited image.\",\n )\n parser.add_argument(\n \"--edit_prompt_column\",\n type=str,\n default=\"edit_prompt\",\n help=\"The column of the dataset containing the edit instruction.\",\n )\n parser.add_argument(\n \"--val_image_url\",\n type=str,\n default=None,\n help=\"URL to the original image that you would like to edit (used during inference for debugging purposes).\",\n )\n parser.add_argument(\n '--val_mask_url',\n type=str,\n default=None,\n help=\"URL to the mask image that you would like to edit (used during inference for debugging purposes).\",\n )\n parser.add_argument(\n \"--validation_prompt\", type=str, default=None, help=\"A prompt that is sampled during training for inference.\"\n )\n parser.add_argument(\n \"--num_validation_images\",\n type=int,\n default=4,\n help=\"Number of images that should be generated during validation with `validation_prompt`.\",\n )\n parser.add_argument(\n \"--validation_steps\",\n type=int,\n default=100,\n help=(\n \"Run fine-tuning validation every X steps. The validation process consists of running the prompt\"\n \" `args.validation_prompt` multiple times: `args.num_validation_images`.\"\n ),\n )\n parser.add_argument(\n \"--max_train_samples\",\n type=int,\n default=None,\n help=(\n \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n \"value if set.\"\n ),\n )\n parser.add_argument(\n \"--top_training_data_sample\",\n type=int,\n default=None,\n help=\"Number of top samples to use for training, ranked by clip-sim-dit. If None, use the full dataset.\",\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"sdxl_edit\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--cache_dir\",\n type=str,\n default=None,\n help=\"The directory where the downloaded models and datasets will be stored.\",\n )\n parser.add_argument(\"--seed\", type=int, default=None, help=\"A seed for reproducible training.\")\n parser.add_argument(\n \"--resolution\",\n type=int,\n default=256,\n help=(\n \"The resolution for input images, all the images in the train/validation dataset will be resized to this resolution.\"\n ),\n )\n parser.add_argument(\n \"--crops_coords_top_left_h\",\n type=int,\n default=0,\n help=(\"Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet.\"),\n )\n parser.add_argument(\n \"--crops_coords_top_left_w\",\n type=int,\n default=0,\n help=(\"Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet.\"),\n )\n parser.add_argument(\n \"--center_crop\",\n default=False,\n action=\"store_true\",\n help=(\n \"Whether to center crop the input images to the resolution. If not set, the images will be randomly\"\n \" cropped. The images will be resized to the resolution first before cropping.\"\n ),\n )\n parser.add_argument(\n \"--random_flip\",\n action=\"store_true\",\n help=\"whether to randomly flip images horizontally\",\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=16, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--gradient_checkpointing\",\n action=\"store_true\",\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--scale_lr\",\n action=\"store_true\",\n default=False,\n help=\"Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.\",\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"cosine\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--conditioning_dropout_prob\",\n type=float,\n default=None,\n help=\"Conditioning dropout probability. Drops out the conditionings (image and edit prompt) used in training InstructPix2Pix. See section 3.2.1 in the paper: https://arxiv.org/abs/2211.09800.\",\n )\n parser.add_argument(\n \"--use_8bit_adam\", action=\"store_true\", help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--allow_tf32\",\n action=\"store_true\",\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\"--use_ema\", action=\"store_true\", help=\"Whether to use EMA model.\")\n parser.add_argument(\n \"--non_ema_revision\",\n type=str,\n default=None,\n required=False,\n help=(\n \"Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or\"\n \" remote repository specified with --pretrained_model_name_or_path.\"\n ),\n )\n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--push_to_hub\", action=\"store_true\", help=\"Whether or not to push the model to the Hub.\")\n parser.add_argument(\"--hub_token\", type=str, default=None, help=\"The token to use to push to the Model Hub.\")\n parser.add_argument(\n \"--hub_model_id\",\n type=str,\n default=None,\n help=\"The name of the repository to keep in sync with the local `output_dir`.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n help=(\n \"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to\"\n \" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.\"\n ),\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=500,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\n \"--checkpoints_total_limit\",\n type=int,\n default=None,\n help=(\"Max number of checkpoints to store.\"),\n )\n parser.add_argument(\n \"--resume_from_checkpoint\",\n type=str,\n default=None,\n help=(\n \"Whether training should be resumed from a previous checkpoint. Use a path saved by\"\n ' `--checkpointing_steps`, or `\"latest\"` to automatically select the last available checkpoint.'\n ),\n )\n parser.add_argument(\n \"--train_text_encoder\",\n action=\"store_true\"\n )\n parser.add_argument(\n \"--enable_xformers_memory_efficient_attention\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--do_mask\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--train_from_scratch\", action=\"store_true\", help=\"Whether or not to use xformers.\"\n )\n parser.add_argument(\n \"--mask_column\",\n type=str,\n default=\"mask_image\",\n help=\"The column of the dataset containing the original image`s mask.\",\n )\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n\n # Sanity checks\n if args.dataset_name is None and args.train_data_jsonl is None:\n raise ValueError(\"Need either a dataset name or a training folder.\")\n\n # default to using the same revision for the non-ema model if not specified\n if args.non_ema_revision is None:\n args.non_ema_revision = args.revision\n\n return args\n\n\ndef convert_to_np(image, resolution):\n try:\n if isinstance(image, str):\n if image == \"NONE\":\n image = PIL.Image.new(\"RGB\", (resolution, resolution),(255, 255, 255))\n else:\n image = PIL.Image.open(image)\n elif image is None:\n image = PIL.Image.new(\"RGB\", (resolution, resolution), (255, 255, 255))\n image = image.convert(\"RGB\").resize((resolution, resolution))\n return np.array(image).transpose(2, 0, 1)\n except Exception as e:\n print(\"Load error\", image)\n print(e)\n # New blank image\n image = PIL.Image.new(\"RGB\", (resolution, resolution),(255, 255, 255))\n return np.array(image).transpose(2, 0, 1)\n\ndef download_image(path_or_url):\n # Check if path_or_url is a local file path\n if path_or_url is None:\n # return a white RBG image image\n return PIL.Image.new(\"RGB\", (512, 512),(255, 255, 255))\n if os.path.exists(path_or_url):\n image = Image.open(path_or_url).convert(\"RGB\").resize((512,512))\n\n else:\n image = Image.open(requests.get(path_or_url, stream=True).raw).convert(\"RGB\")\n\n image = PIL.ImageOps.exif_transpose(image)\n return image\n\ndef main():\n args = parse_args()\n\n if args.report_to == \"wandb\" and args.hub_token is not None:\n raise ValueError(\n \"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token.\"\n \" Please use `huggingface-cli login` to authenticate with the Hub.\"\n )\n\n if args.non_ema_revision is not None:\n deprecate(\n \"non_ema_revision!=None\",\n \"0.15.0\",\n message=(\n \"Downloading 'non_ema' weights from revision branches of the Hub is deprecated. Please make sure to\"\n \" use `--variant=non_ema` instead.\"\n ),\n )\n logging_dir = os.path.join(args.output_dir, args.logging_dir)\n\n if torch.backends.mps.is_available() and args.mixed_precision == \"bf16\":\n # due to pytorch#99272, MPS does not yet support bfloat16.\n raise ValueError(\n \"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead.\"\n )\n\n accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)\n accelerator = Accelerator(\n gradient_accumulation_steps=args.gradient_accumulation_steps,\n mixed_precision=args.mixed_precision,\n log_with=args.report_to,\n project_config=accelerator_project_config,\n )\n\n generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)\n\n # Make one log on every process with the configuration for debugging.\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n if accelerator.is_local_main_process:\n datasets.utils.logging.set_verbosity_warning()\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n datasets.utils.logging.set_verbosity_error()\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\n # If passed along, set the training seed now.\n if args.seed is not None:\n set_seed(args.seed)\n\n # Handle the repository creation\n if accelerator.is_main_process:\n if args.output_dir is not None:\n os.makedirs(args.output_dir, exist_ok=True)\n\n if args.push_to_hub:\n repo_id = create_repo(\n repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token\n ).repo_id\n\n vae_path = (\n args.pretrained_model_name_or_path\n if args.pretrained_vae_model_name_or_path is None\n else args.pretrained_vae_model_name_or_path\n )\n vae = AutoencoderKL.from_pretrained(\n vae_path,\n subfolder=\"vae\" if args.pretrained_vae_model_name_or_path is None else None,\n revision=args.revision,\n variant=args.variant,\n )\n unet = UNet2DConditionModel.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"unet\", revision=args.revision, variant=args.variant\n )\n\n # InstructPix2Pix uses an additional image for conditioning. To accommodate that,\n # it uses 8 channels (instead of 4) in the first (conv) layer of the UNet. This UNet is\n # then fine-tuned on the custom InstructPix2Pix dataset. This modified UNet is initialized\n # from the pre-trained checkpoints. For the extra channels added to the first layer, they are\n # initialized to zero.\n logger.info(\"Initializing the XL InstructPix2Pix UNet from the pretrained UNet.\")\n in_channels = 12 if args.do_mask else 8\n out_channels = unet.conv_in.out_channels\n unet.register_to_config(in_channels=in_channels)\n\n with torch.no_grad():\n new_conv_in = nn.Conv2d(\n in_channels, out_channels, unet.conv_in.kernel_size, unet.conv_in.stride, unet.conv_in.padding\n )\n new_conv_in.weight.zero_()\n new_conv_in.weight[:, :4, :, :].copy_(unet.conv_in.weight)\n unet.conv_in = new_conv_in\n\n # Create EMA for the unet.\n if args.use_ema:\n ema_unet = EMAModel(unet.parameters(), model_cls=UNet2DConditionModel, model_config=unet.config)\n\n if args.enable_xformers_memory_efficient_attention:\n if is_xformers_available():\n import xformers\n\n xformers_version = version.parse(xformers.__version__)\n if xformers_version == version.parse(\"0.0.16\"):\n logger.warning(\n \"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details.\"\n )\n unet.enable_xformers_memory_efficient_attention()\n else:\n raise ValueError(\"xformers is not available. Make sure it is installed correctly\")\n\n def unwrap_model(model):\n model = accelerator.unwrap_model(model)\n model = model._orig_mod if is_compiled_module(model) else model\n return model\n\n # `accelerate` 0.16.0 will have better support for customized saving\n if version.parse(accelerate.__version__) >= version.parse(\"0.16.0\"):\n # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format\n def save_model_hook(models, weights, output_dir):\n if accelerator.is_main_process:\n if args.use_ema:\n ema_unet.save_pretrained(os.path.join(output_dir, \"unet_ema\"),max_shard_size='15GB')\n for i, model in enumerate(models):\n if isinstance(unwrap_model(model), CLIPTextModel):\n model.save_pretrained(os.path.join(output_dir, \"text_encoder\"))\n elif isinstance(unwrap_model(model), CLIPTextModelWithProjection):\n model.save_pretrained(os.path.join(output_dir, \"text_encoder2\"))\n elif isinstance(unwrap_model(model), UNet2DConditionModel):\n model.save_pretrained(os.path.join(output_dir, \"unet\"),max_shard_size='15GB')\n\n # make sure to pop weight so that corresponding model is not saved again\n weights.pop()\n def load_model_hook(models, input_dir):\n if args.use_ema:\n load_model = EMAModel.from_pretrained(os.path.join(input_dir, \"unet_ema\"), UNet2DConditionModel)\n ema_unet.load_state_dict(load_model.state_dict())\n ema_unet.to(accelerator.device)\n del load_model\n\n for i in range(len(models)):\n # pop models so that they are not loaded again\n model = models.pop() # pop from the last\n\n # load diffusers style into model\n if isinstance(unwrap_model(model), UNet2DConditionModel):\n load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder=\"unet\")\n model.register_to_config(**load_model.config)\n model.load_state_dict(load_model.state_dict())\n del load_model\n elif isinstance(unwrap_model(model), CLIPTextModelWithProjection):\n load_model = CLIPTextModelWithProjection.from_pretrained(input_dir, subfolder=\"text_encoder_2\")\n model(**load_model.config)\n model.load_state_dict(load_model.state_dict())\n del load_model\n elif isinstance(unwrap_model(model), CLIPTextModel):\n load_model = CLIPTextModelWithProjection.from_pretrained(input_dir, subfolder=\"text_encoder\")\n model(**load_model.config)\n model.load_state_dict(load_model.state_dict())\n del load_model\n\n accelerator.register_save_state_pre_hook(save_model_hook)\n accelerator.register_load_state_pre_hook(load_model_hook)\n\n if args.gradient_checkpointing:\n unet.enable_gradient_checkpointing()\n\n # Enable TF32 for faster training on Ampere GPUs,\n # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\n if args.allow_tf32:\n torch.backends.cuda.matmul.allow_tf32 = True\n\n if args.scale_lr:\n args.learning_rate = (\n args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes\n )\n\n # Initialize the optimizer\n if args.use_8bit_adam:\n try:\n import bitsandbytes as bnb\n except ImportError:\n raise ImportError(\n \"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`\"\n )\n\n optimizer_cls = bnb.optim.AdamW8bit\n else:\n optimizer_cls = torch.optim.AdamW\n\n optimizer = optimizer_cls(\n unet.parameters(),\n lr=args.learning_rate,\n betas=(args.adam_beta1, args.adam_beta2),\n weight_decay=args.adam_weight_decay,\n eps=args.adam_epsilon,\n )\n\n # Get the datasets: you can either provide your own training and evaluation files (see below)\n # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).\n\n # In distributed training, the load_dataset function guarantees that only one local process can concurrently\n # download the dataset.\n\n # train_dataset = load_dataset(self.data_args.load_datatype, data_files=self.data_args.train_file,split=\"train\" )\n if args.dataset_name is not None:\n # Downloading and loading a dataset from the hub.\n dataset = load_dataset(\n args.dataset_name,\n args.dataset_config_name,\n cache_dir=args.cache_dir,\n )\n else:\n data_files = {}\n if args.train_data_jsonl is not None:\n dataset = load_dataset(\n \"json\",\n data_files=args.train_data_jsonl,\n cache_dir=args.cache_dir,\n # split=\"train\"\n )\n # See more about loading custom images at\n # https://huggingface.co/docs/datasets/main/en/image_load#imagefolder\n\n # Preprocessing the datasets.\n # We need to tokenize inputs and targets.\n column_names = dataset[\"train\"].column_names\n\n # 6. Get the column names for input/target.\n dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)\n if args.original_image_column is None:\n original_image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]\n else:\n original_image_column = args.original_image_column\n if original_image_column not in column_names:\n raise ValueError(\n f\"--original_image_column' value '{args.original_image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.edit_prompt_column is None:\n edit_prompt_column = dataset_columns[1] if dataset_columns is not None else column_names[1]\n else:\n edit_prompt_column = args.edit_prompt_column\n if edit_prompt_column not in column_names:\n raise ValueError(\n f\"--edit_prompt_column' value '{args.edit_prompt_column}' needs to be one of: {', '.join(column_names)}\"\n )\n if args.edited_image_column is None:\n edited_image_column = dataset_columns[2] if dataset_columns is not None else column_names[2]\n else:\n edited_image_column = args.edited_image_column\n if edited_image_column not in column_names:\n raise ValueError(\n f\"--edited_image_column' value '{args.edited_image_column}' needs to be one of: {', '.join(column_names)}\"\n )\n\n # For mixed precision training we cast the text_encoder and vae weights to half-precision\n # as these models are only used for inference, keeping weights in full precision is not required.\n weight_dtype = torch.float32\n if accelerator.mixed_precision == \"fp16\":\n weight_dtype = torch.float16\n warnings.warn(f\"weight_dtype {weight_dtype} may cause nan during vae encoding\", UserWarning)\n\n elif accelerator.mixed_precision == \"bf16\":\n weight_dtype = torch.bfloat16\n warnings.warn(f\"weight_dtype {weight_dtype} may cause nan during vae encoding\", UserWarning)\n\n # Preprocessing the datasets.\n # We need to tokenize input captions and transform the images.\n def tokenize_captions(captions, tokenizer):\n inputs = tokenizer(\n captions,\n max_length=tokenizer.model_max_length,\n padding=\"max_length\",\n truncation=True,\n return_tensors=\"pt\",\n )\n return inputs.input_ids\n\n # Preprocessing the datasets.\n train_transforms = transforms.Compose(\n [\n transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),\n transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),\n ]\n )\n\n \n def preprocess_images(examples):\n\n original_images = np.concatenate(\n [convert_to_np(image, args.resolution) for image in examples[original_image_column]]\n )\n edited_images = np.concatenate(\n [convert_to_np(image, args.resolution) for image in examples[edited_image_column]]\n )\n if args.do_mask:\n mask_images = np.concatenate(\n [convert_to_np(image, args.resolution) for image in examples[args.mask_column]]\n )\n # We need to ensure that the original and the edited images undergo the same\n # augmentation transforms.\n images = np.concatenate([original_images, edited_images, mask_images])\n images = torch.tensor(images)\n images = 2 * (images / 255) - 1\n return train_transforms(images)\n # We need to ensure that the original and the edited images undergo the same\n # augmentation transforms.\n images = np.concatenate([original_images, edited_images])\n images = torch.tensor(images)\n images = 2 * (images / 255) - 1\n return train_transforms(images)\n\n # Load scheduler, tokenizer and models.\n tokenizer_1 = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer\",\n revision=args.revision,\n use_fast=False,\n )\n tokenizer_2 = AutoTokenizer.from_pretrained(\n args.pretrained_model_name_or_path,\n subfolder=\"tokenizer_2\",\n revision=args.revision,\n use_fast=False,\n )\n text_encoder_cls_1 = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)\n text_encoder_cls_2 = import_model_class_from_model_name_or_path(\n args.pretrained_model_name_or_path, args.revision, subfolder=\"text_encoder_2\"\n )\n\n # Load scheduler and models\n noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=\"scheduler\")\n text_encoder_1 = text_encoder_cls_1.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder\", revision=args.revision, variant=args.variant\n )\n text_encoder_2 = text_encoder_cls_2.from_pretrained(\n args.pretrained_model_name_or_path, subfolder=\"text_encoder_2\", revision=args.revision, variant=args.variant\n )\n\n # We ALWAYS pre-compute the additional condition embeddings needed for SDXL\n # UNet as the model is already big and it uses two text encoders.\n text_encoder_1.to(accelerator.device, dtype=weight_dtype)\n text_encoder_2.to(accelerator.device, dtype=weight_dtype)\n tokenizers = [tokenizer_1, tokenizer_2]\n text_encoders = [text_encoder_1, text_encoder_2]\n\n # Freeze vae and text_encoders\n vae.requires_grad_(False)\n if args.train_text_encoder:\n text_encoder_1.requires_grad_(True)\n text_encoder_2.requires_grad_(True)\n else:\n text_encoder_1.requires_grad_(False)\n text_encoder_2.requires_grad_(False)\n\n\n # Set UNet to trainable.\n unet.train()\n if args.train_text_encoder:\n text_encoder_1.train()\n text_encoder_2.train()\n\n # Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt\n def encode_prompt(text_encoders, tokenizers, prompt):\n prompt_embeds_list = []\n\n for tokenizer, text_encoder in zip(tokenizers, text_encoders):\n text_inputs = tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(\n text_input_ids, untruncated_ids\n ):\n removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because CLIP can only handle sequences up to\"\n f\" {tokenizer.model_max_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = text_encoder(\n text_input_ids.to(text_encoder.device),\n output_hidden_states=True,\n )\n\n # We are only ALWAYS interested in the pooled output of the final text encoder\n pooled_prompt_embeds = prompt_embeds[0]\n prompt_embeds = prompt_embeds.hidden_states[-2]\n bs_embed, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)\n prompt_embeds_list.append(prompt_embeds)\n\n prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)\n pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)\n return prompt_embeds, pooled_prompt_embeds\n\n # Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt\n def encode_prompts(text_encoders, tokenizers, prompts):\n prompt_embeds_all = []\n pooled_prompt_embeds_all = []\n\n for prompt in prompts:\n prompt_embeds, pooled_prompt_embeds = encode_prompt(text_encoders, tokenizers, prompt)\n prompt_embeds_all.append(prompt_embeds)\n pooled_prompt_embeds_all.append(pooled_prompt_embeds)\n\n return torch.stack(prompt_embeds_all), torch.stack(pooled_prompt_embeds_all)\n\n # Adapted from examples.dreambooth.train_dreambooth_lora_sdxl\n # Here, we compute not just the text embeddings but also the additional embeddings\n # needed for the SD XL UNet to operate.\n def compute_embeddings_for_prompts(prompts, text_encoders, tokenizers):\n if args.train_text_encoder:\n prompt_embeds_all, pooled_prompt_embeds_all = encode_prompts(text_encoders, tokenizers, prompts)\n add_text_embeds_all = pooled_prompt_embeds_all\n prompt_embeds_all = prompt_embeds_all.to(accelerator.device)\n add_text_embeds_all = add_text_embeds_all.to(accelerator.device)\n else:\n with torch.no_grad():\n prompt_embeds_all, pooled_prompt_embeds_all = encode_prompts(text_encoders, tokenizers, prompts)\n add_text_embeds_all = pooled_prompt_embeds_all\n prompt_embeds_all = prompt_embeds_all.to(accelerator.device)\n add_text_embeds_all = add_text_embeds_all.to(accelerator.device)\n return prompt_embeds_all, add_text_embeds_all\n\n # Get null conditioning\n def compute_null_conditioning():\n null_conditioning_list = []\n for a_tokenizer, a_text_encoder in zip(tokenizers, text_encoders):\n null_conditioning_list.append(\n a_text_encoder(\n tokenize_captions([\"\"], tokenizer=a_tokenizer).to(accelerator.device),\n output_hidden_states=True,\n ).hidden_states[-2]\n )\n return torch.concat(null_conditioning_list, dim=-1)\n\n null_conditioning = compute_null_conditioning()\n\n def compute_time_ids():\n crops_coords_top_left = (args.crops_coords_top_left_h, args.crops_coords_top_left_w)\n original_size = target_size = (args.resolution, args.resolution)\n add_time_ids = list(original_size + crops_coords_top_left + target_size)\n add_time_ids = torch.tensor([add_time_ids], dtype=weight_dtype)\n return add_time_ids.to(accelerator.device).repeat(args.train_batch_size, 1)\n\n add_time_ids = compute_time_ids()\n\n def preprocess_train(examples):\n # Preprocess images.\n preprocessed_images = preprocess_images(examples)\n # Since the original and edited images were concatenated before\n # applying the transformations, we need to separate them and reshape\n # them accordingly.\n if not args.do_mask:\n original_images, edited_images = preprocessed_images.chunk(2)\n else:\n original_images, edited_images, mask_images = preprocessed_images.chunk(3)\n mask_images = mask_images.reshape(-1, 3, args.resolution, args.resolution)\n examples[\"mask_pixel_values\"] = mask_images\n original_images = original_images.reshape(-1, 3, args.resolution, args.resolution)\n edited_images = edited_images.reshape(-1, 3, args.resolution, args.resolution)\n\n # Collate the preprocessed images into the `examples`.\n examples[\"original_pixel_values\"] = original_images\n examples[\"edited_pixel_values\"] = edited_images\n\n # Preprocess the captions.\n captions = list(examples[edit_prompt_column])\n examples[\"captions\"] = captions\n # examples[\"prompt_embeds\"] = prompt_embeds_all\n # examples[\"add_text_embeds\"] = add_text_embeds_all\n return examples\n\n with accelerator.main_process_first():\n if args.top_training_data_sample is not None:\n dataset[\"train\"] = dataset[\"train\"].select(range(args.top_training_data_sample)).shuffle(seed=args.seed)\n if args.max_train_samples is not None:\n dataset[\"train\"] = dataset[\"train\"].shuffle(seed=args.seed).select(range(args.max_train_samples))\n # Set the training transforms\n train_dataset = dataset[\"train\"].with_transform(preprocess_train)\n\n def collate_fn(examples):\n original_pixel_values = torch.stack([example[\"original_pixel_values\"] for example in examples])\n original_pixel_values = original_pixel_values.to(memory_format=torch.contiguous_format).float()\n edited_pixel_values = torch.stack([example[\"edited_pixel_values\"] for example in examples])\n edited_pixel_values = edited_pixel_values.to(memory_format=torch.contiguous_format).float()\n captions = [example[\"captions\"] for example in examples]\n # prompt_embeds = torch.concat([example[\"prompt_embeds\"] for example in examples], dim=0)\n # add_text_embeds = torch.concat([example[\"add_text_embeds\"] for example in examples], dim=0)\n if args.do_mask:\n mask_pixel_values = torch.stack([example[\"mask_pixel_values\"] for example in examples])\n mask_pixel_values = mask_pixel_values.to(memory_format=torch.contiguous_format).float()\n return {\n \"original_pixel_values\": original_pixel_values,\n \"edited_pixel_values\": edited_pixel_values,\n \"captions\":captions,\n # \"prompt_embeds\": prompt_embeds,\n # \"add_text_embeds\": add_text_embeds,\n \"mask_pixel_values\": mask_pixel_values,\n }\n else:\n\n return {\n \"original_pixel_values\": original_pixel_values,\n \"edited_pixel_values\": edited_pixel_values,\n \"captions\":captions\n # \"prompt_embeds\": prompt_embeds,\n # \"add_text_embeds\": add_text_embeds,\n }\n\n # DataLoaders creation:\n train_dataloader = torch.utils.data.DataLoader(\n train_dataset,\n shuffle=True,\n collate_fn=collate_fn,\n batch_size=args.train_batch_size,\n num_workers=args.dataloader_num_workers,\n )\n\n # Scheduler and math around the number of training steps.\n overrode_max_train_steps = False\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n overrode_max_train_steps = True\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,\n num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,\n )\n\n # Prepare everything with our `accelerator`.\n if args.train_text_encoder:\n unet, text_encoder,text_encoder_2, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet,text_encoder_1,text_encoder_2, optimizer, train_dataloader, lr_scheduler\n )\n else:\n\n unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(\n unet, optimizer, train_dataloader, lr_scheduler\n )\n\n if args.use_ema:\n ema_unet.to(accelerator.device)\n\n # Move vae, unet and text_encoder to device and cast to weight_dtype\n # The VAE is in float32 to avoid NaN losses.\n if args.pretrained_vae_model_name_or_path is not None:\n vae.to(accelerator.device, dtype=weight_dtype)\n else:\n vae.to(accelerator.device, dtype=TORCH_DTYPE_MAPPING[args.vae_precision])\n\n # We need to recalculate our total training steps as the size of the training dataloader may have changed.\n num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)\n if overrode_max_train_steps:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n # Afterwards we recalculate our number of training epochs\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n # We need to initialize the trackers we use, and also store our configuration.\n # The trackers initializes automatically on the main process.\n if accelerator.is_main_process:\n accelerator.init_trackers(\"instruct-pix2pix-xl\", config=vars(args))\n\n # Some configurations require autocast to be disabled.\n enable_autocast = True\n if torch.backends.mps.is_available() or (\n accelerator.mixed_precision == \"fp16\" or accelerator.mixed_precision == \"bf16\"\n ):\n enable_autocast = False\n\n # Train!\n total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}\")\n logger.info(f\" Gradient Accumulation steps = {args.gradient_accumulation_steps}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n global_step = 0\n first_epoch = 0\n\n # Potentially load in the weights and states from a previous save\n if args.resume_from_checkpoint:\n if args.resume_from_checkpoint != \"latest\":\n path = os.path.basename(args.resume_from_checkpoint)\n else:\n # Get the most recent checkpoint\n dirs = os.listdir(args.output_dir)\n dirs = [d for d in dirs if d.startswith(\"checkpoint\")]\n dirs = sorted(dirs, key=lambda x: int(x.split(\"-\")[1]))\n path = dirs[-1] if len(dirs) > 0 else None\n\n if path is None:\n accelerator.print(\n f\"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.\"\n )\n args.resume_from_checkpoint = None\n initial_global_step = 0\n else:\n accelerator.print(f\"Resuming from checkpoint {path}\")\n accelerator.load_state(os.path.join(args.output_dir, path))\n global_step = int(path.split(\"-\")[1])\n\n initial_global_step = global_step\n first_epoch = global_step // num_update_steps_per_epoch\n else:\n initial_global_step = 0\n\n progress_bar = tqdm(\n range(0, args.max_train_steps),\n initial=initial_global_step,\n desc=\"Steps\",\n # Only show the progress bar once on each machine.\n disable=not accelerator.is_local_main_process,\n )\n\n for epoch in range(first_epoch, args.num_train_epochs):\n train_loss = 0.0\n for step, batch in enumerate(train_dataloader):\n models_to_accumulate = [unet]\n if args.train_text_encoder:\n models_to_accumulate.extend([text_encoder_1, text_encoder_2])\n with accelerator.accumulate(models_to_accumulate):\n # We want to learn the denoising process w.r.t the edited images which\n # are conditioned on the original image (which was edited) and the edit instruction.\n # So, first, convert images to latent space.\n if args.pretrained_vae_model_name_or_path is not None:\n edited_pixel_values = batch[\"edited_pixel_values\"].to(dtype=weight_dtype)\n else:\n edited_pixel_values = batch[\"edited_pixel_values\"]\n latents = vae.encode(edited_pixel_values).latent_dist.sample()\n latents = latents * vae.config.scaling_factor\n if args.pretrained_vae_model_name_or_path is None:\n latents = latents.to(weight_dtype)\n\n # Sample noise that we'll add to the latents\n noise = torch.randn_like(latents)\n bsz = latents.shape[0]\n # Sample a random timestep for each image\n timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)\n timesteps = timesteps.long()\n\n # Add noise to the latents according to the noise magnitude at each timestep\n # (this is the forward diffusion process)\n noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)\n\n # SDXL additional inputs\n captions = batch['captions']\n encoder_hidden_states, add_text_embeds = compute_embeddings_for_prompts(captions, text_encoders, tokenizers)\n encoder_hidden_states = encoder_hidden_states.squeeze(1)\n add_text_embeds = add_text_embeds.squeeze(1)\n\n #\n # encoder_hidden_states = batch[\"prompt_embeds\"]\n # add_text_embeds = batch[\"add_text_embeds\"]\n\n # Get the additional image embedding for conditioning.\n # Instead of getting a diagonal Gaussian here, we simply take the mode.\n if args.pretrained_vae_model_name_or_path is not None:\n original_pixel_values = batch[\"original_pixel_values\"].to(dtype=weight_dtype)\n else:\n original_pixel_values = batch[\"original_pixel_values\"]\n original_image_embeds = vae.encode(original_pixel_values).latent_dist.sample()\n if args.pretrained_vae_model_name_or_path is None:\n original_image_embeds = original_image_embeds.to(weight_dtype)\n\n # Conditioning dropout to support classifier-free guidance during inference. For more details\n # check out the section 3.2.1 of the original paper https://arxiv.org/abs/2211.09800.\n if args.conditioning_dropout_prob is not None:\n random_p = torch.rand(bsz, device=latents.device, generator=generator)\n # Sample masks for the edit prompts.\n prompt_mask = random_p < 2 * args.conditioning_dropout_prob\n prompt_mask = prompt_mask.reshape(bsz, 1, 1)\n # Final text conditioning.\n encoder_hidden_states = torch.where(prompt_mask, null_conditioning, encoder_hidden_states)\n\n # Sample masks for the original images.\n image_mask_dtype = original_image_embeds.dtype\n image_mask = 1 - (\n (random_p >= args.conditioning_dropout_prob).to(image_mask_dtype)\n * (random_p < 3 * args.conditioning_dropout_prob).to(image_mask_dtype)\n )\n image_mask = image_mask.reshape(bsz, 1, 1, 1)\n # Final image conditioning.\n original_image_embeds = image_mask * original_image_embeds\n\n # Concatenate the `original_image_embeds` with the `noisy_latents`.\n concatenated_noisy_latents = torch.cat([noisy_latents, original_image_embeds], dim=1)\n\n if args.do_mask:\n mask_embeds = vae.encode(batch[\"mask_pixel_values\"].to(weight_dtype)).latent_dist.mode()\n concatenated_noisy_latents = torch.cat([concatenated_noisy_latents, mask_embeds], dim=1)\n # Get the target for loss depending on the prediction type\n if noise_scheduler.config.prediction_type == \"epsilon\":\n target = noise\n elif noise_scheduler.config.prediction_type == \"v_prediction\":\n target = noise_scheduler.get_velocity(latents, noise, timesteps)\n else:\n raise ValueError(f\"Unknown prediction type {noise_scheduler.config.prediction_type}\")\n\n # Predict the noise residual and compute loss\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n\n model_pred = unet(\n concatenated_noisy_latents,\n timesteps,\n encoder_hidden_states,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n loss = F.mse_loss(model_pred.float(), target.float(), reduction=\"mean\")\n\n # Gather the losses across all processes for logging (if we use distributed training).\n avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()\n train_loss += avg_loss.item() / args.gradient_accumulation_steps\n\n # Backpropagate\n accelerator.backward(loss)\n if accelerator.sync_gradients:\n accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n\n # Checks if the accelerator has performed an optimization step behind the scenes\n if accelerator.sync_gradients:\n if args.use_ema:\n ema_unet.step(unet.parameters())\n progress_bar.update(1)\n global_step += 1\n accelerator.log({\"train_loss\": train_loss}, step=global_step)\n train_loss = 0.0\n\n if global_step % args.checkpointing_steps == 0:\n if accelerator.is_main_process:\n # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`\n if args.checkpoints_total_limit is not None:\n checkpoints = os.listdir(args.output_dir)\n checkpoints = [d for d in checkpoints if d.startswith(\"checkpoint\")]\n checkpoints = sorted(checkpoints, key=lambda x: int(x.split(\"-\")[1]))\n\n # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints\n if len(checkpoints) >= args.checkpoints_total_limit:\n num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1\n removing_checkpoints = checkpoints[0:num_to_remove]\n\n logger.info(\n f\"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints\"\n )\n logger.info(f\"removing checkpoints: {', '.join(removing_checkpoints)}\")\n\n for removing_checkpoint in removing_checkpoints:\n removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)\n shutil.rmtree(removing_checkpoint)\n\n save_path = os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\n accelerator.save_state(save_path)\n logger.info(f\"Saved state to {save_path}\")\n\n logs = {\"step_loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0]}\n progress_bar.set_postfix(**logs)\n\n ### BEGIN: Perform validation every `validation_epochs` steps\n if accelerator.is_main_process:\n if global_step % args.validation_steps == 0 and global_step != 0:\n if (args.val_image_url is not None) and (args.validation_prompt is not None):\n # create pipeline\n if args.use_ema:\n # Store the UNet parameters temporarily and load the EMA parameters to perform inference.\n ema_unet.store(unet.parameters())\n ema_unet.copy_to(unet.parameters())\n if args.do_mask:\n pretrained_path = args.ori_model_name_or_path\n else:\n pretrained_path = args.pretrained_model_name_or_path\n # The models need unwrapping because for compatibility in distributed training mode.\n pipeline = StableDiffusionXLInstructPix2PixPipeline.from_pretrained(\n pretrained_path,\n unet=unwrap_model(unet),\n text_encoder=unwrap_model(text_encoder_1),\n text_encoder_2=unwrap_model(text_encoder_2),\n tokenizer=tokenizer_1,\n tokenizer_2=tokenizer_2,\n vae=unwrap_model(vae),\n revision=args.revision,\n variant=args.variant,\n torch_dtype=weight_dtype,\n )\n\n log_validation(\n pipeline,\n args,\n accelerator,\n generator,\n global_step,\n is_final_validation=False,\n enable_autocast=enable_autocast,\n )\n\n if args.use_ema:\n # Switch back to the original UNet parameters.\n ema_unet.restore(unet.parameters())\n\n del pipeline\n torch.cuda.empty_cache()\n ### END: Perform validation every `validation_epochs` steps\n\n if global_step >= args.max_train_steps:\n break\n\n # Create the pipeline using the trained modules and save it.\n accelerator.wait_for_everyone()\n if accelerator.is_main_process:\n if args.use_ema:\n ema_unet.copy_to(unet.parameters())\n\n pipeline = StableDiffusionXLInstructPix2PixPipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n text_encoder=text_encoder_1,\n text_encoder_2=text_encoder_2,\n tokenizer=tokenizer_1,\n tokenizer_2=tokenizer_2,\n vae=vae,\n unet=unwrap_model(unet),\n revision=args.revision,\n variant=args.variant,\n )\n\n pipeline.save_pretrained(args.output_dir)\n\n if args.push_to_hub:\n upload_folder(\n repo_id=repo_id,\n folder_path=args.output_dir,\n commit_message=\"End of training\",\n ignore_patterns=[\"step_*\", \"epoch_*\"],\n )\n\n if (args.val_image_url is not None) and (args.validation_prompt is not None):\n log_validation(\n pipeline,\n args,\n accelerator,\n generator,\n global_step,\n is_final_validation=True,\n enable_autocast=enable_autocast,\n )\n\n accelerator.end_training()\n\n\nif __name__ == \"__main__\":\n main()\n" } ]